Tracking_Coordinatess_YOLOv8.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MJAHMADEE/Yolov8_Track_Coordinates_Center/blob/main/Tracking_Coordinatess_YOLOv8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "_IternVd_oVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "HOME = os.getcwd()\n",
        "print(HOME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "984J4pv4K2D-",
        "outputId": "1a8a38f3-5a38-49b4-c706-50020774c670"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %cd {HOME}\n",
        "# !wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1pz68D1Gsx80MoPg-_q-IbEdESEmyVLm-' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1pz68D1Gsx80MoPg-_q-IbEdESEmyVLm-\" -O vehicle-counting.mp4 && rm -rf /tmp/cookies.txt"
      ],
      "metadata": {
        "id": "ctvdpAiRSb1_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SOURCE_VIDEO_PATH =\"/content/BEAUTIFUL-British-Airways-Airbus-A350-Landing-Heathrow-Airpo.mp4\""
      ],
      "metadata": {
        "id": "gtxQLn33TBWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pip install method (recommended)\n",
        "\n",
        "!pip install ultralytics\n",
        "\n",
        "from IPython import display\n",
        "display.clear_output()\n",
        "\n",
        "import ultralytics\n",
        "ultralytics.checks()"
      ],
      "metadata": {
        "id": "IGckxTNGLKDh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1ae0850-dab6-4e02-8ded-329d41dd4964"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.112 🚀 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/78.2 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install loguru"
      ],
      "metadata": {
        "id": "a2RXYcwNAN25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yolox"
      ],
      "metadata": {
        "id": "Mb2jAhTWAS0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install thop"
      ],
      "metadata": {
        "id": "uwa0GEZ8Akn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lap"
      ],
      "metadata": {
        "id": "uSlCyqYsA0gQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd {HOME}\n",
        "!git clone https://github.com/ifzhang/ByteTrack.git\n",
        "%cd {HOME}/ByteTrack\n",
        "\n",
        "# workaround related to https://github.com/roboflow/notebooks/issues/80\n",
        "!sed -i 's/onnx==1.8.1/onnx==1.9.0/g' requirements.txt\n",
        "\n",
        "!pip3 install -q -r requirements.txt\n",
        "!python3 setup.py -q develop\n",
        "!pip install -q cython_bbox\n",
        "!pip install -q onemetric\n",
        "\n",
        "from IPython import display\n",
        "display.clear_output()\n",
        "\n",
        "\n",
        "import sys\n",
        "sys.path.append(f\"{HOME}/ByteTrack\")\n",
        "\n",
        "\n",
        "import yolox\n",
        "print(\"yolox.__version__:\", yolox.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KdBkOflo2xY",
        "outputId": "e5e1ed51-10a9-4a80-8a22-1e410e355596"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "yolox.__version__: 0.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yolox.tracker.byte_tracker import BYTETracker, STrack\n",
        "from onemetric.cv.utils.iou import box_iou_batch\n",
        "from dataclasses import dataclass\n",
        "\n",
        "\n",
        "@dataclass(frozen=True)\n",
        "class BYTETrackerArgs:\n",
        "    track_thresh: float = 0.25\n",
        "    track_buffer: int = 30\n",
        "    match_thresh: float = 0.8\n",
        "    aspect_ratio_thresh: float = 3.0\n",
        "    min_box_area: float = 1.0\n",
        "    mot20: bool = False"
      ],
      "metadata": {
        "id": "rwg-lY49o7Sf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install supervision==0.1.0\n",
        "\n",
        "\n",
        "from IPython import display\n",
        "display.clear_output()\n",
        "\n",
        "\n",
        "import supervision\n",
        "print(\"supervision.__version__:\", supervision.__version__)"
      ],
      "metadata": {
        "id": "d60yX_PFQ9A2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32b2075c-2751-451f-81df-ed5b505f919e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "supervision.__version__: 0.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from supervision.draw.color import ColorPalette\n",
        "from supervision.geometry.dataclasses import Point\n",
        "from supervision.video.dataclasses import VideoInfo\n",
        "from supervision.video.source import get_video_frames_generator\n",
        "from supervision.video.sink import VideoSink\n",
        "from supervision.notebook.utils import show_frame_in_notebook\n",
        "from supervision.tools.detections import Detections, BoxAnnotator\n",
        "from supervision.tools.line_counter import LineCounter, LineCounterAnnotator"
      ],
      "metadata": {
        "id": "7YDohOpMTWH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# converts Detections into format that can be consumed by match_detections_with_tracks function\n",
        "def detections2boxes(detections: Detections) -> np.ndarray:\n",
        "    return np.hstack((\n",
        "        detections.xyxy,\n",
        "        detections.confidence[:, np.newaxis]\n",
        "    ))\n",
        "\n",
        "\n",
        "# converts List[STrack] into format that can be consumed by match_detections_with_tracks function\n",
        "def tracks2boxes(tracks: List[STrack]) -> np.ndarray:\n",
        "    return np.array([\n",
        "        track.tlbr\n",
        "        for track\n",
        "        in tracks\n",
        "    ], dtype=float)\n",
        "\n",
        "\n",
        "# matches our bounding boxes with predictions\n",
        "def match_detections_with_tracks(\n",
        "    detections: Detections,\n",
        "    tracks: List[STrack]\n",
        ") -> Detections:\n",
        "    if not np.any(detections.xyxy) or len(tracks) == 0:\n",
        "        return np.empty((0,))\n",
        "\n",
        "    tracks_boxes = tracks2boxes(tracks=tracks)\n",
        "    iou = box_iou_batch(tracks_boxes, detections.xyxy)\n",
        "    track2detection = np.argmax(iou, axis=1)\n",
        "\n",
        "    tracker_ids = [None] * len(detections)\n",
        "\n",
        "    for tracker_index, detection_index in enumerate(track2detection):\n",
        "        if iou[tracker_index, detection_index] != 0:\n",
        "            tracker_ids[detection_index] = tracks[tracker_index].track_id\n",
        "\n",
        "    return tracker_ids"
      ],
      "metadata": {
        "id": "SE0G6LvFAXlk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load pre-trained YOLOv8 model"
      ],
      "metadata": {
        "id": "c_417m4g9XVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# settings\n",
        "#MODEL = \"/content/drive/MyDrive/best.pt\"\n",
        "\n",
        "MODEL=\"yolov8m.pt\""
      ],
      "metadata": {
        "id": "m3FMq5FcUsRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO(MODEL)\n",
        "model.fuse()"
      ],
      "metadata": {
        "id": "KFCV_2TR9eo_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffef970e-a848-4191-faaf-86d72537550e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to yolov8m.pt...\n",
            "100%|██████████| 49.7M/49.7M [00:00<00:00, 196MB/s]\n",
            "YOLOv8m summary (fused): 218 layers, 25886080 parameters, 0 gradients\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dict maping class_id to class_name\n",
        "CLASS_NAMES_DICT = {4: 'airplane'}\n",
        "# class_ids of interest - car, motorcycle, bus and truck\n",
        "CLASS_ID = [4]"
      ],
      "metadata": {
        "id": "yKuDnOIxsN6l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TP-NxWD26Ct0",
        "outputId": "97b70be3-5f66-4d22-8cc1-8be4d6829e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'person',\n",
              " 1: 'bicycle',\n",
              " 2: 'car',\n",
              " 3: 'motorcycle',\n",
              " 4: 'airplane',\n",
              " 5: 'bus',\n",
              " 6: 'train',\n",
              " 7: 'truck',\n",
              " 8: 'boat',\n",
              " 9: 'traffic light',\n",
              " 10: 'fire hydrant',\n",
              " 11: 'stop sign',\n",
              " 12: 'parking meter',\n",
              " 13: 'bench',\n",
              " 14: 'bird',\n",
              " 15: 'cat',\n",
              " 16: 'dog',\n",
              " 17: 'horse',\n",
              " 18: 'sheep',\n",
              " 19: 'cow',\n",
              " 20: 'elephant',\n",
              " 21: 'bear',\n",
              " 22: 'zebra',\n",
              " 23: 'giraffe',\n",
              " 24: 'backpack',\n",
              " 25: 'umbrella',\n",
              " 26: 'handbag',\n",
              " 27: 'tie',\n",
              " 28: 'suitcase',\n",
              " 29: 'frisbee',\n",
              " 30: 'skis',\n",
              " 31: 'snowboard',\n",
              " 32: 'sports ball',\n",
              " 33: 'kite',\n",
              " 34: 'baseball bat',\n",
              " 35: 'baseball glove',\n",
              " 36: 'skateboard',\n",
              " 37: 'surfboard',\n",
              " 38: 'tennis racket',\n",
              " 39: 'bottle',\n",
              " 40: 'wine glass',\n",
              " 41: 'cup',\n",
              " 42: 'fork',\n",
              " 43: 'knife',\n",
              " 44: 'spoon',\n",
              " 45: 'bowl',\n",
              " 46: 'banana',\n",
              " 47: 'apple',\n",
              " 48: 'sandwich',\n",
              " 49: 'orange',\n",
              " 50: 'broccoli',\n",
              " 51: 'carrot',\n",
              " 52: 'hot dog',\n",
              " 53: 'pizza',\n",
              " 54: 'donut',\n",
              " 55: 'cake',\n",
              " 56: 'chair',\n",
              " 57: 'couch',\n",
              " 58: 'potted plant',\n",
              " 59: 'bed',\n",
              " 60: 'dining table',\n",
              " 61: 'toilet',\n",
              " 62: 'tv',\n",
              " 63: 'laptop',\n",
              " 64: 'mouse',\n",
              " 65: 'remote',\n",
              " 66: 'keyboard',\n",
              " 67: 'cell phone',\n",
              " 68: 'microwave',\n",
              " 69: 'oven',\n",
              " 70: 'toaster',\n",
              " 71: 'sink',\n",
              " 72: 'refrigerator',\n",
              " 73: 'book',\n",
              " 74: 'clock',\n",
              " 75: 'vase',\n",
              " 76: 'scissors',\n",
              " 77: 'teddy bear',\n",
              " 78: 'hair drier',\n",
              " 79: 'toothbrush'}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create frame generator\n",
        "generator = get_video_frames_generator(SOURCE_VIDEO_PATH)\n",
        "# create instance of BoxAnnotator\n",
        "box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)\n",
        "# acquire first video frame\n",
        "iterator = iter(generator)\n",
        "frame = next(iterator)\n",
        "# model prediction on single frame and conversion to supervision Detections\n",
        "results = model(frame)\n",
        "detections = Detections(\n",
        "    xyxy=results[0].boxes.xyxy.cpu().numpy(),\n",
        "    confidence=results[0].boxes.conf.cpu().numpy(),\n",
        "    class_id=results[0].boxes.cls.cpu().numpy().astype(int)\n",
        ")\n",
        "# format custom labels\n",
        "labels = [\n",
        "    f\"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}\"\n",
        "    for _, confidence, class_id, tracker_id\n",
        "    in detections\n",
        "]\n",
        "# annotate and display frame\n",
        "frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)\n",
        "\n",
        "%matplotlib inline\n",
        "show_frame_in_notebook(frame, (16, 16))"
      ],
      "metadata": {
        "id": "hZQsgCa0cFvH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8040d3f5-84e1-40e5-ea7e-f9e6e584e0ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "0: 640x384 1 airplane, 76.1ms\n",
            "Speed: 16.9ms preprocess, 76.1ms inference, 20.3ms postprocess per image at shape (1, 3, 640, 640)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 1 Axes>"
            ],
            "image/png": 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OMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnHoJ6IiIiIKOcY1BMRERER5RyDeiIiIiKinGNQT0RERESUcwzqiYiIiIhyjkE9EREREVHOMagnIiIiIso5BvVERERERDnX86D+N37jNyCEwPve9z7vb8vLy7j77ruxdu1ajI2N4Z3vfCdOnDgRmO/gwYO46667MDIygg0bNuCDH/wgms1mrzeXiIiIiCh3ehrUP/roo/jEJz6Ba665JvD397///fj85z+Pz3zmM3jggQdw9OhRvOMd7/C+N00Td911F+r1Oh588EF88pOfxJ//+Z/jIx/5SC83l4iIiIgon2SPzM/Py0suuUTec8898rbbbpO/8Au/IKWUcmZmRpZKJfmZz3zGm/b555+XAORDDz0kpZTyi1/8ojQMQx4/ftyb5uMf/7icmJiQtVot1fpnZ2clAH744Ycffvjhhx9++Mn9Z3Z2Njb27VlN/d1334277roLd9xxR+Dvjz/+OBqNRuDvl19+OXbs2IGHHnoIAPDQQw/h6quvxsaNG71p7rzzTszNzeHZZ5/Vrq9Wq2Fubi7wISIiIiI6HxR7sdBPfepTeOKJJ/Doo4+Gvjt+/DjK5TKmpqYCf9+4cSOOHz/uTeMP6N3v3e90PvrRj+JXf/VXu7D1RERERET50vWa+kOHDuEXfuEX8Fd/9VcYGhrq9uIjffjDH8bs7Kz3OXTo0Iqtm4iIiIion7oe1D/++OM4efIkrrvuOhSLRRSLRTzwwAP4X//rf6FYLGLjxo2o1+uYmZkJzHfixAls2rQJALBp06bQaDju7+40qkqlgomJicCHiIiIiOh80PWg/vbbb8fTTz+N73znO97nhhtuwLve9S7v51KphHvvvdeb58UXX8TBgwexe/duAMDu3bvx9NNP4+TJk94099xzDyYmJnDllVd2e5OJiIiIiHKt633qx8fHcdVVVwX+Njo6irVr13p//7f/9t/iAx/4ANasWYOJiQn8/M//PHbv3o2bb74ZAPDmN78ZV155JX78x38cv/Vbv4Xjx4/jl37pl3D33XejUql0e5OJiIiIiHKtJw/KJvnd3/1dGIaBd77znajVarjzzjvxsY99zPu+UCjgC1/4An72Z38Wu3fvxujoKN797nfj137t1/qxuUREREREA01IKWW/N6IX5ubmMDk52e/NICIiIiLKbHZ2NvaZ0Z6+UZaIiIiIiHqPQT0RERERUc4xqCciIiIiyjkG9UREREREOcegnoiIiIgo5xjUExERERHlHIN6IiIiIqKcY1BPRERERJRzDOqJiIiIiHKOQT0RERERUc4xqCciIiIiyjkG9UREREREOcegnoiIiIgo5xjUExERERHlHIN6IiIiIqKcY1BPRERERJRzDOqJiIiIiHKOQT0RERERUc4xqCciIiIiyjkG9UREREREOcegnoiIiIgo54r93oBBIDR/kyu+FURE1A+DeA/QbRPQ/+0aZFHHLMnqPaYC8UfFWqkNoRXCmnoiIiIiopw7D2rqwyVV+zeJ1Vw+jyOEvuQuZfePh39dvVj+ytLVemRNR/2sSYlb92q+PrLUZSSdj7hlr+ZjGic+jYuY7yQs9OKYiRRLFYnXZhz7XOfzbGfZ70Gu+W3v2gzeu4BwHOFGEhL93e8s+Vle63WzHu9e3gN6te5094/zIKgnPwFEpos0N7purYuIKEm386S8iAupz9djkqTTYog7r1R+jzvIQvmZ54MGBYN6dL+s2uu626zi6mazlEF12+3/XSo/5yUj7OX5GOR6rV7qdRpfbfqZp/R63XHfZVlv0nYLxOf93bg2Oz3meb0++pmfdfuYRd271O97cd9MK/naTGols3KZzs5HadPZeRDUh5O8WiLvdnA56AFrXKNw94/Fyq1rZazkEVoJ/Vl7f2u3ernmuGVnW2+/jld38rP+5ANJQX38uvt9bfZT53s+uMesvWtTxkYKIvB9f6/NpLX3Lk/Kr37ud2frTpsbrfqg/tLLd6FQKABw+8UBrYMa15fT+T6hGBvZTzyi33oqPex7HtWf3mVlWLcQ8f1jXVHHLNted3a8vbni9jvxXGa5AWatJ8lah9ofIrTdumcVOl96/LKyXJsJ84osN9j47ZaJbYrJdXadynQ2Eo9Z79aefH2FOl10Zb365cWtu90ld56mpW/VwaxNJG9WwjBBSac6zZMMnc/fu+OdNH/23KrzqzPLVZ/fWvpeX5uDt27TNLH3xZcSp1v1Qf1P/exPYHhoCBISpmU3XtglHuFl+K1YXwLC/r0pirBgaDMpf0boLtO+fThfCAAiQ6ceq3eNmMIwYpNUU3a+7gJEYqEBACzf/rkBfue1gUL5tMdw7nBCF9R7AZphp5XIgM1se70u6S47UlLnhKTzFXdUCwnz9i7LNzIcM1unGWtSp4uY+byH5eIiGxnTIbfThnp7fVbi+YpZQsaKgmxBixG9gMSKAAmpHDP/9Im1VzH5UVIAKoV0lt/e3rvbZ1gx6Sxp3WjdV3SzhY6ZVKfKcu0mXB8x5aCk4yUhO04LicVlI2JeL9+OvvaEFIAVd13rz4fLEp1f14YwUlWGRS3DjE3IIjahFxIrIQaUyPIAvQBkhvisT+teri7jo+//cOJ0qz6ov+jibRgdHQEANMwmgNa1K4Vh32albAWXzr/VwhCaQnN4hLRrpJ2bhRugSilbAa0QEFEZTArSkj2rrTeM+ARlZgjqDSHarqkP/NzJIQtdIO1dMEI6xTspw7GY4dzUnXVYRtSxyVAIc9JgpKQMIPZmkhSQJN28ezcygkDruvFW5/zsLxjqC4n6/Uq/rA72yzsWUfMq50F7XqLPh5r/BPfbcOY2AtMEVpd0zKzg8tvV0bXpbk9COoqvCJCQTp7U0ahdMfmwMASkskzpvxcEgvr0x82AHaQJbZBo70fc+ZJSwhIWJCSkDBZ+DenObxfw3JjMfwwEWt+ry4363X//EqKgPdb+NOpOH9p2WF5BS3dektJfXFpIrDAqOPl0aEb1L8HfDRj2n3SXrFDPvT6/7TyoFyiIQsdBvQRgxq46voBXSO6MMKCsjJXtWYP6TtfbeYG7uriUarpVH9Q3Gk3UGw1IadcSuPU+9u+WnV3L1gcAIAWaxQaahj9DbdXCu0G9AGBZpjOftIN5OE2+hoisJPKmd5esTmh2Xm+dxOphTb0hjLbCpcBNBZ0GDm4NqtH6vR1OxaoX1PtrLiylJtByt1SzDR2yU2Tc/AnnI0sGAxl70GWGtJBEiHBQHz2trltEloAha029jgxMGl1T3/52u+tsdb+J6nqhDxSllDG9gtRt0mxfcNFtExlaGDJngxY0tfWy9Z3yt8D/WzcEZX61ejr4uwW3YKZJZ86iQt1eQou0awNDgbi3/rgHHAUQee1Kpb7I/cV/v7KQ1M9GavfDVxBSKuTdUMYrtMZte9w3MelQmkrLuzej+pfg75ad0QfSg7dfvqDe3nQr+L27xI6620oAwjkmGS4wXXYWCh71wWRTmqluaStZnx/aSu32RcRIXr2qLy1oLn8ZUeBOtX2G7Px0RQT1uvxZ/ZvZaKZaxaoP6u0M0wDc7jHSnxiEd08T0m0WsQ+6CJ03X02G85+USgcer7bDrcnQBw+6YCkYuPTuEhL+FgVFa3+6v2zdurrTGuEee++20f4SpDtnVGHKCqxKr/Oals5lPH4xs/f2nQJOsJLykIW3pbPCRnSQlmruLkzaaWHdLVpamgDVWa5y99IFgxFXve+LmO3LVJ1nRdZEpktnPWgxCtwxW/sdCINit81t7nV/1uxfxDELl/mcQDtwC5DouDuKTEhnaoEitLwU58S/+z6We11HlKOEEK2fk9cSFrdp3jFLmkezZhkshUjpbqu9TDXv8L53/yIRcW0mkxkrnfVhRrqCgrQiCvHK71la6toilas96jYRcdAC15EIP3niLBKJ3fZidF7X7q5dP7dQJ+vQqg/qi8JpgRXCewjUvRRbh1dzM3SrBdxM25cZQRi+gFQ9QWrpGE5zZdQ07u/h+XojLknaNUC9WXa319VNScFWXPUQkC3oEB3fDFrr71TcdndWQErHOfcdL153505JW3WzgjKtOqZvesdEKyrJkg7jxDY5C3SncN8m6aRvb7M62PcVi3TyI1xgUfThVGfjuz7c3wP/rlLK7q541/ss63MCu0BdRReXb2Scv9fXx6oP6oUQMLy+7oBlSRhCwJJOQhXCa6IVUnjdcFo1JE7g4y+JBwpbyUF9a6a4eQYhqM/YDNhWUJ91XV2UuBlptrPTfclW7s+uV2khTjfSWRYDku460u1td5cXVUnRrXVELDdr14OOqPlyP9Pi6tLLnLI/0t7fV4+VyBHa2Y4s8+hr+TtYsDJ/x/Uf6RvBOrbqg3r7wVansUVKpyeOhCHsunr3gR+7S4zzs3PWWl1p0Kq5h1OvG1FL06rpF4FRXrTTRPyt32Fep6SUgSEx1Qep1OOh7y/a6co7r1m2z3iGtWdYd/aCeZYlRD/3QXkT39qU3C0uutVGIGt3rKwtQj1sMYqrbQ/U0FIq9s0xfppOHnhOw3eP7jr1wUp/yz0AmaHlUWS440sA0sx4D0haQZ/Er9qIncKQ0a2adn7W+XZFdedPRcavOy47SnuaV39Q75FeXmI4fa2E8+Cj00vON6nv59iCeUTmpP6ekIICI+ecJ7oa0HtL6Dxj7PjotzpUdrqEDBhwkE/C3Tn29h3TLUl2JX132iLUed6Y7uqIK3AkbdugdB8cIKs2S1IC70Af+l5eH0naOeC6dUSvt58RSXIvyYRj7nvmIfRVVD/9tDLU1CeerS4kpfMoqHcJOJX1Xt2W5RSfZODCdWv3jdZc6hmJOPhqv0Ih0/XY7nd334EmfXV6PEb948sQ+9Wl2L2+mBa6IM1J1GVeagX7IJ+Hfm1nv/svrKS0saVXzZnX9uh+6LTw6I5i5F6s7b4bpN0q6TbOaUxgLQMBUfpFdjxPpKjj3uO0m3EfzrugPtTnSjhBviGcCnoBURBoDRXoPuTaGhlGRDxVHbWeNIWvzjtwnB/8WRP11yCch0HYhtUj+e6h5mf+vw/qeVD3aqW3c5CPTS+cb/vbLrubb1QJ+fwUdY2qcX2WZeZJlv12nVdBvdqTpuj0n7ekMzKOZY9hb0mJItwHau0afMsZ41K6F2ZgNBxNJb5mqCjthqh/tpsOYk+qtB8IiH4K24ie2/9ylaTtS3rJjfq9UEa5UEYJCyw7/GcR38/NqTQwYJfkLU1hObxof01DTDN71irfdkp4uq8kEFUrELdpoQpIqQm+4tYtoxOaAEIDhLSGdHWX31nXhKTtTiKcqhw3LbRz+ux1B2usAt1SZPwL1GLX1eZ+uMtqHQ/hfSGAwIgTAhEvvAwsML4bi/Qv0JvW+Vcd1979VtrHx3vJkXC2Rd2TmHTUGrk9+E1rlfE75s8X2rpUJWAozyqFDlHiy9tSrkp5aZgAAEsG1+m8CMuuI9Isu60WhR7mV5CIGu5ECKEdBrFFedum8P7nLFk6+Yo+Xw4UDET8Xno9H5X5o6fuSkbtW2K69GFXHNqxRESHkNht07+QLmFeCbT6nce1jGR8z0lg2cFtsbc3GHUFhwaNSGO+vMabtN0tU+Ic9aWbcd1npNTth2/DnG23lKHJjYghzDum7nfK98as/qDePR9KgpbCfYOogHDCE+8YSuetsf5F6Kqn/DelQDSlBLva7Qr/NZiGoy9/9btgINd5GS9rLYt9SLKUk+OuNM1hT7vIxB3LfoOMifMyrTnVpqv30IR5vUwq5r0EuvjC/2+3a+TaOwMikBba2xbN1L6Vh99PkTh31KLa2xJvR/TXtnS2LXoFnaSkcGAfeBGOeyyUa0/djOR2y6jtcQN7JW+WutTXiWBuGXcEoufunDZtthGoxF+bWY5L5/lV4po1X/pvdf6APXZlrZqE6Gna7Ryd9WTHxvzJGxKsEumO7i6tww2IGcEq9JKuhOOsXh692L+k/vDRlZm6WjP/RClWnjJT0S0q7bFY/UF9BO/lUVI6NZatUqcw3H73FtzXVhko+GpYhF3rJZGcQnTSZGarkPumwcjvurrzeX+Izd3+qBqWfktT80WDIlxDjTZqOVuFHa+Xrq82La72PLrLQTZprw7Ry+hgVXKfJdPXDEsr2wsK05w5ZWCZxLrmwPYN4qATUl/Z5r28KvH6cN6+7bto/W+lj64hVvvUt3sdtjOPf12rhZNWvRaHQb8n286roD706nRoClv+sejRKoGHA1KR6bzqZs1DiNTbN4122eBed+nkfftpsKi1TG2kL10NfVJLSc+Dq140GVEmhhBQu3KlJpzKNmjqyto41wMZ2EdwC73pusSqRZ2k6ZMnSyKM+OMYeDP4arwW1WbqHFj1Qb1pWqkyFuHLUACg4FQXWM7fiu6LqUSr/Gp/ZOhup7Yo+wrVvj9GVlkjLgWptRjdJfXb2s01KOPY+4m42nVlvwvSdw4EYKhvrVRuAqsxv+kt6TVeyeCfW//kKKOjldVeUKVe9929WqWSd3Src09b2wCE8vxBv3zi+3LHzxPFvsc6N0xNlbz6xpB+BueBfdF0E2tjSdDsmTPgRorSitP1w9/q1asWMFXsOkI3AaVmX9OnfqXuxO2mWW1cIuLHwh9Uqz6otywLpmkGXoSkb/ZyLxzn55hzKaTbnN0K6IPNvGpi1jVjtZdBeNvc1lwrr52Mxl+jkvQaavfG7HYDcH+WvmMftQyZZgUDf4sdNGnO82Af07zU5nVLll5/Uvk3bpntHdeorepO6O1/HlhtaIVsv+UxbqCArILHbXBy+m7XfLfuv91Zjst/PtRt7mtupa3Jlvbb7Y32Hq70xy7dOCfxyxCwZDP2e6HGOYEWBSUwWuEknen4eH3o9XHXIN85Vn1QL6WEZVmhgN79Wf3d7TFoCLtvfdNyl4NWmhWABctL0v4afGdqCBR8GwFYvhEDhP8p3NUkKTDX/c0f2CfM6x+NxfC1+tknwXDOhtWaIVBxz6C+qwTiC0r9GsQ+gX396rfNuy4HJ57qGu/6iZAY1Lv5m4heVsc30VA6UpvYOk9LlprVDmayHHh56NKi1l6HAvv4+DW53icjXe26ZVnOhsVsnNPFxd2fbp+H6Jc0dXpA3Pny2c9ewneL0G36gN8fVn1Qb0nLbviSbvNVq6tNkNs4Zo/I0ErP/ip4J1KUEoCRUNMgY35NUWcWyGEE4ka1aSeNtXehaqbVDmUQnCdpS9WmLu/5hsRNE/r1hs6VDF2MyU3JHR5fAQgZV2RIGo+ou1Fku03G8WsOt2T5/9q7YlLy8QhNkbYQoTnc0te0LQLXWsQyk1qk2+EWPkNpWPdbxptj7HWj5DER2Zd+/zqrefX647Y9I4LHrOMsTTOjmo7aOORqHhNOo+nm99Ybm84SFqarKEl9nCLqJVPMH8whNN8rvTEC9wLAq1QLXRa65cb/av/NXX5CRXFSO7ium4yI+U1duhDwDQWqrCd1kG7vRHtvY4+7Yu2fdNe9UH5Pu3n+WexL238mhfJtr+Nj/dJl4oqVO8BKBvFx60q5Has+qDcNC6Zh194GAzqBQPW695Xp/WpZFtysUXid6e0aYWkYgYvRGwpPc3MWUgYPtDQDNzIpDHsdzlPs6qjObm88t8tPwbe94cxdQMS++jyef07dvdYINGmqmZOMnhGtzCg0vr1b2FKmC6zX973/PBbcVQozMs0H7v9K7Y0QbqaWENQnlAWihjSzk0zc3dmtzVAln8NArXMHtSpJcbA/nam5vf1bl8fl9YurNhPBUQiMNloFpBSQogB459z+eIV+r3+Xv6rGf51bsUkhflP0tVatXbUCkwSW5U3TWWAvpBGTQt0fhbc9gXxApNmvOHHpP1wAT7OmoleQSJ/utasJjN0PGE7gJGDX8ndyP/feY+JbYdtnTRqRc0nI2OvDy680hzZpO0KhlrMwO890+hgnXvYJwbG/+6vh/w6Be1d4Ke4Tbu736hQFxO1h7LkUgH8QmXC60sytDKoRVwkgQ4Out5ZnwUTrGCuzSgkDhdb9RbbeT2OfYgkz9qRaSlrxj+hiJ7NAd2Tn5TKtWCb6ZAvlW0v5sjUuv9maQjiZSYfXVuS2hFowrNh7duyjAgKAIUPvCVGPRbtdp9LQ3yGgWXu0VR/U++O1cBkxJgPIkuL8ATs0GWVoesBtAIgttIcKIO4vK1mUjBZ1g9UG877CT+sYJWXIak2qfx3JmXYmmdJDxnX3cgXJ1Vc9WW1f+bdbFzhnTSu5NagnNFxxEfxb0na3nakOiJU/H/7KD6BVWeENoSgAacUNaCCTK5599QOdNNLE/R4/c/TUSfefblmpB1x9a2ztd8zu+R8WHcQrYcX1MSuMWnXaTVr9QT0MeGUcL/9OkWzd2qlOT640lVVqq92c35zSsfDV8oQ3x5fxqMsa3MvQzSx0T+0L6SvIJDxn0CqbhaP3pD6GWa/P2BJy4sLjyt7d185NI+lFS7Hzdjhfv9lnw62pt99P4d3zhIAV+4bR1WxAA/pg86fDn0cmVJakLpkOWooetPNh32cCQxhqxed3aktwuy0J/glCRyjikLmtsYUeHVIZ/F94/b7/u9sT933gu8zD3bnnI+YN32mG1DyPrOwdOyiuRTRtg/R5ENSHyRT5uGyFkL6/6evFtW8gC9Qn+0rLgaX5vncCeu/NZYGvB+eGk/bi19XOB+7P/qattPvne9jBfwpTvUU30ETqRXEZm2T6y76P9Kl2Jc1hi9wo39086qLqKen7qH9fxQZ199rdrnYr6mNX7E+AuoQYTMQrF/xkaX2A9jmlTmrGO6JrkPX9RQR/Vb7152fKPmbI5OyXTcZPk+Vt7PD6juuW28tbuIhdtvQHO63ai4hpvL/4jtXgxB5Jgs+kZGi6lxLqogJfJ2xHvx8oPw+CesPXn81ml9zjmZYFS7ZqJyxnnnT14jJY8SyDGZl6Ebnvd7Dci0kGE4Y3zCZW9r6sGzJM1x8+SmjsV4nAsWgr8bvFZ6UPearj4fYTdApM0v1ZylZl3gAVnNoW0XLRy7SSVJuRXN/taxJe8cYmC4HAXgT7ma5Gve1JlSU1dFAv5o+SMkepbjpwnjkIbEp726UbNtlbS9vbmaZGPGEJyiLSPb4gnTom4f0KuPvj5itxrSMieB3FVGap2yKlb3oRDlZDxa3Q9RoO3APno0cZYtZcQ8A+ZtrKwa7W1Avl77Af3o3YgcTVynBakG4XLe8+39nRSbpeDMMInH9L7RKWkNgNo+CbVIlrpAxmWbqgvsOTvhJVcOdBUB/m1SIn9uHW3zN6HVgnnvLABii1zSscl+guPm1zXuDmELUwZxpE3atjq4Bi2aXtqJlE/HEb1BrOjJKHcOtlYspWE5mNm2P7a+ulXeD0DWu7qiQdbvdh7o5bTvJ5zPRdIXxB7fnOF+CvfH9wRcZV9zTHSdMK4N3YUjx30HVK7UlURYqUkV+FJlWm83o3tPnweyf6MsSqr9EjdrLYp3DjFxCXjNIm//MyqAfsAxQ9joVTuet1IVG+7FFa8vc3dMvWiVOqzX4rcEG1VhWRzKRX7+X7U0QthO9fL67Q7IKl9H0O1dHE7bZbA+RvAfFlCnZaiBe17HQXWlyv/P724Y7eL4H47fYHxJ3oV1AvEWynk7BHILJvtBLRo47kWVJ9uDd2VFTLSWyhN+vx6mU6S6tVY78az387AsGSd+jtVggDRmh0tpWSprUpNtDrYcVN3PXlpWDfjW3lg1Jfjb1EfI3OKq3E6oYVOWNRK0m58vMkqA/2cPeaQNyrzXewWrURApBurV1U+5RUwwPtz/p5A/mlbx63L6Rv2dKA8D9oqm0P8i0rNk5M09wWV5uuqS0X2l8D0xrOOZDqtsIN+AUs2RqqUFsIcE9WRPNJmrxIN1691J0M72y4cU5S6Vq/dvuvcScka/WTWnhSfwuXSIPXQqd3uoT67JibRmA+3WQJQaJ0W1akP+G1GjbtP0fnjMGnXdzCi5sG3FabiKvZq9D2BcFtbHusjmrI9ULd5Hz/165aXW/b29GjAp62S4L/79kOWLCRU3hJwB1NK/ktE8r1l7omO366rOWk+Ks6Yt1u0vfugdLXiClhSVM/n3/ZXazJDwe+IuGwxRQAhdE6qIH+1mnv2iJ4UtTmZN19GXHZoD7dBF7G6P6r5vFKdhSdRoP3ssAyvP8px9TtpeOrQIsWzmO8gS+cZQfvja0uVcF7bpp1adYel9YSa9Nb92Tt/VsT5gSPtH4Fui0K3U5i6Kb19tNKt5BVH9RLKZyQqlUr6yZoe/SV4EUkvGDffbmU23fLrcmzCSkhdePKOgla+mufhFI7aIlWvuJbpuVmomrcbZkIvuKstezQFiTdDRL6XMhWLq6f3ReftoJ3+9g1nTqcgvNtYJxX90L39Zl0j73l3kAibtT2eXGPaFzwHHGhJdxopASk5XtIWflSGtEPI3mZauxh796Nzk+4Ccj7g7omdYxiZat8/Qq9BQRuBHHBVlK+GTOBRMy46a7o2tvWLQLhOxyQ+IJG/7KldJ5p8Rbj9q93F6imB7cmV7aOt4AXKLQzZr7KkkbPqoIk4hdtyKxjLvcoqIcmJfhPeQdDOLXG5Qb8M9t5n7M2kRzQw2rN237XlBTbHVf1mzCb0OXj3u9xQ1IKtPpFu6sLBvOBt6UnCAVqSXFYbGCXlEZjCh2WBcMtvEM9txK6az3YYhG/H9KQ8EWsXvYRuG17P6gVMfHPqgm4764P3svce09MDu882xee190f9bqXbmnWaQ2P7YYm1GPmFIvdGNTeEYQOVqBQlf7ijXtORfumXdGaLvzCLks7rf2leo4s509SM7FuQwMr8ripL7boqMkW3E2Pe92N36oP6i3lgvEH9V4Q4P9eSiURyLgH2yPFnn43wxTBaQdBbKlSc7zUzCcwc+zoMvYBaJXYwyV39eKIzb46HH9USunV7OqaRPv9JHtvBTPkzufthaS7f8I07gWmyecT93qQLkhaeVkui7zzZ9+IyL4H5JhoC1Fd27buLCgpiPMmiv06eFKC8UzcPdHOA2NvYaFRYtzAJKKSC+q0af9KK2XVB/Xt8i4YL7AXrcJm2uvcfdhOqv13nUUDrQK7EClqFleGWxCNrCDSxN3B+aOLMv6Ckv1kvPBXeNo1puo8MWP7a7YuZsuTZ5Xow4M3/ea9AbiD/Y4t/WWUuGzfa7w1my68pCC1NU3aCkzh/ixCzd20eqnd8eyaSidDTn6SfFXxj7Ti5smhWkOgPw8p9pzuJpxxH0Mtp1H0lWTet750qHsINZB2/dOlXHewlanzh3jdMElpAKMVtvqDem3Ti/2/VvNisCnfzbjcb718XtphQqt9LWKVgLtEr4ua+gplwF5mHm8b0bXZTgk/aX4E47bWg1f20XLvLcJ/3pyTFn284teqa10Itcb4l5Y6Z3OfCNAXZNpb1kpzs2BD+Tc7u2W20/2Ov77aWpISgKg1Z4mVUSEi8I8M/i/xZho7BKyutaqN+WPn62iuwdDbR8ndvMD51YlIWkM49jGHzuPNYUBJ/w0/8sCq3TvaWkEwflDqo9Jcf9HXti/Ximr6d+bvaOhU3wa6A3cn19Lbd/pgXur+7hSIE/P/7uVKahcb0buejANv1Qf1arJUL2vpuxD9F0UrsFebr9LWCLea/tXCtddnEa10v1IJULobFEFI/QM/sct0AyfhC25jFuE9w4DWcYcmwFbfwiu8n3TbkLBSZXvd5XsPJCvNmuq2ptX3Yd864VZtd9LPLEqagD7i2NqnMuYNiIB2M1NvuW9C78GuwMLj53V7m7ZKpr7agQHRTl/mgdbzjNGfGFq/d/NS6ETcqtOcysQ8KKK2N62e1Nb77x/dJkM/6DegDboa8s4EY4yovuHKj/a6NdvU9va0QpXW8lIcCvveGbFx3j1e1wLibx3Vr2hwK8IG36oP6uPYD3L4Y1xfyK9eGB1m8m6it+ugnd+E+lT4yvFGNYggEp6wjjoEAvA9SOx2WZKBeZLL/sFAzn2QTQi0HmKO2IY0+Zg7iT+bcVtRrIjnBKQT9a3uLEZXY59mnpg6VFlA7AWT+L6CNHeV0EKdbgOaGm9/QdJ9FlJbcScR3+0i/UOClF2aHDLztemlN4F0ab/34pKgQOAZ3a5zH8jzZeEB7J0W5G917tWxaTdS8Fec9ZL/nrqiElo/DGcawLle2m118VMCl7S3p345r4N67bB0gUpjCSHcB2U7OYtKU720A/tWohiwYDFlM6H2SX1fbb2vGaQ1jT1hoInS/bvaaqLW0KereEjecnUKd912jYMMBfPt1Basin6mqZpMUy7K9/+kqdr/roO1Sviah9FK66E0n9cq7VWs15eVEID0d7mx9TslRO12t7dLl2+5Ab22AsX9dzXkeQNGezzbaLZZ0VbiuKbzFVh1bMrztcBGpePUlArdfucLSc6DoF54gUqg64mmNs8NPO2aEDtAtZSLzK2bNN2XOLjzemPK2mOy+8f0FUpTlG9APQQ3ItiM7wXCgNe/PKmmPamm0fCNpxu+/iVMmBGZfHA7QutEazgrN1B2a+slgjXh7s2gtUx3v0RrccriE4eNbLO52T+cnYB0aqecjXf33zsOBV8u0iqoeRmo/4JXCzyAV03gP5+pqWMUG+4gZvbxEF46E75POoZX0a60THhjaAULV/ZZsP+NaTl15pWIGmLOXoLh/V97HermcZqDvHMVmKN1javdx3wjzTkPndnHzm2pU9cd/14Wy/+Lvf3Cl8e4yUNzWas7p6YVI2m4WW88WbU22QivQs3fEpZttZl22pHteVMZeFBQ5Q3zFiqMOgPginBrUuuwt/Kg1nUZPL+xhVwRHFCwVUlkd4EwvFYd6fukox9iwfd96JoRXtqVkPGbrfaXViphvLeFiPB6/NPrVmE496fWIAfB4xO/V/YdyPtZc53HZ5/CV1sU3nh3cGtdC19y7avaihysbBLqS8vaTfO+25/7IriotB2+PyvjTHtRrHCyOm9BziTJlWZu3mSFxlE0Wtvk1IoI5/dWqkqXj4i4a0L61hXxdWBO7zq3/zWEL08U8L0Dx2Yoyw6/t0a3XSnzx4hdsvw/+YbglNKKKIx5g5GGlhDn/Ajq4esr70vfulPkhd/C119Ml5OEOuMGlxf5c6gDr/q7bnuif++MvojdKu+Ea2Dch/icuCq8ROHErs617gX2ynTqE/pef3pfUB+aJ2UOmaVmK3haWtvl73cvICClZQe7vvJXVLDuLSPFNiZsnbMuGfyT9Bfg3KMen570S7Z/klImBLTuTdm/zqgFq9sSXpr/ZqUrRvqm9HXtcr8VwYIVEF1giumjqtus5MKjet37sl7nNAhl29Ke96hdiH7ZVWBDoCYRZUP7pperjkpFdkwoOl55Ujuqv6IhOCqJek1qNy/VFujXG/xOuNsq1asmbsnhZatzaANd5b4ZfQ+1LwJ3Gf4KkMh8OrTJui3yza29FkT4Z+cP+vWmPWLx3DxKt1lJ4u5b+rSt5PHKvcufDr20oSw5+K6GqPVHBEnezkrfPvtjivRHoNN8QZs2A2Uq/z6qx0izvHApWSN5ayOnCH2hjjqko8SYKQ/Wqg/q1aEUvb/1c6M6ZCcCoNfjQahaw3y617PmhuAFryKUU+gSrpQJiVoqP7sXpm/dlrTgf+lEp+c0tuDkC8q90n4gkOpjpGRvQIffAdluY30kgoXAboqpO2qtvutrJWqP8Je+QzXa7elqlw2v8O0LlvM2cMAKyzKycOveKLxlxVU0dbT8pL/x9HbEsnoTx636oF610hlM4OVMHYykEv5j1i1qc30asX0p/d2bsm6Pf7HK30JLz3hc4gJ7S1qtGj9fQcStiZd9e5A2aa2rNKgHQpWgkZO1e2NjxE55k/JaWDFqVwa/xAq18/MCzHwK3UoytI6gQLajGXqZ5CrVjVHvBsl5E9T3M5hvf2YEW237lMD0L7MQgd/jXt0cvQz/klo/t1NulW4p11+DH9eOqQisW2omCYyG46sME3atSKtfqnD6pvunD7QDJndWyXR6/etSs/GI5tPcs+DVRwk4+x0eY3/QMmY7DcU3t4ZagQdsH2ilJLQZRdV4pG7A6s3QuzKQ9YUz1qzta9pBGlZE8Kbc1fW6i7UACOdpI4GI0bmg5CHBPL7dIS1l7H5EdMeUrR9aL2uTgLC7Z4Z6QfkSaGt1EmryUN8nEnomos2BK+KOQ1vHSNs1J31LVOy7SXpwDZ43QX2/dVpLn6ebelxAr5PQzS1RO0dGN23qXoBSCezdmd1WT6XlQt0vtfCiLDqTYP6urC2mnNMN8ec35qE22crso577TpXuA/fZ1r7HFTQHreNd6EbWvSUrx3+w9psStHvhqgF9P28bInjNdbopSRVG3RYayjhhe1rTdmHdEb93cuy61jqZUCfUSm6dFdV604EyP3qVnhnU91gvgnIR8zSjf1SX7OsRqUq8aTPfyLzDrfUH4h/UVJ6NWGnu8/ISgLTsESaklCgIwx4VSNMNp90WiO4RrRqUgeTW8GRchldj79bWZ1tmtpFaiLolKddIGE8/9tqSkYXbbrwFWxsQu70ygfM7kutAmjxJd98RIvlFkrHJpMPbhwBaFTc816l0876z6oP61gh9/qoM2YopAt1cfKVu0foqfLzjy5hCaZ7paLsRvKYCtb8JNaSprkTtzrVGeVE3P9wEFazVEInb5U1sD/dmGOGRcFqP2oarP5QDEWi28/4S08yl/G4fJRn5fWDZ6g1OSi+teM2Lbv96tDLUwHLVExrcEO/ncBclZSZ1ORkzg8Ds3nKdkZ+CY7a1v+zEbYs96s4ypFILKcLL1tZIu9Np1hG7XTLh++CNUii/uz9GJV/d9Rx3GIL1nRHbFpW2QiuMKzEr+9VhrWXk4mO+U6oE2pzb/VY9AM5SM98tMwS3sce7vSWHgi910aH8IGLpgYhLXYQmdSYcvlDrrDt73HzpE0MH1H3w5/Haq0/zffh3p0E2fs0ZrpPQUdRnzFErdpuMlXmit0s3HHPUouO+aKVL3ehCMrxJStch99oNNbqqy2mzhBF3rpKW5LsN+o9iYIq0XXv83aG8tCBbe96q0Ix+Lq/dLlXnQVBfgCXtMUvtug/pC8ZsvnQJwzm4prAixke2/2Yo3WPUkDJdvqWexoK7hfb/RSuxt5GcY78VMpzdtbI6Z6z50Jea0YW9TKG1D+FezboNCNfqu0+BFwzlbZ2+8d3duSzLtwPOvxZk7EWhu4qTt9P/YzC7d3fBvXhMqw4hBAzD/UtwPwz/sQzdnIWTMapb5K+pE4GfhTTsPZaAiK3RS6rtE+F//Rsb6CCri0Ji7ggJtfCG/6SGJlXX7UwmnXMhhC9YMxCa0H+FKRliaIxuzarjSCN4Ei31fRUyZmxl0br1+f/fEryC3Gm9PZXRb7MVMvxODc0WxHyjS5zdkdRgpA0G3E9S0OEtRH0ngvt7ljfEpj8eoRuvdpD3NpYn3XsBlEKthpKRB/J4TXwuIq5Nb2jW9uInZVv0M/rvMbGzd7ja0NyhB6X8rRPqNoTf8B5+wNdoXY3auKDzgya9Ue1CJTPne0uzze5qZaiCCv7fZfwxD7/GwX1eyT1+pejthi99aoqwatQgpBW4/6tp01clBsB9B4i6Xe7C/DeQ4FqDS9Vtd1TXUGHfj0NLCq7b7uWasrDmC+iFrw+vmzwNZR8KRgHSsmBluBJWfVAP343UTja+i0/pLmEnE/d2KxJfxa32o047tGIrf46+UN16a+ncLLxCXrZcT1lrMCOyt0vZJvfmpGxA1D0m7pC13nWkPKUvdEdDwDCijo8/M0kpbtqo1QQeePUdeJm+BkZNDF5KDBSKUt4QRGsJbnEv07g72n1o1Sak3KDurj5m3Wr6aC1HWZD/VLUT0CNlAVq9b/hKyVH3isDPmhW0/hwOOKJ/029G9LJXTqimv51z3a3r2ltBh+k0EGjFTeNuSkIm0+ZJyHLedOUAqXyvk2590VNFHgFdKaNNiXlu/KWj5y81pbrwe1T4FdE5QOLqstwCujBFO3P7skrvD1HPhXn5dVzGFvnHhPOkS0spg3VLShhCwDAMGIYBy7JgmsFKhfB9J/Cl92+oKs+/HNGaVvjnS7Dqg3oLbg2Z7+MLFu3SErxU5uY5VmRJ0Kaep242R7tvunTTWCDQ6EJ+Eli3v4ZJOg/gx0wvDPeNcvrG5bjNU6cPtHakiWndQphXGvAtu8v97Lsy/GgKvawh7atU3R4Gb79liqSYbaul8nM3j0HSlmeptT4PJaTh2DO30qWodnTSSbtLMj9G0yPh4HNAN3SAxEdIaUjIqAQhnXujL7AN19T3hyGE17vA/dc0zVAMYlkWLMtCoVDwauoFgrXzgS45lgW48ZVpv4PHv5/CYlBvE0pA3IpftWU6r+klbohEb2FBgWEfY+ZNc2rUgL6bNfVAeH/dCgvt8I6+pmTp/5uzQf7mqk7ClKh51Jol9dIelPtm1IPCUS/paotTWygjDm5vA9B8CjZl+/8uky7NlMuPO+rtnF+hth12vg2payVDP2oX0FY61T1v06eH2QdLzDFou6mxS+tN0G5ajK2RXHWYprMKPEdnVz8jPlhya+rVYy8zlRDjhkb1D9zRmqwV/zSaTTSdj2maEMKAaZpoNhuwLAuNZhOm852UQKFgD+BgFAwUjQJKXpBvOP/ahUjDrUgWQAGtlgC3QLBcXU61b6s+qG8agFlwg2P7xLknrSBa/RbdtNM6uVZsYVCGmol836Vo4k8M+n019d7fEuZrh1pb7z5RbKG1P4F+olEHQ/iPaat+QyB84ags//xqsCWDY3b7CzkQ+t7i3Tg0UTemTm5YnbQetNbjLzQJfR6WUKuc5XjYLSLtpevBEXOjiOsK0qHgC+bitkpzY4J/azuv+5JSJlTEK8sO3dSgXMvK3LHp3x6f2verwog5MMrY/GrNXKBpLke0gYjva9nDumD/ujV5hrb7l9d1NP7cd6NFVMSks4HFYbG6yqt36fAicF4XE0l9wqYT0gkMC1Lao9tJidpyDTNnzuH06dNeUG9ZFprNJhqNBprNJpaXl7G8vAzLslAqlbzg3DAMFAw7sBdO9x03YC8U7FjUP70QAsVi0Zu2UW+k2u5VH9QHUo1bgeR7AFWdSm0cT1puu08m503S/kV957YweMvp7maFeF1zOpBlGLe0XYdi38LboTSprn/hUE7bEDId1NWbDyRKGm6ineX4glKZ4cafWabTmSZTSNixTPvdXotR3J0vfGp1hdOUG6u0nHfS4NQzOc2y+qXvh6vTDVDnC3QDkL5p7JH6pGXBMptYrlYxc24Ge/fux4H9+zE/vwAhBMxmE5YlUW/UYVkW6vUamk0TkBKGE6z7g/qCMJznBUUguDcMAWEYKBQKXpBf8NXqey/cTLDqg3oJ2A+bwh4lBQJeV4aCv3JJOKNcOwnBSuh/mtg7J2fcV0zrbs7t1tSo3WO8uucBbZ6N2y61Vinr+M1dPwbxFah91PcsvyNJdeXJ2Wo+9zsLIZWaekV+izrxqUHASjibcd1v4ishsh0ztUolzfS6aq3kVpvWjxKGIdwG33itRsjg4e1zQom74/fnXSODL65cqo5I183Kz87bNG2Bcy19PQalv5eBhCEkrEYd1YUFzJ49hZlz57Bw8hTOHj6KmTMzzrYUIKVE06zbi3Pu80IIb6ATN3BvSGm/40YdClbAm14YAqIQTI1pKw+B8yCo92dw7tCEIvCdX9ytSb/kqCUlnYG4b93Xa+tqyRMDw8Cwavr1pB9WTNtOG1hPIOhVal68t0e7S1JGuWnrIvftS9Rc/kJEN7IP/3WkPebe/iQ/gJhQFxY5n7fP3mFXj0LwfIfWmnSMAz0y9N1DEmfsUPwr3+POtq82xTlJMjBtt1JABP8hD3QX0dQ8+hsKpT58CnZBV9q3lO5C/lpO/8qUX51tCfbXj09zCeczqSY+7muJ9rpuuefVW3V75zJ8rXaaFhJ2TBksoJ23A4cfO9Dn1N5XsZsivcnt5JKUuyjbDXXQ3rh8QJ9O0nepUW8SaqLtZetFFDtG0G958r77r8lO49bY3UpMJ9HnW4RSQ7B3ljdSfOCacW/cCb0Qkk5VxLNmkRsb/8fQytPeQ9NodadudQeUlgXLMlGrLuHcuXOYPXsOC/NzqC1X0WzaXW0sy0LBKNk1+s5yLNOEUTAC+29ZVmiUP+n8z5vXDZaEAEw7MUkn7xSwR91JY/UH9cIEhBkq2blhvp+VGO6q8wd/1gclum0KrVq7cDWAT3tz82fR0l2f+11SpqmMDBRatlB/cNepywDsabxaDgmva6KddoPHy/AXJ9S8HgL+11sYvpuIlBLC/zIrzYN60lC3zfD+lVIZA9fbReGbMvq4GaIY/727TWgVMb3z4h9UWrN9ln+7Q5so4a9DUlOvIUXCuOlJ9U9RTy6kuUqyjLQi0RpAOapg6XzsKg4EqlkS+792HhmE9iq0KnXZahpXvhLCG+3KsJS5pX0M3MKAdDN8AHACdn+hWVjKmP0CkN45VM6brgAYKb4mHgDgf8eEMkqDYdlDr7r76WxdxCrt7fTGTIeFbD1kM9zshQUh7GOqf4FP3FETkIh+rwCgZqHuwpzz5YxTb/i+9v8Yfr7GCQucxCLUbMNfMBTBdVsyOh8I12X4i6W+v8r4N4/7JmwtW7Noddxuv2y15RH5li/CjQ/q4/MMy39tqnPLhNww4bD5t1zXFcqQrXOsO9eBZfnm15cTW/dFO3uN37i4+MQMhQO+oyAjz0jry+DMgamVcn9y3ZVT+WO/CSRq4lZmKiXQqNWwXK2itjCP+YVZLFeXUF1agjQbgNXq324GdtR5X430573uNjp5cqDwr9lXKQMHz/uao98k6UmRP52kc+OLTaFehO3co6Q2vu2Z5DqN+GkD76RTCw0i/HqpQJcY0VqSDM2vL2ykTQPdOHz+QlZXT0cHC0v7NsFeSdxkX7qXagIe0C5cuSd8KbPtjCaDxJtxik0JZBsrmz5W6CjRiuskY+3+VqhW+zN8KybhEJpmEzUnqG/Ua2jU62g06pDSQtNsPSArpfQtS12opiIA6StnQ/Oypt5lYBDHZ05z6wm3G9jRTvy8bk2XQ65gHOTUFMQW7oWS1L2CqlDrMZUbdLieJlBLIFrtE/EZn6H8G7GZalN6p8dQrWGDppYtA/9eCABjy+ewce6Qb92albnHxlD/vHI3C+0x0CVUXZ9ewN/U4TaaIu1dNS7wc7u+dYtQa2WU0qy/1kZttAl1vfO/jE3CHpEB/jJQqMoHrRXGJzoZWrmyH/GdZ4N5ldqtyhn8X12E+7v+BZ2tor5a46Vbhn9busXfrU6bJOJGBkvINEINSt4vzjmLeXswgPCLEb1Cmf22SuGrUFfznFDlyIAUTewRgeKuze7mnfHU/CS6Xrkrq3KvYi9za63b0LS2BoYS9RXGw8couN3CvwrY5z4yPxTho6AuWbsrKe1bd3VfKmmS1yhhCEBaJmrLS2gsV+1a+uUl1OrLqNeX0WzWvTHq/Wk2mGdrgnpNPpgWg3qXFCuZE6SSVBHWeqtt6/82J6duo+YqJMV6YyUE7O6/Stf+2M3SL1Jzt1bu/sGQJvjqZzuw9y/LbC0zIRII5zPxNxr1J3VZ0nn1tP8TtfTY8ceVlhuddQvHcPPeL8VPRERE1CcSTlA/cJyCsQCEkGg2G1hcXMTZs+cwf+4cLKcG336DrB136J5lcn7QL73ToJ7db1wGoOvbKCX69Ux76jK/Usse34fTmyVTzJ+FgNBH8DL8Z38XEOlME64999fEW/oqPbfbjQzWrAGtY2evS62hb00rIPXLjtiWIOeIR3xtt84J72Jut9bf3j1flZt/HPvBqGAjIiLKLeHr62w/H+K+p0hiaXEJp0+fwdzZsygVS6gvLTkPvhqaZ0nc6M4fc6ys8yCoPx+Fo0Y32Dd62NgatdzWumPiUH+boDufL9CWCdttIPggbcQKOpMYP8cVDkVvi47qhjHQJyIi6oiA/bKpgt0HB0uLi1haWMTycg2oANWlKizLHqbSsgBhuJGNLshfeQzqVyV9UG8Hp0l98nu3ORKtCvHwW3gRG3drnxL3fx/XHzP0sCWCwa8QziSxfZM6lKJ5JQO1dw4RERF1SNqBveHU3lerVTQb9mg3jUYDjWbT7jXhxg3OPJlX24V34QDnRVDf+cNjvZLURUbtntP2OM3SP8yhHc8WnF/a7f7R1nqBdH1/pH4y4Tx44vU5E62ZRGJ1uaY+vPWsXbAfm7904fzB/6ySvxdRopQPxMVNFnd+w8c0eqhH94UZREREAy10c4u+d4UfNTOUWdXn4kzvXm4v2f7BCvzmX7N785cQwoKAhYIhUCoYMKTEUKkEo9ZAvVaHbErAEnYfd8vuxu3deQWAmCFiAX1PX/edIt2Izc6DoD5Od6LbdktWiYF1hrhMt1jvb9J+p0GvpF50xGPz/u456iOxIulJhJgx11sP1PrXH3GQvQJFOwcq5hFjGShbtE9GbUpEAM+YnoiIBpzw3dySKy7Vp1HtJbSWpSzbrcjzKvRkYCn69y8oH2m/MbpgCJSKBVjCQMO0YDkf6RUkLHg1gYm7Ea6N914SKCVkzEI4+g3ljPv2NLQZUHfXSo9zTURERO3o3UAnwhkeu96oYXFpAY1aDVajCcMwUCqVABiwLAnLMiGEgOUE9+4ws26Pg8gt17xU1A3Y7ddgRgT1HP2GKD0G84OnaUkcOFfHI4cWcf/eeRQMgVsvHMPNO0axfaqMosFzRkR03vG/iKHLpLTQaNQxPz+H06dPYWF+Hs1mE0XDQKVSQbFYRb3eyBTUA+6w2/4RdyRMGT0kCIN6yq3WcJfd6h4F5Pkx0sQ+96tUw5T4+v55fPT+4zi92IQAcN+eOfzKHVuwebzEoJ4oJxbrFk4vmViqN7233Y+UC1g/WsRYefBeDtkOKSUW6xKnl5pYapjad9AYQmByqIA1IwUMFXuzv3VT4tRCE7PLza70wlw/WsSGsVIXltR9qd6p0+mynWC7UW+g2WyiVCpjeHgYFVGE2TRRnF2EaS5ASmm/VRbBoB6mGVhe1P1b+LoeecE9kPmmzqCeVoWk/marsiJ+lfafNy2Jfefq+JPHTuOvvnMWZxdbN6nDsw186MtH8NzJKt5zwzrsmCzbQ49RwNG5Ov7XN0+iWBC4++YN2DwxmDdnWr1ml01859gSvvD8DB48UMWJhTpMXz5tCIHN42XcsG0Y33fFJK7bMoLp4fyEJGeXmnjsyBI+99wMHjlcxZnFhvPIZJgAMFwycPHaIbzhojG8adcEdq2tYLjUnQB/dtnEJx8/g9978CSsLg2pOFw08JvfsxXfd/lUV5aXRWi0vB4MKucONGGPQS8wPDyMNWvWANUmqvOLMJpAvVZzRsUJzNl69FZ9C3jMuejV0Jf5uYLovGIPExXzCmta1Y7ONfDlF+dwZrEZ+LsEcGqhiW/sX8QPXDmNHZP92b5BduBcHf/jGyfwl98+AwFguWHhfa/biG2T5X5vGq1yTdPC86dq+MzT5/C1ffPYc6aGxbqFhiWh6z1wcqGB504s4TNPncPONWXccsEYfviaNbhm0xBKhcGrwW80LTx2dAl//Z2zeOTQIg7M1LHclGiY6d7/cmi2jgcPzOMT3zqFV22wCzNvuXQCG8dKMDqonJASOLXYxG89cByffOIMlhrd7Wf+vs8dwtrhEr7rgtGuLje7Xo70ZqBYHsL4xATqtWWYCzWgacFabnpDW/aKN0RmBgzqaaCpD5XQ6ndioYnPPH0OL51ejpzm0cOL+Ob+BVyyroLxiuaN0eepI7MN/O8HT+LPHz+NpnN//z+PnoFhCLzvlg3YMsHAnnrjldPL+NjDp/GPz8/gzFITZkQg72dJoGZK1EwT3zlaxTPHl/HXT57DWy6dwPtu2YgrNw6tzMancGK+gf/85SP48otzWGqYMK32AzDTAqqWxOHZBo7NNXD/vnn88aND+OXbN+MNF42j0mbXnKWGib/+zln8n0dPo5Gyz3U7ji808RN/sw9/+UMXYveFY11ffsdkuBCVPU6wR54RhoAhCiiXKxgZGUGpVIJhGBCFAgqFQleaCEKj3/j610fuR8r1rvqgXtcc5r6mqKdv+sygl28kM7pUwLXUBNaFhG5lqpiJn9l/TMMvvkp4X62Mf2FXQXqTheS8O39frBst4q7Lp3DfnjnsP1fXTnPt5mHcuH20a83Xq8VwSWCsYngBPQA0LInJSu/68hLVmhb+73fO4VNPncXsspk8g4aEnVbPLDXx2edmUCkK/Pb3bBuIdFtrWnjf5w/hyy/PoWZ25/5sSsBsSjx1rIpf/KfD+NMfvAA3bBvVdO+IJiBQLIieBPQAMFIy8Hvfux3Xb+9+Tb2MemFNCm6s4L4Hxl5e62+hgF/5gzb2c27WUtovnpJCwIIBS0jIgoQoSkhhomAUYRhFWNJ+eFU6Y9YAwn4hVSG0UN+vBiz3ZZjOMN7+jY3trqMb4F6j/1fLiot58+h5QHThoy6nOxsmAh/h+6SbXUR+0i0g5hPHN7B+u7OSniGAC6ZKuHXnGAqF8FEsFQSu2zKKtSNFHmOFEPbxUxnO2w+JeuHPHz+Dv3jidMcBvWqxbuGzz87gd79xsivLy+r3HzqFrx9Y6FpA7ycB7DtXx+987QSOzLbXvcOUEicXe9MlpFQQ+L3v3Ya3Xj6BUi+eW8p0KJ1YAf64ITyJO3x86J7cxv3ejSEsy4JpmZAACoUCisUiSqUSCsUCCkX7d93HMIyufdJY9TX1tFpkaWIIldtDU6QsBIeleeFED6mrlqsgcCsI4PL1FfzWW7bhsnXD+G9fPYqlhr2nIyWBX79zG9716jUYKa2CnSXKMSklXjhVw3175nFKef7FtWmsiFdvHcXbrpzC1rEiRsoGak0Lh+eb+PzzM3ji8CKOzofnnamaeGDvPN5y6QRevXm4b90wZ5eb+OreeczX9AWWrRMlXLfN3r+NI0UMK/mSJYH5uolnTizjyy/N4tkTVcwuh+uKHz64iIOzdWyZKKV++L9pSRxVCgKjZQNvv3IK73r1GpQ0lSJpSAlsnSpjy3iprZaD1UBCwJROZbqv5tyyTDSbTQAWSqUChOk+sFtwngF0X5EZDKst/xCXwoBEq6uNW1Mv4Nb6R/cZsGJesOnHoH4AqE0u59cl1H9pAvqoSYTzfzff62XXKd2yV+PzBu4+DZcMvOmScRTEFnzu+RmUDIG7Lp/E63eOoVxooxWGiHpiqSHxt0+fwz0vzwX6zwsBXLSmgvfdsgFvvmQCWyZK2vvau66dxvEFO2j+nQeO46Uz9cCLeB4+tIg/e/w0fv3NW/vy7IyUEp99dhbPn6gGurQBwGXrh/FLb9yI110whvWjyaHUnZdM4L03r8eLp5fxF0+cxd88fRZnl1oFhTNVE//322dx2bohrB0pJOZvUkqYFnBmKVggGi4auGz9EHZfMNqFYX/Pxzw2XFNnD1/pDGHpDGXpdnSR0unkLd2a9OBzgIHz6LQu+Hs8uP8KQyR1BE6FQT2tEnEdXpLfPheXdUnEdF1QrkD/2LOUTakgcNXGYVy5YRjv/a4Ndq1IRPcSIlpZlpT46t45/P2z50LdUr5rxxg+8fYd2LmmkriczeMl/Ni1a/CmXRN4z98ewFf3zHnfNUyJr7w8jxu3zuBdr1mz4rXGy02Je16Zw9lqsJZ+41gJX3j3Rdg8XmqrcmGoJHDt5hH8P28ewmjZwMcePoWqb8SaL780i7t3r8eakUKqcHqpYeLkQrCmfqRkYN1IcSBHD8oiECh3IfiNo96/LcuCaZowTdMO4qUJS1qQEjAtt1BlH++iiDnu0gqeV7fbrgSkZQFGdGEubTpbXWedVjGZ8Ek7f9SyOqe71FaqFrl7ezG4DCeQLxgM6IkGgZT2A55/+vgZ7DlbC3w3PVzAB1+/EdvbHEJ1zXABv3z7JqwdCdbIH5qt4/9++wwePLC44pUljx9exPMnl1FXCi2vv3AU60aLHefzIyUD12waxjbl/REnFpr40ouzgUA/igSw3ATmlG5BQyWB8SGOCNa2iJtos9lEs9lEo9FAtVqFZZne9Lr02KrJT7muLidpBvWUE90K6i10O6iP0taDuh2QsPvQqx8iol6qmRKPHVnEN/ctwPTFL5WCwM/dvB7XbR1Bsc3+3AVD4PL1Q/hPt23CkG9eSwJPHK3i6/vnuz4Oe5KN4+VQH3kA+MFrplHImLdfv3UEl6wLt2TsmKyk6jYjJbBUt7CoHJPRciFUMKJ0DPgr6QQAA6YpsVytY3GhiupSzeuCoy1gShn4Pult8IZU19mdfVjl/AfX/shAESkY2AVPRjuf9IS66jxSt7/dfWn38EqRYhqp/QjoN7WtTc502mM22hnaKs1HTTehdMQuP0S0Ao7ONfDwgUUs1IMB5eXrh3HnpZNYO9JZz97xSgFvuXQSV28eCfx9qWHhscNL2HtWP8Rtr0TF7ZWCkTkQKxpC250obWukBLBQM9FQnt8dKxu5eDNvqls6/Pdt/3++5fi+U6eSgW98t+y4+6vH7tLbbJpYWFzE7Pwclut1QBgQhuE1IQvDcD4ComCERvILJxTf33zHQD9t+wb/zGck4NbO2r8B7gEMHr9WEO//mzOX9sr2L7O9AQzDUwd/621vsexC2+9sriXai+vdi8hAK25vY60BUgZvLoGXOwCAUfBvanjpMRseu1lOTJ7wSFPMd+0MsKo8wCMB4R49ZwcEA3si6rGGKbHYCOY1ZUPg39ywFldsyPbCqO2TJbx39wb89N/vD3R7qTUl6s2Vzd+aptTWlZxZbGS+Sy/UTSyrT98CqJu6t+uEWRI4vdRE09fVo1wQWD9axHhl8Otr3Tgs9bS6v3sDVDh/iFhe4G29Utrv69EszH2DkX1PN1ApD2F4cgIja6ZhFcoo1usYbtr96+v1ptPNxj7+hvOgbL1eb1UmOhXElmXZ00oAVmv0G++hcKd2v1AoOL+H053ZTDdc7KoP6rsh8U1f1JKUG/XrECZt16But8L/5rl+kFLClPZQapbyRsWCAIoFgYLI37Xi7ZdpvwXTv19FA5lG24k9ZoZdY5enYzbIacCSEk1Toqlsl4B9rEsFfe1ot0lpp6OmJUNvHjWEfc6LRn7OuZ+U9gui9p8L9qVfN1rE1okSKh0Oo+gqFQS2TBSxebyEAzOtmvkjc3UcW2is6FvG5+umV2njP4d//dRZfP+V0xgudXYOLSnxyKFFvHQq+NbsomH3kU/zLilLSpxYaASGxxQAhooGxsoFb5qmBZhOkDhI1+ogE0LAKBZQGapg69ZtmBgfR6NmApAwTcCSFhpKUC+coL7RaNhBfLMJ03nAVkoJaUn7Ydu6CdO0/95oNmCZFixpwTLtv1uWCSntwN6SdmGg2WyiUW/g2N5DidvOoL4NDO4TyOjmSufrvomrtxAy/vtBe/NwPwJ7KSWWGhYOzzZwaLaOU4tNVBtWoDZhuCSwYbSE7VNlbJssYaRkeNfKTLWJZ08s4/INQ1gznDxcm1+taeHAuTqEAHZMldt6lbqUEueqJl44tYwrNwxhSmmWtqTEQs3Codk6Ds/WcXqpiWWlBnJiyMANW0exfaqs7esqpcRM1cRzJ5dx5cYhr+k79TEbK2H7ZPiYDRopJRYbFg7PNHBoro7Tmv0Z8e/PVBnDRRFIA8+csM/DdJtpYLlhYf+5OooFYPtkOA00TInj8w0cnK3j+HwDc8tKYCSAqaECNo+XsGOqjE1jpbb7fKchpUTdlN45P7nYwEIteIwqBYF1o0Vsmyxj+1QZ4xUjV2OBNy3g9GITp5SXHl04Xcam8fRjrEcxhH18dq2rBIL6c1UTJxYaqJsSleLKHK+rNw3j+i0jePl0LdCf/7EjVTx0cAG37hxDqc3CmWlJHJyp48GDizi+EByO8pK1Q3jjxRMYSrF/lgUcmQueA8OwH5RtOus4sdDAsfkmZqpNNJSHfYdKAhvHytg8XsSGsSImhwooGRwq2GUUDFSGhjC1Zi3GJyZhWRYMw4AURrD23eGvffd/3/odkJYELOEMj2kH+fZ09qg6zaZdULCkXYBwl9lsNlFfruHph55I3O5VH9QnPaygTusXlbil/caBQam8pYzCHa8Ucd2KRIYXV2UghFiRQpIbpOw7W8PDhxbx98/M4LHDi5it6Ys6ExUDr902indcNYWbto9h55oyzlVN/PWTZ/ErXzmK/3jrJvzkDWvtcatT3DyW6hYePrSA//61EygYAh943UbctGMUw6XkwF5KiaNzDXzy8TP4na+fwC+9cTPe9eo12DhegpQS1YaFV87U8LX9C/jHZ2fw5PEqFuv6/frRa6bxoTdswkVrKoGgRUqJY/P2On7jgeP4r2/cgn/9mjWYHi60dcxu3DaKd/qO2SCNwy+lRM2U2HumhocOLuIfnp3B40cWMRexP5OVAl673d6f124fxc41ZZxdMvH/fecsfvkrR/Gfb3PTQLrRURbrJr55YBG/87XjGK8U8P5bNuCGbaMYKhmwnHP89PEq/uHZGdy3Zx7H5vVv2BQAtkyU8KZdE3jbq6Zw1cYhbOrSy3Xc1osD5+p44sgi/vaZGTx4YCE0FKLLHf3kbVdO4badY9i1rjLQBTq/umnh1GIjEOQWBLBrbaVrD2hOVAq4Yv0Q7t877z2IW21YOLPYxHLTaqtgn8VQ0cD3XjGJr+1fwJ4zNS/Pnama+OAXD+O/3r4ZV28axrYJ+5o1Igo07pjyszUTe87U8OmnzuKLL8wGuheVDIG3XzWVOk2aTtr3ExA4vdTEPzxn5zmPHV7C4dk6liO6LU1WCrhkfQW3XjiGG7aO4oLpMrZPlrFmuBC5L3kU9y4gr7IWbjcet5u2gDAEypUCrLJld80R9t91y/W60kTEm24Pb6l5K7FdOPB3uXErAlrTLi9VE/cTOA+Cer+4mvaooYmiCOH0fJfqY5ju94N5QaQp4AzqtveMsJ8H6IjTphnbaz6Hx9MN5veerePxI4v43HOzeGDffOjBONVczcJX9szj4UOLeMNF47jr8gk8e2IZf/TIaTQt4DceOI6z1SZ+4ZYNuHA6fgzrpbqFB/bN4398/QQePLgIwK6Rff+tG/C6C8cxkhDYH5pt4He/cQJ/9MhpAMCvf/UYZpeb+Inr1mKxLvGtQwv43POzePjgYuKoGp966hxGygW875YN2Lmm7N10Dzvr+IRvHacWG7hy4xD+6fm51Mfs3j3z+JZzzL7vyklcv2UEF62p9DW49wfzjx1Zwueem8HX9y8k7s9szcQ9r8zh4YML+O6Lx3HX5ZN4+ngVf/itU7Ak8NH7j+Pskp0GLkhIA4t1E/ftsdPAI4eXAAD1psT7XrcBF0yV8cqZGr700hz+6YVZHJ+P7+csYdds/sW3z+DePXP4vium8PZXTeH6rSOZgsSGKbHvXA1PHVvC55+fxb+8PBdZ4HEtNSw8fGgRTx2v4svbR/D9V0zh5u2j2LVuCCOlwSnQ6SzWLRycbaDmCxSHSwYuXFPGRJdeEDVaNnDx2gpGS4Z3LBumxJG5OmaXLUxm67bfllt3juP6rSOh4PjF0zX83D8ewu0Xj+MNF43hgqkKtk6UsG60iIlKAQUDqJt2C9XxhQYOzdTx9Ikq7n1lHk8fr4YC7Quny7jrskmMl9NVWDQtGRqjvta0cP+eeXzl5TnvLdxxZmsmHju8hMcOL2F6qICL1lbw3ReP48YtI7hq0zC2TpY7fittdunbz9XrJTbOifhKug/LOqPeWM6PUgAQ6dJ13HMCwllJIfzYLuxQXChTK/tUTLcN51VQ346kt3daluX8Ltt61HE1a5V0Yybolyx95hPmTX5QNp9OLjTx3756HPe8PIv5hEBOtVC38IUXZvGVV+ZCN68/euQ03njReGJQP1ezAzo3oAeAr+1fwKu3DOPaTSOxQb2U0itMuJabEn/62BlsnSjjnlfm8cC++ciaeZ0XTlZxcqGBC6bL3ggVz51c9gJ6AKibEr//0CkMFUVk7VgU95jdv28ed14ygf9251ZsVcaxXkkSwPF5Ow185ZW5ttPAfN3C556fxT0vz6GqSQPfffF4YlA/UzXxlVfmvYAeAO7bO48rNgxBSuALL87i8Gw9VR9klyXtAt8fP3oaTx1fwn95w2bcunOsoxp7KSVmlk385v0n8KUXZyJbY6IsNSzcv3cBjx5awusvHMevvGkzrtgwhL7FUSnMLpt46dRyIH2XC3bXu5EUAWkalaLAprEShoqtoL5mSuw5U8eZpSZ2TLU3Bn4WIyUDP3T1NL59dAkvn6kF7nFzyyb+4dkZfO65WVwwXcbl64ewa20Z2yYrKBeAhZqF/TM1vHiqhpdOL+PUYlObVkdLBt7+qilsnyqnriGvNiycWgx23zElEguUUc4tm3j8yBIeP7KEDSMlvO1VE/ivt2/peCSjTNJczz26RiIfynU+htSvWo0DdMsRUV9Ers3/p3TnlUF9G9Safuk84bwqI7oOJI0BlO7Z7e5LOj2Z8o9+PijQI1JKnFps4uMPn8I/vTATeltkO6IC23WjycFquSAwpgkSxitFlFNEPetGwzUbtabEfXvm8ZVX5kIvlIlz8/ZRfOT2zbhu62hgfOqoG167Ab3fQs0O7rdPlvELt2zA+gwvuemUlBInF5r4w4dP4osvzmZKA2pA71o/kpwGKkUDo5o08MyJKvaereHwbOejkDRMiccOL+H/PHoa2yZKuHhd+9W/56omPvbQSXz2uXOZzvliw8K9e+ew7bES3nbFFG7cPorRAayxt6TE2aqJvcoLp6aGC1g3WkSpS102iobA2pEi1owUcdIXuB6etfuJW1Ku6HMIt++awL85XcPHHz6Fw7P1UJozpcTeszXvuFSK9kgqltPiGWesbOCtl03ix15td9tLQwKYXbZwZqmZOG0nTi418KmnZrB5vIR/f9P60LNIKyOugJjYaTYbTdKynADHaIa/9tfQC6flXps8A60Bcek3HMCn7XC76oN6KQWkM+5RKxjXTZn+4UN7+HOhnLV0XXsGLZPuJgkZKqqGhufUHGLn2XF0s3SknkvDfbF0ZPE58EPw6/ButfrZp+hTH7o8kw9JG7r7GK+UEqcWmvi1+47hzx4/E/reEMCm8RKu2TSMDaNFr1bJtOyCwFPHqzgx30BSDJjmMhDqJeb+3ftfwvyavy02LHz+hdnQ34uGwI6pMq7cMIQ1IwUvYJDS7gpw12WTeO320dADemn2o+Acs6uVY2ZZEicWmnj6RBXH5xuBGrxaU+J/fvMk5pZN/NfbN69oYC+lvV2/du9RfPKJs9r92TxRwtUbh7FeSQMnFhp4+viyE3jFryd1GtD8/YF9C9rpR0sGrt0yjJ3TlUC3gUZTYs+5Gp46Vg10tVpuSnz76BIePbLUdlB/ZrGJX//qsUBLjbfdANaMFPHqzcPYMl5CwdkWSwLnqk08d2IZB2fqaPgOUt2U+JPHTuP5k1X84Q9cgJHp8sDVF1kWcG6piVPKA55bJ+yHj7M+JOsyhB3UXzhdxgu+EWLOVps4s2iiaQHlFXy/Urkg8OOvWYOJoQK+9MIsvnFgAbPL0dVUdtek+AugXLBftvWmXRP4waunceF0JXVBRUq7xWQm4pkNd/nrR4u4eG0FW8ZLqBQN75qzLHtkn5dO13B4thYYQcc1XzPxmw+cwHzNwi/euhHTXa6xT6y1dvuUi9YDqK153chZM4to/SK86f0ThOcLfh08d+5vwu0Tr2y2ly+7MYHbt16zfOH21o7krS1i25Kt+qDe7R/V+lnPTjD+75OCJcMrjkmZ7gY1CALjt3d5BJVQAC9if1X+mK0TS1SfusA+JvYN0he+1OJGW0dNiUriSvjZZSsYLTctPHxwCZ9+8iz+4tvhgH64aODG7SP44WvW4PotI1jrC34tZ5i7x48s4dNPncOjhxdjay7rmvGZ+2WiUsCtO8fwjldN4ZrNw5gcKsB9tYyEPULNeKWgfYNk0n54x+zqNbh+2wjWDmuO2dElfPpJ/TH708fPoCmBH71mDW7aMYKhHj4gKKXEclPiW4cW8aknz+Ivvx0O6EdKBm7aPoofvmYa120ZwbSyP6eXmnjssJ2GHjuyFFvD305LSRIBYOeaCn7s2jW47aIxbJ8MjlTUMCUOztbx9f0L+NhDJwMPsJ6rmnj5dA21lA9gLjftFyH9zVPn8KePhwP6oiFw9cZh/Nhr1uDm7aPYMFr0gl0LEnPLpvdg7/17FzBXa22LZQGPHFrCF1+cxU/duG7FRnlJa7Fh4vlTy5hRAtrRsoEhzZtXs6gUBSaHgpH73LKF509VMVMdx4axle2atmakiB+6ehoFAC+cXsbcspmpQma4aOD6LSP44Wuncfn6oVRvkXVJCRyfb2jHuR8qGrhiwxBuvXAMV28axq61FWwYs1s33a7ClrTP5f5zdew5U8MTRxdx/555HJtvBvapZkr84SOnULckfvCqaVy7eaRraVLE3fJDB1bCH5PZdzo79vIvI6rOzi1AxMbUcYG+aFXuSaHGA7J1n3c65kfd15NjxUBxQdm+dMf9PAjqV0bakXNIT33SuxvHzzsnuhYD5Xv/6uya4GBu4Z/bfTZaxmQmIm11cgbdbgGqNi38zteO4+v7w7Wg60eLeN/rNuCWHWO4atOwdvSZrZNlXLJuCK/aOIwHDyzgd795EqcX9c3DXYznMtm1toKfvWk9brlwDJeuq7T9wGTcfqwfLeL9t2zAd104hqs2JhyzDdHH7P/7zlkcmqnjL3/0wp4G9QBQNS389teO4xv7F0PfbR4v4j/cshG37BjFqzYNa7dl62QZl64bwlUbh/DNA4v4H984gXMRtYlmOx3hE/zszevxxovHsXvHGCaH9KPIbJ8q4bL1FRybb+BPHm0F4/M1E8+drOLoXAM718T38QeAZVPi9755Ave8Mh+qJ6gUBf7zbZvwXTvGcP3WEW0f860TwEVrKrhywzBuu2gBf/zI6UBtdN2U+L/fPot3vMoeCWWQ7iVS2tunFtamhgsY7XLV+XDJCHXTa1gSyw3pjYiz0pYbFr788hxOLzYzBfSAnd8+dmQJ+8/WcMnaSltBvSGAXesq+KFrpvGpJ88BsO82l60fwntuWIdXbx7GJesqWDtcjBm6tYRL1w1heaeFt8xP4G1XTOGJo1X85gPHg9vZkPjk42cwt2zht+8aQgUr2EQyAMItCsGOMMHrUyD+1WFJqSb7tc6gvstW8sUYg6hbhZssx7HtFgi7P1Xrd+G1o0EIEWqzkcq/9khXnZWqu6Ub6e7el+fx4MEFNJVga+1IAf/tzi34viumMJ4wusVwycBrt4/iig1DWD9axIf++Yh2WL9DMyv7unedi9eU8Wtv2oI7do13HJAcmtXvx9rh7h2zpiXx4MEF3PvSPH7wmumOtjOte1+ex0MHF0NpYONYEb/+5q34nssnU+3PTTvGcMUGu3vOf/rSYe3De1HHzs+0ZGhbVP/x1o346deuw5aJ+KEAhRCYGiri+6+YxGeeOuttkymBl07X8NTxaqqg/mt75/HNAwuhcb9LBeC33rIVP3TNmlANs6pSNLzRRV45U8PRuXrgGL14ahmff34W77lhHUoDFEMVDYFxzbUyWSkkjkbVrqGioe1jPlo2UO5DC8bJhQb+y5eP4J6X57CYMFpWQQDlooFa04rshlY3JZ47WcVH7z+OckHgtovGU1cqCGF33flPr9+EkZKBv316BrfvGsfrLhzDj1yTvm8+YB/nndMV7Jyu4LsuGMO6kQI++KUjgWkW6ha+sX8ejxxcxO27JlIvezUIpTSlws7usOF2wUnT4yCpx0A2DOojJXZ+Oq+D95XQbqCqGzPWJYITBjr7CADCsueRTp2+EHZTpduv218rH+qXZ7RK7m4NfUwjWs9k7U71lVfmA8PUAcDkUAG/9dZteNuVU6nGhneNVwp4x1XTOLdk4lfvOxYaMnJDigdle2nHVBn/751b8eZdExjKEIys1+zHaMnAf7x1E97+qumOjplhCHzgnw5jztfFodaU+Mqe3gf1X3llLpQG1o4U8Ztv3YbvvXyyrf2ZGCrgnVdP4+ySiV+772ioa1GaNJDUM/k/v94O6DemHNu7aACXrK3gsnVDePRIa0SdY/MNPHdyGW+9TCY+hP3A3oXQqElFIfC737sdP3L1FEbaKCBOVAy85/q1eP5EFd84sAC3N0XNlPjLb5/FD187jUmjvRd19UPU8y+ZlxuxrpV2cr6BD37pMD7/wmzo+nCtGyngsvXDuO2icVyypozRIQOnF5t46lgVDx5YxL6ztdAIUqYEvnOsiv/y5aP4rbduxet2jqFcSL7GhLDT3K61FXzw9Zvwg1dNY8tEGWtGCpiodJ6fTQ8X8BPXrUWhYOA//tOhQIHkbNXEo4eX8hPUi9bzbt4gJ1arC03WZUcJHH0Z7lAjEr6PHRIzBQb1jnCmma6Pty6zVf826BlyN2XZV917BNzAPmvAqsbkQvlOOOu1nFzM/h1eJzh1ejhfuf3nvE1W93+Fu5nouhmlMVNt4tHDwW43hoD9INcl7QVzruGSgeu3jdoPLCrvA5pqoyap28oFgas3D+P1O8czBfSA/ZZSVakgcN3WkY6P2ZsvmcTl60/jscOLgZvqo4cXMFNt9mwkipllE48cCna7KQjgig1DuH3XREf7M1IycOO2EZQMgWUlbU6mSAMLNfuFQzrtBvSAfY2Plgu4eG0wqJ+vmfj20SXsObOMKzYMR84/u2ziiSOLodrXXesq+N7LJ9sK6AGgYNg1rjftGMN3jlUDfdWfccY0f/urpgbugdnzxXLTwv175/Fnj53GvXvClR4C9vjyP3LtGrzugjFsmyxh7WgRYyUDhiHQMCXecqmJE/MNvHh6GV96cQ73vjKH00vB1svnTy3jP//zEfzr16zFj1wzjQ1jxVRp2hAC2yftF0YB2Qs89vVh4PuvmMTHHzqJl8+0RjmqNiw8dXwJCzUTY114H4H/oVLNlmRK88JtbXfv4c4KdVW17a4nvp49uDQJGVq+f5qk79P8XcWgvsvOpwC+V3rShcnts6+9IoWXGRqGEXj1MwBviKrW1HC63CAQ0Q/Eue+wEPHYkSXsPRvsDlEpCvzkDWsxNdR54DtSFvobTR8P1fRwAf/qmulMNVou7eg8AhjOMF731JCBn7xhLZ46thSo3d57to7Hjizhjh7VlD12ZAn7lKEKh0sG3n39WkxmOFYjZUN7vtMkgdGygWlNIebdr1mLH3/N2rYCetew87Dvp55qPQhsSbsAU0moJX3y2BJeOVMLBfXfe/kkjs83cGpB/zbbJJesHcLkUCEQ1NdNiZEVenNqVpaMic86JCEj3865UhqmxKeePId7X5nXDs36R2+/ADduH8G60SLGK4VQv/iiITBcMrB+tIhL1w3h1gvHsefGdfiDh07hH5+bCUz7/MllfPzhk7jtojGsHy2mukB6dctZN1LET792Hf6TrxtO3ZR46XQNL55exvVbR7OvZAVGGGnV1Pd0NQNl1Qf1QqzMWL9ikAI7CvEX2MMlY4RaA/ytBqFWAk1xvx/nvZsjGf3tMzOh0Ui2jJfw3RePZxqmzhCD9Wo2QwBbJ0rYvaOzlw2lXk+GeQuGwBsvGseWiVKgoFU3Jf7umZmeBfV/+/Q5+HtJCdjH6g07x/qWBoqG0I62sW2yhEnfyDvtKBjAhKagWjRE4tszv/DCrPaFZX/62Gn89ZPh0YLSapgSZ6vhFomsLUndttSwcGi2Fvr7oZk6Ti02sGm8e93qZpfD4+EDwNH5BuZr5oq8FOnzz8/gkUOL2oD+Mz+2E9998QSGiskxhiEEhkoCm0oG1o0WsWG0CAGJzz43600jARyea+D/fOs0fv0tWxOfy+gVIQSKhsSbL5nAb3/tROAFVycXGnhg30J3gvqVInUPu65e50FQb0EIqZTUdF1tZOBnKRIyU+FObc9nStPrhw0AhbgnxCMCMF23bZul/Bt32pJ6oYZ6dWnWav+rjkhjPzgavWzheyeAnv6YuKPJFPxvTJPB9RpAoFSvBrGmaP3dH5B77yYAfOcMgd2ya4R8zaFG6++t3vetfvKWdPuQ2gs0TPuYe1uvBECxZ8PXl8fbJzcdifTBuvCeE2g/55qvWXhwf7jrzXVbR7F5hYeN66aq5jXpRUPgknVD2DA22Fnf5vESrtsygv3ngm9L/eb+RSzULe1LubKw04DS9cYAXrNlpKuBWruiYqVKychU0OikPLdYt/CNfQuoa558PFs1gZgxw1eLiUoB124exXjlLOZ9Q3H6733d4r0Pxme0ZODaTSOpXl6X1WLdxP17F3BCaX0xBPA377oIt180jlKh/UrDoiFw8ZoKfvVNW1A3gS++6AvsJfDlV+bw3vn1GK8MregLtlTbJst49eZh3PPKvPe3+ZqFJ49WUW1YHXXHC4jdNwnp9tnU3NKcKE3/vQSK/hs+lHEw9IsMLNvdgtamtlJ3KJ3L1grs1aqF/mBkF/uIdcwhSXtnH6xqgB5yH+Rxg7HWR01bvsA2OJPygffRvWDJDS51HyAwe3yYLf3b040j0Y5wMtJtt3/74763P8H//HMnLdsdpUZaVmvEGt/Hf3ztyf2BMhA42YGFK1d83MnxzrnzqzKevbvedkjLCqYPKSGlvY/JxxNehtJp8vjyy7M4qIxGYwh72L20rywfRE8eWwr9rVQQ2Dld7uvNMg3DELh03bBaPsTBmRo+9/xM19f3zy/N4oBSA2sIYaeBATxW/diiP3/8DPadq69oU/7+FV5fkmJBYMNYMdQl78hcvetvN51dNrH/XDBNjlcMbBgropLijdJZfXXPPB47En53xK0XjuHaTSMdBfQuwxDYOlHGLReMYqNSwXBsroG/fOIsljWVEitFCIGCIXDJuuDzJXVT4tmTVTx8KDzkbdvrQHs9KfxxW+wy1Z+lchtPWl1UQOaLH4IxjJ8mZhLQxJnKutIGUSmcN0F9r/kTW1IilbDr3KM+2S/ljKkiRtx2J2vtuYQFy/nX+1tMQcj9WJZ+OsOyPwUJCNOCMC0U3L8n3hUTw+aYXfIVLAJ/Tt4XdX/Ci47f7qj52nXhVCVUMC0IgTfsHMu87H56jaaJeKRk4E27plZ+YzrwxovHQwG1hMSFk8nDLrbrwqlyKHgsGgKvz3ka6KbdO8JvFO6lX7ljC37w6ulQwa6fCsIe4nbzeDnw91MLTZxYaCYOP5qWadkvZjs6H6wlXz9WwobRInr9qEHdtPDAvgUcmgmuf91IEb/9PdtCgXgnhooC/+b6tXjjxROBEZckgL9/dgYzy82uvyCyHQUBvPGi8PU/VDCwsc+jlyXxx1hS+OIU0YqxtJ8ButY6xaC+C9SAPunTrvau61R1u32i6ZvexuYkZXACdq238H6WgZrseJ0fMy9DiGiViZ23jWl7JerZwMzNq32mG6BBCGBosO9Hnqg3dBZ70NU2qitL3tNAN63k2Oi//qYtuHv3eoyV+51nBwkhMDVcxKXrgwXLhbqJs9Vm194U3bTsZwz8XXwAe6SZDWPFnj/D9PTxZXz7aDU0FO+N24axbbIEQyRX3iURwn5j7g1bh7F2JHhRH5yp47PPzXStkNQp3QP/QkTfM1ZKUowlfb0pvJ44XaoJH3SD3bG0LwRaYyF1RkqpaZbxfY9gH293da2GHRlbXZ8Y3CY8QBmfGanPH6hfZ0v5ST3+I+eL2Ch3pBoDdvuWoR53t9tdzBj2aXs2BUbJ8i3e7c/u//jXldhy4xu2U522n8F+xHDMuZeXvLtfb80ctG0YdL/zPdtw7eboYTDbYVnAxvEitk9VUClkDxx7Ybxs4JK1FYyVDSzUWy/wOjHfwELdantYT51qw8LRuUYgqB0pGbhoTVk7GlK3rR0pYrQcPvY/+Ko1GC7q31jcCSEE3nDxBD791AyOzQe7L10wWen7+e9SGa0r2n2xpXeP9t+w0xxOt8eubN3v4zrZBJbfBbqhvdvBoN7ROoCdPm7ozC3tlBA3XrjyDIevBOn8EEolMhRM9kN4RNWw+OJC6/9ZI6vIYLeLmW1onQgG9mmlrbH3/7ySoyk9f7IaKsiZUuKel2exe0eORjlYZb788kxoSD8J+3y9dnt3z8vzJ8PPHzQtia+8MoebmAYAAK+cXtbWnB6areNnblrXgzUOXkAP2MH15vFSYPjGxbqFPWdrmF02saELD9cv1i28fLqGed9bdguG/cKy8S4MRZvEEPqjP1TuXkDvKhcEDM0uFfQjwa4YU0r8y0szob8v1E0cnKnhsvVDXVtXJ5VZqSs3dTfsqFl9512dzf/3wBfZ6oC7jm2rXaDrchHXj1rNE3T98UMZR+poXsA+rVGfjDXtwoj8JM4LCSmkXQTW9mhrYzvUh2Iz9ziKO2YJlAevOxWVjuJXnT03+Z7LprFlMthHVkrgwEy9ry0F5zMpJfacqYfGQ986XsbbrlzT9fV9z+VrsFkZ5caSkmnA57svnsQFU+VQH3d32MU0XS/b+/RhJ1MoFgSmR4qhEZj2nrUfls2aXqSUmK2ZeOHUcuDvIyUDa0aKqd64utpJaY/h3zDtf7t9jdrLB15R3l1SMgSuWD+M3TvGu7q+lRQbKqyCrO48vTpSBpKRT1MEP6FCoIyfwR3hBECrz7d7UQaCOaW2XrZ+jv7o+mmn21//M5+aZz+jJ4qcWDd/5Ga3NWvb2txOGfgPrf87y5CRyxNtbaSAb9hN3znzgnURf8i6kZlPDRu4edtI4G+WBJ44shQYo5hWzqnFJp44shQK6m/aMYqp4e5n29PDBm5WauRNC/j20aXQ2y/PV5NDBq7fOhJ6wdDjhxdxYuH8uU6KhsD60WJoqNNDM3Ucm2/CzJglWdJ+8FYdo37daBGbxkqJ7xJY7aSUqJsSn3nqHHb99jO45+V5nFjo/kO1x+cbePJosAVvrGLgms3DGOtCa4lEdOVnN0QOQBHz8U8Tns+NBnQxVvJ2dLpf7c6/6oN6AV1zWVxEadfOGhCxdbfBj0DB//ENlan7GJD2R1oQ/o9lApYJSBOtZ7Td5kdhV0d7XXPsj7rs6PF00obEatnVv5eite3aj4Ch+a91TIP/CSmCn7iHi52PtweGEfzEVWuphTAJZ1udjxAQBXgfGM5EzqcV2DuLsyy786t0h5wUoT1Pm3qEk2aKwkC5UETBKMBQ22IjqhW6mYX/0DVr4L9XSgCHZxt48OBipkx2FVR8dCTLfksp8c0Dizg8F6wlKwjgh66ZzrZhMX74muBIKxJ215KHDi5kTANxnRHz5fuvnAo9PHx0vokH9s1nO0ZOrWu1YWHGeeB0kFtI1gwXsXNN8GHZs1UTB2fqqDaydcSumxIHZ+o4rVQobJsoY+P4yvQYtqQ+zS42zK6/5Xa5YcHUlIQsGc5HpJSomRJ/+/QMfvLvDuD0UhM/+td78QufP+Q935CVW0v/tX0LOK4UVteNFvEGzYg4Ha6oO8vpRNQ91Q2xBOw+WM5HBirXVma7Oy0IrPqgnvqn3Vr1NCXojkcQUpbVQUNB++J2qJ0VZ5k3pZt3jGLHVLALznLTwie+dQpztc5vFtW61de8ux+kRKbAZq5m4Q8fOYWa8qTyBVNl3NzlvvR+u3eMYYfSDWupYeGPHzmN+QwBQ7WuiU5y6vqtI7hwuhxqnf29b57EzHLnx2ixIfHZZ2fwK185iiv+x7P408fOYKE+uIF90QCGS8HhICwp8YlvncLjR5Y6DnwtKfHy6Rr++9dPBmr8BYBKUaC0QuN7nqua2mv4M0+dw1Kje+fFkhL37pnHwdnwe0KOzDW0eef9e+bxM5894P1eNyUePLCAP3z4VNcKHGerJv73QycDfysZApesreDKDd15KJx6g0E99Uw3g/pQJ5d2gntNabzXQ1klPdngfgqwx4QPfJy6f/ctjZkeFUhp7UgRNykBoyWBZ08s4+njVTQ6aFNvODVupubhwtogDauQgW4/TClxeLbe8TF76ngVzx2vhrre3Lh9FGtHeldTuWa4EHoA17TsNPBMhjRwYKau7ZKRxzQwPVzEDVtHQ0P6PXdyGd8+utjRMWpaEgfO1fAHD53Exx62C9G/+MXD+Ktvn8VCbTAD+80TJbzuwjGsGw2mxyOzdXzuuRkcm2+0vd1SSpyrmvjbZ85h39lgf/qp4QJu3jGKi9amf0eDJSUWaiZOLjSw72wNJxcaWKinq2m/YsMQrts6Gnpu4PEjS3jx1DKaVvauj5aUOLHQxKOHF3FOeRvxRdMVvPWySe14/DduH8Vl64LHYa5m4uv7F3B0rpEpsHdbjB4/sogXTgTPwXBJ4JpNI5gY6sGYutQ1qz6ol+heHy1Kpxe131HdcgzDgP8VzknbpKupz7pNEiKwrLZaEoR+2/rxNP13XTAeuomcWWriJ/5mHx46sNBWEFZrWnjwwAL+05cOa2t5X1Iegsurl06H92O+ZuHDXz6CBzs8Zj/x6X04q9zkiwaw+4LevwjqlgvHQmngxEID7/nMfnzr4EJb45DXmha+uX8e//GLh0LjfQP5TQM3bR9FRYnqG6bEO/9yL766Zw7LbRyjhmnhO0eX8O/+/gAeVZ6h+MhXjuJbhxYHspGjXDBw47ZR3HnJBIZ971NoSuAvvn0GX90zH3oTa5KGZdc4f/zhk4HhdCtFgVt3juG2neMYauOtU7PLFj7xrdP4t393ANf83nP46b87gL94/GxgRJ0oQ0UDb7x4PNQqc7Zq4pe+fBTHlJdidWKpIfGXT5zB1/YtoO4rDBaE3RVu7UghdC9xx7b/0WvXBgqWTQt47MgSPvbQKSxmaFVbbkrc8/Ic3vl/94ZeKDleKeDqLg3dmlm3WsFXoVUf1FN63XhRFrDy15Tb5z6pP77XL1/3e8Ly/Z9CoYBCoeBbNgLLM9CqfTcSRrjwFwgkWn36vE/vD5/nbVdO4lUbhkOje5xYaOKn/v4A/uWlORycia+Bdmvnv/zyHH7q7w/gZMSDthev7d5waP100Rr9fpxYaOKn//4AvvxyG8fsJf0xMwTwqg1DePuVU93cdK23XTGFKzVp4PBcA//+swfxLy/P4VCK/TlwroYvvTSL9/zdgVABxZXXNPDWSydw9aZhqM9r1i2JH/v0Pnzh+RnsPVOLLQA1LYnj8w18de88PvBPh/GdY9XA94YA7tg1jms2jwzSaHkBl64bwo9ftxbXKttYbUj8/kOn8LV9Czi10Ih9PsB96PP0YhPfOrSE33zgOJYarWkFgMvWDeHHX7MWr94yol2GyrQkTiw08PChBXz0/uO4b888LAl8Zc88/vs3TuCxw4s4vdjUtiD6ve6CUbx222joGYpvHV7Ev7wyh2dOVHF6qYmGmb7i0JISi3UTB87V8OCBBXx173xoMIIdU2X88DXTkW8vLgjgx149jYumg7X1s8sm7t83j6/vX8C+szXM18zEfQTs6/XMUhPPnqjiH56ZwQ//9b7QNENFgeu2jOANOwdn1JvYFuxBvWhWAMepp67J9HBgwvd23Cz60uqSNIauV0PfhXVJ6SzIWWjiEFtdzLyGSwZ+8fUb8YlvncI3DywGvjsy18BP/f0B3LFrHD95w3pcsaGCMd9LZiTs8YtfOLWMP3nkDL7yyhwWY/qVr5YBLOJeMnpkroGf+rsDeNOucfzbG9fj8vWaY+YM3fd/Hj2D+/bMaR92+64do/h3N61fkbe7jpQN/OKtG/FHj4TTwP5zdfzU3x/Am3dN4D03rMNl64cC3RPc/Xn+1DL+6Fun8dW989oaepdubO48GCob+A+3bMAffesUHti3EMi7qg2Jn/q7g/iuC0bx3t0bcPWmYUwMFZSg1x7T/VNPnsNnn5vBmaVwwfeGbaN42xXTkW8VHhQ3bhvBD18zjQPn6oHa66ePV3H3Px7AO66axi0XjGHndBkbxkoYLhle9lZtWDi12MSBmRq+dXARn3rqHI7OBWvA144U8LZXTeG7L0ofTC41JP7k0dP4ra+fREMpWB2fb+A9f3cAH3r9Rrz7+nXaF0y5RsoF3L5rAl/bv4BXzrRG4rEk8L7PHcIFUyW8/epp3LJjDDumylg3WsRwREuCKSXmaxaOzTfw7IkqvvjiHL55YAFzy8ECb0EA33fFFDaOl2BEVDYJIbB2pIh3XjWN3/768cAL4p48VsV7PrMfr90+grsun8I1m4axZaKEiUohMGqThEStKXF6qYlDM3U8dHAJf/vMOexXRhwC7HH0b794HD9547rIgsZKE0Bs0CDcijaN1d5zg0E9dVWmwD4uv5AIPC0fertchvWqdC0UcW/pjQvqk/q+u9+roY9QvtfpdovIaNnAO141heu2jODnPnsQX9u/EPh+oW7hCy/M4tkTy3jDReO4YLo1ZrdpAQfO1XD/vgXsP1dLfBNhZQUC1JWQtB+L7jE7uYw37BzDBdOV1jGTwIGzddy/dx77Z/TH7PU7x/Dxt+3ABdPlnr+ITAiB0bLAO6+awmu2jODn/vEAvrE/GNjP1yz84/OzeObEMt5w0Ri2TwXTwL5zNdy/dwEHZmqJb6NtpyvFIBkpGXjblc518o8Hcd+e+cD3DUvia/sX8PKZGm6/eAKXrW+dc0vageU3Dyzg2RPLgW4Xrpu2j+D3vnc7rtw4hMIKPRjaqUrRwPdcNomnjlXx6SfPourrN3Nsvok/eOgU/urbZ7FrbQU37RjFprESCoadVk4sNPDIoUW8fKYW6lMO2MHk6y4cxw9fPd1WgXZ2uYkvvzwXCuhd88t2//N3XDWN0XL8cr/74nH8y8tjOO68LdclAeyfaeB3v34Snxw+g0vWDeHmHaNYP1oMtXJJaQ86sPdsHU8cWcKBmVqgNcIlBHDJ+iH86LVrMJqwv6WCwL969Rp84YUZPHdyOdBta6Fu4b49C3jwwCK2T5bxmi0juHT9UODBZtOSOFM18dhh+xmBqKGLiwbwlksn8N/v2o4tE9lfKtY1Mr6bSf6e1umeVR/U26MS2ileHYooLohM9RKCDvPbbItWA8rQI6SxcwPdKalGNqfGHVMAUb3fBcIjXLUbw3SzpUAN4gMvugrM6L5MTD/CR1Lg7Z6t4HRpd1wGjlk3gj4hBC6cLuMTb9+BD3zxML7y8hz8la1NC3j5TA0vnwnX6JBe0wJePl3Dy6fTH7OSAdxxyQT+x/dsw/ap3gf0fkIIXLSmjD/8gQvwi188jPteUdOAxIunl/Gi5nmC88n2qTL++O078P5/Oox/fmkWdV9cKiVwdK6Bv/z2mdTLKxrArReO46N3bslFQO/aPlnGD1w5hedPLuPxI4uhwunMsonHjizhsSPhtxZHKQjg0nUVvPOqKVy0Jv3DsQBwdsnEyfno9waYTten2aoZGmtfNVQ08K7XrEGxIPAPz8zgbDW83LNVE986tIhvHVrULCGdomE/z/Kvrl2DrZOlxHufALB+tIgP3LoJf/2ds/jq3vnQ246XmzJTXj1cFPj+K6fwG3duxYaE49Rz/vtc+E+ayWU4oAjIem1FLFvGL1k7V5cv81Uf1LtjqEtnTHGX93bTSCIyALXnt6fpRNKDkMldLgaz+SipdJx0tNSSt//4J40NKxFfm54kPoOI/91IWJel7HhgOzXT9/tWLoTA9qkyfvMt2/DztYN45PBioAauHaWC0Pa/nl1OflmPO263aqlhoplilJHZ5XDtn5TAfL33L1MqFwXqHR6z4aLAa7eP4rfe2n5An/WYuYQQuGC6jN94yzb8h88dwGOHlzpOA+WC0NZIp0kDdVNqHzxdalihICYtywIWNA9L1kzZ9og8G8dL+O93bcdi3cJDBxdjuxvFqRQEXr1lBL9yxxZcsXE4NwG967aLxnF8volqw8ILp/QtEGkVDYGL11TwE69Zi7sun2p7/gumK3jLZRP4k8fOaPuUj1fsbjVblSF8dSpFgVsuGMOla4dgWRJfeGEWpxebXW0hHS4KXLN5GB+6bTNu3jGKcoq+ie4Ds++8agqXrK1guWnh8SNLHac/1dRQAa/fOYaPvmUrNowNRg29gFNJC8BCwrNwsundSHWxgBBZRvCJP/tGzNfuc3PplmRz8/+094FVH9THDuA/mLFxstjtztfNYFWI6XOf1yQGAJvGS/jQGzbjK3vm8PV983jqeDX1iBZDRYFrNg3jph1j2HNmGf/y8pxXg3fHrgmsG02+UZQLApeuG8Jl6yp40anlvmL9EC5ZN4Ryiq4ba0eKeNOucdzzit09olQQeP3OcUwP9zbbGy4KvONVU3j5TK2jY3brznHccfEENnZwM816zPwEgC0TJXzoNjsNfGPfAp4+kX5/hksC124awWt3jOLlU3YacGO9tGmgUhC4fP0Qdq2teP2ar9o4jIvXVFIFPzoFA9g2WcJrt43gkcN27fGW8RJevXkYIwndMXTWjBTw4Tdsxr175vD1fQt44mj64KpSELhs/RBed+EY7tg1gYvXVkLdN/KgXBB4y2UTGCkLPLBvAY8fXsSBmTpmls1U76kQACaGDGyfrOC6rSO49YIxvHHXeEd9uCtF4EeumYYQAp98/BT8Zcep4QJ+8vp1uOvyybbSz+RwAT9z03pcvm4ITxxbwneOVXF0rp5qJB2dcsHuF3/5+iG8Zsswbr1wHFdtHNIOYRmnIICL11bwods24v69i/jO8SU8f7KKUwtN1NosWI2VDWwaL+HVm0dw7eZhfPdF45gaGtQQMcW+uc+lDVpM1M2H8DQG9Yx1jT+oD5Z07LeJ5k9y9xo/fSk1vka706b+uIdT7JV1tNiB528lkFIGjkHSIRlUbv/qW3eO4rqtw7j94nHct2ce9+2Zxwunqto+oQAwWjJw2fohvHHXON540Thes3UEh2fq2DRewicfP4MfuWYN7t69HtekGBptcqiI779iCkVD4GMPn0LREPi5m9fjzksnMFFJqmkRuHrTMH71ji1YO3oSf/f0DP7NdWvx7167Hpdv6O2oK0MlA+++bh2aUvqO2XJkoKces+u3jWC0pHsTdrJsxyxICIGxssBtF43hum0juP3iJdy7Zw5f3TOPl04vx6aBKza09ufVW0Zx8FwNG8eL+IsnzuJfXbsGdzsPkSaZHi7i7a+a9vZntGzg7ps34E2XTAQeOm5HuWDg1gvHId4A/O8HT+KVs3X8+9euww9ePY3N48m1t35CCAwVBW7eMYLXbB3GHbvGcf/eBXzllTk8c7wa+dKuoaLARWsquG3nOG7fNY7Xbh/F9HAh8uHIPFg/WsTbrpzCLReM4ZkTVbxwahnPnKjiyaNVHJipa1vOxisGdkyVce3mEVy9cRiXrq/g6k3D2DhaQrHDQttQ0cDNO8Zw4fQQ5pab2He2jkcOL2L3jjG8auMQ/sMtG7B+tL3Qp1wQuGrTMC5bP4Rj8w08dbyKl0/b72948ngVh2fqiS9oKxp2IH/pugqu3TyMKzcO46qNw7hkbQVjFaOjc+/W2N920Thu2DaKvefqeOrYEl44tYwnj9nn4NRiM3K0qtGygW2TZVyzaRhXbxzCrnVDuHbzMLZOlFEa+NEMogOKpC3vVyii727b5XXIVfoo8NzcHCYnJ/G/P/1xDI/YN49Qt4cMadaev7MFWAnrdpuYYqZIWEPn3VCAuKC+lRR1y5XCiD0m6n5lfa16aPltdL+Jmza8/62CoWEIWGqTrjCUU6L2t4nf7jhqU14wDctAlychBC488zxet/eLba0jDUtKzFRNPHmsiudOVnFysYnlhmydbglUSgIbRku4coN9Y5jyBSkHztXxLy/P4vZdE9jZ5kOfs8smHj28iKIhcP3WEYy3EZxKKbHXeRj1TZdMhN6am9XjR5bw+k+8GPjb9HABn/+JXbh2y3DrmJ1axsmFBmqN1kUgpR3cbRgr4soNw6FjlkWWYxbFkhJnl0w8eWwJz59axqmFJpabrTQgpV07v2GslQYmh/xpoIYvvzyHN+2asMf/bmM/Z6pNPHJoCUMlA9dtGcZYF/ZnuWnh6eNVHJltaF+k1AkpJRbrFp48VsUzJ6o4sdDAYj14nZQKAutGW8HdxrFS7rrbpLHctHByoYlXzixjz9k6js01sFi3IAQgLWC4LLBlooyLpsvYta6CTWMlDHXxAfq6KZ2XTjXx/KkqXrVhGJvGS9g+Ve64lcev2rBHs3nlTA37ztZxbKGOak1CqLvg1PNMDBWwY6qMXWsr2DldxprhYscFlzhNS2J22cT+c3W8cqaGgzN1zFSb9oPrvmu1UhTYNF7GzukSLllXwZaJMoaLnQ9l3Q4J4K9e+4sx8YIS9spw95v4+Cu+gCVDJ6lLJFBIuMX7g3rtsNURu1VdXML73vHTmJ2dxcTERPTyV3tQ//t/84deUO+XNahP7D8ek+CshB7iBpKajDpPkFlPtxC9CeqTho1Up+lmsk1urbB8Qb0R6l4jlQwm7ty3u93qMetXUO+ynHGllxsWGiYCTy2VCnYtdbkgQoGpJVuZWbutF1keoJaytU4JdL1rQ1xQ/5qt9rjaccesWLCHEtUdsyyyPnQex92fasNCMwdpIG6Z7rZ0a5mtZUvveYBaE4GgvmC0zvlqDOZ1ak33WNgFHCnt505GigbKPRwmsdfXv70OiboJVJsW6r5CbmsC+/xXigJDRWNFa8CblsRyU6LWsOwRcnxBfakADJcKqBS6M8hCO/oZ1NuxX/+CegOtvVMreOPi0rRB/arvfhMraQzF8yO/bUNSQOq/ReZHcobmXH5C2LmKVDOM8+e12YbT3aDd4Qjdm2knqSPL/cZ9N1in6+6GTo9ZFr28R+ctDcQts1eHSQiBSlGgktNhO7vNPhYrn0+uxPVvn2v0Zf+SFA27C91YB8+K9J/vPuuvDfAi3/gKxMjvEB68oh8EnABfZq9k9jtvg3rh+79O0mgrtLrEtwK4BTyJbhT2krv7UJ7xbBIRZRRo1kcwsG9zdr9BiOx8DXf+d012xaoP6tUHEoK/h5to7PhKpDjI8SlL93Ikt/RoQDPMoX+RMqlQET8kX3KQmLLJSzeFjKn3kAi3k2sm0S83+YgPQk8x3XmV3vkwnGkQ+N1fa6CtX8i0W9Gpm/qDZ4CIKAO3w7nuz4ivibenC8Zw7qLcfwtO6GfFhDPR4ic2YzYu6iuvzlDEhFApbyyrPqgHkOqEtYI1YQf2qQ5g+rKgP+1EFTbdfvSD3UoQ0+TV7TVl7EMfVbhp5yFawM4g1CV5o9245yrmQgxvRfeOFCv5iYhotUkaijKh87R3c3S7tnjlBOXZns6irYi1p3mHkGYSrz5XRt/T097qz4+g/rySVFubx751fabWGAjhO8T2YzuDYOPcQdxw8P5+bwYREdHAynbHHuwWcQb1HbIre6OrZ/tag5pYhKX2KY+o+4ezGADr5o/itle+gLLZ2SvBiYiIzguruIWbQb1G9r7b3X70oT397HverYc+pZSwrGB5ut1ld3ocwvO5rRvSax6T/pr6FcwggsNpSq/7j2E1GdAPiFV8vyBaca+svwqP73hDq99xBJnQcTVpRBQh0Ro3XARXJRK7VfS+BVz3Ek0pJYQQXX2J5Irp4/ap/e0D5xpJeXhSPX9/e0MwqHe0ewG4D9TqDUbt7aBpty/7ILEzTqAV2Pfu+YdejcdPK4NnjKh7LGGgXqgkDjwWOyxg0qBl0jd+uNAtK6n1PTkUzEL3Tpc094ZBvqf2VeghOd/POc/AGdR3LO5iyVZTn+VClD3u7sVMIj+e3LIbz2y+CYD+BpBUn6DWR/gLMOGHmOyEZ7/TQwKyEHiKXwhhzy9bv7fDrZESQtg1bjE3NMt7sYi6B+4rFTNcX8qs+yf3AfiVwN/qxSF86VXvwrO7LtAuQ61x9A6Z6PzS9Y+upWUoo0E456LZbGJ2bhZGoYDp6enIQCHN81/etM5IA9IZxctQXvQSG4yI5AfkVqygm/VNReqbp32SaobVtOC+WVo4T/epxyhwTBLSUVKNd5xQZZZvIHgpJQwZTCv+ax7QjPqm5APjy2fwfc/8RfR294tIqp3t3Rj17guK3FPsdfx0woxCRE09tUetrc+r8yaol4GMJvwT0EFtfdS64rYj7ruUF2bnQ1Z2g+/odTkfSdp/N7DrhTTr7mCpvv+rX+n75OuaUmPXHNscLXwBrn57ogIo9W21UattnQ97aincUmXr7cKB8yZa8yedS12gIkRrjKjI+YQBqMFHYMypuOJMQjpwN8Zx6tip0DSWZeHkiVPYfslFkcvw77r7psfAmpVHOJKImFET3OWFliMAEyZMKdBommhKoFAo2sddtqaJHK4rgr9nmpQSlhBttT4l7u9K3XET1uM/RFoJySwU0PvThDK5JVoBvX/dum2NrbEGYkfY0Hbj8C0sNMqgP6h3rw7lspOBbfNXmYvg8qTzplDN9npdT9I8MBaZgOLnDc0WivKiFuyewHbuEb5jmjSfO6mXAGToO931JYL9RFNtS1Bn12nwcbMetl508r3UfKnL4wIzy/C0Orpd7Txs6HhZ50FQ76+Z615nCRHz3nLh+7/OYA9ZmURG/OzmH8H9Xrma/XbWo69lTqIPRgSAkvOzeytu5RwSrRuVUObS5gFKwSWpfkh4yw5HEKHaW6mJzzo8PeHzGl/v360CmRtwxi/JrZH3S7vupHQQXM51r3st/vUv1PBX/+tPIKVEZaiCH/3Zn8B133VjyvWFg8OO7gNJzS4RC3VbPxqLVZjLdRQrFRiGSHgTRsKqfEFFrlv2Qm+ORjhihRMkO98JKQHLyeGN6NpbKcL5gV8hWMHdKqP2+NYhIBD7lhYlSIcMbmhiTupLp7pqA116af0pqdd8kuh7k4SML2xaMZmOtHN5ZeFtbUssJx3a94xWiUyNOPTzplh+7KbEpYXW+QieN6H8231WB8c3UDby/925KQrpKzwHuH+wz4MhC95fNUPp67cm8Z4VtdXpvvM7D4L63lAzACOUyDSnwJc59VdiHZOnnzdltfZB96BQ58tu/Zy5xcGLlDV33SyRmrqM0HqTOipE11V3tBnKcY89D27t3UA1C3ch3Si/FwoFvO7O2zC5ZgJ/8hsfw8/81/fhitdc1dlCZUzcluqOkFA9G5o8qroWwVFbM9AHaTEVHiucXtrpnwxoKvPClaatn5OWLaKvXwmpKZbaJYHeH6POrhPtXLo/RuWJcdlJmpfH+EoU7e6B1z0wblNiVx/MXdtZf2I1QsQtJeoP3epxkERbxaXeIzpcdjrtFIyCc+juizLie/2aRWD6uHPYThGHQX0qEoGrfRXIlqmnD+gHibvPugeGnCnaWRo0t+cM4ucXvpqV1vavnvTYK+0GXP101Q2vxv/8uz/u67YmPoi+Qttx/lDb29zQYPDTa6QuFeZ6IzmwbyuYDlwvSXvdrRFNOltOVKtOP89V/GAhPV53X9aaD+dBUN+ZpCGiQm8dVabvXQ135vY0zbIGp+Ysar1Rw3YNjqTAHk4NaPJ+BIatjLmPqY2wSctKEpo2QxIe7HPVXf5uJnnqbrISw+BFDcOXV8FtX9lzvRLHrZt3l9j1xLRe6NbYqqmPX3uwZ9AgnatgnW4711mWLdfdP8LLy+/1SGEM6jtkN4/26QYeNxSFBNJlA1EBvfDy1W7fRLIOaWkY7dVyxNX0drJvyRlx9DLdfrdtrzfmPpa0pCznT6J7AV7UcjrZPin7eN2tMv7WL/8zD52kmjwVZiiF6H5BybPGda9K6rveM5k7HybMLxPum212iVH6qGe6umKr9rOfC177g+U8COqd5lA7olK+y2tiTNODq1vLo/Z0uXYor0m0x+IDB2qHlBKmZflGGGn/GOa59r1z7XQYP38kDVmaTzEPfbvjSPv6bcdeDkkXWCcXYGib0n2V/FKt+F7efYvnsx6f3KbDZKs/qBfS/nhDFDhS12ivvKQ6gdga4dhv45Y1mMeiF3re7UDIjtOX9kGjHrWc5BFrhbrDX0vfaDRgOUF9J6MU5fJtll0R1aceOJ/yU0LonQStgTM0rcsy3MtIJnzfCW3ZILRcGZ1Ucx0jRcv5Uy+JVn9QLztv7lPrXO2mf5eR8DB8Ul9StaNbuiQqIRA1xnl3HnKSbY8GIyN+E75sxX5a3A0kWlMHd7u9/uFtjXOeqJ2+58ofArmn7xffP8oTGEraim+u7vSkpuuE5eu/H/4yce5Ovsp8n5DqEVSXH7MC6f0vZv5ONqo7IrcsqUW/wzRSgICwJGTThNU0nRdFydh7fRpq4SAunQn1jyIif2t3GxK+V7vDZblL+P8vhDvUn37/7XVnq47NcmRSVRgFVtDNC0JZvrLoyIpjL8Bsuw0p4ufkqb1tcr5J6DyqLCGpu098ugvf6+IWFb1lQpdXqgWK+K8z0m9bmrOYvB3xlZudLz35vqadIjbtphU3cboFrfqg3h4L1xnnVRSc42IkXqKhtzv6bnJe1hLTZzu+0Spp+vhyqGy90Dq4gaHl6vqgR418ni7BGE5hJpRVCkAorw4RkIHbWjDDa02lk/QgcpK44S/Df2u37K5kuNJwluk8BCuD41NLN38Xrcy5ddsXbfWYDGx7YtQS/9KfUEoQvuXLhHfnKONTqwrOture6dravO71tQ+Ku34kDGWrgg8mC3RvpIv2RI157Aa9wjddUoyfxD32I7KAEctAdakGa6GK8tQUms2mty2Gc5rNmIUnXavhFxb5vwRax9twvnZyWCEhpYX2rk3dBur/rDbeutO6b/AU0mijxdS5ooWEdA+au1/ahSQFfeEZW888dHY8AgM9uFuhXmuaRXe10UU55mnzPuH9v52NcdKO13KacNwMoQ+QvcENYuY3Cq3gX9qv7hNOfqp9piu0iuh8Oo67zmgRQ6N669Icz7ZOeFxemfSWFSS0EnS+bK8M6F+VF68lbVTUBPb2aA+Zb9Ysdw+hOV/BtSdb9UF9O0K1Nr6KgTYbpFN/19tm6ojqkD6L3ud2SqnBWptu9kzpajcX92biDPytjo8vgEE7PQMi6zmIrjZRv+ni2c4dYQgYhgEDAmazCbNpBq6sFLflZAnxq/eWUe9lT9lqsbulO7WJ+bL6uk0lpCWpmcw3uTfoTlJi8GrE+59us1jx89/h4ernUU58DCFWZ61F6ea0nbdBfdqaYKF80nObn1vLtd/aHF2D3H1paoNWTnLNbDtBfXDf9BUsg7DvdruKkM72+HZ1ELaun+ILT53nnBJW/Oxq83P816uaIQwUCgUUDQOLc/OYrtdRLJUgjWDFRietAN75jSu5us0Qbo2qvQB36+BES/0R0SLifZ1hu2RizfH5lArT6aiyJcVhNOBrTdXcZpJuI/ZmmcrKjHQr77HBuAeGZd6qhGuzl+LObLpiXfQU3bgjnrdBfRJ/VxsBTQYemyqjnwrv9UVm3yviAuDkpBE1FKTbL96fcLvzdlehfWNslj7xre2Kyq2j5+2mVjqSXtW82vsgcT97lWTaWG7bN1TNOUyfVtJkjXFVv70rMcU9FJq3B0bt8fXtoWILhQKq1SqWazWMFApAoXtdkHR5kv+YGdK5TQrTmcEZttTp3y+7016Qe4H0NbjJqsfSF4TclyOpre7aqbtyPMP32dY5G+wTNsj5lE5fW9FiVi6g756zkhjUR7CDefumZ1cmBc+k/zd9H23np46Cmm4KbGnGJfV3jPD446cLGtK0AESvJ+rctRXgCuH2xINb6+h/WZG1ghWRcSPrdH1dmRcQ12SetPQMBYLzjJRAoVCAYRhoNptoNBpdfz+Brg+4/9kNYRlOAGb6p4CQBiQs73Tn6Q3DvZTvvc8yDGJSYV8z7KSw71m6JGMoNSydHtfwYA+6+45a4Ig+CHHxRLf17yWaXZA+HOj6ajvpQLxSGNRHaL+7jcryh/aAU8sturT09g1Wfz/9jdlC8Lj4auiUrkyq/mZO7rFNU7vpTtvdNKBdksxZJp1a0rGLT+ehb0WrzL4ajk5almVfb4Zh2A8IGwIwjFYQ7esR0+3aJ3vddsVJyQBKpSIKRYFSqQQIwGxKNOpAw5RouLnmSr9Vml3kBoxE/FMeKfJf/znt6snVLcyf1w+YiLQtEKy/TOiQ0HlEkXBtea0rAyh7DhS9Y25PCJ207ZUM6h26WsxMacq9OryF+PqLDk5s3Rf6G7N7rKKu9vjOjcL3f906ehvctplxJ/UGamM5rZaT8Po7HSUjHzJU0yizygG9764UwzAgCgaK5TKaZjNw9Aqwj6YZMW8n6vW6F9QXDAPDxSGMDlUwMVXBxMQEyuUyTFOiWTdQrddwen4Wc3NzaDQaKxrU96PqhWJ06WT07pxqlqxGyQMiKm2rPRdjc9ku5Jt9qmzPJq7nZ5aZ4dyzoyZJeaxXfVDvDRkoW9Vx3kB7sQfJ6RPnBeO6Zba+s9SzGTUcmtPdIrbmyw1uvXn8P7e2TL/JCR1klNqudh7ctbtr+IettOf1HjSSwcE61dg1mGA1TfLSqWmRrW5PrXyjtd26h8zsXQ+2jfjFDs8I6AsMTgFMbePQLTv6wTd17mAQLpRCntdwm+YClrKVNrTDqqpN0YZzGQinXcFqpSfRSgvecIYZcuyoWgU1aUeKOwAyXGDxp1/Dmya8qMC1J303N9lKb3HLzkJ3rfn/FjyF4W4KoUJaxK+6srE73KK3dAlASjSMAppFgcrwEGqNOuYW5rFu/TpImHaNuZvFyfAVpNam+TfHEAakBVhSAiig0WiiUW+iVq+jXq/DNE1IS6JSKaNckRgpDWF40sD60QrWTkxAiBIsWUHDkphbruPEqVM4fOQw5pYWYUkJSwRr7vU1e861F7j8pO87IHQLlP7rWbfMOE5qiptZqtN3qJOIJ/oFIRlXrtsY98qylN8TYlzdl9JfKRO33Rakkup1x9sK/OZNGXHeot5zo1amuEOvGr6/C9gjOoVD6GBtuDPstlSnc45XtuFWtFr3VeW6DuU5KRYU+V24WildXuoMaqsEVYF5RUI60lVyae6zobkkkPQOoqTBNOMq09odwroTqz+oFwaEb1x65zJxfo5vBgmUspVrTVity0I/Tn0h9LfWaBDxl4r01Uq7L7wS/u+i5pOAgIRhGL6/aaaPaMYOtVZo+va5r5UWyrJb2Xcw5FeyMiiTBL4SvgzGPTfefgv/VaxbsnKphXJsZXWhl+NEpIWk60/qtiZpntbUAsHj7O6FF6SkruXRnWclgBR2MO8vloULXnrtvjfAPb7afvypxF2b6efUFrOcnXaTlFt4MdC6NafeSv/Y320GSuHCtf6mbv+o3ODUZbl/lPpptP3aYY8/LwsGSuUyJIClpSU7EHcHnnGWGVUoDi1VwAtOpAQs08Lyct1+CHe5juXqMur1OoQQaDabKJfLKJQMGFJiengI5vQEhqfGYBiAJQ3IYgnD5XGMD41ibGgY+w4dxJnZc2hYZmAf48Ku4Nb6P9FpU70ukkWct8gpw4FeUHTo0NuQIF5wSN5Ogs34rdcvUVcp0s4SfAF4RCE0eh1x+xh1Dv2pUg3+w4V7KS3Y73jRXWWiZy2JXhFGRKVdiaSrIG6zOi+KuFeIWmshfccsvlzqvkZFlyfrKwB8fxLx16alvI+nHVL2/rnEVR/Ud85/MWpK8HbRPrJC0YJpnzxhz+e9vEIoy2lzi/x1H/2grru7dQitpXvlH/cCi7gYAjf17ldoDBy1MBLHfpGFW3vljDAEwB0P3H2JkQCClVqr/Tg6l6FIvmfpZ3bYhYLgFSFEIfHh+OiWMv8GtRtWtr8zgcKkdF+aI2Gapt2/Ps0yJGAJwDTccodAQRQgLaBZb6Beq2NxZgHVpSrMpTpq1WXUrSaGhofRKEo0lqsoN4ooLCxhviZQlxINaaEIwLIKgBAwChLDlRK2b9qC4XIZr+zbgzNnz8I0TdQMp8DftqSgutc6Snw5str3r11qPgG4xyf8/mF3mpVPn70ccrsby0x//7OPaDv3y+C8+U27DOojOaVFfwUx/AnTeezVrc2KXkTrxu/+3n7dbvoa2xXMCHqV7MMZS3zpVgjR0ZjR7gUvgcRap25mcnHr8toiYmI67/ikWJd/Mf50Z1kWREF43X/C9Umrl3s/7SgUzHiAZKC2KS7N9ah6Lsbw8DBKBaBSqaBq1ROn965Ot9ztNC1bpoTZaGJhYQGLC4tYPDeP6lIV1oJdY98sCBSEATlWhiUtNEwT84uLWKoaaJgmLGlBSglLCqekYNeMlYslrJmaxuT4OOZmZgDT9K7f9pwPqTxelmcTzvfRh7IK5SFenKDU4Ls/83AHyDaHLO4ssM+v8zqoj82cfDXqHZX0BCBh+Wrr/f00Ep7St6NMbzO8XnrOIgxdLZqUXo1V3H5ZcQFlqFlQnVZC+Gom7UPka1aMXHL3aF+53eObjPCdD8A5H73OI1LEdHa40/pZZY+N76ZBAWG1FuvvHOZ1RlAX0oUAFuhFrU+G5UkJNN0aMgT+9cZjSbm9adNi61mQ+GtPBE5AhzX17mya2aX6cAEAQ0gIKWEKiSYs7NpxMXbt2Inn974IwO6eE7U/roIlYFgCQgpYpgXUTCzPzmNxZg7zc/NYWqrZfehrdSzMzaMyMQpDArJhApaF+WoVjeU6Zk8VMX+6irnSLEZGmhgekSgUxwBZdg6JhVKhgE3Do1gslLC43EC9YMISEtD0+W8dR9/PgZaVQa6pX5mWhHav0d5ktStfM7qyL4EEotrX7UKpCHSPc8fXB1biqEilVdz5q3d8sizbzoTyF1AnpEcZX5HQ7909r4P6OO6Dj9HN50lnzr1SnKTdRsWbbjLhXiDabU1fcxtabsYU6J87awbkvoBKyuBzAYMgXLniZrp9rEZJsWq7pcmtQ/V/ofm5jQqQdvrYd/sG6r/pdTR/hnWvXC1l+zX10t+HSgaTR+QzI85EUkoUikVMTkxgfGwstkgRuvk7hV6zaaJeq6M+X8W502exuLBk/15reK1phhAYHhpGoVDA4sICZmZmUD87h2LRwKmDdbz8wgvYvnYUO3fuxPbt29FsGmjUDNQbdRjFInZdfDE2rluHpflFHDt2DDOyCsB0SqQJTaZ9aP2IxupXUvgGZrBjD3fgiH5tULYV6/Jp9Tm8lboe26mt97pKx4juOdD/65pBfSTnApPBUShSBfRCOKOMSEBNTCma09Qg3U38XlOzMr9QbuDdqmENfwEYvq/Ua1YC2cazFm7dsxNdOm+UtBduRB43AbsLcFJnhk6pxQs1Hu71Mw5x43PHrbtYLKJYLNpDAVpNJyMSrT72/mQJ/c+rjdto5tdemk0628HU0r1CQNKNpr31+B6lBwAIS9oP/5tNmPUaWo9Tq6nf8OaTErAsCVNaMC0Jq/n/s/dnsZYl61ko+v0RY8w5V5N9ZVdZtWuX9y7vzhhvs42xN/dyfTC2hawLCIGs4weLB1sCGQR+ABmBEBYyAvFgzIMt82CBBG8IDlzJRjRXnHs4xmxszHazO++uuqysyspmdbMZI+K/D9GMiBgxxpjdylyZtX5V1ppzjhjRxx/f38QfGrP5AtPpHDOlQFJisrMDMZ5gNp/jcD4D7U8w3tvFo6NDPHrvXRwcHmLGhyh1iflhiXffextfA+G//bdvYm+3xGRcQJICC8JkfxevvvZRfP8P/AlMrl7AeH6M0UENVWnUTrbwltGnTX3j9XQVFqchmIaHPhvh6UwMxFMk8kqV3lRuI+X23F3GvWwTZUkUEeaMuVVt+7K5D4obzjmo7yVK/uaTpG4oWWa+wmQKQb2bhF01SQWATab/UhFx0ucblNfKiOPwYXGXmo3CtzOj/T1N2bmr3U+KDWaB/UAljo6OcHh4iPF4DCEEbGjwtgCYyfZssfftUrpSu6M/tCk2k1P6cLV6BGMa34Dc99JqZSyblVLKx4EfjUbZORBaL5nNO0opKGZUSqOeV6gqE+d+MtlBMSEIEjiazjBfLECCcGH/EhiMxwePcXx8jLpagAu2loISpBmqUphOF2C9gK5HKAsNURbAbIZvfvOb+OKXvoQPv/pRXLh4ETv1FPPDyggkXrnx/G/c26atAZ4nzRjPMLnuzPHubF+3Fh3jjOHsjegsCA08FDbnOaDnHtSzFtaNkhDFaYbxAQVM9Aag25Ui6ydrVdYNM3S+C+ZzGuEiDp/Yz/OkW91eLW9voyUCs47j1mb9cYR/tpIWOafGTH39uB12snnOaIWWTLNPcg77QgCAdbthACrqd/d2cscAudB2MvFCjH3/o3CZiQZAstNeu/fjNihqgnWmXZ/mnWuXy00E7zSZBFGTucnJhVmssRwz1Jl5UNeMg8cnEGKGsiyN5r4sMB6NwcyotbaV0UZJxFOQkMbqwQyNkbE4kam5vYDU93VktUn6lNh57TPMVe2+pvbXdshXR8bHtG8eKZC/fTj817wfpQ/rmZxApqZK5l3iVlzouPt7wDcRwD31boXbadc9Ljctp9taZchq14mdodH70adx5qOaCIGCGRMpsX+0wEf2ruDzlXl0YneIyuenwdpEyNGKoZSG0hpKKxNBRxCEBKQEwAKKGQfzGR4cHWC8s4vxxYt49OgRjqoKXJbY2dnBRBAuX7qE2y9cxNXLlyCqE0xPjrFTEm69cAc3r76M3d0LgBZ4fPQQBw9rVLcY4wlhf3cPhyfHqPUCQpC5JIsZwoXVdN0c9fmS5FyL/KsdQCzq0RZHy1BujnRxx6YqMS/uXj/DO0zfe64uq6lImrm6ihUirSeDROaaM2+F3TQYYK7fc+1pp/M3K9v6KLuWnZVPOlaaieCWG48Yu5N3tYl6g9Mg0cvRauC5b7yoFd2rTf3X0vVp291OkB/V+PxeV/26qWcNMCx/gFEkZvJlr0xd0fJHhN4+ocydRmHVKEqa7F3LVeG5B/WGzEYcScdsBoz9U/dpeXKx7DnIO42Wk31viWJyk8mHJByYy+tqWqn1IZd3qrmnNMFw/ujQFtPQxVk9v3P0oZU+Gw4zjDIw1Gu+jrlHzYNWuxxlALB/0Jl3vk4tRtnRL3u7u7h69Sqm0ylOTk5wdHSE2WyG0WiEmzdvotgdQTODhDnDsDceQUoJXVdQjvMwoLQCMyCL0p8NYZ3XIjEztNaQJCC8+5Rdf62u6Jtoq4Cv3I8U/D9OPHg/RQ8Z0LwBtEiHfuWsuvvMn7tJ+FzTF/G7bv4IITAajVDXNb7we7+Ha1evRlOy0X4bZYVSCnVdQyuG1gyN5iyMuylWCAFdM2azGU5OjlGORtjb20NRFNjZ2cGFCxews7ODixcvYvfCPvb3dvHyrWv41Cc+jtc+dBvjUYGdUmBndBEjuujrXKsaX//G1zAaEx4/foCT6RTKRcFpTvz1zol1KQe7OxOupZTs5JDrZpgvZbNuWKaEtd/s3DsJy22c3TnH2a2RVWtYVwJ7yybIKBBXLGpV6rLQbNPtpfe54zAtvtWXp0m0rnUp7O3cug55ZfsSx96aDcsaQ1XugDHLtvQDAupPn3x4xCTSRU5SXVbfsG2fsk2ppcOm/m1oWdrGhXncuk0wpj691tMkDaBByqkWl1v4ZBUmpglAIbF36SLGe7u4cOUyHj58iIODA9x/9BCzNw+N2wMZUP///qEfQlkUODl4jPl8jgXPobXGYlGb2OWsDHBT5haGikLrB2DYM4MFg8lp0h2ldoo+baKJyNJFDAJHmspY20Q9d+EOWQFy0aFiK1ujvW2735z29rt9IgCllFCTCRbHx7j8//sqqvcKTL7nQ2AAR4WNzsEE1oCqGUoxVN2AePccWoO00V5qAha8wLSeYjwucO3aZYzHYwBAWexhf29kIuIwg8oSVb3AvXv3cOniPu5cv4Dr117CxM4XfwM4JMqixGsf/SiACscvXMHDLx3hsdbQpC04OKsrfYjW1a4/X/SsjJ4IjTjP1pJv0Wnii1XybtJy8P/l3jlTvvIbCZ+b0zmoX5P6YqUOT+ShuOubGxpPi1Lz0EpXWKdJV7RsrVwAhx9D03UqLK1ooQnOMaRS/ToUAfumFP99XYYlhDCad61BQmA8HuP27du4efMm6rpGPZsBYCxmJ5jNZnj7rbdw5coV7I3H2Nvfw1zNIISAEIZNzGYLsNaolULFjJNKoVosoLSG1gpaK983Otz0vLgV2ER61khk88g0nYPH0SQKxztU2ramxXY1UGdqQ1mDtNbeP/7w8BAPHz4E+EPWCmmJTbrwn49WBWu1YafcEHAm6LIsUYzG2NnZhdP0EwkQGXewWilvrK7rGnfv3sVb1y/iyqWLKC/uQ2gJzTWEKC3P1SAywtdkMvHa/3k9b8bhGRSuzukZIju9iNF7n8jKeeY+nzr1rZWnKWQOaLzT1Ns+BBtCgxX3j6e9H5yD+i1R60Bpx8Ca0G5DMLCd15nQ1hOi2xvbWs3hOlLH5431VKlw4W7EyVA2/n7PYckuvh26J0Rv+xNSefAanbdwbtItl4HQFNkDgEPfv4Q0MxSs3yeR92GEIIiywLgwoQt39i/gMoA37j/C19+5D8EaRVFgbyJQFAUu7u9jf38fhSCMRiV2JhMIJlBtbx+FBpEGiKF1jaqucMw1aq0AFqgq46BdVZUHkBq7HhgKIeI+AaMWPmp8RiPeHPoVgowFIRQhtBFkhBAWYMZ5p4LoKmbf54kocHcbz4HJgvD44ggnV/dAIOMrrxiajcOwA/51XaOua7+REhiCNZgVwBVUVYOYMSaN8aiAKgighREuARghTwFgCNJQ/hSRwtHxI9x79y288vJtXL+wDwgNafW3To9CQf1v3riBt+/dxUk999HGAHrayrJzWoc4PqdzFsnZBLVl+nJL9fXT9Yy3/yxTCOx7gfVAH0d234yLZP8pglYOmcJPd5DPQf0WaNuSWRoIo+W6M+SzdYo0BOP7qta6W2cV8taL9EWKNYquXk+ROTorTpfPeS5tn6a+692l5l3km9dcciKIPKhzuezu7aEsSwjWqKoF1OIEdT3D9OQE7777LqDNbZ+jssCknECyAKBRFgV2dscYT0qMRgVkIbE7GoEBlOUYUkhUVY3ZfA5V19Bao1YT67rRuKtNp1NXUQh7gNi7eFiGre3hRXc4nVmD/QHRuI2AAaTtXoon4lO9a+AMkLm9lTFfLPDyzZv44z/4A/id8TG++fobqAszDzUYdW3csMJxM0CdoVUNVdeoFguQsn2u2c+zBnADYPY8jhw7s+YXpRQePnyI++/fx50b17FbjtHN1BjlaISiKEAkwKzb51W2orEPxfeUzqWH9Sg/pmdCebUsbbKfdeWT+75p/s8gLTMLcl3yQQlb2UfnoH6L1OtSsPHBP6uxf4oAxGiUk+8BDbUw4yWxNWrVzX8W8L4BT4I4hJSGBrUGy2DzDo1/X94EozMIXVGA0OpDkd6ARyPMwcYnfbwD2t0xAEwrKFUDWoFZY8EatZYQtURVzcE8Bz8+Rq0qkNCQUmC/HKOUBcqyxHg8xoWLO5iMxyjGVkOPAlLKSLNyclJ6d46FNtp9IYQPt2jCcmqc1BoLZVw+Gjc4Bluff8gxZEHWPSTjPx8ZQXTLQpJGM3qeiSGgIXBCjEUhcfejV/Bb+wscVQoHQkPVZoZoq6k3B2EZMMdjjRafGVopqLoGK3PrK7SG5gpSFCCtzTR3ZzJgYh8pVpAAapKwvjtgrXH0+Bh3334Hr965jd2r+2jur4D9SzBRo4zGX7i7LdxzH4XI6/Q37qX8Qv1gg4f1qZsf66p+slVZk0IOwdjM48vxaaDh1SGd9j0oZ5bWVF5uCuxT1WGa07Cm/unScw/qDftIYVas0WO02czwlQ9BGcx+IjBn/HhTzTt4CYzZo18ceLf/8RLt6kqSU1YNZbfKwlxpETukirwircGtGNLGWp0vek1jHY+YnbDlMjODHzF5HuD3Pu+o0lGBWWU+hjRbLrRq3EHNhSgdDbJFu9CFhRQoZAGSZuuRBJAWgCCMJiMIARBpKF1DqQqABmqNhVJ4fHiERbVAUQCjkYmuUxQFhDAa1rIsQUQobXjDoigAIkwmu6jrGoII4/EEzveCQJgoxrxW3h1ESLK+/gsopbBQEsKOifP/bvrDwFHXu35mRBaNABASgVphz4JxablN9Y/H4PrJqp96ssy82xUUqVvfDVR1jZ29PVwel/jK174GLggkjduL1ho1234Mz5NoA8JZadRVZQAZMwopASIsYMaF/YFqC9zDOcnciJY279l0hrffeht3P/Qirl6+iXETobdpnw0puJgvUFv3Lqf6b3dpXwdmzHyZXqPgc/xuH7pokuV+Rv5xNhEN8LD8q+upfKm1ICzvCNvj2MRA5f1Fv9mqx6ENpBDY2dnJZ5fWaWNatW/yoQ83Ki587AJs+P8vn/fQHtBPnYOzRMHrvdYqvivjDfJvLOC2mATk56BCV/VWrcbwebHhAr1Xg917VhFSnntQb+4mTxcMwxxVlRHcjzuP+8NetZiu+U0AVrQWrbThwu3z6zX1iB7GeSVByVvROYLHqc5R9yFMdi3JJyD/3H5P0hEYomdL0YHn0KrYhdjFNHHbq1u0JqqKgIoy1exM+xTsLLac1AUGzse3q+w4mkps4HexislqxZ2fcbvvs4udqXUcwEWSMe+YvF3LXRhWN4vduQad0Ugb15Qq8PEPxi4jJciofgZkCQGAm7sAmAHl4vpP0haO/KfK9mdx0TAZXdVQzJhVlQHi0xqAAvPM90td15BSgjVjf7KLUVlACmBnUmI8lhiPShSSgFphJzhnsjPagSgF5pXEnBeAPSAsCtmsOW7cR2quTVx1W0fjfgIo1dwvwSRBQsLdy6BtCE+2OK7Stn1F4ZeMGQ/zu/ZdHq9dkeELjjTSOWLSxvcBIEuNiBaAVO/6AnsWpr0GzMwniIu7mBBhLgQqIXwwZbLaedIaQmsAGqw1UFeA0hDMqKoKi+Nj1PM5BAhcSgibx7yuIQpCOSohhUCtakDbGCdk7mOYKCMMCAvcFCvcf/chvvLFt/Di9Y/ixrV9mBkn/Fw0613h6PFjqEUFYt3cKh3JxMOghpCPuqL9ux08M7IGNPmB7RrNyQtLSlp5LtyIFoNAwzPaIOUyBw0YiG7tYIYUElqHu6ewWRvep6jhWQTVCGrMIAYkSUAThBCobc2lleFG3ByovvbCC7guLwD/q6s52wT18IKh4XVds8BQ9maBpXFW954KuLNpKR5YNu8h6tnvgY45vDyta9Psn8N9ceyWJR0wgzA3YfZs9zXT0e165cIEr0vc32fc/XXZOfHcg3qiHiknh7FOwSdrWalw1byeDm1Wg7XfJh7QSHQX0N5Y44PH2x3tTPln1E+UV9R+xfaDfrbsBT4ryDlXm7I0LjblrnWPsRdgCSH84UtV1eCacTKbYTGfQtULFAWhEEakqecz6LoGCYKUEjs7O9jb3cNkz2j6pTQAshACQgpfF1UUUFqBJEEIgqZGi88MLKoa89kcSisIOYKQBQgCWjOqSpl4/TDg3V1eEt51ABgwz+GmFLg6mf83l43lNo/e7+lhm9533bxrgH3zWwjqjWBYFEWj1PDRoWyUGwfo2bRH1wrK3j4LBurFAov5HPV8bgGgsbCwZqi6BhWAZoVCFjZfl79pE8GFxzSKFEECqlZ488038eUvfwmXv3Mfk/EuWCuY69g0IBSIgZOTY3tYG8FZliZmf9j3ff3W3asOFOVS9OvxuAOKtnC9s36stO90MzpyvJI4M6WG+oTdf75ern9t5s0fBlgzhBTezY+sUCnsvisEIM2goiwLSJg7BaQGZvM5Cimwv7+PmzdvYryzg3e/9Ft4bYVeOKdzOqeYnntQvw4xD2jph4iQaNf7N45VKTUERpr6U8ePfQUMFN7Tp0tVe6nwmaFk3vzlqI84EXs30wx4eBQAp3XMdk+acsJGOJdSjULYq8aLuaeFQd6M5mCuMIWgttYTdppwIkCWIADFaARRG83eLrPx52fj5qGVwuHjb+D+O29hPp9b4CDsjbkSUgpMRmOMx2Ps7k28T39ZlijLErKQ2NmdYGd3Bxoai8XC3LRbFBhphVISZDkGkYSUEmBrnSgKo+0n4xdeBeE7nZ84vIsEQXEQx92qbRvtrfssIr2NZorXr9Mk2o4f9q3N6YAY6cJzoSgBczOmBgNSgLW1XWjj8665KXNUG5DPWoHrGvViAVXXULXCYjGHXszNMwK0AiDIxKzXNbhSRoAqCnMugk20I2XNGQLWX782Wt5SFgAz3p2/i9/+/OfwwkWFD7/6GsajPQgwSAgQNBbzGaYnR9C6hom8pK3hzglQ/RpS1z9MeT9ZpubG5Tx1aznNyGWeDShOl6f82hMAwAyRA/Tuhx6NPTNQ+8hR5CNTOb4g7HXBUghjOdUak9EYggi6VpCCjEAtCYIIo5FxwdK1wmg0QlEqFIXEYrrAW289wk6xhzs3ruLd+/dweHQEOn60acec0zkN0mmGIx6OvHO6yOAc1HeQ6fv1Oj99N9XUbzqN2sqXbU+S05l0m+Ta+LynuZF96tRLDk47baBXX/VWLLwVeJn+jNJwuzz3y1Jjk/r7tQ5ktJ9x/nEvDR6sTcJ6doF2/+sy/sRh2aaQllDlQCYFQ8dSGLApACELsNaQJVBXFQ5PTnB0cgIfDrOuMa8qEBltL7QJySkF+XEtigJlUUBIiZ2dMS5cuAA5ktBaYVSOPPjfGU8wKkc2lrqAoBJCSBRlCSEFpDBAUUgykXWssOIqX+sKLpoMswXCbJ2zyLxjfMwJi5pRawZgBQhqANR4NMJ4p8R8PsN8NgMIqJS20WbIR6Bh53PpxEjXr27zsCE9iWWzn2hY/3hAk/WLd3HnrRAiYMAYtAYxo1bmkDS0NpGMZnNUiwXG4xEmkwkq61altYaQ5K0jUgjTZs2oK4W6nkOLAnWtMF+YQ5FK1agrEzlHK+vqIyR0rXDv7ps4ePAuvv0PvIPbt17CpYsXsH/pIi5e2sH0+BjzxcIDaOeqBpiwwaYfOpxYAjfL9ryNAqTa9Ol0Tzl5omgxEzv+zZ+/CHlH8GyAhoRwuDKtxcLNTf8PZIVr4d914B12TZqzKNR6XliBrCgKlGNzCH5cjjAejTApRyhkgUJKSCEhBKEQJtxsUZjxrRbmvMu99+/i4OAxTo5ngChxUil8+evfMHNBEHYG9QTdCq1tUmTJ/YBHUzmnValf8blWFDssD0dPBdS/9dZb+Bt/42/gV37lV3BycoKPfvSj+OVf/mV85jOfsZVj/J2/83fwT//pP8WjR4/w2c9+Fr/wC7+A115rDG8PHjzAX/7Lfxn/7t/9Owgh8Gf/7J/FP/7H/xj7+/sr1cXwuBYMRqjHyfaVO2jRQfnbJBvK6nZCLWiqOW4V3zPwSdlpWWvH7siYmfsmXKveA5NuU1EhBpl2fJxPoN9j7dj2uMDlxm4Vt6tVhaje9GtsGJz87c57OYbhgWlURgLp2fV/A4b6bn1NtaTunIEDoR5rWaDjf7dMT5TJAArz/qJm0HiEnYsXPZBkraEs2DW+PgQlhNGwK2NTWNQVBAzgPnh0gAf3H0EW1MTKJ+OuI6UB2A1IGaEozCHe8XiMYmT8xcXIgB0pJSaTCUajkWmjhBFA7DN/xsJamZT9zhB48P59PDo4xmi0g73dfYwmEyilMB4R9vf28Nq3fAsePXqIhw/fAwBUypwLICIsLEgyh1hN/2obZYa5aRdDQSkN5jEIEort2Ch7iJgZYBOxSICNRp01hGawUhBWkJhXC69xNf2uIYsC3/qxj0EQ8PnP/y+cnBwBACQXYGk0ucwa9UKjmpv3jo+PoajAYrHAbGEiGi3qBbRSVsNMEO48rrn6AAcPp/jSl97FpYuXcenyLm7dvo7v/M5vw2RnhIOjE9QMsAOjbMCrZIIg4Y4GZNZ8fum5i7SY7X0GjbzWpIkAn/utlVGGD7dVExrI7jPkAHaiURTCnO+RgSY9BO7G2YwgyWjTpZAYjYzQWgo7twtTs5EoUBYlpJTGLY0IhSwgIH2dpJQQUqCUpVkfhcRoXEAQoWBbNgOsjSCram3Oq+gFdK3ACw1WFbiu8PjhA3z19bcxn8/tnC1RkzCdUBh/9skZBdDnwP6DTctr9od25TUVxdzakTtp66D+4cOH+OxnP4vv+77vw6/8yq/g+vXr+MpXvoIrV674NP/wH/5D/PzP/zz+2T/7Z3j11Vfxt//238YP/uAP4vd+7/cwmUwAAD/6oz+Ku3fv4j/8h/+AqqrwF/7CX8BP/MRP4F/+y3+5Yo1ygB6WqTu/1tU7ugW6V87hLNIy/XDWWpqrs9eTA+hxiR3IpYvSQ83PK0XCou/OWCBemZwstsL5CJPegNSylHjxpZegrl/D0dERptMpqkWFWikbVlFBK+ObX1cVNBO0VsZP2woOIwaUshrEusJivgBgtNMkyN6iawARQRqNvf2tHJmzAVSQB/VFUWBUlijKEju7Ji5/OTJgqZQFZFGAACjbBhIFFBO+8dXXce/+Q0wme9jZ2cN4XEIIQlkC9+6+g1EJjMcj7E12MJ/PISRQwtRvfzzx2mMNExKyVhUAgoYBg1VV4fHBAQ4PDjCrS2gugjHlCIgKKVCUpdHi1xpQjEVVAfayKa4rE4e+WkDVFVhVGI1LHBw+xnw2w9HRIZQyAsdsVqOQEqrWmM1mmE3NxVPK+tizLLGoKiyqhRldYnv7sRMwm01MEIEF4WSxQP34MR4cPMTb776LYlzizku3UUMbB24Ic04CxioihISwseuB1EWRvSY9PdngUwkBhrYhO407SningkucCrZCCK+wEZCILk8j+1xIP8/IzisnkBXSCI2F1Xg7KssRpBQoRQEhyAiRAKSdm0SEsihRypGtg/lHRB7UC3KaerOCCxLmbAi7w/5sYwqwjSK1CNagSVerBQ4ePTbvKHchWY1amQvJ5vMFqqrGojbuWTXMnRdVrTCbzzBVNRgCyvIRHYGlD/qNEed0Fqhrb1r2ElDucZSk4P+nRVsH9f/gH/wDvPzyy/jlX/5l/9urr77qPzMzfu7nfg5/62/9LfypP/WnAAD//J//c9y8eRP/5t/8G/zIj/wIvvCFL+BXf/VX8bnPfc5r9//JP/kn+JN/8k/iH/2jf4QXX3xx6fp0aerPGjQ9OzQ0ac9Yz7nNyH10fvfJqf6+WusNNpNOS09AQ5r6M9ajnjgM1BIAeqel744VMZiztfZ3MUkD0twncuCLABKEcn8ftLuL3UtXfFhLAP6mU13VqKoK0+kUi8UC87m5bXQ2m0FVFeRCgQBUtUK1YFSVdeMRAsSA0qZ9pO1lRtwcqC3n0h6+N/WWUnqtuPlcYzQaYTQaAcwedBkAZzX2VIAh8fDxCaqpAczz6TH2dgTG4xK1ZNTzBf7Xb/wPvHD9BVy6cBFEhPnJ1J8jKMvSRJgBAWIMWQiMJgWKskRZlNBsLvN6/N57eO/ePTxalKgwiqwyQljYafPb39/HZDSCroHFdIHpdAqCxmw2RVVNIaWAVcsCrDCdVvjSl76IujIHZQF7qLJmLGYLnBzPcHJygvlJhfF4jNFkF4IkajZ9KmCj5AAWgNrINoW9W0LY+VZIsJRQokBRCFy7eRsXrtwEy10U5QLgGswFNAM7o5ERBhSse5bywlfY7kJYgOsNsmYM67o2Zy+kBIjtYWyCkALa3tNgLB9AIUv4bdr2oRQShS1LcGP5MZp3IyhKC9yF1XxLKaGVmV+FLCBImFj/AWMoCzPehXWjkYW5hVdCgJisACSMm1fwYnTpF+zYkZnni9kUs/kc0BpMhEotoKoKC2Xm2ayaQSnl16nWwKKqcDI9gdIaVa1Ra2XCnVoeqpVxgdNsuEUNq8GHNACezPmmJjpUo1YzFryzyg3P6YNO29DWNyqx06Otg/p/+2//LX7wB38Qf+7P/Tn8l//yX3Dnzh38pb/0l/DjP/7jAICvf/3reOedd/D93//9/p1Lly7hu7/7u/Frv/Zr+JEf+RH82q/9Gi5fvuwBPQB8//d/P4QQ+PVf/3X8mT/zZ1rlzudzzO3GAgAHBwfmQ/ZwpTHVOonKdLEz/pt3GtifaGVNAg8yUloWHKayXPjeaV5946vM4bewjXHNUvfuNBRnWu++drXqkCZIPC7CvIg63D1sO4wZOx0QNhtz6GPbQcJdcOP9XpuNJt4mXX0ydnerAWQLAIcONnpf1l7pP5yd7rv9F/RJ/gwxQQeziZAW1XzJgusgdGrzbuhA0NenqaNB8KtmGz2Ogl8Dtx7vVmV/oYYRMgAthGl+ISBRQML05cjVU8No7e0/XRuQv5gvMD0+xuHddzGdTlGThJjsQsOADBIGEgmrCCCCvzjJ+BozFvUiGDOClCK4kKlpS13XWMwX0FUNBoOEBAkByMKcEWCBqiZoTVBCA6RxcDQDDjUUVwAzvvFGhd3dXdy8cR03rt/At3/kw3jn7l28ffceFlWFyvrtazIHhMejsYnYU5SolMLh8QmOjo+wWCwwhUDNlQnNCVt/IhRWy7tLCi+9cA2f+NaPQmuNN775Jr761a9iXmks5nOc6KkRVgrjpkMwAH6xmPqoRazNweNaaeMzX1WY6xplKbC/vwshSyNgaTMri6K0gFGjKOy5BSFQFAbk1nVlNM5SoixLXLxwAS+/dAef/vR34FtefcVcTEYKDA3FMO0pR2Yua+PGVSll8pQFyMa8JIIH3k58lESolcLR8REKIbG/dwGyVADPMbeHggs5wng8BqQ5NF3IMdwaEzCHtUGAIAPIhXMncgKdm+Os/HwndodxCdBGU2583rVZB35ZaEBpEBSgNDA15xFM5FATBlSRMIAZwEIpzBdzKFVDKw0XyUhrY9lgpXB8fIzZbApmM4cXVYWqqnAEIxybcxTsQb1TkDmXJr/CyfQ9A2CmaCVouzbtCRDvdhemaci4T+WoSzHS5aba9omP+ZFhZUGaHl62rLtlN9hzZefsmxZLtPSOYf2Rec/lnHk3rlRyk3vY8w3EbEdqEuiAOFHpA4V31nuQBsseyDqZR803HfyQq3/jutt1hmWpeB2d9Rp+HCVJMNEytHVQ/7WvfQ2/8Au/gJ/6qZ/C3/ybfxOf+9zn8Ff+yl/BaDTCj/3Yj+Gdd94BANy8eTN67+bNm/7ZO++8gxs3bsQVLQpcvXrVp0np7//9v4+/+3f/but3o1VrL6QAoVtiELEBtX4RCZ++GWA3UTPa/oBhrSKNtdYzeuZrotld1b87hmnhPyfSdIsU3s+0nZl9f5165LXdnCbq7BRTf04ZCDWLl5khesdDQ5C2eQgP7F0dKMdgOGJ/vopLHWYLfh8evzbT8TMsHIrcuYxoDmdyIt3ZK+kc9i4LQS3WI7vGol5r/gaeCvFbwcZmtHkS8Gs1LYEAUYDKAgXbOPlaA5pxdHiIk4eHqKYz7Fy8hOs3bmBaLTBfzLGo59CzBfRsYS/RsnPegkStlBHw3KFDp2UWEu42WykLc8hTG0Gims4M4JESLCRYAFIAUhZglkbYZMZ8PsfDh/cxnU4xr2bQWqMoCUrVuHDhDVy9ehW3RgVe+8hHcP/dB3j9jW+gkqURFAoBKQpIbQ7baiFRMTC3fs4kCmhRQLOw9xnYeceAIuOFPaYa3/baR/A93/FpMAO/KQrc/eY3oBaMUhRGKJFmMZj1BH+LrFIKmjWqRYXFfI6ZUiaCiiywc+kCbu9dxng0wmy2QCEJhQJmsxkqzdZ1hLGzM8EL16/h2rVrqOuFt7CcnJxgsVjgxo0b+M7v/E78wU99O15+6SUDrsEg60NPpKy7TTwhtD0UGtritHYHnM0TCYHC+t/P5zPMT05Qzyss5o9x//6bePONN7FYVLhy5QZeuvMybr/4EnZ2dwA5AVhD0QiCjKuWiaMjoaEhbcx2EzObADJzlu05BtYKuq4AMHSloBcVuK7N+QA2v8/mU8xmMxwcHWAxm2FRz1DN5yiPF9CsseAKDMb44j6uXLuGBTNqMOZsXJ/82Qo2t/9q+5fBqLSJGKSVdf/RZl4cTkpbRxEvUbdA+85ZkV1/jv9ysM6XYRq9ypdN0BSS/aRDm3Ra5LUiKRc1gkzvBWnc0+VLbB9xjHU3Dk5FZPi69t/a1e6lJzRc+ec983AoL0oVXPGb3Xs2YJR3pzNnWrgnwjTLlbl1UK+1xmc+8xn87M/+LADg05/+NH7nd34Hv/iLv4gf+7Ef23Zxnn76p38aP/VTP+W/Hxwc4OWXXx4WJhPaDnDZPkXgbysmStcxAmEHDea84UJdNu8Yjg5TwKc6nvdd7uwOFdpM2itrkM5iLHpi9GoV+vqX4O8f8t/TJuqeLtpAR7MCUXbMnY+yIeMOYMCj4U9TrqEKgQvXruD2Ky9Dg41PsKqhqgrVwt5SarWUdV1jasHl7OjIpFtUqLSGqI3ftVRGO7szMtrbaa1RKQasTz4gjEaUGQQTUYbIuO9MZzMcHBzh4fExqrq2h1eBWW0Ei8XjKR4f38P/8f/59/iBH/gBfPaPfAbvvfMWvvr6myhGJRZihFJKlEQACZAogKIAC1seSQhZoSCR4DPjImKu4ZtB4gjAAZRSmE/fQ0HHkKhQc4URkRGQtBV4a2MBYaXBtfGrVlbTW1c1iqLE5YsXcPHiRezJErPpCYRkXLiwg6KqUddTQGnjNjQq8dJLt/G/fd8fw4dffgVVfYJqUeHhw4f4zd/4DVRVjT/83X8Y3/EHP43LF69ACoB5YdYcjWz/FuZgbnj7LMNfhhdFXHJuS8QAK1A9Ay+mqCzAPnrwEL/727+H+w/fxfHJIY5PTiCEwMHDYxwdHOLxowf40MsfwrXLVwGtwKRBskA1nZoIPosai8Ucs+mRdVtRdu0Y0DSfz3B8dISTk2PMpjNztmO+8H0H2APVgjCbGxemw/kJFosKgDAWjJqhtMJMKZAUuP2RD+NbPvpRLJjBUkBJE+vfCedOIWOq4SIfkdWwE1g07j6cnN5dJWSyf+zBea+K6lQoG3jCPDg/8LpFWhFanTnqi3bXZxlKbRqbUozt/P/Wpq2D+tu3b+OTn/xk9NsnPvEJ/Kt/9a8AALdu3QIA3Lt3D7dv3/Zp7t27h+/4ju/wad59990oj7qu8eDBA/9+SuPx2GpvYloVXJxFUN+aYNtgTJ7nNkz3abY3V3b426Ary9DzHnS71Wg2Z4yGtR15Srdir+AJnp+mlmY5Wn7GGqGEUdU1FlphtLeDS9euQo5KSEEoeGRdE2BDMGofW5/ZHPDUSmN6fIS6qjCfzlDXNU4ePsZ0OgXbw6CSJKq6xnxRYV5VmJAMLCbGhUczg1hjVJibOg8PDnF4cIi5tm4klmtpG12EtQA04Z137+Pf/4f/hA/dvoo//Ic+jbfvvo1a1ahqQo0FWJINpUmAlEBh/MxJSHNeQEgTRcj7+At7zTPhePoQv/E//itODu5iNpvhG19/EyfHD1BXBFXVgNy17hQErWrMp8eYz6eo6rk5BKsUFlWF+WKOvb19XLv2Ai7s7pmIN0cHqGtz1gCkMZ+fQEoTb15KgpSMq1cv4ts+9XG8eP0WlJpCCIGH7z/A7Ogx9vb28F2f+U7s718EoMF1jenJEUaTXRTlxPRtxsJj4CoZK0pVQytlQfUcBwcHODk6RHX4EEeHD/H40TuQZYFrt27grTffw+/97pdRWd/545MjnExP8Lr4JoqiwOUrF/Hxj38c/4/v+A6Mx2N88etfxIMHD3Dy6AFm0ynUdI7ZdAqtKhOdyLknWcVBVZvfqqpCXdtzG4LsYWKAhEDNMICbjHuNlgSlFMpiB1prjCYTkJSg8QTFqAR2dnGsGUoIKAYUwwB1bixKis0FagQJErCHVdEIOYAVhGz/bbTPnJXd85xOlZ6DYV4llPUwPX1ssHVQ/9nPfhZf+tKXot++/OUv45VXXgFgDs3eunUL/+k//ScP4g8ODvDrv/7r+It/8S8CAL7ne74Hjx49wm/8xm/gD/2hPwQA+M//+T9Da43v/u7v3k5FM9bAtgvImgPEw+/2MUxvsGT3P/eO0bqoFSZfy7eQud1Qd8kNA+klxmk9o2+caeU2NSGJccI7akR+7K5eFL0gvMQ73FfDbjBDbVp/IbdCSXb4iDbPV8gbq/HcvrLTfDT1C1rpzBB+nEy+SilzGLGThq9a6iOttQkpGdRGaYVa1QARbt2+jUuXLxvQBOs2IIU1y5rIJyyMjzIBkMUYEkA5nlhfbQvUbpmoMMfvPsDDBw+xePwI81pBTyYQRYG6NjHaC2HCW2qwCb8pgZ0Le3jw/mM8PjrAoq6hBEGLwEbFI1i9PioFHOkRZu8e4l/923+PH/qhH8SF/Yt4/+EjCGhISQBVcDd6sjbuHM5iQeaKT3NxkxBmgQThEGs1x9e/+lW8/+br5qCjIlRVDSXHqJTCDDDCi40aZNw6FJSuYawhxv3lxgu3cO2ycbepFguoxcKEUCSJeW3OPWmGjfMP1LrCWAOHD+7hC5//HNRHPoLH772NC/t7GBcFRvVjHNx7F7/7G1NcunQJo2KEd995BwePH+NTn/o2XL9xy9bfhDatbbSeo4NDnJycoJrOMJtN8fjhIwO0WWM2m+Pw4AAnswWOa4nZ/BiKZvjUt30K9ajAV17/Bg6qBd5/fIKHj49weHSA45NjELENYTrC73zzbbzx+tfw2c9+Ly7t7+Hzv/U/8aUvfNkA8Lo07khFzFtcBCCvtScBAXN/AtjMvcXInC0Y7+9Da8ZoMsHe3h7K3R0AbC5/AjDemfhDuEIQJqMxZigBNm5EisltGP6SL+PvLr3rVcO3ZMK39YDl8xSBS+/+MczPO7Wu1HymQFj3aczDlau7DaIltqlnSZHU0JAufzN77ip9soxidLvgfjkaqlc0R5es19ZB/V/7a38N3/u934uf/dmfxZ//838e//2//3f80i/9En7pl34JgOm4v/pX/yr+3t/7e3jttdd8SMsXX3wRf/pP/2kARrP/Qz/0Q/jxH/9x/OIv/iKqqsJP/uRP4kd+5EdWinwzRE9d0dhDDqSGGlMfh3ud/Hqc8sKy+sylOSGopb3dEg0JXO2Uq/m5tULcuQW9dI2WqNmAgJN73ueTvw6v2ZquLSfcrbCAhozw2zSLh+MZRnthALIo8ML1F3D9xnWMxiMDwrzG0mwyxlVB2EuibN1dfkKag5U2rnY5MiF4x0rg4PAIldKQoxE+9onXUAgJNZvj+OgIhw8f4/D4GPP5HEop7O6ZEJizxRyzxQJaM5T16yZykVqCMz2aUbOEqhnfeP1N/N//7XOYzUxeEC7qidlE3cFAYkB6lwojhJAQweFFNJF5YKwTh7OpTS9NlJcRsKgVHpwc4+DgAHVlNMuTyQTMGkrXKMsCe3u7uHH1Ki5euAC1mGN2MgURY1yWODo+QFVVmFt3oqq27m7CAUzG8dFj/PZv/SYe37+HiyPC26rGe++8jXvvvodZVeFL4zF2dnZAmnH4+ACsNO6+/nVcvHQJDDYHdGvjulLXNY4PTbjTejoDM6NeVF5brizwr1jgmC6AhcKla7ugosC79+9jOl/geDbFG+/cw4PHx9CsjFKBNUgxpnWNR8dTnLz/Bt569x380Pf+EXzPd3837t17H2+99TaISjAJzAL3sCYMqg03KQsUozEkSdSLCpVWuHT5Mq5ffwHj3R2M7X0s5djcgTCra4CA0aiAUgqisBF17DgTOzFY+L9t/Y2bT+2bTSjgo/2OeZs7XWwCnob4RGeeVtEWKSqWOtPUnW6bPCvdj54LOqMAy+hKTQWeR3esrYP67/qu78K//tf/Gj/90z+Nn/mZn8Grr76Kn/u5n8OP/uiP+jR//a//dRwfH+MnfuIn8OjRI/zRP/pH8au/+qs+Rj0A/It/8S/wkz/5k/jjf/yP+8unfv7nf37l+nT6FYe/5ca1a9K5309xLjitZso/nVyrViw7ulxI23Bx5CY1wx3mIgCKBzzZT6ndpgZxMUM+9c2CpNZ4GRdabjLvy2c4ycqUu9Bp2feeFA1FKhoMWdnTaV1LLjpUDGuB6bIcdWZOgwKc24jdBT2AOWy/u7uL3d3diNdIB7y0LdUGs/Htp4aHCLv4dGH8o2vBEIJA4xLH1RxHqsa1q9dx6cVbKITEiARUVaM6PMH999/H3XfewdHhIfYvXgQk4WR+gpoVNAika0iYtUmCAF1a65qpRMUlAODhgvDFNx5gxDUUj8xFUUwwHuHNmjDnMdmYIUiAwFDKzU0BKQWkNBFnKjWzoTzNrGA271dVjeOTGR4czFBVNYQoACKcVBoMDa0Z5bjAlUtXceHCJXMp1nQBARMicTab4nh2bDvaRLJZzCszFwoJISRqPcbxtMDhMeNbXvuDePWVG/j9r3wZ/+P3voiHhzMoZoi5Bh3MTJx7ZS4au/flr6MYFSZQh27i2iuloZUF8JbfMZvbg80luQJam5j9tZwBTCiowJRNOMmKgbvvvYMHR0eYk4Q5F2EuDNOKgdqIgbNK4eEX34F6/F/xo//7/45v/94/hnv/8T/iUJi55QD5ZGeC0t1aPCoxGo9M24sCNK/w7tt3cf/+fexcuoTrL91GORr5kFtmDtcorWepkBpCCoCDuPnpaiENQPWso0BDP+j4mLzHwfONwoBsnxjGamQ+Nxt1t73xnDYhbm+7CfVYWwkAy6c+IjlwPyzsbWZFPm06lRtlf/iHfxg//MM/3PmciPAzP/Mz+Jmf+ZnONFevXl3joqlMWehgWw7JUcyn3IPTc7hYjlZhtb35JJPV5+RMktwoYXPa1NPSxGcpKGxIWdFc6NLFWZyZ2zxMo9+cdrvWBefPkuagVxGTPHRzi5u9Nl57fROvlfkqtWz6VEqJ3d3dyPweFhOwhLgKwRrx4oT9rdY1hCxRK4WqriGKAtdv3kA5HptLiBRDFgVGFy6AiUxccJgwvgZgGoBIVlsO2L5zdWPPkqBtpyoI1EwoSYJhIphIGHcWdmdkrJq2qmvUlYIQQCFF4PLU3BUghADpWItLEGDSqKoaJycnmM0rlGWJ3d09MAPzqrYXJY2gNeP+/fuoF3PsTCYYEaGaLzCdHUMphUIWUFr7UJeuXCd4LCoF5jnmixqTnX1cunwb+xffRa2Bea1Q2xjuSiljfbC3qhLIgHzbB04Lr5QC2FggIEb2sKoLyWi5u5Cmn6VApWo8OjzCe+8/wIvXruPk5ASPHj3CogJqEMzpWzbh+e2EJSJUmjCrGW++eRf/1//93/A9P/h9+APf8Wm8zwVGoxGEKJp49DY2velvoGYTWpJGC4x3doy1xT5XbG4kdq5TQsjEMsbx/Gx9QQPo+4B39tEyFsnTUINsgzg03Aa/UWs/CYV+YD0r6DlhfTnpjPX3kOY+er6R+XtQEtqYTgXUnyXS1BGlg9nGJjdEbqNxKIOdBiRmjA6s1Pav2wpFEg9chnG2g/cAc9mR5mACJTxShdqFEOiGP7mDV8wQgW9yLn6+cz4w9eiSMq2rQaDdMdqpVAUevGG7xhsWyAXI6qJ23VyW7np2Y1JugIwPxwY4R/nW20SAhvaMORfClHWi9w/NnWjuLAjzFUF7jSUhiT9v+7IIdUHU9LenoTAxveSWqIlbTX78TP7OakOZvjEgsW793qlnSLJQHuA185Si1gV9mPrjB4cXw/qa5UWQNg5y9LjH8hJTP1ckhKE62X9TWpmwjEEs7VaubGKoMzXjDdhIQAR7y2cD7CckIZkwLwElNa6+fBuXbl2HqO1BWyGgBUGNCGokUQkBpWtcmJSo5hpFrVDwCFUtoCXbeOZGC6uY7WFdE7qTtAIIUMUEtWTMRQFFgNDAQit76LQwFgfFULXCbLrAyckUspQY745RkpkromYIpaCYIYWAHJUAEWqYyEFFMcZiUWE+rbCoGUoKc2vvfA5JhEIzJuUYxBqqVpjOa+hFjf0L+5DMUFUFdyFSpZVZY2TGXJRBmFVm1FyhXizw8OB9/Np//6+o9GO88frrGO9dgDya4ujoGEoxlBsOZUa0kAWEvTYA9rAuWzMLw8SeH40mJpqR5T3KCXQwmvd5LQBVQRwDs2mFh8cHeOvhAR5VEjUjUgxoEc4qoFATQI3wAFP81lffwCcPTvDyy6+iOHgIIQucUMQZvGZTN7+ApESxO0GxO4GYjAwP1AytTHhSBqCtvz0YYE0ZIOpu0rW/kysrTBfsdZSECPL/qK31T4lc+MVOdVnf2616tx/muFPTaznw7S8Vi8L3Bgmd/qrXxYUQhf9tgbx+4ai/1Qzto6+F9bKCbe+7bQq3t2hoc3jTq9NdInenhrayf2iPDd2T3F7XYy9ngGpXdLtwJtluWxTpJY1IF5RFdt53UMzjVyfqytuNe6s89zz9YUUKApPknyPqo1TptAw996B+FaLg/+76qSFlRtcQrTzm4QvRxIlz8hrPnrIGlb0dKp4Wr03zaTEvU5OQ7fW7ReQ4cvyoCT2XtDLINgbt3Kp37DsZCGrZgpua51J1UtRHqT9/RhLrzWuZUl1/JOM1IPn7XlyiiKhHWmG2ks9LtIt65lZ+zSzb+8OFU1LnVULC9nUpUwxnXMzzwt7I+uKdOxhPJj7SSZip1hrzxRzT6QysNXYmExTS+JULEtA25GB4sFIUDtCZsJtFUfjCF1UFKYTfs7W2x2o1QWsFVSvzmxXOtWbUSgGKIUyYHVRVZbTJVYGyLFEUxs9eaYaykgwJc5OsVgrz+QLjskQpBFRdm5COyggwi6rC0dERpLUyOC11zcr3HRDLuOam3zlGoxKPHz/G//yt38S9e9/AdDrFyckMi0WFxaJCVdeoauU18sTG8jIZjcxNqvaeABKAMiFkIKXpc83mcDJgFCYAIGRp0hQllFYopPF3OT45xsNHjzCvajCaG2Nzc8dEJmIoMB4fHODtt9/Gyx/6EOSRGRPn9tXt520YqizMTcCj8dhr82F6PtjjHWhKwXTAiEIA6v3a2nzD1T2qx1Jrj5q8u2hZZtP58qoQN6RuDmJ0Bt31Ml3bBfqXa09f2SvR4P6djB11PPOFdyKZVoHRHjBUD457PO19zkwVTj/1DthA+WvSel5jp1OZVSzz56D+lCnbwW4BJZqR7ZJlfF6j3p79p2lK3FRQ7aNwuwpdI9zNj8uB3zYxrNaZQsYTq4lPiX8sh+mXymEdPz5agjP3vWu16xzesuc+94AEAEuciEjeX7FuK6yvbR48C1ccwQI71mACXvnwh7G7aw44SinbfaIZelFDLyqo2QIv3rmFW1eu4OH7x5hVcygpA20iQUgCCRNxRSkFSAEWBFEWKHcmuHP7RVy+dAnHJweo6wpHh4+wWFQ4OT5BrRk8Eqi1RlUrAAK6NvfvEhGEhrlpVNVgrgGhMBoxRmOBoigwKoG6NuCUqTDuI6SB2rj6CACsjbZMSIFSFiABq3mHmTtaAVAg6x/egPlGQ2dcZhiLRY0j1ijKE1y9fAHv3z/A44NjY2tigVpL476jTL1BAgUIVEtIkgAVZm2zOVAqhEANgbpWICEx3p9gPB7jwoWrJrrMzi5GozGqivH7X/oC9PwYUgpMp8dYzGYwjk0xKnG3d3rQLSUWqkZNGkcnU9x77z3cunMHIHOBHUEB3M+PSQiMdyYYTcYYTcYoCgllXbJWmbXaz07z97T42TAN1TwVSs4KhTYUILSnnM36LktuPGIL79miWAm2gt7onDJ0DurXpH6Z/5SnY1rEk+I5p7zaWGujFQyLdL6PosNlYlnARsnfKJNVarkGDYxP6NuZeXq6hXdRS3vXoR3coOxn6fyAp2QNCCHArDEej00cdhYwN8sas3ZzCZbVXJO5zOrevXv41Me/DR/72Mfwxt1H0MczKElQsDfYgq27Ffl/UkoDuEcjjMdj3Lx5E3fuvIjFYgohCAt7QdHx8QmOD4/x/nvv443X3zAgkTWU0qhrk0cBG1GnsrHUtcJ8Pkc5J5P/SEEIgXK0g13NOF5UZsMtrEGezYHNQggU3ufb1Bn2ptLK5l2MjCCRd4M0ApDWprzrN26ASGA2m+HxwQFISBTjMchaJNwZhAYsGw21910vJCYTE+5xsTBRbC5fvoyPfutreOGF6yAxMmMjCxAJPHp4iLff2MXB8WNU1cLHj/c3sdqBzs1VNy4ubV3XmM9m3jfeKSJk5l12bibMKMsSL7zwAsbjcRCCVPTytnYovM6k5/QBpubsYGix4eD7WSBqfTsrNXsW6RzUr0mRqYmdRpTsEwJ6bjCN73AdptC/L/Rzz0VY2ST0FqFfsRvqYIR9YZtGCQGKTGONy8Yp71jeVL19Yhg3BiHymms3a1r3CQQ5GMpo6kNs3XFWwsVoTpvnLS49fRsfJIs/N+4AQXUi83VntoPlniaZ+ZqvnLMO9dbN9YHdeeI7IwjCCqEpmCciFKMCxY6EFoz3Hz3Gm2+9ge/6Q5/G3Xv38Pnf+V3M69BT1FlJFOra8JKiKDCZTLCzs4OyLHDphauYqgq6sMB2bx+jPWB05Qoua2B85X3cOzxGPVvg5dt3sDOZ4N4772B6MoUCsJgvAAbKcoydYoy6rlCpKUqSqFFDQIDVAuWkQLkwl20VIH97bSklSiFBUngXF83anGMSABcmWgwn5z3qukZZlv6Q7kwrVKrGmApUWqAYX0XND6B5DkEC80pDs0JVM0zUSoIoCpTFLnYvXcO1ay/g+s0buHzlCmwYd1RVhc9//vOo5gpy5yJu3nkVIIIiQuVADgNCArs7EyzKES7u7EKwxC4RTmoFFubMiteCO0xkD75q1tDQmLPG3s4INTFO6jkq50blgVNz/ilHxXiEqzsvuCnU4nsu/ChzEj0q2Be6FANdcdvj38MwAt2RP9zh5ucXcjVrlkQTaMH2Mnp9Rbi7VxqPpBbDfAJdycFRpXDDsPNy6O0eRr45B89r6HvfeBrKzGeMzkH9mkTu/941jILVOzRDm4N2a1Hiy7YpY2jieC+v9WY0C8z5GW9UB1MR/zllOczAuuA+5qeNA9EyZ6BOnQLf60Ftfc4ZsGeP9ZtSonQPxIfulxGChuG0bTAQp283LRnbNLezzLDtZsxBE/u6SGtttd4lLl2+hPs7O1g8PsJvf/7zuLx3wWjr334bj+4f2AP7pk+cC4+bFzs7O7h06RJ2dse4deu2j5XvO4sarqDZXL61s7uDvb09vPLhV/DSi3fw+1/5Cr74hS9ienQMBjAqS4zHYxSiRFGYKDWmXOObruoFGI1/vAChKArsTcYoZQEojUVVoaoraCdA2kqEB/hDwbkoCm/NUEqhLEv7ucK9e/fw+P2HODk5MemIsbOzi4uXLmJRVXjvvfeMcFGYyDIf//jHcfPWLQPyyxIMBSLCdDr1YUvNOYRgQpFxUCGwtYAIL9DduXMH169fx8ODN1CTW5NWwHLuNy4bQYACRqMR9vf3sbOzg7qqsWAFISVYxttrbvWEQqRR2CCQl9sazBz1W/qWIHa8ltyXfEV76tDKsJPO6MJOq8Wr8aDUrtnKPgLU4UvLxcpfmyKgEQomQzVG/zACLXf+1SkWanrzWjbdOZ2D+o3IcWAmOB3KkCexfWND6ivFnW4/XTKaqIRnbESUMFH2//fZZ8JOLXWRCQEiqWR61uhp4fpcKK313HFyyJgbQSCbXd/OxQDZcItkQ/pF9WTE0SIyF9ossSt2j1vfu2dAU2ir5wBsrraubU4zyoKwe+0yrty+gfcWCzyeHuP/+3/9n9i7uI+9i2NcxUWczBdYLGp/wJXIgGgiwsUruyhK4OVXP4w7r7wMRSYud7tsc4iVNGFntINCSCyqGkfTGY7nC8y1Rrm3AymMX7oggaqaoSxLTIodGwpyAfbx3k04zEJIFEZ/D8dnjMAiMSolal2hVjVqZsMbiIBCNoJPiKuJIGUJtjH0zS29GvOqxsnRHEpp7O5cgBwVuHjtIj7xiU/g+PgYJ7/5mzg6OkI5nkAUJTCWoJ0CLAgVKTCbg78YC1y+eRV377+L3UuXUMMeALaRj1zYUMUaJAXqeoE333wdH/vIS/i2T3wcb735DhZzh1oMGHIH/4gkAOGjp5XjEa5cu4rx7h6mVQ1dGsYYHQhGPGspFA6C390qEtbi6y5GO139eAjoKa+TDrqitx6Dd0xsUs9zWp3ceKQjGtp8ni4tg5madKG16OnX/SzSOagPqO82Ok+eu1LyjFtJN6xN/JkaptsqhTpec88GJe5QDA6k+h6NTb62iX4p6U+3kfqstqSOXWrD61Yib0apFrzTtWNFzU/LZ7bREvonq7ZjjbkRvUfxkKaWnVQYaYW57OqAZeq1ylTh7MfNyWOftIDujgzdcABgb28Pt2/fRvX4CAfVA8wXC5zcvw8Iwu7uPsrJDuqaTRhGpbyfeCFN1Jvbt2/jlVdeQVEWYFLeokVotK3EjMV8gZPpiS//4OAA8/kCBwcHuHnzJljVqCtzYLeua5RFgZ2dHUzGBQBGrY3vfw1GbS97IsCH/tVao0aNUhYYj0ZgCCyqOWaLGeq6auaG1dCHvTMejzGfzzEajfAtH/kIRuMdfPWrv49Hj2Z44YXrECxw9+27WFQLXNyb4Oj4CHfv3sXly5cxGhmfeGGtGIuqMpr7kbmYSwgJEFCORrh+/ToOXz7GjRs3bEQf7W8JBhuBaTQaWXemEu+//z7u3r2LT33yk3jn3gN87ve+hoNZbZdevAjMdDQWmMuXL+DWrVsmRr5WKIqJj7az0rQKp1TvCx0q0lAh25qjq9ZmKM06z1ZJszw5F8MwvHKOf2yuEY83kXBWD/YYp6PsKzVQzir1Is8L8jktM7brltxRa06+hFCDkp9zkmREFP++pWm01duCuferyXvtii/33jmo7yQjETZSpDvYZH7XLX0GBzi4g+kuXXQ6eG7r7qppUlgYg7X1Ul/d2Jv/W2ZCCOt1oJM3mqThAl+97H4iwFzm0/V8KFxZckqPg3eymqkoLQFEkPaO0capwMTIANedo8MMaA8kTDmSRFPmsmoKz48LvxEYEO0+514igIJb+5z7g62H6m240eC26hB+DN51oR0zFV6KRPjXzkGni1ldJ9MfSrCzy4P6p2m8XqtKwJowc4Ozb8SX20iY6DPFtevgjwu8sfM67r75FqbHNYSQGNdzjITxU98ZjzETNvZ6QSjGBT7+rR/Fq6++CiEqaD0HidLWG8YVxN4OLVhAkAYR2wOyCm+99SYuXryIj370o7hz5w4evvc+vvTFL+JgNoUgjZcuXsWdO3eg9Bz37t1DpUaYL8xlTszChoA0IFopBQWNSi9wxDWEmoDJXMBl7gIwgNvdoVCPAFlIUCGtFn2MG5fu4OWXX8atW7cgWeK92RHuL45w6UN3sL+/j28c3AcxAxdK0HyBu+/exeOjx5hVc1AhPSCXkzEq0Qg2ZO8/ICLsXrqMj3/q4wCAuVpE51qEXcU0lti/dhm8u4dH772L//Zrv4kf/N8u4c/+iT+G0WSCz/32F3Dw+BBaM6asTejJwrj4lCOJF65dwO07d7Coa8zHAqOdEqUoQKx8GW6MQmDJxOYfzNxuiQBOJgL5fSVcTSJdgIHSwJyz5m6+xu0njQum+z+1nrnMRU9MwGE7se6q1RKU1ltHfzm6aTrdEzmzr65SdG5P7qtbjloaoI533D7ZEYGHkrTp5+RsE0djmdZ6+UDCTR7UqkcWvCbbfSyLkgmVH9bV5UPmG1m0H98AFKKM7Qoo26MQf2XmjTgH9ef0RMldArVEytbpS24BsJUUrJtEdODYPSQ+RNZOTi5NAOxPg4isMOI3aWCrmqolsooO4W6v5FjrEv5Gmecdqpzo55zaJ5NXXOAZoBXMMGyjnVy7dg07ozEu7O7hrTfv4vDgCPW8Rl3VYCkhhEQlNIqywKVrV/Dqq6/ilZfvQJCAUnMTdSejFnLYoyxHuHDhAiY2Zn5d17h1+zbu3LmDyc4OXnzxRYxHI3z5C7+Luq5xcWcP09kMVT0F2YOgMvBF12wupVLm1jwIWYAEQwgJRgEmidFkDFlIAySsCw4AFDslJpMJLl29gslkgmvXrmF3d9fXraACn/zkJ3Hj5gu4du0qpCzwqU9+EpPxGNeuXMH9d+7h9W9+E/fu3cNsNrfgoMaNm5exszNJDqGbCegiCBVlmVEAWruGdYva29/H9Rs3sDh8jIPDQ/zH//Sf8ZlP/0H8iT/xJ3DjpQ/jc5/7Hzg8PMJJXYPJXEBWFAWuXLuMvb09I8gQYTLZ6TwQD6AJdMARM+iYKLEwu9L03yJ7WSv/U1+bGSC77RK2qb19grT1OuYm3mnPr7TcUy7vNMc1n/fpNugc1D8DtLQbw5OoBwWf0+fB39VryD3fVn07Q2mFIu3TioUlBafgqj0+5nmkjd6AfLSLMM8t84lh60XcbdpOjS7wHmoSo5/te64d5DIHd+a1TOj9Uz18FpajOXubbytdcOjY3Nha4NKVK9jZ3cXV69fx/vsPcHBwiMWiAgmJ0WgMmoxw5coVXLl6CRcuXABrBQWFYjwyceudG4gD80E1RqMR1EL5kJU7+3uY7O2CywJzNrHjL9y4hm+/+Bm8+eabmN27j8OHj6B1DSEENICKCLUsABCq2txwq4sRRCGhpAmxOdndwa1bt3D1qon/XpQldLIiitLEvnduLkRkLQD2YiyucOHiDi5cfMUD7Y997DWjFRYC+/t7KCcj/M7nfxuLusJ4PIEcF9i5uGO09R5puE5IeEnk8gfbWfbWXiFR7O3jxiuvYF5VePDWm3hvOsN//PX/ia+9dR/f8qFX8AN/5DOYVwscz+ZQSuF4OsdsPsejeoHpdIqr11/Aiy+/iPFo4q1bQmZu0/R1sG4SPXxcUaLlRKBgZ0AOTu9Nucy6eT+JM10uf322hPs1KafMemLN4m4PACCOi2TSL62/aNFS59+isgHnJXBOy9M5qF+SXCQRx1XT+2ZTM9JgTL+EIg1Oq+wnBeqbNuUOcObqsgnlNP3R1+Dfum024HfYLNrbrIEIBUPjQ/Z/IWBFO0Vv/YYf5fPI1tsxcvL/W4syhkUYs2nba9Ctmtav5EzE+QLcLecrzbqOMH5Nsd1tXm5+Z9ZEakZ2bhZxykBLay6tKsoS1154AdeuX0ddKVR1DUBgNB6DpYBmjdDlTUqJuq6iqDiuHG/UYKNBn8/nODw8hNYad+7cwa3btyCkPcDMxl1qb28PL3/oZfDlqzg4OMDjxybyzGKxAIQAK2XGRytIUWBUlhjtTFDuTLB/8QKuXbtqrA47O7CnSC34JD+96nrhuz0+Y8BgzVH7lFIoCgltw8Bqe65gb28Po/EYmjVG4xFu3LqFq9euoZCFB8mu7b5fgs535RpNOsHE1CcIQdC1wuUrV7Dz8Y/hDSnw1jdfx3w6w5e/+lW8/vob2NnfRVmOQEWBoigxq2pMT05QlQVuv3gb3/It32LuKBD9h8M7b2lOnwGtNcRhk56UtnQJSuvNxNnfgwTo2jeXKC3zOfztDHXME6a190f07AADXXlqBpsu89RZojNct3NQvzSleuj19NFD5HyT00UqBjaMPmoJHB3Pn5R2M0dsbPbxb3Cb2Pb6eZ0+TEFb8tRo53qyJQtayH8P317SbQOxcELepce5Dy2vzWjGeTNAH2rMtZVYCGkUoyZ9SkGUQ9+UlnYyU5ZN2lu3rNEzANSd7w6ugbaI6IR4IjLa7Jz2KxEMybqnNO1i0FhiNC5sexmAggBAJMwcEgCzshdeteOem/60QpXWqOsai2oOkEY5kigKAUCZOSc0FABBEqOdMcTOGDs3r+ImfxhgxsnJEU5OTvDo0QGm0ykY9tKrnR2MJxPsXtzHeDxCUUpAM2poh+ktuG/G3J5dtT1VJ/0Si2yyYGiuzPtEEIW5u0KOJHYv7kI+LLFzYR8f+di34tKlS40ypG/9kbEqNP1l+x4MZgIEoWZGefEiXvm2P4C9G7fw3jv3cPD++zg+OsKj4xMAJ9AamEwm2N3dxeWbN3HrxRdx8eJF7O7uGuFlrVuf85SzRvn5sgabPjuuI5vum4xGQ5/0N23ir59k9QT7q8uyu967T3IfT0/YxeROc61Sn4Yd5m3F7tftrbR16GxbDs5B/Tn1Uqrp7tLgb5OEvT0yPCy5LHVaOwbeO20WvtE2xgGoDzLK6a2GMloazj/tDtsSPTE3HO53pwByVi8k/UjB/9NfEQWmypWvlIYUEgLmHMnu7i4uXbqE6ckJpJAga/Ywmnyjpfb1tr5OrAxAnuzsYDQe4+oLN2wJBhTPq4XR9hfSykbaCmMmv6htG5LjOyQEdnZ3sbOzCyEkpCxMe4wEASJh4MUAXowPg7oP9uZeKxbL0Qgvvvgibt24gaPDA9x/7z0cHhwADIxGE+zv7+P69evY290FM2Ifelo/rkWLckatLotWi1Lkf5qL9Ulqxzcs5zR42hnW2D5tShVRS7xg6BnZW06LuvaRZTHXOag/p0HyeyX3X5u+CREo65scRqjZrIBkQw/zz7rEbIfy+obVqFsTSdDuIOOy+axQn750qQ/3WaYnCezXpcY6R62oEeH87BNyicyNttPjExw8PkK1qMFKgxgQGoBi1DMTAnJnfwwpS1+Q84T2gbOEACD8DbeCNQiMifUV10K3yjYaLFP/UJelgaXnigfeukGwWjCKcoTJ7h6K0RhFOYYQBaQojWUEgDBmjBXnpIsQE4Yn1GBiiJHE7vUruHPjCurZAgRz8y4RobS2twLuBmwzKtucZSnf0GyUl42f8RCl2sQoty3WLGUEq6phVqFVxzelPg3ruvWOzG1PnbbN63I95lq77CxaxhPAzSozCgK58Tg7+vG+Xf005/9y9NyDemI2odGQujrQ0mw40D8hdr3h6In/zoguDYnr436k1kPOvTBA7YXSn4E7G9AFypvsQvBuJyrBaPs6lDO5Hm0lW3K+Oyi/7D6dde8InuYNhdSpUqQgU9MFTdsi/1abljj+LUwnopRBvpzsBx1a+PDhquOdJuWknt2sqTlnYKZr3hQafnZeSk3fcecLPvRgNGvyloRwLMwPvHR0uu3rLDma5W7slTu8TLCQLydEZvrDh4dzmwHnUqZV8P3tSBBBaODhg4f4+le/hrfffgfj0Q5mJ1PMZwtoxXj4/kO88+49HBwe4GOf+lbcunUznnPJBWLGIy4++Ojca6Lx8QJJIHj0+I+nbfF/fDp3AZKJh68XC2jNGFOJvXIHO8UIBQPSuSAxQBLQ/uAw5YpoEQUJ3DZNRCBhjwdqhUIKjCZjO09daFpz6ZY7OxCu57BMpgHY03dOJ+Gmq+HGQMhgBOe1UvDdRf3a95jLx+mcNYGYMmndyHa93geI7DxcFy8ttREtMVGzPw9VKmX07RxbT4MFkbpo9vG+kJ9SMofIM43u8UtrksNI/vxKMprZ41sdfeOax5leYDf7k6ht/MRcopaZZH1plls7Hfr4TrawrLz23IN6wQzBQbhDBlzkA+4V3I3myftDk9swDHPUJshq+ELwyW78GYTrNivnT+xuXvRaVCFa7/WFSWvXO2Xe6WNucETWHy9uT1O2ab+0tz92EYUcJ81+SYAevr5s+lQgctVwMn9+oQUgMozJHoDZZswbjaMpi3y+lEykFJ6JIN4wMSBYRH3hUdNZbgABAABJREFUDoO22mPL7xr/Vfc3z0jd+A+mb3oud0dAHBnBRvTwc7x9WsBvAey+mXlvvrpwqG5D6hn/FRrO2EzDk55v0Rz7hDvcJgLtEll1KnMG/gTWKJN3mJuNt51rNBug5OJKM8ftIgXMDk7w+7/7ZXzpq1/ByckMo3ICwQLEwHv37uPu2+/g7XfeQVVXuHH7BVy//gKoIF+XdNfwXjkgFMLwDk1uXLp6laET4aCpdY6aUTb4yMwhQQL1YoH37t7Do3ffR7VQWCxqYFpBLjRkXePgvXfx6OAY+/sXcPnmleiOA5dfyhfSsqMY2Baku3ErAAhl+Ty5eWrSCSmgeW5fKVo9wgBq0Q3qU2tMWkVF6f7ST6FQZSYl+8bHrmH9gN3Vvn3nRPi5AVytoABMET9cJTQk+7pnnpBuW7BWoJSXtKtFPXMa6NdL25nWUTlzsbeMU/cOR7qfsM/HZ9BBRM1aMwfIA6Gcw3boPIMNfdlboLtNIrxVfMUdKbXgNdOC/V7S/BLTEDbYzOd+aKKtK1laEuSLCK3nBGOR6y58udn/3IP6lJZYF9n0lHxONWXhnrgSEEWe8Z3TatQajxXfb8dy785hmW1xqTK3lM+zS+FqPJu9kYIXHYiJMVmuEAFayjCJ8B0RRNUyz1t7bMT0G2GQQBF41krj4fuP8Oabb2M6nQMQmM8rCCYUosDdu+9AaYVFVflDo1prCC2WC8kJoymjzOTfFv9q9AmMRV3j7dffxBd+9wt4fP8hqkphVI5BJHD5ygJ337mH1994HQ8ePMJLL7+Mb7/0BzHZmSRVa7TVnWX2REpy93M2vZPmjmCIG2UKuf9t1C2rvdwWH/uY4UDeuUEO345QaZprKszn4Fhv4QPPt0N90eby9PR4k5+BhOBySEPPPHboqv5As57xVp8qfeBAPdJ9dQmKNJupFtcnCjSvaEBAW1sZfE7r8mT42XNJ6XisouUHp8wxH8Elm/e63OV8rC15b+6nXZGliElEON15ozWa+mjHTV5Gon2N3SLMQegEErH2GisTBcdp6jmylDAzVKWhKg3WBGaCUrYMAqazOQBAysLkQ8JE1FoC0Lt2dj7bFrAIyjh4/Bi//5Xfx7v33oVeMOpaYT6vIaXEO/fu4Z337uHR40cAgIsHB1C1Mn74Msmoj1quCO5nbfsX9qZe/yROZ/k3uUsi/Ebh3B7W1fxGtou+BLa+nCTlOBKRy6uRmoY1kX3umRmfwSayFCP2D9kWqH/aHtUbmAk2pGWL3e45t9Pv7/Rc1sqKuG1W5jmi5x7Uh2xbB78B/ZPITxiKf+vS08XUgMI+UN/9dkedtrB55qT8pWMrL0NrVjEN0bcqg6LOL8u9HIWLTOuGuFlGMbdeQ5loe+G4GF5b+zQUNqvOjVZIR/Or/ZdouE9xBw1jpefCx6YUzk2H8bQFUsJjGffB5u2wYHQrF+y5A/Ia5CheClPLX1aEmk+Ly1IXOVcuSYAkIApT7O7+HvZ391BXNWbTGepFhVopTMoSk51JdIBt+C6M1cY6CqObWdsUgE1t+0hreKFlNpvh4OARaqUBlCApAGIoMB48fgRAACRBpFGrOghZSe3FsDTGj+uZ2o/aYVVD4X8NpsNWw26FvVTh02Y6PXlFCez3xJWiWVfDmq3+pd0tmILTq8eSeWWkru4oZZmfGeQNYNvEre0DnEu42HTnhtPiWSlPyK3MztCWQ1VqSevO/dfNm2cDOve7BH+wNGgfAFDfHGyL//IwEqLmr2NbTjO3DTExe3bplOffqV6JvMW8lgkRGFLj+b8eRWVlFKy5cVqrvZH2DM6Jbm2iLunxCdNKAJ8B9nFV7FqM+vcJLARfVP88SwVfYlNvYQGg0BYMmkD90KRi0OpODwv3PgBqwkmGeedmr8diDDArdPuma2goaCgUpcS1Gzdx50MfxuVLV1AtKnzli1/BO2/fBWuFF154ARf2L4CIWv7vvtxOn+phal2MtcTcaPC4cQty/0BkXJ6IUY4KcyGV1tBaAExQuoZmhtImfr8OgMwmKysMf9mpMGD2w9tOGVv7Og+4e4kBEV8gTgSjntZQu2KZp+Epl03XVji2ufzWVHgAGZDpHqSCSybZ0DzrOgew1D5zeuB2VUVfPB+3N5b+uwM6AFZp9zLKkXM6ffoAgPq2hn7T/HIye+ac2XBeqYZM2MODeOoYbT0aUAxk+f+WyEefCXh/7gDqOtTpeuAyD54Pbz3Nq26c1+6SwMKQY6gffvAlXD25FxeavD9YySXTtiPl9PcCJZvGqUaFGxqQlfArtz8yB1/NqEZZermL2h44K1GP8KEZ09kU3//SQyhm7Oy+i71iinJRgjXj8Y1HON47AbPG/t5buPTgAMVhic4Gr1rHvrkyNO/CtQuANGM2m+GPv/QI9S0N1nMADCkF/EFW7V7V2Jm8hWtv/3sUReFvNO0qOiVKxjNb1U5tfHrZUUuVn61Im7+fBsfPMKdToZVMCtaXvft5oRf9ZfVm3/Mw7P+sID+kiT9FopRjJJQ3X7hXA6+vzDmBoanly3ZCpnCZmZf7mNWG3ZUqR1eZqelo5dbtM4mhtkDPPagH0PCCZRHXQF45CgHr8kV0mCC1jp4s4yJwFmhoIZ02yyTA++ltvayOhjX7vdXsLSnZbat+fXH8L80f4tL84ZZKOqdngi66D4+B+WNgbr+O7D8AwCNg+giYPtmqrUwXlk14BJwcnWZNzunM0Cnug2d1i81F0UKjyKLe6Hir7DTbsuQsX1LXb4NGl65MogQfTHr+QT2ZS1TampEBU14SCorYheyLQVQnmO/IPjJ2Z6JdgBGFvRvIrk3UlBLGTo8UWEn2fUA09D8GGh9ik08S7o9ixQAn74bhtrZNWetJYDnQg8U6rRs140nOZN3ha+l5YFA6wdyN4OrFgA4Kp+AgmZs7y+u52sSs7N+ne3H2OZ3TOZ3TqRA19vahXTv/2WZjnTRz1najEErdikI1cqoRlP07GeVKaUoz4WkBMIFJQwehPKNtJdcWIYJfGQzVV5NW2YZkCG+CcjKaflvPOA/THxTUu+0iluz2WQvdenigfTKty+LY1uSaoQ335AQdcuh2kQuaMeCSMES62f85svK6Ertm+XJ7/NM+Un76ZIGnA5Tt2NDLkelmB+h7wCk1f1opKP5HYd2oybWppwXNNjbySv8ylRhq9rBPYuuFqEiGWSucbfwpU9L2tCuWejdl4k2g7vYrfmzaeaRDFn7ihJFy8m9dur97E7/+0v9rgxzO6ZzO6ZzOFr156RV8/vZ3Lb2f9LDs3v2YezdQCgBox/O+HSfd931pAY5Iiwrr3LPPt/aMweosWeeQOts+lAUneKd/fFahQQgU7ukrNLVdQq607dQ7T5n6rljs86+pP6cnR6y9X+qT8uhclobqkcrAafrTakdqCVnX1UrJEl+99gkI1viut/7PbVXvnM7pnM7pqdDdCy/hv374+zEvd592VXrI8eoulUwfL0/UOfaPGNKt2TMB5wdPzylHzz2oz4Vrs0+eeF2ee+LYBEfB/582DbHW54GUKPGVF74N37jyGoC2gM82Wkf4vfN2R7YHEzuodVDJRwIxkV0oNV8ntJKzkLFTNoqXKHb4MOlVIgzZUJXOlUzPF3j44AHufvMNvHP3Lt78ytdwcnQM5c2m5noi9jcSB+booI7s8u74DjjNXfiOe27yFsnNxe4W5MjsHfS7iSRjRkqWEgygrmtIKUCFhGaGGJW4fPkyvuf/+b147bXXIEYliAhKWKthLo49I4ra1Jpn7p/tQzEUC18leQXpGfB97Uha9wNnKXP8XSvdqm/btL7+ahdufrtq+9vGbc2T0959Ln8CnbZe8652Fj/K1nvInZDIrfUwobkNXBIAzwsyPCC61ZWQOmhoE4zIPOWQzzNM7wTj1wov2u8qoiN3TolFMelNvzS1LoV5ErT63pfowc3ncwB/TivQcw/qz2kzChk+EfrD1qauf63MtlWrNain3o25bnnGHzFZ4pU2DG0PQjPaoGNVSt/WosBcFNnnqVWg654CB/ZFcgArFJBbPqkUh2gUyXmRtOwU1KdlJQV7sG3wEy19aVKu7D5yF/l4PKQK3D+8hzfvHuD+e1O8eVBjPm/Saw+iLbAcUrP5glwBfWSfW7AoUtBoz1FEoJkNtPJnLJwv3ExDCjKhIMGgkYlCImqCmggcYxcnYh9FOQYI0En/NsNjxiLkA4JMPH3A9rUwZbpeEb32Y4aWSauTUI46YToUfgh8/Vjm53MXMF6VHIB1c1txamVrPpuS+r1bexUNPXeIOIGp523AgXoOLX9mzG2k+I57QeyodeTPMAKF64u24kCgv2VVM60z48FCtn5bnrrK3cZJrnRvELa4tjBv0g7xnOQ5Nxr6dNZkVZJnBOSvUo/OsK7ntFU6B/Xn1Etp/PY+DJoD/Wdh2S5VBwfoOzbRbrIIcMWGpoB4Hdq0b7vK7brIZFsbydnYjnooMAQsqhqz2Qzz+Ryz6RRKqSaR+2MBOq8yEWjY9co/9wcxhk387MEaxY8iK00KUAw0C8Ffvh2NU108RbSvL7MJJkAiWBbdGNHYOQa1zv3Pm3RPmtsk9ong66Z39pxWW8gdHNygfqk2ObXSbMKZVr3fIJNDPs+NmU6uTdTxeYgPtBfEcJ8+Z7TlRj0L0QGfFJ2D+nPaGjkNrSdnoj/jXIlgLc4d9VQu0VmjZfq1Vwhrb6CrAPrYsN/oq5/26ftVNPOdZJurFxWmh0c4OTiEmi8CoJ0BDwyQ6u5wBhB50HB8P7Vgo9N2GjvWAv6iPABa1Nl8NS9hoicNhIDOom1BEiJ0t7AVjO59aH3nyKVJdMgA21oyNBy6quflLVViCTIWLqCBtv3ua6ddl7Y1qG3BSOeMkwf7uq1fYGGs6FwXVmi99zyJpmNt233wk611uKtjOrahpaPvvQ4+OmBJdvcznIPXcxqi5x7Uh4Bk5fXQsdB4+GGy6XM2SSdRrJRLPW1z6ZvHSSM58zH321LE3cpCW4+hmyibOmQYVKS+bismh7QXa/M77vpCzfeM5ZCDVBttGgN96pO1fLCTBGvQOrcrtuoRumg1iXq3t3W7y+HRlSI1UdeD9tfoEjlmPH78GPfv38fjx48xn82sxtvWItLQ8/A8yMzp4fHrAQu5udFTvnedCirqtOvpi65VTYe7vxzk1fhla9ae1xajMlFGhtrhAcSYNnGpiZJPZNqZdHofU8lmm0nQNbcZrtdORZbwY0z+f/l0A/mEINH90HqnIxPzbt8GEterdzm0fPmpfcF2/3LtKbk9z5pRo+hb8+42Ro2Tv8P5ZkNXRqEYbe06zodsBPRXYsRx/w2+n+N3nRON0F5Vy/fhNmjZrjAsr0nd3B7ufkhfyGwy26hIBz33oB7QPpY30SY6RDMQTX+HDLb5yLAau2jHjUFI116RZh1l3AGWUyBBIJA2/n5hBNn06JN/ouOFM9RFbc1O8F0kkVtSv2f7nZnB4FirT+TPULlbYUNlEyVmek64/yZD20CadFD6NU5dazgkArKqa6ejFbwcu8ppyxmI+3AD2mRjcBq/UJPUB/wBQHC8xQ6VztFn1Qv2hDY5Mtoybi63FhEgITE9meLho8c4ns6wEAIH1QKKCbViMNdRm5eNWhRO2/QcgUazNkyCpF1JbOWQx7h66C5fXoqtAqRtfTVjPp1hengPanYDo/ISiASUGCcCml2QwQlJVdeYz2aoH52Aj+bQqFGMCuzuFRhPSox3xyjLEjPaARNBe8/7oFq9vdW0bR2iAGTnyiIA1OPCzchHjG5HFHeftZlwpMFLtWx5ivYPJ2B1JiY4z/lcQtUCjwFRqkRqE3X0gPnO4GBFs4sNT3Ea8yytd7uk3seZsv3nePsFiKP9MPf2UrPRWrWaPnB/+y+AEggnWlxWtOYzCojTExPXyJcA+Dt8qIvBGhpSYERfl7HuhP22vmpIZNyxQlGMBrMOXTBDQO9Ew/bLy9e0bx4tN1YfAFC/HrW7j7oeZN/LQ/Bt1sfmnROFOTTaB4AqO1n7AU50mAoNEElBXLaWAyAnzZs6NlFXN+4/HbYhdefdErxXeNszidzvXZmtQqevwGgXuWY0hhTw98yciHiZRKvUI5d3+BszFos55vM5tFY+oo/WGkopr93Ogfp+F5h46+pyE+rOIwb16ec+t6N0H/NrlxmLxQJf/OKXsFgs8OGPfCtu3rqJQo5NuuzkN+0+OjrCN7/xDRy8fR9ypjDaKXHp8kXcvnMN+xd2sb+3D8U6CngSsqBlNI2nvTyG5t3wlHMpXOrl3no6lGoMc0lWEa+XJL8pukm4Zv8MovoODYlTIKxQbPecbJfhrFWdeeU23tZQrA5St+eKs0o+bs26dq9bhyYfIKPR7812tTJz/FQEWsDoKQHmcPlKRYQvZ/FCyCWiN/q8Fdakc1D/HFCsxdEQbKIcMK2+8Fl3q47T7SqnaVyprET71HdHX05T/Vz4Fz4HTThrtA3Zzx2IvXLlKu7cuQPUjMV8gfnsQVxW5N43LPFHFrOecKKtV4mgOywhy0R3cXrTUAAgIpBm1PMZHrz1GAf3fgezE40Lkwu4tHMxUgyIKB+AWEPN53hw7z3ce/MtjFji2tXLuPHCNezu7uHKtau4dOUSZvMZTh7N4k07YCRbcSE4p4Q6OntruQeAiGLdYqN4cXUIburekNbJ5clFienu525HxGeVnu32nOnxWCW8YAedg/pTotNiJkNagc66WMmafB7rFN5eEBGgIKCP7TKeJJN9dqivR54FqLPsmD6psc/YrgAs15fMTT3LssTFixdx5coVPLr/EFJKCCEiABoKtkMRgpygHVIKZnPa9qHoHd5Xvq9sG/fcpRFC+IOdRAJHx8cQRDg+PgYJG/bSm5OtFtGvfwZrhqpVY7mwDmVCCOzt72Fvbw+TnYlxxqB5i+nk6vxcAfsMr1yVnt3+CNsdWi/ONu9fij+1NtAluMsSc6HVY83BHvNLZ/bL9Ol686hvbUYRttaiFeeD70P7Xl+XfMDpHNSfEq0CYDpD2S2Zd6QxT6K4NJsnImC/DYo17UPMZ9OoJOuaxM42LcuStxFruzcW/IqUO2Ab5r8MWD01CooOtc2rTJ/QpabLZ35b8c+XqYspqDvNMnWI7hGwoF4pjdFoBCokrl+/jtc+9jHsX7yIUpr4906Q0fM5jo9PMJ/PARC0ZlSHc+ijGtW8Bo0INRg1MRQBSgBiVIIX885quzkxBOw3AbfhvGuNIRnLZjcNuZmlwCQGYqfJrjaZc0PvUvD/lamlaXSaevd3fSIMea7n29YcCO5TOPFyI7bNDdSXDVs3s3/qCNAb6i/yKamFNnDBTOs8fElcKBiu1qbTcHE5LepVzJxr6p9d2ib4Wpn61kvONfBc835mqMtt45wMdc3U1KeSegz9XW5gzkd90/WQW/tuDDcS7sJIJ2zci6SQKEcl9vf3cePWTXzyk5/EJz7+MVy6eAEoTFllWYIAzOsaDx68j7feehtVVUPVGo8fPsb9++9bV6VR4FKjDZiOXGsCBcDQ2Z1UIFwKhOaanLcORH3SY+42h4771k/4nFv/NgvMsAnlNOXbzxlA/t69jbTK6xMngPvJ7k0988TzlDyFigcg08eDZQ+1c/M9wPXt+W5y2tQ3lueg/pmmzS/hWKtUq83qeMxxQJttXkh0Ttuh59adYQvkNfbU/i0lf7Y8ANHOGkUkW+vT/ZOyG9gzut1vlhmrrkhHfVGHgrebSgAACwhRoChKXLp0BR/58Mu4feMaimqG6YMFRsLkKWUBCMIUGtOjQzx87wEODw6hFgpHxyc4Pp7avjLhCDUBSltQrxUkdPvgWNBfYduz5wTQv5Wdz/AO8sIKtzXURjW8bsbRUUBGqj3vsnw85X3iCfDCzjU80PSIL2Q87QZr3ueHvc3gEqdgpfigUbe6yFCfNr4dOyxPH1hQv5xWs2+xdPy+xKRfRnfSdm3pS9v6JTVuBfVaxkwffx+KThEZ1NYG+RT9WW8T6BaEhkHTQB93lmVGs/ftDi69uiExk3XWv7P9jVpprfYomYzNObcldqKhBJR8b9VpOcH1SQuOZBzN7ZcAWArjtiIEe996VzcTpx1w4XOzbgA2v6Cg5mP0gJtfqflbCunLdJF4GkGjAff57orzlFJCSgkiASIBrTQePXqMo8OHADMEjCa/EAVISixGEoeHR4AgKGYsqgqLuvb9FRaptUJdV2DWvi9dnWIgnxeAot94CNbnaEmFyBKaxzRCV/x+7u3ARYCTWOhJP0Uj3mH96ah2u0fY/y/iRnmHhRV1v2E9qLMG7ffSsjZcxkHgx2CbSOuWJ2IGZ8+mhDkuU8HV+JSzsWWtU13Z5x5QciN0q4we2gCrtF8Ybv9Sd524/w9tnJ5p5ixi7bLDmiJM1XK7a++Vy3OaxPLYMDeb1xBeO9397LkH9amvb0j9wK+/46lLI+p+7jlAR0SgNIZ7D7UngUiep4+TsoKitFboIxO9IGEagR9ceJOk0zCQC3OpdW+3CcjuBU8MFE2EY3dg0aceVlck7VgB1BPQW/Go/zTIxukRoGyauCLJ5h38ZTLNXtZIn/oEMzOUjZnr8hBSNMyJkw2dYyZKIt7knFDmdW19IRIRA1ETNV2gYcAVPHsjeG2Sq1dqjj4tRjekGWmlb1T0YEHQQkARUBFDSwFIgiABmczlmp1/uIlHrdFscOFm16tRZxggaP8ZAcL8k1Li8oWLkFJCKYXpdIqTk5MA2KPFY3y2iVbcgHk3gwuoGnjvvffx8OFjkKy8cCCEgMAEEISqNLfbnpwcYb5YQDNDuTKJLOBq6q5UDWaNspQQJOE0uF4IEQJCuPsNYp4U80tGvzeim6N2pFfRTA5pHjlaPU0pnseHj8OZJhwDM9m4FG7sqa1cXcV1joiQhtc3fd/k02h+7X0GoQCZZJuW7S7wdWwxFj7Q/BA1wo4vBGKOlnKKDc7WkIaLgm8LAwIGJuIBiTg6I9oWm985SNE7dbrrbfit6ZjcPbMM9vzA10uQ542kkwhC/pPwvzTLIcPRqAfG5dK3C8rSSkrOtah/Lti7rRH3jjICTuv3bvJ7kP8hhu+rtyrfpxzFr8+8ZSOY9ZVHMapIaLn2Pveg/rQo3CRD8/cHkZZeFEPgeYv0VM8lPEmKVYH9sgmJXiu0YzdsAUkv3lmiamdxReTqHW3+VpAh0ahxte0ZJoBKCclWg21BulIa0t2oGujmDKBKC7PbvwUk7L67ijjBixlMgHZWAwbKsYkdrwCQLCCKAkwCfYc9m7ICIncxlwDJAqPJLlgUYFmCpO0DIigAmgmsGNN6anmdcckRAMaaoWsNkPEfd6BSkkAhJQppfnNWhAbiWqizJCsYFviaPP3Ndb6p/bOwX5MvEsE1wLOwkKTn/dOa/0u52LF7biH1gM4iwscbVNyB2DaSWiGDAQqruWmVn8q+/QSLbMbitNqaik5whbXXRnK25nmjs4IBz0H9hrS6m8dzSl7F3pdIt9VEzzTFEv/ToUB4YaDval2/wbvkiYY3NG9nAWkfraoSf4rUsQ15apSvBC2A8e4OLl+7ipdY48q1F8AMSFma95mhVBPaUdnxcBdU+XKYwVqDVXNxlXa+5/a5rpX/PedzPrPvas3gosB4/0KkeQ/Lc3k3Y2yAnSvPvXf58mW8+KEP484rt1CWJYSso3e1KsCsUatjKKVR2zoyM6YnU9x/9z4ODw68EK21BjEgmSACw5AB9U4bqq1FML7ldj3i4C8Ff7dB/Xn13emBDWuR20eyZw5SCzBRPHe4fbF3V03d+t9kRBqP+9Vbv0rZkcywonD4NPboVnQX+//GWHRKdTrN7YnSEXPSg/3c1mg0+/+Gltl0uxlS1jxJWtl1bsv03IP6J+WPu7I/WY8wMBi1Ad3v2l+y9du0L7K1CJRA7DSNvXXryJvj2znPErUMZr0XRDx5aiztycbRGu88S4nSrdn1Zvw0AGMNOGNDuBK5/pBSAiXh2vXr2Nu7gFfrj2C+sO4paG5W1qwN0NYMzQ7AOz97u+4soNaq9hrt9GbaqqqMgKAVWDNq67PueYsOx4lQFgWKosBoNDJhJ1n7cpRSqOvau+cQSYCBqq6hlYIsCpRFgStXzeVa124a1x4SdSzsWVBPVEFrhvHeM/WdTWf4xu438bUv/z4W0xmUMsKA9r708PHxE5MSzoZAPLTFnp1JnHPh7KM+PrpKPqvT6Y1pV86rauxTy/o2olatSt5yRc33ntT272ZjlccoQ+1eQzjrwUIr9TMnf111rKz9NIW0kHLzpxPPDSjL+nRjy6rZzkH9FqnPBzJ8puyhuq73hhd4/0aUcz2JNX89rw9Q6LLaaHXXzy/Ke4VF+kTBf+B7a7r2aYMRS0Q+IkpOP589PJTps65NrfeAdKfg+ey7ojlAKqVxT5GjEXb3LwAAFDhaS80hKXNw1gFv5kZj2uWel2rza62g7E2vDvSHZeiqRlEU/nfna18UhY8779KH2nbjn18AIG9R0FqjLEuMx2OMRiMUO9aFhxqfTgIgtNkihN0qiBthRo5G2LmwD5ICdVWhrivUde1dmJoOdZZ3p8lzffa011H/XH0a1dsGX1sXTJF1mdi82YGWdst9GB2ZCLDuabjfxMBszQICaiyiiCocbC+nVnZPrbo7L63oqjn3uiUP7Ddd7p8e0G9Uta3TWdrznntQf9q0Ski67VF02TyiBfIEYiOnAvTyLT8L2rk1yJsZz6KPyZDcv9xmTkTQ1nSuUsCaodxIuh46Y/x2aaLkC9ljS0wmjCUAFBbwa5W5OMn8EGhUAr9xApwvuxeuuPHVByzIB0BSRHn7EmoFIWULNBsNuwX1zondvtXUxcxdl5dWjTbdKAFWO8AohEAhJEohUQjTN6yNa5DW7PsOJCDI3cJrDv+acwBsLQDi6WP7M0hdlstwv+lbn9Tx2eXt8nAcbZtD4Jx5zD/nYrUc71ynHmFJK7/bAzy3phBk9nctnAXwF7aZ0R/con30N6X4cre+56tRE/nLz6iEtzlvnlBfkGTxVOhpWH5COgf1a1KO0T65wsMvbqI/YXJmw7Q6YZLWhtSzCT19XtdDvP2db2u0vY4jmCgmHjj0JU40eqGgdya7aQmKzfi29faQqmPSzlAjKXgOwG9e2iZw39068X+DXiUTe17ZfUvaVxTpaKcyHxlyRAA0KAl9IqSxIGnNAVhOVaXkW2U094A7YGuC06Ttsb3ggZ+NouI8gImM609RoigKL1gABNbm/ADBWJKE4wPc1AREYDrL6+rpUssSZCnnNpKzcMZbRKz19MICnKxJfq5tOhQt6E5OtbphxlukpVxks5bLzctmZssLnu6G1wD7bQ5MLCBtBGytUsVEJwiCkZCdYYlmsSW4rl/yViiNevYk6QMN6tPOzl0p3nW9vb8IQCQxaC2DbL2XMON+/6g8NGpAsj2kh8ZfN0gFcKzJJ3JaMzIbeZDeWdjcT2JA0+82A8OqrSYyOHyX9mm00SRN6lr02T73pkCG86djb4NrwIfPI8p7qD8HmAC7DhJGS8txHxG4h+FHx0/BsEfJGCDuC/W2AVuyWEmSKc/Du5ZfrmiDKvLe4u2zBF5rY8FlEIqQot+d1t/mEC6P9VvliQONcxcFW0tQgVR73YMp/VRj+9HcHppzmzN/hFUXNmHNwrY3MkBcMwD+Vk4Xzoy8EBFvkNKBcrvh+RnDFkj3bCJktfg5FwvSYcA9M8b+oivhWiOgtUYTBZWhSQMFoRyPUJZjABLMAmCCEDb6DYCiIAAKAgLEJs69OVhAS06IJFHEv5oRdPt9850MKwzHLGr4AGhOauH7JJgbXe+6ZLGgGGsVw9Qub0q+h79xhz9ua7ytZrjTlceCI7cyKAH0zbpuQiqG3xuXM/NH+2cMkemXZg02YRy7iNhJrPad1qaRvtz08JBCwQkwrdptQava1aQserCMwTm6EShqZigAm5RpANMku776t9BuvBf1L0DLz30+RllBbowdBAJBktWss4aGC+HpImKZDJSzctrcC8TzqLEyputGe428WQHCKATcRpdRummGOciedARZrGCNoY2Loq+CydctGzNUcR81+w95PkygwOKqvADCducAs5dv233sxtv0d9doLusY94EF9cv4sXcBevO+fQ/2UgufHTcDGaZPvnXdDunyyJUlhAMRsjG1pwOdfPWLwWaiWxO0Xcm+swFhUnaoJQAu/VJpDDBT4WLo3SwESwBpmDp8t4khG7zqpf+eYgGAKSjZJfYt6X+3lbdZ2aHWM592iQXckaTpqeSOgtYct5Cf4nd91hQlbZUgg8zjuBnsMVuubptSf7DNDr5p60dJipTP+jloNUQueRuuZb63EWPctynPSXJwm1ZaR5OWIc0i8e86xwZmO7YDLMVYGeJZDPubH2+/kzZtd2Uwgo3FbliQBOHcfzSgFVu/fXdwWkMIYwMSXstmNlKym37/cLYVIO3NLZ6oOSuFmwJRPHBGK6euXP1v4ZzQ8dtdPL1jtuTz7fgevscD6UItfzYfr/Fs52fkLI6fZNhuWCOOvmt0D2g/T3OcsTtdF0PpKC9p+yqxltr91v3m4PaRS89ubyaLSSl6Id5rUz67GlGcdVKz7tqb1crw0yXAG8ImIDYuiYVw4ZIZmgyo19qFzjXKEE2xUOcMdd1VcPPLiR9u1xRgUv0LS7Ub7oRcIkBaLKVB8d07rkuoUeKkPa5TjsRO3Alr3NSJHW+OXDNzbaWBEVmOPrCgfoiGDw52rhTDGQNteR5kfPAoEkHISNMb5gJYLUYuOoTXwidjGX73GtHTGpO2suCZoV4Ry21Ilp7ogfQnVlKOYnG9Tw7dptmVQN5DpvXM7hmnFpTJ43hqfIPND8YKaC/IAlnNl9aA1iDWEPK0x6sbBIZT0oEJx3M+ILdYnAoNzbNTm4bLHvbtoHDlrhoY4ExT74VrybNU6Yc2OI5vGnfg1vzgQCtRo6GXRJCCIMjyCGKAGFqZszO1swQSrMLTrD6vqXfKJ27gMAPQpIJFbEGvl2xVvMCdoGDHMBfG1fAq86Cw4J6JwYKjTLQVTBxYN1ZQUytmE66X2alvGWwt+P78geXHwgpuwrniOEWIh+6xMmJbK+cc1C9JrQOxoTKjNYHausQOceCDRaEctA2RdIVyI5eLwK2q/4T+1oo/tY3utGjVHnmSh4Oe5voRyW7xxISZLkAffO4Dqhvc49lL3gKoNVgLE5KzVtBKg1nb22RPj5xwMZwIDa9+1hbjGaKl1t4zssHl3G1Pg9aNRrRk7hunydQOxp0kBJrh/gnv0ivhAL0ByUIQpDTJFcw5HwSgVge3zhPglQIhmYAB3Hi92jqFnygBwW5/d9YyTc3YuvxDd0npPUkbl0qylkQWZC79s7fxah0rDUlJKKWhLOp33lJO4BHWTS62ZMMHonBa+6jnqfWhRechLS2F2tp0IYWTKV3U6YIP/65ThzREVXgLZOi/7zTHkeXI1lspO6mo7UrSRbl2r/Ku1hpSSp9P302tq5blwvAt9w4H/0KyHm094CHd+NuaexOF47TIaVK9ee4Z2fRiu3zmearhoeDg3jPV0OWJOWP2jeh05pHRXJnPTlMannDYlCgRVlquh7bM1G9f2ptjCwIEK5CuAFWBqwXqaoayGA+UvPzNsstSKKinwoxzG9BPUqlwCjTEM7cBULt4/ml1G2H4rMwQ9Z2RGHpv6VjjK+bdl5eZq63aJP2QgL9VFkzKh90BnmXq6jT1ZHYvgjlLaMA4ICAgyWjoJZnD+ia6FYOJIYhBAhC2b6UVFBgG6wjhsE+yHwtz30fNwmvLgdggIa1enFLA7v4nQswVRtIxdS9gNehoALmQDkNo+EaCrPsQQ7mLA0mDJUPB4KMa7myjAJghpKuL8v1oxtyEMmcbUADQXnCIBmzDtfuBAfWrpg3jPufycWHltDMbZQBO1/fl1uTZ23GeZpimXlc093HNsT5tcsrB8O+zQKvUs8vV6Zklp35pfgCQGb/wy4ZN7ztAb7aAQMXVtjxvVrapAIDMGHa0yykjBBHADK0UtL30arFYYDadQYjcgeuBumwJkNrcgozdQ/P5OZmpbaIVb4MOX8Vwv2w6Ol3j28c6wjsf+vI9q3vAUNmxEi9NzU2CobMz2cLirPrezx0UD/96QO/XvcuUI2Wlf4cMljI3TRv0r5X5jUT+MDezVVyw9IpFUCygCyGMgBHWN/M5p5B17fAKiqBgd8bO1MtF87K2C2EVnfYtAoMFQdggAs4IUbQUnwylufH9a/kFBrSFOfncg/pNaOjgppvPLZc2ho8oYFIiPt2OIcYp+rWkTxEV9h0eHqJWmy1PyEWDyL9tpN1WJsbYtVJdorF9Qv3p79x5MsU9v7RMB24wpj76RGv9G13V1lXLPvflbgF1FPkIo79bUn3QtshF01HVAqwrVLMp5ifHODx4hMf3FXi+C9b7p1DyZsTAsyNdP0M0tDusG0veUd+evCpIPzs+9OnqzW34fc+3Yx3M9YfQDpgbP3Vhtd6SBASMj7qwIJwAQNuQncK6uDirIgkQ7K3XjoVas1nq0uhcdrS2t3VbxXYUo4t0oGZpUwEnMNj0YRm+ss37Jq2xpUtoCGiL4Zy23rj0MBgkhQ1OIKChoYjAJKCkEWzIRkDTrg7MkKShSUFp4eIJpb1vasPd24tYcts5B/VrUx9D6Pep3yz/pwsJ1zVvNi8EeYW/DTarQzW5RndkfRxXz2a1Mp9wec879a++p1n6Eyo11DpnEyS0pU5xWjcA3h+WWWOxWEDVM8xmM5ycnODw4AA7oxqsKvD+3nYKzxF1M49O2eusYLlzGqRlAf0yz59dCtXr2+VuLtpQZ8Q7WDDvfOatFluEVsWWVbP5ucnFvCe5CWzRhVKllHAO9SSl0Yg7P3+vXW+77aT1BhpLQNo8524Y+tOHLjwO9xv+Zq0H3nvDXfQnICB8Q8m64whp3ItC9x8WBKkJpIBKhQJGYAcZ0DQsO7s/AKC+8blyHe3cp3Na56VNeKJtngJsfOCOueZNSXCmHf8AThAwSyyR3Aeqk2qddU9YGVeGa3l67baPfR9Qn3a+K+JMNi2Wz7dNLl+ZZSCKVcZk0lAhBzoxtIgl9WrWGjcmumBQ0iY34+yYgwvt5QsI8hSANfPF7bKWB1XbMqjxe+am33NHHBgMFQCvziYPmLMltWe4r0AyCO4bC9M3lFibXPzfhjbbgHu3NuqaV+3Np2+2Npvp6YAFs2GStzEpalxVNLfbkI0rTWgFa3VTtVsfFHx3/aFNLj50fMCfiGBM5zChM51HKMFo7CRJcK0wm8/x6PAA7z+aYLJbohAzEDSU2kNBAifQEEKCmazNzc2VPj6QsUH4Sg+BPPdm835L3xnxjNhtxdxREPLhZN3bm8hYt12MGCbqh3ktrqfRWg7dVbI+pZFVHenMp+z7DoQBADW8ywdq3gBXupjdEdsK5yrVPfUaoAz/D/muCCq+TEjrdund7w9aKHQcSMDMHRusAQTFzZpy6TSc20lsHUznzZCevq9ttT2f5/IQLn/r6y4gbHQrWz8KFZZsw9U6DX4Q/hIAIK0l3q1CDRLkLZJuzcR96c7YaPM2w7u1sAXHDIam3u3e3CXDABWm/qQbpxmjPU/05cFe6IQYYRUGbo9VSpl1TY5vmfSFxUyCTSQdVWuzd7p5bTIES6CUjFHNrkjzTwNa12DW0DHH8dzP3GpedTc4oOce1JOVLGPwuQULOnUIBcu/3vFb+4KOoTxbh3CW1dilmI1jnUCa9zKMsFcg2qjTN9kAuSXo9B3+WqaWyyqFPFMbfKHjuZ2sob/1OrGK16O4TtE860zeCB7tw2FbAjHLZNNKQx2Pkjmd/nyKXe2XX84VLCm3Va8uWkIX0AL29j0mNBekJAvC8VAH9MJN0PnNEoC6rjGbz4zmXpWo69oIyYIaLT/bK73spjkIbnMyJZollWMrREvEfM5qxtKGdz8mV7bDe8syERp4vgXarAjyS5njn9vtXJM6s+kY667H7aRdKfp7Y9gK0M7bnyPqfTOfIFVy9AdqSHpr1c2qL2eLF9wqZKAB5gEPZ+ZGqZTrq4RnhPOvdd7KpiXk+j2YZGT2TefFoznMsx/Us627E1Bc3ZzFQWbcdV2gkgbQx01txjtRZCJNI8MfXEP91SeOMzlAb87XGu1/pXTAV426w/nxL7vsnntQf1q0iW/5k6SWu0zw/ydNSxy8P7W8nxwQNuRBkOVR7ta61gGpgXzMSXmO/PB8qC8GRDIP+6ITrUppuM+WEPd0ptFzSwINQBXJfCY0h/I76SmNh2KgZkCUI8hyhPFoB/t7F/HC1ct44doVHMpdTOcLa31Yr5I5BcJQONplxOgOpXbzPUjQBmLkFUTEAKtmLdro1e5F94L5q7W1VuRreJrhFc9pexQpCntmGyG4KA72iuNgLnm3DzdN7Np3tzeHvCA3R/tmytCdAqmqgywYdrYkAW1dbQSITZQbk1Ibn3rAW+4Ek40P7yqsmznv9FrEaMRt0w+hoqCpN3upmQMBndhe7snceW8HAEiyeVsNuusnAeNJ0d4mGSBtgLdrUNJJRABrhlCxmCVcItukmoI2Af5yUn/jrGz6xBlhtDYgX8qYLzgboGJGvVgOwzz3oJ453RCaEGotjHLGGOkq/oTL5GFz6uUC2+yBrNmvR1u/Gew+LWP2atQas6S9OSDSW29qh3nzGvsOoLMV4rYg1NLUh2UFTWWcldF49sj1Wk6TSU6SyxCHCHWdKeD2JLffJICld16xCXtYihKTyQQ7OzvY3d3Fzs4udnd3oYs9zKsarEy6qBmrzrNW0QMa2L5qp8+pnT5WisY1dQeqHQhrnzfKFPhE6MkqMNahdblDo+0dzqFx+3B4a7V+aUes634/vZk7n8gAWuP2mChj7MQL9NTJ33A9piVvON5egU7t9dDpa0+Zv00bXLbUZN68SXEKp/92ewiF+Xh+1Ci9GWgJASlpbfraGAmtTz01ZbWbRT7ioRfWk7qTTUOagva22+zL83UFnMGAQQg9gdmCeS/vQwDEFuhr31YCUJbLBQN57kG9UjWUaprJbHwgTW9tT6sZaUjN0ehWGiIyh0AG5eu8T+1qcd0NSSnjhal6LAxkF3ZgLhsKIZZ+b9/Wis7v26VefQXSewGWvZ2QrC2SwZF1Jidw+U0keCZIIPW4XM4dZ4m62XBaYb5pHPFNqdPFimOI49wQopae4/qVaMgAL3o2b+YG2If+sY6G7IpE7lZEeE1YkzdH86z1rpQQozGKYozJ/iXs7F/AZHcP5WgHJEa+1lJIk2/vDZitlrV/WXaOp3hiKOtkWba5dJKTDjf0+Fmz3S9X9Co0uHs4bWm7WmeCmjjoq/WCtvwtdXnweeZ0SP4vo//sBjC8AjNlus8YGpPGW9oBzCbrRBgUDgDCAt1UxOfk3Z67Y4bqzdIqxM2MdcCeXN4yXG/CVjsVXptzi/HzXNnmdyKy52QTa3Awb9kqYJ07kPO1J9fs3h5vThoxlM9ECOl/Dfuiyd9p1Z1A2KRz+2shDah3Lr0xFmAUwXAIa2l3/IBhXXwcXiSTjytb2XozTN+7iD+agFou5x3y3IP6p0LO5+IZpXXBd9fB4ybjgQw22oD6wAJHea8qGLVyW1JzKERf+MOAUfdVxZoRMw+GqrkhNRat5y4G/TNJy/T900FwQkgURQFRAGVZYlSOUJYj74N7amcrzjAR4C/GATbnOREtNRVOldk+NYq17o045VrbAX07f2m/dQqUc1vMCJT+b6qmHxrKnudZi2kuvQPPSUf2ubilASOivLOvUPC3UR4Kq1AwW3h8H4EH29zk4ddRX7ut4Cds4IZmuoTa+iDiTfCZ7TMG+/ciS0yrUnFFRIR5uNUX3jJhvwlhbq0Naye8kqUpY1nX2nNQvyE1IZPigaQt+jY/MeLgKuNToD5txjK8qzfv9tpZOu9VD/x2hfXs2rSJmm0orBOsz31vf2sD6lPtpBCb3764LOV963s6/Jy2TmbtdPc5A1BPeEAIxt+3kBKj0QgkCVKWIFliNN7BeHcXclSioMJsSJsHT3lmye0R2wL3/SM9xPGezYXLMHob5TTKIchCW45p6Xhygs6gxnc7FHHrDsV7oyE3TXK3HhfJsm8pynoUuENRYpr6wbv3NYJAvGM3xVofezQeCkZ3ne6L5n/xfCcPuCmomIfa2o4tyNQ97DZ/eZOrW4/1EG6d2QukmEHa2DpJiKAPqbV/e7fZBLO7ejE5t5hG8AhZcwiqOfhnLADmTIWPGWT1ftI2xYT8bOqjYSLZSd/jw/Tcg3pOFZ2hNLpBnkAjqYb+ds5dY20/UD9uCWPmoOCkPe2yUnE78yvFSdznJhpgWkiO+nUhpmocfPfGLPP/IHFvly1h+PDSb8rYO/PjJF3TO46xurTODNjOsKcwsmDYVylpBAONj18+E+bwwg1Cy90FjVahk4bm+pr7GUeZZywOtIlm/zRh3zKetetu8svUOxCO0J7a6ZqJnlBgPUHS+8nGskoLPMbgAAB0sZQkcx8iThiNk3MxHI/HuHjhAvb3djA9sVrAsJLMfqNrgbFOftZZ8yhJaCiN805AR7KmlmI0nfVq93jTlSFA4Fh7l82if/9ovZcp2BlE1l2C3PG5nXCIMcf9svxOkssLrYpRyMs53WE2Ix/tKVPJ1N1n8AzR0HbKScMC5tBKvsKgUgLMo70s6DviJpBDaAVx2Naf7WT264Zt2lg+CTdKMwETiOH5lfF7h1dwubq48t2bbMtlNm6BHHRO2hMU1BMw3gNSSFMemwPq8AJh6i4Uvpv7zX7WQZ0DEtYNabmzG6Gw1Aj7zpXJuZppd7A6U14XPfegHkhAuIsrlILmFsWLweTDwV8RS5H+s/lQ9KxxbSUwXycvapu/EoTCmq5dCDj7YrtWIdh3P9i4dEYibiRVF5Yq2AqzoDLFvDnMmD2kmVSOk3MF7rS60wo4OVtawSiU9N2G70apPRoxSUH2YEnYr6YhLkxVlHm4SInNrXUw8XahCcTWA1NbrkqNZoBs/GDPsNzdA1FnmOcaCBQKphWCgnMVrbMTjmmZttQwaZ1G35Whg/mWmhAb82Q/E+h/2r6wo/V2aKu1g++a2mXNQMfzdvGnBOwzTDyToPup9ys1lLqc9dlPGBSFXWCto1BlDtCn5zUaTVJwNiSUgslFq+guu1On1bAWV0mYcYzXS7wxW36i7fQlQBQEIgmGBCBAJDGZ7ODy5cs4FFPw+wq1qgFJYLe2lsHQbNaC64ugysG7yZh5sJfl4vHn0AE2rYx2eeWj0WgfUz19ZvxoRcTPzJmcpj5kV3gw1l5AUXkwF9Wzr+Piw8iOlzbliKg90c3EHJ+nyOYenNdzgCtHOd/3sCrp8A1NBQGK+LjbR1xfqXAtEqFojVmRtZSEbg25njXlJHrSCFP4VP5hdP4lGEvDn01SCy+huY6soa7XJLt2d8kBdqcgHe0B7pkh6bmySx9UHzq4BwUwc4GIfFx6ZQ+aaoud2EZSJzKRbdjd10NunzatYgaEru3WGexPNqEggZoyMdmZoYI9XGtt/7HfKpnZuOlE4+v9VwIFAiBJQsDcBWLa5day9nw89qt3GC/T4a6ekcXAfCAyq87E0FdNe63Cw2EBYgHB4YFXaoQpZjAb/38NhwAsl2AF5vM49Vuj3AHIgTcSbX5Cy2xmQMBh+oFGjpzBwIDaoA2au7PjBlhEP2fdL5asRKSpN/+nZCWnp8zTSuW15EkqSroqymu5/gvrlRa5+ggMUUYoYgOUc5qCvjzcbXuuJ51mkDrKiWn7LctR67wFPamSzyA57Vjm93WtG0/j3IPTygt3si95JqTxtfePQkUBllY8edr2IfAlSrR/Bxh216ONqrl+EN4ONho87M45BDbdeQ83bCgsZ1cZT36Mt0kpRLUU8ToXecXNKQuFE4C5DDU+6dT6zZcW7v2BdSyyHuVawuzXNLPl3xYMCxELVw6MZxxwmnLIKQPMk+ZwaFwmW028s5JoNoFN3Fmc7lVhFV/BJp7rS2aGu2HP4Zou99suHOT89P2eZs0ZTK7NvkZGkcNGoeMtP4GVx80ANzb+M1MkYDNTS0naReegvodWB/PuxTZLjk/Kp1pQp1ayEyZSYYT3Ny5HDUPlQBe0Wh5dtBFo4KYWLZN79yv9CSzVrBLfQXfYxdCyJxw8/kiFhFOheH5xappf6m0NMPsrrP34WHmqf494WhtnnxD3/JKb+80mGK/KIQ3pYP5PCNiH+UspIQpptHZ286+1Rs0m5rMLth2eeRGMxkKxRlWfHPDz+kzkJqvgNh+LaIPqDce56M48fZLKVGmE/DAeCIN9eMW1yu4B88/tgXs3kbnZZyjZi1zvtyPGIQD5iNL0nfWKn4nMs1i7FbmPMLfwSfissXLHbfGAWwPwngRuiZO59TTQ1GsElz/B3GcR5p3WSVthg9leFsnk2xLWOcY0rR7ymnrvCh14ZjA3ZTjgnioscwrM9FxATMaazlZX3yQzyg7H7522vhGunOWi8aSouYZmDa2FFWacS49ArZaD6x9IUB+erM793nzfXlmOvEkmSWcmYHqSzE7G9KRLz2ZBHiU4DX1SnwEJPaNUi37pYAXBn4A5hd8DsOr3jNTUFQ1I4vs3RIlVwOQbaj5X32HDNzfKJeEHUXhNzmsKVi7FaTkiy8iTO0y7Gg1oPp9j8oJb0Pz1dbJtOk3g1OJlduM018iLJsSbNZlzcEMjAYDmwGe/ATr9ZeT5FfVMob5nUVm+HgjcKNIWdgcOEEG6TC3itmTq01xI5NKEidYH1im/SZ+Z0KeZvu9m/nF+jt07cJLZYzq1pLnqhr+fWeCfq4/bn0MIHQDiQGrNhX92CZ2POrk8M9Mg7VMPSJsSYtzAHL0P2PGioG87plD8vImj7kG9zd5pj5ngL4iK601RFaL9iOOw3czOj9zsj2wLSTXYaT1D1xmDrZrb1/2B3KAMjyuoeb8L97XxkxMUzDfntqVd9ExuOEbj+ty0X3veb9rkwms6IYbBqGsFpbXpV83gII9l7zv9QID6vOZgCQazRVVimK+2EpsvxRfjlmeonU819fnNMCIXFspzFgpe758Z5vKDTP193plnjgcwIHJaNDvZY2bFWXeXdcnwn/U2BNfLKXHwNxyBlesVc9a4jPQgwhrUuVkO9Wp0Y1GbBg9/rUCUCGwfSAp2iVCPFplVt9Dl6wD7wbSZx53YgBrNExGhEBKSBLSq7abozNAdGSflZq1s3FbKLPUsSah1HNouLMv83m3jk9EKCQs0IE2l6XN5OV/mZMeuQ6SYpf4GtgSG8A3u4Az9WM+nIViroIvTT6FPenLGZElrSgMkt8dztku5HvPI3e7SMT9t5niTXAAxsLYwMB/ZpM/dBN4STUguJQq02+Zrc8eKB7ROGMkoGD1Y5qCe3Ai33GTiI/SQQCCWkNeIg5J9JGhOapnU2goOfuMND8ZmmxgA+0Cs8haD1lv+b3puqWmX/9UrKVz+TTkqSuc/eam4sLIeOZkPSitUWtk+NekkGGSDHyitoJmhamelELYPm7axOr986kxSc1iFot/shwwnWLOM1ibNyd+e91vpvA4heTtO6fdorwmy77WK7K5DrLUZrGqUZagt8daYZTLh4P22gmMLxJHmYrvZ6oAp2R/d48FYZrxc/5wKDfXwWd3ct0enqY3MhWnrA1hKpRA0oKSazdrypcXgwIKIWinUdd2ARQu2k2m68lBv7HaTsBin2c/n2j9GXU+HRrYFVILIRqZePXCu1YHhewku9mw4Fj+6xfm2r3OUf+DKEIT2aufSkclaZ7POBGXqGk6chNe6fiKrOGk08QhlgSB7szhC5RScJrlVLEVrMCtuBFbwWGBsxtfXieL3QitCCLAdiAZMniY0ZPMes/ZuoH4Gc3ucqSXINvtXZHFmCrZNCozxfYJOk3+oSDTtCvomaXNKkZbe8S+BZsz9ng7Au8g0lqsGBjFqNpZLo4Fv8tWaIYQEo7k9llmYdnPfVYP9dA7qzwC196hQ8lxtaJ2/WNfTPifQBqJTxNgTF7smfYTm0QLG7r1UF51WL+cis/KE1mn4sQbMdHsQNsW6evv7X7eIKZvl2c50SYtaD3HHZyfl98XyfZp0DuqBdBPdLj0JH2YigqBg3cBuecyoqwpVVSGyyiXgeZWabbOfIr06exfhhDqRPoAmMFbHm3F5Sd054cVpAAPibreffAnBu8Ffr44RzS/CRuHJ6Rm8hrWzWLYRnNgDvYTxdgoFXT7MffTkzk8MUHbfDKXUQGiiZgQEXMSYHk7PcVQ7n5P3RW+XHStywqw4+hw+b3y5+8fYg2pfhg5wbBv0uxMnzgLhlGpGE58H9HFdGz4VgXqSwXtAY3/KzwWjuHA5Uqu8sE/6feoFCM3FkdrPQQFiAtkbQdy4kLW2uz7VcNHBDJivWUMpFVhLXLkaQjb80rxc2JCZ0udmHyzNx597UG9coByscp2yHJTq4yND/at7WTLDXxkdVsuZa4RdINotQrM4RLhAkS4Xlxl5yTF80EwI0YnrGxm0gcECNNxY977bp6j5HpLL232O6w2Qi1ubyDPLsHMKGUTAIcMRT7US0fv+OINbqI1kD6IgfwTqkSVqxuFsc50Tzj/HqFo1Cv7vXk+N2wCxC1nazsbpiLqJokkeuYTBxcjtfBM+Dmhnp+Z/5uTD1jfsQXlhlfJcx9qNx5u83S86Sko91hF/+NrXI1fdWPdmNmIb0rHvOvhItZRrgl19FP+8MdkTsARAkDEnwxanmFEpBU0Cmqx3OpHf/H27/Obo+GFTMwJAoUQQPDErKaPJRDPvQ5eXUMvnuG+o2UstnDmgHwEAOME5Jbd5dwMaw9Kb7yLh2Txwm56zwiV4umlHVK6Zl6kgEb4TZxTPwRCkMtquQunZAe87nWwd5hBu6nsdSjIwPhgZq1K8Twa1dMJPoDU3hzNThkgtjXWzrhXc3pt2ebMVUauqzs3EzzdywqtBHEQu0lv/ShPkuUum5LywEAFiH+bW7gUGeoPB3k/dDYjLQbjiWtu7rYkmo4kP1qYpwoVDtb7jBunDzQ1mwAOVzB5phADLi8IY13afZP9O6EKTMk6TudF0u0PCcf3dLAt3KL9imKCV4UUxJrArmlwsW8NT3TrXWoM0Q8Agcef7rm1cX+E0+cpiKG0ujlLOR8mHQbczhgGlGu2+CTfOPv6/Wec2fDYxNC2HW59/UA8Ynz/vDtLDLMP3YrzTol6cOwAaurUwDjg0h0gCobib4yBYP9z2J/M+cICVNPPkgAEBfjUIu2ZzWCVXnZD5pt0Q8Jb2m5455vMeItfq4AhqCzQxOgBkMiW8wOWnTHJIKcNwu8iBh/CXtn95Lp9m0/am1Fy9WQQP2vn0T0V3CKqdyPRVNxNhJog+kNlXrC07/3k71I2HViwrYvjut2R3D+ZPnzuT2fD6aulWUAq8mgLSw3Jxuny5dk9wU7nZIykGZF3h3brICf9EBpiYra1ZwZqNNlvDRMbQ3teV7Z0Vwerwi5QT3KkDoYaCdgdroqcbsu0J0vddvd61MqOMOhbYUD+2+XD8TYvufYrZvB/OO7bIjRw4SliMVyhnSuTsr82TZp65fbTbGkx2/wmtNtE+FTUr7gPvFsHNeYw0Ln2YtuWH7w5JZvvNgKzQDc31WbOGG57bejsRRtwM9G84XmnrIOFcZxzH6Y6FT9SHB5o53xlVyIqwzT7hAL0brsBdhyhqq+6c5GSxhCXNPpQj4I6Ph2535AEwAUa+0O2MQ7cg3yeMQBAN1ngLdLjxDvNJNe06wHtRa4LxJevm4vY5k7eH72Hcf8fY2CgpmBmsXJ0JSmko6wIrbF1Im37RWkMpMy+EtW75uUDWkhGcqWPtimIA9h4A32Zu62o76LkH9adHQ1LTskEUMzlnGWZjSltFs7mO6b3ZllcDQHYq2nfRTEhLsSZ4u+S0oI6P5Frd2W+ZDS8vfKxLPdLYEKV7O6e9Ovx+/0O3o20fWH8QqZcrPMUuDnBw8nsM5Fd12THAS0CSQG1NxM7sTDCbmQDsIXpTD41AYB4uwW/u7HZg+5d62uUoXS3pNG/dn9B6P4agkcAQ/H9logHFUI9pkWD5nGu7F9RCjt3OPBSe1qy1ySfUkqxK3B6TzqQBuO+ypoT+2d4KnOlYIZoLt8ycX7XiCeAP3jd7hYNroXop9AN3KRvoumwV8i4k+bc5SBOV2LMv9pHfz+26dW52zkLS/CPY/6CZEQr3Yb3b9bXuMC2RiXrr66yWwfEDk9qE4rHgnqxVgLxyTPh51H7XZeHMGOQuiRRkInlpc1ZI1/D5hbxJeeu++eutJDZfM+ZOKHK/2+8UCjZNv5E1F/IKlzGeg/oO6ls4T6DwpwqyBFHkArMsAwqnZLOUcgmtRL/FNnLItFrZdosnXVrqjMJqA+oG9V5b1PtmAryWrNeyvWuY2Tmw3wr1dOFT4iam7FQ4DMgB+3X5nbAaVbfRKe38PxswFs7Z1WdZ8EYK7JcWDtzLwbdMe2N+0IALX/SyjCFXp/TV3nq3NQ0e2KLhtRT8z39mzgtwfXVbgULhYFXqa7ZxObDtyvheG1YZA/p0DLvOqOR888P3w2g+OdJkXHilEMmscK2yeaJP39/AuvababrmrxnXvFATuuGwVc6EbTJltEXRpfdeXxXrXhMMvmZ3VIPcfU52fPvFtv76hYXmv7r6xNGGvGQLD5rtnhbaS5pynZU62H+5mWPNWiPb9uY278bzoS1ChwE6GlebpP1Bo7wsAvJnEBprE/lbc33c/SXoAwjqnz3g0o42sfw7qxJZvyPvouj3luH8cnJ4ttarqCmWJN3eA31RT5KW6fdVxkboRiuSzWuDBlLDy8z3c439RvQ0Qfuq5Hw2N6FOlwCOP7AyMespnF5h8UNTLtVU+r/JBB6oK4KNuStNCiYjH3sf2cNQquXvc+Vx+UXlZcqIn8btjrLv46EtPQFHdRUU5E05H/lgkHI1W9Gak2S+nIIoAeE5HtipreY4/nj6OX6fA/DVJ5E3cxkw4L55FLi8UsNNrd7aa2/TgWmgYS4EYwPsOWlrl8a7C0D6yMXNkLf04l3UaJaN5pvDsdcMEubqJW3bLSgA/lbobs7RxcKFgvNHD258ICcdNAKe17IFmx2T1WJHbMzUVSkGoLwVMQLk2s0HadrtD5AzXLQdwYDmJoSltqDbAW8djpcDShnlXCx0WsGg1b9stfDh/LFCCDmhw/XdcuvtAwPqG8mLW78B64PgJ0ZB9XI+1tyRdhUKNUGdhQ9mEr/V2vPtAm1jAWeG625Xn6SaFYp7qv3UoymsQN2are3k7XiK0248S31zpuiMd1t6gDJaqxmXhi7yz9llk4JuAyLNvuy9b/07IcZYag4/oX4d2gOGXHVWBfl9ZXp3AoTAFt5Pe4h6gZq2wCEzAGG5jtJ2nRp/yMmHGXDfVYcciHe/9+4dfuMZmveAm7mh5cv87FxtrE+3z9u8493+kdmrlhRKuwRAt66dpcOA2CRd177cW3ZTppDNHPRLme09D9bFLjUukatTWl/7m2Ldmufhgf9o9YQF26/mjETwKzVzQFtxhLU7jN+8J4RxkmoUG3GtTUkGzANszwYFsJqSs2gcFIDl1n3YF6FvfSNvB3XybTwH9adMp+dT76lHq9E5vD3j3mg7lmfKHghsi487wTbNMnY+XJ9sFiLJqo9fc/A3V4ONRnKw83iwb1c9wLhC1iZ/m5ajH87peaJQ08mcB3RR2lWAW6cwbTdnQRCi6wDjqpk/AeLoTy9RAHBSWgfkO1KB9o7cZ2p8gn3eKXbjTF0SpKWTBhre2KDOdlMaDkmwwRMCgLnaXOkJ1NDT48ytHSPIMhYylyV/uNithSGzZ4CGTWQ6gpDCAy6n8WeNRvvqtfjxabNVpnXOxQjI7wvbVk26NmkVHJS1mqDOrc1KPDrY5dvaeg4OU5vTIMpPSAPsC+9T69aNDobJ3NjqBChmBId17JkTZn8tlzskra22XdjINtoJBsJFwjL9qq2AwFr7EJVKuxXQnvN2Jtl3Q7DfwPFw9ZsTIPFomfqZQPaC4uvIFLcj33XROajfhE5ThbplWs8SsX5Dehl9AihoW4A+yD78G+1pXfU6VewwAOqXyWENYO+F/iXSARYQkAN1KxV1TmeeGktYqCrv8itfB7A1QI9dRgAAIUUQOWMdenKT0WltGxYVl92OXDWQ1ybUxgYx6E6z7ylu0CrihRP0drfpnqd33qwzAsxWGFZ3Hv6JU0iRiL47uNfSgtv+EhTlglX21tQHPfvZafJdsZFWf731bPK2OYSa/sBTYPAsTshjIr4Tb/nuwKzLv+swdvjVlW2bD621cVFzJ/OD+oPDiEoieNdaIqzrnXM/Nq9oa41wQTjbPMz0uRO64bX4vvmpVdP1h503OhhbZg0mF8Kyu0uH6LkH9QzAxCAyRID1C2NEF3lHi254wbm9y0wEgbavZm3/iuRvWE6SH5rFkt9SwgWSbjbBhh2md/+zv2twFHPdf/br354qT7rGZZuJUtWQbHzGcj3oY926+oSLmgBdNJmbegf1dBI/IfrbUDyV2zHB6856RcmQGZ1cn3YlCdawaSZBtERs8v9MqLSe0JFh2ZmKdc6PoA4+tUiP+SVmdRQrbDsMjdwtpK5tNu5XauewWhcXA5/CyYmgRbSZpWttyNGpfjIkdNyr6aoO1wcn/whIL3RPqOdWVyDmV+kzBrri2DObN91hthCghHVH5nek/CF6nyCpgJQlNAiKCUrbiBPCmOyLkYTUAILwqByEyiMwdB/T2DLF+KM/vlfrGccZcKSLSCwRSVpYDWHk4kghFIzrl8Y318K8627+Vbas3E0RJrxeUvWgj9MybUUybc73Tno5XzRdSDffM1r5nF9xVGLfPQ/Ji2FAEHdINEoXlG+EFU5uTrY82N/y2Tzz69Z2jYSOyqttzo63S1ECZHyx2fYnEVk2RmA2sfDdAUjoZuwYjDBku6+wt/RYoIfmtlYTtlHad3X8MmsPSM35iIAvcDzQBkQG+64D/yBoKAhhYsE3IDu0EFn+TsL0obBZMdtYmcKH5m6KNhp2rRTCPdt7BLB5j8FYwIaZsXuJw1zMBpgTERRrv4oJgGICFEPWElJK1C48JTXpNDf3zhAJr6mHdW1TsP7zRNCsPPg2vMo2NNqzYncrpiYEb2iZUDB4SygdjG+A9ggACojg0i2vI2ER3AvTT889qI9iraILKhtaSYj1oxCoUMKHvaqRXEHc8Xs3pRehZOtvTWHNKuZWVcMaeX7YUcYmEqTlq0ao5bS+lNSrlQDhpSZRJV2+aR5hyUsMbroxLkWtMY7rkB/VANS3Su6pVDanNkPuST7YD154GkJYXdlw+iHtgTRGUjvbjbHdBnO0Q6wLqOfQ8lC2yzRsqO4rFh5p0jIsys9X7im67xlgN0OjKdO6ufnQmLSd6w21Do5FXTJUxoYURVCBBVyI109KbUCaIuU0UcpY4xy6/MOzDDlXPrvY5/ZpF9MenGdJ3Xh5ftfw33x6Tr+l+Q6ZAlbdA6O80z0EQfm5TskuiCi1b2+ry/zVS/axA9tWU+vmugO5jq9m+rodNQ7t54xAcWhLtGu74jp7PjB0WernaBl3O2r6RLM24WhFWm+YMI/Q9g4KC+xz7QrqopSGC7/atjpZ/ts1bYiM4Bew6XAs4NdIHH0m1sCHBcPWybgXkTDyiGLt+VlTf7sGEbrGUBOC3P0R4R0FSTQizV7730W5801DvCqk5x/Un1M/pdqUYOGmc3+TSCtNeU6abfzel8l2OI1eKtXpU+jmYOqTX75GBF8x6vxKtLxgEmx8ZLUv55SldWdZ3wWh26J0JnEyv9pgYTskBIGkgCZAgVHZq9E1MSABWQgIt9kn5ukucgqqTjl9KWpa7wUIar4DQ5aTJLcVJE5Gh1WTrA9vx3xoudnZRI3ndvRzZ9lqIEGfbHh6HGlzWqb9p0NpiW2BzQhd1sfb/d8gTX+OoQmwSH5ua5C1oMOODXsU65XeyO0jGkwmFGfuHMx68fjDRsGDbw1zoLOI1o/xUQfbi4DhwLNZ7OwBsU2rYsDKcG4wbt6ZDjHWEfb9J6y1oXFBa49+2n4HnIV0WnXHue3IUDNGTj5Q2mjQnUCQKilcvgUVFsc0YD1MI9wl8QR7KN1XHIBz5yFfeoQB2PJtDm/LXW22n4P6DzhR8jcEoy2t/jYLZYRrrJ2kpeHZpMCnvE09wR0ox9iXfBN5PdE5RdTTPV2z7In1aKhJTDT0rTpsqVLO/TC83IfZabgYUkpIKVbemKI6rlnX2PuF4h/WyXOV5bFB/5I7uRlINSmr7M1+qOxl2OFpTVrvJ7rOu/bvU2HnWRHMfzIaXDJabeHAdAPqnTY8BPQmC7KHbuGFLXeo3Wm3nVZXez/+Rr3ddSamqaFouy2ttCc4TX7cZALF7k8WnLuDp8JdmuTbwIiV0y5UZ1xWoH435YY3riZWjq4D/cZHHt533TYa/qyYUywmYN2UaAC31rE2PRZGms+pJh9gI7y4pjAQhezU2l2b1VhDwnlk5wz5+q6+ED8woL7li54BP2nYqP7Dng1z2vahIZ9ftAfl67LqAcr0UGqIrZ08e5pcM1hjzZ61leKelo6JPe/O3fznufVTonRuRExRN16dADdgwvsyrkHR4Y6zrPNbn3pbleLGJywj5Q7VmR/630t9sJcpw9/qaN1rwPnyjQuOaPxm3YYeZppgPW88ouVCOGbJ83rzv2h52i9qlaxzegZu/RIl79pDes8m2fo5bb7vCm54pYrj/bW0uL2m+kS+SYW+IYvsJlPaDMV6OYTOJ4CpZ7h3pPJC+yKxFMQ1adJ9tMW9Wp3SgE/3gckCe+0OxrJfExqAi2JkPEHcZ0YdavL9wmA405Bi6fNqCacZSsFgbg52h69O8rIz0AF7bXk72WeCyIyLQclwoT0BgrvvQCt3PCAE5IBi5de2qU/DI5wwwDDWPxvrJhICTFeE5QVTi6Wpr7ZRp6y5w5290OwEkTAGTyAksauP66+mTzS78TPle3uoHR/nr98cJ9CJMEL+PI5OZ1oitCil/LtDIXUdfWBAfUhdsX5XAcibMKdNaNnFmKb3lEp/CcCPJMdtN6+lbqKGd20KAJ/8ULQo67sMDvaDjJbqqda7Q/Xl5d6YwbTezs1/y9ieTzhvqWvMAtDlhvpJDa/ZVBpAHQOSJlZ8guXaQgjyKzE7nh2NS4F9eqmTZwPkQENThhOEGlbBG3diU3YMdJh5RZfCNi/tmwvmT9cs4KwiIHrfdgKjmVehAiYpCsHU6yVOEhG6MWv+5adM1PSJq7eF5q0tOXc/TSu7jFY0jwO6Oqk7b5eP1i5+PYMoKUsYv3RiIxA0Em+4N6dCRLNWXDSXXLmbc6Fg02bjhuOAvEAjpLgylXIHiq0bUqKhdxryRtAxZXAwydlf/NQ4x5H93dXFCRUhpHC/maqaycEAWKnAemCED6WbyDlNh2sjHgfWAS88IJ4bkVtyOLtavLdJA9iLreyidsdkcy6SIe/0c6jn4ryQPpCgHoDt/KAznwJA/0BSwJscUzKS+dOr0jap1YwIOeXn2ODFKKdOVu3nwmmd09rk3CmfxnTWaM8X9z2K1xDshOH2Fb3XMw9cCYIYENZH3Nm1IcBa2n8CaaSILIU6BgTa08zzVSi1FqR9I4SA6llfy6y9zqpF8nvbjO4Vgh0mdr+5O9C6pGzjx6YvTSqb4Knop9amQFnbEkzPBjVhFgE71txofRmNewpgLRBCmMuYYFxOyE4gcs+d2BI2OCigSyG5na4ha5UznE07NxYYrbQGQMLJuOwjhBEL5HAoswPxTbtiOSZ8HrYjXM9eJ+i/uzREBK2trcrF02dtfrMKBsWNgOEYojM2NLzMKiQQ3/irIogfkvCYMmcpJSIo6Cj6oAP1rv19Z3yWxQLPP6gn22WRljgz+ZPDDq1kgdYk/KmTetQnXVqD1mUE7q9fvF1lpe3rp0yzgg003Xy6/bqW1t7mCs79MFD/tEuHmruSW09ftbcOrOMR2CT/Pg1U+oQG0qdPh6rVqc3KWixy1J60Z25/DshFs6D0N0upJtwkSL6vC546OiYer/7e44QZNdvZlijkcd5HNXhMzQwk5s6tcVtdthSFfbYGsm2x96U3iOXyDX/hINPeMePMXnJGUXvbPbA7baiJbeWT/PXpo/EdrE2cMFkg0dPMJPXRcFz/Ry4wouEdGUt5Thhv+IkDmRyAzmDqBi45afu71nfrvE3YgSlWis54NO8brb1oAL5XhNtLnRhegnSHdtvDEaP3dit6hi30jQk+x5GuAgDNDJCLSGMxXwcWbKyLSV+jPa+atZVMlEx1KcGQIaBv6phqOXqzbdFzD+qtq1ZA7H+nJE6qVkCku4pMxu5a4SWYBBMkSyd0RoNm3uMmDBLg/U39dzRiaLhZhGPtFiJr45W1LIDVieKMZbO0mia5iRxP3HXAZ5erT9Q29CxcS84lT8PwCfe9M4pE+JcBwcZY6E+9Ry+1Rmg1ylQgkPXjBOmO0K7MCpSrd6Kja2k7ApKc2182I6/5SNrt68N2zCTMRpBr+4bjsQnZzTmqzZIdk7pzpC3bvEVxn6b1kpSpKzVrOVrjNhvv292xjhoKDnuRhon7bFwJIGCicJANBWc/a7g7ILVJ52PV29jYIu1pkRl5nROVlqJlgGwfD2qiSOfzdDw81Yp3KUhWqlvyuHbGtMxroQy9DCeJDmr21G8tfr9E3t3UPwmJ0/niCwXQjg6TqguiOyVSl7AoY/Jg1IEqFzvFJcvZn/xhWCIoBGFcLW4QZB1WIpcKhq4bcBmFKE7r4r4m2wcLF+o5s28PrGuhTVylxuU9xD4WhYJ9vfwhTxAEGAW0Wc6Ry4y0oDmI365TvsRw9wKYLcOVCwQHC+yfjN3J+vSEZwTDdte9AJv97bWRax5JkChafRbL/QRm3aHYafY+E+c/V7JA2hUh1TK4uYUB6QAkAOblYnU996D+nDI0xKuXZMpnVfsDLC/knA3aZEB4iTRnl5Y6lP4kiRFpb85MvRJa17rT1Z5E1xRvWr3p85ourc1FLTmN1rPiahcKGBFg9krBjERMGF7OT4DCKpyRKp1parS6YW/1rH1uz/328TWr8Q0fkn3PYebQh6TJOqFgdTLQqHvJgvxTHN2BrggUyTlOgDBOe/oeR8unsdy1524oxYRqR3g3p7iIoVCe7K806Cpr2ZjwznLiotaE2tdcFbIeIUl+/inHeZy735xTlihchQk9D4w/3HzPJhxLaEj6GDzJ92yD+rNGnHDSswrqu6hLw5paA9N3AKDXjS8FLASjkRfcmsNs1X5GU5eLtbx8n1Lr3eVpU7DjrHrhIdWQt9TB59iqa9/fqPQNKB0r9BoNsnR6QDGuyLoWge1TArYG+G6X21DXgVttbz5NLwuMo70IX3STe594ZvsuEZxjYXMz/jWkTnL/jGY5uDGZ2UarCVqS9JkOtfMW5SfYPM+K3K29cLzBZRCU54Sejnqb27So/XvnWyHFvFQFZTFcRJsOSn2QMo9hXZWckLGsgOHogwvqs2JU8PmM7uXbYIC9eazgP79OWet2K4FjMx2QHUO/AZ+FfeKJUKhdyuke+t99KvspYePN5knR0LwPN7EnoRLdZP1Hdc0nALB6OwyQCLe15p/Zcy3QIIB14xV91pdouKo8BTIMNY2OtPhO+dp7vOiUF14/FNyAaAkt5mABMbCNhbahftlev/VZCT3QpkbWHYa4mXy4uQWYWWeqT4FrnAOmfUWZCsUaXCdYNnhxK9w1RLlpvW0d27tP0xJOGsJpBpz8Hp4L6Jhn3r0rlL/QuC+F/d1H4YHjdCkykKyX/z97/xZq27Kdh8Ffqz7GvKy19lpr733O3vscWZfzO/qVYExiMAgFOy8+RE6CkSEvMocgHBMFjB+MwY4NkUh8QbZsjJDiRMljwHbISxwwxCBkgzAWJ0bINpH1y4p8JB0dnX1f97XmnGP0av9D3VpVr169j9ucc6zVvr3HmmP0Xr2qel1afdWqVavUz91G5oa3G6QyGEl0kGA+CXQxJNt7yt5tDt4IUh99JBeFIi21ahWb/b5tZgINtFxzcZJQG8d1o/DZNp4khAqT2rMAKq/fdvawA5yXNC88q8sw4/U3LfjG29F0xobu5bKEwyhUIPkz7zdLT0Y/k4hX74FG2VhYZq3Jgtoy+r4xtwqmtJ/V94/kNLzHdGIEV1+mI4B6gNauQVIP5317DVCPrltELxLMNh43f2gt7a7xE/zOjzCmcy5btm+hM9Mv25fHUEucu2Ss8q+izke75lTfObC2a+w95QRq9NnWzRntIGzmTOEbBJuG1u8pqUJb7809alkgAtjkNuOUuVRM5C6x2bDBwscZNNyMQRsgtOt0arLi9g6k+x0F+eyJppjAZhONyPqHaceNrCJey262HAk1A4uKLT2L8KFJSAcF4azWad4yTpQZubZ91HXzaKz16+F9m56p4v4AMVnakIO9EaR+FDXpF3BLuOxB0JJvx/DeUpl49IR9klpvEI9UYdzeikwDUOUel3qS24aUt5ZnqMOlWrm3pcKhpbmX99z8S5KMIUGUniJsUKAwozMGBIOu67Zuki1SYkxriNxNI156qsji3TpWH/eO7aZse4duh5Me2K4NN56BUYzJgzltsNRqS83/mMcfh5oaXca7v5HAkNgs3wiXiGl9RhRXJogcOR/cd0/ZqqQp4SdeHAiwaYQV8XD41nYFOrTX95MuaVY08vBgMZobAmWPeKNJfVbmpaZ+bIZ+5GjOEnGbRaYDib9UXDtO7KHEgxpR2GUeQ6nUBsFI6m9/9m8AQ6HUspOftKEvfo+R+8kWSsbptNn5pWd/sAvBafbILCbJ9ximxLA71KceYtdVAEJdXt605djtsUO/buw6Qh2w4oqGOmZbX/MolzTXtTzKlcOQSJ381/vBvAOLqqD6ygzBnSTrnFo2JhSG6qS5c07tbXzW+cCPwchp+sMhTdWpRPDlL1gAS484VJZTljN0LO3ik/yLLiVNuCbeW2z0JykZCiuQOM0qkqc4Rh8Wrz2pZyBzH+kuut/x2Olo8ybCUPnIuDZrLN2soWbLaRTvR5+p5bN+UIwdnfxJx1RmtMjTyMwzvYdPt2nwmXI8iGonucjVX0lkzYg85L8WZXv9NQsWtcUAuFTJTZRxDDZlozwLbU2Lw7i+kEuGwfmXaUImHp2yQWs8O/XQWBnlp+nJweumsFva7XMAtskDCRZZITVZW22t0kjNUkWWIZycWKY/fD5aeTHSQTnRPMJr63tve8oWxqTNe2So0gHTb9cf89fbFpKQhNUGuZHOEQeK94eiblyD18qhlP31SStEqo3YG2Zgu2jmpXvTmIuWSVq2SpO3pGr4Rq1xrQ1LBH9/27ze1FgwmixHnUhVN8yuTTIxjB8q4r4JyUtRyDOW18pTSJEI5CB/nH/32mdpr57lMS6ijMlYQX0rIp7jAU1IfThwJErmLBY21m2Mxh8Lm+z6Kcug3PQ7GAJG6oMiI+ZY/kAy5wx9J5vaiLIM/CmTWVk5O7mUZYMoc5Ut6yBK39B3vXvs8LQtK3Zi7LWFXKKivNLbu2+ldJ/Ca0/qw6wPADrjZohs3ZXgozls5trXps6YrohOanz6lKgQcWODg+i0YSBt8XFmb5M3GiJlsfbOQgJUl+5m2lnXbSNF+CAUkY56bjXazG9AJV9NfYTvpGHuLtMJJulZkdUyUs66xcC6O6lv1Fdrxy8D4HEN6E70UginmJVicK+C5N0N32unDiei2cUUIct/Be1G2q6v2SjLL9jQrgchE4+WPch4OSHbxtRpwaE3YqCeJi4GnkBYQd4/tQGTAQfv3ZbA/shG7tfAkgHjBk3Tde40RyJkrvngpCEPkt/BxIncmR+yn8rasQxwJExDGOozYm/lXwK6mt90ITtDmlNuRANaSqU8XLtMpuRRjVuVsUUSWKbTImmYlGbD1Au5G0hzzJOhInBj8iHHtqkJU3HLnXJakC0RZG3coNNz6uYEMUZSiitFK/ffsDfloNjkrQ8VT4/1Mjf5SffEmq3rL0DhGWfivVzCSP0LIm4HW46c2X1O6cX3SO/qDpQ2GVGp7jSI/U+k5fcNxHZWknQkhVW41wnJwLBOvvgXs2WpFGY9uSzkQeO15Mu2M7BB44+ijov3KjX9Id8xRKVqLJDtoYj14f/Gsy845CtVuJ25RPj6k3rFzWMfPEehOHLMmfzdmo3pG8ER+7AcHl31eT/1acyf8h99vRjO3w430bxOlIqLGmGfur9NOps8Owc5mbxZk6Mw/xyds4+oU0mUUraqKsl/mCDIeW58/nYhd/sJtHJYugfeOk3kbY0QJrYbxFFMhOWCEANO0RtNDb1GdqROQ3z7BhffCYgTDBTfW1BSrzg4bptg2hemDpJQKPaFqGsb6UzBrO5mEHRaBszkDp6yFn1v0fd9HGRuz2bLhJYHtLZd7na4LnI6esot2pr5qZzJEqnFcyhsWmaH2MROyN8/JdZeQ2BQ5RRReUKzeNapa9sTiCPBrivYpWVSmPRsm5esTbBcCwGCCRHHiUprJWy7PDQRRSPHVZs0idmsHb85pJ7dMhYQNDSMjaZ62yY7tkZ5Q2i78/NLgqP2z+P3xh8Yv0e4lio4CN50Ml/T0imOGH5SwGMV2/D2UPbhpKlPXnCCS8ve9gC6tHQ+KQyHE+c8vcYAzLkJRMibfLYlfw4lmq7D/elOmJm3YJpQXrwp2VjzAlVuYNw0rhCfNJEgIKp6I8nMmhVFM6V4OTOdIAS3Ki5MYSoi8zEjr6332qYmsv7iN6LP3YyePVtJXW4OJlBz03lWpOxMbgyLu35W6URMnlZwvTmW37ifUqTFKNpz0OYPFhzSdHabtl4+kS8WpraTjKYC1PymjjDYDC63K+c2LYs388pTM7t8IBvaSe9X/9Iif/vXgyn2Bhq2o7Kt3KIuodgRcqzcxBy5FRsLWZv5Gd94Vb7CHDd4MiZb2al3yCYcD8I5gNa4hV3S2sQUp7bnig+gvt9WSz80FdkeYa9ZbcwaaI+lwpCk9jd7Sj6QZ4/SuDmrKKfKZ+v2cNjVkZZ5TjYhAvy+nuD9nuL+g7Eoat6GJAKpN0g+/mWag7FuJP6NUTSgYdy18qPGvSHePFKvuFlUBgKFYiPsqgg8wgYYJ9yj735Y7WhaCvYDLAHusHb/CWvX4Y9nd+xV/2RceLAF0G2YurAlpVJNsGlcG2BMjRizcEvV7VPZKu6XRSpJaq0Ers+l5w2deF1BNIuAK6qgMc43kCc4H+tpU2xZ5vkmy3HV1+BqRSE5K++4HsXkgOw2ktzE2WYgv07EMIyfkIffLkx6T/blPxZXaMNj7YuFTX3rvIptYNFQaMaMhQs0vDcBJfWKG8ER8irFLcL16J72i9u02rcxSt4RiL3XKgZPldG0MYPTgRk4/9YW2KAiSi2A0IJuFM+2qNRZVK9NJ37dWvqAjVKURRqIzEg8t4RjXz88ecy0x2XpFHwsHgw7qo2eWKsOt3yHqXO69mpT1mWFpvvWYNOshAWQmQ1xdA9L9EmaX6p5Qdzn4hOLv+Nxjq3qKKl3sBw3nmSVJVeDK5VO7oHxeBeF4zWbx91zLh2HM+6JfGerc64JBO3AtPZiag481ji4+JuH9Tq3RqouXZPWHN1zYSmslg8mxMMnJnpqq0mn0+fkslnyv9sF149RUKY8MZU9GzH/zNw+sauCoQ0wjd4v81xFU2pXpFwsUwvQ0I1YkoztZIfULFdLDaqr8AW8MbL+eTsGnpbN6By3pqUJQHyr5vuFNpgfu8LiVu0ZGtiR7nDwTBN+oZqdxnLZdVgYA4IFcw/iJezaYr1ag5mxsIxz4xzSGb90bji4uPa9tEF+ou9h4AD26KmMCKUZRJ6pkItDrA1s5h7Xl38rRDOeUqCF+LzbU2ti+wyTknjOAIDghIPgTgQdTLoEKP5Tf40yl2WLLedOg7ciYR8tXIwmRbhwmVybYAVNryxST+5kmqEJWtFUGfnBSMyFopVdGTHnvslTK3OxEBdEe6Dtrnv3NzF4LpPkd1M4c2B4l9pFGWTJNcYrUHLDbajYaF7GVZsRMgu7eHfNmsZjI82rhzxXI9RIaLOIbaucCFkZDVXKooixKnN9pAb5iwzkRxWl88783Q2nU4xC+BRCvd/kKAj9HDRJZGuZSQqHkVjmzq5j+CBMykZ+cHDxbTp1qix91cfskmQ2usREb4kT75psJCfwWdzIhah4MAaqVehhsGlbGD5dbTETNTU8WmdXjWKrxLZUyNx6bG1b2dTg0uD7xqkcsgCDQoQ9cSATl7wB5/mGrTuUxVoLYkYnSGE+BFcuFA2p7Kv71HyXbXYw3EqStGncW+QzkvuNn5SYITVoM/mWn6bpn2JhYhLCVaV8vRzKDaI+8OA5htibMJnrIWkPm1KzscbnXx5GJiVpNe6KnTVDDlvDuov29JkGuJLH1jsUzDbrJiPyh3Zot00U4+tU1stDn8qg3LxQjTIn3COhWH6RfYrS30k0w1DefluTmSJnVKtM8XNUBs2syDeH1O8ZU9rY/STi447T/ttNdaZyN3o/yKwdRrLYUUSZhVMywQCTTTN3Gidj+eEZs3vqjSENKHIWn6Sjazoh75Ulh1aZ385XvlaMngg68/oAQnXEXHpf2W3GfiivI3IA7ZB+uMm7hSFn42r8PWbnytL2DNsDZBggi44YPbaxqW/nbTYCtxO/s7gqpOK6uoAkmLfJTKvMSzC5CmVpvWbxUGtCMg/7cgcqN1Hmq6ZpDElT1HDPIUhQs1E2WJwuez04tNkXAf7E6OJ6S8uPWzSkXFdFFAiK2UNCSf0GGMw6r6GFyln5dbsK29Qd57akfqC52yJuOedJR7GT1ySlWUNNER82xcQNNlJrf2ukUB0mLI1TyVb8sdfs9QmVF6fG+92QzLt1KAcpY8yAfG3WL2t6p4DDlPohfKMHjVJ09wckrS0nwlSaBphDuHOc2Uernlnaqvo3EqUJhyTV7ks6yTOQlNvupTOg5hUlrgCItiw18QE08nc6zS0zuyGmCPXe0pkZrk7ob0EHm1MuB54QHRJK6j2qdvXlQogMUyg9h7az21ddqRWoLMbthMnOPpr1GZK78uyAbx4AQdMfBbQYoN3AYwPVrT7LtKHh/J7AgQUJ1Ow+4xe/nGiIYAxh0YcA3hZPTla8jwBntWwjua9NbGoY2hseP8LgPVczusn+iG1waO3stWh9idB1HYwx6CsqW/L3m2dkeAY1IIcV7Xlp49vOWk5Q557KeHgkLWetTbVb1dD4oLzdLpVcw2qMp1v++PpgK+yUBTateBax9v7E4Nw6oCK7POs3ZV0RxbNjZPjam4V+C+Tlw/A7skq9RkoilkfWbIIo5XQ/2wfm44xx+2xKDX2enCle/UCrZoV9/I6Le6NpxIlcLOjK+9QuXbPi8dbAm3hVxFUeDDTYUyB/lmuYkkfOZShvDKk/xOlyUshsk5+xDS6DdFwi7s8henFIokx7wzJrN+YDa3Kidj6lFz5hUaVqwwmAhTbqtiFTJHK6ZozxmtI8tJy0EBE4bp7sEI4pT/7Dx1WTmztOK/J9S8vzTcJcGQMAmCSS7XSkxn4sjDG1YYkGhJ5qjX4HyBWVjWXaRPpbt/NDCsMthG3aCMtRAx/JuvwrzEhCnQ/lavpVHjFWHgxlSmUKYXTiJV8rfJd/85RjBqJJfW1RZkzfNIybxtMYxHB4YkvyywGTK8tkFwQF1nUo+a4brr/M3yobUOMkw/2IZQts440h9ceE6mEWtwFT84kZivxWXPs6FjsMNoFEGKIY90AzBABkYDs52DjNftJo3ZzkGfqMJhgQOt/FTTAXoqBNkw/0YPRRbKS/LuLV9Tmcfm0wfjrpnIcptqfBYoxnD/uqkX2b3IQJcv1qe1JMFTLvn4IBYc0cHQEQCh8PE69gJpTWxoj7HJ1e1VFozZhwLT7Zt1UKNSLcITcARU/akkLL306hYHyd9Ujtd6N0hFlPOfFiv8l67Jm9tu24kXYEDJgZDYGDHZKLtZ5WinIrlBtgQznVJsw7rwLum3x7xUHwICT+uVaUw2Q9wIHSLnhPzaGIhKzXufX4+pP6CaE/+XhL01WY3GwTb+30uzDgjUU9lVSz6mc05tYm0lbcHP/ZLNmkzZGEaSKOIiOybowxfthJT4Wcd9QN3i9q6oUqnPzQFnNXLnuOZyV/AR/vWB3Wn81/RGJf0YYaE5Z9rdCoCfIOqb+XZULtCiszkgn4Ee2+WEmY7g4TIfZIWmrCMOu6syp0XtrVvlNpuLU1o/pg4+pqskwjqQqkJ12X3kM2gqjPMnXZdCIpG2sXgpyl/S6IkRs/myE7XDviLO26brVVXcnJQCiiogzkxuVa2pPycjzAWK6zx1tPNx4a2itvw0REDgtV9sBSpgjuNODBI02poRxmPBwGFDWTDK+IoGwVR+7FkOQ+KiwK2Zh/HZY0hXdzkWfv47JAlTF4jJCPl3E8ACmI1rKZTfa9RjuKq/R57mpjSi2Hw9GHKt9monyv1s0KQv9j+RYh44MmNCJP5uRz5JmQ1LY8Zk4am4Qs29RgckYj3xt47Un9VAVKyHZEkfSMxCsPOUTmSnljTC0Ls4HwfzuRL6Ct8R602KQeJEbTV/zU0pL0EzzMVZExgtOGj772BHnykYRyORFpEPXeNs3P6IgBWP+VQIYALH1EnvLaHoDzle+eYoCcCFpbGzUMbk9tTmAyylyZ+NnUqIYg09xzTes1CHA2y0gbY42PzuAyycUwUMaic63fwoLB3gOQJzjMOO0WflUilagsX+NtXpNv37FdHhDkqPMC2g7izF9s/NYhwQz0cbOAnz5neZkzMG2SoCiziQcrujb/t5+VL3DDqwzbCcHg0eiPuSwlWJB3FWsA08HQwq0cscH6ssfl5RprC/RM6BYnUcZ1cOdKgF1YQyJrhXhjgltr8ntIrE17IYwhULHxo9zU2fE6SAFXz77tu/SFHKi8eseJguSyxMFOyqg0HSrjJjLNKu2KHS1WSN+QG6BuZ1tqAysh8l/W+6JnN+51VLSjAdla+8lYiM2tHAIE2wHWcJQpbG183hoLw17e+bQMGbc/yNrMLNCdt+Qe7PzJZuzDhHYY+m3vE+hESaczSmx840Fx+7bAFFaHxCRxOLMcRcxvyLMTnqnVsB9/8qSzNp9maylMVrec7PttfhnoDCziaS/DuU6B2fKrKLT6hKGVVq66IL9q5vY/cBSJYbUtchzJwXYFid4r4m+JwpYDiTxfroAoc21ElVMgBIkv6UFRatbvYam1v7kGkkrqPbLimrGVvyzzw3IUQQq3WefMMHxe+gPOkhnJxiDGKe2l+JI16k1eY1zZkC55o8/Y5USYweNRYKVnpMLAaW7gNUl5klSSer+mXzsYJ+PXIp9xgLacd+Li+WzmLjRbY67C0rtL+uUP6iCDOGVhipsaa5vgQhyGOgAMazkdlz0myZmKgWCsgnm607TuzZNth4xgFkof3v7iteYhS3KLQAMyKv+J/SNo6ofPW0/ESoTNmVz010i+RYqdSZtsjTFecyvSDPkQGZb9gJCULsFEwvg+zA2zCqlzYCkcZGFsXY2lMKzc3iC2UqLPeyJNWeJ7hvIcbRApNFW+ZUNV8ag7u8DGM0OI2W2gFhPr4IksG4v884YMrNemSY81BhTJLLF8h2F1xbyUFzn8HLCuooT2AFG3MdbYjofhSu/8g5y0BroaZkxYUvkObvgkw6RlC6JUFGc59tYebV+cES7n4Rs/PrwvKyw2HnHPIaxkbRS5e3BGoDpee1L/umGyPezIFUyLb+0s28Y9PkyBOJ6RChBAFnEDVD6m2KixlpCmtVm84S/l829QshU1xvmDMHAkJdFs48OLJeUC4bRa9nni7En4lRH3EuE1ZJ6YArk2oxv+2mXJMF63Y9mm+SoFjefw2XDd+PwSHAmz7DSloYziYCrf3T8b50u3DSTWG6RAnontu9f0k+6E1V3SbYU64CSCKLZz148SYQ8kLWTBWsGUKScooY9mAzsBFA638mZsnZGa5DCZCO2S4nNA8laS8uQ+fu0K3HI47gOnA4oKpj2xCVlq7GrVWvPENQdSRowF2DRmS76s56Qf6tp/XJf35c/ypFNPxGv7mEK61sItWiSPVCzKOZkSclS2BC9GuSyluEIR9k1ssvXbvcuQHO/bm0tjrXMUh5SlrSF9uvym7lPGTUNbiZ6HtrX1P3K02lTnJ7i22PzjVtLmtcXXntTf1kM9muC8QwXNQ7gyVbmt24MJbKHRZfK2j1FDMcxb9UfMXDNrIo85MZ3jZlPaCQ+m95KfRa2PGDgHGgfxhWgQLgo7EoSArSe606K5LNeY/1EeKSpYUhCCNxdKA1sl1CA3U9UxPFQmnzAk3/0pFkPOdIlB4OaOwxmYaCeTXkf2kfRYGg05MbWrJNgOl9crqddj2KTz3iJkfVl8qclezv9Jq2GMocY/9D8WXSdr3OlHMK9J/VYE8/0vX3XbZFLHxV9kjGhzzzqb958st4PHN4lvqDl0RZfKciy2SMzGUhTlMQdpM2ySP/JJSfCd8wKXMhlntsWDeCAyt+Gk3ddn3CNQVvkB+1/QjNeUXuVi0gGn5wNw5eUJw7Fo5OH8Jw/bT4y1No7fVsgiqVKkCUVBK2rZCASstbPd8b72pH4uIvHyv0PD29dEoL5Zr17Dg9kzi47NPMs0djwjQ2KSb6yMepYsF8G0pKaoIvHEJthkshVkdLQXDQO0T9T435ZtZjYT7d1NWJ43kSS4G86Tu4k6PI62ssazBLJpyTdqGsnZqoMx9L8sEIahaHZXVG7QGPpcZVq4MMkCLCyFg7TizeB5HrLkU1kG29PxvHWUUksHy1A0UQDIH14VSiYMvC4fUzbFbQw9wATs6k6znapLYQx1p6e1OObE7yo724DX2jvTSD2S2VtI7PONsgwIjWvuVztcC9r61P8Td8t9qJdbhDPZxwBLo3rRhtPziPlyK3s5U2tawFAKU7OLHoSX8oxTO66by23Wxp0sCK9SsqVSLk/LVXmL/H/BLUlwEFDPB6KmNdpwZxOd+W8W2oftLZgoO9iNRf6Zg1x3ablVS5ebeMKr6aKiIU7iMkVJG3GFwXc0MdzOeToS3epd8pvBRT3JVZzeN/7W3qo0sRm+UjGaD9v0DjIjTrlEHIkj1cadMuFWBysUABS/+vJs97fmPsSi4nKJvBuvi29Vkce1MybKtFr+5tkOR9VN86qkXpGheeAWINiF/B0f3gpztTplUkMdAmf3RyOp3WQgnkQSLnAgvEm4G2MGokYO2rU3GYj8qlTOMxYmFAx5efig1KzLv1nwgVoEflJECHbwkgalSZ5vDZw2vsUWEsyVBHnZR1s4PNyGy5JwZthpTjFslfm9duS7TWcONxlqIbOrF3kI9vQkCFtLueE520ZuJCXpT5Nhed9FHDffhb+JmcxLh6QpQU5ih+DsXn315gDYU/W3vKWEJpxJtEYRzM0bM6MPO9hJKhDYKxYC402yNihT4j4jE8wMGVIFwrVuR8Nrtfa5F9ewB+iW+xK17WelV6OWXJs3kWwh647UVnBsHbdMY+cIOc1uBSrNavOoy/h4MHVuQkm9IoI8g3QaAy8ww03fkI3QpJWdbsLfyY6Z89kgMduGGJyjkX0KPqY7qGUyKsAEsY8W5b48ho/5QYMICIeoVMhisPkEci8WMZ+FvAx2yQQ45ykhTwWBYM43IZau2YiTjxxZGJH8MKKZDQORlMmVm2BXDzDcgkWaHWRt42b45NaQG45Ld7K7RRwEPdUHj30dxjCIF7gNlSBlADGBLKHrDIwx6Iw7dVbOiqP7QiRbaCv6gfxbAyXfUnG0HhLo3COFdCdsGjXekzu8LSx7p77lCOao7/JSPnKu6Qz3DnGGddQw7xDHmJyLKQQ5UHOzU7kkm3wrX7If2nKFx6fNvn91pQIDqYy9taLb3I8gu0sGn4aLKNYwTux3xSGmdHs5Q4Fq/cXHLyoz9ulKfb9poLBcIlbypB6tVSRT/X4dAnnIfjB3Zfy1J/WDQh77QUD0dRQ7+Lap0qAXbzTbL5dw8mxNPjuSI/+3FFjlMyQY7iDywfL1eEqboWbDP3fXeJqBpwkHU/4K4+4CA9mWlz2FjzPyMICEevTkF/D3XDjO4pWp1MtyxqvFoMmuP6U+NYAH95wpkvRE6BdZiDCgDSYZflOsIaH1St4RZN1l6U1gfLmZpplAI3o52AVNR/geA3iVStSszOtcKdImKMXNnBpiocYpkyymSqNRb4UJ+VPPS571YTTJmSuRNNvLLW4ZcAdQ+dEv6uB8X2KvKJAbLGUzbMnOkrBNacTLCRz7BOPVws4g+fi3+eSeQwyNog0JVNR30quWHC9ifgr7WfbjU234yuZzUn6N5anIc1yJHJH7uTcPiyAaTcxAqWwoImiUT9TMyr8u8iTPY1vg2HfZWsA4z1zBFz15Lw/G59O55A3ES6ymSnO4VCQACu0wlRPzdh+SsqaUofLnmNyutdzZo+xo1mrEYTiutlnDWDplw84jqr1f6g7DFOdckbG1zH8mxfmcwh99tpLTJEAGipuBzGlkzvXFVK7Df6fx+pN6AtgPOKlQnDWgkb6A2c3w2YpK2GCGnnOQUbYyfK62HI388RD3XCWfFOhuZpjstQdWaoN8e5/YVQkzlYGpl25ak6FstmGwCv55a/EHAmctZ5s614G4eRGykGWSS24wGGvA+cy2BobDVljn23stXDoQ4F1YuhJlBtbExYARLT0BMCxsrOdghpCTpVA/w/fruEuTCXZxutQ7R4aitoBiKQa482ST+z8CxbSlxjErTylu4xfj8+snQJ5xOW9E3k9vqcUcvIlIgwHL0g47h6B9MU95BHJorKFiiyleNdnOury0HKCU+W56SwGwwMKXYfD8ktvnDhaV581bZwv0MZDN3zNUZ/AmlSVeIWVjhMP62iJKh6C5x617dwaYDdwaX/jEXIlJsvBa4vtt3PvSqGoLymSoSy/0h9Cah8TG+PcN3SpKH8nphY/1TB6j80viof3WayeWN1eaOcvTbRnonIefnm3gtHkfyF6Bsm9xZTUS2owJ++6SRgUbpY1fDRwcF5JXeNioH9+rYxkyJeUHGOnsxtVD/UAdUfVudYbFRIeyV3CXwjKtn/URLDoyIC9/O7N2stL68YCX6Ll38gqJ2MtJviuDDkwW6dCDYM0dvDqN13F8EU6EvaC5noOk+oEM5zlgbXiraeKJCNL3kxw1q92kku1RWiPbvp/p5akN456kOSLA8CyflolN2/xmzHNbvF8rO6Qxtgv9fnRC00Bc+K5xt3Zsk/4lAg8VlSrnC3Pw2pN6xeYohozdbdCuAVMCLSPR8joFcpK71LNRYkmPyXUMNUBl3sgP7P6vKNcg3ONYRpHO+GeD9BEsNL5cEulVwU4clwbJPxvvS8ExB0F1GlgEwlgm332m1LmFGGxkrE02iKrX84h8/QXCXK66NUfgwyO2njmzh7ntg9xklbxmNB4QxQBbMU2kRjvh7E8iXjMUK0GzH56PvYcJ7c12lUG5Yn6xT4zGX7aTcLn4O9Z+GIga98qcbDNs+Pq5ln2CAO8JYXXH2c+Xexb8QWUMdJ1BOovA5SvsqUkCzU1NczKe3PnO8sXO23WdQ+NoPP7tGaVlg5s0hfF3d+xSv61n3fA6NNHdJD0l9YqIQHhzQZDEHU9qSG8OIWfFAZO5J3nOr8uZerQFJkdEDAArbEZbGgWgRej9RxDC6C4SwrtA/BRecAJxjqN1GuaDAAByUgMkTWUYuLIBJ5LyhhZGvgMFy2OflvFkzYY0j4vQzyGMY+SLBIGsQU6I3UpGbiaVT8VuCFKzKiEnvh5z7L7jGxJ5szfvJco67SqHE0sP0kao+OsQJ8wbTA7y5w/ng6k2gUwb7UuPQUDWgGre/EPfD+S6THAjsU3xYLCx+zKyQK5D2tchBcL7WThPSs63d0o5nDwczHE6pBUh6f872yMBVNpn0FYXK1pjGUI64XzXgshdwN6WKUKBKbbZLIOGidisyNsoF1Qzs9Idi9Pu0s6nVgYC5SDHu+KKwAYJKqkX2PdBE5ukc30z6vY71nIhtcmTJTQVYMvXHPCPMh0RIPN1HRXcgVADIIpGAizWiuMj5DRAwbVbuZku2ZALkl5B9NLgB0npZzbzltMok+QGML5IejGh+ZSa/pSvOgllf2+yuYeBOtp/5feYeaDxlW7kptr07P7Gpd30LhCmT9Suv7lhhimEh1FdEaFaWP+jNYDvogSNU7xKWxgmtGHcwbwrmoBx9KuclZtvr+nSbvKWyJ+XQMIlZprVBr3seARRJvjSISDarZebcnZEq/0kF7LimvikPAak/BYn1M8q0kyLOWiMw5XZgb2yz1hYg4mylJAZV9VQupkcuvqcNWOOMhrMfr8Go/OyPeaZnewir7IhEMg40xy3V8KLuKKq5b6pVAaNnkmAbGdj4YKehWRzq4abL+Emq5zyErWD8p7/7AATCo52Aty6iVo7vDUoiHmZzylFyNRhkYIZpGcYs82vldRX8PovWTVIzMhtqfdtRdsquua8XJDRWuBkARpu12ML5CL6jo+P5D2RZeeRds/hL6XBypVJbRYxLngkcUk27JXBPSPhchBJ1wabrzxs1ZQhqYqCP/maBoo5aNrrFRa9khB5m3k4+32x+SzmiTAgb9V3HUB46hnMPERdF+8pxu4UE+X3W9jUxKK+yW40cAwXtb2xKZLTXPtLFnnpNwl9+GdL0RTJi8hPyjMiyQjYyDMLJfMbAFFTL4m93NMxJX8SkeXIrcc9kpCzCfckPp5WGd+mi12/RiDJJ+BONAWyE42uUUOaFAUOYYUnlkXUkDtNPUn5xql9xGJpZD3EVLsOwE+UxPVK34x7c1wAkU/2+UgTvSmlx1yEtMKvcE5HIPcc6pOc+ZsxQXa6k7QNG0fmTWhTSRbYuE/KpWOLNjBpdedPxY2yr7hv4iQD8W9NjgVkKw+oeO8J6cqVWh/EmGKCkbP69otcN8bKlTFuIujvzxWF++ZzYY9K/F2OhxOT4mbc3jTM9dH0HLFbcZoDJfVvHKaoecVPjNQqNwdkavbEvbjgmkAa9mTCiWQ7jzAVDYQkzpQL8uHg034R6V0hljaV5SizlwaUuEnUsrgn4vSjUThVOG4CKzl1lOY0UmXkn2lOtbKFAetda1riuGkttaYNBNesUFNanHwCVJtUTOaDeTaxSL7/ufkCloerMem3yJoY4KtEe9+gfDyvvkKRv/L50aiJIql32nObSB97+uV3Y84p77I+y4nfUHOcJqChb8e+JpfdkS/4E4COwkqYd4m754qYb/7DPn/JxSdn/TcnauXEcDBPa7WrJJ5inQe5CQxNGDeBm7MdmjimMnLtmhGOw3ODjKM/5A8W5JSxjETHeRG51VTy8o6JosOM1OYmciQC0JwH9oWgTed6koPRvrZsewOYYiFTpTd131J41aGRD4WOsyWaq51UU6KNhG2l4f8NbnM3aU1K6g+EORvvbgaNKS4nTQtQIUsTuA1vODaGxdMGjR8qaw4ZBIHdtrpqpSUX1IYaF4phgrZRmtSILKV6EXFZUVcyA04LNeIr3aNjO1FnyVdD0swxLDk9ojmIp+2ACTWN/NWyb2nFUimcMRImT7dsFmop8LP4Ur26mxifc+0ZVa3gmJawEBFT+YtnWnjSHk7/ZM8qg3Z+F5v61upKS071jcHbEJCta5e2arIDXjNifWXMPM9bmCQOsijqbyz7Ud5RfVKwK7bdy7BRGmGsIqC31h0+JdTwbl8UnAIkU9AkBwIuj6kMgtcbNyFou02M+YjlGGT5Zu+9Fz/4c9vqgNTfUAOfQHReN4ap9yWqKhH3cphYSL8G6cEJI0qMieqOXpqK4HPbyWtP6p3MLiV7W9uWHt4y0drAP+V3W94PGxCDICoqc4pKST2P018EDwHAFIvILD2Cqo3ik/HfsFwciejoy2UtvJm21HWURS+Xp2uN24pMUBishYYuxN5bdt4P5IkjoGiz6lxEuhGTuYta9Dj+i3yGEs0PSpHx2qh5k1nJcs8sqj7FE5bf03AuBnb/rkQU28ag1CuqkDhBiC+Qt6SwuSj8tdFg17UBG3PkrFQtlS+T0mh5fhxwFdSI+Xo8ghESL51c1EhNPDNnsvNzTCaSyXg66MTR5VkrFoSXU1+sJFWda5cu4JKLx2yqF7+2zHeIxRmbZeGI8gurN2llKwQd9jnf+l2fMS79ZBRhwCD0lgG2MN62mcBCnvn+7LtcLfuOj/mUbEiXs/vpOw/KN/Tr2mDeM7zZRHqbMPl3m9lTvVexCxcbdAD303hZ1BsbCeigxTKwCN0jn8vnq0EQcmBgkJv2w9jsd4owPFJOjIx3uxn049INLklnCkywNow8YuJUKw5fN31RJBLOE1ioLadWAODOHURytUtx/SIcGuaeMZ7w5/uZvKz3fTwetufM9N3eKmYsTOfbaN7LnGxnmOA0eWCmZ0QJhVLzb0AyDMVno1XkyPyylDHRCUTRFgA3nrmV1erUcEjywyr18Nx0kdcUduNNvBmXaASbYO0sTL4GY2rlyUznRV62zu6/ZdslP26n8TDNEAH0yY00g0GcpJIbA9qQ2UoSQA+fiqjXnRTjdWzYVKvpjkVYTbeWoOioVZZSTVi2XkkxkzAce/OhuUBRToLcpMwlolDfAFy8zxjE4O2eG3bT9mazPN/DkwaDkPB5TqOQI28W3jRHEOsg3T2MHzzCFVv8rb1UVezJvGWkvnzW5zYrC8oJwZj2KIUu0paCrdinwGniUs15EKRhIjHRgcZuT/ctRrPDcE4xZZ5aY0HoB61sV5ewk/FwPT9lWBlf+FtEUQtTz1CIGEXfk3+n4wm9vmwXcmBszDeqkUdDNrHnIl+VCpNtt1HRmTiEAdL/wxjYGQOVNi8yNCFG8veWRLWS/760hw1f/QuMl+k8RjCuXcvlMIn0CcBayv7KG3fiIKrYNMruKn/Id5MrEqLMw7qd22vgww5kruyXSaamq0mm5KNH+NumNC2ZwkJWEnn7ecCfG+J6to3tKZjncJ4DYedP4f2igbvJw8WJHdAZ+U55aQyVOvn3vITC2OTP9whSnoPEF/2gXH2N/4RwFGX2aHMUvBMs1l5D/fpIxSiAqkZINpQy/o0gTJomGdj4/ax8QuhcRV7kU96jfG4ylWVZkwTI3DuyLYg9+w3ZsR+k3TDAcB/VGJJHsZSDuZz0tSf1LexK3G8G83aFj2kjN116ko0wjsNJDm3ep2eitdw+x7RJPm+tFWEYfd8ncwjygnuDF5LBgqamFbZJAtFuhw2OujNC2pkbNielBmEOmY/XFnXuPYmM9mScU05I9jLCVtPZJY5gdiM3ylrrDrRzG643NGYVZGfkdvbahOICx3/qz88wrxiDFf2mJlZ3MU7btCbLheDJUmYeTASCHDATQitokqXskpOSdrL1QHKj8/jDKa2oec7CJ/pE4mZY7WmuZAWiH7iUz0w4BMz4JZDcC5cz8XBOGZzfnx7J4xPBTaDyNunpnmg7YaUhTa786/oJR5x4ughFcUwLGJczT0BznpgnJh/g4vdtRI2z12ZUFez+Svnp2fG3r5ve83tj/OpiMAkTvGkrlDbDI3hzSf1tbax7wl4Id6lkkYSei3s3gLg5rhiYa55K0vey57N/GRNPZ712TJD+gxUx5d+j+UUpeCjNexTXBErFPb9V7lZB+2z+YfOqtRYs6F/mL3peTG0lRlJf+tBCwclA+fA2ZxM0IWRiHtlm0VSi3S3snAjKfIs8Nx8PVSiI1F5Ew1RdCQ2vI6v+sbHwWc6oWr/ZXhlZIEHnkzJX/HbXUvNLg2Jy7SoVxKGwvOEQBWHrtb+y7DMtSjFbQzJ5HbziCEh8ymcGj4oiuM1KnFq+W103u7WXF5OziFCvSOUX+0g6IyGtwGyX4txsv7mk/paDIXdwbxdBWs6bF8mU1thW3GFNaf5nrwxUVMFys1XpCk+6zyMiv+stvIPXyoj4TLwr4w4fcUBQNlE4DIsdM9GohkWlTgYDztY5ySKyI3ZFQRDdZiG/LeomYzS8LyaxY+U+iEvIfNnaxtKS9sQk4ku+yMNgXpugtg93Cu09e0KkHfZASLPRLC+jMde11AAyP/XL5RLdoku5FoSFi3LKI5/oHyXxQVkPhzr0agrT/t6z0ANCW94X9/JkXFqZylVS3jpaJUJ+80naTyGSy+RAMi2IkpQ4euaq7t5szByJ2B1U5nMYHfJ60uzcoiatp/EvQsafJBL3f1XcAFPep1x6qRTi6OHNcqJnU3LvwTJOIG9TBPToQQR0ntn1fe6EgNm9Dxlv2y8ECREDbNNklHw+swkBZXkupiajZWq4XuR5PR4fqr26NSnd48tSyUUQzkK4HVBSfwCUdpxz3bjF8JnY3IHYbwshTA6ZdNMGbgI1rUuMISlFihtTkaIqH5Pmfxj80KjWv8/nzi5CqfI9aok418iV4XeE4yJjJdieXu6UDTp0q94RTQbNI9/lw3OeHUladiCa332IfF8OfxG09ByJfXAZOIixNj/ZF2bE2fKoc5Osp+IqYPC1WT+7NYU8aKnipfw+yXCZDOWB8GqNZZLHxjYFxE2H5I1hQhg5bRlor5H2TQXb8zkabRlfWvHhOMUYW80J5396s2qUQxvJOKMMSpOQcEBh5ApS5orN3nma03IsTQdypK0ENHzA5y2Jojj7uFUzgfb7H07GU97I/YQyFM/NF9BrT+rlSYcSzlNC4zkcsllshorybLPnha3gvvIyhl2SGXODNmdSZH1Hq3aqEaIUiWXwaGCGaU2uROyhE7diOGQbNN6diK3I6kyYHwQzRtkDJTtVZ3txebYjGE2l5rXlIdiNt2sqHPhm3ME33Hsy72zq44bDIu4Y8Q7vWXoIKrLV9MK0K+RelEGToQPUXyS9SRvOgVFsFI80iBKXA4kFUNa4C1+uFLlg+QKu1IQPk+66CiGWAUcOR3LWKulwKbm/JJz4vfY+w6KSPkYlVw5ETgVBJx9PpsEWk4rWPi5H2l3pWHaa3HBSedyISgZkpMcbG8kgG6nnTSsUlrk6iRjymQkmU2p/xE9TXihmZql/0UDDNGgLM3B7XHvvAPnKcRKUz3QJQMeFVz7/ddtzIOaW3GtP6kcxVUI3P66PgJoEfZSQ7IND3WiZTREx5KOozOuOmvqD4oZlHBV/hwLr9UN7gB42lmsfiGj49cbE0QzSbQyh6zqQSRl3G2QFwYthTRrVdiT0g8hLXEehBULfUKzvOblIGvaeRCDDNFakQbtcPFNwwlzjPYxjaCofNPEprppyJWi7SzLuY03h5C5U1ImxjDe+RySu1aAxr5VYom5bUuKojCOXPxK/Q5pRJ571+fC0nF7VJ0OTCohKdQ3iKcLHOmjH7Otrc9m4N1/xN4ix/leWRtYOsCPFmFlkbyypd4t5OzSsKduHFvEu7w86hhspuhA4ZDO4sw2hxrLPQTDks2r3sai1jig4Wq/FaL83TehARfQuLzJ08iJTS8FpUhKCb+U8byKwXB7j9MQQue0pCdVfKMHw2jXtFlAu+9YwHiLkKtbWIGh+Kq2NUrr35UnpLYJGgMfLECyPlDKZ1Mn4PFXI76YKwS3vubTG29mgHrg80KnRDolAXIi9gaax9rRvsWy9BtD4lfPyFDM7eArkeOyg55XdrTB0Heakog4Wz7Z7X+Muey1sbaSKZ2aM981a9pz5TY++dx9jHHGzdnPb05C3KfEzACU3jQFdugVXJg29GZlmgsH2vBpiimuF9xlVwpR9T5QbAX1ufNJOrERoZmXSQfsP8l5bhtpHwlCSZnKDUzsjIuGww2YuTY3XqhuygqBTOgHY/7G+/G2wz/cnE7u+OOwf0utNOEgKlbZXnk7MYPRks3fOxlB/SFnu3jYcyscIPmbId2r2pjbpxF+xV6Bgdl3Mg2+t3v/twpdBb9fpfUqGzgC4y/pG8Lk/Sj1Zjher4f3w/gyQ3+jDUabmY/Y2+o75hJ5GvsPvQ8jzItFPdECKkgADOTzsG+FuJ67kgyYR4iqTAfxKmk37SzL4HX4bTGw2USy9MaS+po1rH+c7XeBjz08+6VQOZQZFBOHAm0FTTvKE0yP5Bh4Z5fyGwJUs1bM+UWZTcWSuKcpb4zP4jWQHb/guE2GjDK6Eq5d8PUQjI1mwMqYwHAwmg41n55bXYMORaFM7aainJrUtbLVZQFpsN9oht9swjTUeORsNqcQjvOVNGj4jEx+LF2HSJORU7dl4e4syarxW/FWKJYjynNGUA8kJNvXr9Rr9ukfXuUN4Mk397DwX/XTuqzPqWudNJqeNtFrZ2HD+O+ud8n46qLnZiHmr1LW7H84fqN1rZQqZXfxIViMhCkR3QN1EYq7Z5Rkhn85Y/gcX5g0GPskk94LZarTXZxG21pV95qOzhXBNlEn6W+trPnDkJy5t6mhgpy+fYmvihCWfeg8zOhJFFaGIm6qCDVc9B0mP1s10ne2wJa/dSAeNeDgiVxt+mrYhtNpKyIPjtSf10r3hvpd99hdTES9BbuQXAieH1PJmeaJKZ6zEMTjgoOQNVdjdN2hWMSU+NphADUeRMVPNWTjM++4HpfY81P+c9zVA9LIQZNK1rIpOEOtDo28lPZGtwSqI10zGwWlyBW+8gAeK07JL1AjqTAz1yfnv5jrWZKIUN8pGN5bMWK2ucHV1hfXVCgtvmiNJfUizq0f6hsPrma+jPxbIHRgAQwLTeHYqcmbPmfzKkLBFcQQ6yCBPT32bcj/N0FlB4elokBwQ+2Q8kTgzz8mfdRMG8n0tkHtKFRH7txHPyTzkPSk8FrTsJP+KGEzcYD6MM+S33I4QVyKIYIwZrILNajpU5l/eorTJN65+BH399crvTXmbnDTVnx07MXfs93yEFdnex1NahMQD1HheHcXWOrMIXntSL7FXO64Zdb5JeuUmUekz3N+I8qTUZog/In/1DBKZ8dZBAOxEQ6vJwz2i6lnAr1qM5au2D4hFPPF8qR0w/vzMnrkDqJF+NqEL7UO8bxLZPHgiCOyM0F+HrKZpAXXQIm28Y3U1RMAMCAGl5yanKhNv1VQITU16p1DLGbVvC1OK4Oovd62YDuxxNtGBPFmnoe9t/G7t4tDdZBoctNCirV9zppJ3k4EqZhTXRZ8GsndEpU/VMPMQlWxIJ26GtMNncL6I75TuRFlp7pjLXsv54NVHFbnzCy8VfMYY5Bozjt5gcmVaq9/IBkRZ3xiOx+E6D6RERvAzci+v57/DKkDWH8UquPRdUwQavtHofgN5K+RcePmpPnVN4LYthczfNsrcqQMuh6OpuOfZeiov+dThe/MbReo3A2GfFZA1ikxDMWxAkpBlR27XBiEa6AaGrr7EhMGOmRe4jAy0CGVaU8Rl7vJba+NS9Z6pXKNULtlAgPwVGbud7jiwZZRg8huh/M8Nlxyl5qY+X6I0DJRNspIUU6X9cBokNuASB4Nr/lMa681qLO9frUYccjCWdvtZNnIkLWqs1OIPUp3KVxF+p/XlMi5gtN9PQazmZM2H/bBVGeGYGdZ7v3n16hVOThbx+r4xf8nf22GH92B2xz6Ox7yXPNWzYv0BRBNpz8zCpmYPreddJECtDxKStxl5LcBOzpSkhhtRWPn1rlg/4eNO5Awy0ESTt+o5ImyKFflwSi35uG18V+YwgQi5EWYzHMpDfp/eCxK8AzG4rUTiXM9NRIP9YlPtR9Q23B4i16gtA71lnwcD4nxXSRlr029OOYsHuzoAw521O37uxb5R84g3UcTZyxpjxP6HNqb3HoeRdVh65Tw4hJXn7MS512ROUri4TWIGlNS3MCmbWqP/fgav6ox/hpbTkcBggy8mBnvMUTGX3csUqKWpj2mWnYKLvzU1wq5KzhtXM46DR74fXiewLaYK88A5byQ/RwMlB/zJWdae8uUTHp9bznl+m+zN1GhL18GDw5+8eYAJBDopUA+LW95vJ8eITcpnh/fcioyNCR1gfr45kXlXVWkvRoiW/QZwBtzBVOzIeb3okiYePj6EyQFzOoF2oLFHQbpyRVh+EFRIJ29cnDGuSsMrTnUr6LBIb/y96qD4XMimMQY9lxUUvA6FdPxGfzKj9dUkwaJMWmc9bKPYux4UmgiRTzeXmTGpLqMYCTSLCpYDdz5b8p1hRjx4Q0j9Vpv+2LuaGbvdUstNaOymEDR6Yed8vF6ut9UfjvV/mD6Tpx/88Irk94rc/jHFPpgjl2ufA7vQHVBRCuU3b545RMcpt0ATPw9tjepuayttTLXRuW24uoFuh0KfTLfZDqeedb6yx9DVtE5ysrxVugZEBgbkP+w/h6vh9upGcqfoxs2bIxU0kfqmraiM6zq6fmjush6npCGRqc5A2Quwvk/VxgB66+rJcnD+5hrjOFmUSqcUUY2oZ9diPlKY+guEOPPROdvyy5ytZjitedoBV5YR+QlH2D9AyIev5PGs/r7SDA7ksrgIk5mYGsdI3QR8NzkVfPJzQdw3WTHa2RnD1mBBnAOnEp6R2Izo4UsEJYXbZzj9KlOrgnJFSLYn+MjnScw3gtRvg8nV0/jPXFSmYpzfJ8/Ih/bQIYgXOMUMcTTFWqdqPUMEE3ZYjnDiweNc/mypcFoZ4GqQmuUcF3+pCOfIVh7fzgsnTVeeIvcV7cTwSR78ouJv+XSWPA+/1l5vMMCOLGDIQ3TmYro4tyjwOHaRcKFYy9SQrA0G89CMKx151wPDOPtGmbJnk5jHNDujmMX6Gw+3OkEkKyl42a+GyeQZciRP9EN29WeMcQdTUaGpl0lS8buS5Ka1NpBbRfkRKkUi+0ilvG7P4TmV8aR2awIlCctP0h1rodyQV5Xwoi8yJ4NSrpBzudkzroRFU5p8dlkbGaraYc7bjyT0cdMiASY87xMf2vcXE93wDoUUDuR8Ljjah1een2WrUYwYQhEWCH/Z9rn8NoifhpdrYneQlfnvXcqLkRAb3pF5keOmLFuXbjAdJBmYRPDRRj5sg5kZ4iCnXPwqxqpKOPkSwTJ/7rj1RpH6jewOJ2ayhscqEQAYZMZ9uFJ2+sowULhb5ZEMLJELqSiIwijVzHpjSlmzU5R5KWRDmXtmO2h4+SDYJsatMrdFYiURkM9Kt2Rz0p5qC2RbZg9BPeLywPnuIoDdqZYxv+D8Pf3jVhCdLC05IIkbQdtpSzfpxd6N6CFhRLs1KKWqCUUdbRu/+vMpf8EHuMk1PeHZUGhjnUC+ZiMfA23QRDubwvBY9dx/eLOdFbfZ5y/s2ejA1ayNacIqsbuiE2Gj1w07Ry8tz4oQkxUALHzUSA0oYADqAcNYgHBiCWADWIJhgw4dzpZnOF2e4vTkpfdPHoYqP8T5jWVEZlDlZZm1y6CCYqyU+2OiRtOnmVGzirybTGrG+BJse6NP8xHU/OePvne1X0+0w7F7zF5TCd9P8ukUGQNr0hVbKjA4T9v27DXDHdhS9PgR2lMPk2Uosy4nANa1lw7OV330Px7qMFNpez8jYsOoC+p+r4uVqqBN70CwbNHDbd1yn3Bug0u/78PaSsVLPwFE6xhrfoJ7sD0f17Jatk7++6GZxCuQL5PRXs+yfigjykTsHEZQ7l9K1liwR4jTCvbjRNQs5vleiJz0LDKKYZtv7uNhhukn+ldTMd3WWofx0bUHBlMPBmPB7qyRkz6cc7B0Pvg7L9ttF8d0kwkEkXXPoVjUMaebIzlzubFB3hU8wO19GJaGy2Mg9vPwRpH6vSOQaMWbjYmRn0e+x99SeFxzc5LJZUo62q1p1x4d2qvzrESyPA7i3IR2TeNaTzosTMbcGNg+kn40qhC2ca+FoMHa+u2JQMbEwS5u2Ba29rUxvtbeDlEDY3GWtFX+LXVntf0WG81rZYTj+pO94KCtuCrEZjw2o4GxPGSLxQQSw/Y5Zcvd7DeBLDXCpDYgK6tsFfuDI/U+xSkzoLlxYkLEislRmowYr8EWnoB82LSx2GFXeTkja3tDKIu+t3j2/AWeP3+B3hqc3z3Hg3fv4+zsFKsri3WfH205zMfE4Fi9JYh8nIiNx8Gx5YXVI9XUHx6hcqotb7eGfjhr4mnEzrprPLdmmfrQKKTcAF79EjtxYU1bYw/XhFLLDwjStY/8VAhcJI/RlGzm4FUpo9eljZXVsMlqSf5gMUn01xAIdgNBOy1Jdn6zAUPgjsDGfSwTegOs/cd0xm+UpbriztdtdV/IPjCmLETeBxh5HsoyKxSTzQWfsWGBrqGvM8F5aRpDX+9zySViO/7Mhn4TQo/GkIlhey9JjN1yJjQwx8wTSeS+EMtuRSXQq81PQt4ghxgr9G3szqX8MJjiE6lspHkUGeOvuwrL3PQ36nATVA+GOyR8uVxervCNX/8tfPjRRwABv+c7v4y33/0+3Lt7imf8Etb2CBu05XMBZkSrDqChqfe3mUW5iglrKNTgqYl6P3dI56CwmXdq3xtD6msDZHvQzAegORtzWnEPzVB8k8nCiR5zyBOPRl879bK69cqEpoXiP6PBrqMT3y6yN9xlcF38fRctSlXTvn1W5mEu4aERcngMGLDucWzjpjAUYU2rvG+QEBbGGJHfXOVOIHRdBzLJ1SCJODbd47DNSsZoXMX3qZwMTcXG62jbXNVsoAdp79IBqO2pLBl7SBLN/l5FUT+xfLGJ1rlsCxu1jMZ7DcwXarLGLyVlGtq4WjA0f9iXDJr0o16adA5u13XKs8qb0kbbSs7iuRzBzj95FvJ65IaiZchvytjn84R99Hny8bC1ePnyFZ4+fYrlconV1RXAaywNsDCMNfVuKhcItdR0UJBe4+nTYHIq85D2T5QtKu4hQZj8uzwQwe3LtmvMwRtD6oFNSc6UVmCHmXtauyrS8ANeK+nJV9iB8OzIAqa0bIc8HXFwrHlh135jyPr/6EiyffS7Ls+2nh9M7LJZ7k7lGoRWNM3wLDTaklIKWE6HD1mhO5XnRLbCoCJ/D8xRRgavmp/mWrgQMUM+C1+OrXdjBFOZyT0mIq2wuT/PW9JISUJHYsBKB9hUiPAsAjON6bqsbOvf12Rxqi0IsyR5LRCnUp5dl1kYwdmTu3qQFN5lxhQ21pZtljfbmEyGjamtNxkQ7+znfFk5aMcV0ZvMwiht8fGf1FfZk7ucgmU+7gP73wHpMKxAoN31sEdr0423IY9TaE2qCUDn68uKMEzu96R+YoJCscGoTGqt6MT8baH0CNmyznUPCAbEjAWvYOwrnNIK3K3QW0bfW/TiaHYicm5Vi30gA5THuYu8SRkjQ1kOpjYswlG6x4z+8tWs93ujSL0CByW4FDcXiYYp7x8u6RtF6aK4tDvMMaSlN1UurbFortK8icEgirQQNQhUkNbq1SOHJDUztZlzB+haX5Oa58n6HGnD4ff4hNxvKwvL9pw+xn/COwy1+Yet3db71lIu+8MkaRmLaCQDJP8e8PV34phE/rCmEElqOftYAW1n64CD04yogz27C85ZOeZT8fy7a95Byeefjx2IpyeLQS6OhGnq+KL2nAYhg+lGa9E/N6VKOmRIucPpnbgQBIdbFZ/p332LeEM5sbhkiLDoOoBXWF9eYLW2WK3WWK/73CMT2M1GEGt2KHsLUs8FqQ8mTAyGtdZtNhftpWwz4eyGV68uZr2hkvo3DE1dx46dKDbEaJtZzlh3i/9WQ75yxqCGJZ6Zrt7wRuvRweIQ8XLQ0sqAw5RKMnqdmKPx2WUgk3SplmaL3M8i9iSGd8qb4WSuyzacRTyeKjHQwaDzWq+OGaZndBYwltHbPr5TOp1zvtZ1F4xlfax9bmMvPoaabW3UeRzw1cPkagy5G8byWQKZUlO/x11eLc3tTYrCMu1I5hnBDAOQB6yNy4FtVlWyVbVihWvK/CZleEjsLdrdtzQVCXGlVQgS4dLOAkfwaWtt+c1BTFxEOREIFy9f4Hd/5zfxycePsbqyuLq6wmq1grU2qw+m5BAA8J75BAKpj/sQ5KQIrk0x3GpH3/fo+x6dX8mMmnq5+mXdydyXF1ez3vC1J/VkKU1VZw9UoTOFHznRGEbmxZ6c+k0JL9EowKmpueZmZpmqDMJ41ZLc+CJuiHyPL+eR+Hf4zjNQLj3tCeSnuDWf6kGDkFtD5u/cdOhXaiYriQ/NBGQGZHIU60HOtGVaIX/zSnmC/OxjInaNz0Yf8l5dkeswEuGhmnqpUg/Xjc005sXURG7YE9HM3RjLDbeUsVnVrqPSygb233V3miM5QSIP5Ek9AUSwxoCJYAlYcY+eLHrrFu8NdU62+d6Ttn1RFvMwb0VfK9+QazcCDasjXrecBcqmG1RYvXKeShgiqMhzKEdpshDMaeKGOMLgPZNc3rCNk28byWZNHDvAw+gaZg+OQCatM/uylWMJhSONhk05ecH0YYfRj7+bI0yt/lWWTS7n3WqRcO8cCBL5Wq2Uea2e41jMeQpBYkWvMIEysyhPqbCBD8N+Uu4DDF+RIjGkyA3S4Ze1g6LyciwjTAdjGQBc7kgVZNM6H6QQlkaFkmHME0womXLzpsiX9BNbgTTlqrbTZjdojwRDeQc/rjie0HULMBuwPYHtDS4vHYH/jW98E7/7O59hvTJYr9dY9WuwDScYUyT1hMRFXF9I8mZhjC/PIUGPXDJMCHzcJvTJKBtCHXJcjVldqU09AK95QLklIdxrgJFpuoYcoz7QJCEw3uR4IACGgmZqXl7eD7/tsDmP52PO0v4mGqXCEG6Yx1ZkJRGvPUuJ9PmriebJPQ5xqPUPF78r2Z7izeXt6GoqFrecDskn65FRbCW7kdSpIbAFP37U7008LF55f+D0d/TQqRlpttvZxLM7bUDMf+Y2txj82Ciloj5qz9byXpvW137TyPdq4BiIASavnYcj9eTJPdiTenY2oQR0poOzYvUyMMxEJtralJyOsoPyG6PPccp+5HmUR5WilhMOLqNplFWen6H7xHGiVtvkvEm7zMaPWtmKjA81rQzmfjBPyvZReBuydHhTImcy7REl8CiYGeiavkPGX0SknYXytmDuVFcXQJpHMAALi7CfJMSX+d8n7yk+TL4BEIcpmxt3jDDJkHkIhIxtnobcP8FksrE4jHXx/BlPnEcVcZ4c5/dTI/axDZ5jAOuQh8hQw16BRFIHD2XXK2lChpkYWBvp7DK8SOIc4iefHgEwpgPYgO0Sfd9htVrDdAbrnmB5CeAU1FksTCLVqYUNGZqsv5Nu4U1mKqd0E+qDrm+jZrEQq5l5na6uVrPe/bUn9XuBaGAZmZENr+hvsjrbdPUNAo0TrumlOyd+4+PR/nHGJGYGadhf/RT5YRF/ybD2TYoPjH0ur0aSkGaj6U/WqaQwvXlUD/A6mF3p/nC4HAayIjWLjpAYk347l5ZGbAhkAN1YpIfBjkRh39i2N00dIpdJIGknzsPwwawrW+5vTPkS3ahLXnltQ04/jXJiWwjujG4xou2yuzduox3InmSBJqpd6moaC3gtdiBfXulUyaplRyVl3mIeQZDmNZmZB4dpA2f3xpEmHTG/7A5biqZv2YZ6KojnZhty67PamY+Ws0YM5ei8t902feHJh92m2PM7d/D+++/D0DnseoGeLXq2YOsIt9tcG/KeT/bJu+w1ZFzbYQYsD8qTwW4zefH+QWHQLZfZqph8/upSzW/2Air+1hoa1QaLTEszrhGtglMlHwNhmIPMnGmrCMrvvFGhtkLufcJF9Z+Sy7/RkzwSX2Rh1CpJqlLfZOzYaA5WehRIfNoEG0h8IBJk8t8HdYM1nd1bgb16LK4OSrUf88p9QOnFnDr0xDFVSsF39o4s6mIIKHWocoF2TIIQnNvGwbsED0A1J55+KJN7G7OJzoDIufgGk4SZ41d5au8Q4/GUBotlyOT9ZvNK23VIb84KrqGjypIhMjg5OcGDBw/Qrwz63sAyo2cbbdqD/XupZLJgGGOwWCyc0iKQ+T7X1LNfsYx9p6aw9wf41XB5sZz1Xm8Qqa/4VG20HMp6rXveP1RdcZJxRR6SJc/5I81VKY7xlSYy03rp+aP/bZ4wtEyD8t3+8Wq2uWdfPqNT7DmiQKNglsvjMlpM+vycP4/0QNUwpyXs0+f3XNTacMlBSs2jFSOowe1uu9uiebqlM3bNB+gbLoPozSgQe8iNhEnLtFwsffjt8rvzCtGEhl7axu9comJZLqutgVKijW3eObfdLbWegsAgaejL52oaVFmXAKOPdtTpaKaUTj3NuThci6bBRMoNIwwr2q24K0grlS8GawnR7EhoXmOwsv6yzWBCy8sAUWEq5s2gyLiw/UQxEg/NPLL7lT4Z/w682eRZP+RxOe3JSHvsmtq6HTgUF3VDxfvHnBDBWsD2fvChtVNUWPcsEaPrTFx9lHH0zDAGMIZBZAFybYeycK69MAAmse+jyEPYwFtfjZu3Yf0NIvWbozqzl5PLEZk9Ne/dWU810dHm2BRXB9g9KtAksZYnFY4L+vHEgx1i3s5TKUufwUA+YO0FI0WVCUAKv2udMW8T16ennHuA2g1hJtEpKYOT0eOTvkNiI9vm13DiEZCfKxA08PWwwV/9HJLnossabT3t8dvj8VYuhv1WVGmL5aS4JTOljHNFU5lJXGdzCNmp2NxwJWtTkKQ+rbSUkm1+vq4fw9aX9HbW11s5irNwZpETe69aiA0nxi50f1lUaf2g+Ov2JVSblnV5GJpD+RhCH6zeHUdG7G+oPtL7NjIwMSbMz3qom6JyBhMvMQHgHpYtrEXcLByUFwE2eLlBeB9OcQY5wBzveb1M9EkP8ofyea5krQXDRL4Ti4EIi8UCE3O3CCX122DGQGJaFUBDN0hl/C3zm6nkD6ntaO+pzbVTQxu5tAIhLsoIGpBz99ClZ47oM2RXSyNRSyVuYqIiXKHpkFocMAtRjr1U1E1o2veB0P7D9rJUytIjwrTGZpODVo4ZA7K7KWqMpprI5kg0pU7uV6uVdw3XxwHyBtldHZKn7gnZECCLZebsftusBDeM2bVM6zHyXEl69pyvXUFAZrk1mDPJ7IfyFteSf3CPyL+CfpSEDOcsMPkbSW6H1VYvr6rhR95BDF3BjUe7TOsaWiLjuETR0Iwknmzh1iJixtOYNJKotAqY4pF7dHZ6gwjb9wFQD8B7xfH7EYa1U5RnXH0JH/ahABD51QF/LypBDMibJIapARGwXCzQdR2Wy1MsFgssl0uYzmC5WOLy8nLW2yip3xBzBNquQq9GfjPt88SE4KYxSrBGNPW01YC62Vy9XWTtyUqtSJ12b3hq4UBYivkH5T/3gIpJ2bGS2x0L5fUn95QRw618Ye83Q3ncwo7eBq2Tz2KwRbW2x8aa3WvATI69fcRzf0vsMTNjbaW+ojmP2N8IZix+0Mj3OKIS0irfIP60EjhwLRrSJvFkKbP8AWxOJTvMfDrIafBgGXj0pUpvRURDWm2Z/YZ0RA1wOFgr5xAkJtlhhlPkzdzGhrBnZIN28gbk/gJB8Ca3lZLMh7+U/06CGrICDRHIdDDdAqZzpH2xWKDrluhMh8VigcVigbOzO1gul6LugEWnJ8oCcItXYb45nG+NI87c/UOlZr3zN6JdVdExF0izXfbPxxW8xlK1u10TCj4NBrCuExgmR5A769MNagERrNR8xsMnuP6eUcb7tFplxn7ZyHkGkEtTQZDUhRdVvsnvckwySDKTs6BtnUGbBLH3ry86J1GMk9C7iZaPoouJ+hm5YAVizIgXrRiNQgpGvABX6idcM2JwiXMfQ6keitfKlmo5L3JTaJHqqxOp3uJ7FeEMQltolenY8Op+jfs9CR6Oki6EZKYnsCu5D3an6aCkdjoBtX0cm4J4Qu+V2dRX8la6jJaPUl5bXMiYeOBQNWNASUKCeYll9+4WFuyPkA3/Xa17XFytse5XuLh8BV6vYdiiZzHs+M5MEO0yczmY3jnuM8oy7mSY9HOdzPVSyPC3K95yIM+4+Cr7ZlnnZVj5nRlm5T2jBC9AYIDCmOKPhBdtjMS/0UXiSJuyo5oQbj6XhaLwXZbnlPGmgW0sa9owxglZk5Hr7FEnoEi0L5JtmEq5EaSBk4mpf/pyJgNwOiDJiifBAHpZwjkYQJ/tdcr7InthGGWioazSV+RWqYNr17Ind4xEEMPbRI14aVqao32QGAZ28QCSVxseTgQyDztREpMUuzFuKhp26Key1YbxKvVPKYcbGR+wkSDr3epF68gbcoctpPcN0WZjltSx+3GF3MqF+w+w7PzO94ECwGBte6w59REbib7LkImpdU6bbxewTDA4cRyInbkMAJAxYEOerC/RdQssFqdYLDp0C4NF1zlb/c7Xgy9wYwg9WZARHpVmOgt77Um9JFMSk8PvgJNUiHiz0RU76geTurboLG/LYa7GtVh8GYplysNlA1VFyI1lbZIn0YDVxqFnbCITuHQZ0+Da0PVaotUh/HZErp6Z/G/9qngiDJAT5Snvh/wP6rEMUMYl0trUJKOQeaNFFm6NNnEhxOMzpd1R+chIXvPJmYy/vE6VdPaLm9530Hb5ukv7LtlVJbpNJ0IiDjcJSv1cDuwMN2lYLDrxaEFmeaK9xYAj1wcPVtpfM5Lt0Iqt1r/TM2EGU8pKQVarstyR8HIqIGPYKNOZEA1/p8qofV+cv1q9O0hfCtNWKlHmpFJN5gvwwrV4imRpt/I9p39RLhhF3NGDzKhQFz8H2ZzgAs1c1+s89T0eXBtNYTCJGs9Lqy9tskG6GoMfJKda2SBEMbhZZq+ASpOYUk4BzjbeWovT01Msl0uEw8Wstc6uHshOlU3TRhfBcnmCxWKB05PzqHUPcYEIWHTROw5RB7YEhvX5AGQD4XB6m9irMctCQ+C1J/XDuWC6PvXcLgjHM4dZ7D6Q2RNWOqCUjzcGRjxVMHS7G8/TDBCXJxHKsyXz1hBs5G/a1KMy79m53c5GmVBtwKAySBKuipvFJnUwJkMBuSciLRO7DWFSu2Vh7RrrvgfIDlb4953fRix7iGPDFClXbCQCSs0y6MXAniBOeJUbV8X9IUMfy9hUzrfD9psvp0eKIJWHV0UUPBxvLTma2dznhkPKJUbvB+98TwBlfw+TMlLaQy1JpnUfeNqrbCQNfzNlnQg7v/45cYTavkGu7zvMVrA5LevUegMAf4Ksi4uQuh2jd5th/YrGcnGCs/O7WJ6eAGYBQx1gfD8jgDqA4M7ZOFmcYtEtcHp6jq5zJjOdMTg5deTe+EKN1g0EgMh7M/LmUCFeCpMg9lw+V1rm0BNlHcZkxZxB5UDjSNw4uUGymWZmJF9Ji3EzKBUuYwqh2wYpVDOt8W3NsECe96kNV/tOO2FCOfXaYR/mNjeNnUnMBo9fXl7i8lIMwrOfHd/vkmyUm4/HlYDrRkbih3eni28wKcZsuTSlkz4UwiQmbAalWfndoCGNBS015JSvFO0W+WaxjEY7shgwyyvUjL5ak0ecawIrWcsnl/nJwcXK+EidBu9Wm+Z3Eo26npo7pPzQID9hv4H8fXJyiq5zPuK7boGOTmAWnrCfnsB0HQx1IDI4XZzCGIPl8jRq//Ny6vNVSm+aFb3leBV98AiWbbVAtZra5VHg9Sf1I5gy1wia9hgeGPTr0lZt8Ly/HWeHMyuFBgkLtO4dEHMIjPQ+kGWNAbD0+yue2Rt5Hi+MyTREvgG4zUNZdJT1NKmVoGAje7CRsnyvfKpkgla0ErKQ1xsOtOmZLCinyQPF35Vm2mibrbY0fhT6Zo19E8I9NpC14piyud8KvMnC9T6S224glkoJqZ9IZiN+kPeb9C4vL3F+dgJrLdbrNZiXXiQw5AaZoGcudc8lSu3gdK4ptvmNJmITY4TsXwM9DQ0fD2NA1E5WFaEpRjkluM4J5KhCe9YEiQZyhooyyCKU7zX1iiTbR6j7pOSyInPsw4MRV46n0Ozvg7BFhg88Ft9WBcJcV7WD57Lvoi4F5N6vED5Zs08nIPtfazwiENbrNVarNe7evYsvf/kcbM9hgz17Z+KGYSKCOzOWRFtce9JjU76zdOWhUza1+ZhBqagdn+zbmY3stSf1rf68kaaEKwKNEgkYHRCLRhX+TqUdeVc+4czvhd+3yJyBKt8C4mBWXCTaj9Aa97ozY0ICT1Y5dcT87kjU8A8csgrK+CsSqpb8oLyLgXZKGAdNY7V+GuNZWoKt5PMINNybbJDdJ0YUeUeLUFKdcac1nqyMszO9KgIcEPssz6GLXvmDs26ZBvbKoUcU7ueTE/9kij27zkWCLZm0G8YIUBh3ditTEpGUg1s751wLE8ZVH+lgcgXM3GM/J1D9zdPUkoevFr5SPm7vUw/Uqo9YHgTRnnIFFRW/R+PiFA2VYbdoFFl/AfyqgmAQYSzeotERAcRUfTbZx7v8m67z2volTtYnWJ0wmE+cLT0lJycpX7V4i8LJMiNfEnmDTKdjoaoJqEbUxutP6osKSTe2F05bPSe0DHOfb4k6N79rLe8qNoUfYuPudpsujuPQTIz9ISf5LE4kfjMtYOy1bzth3xf2fsAZ6trbYwATnIeXsHoRbEnRw1IPWi7RnZzidN2h65KOLeMZM7B7eR+gcCOprKWUPINk16k+9scwzFj4jXRVkwoAk4Z2uxZV5fn9iLpQYFsMwDXGLH4FSk0iWJSUOxP7icxyTLUSC4PJ+ccz8QqwF/HBQ7fGQOorJhVOlp8QxorVwW36F/tdv5nVwpwHC1HH4hsROS9+fl7L5DzTsJE+iVJ512DYZu1gIK95AYJzH3lycoL79+/j7t27uFy/wktzCeefPjWgrGjYNsvKeF+I0hNPyMtQkSb37iEp3qrxzsNrT+oHvCcTsC0dPmez/myGCk+oS03KWPq1aflEBxrMqSvRB41oOWFmFK9dCzAn8Zp4amR7OuodpVht5GzM5Ocvz48NyvIHFzfK2fV8lE1iWgjykBhgrgaqiGeDtEP/GBsQx54t487k4b5Iv4h8Tl/JcMhJWAsT+UoebuaV0aSGbkPkdqGywc1pAJQdoR5grUXf99GHfXzHbNm+1lI2ybjMhcivJIHVVxgdGNKlVnsVVVULWq3OrPNJjV09d1N9bAyD4WYsXGFqFvyZ1zVJddKa8exNBNsWjbS08a7tT4tmoOKeG68nIp/i7XK8DRrVkBa1+AQPvpXtRViubY1t5kit37X4qxKqoosY69GztPvFUJsNuT7xselTjFp4kArhgrlf2MgfJiPL5QLn5+c4OTkB4DX5sCMiYZjpMY98DRrVxLAdTb1tjtee1BvkM5zoS5UY7LfDc7DrDEc/I7crLAm9e6bPqrc2c5Mko/PPupWbCUIfeGJRi3XNaH5jdGUixDHWMoqOGfzBw+cjvLttGLNZstnu7SEWGwueCDHtFd6oknv51qMTvcESnEI8CgtHQMh3bLI278zFxNDZdfp2NUis0ChQagfu+WH+Xdr+/qIWJ8d/NxkEYpueUQn1MV0U/IiGKGlp87ik1iSFHUljAmFDeD6wBFMhAvpx68PJgWsi/d1Ncxr6FikwsmSkCzXZlhp5reSltQdoCpmsE6Xo6tSdkBhCEblj1nvrNoxF4mjI2T2Pnne+uXQwsaiG70bA9JGYVfrhyplsubcmR3tSnTp4FnOF8JZJEAh9sBreqEhcvi1yeTY1+ZdtwTJ75WFVSzKUV+L9CN7DTCPPZiAA5MAzcTbEoK7CX0djFnbY79OrVU4zz+Juywb2mt803oZ+HFrfOiuysszZ+1UPHHOcLWzWL5N5dooxjB8GhB5U9WOfwrq/clU61jED0uM+w/OC4j3KNhHiotF+7mC4LU+tqejl46u047YEZyYjx2p2zKvrnO94JgLMAovTMyzOFqBFF+tJys+4CDO1KdJ70LNmXMYHflFciCjPOwLEhG+mLHjtSX0TU9PNa0t7T3FtzZi3SKuG60j/IBhqpia4596TzxITNnY17Y8kLdfr7+a2II5k6QqncwzexBK5UZAbnDvTVezOHQty94vBbk9yq6VxnCS0E2kfsi0ddLjZNfKG8kferymfDpHsbYg3j4OGwwYPvx5aFuUa6XlKkU0wGJqK38cLqnxr4Dq54Y54Y0m9nPlxPMmxOEXugGnvK/x28/vtcdwduQEWyimpED3wCw+ilxq8iXmm1PjPjv91wMgEbBNzK8UeQQTqDMg4P85Ba2jRu5NmiWAWS3SLvGa8Mnkn2RX8T0vIZe9ibWMQbsqc8GA4YOQbjy9ilTGMiS2fA9FXeAi7XTbr8U+0huw01Np+A2E7XZoWtQ9sbrUUgONRt2F1geLqIJe+xYM2/yaJIPuy3KFypNeZ0FdLbG4CehtAMLQEYel+FSuYZbMq9u/eeryxpB5Avm60hxOiRm1Sq+luGrn43hqlWve3xZiAKq9Npct1ob0PM2v2EW3jXSXtss+F4NziDGmP35x8epASFUuAg7D+a3NpdSLnmxb7wAXihs/v72GPPQnbqfZye7xL3ZZ85CBhNkEUDlmBV5g48xvjCb/zEW2jSNkH9xntJT7yqYnxGG5aS79T+o2Vx6lHbut+7WQm68aR2onW8or0TLet68Wt8wrMan+7oDbWxffFHtrvnDH/yJFOmZ1bWjWpdbsK5Q0m9TV1bJrKm8IraGk3tks9tnUC42hptg6hsc+OK/dfpUl9KSRL+/4hKgH2lOGahmYTMmZ4WMezs8YN1Rbau9Zrm6Vk4iTylLm3xTwt52QJtDSVLRvsHdu/T6AeNzaru5tWil0n5pbLTU5E2MrTZF1e+r7HYrFwx7DTVTDK2Z1otV7TT9abOpCW7mXPWuhN0t5FA1qOLzUvXlXlx8hG2NuCrEwqIlfuRSoJvfdxuH3ijUdJ5C3YX8fVwwM0oGrfHhWC278zoRi7+DCrMzeBjMjTXHkZBuB840j+6G69R567sC3eXFKfqXPKa0W5XncLnmoXNRXyobT0bwriBLxN0LeOe06wscmIWwrIze3hszsxiWopoXeeENw00gjuy+a20pE3CFkV3A6V71gWbkHWrhVjmviWkmgqvr1OqDdYTqCxhKMChLO/U5GzfHgEQsUh7Gzq+Zs6j2Zfw3V2cN6OcdWQrSOTUNwVCqZjQ7ayCMrG3uRVqYXbKz3eXFIPIO7sFst66Z77TDpPOBDGOkvIV11Lazwpvb0N7jYilnUQyHsy6xjEX8FUTTkvRClUOY/bpaaPtpUIjZzkjcdwsNXrDMdzRFtld/rijYHCKaOKCN9fiMI/yBnM3O5DSf7ss4anFnqlrB7qXupKjCgTJsbF1hb7jNADACxAgUHk8pmB9injoQ52XaQ6tLwrdUtiXAwHqB031fATP7iTr4GkwW8X6+1+6def1DeYz5irQPlg8G9LhNwTWyXO3Hd92dFJzHCnG8UgBJFz3zgIIy/OmIJkU++ZzdPne0orQ0A2ig6WIEu1sv9pywcwXEKO4Rqqj6i9kd9nwC2F5+7apE9irhiZBkVN1JaPnVnAmx8QJttlUV2VzaDtSmyaN0yU0dx8V8OVVV3jDdsOSN61Xhg4Dy1i97qXoJ3SdIidHFkP++aMBCsiKzdlcH2VYYj8AerejMMy+rX1/XHYTgdK/TE5PTOr9VVwoTL1LyJzLn3l5/XqrttN1rMabb6d2WE/DXbRW2FMVs9puDP6VBlNGXbr/jFjgIn8vFI+A61yGCGjbUw78UHychG/TK+YURhxk+AtNGQUrUIRXaOWxdEhj9vvFajP7D1DoWyzOMafHVKrNDa5rrbbwVYtTHJuguuAhRxwigZKCggGYJ2sIsCdtUHDU6A3Sb/+KDfvjj2cTWJnCoTXntS7KhS6hMbseUiQGcR+kOJy9lZ3zxZFQ0HMAskkKh6ooKRt8M/XGhpnbSU8Ob2MmAmRKSk9E2QrOpvBkp0cBCkJtJpdtb9JcD7wW94LIsmm/DuA6ffJyjYJb5ZrqFIoFbMrI4xvaxNF3kGXVRUFcgCpvFtmS4okUMuN3GZGuTTvNU5MIZdIpr3NqoPRjr8B9qwjW1HZYdDYyH/7LmU2CR55fnddaIsAjT6TkQaZB3a/SWSXGUtjsIiaLoLtGf2qz7Rg2YTVNRJBfGggIpjnWd3Xio3iv779i0bHxe/8mUAG2+U0FGeU3eyK9xgkJNtdIVMs7UCELOc23jJvlSgH7b+VbIXBZ2S6UmCt/jXrkLMUOMnZata8j3E/FbPEwsc40NXGJ5FvOUSV6rFwYrI8OdbBxgzJq92gnGoFL2JoybFWlYzdoxrpHsegZAIfnmKSWeI2ygRXlr4HincyxuxldaHKW7JL+chJvjN3bNChgyH2vJ/APQM9YGDQGc9JDImni6kLlW0gz5iUMHnmpvkZFe9Fon67mYPLa0/qxzDbU81riI27VBzbp220p0tyJAIqb918nQyydBvBwx+SSB87miRAcTBs7EGq0PbF72LDbBbuBhH6tTw0LlzfV+42jWencqnMF24TpBODTe4Nworv8+yec7TWsTdZ2R3D5trb/eDmJD17K4Z5hbd5G5+p3R55NvZxeZWTwjVs5rfWDkxco5J/446MGycNbyypf2OxQSPNBvY5/avUqhXTzqADj51FxrVVDzos9jnIHwJRIzLiPcY2F09vN8ZIpXRrp7hdCIY3UekIpzEOg6YxhK5rHEl96PwJ7y7xW0E0KFyeEd8+PZvsOtkZe/o2TKKA9iRxo/Ml5Nxxw1dr+qmfqnNKrXqYH3dqa3tobAitUj27CaYeuxZZydMa/RByo5nYDqSei780vBVX5Dy5J786RuzMqezESp1EUmpMr/ROmsTuyDqU1EMIlQMKwLgbf+T+dQnfclGoNjBtnZOJWSpxHq5ccgaorvGbkzQDxsdLYazeII5B0Moys+KWIKlaFTPBwLWs3pBfmmZm9H3vBkxP6gnk/dQfvurmxp+F88WTTDGGw2/0ec4MMiY3KbtuTCVaFsIt7y83OVdn5Jr8MLnLLlSJfWgpjbEdhy/6sfSdxdL2qTe5Ost38+UgwsfrkWCHL66kbcuMJUNwISmMa6Ye8/dt5F0cn2MK/TifZEiX0bL+TZHevDPLCSw2Yaams8FMYUsoqb9G3AYFY1XDMTIylX7qGbliazjrpvz+SB7iYU/l9WJik2llZ6nOghBL3+dgUCYzNXW3FbdFO3coZL58Dy8jFVsiaOidFY63YQ0290gEed+I7uqi2WAjbOV3XAGbSki4v53kGKVsm4p7JurpJjlc8lJMlHlVBk/loXSruNNm7u0fTXmpa6okua2+V2MMmHyjtpr/eoRULQ8HrQq5Mhe0LIMWV40z7meZXd8VLc6MZznrB8NJepigczEbyVtRPi1yeyBG18a8aVh99XxAosr8jN6ZDyX1ApvY971uCHPKwzqgEydJSsueGUtOh5aJQ13A8aDaZndYubytmL3EqTgYapptgEHEICMGUWZYJtevhNmL8drtQPRvBQoNvbycURRK125koWjn4rqeHNc25pf3a/da2u45aI5dnHuo4fiP+L0LKaaGPpzbipa9TfD23Z2mZ7WOABOBxAbYZPkA1ArV9Z3gRGGTDOWjdPNRn2yo8z5cjiv5FCeA1gLWumtOPlE2qQv5DI79ghXAaJu5YSipL/AmE/vrQiT2kB1zMIe+xhylFI+95knsIJulbVQoNsDYIh8IIOMHSqEdCxZ2btUt+PlLbfS2gIq/gxuYVLJdC7ZNficXmbukuSGxP6QArk9Im5eGz91SHCqP0ytQiMR+AD/DT77fOZrh8UwjljFMjtWl1YG/FlfiBKlPmnpP6geaeh+HJC23eIX4tSf1lgceCD0qlGfEP3jQIbiGKMV/q1mN3AsqguJ222d+La/iTljaZjh3S42Rp5wdy8burMWKNetyGVdepzKXgwvZX+v3yAWNQvmXjbBqpGGa2wqBKQ2QNAXKD4B238PR2GOxl1qfIUq/C6PUaIDqHVEnbCptQ5gfGFGdQUfKI+2jPAI8cLDBAT6u0Q33REjXdT5MSLv2Ci3NVhlW2iKWPu9LhVBpCz1lU3pdGmPXDsd9cNzGlaKUn5F8h8MCAqk37KVI0tayBVZXa4AJ1vZe02UBdJh641GyHZIXfweKApJ3HAxKXZ9oo5Q0eu5awxe6WH0YQ7n6OXiHxsPlmRymaPtTNsXOITPHNIKrStpwd6/oxjH9Uj5uiry/WfGXBvnL3GECTSqY5FT4km/K7oOmtWCE8Z3GBBW82+KoiU7lz0IYtdtCL9oyxajLbzXIti0rg+BMPdbFo9Sor9p5CKPpUiiz1I6Cht34uNhaGEOw1kainAizj8cPJJ1/Vcsu8rV4k/E3r13jAddgL4OYQlrupoG3ncfC9W9aYYXgYhdw7c6CuQez8IJTFhxSP2b2/0SCT/n4w+UT/noUoT2aqFM92KnnPF57Uj/eYQatfeTZ8qk5mvz6/UiWNp7j1eelktBnZGzjnA21GMPBsXK9fJArQiK0d0oC2Z1Gx2XU7cxNFNnk4NIIMIi6UpDVpkLYSuG4kWvmwbPD8m1NIF3ReRtbFpPShj/kKKswbHmz21apzdhw9B8SoLzf1fI2mseyyDbxS79P3DbGPgOt5hVDUC7RuPhrLcNaBqwFsxXL1xy1ZZN5GAk1TrobEY7Jt2pclZsDLzrT+apOVDcElfkeDTiSCZ/hTdKuua2f+p3dq8iZ4QGNYcIxLNEp2VOS/rzByjMQPDUutQCMaANde4+4PyN/g/zslZl0IJblnIG6fL4SdjCHCXGWY3Lx3FSy2SpKGOOozDYBLPZuePkcPMiMvQYAdOQmW31UCiXym8vj8ZwOFDWUiD2AXJNLAJE3DxpMOKRLSzFhoqKuJHyBpMlhKnSZ43o5tEt/H0PEG0DqLWadtOpBwCwfpVP2a/t0d3Y02JLkRrSeFe4yqwMFtu8Qg0HqGnleLkqGmKY8G6QlBLSM/9CoeZeaeu+bc3x4WBy1WCiJKYe+4mzqQbmkZbjf677Her0GCO7vDSCOvZs+c0O4ze0kG02LScIceZVIo2CrccAsYxO/JiZG1zHm3pp9IFOQSr5M/jrjlxQuHxCIwuTRmciw/16uqoMcFQ8nSBMlh7bBZr2cdYgRHABgOn/V9j4rxk8S8nDjIITWyGQRp18EMPs4/YQh+CdK7itrI1CQWPn1TSdFN43Xn9RvwYrGtCElmZytpXmTsItgvUGBuYn2ab8JT2gpG0WylXcsOXbuqbivzR3rke93Od6cJ9T6iZxsD72nOC841u9Gc8v015HTHC1t/9QzN4Fb21aq6mzxfUrZJQhmIsjjRL6M/Kbk9NGQeYFyIpvk53ipSbez7iRoThSK835ESH2+uqJRTsIKpQ7Br17401vj4byzijofyNyeEZnB2kbclNfoWnemMLq1/bGC15/Uj2FE6+Qa6jTnmdI0tmFmxH58kLbXm2q74274Ecjy3vVcgcEppeC4OnPtKyyja3xFkAputpVQk5xVW3hY4p6ayOyWMcVNobL0Hk5wXK1WMNfN5muH52Wr8gxuLAntQuTcSm9OSMvommZgM+TCITD1zrW70vjK7Vlo5JuD1tZL/B1Xd0tXhLLMWqdRs19hChY5e9VyXCMKq5HEYWa8yuT4SUAXNUeC3EfTk0SoU37mlKHX1As/7tZaUXPGpzlxWph70rUha91KtL/ahTTg22coKGYsKGjke4DWAK1hOgsy3ovXROEFH/ehaKzYbwEA3NgXch3YuwfDvu/xYz/2Y/jKV76C8/Nz/N7f+3vxV/7KXymc/DN+/Md/HF/60pdwfn6Or371q/j1X//1LJ7PP/8cX/va13D//n08fPgQf+pP/Sk8f/58fxklMdsc3sp/VzRQ2cO1z0gcaVlqg4ePCYOpPEZeKd9U44pl/L/ryPaN1cBNNQXhozeQL0Z+bfTjxefYfTkIlK8z3XXG03XaI8Vtwvjpv4j1ZYyB6Yw/UfZ6e1gco/kG+/gtQqtfz8LOMkqePZyPrWObLfeDliTaIcq5QYtJb/i6SeqxbMRTtbeqxVuW84zURE1R9nsq3+32Fbzg+DiYYYj8x7i/xohrjY/Jw3f+emc69+kMOhM+HYxJbWvbSXty3Vm7t1WUe8XeSf3f+Bt/A//T//Q/4X/4H/4H/Oqv/ir+xt/4G/jJn/xJ/MzP/EwM85M/+ZP46Z/+afzsz/4svv71r+Pu3bv4wR/8QVxcXMQwX/va1/Arv/Ir+Lmf+zn8w3/4D/ELv/AL+NEf/dGN8xNmXoNPtOVzQt/Zh4kOMdHoeeLTfpgmPhu/5q2AtEiT5ZBsb/ec3r56kM9szCdnl98YcPxnT5F511NUdg7LoL7xeV15+5TQOPLGNkbGiAjL5RJnZ2dYLpfXm6ddyvQ1r6+tsa/yEM3kMCS+QDbGmuG4uwUk1c3+o+EHIBh2dugGztPP3hRWgcfMqIdWOTuXjkEf7/NKwVSlEh7FpMKb2ZEg7QbsPxDXxHdPxt0H/sMThJ7dxz8f7ft9mXYG6EyqHxfWoiPGwnjyP7NoJTLlNIIybIuIDoi9m9/8s3/2z/BDP/RD+M/+s/8MAPA93/M9+Pt//+/j//6//28ArlB+6qd+Cv/tf/vf4od+6IcAAP/r//q/4v3338c/+Af/AD/8wz+MX/3VX8U/+kf/CP/8n/9z/ME/+AcBAD/zMz+D//Q//U/xt/7W38KXv/zl2fkZ10AUG0byp1yIsV3cFJ4ceX5wmYqL++nIZTI8dqPMyrxYRuKidL32GoEZ+leuek/gIniMeZhxHvzK03d2giJEtuTOKcsTQjvUUDzttni/8flDWbdbYOxxGr8dNNp+nX8iUiF44rqh2La0ZfbLftUyi8quMNoqDRo8kacD6Vp2c1Tlwezu2fZd1XK5V7tDIm2eaEpTvOM6tM9S7oXNdCWCdl7K3sViET/1OBv5D32xaoNdpJ/ccaRLY/EWj2/VDVrthjkLMri9Q3JTz4698yxFyFTX3CCCoXjybYaH3XxOeUyGKd+v9sAeSVjNRehUP5bjHLWEvAsQy5CkzJaP+ARrGnSakKUmHBpVWFKI5F33E2MG+YjDygPF5xoThmxVgkR/9lcGqwiEvp+aneT35bBd8gIK8ZJB6VKyehBa5BdA1p45fya4NB2WWP13Gd/wpOmQQHq6SQQq2Lum/j/8D/9D/PzP/zz+zb/5NwCAf/kv/yX+6T/9p/hP/pP/BADwjW98Ax9++CG++tWvxmcePHiA7//+78cv/uIvAgB+8Rd/EQ8fPoyEHgC++tWvwhiDr3/969V0Ly8v8fTp0+zThgXIAsZ92P8l469PlGBLuA3bda7OCF6cRz8U1g38/JPK+bD4kHEfY8DGjPogH0fIV+8/HNXVFGfY7tOBsYCYCZbjKZzXkpRbBmCj/6HOEjpLMOw+KVQoEa5+OOYllUoHoOOUXkcAsQVsD7B130N5c17eZvCR4rCDM7TtIosqbeSG9bA9gnZljFhbSh9ZKi5jrvmGcogffz004/TXk/vwt3gbA992G6811rw2WzVphz3kEqZrkZv4w8phwfmH3IcrdVR+xtKMRT7x3kEpNPZpoSlvGp9qPuL7cCxL2U7Lz1W/wqurV+iIcbIwINuDuAejB9M838v5y3L8hL4TP5bdJ/zeKN55WQkwE313kI7EFiIlLOIOzo3YJ3j7tuLasAXEZ/AfWTAxmGx1Qthq31wkFsojfMjmH2NTHbl6CuOCBcHGkSfNMIYjw1ilGAqeX8Ld9F9nDU7iB9mHKo0t5dH76edaI/Byxv8X5I4laevuGiOZ9JnqANzbNB4wgN7GD4VDfgr3kKHcewA9E9YWWFugZ/c7fJKcIFhpgcAWxH0a/aONu5MoPffoeQ0mLyOqHwsW/8WyjLXWAWzcHQKI1yCsotmNs86gTHaEeoDPe88+V+zy31uCZf+eMQeuHJIcLNv03FHAIcSzyxC4d039X/yLfxFPnz7Fv/vv/rvoug593+Ov/bW/hq997WsAgA8//BAA8P7772fPvf/++/Hehx9+iPfeey/P6GKBd955J4Yp8RM/8RP47//7/36jvBb7mCY7wHBZeSQgN+61nouPtzchukjG057CcMNQJVrPsYdjTzoNTpLL+CyF92P/nSAVAUGzMVRwcD3/VHzldNFt1uHBMxTKn8WDs5kiVb4Nfw3y3YqxUZnhTtn8WAbgSuBKNmp2guHx8m8ZIHvrkfIKK1Q0szxbG6hmdLetse0y/qBsxgJt+/BwnB48vkO33ijpnSKZIqgRM3PdKLs5+d7+3Wi8b+1YYLOIfvbeU4OPC1v1/Fhi18YynZvdIhYDRyYWCBODY3GvXAaeM4lrRNmU0+WJVRORUaVHx1NU5TuLsbMqn0WgsTY1GKPLSHZwTBAOLRuKex4c4jV8dph0INRhHNla3pbBBhpv6yPnqB3vrdsca4JJlPDys16v48etNC6y2Djrp+1c72xFNsLJ5ka7d1L/v//v/zv+7t/9u/h7f+/v4ff9vt+Hf/Ev/gX+7J/9s/jyl7+MH/mRH9l3chF/6S/9Jfy5P/fn4u+nT5/iO7/zOzeOh4u/R4ddGhSFPyPEtiFUSIRx+xYkkTMAkdOgIZ+r1nQSZZpupcKKu0ZUlDuhMp4twvI5/zWQ/9cUwVVZ2sBz+9/1YKThBlFry/vE3pdV9wSv3Mo/zFhbC1toJXdOa2SyuQ1CfYUc1uYsexCnW4Wrltjr2GkEWFbCa4LgBSy8Wm08tfG+YPgioOGWUZ9v/3FCk2utd9X6Aq16mYo5n7SVSkBgLF5/q9GvMxOhhnaV42p7GhutHy+XiwUWnaPAwe1uOohKpjWicNsDyrT2IeP3Tur//J//8/iLf/Ev4od/+IcBAL//9/9+/NZv/RZ+4id+Aj/yIz+CDz74AADw0Ucf4Utf+lJ87qOPPsJ/8B/8BwCADz74AB9//HEW73q9xueffx6fL3F6eorT09ON8lq2rU0qK9oFXrO7oimMzfIDRm2RK4S+ViiDSU+UQ14SxZ35ydVVmCkPNAjtjDfuz1WDHR5TNotHhaDZGLtdaG22fmuxEauZFcUQrYJpas3yQfAQE7+abGFpBzxLVlZcP04/0srU9HNUfJ+KcxZGXFhW5FjsT8XS8bZVNHMh7VbiGBQS+4VnzPK9x8bJ2jXy/Y6kYo3l7c2G1mH0W6OsSyvGjbQos+WEfIYsYQR3k4OMeft6igoxYwzIUNwozDaNdRvlkFsj6LznJeYoVUvsXfnz8uXL6Ng/oOu6uHnqK1/5Cj744AP8/M//fLz/9OlTfP3rX8cP/MAPAAB+4Ad+AI8fP8Yv/dIvxTD/+B//Y1hr8f3f//37yyylzx42wt8eGIqfaOuFujWXdJEV3G2W1+SHQzyuTxSWhwxi63ezm/hJYaRLz0FOQN4vwNCuMYTwaSDZ+VPQ3FdtIWvXx8IeN/ahpZ+jU5UrIS3UPKCkdnW8qLfMeai5/az9rbqC8x8hsrLP3LQPAxdv13XeZaWDtRar1Qrr1Spe21gJUiHG8jMmMWKvnmqnQPR+Jpe5Z5Wrr5PgBzt3+ToOE/LV6HC71NQbx4tvOUZHoujBhaO9vwFjwYxOfKRHtkHf52Fb3UQ+DfYIFZ/o6rgmkyaRv7H0h0O0m2vb0VGf3R46y4klhB0UNjxFHZiAdb/ONfh9rqW/rn5Ujo3lZ9PxZu+a+j/2x/4Y/tpf+2v4ru/6Lvy+3/f78Mu//Mv423/7b+O//C//y/gCf/bP/ln81b/6V/G93/u9+MpXvoIf+7Efw5e//GX88T/+xwEA/96/9+/hj/7RP4r/6r/6r/CzP/uzWK1W+DN/5s/gh3/4hzfyfDOFUjmjaGO8jQeXXYLxVbUKcplwpNOU12K0NIyX0+VSI0HZ9xlar9egEVy7lmugAXFVPIdMvU7YRpsCJC2Q/Hsr22HrnUbuyaXs8DHGDBQ+QbO101RvJA+EDRQ0Qpakh2cmy5V0SsI+iC+/mfeZTZcqXjOMaKT3HmcZ/1iYfaUd0hDxMTNWVyus1z3IGCyXC3DZRyj7U89cbQWo8VSZtW3CTMqryvgQDel3tXCoZirxA5ZlEIqIxWqiSD5q6mOebtHgFeUjZs8y9k7qf+ZnfgY/9mM/hj/9p/80Pv74Y3z5y1/Gf/1f/9f48R//8RjmL/yFv4AXL17gR3/0R/H48WP8oT/0h/CP/tE/wtnZWQzzd//u38Wf+TN/Bn/kj/wRGGPwn//n/zl++qd/em/5lDzxTZOZW4PqNnrOG01wb8dpo2q4bwjWpk5mfNixfeBSpHEcMIv5q8+E8Ywo3KkRe5fmeIeYo6FW5AgahIC0ERppA9VIx3qdNImhDDbhjseI6vtxshkGksYpHBKWPgTb+5W9Pa8NtzzczC3rZLbg8ih370xxlkmNfBG3TFM4CIyrBSHOIPKOtb3siky2YD/l0OyjRf3ItLdKqDYvExPHoIB58fw5Hj95jHXf4+5bb+HBgwcgk2fEiPDDla6UUE0WTeZ/ooHH9Crhxl2Cy8iL3ATixWMr9vMxKAkhC/q4+mkyMx/moPP2axFe2bBYLGA6ch6DbhHkvGluce2d1L/11lv4qZ/6KfzUT/3UaBgiwl/+y38Zf/kv/+XRMO+88w7+3t/7ezvnRy4VZb5QQYldBCKyL0xoeQL5HVUjSRXTppPHqPKrK4bY36cscpnnvKtWN7oUybkbnCbhZSAGDDl3UNJzCo3GOny+FC5RXPgvFJbBQ3yU+9BuLYjnGg3OtaaTIwph8rSkSXVh2FicRyvTTe1zXKiPxDx2oykluPI9ECgOF7OkKRVaZXQcer+R6qvNMXjrVjG0yFH5ojM0tLmmDVndTT4+cjP22EY9E4kyHgRpRpwHD+Y8TYzXS5SnTHGfRZpuC81h0NJHv/VuAZyIQPE5ThvZ54iCSr6rtutZgPx1UhHnaeZyYEZeJi+IazWNg8wX+z4RhE8hmjNpWdNcXBPmtJrK6DM/BKN+bzdx0cxRnqvKU76e8pOyyzMa0vTWlKvWYswLfYKI0Pc9Hj96hN/91rfBAD74jg73790HmeDGGQh71EJTcfbeNgkdIgBWjKu1Aasl6NthW+NmGvtbSRUC1ud7sDYn5Gf8RiL9SjZYZD+UUSz1VG1xT1+If4zfCI/7w8SGbxKH9Wx4nzN+NFArznBtrhvmvZP624xM2DOwsEkfUJL/qYNdw+aU7ZD2u+cfh+Beti/i7zhvTFXIPlAMDK4Tev0QBWv0sPQr7OXFg/WGRNHjTPITzaAO0U1UzI4oV0N9RgJKTZazd0vPlfFkO20pz5+BhfEKABC8140Q9cTWFQ4l4UJTdn26krk1I2QpLEYCIJH2WG9pDMjyUD8DYQQVu3YZ25SHcFuoy4z4DiYY5P0ny1KjTOQqzTarsLW23QxbfC+VxWFwMvCbuZqTneE7l3vcRjPFI5cDRxedLRNVkUSHodD9tl17ghdOO0ytWwi1iYFnTr3IoTnuv0Ga/EX/zz7v/eoSBBtLsAOBrc8MU7vsxvIwUqYBxkU9eF0u5kcx6U3ao1ActLplaF/5PthAEMlPfEJM1psDhLYV6nDhJFNtcnLNaI0/6d5YY7e+3Gr3xGoFDa/vdND0RL2OTzNsqj//G3DngWQTQnarUkSEruviilToC4bc5JXDgUrMuLq8xLNHj/D0o0c4W57j5IMFTrD0yrF0voojr06jvFwsottFay26zsBg7fe71WzdZ714Cp21L0bv22MYOzrJU5CL+QH5LMcA/16ubnsEL3ah5xsvnzqftqXek/Jxkr0OdIA6EBnvV94mHiMmY2D29wQ3YGC16rFey6v1MgvjDhe/s0d27ZRF0vJMin7mKudrT+qz46drg+8hMDVgAo3Rwwv5ChG1IszeEDZ1RVbZjtuVp+dtgYzI6IrwdiNbi6KCeHTIHD4WHq3OlKenz1P26JO+5ucyyz2jPfFs53u3jBUtlDb3WnIjnqNqmxhZaHQC2RuDr+zJ/Qsbjq0uTGP6V+uetRU5n1ZthW3r0q7UK3vCnnM3yuo0HyRdH2TejJrN2SciQ8zyfb038Gg9l2UdiGEtjLUWr168xOpqha4D7t69iztnp+h7i/V6haDdjPra4LUD9XfN0toCo/PxkriMCv7QoSpxRG3yePSj+QpPXutshuPHGGc4aozBiemy/D5/9hKXl5dYLBY4uXsXi4UzSCWf4ehy2VcOM3B6YvDlL72Hh3fu4Wxxhofvv4OTJeFidYWTkxMAFoYMXl1cgJlxen6OBQhsDJ48egwYwv379z257+LpsMEpicv+kElsJncLZYCARd1MKuuPjZgtc7UJybNxOVsyz5FkwwzFm1gpsb6MOJD80pXlhJIuJJnlak9tshXN3CRee1LvvCMcljy0jp1vdqAgfTm7AOtVdtnJgeK7QYd9IeXd0+dw5PRIviX5AZIWrNbtSi8QkmQOyqwYiQJRlpYCtRyZ8htXAjI328BOGiCgLQQE0dl1I+ugTmYQmIO1/KCK9QgrMPNSHyev+3C3WDvoSv6lys2Q/cnNX5X4ZVxja0Jzhh+3yViQ4qpccZouZ8KSa4so2zkSCGCRgS1R7p+QCqoQtalsOLPWInq9iBsu9ofSNFcqGw6Nmj3/4IwP8qdUeiTSw3CSh7Ber/G73/oWHn32Oe7cPcW/8+/8Xnzw/tu4vLzEi/WVb5R+TGAzPpnbB0ZkrWy/oc4Hst3lcGJSMS5tZVuudRRq386f3xf8JJSxRm8ZWF/h/PwcD9+6i9VqhefPn+Py8hKffPsjvHjxAu+88w4e3jtD51fjTSgpz+qZHHnsbY+OGG8/OMUX3jqDgQEv1nj1/FM8enqBt995B6eLU5BhvPjsMYgIZ+hgux5Xdo2Xz55jcXqC9dkKTI6ULhZ1OjfPp/sQ0RdL1qny3iZPULXF7bldnYv2bGNFjxAgnxrPENb5CnIS9sy9/zgZ1fe9Mw9sZDr3w5fy26Po4Dtg3ipYG689qR9Di1TsBVWBL//h/Ib8Gcwkhhwuxt1KmL1GoMmNBAHJ5hbFQ3P9wYZumfWJDchZkxZPvjhFvXHQ5GQChqYI9W51PmWKFcok2P0lJjl3IKpPm3ZuqSNaxdGwmewaN2qi8O/IhgzCfAG1K0pCv6/4smty0KyUJsd/ZqYh4pMDniRWwS4XyAfP0tJ3vi6rmaNZXTlbFW3cr01yWaSxa10NFQZ5nkJTdhPIqSl9a5TNg1E2y2aYsf4VtXzutG3bWzx/9gwff/QR7t+/g6vv/D1YdgAvCJcmeA8KxnJhf4LT3NuY/yAjyP/vTBmt9asjJQOXucrsxyiaLJUSp6wj2YdpqpFXKleu4kbCJJRFIuHhVKCW1FQjF6anUwgrIaGvvXr1EpdXl3jx5DHunJ+Dv2Tx7OlTfPOb38TTp09xdbHG1dUVsCacL+9gsTTerKoHW4b17hNt7wjkarXCarVG31unSGMAyyUWyyV4scRVb8HsXMGSJ+sX6zWo77EywN0HD2CWC9hgc89wcRXvVnaFnRQmnjPE/otciVGVjY3onJkNxYDBACYO2xWlVdJBBlOj5KADQFJ4IMmY2jt3nTPXAcMrDjkmNktWiiF5MP/YEPn40SizmVX3RpH60qa+vJcvHc9v/MONWcnytqrJ8SeuFjcE1wuCWUTAyEzKJyEmsdL/qcjiVu2QfAPMhCyHpbgh6ZyaPOUDey2gu1PGFdNnyn+LuCJhnhRk0zP+9tM8Wi/EaaUobn4Sv6dyNjah2YdGO05GQlsrV1Zyw+7B87n5jf+TsWgqb6dbZV5uwhRnG2zRVDYJXiP0IZIaQZfa4kF7KgbFQ5dwbQB2g6rTItY0iVx82dfkqwpKE2uiMDEazRGmSiy7yxjEZdJs3gURRBHkzhABA2wtOnIa+H61Rn+1AtkVFrQGry+dDbU3qI2ruNbCskXPV4K4uNiJOhh/Umbf9+j74e6Z8gRquZ8syHi56pJ5NAKyZ2MRMGcrq1nxRFJvou25vG7Cd1tvAeGUYsBNBhK3q0wMRsCV96rmEa7cAjlka/Htb3+EV69e4eWTZ1gul/j4ix/h5cuXePToEVarFezalQ+tDZ589izz8sSWAWt9uVAsb2sZbN3axsnpKR584R188cHbOLt/jm65jMqAuw8euDGWABCBOsLp2VmU28tWrxEEN7zjNuOGa7t5nTHVFQjyd6sHWXG/tOV35TRkEHESG83PQtjQHicShWvjTh514BXlXqmoxVocQtWG/VfSCmAOb8x4ZqlEbaR+Y37qbxvkiu/10QYaCPjS805+P6/KQI7LXdVhGdce6E28zMjyAiHs3e8UNmprKEwSxGy3RtKIhjb2wnXI6FsNXMMg60apK90QMWy452IgGzBR/C41q0MU05Vod82+TbTe+bDlsWdLihTvsRB87Kj5GsSFQTsfQJJ7EdQRgGIAROod+zPYa2ZtqBzh5O3DmP3nIuno6pgeCFua+omD6Vp7IGKQyOjjZsaQY/L1TUJpET627/Ho88/xu7/zLVxcXGC9crI1LPVby+itRc9u0yQioSaQMZ7UE9Z9n9tY+3wj5IKdjTGLMJHUd8ZrM10d9qt1sj0u2mq65CZNvbVF3zAx34tugfPzu+gWC1g/4SA4si4PKpODZByXuEIkeRC8Wm2B8BERDFUcOoiYbW8jYeutxbOnLz15X+Dl5RqXFx9jvXYbLIkWuLpcwVoCP34FOnkLZ2cnWCwWWC4X6DqDbrFAZzp03QLGEIhM/EsEnJyc4M7Dt3B+7y5s5wmxXIRBasudQdTOT7uVrJfFfDQKtUx/OEw3cxd6QngveYYFw01iUnxpMiKfyXIa9vv5CchgZKXKgZrGwJA/wo5u/iBKedDY4N5MO+HXn9RDzAanwk5oJrfOQzSnCX9Lm3WpLgVMmIyGW/6H9Al9KIwNU0GTWyqygmwPCvExPhZOo61qmONGsPoztXem8suhC6YBObCUyIjXCLmfRKZJyBNutelyT0M93u2E2L5E3zER+IOjov0ERBlJtVa17vOprvvr1oc2WXncGmMTW8t49eoVXrygIcE8UJrznp0ok+n5Vft+xexHRht+S69SQQn17OlTfOPffgP/76/9/3B1dYX12gXpOcgO8lrzUtnRgYwBdc5bTiDXgw3MDU19lWRZC6ytZO/JtwKHEzwBs3CTiZXt882HvIhhv/jFL+L9L3wBp6enA03/2vaDsstOOC+uybCZOVDxPABcrdejWvqQflaWPg1rLfjLroy6rsOzZ8/w2UcfY7VaIXilceSe8MEHH+C7vuu7cHq6dP7Pl+7ANbPo4tiXSKU7w4Wt8/LSLwFryHm9E5SgD+3CX970bIzdUMoUqZzMy6sc/zkPPgAjdUEGomwI7U4+H6mH+F2LukbmpaIykPrYDsIHlDSUN0jsW+duqKbew2mPgs9XFtc5ak9Q06xzrsNJ5JLLCzEhl4aLqI9C0qAjvxQblwx41KaYQNEWM51BEfwe+AYb1jijxiUIZ581ShOEoKmLAiHuvvULV5yOQAkkuoZAEFnEWwZmUFw9HYyX5OrBf62nUfydIqyDBIpfxKJMqs+031nG1STgtsxprsHqS22gqPxShJQmVpYKe9hMqNYEUM76qzbDsoCZ0jIoZS0c1ut36y4gZ+mIhkuyJT8tK734EW9n8+1NCWq2AF1ptw7xYLVG9Bzqh1MbS6mUk9pxW/TBilh2r07XubiX9ZNMFqXdG52n9WawlF00tNpLxwGGcmFIDDbJvSYRQDa54pVuLMmrR1+9vMDzZ4yrqzViq+cYe5FeNSvD7Itsj/VADhsU/e/UVUPvMVm6A9esVU2+HzNqjZPCeGNAGNo4h6c5ynDAkAVZeNtqC1iL9crZaF9cEl69YjDOfJZMTNL2FmByGnWfp27RwXRdHDwMDadz5e9134OMIzqdJ1TBNWN8xgkywFrQqneHCTpWCuvbOXULhHVmeWAS/CoNgXB25wQffPABvuMr34nz83OA1pnJjiWL0T1Kgdz68W14OmiNzKUAvbWx39Xay8ADo+/Q7j0MmB3hvLq6wrtffIh1v8bZ6RkYjPX6CovFAvfv38f5nXPvqx7pIKnQDt1ynG8X3rWvdaYczHBf/LtlTiioMqZ6tKbJYf1h8G5iXJSFMZzY5ERHWgkQCMZ2Po6CEFFgLSHeYc4Edc/egyOJEJGFPsoxZTjGQTFfvQVANiP15OUe+5yTaEPkLQfchIHARLBIrr2lK9zaEJzucfnqw2cgudNwfAvSmUs+EvuQjaZ3U3jtSb2hIN9y+8lAhwEeHl3uAxBTRmIdMQwBisYvppB583bCz632uTyUu8QlGAzpftrN7immTex95ApCH0xaCMFOjQa2ZaKZJ+YihEbY5NXcfY3hfSruC+OQwcOyudbsyjalai2Q+FuIm/YDFYxpBfxdgNOksLYjf5h2fsUUFZWXTR5dzhmHaZWorYxkB3pIAVVtLynlMOEgH8e08cEw0yEdt9CZEs/akcwe7d4usrbQIuxzIqvUR41rAhPthssJACoXRpNNiVVIbdq54eWb6OV5PuVTtppZOZDKvID8GkCxakDsvODETaNeaHAgeTGuKHVipOF1msRe5kxwulLD5QZlLrLOYL/cSV6QhbMWJH+YXknz04HQTm1e+oCrSgIiuZODd/jdB6IRlE5+VslssVgu8NZbb+Gdd97Hy5cvweaef9Llt2e34dJwl2mfu65Dt+wyFlfaUZfyMNi4LxaLOBYGUh/kBzNgewZZhrn0nmC8BpqNMyAy3QLGLECmA/xGxJOTE3Rdl/6edzg/P8fZvSXIGHSL0Ar8e1Fu/59tIpTtrNpX2qSnK4zQyvqteUhz7YcA7qLsOO1O8N6X34O1Fsvl0mmYaR01wICNOaEoNPOOmpPE9P6pL3lVnGjj1bG5FNyD22lsHYibMh8jMQweErcoepUPVFjco1TGYzmsv5IYAHyoXHKF8uKsP7kvqZGQL5vIS1iYuQXtvMwswgQgF/Bl2Q2KfFBEad9O+Uzd61+KgwvzRdh+GHYCrz2pT0hNOGwcdURl6LFBEjNnb+VsGYkZtmY+UoITmUoCV2jLhRlLXZNTkDz/Jc3waEDWWH6i2iO9dpLx6eRX9pq0KLgObAohl+fG3A6OPluE2ecE4CZRiEJxNaAmiudBLvcCQsDOacMhpQpprOVy9PkRhPqcu6S4C/Zp9368qLe0XWH9aZZOy9sVChJX7pZtJHZu0+bV3vPRRHWmEIRjfkPa2/KEDjSselrvmSZKZ7FSElp6Eq3ud9ik64h/mHAm8m2Mwb179/DW/ZdYLpdY87l43h9yb51e0flP933da9zL8wwYiKwwEc5E4JONNyXzBw4rTa4Aw0Tc3He0wXQdlidLLE5OsFgu0C0WMIulsxvvnBnQcrnMJgjktY1kVmBYWOoRSFr4N6TZmhTX+/Ru/TwfX7i4IU+GA7oFocMCgIXpCECXKw3LsXTGqeKR8GdtZeIpavfqfJvsSLoboFzJYubRwxfbJn9zxrH9yO2xyUtNmVge8DaKFhnZhEaNtfEdXv2NIfXRLDWQ+My2TWihOGzqCruvkewMZ5KhQSsSaqDSaGHODNav2FUbJ40n5geKdMmFtFmYbLwTy6WHRGlb/sajWQxtUj9VltuWMZXfNxVc0/PejWTfthgzuXrjcIhXLrV2JvdTH7Tz1nsSWS6Xzu3fgbIzlr1qU4zCcYhArjkZZNURjlym7E821ozmjZw9NTGcyUslqLUWnTE4PT2FsSfhKpjZa+qdeU0o86BxJ0NpFQWpHoLaUai3Ms18eHfATSqC5j4Q8oVxGz5Pz+4AAJbLJRYnS0fmO+MOEzKO0HNRBnLDcF5YiAOcI/EUw1x7L5W6tMILj1TsOk2vySY97TqffpO4IinLZYt8DzChyd8LcR4T5i0hXyrjUZZdO1/DUZGmX1Us+w7N7AKhLzT1M9LeBUO1Qn5vm9p5A0h9IO2cEfikra/DkfjwK8zovGkMkd8zNFLkjDgJMOw8lBhDcWEn28wRBFmWmahLQdBnkmdW7IwXo3Y9boSLNgvC9RTS5lqpqQ8/iLxpEstVgMPqTmsu+8ISfl6cZZmkAcqVw7DJ3xxXm+fHe/Tp1sPxhKHRADGOKbIqNfRxWbgFm2sywkJgPMq98WgXoh+bk3j5Gt2ClfNSrgx0lfeYwpwyATbQ5pcEbt5TrzciT6OBKaO1Fmz9pkr/92SxxNXlVSanBnVeotSCjoUbzWCQj976PInB3HRngwplrIfBKTWQ1PSCaRPlwSzDeT/Jx6JcUy6VTM4XuRsDLMgZrsMdoiNegN0Kc9Ckhndknw+CQUcGZDqQcVp0aSJzdnbmTWfcZs/T09NoX0/RlBRg40lQMC0FXL6qtCgIhJDRNQR/r5OtsrznrC7uqUMOZUypD3dmNkm+DPflRVwHsQ4xlWXEwhQOafycO9yPbtqvhduo7P0krmKGO1fpMtyI7ikyjeRbKC0zsy5K4+E+VnXLFYDq+4Ti4nI9gyvfNsNrT+rTDNqgdlrYoGFI4iOvI//RIgM0fCKSed90UE4piq182TeiRBqNIVhOk5OgURjLt+RV2fuQyCUhujHbqUkzmkturdPahjkcVaQhDRBvAKWaoe2IPxtCUZrixGX+XYtvYpwaeG3xX0tXrYN39HsUGGKQFvfnTGA2xXDC0wqcZ2lU01I169syf9jn0L9F+oUWrcxL6RIwXAt/rbVYeZeIUvZwiFsQ+tHiL4RwUHSEZ4cLlu7iQI7zsO7mlm9WDpV8GiOlP2eJVS0yBBkP5RT8yifvK86RpTTP6RYd0APcu43HtXFMxkHkN9Qag+XiBIvFEqdnZ1gslzg5OYHxpjJdl2z0F4tFFq+zHQfCJCVuJiy7ebX+WPwpBZd/6CYbeAVtOZBmI3NkUVuFeOgXH2MEN49DrdonWZuXfG0SEa533cKvTm2Wl233B86SOVsUy2tP6oEg2PLfEANOvaKHukg3cASCPa+0DerejsvZmetqLpSl3s+uw8yTPPF2Q5Rhk3JnDCgcROInCx28j1cUA6VINBK8OOgF52iH09SPdeCwjFkekRLK4zrsrm8KM3RPhecC2VbzGGoTzbHJp/Plm8c3msciyD4ts0NXCqvdr3Ndz8GoJmzngX8oz4qUsfEIUswLpQ9oAK5SLcP0DHu5wvrVCv3VGh2EpxxUnEeV2UaZTnqbcE5DqS8InjWm2qrsB5kD3VFlDRB6wCKuSng78YH5QMPzVJln/2jf91iv1+i6Je7cuYezs1dYrZ6lB8n7jO8RfcsH15Fd12GxWGLRLXF2cgJDbuPq6dkpTk9OsTwzWJws0C06dH4za7CjD5MkuZ+MKEwu3Npc53/7M21h/JiR5AjBmQcRxtpTuaKQNoiGsa+lFNqvffgwb4cj19Q4uTijEwxQZeV3LGfu2akOZMUMmhOv3/PrbkrQ2crxSX73fG3Xg1BCV6aGZGOCta4PGQPUfKaUaGXL9YAtEPJqhroJyoNM4rUn9WF5M2m9d58RRu3OmLa+soYsbd/H9sxIbxLs1U+5ciro+t2/fU1jSYgrAiWhd0Lbxg4U3GulDFRG0D0iypK5mtYDCJ5biaZMLm+WxEH8qpTrWFnPMr+phdhn25DS6k2p69cMoT9Poe97d3iPPORokzQq4WnkL+AemNybmIIO4hzTZzq5Kld10lOZcqiRXtJoE8KKVijHoK1fLBY4OzvD6dkpXrx44dz1hZjJaxaXCyy87ftyucTp6SnOzu7g7PQU56dn6LolFt1JtInvcQXLPcjb8DPleyDCKarB/CfUFRUV4HVb1fKZrQeOTCuMld4kdcYE7xA49N6bOa8l+9Lekq7J2LEOtSfMG9+HZL5UWO2nDKZe1O1FWa/XYOv30liLKfXSWIy7NNE4BI4N8TPL47Un9YwVrPW79Y3vNP7wp5qPbNkHSp+ssuN33rAwaJeDOYPXncQGaRnO5RmVjYQG5ijMPfreOj8A/nmmtAnHhnkbJWHrBK8FUTo10vm8dVqy0EjIux0D5VNBhrfPDz7Ji1lt+X3kFO8qBnZtWHhdGMfBLOXDvWENNqY+3tHkprY0geI0rW6M8CZow1nkOctb3d3fdWFUSyOIcGy3zFGDGupe2tXKUly3pCYDscUVRZiKslEf7CIJec/cuDIG9RHChf7UytrOCpy2jJ8WzIWgzcRIVWMcg2ZRlK2domPkQG68g1oSl2raTwQNT4hxEPPwHax7Mq/blPnB5LCILjtUjQhMxuXBMAwxiHtYS7i8XMFyj8XS4Pz8HOv1CkSvAFhYmjP85Pt9gkojeBDLvVKndw3t/MR6ieMPrWDu3aZOWFjL6LAAsUUHt+GU1xZrBmA6LGBhYLEkA9s7G/auM1h7DbVZu42kV2bh+xlgbY/OMAg9GJ/53C5haAnql7BMMOYk2zVlhJKFibE46QDDoM4FOL97B+f2zJnInDwEEeHk/Axd1+H0dBlJfTCd6Yxx9UMM16PCqa0GjCWisCMGhx5HwCJ6fHQlarq8DVjrPboBABFYtm4OayNpFTmqjKqTvgMI1OK8gCmNqd8K4INPnbsxVKBsgm20t7KNNEtLDMplOMOFDOL8TcrDy2qQe/7K3mbL2g7yhBlkbOyNXJX54S4BwUxMZDT1fM6eqsHE9SP/uC80R8tc3hzfsoI/OVNmwMBad14Do58cYGK7noKYo4ZxlMIeBxGXrANj8xrp0tMw/bw+89qT+hY2UQrUho5sbCcSDTAcdhA6A1dOUvQuzQJh9+ScmWHBzkc+2B9eYX2c5J9LdtHB/tLlIcnPaEPtc8ReyFMgCdUCqWgrijKZK4prQk8cc+XDpLyG9xrNQHF4Tt0cgVI+RW/K06kj27yM4XvfGIosD4++RjYZKe2VZ0Y7GSbU3F7KIkbS0HdMZHBbStAsk9CHJwTDoFxCuyrVl4PE0nQzLuYNwpelnHpdNVsjM3CqBpBI+3oin68+X79ArZspiUSaiNB1BtamTaCgKSI1HnW4Vd4e6+KhVFerFdYXa7x49QJnp2d46+4DAIQuuoJ0y/G9z6NlC7NcAMy4Wl2CeQE6cb653WmgFhYWppPeUICrqys8ffYpLl5dYrm8g3t37uNkcRd9D5yeGX/yaiqfvne29MEUIPhA/+IXv4j1ukdHd10ZLlw+l2enccxI756b0pSN1VVxmCEGDzm1sh5K+lKGurFlUPrpL4kHi5UNzhOLF8MG6BD/5hj6gmuJGBkkGzOmHgjPbaJKnhk0mMQOMrdltGUf2WQMn53ICHKnGBgKPPL/FMq9QRnMzIssqmBKRlmbHs9nPn5uP/jUjB2GvSTnMdmkqTL0pamPkvqdITd6DTbSESDNY5Lf4SD0xBHbCJVdVgpnabi/lLQGnpA6pVRwsQYYWkQvCMkLDPn/TRSi0WZ+zuCZXjp1DAwF5ByNw9yBOsUfpvctCcwYWt038sBTQvr40BroNnnV2ZqGNxU7FI7sO689ItGTgynFvwYAWQaveyzI4KQ7Ba932+Qs95HUzDWypX/ZX9h6ZwCv8PjRJ7i4fIa7738JnV1g2XU46QyIOjAM2FpwDxBfYdkB66tP8fzZY3z++CMsl0vcu/8ulicn6HAPV5dX4NO7eOutt7Bii4VhEF9hdfEIzz75Vbx6dYlucRf9vXdw7877ODu7h447YH0CokU0twGz9zSzxL179/Dg/n1cXF4A9sx7EDr1Bbz2cngNMm51Mh+b0iFGQ+STgE3R3EweV4ApK3a5Qd9VyRg1CYOd/8WVV+BRqhdiwE30vFkTkIkgsjz3pjw5OLzme5snhewY3hP9ewYyM2gqun38zvGv3EQLGDi7eteXjAG6jtAbk+TLXvdaWABr/934Q/AM4nZ+XsApXPwoTcECQT7XxhtN6lt1Zf2GoaB9kbM+a61bGkXeGaMGTmw2CjOvMinXVlg8iaiJT5OIdNtVqwvRo4+bo7KZJQ87QiD0cSLSap+cz+TLWb1bRZipoZAaItGJ5KNlVmpllL7x2M2Ri/nBKVM45CapXbDdgOHfnfJb9eofn0hx8XCM0w/eh5w07WsCsyl2ituX99hetNeR6OfaMe/zvPAewZ6whk+2mXZLpA3+lXtSQxgU036QtH0PEHDv3j0sFwsny5fJy4vte6zXPdY9sECPV1eX+PTj38bVxUt8/vhjnJ6dYGWd28eOLZ49e4bFvRXu3bsH8iuhofIfPnyId95ZAHQGwhIGBqbrcHFxATIWy5M7CO6IyThvM/fu3sPDhw/x4MEDmOcGL549c+Vl8uOsnNj3bhURLvi7LZE3o9hr7oJdEsEbz5DIkSjrUAd5DH4y0JI3Inu1V7htEnr2mLFxxm8XrW/ayA9W9+cS8Zx/yQljjGemiAietyL/HjwnFadOS1rytlJbH/e5gAeZ3TdXoBFpFq8UpxFP4Y0l9Qygb9ZNJ8h5rn9L8nNsLkmh7aSlvUEQP0vMMpWT/HQ5CXQ32bADDX/TxaZYWdhl1hlz1+rkU3GMaHo8TxwmNn4h75jV12Jx83WkVG14ERYRzm7cKi5J6I+gKFv94ZBgOHITfPmX2HU/wG2HGwxz0r5cLuJmtBBmH4jEUawOcDH4rv2ydUfWF/4LXL76DCtzhbPFFbrlQ/S8RN85E5gXz1/i488+x517b+GLb9/Ht771W/j2t76Bs9MlTuwCd3CCxcUpnnz2Am+9Y3HSWbx6/imweoCTs1NcvHqF9eULfOsb/y9O+Anee+8DXF1ZsAFsB7ziK3z6+We4vFrjgy99B966/yAqjhaLBRZ+gmE6g/X6Cpa9do7WvkEJc0twbFBDHrOdHymnwGqviNarz8YxrykjyDZuKgLmOjJIYUcwd7f4BMY928g8brb3bMy9ZNC4b5pzqalPsiAoPZMTkhA2EvkibWvd6lmy+N1MaLflW/CHGE46CDsHnLIhnu8Sy7IHEWDIojPzVkXeWFIP5gl+m4RPrZLYWrdBbHDD/XF2UjzZHsbywIMvAQSuuMfKl5RG4uORAWALzCH2Q0KVpP3mE4OhkLulyvUbRXLR92ZjazI/5zEt4BwliaNEShjCXrzvMx/sOyVZaef1OvfEnwHLFk+ePMNv/9ZvA7jEp3c/x7m5h4f338KDt85xsjzByckp3vviF4FugX69xmp1hfV6jUtYGALOzs5w5845Li4ucXlxibfuv4XLl5f4zd/8TXz86HO8++67OO0Y3/zmN4HLD/Hhh59gtTK4//AL+K6v/H6cnp7h5OQKjx4/w7Nnz3DvrftJK+jNT8L+g3CaK7MF27GmWYwxtbFqqxLeEEUiW0/+eeQ7wpg6Pw9HjUBMGytSMuyh8pDhAAsIo3KgZlzeQNw/KJVP7k4KM6csQ0hpylNkpeZhboOcujiK3xL5ngJ3vochOzudN4rUB/+58yzM4zQtv+qXZnq2gxBBM5rKnkRHKBvCWJp+GCptH7KFyRaG0rTm0WUf2EVjH54H5g7w9frYC16nwQAby0OFwFQrm+VR4w0DIXhwYW+C4rVRxmm72BJs7+RaMMXZZ/usyZHkrjdonU/Ado1PPwHWV+/gvYdfwPlphxdPfwcfv/gI/cUdLE86nJ8vce/BA9g18Pj5S3z04Tfx4bc/Qtd1uP/WQ3zhC1/E8uQCDx4Cd+58ERcXl3h4/138xm/8Bp48e4YHdx/i8yef4NXLNZ59Apyd9zg7u4vlyV309i2Yxae98f4AADbrSURBVNt49723sTh9C4tlcqfX9300wzHGOI02BecJLVt49+7plOcaqfemLdJ7wEQNbD4pDjb15frgfGQ5qkTxmonpUYRyMK7SsmvbxhV/F2Ot3JwcES0MypnakELk8ZVxkQgXfh9qZKpJ3iFncu8bypRS5pzrLhCbuLrRmpjWVhnm8Bgid74DRVOOHiDnZcsVzxrOoUmaqCyNP7yvfzUZP/AmkHohY4Kgd24izaSmt1lHJecWHSFOGzgdKjWeVo1wDzuTy3f1bhNZw5zQ5g9Sry00FI/uQ1s+NoCUl9udZmrJo/XkMNDY6Zij2EVWtaKeyn+lTbQUKjXly9aYKJOxVayUcuP5Q47ge457PgFKrar+hPTVnTQ6+db1ZvSz0k7xpr/DVfo89JgihOO/yewmmODIWJwtfbKpR7FnqPkelN/I8j14wzzd+K4MAAYP334bd+/ewxcfvIuOenzy4QV+8zd/AxevnuLsbAnwGmd376BbLPD408/xO9/8Jl48ewFjDGwPfPzxJ1icLHF+fo47d87RLTq8XAFf+tIH+D3f8z149uwZLi5WePjwXbz47BHOz+7gi++/jwdvv4s7d89hDOFseYLlybsAGMvlCa5Wa9hgF0wGhjrY3pkB9H2fvUx08hcO3YpXg4rS+9gWsju0qTQWkIywlHRR+bWJcA9uijm/OBp6FDspniqSfJb8vwUY6+KMaZO9XVX5tdssSO/cZ2siXV6rpdN6uQ0rpho8KFSF1USQUwzXx0pzwSyuSqRUXI9mPWFSO7MJO67IYLawvIa1Pcj06DonK50tm8W67/Ht3/0Wvv3tD2fF+9qTeoLxmqMAd7hAGjJnxlNs7srnpyTFKsCJ2DPSgCIRNUk0jG8Q1sftpyQY+ryfgbLzjQRpLVBtbtixYfgp2dMcZLgyHszUDJBbmo+PbICdB4QJGTBrGlNOtMr7cyIbhM21LlT8TZrQ8bzJ/jJssTdjW3tIXVGIfwyuTHiERANsQpnY7BkZ91j8m64wFC6949hXSxfiDIwEg+jv3LrBqTOM02WHDgawBO6cV4fef5ZnJzg1Z6CPLxK3LNp/hdPkvyi9Ty3fTg6EmS4BWCSySYQPvvxdsfzZrnHH/h7ce7kCrVfA6gqXz55g/XIF2Fd48uwViO9gYZz7yn5NeP7sFR49+TYurs7w0ccfous6vPPF78aX338XK/s27NUS77zzFhYd4eXjFU5OT/Ddv/d78e57X4A1FhYvQHQBY04AXmC9ZjA5P/dOi3cO8Dns1Rn46hy8vnCbU43bE8B+PxVh6Q6pMgTLPdC9cHViFwAvAb7jPNQTgWgFoh5gcuNh7MTe05rY+JqtMm/SUwrZ3DpRpHUn+i4f3ZjZyELRymeNkrIfNCcUzYTbISaKkUQfkETa+T3hyfdoku+BEo6L2/V6HkszT6sYA8p9HJQ80deG7jEf+aka5vOc6lkyQ+GAsKZlOuu05Oy09AYdDBO6cPhulZela1ReoVC2ufwuU06NhQFeY9ldYXX1HE+ffIqPP/4QJydneO+993D3zn2sVmtYvsLnn3+O3/y3/wIny7NZZfHak/qAvDFLPdV2MOI8YRn3hP6xCD+m/RoMZTeGW6HFODBuq+ebXfD6vdHrgfZUCJN3D4F9pSj8Rbh4mYWrOOf5Zbno3HzR0C7eFV38Y9cr/TmM7+Wt5WKJL3zhi+jYor94iVfLBS5ePMOrF89x9+593Ln3AJ9/+jFeXbzE6ek5rAU++/Qx7t49x2efPcOLFy/xwXdc4Pv+v9+Ht965jwcP7+HOW0v0qys8/cIXAQK++N57ODs/x6dPPgWDcX73DgwBbNmN9OQO6lksADJrkOmdu0rqEU4AhzFOg05i0k4EizXW/RUMrnCydL7r16tLMDqw6UDdwlkXCDUWS16RkbJUUFu3CRU8m4PGiXnYR7AvRcTA/MbNJvNAPH/BZHL1nOvh5rqr3AgbRClXE6OnQc6vbwq5j2g4eQIQNPrktPPBk9TLly/w0Ucf4uLiJfr1Gp98wrh6a43l8gRdx3jx7Dk6Y4bCawRvDKnPcRh9nRSYu2Aw1l2/QjOmqzL6CHFT7UXRxmR/ugFCv9e24kdG71VizRZrtuj9B5RI7M5ugDZadRIrppTfOD29i4cPO7x89gRXfIGT87voLeNqzXj73jsAExbdGVarK9y7dw8AYBaXIBgsukssuhWePvoM3/iNf4MP1j2++MX3cOeOO632u77yFbx48QKrFWPdX6BfG/Tc44TdpIYW7uTYtb3AydkK5/d6wDyCxSfo8RbYPIXp1iAy6LGEZes1koQruDh6fomXV09x9fJ3cO/OfXT9OWxPODt9gG5xBz0WYHd+LogXfkXSkxmpjIqukbcdG8MzO87UFBG3aX9OLS/TPfg636CV1oTUtQzbM2AJHXeFZcd+wOIbMaNfr3B5+QJXV0/x6NHHePL5Z3j74X3cvbcE8wWuXjzHyd23cLo4x1und/Hty0t889vfnJXWa0/qpf/RwexpA+8tQz+q42FRakAUrzduiETPWRWawuu4SvHaYge5Ul2JPhDChthwMup6vcZ6vcJqfQW3wGmBQFCB9syCeCCHB+k12nDLARkRwSxO0a0tnr+4wNMnz3H3ZIHF6Tkenpyi4w4Xry7w7rtv4/6Dt9B1nd8PcIFPP/0EDx4+xAcffAmWCReXF/j042/hZAm8/8EHAID1+goMi4uLCyxPT7BcLtHBgGABuwbA6NdXuHj+CCdmhbsnjJdPPsLH3/oNvP9eh4tXz9CvT2G6DtSdgizAwYUlvXQn2vYvsV4/w8vnn+Plk8e4egYslmf48pe+B2+dncJ0J1gxe9OItAmwnGAG49FsX8IWsoGjseh2mHy2efuGZNnOgngfknzbpCtpB7O4irmxaxKSou6S7+t852QdQeF3sNKT/81SOJSnz4cYy+dq8TCILPr+Co8ffYZPP/0mPvnkd7C6fI4Hb53j9OQE636Ny5eP0K9f4OTkA7z99h10Bnj29NGsN33tSX3AruTFWjs6CRjd6LlTitePffmP3gbHSi5jie1Qdts+ybsle7Rl/qYiHil+LPDtq7dXWPeXuLh4AWMsGD0s1kjatfFhqNSulx63mm2Yp4wsO5juFN1ygTv33kW3OMOd0yVWVxdYry6xev4Ui6XB2R2D5Slw9+4ZlsslXr1aO9MYAOd376LvV3j2/DFW66d4/OkaX/hCh6sri08+fYL1uke3WOILX/wCTu+ew5geTFfg9QuAGZ29RHf1Gc7tI2D5HN/+6N/i0W9f4fPf8wmoAwjfDYMlmB+C2PplewD0IXprYa8+h8ELvH23x8vnL7G6WoP7E1y+OMXdu2fAgsBYgGkJ4s5RGnKrKVKn7swwnNfsaXfP47C0ma9yCRb/jgWYtHrfJe1tV492s+TdOYJdxm2qpR3Nb4blkR8eaath5iA+NZZ13l3Pn8ol2ZsZb07hblnf5nvAMJgsmLwjFTMuY0p3uqWxtDOZG1Mgr7FevcSrl4/x6NFH+OzTb+HzT38XJx2A/gLAJa6unuDx4wusVmvcu9u7lbrVM5wt1fxGoVAoFDeEZFvK6Ps1Li5eefeMyUxnq3h3nYzGZQs30HaLBd79wntguwbxGs+fPcEnn7wEgbBYLvHy8gXWfIWT81PcObsLy3dw5+49PHv+CT777FOsVyt8+sknWNtXuLq6wvm9UyyXZ7i6ugBgwNzDbXB17usur17i+dMn6FdXOD8xAK3QdRbG9DDU49XLZ3jy6DHu3b+L/sqClkBvep/dNdgyLl89waPPPwNWz3B+tsSDewvcOVnigwdnuFoxrlZXuLp8hqU5804ejJ8SVmxzxaE8+ZLONuVc2biwybM7keNd0nbYdkVrF137XCfbh0DV+UWlGOv5yzXWm6ddv5i1vomJ3Chkm47tirzlBqXrID/RNYBxG/zLPSVc1G5Z17mmPu1RGFhv+AfXqyt89tmn+Pzzz6I726evXuC3vvnb6BcnWJ4wFmcAL3p889u/gcvLKzx98Qzd6RLA1eSrK6lXKBQKxcHAHEj9S6xWl3DDjvOYM5cTDE6c9Kj63Z6FTsRFMIszEHpwf4VXVz2evrzAw9MlVlev8PnjT3HvrXO8t3gflizWDKwsYG2PJ08e4/LVBQiEs5NT2HWP3/2d38Xde/dxfvYAp6fn6BaE1eoCfLlG11tcXD3Di1eP8OrlM1wsDez6Cq9ePQLbCxizxqPPP8JH3yYsF98Bw8ZRhTPnfW3NV1itrnDx+GM8/eQjrC+e4fS0w9X5BTrq8N677+PeWw/w8iXj6uoJLJ2iOyMYGDAZOMZW2M1nS35Sc7sFnWK72/LhTvrZ/XjU2mFKskN6u+R7x2crG2Uzi7iRbmphMe2bp445ZRzCbPV2gzbtvN4wjCff3hyNwgZ0AzIdyHT+uVIXn36Xrayk+xwmBYPNwc6dL1uL1dUVervGarXC1dUVXrx4Beo+w913F/jCe/dx950zrFbAZ598imcvnmPFwNndcyipB9AHy00q7aDC8lGO6OLJK5Pi9bBDmjhr8NFvsGyClFlu+bv50dvSYzWVeSvs1gYoPfmUU9v8axFUTMOripEZ2pKRdHbddDdqxjSUOQOQ9As2E9lq6y6qEnJlusn7h7RnHDrcxG02oJnWqN7m3NcRtC2baPRSO4viPo+zElfRw30822jGkhyqHERdMTkY08lVJQUgTIKI/MFJxoLNGuyHfQBYLJboe0JvLXq7dq49e4bpuuROdhSlSUegQdTOPw2uiBtrwFm3+8G3B8BY9RYvri5xse7BZ+d4/vwZHj1a4+zsHMvlXTx+8gK/863fwKtXrwCzxtX6JWBe4uHD+7h3fg5mxqsXT7EixsnDc2BB6JaXMItT2LXByxcXePb8Eq9e9ujXBFwxXjx7icvnVzg7WeL09G2sVpd4+vk5nt29i3vv3oVZnKI3a6dxtz3WqwtY+wp37hisOgLbK3z88adYXfV48tkr3HvrbZyfvY3FKWG9NqB1j8X5Q3SLu768OmQFygQ7chpvXmbFRKCq5aW4OrPVeLDp2RcQsrSeo7kJJ1OvLcQSiX83Txlxr8Q26e40ftTS9d2KQvciOMFXvN4ue91jXc2Io1aq44+xUMxLEie5H4HQwZDvBwR0S4JZutI0HWBI7g7hIop2SzNmDQJwtVphdbUCg3F2egqYDn1/CRDj7bcf4sXLJ3j66BNcXTEuL4HuRY+XL4HVCgBdAGDcu3+KlxdPwbQecMgxvPakvlss0C0XIxtd66SeQG4ZpiD1ALwvaRmD+53i9t5IKffsPKZpknHH33aHJa0Zy48tbdYm9nllWm6AnP14M654vYhzNA2q+amfStTXlsVuMxJvf2c3JPVBqO2k4dldMaXYAKH9bdRcYh07PdFYnCJ49juSln6bynYDUM8jfIlKGViTiUBXOxujaLvE7DaDLgE2K3etI5iuw+npGRYnSzAzuvUaJ6cLLNYLLBYLrJuNmLHVuRxzUfjW7swJ7j54gAfrNc67M9gL4MFlj+XiPrruHMxXePLiCYiA01Pg7dO38Naduzg5OcG9kw7LxRLPH13ixcUF2LxCtzQ4Pe9x994CxPfA3OFqucAFr2DXa9y5dw+rC8aTi0cgLHB25x28e+cc9+5/F5b0NujkAbBcgrvelSN3uLywAF1hsbRYLAgLc4pTegcXL69A9gTcEzpjsDAWFy8/x8WLS9z/wgKnp3dAhsAFqbelf/FRyHD1TmC9XT4P2tVcbF7XoX90O5qChAnoNkPBLmck76oM28Wm3pp6PRkOkoPi70rKdUXBTOzSqwuda4In8hRnHCGDiY9Zwzg9PcdicYJ1t8Bi0eHkrMPZnSXs1QKLkw7LtWsPOW/zUzfbznnXXUW+uV6tYPsey+UJ+v4Cn3zybVw8e4qz0xMsuwWsBdZrAtEShs4AEF6+XOHi8VO88859rFYr9HyB7oRwebGaVTavPan/rX/zTZzfOY/a1BxjLZLcWCd6W+w4vrHY2Gi8IBCntbpBWZL6cuNR/jsdoRxuc3sWXPbhikZ+FBNybyNSX0R4ME09EBdP8hQTqC51moiaAqadtA5s5KrMBmlDkPstsclEQrE7tjnVGZD1XZE5uf5g0I/S+LQtqXcDUqN3iUSqvatxGA08uSf06zU+/fRTPPr4CZ48eQZDhIX9DL99egbufwfn95YAGE9erfHq1QVeXp7AEIGppYHi3VSCUxCENpyKenl5hfUri6d8AdgTMN/Bt3/3CbpuCRDj2aNTvHz5AnfOFnj33XdhAazWHZ6teiy6BZ6+uMS3vv0Si8cr3HvL4vNPnuPu3Q7L5RrWAr01ePYUePz4Apevlri4MPjoY8bCrHHvzim+8IUHYF64QfzF5zCmQ79w5gP28jEePfocF1crWAu8/eBtvHXvLTx5/BnWVz0WfAbQPby66vDy5Qs8e36BHi/xzhdO8NZDC6IukvpWjQ+Ra/bHpBYHUr9BzHkam7fx0DzMDu3Ery3E75tiF1Jf9v/NHp2xst4Am7q3ojCctkg9+7FzW+xqaFWFn0wmGjMMaS3j0WdP8fTTJ3j1/BVO6BX+3185BV9aXPWv8MnHn+Pqqkf2ekLTQiOT4LBBlmgVTQ6d5681Hp29wLNnT/GNf/tvsb68xJc+eB8vXlzg42+v8eSRge3P0F+d4MPfXuJbv3mF3hq8806HFy8vYPu30Jl7uLi4AvDpZNkQv6YuMJ4+fYoHDx7ge/4/3+2OJsfmS9ebcePiSr5NfOJZVbgCuxpj3KDKe5fK25HU79fPuOLw2GX2uItxwW4h2l533Bo9M2O1WuHi4gLrtdO/LxYL3L1zjtPTU3Sdi2PVW+/u0vjGf8t8mzPA4fh4AKvVCqvVCqenSxARLq9eYb1eYbHo/HsxTDjWHYzVyuLi4hKmW6JbLEBk0HULGBOW+wnr9QpXqwssTAdmxsWrV768OpycnGJhOpAxgFk4kxav+eP+CldXV7BYgwg4P1tisVji4tUrv/LYoVt0YDBWqzX6dQ+GcXEuToJdQnjNLTHeFm7SlfOuonCXnN+oGN6F1M+J8kBVuuOwOXGHit851qs1Xrx4hX5NODnpcO+tc5zfOQczsFpdga0Fjyg4nSJjeI/D+gEHBUE4iMqCyGC1WuHly5cAM87Pz9H3PV6+eon1egW2gDGExbKD7XuAGIuTDv2qBxGh65awvcXjRy/x5MkT3L9/f7QEXntS38YOKu3JJinvl3FtolpXzMOuzVjrQHFo3GQb3VWe7YJZ1GHDZ/eFOWPA2NJoqbErf4fJymY68XnYR1vYSQtyIByyjSsULRgAS7j9NROrh1WUbbcmN8aenVq3MP7ZfpLUv/bmN0BLTOymcWhrr9K9Sa1+JS/HSDFvcvjdJXWeEfsUbqK+dAi7GexGhaY04odJfZ4GdRtJucsqwNx3uSkSKdOtKWlKcxFThDGoEvpakhu/4m7kVxiWbpj0nHFxF+yyGnWzEvE2TpFuO26qzOrpGridGVNelKqOQDHsS6U8qH0fCz+M232mN8u+EaT+UNhV36FQKBS3A0GLtJ1d8zy0l8SPD+UAK4dTrtwXtzZEGO63M1QaaghflxpQKPYHC2CF7c0Bw3NhMr8W96bkqp1IlzC31yqpn4OWJY1CoVAoZmL+4KQYYjslfyWEVoNCUYBRmwDvL+7rMS17I0j9eHHMnJFVI0iVv3sTUE3KHBy2THbbrKf19ebgkHXdjvtQA850GvtJtYz7NmyQnZOHueUu49pvPVV8eOwWIYd/9t+edovxNrSJ7aBjwOa4qTKrp1tq1zd9PmCsDV9P2z6gA2CFQqFQKBQKhUJxHXgjNPUKhUKhUBwWx6tlVigUrwdUU69QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5lNQrFAqFQqFQKBRHDiX1CoVCoVAoFArFkUNJvUKhUCgUCoVCceRQUq9QKBQKhUKhUBw5Nib1v/ALv4A/9sf+GL785S+DiPAP/sE/yO4zM378x38cX/rSl3B+fo6vfvWr+PVf//UszOeff46vfe1ruH//Ph4+fIg/9af+FJ4/f56F+Vf/6l/hD//hP4yzszN853d+J37yJ39y87dTKBQKhUKhUCjeAGxM6l+8eIF//9//9/F3/s7fqd7/yZ/8Sfz0T/80fvZnfxZf//rXcffuXfzgD/4gLi4uYpivfe1r+JVf+RX83M/9HP7hP/yH+IVf+AX86I/+aLz/9OlT/Mf/8X+M7/7u78Yv/dIv4W/+zb+J/+6/++/wv/wv/8sWr6hQKBQKhUKhULzm4B0AgP+P/+P/iL+ttfzBBx/w3/ybfzNee/z4MZ+envLf//t/n5mZ//W//tcMgP/5P//nMcz/9X/9X0xE/K1vfYuZmf/H//F/5LfffpsvLy9jmP/mv/lv+Pu+7/tG83JxccFPnjyJn29+85sMQD/60Y9+9KMf/ehHP/o5+s+TJ0+avHyvNvXf+MY38OGHH+KrX/1qvPbgwQN8//d/P37xF38RAPCLv/iLePjwIf7gH/yDMcxXv/pVGGPw9a9/PYb5j/6j/wgnJycxzA/+4A/i137t1/Do0aNq2j/xEz+BBw8exM93fud37vPVFAqFQqFQKBSKW4u9kvoPP/wQAPD+++9n199///1478MPP8R7772X3V8sFnjnnXeyMLU4ZBol/tJf+kt48uRJ/Hzzm9/c/YUUCoVCoVAoFIojwOKmM7AvnJ6e4vT09KazoVAoFAqFQqFQXDv2qqn/4IMPAAAfffRRdv2jjz6K9z744AN8/PHH2f31eo3PP/88C1OLQ6ahUCgUCoVCoVAoHPZK6r/yla/ggw8+wM///M/Ha0+fPsXXv/51/MAP/AAA4Ad+4Afw+PFj/NIv/VIM84//8T+GtRbf//3fH8P8wi/8AlarVQzzcz/3c/i+7/s+vP322/vMskKhUCgUCoVCcfzYyN0NMz979ox/+Zd/mX/5l3+ZAfDf/tt/m3/5l3+Zf+u3fouZmf/6X//r/PDhQ/4//8//k//Vv/pX/EM/9EP8la98hV+9ehXj+KN/9I/yH/gDf4C//vWv8z/9p/+Uv/d7v5f/xJ/4E/H+48eP+f333+f/4r/4L/j/+X/+H/7f/rf/je/cucP/8//8P8/O55MnT258l7J+9KMf/ehHP/rRj370s4/PlPebjUn9P/kn/6Sa0I/8yI8ws3Nr+WM/9mP8/vvv8+npKf+RP/JH+Nd+7deyOD777DP+E3/iT/C9e/f4/v37/Cf/5J/kZ8+eZWH+5b/8l/yH/tAf4tPTU/6O7/gO/ut//a9vlE8l9frRj370ox/96Ec/+nldPlOknpiZ8Rri6dOnePDgwU1nQ6FQKBQKhUKh2BlPnjzB/fv3R+/v1aZeoVAoFAqFQqFQXD+U1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0m9QqFQKBQKhUJx5FBSr1AoFAqFQqFQHDmU1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0m9QqFQKBQKhUJx5FBSr1AoFAqFQqFQHDmU1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0m9QqFQKBQKhUJx5FBSr1AoFAqFQqFQHDmU1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0m9QqFQKBQKhUJx5FBSr1AoFAqFQqFQHDmU1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0m9QqFQKBQKhUJx5FBSr1AoFAqFQqFQHDmU1CsUCoVCoVAoFEcOJfUKhUKhUCgUCsWRQ0n9/7+9e4+t+qwfOP4plhYQ23JZKd246RCcXILgsG7TP2jGkAw1RheCyi66MFlkyWLYNJP9oxCXLFEz0XlhJjMSNQPnHMMOGIhhZUAZN8OYQyAIVEFK2YVbn98f+3HyO2O/ubm1Z095vZIngfM8aZ/zfA+cN137HQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5t5y1K9bty6uv/76qK+vj7Kysli+fHlh7syZMzF//vwYO3ZsvPe97436+vr48pe/HP/4xz+KPsaxY8di1qxZUVVVFTU1NXHLLbfEyZMni9Zs27YtrrnmmujVq1cMGTIkvve97/13zxAAALq5txz1L774YowfPz4eeOCBC+Zeeuml2LJlS9xzzz2xZcuWeOSRR2L37t0xY8aMonWzZs2KnTt3RlNTUzz22GOxbt26uPXWWwvzJ06ciGuvvTaGDRsWmzdvjvvuuy/uvffeePDBB/+LpwgAAN1cehsiIi1btuwN12zcuDFFRNq3b19KKaVdu3aliEjPPPNMYc2KFStSWVlZOnjwYEoppR/96EepX79+6dSpU4U18+fPT6NGjXrTe2tra0sRYRiGYRiGYRjZj7a2tjds307/nvq2trYoKyuLmpqaiIjYsGFD1NTUxKRJkwprGhsbo0ePHtHc3FxY84lPfCIqKioKa6ZOnRq7d++Of//736/7eU6dOhUnTpwoGgAAcDHo1Kh/5ZVXYv78+TFz5syoqqqKiIjDhw9HbW1t0bry8vLo379/HD58uLBm0KBBRWvO//78mtdauHBhVFdXF8aQIUPe6acDAADvSp0W9WfOnIkvfOELkVKKxYsXd9anKbj77rujra2tMA4cONDpnxMAAN4Nyjvjg54P+n379sXq1asLX6WPiKirq4vW1tai9WfPno1jx45FXV1dYc2RI0eK1pz//fk1r1VZWRmVlZXv5NMAAIAsvONfqT8f9Hv27Iknn3wyBgwYUDTf0NAQx48fj82bNxceW716dXR0dMTkyZMLa9atWxdnzpwprGlqaopRo0ZFv3793uktAwBA3t707WT+V3t7e2ppaUktLS0pItL999+fWlpa0r59+9Lp06fTjBkz0mWXXZa2bt2aDh06VBj/90421113XZowYUJqbm5O69evTyNHjkwzZ84szB8/fjwNGjQofelLX0o7duxIS5cuTX369Ek/+clP3P3GMAzDMAzDuOjGf7r7zVuO+jVr1rzuJ5o9e3bau3fv/7uRNWvWFD7G0aNH08yZM1Pfvn1TVVVVuummm1J7e3vR53n22WfT1VdfnSorK9Oll16aFi1a9Jb2KeoNwzAMwzCM7jL+U9SXpZRSdEMnTpyI6urqUm8DAADetra2tqKfU32tTr9PPQAA0LlEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZK7bRn1KqdRbAACAd8R/attuG/VHjx4t9RYAAOAd0d7e/obz5V20jy7Xv3//iIjYv39/VFdXl3g3F6cTJ07EkCFD4sCBA1FVVVXq7Vx0nH9pOf/Scv6l5xqUlvMvrXfy/FNK0d7eHvX19W+4rttGfY8er/5HiOrqai/mEquqqnINSsj5l5bzLy3nX3quQWk5/9J6p87/zXyButt++w0AAFwsRD0AAGSu20Z9ZWVlLFiwICorK0u9lYuWa1Bazr+0nH9pOf/Scw1Ky/mXVinOvyy59yMAAGSt236lHgAALhaiHgAAMifqAQAgc6IeAAAyJ+oBACBz3TbqH3jggRg+fHj06tUrJk+eHBs3biz1lrqFdevWxfXXXx/19fVRVlYWy5cvL5pPKcW3v/3tGDx4cPTu3TsaGxtjz549RWuOHTsWs2bNiqqqqqipqYlbbrklTp482YXPIl8LFy6Mj370o/G+970vamtr4zOf+Uzs3r27aM0rr7wSc+fOjQEDBkTfvn3jc5/7XBw5cqRozf79+2P69OnRp0+fqK2tjW984xtx9uzZrnwqWVq8eHGMGzeu8H8IbGhoiBUrVhTmnX3XWrRoUZSVlcUdd9xReMw16Fz33ntvlJWVFY3Ro0cX5p1/5zt48GB88YtfjAEDBkTv3r1j7NixsWnTpsK89+HOM3z48Ate/2VlZTF37tyIeBe8/lM3tHTp0lRRUZF+8YtfpJ07d6avfvWrqaamJh05cqTUW8ve448/nr71rW+lRx55JEVEWrZsWdH8okWLUnV1dVq+fHl69tln04wZM9KIESPSyy+/XFhz3XXXpfHjx6enn346/fnPf06XX355mjlzZhc/kzxNnTo1LVmyJO3YsSNt3bo1fepTn0pDhw5NJ0+eLKyZM2dOGjJkSFq1alXatGlT+tjHPpY+/vGPF+bPnj2bxowZkxobG1NLS0t6/PHH08CBA9Pdd99diqeUlUcffTT98Y9/TM8991zavXt3+uY3v5l69uyZduzYkVJy9l1p48aNafjw4WncuHFp3rx5hcddg861YMGC9OEPfzgdOnSoMP75z38W5p1/5zp27FgaNmxYuvHGG1Nzc3N64YUX0sqVK9Pzzz9fWON9uPO0trYWvfabmppSRKQ1a9aklEr/+u+WUX/llVemuXPnFn5/7ty5VF9fnxYuXFjCXXU/r436jo6OVFdXl+67777CY8ePH0+VlZXp17/+dUoppV27dqWISM8880xhzYoVK1JZWVk6ePBgl+29u2htbU0RkdauXZtSevW8e/bsmX77298W1vz1r39NEZE2bNiQUnr1H2Y9evRIhw8fLqxZvHhxqqqqSqdOneraJ9AN9OvXL/3sZz9z9l2ovb09jRw5MjU1NaVPfvKThah3DTrfggUL0vjx4193zvl3vvnz56err776/533Pty15s2blz7wgQ+kjo6Od8Xrv9t9+83p06dj8+bN0djYWHisR48e0djYGBs2bCjhzrq/vXv3xuHDh4vOvrq6OiZPnlw4+w0bNkRNTU1MmjSpsKaxsTF69OgRzc3NXb7n3LW1tUVERP/+/SMiYvPmzXHmzJmiazB69OgYOnRo0TUYO3ZsDBo0qLBm6tSpceLEidi5c2cX7j5v586di6VLl8aLL74YDQ0Nzr4LzZ07N6ZPn1501hFe/11lz549UV9fH+9///tj1qxZsX///ohw/l3h0UcfjUmTJsXnP//5qK2tjQkTJsRPf/rTwrz34a5z+vTpePjhh+Pmm2+OsrKyd8Xrv9tF/b/+9a84d+5c0YFFRAwaNCgOHz5col1dHM6f7xud/eHDh6O2trZovry8PPr37+/6vEUdHR1xxx13xFVXXRVjxoyJiFfPt6KiImpqaorWvvYavN41Oj/HG9u+fXv07ds3KisrY86cObFs2bK44oornH0XWbp0aWzZsiUWLlx4wZxr0PkmT54cDz30UDzxxBOxePHi2Lt3b1xzzTXR3t7u/LvACy+8EIsXL46RI0fGypUr47bbbouvf/3r8ctf/jIivA93peXLl8fx48fjxhtvjIh3x98/5W/7IwAlMXfu3NixY0esX7++1Fu5qIwaNSq2bt0abW1t8bvf/S5mz54da9euLfW2LgoHDhyIefPmRVNTU/Tq1avU27koTZs2rfDrcePGxeTJk2PYsGHxm9/8Jnr37l3CnV0cOjo6YtKkSfHd7343IiImTJgQO3bsiB//+Mcxe/bsEu/u4vLzn/88pk2bFvX19aXeSkG3+0r9wIED4z3vec8FP2185MiRqKurK9GuLg7nz/eNzr6uri5aW1uL5s+ePRvHjh1zfd6C22+/PR577LFYs2ZNXHbZZYXH6+rq4vTp03H8+PGi9a+9Bq93jc7P8cYqKiri8ssvj4kTJ8bChQtj/Pjx8f3vf9/Zd4HNmzdHa2trfOQjH4ny8vIoLy+PtWvXxg9+8IMoLy+PQYMGuQZdrKamJj74wQ/G888/789AFxg8eHBcccUVRY996EMfKnwLlPfhrrFv37548skn4ytf+UrhsXfD67/bRX1FRUVMnDgxVq1aVXiso6MjVq1aFQ0NDSXcWfc3YsSIqKurKzr7EydORHNzc+HsGxoa4vjx47F58+bCmtWrV0dHR0dMnjy5y/ecm5RS3H777bFs2bJYvXp1jBgxomh+4sSJ0bNnz6JrsHv37ti/f3/RNdi+fXvRX+pNTU1RVVV1wZsF/1lHR0ecOnXK2XeBKVOmxPbt22Pr1q2FMWnSpJg1a1bh165B1zp58mT87W9/i8GDB/sz0AWuuuqqC25j/Nxzz8WwYcMiwvtwV1myZEnU1tbG9OnTC4+9K17/b/tHbd+Fli5dmiorK9NDDz2Udu3alW699dZUU1NT9NPG/Hfa29tTS0tLamlpSRGR7r///tTS0pL27duXUnr1Vlo1NTXp97//fdq2bVv69Kc//bq30powYUJqbm5O69evTyNHjnQrrTfptttuS9XV1empp54quq3WSy+9VFgzZ86cNHTo0LR69eq0adOm1NDQkBoaGgrz52+pde2116atW7emJ554Il1yySVuKfcm3HXXXWnt2rVp7969adu2bemuu+5KZWVl6U9/+lNKydmXwv+9+01KrkFnu/POO9NTTz2V9u7dm/7yl7+kxsbGNHDgwNTa2ppScv6dbePGjam8vDx95zvfSXv27Em/+tWvUp8+fdLDDz9cWON9uHOdO3cuDR06NM2fP/+CuVK//rtl1KeU0g9/+MM0dOjQVFFRka688sr09NNPl3pL3cKaNWtSRFwwZs+enVJ69XZa99xzTxo0aFCqrKxMU6ZMSbt37y76GEePHk0zZ85Mffv2TVVVVemmm25K7e3tJXg2+Xm9s4+ItGTJksKal19+OX3ta19L/fr1S3369Emf/exn06FDh4o+zt///vc0bdq01Lt37zRw4MB05513pjNnznTxs8nPzTffnIYNG5YqKirSJZdckqZMmVII+pScfSm8Nupdg851ww03pMGDB6eKiop06aWXphtuuKHoHunOv/P94Q9/SGPGjEmVlZVp9OjR6cEHHyya9z7cuVauXJki4oIzTan0r/+ylFJ6+1/vBwAASqXbfU89AABcbEQ9AABkTtQDAEDmRD0AAGRO1AMAQOZEPQAAZE7UAwBA5kQ9AABkTtQDAEDmRD0AAGRO1AMAQOb+Bwx9PkxrK4quAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# settings\n",
        "LINE_START = Point(50, 150)\n",
        "LINE_END = Point(320, 150)\n",
        "\n",
        "TARGET_VIDEO_PATH = f\"{HOME}/result.mp4\""
      ],
      "metadata": {
        "id": "MjP8Pn10XuJm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "VideoInfo.from_video_path(SOURCE_VIDEO_PATH)"
      ],
      "metadata": {
        "id": "3btq7JavXknU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8775e687-06ab-4a42-ae2d-c83e3901cf85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VideoInfo(width=720, height=1280, fps=25, total_frames=1326)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# create BYTETracker instance\n",
        "byte_tracker = BYTETracker(BYTETrackerArgs())\n",
        "# create VideoInfo instance\n",
        "video_info = VideoInfo.from_video_path(SOURCE_VIDEO_PATH)\n",
        "# create frame generator\n",
        "generator = get_video_frames_generator(SOURCE_VIDEO_PATH)\n",
        "\n",
        "# open target video file\n",
        "with VideoSink(TARGET_VIDEO_PATH, video_info) as sink:\n",
        "    # loop over video frames\n",
        "    for frame in tqdm(generator, total=video_info.total_frames):\n",
        "        # model prediction on single frame and conversion to supervision Detections\n",
        "        results = model(frame)\n",
        "        detections = Detections(\n",
        "            xyxy=results[0].boxes.xyxy.cpu().numpy(),\n",
        "            confidence=results[0].boxes.conf.cpu().numpy(),\n",
        "            class_id=results[0].boxes.cls.cpu().numpy().astype(int)\n",
        "        )\n",
        "\n",
        "        # tracking detections\n",
        "        tracks = byte_tracker.update(\n",
        "            output_results=detections2boxes(detections=detections),\n",
        "            img_info=frame.shape,\n",
        "            img_size=frame.shape\n",
        "        )\n",
        "\n",
        "        # Draw bounding boxes and write coordinates on each frame\n",
        "        for track in tracks:\n",
        "            track_id = track.track_id\n",
        "            coordinates = track.tlwh\n",
        "            print(f\"Track ID: {track_id}, Coordinates: {coordinates}\")\n",
        "\n",
        "\n",
        "            # Draw the bounding box\n",
        "            bbox_color = (0, 255, 0)  # Green color\n",
        "            bbox_thickness = 2\n",
        "            cv2.rectangle(frame, (int(coordinates[0]), int(coordinates[1])),\n",
        "                          (int(coordinates[0] + coordinates[2]), int(coordinates[1] + coordinates[3])),\n",
        "                          bbox_color, bbox_thickness)\n",
        "\n",
        "            # Write the coordinates on the frame\n",
        "            text = f\"{coordinates}\"\n",
        "            text_color = (0, 0, 255)  # Red color\n",
        "            text_scale = 0.6\n",
        "            text_thickness = 1\n",
        "            font = cv2.FONT_HERSHEY_SIMPLEX\n",
        "            text_x = int(coordinates[0])\n",
        "            text_y = int(coordinates[1]) - 10 if int(coordinates[1]) >= 20 else int(coordinates[1]) + 20\n",
        "\n",
        "            cv2.putText(frame, text, (text_x, text_y), font, text_scale, text_color, text_thickness, cv2.LINE_AA)\n",
        "\n",
        "        # Write the annotated frame to the target video file\n",
        "        sink.write_frame(frame)\n",
        "\n",
        "# def calculate_center(x, y, w, h):\n",
        "#     center_x = x + w // 2\n",
        "#     center_y = y + h // 2\n",
        "#     return center_x, center_y\n"
      ],
      "metadata": {
        "id": "caPIuyWIKDFQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f35cab88bee24ec098ca937cd3c5e931",
            "a7c68a1f9a134af0ab870cede8a85013",
            "d6d4692c5d0f460ca4ddc934ab95cc49",
            "f2eba7e792874bbca33e861efd3cc883",
            "2ff9c8e612844273908c2df72b166aa5",
            "fb30d6934136440da687d5929ab09caf",
            "b2420854ae27441ebe87c86042f4c5e6",
            "c00c97ede80f4afeb32e777a137bec0b",
            "df2547d6d94b450b8f8a10ab4bbc6c39",
            "281a0e36c8f84d549ca4e3fee667edc8",
            "4938bd033aea464682c8495a665599a2"
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      "outputs": [
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              "  0%|          | 0/1326 [00:00<?, ?it/s]"
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            "Speed: 2.3ms preprocess, 16.0ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.9ms preprocess, 16.0ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.6ms preprocess, 16.0ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Track ID: 5, Coordinates: [     3.4178      567.43      617.45      184.14]\n",
            "Track ID: 5, Coordinates: [     4.3299       567.4      624.43      186.65]\n",
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            "Track ID: 5, Coordinates: [     -1.077      557.95      622.46      179.89]\n",
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            "\n",
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            "Track ID: 5, Coordinates: [    -6.2475      554.92      615.51      179.56]\n",
            "Track ID: 5, Coordinates: [     -5.163      554.09       605.5      176.93]\n",
            "Track ID: 5, Coordinates: [    -11.447      553.64      611.66      179.66]\n",
            "Track ID: 5, Coordinates: [    -9.2949      552.95      606.12      178.53]\n"
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            "Track ID: 5, Coordinates: [    -1.1909      550.02      583.75      176.37]\n",
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            "\n",
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            "Track ID: 5, Coordinates: [     2.2695      540.34      565.47       175.5]\n",
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            "\n",
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            "Speed: 1.7ms preprocess, 15.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 1.9ms preprocess, 15.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Track ID: 5, Coordinates: [     176.19       553.1      463.09      139.22]\n",
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            "Track ID: 5, Coordinates: [      186.4      551.83      458.34      139.39]\n"
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            "Speed: 2.0ms preprocess, 15.9ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 1.9ms preprocess, 15.8ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "\n",
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            "Speed: 2.0ms preprocess, 15.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.1ms preprocess, 15.8ms inference, 3.3ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 car, 1 airplane, 15.9ms\n",
            "Speed: 2.0ms preprocess, 15.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.0ms preprocess, 15.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.0ms preprocess, 15.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.1ms preprocess, 15.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.0ms preprocess, 15.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Track ID: 5, Coordinates: [        174      546.15      469.16      158.05]\n",
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            "Speed: 2.0ms preprocess, 15.8ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Track ID: 5, Coordinates: [      172.5      541.75      489.72      166.49]\n",
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            "0: 640x384 1 airplane, 17.0ms\n",
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            "Speed: 2.0ms preprocess, 16.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "Speed: 2.1ms preprocess, 15.4ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
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            "0: 640x384 1 person, 1 airplane, 17.0ms\n",
            "Speed: 2.0ms preprocess, 17.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Track ID: 88, Coordinates: [     249.05      469.47      76.709      157.57]\n",
            "Track ID: 89, Coordinates: [      338.5      469.61      170.99      47.556]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.708      157.57]\n",
            "Track ID: 89, Coordinates: [     338.49      469.61         171      47.556]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.707      157.57]\n",
            "Track ID: 89, Coordinates: [     338.48      469.61      171.02      47.556]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.706      157.57]\n",
            "Track ID: 89, Coordinates: [     338.48      469.61      171.03      47.557]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.705      157.57]\n",
            "Track ID: 89, Coordinates: [     338.47      469.61      171.04      47.557]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.1ms\n",
            "Speed: 1.7ms preprocess, 16.1ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.7ms\n",
            "Speed: 2.0ms preprocess, 16.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 15.7ms\n",
            "Speed: 2.0ms preprocess, 15.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 18.4ms\n",
            "Speed: 1.9ms preprocess, 18.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.0ms\n",
            "Speed: 2.3ms preprocess, 16.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Track ID: 88, Coordinates: [     249.05      469.47      76.705      157.57]\n",
            "Track ID: 89, Coordinates: [     338.47      469.61      171.05      47.557]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.704      157.57]\n",
            "Track ID: 89, Coordinates: [     338.46      469.61      171.06      47.557]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.704      157.57]\n",
            "Track ID: 89, Coordinates: [     338.46      469.61      171.06      47.557]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.703      157.57]\n",
            "Track ID: 89, Coordinates: [     338.46      469.61      171.07      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.703      157.57]\n",
            "Track ID: 89, Coordinates: [     338.45      469.61      171.08      47.558]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "0: 640x384 1 person, 1 airplane, 16.5ms\n",
            "Speed: 2.0ms preprocess, 16.5ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 19.7ms\n",
            "Speed: 2.1ms preprocess, 19.7ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.0ms\n",
            "Speed: 2.0ms preprocess, 16.0ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.0ms\n",
            "Speed: 2.2ms preprocess, 16.0ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.1ms\n",
            "Speed: 2.1ms preprocess, 16.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Track ID: 88, Coordinates: [     249.05      469.47      76.702      157.57]\n",
            "Track ID: 89, Coordinates: [     338.45      469.61      171.08      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.702      157.57]\n",
            "Track ID: 89, Coordinates: [     338.45      469.61      171.09      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.702      157.57]\n",
            "Track ID: 89, Coordinates: [     338.45      469.61      171.09      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.701      157.57]\n",
            "Track ID: 89, Coordinates: [     338.44      469.61       171.1      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.701      157.57]\n",
            "Track ID: 89, Coordinates: [     338.44      469.61       171.1      47.558]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "0: 640x384 1 person, 1 airplane, 16.0ms\n",
            "Speed: 2.4ms preprocess, 16.0ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 16.4ms\n",
            "Speed: 1.9ms preprocess, 16.4ms inference, 4.3ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "0: 640x384 1 person, 1 airplane, 15.9ms\n",
            "Speed: 2.2ms preprocess, 15.9ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 640)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Track ID: 88, Coordinates: [     249.05      469.47      76.701      157.57]\n",
            "Track ID: 89, Coordinates: [     338.44      469.61      171.11      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.701      157.57]\n",
            "Track ID: 89, Coordinates: [     338.44      469.61      171.11      47.558]\n",
            "Track ID: 88, Coordinates: [     249.05      469.47      76.701      157.57]\n",
            "Track ID: 89, Coordinates: [     338.44      469.61      171.11      47.558]\n"
          ]
        }
      ]
    }
  ]
}