{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Lens Flare Perturbations\n",
    "\n",
    "## Introduction\n",
    "This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.\n",
    "\n",
    "## Layout\n",
    "This notebook demonstrates how a particular condition (in this case, <b><i>a lens flare caused by a bright light source</i></b>), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
    "\n",
    "- **Traditional vs. Relative mAP:**\n",
    "    - An overview of the nuances of what we'll be evaluating.\n",
    "- **Setup:**\n",
    "    - Notebook initialization, loading the supporting python code. Depending on if this is the first time you've run this notebook, this may take some time.\n",
    "    - Loading the source image, which will be used throughout the notebook.\n",
    "- **Image Perturbation Examples:**\n",
    "    - The NRTK perturbation is demonstrated on the source image.\n",
    "- **Baseline Detections:**\n",
    "    - The object detection model is loaded and run on the unperturbed image. These will serve as \\\"ground truth\\\" for comparisons against the perturbed images.\n",
    " \n",
    "At this point, we have the fundamental elements of our evaluation: the model, our reference image, and a mechanism for creating the perturbed test images. Next we adapt these elements to be used with the MAITE evaluation workflow:\n",
    "\n",
    "- **Wrapping the Detection Model**\n",
    "- **Wrapping the Reference Image as a Dataset**\n",
    "- **Wrapping the Perturbation as Augmentation Objects**\n",
    "- **Wrapping the Metrics**\n",
    "\n",
    "After the evaluation elements have been wrapped, we can run the evaluation:\n",
    "\n",
    "- **Preparing the Augmentations:**\n",
    "    - We specify the range of perturbation values to evaluate and optionally specify which ones we'd like to visualize.\n",
    "- **Evaluation of Augmented Data:**\n",
    "    - Each augmentation is run through MAITE's evaluation workflow, computing the mean average precision metric relative to the unperturbed detections.\n",
    "- **Evaluation Analysis:**\n",
    "    - We plot and discuss the mAP@50 metric from each of the perturbed images, as well as per-class and per-area results.\n",
    "\n",
    "To run this notebook in Colab, use the link below:\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Kitware/nrtk/blob/main/docs/examples/maite/nrtk_lens_flare_demo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2e1964-46e2-4497-88ff-274eaca17960",
   "metadata": {},
   "source": [
    "## Evaluation Guidance: Traditional vs. Relative mAP\n",
    "\n",
    "This notebook will be evaluating the perturbed images using mean average precision (mAP) **relative** to detections from the unperturbed image. Traditional mAP scores the computed detections to ground-truth annotations vetted by an analyst; the mAP metric indicates how well the detector does compared to that analyst and thus measures the detector's \"absolute\" performance (\"absolute\" in the sense that the assumption is no detector can do better than the analyst.)\n",
    "\n",
    "In contrast, in this notebook, we're not concerned with the **absolute** ability of the detector to find objects of interest. Rather, we're interested in how the **perturbations** affect the detector *relative to the unperturbed image*. It's expected that the detector won't find every target in the unperturbed image; instead, we're measuring the **change in the detections** (or classifications) caused by the perturbations.\n",
    "\n",
    "To support relative mAP, we'll be computing detections on the unperturbed image and using those as our \"ground truth\" dataset, and using the MAITE dataset class slightly differently than usual. For example, there's no on-disk json file of reference annotations with an associated data loader; instead, we'll be taking the computed detections and manually copying them over into the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9268412-0d13-4fed-ae78-f6ee02c94c9c",
   "metadata": {},
   "source": [
    "## Setup: Notebook Initialization\n",
    "The next few cells import the python packages used in the rest of the notebook.\n",
    "\n",
    "**Note:** We are suppressing warnings within this notebook to reduce visual clutter for demonstration purposes. If any issues arise while executing this notebook, we recommend that the first cell is **not** executed so that any related warnings are shown.\n",
    "\n",
    "**Note for Colab users**: After setting up the environment, you may need to \"Restart Runtime\" in order to resolve package version conflicts (see the [README](https://github.com/Kitware/nrtk/blob/main/docs/examples/README.md#run-the-notebooks-from-colab) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "796743f8-2f1e-42aa-ad6b-f71e721602f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "# warning suppression\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2561179f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning package installation...\n",
      "Installing required packages...\n",
      "Doing a fresh install of opencv-python-headless...\n"
     ]
    }
   ],
   "source": [
    "import sys  # noqa: F401\n",
    "\n",
    "print(\"Beginning package installation...\")\n",
    "!{sys.executable} -m pip install -qU pip\n",
    "\n",
    "print(\"Installing required packages...\")\n",
    "!{sys.executable} -m pip install -q \"nrtk[maite,albumentations]\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"matplotlib\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchvision\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchmetrics\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"ultralytics\" --no-cache-dir\n",
    "\n",
    "# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\n",
    "print(\"Doing a fresh install of opencv-python-headless...\")\n",
    "!{sys.executable} -m pip uninstall -qy \"opencv-python\" \"opencv-python-headless\"\n",
    "!{sys.executable} -m pip install -q \"opencv-python-headless\" --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "from collections.abc import Sequence\n",
    "from typing import Any\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# some initial imports\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"jpeg\"  # Use JPEG format for inline visualizations\n",
    "\n",
    "from matplotlib import pyplot as plt  # type: ignore\n",
    "from PIL import Image\n",
    "\n",
    "from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e9bfa-fcea-4a1d-a8e0-9455b28777a2",
   "metadata": {},
   "source": [
    "## Setup: Source Image\n",
    "\n",
    "In the next cell, we'll download and display a source image from the __[VisDrone](https://github.com/VisDrone/VisDrone-Dataset)__ dataset. The image will be cached in a local `data` subdirectory.\n",
    "\n",
    "### A Note on Image Storage\n",
    "\n",
    "Typically in ML workflows, batches of images are processed as tensors of the color channels. Both our perturber (NRTK) and object detector (YOLO) accept numpy `ndarray` objects, and we will use [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) to view them. The complication is that although YOLO inferences on `ndarray`, [it expects the color channels to be in BGR](https://docs.ultralytics.com/modes/predict/) order. If we naively view the same data YOLO inferences on, the colors will be wrong; if we naively inference on what we view, the detections will be wrong. (Our NRTK perturbation is agnostic to the channel order.)\n",
    "\n",
    "In this notebook, we'll convert the channel order to BGR when we load, and convert back whenever we explicitly call `imshow`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20.0\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nrtk\n",
    "\n",
    "print(nrtk.__version__)\n",
    "data_dir = \"./data\"\n",
    "os.makedirs(data_dir, exist_ok=True)\n",
    "img_path = os.path.join(data_dir, \"visdrone_img.jpg\")\n",
    "if not os.path.isfile(img_path):\n",
    "    url = \"https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download\"\n",
    "    _ = urllib.request.urlretrieve(url, img_path)  # noqa: S310\n",
    "\n",
    "img_pil = Image.open(img_path)\n",
    "img_nd_bgr = np.asarray(img_pil)[\n",
    "    :,\n",
    "    :,\n",
    "    ::-1,\n",
    "]  # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb\n",
    "plt.figure()\n",
    "plt.axis(\"off\")\n",
    "\n",
    "_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
   "metadata": {},
   "source": [
    "## NRTK Lens Flare Perturbation: Examples and Guidance\n",
    "\n",
    "Lens flare (i.e. \"Sun flare\") is created by unwanted scattering of light from bright sources within the optics of the camera. These flares are present in most outdoor images with the sun present, and since they are not actually a part of the image, they should be ignored in an object detection model. However, because the flare is typically very bright and extend across many parts of the image, they can easily obscure objects and confuse a model which is not trained to ignore them.\n",
    "\n",
    "While the flare is a complex phenomenon which depends on the camera's lens, it has two main components: The first is a bright glow or haze near the bright object which reduces the image contrast. This can happen even if the bright object is just out of frame. The second component is a series of circles or geometrical shapes which represent the internal reflections of the light in the optical components. Most often, these shapes are colocated along a line emanating from the bright source.\n",
    "\n",
    "The [AlbumentationsPerturber](https://jatic.pages.jatic.net/kitware/nrtk/_implementations/nrtk.impls.perturb_image.generic.albumentations_perturber.html) serves as an interface from the perturbations available from [Albumentations](https://albumentations.ai/). We can simulate a lens flare on our input image by using the [RandomSunFlare](https://explore.albumentations.ai/transform/RandomSunFlare) perturber from Albumentations with the following parameters:\n",
    "\n",
    "- `flare_center`: An (x, y) tuple of floats representing the location in the image to draw the flare\n",
    "- `src_radius`: The radius of the primary sun flare\n",
    "- `src_color`: A tuple of integers representing the RGB color of the flare\n",
    "- `circles`: A list of additional circle overlays. Each includes an alpha value, center, radius and color.\n",
    "- `p`: The probability of applying a perturbation to the image\n",
    "\n",
    "If `p` is 0, the output image will be unchanged.\n",
    "\n",
    "These parameters are all optional. If `src_radius` is left empty, it will default to a value of 400. For any other parameter that isn't provided, Albumentations will select some random default value. See the [documentation from Albumentations](https://albumentations.ai/docs/api-reference/albumentations/augmentations/pixel/transforms/#RandomSunFlare) for more information on these parameters and their defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a370d2a-2489-4c48-b29a-ea8ad3ef98fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
    "\n",
    "params = {\n",
    "    \"p\": 1.0,\n",
    "}\n",
    "\n",
    "for idx in range(0, 8):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    factor = (idx - 1) / 2\n",
    "    radius = (idx + 1) * 100\n",
    "    params[\"src_radius\"] = radius\n",
    "    perturber = AlbumentationsPerturber(perturber=\"RandomSunFlare\", seed=idx, parameters=params)\n",
    "    ax[row, col].set_title(f\"Lens Flare Radius: {radius}\")\n",
    "    ax[row, col].imshow(perturber(img_nd_bgr)[0][:, :, ::-1])\n",
    "    _ = ax[row, col].axis(\"off\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30eb653b-6be9-4d58-8d69-db4b64c3d81c",
   "metadata": {},
   "source": [
    "## Baseline Detections\n",
    "\n",
    "In the next cell, we'll download a [YOLOv11](https://docs.ultralytics.com/models/yolo11/) model, compute object detections on the source image, and display the results. As discussed above, these detections will serve as the \"ground truth\" for our relative mAP evaluation later.\n",
    "\n",
    "*Note that here, we're using YOLO's built-in visualization tool, which automatically adjusts for BGR / RGB order.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55600de0-089f-4dd6-ac4d-7896b8df5494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics 8.3.85 🚀 Python-3.10.12 torch-2.6.0+cu124 CPU (11th Gen Intel Core(TM) i9-11950H 2.60GHz)\n",
      "Setup complete ✅ (16 CPUs, 62.5 GB RAM, 329.0/914.7 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 55.9ms\n",
      "Speed: 4.0ms preprocess, 55.9ms inference, 1.7ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "# Import YOLO support\n",
    "import ultralytics\n",
    "\n",
    "ultralytics.checks()\n",
    "print(\"Downloading model...\")\n",
    "model = ultralytics.YOLO(\"yolo11n.pt\")\n",
    "print(\"Computing baseline...\")\n",
    "baseline = model(img_nd_bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39050929-bf48-44b1-9247-5cac7f44cd8c",
   "metadata": {},
   "source": [
    "## MAITE Evaluation Workflow Preparation\n",
    "\n",
    "We'll use the [MAITE Evaluation workflow](https://jatic.pages.jatic.net/cdao/maite/generated/maite.tasks.evaluate.html) to evaluate the performance of the perturbed data against our baseline detections. We'll need to \"wrap\" our model, data, and perturbations into callable objects to pass to the `maite.tasks.evaluate` function:\n",
    "\n",
    "- We'll wrap the **model** to make predictions on input data when called.\n",
    "\n",
    "- The wrapped **dataset** will return our test image when called. Note that this will be the original, unperturbed image; we'll apply our perturbations via...\n",
    "\n",
    "- ...the **augmentation** object, which applies the perturbation to the image inside the evaluation.\n",
    "\n",
    "- Finally, the **metric** object will define our precise scoring methodology.\n",
    "\n",
    "The evaluation workflow in this notebook is slightly unusual. Typical ML workflows apply many different augmentations / perturbations to much larger datasets, and only call `evaluate` once to get a statistical view of performance. But since the goal of this notebook is to drill down into how perturbation affects performance, we've essentially flipped process, calling `evaluate` (and thus our wrapped objects) many times, once per loop on our single image perturbed to a known degree, and then observing how the metrics respond."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da908c7f-b499-4361-8e2b-cf7647b45de6",
   "metadata": {},
   "source": [
    "### Some Helper Classes\n",
    "\n",
    "The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:\n",
    "\n",
    "1. The `YOLODetectionTarget` helper class that stores the bounding boxes, label indices, and confidence scores for a single image's detections.\n",
    "\n",
    "2. The `MaiteYOLODetection` adapter class that conforms to the MAITE [Object Detection Dataset](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Dataset.html) protocol by providing the `__len__` and `__getitem__` methods. The returned item is a tuple of (image, `YOLODetectionTarget`, metadata-dictionary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "346f4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import torch\n",
    "from maite.protocols.object_detection import DatumMetadataType\n",
    "\n",
    "from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset\n",
    "\n",
    "##\n",
    "## Helper class for containing the boxes, label indices, and confidence scores.\n",
    "##\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class YOLODetectionTarget:\n",
    "    \"\"\"A helper class to represent object detection results in the format expected by YOLO-based models.\n",
    "\n",
    "    Attributes:\n",
    "        boxes (torch.Tensor): A tensor containing the bounding boxes for detected objects in\n",
    "            [x_min, y_min, x_max, y_max] format.\n",
    "        labels (torch.Tensor): A tensor containing the class labels for the detected objects.\n",
    "            These may be floats for compatibility with specific datasets or tools.\n",
    "        scores (torch.Tensor): A tensor containing the confidence scores for the detected objects.\n",
    "    \"\"\"\n",
    "\n",
    "    boxes: torch.Tensor\n",
    "    labels: torch.Tensor\n",
    "    scores: torch.Tensor\n",
    "\n",
    "\n",
    "##\n",
    "## Prepare results for ingestion into maite dataset by puttin them into detection object\n",
    "## Images must be channel first (c, h, w) in maite dataset objects\n",
    "##\n",
    "imgs = [np.transpose(img_nd_bgr, (2, 0, 1))]\n",
    "dets = []\n",
    "metadata: list[DatumMetadataType] = [{\"id\": 0}]\n",
    "for _detection in baseline:\n",
    "    boxes = baseline[0].boxes.xyxy.cpu()\n",
    "    labels = baseline[0].boxes.cls.cpu()  # note, these are floats, not ints\n",
    "    scores = baseline[0].boxes.conf.cpu()\n",
    "\n",
    "    dets.append(YOLODetectionTarget(boxes, labels, scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd8fb3-2ea6-492c-ab84-f3432b9d3440",
   "metadata": {},
   "source": [
    "### (1) Wrapping the Detection Model\n",
    "\n",
    "The first object we'll wrap will be the detection model. The cell below defines a class adapting YOLO for the [MAITE Object Detection Model](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Model.html) protocol. The `__call__` method runs the model on images in the batch and is called by the MAITE evaluation workflow later in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1131aa7-3716-479f-838b-0464b74106e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import maite.protocols.object_detection as od\n",
    "import ultralytics.models\n",
    "from maite.protocols import ArrayLike, ModelMetadata\n",
    "\n",
    "\n",
    "class MaiteYOLODetector:\n",
    "    \"\"\"A wrapper class for a YOLO model to simplify its usage with input batches and object detection targets.\n",
    "\n",
    "    This class takes a YOLO model instance, processes input image batches, and converts predictions into\n",
    "    `YOLODetectionTarget` instances.\n",
    "\n",
    "    Attributes:\n",
    "        _model (ultralytics.models.yolo.model.YOLO): The YOLO model instance used for predictions.\n",
    "\n",
    "    Methods:\n",
    "        __call__(batch):\n",
    "            Processes a batch of images through the YOLO model and returns the predictions as\n",
    "            `YOLODetectionTarget` instances.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model: ultralytics.models.yolo.model.YOLO) -> None:\n",
    "        \"\"\"Initializes the MaiteYOLODetector with a YOLO model instance.\n",
    "\n",
    "        Args:\n",
    "            model (ultralytics.models.yolo.model.YOLO): The YOLO model to use for predictions.\n",
    "        \"\"\"\n",
    "        self._model = model\n",
    "        # Dummy model metadata type to pass type checking\n",
    "        self.metadata = ModelMetadata(id=\"0\")\n",
    "\n",
    "    def __call__(self, batch: Sequence[ArrayLike]) -> Sequence[YOLODetectionTarget]:\n",
    "        \"\"\"Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`s.\n",
    "\n",
    "        Args:\n",
    "            batch (Sequence[ArrayLike]): A batch of images in (c, h, w) format (channel-first).\n",
    "\n",
    "        Returns:\n",
    "            Sequence[YOLODetectionTarget]: A list of YOLODetectionTarget instances containing the predictions for each\n",
    "            image in the batch.\n",
    "        \"\"\"\n",
    "        # Convert images to channel-last format (h, w, c) for YOLO model\n",
    "        batch_transposed = [np.transpose(batch[i], (1, 2, 0)) for i in range(len(batch))]\n",
    "\n",
    "        yolo_predictions = self._model(batch_transposed, verbose=False)\n",
    "        return [\n",
    "            YOLODetectionTarget(\n",
    "                p.boxes.xyxy.cpu(),  # Bounding boxes in (x_min, y_min, x_max, y_max) format\n",
    "                p.boxes.cls.cpu(),  # Class indices for the detected objects\n",
    "                p.boxes.conf.cpu(),  # Confidence scores for the detections\n",
    "            )\n",
    "            for p in yolo_predictions\n",
    "        ]\n",
    "\n",
    "\n",
    "# create the wrapped model object\n",
    "yolo_model: od.Model = MaiteYOLODetector(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cef0788-137f-41c1-a9e4-80e7d08b47d1",
   "metadata": {},
   "source": [
    "### (2) Wrapping the Dataset\n",
    "\n",
    "MAITE pairs images and their reference detections (aka targets, ground truth) into **datasets**. Typical ML workflows have many images per dataset; when these do not all fit in memory simultaneously, a *dataloader* object is used which can page images and annotations in from disk. For this notebook, however, each invocation of `evaluate` will use the same single-image dataset (our reference image with its baseline detections.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1dcbb5d0-b899-4355-9922-9d52fb866b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our single image, its baseline detections, and metadata dictionary\n",
    "# switch image to channel first\n",
    "single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id=\"visdrone_ex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7963f7fa-052c-476d-944e-5bbdae3181b5",
   "metadata": {},
   "source": [
    "### (3) Wrapping the Perturbations as Augmentations\n",
    "\n",
    "The `evaluate` function will perturb the image from the dataset using instances of the class defined below, one instance per perturbation value. Note that the object doesn't perform any augmentations until called by the `evaluate` workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber\n",
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "perturber = AlbumentationsPerturber(perturber=\"RandomSunFlare\", parameters={\"p\": 0.0}, seed=1)\n",
    "identity_augmentation = JATICDetectionAugmentation(perturber, augment_id=\"identity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81020be7-76ba-43e3-b231-7d2bd7c30387",
   "metadata": {},
   "source": [
    "### (4) Wrapping the Metrics\n",
    "\n",
    "We'll compare the detections in each perturbed image to the unperturbed detections using the Mean Average Precision (mAP) metric from the `torchmetrics` package. The following cell creates a mAP metrics object, wraps it in a MAITE [MAITE Object Detection Metric](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Metric.html) protocol-compatible class, and then creates an instance of this class, which will be called by `evaluate`.\n",
    "\n",
    "This code is copied directly from the [MAITE object detection tutorial](https://jatic.pages.jatic.net/cdao/maite/tutorials/torchvision_object_detection.html#metrics) (with the exception of setting `class_metrics=True`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e33407c5-f1d7-4c7a-9a31-2d500f97edcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from maite.protocols import MetricMetadata\n",
    "from torchmetrics import Metric as TorchMetric\n",
    "from torchmetrics.detection.mean_ap import MeanAveragePrecision\n",
    "\n",
    "##\n",
    "## Create an instance of the MAP metric object\n",
    "##\n",
    "\n",
    "tm_metric = MeanAveragePrecision(\n",
    "    box_format=\"xyxy\",\n",
    "    iou_type=\"bbox\",\n",
    "    iou_thresholds=[0.5],\n",
    "    rec_thresholds=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    max_detection_thresholds=[1, 10, 100],\n",
    "    class_metrics=True,\n",
    "    extended_summary=False,\n",
    "    average=\"macro\",\n",
    ")\n",
    "\n",
    "##\n",
    "## This wrapper associates the MAP metric object with methods called by the evaluate\n",
    "## workflow to accumulate detection data and compute the metrics.\n",
    "##\n",
    "\n",
    "\n",
    "class WrappedTorchmetricsMetric:\n",
    "    \"\"\"A wrapper class for a Torchmetrics metric designed to simplify its usage for object detection tasks.\n",
    "\n",
    "    This class facilitates the conversion of object detection targets and predictions into the format\n",
    "    expected by Torchmetrics metrics, allowing for easier integration with existing pipelines.\n",
    "\n",
    "    Attributes:\n",
    "        _tm_metric (Callable): The Torchmetrics metric to be wrapped, which takes lists of dictionaries\n",
    "            containing torch.Tensor objects representing predictions and targets.\n",
    "\n",
    "    Methods:\n",
    "        to_tensor_dict(target):\n",
    "            Converts an `ObjectDetectionTarget` into a dictionary format compatible with the Torchmetrics\n",
    "            metric's `update` method.\n",
    "\n",
    "        update(preds, targets):\n",
    "            Updates the wrapped Torchmetrics metric with batches of predictions and targets in their native format.\n",
    "\n",
    "        compute():\n",
    "            Computes the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        reset():\n",
    "            Resets the state of the wrapped Torchmetrics metric.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        tm_metric: TorchMetric,\n",
    "    ) -> None:\n",
    "        \"\"\"Initializes the WrappedTorchmetricsMetric with the given Torchmetrics metric.\n",
    "\n",
    "        Args:\n",
    "            tm_metric (Callable): A Torchmetrics metric instance that expects predictions and targets as lists of\n",
    "                dictionaries containing torch.Tensor objects.\n",
    "        \"\"\"\n",
    "        self._tm_metric = tm_metric\n",
    "        # Dummy metric metadata type to pass type checking\n",
    "        self.metadata = MetricMetadata(id=\"0\")\n",
    "\n",
    "    @staticmethod\n",
    "    def to_tensor_dict(target: od.ObjectDetectionTarget) -> dict[str, torch.Tensor]:\n",
    "        \"\"\"Converts an ObjectDetectionTarget into a dictionary format compatible with the Torch's `update` method.\n",
    "\n",
    "        Args:\n",
    "            target (od.ObjectDetectionTarget): An object detection target instance containing boxes, labels, and scores.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, torch.Tensor]: A dictionary with keys `boxes`, `scores`, and `labels`, each mapping to a tensor.\n",
    "        \"\"\"\n",
    "        return {\n",
    "            \"boxes\": torch.as_tensor(target.boxes),\n",
    "            \"scores\": torch.as_tensor(target.scores),\n",
    "            \"labels\": torch.as_tensor(target.labels).type(torch.int64),\n",
    "        }\n",
    "\n",
    "    def update(self, preds: Sequence[od.TargetType], targets: Sequence[od.TargetType]) -> None:\n",
    "        \"\"\"Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
    "\n",
    "        Args:\n",
    "            preds (Sequence[od.TargetType]): A batch of predictions in the format expected by the Torchmetrics metric.\n",
    "            targets (Sequence[od.TargetType]): A batch of targets in the format expected by the Torchmetrics metric.\n",
    "        \"\"\"\n",
    "        preds_tm = [self.to_tensor_dict(pred) for pred in preds]\n",
    "        targets_tm = [self.to_tensor_dict(tgt) for tgt in targets]\n",
    "        self._tm_metric.update(preds_tm, targets_tm)\n",
    "\n",
    "    def compute(self) -> dict[str, Any]:\n",
    "        \"\"\"Computes and returns the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, Any]: A dictionary containing the computed metric values.\n",
    "        \"\"\"\n",
    "        return self._tm_metric.compute()\n",
    "\n",
    "    def reset(self) -> None:\n",
    "        \"\"\"Resets the state of the wrapped Torchmetrics metric, clearing any accumulated data.\"\"\"\n",
    "        self._tm_metric.reset()\n",
    "\n",
    "\n",
    "##\n",
    "## This is our instance variable that can compute the MAP metrics.\n",
    "##\n",
    "\n",
    "mAP_metric: od.Metric = WrappedTorchmetricsMetric(tm_metric)  # noqa: N816"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0102dfde-cb9b-46a7-8e4a-d12ccd948864",
   "metadata": {},
   "source": [
    "## Running the Evaluation\n",
    "\n",
    "We now have all the wrappings required to evaluate our range of perturbations:\n",
    "- The `yolo_model` object, wrapping the YOLO model\n",
    "- The `single_image_dataset` object, providing our source image and its baseline detections\n",
    "- The `augmentation` object, which when instantiated, applies a single perturbation value to its input\n",
    "- The `mAP_metrics` object, defining the metrics to compute at each perturbation value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a7fef1-a3cc-4e63-8a2b-6133274e4b1c",
   "metadata": {},
   "source": [
    "### Evaluation Sanity Check: Ground Truth Against Itself\n",
    "\n",
    "Here we quickly check the evaluation workflow by creating an *identity augmentation* (with a cn2_at_1m value of 1.7e-14, leaving the image unchanged) and scoring it.  The detections should also be unchanged from the baseline and thus give an mAP of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0af9f36-c69b-400f-82ec-f1fd7e9d98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 21.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check: overall mAP (should be 1.0): 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from maite.tasks import evaluate\n",
    "\n",
    "# call the model for each image in the dataset (in this case, just the source image),\n",
    "# scoring the resulting detections against those from the dataset\n",
    "sanity_check_results, _, _ = evaluate(\n",
    "    model=yolo_model,\n",
    "    dataset=single_image_dataset,\n",
    "    augmentation=identity_augmentation,\n",
    "    metric=mAP_metric,\n",
    ")\n",
    "\n",
    "print(\"Sanity check: overall mAP (should be 1.0):\", sanity_check_results[\"map\"].item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1a72bb-dbf1-431e-892e-132771cd7b92",
   "metadata": {},
   "source": [
    "### Preparing the Data\n",
    "\n",
    "Now we'll prepare the augmentation instances for the evaluation. In the cell below, you can set three parameters for sweeping the set of perturbation values:\n",
    "- **sweep_low**: the minimum perturbation value (`0.0` is no haze)\n",
    "- **sweep_high**: the maximum perturbation value (we use something high to see where accuracy will drop to zero)\n",
    "- **sweep_count**: how many perturbations to generation\n",
    "\n",
    "You can also optionally select perturbations to visualize:\n",
    "- **visualization_indices**: a list of perturbation indices *p*, 0 <= *p* < sweep_count. These instances will be rendered along with their corresponding detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 100 perturbation augmentations\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 100\n",
    "SWEEP_HIGH = 800\n",
    "SWEEP_COUNT = 10\n",
    "VISUALIZATION_INDICES = [0, 40, 75]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentations = [\n",
    "    JATICDetectionAugmentation(\n",
    "        AlbumentationsPerturber(perturber=\"RandomSunFlare\", parameters={\"p\": 1, \"src_radius\": int(p)}, seed=seed),\n",
    "        augment_id=str(p),\n",
    "    )\n",
    "    for _, p in enumerate(perturbation_values)\n",
    "    for seed in range(10)\n",
    "]\n",
    "\n",
    "print(f\"Generated {len(augmentations)} perturbation augmentations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549d0d4d-eabd-4f54-a487-7cfbc99916c9",
   "metadata": {},
   "source": [
    "### Calling Evaluate on the Augmented Data\n",
    "\n",
    "We loop over all the augmentations, calling `evaluate` on each one and building up a list of resulting metrics for analysis.\n",
    "\n",
    "The location of the sun-flare will have a high impact on our results. If the area of the image obscured by the flare contains no objects, it would not interefere with detection. Because we select the locations randomly, this results in a high variation in relative mAP depending on the random seed used to place the flare. To get more meaningful results, we will repeat the evaluation for 10 different random seeds.\n",
    "\n",
    "Any augmentation indices specified above will be rendered in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  5.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #0: Radius 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #40: Radius 411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  5.92it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  7.38it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  6.32it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  7.02it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  6.99it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  6.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #75: Radius 644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "image/jpeg": 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ch2674ScnJP8AMn86oXwuBfjUobt3miX5YxGU3nn29zXFHTriHXLdIbq48v5PMjS4IwCMZIz61PawaoI9RI1W5IjDeWfPyFINLlY7no2k67dG7liu7qNU2K0bMMDP8QJx1rVXXIC7INT08uvVfNBP5V5V5niBE0/GpTkyORLlVPHHtWeksw8QTiRi8plIJIAz2/oK0p0Od7jWp65/wlunrydTsfxJFO/4SyyCb/ttoV653ECuEhuJQFUrn6gGqzWxkkkZ4CwLk/6viuTFylh4qW5vRpKbabPRU8XWDf8ALzbN9JRVu212K7Vmg8uQLwcSjivJLvybKLe9ukeOfugE9vSp9LuoMx3ojHljByR2yAQfzrFYiUpaL3SJRpxvFy95K56LqHiS4tJXT7MMA4XnPaq2m+JLmOBZbxWaI5yQCT1PtXFXN9pp023dpBvdg3lAnJ3Hu3p7VZFzYpLFp7B/3y7kKnj1wDmu2NranPGrCTsmd6PFdoyBhFOR/uGnWWvx3dwUit5vm5OVrhptOh85bSKS6hlkGVbzMqOfT/PWp9McafdtLHcF2jG143mbGcnng1Jrod/HqJaWRTBN8pGOB6fWn/2ggYAxS88/dH+NcQmtzXLSmO6mhYkAjeccZB710q38D7SjzE4xkyn/ABoEaf8AaEY/5Zyf980n9owjruH/AAGkCRsQBPJk9B5pp3kj/ntN/wB90CD+0bf+8fyo/tK3/vn8qqveWscgQ3smT6Nn+lVNZ1ZNIt4pyZpllOAEcZ+uMdKW47Gr/aNt/wA9P0rBuNZjE0bPOx+Y9sdKzv8AhMo/Nz9nvsAcjK4/PFZXiF1k0qJTcOoeUN1GDk5x09O9AWO6tNYtGgLvcqQGxuqc6vYgkG4UEVw26xi0U2trLIIJpg2Yyuc5x6c9KV9PkmaF31CXdetnmJTyMe3FO66gd0NTtGJAmBx7GoXuopH3K+R9K5S2huYIHul1WWZZXwPLQJznHpWssLxQhpJHyBgnjmtKe5LNJrqJer4pFuoXJ2vnHtWMWWQFhJgDvnp+lKLgwAYnIDegzWxNza8+P+9R58f96siO5klO5biT0+7j+lI968LBDLMST2UUDNfz09f0rmZpwl/cvgtufH4CtP7VKOTM/wCQrEuDuuJT8xy/WgC95i7A2TnOMVLcy7rVV7YHWq2AO5/OnXH+oyWyAOmTRImIkLhWHHUDoKfLMGfPPAH86ihIfGFbOOKlMcuMDtQrdSyCba7bjnOPT3NLHjeqKpyRTniDnOVGAOpojVVlVvl5OMUh3RPHcb5GQIcr1pJZMggqelOig2MzEDJxQ0e44AA4pkMrb9u3AH51FI3IPH51aELMeMcHGKJIJFGNucGgLDYn3RjGKfk47URLJ8ylOlRzsYHIfjcKYm7EqOSvFOjfMp+XnHWqST7V9BmpYC5kyDuHJIFKWxCnqaAkJ5xzUgmOOlU93y8daUO2egx2NYFE7uWPb6VBI/OBijcSeRTPkBAxmgdxGPGOKiLHJGelPZDyR09zUZIyARximCYhYjvQDuBOM0pC4280gPGAOM0E3DOBkjrTl5GCOtRsWYgdhS5Cn5uaAF4B2nj1NL90HaTjvSAgnIOPejdnqKAEJ70uRg7hkUxiCKBk+nSgqw52JHt6VHkswFOz0/Wm9yQcCgaKlrP/AMXM8LWyyD71zIyY5P7hwDn869arwDw3qH9ofHfTsBdkAmhQ5znEMmT+ea9/qQg73CuG1Bi/ifUgTykiKPoYkP8APNdzXn2oebD4x1hnGIpDAUJPUiMA4pS2LMHxNAgNrK5+UTbG+hyK8f8AF1sLXxVfoPus4cfiAf8AGvaPFKbtKmbj5CH/ACINeTePlRteiuI8YmgUnHqCQf6VKGjlqKKPWmhh3qSJjFIjj+Fgf1qOlxwaAPTPGlxLDp+ky28m1GV0OPbaRS6K0k/gadllO6OcEkehIz/M069sbjxD4J0w2oQyxMjHcwGQY8Hr74qx4c0e807w9qFldiMPIuYwsgO45zUkSR0XhdTHp9upOQoZPyOP5VIPklHqrY/Ko/DyPbW0cU5US+YeAc9QP61NcqEuZB/tmt6ZhW2TMrWoRH4strnkBjG3Xj+7VbxBCTqMbM7DEROAepV//ritnW7WW7ms5Itg2KG3Mf8A630rOv5ItQ1N4FO14cqSeh3gH/2X9aySabNLrQ09cTf4d1NRgnymYfXGa5jwqWSzsgxJKs4yfcBq7DyhNp88D/Nvt9p4/wBkg1x2gqbcwQFt22RTu/4Bt/wqkmUtTCubcroevwISrRXQK49N5Fcx4bvrldaQXEz7XOQjZ612+qRrZ3OtJksJ8uf9nHzVwml6nctqgEk/7pmwMpwfYelepgk/ZyTMKyunoep2yga/qGDw3kOPyAqjdxbfFWoMcYaBWA98Katxts1KKYf8tolDA+x/+tTdWjWLWlm53SRBT+RH9K8xxdzZaq5bChddJA+9BEfycj+tNgi/0TVYSBtMlwOnsCP50+T5bm2nB5aEKfoCGpkU8g1rUrPyj5ci7w2O7Dnn8BSaY7FyRf31qwAyPMA49QD/AErltSfyvE8zZ2jepzWsmpTtpVhcGIiQXCxyDHYhhx+lYniI+TrszDrtVsfgK68J8T9Cooht9QuG0+V2knCq6nzEkOSMMDg+3Fbv9qlLNZUuJDtjViN5zyB2rkIZkFgjAbU3uXVe+Dx+GaikumlUTvMAhThVGTxxiuWp2Z5lXFTU3GJfv9Qubu4eO5Sad1+4pyQqnBz9OKVLu5jt2g84pburOFQ5B59O3GKyv7RQlmAILHG/POPT6V1Xhbw+2rxw3V35kVtH8qgHBc57e3HWoWuhxctRu5i6fpWq6rbwJaL5kWSCzkgIcdTXfaR4XgtTDLeTPcSjHoFBB4966CKKOGMJGiqijGBximyA7twHyjuOlUo2OyEEvUs20Noblt0KBi27JGSakls7AyvvtYizdcoOarowLh+Q3rTpX3qHOSfambqo7WI7jTrCUMBCFDdSvb6elVJI72wR/s4W4hI+4RhxznINXFlw+3H4Uu4g4z9KTVyueXczbPxRLDOrGFNw+VlPBBrck8USQwPIYgxGMHtVF9OiuXM4ASUDAb+8PeqNwkgUQzRqCQeOefp61jJNHVTd1qUp/EhVnIjDsWyNx6c9KoXfiJrkRhlbKnGB0Aqjq6QQROYraUSDoeePfFc614TGecMTgHpioU2Z1+eL91nVXWo20iR5ndIiTleCePfvUl1f2OoaVmF2domC7XGCccAn61xf2iWe6jCnmJsknnbVv7Uz26mMohkG1to6AEYP64qlJ3MJYicNWbDavG1tBAUVEjBZdhI6dT19aT/hJLhFidbiQeUQIstkDjtn2xXO3JKECMFstjdjGF4qpdGW5jCxAjbgBF6YJ61fMcLrVpO/MdbaeI78x7YXfyQQ2xugJIOcV6DZaqt3pMUskhZ5lVyOy/SvJo28tHiRXkwMMoGTkDH/ANeu40ueRrC2yhB2BeR6cVvTkm9jShiKkp67G9HcJG5YEDAyM1QkvWkk3MCeefrTbiYtcblAXouMfrVR8Eswbvk5rVnZKbkbkOorHHnaTjjNWRcLKEk2nnNc55hwuGwGPI7CtSwlDKI8HHOMmkVGT2ZbmvER1Uq2fpTYsFpj6vTLqDzX3ZxxVOKRzIQJGALYNCNjSJGKhuWAh45JpznCsfQZqrNcJLHgDaQvr+lVZdSU79CxaziVlCMMgdh7VZLuqklsjGelY9q7xAMmAcdxVkXM7MFbaQeDxSGyzuFCcyoPeozyDxTZJzAVcLux2zWjWhjHc1+1NHUn2qiNQkI/1I/76qNtSaMkGLr/ALVZG5owcMPqTVh+Rmsqz1API25NqqOST6mrUl2hj4bORxigGSxSxszFGB2nDY7VUvbiKWIEKSQSAfxrJeR4iHjnCg/fH40kU5kjEZYkr6+lMy5iVgBweD1FT2crRzqQearFc85xUlscTgg4wPzoewkabkbsqOOoA5o3tjk/nUO/dySRnpSEnBw1YlEhZs9c00MFJyeajLHuaYT3oKsStIee+aYzHpgA1GTxntSE8AUDSsP3AGms5H404lWGCRUZUHoTmgEiQuChHPA4xQiNje5PtTDwABzT42bIOeD1oC1h+3YTk9uKYQQcHnvT3lMjEnHTtTCPfOaCRGz1oHHfPHWjB/vU0thetBYqn5j/AHe9Zuv6mmjaLdXzbQUQ+WCeC54A/Or6nHOM+teV/ETXP7U1IaRbZMNrkyc43Pj+nT65pN2FLRC/Ca6Nz8W9IZixZjOSWOST5MmSa+pa+VPg7EV+KWis4dZAZ+CMceRJX1XUxCGwV574lYDxHcjYp5TnH+ytehV5P4u8dWGmeNLrSJdOnlmRok81du0lkUjqc96pW6jkm1oXtWt1uNPmjI4eIj/P515P4l0y71W2sJrK3aZlDB9mOAQv9Qa9dEwurJHKFN68r6V5L4nmsdMvxbS2U4cruDxy7QRkjp+FQO5zP/CN6yOunyj64/xpy+GdXPP2Nh9XX/GpP7RsXcKLS5JJwMz/AP1q0tIgtdV1H7H9keNthfLTZzjHH3adht21Zk/8IzqwPNsF+sij+tKvh6/Mm0iBT7zp/jVuXVraB5LY6TGWiYoS0mTwcelO0+5iuL+3iGmwIJJAu4sTjJ+lFg5j0Pwja3Ft4fW0mA8xCCNh3D8/pW15EpH3H/Kq5afTvDV5PapH9ohheRARkZAJ6VxuheNfEGq6rFbTyQRo+cbYcc4JHU000tDOS5tbneRQyxzxsQwAbnNLfri6kPrg/pXK6XrWrX2qvb3F7winKqgHIYD0rrL/AO+h9UH9a0huZVF7o25ZRZwSOwVcFck4rJlgs4ZzfzSIFYIdzNhc9P6VH4rt1ufCceQC0dwp5/Ef1qjdFbjwzaWSbRO6Axhunytz+gNS52dhqF0mdRYTx3AV4pFkjdSNynIPJrjNNuI3vZUiJYwOu7ggD5wP8a6TwvmKwhRiNySSK3GO4P8AWuctFMWr6yvTBlOB/suD/WlzGsVYj1n99r95bKjbjAXJxxjy689g1EprAg8qBYw/BY4x759TXpt8i/8ACZRn/ntbbT+Icf0FeXS3Npaag0Btd/7wFip716WBm3dGVVX6XPTPPY3emxKq7ZIGO4n0BNQeKry6i1jTIbeFZFmXJ5+bhug7dKLNvMOkMezOg+hAH9anvT/p2jzSD7m5WPpkL/jXnzbU2jSm7xRcuHkK6cFOFaKTcMc5C8c/hVu0+bWLlmYkERhPYFf8RVOaVIrSylkYCNJGRmPYEMKI7sW+qwxMR+/hQJjuVz/iKh3LZLHCDpVohySJY2bJ6nBH865zxY6x6mJ8D95CuFb1x/8AWrafUVi027K8tZS8574fP9a4fVNTfUYBI7k+WSu4jnAP/wBcVrSk4t2Mak2lZdSlcTecjSK3yhuUAxx7022tbi7gIgA8tGwzswVQfqeKhuWwSzA7nwzrtwFq3qPmRJYxRcQi0EuM4yzZJPuf6CudvU5IU7K5s6F4bttauxbvesXhI81YkJUKOoyQOTXqkMcdvFHFCgSKMAKvoBXM+B7H7Lo5u3H728fzPu4+UcL/AI/jXTkgZXtnPNXFWNEtCQNg5z1PSkk+ZScHg9MUgb5cgd+eO1OLEMDnCngVQrWEVseo7U8nIHox5qJvl5ByDU0KLJw2evGKRVrkZPQn7w4qaLEkgBPHepTaKXUfNjvU0cKpwqnnvQWkSphcBSMD1qHUbVLqBX2DzE5Bxz9Km2jOcY9amPGKTRom07o4G9ZlUmOONxyVyT83/wBYZzXIXmmySEyIPLJO0Iw4Ld8fnXf69aJA8wHEbLkBeoyR0/WsW5hYSNCrgx4B2n+L6ehwPpWLidGkkefzyGBXiH+sZiG/wqxFKgXYRu2dx3I4/wAa6K/07T5Ek2xHzoQOMdDg/n2rkAQyMN5Vc8HOM8880WscOIp6E7ljIsDNtYgAgHPJPNTK5jMkiAYQBSoOenQ5rPSYxvLKxO44Xjt3p81wSrALmP5VJA6/54oRxTpu9kWFMlyPs8UuJyQ4BOOep5+gruvC1482hIru7FTklnyRnn8K87V1a6D7ggB+Zh1PWug8KXq2+syWYk3JMhBx0DLyP0zWtN2kVCPLJRO4lb96MtuApNwCuDzk8YNNkyzBscHn8KQnOR05yPaug7lHQlWOM9ZgDj7rAipoWlt3V+CMYDDkCqRZSOvSrNiC0oU/df5SD3FBTRomWdpo8yZViD+BNLbxx5dyOfMOKrtdKojK4ZgBznpUSzyEbApwWznNC0KczWcblZR1IwKhntylmCQpbdt4pIrgbVy4Mg7+pqW4dmVVyDl9wz705bjhK6ZVETKoXBztx0p6oVkJOewqfdJ57FwCfbpipGJdT8hGSKdgbsQ7j6moLgkgd6sbB6kVPEEwg2qWz1I7VcnoKLT2KakhRUUjEyLkegrcMER/5Zr+VR/ZYQ4fZ81YlmWm1ba5bHdQefesj7QxdmOecDrW/eQsluVthh2cE579ayh9qjceZbJJk5BK5/lW9O1gsQORmTuA2B7j/IotcvJvU4yQPrzRLMgYo9uUHPQ4z6UhSNVBjBBJ557UpIzmktTQY5PytkDg1PbquWOOnes2GcI4yPlPUVpQMJIgyMGDHqKxk9CY6lhRyMjOKaQQevWj5gcHNOwTkMGBFZmgjdcDpSdF+b0p24Z79KCm4DLcHr7UBcjOcAYzQSOOBwO1SbQp4P60hTkAcmgdyNeBmnKobscUgjy3PTvTyOiigGxm3CnI57YNSAHYBmmqRkHHfHPengjeScAfpQJibMY4PWjYVbnke1LkcnOM9hTQ3ODQLUjKEsAMkGmspBqXdgkD1rH8Sa9a+HdOe5kzLLg+XCCMuf8AD3pFXsZfjPxPF4e03y4Zk+3zjbCpP3f9s+wrxy4usSx/Uhm9T6571DquqX+u6q+oXjF5ZOAF+6gHQD2FI8YcI5Y5HapepMtT0H4RPn4oaQzfMz+cMk/dxDJz/SvqKvlX4Qlz8VtDySR+/wD/AERJX1VTKgrIK+avik7L8TdWZSAUaA9OeIYz/SvpWvBPiF4b1K++IOq3UMKmCURbWMijOIlB6n60LdXLOy0+ZZdMjkHIOG49Dz/WuH8Y6PBfa1ZiUldySAEd8YOP1NdZ4fjli0eK2mCiVIgGGc8gYrN8T2Nzcz2ctuql4pQ5BbGV2kEfqKmXWxK2R5DqdubDUpLZQPkb5Tjt2ra8N7rbxRYszEiZWXn1IP8A9atXWfCOo6percRLDF8gVt0mckfQVZtfCOoRX1jcmaAfZ5A7YJJIBGe3tTpWW5UtUzM17SojPqkkIxNFKXP0OG/rXPWDlLm3m3H5SjjnvmvSbvQbiXV7u7hmTyrlFVo3QnBAwTx7YqlF4EhVQHuXOBj5I8Y5z3pwSTfMKLtGzO1SPzNMlTbuDoRj2P8A9avLrWBLTxZpV3GMQTlRx0DdDXqmnrtgKHOEAAJ71zg8OWMMgX5mWKUvGrP905zxioad9BaWKsMaReKpSi4BklQ4HQ9a6m+X5IW9QR/KsprW3+1vdFVErHcSAeuMZ/KtFpo5dNgYSAkNgc+xrWGj1MptNMoapE0/ha+RELOpVgAMn7wrCaxu5tGtpIQ0d1D5qorLg8jI6/jXW2ufIuU6ErkVVHzDk/xChxvK5MZ2iitoUM8EIW5ADtLuwCD1XB6e4rLvbN7TxBqFzvVo5mKFB1G9FrchbbL15GDj8RXOeKb1bLxEiSXARZRG4Qnqc4/oKXLqawfNuR61IYrvTNRT74iHXocc/wDsxrz7UbG2fUp8qwYSnOwjsTXc67NnTNPjhRp5VDIUjwSOAOfyrN8QwRWmjfbY7XFwJsOzrjcGJP8AhXTh68KTuwqU5Ne6K00h8Mwy2+4SLIMbevetu6lZ9Htrh+Du5z/u/wD1qwtSkePwak1ufLO+NjtHqOf507X5Fu/CNu+4485SR0AyG/xrGc05XQ4R5YpM2NSaJ/D7+dJ5aCf73pz/APXrnpdfUXcG/Y8lsuFY96wbrUri4jaMyAx9geg/ziknNtKIiMg/xtznHbjPOBWUpMiUm9jYm8QNOlyUWNXuH+cDof8AOKwHl3sysSByQB6n+mT+lQo5XcE37sfdNRO2G8wEk8fdPT2qbmXK3uTmUhwHUHcu3B54q5qrFE0mYASE2Sk++Hdf6VlysrsuM7e+eMVq35VNK0a4yOIZEznsJnyB+dBpGOh7TYwJa6db2yfdijVVPToKsMc8Hp1wKjUjaGXjgdTTg/pknFaGdiRMkMMc46Cg/dOecd6aPlxTjyrY5oE1qIeQPSpYD+8Xrx6CoQGweKt2kBLGRsgdAPWknc0cLFhmCEZXJJ6VIDlc46U+ONWHzMAB6mnNGo+UEMDTGMwdgIUtnsMVMOUGRg+lIgCjAGBSk4B4oAydbiVvKfAyQRXLXUYiIdoh5mSoPXC9a6bxG8i2AMCbpQG2DOMmuVsbm/naY31p5K7QI/3m4579/pUPRm0NiN7BrK4V0tws0h3DYxVmOPp29q5fxBoYhikufMEc28P5R5BySTyOmK3p4Z1uPtLuqKqliXckKQD0HQDpmqWr6mlnpxecWbNJjbGBsyCPT86jdjlHmRxUqskTsxZdxONw64x0pZW3KeiqBuB7g/5FTSxST2vn2+XhGzeobdsJ7/TNVJdgLFFbAPr1H0pnJKm0ySBpNwYAZfkgc+o/Ckgne1lE8O4So4YE8HjpUUPzsHMnzRjGAOtBDN5jsSMLu5B5PpQZ2tI9YtLyO+tIbqE/JIu4c9Pb8DxUhJPFcZ4M1QRudNlyQw3Izf3j1UfzrtcKeTx6811Rd1c6YjMkMVA4FXrJv9JUHoFLc+wNUcEHj8Kt2J/fsxHAic/+OmmNoh3+X8vynnvTjOShA+tV2UqB8wJNLjC7jmiwcqZMJWVgcmtCDUdiBMA+mRms1oXMayAqVJx1qQ27JCJAQeBn2oauCXLsa/2xBtMkR57oacl6rtsUNgc/NWKcx7Rv68kfhU8MxQ7iRjpk01GxTbsbqNvGMfrQqMlzv2FhjHB6VkS3D+WNpB5qKO+ukPErYAzjrSmpdAg01qdMZlVcsCPwqCW9QDgOfoprFN3csvzXLA+wFMLMwOZHb3JqG2lqaqKk7I0J9QRAh2sCXAO4Y471E2pQjbkEgofzzVGaJN6g5AwCSKhZUkIEcijAAweM81tC0ldktJOxo3F3EyLhQ3zFeaz5XXcxUkZ5xilZJF2jbuIyQeuKiYEhWcHntQ1YxmtShql+un6ZcXRblF+X3bsK4jwt4uutCkkMrm5t5WLyRO3IbPLL6H+dWvGF8k92LUPmC3bJQf8ALR+4PsOn51ykl1FKjtIuH+6NgAwK5pyuzJybdon0Dp+rWmqW6z2twsq9wOqn0I7GrXmYOM8V886RrF3pc5u7OfynXjr1+o7j616ToPxJsLsLDqe23ufumReYyf1IpJmi8zvWKkHofwoDEcBRVWK6iuYxJBMkkZ6MjBgfxFSAHOBnJqi+Uk3A5G3p1oz3zjHNAUAHLCmtjdhTyaASJS3yZ2k5pocEjA60gfacsfpQNxPy9P5UCtYevyxgYyeWFN/hOaXGD82RxTC/Y0DEJIFNDAnmqeoavYaXHvvbqKEYyAzcn6DqehrzTxF8Tbi5LW2jIbeI5VriQfOw9V/u/wA/pSbGd34h8UW2hxuqqLi9KhkgU4OPUnsK8i1rULzVb17rUGLSycoucKi+gHpVSO6Ny2+aV3cgkux3Ek+9GR5m/ILAcZ9ahu5zynJuxBKAIlhjVUKDJYDkk9qrwiS5YruVCg6nkZ96uGRXfftyTyFUZ/MUiThW3umQ2c4GOf8AIpAm7HXfBzD/ABV0klgShnwQOD+5k6V9VV8rfB9UT4q6OI+jeeTkf9MZOlfVNUjohsFea+Kr6GHxLdo7KCNmcj/YWvSq8G+IviK1sPHep28j4dPKz07xIf61cU29BVL20N2x1mBboqSNpXrjGDUl9qluDHhsDBBNeZ/8JnYITzuP+8Kjl8c2ROAg+u7/AOtVKlO+xHvWseif2xbgHDk/QU1tajXHyuc15q3ja1BwAMEcnJOP0qGTxzFt2rHkZ4ODVqjN/ZJ5ZHpTa2CSBE/4tio31lmGBGAfc15i/jhsYERyP9n/AOvVdvGsxYHyicdegzVLD1X9kagz1u112SEuNo+Ycc9DVK61OYyswbG45xXlT+M74/dXH/Av8KrSeKtSfPzgA9jk/wBapYOs+g+RvQ9Re+Yj55Tz700X8QjKm4AUHON3evJpNe1GQ5M+PooFQSajey533Uxz/tmtFganVjVI9ij8Qx2zEC5kOeMAE0yLxQkU7ECSXdj5eMDHpXnFgpt5HAlZvMiSXLda0kunW5jIDYDD7o5xWFSlyOwuS2h2nhaTdqU75ZleNmDO2T1BxVD4ofJqunTjHMJBOP7rZ/rR4RnZb3y2RgF37mI6KVIGfxpfiSgmttIuA4CfOuQM9Qp/pWDNIljQ5lkM0iscs7ZGeKh8UMk/h26ijZGmXa+xSN3DDt+NcbLcXd9LOlnL5MRIJQnGQcCqq2t1bXjOZg6LII8sTyTjtWkcM5R5rlupDZs6hrmObwZJZs4E6gEIeuARzWLLrom0qPTdjeXkFpB7e1LK4RX8yUeYy7QAuBVFViV2EjEKTwE49aVOKSfPuZOrB/CRRxzSoGjicr/tcKcVLJ50Zw21SOAq9Of51ovNbwaPBC5U43gbjnHXH49KyPtYOAVO3pnPvWF9QdnqixLp1/Ikd2yhQzbQwIHP0qJR5M7wOBIW+VgBwSKs3WqTy2cUAYiKIZ/GqDTbsSSBWJwQ2cEGh2uKSurIeygICxDAnAVe1Xr8qPDujOuCEe5Qd/4lP/s1ZjXJVt0RPB64rVnjkl8J6c5yCt7OBx6pGR/KgIJo9i0i7/tDSLO84zNErHjHOOePrV44IJ5zXDfDjVmuNOl06aVTNA25B0JRufxwf513W3DAE9eTWiJasxqgnjPFSKSpAyR9DTQuSwJAwMjPepAvOD2IoJaLEQlZuJ3wDySatmQ7fvH86qyMI02A45+Y460QEyKC3WmUSltrh2k+Tpgk1OjbvmDcEcc1CsSsDvQHnjI7U3Y6yqiDEeQSAKBlxG3DgnGccinkcVHwik496hN7DsHzMPXPQUgMzW5QZY4fRd35/wD6q5bV7i6SzYaeV+0K43Ky/wAOOTk8Vp3F19rv5JR0bp9ASB/KuZ1bQr28v7ieDUngSZQrRhARjA4qb63NoLQ14WSbTYRewyGZox5mxwBkjnjFVL3TdOez3S4NypUrk9UJ5A/2gB34qSzhltbKK3llMrRqFLt1b3qK5iV2Bydw5Ug1lUg5tWdiminFBpqTIsaXiYJyGVCpGDwQME/SotT0bw9FNdlZrsW8e5Q8cKkEHocFuOn8qebnM0sflFNmAGJzu4qGScvGygDkYZezCna7uJohtND0CeOaQahcqUGVLxbWZsjgAZHTnk/oKzfsnhySY41y4UkbcGxOM/g1O1a3W2tRLEkclsCxXd1Vjjg+uMVzUhkbLxgABuMLjB9RQkzCcVfY7G/0HQbFYriLxE8EjgF2aF2CPk8cAfmK67SLjTdUtcQ61bTyooErLE6jPqARXizb/LbdKCT0HvXU/DlGHiKUPHhkiOQ3HG5cjBo9p7GLk9jWnT5mkj1RtOgSwuZ1uUmKAbdmeOe+aq2hBNw3PED/AOH9a6G1FrLAyrCq7mG9AwIxVG8hWOS5ESBR9nOAPdxW9OtGpblLnScdzFG0AFjzTlmBRlIGD1zTNoLADrUgjRWGWLe3atbmLY6MhhhF/SnEZ48zn3pDJnjgD0A6U0KwGevPAz1oI1JWtxI67GA9QanS22AK+NvfI4qi/JByVywXGatl5FhXEm5G4w3J7U2pXVjeLTWoToiMwRUwF+8nQ5qFOrfQCr8EcLvKEmXezfMjjaKjfyHDjCllHVWrTkXcmTs9hI8i1lOAfenW8CSJueQqOc8elM2f6KW2d/vZpTltPOFPXHH1FY2voaJtbGh9ltwwY5LYxnNQy2UTtkRqf0NRecVc4JHmXAUD/ZFCX5bbtXG+XYv09aHCa+EE11IZbFlYmN3Qeh5Fcz4q1z+x7b7Okm67mX5MfwjuTXYahepY2wdhvduFHYmvKvEYOoanJL5u6XgHv0/hxWcqrWjIqQ5locmzllZpCxYnqTnJqkNu8MxYjPPtWpPbTYy424GMY/pWdOwgjDMp+Y4GayRjCDRFO4BKxDK9c1LbWM7oJBwp5OPSm2Uwil3vkxMMMo7ityy8SXOmosSRwNCW3ESQhvyPWmOV72RBaajfaO7vplxLBJgMwB+XA9QeDXV6R8UL6K3T7fax3bk4JjBRsY79RnPpVM+MNB1WFrfV9LEG4YE9sASB/MVcNn4MfSBKlwJI4E5kgbZPj1IP3vyp3KUWla5vRfFHQmBFwt1C6/w+WG+vINXk8f8AhhgD/aiqSOhifI/8drzs+D7PVMSaBrdrebuRBOfJl/I8H86wNQ0LU9JuAmo2E8A6AunB+h6GjmKSaPZz448NDaTqqHd0Ajcn8sVLL4+8OLCHS+8zttjibP6gV4Yn7mRWXqD1q5GrAnMRJzyO49aOYyqSa2PU774m6dECLWzuJ2HRmIRSP1P6VzWp/EPV7m3IgWK0B4JQZYfia5KV1jf5YwQR0Y5IqtsZ5GYMoC9VJ6UrshOT1I725lu7hri4kleR/wCN2JJ/E1SIJbAALD34rSaeFwwMRyBx6ZqoIZD5n3EHJOOaSNYsmt7aeONWyoV+4OanvHhWQCLcEwASxySfXNQwrLHbK8sqYJwAe1OmdJYgm4Ak5Wgh6u7Gsys2PMO7qeccUZKjKjv0qpd5gnx95WA7U2K7dMHbjFFi+U9E+D7j/haeiKVy588lvT9xJX1TXyf8HZml+LGicjGZ+n/XCSvrCqRpFWQV8l/Gf/krOt/9sP8A0RHX1pXyf8ZFU/FfW8nn9xxj/phHXdgP4j9P8i0rnn+DRtJqwPKjPzZJ65Brql1lJbE201zbDFqd77iCzEEgAKACRwK9OpUcLWVw5TkYreWX7kbMM8kDOKsGOZVxCu1exHVvxrrND8RWVpofk3F88Ui5wkaNnA6AEcc81hi7hDS7r92QuduISSR69ePpXPOvUbtGINGZHDLMH3E/IMkNyavHw3fCx+2FUWPZ5gVmwxXOM4pUvIeBE1weDuICqB+NWF8SlbAWbRPIqhlU+btU54BIA5P41XPV6ILBL4Vni0o3v2iJ8IrmNc8Bvc+mf0ptxolnA8UQv90rFd/yAKAe4Oc9Pao5fEt1JZC18mAKYhEzFSWPGM8ng4rKluJZpjIzZc46DFXD2z+LQLo3Nb0Ozs7e3ayuPNclhIWcYOOmOBWIttyN0iJ35q3DPczwCGdruWJSCiBjtHrTzbAxsPsipxjc8vOcdcfXNOLlFWm9Q0LFuzBkBdiVi2A/j2qUlmXJBJPbNJAFSyt5Hmz5bOh8sZNPgljknChZVbbuVmXA69a82tFuV0Q3qamlaktqsyx2c0xkK7VHOOvX8609RbVtb063t5bNIoom3Kc5bkfgO4qjbSGznSVYZJydmIolGScg/wBDTrnV7+RlElzb6eihcRqPNk4C9h0+6OtcwkluYVyJ9OvpoUX5wAMt6cMDx+FVU89pt8kp4YMFA4zxV2W7t9hQefcYlVyXblsAg89u1VmurkeakAWBJG5VBk9hjJ5xxWyquMLItRjvYml3SHLuyKTyQP5U3yQZTtlAjPT5eTVSC6HmNs2mTuxJII/GrJucy70jVXB3HjrXNzyRzzaTskC28ilo3dWi4P3v0xUU1tj5oFYqOoYjINW1lllAaRkPGcYOR9KcZ5Uj3decABRyPU1ncnnaKaJJnDrgYqIwszuQMgc1ZlWSUx+YcLydw9afbRxzahBAszNvJyO2RzVwg5PQ1hrqVWjkXKGJtpGcqPT/AOvWx5hl8KBBuZkv92ADkAxkZx+FU5lN9dMpiKSYxIEQBODjj9M1rQ6eR4dukjQbo7qB2+hDg/0qnZKzWptZWM3Sbm80XU7bUIInXb6qQHXoRXt+nXsOradbXttkxSJ0PVTnkH3rxdhNcWksZkEYQZjbPAyRWppWu3vhpNPZUMkJVkuow3BOSQc+uO/tSuS6UpK6PX0QyNt6kA1PsCRjcBnGDj61Q0jUrXU7MX0JOGHEbcMD7/41pKqsQGBw3pTMWrDSMnFSIMVK0abMgMceppgRR/8ArouBKvXB7dadn5vwzzTEj2tku49QKij85pXaQ4ToMGgC0PesfWr4JE1tGVLN9/B6D0pmpap5avDZEyS52lt+AvbNc0tszqxNzcbick+ZnNJsuMerJEYq8Zx1LDr9P8ameQ5zj9aoNA67MXUy/OehHp9Pap2t5CMfbJvxC/4UjQJJD12/rVaTVLO1eO3ubbzZZ2xFiQqR69qfLazM3y3bjjoVWs66sJXmhl+0EvGeGKCkUmU/MW5urhYWYtGwVl4xnHaoWkETZeRI/wDacZH04/GhNPubcXbRykSSsWzx8x9aoawssOnIZnz+8RcMMc/hQMvtKJ7d0VRsdcMp/wA9az7iwTTMXKqtxZbHOD2JHTOP1prpeSEwWchmvFZj5cceTgY4/Wqml6lealb3NrcSGJV+Vl8sEnPX8qLktBePbT2TSW9mtrlcsodmOc9yabPbzrMrxTuskkh3ODjAOPT6VJc2NzZMETEkMgILKOF+uelWADOiSRzErnOSoo0Ytjp9DSXTvC9/NFcyPK11EPMbnoDkc9jXRaLrlrqVvcxvhLgERujdznPB71zEZuIfBc0q3LYN6FC7RjITNYGn2UraPqEyzlJGuIeSO+WOf0qoNR2B6npElsEkZ1IVQentUbDJz0HauR0nxRKk62t5MJwxkIBHz4DEdcYxwa6W01K01KYx2twjMONpOCOM/j17VspJmbiSMPmyDSswIG3qKnW2la4WPAGW2hqsXWlSxIGQrIxPRaoLFDc2wE4IBBANSLMhAXlfnBP93H0prwXKKSYW29zioU+YcimhpWOpmawuLCRWHlSopfYQV3EDj61mq7rF5Z2lSNucZ/WqBuZnQRsxKqeM9qkjnVl2mQjHY0irk+CRty+39K07KLbbJkEbsn9axkOGJMoI+tbAuYhHbMXkaOJV80Dnbk+1N8qe4MlksEnYMR8wYNkcGuY1i8/sq4t4o45GcSHlhhVz79+laeqz3Ut1/oFxi2SUhyj8sgXkjPXn+VRfY4r6NlN3M6sMEE8VjKq0vdLUe5zl7q1zNdQwzkFAhbPfOcVzVq0dzrEoki+VrgsAe4610Op+Goftpxd3JCABQz5wPTpWXceH4UBkNxIpXndurnk7vQqKVtTmpZoznn8ay70h3iCjcMnOO1dLJ4fgZQ0cjMp53A5zWfJo+yeIBsjzFzkdeaFpuDSMNtiXciyfMinHy/WmSuJiQFwuePpVp0E0srBRlpGwB9aiNoWBwQvoKs53HUoNEu45bGOnFMlGD8h4Aq8tkXBLOAo9DVZkVSApLetBSIQXVkIJBHQg10uleM9c04iBbvz7c8GG4HmIfwNY8EKSsByMg4qcWnlSZUkgggAjmkyjsII9D8XDy4Yk0nVT08s/uZvbH8JrnNStJ9PupLdw8ckbbWV+o9jTIs2xRkyCCMOOGFdV4nX+1vDmm69GF80/6PcZON2On8iKkxnG5w887Kx5Vj7VXjlZnIweATj1qzLGVGMIuR25qg2FfKyDI71YRSJoZlE211bPQ80k4SVWERAO77pH9agfevzY5I/KkjnVOifN3bNFikixBLLAwBY7B146US3G5/MGSfepFeNAWRjt7qe5qNgk5ZmKjjOBSAasgkkGQWY9yauW7MiSLIqgk5AYD09aoK6xvtYEDBxmmC4Y55JA707CaPRPhD5SfFfQlVf3h+0bmHQ/uJOlfVlfJvwYuZJfitoiuS2PPwT2/cSV9ZU0XFWQV8o/GK3nk+K+tMiEg+Rjn/phHX1dXy78U4rCX4q+IReyujAQbMH/AKYR124KTjUbXb/Iblyq550bO4Y5IXPuwoFjJtBaSJc+rVfEOmf2dAxBM/mKZBlsle4//VU0o05b9mt7NmgMeApU8Pn0Nel7ST6mftNdjOTTjJuxcxHaecZ4pGtoIHKvdkMDjCoeat2ULxTzMYXjhf7ox2zwOafNa3Bv1ngVWUDacnGeMfqKzdV8/LJ6FXZVMKSKMG6kGQDhDjnpT0sH/eEWMp8td7h2C4X1q9/pgDbjDGSFYsTkk8c/XgflStcztJI73yKZI/LbYg5X07+tKVWK6kOU76Iji02d5I0W1to/MjMgLsW4BH+NMd5beG1nEkOyR9rKsWMc89akY/u0PnzuyAqCDtwvT2qJfK3furUttx8zHIHP496ydeFhq/Vk1yFF7bpM7pESQ+0kfTp+FIEsdmFtHdyrqzN3Oflbn2rTs9P1G/BaCOOOMHG7ca0I/CN1vAmvWAPP7sYrn+tKKtbUHG5kQ+YnnPEgiK7GCk+xHb14qpHMIp0leYSMi7VQL0Gc+9bF/pDabOlom9knG7Lnhsccn61pSaDqciRxO8YEYBUZxjjjoKyeIdrdx8pg3V0jW1nmUxLOpXzMcLg9xWzY+CjNCk9xcrJEfmAiPBFYV7ZzKjQyZBilK9c8nrTtAmniv3tBO6xHOBu4zxULa41sa2o2GiWzskM22UqQcZb/ACa54gC+mUnhZf0zmm2NzHDcBXzvOT9eDnNLERcSzSHaJjIEEO7kjGN3vjFQ3cpId9mghkaSL5t3U+/cVDNI0cW7awBPpVl4IbRgkm9s85DYx26Ecn8qu26S3kDuIdtko2SSSybAe+M9z9KhkOlzO5kW8i4+aVVlzgHBzzVi3eHLqxwV/jBPPPPGat29pFfXrQWZD/MDhATxgAnJHTNa2rxWrvFA7M5gTYXdsls80rE+yTO7+HGieGdQ8INqWr2qPKt48KOrOCwAUgAKee5rqz4G8AW6R6n/AGfH87HY6yysWY5yAoOSevGKy/hzYPY+F7K9txE7faJykMkgTzFYKCVPYgp+VdpPHdeZa6kIrYzwiQPbiTAw2OQx/iG0dueapScdjenCMYo51/B/w9Nut81pF5ckjKr+fLy/dQN2c8dK0Yvh34QmsZETSQYLkKzgyyDdjlepyOppbKCN2t7ia6sd9teTXsqRzB/LVlYDn23da1T4p0JSwOrWgKruI8wcD1oV3qypJLY5a88MaTNBdTxaPbSWFiXjMckkheQL9/ad2BjoOvTtWh/wrXwfcQRsujx7fvoPNk25I9N1QtrNo9vf2VjqNhLa3bswuDPjyt/3htxzg5Ix69qvnxVpsIkgh1TTUijiAhdp8ksOxHYe+aSuN2toZGl+GtMl8SS20sFqxsUBDQtLGwc+gLnIA6n3x61oR24HidNNl+xunlmRwqujAfwgEsdx9eOAPeh9etZru2vry7sbFIFcRsZxJvZhj0HHf39qaPENpqU9p9qu9PgS3lEzSJcBy5AIAUYGAc9/pRqDSe5T0LVbG41q+guAUitlkJEg+UBWAzmukgu9Kkilke3NusS728+Ex/L/AHhnqK4Lw1ZxX2s65M1ysVvtY+b1Ay4YH3HFdibtdVspYLvUtPj3geUIH3fMDkMc47gcfrTbZEYxsWY9V0hvO8yF4DDCZ3E0DIfLHVhkcirFomnapbF47QmEngyRFQ46gjPUVnPp15q7XklzPZjzbN7RVgJcZbncSf5frV6y1zS5JY7CG6DzqNm0Iw5HHpSTY5KK2KV7pls+pR6fZW1tbs8LSyzGIN8uQAoHuT+lSaPpunz2skc1hbCa3laByiYDFehA7ZBBxVy9trk6hFfWJiM6RmJ45SQrKSD1HQgj9TTbOzvLONFSWFpZZzNdFlODu6hPpwOfSjqPSxh61psNnqNmsawR280w37rQFI1HUluxPQfWn+I9Nisoo57VYIld1TBtA6p6ux6gAVevftV3HPYy31h9klJDTb8SBCfu7eme2c/hUl1PdP51vBfae1tKNoeR/niBGCMDhvbkUmik1oVpfD+iX+oGDymSRYVkzEdqspJGR+X8qy9e8MadpVk14kIaJB8/nXJQ57AYU5JNbUfk2EpubeWGcQ2aW8MXnKrNgnOSeB2qS81hRhHs4rqJ0BASeM7W7hgxA/EZp9CUlc57UfC+mWGg/b5tPuZJ1j3vDFccLxk5bHQeuKwviB4L0ez0a1mtrZjIZhnzJCQeO9do8SDwfNpourY3DwOqqJhgE5IUE9hnH4VlfECeCfSLNIriJ2WblVcH+E0A7LY8HvtM1OLUpbm0ZQGYsrB8EZHP0pkenz2IgnjkaW7nU+apfJLbjz+grsXh8xNyZHUcjByOOhrmLP8A4SHTfEAmtUlUNIE3qFOU3ZpCOp0zwh4pkjkhutHkjTIOZVJB59Bkmqd18PvFcl40yaZKI3OAAOgB4yPpivd7a71C9nkuIGt1tY5mh8lwd7bW2lt2eDkcDH86qXOraiIpL+A24tY7kW/kupLN+8CFt2eDnOBih2aHyu5x8HgCaDwwmlNG8xEhncsDhnIx+VcZL4J8S2mn3tvBo9zmW8RwE5+VUYcH6mvbrG8v77VL1T5MVpazmEDaS8nyqc5zx973qTxBI8eizFHKbmRGcdVVmAY/kTUQp8rcrtlyk3aNj54g8A+JftQul0i8MiAqBjI56/zNOtfBHie0uRI2j3caBmwDwOfxr3yO0ttN8RWkVjCkMc1vJ5scYwDtK7WPvyR+NXdVsY9RspLcLbNPtJiM8YcITxuxWpnZXR4xbab4ztnSSOxuzEuT86hh+Zrd/tHX7S0nu9Y0ho7eGJn8wDYCcdCeetdVYwQONK0iS3RbaCSaOWMcpLIgGD75yWwe49qxfHC/Z/BviSwtcLAs0KxJnAUttZlH8/xoU2NwSOITxvZXG1EtTGWHAVwefTn6Gof+ExtPtYt1tp2lLEKowcnt3rkrO0nmupbizspFig252gEKM989T9K6TTbHTJbg6vC6LcCcxR264KhQvUH1q3OVtAVnoyd/FhjvVifTbhX27x5hAHbjvWNf+Nr6W+FvplkhaVcoWyxz1Pp2q34guxe3VlK0Pls0fzqOdoz1NZGmWyjxRYeXP5saqXYLyeBjbj1wM496ITm4XnoxSjFP3ShNrms3s8cV1cSqjNkKo2A46jAxmu703xAmn2dsl7/o7HCbw3AIxwT/AJFc54stHXWbe8ijcWi25BkK42tyTkdjWVqkxurOyn4xOhK4PXGBk+5NQ3cHFpanrInF6IzG8ZkLliSvO1hglMexNXrKeSGI20ao0ZIwehXnJz9cV5xa3FxBaRt+82ou1fLbaynruHvW9pXiksy/aCt3sx8wASVe3I6N17YrWFGUo3g/kSm2tTavfmu5D/tYrC1eyiuoozNZ3l7DHcRma3tWALId2c8g9sD8aW78UWCO0jC42O52uIGIb8qy7vXtD1FcStcFk5ykcisB9Rzis40aqd0jS4270uPRbGKO2M6tLAk8kDHc0BZ2+T/vnH+TWHDLdyS2onB5ck7uO2QP0rXOuaNDatHA8gyO8LEn8x1rMGtLPdGK3lBj8pi2Ywp4Un0reM5Ri+eOvmQ5tO1ibw6fCsdtGupRzNdySb5JsNtVd33eDgjH86qa1baadUf+yJ5ZrMgHLrtw3cD2HSktddaCyhSO5J8tFwgjGRxjuOe9K0ks2JzAMFQRgouQe+M1k9tibXloZj2cj5D4VM5+Uc1XaxKtlA3TniteO4SV2QqVK9ehp7BSOTge9ZajsYZDQRphfmRjwR60+wuLOfYs4mF05b5xIAg64+XHTj1q3qEQ2LIpBwQCPWmWzRi3iIgiLKW5ZfmU/UH+dUMhuUuftpRfLKI21trgkep+ldVMrW/wikeUcvfgoD6Z/wD11y1xZEOzwuoBB4z3xyf5V22ryXOm/DfQbdVYzSgyyBBnjk/+zCqqQSimnuS11PPjIs1uCCqseOlVJY4oj+8BLexq9KUZk89SobkHoeaabKASKyOduOSecH/JrMxTsQOYp4chlAC8561TH7uM5Y8jp6ipryBfPIiXCngDNMMLYB3DOeTTRomVy5ZxgYHSrKKqsjGRRjrUWJWIJ6Zq9cWEXlxtFkseGOc0MTlYpXUa+YpR924ZqJGaJjxweo9a6ew0qGfT3e4XagJWN1X5j6nJqrr2gppMccguPMV2wq7MH8f0oMliIOfJ1Ol+C8wPxW0RFTaD5/T/AK4SV9Z18l/Bbyv+FsaMMndifbjpnyZP6Zr60po6EFfNvxQ0vU7n4l6vLbQK0R8nazEf88UB719JV8yfFm4vF+JmrpHOViHk4XfgD9ynbNPmcdUDRzg8O6uGDMkK5OOoqwnhnUWx5s8f0Bx/SsWCS5E4LXIYkEDDd8U+3lkFwHa53HB4BJ5odWTFymrd6E8Me57tXO4BkVjkg1Uj01JJ4o5XlUynEYI4J+pIH/66hnlAmDFmLHHTpUF5JJKsMBY7YsyqhPBJIHA7k8fgK2o+80mKxc1HT7ewZYVdfNZQ+fMRgBnGDjPNUiyK3Midz90miy0+e9F1NAgPkKS2SACOuAfWq2SyKQe9aYmkqdRwTuMsttl+ZicjgbFA/rVxT5doql5jNI/zoWyu0Y2nPrkmqUSlItxywz0zxVozKlsoVEUdSc5bNc7ugsrHbeGSv2eZEVQu4N06ZFbZeJnVROhYHlQa4vw7qCRCSOeJ5RMFVVVM8j1P+NXbnxFbi/MZtI4pYfmDueM+mBUOPNKyLppPcn8SWkb32m+YWERdlfB+h/xrrNUfSl050s7t/tRXCtjIBx1NcNfaqL25tkMisVkBAVcYODnmoYdC1TV7YTveRD522lnLbgGwCQOO1Qri2K7Iyo9wqSSHADHOQ5/vfyqA2iXUiH7RbWkiPvVywy3t+NaA06a0stRsUuRJNBCGErPtAUHkew61zgIij3Ld2fA6eZz+ldH1ifLy9DX2snHl6HQp4RglBkuG8tQcmQkAAVz+pW8FldSJp85lhQKwk24wc44NbVvqVhNpv2e6QSNj926zkYP0JqDSLua4W80iCWAQXxSCVnTdjDDBXnjBNYu7V0ZWsa2meEtNvfC8PiHWry8iSTIYxqPnwSMDI9BUd/JpzhbWHSSLa2wEjuz/AKvjrx69TnOfwrKWK6hsVtF1GZbf7/kqigZ+uM0T2wvZnubsmV25Jk+mOnSlYdyxa6rGtwbY3a2sM0oB8hBgLtxkYGO2Kr7wWYglxkjewwW96I0A3iNQAOg2kCo3812wABVJCbPWtES1ufA+gwTs4Z5LgoUHUhuRVqbTrTTnhmFtfToNzSEbdqLgglsnpg9s1z2ja3oQ8Gadp2rNqEctvNJtktUUjJO7PJ/pU73Hh68iaFvEXiJlYYI8uIZH/fNJrW4C+HH069sddi022MUf9lTL5gHzvnbg56k1y2k+G2sC0s0zSGRCDGTnI+tdZpuoeFfDtldx2DapJLcQG1HnomPmPXg1gXN19rjMSlgoOP3bAZH+02P0FVSqTVNwfUUKklGzM3XtVWK3+yWfBxtLqMBBxkD3qlY6hZW1vbwT6a008oLFy5Hc4GMdgKvT6akARpRyy7kwOMe1XrLVYLa0jt5bVSIX+9xllYHPuMZPNOLS3FbmINNkjvp7nzrVzBI6iOKJx5iAAscDqcgc1ba2ZGOImjBk2BOuD6VR1SSe+nW601Y7Zw+9dh244x/L+Zqxt14adC8dxap5hMWZCSfMJBGMdB2zSbT2Gkdb4fU2/h7xBKQSPsnAHU81iWbTW6b4JrhN3UqDk+vOPWuh0prqDSfECwReddQ2o2xou/cwPQAda5Bf7UmS4kuNA1pJnJZFjt3C5P4cDNZzRcTrfCes3lv4ugtZnuJYrxdpaTsRk9/fH5mrfh4f8VcGPU3DgfrXPeDf7cuPGOlrc6Pe21pbBh5ktuy9urMRzmt7S5Es/FCTTuI4hcOzMe3UU0tBPcsnSNSk85ra42xvIzqBIRjJ74q3oun3tnrtnLeT+ZklAN5JGQT3+lVorbyA6f8ACSWIjLFtu0568cjBos1trHWbfUb/AMS2LwwsSEC7c/KR+g9TUqL5h6HKR3sV9c6qsccgaJ3QgvkZDA8flU8RygNV4Et3kuLq2TfDO7FGVcbt2QGxVmNcIOMcVq7kdQbkdKhO7pjg1OeRULE9R1qRlK+vrW2jt4/IZ7tphyCflQ8E46dx1zUV7LJDbvLFjzAMjNQ3VtcnVIp1KGNRghuoq28QliIkUEEc1TtZaDG2017rKvcp5EmyTaxL7MJzg4JyTx0q3pUcc+r2cUnKvMgI9siuSuLq5069az02b7PBOE3gqMZB7Z+prrtDcHW9P2nI+0R8+vzChpAehX2p6RZak0t3bSpL5rHYrsUfacbyo4zkdx2qvqV7p8trqGopZGQ27QzBTK4jlLYZXKjAyKZfatp9jqFzBLcrJK87j513bcnO32AzWddTq2geIWSYSRCWAIVXCqM/dHsKnQfM+46x8eziWfZp1vGZW8xiGb5m4Bz+AFN1z4h3VtZ7m062mtpBskVySeeMY7iuKYb02IQqkY6449Kr6hNcS2XkvD52GG1t209+uOv1pSvbQm5uWXxElileOLSIt0sZQtLNI7bB2BJyBzUcnxFe6to47XS7dWtj5Sn7TJGyr3ywbOPqa5G0S5W43T2+Ai7VcP69sf56VLFpcP2iWXLYlGGB6GlTUnH39yac52vI6uf4lSPoUcVvoUEjQyFtkUrgpycOHBzk+vfNO1TxDNrPhebTDYxWyNKkhZWZ2LcnJLdeneuTL22h3EUkNtDISQGSUnZjtn8q0F+INhGn+laHAI1IXKOM5PPAx/WtoUZNXRq22jnbGZ9Llu7STcfNCqScbeDgcY44JrU0R7Yy7YFRBbTlxHtI6jaOv0rRuPFvhK42mTT5FmHIUHgk/U/0qppM2nagLm4tB5LBwvkE7j3+bPv/AErSTko2sIx9fuXbVJ28sszR7QIlPTHfHXvUPgjSLxry01hUX7ElyYGcuBhjGe3sDmtTxBceXC0AOH27mwOcVkwy/Y5rY2cEpV7YSvCrnDMynkgn0IrKcbwVikup0Wvy50q0CF13Ft2Fx5o6FjjjB61zmq2EkejW13GP9QSgjCn5TncSfTgitO8u7q502zmUBWgQh0jkzgZ46ew5qlNc6vDbzxoxlhmjKjbt7kZ/lUwhpZDnLmZcs5Hlt9PQlIUld3klcjC9cA+mfXtVDWLCRNeMJQB1h3hk9SxOM554IqmRcxTRS3Ree08va0T4z93njNaYh/trWdI063ka3jucQKzfwk8fzx+Vbu0H7u5jBPW5neIbmJLeNrBJbUQRpGyCZmG/nLDPTIxxVKxjvrq1WVZJ3dgQSG6rkjH869XuPgXezaabZdZtw7Sh2YxseikfzNT6f8FtRsGtz/bNs4hQjYYmwTk8/rWkqzVNWeo7ux4mhkN5JE9yyoGCHJJ2gHHP0ArStYrdZ5nhvhOohfcwjYAZGM8/WvSk+AWofaJJm123DO5b5YmHX8fenD4KXGkWl1JNr0PkNGVUeUxwSR0A6k4xxzWdWpzdRr0PMZtLshBvTUo7h1GFRUkUkZ9SAO9YaR/8TPylOFDnjPpXsFl8Gb2+dhDrsKNFjfFLbOjDPfB7H1qK6+Cr2N0PN1eOSdtzlYLWSQhT0JA6D+dZxklrIbTbslY8vnBDKqupyCciq8quy7d3v1r1S4+CsyCGdNYE8Mq5Rra0dxj3xWJ4h8Ar4f0OHVjfC6hecwbDE0bbhnPX3FRKaUroI03bU4jz2xHG4VQP4h1rVkEcNoh+Ulwdw9W/wqs7RylUW2QbmCjGasXcXkXkdtKSZFGQMcciq5+bUUlqYyOMvH5e4tnHHQmvQ/iNKbBtD05Pu21lhgDjrgf+y1yWkWTzeINPtJI8Ga4iHXPG7mtr4n3In8YzJ1EUSJ+OM/1ocrtAci0vnykgHnoParLANbJshlj2/eYvlScduKqwRP5+wBSQOhNaHmhYxbkAFc8HnGaUmiJFaLfLbtliB1zj+dRNHGhH73epAwMYwaszPwERxwOlVyREBtTMh71BmSStHaxLlA2/p7U+2VpgAQPK9CTxUAgO478k44Bq9pyiFnE8IZAOucEZ9u9DIm7RuasEaBREjLBCi7iM5/GqOpzxS27iSJTEr4jck5HvSQOqXUpJbySuAoP6VUu76K6HkRRAoBkOTgg0HHSpv2nNudb8FY0b4raW+SConKj/ALYuP619YV8v/BrTntPiTpzOwYhZfujIXMT96+oKpbHo05qV7dwr5s+J9mbj4o6oMQqHMI3yNgf6lPevpOvnj4lak8XxA1mFLeGQIYj+8OeTDGcgGhlSv0MO+0zQNN08f6QFv2T93sIZnbtx0wc1y0llqDqZkhJUP83lw5+vapprgTTQid0Esc+wl1x5YPGeP88V1V9ItvaWltpmoxTBVKSSJHncevXvjkflSQ7aHOw6Jq2pSqlpBK4Iz8vTHvz9Ksnwb4obY9tZmKYcDfIqnB6jk1NaahqWl/NavgrC8YYpx69fotZ51nWJXUxyrHIOjxgbvrkc16UMHdfGkgguZXNjxJBqegabp0i2sBR4vKdDhmVtvPT+tczHaM+AoznsO1ehysNU8JWEdyzPdRSBmLA5PBBzn8KoR6baxSeYIF8zHLd64WrNq9wkjlm0u4Fu2FPBzSW1hdsn+r+TOQM4Ofoa68QwlcBBn6ZqRYhnCgkmlzO1hwfK7nHqPLujkNDJGmzMR28evuSaWIC5lhXaWBJVlCfOP9o+ozmuzWyBDbogT2pBZ7crGqKT6CpjdO429bmHK8MV3YQSx7YvtCmRtuMKciuh0BQNOlhLEeVcyqOe27I/nWTqWiy3USbGXzFcMCewrotKS3tbUrIf3sjGR+cjJ9PwAoZJz1/Ft8QaiCHKXWnvGCBxu6jP5mvO00TUWGRaSZx3xXr+pwLczI8DIv8AeJBOeKgW0RR0zikM4C70V3sbUW0FyZcfvFfgA49as6Nplxpkv2mSLGx0fls4AYE/yrtDbmdQOEX1Hf6Uya0X7JLEvVkYDPriiwXMl7RknZiARkgcdOahvf3Fn5y53eaicDOAe9ayO/2RXnKCZ8syg9ySazL2zmnlgmihGYy2dxwGBBGD+OKaEyOSHOWJyBzkHNNEAZAyjgjIq5p9i9nZRQyFTsG361bCIoA28AfhTuIyliuiViGPJzuxt5z/AJzVtYGTnoalubkRQOsbBJCMDuc06GQXKq6Sb1PdR1pPa5ST3Izvfgjdj1pZGKRb3O2NcZA9PpVkDbnAINKsi2YEk2nm4ibJJeMlBgHBPIwM9/8A9VJS7glfQfZ6el7bPGkiIS/m7nOWy3OB/sgEVmXdvc2WpLbxopeclGm37k2DkHbjqDkc1QihvNbuJ7i02okx8tSz7ME4yOvAq7pmhXdjqOZZQzxFkPJOSf8AP61q+VrQrlSRq2Wlx20Kqu445OT3PNTyabDcFd/mYXsGxV1x9mgAIJJGSfSlYqLDLBgzjkDqKjlaVwdOajz20JIGvdLd3tWuLZt3lsytjJ64qyuvazn/AJCV2T6eYa5HRvElzqd1Db3KjaiErgk7cdM/nXQxngkdaTJNSHXtX3Ya/uWz2MhpkrEtggMfXPeqtsMzbiCcelWVIe4HXg/yoEyncboY5H8hnYKWEa9W+lZWvQyWV1ZX1sY7xEcOYJOoBUAgDuME1s38uDIckfL2rnI9TkWSJ4vKLTZG2VSw74446A/pVR6jSv1N7T7mG5tVeCBo4cYjDrtIUdM8mrWwhMnoelNjmD20TKQQVBzjGeKezDyV9qm4ERQhc9qjZSBuxxU+QYSOc0xCCrL60gKkiF/mNIEOM1Ovoe9Rpw5B6UDKctrFIw3ID3GRU1urQSJLD8jxkMrDsRT3HzEf5FNV1V+TQBuf8Jdr3/P7/wCQ1/wqnf6/q2qWzWl1dF4WIJTaozj6CqLlVYEEU19owwNAiusQ3EAAEUkqrEuXPy9OmetTvhhvGc96in16PQ4DdywGWPODhQSCOV69Bnv9KALP2/TLfT2tXige8R/KC+WTjd1ycdcYI5/KqPkgJwuMdKwLmC+1G7N3ECEuLo3TAP0yR8p+gAFdCrOwywwaYHG+JNya3bCRN0EgUdwc5x1HpnNaWp+AbyQo2lxSXNsXCNIQQVPBz75yB+BrVvNMj1FkieHzHJwmODk+hrcj0bxzHp9tYW9nPbshZ5LiTEnGcBRzzkc+2K6HVvBRSH0PI9fsF02/MAYnBwc54x9QK3vBkRDtdFmO5XXaTwDlccfTP5V0Ou/DDxXqGpmTyjMMYMoAG78D06CrNh8OvEljCqJZS5XoTtP9audaLpKFtRN6WGypBdRCY7WVed3sDz/Ks83VgIN40sbYti7lbDMuAAPcDiugHhHxIluts2kO0J4O0gYHU9T0rJt/AXiCfXJHFhMbJThQrqU6Y65+ledVV++hUSC3u9GeWVE0+VHkU+ZjByB6n8aSO60GK1dLQEEjcqepxwOtbSeB73T5WJsDE0i4+eReRkZ6n2qG98B6j9mjuLTS4gVdS0mVxtzzgg1yQcnJLUtrQxLnS1lGR37dqZ4c0lo/GujuOVW8iYAj0YV0usaTf6Lpj391ZOtuoyGBBzwTgY+lN+G09rq2q2VzKz+bDdkR7mALfIT0+h/SvTV7GNj1LS5bd7iJ57u4/tdmImh3scexToF6YOPTnmqMc6/adOZru4/tZ7pVuofMbhecgp0C9MHHpzzXB6x8TdVtryaCynd7mMOrBoV2KQcZ45I/GsCL4o+MYjO8l9GxQE82yjojHHT1ArNq2hrz9T3Lw/ayMLi8upZ5Jzczou92wqCQhQF6dAOan14Fbe1uCpaK3uo5ZcDOFHU/hkH8K8Ih+NXifyDu+zSOqqW/d4zk4OMdO1dNpnjDXtT8GeJb+S+nxFBDLayBQjR7mIYZAGTxT0tYly1uejxahayaxd6nHKGsobVUkmTlS24nAx1wPT1pdW12zt5ktYbq3guriMMZpWCiOPsxz1PoK8p8Ip4w8ZxXKW/iy4tBbhCTliTuz6H2rp5vAXjOQoV8bzAqoGSGOT781MXzK6FGalrY7WaVLHw9FHpRErOqw2zKdwLHjcT6Dkn6GvN/jdANL8CaTbW/3Y7oLluSfkbJPuTzWzb+B/GUVndQyeNZ3eUgxON/7v2HPSqU/wAMPEOpQeTrPig6jGG3IsyuQhwRkc9eauw+Y8P0mfz54EMSFUYkORz/AJzUiyi4umUkNK78E98CvSJPhG+kyNCdXSWUru2w2jyFF9Tg8dPxxUtt8DLqSOO5t/EcGJCHEiQnkdRjmqbT0QNvRs5fwtpwbxjp6sIy8TvISByNqH+pFcp4xuRdeK9SmHzD7QQPw4/pXsnhrwK+heJyLrUJpppkeGHdZlR1BZt3ccdawbz4RNc6pM0muJmSYhphaP5Qcn7u7OM54+vFQNpnldqm8y3LphT0qbcxcH1UZPcH0q34p0N/DHiK60aa4Wc25HzqCobKg9PxqhbwKYlO0pvJJYcnHsKbMtbalZizyMc89KTy5IwxZGPIXd2BPQZq9BBF5kMTxkszDdj0z+lb0Js0Q2QhUxF97B+V4HWkjkq4hUmla5yMwkiOCcEHt1rRgSVYzI0mQyjGTzV680rz779zJGysMhyQAPWnahbQedbxwyFYnAQv1O4dT6UmRVrwnZRZSSTyySCpYjGMZqez077NJaOYPNiLZlBGeK3rSKCxtvJjVZNxDtI3fHfnpWfNcONwTjJ4GeDRY4/rLbcYbHoPw2nU/EHTURUwwlJA7funr6Ar5q+FN20nxH0mLaqqTNkqOT+5frX0rVI7sBDlptPv/kFfP/xHsFm8f6q3K7/KyfX9ygr6ArxTx5Hu8bahx/zz/wDRa0zuPNZPDlufvSSH6CtG1sIoYhHHGAvp3rX8gYxwBT90EA5eMfU1IFWC0WJAojBHZSMgVbFsOowPYCmjULTbxKrf7uTQdQRhhY5T9Ex/OquJKxN5K7epprW4znNQ/wBof3YD/wACcf40hvpMj/VD8z/SgC7Ha/7IFTpEqDoM1nG5uDn/AEjGf7qAfzqPe563E59twGfyFAGuwVVwVz7VF5cKHO5E+pFZZCHqjN/vOT/WhViByIowfpQBoPc2cZwZCzeijP8AKoxeRgDZBKc9MjaP1quZXPGcD0AxSDJPTNJgTvdzE5WGNf8AefP8hUbSXLj55YwPQJn+ZqN5RGPmqJZWkfADYpDJAsoXH2iQ+wOBQsahdpLOfVmJqUJ6ZJp9vFmZDPv8kH51QfMR7UCsVBbRBtwQZ9aczMDwpNbE2raVYxs8elO20ZJkbPHrjNVbXxrHdSeXaWkQxHvz5WBtzjOfrS1IcrFJY3IywYE9hTxbnHQgfWrk2qNfSeZclV2jAAwKge5hPPmJjtyKCou+o1m0+0lBdcuyttZx2HWsuC70qCK+nt70+X8rpG/Cx8chT6dDVbWt5STyL5Yo8F5UYnDZGOMA/wBKntdL08RSQRBWMi4JJ+XnpW0prl1R1TqqceXlSL2mX9vqVoHjKNKuBIqtnaa9A0ae61O0htI7WMWwQRuSwwRjBBHXmvN9Nt4dK82JXV5JDvZ85JPTkk+1VdS1WaGUeTczwMhGPKY/NyDnj6frUU4e0laJz7GvqOiXvhyO4tYbQGJpWZJQc7snI7Dtjin+HLHUL2KfzpBFOEGzcfmZuw/Idap6fqbzQILq9e5kGeZGLMOe+a0bfVba2uI5GmaJQeXUdKqcXTlysdzYuLeKGadWf7SUUAIFAD9iDnp+HrVLVQr+WIdyYTkEjg+2O1RX8Op3H9ozpFIsNvhHZxtLnAORycEjsaw7HVRNCOJDjjLde9ZuLve4c2lhmnaEbS+aYbdoB2+oz1roo1Kj3qrDNv4UEmpDdLH948+1BJfhYx5x3qTlW3gke9Z4v4wBwTTxqCqhIVivuKAsWJQGJJ5NVDbRs5JQZ+lSm5VlLIkjKOpAqH7WCCRG2KYF1DiMKOABgDHSnM2UwO/8IHSs7+0EGMIckgVO11tYfKfzpDLIYheDxUe4rnBqNJpJWCJGfcjmo7hri3cCWMDI6ZoGSEkHPeoyW3ZzzVWS+dImYW7yMqk7E5JwM4FRR6hLJbRTPavEZFB2P1UnsaANDJJyTUEreVucgkAFiF5Jx7VTN3OWJVcA9vSrOl6l5WqIlzcQwAodpYgF29B+tAWJ4JRcQRyqGCuoYBhgjPqKcSRxniodVunfUJTBcxypxho8MOnIyKpNc3BGCRk0risaDZA4rn9Qha8urmB4W2hFC7hww9Ppmun0r7ZqOi3un2tjFLcAiTzSwEmMj5Vz/SuQs9amm119LurWS1dJPLd5X2FPmxg5HHXNXFJ7id0bNrGY7dEwBtHantMkeTu3Y9DS6vpF3o96YLpGVTyknaQeoPesqSM9RzU3Gka+n36nWLJVXIM6Zz2+YV7NZXWoXr/bElt0tfNaPyGU7sKxXO7PXI6Y9vevEvDMaT+KNMikXKNcJkfjXYan47tdP8QXQXRkeWCZlz9oYKzA43bcY3e/WlcpWR2T6rqRgj1GOS3Fs90tv9nZCWC+ZsJ3Z698Yq5pd1f395dySPDHawXDwLGqZZ8fxE54+mK83svG7avfNPH4WVmRxJ812VQODw23GN3vjNdJP44+wxxCz0STfK5e4DNgKT1IxnJz9KXMluym1bRHTeISRpO3cVSSaKOQj+40ihvzBx+NQxW8Fj4ljitIkhjltGaZIxtXKsoU4HflhXL6l8SrBvMtlsRcxNHtkRmxhz/CRjge9cxF8UbS2W4hi0AlZRskM1yxdgOMZIJx1wKd1cE9LHq2ufZY7B7iW0huZgBHAroG3Oxwo59yKzodKgtLzSNKlRJLdIJZCrL8ry5XLEdP4mP415//AMLcjWOBB4fjKW5BiBnJ2kDAPT0Na1t4+HibQ9YuH0pI5NNgFxEfObO7p1GCPwNG4J20IPiN5cHgnU4EBWGHVEW3C/dQ7AxGPTJbjpzXCfDRYbXxrpkPlsZZXDhtoCj5TnGOlN8Q+Ob/AF/QU0ZrC0trVZRKTCG3EjPUknOScnvWL4f1iXQNes9UWLzmteiMcAjGMZ/GrUuVGc1zMfL4mfT9YuYblrhSl1LiWGUxfLuPDYB3flVn/hJbC4n3Nf3Cq/Dbmik+v3lB6eldN/wk3gy7Ly3PhK0Ejks26ZvmJ5J/Wqs2v+BUGV8GWT/9tWprEx2HzLY56TWNPkeWKK0spkRcGaW38xmPXJEZAAH410PhfVLu8+HHjCCbysQpGU2YCjL9AOwGBioV8WeELQObXwTZjeMMBO4yPyqjfeONLXw7qOk6P4XttNF+qrLJHMzcKcjg03VUlog6HRfCeWVbLV8NKkHm24uHj+8sfz5II59MkdBmvUZZLdNMxp9zcNp/nqJ5Fdm2R/xbGPOM4yQeOa8b8Kazf6D8KNbv9OnMFyt/EokCgkAgZ61r+FPGOuXMEsmvavfQx5AjMSRgAd8jZmsrJbDi+VWR3zMz22qRaTd3JtvOtVikR2faC437GPbGc119raxWcAhh3bBz87lj+ZOa5CDWUXTvtUOsvcW68GTzEwPqcACsu78faZCpVvEUKt/szxkj8s0JpFSbaOse8t9L1y/kvnESXEcZiduj7QQVHuM9Pen6LOtnptjZTh0uZI2kWIqcgZJx7YBArynVPHfnf8g7xsYZOwdY3X9VH86hk8feIrTQsR6vFdXPVrsFHjx7bRx9DUymo6kuWh6hPdWd3qtpd6fNI2o7lieHJ+SLPzh1P3fr6gUkur6dqepfYReW8NpBMN4LgNPIDkKB/dzyT3NeeyeN9dg+HsWpyakReTnAmSFWP3iOBj0BrIu/GWu3nwjuNVGpXSXqausKzjEcmzy84O0DvTTuVzI5/wCJ9tFc/FXUI33nfJGuAP8AYWsk+F7mzj81Li1Mcn3My9vxrLsNQmv/ABEt9qdzNPM2WeWRyzMQMDJq/dSs8xZPnjfLgEnjJP8ATFaHPVkoR5iXTtJuUklmMkTqFznOST0wD/ntVWfzYZn6xsvX8e1W47yOOS2AkwvljzCq9xk4rOnkWRZZHbMrsSc+npUHlLmnUbkP+0u0ID7SoPGBzUsDyyI5RdyqMsqkZweOM1Qt2PzYPLcDirdlKI5PMkUnAKEKOtKw5U1G5YkBVAHfYp4zuqnbTqszGRyYkB2//rp9/NATvQ8kYA9KgGRAFQKQTktTCnD3bs7r4SuH+JmkEuCxExwP+uL19OV8y/CTy2+JOkgZDJ534/uXr6aqkd+F+B+oV8/fEm4uE+IWprG2FHlcFj/zyT0r6Br58+JbKvxA1TJA/wBV/wCikoZ0nNeczcsuT7kn+dKsh7Ig9woqASxj+MfnSNeQIOZF/A5pDsWvNlP8Zx6Zo3N61ntqdup4LH6CpIr+KUEj5QO7UwLylicA1ajjxyTk1mHUUjOEXd6mmvq7k4ijBHck0CNd5FQhc5Y9FHNOXKrudvyFYkmovj918pPVj1NIL+ZsFnJPagLG0X4OevUL7VIpTaDu4NYklzLIqg4AHX5+tL5zEf6wAem+gLG3vhAyWHHtTvPiRSxI244wOtYLT4QKHBJ6Dk5p4bjr1/2DQBs/bLbbuY5P0p41GAdM1j+nL/TZTw3BH7zk9gBSGav9pxlTjP5mkTU0QqZmVV53E5xWUzN0bcV/3xxVW7j82BkULjg4Z6AsRapqb3kU/wBkA3yz+WABk7MYGM9MnNN0lP7MtJXlYLKhyVOSdvHA/E0aH4a1LVrwQackJlQ7yWcgKMjn37V3l58H7iawSVNYjN8+DOJIv3bYOcA9QKHLoRKLasjkDqdvdI4hcN0OO9VzI5Bx/OkutJey1y5i86AhCEP2cllJHocDP5VZS2GADuP4VIQioqxj3clyyMi7sOpX5Wx+dTWM11ZZlVA5CkBR0zjj9cVptb8Y28j2pUth3B/Cq3RehStp7+ZHWeIBWYMGICkdePpz+lSvM9lPHdIqM8RJw3IIIwat/ZlzwpFMnsxKpXYxB9qcHyO6B2M6zv5bmaeeRArO5OBwBWvpEkx1i2VYBP5riMxED5txx1I4+tVo9PMSqsUDgdzirtnLeaVf299HCGkt3DqH5BxVTm5u7Ejr/iXqOpaVYW1rDbskF2u0uJC5cgrw3A65xmuKiCIwGOc9Kdr/AIr1jxk0KT2sapG4dCpOAPQnuasWtnKoBZUz61A7ljzfLjAC4zUHLkkk5NXhZ5IJIz9KnS2GQcjj/ZpCKUcYyNqMfWraIX4EJ4qyI8Zw5H0AFCgL/wAtm9+aYyJEmjP+pJB7baf87YHkOh7sqf0p5KZGZif+B0Axj+Nj+JoERtYKxxLcxLnLfLkk/gQMUxZfKX9zCZOOsjdPwFPbyt/8e49M0qxpgYSTBHrQMYbq8cbShVT/AHSBVR4JB8zNn3LDNXGSIjHk8+vFQyIgGDGx9MkUALpN39n16yt2lKJcM6lwQcYQtj8cVJr7E6rIFufNhwNueMcfWsPU9T/s/wAp412IW2SMqgsFPB2nsferOk2cuoWAuLRgbVGKLLcSqM8nA3Hqaxr1lShzNXO7A4OWMqOnGSjZX19Uv1GFUBGZPqM1i63DbzfZ0BLzCTgkEgA+3fP9K6SKwvJZpI9ixmI4kaUhAp7Ak1LaaReS3mWhVRBKokaQhQpPTk+uPxrkWYxf2WerLhyrG7dWOnr/AJElt4b1iOBFGk3oAGP+PZl4+mKlbw5rBOf7Kvf+/Lf4V67b3uoXk7zW8dv9jjmaIo5PmNtOCwPQcg4H6iqtxrOoLHJewRW5s47kW5RyfMb5whbPQc5wP1r0ND57ldzyux0LxFaar9sttIuVljhYB5I3AHOcLjHJoufBt6lnPrVvpd6J7uEJLZiIli5c5Ygg9vyr16yv7691S8jEcMdpazeSScl5DtDZHYD5ven6/LJDo0xjcxlmSMuOqhmCk/kTVaWFy62PGDaeL73T7ayutO1B7WEARxG2J8rAwACRn260n/CNayMMdJvB9YSK9fitINL8QWsFlGIop7eTzY1PBKlcN9eSM981d1ewTUbGSEQ20k4UmL7Qu5VbsSKmwWVzyPw94d1e38T6bcSaZdJClwrO7RkBRnqadq0aafrWpT6rpM8FsZ3Iu5oj5TkscYPQ5GK7yxVI4NI0+CMrLa3TLJEGyr7B8z547sCOOpxj0yPF52eEtailyHjvRIYuyKcFSPr1+pNS3GKvLZGtOhKpNQi9Wc7bf2dcwPPJeGOILuiiCFFPtXK6pqWuveOLW505YBxGBPtOPfOKwLC6l1m5aC0uzDGi5aQnLE+gHp9BVQ6zNZXc9tNJaztG5Qs45OPxFaVKFNS0l+BwxqVmr8v4mv8AavEKuZGMEhIwUW5Uj69etVppr2VGe+07yyuNrRkNuJ+lZ0uueYQQtquDyAw5/NqWfXY5YwotrOMr3Vhmo9nHv+BftanWP4m2mlSm2DOjxSc98g10Xg2Nv+Ef8Wx5G/7EBj05NcVYTXOqW0xjuktYIgc7VB3HnGfbg/hXV/D66MvhvxTIgj3f2epYg5+bcaaha+pSm9Lo5VrKfHUfnVe4tpo4HZsYA9a0PPnYZ3x4/Cn3dpqqaa941sj223JZsAY9aXoabmVYQLeecskjqI495Kn0qVILWS1kk8zy5YIS8oZiNwzwR68EcUltdWsJkWHfIJogskKkqwORlTn6dutdLefDHxTdIs0GkKVkG4KJF4Hbv6Uoy5Xawku5y6WX2u1kuLaQFI2Csjcke+RVZrGbPb86662+GPjG2Ty4NJaNTgtmROf1qZvhz40AOdNcnsQ6Y/HmrGi54d0yab4X31u0bFZ9Xt1yBnjIBPPpXYano+mzQ3E99BPbeWADcwAlZB0yQM88dx+NTP4c1yx+FTaVaacLi/fZvgdgM55fnPb61y+ij4jWavCmki+gT5THJcxl09twb+dJoeiJ9R0LSfE3hm303SdWlis7eQlnER/ev33ZxmuVk+E+Fwmtr+MB/wAa7zVvCeqahp1pcXe/S5433lEuVCAn1PGWGPpn1rO0zRfGEF1L9phGo6c2THdRyRApj+9hsUmuwziG+FdyhJTV7dv96NhRZ+AtY0yYzW+pWYIH8LsM+xBGDXcanNf6Vpcup3Wk3gsosb5keJ1GSB2cnqa5mDx5pt1dxWyQ3e+Z1jXKDqTj1qWmLRHYX2nQXWi2ljdKxVY1JCnaQ2OvH1NYXiDRU034Q3NvayyzK2sJJmTkj93jGe9Q+Ohf6hrtnpljctBEqGSVt5VQM8biPp0pWkY/B+63TM5XWQu/JOcR1UY6XMatTlR5jJplxHEXKLIMfMo5xVkssdvFtBGxB0HJ4p7sxQoXYIwxxUawyINsb+Z1wGwTV82ljjdVzjaRMk8RtiEUmQ9yP5VDLh13shyBjHamSuVCBojEwGSnrUTXDlSChAPT2pImFPW6H2U6JLhz1bAP933q5E8UkrFicL3U4JrHErKWKfhxU4eXyuEbHUtigupSTLNy8DsV2nd2wanhEzQuYVDui7ivQ7R1NUFtXlceWhZ+vXrV+1mmg2xFTHI4KFiOo7ilciUVCNtzuPhNJv8AiRox2qATNj1/1Eh/Gvpqvmr4ZyW7fFHQ4ogzNH9oUyZyD+5evpWqR0YX4Ar5h+LTkfE7WBngeT/6JSvp6vl34uKT8UdZ9P3H/omOmdJx++kMhHemKpPbNKUI7c07DFDnuafGzMcfNUcdrNM+AhrTttPlQAFVA9SQKbsA5ZG8vYAce+KTlAMBcn/aq2tkeu5R+NSfYjjJkQ/UmpAob277T9CasxSoqDIXJ65TNWVtox1eMH2FSiKNVJ8zJ6gKOtAit58aIWO7A9IxTVlLvuKzlMZUDA/OryJF1LyMT1G3gVMGiB+7J+g/rQBRDSM3EcoH+9UgWU/8s3/F6uiWLPEb/i3/ANelF3GD91R9XFDAq+S5P+rH4sTUyW7cfJGPwJqcXyDndF/31n+lK1+jLgShT/sjmkAwW59E/CM0/wCztjofwip0NyFUkySNnuVpGvJC3BUDPGRUtgjpNOmmjukh8rES2iNuBA55/wAP0q1Dqpmt0nMUqQysqoWYZbccdKoy3lpYzwma4ZZPIVXATIYdvp3qe3EV7p0cFqtwyxbNknl9weD79K71NJWufnVTBzrz9pGk2m1qlLbW7+eny8yxJdQRPKhiy0e0YCj5i3QCobm4kW0LtAYHEiA5wQQWAPNRTwC1Saa8N0WkKnzRFwpXpjH9arSajY3Fu0N1dSS7mUkCLAwDnGKHVTW4o5ZVhKL9nJ2avpLyvbp33Ni3miukMkcf7vOFcjh/ce3vUN4C9xb2yHyxJuLMvBwB0B/EVVt9T0+2LrHI4jY7gmzhfXHt7UXOp2UwSRZ2jkiOUfZn2Ix3qnUi47mEcuxUa140pcutrp9tL+j3/At2WRLcW7nf5TDazDkgjPP61U1G/CXCQwSRoI5E85jjuw+Ufhyaij1G0WJyt3J5ruHkkEf3u2MemBipdQl054Y5JIN3mFZAwjGTg55zUuacdGbU8FVjiU6lOTvpt1SV3Z282hbucy37W8azskcav/o+Aec8kn6dKpavcqFtig84NHndjGfwqSXVLUy+bBJLA5UI37sEEDpwe4yarXV1bsIUgDqiLtG89ayqTTi9T2MqwVeliKcpQcUl+mt/V7W+ZSDTD7luijrSrNctkAKMcfShplIPzHP1qOMquTu6n0rmPrycSzEkM5GPSgyYxunYgiqxYFzhh+VG1gu7GV+lAE7TLjjOKcJlB4FQeWCm5RkjrVvTbSW8mMcKFiF3EDHT8amc4wi5Sdkhkf2gchc1aiiupVJWNtuPvMcAVrNo91AgW3s1Ykcu7AkGqs2jatKBviLe24Cub+0MJ/z9j96EVRDDG+XnMj/3UHAP1qU3Dhdq20YQ8cjcT+JNPGiakEC+QFJP98c1aTSL5UKm2ycnGXFH1/Cf8/Y/egM1ovN3FYsAdielVbm1kIBEfQ9cVtPo1/jIgJb/AHhilOl6i0QRrMZ9Q4pfX8J/z9j96Hc4+6tFmj+7n1Bra01bC38JtFeTPAiXQYFAD/CeoJH51OPDWpYP7kAn1YVT1PwzrD6ZMtvbI0xBAEjLsxj+fpXPiMXhavLD2kbeq7ep6eW4lYbnqr4klb70aFp4lhuBcCOZooZXQpJEqykbV24OevH60+yv01DUVs5DMy3V9AxZiM7VyvOO/Nc14b8IeIrOKaK7sxHGWDIN6k55z0P0rq9J8P3drq9ncSoQkUyOxyOACDXNCeEcmpVlp5o9GtnbjTjKnGN5LXys9PyT1Om1Txbomm6les9telraciVEl2wM4P3m5xn8OtZ0vjnw9d+ZObK93CUPJbpPiIuCDubHfoelcX4n0nxDL4w1V7FBc2l3K0kUTzooHzg52E8nA7+tURovi9FnMmjxfvfvv56ADjGa9F4nD/8AP+P3xPGWJpxSvTX3y/zPYbPxlZjRNS1aCwkj8uZTKjPnezADP6CsC4+KP2iF7efSoXjkUqymQ8g/hVTSrRT4R1PT0lje4meJwiuCcDr0NYb6A8sUnkbWkjBJ2SKSPwzUyxFFu3t0vnEqGJpQbvRT9XLT8ToIPiLpFlbzs0E73vklWEtwXKp7MTkD8OtQj4maFJJZWsi3MEka+WzwzsX56LnPzdutedTaNrXnPqE2kFIpI0TejZfaSDyM/wBKz9P06KPW/kZdqF3DS4Iz2H154969B4RxV3U/L/IPrlL/AJ9R++X+Z7JL480uCO0FnYu7QSFwzMUbn7xLZJbPfPWk1f4kWs1u6WulwStIcyfaowVOOmQDz9a4xLONkHZvamf2e5YglcetczpS/mf4f5DjjKaafsldecv8y4PGzxEtH4Y8PA9MiyA/rVc+N2ByfCvhzPr9hH+NVHsHUkFPxHSq0lpt5IH5Vtc4DQl8c7CCPCvhwg/9Q8f40z/hPRkY8K+G8f8AXgP8azHtUYEEqc/Wol0xmOY3jIx0JxQM3F8fFVO3wv4dAPBxYjn9ajufHt3Ppd3YW+j6RYx3SeXK1rbbGK+nWstdJuNnCRkfWoW06XOBtB78imOxUtlia4iWd3WAuA7IuSFzzgd629S8TWVpZ3Npb3M08CIyeZIm1mJyNpHTjP6VjvayBwh2qCcbs9KS9jFxfuqSpNAuAN3RsDk1tSoOrsNa6GNZ3Cs8FrECqP8Au45GPzZPUEjsc19UWFxf3SM8NxBBBaMITE6Z34UEktn5evH58185WNhaQ6pbs9sQ0syglX45IBx6fhXret+Lo7LxjLp66NbSfZXRPOeZgDwCNwHHHOM+lE6M4620Ha251t/qeo/ZtRvraeCKKydo/JePdvIAySc8deKt2t1qF5rl5F5sUVpaOgChMtJuQMcnPA5rhfEPi+3+1Qzx6JDdrKnzF5SuSOmR0PtmmW3xNvmuSIdDto5Z2AZzMcE4wCT9Kx1BtW2PRNfkki0C+eJmVhC3zL1HHJH4VUktray1jSPsUUcW9ZEcRqBujCZ5x1wdv51yFz4/8TW+7zvDFvJAVyHhnLhh9MVm+HfHRuI/EJh0OCzuNP05rhC0jvnB+5g9F9himlzPQSdlY9Uu0juoGgDw+a6kx71DgH1x3xkVybxzQaVb6NDHAxi1BYJZCCI5crvyw9yRkdzxXlH/AAtbU5dGZTpemIlsw8sIki7Ce+Q+RnmpD8W9bjsodPfQtPWCVd2xoXAYf3hlsk55zT5Gxqaij0/xJq8nh3wvrko0+wnubXyXZRF+6l3sANyZ4Ix6+leV23xQleP954X0FHHzKRZcH6c9au6r4+iv/DkmjzxWsMV1tZzAjlmIYHOSTk5A61wkkaNIyXDGAEZjYrn6Dip2PPrYpTlyw+87W5+JV5cP5v8AwjmgSOAADLaZOPrmsnXfH15rmjDTW0qws7PzhK4tIdmXAI55rl1u2t45kVkcSfLvIz09DUG4zMquQFwST0IpshubVmywyllikCqVJxgc1EzLC7MOTkbR6U0TCICLbuGPlPpUMj4iB2YzSRmo3dhLiZbg+ZjGDg4NNmSTZvVe3T2pEQFSowVI9aVy6R7FlPGDg5xQbpW0RVAZChUcZHzHpXRx3LW+jm2OGEjks2Pl7dPyrCF0QB5kYkiBxs6A+vStWSWGZUhU7IscAjp9KRnXu7Ip3cqC9CxABVxgKana7DyfPGxGPuqePeqxXNyBGn7pRjLcEnuaVrlGkxkZ6fWgHG6R2nwmlRvi3oiRAqg8/gj/AKYSV9UV8tfB5Hf4o6XKS4A87/dI8mSvqWqR10fhCub1mHSf7RIk0aG/vZFDsFt0Z9oAG4lvpgc9uOldJXH+J9YsND1dJ5NUhsrqaEIUnheRXQHg/L0IJP51SjKWkUbxSb1Fnt9Bj8pYPD8FxLInmeVHZoGVemW3YxzxjrUQl8Osln9m0OGaS8jaSGJLRA21SAxOcAYyOtVxrthO632nawrzeUIppJLSRkcAkg4AGCCTVHTvFPhOxlsi3iGKWSzgkgb9y43FmUk9OPu9KlqV7WKtGx140bStuf7MsxxnHkL/AIVzgs4hpya0be08l5Rm1+yx7REX29cZ3Y5zn8KuD4g+FT01eM/9s3/+JqKyW3vbKJLfVIm0bzw8YaFldvnyE3HAI3YHT2pMUWuprXmh2T2zLa6fYJK2AHe3U7Rnk4xycVijTra2TVIo7ayuhbQblnntol2y4J2kgAHAwfbNZuu/EGw065v9LbVWivDL5cRFmT5Y6EZzyevNV4/F+jnR5LG4uwIGACNb2zZVs7stljnkc5602mEWral3TIbC7mniW0DxG0eRvtljHEwbjBTCjI656jpzXiaxHexaaVhnoBxXrx8eaPcTCe91IO0MbrEsNsyZLDGSST+VeVkIWJN4AD6LREU2m9CJYwcfLKRUy2wA/wBQ+T0yab+4Gc3UjfRcU8C36kzN+NBI77OB0iT8Xpfs4XnMK/U01RbFuIJX+pqdQDwtgP8AgTUANAA48yH8BTwsY4NwB9I6lAmRS32eBAPUiqsly82AwVQOm1cUhpAzueCzFQeM037xHNG4U9Inl+4uakDrI7vRrDV1udZ2BDEiQs/3VbGeR9BXd2M2nXUKvbTxupHBznP415F4y0y61AW4tQrtGnzxEgFgVHTPHaub0u51Hw/pOozQPLbzrJEixygjaSSTweOQKurH32cWVv8A2On6H0g1uhGGQEHgjFY+oeFNJv8AJaDyZD/HF8p/wrzrwh8VL64ka2v7dSIomlaRTxtXrwelei2HjTRNS0lb+1ka4y/l+XCpZt2C3PTHAPX0rGzR37nJan4F1C2Bks3F1GP4ejD8O9c1LBJbu0VwjxyL1Rhgiu2v/HkhldbGKKIbeC3z5P6AfrWLf3N3rlsJbmcyzLkD5QBj2wKaYWOdDKD04+taWpyMLOw2nGYqp/YZ0VGeIhXG5TxyKvanExsbH5SdsVbQ+FnnYr/eKHq//SWZTHeuS3NKTmMHcOPamojMW2qSQKmigmcYIOD0qTtImbIBzk0KeKesEhkaLuBxmpY7aQ8EYBoGMIXbkE578VIGG0qmdvQn1qwtltQKRknq1PitBHIScbaAKqh1LYHGOa0tFuZLT7ZPCFMqQEru6feFIyxsMdhWhpiwwWt9OIBMUiH7s/xc9K4MzV8LJWvey+9pDTs9TWzqf2/7J9sjwYvNMgh5HONoGen1ogvrq/S1jjkSB3h81327s4OMAGki1aNw0zwpHOq4+ZuSME9MZxkYqFL7TtSsJxHbOgtDwjoUK/TocHmvkp5RioR5qkElor6bvTb1aHzLqhWmuLy50yTzQhSeRG2rkOVDDIz0zippb66EVxerIiwwSMhgK8tg4PPYnt+FZr6oXhSI28WyMgoApG3HpzxR9uWSdbh7eEzDBzg4yOhIzya3/sLFO14r8O9/67D5l2NRbu7a4vpSyi3tTgRqvzP8gbk9utSRG/aDzftUDtLHuRNmApxkYPcVmrrMqM5SOIFzliFPJxj+lQR3xtmeS2t4FlYcEg4H09B9Kn+wcXb4V+Aro0RrTzQyzwovlxRLvDdpGOMH/d7/AFpdSW5i0m7We5SbKZGF2kc+npWZbahJa27xbYX8xmeQlPvM3Xis59Zltop4oLa2Bc4w6Fgw7Dr0pTyavSknZJX7/r0Omhh51m/ZK9tfM6C61HUTe3C2sDmO3YLjC4c4BOSWBHXtW4DkA158mu3tzMJbu0tWkU8FVbkDpnnn8a3LHxDcz39vFP5KQvIqu2MYUnk5zWTyTE1UlTS08ycThqlBRVRWbJnSM6JeXDKPtQmkbfj5g4chcH8hW60iKn7xlHQHJ7msTWtE1z+2pLjS30E24bdG1xOQxx0JAOM471R/s3xezTF5PDBMwxJ/pH3vrzW08gxc+33+mhhJpl6ZF0+7vTNtdbiB3BjXa0argbR7c9fWooLa4tbi0N79n8tkaKP7Om1gSufm9Rgdu9Tw2lxonhi7ur99Ma/LKqLby71cE8g5Oelc22p3McTPZwW0chUrllZsA9hzxWqyXFrRpf1p8iudWM5PEqarqFrpyiSK1J2RuH3MWAIyx757imvpMDzK5AyDngVlPqOrW2mDTXtoIrW2bzY5BCoYNnIw+M5yfXvWrpN7JfWttMYXQSK27dwQQcfrX2b1tYxUbGkkRUDBH5VIFPfFCjgU/GKkobz0x+tNwDwy/mKkxmkxQBX+yxByQgBPWoZLCKTplSO4NXj0pvFMDMe1mjYGHO3vhuahk0y6ny0MMjkfewK1zUo837GfJcI32iLJIz/F9auCu9Tkx2IlQo88bXulrtq7dDktT0XV3tSsVlc7iQMopyOayY9E8RxyBF064KHgO0Zz16mvWBPcyai9unlrHEqMzEEls54Hp0pqX7Na2kpQAzSbCPTr/hXoUpOkrRZ8x/rJjUlaEenV9VdfgjzfT9I11Nes2l0icxidDuAOB8w5P0rqPHGnalJ431p4tNnmSSWN4pQPlBVADkdwc9a6fTZ7q4u4J1EXkNIAE53AZxnP9Kva7eeZ4ru7WMD5Dudm+g4X19/Spqy9rKPMdH9v4t0JVeSN4tJ76aO9/PQ8/m0ybUlilvLfUYXjUHykT5GbvyOaQ2N7bTM8VlevCB8oBywPpjrXZ30jxWFxIhw6xsQfQ4qn9nitJ7FrdQrSNscj+MbSefXkCs5UY7HPT4nxFSHM4Jb236K/c4WC58XackpsLC7t0LZRSpYAemDxXZeEr3XtX03xFHrGlCGQ6a4SVYdvmE9vetO9S3aHfdH9zGdzA9D9R3rR8NW7f2VrClFjWW3cpE4yEU9AR+uPerqxjayjqb4PiKriGlKKW/fs33+/Q8H1zSNWtmeT7BPFZgIJXC/J+P4mrWvXUd/rFsbZg0NvGFyvQnviu/123J8O6jp5jiRtiPuhXaCCw7djxXnPlJAJFVDvBAEnrWMpKGx6mBzGWJoynNLeyt1Vk0/xJFCq6ATKCASD6A9eKu6Vp8uuaZqM91cMjxsogjHCk8k9Rn0rFV2DZJHDc981saXqkkEZAYsGyNh9R0PtXKF/ZxvuzEmia2u/s8mcg4PGP51YtDHG8w+yLcybcoH5x+A9s/lT74GdhLJIDJ83UZOaSGG1FiXlV45hxkdPrR1LVT3E2UkjWfLs6rt569KrzszuckbV9qulI9ys+NobBHoKzpFceaq4KA5HPJFBtB8zuNiV/OzCjSOMkADIxU8k7thioT1B5p+noE3Os20kbSuOtSTRwqx4V5HOCvTbn2pFykrjIF2ufNx5Z7gdKmzEI/MVWyeADUA243FhheAoNNWQNEzyuMZKomP1pkSi5DrqUShUQqNoyarRo6vuUZZeQMdacNqRtg7sY5PWomuHZ1OM/Tig0jGy0PRfg6sh+JWjOWBGJ8gf9cXr6kr5Y+D06n4n6NGiMufOzkf9MJK+p6aNaN7O4V4Z8cb4Wev6YGU4a3bnHH3q9zrwr46Wl9d67pi2trcTx/Z2DGKMsAc+1bUavspqZtF2Z5nFczPbCNtTuREMlYhOQuD2FWbayVQrKkcaDkk8lvqTVC78OaiXB/s66GYyflgbGfTpV6z8NXq2u2e31B0BIKeUwHt25/8ArV3fWqPLzJamqa3JJLq1UFUlZmBwfL5x3rp/BGtX9xrVppQuC0Md1ESr9Mbg3H5VmLoOtTxGP+yvItmZXMs6kHj0A+arHhDTJbXxJYL9mm2pfIolMbLuUMMHkelcVWqqutrEN3L3jSwsLfxdfyak00MUlz5kBkjO0MTzhxxg+lbmj+FItQ067W3hjie7O1jDKSEw2eQx9s8Dv1rifGemX1z4w1gmC6eNryQrhGK43HGOKx47PU7QsYmv4SRk7Nyk4+lYOXQnlOh17w9feHr0211bqVPMcqjKuPb/AArOXcBjyVB9cUmkahrV+os9QN7JDGC0RmVjtPpkitcQbHyYpj7hDTimxWM9BJjJ2KPcVMsU0nSVPwFX1hSQY8iUe7Rmpkt1XpDj6DFDutwsZzQ7FzJPg+ymnRWSSpv85znpT9XQppdwVHzBRgd+oH9a4/7CP78g+hqGykjp5rZo5Qqq2CcLnvUsNtJySwBHXiuNuke1tzKs0oOQB8x6139lIbjT7aVwcvErHPXOKLhsUBbb2OGz9BStbyITt3bTWqVHpio2XceKBXF1u8gsbhZJ5hEhRASwJ5xxwOe1QyeRf2YjuI47i2bDDPzKfQg9qPE1tBLeQvcoxiRUZiBnAHX9M1zEWoeSAIZpIkGSqqeBn0p1fjZw5X/udP0Na38MWls13JYySIbi3eEI/wAwGfRv8a1vCOgX2kRS3AnhSK7YW8SLJkxuCTGxGP73y/R65YapMpyl1KD9aP7YuEG77XLlPnHTqOfSs+bSx6CVjannghl2vIiv/EueVPcYFaukW2qXxH9m6TcTdP3kgMafma7LwNLp0mgWd3dWsMl9PukmuHUbixJOcn6itm+8U28EjQW8ck8o/giUsf0pBc4248K3+n6U91eTRGbfnyYskIp9z1rOvo1a3tB/0zrprzVftkm3UAsUY4W3Vg0jE8YIH3fxNc9fJj7Oi9ApHPPetqb91nnYr/eaHq//AElmZDbrFMxHRgMCrSpgV5ykclynmSSvuLHnPuaeLeVfu3c4+jGpud9j0LaA2e9LjntXPeEria4sLjfK0nlzFQXOSOBXQqjHksaYtB1LSFQOpJpAoPOSPxoAXjFaNgcWF/7Rj+dZhXGfmNXbEy/2dqRUBiIxt7ZOa4sw/gfOP/pSBm1q9idO8M3GowXcj3lvEkMka7csMg+Xu6gc/wAqwdFlik0/VpI23AkMDnPGf8c1zet+I9X1KzFoEwhlYSRxg/MwAG4k/wCeK1vCVrPb6BqEM1uY/lUgk9ef0/8Ar0Y/l9j84/8ApSJWurLbuWAcqPm9KemSo9KjjU7QD+FWVRdjHHSu0oFOeDR0NIoywHqatSRoImIHPFMCp3xTNoDA4Bx61PGoaVQRkGprmNEjG1cHNIVzPkhKtkrgHkCoig9K0YkEgZWGcdKrTqFlIAwKBlRoFbqKqtZx5+6PyrXt1RyQwqK4iCScDg0AUo4FUcLUiRgSFMcMM1KuKjlO0Bv7tAGrpsDrLHKqqQCd2RzjHWmaq5luj+42KAMPtxuznNR3eoyppSSaeI0vZIhEu5uOCxBx35J4rNtL3UpoNmqFDMpH3OnSr6GWvPcsAcVFczNCqlVyScVaQKyZGM1NppxrdoAwQgvhj2+Q81y4htU24uzPSy6EJ4mKmrrXT0TZjveTRth4trdcMCKGu50YBotpIyAQRmtaO7itpbMapOl3Ik5cnf5gRMdyOvODj296stqUaajp7XJttsc/mFhM0xC47k5wD6V46r1v5z7R5fg1tQT36v8AAwRcXDOFEBLEZACnJFNW6md9iRbm9ACTWhpeoXUstyZLhXaQDIlnMTEA/wAL9vpUzzB5b2Ozvwty8qMJnk2l1A5Xfx0P0ziksTWaupsp5bgoycXRX3vy/r+tMgXUsjbViyTxgAmnrqDxRSQPApDEZySCCOn0rSn1NIY7821wBPIsKmVOC7AfOR6ZP5/jWdq06XNzFKj72MEYkbuXCgHPvQ8XXjqp/kS8ly/ELkq0Fy+r3Vn5f0jRsb93SW6k8mCMbY2lfcxY84GB+NVLmY2N6N0Eb7SJYiHYphuQyjPGc020C3elSWYljjmWYSr5jBQwxg8njI4/Wlv1iuJAkdzFttbdU3Mf9YR12+vJq3j8S4r3/wAjijwpk0arXsFro9ZbaW6/I1dG8m7uFuvOjjaNhI8ZV9gOcDvjJNaXiiSZtd1OdUt9tkwddzHechQcAdvmHtWPoKmzvraQahCbKR0MsW7lz/dKdc+/Tvmt/XdC1S41zXZobGZ47iFViZV4Y7kPH5Gt4YrESpXTd/8AgM4a3D2UQxHs5U1yNX1b7pd1qk38mef6j45vLOSdBYQSRxKpYs+Mg+1VJPGLWN/crb2EcqW1sJN7TMQmVB2gduSBxUWs+A/FU2pzPFot+0booIROGx2NVrTwD4thhvI08PXqtOVVSyjAUHP+FfS0akXhoTm/esr+p8hjcky6nXqQoU0o3ezdrfedPpF1Jrem200lstvCp8xSZ2GSfTua3vCGrTXlt4gtxayL5dmzR7pyzP269q4O0+G/iWIbm0a6J7hkFdr4b0PV9D0TxFLd2E1op01wjMMZbrxWEqqadjGOXYaFmo7eb679TKnmnexuLR7dLeWQDc0khcnv+Irz5n8pJEkhDh+mDgCtJr6XVZdl421o4c74l+Y9AMjvWFdl4XWOYFXB5DKQTk9wfas5yv1MY4anCP7pWT16/qaVroy3NiJzI6S5OExkexzVGJmji8tsqwJzjjJ9zVm3u3tmUb/MhOcJk8fjTb65ZlO5FUMQcKPSszFOfM4vVECqbgPudUA6uR37D61ARIAXVt2eq+ho8z5lY5CHkgH0qNmaa6cxB8HnbnrQdUI6WFaRmzuT+HaTVVmCqACOeSe/0qYygSsCrAZJ5PT2qJmUo+MLTN4KxFHKY5Ax+bJzjPSrUcInUXPmnccgqBnH41RVVd1Qbg54OTwT7VZCR277Nzx7sbgcikOcXuiYIif6O6b0LDa57ZqI20scjKy4AO0KO/vTnkMZKjlCvXNKtz+7IJycdR6UExuV5lJjBztK8DHpVfJkcKuMn0pVIdjyQcc0sGxMknLdgKDa1keg/BsSL8UtF3jIImwfT9xJX1ZXyv8AB9yfijoYGAMTgj1/cSV9UVRUNgrz/wAe+PdP8IajbW955u+eMuuxN3AOK9Arx34paT4a8R+IY49S8QNYXNlGI2jW1aT72GByPYijTqWin/wu7RR/z8/9+q9C0/xC+p2VtPp1k1yrwRzSFnEe0OMgDPVsduB714PJ4G8EKfm8bTL/ANw1/wDGvVX1bw54RgsdNfxDNaTLZxK7LblvOQDCt90hTjNbVY0kl7N6lJq+p1t1q8qSSraWLXHkIHn3OE25GQBnq2O3H1qOPXXuby3trSxaR5baO6ZmcKqIxI/E8dK5O88deDnkd4PEktr56CObbbu3mDGAfmU4OOM1LB8QfAumXQI1tvMS3jt8NDIflXJB+715rC0nqP3bHeXMnkW0soGSiFseuBmufiE9rbaXqRu5pJbqSNZ0Z8owkH8K9BgkYx2Fc7L8bPBu5oi966klci34b9aj8O+NfDmr63YaXa3uoShXP2W3lgVVQgHGWHJwM4zTlFhFpI9BvrZ7qARJO0CFgZGThivcA9vrWCs11Hpt29pNO1j56LHM2ZJFi4EjKTyR1wee56Yrz7Vfi5pLmfSnbVB/pLeZIVjIIBI2f7vFSH406PBYywPJfyuceW8UcaGPHpjj86bpzfQqOx3Vpq8NvPqUdrcS3dnb2guFeQlyGywKhj1HA+lZH/Cexn/l1b8hXIn4qabqmkatDBbX8l9dwGD7RJsUICrbeF7Akn8awtN1RLqzhZyDMRhlHqODSdKSWqIk03odzN46gtL27vp9PZ4fJUhgFJULnIwfXOazl+NOguSGsJV46tCvP61huwniZGi3KwIZW4yDWK3hbTH5+ybT/wBdmqLCOnh+MGgLqF3dSaVIyyhAn7lCQADkdfU111p45s7uxhu4tOQQyIGXKLkCvLo/BGmMm5ldM/3XJx+ZrWjiFvbR2kEhEEShVAHOB70WA7tvHFnj/kHJ/wB8LTT4301R82nwZ/3VrjYLJf8AWMu7/e5pspTO1FAA9KLCZv3niew068tTc2EU6R+ZIwZVJ+YcdeuOfzrPHxW8KN18Op/4DR/40PJZ3G2WaF920A81zj+GvDG9iLS65PaXitZwUpN3R5GDxFShQhSlSldK3T/M6V/ih4UUDf4ZUBhkE2sYyKpX3xI8KzfZzH4ciVY5g8g8iMblAPHH4VlHw14elRQ0F6wQYUGc8D2qM+F/DQ/5dbv/AL/VHsl3R0/Xpf8APmf3L/M7ex+Jvh6SV4E0uK3aBVYK8arx7fTin3fxd8Oxs0b2yyg9diA5/CuHl8N+HLicySWl27nuZqdH4d8NRniwnz7yU/ZruhfXpf8APmf3L/M6m1+J3h+eQLB4aI54PlIMVdudYsxp1xs0uItdRsYQQB5WRwB6fhXLwadocR/d2MoI9Wq7cywyrFHEhVI1wM01FRi9TJ1J18RSapySi223bs13ZVHxQ8IRjZ/wh8RI4J8qOo774n+GDBNBD4UtxIyFVkVUwpI4PFZUng/SpXZtsqZJOFfgVA3gnTuiy3C/8CB/pWdj1bmrpmsWWuXF5eWFgllDvRTEgGCQoy3HrWr0FZ2k6XBpUTxwl23tuYt1Jxir7GmIQnJp1Ig5zSk9B60AIRuOKuwXMtoGML7cjB4BzVZF+apG4X61M6cKkXGaun3ETf2vqG7HnD/vgf4VNLf3UsDRySZU9cADNUok/eDNWJBhMVzxwGFi1KNOKa8kBEijIHrU3/LNqai5K091xHketdQxi4DLyOtWJyFhYngccmoExvXPrU8v+qPfpQDIYv8AXJ9atXK/uD7EVViP71D7irlwCYX+lMRVtTib6ior1cSZ7U6L5ZV7c4qS+X5AcUhlSA4kHvUl2o2hveoEOHHtVuUbozj0oEzONMkXdGwB6ipSOaQDORQMyYp7+GdJ9gYQ42oF3AgZ7fjmpl82aZpsFA7E42Y/Sr0Hytn0NXpIVaM+vUUPUVihAWBIJ6+1LI8kd9bvGWDYbBUZPSgqQfvEVWvmdE3xtl1jkZfqFJFYYj4PmvzR35d/Hf8Ahn/6Sy/c+EdeGqafdQxIbNo386EFcg4yMj1/wqtEiCecLyA+Bxiuv0i9Sfw7Bbxakk9zHb7ZZnfBEmMtn3z/ACrkInMk9y+4MWkJLDuaicUqkLLv+RrSnKWGrXbei/8ASkS4HoKGA9BUeZB94Z/GnF89VNdNjzLsQ4HYVZGDpTHA/wBb/SqJkBJAbn0NWQX/ALFfauWEvA9eK0prVnDjpO1PX7cfzMPTE1Nbee4tNR8lJLiQ+W6K4wGIB9R0q2ZtTDkTQ6ZeEdQYyhP5ZqKKdbuOAS2FxbnBwyxYJAwDnHPp19607nTkht1vFeSO3blXL7gMHkc5rpm5U2k2tdj0VSc7tM4fxE1z5lztsEsI1iBby5C4OWHGex/pXYeDNU1KW1RpNSupAY+N0zH+EH19657xjqSR+HlhLiZ5ZclgR0Cj0+lWvh6oSwTDsd6yPgnp24/KnJyc1zHBmjlHB1NdUmbTX+owzrMmo3LOhyA0zFT9RnmugtfGUEtt5OsRSQsD8ssMj4+uAQa4a6v1t8lnHufSnKwvmUSMWQqNrImTn39ulccnY6owcjvpvEWhRWryNfNKn3QsVxJvB+m7Irz7XdUu5LW5h/tm5EEyb1t5ZDIHH+8f5VRNoitIBITv4wcCsW8ja1kETsGDc4znFPm7HLV9rTd+gy1nngnR42ljyRyvGT1H1FF/fPqd69zcysZX/wBYccH0pjmNFG4tgdDk8Go1aN3ImVWDKNpHrSMYSfKLGBFErbwQBhfao533yRsmCeDn6UYJPlomSB+FNuJMJHGycrgnvzSFGHvXEdf3rHbhiN3AqvHOYroSFcIDyRzSvdtJNkYUL2zTk2PcGNtgU989/Sg6Iqy1Eu7lJ5mZV/4FjGaqXDK8a4UD3Hert/5RhiMYTe+CAnOP8KgNvJvjMg4XqM4IplxasQQyyRAKdwLcfhVqXzZj+9TdJ/ePU8cUDEt2N7Zx90VvWU9kUP2qDexUKW27sgdfpx/KkTVq8qvYw4GRF23ERBPT0IpXZXjfy0APYjirNyIXkIdQsbElBuyVHYVSYguqrMuTkADjFBEfedyltAJY9D1ANJG6xnLDccdasi3XDMzYxUBRC2ODn3oOlM7z4Msw+K+i9drCf/0RJX1lXyd8HCV+LOhr1UifH/fiSvrGmio7BXhPxDgVvHOptwSfKyP+2SV7tXifxCkgTxhf/Jul/d/h+7WmUcillGxAYDPoa6bx5YRTa1aM0YOLKIdPasKOZHwvOOxrqfGh26ta5HBtIhn8KBHnut6ZEujXTRoAyJuGB6c1wktxFK2997PjGBwK9O1oJa6cjXTMkV2rpHt5JwME/TJrktGh8P8Am29tPbTXF3LKqENwBn8a7sO4xp3mr9irHPXmY44ALbyWZN4bcSXB6HnpXo3ws0y4g8W6VPd2bpKZiyyO+DtKHouf1pdSsLFrS2ZreLzCcLu/5ZqSBgfQYroNNRtK+LWiWNvOJradN7MeSGCtkA/lV1ZpxtGK6lpRSbZ41rXGvahg/wDLzJ/6Ea0bS00g2sUzpeTHYBJyqKH9ASenX8qp6vbs2uX+SB/pMnf/AGjUC2hIA3ZrpdelFJXM0RRStBMdpcLnDKrYyPrXWaTM+l2EU/2aB1J/eSjPmAHtWLbabvBXapz+ldBb6ffSxRxSzZhU524AzjpnHJrhxOIjUXLADqkcMM5q1bxjducjPYGotO09SqtIST1ANTXEijMaY9ziuEBlxOG+SMjA6mkt4iwDsOOw9abFAJGyeFHpU8sgjXC9f5UAJPJxtHXvTIYvMJJ6AZpsSGaQDPHc1akZY1IUdOBQBBI+MqO9Rom4+1IMs3PWpuEUUANfG3A61EBluaccs2TUqxjGTQA3aAM96Zt3HpSsw6dMUqEdcildD5JPWxIVAUU3OBQzg8ZH50mQeMii6Dkl2HgjGaOpzTSwHcZpVZc9RRzIfLLsSKMCkJ5pxZQudwz9aj3L6ii6Dkl2JF4UmkAwfemqwJ5IA+tLuGOo/Oi6Dkl2J4sjLGnycED2pImQAZI/OkYksTTuJprcmgHJPoKdLnABp9qmYyT3NNuMeZgelBIkZHf0p8hHkgD1pbeGS5nSCFd0jkKozjJNbT+EdYKgC3T/AL+LQBhIfmXipZT+6Na6eENYDAmCP/v4KfJ4S1dkwIEz/wBdBQM5+Ph0+tX5RmJ/oatN4U1WBDLLHDHGnzMzSqAB7nNWodCvL6AyWxgnTkbo5lIz+BphY5vJDA+hzVm5G6E+tS3eiXNi/k3UtpDIRkLJcopx+Jq4NCv3sBchYDblM+d9oTbj1znFIdmc0ODV1WzCPpVq88MaxZhXeydwxwPK+c/jio4tP1FY9rabef8Afk0EmY4wxqMttPNX5NL1MucaZeY/64t/hTTo+qMP+Qbd/jC3+FDGZ6tiVl9eRWjA4eIA9uKgk0XVlmVl026Oe3kt/hV630fU1bDWFyAR/wA8jSAzZhtcj1rOnmMTq/Hyg9RkdK6GfRNRYfLY3JPtGazLrw/qzEbdMu29f3Lf4VliPg+a/NHdl1vb27qS7bxZzMzWlwA8NttZeGkimMbODzyAR/k1q6XKVgAK7cgfKTnH41HZ3d3pEc1mzhAMqFMfKnng59M0lsCoHfjrSnWhVnBRW1+/bzOhUJ0MNV9pZXStqn1XZmqWDcgUmcVEhPpTySK2PJHNtY8qDVlb1NN0l7vylkEUm4I3QnFVo3GOmamnsjd6HPArBMsWyfZc/wBK1p9fQ4MftT/xx/M5wardfaIpiWOWDyIHPP8AeGff/Guuj1GQhYJWL2rsHcLz8uc4X2Oa4AXEDuY/m3kjbs5yxGf5GtHTtfjtQi7GmicMEQghgR1x9MVpjMFPEwjVpbpbHtUakI6NlTxhpFnJrDR2UiPEyCSRQcbSwOP0qbwdLJbzw2BAKpDINwPU4zXOrcNLqFxcSyjZdBuSCBx0/LpW34OdH8Rsu5PltnIx3Jx78960hTqxjF1dzys4cZYWq12ZjzSrdNNEXCmNgCTk8854rqPCqMnhy7uVbNwJGUHrtwOB+uawLqwkjuWljLKSSeTxj09qkin1DTNPuLe3YGK6YMXHY965Z++rI64Pl1ZPc29sbpJrr93KnzDJxn6+tUrqAzQfbVZ5lQgOUXAUH15NVd1xdtukzKUHGcAA5/U01ZljieNSyyZzIrcfhWjjaN7aHDWhUbvU0TIrjDN8oCpxw39KqSHyrjch4XBGRUs28/OOUHYdqSFEkYO25VHAPvWJlTXKrks1lNawRy/eR2yWxgGqjynDB0G/PVT0q607y23kSSt5ZPPPGazJ7cxkyxlgGOMUDpO/xCSWeY/PWRWbOCpqeRont1jCIjHpmq88rNI7bcEqDntUNuplmWM4AbjOelB02JjJEAY2UkrjHNQtO2SfmJ7CppbB2miiDK28ZDZqW3sbm3njdkk65R4jlieQCPXBFAJxvZmesro5bkHOM+hrYimaPao/dlgGJf0PNRzWs95O91cSkfPuZ9m1ie/8qrSSPI7zPJI5HALnJIHTNOxMlGeiJZNsfzbcqx/iNUC4ScsicnoDWgsXmQxu+0vkEc9qjulJlEjJkbecGgqK6FNC2CGyPWiSNcELkkU0gscDJzzUkE0cfJVi1Bodt8FWx8VdEXnrPx/2wkr61r5Q+DYT/hamikH/AJ7kKTyB5MlfV9NFRCvBvibeQWfjK/eYkBmiUYGefLSvea+fvi5atc+JNSMUSyyoYmVSD/zzWmld2KexkDCEbR359663xs//ABNrVScZs4j+lcJplxPPbu1wEZg5VWTIBrpPiJPKmuQeUQSLKL5Sf9njFWoXlyglc4rUL6bUbhBOfMht1KxLnhQT/wDWzVW1hgtJ5b0NiSKN3jUHPzbTj9SKeyGzVy8ZkkLABR6Y9cUxppJUCi1RQRzuBP8AMivopRUYeyjFnVaysaEWoDWVs/NUrKzAzr2G3k49j1/Gug+Hs8Oq+LtFuPMzPBc3G4Y65yf8K5W1aC1WaUpskZfLTHT5uP61sfB5wvjS3hJwyzyDnqfl/wDrGvPxtJU4Kn8zGoraEN9okA1W8crkmdzz/vGnxafbp/AoNbF+gOoXQP8Az1b+ZqFVAHpXkGZUTT7cgkRJk9eK0tP0+RFJckqp+QHrj3pqjH17VJ9ol5VZCPUigCxc3AU7I256E+lQxRmRiO3c1Bwg/rSrJtH3sfjSA0HKwx8Y9MVTzuf3NMEpfoGI9cUK/X5WH4UAW428rOD+lRyMZDmoTISp2q2fcUgdyOE/76OKALCBVxk0M2SarhZicl1UewzSrCCPnd2+poAmUqOWYD6mnNJxgVEsUS/wLn6UpOBQA+Oxglgmu7m5aKKORY8Im5iSCfUelTW3h2eeabLM0EQU74k3FwwyuASO3r0pIL63t9IvEngjm/fJJsfcOFVskEfWqOm6+dUFxL5KSwMyq0MgKgbR8u3ByMDivCqYdzm5qN1d/mz73DZhGjQjSlUUWlHe3ZbfjuXW0NYGvGuroRxWxjyVXLNvBIwPXjnmmjR4zpouxPIQVLArDuRcE8MwPyn8O9Q+fLPbzoiRRxTOrFFXG3bnGPzqxbXLWsG2O3gE2woJvmzg5HIzgnB7is44WUldR0N6ubUaTcJVfeT2t5a9O/qRx6SjeXA1ztvZUDpFs+XBGQC3Yke3enw6NFJFah7vFxdIXijWPIGCR8xzxnHbNOS8mSJG8qBrlF8tLg53qOnTOMgcAkVHFPcxzWkqmPdartTI7ZJ5/M1X1Of8n9feQ86w7TtWX9X8ttvMyq6GMRR6jaaUbaJoJUjEjeWC5Z1B3Buoxnj6VjmzYfxCtOK6mSOM+VA1zGmyO4OdyjoO+MgdDipp4Sst4mlfOMDJe7UXXvv0e3Qz9Oa2h1W3a7wbdJAX+XcCB7d6t6kVu7b7VBNHJHEwVwLZYWBPQ4XqODTY1aMQBYbfMROWKk+YD2bmppHcosdtDBbxhxIyqC29h0zuJ45PFNYSty25SZ5xgnUU1U1/D8r6+XzHvcLe6Ayp9na5jYSTAW6oyICANpA55PP4e9PHIFQylmheKC3t7YS48wx7iWGc45JwMgdKmTgV6OCpTp3512/U+bzzFUK/J7F3s38r2JkmdFCjGBSDMsgHc96Z1NSRhi+U6jmu8+fNDRSU16yUc4mX8ea7vS7i6uYrfUJdQTFxnNoVUBf9lT13DHOc9+BXFeGFWTxJab1B+Yn8QCa1tP1mW51yQ2+m2Mc2T5lwVIwucEk5+lLYuLVjTg1C+c6VetfqVvpwj2uxdqAqxwD1yMc59+lX9Blvr2KS9u7rIMssaQogCqquVBz1JwKz/wC0dEhvTdCCOa6ByZbezdjn1Bx1qSPxXZxLsj07UkTJPFiwGTyaSZUpJrRF7XtpWwEv/HubxBLnpjnGfbdtpsDRQ+JL9kZEhFtEZjkBQ+Wx+OP6VRm8UabdwtDM6xo4wyXdrIqn2OeKoXmo/wBm2ywQWGnT6fcHrGhKN9eeTTuriT0sdFrc7LZpBblftV2whhOM4z1b8Fyfwqn4i061i8IT24hBitoP3SnopAwD9axdf8RXmm649vbpAEgUCMtGCVBUZwapSeLdUms3Mn2d0PysjRZBFNgpWsdvf3s8Mlta2iRtc3G7aZSQihRkk45PUcVDNf38Yt7QQQC/mLYJYmMKvVvXuOK5248Qxf8ACMW2pamshl85gktuQjR47g/Tj3rDu/H1mltFGnh/XJNh8yO7Q/Pkjk7vcfhRqCcTsjr1+Io4/skLXb3xs+HOwYQtv6Zxx0rfh83yU8/Z5uPm2Z259s15ja+NYIPLjXwprTvHcGffI2SZCCpYk9eCauz/ABXitGYXfh3VIQoBLFVI59xQvMUmuhuy20d9FrF7O7C4t5HSGQMR5IRQRjn15981u2p+2aXC06A+dCpdT7jkV5yPHeg6u8s8drfKrkLPCkoVZSB0Ydzjj3FbeleMrC+1qNYrW6SSbbAFaX5FGeDt6ULccmmi/baXbHXriXTreOBbKIxqy8BpmGefUKMf99e1VLaX7JqOkpHb3kd9LL5d68oba/yMTyeG5GRjp7Cq7+L49IuLmyh08FUmfLGXliWOSeO5rMt/FkNrcrP9imlMeREstyWWPPXbkfh9KViufucxrsLHXb8jB/0h+v8AvGqiR4HIx9KuXtx9rvprgrt812fb1xk5qucjpTMhVAxwaOo60inax9KDSAbnHStjTCGs334I3MMHnPy9KxHOM1ML86fpMd3jd5d2px68VtSV3Y4MdtT/AMcfzOM1zSL2BXgtIHiaOckyhsALtG05+grMit9UhlTmaTAPIyR79a+jvsnha70yya9tEmM8CSEqrthSOGYr0HXk1I+jeD9FuA62MccmzcWiDvsT+82M4HuaHVktnY9Bxb6Hza73NkhlUsjKNqAdgeo+nSuv8MadHbajFIhIZYWDA9/l/wA/nXtV1Y+Fppkt5bOKaW4jVhGiM5KHo2B0HHWrH/CL+H9PiluE02JAiEsQTnGOe9WsRJ/E7nJjMNKtRlTjo5Kx4cIRN1OeelQS2EwikiWNyrqRwPunHBr1238P6fGbG6utKsvs166qI0Dh4iwyuSWwfQ8Dk1t3XhXSvs7/AGezt4peod1ZgPwDCueN1qdkl0Pmh4ZraMC6ikjUDC/L1PtTBYJdK88cw3Ko2RkfMfUH/GvdrfTNLvtFklutOsbmZ7oQW8flvHtycAuu4kd2x6VT1rwpo+m6PrEUmmWi3cNg9zb3EAdOmQeCx5Bx+ddNbFOqkrWSDG+0qwstLHg17HJbzFChQYyM9x61XZVS3RnbBlG6Moe+cEGtSTVHS3MbKjqy7QSgOB3xnpWDNNI0ig5CjOwE52/SuY8+hrHU0nRY7SOWSVHQxhlWN8ndnkMO1UftWdxk5UdAKIw20sBkYwcdDUDqVjY7SrFuPShGvKrivcZjOFyrcZNTxSRGFv3YHfPoarSMsijZGE55wc/571IURowsbFiCSSRjI/zmgqSNLTriIxmGURqQ25GIyQe/4VoRX32Ox+RJAXlJjkByoBxx9RWGssZyyqeRyAOlSxyzpHHAnmShsDYORnPp607nNKlzMSe8y8iGWeTexY7zyc+pqulu8kbFWRMHdhs5x0xUl7MWwmMlOORjB+nrTAsxtnm2OsTtsD44JwOM0G8I2RFJG0MXnfNtZyquOhI//WKied22MWbAHOO9Sm5lFuLZhmMOXA7biMZ/SnXUodYGSOONkQLhF647n1JoNUhgkJYtCCOe5zRsRZEbYZDzvXGMH696jikaJsAgA881IZVZWYv8woEztfgy274taKCOcT5P/bCSvrOvk74Nvv8AixohKjd+/wCfX9xJX1jQjRbBXhfxHuzaeNb8gja3leYvfb5af4V7pXz18WbtoPFOtmEnzUjjIKjO390tDv0KTXU5SS6jWHdFcOEkyAFQfu+Mj8yOtb3xRuHtPEtm8jJ5J06FsD7xIB/xryg6zdS3izTyNIBgFc4yMYr0z4twC41PSZt4Vhp8IJb3XIrahJxmpbsd9dDlftt1I2IrOd89zxUsdnrVxIB9ijjQ93au/wBN8OvFp9ujgEqi5eRxk/5zV9NLiOTJdICP4UXJrpqZpWv8VhuvbqeVazZXlhbBrh/lLKCijHPJBz6cVufC92PxSstvAlbzSPqhz/Otbx5BZLosaxSsz+aoG/A471S+Gv8AyUXRWRFDAFGPfaIz/WoVSdWDqTdyebn1Na/kH9o3P/XZv5mod/pUF7ITqd1yf9c/8zSI5Y7R6cmuIRa3M3yqcHufSjY2MbyB7DFMTCjAp/NAxnkIT8wLY/vHNSIgDHCjNNLH0p6ECkIlFLimOdqEjrimyLdxRLJLbukbcKzIQDXPWxVOi7SPSwWVYjGRc6VrJ21diTOKQdaiYXIgE7QuIWOBIVO0n604LeBVYW77WO1TsPJ9BWP9oUfP7js/1dxv937ybvwKd2qvJ9rilEUsDpIeiMhBP4USG6ilMUkLJJ12MhB/Kj+0KPn9wf6uY1/y/eSluaQVDMLm3YLPC0bEZAdSMj8alsVku5im9I0RS7uQTtUdTin/AGhRvbUT4dxqjzaW9TF8QX8un2EjxKG3yBGJHABB/wD1UeF7y3udPkWFBGyN8yA+vQ/zqxq2nQajA8TNI1uZOHA2kke3OK0NA8N21ppZuYvtCqWMZ8qDzC2BnLHIx96ihjqKpcmt9fzOnHZLiZT9ppa0Vv2SX6C2/wDqh9TUvaks4VNtJPPN5NujhAwTczMecAZHaluIpLa4aFmV12q6Oo4ZSMg/lSwuJg4xp9TmzXLK8atXEO3Ldv73/wAEB1z607FIMDHNA6V3ngjqcDimj1pRyaQDxThwKb2pRQA4dakzgVGOKcOTQBNGpdwo71aSEpnDdRjpVNSd3FKHOOppjN7wvHt8S2vOeW/9BNc5qsOoC7S8tdQlhR7tIXgUkq/PBAzwc46VueFGJ8UWf1b/ANBNMS2W4utGjLZU30tzJnjhD/L5aTY0eqWoAt1wAM5P61Nn2H5VWguLYwoqXETYUDKuDmpRNGX2BxuxnHtWYWG3KI0EhaNThSeR7V4lLLrV/wCLdWigv0g063ugpiZN+QpwAAeF69q9tmZDC6l1GVI6+1eOpCYPEvicFgELrKM+rEMPzyKaGbPiqCN/Ed07SYOE4yP7orLMMYhZVmbaeSMipPGcmPFl2PQJ/wCgisyB91vOPYH+daElnxpKLb4caeEJYNeMDjnI7iuO0nWJ9f1Fk1C+vYoWRvLSBzGEI7Y6YxXUeKpR/wAKy03d3vGP5c1wqn7L4jl2jCidwM9hz0+tKTsNHTf2NaB941bUueoLg1NJY/MDaa5fRADlZGLAn8xWYbz0NH2z3NZczNORCagt1pqPczXFrNG/yNKqbHzjIJ9enWtbwPq1tfeJbBEZt/nLgEYBIPOPWsDVJRdadLGxOBg/lW34IWFfEunbY0EguE5xzg4OM1UXciSsdFqwg/tm+3Ng/aHzz7mqMiRBcqcj60utMBr+oA/8/D/+hGoovnV4/bIrQhkLBcnHSkyu3pQ3AqPigY5SueRTm8vbx/KoiKUGkAj7MZ20+4s/t3h+WBI2bdKOFGT0qNuaRJZoTiKV0BP8JxVwkovU5MZRnVhH2bV009dtD0/SLa70LT7WKOSzaSS1hSVZ5thjZUC56fMPbj9as3fmRvefY7jT5RexhXMk2zy2C7cjGcjHavI/ivrGp2viGKC0vJII1tISxR8MSR2Fc03jvVbbEKIpWNdmZSxc+5560e71uXKWLT91Rt8z6H02C2s74XD3tqcWUNt8rjOULE/hzWrJPZ3cEsH2iJg6MGCuCcY5r5ft/GOo312kc0skYyRuidhyexGfau6+H9zfP4qdZLuaaCS0lO13JAYLVWiloTGWLc1z8tvmduviHT1Wygv9WsEtbJ1bzVkYtKVGFyu3j1PJ6VbXxtYyqqw6ppb3JdmWISMA8fOPmxwe/THFeK6qsk0G1AMgk47mq/h+BrnUbqbYrJDau7puwSMYO2s9jsc7s9pl1TTrn7RezatYQ3jPFJEiyFkXyzkbjgE5yQTjoaztX8UaLfWerfa9WsFupLJrWK2hlLnB5JyQOTx+VeQt4ksSrrGtyiMcglUbaMcDn8Kxbgrd3UkyqQrnI3CnV5YRumTUqWiQzRL97JwcgL2ArNcL5mcsjKOCOua2DbsYiqjcSeAO9P03RZNX1KCziIjdkdmkJzjaP06gVMJqaujlpRfQxDdyxzNHIrAd1IAINRNdNJFjy+nANad5apDetCV3Tq5Ryc8kHGaplWgjkHk7sn723p2q7FrcheBlhciUHDDGBw1XIVtvLK7sEqc+p/8Ar1VMTtCCCSmcAd881WGVf5WOR39KAkrqxekjbYoQfLjBIq1GHEBxEqPtyknOf8B069aqkFzu83IUCnSXEiRgDhsYPPWkSkyBcxyKWiDKv3xk/Pzmu9trrQLPTvszywGGTMu37/J9R61xgjafMaSRYQ8sTgN9M1UVU8ss3OCeAetBoma+v6dY2oWfT7hZFz5bRDkggfeHt/Wsq5k84KETCqgCgD7vr0685qyXjSNlU7y4A5P3RVUwuF8xVJDNtHuQM0AncQ2srKGGza3HB6UQxIgZZVw2cDjrUYZ3AVepPWtK3t7jyHlCNIIhuk2jO0Ggl3O2+EUUQ+J+iMgXI8/J7/6iSvqOvlP4Sbl+MGirvRlxMcocj/USV9WUzSmmlqFfPfxWWWbxZrlvGFBkjjUdufKTqa+hK8E+JMb/APCcam4AwfKAyev7pKCjxSLw/qM0rIsGCpAYswAFet/EqFDeWkUpGRpsK4HrtrnEsmjleV5GJfBxngV1PxGiA1y1kCbj9ggBzyDxRGVncqxxsXjS4h8Ot5k0v2/pEfLBQD8f/r02fxIdQ0CGFJrk6pMwjEeWAb/aBBA68YrmLq3aW4uFQ4RZCQo6A+g/z2q8s/lWtiF2F4V64+YYcn+VdM3RVnGOpKjBO6RFd69c3+lR6dc7FW15U4O9mHHJJ9/0rtfhXmXxzpkuOCT1/wB01xBs0uL6aReUeVmHHYk16R8NLUQeLtMwMfMf/QTTqYiMocsVYexQvgf7Uu1HUzPz6cmpIl24FS3sarqV10yZn/maREPXNcgD1+lPHNIF9qeOKQxMZoAweaU4oHLZNAhW4jJNbusyrb3+ptNeJOsw2JACSQ3GCQeBjH+FYM3+qNTG3utRMoiLNOyElz1HvzXBXoOtUcU7WS/U9/AY2OCw0ZuN7yl5bcr7M1tQ1WGa0naH7GsUsYRYwHMg6cbSdoxjrVebVWn8QrPFdKsSqqIZASgGwAjA5A68j1zWJ4c8P3LXOoSRzSSRoVRUnYliwyW696nUH7Q/bB5GKxqYKSnFOW7/AM33O+hnVJ05yjT+FdX3cV2NmS6to7qIC4VZGhkTdG7OkLHoQTyPfGcZpsF9HYy2qyXQmmhEx85MsF3JhVBPXnn2zWYTTSRir+oa35vw/wCCcv8ArCrWdP8AH18vMknuhPpEEbyM9ws8jHdkkKQuOfqDTdMljjlnimby0nhaLeRwpOCCfbIFNHSkIB59Kj+zdb834f8ABNv9Z/dcfZfj/wAAcqqSLF541Tzdxm5KjjHYZqS1KvF5UWsRwlJDmN5GUf7y469PrWDql3cpMtnZr+/myd5PCAdTVB9CMMtnLBMvnwyGSSSTJLnjsO3496ilgFJc3N3/AK3OnE8QSpT5OTonv3SfbzOvtvKutNezeeOGRZvNR5MhWBGCM469D+dPuZY5rxVhbfFBCkQfGN2ByfzzWfDnZzVmNSseD1JrbCYS3LUucWbZvzurheTra9/NeXl3H5yc0dBSdKU16R8yAOe1PU88VEu7+LA9hUy49KAH0tMB5pwpCHZxTkOKZTlPrQM0oYIjo9xcsrGRZVjVt3HIJPH4VRONuc1syqLfwtAneaVpTn0ztH8jWNOAm0d8c1VhXs0a/hI58T2X1b/0E1Zu7R7bRJL042x2UyKc/wAcsgA/QmqfhA/8VVZfVv8A0E129ro1tqehGwud3ln5WKHBDK2ePxFRLYpHlKwnghQM9MUHKsQSQcdjXfT/AA4wSLXVGVewliyfzBFV3+HN2RxqEO712moKucPIGKYY8MMcmt6W2keK+1DYdtzYWbBvUgAN/IVtxfDWR2BudVGPSOL/ABNS+JbCLTtDWyty22NEiXceSF55/KgdzmfGzY8YXn0T/wBAFZNs/Eoz1StLxyceMb36J/6AKxrRgZSCeqmtOpmXfE3z/CqybuLiUj8q4WaVpdbY9nCzZ/3kB/8AZq7rxEVHwusQen2mb+RrzzS2aW43OS2yNUy3oOAPyUUpbFRNjbx1pCCO9PPSpHtZhHv2jbjP3hWJrcqyK7ROuOCDWp4HlDeJ9Dl58x5Ejf6q/wD9cVmGQlcVN4Jdl8e6RArfKLwN9QSDVxJkdJrNzu8SanHg8XUn/oRpLeXbIrE+xqLXYx/wkmp8kH7VIcj/AHjTImytaIyZbnG12FQkVPId8KP+BqAnimAEZXFNG4AZOTjmlzTCQHxmpAcTxUMjEEYFSdzUEmeBTAPippsl14qSeJgzR6dCXToQvqPyribHR9Tu4TLDawzqW3FnJzzXovxEm+yeLrO4JIX7JCrYGcgjkEdxXIXnhm8m1CRNOklEbRtLGqS7RgEZB+hNODXtOWTsmXy3jdGXHpF9ps4nvINka7nyO/8A9evS/h3Isutbl4LWch+ny1yVh4bnSzZLi5F1dMvypIzAIc9Qc8+n410fw0ilg8SyR7SYvssxHfZgYKk/Wqm1GfKQlzK6OZs7qae4uobhszK29OMdOCPyrY8MKbPWru4CoyG3zt4IIJ5BHuKwbk+Rdrdrw6tg+9avh+W3kvrsW+9Z5UxtJx3zgflVNaEox/Fvhk6bfrdafE76beZkh7+Wf4kPuP5Vgpdi2uXhmRgyHHY/pXqsH2TULOXR7yXFvdco3QwuOFYfyP1rzzUvDMsGqz2twHEsLbG3deO/09KycFJWYpq6G2t1A06OeY84YY5GeAa6jwtp0cOoXtwsYRki2hh3yRXN2mhrb7wWLh8ZBPYV23hy3aLRJLoTpMbhcALkFApIwc9+aIwUI2QoRscVrMtudVkfKiQNkkjnFNjntreWQsQjK4VsgH8celQ6pEFvmuCjN5n3gwxg/wBazZNrjlCCP4t5Jq0zJ1LTsyC7WO3kljjkDLuxuFRpNF9mmV4281/uuoGB7UG081XIYBlGRnvRHFIFMZ6980rlX6jI1ZVIPQ96en7ybyyPmAGDShZiSkalAOpPGaS3g23KbGIff1AyQPWiwrpluOP5z8oVwCevBqhcJGZc7mU8Z44FTm5/0mSNd0h5UNjBP4VH9lklWNlgfa5A+XnJ702KMXfUiKorYDkruzn+9WnPYyRR6dbzsYRNvnYn+BDgZP4LVP8As+WCQRyApMCD5Z6gf0q1JcbbuCV1MxiXbsmJZcDoD6ikaJpGrLZQ3l1AnmQ28cmZNigAxxbQo9tzYBxVnVbazt7TNsJSXZBJJJHtwMjAHQfkKZpOtWVhZJI9tJ88hEkqrn58Z+mPasnUdUl1a6eWbAT+FR0+v1pFSdtTvvhrBbQ/FfR1EEMUv75hsYsT+4fPP4nt2r6Ur5X+EZcfFfRQTkHzuW6/6iSvqimhwd0FeJePoBJ421BmPP7vGf8Armte214r48YDxpfgnB/d4/79rSk7ItHKNaDPUGuj1jxV4duII59U8PzXE0MSR5W5K7sDAHH1/WsVjhWbHA9e9Y10pvNQjg/giPmSehPYVESpbG6lz4IWEMPCM43cn/TW+pqMT+BpCP8AikJxjn/j+as6WMZCDovamiIAgAVqZm/Dc+Ckxs8KTL/2+NWpp/iHw3pd0l1ZeHpo54+UY3RODjHeuREeO1P9hSGOuHE9zJMRtLuWwO2TmlTpSKqingc8cUhgCScY/Gl74BoAP5UqjnpQIXGMUgHOSaUnmm5yeDQMSXPlmtTT7oWyiFFJmlZcsegX0/GsuX/VmoV1m30m5YvcDz9pdQ8ZcntwPWsF/Gl6L9Ttqf7lT/xS/KJ2N0/2NVaIAFnDNjv6/pXNXVxFc6hczQgiN3yAazLPxWv9tXservKsMQBiRBnPTggcc1NHepebrpBsSZi6r6AnpSqfFD1/RlYb+DW/wr/0qJZLU3kt7VGrbu9SD6V0HnjieMCjGBkmgDJzStSEVfKzKXIHTAOOacIxU3YcUEcVi8PSetjtWY4pJJT29BYwFGB0qUdMGo0HFSDrW0YqKsjlnOVSTnN3bH0dTTeacvvTJE71IKYOtPoAXtTh0pgp46UhCjrwaUsNpx1pv41YsIDdaja2+P8AWzKv4E80xlvxRPNa2dlaRhi8MUSuqjJyF3Hj6t+lYsd69yqmQYkUbWXGCO/I9eRWzrc32rVXYDh5HcfQsQP0ArAVFju7hx1lfd+A4/pVxiuVtiTTdjpfB5z4ssvq3/oJqK/8QeJfDHiWdvskl14fupfM8yMHdB2bBHTpnB60/wAHHPi2x47t/wCgmtzTPFELSPbbW6kjch2nnBw3TrkYqYu3S5SNxb+RYFkW6neNgCGBDZB6GmDWJSSReTkdMeV3pov7RhhosA9QMEflSGfT2GDEv/fsVzS5r6Fxcbaoj1XXn0vTJr+5vJhDEucAAFj2A9ycCvNo/EuueLrsXVxZvZ6fGVit1YnMjswySe/A+nNek3N9Ysh8yBZAvIEgGBXIatr9rqGoW0ULxlIZkB2fdU7gevfgU6jSoyutbHTgVfFU0l1QvjuykfxLeXETJIB5YdFPzJlQBke/tWJcaLd2sRkLKWQhZUUnMZJwM8Y6+ma3/G1+tr4mvYrdGFxIYjJIzcYUKwAH1ArGu9aOoISpumeRwzK9wWjBBydq/X16V5NelNzk7PfzPscFiaKoUouUNle7jfZb/ib+paHM3gux0+byfMiuZjLuO5VAHOcdfoK5JdFAuDFE9qYgvmNMvCKvTJ4z14xjNdRq2swQ+DbLU7pZVR76Z/3Mm10J7g1ycvjTS7i6lRlleJohFKGb97Jg7g2cYJ/DpxTq0pSs4p2su/Yzw2LpUuaFRxTvK+3f+rFhNDmkufKjMDIYzKJt2EKDqcmoLzTxaGM7opY5F3JJH0ODg9QD1FVW8caWn7qJZY4FgeFBIp3fMckk461Wm8V6ZPaWtv54XyAwzg85OfSsJUaiWz/E7aeNwrknKcLfLz/4Br6fbWxjurqeFZEt0BEZ4DMSAM45x1P4VraLZ2n/AAkeh39vbRw+dcBWQDO1lYdCecYIrkrTxfpFo0sM0plhnTa6pkHgggg465FbnhfxXpOo+L9FsrZJhFHMFiV+pYnO4nHrjj2rSlSrXV0/6f8AkY4rF4NqfLOOz6rttbvc3tUt7E393j7C93LdTZWeRww+chQNpwPx9azbCG1hspPtP2QXYmKGO5dxtAA7L755PpRq17pv/CQah51rK80V1IMpJtV/mOMgg/pVRbm1u3llvbaRpXkMm+GQLnPYgg1U6dbmuo/mZUsTglTSc106q/y12JIo5InuYJl2urZ25yB9PaozUi3TXF+8zqFD4UKOiqBgD8gKZKu2RhXrYaLjSSZ8lmk4Txc5QtbTbbZDeopjD5t1L2xTX+7WxwB05qNxlhVuwtTfXUdsjAO/C5Peq1/o3iGSyvlh0uSCSNWZJJGUAqBn1znGaQ7XNX4jReXr9hcOAUFvC2PZetcMvii4g1O6e1vFSHzD8hxzz2/CvQ7bxG9/4esNV1Xw1Y3DSwqol3lgQBgfSsi413RElOPBGlMTzkk0NJ3uVGTizBk8VQxMGjuWJIDEgqMHv2rpfh3qk1/4mlMbfuHtZi5AHzNt7kCqP9v6Nn/kRtI/Nq1tJ8XWlm7LZeGLCykdCgkiY8ZFRCgk07lSq3VrHGyW6zB1JOTSQ77gkhjBdxMBvXjPoavtE0EpyOQentUN0DBIl2qkheHHqtbsxOk4urQXBCBjxMB0D+uPQ9fzqzqunJrdgs4z/aFpGN204aWL/Ff5VmaXcqkvJ3wyDDAdCvY/UVqJcva3iTQfMY8BSO/Hf2rMZwF60enswDu0rjCpnP4109mz2Hgp5yMSlHcr74rK8XaI1rqy6xZjfZXfKKefJcdU/qK3JJYU0O0hk+fKKrIMckjOOapyuhcttTkraBtTtUur2Bo7aaPHmdAGPAI/GsfXdKGmSRqtwzwupKsyEc+hrtPEXjErA1nNZW8aMoQQlVJ6dgOR0rzW+nv5nBnjuCijCK+4hR+NO99yXTT1Gm6ERI3ZBGcD9KuPqVtdW6BY3N6OrhVAYe+O9UYLVGQMJgHwcoy8iry6XACp88epwAM/SrTglqNUlaxbnW0S2t280xzNFlyITtY9uR36g/Sobq7sJGtZ7RhFNAqh8Z/enHJ5H14rT0qOy+0rFJOYrcPxvbdtbjI6dCMU/wAQ2GiOojsSn2oN8xXpjuDVxlDqVyxTMN7WOW8Jt287zMNlRgBie/oKv2D/ANnXD21ywQMQ6HPHvUenadi3ZsSK+7hkJyKZcxzPcbph5qRI/JGD04z+JFZu19BNWIb+5e+1TzE3NkBFGOTjiq7W80N89qUkkkAxtAyQQMn+tSWblbhSBufOFz/e7V0ejWcNt517NL/pnzNuY5Ur3H196CGluYosmg0kXIguV+bezMoKY7HB9c9e1R6hLcG1hllSNN5yijqB9OgHeunvfEcH2Em02u/Vkcdsjt361zGqSJeTs8TDfIAXAXCgYGPqRUA2mdJ8HQx+LWiM0gb/AF//AKIkr6wr5S+DURHxS0VvTz8/9+JK+raaKjsFeDePvMPxD1Xg7R5OD/2ySvea8P8AH2B441In/pl/6KSlLY0juczPKIIGeRvlVc1Ws1ZITI5O9zuY1Dcv9sultIjujTmT69hV4RM7iNcljwBRFdRSdxmdxzjk9akwF4q3LFBaxeVgSTHq3Zaqn1NNiG+ppUGTnFA+Y/SlB+U7euaQDunvTlB/GoUZxw65PcipwwHY5oGL04pRgDNMLDPOaRpAvrSELIccDrTVHFNDBjnnNKWpjCU/Lim3MRcx3dk6C4UeTNF5Yyy+zHn60x2+YDNYGrG8nlMcYk2KAS3OSSeg/KsE0q0rvov1O+UJSwUOVX96X5RMvWtSTUp0e0txDJEWWXA+8d3H6Ct+0LLYWgIx8pyB9axrXSXXCeW4QHPIrooocQQpjG0dKVRxc4WfX9GPDwnGhW5k17q/9KiWIsY5qxnuKZGuMZqVVOM10HnD85p3XrTQOcnpQTSEHWg+lMaREHLAfjTBdQ/38/QGmFiyowOBS/hVc3SjG1JG+gpTcsB/qZOKBk4FP6Cqq3a8BkdfqKl+0wk43gfWgCZRgU6oBcRE4Ei8+9Shg3Qg0hDuhp2eOmKb3FK3SgYoNavh75dSkucZFtBJN+IXA/UiscVuaR+70m8fycmWRITIDyFyGIA/CmBy/iXVLjTWD28KyFcISegwPT6ip0srltO0zU5oGgW5g3Y3Z3EnPPoat6ILbXPFb2FwVMYQzyAnLN/hywro/Hdx5Xhmf7EoUW5UrtH3BnGR+YrdxcWodxxi0zF8P38Ol69a3txu8mMndtGTyCP60y4sfComkkh8Q63b+Y5crDHgZNZFrOLq0il37yyAk+vFWrW1V8uRwOBmsmnFtCejLY1LQLRth8X6+cDo9qj/AMxT/wDhJPD4HPirV/8AwXxf/E1h32nqZi5T5SOtZ7WMYBKpk4zj1qbAjppNZ8O3IwfF2uAH+7ZRr/JafaWnhGS5W4l13V7h1GB5sAH8hXI6dCJomEkYSRGwVzzWqkSw7GKhhuGR6isa7tTbsdmAjzYmCvbU6/W9An8W6pcapZSLFbzbdhkOGO0AcjtnBqjF4S/sq0kFxdoHRd/3T1JPfofpWxo+qtLPIFCfZmkJjKqEKrjofXHTNYtzrlp4gk1D7BLJI1u6qyiQFMKeWAHqM1s6j5bHPGC5ky5L4fvvEXw5sbXT4FmdbmRnUuBgfia4K5+EXi55CRo6t6EXMYP/AKFWlIZFiTY7Llexx3rIvIp3JPnS5/3zWOH/AIUfRHRmH+91f8T/ADBfhN40UYGlTL9LyM/+zUv/AAqXxsf+YdJ+M8R/9mrONrcnpcTf99mmPaXI58+X/vs1scly+3wf8ascnTWP/beL/wCLrf8ABPwu8T6N4t03UL3T9kMM6O7GeM4APJwDXLLYXIhD+fLg9/MNWbdZ4FJkuJAAeu802K9za1aTd4r1gAgj7XIQR3+Y0+McdqyIwI7sMpyrDJrT8xVx8wqBstoCSGycjoKt3WCqSDjIqikgxx/OrsTCW0ZcAsnSqEQk5FMIyKXcAvJxUTTRgH5x+dIBC7R5ZH2MBkMP4T612mk3N5c6HaXV3MGkmiDFlPBB7/jXn13dR+Q67/vKRwfWrtp4vm0XQraHUbN0iCmO1miIKOqnAB9CKlouJ0eq3rQ2H2QeWIQCqRooAH4CuUmi3LkDLDkUp1ZtRAllKIT0UNnAqRSkkJ+dd69OetNCkZ9vJ5qsxQgAnr2q4iBgCvWsy7vDA7eWgYEc+1U7fVZrZj0dCc4PaqJsdVKouLYSgfOvDCqjn5CpGRjpTNN1q3knWJ8p5vykHpmp7uIwyFewOR9Koko2MptZGtyTlfmj919K6O0nFxCIzjcmSg9V7j8Otc7f258pLiM5aP5hx1HcVLBcyMqz2rhWzwW5CkVLRR1Vs0M9vNYXSmS2nHTurDow964rxy8cbCxcsqhgQHODjHGa621dpbmCeMjynO1lUcq3Uj6VQ8TaYniHTZJYkLahp6ntzNF3HuV/lU21GeYPBLM0cwSRoUI2gsSR9Pat1Jy2TDMm4jHB554xWbDHKHWKPgswUZ6c16t4E8M6KbddRumF3eRsVKyj5Y2B7L3+tUwOe+H2hahb67dalf6arWUkfltJMBnk9geo/wAKseNPAlhbRzaro1qjLgyTW+8gAdygz+lej6hd2FsJJDJt38sR3/H8K8f8X6y8+pNJpGoC2tDGI5cOSXPPQduOM1K3C5yMmoLPHHbLDHGFGOBjt1P/ANercJt54hLJdRRzqCHyGO5R0bgdex+lT2Xgy9v/AA9Nq8TAqm9kTq0qr3H6/WsmLSNYh2XS2jqA3AfHPrkH+taJOWiE2d14XmvJdbhntNQaCziVfNRG3IwXHKj35BB6GqPiu+sdb1/ULiIeWC6xIRHjIXOcD1z371nxQW2nySCGLIkG+MzDIjPsOntzSBhG8e9VIDLvX8ycVr7CUYc8i3H93z3I7KxgjiBZA8m7JJ/hPpV6ZHeBkicIxUhWHasvTtQnu9Qnt2gPygvlR0Ge9aioAp8s7Sec9ayM1sZH9lXBLRiPzGVARz19as6doo1OOYszQNG20Lt6HHf8a0k3KQd3zDvWnaTpKSwA87HzqON49frRYz9mou5f+Gnh6bTviZolxHMZYR53mZGNp8lx/M19IV4v4BWN/F2nyof+emRn/pm1e0UkaJJbBXkvjrwj4k1TxHe3el6YJ45dgjkM8agYRQThmB6g161RTauNM8Jsvht4ls4MDTMyHliZ4uT/AN9VpR+A/EVrCWi08STyfePnRjb/AOPV7JRQKx4f/wAIB4pLEnTP/JiL/wCKo/4V/wCKP+gZ/wCR4v8A4qvcKKVhnh4+H/ijGP7Mx/23i/8AiqP+Ff8AicdNM4/67xf/ABVe4UUWA8RXwB4lHJ0zn/rvH/8AFU7/AIQLxMT/AMgz/wAjx/8AxVe2UUWA8T/4QLxN/wBAz/yPH/8AFUw/D/xMTzpn/keL/wCKr2+iiwHiI+H/AImH/MM/8jx//FU1vh/4nxxpn/kxF/8AFV7hRRYDwh/h74rHK6SWP/XxF/8AFVmS/Djx89w5j08JG2Ot1EduP+BV9FUVMqUJayVzaniKtNWhJpeTaPnhPhp47z89tge00X/xVWk+GvjAfetSf+20X/xVe+0UlShF3SQ54mvNcs5trzbPCR8O/FuR/oWBjr50X/xVPb4e+LR92zz7GaL/AOKr3OirsYHho8AeLuM2Kge00f8A8VSH4ceJW+/YO3/bxGP/AGavc6KLAeHr8O/ESAAaSDj1nj/+KqRPAPiVf+YWB/23j/8Aiq9soosB4sPAfiXvpv8A5Hj/APiqP+EE8S5/5Bv/AJHj/wDiq9poosB4sPAniXvpg/7/AMf/AMVSjwH4jzzpg/7/AEf/AMVXtFFFgPF/+ED8RZ/5BYx/12j/APiqafh74g6rpm0+qzxj/wBmr2qiiwHin/CA+KF+7aE+xmj/APiqX/hCPFY66YrfSeMf+zV7VRRYDxf/AIQjxOR/yC8H/r4j/wDiq2k8J63F4aW1Ww3XLyPI6iZBg4AHO7HrXp1FOwHhvgj4f+KdM1zVNR1TTTG00Zji/wBIibOWyejHHQV1moeFtWutFurQ2PmPcRNGR5qDqD716NRVTm5y5mVzM8F0b4ceLLK2aCbTflGNn+kRccc9GroE8Fa9HBtXT/m/67R//FV61RUvV3Jep5C/gjxATxp+f+20f/xVU5Ph/wCIT8yabz6edH/8VXtVFAHiY8AeJQn/ACDAG9RPH/8AFU248AeKHiKpp2GPQ+fHx/49Xt1FROmpxcWbUK0qNRVI7o8j8O+FfE1np8dtqOjASQgqs0c0TF1OevzjsQPwrndD+GHifRXvpIdNKtP+7VTcxHEZOcfe6179RS5PP8v8hqsl9hfj/meIv4A8TtHEBpnKqQf38fqf9qq7/DrxQw/5Bf8A5MRf/FV7tRRCmoxUV0JrVZVqkqkt22/vPBB8N/FIP/IL4/6+Iv8A4qn/APCt/E5HOl/+TEX/AMVXvFFXYyPDf+FceIvIWM6bkBcH9/H6f71RH4beJEUBNLB4wf38X/xVe70UxWPAJPht4rMoZdJ4xj/j4i/+Kpx+GPih0BOmkN3H2iL/AOKr32ip5UO54RB8NfEsed+n5HYCePj/AMeq1B8PPEiP81g6qRg4uI//AIqvbaKdgPED8OfEHP8AxLmb63Ef/wAVTf8AhXPiEdNKH/f+P/4qvcaKXKB4Lc/DXxM6EJo4J/6+Iv8A4qsu5+Hfjua2Fr/YJ8lCCgN3ASjf3l+f2HFfR1FVH3R3Pn8/DnxjPKJLrTDK4RV3meFSeOeA/rmrdt8OfEscoZtJAHvPEf8A2avdaKHq7hc8B1X4YeJpmEttpYLHhlE8Q/H71ZbfCrxkw/5A3/k1D/8AF19JUUrBc+a/+FVeNRjGjH/wKh/+Lrpo/AviqfTovtOlbblBtYfaIjn34avbqKYmeGx/D7xQFKnTeO3+kRf/ABVU1+GviqCdhFpZML8kfaIuD/31Xv1FAHjWneCvFFpI27S/kcfMPPiPI6EfN1p8HhDxVb3SXMWmYkDbiWnj49uGr2KilygfPviL4U+JZNYa60nTc2048xovPjBhfuvLcjuMVFF4Q+J9hhLTRY3Q8sWuYRk/hIK+h6KOUdzweLwV47u3aa/0siT5QqC6iKgDuPn96ujwB4glUCfR1YY5UzREf+hV7XRS5UTY8gt/BfiSCIRJp5SMDAQTx4x6Y3VQuPhnq7yPJHpQV5DlmWeMZPqfm5r26iqjeLumDSaszwf/AIVn4ihmDJpm9Dwym4i5/wDHvofwrKsvhR4rbUy97o+bYZ4+0xfP6Dhs19G0VUpylrJiUUlY8csvAWqWEkktv4biid0CMUnj+YDp/FXPv8MfGDyySnTIRuYkItxHgDPT71fQdFSOx88/8K08Yd9HH/gTF/8AF0D4a+MQwddJ2sp4IuYf/i6+hqKBnk3grwh4j0zxRZX2oWBt4U3+cfOjYHKMAcKxPUivWaKKBJWCiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcmPiX4S84xtqjxgTGAyy2kyRBw20gyMgTqMda6yvCJLnXj8K9Ws00y0GhzX93Hc6iZzJJBE1y2+Tydozt56MemcUAe70Vwfie4sQ2k6RBceI7q4FqZI7bQ5xG0kQ2qJZH3LxnAHzDOTwa5618Ta3L4Hew+13tveP4iXQ1u7nYbmGJnXliuVMgViufXB60Ael3Ot21r4gsdFdJTc3sMs0bKBsCx7d2TnOfnGOD3qbS9Q/tTT0u/sl1abndfJu4/LkG1yuSMng4yPUEGuCh0aTRPizoFuNVvr63bTbtkW+nM0kbZiDHeeSD8vB6EHHWqmk6tqUnh/wVLJqF2z3HiG6hndpmJlQPdbUY55UbVwDx8o9KAPVKK4m/utVk+IGs2Gm3bCUeHEktYncmJLgyzBXK9M8Lk46AVneC7iO11m1stVuvFFtrktu2621W4MkFyygb2iIJQ46/KRwelAHo9V76+ttMsLi+vZlhtreMySyN0VQMk1BpNnfWVvMl/qRv5HnkkSQwiPZGTlUwDztHGe9cd4s8RaNP4vsvD2rarZ2On2apf3oup1j+0Pn9zENx5AI3t/uqO9AHUL4q0RvDK+I/t6LpDIJBcujKNpbaOCM9eOlS6F4h0vxNp7X2kXJuLZZGiLmN0w69RhgD39K5f4QXtnefDHRobe4hmktovLmRHDGN9xOGHY4INWfh1/x7eJP+xiv/AP0ZQBp6f418Parr0uiWOpLPqEQcvEsT4GwhW+bG3IJAxmt+uMvwB8X9DAGB/ZF3/wCjIq7OgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiue1TXbuy1CW3ijhKKBgspz0B9fegDoaK8R1n4weJtNvvISw0sgLkl4pDzkjs/tWX/AML18Sqyl7DSduecRSZx/wB/KV0K59BUV5Jq3xS1vT7KK4isrJxKwAzE54wT/frBs/jd4hm1KG3lstLEcj7MrFIDz0/5aUXQXPeaK8ak+LeuQ6tFZy22mbZXAUiKTOCcD+PrmuquvGeqwaVPdpDaM8UbSbdjc4BOPve1Fxp3O7orxHS/jF4j1C1dzY6YJEYAgRSYx/33WxpvxJ167uzDLa6eo4wVjf8A+Lpi5kerUVwMvjTV1to5FgstxbDZRv0+all8b6nHpz3AtrZnQAlQjeuP71Oztcn2ivY72ivPNK8d6rfpcmS3tEMT7QPLcdu/zVDpXxA1W+1KWJ00826Rq+UjcNyeeS/9Km5Vz0miqP218cBPyp4unI6L+VFxluis+8v5rezlmiiWSRVJVCcbj6Z7VQ0HxGdatpXa2NvNC+ySJucZ5HPfjFMDforMvNatdPaIXU8cRmbYm44yat+e3oKALFFVxcE8jae3FI9wyoSAuQO9AFmiqyXDNGrEDkZp3nt6Ci4E9FQecfQUvnH0FK4E1FQ+cfQUecfQUXAmoqHzj6Cjzm9BRcCaiofOb0FHnH0FFwJqKh84+go80+gouBNWRpXhuw0nRJ9ITzJ7SeSZ5FnIJbzWZnXgDj5iPpWj5rego81vQU7gcsvw9sYrXTI7bV9YtrjTrY2kV3DOoleAnIjf5NpAwMcZGOtSW3w90O28O6hoebuW0vro3btJMTKkp24ZX65BQHJyc5yTXS+a3oKPNb0FAHOaZ4Fs9O1+31yTU9Vv9Rghe3E15Or7kbHBAUAYxxjH3jnNObwJpbeGINCE96kVtdNeW9ykoWeGVpGk3KwGOC7Dp0ODmuh85vQU3z28wLheQTQBz1j4EsbG9v74alqs15f2f2Oe4luf3hAJIcEAbWGcDbgDAwM5Jl0zwZbWGrwapdarquqXVsjpbNfzq4gDcMVCqvJAxk5OK3vNPoKPOPoKAK2k6Wuk280K3l3debPJPvupd7LvOdoOBhR0A7CrEtlazvvmtoZH6bnjBP60vnH0FHnH0FFwMbTfCdpo3hKPw9pt5eWsUcflrdRsgnHOc7tu3Pb7vSqvhrwPF4YvZ7i317WrtJ5JJpYLuWNo3kc5ZyFjU7s+9dH5x9BSee3oKVwOTvvh4l9rzayfFPiKG6/eLF5U8IWFHYMUXMRIXgdSeg5rs6g89vQUnnt6Ci4Fiiq/2hvQUn2hvQUXAs0VW+0N6Cj7S3otMCzRVX7S3otH2p/RaVwLVFVPtTei0fa29FouBbork38UXayuojtyAxA+U5/nTW8TakV+S2hJPqjf40XQHXUVxg8Ta6RkWdnjr0b/AOKrLvPHmtWUhjltLNT2JjfB/wDHqLhc9HorzAfEjVWJ221kcDn92/8A8VUn/Cw9YA3G2stv+4//AMVTFdHpdFeaD4h6sx/497Ic45R//iqcPiDqoOGhsc/7j/8AxVAXR6TRXmZ+IWs7vlt7DA6ko/8A8VTW+I+rI+021jn02v8A/FUCckj06ivMv+Fi6xtLfYrLGM/xf/FVG3xI1ryt62Vh0yM7/wDGgOdHqNFeLz/F3X4kJFpphIJH+rk/+Lp4+LOveUG+y6Yeeoikxj/vugw+tUz2WivGofi3rhuhHJBpew9MRyA/+h1BdfF/xFbXDxGz0v5TwTG/T/v5RctV4NXPbKK8P/4XJ4i2Aiz0s89fKkx/6HU6/F7xBIUWOy00sxC/6uTqf+B0XD28D2mivKdR+KOqWEKZt7EytwBsf8TjdVW3+LeqzBcwacpY90cf+zUXG60E7HsFFeL3Hxh1iNVEcGll84YGOTj/AMfqu/xo17y/3en6cX9Sj4/9D+lFxe3ge4UV4jL8ZNeyBFaaUDsz86P970+/UJ+NHiOOMO9npTZ6hYpBj85KVw+sQPdKK8k8F/FLWvEniyw0q6tdPS3uPM3NDG4YbY2YYy5HVfSvW6ZpGakroK4bxDJINcuAr2+BtG1mwfuCu5rj9aKNq10CMlSnUf7IpMo8j8VaPJd3okEttGy78gyYyPvH8ua5SfwzeLuBkgIXdnEq8YGT37Ag16xqWnQT37s8UZBznK+q7TVNtHsXUh7eLkYJAx1Taf6VNhGTfwSz+FLe3eIrcQhNxOMZHB5/GuL/ALDvo72ORViykgODKoPDf48V6YdOie2khYMYm6r5hx0/xGfxpn9jW7OZIyQdxYYkHXcG7++aLBY5rW7J5ry0ubeMsU+bjtggiu8t5reeyaNpFw6lSCfXj+tY8+mNIqqXc7VwvK9s+3staelqBEbZjEGTOBJFyRng5zzTBaHnPhyzeC6vIZY2UZBBYYzgmuosEWPUo2GOR/n+VXJtOZLibBBBZsAKcfhzSHTyZAxTp2Jb/CmthW1NuaH/AEM47PmoEUNaTRnoUIqJ1gNtJthYkAH92+cVUWNpFdB9oQMMcMpz+taJ3i0YTXvpk+hk/abpT32MR+f+FVtKjWLUAmMFoHjP1U4qOEfYZ2AnO5uoJG7HbvWc9zfW2swtHHN5TK7tLsLBSecdKz5Wa8yueh2filpXjimsZd7YA8vnP4V0j3UNvEsk8iRKzBQXOOT0FeTwaxeWF7b77gZkZVhJiAwec7vbGK6q51jzrYRSrZzAEMqyHjcOQam5Z0et3tta6Xcy3is1qqFZAvfPH+frXLfDy9tkkvrIXLtMSrJFLHsIQDjH97GcZrMvLjVdStXiTUI5Y3b95EXG0EHPHGafpkmoWOovffYoZZvL8pT5oBAOM/yq4y0sxo6HxpJstrW4y/Epj2KM70I+bPBwO/4V0VvcxXGlwy28okjkiHluOh461xOq65qV3bCNtJxGCNwVySQOoGB34qTRPFExhttNXTZVljj8tflKDjqcECi+gzX8Fzs1ndxPN5hinKn5ywDfxEEknBPP4mpBrlwfFr6W8eLZk+QlT8x254OMcY9f4vasvTI2sdSuLk2UsazIEOwZAUMxxx7EfrVW61eS38TQ3bW8i2yKd7MoUKMEdT9R+VF1cDvYD+4T6YqXNYeneILO8LxRiTMYBbIHfkd6vjULf+83/fNTcRdzRmqf9oW398/98H/ClF9bf89R+RouBczRmqq3lueky/nTvtUH/PVP++hQBYzRurOm1exhZle4QFeozUWn67Z3+NjbS2SASOaYGvmk3VF50fZ1/wC+hUUcyea48wEsQQN3t/8AWpAW80ZNQLKrMyg8rwafuoAkBpc1FmjdQBJmjNR7qXNAx+ajJ/fD/dNGeazZNSAuE2p1JHzUXA1cnFGTUEM4mTcBjnFSZoAdmgmmZpM0CH5ozTMijdQA/NJupmaCaAHZqvPf2ttkTXMUZHUMwz+VSbua8c1S+I8a65K0/lKXWMqXwPlBHrTA9Mm8U6XGTslaUj+4v+OKzdQ8XTx2LzWlugOAVMuSOfpj+dcQGbYsu0+Xu27+2cZq/rl0i+HURJdr7E564xTqR5UjOnJybudLZ+Nh/YkNzfwKLkuI5BG2EyW25BPOOQauzeKLZcbXjOc/dJfp9K8s020SWZxLI8vmR8I/IGccjP1rcvL0xyrEiIV2Bmx2y2MfkBUI1Oqk165eYtHcHaR8q7cY/Sqt5rVwkTSS3LKnTg4/lXDanf6m1zm2maOMqThVGQMkdce1VFtnvPLF7dzTEgEK0xYE8ZAH502UoM9VSe1ihRmliTgfeYCoJ9Y06EfNcpwM8An+Vc3G1sFWNWj+UAbcjIqC/ZGhk+ZMbcDkdzS5TNzsdGviLSW5W7BBOBhW/wAK53xDdQ396rwSb1VAM44zkmsiNY41jDOgOScg/wCfSpPNXgBlpqNiXLmFWMhc7xn096eS4XhwfaozImSC6/nSedHtbLCqFYliJYDB4yTyalJG7tjHXNV4Z49oBGSO9Z15dSJcsY7jao52bRnH1NAnoaN1dCKOYgjci8ZPU9qyZL55Z4ZefkRgRnqelVJ7ozuBLJuXHpjB7VH5zA4Zg4A4OKLnDVlOWi2L82qtb2ygISHQ5571AmoyCxdc87gc5yduKoTSs+QWXH04qBQIl2+acH0FK4KnNolmjaUIcAZ9SAKUMi5BYkbduScU0rG43kszHpxSboznIYnrzSuNUtCQlI51kboozx39KLh4mPmooLOfmDjOaarpwCpIByB2p7kTfMwTKjjd6UXGqduox9kcW6MHngjtz2p0Ra1G7Iyentn3oDb0YFE28ew/KpAPOjKgjIHGKLicbFa7Z5Qqgljx1OcfShkSGMZZ8jlgB3p4WTB2MNzdFFKvmRg+crM3UCi5VklqV9i8SMjebwwJ6Yx3FVJYpUkLE43NwFOatS7Swz68j0oaESoyqyhhzknGB/n0pDirEDsH6sowfTmoZW6jd8p5IxVi4SJI/LXIKnk+tUmZVwoy2e9BShF6nbfC5k/4WHo6KBked07/ALl6+jq+bfharJ8StIGD0m7f9MXr6SqkdFJWQVxuqvnXL5PTZ/6AK7KuJ1KQN4mvsD7pVG+uxD/7NQ9jR36HIayRFr6vJkpLDjGe4PX9a81l8batbzyRNDaZRipzGexx616X4mhd7mzZeMlo/wASD/hXjniKBrbxHqMRGCJmP58/1qUUaw8e6ljBtrTHoFYD+dKPHmoBsi1tgfbeP61y1FMZ1o+IGojpbQcf7Tf41b0vxxdz6pbRvbxJ5jhC6scgH61w/ep7OTybu3l6bJFb8jQB7zcGPKyuY1VlXLPx1FMMaMuQqFfXtXLeMruOLT9OiuFZ0kjbhemRgU2wujceBHVhI6+Z5JGecEjH8xTUkYtO52tqh+zXKYXBU4x9KqJGzzRhJGQk4yv/ANek8LMzaXbo4O4oQc+oOKfGcTIT2IrSGqZlU0aZl3mnWz38n2hopJC+3BxlR2/nVObQbV/NAVVXbtG3qfU/h/jUur2qL43ilaJSGdG3Z55XFVfENpG2qxsIAfkViSemHNSpluCbuGoaXKnhBQhPmwMqKwySBnrnrnmuc0dL4arHZXGoX4EyN5YMrEZAP+fwr0HXIFXwtqUaEqqqX+XqMHd/Sud8LBZLbT5XDF180Zc5IyQf5U1JWs0WtjI0eXXBqEMMupXJjOcq2D0HuKn+36+muNbfbI3iEzIA8CE4ycdqpG4urTQdUk8+Zpba6CgmQ5AyRVHQvFE1/qwhcyZ3Z3M2QRn/AOvXTSo+1i5LoROo4dNDpf7X1tdcayxaNEJCoJgwcDp0NVpvFetWt/LbmztnEcpQEM69DjPWtm33HxBf7yWCvEVB/hDLzis66Rm8U6ihOUVBIqlRgZ2n09zXN7vY0uXk8UalFqMNo9iv7zbhluXAGRmpV8VzXUN4k+lzFIgd4+07gwBx3FK0KvroEiqyxxxPGNo4O4jOetFup+x6qcKZd0yj5B/D0+vWpdh3JbfxelrLb3EdnqEUlyPLUqyHgdM5+tOj8b6y+qzRC7cQbioR4kyv4geuaWS1hElkpRdkZcjjkEbSP61zt0Ft/FE4xlfNzj610YeEJN3Q0dcmqX8iAHVr1CfdG/mKrPq2rEvGmpS8MQGZAT9fSsOHW1eBpUtkZlZV2eZjqDjnHtWqLi3EYd7Y5Ybj+9PcZrhxlJuK9jujelVpxb5x39r6tGMvqe71/dKK0dK16/E4ikmhkjdgCzxAlc8ZBri9V1WO5V47RRCi8OzNuycjp+tNstXWCwaBEeS4UHEgbsG44/A/hXOqVVy55P5HHLMaF3CKurb9bnb6hc3FzCtyUgAd+SMqgHQc57+lLELgWIiVUEv8JDMuRnOfyrziTxNdNYW8DSOgTB2Z4OOenrkVr2ervd6laXMkiRfKAfRx0JIzXdGVjGGMjf3lY6uR9St4CWtxLtH8NwST+FXdDuzFfKbuCOOIrne0rEd+OlZlpqMd/q++3VCYW2EA9FHOTUspvIbi5CSJsLfuwcVJ1xmpK62OmOqW0UkrrZGVSVwVc9x7j2rXMChlYxIBtyf3v/1q84E5s2dRIFdyWGRkEevtWzaa/AwZJZwsqjIB6Hnpn1xSE5x7nZG1PaD8pf8A61L9lb/nnJ+Eo/wrKtPEsEs8ayXMYQjPTr+Nan9rWYQu0wVR1JpcyGhfs7j+Gb8JR/hUcoWHHmSSpnpumUZrnp9ezKztelAjYA3dR9BWdr+txana28LspRGySDgscdf1oTHePc637RHv2+dJjGc+emP51yetXVxa2KTwiVn80gZkBXBOMnBrBngjjKrHCzyvwvzZX8KuazJcvpsRyE2EFj9Bzn6Gnox+h0EN1qtro8jSyO135uFVHUDb+J5qKXWtVLyGKG92Mw8rIByO+TmucbVpDY26G4SaQNucnIz3H86tL4piX7CWjOLb7+1/vf5xTu10MnWprdo6O01u7keUyWt8EB+UuQmalGpXzEtsm2D/AGxx+tcxF4vha1W2uA7Or7zIWzkbuP0rbuNTW70i4khg8ob0EbE/6wE9R+VDTW6GqkXomNutYvi/ySSYA6JIMn9afY6zLFva5+3SFsY2sOK5uy1CZZHinnIUYYyAHKg+nBFY+oeJPOuIoobhgobIZm5PXnii62JqVFTWp6MNfzJgWuoFcdfMTOfzp6a18vzw34Oe0if41y2kXMKwK8l0TnDHfKT1HHf3ovrWC7vIpd7bcsTiQ4PA96kqMlLVHVrrMbNgQ33XHMiivGtekaTXNRcrw10QAzciu/nbaqIrYB7Zrif7MivdQ1GSXedlycYYjvTjqyi6POZEjIOxfm/1nGcfSrOqTSNpsaKpXaB8zSEjP0xTWiOzOcDFV9Yup/7MKu4VcDB9P881pV963KZUoyjfmH2sUhZW3J8qjI8w57e1OlWPIZvN3EfNg+n1+tRaSLaIwkK8oIwRyf4R61sNc2uxs2rZ2nG5Rx+tKD5dLG3MkjCvYsOgTaRsGc5J5Jqewh/4mcTMUK9Bwf8AH3qQXEoY7WxkAZAA4p8EshuYELnb5ijAPGM1cqT+JmccdF+6kTaRYL/aF5Mdm0gY+Xjv71NdW3zEhlOQANq9fWt8RjbioY4184sAOEx+tZJjcbnMyQbigJbCdTsGB19qsJbptALsD9B/hW5p8SSy4kUYMhJyK1ZrGBpGYxglsE8UJhY5AwRIfllJ/Kqk0ux5Y23YAynPXiuzGmwbmOwYYmua8XaXGYkuoZvLCDDBT1Gccfmfyp3IqJ20Zj214qx/OSSWx949O9UZponkdzEfm3AFicdOuKoNvWLClcBu3XFaGm3cDO8d0heJ0KEjrz6enOKLnEnU6sz2jJj3nb6AUIyg7Tj5R6VantdkjRlwQp5ZTkEeuKcsUKbWEbBxnHck0ir6alXazsOVUc4z3oS0d2GSoJPc4qcAK/3NmeCTUkKfOXbPHTHU0WHzNbFZ90bbTgY7ZpQrbvlwXdST6CrolRVcABTnOSvX61CyL8sgbHUYx+tMqF2QtbymLlcP1NSRxhIip5fPWpBvLhiRxTAVT03HlSaQrN6EU7IAqRqenzHHWnQ5LYVQu3qSuKleMiMyKoaRRk84qFWe5Y4Rtijk+lA+S6EaP99v3NjodoyaV1DLgNxjgk9BUkMbAsAGVguTn0qA5ztAAz1zQTyaleZAU4GSOd2KYzAggHqPTmpZSAdp3cjj602MFeSDkL90ika8uhXkHmeWD/EOncVVCiG4JwSF4yOQTVwDzXEZJDZ+X6VEI3V2jLKc/wAXtQXax1vwtkL/ABB0jD5BMxI9/Jevo2vnP4ZKE+JGkquMATZx6+S9fRlUi6asgrgLyYHxhrMR42PCw98xAf0rv68+1/bD4mvJE2K7mMMdvJwq4zQ03sW3Yx/FKkWHmKcGKVXB/Ef4mvJfHsBi8UySZyJokkB9eMf0r2PX4Wn0q5VSMmMkfXFeReOFLyWFyTu3xsmfphh/6FUlI5M0UUd6Ywpc4pKcOTQB6N4sP2rwno94DuIfBPpvTP8ASn+E5DceDNSiJyUIce3P/wBatXwkYr3whAkqJJsIGGUN0GB1rZSCOKJ444kRHGHVUUBh6HAo5W9jOcktGw8MSB7CMjp5jj88N/WpZgVuHHo5/nS2eIZ41UbU3dAABT70Yupceuf5VrTutDCq01dGf4jVY9RsbgEBiFz64ByP61BrsXmXasnOEkVgOccqR/X8q27g5igcDnZjNYa2twNamnBAR9jZP07frU8mtyudGxdxte6NeR95bYnGOpKVyPho7YLZGUqwlAwevMf+IrtYG37kP8UZH864zTv3d0mf4XXP/fQH9aFE0i0zNu4TGviK3kG0PLvXPfDknH51xejvYpqyiD7SrKcDnKn69677X0zqN8ufvxsRj3TNefWcNpBqaL9sYTh+Sf4s9s16mDjaDRjW21PUoG263I7fdnijIPqQR/hUGoxtH4hmlIwksAX8dv8A9ajzY1uLIFwJGhyF7nGab4jvba21SzSSXbJIo2rzyNxBNea4rmsbR1SNFvl1OGYj5Xt1A+ocH+VLC8f2jVbXcPNDO4Hs6jH8jUVxMqixGCWeJiMD0XnmpLeCOXxBdzCMhwqKWJ4Py54qWirD/tsD21hc7spLJ5YPuVI/mK5jXGMXiGZycfdbP4CtiOyT+xLNFTANwjgE9OCf61jeKRt1pyWCoYQQfwFdOEaUnfsF0tWZkBAsDHhWaZiC68YCk8/gDU0urSGM2se+RPLUZI6YHXIrHmnMEP2ePLKGI3HsTyarG8cWiRlumRwexNcs2rux48+apNyT0NWTyJLh5WVW/h2biOcfezUiOZo4mRd2Uw5U8nJ5+vesmKyvbmFZLW2lmXIBZRnn0qwkzW00cIYqdwOPTr1qLmU6DSuXJLWKSNIGLeSPmQMeTuyOv1/lVN3+wzxRxsriP5ge+D2/OpUu2Ejo6gBmKhscA5z1qDU5f3TERJId2wzEHIGeMc4H5UMKSblyy2N3TLiaz1f7ZbFjC2AyRnlvUflj860NU1OZrk3Nv8qE5dWbHHTvXL2l00fzLg4Uj5Sck8fn0qxNIXtfsxO8FsoTxx1GaRopTj7nQ1Vv0uJdkkpL7Thlz+XOKZ9tyxyNrrjIP15rnzIRPFcof3ZADY9R/jxWlDvurqOOIZdyEUevFQ2bxp+8dTo9ybmXy5GbYMEFccfWukuNkluQko3MOD06VgaXYR2kaxs7pKWHmNtz83/666LYPKgEcgb5Rk8/OPXrS5T1IR5VY5fUILi2RpJXjKryRuyetY73Y2l8ltpzjOOa6HU7S0lVhMwQfMTgngevt2FcbdwvbNgAtH1Dgcd//rVKUluzKvSUtUi7NqshaCIO4XcQ2OTz6VZW/aCyeLznmjc7kBHVs8j+Vc+swh3ztgndhAR09/1qzDI7QxLuHy84PbgZ/wAKpHDUvDVE8txJFjcSCCUAIwWY45qrd3jQwqEJc5BZux7DioppxKFOxXbqPTk8UqLF5jLMFZcK2c8ZHWruzi5VuzTtlEUBDKNxAI3HPbmpRq7xsLX7Q/lhQBz3z6dO1ZUl7thkmaESKWzgnACngdOf/wBVLcPbyFLkRbJDhgvm5z6VXMyEpJ8+xqX999hvjDHcO8ZjH7xR3POO/r+lczcSmKZspkCQFWyTx6U66vGN4m1fLJcOd2etV1lLw3JD5cHnLAcZ7UnJt3OtOU3zTNT+0GWExsGYySfLIO/+eK7HRJluLCKF9nmxk7lyPbH6CvOD9pjEQMciAKWR2BAIPf8AGt7w7rCWt5D5gPlhWzxklvQfoKTZvTvTkn0Og122k/tBHijZwBuyO53ZrLg1lor25i+zuxlm3ZD/AHeR6/Wti48Q20t1HbrDKDI4VWYcHJxmsCLTrl7tp0ddkdwynJIJA6/yp0ndnoSVkb0sxEbgnAAPJ7cVna4wOnoVmSVRGDhW7dz9KsXhK2dweD+7bp9K52e0aLSheiZiHXygCOBgdP0Nbymo6JGVFSabbuSeGNS+yyrLdzyvGIz3LckeldG+v2FyjQxyP5jx7VBjIyfyrkbJTFaK2zkRZwa0LcA3gygBQIR9T/8AWNZxeqKlszadyoJDgEA8VPBcQW15bSXEyRxhslnOB0NVRODkFD9c1mazIkluq+vrXZU+E4qVOSkro9CXW9Ndflvrc/8AbQf40xdXsFL5vIBwMfvB/jXFW/kGFQyLnGBxVS9SJ7lACoBXHTuTxXDdI7z0fSL6zuLrZFPG5ALYVgTW206iBnUE4Xd9a8s0lJLK21O5ikVJVWJEfsuX/wDrCnweK7qRWSQowGMZxlify9a0hSlON0Dva50UXiW4s5WS6WSUzktFhegJwAP8+tc1fXTXFqiukgbzGJLP1JJ6DsKSa+upEljMgURsF/3T9R261STzJZCVBcDgADqTUuLW5ySUtmJMXeRNqfwBcqMbscZp9vtWYK6npztp8jmNgmwg4wfalSGRFYggtjBX6UiUrqzJ5P37lsF9vHXtTTJs4SMjuNnQU2GMsyLlUGMEk+9O3Phl2jDcCgcabQ0ncTk5yOnWmGQh8rjJ9KsPBsADEhscgDpTo4leM7eMDP1NBqoorsHYcrwB1pzAlQcqp6YA7VN5X7sAtg96k+zr8h354BJ6Ypl2IZLfcu5GPHqKglgkwpI46AgdauohJ3MzY55B60Lblj8vPsT0pAlYpmIiMFm5b1HQVYhZdqxeXhc8Mo5JHvThB5gaRiCF4wT1qZIAkSsAxYe9MUmrCXjQtLiIsUAGPY96oyIxJbb0GMHuKuGOQY3DJbioxGZXA5JA49KRmkZ0gGAHiYZHpUbAhC2O2MnrVqR5FbYygHtmqcyvghTkeh60jVIrwHe5zgFuA3cUsi7ThQu4AjcO9IY+NrjBPT1FWQi25QN8xYY5HFMGbnwujb/hY+kyAHZmYH/vy9fSFfPnw0iQfELTWQbEHm4GScnynr6DpouDugrhfEMFq+u3DPdIjnblSwyPlFd1XzX8TjHF8VdSnIyySW7Hj0ijqk7bDkk1qeo3IWW1+QhgykAjvXl2pwyRRxwzw6bdLHnaJ3wy/r7fpXplmiHT4gvCjgY9M15h470OW412BraPcXjbIGOx6/8Aj1RsK+xlMIRnGnaMv/bQf/FVJCglfZDZaMzdcLtY4/76rmZLcWtzskzvRsMuPSuj8JkJ4ohUoUEsTKM/n/SnH3thyulcmL2ax5L6LHIOCvlg/wBait5o3ukVLjS9zNgKlupz/Oqes6I1peX03WJZ26dgT/8AXqvpTCLULSQIMLIjEk9s01Z7ArtXPW9P+y6Zos93cELBEheTYmOBzkKKqWnjPw9fXK29qLiSQgkDyiM4+tbEtslxoV3btjbLC6H8QRXlmh2v9m+MLK3wNj4Ib+8CMUuazsS0mtT0C28T6fd3QitbW4LqN2SoAHOPWtbUAPN3f3lBrkdOtPsfieePdlSZFH4HP9K6+/B/dHHBXH+fzrSD1Mqi90yPEd9c2HhuC5tTGHEwRi4zgHPv7VTv724TwvDepKEmKpuYKD/Fg8fjV3XFMnhO5J6RSI3/AI8B/WsGW5aDTLC5Ks0CLKJFUZ6cj+YqG3zWKik4pnS+Gp5Lmxt5J3ZnYupZgAeD/ga5XTY/L1TVQzu/luxUseBtk/8A1V0XhycPbEx8L5xKj0BUGsQr5PibV4SpCyCXaSOuVVv50XZewl/bA+MpcrkS2hAPvsYf+y15rFZMupGb7TEk28YHA478V6bqkvkeItPvG4heEBj6ct/Rq8v1PTLhtUlkUCQCTAHTgHpXo4Bu7RnU9bHpEB8y50iX+LY8Z4/2aXWLaLUdX0SSVQylXBUjIPQ/zIqjHdiz0myvCu7yZeAO+f8A9Val75cltZXKZHltlCD0+Vf8K4akWpsuk7wTLTqGj049MeZGPxUj+lT2kyrqT5OHaKJ8evDA/wAqztQeYaIrwZ82O4+Ujr1P+NUpNTibULC4EoQJH+9U5/pUtFNpbm6sscemxsxwsUihvbD4/lXGeJdQjvDFJEGGFEbJx2JFXbrU7dbbVEE+6O4kJQgcg8H+lce8pMUkSZblmJzyB1/p+tOMrHPUlfRBMSqOgJMbEFGfjPNW/NTS4oE8uOS4mj88vIAdoP3QoPGcDOfes52SZgoIQBPlAyRn+lXNTlBOmltqrJYoCWGSBlgf5VkzOMNCWTUJnadZJpJYnO3Y7EY46Y+uapgeVGUYbJgwUZ/hHX8agY5RJRHjb/rAX+9zSyuvmYgYmYkgnoNuPfpQRyt7l8KFij/fOYwxVSvTP5+lOdYDI1oyuw7jOCGzniqMDB7dyMExruB3Y2/41K0jPArLtQp/F/F7UGMqbTAjyXIaMrtyR2yCa0bWG6vI0WAB2hbJ+bGB65JrJnl3pGhYllyM47HrWtoNxIt3CipuywLDb1xn/GkbxjzasfNot+ZUiWEhpsNw64yOvT8K3dH0O4tZvtEpAfoqqw+X/OK0hJumjdvJXDMFyD1wBjr/ADqwkgPlsqRnGckL1/X2pWO2EEidRcRSDa6uDnHyjIPrn0rYUeWsSnPGRkmsYMwRt0aqXPykrj+tajSF4lZSOoI/lTNCrfxMImBAd9ueeN3Ssee1ijUxeWVfeD6qc8bT6V0U6NcQFGPDAZxWbOGaUylk2MCjjGSSM8e/eixSZx+oeHbhf3wddg+ZgOnTOB+VY/msA5A3MTyM4HJ6Zrt/JZp5vtE0bQu3+rDbSgx75xXG6hY3Gn73RXaDfjzeowScZ/Ck0ctendXIUmEl20mAFQDaBgA89PyNSyzKqSR5y74IBGACeSP0/Ss9yQknytjdke+P5U+bbhh998hiO5A/yaEjhnSuydGxeKuPNRuPLx7GklnbylhZApVMI3c47VDDIDIzb2wTkMPTn+tRSO7tJLKed2Rnt6mmS6d3YivZC91kPku20E9aJmJV9zl8PhvfHt+FNkZAUBQEKME+pznNQNOFww4bt7UHXCGiRdS+vLZCLa5ljDdUVyBnucdMdqvafP8A2pMIJY40uSCIp0Xb845AYDg56ZxmsBW/hdzuIxjp+FdD4cCJqlui5BHz465wrH8DSLkrKzOh8yFGsppJFARUPz/eOOTxVeHVo4bWRBt3POzYLfwnPNc4t40qL9pmIZPlG5c/hipRPbQo0qorOrZDE/0oi7ESnJaI66GZG8uZtwiJDFTwcZFO1y4il0xICNo+2M2GHVcnHSuWXV5XZQWYfy/zxW3FeWl1bxy3s4SRDk7Vypx3qqtRXTNMNJtNS0ZKsen3F86xq3kplAzE4bnpz0q1PYwL5kkTbvmUYznmmx/Z7u4863ntpGfqofaemMgetWN1tGqwuiRylskBgeBWkatNtWZrUhNK6KPlygZKbjjnDe+atpocWoRQSTXBj3NsxsycjHvVtbeJ1GxueM4IPNPsSf7YFsXTYi7xuPJOPT8q1qzTiZ0/at2ktCUeFCigLcg7R1K//XqkfCU0l3G7yIIVAPynnIrswwA601sKBXKb2OLuNPOk6Vdm8OY5p48eXgkAZPesO2tNLnkx9veFd2SJI88Dp0r0C9aHy4lnC7GlVRu9SDis6TSNMleP9zEG2lxjjPT0rppVVCNijmpbVXaYJdQyyuzMx3bcEZzwfw/Km2kdzp6jIjKlwAeuSDnn2rW1Dw/YJtdJmiZ898gmqMsJgJQguqn5TjqPWic4taPc5qzS2GyR+bMTuGX53Y4X6VG5EYIAJPqe9KshTnHI5FSwxh13vwQc4xnisDKMf5hImDEErj2NTCPzAHRclSO/SpxbwySBlDKO/P6VOkcO7CoVLfwjt+dA+ZdCEW6lyzMSxGetI0bMgEUbfNwvHUc5/lVk2wVSdxDDsasRgbQNwGPummNSKItslfNC49OnGKRrdRjIJGPWtDyyf4wSecCnEDep2BhnHSkUpMykiy4RF5PHNTLEsKj5RuI5JOa0vIUAtjHpmjygXBPcYAA4pi5rsz/JVyASAp647U8Q/vCisNo6E8YyetWYrZDCrMQp3Ekd8fSmmPALI2D0NIGioy8nKggHhhnBpj4mb7oJ6AAVYYsq4OcD0qsV3NlDg+lAKBVaNNw8xAWUkDJqpcQOjkyLtz07mr7BRM69uP8A69Mmi3K7YBHTkdPpQXsYLHdMGBOQeppZ0Z3VsHOck1oSWkaRK/3n6lQKruVdfl5xycdqB3udF8N2/wCLh6ShB/5bEZ/65PX0JXz98Nxu+IOlkD7vm8n/AK5PX0DVIuOwV82/E+Pf8TNZHP8AyxI/78p/9avpKvNfFNlZSeJruSSCBpW2ZZolJPyL3NC3Q3JLcqaBP9o0CByeWiDfjgVieLCIdQ0+4zhfNaNj7Mv+Kiuh0wwxuYEKqu3hQAB+FJqUMMjR+aqtkcAgH+dKSu2TdWPGPE9uF1kyx4dZFD/Kc896uabmHW9JmVWx5oU8dASB/U16ettaryIkB9lWnjyF6YFOmnFA6kWrXOL1WJm1jVrSaJxDMitG4QkZKgdu+RXPW3h3U9qFLSYlQBkrjofevU2mtx1Zfxeo3vLZV++v51Ubxba6iVWKVjSssy2ex0H3RuB7VwaaDftdWMsixpNZSn5y33lB9vwrsrHUrUCUF1BxnIOM1n3OqxpO+MkZOMVDjdg5pIr/AGCQa1JeKymMyF14OfmXBH55reuW82ygfHQkH/P4Vzr6sW+6p/E0qazOLRYcA7WyCe49KuOjMpT5lY2RBHdaXe20y7o2AyP1/pVJLK3S1Fukf7nd90nOcjHem2usqvmB4sblwcGmwalC0jK5CKNpDEjk96el7kq/KkW7QLCyqiKiqQcKAPasfXrsR6zJbMp+d45A2eBwB/Q0mi6tPqF/PFMkcYVSVVTk8MOc1ifEW4ms/EFjJFKyK8SkqB1wxpO1zemn1LOvY/szTpDwRGQST7LXO6xpH2WNtSmmUQyysAMcg5OP5VuxxR6ygivVEkEDt5Sg8fjxmq3iuCMeGJyigbZEc85zzjufenDESh8Jc6cZLUrXXlWXhJDIDLGZFxgeoJFXNbvJLPwtbPbIp/ehQX6YIb/AVQuyJ/AJI5KbPwIIFVry/jm8LQ23mA3G9SiZ54HPFJylN3EkoqyHah4nLae9rFlG3B9469ulY10t0JklEmTLwFGcgjg9qzjIcZJx2ye34VKZ1woDsyrwSOAD7VnIykn1HJPIinzMhc57c1XaQiXIHy/XGfemPJGk37pWaI8EEYNPSMThlcsDgFSw/n+lInlS1EnJV1A4OOh71o6oBJYaPJKcp9kZD65Eziszy8j5S2V7np1/+tWlqBb/AIR/RtzY4uFYgekmf/ZqC42a0M0jyJNkhAUcZB6ipBsf7+VXYcgd/SqgkbJRWYKeOnOKla327nX5kA43nBIoE4lq2lKM7OikSjBXtViUbobjduL8HJHIAxj9KzROXxlcsBgDpir9vIsmEdTIWOSobbxjpkigxqQ1uR718vax6HrmtTw/ayXeqbQjNEili2eAccUQR2TSiJtKvgZG27ROOvXGNldpY21hplo0dvbvj7zZl5Jx/u0kbQgK+mNqHlrh8IchF6df/r1cOnyw2skJURu27bvXI5PX9aqW2uXNvfLZwoIfMUEuxD5PJByV9sflWmdQe6l/eES+UAN+4EH1Xgdj/OqN0OgsopbOKG7WKXZ/sYGfp+NXERIovKRlSJRhVC4AP+cVHG4C4MbK/Tn60rkmNgRSWgJ3JZJkt7R5ZGAWMZY9hWcLmz1RnSCRGMQDEI2eo9fzqyg8yF0kAZWHIIzkEVn3UlhodqZzAsSMwUmOMcnmnZt2QEV1qfm3kUbQxCFRnaCxLAA847YweAap6s9rdWEuboRRcA/uigJHTIPXrWqITeack9o0aCaPKMXVSuRkHBNYur6PqF9pyMJ9lsGCMvUs+cDqOnv0rOTUX7zG7NWZxVyFSMRhQpbDbkbIcGo5N6ZU44+XryK66LwvdC3W2uIHlRiMSZUmMjoQF5PpiqF34J1iKWaIWcsjJnO1M4Ixge3c020jCVLqjnoZCWi2x5TbycdD/k0xiMuAvyj5vXityPwprSFwbOccAsknB25A/maibw3rCF0GlX+1kA3CB/youjBxalsYBRgQSdoYfID3phgYMCm1gTwWOMGt258M6tboXl0ydo3UNHtG7K568duDWedOv0R8WdzG2PlBhb/DihNM3jczMjzWLrliefaug8OkHVSwIGy1nPHtE1U00W+exu737K6Q2YXzGZCASTgfWrOiDZNeuqjamnXHPf7hH9abKtfcxZLl2+dgQD1IHFSKzsg4JXpzUrRSXKRs3CAY4FTrBbvbSKJMPxs9z+HSmkg0K0su1kAGABnFW1kkARpNwUgYBqypP2GKGRoysb7lOwE56de9TzXTtZfZ2hxGAqqSvKgHt7UupEpRvYpzMZxEDGFO/IIGMjFXNLQx3ZbOOO3eobtZ0mgXHb5QB2xV60t5niC5eKUnO7af5VtommaSg+TlRsnVXsoldY43O7Hzj2PpU9t4os7iQfa9NwR/GuG/nWRcxzRIElmjlwpbhCrDjuDVWGNWZ1x0RR+PNY1qdOo7l0JVKUeVnYnxJbNB+4srwr0+VtoH5H3qnNq0s8eHt3wP4ZJm4/CqMESPaTOZSpT7qg4zU1raXNwuY4JHBOAQOPzrmnDkj7p2Upc0vesRXYuj5SRKq/MrhUz17Hk9qhnM8QjDvIWEe3oR1bOK6J9CvLi4V2CooQdGwwNVH0a7tpdq3eUJGUlGQfx6V3UKqjC0jGbvLQpfaGkgj8yRgPMd2DcknP8A+uozuY5XIBHBPpV2eOaOfNzYhwq4Dx9Oc56fWmlYmREVSoTJAB9ac9dTkrRSfMQW8Eu47l78seg96uLDhCY3JQdmAGTSBzIpwAqd8d6YoZ22AYXPWszNJyLCsZm5YDb+lSomWyScdc+tRLmJTGD3qaEt90Atk/rQXykpYAHJLN9akQsxyyrj3pTblAXYjNWbeEv87DAHAz3pjSSRHFACGbGcnr2FSFWXcNvB6Yqxk4OM59T2pgaR23Hn+VAJXBY94ClMAdTnmmLHtYBhkg+vWpdkpIxgfjVowbO4c4wcjpQS1ylTaxAXZkBSOffvULIhVlXg+1aDqRyq7RjoKrvCGXcSBSBMzZFP1A9KqMqlsqcGtKSJkfcvzAVUkTdyMA/zoNEhgjVsbowSV5zz+NQyr+8XgKoHYVMjdmBz2xROjxNsfBJAOAc0GbWpQliBcsv3e5qukCJIzI2Cc4J4xVqQFCAQSvQZOP5VWdQpGemaAsbfw5Rk8f6eXPzMZfx/dPXvdeE/D1MePNMfdwfN4/7ZPXu1NGsdgrx/xvetD4vv0811A8vgHGP3a17BXyr8YdRuYPinrUUcu1B5AAx/0wjNb0aUqsuWIpx5lY6uHURHLv8AtOD0+ZhS3OsI+3N5GCOmGANeLnUrw/8ALdqYb+6Jz5759jXUsBPuR7Ox6+dWiPBvASeQN/Wo5NXtdvNwNwPTk15H9ruWIzPIe33qc7bf9bLI79wG6fjT+oyW7D2SPUm1uyUbvM6+xqBvElkrKBKoB9SB/WvLyBJnaGH1OaZsIOKpYC/2hqmemnxhp8ZyJVP/AAIf0qrP42smJx19gT/SuA+zTeV5vlP5fTdtOPzqw2k3yRxyPaTrHIQEZkIBJ96tYGn1ZXszqZfG8OcRxyED/Zxn9arSeOLjBEUJAPTLD+grn77SLzTo4ZLmLYs2dnzAk469OlUwDnpVxwlG11qNQR2Vrrt9cu/2nfGRtYKGPIP41aS6V7mMOS+WAx9awrabLRttIJtwpyfSpjPk7gqnnqzH+lcNZRjK0SXHsdz4RmBuuo3lXjI75C5/pUPxQzI+lSAElkkHA/3T/jWf4duBaSzzsYY5I8bSWJAzkevNTeLtWtdV0nT4re4EtxC53KnJxj2+lcrQRetjNHiM6K00KR+e4fIY9OlU5/FV3qBlsbyJEgkwrIVwRyDVG3mWxnnEw28DKsMnORmozqJnuDsiODMsjPjsAK66dGDp3aK9pK9kjXF1ItjLbxs/2XYflIwM8HOKxo7VpHbbuZlz8xOPWrNxcSSPtjDtyMAHrVd2k84oVbzB6DpWELQTS1MnUqS30NCHT4YtLhmKKJm3Bt/zZxnt+FU5HSU8FQTjKqRgnNMa8luIRb7tjLnGMDg9fw5qpJE8DbZAFPXOa5+pV7m9c3VjFptvHHAvnZ3MdoOPbNZUjrNdGUMvXO1uM/SogRKu0ZJIxn0qJt3Kf3fbmi92D1LEki/Kku4qD0HWrt4fM8L6Yw3MFurlT64xGf8AGskoCrAsWfAODW85Q+EYAF/1WoOOT6xj/wCJp2HGFjBWRTINqnYOmep96kUFsJ5Zy5+Q555pFXEqlwGHHHSr00eHtGUgkwg/QgkYosNoqRh0yUySnLED7vOOauIjSEFFJOR0HPP9adCrBybbcGkjZZFPcdcYP0r6asZ7NbW3ih0fzxFFGJ5I4kxGSoOMHljgg8UaC9lznhkRGm2puGVmup2OxduTGuOfxOOantr6W9hUwECP1wa9yv8AUrK0Ny66SbiK1GbiWONPkOM4wcEnB5x61LHdWT6m+n2ulh2iCNK6xoqxhhkHnr9BRoaqDSPC2hv72NktrsQhJCrnyySSD0znpU8OpzWctvYXG6e5MixeYEwCCAcnrz/hXu+pC3sNLu7pLWEtDE0gXYMEgZrMSzbTr3T3mlFyLtjHKskaYVtpYFcAYHykY96NhpdTzu1ia2jcMSfmLdc/zq3KzrbqxiCg4y3mZz+FenahYRT2bqiujAbh5AUM3sMjHNc5YtN/ZdtZSO63s955UxljUvbjaXwOMH5QMH3z7UnuOMbrQ4+JjvPHBA/w/pXM63qlzBd3Vr/Zk9zAQGjIQsu7HUj2r1rUE+yWuq2EjecscEc0Urqu8BmIKkgDuv61xMgjJ5xz70ybWZn2Eom02BmgMDFBmI/we1JdzXJXYp/dkBZFzjcB0/LNWn2A8EfnTVt0uEZxcwIVONskm0t9KiUFL4lcdrmP5MEcxYFC69QOSuR/hUd+63STvJIzSzAky9weev51HIoiupmVMb8ZYY54qHcM/NkqeoC5p2QmiudRvtIhkt1uzCzlvNUliGQYIxjqDj9K506reW8mVvrrg54kYZHp1rp9QjS7sGgkfLAfu3xk/T6Vz6aZtn+zXTCGQ7nLOcjgZBz6U1EylFti3fjXW7qFQL65i8r7pjlYFR6Dnp7VoeGdY8S63fNbJ4gvYyqbsmZic5A6Z96yr3SVtYGYXkU64yPIbKgZ5BJAqxpV6fDmoyXdta+YJN0SpJzxkEVnUjJQfJuaU1FSXNsejPomsXug6hp9/rBvTcukMczsD5ZBDHHqDgVxUugf8I9Jq8LTec39mybiBjkyIv8AWupsdcv7rQrvUJbG3ikhnjSOMpwwYck/Ss+VpNXi1O4lESsbYQIFXAH70HPueK56McRze/sdNX2PL7u5523mjaM4BGOKkt7O43bSrxoerEgcU9raa21Bo5FEiq+zcOFPvViYuJSA27Heupo8+TeyJI44bb/Vu0zr0Z+AfpQl1JnO8jnueBVMzOkoyMjpjtVqWcxRoGClGOWXFNGXJ1Zb/tS5tpQFUOuVAycjJxkZra/tOJIx5lu9vcnhDjcC3HTH1Fcz5kbpGFYx7ZVcjHA9/wD61ayyPJZxRkq6C4WNXTvyOq9e1U6UJyTO6lUcVy7mu9ncXM11JfLMmSqRztHjOMjJH5VE2lNHG80MqumAWz7eldS/h/UdK0qXUtPvmmjOZXEvMcaAEn5SOuQemK56Bop7aaWW5mNxIudhjXaT6AgCt1UpWaaJqRm5KSYkJZdKm+eMKzD5STuNXLa4e10czQOpkAI6/dJZR09eaqBz9hMHkLuY58zjPUHFbWl6Wt5o4hlBIZt3ynBGD6965UldNmjLY1Jt8yyAnbcpbIQepI5NTre2xVgXRgJPKO0/xen1qndaNcLNA1vhkS6EzI3Bycck96yobaW3utPhmjIP2pnbn/d/D1q5YenU1uVGo47HUm0Rl3BfLJH41h6pasl1kF3wAK6RpYSsUQJMpBZqyQrXV/Iu8HEm0fSs4Q5NjOo/abmKUCjaufU05Ygo685q/wCUvPygDsBUUkC/IOnJxWiZmoWG28SyPsbO4jrnFbVlDYrEEmkdZM4BCZFZsMHzYHUdM1bCYOc8jimS1qbX9iwTRs9pcrMwGNhxSLoN15aYKlupjJ2kfT1rIjWQvuBIx3HWtNb+8aEwtKWQjGW5I/GgpJEM1nNbvtlidD6MKasDE+ladvql1CqxlvNT+7INwq0Dp9yf3sLW7+sRyPyoHYx1gG4bm49BUqw8Y38dBmtObS9kfmwsJ4v7y9R9RUDRIhwVx7ZoJkU3iYHkj8KryYJ2EY96vugPQ/nUJ7qR/wDWoEkUJLbAyGGByTVF4wSxHLdgBWoYzuPoe9Vp1MQJAGCcdKBopBhJGBtwV74qvcJuQnJ3A1eLblC8r9KqzjByCcDrmkKxmSlgxVjg44yKr70cbXxV25QTOH6Y681WNtEwZtzDB9OKYza+H5X/AIT7TQM5Hm/+inr3avDPAMJTx1pcm4FW83Hr/qnr3OhFIK+TfjKhPxY1sgE/6j/0RHX1lXyb8ZH2/FnWxzj9x3/6YR134D+I/T/IpHDpAW9PpnFdB/Y2kPZLJFcTmd4WkSJ2APGewB9PUVzjyFjkE4q0ur36hwlzIodBGdv90DGPyr0qkZu3K7Dujp9D8O6be6ULporqWXOMK+0ZHXHBGPxrIW0SRpIxa3BdWKnKHrnoeOtZkV9fxQGCKe4WIggorEA568U4PeyAhhOwJyeT1rnnRqN3chNpl+Oz8gNmF8SA4BYLwPrW2t5p7aALcC2ibymR9zqDkc9OWJJ71ywt5lwy27ZPQuRx+FR/ZJS2XkjXJ5LNQ6SerkFzrLrXrCTQvsrXKu7W6BU8sthlAwCenUD8qwbvXJpb0TIxIXbtLAZ4Aqn9khHDXaZ9EBapVtLQNHue4IkICsI8A/iaqMKcFq7i5jRutam1i1RGjJkjbLPI67fmPpjj86zikkaFhcwqQudqNyeM/nz+lWDYxR3EUEUHmPJnG98Dip1sZxHvH2WMMjMMLuOVOCCacXGKtBaEuoo7iWymSO3lVXPmBg31FTrCSQpX5ByRn3pURVheGed2jRwxZDg4YdOPcVDaJGt5G6JIi+Xh3bPJz1Ga4qkOe8iXK70NWw022e4VJ0zH8hyx2qMkA5596031LRrbZbW1t9qkAUlYIt/ULnn/AL6rKunTyImaAThlDCM55IyOg5qGBdWvV8q0hMMJ4IjXy1/HH+JrlsVFjL2D7RK88kKW0jSovls42qCvU+nIP51SMlpD5qs7yyA4Tyh8p6c5P49q0rjw1fQBmmAkXBYBDnn6f1rL8pftMke37sm39RQ5WjylqQJOrZLFVT/Z5bP41P5ixyL++zGWxkZzj3qpFYzQ3DmVzgjjPXFPZEQMSW54xmsGctRvmLZnikcmNmyRjleSB+H86TbbMh3qxA7kD5T6VDBKWjyNzIAQVAOakjjM0WCFwDkqMhv5UiHdEMjRqUECct2xQkbNMsZh5k7nHQHJxXd+Fvhve+K9L/tG1urZI0kaE72ZWyME9FPrWtbfBTxHBqkVw2pae0UbMceY+cHOP4cVrTinuzenqrs8xu7eMS5t1EqMo2SFSM885GeMdKvxQuPDEokwVW9iIXJ43K3OevbFegD4Ja4l00qXWmEFmIDO/GTkfw8VoJ8JvEC6ZeW/2rSzJK8TR5LlRtznPy+9Dk0uVPQ2ueTzR2osy8UBaSEfvMMTu5GD7d6kaVUsdOJhSTzkYqCxyPmIxXo1x8M5LOT7KurWsVyVAkLb2VScbckJgZ9zS3fwZ12eCJTeaZ+6YneN4wvsNv449qncpRVrs4jRbQz6tKFWJreMfO8nIGeMDj3/ACr3ifUrDRtSbT01Y2zts8+IW5b5goGUPRcgD1rgrXwHczmHS7G8to9h86USRyozAEZPKAda2tf8MT+JfF0t5p97ZBXGEEvmLvwoBIO3DYwehqegWktkdFeXlheNcW1rqzQQ6gwSVDblzuYBcqe2eM5zT28U6PpGtX6SG6aZmWNwIxgFBjg5rn9G8MXa6rbpHqWjzvBIrzLDOWcBWGflx+HNX9W8B6jqGq3V1Hc2yJNNvAJOdueQePShX3YuZvQ038baTqMMlsbHUXgmQqziEbSpGDzmuf0z4gaLd6qsD3t3dmwVvKxbKgB+7uY7vmODjoByavf8IBdnJM1uCfQn/Cqlv8N72As/m2fmO3zuMgkZ/wB2l7zeo72WhpyeLYkt0xe3QnjY5kkgTYwPZlDDp6g1D/bmmvbtI0l4180yz/aBCgAcDAwu77uOMZ7nmq114Xm08b7u5tcyMfLQKzkt1GFCknFPsfB97c2UcyXdm4dc7kZiM9+1VZBdpXG3niO0k0++LpdXN9dhY9wjSNQFOQANxwOSe/WuZeeXH/HpNn2Kn+tbf/COSyXn2SO7jaZJdrbYpCoOOQWC4z+NTXXh97S48ia+tg4wWwHIQHpuIXC/jRoK0mzl5pnBwbSf1yAP8ayNQMsl5ZyeVcLFG2XjAHzfXmvRJfBmqiTEf2eUY6h8fzrKv/Dl1ZSKl3PZwOwyoknUEigFfocDazvFLfzTxO6mTcq9cYGMH9Kgvrx4bVZYi8LswGSMcHqK7+78G31tAJp2s44pOjSTqA2fT1rnvFvhDU7HR7a4MtottLMpDxybhgc9utAznmv1trfe8chjyUOV6Y6/zok1S21jTXijSaS4VSquoGefUg/pWdPq62msTJeQLcW6sw8hyQvIGOQaq6UxsGeZoTFa3QLx/Pk4DYH5YNAmTbgkD2sispC4UYPH59qvzAF4iYpMq4I4qzvtNViDyFRNGfkfGcZOOnfr+dUJLo2ty1sgMipwHYfMTnH9KL23JOttboJ4SuS8M+Gu0y3GOFzisGx1C5TT9RmELmPz48DBPBLcdeOldRb6Vfz+A1jli8qaW6M+GB+VNmBmuZtp2stFv0YIT9tiRdh4OFck5/GpjUjJ8qepbhJK7RWmeW5LpbowLs/8P3Rux1zWZcYhk8vncuFPuR1qe2uLpNRjfLC2CvuAbgliT0/EVPZXTtqG6ZEZtzZfHTgDge+Kpoy5TKxumCfMxP8AABznFOmtZbXDyI4jY4BYYzxXTQi3i1W3uREoZZfNYgDkjFdXdzWGtr5AAnRA0j+cu0LhTgk/XH4UWDlR5X5sawmMsAMUsDEMXjkw2QQR2I6Vu3tvYrE0f2eykmAJLQK4wPXJOKyYYBGwQg8Nk1S0NOTlVzpU8UeXpz21nLPAZVaKaEybkKsMErnkdack/mRIfJO/OT0xzn/61c3DtkukiUEK5ySeuMZzUxknsLmSLeVUchetTbQOa5vwziSXb5DbhgZJP4+3rXd2UZhh0m3jt9rXEanczjBLHOeOnU15jb39xJIqmQZJAIAHSvTtFuIH0+Egq+EQBg2CmPStHO7TsEnc0JJ3s7wQTW4O5tgIYEZxmorjS1mfzI7QxyA5UhhjP0q1NNuzI7GQySNy6ZLEjjnsenPtVqykQQESyt5ox8v1OKytdaBoczcQ3yXIP2QKQu0ncD71XjgubeXzFgBJbcQTXTXQ3XDfXFU7qO5by47VY90kgj3SNhVJzj+VLWTGtFY582t0qjKc0427l41ZTksAK1k+0xxA3cS7iMqVPDjJGR+Ipkc0csiFRglu/tWipy6AUjFulfB43HmplQ5AAq/pukXN3F54MYRnwoJ5POM/SnyWctnM0cqFW7e49aVjO2pVVGY7VBqysZPygVJGCqnbyx9qmSIqOQcmgBEh7KMmp0gw2Xz9KlijEagnr3qzHEXG/A2+meaBiWsjWzhl/EetS30EalZUUeXJyPY0mBu2hc/QVbYbtJyw+6+BmgTRiSRZ5B5qqy4OGFakifLuX8qqvGXHB5oEjMdQrHPSq7KVyGYbT2rSmi3Jx1HSqEke5CecjpQMqNFtYhhj0NUpo8MVHI7VdmY7etV5N2Aex4NAjO2Ycqeoz1qu0gwwAAPp71aulKSA4xkVRcL5h3d+hoBo6DwC6y+OdNYZ3Dzc5PX921e4V4b8Po0Txzp/zZP73p/1zavcqENBXy78VrS4u/iprxgtopBH5BJbv+4jNfUVfL3xXZYviprbC4ljLiDcE74gTHaunDTUZNvsKTdtDiBZ3X2SK4CWoSVwgG3kZ6H6VO1hdwXbWrz26FI94ZBnIzUIZDGsHnzmIYwo7Y5qQRLOwYRzucYzIx5Hp1rtden1M/f7kVlNO89xDJJvZScbememRUV0xGoCOSeRY2GThsYOP8a0PsZiH7u02bsDcSf1NPWC5mc7LQSEfxYLYrNV4c/MloUUVWwwxJeQ5VgMsTjjI449asj+z1e4EVg7I8IWPMX3X555/CpZorq2jLyRxxjAGFAz/OoMuW/1rjOfur2/CiWKXREund3bLEd08Rgljs1UpEYjkgZPHPGe4qpKkrwwRTzxokTbwQcmnyIzgbA7qBzuB4/OpYrKP7MbhoCFZwiSGQEBhyRwPTFZfWpPYFFLWwkpt5JkcSMZEOR5RIwfyojspp2+SyuJTknLdPc812fhvT7f7PKXVWkD/eC9R+Nb7W6bkIQ9xXPLETXuplpXPNvKmtI5Y5oUhkcDCjBAA55pJbWExx7XuS38YLAA/TArp/Edo/8AaeneVCnmuGUZPHUHn8666+0aSwsHvZLbKImSqYJ6Vn7RsdjyC6d/s1r5ZYSWzMFYHnnkV0ui+Lrm4Q2k9rE0qA/vFGCfqKpTwpNLK7osccjeaNpHyn+6f1qnHAdPvkurcmdjJteNVycHvxXQqUlC5r7CajccmqXmpzlp52VeflQ4wCOKzi267uJUYFC2d+eA23P86nTQ9QMwkgQnBICnjIx7VG8NxpUrwXSOmXWVocYVvQj/AD2rnbuQhVFxMMOEznjLjnjoBnmny2Vu8Yh3hZSAVHmcBvetCLQdb15UutO0+SW3kGEYuM8EjnJ6ZrSk0RtGtRbi5s4r3gzTuPMxkcrGB0x69SfapbQci3OZFpLDKUWRiQwI2EY6A/1rTvdGe08p4XdZJULOsjhipzwOParmnRQRXUjE3WoXHmBYwSFVmwOcev49BUVxPJcTvI5KMT93uD6UlqTyI9P+Gay23hSC0ubqS2tZr2ZpJlfYSwVNq7u2eT74rt551Isbc6nL9gkMga7D7SxGNq7/AM+e+K5PwzcXtr4A0oWMMUomll85JEDB/m4JrXXV9flVLe4srJIHyvK5A444Jx1xQ97GkWkkixb3F3eQ2NvFqUzQyajNC06sCzRKrkANj2HNdbFGIoljDMwUYy5yT9TXnA17X4PB+tXclzay3trHGYY4IgqxEtgjPfiuM0vx34y1CWXzrp4U8shWCKVVvUnFawpNwcy37yuj1Oa4toNJ120uWUXUksuIifnk3j5No6nIwBj0rcS/t7aAxTzqZ7eFXmVeWUY6keleOX/jDVtKtVnn1Fri4HCM8a7i3txwPWoLLxh4kvbZbltft4GlzhW2hiAcen1xUJPoRKa6nq0UlsniIz2l4tyl3GWuhvV1iRR8pBH3R14PXJNFjeWer6hbSW80CWVoT9mjVxulbBXcB2UAnHr16Yz5lB4p1u8a5srC+hSWJgCY1VRNnryRjjmmReIdYtpcpcpvQ4DpEn8wKXK0PnRsaBLPa+Jdbmtl/eqkuCF3EDzBk474GT+Fdu862mn3b6Zqct44jVjufzvLyeXBHtk49ulcZ4RvJrW21zURtNwkG8OwzyTS2HjfVIc74Igrc7IURc55yeOuKTdgizpZ7trWTUE03VZbpV0yScFpRLskHQ57fSuj0y2e3s0MlxNPJIAztK2ecc4HYewrlvDPiuPUNWk0+S0jty67kwB8x5yDj1AP5VBouu6lc+Jfs0tyzW/msmwgdOf8KF3CUr6HSXc0Vp4lt57p1jha1eNJHOFDbgSMnuQP0NM0a7t4YpneZEiu72T7Nk48zP8Ad9ckE/jWBc+MbgedDcWFvMI5WXB6YB44Oeat6P4on1TVbe1a0hjiIJz1II6Yo5lcL6Etx9jsUu7mw1Sf7cZ2ZbVpc7pCfueX7/njnNWdTv7Sa4n0mGa3hlmAN7KzgeWpGMe7kcD0HJ7Z5WXxXqX2m7lT7GXhyFkCKWOGAxn86iPiS8nYySw2buerNbqSadgU1fU7mNkt9fmYOEtkso97E/KPmbbz9M0y7sY7rVpLqLVnt5RAqlIwh+XJIJ3A8HP6Vx03ivUpYWik+zPGwwyNCCCPTFU7nxFdXYQXEFnKYxhA9upwPQUApanZaddm+1axuLwIC1nIIjjCswcBiM+oCn6Gua8UgN4c2xjNudSl8nHTbz09s5qpe+JdRuLKJrk2Elv5ojEckIJBwcYGOOntWZrHiS5u7GOK7MS2sBDKscYUDt2os0DkrHNXNlE+WkiVj6lRmsG11TT5dVjstRhnNrG2xUhCg7t3fPbmuuknsQdsEjeXv8seaTkv3HIFQW3h6wvdVt8WkXnSTLg4xk5oJuesQaH4QsL9re302OKUtseSNHChj/CXHAPTjNJc6V4NguRDNpsOUcJ5giYorsfulxwDk9M960rZLq0nlt4JLN7Vrh5GldzvQs25lK9zk9c1n38UtvZ3NutzZCxF0J5J3c7o8yBypUDk56HNDuWlG5qI+jzXz2EcXmTIdsgVGIQ4z8x6Dj1rO1nwv4ct9OnnfRbaVi4ITkBpDhRnn3FSaZrOi20+ouNSt2NxcecducgFVUZ4/wBmna1r2hzWT2kuqwwyOA8bEE4IOVPTpkUlFLVIV7tLoZVr4M0W21KOyvtI0+Tz4mkjeGNkwVIypBY568H2NTat4J8P2lhJc2ul2kRiBd/3DSluOgAYc1HF4s02e/W8u9U09JYYXSGON3dSxxlidox0Ax7mpbnxzZWsMEp1TTGYrtljZ3VQ3Yq20/kRT6BbVEFn4Y0mTw0l6NI064vTGW+TcqA+h5J47+4rO8T6Zpen/DiXV7bT4ILuW2j+aPIAL7Qe545rZtPEugWmjzPca1aA3ckjs4JCBj1AyOg/XmsXxVq+nXHw+TTrG+iupolhjcRg/MBjOM/ShahKx4/b+ZFerHJMJFTAYoB3PTnr1rXt9Bnk1gTSfPYLJsaVVwrN1K/zqtoqWphvRcWsfmYXy2bGVPQ4GeOcVtaO9ykMdtJcB4/tLGXauFxtrR05NMEytq+m2FnqNvLZBSJI9oK8gnIGP0rLs7FbjxDZQTxO5fhgxzn5e/4n9K0NWeOO98uI5jt48IT8uGxnr3qr4cvJb3xFaC5IYENGO2BtJwPxqIr2cLN3CT5mSanpkWl61BborD90Xbd1PJ4/Srt3fSQRwtaOI8ZZSnbodtaWuW0LWsEzREzOpRXzhoxjofWsK5RotPsg2WSNSGfHXLf/AFqSfNqglHl0OwsNakEUZIVjtyyk7Qx9Bnoa6Cxvbe5kQKxR+vlzDDfh61xkBjezhU4JldgoHXAzzUb3M9rcCFCJYwu4q69MNjIP4VtCMJKz3M0d3KQZ2P8AtGql3CZ02+TBMuQdspIwRnkEfWuY1CSS0EJkcSFlEjPDO3JOeD6Gore8dvmDXG0jABnPBo9grXuXc6142mhAmVQI41jRI+iqOgH41WjSCJgo3BgCQMe31rBivJXZ4y0xIAH+sJyav2nmh2L+Z9xsbs+lDi4K1yXe5qWgjjgibzLhWwD8pGPwqwfnYFpGckfefrWSonEKjDbQMHdmrMd0wZI/Lj7DO2oC2prKgJAFWEiBbp0rPjlKSZAHPtVqO4fJwBU2AteX83Tip4YhlW3dO2Kgjl3rjgn0xVuIYiH5inYB2GLH5R69KtyKBpqKwIDNnioFclR0q3eELBEhA+7mmwMh49uNnSqs0bbxkYzzxVyY/Lx2qvMSEU5B+hqSTPn3RORnrVKQckdq0pVMhG1WZvQc1H/Z13K/+q2+7cUDuYTqvzAVBMkqxZzweRXRjw/Kd2+aNeeMAmmNoLuhU3C5x/c/+vQJs5h7SS5h3KSzJ1GKzLy1khI8yNlPQ5GK7tdAdLfyvtCFickhTz+tZ+s6HfXUKqkkJKN93cRmgxU5c1raGb4Ajb/hPNPbHAEhP/fthXuNeNeCtKvrTx3YSTQOsaCTL9V/1bDrXstCN0FfOPxQ1e0s/iPq0TadDLKvlZkYZJ/cofSvo6vmT4rpu+J2r4dMnyeCuT/qU9qJX6Azn4vEqyybRp9uqj5vuY6D61Pb+KJZJtq2kKpgnODSDwxqcNr9tkAaJV3lFA3gY9PasJrkJKNskrjdtHy4AqdQ0Ny81i6nJjdUMOQeBjoaz7i/EBs3hiUSq5L7+UZeMDB9OfzqtcuA4JSQt04OP6VFNPFC6yyq5iK7HVMA+o57ciuii1Fpy2ETXep3V9cCWURIiJ5Y8uMKBzkVWeaQgHe/PGN1dCsOm6JaD7RIC99b5Jkj3DkdFxWAsJ245yK0xNSnUqN01oN6DofnTLDHOMk1dIj8mMs6kjkIucD3+tVijCA5XOD1p0bCWPy84KnnGOlYNaXHFOWiOx8L3JkjuEjIDhVILjI/nWvPfeTcITqETRg/OhAyB6gD+tcLaH7PNHtUToq5I3FMt/d68gfTrSv5j33mQs0UswI2qu0ADsD361MUpS1ZdO8G0zqNauormeyMW7Mcud2CBgj1/CqN74z1C6X935kiKxUhiWKkHB+UVAbR2ubLM7tNJOEVW4HOfyrpPD5b+yQAo3pNIjheMkOeaztYhnJo2/SLqa7tirxqrOjHDEk9cdsccVnwaxPb8rMI+MMFQAEenTiunu3mGs61CFBzp7yIWAOCDnv+Fedf23qJjK+fhCOgRR/IVSbGj0e3vpp9LaezNlHMq7juY7gPYEVzpS01G21CS9nuX1Ion2UKpYSNu5U8ZHB4rOuJNSsrO3mkxtlGQzYOeO/FWPDNxMNUSeZwB58bE+vzr+mKHqtQaRt2niW6tfB0ehx6fOjqxJuIzt3KST1Le/YCs64d2u5TYwRwwnG3zcsy8c8Zx1zV5oS0hjJIVfl/KiWFYIN3zbNwXjrk0JA2ZsSSxzLN9qlWSNt26P5QT64HtxSO6JnBLHOSTyavGAKzFdzZ65bNMNtg5I5qrE3PUPDtpqN54C0O4023NxLBLOHj3Bcqzcjk1NqNrrt7bgf8IfO0yA+SzzRHy2PRh8/avMo7ry4Et0eVZFkLjZkcYA5Iq/FeXSHJnlI9C5pNdQO10Tw7qej+DdbXWrZ4nuEhA3SBixDdODWBc3Kw25FvGjsg2sMhVT6n/CsqaQygZkcYcPyc4Ipd4ypJMjZ4LdB9B0pQjyx5SYpxVjNvLSe+l82ZmPoTxx7A9BWhZeHdPube0ma8ZZlYxtGGGB94jPoORzU8yzT228xhXVioVASXTJw5+uKzGeOKVxI4USEBAhBcyL229e/HT/DTWOpS8y1eG08P3Owbby180bxt2lhtxkEehPr2qw+uaWlskkkci7szKiKTkA4x9MHP4Uyxtrl4F+0ImTng88dq0JPti28NvbwwbUl8zJwD06e49qi7YKyOm0c2w8Pa6szFYPsgMjAcgE8/pXMpcK8c7W+2WCEk72nKkr1BIz6Vt6fqEVumoRalZLc29/H5csUUmwAZ7Y6VXXTvBYGB4buFHcC/fH86mUblJ2GeF7qxuPG2iiI7roBjJg5CkjoDnmuh0E7fF0a+ty/9apaInhTR9SS/sNBkhuY87Xa7ZgM+xpTO0V2LmGTypdxcFT0JJP8AWmlYTZrWussEeN9PmLhyATCduAfUZqbTdWmvvEdjAljNHEshLs8O0DCnvn+lY7+I9Y+b/iZOAO/H+FZeqeMNdtL61tbjU5rOOaQqZmUEkFeGAx0yR+VJQ1uO5StLJ7XUNVywbzpZWbcMbc5OPzxV2MEIOc8Utva+VAIZ5TLKhHmM2cs46n86kCALgdqtonqRt0qI7QckcVNUZUZINSM57UZEm1a2glL+Wp3qOcbh/wDqq9dRm4t3RSORjmrU0as4IXOKAo249apybVhmLZa0ugpPZ30JuJpCrxMsatnOQT83PcV0Gif8hzT85H7+Pj8RWDdaMJrwXCyFZFIzkZyBWvprGyv7a4clxDKrkDqcHNDdwO+v9PF3dXDSy3FqElkCrG2NxLE7z+n5Vn3hB0LX1fzS0Zt4meTguVGNw+tMvNX0S8uzcPc60hLl9kbRhQT26dPrVPU9Y0x9IvrWz+3PPduhZ7grgbTxjFSI5lJTagsu0tjHt7fh/KotZuIbzSwpdY3RgSsjYI6ng+nbipEDqQc9O9MltklXa6Drkcd6GrqwbGFYtC92pjdDhCGTdzk9P5frRb213HPPHtBjJLRlgGAP4108Ph61/s83nnRRkZD/ALvof4R+PeqiwbBnPPeppw5I8pEIcisZVtp8eBb6tcQpbueHIOFbOTgDuea622sNIki8mHWbUs2CVJweM+/vXDa7eONThtEZFfAK7xx3/n0/CsvUrS+sT5E8XkyOQ23GCccYH05zXVGglC70ua9NT0G58F7pjdQXsBDHLMH4HIPTHqKpafYzWk1wRN5sRYfMrblByeAfWuGup760lCPK69PlLZBrpPCF1cMZIGY/Z2zJy38QwD/MfpTnSkoc19Aexb1qzt8NcPvdyMBQcD6nFLolz/ZEUNo8NqxZ/tSXP8ajaQFPtxmr2pad9sYMJGwQFKjtz1FQ/wBhwsYN91bSbIki2MemFxnPvzXPOcFFKTGrE9/dW8tnZBW3oysWKtj5s8n86glvbGbTJ7SaOVfLBdBnHzcAfh1NXo9GmeB4Glt5I9uIgoHynnk4pw0OQ27G7SIlf4lGBiojWpvS4SvczrS+ZJrW3hTyJo1JSSQZwSM/kav6jJm+ilaJXneHaVT7vTsPTg1VlsSItixjaOhA9qryGdDFhQGiHDEZyK6JO+xnG0bk90WexMz4G6VVAH0Jq9pZhxCkirtKksx7cmsO4u5pAYmPyCTfj0OKtw6hJEqooGBxWsqbdNJDLtuzG6lZM4DkjBxjnitOylmdJ/Nd2dUJ3M+fSq+naPLNAs77dkgJXrn0q3a26W73ULuuNihiM4GT/wDWrKdr2BuxOt7I8ao80jg9QzEinxKTKJBgjJqOKC1yQrMWU5BHQ06yDBnDK2VPIqdgUubUvqzeaverkSnfzgZqqFzISqNgH0qz5vyZYFT9KhvUasW0UK33xVxWPCnpVGHZIU+bnPIqzvEkhJ4xwBTBliIneFHOSBVnUnJnCDoFFV7ZSbuLB43dMVPPbvcXcjOSsYOAO5oEUArTOEQZ9atrpybQZTk9wOlW0VYxtQAAelBOOaBEXlpEAqKFHsKjbk5J4p7N1NQseKQiNzkGmoMgjIBpXbAppYYPGKBMTeclehx1FV58BQNw3dakYjPJqARlpFDHBbgUiEupc8Pg/wBtQn2b/wBBNdpXLaQirq0Rzk84/wC+TXU00XCXMrhXz78RjaRfEXU5mu1huEMR+6GJHkpgYI+tfQVfOnxMtJG+IetOF3CQxYGB/wA8Ix/SnYqSucrqetXd8sCC6kjt92ySLZt8z24681duNMsbPTrf7dZ3BkuFL7AGBUj8enQ/jWA1jqYkDwxMhWQSKAeFbPbP4flXRCTUbxFbULozzDnJ6e36VIWVhmj3mjWkqSajYLIFhZj+76MCB6/Wpf8AhK9Dtihj0WykTvH9m5+m5if5UkelJKn73AbBBYZ5znt+NCeG7VWBzKcemB/IV6MamFXxRuFO1veNXxJaW3iDwnpd1DbJEnmgBFUDYCpHX2xWDBoTrKC8kZjGcKRz+ddJFG8Vklqu4wp0VmJqMo/QDAribV/dQS1M06UjRFdqHPX5c8URaVBGu3ysjr83NasdsT1NTx2YJyx6dqgFoc//AGDE8kkqyMue3GPwpIdDgt9pZ3kVG3qnYN611AgULkbR7moxE+8ltp+goSsM5HVY5IXgu4lKtHKhHXPXrXXaDayfY5pNwCTTvInuD/8AXBpxtSw5UY96cm+JeJNqj0NDEZusWKQ6ktzgs7oYvlBPykdD+VZMfhewAH+iRjHXiujmu4A37y5Ukf7WTUDXcJGI1kb6IaQzJk0ayMcaLAJdv3FJJA+lK+mRQ2MxWJUYISNo6EcitFLplUn7M31ZgM0nmyyJ8yIueCCSf6UCuVQIbmL7RGcozMVPtk1k6q3EMDOnkPJuZuhQqCQeO3BFbGyfaI9yCJeAFHalMMPG5ASO55poNzLsd95ZRyTDbI6ndgYwfp2q2lmBGqkk4AGTVpRuX5FGPajyyewyeODRcVis8MEEbSuu4qM4XqakwrYCqeeasHSZZmIlm8uPHKg8n0/xqvaW11brcJdoxWAjExORICM5Ht1qnF8t2bSoThHmaFWFQTuGfapIrRbpGie5W2BGQ5UnGMk9Oe1TRxRzwrLG6tGwypU5BFdCPDWhXthCDO5vWQFWVxhXI6kdwM4wazTsyFa+pxH/AAkc2k3NzDbLG6IgxuXBY8YJ9PpVe21GbV9YguLq1jRl3HcinII45q1pcEejWswujEbiKdvlIDHAOOeuelaOn3sV+89zHAcAeYyIPu+o+me/vWym2intY0kjAhDMMZ6A1IExbNKACcZX0NTTWsk/mIUaIRqNzOuNmRwT7VBqUbWcMUQXKFNwweD9Kj3e+oOEeS6epXg1bTdSdY7NYxIxMkm3OB2IHtnFWEVepHFcVoNrNa6r8w4jVgBg9D0x+tdpEx2nPSkyCxAgaYADirbDfOF7EgVWtioJLVIrFJslcjmkJkd/HCySRvEHjZCCp6Gse9vLfUUSDUJT9nx8m1N5O3j69QOlat0plLY4BGKxhpRVowszlYzkBuc/WrTsmNW6o2bKygsbKKKHdt2j7/XpVtseUpGKqxfJbxoWZiqgFj3qVmHlbeuO/aoAcVzDkCotu6M8cilDAIQc0xXCk5zQAwKGUjvTEAOV707cAenFR78SZAIFAxrLz7j9aWMDODSsQzZFQknzMZPtQBMRhsGmFdj5I4NKxL45OaGwV2knPrmhiGMNjbhypqaCG2uGKz3Dwjggp6A5PPbj+dQnhcHJH1rnteU3ltNaAgBAG3eh55+ntQAuo6zKmoTQxTSSW/27bEgkIURggbsD15NbodGXgcVnWVtCbWAuiOyrgFl5A7VeXA42gCmBi63pBvZ4biFlWWLoD35zXSf23o/9kWs9/YG4uJJCVRcyMMYBIPGAMAfjTLQQyajaxS7SHlUFT3Ga9Lj0rwouoeXFpUIkBMXnpAdgbuu/pnP6+9W6jaSbGrs+e/E+brV2aOG4AOW2ODuGcdc/Q1q+HhJY2ih4W6kkYyRnGf5CvcbmPww19iewR2Mgi88wMybycbd+MZyakt7Dw1c3slpb6bE7xMUkZYDtRh2LdM1TrNw5L6A4yseURXObIR+bm4IONwI6k9OOw/lWQrSyXsmnCFWGRvkycZAyP5V7dq2h6NZ2Pmx6TaPMZESIMnAdmCgn6ZzVay8O6da639nvLCxmlmiM0c6QbDwQGBGT/eGK55pvYcVpc8rs9OnSd2lKY2naFyeSRSyW11aCNpbsvE5CFdp5zxXqGtW+lWd9b2Udva27SRtK0htzKcAgABR9evbFRanDp1tptjLIlgsM52yXaW5kXI5XCg55x74xXOqU1LVovlbXqchsCg7iPfNV0t/tVv58exUV9shYjKj1xXQeIRBJ4Ge+iggINx5YnSMoZIuRuC9Qfb2riNOubix0qddwWHesnK/M2R9eK7Yq6MZe7ua15o1kIY5g+23aMOHk+8R9ByTS6bFoSq5uBI/1iwAME+vtVTT5LS8CTSFiWyrRtOse09eMjmtFdOgaSRI4rghxwUkSTqCPX3p8kyktDZi1DShaIIrlUVFAjBUjv344rMnCB7gYYs7KSSQcj8KrLpVvFvja8ZHK5MbRliuD325xTzPaNawpCzM65Vi3GQOhx26mp5ZLcTJQqQrmM8sMHjFXJJFEsbdPlG4D6VreFbi1jtbuS6hExUokabAzMxzhVHqa6U3Vglo0s+mGGUMIxA0Kl2Y9AuODn6/XGKiL7hFNo4+1kZLeZvM+7gAHqR6ipYlM3zM/A4Ga6RtX0yG2uZLrTmt2tmjWSJ4VLAucLjGQc57VqWkcU9uJHsBbk/8ALORFyPyyKq6G4tbnH2zBPnDdKljm+YMQevStma3kv9Vura3lW0jtkTmOJGLu2TzuB4Ax+dXNJaO/0qC5kt4RKwIcBBjcCQcfiKLjasrlfS4GefznBUAHC/WpnO5mOeM1V/06z1GzV7zzprlz5tsEUIkYBywOMjHA5JzmotUtbuK8torXVLnzrmcYjKRlVQcufu5wBx16kUrj5bvcubwBgU3JPJ/Cn3XFyyqKgye/amZiEFqUxBhyQDmmg7m7gCms+GoIk3fQilU78dqSQFSFxzjpUjSdD1b3FRO2cAk57UiXIRYcrudiOeBUbbVHI6dDT3lOPeq5fc2386DPVmjorg6tAM5PzcenymutrjdDP/E6t8Z/i/8AQTXZU0bUloFeH+PogfHGosOp8rn/ALZrXuFeEfEPU47bx7qMTKMjyuST/wA8k9BQzQwWt93Xr6CporNVA3HHtVT+02PK+WPzP+FNbUJW6z4/3Yx/jSA11gTHuO1SHCrwawPtTkYMs599wGfyFNEwzyu7P95if607gb5kjUfPIg+pqP7Xb5A80H2UZ/lWKsgB4jQfRBVhXkfBLMF9M0xGuL6Jc7YZm/4Dj+dM+3yHpbH/AIE4FUt5xzTUcvyAcUAXXu527Qr+JNNF1ck/66MD/Zj/AMaqbhgkc4OM04bsdMUATSuZD800zewO0fpUYjjJHyZx/eJb+dKFc+n5U9VGSO468UgAEKOBgegGKYZgTgcn60rweaMhiF9qclpEhBySfcikMTnqadAjXNwkMQLO5wOcD86l/dAZLD/vqiKSGTaqkfNnHJ7UCNH+wRGM3V9bRewbcaadO0Vf9ZqDsf8AZXFclrWoQwQXPAZQ3lJsBJZ8Z69scVneHpJHVrmdmb5fKySTznO7H6ZoaId7XO1ngsfMC2TOyAfMW9ajZVX5RisqS5eBH2MAePfiqjatMAfn6egFJMKcrq4/WdTksmUMsgdtwjZG+U5HBYd8Gq8Fnq4sLkSXrqzpw2cnj+X/ANeqd/rMphnUSOC8ZHyvtHH86s6brhabdcOAoXcWI9BnofpWjm+Xc6HUlJWbL3h2O6itphdysV3jy4252Lj1wOv0retvFzeH3Qx6fDckHly2GXkcZ+mT+Fcuuspcs7pIfMUhQjKBkcnPH+earzCGe8Q3czxQO37xkPTjj9f50ULTmlMzZ1l3qlt4lH2iTTY7d2J385J+p4q5oZTTpxFBCDHJ8rRqPvDnj/61cVa36+bKkNw8kEbEIW9Mmr9lOLu+jt3umg3n5ZQRhW7ZJ6D3qq0VCdo7AdJq2qhbm4Eis52g7ZOg/wBk56Hnp71RW+W7QFgqED7qkcdfStnxO2h+HtMvECQvd3w3kLKHKDhTn1Hf/wDVXEWdv5C7VJIz1zWTUb3Hd2sdHGIxyAPerCsprMiYRruYgmopLiSRsqOB0oJN1WA5zUokQrycH1rATzDjcQM1Y8sFcbxk985xQOxqv71CSoPUfnVSPy2BSTB9+4/WnfZo9vykvnsBzQFi2JFA5YY+tPLrwC2R6ZrHMDSHEQdyDnAGcYq3sQc3DpCR2JGT+AoGW/NjwAGBJ7DrURuI8/eHNMWazj5QGR+wY4FVLm5adwfs8cYHGFFAFtpo9ud6465zUf2iEqGEilSMgg5BqKxtbW/vBZXMLuJwyhEbBbg5/T0qPUbG20y4/s63tpYlgVQBIe2PpQOxKb2JSRn6GpLVrXUbn7M5kO5GJCcHb0Jz261lBSMfuhg1Q1C/1LS5ILuzkSEFvLkBUHcOuMelAWOjuGt9Nk+yRiUpCoUb+TjHr3qFtQiwSAfbiq3n3N5tnupDJMwwx2hcY4xgenSkZMMMAYqbisasZtZNEmvWukWdHCLb45bJHOfxrCjlsZ76RomiNw+FZBIMkjjP8hWpouqW+nalJHeQWsllNFiRZYyzMcjgYBwKwV0B9M8QXeqQRLDblPtNtJEQ0ceZOFO888jpnNaQdlqS0aXnyRMU8sBgcEHqKgluZTkNjBrV1fW9N1qK3uILR4NRKjz3QfupeOSvcc9OOlZhUscHp9Ki5SLGhxPda/p8Kt8zXCYyeOtetQ6tbaVcPYnVdNW2SZ3ZpJP3i5YkoV6Zyeufwry3wvGU8XaXn/n5TH51d1HR/t2v6k32pFzcSHb143EUr21Hex27anp5jSwi1zSfscdys4lab95gSb9uOmc98/hW9aXGn6PFI9xf2yLe3LyxEsBv3cgD1ryzTdBis3lNvZicry8syjaB6gdqo6j4k0O3uTAyq8kfB25AHsKj2lnohuTeh7JrFxY3Fo1s9/BBKAs6M7DjawIJ9sisqPWrRrx7y61bS47pITFBEk25VzglmJwecDj0FePP4isbqcs88xT+FmcEL7dM/nVNhayEtZXAdc5YE5IzVc4Xtoe2T6xYfbLfUYNW0pr1YDBKjzYRgSDkHkjBHpzmmQzWttCNQs9SsbuS0WWS4UsQg8xgxI2gkYIwODxXjKxO0QmC7kPcHpXU+FMf2F4l/wCvMfzppphzM0/FOt6be+HLiKC+imvLm8WdooVYKgC44JAz0/M1xJbzApCDKoFP1qIEA9KdvC5OMcVV7Il67lqLTLuULLEkisOjK2MVYNrqUTKxY5HQkA1Daz3BysGemSAasZuWRWZ9yOpI+b8KSlNvQV2SRPcRl2n3knldp2/N+FEcsoO8hck5OPWmFpE+V92D75FAbafaq5n1Gdt4ZWD+x7q+nuRbGKeMxybSwVxnGQOoOcV0ttPHrVqWXVYpbqGUSxtFEVSMgcfKeSCCc89+1cdpiGTwVeoo5e7jUY962k0ea0jJ0/DKPRsOD6UnYabRtf2RJcC6e8vY3luJIXPlxbVXy2BAGSSc4rcNxEOriuRvbnVrXSIgsLT3TH5sru2D+tYLanr5/wBYs6/7sOP6UIG2zuLi3ma/lutOvIopJowsyyRFwducMORg8n68UtrH9hgtI7e6xawxlWR48tI3Zs545yayfC7XrpNPdPKSyNsDjGMVcW5kY4kGfcDBoE5PYTTbbUbWZ7u4u7WfzmzLIIGDsOyj5sAD0q4r2y6tJfSSMzmMRRrt4jXOT+JOPyFSSRk2iIvXrycVmSBo2wyEfjQgcmy1LMJrwsn3Se9Ek56HHHfFUhIwORxTyxwC3PFMhvQsCToAPvD0qu7AbiBjsKQscbs9OlRO2B1oM1uG7g5pEIOc8gjv2pinceab1OBSBjpWK89h6VWzgFieTTpdzd+B71GWGOTxQStjT0EH+2bc5/vf+gmuzritBIGt24B67v8A0E12tNGtPYK+ffiUP+Lg6of+uX/opK+gq+a/irfyQ/EjVo1xgeT/AOiUoZZkACnAAVhf2lP6qPwpj387dZD+FFhnQ7gDzRuB7iuYM7v1dz+NSwXTxghM5PenYDp1aNCDI4HtUr31vFjdIoPYVgF9ybmY7/c8VGrgcs6k/WkFjenvI1IMhJHZF/rQdTjdcKrAd+awvNQ8bxVmKNWXO8j6KTRYDTkv0CqEIOR90txS/wBokKAMZHfdVMRkjgN+CCkDP5nljdx1JZQKALh1B/LPzcn0yaU3krptyenZTVNhuIUuxx28wYNPyOMkf9/KGgLHmTFQMyYPfFOBkweZPxIFV/l9U/76Jp6qncp/3yTQBKZWxtVjnvlxVa7u54ICyTKjr/Fu6VY8pWxg/lHSm2JBH7w/SOkBy8c5cKLlhIhm3uOeeefz5rau9P1q2003Mel3UWn3IHlsIWIAyMk914H412nhuysNOv1k2rcTmBZNk4DBc9wOx4/Wu0/4SWZhsCwErj3x6d61VGozwq2e4GEuWpfTyZ4TA80d5Jb+c86BRhuo/Or6wuwzt/MV3N54fhv9Qnvbm7maaVtz4wB+WKqyaDp8UImE0rruVcqwPJIH9aHh5oUOIsA3yxb+44uSxRwCwzg9MU9bJXXbsUj3ruP+EZsySPOlz6ZH+FQ3Gg2lvsRGmklkOEQEDPc844puhNIcOI8DOXLFu/ocjFZpBwkUQ98U28glkjYLtyR6V2NtoVpcBwWmSSNtroSDg9fT0pt5o+nWjQrI0zvK4RQCO5xk+3NL2M0rlrP8G5+zV7+hw9pYtax/NjLc8Vs6ObKPWrGS+wtqkytISCRgHNbc+j2UV39njiLuFDEvKF6k4A9elM1TSrS22Lwu8ZOcUpU5q7Zvh82w1epGnBu8tV+YvxL8Q6DrqWK6dIkz28u1m56HBPPfhf1rDt1ErBi3H90Ckt9K061cMZBI44yeeK1FuLdBxk/QVmekMMLPtwCAOwFSJZ/MPv8AvxUovEONqMacLt+ghOfc0APW1Vf4HapY4lXP7g5PvUJu3UdFzQLqTuwH4UDLRRSR/o6j/gZpFhXOTAgPb5jVb7U/Ut0+lH2skH5j+dAi9JPdsf8AWEKARjPXj9aqrbHHzIh46k0xBNLJuEZCjkk5xVgC3VcGZC/TCqWGfc0AQtb4GQsWR/s0yRHKfdjGPYU6TKsQPLOO4FVLiVkwdqYPB+WkMjMyWOp2V20jfaEZxCkaj5mZSuCew560T39zeyCW7ASbo6rhgCOOD3rI1a3e7hCsQOcgqO4ro/DdrdxeFi9rbJcXQudrO0QkbaQT0PvXNi6k6dNcj1uetlGHo16zjWV1bvbW6W5nF8nO5vwAqu9il7dwb5ZQA44B9xXX2sFik961tC0twroFSFFk2gr820NkEbuM9qLcQrc3ItLSJLl72CKISbW2ZDbsdR1/KvOWJxGnvfgfRPK8vs7U306vrb/M7X/hHPB0N2LL7KolyFx5khAJ6AtnAPtnNE+ieDbe6+zS26LJuCH55NoY9FLZwDz0zWjD9o095oALNrR7h5WuJJQNm5skFe5B4HNUbmGY2UtrDJYvZyXYn+1NcABQZA5Ujue3WvackfGKlfp/X9dAh8O+EF1KS1hs42utpjkVWdioI5BOcDipdV8N6DaeG2tX00S2se0Rwea/LFvlGc56kVf077LYnUrprq3Mdxc+dvVwQAUVRk/8BqpqmuaJf6dLbprFqkhwyMX4DKQw/UCk6kUtWio4epJ+7FtejMiz8FaJb6hFY32lWrGSJpInhklAG0jcpBY+owat6r4Z8N6VYGVdJWWZvlhhEjZkfHA69OMk9gDViHU1upl1e5ls4oreB1iCz71ZmIyS2BgcY9eaWcabriWk09+I75oXWIWtyQvP3sY69KXPHox+wqXu4vz0Kdho+jRnQ7m30mFL25VZwwdsRAKGYjnnqAPrWF4v0fTodP1bULW2Nnqsd0oknhlb51b5gcHjkH065ro7eO00a10bzNRUSWw8pzcSNhgygMFJ9CAQPaq3iazsrzS9RguNXtbaW+lVlZzwqoAAPf1z70pVeVNxauvQ0o4dSqRU4vlfk+/+R5Qniu8tIxbQmZ3ZTlUcsxX1JzVNNeiupG8+cRvnBWW3DHPvxWzpfgy10q4eZfGekylhj592R6YPUVWm8B2lxdSzt440oNI5YgIcZP1ronKDd1FHlxoyS1k7mZLf2nAzayA9/soGP/HRSTtpqw/u5LXLfeEURU/oa0j8P7VTz430gfWKnjwDaA/8jtpI+kdR7v8AKWqcl9plWPV4vJaOC1ErhST5KHAHvz6V0fg25SXRfEbF1KtZq4A7DNVbDwjaafFKkfjDSWMn8RQ5HX39+ntWjpen6b4Y8Pa0h8RWV9Jc2wiiSIc5B/8Ar0JRV7IpJq2pzouICMYP/fNNnmt3gcM2zj7xFQ25llkSKJSzuQqqAOTXSXVraQ6XJb3Fp5N0Eb7wG8HHBPapNLGLp6NCbhWIDtDhCTgckc0STy2yTRRyjbIhQ5BP1x7mqunKstzbRXDtJ8wVoC2QAT2Pp7V7K+g+EDeiya1Uyg7Mb5Cob+7uzjPtnNJJp7gkeVWN28Vo8E43FjuGB0q0k8bA5RuO+K9IfRfBttefZ2tY1l3iPO6QqGPAXdnGeemc1MNC8J3F61mlokk6EhlQudhHYkHj8aoLPsY9nHb2HgZrmbCFpkmG8HG7Py5A7U+28S6Zdo6TMbWd8bnViAT7MP610Wrafp1roEkDWYmg3IqQF2wWJCqM5yBkismz8LaXBqAsL7TbVmliMsbwtIoO0gMpBY/3hzQOztdFdrq4tbaJ7VnvY3c5keXOPQCn2Wsre3DW4WSOZDgo/BrY1exh07SmmtRFDHAmWDRvISAOAAGBzWTKttbaXpeoTx2EU0n37w7mii4JA+9yT0yT1oYKLex09scZUHgLURc+tU9KuoLjTJrxxCBlo0niBCTD+8oP5fhVGILkOGbhs9aCJe7ua95dfZyiqmSapyzmUjK9PemT3jGcShRkDHPaoN7Ek560ESfYkZ8qRioy3HzcU0npzSbgDz0FMi4pbAGDxTGdQDzzTZG5+XAFRMVHWkNC78HJPvSGTjg9aiJz16VMEjMBbJDZwKAk0tyB3OaejLtO7kEYx701yI3AIDeuaQ7Q2Vxmgls1dDQprVoPXdxnn7prta4TQWJ8RW245Y7/AP0E13dNGlPYK+Xfi4xHxQ1kf9cP/RMdfUVfMfxZjjPxO1gtIqn9z2/6YpTLOFDE0E8ZzU4jhHWTP/Aasx21s6gs0mfQKBTuMzN5JwuDVy2hBwWHNXY4LOM7hG7H3xVgXESH5IAD68Z/lQ5AV1iPXaD+FSGNmwOgHPNWFvDzwB7Ypxv3xgZ/75pBcgW2ZxyzEH0qdLNiP+WmB79KDeTjnDkUq3NxNlfJfb05PBoEOFg0qFSkgU9yetTJpUYAGw8epqNXmIAKj2yxp4EpySIhj1NAFhdOjU/dUf8AAqkW1t1PJQfiKqAMOphH4UAup/1gHuFoA0Fjtc/fX9KkIhRcqXYjsvFUAzEY8+Un2WnKkrd7hvYDFIDQixIDuSRfTLE5pGktgdpDEj6mqBkwNoD7h1LNTTI2RiobA7G2g8u6ilEBxJaqisF5UjPX04IpI7E2+j2wWHEqGN5Ao+ZsEE/Wmx6Vdazq/kxXZgijgRnw3Jz6CuosvCtvbR4kMsrHGTI5J/oK6nWjF8tj5Gjk1fEwjXVRJSs7WfS9lv5nMPHcXL3UogkWNzFhW4MiqTuGPf3p08JuLKQWdqYGMiEFl25ww52+1dTdeFbO5iKq0kT9Q8bkEVz2oeC9Wg3PZ3bXC/3C5Vv8DS+sR7Gv+r1ZNNTWlraPpbfXXZf1s7T4ZLdJIpVJk3ZM3/PXPf2PtReK6XFvcqjOse4OqjJwR1A/CuYuft9nL5dwLiJxxhyRUf2u5ZOJZBnvuNV9ZVrWMP8AVis6rqOotd9H1Vn1/wCGOogaRHnujBIfOkUKgHzBQMZIqtf6dcyStPFc/M8kfymMHaoYHg5/GueW5uBkGd/xc1f1KfbZ2W5mJaLk560/bKUXoL+xKtDEQtNe9p8N9Errd+Rs3HkrKfPsvPYoAJFjDbvY+lZWo237u0S4OXWPB+bOOemayGncDKs2PrStO20EZz3rOdbmVrHp4HJXhKsantL28vK3d23LJWNVJEa4qKLgtlAOeOKhaU+pANCufU/XNYnuk5kIZsD9KYZnbHzEYppEg+Y7uakwPLwwG7tg80DGnftBPSpYkMrY5J9qahXaVfn3rU8PtbRz3E12qtDHCWO5c45HasMTX9hSlVavboMh/s5Il3XE+09dg5Jo+0Qwr+5t1J/vScn8q6hrzTXcRmwlaUDcsZt/mK/3h7U+RtL8qF0s1m81d6JHFlseuO1eG+Iorek/wDll2OTa5eQB5HLY4AzwPwqZUR48kKuCa6CaSyWewW3skeK4dgxWIcYB6+hyKsPLpkcxjNqCqtteURZRW9CaHxFDT90/wHZ9jl8hFJCLsPXIokW3kh3Axk9x3rqPM017uS3FqrNEcSN5Xypxnk/SmLLpjK7my2oqlw7QYDqO4pf6xw/59P8AALPscHs3KQV4B9Kjv7+Wx0CYCTyoQ+4OFyd+MAY716M40uI4MUIHl+aSEGAvrVK8/s99NuXXTkV0TOyeDG4Hg/UVlLPqdWUb0nb5dTqw2JlRhNRunK1mump5h4c1SW/glWR2aeJhyBjg9On0NdXoMUo8QacxQgfaY+T/ALwrpfI0HTZWii06BNuPNaKAbU9NxFa0dpbRuskcEaspyrBRkGs1ntCm3J0XZ7bGlfMa9SnCLlK6Tvq9df8AI8y8W67HF4s16xnDQuLlmSdmHTeBhV78EnJ9KyY9asStz/p4IzmIOwB+70wPevQ7yxt70XGsT2lpMEkfKSQKxdQxDEsecnk1oyaBoMURmfS7FUUbixhXgDnPSumfEGFW9L8tzD61iIpJTf3sytNjkPgDWhtwWmhIx9a5WW0uI08zkgdVB5rvNLnkuI7+D7NHHBlDFBjAI25Xd7/yqM2ZS4FtKsM5liZnQRBfL44wfTPHNH9u4bm5ZUtvT1D6xXi3ab+9nlUuoySX88EN8piMICxr1DkjkGq1pNqcmsIk9zLGqudypzwvtnua7a7tNEaGLT7fT7N7zapkWIjK4GeGHLEHr61hJp1zb6gLiIglsqxK/wAJ6/pxX1EoYdR92H4IlY3EP7cvvZo+VO6BtxYfXNRMjvkZYkCtSPcAM5/KphtJzgZrm9lT/lQ/reI25397OeNsOQQKiNqueldK0EbndgZ9qjayjccMwP1rQ5zm3tNy/wAqhWAg4bjHrXSf2e5bmQkdsGkNtPGPlAce+DigZhJCu0jd+lOWPb2PPStzzAhCSQxkn0FQSko2RGmO2VpjM5HKMu0ncCCuCQc+1SagJGuJbd551d8bmkbOB1xReXHkRecsYLgjAHHOaqR6sGmZp2Pmn72R3rrw9BTV5DRo+H9JuG16xCSIwFwuwhuTyOD6V7ZbXBsLmSwE2nvCbh5DK9wAw3MWKlP7wJxnP+FeO+G76OfxbpUYMeRdR8/8CFaGqXhg8U6zKCitDeEjd1wWbJA784NKvRVPd6jemh6TdQTQxDTvNsRbm7Fz58k4U7fN3kbccntnNaOlyWVk18zX1oTcXTTgrKvQgDn8q8vv5ru8Fs0Fss8e3PmuRgA023Ihu1DRRrJHhvvCua1tQk3seqanc2GoWEkEOpWiyhldD5ykBlYMM89MiqsV29xdyalPLYxtaWzhI0uRIOcEsxA4HygdPWvOf+Eh0mXeNSsoWkU7TJbjBb3/AMmtXQo9Jmtddl0y5kffYMrRuuNtV7OW8kTeyOsfxZEsUc0d1p03yYljFwV2N6hscj8BVW3kgIt7m3ubK9kjklkkt/MwgaQ5BU46jBHI5yeleSzzpbK9sDkybWBz0ruNFt4bO4kjiH8CbiWzk8/lRyaBzWR1iwMLG5ZvJRricSC3ibKxjAB5wOTjJ471UCgZ2dutN8xmwCX+gPWlB8xXeEMAmNxBqDnc3N3Hh1wcnHoPWoyQeAcmoi2TyeTTkUHJLhcDvTDQk3jaASMimM4I61GSCcjmms46AUgQ7JIIzSM4CcioxIc89KGdexJ+lBQpKtgZwfU9KkkOFAHLetVgN/H4/Wn5Yd+lBEhjn958x+Y80jYJzSSFU5HJNQBiWyScUD3Nfw2M+IrY4OBuH/jhr0GvP/DLg6/bYHXdz/wE16BTRpDYK+YPi2q/8LO1hjkn9zwP+uMdfT9fOPxQWA/EfVvMJz+5yO3+pSujD0fbT5bmkVdnn0EAkbLAgDtV8KoHAq7barbR2m3+wYppRkGaSc7fYgDpVOGORn/eOpLH7sa5Vfxq5YSd7IfKOjhMp2rj8TVlbYxjkwj3JzS/Zdq7vLLD64rqNK8Q6daeGpra3sVgvmG1rgRq7tz23Ajp2rCpSnT+JCaaOSZZklBaSFYyOPU092AHMpJ6AAVZvdGvLy+DJEcIyO2TlHjbBBBHHGeQK24dAuLnT7yceRcGHH2docgk5xjBHPBPTmk7W0I1OaVAz72eVj2GeBVhYyeBDM3p1oYyRMVaXY4OCu3BBp4kcrk3TfhUgCwv/wA+rH/eyKmEUvGLaMfU1CMOTmaU0/bH280/hQMmEdxnkW6/XFSfOAM3cC/7oqBYF6iF2+tSJFJ1W0XHvSAkDJn570nHotMlmQYEU8jeueBQ87x5TyolPQlVGRUUcYY5w+PUUhpCce1HQ9aXy2yQoJxUqN5OQ0QY9s1Iybxhqd3pclm9nNJBI4B8xPZeB+pq34d+J+pQafczaqgaK2KAvGOW3HHTp+VWtVEcswinhSSJo1Oxl3Dp3FYd1oFrNpc9rYOLczOrkOS6cZ4Hcdferq252cGVu2Dp+h6poXjjRNeQeTcxh+65wR+B5FdMsYkQSRuCnXIPFfNWkaJqOjXV/NND92zlEcsZ3KWIwMH1rqfBmt391YXWmancz3MNv++X95y5UEOh/vfKdwz3SsnHS6PQTuepatqehqrwX7xXAAyVRfMI/LpXnuswac4+0aTDcRwH7ySkEA+3OR+NZ0jys5LSE5HGT1Har1tdRxQ7bmSJYz13nFK47GMAT0rT1NSbLT/+uPNPjjS5tBPFajyI2MfnBDiQ+ue/p+FT6hCGtLRdo4j9K3h8LPNxX+8UPV/+kswQTgjccD3p6nehUbuPerFva/vXWQDgCrkVtGgHyjI9qk7TJ5Ixg5HWnIpYEDOa01tUE5fHXtT1gRCMAce1AygsMhjyeCeAO9SQwO0hVuvU1o7RkHA4pcenWgCs1oAPl5Y9T6Vf0nT90d75syxxmHaXboOQc/pUO04PzGr9iivp2oK/zKYwCD6Vw5kr4Zru0vvaC9tTdBtze/bRcx7fJ8vGffNVls5LCCKW3mid4YvLffnawznPHeqkltLp2mNfSQXMdk0K5IKgBzxuLHkLg9OvNUNHVkstXXc+0PgAyFsc9Rn14rxK+R0aMLqbesVbTZu3bzHzNaGkJbaKO3aO5UzRStKxZCAxbO76deKR5I3jmt47qP7HO5ZyUO9cnJA7fj2zWSyoCdrZHvTl6Drmu3+wMPvzS/D/ACHzM3EmsQL1XnJW6bJwpyBtC/0psGpLCm26uojBGm0FY2y/ufT8KyRGD3Yn60bPc/nUvh3DNWcpfh/kK/csWP2T+z7hJp3VpWxFlDlI1PyD+v40661mCawuY7y8jUEbP3UTEA+p/wAKpFM9zVVtPjkk+ZmG4/NzSq5DSb5otvXuvw0Omi6MnL20mu1lf/IttqGi3l7NNDJbSiZwXaaBiyEADjjngd66O31mxuJ4reKUmSRgijaRkngVxY0+KNiBng9RU8O+1uI54mxJGwdTjOCOlQuHqNRfvG9NtV/kLEex91UpN97q33b9C7q+tafo93c6Pd3qxRGRjIvlOzqCcsoIGOuefekfxt4du/OhurwSWrAbU8iQH3ycVem8W6tKxYpZsx6lrZSapnxZrIPEdh/4CLWr4ewz+1L8P8jn5mXNJfTGsb/XNNcGAbVDOrgISMDIPJyaqxa9DYQyPcyxSGQEvLGjszNjjtwP5Uy78RapqWntZTm3SByCwihCZx0rKSDKtGcEjp703kGGe8pP5rp8huTe5zdnNpVpBaXy3tzJqsE26WIxgRlcnOGznofT1roLe5jnWN1YESDK4I/Gnw+GdOu7jEivEZvlLR4z+oxVg6FY6KsVtZyM6xKceZyw3HPX8K953ZnzK9hy9qkpiggUskqRgF2wKzk1FXZpCEpyUYK7HfhQAAc4qEXcH/PQUv2yD/noKz9vS/mX3nT9QxX/AD6l9zJ8Y5oxjnioPtkH/PQUC7g7yCn7el/MvvD6jiv+fUvuZMyIw+ZQR7imyJZxW/mXKDaZFQfNjqaj+1wf89BTvtcBtXHmruEiOF9dpzirhXpX1kvvRwZlgcb9XfJSne62Tvur9Ce50bRpSkMxwzEFVEhBPpTB4W0XMcqq2c/IwlPJqxa3VrJqU9wrAtJGgAAJYYzkcfUVSS9RRa2xKqtvNuaRj1HPQde/euz6zQX2l96Pjll2eS0Uat0lfSXVNu2nR6Glo2iaLH4nsGQ/6VFOjKPM5yDmpdS0nTJtbvp5IsSy3Uo5fkncQcflR4duVGqWMAh8yMXQ/wBIx8pJbj/gXNMvylp4jv7hpfMH2mVZAQf3QLkjacfn/wDWpfWaXMnzK3qdTwObLCyjapzp6aSu1Z+Wib7+m419NsrS2kcCRI0Us2xyOBVUWSSPDHdQ7En4RkkJYHGcE49M1cuL2xurOWL7bCnmIV+ZwCMj0NVxdrLcW63Dww/Z/wB42ZlO47cDHPTBzk10O0rNbHjQxOYwjKNWU1JXvdtNaaaeu5Fc+HrNUVILdmZj1aQgD3NbfhLTbeCz1vyIgswtmifLkqeh4NZs19cS2ifZo1MkhwxjlVti+oycE1ueGpkGlassCBHjtW+V5FJJ9SQT3pVJXVjqwNfH80VUk3v9rye+u3b7/XjLvTo5Ir2Z4F82AqFeNyQW4yMH0GK2/CRld72WV9zfJn1HWmOsUOjtEJVkbGWKnJZick/nVfwnOjX1yvmAM6Zx0zg+lc9bSyPbyjFVKlKbqN/FZXvtZd++52ImOevtVu0mWON15Abkms8LIV3Bcr6inRvhc9fauc9Z7aEkxUyblBAoUMV34BU1E0hI4/Km+bgEAYH1oGr2JASOF78VC0nX1pTJxx9aiLHBz1NBSJIxuJLgkYoZcZxgDsfWokkwcZ4z60rSMzcKStA3cduJ/wAaaXypC546mm/MykFsMD0NRb+NoPFArDyT1Y49qYzrnjofWmsxC9c+tRM1AzoPDJxr1quQfv8A/oBr0GvN/CpB8R2mOvz/APoDV6RTRcFoFfL/AMYBEfiXq2I3SX9yd4P3v3KV9QV5x4r8FeFdZ8R3F9qcczXbbN5WYqOFUDj6AVSqSg7xLR86SXN3bnbGwYbN2cdRWlZXl/NbDZaIjnI3scDj2r2STwB4KkKlkuMhdoxcHpWhF8M/DjQAxW95sf5gROa6vr1Xlt1L5rHiVxHDsYTXrtOZF2Rt0544x371Pos91p88t8qq04bBjPZl4OPSvcofh7oVsP8AR7CSJ8Y8xWG//vo81nN8MdI+2ALFemOTdJK7TZLNwAP6/hXLKpKXxakt3PGtS8Q3WoSCa0tUsZFb70DlQ2DzuXO3J9cVvaV8SNT01Nkuk2lyFOULcFfxr0f/AIVJ4dA4ivv+/wAP8Kp/8Ks8PzaibeN7sRpHlws6ltxPTpxWd2Ghwmo+JrDxhKtyumLaX6/68xtkSDsarrYgtwgA+tek2/wl8NaSWuFkv4xjBeS4UAD8qtR/D/QbpWkhvLyRV4ZkuEYD68VSa6iseZ/YmVfkOPwqaK2dfvOG9ulejw/D/QhE08V3ePGuSzCZWA9e1M/4Rbw5/wA/1x/32v8AhRJpbMVjzbUGa3spZQxGwZwCawI/EGpxoFFtCw9yf8a9Ym8K6Df3N3YPqMywLGnRl3Ek5znHtUB+G3hftq93/wB9J/hUXKR5VNr90MSy2UICnkhjzXWW6pJbpLHwJFDY+ozWw3w28OX15dWba1cxxw7CCCm5iQT6Yx07V0Nv4M0C2tYbdNWmKxoFBO3OBRcTOHFsBySWz602SFP7orvz4T0PH/IWlHv8tM/4RDRTz/a7/iq0XEefeKIbya7ghtJmiZhGNyHDemAR+FZ8GqOkapPF50i8NKpClveu9vPDmnandW0U2pG3eTeu4DkBBwRz15/Sqw+FmjYGPE0n/ftf8aqt8bOLK/8Ac6fockmuiM/NbMRyPvjke9XLfxebe2ggW0AihnWYcDPGcjOe4JH410B+Fmkf9DO34xL/AI1UvvhrpluIEHiUN58ohOYwNoIPPX2rM9C6NDQfBMXiSyi1RtUmtracs0NuirvVNxABbucCuos/BvhrRf3rQCeUfx3T+YfyPH6UaLo1po9hb2kOto6QgDcUGT69+KlvNE0u7ld5tXeTPSN3wv4gEE/nSaFzFbWLuDUoTptn5bHHIA4Qdjx0rjb0FY7VW6hCDjnvXW/2FZ4Ef9vW8cOQTDDEEU47HnJ/Ems280i1+zXM76lEptUYp8uBJ3454FbQ+GR5+K/3mh6v/wBJZ5cfFF4zb4bWIrkgA5z1+tOHivUAOdPjP0Y12cPw00qOFR/wlVqO/wBwd+f71NuvAGj21rLN/wAJVbMY0LBQoy2B0+9UXO8yNG1c6rbPK0Xlsj7CAc9s1pBiTwpqjp2kW+iXF5Z2uoR30RdZBMmMAsoJHXtWkAMVQhvzen50ZbsufxpWPNKOBQA3Lc/LV6ylQafqJJwFjBbPYZqk3oOtX7FoIILiK4DlJlwxTGa5MdGUqD5Vd3T+5piZB4n8bWM3hyfT4mWdblh2+SNAAdvT1/nWf4TuJLnw/qLySK0hVc9u/XHb/wCtT30Pw7K37z7e43s4DFcAnr2rVtodL0+xnisYZFMqgHcB2+lc+JryrQ9nGnLVx3XaSYloiivzRqCOn61YWMhe2BTUj6Cp8ARvXplEag5HvUrwEIWyOKYi/Mv1q1L/AKpvwpiZUVN8gUHGafLbmNQxIOTili/1yfWrF0uYQfekFyp5YmGVAUqMH3qtImxyp5q9a4MjL2IqC7TbLQMhihMmcEcVDLDscg1atjiTFF2nIb8KAKioMYpGbZIr/gadyKjmyYm9cZoA2XurXStPN7cOr24i3jbywck5A98BeKy01TT9Wi+02auCcbiwxniqMepwTPb2srH7OhxI20MATnPB/AUCSOK5kSGPEYbA2pt4HHSqvZWM1H3rmkE+XOeKIrWK8vreCYv5bFi2zrwpPH5UyCbKkHOB2Ip8FybTVraZQx27j8vX7pGf1rlxKvTa72/M9PLG44pSTtZSf3RbGW+mWuptbvb77VHm8lw7b/4d2Qceg6fSpxpOmXF7ZxQ3ACyzeXII5DJhcfeyVGD7VkXupTNrdlZyySB5FZ43jQIqEcngY546/Srxnu5rkmWdleB/l2KEwfXAHX3ryvqs01Fx39P6R9f/AGtQlFzhUdorz0vot1r09BLSPSriaXKNGFAEaSzbQ57ktt4Pt+tElja2f2meeCZ445VjSHzAD8y5yWA5GOmOtOtY3sy5hlOHGGVlVgfwIxUivPHcSTi4dnl+/vCsG9MgjHFUsDVtqlf5GTz7Ccz5Zu3z8vu/MadOsbZLq4mE0kcaRPHGGCk7xnDHHas/U7aO2ulEIYRyRJKqscldyg4z3q5JE8vm+ZcSt5pDSZP3iOmadJYLPbCaSaRmQiMZI4UDgUnl9aStFIX+suCoyUqk27tLZ9bfrcht5ZLbQZZbd2jka4VHdDghdpIGfQnP5U3WAXnglYfvZLeN5T6sR1PuRg/jVbS9VWza43w3mzzWjDQosiMFOOQe+c1Z/tCylknb+1poWnGJPtEBGR6E4/lWv9lYlxtb8Sv9YcDGo5X/AAfkbXhWe5Gr6bPczNHbeYsMMKnAkOcZ2+g6k+vvV69kS48XGJbwQQLdMk1sQf3uXOTgDDZHGT6e1clceMbvRruF4biw1D7KqlAkSnABGAWxkceldfpPjO01mNpJdAt0MgZi3mEn5hk84yM5rpjl1aMVB97nlYzPcLSnLENacr26fejy/VJdt9eQpEGjfAVuPl56801JmuP7Qubgr5hjSEbfwHH4CvQHvNAkuQ1x4UswhOHfzGJA+net+z0nwte23nabo2mSMDll3OD+XWvWpVeShGlbVKx4OMxEa9edSOzbZ5xo2uX1jbLBFBAigY3lCWb6nNdd4QkSG012Zo0JFkzsqjAPNdCmk6aEZU0LTAQOhkcD+Vc1f+Jraw07UbWz8PxW8k0XlNNE5dNp96hyb0OXnXRmVLrAdx/ZsSksm54whBBGPzrN0e/SDWrV+FDPsYHIIzxz/Os6zulSc+YjMrrtIVscZ5pNR+zvelrFZVg/5Z7myRj1qG7nPdSXM9z1+GYKvlEY/Hg1E+1c7WJ5/CszSL8alpkFzuySuH/3h1q5I3IGeDUiS10JFkIJAPJ701nIYrjI7VGScnnjtTUYebgnOaDVEueccVG3Q/NnFJIArnaeKjc7R94HNBSFJ6fpT1d1XaRkdqhRlznmnOwJ3Ido9CaBvUkLc5P3hyKCysCQB64qMDzBnIzTWACn5s4oJuNZyaYuSc9qZnBNCknvgUFG94X2/wDCR2fr8/8A6A1ekV5n4TfPiW0B/wBvH/fDV6ZTRUdgr5Z+L894vxR1lYrmdEXyMKrkD/Ux19TV8/8AxJskn8faozqDu8rqP+mSU1o7lXseNPe3oOPtdx/38b/GvqTwfdahe+GdMgtrmKD7NYW7OZU3mUsmeeRgcdf8K8Ofw9BN0Xb9K9X1vWINB/s3TU0iKeOGziCsZWUlcfdOOo46Gt61f2kUrWKUlfU7K5vdQnmu1tJ4IBZxK7bk8zzGK7vUYXjr/hUdpqOpajf20MckMERsYrqRtm4lmJBUc8DjrXnet/ETNo11ceHreUxJjAnZdy56HHUex4rNT44TQOHt/DdqoESxh/PK4UdB06DNZQoznrErmVtj3G6Z0tJmj++I2K/XHFc5HBbwaXoN1bIouZJYsygYaTcPn3HqcjJP0rzSb4/XUMaN/Y1m+7qqXDEr9flqz4W+Jiarrdm0Wg2kBubtYM/aWLJvI3Mq4wOvtVyw1Va2FF20PZZvJkTypDE287Qr4IJ9Md65H7OdPhuNLm8p993BJcTRLtDpI+NrDt0xjpg15Z4m+K2paTr2raLDpOmNawXsgUssm4sGI3ZDDB9xin2XxI8Vtp/kpoWjpa3KGQmbefMHAJJLknt1qvqdVpOw4ySPVfEPl6aurm1hVY/7IklkjjG0FlPynA743D8K8vt7mO4gjmjbKOoYZ9Kw0+L+rNZXmn/2Zp0a3aGN5AX3YII5ZmPTtVHw/fD7PCtxeIgYkJBtIPJyOelKeGlCN56Et3Z0t9bpc20yblSSSJkWTuM1xX/CNatES0d5CAeMiY8/pXboqAcKMe9TQxeY3AAUdwK5xM89TwprTIdhDj1WQ/4V3tg0tjo9raSKzXEcQVj2z9auyyCBAoxn0qsgaVuufU0CGxrPM3JVR3PWnyiRfl89ifQAAVZZ1giwAPbPWqgVpH46nrRYC5qWkvqMiOkwCeWACGHBxjNcL/wg3iBWIElsQOh87rXakhEIH4VV8ve3JJJrRyi3do8yjhMVRpqnCqrLb3f/ALY5iXwVrrLH5aW6ELhz9pzuPr7VG3gjxCVI3Wv/AH/FdgYwiHuOmKj27jil7nb8TX2WM/5+r/wH/wC2MGPw74os7yWSxlto45VVSrTZ6Dr9evNJJ4V8U3Uhe41GI567ZsfyroRDg7jjFNKDPHSi8O34i9ljP+fq/wDAf/tjKsfBV5buHklt2I/281015GYre2hJQsse1gORVNYdoz+lOHrmjmjZpIUcNXdWFSrUT5b6KNt1buzz6bwhqwlcokbjcSCJBzzUUvhjWncvJbbmPdXWvSRxSdWqLHoGH4U0u4020uFuVKmSXeASCeg64roGNNQUrdcUAIvLe1POBSJ0JpOSeenagByLlqmP3DmmRcsewqSQdB+NAiJE3SDtVl1xHmmwKC/0FSS/dGKAGIPmWpGBEZz0JojHIx2p8gIgGe5oGRoCWXBxzU0/MLDO08ciok+8vNSy/wCqNAMii/10f1FW7nmFx1xVOL/WJ9RV6YZif6UxMpW52zKfwovk/ipFIWRT71PeLuiz6UhmfEdrg1ZuF3R89uaqA/NV3IaH6igTM4ik25GKewwxFIDg0MZUjsoJcrIgKscGrf8AZ0cMX7sHC+tMQgMy+hrRRvMiBB6jBFAGauVbhTUV3KsbpLImVjV2YH0AyasuNrEVSuGR2VJSdhV1bAzwRisMR8HzX5o7su/jP/DP/wBJZ3sE1leeH7G+uNLgiYW/mRROo3IrDoD9MVxcTKbi5K5K+YdueuKhi8S63pkapDeWlyrLtEczFWi9B0IwAP0punTecru7As53Eg8E06sJRnBvz/Iug/8AZq3ov/SkW1lU+31p5PoaYyrnoMGkAX6fStTzgZ+tWBIBpLMTwJefyqqYsH5XPPY1etooWsdtzKscJly7noBitae7ODHbU/8AHH8zNt0aC0SO1dJlZi7ZXBBbnr36n8qmlS4SQiWFHGeiMDxn3xWXAdLhuIY4YNiM2EcSkcN3J74xXWH7NdWaWaF1ui2yOQYywJ6njHAFPFVKdCUU1uevTpKa1RxXiW2s4dKmvY4xFNKRCAcDAGG7fzq54IuJJbBfNQAgMFI/iAAGf0rF8ZWt/FMLCcggEvG2Nu4Yxz+lafguaNLS3h3DzfJclc9OtbWXMmndHl5tDlwlVeTNOS6H8XSozK7P/oRkWQAOduQR+Irnb2+lkVyjcg87eoFbWhYvNOkvpw2yE4XacFiOSf6VyzvFXOyEUx8utaxNBJBNdSsg+7kjP0z1P41i3OpzzqUZjGSu1gOje5HrVyVrs3KeUEaEn593UD+tZ9+rzTJJsdIx8oLLt5/GnZvU468IxfMnqQhSoUhF3LyQT1pq5kZjG4yFzz39ajuAUO0Od2eCDmofMZLhQ+RtxuI60jOneSOy8H6tslbT5ZOJBujz2I6j8R/Ku0YjCnPJ9fSvHYp3hmScqUcNlSRjbjvXpuj6tBrOnrcRsodflkQdj/hSNYGozoH4I6YPNMwCSAeahJKnOMA+1SkbBvDjNBqLImwbt2ajJDEAd6cxLqoJHy9qaZR0CgAUDFKguAo6datx2omyVYDgcYzVBZcPmrKsAOSfwoJnca8eGITGQcHHeoy2OxNOZjnBIGOBUG9g/LcCkNIMH8KYc/SnCQkHFMYsKZaNvwlj/hJ7Md/n/wDQGr0+vLvCJ3eJ7Mnr8/8A6A1eo00UgrxP4gQN/wAJhqMpddh8vg/9c1r2yvDfiJubxrqAJJA8vA7D92tMZhIv93qOq10/jRQ2rW2cZFnF/KuPjzGylj9D6V13jYg6ta+otIiMfSgRyGqQmfTbqEIWLRMOPpXEaf4Rv9RhSYyxQxsQqmRutdR4g1Rb5LOytf3Rtg5mfpvYkfyAxWBplpLHqsc0jE29uxmZd3BCgt/SvVo0K1OjzRNFTdrlvUvB8awW8qToiqvlHj/WMDjP4nP6V02lwWmheNND01rFbaZ7q3ZAFyXBcfMWP41UvL37RDZfZGDROU8knuCc/p0/OrcNzPr3jvwxqrgMVvhESo4CrINv9ayrOTipzehSlyrRHCeOwT4+1/A/5f5v/QzWZHcTLAE8pGx0Z13Hjtzx/wDqrs/FuizTeNtbl4CteynP/AjVKLw9n70hOPSj69aKSiZHLpZvIGYkA9cYro0lSbT4rRIZQ+4fM4ACAenrWjDojR5IdSOwK1rafaLJwIijLw59K5K1eVX4gLdlHcTgEKAO561ot/o6cHBPQYpwdbNAAO3AqmzNK5J5Y9KxAbiSWTA+Yk81bVVgj5HHf3ojjES5PXHJqCRy7deO1ACMzSvgDJPQVaSMQKckZxyKjtgEbcwPtSTybiQvTPNICJn3tntUiLtGT1psaZOTTnPb0oAYzZbA5FKsZ60KufpUhYKmO9ADGOBikQAmo0ju7iVktrd5cdQiE4pkBu5pvJhgaSUZ+RUJP5Vxzx1KMnF3+49qlkGKq01Ui42aT36MtuflxTCOKgT7bNM0ccDtIDgqqEkH0x+FOCagYGuBbSGJeGfyzgY96j+0KPZ/caPhzFrrH7ycdMZ4pQM96qg3r2zXIgYwKcGQIdoP1p0Yv5IWmjt3aJRlnCEgD60f2jS8/uD/AFcxi6x+/wD4BdxxntUZqj9vmxj5fyrTjtS5iga5C3syB0i8rK8jKgtngke3enHMKUtrinw7jIfFb7/+AMB7CjPeq2nme+1CC0UqplcLu25x74qfUg1iyKouAWyf9It/L/Lk5o/tCly82tgfD2LU+S6v6/8AALUS5UUOcufQVWunms7azlE8MvnqSQinCYOMZ71P1/Gt6OIhVuo9DgxmX1sIoupb3r2t5blq2TKsR34onG1sdDilglSOMA9e9RyPvkJHPpW5wDl4/KnSfcAz3q3oqo+tWccqBlaZQVYZHXvXfW0+nXc6qml/6O5IjuTAvluR6dwPcgA0DSbPNI/vrUkv+rNehR6hpb3MCDSyILiTy4LnyF2O2CeO4HB5I5qbT59O1OSQW+mgwozIZmhUIWU4IHc8jrjFGhTi+p5kh+ZfrWk/+rYD0Nd3q1vBDFbw21rbJNczCFZPJU7BgknGOTgGmWFssOqXGnXKxXIWJZo5XhQNgkgg4AHBHp3o8hculzzIt8ueM1cch4e3Iziu81zTLWC1lvRI0CxpxFDBEd7dABuQnJJApBob/wBn2qXMtvCY4d1zOkEe4t+K4C9e3agfLpe55gfvVbiO6L3rvRZaRdaFb3Wr20EY3HY6p5ZkGSFOF5yRg4praF4TSxW8Ma/Z2OFYSucn0AznPt1oE4s86l4cjNQuOK9HOi+DjaC7Ii8kvsD+c/3sZ29evtV2Dwj4engSWK0JjcZUmRxx+Jo3CzR5MW2XAycAir9sxIKj6iuyuvD2nSPeT2ulwvbWLFXEksm+QgZbbg4HXHPU1rN4c0RNON7b6e0uIvMRFkbLcZA60kNxaPMrkHO7FZN4D6kZB5r0nTrXRr268uRLK6jMTSyG1lfMGMcPk/Udjx0pYvD2myfYrq70qEWd4wWMLLJvj3DK7ucHPf0J71nVi5wtHy/BnXhJqjV5qm1mtN9U0eWabBYS2kovGufOQZYBVIzzyCRzn+tTWREaKFG0Y4Aq1rFjFbavdwxFo445nVRnoAeKZFCAMg5pP205R57WXmzR1sNCjOFNyblbdJbO/dlpXBGM07cKYq4HSlPI6VqecSooYdabfpIfDtyqBmbf0A7Ypitt4rWsVE1m6jjJYZxnqpFa0+vocGP2p/44/mcQEZgAoVgvb0H+cGuisL2O5IS4dY5guck4Df4GuG1CeTTUE0u2VvO8to1bbnaoBzj3qvb6orNCjxyhRuJBkznPQH6V01KVCvTUartJHqxrOHS5s3utz6nrF00h3hECQlh1C/45Jq14YfzddAOMiCRjwfQD+tc7FLHGyyOZf3akM6MMknoefp0rovCdrcQ6ys0mcS27A4xjpn8KcaEKUYqErnmZtV58LVfkzIdZbW7mLb9jv1Dcj0+g5rY0jWoNP0S9tbhSGMm6NCOSGx/hUstqspO1cH1qtPbq9uwkjHmIpKH+9jtXF8ejO5XitCrNqbSsDbAwptO7I/lTRcbrBkuI1YMf3cn8S+9VrcKVyiB3K87hkY68elQyRzJJJsRxGAN3BIXPSuqtRlTinJ79DDE4b2KU29wklw21fz9aZskmlwBk45xTJnVcxuQT6+lMikdYWaIBgpyxHYZrkOaC00NS4uUnskRYB5wOQ2eT9f8APao9I1y40TUvMZCbdiBIq9GH+IqojuyfJywG5jn7oqFpEmXYMfLyfemOnFxdmex29/BeWqy28gkhcZB/ofenrISQB/8ArrynRPEEmi3BY5a3fG+LP6j3r06wure+t1urSZJE7EdvY+hqTpJWY56cnsaWMb3AIJHcZqxHiXDlhuXtjtUyRxtGXkUcNg7T1pk89ijIo80iJXxngEc4p5fD/KpVQMdc1IzBVZQhXk4y3SqucA/Wgq9yUncoI6ZqORBv4PHXmlErBcYGBTXkDEfL1oGhhOOlOUb+uMUmFVvm6U3YdwycA9CaBm34QXHiiz9jJ/6A1epV5b4QP/FUWYB4+fj/AIA1epU0Ugr55+MIdfE99Ks3lKkkRY8/88144+lfQ1eGfEZLebxjqUF0CVl8pVB6Z8tOv86pNJ6jtc5Kzu4b6NpIWyqttIIIIP0NdP4+vlttbtg3AFlFz2PHSuPiJso5F8mV2BLBsff4zx7AA1u/Ee5QeJ7e3JJL2EDFSvGNpz/IVrSgp1FFFJPRM4uMbzJJIVVchSz4HPU/qTTpHQQOi3QIlQr8qk5B4PQUXU1vJkSlUGc434qkbywVwqvGWPblq+jqxa91ySXmdLWm5oaTA9uWfeDBbozKB2Yjj+daPwymkl8RafG7kqt9kL6Hctc7cXxigHlxuAxVgxGFPOf6Vr/D2YW3xCs4FOUkvkK49Cf/ANVebjXB+7B3VjGpa9kdf4kiB8TamQOftMn/AKEazli46/lWt4jI/wCEl1L/AK+X/wDQjWcCBxXjmQIgA9u9Xo7qKFNoiJz0FUy4Vc4z/WmhzknYzMfwAoAlZ3kO5zz6Dt7U+KbyyW2gmqrGZugRfXnNOUNyC+B7CkBbknaRdvQVEuc5/SmrEo9/cmlESjOBQBKzkDJ4FMMigZYgD3NNEaAHjj3NIiqv3VAoAX7UCcKGbHotAeVySIgv+8ak6cUo6UANXzv+eiqPQLSn3oLYpM5oAv2qCXQrxPtaW2bqL5nyAflbjgGpotWtXku4h5G6QRATXCttk2Lg528jJ5/nXN39+ljY3AmlZI5G2gD+8QcH+dVvDcAh05z5wl3uTuBJH615qwcqqdVO2r6eb8z6l5rDCpUHBuyjre3SL7eR0tzqu+21JElG+doVBiQqGVAQcZ5x069afbXMLacgubmMlImVdodZk64UY+Vhk9+xrMtz+6H1NTVFLBc8VNy3X/B7lYrO1Qqyoqn8L3v207dkXYrqAS294bnbHDCI2tMNuJC4IHGME85z3NMg1NFuNIDyP5FtGVkXBwCWYnjvwRVXOcDsOlLz6Vf1D+9+H/BMP9Yr70vx9fLzZmFW/un8q3o5rV7221R51Uwom+Da28ugAAHGMHAPWqpOaevSpjlqj9r8P+Ca1OJ5T/5d2+ff5FWGKNXgmN20buTu2I26L0Pv+FWLnbb6ZJam6F1JJKrrsDEIADnkgcnI/Knjk05aFlqS+L+vvB8TybTdP8f+AU7iVZNOsIVDF4t+8bTxlsitJOgzUXfpUoOK6sNhvY31ve34HlZlmbxyiuXl5b9b7u47vT4iEkDHnFMHJ4qeJcFiyE8ccV1HlGhoEX2nxHaAED95uOfbn+ldXYala2kkdkurxPZxZRIhA3mEdApPTj2Fcz4VBHiW1yCOW/8AQTWQ+vtoet7106ScSyBI5xyqMTzkY4OBx1pMpM9Et9MnD2MQvVNlZyCSJPIYSEYICsSccZ64rV060TTrP7OHLDzHfO3H3mLf1qkvh6G7TzLm/wBQkZic/wCkMo6+gwKiHgnRs5/0sH1+0yf41KlYbk3uXNVWK5t0AmaKaKQSxSeUWCsPUDqOo/GsoX8EEt1LPq0KalMqxq32ZgsagnA2nk8k96st4Wito2a11TUYdoJx9oZx+TZrzzV/FtvPrkmjzW9zcXdpMsYuIo92efm3YwAOlPmuwu7WO91fWNOi1C2t764kzauJXjWLh2x8p+gznHrj0rN1rxBb6g8Hk3iraRndJDLbswkbtkgjgelY/i2ORvE10VQlcJz/AMBFZeyVbR42iOScg5qmCk0d8l3JfWthq0bwTPAzqYz+6D5GPl3E4I9/enXNyBJa6gWsRcQs5a1+0KMhgBnceNwx/MVwfiW4ex+Hdi33Xa6ZAfQmuNMekaxdtb6dor3csaZaaS6MZcDAyB0oBSPY7eKKZ4J7i5sI2GpNeGMXCttUoVHPc5IrpV1Gxb7t5bn6Sr/jXz2umyrOWPhCIjOQftDMOf8AgWKL3TIkkJk8MzQF1GZLOcnH4AEZpJpBKTe57RM08H2+2tXtZIb12dZmuFXySwAbI74xnj1xVmUPNZS6Xaz26RfZhFDMs3zFsYxgdB75rwW1uobNpYhNcc/MsVwhDD1Ge49+K6rwo+/xFYleVMyN+tFx8x3gls5byKOWOy01bdXjl2ToS4KldmB2zzz6dKIb+OWOwsbm7skgs3RmnFwD5uwfLhe2TgnPTHeuR1eCRtaviMY+0P3/ANo1nvA65ziiwObF1dkm1i8kjYMjzOVYdCMmqXAqQoQfekKcZ4oIGoeoPagnFKqEk4IFOaIgZzQBAWxWjYXSw2iM7bI3uPLdj2BXrWc8ZxncKS/t3fw46xnLi4Vh+VbUrX1ODHbU/wDHH8zrtR+FbX26eGa0WWQh8urfK2OSMevFUYPg1KZw15c2roB/yzLA/qMV2VtcRSSE6jfzW1zH5Yto1kK5XauCFH38nIPB9Ki1q6CR6pJc381tfQ5+xxpIUyu0YIUffyc5zn07VnKV9z0vZ3Zyt58FI5o3SHUAgIG3PqPXjpWhYfDe8sJDNLf22xUI4DYHHXmuus4ZrvX76S4uJzFbmLyYg5VQSmSSB1+hq9rkMlxoV/DCpaR4HCqOrHHSnGTjsY1qEKsXTns/1PMtP8Fvdz+VBqcBYjeoeKRN6+q5AyPpV28+HzWsXmzTI65AxFFI5z9FGa6431pqOoaSLGVJDAWll2HPlpsK4b05I4PpV6bV9Nls0Z7sJb3AZFnB2jI4I3dj6fSoVjdx8jys/DSDUtNe7sdQhtokZhI8qOg+XrncOxrPvfA13pHhWbWV1O2u7BEDbFVgXBYDjI9TXrnh5I/sM9tEfO0+GQJau4B3IAD17gNkZ9q5zX4mb4QXURBDGIAAjv5oq3OUviZniYe0i0+h4fcJYzo0pZ4ZQh+VVBDH354rGnuQRDEDzEpXOc7hkn+tTzK5O0g4Axu9TWe6fOpC5AGSFODSOKgmo2Zea8muLdIJH3IiBVOBkAdAapEsI3bcSueoqZJIPmiyS3XJx/SopDCYgVYAjkgetJI2W42VQiDEivz1U1paZq+oaIwltnK7jhkccHHqPxrIyVjLFW4OCfSrsVvK0ZJJYkHC+/vTHKy1PVNF8UWmolY/M8i4P/LNjw30Pf6VvCRWiUMuG/vZ614O2YlCklmxnBPSum0vxVfafCC8yXUMfJhkyWwMd+w/H8KVid9z02UKpBBOe+aBJsVgvTGK5ax8c6TetGlw7Wkj9nBKqc9Nw/nXa2llBdWh8udGkzncjZ4oLSKLOjwgfx7iSfamyIqBMPuBUEkDofSn3NtJbOBIuARkH1pkoCgDdnAyfT8KChqFejZpTzzjgetR9+DSqrMD81Ajc8IAf8JVZkH+/wD+gNXqdeTeELm1HjGwhFzE0zeZhAwJ4Rs16zTRUXdBXgPxUmWHxZq0shfZEkbYXr/q1r36vnb4urGfE2v73ZmEMZC9AP3SD8aGky07Hm76/FPNFbx+YIgRteVuVOOTx69K7P4wyy/2xpboPkk02INgZPTNeSY+avaPiZA8tzYGNiGTTYHH4LWlLSSsDZiaf4UtZrSCea32yOoJRmyR/nity38OWyMDb2AJHRglT2fi60XQf7RS22QRABiqDJIwOM/1pl54vnh0L+04Yw1u4G3fJgEkfd4B5/HtUz9q5W5SPaSbskzH8b6I9rpCTGEx/vVzj1II/WszwTFHD4/0KVnBEk0K49G4o8ReKY9Y0G2EUu653iSaLYcJgep/CpvAyh/Gujt2W6ix+YrqjTlTotz3Ki21qdP4kl/4qjVB6XUn/oRqgJPz9Kn8Snb4q1Yn/n7kwP8AgRqpCOcnr/KuMZZTOdzYz6elP3VGvXrTxSAXcPWnJycmoyM05aQiU/KCT0pnnx+v6Ur8owrTvrGyFzf2cNs0JtFLiYuSWwRwwPHOeMfrXBi8TUpSShb5n0GUZbh8XTcqzle/S22nf1MozJ6/pSiaPuf0rXv9N0u1t5rdZ1a5iQMGUSFmPHUY2hef5daiubfS7fVkszbyeUgDSS7iSxKZ6DooJ7c4rmeOrrex6kciwMldc/V9Nlby8zO8+L1/Sl+0x46/pWjJp1q9wkqRL5AheXEEhZZSvYbhke9JbWNlePbXDQvDDJ5oeFHJzsTdlSfy/Cj69XvbT8Q/sHAWvef3rz028jMM6E9f0p8ciuyou5nJwAASTUl7HbyadBeW8HkbpXiKBywOApB5+tLo5Kvdun+uW1cxkdQeM4/DNT/aNbmtp+Jo+HMH7NzTl96/yOf8T2FzdWj28cJ+0LKG2v8AKRwfX603wvYXWnwvFOxYzYIjUZ2tzx7k8dK2dqvZ55acy49SRit3wtFBFPFMLq3W8eYRhJCQUTuRx1PT86dHMKyXsla39eZpjMkwri6snK6stLdFbs+xj2avMPLiikkcZJVFJIH4UokVsgZyOoIqe0ljaxmtmuGtT5wfzgpIbj7pxz7iptYWP+25FjJP7uLczDBLbBkn3zWuExM24w0tt+D/AMjgzfK6EFVrq/Nv5bry8+5WAPFLSZ59qBwK9U+SHUvfimg8YzTlIzSEP6ClFJ7UtADhTuvFNpycmgCWNgkgbGcVYF0cfdFMjtZHtZLkbfLjIU5YZyfaoOcUxm74WmL+JrQEDkt/6CaoXFu1wdOg2kfatTyT/sKRn+tWfCRz4os/q3/oJp4L28dvd9fsltd3K56Bt21f1YVL2Gj1GAYt4weu0E1JXlUfj/XExuNq+PWLH9akHxC1cSF/JtSNuMbWx/6FUFWPTZxm3kA67D/KvGLTMXiXxMCPnW4MgOOxYVsy/ETWCh2wWYOP7jf/ABVZ8xP9qa3dpwJ7W1myP9tVz/I0IVi74vvJIvE91GpG3Cdv9kVlpdu8EpJBKgY4qbxq2PF94PZP/QBWXbSHZMv+xWpJZ8YN9p+GmnM55+2N0OOM81wWk50vXTArECOR4+SSTwf8K7bxSS3wu09wfu3MpriLlk/t6RlJyZC+PUMCc/qKmWxUTqDfsP4jSjUXU8OR+NYe9vek8w981jdmtkXdfu5rnSWCuS6sGB7jt/WrngGye28TadJ9qdw04DI3OcnINYU7lreRcnlTitvwTOx8U6QRjyppImHs27GP0FXFkSR0WsTyDXNQAYjFw/8A6Earh2kibJywpmtTD/hINRweRcyD/wAeNMtpP3oz0YYrUyaGMT1zTMnFPlG0sPQ1ESaQwyV6U7cSOpppJ28daQMCAcHmgAfkYqzY3UaeXbzQeaplVvvY5qselELAXcOf76/zqoyaehjXoQrR5Z7b7tar0Ol8bfEybwz4in0+KATNDt2Ku3IyAfTiqn/C2YwENxdRpOFyy+WG2H+7nb1ri/iz5g+IOoqVHlusR3be4VeM/nXE5t5mZmlWLLkBcE4H1qlN/wBIzlgqcnzNy1/vS/zPZx8XLiaVUtXSUnORwp+uNvNbXhbx/f65rkun3EJjAt3lVsg5wPYCvAtMAN/GVY5Dnn2Ar1r4fov9v+b97FpKQfbbTcm0KODhCSknLT+9J/qUdT8Y67bwkw6pOjM3JXH+FQab4z8RXeprbvq8jRiJiA5G1jjjP5/pWWtxBfrMEBKRttKt1wf/AK9W/CdtF/auoWs7YhktWjZsZwGOM49qhqx1X1N1vGHiFEdf7Qm3rhQI9hzgDkc9Otcnq/jLxHdzXNpNqs0tsWGYmwVOMHt7iub1bTLrRdUnsLsESwtgOOjqehHsRzTrYIx2g9ODg81Nap7miJqy00By7RHPGOlUjau8qrGrMzgnYuSe+f5VtxQCWdYWwd2eo4PFa/hKwaPXZLhljZbeF8ALggtgfjU0pOUbsypxucTPY4m34EasThckke1QR28flPvcAqeBiuk1eyVNXkcMTGZSQuO5OTUC6PBM7CXeASACvUd62sUk72ZivOzRlSCXY5Y56imR3kqHGTtxgqD2qd0MKNFIhyrdD1J//VUaWgkt5rhWJWPop4J9/wAKQWvoSvICVXy88A5Pap/PijiDbRyMZB649apRzMyMDycUrfvOB2AyBSJsPkEQQEZy/b0q5/burSxoFvHCqNgKkLx7kVHC8iSPJ5m7fyzHk/r71QlE0ZKAbkbv3NFhxkuh0UniLWpIJBJqU6c7h8/DNVAeJdZRju1GfGMDLZrNaSYkB1OQQAD2qZkza2wWLfJLM4UD+LACgfnmgpJsvHxbrAjA+3z9eMtyaga8uLwEz3VwVdtxDSEhj9DSzaOIJ1igLzv5nlgheGfaCQD04OQfpWpdaTJp9nKbi3U+btWMNncD3I2nA7/lQDgzo/hRcf8AF0NHh6j99yR/0xevpyvmz4Z2Lx/FLSJzcxzY84HYO3kPjJAxX0nTLgrIK8A+JYU+O9U3YwRF1/65JXv9eGfEOEyeNtRAUbT5RPHJ/drQUeb21hAl1My2EYUsCrMo9O1dp8SFd9btVZgsRsIenbg5rJa3YkDAH4V3Xi3wjqevXVrd2EcMsX2SJCxmVeg9z9KlS7FWsfP90XiSS0TcVRzyCcMPXHatC3SOO00/fLI0JIkltyTsZtxB46dBXbL8I/FLNJJJa226RicfaU/xpj/CbxfsjQWtuyLkAfao+Acn19TXTPESkkibnnklpIl5cwxkqnmMmPYN/wDWFd34AtDD4l0gt1+0xD/x4Vp23wm8TK26WCAknJP2lDk/nXT+HfAOs6drVhc3EEKxQzo7ETqcAH61E605qzA5/wARxE+K9VYn/l6kx/30app16VpeI/m8Tak6MGU3LnIPB+Y1TQcdqyAFH0p6jFIMD60ucdqQxcYo6txxRgnFA60ALLxEcVa1jVbm+8+R5JTDkusIOenQe9U5jmMitbS4oFjee4VWBIiRW5yT1OPp/OuWVKNSq1NXVl+p6tPE1cPhISpOzcpdu0Tn9O1bVL8XGn3ayQwwBflD7hznAJ/WrscsqXpmWWXzlIxIDyOPWuhsbS30gTD763EvIbng8AfhWHdwra6lcwqcqjYB9qiph6KnDlXX9Gb0MyxcqNbmnsr9OsoodLdXMtxHO9zKZY/uNuxt+npTJri4nm86S4kMgXaGz0HoPzNRlqYW5A9a2eGpfynn/wBpYtbVGLsJiEZdtgJYLngE9/0FEYaCZJYXZJFOQynBFLnApMml9Vo/yor+1cba3tWVr3Vv7Oke+knYThsoVOGLe1Yf9r6rbXllczgIs8xJCgmRBxzx35rTmtEuNRjuJPmaIHapHAJPWrJRm7kewOK5YU6KVpwd7vo+561bEYyc1KlVXK1H7cV0V9L97luyu7i13tbzSRlvvbT1p0JLF5mYs7k5LHJJqKJcLg1OD8vA6V0YehCMItx1sedmOPrVK9WCneDb9Gr6CilNJ1pTzxXUeWNRlPTn3qVR0yaZ9KeOKAH55p1Rg04GgQ405eehpg64Ip3QZzQM20j8vwsztw0tzkfRRj+Z/SsmQFVAPcZrS1q6Sz0TToSDkxIxx6vuY/pisiW5jn2PGfl29+x/yKatYl3uja8It/xVNkPdv/QTXR3Gk3OpeFJILSNDcvEqgE4yocMRn8K5nwec+K7Ie7f+gmt7TfH2m2eu3Xh+6YQ30c+IRJwsqtyMH15xU8rktC0ctNo2q2nyz6VcgjusZYfmM1WksLsfP9knx3Gw164ussQN1o4PoGBxR/bS7iPss2R7VkVdnkiaVql0VW3026b6xEfqa6bU9PfTPDii4iVbr7LBDLznlT/TmuzfXFQZ+yyAdSSQAK8+8R+NNO1e9/s7T5UuWRd07RnKJkhQM9CeSfwpt2Tk9kXThKrNQju9Cj44P/FYXv0T/wBAFY1qxLOB3Wtbx5lfGF4fUJ/6AK5+KdoXD4zxisJY2jGTi3qvJnoU8jx1WCqQho1dar/M1fEQz8LLJf8Ap5mH6V5/aSC6v1mwVPkIpz6hVUn/AMdr0bWbb7V8NdOjDFS13KM4zjINcZaaMlozFZGZmx1Hpn/GipjaK0b/ADKpZJjqibjDZtbrdfMk4AFNYirRtu27n6VK6kp5bLGOMfc5rH67R7/mb/2Dj/5PxX+ZnsU2dOcVY8Dz7PF+i2xzlb3aPYZBFT2mjtdyMFmVERd7yPwqj1P5ir/h3w6dO8c6RdfaVlSSdGRkHysM4P8AL0rSGLpO1mY1MmxkU7x213X+Y7W1ZPE2qMBnN1Jx/wACNOiYgA+laOtabnVtQupbuCFZLqYIrhiWw3PQGs6yjimQ77yCFs4CuHJP5Ka0eMop2bM45NjZx5ow09UWZwG2uOjDn61AaszQtC89o7IzwNtLIcg/nVU9K6IzU1zRPPrUZ0ZuE90LTc84NHamHPmZzxQZDie1NiI+1wcc+Yv86M80RD/TIf8Arov86aAl8ehH+IeoQTKWinVEP+wdoww/SuJa7m0Z5bSTS7e58r70jnHHY/lXbfEa387x1cKWKBpYhuHUcLzWWmsaXFq0sd7btM6K0KsFBDKT1PuCKSlyT5mrrqaRXMrdTDjaTVLCS5GnRWEYQ/vMMA4zg47ZrsvhjMx194JPvx2so9iNvUVCL23gcB1fZwQhZcEH2JrQ8BNYr4plSISNL9ln8s8YRcZwcdenFS68ZVPc2Yezlyty6HFJi01Qv/ywlyGFbmitKt3evIEKeTsRl4LDPrWJdRySI4Ud8jmp7CY6dMLy33XEDcPEx6eo/WuhvQxOk1XS4/EWj7fIV9TsUPk7uPOi7p9V5I/GvL7uzuDeu8cIQnGU9DivVk320sN1a3DkYEkT+o9x69jVTxHoqecusWUK+RdHMseceVL1I+h6is9Ngaujz+wgvGDrIpQggpz3rvfDJBtr65MZTcqgkjGTznHtwK58yyRbzLEqqoPOe/pXTaRdOnhFZZ5C8UatsLHOF6/zzVNWWgo2ucHqnnnU5SXKRtym4YB9wapzXM2WaORBufeVR/ukehrWuLVdSb/Q9rtsMiZ/i9qxLuK4tH8ua3eOTGSCvQUtjCXMp3RSu5p5meV+SxyTio0VGTdtIP8AOrccwXzFJOWBz7irctmsdut7ESYG+UhhgoemOvNFmVd2uZxdEGGGX4xtOMU233i6DsmVZgNqjk/StNtOaCGO5ETtHMhZSEPGOo/lRcWJsbizlZllhm2uQjcxk84PuOKpRY7N6WIRKEmk42EKQUYc/jWfLvZgVYEkjBVulTS2rx3shcHGSfn4+XPGa0dMtLa6TZLGC8T54ONw7ZofYIwSZjL5jkM2cZwW9a2Z7hZLzTwBHZxwRbFcHdtPJLcck/1pmpGCHUzHbx7Ygi/J2B71R3GSUhl2lVzjP8qVirtM7DR7drhYJnulQrGUgiTaCEPUnr8x6msvW9QglmNvZs0gQqRJIzMQQc4GenPpWZHBONOVnRhH524SI5zwMEbc44xTLhbWFcwTM5zhi3X61I5N2PQ/hfqD3vxR0dnwhKzAoo4P7l6+la+Vvg9deb8U9Gj24H77r/1wkr6ppodPbUK8X8dnHjO//wC2f/ota9orwvx9dEePtTiHATys++YkpT2NY7mQRycnFYuqu8pitI2YGZucHog61qNJmMjpkcms60Alnku2B+b5U9gKiK1CTsOaNkjRAzEjvmmJGyn7zfnVlsM5bOQentTggzk/hWjIQxN4/jP51LuYD7zfnSd6FGW57UDFCk9aeBt4oGOe1KtIYAAYPelXk07FCjFAhTTckUOcDNMXPWgYsv8AqzVmylQ3SyXAkSND8oRSxOPp61VlP7vFUdSsytzFLNc3i2k8XljyOFLdwx9P51hH+NL0X6nbU/3Kn/il+UTpI9Zi1y/msbVdzW+DIS33eev4VkttS4mVXMgDkB/Xk1x+t7NKvvtFrd7lu1YMIjt2gEDB/wA9q2bCUpptqAc5TrRU0lBef6MrDa0az/ur/wBKia+/NOB5zVaJmNTg+orc88f1NK3THrR06UuM0hEe0Ae9JtwKk2jpjpSEUwBKlHWmqMCnD1oGOzilXmmZp46UAIBzUgpijvT/AFoEHUU4dKaKcOBSAUHFPijM0yRLyZHCj8TimZrS0BFfW7Zn+5CTO30QFv6Uxi+KI4rnUGhZQ0SOUQdsLhB/6Ca523Rop7iMYESOFjUdhj/Gr+ranbWlws11KqZAJyOST8x4/GqIkMkkEqFZIpoy4ZDnHPf65rWOkH5ijfmZ0vg4/wDFW2Iz3b/0E1r3vhrw/wCIis99FtvEJCXMTbWAzkc+3vWL4N48W2P1b/0E1Cml+LdOvppI/D91cZcgMlwFDLk4yvIzjjPpWSutmUjv/sYeAR+cJCAAJOMnHc4xUP8AZbgYE7nJzyD/AI1zsNz4iOBL4V1JD/syRsP5irPn6zj/AJF/Vh7bI/8A4usJUru7LjNrRGprGjjUdLlsvthto5BtkkXG4r3A9M1xf/CN6N4aNta6YpZpJ4zNLIcl8MMD6cnpWrcy+IChEXhjVGJ7nyh/7Maw4/D/AItutTWaTSb1EDB907oSCDwABwAOaKvMqUoo6ME19Zg5OyubvjV1XWr0XzK8JaLyE3AsPu78dwMZ+vFZur3UL2zqsMX2cyL5DG5DhVz/AAoBleOuf51B4+E3/CYXrRRmQgR/KOeNq1jafYXctt5s1u/GSfkOMZPfpXLVwNSTlNNWZ7eGzvD04U6cou8benTz8jv7+88zRLaW3EBQ3k6xAOIwF5xsboDjoa5yZgL+QpcKb5rceU0rqSj7uQXHBbbnB/DrVXxCjv8AC3TlRyrG7kwRXlE13fQyvsuOG+8jsfoan6rKrGM77pfkP+1aeEqTpcl7Nr8T18XSW9x5k8kLXy2cnmOcMN+fl56FsVm6lc/a7Sxmkl8y5KOJWJy3DHGfwryxLm7HIeYH/Yuc/wA8077dfr/y2vPwfNRLL5tW5janxFRi1L2bv6rz09NT1DTNstve2m9UkmjXyyzBQSGBxk8cjP5VtaJHs1zQbIyRmaGcySYcELkg7QehOB0968NlvtRd9xmuuPVjXSfDh7x/H2jmSSTYLlMhnJzzVU8vlFq8tv8AO5FfiKFRSSg9b9etrHqOoyal/bF2sF9ELRLqXdG8iqE+ckhlPUfnWPbSWkOq3V7GVEMBaS3jP8TZwgx6Dr9BVDWZN3ijV1JPF5J/6EabHjHT9KJYCTlfmJp8Q04Q5fZ9LaWRb09zJLJvYkucknuac4wSPQ1FExSRWUcDnNWrpQrhuoYV30KXsqag3ex4OPxKxWIlWStfp8iDOBTX6e9OIxTW6VocgKrSYCqSfQVWl1C3sla6kkBSBxv284weelXLK9On3aXQQSeXklCMhhjkV0lpaaBqNg93a6Rbql1EQWKZYZGO/QilcpK43XrPRvFl/JqVv4m0+O3kwYzht3QdfyrkJPAenxzSsfHenjzG3ENGa6CXTtNsfDVvpyZaa1Tb5xQKSfoK5W4tkY7yvA6/SmmDuiw/gqwf73j/AE7gYHyNxXT+D9H0bw7eSXr+J7G+k8iRAIwQxyK4lbeKTJUDg4q1BCIXV1ABBzQoxWyE5N7kaIBId2QpNRpGtnejHyxTH8A3/wBetS6gUgTKPkfr9aq3EMc9u0bcHHX0PrVMk2tMl86M2jHbk5jb+63cfj/OtS2uI45Htrrm1mjCTJ3How9wa5LTLtmT95/rIztc+/rW+GW4iEpO5s4fPY9j+NRYZxPia2ns/EMun3OVgjAZWHSVTyGFdY1sJPCsNomEE0RHToGq5fabH4h0sWzgfb7UFrdmI+Ze6Z/UVzvia9uNMso7eNVLxsqlJeRgDpRcC5eaZ4f0fSlt7K9YXUaArM6ZLHrz6dxXn+ua4NRliUJ5bRKVaQSE7j/hTNS1Ca8uY4/LihUkb0hGFYfXGatPpOmnJeFwMcbWPX8c1adgsc9smlCs6uFI4OOvvU0Vlco6ny2bd0DDiun8G6Fba9r0+mSXsttEsZMKjBJOfftU/iHw5rPhy8KXU1sbdifKnAOHHvxwfaqVVrRDSRRtbK5vXity0gmQbf3MmQydAfw7+30qHWdA1PSrbzWujLC7bSrHJ/H8qVb1rFI5ba+zcMd5wMAdOAO/Sn3N7d3rrcSTPJC3RSPuMOq/4e1WqskDZSsluZ8XEyifYQoDNzxUlzPuv0e3Jgk2NvBGOQCa6HRLfT31S206W1u5knwVmt5BjJxu4wcYzzk1D4q0aLS9YvLaxY3EUYVA7rlst16emOvvUN3YmjmbYJLch5fug7myeo64rU0/Shqd9Lf3EAWyDMoVT0OMD8B61UsdOlfbMxVV38Ajritw3L2VrIYy/wBwghOpoIt1RYvdH0e10vZKPLHGJc5bPrXM6yBEiQxA/Zk/1TBSNxwM59ee9Nae4ktzG8pKYyQ5zg5HIpRa3VyWljR5wgClgMgYHb3qCFO+h0nwbLt8VdELdP3/AP6Ikr6vr5a+ElvPafFPREnt3j3icqWHX9xIa+paaNI7BXhvj2Jf+E81STaMnyuf+2SV7lXhfxHmS38Y6lK77QPLyO5/dpwKUjSJyepTt5S26HEsoxx2Hc1Kq+VEEVcADFV7CB5Ha8ufvN91T/CK17e3UA3M4xGPuL/eNNKxLd2QrZutv50jBF/hU9TUZyT14qWe4e4l3OenQelR5oYCewFKuFBJpBxnuTSgY4PrSAEkEgDDv0BFTKO9RLGCSdo+tP2r2oAcck0Z456U0qPWo3GeBSAcx3NntRnHSmqmKRsgUxiSNk4NZmpa41ohgCCUMQ3lyEsBz+lXmbByTgCsO7l043Mol85pDt+6AeBnp/WudycKrdm00tvmejCEa2FjBTSalJ6u26j/AJGDBZySFvMXLu24jsOc8V1sEW20t1/ug1TgeyDZjhuSfUpWglwMKFglwPVaUpuc42i9H+jKhSjQo1eacXdJKzv9pP8AQtxJxjJH41Ooz1zVcXHIxE5OM4AqT7UEGGicA98V0nl2Jx1xTs4qAXScAK5P+7TTJcN9yIAf7RpCLHNA55qDbOygtIF/3RQlsT96aQ/jQMs5wOcUpdAOo/OoPskbHLbm+rUptYTwUGKYFhcEdacfSqotIgcqWH0NPELA8TSfSgCx0pariJwf9c2ffFL+/T+64/I0hFjvSngcVX+0EH5onGPbNOFxE38WPrxQMlBra0NAtpqFyXTc0X2dFzyS5AJ+mM1heYmM7hj61v2iLD4Wa4ITdJcF1Ldwq/4n9KYHO3ukJ4m1Q6ehBnZyyDOSq89T24rpPEmk2HhzwrZpGvNkqxbkAzIPf371y/w+v57vxlqdyrEQQW7KB6ksoGfwBrr/ABJEdU8M6isjDc0TMmf7w5H6iuiolGcYvYvlSZykcpZUli3ISAwOcEU5ZL64bBvbkAcnErf41k6DcSTaeqSgiSIBTnvxxXSW8XlW5J6nk1jJJSdiJbnO3w1EXDBdQuwMDpO3+NUm/tMDnUrzH/Xdv8a6ieBZPvDBHQjrWdJaYyG5UjFIEZFpLfzxB01S7YZxxO3+NbFrcX8ZUG/uRkgZaVsD9ahtrKO1jYxk7GO7b6VLcN5cO4AnHOPWsMT/AAmd2XK+Khfuei6Nd28IkslPmSROVZpEwzepyfvA1S13UZYluFgVVjwiF0k98Y29jz1rI8K3Ed/p0N1DMHmG7zE25aN8Eflx+Vch4YvL9Z9YbVCJVdj83/TUnGRjtx+lW72ZzxWqOns/FUunaRFYSabY3kCEuouY92CT9azrvxjGhyPCPh5/960z/Wq8gBih91P8zVGaINniow7/AHUfRG2Yf73V/wAT/MnPjhB/zJXhr/wD/wDr0h8dKOngvw1/4B//AF6oi2XPKilazVh0FbnGXx44BXJ8GeGvb/Q//r1oad48MMqTQeFdBglRsq8VsVKn1BzWQLH/AEVQFAfbnOM9qiNowTk7SRn5R0NDEmD3Ut1rNzdThRLcO0jbRgZJzWgpK496y3BScZ5O3rUrTXO0Mq8E9ai5RrowAq2w82zVg3K9axbeW4fPyAEdcir1rNP88flhgw9aoRJ2ppPFQk3OCNqrj1NRFbk9ZAPoKlsBbiUpFIy/eCkqM45xXT6BrNlP4atVS4jaS2hEc+Dgqy8EkHscZribyObyz+9/SsvU5LKfT4I4wsV5EctJFjMyk5IPv1601Fy2LTOz1HVI70jyJA8YPUd6osu6ItjK9CKykfaypa+bHGEBKzqAwJGe3tV+1eZnMZdQrcH5aOVrQT1KQMFnvQvjupNOtNVhYlJTsIPGehFVtXtZIJQX+ZD91sYxWSyHGQc0xWO4tHSaNoNwZWGVwe9U5U2MVYd8GuXtrua0mjliYhkIOM8GuwmeO9tY7yL7sg59jVIloyrlWtbhJwpCN8snHbsa1LbUBZvukBMJGG2jO5fYe3WoGAuLdonGeNpqjavJbk28oy6nKN7UmCOut5Al9HGCzBgGUjoVPQ1znj3S5Hjg1S0JFuTsmTGfKk/wI6Vs6TcK7LFKRlCXhP8ANf61JDPbGW4t7wbrO8/dyoTyR2YehHWpKPLIxEsJSWJTznd3zXb+FvBmqa6Y7iWT7NYHkySKCzj2H+Nc/rGmSaFrr2kib/LYSROB8sidQa6rQ/iBZ6LaG1nmlAdy6q4J2Z7DrQ2B3Nl4X0/QhN9jtIwzH5ZiAX/FjXG+OfFa2bS6Lf2aXq3EG5BGckHpk56HPOanufGb6qzfZWIgAH7zPJJ6gDtjisO60ZtQDE6hOXfku2MnH4UkTc86SG5CtcJG/loSCQCQpHY//XqW11e4gfy/tEscLH5jGcY9wPUV6xpdlaWnh59IeESxyKwkcnBYt1b61z03hiGBXiS0tGiDZTCENj3JzmtIWlKzdglKyuUtFvdQ0ma5e4aFZR1kZSzyD1XBGDj9MVT5nu2keeTzLmQOzEn5snv7Vp/2UzXCrIWjONqsD/P8Bisy3hlv78WkRVeerdFwDyfbmuicKcI2WrK54OmrbhbXVvJI8UMm4oSMHsB6VbDuqsXG7/d61p6R4FtbW/nuLm/jkDxfII8rtY8k81mkxiV1jfeisQHx97BxmuclCLHC0m94FYEYYEdRWnplnDZBzbn9zK2Rn+E+hrPBHUGpILgwP/ejbhlNPfQTik7o9C8C24/4TfTJGRWKebtbuuYmzXtleH/Du73eMNOjGWjbzNjen7tuDXuFSlYq9wrPu9B0e/nM95pNhczEgmSa2R2OBgckegFaFFMDN/4R3RMY/sbT8en2VP8ACnPoWjyAB9KsWA6A26HH6VoUUAZn/COaH/0BtO/8BU/wo/4RzQ/+gNp3/gKn+FadFAGZ/wAI7of/AEBtO/8AAVP8KP8AhHdD/wCgNp//AICp/hWnRQBm/wDCPaJ/0B9P/wDAZP8ACj/hHtE/6A+n/wDgMn+FaVFAGb/wj2if9AfT/wDwGT/Cj/hHdE/6A2n/APgKn+FaVFAGb/wjuif9AfT/APwFT/Ck/wCEc0M9dG07/wABU/wrTooAym8M6A33tD00/W0j/wAKaPCvh0HI0HS8+v2OP/CteigDLHhnQB00PTf/AAEj/wAKUeHNDHTRdO/8BU/wrTooAzP+Ed0P/oDad/4Cp/hS/wDCO6Ieuj6f/wCAyf4VpUUAZv8Awj2if9AfT/8AwGT/AAo/4R7RP+gPp/8A4DJ/hWlRQBm/8I7on/QH0/8A8BU/wo/4R7RP+gPp/wD4DJ/hWlRQBnf8I/ov/QH0/wD8Bk/wpP8AhHtF/wCgPp//AIDJ/hWlRQBnf8I/ov8A0B9P/wDAZP8ACj/hH9F/6BFh/wCAyf4Vo0UAZ3/CP6L/ANAiw/8AAZP8KP7A0b/oEWH/AIDJ/hWjRQBnf2Bo3/QJsP8AwGT/AApD4e0Ruuj6efrbJ/hWlRQBmjw9og6aPp//AIDJ/hUp0bS2hWE6bZmJfuoYF2j6DFXaKAMy18N6FZFzaaLp0Bk++YrVE3fXA5qd9J0102Pp9oy/3TCpH8quUUN33Ay08NaDHny9E01c/wB21Qf0qX+xNJxj+y7LHp9nT/Cr9FAFA6HpB66VY/8AgOn+FNOgaMRg6RYEen2ZP8K0aKAM3/hHtF/6A+n/APgMn+FB8PaIeuj6efrbJ/hWlRSaT3GpOLujMTw5ocZJTRtOUnutqg/pSDw3oSggaLpwB64tU5/StSilyR7Gntqn8z+8zf8AhHdEIA/sbT+On+ip/hSf8I3oX/QF07/wFT/CtOinZIzbbd2Zf/CNaD/0BNN/8BU/wpf+Eb0L/oC6d/4Cp/hWnRTEZv8Awj2i/wDQH0//AMBk/wAKQ+HNDPXRtOP1tU/wrTooAyj4Y0AnJ0PTCf8Ar0j/AMKcPDehAYGi6cB6fZU/wrTooAzf+Ee0T/oD6f8A+Ayf4Uo8P6KpyukWAPqLZP8ACtGigDOPh/RT10iw/wDAZP8ACk/4R7RP+gPp/wD4DJ/hWlRQBlt4a0FhhtE00j3tU/wqA+DfC5znw3o5z1/0GL/4mtuigDJHhbw8v3dB0sfSzj/wp6+HNDU5XRdOB9rVP8K06KAMyTw3oUy7ZNF0519GtUI/lUX/AAiXhv8A6F/Sv/AKP/CtiigDG/4RHw1/0L2k/wDgFH/8TUqeG9Cij8uPRdOROu1bVAPyxWpRQBmDw5oY6aNp3/gKn+FI3hnQWYM2iaaSOhNpHx+lalFAGaPDuiL93RtPH0tU/wAKB4e0QAAaPp4A6Ytk/wAK0qKAMy48OaHdeX9o0bTpvLG1PMtUbaPQZHFVm8GeFnbc/hrRmPqbGI/+y1uUUAZEfhXw7ECI9B0tAeoWzjH9KlHh7RAMDR9PH0tk/wAK0qKAM8aDo46aTYj/ALdk/wAKDoOjnrpNifrbp/hWhRRYDNbw7oj/AHtG09vraof6VGnhbw9HIZI9B0tHbqy2cYJ/HFa1FAGb/wAI9ov/AEB9P/8AAZP8KT/hHNDIwdF07H/Xqn+FadFAGT/wi3h7/oA6X/4Bx/4Un/CK+Hf+gDpf/gHH/hWvRQBn2uhaRYzLNZ6VY28q52vDbojDIwcED0rQoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArwVtbt4vCmo6hH4z1RfFcd7cLa2Cam8pdhOyxp9nJIKkYHTpXvVefaV4Fe68BXuk6nbJa6hJd3U9vOCpeF2md4pFZc4Iyp657UAdLqOpa9a2lkbDQo764kj3XAe8WBIWAHGSGJySeg7cmsv8A4T+3XwpNq8unXC3sV3/Z7acGVpDdbgojDDg5JB3ehz7VgapoviDWX0LUNe8Mrq6R2DQ3elG8jVYrrcP32C2xwQDxkkA9M1W0zwNrlt4QvoY9Os7LULfxCNXsLOOUeQypsIjDDoCAy5IByM4FAGjFqmp3/wAVvD8OraOdNuYtOvG2rcCeN1YxYw4A5BU5GOOOuat+HvEtlY+HvDyafpH2e21PVbmzWH7SX8kiSdmfJGWy0ZOOMbuvFFpbeJdW+IWk63qGhjTdPtbK4gKNdRyyK7lDk7TjBxgYz905xkVQg8L69ZeCtB8mwWTVNI1ia/Nm06L50bSz/KHztBKShhk9sGgDqdQ8WQ6brupWFxbN5NhpA1SSdGySu6QFAuOuI85z3ximeHdf1vWvs9xdeHo7PT7mETRXCagszYIBUMoUYJB7E1j2Fp4ln8Y6tr97oUUUcujJbWtq90jbnWR28uRhkAndnIBUBhySDVbw14d1C18X21/ZeHG8M6akUovbcXqSR3TMBs2xISq7Tk7sKe2KAO30nU21S3mlaxu7Py55Idl1HsZ9pxvA7qeoPcVw3jbxFO/jay8LxNrHlGyN5JFo+FuLhi5VV3kjYgCsxORnKjNdzpNzqF1bzPqWnixlWeRI4xMsm+MHCvkdNw5x1Fc1r2k6tYeObTxZo+nLqbGwbTrq0EyxSbN4kV0L4U4OQQSODQBLoPiLSLLwTd6ot5qcltYvItwmpMXuYpFPMTZ5LZIAGTnI5Oay/AU+uN4y8SR65eTyTPb2d19kaUtHaGTzT5aDoMAKCR1K5otPAmoappF3Jql5JpV/eaz/AGuY7QpKISqhY423Aq+NqsTjG4DHSl0Lwdr9l441u+vNev5rS4tYYkuGWBTcMFkByFUFdhYEEAZz3oAoa/rep6j4x0O7sL64t9Fg1yPTNkMhVbxykjSlsfeVSioO2Q9eo15Xqfw21y1tfDljpXiTUZ7Sw1GN9rRWy/ZUCODKPkBZgTjB3Z3E4PWvUkUrGqsxcgAFj1PvQA6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKrS39tDIY5JMOOo2k/0qzXN6pLGupygtgjHb2FJgWZPF2hQvse+w2M48pz/AOy0i+MNBY4F9z0/1L//ABNeeaxAovN6AEEEfr/9es1UYKcA0XEevvr+mRrue5wOmfLb/Co4/EukSOES7yx6Dy3H9K4+7iWXSIJu/wApP4isuH5LiIjsw/nRcD0ka7ppfaLn5s4xsb/CpzqFqF3GX5fXaf8ACuJuYyl5CwJxnn866DZ5kDJ6qRQNGius6e4ytxkf7jf4VImpWjnCy5P+6f8ACuSsEyZFPatO3QLLmmI3vtkGAd/X/ZNL9qhxnfx9DWdt/d/jTguYiPamF9TQW5hbOH6exoW5iY4Dc/Q1QhAAYD606IEN16ikM0dw9aNw9ahDA9DTqAJNw9aNw9ajoWgCTcKNwqM06gB2RRkUwUtADsijIptFADsijIptLQAuRS5FNooAdkUZFNooAdkUZFNooAdkUmRSUlADsijIptFADsijIptFADtw9aNw9aZSd6AJNw9aNw9aZSUASbh60bh61HSUAS719aTevrUeaSgCXevrRvX1qHNJmkBP5i+tHmJ61XzSE0AWPMT1o81P736VWJxVafULW2QvLMoA645/lRcDS81P736UedH/AHv0rNgvra5tUuYplML/AHWJx7d6VrmJerj8OaLgaHnR/wB79KPPj/vfpWJJq0KSFNrnHeoJdbRM7YmP1OKLgbB1iwVipn5Bwfkb/CmNrumqMtc4H+43+Fc6LUSfOzt83JA96GtIRjK5PvS5gszcPijRgcfbR/37f/Com8YaEud19jH/AExf/wCJrFFrCOPLXAGOlcp4kijgvQsKbdy7mPvk0KVxPQ9DPjPw+Ouof+QX/wDiaP8AhNPD+cf2gM/9cZP/AImvIR3OGwR1J7U95EEfzKR71ZPMet/8Jp4f/wCgh/5Bk/8AiaQeNfDx/wCYh/5Bk/8Aia8iVumOCSe9OLHfw5zjpmkHMesN468NqcHUhn/rjJ/8TS/8Jz4bBwdTA+sMn/xNeQSyCNXdmOFXLc9Ky7i/T7VCqk7HQu3P5UETrKCuz3I+OvDQ66on/ft//iaY/wAQPCyKS2rxgD/pm/8A8TXiJ1CBbbc/lhihKn+VVRqcLWDOcAhtm0H2z/jQZvFRPbn+KHg6Ndzaxgf9e03/AMRS/wDCzvB+wN/a/BO3/j1m6/8AfFfNV7MXX7wIY7ie9SrOzJsI+bZkbuufrSuc7xdW17I+kI/id4Pmm8pNXzJ6G2mH80qN/ir4KjYq+tYYHBH2Wbr/AN8V85RzSfboyshA6n2/Gmaioe6eV2VFdvlKqCPxoLWLlbpc+j/+FseCduf7b4zj/j1m/wDiKkPxR8GqATrIwen+jTf/ABFfMvkhI/3p45wwI6/T0qeyePeHmJKo2QPXHI/Ci4/rUj6dl8feGIYfNk1PanHPkSd/bbTE+IvhRxldWBH/AFwl/wDia+btZ1ee6jjyAig8KvRjmoLe6vII42G4AnKk/mTRcr6zJ6paH0pJ8TPCESK0mr7VboTbS/8AxNMf4oeDY4/MfWlC/wDXvL/8T7V80vLcTHe02IQ2MZyRx2FUTPiRhtDrnAP9aLgsRNvRH1JL8UfBsIBfWRgruGLaU8fglMT4reCnAI1rAPILWsw/mlfNDzsGG1iTjaflzxUM0xMQj9D8vHSi5P1mfY+qNJ+IXhbXNUh03TtU8+7m3eXH9nlXO1Sx5ZQOgPeunr5k+EqY+I2iMWyf34+n7mSvpuhHTSm5q7Cuf1JGN/KQ5AyvGB6CugrAvsHULgZ7r/6CKGamBeQStOyrIo6/wg9V4/WoGsJHU4aLJBxmMd14/Wr9zKy3xiUDBTcCfWqC67Z7sGbH/ADxQBbMDGAxhIwR0549elILNvN5jQgHsB0z/hUaazZk4Ey59dhqePVrbn98hH+6f8KAJWiL7SUzgd/8+xrRt9xj5Zgen3az01K1YZ8xD+B/wqxb30O8BJB8x6DvQARQLEzlQRk1ZUFSMYqRl+b608DmmIk+bb/CaUFumKRQGBzzSqBnpTE9x0ZIY5FKrAP+dNw244xj6Uu09cDNIZMhXPQ1Nu44OKrchA3cU9ZGPGKBkrPlfrQnB68UxXycYpwYZxigCQketKDxTARmlGM0AOFHekyKWgBaWmiloAWikooAWikooAWikooAWikzSZoAdSUlFAC0UmaMmgBaSkzRk+lAC00kA0ZPpULvtGSKQE9FQiUbN2OM0GcAnjpTAlpKjE2SRjp703zxnGP1pATUlVnuwv8ADn8aSO6MmfkwB/tUAWaSofOOcbRn/eFJ57HkKMf7woAlzXOXes3A1K6tkIEcWNpXr71u+c2fur/32K4LULp01K8ZGVSZCO1AGjLdzSY8yVmz6nNLqBWHShJgkkLx61ntcjyQAE35yW3D0pdSuc6egLjoOFcE06myIhuyrFcXU0bQKgjVcSK55yQQRWlcXLKwV7lixBwBx3wDx+NZEE5SRCCp+UDlvpRcSzSy79qkFQBg+5NKKb2NLXJrvXI7SXyik0hA7EY/Mn2rOn1q8vIfLtrZYg3JZnycfQD3qC8fEgDou7b0wTjJNJbu8l4kIIC45wnX26UmaKGlzpv7QvfJRRMqkAZwg9PeqGoXd2I2YXcoIU9Dj+VZ9nd3k95PEzDy4wNuIzSXk05DKeRgc7MUWOdtkEd/flUJvZzk/wB80yaZ5m3yyM7HjLHNV5nmBj27vf5R7+9JtnYA/OR/wH/Gmkg16jy5wQDUbMSh5zimtHOGO4OPxFVpDtWRd7ZUfNk9P0pibS3LMP3FIYe9UbnVFt7gxNHKx6blUY/Oo7ebzULFsc45b/61ZN9JI8sv75AgB6nPY9qGZupHoyW+1Jpi6RbgXHzbx2HYVmieI4ALAgHhgOhOaoSzyZDb+g60wTtIR82WI5PPalc4ZwcndsmvbhZolTJ+UYU4qslxIbdo3RsH0qOWTL8ux9T6VVeQuw+Y8GlcqNKNrF1nfIKRgKvcrk/rTGLnJBxk55IqPeioEJJHOfmwKiCgFmJZUwSp9aWpapxLTM02ATg9C27t9KJ8bVVZD5Y678nmqbnEXmAkjpViDzDbtJu2jG3jrRqK0VqSuUeIp5mSMc4/pUkjK8IK5+Uc7R+VVpFe2jXJG9xkAjkVJDIEIDncCMEbaCHa10hC0bFmfJCgH0qQMt0rHJRR/tdB2wKi3N54iCF04O3A5+tPlACnCBemVU9TQVzJKyI3baVVVUcbR1/Omz58rftXCnlQuM+tRzEqgYkgZztx0qRZWi3MjZO3jIzj/A0FrTUR1lihDFhk9lPSoGd/4iQfQmn3DmVuCSzfMcnvVRFaS4UPnAGTg8gUjWKfU774ROW+J+jbic/vup/6YvX1FXy/8JPJb4m6M4Rg587BzxjyJK+oKtG1J6BXP3h/4m1wM9Qv/oIroK5q5IOs3o6lXT8Mov8AhQzWxk6p8tzCw9CP6/0rkLhBHfzIBwHb+ddrqihRFJj7kg/I8f1rlNXQR6zPgYDYb9BSQEMQ5NWY+hqtF3q1H92mMsRfcq7bHbJEfRhVOP7gq2nBU+hFAHTTysiR7CASOpqSOYvb+Zgbun41TvfmigftyKltMNYv7HNFybGhbtvTcepFPWo7XHljHuKkHFUhMRmbzduRjNOYkNjikcfvFNPYfMKRQ8j5DSIMsDTjyp+lJH0FAAuOuOlOBBOaQDhvrQoxTAkHWjHNHel70gFxzRR3paACilooAKKKWgBKKWkxQAUUUUAJRS0lACUuaSkoAdmim5oJOKAAn2prtjGKazGmMxoAUuw9PypkhGzpQW56U0vkEEYpBcGcFBwME9xSFYzs4XnrULOcVC8mFyaZLkWgItuVABJodUVGYbSRVAsx5zinG5wuzbyeT70C50ODpISNvPTHHNV5po4wBsBPcZprTFJGBBBx9KynmbcTu6HFIJStsa8YWUZMSj071FcbkkVUiiIJ6mqIv5EhO0jg4A71YS4aWFHyM5PagIyuSuwQj92uT7Vy9zFLNeXBRVwJeea2L28limRcJj3+tUY5Y/OucsuTJn71CLGsMAkDnFQX8sYsjt3bgBViSQBW47Gs7UpCLTAG3K5zVNOREUlsyS08+QKr7V3D8uM1LNYBgSZOFB681laVefbXVHjIAUnJbPbFX7hESJmAHCZpLfQtuyKcoid8vvOAAMYFNhZFuYyAxcsBknio3kCsSQTgZogO++gHq4P9a1lTSVzmhiKjaV9DZgs1t/MZCSzgZJ/H/GoHthM+HY427jj8hWgWGKrqcbz6AVgmdFkZkWmpcyYDMCXKg56D/Oasy6KyyMEkO3+HP1qawU+cADjktWrIw27mPAoA5ttJnaRgZc88VzPiW0uNKnV44jIswwWHc9P8PzrsLXXbS5aZc+WYyQocjLgdx+Oa4zxB4gnvdN2kQ7XZh1+baGyOO3Si5hUlBxOXOoOkQywDBjxjvU9nbLqJkjgk2ThGdQxxlu+T9M1n3TqoCBiysgYjG3ax6io7PIlHlN8x560XOL2aXvDZd/liMDJJHAHOfpTRaTb9xYBSOQpyVHSrl/I007SFhlx8xc5w3rVZmQDBIYAnOw4B9qBqV1oQhTHLzIpODjb1P1pbeUicCMABcseBUMmxXYqduOQQc1GLpIiy4yp5OByaRXLJluSBH3uzMvPRFHH19aqt5iTIxQlQhH3cAf0qF7ggHynK7sk/XP8A+qmySkRIPMZkzkEAgbu9M0hTkl7xc81WYRqoC85AHApqyOdzM4Cg5I78d6rSvJCAxUhe5PPNVZZj8mxzjHIouJUrl2VM4lkfERPG4808TLDKREAXPUk9B6VWNy727japRhtG4Zye+KsWVrGkRIKyznGQR90d8HNBfs9PeJI3EoJkXtkBT0pPOKjmQZ6D/wCvVq5tfsMzJ5fzFBkZyMHoRWcVRZco+Bt6jnBpGSir6CyyEgqy8gdz2psID4O5gu3JPamSRlsEEHA7dDQuwKSoUKV5UHvQbpaDtxKoQCfLOCKgyrs7uG3k8diTRbSgztID93nb6mpTIY5fPLjO05AHNIqx3HwgBT4m6IhctxOR7fuZK+pK+VfhDM0nxZ0bdjnz8Y/64SV9VVaNaasgrlr5fJ1y8kLZEnl8AdMKBXU1zOqxynVJSobb8v8AIU7XKbsVNTXdaSHngbq5fW/nvYpcY3R4/I//AF67G4TfCQehWuUu0iuViV5xE6A53Kfb/CkMzIu9WY/u1JHZwjP+mIfohqzHaQ7f+Pr8ozQMbH9wVaTqKVLOIID57Ee0ZqwlvFvA8x/++KBGxDGLqxjBJG3HQe1WLe3EMToGJDDuKbZRbIdgJI7Zq2sZ9qdhNhAuwbevNSngn60xVweop5600JjmG7bRnMmDSMwVATS5AAbFIZKP6U1ODSoc801ep9qBjz3pB2pT940g60wJO9O703uKXvSAdRSUooAKWiigApaSloAKKKKAEooooASg0UhoAKSg0d6AEooooAY2cVE1TN0qNlzSAhP3qaTmlfIOKYTyaCWROeDULjcuPpUp9Kic/KTQQxjsB3A7VXlB3gggj60+TnC9PeoZG+Uc9ByKCWiK4kzNknngcelUZMMrtj9anmOJjjiqrnCvj+L1oHYgkcbAT1zk1dsp8sqZHOSBiqTvEE+eM5x1Vv6Go8ALvhcsByynhsUgtbU1ruOKRwXwT061zjhftuSufnx/KtJ4h9ogbJBJUkZ96YroEkYlcmU96ImrasRztiOUjghSaxbq7uJrUiZDs8vhsdT6/rW06+aWiPG87c+map6jaJFpSxozbvOKc+nOauTd7E00rM5+3BSAEOV+XOQcU9fM+0RgXEhUY3AucEGrkmmun7gFchcHnOOabJZvBIzkccDPbikk7jew6TDAjfgEetZ+qXU9sEkt5Ckg5DCrDEZzkcjPX3qneWNzchBDFI4OMYGfrXROyiYRpWadyePUNSaPP25jx0KL/hWfeaxqcEmyO6zuXJ+Rf8Ksiyu40AMMoOMkbTWZPb3Ul5HGgdnIXI9s81yNnQaGi69eQPeXN1IZIreNcgKMklgP5ZrW/wCEvs5YGjD4Yr8wcZwD7g4Jrn7dJm0zUQgPmGaJR07MScZ47Vzo07UkfaLaVizheF3A4PNdFGlGcHdjcbo1b+7s22ShZS8QAIVsCTn/APUKw5ZkdjHtVACWJxz9P1qe5Z1N1MEMatJuTcu0jGcfy/lVTTmtpmY3WeWXIxnAzz9eM1E6TjfyOOdKwyVYuC+C2O5xmqwkVXMhzkAbSOAKs3sSmZvLVtoxsGCCwz2FRyvsjYOAB12kdM+1ZGSskRCboZNrlxnkdKgleGXfnG4DjPSp48RyK6OAR0PFV5owGQjATI3HPU+tMqMY3IXLKAxBPHbiomjZlB+8cZ49KuSxNLKcJhAOFDdKiZTbQOMkjGGx6mkaqS6FVlPlbivLfpSMrhUGGCtg49f881KIS+1UfAPHzcDNI7yjaN2SFwpJzgDpincu45JxICixlsgnrzx0qu5RwA6KuD94dW9qFdUUhhgn0FWrbAUPKFwV+UYz+dAnaJVZ95RIz5aqDgkng1ZtoZFEU7YHJJO/k5pVTbG0MeWLnPK9T7VYWJVHkMCGQEMp4/yaCJ1Loia4aVi8jM3UAseeO1DbJOI0GCPmJ5qbYuVwUwrDIYc4+n+etKYYUlLRENgc7x3+npSM1KNyg7W+ORjAxhe9V3nCxlQMEirbWvmSpIjKq8h+ehqq8as7BGyB1HXNJHRFohgkdMtHjcpzg96vxq8jAynYGXg461nl0M6rt2oCBtHWrdzcN5sWDwDgc8496Y5HdfB+Dyfilo6HYzAzncpzj9w9fU9fLfwhkK/E/RkDEF/O3c9QIZMfrX1JVpWLp7ahXnfiPX7+08UXFrGYhArRjlMnBVSefxr0SvJfGQJ8W3/Xgxnr/wBM1oLOxjLvAhfBbvXMavNcWt4sS7ShXIygPeuispfNsUcdwGrP1SJDf25dAwbcvPrgEfyNJ6CMKK7nJwQgzj+AVp6c7TXPlyFSuCeFA5rMuo/KvXQcKeR9K0dPAivoCBjJwacdRvYe01yjOm8gKxHQetWbeVzLFukOCwyKknjEj3KAfMpyPyzVeHICNjoB/OhagtjpSp+yyBSQdpwfwrPsy/2kK7sSexNaicx8HqKzk4uIZR9DSEWrdcTMpB49avntVReLpvqatnoKaBiScw/Q0ucqq+tBG6Jh3oCfIAeooBEsfA/GgffahBig8OTQMd/FTc0p7GjvTQEgPSl9KaPu07tSAWlpueKWgBaKKKAFopKWgAzRRRQAUlFFABSUtJQAUlKab2oAKDQaKAGtTG61JUZoAYeSRVVjgnNWjjOM1DKg2HjJpAyqxwpNQyE4qWThagkOeP0oIaI3OWHGcVXlIL8dBUrffxntVdz8xHvQK2pXk5fOfeqsjDa2eh5qd2zuP41Tk4XnigdiGR8x9OO2adaLieIn+929MHioZSc8HjtUtqf9LX0CMfyU0gZHLeOyoI9ygDBwM1TYyZO6QBA3I71FLJh8ISB7VWeYtuGaCHFsv/bI1QKucDvnke9LMrXUKmDd5ZbcSPWsR5NnPGad9ulWPajsue4OKUm3sOmuTRmk0ZhuWYSE7jncxOT7VJIskkILSKwZh0FZEmr3UCJslWUbsESLmootUuJrrDBFUDOEXFWpTurouai43uaUlsTjKKRgehqa1lxcR2i/KyNvI74qFb6JBmeYKCcZK+3tUkHlyXpuLS9hZmGCvDcfzpVq6jpIVChf3os3jggk1WdFAyVGfXFQy3kkUWWaHOOpk2ism88QBUARrZW77pCf5CsFVi1e50eyntYdrenpqFktvv8ALzIDlRz0Ncv/AMI7qVnIrWmoOobkjkYHHpn1qfUvE0qJAMoSJVcmNSPlGcjn1qnL4oukMRWJR+5IIYY5LcH8q7KTm4+7sJxa0Kd/b69bzF3VZkO7b0bAP1571RndpBsaNUdH3OQuOfQ+vNbF5rlxPHCEBVzIwGw5+QEdawL263s5dF3nliOpPrSqSezVjixDbfKM+0AOGLcjqenGKoyJ55DKCQTtJFIHEintngA9TUm1reMo8boxHPpWJkoqAx7CaGYKoVh0HOfxqVLWYb1PluvZuv4c0jTBWXy8v7n9ah8+RpwI1OBztznAoJbmxzvLFlmj4xg/L1FKlvC8cZaPOzO8Hqx5xn06/pRJOAjlV2hh0LVALndwLjAGc+/FMSUiR2jXlAwA6YGO2KbJFG8iqG2gjGSveo1nlJchson3RjqM0hkVt24KXX5gScfypXKUZAll5UvmkAhCOvIP4VI8JkcBQdgB+YDGPrUu+ZokWN0EjDO0feqKKaeR4j8oK9Sx6c96CU5Seoy2M8ixuq7iXZU2/eXAzn2qdJTFKZmJJwNpBwQevWnpIYrNYA+1djOVI6HnH9PzqJspA+eTng56Dmg0krieaGViQWdjlnJqFZtsgDDcp/KopMDABOTyahR3SU7hx+lBUaaLBaQTumMfNuwPelulWTcUVYcDgKn3jQlsWl8wzAB13DPzc+h9KlkjCsI0YOWHOF6e3NITaTMdLOQqsh4U/wATVaZV2qCAW7VZnVnxC2dq8KvvUEFq5d1uA+1eODg+2KZpz33O1+EK4+K+ie3n5H/bCSvquvlT4MlW+Kel4U4Bn256j9zJ1r6rqkbQ2CuA8RaIt14hu7kzMN+z5QmeiAetd/XGa5dGPWrlNpONvf8A2RTCTsO06Ew2qwZJ2LjOMZpt/afaGiO4qVIcEDv/AJNVLe/ZZCduBjp1qeW+3BSF6dqLC5tCKTR0nlDySOSBjgAVPFpUKOjbnyhyORTBenGQgqQXb5BGMUJWDnLJtI3maXkMwAOGqRLSBR9wfjzVUXMmfvCni4bOC1MEzVh+7gdu1NwiscADmqMdwRn5+tP80MM7hSHcu7lBqVZF2is/zVx1p4uEAzkfnTA0UIwRmlBqks4J+XpUqyHdx3oAsqwboaJDhxUcJ+Y+9OmPzLSKJOoGKceFqNWA607cCMUAP/hp2eKjB+XFKDkUAPpaaKWgB1LTaWgBaKTNLQAUUUUAFFJmigAopKM0AGaKTNGaACg9KSgmgBp6GmYIzk5pxqOQg5GcYoARhgZqGViFwM5NTZ4HQ0x2GMUgKbqW9j6etVnzuPHSr5bnO1fyqOVx87CJT6A0CsZn8RyOe1QN0YnritHzYlQloVGeBiqb3EPe1Xnj7xoJaKEmQhAwMVVkOF5Gcc1rXNzZFVAtgxAwcNjPv0qn5tlK+1bOYkDosuf6UrjSMiUZYfn9KdasVuJDnkQSH/x01rvDbvYXhhtpUk+XAc5J57VkQJIhujIhX/R3I3DHcD+tF0OzMmZV3YQEmoWidYXfb93kmp2lCxkAZ4qqZnCbcEknGB3ouK5HLBG9sjhnDk4YY4x7U24hgW0GxyJVAyPU1K3mbV80eWM5PrVeVomIG0j3BpXM3PUqXOxGiUA+p9zikjbZmVUZu21etWZns5Z185iu0DkHg59atJbQRIZ0YNEvJ2NyRTlUUWkbqnzx0Mu4mWVNoDqwJYhlwelZiRkFmXqFBrau91084hEhWJQNjnLgnrWW0bwFzIpXcFAz37VpJSerRKgqfupii3Rrdz5AkVeSxPTp/So8IUIVQo7AdquJIqabcLvIZj93HB/T+tSWhs4LIXF1AjKNxJJPPIAGPxrkqRclyo6qU1CV2Z14iveRosayYRW2njNZdyqtLsu7SeE8BWB3KOfftXoIurXLrHHGPL2qw6YJHAqvLbxzklGCnrhen5VUcQsOuVoOV1G2jgnhhdhHb3aKgViQ/wApyScce3FVJLZ44omyjvzuIPGM119/oMFw7O8KM56mP5WrmNTtWtbryVRwmASHYHmtVWhVXunJiqco6lNvIhJdNrSZ+VhxtPqKoteMEZCFZie4zxU8m6NMN1P5/WqPIYMEHByfeg5oRvqxY5tiOSWDHjj0poulbeq5U/3z6Ux1kuXYxAAKNxGcVp6f4ZvLu1FxHBI4yRkDjPpQXJIx5XZmPUg9qkhQyuApATGWOM1r6nouo2kLm5tJYY9u4vtJGR2z2rNhWW3tYyYTH5h3eY4IDD2NFg1cdiXdBBGSEY88M3U//WqrNcQzzYUELnJYjr+FRSzBWIijGc/fPNVgWDfMOvpQKEOrLy3aWxOMMWP41aFuzSeY5aLcu4Ky/ezxn6VmxKXkVUUFiep6Vp/aGmTDqdwGCM/ypGdVNaolZRBJzskO30/x6VTkkkB6Eq3UGp3kLJtXlQBwOaY84EPlq/zdwO/40ChcomQiXa7YQ5J9qrlgG+8T3FWZlkyCVBLcYHUVU2sSw5Cr1JFCOiKNK0uAwxgEAEenWpfMUEkZ6Yzmq0NrCIkYSM2fvKRipb6dnYSlVCgbBgADA9qDJpOWgGYg/MVDd2c9KSOaRCGRhu5Bqu0qbtpXG3nPvThlwNh59T0oK5Edz8ITn4q6NhcD9/nHQnyZK+qK+VvhAzr8U9ET+H9/n3/cSV9U1SNobBXk3jHWjaeLr6Dbnb5fb1jU16zXhvxA48daif8Arl/6LSt6EFOVmU1cI/EUgYgLj8KmXX52JGOPwrnE+8DWoNOukG94tq4zksOn510OjTQcpqx61Mw6dO1WF1O5dARwPc1n2emXMqeYFUIefmcCpkgZgeRx0GelRKNOIWND7fOQDkfgamW8m3Kd36VRgiJVg3bpzWmlkn2QSGTLFcgDpU+4h2HJcS7vvnmrEcjliCx596X7LF9k3ru3hQc9frUjNCrKAmMYye9K8egWHxtnINTxngiiVoiqMgAGT0xSKVVs8mle4F+LGPwFTq3Q5qpGcEehFTocHHrWUtwLsLd88VLKcqpqrGWyQMc1OFJGGapEKDuOaeMZyDUXRjg8U9eKYyXNOBqMGnDrSESZ4pQaYKd1oGOpabmlzQAuaWm0Z4oAdRmm5ozQAuaKSjNAATRRSZoAWmkc0uaTNAAelNy3pS5puc0AFMZFJyRzSk0hOe9ADfujAqNulPY1C0rKwUdDQBG7EZFQuSeKdu3u/QYOM1C7BfmOce1ICO4BKEpkHuBWa6lzxWgXyCTxUEy7UM8fBAORQFjLdCdxweOM5p+nMtvdyNM4VcHn3yKfNK8sB3sWbFV54gQoxwW5pSV1Ya0dzbF9C0LyCVWRXBZtnftWRflZ/tbxnI8jBO3HJccfpViONV0qUKoAMq8Z9qox3KCK4XPRgpP1NRGkou5UptqxzjkMwQ8fWmSlI3AWNQfXGa0LyKPzXkIJcMeQO4rOm5cs3cVbMGrlWWYNLgnOepJ61A4QHhiGJGCRSuNzk4OevtVWZ2ZSGGMdPrQTy9hswztkZdxMijK+n0qSTyjbq8RKSFwrqp29SOCOvrVRyVgJDEP14qM3z4RZFDqkokz3PrzVp3aNYOx0n7q0jle+sZbVGkwJo2yXJ5HBOen1qpI881nMyQSm325WRwOn4Gty48RL/YTw3cEcu+F0guIXzhiDgHPIPNcuMJGuyQ+W3ykZ6jnH8qarSSZU6cZasY5A0tlE6j5v9VgZPI5p8NrcXuiMkXzMX2gNxxuBOD+H86rnYznmLAA4zzz07V1GmQra6baeY8f79N6rnPU8cdu1R7OUWinoc1dPJbXaLMuxp9SWV25ACgDAz39aowapdztah5ebq9OcnkIMfKPQc13/ANntJ22SSR5BDKCQea5vUfDwguoLiMwmOCQyYjIHXqcfgOlUqkWveQJmnIhjtInUBVfgDvXH6qI7y8kTyzlX2dP4h34rWu9RjivIQblWQRnCg5Gc/X0rGt7xY9UaWeVQGlL57CsWlHQEnNGHPZAA5fcwGMjpWXeLLCi7QCzHn6Vry3ikn06VnXLGaSIpxg4+uaaZPs0ijC0ltMJto8wdm5qz9uubRxJHK6dzsYrn64pkmYLyUqhwrEAMPeoJJGkJZz1OcelUYNam5a+P9Vt1MN0Ir63b70dwufwzWyfHulNpbRjTFSRV+S2lUSQt7e31FcC4QEnbn05qKT5zkelBojsBd+DdZ2/aLe50W4bq0J82HP8AunkfnTJfAl9Jm40e7tNWg/6d3w+P9w8/zrkChypHarNnNPBcq9vK8bDncjYP6Umh2LM9ncWdwYpoXhlQ/Mkg2kfhU8aspIEq5zn2NdPpniOPWoo9O8SxC4gPEV0wxJEex3dx7Vk+I9Ik0nUHt5Nu0YdHQYV1PQj/AApXMKkTOnkfflWwcfNjiqoEbOzOzbuxB4ao5izZ8vfj3qvG580gt1zjI4zTCMNC6LqV96gqflx6nFVSkShzI7kcgZzgk02Kd458HaQO+KHkW53KVIbOdwoLSsWLeGJbcGIu8hOSAwHFOlfP7p1OT156VVhiZWLx4IHUZ60kzuCcja3egTiF2p+0DyOcjBAOcUkUN3tLKrEKcEiiKRiQTwtWYmWEOPM3budy0xt2Ow+DZZvizom4NkefnP8A1wkr6yr5W+EbP/wtTQEAXyx9oIOQW/1EnWvqmmi47BXh3j9gfHOpgjkeV/6KSvca8M8fKh8e6julC58rjHT90ldOGa53coxEk2jAA49a14NSRYAn73YI9gUEAEkHJP4msaJYRkGU8/7NWoEiJ25kZuuAK6pcr3C5tWeri2tfK8gOeuS2Bn6UsdzGHLCIcnPLngVQtkglDAB/l55PWrSbA3lrFkjplu1YuML6ILl+OfDDKxqPpnNWIruVfkDYU5HQVVjjYrwkakHH+fzq6kEnzfOoKru4FDS7E8yJY5ZmGws5XGAM9qsRRyHjaajRV+QvMcFcnnHNSKylY2TcWzzzmpbHctRBlGCVH1q0jblILE49BVcZ8xHABAqdZGGPugcj8DWdwuWouUAA5B71NGxJAIHTtVdW4Lbs9ORUqgrhgvXuTUNoC4rdCCAfU04OD1JY/lVffwp6juKtReURuU8+9SMcrZ7CnAknBpvmK33RmgHJNICVW7Gng1EGBp2SBQBKDTs1GDTs0APBpQeaYDS5HpzQA6lzxTRS5ODQAtGabniloAXpRmmk0nWgB+aTNNxS4J4FAC+9MxzmnkEDOaYDkUgFNNpGJA+UijtTAQ49KaQPQUE4B+tIT3oAYwX0qJ1XpipGPeo2PegCu8YwR61VucJGAOpIHFXGPUmqzSvKxDYCqOAKQFOTY7eTuCcn5ieKqRNlnjchse/FWprYM7PuIzVfyd+Ei/1igkngZ5oGMng3ENGflHUVWdkLYYbWHapIphEkjOWODgAHBPNIbQTKJULEseg+tDESERto7yKAf323I+lZkSQtZXLNjiRBnp6mt6S9sYLRIVR9uSBjse5zWBcL5lvKsG5w04JOOfu//XqYybexTSSKKTRfaAjICSGOc8Dk9qqmOG4mP7wqGY8YwanlieIBniKn+8RUNrA9xckJgEc/Nx1q7EPTcjGnIbuNHcmJ5AMDrt7/AI1d1bw7b+SqwM0UmSS0nIwBk/oKhuo5bedBIuODgjp2p1veun2iVnJ8uFmBY5wenH50mh7nNXOnERb471CB/CUZT/KsxUctgk9cVtNcvfT4laVi4xg9B7j8qpratLqYt0RgzE4yMYHrRokXZdDM+YTlN5yzYVetLFqCIzpNCMr1KjrWhcaJNpmoRmZsjZkEevAzWVLaSX+pRRR7IzOOec4OM0K0ldCacXqPjurQHcqOcngHpmvQtOjWaDTbjbCklrHHtXAHm46815rNYT2N5DBPgFgXC+mOM/pXS/26+jwQxSN56jIbscDuO2PanzSve4m7nX6jBBc3hnKNCwmZgpXOdq9GPYcH9KntoIrqHzvsiBfXA/z1rIh1O0uo4RJJ5asd4bdgHPZq04RIkgQO6I+MqDlSByMVNrR1DmuY+paZZNeuRaxZ4/gHWsLU4tNsIDLcoirnA45J9BXT3fzXMh/2jWLqlwtqIphPaW88U0csb3URdDjdleOnUZ9ePSoja+pZz0UenX9v50AQoOoIwQfeqE+n26zwkAACRcAHrzW/fxQ3NnELBI3YQJ58qoVWaXcWZgOuOcZrBh0y6gaBn3bYyzsRwBkHnn6V0QpU5JyvZCcle1zn1/0iWRRksXbOB054pj2yAkS53Z57EV23h3U9T03TraGLSIZbVmEpYyANI2d2f5D8KydbkXUdWa5GnJYEqB5Sjr/tH6mocUlcyfxWOZ+zQopeQN+IqmV3uAiYx+tb7WA3Fjls9ieKgksM87UGKi40jOt0LFCyDDAgVK0UKz7U2hyMEA06eJoUCA4KnII96dp1xcxxwwyWqtA5cGQxAsTz0brn8adhjyPKwq4AJ+72rqtYZdV8AafqUjET2c/kMw6kHt+g/OuOnWCW+d1uJMIxADpgMBz69a7SWzlj+EgSAb5Li98wcgcAn1+lEouO5DRw86DH3nPAzntWbIyB8bWGOnNaDsywhLhGDcg89MVVeQBtkIDk+opXJiyKWJ40zyMjk1GskuzAI2LyRxVo3UbxkSqQQvQd6rHCqAoyx70ykWPtK9doV+gwO1IsiuxDgliOM96qFZN43cY7VMJIkK5VsrQxWQhl8mU/dPGKjRnfdtxgdqnuI/NCSJEwTHLY4qqVZGBBxQNHoXwVEv8AwtXRSfufv+v/AFwkr60r5K+C8sj/ABY0QM2R+/8A/RElfWtNFIK8W8aSGLxzq2bcShxFg5H/ADyWvaa8d8a2UMvjPUHkvlj3eX8ncfu1rSE+R3CSujnEmk+yxRiH5kYNnPGRVt552uPP2ohZdpGaSOxsNwX7czNn07VcgtdMB2C4LMP8+laPEE8iK0SmKVpGkQluSM1Z2oZxJ5u09OOalcWIGyNm8wEYz0pyGGMRNJsKu20nnIx7UlUlJ3Qx8e0HBkkYEAYHFWY9u4goxz6moJp7dyEh5GOTsxzTlkBAOD+dKcpJ2Y7F5CEwu0LmrMZc5+ZQBjuKoo4dc4HpzzV2NsxLtAyDkt0P0qLsDRtbYzKSztjPSryWcakDGfrVexkBjYkntV0SLuA2sD9KhjSuRSKqOq8AEdqtLbgdScVBPjfE2BwatSXcTJsIAoAgxgkA5GakiOyTnoaiQ7k4I4HH0p6Oh4bJFVoPQkVip9qlVs8989aRTGq5JBFIQW3OgwoAzSETbhnFSAkAFvwwKbGYxAGwC3oaUyHcQxAPtSAkXJPoPenbgTUIbnOMinBvXimBKOTT9o/vfpUcbY9MVKWA54oAZ360ueozTC27GadgCgBQPWgnNMZs0oJwMdKAFP1oFIDg89T60ZoAWlBGetNOce9JnHpSAlwCD83Soh7U0kjkY5FLnigBVHB5A570h475pwVSuc0yTagzmgBhPXmmk5piuW3g8elLnIpgIxzUTHP0p7HsKjY4pARueQPWoGVd+QOe9OmmddsS45OTUM5IjOCc4pgEqH7rYB9jmsmewkaQlZFwTnpWjCRNG7NI6lWz6/hzTGbA+tKwGbFZOkhy4I6dKmjBhJBPy88+/rWzbKUtw0VusrNksSenPAqhf4W4YKioGUMF7gnqKAKF6qurBVYggOWQ8ge3tVRYhDAMsx+fPv0q8kvlqSxwpH5VX1Z/3CSgjORx2PvSk9BxZi3lwxZt6gAH5celU5oZ9iuGCAMCSrcirpy86o+OVY8VUkmE4eJyyGM5OwZ4oi01eIuZTIlF+R5cxlkYv8obOR7YP55oeKSCG8tZ2PmBAmVOQCXH+FSR3F1PCsNm5juEYsG2/M2en9KvtYXDW8glikYyEEkqeeuf1pp3Wo7WOct7WS9+0TmZA0YQ4Kkk+mPTpWlY3UNxEspgb7XNcECZhg4C5Aqi1pc2VzIkcP7tyAMKMnkH6+tS2E7tPL5iMpR9yNn14/lWnLG17jK+szytcW6XDZkij3SE8ZHXArK0uS2u/EdmbdcFA2FboWxn8sAVc1eG6a4mnAGwry7t04wBVXwxbWiPFfS3my6jnINsF5Mew5bP44qH7sdA3epZ8S2Mk13FqK7FCxGPyskbiMn5c9etc/eu0tnYM5YSSIQVP8ODgCup1omTT7JMRg4LIC2MDsOe+KydR0xp9FLxFftEGSxL4wg5JA+rYqY3tdlTtsiWFZFtQ8bAnmPDDIIHbB9xTrLxG2lyiHebc9RG4LRnnHH93v0qpFcRRQ2P2mUuke6SSOLljkEgj6elV9WghOqCRJg1rJBlHfOc5yTjt16e1dMJOCtJaGUHdaGnc+LJtqSolofNJIRpipA688VTPiyeTcjWlrwu7/j5yCOfb2rE8RahJqVoZZHEnkhLdX2gcDJxVfS9OF1bQrvQO4JAIyep/wAKtqkocziVc15/FbSxMn2SDlQf9dnGR9KzItRa7vmPlCMJE5wr5HCn2rGURLqUoZchZCpGcAgHnntwK0bSe0maeaC18kLC3ymbcewI6DHXrUVFBaRQrX1ZENVjigRPLVWCDndkHtz+VSJqdqwB+2r5rAfKIScH0zntSyS6a8BWCykhkPRmn3gfhtFYAUHVTnO0Of0rOKUtLDaSd9zfTUVV2Ezjb/CdpGaV9Ts1XLSgj2BrFneMuqgsowetQyKrcZJFQ0k7MUbvU176eCaFGUkHI2kqeRRBNIlsBvddpZW2EgNWQVmTZuDtGOx6VqzyqltCiA7yCoPXv1ptLoD0Ip5bKfdIHcHBBIQ12niRBZ+A/DenxSqpMRm+fI3cA/8As1ecRJM03lJ/y0O365OK734oS/Z9U02yRtotbMLj6nH/ALLVTfMlG+iHucbJN5Lpu2v3YY4FPZ7Z9kuxQMYxj/PNZyu00pLYy3JOK0Ck0lpGzlGUZChYwG4HfArNxsYyikVruI3Eplzxnk5quY4yg5PB7datxw7bc+aBz0BPP/1qizGZP3UWGxhuenvzSEnYrJbMw3jPXmtGdYZbeMhVUL7YJFQz3OxFWA/MfvYqayXewcs27kkCkxSelzX0uOBdMeRwJfMYhUfOAO9V/Elpp0EEUtlFhnc7iC2APYH61aiuIZZS0jGTYgIUEDPOMVS1G5nW3d2LbJX/ANX2A/zinfQ8+nKbrXub3wXkx8WNGXYvzCcZ9P3Ehr60r5S+CmG+KWmuVBJExz6fuXr6tpo9WLuFeG/EBQfHOonc3/LPgD/pkte5V4540tWk8b3zt5piJjBEfJH7taGNuxylvGBKpRXyR3FWogIpQ2xxjjJrVvdStYrVLKO03GRdqsy4C+/rxVGLRppYBIGXruUsx5H5UrBckdyrggL7knmpOXlUDH3cg8D/AD3qxYaP/aDqpnVAV38g9P8AJq2NBtXVY5dSC+jCIgD8TxWsVJdAI7Wyco8kmUDLujIGd1NjJGOOK0dWsnhsrWS3uGY42ErwGAHtVKCEs3lkEH3HFE5ub5mDLEZ2DI455q0sjbQck9sUxLXKEZOe3FTQWjAYZufSpGvMuWc7QuMbRuHO709asm8l3k5JTsQMZqgkcquQV3DsTU8Mcm5dy4A65PGKSeo9i95jMyNg4z1JqxBBHJEHLNyT36c1SaUxSRE4KBgcVoWpwjjGQHNIQuEHmxjIAXOBUKzRkYy2alcEXDMAMMhU1XW1Zh94ZoAuJc7U2knB9eafC4Z8ZOCcGoTExRQdox3FSRxmMZBz3oAnAU9BzUgPrUeOeKVn4A75pgSg0uajUgjKmnA8cigCQZxwaULTAMnrTsmgB/TFO3VHmjd6AmgB5IxQH7DtzTQ68huKbuBYBTxQBIW30hxgZJ/ClHTgUUAI54OKi81+QFz71KceopM+lAEal2fLDipT0pueetBPOBQBGQ69H/SozkMN5357YqV8gHB5qGUbyDHkMOeKAHgkrkDGe1ITxikVmK5YAE+lBOBQA0nAqNjxTicDNRNzzSAgkjHmB8nIpp6ZNSNzzUR+agDNuY2ecMVO3gcVMWJ5xipn5PtUZ54FADk1lbeJYhazYB+YgfrVG4u1ubl5Y43RCf4xgk+tWG9KgcAnFAELMHOTmqs6yGJgkhUZzjFWnwG20xopJOIoy5BGcHHU4oAy4rG4QGXLtGc4GzjjrTREm8yFBk98Vdn1f7OslmBgrMIi5f6ZAH44qNxxtzQklsK1jH1C/NlIhjlaJxyWXrzn/CqTeMNUtkLi6MnOApUYPvTdcjlj1CCdM7OA2On+cVtXPhi11WOO5guIoI92NgO4MBj8sEmuj3YwRXQyZfHt6E2SQxMf7xXNJpOqrqJmWZF+1E5DLgfIPYfWsLxHbrb6iYo1AAznBBB9O1W/DgVFEhIVzuXPqCQR/L9aqcI+z5kD2LetpM6+UkZZcZB7E1lyJePJbOg2Mtsg3shIyVJ/9mrozOklp9oZSFGSAeTwTWU+tTlWX7RH5gIKRsoOPWuOc5JJJDTKcyy3VkqF5TNaqSS8eAcnOB69KoNp7XEUj2spVXG11ZCMd63LXXbq4mddkLbVLE7D9P61C/iGR0MTWJjdxgMUIwTWUcRJP4RtdTnntTZ3EdzCwE6LgqAcfdx/hUd/fR3FzbS3ADKq7ZFVTx2zj8zXUzWiSLll59TWJe6ZEHUkcyHYoXksfQV1OV9yE2c5qV9btpps4929bjduOMFQuBTrG+t7aKJzKyzIm0MMfL/nNTan4ZvYJwrqFkbBKE5xn0IqTTfAmq6lvOPJUHAZ0Iz+eK6pTpSpqLYGHaRyztK8aqzZLEsCf5Vf02KQpqHyoHaABQBgElh6/SvQ7LRTpmjW9uyAtBHiZgNvJY//AFq46/a7im1K4DFHLRpCd2SoGeODxXPOpGUtBSv0ME6TqTOJTGDGhG8gg4qHT5WnlZWCkbjtJOME9autJdx+ZcXhaRmXgsDz06k+1JJZRWl0ggRXW4Ckc/d4yR+Zx+FTzxs0hU27akEtqI9SdH8shTggEkdM/wBamZY4gzR43LjGKsW7Wl1b3F1PauGjwH2scbs4xnt/9asy7T7QALOF0AGXwxOaxlFydzZTt0LAW7uHiGWIkYD14zU93D5V/wCVEoeNcAyehIqPR3lM0bOxEalmKjt2pbecS3RiPCF+WJ4GeBzWnLyrRidmybQtOeTxTpdudzJJcx7iVx905I/IVa+JF0bjxpdgHiNUi/TP9a6Hwhaxy+L7URlmjhWWXd24XaP1auJ8UTPd+JdRmxy1w/6Gp6iasZsMQFyybmXAHIFXiZATF95R/EKgtisUck0jgyPxjHNOSVJDlWJCqAw28E+tNmb1Qydwz7CCNo/ComkJUJGwwOppuN5Y4OCeuKsLptw1q1xCm6LzAmQvzEnrgelJGd0tyICJScMPmGM1fsC9o8hjlBGB8o5BFZ11bPFKY8MTnvV+OFIIdzEhmAyO1DMq7SjbuTQM32uSZUDOynj+tUTd3WoSFVDCI/LsUZzWhbWtzcxs9vE7qDsLA4HIq3BYtbvazROkbwtllPGaRze0hB+9udj8I9I+w/ETT3VGAxKCXPJ/dP2r6Ur58+Glw0vxB0zBYLiUNngMfKf/AD+FfQdUjfAzlODlLv8AogrxbxzPOPGWqKsroq+WABjH+qQ17TXknjKBX8XXxYAg+X/6LWmdjOIFyqlR8xVZMkofvLn9O9dDNc2t/DB9ngkiijyAGJz7/rUAsoB/yxQj1Iq7BCeABx+lIZWjt5GXdCSPlIABOcULp91Jjdv/ABb/AOvWvHGU6dKsqgHWuhYma2COhJE2dLhtnA3RkHOc+tPXAHHWkXA7U8KMjArFu+rBkiEsOlTopboMU1FA5zUokRedwH40hEiL8pp6gN8uTUX2iPHyuD9KkWdG6BifYUDHNCrrtOeuauQv5SBQM1U849PL/EmnLK/Q7R9OaALj/OQeePenCqgZuu808YPOST9aQFkFSPmI/OnbhjAPFV1K+gHvUgbH0pgSqw2gZJNKQTjJ6UxRu6A1II5f7poAcoAGAaeGqMqVOG4NLyKAHls8YpVbI4qMzBe3HemCdSrsFwaBtIs5NP2yhdygVWilLr9OM+tWY5Bxvkx7UhEKZcEtxzinqAG4psijny5AQfSiFMZDN1pgSkkDignC4JpGYKTjj61G7q3cfhQMjilO/BJNTbqhAAORT80CHg80buaZuApCc0AEjde9VSXJGcj1wasMQKjPNAD93yjtxTWbC0wt2FNJ/OkA4t8pzUe7P0pCeMmombP0oAC3aoi2GIFK7dhULHHSgAc4OBUJfafenFgOT1qEq7k+WAWxxk4FACu2OQKic/LkDmlw6gecAr45AORmonYckmgBjtleetZ99qE9jA0luQZFGdpB6evHer5RniaYKTGvBYDgfjWRPE0txKSymMgbT6UJBcrmzN1tnkkIZnMjAj+POSfzq6CQOWyajWREULv4AxUMl0uTt6j1FAD5U3DHXPrW0fh/dTW8MEupK1umWCKpXLHoTg9hxXMtdyNKpXgA9PWu+SS3bUJTqFzcx34mxbxo7DMfG3ao4YHnJIPfPSi5UVcwL34Vw3VxvW82oBgKwJxTo/hl5UexL2MD2Q/41o6tKoW+a4ubmPVVmxaxo7D93kY2qOGBGcnHrnpWvYWjz6/qVxcyTssMyC3QuwRR5a5IHQ5JNHN0G4WVzmJfAE9vCZG1eNIUG6QupI2j6msiw8Bpea484v8AbI43os1s6My4xlc9eoz+Fek69BJc6HdxRIZHKZCDq2Dkj8ao/brbUtZsJrOTzI7aOR5nUH5AQAFPvnnHXionFPRhFaXOXvfBQsEVpL5S0h2IkcLMznrwB16VTvvBkdzpsUp1F4/3oCRiBxIzA/d29ex/n0rrNWkhvbvStQN1NDpu2UPNHlCCcbcnqo4PPH603zW+xR3sl4yLbXLCzubhCwdCuPn6HHUBuOgP1yVCmndIu2mpxeuaEbDw3darBfI7QkJ5TxlGVyQApB5B5FcPo+toui3EdwhW4E4KbYseWCOue3UivSPFPnt4D8RXs0glW6uIijopVWA2LlQecZB574zXjc0nk2jW0flrFLErMQPmyPeuqDVtTGonHSJ0D3uo30MMFuYIY/JMaTtMqPKM4GGY9fXFZi6Rq8BufK8wsFJBjn3nOxgDkH1IrHs9RvdMiSOBVkgU7irBWOfbcDirX/CTRtMslxpkLkEZzbrn81IquSm3uUloTLc+KbeBkL6igRV2kFjnBwfr1o1C2uI7eK6mjSN7n76Dgq6/eyO3UHHvSReI57qSYW7SQQohxEkpgAXvjaeT7kmsJbxXXYbiRkDsyhu2ep+ppOEUtGJrQ9N+HfhfQvElvqUmtCQxWqxnJmKKud2SfyFd1/wrbwJd2RuwGa2jXBcXJCrj+Vcl8IrG5urDU7mGNZUiuIHCMcCUruJX2PII7ZxXqlzb3V9brciwSGSOdJvIdhulC54YjgHnI5PQVjCLirCpwSjZnM2nw/8AAp0+6+zuWtpSvnsbo4B7Z9DU+m/DPwcYDLYRzPFIeWW4Yg44rSn0m71NdRklslgW6mtmWN3BYrGwLbscdB0ya6kAAYAwKtNsuSS2PMpvhp4fW9ns9M053aNFeZpbt1HzZwowDzwf0qxYfCjwVqFhBcrp84Vxv2tO2Qe4P0NdbMl7Y6pc3NtaG6jukQbVdVKOuRznsQR+VSadb3enWtpZeSkihGMswfAVyc8DqQSTSW4NK2hxeleDdJ0HxaLWzsrZXuI2LkXLl0iB9DxknHA96zpPh34Wf/iYSaXJ/Z8k/l+Ybx/NyX27iMYxu984/Ku5m+16jLbpPZR2zQTLJJc+apX5TnCY556c44JqleaYt3eBPs5gtxdLK0v2oGA4YNkJn75x6dSTS16F+71Pn/4m+H7Dwr4yk0/ThItuYUkVXbcVJHPNc5a7JIlXIfGWZMH9a9D+LTw3PxRiRDE4MMQLZ6de9c7cabokce6O6uhI5w6Kqnae/TFUzneiuY1ojtcRRxgMkbAsTyBzWxHfsLjZkBASeOCcDpRp+m2aM8iySq7J8ikY7nBP+fWs27QJNKpffs7j171Ox5FaUa1Sy6Fm6tbSe4EzCSOFhyAM0t6I7uaCRYR5SDY8ecAKOlUI3YphSSRyQe1T2ytLvG/bIq71DLlW5waQvf6vY2FvYIIVSJhHEoztHJ+nvWbJL5x+V/vHII4xTZmjiwrbmPUqBVK3kaKSScR5GCAM9DTM6dG/vHefCmSU/EzSEkdiAZuC3T9y9fTNfMXwkb/i5ej/ACEZE2ST/wBMXr6dqkephFaD9Qryvxe8Y8V3oaRQRs4J/wBha9Urxbx2G/4Ta/YMwx5fQ4/5ZrQzqIxPAf4wxHpzUwvExhUkP/AcVhrI/B3E/U1MpJ53UAbK3zEY8vH1YU9bx2GDsHp1NZCnPIPNTqe4NAGot3IeDIB9F/8Ar1IsxyBuc/8AAsVnR/PVxNuMd6YFxWXuM/Uk1KhVTwq/lVIS87R/30elTK4QYyDn3oAvCQjvxUivnkGqPmhByeD3zUyzIACCKALquD9alVj0NUluEK5H86kFyuPegC5uYdBmnoGJzVNbggfdzUouD1ApAXQc9eKkQlWB4OPWqIuTj5uKUXPlgNkkD3oA0HvHAbDAY9BUcN5LKeTxj1rMa4FwME4LP3qVJBAjLnJHNMTNQTDqxpfOHvWctzvJVlKmnhs9GpAizLIrZJHQZ4OKlSVG4AxVBi5//VTlJA4yDTGXg6r04HvTGYu20YJ7VWTjq3XtildwOQcEd6I6MC0jhfl6EdeakWX5uBkjtWfG+cknrU8OWnjCtgkim9wLskRCO7MAB0x6VWSU46VNqu5Fj+fg8YqoHyeBSAsiQn2ppkx3qIu3QU3nvSAm870oMrY44qLJ7Cj5j1YUASFzjjGajLOevFNPy9GFNJJ6spFACNMw6UFznjmkLRA4AZvqaiLnHDBfYCgCU7zy5Cr71XlcA4STd74phJJ5fj6VG5UDg8/SgB376QFYiu/BxuPFQP58KKJnUyYG7ae9LFIY7yFypMY3F/YY61HdSxTzmaIEo4GMcg0DIWckkl6gF/FaXSNMXZWBChecH1p7ZJ+4cCqF4klwUiSL5d4JIPNAF24mS6mMkYdUbkBuD9arOTnbuOK3l8K3oQL5tuAO2W/woPhS9I4mt8/U/wCFKwGVYMt0sumPemEON6qVyuc9z2rm4JrtNZnt5XEsEDZfy0ByobqAevGa7RPCGoLKzi7jT5CB5bMDntk46UXvhSc6Y00ktol4sQWSZ87AoOSen61S0Fa5i6tpyWscV3bSrNZzgFGU8rkZAI7VjSAHgda2NP8AC9zfORDf2zyqu5w4kUsD0bBUZHvVi88KXFhGr3N5bKXO1VUOzMfQADJqB2exz1kSNTtR/wBNV/nXQ+INe1u3127t7SaQRxthAqA44+lRx+HGiksb439u0ckyhAiOWYg5xjGc8Hr0xVrxNBqWmXN7q7eRLZGQIsa5WQE+uRjrTSb0QPTVmXa6x4kLFr3VjAP4f3SlvyxxWlfeIdY2QKl/DDs5ZguDJ9c5/TFY1vrmnpDJLNChmdeCHztP1xXL30Ud7dSTi91Abj0XawX9RUyhVTtYzVan3Oyu/GusSTkW8iR7UCnDKwP+0Pf61z1z438U28hEl6Y8ngKi4P6VzrWKod66jdB/7zwgk+33qY0N3bxFnu2nDfd8yFlx680veRanB7M3X8feJjwNTb6FF/wrd0HxVrep6D4ia6v3Z7e0DxMoClDnqMVx6wWwssyzIwGcMVKn/wCvWz4OVJND8VIpJH2MDPY804ybHdPY5i/17WNQtmtrzVbqeBsFo5JSVOOnFY7KFBPykAelaj6eMfeaql1Y+XbyMpYkDpim2MdDe2UMf7yzhY+uOn5/jUFze2cp+WyhVsZ6AZFN0xY3W6eVFZIoS3zY65wP50PdwRWssNzE3mRREQ7RncxOdrce5yaiME9WyUrlKYwOvywKh9jkVReNewWta1W11KzllOUmRgBgYBH09aY2nJn75q1oNHc+EHmh+Dnit4GdZRMmwxkg5yvTFO8P3up+FxMuqE3Mz4YxzksQMdVY/wD161vBumC2+FWsSXBAt5rmORiQT8iuu4kD2BroWKXmmSywLZ6pbHBgjdgOPTdyP60NspIgn8dadaeG01icy2kTuURHXcXI/ugHke/tXLXPxo00AiG3vpB6kKo/9CNX9Sm0XUtPtrfxDpVvYoshjtoHmzjGORtxis5/BHhSRflsDj1Wd/8AGpem42YOo/E3TtUBSWxvV/245trfmCK3fAXimHUPEWk6dZ6jdeQsufKuWxI3B4JzhhVWTwD4Z6rb3Cf7s5/qK1PBvgzRtM8X6fd2r3fmRyZVXkUr0+lJ2ZLQ3xY81h4RnjYNvmu3JDyFeDIx5I7YArmJJZZPgfds7KG/txR+7PAHldq9B8TX1lp0zJeTQxxlyo8w8Nz6d657xDb2Fz8JrgacIY4m1dGPl8KW8unHcmTUVdnkul3gstRS6kBYIDkkE9RjtWnNcC4dZ1cp5iliMdSST/WqcumwrbtJHPtZFyS3enFy9umwBtqgAZyBgVrsjlq1Y1IWiaC3Z327iLKwoFOTnJ55J/GqM0wSNkZfmZi2fWlW5k+zeWyhA3tjNMct5RbcN2MZHpUI5IRtIZa/PlR1Y4PPSrVrI9s+eN4yF3dhWfZzPFIcLlSfmGeoq5DcgOzsox/CCMimXVg+iHX97v8A4MOep/wpqozxhVyq9SuarzX2+RkwMHjpVy1tmuYZESQxsqGQN1Bx/DQJR5Y2eh3HwlZv+FkaSHQAfvtpx/0xevpivmT4UF1+JWiCRjufzjjOc/uHNfTdNHVhfgfqFeIeP7uKLxvqKMef3f8A6LWvb6+eviW+PiHqo/65f+ikpnSU1v4Rxk/lUg1JF4VSR71gJLkYqZZM8UWGbP8AaTg8IMfWrMN+cZbH0FYKyAcGrMGSe233FOwHQfa5CodOFFOF3LLyHKj2rORiOMjH0qYErjax/SkBomdpAAT07CpYncjGCfcVnpuPKlvxq5Ez7QNpz/vUCLys/AJYj6VMH245PP0qkruARsUn3NSxrKrbtsfPXigC6HJA25/FulTq4PBxn/eqmiuTncufpVlEdurYP0oAsq+OoBqVWz93H5VAqOOCxIqZY2HO4/nSAlDHuP8Ax2nEMV4z+VNUZ/8A2qkEfcY/OgCGGSz88xXNw0OOemc1qNc6AYVTzsFf4gGz/KuH1FJf+EojIlwghfK7evK96ppeXf2CS8dkxuKrGF6APjJP0rpjSg1d3PksRneMhVcIKLV7a36tpLfy9Ds3vrLz3KSMVzwWHOKf/aNoBkygD3Brj7u/a3nmQBSI4RIAfUsRz7VHfefFpl3586SDyiRhdpH+IpujDXcwhxBjpON4xXNa2j628/M7X+07Yf8ALb9DSnVrRFLNMoA6kg1yNhdPdiR2GxVO0RsMMPdvrSagAz2kbjMbzAOD0PBIB/HFP2ELXRC4jxqq+ynGK76PtfudgmsWMihknVlPdeRTJdb02IgS3UaE9AxxXI24WLU7pIwFj2IxA4AY5/oBTr+1F9bvEjxhxgMWXOB1x7Zo9grXW5S4lxCqqM1HldtbPS6Xn5nWPrWmxqrPdxIrdCTjNWra8hufntpt+3BytefpAb/7PcxyRRYhx5RjD4ye3txXR+F53lt5WwoYHYdvQ4JGR+VZypJJs9DBZzXr4iFGSWralvdaNry6eZ0BM8x/eMxHXk1Mox3qEl/71NDE9WNYH0pa47mk3IOpH51VJA6nNIZFHQZoAtGRKb5i1VMxPak3k0AWjKvYU0yjvVbc7fdX8aUqFGZJAPagB7Tc4GKOcbsfpUXmIPuL/wACams7kZMhNADmlzxnj6VBJMq8Aj/vmnMxYnc2D/OoJY8rwaBlG+uGUKy/NtbO3HX61BbXbtuZhsDMTtA6VPIAy89RVVz5aMxUkKM4A5oAsNcEjjPvxTFuWM6AE8sO1Vba7S7j8xFKjOCDVqwC/wBqWqkA5lXg/WkB1ME6S30sl3qUsF4ly0aWwfA2Zwo2fxZHOffqMVSvr1gs1xJqU0F+l6sSWwkwNnmAAbO+V5z71V1LxFc2+q3ZVoiYJDGqqimXbnHGe1VH8VXxmkkMcYaIY3SRruxjPBx0ocWWpK+x1WmRz3OrajPcXU5WC6McMQbCBdinkd+SetT+IUZ9En2qXCsjOq9SgYFh+QNc9Hr1/ceHb+684LLE6BXRQODXNt4l10Sg/wBoybfTA/wo6WJ5tbneG9tbrxBa3FrPHJFBaytNIjAqqkrtBI+hP4GquqSJdaxpl0NR+zWD28oW5QgZYlcAMRgZAP5GuDfxlqVq81tDKRIsfmfcAVu3asZfGviG4v0t1vnmgaTBTy1249MY7VapuSuNSSZ6tC1vfw2T393IvlXb/ZZ0PlifGQCSOOQT0xnHFUPGser3egapb2tpLcslxEYFVDllwC2MdQDnmuVfxTrDIEN4xUdiq4/lVW78Ta7PHs/tOdRnPyHafzFRF2d0TO0lZ7GPZeGtbvrojUdC1BIlX5U8lsE9znHWqEnhzxNb3UqWmi6wsAc7NsbdO1az63reONWvgf8Aru3+NV213Xl/5jF9/wB/2/xqpTcndszVOKVkjMl0HxZIRu0bWiR0zAxx+tNm0PxhKu19G1l1HTNsauz+INfA3Lq98PUfaH/xqufEviA4/wCJxfj/ALeH/wAaXM+4/Zx7BY+EtcuI531HQ9UZlBEaGBx2PIx3Hp71v+E9J1jTvDPiR9UsL20BsAimeMruIJzjNYH/AAkniHaT/bGof+BDf41Bca7rN3A0Fzql5LE/3keZiD9RmnzMfIlYpZUjJeStG58OT/2Q97DekSKhYxqeeOoyTjPWs2GRYLiKV4kkVGBMb8Bh6GrWsa5cok6QWEdvERtjgifK89wPQg1KTNEZMF7LJslhiWMvHgzMco5HcA9OnT8q9Sl+C738Ecp1wo7pk/uc9ecda8espJJr+OArhC4SWNRgIB3/AAr6Yh+zfamGoyXK3odRaBCwJjwMbAODznOfx4pOCTuxJXZwo+Bj8btcXA6AQEdv96hvgY+CF1tMEc5gOfw+auy1lk2aibqS5XUwx+xiNnBKcbdgXg98/jnitWysmn1/ULm5aZlieP7OrMQi/uxkgdDzTuVy2V7maPBcsPgSfw7b3/kyPEqLcIh+Uggk4z3we/euE0j4Wa1DcSvZeJbqyfqd1kyI/wCBbB/HmvXdXS4k0i6W0LfaPLJj2nBJ9AaoLqaajqNj9ikkMUIeS5wpAUbcBWz3yc49qHbYSV1c5m/8Aq2lwJqEj6lNGcmRLdd5J69xhT0rAsPhtPpcsmoaddXUcEzlXsXs8lTngqC/A969Nm1ixa0jkleWO2uAwEpVkCkdieqn0+lV9Pu5LfTL65xcT2sblrXeCZHXaOBnk5bOM0aDSdjg38I+I57a4ezng86PbtiurNo9+fRg56U/w54Z8ZWfiOyuNQi00WiSZkMRbdjB6ZrtL9Lmz8JObm7kS6+V5JVfGGLglQew5x9K4W61WdJLh/7avI5xyE88jBxxhe+a1hS5o3PEzDM44bEKm+bVX0Sa3t2uY/jjwpqeueLYXk06/axhVm3QQMxYljwCBgdBVXUNNvdK+ElzDe2U9s51cMiTKVYrswD+lU/FWv8AiEajiLV9Qt9tujyLFOwCnvxmuQ1DX9av4FXUNUubmNTkRzyMw+o7VE1yuxpSxKr0Yy6tGc6njKg7v4TzTVhjZtsZ8snnIHAqfIkihkV23Zz+FQSy4LMg5Yg7v8KhCV9kR3JkiKpI6u6gYcVXYy85cHdyac8zTIZG5IbvTpIxJDvEi5Pv+mKZvFcu5UGXJC9+laEem3sli1wigwpwcNzn6fjVN7eREV+iZHAPNbZkaDTERCRCW3EfxH60hVqjjblM0W6l1SSQRZ6Flz+dWkjkt9kbOGiGc7eoHU1WuZnnvBImdvYkelTGa4WUEkE46tzQiZ3kjvvhfffafihoiIqpCDcbBjBx5MlfTFfK/wAJmkl+LujMyqNon6HH/LCTtX1RVI6cPHlhYK+d/iYjN8RNVIBP+p6D/pklfRFfO/xMklX4iaqFYhf3P/opKZucwkTnkKR+FWktJpFBVD9elU42ldwFYn8a0UUqvB4ouMfDYSMfnIH4itCG3jj4aUfQY/xqkCcA1PGA/JbB9hRcDQQQjgsTVhfJToXrMWSPzPL3NuxnAFWAoH8Dke5osK5oK8Q5G78TU6zQ4O0Lux3PSsxQS3yxfL6lutWY41XkIoPuaANCOePA+VAfX1qZboL/AHcfQ1RRh3KA1YWQKMGROe4FAFxbonkMPwWpUuieufwFUxInaU/gKlDxnHMhPsKALqzvjo5+uKeJmb+E/iaqrnoEkI9zUnypgmIj6mkBcWbavKqM+ho83nO4/nVISY64/CpAwPQ0gI/sK391vZWaVHO3b1FaMXhXzLcxMVWNs5TGc561BY6oNL8yRo/M3tgjviujsdcsb5fkfBHUHgitp1JRdkeFg8qwleiqlWN223u+78zBXwYlsrmEKxYYbIyWHuT1qmPDlvbK8bRlQ4wwYZyPTnt7V3qsrjKMGFDorja6Bh6EVHtZHW8nwbveO/nL/M4caTAJTIHw5G0nbyRSy6NBPEUkfep7EV1E+jwyZMRMbenasq5tpLRtsgX2INV7afcj+w8Amn7PVeb/AMzGHh60W3eHrG3LAjO4+5psWhWN9Es0yHf904bGQOx9fxrU3dcH8qr2ZP2Ycnqf50+eTgzB5bhqeMhGMdGpN6ve8fMin0Cwn2+YrkqMAq2MD047VesrSGwTbACFIAxxxSbjjHP50ZO3v+dQ5ye56FLAYalJShCzW3kWTIabvA7/AK1XJ46/rQGGM5qTsJy65pN3GR+tRb1xwKPMLcAfhQBLksvpTA4Q5PNNIbkk8VT1CR0065aMkSLExUjscHFc2MqSp4ec4bpOwI0ZLvcoCgqO4qEvkd659LSQ3VpE13dFJ4Web94csw24/wB3qemKas7tDBFdTyR23myo8u4qTtbCgt2yO/fFfH/2zjuk193r/kXydmb0lwkQRXbbvbaue59Kl8zHHNcxMBPbW7zTSG3hvQI5t5XKY4JI68nGamu7geddtLdyQzRY+zIr4DDaMED+PJyO9P8AtnHaJSX3f18w5DeZiRgcU3LY27jj3FZMCT3OqStPJIoiijZYlYqu4g5zjrTLB45vLaS9l+2yAiWLd0Pcbf4cetS87xyT99fd3Fymn9mHOSaq6hZiTT5F8wRgKzM+fbj8Kow3l/MJ413GaxiZXGP9ZJ/D+gz/AMCFOLWzafd/Z76Sd2tnLqz7+cdf9k+3H0qv7Zx6esl9w+RrcdpmitaxMxnWTzACMdB/nNa1hZBdQt2+XiRT+tc9dy3XnLGZ44IkgQxb5mjyccngfNjjj9Oa6nTC5ltDIQXym4gYyfpTWc45TgnJWbXRClFo5jxFpyf8JPeuL1YTI583bGW25bIycYHbqagbw1forH+1QsfV8rxjvWzqlxBb3muQTsolkmcrGTzIGUAYHf0qSK4t4bQW91KgeKANMrHouOSfatMVneNp1JRg9E2tlsEo9ixpkcdz4Yv0jzjfHklGAPuMjkVhSG2l22/zo8h2JvgdQW+pFdLot8uoaFfSxmPyfMQRqrZbHqfTPpWbGDd6s8pB8m1Hlpnu5+8fwGB+Jq8RnOLpKDva8bvbe7Xb0C1m7nM3Phy5ghFy+qJI8m0BGXCnuQPyrFjijh1PfJGUVt20KMHcen4c1uCbVJbqDULy3c6bJKYt7qAo5IGPTp1qQxRM27GffNfZutOUFchX6iqqGMAjP1qM2kYbOSQe1WAq0Y9zWYyg9jySGyDVWSxcZIUn8a1z9aZzntilYDBaAsSPLbjrk1AbODdl98Zx25ro2UE5wDULxRvwygj6UwMNdPgdeLlgfQ1VktIFbDSEY9BW5NZh2BV9pHbHFQvpT3CsUeNdnUscCmlfRE1K0KUXObskc7PFDEQ8kmIgRuyccVBJJb3V89zHujU/cCnoK3dR8I6lc23lx+QckZBfHGayv+EC1sSghoVjJ+ZVl6DPQcV6GFUaavJO/wCRxRzjAL/l6vvHWM4TVrFFdXLTxj5k5+8O9eh+JfGur6b40vbJL9o7SCRFEaxgtyueOCT0P51w+n+C9dg1yznEsRjWZWYFxkDcM44ro/GfhzVL3xjrE1tLAtvcOmQTk5VRg57HrRXjF2VNDlmWBqR5/aLlXW/Uua3491S2Nu0WprbFwQyOgbnt1qta+OPEU00YfWB5RI3MsS5xVBND1IIhntLG4mRQqylirDHfpyaqPpt/Jdu1qbI3OMGLIwCOvNczozSI+vZfK7jUTt5nUxeIPEF/l9L8VxT5H+pmgVHU+nT+lTaTrPjP7L4kXV5mCwaa0lrIiqP3nqCK82u/CHiLyZZJriBQzbmPnbQP0rsPAGmaxbaT4i/tG+jnt309kjJmL7W/HpVyocivzX8ioYvBVP4c0/Q4s+PvFUVhcedrd2JlK7Qz4JB9Pxqa88X+NLCa0huNenBmXeAshJHseKzdX8I6lP52oJLbvbW6hmw/OAPpVXUbw6vqiXIVkSJQqBu340kkl7xpGeGqxk6dnY6mXx7rc0EduZp7hXHIlmyOOueKz5vEOrWkpknufPXGPlIBQn8M1jh4S4Xy2Ixzjua1vDumW8ul6kt5Gss8xQI5IZowM857ZP8AKufnlsmeb9XoyftKkV81cxL7VLvUJnllmkdnG35m/hHTOOKqRKzyD5XfAOVUZ5/z/Kpr63+x3/ljBjBIBBz0p9rdyRNKkM/kCQfewRz+H5fjSvrqdcOVRXIrIqNM8TbVIVW6imTs6RgM3JqWPYAfNVyx6cZzVWQhmaTPA6UjSMVfQIWR32A/MRjbjNPmh4K7D07HmobeFrmVhEyoQNxLH9KdIXBIcszr1HpSNHuJGblD5kJKuD2PQVpG686QeYmeMMB0qrDthcOPm3fwk9amaVhDkqA5/urnA+lMxqe81oRSownE8h2JjaoXp9KhFy0koXaTk4FLNL9oIB3EAcEd6iEOxiGIVuoNI0jG6949A+Dsa/8ACz9Kd9hYmYr6/wCpkFfUtfLfwcjX/hZOjPltw84HP/XF6+pKpG9F3TCvn74k2hm+IGqHoD5WOf8ApklfQNfO3xQmuIPiDqjIUZP3XAOGH7pK6cNyc/7zY3ja+pm2mm2gthO+q2cKnOFZ8tx7DpVeKUSlvLDMgP3sHBrOju7WMYljxIec7ec/1rShu7Ty1me4DBegHUfhXTLCwfvX0K5S3EHRtw25/wBqupstHgk0V9RuL7LY+WG2Vd2c45J4rlHnm8pnW1xEGCln5zVjR7iKWWRLiV47QkHcpxkY9+K46sIx+B3JasOu5njnRIzhS+zfweferqwieOVklbEODKrcEdv54pb3UdPsrgyQm3vA21XMeUddvAO3p26g10mlavoF1avBeaooLn94JgfmHse1ZuWhFjmUKL2dhVlGi6iBz9TVvWNOtreRZtMv47mzlJ2YbJT2NUVSUHAfP40hllXB6WwB9zU6yOOsSflVZYyBk78+1TxxK3DBh9aBE6yyg5Hlr+FTCZmODcAfQVAwjhjLbcgdTmo4tS04rhpFDfQ0h2LwkAB/fux9AKRWZz3zVNr21LjbcJg/hir6QLt559DSGKHYdalTa4yX24pixEHkce9KYx7CgBTarcxkbyrKxw2Mj8RUT20trYXG4csy/NHzwMnPqKR7o2qAhC5LYCg4q3DeI6K4YDPZjgirqP3jgyz/AHWPz/NkWjaxdxvIPPLxxxs+Dz0966fTvEbX9qdkSrOpAYOeADnnjrzx+NYAW0kLkiNXdSjMDg4qxYQWdsu4TsZJW2SDjG08cfTg/hWeh3ouT6rPMx3MwGMYBx/Kmo0csZQ9fcVUWC8lkKQWsku0lSwA2kjjrWlbaJqDjMzx26+i/M3+FIZT8pQoIkBOORjpVa1A+zDnnJrcutNt7OzYKztKTncxyTWHaEeQAfU1rH4H8jz6v++0/wDDP84kijeSA1SJF6sKq/b7NHOZQD34NPGpWR/5brUncTCMbyD0pyoBTY545V3RsGHqKdv5oAfgAAAcUYwcimb6QuO5oAkLZ61HIdy4AwfWk3r65pu7PAFcOZK+Fmn5fmgLSQDDbkyQm8MGAGPr657VTV1k3q8UZAOCAcg/nWnqptY9El2hEmQhY5COp4Of51h2t0s0Ekqjgvn8a5sXgcNCMXGnFe9Hou6EW2HGwoAPSkwnykopI6E9qYXZlHzdaAQBzXd9Tw3/AD7j9yGPJYnpio2H3igCuRjdgZpdxNNY46c0fU8N/wA+4/cgILeH7LEyqWZmYs7seWY9zUcrSlWESspbuuOasE881G2QcgY9KPqeH/59r7kDdygjXgP71y5ByN2OKt299Pb3MUrszKjhioPXBpZEwcg5J5qBhzzQsHh1qqa+5AaN/qGj3V01y8eqB2JOI3QAfTNUG1LReQ0etnPB/eJUJG7oKrugBxjmm8Jh27umvuQGq2tWEGjzWemxXiySspLzleMfSslry7eI4uHUjuOKAuB70nAk56NRLC0JbwWnkgMJtF1OaSVYpXmVslUDYyfxOKvWlnqNtDbrqCCOVVYMoAIPPGD9K6Gzg+dCSVZR5gB6FTkD9QaraiJWlLs6mM4CgHpXSloRze9YpA0EjtR7VWuknlWOG2z50kgRQD1JrKpPki5djpw9F16saSdrsn68U046VmXFtqNuFJlEgLbMwyB8N/dOO9LLZanGUHmByziMiOUMVY9A2Ohrg/tGP8rPf/1bqf8AP2P4l9jxioyarHTtSE7RNNGrIN0mZlxHzj5jng57VGLS/a5aHzVG1QzSGUbAD0O7pT/tGP8AIwXDdR7VY/iWmpsimTTrsBmXAUkr9agSw1KS4khB5jTzGYyDbsyBu3dMc1DcC8sJPLeXiRchkcMrr7HvTWZwjq4s58VwpXr0/Z06sb6Pr0aZvt50mpJbrcusKwByVxuY5x1xUb37pbEtIokN35QHfG/GMfSszT5WjtJ7uZ5Wig2xrGjbdxbOBnsBg0zUD5UsF5bs4FzHvG45ZSCQRnvyK6HnMLXUWfMx8NsTzqEqsLK3R6ta66dV/kb1lK8uoLK92YilyIxFxjGeM9yTVnW7mVfFd3CQYoDcf608722jCj0+v4Vh6bavcQz6jc3EiypE5tiPvMyjJOfQfzP1pTelbeC41GW4uZbsliRLt2qDjPQ5OQfypf2tC6k4s2XAGIjQnQVSLba6PSye2mtvP8zcl3GF9n3tpx9aylaNrDTFiI8wOmAOowPmz+Gc1yviPX9V0PULyzh1DmEAoGjVi/zEZ559Ky/+Eu1Ka8v7iwmiWOK3Uqfs6bmcgDrj1yfwr2oN1aSq7JnyVThXF4OpKk5Rdno03rp1021/q56bcXFqsJlkZWWN+Mcnd6D3rR0CAyWOtyzMI2ntjkKR+7A6e2a850fxPZXVgg1W7SSY/M0UdrnDdyTtxn6V0/gS8muLXxEGkhWH7EzKRAEC89x3/Gs6krrUrBZDXwslPmXX7vu76v8Aq8WoBvsd7ZmdriNrZmJbBKn0yB3/AKV5i/yxTBceVnpjkV3N3fpp0Mlnc3KiKZMgxwhAQR7DrXBPO0G4Ry/eyDxk/nWNWVzrwWDq4SMoStq+np6IqBl3Z3E88Z6Vo2F1JFFuB253LuzgYPYetW7G1sptNHmrG8xJ/eZ5x6VmNbzW0bK6fIpxk+/SsLHQ6kZ3j1Qtz5RXBDEjIznjnvTorlYrJoGWORgdvvj3qCMR4bzslQcBN2Mk9/pUDogyNwWQcNk9aDZQvGzZKzCNgxUkqQ2MdRWa6BzKysQCcqMcfSrWWx98EY2j6VWYsUGFO0cke9Ox0042JrNoVyrIPMPy5zUs06CQxJlWP3mHOfWs1ZDG+UbB7+9XLZraWDcyr5w+8ScZ+lIJxtqxRIoiMu1j23dRUUcirE52bpGPU9hVhTsYmPHkkgsvt3phtFWRmV93PDEcBaLDVmQMypHjG0Nyo7Gq5Z9wJOPc1LKyMhUtuIOagT97IByBQaJHo3wfllf4paIG2YxPyO/7iSvqmvlT4NwmP4o6KVJ2kTZB/wCuElfVdNGlNWQV4t4++H2t6540vdRsRbeRN5f35cE4jVemPY17TXiHxA+I+o6D421DTLe1ieOHy8MzHPMat/WnZvRGiMmb4V6/IQQlqfk2nM3f8qt2vw01m2jwLa03g8HzQc/pWJ/wt/WP+fOD/vpq9j0vWNPl0LTLu+YRz3VskzKu5tuRyTjoPc8VrUp1acfe2K12RwqfDvWPvT3KleCYYGC8j1Y/4VXXwRrULtALaECV3ZE80Nhfc/jXqd5e6TYMq3Eu0su/jc2F/vHHQe54pklzo0d1BulBnljBjVCzEox64HbI61hfoKz7Hk6fDnX1H/HtB1P/AC2WkbwFrYmEP2OIyFS2BIuMe/PFe0SwW8UTyOp2opY/MegrDt5p1ks7+5ghFvelY1VC2+INymTnB9DwOtKw1dnnmk+BvEtrK3n2kflsOgnU8+vWtgeGNbQ5WxQj/rqv+Nei3VqTbv8AZ2SOXHDSBmUfUAj+dc/YatLNpM10/kTyG5Fvbqm5MkkKC4JJHPP0qr2BRvqc+nhvWTw9kgHtIv8AjVgeHdWAwbb/AMfX/Guhmu7iyW8gvPJM0dq1xFLHuVSBwQQSehx371zi+I7/ALuv1xQ3fUlqxXvNA1SWKa3itmabYDgMMYz9frWaPB+s99Nb/vpf8a0p/EGp2/2i6hdGYx52FepHTvWEvxL1cMRLFD+Gf8akL2JJfC2tfPFDpztKuCVBXgH3zjtXSW2iaqtrCslm4cIAwyOuK5SL4iawkssyrBukxkYOOBj1rrbPxLfT6dBcyOqs6BiAOKAJf7H1Mf8ALq5H4U06RqDHm0kH4U0eJb0nCyAn2UmpP+Ejvgvf8v8A69AigbG7aTEVu0jpJt2Y6kckVV/snWc5bTJiT/sVbXVb2CczwffJZyOxJH19hWcPiDqwOCIc+mw/41dX4jhyz/dY/P8ANk/9k6r30ycf8Apr6ZqUajNhMpPC5QjJ9KG8f6ogUk253DPygnH15qKXx5qM3lktEDG28YU9fz96ix33O60M3dppdtBLbyqVHI2HvzVi5l1JmZYrfYv95v8AAc1xaeO7uOZ47gqQACpjHf0pH8fX7PiGLI/2hRYVzpibveWME0r9AWjKqvvjqax7WCZoPNELmMZJYA4qpB4u1qZsN5SqfRDVu31K5j09rVJNsbAj7vrWi+B/L9Tgq/77T/wy/OJkx6VflBusJyef+WZpTpV4qknT5gAOSUNRnx3rakrvhGDj7n/16ZN441mVXQSp5bDB+TnFQegXtLSaCOaOeFonD52sMHBHFaI9cCsvTdRutSM890wMgYLwMcAVobj60xEham5HU4qPOT1oJAoAcWA7UigkMc7cjA9qZuyelPLErWOIoqtSdNu1wM+6s57lVV74EqTgsSeKs2dqbS1MRlD85GBS7QWqUnC8CuZ4SrNr2lVtJp2sujv2EIM4qTOENRg9M0pb5Tiu8YAkkfWpZG/dmq+7lc+tPlbERIGaAY1D+9Uk064J8sY6ZqFP9YufWprg/ujgdKBEUPVh+NVbgHzMmpoWxKPcVHdg780DGQPgkdjUVyAr5FEbfvBT7nlMjqKAKm7HJqGZvlLenNPY81G43KR2IpASXVxJqGnxWAkdZJV25TG7bzgfzrPs4TYK1s08kpUgZkznge9QrZTFy8Up80HKEnoam+ySA+ZKBvY84Jqm+xKjrcvo4K470yJ1TVdP3dPtKfzqGMbD9feoL/l7c5H+sA56VzYh/u2d+XK+Jj8/yYv2tNOmCWEczyPMsx84AH5CSAAOvXrU82qSTyxMj3kwSdZXjlZQEwc4GOp9+PpWpN4Lsru4sNWi1IF4Yn3MFyjZHBHPTOaw4xi7uwVCkSEEZ6VxPCRjJR5nZnuRzmpUpTquCvFLvrdpf1YbatKmoz3L/aIvMLEPEVJGTnkHgip5po55Z4nt5Et5lTdIgUPuX+LaDjnJ4oz2pGNarL6aVrs5HxFiG78kfx/zI5bl/Jmt4rd/J+zC3jLMM8OHyf14qtcxz3VrbBYCFtovLY5HJLE/1qwzYqeFs6fcn3WqjltKbs2/w/yMMRxTisPBShCN+aPfq0n17Mo6QBdW93bFGkgZwHaNl3RuvsTz1I/GprtFaRI5LO7FtBCY4tqgsWyTlscdSaxItLsxbmW9idJ5ZZHDYK5BYkcj2IqeKARyYs9TvIyOBifcB+BzWqyvDJWbl+H+R0S4jxnNzckfx/zLdt4wk02J455l8uK3aNQ8K/IDwOSOmT0q9ZRR6pawrMUlaElkME8R3qTuwfm45J59+lcV4psNQ8q4vL68e5hVFRGfAyxI6Y7Y/WtjwLLDNaK0bZOwhh3BCjP65pvK6DaUZNr5f5HNi+J8RQozrwpxTSvs/wDPzLd74OHinVJL2bW9MsrmVyqQSN5jdc5G0/h+FSf8KjktoJhN4m09I53BY+Uw6c4z9agkWPdlOGByCDyK1LXxdqWjw+TO5u4j/DITnB/2hzXXTnKnSVJPRHl4ivPEVHUatfsVofhjZ28e7/hJNOGP4grYrXsdP03w34e1+ZvEFhdCeyaFVhJyGPSq8nxGjW3ka3tJRMOGWRgVx6j1rgtWv7G7EswhEksi58xflKN6EdPx78UtOpxSrK/JIrWEjTzmGRhMoj2xrNJhdxIHHOfyrO1Wyn028a0mCl4eoSTcBznApifI0bFoyCcY/rTZZW8wNNHu2j7x681Or3M4yvGzJoZykqTW5ZWcZOaS7mdmALl3Yjr69qiVo0iyituPUd6ZPlvLc5Vc4BznntSsc6p3ldjN7LKCcl16gjvTFDXN2clVc9DjipZFxIQWHzLnOepqoDKk3nAltpHynvQdUIkspmhuGEgGev1JqvLKAjeZn5uhxUlzNM7lpVKg9AarTs7oit36A9qZrFdxYUaVlBVdij7wwDjrk+tWbj/R5grxq2MZKnI6ZqoIZYXSMgEseg5NX/sNwAYwHKqN3yrnANIcmupE5JbdHnG3lcUBpVQrsIGM/N/SnoZbJBuKurd8dKHdpI2Vjt3c80GSeumxnK43EOBxxToGAyqpye9N4Bzzu96armL7o7fWg6N0ejfB1g/xS0TnJUT/AIfuJK+q6+TfgyrD4r6I3YifP/fiSvrKmhw2Cvnb4naSLn4hatNkgt5P/opBX0TXifj1AfGuoHBB/d89v9WtMs8rk0CYHKfNX0Bocc+gaXZR7rJ5ZbOBZEmuBGYyq4z3yv8AnvXmqQquCRx69q6bxlEG1a2PHFpH/KqlOUlZsadjr7q5W1ku5La602ZbuJUYvcrGImC7ffK89O340umSabYXsU8uq6dlLCK24uFzlSxPfpyK8g1iBJNIulAGfLJH4c1wSXEshwsW5sYzt3GtaOGVRc1x87tY+sptY0eaB4m1Sy2upU/6Qnf8ax7OX7YdP017vT3jtHVhJFdK7zbB8uEHTsT9K+db6y1FI7ZZBJIhj3AbeE9QfpxXoPw90uTTvEVhG10kn73e6IvAJUjk1VXDwgruWo4qWtj0abxxp8SODqelCeOYqYTcfeQcdccN+FZ0uv6Nem4vZNZ02C6ZonhiE+5R5ZJG5sd8kcDgeteC6s3/ABOb70+0Sf8AoRrTtdWihtotkNrG6LsJ8oszf7ROevH61t9SjbdiUux7Jf8AiTSNRtNSubnU7GORrF7eKCO43k55JzgdSAAPauJ0fU2nto4wrsVBUuQcHHTmvPo2bzDIgOQeuM11Vu09lpsMkczqAwxGW+Vyfasa1GFKNr3Ym7nWqzkfeA+gpRGHO08n/dFVop1fG05J7Cr0OIgSwP1xXIIctpbomXjVvXIpMoxACYA4AqKSfzG4yB2FTwjaMt1/lTAsRqsals4NRtKWbkcVHJJu4B4FSRKRy3pkUASAALknmoDGhJIiT/vkUryAnFKgzzmr9pLucLy3CN3dNAII8cxJ/wB8ijyYv+eaf98inM56UgbnpR7SXcX9mYP/AJ9oBBGW+4n/AHyKd5ajgBfwFO3DHoaQEHvR7SXcP7Mwn/PtDlQ/hUqkqAB0phIC8GmZx3pOcno2a0cFh6MuenBJkphic5aJCT3Kio2tLYnBgi/74FKG496AfepOolhRYl2xqqL6KMU9j2zUWcDrTSwzQBMpAzSGQZwKiDgCjdjoKAJ0bLUrk4xmokJHQ8mhzzjPSgCSPG7inyHC1HEepolJ4FAhV6gk0rkeXketRq23n2pZHzHjHegYKfmXNPlP7s4qBSdwyafK/wAhFAMYh/eKSe9WZuY2+lUlPzKferkn+rb6GgRUU7ZF+tOu+UzVcsBg5qxOd8R5oGUA3zcVZk+aM/SqJODVpGzDmgRTPU5puadJ941C7EdKTGEZ2k46g1ecK0XT7wrMD4nI9eauwSfu8HtQBVZQCSQKz9UQzWypECHYkDHritGfhs9jWTfOQFwM4OcVhif4b+X5ndlv+8x+f5M37HxZoltpMWm3JksUhg8oBh98AYyCRzk571kWTrMZ3Qko7ZGfSqMEWqanaCSFT5ScqPlIAx0GTn/9VO06R40wzbm7nGM1daCjKm0+/wCRVD/dq3ov/SkaOwocBv0o3P04NBcsMmkzirPPIX3AkMhHuKt2kMtxYXMUWS7MoXiosseQKc15NaaPe3EDNHLGBtYdQa1pfF95wZh/Cj/ih/6UjNgF8CkV1LZyspIIDngjgAHvn+lbl5ppk0/7aYklYH54vlypB/LOc1xXnyrLDJ5p8yMg7iM5Zeh/n+OK620u2keO4gbegYOVboT/ALQ/GjMKtWk4Shse5Qjdas4XxdqeNKFlAjRsspaRGTBHy/8A1q1vAKhNPhbaF3xyMSO55qDxRc6dquuStCnleRGPMxzlmzkfhwPxpPCLGPVI7aN2MSwSBVI6AD1/GtoVFV5ZxVrnkZzG2FqryYy+1NYQSmWGcY9au6ZnUDuiRZECgOjsQF9Me/8AhXMGTzbieGUZAb5fmA6E557HpXT+GDEPCt/HuCukrByT6gYrmq2S03OylYzZpbSC58lhsklOB8p5/HvWRqEKwXKpFk55OTWxdXUNpIgcCUkZUoOh/pVFvIubNpYSiTRkEo5Ylue2SRVclldnBXmp3jFGdI7RpkIMdNtQwzMkwC5PmKFKnrUtw299znk44UY/OqkvzT7kAyRwBU3MaSTWpJneSocBBlcmmTmQbEByE4B6VeurOGGxjuIpVG1uUDZI7f5+tZZuCd0YkLc87u9IunaWqIHnZpzvZtw6YqwrFJfMkWQxH/OaJYYBGVIMc6nPXtQ12JIljdiw7kcfnQb20C9nE8ESru5xvLdjUfkxxup8wFl59jTHujHleNp6HFQp5ksgSPG5zgEnAz9aBqPYsRMjXfIByCPpW9p1/NCmxESRQOh4wAeOn1rmZ7aezuGhmXa6vsIyCCfr0rRLiKRYZHVsKGPlEEEkdM0LczrU3JWJJboNM8mfMLklxjHJ9KoeZFJMECPnncc1PNIVKlSQ7H7qiqUyzJOQw2sfSgqnGyJR5IRj1NVy65HGB1NCkIDk5p5US5WMCg2Wh3PwbIb4saGyfd/f5/78SV9ZV8lfBZWX4raJlRyZzn/thJX1rVFRCvF/iBcsvi6/iUAY8vLf9s1r2ivBPilftYeLr11j3lniXvx+7T/69NK5RnQzksAVGO4/wrqvGJK6pbEdPskeR+FcfuyRjjB6iup8aTBdXtQx/wCXOL+VFmBzOvXEdjZ2mAs0l0r74/7qjgfieawdE1iaG7trCG1gRWlG+XHOO+fwzULSvPcyS7jn6Zxnn+WKkRhAk8giJlaNlVsDgkEZ4+tez9SjGjvqbKmrHTajcLFbwxOSG3gybe5LdP51qWT20vxT0KawylswdGUcAugYHj8a4vTbyW5NrDcfM0A3uxH3go4z+groPhteLqHiTTQ8f7yCeYq/sQf8a5K1N04J9SJXSOL1C2L6ze/KxzO/b/aNOi02U4Cwt+NdheWka6hcnYBmVuce5ojjAHC1yyr1H1IOdt7A8hkdQOvy1s2ekxgow57jBrRSMHAIwav2dpFArncASc9eB9Kybb3AsWcEcKfMBnHX0pJZ/MJA4UVBLciU4T7o/Wli2k5Y8UgLEMeTvP4A06SbPyj8TTHmTb8h5qENk80AWbdRJJk9BU8spUEevSqokC9BzSFt3egCVTk4qUsAABUCyBccU1p0ycsBQBPznrUoOFzxVRZ0/hDH6Cnl2IoAbPfwQPslkVWxnFMXVbLvOtVLmC1ke7urpJJBbxptRG27iWI5ODTINGtJJWneXZbCBZxHI+0/M23aWweMjrj09a8urjKsajjFI+pwmS4Srh41akpJtdLb9lp5mgdWsT/y8LR/atif+XhfyNZp03TVlu5fOaWCG3WYJE+csXC7dxHTnrim2dlp13b5RHad2b9154VlHbaCPn/Osvr9a9rL8Tp/1fwdr807fL17Gp/a1j2uF/WlGrWP/Pwv61jwWFoPscE6TNNeAFZEbAjyxUcY+bpzUlvp9hHHZpcLNLNdTNFlHCqmG256HNCx9Z9F+I3w/g19qX4efl5M1jq9ht/4+F/WganaMpZZMqOpAOBXITR+VPJHnOxiufoa21ubi1fSIbV2EUkasUU8SMXIbI7+n0pRzKo90v6+ZVThrDRS5ZN38169jSGr2Wf+Phf1p8moQRLmSQoD0LKRXNMI7fWz5cZkhjuOFX+IBug/Cr95cR6zFczqbqMwsJCss3mKQzY9Bg801mNRp6K/zFLhvDJr3pWfXTrt0NpdUs1jDmb5TxuwcVPvVhuByDyKydV8uS5v7C2ublRAh+RseSVXsB29j3/Gr9uf9Hi/3R/Ku3DYiVWTUuh4eZ5dSwtOE4N+937aW6I0YP8AV/Wopm3OR0FQbyOhIpU+ZwGPFdh4xas4ftV3FBv273C5xnFdG3hSJvl+2tkdRsH+Nc9pf/IZtVQf8tRiug0iO3SWFZbW4GrjP2iYo3J7ln6Mp7DntgcUikrq4DwlCGx9tYkdtg/xpW8JxMNv21s/7g/xqjbwILvSh9kuRqqXGbyby25G1sln6FScY/Ctfw7potraS4nib7ZJNNukk5bb5jbRz0GMYFCdypQSW5l3vhq2sLfz5buVvmCqiRgs7E8ADPWn2ejw3zTQtJPbzxAb4pUGQD0PBIIOD+VbGtpII7S5SNpVtbhZXRF3MVwQSB3Izn8KrQ3YfUr3VlguDbJbpEuIWDyEMxOFIycZH60dQUU0Yl/4bazYhI7ueMLuaSNYwo/76cUw6Ru0eC/jF08Use8psRWRcZy25wP1rptXSW/t4LGJJBHdMBM+MbIhywPufu/j7U7VJ7OOE2d9ayvaSptJSJnU/wCyQvIoYJLTQ5OHwpNf6db3lnKyecN3l3KbCB26E09PB2rIuBLaH6s3+Facb3MVrZ20889nZzzS7ZGbDpH1jQsfu59+eMdannn+SztV1SQ2ckjq92GAbIHCb+nJzz7Y60XG4a6HPv4J1d2J860+m5v8KafA2qkczWo/4Ef8K1I7q8uIobaDU5XjbVWtzOCCxiEbHAOMdR1rrYYvJhSPe77Rjc5yx+poWpMo8p5ZeeF5ra8WGS/s45eM5ZsLnpk7cD8a1V8FahADJJcWoQLliXOAPXpW09xbW9nr8F26rM8sjeWx+aRWQBcDvnoPcVcubqOHw9LazbJ7qOyDS2+75iNuDkdcZzSKcTi4fDralKYba8tmkxuAYOu4eoyvI6dKot4Qnvrn7Lb6hZGYE4GWwxHUA7cHHtXVwwTQXloF1f8AtSSSCSOIfL+4JXO8beo4C856jmrMc9vNpWgW1sym4SWL92D80e0fPkdRgZB9zUzipx5ZG1Kbo1FUhujyGeyn0u6ubUclWKNtbjIJzinW8TIB2rZ1yEHW74gkfv36fU1SVcDrn61CoxUlK7dvMqpjak4OnZJPeyS/IEBHelbPrSqcjpScYrU4xUkIHarkNrHd6dexOCysucL1yASP1rOJFaujNmOcDliQFHuc4rWnv95wZh/Cj/ih/wClI88/tIKfn2LExVd83yhTjJz+dT2WuXEMsZtniR3DKwDgqAPX68Yq74i8JXkiPnf9mEplTZGcsGHTgdsVjWvg+/uLlEtY33AdHUj9SAK6liIxh7OUeZHo88ov3XYpWyu94Zgu5p1dnBkAP4k8A9wK2/Bkm/xEzbX2tauFJPf8/aoL7wbrttDKf7PlZwATgA4HfpXTeH9Klivkb7JIrRxMA2wjtj0p+1pSSUI2sedmkpPC1E9bpnN3mmh5C+BnOcrwc1XlsWSzk+zzMkTYM0ee4711EdhcM3zW02c90NLJ4eu3DKsLAMCrZQ8g/hXBGWup6DVkcPDB8pdn2BhhWwST/gKY05QeQygbeVZf4j61u3XhrVNPi3m2acH5RhSce54qsumM8MrXFlPFKV4kKtsTHf8A+tXZX9jGKVPfqzPHRpU4RcN+phSKGy4Pz+h70kMsY+eUKecYIq7e6ZdFmljVpYghYsg+4Pes2Rkjt4l4cyqd4JBKEN29K4zjprmjdFjzAI13AFM4AqrcwKyhwBvY849KnM9uLZTB5jTeWAyyAYU55K4qgszqXIySeMGjQqNJxegswkYu27I2gHFMto2EqtIGMWcNximPJIU9O23vViO5ZYSGOM8fWg2d0T/2ek7wvEsnkHKscdG9Ks2uiuxWS3eORSSNsuAB1BB/+tUVjdNb745FdkbqM4xjv9avC6lOnHyXiZJJN7p0cZ/pxTOac6iloUWsIIxLLKY3MbEbFfK5Hoe9UMqQ7AAMTgY7VYuDLE+fJjjVzkY/wpiQxNFJ5zvkfN8rcbv/ANVBtC71Y6OSEQKnmHfkE+xptxjerCRd20jnnNNlgIsRcJgq8rJtP3hjBB/X9KrPFMvlb4yCyhkDDGR6/Sg0SAQtI+BwfrRHNJGdoUbu3FPQPc5bIBB7cU87kkQphGXILZzuoGdv8G2H/C1tGBXDnzycDj/USV9X18m/BpXX4taKrcAef+P7iSvrKhFx2CvCvidZSX/iXVYonMbgRNvzjH7tf8cV7rXhXxJkePxrfNFndH5bE7sA/u14p83K0WlfQ5LTFe3tnWSR8b9wEjZKjgflmui+IbH+3YQGZG+xQgMPQqev5GuMub6FcI8USzNzKm/O7Knpg9R1xW/8VLhbTxJYTKzFn02Lap+7wD/jW1GUZVE5aIaSTRgyRmFWW2dMs2dxxwPSoybhgBJcHgdiefyxUcOn6rdIksckKxvyCozV+PwteNIGuNQkIHJVeK9arjMNe7TZs5xIRefZLeUkg78IWPGATjitj4UyCLxvDbt1WdwPxU/4Vh+INGGm2aTJlyWC7jzkYP61qfDVXb4l6czZ+dfMz6/Ic1yV8RGv70VZWMpy5mdDe4+33Of+erfzqNeKZfS/8TC5Gf8Alq38zTA/cV5pBaUjHXFKGzyfuj171XVhJx/D6+tPCr1Iz9eaAHmeMYAYH2Ap6ynkAMfwqMYHoKch54pATKXbrge1OUyDP3aYGp+cDrSuA7c5Ug7Qfagbj/Hg+wpnmYpVIPegdiQRD+Is31NPQKo4QD6Uzd70u7jqKYiUMKQt6Goi4pQwPemMjjvJIX1JEI3tEmMoGBAOcEHrVDTJby8aS+acpIflIKghl9MHjHtis/X5pIhM0chX5k3Y7jBqz4cv5bq0lWUHKnIc/wAQP/6q5IYWnUg5yV3d9X3Z7dbH4jDOMKTsuWPRfyrui/BHJfQyTS3EgMo2Oq4AKg8DGOBwKt28MlrCIorhwoyVJRSVz12kjI/Cq2mtizX6n+dXN3FRQw9KVOMmtbCx+ZYqGIqU4z91N2Vl/kRwwyW8HkxXUgXnGQpKZ67TjIz7VEunhfKxczDyW3R8j5TnPHFWQTS5Na/VKP8AKcv9rY1a+0/L/Iovo8LsWM0hZjkk46/lVy3t5baDyorqQKM7cqpKZ67SRkfhT8ml3EYGaFhKK1UQlm+NkrSqN/d/kV104rCkK3UwRH8xcYBDeucZqW4tGu4vLluW2Z3EIiruPqcDk/WpQe+aUMAKPqtHblF/a2Mvfn19F/kQyWry232d7uUx4APC7iB0BOMkexq5HhEVR0AwM1Du9qcWPatIUoQ1ijnr4utXSVWV7E24k09F3E5OMDNRJguAxwO9TgwgHB/WtDnL/hxs69bfVv5GpLXVpTqUiXeqvHHG3zJ53zcnAGM8ZqPw6yf8JBbBcdW/kawtT06C7ktrh0LXBvlhjbJzjPT6c0MEeiibVnGbbTht6AzXXJ/AA0zd4nz/AMeen49POf8Awrbt/wDUIfUZqTJ9azuxnOPca2i5udLJA6m3u+fyIH86yNQ1GYyBrO+nVlZRNbtLl489MgE128xIgkOf4T/KvFTplvqHizWru7aVzb3Z2pvIUYbg479aabA6jxPdzxeILhEnkVQFwAxA+6Kzftc7WjP9pl3g/wDPQ1f8Ty26eIbkOuXwvb/ZFZZkt3jZgg+XrxViNe61ybSvBMNydkvmTOriZd+4dhzWHqPirxLBCsL3egwxMgxbTAZAx0203xlKW+HmntAAB9sPXjmuH8P3OzVnF9DFM8gdZFkG8Z65578UN2GjtovG2uIwiTWdBREPAWE8D8uKkk8deKI2byNS0S6GBtUfIzevBNZBTTM7v7MtM/8AXPFJOml3LAyWEIIGAYyU/lUc4+VmvF471qaQvfWdrHPF9x2hB49VPpn0rZ0Txhqd9rVvDN9nxLIqMyxgEqT615vq0Nrp9m1xaPcKM4aIyblII9+9afgXULqfxLp6T2pRWmUK/Xocc+/0pp3YndHXXmuX1hq17HaPHCBMy/JEo4BPtzVMeJNUjleaOdFlb7ziJAT9Tik1a5RdZvlK5xcP/M1SeVWQsq456VQrlWaV553mkbc7sWY+pNREE/SpHfknFM35FADBkMcUN06Uok2mnNLlfu0hFZ8+lL9pkstEvbiPKvG8bA+nPWnu5xU0JguLG5tp5Vj8zbgsM9K1pO0jhzG6op2vaUXpropJvY9R0fWrm90qyNjZCbbbRSTF5NmNyghV4OTj6DpzVy71e5R5xZ2QmW1QPP5kmwjI3bV4OWx9ByOa5m81TQvC0Ntazarc2bR2sKSmIL+8ULhScjg44yKlk17RNSiNzaX17DFcpskMQX98o4z82TnHGRUuMnsdbxNBOz/JnQR63PdX8NraWe4tbR3LvI+1UVyRjoctxWndzG3sp5lGTHGzgeuBmuNTxl4X0q8UtczLKbdIQGGcohOPx5rU0zxtoeuXEtpZTmaRY2ZkI7Ac0csktRLFUZSSj+TEiSWyi0q+F3PLNdSRrOHlLI+8dlPAwcEY7Ct+6t0urZ4n37T/AHJCh/MEEV5c3jzTdMMMklvqE8Fu3+jwPIm2M4IHOMnAJAyTSQ/Eyxu3FqseqJKC8/neahaPg5XGMEY7Ed6lHTJpvc6e1e5srGWwlkuIb2a7jWQtOZBHG7YGxiT2BH1pniWNrHSNcsI5ZpYJNLkmCyyFyjDg4J5wcj8qxB400qSzuPMtdQuJZSjPcFlDgjBXGBgbfTHrWFqHxP0vydSs5rS/nubiHyDPMyYVcdMKBgcnNDi4q7CpJOLueTS3MiptQ9RyM1ky7mnGWBLevArXl2MpkC5OfmNZkgO7ayKwIwKe551FW0JUgcIWPysvGD1FV5YmWHn5gxzn2prebFdMqlWIyMp0P0pq/aJImGTgHB4osbIV5HmQeY+4A9/8+1SjbLHtCbFQluP8+1MeODyGKgjLDbk8j1qzDexbRGUB4IGTxQgk9BomYpuYBSRyT3qSNGZo0iZUHG5iOgzyT6Co5UVtuXAIGNvWrSq32ZommJjK52Z4U9z9aCFbco3EvmkqhGF4GOhH+FXINIvbnTHvI4zIpkMZVAdwwM5x6VU2TQskiuR5f+rOeneutTxpaW8IWK1kbcvzKxAG7v0oNYpHFHzEO0Mceh7VNeGV4oTNvJ2YRnPVRwMegrY164stTjE1nbNHMnBbPBjAwMj14rEldrhiWYbioB469v6UDuQjMbZ56c4609Zt0bAIfripjDCUDGV89wafDG8YZVAcMeOeg96CXJHZfBoSD4raJuB2/v8AnHT9xJX1hXy58JJUb4oaKhyGXz8D/thJX1HTLg7oK+evi35reI9eCrhViTLE4z+6TpX0LXz78UYftHjPWIWdgsiRIcdv3SUFniUdxJHMsiud6ngmvWvinCst5pkjozBNPh5U44K81wUPhhGuZI5bwLsI4VeSMV6X8Rig1S1t1Uu39nQqM+hUiqg7S1GaOn2FhDY26JONgRdqxqMdu/50+S70a0jZ7h0BHO6SUY/KvJf7buLbQJNP2oS7EO5J3jnofyq6mpXt1odnpjwrGs7DF0CeEztAYdOvetJ4WSd3LQzUZX1Z1vjXUrS58PwSQwoIZJVKyIvBGD79+ar/AA53SePtI+Y4jJ6f7hFefyXVzsezneSVYcpGpclUOccD869B+Fqs3jLTZWHJJz/3ya2lCNKlyp3LirImu3/4mN17TPz+JpYmLn/Z/nUd6C2p3Qx8vnP+PJqSPjpXEBZVu2MU8fWolOKkXmkMUk+tSK2OlR04YB4oEOuGzbuB1xTbvSHtjPGl5HNPbjMsSbvlHqCeDjPNJO223b34q9q11G97evZ26q0+VebzMgr3wO2cc15WOpuc9Fey/wAz6zIsRGhQ9+SSbe9unL39ehWuPD17b2bzOWDxqHeMowwP97GCeelNfRJEvY7M3sX2lwCUG47QV3DnHJx2FRtq8eqxyiNUluiAJZFmJX6hemTj6UNK9zqr3M0KMRgNHuI7Y4I78VxOhK6Si9fU9uGOpuLlKpHRNu1vJL5X9PMJNKcXYhjulZdhd3ZWTywOpYEZ/wAaVNIknniW3uo5IZd+2Y5UAqMkHPI4/nVyS7laWLZEphSNo2WWQuzq3UFsfl6VH58kLxrawxxwRh8IzliS67SScemPyq/qs7/CzL+1sPb+Ir28vP8AHbyKF5Z/Z4Y54rpZ4XYpvUEYYYyMH6inaZGjyTyzDzEghaXYScMRgAH2yRTminawjtP3e1JGkByc5IA/pS2aTWc5fbG6OpR0JIDKeoqFhq3NfldjV5pgvZtOqr/1+hSuJftMjSmONAcDagwPyrV02Sxi0v55bVLkykYniZ8LgYxjpzmqmyFdQLC3XyEwWikkJBGO5AFU7bW9Ne4W0lsTKWlKxSI5UfQnuP1pU6Fa7aRVfH4PlUXK2z6bPbc0NDWCXWLeG5iMscjhQoYgZJ6mjTCfNmGT271Fp4lguobyMITG+4KxOODViwgaKSR3K8+hrfBUainCTWmv5HBnWMwzoVaamuayVvNPU0s9vSgHios+9KSAK9w+FJM8daUHmoQfUgU9TnmgCQnilzTM80u6gB+70pwJJqLdTgcUASjr1pN+BV6EFdAuJMDDTqoOBngZP9PzrOJ+XpzQBr+GGJ8R2n1b/wBBNEYj+0aZJIMJBJcXTnrwhPP6Co/CzZ8SWg92/wDQTV3ULdIPDc12GOfsxgHH/PSVcn+dTLYaOhh8a6GVVfOmQAAfNC1Sf8JlonmY+14XGcmNv8K8wChlzuFMcbWx19Cagqx6jL4y0IRtm9JBBziJv8K4J0SLxB4hlGSjpFIMd94BB/M1kSbVAGevWugltf8AiUyagzj/AEmxtV/FeP14/KmDRF4wkx4quh6BP/QRWZDJmKYE/wAOf51c8aNjxbefRP8A0EVkW8n+sHqlWQW/E8pX4baeR0+1OSfTHNcVNuh8QSuM4M7njp3x+ldj4jO74XWfr9ol/lXCl/O1YSBsq8UcnB7lF/qTSlsVE2DdMaT7U1Q44pjKB0rE1uOvZvNspVYZGM4+nNbngy5U+J9Ohz83nRuPcE4z+YNc88eUOW4I9a0PA82fEmhpxvW4WM/QN/8AXq4kTR0mtPjXtQ9PtD5/76NQwMGLIejCqmszsPEuqKxG0XUgH/fRohlKsrehrUzZK/HB7VEWqa4B3nHRuRUBApADYxk/WlBpOMUwAAAA9BQA49KiIGakJ4xUEhPBBoAp/F9g/iqKNnY7bKAqn8OO5Jrj5dV1TzGWG8m2L+7URMdoA7CvQviVp0WoeK41GVuBp0LRnP38ZO2uDsrGy8n9/rRs3zny2OOe9OPvSstyulyGw1G9e9jWaZ5ELEbXbPPr/KvS/h7bhfFZnUbc2kquPX5a89+wWlk32i01FLs4JAXP3jxg8d69E+G1wLnWM9/scoP121T0dmT5nOahCblDHu+YNjrwD/8AqpvhaylutUvNu/7VFauY1AB3N0AI71TsS0F/cWchJ805Qk/xdq2/DskcerXs0MhEiwDIIxtYH1+tEo6Erc5GXxFdOziW0teWO9DHj5vfmq+XupmldV3uckLxius8ZeHku0/4SGz8uJJTtvEA4SXs2Owb+dcNJdPZ3boGV485VscEGs6vPOFiat2rGiLUuNg4J7kdKteH9Jh1bW47W5LeRHFIzjG3p0we/JqnZajvl85FBaP7w7EHiu48M26iW+mBDL5Y247A8/0pUouMfeJpxtucJqVpJBqRtBGVVJCFb1XPFUhp14/mR265OeVB61s6xeBtUmTy2JU7sjoahbVIoXYBVceYNpB4Ze/PrWqdxLl5rGB5KmMseGBwQegHv71B5DgtlMBeuauXs8Blk8lCsZbIU0yOaVLWWEBGjk+9kc/WkMB5TguBjAwBSyltiqCT6H60kcfykggj0Jx/npSW7CW58rcCp4HFAixGkUshSQSoqn5FBBP41U8xYgVI/eA5G4dPrWkvysyO3CqSCO1Z08iM+SisB+BNNomMm3Yma7UqQmFDcOfWmNCPISbeF3uyc8YwASf1qHejONiAAtkA9q17i3t4pdMgDi5G0zSrHzuZjnYPyApGsUZjWVxGFE0bxgnPI5xgH+RBrRjsTHBMS4jeJQ2HB+fPbpx2/Otq3tk1O6jkeCWYKxecnhTJ0EYBP3VHH4VL4gmSCBbdoYLfa6s0UJJ3cjkgYA4+uaVwcV1LPwlj2/GHRiqShf34+cd/IkzX1ZXzZ8Nb1J/iro8aSyToRMQ54C/uH9P8819J00VG1tArwf4jw7vG+okMdzeVgAdB5ada94rxXx5Gp8aagSOT5f8A6LWhuxVrnBraRRMSqje3U4610/xEjb+17Rl4P2KEA++KzzCmeAfWtbUfH17BbRiTTNLuZVCxRtNb7ie3rUJ3LZ5FLai5uJ5cEhnOD6+/86lMpW3togWUxjHJ4J3EivVR4ylWFD/YGhgnqBZj/GmL4xkY/P4e0Hjp/oQ/xrVyb3IPM4bU3Ny83l48xy+PTJzXonw8t/L8WacQAAGP/oJrRj8ZSDpoWij6WgH9atReN7qFxJDpWlxSD7rpb4I/WkBzd8Auo3IxwZX/AJmkRQBmiSQzyvIwyzMWJx3NOWkA9aeDUYzkZPFKOTSAk35/xpVIzTemKAQDQMLo/wCjsPpV2008aiksTMFj2fMcZ68AVn3H+pPNaVhPK7paQYKlwzleuR2+grGP8aT8l+p21P8Acqf+KX5RE0Tw7FFPeyyr9nd2EabANuAPT3zVQRtFf3CSD50bBrob64QFEjkHmBg2M88H0rnZZ5Z9QuZZk2SM/wAy+nalVd5wfn+jHhlajW/wr/0qJMWppaoy4zQCC2c1ucBMDx1pN3rTCe1L0GaBGZqcEk8qorskMnL7erAdQKjlsraRLdBDKiwEsgQ7efX17Vo7OSx+uKcOOcVzqjNX5Zaeh6LxtKaXtKV2kle76KwWilYcEEHPerabUjwDx1NQI1SKe2K1pw5IKK6HJiKzrVZVWrczuTZ9KCw/Go93tSg98VZiKABySSfepQ9QbuakzQMkDUoaos8U4HigRJnNKDjqc1ECM0pYkbQOTwKBm9dkW/h60j7yZlP/AAJsD9FrJuTtZV/2c1RvLHVbhmR5g0UUrRQiSUKWKEjCgnJxmqdta6hcoSr7Ap2DzZAmW/ujPU+1ee8xinblZ9DHhypOKn7WP4nVeFD/AMVPZj3b/wBBNdxpL2UllJBIY3SN2hkR8EZB6EH25rzzwTa3x8SWlzKCsKyPGS7AfMFORjueay7qLVdNvr28S48uSR8zKk4LY7blz9O1Dx0fZ8/KyY5BUdZ0faRukn97a/Q9Sm8L+HLkki3iTP8AzylKj8gaibwZoTLt/eY/6615nLca9b2/mtdOAoBdFkBaMHpuUcilmbX7eFppLp1KqHZPNG8KcYbb1xyKz+vr+Vm3+rlT/n7H8T02Hwf4dgO4wCTH/PSUsPyzWV4vktIbJLdGjjWQBY1XgYX0/EgV50mr6nLIiC8lLMQBz3q/dNb3Wq+RJLcvdQusInkk3BhvAYBccDPP4UlmEWnZFPhurCSU5q3W19i144cL4vvMkdE/9AFYttOiyncwAII5NdJ40sorzxJfNCWW4i8oPvI2sGCqMemMisnUNFs7SJ1S7QzROEI85G8znBwo5XHvmnUxtWM5JRVkXh8jwtWlCUptOSWmnZf5jtf3t8M7ERqWJuZgAOc8GuC0y0uIpGeSJlAAUcdev/1q9d1HTrCLwxZ2L3L+TDeTKHYhPMYZ4yeFB9TXMS6XbxXEsjidLaKISsu5WZsttAVhwRnvRVxlWOlkGFyXC1U25y3dtF3sjnSrEfdP5VI8Efl5WRi2Omyt6LS7OWRZvNmS0e3ecDALjacFfQ/Wql/bQRRW1xbCRYp1JCyEEqQSDyAKweNqJXsjrjw/hZS5VOX3L7vwZh+XKRjY35VP4PtbhPH+lb4XWJbtWzjjJx/hWzpbGG2v7mPieKJdjd1ywBI/Dj8a19IdrjWfD13MczvcbGc9XCsME/mR+FXTx021pv8A52Mq/D9GCk1J6X7b2v2MrXQP+Ej1P5cj7TIen+0arxyrjBYCuq1aSGxu3K3c0EtzdzsxiQNkiQqN+TyPb3NYmkh7TW7m1kSJn2TIzbQcYRvu+lXLHTjLlsjKlkNCdJ1OZ6K/3b9Bm8S2wZSCVODioieKZpxykqevNK3Fd+HqurTU31PBzDDRwuJnRi7pf5C5qMt+8xTweKY3AzWhxCiKa5mjgt1LSyMFVR3NNn0+7gaPcFkDtsBicON393jv7Vc0dkXXLBmPy+cCTnFEepW+lGM2YlkCzrO7TqFxtzgcE+p5rycZze00b+R9jk3slhkpxi3q9UtdX1foavjjS5pvEFlKkG52hhiQg8h8cD2NcvN4VE92/n21qZVG+VnkXEZz/Ec4Bz2613viq6uTrVhJBDeTxxiG4KxpleOeoHJrkLKaWC+nklh1KHzM/NHas/fOGUjBFZ1VU9s+Vy36HXg5UI4WDqKC0621KNp4fNtPPb28EUI2jzX8wBNvUZbOPTFdL4D0p7XxNcL5AjK20jOQeMEYBz0wcjms6W7huHuIGsNQitpSjb47Rt29QRnbjGDk8A8V0fg65kbWLgCzuo7cWDQo8sZX7vOTkdTzTowq+1Td9/MnGVsK8PNRUL26W8vP10OFvNO+y3e2aNN4wyspBBB6EEVc0+GCG2ur6ZXKBlj2RkKXZsnk4PAANRXt7HeG3VAQYYRGc9yCTn9adZ3lsIptPujIFlKyK0YBKsuRnBxkYJ71gliOb7X4na55e6avyX625e+v4Et/m3TbbyP9kvYQxjcg5GSCp9cFaZpOg6LfQXHn6Z5kkUZb5JQrOewVcc+/sKsSGG+cqkU5gtrfZFt27jzncw9yT0qxp9rCtzbSxwXRu0IZYBt2uw5HzZyAfTFVFV1K+tvmZzll7pcvu3/7d/zMy20bRbK3tmuLRi9182YmC7FDFQcY5OQTWnZ6fHpltfQr8rrIyOR0O3pj9azzq+ltJDa6i8qXVqSPLiUHeCxbaf7pyT61avriefQL25C4muTJIm31PTFdWE9p7Rc97W8zyc5eF+rv2XLe/S3fS1vLc4DVoWjnMwCqw++Rk59DzWTJIxT533DsCBXcweHNW07S0v8AU7dn/dbZYdpZgp/vfhzXLeIo9NjlhksvK2umTHHJnb6GvX9D4qdJuV0ZKwRSJIGB6fKQehpTYPbv5UisoxkE8bvoe9VDcEt+6Ukkc1ZXVJ5LVbR4o0RTlX2ncOc4znpmrVNvUpQlYDaSNIRISFHQYI+h5pY7b/S4oWTBdgRu4DD0B961GlW5srVIrRJQqNHuEpDbupB/p9aqajqcsywR3do8bwKFhJHQADg8c1apXK9m09WUWkkF3LGqiNcldpyQOas2+mzXscbo6MQ+1twxtwKfG1ve3IaMLApADbjnnuR71cVxpF6VAMsUq7uOoxWbVnYFGxRudNWwuvs0jAuAH8wHg59KYZ3EySQvsdBw6cHpT3L3+oBuAXIUegFOi0+S51iWzsmjcAEFj0IA6j6mkK2pf0/X7u2sokgEDlH8vyT1Ix97GPXvmqF0LwzNNeBi79mbJHt7Vsv4fvl0fHlW7SKdxQD5j6DPqM1jalG9vEsUksjXC4Mqk4CemPXipHJXR1nwiKj4s6KA3J8/gdP9RJX1VXyd8GxH/wALY0Qruz+/6/8AXCSvrGmhwVkFeJePZ0HjjUI85b93nHb92te214R49hY/EHVX3fKfJ4/7ZJSlsax3Mhn+QkHnsBWVsN3qQc48uDge7Hqas3k621q0g5OMKPUmo7ePyLYA4LEZPuamK1CZJIAZCMcDigR5PHTvSqGbnBOOpqQnBwBVslCBQDinDk47UnTvSoMkmgCQfSndetMz6U9QOmaQxQKVaQnBxS54460CFJpoOTSO2TgGgcCgYTn9yfwqu91dwXLQWtg7kxeZ5gk8tR9T3+nWpJmymKi1C4s5IYmuXWKeFhsn3Fsj0IHasI/xpei/U7an+5U/8UvyiZsOt3eh61dyzQiYSj915o2gEY5H0q/a3r3EX2mb/WS/M2PUnmuOvb+61nY075WEssbDqQWz/hXSWilbS3GckLRVXvw9f0Y8K/3Fb/Cv/SomukgOKmDe9VIsgdM1OCT0HNbnATj1NKeaj3fnT8juaQg5wOaDnFICDRnnrTAenSnimA8dadzjrQMdnNOVsVEKd2xQA8Ek07J5qMcClzQIeCacMY5qPoadnA5NAx3FXNKh+06vaRH7plUt/ug5P6Cs8NWroWVmvLnp5Fq5B/2m+UfzoAr3k1pdW8F1L9oL/ap5VSJQd2WBwefl+vNQwa+7RtHK88DGZ518hFbduPI56c9+a5zUY797ILZSMrrIQwXqfxrpJtBurHQ9LvL198/khZQVAMbHn8a8+nglOCqczTZ9NXzh0asqHImot2uX/CN81x4j063ZZNy3E8xLHOdyfz4rLv8AV7KRbiPzzJMJNqLceWpiIPZicsOMY6VHpmoSaTrsV7EiSNEuQpPBzx/WrlxremXEhY+EtLkdjkls0U8JGUHCTejf6InE5tOjWjUjFaxT692/1I2Czi7ubSNpLi8Uhk81CqZOSQQ2W9uBinSQX8l5eT/YZcT2/lD5l4OFGevtWdceINKgmKjwTpBwM55qA+LNOA48FaV/301N5fD+ZmceIa6+xH8fLz8hW029tmWR4ghU5BZ1H9atS3th/ayTo0gupZVleL5SqjcCSCD69OBVNPE2lyjLeB9IP4sa1bLxDpcJDJ4R0uJuPuA1lLL6dODfMzppcQYnEVowcFrppf8AVmh45u0/4Se7tppzBCNjExxAlm2jGTkZrCuNRa8jUW83nKW/eSeQsbMR6kE5rsJtEtvEk8moap5kNxNjMSjCrgcYJ5PA5+tUbrStH0e3mhQb2VBgebzkk/w9B7GuipgaU7ycndnJh88xFKMKahG0bLYratq1xafD2yv/AJZZWupi4dAwfPXI6V58/j65S8LyRRphPLRQg8soecFcYxn/ABr0GIaLqvg200y/1cWMsMzyHMLPkH6Vy1z4M8JBznxwiexsHI/nSpYaE6cZS1ul+RGLzKvSxFSnTaSUpdF3fcwW8dXs0rSGO3wYzFtjkCgKewGOBTG8XzSQxRPa7kiBCASDjJya1/8AhD/B38XjuA/9wx/8aYfB3gjPPjiD/wAF0g/rVvBUX0/FmMc6xsdp/gv8jHi8czWM5eC0UZUqwdgwYehHcVv+DPGt3q3jzSIpIIVBmWMBANqKT0Axgdar/wDCFeCDyPHMX/gvl/xrb8MeHfB2h65aanF4ximNvKsmwWTruwc4zVRwlGNrL8Samb4yd+ae+my/yLGp6zcweIdVij8sql5KV3oG2nceRkcGqEE0sc5nWRhKQwLHknIIP8zVO9uo7zxJqdxC+YZrh5EJGCQWJFWEwOKl4Wi3dr8xRzbGQVoz/Bf5Fm0xBMvPU81POm2Vh68iq6AHk4zVu4/eQRyYJPQmuiEIwiox2OKtWnXm6lR3bK+D3pr9KceRmmnkc0zIW1WGW+to7h/LiLgM3oKv3vga5urO9hm1mFkkVmjEUeDnGQM5HGf51i3LIqMW+4ASfpiuz0FFTwzpzb2cyW6v8555Gcfh0rnhpUn6r8j0K6/2ej6P/wBKZn2l74n0nwfp88+sXQlKBXimA3r2x+FZNz4u8Ubxs1m4APpj/Cr2uzr5flBmJY5wT0rDkjDJjpkcGqhJ+1kvJfqTWjFYSnJLW8vyiWP+Et8V5/5Dlz/47/hVm38WeI2Pl3Gr3DxuNrA4HB/CsG3Eqq/m4yCcircDpMmVI4OD9a6DhFntjDKQO/INQXcLmNZ4v9bH8y+/qK1FX7RaFT/rI+fwqixIBGevSmSWtKvF3x3ER684P6g1ru7F1mjJRTymecAf1rkonNndGLokvzIfRu9dDYXQkPkO3LHK/wCy3+BqLFFPxZoY1AHxDZjbM2EvUX+90Eg9jjn3p2oapDpej2izkRhAkeSNwzjrgc1uWEzQ3DIFUrIpR0YDaR6EVxnj4S2t5HE0X7qUeahjHylfakMoa34tuZXVIb43BkAUOg2rj3yc/hiubl0TUZJXdXiZuWJ3Y/nUi2xuCJ/MXzQR8pHGPf3ras4pdRuUtbeCd534Cxjcf0/+tVbBYxdK0S51O4ksrWwkmuoULyeU2eM9f1pX+wwTmGSBo5YjhkZSCCPUV6p4O8F3nh3U7nUri7xKBta3Vchh7t6j29ak8bQ6Vq1vPLDdpaalBGXyfl3jHRv8aV2x3PPLG8htJVu7qzYwuwwNgUMBghse3NT67qtvqYFmtr5aIQ4kIHPoQfQg1y7ahNKypLIxRRjOe1XrS+tpI1t3gllkUnyysgUkf3eh98Vd7aiZtaNoFxdJ9miWBpXO4RvKisQenBOaoarpVxo+oywXSNbzRRkiJhnAYYGO3PP5VqeHLvTLnV47+68wyQYCJsO5iAMK+0dsZB/DtTdZ1ObW9TvLme2BE0o2gMcgLkDP+FCu9RNaXOas95mAQEZO0kDoO9dPp3kWFnIpi+ZsyCTgMD9aq2sYgiEQULg8n+8aldYrqJ4zkqQQR0pk2uite+I5bqz8tQYX+8GQ/e6ce1ZF9N9scF1K8AlySSxIGfpWqukKXMaTbAygKSM4Of8A9VXtL0VG+0R6jCrybuGz1XGMiptYyXNezLPwbjVfijovIyPP7/8ATGSvquvn74e6DaW/xG0e7tl8pohMGUc7swuP619A0I2Sa3CvD/iAdvjfUeOvl/8AopK9wrgfFPw2k8Satc3q60bRbjbujFtvIAULjO8dcelDVyk7Hi6E6lehlBEERwD/AHj61pw2xuJiij5V+83oK9Gh+EKQQiKPWtoHQi0/+zq2fhcq24hh1fy8/eb7Nkt/4/QiXdnm088aR/Z7cYjH3j3aqtelf8Kk5z/bf/kp/wDZ0f8ACpP+o3/5Kf8A2dGozzQDqT2pQSy4PFelD4Seut/+Sv8A9nS/8Kl5/wCQ3/5K/wD2dFgPM41ZPlVsgdM1OCw64r0cfCfA/wCQ1/5K/wD2dH/CqP8AqNf+Sv8A9nSswPOMtnpTXcjoK9J/4VT/ANRr/wAlf/s6T/hU/P8AyGv/ACV/+zosB5spY9qC3rXpX/CqMf8AMa/8lf8A7Okb4TZH/Ib/APJT/wCzp2A8xkbJrntShlubl/lk2oFCgA4JOST+gr2p/hCxHy66FP8A155/9nqq3wXmaTcfFEoH90WYA/8AQ6wlCoqjnC2qW/zO+nVw8qCpVbppt6W6pd35Hj9tYENkxlR6YraiRQiAnGBXpafBor18RSN9bb/7Onn4ORMQX1osR623/wBnS5KspRcrWX+Vh+1wtOlONLmbkktUu6ffyPPUwO4qVduOvNd+Pg9FkZ1kkAYx9m/+zpW+EER6ayQfa2/+zroseecCOOTUb3MSfecA16GPhEg660x+tv8A/Z08fCWNemrr/wCAn/2dKzA84+0qR8is2fQULLMekB/Fq9K/4VVxga1j/t1/+zpR8K8f8xn/AMlf/s6LAebFrhiAFRfqc0pFyeAyflXpI+FuP+Yz/wCSv/2dH/Crf+oz/wCSv/2dFgPNle5XGVRh7GpPOkJ5hb6givRR8Lf+oz/5K/8A2dKPhdz/AMhn/wAlv/s6dgPOvOfPMTgfhSrcxk4JKn/aGK9F/wCFX8/8hj/yW/8As6D8LgwwdXB+tr/9nSsB5+HU4wwNOJ4ruj8KIu2rYPqLb/7Oj/hVTL93XGH1ts/+z0WA4TpW3pxki0G4k8wCJ7lQykZzgbj/ACH510X/AAq18c63/wCSv/2daP8AwgbLoyaemqbcFi0n2fO7dgdN3oKdgR5h4OuUm8YPDOrOkNu02P4Qcgc+v3jXSeOJX1DwzfmPpEvmfUKef0zWzpPwsbSL+5vIdZV5Z4hE2+06AHPHz1pTeBrqayktTq8QSRSrH7IckEc/x1FOUoRgrbJdv8z0MZKlVxE6kJqzfn/keNaZcrdQQyqMDygMemK2rSEGMuR16fSuvsvg8liu2PWsr6fZf/s61f8AhXGItg1XHv8AZ/8A7KlTTvJtWu2zLG1IVJQ5He0Uvn8zzG7sxI5dRk9xWc9vkFQuGx+Rr1xvhqG/5i2P+3f/AOyqB/hYHH/IYw3r9l/+zrSxyI8ksLWaKJ0uMsQ3ysehFXmxCFdTtIYHPpXpX/Cqfl2/21/5K/8A2dNm+EwmjKHWsA8H/Rf/ALOsa8JSptLc68DUhSxEJzdkmYWiXksrm8+fy7lml28sp4JJGehPpXOad4pXxVLqiFFiMX7xGZAGMYIwCfXI/WvSdF+G1zo1oLRde+0QKSUWW1PydemH9zWdafBuOz+0GLWgHnfczC0x3yR9/pTabvp+X+ZMYwVvfX4/5HncqZhi91/qazLq1V88V6+3wn3Ki/239wY/49ff/fqJvhAG/wCY5/5Kf/Z0qMJRpxi+iDGVI1MRUnB6Ntr7zxf+zkbtSPpi44Fezf8ACnRn/kOf+Sn/ANnTv+FPjGDrn/kp/wDZ1scx44NKjFssmCeM4AzT4bYRJlFLDqO1ezD4SoEC/wBscAY/49f/ALOo2+EKsBjWsY/6dP8A7OhiVzyIYW6D42nGSDVxryJccn8q9Kk+DO9939vY4x/x5/8A2dO/4U1GUCtrQJHf7J/9nU2ZR51HdxMPv/pV+3uo3t3jLjI5Fd3F8IIosldY5PXNr/8AZ1LH8J0jfcdWVvb7J/8AZ07CPNzdRqPvZ+lRG8Qg4Rj+Fenf8KoTnbq4A/69f/s6Q/Cf/qNf+Sv/ANnSaYHk88++GX5DgKeo60z+19W0XRIGtblLu3ndl8p1+e3JPHOenPevVn+EIcEHXOCMf8en/wBnVST4Iwygh9ekKsMMv2bgj3+esYxcZybi7P07ep6MpUqlGnHnScU73v3b6JnnCzXKyLDeFjOy78nB4/Dip4biOVGiw5bsQK9Fh+DEVv8A6rWioAAA+y56DHd6nh+EflSB/wC3M4/6dMf+z04RftJStZWXbzIxEqaw8KcZKTTk9L9eXul2PG9QnlSZlVmjbHIPes5LmaBi8bspPXHevcNQ+DaX4U/235bj+L7Jnj/vus8/ArI/5GP/AMkf/tlbWOI81sNfeK4j88KUJwzDg4ravotr7k5RvmU11x+A/wD1Mn/kj/8AbK2Lf4SvFYR2smu+YU4V/smOP++6olo8tu7dbm1DRgCQcqc9DUEEy3cBLkowOHCnDV6wnwjCZ/4nfB/6dP8A7OoG+DSm481Nd25+8PsfX/x+kwRytgwu5o7gEmWIhZgT1HZqbfWaeIbKfSpNizoxksXJ6N3U+zfzrubX4WPayiRNcycFT/ovUf8AfdKvwukVlYa6VYHcSlrjJ/77pWYz57NoYrowTZjKvtkGOV55/KvXvA11pmm6UyC3jgnVipkzkyDqDnrWzr3wYh1vUxqA1n7PO6Ymxa7hI397G8YOKyn+A90HUweMZoFH8KWZH/tWk02Mv6r4ms4mKII5JcZ2ggn2+nSvK/FsV1r98t9NYz5jTYkSsuNuSSTjnrXqtp8GJbdW83xK0ztj52s8HA7f6ytKP4WMgGdb3EDGTa//AGdFmTqeN6Z4HtrrwzLNO/l6k4d4kZsbPRT6/wBM1zR8LPHkPehblHwyiJhj6E4z9RX0cnwyKf8AMXB/7df/ALOmzfC5ZhhtWGD1Btc5/wDHquDtK8ldA720PCtk3mSiCMKsy4mWL5STx+fJzVMyeVMmwsXjKngdcc/n0r3dvhCglWSLWQhH/Tpnj0+/VCx+CC2Wom7/AOEg8wgkopssbSfffzW86yceSKsh883DkZ4toemazd6ndJ5chiVPMJkBAOTxjNaDJndHIpBzgg9RXvC/Du6HP9vEnGMm1z/7PWIfgnGzO7eIJWkclizWwOSef71YCR5GFCjGOKv2l3ysUh2sPuOf5GvSx8EyP+Zh/wDJL/7ZSf8ACksjnxD/AOSX/wBsoBq5R8ASpL4vsAwAlXzMj/tm1e0VwXhr4bHw/rVpqL6v9qa33YX7NszuUr13n1z+Fd7RawK/UKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB/9k=",
      "text/plain": [
       "<Figure size 3000x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perturbed_metrics = dict()\n",
    "_, ax = plt.subplots(len(VISUALIZATION_INDICES), figsize=(30, 12))\n",
    "for idx, a in enumerate(augmentations):\n",
    "    # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    src_radius = a.augment.get_config()[\"parameters\"][\"src_radius\"]\n",
    "    result, _, _ = evaluate(model=yolo_model, dataset=single_image_dataset, augmentation=a, metric=mAP_metric)\n",
    "    if src_radius not in perturbed_metrics:\n",
    "        perturbed_metrics[src_radius] = list()\n",
    "    perturbed_metrics[src_radius].append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: Radius {src_radius}\")\n",
    "        datum = single_image_dataset[0]\n",
    "        batch = ([datum[0]], [datum[1]], [datum[2]])\n",
    "        # Extract the image from the augmentation and switch it to channel last\n",
    "        aug = np.transpose(a(batch)[0][0], (1, 2, 0))\n",
    "        # Plot image\n",
    "        ax_idx = VISUALIZATION_INDICES.index(idx)\n",
    "        ax[ax_idx].imshow(model(aug)[0].plot())\n",
    "        ax[ax_idx].set_title(f\"Radius: {src_radius}\")\n",
    "        _ = ax[ax_idx].axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0303b6a-f587-43c8-ae9f-0057a7884241",
   "metadata": {},
   "source": [
    "## Evaluation Analysis\n",
    "\n",
    "Now we can plot how the metrics (for example, mAP @ IoU=50) vary with perturbation level, keeping in mind this is a **relative** mAP against the detections in the unperturbed image.\n",
    "\n",
    "Because we repeated each perturbation 10 times with different random seeds,  we will plot the average, worst-case, and best-case relative mAP for each flare radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09b6804b-c98c-46fb-af98-ab5598892ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x70d34161fe80>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map50s_dict = dict()\n",
    "for radius, metrics in perturbed_metrics.items():\n",
    "    for m in metrics:\n",
    "        if radius not in map50s_dict:\n",
    "            map50s_dict[radius] = list()\n",
    "        map50s_dict[radius].append(m[\"map_50\"].item())\n",
    "\n",
    "map50_avg = [np.average(map50s_dict[m]) for m in map50s_dict]\n",
    "map50_best = [np.max(map50s_dict[m]) for m in map50s_dict]\n",
    "map50_worst = [np.min(map50s_dict[m]) for m in map50s_dict]\n",
    "\n",
    "plt.title(\"relative mAP@50\")\n",
    "plt.xlabel(\"Flare Radius\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "_ = plt.plot(perturbation_values, map50_avg, label=\"Average\")\n",
    "_ = plt.plot(perturbation_values, map50_worst, label=\"Worst\")\n",
    "_ = plt.plot(perturbation_values, map50_best, label=\"Best\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8720fd-32dc-493d-ab5c-81c313615504",
   "metadata": {},
   "source": [
    "### Evaluation Interpretation\n",
    "\n",
    "The general trend is that mAP decreases as the flare radius increases, as the flare is able to obscure a greater number of objects. However, the most important factor in effected mAP will be the location of the flare and whether it could obscure any of the objects in the image. It is possible even with a smaller lens flare for it to significantly impact mAP, if the location of that flare happens to obscure multiple objects. As the size of the flare increases, the probability of each object being obscured also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}