{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Automatic Number Plate recognition using YOLOv8\n",
        "\n",
        "🔥 In this Project, we are going to fine-tune a pre-trained YOLOv8 model to detect the License Plates of different Cars\n",
        "\n",
        "\n",
        "✅ Dataset used: https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e\n",
        "\n",
        "\n",
        "🚀 Full code: https://github.com/HarshSingh2009/Automatic-Car-LICENSE-Detection"
      ],
      "metadata": {
        "id": "bRs7UuO8lQQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics\n",
        "import ultralytics\n",
        "from ultralytics import YOLO\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "import numpy\n",
        "import cv2 as cv\n",
        "import PIL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abOzH6-ymqE-",
        "outputId": "b7c62138-335b-41b5-8d79-01305c3467b7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.0.237-py3-none-any.whl (691 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m691.9/691.9 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.23.5)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.16.0+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Collecting thop>=0.1.1 (from ultralytics)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.5.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.12.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (23.2)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.3.post1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2023.11.17)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Installing collected packages: thop, ultralytics\n",
            "Successfully installed thop-0.1.1.post2209072238 ultralytics-8.0.237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mount Google drive and extract Dataset"
      ],
      "metadata": {
        "id": "zpO9CeUhmLSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUdGAIXKlws0",
        "outputId": "9eb72966-c9ac-40b0-9450-742fececbf1c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir license_plate_dataset\n",
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('/content/drive/MyDrive/License Plate Recognition.v4-resized640_aug3x-accurate.yolov8.zip', 'r')\n",
        "zip_ref.extractall('/content/license_plate_dataset/')\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "earWedsrnZGI"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## YOLOv8 model inference\n",
        "\n",
        "We will load in YOLOv8 models and we are going to run a quick inference with them\n"
      ],
      "metadata": {
        "id": "Lmm28HI3mP1H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "# Load a model\n",
        "infernce_model = YOLO(\"yolov8m.pt\")  # load a pretrained model (recommended for training)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwbDaV69mZb8",
        "outputId": "5316c53c-9a02-49e0-9327-33cfa82c0979"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to 'yolov8m.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 49.7M/49.7M [00:00<00:00, 223MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = infernce_model(\"https://ultralytics.com/images/bus.jpg\", conf=0.5, show=True, save=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aKLFsoioUT4",
        "outputId": "3366e7e3-7b6d-4ec8-c2c5-922220872677"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "\n",
            "Downloading https://ultralytics.com/images/bus.jpg to 'bus.jpg'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 476k/476k [00:00<00:00, 16.9MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING ⚠️ NMS time limit 0.550s exceeded\n",
            "image 1/1 /content/bus.jpg: 640x480 4 persons, 1 bus, 176.9ms\n",
            "Speed: 18.8ms preprocess, 176.9ms inference, 1068.7ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo predict model=yolov8m.pt source='https://ultralytics.com/images/zidane.jpg'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNEIfhAVokOe",
        "outputId": "22b4a609-4a5a-4c88-d460-abf70fc3e700"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.237 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8m summary (fused): 218 layers, 25886080 parameters, 0 gradients, 78.9 GFLOPs\n",
            "\n",
            "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
            "100% 165k/165k [00:00<00:00, 11.2MB/s]\n",
            "image 1/1 /content/zidane.jpg: 384x640 4 persons, 2 ties, 115.8ms\n",
            "Speed: 2.1ms preprocess, 115.8ms inference, 568.8ms postprocess per image at shape (1, 3, 384, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict2\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset Visualization\n",
        "\n",
        "Let's Visualize some samples in our dataset"
      ],
      "metadata": {
        "id": "hm7uCCOymZ-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "\n",
        "# Get all train images path\n",
        "train_images_path = glob.glob('/content/license_plate_dataset/train/images/*')"
      ],
      "metadata": {
        "id": "EGYM6fmao5EX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# take random sample\n",
        "rows = 2\n",
        "cols = 4\n",
        "\n",
        "for idx in range(8):\n",
        "  sample_img = plt.imread(random.choice(train_images_path))\n",
        "  plt.subplot(rows, cols, idx+1)\n",
        "  plt.imshow(sample_img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "rnDsQ7AzppKw",
        "outputId": "f039e82d-f6f2-4105-900f-ddc8ee1e399e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 8 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAFmCAYAAAC2vDqJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5Bt2XXeCf62Oeb69M/Xq/fKogreFkASAiWSYIuiRI2aI3X3hDgchUQxpI7o4cSEREXIx4ihP7pHESNFq3s61JqWFCGpR75FoUnCkQQKAGHKobx9r55Nn9cct838sc85eTNfvnKoKgCcXBHvZd6b9x67z95rfetb3xLee8+xHduxHduxHduxHdsPiMnv9wEc27Ed27Ed27Ed27HN27FzcmzHdmzHdmzHdmw/UHbsnBzbsR3bsR3bsR3bD5QdOyfHdmzHdmzHdmzH9gNlx87JsR3bsR3bsR3bsf1A2bFzcmzHdmzHdmzHdmw/UHbsnBzbsR3bsR3bsR3bD5QdOyfHdmzHdmzHdmzH9gNlx87JsR3bsR3bsR3bsf1A2bFzcmzHdmzHdmzHdmw/UPZ9c07+wT/4B9x5552kaconPvEJvvGNb3y/DuXYvo92PA6O7XgMHBscj4NjO2jfF+fkX/yLf8Gv/Mqv8Nf/+l/n29/+Nh/4wAf47Gc/y82bN78fh3Ns3yc7HgfHdjwGjg2Ox8Gx3Wri+9H47xOf+AQf+9jH+Pt//+8D4Jzj3Llz/Nf/9X/NX/7Lf/ndPpxj+z7Z8Tg4tuMxcGxwPA6O7VbT7/YOy7LkW9/6Fr/6q7/aviel5Cd+4id4+OGHj/xOURQURdG+ds6xtbXF8vIyQoh3/JiP7Xsz7z3j8ZjTp08jZQDr3uw4OB4DP9z2dowBOB4HP+x2PBcc21Fj4Ch7152TjY0NrLWcOHHiwPsnTpzg6aefPvI7v/Zrv8bf/Jt/8904vGN7B+3y5cucPXsWePPj4HgM/P6w72UMwPE4+P1ix3PBsc2PgaPsXXdO3or96q/+Kr/yK7/Svt7d3eWOO+448Jnzd1zk4sX74Q05ziGTpXWCQCGEoiwLLl26TD7LsK7Ee08SpSwseLJsi2yqOHHyNItri2gtybIZKytrKKVwzuGcQwjBE088wTPPPHPLHpeX1vijf+RPsbu3w9e+/rs8+J6P8OzTz3Dl+osYNzn06fmTODrrNuyvcGL1AtPxmBubr3LHHefZ2r7G7t7WLZ/t9YYoKalMhbUGYwzeu7D1dzGrNxgM3vJ3bzcGPvt/+C/QSQcVac6ePcvi4iJFkSOl5My5c2it2N7eYf3mBsPhiF6vw9b2JtPpLNw3a3E+3DspFQKBjiLiSKMjTZp2GAwGREmCc46iLDAWrl/bYmNnGxFJFpdX8M6jpCJJYsrKUmYVVBn3XLyAUI58lpHnGds7Gc9c2mA6MzhRYWyFEwohYgi3BIUFAUJIvIrwSoMAhUBpjdaaCEckLKNRzIXzd3LmxBqDXszN65f56pd+i5eefoa777iT4cKIwlREccpslmHKijgKj733Hu8ceA9CgPfhvfpfG9lWJUIIXn7xBaoio5tqOkmEEAKHBCFQUuKcw1qHtYayKtnb3eXkqdMoKRFK8qUvfel7GgOvNQ4Om9aan/7sJzm3PGUkXuLhh3NevlHwwff2kMkF6K6wevIcZ8/fwWhhAScUpfU88sjjWOd48okneO/73s/uLGdra4d+p8NPf/Yn+ef/339DvxPzoz/2SR544AEGgwGmMjhjAdprdrvnynsfrvdR77+B181PUd8vEHg81vt2PJuiJMtmjPfG7O7usbm5ydbWFttbW2xtrbO3vc3e3h55nlEURT0fvLvZ/XdiLvj+m0AIkCoijhPSNKU/GDAcLbC4tMLS8iorK6ssL62xsLjEcDQk6fVJkg5axkipAI8XPvysn0sRXoGvx5jS4BV4hfcCcHgMiPAd51x7P62FbJYRJzFJ3OwDvHdUxmArgxCinv883nnKImdrcx2pPFGasrm+wc7mFuvXriBkQVU4FDFKS0xVIRDEcYK3HqEkvcEA4TWzfJd/+b/+z5RVeeTVer0x8K47JysrKyiluHHjxoH3b9y4wcmTJ4/8TpIkJEnymtvtdntoHb1h50QI0DpqnRNjDEJIhAivpfBorVHKABDpCK0jkjjGC0ecpERRdMg5AWvtbY8vTVN2dj1xnKCkwrkwCA+aqP/5udf+0N9Bq5hEdskokEIz6K+wtXMreUwIxXsffD+raytY5zBVRZZnOOuoqoKyzMmygiIvuX79Wv0QgLEV3nuEEO1idTtH6bVt//jnIdc3Ow5uNwa8gChJwmIuJRfvvhutFNvb2yitqYxhmufsjScIqZBK0u328R6KsjywEIf7rVFS1/uL6fV7JHGMlgqtNdvjMa+8/CpbO1N2tmdMsinr2xlojbEO5yxCSiIV0VMSXnmV8+dPsnzqBFk2oztyyO6Ily9fI6sKjHNYAB+jZIRzDmUt0nlKYymxWC1BOpzwgEWhyZ3HRRE7BTz6wiWubOxy8ewJTp86w8/9qf+K5x9/nC9/7jeYZCXnL9wJSmCdRyHpRBHOh+2FxTJcS+fcobET7r1UCu8cQnjSJCLSiiRNCAtjcOikUuA9xlqsLXGuJI41kY5QMiKu7933MgZeaxwctkhrnJ1hy02iQcW4sMSpRukOOumhuz0WFxcZDAb0ej2cULhZRq+Tcu3aNZIk5v7772c0WmJ3PGVzc4OlxWXiKOGOO+5gOpnwzDPPcv977mc0GKJS2QYozXWb/9mO1/ZZOjSO36Bz0r4+/H3rb/F5PB4hJQJRz2sS7yymKhmPx8xmMx57/HG2Nze4/+4LPP7441y/dp2rV6+xub2Fc4aqLKiqCmfdEXt96/ZOzAVvYu/1zzdzPuE7UimiKCZNU3r9AcPREkvLSyyvnmB17STLy8ssLq+ysLhIvz8g7fSIoggpFV6IW3fpCI4GgLAHj64ZO4IQONRzs5QSgcJ7ifeS4MgYfJhJwrNd78ia4MA212zeea6qCldZpJRIIet9C7aLLbbWt7nj/FniNKLsDaCy7GzdwKsMD/jc46yh2+lQVZY0SZEInKANVDqdHnGS3tY5eb0U3LvunMRxzEc+8hE+//nP83M/93NAmBQ///nP8xf/4l98i1sVaP1mB6uoo4/w01obokjqRVgIhPR4SpzzKKVQAqT0lNYglZ67uA5weC/I8/zIfXXSHgBFkRHpGGsN1lYtgnG0HfXweECiVIwQgtJVeAFKCUxlbt2zkKRJD6kEQuvwL9JhMRYS5yqsUZS5YWdnTJokpGmf7Z0baK1YWV3FO4+1HmstxhqqqsQagzEl1lmMsThnsdbi3Lwjc3uH5u0aB9ZUSCEwpmI8HrO5sUG320Vrjfee3ckYGWlOnD6FK8NnpRT0uj2U0jWaZBFCEEVReF9qOp2UOI7DNfSCzfVNbmysk3S7jIYDJtOSyhiKwkBhEV7gAWc93hkc0E/77EymlC9cYm1a0u0OmEwr9ooC1esgCoH2lrhyOAc6lljrMFlBqiSpiplVnllVYITAqQhQlNKglKIwDu/C+dwsxuxsT3jqhRe549Qyd9/3IP/Hs+f4yuc+z1PfeYwL996FVBKEwgmNEBaJxSNwPkRaSjVRlT+wuEZRhHMWD1jvkTqq6/wEUsj6sx6hBNKDdWFRVCpGKY0QEq1ufT7fmbkgmI4i8mxMle5gO57dzKFSjZd90BFp0qfX66Hrc9ZKk+c5aRzRTRM+9tGPoCOF945YKU6srHHt2g2wjpWlZT724Q/wncce46sPP8w9F+/i/Lk7wiQvZXsdGzvsoMy/D68/Sb+u+TqynotjPAEME/g63BFIIfBSEcUpw5FmNBrx6Hef4g/+xE/ygfe+hwfe+0F2d8dcunSV5196kQt3ncFVBbO9MU8/9SQ7O9vMZhmz2ZTpdEZRFOR5jjEmzJ9vMYB5J8fB0Xb0MUopUToiTbr0ej2Go0UWlpZYXV1ldfUkK6trrK6uMlpcoTvok3R7RFGKUgopw1W+nfMZdnvr38Irh5hzmNpPCI8XdZDQfFiEZ7CZW4VwzW+1c+pxc8+vEGEcOGfrtWZuP96F73gZvi890kl2t8dceuVVzpw5ifMKFSmQ4CkwZYZSEa5eb2QSg7HYwjJYHOEkICXGVCjZoZN2mUz23vQdgu9TWudXfuVX+IVf+AU++tGP8vGPf5y/9/f+HtPplF/8xV98S9sTQnxPnnTrnLTQabihSiq8B2sNsQ7wtZQCWxjiOL5lUvHeUZa3eokC6HS6CILzEsdxvdBb3mo0opUKi4W1YcFVEmtvdU6kFOhIIwgD/PCE2LxuIHkhBVJKnPNonZAmXZwDKdQB8lLzUDUwtavhRGuDkxImrILKlFRVxebm+i3H9naMA19ZtANrPXvbO7zwwgucPn2aTqeDqY9Da00sIzb3xiglGY2GdDodFhYXmc6mZFkWrqOUJGlMv9djOBjUi3O4/4uLi5TO8urVawgZsbI0QCvBiy+/isFSVR4dRQgpMRi0FMyKklLA9s4ON3dn6DhFoDE4SldSmhxrHVQhpRAj0FqDjsjLjIFQLMQxqRHkXrBbWZyStDOVBysjUFB5gRWKcup59vIW1zb2+PCD9/Ez//nP8+y3v80XvvRbdPtdTp+6g/0lS6LqXNJ81N+Mi4OLqsAaSxKHtJLWCpB4oUKELmS9OEpwCqljZAQqirDOo5PoyPv3ds8FjSkBprBgHVUF48KzNtJEyQJJp09/MCBNU5rJWkjRwt9LS0t0Op0WScqyDCkVL770EgBlWbC6uspDD32Cly5f4tr165R5wYkTJ+j1eiGQUQFpm0ceD/8+b6/1t6PsYOLX3/JcH7WFZh/Nvc6ynPF4TK/XY3Nzk7IsMcYQxxFRFLGwsEisNKxYTGkxtkJKhVYK5z1RHDHLM6IoxhvD5sYmu7u7bG2vMx6PmU6nzGYzsiwLUXo9Pxxl79Q4aM67QUaTJCHp9BgMBowWl1heOcHqap1uWVlheXmFhYVler0eSdpFR0l4LpBAcMQ9AoPDeoeoHftmzbjdNT/KafF1Wo4mQ7f/cu69/fGw/21H846vU0lwcPv7v4dnukH3D6cd22PDt4H67s4eVeko8oq4l6KVBgSJSrClREcKqSVZ5bBlifISVxmyWQaRpD8cIqVAENHpdt7yffu+OCd/8k/+SdbX1/lrf+2vcf36dT74wQ/yuc997hZC1Bs1IUQbAb3Bbxz858EaO3dDPQJJSM85rAWVaKSSCCmx1qKUumUScc7fxjkRdNMeHk+Wz0iSNOR5XYV7TeTk9sevVAReYG2BVAoBWFfd8kkpdUh34UEECPAWqxdg7y2gEMJj64EshAzvy/qD9aLWTIFChIhNAiiB1/uTZMiZhm0f5Zy8HeMgFgpZWrQQFHnB3tY2vV4P6x1lVWHxJLI+5kiyPd6ldBW9TpfV1VWWl5fJsoyyDE4UQBxrdvd22NnZYXV1lU6ng4wVw+GQ5aLEVBaPod+POX12jZ1ZwdbumEgmKB1hizFSePr9HqdOnqCYTZE6xnmBUhqEI+nEmLLgxs0NNqY5pjAUZUVXJUgRU7oSmc8YJRGLscalCRQFu3mBE+BdiNItYKxAKovwEmkUFYqizPnGd57mvXed5f6Pf5zlO07yhc/9J5555knuvvc9JEmMNUWYkJREthNwiNyErBGReraUdQiWxglKh7SVkApTf16KOnIEvBfMcoOIIpxUJElC1Dl6knq754LGvBQImxJjmeWCWeGI4g4i7hJ1OgwXBug4DudZO2V5loX75yzbO1v0RwsoKVlcGJFnBVdfvUKRz9gb72GsZdAf8P4H38ve2V1efOFFrl692qaKoigK5x1FBxyH2y9SR5zDG3RUXtcE0ETY3tbcBMerV6/S63WZTCcUsxlpkpJleXBgMAEN8L5GRF0YHz6kirtpTJQmxN0uo+GQ4WCALSucsxhTtou1EILZbMZsNmNvb4+bN2/yr//1v7rlEN+ucaCkZGl5hdXV01RGsLS0xic/8VHuOL/Ge9//AP3BItt7GSqKkTpBSB1GsK0Xb1EjTHW6UwjZplMQ9X3E1e79rU7hgcv+OvdOzKV52hHgQwp33g47HeFrrj6WBrFxtzgcR23j1p/hdylkzTsT7OxOkDomz0uGPlxThaCTdKmyLtpGaKmpdAXCoKMOznqm2RQzs/T6A5K4gzGGXuetc4u+b4TYv/gX/+LbBtlJqYjiegF+HdLJPtzVgJ0AHmft/gjxAAJZL8jegZK6hW29cyh50Blq0JdmgTuwTzydbhcIE2Ca9LHG4KxpB9mbMSFEDR0LrKtQKsDPzhkOc1S01gEB4iABtkFSmtM11oQJpVmgapi/8crD+0flam+NAA//Ll7jnnyv42C0sIApDTqJcMpjjaUqq/DQicArMcKzNFxgMBqishnOOmZZxvr6OsPhsM3FqtrJM1VIl5SF4dXLVxkOh8RpilCatbU1jCnRWmC8I+126I4zZtMZSni6SQIiRDa+LFnopHRGfXoLA3QU4Z3FVgXWGKJomfsuXGBiSq6t3+SFF15hujfGZhW+qii9oagqFAotHacWRsR5yeb2Ft4J0DEG8F6ipEdYiZdgSoEVgrF3PPrsq2zuTXn/Pef4+T/1X/LNb3yd3/nKVzh94iSnT5zAWQPWgxQtLN+QLZXwoGokzQq6cUIaxfS7HXppglCSKsTtdUQmKMuKSlXEUcJgYcTZs3ewtLCIVop/8c/emTFwlHW6Q5SNSGPL5jiisoI4HeFVhI5ShoMBURwTJzFShsiyyAsQMkDpUjAZTxgNl0h0hOiklHlJJ0mY5TOAmqcmGA6GPPDAA2xtbbG+vs5kMmFxcbFNFTYRa4POzS8Qh18fZa9HVg2OwOsthB7vLc7bFjl57oUXGAz7ZNmMrb2MxcVF0jRhPJ7gvUNI8M5jTNWirs450lQihKAqS1CSqqow1oBwNERuaw1RFNHpdOh0uqyuytZROco5ge99HPR1xNLKMr/wZ3+JD334R/j6N59E+ZSf+6M/zXDBcfaONSYzwyuXr5NXFudESEM7j8XjcTgvkHXwtX/VBW4emfAh3cIhp6H5Oe9Uvp7j2YCg4fMNMi1qfvrRzqlzzfuu/c5RqFuLzByx7/bMRPMzBB/ewWyW4ZFkswzvHLoOxOMoQfoIYRVeKITQeBlI+qawAR01Emssg15KQUa/P7zN3Xp9+6Go1nk9U0rWLOQ3E2WIfdfEc7CCpf6ElIEJ75xHquCYIATOe5SSLVzWWFMJc8uehCBNU7zzZHnOoL9EkQW+xluxprIkpBwsOtJYZ2uC7UGLtA7HyiHCnADc/sNmKtPmJ5uJKCAnot3nD6KdOnea5559mb6I64k+3I84jhiNliitYTqdIgR0ul2SNME7R5WXxHHcLjBRFKBsayxZVtbRXkGn0+HGjXW6gx55XoWF2ld0uwmdNEGYjOVhl9mJFTa39sgmY5JOD2cdWiqcq1haXmF3Z5NOJ8VZiy2LMKaMwYmMQUczOH+Ks2sneOnFV1l/5RqyLKlMQVJUpMZiJ2OIFUvDEYIhOztjjC1xRHgCGVXUHCZhTXA4K8leJXnh8pRsa50PP3A3H3voEyyuDPj8b3wBrOXuC3ciPAglsDWhTrjgrDvrWp6Rd4a7LlxAeofzFdI7bGWx0rXpRQjjJYkiVKKQWDa3bjLb20G/FYDwe7DBcIgvpySRZHvXgVB0ugNinZImXaIoEASTOAkLkw/nbL1lZXmZjc1Nsjxja3OLXrdLpEO6LyszstmsfkbC4mCtRWvNysoKnU6Hzc2Q3ijLEmstvV7vALQOHHi2DtvhSPn10z1H8xxu+3kBWZ6zvr7O0uIiRR7GebfbDU5acbVGEOQ+yd97pBRUVeBnGWMC4qaaKi2DqtMnNDyemvc1H8m/k1VBd54/T6ffQ+uAckeRxhSGKJakSYT3zHHiADzShyX+CMZHfdx12pPaCZzb3/yZHCaTz9vtz7lxHpr7e9CZeCPIi3e3d5Boj3z/GEMQsb+fA/O7EFRlRRzHdLsd9vb2sO5kcE6AKEpBKLwT9XWKcA4MAifDGtmJYrIsY3FxmSiK5tLjb/6+/75wTnSUhPKqN2ltrtw35VdzfxQgZF1m6SVKqpolHaKJ+QvufeAmVJU5MqcqhKST9vA+lGlFUcp0PD0wsI62o5CKAKtKGdWRjCHSXYzJ6gfp4GejKAkREAAST1VHxpIwcBWgqKqqRZO8D5H/a02gPyh25twZnn3qWaxPUbEOmStnGQ0X6A+HeCWZ9bNA1kRQZVNwjjxJsc5S5CWTyaRN0zWRrDGGKImIEs3exHDlynW0jlhYWqA7GNDv9VBS0q0cZ86eQ2rNdJqxvZ0TRx5TFeyVE67fUNxz33kQC5giJ9KKyul6grfMZhPGE8vMgY1SVk4uE0vB7MYW2XiMzF1wYhCYSYHXUxY6Male5ObmFkU1RagY5SK0gBhPqgSpViRakfZiokjQ9Y6Xnn6KOIVptseHPvgg+bRgd2+TKs8p85yimIX0Vlm2YzPQSATGWRQCZx1KeFQ9PRnrazLe/uIpvUd4h/EOp8N15wiy9jtlQgi6SUSZbyG0Zm/XEkcJ/U6POO2SdNKABTYcKiHBS4xteFOghODE8iJSaKzxSBz9borSNgiA1Yii8xYlQqCglGI0GpEkCZubmy2Ksra2xnA4RGvdkq+Bdqy9Hgw///utPLfXIGAe/GT9OYeUghdefhklBGmcUlWWO06vcPfdd/Pcc89TlhVK6xY1raoynKeUKCVuWWy8n0sD19c/cPIkzlJz2A5ymt4JW1wcEacpZT7DOINUYQ7v91N6fV1fb4EUEVJUAU0Wfo7wEX54EdKZ3gen1gsX0AofAuCQ5vE0M+gbXXiPRlPmXaP6AA4HknPfP7wv0aL7+8j4vDPblBUf5DTV6T0cQjlkgG7QKKbZjKXRiG435trNSxhnA51BKaRSGFcihA50BBG4hta6uiJJ1qiZJS8KlFJ0+0MOo/lv1H5/OCdK16jGG//O/DPSLEbNoBBznwmOs9hP6bR/k7cMlrKsjvSgpZShDtx7KhPSMMaY1+GbHO2YNNtTUodIzIdqisqUR342SZL2XOefwf1zB7ygrKp6cHEgujv42bcpB/422sLCAmmcgHXEHU2UxoF0HMVIL8hnObNsytKJFWKpKIUnm0xawtl0NsUUgScURRHdXo8oSdBJIDwXRUHa7WCdb8mCZ86cCSm8oqTf6ZGNJ4y6HZYGHYTzJDHIbsx0mlEWUzbX19FKgq0QAnQkiCJNURhGcYcrN9a5sbVLLiQLwyWG/RHSeYo8w6qasW895Dk6U8goRWtPvJAiXEK3161RgIhOrMFVuLKgzCaMt68xnY7ZzUtm0ylVVeBcCc6j0PiyCMXAHvAO6R2xD+z+EGUGB127sAAJoLKW0tRInVVt3hrqn87hncUJsMoTpQlpJ33XxkRIe0q8mCGkYndq6A/6pHGKUkG/xhP4YUIIvFKByGwMC4sLvPLKK5w6eYpPfOxTxHHK7z78dfIsY3Vlhdx0eeXKq+E5luFZCM+LaJ3bTqfDqVOnSNOU9fV1XnrpJRYWFlhdXSVJkhZRmCcozh/7YZTh9Z65efLjQch+flv150QIyp57/jmGwxHeOUprcM7R7Xaw9e9JHNcLsifP8xYlmj92IQWiDtRqP+YAOXc/iGumnTfqSL1F82GfRVHgvENpRelLkkTTSdP6OAVKamDfWRYEH0XO57kFdemv43P/8d/wyssv8vFPfJoPfuQT9Xf2HfKj7lm77TfMM5pzSA5DNIe+dysH5WCxw7w5d+tn5y0QV/fH7mSyy9raiL2p5KXLOWVVEUU6OKtaYbxBS42aSycZY4iiDlI0Tqgky7I2pRdSmm8eOv194ZxEcRQQkDdlB3NzzQQTEoB10kfQOhBCSKQKK5qQsi4dm9tavZAdNUCU0sRx3ELlwTkJ5be39yhv/xCrui7d+VD+HMXxkVwXgDhOap6T4Mh5wft6oi5aaC9MPHJu4vzBckjmLYo1p0+d4urVa+AckZLMshk3b1znjjvOMx2PefnSy5TesLa8Qr/bY9QfsDuZMZ1O2NvebpEzQxAcU2lMEteaAFIELZVIY4xhaXGEqhcg3Rc889TTvHj9Bp1uj/On1jh7MmjEJEnENBvR7XTpRglZVrC5vkmahPJaKSVlmeOB8W5FNnGMq4Lx9g1W1gyrCwusrq2w50oSa+nFnZCKGPZQS12kdPiqpJplZJNdttavcWNzgyKfMRvv4W2J9wbhPNo5vASFrkM9gXKScjrD5Bm+MtiyxFmPsQ7jTCvMZX2AvqWvJ2uaVHtIbyIkro7G9qHi8M/U5YtCS/pLo3dtTEgpKYqCYVxhqojdmWXxxCJSaqRSpGkHpXV4XgUknS5SKuI4aJs8++yzXL++QVk57r7nDpaeeZarr15luDDi1acvkU1n2MoQd9OQ/hT7KeUGGZFSsrq6yuLiIlevXuXq1auMx2OWl5dZXl6ueWJBS6l5zm4n5T2fani9J/F2XId9vgRkecbOzg53n7+L2WwadF58cNa0jrDW1JVMYR4sy4AUhQBNtcGLAKir+xoenLOuJUcj5ng170JaT8pwDGVd2hxrRVGTgaVSgKOqqnqBP3hdpBe1HMC+Axc4GI6Hf+fLfOPrv8vi4hIf+thD+xy6OUfBHeKIHEa+jkK/GtKtJzhHrd8gbk3tvdbvDVfsaJv/Wwg2pAxrgRANk1DUnLsgMXDm7Ar2SkFZFBRFSbcTyqWRQXzAeNcS5cHXxRS+vragVExRFIFzlHaQUh1ZSfp69vvDOYli3uwCKhqkSYL1riXE+rpMSwqQGKzzIFV4CJWvUx7zk0ggmgmhKIrqSOckimKiSGFMUQ+KwHGx3gCvxTs5esApodFoclfg8cSRpiizIz+bJAerJDy+rbWZz0tWVd6K8RhbBgdIytqJl0ee1w+CFVnBxQsXePXyZZwtcDZCATevX2d1aYnxZBzusakQSlBZSzdOWR5FaCGwS8vsbG1RWVuPAYd3Fd3uIIwDbxE+woiQFMumE/q9PrrTZW9nG6kkw9ECk/GMTmpZO7HKLJ8ghWBpYYRzjlhC0hmwJ3aYjAvyfI84ikGAjhMS3WEYWWQVSm97aLpAb6HPHSv3k8ZAVZHt7LG1u8mlx77Lzu42O9vbmHyGMTlCeFpcz4cqKu8M3ksqBM6DFB5RQj6ekW2N8TODUwKJQ0sQUuGkRihNJOvFSKnWUdc6QtSKtUIplJKhcicOi34zoTsc1lmqvKDIcipnmJXF7W/i22oCqSWT6Q4X1xxlYamsZGVlEaUj4qRHkqSh6s47jPDEIqBmURxx7txplpdWeemlm0ynM3SsGC4NuXTpZRIdsbW1hRIC7D5cjmA/DTbHQ2v+fu7cOZaXl9na2mJnZ4fr168Tx3HL82hKjpvvN9/dPyNoKkUOTglHkx1fi8sihOTly5dRSrC8vAAEgUnvYG9vzGSSYaxh0OsivQzpuapCNNy7+vyauUIIwXhvTKIknUGMd4FMrkQjLFb/aIJH/84FOqJ2Kgpr8cKTSkEhRQgq61S2rcJcLdAIbA2S1PcQQmzqPa4hqwr40Ic/xsJoxJ133oNvj9+D80hXV7cJf5A0y2s4iO3r+evh698PoiZNOmz++0fNxQEVu/X9UE3ZaKFIQOEPBOauduoCxyiJFOfPnmRze4PKWrKiYMH5Gq2XaKmobIXQrnWKXK2fUlWmFiWttbCsoZN2iaKYqip4s+md3xfOyb7myJtdQENusUmPzOVs2ijGOV8v1I3+hztQRtyMEyEEeX70BBxHIVrOsgxRIy4hrfPWFnylFNScBQgT62R2FHIScr8NnNviq3NpngYOrKoy5BZrpr0QNXLSPKG8Prz8/bDpbMrq2lpAPoyhKkripEsxnTGZTsmyGQ3zXWtNlRVsTDNmsxndbpelpSXSOCbPcsqyQOmgTxHZoCUTJx20A6MkstcBIZlNp0zzDKkFveEALzR55XnhlUtcunKF0bDH2toaCwtdTFWQjSdImSClYLQwpG8q8jynspYo8iyPBgzuPEknSRHOkxUTtrc22bl+k5ubN9jcvcFssofZm2LLqoXORY1agMULMD5MZl6E3Lr3EdaFydfhiaQk9hoZRZy/9yKxiELeUnlUJIJQk9Q474mjCCkFxjkq7/BaYryncuClDkiccyjpsd5RmKp20muCnzX4TkLS7+GLAplN37UxIaRgb3eXwXlBYaPg8CURKo7rNGdYjExliETQOknTtJ5HNBcv3sO1a1OshekkZzgcoSNNWZYMBgNu3rzZEqlhf3Fr93/EYpKmKadOnWJhYYG9vb1aPj6nKAp6vR5xHBPH8ZHoyYFoe44b8ZrXQNzKDWm28dRTTzHo91leXsY5x8bGZqsYW5YlSqk2DS2o5yrnEMLihKal6tSHUZZFe4xSSswchH8rR+a1j/t7MSlFS9Z1taCmUpCmodWCtYEvJETjmDSeU5Omb5wLsT85Ivi5n/8v6zOtU5ht5aNj/3b4AymtN1ZafOj1XApuHj05Ol2zz+F5TTIu/oCDc1QML2RDUYC8mLK41CXtRDgL0+kMVhvUTKKjiKzMcHOZg3luYpOFcJRUVYGOQkuP2ey2h3hb+33hnERRzFFs56OsuaEN+RPA1yz6RimzyR9DKLvTMpDDhFQt8euwBbLr0c5JkqRIIUPKQOpQnmct3r+1ah0lI0BgXS2tH0WU1e32HdNih3VUIERTKhfO1QNVVR1g5wfVwway/8FzShqzpaXT7TIYDRjPJkHdNe1jvSebzTh1+jTl1Vd55eWX2d7a5MTyKtJ6ptMpxhiWl5bppikLCyO8CzwCHUVYa8lmU6wJkWPST4NuRZxgHExnGWVRMM1zZlVJMughtzTb2zusrq7Q6fZQWuNsFVAEM0HHIDUkaUKvH1ohSDzlzi7Xn73M+o0bbK5fYzzeJs9nIdXiLVYEYWrlFMJB6ersDB7ng+qCDThPHdHGqChByVosTYX7nUYxwil01CEnYmI8PnKIGIT2DAYdVlfWSLzD7O7w3ccf5dT5i0gdsTWZ0hstIIUkijtoocLIqSs1kkSjpEcKjysLXFVRZDnTnV18XlDpN5t2/R7MgS1L0o5ib2rp9PpoFRz1SGmk2EcFtQzS4qEFgmRjY4sPfOCDbKxPGY/HvHr5VTpxQhxFXL1xk0hrqrKizAvkHKIo5mKbfec/vDPvyHQ6HbTW9Pt9iqIgy0KKRQhBt9ulN+gHB0qK/Wo6mnXr6Ih5P0Dah+8b16FxMACQgr3JhJvr69x14QK9bpeFhQWyac6NG9e4eeMyN29uUxjH6snFJnKhURb1zuOEAYLInMe31UitmrKQIA6rkb47JqVCSYHyHucsOtYI6UiSCClCubC1jlbO34deNqJJ5bQOIHgvEHPVm00PG1GvD226hn0H4nClT7DDDsj+L6KG7hutkpr4VceR/khP7ig05ii0bT6N5+veYUFuwtbBLTVaJBAy6IR5E57l3qDLwmgEHiY70zrtExzwNO2QTzO8I6wX+DpNNE/EDWNxlk1JkohO2mWbTY4av69lvy+ckxANNbm1134g5klMjbkaog3Pf4CmhZR4SqyxKBkqXoIAW3mrAFvtaGf50amVNA0Rd1EUxFEUcvuvS4i9vSkVIxAYW4XUgFSYI/oXSBHy6AhfQ3rtmAwPmQgkN19Lz8dxFJwTZ1EqmtNg+MF1TryByljOnD3Ho48+SqxTZllO2uuSZRl951hdXcH5Am8rtrc26XZ6SK3ZWl9nfWODQb/P2TOnWFkcksRhsTJGMJ3B1u4us2xGrxqwduIEygsirel3JUIPkJMJKqro9AfMphOGnZReJ8FWJZvrG3hrUCLo4gx7XYRUaCUpZhMuP/cMLzz7LJObW5TTGZ4K7yvwLjglzuG8pHJQOYF1Fu8kXkhkFCEjHVjyStLr9oniJBA+dUpVBc0boyU2pNsppUbrBINAyNBDikiCFkGjIO1w6uJF7jp7AjUbs3pqjd/84hdYO3snJ06dJkME5VedYCqDFg5hPIPBgOWVRbT0SO9IYsXCcMDO9jY7G5tUZclkPOZbv/eld3g01KhkZehKT5xqbtywDAddokgTa0UcRaERoQhOqJYRQiiUjpBKMx7vcd+97+Huuy6QdhKuXL3CiRMn6Ha6xHHMoNdjsrdLnmWBo+D24XRBQObn14+jFpOmbL3b7dLpdNjd3WVnZ4ft7W10HLQ6RqMRnThp53PRBPNzi17TEo52gZyPzuccFED4MKc99+LzWGdZWV4JSKGQpHHKyy+/xD/9X/4nkrjHT/2RP8697zlfZy7CtpsA0HmHRLW7kELQ7fbaOVGImn8kaqKmO6jl8k4HOlpKpPfYusqkrApoSri9Q0tBXpk6zSQRXtTzoGudAze3sNNcRRt+q690SF/h8bIuQ28+eKQd9Ycw/wrfiB/OIf/tfpvE0xFbPOI6zhORm9fN8Tb/glibaMnBodtWKBUuq5IoSlBaB20nrZmNp2B9G7gmcRq25QTI/REYiNSBQyllUJSdTCZUVVJrfL15++F3TkTQqHgzS+jhCaNVQKw3IEXosup9XSalVJvmKEuHrvvaHNyOpyqPJqWmaadN+8Rx0kq8vzUeh0Br1cKUsiaiNSmeA5+UdbfIBsCcm+gC2ze8Z+uUSAN5W2OJ0qTVDRFCv+OTyls1axyzWc65c3fyrW89gjEBuo3SpEZGlkj6KffcdZYsy9jembCxtYMxluHSYpC3V4q8KsmqkrSb4gEnobs4ZCef4UzB7mSGl5sMBkNm04wbN29w/s47OX/xIrOioqpyJjtbDJKUXqRRxoSIXEfEuk4L4jDFhJdeucSzTz/J5s3rOGMRPghA2bpDsLcwq8AIHcSOdIxPYpRO0CpMHirSDBZG9Pp9VByUWI33TMYTnIoopzMqa6mQGB/Gs1QReIWWskbQDMJphIsxpcYbx6NPPsPO1gYP3nGW937oY5w8fZp/9e/+I+NZxYmL97CT5Qhfojw4HEujAYsLA4QEU1QMeimdTszG9hbj8Zi426G3MCTuvbUJ6i2NiZoMqZWgMCI0b0ySA6mTJj0rVXjOI63ppB3G4wlRpOn1ulRlxu7eFqPRiOFwyIULF7jnJz7DxsYNinK/h1b7HDek80PIyVG/N/NHkiScOHGCxcVFdnd3ubmxzgsvvIBzjtXFZZaWluj1uqSd5ABi2+T758tG57fd2Hwg5r3nmWeeYWVlhQcffJDJzi43btyk3+9z4sQad999N3kW5gFVSzPYmhjdaAKVZQkqpEmkkvhDInINIThc39sLhL1T1qQVmxLaRjYfAUUxQ2lJvpujom67sAanMvSpCeqsvkUzvJP7DsnrIRmelkJypDvSoPJzn5/7ceB7hyuA2qyMCJ86qiqo+ayfe08c+szhrEFQwJXgFabKSJKYOI7o90Kj2tA7ybapm0AT2E8XeeFByFs6W1tr21Rp7/9fnROBINIxwsvaA36dzx+6OeCxzrRRSah9F0G6XjiclchI15yT4BDE0a0QtXOO4jakv06nC16Q5xlJnIbOra46AAO+mfNVKgZk2+tCiBpFOWRKqrrzckP4suxjJ6793VmL8y5AnUK00F9TAna4MukHyfIsJ5tNWFlZodfrYp2jKAqSqmJWZFRlRmoEIrIksSBJInqdFOcEJjFtbKllgN9nZY7F4xwombB26gyjxWV2dqfMsoxqb8LWxibXr99gZ2+CQLMwHDGZbPK+e+5C+boPUQ2JS2ehCL12NtZf5bnnnuDK5etkhcVXYTYrnaW0HuckSkXopEOvF1EpiUxivJNkxmK9wKECabm0jDe2cRvbOEKZr6eJmppJSSBEFMZzXVESnGyFlgIpHL4mtkZS4aTExo5iZ53tK5e55/xpVpZG/NE/+kf4rS/+Dq88/SR3PfAAe5MpsVasLI7oJBG2mFHMZiRJhKsk27MJWT5rSbLTSUiRvPO2/zx1E1knuyJ6vZQ0TYgSjYyaFhQOpMLhg9KPkIwGA3Z3d6iqijiVXHrhBoPhgLwMxPhOJ6Wbdkg7Xaaz/IjFtkFfb3N0B5wIgIZcGib9EydOsLy8zHQ2ZWdnl8l43HJBwkQfobQiinTL76h9lBY1ad5v9tfwzaSSbO/ucmNjk09/8lMkSUquZ3SSLmnS4wMf+Ah//I/9HP/mP/w623s7CBk6UZdliXNh221DQ+GCPJIWaCVxztTR8776bFEUJHEjFvnuOCdeerwK1XKuqkJ/pbKqg7gI5wK52xYFke6F8uHGERECIwBpwYFH4WtdE/zRjsm8ydoxef0ZfR/pCnsPomjiwPu1NdP03HdbJEQcerflXDZohms/E667a/95X5dECFGnZwJVIJvM6PXCOBMuIKN5UVKWQXRSCoXWofjEC4LkPw6hQ3q5ldcgrIdxzfF6qyqxP/TOiZSyrtZ562Zbr29/cEjVeOA+SNcLiaidk4Ock5Bsds7dtpy31w0difM8I44TTFXVlSFvATkR4kBDwoa06o5Qm22ajx041ua3OXi4UYBsljTn9tVhm8ntKAGgHwQr8rztZ3ThwgWeefZZHCFislXF7tYW/V5C6SqStMPSYh8pEjY2tnF1O/Hg3FmmkwzrLDqOMAacLYjjQGYe9fsMOl2qssR0Z5iFBZI4Ih9vs5tPkd6h6mvlZdiucyY4rMbyyo3rvPji02zvrGMqj/GKwgmSTo9uGjPQCf3+Iv3+ECEV2SzjlZtX2d7ZBS+pbFhGtY5BeaJIh75JsQ7oTBShRaiuiXSMVkGbQGuNVgolNVHdSVupKHBrhIdIkkSShX6fYbeHsQXT2ZTpdMzGxgY72xvYsuLs6gKJ9IjpDiOtkMLhxltMp1AVJRKIXI/tugs0WmHKirIsA0/lTZf6f2/WTSWl8wjdCdo1kT6Ya6+r7lr9Iu9ZWFhgc3OTyWRMf9CjMobFxSWyomB7Zwdb5tgyI88LJtNpi8DML7zyDS1Q+xHwvA6G96FlxGg4ot/rY4whz/O299N4PME605LztdZtikigDhATm54+QogQsynJ08+/QJp0uHjhIuvr6/SSlG63x8b6NuO9XfB3UBmH1qrupQTVXLq4CViAfadHBs5CQ4Rs9hkabtr2HN8NE8qhlaEyOVVZ1Y5TaD/iEaRJl4kqsbZEEMTHmlQUokE394XL3ohTsm9vArufh2zY57U0vKWDRGvfOp/zeaajj2ruGMScc8r+UuNrDpGvU0kNl0QKQZVlnDyxgMQRxRKtwhoTnJNQ4daUvjcIlaiP1xhDpOP2uu2Pa0ev15s76Tduvy+cE1V36H2jj8D8zQ/cDXPLIGyaXjnn2oZ/+2V0+7oGzdeMMZjbqGCmaRePJ8syFkZLVKV5G6TrA6lWR0Gt7yi1Wa2juq+OrY/16MFR1ZL7vv6M803Tv3qfr/Hd77dleUae54wnYy5evMgTT3wXoSR7e3vEacyVy5dZGA3QqcLYnKzIuXFzk+2tXbzzxHEUBNJcKOvOZrMw2TrZpsriOMZbRzbLEISWAOdOrAakwFS40qCdRGqNV0F/xvqQ466qgpvXrvHKS88znU4wXmKkoD9a5tzKSaK0S16VOGcZDkb0ekMEHmcH9NdGTKcZ3kuk1CgVIYTGi6rmQAlUFCYLhUC5eoGUQc3Re4+QAQXTtYKjUgrrggIq3gQJ/CpjZ2OLK9s7TKYzZllGXuQY64giiZlNKacTvAenNNZ7vLN4W+FrzRZvbJvL1kmCTAOBuJd2SOK4VbB8t6zfkRgg7i4QRXF4FpSq+SYyxKz1YttojoxGI2azGTdv3uTUqTN477l58yaLS4uBN7O3y87GDYqioCjLwE07xF8TUhyowjuqWqZ+xT5XZL8zdOOgCBE6VA8GAwbDAcaYsN88YzqbUlXB8QutGWRLfm76jPXEfipLSYlx8J1Hn+Dc6bMopbh69SrLd99LFEVcv3EdJQR5kZPNpqTdEB0Lmkamof+MUE0qoI7WG7SmPnY5l1po0mfz59zMLe+UKSXQyiBEo3MStImMKRHEKC1RSpKmMdaWIQC14gDqEI6zDsYaXkaTjmnTdQ0HqFaSPbTo3lrRW/Ntmnvu58bCHEVof19z22pQ7/nGfvNbrt+T82Nw7rj3sZbGAT64bVGfvhCSPJuwunwBgWVxcciJE8tsbO1QFgWCQesQS6nAi1o7JvDXjDdtibn3vp1vjDF0O92wdr7JNeSH3jnROg4det9QA73mBkoEgbTTMM7DzZu72dLhseFmSolQoQ+lc9RiNXW33tqMMaH51eE9ChGk650jLzJ0lJLPxmEyb1UK37hXKYREqZTQpKoikQmVreZSNvvbiaIkDCQRSk1dzTU5GK25uq9O/WBiw8CTdc71B9Qpaay0hsJVzPIZq8vLJGmM86GV93hnhzLP6Xd6nD13Ft2NuH5tnceefiZE+97R63YRziGdAdvWwNRtCAQQJrROGvpNDPp9Ui2Q3mCyMkxpSoU8PGBLR+EsXhqUrbjyysvcuHGD2WxG5QVWdzh/8V5OnTtPFHfRKkJIhzUWawL6VrkSbyJ6OiFNh2HRQtawKXgMTYsBRYDspZBESqNF4FqoJMbhSCKJdSV7uztsbu8w3huTzTIms4ysyPFlha8sriyRXuCUwPlAbHPW0e/3MaYi0Kkksh7/OkrQnQip98nhTWSttKpTCcEpEipUub07JojwDHsJzhlU1CWKg3MSrrUMJdNNxQ7ghKAyhm4nAe/YWF/n/vvvJ44l65vrrKwtYSvDsN/jhWcvYU3FjY0byBphmM+3B1kJH5yU+aM6hDw2yImU4P1BwmjD3Wis0aHodlP6vS6LS4stZ60pRy7LkrIsyYuM6WzMzZvXKcuydlAlWztT1m/c5APvuZcbN28wneYIFN1Oj8l4ytLCiGmWk033GI1O1Zo5HlPmKGlxtsSa/USEJKgJS2eRPjRDpXbynHMkSRLm0zn9FyAgxu+QeaFAepwLVW5CCqoqIIELS/1QVYKl3+2wuT2lP+hgHfjQ6bFOfcqa1Ns4BU26bA5Zn59mRePG1ffq8HRZv5YirDWNmxIGx0G0/iC6UTsVcwhIy2eadzCa61o7Sk2aev/vtYPo6/NsE741RQAfrhuQlwWjxT4I6PVTBsMhprJkszGwEBwgqRBRjDMVAoEUOqSN5b4r4b2HOr3nrCOOOygV48w+T+uN2A+9czIPNb4x7ES0ebGACDSNrQ5+XTSenq8nfzXHUJeKhj3f7Pb20vWqflAbBUZNVZkjP/tGTIpaHbbuphlFUZDeP8KHiOK4Pac273voM95Ty3g30vUNXCtrj/pg5PODRowtjWGa50R5xrI0nL3jJM8//yJaR0zHE/JZxkvPv0QxMwxX1rDWMhtPuHblKmmsWFoYUUxnCGPbicWLRjFRknZSFhcXWVxYoNsLEYBwATlosDelI2wUYa2gyHIclsrlbF67xI3rV5lOp5TGotIuP/LpT7O0drJt127KiqKomEymZEWBsQYnY5yQOOERUV3SKPZbKOgoRPudJA1lrnGMVhBrMOUMLWA0HNWdr2uBpNVTbO/u8Oqrr7K7ucWDDzzINM+wecl0b0w+neKNQ8QaWctVx1EU0kJaE0UxUoT06XxEbL1pHXNnLcZYrC1xpmoXKmst5RENMd8pk0KQ9mKMBxX1iOMErYIirDGGOBUoreY6i4dKqCTuoHUU9IiE4PTpUzz+3aeYTTOuX1snUpZXX7mCKS3f/Ma3+NAHP8z7H3wvURSFfjvN1lpO28GeJo011wUOqsI2TknTwbjllQC+nuztXFdhrUOqqt/vA7WwY022z7KM7e1tNjc3KYuCF196nrUTy1Sm5NKlSyyMgihcrJMg9+4cRVESxRFJErdzRdNzy3uPMbYVjGtIkfOkzeZfVVX1vKwOnBsclFN/u63ZhzFFqKrUfTyO7a1dzpw9iRBBXTvtROTXpowWeuBU6wgKIeoyc1cfp6fhMQrZpHrqO9ukZLyYdzkOBXPilvnzcBHFPnrGLZ/z9X5apP/Q1OsPvWiO79Z70xyOrVHDppdxEKQDyPMcawydTgePJ9IRkdYkSUKWZXjv2/kniiLyqqznpMBviWOFMe7AOPA+OLI6ilBav+n2Wj/0zkkURW9iwZzzJmpnpKnIae5q8JIJnrLbX6gCkrD/94NDQ1BV5ZHQtdYRSZKESdoYtNKh7Ip56fo3/sDKugFhQ2LVUUxZ5fUDcnA7SZzSdiO+ZWDv+9dVlYc+GSJElbIl1clbv/iDZlJRlgZrDOPZHnfdc5Gnn3kapSTOOsqi5Hp1nWyW0715g7Q3IFaKYjol8jHSD+l1OyQ6otftkSYJadoJ/ZpU46QohJYUpanblav62lm0kFgdoZzHFiVYgy0Lrt14hb2N6xTTCRiLQ/ChD32ETndANp5RmXGQgraOIncUZQVCYoUMcLKW4EBqTRRHdWqmrpoSIcXonScznqwq6PcCAhPHGqxnb3uPcjJlVmYYYzHO4zx04pRoeYUqL0iUJhomrK0sk+oIrEdEGitEXcFVYWxIVxprKMuMsh7nRVnirKUyZd0RO/TTsc6BcaHoAdoI/6hu3e+ECQJi0+soPBFp2iWKI6x35NMpSbdTNyOjhaWlDBGe1opOJ3Rj3dvd5eKFCzz/4ivs7Y357lNPMepEjHfGlNZz6foV/sf/4R/yf/k//yIf+chH6XQ64ZrUPWdChci+cCPc6twHXaV60ROiRsBEnVYNDo5zjS7HflfZZpvNdpufjSMJ0OsFIcCqqrDO8eKlS5w6eZoyzxmOFkg7Ka+8+iqzrGBnd4fl5UW2t7epKhvUi+vFpayqZiUOVWJ1b51bpsB6/mlOr0l/N2T6pi/TWykCeMMmRd2B3VCZEiGDeveN6zd58H33IQT0eh1mk4xIefA2zBPe1ghJg574+tw8AhNEDVtuSLtwtOfcOAa3npm/xek48Nd6H8FEm95rnFvvXZsimksCtt9vnKGA+Lv6PjXObqNPs3+cgiCkJwR1CXObnwpaPDUxWxAc3+FowGDYo6pCwYhSTQm+CuiSrPchQGoZOjwfqNYKCGocRcRxTP7mgJPfD85JfEve93bWlGQ1n2wuojX7Urz1X8Ki7gApg5S9bBo9yQMebPNenuc1HE67DfBoFaF13CrCSqGoTIX1FbyFB1XK0EfIOoPzlihOqKr8yG0lcQdEk544fC3CcMWHHi9Kqto5KRA14Q4f/jU51h9EU17gKyizgmyasbK8wtraCdZvbhHHKToKqoZRHDPodOn2ugyXF7l49iyjfpe0k9LpdIjb/KlnMpmwsz2mLMugkGot5OBlUFr1wuClAGGJvSQ3DlnEKGtIpWBz4zp7WxuURUFVeQoruOO+9yDSHtuTHCmCwxNHEdJZul1NkliQMoy3OAUhQ3fguUnG19FKZSymLHHGYqoKbyzXTIHwlsgLIi1JVUQkJJ1u3RBQqcCJ8a4trzSVwZQ5VZFRzkKXbOMsWV5gbHCmjTf1IlUg5D4HZzadtA5JkDZ3reAcTuJ9HWHXPAp3RDXZO2GCQBZOtcWIhE6ats3qmnx4QEVBKA1CIoRqVUVPnjzBiy++xPXr17nvvvvpd3sUecHW9jZdtcBwOGBWWVZXVnnm8Sf4X/7n/5npZMLHH/oknX6PoiiwxrQ8lnmSaONUtKXMslGnDkqjQoi6eUajdrpf6TKPwjTWICgHItX6HJ0LnWKTJCHLc2bTGWsrK9y8fp27LlxkfXuXm1vbvHzpMq4ybG9vUVUleVaglQ4LrgvpxsDQMSgdI6TGWYMgILhAvV9RH7Ns0+NlWbQK1d6H6qg3ln5/qyZRQqEwmKKsj8Oysblep+ODKKUzFYvDDq4qiNK0bnBZOwQoLA4hXOssWEKQ2gR0otY8qfNWDUZ226N6rdR4M7c2HJbwXgN3g6Op5GFuGj+IVrXv1akAD/tzhm+qdEKqpfFVmuTO9auXePKJx3CV4L6LF0nSIMshpeLEyRXiSGGt2+da1iXXxhYoEdXCdSF9JkVNOGcePQnZjSRJ3tAdnLcfeuckKKC+Uau93rmVtil/C39tJpEw3FqJ4Hqxtm6/3vsw0a2pGDlsId+tyfO8HVv78spv3lRdOtzk8KMoYjLZO/KzzYDYJ3J51C1VE76FbhspfyVVHVEchKJ/0FI6QOhI7AmLgu2QZQUf/tDH+M53HkMpyWh5kU63x6jTJ4oiVKQh1iE6INxbV1UUtqQocnZ3d+rySQmuIT4LhLU460FJnHA4L5DSB4dUKETl0VKws3WDnd0blEWOcY5CKO76wAe574MfITeevKjq8sVgsQ7KxlKJIIXkPFLUjQG9JStmTKczrDUURR2Z44ikIlUBVekO+ig1QkU66PPYAO9XZYnwliybUVUlprIB5bCWygS0qekYb5zBeouonXUtNeCRhMVVe4+1kl6vF65PBQKJ9iFWc85hnEM6gffiQCv5d3fcCKJIgLI4YoQOxEwlNZ1+j0G/HyLkVjSs7mDswwS8vLzMk08+xcbGBh/4QMoDDz7A448/ERZ7rRkNB9x57308//JLvPTCszz+6KOMJxO2tnd46Ec/xerqGmmSBMd2XuK+njPmBckaiByox5ybP439zx9Knxy+ps15NGkhpVTbwl4pxTPPPBPmifEYgSDtpLiNbba2tsinM9IoZlrriwcti3BM1gXtnRABh3nP1uKR86m9xvFqK5bqVFVRFC2Sc/gz75h5kN7jTYXw4fru7ITycK1TlJIMhl2kkly9eoMTvZMIvd8110vQOCyuDkQa8UrR3pMWPZlP27Q+QuNIvLHzPPpTvqYbGP7JP/rvuX79Gp/60U/zY3/wp25J089vpw2taz7TQR6TwNfBZoOICwHPPvk4/6//5/+DTtLlH/z9/wGt63skBKsrqzVCZzGVQarmeakdFrWvZhsERhOkjA4cX/BdJJ30zXcl/yF3TgRR9OY9snlpmgYe3c8nBuIhwrRlxIENL9uOwoetIaYdeheAOEpQSlKWBVKGyNXY6i1XLygVIVE4G3gmcRRR3YZoFCSlgyKgP+R9CwK0Z72jMnN6Kabp2SBfwyFp3jucSnr3nZf+oMesLLDWsbMzYbybkcQdPvrhD5OXJaU1jKcTrl+9Sp7P6qZVEWnSoZd26A/69Ps9dKq5ceMmjz32CHEc0e0O6XVGJEmHOIqRtgqTqwvdqY1wREKjvAxli5Fmb7rB3tYNbJWBkBihSYddhmuneOKly2SFARfE2IRz4CwKj44saRwRIVHG4Usfmq0pSZRq4jihk3aIBoMQ1crQjdpZiysMJp9Sehca2RmLEwZjg76DqSq8C92wcU1EJcA7nHG4usInaFUElE3LWpuy7j4qgEhGeOvBKq68co1+r0ecJKF8GhDeIl1NjKz7z7sa8vXetdLg77R5IE0FFQbrewiCs92JI3r9FC8Fk+mE/mgBqPkzPlADrfUMBkOMMUynMza3NrnzjnO88OwLdOOYEydPsTgasLK6wiSfoevA46UXnud/+cf/iBdefokf+8yP8+D999Hrdmql3or9KNIH8TsEcRQhVMPP0HS73drBti26sr/AHAyE2nP1R78//3tlDd965BEu3HmR6zduMuj1yaYzJJYTywusW4MSQU7fVBVpmqB0IPtbYwIyEErDWF9f5zd/87cQQvDH/tgfY21tjdAyIcjZK6Vq/lqQMAhS8fXiLm89trfbBBLrRUhvuALnHd00YZZN2NnZxrkh3W6X4WhIZYLEvcQTKUKPKOPCuq01lTVUztXbC1okAVwJGh/eBUSjReHr34Prsu+wiAbQgINTJfOz5S1MwPC/Mzz93e/w0ovPceedd9y6gVvM1b7Rwe0FNdya/uokri7/VgKSTsrq6iliqTh95gTBrwyFH+fOniJJAtLvakcZIerUngtzUM0rCvwihZJRjUQdFAjs1nIab8Z+6J2TJOm8+SVx7gutY0KNnCBrVrepo4a4ruU/qunf/uJd3KavTqfTQcjgnEQ6wvvQv+Ct5l6VVEgEtjL1QFFH6qsIxByUtp/PPGA1/FoZU/cPEtjaAWtKqeejsvZL7dHPtU7nMCLz7pgFHIF/MZ3lCARZXgaxKp0QacXJpRXSlVNEsURIR15WFEVYgLLplJs3rjMrZkwmU4pZzmRvj105Bq63fVjSSBMnQVQo6XaIuimVFMRJSpR02JtssXHtKsplCBdSIQWC4XCZl69uUFKXexsQOLTwaOGJOjGxioiUYJCkdFVMIqIa0nUYV6Ns1lDmObkPCIWpkQ9vLM55HKYmS9raydifHICaQF2PAQHCgbOmnUhDzrqeScV8eWRAjxpo/sqVV4FAxI6jCN/wtYJ2Fcp7vAjdfqwNs7OznndDx69ZHDrduteQ7NWpBFGXWgZkQUdRmJxVKLmWBO0g5z1IwdLKCts72zz22OOcPn2aNIm4+64LnDl7GlsZbq7f5J577+Ge++7n5MmTfOW3v8z2+gb/6d/8W5767pP81Gd/ih/51I9w9o5zdLvdUIFVo5PUyKT3nkcefYTHHnuMTqfDRz/yMS5cuDDXpG3ugonGRTw6RXAUigKhJvHm+gbbW5t84L3v55knn6R7551MJhOUELz/fQ9w/dpNtjZ3WFxYYDqdEtWoiRC+TUUHscdQlru1tVmnpUKKL3Bg9jVWGoREShnmnzowUvLWktu33TwUVUWkJMaUGFvRSVOMKdnb26sXyS5xkqCj4JDleUa3l+CtD1VWPvAnhJC1895wOaj76oAXwQH3cJATUnNxDjritX7UbVI7jbrU4b8KERb8D330Ic7ecZ7zF+6hKd8+8P1DaZ35nkbN/QjOyTzxsHawvOMTD/0Yn/jIp3j6scc5d+4s89zF4WBAv9dhdzwNpdkiDmip3s9WNChLFMcoEdf72qc3hGsi6b4FldgfcuckyE43v7+RyH3/QQ43oIlU2pssaIXNAnM+qAoqKSnqKOew9380chIsSQKclec5URxjrfsepOtpYVLjAmSslcaYI5yTmtzUZrGOHNSizStHyX7nzjgOTHvnm4myIbo1jonl+vXr5EXGaDhiaXH5LZ3L22E7uxOckiHa9x4l60oaJxHOQukQpcXJAjHzKB3KPCOpWVgYsba2ilIaH2AFprMJ0+mE6TRnPN5jd2+Hvb0xRZkzycehbLRWzsQ7dBKjky5IQSQEoioRZUlpKmS/T3ehoith2O0w6PXpJl06cUSsBUo4jCsCYdUYXFlRlBV5GaJWIQTGVTWfo67WsAFWt9bVvXYCAc2LWp9GyAOORpNbloSS2flxLur2BKFFQYiw2jRCrTQrRd26ARBas7i4SFYrlhpjkEpircdLgXchnSMFKO/qMllfQ+TvfClxM8Q7XY3zCqk6IBpV0/D3KIqIkqRNj8o5eN57j5OCU2fP8Ogjj6B1RJqmpGnMRz7yQdIk4qUXXkJpzdbWFmVZsri4yGd+4if5yu/8NuOdbV58+kn+P5df5etf+z0++5/9FA899BALCwukSRLSJJUh0prf/p0v8z/+9/+AmzdvEsUxj37nUf7cL/0SZ86cmSMP1w4JtyKYTQrodny74Ah5HvnWtzl/7g6kd1RFhvCOGzduoJRiZWWJ1bUlhBDkec7Ozk4r3hbmtCJExwIglJV/8pOfbBd5Pxe8NMcUqnkU++q1++Tgd7KMuDnnsizJfOiy7r1HRxpTGTY3tzh58iQQ5vc0TRiOBty4doXh6EwQnlRQ+UB9dT6gKVXtcEgkTRNiL3yQua95Kgf0ScRBZ6O5g0ehHm1IPI92zG1Hac2f/D/9GcDgvTpiC0dfA+rtNePmdqgaBHX1SCvSpMviwmL9fdHyVnq9HpNZVq9ZEoQiipLaCan1VWTDSzyo+9OsHUAtxPbm7IfcOYE4SfbdVw5FHK0dhD9F+9nALm4espDjBSVBeIczgjjSrQibtfaA1wj7ubXbcU66nVDmlxczkriDMQF69xwmz74xU0qDlxSurIW4PMbcitpIoVvHzc3lrPGy3p0HH1JVgeC2L10vZRQqGJxjZ3eHqipIOx0WhktAgHzHk21msylJHNNE198PK4qCtN8ND4cTNTTrKaqSQhq0CNLsSjeEZh9Y59KgVVDgbFJ1URTUVJeWRiwvL6L12X1o0kGWZWRZxnQ6Zby3x+7uDpvbW2ztTcgry8rSMvfe915Ora6xtLpM0ulhvMBAHckZyqrEZVNmZYU1BdaH9Is1DmcsrjKhakbUKce2PfscIbIGqZpyxwb5F3icN3Px0X5uGRHatYVt1uCzlkR1Z92G36C1Dqkh5/BOhrEgZJiHvWU8HlMUBbu7ewgpWDu5Vo+b4OyIOtKUTrXS7Agw7/DCtG+CbifG+IRYaWQ9SVsRqmNkLcQWnC6F8AHVaSJfbxyrS8vs7O5w/uIFTp8+xcJgyN7uLs8++yw6jllYXOAb3/gGVV7we1/7Kn/wD/0EP/mTn+Wb336EV195kbwo+c63v80rr7zIN7/1HR761Cf50Afex+rSClEn4vkXn+P//T/9Qy69/DJ4yKczvv47v8PJU6f4pT//52lKdYXYB7NuOcu5BaAZF83cJn2t+OkMTz3zNBcuXKCyJpRPa0mRzVgYLTHZmzAej+kkXV6+eonr169z9sxphAsLVJWHFI3zjrIoUErx/ve/nziOSdM07EsQWiHMkY0PLE4iBAvOibfMs3szVhmHr2a4KkMgieOYIjdsbGyEYxZh4e10U0ajHjeuGKqyINYKvMOqUEkpnUMLgcLWjn/gn3gvsA3BFIKDcugGhZR5gy+7ufn2KBelXodqbSFZP97e12NWgPONxP1rzbGiRXgOHEOdsgwp1joFi0C6UBkmvQql81rSPeRAlFXO8vKIWTbDOIcnpHmjKEEoHVrGzLMFfAj84FYnqNcb1lSBNz4Gfqidk4AchGZmc28e+tStnIh5jMW0Amx1SRZNZU7oVqw6ek5bxLV1/rAPqbkmkj7C0k4HgDzLiOO47t7ojoD53sjiXvfVEWBd2S6qRwlcHZSuP+w57+/b1uq4rW7FXF8d5yzbO5tMJnssLa4wGi6F3KqQDPqLRFGHNG0G9BtDrt5uWxj16Q9H4f5UhulkEvo91MQt5x2FM1BQT0weWaumRsKjlaIiEJ1N5RAqoC+BCB3GghQSLSS9bpfhYNBOxEoFSaqo02NzZ49f/w//ESE0i8snsc6zu70TSotNqKwqTElhqrD4lxXeGlydtmkiFWE9tq4AEHWOt7H9ib9ZjGgjliap0U4WoiF2t99uIXdH3aAOD1KidXAuVU1kFMZgjKsFlgK2ECcJr756mfF4TJ7nbGxsoJRieXWZbt3Ur9EHCdNrWPitdUHO3707Y0MIEXQ6RAdRK/bOp7dsG4w0jtv+YtpoiaRJwukzZ7h69SppmnLyxAk21tfZ3Nzk5KmT3Lx5k6effoaFhRFPPPEoD3/1q3zqRz/NT/zkT/Hod5/g5ctX2NvZYePaDb74ud/kqe8+ySMf+QCfeuiTnDl3jv/1n/9zrrx8qe59pQMSZyp++wtf4Kd/+qe5cOFCIGj6xrEUwBy6y+HJf85ZqT8ipeSl519iNptx/fp1FhcXg96EVAwHQ6IoYmNjg+2tLdbWTrdN/Tppp50PXF3+69zBqqPAZYM6L9xeOz93HIEzEzgQ807JO0uIDcdsbFWTkTVJ0qEsd8nzjKqq9onCWtEb9FleWWGys8vJ1eXaK6gXbyTOQxG8Aw6uIw06OefUtsHf/jzYzrSHyKuHzbeOzEG8RdBUl4oDFV373zt6m/uzwf5c38z7LZ9JNnMETCZ7JGkU5oHaKxYikFzTNCHtdKhMBU0Ao6IguuZEfS3qddB7hDs4ThvOVLfbpenV9kbth9o5UUrVPI76hgl/y6I//yyEyTzk5VUdnZiqQgqN1p44TajKCiFC6qLpMYMUSBWEmprJbL7J1e376gi6nV7Qo8hzer1RK5R0dP7V3+b35vglUjRMelPnrt2REUlQxNQcVboXxku4VgHF2R/0TQTdRGNxHJMmHXQUz7kekpMnzu1HBN8nxwRg1OvQTROkiJApxEoF2JXQzC23FVlZhLLZupQunLrHe0Ppw33b14hwRLFGyrqKRoJUYRH3xqF8eHAjrbE2SFzvbe1iveChT/0I//Zf/Su89Jw+exbvSnyZY4qSyhgKW1FWVYCH635Gzju8dWFfTf65XpBc3f9FUBM358QGw+2qS77rlMw+Jlj/lGFRa5BeIUKaxoeZKSzQOvAuGoDa41DOoW2oGgLAQ2Uq1m/eZNDvc/r0aaaTKcYaZnlG0g1RtHE2KJVWFdbX6cvaDy+KNyly8JYsNKKL4xihushaWh/2U7VNENLCJXP8jmYUe++55+67+cIXv8jNmzdwZcUrly6RJCmXX73Mv/93/45r164yGt2PUopnn32GojT8gR//CX70Ex/HV1/l1aqikJCVJdeuXOXapVd4/Bvf5sTZUzz39NMsL6wwGA645557mM7GfP3rX2d3Z4dHvh3Iq7AflItmuRHN7/OT/4HQI3AVRLi/3/z2t/HA5cuXuO/ee+mkoeN2mqRkWUFZlMRxwtbmFp1Oh06nQ9pJ2+03BF2EbwOdZnHfd/CCOedrxz+s8K5JNzZzDdQVUm/vHZ83620g4XqwpkIiA5ldBK7RYa2dKI5ZXlnh0nNb2KKgqwMXyQuBEsGJl+awirdA+ECibsdSjV7un1vtsPkG2by12qrZ5gFnoxmB7cbCEzl/yV6PDiCaiGXuOJqjb/5JEZA1ISRaSyaTXUajoAy7j88IIq2ZTKbs7u4yXAyK6k1rDFB1Q8W58/J1RWN9HC11wHvSuqTfHqGifjv7oXZOmvy4dWGBaSbsxsJ9EnODJkzW0JQEO5QOi9HZtTMIqXn5pZeYTDdZXBIIpUg6HbrDLipSaK0OlB431pQNHjYhJGnaASnJi4LhUJOXBd7PC7AdtqPer/1gEfqrNCWDSRLXHZUPo0OeSKdItU+EOviZfT+9MlV9xYLQkPc1z0aENtpnT1/Y/9aBazl3qO9oNPTapoVA2ArnKpCSNG6akHmQjlRJpIooNORZFbx9D840MGdAGCprMM4igiA8WguErLlBwlMJQWU8GlBaUhrHeGsXgcSrCJXGDFcWeegzP8KXv/RF/vAf+Rl6SYStCvIyozQuaIfUkXsTiUoCybkh2HlAqCDEhg8LTVuGWU/8Lzz/HHt7e6yuneDCxfuAIJDU5Plli5IIguiS2K808I2eja0XsTr9UldnGQxIR6xjrIWqtOR5zquvXg6dk4WkrCyrdf7eetjdG9dqpZpuP6GvJF4GtFFLFZybquBr78J4iCKN1gNk1CFS+/1dtA7aHVpGYQKWKlzjA05KPaa0ZnlxiaWFRX73d7/Cxz76MbK84Iknn+Tb3/4Ow4URN9dvsLG+HrhZtuClF57HFBUf+eTHef/738uPf+Yz5HnOv/33/47xbEonjtna2ebSpZe479738PGPP8TlK5dZWV3jPSfeQ9Tt8NWvfI2nn3gK+cclpbN4BQpRoyGyJvU6xNzi5w/B/Q3fY29vm5defpEb124QKYW3jlhHdNOggqu1wVrN3t6YPDekSRp0UdKkReTW19cxpmI47B1MHdX/oihqq3SgWfBCalhQ9y7Co1UonpdHVDq+nVaUBb506EhgyhxcRRqHJphxHN9S2g2S/mBIbzBkvDelv7RIGmmctVgEXkGq66abTrSq3Pj6nrRT6iF5+1t+7tut/I99xyY48vvVPq1iNYeLEjiAdt/OwndsO7Q1dTNpD6pGiLzz7O7scu/dZ9EqYEbNXnu9HvhQUYZ3IByiDtpCYGwxlUehaXhq3pu2aGTeeWtUpt9MkPJD7ZyECVECBtiXm6+DjDmiUcMkDzl0IS1CZyjlWVoVlJXh+o3LzGaWPJ8RRRWeCB3HDBcXWFpaAizDwQAhdOi0OheR3bx580jkpNsdMBqN8C547Z1Oh8nepF1s3mzzv6ZypKk911rPdVQ+aFEctZ1FYW4wi4O5yaqqanXYevIhpENa/1kcfjDqvxxw+L5/JmoIEuUJtbIBhRDSg5MoIYiExEmFlRbrA/wYiJrB8XB1FCSkQAtJkRcYDUoTKnycxHuD1hGI0Ifl8uXLUFmWFldASDAl3nou3nkHm/fex8O/81V+7McewuOo8C1+NZ+bp04zidolav5OrcQoatxESolQqu1j89TTT/LSiy/wwQ9+mPe9/yPhe0IhZWgCKaSnEW8T0gc1WRF0DkLpsMDXMueiTkkaYylMRekKsnxWoyYKQYQUirVTZzh38WKYZLTmHHfXFV1BSbaRqQcwpmrTl97Vkurl0dVsb695kiQmjrpoHfp5CIKqqZSyjdwPjOkjoPLQ7j3hAx/4AL/7pS/z3DPPsru7hzGGhx56iAcevJ8vffELfP3hr9eRoAcsl199hd3f3ObjH3+IB+69lw9+6IOcOXOSL3/lqzzx2GPESUIcJ9x9993s7O7wu7/726SdiPvf8wCf+tEf5+b1PV555TJZntNfGFLaKiC8xrVy9m1kXVuLeBKcAwloBM9890mq8Q6b167y3vd/GGstCwsLrK6sUhQls9msJbVWZUmaBD5GEsd4W2Kt5erVawwHPYbD3oHFsJkLoihq1aRV+1PWTr4PwU79uaC0rQ6gLW+39foDvFIUsxllleG8IYkiIh3R6XT2K6bq6yakJO6knD57hldfepFpnjFaHJBagamCBEOkBJH02Lo81s2lbW4xv49qvVZl0q0pubn3b4OM3Joqub2D0lT1NHNjaGTqA/1lLt2rlKQoCmazGadPnz6wBYRFaYgjQRpJsBVCGKSyCBnWHiNCf52WhCv2j6kJoEOADFor0jS9rSbXUfZD7ZxEUUyaxiDFgRsW0DTR3ucm7ePxCAVKGVAznMiJY8/pM11uiDG7u7tY50glVGVFtztEKd06A6rOtTWpGSkV3gkuX371SAfh/B3nSdOY0mSMRiNeeeV5qplhZbTMidVV9ia7bGxfp6xy5jOF9VEf+l22XVStCzBqHEchF3jEgxBFEUh3y0MimkQpso1oQ3TuD5QH3vK97yM68loW+hUpdNOum/00VzMRRlKBUhjlsM4gajGhlihc3zvhwfgKaYPDYh1YGx7mKOkgkVgPV65cYby7x2g0Ck6Hs2gHvgRbVrz/fe/nN6/8Ji889zx33XORSMcI5zDOthyWBpFB0jogqpEm9x6pwkQvReA4CSWQUeDQLCwusbI6ZjhaQEcKVaucNpUpQtX3S4AlSMpXRUVeFJRFRVHkOBfus45C/4yk06UXxygNiKbztcBWdR8qAajA2ajqvjkCga9KnPNUVeDWmKoK5D4fnB9va02W23Cy3m6Lo8YRiQMBVAhiFRZFpXVo4ilFndbaT5XMIwLGBFLx2soqH/v4x3n18mW885y/8zwnT55CSsEf/pmf4Z577uXXf/0/8uzTT1IWBWDZ29nmS1/4LW5cv87u3h/nPQ8+wC898Gf4/Be+wH/4N/+GxaUFysrwyDcepchzimLK5UvXeOGFS9z3wIN8+Utf4msPf4tP/4FPEvVSYh3hI4ete3eFoS3a8dPwFRonV0pBVVY88q1vM+z2g5ZPt8dsmjEYLSCUYmtri0uXLrG4uEink9Lr9XHOMZ15lJbYmijf7/XodLv1/tyBOUApRaw10gdnSNWCb0Db+TksVCENEPhZ1AjDO2N3nr/AzvoNruebmLKiNBlJOkJHimF/QK/Xa9ObAkJwIqE3GLJ28jQ769dIqwQdaxCgvUUbTyShxNLonTRE0CYFCNRTcO34Hy6lnUOdXsuaS+PFHJDn5/7QfO515+J6XhOi5pY0WSLZ/ms4c5PxDKk8/UEvdGlumSoe4Q2JlqRxgje1GrQMmkVKa0ydtiuKopWt2O+95FtHX6nQnLfTeXPlxD/UzkmSpCRp0t7JA96l2xcx8nU5m5CeKPIolaPiMdJbpEvpdCv690TEacIzT20QRx20kCwtrbC4OALcHCS43xbaexhPpmxsbNxybIPBiNOn76AocnSkOHXqFF/4wm/gK8PCcIG47HDPne9hefkkV65eYpaNKavpnOMcBrqSim5vSL83Ik07CAFZOUYQ8nj7/RMOWhzHARcUR+UpRcu9COJwIaJ09eLZ5op/CKzRl4lj15ZBtlotDQIhZYB6vaOsCb+N+F4T7R/ICddplUbUTCsdxM4MbG8HZc0kSVBREhyJ2sGx1uF84Pv8gU//Af73z/17locj1tbWsB2JdWGCUu3iUvekqJ3OfU5JPaS9aJVaPR6UwOD5w3/iP0cQysi1itEBRMf6cPxZnlHkBUVVYpwliiK63S4LC4stZ0CrCOcDr6WsyrZCR4jQG6bpyixEXW5uPbY0Lb+q6cQrrAkS5wLAkZd5zamqZetrNeT8NjpAb7clsUZJTRSlKCnQKvBQglaRah2vJvhtHJR5U0rhjUErxfnz5zl79mzrOFZVqK7q9fq854EHWFhc5OHf/R2+/KUvMB6PQ2rMVHz3icfY2d3hx3/8x/mxz3yGT//oj5CkCc+/+Czf+PLvsrG5R/McWuN5+smnee+H3osA/sk/+yc8f/kF7n/wAe48fQcrC0P6/W5dfacPpAVb/lqdApJCcvnaFba2trjvnnt4zwPv59qNDaaTKYvLK1y7fh0hBB/96Ee5cuUKZVkyHAaxuH6/GzC+ei4YjUZEUcMTubUktSUTz/8j9OBxziH8PuonapTtnbSAfgYOlq0spipwnXBYRV6r1c7d66b9q4willZP4LFM8zGL3Q4qAic9sgTVkOTZ5yW16ZiDBzAPSod9HEgjvT5nJKCp++dTh011V2PaRf/w9utvENL/gPQ1Xrt/n5rWO9ZZYq0Q0rGxeYMoluhYNbuf27YnjjRJlFBa16LLCIGKQnrUmoqNzQ1OnTrV8pIO0B5qDmMUxW2DyjdqP9zOSRzX0soH81tAG0U2aZ2mjFTrnCQtUSJH1NURIqmIE8u99/W4eW0D6Q29zhL9wRDnS8rC4J2vVfAOPmDrG+tHpHQEF+68h9FwKTwo1hBFEUtLy9y8cZ2bOzegFti688xdDM5cRCWKndmYvb29UH3jg3iNEJLR4jJSS67fuILwGbt7O8SxYnV1BY+p5YQPpoga7ZPDg7mx5nqFFFVD8nWIehKed3ia7/8goicNqtV0Vm2uRcvpkBKsBSVI0ojCaCpjWx5CkiRUVUVRFC2aEqKHZhpSOCHBC3Z3drm5sYHWmrjTJUk7OO9QIkSFYZ8Oj6Q/GPDJT32Kxx5/lBOnT9GJOwHJ8QQxp/payuaBlqHxG2Ku1N37mrwWeu54AQpP0oheAb6y7O3uMZ3NsHjSJCFJOoyWBnQ6aR21qpBmqVEPZy15ZfE+6KNY51Ai5NGttVhvQhWX83McGY+twhhz1mKr0BSQmmhYVSW2KimKgrwomBUF0+mULM+x1lCW705vnU6n06Z0GgmApjKnWUoOLhiHIXZxa18caB3ZeUXpKIo4f/48ywtD7jx/ji984Qs888wzdXsKy6uXXuZf/a//gudeeoE/+Jk/RDrs4UXMbJrzqU8+xDe/+XvMsl2UUFR5wc2r17l44Q6+9tXf4erlS/RGI06dOs/dd53mvQ+8J+xrZYV+r9cSDFW98HvpkTVs/8QTT9AfDLj3vns5dfocv/XFL5MXJeNJKAP/Q5/5cc6dO8d3vvMdvvKVr7T9b/r9fvusF0V5gNB4mPMQgp/Qo8jLuVBfiBo5qYXjZNBMatCEd3IOyfMMYy1lWdUtFgy2PtbygA5V7ZTUaIeTQBSxuHaS7U0wXpIkglhpokyijEBW+2mXtu/OfH7bB/HB17KjUjNwKK14xN/rD7UjtSnCmP/8/pxFndJuegPVej6yDnG8DduSkul0j63tDQaDDmmq2+vSOF1Nil8qCbbm29REmEg3PBNHlmWUZdkGhwfOuT4XpVTdOuIw//H29kPtnPT6QzqdAdB4mZamjtr7hqDT5N893npuXF9ncSlneQGUKhG6Ik48MvaMtwuMy+hFXTqdPkmi2yZpzQA6IHePZmtzg8PIhdYJK0snkYApHV5UPPb4t9nb2yGNUkqjKe2Yyc41Xp3uUgmPSTRpOmRhYYncWLwr6addTGkpJ3tcWn+FvJghZIdYJ6ytngrse1+RxB1m2aTeeziWvfEWxgSnKJR3hTSHgLqRk8CYiiyfBJItnqoq6HcXSNNOcFTewfzw22UN98fXxK3GSQtlawZrw8PsDVA32zPW1iXUok3R7d9jjxce7zVSaRACJyT5LOPGzetYWxHHijhWoMMYC5UJUYBFhUN6hReKMxcvcm3rJt/4zrf51Cc/jVK67XB8IPKEOqqv328jskCYFSI4L5aA0nglEc7jrGN9a5N+2uWOO+8kThOECMTdgGxAVTlsVVcpWY+2Pki7W4szNqjKNlLUNqSejClx1oTXVRkaVRqDt0FssKoqZrOMLJuRZyXTWUZR5FRliRXB4RJpQhylJJ0e/SRGC8+LT76zY0EIwWg4QsehRbuSKgjNCYEXtZPiPA6Jr8ukJQf7vRy5aNSLUoNUNGOlGXuLS8t87OMPceLkKb7+9a/x9a99jfX1dbz3TKdTfu+rX+XKpSucuHgnvqw4ffIUf/6X/zx/+29v8cILT3Ni7SRlYZlNJ9xz/9383jd/j5VTZ5ns7vLck4/z/JPf5mu//ducPHOa0+fOcurMaU6fPsPq8gqDwZDBcBD4LEoxnc747lNP4ssJWZaxub1FnCRkRclkb8xnPvMHeO9734sQgt6gT2EqkILZeMrJkyfb8uE8z+pz378mzfk2ZNg6JxbmVyHq5gchurYeIq1qRMXVz4l7wwvTW7r/WHxl8aXCFBZb5VgBcRIhKhPSn3XM4fA1ZypkTYTwCK0ZjBYo8xmiaRgaBRmBtpuvDJw1F3I6++NGeA6f2u3OdX492f/+PhWw+Vo7/KDW6zkYKN6asm82QM0x238tpcQJi3QehQyKuDiiSHD3PXcRx8n8hgCFQ4aKN1XLMqi6qlo44iSoWEslWFlZabmQje0rBvuQCsIwGAze1P3/oXZOojg+kAYJhNfbe+Z7uxNeeO4qkS5474Nd1k5BFFdEcVCG3NsuwEToXoel5RWEVBRF3kZTEBq9OWdrXotkd3fnlv30en20jgIvxUtQngcffIDx2XNcf/kar1x9GeUVH7lwL3/sJ/8zfvPh3+Erj3+Dre0N9rZ3yKoC72akUrLWGaEQVNUESYSqO2/2ev3aixcsLCzMOSfBsiyjKisiHUqPZetoNBcslEaXZUmvmxJpzU6es7a6Qq+zxCybIIQ98kF6o/ZuIC1VVRHH++mcBi0JTsqcFLhSLUVvv/W8oqxz+bfkhOdyyMZYrt68yqyY0O2kqFgSdSJUpMDKWuhNE0UaKSMsEi81SMNHP/kj/Pqv/zqvXLvK/e95T9tzJEQhdY66jlBFq8xYXz9EnR6pSbyiFl5zDi0lk+0xF89eoNvrYutzKMqSwhaUtqiP3xM5AcZiTYUrC6oypHsKa8iNQYjAsXJFRTGdMZtNmGUziqJgkoeoqDIlZVlijUGriCSuqzviLktrI2QS0RUa5zymoxFxgnQSZR1VPmWyu/WOj4UkiVhcHKB1QJYaefp5rNq7+f4jYj/Cr61xGJVSbeoqiOSFtG6DMhRF0aZ6hXckScLFi3exsrrK2olTPPzwV3nppRfIZjNMVXHp5ReZVjm9bp/zaye5++67uP++BxFofuonfpov//bn2Z7uEnd69AaLpEkfsaCRUjHd3WZzfYuNjS2++/jj6EjR7fXo9kf0B0O6/R5CBrFBiaCTxrz87FOcP38HK7O8Hd/vf9/7eN9738elS5c4c+YMN2/eZGVlhV6vx9bGJsPhMChde5jNsv1r5puszX5qIqTJxX7qoX7MmjYfTQNCNdfo8HYo7ttlG+s3cUVQvDZVQAgFkEQR1txOvqE+x9o5iDopXlqEDqT6JNWoaUXoxktY+Gnk6+fmDFen10W9oYY/cgTqdItjUl/DNiUk5riTc5+Zb6YZPnZg5LYTh5+r/GtgkBCYBlmBgAxanDN0uyn33Xc3Uh68Nh5qBWiB1gLjQtowVCoF1JB6W71e78iU38GteXr93i3X4rXsh9o56aSBrNXeFN+0mIb99E64hc47Xn75Rfb2xigiHvnWmPd9eMCFeyKStCQvPbM9BzYK5McoLCLznTTDRfXNnM90NmUymdxyXEkcs7m1HpRV05Q4VSip0VHE+u51MjMGBI9eep5PF2P+zH/xJ+jtrPMfX36BXjrEmC2W0z7/1c/9ST7yvg/xG//uP3D1936DDEsviSmNYZZNGVQdokjS7w9oD6o2Yyr29vbaPJ8QopWj94C0ltlsBg0Zs+7R471nceFELY+et6gT/iAvw7d73Hd2DvzfRhbvLH/FVFXo+CzUgYHf8EogePuVqSitQUhFWZVoFUpfo+hgDl80w6lGExChpHKaTYPmiRbEaUS3mwSZ9jhG+0BeVSI0BfRSYZ0AoYjTLj/+Ez/F5z/3v7OytMypU6cQptmHqJtwSVR49AGwraMU+FLNBRQ1RwHnyWYZi8MFYhlhSovxNvBMigIROaQzKCHI8pzMGMosp5zNKGYT9sYTssmU2XjCuCiZFQVFngdSqwhQfJKmRFoRJZru0hJREmFVaDYovQRLncIp2du5STmbobOKcjojo6JrBZEL5xR5T1p3330nbTDo0RuEVFYQTqwrz+pUpaidwfmOxIeteb/pD+NdqEZSUtHpduj3+iilyPOcySSgE0VZ4qPQ/mEwWuRHP/0HuHD33Xz99x7mGw9/jfXrN/DWsjga4pxARxFpmnDX3XdQVSVnz51BSs90OqO/MOD/9n//v5JPCi6/8jKvvPwi165dZZLlyChib3ud6c4mu1nG7sZ6KIdu0DfvWVpe48Jdd3H12jW2d3aobOgzdPHCBT75yU/y5He/y1NPP81P/uRPsr29zalTpwJsLwPJsR6Y5Hk+N/c5mtYGTdq0qqpWcfVABUz9e1NmvB8ovPOWZ1OqLGjrVKXBViXSeSKlsXVnXTq3fi/MjU0FnSRKU7wvQAjiJEKpHK1kUJZ2bTcvmjSIqLkctTcQ0qW3SeEc4LbNWetMHPG35u/tRNqkXUTtoHhoGCYNl61xppqvSCmoMzpUVY7WkjyfcfrUGivLiy2XpN2f85SlqR110AqUEGgVk4ma4Fx7rb5NMe2XtTcBoK8d3SiK6HV7SKnesNbJm3JOfu3Xfo1//a//NU8//TSdTodPfepT/N2/+3e577772s985jOf4ctf/vKB7/3SL/0S//Af/sP29aVLl/jlX/5lvvjFL9Lv9/mFX/gFfu3Xfm1O0fQNmtOYohaGkRJZY+DhGs/fYE9Vzbh+7SbWeoRKmJWGR76zjafHg+9NmU1mZFMQKmG0uBhypUIQNdFBvYCFKofQi2V7e53KHKxCECK0k37k0W+E5mJSobTm1MkzSCmZltP6yDy2qvgn/+wf8y9jTc86lPDc2LlMDHzozH0MqPh3X/jXfPE7X2VWy47nZkKS9lhaGSKUw3pPtzuY453sD/69vT3OnDlDFMcBum8Wbhfk7bMsI1Id0rQLWJIkEONOnlhlZ3uTyubh8RNhYhfA9euX2d3dJM8zpJT0egPOnDlPkvTb/T733GNMp69dMvZ2jYHpZBoiZbWfh20ULCOlUXEUxNdwZGXBbDJBKoWxATGIoohOJyZNEooiSMw771HegSmZ5hnTyS7CeCKt6MmEUdIl8QqBJEpSYh/hjMF6Amri694yTqCFJukv8mMP/Qhfe/hh/tBnf4per08kY7SOW5hZSXEgOm0m+mYhaM5NNe8JQdRJqLDYsiQrM7CWyWzC5tYOO7vbTDY2GO9N2CkziqyJ9B1OOLRUpFIRdTskiwO6YoDzYCqBcALlHabIcTtjxtfWyfcmeOuIlABTsZXvsZvPyE2FFpK17pCfOf8+7j1zP8SSUafPf/u1/8RTN68cuF//zX/z3/CP/tE/etvHAcBotLgf0YlAIlRCIGpitBQSoSKk0vW4Pnit519LKUNjQ+fQsabX6wY+i6rHVhSFBVhrtja3sLYM6EUU1DDvuusuVtdWuHjhLn77i1/kuWeeZXFxBVs5ptMMZ+Hixbt45qmnOXFimaIsak6S5GMf+hCn1k5iTcmNm1d49dWbvHjpFT73uc9RlVPKYoYtKrw3ICQ6isFbvPN87KMf5bvffYwk1UwmE4zVrJ48wf3vuZ+N9XW++c1v4gnE7khHDAYDLl15lbWTJ7HWtGOuKPKai1brW9RVY62miZR8/vOf5/HHH2d9fZ0oirj77rv5+Z//+Rbm997za7/2azzzzDOved/erjHQ7UbslRW2tNjSUpU50lmiNMZagavRkxZxuMUHqIX6hMMLhUCj4pgk0sTKUiqLMhJ8LZ4YBtEhhGP+930BtiNLfg84IftpmiM5fm2A0tY5zH1z/2ezttRRDKBaNNkZh/AGKaCbpJS54j333keadFqRwmZfznrK0hHHaXgW0qAXFYRPEyTjOqW3H7iH8agQqDl6haAoytqZ7dYZhXfAOfnyl7/MX/gLf4GPfexjGGP4K3/lr/BTP/VTPPnkkwca+/zZP/tn+Vt/62+1r+c7Elpr+Zmf+RlOnjzJV7/6Va5du8af/tN/miiK+Dt/5++8mcPh+Ree4/q1jXaiiOKYKI6Jo5goiuv3NTpS7O1tMx7P6iZUM7zTlOOUb359gnAR3YFkdy9HqcW6B44B5IG0xjzU5pzj6tWrt3i5cZxy18V7KMoZs9mMLJsxnWZcvXaFssyxdt+ZGbuKWW7wuccJEdrPC0MBfOHSU3zx8pOkUjGzLpS9AYWpuOPECWKpKIqiPfc4Tsiy2YFj2dzcpCxL0k7aTjqBiwPOwmw6I4oi0iRlPN1hOBrirWN5eZnBcEi1UwSPvFFWxTOZ7LG8dJpuN2gfXLv+Cs899yT33ffB0PentuXlk5w+fWd9zw1PPvnNd2QMzGYZOtJ0ur02J96I4hmlUKZCRhodx4yGIzqdDnmWU5YlxSzDaUOSJGidoOKErBIUVRmqUsqK2WwacuxEJEgSETPqjEjSLrmxCBOqEXSikJGAoPVFOc0Y747JpzMmkzGT6YTp7pgvf+mL/Owf/7mwsHmJcnVutm5PdHih1DoISDVQeVOdVFUVlTE45SnKAlNVPPHtR3jsu0+wW2RYLD2p6OiEqN9leTgiVpqyyhEayjynzHKyrS1MWaKyCu1AFg5lQxy21O+ykGpGUYfRySWGcZfTa6tEwvH3vvF5/uT7P8a9K+eJ4ph//I3f4t9fepT/+Ad/lUp6KlPSfyTlZ9/zAT67eo446vLnPv8vD8wLb+c4ECJiNFxoEY/5f6JGB6WQdbNDf/QCwEHkRCtFHMdIJYiTuE5Z1B/Uik6/R9LrkiYdNje2QhWTd8zKAiUlg8GAD37wg1y4406++Z1H2NzdY29zm92NDZ5//gX+t//tP/GZT/8oH/7wBxkOB1zd2OKxRx9H5BUX77zA6uoCo1GPc+fOsLQ0wlcF02zKeDJhMh4zmYyxdb+Xq5deQXn4z//Ez/Pcc09z4eKd5HlOUe5y6uwZrrx6hccfe5ydnR0u3nUXWZa12h/Xb1znRz75KUxZwZxDLOuUaFOi3DgnnU6HKIp44YUX+NSnPsX58+dJ05R/+2//Lf/df/ff8Tf/5t8kSZL22v7oj/4oP/uzP9umw/76X//r78gYSJKEbk+w40ORQlWUKF8v5kq1Ugkh+3LrfQ9IgESKhMDIkAhtSfSMSBQoLApxoCvaYVSkQUSP+vvtrKmCmSe6tu8fTNwcjrk57PMEwDpoGnnPgbGMkJRFxYnVFeI0ZjhKOX36VJvabGuDRCiOqKoqIEfaE9cK4VIq4jhqn61AQpk/XupzkbVTJEiiuE0nRTqiKDLeiL0p5+Rzn/vcgdf/+B//Y9bW1vjWt77Fpz/96fb9brfbdoA8bL/xG7/Bk08+yW/91m9x4sQJPvjBD/K3//bf5i/9pb/E3/gbf2Oub8Pr23BQ4P0OWVaxt+sx1ocOqfUtFbUwldK69uQkXgStC2zo7CpKzbd/b4NzF2LyXNDpdNE6IehlNJNYKBG1NsiJ4zzWGPb2bkUHet0+Kysn6HRDc6woisiygu2tXZ5+5glevfIScZSQF6G6QtaKjqnznBKeyFt2EWwi6HdHbM/GlG0JXtAfuXzlFa5du0JelnQ6HeI4xTWt6+dsd3eH6XSK0jqQKm1wcbwTVMaSZdMA38ea6Y0xKysrVLkj1ooH7n+Ab3379/B1hFAjlrzv/ofaVI/D0u0OeOTR36EqSpJ+JwxwEVIVUoWV2rqDx/V2jgHrLbN8hhO0KoQCETgYAsqiQhmBKBVRmtRRUIeyVJiyxFSGfGpwbhqivTr9ZY2lqIq6JNKTDLp00zh0mO2mWB/qeorplInZZZxNmE7HZNMps9mUMi9QVZC7l5HCRTDoddi5cp0v/uZv8Z/97M/SiVOk8WHR1LLuNtz0fgmIFewT6JRSRFHU9uLJygIdx3gp+O53n+TR73yHtVOnOL0wZDbbQ07H+L09yvWbzKY507JCOUcUKxajlOW0R0f3GHZWOLUyYrE3pJumJF2F7mp63Q4DGYUGelqhY01Z5Ngi5+8t/QniuEN/sMa3n3iEnx6c4q++8gyfe+4Rnn3hZZQQ5GVJRcbS2SHr2zvhmR0O35FxEEcxw8GISEWhNL4uzW4QklBpJ2qSvAtVUFIcjEAPzfQ6UqFjtQhcJeEdTux/R4qgo7K4tITWEZPJhN3JLlVZUjpPEsfEccKps2f4VH/AlcvXePw73+HGlVf56sNf4fOf/xz33nMHUaKJkpgzp09y88qr/PNvfYvRaMRo2GF1bZmF0SKnz5wOnBelWFta4uTaKjiBQXD95g1sUfBzf+SPsrCwyOnTp5DKUhQlG5sbXLj7Lh555BGMMSwvLyOE4PqNG6RpyjPPPUsnSVlcWGTr5jq0xHKHisO80YinNY0hgzOv+XN/7s+1vJzl5WV+8Rd/kV/5lV/h0qVL3H333QeQzCZ4PXxP384xEPglIZ1nK0tVFuA91kHajfarIOWtRP9WiwQBXiJEVMMUFVGkiWTT3rRdXQB/hPMRCjPaBPfr8O7a79cIapPOraNBwB+QOdl3rG9VjRVizkEI36xF44L2DFKTG8Opk2sk3YROJyVJ4oPHUZtSiijWJGmC1pJut4MBpAzNZxsBVOf3u2M36Xwhw7PhfVCSTbsxURSuXpImTKaveUla+544J7u7uwC1guq+/bN/9s/4p//0n3Ly5El+9md/lr/6V/9qi548/PDDvO997+PEiRPt5z/72c/yy7/8y3z3u9/lQx/60C37KYqijRiB1in4zGcX+f+R99/BtiXXeSf4y8xtj7v+PlfPlTfw3hEgAQLqITUUKbXsiE1oOqgWBalDlDShUIQiOhQzHZrm9B+a0MSIMj2izECUKJGCAFIEYWhgWAAKQFWhvH/eXXvsNmnmj8y9z7nPVBXAehGDVla8evede8w+e+fOXOtb3/q+3iDBaEFdQVVJytJRFppi5ihnjtnMURSGvV3NbGaDFoXBWS88FkUdpEyoay+6tRKIsNbZ4PpKOOeeQFnXFcI6ppPpTVuI19Y2ApRVz9Gc2BPbimKGUgl1bdpIVUhJnuSsGM37hWK5mnA26vH7pibv9NkN5REhvK11WU6JoxhjHSqKqaqaWVF6N97reCdFMePixUukaQ7YBYKgZDweUtUF3V4X8N0dURQRdQRXr13k3e94P88++ZwnSrqF1jnXSD45rHTY2k/ObjqgE/ewOJRQ7O1eY2/3GnGcsjRYO3CW3sg5oG2N0456YvBeEbG3AU9iIuEQVhNpj4PW9YwocAOiKKLTSRAo6toyGk2wRqOtw0lJUZVoq3Ghvmyo0cZRDPc4s32V7f09pmVNbSwiUnSynLVunyNZn+FMMy5LROnoJgl50kMuZVBUrG12uXzmEl/7vd/nYz/+cc+PsBZjHdroAyWceQvsnITYLE55t8O1nW02Dx3CGcvFS5c4evokm8eO8Adf/D1mF8/zkZOnuG9jnagfszJYYjXKWM27ZGlKlvg/UeLrxypLkE5hh1Oc1cTLfYi8eZ+tNK88+zyn77mbWGV8/WtfYzSesNpZ4s0PRujJjEMnjsETUFrD6kP30+kMKL94loevXeDhrQt0peecTKfTNkB5I+dBp5vS7WZeDCyKkCpYzAsRzuP8XDbeWVKAusXeIaRoZvmcXSA8GdBfEAInyWeZUgn6gx4qEsxmM6bTKVWlcTi0NfR7PfqdDmmcMJ2Mef755yjLEefOnWFazhhPRiyvLHHmzIvsbF9mZ/syXlPJZ75pmpKmqUc6s8yXmOIUbR1Xrl7hLW95K6urqzz22GPcc8/dPPHko0zGFdNp4QMIoD8YcOr0ad92bwxFVfHss8/ykz/5k+G7EtAN30YsxTywa8o0i6PldFmvA7Wz40nPnU6HoigoigLnHN/85jf5xje+wWAw4IEHHjjwHm/kHJAYksh7VFnjqOoCi0Vbh5KgX8N0bh5IOHAidHIliOCtJoOGmTdkFNfngnNOIo0E/Wt3Ox74zJYc71pERMy3IF/aaUpFzAOU9r0c8/kZiCkNaTdSEdo50ixjdW2FvNs50H11/feQUpBlCSqWZHnK0tKAaWmotSdCJ0niBUoDrWHeneM/u+UaiUYZWIS5exPSzy3GDxycWGv5G3/jb/DBD36QN73pTe3jf+Ev/AVOnjzJ0aNHefzxx/k7f+fv8Oyzz/Lrv/7rAFy+fPnARATaf1++fPmmn/UP/sE/4O///b9/w+Mr3RmDXoFx0iMDYVI4631iPFylwMTMpjFf+f0pTz6psTrF4gJXQaIyiRGONB+wtr6KtYZazz10ZCDSjUcTnn/+OQbdHo3I1OJIk5SjR46TZ10MNbPKUJkZxWTC9554lJ3dq6yuHGJ3b3sBTRD081X60xH3xzFrZQ3ZMrIcMykq9IKuipSSSEacPnEvg8EqIhKoSKHrmvFkn+eef4bxeJ955dExnowRAqrKC2d1uj2kVIxGQ5zzngdaa2+WJgRRFnPh4jn+2I99gpPHToTX1b79NrjV2uARY2zNM889wnJ/nbX+Ed+O6ixH1k+SJR2SOGM83efFc08cOE9v5ByoG20R57C2pnaOQtfI0kOZqQoS1EqhXEypDULJVt8lkjGgSNPUZ9VVxWxWehQj9PBPR2PK6ZS4LKmJSZVCCccdq+uI9T7xcg8QrOc9BsRUdcR4LBhOhgy3dtgV23QOrXF8Zd1zhJKcF598lk6W8+53vQdhHSJWYSMVBywKDrh+NqTdSKFEwnQ2QxeV51VYj+7keQ6zGZl1vO/kPXz8zW9FJSlSRURljRnPcImX7X72uWfZ2bpKFsXc/8F3oZKE519+EV2WvOW976WWXndFOkmxO8E6iUkj7n/zWzl8aJNLTzyNrGqWV1f5fz3zVd55/E7edvJ+vrF1CRy8/djd/Pg738GRvMdvfvlL/OMnv8bP//zP89nPfvYNnwfdXkbeiTw/aqE0JsS8i0/MWYQtlH2rLHpxuQ6J6sFS0EJpIEni9rivXr3CtWvXAN+mrk1NXWqE8O3bXthsTg5+4YUX+N73voc23t5if38XFgoHjSOw1prJ5GDKKQAnBIcCSv3SSy/x2GOP8eBDd3FyfJIvf/mrHD9+iizP6Q8GJEnSElkdjkcffZSjR49y+PBhyqqiQQIazR8hb8z+4xDc+/MyPx9VVfEf/sN/4M477+SOO+6gqrxE/rve9S5WVlbo9XpcunSJz3zmMwe+wxs5BxTWm36Ga1UWZejO8gamLG7c3Lghhx/874TDOYUgJYpj77UlAnK++LqGdbGwFfj5Ia6LXW4s7xzUN7kOhXELx3gAHbk12bb9fQhIGhl7KYLQo5Isd7yYZ5PsXB+gLF5TX76L6PU79Ho5Vmqq4QQlJXEcE0URdVkd/B4tzyYkWBC6W32AskjxeK3xAwcnn/rUp3jiiSf46le/euDxv/yX/3L785vf/GaOHDnCxz72MV588UXuuuuuH+iz/u7f/bv8zb/5N9t/D4dDjh8/Tj/XdJKGQQ0WiXUClGdMCwzCecndfl/ysQ9tYstrPPnsLKhjdij1lF6sMNqS9w4hY9HCmn5zcOB8AHDu7FnKouLy/mWKenbDxOj1luh2ffZUl9aTEOuanZ1ttrauYI2jKCZoXQIOJRKsgyyJiVyXleVNeuRMOwmiLhhPRi08B47NjSNsb19jbzikv7RKt+Nbs2ycAI6V5dUQnDTDksQRSRIzGnlvEOMsnU6X8WQfKRVZ2mU82SNNM4RQRCrn4oXLlGVBdxCRaMAlvkOjyQaCjPOXvvZZimrCz3ziZ8mSvmfEa83RQ0fRjY6G0Qw6y3zjiS/y0ksv8ba3ve0NnQOmqoiVVz8UDr84SYUVhqosMUIEhMx3JKWB/CUi4d2mqYhVjFIZCO+sWxczRrtjru7vUNZToqpE7415cHCIoyKlv9xjpxizf+YS1dYe2R2HOXzqOCvdAa986zGOJX1OraxS9Aecc5KtrR12z11kyWziYgnWcHp5k6e++R06Scab3/ZWJBJnQTofmCjnRdd0gKIdcwa8N1azrK+ucOXiBY4dO0qnkzHe3kOdOEFvqcfla5ewxiG1RUmD0wZTVIy2t0mWe2hnOP/Sy9xx+gTV+W3YLhCrGc9evkiSZLxFxaAgkgIsrHb7OGeJs5zjd57C2prSFPQyyf/6lc/zzP5V/qeP/HnWemuMH/k606Lgx4/fw0dOP8D25Qt84Og9/OMnv8bnPve527IWDHpd0kiAUi2ZvcnmVCDEIgRuYa6IloTsp7aVIKTDCi9G6KSkMYBopfGENzIUMnRJON/CrrWm2+1w5PAher0O49GYyXTMzvYulfZS/nkWk6Uphw/fgVIxQsCzzz3D73z+t7n73vuYlAV33nsPl86fZzaeYMLr5qPJzJt/CbI0595772P72mWQButKLl26xFve/Ha+/e3vsXH0MJ1ulyR0z4AvrZw5d5atrWv8mf/2T6OExNR1yLyhKsvgh3jjptMEfItdJ0opfu3Xfo3Lly/zt/7W36JpuU6ShA984AMtwnzkyBE6nQ6//Mu/fFvWAqRFYHFS42yEns28Xg+OPElJhAo8mlcZbaTRhCESpXxpw+sYMe+OCWZ/wglcaMFzTjJ3ygooyoHSx+I4uH947bR5Mhri6JuENeHV1/Gm2v0olIYUYJs3CW/U7/fb7/fqbb2OOFHknYxe3iXNIuKqBguRlMQqIY4iChZ4Wm3gj688OIvFIGUc0EtHt3ubg5O/9tf+Gp/73Of4gz/4A+64445Xfe573/tewGcId911F4cPH+ab3/zmgedcuXIF4JY8lQbSvH7I0GtOcF6VmLbFCj9NPV/HagyCpDfk/R/pUqN59rnSGyHhNQqEcywvryxMJEIrnO+4mYzGnDt/lkQpVgfLTHZubCH2zG0b1DU1WE1dF+i6ptPpMJ1axuPx/DOkAOfYG++z0l+jXNmgSge8uHPWM5qdQYkY7TTOWS5fvQgOLl4+T38woNPtEEW+ZBRFMf3+4IYJV1Zl28kjhGA6maFrb/YWR54TM52N6XV7OCeoa83u3h7D4R4/9rH3U+mKsigpZmWAakvKouLXP/drvHLxef78n/wkve4Aa5oWOo9EAMjAfl9eHxwITt7IOVCXJQKDklHYjOa+HghPNBZSoA3IWlJMSy9nnypUEpPGsecfSU/6vXrlErs7+9jCkMSQSkVcW1bSjOTcJUTUoYvERRHDYcVse4qZWCqdctFeRlwbMVNTVL9PHUnWVvrkQnJh9yqj7S2yjRVGu/tEacqx5XW+9fWHifKUNz3wkA+GnZc/aXRLsL59F/Bmff4HTF3RSxIujYfs72asrixz5dw59qdD0pU+lXRMRxPUpKaeziitZpB3EcJb3ee9Ph/80R8jWcq4Zp5HD0vqbMaL011Wk8PURMx29okiQd7vkkYptihJ1QBhLPs7QybO8i8f/z2+t3OJn37/xzk3nfKBLOV/+JmfQeuSS088T7W/z3PPPsvdD70VvuCv2e1YC/q9XuuKK0LbcINCLWbJze8XJQJkE6EsLOSLOXbzusUHxMLjxliuXLlCFEVsbq6xubHBoN9jNBqSJhlVrf39huAF9wLveMfbqeoKISLKsuLChQu89Z3vYm884l3vfAf7u7tsb22xs7XNpUsX2dnZYbg/ZDQaUZUzfy8DSC+vX5UlFy9eYDjc5+577uKpJ5/k6NGjPPCmh1g+fBiVxuA8ZymJY/b29nj4G9/gIx/+MOvr65ig29SUDnTlDd2sta1s/6IA3fUdKJ/5zGd46qmn+IVf+AVWVlba7pDGZyWKvFkqwIkTJwBuy1qAsKAqhNJYZ9FFia3r4O/kPO8CR1NuuWFTb0f4bs7hpEDGCVEkiSQoMVeI9dahCxwR4Zr6X0A7rgsmX40cu9CN0waBt372TUcjE9HKO7Rz1U9qFUUMlpZCyXMe2FyvUQJetkUpSd7JkMHFXKm5jL63zjgYPohw//jr70+D0TXOpWitgybK65ew/76CE+ccf/2v/3V+4zd+g9/7vd/j9OnTr/maRx99FPBRM8D73/9+/uf/+X/m6tWrbG5uAvCFL3yBwWDAgw8++P0cDjiJtaqNVEVr8OeDDiMcRjgQFivAKkt3sM9HPrKElCNefL4kT3ooKel1l+l2fS286dFvo8EAC/rHHJPpCG2vb4fy6pRS+S4a6wwIi3MG5wxJkjAamZZABGBMjUQwme7i8h71IGM/S7BDyVQP8bhNaOMAZrMh3jo9YjwZUlV1q9+QJAlra+sM+gP2h3vtZ2itfVeHc/S6feraeElxDVnWwVhNVRXI/iCw9L03yuNPPMG73/fOUGuVrTSztZb/5X/9X3j57Iv881/+5xw5fJS6roNkdElRlMyKyncKzCrKWckTT58D5ovNGzkHRqMRSamIk6CrIJruDJ8hN5uTVwsVCCRKRWhX46qSQkqfVUUJly5dYjibIkREkiq2dy9z1x2bHBp0UM9e5HCnSywUXSdwIvH+LbMJ+tqYabJNL0/IrKKqZlwqp1TKsdrtsdTJ6WZHuVCPGEaCXqeDNRZXaTZXVviD3/td+p0Ohw8dwjnPPTHaYJvNIJSuah3ct4032TPW0MtTnn/2KQ4fOkwsHJeuXiLq5bhIMp7OMIWhimpkJ2K/mJKtr5IuLeN6OXEnR9iK7XJKHK1xdbhHvLECKqYyM86df5mV3grZ8gpJkkGlQTh0WfPsE8/wn7ef5/dfeoaffd8nePNHPsqZx59jbzxET6b0soy6Ltnb2mZ/b48nX3quvWa3Yy3wxmXKm/2pRlpAHlh8m+4FIWSrg3IgcGn+vsWCvbhdGGOI5EE/kqKYceXKFSaTSYDD+3Q6fa5cu8ZgMCBVEcvLA8bjMf3BCuvrm6Eur3jllVf40Id/hLWlJcyJE1RWUwfdmp3tHfb297hy+Qpnzp3h/Pnz7O/tocua0XjMmXNnqcoZjz/+Pf4P/80f48rlyzzxxBOsrW2wurKGMI4ojlhaWqIoCh797nc5cvgwb33LW9rz0pRGEVDrytM6tUbIqCVkL0r4NxL/n/3sZ3niiSf4xV/8RZaWlsL7+Y2v8XFKkqQtFV28eBG4PWsBwgIlUexwlaUsSkxZQtdR1RUqUm3scP3Wf1MUQXh0SsSKSEliBZH0NihCWr+DB0R53u0yD1deHfc4GKy8GnH2Vtoo15en5shJ+DlcT7+GC5I4Znl52RuPildvb5bS+3V1ck9kHg1HoFJv16F8IhhHkU+cxHzfxXrAoCkXFWVFUlYtbWCxq/e1xvcVnHzqU5/i05/+NJ/5zGfo9/ttTXBpybdovvjii3z605/mJ37iJ1hbW+Pxxx/nF3/xF/nwhz/MW8KN8IlPfIIHH3yQn/3Zn+WXfumXuHz5Mn/v7/09PvWpT908Gn614QTYJno96HojEMGXxLOrNZrKCSJiVrozPvojPUw1ZnfLEAl/4YR0bdvm4uJmtWEwWOKOoyd46cxzTFxx4DAE0Ostc+L4nQgp0XUdapwgqCirgqKYBcjOhdNuECj6aZ+qmjE1NSaJqE3B5dFu+C4S4+rwzdqlE+ccW1vX2Fg/0rKqEaBExKHNY4xGQ6xzRDLyltjFNGg7qNASGZOlPdIkpiwL0jTDYhEYnJBEacZvf/kLFHXF6uoqy0vLrAyWGAwGfPo//ku++od/wP/tf/oHZEmH8f4YqSRLS32ydINzF8/zu1/5Eh/6wIc4dmyDZ59/ji/8/m8BtNykN3IOTMZD6sTDrlHsYXopfNiulPSdV4HA1khuKxURmQgigZGSSVlzaW+fqirpdns4IXnl5Ze5++4TPHT6NC/8wcOoK1fopH3yvIcoC0ScouKY7lKfWVFQlQU7kxGpsHSTlGJcoFXFznhIrRJU3MMlEMmIpdUlRKToH15jTMX45Qmf/+xn+fjH/xh5nge3ZK8i66zm61//Cts729x3/wMcP34KW9VYY707ML4rYX9/l8HKMi9duMSySpEOZraiShVxf4Xu+hJFVZDFHYSKmClHgmR0douhqbjnyBrPfe8JxlvbjIbneHljnfvffA+R7FKrGKkStJ6hZMWlF1/i3774bf7g8ov8P//MX+Hcxct8+w++RLU7wRw5wtPPPcf5qxd4fLbLn+7/KC7v8M1LLwLwwQ9+8LasBXkevHSUI5IS2SJpc/0Yn9kpHGIenDQwvnDhPrs5ibHpRmjLO9dJuXvlX68eOx5PMdZwNE1RSnnncAdplnL8+HEee+wxptMpD731zYxGo6CDdJhulmO0BilI00B6dYJDG5vUWlOWJbOiYDKZsbe3x87OFpevXGZ7a4vh3j7nL1xkd3/IAw+9ia9+9avsj6dsiMjfn8t9aq158qmnOX/hAv/nn/skeZKincM423aAOeuoqxKHbUW9GrJ8s4E3SMpnPvMZHn30UX7+53+eLMu4du0a/X6fwWBAnufs7Ozw3e9+l7e97W1tYPIbv/EbwO1ZC/wFqYhjiS09z67SUxyOorZE2Zzrs1gSuZ4zMf9HU/KLkHFKrKYIEfyuLMjQsehkIEo7fELaIioiBH43BgI3iLDd5Dm3er4TXi26EWC78bgd0s0xIoEhUgYSGbpmXj0YCicSQUSn0yeJUvauXSNfjpByXqKKosgbn4Z5oeu5f5IJ1QxrLZPxjGQlR4iIXrePlBH2huT+xvF9BSf/+B//YwB+9Ed/9MDj/+Jf/As++clPkiQJX/ziF/mH//AfMplMOH78OH/qT/0p/t7f+3vtc5VSfO5zn+MXfuEXeP/730+32+Xnfu7nDugfvN7hKObIgptv3zhwovFcIdQCwWnvSUKkiTr7HD2Ss79fg5AoBXHsb8BGKTVJksBPUGRpzgMPvZlpMePS5XOA9z2JhcQKxYk7TnH82AlKU/uaaxwznYx9v33tjaiaFrAmorYYxtUE4TRXx9t84bGvoyxsTYbkKsU4hTbjhSic0BIJVVXw4kvPcPz4nWxsbiLw5FaMF/4CL0Q1GQ05+8oZ1tc3sbbEmJilwRr93pDpdJ+qntDpdLDWEkWgVIyJ4dzZl/hX//Z/g0ZpM8C0k8JzWn7x7/yPB67FB971Ed7x1vcgJfzOF77Av/43/4qqqlhf3+C97/4g/+mz/+G2zIGymuKcodaCSMd+0wmqq86BlKXnHyiJqhQE5CRSXhwvUorJcATWsLGxgVKKrZ1dDh/Z5NCho1x65QpXXr7E6rjk5VnFkrVsuJhURsSpAiqsqDH1jL1tH+BsLK+Sq4RIxsQIhBHISGDKEmFjVo6uc3G4zf33neaZJ59CVhpdlTzyrYd53/vfj5AKE5RJTV3w5BOPce78OZYGfTbX1rB1QFWcwzjN6tKAM2df8df42kVmdU1sFFNtUOsDspUVSiWRaYpzXgofAZO9Ic9/5yke+sA7ibpd1pdWePvh05TqKsudYOcag0Ki05Sy3KcjBJWp+a0zTwLw3//rf3jgepw4fIgfPfUgu7HiO488y2/++1+mNpo02Cj86q/+6m2ZB3keo6KgbaJkm7nBfAPyBEG/44hG96QNUG6d485Jg7RPFMKrxzbk5WbTbj4P7doum9XVVYZ7Q4yxbGxscPr0aa5tbXFaePXhzc1NTp48yXQ6JZKCKImRzoSAwHdMNSZ/3V6fjQ1/3Np4o8WqrCiLgu985zs88cQTfOyjP8YDDzzAaFww6Ho9otms4Py5izz22GP8yT/5M2xuboZKggvBsN/NbLC08CgxQe+JFo1aLOs8/PDDAPyjf/SPDpyvT37yk3zwgx8kiiKefvppfvd3f5eyLFlZWeH+++/n61//+m2ZA9IJEBoVWSqtWzTXBVdsE7qWxK0uNDfftJuN2MvxW6SzSOc/zzbUgpuMpvPmZkJsr0f/5GbH1bxXWfhupTRJF6xJwnOZk2KbUpMQgjjokzTvcz1/6PqfAbI0pdvtYi5d9kGP8tw4IbzSsfOqpC3BFkLnjm1UlhU6cKcSEdPpdIN69xscnLzWCT1+/PgN6rA3GydPnuS3fuu3vp+PvukoC9+jLUSoceGdGYUAK2ucU+BiBAYrHErWgMEKS1nF7O9WaCNQMiWOcpI49d4gAT431gWCpcTg6A0GPPDAm9nf3w3eM5AkKVIlvP0tb2dz4xBnL57zxmNKkaYJcRK1ni9+NBkagEW7GoljUk05c2VGCQwBY2tsW/uew4PNRa21ZXdvh8mkJM+79PsZQkiW8gGHlzbI0pTLu1eoyhnnzp5hOp1y192nGY9HXL58kcm4YDabYW3JSjpgNpuRph2UUkTKEkWKSk/9guVAWyg1eAuqBsJsJjf84be/zh9++w89AVH6GzlPl9CV4JFHvnPb5oBzBmMrrBOYsCE0NwX4vnypHMI0HIO5NLkUirqsiKVkeWnA6soKu7s71MWU/soyLz/3Ap2hhhlMjPYmaWPJKhnX9obsdWPSlSXQJfVQMHARE2uJXUQmYl+fDkZqVmtUInCxJOlGUMBv/pfPYWeGPE+pOikXLpzl7JnDbG4eoqp0cAiuWVldw1hLkiQURYkMJn3WWcraw+VpFDMa7bPc6XDtwjZOSbamI4qyInMC5RQ4jbY1KojlP/HUU3QOr6CtYX9nh7seuJe77r+HV777OOurGwiR40yMw6DWe4hpwc54Rry+ym//2f8LV82Ei+WEnfGEncmY7f0dHnn2Kb703W+xa2sqkdBbvwOEQlvN9MrLB3RO3sh5oELZNYqiVm23GV67QlBpTZyAaBdm2dbjF0s6LPx8/WYlAaQIsva6NdeEoEcTgqM86VAUJc55PszKYJnZdEqeZxRFQZImbGysc+89d5NlGSeO38F0OqWYTimLAl16DaQ0ioOAlt8go0giIxESoJQkTqAHkVJ0Oh3+yS//MpcvX+HE8ZOcPXuOcjZjMpsxHI95/PEn+NAHP8hDDzwQ5MrxvDvrNzCHQweXaf+dnJc9WNjEyrIMflYJv/RLv9TC9VVVMRqNOHLkSIuybGxs8KlPfYqlpaXWYPPs2bMHgpM3cg5gJU7UKOV5f3XlrS2sM9R1xXA0pLPUP/CSV93TwqUXQoWERqKERfopEMqCFmevf2E4ny0/da4yfsuPukkZcTGgWfx55+pV/h//9/8rVVXwV/7a3+a++x+46fvNvX8cQvpkteFT3Ypvc315K887rG9s8sILLyHwAbnAo0dJEnsDRTdXUBfC3xtGA3G4u6SkKGYkSUSaejftui55rfFD7a3zyDeg24vIU0maRaQpJIkgTiQqMajIIlX4E2mU1FhR45CMh4L9kcU6Sb8/wGhLrauWCCZEjLUSEUUIFaNkhMDDrYP+CrquqXXJuCzIMq8WKZSkLCukVLhQ+06SlH5vwHQ68bbybWlHIlqvSdBoSgRjHBqPywgXyGiYNiiJVIy1LiAEEXVVceH8eR544G6EkmxPdri8fw0pvL6CxVHMpiRRxMrKGkvLMBnN2Nsbc+3aNepa+o6hLKffW0JFMRM3ZdBf9Ru+bYwOdVuXDi4OwPwGnP8tfEuvhqJ0MKF97u0YjUu0l/7WWKsC9Ki9AJ/ygmxKzV/TdC0UpvQCa3HO0soyUeyFhYypmA732Lq6xUqdoLQ/jwqDLGdgdlAIlItZWV0l6Q2wlWQt61BUmtppdD32wnDKi4GVVmPiiO76ACkNd6ysUQwnnN/fpnZegbGbd/jutx/hfe97H0pGARmxvO+9H2izsKryx2ztXNY+imJWlpZ56eWX2Vg/xJbaoUYzmo2ox0OktujphKIckyUpw2lJZ+0QXet49sWnePn8S5y+624Gy32Ms+QbA3arKaNLY3bGU67t7LM3HnNm6xI7ZsbezhBZWrZtyX7kKGRwaZWS2hlUIolQRDIh7+QsrS4TScWjV16+bfNAJQlSNgJsYk6IDc7Dbh5+eDt5vIKNQrap9KupUix2JKgg1pd209YgUGvdkBlASrJOh1gqdG2YjicsLQ1YWh6ANRw5cog8T9sW/qWlJe644w6SJGE8HrO1s83VnW32dvaZFUUgcnoOR5o6YjcPwmGuMXH48GHe9ta38bWvfp0Pf/jDGGMYjoacu3iB5559gbe/45188IMf8AhrIAB70rVr9TGKsjggodCQihvUpAkyFs+JEL5temlpqX2OD6SitgQkpWwR5Ns2pABKkgic9grPRVn4BMYY9vb22Dx2tC31N9/hVkGDC+u0w7fixlIS4zmMTVAbVkFCnaV95c3YrK9VSnktDkrze601V65coCpLqvJVNnnXBCkWKfza7QPLm2/7NystxVFMrz/AWR8sN+waKS1R4sX5muNq1tUAz/rvEeZNVdUeGYwS0iRhNnttJbYf6uDk0SccQpVILFI6lPAGRVEkiGJLkkiSVNLrC970plVW10bIuMZawXgomU0t3W6PPM+oSsdsNkMFGEoKhRAWEXmJcRknZHGCXI84ffo01WyCNl6iWcUxly5dJIoy1tfXKYqC3d19sjTBWUmaZnS73aBhoBruNKlMyFVEHAmMLaiEpqprcmIgpjKGTtYhShWjyQhd1wgRIWXDnNc4J7l27Sp33nmKTqfD6qENdobb7O1ue/KnkKwur7K5eQRrBWVVkmU5R470qOua6XREUU7pdQekaYcsyymmE9ZW11lZ3fAsfWtw1t/gOpAxGw0HretwLP731tlWW8Bh2s6k2xWgzDOSwBdwrhXPE8L5axnumZbYGEp3QliUECz3uww6GbYu/Q1hHGm3w4NvfQB5dcTuuWtsGMWS7HMszlm2iqm1XCos9toOnZUBVRaha0s/ihBRRK1mVKJEoUBl1CqijiJqFXF5d58oyVg5fIgLuyOQKe96z3t54fmneerpx3jxxRc5derEgfMnCKVJazHGtRLj165tMRgMWFleYSXpUI0mLK0OuHYVKlMxnY4Z7exSzEYU2tLprVBZg6s0K0dPct9gmZ3ZmJcmYx753S+xOx1xdXeb3fGYUVUzrWum1lIiKGKoFSgrSUSE7WQQxfTjlLTbIe92yDod8n6HTienk6fIJCJWMZGxPPrVL96WOQC+JKta1EwudO7MN9AommvJiPAcuZBhHpAKX5grzd9CCKJABI3imF63TxRF7O7u0nRXNMrl0+nUox7RQWPJXr/P0aNH6ff7lGVJknibjcZor9vtkve6bBw9QjEpmEwm7O7ssLu7612hjWkDhcUAxRivkPzBD36Q737nO3z+859nbWOdrZ1tLl25zHvf/V4+/omPkudzsz4Rvldzv/rjnh2A/JWaE4yb8sb1owk+8tzbfjQZdFPWWiTUFkVxw+vfqCGFw6BJIhDWomvNbDoN596yt7eH0RqZqNd+s4XRfBd/Hvy+ME/OgldPi3LffJ27FTLyWs+dPzh/6/7SMn/uL34SWzuOHD12w3NuCDIC/0RFke8kfQ1+y+IxLJLK/f0l5mWiKPYGgNa1zw1fDJwNcfo85K+qiiTJSNLsVT+7GT/UwUllGtJpgK7Cn0YYR4QShMSxdXXIj31snaUNS1Va9vcsdR2RD1IQiu5g4FtOdcAtgvAaTmKNoy5rnLbMZhOmxZQkT4hMREMjeea5pynKChklTCYTsjRlMqE101JBsTJWKe966/t44L77yOIYV3rPhjST7O5f4eKlLa5eucJ4OmFvWjCdlZSFJU87TIK2ig3OmA6Bkl4l9vLFK9x552mWuiucPH4ndVljrGHQ7bO6tkqSZtSVxdSOSbHfOoqCxOjGi8Fn59Y5tDPtBFVSIFQcpKTFDQv3XHjHtUiGMZ4XobXPlq5un7k9k8ABxuGEF0lywsvtSyFxVodgCawKZFmlfKnFWiKnGfR6HNtcI0sUxWyGqWuMge3hkMH6CodPHEU/+zLpbkE3SugQI7RFB6PE2WzG5XLMMEs5uXqEXMbEgDU1ggqIKeqKPVuwfPoe3vajP4pVltFoyNkL5/nTf+HP0e8sM9oacuaVVxAy4syZM6yvr5BmEVLMtQ/aTc6CDpC1FF6LZH93h24n48z5S6wfO8zFTocr+xMevXaZQ90OV+sx2/sTds6fY1oVTOtvMa0Me+WUfV1QSb+QaCwlDuMEIkogzZBJQtrJWe2kZN0OWd6l2x3Q7XRJs8RvwkmMiqPgCCwAAxIMlkgIpL0B+35DRyRikB6NbHhd7fwUDbFxviErsYhbHlyob1bOae7h3d0dtre22Dx0iDiO0UZ5DEa2T/TCacYw1boNOKRU7O3skSYJnU4PbSwy8puktgZXO65du4Y2hjhLyftdlteW2Ty8yaGjh9jf22M8mTAZjz1pFi/QVlUlBoMUklprVldX+cmf/Ek+/elP88orr6CSmI9+4uN8/GMfYzAYeMpN2HA8Hc/f7839O51NccHs1Dt9+w257V6UB/k8hPfw1hFxi6w0AU3TqdOcw4bDcjuGMYYosiSRP/66NMwmU78/WMFsMsXo0KzQdrqIWwYLfldpdpPYq/UqhxISr6ElEFZ5xEb4DXmOpiy8jxBtQPD9lHac81oqTtLuM85B3u/xiZ/86UD0989vfeLDcxzQkryFFyntdFJUNO/+XDyuWx0H4UwppbzzulTePFd4e4eWy3X9iQvvLYVABETFhDXg+hbkW40f6uDEOot0c/8Mh8K1U8o34goJtROcvQy/86UtfuTHujg7YTSU1MbS7a2gVO6NoZxuN1crDEjfZqtUjDSW2jr2R/uMxkO0NZ4gFDw8hIBZMWV755J3/AxtdFpr4kihdQ3OkWU5f+HP/XnuOX2Kh//w65y8+yS1Ljh+/Cgry32GW3uMx/sUVcWZ89uce+UCj3/vUc5cOEdtDLXxwZNnSpt2sb106RInTp4gSWJ63Q533HFHm+lkWUa31w0ZhOeIGO21TsqyIorm0uJZltDp5jjnNTdwQaze087bn30c06YLQLjdpSCSAdINm4Ex9vYFJ9YiVeT7mRbuKb9+erEya5vsxndwgQBjiZOE/mCJsqqRcYJVEhsrbKSYzQzPvnCO1fvvZ/nOUyTnd1irIzYLAZEB6VCd3JfOopqp0Bx6890MXzxPXWpsIXAkDKclW6N9enee5H1/7ON0Dx/FUNNbXiLKciaziv29XV564QWefOoJxmNfgnv5pbPce+9dNPLp1tpQcgRTV1y5dInl5WXyOGZvZ5vZdMrhQ0dYXh0w3t3j8MYRzuy+wD97+Gs45SiEw1mFESLA34HUGQlcnkAUkamMtJPR66b0Oh36nQHdwYC830XFEUnkvWRklCBERCQERvlr3mxyCC8mFQnPB7NoT214DenwP+oQoQurbSc/EGCIMFX8HG4MyAjn1SNrokVVbvr+QrC7u8M//+f/jFdeeok3vfkt/MiHfoQH7n+ALMtolHy19Qu5WyAeTmZT73hdlm2m3ThpO+fodrqsBn2QWVkwmk0Zjkbs7e2R5zlZljFYXqK/NGB/d48qvI+11nNAjEdcfdAledvb3ka/32d7e5us1+H03Xcx6PV90BD4YM2xLaI6zjnKqvI6GM3aJkQbnDTn4XozVCF8x1IjyDa3XxAtSmmtJc/z18zY/yhjb3/IxrojSixCeb7MZDLBGd/5puuauq7IXKc9vteDIvgAJpTQhG/L9d1e3h6wSYnbsZgwLwQki3+/rvMQ+CIHFrbFzxGuLc/N6QghqW6et0DYTdKYKL71ln/TspLwvjxZlpHEsfctCrzOKPI6YEbPv0/z+Z6KMEfQGgS+KAuMNTf59BvHD3VwYkyJdeoAHNtEj80jrjGiNJJXzhRMf7vivruWGA79a+Okj7UR0gqM1UEZ1iGEBhtkxA046QOBpvvGaI2zLpR1IsBR65pTp06xsrJCWXqL+qIowFlmsxEQLqhybO9f48VzL3H3/ae5trtLvpfRHfT52je/Qb+fs7qxQaVr/tTP/AzrGyv8q3/7/2V5ZYVrW9vEUUYn73L06AmsNUSRYri/x/7+PmmuME7T6+UI4cXZNjY22Ng8xP7+GF1bNjYOceXyFa/HYi2HNg8RJ3Egj2mSRFGn0XWGU9Awz/2/Fx2aF1npIXJ33ijN/+b2ZUvOucAYV+3Cj8BfG2lpyMfSej0YjAUrkM7/eHVrl/GspDPosbG5yVhXFFgiEnYu7/PlnUfYSGLu7negzKicZjIbsxdHXK1KVu6/h8xMWYoMu7ElPX2Ia+cvIrQAF7HnZizdexd3fejdPD/dRT+5w3hvh1fOvIg1mlpbLp+/gilmnthrLFZIdrZHDPcm9DuZXwwbBVkke3tDBr0eO1tbaFMzm85wOEZlj04/5/LVa6wcOsIrvT6XjCVOIiIVE0cpeeoNEDt5Sr+bk/dWyLoder0+g7SDSiJkqoisQGpvD0CksAqsCnGZjNBAJCSx9MGJrjVWe7GxylgmgaFvXE1dVYx2d2/bHAAw2ty0DNGUdoD27yRJGI1HrC2vL8DyNxe9staSZRnaGP79r/47Pvcff4OyKHjqsSd48vEn+PGP/zjvfOe7OHz4MFmee4aCDOXFZvN3jsrURLEv8Rhj5gaCQrQEegR0oi4qiYmzlP29PYZDL74WqYgkTbDGd/H429ERJ759eXd/D1NWlLomjiLuve8+6qoCJRGxankislHL5SDi2QYqxhKp69fU+X1vAmHWH/rB7o55R6J/76YMNZvN2uui1PdXUvl+xv54xNqGJU4sKhLoEJzoUG7WWjObzej1l14XmrH4qJSyLRsKbAO6+GAt1E1E+yoXAt05+bRth3BuXgJaOAax+JnNOV04hsVg5vvt9GnKMC0H6Cbvc+vW5kaGIbnOhNFrRykVocU82BDhe1vngyXrHFF7jgXFrLhO9fjW44c6OKmtRthFDRA/hMBDbuIgZOeIuXZlwlKnYjpzJEnXBxzWIKzCuQiBCY6KEpwgkh78bWq9SZKSxBkIhVDBv0c4qrrE6JrlwRKrKyuURclsVjAajxgN99jZteH1EVGWMi6mSKF45qnn0brinW96G5fPXODpJ57iE5/4KIN8hUjtUpgZ56+ep9AFvd7Ad59IhVIxeSdjf3+PtfUVVteWGY/3me1VKAXe2CxId0uBdRpHTVlVWGtZW1tnNBqztXWtFUgypqaqCqJIhfPma6t+3h6svR8834vZQfPovD1Rm9uXLQkHLrhFiwa+D/rkLY9AeO6GVClIyf5oSCfNGE5nWBRud0Te2YWky6gyZL0+buI4dvQkZ869hNY1LnWkvQ4jJyHNqZd7HLvzbuygw5E05shKn163hyzGuH6X88++zOmjx1mZTdkeDfniI1/hwsMTuk4RCUeURpw4fZy97WvkiWFpeY04Oky322O4u8/zLzzPpQvn6d59J/NqtkdOZlWJNhonBXGU+O6RQEKLM8nm4TUubV3m6NENtnd3edNDb2XQX6bb7RElkm4nJU2TcC2jOVzflD4C+dtZRyaVR9qcpXICIfEGhWXFTBfUZREUkS0430U0nkyYzGZoXSKwry0Z/kbMAxwCAy4saU1QIv3GYKzFSpDCMpvO+MoffI0/8VM/RT/veIHGsNlIbgKv4/jmo9/hM//x1ylnnjMxm0x45BsPc+alF/n2Ox7hPe9/P29521s5fuwOkkjhmNvJi7CJYSxSSKJYhWDA+oUchxYe8zU4RtMJu7t7RComiT2PoywN2syCiahsdj2/QTroZjl7RQkStAsZq1JYHEYblIhacclFpAQ4EHhYo4nlPIBo+C2NW3bTsdO8bvE9mqClIb9Op1OMMS2KbMz1cvxv7DA2ojY1kRIIJTGlZlYUlLoiNwZhNLPRELdxqL22rz4a9ECAjBAqoCXC4VAId4ukK5R4PLDsgwEnQr1FXFf2acprYl5GEgdAxhDkvI6A5MayUOhGc4Hc4PxcQIKM1Wt+/2b9FkLR6/bxYqdNjcl5lDyKKJmTclsqQCgDuqYS0ZSjtcaY6w1zbz5+qIMTgWcL3/wcH+RBuAA5dzrehbgsR6ysrITo3p+sRh8jVjEi+AH4DEy1tVRjDEmaEkUK50RrIb40WCZJsvZm9H4efsGu69qrFVpDEucomZAmOXccPU0ny9jZucZ4PGVtdZOVlU0uXLwGsoeMUyrjuLa9zXQ6YTr1C6OQllm5xwsvPcHxO04RRap1QrXOICVEsUCGkk6eZ+33q6qZV6jsaZaXlygDO384HIKwVJUn4yZxjLU+03DOkSb5gVrzrVrjmhvpACz4Otw5/2jDBXRm3i4cKFrhugtcFKFlxHQ6pT9Yppd36DhDpzfgxKm7OH36LmSc8vxLL1HMplw9fwVn4dSpU+zv7nBpNsaxT//O+8grwdZoyPkrl9kcHGPt6GHqvEspJGfOvcDWlfMsbyxx1UxQSqPWOty/foy7UknqJL1BFyLIOin33H0KW3vOgdXBpEwJLlyI2d/fZn+4zMrqwHdMAcY6Bv0e+3v75J0udVVy9NhhpmXhS0zWMOh3uHztCku9AUpAf6nL0TuOksaZd+yVoCK/wVnrPMmPg5miAypTY+u67XrQddXeA1r7ck1ZzJhOJxRFia7qIAqYkKYxnTwOZNNb3aNv3Oj1+j7gUs09y7xMI7xfDFFMlsDlCxf50ud/h/e88510773n+tzmwKItpWR3d49f+zefZndr5+ATrePalat86fO/w8Nf/0M+8OEf4Sd+4id44IEHWF5eQUZ+U7YEk7WgGTQXY/T/H4/HgS8R+eBkNKKuK2QbUEREkQFM4MIRXiu9KrZzpEmHOCooiqlXCAslWYvDSkhidaDlGeYcpkb11aPCmkTJA2WcBu1pjrHRgVp8r8VST/Ocqqrakk9jGHg7u3WSuEdVCWRsELHAGK/ZUhQlruu/62QyCR1u0esPTnAgIpxUoGw7l9ug8waWSUBPFoMbd3155royCg1v5MbfXT8W9XSadfh63swNLxd+b7PO4gytGNtNv3VznM7zbaSU9Ho978C+wNNatChoPrfdI4SfD1prTCBvGzOfd69n/HAHJ8LzPZpIriUCEbhAgYEilRfk8jeiR1qSJGVtbZ1ZNQYBZVmQ5RnCNYQ2HfwFFCogJM75umUSFgwXhJKSJEOpiLrWLRvaGE1dl8Sxot/vs7S07LU/Ol1eePEVtrav8ta3vJPHvvNdPvyR9/H82TOsrm3wiZ/+aS5evIDq9NlIcvZHM/b3pwwGy+R5l739fSazfa+zMhM8O51x953eG2MynaIUpGmEtT6limIv6Y0QJGnK5qENcJKy1OztDlvfnST1G0q308VYHXrTK5566km0Njz4wJtYXl4+cP5vBQten5HdRg4c7ULgnK+vuoXF28VtjRgRUVaWTKUc37yDzbUVpDKMplNEVTDZ22GwtMKpY0coTEknj9i9uoWpJJEY0K/65Crmxdk+sqjJez2ifp+xLtFXLpAsreCU4sjmKic3l4jt3JNkNJuwu79PbnMchu3tLY6eOIaQMS+9eBZhLFd3ruHwREqnHb21Q0wqw9VrVxgs5SAMxljvll1rnNZUBThr2NneJopjRBKFMgUcObzJC6+c4Z77HmBr+yppt8ORQ8eIVdwamDksQvkApSgKtDaUZeGddOuaxvpMa1/GrCcjr8VRFF5+3zmyPCWJY/p5StzveepgEqOURxRra5jMCq5tb9/OScDq6ipZGvls1TnPR5Oe0KlrQ1GUZN2YSCpeeelltq9e5Zmnn+bOe+8BHBEBbfUnZg61O8fvf/l3+e7D38RdJ2jRzDRrDaP9fb70+d/hySe+x/s/8AF+9Mc+yum772V1eTkEhNJn04ubRxDI8hto0aI0SEmedwDZoiM+WGgCmrA9uIZfIFBK0Ov1KMvZwmbl1ywlo5ZjczP4vlGHrasaawwyiFE2SZ0vc9s2AGk6dhpeShPYNJoni2Jc1lqm0ynLy8vEcXxdaeCNHVGcUxaCLLXIBM/Rq2tm5bSdF9PRGKNr37n1fQwnFE6K0DIPTR3Hc2EXgxHRLkc3fZ9blJJc+x5NSTz8IiDBgvl7Xr++3jBCWdtPMa8mi7PBI8eS3eQaXF82Ei2XxWGspTfos7c3PPA8peQBh+35ezWxsQ9qCcGgczasiTc/7OvHD3dworwcuZ8PAYJqeEDCtotHkqbgFEaXpKmXZo7jNIijAfjaWF1XxFGCc41fwLzPP4qiYISlUFKRpilaN3bRvlWtqgqPlgiDkBAnCdZ57kmkEqSIyPKctbVN9kdDeoMBUeydiWtjAEne6bC9s09ZWB751rcpiorxuKLTGaAUnDp1kudfeJayLHwWZjTPPvckg4F3KfaeCBalvFmVFQYrnc8oI0Wv1/XM9VnNcH9Mv98ny1IQtq1JFsUE67RvE1uIzG/VRnj9v5vnL2ZVt280mgNy3k3MPKOROCIh0c5QTCuOHT0GVjAa7pLnEluUTLVlV0omwyEqTugOugyiGJFmVNbjMNKU9DsxRw4tB8a6p9saCnaGU4rhFrrS3sujKsDBzNRo/OJ+8ZUzDLIOnaUl4rzD5d1dDh05xnhacvzIUVya4HBcPHse6xx5ktPvrzAZbbGzN2Rp0A3wfoGw3pCrqiqMNaQqIHZWMatLojgmzzMOra3y0gvPcu9DbwVtuXLhkid3B+REQJvJe/FBgzE1s2LCZDyhLDXWesQwSRKyWJHECVm2hFJeYdWG8kFDRDXGMJ6MGQ73GY9GFLomSjO6/cGrXsU/6vBWBapVq9TGIFWEIKLWNUmSolRMHKecP38ObTSPP/44H/+J/4a0kyKcQDqBdAvqlgLOnzvPf/q1X6OcTW/4zOtmProqOf/KK3zu6lW+853v8I4PfoAPvv8D3H3nXaytrhFLFTiMwpflwhs0ZFK/aVmsEBhHCKoJQvKNDsvCpzZGc/iNK0kiBoMew/1h0F3xaEmWZq0XzoEjXkCVnSCgNaIt0VyPijQoyPXE1razJLxXgx4DbYt0r9ebI9i3aVgk5UwglyVRQnvvzYoCK8E5SzmdUs7GpGlK00I+Dwzm4nwBaG9DDieakowE6RstcA7JnNdGcCRui7ABMLkZ0fT6AMX7Fck2uWifu3C6bnXmri+1W4LuUFiLo1ghpd9jZFXSzfObvs/NiLoOkFFEb9BnuDf0Qm7WtSX0xWBzkX/UyDkg5p5McRyTZTnydQLpP9TBSR4ZhGhao0TYipqfTRv1V1WNFI44isnyGiEtS0urGOMhuizL5tLMuCDVnvh2vxCYqEASa6zA/YIyr8PWWiOQlNWMvJMSRQKlHMZUzIox48mQ6WxMt9tFCEGe51y8cNEvGlbywnNn2d+t6HZX6XUH3HPP3Xzj4W9x+fIVhvu7FOWQJItYSVY4tHkUJVPG032P3qgMa+zChPeBRJ53kTKmLGqS2ITgwmdncRxz9OgRJpMps9kUHWrKQgg6nS4IQyfPeP/7P4Szjk6n0wZoN2P534yR3kB8s2J22+aAa2rvC+OgdLnfMIu6Js+7dLtdkiQJMLlFG29uVlYlQkQ446iMYf/KNfa2dyimU4rJlKqcMt26yM65F0iyhN7qGvtVSeEEFQ5tLKmKkQ7SNPGBnopRTmBNjYozrmztcnJ5haw3YHltAxFF9JcHnLt4gfF0zNEjh5mNJ6gopjCOw4ePcqGq2Lq6TxqnzGb+PJazCUIQIGqHdl4/otvtMpxM6Pd79Ht9jmxskESSpx97lP7SJoc2j9LtZSSJwgSZ8nJaUJVVIK9Z70KcJ2QqpttLUarXnEhIFvgpraiWZToaMplOqasKax1pltHrDTh95Dj95RXy3hJxkvGtL/32bZsHrQ9WmHdxMC4T+OwukREmdFxcOH8ecLz04ovsbO9wrHssrBrzjcMHbYbf/u3f5sUXXjjwWUr5Tb8MXTMHhoPZZMqLzzzDxfPn+d4jj/Dmt72d9733/Tx43/2srawGGXSPZt543+A5YmbxfW9WOrj+Yz0HoNvtUpUNEuOTqCZQuFkp1nPx/N9lWR4oGSxqqWjtndWzLGtlCJq50PBIGufz5lpo7WUUGr+ypmvntg2XUswkTipUKtACrFPMxgXK+q7BWV2yt7fHYHltIVBYRCUOvuW8D8eGxNP68j9h45YCYdtwInA7bPuvA29+s0MO87WxC2jW7+Z6i/CcxdJjU6pmIRg4eMzh9WJOyvUIucXKg8fyejqW+r0euigPBEHN3toE1s1oBDEXSdNR5D3ePPfo9dd3f6iDk4/+SIe6ckwnjtlUMylqZoWhqqDQDm0EVks0EpUoTF2QpREqilBpgrM1y4MeWZ62iptCeFY2AiIxZ5gvttBFUUS300FKf/KVigL5VFHXFc55+XdvCOYn3WQ6oignvPTys/z25/8TnW6PV9TLrK2u8cyzz7KzO2YwEHz1qw9zbesqK8trnDx5Emsd2zsXiOIIXRuG+0P6/SWiKGM47FIUM8qyAGcwusIB1kiMNkQqRjCHXedOywqpJFEiiXWCthpdGLQJN58K94KMWFpeARZgbpob5uC1aJCrG9j/1jKejG7bHPCb6pxl4jNgv1kZ6+vtXjzOcvjYGjKLqYwmQWIMTMuKXtIhjlNKbdD4TFVLqIUl6mR004R0ljLc2sFYcLGkqDUTo8mX16gmU+xsjFZ+O2yCZVlCJ+2irCBNOuzLMZeubNEdrLG7O2RJSC5fvsRobxena5TTCGGIkFTFjEvjfWrt3a23tvdwTgdE0C/8Ko6JlSSKE5byDCEU63lOJBVK+ix8Y3mFpZVNpqWlLkaM6jFCOqJIEamITqLo5T0QMiCQGikskUpwLiJSXmhQW8uwLCmKitmsoCw9aqiijG6vz9Fjh7yAWMcLsWXdHnGShDZ7Sf1qSpZvwFjsiPAdCSKU+ARxkmDwHU/FdMqVK1fAOba3tnjhuWc5dscRH5hCK94GcObll/nS5z/f6oo0oyGHtp97s83HwWw05plHH+f5p57mu9/8Fh/5yEd4+zvewR13HGdzc5M8nB/r5iZ7rqEnILzBG4DwnW/hbeffOZD2PXhssM6T5Xu9HnEcYx1EDe/luqDgQIIRkoxiYQNqiLPNxllVZVu2aYKRhm+weD68bUfavn8cx/R6PaQUobPw9pV1tIuYTMFKh4xVIDkLJpMxDq+OWleG3d1djh03RHK+/flLLsK6NueLeOkEjbUlQuCd3EIAoYQn4Dongpqu8lQAoVrWiWmCGw+h+PWh5ap4grnT4Vmq0eFh/vlN0Apz5YYF0sv1PkEiPO7vgECINfhuRny582adP7f6GwFxmiCV8o7DzmtK+UTeEIcONI/yWKzxSbp3tJat1s1iAPtfRVnn/T86JUtjnFM4m2KMwmhFXSqKUlJMJdOx5VvfusQzL4CMIMsi4sQTA/OsS9bxngnO+bptXWtfi6ciiqMAQbn5pBUWpQRZnuHwZSFPnPXBSANhZVlGmqYhS4/Dbu64cvUsV69doOmbV0FyO0ky0rRDmiZIKXj2ue9SV4ZOnjMc7ZJlMc5Zdna3SZIpurbMiglVWaLrCrDkeep5AgKE9G6oedQ5IHPt4WoJeJEyoQLsV3nSr3W+tXJ+k4at1s1/bkppc4JP+Ev4c3HwBnNcX6t/I4eMYkQcYY32N5/1ZE2pFDJSVMYwK6ZYJ5mM95kVM1Z6AxKVo5wjSjsYEVFqjYwSNJAmCSubG8go4uKFi5RlQVXMsHFEHCfYJGdaC/bHJSIrwTovTOQdsTzsW/sS4MxajLOoOGZ5dZU0SRmPRkyKGcO9PSajIeV0jK1KJrs7wfDPK/AqpYiFJEkU585e5tixw2SdlFgkYf44WvfdQFADQZJI7yckfIlAWkGUgrOB+xDMMqWUPpDzjo8I6aX7q9o7386mhum09NczUqhuTqfbZ3N5k7zTpdPJyTIfkOR5HoLfEORI0W58piqpxuPbNgfA88rkglCYQ9LwjzwS4cWoRvtDJuFY6rrksUcf5cMf+TBW+usFTa1c84Xf+TyXLly46ed9PwiAqWpeeOZpzrz4Av/lN3+Td73nPfzIj/wIp06c9EFKnqPiObHQk2ejtjypnV0oNQf4ffEGdI1UmH/9nPvBAYG160fzWf4fvqzjgrdYXdcHVGG73U7776ZlOMuytj23MUltkjnnHL1er+1y9O+TUFe3j4BW1hXFzPM14lgihAMhvbCc8KU+ay3j8dgjjQuB0iJw0Kx9803cUJRjxvt74Pz3iZTEWo2Sgtj5eWMdgaDcvsncVJGwTDqvA2Sd96iJIr95a6vRgSti3VybpNn4BXONpuuJtAKBcGIeuCwE2NJJH8AIQVVWXj+BG9GWW41mDhprMDhUHCOqOiSj8+CkeU8RCDeN0B/4gLXf77flwv8qCLEFGiGNJyUJgRQWhSBBkVmfSeAES8f76P9suXDGEScwGHSJkj5p2gWhWwZ5ksT4e7VRRpQLmQEgHFI6pGpq3LGXdhfea0MEDYFmMWhKQk1HT1XPAs/FomQKLqaT9VHSywoX05Lh/h61rrlw4TzO1YAjS1PSfAOHoQyQrdYWrasAy1pwPipOZETTZRRma7tgN5wS70PjFVWldNiF8pTWJmwwNtwYzQ3gRxPBi4Z4urBGehjTtU/0hEpLUd8+hr7Bm+s1sGhzs1pr0NYQxZLltRW/+M4mGD1m/9oWRjuU8sFNkuWkaUKSd0jTnLyxKhAClWSkMmI0myA7CWPAKLDO0On0QAsSlaL6UUDfDLUFUxvKYobWu/50OIu1hqqumBYTlFRIFThIKyukadwGs3ESM5vNMNrQ6/aJY8VoPOKll17AKMPKICNJE6IoQRAyFylI08RzQZRExQLnPBE0qiuUAynjgPb68p4N3QzjyYxZVTEtCpzTnrPSGTBYX+Jw3iHv5MRZStTLg8VBlzhNvbNzmGdN9ux1gQxWG3RdY+uK2XjMeH//ts0BAKWitiMEPNsIPGG+DkaeUil29/bQddggHTz91FPs7u6yvrbZ3gNCCC5fvsSXv/zlN6711TrqsuLiufP8l8tXeeQPv8Hxkyd4+zvewQMPPMDxkydYWVkh7XgDz0ZWv8m2m1DoIFLjEx7nDoqiea5Bk/XfWHa9/mfnvEBWVdchwPfvLYRPaDqdDp5XN5e1b0rd4/GYsizJ87xNfhpuWq/XYzab4WXsM0AxHt/I3Xmjhq4LdC1RNiKJTOCtKmpdeXTK2kD6LhkOh3S6fR+UBNTtVtt1wyu8cOEss2lNnK/jZJ8kUSgrqYzGGIdzEickta2CbYJEOo/gNQFlYMcRCUmnkyOpkQ6cSNFOUOuaShuMnZ9vPxzKNRol88dkE4y7+bprwe9Hwb6k5X3gWnHAVxsH5lgIbLQ1VMKB9PomDVKpFojFzdxoykiLI01TjDE3L4XeYvxQByelAaFpb90GAPMIU/PVBDIrWD0suXzegEhwNg0X3aBEjJG+DOM3uKiN9qWKqKo6bOSyLeFESlEi8IKTfsJpXbcaCc55ASKtNZPJlLKcIYQgjrzrsV8QLHmWkYmEQytHiaOI7eEOO6Md0sRSlGNq4zd1IRVxklKVJbPZlLqeZx9CeO8Sj91ZhPD17DhOUJEMxKQmgpXhLNmQ3Sici6mrOdPeYT2Eh/TZWntW/fCLH21kP0/ebqxbSuHACYqbkAnfqNHIZtNmKP6A4sSTpYWw6LpGSoGKBHk3xzlBLCOctmhj0bpmNpox2tmjqj0JtM1gQ01dKIkRTXa+g1KJL+c575YkI4VKEqI0IUozkihmeXWNWMVked7W/pPE+62kSerF+2TwrIgjH1QoBdbwr//FP+XKpYv88Z/5M9z71rcRSbj7wTfx9NNPsbu1TWkNWSbIMm8+l8RxKNXFiEgglfQCgFGMoMJZQ20sxoCuK6q6ajlXUZKysrzC4Twny1PSrEuWD0jinDhJSBJFnEaI2CM2vq3UttmT0b5LrdaGuqooy4K6nKHLgnI2pZjMqGa3j3cETXAUtQukv18bZ2oPf0dKcfHCBewCknf18mUunL3I+uohCIw1KQTfeeQRrly89MYfqLPUVcHFC+e4ePE8Tz/xPY7ecYy77rmHBx58kAff9CYOHT7CYDAgjdKwDoEWc5XmKJit+ey7icgbhLLZDBt050YIv/m5DSatxWgTWn8lRlct0dE5377sJcvnnJzGM6gJPhbF7xqk1ncyen5ep9OjrjSz27gW1M5Sa4GtIVa+rGBx1GhmuiSKEipjqUrD1vYOG5uH5mjfrYaLMFjirEunt8yTT34Lkl1mM836+mE2Nw/TSVJkHlFpS1kZhIxxQqBxRGG9F060op0Cj87qsuD8uZe4cv4iKlKcuPsuDm0cgUhQ6NrHBWauDo0ITsgBdRGika/3ZFqPlFk/10U0pyZgkRiki/FaJa8WinFj8BCC3Sax9UmgD7OUkiw0jS1wWGhN/+bmrN6EtSXLvsb4oQ5OVNnwQ3wmiKA13jKN7Ld27F2tGG0LhII0HRCpHEGzoInQ9561DHUvSe8FzHq9LkFfKogPOe9QGSe4qdcWaUiy1thWFdE5i3UG6/zG6MlkKVvb1wIxTCMEHDl8B2udJarphGR9laKeMZztos3cICtJEjp5h0hFjEZjtDYL7Xq+f11JQRxH9Hodsjyj28laqeIG7tXaoJQMlvJz4puvEaswebw8foO4NONmMODNyHWLPzc3pa5vH5TbEO7az7VBMZZQu5d+sfbfXXh5bqE8AiYUndyToVWceidgBFbYFtpuSHx1XYNTQSkxpt9botvpkSUJeaeDixVxnhKlifeiUcHwTkaoIGrnjbJ8a7fABxBCeQloF8TyPJmsRsgIrTW9fp/VjQ2EgP76Jsfvvofd7R0uXrjA1atXKcoSW2mELrFBFto5j6QpFSFRCGm9eJ9QqCQm6yYBqu+S5zl5t0OcpaR5FhxrVZgPDYFae4jc+RZ7YWqkNVRVSVkUlGVJURRURfi7KrGmxuoSW2tMVVMXt5dzYq1p791mXoAPoj2qClEcc+bsmQOvq8qKa9euHQiuq6ri61/7+g/sA9Ns3k0J5JbDOYbDIcOnhjz7zLN87Stf4cE3vZkHHniQu+++m+MnTrGxsUGv30PGQfHWEZIhM99jAqo7L/fM23gbWP56bswioR3A1HUQiRNMioIkSQLah/fvCRon7fooRFt6bBsGAleleX/nXHse0jTFWbye0m0a2hh07bBGEsV16/1irWE43mdleRXjfCA93tulmk3Je/2WLweL61xL8AAhkcQcOnSEXm+JcQXnzp3jC7/zRe46dSf33HMPd5w4wdLKOt08xVlJZXwZxDRKqUEnxrkmMdT8/u9/mc//9mc5//IZ4jjmngce4I//8Z/iLe98D6mUXmfIWcQCYtUeG95WgFAicsFnpw0gmxI+AeVXkqooSaK0vT6vq7TToGvhBDWE1qbU1IqtOeeDL+W/bxlay70jcdWi9l6V/L8Chdg0r0lTgbN4JToncTYw9m2owjpBRIyeKeLEkec90jSnDup+PlPyTsTNBY8Ct0DKRogInAJRh4DGOpSSJEnMdOoXoCbDvnbN61UMBj3iWNHt5mi9hA6Q1tb2NfySaXFO8973v4+HTtxPLGCqZ3z6P/87rj51Ho+EhCg7UyA1caLIOx3KsgLmzHjP/ocoSuh0cvJOjgisbCm8N0LTMhpFKmjDCGRwOBbCBj8SvxUt1Gma/x0YN5/Y85un/Z2zCCepqtu3MbVuoQHZIujUtIsvxn9Hq9uaVEOYnEymjN2ELM/p9iKMARFFbXOgL9Wp1i5cCUUqItIoo5v26PeW6C8vkfW7JHlG1slBCaIkIoliksgL+TVGV/MFRgQUK3AlggiaJKiaCsl//5d/gdlsxB0nT7O0sozVBpx/zdrpFU7dcZJa1xTFjMlkwmg0pChnHsXQLvCKBDJOiGJBnPg5HanEiwiqIEIlvPy2x0HmJCIhFBKvTWCsR1t0CDyqqqCqC2azGdVsSlmU6LoGbTDal9Os02BqsBa0AXv7Sntwq0B5bqng54ZrO+QObR5mf3+f2WzKgZc6x97uLs8///wf6Vjq4HXzul9jLXs7u/zhV77KI9/4Jt1ulyN3HOehhx7i7e98B6fuupOlpSUGvZ4vCcq5GNb14l1z0clGH+PmJZ3FklVdVgjrghVBE+TSbqzOegSylUAPn5NlWVvSU6Gs7QPFeZuuc74DEmB39zohuzdw1GVBXWu0TlCxI4r8l9BaMxrt0et1SVyCdTW2Ltnf2SHv9liI8DiANIX/h3APpRJOnbqLi1t7nDyp+dLv/A7PPvE4vW6fY8ePc/zkaY7ccZJjR0+wdugQaZ6T5zngORvW+nkRR4rHv/so/+7f/1vOn33JB4XAzh9uc/XyZX5OSN72rvegQ2NFHS6vCmsRMOdH4flJLKzbjRWCFD4RTRLJdDbj/EtnedOb3vqq5/BAebA5D64R69MHnudRvTnqZBvOnRBUVenVjJXEWI3WzdyB627VW44f6uBERQ7VasA0EFdzU/ksGie5VhpGhSFKBkRRjhF43RHpLaClTAOnxNfGROQDlEj5oMUKT+wLnPq2xJMkMWmeUld168IrlGU8HqJ8pyBlWTIeT5lOx0wmY2yLTDjG011+9T/9CutLa+RpRlWXXNq+hHVesVYIF+q1HZTM0NaRpV2SxGunCOmt4tM4RQpJlnZRqoNSCUL4li5rHPXMkEYDoiQNZQkDViJ8X4gPYJQhUhZn56iMW4Df5pkovkZ7Q4DSuMG2z2olm6vy9ckV/8AjbERC+JshiuaSz40aZwMl+jq6P85Xzr/IZDrh6NFjnF5a8zdzkpAELtGsKPzGKg0icSip6PZ69AcrbG5usrK6Rt7toJKEPE9J05Q08foyNDeu84GCcx7hajaLhiMjVQhWHAdk3t/00EN+kwnEVy18ec/XzWsQkGUpvX6X1dUVn6HUnjPirMJo0MYBEofBCT97PUTs7Q98aUATS9kifUYbtKnR9QynNXVVUFYldVliipKynFE1j1Uzzy8JGxjWZ09+c9II68s+1vhOsts7GlPLZl4GX6dGwAqvEjva2+PY0Tu48/Q9FOWMb3/7m6Rp2s4j4Sznz55hd+ePLhp3fcDUIAuvhsg4Z6nKgqos2N3d5oXnnuZrX/sKR4+f4MTJU9z/wP3ce++93HXXXagFcqTxE8s7Ld+CALt4XIty9M45itkMB2jjz6M1Fql8S7XfPD1kY42mDDB9U8YpigJonNw9OtBw9ppAqShnaG1vq6yA0DMwjqp2dHLIUr8RWGsYT6fUpkbbitpWGGfZ3tphbeMQSScLAd4iMgHgQNiWN1JbR39ple7UsLFasbbS5fKFl9nb3eLMKy+RffvbpJ0evaUN7jhxklN33cXm4cMsLy2zsrrK4cOHiaRkNhrxX37zc5w9dwZnDFFIQq0xvPDii/zqv/mX3HH8DjaOHMfUGixtQCgU7TriEX6CC/ScgNoEhSYQ0qfTKefOX2C4vcdDD705oCzzZo5blXgW55BXhHZYU9Ma1tE0WdBKGsRYokRhxjUWjRIq2KloHIIoev0B+w91cAIabrpBev8DpMRZy2xqqSpF1hHB2M+EFrvrORKNIqbBIjBOBmhct104zQWTQqFkQpLkSKGwxjCbTbFGt7oANmhJVJUOEtHXLxqW/eFV9kdX5w+5g9/HoyIpUZSiIo3WEd1uRlEWpFnStg0qJcnSlCj1ZQmlIpAKGUWoJCXJM6I0BWUxoWe/dhrtNFY6nBIQS6Sdw3Rwk/ojIbS6bgH03QTQTPTmW1jjuwBu17DWYqxuFSC9tbmfB00Q4BGxkBfJedDX3NBRFJN2er5cp2KiOKZ2FpfkYbO1gVUfs7S0xOrKMqurq/R6XTpZTifLSOIgNS6DrTjOW4WLUPt381Iabt4i6oxvIzQOtF1k0nsEpVEJbYIJIQRC+ojcOIczvuNLCIvCUddgRRBhUkH3R/hunsaAyxqLDmWguip9VqxrtPElLG0cTmt0NaVqyjWzGbosg9WD72axrgr3U+OdMf8Z7fBQFDix0BVym0aT5S6SWv2p9iQ+AW2Xxnvf+wCrK+skacL3vvfYvPU1PP/s2bPeNO8NHkL48u5kMml5ba96XhxURcnFs2e5eO4c3/nGwyyvrfPWt7+Dv/SX/hL33HV3CGBf+7NvhiwtdutUdeW1W2Ze60QbQ6KiFvGIlIJItlt4E5QkSXKwAyMQeUW4Fl5eoaYoCpRM54HgbRhWVwgrKQtHd0mSpl5rSkpJrUu0KbE2RQdp/fFoxHBvn7U4hvjmJ7FZ0qwVWOu9qFQoY3XznG6WUEtL4Upm4yF7u3tcvrTFiy88z1e+8vukeYeVlRVWVlbY3Nig2+0icTz67W9hdXXA2EME9uSzzzzFf/6NX+cvf+p/pDvo0nS5+NM7L6X4ddq0+jLOeQfo2WzGcDjEOcfOzg7T2S7T6Q73nr77gOaNR7kaQbWD3//6+WKtRVuLdv6PJ9f6JhB/XB7DM6YiTrzIm9a6LQE2/25R+dcxfqiDE88Pm9cLnfClEBfqcADOCorSYozyfe1B8r5hogeO2fw9oan1hE3FtTUypRQy9rensQ5jHWmSEklBXVcsLy/jrMFYzXQ6bkmxWpugUBk6ZRZLJ21t88ARADKIwYGMSqybgvMBSJ3FSGlJsoyl/jJpkoO0WAwiwnMMhENGYFyJUwoXJVRMEFaRJHGoSTqkVggZYVEoJ8EIXAP3NsTXBe7rAYj4wIEvtlsHfgLecNCXzG7PsLpGF015xLUiXHPmeLg5RFhWQydTFMc88OBDWARp3kHFCUmSEauobUPOIt+aLZwjiyVpGtMfdDm8uU6/2/H/7nTI46TlGcw1NnyAoK3xMgONc3OAPZtzaEWYr43TKeGcN8eLX7TaNtPAHUI0rcMEZn9wZhYK7bQPjqTAaO2DDqOx2ovOaa1963XoHPMLnKasSu8ubAS6mqHLMbbWVKUO5RqNNhXWVEEvwQcgWutQm/aZvwuopXeLDkHh60+YfqBxMCT2ZxHnUU7rPOq0t7NLVdfcdfc9vtpU12R5p90wA/7GpUuXXhV5aJCY73dYaxmNRi0kvrS0RONrdavPaz/LOazW7Fy5wle++EXuOXGKu0/fiWp0UqwI86799rccNyQeDspZhRDKz1ejaUwJkyhCBm4Bync+yWD4prVugxOv8yHm84B5k4EPgnyJWevbh6LaqsRYy2wqQAqiTLW6NVU9o65LtDHU2lDVmiqu2d/Zpd8fEMd5ONmhjCMa9LXtk2oTWye87k9ZTImE7/gjBoUGq5nW3kDVAAz32LpyEYAsKOsK4yiLAoW3ePABtO96U8LijOX3fvcLHD56nJX1NTp5Rt7p0O12b+iKq+qKWtcYbRmPpwyHQ/b39rly5TIPPPQQxawgji333neCU3edDmU4y2w2oipLpFIsLa0gVdwGlDecV+t8d6j1a7ohoDJ4Tp+KFHXt3c+dtaiwd04mkwN8JK1NK/b5esYPdXDSOC7Ox1xG2IUSjHVQlmA0RCoGfMfF4gbRLmrh7fwGAsJZb1W/UMbwpMXIv/8CR0MIQheNb81FuMCgFoFQ6cXZ4jgB6lC/WwxSDg4pJFkO9z64xMqhgiTbYjY1mApEbpgVNUlkiOIcJWKcwJeDrCOSGQkpsVC+nFE6XClQSYJSikzEyCjBRIpKWqxVCFd7VUopcO5G3wl30/TMzRfDFkmZ1/qtAFvXt9WJVFiLMNp3oUiLtLa1hW8C0IZl7gli/lohnF8sVBK6UDyyEUUxMlLetC3NSaKENIrJs4huL2Mw6LEyWCJWkKUxWd4hTzOU9MJDhA4BacKxOHxpRoBxwUq86S5buEmFVAvns2ETyJZTU9d+w5ACj4AAzmiM1cFzpw4mW4Zaa6rSB8amyWrDptFsPA0B0suWF9S6oq5qqrrC1g5bl2ALnLHUtcUa3x3iA5MaZ/1C05RtFiYCPtUklEmCzoK5vcjJ9dGPw2t8eJRKIFXE5YuXEQgOHT7CeDJluLdHnncP+IM4Idjb2+Vm92XLofgjHOYi4ri3t4dSik6n05Y/quAavvg9rnsHtK74+te/yp/80/8tS8vL8/KrE6+yotz8OJquHy/S6INdz5cJvmIIL+iYeF5UUZRU1Zi6rllaWiJN03nppijY3t5BKcnhw4dDlt+Yw3ml6uFw/49w9l59JKlkJiVl4aefiJpSqaAspxjr29tNbCnqmtRZ9vf3WJms0O9EXo8KH6CGOIX2XgylQms1IlIgFaYqwfpSqJIClSYoGaNtAUKig8ZRyPIopxPAeZsE4QnwTviyjAlzVTjfqru3c43/z//2T+n2evT6PeLIt8lro30rbyjFmpBsVFVFMasoygKjDXne4YGHHmB1bZWlQc69997P+sYh0tzbroyubTPc30blCUtLAxxN88SNwxhDWdS4ppW54U1Yvx75e82CiLzrdrB3aRDChhQthKAsiv86ghN3w6kUCz/5SWmNoColzipf6hANn/3GKFE0/zXPWVjwFuHieTvw/PXXO30eOM7w2KLeiLWvFkH6luU0kQyWJcdPxvQHkHdS6qlkODVE3ZSIlHIv47knLnHoyBo22seoikQlRCJBuhRBQqQystyg4g64FESMUn2kESiVYF2MihJcNQU5xtBkN/PgQ7QoymKNUiwQnBaIh2EYLCW3F9JXUUQUK2rja5zWebRMBFJne+6lQzqJdN7OXmkQMkGgkNLiooZA65d31QY1gihS3lsmy8izHKVihPAcF2v9HIviBN/J2ag2WnA1MqBsPt4V7bykCXbDH4tc2N89J8A60LpsPW8aU8lWS8Ra35pnDMY6tHG+nBhKij4LFO0GZKylqivKYsZsMkFYT5Y2psbaui09am2xugJXIqzFaocNLY2+i8wvtgLnhfysaTukmp1RBK1crK9rX69k+UaPRXSmQVN9rOyvaBzFXL58mTiO6Xa7zIqSfr/PsWPHDr6R867ANxvOOYT0JGGaa/xHGA1c35R4kiQhz/M2O341ufdz585x7vx5lhbMOIUQN117bkWIbf4YrSmrKbGKUEqQpEkoFfskxQe4vmTZ6/UoirIl/O7v79PrdZnNZpw5c5Zf+ZVfYX19nb/9t/8WaTqXuRdAMSuYTCZ/pHP2aqPbTxhe9ZYKIIhih6d/SW/R0HbMBM6Nhelsyv7+VfrLKUJ2QSyWnRYC7gWEPQ7yBVma0kkzxvWYaVXjc1NJDCgpMEKhbdgb2vxXeITbmRDABP5Z+BisA2uQwjIdD5mMh1y9Mi+ltdevLWEu8pdk+/fyyoD1jRVW1tY5cuQQveU1pPRu88J537mqHCNkEt7Dd/bdbDRdi4TyT4OgN52SQkiv92W9wKc2hm63G7pb53uClJJxcIV+PeOHPDghCIL5f7cC66FyInE4IygqH8h48qxc2EAXhcq4KfZ84BHXRNGLk8MdmDC2CZncwQWgWThejcvRfqbwpmPFtObxbw259PIhOklGnIwQyoDKSbuKrGPQVYlgEypHmhmcsnR6luV1nz0vLyVI4aP3TmfC/siR9yqcTqgrQaRipoUkLnPc/gyzlyF0yCTdooOkC+hJg4zMz0mT5y9mGi5sjM3ie7tGt5uRZxFlFaF1IL66RgTPBmlofC1cSpy0GCkwCCwaIWuU1uAsSghsLZEiQWiNkBUCRzXTCGoQPhBQSLpZTl3WREJSS4UuPEEQ50Kw4MsbQsYYYRDOH5uxBuPqoAQ5F8+qa++YbaylNjosnhYbHIJpghLnTSqvD4YFAqcFtnYgLMJZjPZt7RaDdibwSirGw31WltfASfZ29nCh5d1oHSzVpUdIbIE0BN6NQweUxM99P5eVr0n5rH0RCQo3jj9O3+lxO0ezrjdtlwECBQQoT3zfurqFihKshd29PTbW1zl5+jSm4fqExb8qX4VvIhVCRUgkpgyB2h8JS5kHo7OZ10Nq/GvmZNP58/wQjMdjnnvuOd705je3zuyvtbYs8nB8wGqwzlDpiv3RkE6UoCJFJ09CV5/X4JnNKoxNiYQgzRp1Yn9t9/f36XQ6AfGZd/k0Eg1SNgJkrnW0vl0jjQAJujJgI6I0IhaSOIq9q7YuQ9eMv8+MqamdY2+4z+p4QFdFiFihREyY1O35hrkZYBR5WYB+Z4lOmnsfp9EENzWUsxDgWxtIyr7Lz98vvlTWFPaxlk6akXeWSOM42EJMqeqpR52wGDdH9+fraFiD26MLQptChUTMO2CrOCaJE/r9PjiPJColcdYxmo6YlSXdLAHrj+tm09g5G4xgq4X9T3i+CZDGMUmimEz999PWMJrUPmmMDnYp+pbmGw0obzV+qIOT64GTxcxdCJ+7WQNV6QKpMPBN5mHMgTdpeYjixjdvOBZNhHsQOVkMUA787zp05bUDk+aYPIQmfWlExFSmgyktRDOkyii0YDxzpHmHKMrY3tLYK8s+k3calc/odZfp9yRZ7gWEBgOL1hGJHdLJIEslaV4h0gJbTllZ15x9AYbbnj8ebDvmBM1bjIPE2PktYxAYU+Pc7QtOZEA2ZBR5rkStW72aRW2AAz8jfGuurX2Joigx9QxXFYi8RxJnREmG6xjqwr9uNhNMRjHjPKeczljq9el3O5RVxbSsUFFEkiahM8ehjaYRwLPWIkJgYo0OaEhDkp4rVzZzqg7H7zNW2xLe2g6LhnSKn1M+6AUMwSJdgNWejWygdjXaNg7DFVaXbG9dJooy6rpC69JnTw2x1YV2UKfb47PGokI3DtD6sdiwqDk3vy/8mBtvGmOpbnNw0mQE199abeAMTKcT4jimqrw/UJzEQWCs4Vi51jfkVkNKSdrvkccJw90d9KsoXjZ8DP/Wr95B0x6v88JmswXROqWUN/SrquB/42ULnvje9/gTP/3TNwQdi+91s7EojOWcLzkL6f24VKyIk7jtcouUmp8fQSusJYRouTLNn1OnTvFX/+pfJUlSoigKIn9Ntm8YjoY+0L5NI4otSIvWDmckQioi5f8YMTce1NoH/nVdk2Q+KNjb20GlGbFSKOVweCHKABGG4Nt/jhIKGSesbh5m+8plZoViJcro5jAZ18xKbzQ6rQpMUNq2YQ2UQgTlWm842+2soOIOWawQThEFFefKVggsceCoCakw4d6z1rTSFWGFRkkveFdV3nZgMpkwHk1YXV331g2OsG40bsnOX6fWsoGbkqmMMYxGo4UuUz/mYmuSNE2J2jbyYL9hLEmgETRrl1IRvV7vBvLtLa/nDzQL/v9+zC9Arb0RYOM9cqvT0mT63h1AtBeqIYT6tuT59Vtss73ujZhfRNf+ea0OmMUxxydiEB0kEcoZlBNgMqRUWFcibY4wfWzksCLC2YgaiyFC2Q6zqo8sIoxUkKbofYu1A3ZHGcuDAREJQk4YuxmyinB6RF1ldDqhjmybDcdn684GEqxrSjhzqBPhDkDrTaRX169frvgHGT4ODG26UvgW8MirI1pjAzrhM8uDQkaA8p01nvSqMbMx47pCxhlRmhGVXo3X63Z4EmqSpt7dOO8yGPRZWl4iy3KSNEVFXpBqnh0cVOF0oa3R6aoNTpqsWYSo1xjjSakNShI2dyFokTeJbzucL/wBDbYGSTBCdNaTcI3AYDDO4H2HSpDeCHE2m4EVGFOBNQgbYG+BN020FhqVYOfPpy+ZNMiEaxp2cS6IQS0gOs66VhHydqJn/vMgFJoWHhUhY7TEym+WUkqGwyEykDt3d3epK88Ha2DxV2OVOGswdY3MMgYry6wMlrh0/jxlWQTit39t49qslGIwGHhfF2OYTKfzEtjrHM0GMf+u/py/9OKLjEZDBoOllrx/43mZByxtCTF0HhrrUcWqqhA0kuM+2G94As65MLcjwraKEILpdEpZlq2/zmQyJYoiTpw4jhBedXWxZVpJxf7+/m0t8QrpEyFjfHcNbjHQFAHd8WWsZhO1zmG0ZTjcJ+l26ScJsXRIkd+wWTeS8lIonIo4dOw4W5cvkc8m7OyNcFaTpanvHqRmsLQBSKbTKTqUZau6wjHDCYMgwpqIOE7RuiaOE9+4oTTrm4fZ2b+GrkrPKzG1J9E43ynaoCkhXcZYQ1GW7Xw30ylbW9c4ffpOHxxYw6yYUVYlnSxlY2ODPWWYlQWz2ZQ8zkAcLOs4HFUVVMl1vSCp73/vy7zaJ2VBq0kptVD2mVum+OAEXlWN97rxQx2cHCCzHvgFCCcQ0lBrQVEp74cjVTixFojCJjvXR2nY5iz6DzRQsXMLDRRhMTYCZw+WPkQLB3ppK+sMQjZlndf5xYQFvPyyRyojvOia8Tc+ERJLRExkU6wWoAMhVhRIJLGLSYiIXIZyEYntedtwK0B00KaLRSJtTBVZsApbreEiLzx2oPu9Daqar3+wnGWtnTtWhp9tWEDL4jbrWziHEl6gWUU+CGk+myjwA8AHMOFC23CcQniVViUVKmgJeKRKY8yUcjT1XA7nuRrW+K04Ut48Ms9yOp0OvV6fXq9Pnud08pw4qL56Yq4LhGjTokgmdLg0gZ9z+DKSs2ET83OoQQL9ufblFB/z+RovzINk6wwO49n/xpPuhHNYE64LDTfFtBmzA+qA6gjru3ZcU6bx0Q6NpwpYnGjQGnBurmGBa/g6QaHVNo819431jPTbOxHwt2eEDeZrDoGVCoQiljG1rnHOsTcaooVje7jH9u4uZVW2JGmnghHizdJIfLtqNdaMTc2Jk6f5yEc+wmwyYWd7hxdeeJ4L588xnc4l2qMooqwqj9IoSeRytNG4sobvwxDzYIDvf966eon97W2WBkvt0TYY581WRp9UNYGJDfcJzCZTosCDss6SxemCNxckeQbSBy4CzzPY399vy05VVXH16jW0rul0Mq9r4RTWOIQInY5SsLOzdVsTFUONw1Jp2Wr8GONwVoAUVFWN0QZTG0xtsalPFjCSYlYxmUxIe32yJEYKgy/l+EVP2wptLDZwx6IoZuPwIWbGINOc5Y0ugyVAd/jQhz7KNx95mPNnL7G1vU0sYpIsxaLJbM1orFld3cCajPe+50f57ncf9XMmjnAyo7/UJ0ksd55c5vnnnkFb7QmzQhC5DIfEoJFYfC9gFZbohbUOwe61q/6xQEMoqoqqrsnTlP5gmf3dLfb2dsmvXuGO3hJKBO6LXy3w+jAjZuXMB1xWt91FzfwpipIkjohEk9i70EHmr/PcBsG1nVuvd/xQByewsGGyQMZsiXGKupLUlTjQb32z0ZJgFx878DkHT2pZFqEWt7CAh6e4kMW0fJPwTv7x14GcNFAeoKIKpMOSYPDQmmoSV+lwqsIhQRic8BLrvgZpUAGcVAjf8iZqFBZhM2IMykVgYyJypI6oTUwVFThlr/vyc6VJL5998ExJOEB4FA2yAoGtfvuG/65zPw+BaMXMfADiI/nm2puwGTvrMOFmVkGIyBPTLFBjLURS4azBmRppvbaHsxZjI9DGc1XKAsoSOyuo84wqQJxxHNP4nQh8G6LXBtEIYRZKIH4bEdqChbm0s8A3GIe5xLx+bwK5tSG7LpKwhTQERB7RCKKFuWlMI0hHA4ZgnD8fGI+SOHxw4uw8qPEX0ysJO+eDHwe+FRkvMicCUmK1f74Uop0HEhHO8e0bLQmWhhsVwpUwVVs0LWTMWZZR17Vvn17I8BtJ7lf9LGOZjacc3tjkofsf4MKFC0QqYmkw4L3veTdbW1ucO3eOCxcusLe3hytLVOURPRFFECf+uKoa0awJP8B3Ho3HvPzKKxw/dfpGZPYma0xzD7elwfCcyWRCI0fuHK1QnJRRyNV8ecM5L0RY1EUoCyTs7e15DZPwGmNDAiVcm0Gr0B25s3P71GH9MEglqIzAmx97VVdr/feqSs/BMtqga58EGGPQwmF0xGQyJZ0O6XVSnIxZ7AStZ2NMWWGMVyBPVcT64cO850M/wvPPPc/VK9eYljMG2YCP/bEf50/+2T/B1Ss7fPFLX+Lq1cvMqil/+I2vM52OiOqMNF9ibeUU64eO4+TT5IM1L/dQCx64716ieMrVqxdYWlpF722RJTGddBmqLlp7RFpGFhsVCOXbwFWkWoPDJMsYjUatxL0LPBiIcChUkiGTlO2dHTaPHm+vb0Nf0MZr0wyHQ+9CDXNu1sLy7zWgGmfieXlxzomzC483ydjrm+0/9MFJCPgDSfXAw+AEdQW6DhuPnAcpN+OVHDjrLWzW/DOUNPC6Ak899T12drbZ3Nzg6LFj843GNf0e17+OdjEIv7n1V2pU+4RAiixssF7l0wmBEsKHHS4HlyAwOOEDF0EWsuLAFhcKKRO08OhLLRwxOQ7lM2FnUFYgjEJKjXQGYf1mbkU4saJZ6D3rZn7kov2/C0GLL/uEx529rUZfELZ26fNFT1vzo1XsDdc0ipOQVTa9+vNOGYfzpYxQwpDGtyM7HFY4IuGRC2m9ZovRGic12jkiZ7GRxEYSLQyVrXFRhK1V6MhadIz1GjgSr8WyuEFgQtnH2IUg+sBM8qJrLnBSArmuebkNOioE5Udn5ohGYwrZBCWgQkuza1sYnTFIp/25sA3K53AslCoWFrBFQq5BtItTi6g1N6QD2oLA7RtzJK9ZYAnzdd4plyYZxuwzGo84tnYcreuAJNTt/AZ3wGn1ZqO5OqdPn+ajH/0oVVWxu7vLlSuXuXDhPMPhPnfedSeXLl3i0UcfZWtri7IsKcsSUdWIxHd7GCWxZYVrfHK+z1HXNS+//DIf/rGPLqCbzXpzcK1pELq555fx94I1VLOZD+gDKtyIqiWB0EiTlIQAaG9vD6DlFGitufPOO5nOPM9hZSWjwW/aeWFhb2+HH+iLvs4RQEW0haJyKOmduG1AvqvSl3Nqq6l0ibYZDtDaYXTCdKyJ90asDAZEKg3IlsAZyWQ4xOja3zfO+Xs4irj3wTdx6OgdXDx/gZeef5GrF0Y8/K2H+djHP8Kpe0/xVx78H0jiiOF4n8effJzd3R3+yT/5Z0ynlo3Dx3n0iaeJ0px3v+dD7E9m7I5m3Pem+3nwwWP863/1Kywtr7G7v0cSZ2RRB2O62Ao6SYc8V9h0gnFjrMMbiQoYLC+xtrkJsinPSawhSGQEo0MhWFpeZXPzMIcObxJJ1SZGZVkwK4og+z/GGoeSEc76En0jz9DcZzK0NbNwlzcCg2VZEgepBq2rA00hrzV++IMTDgS4NKRVEZj6ugRd21YATbYlm4MoC9C2STXQaLtxuYWbDMC50NpZ39gSHN6gyRpuJKiJeanhlkOC88RdKSOvFSFs23zgGd8OIR0Kv3FaJ3BC+fKKNDgZ4goZulSCQJ1FhNKQwAqBlVUQAHNYUYc2M58pCWfDxg8NZOhCnXMex4WMtWmbEmJ+PZylLG+zG20oUwgPNHimOl7RslG3tMYLG0mliKO4vS5GuhbZscIGhrxAOgk2fA8hMU5ibIUynrdhrADl0EAtLVVtiWqQymFdRVl4sptSQYvAmpYzYq1F2oNS9XMp8ZvcuAtTpbmxWxVWAprVdIL5pdYfeyDjCucl6j0pWYQgxgZFWtMG5M4apJeNCuJ5TZeZaaPPm03bOTEOmk2waYKTLYrhDn6R2zBsaNlstDqgqU45wBDHMctLy1y6dJGqKknTFG1rymKGqWuvYisdSjiipHFuvfnwC7Jgc3OTq1evEsexN+o7fpyjx45y5cplzp8/z/LKCidPnWJ/f5/h/j6PP/44L730ErooqUU1Rzh+0P3aOc688kpw3Q7fWzRJxDxJ8FPKhcDEw/W2mZNaY6vKXysI6tbaZ8PG4qRvA5cOrDbszfbY3d1laWkJgMFgwNraWss98eqkk/B7f/2dFWhj2Nvf/QG/6Os8HUYilBcRrCpHkhq/G0iP5OnKoyXaemVsHbR6hIgxRuBqy3Q8ZTKZkeddqmrGcG9CXcFwd8ejny6sf8orskoVsTxYoXNvl/X1DaZDx2gy4bP/5TdZ2zzM+to6vV6fw4cP8Y53vRdrLI898QwXzu/w/vd+lNpp/un/+5/z1NPPc+L0Kf7cn/8/8cD9pzh9fJnhcMinf+Vf8q63v5uf+lN/gizu8cKzlzlz5jxZplha7rK83mNtc8D62garq8t8/RsPU2jD0soaz774QtgNJdZ5vmVZVRiXobAsDZZ429vfTdaNMVZTa78GjifjVtW8LGpw0huIzgmF7Tlv5DFs6D5sJvMcxZv/XNdVSHb+K2glXhxzUIkWqcAqqhKMccRp7Esei6TIhZLLjSWfg/9uN42QQdx11z0cOXyM6JZZ1gK+cIBNz40b0PWfHEo6DdITIiXABRKk/51oIpXm0BZQmkXNFiVCEGN98CIiXxKQTmCIMMyQ4diazarFIuzB7wJzBcXFrF7g4Wl/TP5RbS2z2e1rHYQgFBXIeqIl5QZuiZ2jEE3u3pIDpQj2BU3W3zzXl4UcDmcsOOEt6r27SAttKgfOaqwVGOO7kqpqhrWGTt5hf3+Hbq+zEIjKFk53Ntg6hrnYulgvBB3NEAstuou/XwxOmuF5KD44scaGczFXOBZtcGKCMq1DuibHFQiCaJ9wbcDXCJkJPJWEhWCoyZ4WbR38uZW+DCQlSolWDv12jpvDxfPkII5j1tfXiaKYTrfDdDphf3+f8WgUOmN8WUpGkiR5bYl1KRWrq6t873vf47nnnuOTn/wk1lruvvtu7rvvXnZ2drh48SLnzp3j6tWrzGYzVtfWePNb3sIrr7zCCy+8wP7u3s2qL9/Hd3a88sorjMdj3y7ajIVbc/GczH2mbBug18FFvfm9ElGr6qm1JkqSNkOeTiZMZtP2+o9GI3q9nufVlGXbnXTt2jWyLEMIH6Q7B0VR3FI/5o0adQ1xDGXhqCtFHC9+f9FyvUSkDxCDIxV5t20XoWvJdDLFrlqKomBnZ4eqtLhao0JiY4REyAQZZ1hjqJ2htoas26GTdzF1xLjY58q1HS5c3G6D9ySJWV1dQaiE2gIq5k33P8hf/It/lq8//HX2p0NK7fj2d57lyssx7373B/jy53+H08eP8t/93H8HUjCbVjgsWZ54c9rAa9O1oKoLxnXJtx75Di+//DJqsQGkuYbTKesrSwEBUXQ6PbQt2Nq+Qq+/SlEUTKczpFRMJhNPll4g3jccnOZnAUSRCsFwKAs3u8d13ZJSqu9rvv/vJjhphqTJFHxkW5ZQG0gjXyppSiZ+4b3uxSEIOMA/aco0C4GMc5J+f4UkydG6pLreO8Zx4CIscgOA7+MCiYBcNJmnxUv8NLHnvA1sUWbZb6Ue4pP4/ncpQOOdbb3UdIN4+BKQc+CEbTPgm5yY9qf54tfU9gUHouHwcmEsdXn7HIn9EYiAkEiPay1ukgs3Bng5d26SqDYKso21gBW+tOaE9O25tQ9SBBAFspcSHkIW1viuGK1xQJakdJOEqdaYomLuXTF3cjVNgIc/XBvIqdYd5CrRfKvmsUA0FiFIsM6F9xIB2dAgPMfEBBE2L6av8OJ5zaZkwzwHT7RtAqCgEyN90OtLPLb9nXDMu3EAjOcXOBeQNuFC0Dz/XlGShO90e5GTtqSDxToRWjbnCBDA+uYmxhiKquTitavoomA2mTCZTAlYJM5Bnnon2VeDNJSSbB7a4EMf+iDOWV555RW+/OUv89f++l9FCMGVK1c4dfI09917L5PphLNnz/HII4/Q7XY5duwY9913Hy+/+AqvvPJykMsP52exovc6xuUrl9nd3aXX6+Kkp+E7F+5GEYJu5+blQ0fbwo6A2Wx2oPzZCGu5UOpzAmSkIBC8syyjLEtmsxlSSvr9PtPplDiOPTq1vExRlAHOb+wcHNPpmNn09qKo01KQJjByjroC1yWcxzC3tUDXNSryrbjaegftWDURusAayWQ8oa4K6qomTVOvF2Q9G83YcO+omCjOqOwMJxRCKrSpENbLCuT9LlEvx1YRZWmYToeU2nD2wmXG0xkXLl7g13/9P/POd76dv/izf5wf/4kP8p3Hnmd7f8S1qzuce/Yqd++eoD9Y4szZM1zbvsZgeQltLXEao63jpRdeoChmVKVmPCoYjvb51iPf4ImnniLrdrn3gftbJLlB/PeHQ/ShDdLIF7hBsru3x3C4R5rmbG9vkSQZRmumkxlaG6Ry6NDZ5WHRJqnzKGsUZ0QiRjhvIktI5BCilT1ovMwI8/P1jP9dBSfNvtoEEdYKytJbvs8ldJs+lIPIyCJyskj4bC5qGzE2MHXoew/hBgcIsQuloAZ2X6yJv76VJ3AoWiJh2CBo2iVFaN+6cTUTwr9OBg8hEfyEnNU4pxbeEzz7MdSHaQtXLYIyf+vmnF2/yQjaYth15TVd168uaPUGDC9WFgij7uBVnZccFmt3821HSNFu/k2kD829JxDSIyRCSmrBQveN3wwlnmxstcEZi4ol1himk4kPRMI1t65pbac9HnNdaQZxE4Qk1HcX51KjmdIGuguX3zoN3pILKRpZ7Ib9GhYG5gFGg7o1WWSTUYsQnPgurLkuRTOvF5FAKefzSSnhrSGcd7duukJ0XbeI020bLXRqPUIazB9xvrNAW8PhY0cpy5Ltq1dZ3TyEMI7ZdEoV1E59QOXodrqv+XFplnH48CEGgwFHjhxhZ2eHjY0N9veHXLlymd/8zd/ip/6PP8Xa+gpLS0vcd999HDlyhL29Pa5du8YzTz/D+tomJ06e5PHHHmV3d4ft7W2vB9MAoreSK1gYs8mUa9eucfzEHcHC4uDt2nCPwKOM9YJJn4fwJ9BA8862XAFrbXCrdh6ZVIpev9+KxVVVhVIqKITO526v1+PwYdmudUp5vsN0OqG8DWaKB85F6egkfp5XlcM5hRfiEe1JqYqSJOuigg5L03VijF8/6kozHRt2dnYpZzVSJkRRRF2XYQm2WBESvEhhS7BS+PZ94c+TcQYrw/2hIEljkv8fe38aZdd13feiv7XWbk5fLQpAoSNBiCTARpQokaJsybZEdXaS69g3UuQmfomTvDi2P0RORqL3ciM7uS+6I+PdkYyRyH5fksgZI7G6OIkj2bJaO2ooiaJIsW/ABn0VUO3pd7PWeh/W3vucUwAIQATIAr3/YxQKdZrdzr3WXP8553+G04BjNyu1kIcfeYzlc6f5w//+Ij/2k2/kjXcd5mA3Inp2GZhn8eAuvvGtP8MKyUa7y2f/6x+yY2EXgoAwrCMQPHf0KKlxuSFJ6pL1lR/wtvvuozE1xczcLL7vFekKAL1ul063g9dqMBj0AMHp02doNGu0ux3iOMXzMkYtdYn7WjuFaJvlIm7NzXTiatk9t/nifsS05M4J5OHmy/NOrsg5+e3f/m1+53d+Z+K1W265haeffhpw1N1v/dZv8alPfYooinjf+97H7/7u77Jz587i88ePH+fXfu3X+PrXv06j0eBXfuVX+PjHP16oyV0JzncxxuEmkTjKlfEcy3C5AjCTWxrtL/f88jju5Ofs2Oe4oHMy+u5FzikP6WQMjhyLoY+f8Nbqoq3bLITT8qTP4gNbJ+tRWChf8b7cJXrou9/lB9/73sRrU9MzfPCXfwlwnSe/+41v8vxzz6JTfUFdg6tqA8UKf8K1Kt4b/30ebE43FkGs7OEjcwxdVbmWrlLFtbzKE79GDoK7bi6RUCmnzihF7hiO7nvOdoByfXigYOVcCGlMD8dmvWzkWDgoo+TRabZtF7pzirFZpY8wiPweGxdfz51HS86MZZL2qTsXY0bVG+O2Bzj9k8xErBw5JSfPrnHy3PrE5ayGIW89fEt+Vjx/+ixn151Q1+xUk624mnYwzpDk1yZ/1jSuYfuuPYso5dFeXeeWm27mzJnTJElMFA2dc4dzBhvN5pZn5nzUqlVarRbdbrcIadx1110IAYuLi+zdu5ennnqKU6dPsHfvHj784V9iamqKnTt3smfPHqqVKs888xxzc3McOXwrGxvrPPLID/nu977LYDhw9xUzGj+yFfvWB11rzerqqtOSsHqM6XLOTW7PUgiGw6Gz64zNwLrJKi8SkJl6trNjlTVILTj8Ir+g2WzS7XZ56KGH+P3f//2J49m5cye//dv/HGstw+GAT3/603z/+98nyno9XUsbGA5SpmYcIxgPLVgPa6UrJXYNwhkOhzRwi9V0rKN2qjXKGBCKOIbVlXXnfPljRRHZCK8zh1cohVXSdR9XnptNbVblI90YgGYiZK6EotZosLC4gOf3eOzRp/jCn3yZs6unkCJkba1Dq7XAyZMneOLJJzh06AB7D9xAbxiRnj2HklVMukHgBVQqDWTYwg+k67ouBMoTNOo1vCBAZ/YjlURnlXUW19Npql7j3LlzdLo9hHLyCJvdPl5YIUpSooHTRLGuiBBtXNL9xCI8G6OEdInzxhpkoZUiil/jiys7Nm5eCldsAbfddhtf+cpXRhsYM6J/8A/+AV/4whf47Gc/y9TUFL/xG7/Bz/3cz/Gtb30LcA/Sz/zMz7Br1y6+/e1vc+bMGf7G3/gb+L7Pv/yX//JKDyVPrnf/L14ANwFLjDYMYrDGw/NkNlePsxhbJy2BkW5CUWNZtiZfMeasRXGRGduW+/7468VWhcxEgSSMVT9c5KzIW7mKzMCFFUiRuPwDPFwQI1cwzL/jJhqLAZmSS+AjjJtlMVgr3Wpf5CqFllhYpPGKlefluG4zs7P89M/+bPH3KMkYvvONb3D8xZd49wc+wGDQ5+tf/NLEd6+2DRjh6HgpTCbDrC7ooGyFwLEeBdOWhbnGZfoEBoxGWo0idUnHRiAzsTGJcI0HrUUaA2ni7rOfOTvWJR/DiIFzLJwmF73JxdmsFRMPff4g5+W7ZIL72dm6hG87SpJ1y/4sbCP0REhGWw3qfIlza+2o9YOQrmt3dt42U6E0IuskKyUyez/fRi0MOHLjXnwvwLWFkCjlIaXg6WPHWGv3uPv2WwmDgB8+/dw1tYPcWZxM2hNgDQqFTQw753dSbzQ5cfw4exZ38fjjT7gwW5K6iTwL+9Wq1UvurdFs4vshX/ny1+h02uzYsUC9XqdarbJnzx5+4p0/welTp1ncvZPnnn+WTrvLQw89zE/+1DvwPI877ryDW289wsmTJzl69CgWuP2OO5ndsYOjzz/P4489hgxcjD4IA3TkGjvaNGY8M9kYw8rKimMB01EScH5/vSx8d+7cOR588EHe+Ka7kIHCSEBrBv2+Yw/zkOf4gkpAmtH3QmtkplNRVLlZy8zMDH/rb/0tqtVa0cQQDGEY8KlP/QGPPfYYf/tX/588+9wz/MmffOGa2sBwqAm8AGEtSWyRqYeVrnUEuNFxGLnwK1IgrcIYSLHEGDwLvlXoNKHbSalWfbARiRFZVVvGXWfigkpIhJUo6ZEqH+ELrKaohFII0rz3WNaM1mJRSrB//z6i9lPccfhGXjz6HEpEdDsdZnfsZX11lXhtFeFZuoMeB/bvYufOncjAx5M1lztoneCkIcWFMl2Omck6krvqb+H0WUiwnoDUIpRkY7PDzFSPSrVGkiYIqeh2eyADNJZIa3qxJjGgMwbWZMnUI/Z/cpxKRYrAZOFxp7Nk8gVyZqdSistiA3NcsXPieR67du067/XNzU3+/b//9/yX//JfeNe73gXAf/yP/5HDhw/zne98h7e97W186Utf4sknn+QrX/kKO3fu5K677uJf/It/wT/+x/+Y3/7t3y4EW64EE1PPxKxk0dp1JBaAp7JJHyZWA+5XPqhdyGFh7LN5KGcUoilWxRc4ntHKWkzc1EudkcVNksUKdoylyY89bwU+cerjFE9mGAUzIMZYgWwBJqwAqcHI0ZGJi0/oxUekpNY4n/qOo4hnnniSn3rf+1jct5el06fPW4FebRvIQwdCjCpgLpcdG5/M5NhxFpxUxliQDShp6liJERums3s/Xko36u8zmeE+uYIYD6NkuzrfObEGkzmzeV8Om7EcYuyhz4+nEFezOcMzovi3qpIK4bqf2oxyLXRgdEqadTuWNmuciC3KiJVSY52eBY16FSUDpPSL9yxw+twqdx++hYXZOSyWNx25lT/77vd58MEHefe7333V7cDaCzNlTrvGJUO2WtPMzMxw9Nmn+frXv170eWlvtrOB3Q20QRgWzMvFMNWaYnPDVeA4RmQXg8GASqWKtZYojtizZw833LAPbVOiOObRRx/lnT/xY2xubvLtb3+bA/tv5K677uLgwYOcPn2aRx55hDhN6PZ6LC8vE4TVQlE29VIXgtLJlnHONd8rnrHC2RSuW7RUrK6u8rnPfY4/+7M/4yML8yzu3wvZpBNHcdHlejzROdtKYWN5uCdJkkKvJB+flFI0m02azVaWZGpot9t861vf5Fd/9e9w6+HbeP75o+ddw6ttA0ks8HyBRZPEPlp7aJw4mwv5SoZR7ML8KgClRgtMshwurZFSoVNDEjtWIDUW7RJ6yPOrXIfw0ZihpEQLNyHLnMG1AiVHbJebQVyZ/t69uzn62GPMNkI2uwO+8bU/Z7OzTnN6FqMVexcW2LtrJydOneTQLTdA4CE86SQjlGNKdVZh6ElvNI5kTGqOPGJgGeVMamM4eeY0jWpIrDU6TpxzE7h2L1FqiLRzcoSUrj/Z2LgFTIwZ7tfYYjx7P5+HbLaI0lpcUXj3iuUHnnvuORYXFzl48CC/+Iu/yPHjxwF46KGHSJKE+++/v/jsrbfeyv79+3nggQcAeOCBB7jjjjsmwjzve9/7aLfbPPHEExfdZxRFtNvtiZ8cxZh+XixMoFNnsHl4xHl1W1ZW+V/56/lkNbaDrQ5ITtGPvTLx22LHvuv2daHqigtB5Kt5QZErkr+eG3desktRAMjEair/vMiMI/e7bPaAFvkP4JiVgnm5lPPk9tbe2OA///t/z6c++ft87Yt/SqfdwVo4t3wWYwx79u1DWMHwAglwV9sGRuW1enQt2MpoTWIUyhl/TRYTUlaNCbi8HveTTchy5DRKJbN+TWShlayXTKrRqRuk0zQtpOrd39r9ZH87qhS2lnmaXI+i6ImTkiSJUxfN3stlrZM0Jk4iUu3eT03qtp/lrdjMWSIbXNyxpIWkdprEJHHkmo7FQ9eHx63HXFxLKYTnOVZEeSjlI5XHIE544LFn+fZjT/LkSy+6FZtSrGc9V3bumGN6qsUbDh3iHT/+dgC+l4UEr8lYYHEUfvZs5+NCLlXv+z4LOxcA14Qufya7vd5IN8ZYatXqJR3c+fk5Njc3eNe738WevXtYW1tneXmZWrXKiy+8yCMPP0Kj0cAYQyWskMQJb3rTm6hUXN+Z06dO8+CDD2bqqmfZuXMnb3nLW/jlX/pl7nnrW2k1W+xcWGDnjh3MTE9Tq9XcWLBlNC0N0AABAABJREFU5WmtYRgNityJfOFkjNPqabfb/OEf/iFf+MLn2dzcoNvturwli1NMzVon5PY/7uxO7sftdzAYsLGxUfR76nQ6fOITn+D/+r8+zn/6T59kY2ODwaDPo48+itaa247cDhbWN9bPu4ZX2wbimEyo0BDHoLXEYEltWlyfNElIkxRtnIhcntMlwKlAZ+0fjBEkiSWONFYLlpfO8Y1vfpuHH/6hU53NhAjz8UAp93woz4mhecpVPHmexPNc1ZqSLi9LKUmzWefmW27i9ImX2Dk7zVStQSAVG+eW8YhpNCv0e22iaMDM/BzS9/GCAOkLVCBQAcjA5bMoX6E8ifQknu9lDUgNZG1HivWtzEZwAZ1+j3Pra3QHQ6LUkFqnD5OkhjhxY0cuV58vlPJu3G4eIZt8bRESLObUYhEsHPNf2M/FIhYXxhU5J/feey+f/OQn+eIXv8jv/d7v8eKLL/KOd7yDTqfD0tISQRAwPdbCG1wMcmlpCYClpaUJQ8zfz9+7GD7+8Y8zNTVV/Ozbt2/ifSvIenzkcCGUNBYkkUtslNKJ0Yy+JM87/XwwE9Z5v9KSqVzaiYmP4m9Lof9RrIBdVY2j0MWYM2BfdsIcP/Q8Zi6lhxRedtuLN8jLXkUma2/zGZVciMwZhbsMrpQYnFcvrF/syK2KjdP1GJM+vjDc+S7s3MlP3H8/7/zJd7Fn7z7OLi3xPz/3OeI4YdB3Gfxh6ESY4gtU6lxtG4ji1A0mmZ5I3rF3/DpPXvf8no2YKDcoZ/fLOp0ThULhIYQHwsNTFZQXIrJBx8XkFUj3fs6qOTU3gTGCVLtYdqo1SWpIUp295gTRXA+nrEoE6xyRTCArd0pcP5xRk8A0TYmSIVEyZJgMSHSMNimpSYjTiETH2SQ1UkPVSYJJUkya9c5hxAIK8oHHVfd4ynVxDbwQXwV4XkBQqRLWGgS1BtVGi2qzxY75Od54+Gbe+sYjHD50gG5/wDce+gGp0QjfQ0nJj7397bz1LW/h1ptvZn5mFoDl5eVrYgci0wZyK9vRdc2LC6I4Bik4eNPByUUMrreMTnWRi1G9hHMihGDnrl2kOmHv3kUWF3fzve89xIkTJxFC8qUvfYUHv/cgSRLxjW98E5AMo4hDhw6ilGRmZob3vu997Ny5k06nw5e//GVOnjzJt771LXzPo16tcebUKV584XnWVlfpdjokUYRJJ5uvORgnblUEDUY2PRj0+cIX/if/83/+d4aDvnNos77pUqhRawkx2S9l/LkZbxIIzjHI2dDdu3fz1/7aX+MXfuEXuP/+93D27Dn+7//7/0uSaHq9AZ7nUa3WsVazeQGNk6ttA6lOwHhIbNYuwmQ5X5Da0TkMhgOMSbPEbwqHz2JIbdaZG4GxwnU6TwWPPPQw//Sj/y/+f7/7u+hEI7K2EJ7yMhbSw1c+vlL4mXPiycA9Q1JlPx6e9FDCjR+3HL6VAzcd5KXjL7J33yJvfetbufct93DH7UewNuXxpx7lpkMHmJ+bxfdDpPRQKkApL6uO8pA+SJ/MYRGFo4IwSGVBiSJ0a8SIAZHC9c9y6uKuvlNrSxwlRMMInZgimVpkSte5TeTy+AKKcw9UiDAe4ETebGZQRTdm8rzPy40gXGFY5wMf+EDx/zvvvJN7772XAwcO8JnPfIbqZcRpf1R89KMf5SMf+Ujxd7vdPs9BmYSjCpLEEMfSVa5sCYPkGM9cv9g1G4/T57/Hk3tGNP+INbFjn4cxXYrLuDH51woZ9q1vilHkZvLYRueSV+sUVyPzWHNPP3/D5ivrHBNxqS0HJmDfDTcggGPPv8DTTz3lyg6N5sXnnjsvia3X7V3yXC8XF7OB9bU1ds3PuvCUJesBkh3u2D0fhZfsxGsweZ/kGLsmoGCupJFZEp0edeYdu/82o4TzvMXx7Y6vSLfur3CcMtYExvVMRv1utjq2E3Y7dr7594u/88FIZiycgFwlzWKxwglKjarAQEgPqbImhn6I9D2Ep/CkC6AnSUKjWS/OaXp6mnfs2cuXvvYNgmaTg7t3871HHqXVnMmE6ATGXB2N2JcbCyYS7/LVf8aQOb0GOHToEL7nTXTH7fd7FJ2dLVQql3ZOZmdnEUIwPT3NYDBkcXGRhYUF2u0O1WqVhYUFjh49ype+9CV+/n//eYaDAQsL84DA93327NnDubMrRUim1+vxwx/+kDRNOXHiBJ1Oh/bmxgX2fv74UamE552/1povf+nL/NfPfo5+z6k0SykJgqC4b4P+gDyxPnfqxUXGyRxSyqLyce/evRw+fJg0Tdnc7HDPPffyf/wf/28ef/wxfN+NBdJaJDHdzuZFt3mluJgNaA3WSJQnSGLrGA6bhUIzexcSouGQtdU1rJW06k3IwzmA0Rqr8jBJnito8TyPZrNBrVbPSUiEhdDzMUmajccSKwW5UKUVCm0579n3lIdUkkYY8q73vIfP/88/5tEnHqfRahCGIdFyn5OnTrJjxw7ees/dVKo+Vp2/mHYLWCgY+3weEgKRdQNWnldUahbPvBFI4SM9l/SshRtrjNakqcnE1LIxM9uXtbkkgjjvR0qRNfzLxw8n3DcuGlmEgS7PLwFeYSnx9PQ0N998M0ePHuU973kPcRyzsbExwZ4sLy8XOSq7du0qaN3x9/P3LoYwDAnDCwsjjZ6j8UkIQBInECeOahbq4r118kHM5IuuYmVpz/uc0wXJ2JECk6vxcUMUefgls2ZrXz58krM7QjhFU/e3caqv5JML2Yo+P+98shQuTOOooizb3jlnbiLN6DfhkiqdvporJTY4Mmk8nDXyVEbHm5PmfuA0DfwgxOiE9uYGe/btw0kWDwnCkPgC7eSvtg10ul022m1mZ2ogRJYYttUhGZ/MGXMgdPFwWZvZgXDVh+7eucQ2m8V5hDUoLFq65DNrxtSI8zAZgrwJYrEatVmmexb3LZg460oLrbVjHX1t8dk80XX8Go47VOPJiQIX97ZjNi7zJNjUdVJF5FL9GVOTdemVQqGUzEJXCi8M8PzAORZCklpLnCT0oz5JkiARVCoVdsztYH5hJ62pKYIg4OHHnmKj3WZnpicyiCJqYej2m51DvjK+2nYwPgEUeUTSsYWOTbCYNGX37l1UqtUJ52QwGKCTFGncirJeq6E8j2SrftHYPZidmcFaaNSbRMPj/NiPvc2Jdq2vcuS2wxijWVvb4PCR22i2poiGQ8IwKFaeg4Gr8On3+9z1prs4fPhWPvzhD/HMc89x9uyyY7IuYyQXQrKwsHvSzo3lmcef5HN/8Ck210eMhcDlIBjchDHo9pzNMCp7d3oUasLOyL6X/388RC2l0z4Z9CPCsMrc3Dxnz57j5pvfQJqmDPsR0oszLZlJXG0bsFYSG/ClIkJijXQJ7jYLW2XjQhrHLJ9d4uzKKvfdcx9pnKDTjFFSFmVcYqcxWbhbat7+9rdz5LYjrsuu77kEZCEQyhtJM4hMmBCZjRnSZZSSMwj5/dRY4b4/v7CDd7333YTf+jZPPP44Z850QVp27t7N+9/3fnbv2YPwPOdwiPHxOLf1kc1b6xZmecqQzEP6Il98uJBVajVKuDw5K23xmWIcsqMltGORrNtGcQxigmEDgfS9bBHiLpknJCmW1NqR4rAd09u5DLyi5Uy32+X5559n9+7d3H333fi+z1e/+tXi/WeeeYbjx49z3333AXDffffx2GOPcfbs2eIzX/7yl2m1Whw5cuRHPo4smjXxigWiyJKkwokIbWUNtmLMSyziafnWJlasY4Y2FiqwYwaTf3B8sJwMK1wc+c0XMlvtZvkgzkDyuB6QhYtGzolw/HWRQ5JtBzlSPLU5MzBid8TYv/aC1ybf1vi1sSzu2cPPffCDvP8v/TTdTodavc6OnQtIKTl98gRg6fW6523tatuANoZzq2skicZm4kDj1QRbf/K8kDz3Ik2TTCEztyKXhGrQziHJqrdEJm3uS4svyf4v8IWr3sGkCJNC1r3TmhST5XOkSYROI3T22+gYnUTE8YA4HhJFA9IkItWxywMxibvndnLFtTWRdnz1koeapJKZiBqFGJubeFylkbUSIRV+EBBWK1RrNWr1OtV6g3qjSa3RQHk+qTZ0un1WV9Y5e3aVjc022lhm5+a55fBh3vKWt3DnG+9idm6OIAjwPJ+19XVarRYLCztQUvL9Rx5hdX2dzU6HpXPnALjnnnuuiR0Uq7Ytqzqsc1IEkCYxO3bMs2PH/MR3oygiiWMXxjWWeq3mGjdeBEop5ubmEEjOnFliY2OD2dkZPE/R6bSZnZ3hzjvvwPN93vCGm5memiZJY4LAJ0lcwuuzzzxHGFYYDAbsWVykUg1ptprUG3VOnT41Nra8PIRQLCzsnFB91WnKV7/0Zc6eWZ4YbvLcCIxFGMug1x+Ff+3IpsaTHXP7G1cDzt9fWFggjmMGAyfW1ev2WFtbpdFocODAAZRSPPH043S6G5kK7ySuvg1AnBh8z+V2IFSh+2Ktdvlg2pJEQ4zRnDx1ijNnlgvl0iRJszywvA+RK8/XJsXzPRYWFpidmyuui0Dge74LcUg3z0hPIVX+W+B5At+XeJ7C87JFgBLI0TDOvoP7eP9fej9/4//xN/jFX/4lPvThX+CXf/lXOHzb7YTVKl4Wyhn9yIytEI7d9Ebv5dpXwlonQqgNiU6zOeD8oErO+GBFMS7mrTDM+KwmRkxKPt7k9uAW3pkCb5ISxwnCjEx43JZcu5fLu59XxJz8w3/4D/nLf/kvc+DAAU6fPs3HPvYxlFJ8+MMfZmpqil/91V/lIx/5CLOzs7RaLX7zN3+T++67j7e97W0AvPe97+XIkSP88i//Mv/qX/0rlpaW+Kf/9J/y67/+6xdlRi6FLFox9vdoME8SQ5oqlK9w5MgoufSSyOZ8y/lS4cWKNvv/uItyoQ1dVq7JBb43IZYmxt0EMXHD3YqcIl1kPLk3d8jcSt0iMwrfDUhMnJcL/4yzUe6sxvHAN7/JgRtvoNls0uv1+f53v4sQgoM330wQhtxy5Ajf+cY3CYLggnLVV9sGrLV0O10Gg5igHmSrgAse+iiMVUzsduIBc+EXjZQ4MTFGxiWkRXkCa0YluXnDwcwQCjsx2EISPM9azx2LcRvKVxFutTXqMpy/n6+CyZiRfPUyXro9PhFro9FjTkuuBOt5LtYtlY8RIH3XNVnKkSOjtWaQxCRJQn8YEyVOM0NYSVCtUK3WmJ5pUq/VsErwpT//X9z8hpuYnZphMBzyzW8/gBCC2w/fSr1e581vuos//bOvU63XqFUr/NGf/CkAb33rW6+JHYwvAHI2zPWRIiuDdiJjzWaTG2+8kReef6H4rhtQ4+K7YdbQ7mIIgoC5uXnW1tayzq/uflSrVQaDPs1mk2qlyvETJ7npppuYmm5x7PhLxHHMqVOnsEbw2c9+lr/7d/823W6XSqXCxsZmUVywsrJy2efteYr5HTuAkc30+32efuZptj4Anu8TBoET8UtcGXG+hHE2c+GwTn5+uUMvpaTVavHNb36Tm266iZ07d9LtDvj0p/8AKSVvfvPdKKV4+31v59Of+wPuf9fbGV4g/+zqzweWKNJ4AYihW2xZKVC+j+eneNZDyAqer6iEFQTw4osvsjA/78qP0wTlefieV4S4CpsaC03k/caMNfjSd6xAxi5YGOUpWhAKrHCMnMtzzJ/9bBsYrBRMz88wPTObOQY4ph8yuQxThJfz78uxRNzRvc/F7xwT7MaevPeNycwhX4zmXpubCIx1Cfyu8Wj+qTFWvhhX3PyQX4vxcE8+cRhtMF7mndjJMeqaibCdPHmSD3/4w6yurrJjxw5+/Md/nO985zvsyB6Of/2v/zVSSn7+539+QoQth1KKz3/+8/zar/0a9913H/V6nV/5lV/hn//zf34lh3FJOOreOuZEQ7ViEdabYAzGvRohXPIrAqSdpPZHGieTKBySCQbFvWMLbz0vLx31tXB07csde2EZWSgqdxoMQviMhLQydVjhpPucsYnCw3UDjnWSKcJgjALcxJtbn5baXRczYg7cxi/O8vS6Xb72p19iOBhQqVbZtbjIX/ngB6nWXM7R297xDoQQfOVPvnjBhNhrYQNxnLCxtkEjCFB+iDHZJD72mcx/KK7x+PibOw6QD8Rj1TyFoyPIq6ecdIlBGOOqZ0SmzJnZSe7QWpHJXVvnuI5yQiZZNbcbmcV6nVOa6xbkInwy6yTqBgEPY1Icu+JExgpaPhsMyLpwK+kRSI/Q81FSgZKkxpKmmtikDFOnMOlEstzkk2h3PtVqFS9wg68Ugm6vD8L1n9lst/nCn3yJ4TCiVquyb3EPv/TXP+jyctKU97/3fqQQfPq//Td0qjl000E4fe3soBAcI7/Hsoh6Ov0ViU4TfKW46cab+Lr4WuEcxkk8agtvDcrzCPyLl7HWajUWFnbw7HNHabVa1Go1XnzxRW644QbW1lcwxrB89iyNep29e/cSxUOklAwGA1ZWVjh48CC7FxdoNGto46qIvvinf8q3v/Ut3vVTP8VGVqp7OWg2Wyxk4y+4EOPm2gYrZ89ClndmdNb6Iqs2s0AcRwyHPaqV0F0rM6lfkmPcMXHX1Tm79XqddrvN5z73Ofr9Pq1WixtvPMhHPvKPmJ6aotvr8rM/+78jBfyPP/pT0vT8ce/qjwWW3tDQDJ34GSiU51Or1gkrFk+GKBk65Wcl8JTk5Inj3HTDDeyc34FEoNN0IgG4CGfl1yTTgynYZuvyUXKnQo+P/3nYVyoXSzY2e7adPgo4x0WIXO4dAs8H6ZGk2nHmxmSVMOMMfjYWibHxQ+Ss/ij/MXcYKpUK/cwRzWYh8olFZPpYidFOxE+4sSi7gCMtsWIhpdCkxSLJ9SZy1YxYUEKClljtQszGphePVlwCV+ScfOpTn3rZ9yuVCp/4xCf4xCc+cdHPHDhwgD/+4z++kt2+LEbnPX4BBFjX9M8Yi+epYnWQf2dyospeL5iEzKEk/3vkyIwSGEcTTG6k4zdxtEJn4mG/FINy3go5d1TIDTH3aUVxsOcRBGLyXEcTrGXUZTgPY43RDOMs1EXs6d0feP/LHr/nefz4T/wE99x3H3/46U/T3tw87zNX2wYQsLaxwex0E+U59kQJp0OST/02z/sZ/xIjFmVypeRUU3MBtII5yxLrKJwbt3UnRT+K2cNIhVNnVQKTuSKM/T93UiTO4cxWIpkMtKfyEs/MroxBm8T1QcLpEJDlikghCKTC80KnWukpEAoFpImmNxgS65QoTYhjx5JESVbqbHSRRO37lQlqXxV6EJI4ThlGEffcezee5+F7kqn6NIH0M0dYkCQJSine95538773vBvf9+j1ejz59DPXzA5cKfCk0TriyRJIl7sVxzFYuPnmNxAEQbGat8bFxfP7H4YhtdrFE/zr9TrT09O8+c1vZm5ujscff5zvfe97HD58mOEw4oknnmB9bZ3du/dw+vRp6vVa0XfmjjvuoF6vc+jQTVSrVbROiaKIxx97nFOnThHFEb3e5SeSH7jhANPTM8VgZa3l7PLZwmG49dbDHD16lLXVc1lpq2teNxgM0TpFiIrLTRgLi42HcvLf+bVtNpsEQYDv+3zwgx8sJueZmbmiOixNU6rVKoFf5cMf/CB7Flv8l09/4fyD5+qPBYNhykzDOZZp6px33/MJfInnhSgRupCFBk96LK+c5flnnmPPwi5SNQqZbs0bzB0Wkbd7x43CJntfKZUtUvJFqHWMB3nuE1iZcdxWFeH0fAFVsBvZ/33fTfxKygvOH6Px/fwQ9ngeClmy6lbHc7yhp7VunBpv4OnUokes/IgBGe1bSolONb7yUJ7vztu4ebQYJ9kyj16Bn/K66q0zDqMhGkq0FhntRpY/cOHPuyzn7P+MXcPCi2FiAJh0XEZMQ5HImP1VJEBOfO7lMDL8yb462XsZezYaiPMDyweUXJtjlBA7qsRwJdLFvGrN2CGNBqiJXW49tPM8oQu/lqYjqvxaw1jLII5Zb3eo1Br4UqGtydpP5XdhjHmAiQk//zt3RIp+Nvn3iwfWZKtt11PGmLQI5Y1PjPm9tmPbzl93g4ec2CfkgxOOmRiTiU8zPQmtXTWSVAqlvCwRz/0tfNfTQwpQWUltoi3DaMgwTdBxiklThnHMMI1JtNNNcQ6Y565ORgE7ZsglyWpjUGTdjo1EIkkSS78/JEkSfN+n1Wy6ahevgvQc4+N5XjbA+llS6eXRuK8IlglxOLJJg2zyxEIURyRpwv79+5mbm+PUaUflGOP6xeQrTt/3aDQaWShoKzPqHPDhMOKuu+5CKcVg0Mf3PbrdDu32Jl//+td43/vez2DQ58knn+S2228jDCvFpK6NS3bPOfLlc2c5fMQlW3baHaLLbJYphODIbbeNFhzWTQzPPPMMfhBw8y23cMstt2CNYW31HL7vZ5U9Lh9MFKyILZ6F/BpqrQtRvXFavlqtZnIBIdY6rZDhcEiaJgghiaIIpZwidxiGKGNYWbl8JuiVIoqyxahwTU5FzoJal+CqPJeDlyYanWj6nS7HX3yJwVveggoDl2OiDcobjao5KyKNY0uRXjGpu4RSl9zqWBWDlji1ZqmRGlDOiRkPheSQY4tk3/cztj6ThBhjrfLF0HiDVzcOjRd6jDsn7rtJ1okZxhbGAjcm5ovd8bmtCPcUL0zcfxjlJblu4yna+K6HknU5bm5hmPdsyxZwWT5KHu6+HFz3zsmI1JgcQIyGOBJYK/CVW5XmsX+ZayBMbMe6MjDh1q8Sihbx1trJEMGYVw1jdTsThzC+ErncsxFAPhiAkgphJUJql/2dOxkTXtaYsVsAjSxE2CVKZF660AjjF2ELrESLFFei5AxUTron+YW9wMmJi76W05NpklyRIb4SOM8f1jY6zE7PIMMA5XmuYik/J8EEe7aVwcpXR/l7zjmRRRguzxGxWDAWjQElkXiue6tlIrHWGDtqIDjGzOQht8mBJ3eUbLadsaaAQiClzQZ8JxrlySBzQAWpTUmtINW4pNooIYljEmMZJin92Mmem0zl0WmUWfLwn7SuWZ/MlV+Vct2YlROwcis+iczKR5Vy2js2Y4SSOEEa4bokGEWSJoVOiOd5Rbfay38GfjQIIQsto7y7dK6x4EuFNS4PKEkSpufnOHDwRk6dOY3I7lu73XarTNesl2arhed7BGFIr9OdsJfNzU2++c1vcnhtnf3793PoDYe4+ZY3sLGxwY033ki73Wb//n189jP/lXe+8530un0qYa1wfqMkphcPGSYpYa3OuY11du9ZRCnJSy+9eNnPjfJ8br71FgbDPgrlJlGjeeqpJ3jz2+5lZnoag2V2drYoI1bZCr3TbaOkwmR6HdroYtIBlyQchuFE7k1+DXzfJwiC4ieOY6x1oaIkSfG9kEoYEHgBNulw+szyVbvPl0IUa5RvUMhMI0o47REZYLOQbK6MPOwP6XZ6nGOZYy+9xKHDt7oGIJk/ahFZ40w37kpcJY8SFk9I54Rkzq8UAnQKUmXFkiKryHGhnlF+hhgTfsvH+TG21brkdZVN/vl3ssYEYwnJ7nzdmJKN4dZkjgeQjXUKxwpOMC5Coo1bqCLy6lOXK5f315IUw8TkQjxfYCm3iNHWklrXZyhnWyS4XmFZZajF2X2SJugLhPcuhuveORnHOGtktCWOnOeWC+WMtE4mHZPJMIgY0VIwRkqMwgDFzmBi0B0ff4swD5Bnwl8qMXbC4GRudCOM5JLzJNcLbAOKc8wTA8eW8G6bYiJmNaLbLsgqbaVGxAVem/ystZbhcPiqOSc5BoMhS2dXWNgxT7WqwOpRSG8sLFccrRgJ5I1XwOR/j+K646uOrCEbMKaoAmNN2tzHsiTVsbBOYWd28ljcvp0eSP5dz3M24PkSITRSeYgsNJVaV3GQJDHDZEicKzwmKWkUo9OUYZJmuSPOXpSQTiXZc7kouTZBbt/5oDl+nPlEla+88tV1HubBGgYMsb4B3+AZv2AO84TbnAVKr7Et5Hkl+TkY65L8nKKuLWjz2GgqYZ0jt9/Odx54AKvdfev1R6EUISXNZoNUa1qVimuKtrFR3K+1tTX+83/+z9x6+Ah33HEHt912hAM37Gdqaoq77rqL22+/nSRJ2bdvH0eOHOHkyVMuLy+7znEU4ykX6qpWq6xvbnLj/v3sXVzke9/9zpYyzYtjZmam0FbxhHMu25sbrK+v87+9+6dYPnuOXqdDot39q9freEo5kbR2BylcErXrFaML2x21uFcTbGIe2qjVaoXWCbjcJKVUIdBmrMkcIcHmRodz51495kSnLglcKlOwyEqporOy8lRhk0HgY4ym2+2wvLzMoVtvGcm92/EwxEj63RiDEglSuvJebRzjkZdk+4FPavOFgEYKS2IM1rNEcTwR+nXOw+i5y8cPJZ3A41YxyXFsbTFgASNs0VnaWhBiVLSR709rnS1MvIxZh2RM5LH47Pi8ZkeVgginnSIxhbaJzkKiaRaGgmyhJixWGGxWeJCm6UQJ/6XwOnBOxq5iMWcKtLZEhXS9NxnO2ToJ5xP9ODWf/RQdSjIGxJqsJNPRMNmn8lDMiFEZGcRoUL/06jGn2QwCPwvrZPuwYuz4nCBPkQRrDUXbTZGzL+64cyfGFH2FsgfNWqzQUDRomjyXyatw+RC41X4cxVfU5OlqQFvL2dU1Nrs9mo0GM1N1GrUajWoNRbYkLkJjoxWHG3QzxciJsFzW1wZbsAi5ws3EoCEt6Jx9YcKO8nh0AQtO9yZPjAWRTSw5oyKkKKqFtElcb48kclUlSUKUGOLY5Y1EaUKS6kJLxaSpE11CQB7eG0uec5YjRw0ls4FxnMLPB6LxCUhkVG2SxC5kIoVTqBSGgY7AWCrZwJsQkSQRWidZmea1t4P8fqpcCDB7BpM0QRuNXwmwSUqUxGhruP3OO6jWavQ6XbDQaXfcOQpXGdFotDBas7a6RrVSQSlVOGlpmvLoo49y9Pnn+c4D3+bmm2/mrfe8ldtvv41Dh25menqaRr3BX/rLP8P8/DzLZ5epN2qkmcheJ6vQ6fV6NJtNjp04TqNax/fURFntpXDo0BuohBWGgwESJ472xOOPg5Io30d5aqJbc7VScZU6WcM/ISVplnjp2PxRflV+77fmO4w7evn18P0wy2fxMMbiez5JmuIFHhuba3S7Ay6+oLm6cN1zJVK6FbyXCY0JJQpGIz+O6ZkWlWqVYbdDt9clThKCSsUtQKzTNcqfEcdIuEaZVhs6nQ26cUSzOYNSHsNBjxMnjtNoNZjdMU+t0cB6BmUEgTW0ggBtDb1+nziOM1FA5zy5axgUeWZGZLL72fUVQmBNLsY4eZ+ylFvXC8sbCx+bTI1L66xdhcYTAq2NIzTUaMGcJpnysHHMBxlLLIxrpurKqV14SKDwvcCFb5WLMWjtAlEu1Os5KQZhEVq6PBs5aq2QpH+hnJMcWaAmYw3SBKIEEMZVKYixiXfLM+ImVPJil+JFO2aQ+cRmrfvJQ3OjLeCmsIIhGb03ihO+/MM5cijGFD2L743KoB37obAij+sZyBpSiTHnREr3QDrv2dF5IgtvWOvkjIUNs5DRFufkgizK5cLSHwxelUlpcq/OQekPh/SHQ9Y31qiFIdONJo1alUajge97eJ4aldkp5Tr9CteDJb+3jv7MBPNsptpqbRZLtVkSocxIEIP0cOWBVmZRtyyJLmMMLFnSJrLQOJBZw658FeSO32C0JY2GxHFMagyDJC5WHe7HDVyp1kXYqAhlOM+rWBUp5QZjNxBmrXLkKJ8pd0i2xsO3JtnlV9gNVM5LMyYFG+B5CqsNOkmco+MpEhMT+AFBWCFN05HTcI0gxiadXL7fWhfKiZOUesNVVCRRjNWGAwcOsHtxkaPPPIu1lna7nU0Mjt6emp5B4JrnXShB1VpDr9uh1+1w6tRJHnvsUW666SZuv+NO9u3bz5133slNh24ELHv2LtKaamWLIEun26HVbLKxscGBAwcYDoesrq4SDQasrqxe3vkKwZE7biPvap1a5yw8+OCDBGFIp9NxkvwW9u11CrpepnESRRFxljNUsIXYCXXkfCWd55bkNpA7sXmIrFKpOlFDBLVaDSkjpHK5J4Hns7R0hji6kOz+tYHTMgHPcyEVN/a7Z0/anMV251ir12i2WrQ3Njh69Hn23Xgjh287ghBOqE5Yx44KIYpuz0oIdBLzrW98gxNLp/m5n/sQQVCl1+vzw0d+yFNPPc5NN7+BAzfdyP4DB9izYzf1ep0gDKnUasy0puj2e3TaHff8GnAH6CrrnINsJ3JOIFsiWUlqU/rDQZa8Cp4XZCyPoVLxC7GzvDmojhP6na5LUscxJ1IpkLkQoyBJIjxfFsyJK8Sw6DjF6lELjTRO8Dwno7++vpb1aXJ9eFKd0u/3kFWyEJp1Xc4tLpQj8oV6etn38nXknDgFPJFNLEnick6EtEglJzRD3I2GiVXheazBaKUw/s74gD3uhOTxuPHvjm7IiFG55DmMU+wFk0Kx2nelZyMn64LBFTHyssYTNfMQRb5Kcv/xgGjimPNtX+DwLhvD4eAiG3n1EKcGnQ7p9YYoJQiCgGatwlSjTqVSIazXCMIArEXBmE3k13+UzS4sKLe4IM3uYyFsZK3zDZUrEbQmb+BnndMohOtxkd1RYUVhK7l2RKyz3jtpSpwmjhlJEgZJSqydOFSRUAvuGIVCClexIzNmT0iBkHnoZlLtE3Kng2IQyien8UnJ9/0xJkVODJL5d9y2vbFOrhClznkTics3STAYE4HlR+o4fiUYD9vlzI/JQqvDaIhSCp0xWMYYmo0Gb3zjGzn67LMIcM3sjM7uNzSbjYvuSyk1EbJM05SlpSXOnTvHM88+x549e3n44R9w5LbD3H777Ry86SbCSoVqtYoVjpXYs2cPzz//ImEY0u10+frXvsZtR46wtn55IZDp6WluueXWYpKy1tLpdDh69CgLe3ajtabb7bqk9CyHKQgC0jSl0+mQZo0Q82sGI4dUSlfZpJQqQjrFdR6zpTynKBqmIASep0iSLliBVG4iPHv23GWHqa4OJGlqCENZ5G2Mn6M1NutiLgj8gLm5OU4fP87m5iY/+MEPuOXwrcgxZ9yNyTaP7SIEnF0+w9e/8iWOLZ3mJ37yPdRaM1lYa8g3vvENvvPd79KameLQoTdw+NCt7N27l4Vdu9h/4AAzMzPUalXqc/NYKzDCc6xEkmIzh1Dgmu7lz1qapm58EYJACoJKWNwXiZc5lTCM+pntW1f3Zx17NOj16PcHBYvi+z4I4Zp+phajNX6gMMI9L0IKrDHYxBBHA6f9JNy45vspvW6fRx/9IRsbGyzuW8RajU5jOoM21hpCL0R5HtoaJ5KLzkK8oxDx5eB14JyMYm45ZY8VJJElSixS+jg+T0xOvGMTbT5gjz94BaxF2KzqBosVKVZqhLSOQxRmrCHwuIhWXgkz6qtyKd9kPMdECoXj39xKPSfkneT6SPXVxROMEwdDF8wIuN4OzvESWDQKP4tDZomOFkR+vAIQams2zviFePmDH/+WJetI/OpQuS8HnQVnEm0ZDob0BkNWNjr4nkelElCtVqlWK1QCn2qlQuD5boWZMVES57hIo7A2RViXDOecBFfOK5FgxtgGXOM/hWMwrHAOjXNYDGhbNPOLU1c5M0gMUZJkeiNpVhkksNLLKOVcC8GVOGf0zohdk7m43ihXZDJ3xDkOLi9Dudy5nFYWFE5MGIZFJn7ugDiGZ8QauiofKOSqrSCOY6pV39midY0NPeuUaI21xFdA5/6oKBYIWbjVZg7K+vo6N+w7gFA+ccZCVYKAu9/8Zj7/R39EFMWst9dJdIrv+SBgemaqYAjOs6msgqpWq03kVmmtWTl3lrXVVY4+9ww/+MH3ueuNd3HbHbdz06FD3PyGNzAzN8eOuXnnKMSaqWaLahDy7ONPcvfdd7O5sXlZ53rwDYeYmZkpwm3WWo69+AKbm232HroRaw2DQR+bGo49/zwAjUaDKIpYW1sbG6dGjEjONI2zaeMLqk6nQ61Wc/ZpDPV63VVspGnhHMosT8r3XeXLykYb8yqOAxbDMDKEFccUWqmzRYYbP/OSf2MMMrQsLu7ihWcb9Hsdjj77DJsb60zP7ciWbo6FJmuO55xdw/FjL3DyxAv0BzGnl06y5+ANBJWqq1qLh7T7Xdqb65w5cZKHHniQ2dlZZubmWNy3lz2Li7zh4E0c2L+fmdk56lPTVKtVfM85B0IoqkoSJYljUTGk6Yj9rFQqzM7OFonN6TAmjmJSndLueXS7XedwGMf6oQ1x4pqDmiR1ZfNuRZV1OXcOeRpDakdhpPx29bsdjNGEYZglyCo67R7HXjxOSkpjponvKaw1pFHKUAxdaM9WSE0mXFmI+/EXLeckw4T9W5JYECc26x6bi1eJ4sJvdUPy+5EL1BTJitnqK19S2WyQL5iHbH9bMc5CbB0ILnoKYyWmeV8dwaRin2U0CbntjffqySKQIkuEzQwi361rX50deX6+2UB+cVbkygcWYWF4Abnq7QCNm0QirelHEbLTw/M8AqXwfI9qWKHqu5LPwJcEvkeoPKwF6fTNyS+WcxJy5IlgLnRjhMBqQ2q1EzjSGp06KnyYJCRJyjAakqSudDElz5l3yEON+T7cfc3eE8UnslDdyB7yZpFii5PiVtgmWwV5I+2DQmlSjSh+S9Y0zZU1uxwrtzspJF6WSGuNcPS357RRLAKlrAsfCQ9fVvBUiOeBMdfWOckTcfPmiTabhKwxnD5zhjfefoc759TFxrW13Hz4VnbtWeTYCy+SJoljXjPWcXZu7jyGZGJ/1iV9X4gVMMaFgl568SXOnT3HDx99lENvOMQtt9zCLYcPc+jQIRYX97C4uEgYhtx7z70cuvEgcRzT75/fg2YrpJTcceedKE+hU11ovDz1xBOEYUi1VuPcyjmi4ZDA8zlzxnX4bbVaJEnCxsZGEXbcypzlv50kuppgidvtNvV6neFwmHUcrmKMoVKpFMxurVYj19nQacJmu/OqL0+GccrUjI9SI7FFl2eXMWuM2ldMTU9x44038r3vfhsRe5w6eYrp2R3uvqpc/iy7LsYQDSMee/SHRP0+Eo+jR4/y5vvejkSyfHYJa1x2n7UWnSZ0upt0e21OnDzOk08+lpXft1hc3M3CjgVa0zPMz8+zd98+5ufmaDQaVKs1/DAgrNeA0cIBIUhsxOqSy0sSUmBS7cJ0ccwwGtLv9xkMBnR7PZI4ZmZmBlTmOAqJzMI3aZ4AawzDKCIIQyfWJ0Y2YK1L4vUyIUhjLb1+n5deOoa1liAMiOIY36uO2EpjSLUrK7dWuVzHXPzUmr+gzkkxPwusgSQGnVqUki7On0uNXxRjjosQxWDHmGMxmsxz+njM4Zh4PQsbFVTzpPz9yxyB+y3GNE62hGdy5yMP+djRrp1hba24EBKsGTFDY4bntn2R2NCPiHxTg/5rH9a5FAxu5Z/GCRExDKAteqhsYvc9SeB5VIMQX7leGUplfWyky/zP74rFlf9p42LaWmuiOGaYuJ8oTbLeHRpPelmi5tj1GQvdje61JS9lLkJCIi9RHNnKuCMC5+eRgBOiyt2fPDSUV68JKTFG43l+5pxIXK8NgdGud5n7rMIY932l8sZo7jNY8DyZbVfiqQAp/IJdiZNrTO1b0DodlWhng6XFcvrUKeIoplatuYTdJEEbw/TcHO96z3v4z7//+8zMzhTPH8DszAxB4Eq2tbUkUcRWe75UNZq1lm63S6/fZ2npDA8//DB79+3nwIED3HXXXdxxx50uL2FxkRv27+fzn//8RZsNjqPebHDLLbdOVFf0ez2effZZ14TR81k5t4IUTiSr1+0ihKDRaDiWbtB3E152ncYZk63OSj7ppGla9MfJx7g4jvE8l1eU52XFcZx1JHaOeKd99TqTXy7iyBAGWVhWTLKIRQ4hbhXfqPrccecdPPyDB0mM5viJ4xy+/c4iGTRVBk8Ix1sKwWAw4MRLx9xryuP5548SxxGhp2i3O671QaJJjXMaTb6YtZo4SoijAb1uh+Xl04ALNYdhhUrF/czNzTHVauGHIbVGg0pQZXp6mnq9ju+759NYF/ZRShFmIZ4ojum023S6Hdqbm6yvrdFsNvjpv/xXaE5PufNXCmHdgj2vYoviiM3NTWbnZrOcu8nUhDRNsuc4YyK15cknn6TZaOYfKmzFZGKULqSaOkctzeYc4ebBv1A6JyNWIR/A3cA6jC06lQRVD4Q/ivWLUcmmHbuw1tpCwn68lNjYfLU86mGQ7zfHhZwO91JWVWPFee9f4EzIHSQhRrLlLl6g3f6Fq7ARTj8dYV3pGCI/e4sg62QsJMhcu8RijUR4WT6FVWgihHXCQpcfBbwcONo/irYnczKJEfuV38HUurJcjGaQgiBCiL4bjIRAKZk5KrLQN7DWojMb0NqQpAadpiRaZ3LW7j3f96jWasRDV4EDI4fURSTH84ky6bic9RpzOvO/rcBpuRQ5SqPPbqXmHWuWJ8EKVJYsnTc2tEbjKdc117goFVqnxHFKVVXxpUcul5+HdAqusXDMJTpxz1mcJmhlsMLHF5JEX1tBPmspwmb5AG5xDe42V85xdukMe/fuH3VH1YbAD3nfe9+PJxWH3nAo6+rsiq+mG03CsAK+ReCRJOsIE/9I7rY1hn6vT7/XZ21llaPPPMMTjz3GkSO3c+utt3Lo0CF27drF008/TRxfYmUpBPsP3Mjc3HwmS++cwVMnTrK8vMzBW97gWMBKDaylu9HOnAiPVqvlVtnR0OUVkTlxjMqHJxdf7v95dUkuDjdeYmztSBbfGJOxKnWEAGM1w8GrJMI3hmgIvg/SOil1MVbpIMnUWoFEJwglmNuxwN4D+zl98hRr6y68F3jKVTYZixUWI131Smejzcq5dVCur/vpE8fo9/qEMzXq9Sb1agW/7qGBQRLRH0QIAdpYp1+Usd15HuJwOCCKhrTb7vhOnjxenIdjr5xOENZtwy0CFH7g43muoi/VLlk+Tl1yqklTrDbs27eP97z3/e7ZsJbl02c4e+oMt9x2GC/0QQkXyqwEdPs96vX6+XOiHIX7fOX6DnU6HaSFxnR9jKl3BRzWGoSR2FRjpMFIF16j0HC5fFu47p0TO/JMJqIsceSytpXKar7l+fKwxWAvxrZBRgXa0Q6KGJwdPbTWct7F3nrhRxUPl1etU5wPY0xJccijJktjnMzkNscmr+L8ik+4apTRudnRNye+98oHEm3MZatcbnfkbFmc3xitgWwC2epz5vdujNXK4RgXSa/XLyaVHO4+OUOW2XZys96qPTLunOQrkq1Jq3kZ6DhVL4TAk6PQjcqdXyFGGf7Ze57nGKCiIiP0EZ4EoTOK2SClxVjHVFgyqjgZ9RnJQ0lJ4qoMXpa0vApYXV1HCEG97s4HsgrvKOLUsZP8f/75/8lP/ORP8gu//MtFOaMxhj179vDXPvjBLOQ1mpgrtQbN5hTrJ49nLKjJvbJXdJxaazqdDp1ul+PHT/Loo49y8803s2fPHh599NFLJo8KKXnzm99ciJ/lY8zTzzxNkjgBvFyht9ftcfbsWaw1hGGNVqtFr9cbtbDPwhvF6GRHSdKjEJ8tOg+P95zJq3m01kVfI2Ns0bAv/zu+DCboaiNOsuZ5YizXz1Io6YrMwY6z6rdut8uBAzeQRC4JuD8Y4Pm+S4w1BiMEnlVIC2fPnKHb7bgwampor61x7swSczM7uPnmm3n8wW/jSUklCGg06jDrVJy1MWgD/X6P1fV1LJOdnsevaw49PtacNy7nD9T5oeUcGxsbDKMhWdCT48df4st//Cd0oz5ve/t9pKl7fhuNBpubm0UuVX7f8xwX7KjpYbvdxhjDysoKftVzVXgCPKXwlXCd2XEVpzY7JoPCyWOI8+bIl8N17ZzY8f/lK7msyV0cCbSReF5O601+o0AR6hATU777z/nOx+hnPNdjcmIv8jhGm5n4fWFkOSLZX0rmt2bLCpvxdubj38+7Q6psshJZDvBoknShouy7wvWJyNa+bnIUV2cGSZMrE9u5PjDu6OU3dOsn8pdt8XelWsHzPKJoyHAYO1ZMjpRiTZYn4pwSm+V1jMTyxqsNJsJ142Gc7Fjy/JHi/cxzl2Ov55or+cAYRzFKSCph6AZMBNJYTJLiGyemprRG6tRVinmuZ1EhGIePzBwcrEVngl8uRERGacOlGl6+UvzH//D7VKoVGvU6jWaDVqvFVKsFwmmYbG52OHfuHP3hACklURw5KXmdZs0Kk6xzrCsb9XyfRrOJedlE3vFVzPmQ0sucW8NY1ryDtUTDAS+++AKnT52iXq9lYZOXH7xbrSluv+OOCYZjMBjwxBNPYHGVKC7hNyQa9FleXsJay65du5iZmeHYSy9htM7Y4VwPY6RfMmJERs5JPoHm+8orr6x1oZHBoI/ynH25kI5zmIUUTmX7VWZOksSSpIYwyJ9Wxy4oMbpjJpOBjeMh9WqTvfv3kSQJzbkZhsNBVq1li588rHHy5CniKMIXjhHTccLS6dPcevg27rzzTv77pzyG0ZC01y366YR+yPz8PNV6g7hVpzk15RgJbJFn1Ol0CgclzcZO5+DbLcsc95SOILa8N0Kv2+XE8RPs3rfPCe5ZzanTJ/j6177M4t5Fms0mnpIoKQhC3wnMZRVaFhDG4imVpf1bOu02p06eBqsZDgdIBEFW8eUqYskKLFwYy6TWNf8TKdo6sbbhcHjZ9/G6dk5+8LBhZi6gVhHU6oLAF1RDgU0E/aEhtRZfBeNppBPIS63yFgDjLoawWWyyyAs4X/PB/ZqcssYdGJdMaAsn59I5J/kq19Fpo+kwc1yEhTF6vnA97OjIhciTp/LkSOMky22Wp5DTuCJ13SOtwErjtl2s118Z0tRJqL++YC85xIpscPekm6yFdAyF6z2SbrmyWRNGbSi0ScSIzbiQo7g1l2ScRdG5ZoKLX2KzLrRKTIZ4lFITya+B57QRfOWNVm+pQQpXaZMPVmmSIHSCks4pNybfjiZJNJ7yXH6XdEqtQjhmReAWCVeyYvpREEUxURSzudGeeH2cPfr+gw/y/Eeep9lqMjs7y67du1lYWGDHjh3MzM4y1Zqi2ZyiWq1Rr1WYmW5ecF95lZPOdc65MNvhmJiXc2Dc96NocNlh0IMHDzI/P1/cKyEEp06d5Mzpk4BxXYZx+hbt9XW6nTZCwJEjRwjDkNXVFUyq0WmKlWqMIB6NUeN6F7lDK6UsEmrHm2QaY4niIWESZI6vLdhZY5366KuN1LhKzemaq3jUxiDQTo9FiEyPyDlmnV6b2dmdLOxepFKto42beF3Rg3ugjAWbgk01J08ex6QJVrnxWRrDSy88j/7Jd7G4a5GpmWk2186hrEsW7w0jBv0h7c0OQim8qtMJceEZz7VIyNoA5M+krFSL3mSC0X02FqyxVCoVJ4GQMfq5DkkcR6Q6HaUSGs1TTz3FW9/+NrcN6eaDU8eP8c3/9ee8573voVapAS65NV8/OH/SFj/WWjzfZ/nMEpvra8xMN2lvrJHEEfGgz6Cvido9uv0+cRQXOUpJ6qqB0jFm6ErKyq9r5+Szf9BDygGeFPi+xQsU1Yoi8GEYQbcDpxCst/s0m80s8aiGpwI8L3D5A0EAZnLAhpGjkU/Y41zIRFin+PT4e6NJ3sW+L42cMS5Wx1JO7C/PKCgqM0Y7LPwJIbKKnKysNF8xj8WKOH+gtD/CwuZlBlsEURRdc7ny7YZCBTZzSl3CqKXX67v7Qu5ziKwKZ8RkSDHSQBnH1iTX8e6iW8M24x1k/azZo0S6JLgxxmR8FRwEgWvhPpZYmSQJQkr8ICz0TgB0moLVkGmx+H6ANZYES1ipIWXWHt0KQuU5GlfkVUBXpm9wNTHuFA0HQ84MTnPm9ORnXHWcK3+t12rU6w1mZ2c5ffrURbeptcbzg0xob0t4tcDVfQakkrzpTW+aKHEWQvDUU08xHA4RUrJ39yJhWEEYWF1ZLaj6Rx99lDNnztBqNpDWCbEFvmPLxlPixvtLjbNsvu9Tq9UK4bZxh3cyLJhfI2dLr8V9N0YSRyMmyC0sxxwwRn2t8nCVTjU7d+1kZXX1vNW9G381Ub/D8pmT2XddjqJC8OLR54mGA+rVOjPT02ysnnW2LwWVaoVd8zsQTkGAcxtrGK0JgpAwDEl0yqDXYzAYjJ5xSxFSHX+GpHB5g3v37OHA/gMI4Vp2DIcD4jhmbW2FldVVx7hkUvNLS2echopwjLZzWgyP/eAR3vaWe5ja1yTOWxdkw43NwnU6jon6AwYDp5Py8MMPs7a6Sq/XY2Njnc32Gj/84UPESXJNeqld184JZBniBuIUGBg220k2TrinpL80BM4Vn8/j83m1he/7Tr0vE0ly7dJrWSlpgNaawK8UMsNGa+dNYjDCuPCIMRlDMkmx5auLnHV5eYhs8nIPlRQSF8131JjNOl5K4SSDixSZgjkRIAy2yKGReZPMIptByVxUzGClybQ7sg0V8viXOs6Xc0zc7yQeYs3rzzkZ3c/JFfn4ajP7ZJa1rh1bJbI+PNkAQxFqHIVgpPOGcblFJtMXcZOQsSaruhoJArrfmQOaMSQCVzEkcFVasmiCJ1DCKwT88jbsAvCldNL3iQtveGGIFZn8uZRFPoFj8lzOkueH2GxAC5QHOiWJB3gVJ5ke6xgpJL7vOu9KJbGvQe7B5cKVfWp0OmA4GLC6usrx48cu9S3SJGZcDv1aY3pmhluPHB4dgTVEgyFPPPpovpoijWNmp2fotNtFyXCr1eKll17i3Llz3HbbEcLAd01OpWv4mB9+Xm3jqpRGuSdSymJsBGe3SZJQrzeKCXXc9vPr0+v1iKJXP7xr0QxjJ50urCaPwI+z3k5F2LXZGAx6NBpNUqOduu2gX+TXKJxzLwUMBn2X25TtQ+OSbVeWztDd3KRZa9Gancc8/xw5ET0zNcNb7nk7cRy78K3nmJJGo4G1lvXNDQYDp/ia5xH1un26nTZRPCSNU86dPetE1KyrQD198jRrK6tungpdi4IkSej3+9mYIgspCalcaFEbTZxG7jUh6LQ3efgH3ycIPNY3N9jY3KS9vsn6xjqbm5u022163S7D/sDppKQpeXPSHNc6rfC6d07Ogz3vP5NvW4PWxsXGE7iYHMf45KOUn60Qsvi67yEkrpRUuERDN4ALjNVZV1kXbzSZguPLHZPbz2i/opCRzhmP/J88ryRjVfIJrZBcHzE2IjNCU0xouTRboXQyuXnE5UV1LvT+hE9mGPR7cEln7PrAeILxOMadzXGnxVXvGKJIF3lOwpVNudCNHHXHHt+WGz8zx0cKrMQ5pEJiU4PGVQFZa7Msfg+DQVqJtMbliggnOpdnAEpjIcmcxDQFmYeE3HEZYxzLwTjjh+u1VISDsnPM4p4uzKPJk7zzZmBGa0Q2SSVpmrEyI1t8/eUggbuGr56d33r4MHNZSMdddzh27BinTzmGxxrLE088QRzHPPPMM3R7XWZmZnj3u9/NsWPH6PV6jmFx8WOk8gnG7Ftrp5mROxvjOie+P5JGN8awsbFBo9F0PWGEKJJjc/YW6T7z2jBmhuHQJaBCLsbnnpV84Ex1kjU7lKxvrNOammI4HFJv1Olm8vDe2DNhjWFtbY1ut+deKy6bpdftsLJyjj2LB1jcu48ffP87SARpkuB7Pj/xU+9ienqGtdUV1tc3iKIhUgrqjQbdXpd+v4+Uknq94XK1ECgJcTwgjhJWV1YZDoZsbLR5/vljbGysoZRgZeUc/bhfJCVbS3af3ILEaIOOIx79wUOcW13h8Ucfccn4xuWx/Pmf/Rnf/MY3SFInUV9kKmyTsfu6dE5ejYs3nnBmjHMRLzcunFcAuQTUXJVwPIH2QvvLOl5mvVCMcQJewmoMGk9qQCNSSSpTpHCOT6LTLOFOFZR70Ycl9RDGEqNBu5JAq10PmFhG2KHGxoJUJUXjuvOP8cpWhhZDr9fjQmP21bxvr+YD9HL72ppLZKzFpplDWLzsyrWFgFRkeplFpC2jnLdsN3cGxvdf7CZKzktJyf8UQhQ+a/6l/A5e9CxevcX/Vb9v22UgfTUQDYf8yec/n5WlaiyGo888OxGGeP7551laWqLT6QBuslpfX6fRaNDpuD5AeQ6J5/lFeKYowTamcELGS4vz8nQYOZuVShWlJqcQm+cqoOj2Lu6cXOuxYDg0JIkT/RpGMZ7VmSy7BSSpNfhBQBJFrKyssHv3omuKh8vpiKLIidslToxOYTh16hSDaIjIQmXWgNEGMxjw/PPPc9PBW9m3b2/xPFsLURSxsLDALbccYdDvUalUSdOIJHHl2f1B33XzzVSpl5aWs3CiU+Ctho767vcH1GpNbrzhEEkak6Yxjz76KMdPvcTa+rrrc2NddVSSpEW6wXNPPclzTz+ZlTBPIu/V9VrhkjmY9jp8ul944QVuuumm1/owSlwhTpw4wd69e6/KtkobuD5xNW0ASju4XlGOBSUuZQPXJXMyOzsLwPHjx5mamnqNj+b6R7vdZt++fZw4cYJWq3XVt2+ta0q2uLh41bZZ2sDVx7W0g2thA1DawdVGORaU2C42cF06Jzm9ODU1dU0u3l9UtFqta3Y9r/agUdrAtcO1soNrMXGUdnBtUI4FJV5rG5CX/ESJEiVKlChRosSriNI5KVGiRIkSJUpsK1yXzkkYhnzsYx8r6u5LvDJcj9fzejzm7Y7r8Zpej8e8nXE9Xs/r8Zi3M7bL9bwuq3VKlChRokSJEq9fXJfMSYkSJUqUKFHi9YvSOSlRokSJEiVKbCuUzkmJEiVKlChRYluhdE5KlChRokSJEtsK16Vz8olPfIIbbriBSqXCvffey/e+973X+pC2HX77t3+76Hqb/9x6663F+8PhkF//9V9nbm6ORqPBz//8z7O8vDyxjePHj/MzP/Mz1Go1FhYW+Ef/6B+9Ro28zkdpA5eH17MdlDZweXg92wCUdnA5uC5twF5n+NSnPmWDILD/4T/8B/vEE0/Yv/N3/o6dnp62y8vLr/WhbSt87GMfs7fddps9c+ZM8XPu3Lni/b/39/6e3bdvn/3qV79qv//979u3ve1t9u1vf3vxfpqm9vbbb7f333+/ffjhh+0f//Ef2/n5efvRj370tTidCZQ2cPl4vdpBaQOXj9erDVhb2sHl4nq0gevOObnnnnvsr//6rxd/a63t4uKi/fjHP/4aHtX2w8c+9jH7xje+8YLvbWxsWN/37Wc/+9nitaeeesoC9oEHHrDWWvvHf/zHVkppl5aWis/83u/9nm21WjaKomt67JdCaQOXj9erHZQ2cPl4vdqAtaUdXC6uRxu4rsI6cRzz0EMPcf/99xevSSm5//77eeCBB17DI9ueeO6551hcXOTgwYP84i/+IsePHwfgoYceIkmSiet46623sn///uI6PvDAA9xxxx3s3Lmz+Mz73vc+2u02TzzxxKt7ImMobeDK8Xqzg9IGrhyvNxuA0g6uFNebDVxXzsnKygpa64kLBLBz506WlpZeo6Panrj33nv55Cc/yRe/+EV+7/d+jxdffJF3vOMddDodlpaWCIKA6enpie+MX8elpaULXuf8vdcKpQ1cGV6PdlDawJXh9WgDUNrBleB6tIHrsitxiUvjAx/4QPH/O++8k3vvvZcDBw7wmc98hmq1+hoeWYlXE6UdlChtoMT1aAPXFXMyPz+PUuq8LOLl5WV27dr1Gh3V9YHp6Wluvvlmjh49yq5du4jjmI2NjYnPjF/HXbt2XfA65++9Viht4JXh9WAHpQ28MrwebABKO3gluB5s4LpyToIg4O677+arX/1q8Zoxhq9+9avcd999r+GRbX90u12ef/55du/ezd13343v+xPX8ZlnnuH48ePFdbzvvvt47LHHOHv2bPGZL3/5y7RaLY4cOfKqH3+O0gZeGV4PdlDawCvD68EGoLSDV4LrwgauSZrtNcSnPvUpG4ah/eQnP2mffPJJ+3f/7t+109PTE1nEJaz9rd/6Lftnf/Zn9sUXX7Tf+ta37P3332/n5+ft2bNnrbWudGz//v32a1/7mv3+979v77vvPnvfffcV389Lx9773vfaRx55xH7xi1+0O3bs2Dblg6UNXB5er3ZQ2sDl4/VqA9aWdnC5uB5t4LpzTqy19t/+239r9+/fb4MgsPfcc4/9zne+81of0rbDhz70Ibt7924bBIHds2eP/dCHPmSPHj1avD8YDOzf//t/387MzNharWb/6l/9q/bMmTMT23jppZfsBz7wAVutVu38/Lz9rd/6LZskyat9KhdEaQOXh9ezHZQ2cHl4PduAtaUdXA6uRxsQ1lp7bTiZEiVKlChRokSJK8d1lXNSokSJEiVKlHj9o3ROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtkLpnJQoUaJEiRIlthVK56REiRIlSpQosa1QOiclSpQoUaJEiW2F0jkpUaJEiRIlSmwrlM5JiRIlSpQoUWJboXROSpQoUaJEiRLbCqVzUqJEiRIlSpTYViidkxIlSpQoUaLEtsJr5px84hOf4IYbbqBSqXDvvffyve9977U6lBKvIUo7KFHaQAko7aDEJF4T5+TTn/40H/nIR/jYxz7GD37wA974xjfyvve9j7Nnz74Wh1PiNUJpByVKGygBpR2UOB/CWmtf7Z3ee++9vPWtb+Xf/bt/B4Axhn379vGbv/mb/JN/8k8u+X1jDKdPn6bZbCKEuNaHW+IVwlpLp9NhcXERKUf+8Cuxg9IGri9cCxvIP1/awfWDciwocTEb2ArvVTwmAOI45qGHHuKjH/1o8ZqUkvvvv58HHnjggt+Joogoioq/T506xZEjR675sZa4ujhx4gR79+4FrtwOSht4feCV2ACUdvB6QTkWlBi3gQvhVXdOVlZW0Fqzc+fOidd37tzJ008/fcHvfPzjH+d3fud3Xo3DuwqQCBQShZQeAg/PqOIdKSQCiUGQYgAwWLR1/1eAJEEyZAc+t7GDQ2KW0MIxOjzIGU7QJWEr4ZWvGBTveOc7OXLkCNVqiFIKz5NIJZFSYq1FSvd/IQTGGKwBiSq2obVGG83JE0s8/sQPmZufRSDYsWM3Tz/9NINBxBvvfDPtlVVu3LmHfYf38tF/dulVbrPZLP5/pXZwMRs4cOAA8/M7uOnWm1lbO4f0JfM7d2IGCS+8+BxaW6qVKr7yEELgez6q0mClMyA2EmNSpDUo30OjGXTazPiCmZk659bW0XGCJxXDfo+p1hSyVmHY6XFw7z4azSYrSyvs3HeAdqePTSPmp1oIq7FGsLKyhicFoS/Z3Ngg0TF+xWeoEjwj6eDhYakmBm0tFotSHkIJKrUqejAkNppQKFILA2vB90mMIQgCEAJtJZ6GoJ/gJQlpaIgrAmE0FSGQkWHY76NSiZQ+2sSYNAVrAYGVHkIqjEqIJCi/grUghSTwfVIBVoIRgBSkqQZstuKRCOGhLPTjiO98+wGM1rztvrcxOz+PtRYBGGuJhkP+9E+++Ips4FJ28HKrsB8VtvjtzkUiMHGKh6QR1gikR8ULqBvJjuY0ofSpCI+K8hHWYlKN1hqEdPfLaKw2pElCgibG3Xu0wQLWaDQWg0FjMMKSGIO11v1kxwJgSNzYISzWghACm40lGoohwWLRuHHGAuKCZLl7TUiFJyvUalUWFuaxJmKA4bY3vZWTx17i9LFnkZ4Cz0NY9bLXPElTji8to012IMJijOHYsWPXZCxQSl0V5sRae8ntCCHc9R67lvlr4z/5OJsfm1IqG5M9wjAEHPPT7XaLaxCGIbfccgvNZpODBw9SqVT47ne/S7fbLT6fpinWWrTWpGlavD5+3IXNZMe49f8X+73150Lftdbdy0t9Nj+ujY0NOp1Occ201hM2cCG86s7Jj4KPfvSjfOQjHyn+brfb7Nu371XZt4RsQBif/iUhiqoUpEYzxGBRKBEiRQWBB0Jmw5kA6W6QFQIrAOGGOmFVsV2BgdxxsGBtylAqVjEsWMNeUWGvbTKUmqoNeclu0iPJjkiM/cDmZhs/CAmCCkpJlCdRavTA5A9I7pwYYxFGIoRzXtI0dcbTaiKlwvcDACqVkGZril5viTD0aU3PsLK6ytt234Pv+yRJwsvhlQwcF7MBKSVhpYJSIcarEKcxSWLAJHiexPclYSXAE87Up6anqTRadKLTGKtIE4s1ApTEEwJPKfAssuIRVHw0Fk8otOcTVCsQ+tCBNDbU6lPMzFtqDY9zq23CsAaVCn6g8P0qK/0hjakmM60G8YvHqErLzsUF1trn6J09R6XqM0wsnhTMz82BFLQ3N/CUxWJQYYiyljRJGcYRZA6U8BSpNUgh8cIQiSSVCi/x0DJi6KUIY1BCUhc+FSqIvkYgEVaAlJhUIwSk2UDneYYwtXiBIjYG4SmUFcQ6xXoSPIlRFq1TEm1RKkT5AbUwwMYJSRyTxDHGGIZRTBRHSClRUmFxx/lKbeBSdjCaKO3Y73x/AjdNW4Td+jkBIvuslPieR+AHiNSiYkNd+TRVwM5KkzfUFzk4t5sb5hcJwirPnzyGwWLaffbs20ev12UYDYl1zMAkbERduv0BVlsSCYnQpMIQCw9lEjyjM2dCA2CERFtDikEJgxaATbFi0jkxViNRWGERWIywCCExgLUaKywyO3cDCCy6OP/JayoEbuwRAik8pmbmmJ/fgbGaVGvm55rUpxokWiOVQggLUuAJ5RZbF7mnUko85ZEfNWOfuxZjQT6+wWiy3bqf8c/kzoOUEqUUQRAgpaTVaiGl5NZbb2X//v3UajWEEHzmM58hjmOazWYx3vV6PaIoKpyF8W3mjsn43/lrvu8ThmHhaCRJwnA4xPM8giCgVqvh+z6NRoNqtUqj0SickHyMdmO3IUmSYizPF6DXMryVb/9iGSFb7wHACy+8QK/Xu+DnLoZX3TmZn59HKcXy8vLE68vLy+zateuC3wnDsPAyXy0o3GrRQ9IUAfP4zIuQnaLKHttgTlQ5Ltb5ij3JEAtWYlHum0Lhhgw3kEgEAgG5hyklVgpMxpxky0vADSRS+hg9pGdSNqRmTWp2WKhZnxvsHAtyimmxxg/TU3RJsg2MVjDr6xukGqxRGAHSWKx0xpI7JRM/GIxNwUisHRldpRIgZP4wWxCWSiVEpynCGlQg2RgOGEQxrVaL1dXVy76+V2oHF7MBay3KUxgpGaQCjWD13CrEfXzlIzzlnEOrmWpNUW9UkZ5lplUj3uiT2Gzo1hbpKSphnShpE8UaYQ06jYlj4+YxJTFpikhThr0IIUOmZucZDjoMux06mx02NzdpTNXYt/cAUTpEqCYajZUGPwxBKZQwBDWPZBDjqSqzuxao1StYwK+ELC+dotGoMTW1k9SAMTFnzpymWq1SmW6RGovRho31DURq8cIqtirRXkI0iNGxQKWC2LjpSGuLshZpE5I0RqGwQiEQbnWuMju0BtIUD1BGoHs9iIcYTyAqIUmgGJiUQRTTrHss7FhAYTFxTLVa58itt7pJNfDo99t4QgEBqVVozndcr+ZYkK/WnK1mg6N76kafwdlyKC2hEoTVOvXWDg7edBOJjQnrTd789h/DF4rbb7mFL3/uCzz95w/STAxzXsicrLAvmOPg1F4OLh6kHwr6WjNcWWeNiLn53dS9Dr1Bh6GO8KO+e360JRpGWDQGMMK6RY/1kFZgSBHCFM6HAawQCCsR1p2FJTs3i/uEMIAFmy2ghMCiGG3B2awR+V8WMibrvGuHu2TOdTEgoTMcUK1VmZ3eyVt+7G5eWl5GhYFz9zIW7TzylknWQUqQSmJ16hZfF6m/uFpjQbPZxPO8CUdAKXXe31uZjSAIqFQqNJtNrLXceOONxHHM+9//ft785jdjjKHdbvPnf/7nrKys0Gg08H2f4XBYOCZa6+Lcx38udn3G2YVxFkZrXTgaucMD4Pv+NXc6Lhfjzh1wUSdl/POe513x8b/qzkkQBNx999189atf5Wd/9mcBxyp89atf5Td+4zde7cO5KDRQswHvlHu5mznmrASbEALKeiSez3f1EudsSpxxJCobKdxzKzDW0a1W2OK5FJaMPcENOtZmn7PZft0gYrKhIql42GqdjY5hOrVUjaVmAu4J9yAxPKBPkFo5GsAQdDqdCcO2NmPxmTSk4gESAmsN1pjss86I/MAvHmBrDVprPM/PPHiLkpZKo8YLz73I7OzsFTknV8sOnOErTBojMUgpMMkQm0SIbGBCSvwgpNKo0et1UFIy1agy0Bq92SMMqgyGCQJBtdHEJmASjRIyo001tVodzwriKKYilFu9WKj6IavLZ/CsT2tulmqrReB5uJAHKM8jjmO0sFRDn0inxNoyjDVpavErPkG1ytmzZ1GeYmZmBoMgrDUYRprBIKLRrBJFCZWw4q6dH+BVPTY3Nmm1WjSn5rE6dvdo+TRBHDO/MI3CkpiU9fV1DEOajSZaxyRxSqfdxgLS90l7EVqCCpVbHQtJlMbOphVYnWKGBk/UUBICL2Bx9z6UCtlcX0EaTbNeZ9+N+1jdWCc1MXqY4ktFqjVWVUh0fM1sALJVuufh+z6VSoV6vcbUVIuZmRlmZubo9/ucOnECubnEwdkEGQRUdx3GpLv5pb/9N3lh8yRLy6vc986f5Myp0/RSzcF73shGv8/yg49RSxI2paAWxkyrlCmVMn/4EHMzIes/eIb1do/Z/fuo75GcXVlieWWZeN3gR0Omwhp9bbCpQVuNsQYvY0IUAoNAMznoC+Guhcl4j/z5dvGbSfsXxXNuMcKNIXrMNbNZyPCiz1D2O8VSb1Tx6j7Whze9/V52zs6gzZCK5xwTDXjSA7wtrt8I4wyC53kQpy97766WHSwuLqKUOv/8son/5RieCzEuURQV383DMMaYie2+3GS7dZ9bHZPx9/PfeWhGa104PPDyzsn4Nl6D+paLXoPxa+p5V+5qvCZhnY985CP8yq/8Cm95y1u45557+Df/5t/Q6/X4m3/zb16zfRbevBB4XkCcxNmEnQduYBTAASEk0kpatkKdCiGWxGoMBgEMFSzZiBgw+dBhjcvcELKIAZ9n9DiK1Vi3L53RujYbgIQwpDbBovGkR31mGttsspSuM+hG7FAK3xi82HKjmuZhe5oeo8HHAoPBgOFgiGi1EMjsQXB5BCCdd2QF1i3R3P/BDYR2ZOieUnjKrbARAq2dkSVpgtZuMm/NTPHc8y8xM70AHB27lpfG1bIDX0m8eEgl6WE8wGiSVONJiZTOyUq0xgiJX6lh4wSTWvq9Hh6WuudjPRhaQ+B5VPwGaXsVYQQSRVjx8UMfrWNkFCOFhwpCpO+jHSGG54dUqjWmpqbxpMTYFGMsSkqSdEilVkP5HtEwYhglaKGwPqjQIzUJq2srSKmYnpkBIVHKZ3Vtg43NNo3mDZg05dTpk8jVVVpzs+zavQuLoFatM+x36fd6TM9MoY2hMdXAq4UMhwMqlRpicxMZ+jRmZ5x3LAzRSY1UHvVmg6XnX6Q6PUWr0eLMmSWCMKA1VUdrQ3ejjR4M8Y3ARjFKCRpT03hBhY3OOstnT+IbQzLVBOWRWEsy6FPxJdqznFk6g7aSMDx/0riaNvDTP/0B9u3bQ6PZpNFoMD3dpF6v4gc+vhfy/Qd/wPLSGRI8glAjhaZaC+gfb/PY17/P2emIXXv2sLS8jNYJT7/4EgMsh3/qXnS7TfuR5/BCn7PDLt7GEjMH9jLtKXa+8whWGZ5bWaZdN8we3M/USp3hsQriRYXUmnZfY/3AhWt06jhVKzJO1WCsQhuXZeKcDZcXIqx1Id6MpNDjJyxwyUAZo+ngmDIjTPZ79JbBgBCjALAdPalu/FJIT1Bp1GnNNpnfdyNTB/fx1LPP4aUxmIT5uTlOH1MYI/GsBNyiBjgv9yQf+zwlMw755VfMV8MOXs5ZuJQTkf8edxqGw+HoPDyPSqVyXj7F1n1eDotwoePJ/9ZaI6Usckny/QVBcNnnsx3xo+QDvSbOyYc+9CHOnTvHP/tn/4ylpSXuuusuvvjFL56XEPVKIIUgCALqtTrTMzMs7NjBzl0LzM7OgZV85rOfpdvrI8acE4uZ2IZFE1vtxgGRuxUCIyQDNB0dZd/IVjVoEhMjlYeQngvnjHmzQghSnWC0JtUpjh8ZQQiFI2E1Ast0rcXszBxpJSCdCVjrrKCpsI86FSsJjKSCR4/xVakgSRI225vYNMIYw67dO6k3agiZMSwWjLFIOZ6rMhpoRHbcUggC33fXwrrvVSpVrNEYkyKEIvQl3X4P7IUnn5fD1bID3w+xRuMbTZpCnCTOuRQhSlYxRiKxWGMzx1ExHBiiWCCMxWpNJfSJo9gR71IQpSmetbSaUwSeD8Ki4yEq1VTDOo25WfDcffcbdaJ+j+WVM6y3V5ifn6XRaDg2CkF/GFOrN4njPtYK0tSQWMEwjmkIgYkihr0BfiXAWIOxhjiOaE1NEVZCwKJ8xUxtCt9vEFSqCO2SKJWUdPpt+r0eszPTWGNQoc96r8Py0jI3HbgBozXrG2v0hgNqtRo7dy2QCkG9UkOICogaXnUa2WgSeWss7NlDsxVijcSvNDl3+iTapMTDiMT3mGo2SJOYtbWzJEmMHkZYmzI9u+ByeyzYRDM0grNnzhAPBuxYmLumNnDwphvZuXNHEctvNGqEYUAQBKSpRipDqrvEFnqxohEoPBESRH2C1S5Rv41a3AtI5ud3YBGcPHeWjdVV5vcvYle7rJ9cQ1YCzrXXePjxR+kBN9/0Y9z40/fywJOP0F30kD++l9l0kdbajbz0Jw9ivtlBRwNSEVNTYDzoJ26yz59+ZSzKSrA6C6+4KK+wo4CtRRQJrTkmxpYspyYPJudwCbCjvJL8e1kqnHtfCaSq0Jppsbh3N4ffeAedFE6cXWZj0CeME4a9dU6+dIo4ivDCAJmzMVtYnIn/C4nve/kLL3v/Xo054eUw7lTk/x8MBsX/pZRUq9XLZk6uNISR54rkzEmSJCilCuckD4u8HF4r5uRSyJ27K8VrlhD7G7/xG1ctjCOlpBIGeJ7PzOws+/bt46YbD7KwsEC9VsP3vOxhce5HvxdRrVbp9gYX2aKLvRogFVkmtLUuoRCJFB5tM6DrgjAT30pNgk36eKqKRDlqNpv0bRGwzeLKY98VuJtoshhMPahyw+IBGvUpYl/TaypW6oZBv0OdgF3GRxiLnw1H4zBGc/bsMt946lE67TZ//cMf5mDz4AR1OH7ttq4GJq5rpYLR2XlaSxgGCClJdYrvCVId44eKjY2Ny7lV5+Fq2IH0fBKTIqzGEyEizKqSlIe1BmHAao3QLvHQGkGnHWGsj/IswzSmUq8RGklKirEWGVZIIsesKGsxSYoeRFQ9n6AaMjM1Rb/bRgiQlRqJVARSYbQlTcFo4RKjrcegHzM7PUO7s0a92sRqGCQp/ThGGlCxwRcueRBgOBiwurrKgf03UwkrJOmQ1BimGtPMT+9EKkWcxJjYJdFNTU3RaDSRUpImKWmSUvFDGvU6VkBiNdL30NYSpQlWSJLUIFSA1hadaoRQ6FQiCbB4LK+sI4XH9PQOWFshjXtE2tBNhyxISRoPiAd9PKWwWVJkWAlpxBprIjSS9VgShCGYBN+7eFXH1bCBJEmRUhbOiVKKMAwJfJ9ef4XBoEO318UTHoPI0qwEhJU6HdHmzKOPs/Dmg6ycWaG+sItGo+Guaa3BRqsFu/ayOr2L7/7RV1jvDBDSki6dxmqBqga86cM/yQ133ELSjZHrCdqzmIpk9sadpI9NoXtDhr0hWIgxJMagrUFZjbHCZapZ55BIXL6bCweLwkGxOftQzHej514Ur7sxzn1n3GsAT0jHr25NhpUSKyQz83PceOhG5nbPsbK5jvDr2GHE9PQ0erNN0pPMNKfobZxzmUo2hSxvabwqJU/KBIsVLhxxCdKkwNWcE64EW5mQvKJxOBwW70spqdVq51WmXCj/4nIck63j7XjeSR7WGd9XEAQX/M5WbEcHZTzX8UqO7bqo1rkYdu+9iYofMj/V5Ma9TbqbbSpTu9m1bz/z83NUqgGgsdpmDoIFXAJUvd7g3MrWHIn8ZrtkshBFKCRYR6n6qKxQOKBt28QiRVLksmZI0UZiTESeaGIR+CqkEoQYYRDGEidDUhMXg4gvPZrVOrUwxPcFs9MtZprTCN9ilSD1Fd7OWWbCBseePUPTVpFSobTLnrd2bNViLesry3hK4QcBnvJQeVzVuOQ4o91qMo9l5mVh2JHRK6WoVqsMh8PsoTEEvoeUHlGikSpAWsPUVAOXY6Gw9uXjy1cbQgjwXUmwSVOEr1AyHzzcj2csGoGnBH5QIQ0UyWaMFiClIkpi/DjGkwKLcWXgQQhSkvTWGXS7SAN+JUD4Pn6rgRf6rJ46SbVaY35+kXqlzp7di7hqKEEaxxnjLtGJxqAZRjH1unMOK76k39UkcYqq1MH3aDSbYCxJnLCwsJPhoEt7vUez2WTYSzg9PEd7fcCOhQXCMCSOErrdDo1GHelZtLEksabf7rN7105qu/ZipCbVhvn5BeZnZ9HWOCc7lVRlhSjpI4gJPIXRKWkac+r4caKkgx9UmZnegVcJ0FJjejHoBKU8TJpiohgpXYJnkposx8ay2Y+JhYfxaize9AZkukFgY06evoaGUIQXXA5SJQwIQkVVKHRnFTkcgHaOdS+KCWvTTM/MsFZfZX3tHK1jM/itBqunT/Hm225FSEVrX4WV6SmOnzzN7lsP8U4/5Guf+SPWoiFIzcmXnsN0I2bmW8yJGv3vL5FEL5JWFRvpGunxs8jUMaGeECRIlM1qaCRIC9JahBUII5FItDAILDIL6YynkUoAAdrKbGWTB3qyhHUylqRgShgl0VrACjdWZCEfa50rND0zy4033cSRO49wfPkEm/0OjamQtNvm7JkzzNaq9Ps9hoMBofSwDBkd2dZJ2THQIjsYz78+2reNsxbGGDzPm9BSGXdOclwsRHOhbV/IYZlMHnbXSWt3T5MkmXCC/IzBfrl9XM6xvFb4C+ecLN7wBmyUUJEpgacIlcwypw29Xo8g9IqHMU8mEwI8z6PZbF1y+3VCpqlgM80BH/CF55LYrMW3FRRDzGQ0+DwIDJ4S1MIQTylMkpIg0NYn8AOXFT81TaNaJwwCpHSOgFAS6wmMBc8LOXzbHezbtZtvnf5vrGwMqMgKvvG27MthdXWNv/K//SzVao3p6VZh/G79JYqHMffQczZFbqEqgyCg1+s5zx6Xt+J5XpYU67YTVkK3chcS/Ro47UopvBRkajBxggwk1hikTRDGYJRLTu1urqGNQVRaDK3GSpdLozyfKDHU6iGQYDREwkP5hnoQEgxSZKUKtRDreXSNJj17mmHUwxhDrdIj8D20iVGehxGKYRJjMcRpTJxGaJ0Wg53QFt+T1LyAYRwhqyH79u+nUq3Q7/cJgoBms8m5syvEw5Rao+osWEO702FqZgbleyAFreY0UZSw2eky1ZrKQlg+WI9Op0294aPjhHa0ThpHNFrTVKstUmsRnsAkBivAC3zi1DA1NcXM/DS9/hrr5zYQ2qCsCy54YYCINCJbCVltGEYRnudRr9dRvjv3xFZIZIgRgkAIvNDHaPOy9/CVIqwEBXWslEKqAF8GBLbHbfunOf20JUwj8Jp0hwbCJl5YgUCw1F3m8OBGNp44STpdYWP1HLt27GQl6uNXaizs3cPpl05Q2buDv/S3P8wf/qc/IGkPmQ4k51ZOcOJ/PYxXrdB9cYVTiY+nArymRLY7DNpthv1+EZAZVXLkWSTGPevShWSEtGAMSuKkBozOa2+cC3KxwT2P3zAaA9yYl3/AJdla4cJDGjBSUW00WDx4EHzF9x99hOZck2qtSqvVAiQiHnJg726+9ZWXEKnLg7HF1kfVP/nELkSeB5OvmOWY67T9MJ5vkv/OF2pRFE1M+vV6fbSIgy3l6xfe9uU4C+PhoXwczmUZ8vE5d04ulNx7qYTf1xp5WOe6yDm5WkjjiFB5LnlIa1e2BqhMmyMPWcDIK3bhE5iZmc62MiJHR3DJqQ0R0LQexiZEpNnr0sXVhWTGn6WTrpLawdhjmiW2kmk8WDfoVH2PuekWtaDqHJQ4xfcUvu8jPYnnKZdDgEBKgVDSDfZKILTA90P27j1AvV7D2z/PifZZbhRVFF52Co4VyrG5sUGvN2Dvnv3kz48QIqN2HZNk9WilMLoGk0ZfqVRQShFFEUmSUG9I/MAniiMa2ZDpeR5z83NI5SboVxcCXyrsMMYmKdazYHxEapA6QXkW4wcMh0N0MgApabcjeiLEopBCEYQVhlGE0RrPN5hYEcYeVvcJpaTSbCL8Kt5sk34c00s1Ub+NFILhIOLU6ZNYm7DZXnNUvPJAG3SScHr5FJu9Dc4sWZI4otPtMDQJGCew1en3GOiE5lSLNEk4d+4c9UYDEKysrTI1NUNKQhD67Nu3H60N1UadwXCIH1ao1OosL59FSIslxfNhZq7JIOqwunaWWm0XRmsG/S6d7iYGjyBsoa1FKBe8NMqJ9KXDiLBSodqoEycdlHAZWSZNSaMYJSUyi337nk9YCYnaQ4SQVKo1pOcRxQZjfTyrsGmPloA0tSQ/Qk7SlaBSqeD7fsZcKQK/QqBCqnaFINnErC/RkNC2gkEiGVifWGusD52ky/LyaeZ27qH3/Bk2V9fYv2cRL1RstPtIoahPNVmPhyzsvYEP/M0P86ef/AyrnXVQMc8ffZK5hR2sJG0qGzVkOyGsBRiriQd9omzBlGqX/C6kdOSs1YBGCIsQepQvYrNsFCsK+ZUc7hNjC67xfy+Yd2rHXnaLqlRYjBBUZ6Z441vvYc+ePbzw/HPM7FrADz2MTgmDkI1zqwRYHvzOA5gkpbO6gUJjpEVaUUSSLqh7YUFIipC6sRc4tG2CccckT0b1fZ8oioqxUQhBo9GY+Py4U3G5LMrW/W79fL6/8ZLii1W7XGif2y2kkyN35HJm6HJwXTsnUvgoz0enPZJUozJ6XwlJEFSQ0sMYlz/isuBdkqfAMjMzla1eKCpUHASgCBHsllVXicAQTUKUOQChlSDghvm9KKZY6pylO+yTGoPnhbTqMzQqDWrVamFYjVqdih/gC6fEKi2obKUhMwXOYRIhPVC+hww8jLSkEoQ19Ps9vv3d7yKk4diZM7RMjym/mblM+RrGJewKnHEfO/YiN998CFBOBdYbG0SyESsXYcvDQhaLkKJYEeR6Alpr4jgGLPVaFWs1QuSyTk6wLQirJEn/VbjzI0gp8IygH8UYZUmlRkgPUkPFBqSZ5oxUeSIwJEg0oIwGJMIPwMAwjWkFPtoAyRCpNL4XYHwPr94k8gQaJ2AXeB7SGFKXaeSS2IxBW7DDGD8IMEC7t0mSRqytn0OmhlOnetRqlSLjsTfo8czRZ5mqN0njmF6vx5Sa5vTKKhvDlErTsjmIsMpDeh5CuTudJjGNRp1EJ6xtrDE/N4PRmiAM8cOQc6dXQbnzxFPsO3gTiIQwaIDVWJOASQlDn9r0NEHoE2+s4/kV+r0ep8+cZroxA9LlTAlPEoQeO8KmWwAoxfTsLMN4SLXWoN6YRokaOl1H+AqrE0KRUpOWbpS6/V1TO1AIIfGUolarEVYq+BJqVmOHEcnqGrONKus9C8qn0pxGeI6qx69wrLPEwsIuKi9ucOzPH2Fhbpbde/bQ6fRZ67apN2pYMc+TL7yAX5H8xM++nz/97P9Ab7SpmFWG5yK6qSVcWaISW4K+Y5aSLJHYaks/GtLXQ4yCxBhi6TRonIYJroLHutJim9mWEe6Ztjaf3QVWGAyZ6BfChawzMbaJqU6M55g4/iKRloiU6R3zvPUd93HT4TfyvW8/QGu6Sa3eZDgcMDu7A4kikD4vHDuKHkRsrq4TDTo0whAh/CwMhZNJKHYxlnMiBMIafOHK+XVqtqV3cqFckfHQSl49I4RwtsL5OSo/CmPxcjkn1trzmJOLMQ8XqvrZbg5K7vRdqYLzde2cCGMwiUUb0BrCQJHEKVJBkGl05MTI1ps4NzeXeXJmgvPIUSFgmgBhEycxTYqrvUhBGITyaDWm2dOaYae3l340ZBgnhJU6rVqNNI4RSDe5C5HFl3HJldq4suNMB8BKSGyKNRaqIaISkKJJTUqcgNCC9Y01Xjj2rDNeUgYoprwOvcRgC2d0cul09uxZ0jRFeJlsNgIpJmOXeZy4kCIWEiVt8UBWq9Xis3EcFyuIcTVCnWoqlZCZ6Wl6vdVXlcOVUiKVAiVBKWS2QBc45y82hkRrrJHERhMLQYKPNb4Lt/kSiBBCk2gDkYVejGeHKF+xaXyCeo1mtYZOhiAVtUronMw0xTMGaSSpTPGkk5mP4wFeoPCUAKOQ0hL6BiFjrEloVqtIDZU0IrYJ3U6HXrtDNQgRQnB2eYWBUGjlc3xpFaUk0iREzx9FCUE1cEJ41VqN5dPHSKMuySCkrVPwJN1BwkZnSK1WZZiAFSHVxhRB1SKsx6DtqmuieMDU3By1ZgtpBYNhl1YYEFZD5nfuZLoxTRSnREDYbBBWQrQVDKIYPwhpzUwjw4ylqFRJY0iTFC2HGB0RWifo5qER1zgVqdlssLi4mzAMabVaWCmpKUs4iNncXEd3N5lrGV7qD7C+pdZqENar1FstrPJpY3jmzAv82A13c/yBFzhz6Cb2Lu7lht07GRw7QbvbBmtptGqYOOFU3OWv//rf49v/40859fjTWB0jE8naxgbVnMFMNUIpkjgijlwrghRDqp3kvLHud5qFja0xTglWgJVOvyTLjwfGSl3HqmSKVT/Fh7ZcmTzUI0mkJVGCmfld3PcT76AT9Ti1fIrWTJOdc/MIIZiZmabVanHm9DKDOKXdHqCSlO5Gm3rguxJkm+1vbGJ3MaMRA5GPvaOFzrUN610tjCf15uJq+ThXr9eLyX9cKv5yHZQLhZDG9zvunIyXFAMX1G+5kIMzXuywnZAnTV8JrmvnROseaIMvfawReJ6PZz0C30cpgTV5xvNYVUr2sFaqLlyh9bhc0eghr3oBs6qG0AmxW8OQYFE2IRROGlx4iqASUgs9WlPzWKUcC2NSErrEcYLISnaVkEgxqsrRRoASLk1fCQI/ZHbnHF6twlp7k7VzS/Tb6ySRpVprMYwGGJN5IULQJeZ4uspQpmQa14wi044gXltbY3V1lVaziRRgrEcQjHrouFPNBhjjJJGVlGgpi4qH/IHMH1bf92g060TRECEFOtUuj0MIFhZ2cfLUC7ya3olLwoRYGFKZq2kKhFLEBqSnEEZjjJN9N9bHag9hJVJYfE+hvBTPSPqxIR70qBhAKLqxoFcJ8a0l0IaqVyG1CViBxOJLDxkKSCSxJ4sVridDtLBOxC2BarVGxU/B1wwjzUY6oIGHH3hUPA8tXbWMsZo0TYhTBbU6QnloG7uSU23QnR6+lCT0kVISxxFBJURa4/qTSInxJKsbXbQ29NfarLU7xMMIcfwMvp+A9LCJJY5jziwtQSVAeRXaq+v0un2slTRnp9ixcwGhFSdPnSPGo16fJsXQ2Whj0pTGVBPPD5gK5xDW5fh0OueIkk20jRFCZw6xIlUCLa9tWKfZbNJsNvBUgBDKJW6bASYdsHb2DDYZMBVCoA2y1qLarJFql9eVoomUZKm7xqnOOfZN7efZzz/IwTtuZn7/Agf37OLhJ5+gn8TMz86gBwneAcH3vv8IP/0Lf43/9Uf/g5MPPsqUDpGDPjrwsNI6RyO1WKvRpMRotHAsaWpS2umAb519FqzhLXM3USFAS6c74p7mTK8E9x1tbfaayd531TlZ4BqYrAJ02o/C5f54FWJp2bVnBzcfvo3EAErRbm8yMzfD7Pwchw/fSr/X48knnmRzY8PJBSjD0vETKGPxbF435EKSosjjm1ytFyyEzLVOFLHIlay3H7Y6CbljkKZpNua5BV0uY7+1nPhC23u5HJCXY0DGnRMYMSe50u3lbG98W9shDyWfPy7kYL0crmvnZK4ZsrEZY4xFp6C8EKklSihHXeOE0Yo00PymcWmaybOCaa+OH3VISfClR2JTtE1JhUYJQ2JcVYKQCpH5JQKQyqNSDUiShCRNqWQrYqEc3amkpBqEIEGbBCtStNVsdNY5+dwpTi+dptNpk0YDMJLpqR0Y6UJAjsp1JbJrUWeL8Y1i0AD9fp8zZ8640IeSNL0mWrueLO6BHNNhsa6CxxqDUqro15A/mPnDCpZms0G/33NEcca4pDplemoGKSTGXn5c8ZVCKYH0wPoKmwRIQHoBJo5dIqznrrfL4wnoxh7agocl9AUVXyGsy1dCpKgkQSqPnifpVCoM8KlaRTeK8WtVQr/qyijjCAbOqRGAULmTB0r5dJIhgRegpQcmpRpC6FXZWLd0bYJCUBeCasUjtALhB6RY+smQaCjQvo/RKQqL8gRS+qhU4xvwrCEIPLzQ5/9P3n8HWZbd953g55hrnk9fWd51VxXaAN0NRxAkARoBFEgOhzKjGC4lUoaSuKBiKU3MUFJQETIx4kobitDEzq40sbIrkqPRiIRIgAAEAgK6AcIQphttq7rL2/SZz193ztk/zr0vX2ZXtaG6uETwdGRX5nv33Xvfveee8z2/3/f3/QoJFoWRCisExpbVElKQmhxbCISF9a0u2BQnQQhNTUZ0k5yNCxe82WCmUFawvTVgVLxCe65Gkjh2ugUiCMi2RuAsNgdrDMPxiE6j6fuSBZslbG+veDM6YdGi0u0IfLmqur+hkyAIyLIMqz0JU0qNtCkiNWztdJHCUJeCjhKIZps4ilBCUqvFeC8aTaElL6xe4tTSCU72DJ/71Y/x3/z8TzHTbPDwAw/wjVcukOU5pih4+JG3Mdge8p+/+EX++J/+b/i8cVz/5nMUGIyKAe9345zxYV1rKQRex8b6yKktDJd764DlobmTBNIru5pJhASs2IUjXjPR/2ZxFLKYiDkKW66c99BUJQhJIUGrkPljh3jknY/wyksXOXT8CLVOm7m5OR577DGUs6yurXLp4iXGgxG97W0Crbh+5QoiSWjKCC0kVjisdN7H0LkJy22ad7JnRS989dR3Qtuf1ql8a6p0Tr1en+Ix7o0GvFEQcLfrtEuS3o3KAJPISQVOKt2TN3O8Pyztj1zk5LvOLvHS1VVWVxOKXHgvB+ERgsOU5YWvLv1yzlGr1SZlsvubQKCsRGYQElCQl+HMcgVkM18iOBphM0lSE5PB2Br/wMZKEDfrNBsdlPRKq0p7r5sgCMjylOs3r3Jn5Sbd3iaD4YA0SyiKHOm8OLRGItHYcYrVpUmgEJw88QAzM03OX3iG0Wi45ztOf4siL7h9+zZRFNJs1D25VXrX27uRqKard4wxk3K2CvUWha84iaJoKjTp/+ecJYoidBCQpX+A4ERrhBRE9Rq1uIE1FqHBygRpJCiBVhbrYJxLciu9toS0hNoR4BAFSCtwowGxBRlqXKQRgUIbfImtlCS5IQ40gQQdamRa0LCQO4szEhcojDOowhIJhUJ48T4pEdIRxSFhmFJkikiE2CwnEBBISeAgcxLnQvo6Jw4VsvD9OYgU0lnMOEHkhprSxHGIKvkxNlJlD7DgLEJ7QJAaiTTKu+MCRgrvFxWEOKmwJsOmQ2SRgWjjhMS4gsEgY5Ak5ARIPYOWEf6WWrQKcQhW1tapHYAoislTy87WGlk6IleOsFGDkUEWBc5JbJbi7ndeB/+4SyGIa1FpXaDJaLDdS33ZvLPM1kP0TMenosKQaGaWVrNJlhRk1rFuh3x95SV+6OjbGV9N+My//Dh/7K/8BDOdDo+cepAXzl9AKs3Fq1c5+cBRrFZ86stfITg8y/aLBVr6iBiZxWSZBw2A9COSv0vWq7WGMuQdCycQ1lIXAUb61E7VrHAUzqd59j7i/gVJKSNgfVrF/2emyLHK91sFR0+d4MHvehezix3Go4K5pQV0TXLy5EmMMVy+dJH19XVMbjBJxsLMLF/84lPkaUpcPuPgU1FWeM6dnPLhmkRrpibeqipQa30Psu4fnjYNFqYrGKfN9ur1+h5htKq9ETLsdBTjXpyQ/SkjIcSEe1KBk6Io7skreSM6KH8Q7V7XZhpcvZH2HQ1ODs0JWu3j/M6TL2CNFx9qtiLiMEDYMsS5B8TvPjhRGNBsNtje3tqzzyrBI9BgvOBa4EJyl5HhCEWAtX47OxxhRwE2khgU0nqAkpuc1BlazQ6Egu1en+3tTXa6W3T724zHI0bjEXmRYG2GcwaJKyXeBBpBICQBgtiFhC5k4AoW5pdJk4Tv/d4PcvToUTozM3zhC5/ed8PLlRTexG9l5Q4HlhZp1GuTaEitVkMI70LsJe1385VJmvrwsRUlUbaKsEiM8UQtrYNyeyhsqSNiQAcBcVQnS18N+O5XkyoAEaBURF4YrLWEThJGDV8zpXz6zBKSp46ZwCvIaumoR45AWH8/i8xXR0iNkwGhjKg5QViSlXMHo8ISZxlxJBBSluCgoJAWoxXOCV/NkheE0pJkY5yLiKMAawTk0IwDkjRjlKeEShBKjc4ytHXkzqGloiljCDSxUogIhLQEAm9iWBS0wtiLqgGZsaggwhYJgVLefVhLhFI0GzEmc+TGIrVGWkNqC6zUJM4hpaLRbKGTHFPUQEmc9NYJuZIIXScImwhVrtasj6w5Akap4tK1FRqRIrUFIknRcUzUUEgLzuVoLMJ5zk9xn2emvPCVTg8//DC1WgMnHeurQ772hW9z58oabWnQzjA/0yBYmiOuaYJIeyuCeo16IMjHGXmR8s218zxw6iQnssOcf7nHV5/6Gu//wfdzcHYOHnyQp196ic2dLbpAvRXxyGMPkxUpFy++yPXzlzjXrDHTmmO03SftDdBSYQJwhfEAovRmCkXAu+ZOYZznl1kKn6YRgkKAKS0mvGmoBzi+nk+V8vZVhU9JZK+iw9JDFyslLta8651PMDe/RGEMvUEXFSsOLS+TWR9ZLYwjGQ6I44it/jrHDx7lE7/1W/S3usQERM4gLcwsLpDYlO3xAGFtmcYsb0BVCVn++GhsSYpVf4hRSdmmIxbVv1U5cfV7FEWvUmr9/RJiq33uj8JUPJcqBTLtkbY/avOHKXpyrxTS9LV6syqx39HgxOZd5tuLLM1FbGwlFLkl8mrf5URThpbLNk1ECoKA2dkZbty4sfv+hDwmCADpLKETpEBKTk6OcdZzFXDkgy55z4daR7JPkRfkecJoPCBJxpNIQ5bnFMaDkMmzPDmemToyBIBCEDhBg4BIhCgVEXaafP+PfIQvPPkks7MzOAtnzzzM1772JUaj7quuTbXH4XDEgeUDLC0uorXGlmkbAFky6av0TYXMtQ589ZMOMNYgZIB1xq/SbcndsRJjy9WFKcCBUpJGo0Gvt/Wq87lfTUlFEEYsLBzAGuFN6ijIigJTFDiTY4uCYZqQZ6Ckph0GxLGmFkm0spgAdJahdMQ4UKRBRKRDTwnSmqLIcdbDRikdVgkKCSiLrFukMAQIrBFI5VCRJRICrb2VgbWWQNQR0jDTjMlcgrYQS402ljA1CKlwWmGFpE6EVQ5dGAKpsTYHZ8nTjJoOCZX2EUK8C3NSi3AuB+eIIuN1S7QvsR+NEjLjSeNFXiCyDGP8cl5ISb0WoclJhgorKUnFChcECBUjhKOwY7QMcGVkDeOQhGAVw76vkgqkolCAlJg8p640cVDqCgUBKru/RL0LF17m+z/4QdqtFoUTOGW5srrOjpzh+o7jbS1NYKAeK2qtmGa9jo4jVKBInSUfJrSiBkhIXMGnXniav/ADD7F0O2f8rVVecN/gsQ+/h+X5OU4fOUygFKPhiJs3bnPq9IOcOnKS3/vyEv2NDb52/QWOzR/lPY+8E5kb1m7cYrDV9Sk147xKsfPpkSrCoSTeSkF4GwkBSCfLyd8xTWzd83uJBhyQ4xBhgBOWDEFnYY4P/rEf4NzDb+N3v/gV5kJJ4VLqjYjNzTWsyAgbdU/ybjW5feMagZR87jOfYfXaTSIRoF3Fl5P0+j1Sl/skk6nUneRuSqISepxa2QscOvjDO828HqnUVygyWdQFQbAHMEynZN7o8V6Pi1KJwAkhJpGbSitkWhhu+jPV7/u5P/uP9VpRm9fbZnq7NxK52b99BU7eaPTkD2+veSPNJmC2aDcEG1sOYyzG+nRETcQ4WeZg9zGYfYWHZG5uriRQvrpJAUZYrC0oXEpChqXAkCNcSOQcsS24tX4HleXc6m8wzIYUJvOmb85OvDMc04TVquhXlPqKfsWjEARIQh+sJUDRIkKJEBmEGKVYnJsH4cgLX8Da7sxy6NBRLl7ssUuG3duGwwH9Xo/DBw/6aEm5ipHSy6VLJUtg4SXRw3FAo9lgY32bZnuGQEpkEGGTjNziqzeSBOd2Q5Ce/KYQ1pUaHX9wTQcBUiiE1GgpQYcYaVFaIhyoIkeYgrYRmEKQZwXWFUjpXZWtcDgJRZZj8oKB1GRaIXOIhEQZQyglFkmBwjpICyhCRaAsRlsia73suA5wIse4Agov/Z8XvrTaZIJU+xL3jhbkwxEUaanDpcm1IMEyxiCNRSkwSiJR5NZgcaTWIrUic45Q+O9npPAaLQ6ccUiryK3/O9ABtbhG6ARWKK8DVFhMXpCXK/WWVlgzxBWC1OVY4SNNvgJKAAUSH5HCQWFytDMEyqCtJZaOXCmSWCNcjsoMAkmoBZFzFMYglCR8Dfn6t6L96I/+KCeOHfecKucQRGxujbm5ucPt7YwTNUuEQgmYm/Fl/rV2C2UVnaV5Vno3GY7HxHGNugjpd8d84eWv88cefz+1a2tc/dTXWTi4yJFHT3DuxAmcEFy+doObt28RxXUeeNuDzB1cZtDrs5nv8PydS1x58haL7VkW23PItiAUMcU4xRaKNEkRqUVaH5n0OncCUGBF+R0ADMYWE+7JZBQR3jiQqnpGC5wW5NJSSMGB48f58I9/hIPLyzhrGSVDgi3D6midI4dPs7p+h1MPHEMEGmsFK9dvI9KcGzdvcvnli0TG87LkFE9v0B9gApDR7rQxPdFMa0n5z/gRKfh9+Kr8/6NNp7erv7MsQ0rpVZ3j2CszTwGW1+Oc3I0jcq+0zKT6cR/npQIZr6cSe7fjvlEgcK/zvNt21TlWqZo3Asymz/+NArnvjF5zr+ak11OIQAtfLmutwZkCLQLycsU5fS2qm6CUYHFxESFKl969OybFl/k5LAUpY3IKHAKDxFJ3MQvC8lKyyTBLyWyKcjmiLDneGw/ZjZfsOvS40kdDoktAohB+AEUQomiiwHlb9Uz4KMY4HZMXOagCISVLB5a4ePG5u10cwGIMbG5s4R70eiTVV5VSIrUozf9AKEW9UWOn2y0/XfjqILtrmCcQ1OI6JvfKls5V5XTCEyMFtFuz93z47kfTOsQJ7c/BU52RLkBYgZUFUgcEOkA5iXMGQViGyf3K1Tq8ZkOjDaqgQ42x9qA2LkAJgxQBRghSDMKGmJJ86EXMcpS13qVVagwRkgJZljXLXBAqTVFkKC0IAkWrUSNzATbJaNVbxDrCKEFTCXLtHV+NczjhQc+0fbpSCmkFSiqEkuRaELmMLM0xgwypFTKo+TSn1IzGY/LcYKyjMJbceEKnwaHjGiLUSCNpNQR1AUYoUlOQWuNLr/MMCoMznsip8VXbzhUolVOTgsgJjAxwRhBkhihQKOsrioosRwmHus8k6bm5BZzwmvASi7GW7s6IwfY2xsEoEzSVBaE9AVgKojAi0BELB+fo3dkgHTiS3FCrhShheO7C05w4fYSzy0s8PGjzwr9/iiDWHDh7lAePHaOwlkceeYjnv/0Cpx44yff/0B/j/3P5FscffJSd1g1uXb3KlZ0VLm+tEAjFTFhjttli+cBB5upN2vUmwlrGoxGD4YDtjU3ycYIpfERFI7BWkBcO6wy5MxgEBRIrVAlm/DiTu5wkz8idpbV0iKAWc2BxAYAsN+TGsNnrsdPtEYS30arOOPMu29k4I09GbKys8NIzzxE4r+yrEGX6CG+hYSyEHhDJu6yFplfJlb8OQhLo0Fca39ce8Ptr+6tkplMtzrmJM3EVbd/vcTMdNXmj497+6EO1j2kJ+2qbSmtlNBrdVcBs+rPT5z1NvJ0m2+4/h6q91nv7j7W6usrLL79MGIa8853vfE3QMX3sMAzf1NzwHQ1OpJNoFJEApbzAli+ZMQhpkc6WpBO59wYJfzNmZ2eRSnmBoKnmgJ5NWBcpTWCAo4/FIphFodC0XJ2DokFLrNEzfUJkaYAOvqalzAGXdLhqv96bZ5ck56MkPoqiy8iJRhCjaaA9SVJKCuXRe5alFEVBlqeoIGR2dr7MYd87bB7VajjhK3woAUWVzpnuLFWVjrWWNM0Y9AfUG83J9mHoXV5PnDjByuqtSYrID0Ye6LRarTetBPhf07TW5TWuyFb+ezlAWTWpYBDCgjBlyHk3eiScJ6xKHWFFiJSauhBIJGEgceRINE4LQuVQJkJSpcXAKYu0hY/OqcDfdesIhKCwmddTsY7CxUjnSdomDMiDlK1uF9eooxoBzkgkFmFStAopcgtSIbTPe1fhZiEE2xs7zM3NgRE4QqRQBKLAkVOkY2Zbi1gDzknqYWuSkrJ4oThnLTvDPtQCtJQU1uGSDEqnbC0lItBEaESthjCWLEm9nHdhsCbDCIVFoHRI7MAUOZlRxCpAGwNCeu6DtcSFQ5v7mx+3UmCFQ0q/oBinXp5wNBohdURvFDEzW0BQQ+nyWcATueeX5rnTqhEFimFvwDjp027NkyQJ//nzn2bx//LTHM/rvDtt8eX/76f4np//E8wuL3L62FFSk3Pn5m2+9uWv8Md//Md44l3v5ObLVzl54CDLc0vcvHaT1c1Nkjzj1rDHan+HK2srBCIkCBS1eo0giui0WxR1iVU+cuKcY3s4REhAGGxhMQKviyLBOEtWplSFFBQixyKIGjOIWpvDx06gA421cOXyFaQURFGNEzPH2drZ5uipEygHyXCEzSyrN256YFI4YjRKeJK4xWH9rfSGmNMcPvYSHe8V8le6FGKzf/jgyf40SPVv9X2mCya01sRx/JpRibulUu4GBKZTONU4XKvVCMMQpVQ5BqdcunSJ8+fPUxQFnU6HmZmZScR6PwdlOpI9zVOp2hspRX6tqMb0/a0cm6dLhO8FOqbnl9fabn/7jgYnwjooDNIVBIHyq2bpFVelwsOFgnLl71f3yIqsBY1Gg0AHmGI6j+cv3ICUZ+waQ1EjdylbFNQIOIRGoJAEzLiQQ6JNlyG5cCinyNEUZfqmtPgq4yK7MEUjSYRhh5wQyQIhgfPAJUASoIhEgHCaXEkKfA5SCkmR576MVEkEjlarjRDacw7u0pRUzM4tYCnLadlF+s565+Xq4agY4cYYTp48wSjxMKter5MkCVprtnd2fC40L3x1hFJUURohFa1WiyAIMeZejs9vbQu0BufLx501Xjem1PyWJSi0mBI24vPloqQeC1l6hTiEU1QUAIBAlnGuMmJkrb/+CB+xEM4hCh9Z6vZ2KEyCkxpEgM0K6pFmPB55xn2a+kknL6jpACu9W+/B5cNsb29Trze5c2uFWj2iP9ik05phuzsijGOWludZX19DSsVgMKDdbpH0/YA5HI6IwhrGGlozTdLEMBgOAUUyHuMchEFIqCO0CjFOoOMQaSHKM2IlaLYi8tk6aRZhM4OO6r6kOU0YjkdkaY50gkgp4nqj7MmGcTYiSxKvL2QtsbFEWnqeTl6gwohkPPIrZwlFen9tDZyQvjJKACh6vR7OWvLUr3w3h5aF2YCRAR2GSGnBevGz+YVFnIJaMwRRo7sxYqe3RbPTZDwa8+v/+T/ylz/0F2ivRbxDH+fL/+o/8b6/+CeoLXaQgWZ+YYH1O3f45u99lTPHT3Dh6StkIiWImzzxzneTjFOu37zBysodeoMeSZqRmDEic2yToXLNnV4XkRcYM8JZBcIihfVxVIfXbBKQY0stFMqIr1/uWBHRaM3RnFugUAEPnD2HQ/LyKxd4+fwFZmfaHDy8hNaSuc4s0gl6232y8ZDb127zwjefQRaWwPnKMa00VvhEkgoCIh2ihSFxmbe+qJ6UfZUr0xO3tQ4pFFoqr59k/2AWLG8menG3z05/fjAYTP7WWhOGIWmaMhgMiOOYJEl8CfuU586edPdU9AKYLOjuBlhUqW5srWV9fZ25uTk6nc5EX6VS6wYmpNmqmqfax8RD7S735PWuwetdt+r9TqfDww8/vKe8+W68m/2gpzr/PxJpHSEcEkcUSrTyGg/GVuXDpbSa8wQzZ2Ha3sM5SxSExHFEklTgZJd4lgnDBTbZtF7KPkHQQXBEOJbxpZ1xAafkIpko2HI7FAgyJMXk+A6EQDsPT3x5sEMhWcfSpaCBYpaA0H8jFJKQAOkCxmgINJly1Jt1itxg8pxQe/l0YxxhEJQy/XtLNX2yxZXiaj61paXw3JBJHpkysrTbYTwQSZmfn8e4BCECms0W6+vr/qHDMRyPiOII56pwojcuk0BcqxPHdZJkTEX7vZ9NB4GPNTlASpy0oBzSSarsd3U3JjL+ImC39sFSlFoh/jr4a+GF7RVKhOWDJxEWL9ft8FYIUmItjEZjBClh3KTXH4DJyYaGOG4ghQ/NZklKnqTkQUgQx2xvbzM3N+v5BULTarXY3tmk2Wqx3e1Ra7QoLOA0SsZopZBiTJ6mzHQ65MbQ6DRJ0pS8sAyGCXEcUW9IxqO0nAgsvcEQYZ2P16kIpwTOWCIV0N/ZQrlZhmmGEQKlAmrtFqHQNGoN5toz5IU3+svSjOFwQDock+cJjVDRrjeIM0OQ5pgi82mVvEBjsCODGuVoJ/G1bPc3ciLx98g6iRIBO1s7pMNtBoMuQkIXwY4JOXT8BGEUMh72qMdNgkjTbM6A0liTE8aK5myb8XafXm+bKAxZWbnFJ7/1Bf67d/8J5pKYhzLFM7/5Zd77p3+AZhghpeTQgTm2dzbJOwscOn6Yi1cuEqqYndww35nl4U6H5kyLdJyQjUcMB122ulvkIuD4idMcmD1MPszo9dbo9Qb0BiuMRzteW6kolzUOnJReZweLlgKlNFFUZ+7AMYZFRIqgM9dhfmmBF55/kdurq0gpaLZarK6uMddpEYqAzfVNhuOE3vYq57/9EsqCtn5C0AqENGgliaMGMtAUee4nQlXyXpxDlaka63a5KbA7wSuh/XNSctoo7i842Z+SeaPbgp/si6Igy7IJACmKgm63O+GZSClJ05TxeMxwOAQq+w/7qn1WOlr7lWSr7aYXhPtTKlX0WmvNgQMHGI/HJElCmqbU63Xvx1ZGLCrS7DRJdzp6creUzr3a/vOcfn36tZmZGWZnZ193f9V3rdofqWodIS0CRSA1WhiywkyUTq31bqsVghfCItxuKbFznkUexzHQvesUWuBYESmhCjl6+DiH5pYwN0bkW56FFqM5LeZRwnDR5XTJKGASOQGfr1VUuiXC5+wR5CgWRUAgJDWrqDCxKtemBYIcgQsiikAQdZoMxiMKU/goShnSi6IIJSUVNNlLR/Pff3V1lZMnT3o/GDnV2ZwHT9V2AI1GnX5/gHNej6XIHe12e6JQaKw3nmvNdEA4hHA4VwCqZOULmq0mOzubb/n9vlsLghDKkL6rKrT2XAdK0rOael1Q+QIBCCmwcvo6eCEvEAhZbu+qB/fV5yCFRKuIMKgRhQotDcmwS71eJ88zarUaTRWwkW9Q4JCl02xeFJ6YrDVhLFHKkecFWpWsfBmQZ4aisDRrTUTTYWxOYU0p1y+J6iGoYg+ZrnAWFQQI6QjiiP5Ol1oUeNfjPKU3GkDYoF5vsbaxg5CebNloz5cCfT7CJ1Fo6dNeQRBQr9UQc440HZPmI7I8QZgUF0jCVoRygmA8wuWGUd8QhSHklcbK/dU5Uc7fWysCBBmj7Zdp2jXceIQRMZkIWRk6jtVnGY4SalZh0pwogHrNl9nHKsRRUG/GBAh2Nrcp0oxAwosvPMuXFx/kvacf4sj8UfLtbZ7/zLc495HH6bRqjLtjZuo1Nm7f4dEzx7ly+ybWRQzShLBw1GONqNcRxnLkwBIznQZpnrO2M2Bp4QDf9fh7ePbpC1y5EqDCHWRkMc4xN9shyRKMLbUudEXeULTaLVqtDkIG3NoY0U8cYS3kgQePcfHSBTZX11BhQNCsk2OpRTGBkGxtbNLfGbC1tc3Viy+hjV8UKQGB0kgdoLVCByGZKej1e2QmxzlDWAsnz4nDTaQIJq0aV6aac25igno/uWgVL2v/sd/I31XUoSgKoiiaGJ1ubm5OSoi11pw7d47BYMDa2hqj0WgCBKYjJbALWqr34N5ck+nXKrASRREPPfQQSZKwtbU1OVZRFJP0ehiGkzRTkiQTkFIBo/3X+/WqhO7W7nW/piuaXu+eVvuvKpD+SKR1VEnWtMa7e1pTgCvA5V6PYTLhVKbjuzfMIVGqJDjdxffBTQh8grje5Ed/7Cc4NL9E7eItbv7HJ0lSQ01qOirkKJYdu44VBYVTZF4nElnyUHS5LvekV4cUcISQgy7GOUpZ6Ipf5igQJEAiQcYBuU04//KLXkbfGZSWSBUBOXNzc9QbDbLs1WkUhwBnuXblMu984gmi0Kc/qo5rnSuJOrvM8yAIcM6SpglRFJEXGZ25DidOnWQ4GLLT72GBMAzJkiFRpLDOej4FDh0EtDtzcOM6fxAUOKW0Hw/LKhMry5SateD8FOsJu+UgVPJPnMjxSZ4ArEaIoDQsczhrEE4iRGVYJ9CijhAS42wZOWGyb6UiBqMezU6ETnKkgLgRowNFnvtwrbOWMAwJgoDBYECr3abX77N84CBCac87mJ1hOBwyO7fAeJwwHI2QwtBp1/2qPgqI4xZJlhPX6iBKhWEzotOeIc8Nw9GYMIrQupwMDERRC6kVvX6fdmeGIvaKx94w0lJv1hmnOUIFCKdQpeOsLeNNON9nK5O3WlgnrMWeYN3OKdKELBuSD/soKRG6RmuxTZplpMPEV7Bxf8GJtdZzdkSAtF3e8YBkJmvw2Y8XjIIII0M2BylWRjRqDVqtBmFUQ2vFzOwsg/EYJcc0azG5cQSxpjPTYtQfIaRA2YLPf/HTHDi2gG4d5uTR4/SuXObKly9wYG6e6zsbtOohg7GjSPq8/4lH+eJXnwFCBplhlOfErTnGacGd7S6ZluhQM3/wIKePneD93/0ExlpOnztNq1Xn+vXrfOl3v8RDD5/hwsvPkuY9T5mX0vOMVJ3cwkqvR6+/TWojdK1JvR0y1wzZuXWdhq6xYwxnHjrH1uoKsQzobfdIBmNu37zBzWvXkcYQCu1LhsuyfKVDrHOM0owkT8mMobDWp0adXxA64fV/hJj2Qt9te6IY8AeiEpvn+asiEft5JNPvTQOKykOs2iZJEvI8Z3V1dRKdaLfb/JN/8k+4ffs2ly5d4urVq5w/f56VlRVvB3HnziSCkec5o9GINE1RyrvPVzyS6TSR1pogCIiiiHq9TqPRoNVqcebMGX74h3+Yb3/72/T7fQaDwWRRWollVuXFVQRlOBxOJv8KaE2DgTeS6qnavcit+3+/W7RlGnxNtz9ShFi/Yte+MkJaTGEnfhkIJqTPajUMu/LEQiqU1sS1+HWOIsiyjDTPfQlnI2asBCNhaJCD0NSCGvO2w9haRlgMBsrqG11W4/jKHMoH3K/yqsdVTsizCoMiEYo+0NcGJTO6SZ9bvVXubNwkjmpUFTIAtVqNubk5trfXy7MVk/Ou2vbWFsl4TL0eIoSeXIPCmEnYsUK1FUDpdndYWloCLKPxkDAM2Sl2yLKMne3tvSFLV60UDEEY0um8sZDfW9E8F8dbwfsYs7+Su0EhSalkVU60nm+C1aANTvmoiMgdUlikFFgnkBaUlJMrKnRJYhal4ypV6kwwP79Eu9Miin2IX0mLkAaF93ny19sxMzu7Z/AwZh4pI6wVBGGTdhDT6swhREC9aTBrqyAVnZk5gBI4Ohp4rwTvIA3t1hzCMydptdo+XVWSkou8AOew1rCyuoJA0WrOIIWiv9On3mjgKNNhUpT+T+XFEtLzDlzpmivxnkRWgJKTktYoDIhMjG02cElGNkxI04xCKdRMHSUsSfc+a9+48tnCIfIh8egy5vaLtLUiEakndAczNFrz1Ood4kYdtCYzGTqIaTRbXHvhBU4fOoRqNTASwlqEs4JhMsK5DMmI//TxX+cv/cxfJSiGvP3xR/ndl7/KtUvXeemV5zj14HHmDx5ls7vGwaMn+BMf+QCf/i9fpT/KkEph85zmzAFGO1usbPUh0My2JVIo/uN//HUuXrnON555mgceOMupk2d453vfz3A4JKwvgq1TFI7ReEx/mJBkXYqqSqPeQhoFyvG2s8fIBhtEylPzG2FMIw7pCUFvu8vOxia3b93ixtVraOMISnq3koIgKKUFBJPJ2eAXMa56Etyu2Jtz7Cl0dG6vK/JkkmI3pP9G+Qa/n9bv9/cQQ6fTG6/1I6VkdnaWMAwnlXFZlpHnOd1ulyRJ2NzcZGFhgTiOOX36NKdPn56AmCzLKIpiQp6tPjsej/dU3ozH48nCMI5j7549pbYdhuEEJCVJwng85sUXX2R+fp7bt29PwElVlltxPipy7PQ4Xo3lFSdl/3XfT459M/dlf3qqeu1uJNzpz1TpqD8anBPpkAoCrYhjxdj5EjuhA88RmMrLAaXImJ+QpfAKmZVvwmu1oihIxgnOwfrmNklWkDnF2KY452Wy5/U8o7ygcH3GVOZdHhBpFBpZvlJWkvgpEgBLQVES21I0G87SiwRyJkY2Q5rNBYJsSJ4OWDi0QK1e86Q058OqDz30EFevXCyN1vaiUoFgnIxZXVtlZraFsYaAYNKZpklbVSfvdDpsb2/7MGJeENVj2u02KysrgH9wVAnArPVqLs5VESzodNolsfb+S5YHOvDzqCsJyFZgHSCrq10+ONb4Ki0cQvhEmxMKIavVUglfBQgpvd6HkygpKEsmylWiwJQbe5qLQgmNDmoIKYjjGO9c7Q0ZZeBN8Xb5L7v3R0ovP48LfChGaiQG5wQoRWd+0cc2ykHHlZVHGv+7t2qw5bmJKQ6Q7xdCOQLlSsNIx+zsgk8DRgHWwMzMIkIUFM4QZRmBDkug6XBKUCn1+IImgbMCqSXCeI0Vj5EtwhkQGhW2IHAEdWgYSJOEYTpklAwYy/s71PhJtSRmuxSRdhmur7PUlKz3EiwFUbONlYbMpFhRAylQYQjOcPjIYa4+8zzXrt+ivdBhdmEeGdQ8iAkUg50+uRnieoL//d//Cn/pL/5VtB3y+GOP8dy/+wrPf/WbXL/yCh/84x+mvbjMzeuXOXj4KH/hp36Cj3/6S9xYuU2RGZLU0WjNYdIRQ2cYZZZbt+/wyvNPc+7hh+gNhuhIs9W7zVLtGGub66xs9hkWOaYAhMKKJjb0xPoiz7E2p9WUnDp+iBMHZ1m/sY1WIVJrutvbXHxpTKfZpkhTbly9xtrtO8SmIt9LIqWRwW7VRZXSMNb6PlDysARVJngqdVwClwkZ8x4Tz4RvcB/TOt1u964T593a9HvWWrrdLq1WC1Mu2PI8J8synHNsbW3tIbxO76NKd09HMSoV2cq9vdpWSkmr1SIMw4kg5uRalymp6eqXfr+PlJIHH3yQq1evkqbpJPowzTlxblePZfq7VSnjad7LG7ker/Va1apz3Z822h+d2v/+mwFB39HgRCvrSwdNQiDqSBUgVQ0dRt5yHFBTCFJKNXm4rDU4B7Va43WPU5EesZK1GxtQWDIsY5cSZhIRRbRVjWU3yzhP6JFhhaBwOQkWQ4BDlwob3hNDCs1cq0PhCtYGmyQ4EicYChjWAjonDhHNtn2YMBnx2KOPsTC/xKEjR7zDaJ6SpTk60Jw48QAHDx3m5q0b7PUT8moqprB865vf4OjRw4Tai5Y5QIhiT4eqHuh6vc5oNCLPcob9ATqIOXToCHmeTx6S3FgvfiY0pkhIi4ywE6GUtxYPo5jxaHAf7375DQONkdb7x5gc4QSFMchAIwLvPCyd8N43ZL5KA+8P4pxEOK/HILX3RypwqKnIlCgrDaDAWR8+UOw+iEJUVmvVqtKgpMBZhRPlqtNVcuOlaVspiY/wQFU4iyhTjgjP35EyIAgjTGrQSpfaIhYphBdTK8/FSbkLrHAIpbDGUJT30lcgeTpqpzMPeH6OP6ZHGBEBUd1bGuB8JRKWCWcL6bXikA6FF3GTQoMQpdSGQChNNZz485DEYY3QtWiZFCfub1jfV1VpPLrq4/I+mytdZuoBdsOQpzm1AzVwoJUiDCNPVi8VgA8eWaZA4MKQze0uvf6Qw0vL1OttgnpEzRiGoxG5GXPn1lV+69P/iZ/8i38e0x1weG4BRjk3L9/g05/8DD/0Yz9Os9Hh8sWLnH/lIieOH+PkyWX6wxGLsy2uXbrOcxevImRIe7aDUCHjJCGOmmilWZhvsrm5zsWdHY4ee4CXrl5GhnWctr46SkAoHQ0VUq+16czE1OqSAwcWSMY98iJDByFYh7AG7SQ7m5s8+/S36K5tERSOCIW2EIUhYRCUgpPWr/qNwWD9a8JhKgwMvo+Xw4t0JeW9nG/c5KmpNt0lZMZKen7afe0DezVH7rXN9PlVrQIlVeSkIqYWRcHa2hoLCwt3TUdUoMI5x/PPP8+5c+cmIGHCfSw/V6V0gyDg/PnznDp1agJunPNcydFoxHg8JggChsPhJOXTbDYnEZoqOmKMoVaroZTaw3+pKnmmSbd3+877x/7p9/0cuZdX8mare+7W3oy/znc0OBHCIqWvuAikX91V3gRVCkcIOQEnlTS7lApTdsR2q/26x6nCd9Y6+sMhTSHInS0l7TXOQqxDOrqJLmJyl1E4SBFkOIYCYmepo6lLrwGrdMRQN8itZRw5+iZlsxgRtmMOnDpKbWmO1BQoZ0mNFwJ797veh9CCra0t6vUIY3OU85PWBz7wQ3z84x+7q3S8A+6s3GFzc5NOu72ntn56AJmOMLXbbbRUHDx4EKF97X0cx1hrJ+HTTrM+ubbD4XASGm21WjTqjT8QcIKWOOGQ0uKMIU0zdno9Gp02UVBHSu0jEQQIoTxAKB8OazLyJPflkkqhhcIqUeoyKAILxhSeyjz1XO7J404NtxOt4alVhRBgXLllxfOh0tupBlKHcAVgysVoAcJMCNyVoFVFYvaVWQIhNFQl4mJXB2UyqJQRocmAUB1f+M/gPEAXFTfLesBpSyAkrEMqiS0jTq5MD0kBuGISGSzXaZ6Qai2eymQmg7SSiigK3+Ibv7fJkuNlAIoh2Sgl6aW0NbRFQT9XNNsNCmNIs4zBoE8QebdwJQTziwsUWjISpXy/Lbh+/TqNept4doZOo+lFEEcJIHjxuaf59f9Q5yd/9s9x5fo1ZqMOaSrZWB8iRUg9rjFrEraG26zdfgWtawRxnXTgeOSh07z3ne8gqtfITc7WyipPf/UL9Ht9FjoNOq2Al1/uESlFnvR4xyOnAFVaKWQYk5NlY0ySomRBq+FYXl4kLxLydIgSIKzk1q1bKAdmMOL8+RcZbnUn0ZLA+TL8KPT3JcsyCuP5Ej4SUt5zfB+apIvd5H9lf7KvW77qU8X+Gbp/SZ297W4T4GuRY6ejEFVKqJozjDE0Go27RmWklD7tn6Y8+OCDe6pmqn1V21YRkjRNOXTo0ASQVJyWKooyHo+p1WqMRqNJqmlhYYGVlZVJGqg6hzT1KcsoiiiKYgJ2pr9jBbamgcf0d3i9a3U3zsrdtn893klF1H2j7TsanCAMSlo6nSZ3ugKZQp7l5FlRRg93J1uf0slL1Onfc85NOt1eEbNXP0LWglCSzvwcubjt5cRdTkZQlsuFhEFMbkO2U0eg6wRRGxFKMgyb3R4j5witIQw1tYZmPd3EqYCuSBnYIRkZi/U5dByRJannzQhwLuf2yh0yOyISITp0CGVwGKQKkVIwO7PIgw+e45vf/PLUd9j9HlVZnLMOYxxKKz9ZC/OqTiqEIAxDujtdhqMRG1s7dLt9er0+WZYhyodhpl0vuRO7D7JSimazyczsLBsbq2/1HX9Vk9rrjkg8OXpjc43V9Q0W7DJzkSZohFjrl3hCeEqyFFCYDJOlbKx5ElsjqqOExCm/P60DtPIS/41Ggyjw4MZ56bEJ52YXBJflxpIJObhiOnltHc8edMbTcEsCDJTJPiUNAoO14ESBwCJFVKadLEqokusiQPiUmpClhDm7A+W0d5IniU7lhO1ushHh+7PnrpSrzlL8yweTBNKAL+/yAAohceWqrOIXKKlLgS68Oy7+e7ly4vKrZXc/o/ll84RNbVIYXiHpDslTi1Ypi3XNnR1Fo1Gn1WqipPIh+zKqqqVibmGextwsO90x9TBAO4c0juGwz9ZwQDG3wNz8HFIKsn5KOu7y1S89Bdpy6dpVhAroxLOspSM+/vFPcub0SU4eO8LigTl0FOOcBKEZJ44Xz7/CYGsbpSQ6imi2miitqTfqPP7u9/DwQw/x7Ldeoh5qMGMi5SjSnGLkxe1CJanHinh2Di19tCgb9VCBxBUZysHqnTVeeu5FtLXIwjBOxkRCoa0HJ6GUBGEIOEZJQmY8QXyCwivuUXU/y54zAS62lGfAotzeCdHiKyMlPrUuyvSC50Xdv3LiaeHHu0VO7la5Mj3m7Qcnxhjm5uY4duwYjUZjsv/pibhKzVSeN9P7qP6dbtPnVVXeaK3J83wPf6UCPGEYMh6POXToEBcvXvRaU9vb9Pt9tNYT3sr0+VT3Yj/vZv912H9ur9XeLKCZfm06mjUdxXm99h0NTpwUCJehAk2gvRVIkhVgfJWGd91VZSi/CrOZsiP6fTRbTbSW5Pludc6rm0QHMUjB7JFlbqoXcbkjx5C5AmU0RlkyJeiSsiMKFhsNGu029cUOI5FRbKwjcsPG5hajtIvLfD7f4NA6ZHnxEEVaYB2sra3TmO3QnJtBKsi2UtY21tjobnFw6SDtziyFySiGY5CKLDdcu3IdWQ5U1TnvNk9eW1/bwFhBYcocParcbhcZC1H960jShEuXLpad3FGPQ5qNOlnJiq8IiFX+czgcsrBg0TpkYWGBi6+8pbf7ri1UPs3iQVxBv7/JaNyn36/RmW1hwxglS16KMEgJ1ngy8tb2BhsrV7FGMNBxCS58XZXWXh68Vq9x5PBh4rCJULJMw/jrJISjSrNXz2XlIOuBpQcOVpUuS6XsfeF8H5ywkIS3AnClQYJ1YIQXj1OqNIIToV+FlelKRDXQlcTE18jtTgYn6Xk0Pq1FOReJkjcgUOV7KH+u2JJYhw/hOycwUu5+PynLbZRXjREVIU4gsTjltWREVdZx35sDs4JNn+WVazt84/oOcRQwtzhLvKFp19t4p2o/NoRhSJIkBEFAoxaxuLDI9uAmNgoorPH3Z5gTFoLtzS2yPGd+cZ64HpGOxyR5l89/8lN04gjSFOUcc1FAd2eLF14Y8vwzT3PygVOcevAkc3Pz1GoNFtuzHJhp4k4eAanI84LhsE8cR7Q7LZyAly/epNZqQpYQSEccRxQypAi83UK92fCSCViMsQRSUgsUBY4iK7h1/SYvvXgZM0qpKQnWG1MqvIlfrCOEtOR5SmEtmTMT4CGcmwImvoLHOlP2S1kKGHoguhs7rPphCUQrvKylj2wqgQpUGem7vzy0e6Vz7jWxVq9VqZKqXFcpD2AfeuihiWR99bk8zwmC4FWy8tVkX6WGpl+bjlBPE0erY1YAJ03Tyb5tWeE3HA7pdDq0223iOGYwGHDxoh+Xa7UaDz/8MFprX11ZevFU3+2NpmKmz++1ru000NifQtv/+f3vO+felD/QdzY4ETNYM2bY6yNcTKgj+pkhisIJ6p9GslmWEcdxqWrqO0e71SYMwz1Ke/tbEAbU63WccDSXZyk6AenmGOEkeSnklWIZ41g3A3ZEQisSHJqdoXVgnp3eGvHSLDGSscsJ8iYmN+RpSiOu8Y63vZ1Oe5ZvPvNtVnduknYNh8MTHDh6mMJ40Z+8yDHG4qttJGGgaDX9IHLgwEFmOwd44YVn9nT+qgkhCXTA6uoGm5vbHDp00EcT2J0zqtDfdBXO0tISi4uLKKUYj8fcurnCKElJyvI4IUpeg1STentjDHGkWVpauuu5vNWtEcbESgEFSguUsGhVUGQD6lrRCDTOevVQIQU4i0RihWTY38TkKdYo0tJhWUmJs95B0ylFbhJG4w6NeoySGoT3KMJ5pVTpSvEvfITBE0jL/uXwqREhMMIxTlPS8RDwaolhqMrVJHiKtMG7LfnIREFapqXK6Em16igHASkktiTqVoNgNeAZY6aiO7s5ZD95iMkKWEyIAs5H0aTXivERGoe0MJF6F2WKybhyRSxK51xXRnXK06OMyijluV1vIpT7X9N8lEpj1UGeevkq/8sXr7PUjPm//USb+bmAWr0xuUZK7do3COGIIsWhwwd4+cY1chXghCKVljCOCIb+2ej1+yR5yqH5JeI4xBa2BLoOLSyFg9CC645IigInHV/98ld47ulv88DpU8wvzLJ0aImDBw/SareRKibQmrl2jbc9eJrxoMcX/svnac3P05ytUYvqHDx0iMZMi2E/pd8fkxUZSkmss14RyUooDOlozGA84uIrF3n5xQtkCUROIq3z+jMlKb8Wxl5HJxuTFTkFTEDl5DqWKR0hvQFkPhFPc96m4bUmsIo4XobwJ6RRURYk3Jc7//rtbpPmdJuWVvf8RJ+uPnfu3GRcLIqC4XA44YRUFU1V6qd61qajJtXPfgIuMKm+qaImVWqnUumuwEkVDfWaXExIrlJKRqMRSZJMpO+rZ75KL+0fA+51Pe7G1bkXf+e1ruXrkWj/yICTzX5Ac14zN2NY2RmRZV65r8h9GZbMFVHN1+xneY7Dazd4XSkBTtFotGk02qXi395USNUWFw6wtHjApzvaMSw1GW/20Shy4VUwUxPQL3K6ZsTIJawl2xyvnWRz2MMKidQB2SihETZRStOcbdKqxdTiiGu3rrLx/LfoDYdkNiXHI+larUZvkNIfDLDGsbmxxakTZ5ASAi1RSjMej8EJ4lrE0oGDKBVSFCmVBocQklq9QRDEHFo+QhwFSOF82qBMUrhqppK7odFqAK+8HoIgQByRjJOEre0tsizHml3BnzCM6HRm0DpAacHc3LzPkb8G6Hsr2sMPPcDG+ja9bkq91mBhbpbt7jZ5OsKMc5747rNcuHCZrPCTrjUGZFkiawsvkiel53hgfPREWZzzXBPvfWi8BogK/DV1XjNHlSki8CXI4BkYTpTRCectIJ2x9AcDVlZWSEZDGvUG9UadRrNBHMUolEcJJYfEWYkpQ7uWlChsYI1DK18yXa1e8dW/k8GzShFVhDic86kqHMb5FI+VPi2U5gZnMqTLq52grUQrhcX57+ZcSXYs3b3xbrkOB0751bPbfWqccDjnUw/ClSXcCKQqq5H+ANrGhuNrnx1z4eUtltp1FuuaCMuhpZhGIyCKtBfaqvlVphDC56QQnHrgGF/8xtcorMHqAGdhYXGR7RurqNQgHGTjhJU7d5jttIkaTZwKGA1GBJFiNBzj8oC606x1h9TnmizMLyCd4MLLL5O9mCG1o1aPObB8kMNHjnL40GFm2x0a9RqLBxZ433e/h6A+z5l3nGblziUoLMJCLVIoGTMaWcbJCFcYhoMBeZ6jpaLX7fHShQusra1irMMJ34e1VQROoEVEqHzNYJKOyE1RChdOlQlTJSO9QWlhHTbzTtVVepAy6ryXw0Cpe+Jw0nmtmylwIoRAak2gBQn3lxh9r3avRdI0H2Sigl1O/O12m06nw8bGBmfOnCHLMpIkmXBBKhBQqctOq8VOE2GnwcF+z7FqQVfxUarIx7SOSRiG9Pt9nHOsra0BcPz48cl+g1KuQAi/6NmvEFsBr9cDaG8GaOzf7m6VTHd77Y8MOHnymdv86AfOsTgbYK9fxThLkmVsbm0RRA2UCAjQFKbAWEMYhbt11tLb0YeBpD6p2LnbzRAcO3aCKIq9ZHOkmXvwGOvnb1FzitxZQgzGwhhLl5wUx/poiwu3LnHg0GHOvf1RLl27xPVbt9haW0FYQawjtFKk2Yg0G4PzNoAOypJYx2gwYG11ja2tbYRUXLx0mfe973uxpuDipYu88NxzHD9xgkcefjvGOOYXFonjOoNB6qs/8CvzLM85feosp08/gHMWrUvmvBMot489LTwfBZikC6pO3mw1aDTr1GoRq6urjMe7JoJ+pV7lnn0ZXRzFDO4zOHnPu9/F//Xn/hrPfPs5/tRP/DgPnDrFzds3SbKM1ds3mWm1+MD3fherG1ukSU6e5TgryIsRly+FXs5e+rLyMAjRWiKl99TxZD/N4sICOop8JMD5uUygYcJBMSgfMin5LQKEl/Q3zjIY7HD7+k36/b6Xvx70qddrFNkswfwCYdQo+Sklv0NarM24c/sWTjoOLB1BNDSRjAhM5VlS5pin0ztlOL6KUzjnELbiCXgCk8WSpGP6gxEiT4kqp2olyVE+yiN9pU1d17DSep6SNWALhKguQCld6HxkTmiJExbrfAyIUshNSYW1YjfbeN+axDnF5dsbvLIKIQF/8yfOEScDnLME0qK1o96o0Wq1yIqiLJH2k2hhHcuHlqjFIYPUYLREN1v8qb/45/nGl7/K0//lS6hBQiAVpjBsb22hB33qrXniyHtJtWZbdHsJykJNaPrr2+gwYOHAIg8eOUe93WRnp8vq6hpXr69y/uVLaCGJw4g4rLF0aJn5xQPUW0P6o3UuXXoB0hyRl+Wt1pAmCeNytZwkaRnV2jvpFXmBNo5YaEIn0ZXwIpBkCRZvIugl+Ly/FqJSVqZcxPn0YlGWEgdSY42dgFX/EZ/2kVL6FLsUCCVRZYpkTxMQBIq7j7FvTdufurnbZHw3oiZ4QnCWZXu2P3z4MDs7O+zs7NBut1lfX5/I2leRjf0RkmlgsJ93st85eDq6OR2BqeTrwQObF198kYWFBYIgIC9tBJTyPmadTmcCQNRURiAMQ88PLAHOdHu9iPbrgZI3AmLulkJzbteZ+I2072hwcmWl4Ovnt/iedx0nEwoVRqgwoj9MOFmvEzViCpvR7/cpioJ6fbdsWJRRhSxPfJnwpE3i3JO/p2vInVIcePAEK61vY7qGAull5h2kQIbAIihMyu2dO3QWF3j4kUeYP7DApRdeJE0zcDlZNsRHN0x5Pj5zK8uIh8lyNlbWuH3zBoEOOLx8kNWVO1x85SJ37tziy19+itFwyHPPP08c13nwgTM0m01mZ2YZDLbLfTIhLn7P934P48GA0Wg0CQs6t1tRAT60GUQBUgryvCDPdx/CajURBD7FNT8/z+rq6mQQqB6a6qHy5W8tBoP+W3vT97X52RmEc+RZxszMDI+941FefOk57qyukgy7nH/+Rf7qz/8lcpuTpVBkBbZw3Lx5lW99/SlWhERJTWd2nqUDB8iLjDQZMU7GmDyj1Zrh5LHjOCTjtKAwFlsU4Lx7rGS3SsbzNUTpZQOGAkfGoLdJmnQp8jHO5CSZo0hHSFtwYGYGR+F9gfC4RklDmvbY2VnDCpibXaClZvxkIkpVZLU7IRQl4Vdp5cECbgIGfOqFipRDYSz9QZc7t+6gs4yAAhlokBLlfD+2SrJ44CDxTIRDkGYJ3e1tipIDoaREKo0KQ0IRIoIAh0IHClQIToMtoyiePHO/qQaAQjjJ+nafImyx3kt5dCGipgb0bUGj0fR2DGVEKisKgsgPlMZZhDVEtRrNWp3BsEtYjygsPP3CCzz2Xe9hcW6e//zv/gOhqzhGliJN2EnXvJ9UQzE3M4dTmmRtk7qUOBEwSjJu3rjJrbU7NNptlg8e4pF3vB3nHJsb65gkIxmO6PcHvPzyJYqXXsZZf42dSctyXeddlwEfm9tbeSGEQEgvPZ+lKdI6ak7RECF1FeAkpCbD5OWEJzzXzZXcI+MHiin6Kxjhj2dKPpKOQ8bDDFVuVIGdspf5Rd9UtASmuE748TbQ05y4t75NH/u1+BDVgqr6e7raqIpCBEHA4cOHyfOcBx98cEILqH4madKp6Mg0IKnAxzQYuVfZbpVKqqInFRm3Kj2OoojhcDgx+qvG3OpcwjCkKIrJvirV2f2AaTQa0e/3UUqxsLBwT7G0/edWtf2g627AbLqcufoe0/+ORqM3fD+/o8FJguZb59fITEqnWUOHAVKFDMeZF7SSGpdnjEdjgjDwg6rwVSqePQ+j0YDhcISfyi13Q/aXLr3E6iOPcejIIZyLaM3PsfzAcYpvXQJXIIBMpuQCHBGODJyjNxxya32FL3/ldzly+Aj1RquMjOgJ4YzJ/32ev6zHoN/rMrg4ZpglLMwtcezwUb797Le5eOE8L7zwLDubm9QaDZwTPPXkFzl9+jRxLWRufpEbNy/jhIHS/G5mdo5z587yqd/+OAsLM3serIlwjxQlj6QS7/GaMMYY8izzIXwpvc4GglAHhDpgnCTY0p3X5IVfXVlvSDg3N8vKyu372gdqIuT/8Y/+IYPBiCOHjqCl5fiRI6yvb5JnORcvXGI4Tpmbb+Ma+HREYSmyHjONFoFTaDTveOhRlg4usb65gXOlmZbNmJ2d4d2PvZ2lA4cYDDxoGY/GJEODKwx5npJmKcaUYkpFUTrh+lD3aNDnfHebLE1wWU5QlIOYSRl1d2hGIZ1OjcEo8Smhkt9hshSTDXFIOq2YUBdeujxQYKXnsliHEAFO+CocL9nvIxkV/wUnUM5bPRgM0iQMNtZJdtbJx2MCJdA69NVoQqG1wElJHDeYac9hLfS6XbbXVsmzEUL6dKBzDhUGREENoUManRnac3MgrRffy3OkyNG6VlqE33/eiXGWlbU1dnopmYrY6eV0OgE4RbO9hCy1ZNI0xTgfBZhIgguLEhKVW6JcUpcRCYJnvvk0Na3Jx2PC2Qbp1g515XVrpLU4a0nSIaiItbU1Dh87xU6vB9ZhXYwwkpHLSfKc7uYa25trvPzSiygZoMOQRr1OGITUmg3q7frkmZNCUOQJJs9J0sSDYivIi6JER56k7iu2hI+8FpbAKYQIqClNO6oTAYNsiHU5Rnihvgk+KKMn1fzjEJS+lj7tKQXWOIxzDEaDsgpNYqXXjxKuVP1xgPNp5iqdPJ0ir8rYQ+0tE+5XazQarxIimxx/37/TIGF6Ym02m8RxTBzHzM7O4pxjcXGR8Xi8hzRaAY/90ZP9lTHTUYq7VfBMfzZN08n2VRTk8OHDXLt2jRs3bkxSScCkBLnSQqm4KxVfRQgvCGmMKWUwLDdu3OBb3/oWjUaDH/zBH5xU3U1XJ03/vv/vewGRe4Guu7XX4nbub9/R4MSg6CaOZ85v8s6HTiADgQwUuQFjd8N6xhqaUROED7vL0vwuTTL6/R5FkfFa4cbxaMT6+hqtmTateh2j4ODbHuDy81dwqZ9MUgqGzpH75Dtgsa5gZe023/zWN7h29QqNhneUtFk+dbwqkVOG6SlIsQyGOWIcEAQBWZZjCoPWkq999XdJ0jHO+YdDSY11uznNmZk5D77KwUFKydsffTvGWHa2t1lanJ2qe/dnIErOAUJgTEXWClAqmCglVnnV/eVggr0kzDzLKMIQQcD83PxbdKfv3YQUvOc970SHIa6AYb/HmbNnOf/KJfqbA+7cucOVK1eZnXs7jrLwVgpa7TYHFw+U8t2C4weP8OgTj5KmCVniVzBGOJaWlzl77hz1ZsvfH2ewtsAVDgqBNV5F1zmDtYY8z8jTMcYasjTlN3/jYwx7IygcoZC+8iLLyExBkRfs7OzwkR/+Y1y/fp0kLa+zM/S2e6XOiGBxpkVnpkOaOEwuKVyBUz6CIoUjMz7mpirvKErVX3zqrorsKKmQQpKNhsRaEoQBUiu0CsFJr55sCqwp0DgirTDGkY4G5OMhWZ56nIHBuAJpNXmWoWSAMwVLSwsY57knaTZAa0ejUaPIHeH9i+YDfrAepwnDwZAsGyFqEatjw+H5Gs4IdFhDln5DQRAgrCEpJwMhBEmS+IE6s8xELdJehg4DCpdx/sWXWFicg5omEY5YaWIVUyQJpoxSFbkjjAPWVlY5duQo165d9/dEKqzMyaTBKkWARmSgrCHNE9Jh4qNbEqrwUrWy19KLRYIv//fzksQaf199uk6VqRi/WtUW4jCkJkPazRbjXnd3BS+EByTTWVxKYUgncEpicBghkTU/EbrEQBUhEPgj7Z98qRY3enevU+NENXG9WVfaN9uSJJlM7vsnyLvxH/a3it8RRdHkZzweT6LGRVFMLCTGYx9tv9dEPX3M/UBofzSimvyr87LWEscxvV6Pd73rXbzyyitcvHhxQnitvuO0mm2lSlu9V0V/lFKsrq6yurrKxsYGQviF16VLl/Zcqzea5rkbeXY/IHytf//IRE4sEhXEJFaw3u2zuNT05mRyKrcpoF5vEIZeMdEY71z8/AvP8ey3n2d7u0v+OuBEa02j2WDQH5DPznrRmwMzhEuzmJtbWCcohCNVeMLhBBg7smzM9etX2FxfZabVJgg1SZZNBpQq80sVZsWQYimcr4TQTlPkBVmWU6/V2dwspZQxZFmKs4JzZ7+bWq1GnmcsHTjIwUPH6PcHxFGdd77r3fzkT/4kX//6N5BKEUaxZ6Or3Q5VhWR3CVuuROllmbBUGDzhK0lT74JcFF6sqoy+VA/vaDTCOUer3WZufn4PW/x+NBEoZKBwwiCUolav8cjDD/ONbz7Ny1sXGScjXnrpPI89/kg5WPpUWr1e5/CJY+h6TJ4ZRkXKE9/1Xs/FKQdz4TQI6dMeQvqoEhIhA4Te/U5hSagUQuCKHLIxQknWbt/m8sVXMKkljmIefvQsjTBidWODC5dewVjDtbXbtOZm+NCZM6TjMUVRMEhTrly5TiUguL56mw/9wHcT6wZp39IbjBmlY3LjybbjxJLmudcVcc4TGavVTskbqEqHm/WIWhhiEoFQXs9GqhBrQJey/lY4Hjh9ktmZGbLccOeOAmeQ1lFkKcYWOCmxSQFyTKQjIiF5YOkgaQF3Vta4evUSy0szvPfxtzMcOmw6vG99AHxKcqfXpyhytjZXyWXC+sjSt3Uym1ILI4T24nPj8QipNa1Ws6yU8BoVRV4QyIC5zjyra6sUSKK5DrdvrnHhlSto56g1WySDjHoUEcUBNveibAjlwUOe0+vu0KjVGGz3qYUBs3HE2qBgWPgxy1iHQRCIwOuFODcBt9YWOGfLpcV05EH4fid9H65SSwJVDl1ln0Uw22qjkpz+YECRZV6DpuQBUYIRBxMLAgFlRRmEjTrtxQV64xHD7W2sFKU6rGcz3W0ik8JXjnn+San3I8oInvMgGkGZ1rl/bboq5vWqBF/1HaT314miaAJAgInA5IEDB2g0GpPIhFKKwWAwOe7+dMf+6zRdUnw3oFKBt4ovMhgMOHXqFI1Gg9nZWer1+oR0ux9YVQULE+f4kstSgZRqXG40Gpw5c+bVC8w3ACru9d7+3/df4+mfNzsPfEeDE1c4onqMMw4nQoQMCfUIJyxIjcPrVsy05xCuwOY5GMHWoM/v/M7n6Hb7gMIX5u+/cNWD7BHo/PwcxkB30Gc26mCbmvqZY2S3+xijyB2MTIZzlkiHtDoz9Ac7flVtLN1el35/m8BndGES4Nz9v8MrXbgyrwwWJyWFtaRpRr3e8GF/4SeLPE0QVnDs6DGUFBQS6o06f/4v/GXm5uaZmZnlyJEjOGfpdbeo1WtI5Sda//XEpFJjugPulr95YzmlFdp4ZJ4XOWn5UHm+g/GVHAKyoqCwhlEyJqrFzM7Oo1SItcl96wNCS1ypUukEqFBz6uQpjh8+ypWXr5C6EZcvXsEWDim1H8ylQ0chS4cPUWs2yLa6bKyvYKwjCHWpZ+Ll0HfHl6mHEfBiICV1cCplIRA4HeCk5fqNq6xt7YAKOHPmDEePHaPIUsI4Zmtnmzub64xGI+7cucPZ0w/QbLRwxhAN+2R56nUlrOXilZt881sv8TM/9d8TqQZZVlCUK63COKyxJFlRVtMICuPfN6bAOktmvKCfNZpbt27y7Ne+yMh0McZy/OhxZmbm2OkOGPVHpElCGEc8/tA7OHnyJBs729y8cYXbzhBqhZYBzmkK54G4tQUmSdE1yxNnz+CCgP/wf14g2d6mqGsef9vD1OoLRG9CfOn31xFgY3OLcTpmp78BKmMo4NZQENUCGiiUDOj3+15qwHqLAwApNTrwnihxHDNc3+RQfY7tZMj27XU6iy3CVoNef4QpCqSL6I9TGmFEvdH0TtXCE4KFUGRpQqADghC0STkcHuZs5wB3el228hFbjOipAkxRiiJacpEjpUEY78MkpSyVhctBHoWQ3uRUVOk/Az6SYRHOopwg1gGxUmQ2IUlzCmspJtaifvsKqFZ9WQAFhlqrzZ/+qZ9ERCFPPvUUO9tebXri6e4qZlwFmKYIptYhHRNH6z28GOdAgg4CP+bcp7Y/InEvEuz+yE/V4jim0+lQr9fp9XoTZdZ63YtNLi0tTUp9a7UaaZpO/HemQcb0JDxNbN0fqdgPYCqyqDGGMAw5deoU1lq2trYmSrFVlK+qygmCYM/P9EKxIvgK4S1J3iig2H/e0xL990rt3C2lVR1nmuOzv1rptdqbAie//Mu/zG/8xm9w/vx5arUa3/3d380/+kf/iLNnz062+eAHP8iTTz6553N/5a/8Ff75P//nk7+vX7/Oz/3cz/H5z3+eZrPJT//0T/PLv/zLbzrsl+c51jiKTFDkEi00sQ5RYYRA44MAlRnVrljV2uoa/f4I594YezxJUpJkzOLiQQbDPvEoxJiCZ1cvsyRz2jZEOUlYr2Mznzo4dvQYK6shm5tbk05ZmJwCs1tNse9f/7ub/sP/Yx1pmhHXaiitiaI6g4HveCdPnuDBBx9AatClyYVSmpMnTzIz0/HnP04QwhKGapfVLfbqYsDuA12VyIWR9qobUlGUaPu/fP5zXLp0ke3tTaRSzM3Oc/bsIwghcc4ghOJ3PvMp1tZeWx32reoDVQSqurcImFmY44GzZ/j6179Fd9Dl5k1fKTO7EJfAzpcVHzh0kM78HFtbO9y5c4fBYMDs3Bx+aPV9Y/oZvvtibF//qdQZ84Krl68xSjNUKHnk0Yc4dGCJRx9+iIsXLrO6sc56bwcpJWtra1hj0eWqLMtSRiUPypqc4SDhyc//Ht//Pd/H2849RKz0JGJVAU0LCKXAeGBOFW5HkLvci88lMB50CUuyXLPZ4oMf/AE2NraZ6YwRhZ/oao0ah5YP8raz57h6/TpHDhykv71FkmdkeYLNcy5evsHa+gaj0RghBbdnNxgOehw+epQ0GZAWKZ/96jf51e//wT2X5xd+4Rf4V//qX731/cA5Nu+sMlwf0NtJCQNNLi1Xtg1HOwsQ1yhyQ7s1Q60cqKvqBx0JD9qdYOnADKsb1zHFkPkwIChgfa2PbcVoHaJ1SCEytPR+JvUw9iqtbu/qMstzap0WM7U6qgvHWkscaxxkNd3mdrHDnbzHcDwmEQWJtShnPXerfIadtWXkoSSoSoMtwOS5JzhDWcZe/e6Ya9ZpyBAzHIOxFNanJo1zOLGb2nGISbf1XGXvn/PgqeNYJXjlwgXa7bbv2+VzL8xEpq18FhzXb62wsdNjNB6jlGJuZoa3nztLu9Usz8rx5Fe+xvrW9mveu7eqD0y36TLm6Z8qBVFN4vuJsVW6ZDAY4JybWHJU97XSGlHKm8YOh8NXlRDv//tu1TH7gRTgCxpmZ1lYWPD2IVqTZRlKKZaXl7lz585k/K6iH94bapdtHscxw+GQimQ7GAwYj8eT9NT+KqN7pXWmAUV1nSpjwypKM210uP9n//uVUOfa2trEQPb12pu6+08++SQf/ehHefe7301RFPztv/23+dCHPsSLL75Io7FbCfOzP/uz/P2///cnf087/xpj+JEf+RGWl5f58pe/zJ07d/hzf+7PEQQB//Af/sM3czpY5yMKkQ4x1oOQQHnBMaUEpjDl41TmhY1XcMgL48WEpkhbr25istZIs4wLL1/ggQfPUric/mDI7Vu3+NrLL/CwnWFB1qk5QS4dMpLkowxrLK1Wh8HAr7acMD6ki9kVcBYVOa06D78Sr44PpUMskGUpnZkWSkryLCtJvRalAhqNOiKwIAy1Ro3NzU263R1qcUgchygNSTpCB5Io0qWbbclJKUOwfhzy3AWfxyxKoSqvk9Hr94jjGpcvX+Ls2Yc58+Db2N7Z5ktf+i989atP8d7v+l5mZ+YIggghJGfPPcQHP/D9/OZ/+hgrq3tJsW9lHxBSoVQEpXaLkIowbnDm3BlmOm263R221jfYWN9ibmG5DDE7hNTMzM7TmZ3Facl2r8fWxgazc/M+hUMFaO++Gpu+R5N7Vk4sQoXYccqdlXUMlna9xtkHTnLu4XPMzy8wM7fIMy88xyvXrmAFk/Jk5/yKOC9JkEopjHOYoqDf77NyZ4VzZ86CUjhjKYxBKY2QCqH8hCOcwMmpc5WCQIYIZ0nskMKkxPU6VkjaM7OcOHaCQNVQywEm8xUDcSvm0PEjBLWImXaLI8uHkQjGJsPaHJvlXHjlGu9+1xMMRgOGwx7bm31+9n/8m/wf/+JfkuIgjpFa82d+4if42//D3+TzTz3FL/zt/3HPuPBW9gNjxuzc+Cq1wfNELgUUVik2hynHag0yYTAByMinUmrawWCTSCv6hWWnV1BrzrF85CA3n3+FJDdEQYOFsIa0ARvDjLhdZ0RKJh060OgwoDcc0Ijq1FSEkNKXXJfTeJKkhLWIgyeOYqhhhymtuMmhXNLKmvRqKd1sSDcd4JTBuFLCPC8wOKzzpesGA8749Au7wQcxIdF7EYL5ZofYaZJSrM1N/ivdhfHpIYTEUppS4pkuhYSVzXW+8c1vUBjH9vb2ZFINtPbEf1tFcfwiYKc/5MiBJTqtJkIrLl+/yZe+8Q0+9IHvJQhUqQcEJ48e5tFzD1JYOH/xCpevXbsvfWBpaWmP4d29AArcnUdRaXBUACXPcxYXF4miaM9nqoVBNa/tr9Cp/r5bdGIauEyXFodhyPLyMkePHt2TctFac/DgwYlhYAVEKv7feDyeEGUrHkxFdK3k8Ct5+yiK7gkopl+rANB+YFGljaZBy/Q1rdr+KMu0FkwV/Xkj7U2Bk09/+tN7/v43/+bfsLS0xDe/+U2+7/u+b/J6vV5neXn5rvv4zGc+w4svvshnP/tZDhw4wGOPPcY/+Af/gF/8xV/k7/7dvztBqW+kKSUmXi6W3JuaOct4PMLaMj8nvY+JdY40y1GBxKujViW80yVx062KYQhwhlcuvozFsLC4iDGGr//e1xkXhhu2z6KuMy8aXFlfY+gGGGdJs4xaXPeqotJhpEYIP8hUJFjvDuz2rcinqhqcKIlwxpczC0UYRfR7Aw9OcFy/fo3nX3yex9/5dvJCcvzkca5dvsaXnnqKVqtBrRaSpCOuXbvK8vIyYaDxPine5E2rqrJjN/SpypW5MX7oEsJhrCXNUj7wwR8i0DELC8tIFfCud72P3/7tX2cw6HH27Bm0jpBSoXVAvd70hlX7wMlb2QeEVDhKToSQQABCcfLkWVrtFlhBMhpy88YNzjz0MGLK16Zea3BgeZmgFpEUBbdv3+b0mbPlAL53ELvXYFP1Fd/JrHf5E2CMpdvtYWxBqx5z+uhxZmbnwAniRo0Tp08x8/S3SNOUer1BkRuUchiTMxoNSUsehPcCciByf98kiKAEmKVSq1MCoTSVhxCOXQAqSyNAAVZaonpIo9X0lSphwMmTJ3n3e95HEMZYW3hvqiIjjAKkkiwuzvPhD/8wN67fpD/sMeztsLO9xUd/9mcZJGOefeFZbt2xPPH4Y/y7X/l1nn3pPEWeI4Uuw98xB5bmaLf9atqvyO9DP3AJ3/8exbE45qUXRwyVwghFUjhQAfUoph7H5FlOTWWQr9Nw69TDNqurBXc2NHGimFk+iGrEZP2EXj6moTUNHRG5gDubPWxLMlKWREsa9RpxFNLr9lCBIArDUjWkZJMJx+r6Orc3eiweOUxNaxbqLRqqxYKJqJsc3dtEjwIG4z555mjGs6TCE6YTk1NgfWSjTKP4hYR/Vg3eIViVSr7j3hCnIrJxgrUGVwESqjXQLgneAqZMLRu8uN/65mapBRWSjhO0kohAI6zzxo1JXk484ITg7WdOTeQFdKR559vP8cnPfYmdfpelhTnPp8GhpCfpOiNeZQD5VvaBVqs1ASd7+sZd0hl3+7cqwa1Kekej0SQycrey2yiK9pQhVz93Sx9VXJDq9f3VRLVajfn5+T3nX23XbDYn8vrVaxWwSZIErTXtdhtjDMPhECGEjwKX/MggCHj/+9//KnBR/exPSTnnJkCiAhXTarhZlk0KJaZ/r8qhi6KY/EwL1E1X/LyR9l/FOel2uwDMzc3tef1Xf/VX+ZVf+RWWl5f5sR/7Mf7O3/k7E5T5la98hUcffZQDBw5Mtv/whz/Mz/3cz/HCCy/w+OOPv+o408ZKAL1eD6hCd5K8KMpy1xrO+XruLMtKOXP/GWstvV6PWqPlzb+UwBbFLgCZRC72N88G2d5e5/btmzzwoJczPnr0KF/7+tfZyDKes5ucjAXdZExuCyyeOd5uz3jJZuvBANJXCbhSYRSnoCwdnUxwVeWOoFy9+w6fZRnWGuK4xnic0mnPstPdoihyvvSlp3j0HQ9NOumpUyf5z5/8JF//xlewtqDVarK4uOTDpMKBMAjh8+RSgVQKa6tcadX5xRS5TVCr1ciyjLNnz1LkFXDZLQ0LgrA0sqohhODSxQv8s4sv3/Whfiv7APiBpVx/AAprLOsbG2xsb1FgyWzBteuXca7wofKyZDyKwlKe3xtv3bp1s+T73K0PlL/ty1/v3cyBrbgghiTP0EqjlKYxO4cM4tL5V3D8+HHOnT0L1rE0N4/SAiEsOpDkeUGWZ34yMr7Etd5s0Ox0PF+gcCgZAsbTpaScTEOySmGyq4chhO9JcRTR6XSYmWkhpMWYDBX7MlaEwCAQsUKmpR6o8IJaS0tLtGdmKURBbB3j0ZjtwYCvfvPrXLp0GWEEcVADIKrXsFmGtAVaSX7rU5/mtz75KWp1//5oNJoAlLd0LCi2aYuXaA2vc0BobhSKXKSgBO3OHAuzc8xEmraCul1nJhpiRjuMh0N6GyGZOEieDUjSlKNnz3LxW8+SmozxuM9iM6QTxESiw43uGkXTkeiQVGu0UrQX5kh3emgjKs4pOOd5LUohaxE/8N/+MGGjRjYa45KEYmhRvT5i2GLOZQyHA+5cveXVZsOQYjhESh+tUEIhncSaykixlJB3DuEcoVK+UqswjMZ9pHOlH46PEk4mn6m0DHgMbgAnvPCgMYbr164jUMzNzWHzDOm8kN+BpWUGG1vkrpxgjPGREet9d4QTZKU/WSD9cCdcgEBw/fYa12+vEUfRq8DJW9kH4jieTMD7oyf70zhVNGA6KlCr+fnjxRdfnPTVbrfrU8JlWfHuo+4m51CRWSeKw+yCkf0T8d0iDtMApNrf9Hvz8/OTY08XGFhr0VpPyoWzLKPZbNJqtSaGgGmacuHChUmKaD+YqP6twET1+3Q58f6y6f3VSNV57ifQ7k+nVZGp6Xv3Wu33DU6stfzCL/wC73//+3nkkUcmr//kT/4kx48f59ChQzz77LP84i/+IhcuXOA3fuM3AFhZWdnTEYHJ3/fKRf3yL/8yf+/v/b1XvS5KUmKlTFjg0FqQZqNyJRpgLVgrGCcpN+/cpNns0Gp2qNXr5L0e/vGUZQSlMsF7td5JnuU8/9xznH7gLFJK3vHY43z961/n0qVXWLFD8tyRuMx7rGAYJ33C8AS1WhNLn8J4YzVnlfdDKR9okLhJosenGzwq9mF+C1gnKQzkBdQbbQbDhOVDR0mLlCRJWF/f4M6tFY4eP4x1OdYa0nSMVgIdRbRbTdqthifRFhl5GRIUMickLI3QyjNwrvTwEUznNufnZ9nc3KFeb2IdDAZDhBI8+9w3mZtbIK43SXNDjODsuYcJwoDZmQ6XLr7MN77xe3uu5VvZBxwFzuVeLl0Kujub/PbHf4MvPPk5bq+ukGMQLmdtfROT5eg4onLy1VHE4tISURQy6g9ZvXULZwrP3bjLkQAP6vaAWR8FqwjCrpw0TEk8lPgQrNNlxY9SYOHMAw/y/d/7fThnOXv2AXQgkdZihSDLM4q0QChBLaqzPL/IwaPHiOtt8gLCsCwV93W9gCgdgYVP+bgymSlKmGIMtsSItTCi3WwR6sBL5Odp+V28RopEeIBZeB0QowRFmhNHERkOaxxhs8OBVpPjx44TqBBpHE996Su8+4nHOXnkECYrEIXlu554gh/50R/hyPJhfuU//p/8y3/7b/nZn/1ZPv7xj7/l/QCzhRvdYbCasNQOuLHj01thIIlUgDQ5NaWYMRvUxBVqxjJODNrWsKKBkyHGJbhAs3jiGFub29y4eInIFGwPJGF7gbpQHG8tcivtsl7kFMqSSE1DOOJmDJlBi6BMlziEEyjjpeRzUuYPHWNzbYu5Y8ukqaPjIHAGaQqKLGM8HJGmOUkyZHNjhWSc0u+P2NnukYzGjAY9xuMhw2SEceUKtDBkxhKrwKvw4ygwGGF3Sa8lOHWCMp1jMUgy4fuLkMITcl0lQGDZWl/HOq+lEwtNb3PLj4zOQWFIXEEspR87rAVjeO78RRZm28y2QqQtECrk2JGD1OIazVaTbm/EN555Zs9teyv7wLFjxybltndLPdwrrVO1KIrY3NxkOBxOwMXOzg6XLl3i8ccf35NuMcZw6dIlPvOZz3Dnzh0WFxeJ45ijR49ijKHb7fLkk0+ytrbGqVOn+KEf+iFqtRrNZnMCivI8J01ToihiY2Njso/9UZWdnZ1JxKJK5+R57n3iyvMcDAYTkbbRaDQxD5yfn6der/O5z31uj8bI9CLrXqBi15okvCuom/6Zfu1e6Z/qXJ9//vk3pHfy+wYnH/3oR3n++ef50pe+tOf1v/yX//Lk90cffZSDBw/ygz/4g1y6dInTp0//vo71t/7W3+Jv/I2/Mfm71+tx9OhRpBTUG3VKVgnOOgKpKNKUvCiIRViyGL2B1ezsLJsb2/R7Q+r1xsSvYHdlXI7g5cpnf7t06dJE6CbQmocffpirVy+SkbKWexEtIQOwhiQdIaUgjiOSdDiBOkJq/zAjSm5DCbJKBzZZghMcZXTCb2eMQwhNvdYiz29y/cY10tQzxZVSXL58maPHD3sUnXrRneUDy4RhyMxMh0azPhH6SVNPph2O+oSHwhLRTj/IZk/nrABKrzdESEktihFS8tu//Rt0d7Z593s/QJoUJElOs+l4+2PvJE1HpMmItz30EE8/801MYbh8+TKPPfbYW9oHBP5aCSz93ib/2//7f+XLX/oSw+GQrCgmlu1b29vkeYaO4wmXSErJ3Pycd/rs9VlbW6XIM0IV7Tm2Kwf9qVfueo4OEKr078EhlaJwjsHYKzO2Op0q5U+tFvHe977bq2oqvKLqeOxLydPUr5IlnDh2lFPHjnP63BlmZjsl2HZI6fdfsSOV0hOnWFGmdJwtdUCNAaVBasKoRqPWJFIheZLT3dmB4/78lZyyMnBisvJWWvoUaiFBSrLc4h0Bocgyrty4SVYYfu1f/EtWbt8GB1pp/vgP/RA/+IEPIlG854l38S//7b/lE5/4xH0ZCzA3waT0NiwLnRyxIShsRD0SaGlR1qLHd6iLl5Guy6CvoahjtSATvtRaiQAbOFKd0zlxiJ1xn9GtdQbFiFo+oCPq1NGcbRxkNhlyc7RFrjNS6TVTZsIagQ6w1le1SSvAWZJxQpaMaTWafO6Zz/PjP/4RPvOZz3Ps+Ane+64neOmF59nudXn88ce4dvUGs7VlFk4dxFmBkiFpkvvhweSMhgP6wwGD8Yh8nJL2h4x3eqzduM36zdulB05J5qZ0wxa7E5HBkTsPnJ0ShHHgz1UoAul85aMBjCNwAuUEWklcVlA4z14JYkVhM6QCLSCQlhcuXqHXH/Lh9z9CpAW4HCEsZ08sc3t9hyTPiOstDh08xLXr1+7LWNBsNl/lILy/lPVu5b7Va/V6nc3NTZIkmcjCb21tcenSJebm5jh+/PgEoFhrabVanD9/nieffHKigVKpZFf7VUpx/vz5Cdm3irBUqaM4jvmFX/gFPvCBDzA3N8etW7dYXl6eRFG2t7e5ffv2RMNlOj1UpWSklKRpSrfbnQCeSkguDEM2Nzep1Wq02+094KEa1+8GNu4FLqrj3g3U7E9p3Q0ATny/3kD7fYGTn//5n+cTn/gETz31FEeOHHnNbd/73vcCcPHiRU6fPs3y8jK/93t7V9Krq76y4148lUoQZ3/L8wJbGIyxZDKnyAtCIZAWsiTFNeu75DEhaHfa7Gx36XZ71Ot1Wq12GRL0AEUKX/3g+7WpMsfl0RxplrK6usqxY8fI85wHTp/mve99Hysrt7l25YpfaZQPRZolWJcTRtpzOkqQhAwQsqr9r1xmmaRQtBAlCbYs5cUPUMY40qRgc7MLQtLvdydicsYY1tc32NzYIM1zRsMhzWaLWhyhtSKKQ7wPnGNzc2PCBag6dRzXfCRH7IonTec2RZkq0EozHA6JazU++dsf4+rli3zwBz6C0gHWQL83JApjbKOGtV5kLAxD4lqNYX8wGZDeyj5A5QrsUj7zO7/Jl772RXpJikWhFBPQ1+12ScYJtVbFefD3u9Pp0Gq12VrfpNfrMR4nhHHrVez1PYcsUz++b01gJ86Vg0Z5zaJajJPQHw25s7rC4aNHfX+yxpczB1VZd2lCKBVYw2g0xFiDVpoTx47w/d/3PRx78AT1Zg2lvHbEpJy0EhucDjlb5/uOVN4PR2uQCicFUb1BqzVDGMQk+Zid7W4Z/Nm7WvPCfIB1SA2myHEFZUoSsiIny1K++vWvs93t8X//O7/EkUOHuHHtOl65VNJoNrw/EWpPCeH9GAswW9hEUiSGTgxLtYCdzZzWwSUKA8LltN0d6sVlLA1s0SbJEsYuo58JbF2AERTOkAmLizRzRw563ZmNLbbTPkEk0S6mmdc4FSxQk5qXezfoFj6VEiJQIkAHAQqHcQZX1u0OuwNCpTl2+AjCwrHDR7DG0B+PuHrzJrPtGS68fJHr125Sb8YsLs5w/dZNFhaWeMfbn+DZp59m7c5tHjpzjqMnT7O+tUkYhly/eJn5sy02D9/iUx/7LbAW4SRSSBw5nijuoYpxnoVSCDDCg18jDLK0XLCFAWMRVqGcJqJcrlnPe/NeOuWqukwFaVnw0pU1Vrd6/OC7HkCLDE0NpAEylAppNmPGRmGdpN2ZBa7dl7FgZ2dnIkZ2N3Byt6qU6SaEFwmrRNCqqMXOzg4XLlwAdg33tra2UErxkY98hKUlnzI/fPjwxBjwU5/6FIcOHSIMQx5++GG++tWvcuzYMaSUZFk2KUUeDoccPXp0Ep3odDpUUvk7OztcuXJlYr9SaUjBrotyleapUv/OOaIompQOVyXGx48fn1Qa3Y2Ds//3u12b1+Ld3Y0Ue7dWVf28kfamwIlzjr/21/4aH/vYx/jCF77AyZMnX/czz5RhvIMHDwLwvve9j//5f/6fWVtbY2lpCYDf+Z3fod1u89BDD72Z0wHnQYjWAdYKrJMgcwItSg8CO5lwrbVsbmyQjIaYImN2Zo5Oe5YbN28wHo3IMoMrE8ZetKg8AOD5CXXe8fbHGY1GfmXrLLV6nfe97/30e31++7d/u1TgM2S5z9dlRUYQ6rIKw/9I6R8eK3dvtC4BQVWn7nN+hlaryfLyER48c5YzDzzAwaUD/Or//qv0n+/i3JgwjHDOd9rBYMDTT3+bdrOJktLXwQcapUFrL45UmIxub4dmq0GzVScOolL9tfAqoVNtb87Wk4jjWsT2yiqf++yneP65b/PRv/Y/kRWGne1tet0e29vbEzXZcTLEmZylpXnCIGLIYDLYvJV9wJkCIXK2d1b43Oc/zWDoS8SVltjCD6pOwDAZM04SZowpAYEvE+60O7TbLYSAXrfPYKdHZ2Z2FzhOAKqd9Ilda719xDupvQigM2gdMDc7C0JSOMvzz7/IO9/5XoTwWjhCBx5QlHl7pEOEEW6c0x/2sVISqJDl5UO87aG3EdZDhJAoYX2FlSx5JRMSrPUh+ul+K6AS+BPejg8dBMTtBiIOKbIRW5s75dcQU0JRlETaAqklGIMog3tZUmClIDMZ/8v/6//JjTu3eOzMGU6fegBk4G0QnUMFilZ7BusEVmivpFq2+zEWuGyd8WBMluWoMOPkXIOLdwbUwhmiqEHWX6HeuIU0fXASLSIkMYkLGKSOzIw9n0MVXtBOCfJQIOZqCBsz6maMrPfUjV1ArXAsBDFu9iAX1zOywPrS4HFKEISeYxMolAsJlWPcH4H1abgorrOyusJ4lPCORx+lWW+itKLX73PqwdOMkzEnTj0IQcjKyh2Ecjz6jkfYPnKQyxcvQxRx8ep1Hn/iMeJ2g8s3rrLYavu+J9zEXqCoFmblNTJ4I0qrJEivLixMgSuMN4gsfxQWhZ4ku12ZCqrsMJyx/vGQ8NK1NVa2h3z4ux5iphmicEhXgLM4lxMIaOmA1DqEdmSF1zy6H2NBmqZT6te7UvNVe730zng83rOPaUAwHo+5efMmaZoShuEExBw+fJh6vT75XJV+efe7383Kygpa60nk5V3vetcegGCMoVarsbi4SJIk3Lx5c8IjGY/HrK+vMxgM9pBRpwXW9pctCyFoNBoToFOBljiOJyDl9cDZ5Hm6y3b739//92vt+177ea32psDJRz/6UX7t136N3/zN36TVak1ygp1Oh1qtxqVLl/i1X/s1PvKRjzA/P8+zzz7LX//rf53v+77v4+1vfzsAH/rQh3jooYf4s3/2z/KP//E/ZmVlhV/6pV/iox/96N1XRK/RgjggNwlaS6wBh0aQowOBsaZkWftJRmsvtR1FEc5JVCBIkpT3vPu9LC4cYDhMS4d3x82b17izcpNer4vWAYsHDvHYY+/k9OkHWFldYXNz04OAkpNRq9c597a3MRyMiMKQS5deZjjqkyYFUbRbRu3crpusl1H3K+w4jmi12hw6dIjjx49z/PhxTp48xcmTJ+jMdBDal4wVacqf/ws/xT/4B7fZ2VkjCAOckyRJOiFvBUpNjrHbfHShYqD3ej0WFxd3hXEKQxjsss5hV5J4socShf/OZz7JKy9f4Kd++i+hgwAhJUpJ6vWaZ4sP+ly/cZm5uQWUhJ2dDfolYa3iJr2VfUBgcHnG+eeeY+XaKsp5UvDM7CzdbpetrS1EWbGSJsnku4CPgERxRLPprQ3SNGVna4vDx49RVfRM7h3VIC/2/L57faYfWH+tFhYWiKKI0WjE0898gz/1p/47mq0aQpT5ViHxcuT5hLwK3i+jvGvMdDw/SmqJc366QAlQftoQotLxmUTvfRRElg7HugKdApwvWWw0GrTbbYaDHhvrG6+6z66s4fDRNh+KtYXBCgnaR0H+1t/6JT73+S/w+EMPIQWMi4wbN2+ytbXtU4d5zv/xsY/xJywszB7g2edeAOD973//fRkLSG4y6FkyGyBsj8V2RkNbWrVZwiDEjm4RZWuIdOB9o1RI4QypE+QyxgpNJC1GCKQskCry104FFFoRNAPSPAGbeZkCLE0bsKxbjJrz3BiukQlHqH0kbDDsIXSEimtYoej2h9g89Qq+RUGtFrMwP0eajNBa8eyzz/LEE09w5cplgjDk0iXFzds3cc6y0+1Tq9V46cJFiiwnrjc5dvIB5heXuXPrNklegFalQ7X/qWj2Uw+KT39KSViLKaxB5AaKAml9WbFPCAm/WHJloXLpPm0wnuVauRALuHh9g9WNPh947CSRdqRJSiAFtUghhKHbH3LlzhoHFpcZ545+UnD18hXg/owF1QQ8nVq4WyXKtG/MtAT8zs7OhLRZldRubW1x4cKFyaRvraVWq02qiJrN5gQIeI0ibwpY6ZJsbGxQr9c5evTopCikmjeqlHy9XscYQ7/fR2tNr9djOBxOiL/VvithuGqxXX2Xap+dTmcSmWg0GqRpOkkDVaBof7ub3grsRkpeq71RMLK/iOC+pHX+2T/7ZwB88IMf3PP6v/7X/5qf+ZmfIQxDPvvZz/JP/+k/nYSr/uSf/JP80i/90mRbpRSf+MQn+Lmf+zne97730Wg0+Omf/uk9+gdvtInSK6MoMpRQWCNBSaIwRDo3CakK4UrfhIgoiDh+YgEdxHzt977OzRvXiKOI48dP0W7NEUUxDz/0MGnJYq7V6swuzlKvt9ja3MSagjRJsKVqoJSS0XBEnuWsra7inLduB81wNPIh9DAGhhhboFVMvd5gcWmJkydPcebMg5w7d4YTJ44zPz9Lo94gzQz9bo9eb5u19ZtIrYjrdcKa4uTpoywtHWA0GuA5lpokGdPtdpmZaeMc5EWONz33ISBT5L7K1RQopSYM7Xq9PmUUBUIoH2mY6kwV36KSsn/u2W8D8C/+t/91z7348A//OIePnAQsa6u3ufDSsxhjphyMd8ltb2UfQDqKIuXb336O8SinETV525lzBFFAsrTEc889R5om2Cz34MS6iViEKAeH2dlZ7wLrHL3ujhfAmsJmHuQKnM1ZuXWT23dWWFhcZnZmjqheJwiiKTDnoyECx/LSEq1Gg9FoxPVr13nu+W/zvve9rySqVtwAh7UCNZU+7Pd75XXytgmeMxWU2yucUqXLcFmB4UqQUjoPV8Js1XE8hcqVfFlFHEXUazHOGTa3NjC2QMmyPLl8Xpxz3uG2TO046x2RrbMoLfl3v/KrAHzl6acB+N2f+fMA/Myf+TNYUxDXajz97W/zGx//BOPxmFbLexP9+3//7+9PP0h7bKzHZDYnNoK6HHH8UJv5uRhjh8y1NDXhuTJOjQnrIIxhOM7IbRMhHHmRYqSgyA3CCb/gcRInJAkW7XIQih3TxWoIRJOaDFiotxmYMelo2+vfSQcixArFzqCL05rt3g55NkYpQZqMqcUhYajY2togCDQf/vCHOH/+PGmScOKEJwG9513vIct9Kvfo0TanHjjDlctXkEHIaJxy49Ydur0hUa1BUMrzO2N90ZgrU8eUATo82Vk5iHTEIBnhjEU7Tw6XJbKVCBQ+vezr2rxhZKoMBBqhIQoUWilurngF2c9+49KeW/EDjx3nzNFZtBLc2OjyzJU18sIQxTHz8/Pcvr0rLfBW9oGbN29OIhJVhcndKk32r/KnoxCVIuzc3BydTgetNU899RT1ep0nnnhiApgGgwHXr1/n3LlznD9/niAIyhRxa1JifOTIkUkEpjqvycKjfN2n2jcnXJU0TSfVMoPBgDzP2djYYHV1lc1Nn3quRNDyPJ9EQ5xzE8dipRSNRmNSflwpz74Z+fi7RUJeC7Dsf/1eoAdevfC9V3vTaZ3XakePHn2VOuzd2vHjx/nkJz/5Zg599/OxDqk0pnAUwpLlBheUaQy8rDf4RaRWkjiuESjN/NwsUVxnaXGOi90tfvd3v8i3n3mGU6fOcOz4CVrtNrU4ptmsEYY+f1ykQ4p8jBSlnkHZmZMk4dbtm1y6fImd7R0fSZAKYw0bmxuEcY0kT+jMzvDIo+/giXe/i4cfeYRTp04yOztDGAbkWUaSjsiyMdvdEb2dMXnmCa0vvvwihTW8453HweYoAQeWl7hy5UpZniURwjEcDun3hwyaAxqNGKUEuSnAVVoqnsDV6XQmojzVSt9Yg3WWQIXlPa4ci3dr66tt//r/8D8RRBHDwZj+cMDjj78TJQM21zdZXV2l3Z7hx//b/54oikjGA/r9be7cubkHnLyVfcA6R5qnXL5yA+Mky4uLHF48wMzCDNuDHrdu3eDOrVuYJPv/sfffUbYleX0n+omI7Y5Le/Pem9eXd+3o7mqHa0E7rPBPPD1gjRiZFmLNCLFmhicxGEn0E9ISGgR6S3oLSa8B0XgYoHHtoF1R7auqq8ubW9ebdMfuvcO8PyJin51Z95aBylH34sVauTLznH322SZ2xC++v+/v+2UyHIWROpI+HUWWs7iwCPiy6I2rV32KZK+dgbOMNjd4z7vfzUOPPkWWd1kcLJL1+xxeP8o3ffPXBaja4axBCcH6wYMcXV9nY+Mqs1nF+9//x7zm1a8mzSXOCoSxoCRJlkFdgzA4ZxiPRx5el4rBwkLQcglhRpoilEdLZHuwaMHU82RkG0GLAaeiV3TodwoUsLF5mVpXqFQGKD5eHxcCfo0U3oHWhkHWOcsTjzzMR//8w/zSu99NVqT8y//9f2fQ7fGH7/0jfvupp1hZXuJfv+tdrB48ikTxa7/1G/zP/9sP7dI5eSn7gWPKxraGdOgH4brmyFoPKzX9/iFwl5BmCipBdQU1FcsHlpHPQC5zpJJYvN6HxPOMlfSq05nImBmfPl7uF4y2thlrQZ7kSBJylTJQBZO8y8iM2B5N+cJpr+Z57NgxMIKr21fZ2LzKqRPHyBLBbbfcxOc+91mOrq9jen0+9pGPcMMNN5Apxdmnn+b48eM88cgjaAdveMObcNZw5NBhnnnyKRIpqcoZaMuxo0dZueN2zj/5BDLLSPDOyrYq0eGZ9bI9ksQBWGbDEdppQhYIF+vXYsrDgZRez8QIMNLhVIpME1+doxKkgnd85atZ7UFiSzpZEYJaHXgvhsVOxre89hgu6fPM0DFJ+lTa7gpOXso+cPny5SblcS3OSfy/XYrbfj0Kji0tLTWcDeccJ0+e5GMf+xi3335743Hz7ne/m0996lP86I/+KD/3cz/HhQsXPL+uKOh2u/T7fc87y/OGCNtGanZ2dhiNRrztbW9jaWmJhx9+mDvuuGNXefBkMmFzc5N7772Xo0ePNsFJRHVieW6bsDqZTEiSZBcv5/Dhw1y9evVZAcO1go0XGnxc6/3rVUK1VXiB/eGcfLG1elKT9FKM0Uj8Skdri1IOIS1VPSXNE/K0wCJYHCywtLTEwsICFsGNN97I+fPnqaqaspzxF/d+lI9+/M8ZDAacOnWKu+66i/X1daT0nXjz6pWm48QoeDwes7W1wWi0xawc45yHF6VKGI6GXLp4iSIv+MEf/EHe8XVfT1KkgCcvjUbbABjnSFJJWmSkCbz399/HE0+c5h1f9w4+8rF7sM6xfvIUBw8skSQJx48f52Mf+zjgS/kymTU+D+PxmE4nQyWJH8CANEuDOqyP6CPEF5nTznkV0iyNudDorRNt0OcDV7/fZ1aWvOIVr2A6m5KmGePRlMlkwqVLlxqNGWsNRlecOnWULH3hQkovulnHxtUNLp+5SuYKThw7ycte/nJe96bX8fTpJ3j6iUe5fO4sQLNSEXGwct4PZKnjVxloy5XLl3C2QhBIwRBQE82li5d45JEn2R6VuGHN5sYIIyxfePhBFhdyvvM7vsNX33imMgcPrHHq2Akef+wxRrMJDz30eU6ffoqbbrkVgfZ8ECcRGL+iN5q6nlFOpighyWVCv+h4D5hE4WuTRROIxPvSbq7hGVw7ryuloFd06GYFWMtotE1VzcilQoTqMWzUevETmzUmpJC8mJsIDBY9q3BG08m7FJnC2ZrxeIIxjm6vG/qZD3yte+FW6X+Z9sSZFTZ2xiTSa4A4pxDK0OtndPrLlNMeVjhSZRB5j7NPlqTFJq5eIpcZLkkohUIFvxKjNb1ul7o7o5xU2MqSMKHfg36RMdqaMiy3MLqim3Xod7uM7YyqnFFXvrxXCqiFQwnBM2ee4Vd+5Vc4fuwoK8tL9PsDjhw8xHh7SFF0ecPrXs/S0iK6qplNpxw+fJjPPXAf/cVlnKkRWKRzpEqgMGArVhaX+MQ9H+OR2YQbjh2htJrN6Q59kXk/m1ZfiCk/iQJrg1p15FP5OypD6lZXpZ/EhUArMInEJQqZJKRSkSY5SjhMPfPpH6cRVuOLDA3gkSevGWiwYkbiEoS2uzX3X+I2HA5fcDriWttFdGEwGDQpGvCT+y233NKMbX/xF3/BH//xHzflxDGF8/a3v53FxcUmjRIXsFeuXGlS6WfOnGFjY4Mnn3wyyEBc5uzZs/zBH/wB58+f5yu/8iuboGkymfD+97+fNE3p9/ucO3duFxoUEYiIkMT0UtQzicTeWMG019fmegHF9Tg7z9euRa69Vnny9VJMe9uXdHAizQTnFpCpxLkJxnqJ+kT5oXkyGWOdoJf78tFDBw+ilIevnZCsrq1x6oabmU0fIs8NWZYzHA4pyymf/eyneeKJx3n1q1/NjaduBgRCKtIko65qRmbExuYGV65c4fz584zHY4zx0sJlqUmSDEhwxnDDrbdx+x23MxxukpQZk+mUq5ubrKyu8PTTT9Pr9bjp5htx+Hxi0etw48238Mz583zZa16DTFI2NjdYO7iAUHDy1DFEcCaVQiBUhta2YYpXVQ9irZGIrpVJE8G2ZYVjGXFV1SRJhdbe28V3fE8alWHiklLSHwwYjsd89GMfZjyeMptVlDON0XOiVpolLCwssbKyxPLyIlc2Lu9bH3A4rlzdZjSeoaTkyHFfdtsd9Dl27ChrBw8gE+/mapwNIlnCVyXIBCUMvW6HLFNUxjAcDfFVMDG14zkkONgaDhlXJdN6ijW2kRJP85RyMsTVU8hyhJXgBIPBgDtuuZkHPn8fW6efYGc05tP3foqbbroVJ3QoAzaeQyIkGIfVmqqscUKTJII0zxBJisUhEu9A65z2wYcQ4LzxoS8f9qtdgdzL1W3OAeFIsoR+rw/OMRyOmU6mLPSWvNRJ4L04U1Ob0iMKFpTKcKpGERQftaWclVhb0+v6ChU9s1QVIAULgy5KORKlcNYH4PvZfuOPpixuCW5c6PiULgZMytLygE6hqKoVrpqcg8kQJzSqs8bygVU6W0uosaKyBikTrKu5fPkSzll6/T4qyUmKAjkZIpzFuhLJDgurA4bDmnIyRmmNSjN6eQcQmAHoUxk7ZRmmf8FkNOX+z97Hg/c9gAjPZJ516fcHZHnO0soyg8GAQa/LyvIyyyvLJJ2cyWibp598DKX84H708EH0dMJNJ4+SqJqbbj5FIiX1ZESSp1Q7hpGtKIREYWmciEOeMqYBmySkkAgnEUIhSNC6xgqHdsZr7igBSQLKp8xtROaEpLKGRBUUUpGEdKAVQSHXE/hQSqKko5NLhlMXbEP2p+117H2+ypJrVZhEzkjkayRJwtWrVzlx4gQAV69e5fd+7/eayphPfepT3HHHHXzmM5/h5MmT3HTTTdx00010u10+/OEPMxgMOHz4cLMw3Nra4uLFi/zsz/4szzzzDIcPH+aee+7hLW95C0eOHHmWDsrLX/5ylpaWkFIynU7p9XrNOBvl5ZvCioD8RGQiclvSNCXL/AL2hV6TvQFFu5KzrWeyV+q+LYMfOUDt/+Pi/oknnnje+/klHZwcXZpyuRyRFcukaj74KeVN6JaW1khzgVMSi0EkCU6Adr5UV0rFiROnGG6Pg8S6Q8pFnIVZVTIeT/jIRz7Gpz75OZRKuOWWW7jrrpeTpgl1XTUk1O3tbT8pCNVM/lprpHSU5RQwTCZDjh49hEwy3vvHf8y5ixd47Wvv5qMf/SinTp3kxltu9AcvBYfW15Giw+kzz3D33a/l9LnzPP7EI9x6m89FHz26TppmgEXKFKkEWeYj7Wm3YDKZAAZtNAiwdjdJLMKLMbeplFcljTnL+UPe/tsPZ3mWs7a2xng8ptfrs7S4QrfjoXpjDBcvXqTWJZ1OTlHkJEm6y7X3pW7GOc5vXGHH1eSdDsuH1llYWQGlUGmXvLuIJUcjsUJ64p+LPA0v3tcd9EiynEll2JlMMbVGJnO3YWcjd8dzc5yuwbnWhOvod7pYXZPkBU56PokUGTfdfBNH14/w1LlnMLXlM5/6NN/wzd9M0U1QwdjNVxcbJBKjfYrOYig6Od1eD4RECp9qckbPCa/WcfXyZT792c/wqle+yge6CwOUSuj3+6yvr+9e/QTEKCtyeoMeSZZRVZrhzpiDq/jVrvAokXO++s1UDqznJAgcSZ4jEORphtNe66Tf6ZFnXSaTCcNZRW0tg26HVPoAWpsaY/YXOZmmB9nYuMCxxRxppmjhyDp9et0uUmgQOWe3Flg6WFEkBYurhqm+zKWNnLLqQ1bgcBhr6HY7TQVGXnRQ+ZQkT1FaYu2MI4dW0UZiDGxOt6lnNT29yFL3ANZKFkTNLEsxoxEGhyJ4WCGAqINiqModhsMdhBCcfvoJAr2LNLglJ3lGmmX0el06RU6apnSyDkWnE8rUfXDa7w1IMGhdk6QppjJY5/12fDDrU4C9Xo/hZOyrr0LNmadYS6SzSOE90Q0OLQUm8SJ8Ugq8Bq1qPqVUgrWCBEUiHYkInI6g/aYAJTyh1jhNoQpq46jsC1MHfSna8yEoeyfqebXaPEipqorNzU3uv/9+3vzmN7O5ucnly5ebcfHBBx/kTW96E2maMplMWFlZoSy95MRrXvMalpaW0Fpz/vx5hsMhKysrbG1t0el06PV6rK+vM5lM+Pt//+/zpje9CWsteZ6jtWZjY2MXGfYbvuEbmEwmXLlyBa01ZVmyubnpqyOnU65cudKklgA6nU6T4inLkul0+ixl3Ot57bQDi/bfWZY1r2VZRpZlzfvX+jv+bu+7rmv+9E//9Hnv35d0cPL21x3jDz5+hmFpyHtLmACHSSGo9YxEKZ9SEALjvH28FDIQFlVgXhecOHGcza2rVNUUISwWRbfoM+gvMp1O2dkZMZ2OeOihLyCl4M4778AhG8nfpsJFCIRQIORcqbWaMhrvcOXqZW665QZUolhZW+Vlr341N998K72FJZ566nG087omOMvSgRWeeuIC5axmOpvwwAP3cXXjiofapGR1dZXBYMB0MkIpb3IIMJuNKGcl09mMLFONSdS1yr6stb500LmGDOoDKrkrbzvnmwTOghT0ul3uuOMOirxDlhZYKxgOh16yf3GB6XRMloVAzdldrpkvdaut4ermBlobFhYXWFxeJcszn4ZQgrTIfN7cSZxIQCU+OxJ4G1ZAp9dDpTmlm7A5HmOMIbEWYQCEV581ljxJOXHsKKnyfUdbTxQuioIDy8u+rFcb73+DV4s9cPAgN544yec/fz9XR2OeOfsM5y+c5aabbsO5ugmSvHt2wmxWM5161dYsyyjyHKc1KDcn6YYyZ12VfOQD7+f3/uSPeN+fvo/tjSFWCpDQ7Rb88A//MMeOHdvFSxFC0On16C8uoNKUsqrY2d5GG00iE5zxfdBahyTHCoejAmdIkwSV9SBJ6a8kPHPpArVSZIMFaiEYliWb4wlaSrrLi7gswUk/3ZXVdN/6AMDWZMp4ItguHWkqqaxFFT7vPtneZra5heAUV6aatY1t+gODWzhGcqEXKlJ0eCYsvV4vcBBS0iwh7xTM0gSswlSwcWmKtSWKDoPVBLkkMFenlOWUTt7DpA6jHBsj7w5rgvKvJAjCIVCJajQviOMSDuPwq3KgqmsEY4abW/4YrUU6gTUOkuAJJhRKJgi0T7UYfx6JcygBifBSAB7a9/n/PM3RZYl0zoccQiKoEaJCypxKO4wSiMIvfKQLJFkCBw3vZ+VkghCKwjqEKZkhMHkPIzp00xmyHAE+vdVVktpAvY/pvedKQ1wrUNn7Wpx04z62t7ebVHlM39x99918+7d/O7/5m7/Jzs4OV65cYTqdcuTIEd73vvdx7ty5ZhI/ceJEw1Hp9/t0u112dnY4d+5cUN1eBTyR98/+7M/4ru/6rl2VLCdOnGjG3xgMRakGIUTjNOwpAqNGfn5nZwfnXPNa1GUxxjxnENEONuI5RImLtgFg27AwLnKjFH4MgmazGRsbGw31YTgcMh6PGY1GfPzjH39B9/NLOji58XDN17xqhffecwld5eiqh80kMrGYqvR5feelhBoGu4lurYEiKAW9QY/BwgI7wyHWWYR0qATyXNHtLrGwsNRE0ltb25x+5gynTt2I0RaBJM87CDFubpgQouEsaF0zmU64cPESPjB3LC4uceHiJY4cO8mtt9/B2XNn0Nr7qpTlhF6/z5/9+Z/R6XTR7msAwWQ89oJYQtLrFxw6dIinn54ilESpFCkFVZkwq2rKSpNmKYhIRJJgQ+WN8Ox7v0qQOCeCzL9tghOgyVGqMBGD9ZU8xm+TJgolfCWQ0ZayGnPx4nkm00lj922MwWiNqfcvODHGsL21CS6l119kZWGBTErv+yF0SH2lSJV5aXk80dRbsWqUECRZQZrmOByz2ZRqNqXTHRDKejw07gQry0vcfOMNLA4WsLX2hT8qoVMUFGngqDiPtNigh5IXXe68/TY+fc9fsLE9ZTid8PBDD3LzTbfQeCs5kDIBIai0oZzUYAXdokOWZAi82Booz6ewnv9RVxUPfOFBLl26wpWrI6/zowI5zlmeeuIpjh49GoKfeZCaqoReVpCphHHp2NzcAumw0iBF0PmxIfdcGYSUaKOZjMd84I/+mCcffQRrLJ/7wsOMSSgWl3ECpqZiNB3hpKS3uAhCUteWWmums/1FTqqyQqcLnLu6zeLhhImFUVlz9swFbF2yvDigWH45j1+6SqfTJR9vYseOnc0ZCEdZzjDC+WdMSqTy9VNJKukUirooSOoMgUbXFldPyHKHSwUuTemsd5lsWOrZlH5esDG8SgogkmBO4Sud6pAOEHhFXuE8/J6kSagKc1SNcJrHKpT0qsNS+GDCB1IOLY0X6tUWZw01Pm2pHFjpsEhf/Zv4ez+dTJCp8mXELlCshUJgSaWl31XYJGc81BT9Di6VJDisERRFFlSLPe/JSQlZhpWQGMGhtQNcLSdslDCZalIBudD+PKQgzRyCGVLs35Szu8pw94KsrXgat9n7s5eTF5Fk8Hy13/qt32JxcZGv//qv55ZbbuEXf/EXeeyxx3j88ce5++67+dVf/VUef/zxZsyMAUVMjcTAI6aN3vzmN3Pu3DlGoxEbGxvUdb1LhbV9nPH1TqfTnFOv17vuNbDWsrm52fjYROJs3CaO9xGBiQHFcDhsDA9Ho1ETXLR/ptNpo/fV1l+JxxsDmizLyPOcTqez6/e+VOt8sbXUjHjtHQfYHE74zBM7VLqPMZ4MV9czjKlIRbcZmL1Lpp/QIjsdvPbD2toaly5danKNSvkLaK0lTf0KLEJsZ8+cZXl5lehWWRRFA1e5sN9EeaKusYayrLl08QrOecLS0tISv/wr/5FPfuITfP/3fz+ZUkyGI/I84f7Pf46X3fVqpIJz586RJilf94538P4PvM+XZGYZaZKwfuQIZ848g1JJQ2pNs5Sq1EwmXn652wkCPSHQiLwELHNUxc4f4ki0apedtR/q2PF9SqdHloNzFus04BiOhk3d/q4yPrebiPVSNucMWzs71AL6i10G/Y6XdfflJWA0whmkgqJIkcLhtIEaz8uQXnEyTVOUhXpWYkwkVbbrXiTrx07w1re+jY3LVyknU2aTGVobkiThyPoRVJIHVEP5Mm4BSMvR48c5fHSdB8+dpzKGhx9+jHd8nUY6iSDx1TFSeM+S2YzKaJz0JnoyVZAmoQQ6mVftWNCVZTarqSpLbUY+ZZUolEro5qlPpTga2owNvKA0TcnDKmk089CwBKTx6U6D84Gctk0qSBvDZz73WX7lV9/DaGebWVky1oJaOjr9PkIptNFMdYVIJIv9AZlKvEqtUkxHk33rAwALgwHbasjp7YpT631mTmMSxdqhgyQCpvUE3Vtg+dav4zOP3cuB/hKL/WWujHfYYYh2EisgDQqj0jqUExRJis5yTNElmaZQWipbY7VEM8JZiVQ9ZnqLvEhwdcJkVtApUorUk0xlmqPNPG1qrPP9KwTtxlhsHe6VkljpSBNvmmeNIQl8IWt8EKKkRALKJT7IcAYjABdo0MLrlhi82F9tDbqc+ftoa4wkJGgsHWVY7qYsdBxp5th2knOVQqTSoybBICradkTEEfzhau3QlUGYGmlLcpkhao0UqUeRXbBbUJpEgjD7N+UMBoOGg3GtyT1Oiu0gZa9JYNzHdDptCLFxP5PJhPe85z3MZjO+9mu/ln/xL/4Fn/jEJ9jc3OTlL385H/jAB9jc3KRdPtxG1uMEbQPp+tZbb+XixYvNa5/+9KdZWloKit+Xeeqpp0iShNXV1aaseXFxkcXFRc9ZChU5bQ5IPK8rV67wT//pP+Xpp59mfX2dbrfL9vZ2o50SEZhIvo1clXZQESXwi6Jo5O8PHjzYfGecd4Am2DHGNEFLVMCdzWbs7Ow0BRNPPvnkC7qfX9LBiXWSlIo3vuwQG9MtrhqDdQqc9Ct2XftKi6CYaB0BRnfeGyQE1lE2uNvtMhqNdkFpHjGQJMm8nrwsS5588kmWl1c8/JVmJCpB1xpPPhMhv6YoZyU7W9tcvbpFVWmyTsba2gHe/OavYjqZcuXSRfq9PptXN7nhxpO86lWvJMsKXvGKl/OFLzzC1atXGAz6pIniypUN+kcPIxQcP36MT37iE56sKiUSSZpkTOsZ4/GUqqwYDDpUVQkCTHDDdcIhBI2CrtEmuGnK4M3iPNDS6rTtnKSxhqqu6MseSSIxxqIU9Ps9lsODleUZdVU1nEy3fxw4jK4oZ77Eup8runnix07ncLpGVyVgSBKBlKFMtrZIkXhiqVBkaUqSJgignlaMh2NWVr3iKtZ6sz6pkAoOHznOoQOHvaSItl7yW0g/mIe0iAjiZQQjwMHKMkeOHyO7/wGm0zFPPPEU0/GI/qDnt5cCJyzCGEbjMZWzOCHpdH3O2Bm//LsR104AAIynSURBVBUyaXRLMP7eFUU3pOf8/aorjZIVg84ivW7Hk2UJKzbgnnvu4ewzz3D+mafpdPswrLh0+RIYSzCY8bwsa8hESqK8w3JV1Tz5zDNc3fYVKrXRTLUnvK70eiQWXGWpjUZj6XY65CFgUcZQzfY3ONneuIpOSkZ1zuWRIel2OXjiOAfW1rB1xWxHo3pdtMpZvvPr2b78FI8/8wRjvNGa1QaD8ygcgul4EhAJS1mXUFYc6S5w6uRBrK2YzkqMG1NrzaDTgWpCoXKkWODcZcnVy+fpdHIWe33WDq83YpCbW1sMd3YoZzOqaRmeMxu4IQ6sxDjryfXhuZmhmyos6fC6M84XAAtiOnhen6UQnu+hFAvLS1zd2KR2SejLAuc03SRhkCkOLqYsdWYUhUM7DU6Q7kg/Xkjv0aOUJ1jHVFTsSyKICBZ5n9H2FO0MeaE4sFTgZOXToXi7BSUcmVRMzP5V66yuru4KTq6FksR2LQIozBdjcRJvfzbLMhYWFnjve9/LpUuX+NZv/Vbe8Y53NOPjj/3Yj3HfffeRJAnD4bBJpWRZ1qANeZ6TZRn9fp+v/uqvbgKdU6dOoZRqUjy/+7u/y5UrV3aRTbvdbjNPRe5H/FleXqbb7XLkyBFuv/32xkH+Va96FVevXkUIX50axeOizD/QBBRVVTGbzZjNZs3xbm9vc/78+ea1dnDTRk32+hbF1kazYpsFMczna1/SwYkRGUY7eqrk1bev85GH6yA95rwDsHEkMvGBSIDnHR62FtYLVyXKr0qLomj0P+IqIUaGztkGeYg/O9sbLC0ukGcZSZI2FS0Q0zoi5HpzTF1RTkvGkyndhZxOt8M73vF2Hn74Ua5ubLC4vMTFixe54eabUKQ4Z1lbO4C1jgc+/wDPPPMUp0+f5lu+5Vs4efwoTlqOHVv3qQDnpZJ8QJQihBfxmUynLC12Ax8lRLfhXIQIZbXWMptNSZMseEkEtKCRAXHNCiCaWBV5Tn7wQJObFQKEsORpyi0338RoPKbWBqssShms8boH+9Vq7V04LRrlBT2bigRtDTvDCSJM9HmqEFYihQrH5PMXSgqy4HNTGktdG3AgYvWKqYN7q/JO0rlXARWZQwauSMjnhF36YA/hxdJUqjh56gQL/Q6b4zGXL13m8qVLdPsn/LGEawiGyWxCaQ1SZSx0eyhjEUZA4E41DFpnUVJy9OgRjh8/wnRWUtWa2hpMXXHs8DrLg+XAsZpfr59610/x3j/8Q778jW/i1a98FUZfZXN7wwdAKL/a1TXCenKocBaMxWpHpT0XxSFQeU4qaoSzLBQdqA16WocVukfKukWOQ1Arse+lxKaqyRONzArOXp1wOC84cOgEKi3Y3LjCzs6E5VWNlRkG6B+5hezgMRbKEiO91ICUkvF04kufa410ltH2DjujEaPtHa6eO8dD588zHg0D/0uhdU1HTShST5Ytqy06+RHSXpeF3EPpV69cQhvr8/lJwtHDh+jkRYNaerh8SlWVTKclw+kEY20gk1pMEPWDAMaJYHSKT/Naa1GB06LwdhiZSnDOMikrfEm4Js0MXSWQ0rLUUSx2HYWaIqUBkeI0pDIhVQLZqgKRUiCTWKUhgo6JQynJ1nCbI4MO2iZoXZHnFm1mWKkRzqKNwjqBs1DIlBcG6P/lWrfbbSpV9nJPnuv/9t9+oTZXYIV5FVAkl+7s7PDnf/7nPPHEE3zrt34rr371qxkMBo0+StQ2KYqCfr/PYDBgMpkwm80asuv6+jq33nprw9Xw9iOf4fd+7/f43Oc+t0umvi3JH3kmSinvFxYm+ojqr62tccsttzQBlnOOz3/+803QFp2NY1ARAwvgWUFFu4jieu2FcHn2/v9CxeC+pIOTBjnHMMgqFjPHGEXtKlLZaVb+NkzeHjiJqw6BEhKVBOg0RKaxYxqtcVkGiGaSjrCZlBLrvBJju0Qq1pm3g5ioHLi0tBR8GhZ9gCBTlpaWeOaZZzh27Bj3339/GPh9qmRxcZGq0py/cIZXvOKVbGxscOLECYRIUEpyeP0gWZ41cxViHvUb49MD9R4OCeA9MvBlaFmekcg5IhTZ9krO3S/bokXg88cCuevhJVwzKSXdTo/xdIa1Apc6Kmees3P/VZvR3r1ZSkdRJHPJa+cn063hCFRKkfdI0w7WeJG0mNazoSQvriiss01aRSS+vLdJ8bRlY4Uv/3Ui9agGftXp9SVC2S6RKC1ZP3SIlcUlnrl41bP5L1zgxI0nwn6Isw7j0djzC6RfDakslHEKhxfH81wh/xHHkUNrvOJldzAej6l1Te0MwipOrB+jV3Tmg4yxoC2Dnke4ev0e/cEAKSXb21tUZUmRdRHgxbocflK00WHV0MsLbrnhRi5fvczmcMeTzXXNQq+PqWrKkBJzFj5+zye445bbWTmwhkpSZmZ/S4l3dkaozAdtF0aS9d5BxsOS7cGIpbWDHDx6kjwv0FUdyl39PSyKglltwFqcdiSmwjrLaDJECUGqLMpp8ixh+dAqqgNitMJ0UmEdZEJgrGUannfVl5Qaiu6AzHlUbEH4yV3gkQ9XG2bTMTagCL4sVbOysoRQiZ9UtIHaMJpO0M54rpjWaOMrcXzKwS8krFQgQgWGEGRS0is6bGxvMR6PSZXi8OEVVpYyDvQUerZFJgxLCzlK+qobmQpmZYWdZqjLU3TgTMk9ujo28FXAuxWnMqVSlR9jsxRjDRiNFQIhUhyZ5x4Jhcokqtq/FG8ch/2tfeHBSXsiVkoxGo0AWFhYaPghMTjZ2NhASkmn02F7e5uf+7mfY3V1tUHUpZSsr6+ztrbWuBRHMuhwOKTf79PpdKjrmt/5nd/hhhtu4Pz583z0ox/lzJkzzXfDs5VUjx492mitRI5HDKac8wqx4/G4kYiI6Ze6rhuBur3tWihH+5r8Zcbu5wtsXug+v6SDE+EiJOcQbkKaJJhKoesZznTBCq8Sq6LRW5wF5oxopSQuS0l1ymAwIE1TZrOZzwMbh1LCF9eFyVtKicChVEKaJkwmU1xYxUrpQ6U2IUsIgUoTLl46z8Vz5zlx4ggydZw9e5rHHnucK5cv0u10GA6HVFVJmvnPrh85TKfb4ZZbb+DIkXVe+9rXkCTSE+qkYmltiX6/YDarfAqBkIJJUspKU1Y1k2lJf5BDWMnGwUtI0SBFUibeey4SuCTNOex+oB3R4ddG8pPzwYo/AP+eUglZkvpUg/G8h32MTbCVQ2uLNYJU5qjEowvOCmqt2RpuY4Rm0CvopikyTBhB1B0QJGlKmmcgoDaGMpIW4ykT4pLA3/FQR9CQECJg7ZE8i39d+m2FlDgEy6srHD6wygOPPonWmjNnzvB68eXhczpojEhfqeMcTsCgP0CluU8v4YMTQfTTcXR7Pe5+7Wu4/bZbGE5GjCYjxpMJzgjWVg6yvLw8HySsRSrJz/zbn2EynfLMM2f4yIc/ihWOndGQsppRpDlOW4QLwSkWbS0iEaR5wtLiAjffcBOHDx3mzMXzPPjIYxRpTjdomkxnY3AGAdz76c/w9NOn6ff6JFnGffffv3+dACirmk6eUAqonGOqrffQMTUbG2NWV5KWArIPCGqrsVKi6xKlBFIJksJzx0yW0MkL6llJsZYync1Qao2dnVWubGxz4eIltgOCIvHcDCcEdXhOLJ5H5JwNFgCeX2LBE+4LgTM+NVLkHaqtEpeAkJY8T0AJbCJZKha8Kqv0iG5ZVX5FLXxZuQmIly5t4E84lPQq1qWtSJXCacuB1QM4ucPGaBOpQaEYlg4na6p6RuUcuJzSWvqDAd08D2Z/Ida23ljP4Z95awzWgS16THKJsg5naoo0wSrHrK7QpkZrhXOKylpm2njNlH1qMcUR2/Uqd9rv7SXOtheUg4F3Jx+NRo18fCSYwtwR+Ny5c7t4epubmywtLTXWIFGjxEtOzBd7QggOHjzYVM4opSiKovlc/GycT4bDYbNgjJ+PqEo8lrIsGw+fOF/FIGXXgnJPu1Yw0v77ehyevWn/dunx3sqgGDw+9thjPPDAA89/P593iy/i5pzAWgNOey0rKXFGYKSg1hW69ip/Se4dXefBiU/xxAudJAlJmtIf9L0C6mxGqtKwmnZYXQNBIROfyuh2e/T7fS5cuMB4NEIp6b1J5O78pgM2NjeZ3n8fd955F697w904Z0gTycc++ufccMMtdHtdbr/9dvyiWyGEY3V1idXVlSZC3t7eQioPXVoE3X6Xw4cP8tSTZwm+oQipgqKroNaG6bSi1+/QlCEG0pNK1K6SOSnVXHUwdNB25/OKp4HXYGlKkA0mkDoJqYm4+giCponCmKTh9uxHU8KfU6IyhFX+vhqHM5rJaMh4tIMQlk4nJ08CgTBUTshQLQGSNPEurM5ZTF37vL42CBtULYXEBURNqQRnhc/JN3n8CLs3TBviyC5UQqc34NChNdJEoivNxYuXPLnVBfgv3MXRcBg+JugUnRZaE9i7ESYTEpllLK0eYGn1AMbqpgJN1BbhJDLJQvrBB6QABw8e9Ku8aUWRd0DAaDzyvkP9ATNnMdIjhEr6ehGHQaWSV77iFSwvrfLwo49Sac0T6jRZIun0+2gE4/EQXZcY49jRFdPLl5EXL6KEZDScrwj3ozlnQOaQCGQuWD96DAfUtUbXNVubm+TZXBnZGM32zg5Zp4NAYGoLiUA5RapSOnkXq33QmAhJphRCwKHVA+RZ5gXmLluGwyF1rbEuCBziPKkVGsExU2sENiCvflJzKlR0CYmQksW1VU/kdg6MxWnvBq0ieVOAyBLSboFfABuUFN4/zKlgWirw6JrGAQc6B5hOJiwvLOGyhMrliGQZKXOEkL5MXChmWqNdzWBhQC4sh5XFqRSR+LEIACuoq9o7G8uQFjcWW1acn9WkziKsxE5qnDZIleBIqLUiTTrIBFb6XTr7WK0TJ83nmoCv93ebcxK9aTqdDlVVNQFDm+DaXnzG724XFkRibHvSj/uIv631/adNIYA5QT/uNwYz0+m0QYajF0/kjvjS96Sp0onHGs8nIv/x53pBRSwhbv/dDjDitnv30yYW7+Xx7G2XLl16QffzSzo48QOABgypV2XA1IJaeT/vqq4YDkcspksBlieYYbU7rw82YlpnaWmJsixRQjUdwVrrDc9ijbcVzQ2P5kvxZmF3E4CctRhrGNY7PPTQQ76jWcfq6gH+h//h71AUPQSC1772tX7CtxpHzfbOBg9+/lFOnTrFysoyv/zLv8ztd9zKV3/VVzcd/MTJkzz15NnmdAS+8kgbT9CdzSqqUpNlkQg7j3j9dxkSpcJKKBKZ5tBfu1Jn3tHm1Ty7codi/kvK+CNIEnXdTvpSNOc0KhHNw6hEitMOXM3lCxeZTaakKmFpaZG8KBBKBVE1F5APiZIJqUo9kRDh5dsjitSCQoWUqNQHfzIp/L0OqJSQMuwzvOas557IDJxCZgWrqwfIlKB2jo2Nq15ltuGRWKytGQ63cM4POr1BHxKFUyIELB5BcdD467gw2SUip+EWqcqXbAYzQGttc1fng5ZojB9n0ymj0Yi1pZWAFHr3WW2tL3qyXuh8YXGRu162xPLKCpcuXSKViiLPyLt9KhJG4zHaGD+hy8zLnwuLsvq6E8ZL1SQaEc6yFhZShUwVWafDeDSi2+mHlajwAUuonknyjDQtAiHaIq1E1zUGg3EOqRTSWVwgtApn6BU5ywsLlJMSPdVUDNFBqyQG+LvHGN98ynd+H6IKs68uC27XicALDDuMDKqrRnsNHXyQUtUVxtYkMkEEYrcTPlAXEo/qQaiaWgIHY1thReqPUU2RyiFQKJmShLRfzcgH7EiEgUwSSpwNtbMo6ZVl09APcQKX5ahuDtoirAbnMNJPrBaLtAopPD8p9sX9am0y5i5Zh+eYLK+V3tkbVMTAIsuyXSmVvehyTJtE/ke7eqb9XW0EI35Hm2MSx/f4fvw7zjFpmjblzuvr6+R5vitAiO7JMcA5evQox48fZ3V1dVelTXvx+VzX6MU8u9e6B3vf/2vCOSkxWJQwSJmSKYExNbPKG/2lCqrpFGeWPFQdCJ8Oh7WhxC1UxkkpyYuChYUFzw0xPnXj+SWm6Wy+A2qm0zFPP/00de2JfkIIRNTXYPdNloAxFWdOn2Y2qykShUzg0KHDQSRHI1XCY489Sq/X4+ChAzz4+S/w27/1e7zila/kG7/x7V6pcGMYWPLev+Lo8XXvgOoAJ734VyCzycQLL41GM5YWCw/NmiBv7nxZqTE1eV6gpCcFe/JruLRu7t3gnMUG3QuEn4QjpBgfJpl4SNk5j8T4EmeDtS/On+HFNqEUUgosFaPpGLIuIlM4ozh99jyTskRlHR8Y5L2QupKIJPWDpTE+VeK8pYF13p8J58X6wgX3f4bMjZCCmPMRwR24cQLGILD+vSTzAZC1qDRj7eBh+p0e02nFznCE1jNU3sO7QWsssLWzg5OOVCn6/YUQ9LAr7z/vWMKTomNzPhgWqSeiQjhMY30kH0pYE+voJop+rkilZVppdkbbgEQ55dEjbVHWT44BD8PgXWr7Pa+o6xB0un2ytIOpFDs7MxwWlSQMegtooal0BdpLoe9nW8wlxlRoJ5ESrEgpsoJO0SFdO0ye5WhdU5Y1DkeWp55zoxRSeFNDlSisdBCCaxs8uyLq5Inu3lh0cXHRG43iUEnK1taOJ9LLMPjKUKkSb01rwG4mglDZJxBNpZzXHZGe2W3CWkf6oEOEoEfKJKj2pgjnq6lseDSV8ttajCcyC+PJrFYH0rPAWW9JEIOjLFUkSYYxLpSse16arR1CSawNLr/Wf967LlikdTjpS5b9s4Dvd35O9OidAyFqn9YSIPdRViCu6NvX+9np6d1tb3AS0eW2o29VVU1wEv/fu+8YRDSSDXv23eZ7tIOedr+IQU9723bV5N6gJr4W56CIpEQ5h6htsra21qAkz9X2Vto8X7vWts831r+YueBLOjhR1qCs8+JEKSSJQdgaXYXoF0tVVtRVRZKEmYXYGWi4EC7A6IlK6HQ6nsjkKmIVTFEUu0zyPEHWG/95MpufqAmdaNcA1KyiJFcuX+Hq1Q0Od1YQSnH+/Hk+/elPc9PNt3Lk6FF+6Zd+kbvvvpu3ve1tKKV461vfyp133klRFHznd34Xn/3sfT6tkPgS5+WVBaS0nhDqPAKknCCTiulozMljp7h86RJ2oSBVEmu9U7MLA4611us62N3BSF3XjYiaj/6ThtWdJklQs+QaD6Gf/KIIkX9Y5fxC70MTIiHPchLp2B5tMatr8m5BrWc8+OAj1LWj6GWsLS2TIP1kLQTGWU8rFQZv7Kip0ShLUO0MKFIA2pxsFEbCa63JJnYm4Su0nEwAzzcJUR0EkrM3BPMePlVVkWXdZpAzWgfStF/NdHvFnOPC7mu9N1feoGdCNIFMXKWICBZKnyoQwqMm3aLwvKlqxubmht+n9/MGF+T68SgLxmKlQCUJxmpG4x2MrclzRZ4KqsmEyWSCMdAfpHzZy29nOrHM9IzRaIeNi/vnrwRwsJ9yaexLu7tFB1MbpFTouiZLs5D3n1BWJVJIkkySBkM0F1SMlSoQUiCVX1lmaepXtMJrw7QniiJXrK8dpJvn5EHEb3t7i3I2pqwqplXFYLDUume7J0EHIRXqA0hnbDP5O+dVm8elL9fsD7ooFQTQnMf3fCFxWCgoFehPIvBTHImUIU3pFy9SBTE3axFazZ930WBxc5DQeWK4wVtiGBEqRkIFog29xBFSVzoY/SFCCjlM3njyvAy/vT/X/vWBttDZ3vZcBM32+0mSNNoeMCfZRhJrGzmJqIO1tintjYhyDGRihU0UWGtzFyPC0m7tlFH7uGCOyLRNBdst7isSYuMY/HzprvZ3v5jg4fmQqOf6nhfSvqSDE2ENKcrzBxJDkgqE09S1FzbC+ZTKZDKlP+j4h9v4iUSEBzdW1/gOI+ZVN87inMHhB7lGfCjeQOFTMBBK7WJHbV13If3DLIQfHLSpOf300xw8sgzA/Q88wPkLF7h4+Qrf9m3fzg2nbuDqlasIBAdWVznzzAX6/QFKSY4cWeczn7mPyXRGv+9RioWlPkmqmFYzD81JSSIUiVJMKgNOkKY5UiR44zB/XHs7YVWVaB2DL5ry4YZDkqjmes3nylYeR4SwLwZ7DXE0/L8P9z62JEnpdrqkUnD50kUefPALvO51d3Pf/Q/y+QcfxjnBoL/AYHUFGyDzSIp1BJMya1A4ZOLTXzZohggESAXWzKeWeP7guSiBlO1ouR1HnkhEVDwNkl6vR5HnWOP7ZDmbMlhY8UENklobXzHhIMsSiiIjln7D9QcD/7CHexOPm/h3ONxoehK2TaOtepZjp0O2NgNZz3l4uUGUrAslqoLKQV1VzKYTxpMhCEOnkCTSMKnH3mjTOo4eXefmUycpRzUjPePCxYuhvmj/2lpXcmFDUJKz1O2TFTm11uTGeSO7svRcskyFScTgWkF2o1wphM+GSe8fE7uxVK3BPaAdiZQMuj3EWkKn26fX67G1eZmt4Y6Xqw8TEOx+BtpdKL4iCOkga5t7VhQ5WZoGZGXuayPC8yYin0iI4OcUxhwZAi7j07Yu9g18wBAXWVFocm+KoX2c1hicDOlOF4bAwL1zwpPGlYnDQURmw3X0Vy6MJdKjkXb/RoN2emJvez4eRGyRZ9Htdul2uw2Ssrq66r3LptNGsj3Pc5aWlrhw4QJlWfLVX/3VKKUazxvnHCdPnuT9738/Dz/88K5jaadU2uhJnI/2VlnGz0UKQeQQxnRQO03T/lwaAuwXcu77iXDH9mLQmS/p4MQIixCJn2y0z7MLLJVxVNbiTI1zgtFkh6KboxIZRoXmaQLa0WrbM8CjExKDcAbnBNYAuFAjXiOlQ2UpzgV1PulhTD/Ie5VNP1hIYoXQ2WdOc/frvwxjLKduuJFTN9zMhz/8EZIk41u/7dv5oz94LxhYXFjiQx/6Mz7xiU/yAz/49zi8fohOt8fmzojBwhJKWfqDHt1+l+l4GlARgU3wbrtpwWg05siRo+xsX6GTZz4FRAxMhJe0txZrNHVQq2xPIjFST1Pv6hvPqxlIRTA0C4TRkFfAD1KGOUq1fwOSyrxqopAJ5WjGe979y9z32c/xyU9/go3NEQ7HytIia2sHEFmCtpZUSu9cawXCSBIjUM6RhVUe4XY5F1AHXCOIFQCSOWIf5Lyx2g/e1uEa+DRcE+nAODp5Tr/ooATUtfHEW2z4Mkk9M0xmlVeHzROKLPOOxc+hDrFrwowHZz0835BtnZtXGglwxpAISZ5mZCrD1rBxdZva1KEaLSGRXiXUan+vDWCtDo+PYzyZYKxm0O9RpAlXbM1kOgIhOHz4MF/5+i8nS7rsmBEf+OCH9t2VeCErWe7kXBlO6fYPkRcdEpWCEdTWCw72en1qXfm0iBTULe2i9sRmArIhgi+Nv/yREO+fEc/jEaRKMeh1yfKcJFXkqQqoyCh2mQi+hdukPBFaeF6cc2AwhPxHSO0IrLCkicJJ7zothfTPWislQIsfYcLnvMGgIwxf/tit1zgSoaKwQVSI663dBM9msgzDhLMmHLOYX4eIuFgvqS/Cwi2ep2u4d3aO2jlPot3Pdl1k8Tm2bbc777yTO+64oxE6O336NOPxmLIsG6+Z8XjMZDLh3LlzrK+vN1Lv0Wfn6tWrVFXFzs4Ojz/+OBsbGxRF4avAWkhJ7HMRkWsHUNdDOmL6KKZy2uexN0UVX7sWD+dLoX1JByeVsSihsdYEDwqDTCSuFAhSEA6VOFQiMLZC2ORZNzz+72EyQ6fTod/vM53O0KYKkazDWoGuTZMbtDH/KmUTnPi/w36taQaPNov7yacfo6o1eZpw4sQJtLYolSGFJM8VKk8ZV1Pybo8jx08y6A945LFHOXh4leWVRa5c3uDk8QMI4d0rl5eXuHLhyrMGmCzLGI/H3HrrrfzFPReBmMucq9/Oy4fn+cy6NujgICulbMR6nrUqCYPRnAgbSGS0VgZ7Brv9aHlesL6+TtYp2ClHPHj6ER565gmstliR0Ot0ueH4KVYXl/DG1dIPkIF7g3Mhxe588GY94RUMQoTkuQyoU0A4fBDmCJRsP4prw1OnT/Of/7/vRirFO9/5Tg4dOky8QE5B3snodTJUUKy3UfYzBLSz2ZSy9JB4UeQURYco7Ld3rf2s6+paK3Jj/SQXSbpC4jAhcJzvJ64QhRBsXN1oJhgv6iW9iRwOK/yiNw0eTFWlGY+mWCvo9xcxBmZlxU41xeE4cvgIq4ePYEvI1SJrBw7j9pFrAGD1iBsO5Dy1MeT4sRMsr640PiS+CMabUEao2wlBKufISXvF6ZyX628cua+BXAkhm9clkCeSlcUBeQK1nSFSyWhcUlV1M1b4tEZCRD927bM1TsTjkHjEJgan7ZTFboTj2WOakMJXu4cgQcX+Dg3asgvP2fOMNkEQ85VufKaD1LZPkSICmgJuL/KyZ8KdV0zuT7sW+fVa4/3e9+L/UkquXLnCZz7zGVZXVymKgvPnz+Oco9fr8fDDD3P+/PlGln06nfKZz3wmpPYdp0+fbrgqMZ1y8eLFXaTTvWn/9rG/2LRK+5zaf+/9v32OXwyByV8L5GRnYlnvezn2REpy5chzBROonaXoFDgryIocT/LT8+ABX4HghMAY63VNnN92eXmZ0WjEcFh5wNVF8pJXyLS2RWQKJbyRfBQX2jKkQCR+xePnQcMTjz3GZDQmLwrAoGe1J44przo6WOixubXD4cOHOHHiBo6fOMG5s49irWF1dYkHP/8ITtyBE4IkTVhbO8DDDz6CDFlgF6qK0jTh0qWLfOQjH6YoEqx1JBDOwXgGfu3VVZMkbaDeqqrY2Nig0yno9XoI4asbIktcRNDZ+cEtIk7NQBcmuJhmsvv8LEgpWF9fZ+XACpvbQ8p6hiQhlTlOCQ6srXHzqRvpFh2EsQHFciCl94+xDidDrYfwFRNRDK9BgoRsqnY8bC1wtfU8FBkefiU5c/Ys/+qn/w1JkvAd3/Ed8+BEePXgvDfwk7TwBpRW24DWAU4wHU6oK98fep0eSZq24z/iIflfbv5/EyCGoMnUiEThnGkmJEKwYrTGGYOQgizN6HV74BxXtzcoqylFkYC2yMRP4JEFbIxf3QtjmU7GVLUBlTLoL+CcYDarKesKlGBxYRkrEkgl6Iqy0eLZv1bWNScOCg70EmLVjtaaosgRQZ/HTwBBDyRRyFBxt4usGn5iMo5weV0kiLQHe+lnfOGcF3RMU5KFBabVjFIbBIodu0NdCyyxjDRtVF1bN+fZk0brgu2t+gCaFHKzIGigvYhcEq7D/Jhjys+DNCIgNWCD4SEuLjJak5oLV6AdRLmIJMaUZghOGnhoHqi41mFLGRS596lF0mcMUtqoWPz+ve/tRSsmk0mDgKysrDAcDrHWsrCwQHQAjt4xEVneS26Nrb1YjN/Rfn3vts/X2t/zfIu+vwxZ9YutfUkHJ1d2BLeuKcAgpR8LiyxFCD9Q1tbgSHZ1iviAOmP88yUFxnjEwFjXlGYWReGtpxs4P6Rqmnseb7QXYOt2u0ynE2qjvRmYna9QpFT+s9Zx4fwFNjc2WFpZ9YJozpKl0YpcsLq6yoXzV1hfP8zagVWkgNm0xGrLyvIiw51tjPYlkV4p9nATLMXgBCRJouh0Ch588PPcdNMNLC4cAGeIlTcmlFJOJhNA0Cn6wUXYUJUViUqwhSNJFFVZNSOPf/ilLw5S84fduT1wpPPldzL1EPp+NaUSThw7xl033cLmuSts7YzCfTIM+gNuvuFGXvaylwVyrpf5dir6hfjgRCcC7WKwUONdAY3PfJnAAYhCfgEpac7bau+LIx29TodXvOxOqlnJow99gUQKBoMBg8GALO+wubHDuctXI/1w16DlnGA2mmBqg3CSft4JxGNPJ2xoJBEiic21ohOgKWNGNuWn1nokRTmQSnleiZCkacagt0CiFNvDbaqypFsM8B5BAWWxNgiHSZyukQ4fnJQlSZJ4oTiVMC1LH1hJvPCaTLzWha2ZTsb7dv9jm86gcFNuPLbC+tphukWPLI8VCrbhhaWZ55TFNFM7TWKN8WnZiKw1E3IrC9xMCgE9EK340PkKv6WlZZyQbGfbgGE4mqJNnKj8giYGJteD2oWUwWJj9yQam9aGT376c0ynM2644RRHjh4CHGEZ5FM3YTKOeis+sCAgHvF7fABqnW1QT+tcKxflg5F2kOECWtLw0MRc1LBJ6wgvGUgYTxExdbZ/aZ3FxUWyLLvmJPxCUxrx/Tb3IxJY55Ydz0Y52gHD7ufaPWvb9vZ7v/v5Ao7rBUHX2/75vvP/6vZivv9LOjg5t2lApojgPaGkpQg233Vt0BqvDhtaXPg4QTDPiqZobj7gOP/Axghca4Mf4B0uoAXtfOBc2CYhLwr0ZIyQc9JZJMWqsIgZDYecP3+BE6du8hoT5cTn8oUCV3Bg9RD33/cYAsnhg6t88IMfoiqHmLKmk3dIhMGUNUnhIfaVlWV2DQpuzgbv9XqUZcn9999Pnt3B8WOHEK0afGs9sauqaqRIm1K46HophGgY6CaUEz5rJdIKPOJ7p0+f5sKFC3Q6HY7deCroPuxP07qm2+3wqle8kovnL/P06bPMZjV53uPY+nFe/crXcPDQOlJZEqHQxqBUjqktSnothtoYj/I4TxQVQvl7Lefwewy44kpUCBF4rt55FSt45cvv4gN/8rv82i/9Mr//O7/JH/7eH1BkBXmRk+Yp48mYM+cv+opeKUKwWCOENyucTCaejGwd/W4PFfya4uQ3X+GHdIPzXAIZOrYLnic4X2EFJgSrQfjLGERYtVvn6PX6LC4s4JxjPJ56teEFg1AO4fy5Gyy19cGOv96aaTmjKktSJ1jq9JBSMq5m2KDP0u92SbBI4dBKMp3OXjCU+5dtswpkVbG+3GehX2CtJkny4Hvlwn103iUZfNqV3YOldT5Vca1Bfe/RR7KrDKkza13DCcnzgsXF+UrdscV0Vu9CaXaniMSe34SA1O0OkqAhOzscZ86cY2dnyIHVA4hjh3cddzugcS3Eh2c9i/MxjT2fj+ceUZb5e7TUuZ99HvFqtQOrOE7up87JtYK4vcf2QvcTBczaAUY7rbb3u54rILrecVzv/Wu93g5M9t7nvX2qvc0uVPCLADn5a0OIPbc5Q4gDKDTK1ri6JlN+4C/LyotIiVYnEAIX9IO8E7gnh4nGtM1DpFLFNI3CuTq8FVdRsSPMS8mkDCVoeYfxeOrfl6IhkXl7CkGqUmrjuHDhPFU1pegMGI2GSOH1JRCCxcEiO9sbOFtz6sQRJJqlxT55npMowd9481chgJ3tITs7O5gQOCgjsNKigzZHrnKyLGdlZRXrLA98/iFWVxfodHKcdQEt0v4crWtMqdomUKbJuwfkKHBUIqnLWp+OkmHlBT5Y+9jHPsaf/PEfc+NNN/IDP/Q/72sfcNZRZAW333YHmxvb9Ds9xrOSbtHn5Xe8nLtf+1oQia/AEQalnDe2QwXSo0Ki6A+WSVXGoNsnkZm3drcmlAP7KiYfiHnugvcXCUGCUjgsMvWlx08++STbOztoNwnXyVfBCCHwWsMJytYI4xBW4KRfSY9nOz7ZKAyDfh+Vp9fkwgq/07ACb28Q0A6hoI7LYz9pIsOPs57EnTgGgz6LvT4IxWxWMyuniEDOtNofsxYg0yBqaA2VsUxnNbUxOAG9hT7aCLa2R5RakynhDf9qL2LmtKacTPezCwAwMhakJiVFMUUlC2SZIk0l0VW3UUEOyEkcImPVQ+T+tAd7Y0yUsNm1ffu3cxaraz+BJRmZkijVI00VSvn+koynjIaThngeW3slDiCDa7gMX+gCAdWjFDIYTYKSCUeOrLO0tMhgoe91fJ32/CjEsyewmKhsIT3xnISQSOWfpYZnQ3tCC8id8KGRdPP9tb8jNsu8gnFvcLK3dPaLrcXz2Muza5flxu32koj3BgTt7a43Ib8QJKe93xc6ue89hi+W9tcGObm4UWKsJAUSJKmw5CH4mE0NZanJC+v9dVKapzGKHcGcb+IakiMBMUjnttIuClHRvC9ClUoUPRICik7hybdhhaqEj7TjiioNVRwXLlxgOpsyWOgDjieffILf/M1f5x1f9za6RcbB1SVsPWPQL/h7/+P3UddTnNaMxzNc7di4tMFwOGJra4d7P/5JL58/GODTuYLBoMfa2kGGOxMGA8Xi0hKPPVpSVYYss7tIrmmagqBZ1UVibMzfaq1JUn/c7eBEiKD26HaXWUYvh26vR1F05jyc/WpWkwrB8mCRN979Oo4ePMzWaMTBlVVOnbiBopsiMeAMxlnvpWTBWesDQufIVMrR9ePcdMMtLA46rCwuBgTCQCyLdCEvL2gIxN4UrekVXuJ7UlNNDbZ2GOaaCE7F6cDzTLqdgkQqmionHMPRDjoIuy30+8gsdtp5c85Ba9UvPKQCFi+1HxXETA3B6Tj6CTgLGINMElIHvW6XpeUlEqGojGZ7ZxulTvmVsQyu3mmKdRatPUqgnWNUVhghIIFev4sxltmsxGhNJ1NkKgPnHbN17dVn9xc3gWEpMFKR5jmdXofBoE+SBIlttXtCAT95mmetJt2zJgL/aguxcM+elKUUDefMOIcUCicsRdFp0qCJ3CJXOcPhKKRSQSRzFdA40UmMv/7zPBI4FzhlotGsSZKEN73pdXP/G+dTve3jas5q73mGNIzAB9y2Gc92T37XWsW7Xanta7RwPWRLsRTmY6DYR6GTlwqda4LSPdcwipg9V8nyc+3z+T5zrb51LXLrXs7J9QixcTxvf+a/d/trg5xcHc6YVppuCsI6UuFIA4oxmVlqbemnCVLuVuSz1iMlbUQAaJjp/iGKqR18PX/4Tq/4IUGJUJqnmog6y1KSVBK9LUSo3hHCs9wj0vLkk08yHo0xqyssLi5y8OAqnU5Gt5OwOFjg277lG8kz0Lri8uwKs/GQ0c6Us2cu89RTT/P0009x+vRpLl+5zPb2BgKFtV0IPihPPvko9933aWbTkjRNOXL0OHfccReLiwOm022qqmwkjuNKyTue6gZG3rsiuFZg0oZo2w/E13/91/NVX/VVXncllqbsU0ulIJECoRSryyss9RaoMSTCkqrEy3Qrg5QOIyQmpEmsdZ60rDXdIuc1r7qTPKlZXlrgxLEjCGeAKKIGTaWLcBB1b5y3InDIkCtUmMrSLxZIhO8H0Um40l5dFQfKeVfTNDghCyFxRlPOZr4kVAoWer1dUPou6Fa2BAVN1C8R0c8BbAhMjPYqpx2PwAipcMIFxCVh4fAaS6vLwdresbW1hYslyPh0kaz9PU60R9tm1lGVFQ5IMs+1cs4xGY8R1pGnGd1OhlJeHFCbivF4f311AIaVoDSSvNdDZRlCSVQITryP0u7gouFiCI8YeKVmi7BxPGj1WeeDUhc+H5VV55wUiSAE9UEjqSxnpIUX4lpZWkZawSides7OaORVRwsvOx7Lh306x1NxdyEqMZiwMnA/YoBFPChPqme3pkl7wRA/45Fc0aRl/EAVIh7xbCQHWqhRTGvSTuHE3czT3CSq0QeJ2yoSEpnsa0n5Sx2cxL/jvvciJ9f77udKLb1Y1ON67+8NnK4XfMz76xdP+2sRnAynho0ZLGcZCIeVvhojU5aJUwihSLOMPMtwjmBY5xee/ma2IUbPQWlWMFKQBsSAEMQIfNAio3aJ9JCxTEJ6B0WeJ97vR0q8MaHP1/q5SyKc5ML581y9fJX19XVOHD/G//QD/4BEKaR0WG0YjUZsTSueeOIJHnzQi4k99dTTnD1zlp3hDlrPkErOVQmdwdiK6azk6uXzjEbDMGooypngsUeGOFNxYPV1WDuvq4/n6n2CSi81H8ocr9Wp9wYq1tpGsA3myEq32w11/YaZrYi8h/1oEm/IJ4QjTROvuWAqLp45zXhWccONt9LppXjvGoVVmffIyYIMt3U4BUcPr3F47Ss9kiIzkAm41CMeukZIj6KJyEMJcHcUX/M6NpZu0eHmG05gqamMpjYarTWzWjOrSmbTElNbjh85QpCr9WjedIrCkgtLWiQsDgb4HKSK3XPehEd8nHVBndMHQE0lkrUhTSlBW6rxkAc/9TgWQd7tsLK6zMLCAGstjz75BSw1AsnW1Q2EtgjlUGkKtffJkVLiEkeSQlEJ6skOypb0VY9O0qWuDZPRCM+3yCmKHItHaDQ143LSLkzZlzapYGZzBt1FnPPPkl/BCy8KxnxysNarsYYwoAlMXHNb4yTi+5XBoTyc0Yg0+vLsyOUIAUEQrlPOkUvl0QIHaZqwvLJIlicMFrqsHlhke3ubycw73OqgDCyFwGiDMxaRJMhA3vVjj9fgMT6+DTwuEfSGCPc/ac7vWSvrdmVQ+FzDm7N4JAxACZSYG4HGJmP8EsBCK3w8HKdDFxc0abJLECzy0JRIfEXTfpfvvURtL48jpnX2oibXQtqea38vxTFdL+B4ocfy37v9tQhOSm05O+pwbEVQVxqEAWFJEl89kqYFzkqESHBUQflThZVFQFCu01SrPNjomCeed1gRSmWlUqggbiSEpN/ve4+NJMU6ia79KkxISYLAOcX29jann3qSW2++mfHWlI3NTa5e2WA0mnDm7EXuv+/zXLhwmdNPP8PG5kXK2RRrQw7UK5B7/4xoBoaXXJ9OR4zH410rHPAD19NPP80NN9zAykrfD8xiTk7zXhKGuvLCaVFbI5LCkjTZpQPRDk5igBLfN8bs8nCIpon71SoDBgnCIZVDT2c88dAXUJlgWtV85r7P8prXvoIkVWAlSeIVQF2t/Yo6qF1mSept6s3cQA7n03NO+vRIk0Jx4IyXDce5RmDKKUF3ZYGvePOXs7K2zPbONrOqoq4r6lmN1oa6qjDA7bfdSrfXBRfE75Tk1PET3HnjzaS9DgePrDflneDTUFG3pJ3rnucqXeChEGB+/1HrHA9/+nP8wv/n3Yy18QJv3ZSlfg/pHGcvnvV6H0IwGm3jXE1dOba2h5x95imsrlleO0B30KPT7aC6OZcuXCAxjkFW0CkKyqpiPB6jlGoQoTgplWXJdLr/nJNpbZmRsbqwTH9p0ZvpsXsgj9fNWovRBqTE2Lkasr+eDu8R1arUEBCREuF80IHwDsRzcTMfDEZ/qUR5jNUDEgKVpBRLy6gkEPAtbO2MmIwnOGBnZwdd14yGIybjMbWuffXQLm0T/1vIuVN4XDh5Qq6YBwmtQKxJ/8XrgfABkN+bRz1a6UkR+HTPmkQEjfiiCIF1/J44cUdvl/Y1lVKS4PUE99Nj6aUKANrI8HNxTl4IIbata7K7n/3ljy966LRfi8ewN62zNwX0xdD+WgQnxlk+8fAV7jx1I1KOEdIinCZNUkBjnUJbsNo13hNEcaLrXKAm0g+k2CRJ0FGNT4gGQY9pmkQphJJhAJt3hiRRzUQRJZytUDgrmFnLb/3mb3D+7DmscTz82MM8c/ocw+GUSluck6RJh9WVQ/QHB4ANytkIqVRYtXj+gDPGe+0oSV1VoWRzvhoPmC/OQV2XTCZjjh455Ac9KZrBTiWKolNg7Qxr5vBg9IPIshQp090rz/B7r5vmrqhdChKldq/6X+JmEehQAi7MjNNP3M+H3/dBkv4yb/nmd/D0mXM88cwZbrzpJKkQuLqGRIAzeF0yB0L5wVp4vgAi8ehEyJ54wrSZIx1C4axGBFXgxiBQaijg2O23sH7DSUxdY6xG14ZqPKOcTSnLEofj0LHj5J0u9XgceD+C22+/g7fPNN2VRY7fcAKBa/glMa3kV9E+WHHWV6mJ6J8jYmrH3wNjasbTCQ88/jDnts4zqWscArkjuKAUicO76aYp4LBYSl2xM9riP//ye3jk4S9gjaPIvIX7YDCg6PZ46PFHMdKRd7p0ii7TrR2mkylCQCcos/o+4gOy2XQK+1hCCjCrHZPaIbPCP7/y2QOyMaaB69MsQ1uD03s4BEJiMagkaaTkZbjmGhsIpXFbMU+FSIl0okmRyFD9hWiV1wvh7TNCG3S79Arv5bWysIhzltm0ZDqbMRqN2JzsUFaVF+azAbUVYh6wCBtiUutRYRWCK4QvIxctE7kInsS0UGTG+h3NvXKE8ClAEZST7O6UgeepBJJxqxLSObfL8dZfHv8ZFfqaL+PfvxTDS1EJFIOHuK/4/16X4esFGH/ZIKAdQDzXvl8sKvL/D07+O7b7n7zMxx89yBtuWUSpIdJqlFBoM+Pq1S06/ZxZVZIXaUjVS5z0D51tEeCEgLaAzyxwTtoul845rJQNxNvAlsIPSs7NBz8VVzfWeZl0EfLFCWSJ5NzZZ/iN3/hVnJOUVY1zCiEKVCrIs4zBYMDJkyfRuuKJJ76A1lOSxJN4ratxVmONh1eVSNFGo+sqDILx6rQeViEoy5Kq0kTRNM9VcQ0cb4zXVGk/kP7cn/3QxjLKa60wGg6P8GaK+7hYQiQCkfhV45Xzl/n0PR+jHI64sjHm9JNPceOdd/LQgw9x4uRJlLV+wHUukBwTPyQbQkULc9RJ4ksmjJvj18IRK2KaFEpYVXuth8BFSFNkmpEKh3MaITJcsD7AOZ/vT1N/DMaBdiBTOktrvP6NX47MU+9oLAS4mkCMAGMRLnj9xCDFWZytveptkoMRHtWRIKRPWYzLGSUllfV2DhhJpbz7sFSCVAQRw26fmbE8+uRT3PfgF5hVU4STbE9Lb4ZozpAoRSUMGj8ZJWlKrTWT2RSEo9vrkyRZUDaFqqqZzmbsa4QKTA3UZCSJRDoNLt1V5h6D6LlCLDgrSGGXKZsQIowR3qUaB9KG+xYCDiFb+f0WOhCwwwZ5bJ6FwEURwudCptNZMBj040aqJFnf6wz1e320NmijWZ1NmM1mzGYls2nNeDymrnWoovOWGgRukLfJCKGJBeGsF0AU88kuKiFb4SXbhIkCbiCkwkow1oT+FgTp2D1hxuBIJqoJTuJ7WZbtKr+NLY6D8XrtV3upypRj9da1kJNrVew01V5cO4B4voCiTYiO21+rtfd9Lc7Jtf7/YgpKYvtrE5zsjCs+8ImzFMk6p1YGWDcjwWJNdH5dRltNkg98dQThYRJqnpsX8yg5VujEFUCEz/bCfQpaA1R85sSuAKfRCDEhtQN+BZF4I0Cjw6QPOBxKCdJU4Z1y4eSJEywurvLUk4+HxbkiSRNvDW91WB37NEpjABZyw/64wkTrIEtT7/ewvU3RSREC0tRPps7hbe4HOYJRk8aykTDIHJbcyzuJLb4WJwC/vWQuv74/TQiLkBbnSoydMtyeYqsJshJcOn2WV7zq1XRUxublIUcPHfTBQpgskNITRK1X8XDGIpQAjBcqc8H4zc1TXSjp0TepfFABuERCVXPxzFNUkxF5miM7OWmn443m0i5KJR75ij5EziM2Ks1gOvaabzIh6RSeZpJInDO+AifC8sHM0kWPGxGqiFQ4DsIAKR2oFExGIjL6eZ+jh48wLMcYDaa21LX2qYPgONspCpZW1nAiYWN7yKSsmZYlUimMdSCt1wuRDpkkUGs6/R4Gzc54i2k1weDoLQ5QWRK0RQRVVXoRv31uxknqfAUSiQKUUA1qEBE+n74Mz2RrRbmX/Bj78N5BVASyazTZixNu5KrtQhha8H2br4AT9Pt9qroKCEWA+gMqNp6MGQfRuuFkymw2o9aGWvt9dzoZta6oa4eug0uyECCVVyyOA0ArMIn7F8JXijXADw6sa1XU+X4e+ShOzlGgNn9EBgREJgolg0NySOm0J83d6INHjqK+zH60l6pMud0v2ouyGHzFbfa255t0X+hnno+70uYUXQ8VuV4g88XQ/toEJ9poRrMOH/zkFd70ymUSmSKUr/mvtaG2FbKS3nU3SXwUIb1mQHAcx4uxhagVhzYWbZ0fwJRq0jm0LmoMKvxPYMBL2ZBohZCtUmTtoXYcLoE0S1CJoq4qP+kIA04jZYKSBVVVMh7tIKXgxhtOMhptexvsTJEmOZVUrcHEz3VKKYqiy2QyZB6d+OBEyoROZ8B0WlJWFVme4E8r7MV65KDT7ZKlKWVVMZvOMKYm5qtfaAe31oUS25CbZn8hRVPXGG1IjGZ7e4ueyrkymdDrL3Hk0BodJTmwsszFixscXFslCU7CrqpBGlSSNlLbFhDWowlS+NQJSnmkY56jI9Qi4wISJ5BcPn+BJx5+lEEi2NrexCU549LLuScoskSSpQlZkpLkOUnRJc9ystSRSUue5mTdJZJOhlQgVQrSp5hiOtJZhxA+vWabCTVOmgaBwaI9z0nXKJGSJgmLS8vcedtdTPUEHSvVaoMBaicoS02RF3S7fWbaoS108pTZxFKbGm0tFs+9SKXCaosSOavLK0glmVUzal0jhWBlecUHPMq73k7rmZe1fw7zwpekOYFJF0izzHPOJCGI8FU5cbJpJll8X6117YNvorhgWCqEccEGsUZJ5FpEDdbQ/0K6TeLY2tlmodf34wztACUQzZkHBkqqZwkY5nnuEQkp0M5SGkNZlYCj1jOqqqYKDuSxG3oxP4iBhQ0pxjRNsQK0jcKJAdkLKKhHchzG1g0vzAhfUSaV55ipTKGED0xUEm04PL/EGR8UqdZiTqpkV2WjYTcfohlH96m91Boq8bwiitwO0vbyTZ4P9bjWvuNnr/f+3vfaiMjexeJzBSjX299/r/bXJjixVqOBzWmfTz60zStvWyfNLCoh5AlTny+uDWkuMNrSnLaY511ti0NRa40Jst2yhZy0IcnIOQlveja9lOR5RpKkzXZKKaw0TWpEOjBpSiIlDt0EOB7y9/4/utYMq20+85l7ufu1d6NCLrmuKuh056sX/Ifjqqzb7QEwm03nZFeV0e0OyNKCyXhCWZYMBt5QTsg2h8RrPxSdnCRNSFPFbDbzaIiSzWqy3dq5+qbDiXk62zrXpFH2q3lHajAjGD1xntGViyQ4CmGxwxHSWHqDHhc2LoWKDNHA2eBLiYXwJY5C+ioP5+omEPWy7cGjRkUfnLAKFBYpFVjHuXNnuOu1r+Kx+z7NucuXSLM+07IiKXIW8g7VuATnWFtepdYWZQW/+xu/w9bmVbJUkmYZ3e4K3V6HQa9gYaFPt9dhYWmJXq9H0e2wsLBEXvR9iWySNFwVGdIFCItwhlSJgH45VOq487abSaRlY3uD8WzmHU1rQ+08guBF6BYZ9Hu+ukQl3HnLLYxGI2Z1SakrZnWFNgZhHbouSUXB0UPrSKAua1Kp6CQZa0srXuQPidaWndGIWofU1H424cuck6wAleBCEEHIyD0L2QipvXYAHZ8DWtvHH9OaVpsBPxJi8f3dq9EqrLGkaYJr/G7CgTB/VkQYe/ZOGEIIur0eKEm332e1PuCJ1HXNdDplOp0ymUzY3t72Y1V0unXelRwAY0mzHOPmEuweAQkLBefRuDxNSTqduSdNlmCsaTRJEiGxOpictuT2VZKgnEcam0db+IpEK4Sn2AZEZ++Tv58T5EuJnAC7iP4RFW6rxMILn/Tb241GIzY3N0nTlMOHD7+o/bU5f+1jje/t3faLsf21CU6cs2hm2HzAlUnOTgmZEGSZCnCrF0aqrCN3AmM9Uz0KM4EfO4wxzAIRrSgKAKRU3iY7iJHtbddiXhdFTp5nGD3PWUoVonAhUVZhpfN26BjvTBsefGOMr2B1Dofh/IWnqeuSLMtBCLTRzMpZ8900hNc5vNfrDciywounJYJO0UNK1eRQo4Omv3bz317GvkIlsfIlrL6sFy67HmcgPihRyt4JgR8jXVix2pd8RdNuzlZYUzGutnji0/czFjDNuuQq4cITTzPe2EJ1MsqAUgkbJhPlkQgpRaiEEUglsab0aTUVKzy8VgmhfNTbDOx298U57GyKHo84ffo0xgo2r26QZR1On3mSQdHl8MEDSBznL1zi6nTMW77hGzl1403sbCwic8X5c+dxQrAzHFNWhqfPnKPopIyGQ6bTKUIJZOKRs26W0ykKFpeWyPMuKyur9AcDur0OnW5Otygoii5ZVpB2Eo6tr7F+YIVpWVFZg7aOeloymk6ZzUYMd0YUnS6H1w6QJYJj64cZbt3I1tYWVVVSW+0DGRzSOoQxFEmXQ2trCCfod7rccOIkttYcP7ROgsIZPxnPZjP/7OzzQGmAYTlDJBnaCbL2KhfPLYE5ZG+dw2jT3MX5c+zY3t5hMBi0Su6fTZXYOzkIQlDhCP0+LEaExDTcr2tPHu30DwQnYqXIlEIlKvRThRBQ15pEea+uOGaVsxmz2YzKGGpdU05n2LJmpiuquvaoiNGhPNhSmWBaV6V08k6o/IGMhCzLPclfCnKVIpw/Lh0+E/laGIszHlVqUORYZs/1Uwr7KcJ2PXuAF9vaaZ3opxPH8jbZ91rjf3vivdYx+FRnxWg0Igs6R9f67PXatfgl7d/PKkp4Efv+YmsvKjj58R//cX7iJ35i12u33XYbDz30EACz2Yx/8k/+Ce95z3soy5K3v/3t/If/8B84dOhQs/3p06d55zvfyQc/+EH6/T7f933fx7ve9a5dpVEvtlldIRRokzEsDStdSSoURtd+Msb5h1NbFhaXsXhvEQjS4lagjaDWjlpD5iRCpiSJJc+7ZJ2Cqqo8c7+FFjy7cwpEyHWXZoYxKUni/VEEAe2QFpV40zUlk8BH8ZwUG8loYZe1qRgMUvq9HptbEmtLZrMhed7zwqLWBw3O1mAF1lhkWAV1OsUuhVslfXrG6BJrdaN30pDrrKYyFalIEEBZllhrSdPMly9bH5x98IPv588+9KFd1//gwYP82I//JCQKYy2/8Uvv5pN/8RfUWnPnnXc96369pH1AglFgs5qJLbma5JheH1EUDLaHSOlQiaTWM6AGlyB8WNiyfxdIfHWJcQaFCP4GvhKGBiGznqvUGqAcDqEkvcGAp+57iNXuEitHD1AhubKxwaGVZYw2FEXB8mCBxx55lOlkyGy4QTXZoNuRLB5YxLmafn+BWmt2xhNOHjqFEpJuL2c0HJLmOU8/c5ZO3mOpUzAZD9m4fJFRVeMezyhnPn1ijCEFOr0uea/Dy152J+94y1vJ0pwkyfyzYMH2HQeEl+2rqsojKHlCkqTcdOMJDq4uM51UmLpmNJt69MRUmElJXVX86u/8Dv/LT/7YrltxdP0I/+Qf/xC5KpjNKn70J3+MX/vN32A8GTcB/371A+Ec28MZLgrkEZ9vi6nqoHXUWnUKH+xJ44OHOQJoWVpaet68fxvyF841qaOIhszHhaiMGhcQz97Xrr+FaJAYhBeKFKqVxnMWYUOxu/RifkVesDiwyCwN4oCOFL84q6qaWtcYbKhW0szKqXfVrS3ldObJzkqFocehjUahIAn8Bin8/4Bprh+8/yP38MGP/sWu8zmwusw//offh7V+P3/ygY/ywIOPoo3h5htP8NY3v2nX9i9lH6hjVeVfscV7Z4xpAoiInOzl1ewNwNrplvbv9pyRZRkLCwvPOsfr8UeuFQA9V1DUfq0dJL4UgdtL0fYNObnrrrt43/veN99B6wL/43/8j/mDP/gDfv3Xf53FxUX+0T/6R3zbt30bH/3oRwF/s7/hG76Bw4cP87GPfYzz58/zvd/7vaRpyk/91E+92ENpmq61f4Ckoq4tUgZCHl4TxAYtCSGTXTnEdkmsX7UIOp2OT3EID+Hlec7C4iKmqgNHZHdHi46bHqZ1Id+bUJYycFlCxU8ctJAIFHnhTcm0bpuiuSBqBuCdkdePrHLw0AHOnHvSX0NdoVXq+SIy5sfnn/cdcZ6iAn8uMhBmax1XQF6fxP8YT/SLn3GOyWSCMYbBQJIk+S4ocW3tIN/7vd/n4V2lKPI8sFvgN37lV7jvc5/j7/7Df0jRKfjVX/plLl281Nyrl7oPWKHQWrC1M2Fal1QaSqMY2wk3Li2QFQXb5Yyy8hUnSaq8jD2ec+OCiy9B96VR0xQhSLSA0ZBlRJ+CRnNEQGQenbrjDk5bR7K1xawqmc5qMilJCq/EmwUi8/r6QY52j3Px/CUe/sITqETSvdChk+dQabKsoCtzhhub1Kbi6JEjLC8usr0zBG1ZXB1QzUqWV1dY7x5kZ6opuktU0ylOKoY7M8pyh27H37OPfOwe3viGL+fA8grCGgQCK3zpqUoSEiW94zL41JZzZFKy2Ouz1PccLYPDCF9qnBiHM5YP33MPd9x6K3/wq79KpTXT2ZRMeXVYieVHf+JH+aP3/Sn/8sd/gn/3H36Os2fP7rpvL3U/EEIwGo580NEiZSvlJe1jawZr2dad2NOnWn3d72teibF3uz0H4bkpzF1tIzF+HsjIZ0mjtyc07/nj5mlFQVjcxAlS7ZpsjPEpx4Dz+WO0Dits4z6cSBX4cL5kvNMt/HXQFlMb789FGAeNR1nAB3xGm90TnNae1Bqu28EDK/ydv/Wt+PyyRGbztMcfv/8jPPL4U3zXt34dnSLn9//kQ/zW7/1J8/5L3Qfqur7uBP9iW5ujFP+P/emFoCbXavHY+v0+vV5v176f7zOxtQU04/vt47vesXwxISf7FpwkSfKsPBnA9vY2v/ALv8B/+2//ja/5mq8B4L/8l//CHXfcwT333MMb3vAG/uRP/oQHH3yQ973vfRw6dIhXvepV/PN//s/5X//X/5Uf//EffxbM9UJbVU2xWpPnBcb4srpE+dqFYO1HVmQsLy8/izuxFxZrlxTGXGyWZfQHfSYjL3C2l2cRo2kXBpY8y6nSqhkEJZEgBzKU/uV5TpZllGW561ysDb48Qe3x/IXzlOUMpRKMleBqtJ6hVNCMFNFYzoaRbB6oCOYBVHQGbnLUpA3KrLUOInYVKkmIQLWSyg9O6TxH7py/Rt1erxFnK/IcKQXlbMZH/vzP+bv/4J3cedddWBzf+3f+Dj/xz/5Zc34vdR+ojOXxzz/Cw1/4JGVt0DsV/cU13FIXWXjicFmWYK0/nzABK4uvbvDWxLgocR4fcD8b+N9Z4qt0hMRqDS5OaPNKq6zb4aa7v8x/RlfY2mIr7Qmq2mudlNMpZVUyHk8Y7kz42q95C1tbW+xMd5jujLwgX7mFtpbxdIy2hktnz5MkCTNdYw10sg5b20P645T+YsFoZsnHFYsLi6RFwkBJunoBYQUXL1xkOJ4wrWqQChHE8BrypxBYrOfSWI+iOGsQSPI8QeADORd4CCpJyYRC1xVK+VXksUNrOOfY2Nrm8597kMcfe5zDJ47y7l/5b/zkj/wIRw4eJMsy1lZXOXPuHJ/4xCf42q/92n0ZC0ajMbXWng8mBML5Emkl5qWeDYegVSLrYfsYXvvWrjSJrXnucSGj6pqu4pp9yaaSKkq6R86WEIK6nk/sYS3jjzWgKsY6jLFeE0QEpBYZLJ6EB/HCGKSrCkJKNlFJ1BL2XBhrvU5LM4nGVXRLQFGbJv1sW4FNElRPTCiDToLEPtAgzr5UXaKkZGGh56+CDCXY1jKbVXzmcw/ybd/0Nm48dRwpJd/2jW/jZ//ju5vr+VL3gbquX9JJuE2gbiQi9mi47P0bnps30g5E269dK5h4Pu7JtV7bG6B8MQUlse1bcPLoo49y5MgRiqLgjW98I+9617s4ceIEn/rUp6jrmre85S3NtrfffjsnTpzg4x//OG94wxv4+Mc/zstf/vJdaZ63v/3tvPOd7+Tzn/88X/ZlX3bN7yzLctckvrOzs+t9o2cYXSI7A2rrZZyLNGHifBWEAfJOgUrmfjLxJyInsIdcRKsjWb/6KYoMoyOPxOd9rZt7LlhrEc7S6RRoXQeeg2iRqLyfC86z5WNpWlMjjwNbI5McazVF0eHMuQuYJuccCZnGpycafaUAU0uIvhzOheqOKNwUBmVdB10SSagOSr0di3VobwDjc8f4FWNV1kjhEaTYpzY2rvIz//bfkKYpx0+c4Ju/5W+ysLzM6ccewxjDnXfe6VUogfX1I2RZ5qFjeMn7wIULV3jwoce4/dTNfOqhJxBViRltIgY5i4cPIlPF5nCLXr/j7xneUVg7X8Uk8ERSKd0uGomPPoJpnohqbB6VErHMK1ZfOOe3FQ6R5Dhn+NRHP85oa8yJE4fZvLrFyZOnMBjypQ7FoMuxYx3EyeMkKsEKEE7jhKPUNbNaU01qtreGTEZjhjs7bI13mM2mjEYTOkXO9s6IC1c3KMsa5yTdTg9jK9IMOkWBri2TScmg36foFDitQ+DrESGlEl9pEkXopMBpE85F+EDMWZxwZGkLypZ+EpJC8viTT3Lyy15DnmfcdPIU/9Pf+QccPnyU//O9f0Bd1ywNBnzqU5/aBY/fe++9fO3Xfu2+jAVlVYETpEqSSE9I9Uq588FQKdU88/H59gsSi7U+QLieXkZ7SBX4dcAuorxzvjQ9vOSEQDrXLAxiKsknTxwCi3TM3w+IrQzVQdo6iNVBTXqAxhenkxWetO40SSLDmEWTGXaI5n+lVBjvfL+3VoPTHoHFewoRx0H8/RXSYZz1cj/49LMvW47ltHBlc4t3/ewvkCSKE0fXedtbvpyF/gLnLlzGWMuNp4431+zg2ioLgz47Q++19FL3gYhI/VUm5DYi0S4ljhP9tYTY9qZN2vtp/3+9z12vXS9ouV5g8nzn9KXWXlRw8vrXv57/+l//K7fddhvnz5/nJ37iJ/jKr/xKHnjgAS5cuECWZSwtLe36zKFDh7hw4QLg3XjbHTG+H9+7XnvXu971LK5Lu1lj0bWmrmt0nQKSPFXMKkORKUrjlVWNNdiQzokBQTswaesbNP+3VhtKKZScV/rEjhVLzAirDSmdTx/YZPf7znlxJOPTB1EeP+aW46DlUFgnqOopf/Knf8T5C2dwaFxIT/nOtnvl1zxEgb+ytxM3gzCugT/ja0rN4UXn5sJM8bW4vZSSo0eO8I3f9M2srKxQVSV/9qEP8fM/++/5sX/+z9ne3iJJEvLAL/C5du/wHIOTl7oPPP3keW699Tb6egiTijxXSDdCTUdIKdG548rmNovLB3xpqfBiVbrWpJpgtOc9UER7ogHPL7F+NSialySg5hOAm088LqBX1kqefvxpnnr6LMIZHvrsA8y2Zzz29GP0Di2xeXmTPC2Ybm0y6C9wbH2dRFq6iz26K4sIJzmwuMbagYNoXVF0On7fMpITYVZphuMp1axkOh0x3Nxie3OHndmIndE2o9GERGW87vV30y8yL9pnvey1EsE9V0rfF4P+h2aG0/4amWai2g1xR67F61/7Wv7Tv/t33HbLTTz61NP8y5/+17zz//nD/N6v/S5GeF+q17/pjXDvp0I/9Nfp4sWL+9IPAMqywliv5eGP9Rqcjvl/u571Vo9/1nMT83sxTRK5JTDnIkSiqMMGefndxdPWWhLpjTD9dg4nZKPuG79TSYlLgjhgS521PSZJ2tol3vnYCecpaBBepyGfOpwXa2wtoqSUwY1YNPhf3D7GZn5R1RoTAjLkhzLHyeNH+K5vfjsry4sMR2M+8OF7+U//9df5wb/7PQxHI5SSdLudXRNwr9dtgpOXug9Esv9fhVPRDgj2pnXiNdnLI9nbXmwwsBeFea7P703rXO87r7fNF5sJ4HO1FxWcfN3XfV3z9yte8Qpe//rXc/LkSX7t136NTqfzkh9cbD/yIz/CD/3QDzX/7+zscPz4PCJ3zqDrGbrSzGZe2j3PE0TpB8UkyVAybdCSqqrQVUkWLOnjBNO+cRG+LGclOztDTF1SZOmuAToqyPoJXoTyWYnDhG3mGiExCBDOoY1Py6RJ4l8TAqIJoUtwRgOC4WjIE08+jtYlzpZ4tVALBASFYE8uQGCCIJJsSgrnHIr5MSTKa754tMSPQmmWNZyUODn73Yuw4nJUlUYpxU0339J0/LzIOXXqBv71T/8rPnXvvWQht9+YAaoo7rR/fWBze8zL7jqA25ohq5LFPIdEUY+36BVdtICrV4fcfsttXllVKI9+OOlVW51DZkHMzDpcElY3xvrBWyqEzEKKRwYUZe8D7gtNpUwCpO6oZpV/VXhurVAJpVHI0jGZaa5sbrGzsYNUO4wqzXRrg6STkg861GXFsUNH6WQFp8+c5e7XvpbJcIdDJ48xK2csLS0w2x6TZV36/S6Lxw/ggu2ByxI0Fmsc5aSi1+16gi9+he6sRSRqPuHFaqXAQxJKYeog1oVtymxFCL6ckCRS8dY3/w1qa0jyjO7SGv/23/wM3/qd38a/+/n/g+XDqzgHSZHRX1po0p372Q/AV7JMJ7PmeG1wG45p1yaIcK5BAefBh9cMsvh/nRM4pLc3aN6Xvl+0OCMulKfvrpT26ChC+KAB3898QDif5CMBFWsxzgQUz+uP4DxXxFfq4E3zpGosLJxzXmpeSaSTzUJql0iciKtzr15rg56T56h44UHvphVKooVAqcCVE7s1Mlyz6vfXQwjBnbfd7C00Ak/txLF1/tW//8888IVHg5r1HjT6JVrBX68PvBSE2Hbg0eYGxX7TLCif4/PP1V5I4PRC0J+9aE37dxvpafOF2u//92z7ltZpt6WlJW699VYee+wx3vrWt1JVFVtbW7vQk4sXLzYclcOHD3Pvvffu2kdcSV2LxxJbnufkLVLbs5ujrkq00UxmAmP6pFmGMxW1NojETyjGeDKX0RV1XSKaVY7XJ2hL1ccbuLOzw2QypZ5NyFeWd0F9Uc4YCEh4GJBcrOoxzULcE+NSBJZEK6SELFWhmsePZTjvlYLTnisxG2L0NHjo1DRDSUjZyDSspKQfwGQUlfNLMTzrxvNS5gJCMlQozTtulqbevVWI5uFz1qKdaxQ1rXG+kkUpX24MOOsoig5ra2tcvnyJO+64E601o9Go4fd4x+P5oPFS94HRpEYmkrQr6biK+soYW3RYShKOrx9k68oOs+0py90CWddehM1WCJeirZ+QXGWCaJjXLIlqsEIpL4RmaQUlYZCmreHQ4CoILKb2ku0elXHABOu20fUYPVYkLqGshjjptXRqW1MLqLVGSUmNYGoN09GQS5evcunSFbY3rkI355kzz9Dr9fjUPZ9iZWmVLE34G29+E2eeeYb14ye54bZbKIIT7yDvIaTCaI1UyhOeQ+oxQv7GaF9OHarZRJiYpfA8HCd9cKoidwPX9PMkSzHWce7sRdIs59jRozzy6MO8vP8qX5pe1zzx9FPhCfUtroz3YyzQxjDcGYV748iy7JpqmnMUQqEUGFPNAxcrgjmdALy4nV+o2IbsGtYFnrsTVtKR8N4+WSE8yRhHE/w3pbTNZA9SJeG5tFgJVtvANfEbCIJoGi0ET3jjQSecR/aURDnn+VOta+Ksbfgx8dPCiV0u0RHhhMDbCKqxu9LdzZi4O7AT4brhHN1OhwMrS2xsbnHTDScwxjIrSzoBSbXWMh5Pmu99qftAmxB7rQn4WpPic3FD9qb7Y1rnettfq72QlM/1Pnet1/YGHC8mNfTFEJjE43gh7a+E8YxGIx5//HHW19d5zWteQ5qmvP/972/ef/jhhzl9+jRvfOMbAXjjG9/I/fffz6VL8+qNP/3TP2VhYYE777zzr3Io6LrGCaitY1palPLaJNPJhKos/WreWaQ1pMJPyMZEQpjbNYFHzY5YrZPneZhk9S6+CrQIdtY20KdSXh8l1si39x1JtkmiyPOUTjcjmvB5uNf4oMJpnJ5Qz4boeopzMTjxLa6IVNAk8PsXIUhxCGlR0jv1SunC9yqUks966IhwcUg15XlOp9NpyGDtFaexZhe0OJlMuHLlCt1ujyNHj6CU4pGHHmpQm0sXLzYpnf3oA1NtuTzaIVnosbK6ykqdsDxzHFteoL/a5f6HHuPw4XUKKUmsQVqDrks+fu/H+OCHPsi9997Lpz/9KR55+GGeevppti5v4IzAiSSgJiEQi6tRs3f093etPYCZSmOcxYroFptjtUBqi5uMSWyF1TXG1lTVOEz8Kaay2NoincJpx2w8Q0nv7OtTMAZnHZOyZkvXXJ1M2JpM2RlOeOqp02wNdxCJQuIFtOJk1JjQxcqcMKn4irUo3+6l1XEyVHf5e25xWOHQ7fsOc3VT69jZ3OaB++7j4qVLHFw7yF233YZSinv+4l4ee+wxYF7m+brXvW5f+kG89jvD4a5+u9fm3s03nv+0257Bu4HHw3lbQfDdEa2/92wL7Xi1lXJtp2Lj5ZuT71Wo6mlbYDwL8bG79x/5M/5YgrVC67Nxm12TUuiz7evUvj7t8/DqsC0hyl2Xavd3lVXFxuY2g0GPo+uHUFLy5FNnmvevXN1sUjrw0veBuq6vyxeKbXNzkwsXLrC9vX3dbdppnfa5tp2J96Z19nJTrteeKzjY+9nn45HsRaWuFXzEfXwxBCUvtr0o5OSHf/iH+aZv+iZOnjzJuXPn+LEf+zGUUnz3d383i4uLfP/3fz8/9EM/xMrKCgsLC/zgD/4gb3zjG3nDG94AwNve9jbuvPNOvud7voef/umf5sKFC/yzf/bP+IEf+IHnQUaevxlT4he+jklV0ssFCTpwTBR6VtJJu1jnfGkdgtqBQWCF9vlfKZA2mLgJsNKxtrpEkUs2rspQyRBXPK3JyGmEobEyj0GJbCYW4VMJIe+bJAKlHHmW0uv02FYjdG2IipK+P/nVmm+tkY4ote89gaRMPUETj4r4lYxpvj9JJFIBosYTOtNGUC4GUHGwityT+F5Eiay1lLZGKcX73/en3HbbbaweWGU8nvDBD7wfIQR3vewu0jTlDW98I7/1m7/BYGGBxaVFfvnd76bX6zEej/elD6jCcv9jpzn15ru46fWv4pnRJyiM4thrX8P5WcVnHzjNd37L13ljP1eDMnzhwYf5zd/+DSSZ91sSnl6SpwUHDxzib/0/vpvVwwfw6TONkMFEzgTSYMs+IDrDxgBFAJWuEVJRCIXREpHkVHizNFNWZHmOMI5+3mU6NeRKUYoaYQ1KC6RLSJGMZlOSRFCXE4o0ITEWtMYYidRgNXQHvWD66OgWfYQW3hywMXfEV1E4X5+icUh0qORwTToSZ8mCYJjFoa3FSa+9I6XyhnAqQUmPvP3Ij/843/j2t3P4yBEefuRB/tO7/zNCCP723/ouqrLiK9/wBn7q//XT3HjTDZRlyZUrVwC4++6796Uf+Nvh2Nre8mXCbq+3i/D8sTiJW09GtZ6i1TTZLBLmbRe0L0Rw7g0Bn3W7DZfbMYDwQo5CETybYiAbXMqDsJ0JqR6t5x4/uyYqGfWYDNJJFCGVLKQvGVayhRDtzjBdf0V/7feNMaRJSpZn1JXn8LhYWtxMyP7Dv/uHH+CuW25icWHAzs4Of/ShjyKk5JV33U6nU/DqV93FH77vz+h2C9I05Q/++EMcXT/E2fMeHXmp+8C1hDL3ts997nOcOXOGG264gS//8i+/7na7PcLmafJYFr77evh2PWTihaRpXmjbi4JcLxhp958vFsQktn1J65w5c4bv/u7v5urVq6ytrfEVX/EV3HPPPaytrQHwMz/zM0gp+fZv//ZdImyxKaX4/d//fd75znfyxje+kV6vx/d93/fxkz/5ky/mMK7ZnPUPlZIJVVUiREaaCgiaH9PpFK1LFgY9v1KRElLpBxZnPKve+YHLWhtTxuR5hpQLCGBra6tFUnMEtzaCg1dzLO1VW5Zlzf9SxtcTlBIoBZ1OQZ7n3kPDzZUlG6JlM1TuJTcJrPVpFhny1HEFhqNBe/z3eihSSkGWZ030H4MoR1tUaj6oK6Ua1MOnJwyj0ZDf/d3fZjqd0uv1OH78BP/j3/17dLtdAL79O78D+Zu/zX/6f/8HtDa84hWvoNvpct99n9uXPnDi+CH+4nOPc9sth7nxlXcy2thkQS1hTtzC7/z5pzh17BRHDq3h3NTPFonimfPnmNYlgzzHqaDg6UBPSk6fPc+5i+dZObyKc57fIwShqiVMbC5M+uLZD5l1lqqcInF0soS6npDmApz3RqpM5flGQSslEQQBOIuxBmctMk2QmZ94kiyh1BXdPMcZi5KSqipJnMXokqKXMq1KKmdRWeaDMIMvEcZ3TYNDKs+fUEp6WW7hBQNdgPyNLUPRvUMqjypKlUZPQVSSejuCcO5nz53je//+3+fq5ib9Xo9Tp07xT/+3f8KrXv1lfPKTn+Zvfdd38qGPfoTf/O3fQWvzLE7a/owFgs2NDYz11wmuMTALgTHeIiJiS635do5MMBdcbMbSGDTQTu7Nv3tva8sSeD7JHMlqBzzWWaRKMDiUmKMkcZsYyOxFayEsiEJgbIxB1zVpkjbPc2zXS2nsnbSKoqCuvQNyr9fDVVVTort3Vb+1vcMv/sb/yXgypd/tcPLEUd75/d/NYNAH4Bvf8TX80Z/+Of/t138fbTQ333CSt/yNN/Hv/+MvAi99H4gKsc81EbfRtOdq8Xq2z/m5kJP4mXhtrse1ud5nX0yLaFkk57a/41oppC+24OSFthcVnLznPe95zveLouDnf/7n+fmf//nrbnPy5Ene+973vpivfUHNGoOSXkSt1tsImZBmntQnpUTXNVeubNApMoqi8AGDw8P0NiiBYgDltc2Ezz3LkN4pCh/9e6VGv+IMmlweGWlIcbtVAduKgjE4ybKUXq+LYEplNFmWIoXAuPZg2A5I9izvAnLS+OckCeClqWMgFI/Xf68PUDqdDllWIATNsfkqIprzjIFMfDC1jtoBnqfwN7/lW7y67S5+zjygybKMv/W3/zb/9+/5HrLMIxP/x8/821336qXsA3fcepLHnjrHe//oI9x18zEGi0d4ZHPGg3/yQVaWF/kbX3EXUu5gbeKlxJ2h0+uS5pmvclAKpxTK+vsnhXdbdSH1IaSvWMC6uSOxaz/oc82TeK0XFnq8+c1fyWgyIk0FS4sdirSD0TWXLl9m0OnST69iE4XVkjxRFFnOZFb6Wq1MkmSKWT0lLQrfR4ocBKRJwnQ8ReEQTtMdFJ57ICVpx/drZJDXd56fhBShzNQHokqlgVeQ4IzBugqjKxKRoAOhUsgEawyJ8Fohzjk0IJwjTVJ+9Rf+M1hHXU3503v/gjtfcReDooOzUNqaZNDlf/nhH2Jrc4tHn3gSYwxPPfXUvvWD2K5ubDYk1vbk3gzY1jVig07K0KvFnGRK9MqJz1v8HSeziGrM7z8imsExT+Owt4948vveVfd4PCbvdFDKKxHv5bRZa9nY2KDf72OModvttiYcmlRy+3z3pjass7s5Mc01YdexuJiilIJUpQ1y4OUOouP4vHrxb//f/mbzfdYYf82kaADfLE35lm98K3/zG97S6CvN9jhUv5R9IKbdr8cLAXjta1/Lq171qmZs3Nuulyppp3X2BjZ7Cacvpl0rcLjW/+0gZHeV2bXRsXYwGe/j86W8vtjal7y3TmzOVejZGNVbpNb+oc1STwhTwvM7Tiwd9c6fkVfivOiU9SMT3jPFIYPkYkQxGhSkyNGmQlgd0Bc1r5YRhAlA7upA3jgvTmAecUlTBeRoY0jLiixVyMCfd8IPENfu4/M1m4NG5TYOTvF4O50ORVHsUu9NkoR+v9c8uPGc/ArBAqnPWMh2JO7VIquqah7m2OnblUA+Jd8mGgbOAh6KtvbFPbAvpq32unzLW9/Eh+/5LF94+HHqytFJB9x568181Ve+krXFjicMo5oA8uWveAWXLl1htDWh1sa75pYVotIUiwOOHDkaVrJBhBef4hBYBKqFkkVSmmjuC0BnccDNr7zLD9bOeP0QA1obyukMV1WMvmJINZtx5fIlpJRMpjM2trcpdY3MM/q9DgrH4uLAO/6mEoSjU+RsbmyTJBJjKjpFB1PWYC3dLMPqEpWoGJtADUooH6iEPuu0pRKgTY1yBiEsEr+dExLtXJjMEkSwNhAh7aecAy0QWYrDMS4rHn/sEc488zQrRZdBp8fOxmVknnL2iSf/L815O+HYGY0RMgmuu2J3MBAqVRSeyK5D6bcIE6rn2PgmcDhh8flaB6iQMoncnBCARM6ZsH7cEHHc8M9cDDZiVV571SyEYGdnhwGgjC+37+ZFo0sUr10STB4j0tm+ni5WVIUgOU1TT8u21r8nHYIwHiFCKsj5rLCI9ToQ9Zb8/OWrtaKydhwMXbwyzcTog7mY8XTOXyrr5pNnm8DpD3j/xoK26vf10iu9Xu8F7SseexznIpLSTuvsbXuDlDZvp606/FzfFz97vff3pmr2vnatY/pia/uS1vlibs55W3Hv6ZCCzUjTKdbWJKnAWkGWpa1Uh0Q6v4raHVHOB9N2jbtSiixN8U+1X101ktTxJwwSsWnttVeKjg+I2oupOGAplZClqZ9QtIfjPbk2VoPsRUzmfznnNUiKPKIhHiqNFTcxOImrsPZ77ZyqtabJ1zrnGk+e+HBqrZvPW2Mw0jxroI2ftdahkkCyY/6g7ldLhOSmEwc5tPRV7IwmzOqSXneRlaUB+f+vvXP5sey6zvtvP87r3np1d/WTYpOiZJmWJSuJAFF0oAwCxoZgAYnhAYFobMOwkEEgeOARPfMfYBiaGRpkINhwhoIQh44DxKBhkLZiSVZktUS6KZLdZD/qcV/nsffKYO9z7qOr2NViF7uqeb5Gdd26595zzz1nnb3XXutb3zINRhTWh87UkmhI4OKVy7z41f+Mq0KFR1lVSO2R2qFTw9b22VCiucAvWl5ptk5iG9yfX5UgiBceK6MRr1FZ4PUkgB3mKO/ZuHIBnOep8hnq8ZSmqdGppY5VLtY7Pn7pEtn6Bnfu3mVzsEbTNAyHQ6azkrK+xN7+HptrA6gaisySZTGCRhDawvnAkRACDwOgDttDlEi1DWpROqYORGJliCO1KXVZRScrTHReCUrbrqvvfjVjZ2+P0d0d/kWEIs04d/kCN964zuSd99B+ZXI6Zuzv7+OdD/fTCiRqDIVVfnfJ8Cp005UYAVX+YPGrFsvpksXIaasHEhI+i47Z6oTWjjlXr15FGc14Ou1e17m+cdzZ3t7uxpK2U65WOkZ5Voi2WnWpKYmKwIIgTRhXkjzD+zqWLi9HcQJxelmYEgkl2e4QCX+6a7tcSbJ43pYc1GN0Ulvn5GFg0TlZjEjNqx7nROL3s5XVfS6+5n4O+2EE16XrE/e7qn9yELH2pDgqHznnBGA63cc1NY3XOK/Jc0uoeo03D6HpnjU2DEreY0SHzM4BKrHtvbQYPUm8iaW683TJfHaSOAkorBjSLCEqLNGWAwZugaCNIUtTiqwmy1LSNGUmDbrjnHjEz1foi5jHT9pQpsOaEL4PaaM0VAPlaZeX1EpjE4NNosKhEhpXd9+xqqrYGMx1zolzocJCZC5IJB68a2L1Skh/WAVaSaBliEL7MVoXoBNyxii/HMp9mNASFrcbaUZ+RmHyTYxKUOJJvInCaq6r5DJiUTE8awtLKorB+hAhCrFp1WmadEqwXkJJ8YLAXbSQuRO5eFEwYBe4KQJtGbhq+UnimY1G4Bw+TjhKIDGWTFsMHn32DMOts5w/dyFWkIW9f/LZX2Q8GjGdTBhsDKgmM85fPM/acBAnp1gFgu6cjvD9FCragqhw7lAKhQnTlArfxuIRcVRlhVKahhBltAJeBf5KCN87JuNdjIV0LUfpFJPn2GxIlq2xcfkCXt/rxB4bRDGeTJlOJ2xuDJauU3g4D21rFeJg0qa+0PFCHaIO2638l59vIwitVyK0ERi553WL52Cxys87Txa5HgZCb5yFyGvd1OHamBC1C+TumIIRAi9ItQEdFR3S0D9JiaBcLAWPukuBF1UBauk42u/pYkWa7gjy88ltddLzbpG0f/9J5zjNwHtPXdcPRXOrdToWhdjax4sqsRDOw6pY20F4WE7CarrpsP0tOosniXPykXRORqNdZrMxNrU0XsjTFKGJqppzYTRt2hV9WPWEEHm88ZbCpst5WaNj7whlQehCx21fijBGREO1mkyHMuH9vT2yNCOxdj4B2ASdCkWWsJbn5FkSjzGEj0O5smOxfHgO6QZB5xxNU2Nt6ICc5znD4ZCiKBgM8y5cq9Q8ctR+H+8dXiINspk3C6zrOt5srcNGTM/E1aFP8d6hmhKtpySUrKUWJZ5z+W02B7dx6gwjdY4nN69hZOfYrvkbr1/nwuULFEqjrZBIgU3AaION42bjfSSGggk+QnAaNKHfjI8rTpGocWcQDBB7JqkQGg+mEXcQUzn33GYSOAjdWL1AmhXXBG6TUri64Yf/+D1u3XqPjWJIVdc88cQTzGYzmrphY62gLqdcUoa1zW3EROdEhGIwCLa9uYlYkI0Nzl+8hDFZ15QwOKBRBM+pjtAbJFsUmTH4xuG1Dv1UjGZaznjjX/6FcjQiH2SkRU66tobN1igSy7rNgjCYMcFNcw3+1l1+UReM6xpvDDhN4RxiDRd1Qr3SO+q4Uc4qprMZWquuEifO4/HxAeWaomJG934r2fa/gxcx7V7bpdDi6xbD8DDv0dI0TeAEeYU1Gi2qs5m5gyKIhi5/rAjXE1A+kJS9ErCtA9VyjNrtrVmGCTSxKVM/w7mmS9e28ghhERXSUd6F8bJNXy6mTFrpBGQ5MnLQin3x8XEu3tvx62FMxgeldRY5OO1rDnrPUfZ9WNrpqBP3YVGVxccnJVKyiAc5psfKOWmaGi81gqKuHIM8ECDbVuHtgLAqWT/XEYB2ebqaN21TI+IjEdT7WCUTVyPe07gmiDh5WajMMUwnU8pZSZqmXSlxSFcH1ccsMaRGYZTgJIRrww1w2A227LW3jfysNRSDgizLsHaewml/z9NPAc45jLEETmyYmNtKDm2CUBwiGOXQdU2iEzJzh0wStjfOIG6PZ54o0UzZ3BiyP3uLM2dKMrfHfn2B0SghcbtHWVD93Pif/+t/s3Vui2GSUKSWweY6eTFgbThkPckZ5AVpmjBYG0BiSE1Q9bVJEr97EOPSiQ7iZCIoo1BGQNombfcOAmpxNprH4Tnwy7aLaxV4I+3AMR6POX/+PKm2vPnmm4zH446HsL97lzd+8mMuX/4Z//bf/QeytVABIW0vFB0iIa3+ijSB1K06OmfbbTkSsuMqOpB6BSuORgXirBeH88KNGzf5i7/470zHeyijsFlKXgxQyYBL587xn77yHzlz9izex+OY1cibt/mc2WSXilklKAMDZSjzgt1//hnT3b17z8cxomlqRqN9iBwMQe6pwFmtwlhaYbYXU6mDZ9L3CxCsOqMHTCCLaYJuMooEXmN0jLzO0wXOuTaHG+I6SoVycB2ilKig8IsC8WEM0wKJ0pRVGRoIxoMTmaeKkiRZSuWuciLayXjetmB+zpZ4FUc4MR/WJCkiRyonfhCEMXI+b7Rjeov3I7MuXv+Dft+PY3IYHsTxOMxZfJT4SEZOECGxGt2S3QgVDZo4Adl57nCRM7EkchRtQgtRrdFR12XkXGi0SsOg0biOKd+GcMWH3jqNc6RJGiIVGgaDNXbv3uXurbusbQxZW1+jY88rhzESQq4xB6xR0fExMeTsUa04WzcIzMO+wZkJESBrFULDdFpRDPJIviWGnD2alqzqUNqFPm5x5SVOMHVFbsZUPsPpgnN5RZrd4eknNJNJySevjqlLRZbvMBt7/FSxvlbid6ckWjO569A5pFLTVAk746fR6pVju+SjyZRpXQVp+piucVqT2IwMyBJLZizFekGa5wzSjOHagHwwIEsTNoZD8jQjWxtitSZPUrIsx+YZ3jmSNEEpjUqTGPUiXicPJt4+oeI4qvxKNz9J14gs+i9RaVZcjTWWL37pSyilqPfGbG1uUWyu4WuHEyinU6587ONsrq3RiCexMeUkhHRfJDS2+f5usDMK8VGXQmvwC9yLSMqsqympDuRXr0O6QGnhrXfeYjSa4Bpw9RRVztjdnTCezvhRVfH5f/1v2Dy7FZq/eU81q0n2GzZswealp5hVJSqxjEcjttM1srUEd3CW5HighMbD7u44fHfn0doEQq+6N700H+QdoXeVX1oPdJom7e67B+qAyWUetQjcj+XV9RI3bSnqshBZaf+tLppaPzh4F6FTdJQPEA1eYurOBZtLbEJV1/zfv/8uv/gLv0BRFKGB38I4l+c5aZoGJd+qWkrvLKYz5k7LAdwKQnROx+d9TC3h7k1pdePrMVaMiMhDlbCHqPsSicjtvLFYTny//SwSm1u8X4Rk0R4Oev1BvJ6D3rN4DCfFKWnxkXROvHfs7u2QnblEVYdVpFaeJDEYlYQeO8YssbrbQQCIzc7my2GtFdO6Yjqdsr6+TpJYkLDyKqczqqoK+U3dkqM0dV3GXhPzvHKe54xtQlPVzGYzBsNhiK4Q8rrG6riK11EEqj0uQxApjEnleyIp85VMGwHw3jGZjMP3rCt0PufEKK9AO4xKMUrIVMlmPsY5SBLF2UHDQL3Lk0+t8dpPhry3B89+skRV+2Qy4syWwZYjmpmlKGB9w/HGG+cYpJbEbiAMGc3WqaUiL9Zp9IB3y3UqObhs72Fga2udoshBPL5qqJ0wFfBeUTYNs3GFuAky2gUF1nsUHp0kGBSpsVgVyMO5TciSlKzIyYcFeZ6Q52mnmDsoBmRZTpEPQvVElnX9lVSShgkp5v1VTJfExkdhwHaxOsQacB6TZaCEfPsMl9bXgwPkPFsXLyDOxzJ3h07z7up3K2+jERd4JSLSpaUkLviVWiC7Ev0nrXHes7u3y/aZs4giCol5NGHV6WLaSaugi+LFIeJwvmZWTdBR3807R7Vzh2x3RFWOMYOCaneH4fo6Z7GU+zVSDKiOM2x2AESE23fvopXGt9FJMy+PJ57DeYCkC3vNCaZxEhXvO+KvUlHEzbc9sxYmDFoibHREtJ6n1ph/zqJz0Ap6tRU9i+NR+z2696jlCdO7UD3Wcly8eJqqCn13RKFFk5qE7bPnSJKk+7yDVvWt1EE7qbfHV9c1WZZ1USat55oa3cTtXRyC5lGT8Oc8Ors4mR73RNke98PkV7QE5Hb/D+KcHHR87xdZOSyCskryPexcrl6fg7adJjxWzgkI5WyKiMG52MTKSIgmqCDQ1uaHA3mqiWmWeemr8/MLbIxhMBiQpUXUCrC4qPtRNzX7+3thcrIWbUL6pG5cXHk0selYSCEUgwJfN6RptsTyVkpjbUIStU6UlqijobsQ9DIWl3atyxIGVhHPbBYan6VJQjWbIDkkRkh0zWbhyTJomoqnn1L4cswnr66zs1dx/pyjniTkMsYxAxni2eTtd97mwnoNjUc3KTN3GbHvMRpvYNIBqmgwQ3j7dsHEneduZbDmMvt7JW/fucZbN2/xs7ffO7Yrfv0n18jzjCS15EmK0QaVpeQ2oxhkWGVQAo2Jq2BX0biKRjy+dsyqGjx4mQU+iEgoDzWBIKmVQitFpgKh0FpLYlPSJCXL00BqzguyNKHIcgbDATZNyPOMIg2tD5IiwyZB80VpjUnzwAUwJjJ6FUp5kCq0TbBJnBhD+kQpi5+W0QmOqRxRYANpN0RqfCh9Nborm+7IiirypaI42bnz24gHEycmXzVoEZ555hnOnz/P/v4ejROauo59eBrOntviySevEBpUhn5Qs927NNZR5HlQlvVTqBR7e2OGW2dxpqJxH3wl+6C4fft2SKERyZ1aIS40t/MhZEkorlmcEMKd1Ja9ixN83YSUkG63RzbJ4sQg8d5jWVxtEYuRg/CWe9MoLecjSZJ7tmnVklzDd+i4LdJ+MhBkUoKwnAQC7Mc//nGEcB0XF2GLE2FYwKmOOC8iWGOwxs5T2XE8C/eD7pqiishyJmc1syOy6rYcOx5WZ+IWbQRJKdWNxYtVkKtj9FE5J0fBYfs6qpP3YTiEPw8+kpET0FSzKSKaslEopSlSTZ6lVBjEuximDZGI1iv1Phicl5DLXQ3fWSvUdYMiLISdc2R5xrqsBS0Ar9BRYClNDB5HXTu8BLFwm1jW1tepZiU2SaOwWXuRDDrJyPKcNBkH0qQKJaghfVRH7ltwWLpoSfejKMspk5s75HnOx574WBB0U5rajXj6iuKs3eHJjxUUTBlmNbfGFW/e8Ay0kDjDduFZZ8xOlVI6RZ0MGE+gEc27k4Intrc4e6EAt8EPf2ywec3t3SETv85oMmH26pidu+9w4841bt6+w+7OLvt7M8pqEkm9x4etPCVNQ8RIyhmldzT7QVtFu0AwVALeGJI8wyaWJAnOZKo1GIPY1mn1IJ4GAlHYBa0I54WZrwOptprGiIQNbQCdx6jQyyZXBieCMp5EC4k22MRi0pQkzxlkQwb5kCRLGWQJw+EwXPc0w6aGNMvI8ozEZoF7YA3aWrQJvZZarQ1cWJnTphMkOFVKKZS1YZZygo65pjChOZQBbKwwciZ2KA4Oj9UJT3/8Gb72X/8Lo9Ee5axmZ+c2++NdRuMxF86f5+ITV2J6ITS0HD5xleLfn8GPR9x662dkG0OqqmHzU79AXXvu3L0VNXQ+LASBvHffu814VtEKCtZVhYijweE0eBMI4FpMIMJ7EyfSkK4TiStlBFSUuSekMZRiaXERP5XFKGb40TElG1MecUxpIxGrE2ibQlmsmjl0ElN0qWQVoyehKitwfrwKTktZh5YTxIjsYuWJipyadgxZ2DWK0DFdK43zDU0dW190YpBtZ/Zw3kKzzHkEahmtfdKNn8eJh5XWgWUJ+/b5dl44CEvpq4Xr936T8WHXeHFfhxGNV7cdltY56D2rr3mY0ab74SPqnAh1NUNchXdBVEwRVDXRCU1To1TrcLSRiXkzv5bkuFjHHu4tj7YWIVTo1HVNYi0+Tdnd3cUmWZSpj4JJC6urqq5QKkjMtwJJo9GINAuKkTbyS7I0JcszpFYoZQgTkTCdTQ9ddLRPO+eYTCfByNptImhJOFso/tUTM85uloxv16xnQ/ATfjwbsLHpmU3uMtpL0Jc3wXok2eCt3ZxRYzHJHg7LD988x973b3Fnt+S9u3Dnzjvcvjvh7t6M8XhEXZVBL4aoTLp0dMeLJy5skGc5iI0De5CCD6F5ha8duKBu2kgoj/azMhCYCZkTJ7H0UhxKC84aRCckPohWGRsiHl58CKrYJJRzBlYzFgVaqBEqHOIcutHga9yswjECHFpC3xylFIkWjFKxlYEJ0TNrSdOMYZaRFTlpXjAcrDEcDknTlKwoSAc5eZ5hrCWxoVQ+aGNolLGI86EHlBOUl8BxiWkfTKgiU6JQKoWmRlyNthaUQSlh88wWG2e3AAvKo0zLFXDoxnWTkaAoLmwjZ88iXlj79C+hqhJidMK7huy1f8TdpwLmYaL9pFu3bjEajciyUK1W+5pW86OVOG8t1Yl02+Z7WVQFXdk5KwN93HYQn6R9Tfv6UFnXdKJqixPfIjdhdTLsQhKKjpMiIvOUTUwjtsq3rQMyGAyiMut8Emsr99ovpdq838oxt69pZQW8NDTNnN8UiPVtuvleiMS0k4Soj/MNzjmms+OTFQCWmox+ULRRkdV2A4uNGRdxWNSsfV/7+7DUzkF/H4T7pXUO+/swrB7jcTsqH1HnBMQ3uHoGKqWqPYVvQkmc1Xg394ghOCgilrKczQV2YrVO56BYBU7jlaNpggZInudUZRlEkVwglnZGFQcQawyucfz9a6+ytz/il579JbbPnUPFkK6PfVJCJEdjTJicat+glUFri/gk7Mc38/BtHAzCsBAE4ay1nNk6wyDPV0K3ltd/eoOrwxrtLEWasL8rjPRVPvPZhtHumKnZZFcrrl/LubXnmdaWu9Oand1dbu9c4+7dXe7cmbK3t0tZOpyvgkhV6NjSnnXmzsjycR63k1LojFyn4Tz6GBWQIICG10gaUjOCCuWUxOZuPqiiitI4gnOCb9DicdpQo3C16xrINU1D0wqK1SXel4jyuKahQeFNiGZ4o/ACiUqxJsEkFrEGrRrEeTQWF5REaFwVynHLGleXIeqiFLblqqBIfRANzHSY0GyWYtOEIg9OSp5bBoMBa8UaWV6QDAvyLGOQ5WRJgkkzlNEYbaECfKz+UB5lgpgaaRYE26JzpiTo2YRleIgYqnjOFKGUXtIkrqSrmFYKvapQQNOgtWVMiZfjjZwtI9wVu/v7seleFCJ0gaRe1RV3d/ZZWxui0HhilKHN9RLSFGHiCQEoide/Y5fIPC0ThBXncYflVkvhD2tDf6pF6fPF9MBiee4iCXPuHPmO8xIOL6ZKRKiqKqQxY9rOyVxAcZWIKxJaS3QVOFHELXDrLEmqaLwLZGs1/9HGYAnqxm27jrDfNmAX+DnOe8q6ZjqbMZ1VTCZTJrMpk8mMyXTCdFoxm5VMZ7Pju/oiS9yZhzHJLjtzYb+LOicP8hmr1+VBcVB68H6ft/r+g46lLEtee+01yrLk05/+NBcvXnzgYzsqHiTN9Ng5J847mmaKZ5Oq9jSuoq5qTJIE3gjzVYrWiiyqq06n06UqHrsYutMxqh4HkjRNyYuCqq5ZX99cMhrXRFGtmCeeTqaMR/uMJyPSNCFLg+BaIBxC5Zu4mgvVOAqPMSHUnqQGaw1lfdDFjKF65UnTgvNnt1gr0q7CwCYWbTWXr2xz5kLKz25O2BvvYcw2196dMZrWjPcH7I5n3N2ZsbN3gzujMXv7e8xmkyjudkjOs3v0fiH7DydyMkwz8iwNK2EvaFFhNaw1uq3YUnNxLERCNVQs38So0L7AC9qBQeG1oVEGVQQ+TzdJxJsqIUj+N4aOB6C8dMRGJw5XNzTNhHIqVBKE65RolEnBWGwW5N+tTRCb4E0SIhsCUodutY14GglE77Kqoazx4wmtxJdSYOKMaHVQ8LV5RpomFGnKMC/IhwMGgwHFYI31tQ2KomBtWJCmGUk2wKQpypdYEYzxSC2gUwJ7wYBKIVaJKSVI04QJOUsR5VHW4Os6PA9R4FDjpWZ3Ol5wqj8MCChhMp0hMh/MjbJ4pXDSsLFxJjgYRHl3Y9Ba8I3EiZkuGoIKnb9F+egUCEH/Zj7ILtmW95EM3U5Awbmxdk4In3PNlqsFtdZdVGc6nXZtNrrIRpdCil8ThfLBoWzHmkXCbdPMK5RCeW24V8uqIrE2LpIE7xveeOMNTJ6wvb1N20B03lgO6rphPKuYTKfMpiXjccVof8TeZMxoMmE6nTKb1ZRlRVMvOPHSagPR+o0Lom3Hg7ZJ4cNwTNpI0+Lfq5GTo3zOgxzPqgNx2P4OSvkcltZpnenFvxdR1zXf//73mU6nXLly5Vidk8OO8yA8Zs6JAI6ymkBbfeA0dT0DZ5mLigUDaC/YcDikaRrKMmiRtK9ZHEjaKp+WDZ4kSdd1eNFIyrJkNpsxHIbux5/4xCeYVWWQHZ9OcDGsa2Kb83CMNUEsrgIcWgcJeKWDEzQtp3jf1u8v54fbY21cg/eRb0BY1mhl+Mef7vPPP73Dzn7B7XHN3t4b7Ixusb9XMplWVE0dz0M0mA8xDP8wsFbkFIMsREWcoFyInDTiMYQ0hDGBxOdjGN6qQBkV73GKMNm6wDUIZZsa8W3ZcCQhdhOAIlEasVATBdWcw4rCR1l/RPCFIDaUmde1R7lQqt04mImj9hVV44Iz6z21U7HjMUGIS+vQSTgxQYE3NYDC60Dudj7k8V0ceGonSFPRzGZBsCtGhrQ2Md2YoLXBWkuWJeRpxjAbkqU52XDARpaxvpYHbZPhkDQbMijWMIkhSTQmMVibIXWDTQ1MQ1WasqHLr1GBd9VEYrY4x3g0/rB81A4CjCdjbt2+HSqvsiwGAcI9bE0SnQDVkd/h8ElhcQIKznobbbiX/CpCDCfoyANZdhZC+4egh9Q6IzBfLLXOSZ7nS4JioXQ4fFY7+ULgOyFR+8Qt63u8/fbbJEnCYDCgaWrSNDhIk8mEQVFQz0qq2lHVDWISrr/1Lnvjmslkwt7+iLt37zIez5hNZ4zHYyZlHeXzXUjVBPJR1N0hRiyXr8TS+ZkPVke8kg8OpdRD4Zy0+2o5Qss28P6diWHZflYf38NXep/zsbjtIN7JYn+zoxzHYZ9ljOHSpUuUZfmB1HVXnaWjOFrvh8fMOQlDR92Mca5iVpd4b5FmgjQD0MsCbN77rhw4TdOlMrSOc0IoMZY4iXkiAUyH1IvWFiFUBBCjKouE2qtXr4JS1E3N7u4urmmiWmObhghI09DYq+pY8WGwHxYDJuMxs9Ixz5TP8+Jh1QXONZSNwzehV0xVlVx/8y3Gkwmz6YS6dji/KlDUEmxXUzGnB//tf/wfBkVGkYUUWKIUWZJgE0thNUliybMMTai0SdOE1BhyHRxEE8XqEmMwJgkVXi5UJSjf6hR4lJqXoOqoN5JJTHfEkl+JxD+ik+N17JUkDrGB1xSk431Yt0tIkXivcGJiyWiDuJLGecq6YVpXlFVF7YXaQeMEwQZnyWjQJgrmBdu0NuiXoIkOWeAjVL4BcfiyRtUzvN9FeTBVTCVphdHSNTYLOj0ZSZbE9goZab5GlgSHeVAMQilskZGkCakN51Vrg+DQIrz37p33YSUcB8L9VFeBkJ7lORAqTQRFYpOF7rpEAqywGAGMyR26EIoCpUxM97huAl6emIKKsJegceOdC32VxHZdzL0XvA1poaZ2eFd2C5u6qUnTDBECQReP8/GzRGi8QxtDkqRYExxs7z2+CZHA1j5RLflZ2No+i6s9s6pkOp0xvb3LdDLhvdt32B9P2NvdZW9/EuX+p0xnIbXtvF9wMhaunCyck5b/IrKQypJlDsox8xYOQ+ucPCzexGojwUXnpMVBqZr7TcarvKL7OReHvX/19Yc5Bq1jdBDyPOcrX/nKoft9PxxE2F1NPy22Ymg7Rx8Fj5Vz0g4sVTXFu5qqkrgKblBe4bV0okNtOVjrkKRpupQbbp0L4j6dd6hYSgrgnWBiGbGP1QuosL0ois5BsdZi0yDis76+HuTJY9onCIcJ2HCBi0HBrA6EruB4GIoiZzhYp3Ee56rgoEgcHHTgLezvN+zuuW5VdvS83qqRfMjL3IeAV394ravKUNEA4lAxN4h4bdqntFbYSAY0MUJhdVTO1AYbIwyJsaTWBuVdq6PqriXNEhKbYI0is0F1No8E1cQYMhveb5MEowxaIEtCUEsp0D42noyEU20sSSSkChqnExCNd+CamtI1NEpTO0UVJ95pVTGpSqbllKqugzNTNTjv0GkKxuAU6CQnSVJ0orF5hrUZYcIOYfpmWuOaQFb0En6Hbt0xVOzbhmLgW3Kt0pi274pW4T6IfK22B1CiDf/v2usfsjXMORv7+yN0jE7S3Q96iTDqnOpMfnGyEJmXB3d8D4K8vDugnUQINrZp4NgWQgNaQvTE+SCyGBWXkzTom2gbUnG6jrLyaYo0Dq3BaovzsUmhCh2kvThSneAbR1WVjCeTyOeYsbs3YXdvl53dfXZ2R+zv77M32mc6nVKWJa7yMeW42vVn+fu3Dt7Bp3blnfdMZI9+cdNOfodNxEfBorOx2nNt1Tl5kHTNYirmoPeuTvKHoS3gaI+lxer+2+cOSv0c5Fyt7qd1KNrfbdq6bUBZ13X3U1XV0u/V55qm6X5Go9GRztmpdE4OzcXF32VVUtVTyllKXUFV16i6ilLQIQebZVl30quqOlDOuTXyqqlp6jDwtznIpnbztIyvo6IrS4NdF/qVuQ7CYDCgrirqpg6ORHw+iP2EFXCbww6qrwlKg9Y+ih6F/DciOCc4F3iOpwE/T2jvfvtqe3ws9GZFlgZJWfg/bG2ASrGSwmojUvOn1aGHGwYsHyet0ECvrdxoXaPgCWkVthsTomVaaZIoDGY0JEZhdUpiAsHUi4upmqC5IUDtWwVTwcc+OT5Skr0P6QYvLkT1REACVdUvnAmRVpo9RDZWo2Vd/CwQMhbsNgqTQRfCvz/mRIMY41vKeT9MLNpB+7f3nrfefos7d54O921T0ghUte9W1s55miYMol5CFU1d1SDBIWiaoNESCJ+641857xYctsgzk6BNpGJkzUvswWJMiHK4+eq7iTpIbfNFCK/3Lryn8fE6xiiGDxcY33gm0wm7+2N2d/bYH42YTCfMZjOqqsFLR0tZ8CH8PBLUXbtQXh589nh9jzgpPgwchx0sTrx1XVOW5ZFX5++HNs0WznFFVQVBzrZBatM69Su2d1AKpeWvzHVj3JLjs8prW+SJHPR36xy0nKJ2Wxv5XExJee+Zxq7X7QK8/T7tYv0g56L9jPb7Lv4s2v/iNTjo2hwWUbpvdEk+LKt8iPjpT3/KJz7xiUd9GD0eEG+++SYf+9jHHsq+ehs4nXiYNgC9HZxW9GNBj/vZwKmMnJw9exaA69evs7m5+YiP5vRjb2+PJ598kjfffJONjY2Hvn8RYX9/nytXrjy0ffY28PBxnHZwHDYAvR08bPRjQY+TYgOn0jlp84mbm5vHcvI+qtjY2Di28/mwB43eBo4Px2UHxzFx9HZwPOjHgh6P2gZ+ftZQjx49evTo0aPHMaB3Tnr06NGjR48eJwqn0jnJsoyXXnqJLMse9aE8FjiN5/M0HvNJx2k8p6fxmE8yTuP5PI3HfJJxUs7nqazW6dGjR48ePXo8vjiVkZMePXr06NGjx+OL3jnp0aNHjx49epwo9M5Jjx49evTo0eNEoXdOevTo0aNHjx4nCr1z0qNHjx49evQ4UTiVzsmf/Mmf8PTTT5PnOc899xx/93d/96gP6cThD//wD7uGUu3Ps88+222fzWZ87Wtf49y5c6ytrfFbv/Vb3Lx5c2kf169f5zd+4zcYDAZcuHCB3//93++aIz5q9DZwNDzOdtDbwNHwONsA9HZwFJxKG5BThm9961uSpqn86Z/+qfzgBz+Q3/7t35atrS25efPmoz60E4WXXnpJfvmXf1neeeed7ue9997rtv/u7/6uPPnkk/Lyyy/Lq6++Kl/84hflV3/1V7vtTdPIZz7zGXnhhRfkH/7hH+Tb3/62bG9vyx/8wR88iq+zhN4Gjo7H1Q56Gzg6HlcbEOnt4Kg4jTZw6pyTL3zhC/K1r32t+9s5J1euXJE/+qM/eoRHdfLw0ksvyec+97kDt+3s7EiSJPLnf/7n3XM//OEPBZBXXnlFRES+/e1vi9Zabty40b3mG9/4hmxsbEhZlsd67PdDbwNHx+NqB70NHB2Pqw2I9HZwVJxGGzhVaZ2qqnjttdd44YUXuue01rzwwgu88sorj/DITiZ+/OMfc+XKFZ555hm++tWvcv36dQBee+016rpeOo/PPvssV69e7c7jK6+8wmc/+1kuXrzYvebXf/3X2dvb4wc/+MGH+0UW0NvAg+Nxs4PeBh4cj5sNQG8HD4rTZgOnyjm5desWzrmlEwRw8eJFbty48YiO6mTiueee45vf/Cbf+c53+MY3vsHrr7/Ol770Jfb397lx4wZpmrK1tbX0nsXzeOPGjQPPc7vtUaG3gQfD42gHvQ08GB5HG4DeDh4Ep9EG7LHstccjx5e//OXu8a/8yq/w3HPP8dRTT/Fnf/ZnFEXxCI+sx4eJ3g569DbQ4zTawKmKnGxvb2OMuYdFfPPmTS5duvSIjup0YGtri0996lNcu3aNS5cuUVUVOzs7S69ZPI+XLl068Dy32x4Vehv4YHgc7KC3gQ+Gx8EGoLeDD4LTYAOnyjlJ05TPf/7zvPzyy91z3ntefvllnn/++Ud4ZCcfo9GIn/zkJ1y+fJnPf/7zJEmydB5/9KMfcf369e48Pv/883zve9/j3Xff7V7zl3/5l2xsbPDpT3/6Qz/+Fr0NfDA8DnbQ28AHw+NgA9DbwQfBqbCBY6HZHiO+9a1vSZZl8s1vflP+6Z/+SX7nd35Htra2lljEPUS+/vWvy1//9V/L66+/Ln/zN38jL7zwgmxvb8u7774rIqF07OrVq/JXf/VX8uqrr8rzzz8vzz//fPf+tnTs137t1+S73/2ufOc735Hz58+fmPLB3gaOhsfVDnobODoeVxsQ6e3gqDiNNnDqnBMRkT/+4z+Wq1evSpqm8oUvfEH+9m//9lEf0onDiy++KJcvX5Y0TeWJJ56QF198Ua5du9Ztn06n8nu/93ty5swZGQwG8pu/+ZvyzjvvLO3jjTfekC9/+ctSFIVsb2/L17/+danr+sP+Kgeit4Gj4XG2g94GjobH2QZEejs4Ck6jDSgRkeOJyfTo0aNHjx49ejw4ThXnpEePHj169Ojx+KN3Tnr06NGjR48eJwq9c9KjR48ePXr0OFHonZMePXr06NGjx4lC75z06NGjR48ePU4UeuekR48ePXr06HGi0DsnPXr06NGjR48Thd456dGjR48ePXqcKPTOSY8ePXr06NHjRKF3Tnr06NGjR48eJwq9c9KjR48ePXr0OFH4/2kpeuWYBYjXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tuning YOLOv8 model\n",
        "\n",
        "1. Build a YAML Configuration file as `congfig.yaml`\n",
        "  * Example of Configuration file\n",
        "    ```\n",
        "    %%writefile config.yaml\n",
        "\n",
        "    # PATHS\n",
        "    path: /content/license_plate_dataset\n",
        "    train: train/images/\n",
        "    val: valid/images/\n",
        "    test: test/images/\n",
        "\n",
        "    # Classes\n",
        "    names:\n",
        "      0: License_Plate\n",
        "    ```\n",
        "\n",
        "2. Load in Pre-trained YOLOv8 model from ultraltics and train it"
      ],
      "metadata": {
        "id": "xYohLLFYme8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile google_colab_config.yaml\n",
        "\n",
        "# PATHS\n",
        "path: /content/license_plate_dataset\n",
        "train: train/images/\n",
        "val: valid/images/\n",
        "test: test/images/\n",
        "\n",
        "# Classes\n",
        "names:\n",
        " 0: License_Plate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aZS3HFFmeGU",
        "outputId": "486037f3-d3b6-44cc-f51d-50d837b24254"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing google_colab_config.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Device Agnostic Code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "U13FTfmStUJD",
        "outputId": "76ae316e-d5bb-4e7a-9f0e-abd6b8f066f8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir '/content/drive/My Drive/license_plate_detection_train_results/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsBwOMKU0Nhn",
        "outputId": "1b4df3ad-0386-43c2-8920-446cc93101f5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/content/drive/My Drive/license_plate_detection_train_results/’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "import os\n",
        "\n",
        "# Load a model\n",
        "model = YOLO(\"yolov8m.yaml\").to(device)  # build a new model from scratch\n",
        "\n",
        "# Use the model\n",
        "model.train(data=os.path.join('/content', \"google_colab_config.yaml\"), epochs=15)  # train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv4vJ-LH0L7J",
        "outputId": "6b6a7c88-4b07-4ac4-dce1-0c9e57f7b703"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3822016  ultralytics.nn.modules.head.Detect           [80, [192, 384, 576]]         \n",
            "YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.yaml, data=/content/google_colab_config.yaml, epochs=15, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=cuda:0, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 30.3MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
            "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
            "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
            "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
            "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
            "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
            "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
            "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
            "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
            " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
            " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
            " 22        [15, 18, 21]  1   3776275  ultralytics.nn.modules.head.Detect           [1, [192, 384, 576]]          \n",
            "YOLOv8m summary: 295 layers, 25856899 parameters, 25856883 gradients, 79.1 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.22.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 137MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/license_plate_dataset/train/labels... 21173 images, 28 backgrounds, 0 corrupt: 100%|██████████| 21173/21173 [00:10<00:00, 2100.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/license_plate_dataset/train/labels.cache\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/license_plate_dataset/valid/labels... 2046 images, 3 backgrounds, 0 corrupt: 100%|██████████| 2046/2046 [00:01<00:00, 1134.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/license_plate_dataset/valid/labels.cache\n",
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
            "15 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       1/15      7.17G      1.919      1.757       2.44          8        640: 100%|██████████| 1324/1324 [11:55<00:00,  1.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:33<00:00,  1.91it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.913      0.814      0.858      0.435\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       2/15      7.38G      1.453     0.9976      1.843         13        640: 100%|██████████| 1324/1324 [11:35<00:00,  1.90it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.05it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.915      0.859      0.908       0.52\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       3/15      7.41G      1.375     0.8848      1.767          8        640: 100%|██████████| 1324/1324 [11:29<00:00,  1.92it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:30<00:00,  2.07it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132       0.95      0.876       0.93      0.572\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       4/15      7.41G      1.325     0.8119      1.714          8        640: 100%|██████████| 1324/1324 [11:27<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.958      0.886      0.944       0.58\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       5/15      7.41G      1.286     0.7632      1.681         10        640: 100%|██████████| 1324/1324 [11:27<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.01it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.961      0.903      0.954      0.608\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       6/15      7.41G      1.206     0.6269      1.714          5        640: 100%|██████████| 1324/1324 [11:27<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:32<00:00,  1.99it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.952      0.917      0.953      0.598\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       7/15      7.42G      1.173     0.5967      1.682          5        640: 100%|██████████| 1324/1324 [11:31<00:00,  1.92it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:32<00:00,  1.98it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.959      0.916      0.962      0.623\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       8/15      7.42G      1.157     0.5661      1.669          5        640: 100%|██████████| 1324/1324 [11:24<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.01it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.961      0.925       0.96      0.609\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       9/15      7.42G       1.14     0.5438      1.649          5        640: 100%|██████████| 1324/1324 [11:23<00:00,  1.94it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.02it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.972      0.921      0.964       0.65\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      10/15      7.42G      1.119     0.5205      1.635          4        640: 100%|██████████| 1324/1324 [11:23<00:00,  1.94it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.02it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.978      0.933      0.973      0.659\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      11/15      7.43G      1.105     0.4993      1.612          9        640: 100%|██████████| 1324/1324 [11:28<00:00,  1.92it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.05it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132       0.98      0.934      0.976       0.66\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      12/15      7.42G      1.087     0.4816      1.597          5        640: 100%|██████████| 1324/1324 [11:27<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.00it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.975      0.937      0.972      0.668\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      13/15      7.42G      1.069     0.4625      1.572          5        640: 100%|██████████| 1324/1324 [11:27<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.04it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.976      0.941      0.977       0.67\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      14/15      7.42G      1.054      0.449      1.556          5        640: 100%|██████████| 1324/1324 [11:25<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.05it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.981      0.947      0.977      0.671\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      15/15      7.43G      1.033     0.4315      1.542          6        640: 100%|██████████| 1324/1324 [11:23<00:00,  1.94it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:31<00:00,  2.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.981      0.939      0.977      0.682\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "15 epochs completed in 3.020 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 52.0MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 52.0MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.237 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8m summary (fused): 218 layers, 25840339 parameters, 0 gradients, 78.7 GFLOPs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 64/64 [00:35<00:00,  1.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       2046       2132      0.981      0.939      0.977      0.682\n",
            "Speed: 0.3ms preprocess, 10.3ms inference, 0.0ms loss, 1.8ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ultralytics.utils.metrics.DetMetrics object with attributes:\n",
              "\n",
              "ap_class_index: array([0])\n",
              "box: ultralytics.utils.metrics.Metric object\n",
              "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7bd18e8e1780>\n",
              "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
              "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,     0.99862,     0.99862,     0.99862,     0.99862,     0.99862,     0.99862,     0.99862,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,\n",
              "             0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,\n",
              "             0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,\n",
              "             0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,\n",
              "             0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,\n",
              "             0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,      0.9979,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,\n",
              "            0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99784,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,\n",
              "            0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99723,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,\n",
              "            0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99716,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,\n",
              "            0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,     0.99672,\n",
              "            0.99672,     0.99672,     0.99672,     0.99672,     0.99672,      0.9962,      0.9962,      0.9962,      0.9962,      0.9962,      0.9957,      0.9957,      0.9957,      0.9957,      0.9957,      0.9957,      0.9957,      0.9952,      0.9952,      0.9952,      0.9952,      0.9952,      0.9952,\n",
              "             0.9947,      0.9947,      0.9947,      0.9947,      0.9947,      0.9947,     0.99422,     0.99422,     0.99422,     0.99422,     0.99422,     0.99422,     0.99422,     0.99422,      0.9938,      0.9938,      0.9938,      0.9938,      0.9938,      0.9938,      0.9938,      0.9938,      0.9938,\n",
              "             0.9938,      0.9938,      0.9938,      0.9938,      0.9938,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99286,     0.99237,     0.99237,     0.99187,     0.99138,     0.99091,     0.99091,     0.99091,\n",
              "            0.98996,     0.98996,     0.98996,     0.98996,     0.98996,     0.98951,     0.98951,     0.98951,     0.98904,     0.98904,     0.98904,     0.98807,     0.98759,     0.98616,     0.98616,     0.98568,     0.98521,     0.98474,     0.98378,     0.98044,     0.97903,     0.97857,     0.97574,\n",
              "            0.97529,       0.973,       0.973,     0.97114,     0.96885,     0.96519,     0.96432,     0.96117,     0.96117,      0.9603,     0.95989,     0.95857,     0.95506,     0.95418,     0.95112,     0.94286,     0.93902,      0.9369,     0.93185,     0.92025,     0.91786,     0.91022,     0.90709,\n",
              "            0.88918,     0.88584,     0.87952,     0.87294,     0.82248,     0.77932,     0.75154,     0.72801,       0.675,     0.65317,     0.64132,     0.53763,      0.4789,     0.46188,     0.43028,     0.38408,     0.33747,     0.30704,     0.29297,     0.24261,     0.22528,     0.20795,     0.19062,\n",
              "            0.17329,     0.15596,     0.13863,      0.1213,     0.10397,    0.086645,    0.069316,    0.051987,    0.034658,    0.017329,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[     0.4112,      0.4112,      0.5275,     0.59702,     0.64196,     0.67347,     0.69932,     0.72059,     0.73668,     0.75001,     0.76435,      0.7755,     0.78277,     0.79107,     0.79772,     0.80578,     0.81186,     0.81788,      0.8235,     0.82784,     0.83474,     0.83886,     0.84215,\n",
              "             0.8452,      0.8483,     0.85093,     0.85374,      0.8562,     0.85899,     0.86163,     0.86461,     0.86681,      0.8688,     0.87191,     0.87547,     0.87749,     0.87866,     0.88061,     0.88277,     0.88393,     0.88596,     0.88713,     0.88853,     0.88943,     0.89059,     0.89284,\n",
              "            0.89461,     0.89548,      0.8966,     0.89767,     0.89873,     0.90016,     0.90218,     0.90275,     0.90289,     0.90356,     0.90434,     0.90472,     0.90603,     0.90663,     0.90719,     0.90792,     0.90842,     0.90891,     0.91034,     0.91099,     0.91202,      0.9137,     0.91432,\n",
              "            0.91508,     0.91518,      0.9157,     0.91649,     0.91687,      0.9174,     0.91773,     0.91792,      0.9183,     0.91873,     0.91893,     0.91945,     0.92011,     0.92078,     0.92183,     0.92166,     0.92184,     0.92293,     0.92393,     0.92409,     0.92451,     0.92508,     0.92513,\n",
              "            0.92554,     0.92579,     0.92606,       0.926,      0.9264,     0.92643,     0.92682,     0.92733,     0.92741,     0.92748,     0.92761,     0.92801,      0.9282,     0.92836,     0.92863,     0.92918,     0.92931,     0.92986,     0.93044,     0.93077,     0.93093,     0.93107,     0.93117,\n",
              "            0.93128,     0.93194,     0.93211,     0.93262,     0.93316,     0.93361,     0.93369,     0.93376,     0.93387,     0.93433,     0.93466,     0.93473,     0.93479,     0.93485,     0.93536,     0.93545,     0.93568,     0.93555,     0.93566,     0.93578,      0.9359,     0.93599,     0.93608,\n",
              "            0.93624,     0.93646,     0.93688,     0.93675,     0.93691,     0.93732,     0.93784,     0.93799,     0.93821,     0.93865,     0.93892,     0.93953,      0.9404,     0.94027,     0.94011,     0.94011,     0.94022,     0.94052,     0.94059,     0.94065,     0.94074,     0.94084,     0.94095,\n",
              "            0.94105,     0.94119,      0.9414,     0.94162,     0.94179,     0.94194,     0.94189,     0.94185,      0.9418,     0.94175,     0.94199,     0.94226,     0.94242,     0.94255,     0.94236,     0.94246,     0.94255,     0.94267,     0.94282,     0.94303,     0.94339,     0.94354,     0.94367,\n",
              "            0.94385,     0.94424,     0.94454,     0.94466,     0.94478,      0.9449,     0.94507,     0.94535,     0.94562,     0.94571,      0.9458,     0.94606,     0.94618,     0.94628,     0.94635,     0.94642,     0.94653,     0.94671,     0.94678,     0.94685,     0.94694,     0.94706,     0.94713,\n",
              "            0.94717,     0.94721,     0.94725,     0.94729,     0.94735,     0.94742,     0.94749,     0.94758,     0.94774,     0.94786,     0.94797,     0.94817,     0.94805,     0.94798,     0.94811,     0.94846,     0.94878,     0.94873,     0.94862,     0.94904,     0.94912,      0.9492,     0.94937,\n",
              "            0.94951,     0.94958,     0.94964,     0.94965,     0.94947,     0.94943,     0.94951,     0.94959,     0.94974,     0.95004,     0.95013,     0.95018,     0.95023,     0.95028,     0.95061,     0.95078,     0.95108,     0.95107,     0.95086,     0.95096,     0.95103,     0.95111,     0.95122,\n",
              "            0.95137,     0.95146,     0.95155,     0.95185,     0.95205,      0.9521,     0.95215,      0.9522,     0.95225,     0.95248,     0.95258,     0.95268,     0.95298,     0.95305,     0.95313,       0.953,     0.95337,     0.95352,     0.95367,      0.9538,     0.95386,     0.95391,     0.95397,\n",
              "            0.95406,     0.95419,     0.95433,     0.95465,     0.95455,     0.95445,      0.9542,     0.95445,     0.95458,     0.95469,     0.95479,     0.95488,     0.95495,     0.95501,     0.95508,     0.95523,     0.95547,     0.95567,     0.95557,     0.95552,     0.95555,     0.95558,     0.95562,\n",
              "            0.95565,     0.95569,     0.95572,     0.95575,     0.95578,      0.9558,     0.95583,     0.95586,     0.95588,     0.95591,     0.95594,     0.95592,     0.95584,     0.95577,      0.9557,     0.95566,     0.95562,     0.95558,     0.95554,     0.95549,     0.95551,      0.9557,     0.95573,\n",
              "            0.95577,     0.95581,     0.95585,     0.95589,     0.95581,     0.95579,     0.95598,     0.95611,     0.95634,     0.95629,     0.95624,     0.95619,     0.95615,      0.9561,      0.9563,     0.95658,      0.9567,      0.9564,     0.95634,     0.95647,     0.95657,     0.95665,     0.95674,\n",
              "            0.95687,     0.95693,     0.95689,     0.95684,      0.9568,     0.95675,      0.9567,     0.95664,     0.95657,      0.9565,     0.95649,     0.95658,     0.95666,     0.95659,     0.95644,     0.95666,     0.95664,      0.9566,     0.95657,     0.95653,      0.9565,     0.95647,     0.95643,\n",
              "             0.9564,     0.95626,     0.95613,     0.95605,     0.95596,     0.95601,     0.95627,      0.9565,     0.95673,      0.9569,     0.95703,     0.95712,      0.9572,     0.95733,     0.95746,     0.95738,     0.95729,     0.95721,     0.95711,     0.95702,     0.95682,     0.95691,      0.9572,\n",
              "            0.95712,     0.95703,      0.9572,     0.95741,     0.95743,     0.95745,     0.95748,      0.9575,     0.95752,     0.95754,     0.95756,     0.95759,     0.95761,     0.95763,     0.95773,     0.95785,      0.9581,     0.95816,     0.95821,     0.95827,     0.95824,      0.9581,     0.95822,\n",
              "            0.95834,     0.95847,      0.9585,     0.95846,     0.95843,     0.95839,     0.95835,     0.95832,     0.95828,     0.95851,     0.95859,     0.95866,     0.95874,      0.9588,     0.95885,     0.95891,     0.95898,     0.95905,     0.95913,     0.95885,      0.9587,     0.95872,     0.95875,\n",
              "            0.95877,     0.95879,     0.95882,     0.95884,     0.95887,     0.95889,     0.95891,     0.95885,     0.95876,     0.95868,     0.95893,     0.95897,     0.95901,     0.95905,     0.95909,     0.95913,     0.95915,     0.95918,     0.95921,     0.95923,     0.95926,     0.95929,     0.95931,\n",
              "            0.95934,     0.95948,      0.9592,     0.95904,     0.95895,     0.95887,     0.95864,     0.95872,      0.9588,     0.95874,     0.95884,      0.9589,     0.95896,     0.95902,     0.95905,     0.95908,      0.9591,     0.95913,     0.95915,     0.95918,     0.95921,     0.95923,     0.95926,\n",
              "            0.95938,     0.95946,      0.9592,     0.95901,     0.95912,     0.95899,     0.95906,     0.95914,     0.95923,     0.95935,      0.9595,     0.95974,     0.95979,     0.95973,     0.96008,     0.96004,     0.96001,     0.95997,     0.95993,      0.9599,     0.95986,     0.96004,     0.95997,\n",
              "            0.95991,     0.95985,     0.95979,     0.95974,     0.95969,     0.95964,     0.95959,     0.95964,     0.95972,      0.9598,     0.95958,     0.95978,     0.95994,     0.95985,     0.95952,     0.95957,     0.95962,     0.95966,     0.95971,     0.95978,        0.96,     0.96012,     0.96026,\n",
              "            0.96041,     0.96028,     0.96023,     0.96034,      0.9605,     0.96079,     0.96054,     0.96062,     0.96064,     0.96067,     0.96069,     0.96071,     0.96073,     0.96075,     0.96077,      0.9608,     0.96082,     0.96084,     0.96055,     0.96049,     0.96042,     0.96036,     0.96053,\n",
              "            0.96051,     0.96042,     0.96034,     0.96028,     0.96008,     0.95998,     0.95986,     0.95942,     0.95939,     0.95933,     0.95945,     0.95958,     0.95932,     0.95926,     0.95932,     0.95938,     0.95944,      0.9594,     0.95931,     0.95923,     0.95915,     0.95898,     0.95904,\n",
              "            0.95916,     0.95894,     0.95868,     0.95854,     0.95842,     0.95791,     0.95812,     0.95775,     0.95756,     0.95743,      0.9573,     0.95717,     0.95696,     0.95628,     0.95615,     0.95618,     0.95626,     0.95634,     0.95625,     0.95612,     0.95601,     0.95591,     0.95556,\n",
              "            0.95547,     0.95538,     0.95527,     0.95514,     0.95494,     0.95481,     0.95477,     0.95473,     0.95469,     0.95465,     0.95461,     0.95429,     0.95416,     0.95421,       0.954,     0.95391,     0.95383,     0.95367,     0.95331,     0.95302,     0.95251,     0.95248,     0.95222,\n",
              "            0.95234,     0.95243,     0.95222,     0.95215,     0.95198,     0.95135,     0.95126,     0.95117,     0.95121,     0.95133,     0.95102,     0.95091,     0.95082,     0.95076,     0.95069,     0.95062,     0.95036,     0.94995,     0.94982,     0.94949,     0.94943,     0.94936,     0.94877,\n",
              "             0.9486,     0.94836,     0.94819,     0.94805,     0.94783,     0.94763,     0.94691,     0.94665,     0.94617,     0.94607,     0.94596,     0.94583,      0.9457,     0.94542,     0.94476,     0.94449,     0.94397,     0.94323,     0.94253,      0.9424,     0.94225,     0.94172,     0.94141,\n",
              "            0.94088,     0.94075,     0.94058,     0.94041,     0.94028,     0.94013,     0.93995,     0.93981,     0.93968,     0.93942,     0.93876,     0.93849,     0.93822,     0.93797,     0.93753,     0.93739,     0.93594,     0.93586,     0.93579,     0.93571,     0.93503,     0.93459,     0.93441,\n",
              "            0.93386,     0.93359,     0.93259,     0.93216,     0.93202,     0.93093,     0.93108,      0.9296,      0.9281,     0.92796,     0.92696,     0.92407,     0.92384,     0.92307,     0.92279,     0.92176,     0.92083,     0.92091,     0.91956,     0.91942,     0.91842,     0.91604,     0.91585,\n",
              "            0.91295,     0.91281,     0.91102,     0.90839,     0.90809,     0.90649,     0.90408,     0.90394,      0.9018,     0.90042,     0.90023,     0.89743,     0.89415,     0.89396,     0.89169,     0.88897,     0.88882,     0.88664,     0.88644,     0.88421,     0.88284,     0.88264,     0.87922,\n",
              "            0.87695,     0.87675,     0.87418,     0.87155,     0.86871,     0.86851,     0.86532,     0.86517,     0.86227,     0.85932,     0.85912,     0.85677,     0.85442,     0.85421,     0.85184,     0.84749,      0.8471,     0.84531,      0.8426,     0.84238,     0.84145,     0.83892,     0.83527,\n",
              "            0.83479,      0.8317,     0.82989,     0.82742,      0.8272,     0.82514,     0.82191,     0.82169,     0.81723,     0.81304,     0.81173,     0.81151,     0.80963,     0.80774,     0.80751,     0.80417,     0.80092,     0.80069,     0.79564,     0.79266,     0.78797,     0.78381,     0.78329,\n",
              "            0.77701,     0.77108,     0.76688,     0.76638,     0.75956,     0.75638,     0.75031,     0.75006,     0.74087,     0.73424,     0.72513,     0.72467,     0.70929,     0.70031,     0.69442,     0.68441,     0.68414,     0.67282,     0.66038,     0.64644,     0.63532,       0.623,     0.62269,\n",
              "            0.61445,      0.6032,     0.59086,     0.58058,     0.58025,     0.56982,     0.55882,      0.5511,     0.54051,      0.5317,     0.53135,     0.52577,      0.5204,     0.51448,     0.50718,     0.49901,     0.49379,     0.48468,     0.47975,     0.47898,     0.47352,     0.46857,     0.46593,\n",
              "            0.46219,     0.45996,     0.45604,     0.45103,     0.45065,     0.44785,     0.44444,     0.43987,     0.43642,     0.43267,     0.42804,     0.42395,     0.41985,     0.41776,     0.41479,     0.41003,     0.40799,     0.40393,      0.4017,     0.39808,     0.39505,     0.39201,     0.38895,\n",
              "            0.38222,     0.37708,     0.37044,     0.36605,     0.35849,     0.34702,     0.33899,     0.33311,     0.31629,     0.29302,     0.27644,     0.26238,     0.25017,     0.23647,      0.2247,      0.2104,     0.20433,     0.18729,     0.17271,     0.15749,     0.14802,     0.12627,      0.1213,\n",
              "            0.11265,     0.10422,    0.098193,    0.092215,     0.08963,    0.081442,    0.077095,    0.070547,    0.067839,    0.062528,     0.06127,    0.058028,    0.053704,    0.051728,    0.046327,    0.044263,    0.040851,    0.038102,    0.036089,    0.033733,    0.033268,     0.03085,    0.030101,\n",
              "            0.02876,    0.027777,    0.025471,    0.024533,    0.023595,    0.023105,    0.022971,    0.022837,    0.022703,    0.022568,    0.022434,      0.0223,    0.020888,     0.02026,    0.019632,     0.01875,    0.018392,    0.018156,     0.01792,    0.017684,    0.017541,    0.017406,    0.017271,\n",
              "           0.017136,    0.017001,    0.016866,    0.016563,    0.015246,    0.012956,    0.012323,    0.010826,   0.0098182,   0.0091833,   0.0085479,   0.0081951,   0.0079226,     0.00765,   0.0072448,   0.0066082,   0.0062992,   0.0060262,    0.005753,   0.0055282,   0.0053543,   0.0051804,   0.0050064,\n",
              "          0.0048325,    0.004641,   0.0043219,   0.0040027,    0.003671,   0.0032877,   0.0029043,   0.0026494,   0.0024363,   0.0022231,   0.0020099,   0.0018307,   0.0017107,   0.0015907,   0.0014707,   0.0013506,   0.0012306,   0.0011105,  0.00099047,           0,           0,           0,           0,\n",
              "                  0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,\n",
              "                  0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.25984,     0.25984,     0.36052,     0.42903,     0.47747,     0.51344,     0.54425,     0.57062,     0.59106,     0.60842,     0.62752,      0.6431,     0.65358,     0.66545,     0.67514,     0.68677,     0.69566,     0.70454,     0.71293,     0.71972,     0.73047,     0.73681,     0.74189,\n",
              "            0.74693,     0.75206,      0.7562,     0.76065,     0.76457,     0.76902,     0.77356,     0.77838,     0.78225,      0.7855,      0.7906,     0.79648,     0.79982,     0.80177,     0.80503,     0.80864,     0.81059,     0.81402,     0.81598,      0.8187,     0.82022,     0.82254,     0.82638,\n",
              "            0.82942,     0.83092,     0.83285,     0.83469,     0.83653,     0.83901,     0.84252,     0.84353,     0.84378,     0.84495,      0.8463,     0.84697,     0.84928,     0.85033,     0.85131,      0.8526,     0.85347,     0.85434,     0.85687,     0.85803,     0.85986,     0.86284,     0.86396,\n",
              "            0.86538,     0.86587,      0.8668,     0.86822,     0.86889,     0.86984,     0.87045,     0.87078,     0.87146,     0.87226,     0.87337,     0.87468,     0.87589,     0.87709,     0.87901,     0.87943,      0.8798,     0.88178,     0.88361,     0.88391,     0.88467,     0.88582,      0.8862,\n",
              "            0.88695,     0.88741,     0.88791,     0.88804,     0.88892,     0.88916,      0.8901,     0.89104,     0.89118,     0.89132,     0.89156,      0.8923,     0.89264,     0.89294,     0.89345,     0.89452,      0.8951,     0.89613,     0.89722,     0.89782,     0.89812,     0.89837,     0.89857,\n",
              "            0.89877,         0.9,     0.90033,     0.90126,     0.90228,     0.90313,     0.90326,      0.9034,      0.9036,     0.90446,      0.9051,     0.90521,     0.90533,     0.90545,      0.9064,     0.90658,     0.90701,     0.90717,     0.90738,      0.9076,     0.90782,       0.908,     0.90817,\n",
              "            0.90863,      0.9093,     0.91021,     0.91026,     0.91056,     0.91135,     0.91232,      0.9126,     0.91303,     0.91386,     0.91438,     0.91553,     0.91718,     0.91782,     0.91779,      0.9179,     0.91811,     0.91868,     0.91881,     0.91894,      0.9191,      0.9193,      0.9195,\n",
              "             0.9197,     0.92023,      0.9208,     0.92123,     0.92154,     0.92186,     0.92186,     0.92185,     0.92184,     0.92184,     0.92235,     0.92287,     0.92318,     0.92343,      0.9235,     0.92368,     0.92386,      0.9241,     0.92438,     0.92479,     0.92548,     0.92576,     0.92603,\n",
              "            0.92637,     0.92711,      0.9277,     0.92793,     0.92816,     0.92839,     0.92871,     0.92925,     0.92979,     0.92996,     0.93013,     0.93064,     0.93087,     0.93106,     0.93119,     0.93133,     0.93199,     0.93233,     0.93246,     0.93259,     0.93277,     0.93301,     0.93314,\n",
              "            0.93322,      0.9333,     0.93339,     0.93347,     0.93357,      0.9337,     0.93384,     0.93401,     0.93433,     0.93456,     0.93478,     0.93522,      0.9352,     0.93525,      0.9355,     0.93617,      0.9368,     0.93689,     0.93687,     0.93775,     0.93791,     0.93806,      0.9384,\n",
              "            0.93868,      0.9388,     0.93892,     0.93901,     0.93899,     0.93941,     0.93957,     0.93972,     0.94002,     0.94061,     0.94078,     0.94088,     0.94098,     0.94108,     0.94173,     0.94206,     0.94266,     0.94285,     0.94282,     0.94332,     0.94347,     0.94361,     0.94384,\n",
              "            0.94413,      0.9443,     0.94448,     0.94507,     0.94546,     0.94556,     0.94566,     0.94576,     0.94587,     0.94631,     0.94651,     0.94671,      0.9473,     0.94745,      0.9476,      0.9476,     0.94854,     0.94884,     0.94914,     0.94938,      0.9495,     0.94961,     0.94972,\n",
              "            0.94989,     0.95015,     0.95044,     0.95112,     0.95111,      0.9511,     0.95111,      0.9516,     0.95186,     0.95208,     0.95229,     0.95246,     0.95259,     0.95272,     0.95285,     0.95315,     0.95362,     0.95418,     0.95417,     0.95419,     0.95426,     0.95432,     0.95439,\n",
              "            0.95446,     0.95453,     0.95459,     0.95465,     0.95471,     0.95476,     0.95481,     0.95487,     0.95492,     0.95498,     0.95503,     0.95505,     0.95505,     0.95504,     0.95503,     0.95503,     0.95503,     0.95502,     0.95502,     0.95502,     0.95512,     0.95548,     0.95556,\n",
              "            0.95563,     0.95571,     0.95579,     0.95586,      0.9559,     0.95615,     0.95652,     0.95678,     0.95724,     0.95723,     0.95723,     0.95723,     0.95722,     0.95722,     0.95763,      0.9582,     0.95856,     0.95854,     0.95866,     0.95893,     0.95911,     0.95928,     0.95946,\n",
              "            0.95973,     0.95989,     0.95988,     0.95988,     0.95987,     0.95987,     0.95987,     0.95986,     0.95986,     0.95985,     0.95991,     0.96008,     0.96025,     0.96029,     0.96028,     0.96074,     0.96117,     0.96117,     0.96117,     0.96117,     0.96116,     0.96116,     0.96116,\n",
              "            0.96116,     0.96115,     0.96114,     0.96113,     0.96112,     0.96132,     0.96185,     0.96232,     0.96279,     0.96312,     0.96339,     0.96357,     0.96374,     0.96399,     0.96432,     0.96431,     0.96431,      0.9643,     0.96429,     0.96429,     0.96427,     0.96461,     0.96519,\n",
              "            0.96518,     0.96518,     0.96567,      0.9661,     0.96614,     0.96619,     0.96623,     0.96628,     0.96632,     0.96637,     0.96641,     0.96646,      0.9665,     0.96655,     0.96676,       0.967,      0.9675,     0.96762,     0.96774,     0.96786,     0.96794,       0.968,     0.96824,\n",
              "            0.96849,     0.96876,     0.96885,     0.96885,     0.96885,     0.96885,     0.96884,     0.96884,     0.96884,     0.96932,     0.96948,     0.96964,     0.96979,     0.96991,     0.97003,     0.97015,     0.97028,     0.97043,     0.97059,     0.97068,     0.97069,     0.97074,     0.97079,\n",
              "            0.97084,     0.97089,     0.97094,     0.97098,     0.97103,     0.97108,     0.97113,     0.97114,     0.97113,     0.97113,     0.97167,     0.97175,     0.97183,     0.97191,     0.97199,     0.97206,     0.97212,     0.97217,     0.97223,     0.97228,     0.97234,     0.97239,     0.97245,\n",
              "             0.9725,     0.97278,     0.97298,     0.97297,     0.97297,     0.97296,     0.97305,     0.97321,     0.97337,     0.97375,     0.97396,     0.97409,     0.97421,     0.97433,     0.97439,     0.97445,      0.9745,     0.97455,     0.97461,     0.97466,     0.97471,     0.97477,     0.97483,\n",
              "            0.97508,     0.97529,     0.97528,     0.97531,     0.97555,     0.97576,     0.97592,     0.97609,     0.97627,     0.97651,     0.97684,     0.97732,     0.97762,     0.97781,     0.97857,     0.97857,     0.97856,     0.97856,     0.97856,     0.97856,     0.97856,     0.97903,     0.97903,\n",
              "            0.97903,     0.97903,     0.97902,     0.97902,     0.97902,     0.97902,     0.97901,     0.97915,     0.97931,     0.97947,     0.97948,     0.97993,     0.98044,     0.98043,     0.98042,     0.98052,     0.98062,     0.98072,     0.98082,     0.98097,     0.98142,     0.98167,     0.98197,\n",
              "            0.98234,     0.98234,     0.98241,     0.98265,     0.98298,     0.98378,     0.98377,     0.98427,     0.98432,     0.98436,     0.98441,     0.98445,      0.9845,     0.98454,     0.98459,     0.98463,     0.98468,     0.98472,     0.98473,     0.98473,     0.98472,     0.98472,      0.9851,\n",
              "            0.98521,      0.9852,      0.9852,     0.98562,     0.98567,     0.98615,     0.98615,     0.98614,     0.98662,     0.98675,       0.987,     0.98759,     0.98758,     0.98766,     0.98778,     0.98791,     0.98804,     0.98807,     0.98807,     0.98807,     0.98846,     0.98855,     0.98876,\n",
              "            0.98901,     0.98903,     0.98903,     0.98902,     0.98902,     0.98902,      0.9895,      0.9895,     0.98949,     0.98949,     0.98949,     0.98949,     0.98948,     0.98947,     0.98946,     0.98959,     0.98976,     0.98993,     0.98996,     0.98996,     0.98995,     0.98995,     0.98994,\n",
              "            0.98994,     0.98994,     0.98994,     0.98994,     0.98993,     0.98993,     0.98993,     0.98993,     0.98993,     0.98993,     0.98993,     0.98992,     0.98992,     0.99091,      0.9909,      0.9909,      0.9909,      0.9909,     0.99089,     0.99089,     0.99088,     0.99137,     0.99137,\n",
              "            0.99163,     0.99187,     0.99192,     0.99237,     0.99237,     0.99236,     0.99236,     0.99236,     0.99304,      0.9933,     0.99336,     0.99336,     0.99336,     0.99336,     0.99336,     0.99335,     0.99335,     0.99335,     0.99334,     0.99334,     0.99334,     0.99334,     0.99333,\n",
              "            0.99333,     0.99332,     0.99332,     0.99332,     0.99332,     0.99331,      0.9933,      0.9933,      0.9933,     0.99329,     0.99329,     0.99329,     0.99329,      0.9938,     0.99379,     0.99378,     0.99378,     0.99377,     0.99376,     0.99376,     0.99376,     0.99375,     0.99375,\n",
              "            0.99374,     0.99374,     0.99374,     0.99373,     0.99373,     0.99373,     0.99373,     0.99373,     0.99372,     0.99372,     0.99371,     0.99371,     0.99371,     0.99422,     0.99422,     0.99421,      0.9942,      0.9942,      0.9942,      0.9942,     0.99419,     0.99418,     0.99418,\n",
              "             0.9947,      0.9947,     0.99468,     0.99468,     0.99468,     0.99467,     0.99515,     0.99518,     0.99517,     0.99517,     0.99569,     0.99567,     0.99574,      0.9962,     0.99619,     0.99619,     0.99627,     0.99672,     0.99671,     0.99671,     0.99671,     0.99669,     0.99669,\n",
              "            0.99667,     0.99667,     0.99666,     0.99664,     0.99664,     0.99663,     0.99661,     0.99661,     0.99659,     0.99715,     0.99715,     0.99713,     0.99711,     0.99711,      0.9971,     0.99708,     0.99708,     0.99707,     0.99707,     0.99706,     0.99705,     0.99705,     0.99703,\n",
              "            0.99701,     0.99701,       0.997,     0.99698,     0.99696,     0.99696,     0.99694,     0.99694,     0.99692,      0.9969,      0.9969,     0.99689,     0.99687,     0.99687,     0.99686,     0.99683,     0.99683,     0.99681,      0.9968,      0.9968,     0.99679,     0.99677,     0.99675,\n",
              "            0.99674,     0.99672,     0.99671,      0.9967,     0.99669,     0.99668,     0.99666,     0.99666,     0.99663,      0.9966,     0.99659,     0.99659,     0.99657,     0.99656,     0.99656,     0.99722,      0.9972,      0.9972,     0.99717,     0.99716,     0.99713,     0.99782,     0.99782,\n",
              "            0.99779,     0.99777,     0.99775,     0.99774,     0.99771,      0.9977,     0.99767,     0.99766,     0.99762,     0.99758,     0.99754,     0.99753,     0.99745,      0.9974,     0.99737,     0.99731,      0.9973,     0.99724,     0.99716,     0.99707,     0.99699,      0.9969,      0.9969,\n",
              "            0.99789,     0.99783,     0.99777,     0.99771,     0.99771,     0.99765,     0.99759,     0.99754,     0.99748,     0.99742,     0.99742,     0.99738,     0.99734,      0.9973,     0.99725,     0.99908,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1,\n",
              "                  1,           1,           1,           1,           1,           1,           1,           1,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.98499,     0.98499,     0.98265,     0.98124,     0.97936,     0.97842,     0.97795,     0.97749,     0.97749,     0.97749,     0.97749,     0.97655,     0.97561,     0.97514,     0.97467,     0.97467,     0.97467,     0.97467,     0.97467,      0.9742,     0.97373,     0.97373,     0.97373,\n",
              "            0.97326,      0.9728,      0.9728,      0.9728,      0.9728,      0.9728,     0.97233,     0.97233,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97186,     0.97139,     0.97139,     0.97092,     0.97092,\n",
              "            0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,     0.97092,\n",
              "            0.97085,     0.97045,     0.97045,     0.97045,     0.97045,     0.97045,     0.97045,     0.97045,     0.97045,     0.97043,     0.96951,     0.96904,     0.96904,     0.96904,     0.96904,     0.96815,     0.96811,     0.96811,     0.96811,     0.96811,     0.96811,     0.96797,     0.96764,\n",
              "            0.96764,     0.96764,     0.96764,     0.96734,     0.96717,     0.96697,      0.9667,      0.9667,      0.9667,      0.9667,      0.9667,      0.9667,      0.9667,      0.9667,      0.9667,     0.96663,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,\n",
              "            0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96623,     0.96576,     0.96576,     0.96576,     0.96576,     0.96576,     0.96576,\n",
              "            0.96557,     0.96529,     0.96517,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96482,     0.96384,     0.96354,     0.96341,     0.96341,     0.96341,     0.96341,     0.96341,     0.96341,     0.96341,     0.96341,\n",
              "            0.96341,     0.96313,     0.96295,     0.96295,     0.96295,      0.9629,     0.96281,     0.96273,     0.96264,     0.96255,     0.96248,     0.96248,     0.96248,     0.96248,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,\n",
              "            0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96201,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,\n",
              "            0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96154,     0.96149,     0.96125,     0.96107,     0.96107,     0.96107,     0.96107,     0.96088,     0.96065,      0.9606,      0.9606,      0.9606,      0.9606,\n",
              "             0.9606,      0.9606,      0.9606,     0.96053,     0.96018,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95966,     0.95945,     0.95904,     0.95872,     0.95872,     0.95872,     0.95872,\n",
              "            0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95872,     0.95847,     0.95826,     0.95826,     0.95826,     0.95826,     0.95826,     0.95826,     0.95826,\n",
              "            0.95826,     0.95826,     0.95826,     0.95821,     0.95802,     0.95783,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95732,     0.95716,     0.95697,     0.95685,     0.95685,     0.95685,     0.95685,\n",
              "            0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95685,     0.95678,     0.95664,     0.95651,     0.95637,     0.95629,     0.95621,     0.95613,     0.95605,     0.95597,     0.95591,     0.95591,     0.95591,\n",
              "            0.95591,     0.95591,     0.95591,     0.95591,     0.95572,     0.95544,     0.95544,     0.95544,     0.95544,     0.95535,     0.95526,     0.95517,     0.95507,     0.95498,     0.95497,     0.95497,     0.95484,     0.95428,     0.95403,     0.95403,     0.95403,     0.95403,     0.95403,\n",
              "            0.95403,       0.954,     0.95391,     0.95382,     0.95374,     0.95365,     0.95356,     0.95343,      0.9533,     0.95318,      0.9531,      0.9531,      0.9531,     0.95291,     0.95263,     0.95262,     0.95214,     0.95208,     0.95201,     0.95195,     0.95188,     0.95182,     0.95176,\n",
              "            0.95169,     0.95142,     0.95118,     0.95102,     0.95086,     0.95075,     0.95075,     0.95075,     0.95075,     0.95075,     0.95075,     0.95075,     0.95075,     0.95075,      0.9507,     0.95054,     0.95038,     0.95022,     0.95004,     0.94987,     0.94948,     0.94934,     0.94934,\n",
              "            0.94918,     0.94902,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94887,     0.94874,     0.94841,     0.94841,\n",
              "            0.94841,     0.94841,     0.94836,     0.94829,     0.94822,     0.94815,     0.94809,     0.94802,     0.94795,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94794,     0.94731,       0.947,       0.947,       0.947,\n",
              "              0.947,       0.947,       0.947,       0.947,       0.947,       0.947,       0.947,     0.94686,      0.9467,     0.94654,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,     0.94653,\n",
              "            0.94653,     0.94653,      0.9458,      0.9455,     0.94534,     0.94518,     0.94465,     0.94465,     0.94465,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,     0.94418,\n",
              "            0.94418,     0.94413,     0.94365,     0.94325,     0.94325,     0.94278,     0.94278,     0.94278,     0.94278,     0.94278,     0.94278,     0.94278,      0.9426,     0.94231,     0.94228,     0.94221,     0.94214,     0.94207,       0.942,     0.94193,     0.94187,     0.94177,     0.94165,\n",
              "            0.94153,     0.94141,      0.9413,     0.94121,     0.94111,     0.94101,     0.94092,      0.9409,      0.9409,      0.9409,     0.94047,     0.94043,     0.94028,     0.94012,     0.93949,     0.93949,     0.93949,     0.93949,     0.93949,     0.93949,     0.93949,     0.93949,     0.93949,\n",
              "            0.93944,      0.9392,     0.93902,     0.93902,     0.93902,     0.93885,     0.93837,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93809,     0.93754,     0.93742,      0.9373,     0.93718,     0.93715,\n",
              "            0.93702,     0.93686,      0.9367,     0.93621,     0.93578,     0.93517,     0.93493,      0.9341,     0.93362,      0.9334,      0.9334,     0.93312,     0.93264,     0.93246,     0.93246,     0.93246,     0.93246,     0.93234,     0.93218,     0.93202,     0.93152,     0.93113,     0.93105,\n",
              "            0.93105,     0.93062,     0.93014,     0.92989,     0.92965,     0.92871,     0.92867,     0.92797,     0.92763,     0.92739,     0.92715,     0.92691,     0.92651,     0.92525,     0.92501,     0.92495,     0.92495,     0.92495,     0.92476,     0.92452,     0.92432,     0.92412,     0.92348,\n",
              "            0.92332,     0.92316,     0.92296,     0.92272,     0.92234,      0.9221,     0.92202,     0.92195,     0.92187,      0.9218,     0.92173,     0.92114,      0.9209,     0.92013,     0.91974,     0.91958,     0.91942,     0.91914,     0.91847,     0.91795,       0.917,     0.91651,     0.91604,\n",
              "            0.91604,     0.91601,     0.91557,     0.91507,     0.91475,     0.91359,     0.91343,     0.91327,     0.91276,     0.91276,     0.91214,     0.91195,     0.91178,     0.91166,     0.91154,     0.91142,     0.91094,     0.91019,     0.90995,     0.90936,     0.90924,     0.90912,     0.90804,\n",
              "            0.90772,      0.9073,     0.90697,     0.90673,     0.90632,     0.90596,     0.90466,     0.90418,     0.90332,     0.90313,     0.90293,      0.9027,     0.90246,     0.90153,     0.90034,     0.89986,     0.89891,     0.89758,     0.89633,     0.89609,     0.89582,     0.89487,     0.89431,\n",
              "            0.89336,     0.89312,     0.89283,     0.89253,     0.89229,     0.89202,      0.8917,     0.89145,     0.89121,     0.89075,     0.88957,     0.88909,     0.88861,     0.88774,     0.88695,     0.88671,     0.88413,       0.884,     0.88386,     0.88372,     0.88252,     0.88173,     0.88141,\n",
              "            0.88003,     0.87955,     0.87779,     0.87703,     0.87679,     0.87487,     0.87477,     0.87213,      0.8695,     0.86926,      0.8671,     0.86208,     0.86163,     0.85995,     0.85947,     0.85768,       0.856,     0.85582,     0.85349,     0.85325,     0.85154,     0.84747,     0.84715,\n",
              "             0.8422,     0.84196,     0.83893,      0.8345,     0.83399,     0.83131,     0.82728,     0.82704,     0.82347,      0.8208,     0.82048,     0.81585,     0.81046,     0.81014,     0.80643,     0.80201,     0.80177,     0.79823,     0.79791,      0.7943,     0.79211,     0.79179,     0.78631,\n",
              "             0.7827,     0.78238,     0.77831,     0.77416,     0.76969,     0.76937,     0.76441,     0.76417,     0.75966,     0.75511,     0.75479,     0.75119,     0.74759,     0.74727,     0.74366,     0.73706,     0.73648,     0.73379,     0.72972,      0.7294,     0.72799,     0.72423,     0.71882,\n",
              "            0.71811,     0.71357,      0.7109,      0.7073,     0.70697,     0.70398,      0.6993,     0.69898,     0.69256,     0.68658,     0.68473,     0.68441,     0.68174,     0.67907,     0.67875,     0.67374,      0.6692,     0.66888,     0.66187,     0.65776,     0.65135,     0.64539,     0.64468,\n",
              "            0.63622,     0.62833,     0.62278,     0.62212,     0.61319,     0.60907,     0.60124,     0.60092,     0.58923,      0.5809,     0.56959,     0.56902,     0.55031,     0.53959,     0.53264,     0.52097,     0.52065,     0.50766,     0.49366,     0.47825,      0.4662,     0.45307,     0.45275,\n",
              "            0.44388,     0.43225,      0.4197,     0.40941,     0.40909,     0.39879,     0.38812,     0.38071,     0.37069,     0.36245,     0.36213,     0.35698,     0.35204,     0.34665,     0.34006,     0.33255,     0.32784,     0.31985,     0.31558,     0.31491,      0.3102,     0.30597,     0.30372,\n",
              "            0.30055,     0.29867,     0.29537,     0.29118,     0.29086,     0.28853,     0.28571,     0.28194,     0.27912,     0.27606,     0.27229,       0.269,      0.2657,     0.26403,     0.26166,     0.25788,     0.25628,     0.25308,     0.25133,      0.2485,     0.24614,     0.24379,     0.24143,\n",
              "            0.23626,     0.23234,     0.22732,     0.22403,     0.21839,     0.20993,     0.20408,     0.19984,     0.18785,     0.17166,     0.16039,       0.151,     0.14297,     0.13409,     0.12657,     0.11757,     0.11379,     0.10332,    0.094515,    0.085473,    0.079928,    0.067391,    0.064565,\n",
              "           0.059689,    0.054975,    0.051631,    0.048336,    0.046917,     0.04245,    0.040093,    0.036563,    0.035111,    0.032273,    0.031603,    0.029881,    0.027593,     0.02655,    0.023713,    0.022633,    0.020852,    0.019421,    0.018376,    0.017156,    0.016915,    0.015667,     0.01528,\n",
              "            0.01459,    0.014084,      0.0129,    0.012419,    0.011938,    0.011688,    0.011619,     0.01155,    0.011482,    0.011413,    0.011344,    0.011276,    0.010554,    0.010234,   0.0099131,    0.009464,   0.0092814,   0.0091612,   0.0090411,   0.0089209,   0.0088483,   0.0087796,   0.0087109,\n",
              "          0.0086423,   0.0085736,   0.0085049,   0.0083508,   0.0076815,   0.0065203,   0.0061998,   0.0054426,   0.0049333,   0.0046128,   0.0042923,   0.0041144,    0.003977,   0.0038397,   0.0036356,   0.0033151,   0.0031596,   0.0030222,   0.0028848,   0.0027717,   0.0026843,   0.0025969,   0.0025095,\n",
              "          0.0024221,   0.0023259,   0.0021656,   0.0020054,   0.0018389,   0.0016466,   0.0014543,   0.0013265,   0.0012196,   0.0011128,    0.001006,  0.00091617,  0.00085607,  0.00079597,  0.00073587,  0.00067578,  0.00061568,  0.00055558,  0.00049548,           0,           0,           0,           0,\n",
              "                  0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,\n",
              "                  0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
              "fitness: 0.7115667541140445\n",
              "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
              "maps: array([    0.68206])\n",
              "names: {0: 'License_Plate'}\n",
              "plot: True\n",
              "results_dict: {'metrics/precision(B)': 0.9809674663258948, 'metrics/recall(B)': 0.9394934333958724, 'metrics/mAP50(B)': 0.9771230447216988, 'metrics/mAP50-95(B)': 0.6820604996020829, 'fitness': 0.7115667541140445}\n",
              "save_dir: PosixPath('runs/detect/train')\n",
              "speed: {'preprocess': 0.2594093190376826, 'inference': 10.312552792352433, 'loss': 0.0007755012922389534, 'postprocess': 1.8282861886718988}\n",
              "task: 'detect'"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "\n",
        "def move_folder(source_folder, destination_folder):\n",
        "    \"\"\"Moves an existing folder to a new location.\n",
        "\n",
        "    Args:\n",
        "        source_folder (str): The path to the folder to be moved.\n",
        "        destination_folder (str): The path to the destination folder.\n",
        "\n",
        "    Raises:\n",
        "        FileNotFoundError: If the source folder doesn't exist.\n",
        "        OSError: If there's an error moving the folder.\n",
        "    \"\"\"\n",
        "\n",
        "    if not os.path.exists(source_folder):\n",
        "        raise FileNotFoundError(f\"Source folder not found: {source_folder}\")\n",
        "\n",
        "    try:\n",
        "        shutil.move(source_folder, destination_folder)\n",
        "        print(f\"Folder moved successfully from {source_folder} to {destination_folder}\")\n",
        "    except OSError as e:\n",
        "        raise OSError(f\"Error moving folder: {e}\") from e\n",
        "\n",
        "# testing it\n",
        "source_path = \"/content/sample_data\"\n",
        "destination_path = \"/content/drive/MyDrive/license_plate_detection_train_results\"\n",
        "\n",
        "move_folder(source_path, destination_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jOUTvnTDRog",
        "outputId": "201a6a07-66b6-43cf-ef70-fc5de6708b58"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder moved successfully from /content/sample_data to /content/drive/MyDrive/license_plate_detection_train_results\n"
          ]
        }
      ]
    }
  ]
}