cis6800_hw3_final_(3) (1).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1_ulf83-jhW"
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      "source": [
        "# CIS6800: Project 3: SOLO and FPN (Instance Segmentation)\n",
        "\n",
        "\n",
        "### Instructions:\n",
        "* This is a group assignment with one submission per group. It is expected that each member of the group will contribute to solving each question. Be sure to specify your teammates when you submit to Gradescope! Collaborating with other groups is not permitted.\n",
        "* There is no single answer to most problems in deep learning, therefore the questions will often be underspecified. You need to fill in the blanks and submit a solution that solves the (practical) problem. Document the choices (hyperparameters, features, neural network architectures, etc.) you made where specified.\n",
        "* You may include any code used in previous projects. You may use ChatGPT, but you need to document how you use it.\n",
        "* You should expect that a complete training session should last about 6 hours, so you should start part (b) as early as you can!\n",
        "* To save on compute time, we debugging locally and using colab to train. Or use colab cpu instance for debugging\n",
        "* We should be able to reproduce your results with a single function call or cell evaluation. Please specify the method to do so. You do not, however, need to submit your trained weights.\n",
        "\n",
        "\n",
        "The SOLO paper: https://arxiv.org/pdf/1912.04488.pdf\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download data, if the auto download faild, you can maunally download here:\n",
        "# https://drive.google.com/drive/folders/17cT1ywZeKJ5l-FinBwpWv-EbvaMJ9mSa?usp=sharing\n",
        "\n",
        "!gdown --id 1JD3OaHpq_4KCb7ofcPMkknmdEXFHrCcn\n",
        "!gdown --id 1ssRA7yijGLFmJU-ac-lPyUOq7DYzTAS1\n",
        "!gdown --id 1Rpz-ZuQxDwvLyzc0FD9GZxAKlyka3VC5\n",
        "!gdown --id 1ouMFNT1thia8l6P5vcWCY-nweAexLDsB\n",
        "!pip install pytorch-lightning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPgvajFFMlC6",
        "outputId": "38c76034-bec0-440c-c8cd-c31ed425b35f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Access denied with the following error:\n",
            "\n",
            " \tToo many users have viewed or downloaded this file recently. Please\n",
            "\ttry accessing the file again later. If the file you are trying to\n",
            "\taccess is particularly large or is shared with many people, it may\n",
            "\ttake up to 24 hours to be able to view or download the file. If you\n",
            "\tstill can't access a file after 24 hours, contact your domain\n",
            "\tadministrator. \n",
            "\n",
            "You may still be able to access the file from the browser:\n",
            "\n",
            "\t https://drive.google.com/uc?id=1JD3OaHpq_4KCb7ofcPMkknmdEXFHrCcn \n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ssRA7yijGLFmJU-ac-lPyUOq7DYzTAS1\n",
            "To: /content/hw3_mycocodata_labels_comp_zlib.npy\n",
            "100% 269k/269k [00:00<00:00, 3.85MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Access denied with the following error:\n",
            "\n",
            " \tToo many users have viewed or downloaded this file recently. Please\n",
            "\ttry accessing the file again later. If the file you are trying to\n",
            "\taccess is particularly large or is shared with many people, it may\n",
            "\ttake up to 24 hours to be able to view or download the file. If you\n",
            "\tstill can't access a file after 24 hours, contact your domain\n",
            "\tadministrator. \n",
            "\n",
            "You may still be able to access the file from the browser:\n",
            "\n",
            "\t https://drive.google.com/uc?id=1Rpz-ZuQxDwvLyzc0FD9GZxAKlyka3VC5 \n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Access denied with the following error:\n",
            "\n",
            " \tToo many users have viewed or downloaded this file recently. Please\n",
            "\ttry accessing the file again later. If the file you are trying to\n",
            "\taccess is particularly large or is shared with many people, it may\n",
            "\ttake up to 24 hours to be able to view or download the file. If you\n",
            "\tstill can't access a file after 24 hours, contact your domain\n",
            "\tadministrator. \n",
            "\n",
            "You may still be able to access the file from the browser:\n",
            "\n",
            "\t https://drive.google.com/uc?id=1ouMFNT1thia8l6P5vcWCY-nweAexLDsB \n",
            "\n",
            "Collecting pytorch-lightning\n",
            "  Downloading pytorch_lightning-2.0.9.post0-py3-none-any.whl (727 kB)\n",
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            "Collecting torchmetrics>=0.7.0 (from pytorch-lightning)\n",
            "  Downloading torchmetrics-1.2.0-py3-none-any.whl (805 kB)\n",
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            "Installing collected packages: lightning-utilities, torchmetrics, pytorch-lightning\n",
            "Successfully installed lightning-utilities-0.9.0 pytorch-lightning-2.0.9.post0 torchmetrics-1.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xjZNQ4F-jhe"
      },
      "source": [
        "## Code Structure\n",
        "In this assignment, we are only providing the barest templates for your code structure. If you prefer, you can write and debug most of the components in a regular python and only use Jupyter to train. In this case, you might have several files that you import into this notebook, e.g.\n",
        "* `dataset.py`\n",
        "* `model.py`\n",
        "* `train.py`\n",
        "* `inference.py`\n",
        "\n",
        "This is shown below. All files should be included in your submission."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJQ7G9Jf-jhg"
      },
      "source": [
        "try:\n",
        "    import dataset\n",
        "    import model\n",
        "    import train\n",
        "    import inference\n",
        "except ModuleNotFoundError:\n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDRG7ySI-jhk"
      },
      "source": [
        "## Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "253rxTGZ-jhm"
      },
      "source": [
        "Instance segmentation can be thought of as a combination of object detection and semantic segmentation, the former of which you already explored in the previous project. A visulization of this relationship can be seen in fig. 1.\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig1.png\" width=500/></div>\n",
        "<center>Figure 1: An overview of instance segmentation.</center>  \n",
        "\n",
        "In this project, you are tasked with implementing an instance segmentation framework know as SOLO (Segmenting Object by LOcations). In a similar manner to YOLO, SOLO produces mask predictions on a dense grid. This means that, unlike many other segmenation frameworks (e.g. Mask-RCNN), SOLO directly predicts the segmentation mask without proposing bounding box locations. An visual summary of SOLO can be seen in fig. 2 and 3.\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig2.png\" width=200/></div>\n",
        "<center>Figure 2: SOLO.</center>  \n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig3.png\" width=600/></div>\n",
        "<center>Figure 3: SOLO branches.</center>\n",
        "\n",
        "These dense predictions are produced at several different scales using a Feature Pyramid Network (FPN). Using the last few layers of the backbone, we pass the higher level features from the deeper layers back up to larger features scales using lateral connections, shown in fig. 4. The integration of the FPN into your network will be the primary focus of this project.\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig4.png\" width=300/></div>\n",
        "<center>Figure 4: SOLO branches.</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dU84IoOl-jho"
      },
      "source": [
        "## Part A"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MnlCd9m-jhq"
      },
      "source": [
        "### Dataset\n",
        "#### Dataset Structure\n",
        "The dataset used in this project contains three classes across 3265 images: vehicles, people, and animals. The dataset contains the following elements:\n",
        "\n",
        "* A numpy array of images ($3 \\times 300 \\times 400$).\n",
        "* A numpy array of masks ($300 \\times 400$).\n",
        "* A list of ground truth labels by image.\n",
        "* A list of bounding boxes by image.\n",
        "\n",
        "Note that the mask array is flattened; to determine which masks belong to which image, count the number of labels associated with that image. For example, if the first few images have 3, 2, and 4 labels, masks 0-2 would belong to image 1, masks 3-4 would belong to image 2, etc. The masks are ordered correctly to allow for this.\n",
        "\n",
        "#### Loading and Batching\n",
        "You should apply the following transformations to each image:\n",
        "* Normalize pixel values to $[0,1]$.\n",
        "* Rescale the image to $800 \\times 1066$.\n",
        "* Normalize each channel with means $[0.485, 0.456, 0.406]$ and standard deviations $[0.229, 0.224, 0.225]$.\n",
        "* Zero pad the image to $800 \\times 1088$.\n",
        "\n",
        "Since each image will have a different number of objects, you will have to write your own collation function to properly batch the images. An example collate_fn is shown below, along with expected output dimensions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJYYsbUw-jhs"
      },
      "source": [
        "# images:         (batch_size, 3, 800, 1088)\n",
        "# labels:         list with len: batch_size, each (n_obj,)\n",
        "# masks:          list with len: batch_size, each (n_obj, 800,1088)\n",
        "# bounding_boxes: list with len: batch_size, each (n_obj, 4)\n",
        "def collate_fn(batch):\n",
        "    images, labels, masks, bounding_boxes = list(zip(*batch))\n",
        "    return torch.stack(images), labels, masks, bounding_boxes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DefEnjjQ-jhu"
      },
      "source": [
        "#### Visualization\n",
        "In order to validate that you are loading the dataset correct, you should plot at least five example images that include the mask, annotations, and bounding boxes. Examples of such images are shown in fig. 5 and 6. Make sure that the color for each class is consistent!\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig5.png\" width=500/></div>\n",
        "<center>Figure 5: Example visualization.</center>\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig6.png\" width=500/></div>\n",
        "<center>Figure 6: Example visualization.</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65Rz_Srf-jhw"
      },
      "source": [
        "### Model\n",
        "#### Architecture\n",
        "The model architecture is summarized in fig. 7 and tables 1 and 2.\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig7.png\" width=500/></div>\n",
        "<center>Figure 7: SOLO branch structure.</center>\n",
        "<br>\n",
        "<center>Table 1: Category branch structure.</center>\n",
        "\n",
        "| Layer | Hyperparameters |\n",
        "| :--- | :--- |\n",
        "| conv1 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv2 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv3 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv4 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv5 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv6 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv7 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv_out | Kernel size $= 3\\times3\\times(C-1)$, stride $= 1$, pad $= 1$, bias $= \\text{True}$. <br> Followed by Sigmoid layer. Note $C = 4$ here (number of classes + background). |\n",
        "\n",
        "<br>\n",
        "<center>Table 2: Mask branch structure.</center>\n",
        "\n",
        "| Layer | Hyperparameters |\n",
        "| :--- | :--- |\n",
        "| conv1 | Kernel size $= 3\\times3\\times(256 + 2)$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Two additional input channels represent the $x$ and $y$ positional encoding. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv2 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv3 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv4 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv5 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv6 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv7 | Kernel size $= 3\\times3\\times256$, stride $= 1$, pad $= 1$, bias $= \\text{False}$. <br> Followed by GroupNorm, num_groups $=32$ and ReLU |\n",
        "| conv_out | Kernel size $= 1\\times1\\times(\\text{num_grid})^2$, stride $= 1$, pad $= 0$, bias $= \\text{True}$. <br> Followed by Sigmoid layer. Note that $\\text{num_grid}$ is different for each layer of the FPN. |\n",
        "\n",
        "We will be using a pretrained backbone (which includes an FPN), so you will not have to implement those components. A template for the network with along with default parameters is shown below.\n",
        "\n",
        "#### Feature Pyramid\n",
        "The feature pyramid extracted below has strides $[4,8,16,32,64]$ over the original image. To match the SOLO paper, this should be interpolated to have strides $[8,8,16,32,32]$.\n",
        "\n",
        "#### Target Assignment\n",
        "Some notes about generating the ground truth targets:\n",
        "* The FPN levels can be though of as different grid sizes cut through the image.\n",
        "* You assign each target to a certain FPN level if $\\sqrt{wh}$ from the bounding box falls within the `scale_range` associated with that level. Note that these overlap, so you may assign the same target to multiple levels.\n",
        "* A grid cell should be considered as predicting an object if that grid cell falls into the \"centre region\" of the object.\n",
        " * The centre region of an object is its bounding box scaled by `epsilon`.\n",
        " * Each grid cell can predict at most one object, but each object can be predicted by more than one grid cell.\n",
        "\n",
        "#### Target Visualization\n",
        "You should produce visualizations such as fig. 8 and 9 to validate your target assignments.\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig8.png\" width=500/></div>\n",
        "<center>Figure 8: Target assignment example.</center>\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig9.png\" width=500/></div>\n",
        "<center>Figure 9: Target assignment example.</center>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision\n",
        "import torch\n",
        "from functools import partial\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import h5py\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import os\n",
        "import h5py"
      ],
      "metadata": {
        "id": "kA9CdShQi6HE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Resnet50Backbone(checkpoint_file=None, device=\"cpu\", eval=True):\n",
        "    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=True)\n",
        "\n",
        "    if eval == True:\n",
        "        model.eval()\n",
        "\n",
        "    # device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model.to(device)\n",
        "\n",
        "    resnet50_fpn = model.backbone\n",
        "\n",
        "    if checkpoint_file:\n",
        "        checkpoint = torch.load(checkpoint_file)\n",
        "\n",
        "        resnet50_fpn.load_state_dict(checkpoint['backbone'])\n",
        "\n",
        "    return resnet50_fpn"
      ],
      "metadata": {
        "id": "N11bFW_yi01o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BuildDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, path):\n",
        "        # self.img_data = np.load(path[0], allow_pickle=True)\n",
        "        with h5py.File(path[0], 'r') as f:\n",
        "            self.imgs_data = np.array(f['data'])\n",
        "\n",
        "        with h5py.File(path[1], 'r') as f:\n",
        "            self.mask_data = np.array(f['data'])\n",
        "            # self.mask_data = np.load(path[1], allow_pickle=True)\n",
        "        self.label_data = np.load(path[2], allow_pickle=True)\n",
        "        self.bbox_data = np.load(path[3], allow_pickle=True)\n",
        "\n",
        "        ## creatig masks list that is currently flattened\n",
        "        self.maskList = []\n",
        "        start = 0\n",
        "\n",
        "        for label in self.label_data:\n",
        "            num_masks = label.shape[0]\n",
        "            self.maskList.append(np.array(self.mask_data[start:start + num_masks]))\n",
        "            start += num_masks\n",
        "\n",
        "        self.mask_data = self.maskList\n",
        "\n",
        "        # TODO: load dataset, make mask list\n",
        "\n",
        "    # output:\n",
        "    # transed_img\n",
        "    # label\n",
        "    # transed_mask\n",
        "    # transed_bbox\n",
        "    def __getitem__(self, index):\n",
        "        img = self.imgs_data[index]\n",
        "        mask = self.mask_data[index]\n",
        "        label = self.label_data[index]\n",
        "        bbox = self.bbox_data[index].copy()\n",
        "\n",
        "        ## changing the coordinates bounding box to match with the image rescaled to 800*1088\n",
        "\n",
        "        original_w, original_h, new_w, new_h = 400, 300, 1066, 800\n",
        "\n",
        "        bbox[:, 0] = bbox[:, 0] * (new_w / original_w)\n",
        "        bbox[:, 1] = bbox[:, 1] * (new_h / original_h)\n",
        "        bbox[:, 2] = bbox[:, 2] * (new_w / original_w)\n",
        "        bbox[:, 3] = bbox[:, 3] * (new_h / original_h)\n",
        "\n",
        "        ##doing padding from 1066 to 1088\n",
        "        bbox[:, [0, 2]] += 11\n",
        "\n",
        "        transed_img, transed_mask, transed_bbox = self.pre_process_batch(img, mask, bbox)\n",
        "\n",
        "        # check flag\n",
        "        assert transed_img.shape == (3, 800, 1088)\n",
        "        assert transed_bbox.shape[0] == transed_mask.shape[0]\n",
        "        return transed_img, transed_mask, label, transed_bbox\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs_data)\n",
        "\n",
        "    # This function take care of the pre-process of img,mask,bbox\n",
        "    # in the input mini-batch\n",
        "    # input:\n",
        "    # img: 3*300*400\n",
        "    # mask: 3*300*400\n",
        "    # bbox: n_box*4\n",
        "    def pre_process_batch(self, img, mask, bbox):\n",
        "        # normalize image between 0 and 1\n",
        "        img = img / 255.0\n",
        "\n",
        "        ##rescale img to 800*1600\n",
        "        img = torch.tensor(img, dtype=torch.float32).unsqueeze(0)\n",
        "        img = F.interpolate(img, size=(800, 1066), mode='bilinear', align_corners=False).squeeze(0)\n",
        "\n",
        "        mask = torch.tensor(mask, dtype=torch.float32).unsqueeze(0)\n",
        "        mask = F.interpolate(mask, size=(800, 1066), mode='nearest').squeeze(\n",
        "            0)  ## dont squeeze a dimension here as the check below is doing that in the assert statement\n",
        "\n",
        "        # normalize each channel\n",
        "        mean = [0.485, 0.456, 0.406]\n",
        "        std = [0.229, 0.224, 0.225]\n",
        "\n",
        "        img = torchvision.transforms.functional.normalize(img, mean, std)\n",
        "\n",
        "        ##zero padding the image to 800*1088\n",
        "        img = F.pad(img, (11, 11), mode='constant', value=0)  # pad last dim by 11 on each side\n",
        "        mask = F.pad(mask, (11, 11), mode='constant', value=0)\n",
        "\n",
        "        # mask = torch.tensor(mask)\n",
        "        bbox = torch.tensor(bbox)\n",
        "\n",
        "        # check flag\n",
        "        assert img.shape == (3, 800, 1088)\n",
        "        assert mask.shape[1] == 800\n",
        "        assert mask.shape[-1] == 1088\n",
        "        assert mask.shape[-2] == 800\n",
        "        assert bbox.shape[0] == mask.shape[0]  # mask.squeeze(0).shape[0]\n",
        "        return img, mask, bbox\n",
        "\n",
        "\n",
        "class BuildDataLoader(torch.utils.data.DataLoader):\n",
        "    def __init__(self, dataset, batch_size, shuffle, num_workers):\n",
        "        self.dataset = dataset\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.num_workers = num_workers\n",
        "\n",
        "    # output:\n",
        "    # img: (bz, 3, 800, 1088)\n",
        "    # label_list: list, len:bz, each (n_obj,)\n",
        "    # transed_mask_list: list, len:bz, each (n_obj, 800,1088)\n",
        "    # transed_bbox_list: list, len:bz, each (n_obj, 4)\n",
        "    # img: (bz, 3, 300, 400)\n",
        "    def collect_fn(self, batch):  # should be collate_fn but I wont change it here\n",
        "        imgs, masks, labels, bboxes = list(zip(*batch))\n",
        "\n",
        "        max_obj = max([bbox.shape[0] for bbox in bboxes])\n",
        "\n",
        "        imgs = torch.stack([torch.tensor(img) for img in imgs], dim=0)\n",
        "        padded_masks, padded_bboxes, padded_labels = [], [], []\n",
        "\n",
        "        for mask, bbox, label in zip(masks, bboxes, labels):\n",
        "            pad_mask = torch.zeros(max_obj, 800, 1088)\n",
        "            pad_mask[:mask.shape[0], :, :] = torch.tensor(mask)\n",
        "            padded_masks.append(pad_mask)\n",
        "\n",
        "            pad_bbox = torch.zeros(max_obj, 4)\n",
        "            pad_bbox[:bbox.shape[0], :] = torch.tensor(bbox)\n",
        "            padded_bboxes.append(pad_bbox)\n",
        "\n",
        "            pad_labels = torch.zeros(max_obj)\n",
        "            pad_labels[:label.shape[0]] = torch.tensor(label)\n",
        "            padded_labels.append(pad_labels)\n",
        "\n",
        "        padded_bboxes = torch.stack(padded_bboxes, dim=0)\n",
        "        padded_masks = torch.stack(padded_masks, dim=0)\n",
        "        padded_labels = torch.stack(padded_labels, dim=0)\n",
        "\n",
        "        return imgs, padded_masks, padded_labels, padded_bboxes\n",
        "\n",
        "\n",
        "    def loader(self):\n",
        "        return torch.utils.data.DataLoader(self.dataset,\n",
        "                                           batch_size=self.batch_size,\n",
        "                                           shuffle=self.shuffle,\n",
        "                                           num_workers=self.num_workers,\n",
        "                                           collate_fn=self.collect_fn)\n",
        "\n",
        "\n",
        "def PlotGT_with_Bounding_Boxes(bbox_list, label_list, mask_list, img):\n",
        "    num_images = img.shape[0]\n",
        "\n",
        "    img_np = img.permute(0, 2, 3, 1).cpu().numpy()\n",
        "    for i in range(num_images):\n",
        "        fig, ax = plt.subplots(1)\n",
        "        min_val = np.min(img_np[i])\n",
        "        max_val = np.max(img_np[i])\n",
        "        image_for_plotting = (img_np[i] - min_val) / (max_val - min_val)\n",
        "        ax.imshow(image_for_plotting)\n",
        "        for j in range(bbox_list[i].shape[0]):\n",
        "            x1, y1, x2, y2 = bbox_list[i][j]\n",
        "            width = x2 - x1\n",
        "            height = y2 - y1\n",
        "            color_keys = ['r', 'g', 'b']\n",
        "            category_label = int(label_list[i][j].item())\n",
        "            rect = patches.Rectangle((x1, y1), width, height, linewidth=2,\n",
        "                                edgecolor=color_keys[category_label - 1], facecolor='none')\n",
        "            ax.add_patch(rect)\n",
        "            mask = mask_list[i][j]\n",
        "            mask_np = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(img.shape[-2], img.shape[-1]),\n",
        "                                mode='bilinear', align_corners=False).squeeze(0).squeeze(0).cpu().numpy()\n",
        "            rgb_mask = np.zeros_like(img_np[i])\n",
        "            rgb_mask[:, :, category_label - 1] = mask_np\n",
        "            ax.imshow(rgb_mask, alpha=0.5)\n",
        "\n",
        "        ax.set_title(\"Ground Truth Image \" + str(iter*2+i) + \" with Bounding Boxes and Masks\")\n",
        "        os.makedirs(\"./testfig\", exist_ok=True)\n",
        "        plt.savefig(\"./testfig/dataset_plot_\" + str(iter*2+i) + \".png\")\n",
        "        plt.show()\n",
        "\n",
        "        if i == 1:\n",
        "            break"
      ],
      "metadata": {
        "id": "FqYF1ZxhizLk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQfUPsRM-jhy"
      },
      "source": [
        "class SOLOHead(nn.Module):\n",
        "    def __init__(self,\n",
        "                 num_classes,\n",
        "                 in_channels=256,\n",
        "                 seg_feat_channels=256,\n",
        "                 stacked_convs=7,\n",
        "                 strides=[8, 8, 16, 32, 32],\n",
        "                 scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),\n",
        "                 epsilon=0.2,\n",
        "                 num_grids=[40, 36, 24, 16, 12],\n",
        "                 cate_down_pos=0,\n",
        "                 with_deform=False,\n",
        "                 mask_loss_cfg=dict(weight=3),\n",
        "                 cate_loss_cfg=dict(gamma=2,\n",
        "                                    alpha=0.25,\n",
        "                                    weight=1),\n",
        "                 postprocess_cfg=dict(cate_thresh=0.2,\n",
        "                                      ins_thresh=0.5,\n",
        "                                      pre_NMS_num=50,\n",
        "                                      keep_instance=5,\n",
        "                                      IoU_thresh=0.5)):\n",
        "        super(SOLOHead, self).__init__()\n",
        "        self.num_classes = num_classes\n",
        "        self.seg_num_grids = num_grids\n",
        "        self.cate_out_channels = self.num_classes\n",
        "        self.in_channels = in_channels\n",
        "        self.seg_feat_channels = seg_feat_channels\n",
        "        self.stacked_convs = stacked_convs\n",
        "        self.strides = strides\n",
        "        self.epsilon = epsilon\n",
        "        self.cate_down_pos = cate_down_pos\n",
        "        self.scale_ranges = scale_ranges\n",
        "        self.with_deform = with_deform\n",
        "\n",
        "        self.mask_loss_cfg = mask_loss_cfg\n",
        "        self.cate_loss_cfg = cate_loss_cfg\n",
        "        self.postprocess_cfg = postprocess_cfg\n",
        "        # initialize the layers for cate and mask branch, and initialize the weights\n",
        "        self._init_layers()\n",
        "        self._init_weights()\n",
        "\n",
        "        self.lateral_convs = nn.ModuleList(\n",
        "            [nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) for _ in range(len(num_grids))])\n",
        "\n",
        "        # check flag\n",
        "        assert len(self.ins_head) == self.stacked_convs\n",
        "        assert len(self.cate_head) == self.stacked_convs\n",
        "        assert len(self.ins_out_list) == len(self.strides)\n",
        "        pass\n",
        "\n",
        "\n",
        "    # This function builds network layer for cate and ins branch\n",
        "    # it builds 4 self.var\n",
        "    # self.cate_head is nn.ModuleList 7 inter-layers of conv2d\n",
        "    # self.ins_head is nn.ModuleList 7 inter-layers of conv2d\n",
        "    # self.cate_out is 1 out-layer of conv2d\n",
        "    # self.ins_out_list is nn.ModuleList len(self.seg_num_grids) out-layers of conv2d, one for each fpn_feat\n",
        "    def _init_layers(self):\n",
        "        # define groupnorm\n",
        "        num_groups = 32\n",
        "        # initialize the two branch head modulelist\n",
        "        self.cate_head = nn.ModuleList([\n",
        "\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            ),\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                nn.GroupNorm(num_groups, 256),  # normalizes across all the channels\n",
        "                nn.ReLU()\n",
        "\n",
        "            )\n",
        "        ])\n",
        "\n",
        "        self.cate_out = nn.Sequential(\n",
        "            nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1, bias=True),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "        # instance mask head\n",
        "        self.ins_head = nn.ModuleList([\n",
        "\n",
        "            nn.Sequential(nn.Conv2d(258, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          ),\n",
        "            nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),\n",
        "                          nn.GroupNorm(num_groups, 256),\n",
        "                          nn.ReLU()\n",
        "                          )\n",
        "        ])\n",
        "\n",
        "        # initialize the output layer\n",
        "        self.ins_out_list = nn.ModuleList([\n",
        "            nn.Sequential(\n",
        "                nn.Conv2d(256, num_grid ** 2, kernel_size=1, stride=1, padding=0, bias=True),\n",
        "                nn.Sigmoid()\n",
        "            )\n",
        "            for num_grid in self.seg_num_grids\n",
        "\n",
        "        ])\n",
        "\n",
        "\n",
        "    # This function initialize weights for head network\n",
        "    def _init_weights(self):\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                nn.init.xavier_uniform_(m.weight)\n",
        "                if m.bias is not None:\n",
        "                    nn.init.constant_(m.bias, 0)\n",
        "\n",
        "            if isinstance(m, nn.GroupNorm):\n",
        "                nn.init.constant_(m.weight, 1)  # check this\n",
        "                nn.init.constant_(m.bias, 0)\n",
        "\n",
        "\n",
        "    # Forward function should forward every levels in the FPN.\n",
        "    # this is done by map function or for loop\n",
        "    # Input:\n",
        "    # fpn_feat_list: backout_list of resnet50-fpn\n",
        "    # Output:\n",
        "    # if eval = False\n",
        "    # cate_pred_list: list, len(fpn_level), each (bz,C-1,S,S)\n",
        "    # ins_pred_list: list, len(fpn_level), each (bz, S^2, 2H_feat, 2W_feat)\n",
        "    # if eval==True\n",
        "    # cate_pred_list: list, len(fpn_level), each (bz,S,S,C-1) / after point_NMS\n",
        "    # ins_pred_list: list, len(fpn_level), each (bz, S^2, Ori_H, Ori_W) / after upsampling\n",
        "    def forward(self,\n",
        "                fpn_feat_list,\n",
        "                device,\n",
        "                eval=False):\n",
        "        new_fpn_list = self.NewFPN(fpn_feat_list)  # stride[8,8,16,32,32]\n",
        "        assert new_fpn_list[0].shape[1:] == (256, 100, 136)\n",
        "        quart_shape = [new_fpn_list[0].shape[-2] * 2, new_fpn_list[0].shape[-1] * 2]  # stride: 4\n",
        "        cate_pred_list, ins_pred_list = self.MultiApply(self.forward_single_level, new_fpn_list,\n",
        "                                                        list(range(len(self.seg_num_grids))), device=device, eval=eval,\n",
        "                                                        upsample_shape=quart_shape)\n",
        "        assert len(new_fpn_list) == len(self.seg_num_grids)\n",
        "\n",
        "        #print(\"Cate Pred List Shape = \" + str(cate_pred_list[1].shape))\n",
        "        if eval:\n",
        "            assert cate_pred_list[1].shape[3] == self.cate_out_channels\n",
        "        else:\n",
        "            assert cate_pred_list[1].shape[1] == self.cate_out_channels\n",
        "        assert ins_pred_list[1].shape[1] == self.seg_num_grids[1] ** 2\n",
        "        assert cate_pred_list[1].shape[2] == self.seg_num_grids[1]\n",
        "        return cate_pred_list, ins_pred_list\n",
        "\n",
        "\n",
        "    # This function upsample/downsample the fpn level for the network\n",
        "    # In paper author change the original fpn level resolution\n",
        "    # Input:\n",
        "    # fpn_feat_list, list, len(FPN), stride[4,8,16,32,64]\n",
        "    # Output:\n",
        "    # new_fpn_list, list, len(FPN), stride[8,8,16,32,32]\n",
        "    def NewFPN(self, fpn_feat_list):\n",
        "        new_fpn_list = [None] * len(fpn_feat_list)\n",
        "\n",
        "        for i in range(len(fpn_feat_list)):\n",
        "            fpn_feat = self.lateral_convs[i](fpn_feat_list[i])\n",
        "\n",
        "            new_fpn_list[i] = fpn_feat\n",
        "\n",
        "        for i in range(len(fpn_feat_list) - 1, 0, -1):\n",
        "            new_fpn_list[i - 1] = new_fpn_list[i - 1] + F.interpolate(new_fpn_list[i], size=(\n",
        "            new_fpn_list[i - 1].shape[-2], new_fpn_list[i - 1].shape[-1]), mode='bilinear', align_corners=False)\n",
        "\n",
        "        new_fpn_list[0] = torch.nn.functional.interpolate(new_fpn_list[0], scale_factor=0.5)\n",
        "        new_fpn_list[-1] = torch.nn.functional.interpolate(new_fpn_list[-1], size=(25, 34))\n",
        "\n",
        "        return new_fpn_list\n",
        "\n",
        "\n",
        "    # This function forward a single level of fpn_featmap through the network\n",
        "    # Input:\n",
        "    # fpn_feat: (bz, fpn_channels(256), H_feat, W_feat)\n",
        "    # idx: indicate the fpn level idx, num_grids idx, the ins_out_layer idx\n",
        "    # Output:\n",
        "    # if eval==False\n",
        "    # cate_pred: (bz,C-1,S,S)\n",
        "    # ins_pred: (bz, S^2, 2H_feat, 2W_feat)\n",
        "    # if eval==True\n",
        "    # cate_pred: (bz,S,S,C-1) / after point_NMS\n",
        "    # ins_pred: (bz, S^2, Ori_H/4, Ori_W/4) / after upsampling\n",
        "    def forward_single_level(self, fpn_feat, idx, device=\"cpu\", eval=False, upsample_shape=None, prev_feat=None):\n",
        "        # upsample_shape is used in eval mode\n",
        "\n",
        "        fpn_feat = fpn_feat.to(device)\n",
        "\n",
        "        fpn_feat_coords = self.add_xy_coords(fpn_feat, device)\n",
        "        cate_pred = fpn_feat\n",
        "        ins_pred = fpn_feat_coords\n",
        "        num_grid = self.seg_num_grids[idx]\n",
        "\n",
        "        cate_pred = F.interpolate(cate_pred, size=(num_grid, num_grid), mode='bilinear', align_corners=False)\n",
        "\n",
        "        for cat_layer in self.cate_head:\n",
        "            cate_pred = cat_layer(cate_pred)\n",
        "\n",
        "        for ins_layer in self.ins_head:\n",
        "            ins_pred = ins_layer(ins_pred)\n",
        "\n",
        "        cate_pred = self.cate_out(cate_pred)\n",
        "        ins_pred = self.ins_out_list[idx](ins_pred)\n",
        "\n",
        "        # in inference time, upsample the pred to (ori image size/4)\n",
        "        if eval == True:\n",
        "            if upsample_shape is not None:\n",
        "                ins_pred = F.interpolate(ins_pred, size=upsample_shape, mode='bilinear', align_corners=False)\n",
        "                ## after upsampling the dimensions should be (bz, S^2, Ori_H/4, Ori_W/4)\n",
        "\n",
        "            cate_pred = self.points_nms(cate_pred).permute(0, 2, 3, 1)\n",
        "\n",
        "        # check flag\n",
        "        if eval == False:\n",
        "            ins_pred = F.interpolate(ins_pred, size=(2 * fpn_feat.shape[-2], 2 * fpn_feat.shape[-1]), mode='bilinear',\n",
        "                                     align_corners=False)\n",
        "            assert cate_pred.shape[1:] == (4, num_grid, num_grid)\n",
        "            assert ins_pred.shape[1:] == (num_grid ** 2, fpn_feat.shape[2] * 2, fpn_feat.shape[3] * 2)\n",
        "        else:\n",
        "            pass\n",
        "        del fpn_feat, fpn_feat_coords\n",
        "        if device == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "        return cate_pred, ins_pred\n",
        "\n",
        "\n",
        "    def add_xy_coords(self, fpn_feat, device):\n",
        "        batch_size, _, H, W = fpn_feat.shape\n",
        "\n",
        "        x_coords = torch.linspace(-1, 1, steps=W).view(1, 1, 1, W).expand(batch_size, 1, H, W).to(device)\n",
        "        y_coords = torch.linspace(-1, 1, steps=H).view(1, 1, H, 1).expand(batch_size, 1, H, W).to(device)\n",
        "\n",
        "        fpn_feat_with_coords = torch.cat([fpn_feat, x_coords, y_coords], dim=1)\n",
        "        del x_coords, y_coords\n",
        "        if device == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "        return fpn_feat_with_coords\n",
        "\n",
        "\n",
        "    # Credit to SOLO Author's code\n",
        "    # This function do a NMS on the heat map(cate_pred), grid-level\n",
        "    # Input:\n",
        "    # heat: (bz,C-1, S, S)\n",
        "    # Output:\n",
        "    # (bz,C-1, S, S)\n",
        "    def points_nms(self, heat, kernel=2):\n",
        "        # kernel must be 2\n",
        "        hmax = nn.functional.max_pool2d(\n",
        "            heat, (kernel, kernel), stride=1, padding=1)\n",
        "        keep = (hmax[:, :, :-1, :-1] == heat).float()\n",
        "        return heat * keep\n",
        "\n",
        "\n",
        "    # This function compute loss for a batch of images\n",
        "    # input:\n",
        "    # cate_pred_list: list, len(fpn_level), each (bz,C-1,S,S)\n",
        "    # ins_pred_list: list, len(fpn_level), each (bz, S^2, 2H_feat, 2W_feat)\n",
        "    # ins_gts_list: list, len(fpn_level), (bz, S^2, 2H_f, 2W_f)\n",
        "    # ins_ind_gts_list: list, len(fpn_level), (bz, S^2)\n",
        "    # cate_gts_list: list, len(fpn_level), (bz, S, S), {1,2,3}\n",
        "    # output:\n",
        "    # cate_loss, mask_loss, total_loss\n",
        "    def loss(self,\n",
        "             cate_pred_list,\n",
        "             ins_pred_list,\n",
        "             ins_gts_list,\n",
        "             ins_ind_gts_list,\n",
        "             cate_gts_list,\n",
        "             all_levels_one_batch,\n",
        "             device):\n",
        "\n",
        "        batch_size = ins_ind_gts_list[0].shape[0]\n",
        "        C = self.cate_out_channels\n",
        "\n",
        "        cate_loss_lambda = 10\n",
        "        mask_loss_lambda = 1\n",
        "\n",
        "        cate_preds = torch.zeros((batch_size * all_levels_one_batch, C)).to(device)\n",
        "        cate_gts = torch.zeros(batch_size * all_levels_one_batch).int().to(device)\n",
        "\n",
        "        prev_threshold = 0\n",
        "        divide_tensor = torch.zeros_like(cate_gts).to(device)\n",
        "        for batch_idx in range(batch_size):\n",
        "            for fpn_idx in range(len(self.seg_num_grids)):\n",
        "                new_threshold = prev_threshold + self.seg_num_grids[fpn_idx] ** 2\n",
        "                cate_gts[prev_threshold:new_threshold] = cate_gts_list[fpn_idx][batch_idx].int().flatten().to(device)\n",
        "                cate_preds[prev_threshold:new_threshold] = cate_pred_list[fpn_idx][batch_idx].flatten(start_dim=1).T.to(device)\n",
        "                divide_tensor[prev_threshold:new_threshold] = C * (self.seg_num_grids[fpn_idx] ** 2)\n",
        "                prev_threshold = new_threshold\n",
        "\n",
        "        focal_loss = self.FocalLoss(cate_preds, cate_gts, device) / batch_size\n",
        "        cate_loss = (focal_loss / divide_tensor).sum()\n",
        "\n",
        "        mask_loss = torch.tensor(0.0).to(device)\n",
        "        num_mask_loss = 0\n",
        "        for fpn_idx in range(len(self.seg_num_grids)):\n",
        "            mask_indices = ins_ind_gts_list[fpn_idx].flatten()\n",
        "            ins_pred = ins_pred_list[fpn_idx].flatten(end_dim=1).to(device)\n",
        "            ins_gt = ins_gts_list[fpn_idx].flatten(end_dim=1).to(device)\n",
        "            ins_pred = ins_pred[mask_indices == 1]\n",
        "            ins_gt = ins_gt[mask_indices == 1]\n",
        "            if ins_gt.shape[0] > 0:\n",
        "                mask_loss += self.DiceLoss(ins_pred, ins_gt).mean()\n",
        "                num_mask_loss += 1\n",
        "\n",
        "        if num_mask_loss > 0:\n",
        "            mask_loss /= num_mask_loss\n",
        "\n",
        "        total_loss = cate_loss_lambda * cate_loss + mask_loss_lambda * mask_loss\n",
        "\n",
        "        del cate_gts, cate_preds, focal_loss, divide_tensor\n",
        "        if device == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "        return cate_loss, mask_loss, total_loss\n",
        "\n",
        "\n",
        "    # This function compute the DiceLoss\n",
        "    # Input:\n",
        "    # mask_pred: (2H_feat, 2W_feat)\n",
        "    # mask_gt: (2H_feat, 2W_feat)\n",
        "    # Output: dice_loss, scalar\n",
        "    def DiceLoss(self, mask_pred, mask_gt):\n",
        "        epsilon = 1e-8\n",
        "        pq_product = mask_pred * mask_gt\n",
        "        p_squared = mask_pred * mask_pred\n",
        "        q_squared = mask_gt * mask_gt\n",
        "        num = 2 * pq_product.sum(dim=(-2, -1))\n",
        "        denom = p_squared.sum(dim=(-2, -1)) + q_squared.sum(dim=(-2, -1)) + epsilon\n",
        "        dice_loss = 1 - num / denom\n",
        "        del pq_product, p_squared, q_squared, num, denom\n",
        "        return dice_loss\n",
        "\n",
        "\n",
        "    # This function compute the cate loss\n",
        "    # Input:\n",
        "    # cate_preds: (num_entry, C-1)\n",
        "    # cate_gts: (num_entry,)\n",
        "    # Output: focal_loss, scalar\n",
        "    def FocalLoss(self, cate_preds, cate_gts, device):\n",
        "        epsilon = 1e-8\n",
        "\n",
        "        alpha = 0.9\n",
        "        gamma = self.cate_loss_cfg['gamma']\n",
        "\n",
        "        alpha_vector = alpha * torch.ones_like(cate_gts)\n",
        "        alpha_vector[cate_gts == 0] = 0.1\n",
        "        alpha_vector[cate_gts == 1] = 10.0\n",
        "        alpha_vector[cate_gts == 2] = 10.0\n",
        "\n",
        "        batch_indices = torch.arange(cate_preds.shape[0])\n",
        "\n",
        "        focal_loss = -alpha_vector * (1 - cate_preds[batch_indices, cate_gts]) ** gamma * torch.log(\n",
        "            cate_preds[batch_indices, cate_gts] + epsilon)\n",
        "\n",
        "        del batch_indices\n",
        "        if device == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "        return focal_loss\n",
        "\n",
        "\n",
        "    def MultiApply(self, func, *args, **kwargs):\n",
        "        pfunc = partial(func, **kwargs) if kwargs else func\n",
        "        map_results = map(pfunc, *args)\n",
        "\n",
        "        return tuple(map(list, zip(*map_results)))\n",
        "\n",
        "\n",
        "    # This function build the ground truth tensor for each batch in the training\n",
        "    # Input:\n",
        "    # ins_pred_list: list, len(fpn_level), each (bz, S^2, 2H_feat, 2W_feat)\n",
        "    # / ins_pred_list is only used to record feature map\n",
        "    # bbox_list: list, len(batch_size), each (n_object, 4) (x1y1x2y2 system)\n",
        "    # label_list: list, len(batch_size), each (n_object, )\n",
        "    # mask_list: list, len(batch_size), each (n_object, 800, 1088)\n",
        "    # Output:\n",
        "    # ins_gts_list: list, len(fpn), (bz, S^2, 2H_f, 2W_f)\n",
        "    # ins_ind_gts_list: list, len(fpn), (bz, S^2)\n",
        "    # cate_gts_list: list, len(fpn), (bz, S, S), {1,2,3}\n",
        "    def target(self,\n",
        "               ins_pred_list,\n",
        "               bbox_list,\n",
        "               label_list,\n",
        "               mask_list,\n",
        "               ins_gts_list,\n",
        "               device,\n",
        "               eval=False):\n",
        "\n",
        "        featmap_sizes = [(ins_pred.shape[-2] // 2, ins_pred.shape[-1] // 2) for ins_pred in ins_pred_list]\n",
        "\n",
        "        ins_gts_list, ins_ind_gts_list, cate_gts_list = self.target_single_img(bbox_list, label_list, mask_list,\n",
        "                                                                            ins_gts_list, device, featmap_sizes, eval)\n",
        "\n",
        "        # check flag\n",
        "        assert ins_gts_list[1][0].shape == (self.seg_num_grids[1] ** 2, 200, 272)\n",
        "        assert ins_ind_gts_list[1][0].shape == (self.seg_num_grids[1] ** 2,)\n",
        "        assert cate_gts_list[1][0].shape == (self.seg_num_grids[1], self.seg_num_grids[1])\n",
        "\n",
        "        return ins_gts_list, ins_ind_gts_list, cate_gts_list\n",
        "\n",
        "\n",
        "    # -----------------------------------\n",
        "    ## process single image in one batch\n",
        "    # -----------------------------------\n",
        "    # input:\n",
        "    # gt_bboxes_raw: bz, n_obj, 4 (x1y1x2y2 system)\n",
        "    # gt_labels_raw: bz, n_obj,\n",
        "    # gt_masks_raw: bz, n_obj, H_ori, W_ori\n",
        "    # featmap_sizes: list of shapes of featmap\n",
        "    # output:\n",
        "    # ins_label_list: list, len: len(FPN), (bz, S^2, 2H_feat, 2W_feat)\n",
        "    # cate_label_list: list, len: len(FPN), (bz, S, S)\n",
        "    # ins_ind_label_list: list, len: len(FPN), (bz, S^2)\n",
        "    def target_single_img(self,\n",
        "                          gt_bboxes_raw,\n",
        "                          gt_labels_raw,\n",
        "                          gt_masks_raw,\n",
        "                          ins_label_list,\n",
        "                          device,\n",
        "                          featmap_sizes=None,\n",
        "                          eval=False):\n",
        "        batch_size = len(gt_bboxes_raw)\n",
        "        n_obj = gt_bboxes_raw[0].shape[0]\n",
        "        H_ori, W_ori = gt_masks_raw[0].shape[1], gt_masks_raw[0].shape[2]\n",
        "\n",
        "        ins_ind_label_list = []\n",
        "        cate_label_list = []\n",
        "\n",
        "        for fpn_idx in range(len(featmap_sizes)):\n",
        "            H_featmap, W_featmap = featmap_sizes[fpn_idx]\n",
        "            S = self.seg_num_grids[fpn_idx]\n",
        "\n",
        "            grid_x, grid_y = torch.meshgrid(torch.arange(S), torch.arange(S), indexing='ij')\n",
        "\n",
        "            cate_label = torch.zeros((batch_size, S, S), dtype=torch.long).to(device)\n",
        "\n",
        "            ins_label_list[fpn_idx].zero_()\n",
        "\n",
        "            ins_ind_label = torch.zeros((batch_size, S * S), dtype=torch.long).to(device)\n",
        "\n",
        "            scale_x = W_featmap / W_ori\n",
        "            scale_y = H_featmap / H_ori\n",
        "            gt_bboxes_scaled = gt_bboxes_raw * torch.tensor([scale_x, scale_y, scale_x, scale_y])\n",
        "\n",
        "            for batch_idx in range(batch_size):\n",
        "                for obj_idx in range(n_obj):\n",
        "                    bbox_sqrt_area = np.sqrt((gt_bboxes_raw[batch_idx, obj_idx, 2] - gt_bboxes_raw[batch_idx, obj_idx, 0]) * (\n",
        "                                gt_bboxes_raw[batch_idx, obj_idx, 3] - gt_bboxes_raw[batch_idx, obj_idx, 1]))\n",
        "                    if bbox_sqrt_area >= self.scale_ranges[fpn_idx][0] and bbox_sqrt_area <= self.scale_ranges[fpn_idx][1]:\n",
        "                        bbox = gt_bboxes_scaled[batch_idx, obj_idx]\n",
        "                        label = gt_labels_raw[batch_idx, obj_idx]\n",
        "                        mask = gt_masks_raw[batch_idx, obj_idx]\n",
        "\n",
        "                        mask_indices = torch.nonzero(mask)\n",
        "                        if len(mask_indices) > 0:\n",
        "                            center_of_mass = mask_indices.float().mean(dim=0)\n",
        "                            center_y_raw, center_x_raw = center_of_mass[0].item(), center_of_mass[1].item()\n",
        "                        else:\n",
        "                            center_x_raw = (bbox[0] + bbox[2]) / 2\n",
        "                            center_y_raw = (bbox[1] + bbox[3]) / 2\n",
        "\n",
        "                        center_x = center_x_raw * W_featmap / W_ori\n",
        "                        center_y = center_y_raw * H_featmap / H_ori\n",
        "\n",
        "                        bbox_width = (bbox[2] - bbox[0]) * 0.2\n",
        "                        bbox_height = (bbox[3] - bbox[1]) * 0.2\n",
        "\n",
        "                        new_bbox_x1 = max(center_x - bbox_width / 2, 0)\n",
        "                        new_bbox_x2 = min(center_x + bbox_width / 2, W_featmap)\n",
        "                        new_bbox_y1 = max(center_y - bbox_height / 2, 0)\n",
        "                        new_bbox_y2 = min(center_y + bbox_height / 2, H_featmap)\n",
        "\n",
        "                        grid_x1 = int(new_bbox_x1 * S / W_featmap)\n",
        "                        grid_x2 = int(new_bbox_x2 * S / W_featmap)\n",
        "                        grid_y1 = int(new_bbox_y1 * S / H_featmap)\n",
        "                        grid_y2 = int(new_bbox_y2 * S / H_featmap)\n",
        "\n",
        "                        cate_label[batch_idx, grid_y1:grid_y2 + 1, grid_x1:grid_x2 + 1] = int(label.item())\n",
        "\n",
        "                        grid_mask = (grid_x >= grid_x1) & (grid_x <= grid_x2) & (grid_y >= grid_y1) & (grid_y <= grid_y2)\n",
        "                        grid_indices = torch.nonzero(grid_mask.reshape(-1)).squeeze()\n",
        "\n",
        "                        if eval:\n",
        "                            resized_mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(200, 272),\n",
        "                                                         mode='bilinear', align_corners=False).squeeze().to(device)\n",
        "                        else:\n",
        "                            resized_mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(2 * H_featmap,\n",
        "                                            2 * W_featmap), mode='bilinear', align_corners=False).squeeze().to(device)\n",
        "\n",
        "                        ins_label_list[fpn_idx][batch_idx, grid_indices] = resized_mask\n",
        "                        ins_ind_label[batch_idx, grid_indices] = 1\n",
        "\n",
        "            cate_label_list.append(cate_label)\n",
        "            ins_ind_label_list.append(ins_ind_label)\n",
        "\n",
        "        # check flag\n",
        "        assert ins_label_list[1].shape == (batch_size, 1296, 200, 272)\n",
        "        assert ins_ind_label_list[1].shape == (batch_size, 1296)\n",
        "        assert cate_label_list[1].shape == (batch_size, 36, 36)\n",
        "\n",
        "        return ins_label_list, ins_ind_label_list, cate_label_list\n",
        "\n",
        "\n",
        "    # This function receive pred list from forward and post-process\n",
        "    # Input:\n",
        "    # ins_pred_list: list, len(fpn), (bz,S^2,Ori_H/4, Ori_W/4)\n",
        "    # cate_pred_list: list, len(fpn), (bz,S,S,C-1)\n",
        "    # ori_size: [ori_H, ori_W]\n",
        "    # Output:\n",
        "    # NMS_sorted_scores_list, list, len(bz), (keep_instance,)\n",
        "    # NMS_sorted_cate_label_list, list, len(bz), (keep_instance,)\n",
        "    # NMS_sorted_ins_list, list, len(bz), (keep_instance, ori_H, ori_W)\n",
        "    def PostProcess(self,\n",
        "                    ins_pred_list,\n",
        "                    cate_pred_list,\n",
        "                    ori_size,\n",
        "                    device):\n",
        "\n",
        "        all_levels_one_batch = 0\n",
        "        for S in self.seg_num_grids:\n",
        "            all_levels_one_batch += S * S\n",
        "\n",
        "        NMS_sorted_scores_list = []\n",
        "        NMS_sorted_cate_label_list = []\n",
        "        NMS_sorted_ins_list = []\n",
        "\n",
        "        batch_size = ins_pred_list[0].shape[0]\n",
        "        fpn_len = len(ins_pred_list)\n",
        "        for batch_idx in range(batch_size):\n",
        "            ins_pred_img = torch.zeros((all_levels_one_batch, int(ori_size[0]/4), int(ori_size[1]/4))).to(device)\n",
        "            cate_pred_img = torch.zeros((all_levels_one_batch, self.cate_out_channels)).to(device)\n",
        "            start_index = 0\n",
        "            for fpn_idx in range(fpn_len):\n",
        "                end_index = start_index + self.seg_num_grids[fpn_idx] ** 2\n",
        "                ins_pred_img[start_index:end_index] = ins_pred_list[fpn_idx][batch_idx]\n",
        "                cate_pred_img[start_index:end_index] = torch.flatten(cate_pred_list[fpn_idx][batch_idx], end_dim=1)\n",
        "                start_index = end_index\n",
        "            NMS_sorted_scores, NMS_sorted_cate_label, NMS_sorted_ins = self.PostProcessImg(ins_pred_img, cate_pred_img, ori_size, device)\n",
        "            NMS_sorted_scores_list.append(NMS_sorted_scores)\n",
        "            NMS_sorted_cate_label_list.append(NMS_sorted_cate_label)\n",
        "            NMS_sorted_ins_list.append(NMS_sorted_ins)\n",
        "\n",
        "        return NMS_sorted_scores_list, NMS_sorted_cate_label_list, NMS_sorted_ins_list\n",
        "\n",
        "\n",
        "    # This function Postprocess on single img\n",
        "    # Input:\n",
        "    # ins_pred_img: (all_level_S^2, ori_H/4, ori_W/4)\n",
        "    # cate_pred_img: (all_level_S^2, C-1)\n",
        "    # Output:\n",
        "    # sorted_scores_top_k: (keep_instance,)\n",
        "    # sorted_cate_top_k: (keep_instance,)\n",
        "    # sorted_ins_top_k: (keep_instance, ori_H, ori_W)\n",
        "    def PostProcessImg(self,\n",
        "                       ins_pred_img,\n",
        "                       cate_pred_img,\n",
        "                       ori_size,\n",
        "                       device):\n",
        "        k = self.postprocess_cfg['keep_instance']\n",
        "        mask_thresh = 0.1\n",
        "\n",
        "        ones_mask = torch.zeros_like(ins_pred_img).to(device)\n",
        "        ones_mask[ins_pred_img >= mask_thresh] = 1\n",
        "\n",
        "        max_cate_prob, max_cate_class = torch.max(cate_pred_img, dim=-1)\n",
        "\n",
        "        scores = (torch.sum(ins_pred_img * ones_mask, dim=(-2, -1)) / torch.sum(ones_mask, dim=(-2, -1))) * max_cate_prob\n",
        "\n",
        "        sorted_scores, indices = torch.sort(scores, descending=True)\n",
        "        sorted_ins = ins_pred_img[indices]\n",
        "        sorted_cate_class = max_cate_class[indices]\n",
        "\n",
        "        new_scores = self.MatrixNMS(sorted_ins, sorted_scores, device)\n",
        "\n",
        "        sorted_new_scores, indices = torch.sort(new_scores, descending=True)\n",
        "\n",
        "        sorted_ins = sorted_ins[indices]\n",
        "        sorted_cate_class = sorted_cate_class[indices]\n",
        "\n",
        "        sorted_scores_top_k = sorted_new_scores[:k]\n",
        "        sorted_cate_top_k = sorted_cate_class[:k]\n",
        "        sorted_ins_top_k = sorted_ins[:k, :, :]\n",
        "\n",
        "        sorted_ins_top_k = torch.nn.functional.interpolate(sorted_ins_top_k.unsqueeze(0), size=ori_size).squeeze(0)\n",
        "\n",
        "        return sorted_scores_top_k, sorted_cate_top_k, sorted_ins_top_k\n",
        "\n",
        "\n",
        "    # This function perform matrix NMS\n",
        "    # Input:\n",
        "    # sorted_ins: (n_act, ori_H/4, ori_W/4)\n",
        "    # sorted_scores: (n_act,)\n",
        "    # Output:\n",
        "    # decay_scores: (n_act,)\n",
        "    def MatrixNMS(self, sorted_ins, sorted_scores, device, method='linear', gauss_sigma=0.5):\n",
        "        n = len(sorted_scores)\n",
        "        sorted_masks = sorted_ins.reshape(n, -1).to(device)\n",
        "        intersection = torch.mm(sorted_masks, sorted_masks.T)\n",
        "        areas = sorted_masks.sum(dim=1).expand(n, n).to(device)\n",
        "        union = areas + areas.T - intersection\n",
        "        ious = (intersection / union).triu(diagonal=1)\n",
        "\n",
        "        ious_cmax = ious.max(0)[0].expand(n, n).T\n",
        "        if method == 'gauss':\n",
        "            decay = (torch.exp(-(ious ** 2 - ious_cmax ** 2) / gauss_sigma)).to(device)\n",
        "        else:\n",
        "            decay = ((1 - ious) / (1 - ious_cmax)).to(device)\n",
        "        decay = decay.min(dim=0)[0]\n",
        "        return sorted_scores * decay\n",
        "\n",
        "\n",
        "    # -----------------------------------\n",
        "    ## The following code is for visualization\n",
        "    # -----------------------------------\n",
        "\n",
        "\n",
        "    # this function visualize the ground truth tensor\n",
        "    # Input:\n",
        "    # ins_gts_list: list, len(bz), list, len(fpn), (S^2, 2H_f, 2W_f)\n",
        "    # ins_ind_gts_list: list, len(bz), list, len(fpn), (S^2,)\n",
        "    # cate_gts_list: list, len(bz), list, len(fpn), (S, S), {1,2,3}\n",
        "    # color_list: list, len(C-1)\n",
        "    # img: (bz,3,Ori_H, Ori_W)\n",
        "    ## self.strides: [8,8,16,32,32]\n",
        "    def PlotGT(self,\n",
        "               ins_gts_list,\n",
        "               ins_ind_gts_list,\n",
        "               cate_gts_list,\n",
        "               img):\n",
        "        num_images = img.shape[0]\n",
        "\n",
        "        img_np = img.permute(0, 2, 3, 1).cpu().numpy()\n",
        "        for i in range(num_images):\n",
        "            for feat_map in range(len(ins_gts_list[i])):\n",
        "                fig, ax = plt.subplots(1)\n",
        "                min_val = np.min(img_np[i])\n",
        "                max_val = np.max(img_np[i])\n",
        "                image_for_plotting = (img_np[i] - min_val) / (max_val - min_val)\n",
        "                ax.imshow(image_for_plotting)\n",
        "                masks_already_plotted = []\n",
        "                s = cate_gts_list[i][feat_map].shape[0]\n",
        "                for grid_idx in range(s * s):\n",
        "                    if ins_ind_gts_list[i][feat_map][grid_idx] == 1:\n",
        "                        already_plotted = False\n",
        "                        for prev_mask in masks_already_plotted:\n",
        "                            if torch.equal(ins_gts_list[i][feat_map][grid_idx], prev_mask):\n",
        "                                already_plotted = True\n",
        "                        if not already_plotted:\n",
        "                            mask = ins_gts_list[i][feat_map][grid_idx]\n",
        "                            category_label = cate_gts_list[i][feat_map][grid_idx % s, grid_idx // s].item()\n",
        "                            mask_np = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(img.shape[-2], img.shape[-1]), mode='bilinear',\n",
        "                                                    align_corners=False).squeeze(0).squeeze(0).cpu().numpy()\n",
        "                            rgb_mask = np.zeros_like(img_np[i])\n",
        "                            rgb_mask[:, :, category_label - 1] = mask_np\n",
        "                            ax.imshow(rgb_mask, alpha=0.5)\n",
        "                            masks_already_plotted.append(mask)\n",
        "\n",
        "                ax.set_title(\"FPN Recovery Plot for Image \" + str(iter*2+i) + \" at FPN Level \" + str(feat_map))\n",
        "                os.makedirs(\"./testfig\", exist_ok=True)\n",
        "                plt.savefig(\"./testfig/fpn_recovery_plot_\" + str(iter*2+i) + \"_fpn_level_\" + str(feat_map) + \".png\")\n",
        "                plt.show()\n",
        "\n",
        "            if i == 1:\n",
        "                break\n",
        "\n",
        "\n",
        "    # This function plot the inference segmentation in img\n",
        "    # Input:\n",
        "    # NMS_sorted_scores_list, list, len(bz), (keep_instance,)\n",
        "    # NMS_sorted_cate_label_list, list, len(bz), (keep_instance,)\n",
        "    # NMS_sorted_ins_list, list, len(bz), (keep_instance, ori_H, ori_W)\n",
        "    # color_list: [\"jet\", \"ocean\", \"Spectral\"]\n",
        "    # img: (bz, 3, ori_H, ori_W)\n",
        "    def PlotInfer(self,\n",
        "                  NMS_sorted_scores_list,\n",
        "                  NMS_sorted_cate_label_list,\n",
        "                  NMS_sorted_ins_list,\n",
        "                  img,\n",
        "                  iter):\n",
        "        score_plotting_threshold = 0.4\n",
        "        num_images = img.shape[0]\n",
        "        keep_instance = NMS_sorted_ins_list[0].shape[0]\n",
        "\n",
        "        img_np = img.permute(0, 2, 3, 1).cpu().numpy()\n",
        "        for i in range(num_images):\n",
        "            fig, ax = plt.subplots(1)\n",
        "            min_val = np.min(img_np[i])\n",
        "            max_val = np.max(img_np[i])\n",
        "            image_for_plotting = (img_np[i] - min_val) / (max_val - min_val)\n",
        "            ax.imshow(image_for_plotting)\n",
        "            rgb_mask = np.zeros_like(img_np[i])\n",
        "            for j in range(1, keep_instance):\n",
        "                if NMS_sorted_scores_list[i][j] >= score_plotting_threshold:\n",
        "                    category_label = NMS_sorted_cate_label_list[i][j]\n",
        "                    rgb_mask[:, :, category_label - 1] += NMS_sorted_ins_list[i][j].detach().cpu().numpy()\n",
        "            ax.imshow(rgb_mask, alpha=0.5)\n",
        "\n",
        "            ax.set_title(\"SOLO Output Plot for Image \" + str(iter * num_images + i))\n",
        "            os.makedirs(\"./testfig\", exist_ok=True)\n",
        "            plt.savefig(\"./testfig/solo_output_plot_\" + str(iter * num_images + i) + \".png\")\n",
        "            plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PwUGRtM-jh1"
      },
      "source": [
        "### Part A Submission\n",
        "In addition to the code you used, you should submit a pdf containing the following plots:\n",
        "* Dataset visualization plots such as fig. 5 and 6. You should include at least five examples and at least two examples that contain multiple objects. Additionally, the images should cover a range of scales and object classes.\n",
        "* Target assignment plots such as fig. 8 and 9. Should include at least five examples and at least two examples that contain multiple objects. Additionally, the images should cover a range of scales and object classes.\n",
        "\n",
        "We should be able to reproduce your results with a single function call or cell evaluation. Please specify the method to do so."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvZZkrlV-jh3"
      },
      "source": [
        "## Part B\n",
        "### Loss\n",
        "The loss function consists of two parts: category loss and mask loss:\n",
        "$$L = \\lambda_\\text{cate} L_\\text{cate} + \\lambda_\\text{mask} L_\\text{mask} $$\n",
        "\n",
        "The category loss is defined as (where $\\text{FL}$ is the focal loss)\n",
        "\n",
        "$$L_\\text{cate} = \\frac{1}{S^2 C} \\sum_{S,S,C} \\text{FL}(p_t)$$\n",
        "\n",
        "$$\\text{FL}(p_t) = -\\alpha_t (1 - p_t)^\\gamma \\log{(p_t)}$$\n",
        "\n",
        "$$(\\alpha_t, p_t) = \\left\\{\\begin{array}{lr} (\\alpha, \\hat{p}) & \\text{if }y=1 \\\\ (1 - \\alpha, 1 - \\hat{p}) & \\text{otherwise}\\end{array}\\right.$$\n",
        "\n",
        "while the mask loss is defined as (where $d_\\text{mask}$ is the dice loss)\n",
        "\n",
        "$$L_\\text{mask} = \\frac{1}{N_\\text{positive}} \\sum_k \\mathbb{1}_{\\{p_{i, j} > 0\\}} d_\\text{mask}(m_k, \\hat{m}_k)$$\n",
        "\n",
        "$$d_\\text{mask}(p, q) = 1 - D(p, q)$$\n",
        "\n",
        "$$D(p, q) = \\frac{2 \\sum_{x,y}(p_{x,y} \\cdot q_{x,y})}{\\sum_{x,y}p_{x,y}^2 + \\sum_{x,y}q_{x,y}^2}$$\n",
        "\n",
        "### Post Processing\n",
        "Post processing consists of three steps: points NMS, concatenation and sorting, and matrix NMS. A summary of each of these steps is provided below. In addition, your final mask prediction should be a binary mask based on the mask thresholding parameter. Some examples of results post-inference are shown in figs. 11-14.\n",
        "\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig11.png\" width=300/></div>\n",
        "<center>Figure 11: SOLO instance segmentation example.</center>\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig12.png\" width=300/></div>\n",
        "<center>Figure 12: SOLO instance segmentation example.</center>\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig13.png\" width=300/></div>\n",
        "<center>Figure 13: SOLO instance segmentation example.</center>\n",
        "\n",
        "<div><img src=\"https://github.com/LukasZhornyak/CIS680_files/raw/main/HW3/fig14.png\" width=300/></div>\n",
        "<center>Figure 14: SOLO instance segmentation example.</center>\n",
        "\n",
        "#### Points NMS\n",
        "Non-max suppression for the category predictions, applied to each channel sperately. A maxpooling with a kernel size of 2, sample code is included below.\n",
        "\n",
        "#### Concatenation and Sorting\n",
        "Here, we merge the predictions across all the FPN levels into a single list of predictions, sorted by their predicted score. This involves rescaling the predicted masks appropriately.\n",
        "\n",
        "#### Matrix NMS\n",
        "Inspired by Soft-NMS, MatrixNMS suppresses mask predictions with a lower score based on their similarity to predictions with a higher score in a completely vectorized manner. Sample code is include below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0m1KuTEt-jh4"
      },
      "source": [
        "# Credit to SOLO Author's code\n",
        "# This function does NMS on the heat map (category_prediction), grid-level\n",
        "# Input:\n",
        "#     heat: (batch_size, C-1, S, S)\n",
        "# Output:\n",
        "#     (batch_size, C-1, S, S)\n",
        "def points_nms(heat, kernel=2):\n",
        "    # kernel must be 2\n",
        "    hmax = F.max_pool2d(\n",
        "        heat, (kernel, kernel), stride=1, padding=1)\n",
        "    keep = (hmax[:, :, :-1, :-1] == heat).float()\n",
        "    return heat * keep\n",
        "\n",
        "# This function performs Matrix NMS\n",
        "# Input:\n",
        "#     sorted_masks: (n_active, image_h/4, image_w/4)\n",
        "#     sorted_scores: (n_active,)\n",
        "# Output:\n",
        "#     decay_scores: (n_active,)\n",
        "def MatrixNMS(sorted_masks, sorted_scores, method='gauss', gauss_sigma=0.5):\n",
        "    n = len(sorted_scores)\n",
        "    sorted_masks = sorted_masks.reshape(n, -1)\n",
        "    intersection = torch.mm(sorted_masks, sorted_masks.T)\n",
        "    areas = sorted_masks.sum(dim=1).expand(n, n)\n",
        "    union = areas + areas.T - intersection\n",
        "    ious = (intersection / union).triu(diagonal=1)\n",
        "\n",
        "    ious_cmax = ious.max(0)[0].expand(n, n).T\n",
        "    if method == 'gauss':\n",
        "        decay = torch.exp(-(ious ** 2 - ious_cmax ** 2) / gauss_sigma)\n",
        "    else:\n",
        "        decay = (1 - ious) / (1 - ious_cmax)\n",
        "    decay = decay.min(dim=0)[0]\n",
        "    return sorted_scores * decay"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajIefq5F-jh6"
      },
      "source": [
        "### Training\n",
        "#### Optimizer\n",
        "The SGD optimizer with a weight decay of 1e-4 and a momentum of 0.9 is used. For a batch size of 16, an initial learning rate of 16 is used (this should be scaled appropriately for different batch sizes). You should train for 36 epochs, reducing the learning rate by a factor of 10 at epochs 27 and 33.\n",
        "\n",
        "#### Data Augemnetation\n",
        "To achieve better performance, you may wish to explore some data augmentation techniques. You should be able to achieve the necessary performance without any augmentation, however.\n",
        "\n",
        "#### Checkpointing\n",
        "Due to the long training time, we highly recommend that you set up regular checkpointing during your training in case your training gets interrupted (e.g. your colab session ending)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEgQbp7Y-jh7"
      },
      "source": [
        "### Part B Submission\n",
        "In addition to the code you used, you should submit a pdf containing the following plots:\n",
        "* Dataset visualization plots such as fig. 5 and 6. You should include at least five examples and at least two examples that contain multiple objects. Additionally, the images should cover a range of scales and object classes.\n",
        "* Target assignment plots such as fig. 8 and 9. You should include at least five examples and at least two examples that contain multiple objects. Additionally, the images should cover a range of scales and object classes.\n",
        "* Final inference results such as figs. 11-14. You should include at least five examples and at least two examples that contain multiple objects. Additionally, the images should cover a range of scales and object classes.\n",
        "* Training and validation loss curves. These should include the focal loss, dice loss, and total loss as seperate quantities.\n",
        "\n",
        "In addition, you should include a discussion of any issues you encountered during this project and any changes you made to the siggested architecture. If you made any interesting observations or thoughts about potential improvements, you should also include them here.\n",
        "\n",
        "* We should be able to reproduce your results with a single function call or cell evaluation. Please specify the method to do so. You do not, however, need to submit your trained weights.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_TRAINING CODE:_"
      ],
      "metadata": {
        "id": "QqvpdcAznsYu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\", force_remount=True)"
      ],
      "metadata": {
        "id": "cZkuMU2WRhKW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa807f45-a47e-4a81-8e48-9066480198ea"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/gdrive/MyDrive/CIS6800HW3\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import MultiStepLR\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import gc\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "from backbone import *\n",
        "from dataset import *\n",
        "from solo_head import *"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khrYHbCKs9Q_",
        "outputId": "c85b6356-5008-4db8-bf44-69ae82364996"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/CIS6800HW3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_path = './data/hw3_mycocodata_img_comp_zlib.h5'\n",
        "masks_path = './data/hw3_mycocodata_mask_comp_zlib.h5'\n",
        "labels_path = \"./data/hw3_mycocodata_labels_comp_zlib.npy\"\n",
        "bboxes_path = \"./data/hw3_mycocodata_bboxes_comp_zlib.npy\"\n",
        "paths = [imgs_path, masks_path, labels_path, bboxes_path]\n",
        "dataset = BuildDataset(paths)"
      ],
      "metadata": {
        "id": "ssujsWmKvlmW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01/5#/8\n",
        "momentum = 0.9\n",
        "weight_decay = 1e-4\n",
        "batch_size = 5\n",
        "gamma = 0.1\n",
        "num_epochs = 36\n",
        "train_proportion = 0.8\n",
        "\n",
        "full_size = len(dataset)\n",
        "val_size = 655 # Approx 1/5 of dataset\n",
        "train_size = full_size - val_size\n",
        "\n",
        "torch.random.manual_seed(1)\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
        "\n",
        "train_build_loader = BuildDataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
        "train_loader = train_build_loader.loader()\n",
        "val_build_loader = BuildDataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0)\n",
        "val_loader = val_build_loader.loader()\n",
        "\n",
        "resnet50_fpn = Resnet50Backbone(device=device).to(device)\n",
        "solo_head = SOLOHead(num_classes=4).to(device)\n",
        "\n",
        "optimizer = optim.SGD(solo_head.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)\n",
        "\n",
        "scheduler = MultiStepLR(optimizer, milestones=[27, 33], gamma=gamma)\n",
        "\n",
        "num_records_per_epoch = 1\n",
        "recording_increments = int(len(train_loader)/num_records_per_epoch)\n",
        "\n",
        "all_levels_one_batch = 0\n",
        "for S in solo_head.seg_num_grids:\n",
        "    all_levels_one_batch += S * S"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUl43uG_v4lp",
        "outputId": "d27350de-5406-4e8c-e0d8-964089c0c21b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
            "100%|██████████| 97.8M/97.8M [00:00<00:00, 138MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "train_cate_loss_list = []\n",
        "train_mask_loss_list = []\n",
        "train_total_loss_list = []\n",
        "val_cate_loss_list = []\n",
        "val_mask_loss_list = []\n",
        "val_total_loss_list = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    ins_gts_list_train = []\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 40 * 40, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 36 * 36, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 24 * 24, 2 * 50, 2 * 68), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 16 * 16, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 12 * 12, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "\n",
        "    solo_head.train()\n",
        "    train_cate_loss_sum = 0.0\n",
        "    train_mask_loss_sum = 0.0\n",
        "    train_total_loss_sum = 0.0\n",
        "    for train_batch_idx, train_data in enumerate(train_loader):\n",
        "        img_train, mask_list_train, label_list_train, bbox_list_train = [train_data[i] for i in range(len(train_data))]\n",
        "\n",
        "        backout_train = resnet50_fpn(img_train.to(device))\n",
        "        fpn_feat_list_train = list(backout_train.values())\n",
        "\n",
        "        cate_pred_list_train, ins_pred_list_train = solo_head.forward(fpn_feat_list_train, device, eval=False)\n",
        "        ins_gts_list_train, ins_ind_gts_list_train, cate_gts_list_train = solo_head.target(ins_pred_list_train, bbox_list_train, label_list_train, mask_list_train, ins_gts_list_train, device, eval=False)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        cate_loss_train, mask_loss_train, total_loss_train = solo_head.loss(cate_pred_list_train, ins_pred_list_train, ins_gts_list_train, ins_ind_gts_list_train, cate_gts_list_train, all_levels_one_batch, device)\n",
        "\n",
        "        total_loss_train.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_cate_loss_sum += cate_loss_train.item()\n",
        "        train_mask_loss_sum += mask_loss_train.item()\n",
        "        train_total_loss_sum += total_loss_train.item()\n",
        "\n",
        "    train_cate_loss_mean = train_cate_loss_sum/len(train_loader)\n",
        "    train_mask_loss_mean = train_mask_loss_sum/len(train_loader)\n",
        "    train_total_loss_mean = train_total_loss_sum/len(train_loader)\n",
        "    train_cate_loss_list.append(train_cate_loss_mean)\n",
        "    train_mask_loss_list.append(train_mask_loss_mean)\n",
        "    train_total_loss_list.append(train_total_loss_mean)\n",
        "\n",
        "    del img_train, mask_list_train, label_list_train, bbox_list_train, backout_train, fpn_feat_list_train\n",
        "    del cate_pred_list_train, ins_pred_list_train, ins_gts_list_train, ins_ind_gts_list_train, cate_gts_list_train\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    ins_gts_list_val = []\n",
        "    ins_gts_list_val.append(torch.zeros((batch_size, 40 * 40, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_val.append(torch.zeros((batch_size, 36 * 36, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_val.append(torch.zeros((batch_size, 24 * 24, 2 * 50, 2 * 68), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_val.append(torch.zeros((batch_size, 16 * 16, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_val.append(torch.zeros((batch_size, 12 * 12, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "\n",
        "    solo_head.eval()\n",
        "    val_cate_loss_sum = 0.0\n",
        "    val_mask_loss_sum = 0.0\n",
        "    val_total_loss_sum = 0.0\n",
        "    for val_batch_idx, val_data in enumerate(val_loader):\n",
        "        img_val, mask_list_val, label_list_val, bbox_list_val = [val_data[i] for i in range(len(val_data))]\n",
        "\n",
        "        backout_val = resnet50_fpn(img_val.to(device))\n",
        "        fpn_feat_list_val = list(backout_val.values())\n",
        "\n",
        "        cate_pred_list_val, ins_pred_list_val = solo_head.forward(fpn_feat_list_val, device)\n",
        "\n",
        "        ins_gts_list_val, ins_ind_gts_list_val, cate_gts_list_val = solo_head.target(ins_pred_list_val, bbox_list_val, label_list_val, mask_list_val, ins_gts_list_val, device)\n",
        "\n",
        "        cate_loss_val, mask_loss_val, total_loss_val = solo_head.loss(cate_pred_list_val, ins_pred_list_val, ins_gts_list_val, ins_ind_gts_list_val, cate_gts_list_val, all_levels_one_batch, device)\n",
        "\n",
        "        val_cate_loss_sum += cate_loss_val.item()\n",
        "        val_mask_loss_sum += mask_loss_val.item()\n",
        "        val_total_loss_sum += total_loss_val.item()\n",
        "\n",
        "    del img_val, mask_list_val, label_list_val, bbox_list_val, backout_val, fpn_feat_list_val\n",
        "    del cate_pred_list_val, ins_pred_list_val, ins_gts_list_val, ins_ind_gts_list_val, cate_gts_list_val\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    val_cate_loss_mean = val_cate_loss_sum/len(val_loader)\n",
        "    val_mask_loss_mean = val_mask_loss_sum/len(val_loader)\n",
        "    val_total_loss_mean = val_total_loss_sum/len(val_loader)\n",
        "    val_cate_loss_list.append(val_cate_loss_mean)\n",
        "    val_mask_loss_list.append(val_mask_loss_mean)\n",
        "    val_total_loss_list.append(val_total_loss_mean)\n",
        "    print(\"Epoch \" + str(epoch + 1) + \": Training Losses: Cate: \" + str(train_cate_loss_mean) + \", Mask: \" + str(train_mask_loss_mean) + \", Total: \" + str(train_total_loss_mean) +\n",
        "          \", Validation Losses: Cate: \" + str(val_cate_loss_mean) + \", Mask: \" + str(val_mask_loss_mean) + \", Total: \" + str(val_total_loss_mean))\n",
        "\n",
        "    torch.save(solo_head.state_dict(), 'solo_head_epoch_' + str(epoch + 1) + '_minibatch_' + str(train_batch_idx) + '.pth')\n",
        "    np.save('train_cate_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(train_cate_loss_list))\n",
        "    np.save('train_mask_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(train_mask_loss_list))\n",
        "    np.save('train_total_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(train_total_loss_list))\n",
        "    np.save('val_cate_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(val_cate_loss_list))\n",
        "    np.save('val_mask_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(val_mask_loss_list))\n",
        "    np.save('val_total_loss_list_epoch_' + str(epoch + 1) + '.npy', np.array(val_total_loss_list))\n",
        "\n",
        "    ins_gts_list_train = []\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 40 * 40, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 36 * 36, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 24 * 24, 2 * 50, 2 * 68), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 16 * 16, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "    ins_gts_list_train.append(torch.zeros((batch_size, 12 * 12, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "\n",
        "    scheduler.step()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXZv5NNbJiS8",
        "outputId": "96f36dd9-0f22-4189-b1cc-bd889c1cdcc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/gdrive/MyDrive/CIS6800HW3/dataset.py:134: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  imgs = torch.stack([torch.tensor(img) for img in imgs], dim=0)\n",
            "/content/gdrive/MyDrive/CIS6800HW3/dataset.py:139: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  pad_mask[:mask.shape[0], :, :] = torch.tensor(mask)\n",
            "/content/gdrive/MyDrive/CIS6800HW3/dataset.py:143: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  pad_bbox[:bbox.shape[0], :] = torch.tensor(bbox)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: Training Losses: Cate: 0.0003106972749177853, Mask: 0.5590025176262033, Total: 1.6773182412902057, Validation Losses: Cate: 4.7086181550309816e-05, Mask: 0.5224414531511204, Total: 1.567371448487726\n",
            "Epoch 2: Training Losses: Cate: 3.539511229646311e-05, Mask: 0.4394048010480815, Total: 1.3182497904903587, Validation Losses: Cate: 2.6971394090512447e-05, Mask: 0.44768210722289925, Total: 1.3430732920879627\n",
            "Epoch 3: Training Losses: Cate: 2.2821403314549943e-05, Mask: 0.38972051199026037, Total: 1.1691843528172066, Validation Losses: Cate: 1.915556473277806e-05, Mask: 0.4101819741816921, Total: 1.230565076338426\n",
            "Epoch 4: Training Losses: Cate: 1.7048016267706986e-05, Mask: 0.3583384961946029, Total: 1.075032530673619, Validation Losses: Cate: 1.4912109607755107e-05, Mask: 0.39103749838039165, Total: 1.1731273975536114\n",
            "Epoch 5: Training Losses: Cate: 1.3749910211159638e-05, Mask: 0.32735195548997986, Total: 0.982069610407526, Validation Losses: Cate: 1.2471875258063131e-05, Mask: 0.3721624912537691, Total: 1.1164999429051203\n",
            "Epoch 6: Training Losses: Cate: 1.1649526719526781e-05, Mask: 0.3072861359950683, Total: 0.9218700519010025, Validation Losses: Cate: 1.059190253727138e-05, Mask: 0.3563479798781963, Total: 1.0690545310045927\n",
            "Epoch 7: Training Losses: Cate: 9.95728228747818e-06, Mask: 0.28876394131916694, Total: 0.8663017781187291, Validation Losses: Cate: 9.056280912717676e-06, Mask: 0.3498825567937989, Total: 1.0496567244293125\n",
            "Epoch 8: Training Losses: Cate: 8.708058857654268e-06, Mask: 0.2699037865660657, Total: 0.8097200644313147, Validation Losses: Cate: 8.037510280201224e-06, Mask: 0.3346214877398869, Total: 1.0038725001211384\n",
            "Epoch 9: Training Losses: Cate: 7.738799541964567e-06, Mask: 0.25950114815799213, Total: 0.7785111786191035, Validation Losses: Cate: 7.258307977357589e-06, Mask: 0.3386264887588625, Total: 1.0158867187627398\n",
            "Epoch 10: Training Losses: Cate: 7.080323145960061e-06, Mask: 0.24927544977758578, Total: 0.7478334273129588, Validation Losses: Cate: 6.629112606906138e-06, Mask: 0.31603755278669243, Total: 0.9481192818124786\n",
            "Epoch 11: Training Losses: Cate: 6.378833171996006e-06, Mask: 0.231889417273674, Total: 0.69567462673475, Validation Losses: Cate: 6.0145429248945126e-06, Mask: 0.3120581711862833, Total: 0.9361805283386289\n",
            "Epoch 12: Training Losses: Cate: 5.90348567551338e-06, Mask: 0.2196850794228329, Total: 0.6590611391996972, Validation Losses: Cate: 5.534201539147117e-06, Mask: 0.31810249911464805, Total: 0.9543130293147255\n",
            "Epoch 13: Training Losses: Cate: 5.464232039477821e-06, Mask: 0.20880432262788332, Total: 0.626418429316232, Validation Losses: Cate: 5.156904311494867e-06, Mask: 0.30178403405060295, Total: 0.9053572525504892\n",
            "Epoch 14: Training Losses: Cate: 5.1271928466828995e-06, Mask: 0.19831098145109483, Total: 0.59493806824031, Validation Losses: Cate: 4.878280901200452e-06, Mask: 0.29882068016374386, Total: 0.8964669174365415\n",
            "Epoch 15: Training Losses: Cate: 4.8054851206337655e-06, Mask: 0.18506033396726596, Total: 0.555185805592272, Validation Losses: Cate: 4.650080531522523e-06, Mask: 0.2937048435324931, Total: 0.8811191766316654\n",
            "Epoch 16: Training Losses: Cate: 4.52737189529725e-06, Mask: 0.1749887286160869, Total: 0.5249707125041677, Validation Losses: Cate: 4.379755305759484e-06, Mask: 0.29221457965273895, Total: 0.8766481169990001\n",
            "Epoch 17: Training Losses: Cate: 4.309978522068311e-06, Mask: 0.16711404827562557, Total: 0.501346451940436, Validation Losses: Cate: 4.153597544891384e-06, Mask: 0.29353809322564656, Total: 0.8806184298209562\n",
            "Epoch 18: Training Losses: Cate: 4.081356507650104e-06, Mask: 0.1635957671065326, Total: 0.4907913800343243, Validation Losses: Cate: 3.827064985935137e-06, Mask: 0.2844626478111471, Total: 0.8533917695056391\n",
            "Epoch 19: Training Losses: Cate: 3.851621060895358e-06, Mask: 0.15465099473708663, Total: 0.46395683382776964, Validation Losses: Cate: 3.778872257172426e-06, Mask: 0.2872708993786164, Total: 0.8618164726796042\n",
            "Epoch 20: Training Losses: Cate: 3.7431479298644694e-06, Mask: 0.1497203064832651, Total: 0.4491646608687452, Validation Losses: Cate: 3.6188007540054858e-06, Mask: 0.2789285700512296, Total: 0.8367893256758916\n",
            "Epoch 21: Training Losses: Cate: 3.5476088753886757e-06, Mask: 0.14260260902088265, Total: 0.4278113732678223, Validation Losses: Cate: 3.487994082685616e-06, Mask: 0.28154464521025885, Total: 0.8446374217062506\n",
            "Epoch 22: Training Losses: Cate: 3.401951117506319e-06, Mask: 0.13518364556933044, Total: 0.40555433706305494, Validation Losses: Cate: 3.3430752645965173e-06, Mask: 0.28082655455547434, Total: 0.8424830018109037\n",
            "Epoch 23: Training Losses: Cate: 3.2617044706587307e-06, Mask: 0.13045442207106228, Total: 0.39136652694123003, Validation Losses: Cate: 3.1552591169986586e-06, Mask: 0.2829662947595574, Total: 0.8489020321205372\n",
            "Epoch 24: Training Losses: Cate: 3.149515900881904e-06, Mask: 0.12836565645375927, Total: 0.3851001181878806, Validation Losses: Cate: 3.0640783947758633e-06, Mask: 0.27663956838482207, Total: 0.8299217631343667\n",
            "Epoch 25: Training Losses: Cate: 3.0601887943665e-06, Mask: 0.12197709084984443, Total: 0.36593433194062264, Validation Losses: Cate: 3.0110130316594736e-06, Mask: 0.2709223981235773, Total: 0.8127702028696774\n",
            "Epoch 26: Training Losses: Cate: 2.9572096109126036e-06, Mask: 0.11501689396541694, Total: 0.3450536377499615, Validation Losses: Cate: 2.933059076131308e-06, Mask: 0.2748529906491287, Total: 0.8245619023574218\n",
            "Epoch 27: Training Losses: Cate: 2.865019073362133e-06, Mask: 0.10913516805146846, Total: 0.3274083681033489, Validation Losses: Cate: 2.816322231438366e-06, Mask: 0.27125205122332535, Total: 0.8137589629369838\n",
            "Epoch 28: Training Losses: Cate: 2.788440619568559e-06, Mask: 0.10116770542387304, Total: 0.3035059034881464, Validation Losses: Cate: 2.8365476336294075e-06, Mask: 0.27131768395655026, Total: 0.8139558864458827\n",
            "Epoch 29: Training Losses: Cate: 2.792672715988994e-06, Mask: 0.0996930840357396, Total: 0.2990820438982883, Validation Losses: Cate: 2.8391683591014965e-06, Mask: 0.27128690247999804, Total: 0.813863544518711\n",
            "Epoch 30: Training Losses: Cate: 2.789843535400595e-06, Mask: 0.09883066869815418, Total: 0.2964947948168064, Validation Losses: Cate: 2.830777500704931e-06, Mask: 0.27081640246715255, Total: 0.8124520339583623\n",
            "Epoch 31: Training Losses: Cate: 2.786824908546795e-06, Mask: 0.09752506036240022, Total: 0.29257796634830735, Validation Losses: Cate: 2.825091563731768e-06, Mask: 0.27165333721928925, Total: 0.8149628309348157\n",
            "Epoch 32: Training Losses: Cate: 2.779894806983094e-06, Mask: 0.09649054209391277, Total: 0.2894744053340278, Validation Losses: Cate: 2.8224919961612334e-06, Mask: 0.27297388642798853, Total: 0.8189244795846575\n",
            "Epoch 33: Training Losses: Cate: 2.775900958318792e-06, Mask: 0.09610089674677658, Total: 0.2883054649464472, Validation Losses: Cate: 2.812233737728537e-06, Mask: 0.2711020986201199, Total: 0.8133091091654683\n",
            "Epoch 34: Training Losses: Cate: 2.7732293418071216e-06, Mask: 0.09491612464617724, Total: 0.28475114693187203, Validation Losses: Cate: 2.8121923114598454e-06, Mask: 0.27197624299362416, Total: 0.8159315397266214\n",
            "Epoch 35: Training Losses: Cate: 2.772792516685283e-06, Mask: 0.0953109562182906, Total: 0.28593564067763844, Validation Losses: Cate: 2.811702557241325e-06, Mask: 0.2721644614375275, Total: 0.8164961924989715\n",
            "Epoch 36: Training Losses: Cate: 2.7723090361840123e-06, Mask: 0.09536949892696064, Total: 0.28611126820417654, Validation Losses: Cate: 2.811505556176161e-06, Mask: 0.2721696967500767, Total: 0.8165119012803522\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del ins_gts_list_train\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "ins_gts_list_val = []\n",
        "ins_gts_list_val.append(torch.zeros((batch_size, 40 * 40, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "ins_gts_list_val.append(torch.zeros((batch_size, 36 * 36, 2 * 100, 2 * 136), dtype=torch.float32).to(device))\n",
        "ins_gts_list_val.append(torch.zeros((batch_size, 24 * 24, 2 * 50, 2 * 68), dtype=torch.float32).to(device))\n",
        "ins_gts_list_val.append(torch.zeros((batch_size, 16 * 16, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "ins_gts_list_val.append(torch.zeros((batch_size, 12 * 12, 2 * 25, 2 * 34), dtype=torch.float32).to(device))\n",
        "\n",
        "solo_head.eval()\n",
        "val_cate_loss_sum = 0.0\n",
        "val_mask_loss_sum = 0.0\n",
        "val_total_loss_sum = 0.0\n",
        "for val_batch_idx, val_data in enumerate(val_loader):\n",
        "    img_val, mask_list_val, label_list_val, bbox_list_val = [val_data[i] for i in range(len(val_data))]\n",
        "\n",
        "    backout_val = resnet50_fpn(img_val.to(device))\n",
        "    fpn_feat_list_val = list(backout_val.values())\n",
        "\n",
        "    cate_pred_list_val, ins_pred_list_val = solo_head.forward(fpn_feat_list_val, device)\n",
        "\n",
        "    ins_gts_list_val, ins_ind_gts_list_val, cate_gts_list_val = solo_head.target(ins_pred_list_val, bbox_list_val, label_list_val, mask_list_val, ins_gts_list_val, device)\n",
        "\n",
        "    cate_loss_val, mask_loss_val, total_loss_val = solo_head.loss(cate_pred_list_val, ins_pred_list_val, ins_gts_list_val, ins_ind_gts_list_val, cate_gts_list_val, all_levels_one_batch, device)\n",
        "\n",
        "    val_cate_loss_sum += cate_loss_val.item()\n",
        "    val_mask_loss_sum += mask_loss_val.item()\n",
        "    val_total_loss_sum += total_loss_val.item()\n",
        "\n",
        "val_cate_loss_mean = val_cate_loss_sum/len(val_loader)\n",
        "val_mask_loss_mean = val_mask_loss_sum/len(val_loader)\n",
        "val_total_loss_mean = val_total_loss_sum/len(val_loader)\n",
        "print(\"Final Validation Losses: Cate: \" + str(val_cate_loss_mean) + \", Mask: \" + str(val_mask_loss_mean) + \", Total: \" + str(val_total_loss_mean))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW9YgQOFyjG0",
        "outputId": "8b7a0e9a-8a03-4e5e-c66d-e5b78c37225f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Validation Losses: Cate: 2.811505556176161e-06, Mask: 0.2721696967500767, Total: 0.8165119012803522\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the results\n",
        "train_total_loss_list = np.load('train_total_loss_list_epoch_36.npy').tolist()\n",
        "train_mask_loss_list = np.load('train_mask_loss_list_epoch_36.npy').tolist()\n",
        "train_cate_loss_list = np.load('train_cate_loss_list_epoch_36.npy').tolist()\n",
        "val_total_loss_list = np.load('val_total_loss_list_epoch_36.npy').tolist()\n",
        "val_mask_loss_list = np.load('val_mask_loss_list_epoch_36.npy').tolist()\n",
        "val_cate_loss_list = np.load('val_cate_loss_list_epoch_36.npy').tolist()\n",
        "\n",
        "epoch = np.arange(1, len(train_total_loss_list)+1)\n",
        "os.makedirs(\"./testfig\", exist_ok=True)\n",
        "\n",
        "plt.plot(epoch, train_total_loss_list, label='Train')\n",
        "plt.plot(epoch, val_total_loss_list, label='Validation')\n",
        "plt.legend()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Total Loss')\n",
        "plt.title('Training and Validation Total Loss of SOLO Model per Epoch')\n",
        "plt.legend()\n",
        "plt.savefig(\"./testfig/total_loss.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.plot(epoch, train_cate_loss_list, label='Train')\n",
        "plt.plot(epoch, val_cate_loss_list, label='Validation')\n",
        "plt.yscale('log')\n",
        "plt.legend()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Focal Loss (log scale)')\n",
        "plt.title('Training and Validation Focal Loss of SOLO Model per Epoch')\n",
        "plt.legend()\n",
        "plt.savefig(\"./testfig/focal_loss.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.plot(epoch, train_mask_loss_list, label='Train')\n",
        "plt.plot(epoch, val_mask_loss_list, label='Validation')\n",
        "plt.legend()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Dice Loss')\n",
        "plt.title('Training and Validation Dice Loss of SOLO Model per Epoch')\n",
        "plt.legend()\n",
        "plt.savefig(\"./testfig/dice_loss.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "S8SI50Zd0UxD",
        "outputId": "5055daa8-7211-4020-cd50-68128cd58bf7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Py24fsu9fcnIyJ0+epE2bNhiGwcaNG3Os/5///Cfb9RtvvDHbvsyfPx9XV1cefvhh5zIXFxcee+yxfNUDjnZSR44c4ffff3cumz59Ou7u7tx5553Obbq7uwOOw72nT58mMzOT5s2bX/Y/7YstWbKE9PR0HnvssWynCnJrMOvh4YHV6vizstlsnDp1Cl9fX+rVq1fgx80yf/58XFxcePzxx7Mt/7//+z8Mw2DBggXZll/ufXElsp6DJ554wrl/AEOGDMHf3z/XUwEXK8zXJL/mz59PlSpVuOeee5zL3NzcePzxx0lKSmLFihUABAYGkpycnOcppsDAQLZv386ePXsKXEPLli254YYbnMt8fX0ZOnQoBw8e5J9//ingXjn+LtesWcNTTz0FOI4gP/jgg4SGhvLYY485T7kkJiYC4Ofnl+f2/Pz8cv0v/lLys92s2wqy3XvvvZfly5cTGxvLb7/9Rmxs7CVPSeX3eZ0/fz6hoaHccccdzvW8vb0ZOnRotu1t2rTJeQrs1KlTzqMbycnJdOrUid9///2KG/pfeOou69Rxeno6S5YsARxHyAMCAujSpUu2IytRUVH4+vrmOHVbo0YNunXrlu/H9/T0ZPHixTkuubVPHDp0aLZ2fw8//DCurq7Mnz8fyP9nwcaNGzlw4ABPPPFEjnZkuZ1yvdx3R0Ep3ORT1apVnR/MF9q+fTu33XYbAQEB+Pv7U7lyZWfDrfj4+Mtut3r16tmuZwWd3M5xXu6+WffPum9cXBznzp2jdu3aOdbLbVluDh06xKBBg6hYsaLzXGj79u2BnPvn6elJ5cqVL1kPQHR0NKGhoTnGNahXr16+6gG4++67cXFxcZ6aSk1NZfbs2fTo0SNbUPzyyy9p0qSJs31E5cqVmTdvXr5elwtFR0cDUKdOnWzLK1eunKMBpt1u55133qFOnTp4eHgQFBRE5cqV2bJlS4Ef98LHDwsLy/FFktWLKau+LJd7X1xpDZDzdXJ3d6dmzZo5ariUwnpN8is6Opo6depk+xCGnM/dI488Qt26denRowfVqlXjgQceyHE6Zfz48Zw9e5a6devSuHFjnnrqqXwNORAdHZ3r+/tSr19+BQQE8Prrr3Pw4EEOHjzIp59+Sr169Zg8eTIvvfQS8G/AyAojl5KYmHjZAHSh/Gw3v8HqQj179sTPz48ZM2bwzTff0KJFi0t+VuX3eY2OjqZ27do5vlAvvm9WaB04cCCVK1fOdpk6dSppaWlX9D61Wq3UrFkz27K6desCONv17dmzh/j4eIKDg3M8dlJSEnFxcdnuf6lTjJfi4uJC586dc1xyO5118eecr68voaGhzlrz+1mQ1S6sUaNGl60vP98dBaUuOPmUW0v0s2fP0r59e/z9/Rk/fjy1atXC09OTDRs28Mwzz+Qr5V+qV46Rx3n0wrhvfthsNrp06cLp06d55plnqF+/Pj4+Phw9epRBgwbl2L/i6mEUHBxMly5d+OGHH3j//ff5+eefSUxMdLbHAfj6668ZNGgQffr04amnniI4OBgXFxcmTJjg/KMrCq+88govvPACDzzwAC+99BIVK1bEarXyxBNPFFv37qJ+X1wps16T/AgODmbTpk0sWrSIBQsWsGDBAj7//HMGDBjgbHzcrl079u3bx9y5c/n111+ZOnUq77zzDlOmTMnWjd4MERERPPDAA9x2223UrFmTb775hpdffpnatWvj6uqaZwhLS0tj165dNG/ePN+PlxUgtmzZcsn2HlmP2bBhw3xv18PDg759+/Lll1+yf//+Yh3IMevv84033rjkPhXFYHNZjx0cHMw333yT6+0Xf/Hn1TOqNCqK7w6Fm6uwfPlyTp06xY8//pitMeiBAwdMrOpfwcHBeHp65jroXV4D4WXZunUru3fv5ssvv8zWcDA/vUMuJSIigqVLl5KUlJTtg2LXrl0F2k7//v1ZuHAhCxYsYPr06fj7+9O7d2/n7bNmzaJmzZr8+OOP2f5ju7gRX35rBsd/Vxf+B3bixIkc/1nMmjWLjh078umnn2ZbfvbsWYKCgpzXCzLidEREBEuWLMnx33XWac+s+opS1mPs2rUr23OQnp7OgQMH6Ny5s3PZpfatMF+T/IqIiGDLli3Y7fZsR29ye+7c3d3p3bs3vXv3xm6388gjj/DRRx/xwgsvOI8eVKxYkcGDBzN48GCSkpJo164d48aNyzPcRERE5Pr+LorXr0KFCtSqVYtt27YB4OPjQ8eOHfntt9+Ijo7O9bFmzpxJWloaN998c74fp0ePHri4uDBt2rRLNir+6quvcHV1zXMgwtzce++9fPbZZ1itVu6+++5Lrpff5zUiIoJt27ZhGEa2993F9806levv75/t/Xy17HY7+/fvdx6tAdi9ezeAsydhrVq1WLJkCW3btjU9uOzZs8fZYQYcg0XGxMTQs2dPIP+fBVnP57Zt2wr1+cwvnZa6Cllp88L/iNPT0/nggw/MKimbrEORc+bMyTZi6d69e3O007jU/SH7/hmGkWs3vvzq2bMnmZmZ2ca+sNlsTJo0qUDb6dOnD97e3nzwwQcsWLCAvn37ZhttM7fa16xZw+rVqwtcc+fOnXFzc2PSpEnZtpfbKLwuLi45jpB8//33HD16NNuyrIHN8tMFvmfPnthsNiZPnpxt+TvvvIPFYsl3+6mr0blzZ9zd3Xnvvfey7d+nn35KfHw8vXr1ci7z8fHJ9fB9Yb4m+dWzZ09iY2OZMWOGc1lmZiaTJk3C19fXeYr14p5kVqvVOWBhVvuVi9fx9fWldu3al+xSfGENa9euzbafycnJfPzxx0RGRhboyEaWzZs35zp6cHR0NP/880+2UwbPP/88hmEwaNCgHEM4HDhwgKeffprQ0FCGDRuW78cPDw9n8ODBLFmyJNdxbKZMmcJvv/3Ggw8+SLVq1QqwZ9CxY0deeuklJk+eTJUqVS65Xn6f1549e3Ls2DFmzZrlXC8lJSVHT5yoqChq1arFm2++SVJSUo7HO3HiRIH240IX/u0ahsHkyZNxc3OjU6dOANx1113YbDbn6cQLZWZm5nuojMLw8ccfZ+vx9OGHH5KZmen8nMnvZ8F1111HjRo1mDhxYo76i+Moso7cXIU2bdpQoUIFBg4c6JwaYNq0aaYf/r/QuHHj+PXXX2nbti0PP/yw80uyUaNGl+1+W79+fWrVqsWoUaM4evQo/v7+/PDDD1d1HrR37960bduWZ599loMHD9KwYUN+/PHHAp/L9vX1pU+fPs52NxeekgK4+eab+fHHH7ntttvo1asXBw4cYMqUKTRs2DDXD668ZI25MGHCBG6++WZ69uzJxo0bWbBgQbajMVmPO378eAYPHkybNm3YunUr33zzTY5z7rVq1SIwMJApU6bg5+eHj48PrVq1yvVceu/evenYsSOjR4/m4MGDNG3alF9//ZW5c+fyxBNPZGs8XFQqV67Mc889x4svvkj37t255ZZb2LVrFx988AEtWrTINkBYVFQUM2bMYOTIkbRo0QJfX1969+5dqK/JhZYuXZrr6LV9+vRh6NChfPTRRwwaNIj169cTGRnJrFmzWLVqFRMnTnQeCXvooYc4ffo0N910E9WqVSM6OppJkybRrFkz5ymYhg0b0qFDB6KioqhYsSJ///03s2bNynWclws9++yzfPvtt/To0YPHH3+cihUr8uWXX3LgwAF++OGHHO2B8mPx4sWMHTuWW265heuvv945lspnn31GWlpattM57dq1480332TkyJE0adKEQYMGERoays6dO52jYc+fPz/XAfzefvvtHGPjWK1W/vvf//LOO++wc+dOHnnkERYuXOg8QrNo0SLmzp3r7M5bUFarleeff/6y6+X3eR0yZAiTJ09mwIABrF+/ntDQUKZNm5brfk2dOpUePXpwzTXXMHjwYKpWrcrRo0dZtmwZ/v7+/PzzzwXeH09PTxYuXMjAgQNp1aoVCxYsYN68efz3v/91nm5q3749w4YNY8KECWzatImuXbvi5ubGnj17+P7773n33XezNYguqMzMTL7++utcb7vtttuyjSKdnp5Op06duOuuu5x/4zfccAO33HILkP/PAqvVyocffkjv3r1p1qwZgwcPdr7vtm/fzqJFi654f/LlivtZlVGX6gp+zTXX5Lr+qlWrjOuvv97w8vIywsLCjKefftpYtGhRti6GhnHpruC5dWPlom6wl+oKnluX0YiIiGxdkw3DMJYuXWpce+21hru7u1GrVi1j6tSpxv/93/8Znp6el3gW/vXPP/8YnTt3Nnx9fY2goCBjyJAhzq7FF3ZjvrA754Vyq/3UqVPG/fffb/j7+xsBAQHG/fffb2zcuDHfXcGzzJs3zwCM0NDQHN2v7Xa78corrxgRERGGh4eHce211xq//PJLjtfBMC7fFdwwHN1SX3zxRSM0NNTw8vIyOnToYGzbti3H852ammr83//9n3O9tm3bGqtXrzbat29vtG/fPtvjzp0712jYsKGzW37WvudWY2JiovHkk08aYWFhhpubm1GnTh3jjTfeyNalMmtf8vu+yMvFXcGzTJ482ahfv77h5uZmhISEGA8//HCObp5JSUnGvffeawQGBjq7KhvG1b0mucn6G7rUZdq0aYZhGMbx48eNwYMHG0FBQYa7u7vRuHHjHO+zWbNmGV27djWCg4MNd3d3o3r16sawYcOMmJgY5zovv/yy0bJlSyMwMNDw8vIy6tevb/zvf//L1m32Uvbt22fccccdRmBgoOHp6Wm0bNnS+OWXX3Ksd6nX72L79+83xowZY1x//fVGcHCw4erqalSuXNno1auX8dtvv+V6n99//9249dZbjaCgIMPNzc2oXr26MWTIEOPgwYM51s36u83t4uLi4lwvLS3NeOedd4yoqCjDx8fH8Pb2Nq677jpj4sSJ+XpeDOPSnx0XutTnZX6f1+joaOOWW24xvL29jaCgIGPEiBHObtYXv8c3btxo9O3b16hUqZLh4eFhREREGHfddZexdOlS5zoF6Qru4+Nj7Nu3z+jatavh7e1thISEGGPHjs3xmWUYhvHxxx8bUVFRhpeXl+Hn52c0btzYePrpp41jx44514mIiCjQkAR5dQW/cB+y9mnFihXG0KFDjQoVKhi+vr5G//79jVOnTuXYbn4+CwzDMP744w+jS5cuhp+fn+Hj42M0adIk27AUBfnuKAiLYZSgwwxSbPr06XNF3VpFRCR/Bg0axKxZs67qyGRx+eKLLxg8eDDr1q0rUOPykkptbsqBi8+z79mzh/nz52vOIBERKZPU5qYcqFmzJoMGDXKOQfDhhx/i7u6e6/DjIiIipZ3CTTnQvXt3vv32W2JjY/Hw8KB169a88sorOQZrEhERKQvU5kZERETKFLW5ERERkTJF4UZERETKlHLX5sZut3Ps2DH8/PwKNAS+iIiImMcwDBITEwkLC7vs4JflLtwcO3aM8PBws8sQERGRK3D48OHLTutR7sJN1nDrhw8fxt/f3+RqREREJD8SEhIIDw/PNoHwpZS7cJN1Ksrf31/hRkREpJTJT5MSNSgWERGRMkXhRkRERMoUhRsREREpU8pdmxsRESk7bDYbGRkZZpchhcTd3f2y3bzzQ+FGRERKHcMwiI2N5ezZs2aXIoXIarVSo0YN3N3dr2o7CjciIlLqZAWb4OBgvL29NShrGZA1yG5MTAzVq1e/qtdU4UZEREoVm83mDDaVKlUyuxwpRJUrV+bYsWNkZmbi5uZ2xdtRg2IRESlVstrYeHt7m1yJFLas01E2m+2qtqNwIyIipZJORZU9hfWaKtyIiIhImaJwIyIiUopFRkYyceJEs8soURRuREREioHFYsnzMm7cuCva7rp16xg6dGjhFlvKqbdUITqZlMbZlHRqB19+xlIRESlfYmJinL/PmDGDMWPGsGvXLucyX19f5++GYWCz2XB1vfzXdOXKlQu30DJAR24KydIdx2n+8hJGfLfJ7FJERKQEqlKlivMSEBCAxWJxXt+5cyd+fn4sWLCAqKgoPDw8+OOPP9i3bx+33norISEh+Pr60qJFC5YsWZJtuxeflrJYLEydOpXbbrsNb29v6tSpw08//VTMe2suhZtCUjvYkbj3xCWRabObXI2ISPliGAYp6ZmmXAzDKLT9ePbZZ3n11VfZsWMHTZo0ISkpiZ49e7J06VI2btxI9+7d6d27N4cOHcpzOy+++CJ33XUXW7ZsoWfPnvTv35/Tp08XWp0lnU5LFZLwCt54u7uQkm7j4KkUZ9gREZGidy7DRsMxi0x57H/Gd8PbvXC+TsePH0+XLl2c1ytWrEjTpk2d11966SVmz57NTz/9xPDhwy+5nUGDBnHPPfcA8Morr/Dee++xdu1aunfvXih1lnQ6clNIrFYLdUIcbW12xSaaXI2IiJRGzZs3z3Y9KSmJUaNG0aBBAwIDA/H19WXHjh2XPXLTpEkT5+8+Pj74+/sTFxdXJDWXRDpyU4jqh/ix+fBZdsUm0KtJqNnliIiUG15uLvwzvptpj11YfHx8sl0fNWoUixcv5s0336R27dp4eXlxxx13kJ6enud2Lp66wGKxYLeXnyYTCjeFqG6V80dujuvIjYhIcbJYLIV2aqgkWbVqFYMGDeK2224DHEdyDh48aG5RpYBOSxWi+lV0WkpERApPnTp1+PHHH9m0aRObN2/m3nvvLVdHYK6Uwk0hqnc+3ESfTiElPdPkakREpLR7++23qVChAm3atKF3795069aN6667zuyySjyLUZh92EqBhIQEAgICiI+Px9/fv9C33/zlxZxMSmfuo21pGh5Y6NsXESnvUlNTOXDgADVq1MDT09PscqQQ5fXaFuT7W0duClk9nZoSERExlcJNIasX4kiTOxVuRERETKFwU8icjYqPJ5hciYiISPmkcFPInN3BY5NMrkRERKR8UrgpZHVDfLFYHDOEn0pKM7scERGRcsf0cPP+++8TGRmJp6cnrVq1Yu3atXmuf/bsWR599FFCQ0Px8PCgbt26zJ8/v5iqvTxvd1eqV/QG1KhYRETEDKaGmxkzZjBy5EjGjh3Lhg0baNq0Kd26dbvk/Bfp6el06dKFgwcPMmvWLHbt2sUnn3xC1apVi7nyvNU7P8eUGhWLiIgUP1PDzdtvv82QIUMYPHgwDRs2ZMqUKXh7e/PZZ5/luv5nn33G6dOnmTNnDm3btiUyMpL27dtnmzG1JNBIxSIiIuYxLdykp6ezfv16Onfu/G8xViudO3dm9erVud7np59+onXr1jz66KOEhITQqFEjXnnlFWw2W3GVnS/1qpzvDq45pkRERIqdaeHm5MmT2Gw2QkJCsi0PCQkhNjY21/vs37+fWbNmYbPZmD9/Pi+88AJvvfUWL7/88iUfJy0tjYSEhGyXopY1kN+e44nY7eVqAGgRESlCHTp04IknnnBej4yMZOLEiXnex2KxMGfOnKt+7MLaTnEwvUFxQdjtdoKDg/n444+JioqiX79+jB49milTplzyPhMmTCAgIMB5CQ8PL/I6Iyt54+5qJSXdxpEz54r88UREpOTr3bs33bt3z/W2lStXYrFY2LJlS4G2uW7dOoYOHVoY5TmNGzeOZs2a5VgeExNDjx49CvWxiopp4SYoKAgXFxeOHz+ebfnx48epUqVKrvcJDQ2lbt26uLi4OJc1aNCA2NhY0tPTc73Pc889R3x8vPNy+PDhwtuJS3B1sVK7si8AO2M1mJ+IiMCDDz7I4sWLOXLkSI7bPv/8c5o3b06TJk0KtM3KlSvj7e1dWCXmqUqVKnh4eBTLY10t08KNu7s7UVFRLF261LnMbrezdOlSWrdunet92rZty969e7NN9757925CQ0Nxd3fP9T4eHh74+/tnuxQHNSoWEZEL3XzzzVSuXJkvvvgi2/KkpCS+//57+vTpwz333EPVqlXx9vamcePGfPvtt3lu8+LTUnv27KFdu3Z4enrSsGFDFi9enOM+zzzzDHXr1sXb25uaNWvywgsvkJGRAcAXX3zBiy++yObNm7FYLFgsFme9F5+W2rp1KzfddBNeXl5UqlSJoUOHkpT07wC2gwYNok+fPrz55puEhoZSqVIlHn30UedjFSXXIn+EPIwcOZKBAwfSvHlzWrZsycSJE0lOTmbw4MEADBgwgKpVqzJhwgQAHn74YSZPnsyIESN47LHH2LNnD6+88gqPP/64mbuRq6x2N2pULCJSDAwDMlLMeWw3b7BYLruaq6srAwYM4IsvvmD06NFYzt/n+++/x2azcd999/H999/zzDPP4O/vz7x587j//vupVasWLVu2vOz27XY7ffv2JSQkhDVr1hAfH5+tfU4WPz8/vvjiC8LCwti6dStDhgzBz8+Pp59+mn79+rFt2zYWLlzIkiVLAAgICMixjeTkZLp160br1q1Zt24dcXFxPPTQQwwfPjxbeFu2bBmhoaEsW7aMvXv30q9fP5o1a8aQIUMuuz9Xw9Rw069fP06cOMGYMWOIjY2lWbNmLFy40NnI+NChQ1it/x5cCg8PZ9GiRTz55JM0adKEqlWrMmLECJ555hmzdiE7w4C0RPD01+zgIiLFKSMFXgkz57H/ewzcffK16gMPPMAbb7zBihUr6NChA+A4JXX77bcTERHBqFGjnOs+9thjLFq0iJkzZ+Yr3CxZsoSdO3eyaNEiwsIcz8Urr7ySo53M888/7/w9MjKSUaNG8d133/H000/j5eWFr68vrq6ul2wiAjB9+nRSU1P56quv8PFx7PvkyZPp3bs3r732mvN7vEKFCkyePBkXFxfq169Pr169WLp0adkONwDDhw9n+PDhud62fPnyHMtat27NX3/9VcRVXYH9y2HucKhYEwb+RP3z3cEPnEwmLdOGh6tL3vcXEZEyr379+rRp04bPPvuMDh06sHfvXlauXMn48eOx2Wy88sorzJw5k6NHj5Kenk5aWlq+29Ts2LGD8PBwZ7ABcm3mMWPGDN577z327dtHUlISmZmZBW6ysWPHDpo2beoMNuBoOmK329m1a5cz3FxzzTXZ2smGhoaydevWAj3WlTA93JQZfmEQfxiSjkN6MiH+3gR4uRF/LoO9cUlcE5bzsJ6IiBQSN2/HERSzHrsAHnzwQR577DHef/99Pv/8c2rVqkX79u157bXXePfdd5k4cSKNGzfGx8eHJ5544pIdZq7E6tWr6d+/Py+++CLdunUjICCA7777jrfeeqvQHuNCbm5u2a5bLJZs7WaLSqnqCl6iBdWBgOpgS4eDf2CxWJzTMOjUlIhIEbNYHKeGzLjko73Nhe666y6sVivTp0/nq6++4oEHHsBisbBq1SpuvfVW7rvvPpo2bUrNmjXZvXt3vrfboEEDDh8+TExMjHPZxWc6/vzzTyIiIhg9ejTNmzenTp06REdHZ1vH3d39soPjNmjQgM2bN5OcnOxctmrVKqxWK/Xq1ct3zUVF4aawWCxQu5Pj972OHmDOdjdqVCwiIuf5+vrSr18/nnvuOWJiYhg0aBAAderUYfHixfz555/s2LGDYcOG5RguJS+dO3embt26DBw4kM2bN7Ny5UpGjx6dbZ06depw6NAhvvvuO/bt28d7773H7Nmzs60TGRnJgQMH2LRpEydPniQtLS3HY/Xv3x9PT08GDhzItm3bWLZsGY899hj3339/jsF5zaBwU5ic4cbRwlyNikVEJDcPPvggZ86coVu3bs42Ms8//zzXXXcd3bp1o0OHDlSpUoU+ffrke5tWq5XZs2dz7tw5WrZsyUMPPcT//ve/bOvccsstPPnkkwwfPpxmzZrx559/8sILL2Rb5/bbb6d79+507NiRypUr59od3dvbm0WLFnH69GlatGjBHXfcQadOnZg8eXLBn4wiYDEMo1zND5CQkEBAQADx8fGFP+ZNajy8XhPsmfD4Jv5OCOCOKasJDfBk9XOdCvexRETKqdTUVA4cOECNGjXw9PQ0uxwpRHm9tgX5/taRm8LkGQDhrRy/71tK3fNHbmLiU4lPKfpBi0REREThpvDVusnxc+9S/D3dqBroBajdjYiISHFRuClstTs7fh74HTLTL2h3ozmmREREioPCTWGr0gS8gyA9CQ6voe757uA71ahYRESkWCjcFDarNVuvqawJNHfrtJSISKEqZ/1hyoXCek0VbopC1qmpfUv/nUAzNlF/iCIihSBr1NuUFJMmypQikzUa84VTNlwJTb9QFGp2dPyM3UotzyRcrRYSUzOJiU8l7HwDYxERuTIuLi4EBgYSFxcHOMZcsRRwlGApeex2OydOnMDb2xtX16uLJwo3RcG3MoQ2g5hNuEcvp2blquw+nsSu2ESFGxGRQpA1Y3VWwJGywWq1Ur169asOqwo3RaV2Z4jZBHuXUq/KcHYfT2JnbCId6webXZmISKlnsVgIDQ0lODiYjAyNI1ZWuLu7Y7VefYsZhZuiUrsTrHwT9v1GgxZP8zPqDi4iUthcXFyuun2GlD1qUFxUqrUAD384d5oo90OAuoOLiIgUB4WbouLiBjXaAVA/aQ0A+08kk2Gzm1mViIhImadwU5TOdwn3P/I7Pu4upNvsHDyZbHJRIiIiZZvCTVE6P5if5eg6rj3fjlinpkRERIqWwk1RCqwOQXXBsNPDexcAuxRuREREipTCTVE7f2qqhW0joCM3IiIiRU3hpqidPzUVceYvwGDXcXUHFxERKUoKN0Utoi24euKREkMdy1EOnz5HUlqm2VWJiIiUWQo3Rc3NyxFwgJ5e2wHYoxnCRUREiozCTXE4f2qqs9tWQI2KRUREipLCTXE436i4Qfo2PElTo2IREZEipHBTHILqQkA4rkY611t36MiNiIhIEVK4KQ4WC9S6CYD21s3sOp6IYRgmFyUiIlI2KdwUl/OnptpZt3A6OZ0TSWkmFyQiIlI2KdwUl5rtweJCLWsM1SxxOjUlIiJSRBRuiotnAIS3BKC9dYvCjYiISBFRuClO57uEt1O4ERERKTIKN8WpliPctLFuZ2/sGZOLERERKZsUbopTaDNsnhXxs5zDJ24DNrt6TImIiBQ2hZviZLViqePoNdXa2Mih0ykmFyQiIlL2KNwUM+v5djeORsWaIVxERKSwKdwUt/OD+TWyHuTQoYPm1iIiIlIGKdwUN99gTvg1AMA9erm5tYiIiJRBCjcmOBfeHoCqp1abXImIiEjZo3BjAr9G3QG4LmMDqekZJlcjIiJStijcmCCwbluS8KKSJZEj/+jojYiISGFSuDGBxdWdfzyvBSB1x2KTqxERESlbFG5McrxyWwD8j64wuRIREZGyReHGJPaajvFuqiZthdR4k6sREREpOxRuTFKtZn322UNxwQ77dfRGRESksCjcmKReFT9W2JsCkLZzkcnViIiIlB0KNybx9XBli/f1ALjsmAvpySZXJCIiUjYo3JgoOawNB+0huGYkwpaZZpcjIiJSJijcmKhuaADTbF0cV9Z+AoZhbkEiIiJlgMKNiepX8ed7W3tS8YC47RD9p9kliYiIlHoKNya6sU4QKVZffsi8wbFg7cfmFiQiIlIGKNyYKNDbnTa1g/gq69TUjp8h4Zi5RYmIiJRyCjcm69moCruM6mxxbQSGDf7+zOySRERESjWFG5N1vaYKLlYLH6Y4Rixm/ReQmWZqTSIiIqWZwo3JKvq4c33Niiy2R5HkEQzJJ+CfuWaXJSIiUmop3JQAPRuHkokrc1y6ORaoYbGIiMgVU7gpAbo2rILVAhNPt8GwusGRdXB0g9lliYiIlEoKNyVAZT8PWtaoyEkC2Fv5fM+pdVPNLUpERKSUUrgpIXo2DgXgk/TOjgVbZ0HyKRMrEhERKZ0UbkqIbtdUwWKBmTEhpIc0BVsabPzK7LJERERKHYWbEiLE35PmERUAC39Vut2xcN2nYLeZWpeIiEhpo3BTgvRo5Dg1NeVUU/CqCPGHYfdCk6sSEREpXRRuSpAejasAsPpQMsmN+jsWrvnIxIpERERKH4WbEiQ0wIvrqgdiGLDQqydYrHBgBZzYZXZpIiIipYbCTQmT1Wtq5h4L1OvpWLj2ExMrEhERKV0UbkqY7o0cp6bWHjzN2UaDHAs3fwupCeYVJSIiUooo3JQw1Sp407RaAIYBvyTWgaC6kJ4Em78zuzQREZFSoUSEm/fff5/IyEg8PT1p1aoVa9euveS6X3zxBRaLJdvF09OzGKstej3On5pasD0WWg51LFz7MRiGiVWJiIiUDqaHmxkzZjBy5EjGjh3Lhg0baNq0Kd26dSMuLu6S9/H39ycmJsZ5iY6OLsaKi17P813C/9p/mlO1+4K7H5zaA/uXm1uYiIhIKWB6uHn77bcZMmQIgwcPpmHDhkyZMgVvb28+++yzS97HYrFQpUoV5yUkJKQYKy561St506iqPza7weK9ydDsXscNalgsIiJyWaaGm/T0dNavX0/nzp2dy6xWK507d2b16tWXvF9SUhIRERGEh4dz6623sn379kuum5aWRkJCQrZLaZA1oN/8bbHQ4iHHwt0L4EzZOkolIiJS2EwNNydPnsRms+U48hISEkJsbGyu96lXrx6fffYZc+fO5euvv8Zut9OmTRuOHDmS6/oTJkwgICDAeQkPDy/0/SgKPc73mvpz70nO+kRCzY5g2OHvSx/REhERkRJwWqqgWrduzYABA2jWrBnt27fnxx9/pHLlynz0Ue4j+T733HPEx8c7L4cPHy7miq9Mzcq+1K/iR6bd4Nd/jv/bsHjDV5BxztziRERESjBTw01QUBAuLi4cP3482/Ljx49TpUqVfG3Dzc2Na6+9lr179+Z6u4eHB/7+/tkupUXWgH4LtsZA3W4QUB3OnYZtP5hcmYiISMllarhxd3cnKiqKpUuXOpfZ7XaWLl1K69at87UNm83G1q1bCQ0NLaoyTdPz/FxTf+w9SXyaHVo86LhhzUfqFi4iInIJpp+WGjlyJJ988glffvklO3bs4OGHHyY5OZnBgwcDMGDAAJ577jnn+uPHj+fXX39l//79bNiwgfvuu4/o6Ggeeughs3ahyNQO9qNOsC8ZNoOlO47DdQPA1RNit8CRdWaXJyIiUiKZHm769evHm2++yZgxY2jWrBmbNm1i4cKFzkbGhw4dIiYmxrn+mTNnGDJkCA0aNKBnz54kJCTw559/0rBhQ7N2oUhlnZqavzUWvCtCozscN/z2EtjtJlYmIiJSMlkMo3yd30hISCAgIID4+PhS0f5mV2wi3Sb+jrurlfXPd8Yv+RBMuQEyUqDbK9D6UbNLFBERKXIF+f42/ciN5K1uiC81K/uQnmnnt51xUKmWI9QALBkHsdtMrU9ERKSkUbgp4SwWi3M6hvlbz5+eixoE9XqCLR1+HAIZqeYVKCIiUsIo3JQCPc73mlq+6wTJaZlgscAtk8AnGOL+gaXjTa5QRESk5FC4KQUahvoTUcmbtEw7y3adn1DUJwhufd/x+1/vw75l5hUoIiJSgijclAIWi8U519SCrRdMS1G367/zTs15GFJOm1CdiIhIyaJwU0r0Ot8l/LedcZxLt/17Q5eXIKguJMbAzyM0uJ+IiJR7CjelRKOq/lSr4MW5DBsrdsf9e4O7N/T9BKyusOMn2PyteUWKiIiUAAo3pYTFYnEO6Ddv60Uzpoc1g46jHb/PfwpOHyje4kREREoQhZtSpEcjR6+p33YcJzXDlv3GtiOgehtIT4LZw8CWaUKFIiIi5lO4KUWahQcSFuBJcrqN33efyH6j1QX6fgQe/nB4DfzxjjlFioiImEzhphSxWCx0P99ravbGozlXCKwOvd5y/L58AhxZX4zViYiIlAwKN6XMXS2qAbBoeyzRp5JzrtD4Tmh0Oxg2x+jFaUnFXKGIiIi5FG5KmfpV/GlftzJ2A6auzKXhsMXiOHrjXxVO74NfRxd/kSIiIiZSuCmFhrWrCcD36w9zOjk95wpeFeC2KYAF1n8BO+cXa30iIiJmUrgphVrXqkSjqv6kZtj5avXB3Feq0Q7aPOb4/afhkHi82OoTERExk8JNKWSxWBjarhYAX62Ozj5i8YVueh5CGkPKKUfA0ejFIiJSDijclFI9G1WhWgUvTienM2vDkdxXcvWA2z8BFw/Y8yusfLN4ixQRETGBwk0p5epi5cEbagAwdeV+bPZLHJUJbgDdX3H8/tvL8NeUYqpQRETEHAo3pdhdzcMJ8HIj+lQKv26PvfSKLR6C9s86fl/4DPz9efEUKCIiYgKFm1LMx8OVAa0jAJjy+36MvNrUdHgW2jzu+P2XJ2Hzd8VQoYiISPFTuCnlBrSOxN3VyubDZ1l74PSlV7RYoMt4aDEEMGDOw7B9drHVKSIiUlwUbkq5yn4e3H6dY9Tij3/fn/fKFgv0eB2uvR8MO/zwEOxaUAxVioiIFB+FmzJgyI01sFhg6c449hxPzHtlqxV6v+uYpsGeCTMHwL7fiqdQERGRYqBwUwbUrOxLlwYhAHyy8jJHb8Axg3ifKdCgN9jS4dt74eCqIq5SRESkeCjclBHD2jumZJiz8RhxCamXv4OLK9z+GdTpCpnnYPpdcHhdEVcpIiJS9BRuyoioiIpERVQg3Wbn8z8P5u9Oru5w1zSo0R7Sk+Dr2+HYpqIsU0REpMgp3JQhQ89PqPn1X9EkpWXm705unnDPt1C9NaTFw7TbIG5HEVYpIiJStBRuypAuDUKoGeRDYmom3609lP87uvvAvTMh7Do4dxq+vAVO7i26QkVERIqQwk0ZYrVaeOhGx9Gbz/44QIbNnv87e/rDfT84JtpMjoOvboEzB4umUBERkSKkcFPG9L2uKkG+7hyLT2XelpiC3dm7Itw/G4LqQcJRxxGc0/nofSUiIlKCKNyUMZ5uLgxsHQnAR5ebkiE3vpVhwFyoUAPORsPHHWDP4kKvU0REpKgo3JRB910fgZebCztiEvhj78mCb8A/FAYvgGotIDUevrkTVrwB9gKc5hIRETGJwk0ZVMHHnX4twoF8TMlwKf6hMGgeRA0GDFj2Msy4D1ITCq9QERGRIqBwU0Y9eEMNrBZYueck24/FX9lGXD2g90S4ZRK4uMOuefDJTXBiV6HWKiIiUpgKHG4OHz7MkSNHnNfXrl3LE088wccff1yohcnVCa/oTa8mYcBVHL3Jct0AGLwQ/KvCqT2OgLPj50KoUkREpPAVONzce++9LFu2DIDY2Fi6dOnC2rVrGT16NOPHjy/0AuXKDTs/qN8vW2I4cibl6jZWLQqGroDIGx2jGc+4D5a8CHZbIVQqIiJSeAocbrZt20bLli0BmDlzJo0aNeLPP//km2++4Ysvvijs+uQqNKoaQJtalbDZDT774+DVb9C3Mtw/B65/1HH9j7cdjY1TTl/9tkVERApJgcNNRkYGHh4eACxZsoRbbrkFgPr16xMTU8BxVaTIZU3J8N26Q8SnZFz9Bl1cofsr0HcquHrBvqWO7uKxW69+2yIiIoWgwOHmmmuuYcqUKaxcuZLFixfTvXt3AI4dO0alSpUKvUC5Ou3rVqZ+FT9S0m288evOwttwkzvhoSVQIdIxHs7ULrBlZuFtX0RE5AoVONy89tprfPTRR3To0IF77rmHpk2bAvDTTz85T1dJyWGxWHiqWz0Avv7rEJ/9caDwNl6lEQxZBrU7Q+Y5+HEILHgG0pIK7zFEREQKyGIUeAhbsNlsJCQkUKFCBeeygwcP4u3tTXBwcKEWWNgSEhIICAggPj4ef39/s8spNlNW7OPVBTuxWOCj+6Loek2Vwtu43QbLXoGVbzqu+1SGdk9D1CBwdS+8xxERkXKrIN/fBT5yc+7cOdLS0pzBJjo6mokTJ7Jr164SH2zKs2HtanJPy+oYBoz4bhNbjpwtvI1bXaDTC3DPDMe0DcknYMFTMDkKNn+nHlUiIlKsChxubr31Vr766isAzp49S6tWrXjrrbfo06cPH374YaEXKIXDYrHw0q3X0K5uZc5l2Hjgi7+vvnv4xep1h+HroNfb4FsFzh6C2cPgw7awcx4U/CChiIhIgRU43GzYsIEbb7wRgFmzZhESEkJ0dDRfffUV7733XqEXKIXH1cXK+/deS/0qfpxMSuOBL9aRkFoIPagu5OIGLR6ExzdC53HgGQAndsB398KnXeDAysJ9PBERkYsUONykpKTg5+cHwK+//krfvn2xWq1cf/31REdHF3qBUrj8PN34bFALQvw92H08iUe+3kCGrQgmxHT3hhuehBGb4YaRjm7jR9bBlzfDtL5wbFPhP6aIiAhXEG5q167NnDlzOHz4MIsWLaJr164AxMXFlasGuqVZWKAXnw5sgbe7C3/sPcno2Vu5gnbl+eNVATqPhRGboMVDYHU9PzZOe/h+EJzcWzSPKyIi5VaBw82YMWMYNWoUkZGRtGzZktatWwOOozjXXnttoRcoRaNR1QAm33stVgvM/PsIHyzfV7QP6FcFer3laJPT+C7AAttnw/st4ZcnISO1aB9fRETKjSvqCh4bG0tMTAxNmzbFanXko7Vr1+Lv70/9+vULvcjCVF67gl/KV6sPMmbudgDevbsZtzarWjwPHLsNfnsJdi90XI+4Ae6Z7mijIyIicpGCfH9fUbjJkjU7eLVq1a50E8VO4Sanl375h0//OIC7i5VvhrSiRWTF4nvwvUtg5iBIT4SQxnDfLMdRHhERkQsU6Tg3drud8ePHExAQQEREBBEREQQGBvLSSy9htxdBw1Qpcv/t2YBu14SQbrMz5Ku/OXAyufgevHZnGDwPfILh+Fb4tCucKuJTZCIiUqYVONyMHj2ayZMn8+qrr7Jx40Y2btzIK6+8wqRJk3jhhReKokYpYi5WCxP7XUvTagGcTclg8OdrOZ2cXnwFhDaFBxc5BgA8G+0IOMc2Ft/ji4hImVLg01JhYWFMmTLFORt4lrlz5/LII49w9OjRQi2wsOm01KWdSEyjz/urOHr2HM0jKvD1Q63wdHMpvgKS4uDr2yF2C7j7Qr+voVbH4nt8EREpsYr0tNTp06dzbTRcv359Tp8+XdDNSQlS2c+DLwa3wM/Tlb+jz/DUrC3Y7cU4qrBvMAyaBzXaQXoSfHMnbPuh+B5fRETKhAKHm6ZNmzJ58uQcyydPnuycIVxKrzohfky5LwpXq4WfNx/j/WXFPA6Npz/0nwUN+4A9A2Y9CGs+Lt4aRESkVCvwaakVK1bQq1cvqlev7hzjZvXq1Rw+fJj58+c7p2YoqXRaKn++W3uIZ3/cisUC0x5oxQ11goq3ALsNFjwN66Y6rrd7CjqOBouleOsQEZESoUhPS7Vv357du3dz2223cfbsWc6ePUvfvn3ZtWtXiQ82kn93t6zOXc2rYRjw+HcbiYk/V7wFWF2g55uOQAPw+xvw8+NgyyzeOkREpNS5qnFuLnTkyBHGjx/Pxx+X7FMIOnKTf6kZNvp+8Cf/xCRwbfVAZgxtjbtrgfPw1fv7c5g3Egw71L8Zbp8Kbl7FX4eIiJimSI/cXMqpU6f49NNPC2tzUgJ4urkw5b4o/Dxd2XjoLK/M32FOIc0Hw51fgosH7PzF0aPq3FlzahERkRLPhH/DpTSpXsmbt+9qBsAXfx7kp83HzCmk4S1w/4/g4Q/Rq+Cz7hrsT0REcqVwI5fVpWEID3eoBcCzP2xhb1yiOYVE3gCD54NvFTixAz7uADvnmVOLiIiUWAo3ki//16UurWtWIiXdxn++3kBymkkNe6s0hqHLIfx6SEuA7+6FxWPV0FhERJzy3aC4b9++ed5+9uxZVqxYgc1mK5TCiooaFF+5E4lp9HpvJXGJafRuGsZ7dzfDYlbXbFsGLB4Df33guB55I9zxmWMgQBERKXOKpEFxQEBAnpeIiAgGDBhw1cVLyVXZz4MP+l/nHODvyz8PmleMixt0nwB3fA5uPnBwJXzUDg6tMa8mEREpEQqtK3hpoSM3V2/qyv28PG8Hbi4WZgxrzXXVK5hb0IldMOM+OLkbrK7Q9X/QapgG/BMRKUNM6Qou5ceDN9SgZ+MqZNgMHv1mA6eS0swtqHI9GPIbXNMX7Jmw8Bn44UFISzK3LhERMYXCjRSYxWLhtdubUDPIh5j4VEZ8twlbcU6wmRsPP0ebm+6vOo7ebPsBpnaCE7vNrUtERIpdiQg377//PpGRkXh6etKqVSvWrl2br/t99913WCwW+vTpU7QFSg5+nm58eF8UXm4u/LH3JO8uKQEhwmKB6x92zCzuFwondsInHWH7bLMrExGRYmR6uJkxYwYjR45k7NixbNiwgaZNm9KtWzfi4uLyvN/BgwcZNWqU5rMyUb0qfkzo2xiA937by7Kdeb9mxab69TDsd0cPqvQk+H4QLPyvo4eViIiUeaaHm7fffpshQ4YwePBgGjZsyJQpU/D29uazzz675H1sNhv9+/fnxRdfpGbNmsVYrVysz7VVuf/6CACemLGJw6dTTK7oPN9guH8OtB3huP7X+45RjY9vN7UsEREpeq75Wemnn37K9wZvueWWfK+bnp7O+vXree6555zLrFYrnTt3ZvXq1Ze83/jx4wkODubBBx9k5cqV+X48KRrP39yALUfj2Xz4LI98s4GZw1rj5e5idlng4gpdxkO1FjDnETj6t6O7eOtHof0z4O5jdoUiIlIE8hVu8tumxWKxFGgQv5MnT2Kz2QgJCcm2PCQkhJ07d+Z6nz/++INPP/2UTZs25esx0tLSSEv7tzdPQkJCvuuT/PFwdeGD/tdx83sr2Xo0nge+WMeng5rj7Z6vt1fRa9Abwq5z9KLa8TOsetfRDqfnW1C3q9nViYhIIcvXaSm73Z6vS1GPTpyYmMj999/PJ598QlBQUL7uM2HChGyDDYaHhxdpjeVV1UAvpg5sjq+HK6v3n2LQZ+tIMmuKhtwEVIV+X8M930FAOJw9BNPvhJkDISHG7OpERKQQmTqIX3p6Ot7e3syaNSvb0aGBAwdy9uxZ5s6dm239TZs2ce211+Li8u8pD7vdDjhOZ+3atYtatWplu09uR27Cw8M1iF8R2XDoDAM/XUtiWiZRERX4YnAL/DzdzC4ru7QkWPEqrP4ADJtjpvFOY6D5A2AtAafTREQkh4IM4ndF4SY5OZkVK1Zw6NAh0tPTs932+OOPF2hbrVq1omXLlkyaNAlwhJXq1aszfPhwnn322Wzrpqamsnfv3mzLnn/+eRITE3n33XepW7cu7u7ueT6eRigueluOnOW+qWtISM2kaXggXz3QkgCvEhZwAGK2wC9PwNH1jutVo+DmiRDaxMyqREQkF0UabjZu3EjPnj1JSUkhOTmZihUrcvLkSby9vQkODmb//v0FKnbGjBkMHDiQjz76iJYtWzJx4kRmzpzJzp07CQkJYcCAAVStWpUJEybkev9BgwZx9uxZ5syZk6/HU7gpHtuOxnPfp2s4m5JB46oBTHuwJYHeeQdPU9ht8PdnsHS8Y5Zxi4tjrJwOz4GHr9nViYjIeUU6/cKTTz5J7969OXPmDF5eXvz1119ER0cTFRXFm2++WeBi+/Xrx5tvvsmYMWNo1qwZmzZtYuHChc5GxocOHSImRm0iSptGVQP4dsj1VPRxZ+vReO75ZA2nk9Mvf8fiZnWBlkPg0bVwzW2O01SrJ8MH18OuBWZXJyIiV6DAR24CAwNZs2YN9erVIzAwkNWrV9OgQQPWrFnDwIEDL9nLqaTQkZvitft4Ivd+soaTSWnUr+LH1w+1IsjXw+yyLm33rzD//xwNjgGqNof6PaFuDwhuoMk4RURMUqRHbtzc3LBaHXcLDg7m0CHHl0BAQACHDx++gnKlLKsb4sd3Q68n2M+DnbGJ3P3xX8QlpJpd1qXV7QqPrIG2TzjmqDr6t+OU1Yet4d0mMP9p2PcbZJbAo1AiIgJcwZGbrl27MmjQIO69916GDBnCli1bePzxx5k2bRpnzpxhzZo1RVVrodCRG3McOJnMvZ/8RUx8KjWDfJg+5HqqBHiaXVbeEmJg9wLYtRAOrIDMC0KZux/UvslxRKdOV/CpZF6dIiLlQJE2KP77779JTEykY8eOxMXFMWDAAP7880/q1KnDp59+SrNmza6m9iKncGOeQ6dSuOeTvzh69hwRlbz5dsj1hAV6mV1W/qQnw/7ljnY4uxdB8gXzaFmsUK0l1OvuCDuV6+n0lYhIISvyruClmcKNuQ6fTuHeqX9x+PQ5wit6Mf2h6wmv6G12WQVjt8Oxjf8e1Tm+Nfvt4dfDTc9DDU3qKiJSWIq0zc1NN93E2bNnc33Qm266qaCbk3ImvKI3M4a2JrKSN4dPn+Puj/8i+lSy2WUVjNUK1aIcAebhP+CJbdDzTajdGVzc4fBf8OXN8GVvOLzW7GpFRMqdAh+5sVqtxMbGEhwcnG15XFwcVatWJSMjo1ALLGw6clMyxMancu8nf7H/ZDJV/D2ZOrA5jaoGmF3W1Us4BivfgvVfgv3830KdrtDxvxB2rbm1iYiUYkVyWmrLli0ANGvWjN9++42KFSs6b7PZbCxcuJCPPvqIgwcPXnnlxUDhpuSIS0yl/ydr2BOXhKeblTfvbMrNTcLMLqtwnD0EK16HTdMdY+cA1L8ZOo6GkIbm1iYiUgoVSbixWq1YzjeSzO0uXl5eTJo0iQceeOAKSi4+CjclS/y5DB7/diMrdp8AYHjH2ozsUhertYw0yD21D5a/Clu/BwzAAo1ud4yAHFTb7OpEREqNIgk30dHRGIZBzZo1Wbt2LZUrV3be5u7uTnBwcLYJLUsqhZuSx2Y3eG3hTj7+3TF1R5eGIbzTrxm+Hq4mV1aI4nbA8gnwz/nJYC1WaHoPtH8aKkSaWpqISGmg3lJ5ULgpuX7ccIRnf9xKeqaduiG+TB3QguqVSllPqsuJ2QLLXnH0tALHQIHXDYCWwyC4vrm1iYiUYEUebvbt28fEiRPZsWMHAA0bNmTEiBHUqlXryiouRgo3JdvGQ2cYNm09cYlpBHq78UH/62hTK8jssgrfkb9h2f8cox1nqdIYGt/lOG0VUNW82kRESqAiDTeLFi3illtuoVmzZrRt2xaAVatWsXnzZn7++We6dOly5ZUXA4Wbki82PpVh0/5m85F4XKwWxvZuyP3XRzjbfJUp0X/Cn5Ngz+J/e1dhgcgboPGd0PAW8KpgaokiIiVBkYaba6+9lm7duvHqq69mW/7ss8/y66+/smHDhoJXXIwUbkqH1Awbz/24ldkbjwJwT8vqvHjLNbi7FnhoptIh5TT8Mwe2zoLoVf8ud3F3dCVvfCfU7Q5uJXzKChGRIlKk4cbT05OtW7dSp06dbMt3795NkyZNSE0twZMionBTmhiGwce/7+fVhTsxDGgZWZEP7ruuZM8qXhjOHoZtsxxB5/i2f5d7+EOD3o6gU6MdWF3AMCA9CdIS/72kxme/npbg+OnmDa2GgU8ZPM0nImVekYab8PBw3n77be68885sy2fOnMmoUaOcs4SXVAo3pc+ynXE8/u1GEtMyqRroxccDorgmrAwM+Jcfx/+BrTMdQSf+8L/LPc7vf3oiGPb8b88nGPp8AHVK9uljEZGLFUm4GT9+PKNGjeLNN9/knXfe4dlnn6VNmzaAo83Na6+9xsiRI3nhhReufg+KkMJN6bQ3LokhX/3NgZPJeLm58PZdTenRONTssoqP3e6Y1mHr97B9Npw7k/12iwt4+oOHnyP4ePg5Ls5lfrD7Vzjh6ARAiyHQZTy4l7HeaCJSZhVJuHFxcSEmJobKlSszceJE3nrrLY4dOwZAWFgYTz31FI8//niJb/SpcFN6xadkMPzbDazccxKrBSbdcx29mpSjgJMlMx1O7gJXr38DjKvn5WcizzgHS16ENR86rgfVg74fQ1izIi9ZRORqFdkIxRfPKZWYmAiAn5/fVZRbvBRuSrdMm53n52zju3WHcXOx8PH9zelYP/jyd5R/7V0Kcx6BpFiwujnmvWo7wtGGR0SkhCqyWcEvPirj5+dXqoKNlH6uLlb+d1tjbmkaRobN4D9fr+ev/afMLqt0qd0JHlntaJxsz4ClL8IXN8OZ6KvbbkYq7F0C+1cUTp0iIleoQEduAgICLnva6fTp04VSWFHRkZuyIcNm5+Gv17NkRxy+Hq5MH9KKJtUCzS6rdDEMx8SeC5529Ljy8Ieeb0KTuy5/iitL4nHYswh2LYT9yyAjxbE8ahB0f01d10Wk0BTZaamJEycSEJB3L5WBAwfmv1ITKNyUHakZNgZ/vo7V+08R6O3GzGGtqRuiI4kFdvoAzB4Gh9c4rl/TF25+O/fBAw3D0T1910LHFBJH12e/3TcEkuIAA0Iaw11fQqWSP3K5iJR8xdbmpjRSuClbktIyuW/qGjYdPkuwnwff/6c1EZV8zC6r9LFlwh/vOCb3NGzgXxX6fAg120NmGhxY6Qgzuxdl75IOENoM6vVwDDIY2hT2LYUfh0LKKXD3g1veg0Z9TdktESk7irS3lMKNlDRnU9K5++O/2BmbSLUKXnz/n9aEBniZXVbpdGQ9/DgETu9zXI+8EY5ugIzkf9dx9YKaHaBuN0eg8c+lx1rCMZj1ABxa7bjeYgh0+x+4lvEBGEWkyOjITR4UbsqmuMRU7pqymoOnUqhV2YeZw1pTqayPZFxU0pNh0WhY//m/y/xCz4eZHo7RkfMzPo4tE5a97DgiBI4jPHd+ARVrFEXVIlLGFfms4KWZwk3ZdeRMCndOWU1MfCrXhPnz7dDr8fd0M7us0uvA7xCzBSLbOoLJlY5htftXmD3UMfCgRwD0ed/RU0tEpAAUbvKgcFO27TuRxF1TVnMqOZ0WkRX46oFWeLlr/BbTnT3sOE11ZK3jequHHSMku7qbW5eIlBpFNs6NSElXq7IvXz3YEj9PV9YdPMPQaX+TlmkzuywJDIfB86H1cMf1NR/C592vfmwdEZFcKNxImXNNWABfDG6Bl5sLK/ecZMS3m8i0FWBySSkaLm6ORsV3fwueAY5u5B/dCDvnm12ZiJQxCjdSJkVFVOSTAc1xd7GycHssz/ywFbu9XJ2BLbnq94RhK6FqFKTGw3f3wPeD4c/JjhGOE445xtMREblCanMjZdqi7bE88s0GbHaDvtdW5dme9Qn206i5JUJmOiwe8+9EnhfyCIDg+hDcACo3OP97Q/CpfOUNm0WkVFOD4jwo3JQ/szce4ckZmwHwdLMyoHUkw9rVVFfxkiL6T8d8VCd2QNwOOLXPMZBgbrwqng889cEnCCzWnBerywXXXRxhyGIFqytUrAlh14KHb/Huo4hcNYWbPCjclE9/7jvJG4t2sfHQWQC83V0Y1CaSoe1qEuitHjslSmYanNrrCDpxO+DEToj7xzFNBIXwcWWxOo4GVYuCqs2hWnNHWNKs6CIlmsJNHhRuyi/DMFi+6wRvL97N1qPxAPh5uPLADTV48MYaGhOnpMs4Byd3Q9xOx1GetCTHER7D7rjY7f/+nm35+d9t6XD8H0g4knPb7r6OIzpVoxxhp2rz3EdeFhHTKNzkQeFGDMPg13+O887i3eyMTQTA39OVoe1qMqhtDXw9XE2uUIpUYiwc+RuO/u34eWyjY1b0i/lXdYSd8JYQ3soxb5amjxAxjcJNHhRuJIvdbrBgWyzvLNnN3jjHl1tFH3eGtavJgNaRGvyvvLDb4MQuOLLufOBZ7zgyZFw0fICLu2Ok5qywE94S/KqYUrJIeaRwkweFG7mYzW7w8+ZjvLt0DwdOOiaIDPL14JEOtbi3VXU83RRyyp20JMcRnaN/w+F1cHgNpJzMuV5g9fNB53zYCb4GXHTkT6QoKNzkQeFGLiXTZmf2xqO899seDp8+B0C9ED+mDmxOeMV8TBQpZZdhwOn9jqM7h9fA4bVwfDs5Gji7+UDV6xyTjDa4BSpEFH4tqfGO8YDSU6DxHeDmVfiPIVICKdzkQeFGLifDZmfW+iO89etuTialUcnHnY/uj6J5ZEWzS5OSJDXBMcry4bWOObMOr4O0+OzrhDZzTBLa8FYIqnPljxV/BHYtgJ3z4OAfYM9wLK9cH27/FKo0uvJti5QSCjd5ULiR/IqJP8dDX/7N9mMJuLtYefX2xvS9rprZZUlJZbfDyV2O2dR3/AzRq7K326ncwBFyGt7iGJAwr8EIDcNxZGjXfEegidmU/fagunDuLCTHgYsHdH0JWg7VAIdSpinc5EHhRgoiJT2TkTM2s3B7LACPdKjFqK71sFr1JSKXkXQCds2Df36CAyvAnvnvbRVrOUJOg94Qdp0jlNgy4dCfjrm2ds2Ds4cu2JjF0a6nfk+o1wuCakPySZj7KOxe6FilTle49QPwrVysuylSXBRu8qBwIwVltxu8tXgX7y/bB0DXhiG8068ZPuoyLvl17gzsWgg7foK9S8GW9u9tAeGObuYH/4DUs/8ud/WEmh2hfi+o2z330GIYsG4qLBrt2KZPMNz2IdTuXOS7JFLcFG7yoHAjV2r2xiM8M2sr6TY7DUP9mTqwOWGBaswpBZSWCHt+dRzR2bMYMpL/vc27kiPI1OsJtTqCu0/+tnl8O8x60NGFHaD1cOg0RuPySJmicJMHhRu5GuujzzBs2t+cTEonyNeDTwZEcW31CmaXJaVVxjnHkZwTOyGijePU05VOA5FxDn59AdZ94rhepTHc/hlUrlt49YqYSOEmDwo3crWOnEnhoS//ZmdsIu6uVt64owm3NqtqdlkiDjvnO9rinDsNbt7Q/VW4boAaG0upp3CTB4UbKQxJaZk88d1GluyIA+DxTnV4olMdNTSWkiEhBmYPczRkBseYO73fBe88hjOwZULyCUiKdUxRkXVxcXX0zgqq55hV3VUTzYo5FG7yoHAjhcVmN3h94U4++n0/AL0ah/LmnU01bYOUDHY7rJ4ES8c7emr5V4WbnnfMup4YC4kxkHTc8TPxuKNb+cVTTlzM4uIIOJXrOcbtCarnOO0VVBc8/Ipnv6TcUrjJg8KNFLaZfx9m9OytZNgMGlcN4OMBUYQGqKGxlBBHN8APD8HpfZdf12IF3xDHnFm+VcAvxBGGTuxyzMie2wSjWfyrOkJO5XoQ0Rbq9QAXt8LbDyn3FG7yoHAjRWHtgdMMm/Y3Z1IyCPR2Y8JtjenRONTsskQc0pLgt5cdU0f4hjhCi1/oBSGmiuO6T9ClGzQbBiQccwxUeGL3BT93O476XMy3CkQNgqiB4B9WpLsn5YPCTR4UbqSoHD6dwsPfrGfb0QQA7oiqxtjeDfHz1H+vUsalnIaTexyB5/h22PaDo/0OOE5l1esBLR6CGu3BajW3Vim1FG7yoHAjRSk90867S3fz4fJ92A0Ir+jFO3c107xUUr5kpsPOn2Hdp45pKLJUrAXNH4Bm9+bduFkkFwo3eVC4keKw7uBpnpyxiSNnzmG1wCMdajOicx3cXPRfq5QzcTscIWfzd5Ce6Fjm6gmNbofmDzpmUS9p3dQNwzFuUGq8Y9BFewbYbY6G2Vk/jazrmY7G287fz0+z4e4LHr4X/PRz/HT1LNj+Goaj3VN6sqPNU3ryv7/bMx2nEa1uYHV1XFxc//09t4uLK7i4O+7j4lbynvs8KNzkQeFGiktiagbjfvqHHzYcAaBJtQDe6deMWpV9Ta5MxARpSbD1e0fQOb713+WhTR2nrEKbOr7IDfsFP3O5cOFtxr/rctF9c1zHEUzSEhyh5XKXrJnXC5vFJXvYyQo/VjfISLkowJy/GLaiqQXOB54Lwk7WxermWO7iClhyvibk9hpdsCzsWrjn20ItVeEmDwo3Utzmb43huR+3En8uA083K6N7NeS+VtWxlKL/mEQKjWHAkXWOObG2zwZbutkVXVpWEHFx//fIh8V60dGQi65bXADDEVLSkv79eeE0G1fKzdsxJYebtyMUubhecETp/MV2we/ZjjhlZp+8tahVawkPLS7UTSrc5EHhRswQG5/KqO8388fekwDcVD+Y125vQmU/zf0j5VjyKdj0NWz61jFpqMV6/mL593cu+D3bhVzWsVzi+gXreviBZ8D5S+AFv+dycfcpvNM2dvv5ozJZoScxe/ixZzgez933/M+LfnfzvvKpObIYxvkAlOEIlfZMx09bhuNiP7/cdn551nW4zGuR9VxfcLubj2P2+kKkcJMHhRsxi91u8MWfB3l14U7SM+1U8nHn1dub0KVhiNmliYiUeAX5/lbrRpFiYrVaeOCGGvw8/AYahPpzKjmdIV/9zXM/biE5rRgPF4uIlHEKNyLFrF4VP+Y82oZh7WpiscC3aw/Te/If/HMswezSRETKBIUbERN4uLrwXM8GfPNQK6r4e7L/RDJ9PljFV6sPUs7OFIuIFDqFGxETtakVxIIRN9K5QTDpmXbGzN3Of75eT3xKEXVDFREpBxRuRExWwcedTwY0Z8zNDXFzsbBo+3F6vreS9dGnzS5NRKRUUrgRKQEsFkdj4x8fbktkJW+Onj3HXR/9xfvL9mK36zSViEhBKNyIlCCNqwXwy+M30qdZGDa7wRuLdjHgs7XEJaaaXZqISKmhcCNSwvh6uPJOv2a8cUcTvNxc+GPvSXq+u5Lfd58wuzQRkVJB4UakBLJYLNzZPJyfH7uB+lX8OJmUzoDP1vLqgp1k2OxmlyciUqIp3IiUYLWDfZnzaFvuvz4CgCkr9nHXR6s5fDrF5MpEREouhRuREs7TzYWX+jRiyn3X4e/pysZDZ+n53kqm/RVNpo7iiIjkoHAjUkp0bxTKvMdv5LrqgSSmZvLCnG30fG8lK9QWR0QkmxIRbt5//30iIyPx9PSkVatWrF279pLr/vjjjzRv3pzAwEB8fHxo1qwZ06ZNK8ZqRcwTXtGbmcNaM/7Wa6jg7cbu40kM/Gwtgz9fy964RLPLExEpEUwPNzNmzGDkyJGMHTuWDRs20LRpU7p160ZcXFyu61esWJHRo0ezevVqtmzZwuDBgxk8eDCLFi0q5spFzOHqYmVA60iWj+rIgzfUwNVqYdmuE3SbuJKxc7dxJjnd7BJFRExlMUyeyKZVq1a0aNGCyZMnA2C32wkPD+exxx7j2Wefzdc2rrvuOnr16sVLL7102XULMmW6SGlw4GQyr8zfweJ/jgPg7+nKiM51uf/6CNxdTf//RUSkUBTk+9vUT7709HTWr19P586dncusViudO3dm9erVl72/YRgsXbqUXbt20a5du1zXSUtLIyEhIdtFpCypEeTDJwOaM/2hVtSv4kdCaiYv/fIP3Sb+zpJ/jmsiThEpd0wNNydPnsRmsxESEpJteUhICLGxsZe8X3x8PL6+vri7u9OrVy8mTZpEly5dcl13woQJBAQEOC/h4eGFug8iJUWb2kHMe/xGXu3bmCBfdw6cTOahr/7mvk/XsCNGoV5Eyo9Seczaz8+PTZs2sW7dOv73v/8xcuRIli9fnuu6zz33HPHx8c7L4cOHi7dYkWLkYrVwd8vqLBvVgYc71MLd1cqqvafo9d5Knpm1hS1HzupIjoiUea5mPnhQUBAuLi4cP3482/Ljx49TpUqVS97ParVSu3ZtAJo1a8aOHTuYMGECHTp0yLGuh4cHHh4ehVq3SEnn5+nGM93rc2/L6ry6YCfztsYw4+/DzPj7MJGVvLmlaRi9m4ZRJ8TP7FJFRAqdqUdu3N3diYqKYunSpc5ldrudpUuX0rp163xvx263k5aWVhQlipRq4RW9eb//dcz6T2tubhKKp5uVg6dSeO+3vXR553e6T/ydD5bv1YjHIlKmmHrkBmDkyJEMHDiQ5s2b07JlSyZOnEhycjKDBw8GYMCAAVStWpUJEyYAjjY0zZs3p1atWqSlpTF//nymTZvGhx9+aOZuiJRozSMr0jyyIslpmSzZcZyfNx9jxe4T7IxNZOfCXby+cBfXVg+kd5Mwbm4SSrC/p9kli4hcMdPDTb9+/Thx4gRjxowhNjaWZs2asXDhQmcj40OHDmG1/nuAKTk5mUceeYQjR47g5eVF/fr1+frrr+nXr59ZuyBSavh4uHJrs6rc2qwqZ1PSWbgtlp+3HGP1vlNsPHSWjYfO8vK8f7i+ZiV6Nw2jR6MqBHq7m122iEiBmD7OTXHTODciOcUlpjJvSww/bz7GhkNnnctdrBaiIipwU/1gOtUPpnawLxaLxbxCRaTcKsj3t8KNiGRz+HQKv2yJ4afNx3J0Ia9WwYub6gdzU/1grq9ZCU83F5OqFJHyRuEmDwo3Ivl3+HQKv+2M47edcazef4r0zH9nIfdyc6Ft7Up0PB92QgO8TKxURMo6hZs8KNyIXJmU9Ez+3HuKpTvjWLYzjtiE1Gy3Nwj156b6lenVOIyGYfrbEpHCpXCTB4UbkatnGAY7YhJZtiuOpTuOs/HwWbI+SSwWGNQmkqe71cfLXaetRKRwKNzkQeFGpPCdTk5nxe44Fm6LZdF2x6CckZW8eePOprSIrGhydSJSFijc5EHhRqRoLd8Vx7M/bCU2IRWLBR5oW4NRXevpKI6IXJVSMyu4iJQ9HeoFs+jJdtwZVQ3DgE//OEDP91ayPvq02aWJSDmhcCMihS7Ay4037mzK54NaEOLvwYGTydwxZTX/m/cPqRk2s8sTkTJO4UZEikzH+sH8+kR7br/OcRTnk5VZR3HOmF2aiJRhCjciUqQCvN14666mfDaoOcF+Huw/kcydU/7klfk7dBRHRIqEwo2IFIub6oew+Mn29L2uKnYDPv59P73eW8mGQzqKIyKFS+FGRIpNgLcbb9/VjKkDmlPZz4N9J5K548M/+d+8f0hKyzS7PBEpIxRuRKTYdW4YwuIn23HbtY6jOJ+sPMBNby5n9sYjlLPRKUSkCCjciIgpAr3deadfMz4b1JyISt7EJabx5IzN3P7hn2w9Em92eSJSimkQPxExXVqmjU//OMDk3/aSkm7DYoF+zcMZ1a0eQb4eZpcnIiWARijOg8KNSMkVG5/Kqwt2MGfTMQD8PF15snNd7m8dgZuLDjSLlGcKN3lQuBEp+f4+eJqxP21n+7EEAOoE+zLulmtoWzvI5MpExCwKN3lQuBEpHWx2g5l/H+aNRbs4nZwOQPdrqjC6VwPCK3qbXJ2IFDeFmzwo3IiULvEpGbyzZDfT/orGZjfwcLUyrH0tHm5fS5NxipQjCjd5ULgRKZ12xSby4s/b+XPfKQBCAzx5qls9+jSritVqMbk6ESlqCjd5ULgRKb0Mw2DhtlhenreDo2fPAdCoqj+jezakda1KJlcnIkVJ4SYPCjcipV9qho3PVx3kg2V7STw/snHnBiE817M+tSr7mlydiBQFhZs8KNyIlB2nktKYuGQP09cewmY3cLVa6N+qOiM616Wij7vZ5YlIIVK4yYPCjUjZszcukQnzd7J0ZxzgGB9neMfaDGwTiaebGh2LlAUKN3lQuBEpu/7ce5KX5+3gnxjH+DjVKnjxTPf63NwkFItFjY5FSjOFmzwo3IiUbTa7wY8bjvDmr7s4npAGwLXVA3m+VwOiIiqaXJ2IXCmFmzwo3IiUDynpmXzy+wE++n0fKek2ALo0DOHxm+rQuFqAydWJSEEp3ORB4UakfIlLSOXtxbuZ+fdh7Oc/7TrUq8xjN9UhKqKCucWJSL4p3ORB4UakfNobl8gHy/Yxd/MxbOdTTptalXjspjpcX7Oi2uSIlHAKN3lQuBEp36JPJfPBsn38sOEImedDTovICgy/qQ7t6gQp5IiUUAo3eVC4ERGAI2dSmLJiHzPXHSHdZgegaXggj3WsTacGwQo5IiWMwk0eFG5E5ELHE1L5aMV+pq+NJjXDEXIahPrz2E216X5NFc1bJVJCKNzkQeFGRHJzMimNqSsPMG31QZLP966qHexL46oBeLpZ8XRzwSvr4u6CR7brjts93VwI8HKjZpCPjvyIFDKFmzwo3IhIXs4kp/P5qgN8/udBElMzr2gbzcIDebpbPdrUDirk6kTKL4WbPCjciEh+xJ/LYNH2WOJTMkjNsHHu/CU1w0Zqhp1z6f8uS7vg9uMJaaRnOk5vtalViVHd6nFddXU5F7laCjd5ULgRkaIUl5jKB8v2MX3NIWdD5c4NQvi/rnVpEKrPHJErpXCTB4UbESkOR86k8N7SPcxafwS7ARYL9G4SxpNd6lIjyMfs8kRKHYWbPCjciEhx2nciiXcW7+aXLTEAuFgt3BlVjcc71SEs0Mvk6kRKD4WbPCjciIgZth+L561fd/PbzjgA3F2s3Hd9BI90rEWQr4fJ1YmUfAo3eVC4EREzrY8+zesLd7HmwGkAvN1dGNw2koFtIgn28zS5OpGSS+EmDwo3ImI2wzD4Y+9J3ly0i81H4gFwtVro1qgK97WK0FxXIrlQuMmDwo2IlBSGYbBo+3E+/n0fGw6ddS6vHezLfa2q0zeqGv6ebuYVKFKCKNzkQeFGREqi7cfi+fqvQ8zddJSU8yMke7m5cGuzMO67PoJGVQNMrlDEXAo3eVC4EZGSLCE1gzkbj/L1X9HsPp7kXN40PJD7r4/g5iaheLq5mFihiDkUbvKgcCMipYFhGKw9cJqv1xxi4bYYMmyOj+oALzfujKpG/+sjNF6OlCsKN3lQuBGR0uZEYhoz/z7M9DWHOHr2nHN5p/rBDGlXk1Y11ABZyj6Fmzwo3IhIaWWzG6zYHce01dEs332CrE/vxlUDeOjGGvRsHIqbi9XcIkWKiMJNHhRuRKQs2H8iiU//OMCs9UdIOz9RZ1iAJ4Pb1qBfy3D1spIyR+EmDwo3IlKWnE5O5+u/ovlq9UFOJqUD4Ovhyt0twhl8Qw2qaooHKSMUbvKgcCMiZVFqho05G48y9Y8D7I1z9LJysVro2TiUITfWoEm1QHMLFLlKCjd5ULgRkbLMbjdYsfsEn6zcz5/7TjmXt6xRkUFtIrmhTpBOWUmppHCTB4UbESkvth2N59M/DvDz5mNk2h0f9VYLNK4WSNtalWhbO4ioiAoaN0dKBYWbPCjciEh5ExN/ji//jObX7bHsP5mc7TZ3VytR1SvQtnYl2tQOoknVAFzV40pKIIWbPCjciEh5FhN/jlV7T/Hn3pOs2neS4wlp2W739XClVY2KtKkdRNvalagX4qcxdKREULjJg8KNiIiDYRjsP5nsCDp7T7F6/yniz2VkW6dqoBf9WoTTr0U4If6eJlUqonCTJ4UbEZHc2ewG/xxLYNW+k/y57xTrDpzmXIZjEk8Xq4UuDUK4t1V1bqgdhNWqozlSvBRu8qBwIyKSP6kZNhZsi2H6mkOsO3jGubx6RW/ubVWdO6OqUcnXw8QKpTxRuMmDwo2ISMHtik1k+ppoftxwlMS0TADcXCx0bxRK/1bVNb+VFDmFmzwo3IiIXLmU9Ex+2RzDN2ui2Xwk3rm8VmUf7m0Vwe3XVSXQ293ECqWsUrjJg8KNiEjh2HY0nm/WHGLupqOkpDva5ni4WrmxThCNqwbSqKo/jaoGEOznoaM6ctUUbvKgcCMiUrgSUzOYu+kY36w5xI6YhBy3B/l6OIJOWACNqgbQqKo/VQO9FHikQBRu8qBwIyJSNAzDYOvReNYdPMP2o/FsOxbP3rgk7Ll8ywR6u9EoLIBrzoeeG+sE6XSW5EnhJg8KNyIixedcuo0dsQmOsHM0gW3H4tl9PJEMW/avHj9PV0Z0qsOA1pG4u2qEZMlJ4SYPCjciIuZKy7SxOzaJbcfi2XY0ntX7T7H/hGNaiJpBPozu1YCb6gfrtJVko3CTB4UbEZGSxWY3+P7vw7z56y5OJqUDcGOdIF64uSF1Q/xMrk5KCoWbPCjciIiUTImpGUxetpfP/zhIus2Oi9VC/1bVeaJzXSr6qD1OeadwkweFGxGRki36VDKvzN/Bou3HAfD3dOWJznW5v3UEbpqxvNwqyPd3iXiXvP/++0RGRuLp6UmrVq1Yu3btJdf95JNPuPHGG6lQoQIVKlSgc+fOea4vIiKlS0QlHz66vznTH2pF/Sp+JKRmMv6Xf+g28XeW7YwzuzwpBUwPNzNmzGDkyJGMHTuWDRs20LRpU7p160ZcXO5v4OXLl3PPPfewbNkyVq9eTXh4OF27duXo0aPFXLmIiBSlNrWDmPf4jbxyW2Mq+biz/0Qyg79Yx8DP1rLneKLZ5UkJZvppqVatWtGiRQsmT54MgN1uJzw8nMcee4xnn332sve32WxUqFCByZMnM2DAgMuur9NSIiKlT0JqBpN/28vnqw6QYTNwsVrodk0IvRqHcVP9YLzcXcwuUYpYQb6/XYupplylp6ezfv16nnvuOecyq9VK586dWb16db62kZKSQkZGBhUrVsz19rS0NNLS0pzXExJyjp4pIiIlm7+nG//t2YB7Wlbnlfk7WPzPceZvjWX+1li83Fzo1CCYm5uE0qFeMJ5uCjrlnanh5uTJk9hsNkJCQrItDwkJYefOnfnaxjPPPENYWBidO3fO9fYJEybw4osvXnWtIiJivhpBPnwyoDnbjsbz85ZjzNsSw5Ez5/hlSwy/bInBx92FTg1CuLlJKO3qVlbQKadMDTdX69VXX+W7775j+fLleHp65rrOc889x8iRI53XExISCA8PL64SRUSkCDjmqArg2e712XIknnlbY5i3JYajZ8/x0+Zj/LT5GL4ernRpGEKvxqHcWDcID1cFnfLC1HATFBSEi4sLx48fz7b8+PHjVKlSJc/7vvnmm7z66qssWbKEJk2aXHI9Dw8PPDw8CqVeEREpWSwWC03DA2kaHshzPeqz6fBZftkSw/ytMcTEpzJ741FmbzyKn4crnRuGUCfElyBfDyr7eVDZ14NgPw8q+rjjqi7mZYqp4cbd3Z2oqCiWLl1Knz59AEeD4qVLlzJ8+PBL3u/111/nf//7H4sWLaJ58+bFVK2IiJRkFouFa6tX4NrqFRjdswEbD59xBp3jCWnM3ph7r1qLBSp6uzsCz/nQE3T+ZwUfd3LLPXl1xfFwdaFeFT9qBPngYtUUEmYwvbfUjBkzGDhwIB999BEtW7Zk4sSJzJw5k507dxISEsKAAQOoWrUqEyZMAOC1115jzJgxTJ8+nbZt2zq34+vri6+v72UfT72lRETKF7vdYP2hM6zYdYLYhFROJKY5LklpnEpKy3XW8sLg5eZCwzB/rnFeAqgb4qeJQa9QqektBdCvXz9OnDjBmDFjiI2NpVmzZixcuNDZyPjQoUNYrf++ET788EPS09O54447sm1n7NixjBs3rjhLFxGRUsBqtdAisiItInP2qrXZDc6kpP8beBLTOJn0b/g5k5JBQY8BJKZmsjM2gXMZNtZHn2F99BnnbW4uFuqG+DnDTqOq/jQI9cfb/fJfx4ZhYDccNdsNA1erpdBOpyWlZRJ9KplDp1I4eCqF6FPJHDyVzOHT50hOz7yojtxru1DT8ECmPdiqUGq7EqYfuSluOnIjIiJFzWY3OHAyiW1HE9h+LN75MyE1M8e6FgsE+3lgGGA3DGz2Cy6Ggd0OtvPLL+bn4UqAtxuB3m4Eerk7fmb73Z1ALzfn8uQ0GwdPJRN9KuX8JZmDp1I4mZSWY9tX47rqgfz4SNvLr1gAmlsqDwo3IiJiBsMwOHLmHNuPxbP9WALbjyWw7Wg8cYmFGyyuVEUfdyIqeRNZyYeISt5EVPKmekUfArzcLnkfyyWaFHm4WqlWwbtQ6ytVp6VERETKA4vFQnhFb8IretO9Uahz+YnENGLiz2G1WHCxWnC1WrBaLbicv27NWnb+uovFgtUK6Zl24s9lcPZcBvEpGZxJSedsStb1dM5c8PvZcxmcSU7H083FGV4ig3ycYaZ6JW/8PS8dYkobhRsRERETZfXSuhKVfDXUSW7UZFtERETKFIUbERERKVMUbkRERKRMUbgRERGRMkXhRkRERMoUhRsREREpUxRuREREpExRuBEREZEyReFGREREyhSFGxERESlTFG5ERESkTFG4ERERkTJF4UZERETKFIUbERERKVNczS6guBmGAUBCQoLJlYiIiEh+ZX1vZ32P56XchZvExEQAwsPDTa5ERERECioxMZGAgIA817EY+YlAZYjdbufYsWP4+flhsVguuV5CQgLh4eEcPnwYf3//YqywZCjv+w96Dsr7/oOeA+1/+d5/KFnPgWEYJCYmEhYWhtWad6uacnfkxmq1Uq1atXyv7+/vb/oLaqbyvv+g56C87z/oOdD+l+/9h5LzHFzuiE0WNSgWERGRMkXhRkRERMoUhZtL8PDwYOzYsXh4eJhdiinK+/6DnoPyvv+g50D7X773H0rvc1DuGhSLiIhI2aYjNyIiIlKmKNyIiIhImaJwIyIiImWKwo2IiIiUKQo3uXj//feJjIzE09OTVq1asXbtWrNLKjbjxo3DYrFku9SvX9/ssorU77//Tu/evQkLC8NisTBnzpxstxuGwZgxYwgNDcXLy4vOnTuzZ88ec4otApfb/0GDBuV4T3Tv3t2cYovAhAkTaNGiBX5+fgQHB9OnTx927dqVbZ3U1FQeffRRKlWqhK+vL7fffjvHjx83qeLClZ/979ChQ473wH/+8x+TKi58H374IU2aNHEOVNe6dWsWLFjgvL0sv/5w+f0vja+/ws1FZsyYwciRIxk7diwbNmygadOmdOvWjbi4OLNLKzbXXHMNMTExzssff/xhdklFKjk5maZNm/L+++/nevvrr7/Oe++9x5QpU1izZg0+Pj5069aN1NTUYq60aFxu/wG6d++e7T3x7bffFmOFRWvFihU8+uij/PXXXyxevJiMjAy6du1KcnKyc50nn3ySn3/+me+//54VK1Zw7Ngx+vbta2LVhSc/+w8wZMiQbO+B119/3aSKC1+1atV49dVXWb9+PX///Tc33XQTt956K9u3bwfK9usPl99/KIWvvyHZtGzZ0nj00Ued1202mxEWFmZMmDDBxKqKz9ixY42mTZuaXYZpAGP27NnO63a73ahSpYrxxhtvOJedPXvW8PDwML799lsTKixaF++/YRjGwIEDjVtvvdWUeswQFxdnAMaKFSsMw3C83m5ubsb333/vXGfHjh0GYKxevdqsMovMxftvGIbRvn17Y8SIEeYVZYIKFSoYU6dOLXevf5as/TeM0vn668jNBdLT01m/fj2dO3d2LrNarXTu3JnVq1ebWFnx2rNnD2FhYdSsWZP+/ftz6NAhs0syzYEDB4iNjc32nggICKBVq1bl6j2xfPlygoODqVevHg8//DCnTp0yu6QiEx8fD0DFihUBWL9+PRkZGdneA/Xr16d69epl8j1w8f5n+eabbwgKCqJRo0Y899xzpKSkmFFekbPZbHz33XckJyfTunXrcvf6X7z/WUrb61/uJs7My8mTJ7HZbISEhGRbHhISws6dO02qqni1atWKL774gnr16hETE8OLL77IjTfeyLZt2/Dz8zO7vGIXGxsLkOt7Iuu2sq579+707duXGjVqsG/fPv773//So0cPVq9ejYuLi9nlFSq73c4TTzxB27ZtadSoEeB4D7i7uxMYGJht3bL4Hsht/wHuvfdeIiIiCAsLY8uWLTzzzDPs2rWLH3/80cRqC9fWrVtp3bo1qamp+Pr6Mnv2bBo2bMimTZvKxet/qf2H0vn6K9xINj169HD+3qRJE1q1akVERAQzZ87kwQcfNLEyMcvdd9/t/L1x48Y0adKEWrVqsXz5cjp16mRiZYXv0UcfZdu2bWW+ndmlXGr/hw4d6vy9cePGhIaG0qlTJ/bt20etWrWKu8wiUa9ePTZt2kR8fDyzZs1i4MCBrFixwuyyis2l9r9hw4al8vXXaakLBAUF4eLikqMV/PHjx6lSpYpJVZkrMDCQunXrsnfvXrNLMUXW6673xL9q1qxJUFBQmXtPDB8+nF9++YVly5ZRrVo15/IqVaqQnp7O2bNns61f1t4Dl9r/3LRq1QqgTL0H3N3dqV27NlFRUUyYMIGmTZvy7rvvlpvX/1L7n5vS8Por3FzA3d2dqKgoli5d6lxmt9tZunRptnOP5UlSUhL79u0jNDTU7FJMUaNGDapUqZLtPZGQkMCaNWvK7XviyJEjnDp1qsy8JwzDYPjw4cyePZvffvuNGjVqZLs9KioKNze3bO+BXbt2cejQoTLxHrjc/udm06ZNAGXmPZAbu91OWlpamX/9LyVr/3NTKl5/s1s0lzTfffed4eHhYXzxxRfGP//8YwwdOtQIDAw0YmNjzS6tWPzf//2fsXz5cuPAgQPGqlWrjM6dOxtBQUFGXFyc2aUVmcTERGPjxo3Gxo0bDcB4++23jY0bNxrR0dGGYRjGq6++agQGBhpz5841tmzZYtx6661GjRo1jHPnzplceeHIa/8TExONUaNGGatXrzYOHDhgLFmyxLjuuuuMOnXqGKmpqWaXXigefvhhIyAgwFi+fLkRExPjvKSkpDjX+c9//mNUr17d+O2334y///7baN26tdG6dWsTqy48l9v/vXv3GuPHjzf+/vtv48CBA8bcuXONmjVrGu3atTO58sLz7LPPGitWrDAOHDhgbNmyxXj22WcNi8Vi/Prrr4ZhlO3X3zDy3v/S+vor3ORi0qRJRvXq1Q13d3ejZcuWxl9//WV2ScWmX79+RmhoqOHu7m5UrVrV6Nevn7F3716zyypSy5YtM4Acl4EDBxqG4egO/sILLxghISGGh4eH0alTJ2PXrl3mFl2I8tr/lJQUo2vXrkblypUNNzc3IyIiwhgyZEiZCvu57TtgfP755851zp07ZzzyyCNGhQoVDG9vb+O2224zYmJizCu6EF1u/w8dOmS0a9fOqFixouHh4WHUrl3beOqpp4z4+HhzCy9EDzzwgBEREWG4u7sblStXNjp16uQMNoZRtl9/w8h7/0vr628xDMMovuNEIiIiIkVLbW5ERESkTFG4ERERkTJF4UZERETKFIUbERERKVMUbkRERKRMUbgRERGRMkXhRkRERMoUhRsRKfcsFgtz5swxuwwRKSQKNyJiqkGDBmGxWHJcunfvbnZpIlJKuZpdgIhI9+7d+fzzz7Mt8/DwMKkaESntdORGREzn4eFBlSpVsl0qVKgAOE4Zffjhh/To0QMvLy9q1qzJrFmzst1/69at3HTTTXh5eVGpUiWGDh1KUlJStnU+++wzrrnmGjw8PAgNDWX48OHZbj958iS33XYb3t7e1KlTh59++qlod1pEiozCjYiUeC+88AK33347mzdvpn///tx9993s2LEDgOTkZLp160aFChVYt24d33//PUuWLMkWXj788EMeffRRhg4dytatW/npp5+oXbt2tsd48cUXueuuu9iyZQs9e/akf//+nD59ulj3U0QKidkzd4pI+TZw4EDDxcXF8PHxyXb53//+ZxiGY9bq//znP9nu06pVK+Phhx82DMMwPv74Y6NChQpGUlKS8/Z58+YZVqvVOXt5WFiYMXr06EvWABjPP/+883pSUpIBGAsWLCi0/RSR4qM2NyJiuo4dO/Lhhx9mW1axYkXn761bt852W+vWrdm0aRMAO3bsoGnTpvj4+Dhvb9u2LXa7nV27dmGxWDh27BidOnXKs4YmTZo4f/fx8cHf35+4uLgr3SURMZHCjYiYzsfHJ8dposLi5eWVr/Xc3NyyXbdYLNjt9qIoSUSKmNrciEiJ99dff+W43qBBAwAaNGjA5s2bSU5Odt6+atUqrFYr9erVw8/Pj8jISJYuXVqsNYuIeXTkRkRMl5aWRmxsbLZlrq6uBAUFAfD999/TvHlzbrjhBr755hvWrl3Lp59+CkD//v0ZO3YsAwcOZNy4cZw4cYLHHnuM+++/n5CQEADGjRvHf/7zH4KDg+nRoweJiYmsWrWKxx57rHh3VESKhcKNiJhu4cKFhIaGZltWr149du7cCTh6Mn333Xc88sgjhIaG8u2339KwYUMAvL29WbRoESNGjKBFixZ4e3tz++238/bbbzu3NXDgQFJTU3nnnXcYNWoUQUFB3HHHHcW3gyJSrCyGYRhmFyEicikWi4XZs2fTp08fs0sRkVJCbW5ERESkTFG4ERERkTJFbW5EpETTmXMRKSgduREREZEyReFGREREyhSFGxERESlTFG5ERESkTFG4ERERkTJF4UZERETKFIUbERERKVMUbkRERKRMUbgRERGRMuX/AcEtoDkikbAdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epoch, train_cate_loss_list, label='Train')\n",
        "plt.plot(epoch, val_cate_loss_list, label='Validation')\n",
        "plt.yscale('log')\n",
        "plt.legend()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Focal Loss (log scale)')\n",
        "plt.title('Training and Validation Focal Loss of SOLO Model per Epoch')\n",
        "plt.legend()\n",
        "plt.savefig('focal_loss.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "tX-xYRRZlFnq",
        "outputId": "bf417c58-1cd2-438c-d14d-d3521b677ba3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epoch, train_mask_loss_list, label='Train')\n",
        "plt.plot(epoch, val_mask_loss_list, label='Validation')\n",
        "plt.legend()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Dice Loss')\n",
        "plt.title('Training and Validation Dice Loss of SOLO Model per Epoch')\n",
        "plt.legend()\n",
        "plt.savefig('dice_loss.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YFiSbyAtlHBs",
        "outputId": "70189977-1940-452a-b240-5d2fb5a93d12"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_INFERENCE CODE:_"
      ],
      "metadata": {
        "id": "ERukZ4ssnw5U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Due to the possibility of running out of RAM, we recommend disconnecting and deleting the runtime before executing the below cells for the inference code._"
      ],
      "metadata": {
        "id": "Bry1vinam4aX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\", force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Fd0IdgVnIiq",
        "outputId": "5b660b68-46a5-4180-859f-61274882aa8c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/gdrive/MyDrive/CIS6800HW3\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import MultiStepLR\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import gc\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "from backbone import *\n",
        "from dataset import *\n",
        "from solo_head import *"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-hauxJFnJF8",
        "outputId": "23b84c9c-c3c2-4859-9bc5-07efa99025bc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/CIS6800HW3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imgs_path = './data/hw3_mycocodata_img_comp_zlib.h5'\n",
        "masks_path = './data/hw3_mycocodata_mask_comp_zlib.h5'\n",
        "labels_path = \"./data/hw3_mycocodata_labels_comp_zlib.npy\"\n",
        "bboxes_path = \"./data/hw3_mycocodata_bboxes_comp_zlib.npy\"\n",
        "paths = [imgs_path, masks_path, labels_path, bboxes_path]\n",
        "dataset = BuildDataset(paths)"
      ],
      "metadata": {
        "id": "sux0QRrZnOaw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1\n",
        "train_proportion = 0.8\n",
        "\n",
        "full_size = len(dataset)\n",
        "val_size = 655 # Approx 1/5 of dataset\n",
        "train_size = full_size - val_size\n",
        "\n",
        "torch.random.manual_seed(1)\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
        "\n",
        "val_build_loader = BuildDataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
        "val_loader = val_build_loader.loader()\n",
        "\n",
        "resnet50_fpn = Resnet50Backbone(device=device).to(device)\n",
        "solo_head = SOLOHead(num_classes=4).to(device)\n",
        "solo_head.load_state_dict(torch.load('solo_head_epoch_36_minibatch_0.pth'))\n",
        "\n",
        "ori_size = (800, 1088)\n",
        "\n",
        "num_images_to_output = 1\n",
        "\n",
        "all_levels_one_batch = 0\n",
        "for S in solo_head.seg_num_grids:\n",
        "    all_levels_one_batch += S * S"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiSSAhXFnO6Z",
        "outputId": "dc0db3ba-62de-48c6-f6c7-b7b97aed8f8a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
            "  warnings.warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
            "100%|██████████| 97.8M/97.8M [00:00<00:00, 231MB/s]\n",
            "<ipython-input-4-854d0ab97fb0>:16: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  solo_head.load_state_dict(torch.load('solo_head_epoch_36_minibatch_0.pth'))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for val_batch_idx, val_data in enumerate(val_loader):\n",
        "    img_val, mask_list_val, label_list_val, bbox_list_val = [val_data[i] for i in range(len(val_data))]\n",
        "\n",
        "    backout_val = resnet50_fpn(img_val.to(device))\n",
        "    fpn_feat_list_val = list(backout_val.values())\n",
        "\n",
        "    cate_pred_list_val, ins_pred_list_val = solo_head.forward(fpn_feat_list_val, device, eval=True)\n",
        "\n",
        "    NMS_sorted_scores_list, NMS_sorted_cate_label_list, NMS_sorted_ins_list = solo_head.PostProcess(ins_pred_list_val, cate_pred_list_val, ori_size, device)\n",
        "\n",
        "    solo_head.PlotInfer(NMS_sorted_scores_list, NMS_sorted_cate_label_list, NMS_sorted_ins_list, img_val, val_batch_idx)\n",
        "\n",
        "    if (val_batch_idx + 1) * batch_size >= num_images_to_output:\n",
        "        break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "CwXyySWOnZOc",
        "outputId": "ae698279-e336-4fdc-f0b7-8a5235db1894"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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f+RE8+clPxmMe8xg8/OEPX8zhxRdfjA984AOL58dr97nPfQAAF1xwAS6//PLF382defOgBz0In/nMZ/DHf/zH5fq73vWuuL8ru3KssjNQduWUKr4iG4vnH7g7/h73uAee/vSn4y1veUvsMuHyyle+Er/2a7+GZzzjGbj73e9+3P1/53d+Jy644AJ8z/d8z+LU05tuuglPf/rToar4/u///mO28zf/5t/E5Zdfjle96lV4/etfv7j/vd/7vfhv/+2/4XnPe14YOhdffDGmacJv/MZvlLo//uM/vnjeFfVBJ6m+7nWvw5/92Z/F7//6X/8r3vWud+Fxj3tcXLvPfe6D973vffjoRz8a197znvcsdhWdddZZx+zrtipf/MVfjAc96EH4t//235a+3vve9+I//af/FIfnATc//uMpl19+OQ4dOoRrr7220NxP/dRP4ZOf/CSuvPLKW9z256JM07TgjZe//OULj9cVV1yB6667Dr/3e78X1z7+8Y/j537u5xb1zj33XPzQD/3Q1lOXmS62lW/6pm/C3t5eoU9VxStf+Urc7W53w9/8m3/zeIe2K1+gZRfi2ZVTqlxzzTX4zGc+g2/+5m/G/e9/fxw9ehTvfOc78fM///O4173uVZIUX/rSl+J973sf/uE//Id405veFJ6SN7/5zfjlX/5lPOIRj8CP/MiPLPr4wAc+gH/xL/7F4vpf/+t/HVdeeSV+8Rd/EVdeeSUe/OAHL06S/cAHPoAf/dEfPS7h++pXvxpf93Vfh2/6pm/Ct37rt+Kyyy7DkSNH8Eu/9Et4+9vfjr/zd/4Ovud7vifqn3feefhbf+tv4eUvfzlEBPe5z33w+te/Hh/5yEcWbT/kIQ8B0E9rveKKKzBNU0m+vO9974uHP/zheNaznoUjR47gZS97Gb7oi74Iz3ve86LOt3/7t+Nf/+t/jSuuuALPeMYz8JGPfASvfOUr8WVf9mUlcffMM8/EAx/4QPz8z/887ne/++H888/Hl3/5l+PLv/zLbxYHJ1pe8pKX4HGPexwuvfRSPOMZz8BnP/tZvPzlL8d5551XTs318X/v934vrrrqKuzt7eEbvuEbTig/5S53uQue//zn44UvfCG+/uu/Ht/4jd+I97///fjxH/9xXHLJJfh7f+/v3dbDu1XlCU94Al7zmtfgvPPOwwMf+EBcd911eMtb3rLYtv285z0PP/uzP4vHPOYxuOaaa3D22Wfj//l//h/c8573xMc//vHwPp177rn4iZ/4Cfz9v//38eAHPxhXXXUV7nKXu+BP//RP8YY3vAEPe9jD8GM/9mMHwnP3u98dz3nOc/CSl7wE+/v7uOSSS/C6170O73jHO27xCcm78gVWbrf9Q7uyK7eg/Mqv/Ip++7d/u97//vfXc845Rw8dOqT3ve999ZprrlmcJKuqeuTIEX3pS1+qD3nIQ/Tss8/Ws846Sx/84Afry172ssXWZNXth3b53zOe8Yyo98EPflCf+cxn6j3veU/d29vTO9/5zvqN3/iNJ3z41Kc//Wl9wQteoF/2ZV+mZ555pt7hDnfQhz3sYfozP/MzOs/zov5HP/pRfdKTnqRnnXWW3ulOd9Lv/M7vjAPOeMvper3Wa665Ru9yl7uoiGw9qO1HfuRH9B73uIcePnxYL7vsMn3Pe96z6O9nf/Zn9Uu+5Ev00KFD+qAHPUjf/OY3L7YZq6q+853v1Ic85CF66NChW3xQ21gOOqjtLW95iz7sYQ/TM888U88991z9hm/4hnJQm5cf/MEf1Lvd7W7aWrvZLcfHOtn1x37sx/T+97+/7u3t6YUXXqjPetazDjyo7XjLsQ5qG8tBW98BlFNa//Iv/1Kf/vSn653vfGc955xz9IorrtD3ve99evHFF+tTn/rU8uzv/u7v6mWXXaaHDx/Wu9/97vqiF70oTvK9/vrrS923ve1tesUVV+h5552nZ5xxht7nPvfRpz3tafrbv/3bNzvOzWajP/RDP6QXX3yxHjp0SL/sy75Mf/Znf/Zmn9uVXVFVFdVb8UayXdmVXTmlyoc+9CHc+973xkte8hL8k3/yT25vcHblJCrPec5z8G/+zb/BX/3VX+28G7tyUpRdDsqu7Mqu7MoXWBnPIPnYxz6G17zmNXj4wx++M0525aQpuxyUXdmVXdmVL7By6aWX4pGPfCQe8IAH4IYbbsBP/dRP4VOf+hS+7/u+7/YGbVd2JcrOQNmVXdmVXfkCK49//OPxi7/4i/jJn/xJiAge/OAH46d+6qcOPJNnV3bl9ii3aw7KK17xCrzkJS/B9ddfj6/6qq/Cy1/+8niR1K7syq7syq7syq584ZbbLQfl53/+5/Hc5z4XP/ADP4B3v/vd+Kqv+qp4S+mu7Mqu7Mqu7MqufGGX282D8tCHPhSXXHJJ7KOf5xn3uMc9cM011+Cf/bN/dnuAtCu7siu7siu7sisnSbldclCOHj2K3/md38Hzn//8uNZaw+WXX47rrrtuUf/IkSPlpVPzPOPjH/84vuiLvujzdsz2ruzKruzKruzKrty6oqr49Kc/jbve9a7lLeHbyu1ioPzFX/wFNpsNLrzwwnL9wgsvxPve975F/Re96EV44Qtf+PkCb1d2ZVd2ZVd2ZVc+h+XDH/7wzb5m5JTYxfP85z8fz33uc+P3Jz/5SdzznvfEpZc+Mt6oub+/j99+9x9gvb8+jhaPJ6p1W0a+bklbp5pn6GQ47+9kxNnnGy8nIw52ZVe2lc8Hrd5WfZwM8u14yu3N/7X/1d6Er37wV2Bvby+urddrXHfd23GHO9zhZlu7XQyUO9/5zpimCTfccEO5fsMNN2x9rffhw4e3vtp+tVphteqHCqkqRBpwsyEfxed2ErcR8q3p7/YmuIPKOM6TFU7g8wvbyYSXk3lOduXzW07WRdLJ3ofj7VThpZMBzoRBpBU93YvavZuH9XbZxXPo0CE85CEPwVvf+ta4Ns8z3vrWt+LSSy89gZZOlOmO13NyS9q9Jc+daNufqz6Ot9/PNwy3Vbm5sdzcuG7t87dXORVgPB3LidDLLZ2Tz3X7x+rjtiyfD5o80T5OZV45VeHeXm63EM9zn/tcPPWpT8VXf/VX42u+5mvwspe9DDfeeGN5G+3JX24vQril/W6zWE8fYr5tyhcCPm5ujCfDKuxkLJ9L2jiV6O5z4SXkNj9X9Hcsb8jnA//H08fpwHu3HS5vNwPl7/ydv4OPfvSj+P7v/35cf/31eNCDHoQ3velNi8TZk7ecSgLFy6kI8658/svJEA7Y0eqpU27rMMjnOqzy+aatE/XeHKscL04+16kMn59yuybJPvvZz8azn/3s2xOELcUn9XgIZSdEd2VXejnR3Ksd75x+5bZWiqe6kv1chfyB48PLyYQ/PeD7scspsYvn9ik7A2RXduXWlR3/fOGVk0kp7srtX25dTsxpZKAcDyKO1+o8qP5BsdedIN6Vk72cKkpjx0u7Apw69LqtfD7zWW7vXauf23K7vYvnti23JUEITnxCT10C2JXTvdwSer49y6kG767syq58rsop7kG5LbZUHeQlkWNcO1a8/WRcAZ6IwD8Z4d+VZTndlfjJOL4db9x8uaXzdjLO96lQjsdD8rlOOuY+juf+LgflZsqxJmqboTHmo9ycoXKyCLJbQpAns6H1hVp2wvvkKNsWLbuyK1/o5XPHC18gBsrxCPhRMd/cb792c7sXPp+C7LZUZF/oOzB2RsGuHKscRB9fCLxxULk1C6Jd+dyX2yMfZZckeyvLOGHbDJPjMUpu7/L5JLzTcSX5hSood4r2ti27wxB35fNdTsTwOLWSZk9xA+XmEH3Q/W3Xj+UxuTUhnc91yOT2JrZTMSR0e+Ps9iif78TvU4kePtflluByh79dORXK55ZOT3ED5USRcyzDZPx9kAfleOts6+e2nsyTSdGe7IbKyYSrz2U5Wcb5hR4ivLXldPRSjuVkodUvtHLqeFFOcQPl1pabS5a9rc89uS2NlJOVwE4mQ+VkxdGtLaf6uL4QlO9tWU4mnrqtyqlOw6d6OTWMlC8gA+UgT8m2e0BO4LFCPGxwHK/xcVsYOadCub2E6qmCH+DUgvVzVW7uMMRd2ZVduflyS7YS31oj5Vi8uu2eHuPe9nKaHNRm5UBcn6hxclyN3sq6J1pO1QOsbq/k3ZO5nKpz+fkoO9zsyqlcTjUD+3MMb7DyLevntPGgiADnnTFhPc1+BYAYfrYlvNZPEQAqWUO0o9T+ERm3S7E1eCzPyhZY7Tm1T4fy2E4H6UA6UPHIAZZzAcFHVXuW4fcCxmhjtLQZULVqSxyXdm/mGBnlf9VGtsU5pQUe7f3qMAuarUVbpW1AtYYZth4jpEYXAFStz2FqA0/84EK/1gqyrFDb8WvS64rQ7EliV4Rol+l4S+sSgA9Acseq9fJQttFJrXtzhsVBqyox0pYtdEI4U4NAak86zseW9qNppXpbwFW+fwyot11lODQaGohDt0OYfLSs02VPhT+vHOT9xfIeVV9w60AOIp1iRBQiQLM/EYGI2ncN2nP+V7V5ssGogu4NY/DfxOshVb2BrYtL7aQq9TfdXgxSWIYsKo7F+i2CoM4vA1vkjmyp4GOk68fmlINhW85sDrI+McrrbQL4oL65JxCBaAUgfpIOG55d7bUDZNLxldPGQGkiuOicFTZrm3xxA8X/U5pCXqWJ/c/XRuKY83lj3E6tCki/lzJeSKtZD6VZzdvz3NsbFYy3jbzehXhDN6IUEDWSNwFP0PahO+OOYxaozlbPfw/GlkgoR+gMVSNAaQQPAJ1jPBUax0XFY8psV7FZR1WgmKOeqmCeHV/S76vVN0WlBrsbIrP/jjaBWYF59pEpZu1gV8MmKUPpGk1rvy6JafGHt3BfGgN0XxCKp5X5cpqSUEQwEmoiaABaEzQBplAS+deCfvy705EA4oK8YWmgVGjDiOuPWZs+RpiA1vpMaglqLYvSRdGO01rdqcZpyxVTykQ14ef9zQPKJWgghb7wPRehxJMxpKBDDSXGBoqIGl0i2xensXGQTMuK2ejSFX1hhVC+NCOO79mVtcPejQFX4lramkPOqfoYlvMbYGo1at1YYHoSCKSJ0ZyiNcXUgNUErJpgaoJpUkzTjKkpmrjibcazhh+B8ZpiVgk8znPy2DyrfW/J344TtTE5vOpqPQ2SwIvhXKFQUYg6P6HTy2Cg9GHn3KtqWQTMYTmlkeT1HKcuotW+qNM34dafdDmRxlqnexEl+m1Jt5JzH9QsiD68TtFYCihms5FSB7Ep1OW+WPuS8stpJtAtMa7QpYN9YhOypLUgqv7ZppXN0y0rp42BEoxcFEKfyGbEwQTARgoLqHy4f3aia+FBiXYkJ5MJGcGw2eG2drtMTmERhGbwhceGNX4h2OxTytMI4hiqmaCceeh9jG50pXiI+jnQpLLgdZFyVUkhhGo5gDhzxWTMIgAwxVyoCmYStiEQICG86sqsX591xky8PSswB7tLGBqzArMpillzdaO+WiUlEkKQ8aLCKClFxBWYJK2A9VLlbGfgpCs3SARTy98NZKA0kIHiq13vS4LGVUjQFHHlwDgNaVm4u7JSngNXFj4yGXCgGv3HnMGUojA2qRMzKDv8EjCYGgohz75QGdtynmMeh0Q9d36B5iBwJGlYMCvn+CvWAFO84krRx54oAEzmxMWkGY0+chDiciVkVyoIh1/ouyshlx+pUH0hgTBYCuiSSnS2vt3AFZjhLCQeVKBz73kGsEGDQvp3FYjMcDUZfLnVQGHkpoHi+BNrl+fUF1luYEA6fG5092GaUUvqHDQGFW/ZcRuSGc0anU1JB92YgZNGoHQ8hPcuJWX0rU6T4tIsptd5ejbjUGI+E9/qF53GhTDBRrTzNFGk4wYiIcfYdEk4+1wnHSQ2SBUEz6SRQ7JKJCSh//bSjbylF2ebt/hEymlkoFBxwQdnfp8wATCbZQ5C3ohEdse5aPBp75PWCX5GXzmkZVpbsULKKlVC69Slcwo3J6pCFxw8cfGQ42OJ7cLJNHziwi9FWz6+/HQIw4sxkyVG/+ZaNSQljVbCpupdV3LNUThSWseZMr4dZqWeyeJwOH0la7joc8JtWE1bHemMUH7zLH1VZUZKI6EHkHIQM1QUkCaxuh89Q4FgEsDlGuEkUOf0FDrJqEJs5SroK1Tznrg3pax2BWbMOhks6VhC+M0BXyKK8GzaT73NwDu16XzjllAoQSGhzCKRdL4gFl0hU43+qgFT1BSiRTJiEG05NZKgTBSX1hj9Tl/KE0N07Lj1iXQDyr+7UppLJ7mS58JhDc0medFqhovmolRZ4QIetFZqry98Kz26enRPo/oD7FVDDYc4mzdhXGmhUZ3NKIGgaYMYQGJyR7VFg26gBVtGfykfEx/s+ZLAY2e45O1UmBWveSmNwZSVXkmzPpyaEolF/injt8pN7jjgj8mt9FYMV8m6IoOc4yqkLHS4Fw1LznMRKrFYSoOAeUmVZY1kq6MBwrI9LJWWLbmnskzFsQK/2++cSDmNDBSm3v6bHLxhXgAtCZ6Vr9Ojc39ccsHOHMEKHXHfZUE+nezCBN8/hnVZ9C+1K7gAmnMkvIREKs4e9sluAg5xIUuuQx1J05sbtXxisAIKUioAdI7BC9XRsS0T/P3xQQEOrEmeRbpXLvYmW7qHexDKBbfzrmBuuQJt5loVAZrmitghAtxMk+iS8SkkfOPfhaIbhkPXoq0gkX6lCdBaN0YmgXlPMv7Pq91Qovy79NPd3Y7yNF6KCK/TrdlW1mUPRx0Ih8HY8+H4cS9EdscTR2uxIN/MH9PBuBWRVLCMXDOqKumwkOa5Szy4h8xzi3LlWJ/hNlI5US1WUAlSwqmusDxMQGDq0H70v1yJKgLj2a7xUmSASC4g1HE49CdDuzyMWWzXhBY0AuheRqhCZ/LGOc9syatBPkpfGRgifdjCS1riGIOKG2gvaNM+2VOck1GEaJncwrMkjeIKGX3Ycn1sm9eDQjwNMw5aCLMORxiobgRvkcZpUOSjjrSgW3ok8SWAmt94i5KJsdE9HS90wYqCVoEZxbpsRwi5owq5FeU0MlCAPvOCtCiBPvUzgAmey5B3cvLtQnxKUfZJRGUKR+4hwUwiEcE9HNfzlX3vrArdkBtC9bkPJwLJ/qxvhtGj98kEAJttVVumu3VBVcHr7PocRHhRKCSIDcAw5nIpU8cMHpcjgVhOWhEqjuISnFLtYQjD0ewK1Fb4MzRWoykvevTXc1e6qOxYUEP7Yp6LcFzyYBV0o2IfhuewiBsnHNqhPyAUkc83wmChUJoOCOJLYSTnSqivrrapSb5mgU6/zPOUWmYQlqlqU/ETHQRcLsjJFyJ0n1g5wR8V+IhkF6CJpxhP1UxwyyRHa1xCK2XvM9sUYNbIi6ieAv+u8HBMF/6a3wF4LpmWObLkU4C4dDt1pXHCt8bVt6DaDgsLpXrdAtYlG4qodxp1aGIxSsdY5Y9eAyJPzdHYTfLy2XWfOlHurPrMQm4Q3JU+0gAFuoyQFFAEDQb8Jf0u/QQaz7J0rU8n3Gm0lNsh4yvnmZRW0C/E/EZ4UisE2YDrGbsrHkXInJcAgQ0L98h5U1JxI0g1lHV4Jgye+GcY8y0op5WBUheJMqIWkXTlHGafbiQMKpG+1ZhwbyKVU/52IearC3d9BmsweMN111ZjRrS7wYeJduEt6YdwuoiVU4BEsUlp8OTW0gMxcFKhLvorkskNgjIcFrruOZK8VjXbduY2wesGF0BKWVNARpzUGVkklJI7nZxV3WgU7czqDF/c5+hGDEdaFQgjhYVJUeFOFOALCUcRrayBfRzSMhG2sackoO8Gs3BQMkOO6amo3oVFHLkIeZtnIcUat9jVW29xGXUf//BEv5kal0VbCU+SV9LeKO4rRyJ4MeabxLwbBXWmfNzIdUwhPVZvHrZKre0hCVcMxDQWqU2hzMZJ9O6LHk90Jzpz6d9F1LAQKvIhx58yi3hZvZ+R31juJL6lyVDLKrHHV4QSp5PlfAGUdN7z9QKiUWfTHLH0CS8Ywzk+S1AnpVNbSspSaI5SYyJmIwUI3MPhAKV3lEQoMbHrAqUOkm5HmVbh7uyUXjjPvdtmbIZ+0WzJx0MRnR6Kt/4HFqDCHEeaSl32i7Wr9ZlYiLAeoGvkth3DXaLDc7ewnFYGii7w0Vg1Z6VgNE7MSmQHCyQVRCvVs5LEkte0kEP/mAtDKiuN4AiBexogCp3ntLoVlsjK8CalBr3AJS6xsrRCOP1hMlKE2oNswWF6acJlqC4WlfA5xlcpJ6QQf/NmyV2ayrTsFIldKImHGGLxBnD+T+KyNV/JdqHQBBFCUwCYky48p6XB6ahZIm2KQhYUsY1yvBfoqBI3cpcYdHOPRBKsqOWgIL0qGMI7Vr96ZUgJCQm3QIcYhiu+izdNGkqTVqt7WiR+F6EjpOiG+2lYCgbCCCwGKdG1JQRpiLmRmOqdgNiq0SonSrS41FvB6qS8tLTNUDpNGB+Q58Kfz1BLhUzoH1ob200OPFt7EZLK8fi/vu2XmVY4RhMDl+AZRVYPerKKjuf03LnyFhM1PLcGqbb4KtY455VV1ejQezJvH//M43JPjY0rDNBBjrMTz72dxAXIcHeVaZXczOhnS1X6dV9cCTr/zYFWjbGqyWCn0ZQDpA2EOE8FZcs4zVHH85wwwHGYA/WFVuJAsw9aqIEw1Xc2GiYUgNjOUfKWSMhqZwZH+tJv5AueWMSxJ8rlHCWAb9tWfyLltDJQghhTMieyBIiVhdqExtauFAslrEJiJZW/W8+maHXTE9xcWRvjOQTBZF1FBuN1eeHtDpNISqUD60RdBa7DTWxLQpXwQYI2V5ps+YPcz94myCPhY65whjGl1GUwq4DzZjIRloVnMiOzVbjM/YlBwOSuIxDu3LiRECB9V4j0VfzsCpFc99LDKS6055BTFk6aU+z5c74yBtJ97ruGXEBx/opPIyvHyOYXwM+U8LCOu/i7YSJo0s8RaC4YRDKe7UI7kZHCLjCtgQsSlWCIXB903Gu5flDpesQErqagC5pN9bDdPglB59B4jkZNJiwUr3VcxZgmHFdOoWdIr1oLfUtqt1wraXYCsGsSSaShqDSgAI++k9rQkcEuxGsdXva7uqfUlR8ZZqY0UofYEz5pkjiMwQZr5XOR9Br1UxeC+nPjJFDL9FWerFKSNG1eM0uiRB69J4OzuZdVvQnnueoXzME5fMTvINqVlOy8Ld2N3NImKfVOU8xPnFviT5kXNmBDKvIFPUrQjLfTNKVd9VxuZ7a+i0mx0BEGU+EFReEJ0lYhlSsPCXnDdAmCeNtK8oNNIPbYWltMXLdBOcUNlAELmoSaHpFtrmUJpRnaDABg3gZKse+E18LlriS0UhjaNLmyHQh1FFQxqbpFCRRizD6CSJTalBQrWV8hnmZPDNuFRDqW2WOUq8a4Y8LWNU8aOhFSUpD7mxhODI9ww0T7b8ffwEDVFUjICB08emZAOGS05VhFBO6zEpgfzezJ2frrOEmBNcNOIiBXaWtEGuhbmNUNMjrboQ3b61qoHu+LV6mSv6U/O5mR0sz4EIB++6BMeRCmQqwW2ULGD3L+iOpCQBcPimY/JVyiXrffL4oLaZwWVUUKMtZaQm1WQBbXddnUMOJl4TolkoWqiIuutbHlUIf2lXFsPhGiB2OSNKjVlBFjw4YuNn8zEp++mhYKBYf3woyU8H4Y8C1yQVh2WH/ugYx5YkPBceCUCMrDIBmZlio1nYosckGcHv05TUUnOnePZLTemAQJvZrKPTpjWamxvgyDwAgu8gnVp8J9VsQl7C333+A5dcTURV7F5QB3GCNuFJTAXsENDdQ+HCct8JNodpnsxOseasQ6rqgKp10fXeCfeK480fHpKi7G7upGo0f/kbMiFTcOUElrAIA5vWRJdwfz7PGUU9xAARaTQEzJ8bWFS1wpP0TzeYnDyCjOKEwepaH+VNDC8t52OAM6ROxxsJBHhuERMlE5eQRDIcMZ7vEJ8mZmG4Qbwx+hm1KjGgr1mBD/kR6XPrRkXTkQNyygYv9iNu5fYx7oKRMgY9vjVk92X9a62U9DUk5AHTgFPK8oD+4Syk3pTc32fSYlHyvS6C8Vlu/YcQ+Kn85ZjBPE7CGFH3JaHe/IKVW+b3V4JZiyiZWQUXxcX9IrY98/mOy7p1yzT8evD7gjklqv0n8MUfo/uk27bSlFXHL4k+8437BipBWiI05iPGPP1ZOh5SmrQWPl01aDVs3zNEoUJnFhpSl8X+I8izmuAu4pidRIqfMlJMPiLBgb5LgeSnmU/NvrCajRLL589x7i7B021JfF4a0W9jDPjODyNY2BRFFVjMpKV1KMYJzTgXnyXJJaZ0GBg+rxvCsJaLyewyJQeLK/LaLK5CvNhxa1I9ROEbs2sO1HH1RchId4BN9ZdNA6WXWbPC3NIzyJUVWGNm5ZOQ0MlCz1UCq7Bk/c4pJGCOBf6SmbAbHzCbylKky99cIuYKbZYg3g2FO27dkUQGl+BNAZW3YCpd/pBvV7Up4lkUo9cfScrpuht22cHB9PZegGhNi1beNmJcG/twgpVNhzypTg46dHeIbZYC4znCXqc977gVPeouUPGR54pwYktyxLeMY8pLOU676leDKl1VxpCdLFLrkeZBct73JNATb2sE2iAh5ydJSyEiy5FKGoAtHWHVGgKzprQBddjqodMU+50W6p/slEsBpp7PA4YxxO4lIayhYFiIP3lLm2wl4eJHoi8dtx5jtwiI3cseE5TO4yF+QW02AHAdzLmIvTOfFtMKd4omAQ0UULvkbMpdo8plFuisfnUWCKMZVh4ID4qdPttmVFVb++KEp2I/70S0q7c0IuIfBTs/qyapkOQ2C2yXQzeoR53oyGlA1z0IIuGgz5ujy+aWiP1QZMFZNnuWDLDldpACCzhQgz88Z5Qg3GTtOZD5QomEPWzJDwpAlwIN/5vQ6y5xJmv1uWHESDeScXN04ZSneF5qRIXYxQ3ZJyWhkoUYzRmHDrqjCR16valPlzLklc8FActbLsjAgmaOuEFUJfuYcoOWXEmkJ3eSufw1kqbRMa1ZgIsi6K2UjSBFhvNgkulbgJLxYevhqVg0jOVQqpnADENTiKUZAyxJ+pAidxREKgqsdkFZMMufWOBAqPy7fUjgj0OSO3trcwbnwMOe9MH98RK6KmjjeNYae+TKMlkl/Rj7N35SX8BzdQ2Bhe+oFo0T5gs+Z5hMAvSoJb92u1fcIi/ar38l9Gln01o4Qb5BNkE3aGjejJ55KSHxOnNgGumE2Bh8pJcgjDM+8n/VW3fNJCLFRC2NMobYwZlnFaNVqA5InR0BA1Hvps8WA/A6SspUiW+IU0PHzZRZNlTc2WbJ1jsdvNV/j8nIRSLT2ZwcxFa1dgAyCGFJ05E2l8prQRqk/PxHdOLq60nqaPDl2wlk76GfMkBnSVuxgeLxd9nse6WyAb5ZejA7AjDYpx0q/PxvjC43AZTB4qNqSlIobkLUyGVeoB4au3XbRMvxxAkaYSamBEiuoSJ4y/pbI6oXKaGSh9kjOuvozF+nsfem1Bvl+G1KsQQQDoOR0jWabhEHkgSiEKrWI/bZ6EJHpRpHXqsW4SRNy1J5R1wSuoVFpGgQUxQZAGkI/TgFMLcgSMwUIJP2Arj3wHz4j+aFa6CFU34ELY1XbH0y0z8StDYGxw8PNFHinXyNVRrvJJOLOhJCRYBdmF+phNEVg9pedEpBsjLnxUYuxlpUV4CvyKGSiGbc81ccMlqGrYcgr4qZ9KuDYXsqTQzu2zEgrUUVUKK+0FLRHQFeMpON2SsuUde2dCiGrWl0XrwxVqjmuM7+BpKb2rgHVcKiDqr0vQ5D/nd1aO4t9cWc/Ig5RNqQV9Dd4XMzDnMLYcZf1LHtAFSLPcLEm16OgjbZWf4jonE2i5hMGp9dFmVOW3mGbVZGIDgkfYmGGeWuoWnn8iquLNqOv+PiyTaU4Y/hkeb/ewSmmucLnOIcXZO8QTH8bS4r4iFbLApYG3WIah20hDEDqEKjstsBnQwjOjBhP1apspGFsO/vb31WhFLcx+LZ6tRFZCaa8XEDJRGKee2E7z7vyZ+SuJQh+2e3A4YdY5ogGxAAlJy2x2C8vpZ6DYZxCJ+q4AQYR2zIAYDbzgEWU1z0Q4m1HgD7jAQ9BArqIoySnAS0IGYNvbkFQavSUR5KpECuPxgXPFXcl1/B6UD3pNXBVpNOJOSluBh0TS4BZXEF8aA3jbo/Dh1WoKlTQATGJTaGVLMkCFzYfDxgb/ZpwExAYnu8QHpuyfdsKlgTSHsE0QIqxD41gYcEC4zQPbpKBGr8lytJLIRSpBplNGTSj6UbGFx0Lz8XhmKdh9vhKLhr1o12mP7xKdG27ZOaD0WGFEpWclV9qsh0eh6/OWL5I0US2pLHm1XQ8/UZv/Rr8rWMlbJBeUVI0g8RM4M5oVnjfHJcyrYiGW8PgwUmguhMfd8cPvlsm2U554fRNnQSGN2opcFGQ/NbkMw/eEKwxwsREVUJygAc9/qDxo9Bu0mfjJfAZui3En0VSVIMwPCAMijROff5pLoXrWF5N/Gbty6Mvbyt1d2CJ/E+bkwdHgiOqadVOe9h7LAgpMg0ieNUYX2i2pBHOhq5CvhNMRmQBy4eP6ZhB6UFQypMTfAustL6eRgeKTyr/7n+d2K/82pcP1AMSk1Tg/i0dv3XcC8WWzHYui1Xze2wp3hNWJvkhRlt0rDhuRGmnAPuYljJ24+zVvt7Y5rLKKFAcRaz61FFcDjK4oxnvFvmCBuFRWjiIe87LjJSQp2FjaaaCKz4zojJSKPuWKgLcaxtQYbpya+ByUwttFbwqKsLV+Gs15GCX210xZ5UxrPOeC0Kd7MEnqzJPkG43Bg+eQSJONB/uSbJD0waGRwjLFS6kEN8XXB4OdB5BCX4bbVTnmnaBiuyWFtQoNanLCKLaDlaIv19kaN2mNWXBUVu5cR5ym+5XWWmosJP0FjxL/bMsDyXfaHKAAfNyDzgyRQ4opPYh5nTRm0F5pPP4C0yMqQmGWeqy0XN9SX4phWrmteNIZL4NVPPcdf/6Y5pippk9nNsdjrrScw3bvmg7yNiiSQc3HqLGFWOQ61i43zYZfbWvOi4oMGyZSizzV8nARxN4znK4rD/YwnyKpeczdYTmDAV4e3S0tp5GBAqKADLt0+nNhKHRvZC2Qgvb2+ItrFMob0TRI4lLU9of9fQa0wjIhVFy00YLzS9ZzWFWYCFJxe0gorVwpwxH+Ef1sYe4wgCT+rSMbSxJ89kOoUxOmS55A5oTUsY/965b7dZTEi4rQsKljso8+ZRLPsIyqL/KqopKfZx5M4Z7ho1TYneZc9LLCFNjqnhqWuJe7dyopMpKTzhMpW+ZIbRVV5Q4WczY8Q4328YbhPMxrQFZzqcpWXOoLxiv1qAQW9CGqK6wEDw9fFnWIJ2SsM6LIzYwW8Fe4q9rbSv5Uva/sK55izt2zJC59sn1/G7W3EweEEX7redR1tLkt2GlE85UEIUNyxNVDQEX5qisjxkcaIyPfpcQbaIcVmUNghBKs6iG6kSZ8oget7qFwP7m3N2fUVcZQ5ztDrgP0GndpAePzRfSkSee8gNouGUepNi4ltnLw4qapGENvZeIYRRVVw+KQZJoS7oA8JmIEmxmZ5vxYJXUP49xk+M09fDPl9DJQyAhZiAKboE6PRWyhimo/l5APQQcWpBRmuuRBbWJCb9QTzqzMs9KImKqAjv62eSLio0QLB+bk8RCTlT6cqeekpy1Q0CAKWCk/zYiS7LWol0GZqF0MVmHvQHSQnbrQqBgaIbT5HZ6XWqMDPXv+TVYQre0U4MtKF2DXmkUKyzSlZwA0hRyeSyEX+VGSyia7Y6Wf1sRIrQeIuIr3MFIk3PMxD6MBx63S+S6D7qYHwrFbceCcR3BvoY7a4FZpnzjznwGvT3UJyXLbfIl2j/gVx//AExXHGnWlTDTV2mK8BG0LtyIxx6FzJJsLJRmfirINmIxNNXlWRjSwvZRrDiuPtu6G610MxmbFArbNIWXTxTMlcOKdBBlrfPcWt079oihp7NSl2571Qyal8E81cPvl8mOAhOlCF/xS+8v6GeKltihO6bIovBJLlRG9bkvqX8pAhW/S4IbGNnPh2+rZKvzdDJfoe8toR6NjYawxkg9yGx1nOY0MFCWOJ+1jt3ohN1ZxH1t9FQjydC4d93AY9tPyTAJMMdfApBE0sQXcLFXq58qaI/9LQvFYdPGcOMURrY5WteNK4yQFiu06Hu25KmARdYruphXiQfvxUwwoPZJb/xZiZpieCO1bG5yXUFSi5JiXDOqJbV3QebfsRnfFUXnTRK4yUw9BgkQrDSET66S0ldrDPSZ+PHiZZaEPH5d9jXMtHIFEkkGRzg+KQGDipQq+ELAmLfsJlq5Xeq+OF6eIrncIz9TkVoVDA9km5N3Ijth5GBD9iaAVVkqEfqA42+1/6q8Mucxep3XGv1T6Z29k5n/RBCn/zF547joOJRfDNoXhgS2SX3OAijz4zMcffeoWHFRE9bFLQMZ5HoFLuzEs1As4johO19bLFnZ3/uzVGzGuEv2RjGJiMQXpxyfUiWbe9eVnAlA9PtEl+CqbdMQJjLG8y6ElEhDJP0vLokhapaR1os1gSUh4kOJ6aviAsKJBlmzP80k87JdKk3YluEJIpgWBE0kPJMm4G2k7iAsVp7emnEYGCjwnLZUQv5l3DPFIzqFnBrDo7W95pRUQv2BPgTzayylQc/UBFKYL0rFM9DjqPExtph9WlSAmHrjYxhGvjY/KNV8h7HTlREDnBBK0LMxJiCSDk+FlHXiYSlLqBf6K9vBexBjVkq9YOGfDMg7TVWEKNoSosiuyMI7GWGl8E0QiGa/kQG1x54XdvA/2qjDnx8oj77gwz91XKQcyYVaI2QfCIWWW7wVz+ukGNG/T9bmuyjJhzn0EVZ35fumYn0RXKKVYzcfcW/uxzSlRk8rUaNWv14YTtY6ecr+gIX4FbcLbp0pK9cPFZU/FoWkJR3apEUJdqj4YLyGFhhrOAOTLDdKbWM4gcppjYS9Jo5xNUbQPAOHxgalKg5/idtUYxCHIiYGr6aKxKh+EovG8vToDLityPBScY28n7ZgSuPfBFb/hJSkw+0aQo/1QlJ2VpD3L84O8Lc26oUn045hwXMmW5yo3E60UnCXyRm+Xt6A0KJePqgicJColjRRxiOMd7Na0e4Ft/CKFDbztWHS4oSUEr7XmGzWUPHt8v/Qbv4pUrP0S53BI8ZaW08pAYYZMhCnG16AVdnNCZ78rTwBxrcd7Y7eAIrYOIg6xTuEURAkkYZaS0nIZM0/1lvUIoEJMlSWXvgMfo1dgJiUXv8OtSCXHrouQ0ENfdClXTgmkct16geYiNUwqUGeuA4RueWYx4BRGQjIXroj794jUcZs6zhO1t4Xj3Nhwr0wxpcQPYdv+jLugyyhD0TLtSozIhc9ydaM52IMEr5C5R0o62tjq7lsKm3w5GZYLSWUcSHQQ7IUKe9R0wbpUn3bGjHRFHwprgN/ruOImwYxF7UJ8pBjGUvtxElLmD2zBM7vbuCtWWlsUh4yI5AFofb5s96Q5EH7GWw42JuYbjQ+lx3I5X0BgoglTZzRUPK/E/vUzg3oHy7ZHmUItVTyCcvLIy+vKOqe8egh4IirOiUZYDnoLBvI8IldJtlA/kW8XZ+3Q/Q69YVuh0oY6owxTGkfiJMKSTCi8eQKAn0Y9ykaXJYOJb6JDAY4ekPewMsowb5EMX+GMOd4uTo67nFYGCtQFhxCjcySNVWUrAjOm0QncCDGIi4UznDEzT2VUGpG8FE9IXjABzluDiwUctDBIc/YzG3NlAuQg0nlwdKkKBlIeZnSV+uqCIgdVE2hpTN6iOGvlmKLWKEQXM6PD+AukdSQsMEOOC+G9epW4LoethvUb9STLy7r958jG7KFjpeQ0kkfY67K3QNrgQi20xEKGfokLP9adigwH1QTvmEca6jim+L5NPsmIFgVvRyzGiRNAhNOMfklngT63ikQO2436lXgiiYjW14MHAe5hC6WcwnyrfVrGOYfHzr0g0TrJAMeBs2JQHXvgZBzlMlfG26FY0CAoYDIvPZPJhfG0ta7IeSAZRLAzahcTjiozK6Q5+P48nwtVW1ESFNEXN1bkZ/J4Lp6ogvBIC0rsmeXdETOhJQadGp5aB1Lr06U2j5F5yuUmLX5jN2CBQSL8xnldQu34eUDJ33ZPiN+xxWi14dUwWJ3boo+KrrF2B89x0lrSY9D4KFZvYTnFDZRQheVaycaPc0qcqAHEIdEyPOtterP1fri5IQPx27Tw1sriluM/FkVZu5DKcttFtKTFrcmGwha3dHlyvEkC26Ry+GtCms7Ioy3FQgowPPL4BiMFxpSyrd+xfy8etx7GPgi3XlPo37gIl10pWjUEYMUVPXTcpQqL8n1QrlGbSHNcH8niXyI3TfILQ9kkjnDS5KDCS9UD4O/16gqwOvyz84PbqVgoyshhkbGuzYkvILh/xt92eb9YdCtAnhuC2Q0PjMZXVbeEMeK3NFdTbS3VdNCUYFDwrNqrsgaE7iPIPFlEqGHPXUjerOMgTaMUOonOiDdGwLVioGBjpN+CC4dLaAi5IGCRG+NmAJyutfbj45RFjwJ2J0VTYQCnp1UMD/V5D7kPumGLSKIZKyXCKJq/Q6aI02QybN0V4zqhthleljAmqC4/yYqfdt+EugkvzgC11vbCQ0xyKO4K0XvQ/3LRxHQViFIsDenCo6RLbkUZX1Jzs+U3fuM38A3f8A24613vChHB6173unJfVfH93//9+OIv/mKceeaZuPzyy/Hf//t/L3U+/vGP49u+7dtw7rnn4o53vCOe8Yxn4K/+6q9u1UAAFCZOJDuxA7m81vEBuofyGfFoZ44gLjMKxHNKGvqrIo3kZEK+eHAEUkKYJkNLuZfLONJaMZ7FgAu04WJzGJkBVJD5NhkDR9wT0owjqW7rm6VT4o4PyRpVowztpRwzvJR5SVEUTh71djue1L9T/4E5xjEJ16HqzVwkxhQf20EKfBDWdKUulmWAuEpOp9tF2EEylMi0nNPFCpNwkFep/wFnZc4LVPT0OG7OBvJmfE5ALnjAwy/RfJC5RDJucOhy4Ki7nFipVI130EvtUq1lT7za9JYkwS3oGUlDlHkNw5xUOeMUKr7yZ0NBhnFwiKAYBCMcijxjRoN169hZttA9DTLI+RmGOIoBH2Xy4RYuUObyBTDZB4lgKTzF0oHa8MRYkX7IXQxLBx6RIHGf78TpOC9MCT2Uo4QuPp9mRKk/sxxdyuutZEPuufRmEVzi/NoGOW74EMYqYZ8AH0fpMMwCkpWDvom5OEiq+fAGuqZn6SOrf74NlBtvvBFf9VVfhVe84hVb77/4xS/Gtddei1e+8pV417vehbPPPhtXXHEFbrrppqjzbd/2bfjDP/xD/Oqv/ipe//rX4zd+4zfwHd/xHbd8FFuVUrAT3QHcBTywba3D0y9meAgTiT9rjBNZ5Q1qf71yS/d6PEEu7sLEAzSSdZzaguCdw92Qob9OfA0ytC1hPNFvJ2jP8I9lgYQQUjbqhJhpUBQBtuO9uO9HIh1UmoKYJo0QFjDOnPmM2GFgI8OS0bhFofGXWk9CSI4lFE+ZgKUCdyHnNxg6rlcO33ItATLAQDhTVqhunNAuiu3AcmflVp8XzRwK7xdEgZKUOTyMPB11m3BGejB8qSfb6J6wJ7CkaQpPmFYonOu5HmRLOWvVued7WrFVdNPSsPOasawodEGQk0HCSkq0UmPiZDRdhvwFH4/XIg0e2LMKEu2xuTUb/mawtmYRktcIccH3LY7kL1KJ5jnaonaEkJ28WTBFMgdBDswRNRRe55JvpKHB96oJwDAyTweNiMIN/AIDtNMgDYHIIuXFEp0IWep0XipofGiBl2DyCtLnVYrXyOSz0a80wdT8VRguoP1xfwt6jCj6i3QCZe3Q0dHsd/B3jIlgU+07kvInydIqKQodw/XHLS8nHOJ53OMeh8c97nFb76kqXvayl+Gf//N/jm/6pm8CALz61a/GhRdeiNe97nW46qqr8Md//Md405vehN/6rd/CV3/1VwMAXv7yl+Pxj388/tW/+le4613vumj3yJEjOHLkSPz+1Kc+tR24Emu0yTWp59Y34IJkrFtttRCnFvduwmgHclcPKWunR5JcwRjm2+TEMYYi+7WGFKlc/KcuBVvhCBfiZi6ZD7L/li6IEP0qCZBBeUe3LCZSKPqWW1auS3FtwrMQsAlVTjwgPLn7tMDj42f3qTGou4tTntNcoM+hx3QDVGUmkswjIClUBJfDi1RU4QYtkkpMALhRJMv7OCi/wNt3tPTvs6J495XqOKq0Nh9fE4lkogsCH4uVOYNF08izOQBdlCEM7oNKNBn/JC1RA4sugnoEw14GYHxUTMgrFPNcaTGpvCrS5JgZYZoEfSlmVTR7u42/MJA9Dqk3JTwvAO9wScUadBrvSLFbbiz5bR9wTFzRCDEXidOUAVJqEF+N85tUTBiROq6i/4nRhNvLsbjnLHicG3ccGd8q40ARr+LwN9L78/nKEifZlDPRtNOiz1tQjcS9YiAIeofBq4RboXmA03RBRCy8yvhIbvkCwKVFE6bynOuR3os+UadaZsRhj0xco1CkGTni3n47K8ANn9RHKHTor15IykqQPMmfWmCMObAANiFXxNofJPkJlxP2oByrfPCDH8T111+Pyy+/PK6dd955eOhDH4rrrrsOAHDdddfhjne8YxgnAHD55ZejtYZ3vetdW9t90YtehPPOOy/+7nGPexwAQRJQCOLhXrrJtghTYnSldqLNUndQEON3ZzSvPLjZFbSDxFePrnRmWuWGZk0CUgcoOjUOR/8L84PbYN8ljNS9bZPuzIbB5DIhQlfcJcPSB9w9SUVjGpNoFape5nnGzEYXGVhcxrnwvrwfiX6Gh3x4NNvK97w3HfvjsFdts76iHYHfsqXZAV6MwksKzwxBDfgxOdUIdtnybJNan0+/ZPXj7fqZKwKYBWR1eAxhfGhZAPvYwgsD1Egk0lDwypWjHGdzpfGCuO0l37NTBWUquS6Yy8msENQVHmtL/jmbEnekKdGsED0kh5TQqDdohoUb2iWawyEdR7vLKQVKazcr1zvwnXaGMPKwCysVyHB9NESimpZ5Gw2gGIIqZp230D1VGy6o1S/49AVU67BrqV8XB8cufa5dNoZRVmToMuTeqcNer6hcf5gyQmF6ZvvTfOZSOTuFEUbfl/KN8VR96iap449B7IsnrkMmZyHNlNh9DrAwjnhBziiV0A1Dmwy9dgO/DFn8n5ubt2OX29RAuf766wEAF154Ybl+4YUXxr3rr78eF1xwQbm/Wq1w/vnnR52xPP/5z8cnP/nJ+Pvwhz98IAw52TRpxT5wRSDLp0go9ctpYKSSXE6gmBL0SYnfDkEQODNJAlXcpcWQGJQwJP5zhoz2QfTjQjbG4gbRmNPQ+5Rw9E3ob3vOcJDE8xbqwmR/qQgNVXWs0X6iPY2wAQ7Cc9bNClXFVrzw+Hs1wkcgxVp3/RP/1JUmGxwskQbzJsJ2gzwrhbqlcUi9zxD4dPJjpDhE+QmaWZW6UGHBI9t6LBS4jQu2PLFlhGEnOR3bLBkd1OPaeTiGsIFHhkoDVMNFbzf4p9cTMr6E/1PEu4FYMoi6EkRcrRp2OxRbnAOIQVH9UbF2F77znOT8DAYA2wxBmAFfg3joeGEdbod9bDjwI9R8bSlGsODjYeADq3Su8D5I5tIw8tnw7np/JpuDD3UIr41NbJOTzfCqgL91HWJ8IuZZqHI4+IieZ/k8yhu+FhJCE4JYBLshE3rC5TbjT0M2VjZOyQvMISODJkhWOHX4XzlH1CdiMBi6bCT+oc4jB4uwvjSrEKFGf4t68YgpN7qNGI+vnBK7eA4fPozDhw8fd/0QFfkP+jeFK2snJVaMJlLi02eu4zqdh7Fzp0qofk/4XhIjv4CuKMWxFKHL3+xExqLZuzLOEIWGgiAtUOBY9KwynO0hnRkkW8lnncAVqpvlIOKUwwpjhduRoOZWxBYa3oaZY1/j3BdvjOc2vOQLmmAlQjhToiMM92EsG27lJQsWd/mW36P6jw0eZFRUepEQJDqMYQspElxJ0XzTX48eq3sTojHhMU2kjYmxDtBXWGDN40rEazLgxWsv+ILHH8nnA8kh4StQ5RCqQpMlzIVOaDalVnAO2zryDqNiAAELoANOWd7z56sAS7jV0Bn8PJcx1bHmaHodDZru3QrVH/lV6yIDQZq9b4bNjQxIbcnDobzQcNkW88o7Lh3JEp6TauQqoabijOmqhseY7pnnR2XLciQvqR6wi2ugEREOPPH0jXxnUzzQ/3gwYicDPhLAaE9RHybQFyYAPRx8OMiB7b8k4VbN7eBw2q96rOYFDjCM7tdbUG5TD8pFF10EALjhhhvK9RtuuCHuXXTRRfjIRz5S7q/Xa3z84x+POre05K4Z+9MGWeSWONuO68j+VxxqETfO5FNPo8vfDZlUKhD10xPFCEsAaf1AN/Hnx79BiBZYqJiGXcYmSegRLGEll9R0D9m4J6RRne5FEVlB4p6TbocfZQXSsl+y0osAjoO1XHq0eh9Y4MObyzFXoZbYClN9QNMWnEptYw5mk7hWC48j/zycFDgfHuPEvINKrji8xaTK+pjRoMTUR30XNz61oTznNGc0r1b8FCNOF+d+LECPFT8rtK37AYz2+opPgOIyjvGNA7UqxQE/Cl6bvzy4LsfmI666ziDlxO4CrxNE/xRLhg9FsABPKmxivfM4xvCKJV5SdDQMLQxwda1ExiDDSQirK9MBlcGrzEBOaEmTlfbqnHRYxQLFy8LP8VWfDbWxeb04pNBhIIgTTvZUp3HSYpNB9pMbIHIwPdJlEJClIKXv+sfeneCBYCpvus5HPm+qWupf87YHbJUxxwMMC3P18Gj0v4Vh/Cmpz4b8s27c970NByqIMPsYyUujhosmf4T4lRBEGvzEiDiGMLyZcpsaKPe+971x0UUX4a1vfWtc+9SnPoV3vetduPTSSwEAl156KT7xiU/gd37nd6LOr/3ar2GeZzz0oQ+91TCE+5NX1Joqa1BvCOaiFf7obgsxokAm0rWYDDdYuoVrgk5dWBsDhBWaGkXHjkh51/Ath4kQhk+/PZu6tQz+Im3ymRBnZrzADQ6Z4HFsHYyZ5HWn5oRHzJhxEkphOhoGGv2mt0LiBN5trkBWENxW5E5svV+Lw8OCU4dnIhoesW5D3zKYbnMl8RyQgikpZxsU9nxws9MjochgjRg4wTdHzHhoT93MSqVAmKr9lhY7DDO518WVh/qYKDzCYaJkgrKZK81sFvyslAkj4woQxHtVlo9kEWMSiquJAa1UN8ekObZQHkMoRxxfbHxWqqoOEJcrBGfWzJZ9CpC0VS1BB5YHeJCScgORFHaeAw/SyDGW4PVxLAWVRkcF55S/QQOv/KOBpuDpwgRL46ZOYWg2xIyErOqyobXGSCd5Prbo+NVCewjRajgWhnykVapTlGouYBIV3l/uqMvzoRI/C+MzQomocxx85n1QKIjqJG3UvT2BIqHxDHSZT+eN3DGU4x5DzCnTK27GDSY6Z06glOduuWHi5YRDPH/1V3+FD3zgA/H7gx/8IH7v934P559/Pu55z3viOc95Dv7Fv/gX+NIv/VLc+973xvd93/fhrne9K574xCcCAB7wgAfg67/+6/HMZz4Tr3zlK7G/v49nP/vZuOqqq7bu4DnR0kMPrugnAKl0FGm8qA6EMljlvTHOas6ERFc21cCcUIN/XQE3aBoqKUeirzAA4lEiw2oTYNuE910y/CwLyU51Up51hksyd2lTt7YKRBx/s7GaEyFljps0TtnrSGFB4AIoFWI+y88Q+glXOUKth78FuNQPbBpMosQhS4qw6iUQRMoDOdcL3SMk46z9VEKF7SFux3gjClTXNiuWjkXf3RCXXWnAn4/R0zlVmlCHJ6CADA1Eugte6GbirEzd8D1w6XNJgk6EjIsYH8EhgB9klWBr6d9porCAY0Z83hjPvVaLJrwSC+lcdEgFpoQzlSha3INhcMw5rDrvbFyGMaMQX6TYHKeQZsz5c66pOkwlZEHQOYskBllB96sZOuZecp7HUGTR4hJ7bqoFsaCZpZHjqj5wU2SH3yi+PlPcNl8pCIHZdl0JzHMiCReYxnOMs5qCddmlnE4O4p9hDe44pUmsqTsuNJIafet8GnvF3IrGQleMcxm4oDbBc5nNBP1G6CppJQy4qDfMQshxZ7HK92zgMGjRxjxH+Fik2a7RLp8Uija6Wh30GIzD4p4wb1u3Pnc85YQNlN/+7d/Gox71qPj93Oc+FwDw1Kc+FT/zMz+D5z3vebjxxhvxHd/xHfjEJz6Bhz/84XjTm96EM844I575uZ/7OTz72c/G133d16G1hic96Um49tprb/EgvARbDULVzzER3v3BCgQIwRRCD67jmjGIMYorNmOwVI4mUDRP1PSzRrql2miyJPiU1xrbppHZcnlHsFyruLRPwu1EzYLLQ1f8zAgAC3JvL6+rrSAGab0Fhgp6vONhXE2G4CjSYnDFIsbFcW9uu/dnY9+C0JBbBPLo+QmFwUoq5G9VNyxIx3ZZAvl2xWEd08dsRo3TYYFXZBGC8Y4ZhujOlRJVjJk3olNrl+P7FVUkxSWVMu8QCvSGwnHjQ+DGa85UPpOA6nK8LtCkzkdvlkfk42KVoQt46hyBgO5PCMEKwgVcSSnPFOzE1oQsjC9mD1c04IVBjrXQ3wGFlXrIGm3VY6c5PsZFlzSkdnyuBpoMMecwKVzTlHaTR2iHSBSSCcQv4wKs3NSiUk1m+nMUktEclfN9kUjFYCEly+3Cwi5OVyTvIsfFh82jGbw1hecFYbAsORBJf0V2eTs8r0JVqXI+Xkrwb0BqOkkzWT/m0nE4bKypc5t0XmdKFvzn/XjTc7mZP0KlasJzTEI/jiJ6a09SuR3Kpz71KZx33nm47LJHY7XqNta82eDjH3w/5vUaSTiTfZ+MqDJ3BDKhabIdMKGHaSxkYZMtDZhilrvxoZjhxyz0ePvcXWYAGShEHCbk/UCl9ArYW1B9e13oe+dQqXO8UPjuls61Rhdg4lLdHm5wU2Y8OC4j/0qrVR6rwt2ZflhB96hsAGyIuEclN4dg4FGEJ8OHZBKqkvhCT5d7xVVKSu+YhBzhry2XJb8XyUKPkF7LMkog7ofc3ux+FWN2F7CuKGJGpPYo0rHtY24VWHvW8eJYdMATl41a5X4cigMNlNpVlcX1iYrMwUhU05ZuEnAr7slw6L1Gqj82skgxm6ev15uxWjVM0wSdZ+zvmyeVlc9AIOXeFkwwybjg9escukmh3BDyXRLPYbiZcoPbPkYniUeaexkB9jCPazANZVbsRgWEZjvxmJYKK56YH5urnGNLwCXE8fwveU1w8HsWKk6jX9oWzHgDUvFC5+ARbiq9sEklnXVbGXUaE24gLGHiK0VB+9jJezIuZqJvu1oM1aFtzv3idhZnxjj7lTaFDGQgTgVS5hWiGyeRIqMOTkTPKiktXJelSAqIqD0dhKOAL7XVhAvuez+0KX0h6/Ua73jH2/HJT34S5557Lo5VToldPCdWxjVqL6rO3F7Hec8mzVb1Ip4/0qe9wdxdovaJbqDMnXHmOZVw8ns3PoJoivsLRjlEySCeHWBnRug6gxWUtSkuXEjpxOqwc727tilqGdiyBxImWz1qIWdTJC5c1dsapT5S+poyziO2HHSt9eMrqS5TRKl0c3QeWgh4KgQFqsJbOlTi7nW4AWZMeowEHj8aCzq3bMEXefZJmC2AlnLf9UOfoxRiAsK9KUv3iORKcguMjBRlGKx9Xt1WuRYuZ34X0NiDK5uSk+Cj9iPJWSmr6zSrw8vAGK4kSkOZOoxSxrFUOQMCHKJxXr1x2VJv6JNULZwWw2OTW7EgC2jcE8LXkhcXT2wxqDv+B2XmVYVxknJByjwoynt7/BrD4/rZQyNCnRxYBmZihcXWWvwUaJyh0mw3n82gYtmnLKbGhii1zzC6XabnM+ylgY96FF2jsg4idDwWTMW3lPOj4pfgKYZLuJJ7hmgsLtcqvPZvMJ/PJ+BhfKWaBf4t+pD7r7uNqvwhoMrTGR4tUqT0c0x+PM5ymhgojJoUEjBlUtdjnhA6nkcy2ZaqlsIYApnNQAGwasA0dWNF5xmzztjIBvMs5knhifH9BnOSCWnDKiJYMZEC3jbDROEpz6UwV/EEBJEZc6BRWCqJrwjeWL2M+O1pmV2htMJ8MMGXhOyn1vp8pDXuKwLWY+4WFmjaXnFlIasDzywcYgRpLdQnxl0Wg6wPmUL32eXcYWaBkTjr+mS8lzBUVUCwCe1MEVZS3FqnwewrqLOql2IYMNypvFyJ1vl1LBKU9k/JUaHRpJ28XQyN86UO33AtLEdTXDmWsTUS88VwSD5ifOU9KXI28GA1PXzlNRUpfMVgm4HBEPY5EWpzHDnNjUi9x4qVoZblosCpfKR3fpyg6HfF63h4UMjCSpywVMwmNBQe86/Ss9GPMj7JG0J4DpPX+QeAn8hfwowm81yeOZ54rhPvTHesqAlz7KEs8pHnZOTihL+gZfAkFoSJQOfZexlgi97hXs3KTRoebzfcHP8xFywHYn5YlqDStst6SYk/1osviyaqPIm+Q6ZnXlg8pLWfkXpvbTnFDZQRDTP6Hn5j6jhF0sXOiD6Bir+UybYLG8d7Qtx6o1jP/cyPvRVwCIK9STC1FVaimOeG9WaNDSV6MZMulCtLM60kuBhbuD7TlRY5Ng4kMhmphDyK+FH7bxBzNaYT+Ok7gljZDbH+iAc38HtBZmXZawKCVoLOf0VeDzFpN656uI1WF9uESfTiuOCes514Rgn9xTjxCe/fxXET8GTNOXDm9bme0i1XjPV+tqShKLPOEPrhVXSJwW+nmLqSkYLb/BRri3lHaV5I0BAJFSXo7brXxpWeEJzWXszTwAjOkRW7OOA3hQCH+QgBzw37bc1HisGjmsmyobsF7IKPe0rj3QJVhCJGqF0BM1jqHoy8lnOTHMptqXsgwwjQbG/sV+me455yB0b9ymcH5aLIZKcgo0lFqSdPdIXs9DJIV+s/+SL5R2c7196FBeNdEy+5mKljCe8J8V7g0vmzaFFvoSpfHwOADKOawuWdoDFanl6i77540IE6AsnRz3j6sNQqCO/juIgY+s2mTYchx+t0yvxaF8O1BF3HhPljo/7h+aSHeSRBzJkusBUnJ1BOcQOFi3NTz/UII0QE/GK8Tg0N+Y6YZjjt953YnOU2M7C/mbGZFdMaOLyZcXhvwhmHGg5NglWz5LUNsBHtx7eboWQtIrelFfHffSvOXeh1gtlUAIPFhQaTYBgAwdiShre4YPPe7L0TAASZxNsrbtC3TPtVpRCXZFXDiWem5Hl0VaSWpGGkAoF7bgzIrsRMMVrz/nqAFFo26jg7Yotqdpd1bPlILGchBUrAplwoWtiEo5BR4YI0HsjxhqJMfLlwBT1SxCG5ZTJHB6lUUmtFLRdYbBs5nB1lufp3lc3CXQSxsmbIBvkXUPqKz+RN4koIX5qUSGgZFtqJ24JCxgrFXIqSY0QwXpx0A+IuDX3jZZhloUF4dKnU/LOGfKqXSceJdEBIIWT4gPSmixtQjg0hq9CFeB03/qtkz7nMjkL1FJrn4I3ViNBd4iLgJTouKCB64/aCF2M6FAR50GEGPQTMCJ6Dl5eTNkavwzZDrg+BQgvsCTYjK2Xi0p/pd4N4aKtc0rf3lkwWsCi1KAmJG+YMp/Oge4Rj4RtwD9AZHUbdAfKM2jk+0wxhPeBXnIZcnovTonu9Y9JpfooBLTkvNN9+S4thbjDEoUxGlwqgbZMwx19OIwMFaYiHAhBAG1Q4rJMHqXXviR3ORK+3VhX4y+lmKPbXM46se60j+4ozDmnPPTk84dBqwjQBIg1t00M+m3kOVQ70BNrYDmwr9VmBeH/OVsUb/5RLIxmmgiPJF+910dR4nq1duN1JbCbmcwPFfplg8RXebIlTvgG5E3CmKuYqnEWwa4NU4DGmWIGnsM4fKShH93K242PUlCuS7S1WD9ViqEYI/BrldBAXRoJjUbOuRoVmxXCDDN65wGCxwrSaiiF/VRHkKGHcBmCgxxOCWEkOeIsQhxZYWFnXXqg9k6QuuLh1x0P8YlrQCrU7rLa+Y0WdqrXcZ+8atZSkEnTrGHcYBuUXv4R4xXHTn3dRQjKZxqeFZ+sut3zI5Uni2ubanyUez391OZ1+ZzH1EnhxkbfVtyZI+FLfEF/VcfK9UPaC7i2SHNsIeZVLCOLw8QefN4ebDR7JBwgGN4pyfpiraYjj6qNfjEksipnH6YamkM4u7UvQVvYFWxDR+ISec08oiV/vRI1/Yqep1m4S/yP39anKbcDj+KmpcY4FwdhpPnLriRvjOqJnSW8RTarQ86NxxEvgBaAnWE4rA8WXMOkxYU9J/x2ffuqsiLlpTXhoZt37ymYzC/Y3wHpWHFkr9jeKzazYzDPOOmMPZxya0KYGf5ujiHlRTDmozikIdc4JFbH9++hGTDnOvjJqDNEVSf9FkimlVwhpNwicx3RolQQkaxBvSVXLy/zyCGrL8XfjhLmbmWhQDNE6GxHK5O4GiRp4shUfY9kajw2GRK4CjtVMEZKEowFFXGUUkhiqLbuShSByfPG85LRUMVCvpAIVuIaqdXoT1DJ5ilyQ+s4ad8vy3IyghwKkAUQfCpDVMcglQpqPi8dMLgiBhV8Cx8vt5DR6U5QmPclIGAW9i9VUGCmE1XC4OFVYXUHyCAjHljHuOOCtrwlHUkNx8Dsuie/6teHka6KDrgiVaMavDxq09J8TpduqpHal31R3wTNET2ywh7Ie2yLDZKDp+O79RcgTC8MpO+VL+XxOkMs850A/RI0FgGzFRdlhxbVZ/sRHN9aYQ/szGk9u8zpxO96Gd7agcmdr789p3JC0zdBIFCedStS2XWZ6EF8lXx4k25Cjwghx+i6FcHiw3D6ecloZKBJnjUj5y5f0dcMkrET49f4ZQRnh6e0ho1mBjQrWc1fasyo2mw1mbRCZcHivb2WemqCJYtZNV+66sbCRZ613gyWIJk67rEKmuHSBKkRI0JXwFTwll9fexG1BlqMS1mT82FdvxklsY1MyfGDJsqBdHcagMg8C9wCrYOQAZzpbteXZIObJ2CKpAkWjYByb3ra6onZcKaTCR+KDnskpYWF3rFLVmpD0SyMu2x9mbOidzb1Uv4sSYG0bJykEAIhwggt1Usxkc4y4TsiNnhhXInnPHop4/mBAVBh4bF0yR2jEYHePVmK18kRoYfGvGpvTZKhfPGMLJDoMqLkz8UwaRwrkG6VduVODwcehMBnUmuzq+OvPGR+EB4awwzaAz7XXj7bqiNNAI3z7dEuA1GUlKbiEJ7kgV9kAhzb8rAzQfM9z975K8/A5qsd0nE3JNr2toH+73tePw9yT7g7jhLwApL4rXgT1DhkyxaYpfJXz358mue3Q0vkknEieXkqWywh694cKbqgOw761OL24F9ihdEPREkP4XJPENTezlDHJwstxJ4zWh3ZcbDWKT6CcVgaKc1qZT+LomIy4JvlpDBe7JdQnIE1bVWBWwdENwkhR2UBkDdUVDq8aJjviUubOMTMfeQzzpji7SD810bi7J2CqvyeGIA7frisSJqskpN7LqKqViEQQ5wpYOyXXBHZw/pyeE392LrRI5p1keKKcC0F1UjSn8Nu2SKcOYtExmlId/lHsJnb4ma2ho63d1TyQMHoUJjiQ98A/dAsDjkJw21wNTSli+6cgFR8L/xwHSFtIuem7TtyyGKhoqWcDiAE+IQEZclSz7WFs1ewAeWCMMjS/F+ewFhHnE1yVN+UiBCt6+M0hU02lJOmpKOMkLVzhGfEgZQ7q9A540pyOer80l7QXylAW20izfs39qO1JTAmPsA+PwgYF1ykzCp077ESf9fuoCMcFgUk1MjwzQRXhbfWOoi1TmmPOCYE6IK/O1/ZCPOYhU2q+hC3VjQ8aq2/ZVX+EDIGReRhkCXsiDMreX8I9g551WavcDIewvd1KE8xfLeaHhCQ/R2Fd/zc3VVDwmGBij0diCmEIepOcewaA3lVJC8jAVX/o1h6zdpoYKKS4ReAxwIgnq4V9Iuek1xMlr4pO8GTOXuwdKP7OGV5KyYyNAkfWlpOh697nWXs4s01ojYnIvCdB9BuDVdFE4gVTXXjYQW5mxHSh4syTyU48Zjd+UnRKXE0aJFYwxhTxWuhhJ3Vc9eyZ2RjA+51d2ET3Si9uA3xrYgr8FPRV0C3UpMGQ1zjXgW7C3f+pT4iRUnvhoFLUaVEQGQjIXJFonuLJGohlubXIo9nWs6TgYTwV7MSkjYYAgpb5Yipgwum4ssSI6YR/4cvmCuO3GDfdExPmIfSN1vQgLAh9dlhbYNLlbZml7LhYG0nT4WUJT159sigon+uKRpTHmMlI77mAD/XFrOD5avaoz1VwAIGfPGEyKKagztIycZjrcnGjSgB7v5UfUV60vtsKZNj5eNPGYFVISpo+WakW+tMcd1fa/W5rLWWca3OHi5Ur4x/YslDMvsTaZxlf+FLA0caAXghXObWCSHB30LSAZkYYUX7AEEdYJs447CNJzaHsB3lYjDUheen0XTxYHAKr8snnQ1xnGZHW9Scbhg4vSQkBwptMOOAFrjhOndiNbtgL6vgTIe/iLSyngYGyxIBIkuE2pd5zJkxAWJ5Kno/ia6duIDQomsyI9yIL4AQwq+LoWvFXuunhnUmwmiYcmro7U/ykVentR4KUHfi2mpw5+hZdnfux+inkusdme8QvR5TGjyTRaJ5zXBZwQT0wC9caohUANC1mVTGPyhLH7AapCY3pCRCZjMKdlS2hVqjmIBwxjLd0bYxehRsrJRZmPq7tASAXdA5/Wa2b1O58TiuEorFN6PiqkAyLRUcuoX1+TIiOO0bSOGPPQHbMW0Nz0CwiGU6hKhoCO0c503NIu4joRtET8dm4cmWWNGGiM6RhSsUIO5AAy9OUE48jwpp7gIoXiRSuPRuUL6YIIDEnjVCxMCI1cVFMmlEjpyarFxbSW4jPqub1HRyO3CbDONzLqql4OW4WtaMN56Pt2Kv3cgYcQDEjzUNg9aAvkPs/zZAwRoup5UYOfZAHWJqH0zMfL4bsdMxGERMfzUvvey6DXYR4+EnCmy9ofAGWRmwIQfrUMET6WMgHtZW1eeAt9EZWVgdikD9paPT5drpNMODyhGQ377qK65rtaW09PUKE+tj6bvAV3TEDxJgL4zmNK1eqYdrU+4Hw5fL0RMspbqDI4ruwMLXwjbpA4FwU+uunx/YcknmWFBj9aTTpikTE3GWeN4KuRI5uFDceUaymGXtTgxxuWDXarqwALA8FaGit56qsVs48c1/16Ny1gTpbu4Jsg1AfSYNEuBOl1JpJ1JTj4UwUhOxhHfOkmFyZVboBZffzZV6J/0Zco4b74vpzVaCgBTZJRSRD+UokV1PE78SwoboO4IGEkbBlRgLxV1RmlSphnAC504owHorCeZEFHqIlwOkwgddsIAHtWCwCG5BQWGFmbTNGwBRAT5PNUEVYGqYs5wtODwJReT4SwzK2sNAf1m7QnIzTX7+TnDcZb/PBgEt9sBNvCmFqWElrsbKv2Pb5YWPGQ0Ya+qYUQqsEXMcuxWjbpvmKN46EfjzNfRMVRFiM6XOER4dPMhp4WCGzumeXeRvlCU2YaKt/n4PFwKgXHSCoKtYJwBU4V2Ej3Vf6Cw9U0AYZaN5vESb+NOMcNI+DjCJlnbyVXJCBleRX54cSJik0mp6gEcslnD1MpQDwvKz0CrJB5v1UD3F4Ttwr7ruOECxE6CZd6IzI28k4AU1qAzcfmrv5coobKLX0CcvfybhSa7HBEkQqcS+VkBOXoLUGkZ7wFVLAmp3nHu658aY1VuaZPPNQXz0Uz4MKmjQ0UawmwWRGjKIBmw0m6clkPbRiRgvmIMRSDDbmn2SxJVF0oyEJvigiqjVrHlrktObGyuzXxPNnED48QTdkWuzqsR7MMyPD3rdtZLtQkHR9ofpDMGu0mYKcOkp0pbDgDhEytUAxq+XXOAMqoFuUj4tnOuWcm7b2NQVScP9giHhrYcf4iDMxdFToucSpArwA558uXLYqqOr6ZdrqNZcKf7kNOzGbtWXZbVwItFKzmk+TgPWzQcrcbdO9CT5L6DLckTIWalok1yeMb2XVVFBh4yOvYWnXlWkdZSi2GEev1xhA32EFTaFf2qWAGBHJOO3cpLogoFGPoaPRZFE/NTpWP560TDJHNemcwhIpJrdM1lYhkMYHsbZ92b4eDwOSYRHqUXXoyuuXjA6SOkOAS7IdDNfTOEGhD6b75AomWh3Ylq6DniEc6KJuBUfLv9SOkB2hMyD+vjnOuyFvbdC+myfZM8p3kJygnLAQXSPeT7ycVgbKLPmKO/inohM9JZj1f7v3RHnrMZK85jhYzV3Gc88tccvem7ffm1nx2aMKkY2FdwSHJtvObJaqZ7OvJumGTNB5Z34XAL3JBpl7Pornq6h7WSLk0JNZmT0yKz2FiXMJK8GyQugt9XCSahxFrQrMEGwU2Kif3dJNptYE2lp3LrQ+BlFAZzH693ZdCNYD8Fwp1gPdEISdhepGCzTHJbyDMI76MH2sCBxw01r+YaUpS2PA+2KhE4auljayVGWV+CbJTUK+hnSGR0YFzUKJVmMhQl0gyfCblFJZ0XmlGOswJHuA9Kx5FnN3RRWuqG1FM2lUhXIOGKU8wkaDOgbIM8gCvn4Zc1jyvtIl5es+p6OWJiS6T5DAgs9bPsoKjgELyqKm7cURsVAiGg3N4n3kuHwFLCADWrb1V8fiO+OqgpXYdloIVSRDNS5PlCiat8eWNp2P3PO3BR7CUSjAYAkaDOVz5JuADxibNasBEONqGFvM1AxlS2akgyBVrf0E3MNMjwLDaKekGMiWihQiDinnHijdhjmmN6myc6B9xrSoh2VraFeFeFGHMdGo2cvEbKL0pe7naMSvt6ycVgaKjnQYE41QJqHEinZgFd9/94yT7r3oZoxgkoY0DbrSTm0F7CvwmaOK1jY9znx4hUOrFaYJgJ1/Is1y2YifOzM3SOvehzg+u3UPirsuhASDv2yr7phgcpyLyk3K8TYqprpx0rvyv82s3TiZ+5/aeTFdKUnPmWm9uSagEFHvV218XbdRjN5XxYJgxMJWpHnEf2v+9rn2uHhnMpgCTSJwucnz6mPPuHyiIgUlMxUJA6MhKQIvkwylKHszdUlYKtXPsSI0Ejt4Q7QLJcAZwIkt91KNMCd4mchcsJAKsQw52xhDP1HJjZHBg8cr2GALBqks05lmxerGZHOvBqOHN7N/5TYNJ2xfEbVvMRZ6u3GqqY7znbIi1AD1VYYFzURfDqv6omhUblLbBTR2cPHvDPOYnBpd6aGVyIsCBC2VVXa43CXmzCi5K0ZSKqHoaWGTi53erw9zdhxCINKTYT08lsYJ431UtTQbYQxU3AQemeIXJDLi2P4h2S5EHNEHxRHLe5UGKBUZSmJsB6U4X0hAaM0SoDo0vKWnoFnjg+iP8tSqLE/YOoMknE7XNQy2BIVD3Wzwbws2hZyYMcyvY0IJO+N837JyGhgoJDiUVyN51yNvybipZH06KkH79Exo5r2YWj/23kjCqmo3IjSvrXXGZ4/MmGSDlTTsTStMrUEw9zNCjIj9vT2tCXTedDEnfVcDE0oThbZevwth246Mbhm4sky15cIsXy6XKxFX4JlAm8frN/ix2PYUFGrnv3SbezbcNkssVrQeCiGhEsorjIkhF2fL7C0VmgsVF5Q0PeOsD8Lfusc25nBDKcuofEyngIMaQvhzJepCg+HpA1iEIqxjcUFBAj7WSzb24l1YQF9hDmWh2Q6PhV+PUFQrHcjm49kmmquNRl6vgILGuUQpzUsqARbroxDUEMjWYAkXkAF7gNzrcORolasNY/EtySm0Jc5K8d3I4fpeGFYs0X2ESbx9WoYwR0FUaFXCE9N4GgZ1EG4s+CJLw6uQapC9Oa4syKAS8K/sfwyXSvnIukDIOji9RkiaJYDP34KqBj6vCnBLb4SnwYtEY3GjJdAcOBC4ccv1soekF56GwItDoEC8zd5+Vzva58vhNfmMbESorf4cp6fWnTyLMxaMd0KmbuGBup3X8FLqJS/5ERKRNyiMh1ZoxOWqYh4jgxUMH3sloQUNnWg5DQwUKs7/LjjCBS9G0S2SNzMfo//l9xaK0Ql4EmDVZqybh1f6Dh5pM9pMb9mxydnfKD5zZMaezNibFKupYa95ToPvipmDQHoCat8n1ESwaRvobN4bkX4ivnR4un6dsYkcFRb6aoJC0cJDYV4YuJifI9zSrW/2CWlY3Mkcap4VE3kqnhVjuxG8ZYZiKGGBpNKEYZ0FuJLAH/gzCX1kxFjJS9aULVXBTJytc9IatToCVeKzNXSE4TlWApqrSQMrPRXxSlc08UMCEWQkRE9DRByAhrChS2WbbjG66Fll+NkkyO5yXpCKjnFR6m/TK1Q0Lgr9SviKUotvQ1qjPeqGnE8xU9/MHkPzxsR7bdgoFQbZTnH2qa690nDcEEiDwPVIwclWghj4IUi0zunCe1dIVekaZyu4bNtieMS8jsyQT3uX4S2BFkXnTi3GH88iUVsdXFxLTx+GdhgSLK4qYAc+xpxbb6q2m5JcBYIMRy3ypXzoTOAErbeQXVdaZE+GgOfNaFhR5khtnJXiuWtafIiEh5P5038XHmeZDJ4jg9UN+qhex8smSPxLuWBcM0W/JZxr5oBlCynbSnJ/xBG3z+6JltPLQCEVEZIMbKggCJUnxYmq1803U7pSaaKYQpEgiLb3o3SCIgA0zDr3nT1HZ+ytZkzTBlgB0ySQJhFOceGVOnLq/XnfIpi1u06lqYVRetxaZ171VHGQ1iwRmwDdKevj8/v5gkItBJ3uQURzPup04wMaq04WsLOSd0eSh+OFgDxnYcEXFRnj6ILJp5Q60wAt5qSHyypFpJCoonXUrfzMNgbrYyChq1yXaAw0HskcepMK6S6W4qcZezqQx1P8dXyGMUOeqxSjWcIPob7LAIl+UgQHYaRgLbvcOoKD4dZkTWtgGcZiiF2BOnwJQ0ZCbEa0YB7B+0r9ws/0ccUOLAihzGv2n2PolbqMyDnv5C/ZpiROlfsjjLE61XKH1GfRXoylYAqrSgiADJFcpgiaABq78DisTn5HNxpgiyZ1vDAFJ93GAlC3ePE0OlsOtxSXZ4m4wps8Vr5eRkws6dclYRPGX3C2loeST0lJ+zXm7y2DWQyNwnfjMPmBkRfCWFngyWk7EcSepriP5I98DqWODtA6Cqpnu84n8+9YZxzaLSmnlYFiQRDkuRQ9HAEK54Rwd+sxBJibBlIUn7rnQBpaU8wbDQEn5m2RZl4GewbSMAM4Ogv+6mj3tMyHJpyxJ1itJhSxpALoJvsWRd+K3BO4/Ch5EWDVBLNu+vSLQjeINyd32ZVnjXRCnQ0reRZJkKWFm0T7iw2VnwVCACnmyil9gIRCE0KKXFG6/NAewkoDZI5mnOGT8VO1VKXKuRz8cAqpbaqxCn/HBzEgGQe5atnSkrhC7HRQGE6V8gfs3xAQ9PbqGBrVFxp3EZSDx2KwGnIErEhg4TmJ4ZFWi3912QjhthO9e2EGNQzGd1EKQ6hrgT6nFeq6IGXhuRpEOsPJFwqE3Ebno8wrMeUW21br4zGcxWqy/k4MyOJX5z/P6DD5wWHBJVKKwovnxCVYUkGOCT1EbOHFqlbJzxWrARqLr4RH3EpL44+w2XPcuQe7Y6GrXAgRqsioizbVZ8cVPRbPUVSZ8Jl9pLxmBBI1BzvnhHKINmXSSKcyGCcDDNG0JCtGLBaWJ8gSgSVZtpmhQmp/9MZyp/6d7kUPKSKpnQwdlyd0KU0l5I3VCSPL4FTXnzlu7j9aIxnv1zMBexj4rSynlYECYIm8+K5FELGHJdSZGzauTNxQUc9iYfXQ22mWC9J30/TzAjw3bn8GPrueIUf6s62t0JrtgEG3J7pZMJmAaiaEFMAUbCgQTFPDqgk22t2ZK5g/RDdQSD96f94g3pAcrMOqifJSjNg9sSvqp1/X0NfoO0JWuCIIQ8aSgGd7vEkaVjAcgpS5C7waT6rYHdRAzq9PbQkT8HQPSk/cePCQlrfj844thZQynKxS0LLwT6Z2YzZhKOsNI01pTklLdRgjyg5yVOHO5fcdLaTIYhy1F91SV0NBjuNmfEYgryMzcat1/gRjM7XDbfFyHkvaZTw3S7jjGACYIhNT9komlht/RmpFeXVLIqg/EsAXfQ3+tsUqWFLpBI4dn0MRib5SqRMaqOsYF0Mh1SDzMXd5RQ3a9Th+3ulS6XPLWBiPOvThXoP0+Fr7XNFCl2nounzVlM1bxpodJY1m7qDL4MQh2yfRHMHCeDrYM+gt1F8lmGP/uPcx+GELKyWvjIusZfG5UXG5mHoHhCa1f9joY553WyMEpuE5xmE4dyN3KXn6MwKfTwkakQTAnomJDz2Qzw6yzuvdynIaGCjJkXw6YY+dtXQBWx6Ke1b8L2ei/zlTdGVtAlkUkJkYroUB48TcjAhmo5qeVKrY3wiOiuDI1HB4M2E1TViRMHMvTHpxFKobcmt3g2Y19YPkZFa01iGfWvd8zADWa8WMNRAH1QuATYc/PtXkkRkxCrj3RsEeFB63mtEDYoL+I14k6NnibEfQf30cPGdu8LgLH+A3gxZlzRJx2dCS4dhQ8P61qJcCA/9yAZNrIGPBEIbsNnU9TcL/oHZDnpkwYg0EF1aJXnZYF7njytVXb01IWBcpVTHENpKmAs7t4C6ACKixyJbWKUZekvyy545NUkw1XFIxxuIt/FYhYEFCMRBSno/zvHylb0Yoe00aPP9q7N15z6AwOvMk5FTK+Ui5FrATvAXwzGXIgXBfjv2DQ4/ZB9HfiPZRaUqLC+EBQL7KYhEPtS/BATqbl7jnx80bey0GeR+qz6ADJXG9xUJEGdeEO/bIZD4M0Ufg22RYyGquk/kzfL8OTOPf8v4mQ9piHNF3Inn7u2UohCKVjmuDVYGPO53SnjW6pUmVLfzlvC/iz0rMv3t9x2MLSo/m0QU9GzK3jJ3/eKHu+PRxbaOlW1dOAwOFC+UfBHLN5RsuMBcKQKEOn1hBxurcLSozVRSXN9RRrvQaSQ1PYlvPwNG14shasZo60TQTogiL1YSCM7KtANzjMrXWzxhBz4lBU/iR+Zu5e09kE+8Ytv6doc0VaVzEyrfDbqla2pN3nV6TBlsgSFUtObYKdv6tSGER24l5WhCg9CfjQB8dKo6avGgoZnV6jIJFW/S12S9VMNq12Caojhd/yFoMTcrCdlCCW4RC6oLy5pky1PAYLNQT1aNV+SLkVVL8RyVFFR0AQeCzrIzGh+1CwOzhDK1zlqtpBcbxB7/UwEX3vnA3vc1IvnZlFXJdok73zpGDnYWpMM0VTJTPjjYpVwvZ2ZVtZ9OkY2IwNvxJv8f4ZvOLfOLVM+Wpu2kUjTkOHIAjLrYXd2qea1GMIjNW4immHzPa7OiCANWUeGudF+Y538jewa+GRNBCvRgIcQmcBJ+h3SBhwlUJx5UdSyxruGvDpxAJklDXYX6WB8qlscL1lOdvkP0uT8rAF4ctIWCXMIyYr5MdeZUX/Yy7euKrLacdpuANH5fG8wuWLp3wZFV+22ZnSDxGi7k6EUObt7yc4gZKRYJvFQRSZogkw2ld4sOtPn+RYHeluY/FlaIf7W7eDvRQTqrl6vZMhZ5icDMDN62B1VHFqgFNGvYm26qr/ZQVj2EDgGo/Dt+NmC4gxGU6JofLjBfZbLBu+9g02/ET3pCNucHtBYWY6Z4ZHdIgqmjYYGPPZNjFjBU6uU1cURflnUZPrM7LRHj95QolBbXGpA2ifpgzjblzp5PEZa31XAgVJbityaUSqxkeqfTdi5GrCJYcBlAI4MQLq8GIGRvMLpgUScB51ooL7ypcA0tF8gzK1j0I1YViwtC8gFsRMjTrhgG5CkKpMpbM4GY5m1H6VEyJUyHMJgDpqSfaGnaABGQBvkt5pefozdfi6kyCPoOcpBqGEfYkecLywI2TGAeFDkWorlKWgrCw1+gn85vS9AjKGoacCluHlgacEE267GtunLBhaTLP51OzgU4nov1lf5DyhvMWDK6ER1oYbIHJjZFMtHfos7aUZFijXZ8rOiuoLg5ojlzxbyGVpcfMv9fKIYfpsi8emGi2Hc4XcLNrLeTTFjnkbVdLzG6noRALWV/IEf2ydy/lX+Vn1oXZD/c34oZ5MGlom7kSYcQtntEO4Ha+Pd5yihsoYxET9CwKJAnEY2wAcr83/bEySxkDQMJtnDTklisxpppAtk8nDhden92fsWqKSRDGR5zFEvG/LgLcNdqNEHvplihaA3TuwmWa+jt9VAVTmy2xFgC5sePVXe5/DH+gADrTKZK+eXiCyEyHjjEBs/jJODoLTBboB5OmIERiyLoxG35sJevnL76LpRKgWOoYgihqYdsKefj0Z2W8MDxVcwYcex2zvsqpzzm9uLDnjPxU5qxQTTUsYI3+fE7jnt2xig0DbEHrNI+F/lnpHaSCOGbfH8z8Jh/LQTQhpZ7DwjMfPQhRxQBO9GDjH2WjY0QKoI7YYXIXE583+vPNGR5+tk1sa140UZWAxqWWMDpPEkHnvDKVpwcpH54ZQ3AjMZQKya1ok71XgT8fW78Q79yi8EkeKmjYJEVdxq4IOvbxVnPCR6KJ/xiv050LB9AzHNR1vPL8EJ7VYBoXIALo7G+MX9JyjkUXNyLMJLWfg0rhdOJhH3/mS1X5hIpuZ+rhOQ1xovlIzE14Toj8AtQkhNAPzhejd9jbyoPgSOKowt81VmRNoflbXk4rA6XklSD9IKoeB/V3EvfPzEGJBuA/uqGh8Df6wsIpDZaL4hKA3f3SPRjzDKi0ZBD0a0fXwJEm2LOQjWBCN0QmIlGffFcjvU6T1g+Fm4G5zRA0TK2hNfSTa5ttg27GQABUNz1koxvb5eG5JzMg/Qj9ucRBtF9Xf7Eh4G6pzh9zjDOhVbhB1LHZiOm1cgQJ+SK+jYu6kB+ZlbBS5Txy/Z+KKxV4lrn88scPliqZvHtQ8V5N2FHdBHHowWWGCxQd2DfoqVzo1Ebg5L4Px3ip3ulRSRgeAP1CIG4ZwfJBw6y42bR8Lg0jBW8P3db6tnh+jK+4qW0wY4IoeK7Z/xAReO6t11KGf7zvV1yhU+L4scjBWyObxw2XohAK35AnKTw4g3YaBllCdXXw5alSTdxQcCIis8csgcxDSD5uTfqRCPMyt4MLq6WEYVD86vWcT7eVql5HY6aIEjAq1fDXwngIOb5gspzjNIS9vfp7KZ/82kgII636vGvMb56EXSes0LIZkaX1mDI3phg0LbJOrInBmZG4Qq0Q2xB0OaKkL9dv1o8yBvgHySTDuY/52BlVN19OLwNlkcwpiJV58L+QcjtAEJvAmMEKU9BzPoyBYYEePmygEKikYEAPrqxn4MhasDcJVlOzFxA2M1acAH1CG6D9T7VBZQLQEyNlnm3Vlu8R6t4WT3adY5XXdMY8tx4/hgLYoG8rXhOqPMzgby1NhhAzyPIEIBYk7OjvRo0rJr9eZLu5jbMZxxdCmLiL3OesqPlY1g8GiKYwEIaRVl/bvDMIZhq7GMJ2VIr6lm03Bs9OwEp4Y4FPyifeN2S/RezUmpBAaZSQGZDkhxhSCKCld4jUmNNbwDjWraGPsZmQuXFpe90y/w7n9lYrfImYuhpH4m70WvSbxuPbSGecMxByo9/8miGFKrSz0VS8UWfouIYlpMCTc2eMwnFqr1IMBJo2cXAkYO3j1gCJsWYv20B6TCXG4EZ5N066bJrnTSzSwiSmeXC4IvdFtfB1UiHjc0C9cwsjxGT3woC1+jkKk8u8SCxTVQ2Hag9IgTWn1Mew1A3hYdhGvOM1kgd57VgPkqwq9dh7lTjhpziCuwCHZegiLGUtsHEWKRFbfUhGd5l3wjlLaa8wvncGCsrkkmQKNakId7OUpzwxNYmZmSisUpjfRZQMgxA1sSceAKYmceT+HDtzer1ZgaMbwU37wGpSTJO93bhNZqxoWtva82J6fkqDajdQfJHQjaXJDJsGaRNam9DX3Gt7kVs/NG4NdKNBZmfp7uXxI+91hmW2GLrcSHHazZdLBQESAav/Z6ifXVh6A670JZ9wweoGhcuK6gYdDA3T1K4Y0yypf2wVhSCl0Eoqq0FLAMWDk980fnFOS3m5X3xIAd3b99Ait6eBZvWDM43m4jQPaEuExDuaoAQctTsItgWM411lfsj6OSBWNDmfAthW+hpk475YkQUHlMmtUlUIfxHXFgStZTjCKFgqhCGGxY07W8X7gyF8JeYoTckuIFKRCo15i/QP/pByLzEx1BdZnEvGMIexBUE9bC0xmOExh82rOvygu9RTAEU4V4a0aHSI9Py3WWds7FXsma8E5CKmsmjVswljXjesE0svVZfCV/c+P4kvD3OmxGEMBW9uKeP1PGBuCwTE2wlkr1tzGJXGWptLR07ilg3UhdgRKdO9MMwWYa6UcwXtpVGCp0jKbX5FH7dmHpDmU05HUniJ+iXwqvQcbt6CcpoYKFaCI8yQCOIgRmXhLhJCX0npOorzBBBSgOLZ8v5umrzvtn0/aVH6jh7tqsaPSJu1YX8z4ch6wjRNmNoKkx7CFIm1yZb9gDULKulEVv9sxskE37HQWj+xtskMwQbTBDSZsdnsA7oCbHdO955soLLfDRT1bckb9LdAeZLsjO732cC3KdsIEAJRFL4uU10bLtyYsd9i7cLDRtammqnhz2imU4YLgeaiP0ACIyx3UiKmOZ1pIsclZnKgg0o4WworqAyuuLfDQa3PU/9mbIbXQxODruPiOGnrqRW6BeIUVIOheImreyogdcMrWinyTgg3roBSZAUdxz2/mspzKdZp5LrdOEkXfypczt8aGy1ekQX8ydeLVXLWslm3s38kcRhjFR51qmuUej5g+x2rQwuHbEFGCfUYbMEuPoSom+MZaTNnPmk7aYEVL8OZA+NzS9K7QfVj9P17k36o5az97exhPFktl005gwha8uteb7kCZ5qq/GJIAnuPCkeGsndYmez9gcFSclKLBolObDKqo3XLRCphILwXFcAw0g3fddci8bHf4/YJQalKBhwNXo8MoVZTwL1oWVswTEDijvDpuq8cWkd4qQawJJ59qhoZk07kNBVbnUYnUE4DA2VgUrrSmYLXeKz70qSoT4B0UhJJ/+mZKxKKqhtDCEXkXpMG6YrC3rPStFn/grUKjmyAaR+YpCe69q3EtjJURfdoTA4IFKtgxh5m8jwaRZMZqzZBW8OMue8SWjVMbYN96W8mRuvUMusGm9mUg5hh4SdV5rodTbohUwS1nXIb56J4KEhnQCaExApMztl2ZntBpHt6UvDDQj8p5qrAqHHwyPCH41/Ce+OrYvbEWwdwkZktxa1kMup6DAJy4jPTTIr5TFrjw5X8WR/dHMN0xRUO9GivPukdmcAUF0b2WQQpShE7SFBValukLKv+74hj9RW4GBKZA5vOU75K3iLsWdEVAIoxmgI2FxhOe0ODjj+vJwIZ6wSPSijMPG/IQ542SlqNhAI2Zb1Y7LBiN4Fc1XHFUQGeXOiFkkTgIWo+Q61FO7YbK0Am3Phqnmg+sKy9Xb4eYSEkzU+2U1AV/UWmKjbunN9O/5XOVJ0ONeeEsSFAWdTEaCqmImepzkDgrA+TDRiJ+mEebTvXJTQ/zY0NoBgmQxhN+Z+i+CueecThpAJId/hUSz5LYxw90Q5D8rzLke0pCfw083R6GkMw8s0t7UVQuFzjMfrTo4RKbpiHZ7CFJ0+snOIGiix+JfMnEXXh6rXcFb2Ml25rz/9pEEyi2MhQi5SiZ6d0spnsdk9y1diZ07+vNw1HZA+T7GHVDqG1CSITJnFhMKFPj6+CVkC0A2vHrfIZTQRNFLNs0JpibzWhyRrzfBTTtG8v2JqxmffRPRhruOejCyE75E3dW+LeE4VijTiT2r0r5j2RqLs2ZG1MSa0NORuI9PuKDcQ8MwpF964AufXZPsU9Lf63ZZbdAlGULbhuuFTPhga3xEFlQswmGcdeUkG2u+16eN5EghZScKZ6ifCXCXSlVj2hGuhvj+52bRW0bA4F2bphZr+3eS6CiMP7QXcL3tL0CCEVSikFcx6AVr8T9y1ldFH8Dq9EG6kUWLsKXFTz+pwq9PbUFgLugRTnNwfAZ3lQXj5H6l4YqhfY68cKeDtFqYaRrmEwsnEWHirvloyIxDcZpmY41UPN0kMkDLMTxHhsAk1rvu6D6TArBV7FX9cxAdrPOkHhHUn4TeHRCGM6Oh57HTW8dqLkeSvDr3NiOTGL2nFhLjhYeFjJeCn0Uc5YUrJXcnzqc0xWR8p+e1br93GxU7GShp3TenqLQXKGIRtwtCU0yx4wogoeMpjUnG/8e+XrOqwcXrFy4Owfc1smyBs4wOD0MOutKKe4gbIsXWgpMExuSsZt17qA80VAnQNfMViCaktlNKspFJc8lHeirpRc+Ih7QyYoGmZtWK8bjkpPmp0my0WxpNlZBT0PxalpAmSCzgK0BsUKG1XE9mBpkLZBw4TV1E+eBfbRWsM0rcyQ2XRDCGuDY+59zIoZDQI7VVbnfuaBthAcaZx0Q8INo870as+pKQtnxuomdperSoMffqfmToidM0XwZGimEnoqtw5SVTC8yg/9amAlyyJgSs+Js/KS4azDUPK5usv2FrRj12d19KWXxQVbCsaEaUb3VtDoAfTTimfX4ULCQlFDHCSAEgxXauSNUrj23OqKjbj04lpiJ8IPgQPJfymfJ8Sk41sRhkFZKAjhxtr2k4phuBhd6EVQ+447ooIa2rF2/NwjARAGjdUO5dUqPYqYnU6KXoR69/Y17QfJZ+MpO/un6j9Xkt6mxr1UgEKoz76KQm7drHLjUTXUZcLsaDKPw9S6gdxPiTVYIxSCUkp+GtA9WKQ4uSbDH/dJSYcnGujyR5COGKs3s1vCaH4AKdr2qtUOTbnFcxwhCWvXP326iqJFthv9S60jZW58rN5Q9VbkicdsGFCtgLFgMnFYPnURFYh2I6wnOUYVwqnLszEcRzh23nT4bLvxWDL4RDy2ULgnXk4zA2X7KhcAKVcXjMA49TEXZbb6BefVJq27g2cN5Zir0taNCO0GSL+2B0UD1D4tdDOjYV9XkPWEvdVh7M2HsDfvAdMEkRXE3lrsAhdYwfNZFBM2OgGWQ7KamjGQYjWhGzsyQXUfra0x6Rqq3XMxyQZNNhBZdwNDey7K1NZQ3WCW7i3p79bx/JGNCaPMWekk6S8a7J6YWRWia8NGf1bs+P3+6V6aNSAzVPcN0empycPkeBI03N91rkdmJcUC92oBEI2TT11dRl0KaXirZbVByjtMIBIGXmb35jhcbhApoHM3ZH2lr0C06QLEQyQuplQlvCkq3TDO3RppNnCeS+SzhEw1Q4Bg9TAVoPTGphTMiV4eX2iVwEk3HPKE4TIzwsJVFvfZEOyhMBTdm8YEW1qeS+KGC+WUuGEh0nlHpphXnyuvm7qUQkjlEC2B745Lg8IUqXSsNaKk8Cw6OZGoRrxoU4M/+/hZy6aiYEQtVAaFL5TngXHi1CcIA5ZphREtAKQJ2jRhnuf+BwDiHlrTxjy9qmbQiYWhpSjDqpQTvg5vI5JyWtQt1dMAnXWmuszjaWTDYYUQL1DVmfDDdKj0Efzd4VfCa9wP/a6EV0Touuan8Lir2q/SZcGZC+MkvWkDIAW3PChagDD8eZfaca+/pgdMHZ8VWWnoUiJt3sWimB7dtvA5kXKaGShkx+l4B5XQYiL7Xw89NDDmM4kv2aOJYJKGJpue9ukERasug8Dam5ChGc8rmTBrg2LCep5wdD3h8KZhM/e/qU1mjMD4qp/dkrkisIOGFBwJaBBI62897kze+gsKIcDcDZMmEzZNIbIxI2HCrJu+gtE1RFu/Z/kjHS99B1G+LXltTOZxfFeqnhvjiDYNiw0JWDNGYguL4VYzX6WPcU7qLlZnKqWSVV9qEEOnloGQck82Tbc6YMYAtcvJaNF6kYAAH7LVNGnNjYwudylPZmTa0owpbb/VtQ3cI9cNxYoSpzeQkZOCuv/rid3pencjIYWt0HcOQKQCImXgdX3oFM8pK7LiMvYx2WXz5vg85LHznv8AamfEmVCb5HGQbmC01o3aNFAakwLA5yDROPvce5gWWccMDfac9QWJhzsTkxJyxQ9MTEMptYUpQ98R6HlCkrPGjnwm9Y4/LUZWnD9h/Y68kfPTW8ytxGpnnXhPbqwyZQxGRNCZw+eLAS2TpdEeJ46ywh2K8LM6ELkMFXW4ZxhrdLngiOG3UJWy7xO1P3+eunRNAbih5gZWlQ/BkSSjIowZBogaXoRwPQzLMUiGadoORmvJdmOUCtD07LhDLBdEITUq4wcm7Vv0ZeMNXrDrQvU0pDmq5XnLy2lmoGTc9YDbAIjwyP3H9wVsEZP9ajzT8z26EHSB7CfY5gm1K3Rj5BC6ANmDSL82azdSRA5hoxP2N3s4ur/CajrUd/XIqou2sjqbwvUrgB3AphDTiIIeAuiwObHa+SjSgDZhJYppapjQIDL3rYTTUUzzBjP2IfMGkDWAnjcS7/zRtcnNfsBbv9+NnI7tvnPI73VG2Dcjo7en0j0o3bNytBskchSenwLMwGyJVrIGn8miflBRTBJzFCm+Ld/6NFcVzEJJ3ShwbVsY0pV5thVCOBSfV9FCRiwQaoLs3MN0poS9C6c3f+9JCCNxekqh3+lzMUzESywJNy6G4ojz8AZ23AKMxdpgKmyDMYS90b3QXKivDAeFwMLMcW2Qqa8O2ScvQh5ONxFkUBB9HG4s+cpXpHsORYxX2gzyfUaSbx95C9ymCWXKPgwU74vf6D3DV9m9/xmjz60b/t53VfQddJIbfif+EfCLMwMnRCOJVzYrOxqbG7hOd96uIPDYpIeSVf39OmSIATGPAVbMgcTdNKE8dOILDTZonM79SoY3014iZU5D5pcGelOqqdqZZrYa/OylspDamB8S0EdTttwivRv9aeLCP4RoGXQvk3k5VExPOwuV2atzW0/o5aFt1281o2GUjT4bkjZFur5Yw1VZ4PIsyDcUKLwhZbzYoiLDSrfeQjnNDJQsYVGCrEz7h8UfzUM+6zfti5/50Bl/UFq+4ha/Vz0n3aTvvz3xVc14EexB0V8meNNa0fZnrBqwJ4BMRIhzbyaVje8E6TDN7soWN48aPNYv0jP0oYJJ+gFxvvpZbxTr1q/NWGEd/NH6mS+tdzTJCrNYzokqRGYzTmb73rcn90SwtSG/e1/6uHtui2iDWvKsiIWGJDZgA26sqOFMZnB+SWHWA3yHSpPPoZZUkD5tLHSyff7dSyuiOpVcZf7Shssw+pzVjtBT23KuIb6jGX/1gSK3IoeaDAMYNjatsAc6qlL34rkrqYiq4sxdHZI4DGHowib7TKVKrYRkJ7FctuJn3+HkDu8Tz3GuMFlzsYO8t2V5C3Z6cbOzgKRNxjMtlJF6PlcI5Bb9CiTpTBB8nEyX3swU33MK+8Ra6kYB4uTl6M9vdLp3hV0OeyTPUYaEXLmQkrVHMmdU4s/pOoxScRjNu2Qh4c1mY/kI5HYIzSlEVGyQ5Cx1+LUaA16XFKBNQCrCsL7piQgZufFgd4JHl2rX2wpFTyRfPJ9MrE6T4GtZ8pSX5CSGkziWHu875RJ1BKEbKuohKzCQBasHlWTtYuZQoYwrlnfCnWhMS4YrOVOrz1M5iM/HG/gjviY6ibmSNIjj2q0s7earZHnRi16ESy65BHe4wx1wwQUX4IlPfCLe//73lzo33XQTrr76anzRF30RzjnnHDzpSU/CDTfcUOr86Z/+Ka688kqcddZZuOCCC/A93/M9WK/Xt3owyQB5TeIOMatPHCT+Czd61JH4c5Exo7vsI+kR+ekhmO61mNBkD01WEPtrsoeej3IIisMAzui/dQ/zPOHIuuGzR4Ej+4rNpm/1m6FmiIjzuIUN7A+t/2kzmIyk3J1vw5gasGpdMK3ahNXUMDXBqgn2VhNW0wrNYGxyGK2diamdhamdhVU7B1M7B03ORpNz0Nod0OQOaLgDBGcDehbU/iBnQdH/gLMAnB3fBWdDcSYUZwI4A4ozAByG6iF0g20PkBUE/Y+3WJPI5glEZYD0YLkB5kpTTZlFe2KvPJCW39EwW26Q2h/oL19LYKv08JRRPduh1bN9sh1z5prx0edsA8FGgc2MuO4r9znqt5hzmBL13WCuPPs85wsne1/Wlr/aIQ79C22W8FBSt598U0MfrbSZ40kh5jkGiDGGuEOu4gGlMJNGP/YaCseh5VmJiNUXqmeHE9Jnk8nCFStM06qHNM1QaW2FNu1B2srq9/wuCNPbFDwq0r2cUR90zWgTsgegtxFt8TNGU7EwMVi7LDEc+Xfz4HiYMXYT2XkkYl6fBe27zJEMRYnVhwrNT8+JY2OsTRMUwGbOrceAe36zv6A5C43NURcDPB0WpxuN3YWIGfY60Zd5eXzcpUIs8vg/4nhxj0w+43ZjninD8iF/+yJuIT/cACy5SE7DaR7xPOXrA1JKHayQFX5elQBx3EuoIQfLf5K9ypC493L5X77MtRsZqb76sFwvuNHPRgm8kiPR7mjIB/aSJGgsARyXC8APxMjxlhPyoPz6r/86rr76alxyySVYr9f4P/6P/wOPfexj8Ud/9Ec4++yzAQDf/d3fjTe84Q34hV/4BZx33nl49rOfjW/5lm/Bb/7mbwLoVvuVV16Jiy66CO985zvx53/+53jKU56Cvb09/NAP/dCtGoxAwg1dV5RJQkBX5Bmqt0kJQnEisLnyxETNe94XtvQRQtYEQxdqEwSHIJgAPWRC0d8PBKy1Z9A3AW7aFxyaJhwS88OI9NNE4fCqrci752EWdEWkfZtxD6GT+G/NDv+yFdTUtz9LU0yrCSvtp0ZOmw0m8fwVichXbiRSP1/NEnD9dFveIgwI9jsT6H53VesKKhsA+x032BhkFhKyZF+l8FCcn6ITfCuyxLkpNNf0a0kJxC7C818FFMdwsyVn0tFPMjJh9ptnkXjwhFmZ19eGV4qpz6hvmwVgO50yt8B3lERYCAAwETi9fz56OmGMpVrWF2SYSBMJeYnGRljVgkevbUqNJC57SwIS66vTha/eUnnyytq7F9tpI2HI2LyImEEA+OmnrU1o08p+9/EIGlTm/tYI7TkfosabrrB0CmUfcXsAbhR5vkjQudBMi5+4nNviY+wx3R7u6XzUx+6773x6NBxQSUWJB6cNjk6Ht4WUpcJ2aagQTXV8TatVlx2b2VBdQ4J9PC3ogJWzkUfOE829wmVq8h1ZHPA1Vg/LJXVko9wLfRm0tRsIGeqxysJBp2yDD8YbjZnRAeseZ5+U9D7ZPBBtOksGOw2NDQ6iHJ/zM9yDYUZFJM5w0E/zCPtCBFXaDcNgDISuCHz4XDmQqAbiYiZIdIxe6Qgd8mAJmIN8PSdaRLe9ses4y0c/+lFccMEF+PVf/3V87dd+LT75yU/iLne5C1772tfiyU9+MgDgfe97Hx7wgAfguuuuw9/4G38Dv/Irv4InPOEJ+N//+3/jwgsvBAC88pWvxD/9p/8UH/3oR3Ho0KGb7fdTn/oUzjvvPFx22WOxWu0BAObNBh//kz/CvDGlh4ae/wF0j0WDYK9flxWAVVewumfGwiFj5BVYJPs8dI+GYDMD+7Nifz1jf4YdB+1ttu4R0AbBmegr4jPQV7eHodiDYi+0vphCcebea4Kz9hrOOeMQzjw09cPW7I3FjTivv/a8JwA2sbcdi2A1AYf2+rblWf3AtpXFpXv9adqDqmK9WfcxbGbcdGQfn7npKI7uz9jMfYuzeyMVwHpWHN2fcXS9yXM8VDHbMe39RYWzeWqOYhLFJPv9VNvW80wEa/SckzUUn0VPlv0sgBmi9luOdMMG+4AegcgGiiNmyKxJ+LhAdhc5yTRXMKTMulwVW7m0uJ6aXOxdRf67f6YQzS/JrCz3rIJ6OBDh0VJ02tkE3oD1RoOufA7d7dqk43MKpauYxPKK/CWT7uq38UdcHx4AoRCPKECuZa/jkisT8bAYWzd4LNziMivuVSUl3lboZt4iD0S4IpDm4YE+V6FcpBvaPY/K1QDCiFKR8FI0yXdQdU/hCiszUHr4ovc/q0Jtl0rn+Uw47zCu4rdDoqZV831Xmb0CaF+NiqAb1Rb+jAPJNoblTeAp5svnSN1P1e/PcTaQbxOOCXPCjp1iXGKhY1PXjTAz6DRpbFp1L87+/hqcy+Lfgx4D3x6G6jd8R43TRTVA1BZMZJw76LONWJy2Rl/DOCAEXYvdnsnSSG1lCtaf8YGCDcwsi/N61POI0t4KQ4phNaOCE+ejFUn5bZPR8RGGVf72+dymbut7gTyQ6XMQAwxsx1CBoS+qAx8T15eQF8G1IuaV19JM2B9hALKlI4gNEwK4sR8GNKGqTStccN/7odnCGADW6zXe8Y6345Of/CTOPffcBT643KoclE9+8pMAgPPPPx8A8Du/8zvY39/H5ZdfHnXuf//74573vGcYKNdddx2+4iu+IowTALjiiivwrGc9C3/4h3+Iv/7X//qinyNHjuDIkSPx+1Of+tRWeJIGUzDaHfgFZ48UiCx0nZAzFufE3pwnWw+vbEyZuFs8a7v3xP7UjCIzknx3TigFRbi8N9qNn5vW6xT0dqZDeb+NaibSSRfAkwt07e5bQPuZCNINnFh1NgBzH89q6tenBqymHiKYtGGeu1JtrQOxntUScBVr2zI7a+5gcmbt2RpuFB4yhB1FN0b2IboCsDbcruFns8Tx98FaPQG3z9N+tB/UH/kSqFa+Y9aNv7giJGB8/gWqtNr1dxGJt++xahKCJIxcmYaidcFYjJm6BnUF4oKquIqRyqW7ygV8Dkzyva9Yk3acJhA0YvVdTsXKNXHkBm7gzh8gwy4VFo/C0cTCNj1Eo5ETu3HEDZ0u8PLR3F0jbojRZ8wX5f70EKqE52RqkxkqHsbxBUAvDYq5TWgqUJngIan0dGQYpHv/minV7B+kfgHtCfIA1N78rZ4o7luL6SC3/oZwy7eKHDaK4wMmN4B8KUaiu9K2QcmWefFq5IOO72nVX6mxv16HBk4dbKE/P8UWIFrxHXn+zZ4rOzScPognK9BUDONRXUs1tzOU7rhsTrkgUWdhhrDR7Qo5EEwwsz3h9+y6ZDMBjOdgsOHT6zmM2w0PNlbyIDsE8NGaez0JuJQv1eDIhQTSVjhgN2O3LSTH55fFqZnb19qf8X7iqICHKlCWBhxXvTXlFhso8zzjOc95Dh72sIfhy7/8ywEA119/PQ4dOoQ73vGOpe6FF16I66+/PuqwceL3/d628qIXvQgvfOELjwENIacQiq96stZigv3pgbOSh8yqFXNwq5rXQiCzxfvcKg2F7XFzQXeZmtBUPyg/oXIF4KudjSqOrDeYpHtCNo3UkrlfIXY2pKTQcKLrwhyQIkSQxgn66m9qJm61r8pXk598271EOs+Ypr5SXW/mVOPzjI0ZMBvRwqRdqUyJ6HB5W6Kpu8P92H51o8CsphJv92suNCtD+JyEAHCMm0Qr7x4hgQvCvs9RXXExDCjfNaSVr3B8/UuzykJw6D+9HRor3zhJVbOlUMo589aAe06yLe/LaVqCmLOut9E3M3irVajy847T8MLEVskUWilAK4+xOkm+Mfrz4LsJ67J6tyc7bwHVeHOjzLfkitW1GRX/s5AMJbUmKTZbSLQIRkbui/rbxPtzs06mNDzPh+nEvSK+grRFiRk2ddKJhtywMF73oEQxPkIodR4NElJZ2B9u6CXt1BnwL601SGtYzxtbuICMfX6iwb1v4RDw25qjT+NktBIo5OJXtFIGQMrevI3FsCbSCXYvsNRFx9AyCj7VZSY1gsH4KBfYiGeDhedTa725GliFIaNNDQPioFhFoNHmZfxdOtHgbCM/WbTlxdQR+MDEZd+6uON4c6rvvKcIxrd+VbfUNfnii3YZp+kEyy02UK6++mq8973vxX/+z//51kFwHOX5z38+nvvc58bvT33qU7jHPe5hv6pwHMyLgqHkKwWf5BjPxOo56/kEsYhqAnO7N2NrIL0jfaeOh5MUe8gdOxoE6JCrGQwO+zx3x/C6zVhvFKvJFa8CvN3W9xK7UhypTJOwgkDt5C8R9BNxjX9b6yfPtmanwWy6sbBa9e2IR9cbeN7Oap767p/NjLbpBzxBKiF2OlbLBzpsF88EcBN6Loqgby3ewI/Aj5NtXfvGSwt9J4agyFVJ9mE5GUYCW/SUOJjucFNO4qtzNzB8lhO/TjdOYfEfGUfUWdBT/jfecy9aKnC+3hVSCzWYet2TIHNraAr2mfpJrEj0NND9KLPcnawRBOpYUK9McxveRcBfvZArejdI3Fsigco0Ojw0ldzqMDaBGSgtDVBxE6AQACAtvBz91GNKbhbQuTK9vX4WTIPMDou9zyoSNi3JHZONeBrmC+ieP9pqb1c7/di7q1TQvSYdgTzK7mExui3hLWdiD8ex4eq0uEU9k0dB7bdCzHPak4Vn7fwa+XY0mixuUHlLrPAdBgT+tylbXxiNcjVtBFLgCzBSEfNIR90qcc1o1Ig5PAnFuiL8cTOUJLoM7II8PLVznye3GdjYh/FWtpV9srHFeAvcUiUlIyU4QwbxrsPcDDjK2Sucm0NCx99MOE94a4o808PyAEhusN8JUHRcuNyycosMlGc/+9l4/etfj9/4jd/A3e9+97h+0UUX4ejRo/jEJz5RvCg33HADLrrooqjzX//rfy3t+S4frzOWw4cP4/Dhw1vubEEAbyGLGWEjRcMWceUeiqtQgSun/qxv+4w7ov1IejvkaA5B2WuYuE0lwiA6JCSk3bSa5x5z3cwWTpnzMDYOC7BCBWaDw61adOHtNkk/jx8iwDS5cq6iaLXqwnlWs30A7O2tAAU2OmO1stewz8D+uquBNdATeA11/UwFM8IozJKEuoKgHw7XvRF2ABz6oXW+HVvM5a7mdXL3lOO2y33Hg4Y6RkyhpCtYnZVbPNfH7mdgWFKgtoDZd8nEIWtwk8UFg9rqkOPPbrDMKbwUxqiWlIjuZYoTYSXb7HNGNMf5LEW5Ez2rhyGcKil2bZh3b42bM0FpsSTPei5j82nAdytVo4OFkzOFM5IFA8goae4pEg3vCBRGs3OIRPfCNQFguTbZg8X7Azq/1jDPPQYer4eA9GN2xOnFZ6/zpBsnzTwoacZNZgz5jqcpeC2LGu05B/lnr18WvLEXmL0LnhA5w3fbeVOCPm5XhEFzRudOD077ESYyg8d5XlQwTSus2oR5VqzXa5NfVcYtzpsJ7c/2kG+WVlJQktubQ3JRCMMVGhkJKZWXviMM9/I3K9k0BQnkVMZsHNCw4qir0QMb/EqGRLZO7bjBwMZocpdEJ44Z6oUsBCkYGEazRfWEWHG42bKRoTLjJdrWrdUTS1rllL3ugPVnJuz3Z9L8oDEQbbphxzJzq44+gXJCBoqq4pprrsG///f/Hm9/+9tx73vfu9x/yEMegr29Pbz1rW/Fk570JADA+9//fvzpn/4pLr30UgDApZdeiv/r//q/8JGPfAQXXHABAOBXf/VXce655+KBD3zgrRgKCe8Q5Ao/6KsLH68XIyqCPCgjnveWfUJdgdHmUlFo60ptNg+KusKV7klRNHvnDIJpKqm4LjNFjB7q2WxmO1mWiNQYS+d+jmvP1VCItn5cdU88iejxPHeJ32HWcMup9sS2WbtFsrKV5Gbuo2yt4dBq6jt81tp390z9fTtN+smwreWx7X2FtpyLlOMCyGEoDqGHqvzlgv1tyj15dgL0KIAj6MaMe1s2UByFGzPepCv97CTDFj5Xqi5gPbzjSY9mkOgUgsq9KioT+PwVN25ywuyFhyaUUsO4wiXlL2r5B10QiHSaye2SpOCLeOycrrPGLq7c4ptGUAoKF6LWVnhUQqtY8iT3Q9/F6K/rORP6rmgk4PW//kweIsf62pV3H6snsvbnW0PCKN1IKWZIy/6auAB0BdT52T2JfReZYKMeXtzDPO8BEEvqdIMF8C3i3Tsi8G3XLmQRxpUZyZBOj8gBdwi6Z8/5Tv0lmxHm2fQ6fSO5SZaem6LYhOLPN7+aOLf5F6OZvG5jN4vLvewwgy9DAqlY2rTCanUIutlgvdmg85LPWc0dgkgehKymsOA5eN2TyiGhBo2k1XrcvI8/jeS0CarqjzBX3PcRSBnHts8BdMRJxJIKs5aU5ynFQ5RSf9nuwnSSlGPjAjdYP+RQHa0bLq60oy27WUabeQKkfszCUr7mw2JsER8SzyMWtGlwBP1oYDWupeSkJuBzFQ+UMXTQtYw/PJcjLk+wnJCBcvXVV+O1r30tfvmXfxl3uMMdImfkvPPOw5lnnonzzjsPz3jGM/Dc5z4X559/Ps4991xcc801uPTSS/E3/sbfAAA89rGPxQMf+ED8/b//9/HiF78Y119/Pf75P//nuPrqqw/wkpxgWdCnDl817ZjFc0yayTR9ckmISk8s3dgqTczyrKstYwrPPSGCjknvjSUB9mag0s/IOLqZsbdRO7ckBf9siapNJQS+CrBRgcxmPk2dQDa2BO9vx209bwaA6tzPQkBuRV5v+uFrq6mfjzJNiv11d2WvVn0LcnoxBOtN/73x47IlWSQUmiPafvcpmEzp7NlK8BB8C3Js6zRjxAJeSLc9TV6iLbxR6TERuDqOg/RKDspk19NgAfwQNTJmxO8lc0sYAzP16etM2nZtxkwaKx3Q8qJiyQRQ32nlQryKgUbKWvMzJKQMRkn2WVarsaRMeKpkSQkUajksE5S/fDWEGUQDD7nC6HPczPhInPS8KIplE8/EAW7Bg6Ze7L1SnR/9rByg7wZrPRE2Fgv59u9GHrqOFEtuVw/1gNpcmbEyxTjdMPE56+GgOeZJsQJkLom1cZhimT+fGx+jJvzFOEkF3+WPJ/FK4FngB9W5Rw9o09SNE+3yyftx6s0NMWYhGDnketfo0LCWssqSd4MuWsJaD0kxeUS0G5pQ7P8MxRfzynFHNBkeCsRHGJ8i/kzCX4vS/VH/m0yolgQ9qcht0XTNh0FGS7aHvqCw2al8t+hi2euWIaSH1vEWM1mMFjdkw4ANQxFLnAdrFZ+wtWPLBSMP1y0+f9XoLAhweyj6DVhuRTkhA+UnfuInAACPfOQjy/VXvepVeNrTngYAeOlLX4rWGp70pCfhyJEjuOKKK/DjP/7jUXeaJrz+9a/Hs571LFx66aU4++yz8dSnPhX/5//5f966kQAFG5yglecnODc6+WhY8QfhvcsYV3bRYv9PQNPueQPNQhh9G2QkgkZj/pWYUIiYbYI9bnx0s8Fq7gJxarZjQDW8Le4+9cPbNu4H7o30Bpui2UDntSlW9NV+z0lo/aRTc7m3NmGa+gFv+2I5N00wTd11PuuE2RIdNxuFzj1xWD2vhcIA7EXudN4w40y4kO7nnKzQPSqH0E+aPQLgTPT8lDPRV6P7ZpC40mrQ8GI4ThOPqoj9EPl+FXRBL77TSiBY2RN+wJR0L5T5yNJfRvRjn929bmGtmNw0UNyACYXTgcF4XmWshMVDI1KoLfMsXFl1aGYyNNLVzF7BGaTnQxilkQL4GRK5+iTKJKbo9D4oElqZumdDaWyeD9RDZ54/g4RTATf+XAEqBLOaUeEwW+iy7x5bh1GY4quPt9kLOdV2ovkcd9JP74ifC+K5STV5ulm7gn5YWxpAGmMEIJu+DVf3zFA5auPYdBrWjRlTir6FHoD6jjWWTU4vdsiQ7a5yY8XNHbQMMfgc0xUIFG0SrFarjqfNDNVVyD6hl23GrpMIy7l5KEaHXjeCO9ZD0mnMs3uzkCtvykSAexy4jo8hFZ7Xj9EGrfr6reQ/DLVcCrMNkPfncj+wVQyG2mfecKyQXEHqkxAHkuNMcaTUtMQCqno9aNjxnQwI2NywLtPkUDe6/KbYhZAXMeDEtZLhESIDjmQflM+iQEk35VRtszx6YzaEev1WlBMO8dxcOeOMM/CKV7wCr3jFKw6sc/HFF+ONb3zjiXR9XGURrgFIQy5r9q8u5CsJYyBgkQxnhDA3fkp1ZEZKnNyZ4YjSXLGKvQsJF7vbFxso9jcz9tczVtIyL9ZG0N8enAwzz9qFoKJ7Ihpgh5X0FVUQmq3WNKO+awU2s0Dnhjalm91XEdMErCZgIw1thm1dVkBnzM12BUnrar25oHFB514oQ3Vgw1e1Z6EnH0r/jJcqrk2pzAD2Cd7+NxOHe/zUUetJsbkDIr0ifeW5sjoruJdESWH11bOv0BvSM+Liro9dxTwlfpqj7/LQjT3jib/I796O0w+dMeHiMAlDkQoUpiCdklLpIMRop0Y/XbIfiuYo47YZFjMoF7zC/NCynuFRlZ+ZA0cuPiPcpoJ59tCjDytXmmncdOGobbJ8C0UzYyS2ts+rqFdfqCnop7z2cGoxYLS/g7gbzt1AaWR8RuhM7fA2+MFtDm2HsePXDxpc25gT692zY/wPD2Eq3IsDsTeQu3fN58G3Hwu6USKCfnaQnSls7njHqb/LR5yGgH5K9GS7kDab8Lj4KrmhhRs+PMlAzCdrdv9d5kck6LT+OdU6nxpv+xSNykpRvCvwMXDuIBkE3gh7RdlzEgIYSgo52w/Vboq92EOlrsszl08MzkF6z3nJebPK+q2q2b2e1ZLi21QKoPRLDqhhZoXmHR2fdyOEWws9lsaJ98EL5/iyxfDhNrlHPRB3x1dOu3fxOKGIGRW+MoZbi263FEQi5bQJilCERLARrlBnFIE07btnNXfxiJ2DIuqrQ6+fVJk6g2OynjeiPSSjwP48Y39WHFJFHLDuSgeuiLu7GrrpXhAImnldNqqYdVOYpomL5CThzQysN77StSRVs8j6O3wE02QuX1vpTyLA1Gwl3+v2Q9tMWYaQ67kwG9+WFyGtPcPHYQDrng+CNYCjgBwCdA3FIVtlbkKAdOMkDVB1oy4MFAm8OL7dIxK7PHRl+FsZnVjegXjuQUPDHj1Lh53FKnOT17FBX/3Yu4jsnUN9N5IrN65rBGkGBa/xFJ6LYP25cgXgOVVwxRVU4AYhGUWSAso9fYCaR4lWyZIrWQlCCesJGeJxnDpOAE/m5XNs2J0M9HBkV3rUJkno7smzXCrbSdN5aYONzhHS6a90sITj2IXF893zm9RxJCvqy/KINEN5zQ0RN3QwmRK33T0hjBNmP3yt05qaQWIzphuobtCNUzOyRSFx9s9sc5IHuPX7M/xdViLG+zaPfvBg6mO1Q97muN5aw2q1gkBwdP9ID2+NL0IUD+2yLwBmwFQt2XE3h3IWC79CezscJuzn6ZhnF26Umpm9dUE7Kjz7Ebt/XMZ67cFnEjSopX331mVuV1WVVY2Tgg/7phpdnncTtlxMgMZ1GdriX5IDIMMAsQj1zl0Gy9bWAppU/e4G4RFVe6MCEgbH6Jnx+p7oi9BBOYJlg9Wf4vxhHnTvw4Ay9XGrymlloLDdnFco9wGdueOOGyIUp+xM7G3kvXiKFGDfCWAuWY9ZxymV/ifGz+m+n6FklGsQsGc6qJo3RLrhsL+ZsZ4Ve6r98DX4Gr5bXIo8uVQsfjy3HIe0rsxmyz+ZLEXFhzTrBuvZdVEqQz89cpoEe6uV7VrqitmdJCo9NyVPPvXYrMJPKJxVsMZsJ3pqGBQIIaYdd3KGuaIPm3GyAXAEyl4DQ1YoQlMUffzmBVGfN1c8OReBdVNKGome/ZlMyBdAugLrQr05L8bvroSMsgxXKTR9XJqJh3Y9FZSfSzGMj8Ry4DTkdk+2dIjSGwXDk589w4aOhQRMscW+FQHU6DcVlSY+Ah4T2EhvlNgOrBCVZjj4sd0LV7kbkKFc3GtkuRyRf2PzNfu283W8cdffhYXZIAxrqodUuwdFADFjdm7Rt3uAZju7qLX+TiQ+VdbPQYkjA0oCgs2tuqG8gW/hnx0GXSPe3q3dMIHM3VtnnpNuVObBhM7J4m8Qb767x/FJx+QbbTVR+DZmEcHeqhtm6/Ua87yXcwvEaprfDhwSTZLmU6H1H+nPM0PJ810i/BPSyp51PiEdyDrdceh8MBghfq3zS3mIW4x/Q9ELQrmn2k6PBgdUfPR5gjEGfqVeQk8YoGULsdEzDTByUIrVYDIK5N2UeDwNFzUc+FxY94tAHin7aqJ0QSw58pheN0zEJiRGq6Edg06iJ0njcmm+Arl1PvFVE6a5E/bynng5rQyUnu1uiimwquUjr2owjLtL6WYKZBLWOQfJMP7m3+YreQCx+gwpISbg+tdmhFIndcl8ih6hWW9m7G82ODTB3kYMp+CMsG687R7NX3crB4BiBc8X8dNZWz+xQYDWutEwW5h8mvp7TPwo+1lhSbr+7qDZ2jV4ZcY0dYE/tW6kwJNm0Q2to2sNo6sn+GoyUIS7+qsIcl7W6CPpq1DVjU2Nz4XnocAxgPJivhnop4oKJM6zyOJ5Mg6IH5jWjR1n/x4I8NWkM7QLUqeJED8kNINm3B0fCkMhsgllB1+RwgzYHEE/tMxtLXteI49h7sraE08hUGyMxuYYmyq9LZpEHswLBs7jCaXhW4PTS0KY68aAvQfI5yv5LA0b9xjOOtsOMvd8sSHXlbHzIPkWjWdWZgh0I66TtER+Su/Nd+h0A0XdOjfjssPZzEjqymZWf/lg7ubpoVkFH9zmCqLD6PTHIR3bMq9A31WkUcdHp3GAmyXakjcs5t3kQh7e595Xfyu4+d9cmaHP+6FD3TjZ39+gv2915ZVoXiTndsyBgAzXrL5Kt+nMO9QPVuNkcZ9n8pQwj7hidA+xPRn5DojHiZfIGFnI2oSOH66U6RjP53P+COagW6thc9KCSRG7Lp23CPreB22p7jJAAx6G0g31WCymCUFGQBpFBU6HsVqQhU/DW+TzZO1wnar8mNkZlRLkEqEuqWOpLdG3Y7hJtpg3J1ROEwPFyaaRhQ+U7X+WT8FvEM20rrTABWmcuGborksYAXrypKJpN06mhu5+DtewrzBt1a6wA3hMsUtOXBCAJhO7Fdsf7aGR/fUG61XDIUWsqPm01Nl6VnSjYm3Jq1MTzK33vdloKmZBHN8/zzPmGVitJkyrnvW/3sx9i7LvPtB+hP4cGfudCSYAbRJMdtCbr4A2YjktJgBm+0uY1eZji40ugggz2RH06Zpm0c9zb/eE60oIoT6dZLiaK9i9WvUAt1Qafa4a3CvBL/UbV4wwxc8KgeVszK9O1vZU74un5Vr/LbfpeskwkQZ9uFDsXgjBGOYx7YlIDhfHYv6pIw9O/5kk7O7zGK2HYUgcp9itCorb38zuQUiQckuThzPs5ZBmDHRv2xyGWSg7smPEEtKbHOqKVPdpLpFjCxsqZQHM4xaeVIXJCPQdPkijpAPuOUb7nZ+jL9terG5YdyM7DBOgG6aYIeqHFG6QOScW4oGHesx4RTeCZj+URzJl+9ChhtYm7O9vsJmdT6sB4DQg+XjZUZhKzufeJkb7Yqbn1W2iLc9pisT+WGAYbcV7n5rhx6gj5KfSdSeYDAlzHJ7/LaaJK+eEKkR83rd2DdZyLdrhh2rxM2pSJ1C7pXe6oXnFf6j1zc+nrGG4hZ7hMdCgh+tF7hCfVTTUhhbhPJdTYZCQ/ino0dKU+xq7ve/EJYQDIRhdxx5syBxUThMDxYosf+hwRWGCayRM1y3xmy3xZYTQ8wJ8ldvs5WVAf9lef4K2F7s1Kzl5DKBiy/SZUuhhnm50zIoeThF7gZcGiLHbr82KjZqBIQ0yu4HR9YGiZ/z3lWS/rprKsBsi3a3bzDW+nmdsNps8kRIw46xhtepH5Yso5s2Mja3OVRWbecZ6M2OzmcOrkgi3YWplJd+u3C97gqHNn2+NNFYa2I0+7b6vjGLKU7DEqabxOJmtkqZrx68d31XaSMUsTkAqBYp+M+s3qb/DaImqQuM1JRp4QnkuX1jHu4VgY7LdXoaLyFuKnT4WXoKfaNY9HB00fz+LJecqjVSAEhYBRaXFag3SPMIVanCE8SLA7ImpDte6h2EDDqcP8xzRibkzeuaQh2/Ec5rcqGHjl7enw4wOZeOrj1dMg0egKycmE1ndc8LS3dux8F8P7VTl0L1+ljzN24mRgbo+956XYniPI/475E0Eq1U/emC92cSiIbfFo9ADz5EWOnLcJYxOK4DaVm0zklwCktEXHmNk7ol/rwo8Yc9/JenFbBJ+ZrQ1qnBcKt08EyhLVQeuPDXueZ9pbHHzOScRgsonuWXErqgweIjWtHRbdtUx95dWQ74NYxiUlO8KKp74GGvKNVZ1ox7LYHKVXUmHErAGHjSH5z9U6EHNrzsPCiNgoeErkzhtKP+oX0szSliP7VjohkcXXh6XFTRZoR8C1f86al2Il38wqEU4s6qmshYD0kVYN1L8DcKuQOm44rl/7xEuAXS2pMJ+Tolq34qsgG0LBlR6jkg/6EoBOym2bznuyW89z1Cx3ig26xnrGbbFuJ8Fszc17E39rcuzztC5WzvzDKzdOJn7duQwUEKI2hg10bNQ7qY4mctcOAq15V/ZfugHhPkR55qTrF43Q23dQEMYIoWYfOXHglOqUunyzxMnSTgMojY9MNlFDkFyDA6/reYJG/TDDQrzMvWtHjbXSdUJT8t6UDT1sUltXdPD6ArHvS4hC71vn0tvpVr5PPQuDGffCZLeSw2D3pJl3dATFpies7JhDNj82eH0csiMhKO9L1bE6DSUeSsI3IDg4Bev9UVI0k0PAfSk5/7SSw89UsK0eJJ0f4N352v3QOz377KCqHlQInfHBD3W6FktK8vH6l5gN3aaKKapYZr6G5s3G8/98OdTrTo/ufIk08LgMUMzDKE0/VLWsafSVZQnavc2VPxcGq5vBtPAd45ZYdLQXBTEvAhcIMKNy/DYbPGIxKhJeaoOij8bzrFK0uLCxHEe1wwDOdDlVG/qtCTXloFvCUfZP6H0Y4bUZJQk/nzYWp+v4ZUqI5fXF9hAYMjbJhRUfhbqnwBByo9qyC262tr/zZXTwECpxQkjTQEnsqwjNLGhXMjM1fKPpRRKtsdCnAVt9jmuSqhjt1lZQfm/zndOKeT63MxquSgz9nSKlvPQLnTB6e8cEQ3G2oTh0wVYMwbVzWy7fBRt6s9vbLdNf4lgB6OHZvq92Qy1qTXsrRoOrfpLDQFg3pgrX2fsr7X/bdQ8P5pD9n80STr4QujV3+CVCYIvwpBzgSCOQbHVt82tCRAJRUdhIvWZ7NeaCwt37fr9IoQU8Jf8Bf2kUZHzkGk6vDoT6QaKz7H6KwhCgSIM0zx9lYzXgJ8CO06HBkCMSHkcblR2unWiV/ud+lEC77PjwuY7XPWDgRRGFQtvmsucczOnbaeVoBuPLJidd9TGX9rCCunXSF0x23TrJACmbpDrKneWOYVJbzuojXmexu30BUjkinmVWQSelyOe6yEC0Q3JlxVE5m6ISvJnH0w3JIW9QDZJou4NWxkfzwGHh0umhnjlxHqzwf5a0ROEjZecn2LoNlrCRcq4pOViVJDiqcn++daaemhlI4VMz7Ki1lS9USfmvc+wc3BICWXcge4fozixaJJ5ytOFNEbcKocFuanCPUn5xjlGo14pz2h+L/DT4gEAVOpj8fJJHhdcBjBPsGlAVQ1vyZKOY8ZDGlF5ThiPgTBvBnB6I7fATdAEz4Hp6ZaVU9xAqTM7smG8O1jLdIfghCszIoCQRtam/+fKvV/tx6T3cxO60BLbmupbjL0pFCKpAiAMleYCPnf38FHoah6N/Y3iyLphbzNhz888mPP0CTVN2zQV8qybfrpsMKnFquFhINseuFHMbQ20hs3sL1BTzHNuUZ41Fd6qCfYm4NAKdqrubG0C6xnYN4/P/rqHeWZa5XXFqsSn5j6kCQxmTMlpirhrcV+UVqbyWdIe+23St2yKecB8a6hRSFcKRkeSBmLSSuZZwFg0ng7DwifMMpME3RHQ+JakXcD053UkBZi7VTmU1HGv/VRgWwnHKaQKo0VAx9Uh0fXsODR6b423altiriGST7rtjyl9t0dsDH5Kqcv4oGmBJRoabB5aUqG5TXpI7upISe9Mrx/np9i7flL6OlVNmHWve+vmHJenkcdWbQE8l8VR7sowcO1zBsepSQIFpK2cZG3cPfdEsW+rakUcvqZr85j4rq01oDNmrNB3wm0w6xqYZwg2/SBGhy/ybgC1RPy9vRVWK8H+eo39fcVG/V1BXt/xb15JM97Z+BCbI1ZyodTcUwAxAs7QXKc223qsTqdA37VkISm0SB5Vmyso4N4vR7gMv5LDfB601ClsFso0CM7uJz144RcBpuPMPX+jLO6tlnxUaDHGnLRLHh2BCLoWHpmFfuZnB2vKLgfvk4EGhM2X8o4WSWU83mx+CXzlYrvjrJqAdY6AzkIz9WkSJlrLZWcglfDJLRVMHXc5xQ2UsSRB+s+0hd1SFGOa4Tkj3owhji3b6qTqJBenkYHvOwBc2cCeWdj+rjyiP024RGg7sQv57ok4ur/G0b2prPgxz6XeJpaVRjziSk38BPxYKW5Uuse6NbRZIufEjaLN3HKHkmkWz31orQvIWfuLDdezYrPxM1XMOLGwVIodH74ziuNHQhguBIGhp5874zs05oJRz91QT4T2+uHWJwZ2hhUgjg4LWcKxW3Y7LwI/4ZFwEFtzNTpnyM1utggL9n+amiIUUB8kKM0TJjFXPp9ZvwgbR5PRcXjWAt+a82dn7PRZ8NBMDxWIau4M8jZceYeAp/4d6pDsXUmlF8wNmD6/njcFEejs+Q79kp/xAxhNBk/5itD7CW2FSAo1Rez5U358fV3WOh0IXTdR29mo44BwWbhWcg5i5w8EkCk8dxJGkZ8vMwG6j9iG7ucLwV5yaGeeTBHGmPsrK+y4fegGDQ2HDh3CaiVYb/axv6+Y7UWGCDgFEQ4iARchKyd9Njb9zaCBCT4E0PhQG2bfqdQfQnpVFB5e9NOpXYF5KKXq0j6+pXS1p2aN0KnX9oHMsZB0+U13o22aLaUvbuiKTyGFYIri9QcTr6y0i0FyXHpWuIug08BRwbUb/EgBDTYCEoqMoibwFTqE5zbP0eFQblYOmWH4VT6PxuDkoYYMSK26GDWNatR6Fe7jKKeVgaKEkSQgYxRJZKalWc/A6IIqt67ClSZZiO6e7kemNVMygiYTmtA7QIIwOlFOFDpIgZIX4lRUqTFV1+GKbgjsbxT7+xvsTQ2r1vp5Dq5Y7JnNnDs9wluArkAjWdKZ2rYS++6jfkx493qgqb08sLfFr9xwwt7Y8z2JV8w46QbK2kJIPrRCrMGNAK8gHN+BKzCTOnOnANnMdvRag2t7sMu258q4W1oBnRFpEKihhLT+06DjU3pvluFccYugHKanTmIZDpkF/ZTUaCGNE0BsrlLIOJAuaFIREJHEEGs4zQ0cl+9QSns0I8vhdqPQV2rjVvg4DM+3oQb0Oa3dEInJJAOM8KVZ31GXOiVX+Kw+NdqSLtADLWYYqXkUdbZ8ETcYnI+9tFyHaOLJ5YQDljgT5DuV/LfT18qeW9EM+hknnpfStx/PehR+2izsSPymPaG21wHmed/a3QMwY5IJe4f2sFrtYb1eY3+9wayHEOE3VTMs/CRen05fqTv3Jm8VL0RsGVHENmn1gxH9HCMnMzdg+nuP3DroXtnJmqHdY+7J9XAjBP29ZBq4CtnrtKKuTPOUXt6Fw+o9Fb+JEJMjbFKkjOGP0bgxnhPkQYBMLqDwqMnoqOM8g/GZbBdA6gOYEaAuZ+y+VHgK2BGqQzLm0E3oLNWc/zIC/5fowmRwoJd05DB8Gs9BJc3KZYjslpfTykDpxZQLZZu7exaiVeD6E776CWVQbEagkF8Vm4KePNeaYJr7+SLJDEmUIs1cjsS4IMuX3XnGr7wSgtj24bkbKZu5b29usF0qiOAGvSHW23WCzxXr7MRMGiKNoJ7sKtL789W747JZ3Y2tOGd042SeG+ZZwsCZQ9lZG+QRCNQymkm4uBGXxqTYFmIXUOSTCkGcGifd1T2cARGzprrrOo5yGfgxXZnJqpw/4+tHABHC6MTVQgH48z2MorbjV5cCQLwf1oroL9WDG8SOfzdu/FklRLqwzITJcBZp4pK7VkKeIo4UhAvvpfeAjIrgJYTQ5s39kSisuRrrxkw37AO+sNwSL71fozTXB0jjjkZr8+w4MQMlvD69Xn/dVKq1HqaJo+qQTmpNL5zjhfjY+2qN+i+Kk6eVCavTWz9YbTK69YRfexmh9DOiu3Lch795eZoEq9UeVtMK67VivVbMvkVdCXILx3Rw5jLB7FGL04uCXzQwEGf4IA3UfjyAzRcmuJeG6TWVsyVq+3jDSEE+5zDrjDw51uAT2CLLab+VPopwDEm0FCHxj01N43tccxDzjJcuq/JJKQ3YUoJCjKUwUI7PAqEbt9WTxKGl3J2TtJhgB0clHzFf0/D6u70aqbD0cXkbfr02gBifw7I0Toj2BSFrExzXe1uE7AmU09BAAU0GWGcRjn2CyNKLl4CRm3SgYjHi4rgpRCDaQyOtNbTZkyhdwfgElq6TxYe5Z6J0Q4siU9hoP/5+s5mByd/UKnGIkvdj+Zc9bwGazgXb0dMXhX21NE096XbWGfNGsV6bu3bu1/ztI0Buqe4vJoR5SHo+Q6y+7TCtJGwm7irK3TsQ6NEcvZoS7IqFg2SCzIEQ8o5VBesvP4QbVeHOzqQ/Xt2LWBql+DG8JKxDKGZs2YV4a9LfdBBLeqMRk2zhnh6ESahlQlOws8239++4r9uKCaM+v+J4jSP8qNNUWi5k4zuDFzQo5elow2CbAdsJZM8oKaUuITNZkQWfIvAPW1l6yC36Fu3eGqlQhIg1oRhzKGLn+ZAyE+Mizefc6uHdOog2jDdcfznfaqrlWU3pWf9dCFfhnW8c8sPWZsQ7eABA+/ufpDU06QcRusfJc79WU9+638M6wP7RDfxt3D6H/dMJp3s/xA694wCoG25BUwBiF1YyXjEsAZBxwig1GMT5tnvVPLzd6zguaSJiFhVqZ73krGY1N5SV66ufuJyCtKppAtBnWkkVCxNWPu1yUIxgQ1/4mKK68zwJ78EwKR76hTL3eUqEK6QYJShjWNZ3GmbNxdfC2CjPGJaUm/d2kzGdz3gRygsc1x9K7Qh909I+j3+rP+aEymlkoHTvQGcb3/bmrJWnaeaODDuJ03fp2LkDau8E4Vgo/D7yIKw+by2UcEPP3J+arY/mNDZmzwlBKuDR9ZjCmQQ8NJSSmPKfYWeL2ImsPbIhsfpo6PFar+taTprlPbjWAzrjq/bTZNW2Em/8QDd7N48dhZ4HmAtW0xSnWnaFpnFXTbJPTbDSvvtgngV+LPSgVkOxJXHbvbJiN+GqwXMBE6+sXTlP8T4Wy4GZ5qjXyEiZh9UBb0v249VdYMV/zcNic8AbomT0GsBOB20geSaGN+8Toeyita710hsS2HKhUmHuuTmM36Q1Vi4uyXlV4994l1JVK8lHo1L3JotTd3YFIQXuVAYjDWjQfG8wjSm1I9Y1FIwY+jTG0v+6ob5RtXmrWHNjgwX3guKY3oSFdFWAOd85V87ngdVYfQL+nqeuyPM8k26cKIB1P8nZEl17qFi752Q1YbPex5Gj/d0+vdk8/Iyx2FWe9i2/cP+iST0NRkVR1FrNhAgTiRu4QA+D8zZiw0ngUNDDQfn6hniLNrrcS8+0w7YKPDiszTwqTYDcdu59zeDpcxruooznWrf8+bzWkWZTGi99lzgp12eYnncF7S92dNybfI5iRsK23StpXkj+oq3QxUvoljJyyooBMDSf+XUpDD0PT2PRoMjeaulXDa6gF26falbyS/hyyhLSYqQu+725choZKL0kotJ6zOx9W83AjrSGwpO+0l7ojBSrnWKR+qFWG0R6bCiW/h6NyYyAWfqZkM4cLux6O0Ru4ucrMCGZ2A6ZrEnICtvua8qwc3TZspoHcoX4RzMC9RxFV6aeLzlvZmy054z0/D4JImziLtgZaOZp0dbj0N6uzpg3G1P6DdMEuEE3zylM/EyWjoFUUOpo8VM8DTkR1tEKs+lwjMsQzzmBnQGj2vNhmr2PyMMnijnHaTMfXiZX+LN7XwBFi8P2ZuSK1/EUnhYTmD2HxBNjM1TDu3m8L0WuYMQO/Auq41Vb0Gh/xtv2EF8/vya9aMXodZHPLle10J+m+9/XroyXBMPbYW+WVxE6K4hllIbCXwgpo1GWyQvYSJAD/eyWUD/apyiCm/YqhejX1F9A6x4j34Ze5j1X7f5seAEcxsCn/04BrJpzkSTpRs5kNG8ezyZok71TaXZJI+h5HBMOH97DoT3F/v4+jhxZAzIFrSeleyfsQczzpNP7aE1reSRv+J/2kJN46E463F6a+MF0QIRwdI4x+Tby/qB5oDXfCQVoGJpd95oha4CJy7LAuxt5/BoR95g5bjXG6h4Ef39Y30mVhnHkccTBhInFFM1SvpubuRgGErcoE8wXWE6bavPhWbmMch+FEk8VYyANjGU6AsHtvJNimnDU+SQ9OsFF1I7SMxyUlMhhcdjSCxlqNNpj/hSX19zXFkPtRMopbqAw1w28FwjWtETh7sJNnxJ2l2u+Hh0yQeKtpU5AfkqjCTKzHjqDoD+jM1rT/5+9vwnVbdvOgtGn9d7HeN8519p75ybc5HyXGCuKGjEoqeRURQ1yECGnYMmkYCkkggkECVjwh8RgRSzEYCGIlVQiWDFK/AEt5AeDGBAFq+FCfu6HSfZea873HWP03m6htae1PuZa+5yzz4Hv4+x7xzlzrznfn/HTf1p7WmtPaw1lJMedCjZDlucJi/sQeKhIw00d1nE8j50wPAPDzm+AwXJbw5vAb7nmtM/Z4mHoQwAUQxLoY2AfVpWySEWFoHezvio7FKugqqAPQZkyHuAApY8Rr9UigBTLZvEWJENNkXShJyf0zmkmuSHsQQjMrDbLOwx2TJvFZU8RAap4OMu9SQq0KgG2IAVdXIAKMHcM1lhBhFEZ2tEB4+f4vQFwcEihjXytaHxmziSKDSyCzDZhnQ1bDwQnQbjlclUXslCcOtYqewi9QCeYBNkEOlLQ+y+xLn28WJberx9jnRI8BBvPlYLQ94mOVI5UfHFJiQmfLx/KB+8LslKgAtk2YQDSoIB5/8YUMuJ3uSd8HIqfaA4fsfeKnz0EfTzPPFYcByo1ghSxOkRm/6RqpjYy8Fy9x42B+cGGkzpQAFyvr9GWhtv9LW53hWIx0FoAMqCsQanEWJlCJshSTw3l2kUYIbk2Js9aeEbyPZM5FdbbiSX8yS3hIvS/lanFAzIBF9vCXlQvlLzGT65Sjt2kvsMbwEWBkAHzGoj5CcXMMbJ7YEHCDNcorIfUTHhGzDfnM6QAwSn3cKkx1uaBHz61832c0Ebes/+eexixpk6ZVdP+evc1maZqPtE8JnC95GFwEpRdjbH0/ny8rHg8P7PmJeLZJHTaO7tzugnKsTJ9Zl5nX9vxTQ5QXh7yzn8BUygI993kPDwJdJ1c0BMVckaT81Xk5Tm4scnTcCUMCVdmWra0NlMAy3RiEzZuTbO/jc4b1BceN4dyIZ23R7znpyZDXR1I1FIiNNDVsm4sO0PMA+QeEeNZULCTrAsUhs78M73bjyotVwl3duJEf3/QSuWmsbt0Xfae4kVusbg2pFAwrDEHoOwLVil3ElwOXEoIGy+aprQYc0MOJwjPKoY3YmNo1p5MoaBcc5QYOV8nngv4bEQcEuNnc1/SOqMSkuSfqKoVM/WLjLg2QrBwvfFWCtcch3kWmuIRfypxF4KK/NzLR5tfevl37CYuynhtcl+/+EIQeH0cQvESXIrPl3Jc7ZwkYUsxgRxexXSHJjhxgR3lAKaHydCcex8GlTsBGP/ra1SnVXGS7VlkcA6eqBNYrV8VoDoCoPsNoFbBdV2xLAX3bcPt1nEM8bWvKMp7B0Ykred8W4djN4wEwBinujnzZClBBAA2RDxvONaicTmoJo/MwOF7rPtkz0fFyN7ONqbZ8To8V1S2MzHixOHhgk65GmwSMeA3F6HMQfb54SKfZfkc0nOlGrpVU2roPFZieZqsURWtI5gFyIUY45l70m/UXzsr/1Q5CeVjGU1GwvwduPyXWTBKyvx84SU4muQffB2/5F5xdOfPTp/ReXxO24b6bHqKGWiB0/By03+243MCUEIE4h3WsCuyud25WXM9FqshPWuOFf1HuMKnDRLkt8m9l9dXmOgYKAWojmK7xm34kQssiwv5OSeXNxsChuIL5h5c6VO/8f4n4eiW1BgmCE2Aw70QzPIBaghry9bpw6+txps5OsmYKSAKvHT+0UOO2AI37oqyW7GkSI/NOT/+rJwoxDDJKk13YViLoYDPG8KGgaEhTaUOQAdHkPwSeKl/m8MgVsZMTkrOrY0w6Hw+UslJLAHlVNCrpgkeWM9E+OCxPF+4ccXmKnoHsRZIwUlZjrAMKYhjlUwrfz5vDHbea3xMJ5nHe5vmK8Z4ErD55fPr/lYQPvkGJSAyfEdvnlDSnkU1jANia6BAgWENKCVq6ji3C+pAk3VMJrUWXho57ZlZkdFwoLB1RkQIa87Z+8RsgL3p1hUgnc3urVh6v5Wnp4LbwY7QBUBpwGUF1qVg32643QaOXjGQ5CV6agtrrYg4n4pcFlbqsaOIgj1/UtXzRgdQmFVzxgozMLD56rCaQ8WlYoPVMmEBxwP0Slv126ylojJyjIUgkLNDEehBItUQCmd+zxmETCjbgSw/S4NF4/7iWefn0gF2+87cM/JtGN7kxPtzaAKiAMXTWJ//4t6c/own5u/zLj1/c96kkSLP9+fH56BojmbIiADQPvdiwIQqJ8jd1H1+jkgnj/2Y+mkm0Z4efbpDDcNxkjH64sOf8ficABQeqbA4QWERhgCiNNL8exp88ThuBm1zQzHGa0LdLc9JOlFJleIWDyQUNoUKJahOt2yKm5L1LErikPOPCCBVwjthqbMaglnh9VDEKssSBrAjcaXXQeFpwYznO7lWmCrMTVxYGNKzfUAZAvg5RlcXULxnjQzbuBYVwjuzhpPm5FDEPDp3RApQqoWbLH7vm3MCAUKlUIAyJDekuLLmApH0TgHpAUtSHzCXZge//75UU0nRG54TNRA4K2CmPBPAcG6Lg0jiIhFAi3tO3MuV2SUmcOZFEuTvaWDfr1w5aelRmfCSZzG5xckb4hxRGGoO4SyMAoCFkNWTYIvKztAUogGOTnd9ut3YgbE/xNdhksTHICCfLGlMwyTzPU73P3mvwh2eMGpaV+LXnUI6p9AA4lPDvUDMeGutolYruta9c7h5ygZKKbheFiwLsO0bnm+HVXJGNcXvYUijQ6mvFQ+2KN34fFwqKMs5KsVCrAb08/lEM4QWACLGfZ5ZAybxdHSDOifMzlrDYxfePKaUO1gxmcAOz4CFhjg3ahmIYVyFWo254lqiPNdJNp9U/WyExv3B5X96jCgLeXJx2UYZwLA/sxxzPOCKXkOnvA/88xcaSulllyTkTotwBmHzXuT+iA7rvl5Dhc1oSueLz7AmEY15UGbyP8AQnwXoND/LW2Lk4YS45rWS2tFu7cVr+MaOzxVAUZNaEc+cRSjR9kmBxo6YFqqwoyldlYAISbFcrJadMm/kjMHaRjQvCgKx4uUdTb+YWxkRfsgPW7GilyECaghxxVY0P8PQx1DGoXlVe38MOEDR+HxXI6+ScNjd8urslFqc7FfF+4DQOvVNMzL75+T2k4ju+h6e75Fr3sIadO1bjQp4FlQ6KHUgrVH3MugQ9D5OY8zQjYWvgOp1WQADUKWevR/qAoLCxP5JERZTMQuiaY/G92g9uJUVgnHaquJWrxS39rt13CVfh4omfvxegock6p75vJ4pwhNS8Xmh9wHntcHJCOFm92jhrxJASSeCH2I8Xjy4WlE0EQBl4vDo9Nwp6Wxs5jH190+cER+4SaanQJ3uPx6jCApKgBOedxLPccuz8ojrTa/zntXvQ/z8MboqUfabSoceGJxi8u6hrAXL0lCrgY2jA70LFBUC43ZdLxXLUnEcd9xuA8DqntTicmdMa6l7XR1PbddCx1Q+mGQPIfFKvWkpF5+DMd27TnKCA5XvB5tOhiu9CnPtObHMPSdUcXDvhEBCwYsMl2WTAgxqcBJcY/EIZ0umNaGY9xLHmPvC/vb7gsa/Eh6dzDLyu0tZL27UabfxGOwUzzUiIRvgV4rto/P6mZb7PJ4CTG7KBOeTXkjHqsTeOkugs7LPkPAZUp4OwTTeE/iZRp7vzYZkrrf5gs5v8gtmC4jp2V/ounfv+rMfnyuAcjpOi4KSDjiJIzWrgIhbfCOmRvVFr2k/UenYQZb6Ca+ictZcsWspVi8kBGzcIBjbBASdipJHCOMUplxNOlxZFd6nbaSTavXraaGFZPdDLkYJl5xZn3lZQ9lZpt7vE85PUQNgtrcVow/sR8cxckPPJcxfek0ICoj+5/cktLSDST5KDIc74aV47RGn5rmgNSAjBhAF0AocIjgO66xsRMWJTBxSDhFaGSxOJy94G1EVf96WOZOh4GJ50HpVQOyeDUCZFeh8SrRqyp2hEdaVU6FgFAQ4JjBVW961iCsrV6gUIpBTTx0qeaarBxALDT3zMxArOp5UUzlQ+YSilgliKMIIcBEa4xHVa2POXQXyIv6Yk9iOcT57hyTGoni/pX6MBJon0W7K3jLBJEAZFQs9Iun5Ev7frUe3mE/7UE6/c3xt+XfUIqi1YllapKwfx2GhUf+OFODh8QFLBfZtx/3eATSwAJ/VGsJ0fd7v3NbC1v9sEfO+2RzSeJ3q4+8KTYvvFRoF8J5cmgpXfaaFXJwa78e0MNRkxQ9gnmeOv7riLw5OHPTMPBQHeVZbhieedtfkCYlJA/W3p/kLwPYCDPWBcxcVcTuYqCBSYZly8PdMzhjB3xZfKms5zfX8b+Co+ZiVwPRuevGVy/a0u0K35FTl7zrO+1TyrOcjwUb8/c7YEfAJOOLcqSfDcvo1vIsRbtLYu/NdqOYzhKWFeTa/vuNzBVAUmqR0YAITrtWjOuCID6l20IqDCDCceS59Gn0O9sRmF6bzkUjm1gMSMNg9yJkDlqvTzzmtI8pdLvBAJFwAyXyhS9uquRraJwmQm25ebLl3HJw4+W2S9dPHaUV4ITYnhakA4p2Jq6qV73fvCbsWmyKWsPztm+yQ7KJVxIlmIz4DX/RDGQbhRpqEswObzJCBW9CuIJwD0/uA1hpIkSGbPjrKeCcoB2bwsFMt1DxHFtLKkET1TK6ZXB13GILRBeiU7kqQwTEp4qXu/ffqnhWeh+ewa6UiIjhgVk8RU4RFBH0i9tbCy9rz0IM0L7P5D/JeonvySdImkhMgvEQKpIOF/6XOwcytcfh2WvP2wRB0/qxJs5o3TH6eR4Io86ixK7I9/1lQB7zwtcMwJmR6PQATV4NOFi0VYo7Z2WTQEPRMga+toi0LRAQHwUlXsL9SqRWPD1esS8F2f8K2D6g3EBRhXSIxxR6WfPIrQkkIx/pd5alc00gvD4GgkWgNLEQZA1Enzw9rIhlgb66oyrWsLusAelYUHVK45s2wGDJgtam85D8w7R36IXL8WLIhilPSavd7OTsKNUWkKAQdJMTaub2yrhyoqJBCDzcA/6yFxEesgfSkV7+Yh/TBsMikvKE418qC30++koBPp+8h9tL5OPNYzkBj1gX+Od5KiJlxOkfuc1YwTxmkNDon6yx0zukeTogrHiB5iSdVljckBHazF//rOz5XAOUkVQDYIMu0MObpy8FWDBRaALAiXzJVhggCVmyuJFeR1X76mXPUJd1wOW1+bzF3Eov7tAkBhG1h2iG0l6qRVRcCq/PaCjAzj4R9XUL5BSp5AQYA1vqwBoBVTGhgAEdnCXuBlgId1hTwOBDx1REK1M8r7nylh0VIkgREE9CAQErg3oYRHhzaMQxdqQs4KuMBAUqxjC11AEd35DwOOqJEv82BYC5AxmHW1ADh+TptTJ8KRQoHFsaLYZ0ACbHPCRzAxH8RllB3BVn43eIhOXtHIebVggSosA64EplHFZIUgemKqs4x8gVWpjXD0FNheE3n1NQUXmT0D9UX7txp7vx8PE88K8/h45nNBJHjJhPg8y+eMhklb0qRAM2WLjkG70x4qEPyOOY9Fu0YcvZ9rwPswEzlQPDFG0jOiv23CLAsDUtbIEWw947j6OjdU/pFUZeKh4cVS1uw3Tfsu0LVS95DgehxQ88Eia4JUmj3Il6JzRRjk4UOXJlPskX9s0OsQJrNoys4lQkUJQAgcZyFG+k9zuaIDKGo3zM8xGRKnqEWerhmFcoxT2MSUYlbom7JdD/+C/WrTECCITmRAda1HRiAG6LqxkfuW35Kgy/lviZkWF8CVETbBBMIp1T12dPF8dN4D+/IovmI+eSenK6VX+S/PPcZjodnDdP+m4EHQblMe/O996Iv3s954V9nI4LyW1984xv1n3zeAMp0UMAYVshsgVw25An4QvNQgbk0NRaqLU52CJ2+N22ok7tLk5BZWNfEf0cIYC9pJF7SSDjp/u4k9yeMm3hpWuilCEQLDvfemLL2xTsJ/Ej9pbKkcnVFWpRcDbfTNJyy8V0AEUYBnOfinpM+OMq5KOnSp5yHe1ymriAnORVETz6xJABJayRBylywLciSNYvHjTEiu7yW7CY81FsFqAt4RYRJbFz8KU4bnII81w8BH/kP/LgpozMACFGsOaZUfkXMGwXAeDPFu0pLsarADljMsu3WTgjFC35JkG1TYOe985kM1BhpmPdEgWWiOLkuyfK3kwpdVC6WojggKBR9Lfpajgfl2V14UTBqrCGARbkkxjOHmMAogXt6swCFeHYM12vsoGnfEF8QUHUCoVDW83rLvT89rXMpNDwLAbR4XR+vpVlYRwQW8twPW2cwwLe0gut1xbo0bNuObdttfGPd52bhssvwAOt6IAylAM5jlif+/M4hCnAS4RcbXBEv0ij83RU5xLWkN+sjUAmQUwOUZCiX4Z0Z7KinPivMmzNi1N5V07mvZkKpXbiFt2T+LOfQ5syrgYvCalgBgg4oya6ds+Wg0le8twfI8+qLvzF9foYh8/3ERCGLOun8zvR+QorYOdOjnvQIJnASp0gwOi3b6Ru+bud7AcIombCNyzmdpuIMYHM3vfgS9N3XX6IQzWc5f/8roKJPOT5XAIUDTgwZaXT+LsGF24Hz3Ljic/Y7ykQ8lPhslH+O0IQvaFYvnBZ58UXJvUUlwFD4XMAiyaOxLfxvv0as0eldCitfWLUWHL3HOTSEBT9FxelWuwvBIibo05Mg52qk0/3wGKqQIRjao/bJmJ6Tc6HTQ4V1jskagwtC9Xv0L9MbYFkQJol6WLoSwAgl04UBE1Y1tbQ9v09ZdUKciHlyhvNrWNxF1CreynzvMIBbQpnrSUDQQ2bAK93AxbNvXsox1lFQFv2bwaxnPhRB1qdxzoRU+1wfil6A5u/T68LqmTYGZ89Z+gHdei22L+YpzYJsXC3T64UeN18TWgwM6khQ6vNkRdG5T2ah5Ot7eIBi6gd1Wl+T4J2FM3ea6HxOI4qSgBpq7cWemwbfVeos2XkfAUkR65ATo5JelrhNTaCkNq6tNqxLg4jgOA5sBCc+H61WPD5csC4Lbvcb7ncDJwXpMTH+m+0JSLd5Ki6zZE4i5s1wv/v+63C3pQ1AiWcKiZSj6svLwjzWbauE91hsjaq6F8mByIQe004fYAXazHx0D4woIFZ6QYp69Vi795xKPY3nyYsCOPjgjfN19T0mmXbtReEEh8/rDh0DQ/sky5yDRtktAqZFRwr0JNZB75W+WE2SoPldT8TZCz7NmH/1hXdR8v35awQZ+TmXBQEeZiBHuSWYjWgNUJPAxqaRGvGFfvC0/rzBeTBiF7qBfL7jAG2a18ILWfT1HJ8rgBL8qzhyKmdXMSfXLE1qKQ5w0IZ84b/EggQiYrFRRHR1+lBuMlZVyU/wJiVQcU591oeYr2cV9tUBj4TllNkd7p4vwzJDpvsIRSCm/JpQoWSNPwp0q+6ewIYLOazECQwM53qMQSKtC44zSuFWcQFAQmfCAHIaqKCzaBY8jTrjpn3aUAo4FyXdi6zWwH47qsPHjjFtj2/7OYY11THQogr1LKQ+AzRhvRj33Lxog8zqoBy3Wbfx++f50Jj3EDpFrNicwn+XeEbWZkg3P1C84BeLfknnpbLUugKTVY6wrp1Z7J4smyTBPEZ8NAl+R/GOgAx7me5JwBnXleJ74uzyDTEXygh55QD9cauJNJTPTRE6fU4Etdr9HccWYx8imYL4jFLs2npe41TYcLAy30cAkwj38Sz2U0WwtIZlaQAU+9Fx33ZruCkC0YFWBa9fP6ItC56en3G73yClJu9IqOw8TMG0W7FVkB5TgnRmrpV4psDiI5/bokUaIHpWGFyj4mGe4TJhTHtFQ9mlN/asrBxAOFAwJTXifePkeF8scYASoMdnxL0cbGoaHhiGqxRQCR9JFKurDtALpbUKhh7uTdTEcKjGUYLAOITcmw6UBN4U0fUAnz08wpR59PLls/sCsn9O+iXN3xnAcF9zCMX1QG65BBem62cg4Q/0Qunn6QlFpjUVG+3F/fpGd+z5KSAi5du5Bxd3o0zLYDJGYxBCEr737F/r8U0OUF6gu8kyzFx5XwIh+eyVABlCITsLgYHBwm0eP50dbnnV9Ezksipm7XCj+Pyost4ixTqgk4en8D4UOfn+mcSrkoLblyPhVXgbKBj8XgZGsPVRzRJKwS2uaNK1T2w1e1XpuQg1oYiiWAy3UCkNkDdwzhTi6IsrPqvI2sMnrf7cVGIWvsl7a8U23AiKPb0QThIU20yl5FimMqGCHNBRzMonyd+PMYBDuwtZVurkZst1FAXeeA2496AUsNEkhUiprAzGInoeTuI5AC+iR6jozSzhXACG0nwUmIlC750JD6/sGZlmTsLVTOlVEFzyb0crPgDMeqLi5ufBZxO/27g3GL9gVlRCJeCABQz3ULG/UEwcVpn+jv2a4Ga22rn2uV+Lk04t24zZdnkN+3oKyPN61thndi8S4MQuUqZ5d23CdeyopUjB0irWZQEAbPuB+7aHZ0mHVYj96MMPUGvBm7dvcb/fIVJQ4UGUgnScqACYww6ZbTb3FWZtEmvi1y00k3QKL0PiAJzKnYo0pJU6uLY1VGRAS0exBTqVys+dnx5qlyMAkpPCz2eo+RwW53yaxy+hKttjJCCLfeTPUUpF9SaeJZG2e7aMgDsYilEgQ/MKC0lZllFBh6VH23dszEd4OyOrZ14ssLq9cZ/CMZx9camsxe+ZUpvzpywZEetLnWNCL4kGOXj+DDPyXqbv5rrmvUy6jWsp1WGcM6XaDDD8HmNzzvIamF1F6mMi05lCqyWSjtOeWSgvLvg1HN/kAIXHJFjAidcX7/lfsRleunt9scjpTKAaKpgnVuLfiGcGwdZd6AQAQLg/yZ6OBSWIzyoQ7jUKTk7n2cbMW9Zh3A+URPsBCFxhy0juug6FFoKyieAnxZRncONcJLllZMKY/XW8IBt4jskt66U6HT44YDAF2j3ryO6RnhZxwUKwk0THHGIXooB5O8oAWwnUkhuc4IdghZvauCqZikxBAQcK4RWAcWmGDrRmz5EeJFMbAnPVl2J2W5+KHjVNjkv1Wi21msbog9yH9EoMup18rPtAeHOESrdb3L66VyWek2mbWhE2no87lMXx2NX3bHmyuB09A6XY/Zpni32gfM84aLHzs6w50kHIcALnn+91GwgKLHaNZpVdPkg0eQuplvvvXY/nefEz60gVEdoUXj9FZqiE+dX8W8/F72LfyfRB/1aSa8Jb05aKZVkACLZ9x23bPV3Vxn9tFR9++BqtVrx9+4zb7Q5AUKs9X3FcEXzWAEgy/czKhXWRyJ8Cou0FQ7q2lU0ShWM4z3dWZA42YywLShkwkpNORRo5R7NS5D/pxbHbdMAc8k/BxoX0SGPM1ZCdiMo0XwhKaVZk0b2Lxds/hKel09OShtFZNidnUApB1TCwAgeY8M0gYp4d3puaYSru6dWZKBtp6jkvJ+Ag7x2lF3PqumdaX7Pxm3hcpm+mPpmvQPCQYz995MU9UD6+PBJA5efCK5RSNXY459W8qHq6RuKiBC7n+MNnPz4nAMWPF+NPgc1CPP4CGKax8WX6sbPFtYSizUUy2xECqwlg1R4FFdY+vEG1+e81bsa8G4LZvpzd3PM9UwHNS9AMBiOwiX8o0kd1RC+YoR6HpjUfoFfAZZIN1uzsdKtWIWiaFqi7CGsx4FJF0Ko1Bhuein1aeoLQKuGh8X8JBJgZlJYCgYh4J9rhvAKmvXIWU8kWV6amVO3Cx0EODD0AgNWCcGCE4ZVzy7shLOVcJFCaCWazJ6gUAyhLq1BYRlN3oFFdWIuQH2LjNXRAvC2AQNBqRW0FumcLeYXXoRgK9IECcW6Pp3BqmfrK+ioKlOCCms8y/bCCcXGQZ56fSbiLcZdaM4DdvVAVFXAZilFMiVfOqe8hcZBIoM9xL0AkojCmbbW7HHwp19jZtuI4pJOF64ggCNNzmneqlgJ1wjPJjGmp+5oOt/Pp3Vy8k8CNNTFLdT3rBnr3ApyIuOdki2aZqorLsuCDVw9YW8Obt094er4Z0C0CYdo3gUlERaYwaugkmjT2jOGXD+WuDlI0FEvxvShKUGhKO40yeyjSSuzH9xUoOGbGzlkRnY7I4pn+nix/mjF2b+xjNVK56XAe3Ig1nnwok5R9wIpHqvnm5jVfJM9/4hVi9z3nLirQQ0ZjcvIvijjoJ4zICro09qCsNuvfYzrzBBDSU6ynJTQtM6SvfhpNgXtAJx+LnEc7zyexLmys8iIB7ifdpcJvwTOjPu1Ir37sienu9fQkMwKab+B8v5TZn7JyvqbjcwVQwj09LxHJ93IEJ6s/FmtCCApPxt/MyA6xiVm0xtYNdpEgasL75TIE4yGKGRBT/IxULLkW8hlk2hAmo9O/M4Zi74rep69JAo5IqQWzcDTuQURRdIJMAWwQ12NqZq2mfMdQHEdPRT9ldaSnyUnHQwD3OhS30BUs887nEQu5EMgJUGu6SdlZF2BqqXkoqNQ0gI95SIraWPcBq83inqNSkvAIWGiD+bS0xAiG+Azm7SnuOVEsTXBZzINSRHEMe+KCGimOpsTsPsdQVCnobkXWal2VMQwAWh0XiSygYWSbEDA2XwNKK/K84n3NdJ/vrApKJpUJcefRQFJgiT3/0qorekRbBgsRmofIbooBw2k9uUckXfMM9SVwoTAW7/Ct032HkD4/UB5CwKKn+mq8VquWwdM9g4pOZ+oTHlkcblYcGpefP50iItDI9J1U/nVpWFfrMnzfD2zuOfFRwLpUvH71gMu64pM3b/H26dnUmlQD0gQoAdbElV/u9xIKkj1uNJS//e1PR5mB5KAwbFps6Xu4NLRXjgGQJHCWO/GaTjI8PBzP7rLVAV9YyupKU15OZHog85oSfcCKK9ESsoPwxNZg96Z84fX0+6wuIE7Ebq9FZaNawcA3i4tZRpwF1SYzDABfK/mcYnsTARdctlPR0gs4ycv3QA6OgD/bFOp5Z8HTyz75N8KA4ndwWrIx9vmF9GwgZdh8jVjWfvokJE+vUWeon0kQxkquAZeV0NO82prIdT0P5zdyfJMDlEnQUVWFdZgDmh/Pv9I20Olc5k1Rtzztcz74PoEDFMy+6LX65vIfaRD3oETMVsyN3nWgwNz9BjoIeJx7onmLdBNPPj+/H/4uvgDsvEdnpVRkWqiYLzmQfYGFGjTbtcsYUCdvifM8Qm7LxM1R29ytFuyFwMEU4/sAMom1gJFISxE0FHQZU5YQe6m4FPVW6OLflVKgAziGEwdd/MhIJj6JuoHr1Im+MoIf0w/zBDQPuXCbzd6osDOpuCvj/+rgUlEFaAVoLZ9RFaitYKnVsI4y3GCeoKEFrQC9wtO4B2oZkIXjUgEpXiMmhWjx0vF7t35IgRV6ekTM4TLcS2Rel1jhnF9/nhKAwS1F94g1FnpTV/SRag6kN8aLdw2Z1lOGfebwUr43qXUKOgdLDP28T6jnbrZ97DkiJ5ErktV3+7EHqGf4SN37l7N9iv77vPve0+k74WXlzaQSMpmiaK1ivVxQSsG+7di3HaN3X0cDrVZ8+MErrG3BJ2+f8MnbJ2SAeCrwPoZbycWytBzfkAyqvr8tBZtj6c+gggjhhT5InoJ4CXx6KxnOPXMcciyp0opYqMNI5uaxYFkAdhxXCHTiG1HznUdYYaUZSihwTngRRSkNodQ0+ygdvtZtTZt3TNBC0b2s1RTWu6dgWyo2n2sCTYVj2HMUw+Dzbs44DKioQqVyRE2YmvAPmUWRzNR2LhFySgQTiI915ACXlw60IDkW1EHTXzlRqfgTjPDcfp8cfnz63rJ/aSTO1zgHQ0+hK57M9zGQa3IOI8cJUh2fz/N1HN/kAIXHNHOSwnVGb7OQmuPN+f1ZYmqiSMAn3/glQ8mvsA2ofE/tb3JRQJRLTCtMV83LcX8FghZ45Xo9LSbeWoEJUlq7VIQ8bzy35ndsPQkoyeLcyv4rXv+k+D24kC8eFhviGTs6MPoAmqmL2F+qbqHkAicZktZ8HwNNpj5HSO9LCn9XEENirKyXqgv1ke4h6QZQahELN6kJbRb/Yhl+NjA8+sAYHjflc0qGhXLqTZW0IliXBsCEJYNy9Dg0L85yiLmea2lY12pu4sE0Yyup3UfHATWvCQRH3yFwsLMU1GpVK3uHxb2jp4wtjjIUxzCA0oeldFtxtgIZgmOYp8hc4eodm21+CLaqmBXJar4AUEqCEwpUA2j00qRg0QB9XJGmrKuvc2LRmFL+G14BAbwPDBUl33kpv2jX0Tq3z1AZ2cUsxGcF2sg/4Z7NTCoK8LyFACS86Wn3T/AFdMMkKdJ+DJysqK3i2A/c7xuO/QADD1IKPvroA9QiePP2CZ+8ecaAe6cwwlsxqKUKonO4ed+S2FxANWBj954URb+3Ec8NeGBGHYyEkpi+MMk4d47afnA5YT26/ENjQAu73JoEmkVmeqmN8zGDU0E1uQCARFpBg0iDilWW7t5+ogc48YxAtWcWYTXcGXAob99Au0+QsBIvAEsEUB8NpjyHiwjmTbGQUAACKcjGiBrjY6DHWyUMD/87yCe3jWBhXs9nHVNAQxRc23wSB6HTzZ1VUww2w16cP/E5oCLhWwGRpidB7BEgQ/0slpnnOe8DersARLiWHsp5X9g3XgCo0G15L1/P8TkBKH5MVnuiTC7qEu50psbFcqflN1l94ckAt6sgbTKiaXpNKkQqRIsJkykHxBahk0IRsiEBDGPM/Nt8wSBHhkLdFpmDCQFGscqYQ4FaKy7NvDBDDWyMbrH5mfNIt/tgGXyXV8P/w3sQD2vU6mGDYTUtjzFQjx4Mf2p2phCbLzk3Ds/NcBDHnOEUs+5tM881JYoYV8MU/GHQsjBU5n1rxkARE4Idw0GUjVmBK3FamJqVbHW4B6JZq/vRJ/exFEizOiO1UACmIAkQ6IAg3Ov9gGgz70BJAmetFdIVxhWwbIQiXgRPzdpeFy9TrwOtNvQiOLrPCazRWnHr0ISyxj0NWM+VHu7aESCQPB02VgQEXa1GRDZTTPEhsO80LZn94Z1fk99EAWvzZsrLRZqypxTBagpUcU3Motu2jgluU0hm9RQNhTlvbf7ZSkUrZt0fXggt94pSntv8c72Bc8ZNyAvz/L7+PZw5518qFEuruFwWtKViPzqeb3fsRzflrNZe4MOPXqPWhk8+eYs3b+8GToCAHepjmZm43nwwNQig5tEMxzqB+5zp5Zl3vEWrLzL1rXLlnaDOlC+9BTaWNZX55Fmx6TTFbbJRULQYWIk9n6jPfSovDELzMhf3nDKZzWSJgezjGDgORVerP2SpvhRLfi5JpZ4eHHt/zljM8LxA4j74uo2N8Vds4M3TwdAZQLlu3EIbY3rSZ+5hat5UzbOuoMSPpfsCrIQ+mD4N31dngy03RUCaeF1zHoTQOJeyXZcZd6ebc6BM+TPLRgL3NKZTKuTJwzsFAluK/Hf5ZLEO8qG+ruNzBVDSS5HoVdxFyiZtDNmkW5qCiJYOwMUem9B3jgmFEhteh4CuTHPxOjl2mtuXS/q8rV0AcRrpLhM1Ny5jzAhMDyCVo4U2jJ3eWnXwQYflgIwjyKfiQs+sYQpNU8RU1JWuh2ksRQoWzVDK0Ucs7AlyT3PARa0BVFhjpHrYgiGHqC8CdbKvAckS2TnqitK+S/IsxcDRD2TwvOfedrAyb2Z6vyjPnNwfoGW4Qm9OwuUSqhDrsOzg6zjMIrMQkgnDMQZ6P4BKSykFGMmy6unBtVZo7xBIdDbW4/AxtfLc1T1ZWmxdChS9u2/OKwanWLLrDBKQaOIgS+EbABwnAFgLwFg9xWeBBIiHpDcliMUORAl4TsRkZXhIY/xSMBtoVoUVBXvh8bN1Mgl8JGgxq83WAR+tenjnGCNCUv7wUwadi3cNlYYI+wbYkZO3Epg8F36aAvOareuCWiuOo+N2u2PbD7BVaGsFr149YlkWfPLJWzw934OwnsUdCeSocOF70cKtRefPTAqHKtnHM4pp+UlM9qg7WGLkfD4AKQVFxT2vet6jlJGYfAf0Hgmck+C5Q0wJgibI8bEKUjmBeYDjEn21VBX96OgD2A/F3oExWI6hhHc4NP4EMrkorGR9zjaVKe8/JINSunJMBswXS8DG9eEdjxmSZwNC0Mg0YaHTbDC1P8C6EmFOylindfiCsxK4ReezJiiYWFKxux2bhTyL9ToZffO4nOYR2USV64oGx5hAfXg3Y80h9BO4Hl1PEpAkLJweBDSuHI5NAOnrOb7JAcr54YsIpHqIBQAXVBGGJQrS9eZLJWB78UkkocimQD2rR5VhHv/hInc3I5U5Y4qnRajT/bmFxq0eC3uSygxVpEWkeb8vhNhQWP8ZSAAwFe97E+5I8VoodsIBRCt080godIzwTtCzQ1C31BpOSivrztFPUcGKo+JjV0BAlDya4CEUwVKyoJq4tO5+j0VqCEFLlzTuwlLTe8ICcRCNDcE6SL0j7jPc0+oRaAdXOoBezCocsA8qEKmrvFeBh5NqNSHL0uVuFZZi7ur7dqC16mEzQCtTJgWlVkvvHQ483boESnSMHgPo4srKvRc2wrlGTdggU75BAqS7Y6W4wnbwUckRSaFd/UsxV1yDBCQENyXXUSm8d4BCVE3ug32RslAdPRFcR6k4qis83272zNMWFq4pnzMRhOCca2W05t61viGy0iahCCDAY1qDVM58hvnwIN6wsSFRW2BE53UxcNKH4nbfsO3dx0CxVMGrxyuu1wvevH3Cm7fPsAKy7qlUDZ2bT5l7XYV8LQcp4gp/VgzT1/IRhdAPARWmUwc01/O/NgaS34nXJvCBXNvWXFFiLggUVW3PZIabkZaNvCzhMWbYpveBfgzPrCMIYCE3v6qcYUV6bPzOplBIhLJAbwoPtj5gONmQ8XBwxyN5IB7aUYHiyHUUesDk03CZyPHjvYi/zHngGhXu2VAz5xVH/RAQVPDi0Hd+VV8vn/Y+H0wdFNkUsfGrQgd7rMG973IeaeXd5AkJZqlvTH95qC+XUeo1zR+u1W+Eh/JNDlCAHEz1ehnnjctiTgQrXDRUpEn6MaBhXIWUBqoFRoTlQmbrdiPJKowLoFKgUk+IlvF+os3qXoNBQaITUImJ1ajyyUXDc7yT7+/nYe2FzvoTPA+BjT9nHwmOLKwzsMOIrqKWYSJAdrd1wbRUG8Nz2ehJvcm0kEFUnePNIkuxCekp8tMUFyQBClzoDaQrN5QerzNMmZFgx9CXuIDqityY81yIYIH/3f16xUDeGB1j2C5TFSu0ZqYw2CW6+1yZZ6JgGxZK66Pj6HR5ClorWJtgWSw9WWHjfwzLtqqlOJBSKBz8DAMWtDyrCDpYNbMC7rVptViIA4AV2ZqAscIr7HI2kntVRKCF7uzcJbYOJ69BsZRKCxfaWJBAGOM5ACo0S3e3axUxq0mU85GHndtX8BCgjADL0zaePi3xN9dyLRbSBBAhTN5JKPR0kcQ6jGwxnj68awKCLoZb+ZlagHVpqNUqBW/bhm3zaqVq8/L4+IDLwxVPzzd8/MlbJ8BL7r9pD08Xh+O7IMbb8pU0XmaZbsIp/ggF4SePkIjOdx8DA6Zgh+7VSRaCe1f9ZgZkwNab7ykDixw/CirWNBLUamPUqhHDAc/E6cOVoinD4VWMRaqTRU+aFefR1xgEKlvzFmusi1DsCoyZf6Ka3pcpBdrWxtQIUKrLc4a06qRZ0yg1+VP8mgJ6Eue5lPmeKJ8n4S0E9aETXC5P63Y+53xwFLifqF/idJp3QeI0ax3RSDMjaJy8J2M4kPHUdV6HXpF5fLl2oID1VbLXSHqXAo8AIM8Viub8JJ/lKF/9I3n83M/9HL7ne74HH374IT788EN88YtfxL/9t/823r/dbviRH/kRfNu3fRtev36NL3/5y/jd3/3d0zl+67d+C1/60pfw+PiIb//2b8dP/MRP4DiOz3zjLw9aO4U1KIqRGUPY1+mnSJQKr56uWKqgVIm/a61xLpYfz/AQPQ0FVnPUF3pYySmc8v5IWvR78tdNMQ3PomDr7+kcAliNgnIStBQwXGx9KPro1qSsD/87m9hRQMXyk4IBweFhm+6Klk5GpskWcdJos6qZpU5Vcv3+yYVhiGn4fVvIpKKS8+AbcAzjpXQfI3I2Wq3R1K8Wy4xp1ZR9Lbn5SBhNUWYk3t0JsWxeeIzhPwYM9j5w9O6cjQSEXCPF54ghBIML5EvAvRpszlch0jAGsHdzWd/3gdvWcbsfeL5bP5bDuUDds6x6t3k5hoEaGyufC79/a2SY92jVSln8TdBaRfX0YAt9Ve/qa0KP7QHOu8NCVaXQI2iOcdXiwsqze4rtA5n20VIqllo968gLtvn6Ci4BLTIHSHOtFUgqUSM3M4zmpmakjiXu5h6bEQ7Bk6WL+zhNKEgnSUrQhfAWOIgLICDxnRH7NRWEiGJZGtq6QEWx7Rvue4ITgeK1e05utw1/8PEnbmWXyB4D11ic9fzfVCAS+55fYo8YcdBtSjlNVsoiuKdKgoSez0uuT7Sk8L2Tc5TrLM4p1kSvSH1Rc6hEyfgiti7WZcHlcjHicLUU6m3ruG8Htu1wLpUp+eLnFOeNFb9fMwDzfg04sJ6Quykj5OIcP6URaQakDrjsJRCy90d4EyQ8lwZZJJR2f/Ed1eI/EtdRtQSJuVIqAkxpzAnnIoBgvCsxY6fFLAQqSBAAADrN7wR8IrwzeWLJvaMOOMbAoS5f3Og5uv3sA9iHTj8d+xjY+7B/Z1k5vP5Md6OKf4euUf+O/Xt0dfnrY+q6kB7dr/f4TB6U7/zO78TP/MzP4I//8T8OVcW/+Bf/An/1r/5V/Lf/9t/wp//0n8aP/diP4Zd+6Zfwi7/4i/joo4/woz/6o/iBH/gB/Mqv/AoAoPeOL33pS/jCF76AX/3VX8Vv//Zv4wd/8AexLAt++qd/+ht6EMAbSBEwAADIJUhrPeU2Y6pEu74YhSi5eIxUMIZn7NTmTfL8XxZpkwXQBarV3WRI1OwKn2GPJmY5MOe8hyDT04LOuOYUz6fb2p9gOLgJgStGuFQn2sIVcP4AhUYCEuB08+Paubp9BsNDO5Gi6myFPrB74bE5HGI8F6s9UDRDBoTgWfDI7r4PRR3q9UUEMhS1OfiaLCkqsj66V6JN5Tc83lGmLILDyZcUEJmCnPycrgMNWTq71YohCqnA5bKY5wSKoxtbVcWIq63VEAp7Nx7LfhioKFW8YeAwQd4F9707UKM3R1IDY4TVDCh6N7BSxcKUxdmjpQiWxVKR9z5QBzzGbx6mqgVLawFubMyK15Fx0OxhsFDaQPw3xIcQlFazhsB9Y3NcAUtJP9QLgiPqpnQnUBJoBhym+cVzhVCG91GR6PKddi5wBleYNq2RVUsR7Hv3QnZp3VHYJ+GVMiDBCHdYFMKKDUuAY5lQ62K8E4Hivu3GORnJgXj16gGPDxc83+/4w0+ewFLrp2vENfMeEwxMBqZqVNllRh0psdn7hYrMQEQOi+8BHcbRwGRZw+m5ntEjwUXxbcjMkGldxr6GQLRDxPp7CSzUSoOjOF/NPEuHVZf2sRHyxYqF4myYB4oM35PTohPKvtzz9OiIZgadvZaN/RKkWSFGI/oWMBXelp+Hb9U9uJFhuTsAcbmInuNxAoATXSA4dwkYppUecxrrEMDLYoQBRvik8Z9pn/h8x6s0dBO7xvU5twz9hocTGuEbAoUeSQTuXVaCnLPfihJCThdNnM99xc/w2nXa265y/f5xOvtnPT4TQPkrf+WvnP7+qZ/6Kfzcz/0cfv3Xfx3f+Z3fiZ//+Z/HL/zCL+DP//k/DwD45//8n+NP/ak/hV//9V/H933f9+Hf/bt/h//5P/8n/sN/+A/4ju/4DvzZP/tn8Q/+wT/A3/7bfxt/9+/+Xazr+nU8Qlo+TNlMVjZj8L7IJtTKUEC+4SRZR87k3ukginWwUgrKKNDioIVIWyTQPRxQ2ERxgyIsxqGCLibCir/Zpzs5IfF4zPlefWN5eEOloJaKWq266eEZLkaqm8+SXgiXxQAGujoIccuX3A5VnSp2qsXIa8VeDuy+UIuaorROtSO8TSbE7DoMQVFZMK1xP+ypm3uHTKgRWBkZNOppiHijPnJ5BL1bnwzWBelDkQ3OPCwlJxvHib6KVkzgMmRRUNCaESLhNUHINzGSq3rGj8fQtwMiguPo1llZ3IU6BiKJcZhXrLC+jHjYKOZ0YHTxJmL2nVobajOQUHQ4YdYtVz6HsLmig0sH59LJH3BhDfKZBF1zCTFuzwwaJbiXEuCEYTaCA1r43XkJ5A1kSFKhBQEC2SE4Yha+dKU412J4RdZSLGw2eK+J8EU0rmt/G0i05zaYJMV4Vi+3SQjZKUsutIMrdq4UA/5c74J1rXi4LKjF+uvs+xFAt4ji4XrB48MF923D26dn71pcUplIXAL5T/6hwrAZQkGzn1VoH3/eoSSAMsdpVofzhSQADXlLalZF7GnxomQWc3RwMClNqub82+Z/qdVDN+6BGFao8Th6trCYMsPodU5gKRDU+FuCtDGPjITMhANpgY13yDsIJhJGhMjCa6Qko1P++tyr45YApS55p0q82XvoBBdifOP1mGKZNI8aFJye99Toy583n1ZPzzERVaZ1wlEhCDh920PDfu0JcFg4beaeJCm2D4Z2MsV4xKVGrKFAVZPqEMAzpDyMO81XSK/A0RZmK0Ly99d/fN0clN47fvEXfxFv377FF7/4RfzX//pfse87/sJf+AvxmT/5J/8kvuu7vgu/9mu/hu/7vu/Dr/3ar+HP/Jk/g+/4ju+Iz3z/938/fviHfxj/43/8D/y5P/fn3nut+/2O+/0ef3/88ccvPmFCrTaB9nSmJYHK3tcJNEz7Os8yufeKFnQtGO5uhFYjXo2CIRWqFUCDjgbVxX7HctpEAlgGiExItdumKxIhXhCOUJBSmCowKQDEzplROUCUraG4DSVP7u9ZaGtulpeG6hRNBQsm1SGolFkiUfK+luHFxUwSkuxXhwu+YUW2GCabhW5xiN07sO8HDgcYdVQLr9hDAYKoaULdpaoenqsoagAnBJLhP8CvO4aNB6EoQ96jD/QybG0MRS8DVQzUUDnbuDtxGGZ1dg+dGRCx+WE4DRQCas8Or78w1MGDeobDUM+48jlWBYZ7XZDhJUUSZo1fQze3rYxaNLKHYh74HfdisHUAhGDJBEv1rKjWHHihBnAkl8TOk2txjAzRqAjUy5RaDDsBDMFhkGRTp4KTJX5+SwX3zry2OMHot/pCn5eo1aFpYHn72cMjSKXG0FCq1FzvYdHFPvLr+bq6LAseLhfUWrDvB+7bESXsiwCXy4rHxwfsx4G3z3cch83d0PleJ5nDnTWNAZUofD/S0hWfN89yxVBbu8bd1VAAKbdmSZfrSZwYSr0zgKw3pohZggOQEpa+Bnig7Gq1AbDnO/rAtu/YNgsld2c4i1SXczVkUIJSGoMCKkESbzSuyVHLubCv2r+k0WZ5BA1ZoJF2zc/buYuTOa3Vt/jLHXACvj2jgb6hBWPylqQXJWeTYNEWW04mFTrHL1/L79vOTK/IfHA9CqgjciHFCE0ecJ/SKSw5ZeVAA4CQc8jPDl8HDMcP36+xb2dkP/2aN+rAQwLbmnwgnioacpt6l164b+T4zADlv//3/44vfvGLuN1ueP36Nf7Vv/pX+O7v/m785m/+JtZ1xbd8y7ecPv8d3/Ed+J3f+R0AwO/8zu+cwAnf53ufdvzDf/gP8ff+3t/7qvdWK8M2iaxj0QO2aIrxRoBcXJl+VlzxFAMg2iBSgdKgo1iFRikoWh3INEAalH14xBUPY8q8B0cdClP8tQiqIsmyp7StQCgBJOgALZPgoyJq7qY4jn76HgFK1iyxI1x1YUFlNdCwIqQAw0BoB+On6uNnYYBaBkphvxeE9FeYIleBVXwNLg/JsEAqwGaC7ug4uqIcA0tr1q/E78kOL17mQml0u0ey9wVMnS2ZjSMSXoKXmSKAhSXKqBBV7McBVEEfgqNbkzfbgOTNWPjv6D2UFeeT8Vnt7r5W9Zpy5nE6YGEzwLxKw0MI3OS09IIHI8DoHUogo4rjOFArYgxDAAwLDSXpETHW10yWKgABAABJREFUUAMotS7Oo/FnFglez+LnH74womqo5r2xMzKhQy0CrVbRxJPE0knC9V7S60OA4l75EN6lwLr4FkE1FxbEvRBj5Dr1DQKB9UGqteLou4MyB6ezNRrgZJK0uexwUoC5o1AEWJcFD9cLWqs4jgPP9w075xIW/nt4eEDvijdvn3HsVkG2THuM5UdyPDCFafi6j6bQc+Kp/u+AJ/Vwj7vjqfT4jPryCf1b0/2c07gnr5d6irkmeBOBp5qX+FFlUbWO3UNd+2GVhcnLE443ARAnexpz/s6sK6VMmf49TVO8l0eRGt4nFQNyDKtxvrmfuoM7UV9oAOYKxgCVa4aAX6wWOGa2eyiTDueY+YdmbzcDfSHz4zv64sx5vnf+oNyf1iv1yUsQQpnTVcNLys+Tp0cvSicw4flAcDIDpfk2OP7zjPAXDScRV1ayHCWjhhNX8Os5PjNA+RN/4k/gN3/zN/GHf/iH+Jf/8l/ih37oh/Cf//N//gZu4asfP/mTP4kf//Efj78//vhj/JE/8kdwUrxQtOioprFhpr9MwdLSBsGAT4vzT2xBZshGxIDKkIKiDVoqdJBvsjg4WQFlk0BuPa+7MQgorBBRVaAXoIwRG9sUTrrnAYB8mPMqRoITyToXQ4fXZeDmd0UODVcgY9zqoQ+ZNxyMADqGWYtGNmazPSpY+2ABOQ4FzU9wICtgqmS9kQIA3sittgwVDTMX0VpDqQV3mCt9uDdB4YRQ4Uacq6v6duiHK2MDmxwLaz+f5eD7KAB6hB7gHjVaHUWKe4sMWB1HN8DAEAfS0jg8W4fbkJufoGzEszFcIlGPhcIMw6qfmjD1DKlqmp72shVuMy+RkVcP62cj3otEs5EbYFVVMeDkxWJeDk0hyUrA7HwNadFXiesdUANTJ61nApaNDEXgTReBgWG1WcwJguKeIuMMFVsPzPQ5CfR0D2sRU+5FIG7hdfHQDsXn5I20dQn0TbOsP58T6YWEX8eMXZ1wkq2lPGOCgaU1PF6vWJYFx7Hj+XbHtluJd1HFcllwuV6gCnzyySfYj8mlr7kyi3BfzdwEjT0Z23kOAUAy9V8ELNJjskP4OPYckt6XRELxggHDqASHAJucywAnXp2W4NbmtjpQNu7IMYC9H7hvu3N+XH7UarVVVN1byNkip8b3zpTNIfzb9zHDr/AiajySU5kCKix3gKX+DCTMXZ2pXJWN/gz2FTgv0EOerPxt3+sGFpXy2l0CyjL9xa9evPYZiy0gYFFaGpzX1C8xNcrvuhrPtvExR2dNlp6N8HoZHvPK0eScuCwbwMFQu+Y4Dn3pNXkPMAHyX8xradqzBH++kaJcg6MVEbHO6zEUiiH0+n0j8OTrACjruuKP/bE/BgD43u/9XvzGb/wG/sk/+Sf4a3/tr2HbNvzBH/zByYvyu7/7u/jCF74AAPjCF76A//Jf/svpfMzy4Wfed1wuF1wul095N4exldyoNugCHZKAg7wMfoNscBfkol6zQmss0iL8vQK6GjARApQV1sX4Yht+EhSRKcGOw7BFWmHeExm+oYa6oigBWLjZ6T5LK8UsHJIkSzyIeKXXadEjFz4Xe7gRfQzYIqs6EXVQscHTYIdBDzXz1pRw352BDwdHMDLbGEyScKsCUbQpvCHh9vQ50IFWCnRpmBse6hiQVsIjxvj/UIt1WyYJLWH3SKjDQxVLH8ZUyh8McSCUWe8K1Y7rpbkr2EDL0U0MSjTR8zRatYynMcQbJg7s3bxWrVj9keEeFilZj8SAh9nqxrWAF7sbAQxbow1iMzfcS4SDvUXSg2Ul8z0DrXhDQldmrQiarwvrr2RA4XB3vFV7DZJJWroOhIWhR3eVRxzb8p9jnQAFVY3zNJii6Zyj4unP3UERBSErE9v0q3s9EmAyJGZDdAbpJB4va6YXEwQAw5RQrLsMOQkxSGwopEIBQ0PmqXp8uGJdFxy94/lm6cTqVWLXpeHx4QpBwZs3T9gPC6UFZJ5+t9XDfeD7n3vSAULIiEkt2H5XkABrYa/h4ZOwy+evhMJINZKghKIovJsBmOBrzbpms5Bgay37G/VhKdU7s83sm8WbXE5TldeDpcSXSePN1xb1LEbKNwemY6Q2T5DBKcrQOPl7xfki9JwNn0fQy0fuE+xW1WVPRGUCPCa4jLWkAijDURwzL8qmBYpuxmHMX/oM+N98ZnsIRp0CSw4H3cp5sGO8M54s++97EUB3GdRHejUZ/mE4J76rDP1I7GPqgXkFJcCbxyd/zdUq/nESupHPnGc7jZzi/0YOCo8xBu73O773e78Xy7LgP/7H/4gvf/nLAID/9b/+F37rt34LX/ziFwEAX/ziF/FTP/VT+L3f+z18+7d/OwDg3//7f48PP/wQ3/3d3/0N3IVvIFE32x14qADFFlm0fZbkqFiZ6hxe29du3UVMslqMvjRA7UdxgaW3LVAsANZAzoAjcoVnCli6HhVkESoWOaHLSLskcoYttiICrbmgZ+8JoBGLN5JvDyvtBOpdYokgAAU/wywdc7snXq7Fw10u6U3vW+y/NpmELZUa8h6B8GBcFvMqcTMxfVnECJ5FrX7NsjQMPRwQmJeBrzMVG77pMIaPYw2+RXB13KIOQRLKt0SIiWCn9456FLS1mvB1D0gXy9oBSowJe+QcvaM4tGMGRm0VqoeHrQyoMgUXYPgmQVrpEhVEW8dkxZJTYemCODpYtE21uTAdUTrcmv0J9t16jiyLoDUHKJEtNTwMNGINQNi3yIW+z5eUgkMNjIyh0OEkyN4tPV+qb6H0UGn1+wNxvoCkz+GuNHGvQFG7N0mp58pDgrtQQrDa7bEHUG0Fl3WxeejZOiGeCYjxG2D2BkKppwD2veShgFoLrlczgMboeHq+4XbffK0qlqXig1ePUABv3j5h23fMDSa50WQK60KowIcZIpPyjAfHdE/+hy0nLzHvOHLosFLzmuECAiuVYN2giCsobvy4VCrixDfmbVzXhqWtVjoAiv3YnRBMwqRXVhbFIVbGwJdw8Dr5rFD1opXuJRDEZ8kJUkhwygZR3MRjSImV5xbJhAXK7cI2FmIeOIIUM744L269y0DwdSMUSoDiesIkFuC1fmYCcUAPPrQ64XYGhSedwj2W4RLxkKuio/t90MMHTVk/eC0fj8i8c+9IV/XUbd9fDiGoW0yu2TjMdVDmTB1bO3y+PGT67ztrk3BEkR7+6UPUEeJAbF6CVV9e5bMhls8EUH7yJ38Sf/kv/2V813d9Fz755BP8wi/8Av7Tf/pP+OVf/mV89NFH+Bt/42/gx3/8x/Gt3/qt+PDDD/E3/+bfxBe/+EV83/d9HwDgL/2lv4Tv/u7vxl//638d/+gf/SP8zu/8Dv7O3/k7+JEf+ZGv4CH5Gg/hwgWiGqxPBB11ybQm2yMwu6PcKT4t5j1RLShaoNIALIBUC/WgYZQVQz1nQ6YFW8RTMOPURoAbKbQN+ljKpkwLXCQROWSQVmbnmVeFo+S9D+dK2OdY42NeDFwj4sIm46QJyODEybCzYr+5MiY5VBUyzKMQCghGniTxiidgllOtdCHLFOYxb4l1YDZl12qxFGa/5t5NIFYvIT+CHDeiGFmtzRTwYIaNgzFHYMV/DHRO4S8VjK7Yj25eB4IRH5MAI1pOwG3QrT2798WBnheiUs0S+py77h6TpS7mYXGeyeE1BFrRcENbdVlFx4BUgScTuXeiZCE98XF3rkxrJCSzVotCu7rySs8cDOP5GlBIZdqygQXtrHUwHBhaxc9RvP6nuWxMgdcCrTY6ITgH0xsna0rDOZ9bVtziLuR9UdBZiIsgxcixRtjc7punm/sSlUkZ+i5iv7vMtg/tb/JBTKuWWnC9rHi4XqCqeHq64XbbrHquALVVvHJw8vx8w3bfY+/R3ond5t5B8zc6R0fcwHChHZ4VpBFyVgri68fXeYGfMRthioORHIAsmACokZd93EI3sKYKKpbasK4N61Ld8zhwu++W5t6ZEUZeGusyCVjefJZFcwkDxL37c4qtmfCQTeBMfLyYDQICuynqpZLjE94Hrp5QhvSkIEFKDqbztWRKQ6bHPFYgshibhZpm4DJhielIQzbmncZpgLH0g0dxO9iEigwwtAbOm9ocxa1r1uYJsK7uORlZ90Qxf85OM3NMBvJ390OFDJxxgp5+m0Di9Mwvdck8FvyqItcIwUo/ff6zH58JoPze7/0efvAHfxC//du/jY8++gjf8z3fg1/+5V/GX/yLfxEA8I//8T9GKQVf/vKXcb/f8f3f//34p//0n8b3a6341//6X+OHf/iH8cUvfhGvXr3CD/3QD+Hv//2//409hR9SKjCmCZBpsAKImOJnkyR7bSJqcVWmuIN1v/SYJMM9UlF0cRTMz6egVGe9JfEUsYhBgBCft/uVabZlmmw7Q6gXEBCJFFusvcfKsUXqMVrk4pTT/SGKsAnc8+QWtHrxMLo9B9yqdVfvUFN6hs7TY9GkepjJvTigJ8SE3LqwAJ74XqTb0f4tXiCP/AxFiXRGFiSTYVajDsHu97OAHi8SyNxT40q8CtCcAMuxSRBloOvo3dMZC1aZScMDOjoUbJWQ56DlKq4smqe4H8eIegPqXi7yC4y8ZzVYFPDich3tsLTz5uCKTQ9NuBSMYY3NwDbwwji1CTV6aJa1+lryejhOyC4kD48RgqT37qAe7hmZFMzwhm4esiqF6YupkATm+ZJiYzOcWxMgxQEBHRYGcll9FA7GUshH3R/xgNhgjRN7nW0ErPFkZoLF3qdlFwLZ5je8DuB9wwGX4LI0XB8ukCJ4+/YZTw5OAEWtFa8er6i14O3TM+63u123nMyaSXnaWqAHiLcXvWiGRNYT92nc+4nkm0aDhNBwpTVsrypLyVNwTCCpDAfyLDaiRmZelorr2rAsSxRb3PYDx3EYWAbDcNVT2HVSmmVa766WJK/LB4pnU86semqqBGeB8ykyA6sEk6exmTRpmAhTrObEb3DDzJe4vUTjxOdiWi5Et/ajhNHTTUzvafwl88zDwsh5b4LzV0/eeS3OmUllfz4ydJLhmZlvAg81J0k2a54wi8fVgOaqCO9J7IP3Xfk0KO89ZsLsp2EOQc49R3TMsauv4/hMAOXnf/7nv+L71+sVP/uzP4uf/dmf/dTP/NE/+kfxb/7Nv/ksl/2ajxjeFwOikJOgMkFM9x4ckfvCOa2fDPNYp8vq4GSBFWhbg6Vs8pXn42kd+FBgg5aDJGDgPXJ24cJouo0sZJQbheGN0bspdZVYoImoXRiA3qIUZLVaR1gEaXAeKw1PzOjdgAtsvHo3q1s9s0aV5a4r2MiO9Uroety7lZE2HejCLghzvrmYNiqWSVOLWF8lsTCFdTd2i4hhBLfwBXBeiBPvXCjWMtcCgaXHRtjBYuosQnd0haCjCrBKm7grxkEpUkPajaGQMmI+VL0iMYBRWPdFrUbHFHZTKLb9AITVaW08t71DRHF0EhSneVfjxah4jRm/d2hmUFnXafbnoXQEVGsIdEuNBrT6/HYrTtVg5MA+MFUV9irEo/ueMI5HHxnft7oXxtkSiKdg++ZT32Ph7rbnoeIGPPWbljdiG0bxPxZpKT637UUvpAA4cjoFaC+YQEeCFf8dzrO5rA2PD1YB9fl2x9unG3Y/91IrHq4XXNYFT0/PeH6+BxCPf6e9GP+jrnMlWaRgONdD3dYJr6qebhscCoFEuJcSgkrLGurNXxqTheMXF/cgCfd5QWsVl3VBq+aJ2e6beUycl1CreMFEcrucLE3QqDRkJFKWzfNlnsgyEKB6PmbFGC+EbGToi8ovByRw13SweBvlU3gspouk10BC5isBr3hZAfEFMqXtJ1QqXkepwssgxnuq7D9D8q53Dg9DZFYcvs4LEpCki8XBZw6G8to+pVy7UekWaRDSezL8eyTDsvia3QK9Njke7z8mr+p73jsf+p7XX35Tzq9PoPXrPT4HvXjyGD5bJh/TnT+nadnhi11z0A1IGAOe4Q8ONNU8XYD0rNANKLHw4edFrEkjR57OAlbcLOEKTPx+grrz6tHzAsl0vfRSMMBr7vUR95V1SOh2ddhVBLU070PD2im2vUjytFobthkjrxQ4jadVV7WiYpelAQ4yosAV1FNiB5ZWLdOkVidwIbJfrGDSMOuvWCVKqwMyMMrwe0+odYyBNoxoSwVBy7p6Km3xsJoWhdSKYtQOVDHhSs+IcVIE2tyT5POlgD8/0FpzJTUifDNgHix1LszJxnLyc3QD9nse9x1ME+9q5GBAUcpAI/m12Nm6+vw64CoeRDC3u2VHFVifpEIwBidRrxX74Sx7B4vQ4kDBVvJQ9/qIxlyTnMd7tMaGmZFUxAjptbIFgQcinM+S1mrW5oEvwXFax26XiaseAhrfYwUWnmq1Yl2aGxa2hjnI3FUBv+ldckVEz8m8j5al4dXjFetScbvf8fbtMw4nshcBrg8XPDxc8Xy74enpGereU4PliDrVIU2Ik07KVyL8YGvB7keoSFwWZEjZ/sPwHT0zLFA22z5UyumhcUQGdV+H1Ytpy4K2ONg+Djw/79i2HYCFhUutPoccmhHPkeZTenOii63A1rsXOusBHjJ0Qd1MT1+kSM9KTBIazFiVeOscWkE8pwGm4qHLcf7MBEoNL9uJivJ2J+NQmCYwAXtm+LjXh6AidjZBOEzcDp+PPM980/7EIg585rF04MPbVo5g6izKx05PySAgIc8Lp79nIBLh7ByWz3C8H9Doe5+PC5xrcPo81/Bnuvb5+CYHKGd4PkYHvIiWxSSdeaL95DGZBQMwHGhwAw1AOtKNad+Q8KSQi+JDR0tHE+lK3pJ5Sd2ki0qtvgBp5Qjfg8bG4VU1lprka5rlxZlG3Hm7EYZwIEQwZnDWwjGFaa2KpVZIq9iPA33v4ckZvXuTv2L8DvHz1WLeDg1YZx6TAaAaV2BZLI11jIF+HFAFDlXoblb2UhTrKlG8KAQUR5vKz/KMYYW5FKgmmKjsrHV7hzSbzwKzflGt9HKBFypbKqAd4sK4FfNc7O7a5j5LcSHhYaDFo06qKA5+DO+592nyio0ClOLWoSqqFIiTIhj+YDiC3gCS8+pQaFUIrDKwwvvzuInUy/DGhtYjQ51H4o+MUtRAFuB9p8yrdVkablvHOLznU4f33DHPiY6ewC+q9cJrYZjgjoqhUOOMeNpzxNphwKUUQdVMqQyQrwLFcCIlgqNAy5jijwClomA4L2pp1lfrOA5TapV8AvV5y/lTTStUJzeKbQ0rlf/68QGXy4Jt381zclgxr4KB1w9XPD5csB8H3rx9sv08yWt1hcGeUfyPzB8CwUmCLlVE0bX8lP83gA3PMz1bQQKuAAwejnFgPrQDvtaXumBtVo14ALjvG57vdxs7t+YN4OXcWbVa97O6URXzoqm6ZzVsVKcERjHP/p/hm1RUUcKYykqvMa6Sz8STFxo/7+o8BHQSAjtJREwDyjc1PSoCsXpOPcGB3Q0zyIypKw5ODKR4c1gkIZfgh/dr2dqSa842TVZUpTLQvF843w1I4r+dMsOv6ognPa8Sv9sapCdFoxnjdKkM9UiCtXdH0Mdo0i0vPzVjyTz7S5AyM6BefOY9155X09dyfJMDFCAGRJPAmEQhIk0Xlp7ZkwqRqFMgUm0RufsuQAqDLUKBTC6KDTNDKfOwR32AIEIJWE559ORepDpEAh2hJ4gTjgSpEG8EZzHkSEMTE/4vd3MKPLhQs10Vwk1IQFzMAj567G+FhV1U/F9oWHdD4GCLN++Kd3hzsuPwHkPqpNhc1GModu3AAZTqDcF6D4G1LAtG756S6M/mHo0g5Imgtoa+dWyHhVCaGGF0XazQG4ENinkZljEs60FNuBcnRLNuC8lcCuNSULmJpzDz+kWseZ8CVrwPdj7AxrI5Lwjdw1at+dqgQrYLGRHWGgge1Wa6eTnxLlZxVooVh9v9Gck/aH0AKNDhVXUdIDFkNRRRI6ZWWLbG/bBrdsUhA4IKLR7CgqIqYKnWcO9JkoMZ8tHB1GgTygbiXHiCZGfYHiu07jzNXhneUM/IV0ghcHbgDqCQgOwWdKsF67Kg1opt25x06grN73VQgHJfab7HMKKNb8Hrx0c8Pj7gOAyc3O67y4aBx4cLHh8fMHrHmzdvcQzPbvI9nfsq9+z7xFEaO+/anKmMZ7owIqSjcS0COyThlS074ArZP1OLoLUV67oYeO4D923Hfbvhvu8WBhXn/kzVEMxT6pk5WTb45Kw1xTwHm/JehQXQdIYo00BwHqgpKV/dMIsz0hCYvpq6LjR4qsHAtJMGPP1LOa6nNWAZazTa3JMV5F8f0wAglOiS68nPxz0cZFQV6wANqhjJqr9AXMMUhsT0WUNE6x7NMBS9bjGejuZyjMWfK8OF87Clbvu0Q/AV3355wvf+Ma9qfeft+ZNf87U+5fgcABSAw9BHolkDK2NCnlxs6YQX0HMxWwhW1yHKJsMFjghUCrRUgN2LJScnaJPqynvkdY3shFOZYf4d1paDE4IdyOxtsctQHhqmKrF46UtTSUuIm9hASEmh6rut1YZa0nOThc7SZxNs8eH3pRROLGjEBnEl7lthTe3YQVVglrrJDJsPS6EFFrdmjGpiKR9W36VGrQGxnFnPEhrYd6s9YlYgs0wGSitYmkSYqfcD22GkzFoKsFQwdDU8PNMrO/kSrNhVD+8mXIqH5IqR3DiWYc36GFtoxFotiIMCEeNw0DpNoce1CM+EGjj0QJ0KSBlALqia47h7BtpQQSm0C3PVUjBRERFcQL0Ca6kArCy+dBtndtGN9Ue5jgS36vMGegMmJXr0EWtNHVEwo8zGeljzRoMh0bMIgDetJOBUzyCyvRHCHJbC3Vqz7w/1JnUGKHJ3TmA5JKJLdN8jtQoer1fLylHFm+cbnm+H17UB1rXi9Wt77+n5juNQ4x2pnvY5PTyh8zgHoV1dsWkCjLyn4uEfKmiS5ydgMFgCQMC8eOVzANMa8r5BS7Xu1tVSwI99x33bcL9v2I8DVjuIvBYOq4cJQ5NRAbr8EPLMiGaoVD3LL7TgTP5nSCQBSN6qgco57EB5xwUhEetBpB7T63vuIszVNY29utSOW8t9YXPlXksap5NXxO6D7SycZM6Cm17/JFKQUW3cYF4hAnoJzgv1C/fDBFSF+MTkXPFYqDBk5OmaQ8YEjhKS+FliGAzI5HiGlyU+i9BLOXjvAdSn1973/sv33neud8GLnv79+o/PCUCxw0qOu7IZcD6Du+6mTUgeSWwAhYeBFECHSEORaer9d/U01+JAgimAdtByM1DRCZI0XfvBtlZnZw8uKAcnSle0hCCZey7EclPPfIlCH/G2L36mAJulXIPvAlOaI4mGQy1TxgqgpvvV4JtzF2aoPoW9Sp0F38xAJw/GPs/iTRpgwMmkfSTLnj2L6JIXA3EiFrq7rEZItiwhfp7PQP6AWLhCGkqr0Ra8iKAtDYBgPwZ2rzZbi3iiAvk6Elkc5sExMrIUD2MJzMPk41G8wNvRuwGp7oIWDqBgHXfZ1CbKmpcscsd0ZM+Qt2fuCpGBHjVeBDos3GPKq0NgNVAo5DoUZShKJVmWHjZmZ3BuuTbNwyPFyc/+eHOzNQPXTvgVH3P3hh1dISM7GEdtnuLKyNeQeA5wAnb4nmMdBzci4FwZRTreJcmbLHZI0KgQJERB7M1pofrzWqfpx+uKD14/oBTBJ2+e8fRsGTsiBmw/+OARIoK3Tzds2wGRiphwYEIaHBer66JxA/7+5LVN7tGJmZT1eQLc2XjQUsakjym9QtHBwp+XtWJdrZO1iHmztn3Dtm3YdwfGRVDA2jVwMaOhvcK7JCk8NK4VoxgAQbwC0BBNa99/Kj2roTIR58tZIlcoZS8wybvA/OQOxUDFGPkwxZrh+cll4xrQfMxQ4BYO4zgCVheFYUyCJglZcBo4Jf8uybe8/6FJtle4R08kycowo868jACK7QvLQopuS7bmFVmFtQBC1CUvngtIh0884xkqfGXw8dX+/rTjJVD5tNcQoPobOT43AEUBjH5AjyxakymR/pmpPoqGFLB/bXEPWCFfr+Ap06fFlObQAVKrIOMkdOM+1Ntbd4bA5zSwSUGMLK4UZF7AvTV2ZVqHsbEVYAYNtGTX0HC1SygNjLRwSJQVeBGxfpinwsMIdBmGJUKrI6zodK0O1cisAJCFgpBCZJKD6F7PJCCdAwu7Zva2aa2ZIvJCUcWBx3EckMIsAy9n7ymrxlPRrHXioR3jLYgVl0LBdV0NWAzgEEuxrcU9RSKQVlHEuuQCBcX7JpRqKa6W9dADZAGw8vNVfF2oWUP+/OzsOnRE2Gyo19fwjCbja0iMiSldy6bRY0RlViAV/LBCJBDpGFqwsMqPn3sVGBga2R6gCIu6Ve9+bOCkw1hVEFi5ehJ9SUZWA7mAeg0dco5M8UeNBxVIK8Ft4BGW+xBAFMx+sb2ITAsHQWHxKrbkulgbAIHzdbiXXZEVghRFegf4vt0WBIqHy4IPP3jA9dLw5umON083dPZHqsDj4xXresUnH7/B7XYHPNXcyvvLZCAIwlXCPWvoKJQ95z+eSxD7N3xeBIA8t3po2j2qZ6kWl0MV67b9eFlxuTTUauNy3+/Y9h39OMCO4i0k1wwaJPStb1ZkQTGFFXpkJdVyunaAEVPzFu7109Q4/Vw0DYAr3jlRAXCv7aT/CcrCqzIhzVDA0zlOHIyXoIQ/YG8xxD2weGMn51Csc3kZAnioFA4mgLmirENE8lK8sjQQTi6r9Eo8KyYLlPiGzPGgEyCwbxCOfX4quTpQDPQAQ2xukeuaRuwMzGMmT/9+feDk5Wv6Ke+/BCrT3/rutz7r8bkBKICiHwPaSYhFCJahVvzLP3YetFj0wj8BVfQgYakL68T2AwfMDylxrXm6+hhWFl0lwk2z4o/iO0jhFSiYG8/PVz31b3jpxdh8ng5YRok03+M4TKguxeqSOJeDC9msYLvn/egQICywofC6F2SPZ6ElcWAm4q7kfqCPjuNIsuDo7HczuSF9E1W3GmbPB3dVbaY0992JisWehdbTUCtk9nzf0GrF4mW5u5OhS3Hiq/MvUMkLMTLsqAVtESyLkXsHCnYtzrswq2hUCiYnDIp3bBbPIimCViqObnS7pdSocGrl/KsJ3erPLIrq2TjD55+cHDbEE0m7mhF+qK8dWLfiWrxeDdeyh3gAoHUrdFULrLv2ME4H1+EY7tnx6y0VuK5G9GbpcvNKSD5vtTo4lYXbhlrfDYI/RRC9eecMNnl9sHh+wKzAKgaqVG0h+GPEPaZ3TqElM8+kAIv3hgG8Gu60gano+btM92lcAEXBwLI2fPj6EdfLim3b8Pbt28h6qwI8Pqx49fiAt2+fcbvdnVyetkuarHYxDYVOK99f9zmdFabdo8sNV8JUgiq5Ngg+MxtQA4SRfNxaxcPVCLxLrRjjwL5ZHZM+vI+VFCNlC8FG7kOCgwk+xnsggFE4IPU8JRlhGFGxBmzRDA2riHmwTvrJx0YI2Ow77ExMJWtDNIUEJi9JjPmL1196o3X6l/KH752VZq5XOyjbnHkuFiLOHjwEMhLARDyPbjbAAJi+GHEZ2wP0pp50OBFZGo7KHHQCOocnQxMQ6yCiszka0x2mXsCUvfa1HO8DJp/2+vsAycvP6ruf+wYRyjc5QDmjtTGGdZVVic1pb0lsen4lXavDLRcT3ulqVYAuuUC+RKjZC8SQel4rKmlOrl52lbQzOgiZXsuFp1nLxN+xMINn06iVLO+mVlDFvRPeebR440Adlp3TlZ4F3+QDgNDSdaXZu7lnYQqjj2mxw71KJJJJxswt9RRhmbAOiGrWRLDmdYK1madm99oL1WtaDG/4B1QcMrnES4m+IBhW4E8huG8HymXB9bpa6/fnO4oCZZEASrVWLK1axLgKHuqCy2XB0go27RAMa9BXijsHNFzT1T00RQRrdPx1bktpFh5bKooUHMfA/TgACMoq2HfzErVmnpN97x5aK5EBE6pYB0ppEK9JoiOrd0IMBHSLuVmPIyoJl3ljSBBXj+5iSjH1upEg/woGqgCXBhxNMEYxQrBadlYf1veouIdkGMaLeiQWtfHGh0yHplJywogpCHNbVypWsXBTcaVcpeCQYvweX/KDfWq4J4fH6IsJ9WgO2LuXtz8DkRmoFJgyDF6S2Px/+OoRjw+PGH3HmzdP2PYD4r6Xh4cVrx6vOLYdb988ebEw92iFuJisD0wggsLYbyAdRxO4yLhKKDpWNN679bmJcCufC3CENCBqKdaPDxe8evWApVWMfuDt892L7qUEsfYR+Xemnk4yBhIq2v5vGWbBa4mcaEUOAL8tAbAKqnMlrPS6Gs+dUOJsU886y8/kUD3GJAUz5etZq9E75RgnQ+cEeBMAMW/W1KtJGUIRN9IIEAncZm4YkB4yZtu4n4gupJD7Mdsm8ybOIQDP3JP8OjS+LgQpPt8FgNVKtA9Xpw44g89Ano+DgaIzf4hrZ76nTz8+DZS8fP/l56Yx/ppQx9f6ua98fJMDFGAehN4JUIDzABvBlfFXIUqd3WPcBBSCQACd7kLIsjBKkpPoGUFalxmyYKZNhm64mA3pMlzzYlmF3OOK09hYxTNiotqqmGDsw0IaRRqOflhdE0nPIsmvR2c6KR/Zn09dWWgCLLN4vasn0uJj63LGjc3T4KEUzzA6jh5dgYvUfAZ1+0YV69Kwg/wGMU9KjzZxoHehFMGyNhyHl7Lv3cBErX7agVpahCQEilatMzN04LJe8OqyYttusEJsimsrGBDc9+5ZMDYxS2VBsIGlFVwWAyfsbzOkQBYT0sciKBuscZxb+vu+Y4yO6pyPVqt1xA0hjwgZKJj9052LY2Xtz4fkmvG1yt5qBlAAHN0L2wG1LeiqBmp8PVszOFs7x2HF7A4tOA5v0T6GcylGcH7OBEcL59HxDKYd61QAr1AZcv+Y50nE053dy1WdP3JMFTHzOwhgYGvFlK6qpZIfswcFYXv7f9MmHmClWuDx8YLXr64AhmXsbLspJCiu14bHRytx//EnbwzUTERSnq/IeU44Nlz/L21VkobtThPQFRoZMBL20B7gR+J7RHtWqPDh4YJXj1e0Zl2tn55uXjWaacYSPZ4isyTI61w3LtpCO5bz3YY+PqncaZzPIIt57hHyea8+0wlv+PViXb0Ac/6vcZU4gsAIhpHGKAfvJOSpTudAgOWz/Oe92Aoe3glcErbYp9TADIFbyC12Q0b+bXKHfzuXysOYfSoRwfsxzrvEPIPjpgYEmRUHGZ5Wbmn2kGD22f058Fbfj9ItKJxg/SW0m493Zcu7v7/vtXfn6vzep13xBFM/9a6+2vE5ACh5WBrlCL2e5KuCs3PPA4S+vgVuMWi6b3lE50nxXHotYOFnE+rZUXMwewe+4R20vA9Lit8jXaVAWgPq5K1Iy7MbAeAhDRfsUDhZyxRSLQVNCzpLlANoSo+JWaezS92Aj3ke9s50zeLPNYLsxXg5G/VVZ+EIvDKMV3ntXk9jqdWtc7NyDgzIqUwzvEZHNV4HJCz2KhKlttWnigqjekWp47AwxNLs71aqeS5gYR2SLQWCxcuCH0e3jq1u2RxDsR8D1Ym+BcVqwriF24o1SlubYF3EPAijgnykpv69zUNTYimbx3G4deleGvQMC3hcT+FrxV3bWbrenpuC3MinDIXYiIjLwt4HDjHAIQJoLfY8jGdzLRZ4+rHVEzmGYhwGavfefc6Ao5pfYWklBOtwF0rxHk0kACsE/VBfu3CL1XkyKMEvYC2VCuMpFPHn6ClQU1epj4uNQavFO2qrp5Lb92mSK/dEGqnu7bH09XWt+PD1I1oVvHn7jOfbHVb12KzVV49XiBQHLkaK5ToLuRGS4Kw8WUgtlUxKFgiJr8o/0Zw/pZDobWTABeGSFwfwpRSs6xXXy4J1sfDr/bZh3zdAh9cHMu9iqZ5OjgF6P9S1nwLGx8LkNYidLzg9lj9zkTl7cQDeZ6zEh3R6LpnmbwI1Oq3jkHwWGsEEQngnwXUzYQZKS6EEmMUVEJw9goxU+wkYqbDjmScvTRJwfbzFZI69z5Vk4RYzJvysPJ+WgCXuknYvs3+t270PEU8f9tpIqgFOQnjGmrKHLCLQMqb7yPGDaR8A9KjCPfQe0lQk4JIctnfxw1cCJl8JoLzv+FrBytd/fG4AisIyC7L2P0lRbl1Ybt80g8mNpxuSwGbiwZ0me4wzAs5LMT46hUfStzq54/wgYjYpHds7U3oBlJdgycIE1tSOQX0KJUA7FWWBFqsKyyyRMob3ayleHIyky0mROnDghmb9j95NiYNKFK4I1BTTQdlY3OWvFnKqrZo3C3DeRC7kPgbu9x3L0sKtKX7d5sXUhrtw7b6GW5UmnIcC0geW1gBYNs7arP5JFTpGC4o3zzPABlzaiuIp5HKMII8uraCVCoF5f0ozpV4rsLSC66Whloq9W5iN8erW7H63bUC1J2nYvQmliFeTtTHi86hYLyGGPywENrltQ/nBQ3fp4WJvF2tmSBAgkALc9+4KtCJwyhjQUTxUaFydY9jr+3H4fCi2fWBxoKdTeEDEgBtU3ar11MqIRyWANre7RIgG8C7iYmOgdaBVX0WeRt9hZfIZvjcQnOG249ix7Vy/iBDLNFSxga0Oi5GaP3j9gOtlwdPTDc/PN4xu9X9UFA/XFUtreL7d8fRspNgQtUqDJdWrO41SFQrBkAQgmdc3/xZfX7VYJk3vI+rtkCtiXqjh991wvay4XFcUEezbgdv9juPYDbh4OjE9J6WYpyoI4pBTkS+wIvKkrchdoBuDqj3Ct/OjxDPbd+00nCmfflf2dF2E8gTrgvi13FsQQtZBlHDtwNYLxdr5X76fjBgb5wQz/Ft8rRlw4IPzCcSBzplfgil9mIUgczoVwAHgcHDi3knuEa+TJbA9JWL8RZm87VaI01QOQe2gOuKiA2CVmBUQq9lkt6IQdAPQ4hECUb+PgUoSvu+n4kDlBFKmMTj//pX+fQlQOO+pd9799+VnX57zswOYzw1AAUwBjJ7uP4LaUiZSoiAK9dD9OKNNBSK2zmN2KKsOsLZA8vK55DXSz5hBMR/hagVgXg/GGpO3EdaHf1bji/ZM1rytuHVdzFKGFeDSolMfjwpVc/8rjlCe4QR0wNFHR3hJwGcza5vWQT5j2CgQjzv3fuAgr8Pdm+ouYKbSmpAvlvGigPZhJN1S3Uoc6BESMj5L97bvtTYbJ1cI1bkSgEbjQYGiFWtICCcGo1VPFWfHZFe8LnWNHFqxLuY+L0Vw7Ka0H9fVlblibYLr0lBrQ+lGzCWClVKwLhWf9BuOrliXGg0P2WpgbRU7U98LS9HbGmvFFRis9P/RB9ipF6oYh5XQb8370BzplFa1lOQuXu9ymFLai+IKZncdIeRtfVkoqLUSGUlSCsYAdh1WCXjMCsfGfFkaMAYO73Jsy8cMAHKmhsI5fq6odBJMvuRKscwThXVCLgC2bmRANqAUV2TrskIh3tCuo67uFYvzauxLalIpiqUUK7j2cMGx73j79oZ9o/fUytxfHy643zc8P989bGnnsZo1k+3qCiTLtKdSkdhnYeYEwqH3q3m/LBE2b7RBosfOvCYdpQLXy4JXD6+wrgvu2x2fvHnG/b4DKFjWhqV5L6thZeOLF5akHEgeScovKlPKFPNvhTQKcAIHOCzIBof4SYnliEzW1An4zMdLxTa/rnmtOAX9B9NrPP90dUouGjJwj0R4G8PoEwe/aVBlqrevbZ5JJuAS9yDuVTZOoqADUgEP6bDgpqp1gua6MkDGjuDWeTjCpe4UEU+a0HiqOaGAoNhkawkdMADOr1jDQT1GhI7g4miI1WYZPsd0lOcofi2g5OW/M6go0+/vAxv6Ka9/Y8fnB6AowUPyShQ2eeq9VCgYhr8r0yKhUpi/nJuHPPYCxnDJGg/565sjHHZT6AZIN34cwsuRjT7xX2iV0f3J8IpvHoWnXZaKdVnMM3IcoNBh3QgpFV27eVJo4frzMS4OiIdZfDNBvPKnBhCaQL5npHgoQVKYqJM6a/OCaMfw89rD2njRyi1As26qkYkyrJy9oGNZGGrpKEU9PdZrcsBSISlQKTGLKJaqkFqM39MNjI7Rvcuu9ZxZF8tiOQ4DP2VZcN83AwBqpeBZ7MxSc80r0wogUpwvUt3ytdLp22pK/uhAL/YdxsktrHWge5ltjpNlyIy0zgdCeFafl+6dZotbzsWrzw4XVlaZ2Dxc6g2W6Oodauwo61otOPoBKda5eBzmCSmsMaLD94gRmZfmwF4sy2ptFaPDAbWHUmMt2bqOjAlNQzkKsXGPCflQXufBAU4f6lWI3SpsBZfLBUcfuG/dw5ISe8W8or6NxHYd69o8Xi949fgAVcWbt8/Y9g7CjlIEDw9XjD7wfLsbcPX7YZYSrfUMjXD/J9MlK6kmQBHwP9l8zzwnkqnzOjyt1Js0DuByWfHwsOJ6sTT4Tz55g7dPN1tnraEt1YnRbpUWL9EehPVJXrgBJpUWtrpM9KdQelTOckgAiJJbNr1BQTgDFCEAJZDlx014Zd8vBHCYDbzZezONHFK5cfRf2ueKaLCnM0hO0Dh/OiZkys4kPKOczRToCmr8DH8ZSFModBRAjwAl9mNhINsTZlQMyiIflqFWakK8Z+cYMnFwNXGSzxtvTuBVzWEecyneIwnugq2eaeXe3PBbiVkKw3/lOOR0f9q4fxo4mY9xmp93fwAr0TGDlBnQvO/fr358fgAKEBsy0LGaNdEh6QadJsxC6rSeOGzuL1BHplSsE1qno5Gpc44q4nvF/450Ye5Z94hEjRNh5daztUDXvzMGQdfkGArICCF/uIveGoMJdFhbwuYZLd3JskajScKlCQm7BwoUDcvY3MeB04Z6LBtR1txT/cGS+dW5EkT9IpbC2V0Rt1oynVJGPPvQw5rZtWrWgXawIVZkTI2Oti5YFk9HPjr23tFK8X4uEvdcPV1424d7mszde3QfQxmQ0jB2S12WIjiObunpMF7KAeu+vPeKPgr6EGsuWBXrsuAQlu9XT+2t+OBhQS3Ativ2jvSg1IL7AdzuI0KPquphOluIoeQ9/sOGiEXgmUMjFYLPj3j4o3ul2hpAzcZ73zq0uvu/lBSiYs9zv++eos06NiZMZQwcR4deWjQsbIv1wdnHkTUf3IsYjncXiN4BAingaJEC5EkZ8PN0aVEsCm9WmATgdam4rCs+eXuPYmqBcCajgSn05hUFHq4rXr96RK0FH3/yhKfbEZVsaxWs6wqI4O3Ts/ffQYwblTsg6THh5p32cPpSCCglxE0RePfg5iCTqftTqwgqjyK4XC64XC5QDNxuNzw93bBtVoenLavzUS1rrxq6jGyr6c7iYAVYS4QdqTDhHq1cJphPcPZh5Lxpmls+AEbqxgSKpJjTrAyXGyNrgwgQhGVhVpDLzwCZxDjBoTkbdkpMxLGX8+uhSl1Pnp8h1+fEavG5pICb5lnz2bPKiwMRexpYOMhYNfl59zTRI640Podn02kmSMH1EDll/hAkh59n1SsPQyDFwMqsK8QBYkAUN344jvMayZUwv/qVAMt8HxNAPYVv8J7PTOD3MwCRTzs+VwCFS0vfGRjnQitYdyotDl/VBAHcMbbQmLtgP1E5kosQablwUUa4VdLl52cFC1UxtFOKFSOzBUrXIaW9e2PixP79sEptqdz3I8MLJZnlzcMppbDUvvdKcULrDH6p9MJKA6JIWC0FGJ455PfN8u3VLaSC9DgsjSXhFYCBCRH7rJQcq+pF2YynYYjIiIRGaIV7eAjybEhKCGECJQAezihew0K8potZqYCdozhPo4/Dw1o2z7f7ZumzpeAYwHYMPG1HgACRCnOzdizrGuNXpYbVfcGCUhr2dWA7Om6bYtsOtLXgcSy43Tu2fQc3rBU8m4IJPq9M1261hJDfd9P8Nh9MSU2rZSBbEtRSMRTmNRjA0myNHoc7fsWItNs+YrxIqFY1MLh4Jhafs3kJdUp/9qvi/Y5YQ24gDISGOouqBMAyGQxjwPklI1KLH64XtNZwHE/ow7KrkkxJMEDXvo3CujS8fvWIZWl4errhzdPNwQmgGKhtRW0Nz7cN9+1wvhXP5+pP6E1NL2gSMWP6QH/qScqI8ZaW1pzHgwjZcb8qrGLwuqy4XC4otWLbDjw93/D8vKF3oLTFDZ1sFSGuA0s8t90bAwtKXhUL3CFDznm/DjkCCCCUWaomnzfNz/MDc5YNv8pibeLVehkQOnlpxTYvgWSAgZnYEwtlEkrxX0c4nBUP3dBzlK4C3lqJz2EaBbslpuzSq+Op/J4oYETpku8BUG3+3QXpReh+7sjhjN2o/vwlvEwS3laFV9WWPL+415QBLN5ZgD8/r+EhNVmE7vdRoQ5GVQZCIg5b8+E58X2az82x5Yy9D5y81KEE9ByD2aMyg5b575eA67MfnyuAgnkCJuDPNXxydal4ZpgPop4FagIUiTfMc5AbNYZ+csvM3tOZ4z5iYU4YWAwIwLMLBhirNsFWomBQgmuZ5lvEzrsfB1RNSC813c7JUM+CWMFdoZBjSMyVUIIU459UqShLizomVoDO63oI+TMJuIYqWqleaMzCMbxp1jYZo3tRMCO5hiu9mJfovh9YloZSrCBVH1b11HWkZfOoz7aDuT4U+z7QvB+OYmDvlq3Vu4OfoTjGAYXxMI6DngnvbrwPbAcgMjB0x354M7+xGABoB6qHpOpCwq6iwsDM0oDLeqA1xRMG6tIAWfB0G7jdBzDUsqqKcUCgzs0RE3ahdKtlEEGtDYEptuLp01MFV1+axxjY+kBpLuaG8yC0YD/U+D3ubtgPppObdW/nt/lmUz918gWtux7NHFO8zUqNsX3bK/l7xvz9kw5mGXIwnsZUnA/G+Xm4rlAd2PfDPaLFwYKENS5ez8e8RMDrhwUP64L71j20c0QtkFqrp7F33O63E+8kd3KgD8RWDUMkN7XRbHxPBdCCd1229HJ23+6d1XxNoa614rIuuFxWiFQ83e54+9ayiAjaIeYZHeMARCwMyfRnmSXcfLjneM4gBplj8ys6/TeNptNz+7OTwydU+nZxiM+fuOxidWT1MTA8QpCESK893xnyYj6nwRfhz0keczXxvjMMR8VPr3SuRfJPEqYprwt4iYZslyFlbuaZoUmEgVr9++ysZSAfamCBnEG7JY11Q0/K8LhLRqXZDVvBvlLhlXIoUbjn6J2PEBdPQk9Zhx7i53RZOSaYoLO2mg349wGSl69xFl7+a56l/P1TwIjMv+i773+V43MFUBIwIMC5vZFCMrwc/jEoTu43nc5E5wWFKxy05HQTqftlfWEGox5ZMdZqXI1QKjohIm4SnqOrF/7xdFUu+rAa4nuCpYnzNwa23frQmAVnCs8yKljJVo0FXpH8E1UPDclp+VjNEUVHR10bWmvmVTg6OroLtHDyYwzFtg0cO3BZFxNWLqhKKVDPHmqtBaBbWgnyIKvQDlChFlyWCgm+R45VhnVMQQ0F7vuYMqBMSWyHCYYqYunXu/EbWjXWfu8dpRg7vh8GaPoA9gHstx3Ptw23bcd9X7HtF2yH4mEteLxUlFpRqxUja6XgUAAqWEpDK8ACe01KwcO64GnZsR/qmSTk5NgiE6HVZvVFmjKLRdAOC+OIWAjLvA+2jslbUrVaISKm8KUZIfkYAt079v1wJWfPaMUDexa6Uif0KcG0FS+UavN0LkLFO9Wz4AvpX2KfcV4NWCQ13XUdiijUQ3PN60ysreCyNmzbHce+T94H++L7lN11bXh8XKHa8fT8bB2K/fqAoC0LAMHz3bgdiPGmwifB0mWF2r6OUuXcry/2eSkKFhVcmgEJ24s9awlBI7PoerlgWVeM0fH26S2enjfsByzU5t89tsOaadLryFEXxLiniFKHSSFRMAdlUuE7mNUJkMS8USHn8ycQYlaLnM6WYMlASlWHI/56Cd6HBqiZVw9lJ+8XArf6CTgSpMR1BdFAcF5bI7g2DooDIBAkzFcDiOLCUwEPqxL4vlhf+b3hsp5eE38NJj/iM/GvyaABQEY3jB3AJ8dc/BzpFXJw4sOiXsBRfb4t63L4NxQLBgb7BIlC+vDrF8hgjhGfnOt8DvW87yfHKVfSS7Ayv0bvyvxdnT4vn/LvVz8+RwDlpF4tvUumIYqNmSAi0DRegBM9Dy+PIMme3nDEA1osXHhxtbBYowcF3wvvxXkTzV4g1fRoEAgAyf9oBUCtOGR4/5+OXqjES6Sbigy/CXs+nocKPR843dwCRMjAqnoKLq1h1Bql97MIlafz9oH7dmBdF5TWINsOgQE0PTpqrbisDce+G1HSs1dI0gTDYGNAYMDILPgRStus4uI1TIzked8tw0aHuZstlGHAqy4LxrCCe00qam2W8tmHk4sFUVhPLV29dxN89/2O5/uBN887Pnha8MHjio9er5Bi3IzGrJj9MHFeK651QZViAGnrWJrg0kyI9G69dmxsWSgte7+MY+CuAxWCi3Nv7vsBVSMLa6le5t8ApNUXKxhDcL8f0MXGpQ5gV0XnmhEb0323ORKCC2WM3DgrynXqe0SKQIZCq3s+qLwcgNryekEAj/VrnBgDytyHKR5rAbQOLMU+K1qwtoalNfz+x2/NAyZgLnVoxVSQitYKHh6vkNLw5s0NT8+3CAsqgGVZICjYth3b5nMNeUcZpeg0lUGQEntk/lwALMvSWt3DOLplo9EIEhjQvF4vuF6vEAFu9zuenm/GgZGC1sTXm5O54X1uPDwyG08EJCEjPD2EZFEo3yUsSc4FzxJ/OwJ4yfmg8BNHRSmefK4Jlpk+j+RrDIxTVpf6noLMoQs+kPr9aBg7AnJLUhGGfJwI8cRLUbwQ9Or6tTl9JzntYULf65w/+ylO5iU4i2FAdM3WLP8v7jE2L0qP57d1BbB0QuiXAFcEDwzVu3cwwJr9pxjBz0CyA2X2COd+oE9HVbBUejPU72lA+rDy+xwQv0PSbbPmzFf74SD26XfFOYyTay61KXxlfO1g5H3H5wig2GHWGq0D/qOx0aaIsm9EThi/z4WZkdt4X/Lvk0DWFHbzlE6XAcQ30jhPokKcfK3TfdouCbAtLMaTFsZQ9nsp4Y6mS3AMZqMUlGpxSnoqzJ3J9GrbROGhcM+EFQbzpawKS7czn2HxuHOpJIra51prEaqw/jkdi1S01ozU2ir2vePp+YZarDrm0Q/zbqwNmwD3bUOTgrrUU9ogOxyTo8FxBgXUUG8mV7C2xcGGPWtr1bKCoOGlGuNwALOj947aVmB0Zu1N/YgMTNx7x/M28OZpxyfPO97eNmx7x/HRFa8fVlyaAEMtZlxsDsoAVrEU44dFcL/YPN0ZTx5Tnx6viTKG5t/YQ7FTELcqqK1gvzmHQtWLsYmXnh9WIbYpjqIQ7U52ZYhuOOnUGyAeY1qr5FMhLV+RJCh6BVHT23OgYcpAmBb8cC2hBVAZxlMqEutHBMGTahXeAkFwXRfUWnG77+iqXn02gStcAZjyBx4eLrheH/F8e7aUYucvAerPbp4ygpN0o0vqY533Y5JlXRBAQb8QB8iyo9aleihS0L16srosEFUsS8Xr169xuVxw3+54fnqL+7ZjqMD4DoJDLbvHxtH5WCSsU6nru2ojizj69zV5DPxSEPCVYCDlYnDb4gklwxu2SwCtAU7svJSempeRmWsCSPEKxzrdi4/jJGZd9ua9Qafwx+lwQBn3dV5sjrMCFIVXES9DjDl/xLxRcM95JxHiEWYJ+MyrQr0/kcDKIti+oCeuxz4QXx90vynU+rFJyh/2J4qsoBhiH90SCx1RUJT3LwCzfIqYl9yKT0aSs+sLB+HDjBQMmYZaLBPwK4KUOcU8RhoJTmIlxr/0ukEQe3yp8q58+AzH5xCgTIJEpuGklyORSm4cfm8GK1KQLTe9hsbgZ6ZrgApE39lbjB0WSbKrevaDxHkV2SqegsIb8IXQscVdqxFLu/fw2A+zZJZGUqW7k2sNiwzDYttW6vzwSpZ8YvuOwImWnnYpvoOtKBAQ6Z3uwei9T2NqaafiNTfsuU2cH93u1TJqKlQsa2bbD6zLBeuyYN8O1FrxeF0xunEljBNivUpklFCq7E9TK+OyL6OpRj7dDgtjtFpwaQ1LtWcfAFTM47Ltu/VlcZJuH+alKKXgoMdBLdymHuYwsDLwvO14vh94uu34f3z4gI8er1jKwLKQE1FxiIn6pQGvrhV9LJCyQ7UDh3kk+ijYe2YlcTzHgIfrDrSFaeXdUphbDVUaCsCffqhxcY6u5gZ2IFGqeR+GC8NIBQ3vHMfQNERUKR6KrlYd2O4rrVRuo9Qpvi/ELMVB49D/Lcg+R2zRYOupO9DuqLXhel0sE2l3cvXk3ZvsTIgAl0vDq4crdCjevt1w3yz9s4D9hAqGdnRrXewKCCFEabH7JsasxlmEzbaK5noT21/rpWFdrCDefhzY98P2jxjX6urpzqU0a0R4v+Ho3rtJrKosyZMcC4XGnuNDSmE5e78P8lFmxaFGnFRXtinXdHpWhkAk53deRz6HGntKeNEYF4577DZFerOEe4ZfpXfE15T/LnESCU+zF4sHCbCc7wxXTZ7nSWaHtzWelD3QgJeSwU7on6QzTkqk09ODkqEcAYt5ihzuyar+N1DK7l7kDoRnhJltVtYAXrjNALpO5zc9YKn6LNBoXJ5SFKwQKwqrnaLhdwEZMAxNVdb4EoWgx1oQLTn/7tIbPnQDkgBL7N85gykBCnc318jMOekOSEbsi/RGFZRiDU8vSz3PwWc8PncAZV6aLK1c8y2cJoFCKhBqWi5Elqr593BllUWR/F9NEa35J06QhVanCBChHhcagyBEIoXQzqORoqaV8UemJpug2fYDihogaAxr/na5LMDduAmqHcvSsC4r9n64wJ4sCycbqjAV1EvPI60KeipqLVjXNQiWAvOmCMRqrgx1Yp9Z3KObkj/GCDCwbQfurWBZFkA6SGRclhZpt5ZhMjDGEYqla/dQ0IK2mKUpgHV3dVB3dBsTALiuNWqS7Lsp2nW1FbJ3xT4G1tawe3qtkTUFY+uRQk4Br2rclK6KY3Rsxw23rePtveP5w4EPHys+QDUA0cgbMs/Iw3WNdMN93w1AVIF0gR6IKqesZ6pQ7ENRjuFdqxGETxHBuixWwMwVrwlmrhX3gHXjx0DUCLmobmjTqpyEjwJzOeUAlR1QMQIreU5MlxZMKeq+1WhFmi7SKUpiQMPChDU4NezzZN2UBZdLxbpWPN82nw/vpj3vbl+Hl0vFw8MVtVW8efMWtzs9E3YDbUrz1THC0pdZTrqHaMTmnY0VYj8q6gQnl8uCdTGguB/G8Tm8DtH1suDV4yOulwsUirdPb3G7bf7MrhCUsiZ5ZaZwDLy9VAxhmeYoZBh5khchc2j9M0WVjwtEe4XsyMtEgMnwEv5GZESLWfNML4ycjEVJjN/LjFS7t0Bb8YxZTC7nOIAorzrNTVzzxSt8nbw/w3/yno/7fEKm8edIeSr1pCeyaJ1zcmAelTBo0RFd7cNz5UDAx8raXdA7x7D/OOkLu0/3Fmnev8azkz4LB7kalXpZhFFrAkoRgiL/GQ4WnaBbQs+VBONRYX3moHAyGcqy1gVWxdia1lpSREUrilqGv2ZFBuV0ns92fK4AyunxfdIjIKA5ufzsSzwXVSplfj8XbqEnJXxlTFOb0L6DGMfK5rKHl/IRqwDJ2iQUVnSpMyZ6JlI5zOnGzibfQzzUYUrPyJ8YFgeu1Vz4banYe8d2GNlwXRYstQZfxIq5sY5JhndoBUq1rjtRk8R307pUXNeG+33DfnRbiEtD6QVj7BBhpo2gDquAOkYCsK4Dz/cNUqz+yXDujMXx1T0ZptD70VEal6mFr0YfkKWFMGSvmN7NO7MfA63mPVuXZpiy1u6g1Cah1AXb0x0K4OKbaQzPAMIUhoEDRgj2AYxNsfcdt33geev4tg9XbPuK4wAeLhYH5mJrteJhVdzXiu2y4Bgdfcv0RKvkn9axpUYP7GNADweLIugOPhYHDIc3xjSB7GE91HB1Y5pTGwsXll6OnkqOulmBAJNHt8q+i4fyereicQytiFccs/477uejBcy96MI/gAZ70nS/mFtuRSqWBlwvDa0Ktm3zEOYcw7bvWB8nK3B2uVyw75a1czing+tEpDoRWF0OSOwxnXe/vCs2TcFKAGN6UFqtWNcV61oBHQa0N/PQiBg4+eD1KyzriuM4cHu+YdusGqwwb9zBGZVICA3ALXOJ6A1T6i011v5mKmvesxMxiaJ53iAupiUs/NvlFa+rNK15Vp0HxeUQBGySmgfHcEBgJQMwinVJD/A7nxcTH4Pntc+kkk6vSUzG9Ov8L+EVIIhGpL6iU8Hr+Vb5Xs40aBSoWCg4UeTwZ/bSCVIxmFEm7jFRelA6DKgoRJgZl9WDLfQ3AjQMdPeQZ9ouvSYE+4ktEvxpZKD5wEQM1gzJWpy4OwMTmaiywzHIAKxijgEwgpMI+UQEQfO7fFcKqpiHthVFqwOtWjinFfPqtwIU6WhLS4P+6zg+VwClQKLUPFexxCLM/87DZYKAKHn+vLgCTCQpfv45jY2uYQPK09bRpEQNtVRceDyddT/COAPPeV4OdPt255TAQ0x0fYunzlqasd+DKra9Q8T6d7SleUG0A70PXNcWYYLDn72ywpLysU04tyoQHZEebM3/Oo59w6vHByz1AU+3e/R0WVdLG+4+Pq1WXAAoDlfEw2uSGNHzdtvQPE9z24+MJbu3ZWmWrqzDQgbmlbHCXsc+sK7WbHBEyMsaDyagUi8qJl41lSE2IztbBk/FcQxclmbk3WPD6Da+vZvC3rvt8T40nLiHAuOwcMp2DNy2HW+fDzy9HvjoVcera8HDw2LFq6Aoxaz+D/QBih1Hv+EZCMvl6CNqq1DP7t2LdnnNEu0ddd+xetlzS3M2wDYciI3C8TdlvDj3h1V8RVjsSQIYi3Cs3HvS1e8HGAU+xt0splowhKRvTB4eZGZS4TpFeP0iU0ksNk4iroEJ84Jd1woVxX07IrOCgEd9H4kOLNcFD9cVUOCTN0+43Q8n8LItA2u8pAV9kpG+xmlMcA+GY9THrhIwihGhL+uKZVmgY+C+H7hvu1cWFjxcL/jgg0csy4Ln55t3Hh7mXfTxfccLEgoE4YCIQqwpgqZ//ReGnGSclG2oLQcMePHsSiQKn4MQgLyvSfGBJE67V3u1nLGLOOFZ1UsOTHJWQu2HQSdCPgdbfMzXhevknG96c+bPUWcKNMi0cymU2bucgjkBkAJW6VVyNjSeMGY/15IUq10yrNu4DJMp8PAMZW7EM0+uI8ohm7M0CAz00BA18ClExqDHzP1csYb5SKJuyILGiV9NvFgeBNVlcHhGwdClmiwDMLS4Uc4AFOt2ictIidYT4s9n4ESxFGCpiqXa3mhVAqC0CuuJJtaT7Rs5PjcARWBpeSMWtb1KQGy/50agUJCXkiuEdro4VTNzR2D1U7JHjQvPkRHI2G8OUigYbP0ZcckWzBSTVQMizHVX924Ud1UP2ISPwVbn7lpr6X5k8a1jKMa9ozXF2ioeLgv23VJp79uBRY3U2ppg9APiXpehxvvYd1P261KxFNZU8gCEl3t/enrGermgLQ17P/B8u2FdmvWi2Q/jUFwXXB9XlPszhipuW8e+KVQsW6P3Da0uUAD7dsQ1VAVj76gNWC8Lds/AKIMdZw0QlCpuwQ8HP+6GJNHN5wlKAqKg1iVi//QktVJwuaxYl4LnvlkHY3jl2m4eg71rkEw7gZTP/m0fGE/W1G7bWKX2goerYF0cRACWNq1GlN6PFbcDuPfd++LQgiKQEO8hBFS/l2N0eN/GqFnCZUvhzpYBoxSINCdEW5jP7DTgGB06rMtyKcW8Q16+3mp3GJeDMHxo9+aJ1nq+03DzGiR9DIzuAAJIUK/wNU6Qz3QCvxdmKIjislozvKfdAEqt1SsaexVPv3CtiofrgrVVbPcb3r59Qh+puJgdwdToUFS+p+ewK8UBCeiRteSvFTEybFsaLhcD9mN03PeObesYam0BXj1e8fr1B1Ad+P0/+EPcb7vtz1LCI8lQwmyCmDzwoEExfs88nxxEKjX+rb6GVLp7b129ilvk2hEVSulfCZmocf4ZSCCuQTTkq4WGzwQi7FcFJZ66PDLRpYbmQy9O4+0y04DRAK10m6Lk6eXgIN73J8/wjyM3ZpIRcHO+UyWn4ZccK7gnPAJtfr1yRl9UyjAQOmC8KFFWdSXvRHycaXh6UoF2kJNi9aAc1Qvn1VOE/XUROLlVfP7IPLFxHFRPNFhIsAVDlzPoVTfIgObwo6gDFDGZ3pWeZDvfEHHA4gAF4kDN16koah1Y/GetA0s1oFKrkd7Ng8LrWJsOchLzmH//ysfnBqAA72ZuA3gxLop0W02WNnCe3NlFGadI06G4NWC1Dhyx4wxIiNBJOislUXQsrOEwxYWHkTIpEdQa39VqxNXBhS1e/tya4rGolzX5MyGrql5uvaMfB1qrRlJt9l4fCj0ONC//bcXivPqsuyuNy9GBKllwyrNyjLfRcdzuWJYF67ri6fkZ923HZV1RS8X9fmB7c4N88AqX1QpvSWno2PF827G0Kxp7wQyz2IcOwMmix2GArTYLfbTFxEvvagXW0LEfXqjIuRh0PQjn1hVktWY/UDWuyeEeAhJ+r5cFSxUML0jW3HsjYmPwvLHz8OxNCREOUcV+wOrJ6GazrgdaEXzbt34AL8MBjB1aO9YKPF4bbh3YPMWU3h5RgZSKKgXaajxPgZXi735h9UwrphyzoZgJcRPgBpxtXPtwL4d7m8TJ1ArB2N2z9WLFpwXK7Cnvhj1GKmAIFIe1mRcbi+jBQqBPfQL1ni9snWAAvIl59mqtOJ43rx5rDUz6MNE/Rodg4Pp4wePDFaN3vHn7ZIXnUMHsGYUESMt9f/5V3VKNZAkQoLiihdmUtVqq9+W6eqbRgWPb0XeTERbSecDD5QH3bccnn7zFfTugg4TnEaAkC52VVMSasMN0LuUPAiSUmUcQ+nu4Opy4IQJTfhgQ8To2ItDiYSJnHCQBF6A3hRlz3DeRbRLhh3K6z/S6+HXJu/e1Sh5FQD0xqQkHImnswT3eDp2mKZsB2vwbVbItzQQqp8qpIJiZPDgTSBWBydTCOTlDGIKa0AO8X5WULf6vKknPvCsivzhL/C5zxg4GGHIl4AovCtGZ9xzLe/L/eU8IywLKOw+AJl6jR2Een2L7ssBq1tTijUY9U3Fwj6m3oHCviries35pnjVaB1odWBsMqBRFawOtOM+ssKB+GtLTbOOzHp8rgELMGyA/vCOSmyMWAOi4iMweut5meyWlW7ow7bvuip/SwRVwsiJijZ1ivWIbKQlq/K5EjJwLLvreuFllKaXFWf2WnrZ3s0SNkOSu8lKwtoL7biTW7TCLvjbvIQMqNkXvgmVZcCkLSlGMcXhdhwW9D+zHnsRCzw7pY8cYxXgInjJrHBBf5N3qkKxLxW1T/OEnT/i2jx5QpWBdgEdV7NuBY99RF3hK8nBXpXlEBowDUrqi3A/UaqSw5gThHRamYTx4d+VYi5EiL6vVvhhqcePLpUG3uwGTwwq49d7jmlYi3jN7jm4ApQKr1y9ZmuC2D9x3xW1L1np3fgqzKkdX6H2Hjo7eD5RSsVwu+JbXFyxlwagAVgsd3Y8d10VwXQTbLjgOCQ8EeT+t1ejRA5sC7N0ttlIstbWnyO7DPCLmSZNpXBEhHMrP4O04/2DMPayQhPAE7oJozsZQpVRPgS0o4tYiLTkhKVUDOLMLNfeeeT1hBOn1Akj12jXw/k3DmiAOA6RrAz58dUWrBW+fnvHmeccxBMOBwICBTII47lPuxRP8YniA69sNheIKqxbBZW1edr/iODZs227EcAFev7rg9esHtNbw/Lzh40/eWr2a4UwID5maVTzrOg2PwTwW/KzqHGrieHNa5gCIrT0PFgDkN0hHeE1CzuRz81lNLxJESIAj+wwV9ojvmIeDYzrDBFfyZtrb85o9ACpsuxOuovn8/hmvLWXDMIVj6M2eZCPUsxtpHKpGanHMs4NkC6kr5lsFxzSe3Oc+r84RQfZ+TvgVoFPEAKI3Yz3X1WKDykkbRTiHCQpOuhVrKFnL1FsqV2nOVWT/pM7inh7zM8b37HwoQBlAqYCy7Yn/kF9nAEVxqBj4QYF4bSYDJUCTw70kHc3BSSsGVmrp1opDvPmpHpM3a54YxDN9rcfnCqAA8IWTzOf59QD/gbq5YSiYiTeIIoHAp/wOBXec9zz8BBiZIoyw2GxjFGSpA/XUTVMs1mDM0lkZt7U96pk8OszqbAUYJTIrCvvfiKHc1hpaLbht1uhu794Ernp1WdGoHULewLpU5wqYt+eyNIxRjO/RLYWxNVMyVmDU3Lu9k9fhCN6H0qprKm73HU/PdzysDevFeB7XtbmAGVaKvacbtrsnwcSjYj86ilg/GCtgZYNOnoURYDWU31yAjnVFaGlZc7wXSF4KjjFQlU0CB5ZmfJXLYpt1XYBrr7jdK6rswNaD6C8docwHzMPyVru9XZ7MewXgw4cFrVZItQyiZR+4LB3XpeDWLIx4dI+rD3UlaZrJwjvuXXPvT6vkk1hFWPVCfQr73N4H6q64rAsYzaZ7PMvri4cUXY4UK60ublEPV25cE8wwg1uOtmGYByAYp3i3N5AsuXdOY+/Wo0Ashr0s2PaBfbOQ29KsN5J2hToh79XjilePV+z7gbfPG7auGGDVZPPusBZJEZlquIiviSR8T3gFBCcMT9Ri++HhuqBVQT927NuOfiiqVLx+fcHjqysEgrdPN7x5+4x9txRihnMjNZ+GiHhBvElm8D4owwUeTkXeXOCrd5TXRHwM06kD2hGuIZug+ByzdRhGFj73vB/gBFAlqRPIuiBO+iwuj/wcReCNHumFsnMQ0JpuZriC64P34NoWiM+G98PHpYjfdzxughOFywUIqjjpvhC0uDxQtWcOkDI9M70YU70R9uuxUSw+XtQDNZ6F4TWT2hm6OXnjAYhWAKz46+uQiMzJxaUkOJ4Na1XEtdMjJLxMZO/xiIwkqjRfOBW5p6tKhKn5Y9mJmTwgTuxu1TglrRQnwg5/baCWw9OcD1t7HhJT5DzmoP//AcrpkBCiOm3CDOsAROYz8sWJYZ4vAvOghlA5XdCnodguGyRT8b1Cl5lZvyUWl4c21LgGdAlHall4dhAeFRnMOZe4Ry4+e14NkLEugs2Lkg1jmlq6J+guVNzudxy9WahDBPBY8WVpqKUD94E+OooUXC8W/z+OA9t+eAE470bsTPTuYKoWwfVSsW0bCjzTSIzLgDqszsaw0S5FcDivQuBZQD7mrpchPefwOHpUru1DMYqgqYW8AAMnCsXeO+p+4DhGKF+rFWNehiGW9mykSg1lVwui8WFTwQUF12YAEEWA+/DMKgE8fEJL+BiK533g99/cUQqMOPatH+Dx0brcllqxtI7LUnFdCtZqPXMi/KZR79FUkbLInk334SX6a/VCZKN7cTkzi3ov2H0O173HeiW45loqJUmPxUM+tZZJwNj6W5YFq48FDgsriSuy7gpQinjtBROytXpasSRAsXCYx/6dO1SrYPU6PfuxYz86llY9C4vhkIG1VXzw+hGlFDy9ueH51tFHcWHvHiPvnXQKK817Pzar5t6PD9kA1WLZbg/X1Yri7Ru2bUPvA602PD5e8erVA8YY+OTNE96+veMYliaPkUoT9IByH2uCNrrtw3kvAKucjjLJHR//sKwdRL3Pw5FF1tQUjLjSnEBMAiH3d8oMJwGg5HCINbyDGzSzdp9BVuh0Uef0aQIwQXRk5jOJoxACX/jKoMcDg+RPv6bC08dnmetXjin0MEsxIKJDIdGkUWP/8Jv0IpngIYBNdzj1Rl6txPXs3lNGWmJGD8iVn5vqXXlarnfSDFAm80RzbF/ocYVYeBq2X2IPsz7KvMaEwTjXM2APJTfOBdbwVoFmixT0jA2GgB2skdzemnqmjhFhWx1YqqJId4OWY1YAHLDq0XZNlQKEUYj3/PvVj29ygDIBCQBcdGfsIO98JH71hRpn4driUsu9jdj84IIG6HILpOzv2eIYrrhcOLrED2a8kBxm5+jOMTGr2RQsFYcUZlyYq7R7QTVLe57TCRX7odiOzZWXoNWGi2ft3DbrB6OANfRb7OmPo+O+H+hd8Xi1NMrhNUiqZwLtw7wZ27bjsix4fLhC5Gb9RLRbK3sU7EfHGOKdOBWPlwW9C/Z9xydvFFIX446MbhVexaqdllIwtg0Un3S0FrF6KAr11NNqlTsHAtAcFkhFWQqWahkjVqq+W3ioFNxuG149mlLvfr3iSnMDsHkBOQCePide/t3c36odbTWSrSneHW9uijvgxd1sHYhr9z6A2z7w+282LO0JS6v4wvIK61qCSNaq4LJYA7lPng9EW/ZiVqM1jLS1Vkv1kOKI9GspBc37GY0etLaw3vdD8XTbjV1fjafRvXheqezGbFbyZTEOCHRYmMIBdWsNl2W1bCoX9r13z3KxdEPj2Yl10/F7Xqqlr8MVpqpl1dBw4LmksAO2pW0PHd7LyYRzH0a0e/1qxavHV7hvO56ed+w7w1dWm6f3jmPYa5X6q5ao7JuuZt+ptFZDlAzUCgcnV9TasN03bPcbhg4sS8OrV1e8enzAse/4/T98g/uukGJE8jFoOU6tLdSACYqtIXqt6GiK0BLSp0BChxLIiHlGo8gY4sSIzsRRgC+fkYCWSjMyafxv8WvZ/FNmTffD2NIkNw2EaCpOwDlFCRT4/QidqeZ5A/2U6fl9HgRJEPd/o7t7lH9IIi2zI4dn13VvZGoZjxr8QPC+woMND5kJMhuK9+YggmRZfh+uE2gAwoGbzhOZfCDxcWWlWcta8tIDcU7OIb2MOYYEovR4cv5Y3G1eYyOArM+8OtALaUAjIcfhpARVPFSexovJXWuZYd6TgVqa6xQL6Ygc9mzixeG0QHXF0AMi9MKqgRS8uOb/7wCU+dBPf+uFy0xejo8bCYllXZD4ZBJlzgCFp/BIIsDF49ejp0RYJyIsbBIZh1e4dot2mEUUNwRCInXLzK/HrB66N9WLoIlgWRqqAMfRjTiqEoLTLPyKoR23+4FWCx6uixWdWhXb/cCxdzw937H1huvlAhHBdrvbmi9mXe/7gT/4w7f44NVq4Gc1JXi737Esa6RV0l1ZdOByWXAT4Pl2YBwKadW6BrsH4LJWLG0BANy3I7xQZlUWHGrgaAewLkCrDWgSREhF8dol3UruF6s+O7qidw/1DIuTSmF5afOirCK4lwO3fcdxDDxemnkSxOYIyliuzedjEay1Yq3mwfqDpwOHmkLuYxgwE7HiXQPuSdlwWZ5xWRu+9cMLarEy6fd9YF0bHh8q5ON7KLMAriHAJdboUAOUqAVrqWilYVSYN6PQ9ZzCfdu7xZVVYkXxnCbQ1DsKWyxc+wF4B93WKpYqALrXaWFNlGEdrmtFAyUlXBAXtCLuAXFlCo1ihATZ3A+lwLNjFMfeIapYvXpuhaINYGkLPnj9AWppePv2E9y2PasKF9t7x8FKwPb8rL1Sg6Mn6RaKfc/7G74fVlyvKwSC2/OG/X6HYuB6XfHBh6+xrAvePD3jk0/eYgyg1cX12AAKLLVTvC6qpl8CqkFsDCWUM4CTN0DPhEv2DPKAZoY9+F3ygghgZCBKmAtlX1YpzlsiidGBh7jUk+m7WeIy4JOBdVc49CZwUF2+Eb74trFWDK5sScKdwQyfTlUgQyJ9mJllyTlB7A3WNjqGeT5HeK2n23HCs+Gs9FqYF9X2aeE6EI55AjW+FkDBn5sEaPN80WvIqSRITKU8m9FKz4nLSPth0TMCFO5RDVBGdoyBFMRYxAXA75pXh2HcEnWuWPSwcKEhFAjY6Sefr5bi3l+2jDic57gDcsSFTadVqHYMLRijYGiFFvf8TsD3K+roTzk+RwBlFr92cCHM4Zg5Hm6vj+n3/CAXRWye2KJuRUyLz3419E237gDcTebpwzpfH4BmzroqUT8mdC2xwGw/JEjR6fpA8bCAWeBLq14kjVadZaYcHgop7kLuY+D5tmF0y0Z4uF5xtI7btuHunpZX1wvWdUU/rFKmGUuC/eh4+3TH5XqBioVrjr2jbzsKLC9+XbIgXGuCx+sFqgXP22GVQ1vDcQxIHdBegKJYWzMlBes5A1V0FaBUbMfhtVkqWivAAO73HVJqzI0gFZPFoYF9754tVD0MoZGi26r10CUvpzbjQ3S1Sq5teKjJdiGGDjQBHteCpS0OFhUfP3fswzthhHIcTthUPN0P/O+Pn3FtRor98IMHtDoguAHaw2Oz+dwSplpjQ4//S0S6zUulZr2zG0jz0B3U5vbo5v7vKihDvF1Akm9r9RBR96aTrRqJWEuAZsYWjoPl6JPZb8WrCmpbAPdwqRrXxYymNApsf/kMiYffvP2BtXAo2Hbrhl1KwdKsgWSRgXUpePV4xcP1gtvtjrfPm2dvGc+ptYr7fQ9wUgo9h9XaB5g6i2dKPgrvaWCpRoa9Xi8Y6LjdnrHdbS2/evWIDz58jTGA3//9t3i+3SE+L8EXUYZsiiucEYXZeM05rBOqeTJ0JP6DkEdRSUmTv3DGGa4cxa1z0HPisIFVSQkGfB0bOPTQKr00fu6szZWAKv0l1IqeYsvQQYxoCcWvo6AiQwYJLuSkXAcyVKEKqBP4uUcjpVongDJ5D4g5hxt+nN86j7cgwAkckNUAKe6hwuQNkST2AgPmzSgxCpwBcnSSVuuFJnxdUR8ouTsMq53ACVBlMupkvoLGGBDoz3PBdgxQRN0e1g8iZyz3REWR6p53gDVT5qdBgEerEWXfHz5OdxgBWwBpsGpvBZBqukYPiB4QqShawYKmKoIjvDmps77W43MFUAKqspYIX5usRm7UVPZ836ddJ5CjcOfclBoJ2xH0fpJ8OCCRWkySp38U4QkB08aSCyPim3i4YnLCqMDWAGOn4UERI20ZsdQWj4jxPuAl7OnON0IbnPfQEIXL4NVFXRode/dy685pcHfi8/0OVes58tAsbLLth1c1VTzfDqCI1wbxEvlifWPuOiwluZgyW1rF49Wuf4yBy9qwb2aZH4czwktxa75gWewZ9m73WV1xra1EGX4WvzLlyHHzsIUNFroaybeRI1BNUAgs84lx6tVTnksteHvfTZZcGw5lPxfLvro0wcNFsa4FH74SdG0YOvDxc0eHseD3iBmb4N274pNbx//58Q0fvFrx6vWjlYWuBUtVrIuFeu579ywdE0MiBUtrxrdRhvxKkJt7Vy+UZu81rzC7uzdPxPsYQbGgeL8ft4AVAQSLsJz94WFDX9NDsR8DXYaVdld1t28JRVEmS9gKC84hA3KnAEJ8hXjhveEKzeZzcw9WrdUKxImiysCyWjgRELx5esZ9t6yJ1iqWtUHVQoAmVMW5L4KlpGvb4uLw/SYh60WApTk4uVyhqrjd7rjfN1QRPD4+4vr4iNv9wNPTHbfbHSIGwCU2JcMS9EaYxcpu3yoU/JPFHuIm1btMcR+BeFVyySiCr4cIS8lklYfASus+eA8uCfn9IB1NSEeEn/J5inuxgSIHhl4FI//yi+6lUCdiDjeMQiYRSFjqM4Gs0suD4t5gcfwjAdJDElOuT7/Sq5EewRmMTGGmaWxpm5KAzmZ7IgzPuNzQhAEA5ffMT5lG1ecpFbAkITdPAIGXUPChL074D7AgBLLItRXgMB+etU3y9PaMlQatiCcTOFgsBCnFCkGWEsCFSRmxtl4AFOvL1sE+P3bvBwxgWFFOW587oNUVltctkgJowRDBMeO6z3h8kwOUd588iUnTig5kbLN/9kD4maZNKjq9qOlNyWWKEDJWAh1GzqLQ8F1kbnoSCOna5rWne3HXfnicCZKUgs08DOoBRyOE0dLQCCvEDlSr+OmFV32BmrXa+2EWeLGuxtWtqu04whpffOeMrtj2DUUWXNcFy8U6A9+3zUInmg3PihgZdGnVPAdDobtdR2WgtY51KXjUhtu9oy2CpSzYDvP89NFRqgETwEvdD8DqvVQsTbA6r6FWQWkN27a74nbXPtwC80y+4m5KpmaPoViWgnWtKIely23HQCnA47pgQHA/Op6eD2ivXkq+e7quzW+rgsu64/UD8LBWPF4aPno1sB27Fbk7jBE/8+z6AO6H4g+fD/yfH9/w4Qd3XJeC67IA0jDKwPXS8LwdxqZ3kDo8jCFFoPsRHhorYmbgy7zT9vxLq9i3MRUg9Gynw0imS/OeSWMAo6OqFTdEoRsZoRxYtO2+W00SKQOKHYB4BVsX/AIMT7VUpfA3EANFVA9WbwvB/QQMb3RZARnR5dc8b8MqURbg1cOK69rwdNvw/Hw3ro1YH6dSBLfbBoV4OqTdmxWMmgMoAjBzjmBJgGWxJpWXywodA8+3G7b7Yanp1xW1NTw/3/H26Y59P9zVLYB62EwKuGkVtEoFkBHVooOBEUp+lj3huHd5o/kJviQAhJ1qiaxIfnWFSk8L3wuFm6EWygVMRbPCR6DELOR/pLdNxLlNLKomgPh8MhQ0NIGXusdhQK3oF+XnGAZMIXYezPcxjYtb9qoSKfCW9eigWs+e5GCrhZJPoJJp7inrJzUA8XJrQk4M52CqlRVeeeoRAhUIkjwrgHcmTnPUMnfsvBUJ/NKQI4FcXoKUHBSrCqwawCgN39QjBlDoGfFSFMJq4+6pKZNXhGC+zNcuL0CK+/rE5lO8OSDQA7hamNCSJMzxVP0ZrVCdogRpN1Oup4f7Go5vcoByPjI0qmEhcLI5vVlp0CeXSh0ZvwtL3AfWyHpZ5lzFBTj5JY7+s04BPD5pi7kWi+VaszUKEgDDq3CqWTtCUuHwzSfwlMsGBSLODjFhXEoxhryku5Cuuupk0TEy06VWa/EeinwoOkMDTjTEGI62G9bVYovMALpeL7isphiOoztBzTkv4pZrFaC0CFkdfeC2d9Sb4vWDpRgXWI2Ly3XB2g3wDC+ktiwV+757dpNlrDQnv1rtFSsoV1vDcV3wdPPYKIzUBWRIzaxdA1VwwbZUK59/NGBp5tFZ14ZWgedbx/PduCOHk1xZN2UogAIPlQDX9cCHDxWvH5t3Y1ZvxDi8xsBkQqkCfeC2Kf73Jzd88L/f4P/50YOlXBfFPjoe1oKntXp1R5bvt2J7tQgOkcjeWZoJmsMLvJVqoa/Vqolh7z0KdQEabQBqtbmP2jrNOQd94FAPDQ1L6aX3BDDhhb0bf8jbAxAIMADKMa8O4lqF8QbGgKAHeKa30RoEFqxLg6WbH4AYQAE6ithaeLysEFHcbk/ekA8OTiqO3dJ/i9SsZOn3Vdw1maHaVMhFjNfy+Ljiem0Y3bhXt/sGoGBZLoAUvH264fn5DtXiHbTVvZD0DmlWT3U5E3vQZQJBXwAFcTkE44qcMzrK+X4l55D3H7IDVM6T14Aci7jaZASBe8I2RqgIjQulPBR42ist+hkY5Tgw/CDu+cgYTIIyhrEJPqhsZ5ChMVASspnPlVwVjUcXPrt4sMCNO7tGAoAkGZ8BSuxJB1GU+OlVmt0fEorFyNaZLn6GmnbyDMlMZ6AXze87PB1xjzNQkJgOAoQsazFHBfiKxDXIO5F3zq0OxkoCFAciAZTcgJVpOVgNJjMsiO0L3BgH11QaNk7ZA/c5wlAL9Jdj+jUeX3sw6D3Hz/zMz0BE8Lf+1t+K1263G37kR34E3/Zt34bXr1/jy1/+Mn73d3/39L3f+q3fwpe+9CU8Pj7i27/92/ETP/ETIXw++xFbPwaRA8a3Z3CiOgLpEsJwUb1T38QFTivFY3iuAFWxjY5tdNx7x9Y7dlf4LGUsvN4wzkqNpZQbe0A9PRRpZTowAcyy6e6yWZuT+LzqaYWiinksrAEgvIGcpf/e7pvXhDB36r5345aMAakNpTb0Aby97fj46Y7bdmAfwK6C+zFw2w4cwxVSbdg68PGbOz5+c4OUhnVd3boyRdJaVpm9LBUPS8UHjwu+9aNHfPC44uiK2/0AVLE08WGyLJ/Hq5FrrcmUp7uWalkixdyGlukhYWk0dDx6XZVaWqwEU14lSHNQL+E+2GRNsbaC61KxVOC6WA+Y1qzg1xiKh6Xho9cX/B//r2/B//Gd34LXH10xYGDrGIrnTfG/33T8v//3ht/6/9zxh09WlO26VqzNSkJzIebcmkfi7XPH739yx9u7VzsdA60Aj5dmz7M0tGYseNaqMZDVTEwTuFbB4m5ags5SzCtQmIFEN65kj53h8f3DeSrqIbZIGYf1jSI5cT8G7sew8JP3HjoGdZGHymqmx6+LheFqERf0Dsh9b5GTUEvDujYsraIfVg9HMPFcoHi4LPb+vmHbniFF0Zo4qXZg3w/zKBXrDdIqeQWe4YYce0NGpiAuS8MHr6949XCBjo63b59xu+90ZGL0A8/Pdzw/byChknyAAYlU+qFptBg4MY5Xc7BU6fFzz4o5kGY5ZG1E59oRJiLcPvdzegk6UEMzI6iGQpvDBZRf6uCE1xywAloHoDug5ma0e8tGjsX3nSnIYdVP/V7HOKDaAT0A7RDt7u0aaKVbhkcFSmVYwYp8mf3jxMsAPdN9OlggMKPfyTw3k8IWegJKrO1SLCuOxOysaGpGzVKrvec/xUGN/ZgRh/Adhz8mfrzCCgQNVrm1+Ofse1Ymr7rHpEGk2Wf9dfiPoEFkRZELqlxQZbWfsqKWC0q5opQLSrHXWr2itStafcDSHrEur3BZHnFtj7i0V7gu9nNpj7i0R6ztEUt9QC0PqHJFkSsKrhBcIXiE4AroBaIXABdAV+hYoXqJH+hqP2OB9gYdFb1XHIfg6MXKFwz7t3fBcfjvh6AfVnPr6MDuxUG7e83wYkz/L/Gg/MZv/Ab+2T/7Z/ie7/me0+s/9mM/hl/6pV/CL/7iL+Kjjz7Cj/7oj+IHfuAH8Cu/8isAjJ/wpS99CV/4whfwq7/6q/jt3/5t/OAP/iCWZcFP//RPf723Mx0UjDkI+s77CIsB8avDh7CINIGMkK9gP1ZMy0GGQ9rirr7Z8cbOxDqGlRt2tBqOQAJ3iJcudyHjBLs+FLfdrOnFSaemrBbU2q05nloIR7VMxDKgH1buflmaCZ3GTsCZnnhZzUtydPMSmMVMsHbgvndXNE6ILAXH/cD9eIuPXj/ger3gvm14ft5wdAUOAGPBw9oii2OpA8u1oT+YIoIILg8V0oDj3jG6eTV0sUyb5r2DFFb1sFZgPw4IjHthWR9WLbc24L513GFALqpilAodBxRWLddAnxF2j96jMmcr/1/2/ibWtu04C4afGmPMudbe59wfx7GvX74vcQ8cQ1ACjfhK0AkhForo4Fb0KkQoLctECAsURYpEfgiR0kkHp4Oi0Ioi6IZI5EdCSOAoKHQgkSJAehX4yLXjn/tzzt5rzTlG1dd4qmrMdezEvsnH+2HnXfa+55y9155r/oxR9VTVU08Vqs7WgXeexQRd4NVX7vDN/+/X8OT974Fh4P73P4fr/hl84e3HSHBx6m8HNlU87ob7E7thlqVwFpL6zB74+8EAdOuK59eOx214x5CgwHBqFUstaNW7hgwwoYbN6mWN1po/Y8/bFZpOEdDZaQynK05aM3c4dUZWxX2TAFYEHdSK2YeiNq87G/krhoJdFbpRnr+dGlPvnbN8ivD+leppZVQs64qlEghQD6HCZKa/mb0h6DotDW2peOfZDh2WE1CHMdt3ujvDYLhuV8S0Z6ohD/SuTrT1Ue91AlgCgfiau78UqsM+eXLGujbs+4Znzy+4XAaGuqLt4tlO3TPqi6jVYCg6M7UlQ/gXouL4bP99LZEpQJZpo9UWcJ0P8086pNhjL9IGHewVJNfwjG2RlsdCFRgzEGMJ1Dt+fIo4Z8Ycz8MwZQtYZgsnnXbVxSJn+6rkdRqMLepOtPZOAUTjCDVOnAvmbzHnpWhE3nkdYUMt2TRWjuWkMrMfUVsvOPyec34KjXqCH/XzlHyo/H78/ZBtCaAULbOwyLjPgZsS4DuPFx1RsQpwkzWZ/A8/R1+3c0bOkdwaFxRPwIGU5RnfOLfMSjkyzXXrGQwTthTnUN1Czlw8T5vpJSQPKLqGYi3JXG8Jsh1os5OHrM2Y3HzgS9ye7Ff5+mMBlGfPnuH//D//T/yzf/bP8I//8T/O77/11lv4uZ/7OfzCL/wCvvM7vxMA8PM///P4lm/5FvzGb/wGPvKRj+BXfuVX8Du/8zv4tV/7Nbz22mv4tm/7NvzET/wEfuiHfgg/+qM/6noaf4xXGobjEsdxDWbayxBi0EhjNo9zRHgTUYsImlQUVO/MUpR0QnNxSe4V8ZRfEAI9OmwLpDBN2Mfw7hqCEVM6p1po1IbNDMBl6zyGd6WwVV9cL+Jw3YVLOaLvobsLcHmdEYaYALwsFefz6roinHGzdwKfbWdpaHdBLWSN0idVQvDkbsWyrLi7b3h4ZJ1+Hx2LFRRpHnUKdChaI1F374bxqDifFsjo2LaBXTujdqWTpQ5FRTVDceXG0PxwP8fJyWa4nhoet4HeaTB0DLS1oVQ+iOzHN0Mf1NVoWlBbwbKuKAI8OTU8PDKCrkWwtoL7V55A7p9gqYJXrh0v/cFb+NzbF+fchDlmnfxhA/roOC/iCroVPXg4vrjUCFL2oXi4bHj+cMXTM8EjDUAYIaqBRrQ4huF6pbMcPnMo2jydBeDCeyz5wKN6NUDUkkSNNHrcKDH/CDB0M/KRSvU5T3Nf9G4YomilIWS5c3aQSrY961AHQj52ASxt1la9hLRxf3p7a3TgiBQ8XAislwUIid51XbC0Bdu247LtCIfI0lPP510AL78cfcQha+K7uJbiWiZ3WJeKbd/x/OGKx0vHUMPd3Rl3Z3asXa+Xea/hbsit/TC44S3OLeMnkFA4z4O2KDhtXgo2y2OGXwugkyMlDhHmBCfTb8ZbSgIT9awGnQHXTZSOJs+h+CczqzRFzEJmfl7ssWzkw/HQcnXy+gpy7tXhvAxCPZyjKusx4BOSJuezshtC6ZEbGMfl3DO3I+7vFCDYEbpsxDPwWxd2Pe9kANbIJsWpGebn+eeLg5R5+hOwcE94gHpzgQnP/O/sUEOU9iSuY3bb1Gj/LZFdc/jh2bBaKkqdJTfCxDLXTWad5lpKRlOCi7i3DrUcXMEYnJDozGdZzEFLPjo7AM4BteafVvOqZ2IyMoJ+D2TOkAp/dPDIL/z9j379sQDKJz7xCXzP93wPvuu7vusGoPzWb/0W9n3Hd33Xd+X3PvShD+Gbv/mb8elPfxof+chH8OlPfxrf+q3fitdeey3f89GPfhQf//jH8du//dv49m//9i/5vOv1iuv1mv9+++23v8xZ3QKLeN2AD/N4w1dtLLj5Xo/AUGGYI6gPCUcEai6O0if6R5YVuMYn8QiCFOEqsGy7L4gUvcuaO+CozicyP4aaYt+Hp+kVV9c9adWH2h0QbxplEZIrvWOiONfEbSuqs7zFBpbCkgzOBfuu2DrT+ddtx+Pep/S+dyd1BR4uG8wU9+cT1mXFk7uCvuzY9isF4QbLWmsrsIVkwta4GbdtYN96Rg2qim7MEnQ1XLYdrWqmxVvjs6ilYXTNksDdWvHkbsHV57dQDG9AUL1MRGNA7o6bcwcNfMaCPnZqp1RJ9nsfTPMv4wpBgfYO7Qc9mYNRgn9v6wSSpyV4ELwmOxgPFee2bAPPHq94ZVuxrnzYRZClEUZWXJFmwObCaNm5ZAq1aO/jhbWlofc9pf/ThEYkrqFGK9ltw3vBoWUEsezUKW3qi5hx7cRgxzB6NFBRxowWGeTx4t80toAWwbGWX12vpg/Dw0b+SXS/FBGsywo1Y+lpWGZhdFDfhiCKbfxT48MNrDu6cMxFBKdTw/3dGcuy4Lpd8ez5BY+XHVIKXnl6j/N5xb6HpH2EL34Rcoz+3AnkHT7w2OJex2+7FY/6/BF4hDubeibxe5PfQbn4IMQSjND4+/eF2Sj+W2aHhijUegIRHQSh85kpP9ml4QXhBP1sUnvF7RBGOtkgBos7oIjuozUByOZnEvst7J2gFKrTRtsz+QnmxFt4QBc3bj7SsLkizDQzW4fcD4iMhcQSCF5JXDPynuY9OABGwKb2Cg7XPp9YLO78bmRIspMnUIKY25Z5HwN0xPmxw8btU+XPayFHBAgQU7zxATMz6PuHZ695ZoelNr9rBkRG3c9RZ+rPg+y4Yb74Jm8Zk9QarMwC0wI1ZjAnMLGZHTQ43UShrutyw8maTxVf7etdA5Rf/MVfxH/8j/8R/+E//Icv+dkbb7yBdV3x6quv3nz/tddewxtvvJHvOYKT+Hn87Mu9fuqnfgo/9mM/9lWd34H+lX+PwXsBp48Imt8KADKjGmo6+NC0xJSKoYJuigFmOEZ0KWCiyZAezvk6nkmBWaLkuVHmZ4rD7RJ2QKIeXHEKMSslWIloObILArBMFE7I05smETt5hIBZxxVhlmU3Q1mp0ipw4a6V4kEmuMkGmAEDjCSv+4Bgg1jB3ZnibteNnRV979BSAChqW4GhqHVgXSn3/njZoaCw2kkqrNOgighLDjG0byEpdO87TNjBMgbweLliaSec14qXn6yACPadfA4zcyIkrycyBDGmfKgP3ds6qhjLKoWcARPBOw8dn/3MF/HKTlD8B3/wDt5869Hl2SO5Ox2KuO3aB9fIyZBMen2h5dAguHbg4Tpw2QaeDkWB4rQI7s8rHnaSihXkjZjwfKWFrkxPwyClwAZBKxYClmGRtfHo/RjhKsEDAUqA8YIgVxawc2ZZGvVgRof4fQyeyQxyaXRCwK057yAyS7neHKBYqU4wdkfqZYbrrrhu3fkBBEClcgNwOJ8CUqHq06vV1X0rMssU/JejIYzrrs4Nur8/YVkWXK4bnr3zgMfrjtoaXnrpKe7vT7heLnh49ojddXj47PzeRTjujzIcRnaXusMwj0Ld76a4mKZjzMCX55uZEwkUy6xYohvvznFSKttiDbXawVbUBPK0Xz61e3Tu26E574tGxmZXhwTpOfRtZov4fMbm4IkD8MQKoouIvi0YHdPeik0Cq4mRHOUdOdV1mESFoBXCSyySmcTEKAegZHH8IpxvFLdMAyyGDY1VwHMZCUbmccxtZUIY472OrJOEzY/bkBZUkhRdJKLIuE2GKGfEswgHEzpL/sg8E12zjFPKbBOO/5XDdQgiyzVBs/hemWDK94DRBuEISEF/FiRfOMkbmojOq2SW2VaIZ1gQtTjyb5g1nRo0cWcCCEWzXimRrQuGz/8NAOW///f/jr/39/4efvVXfxXn8/nd/Oqf6PXDP/zD+OQnP5n/fvvtt/FN3/RNt2+y4z8ijXjYaDIfdxjvJER5Tz9JUIGNJW/wALyrZJAc5/g15lLOhcDjiAMSNYUxJ5yfVtwJanBYEEqJ8TAN2mLDDyeBsYtgWSrOZUFfh0eWI40Nz5+ghY9fvb7rGSBOlEt2OyNLLh4VwXVjzbC7FHpcPx1Pw+iMZMWdRwTN1PnoKNvAfV3w8tMnOC9MzbMMMNBHxxjCe6FAW5jev1wGWjWs6wIUwcPjI1CKZ40IUgBG9zDD9bphXc9oS8W+7bheN9ydTzitFeduEFGsrbDkALB1GcisgQhrsH3A05bAaaFWSBPB3drwcBn44vMLnv1fVzz9DNOa7zzsePbQM1vBzSyHFRRGXCi73wfJsgWcufTCelZjh9DlSi7F2ti9MrTgbl3wsHX0QSnpgQC85pEWwfZQ6sxAWNrZ98gYeMt1oQUXM58+DCdle01ZyduotUJlOGNfkmgaPpPdNtQb6cNXu5f6TAqGv2ddpr4CRFw4y8s5TDUy7Xxw+gbB44UZi+W0kBvhjnTvA+bdSGrFdVKGtzwjA45S8oC+Z5D7rQioAnt/QmsFD9cdb73zgOtlw7o2vPqeV3B3d4dn77yDL775tkvxz76BGcHyxC0iYRzLMW5CPIMpkSHwWxWtzTOKtzx2ZDzCz02XqSgy/JnHHgfnotRDhw3ESySKYcMlBHb0fUcf3bO5fvwIWsC1MQf6cZ1OMxnAld8g4HJgKaDVSy5GemIEYZS/RmdP3oj4OxTiJWwpwMhajQNk5/bNjONhsdjNTkvbbYBzWTQ5JYI4N/NM3tx9WfJwDZbZaGcIhe95PeD+OfjSuOScdxNoJn4Y6rzuZUw0109wR4qDC/E/ixRUX8clsiSHtRWAJe9LApXDsz3cswALQFxnnj3C11l2oTkMNvOZSX6uTh+YoFpu/lRfy3FM3kuZn4vi2Rzh3Lj47FwvX31vzrsCKL/1W7+Fz372s/hLf+kv5ffGGPi3//bf4p/+03+Kf/2v/zW2bcObb755k0X5zGc+gw984AMAgA984AP4zd/8zZvjRpdPvOfF1+l0wul0+qNPLjGIL57DBiql3DxIAJlO8+3nzhy5eGfXHEHEPhTD5yuYg89QORyIDhEHKH7kfJiO1osAKL5xLSqGTpy1SBy7DoaRqNgczOi+o8pOobJa3DBXOpHacC4Fa1NG2KqZ6u8e9UdUsKv4SmQJpVWSpGL2jJTgM7CUYuLcmcoJvxCg1IrR6eCHGa77zvTfGDAF5fMXDhS8XoFt25Oo+XDdgSvv7VCgjx3nUnA6naCquF6vrmzaKFXfDfvWUVuF7h3Xbcf5tKDVhjFIkhU3orUA59OCXgWPVw4TNCeXwkFEH4zAW6koUrBtBHK1AE/uFjy/Kr7wzhXPtx1vPWwA5oY8ZITjPzOwOXyP7dXhRPx9bo8LmIG6dsXzS8fj1bB4q60IpxRXN3bFs2fDs2UBNsiW7xlFxbM+reR78PqmZRXmd31Nih8vrmlkx8/SGFUDbK/mlO2aXBhzboz4GmBmw7J7hTiZIJ/daZLRYy0CGblVqXMD4PnD5tGdE/WMejWjUxhKzXDdOi7XnWC9seQ5IiMQz0SmCQzwcjoteOnJPVoFHi5XvPn2Fc8fO+7vTnjtfd+AViu++NZbeOutZwx2HXiFXUinLGFf3CAnYATCGE/3JkAGOVwTM2sywbKfqP9uOIzpflHI21p8MnmtQK2uO6Hm+3xznZmOoR197Bhjn0Rqk8O5uyvxaxQxSPHnJpPnMtuQZ0kRB1vK6461xAxpbAA9vsfvRRGBFTjfwTIgj1ScOMFfFCz/HB1u4gTnkWmAHct7SSBdvJwFHBskcs/GJj2ARLV4jm6rM/KI35/HiXsTf4+sg1v8wz6bjlccLEU9P7gyBVGqmetV8ns3R/P7fwAf/ndBZIQC8MrtKAAHC8dVRfzm+c1olS7pqTKLVQBwYsYUy8u1hMN5S5yfYOp7OPhh6gwwcXj4pdf21b7eFUD5a3/tr+E//af/dPO9v/N3/g4+9KEP4Yd+6IfwTd/0TViWBb/+67+Oj33sYwCA3/3d38Xv/d7v4fXXXwcAvP766/jJn/xJfPazn8X73/9+AMCv/uqv4uWXX8aHP/zhd3M6X/4lX+byo0R+fHC56Ur+KSisdUc0IGwdg1WIuGqlRUugeKzjC8MRvOqUID5WQk3ZniwxldozHa2xXtn7yDqsoEy2v6P4mCFz7YNEBpnXoH2gesRVhCl6g6RibGiMCBwUOEl278DWWRM9tZKRGfkqHA7FDMiUnR6qbkgMkIJWGzh7ZkfvJPOuS8X5tGJdF7RlwbZ1PF43dHjbaN8T/asqtqE4deqRLNqgo6NKRWkV29ix7QOLEIyNMXDdXAgMgHSvxxrbh1uhZL0ZP5cArjhfCNj3HUtZUAtn/wwFrCtQwLbEFlOZw3ncYI8v+5qRQ2TdCAwX1+NQM1cGRSTKsXfF49bxeN1xPgmjTmELNn8PqUxMcCs5qbmYcZp0tOb7cwEWGmH1YX7OG+G6dFPhjrHW6mJq7N6hFkxxoOHD95wkyQnPB4GnVrI7rO8bFIrFnXUIvMVclPBLJDpSE6WWBkjBtiseHreMjofvHarVCqRUPOwbHq8XqHacT3ckxHqbM8upYeDdufq1351OeOn+HlWAZ49XfPHtRzw+dDy5P+O1194DAfC5L7yFd955npmiqe7sgOnglKKUF+6RGf3Y3VEeDm6GOxRFlnvh94+XGsFIREWS5Z62LGiLoC2D2RLp7BmxjjE26OCffN7RPdGdYD4goiglAAqyyzC7dTxEzjIDfCpvXL8AkVFmdiNcaLi5INi67UnJ82NniwM5VHf84PouQFFLHTSJIM9sltNNcu+Fs0XsP4lrOTjnBDLxmYbgyiVEPAQYk0DNwx55FAd8la+4ZZH5yTWR6+KQeQkAEMAlgcwEVNNoeMbBnbol/2eWeYAZ5GbWwvw48WwjS+KGIrGfzc+SJPmYPzM/LQU0BPkAwDy7b5hlLsQ99kyuecmJqwYqLPvllYegn8YDjhLgMWvyvyiD8tJLL+Ev/IW/cPO9J0+e4L3vfW9+/wd+4AfwyU9+Et/wDd+Al19+GT/4gz+I119/HR/5yEcAAN/93d+ND3/4w/i+7/s+/PRP/zTeeOMN/MiP/Ag+8YlPfOUsyVfzMjsszlhRk8KVdWp/0JmOxXy4gRyjG4LmJMCLbyAEadK3ZYjcwDxi4edFClfjAzJSmdwQgF0NOcPHQDa8T+41U+cHzDSaOI8EDj5G8k9A7kjswVjwQlIWOyUqhrKteO/Ux7jsI41wDd0XLxWYsFNkEU45JieF92hYh9SKtlSYa2uMa8fWFcvWcVpPON0/hV6vuO47y0FgvGUanSqK/eERd7pS70SdsyOC4jN5pFQUn/nDtmNvWz45D6G48Fer6F1xty4QEzxerxjDsKwrhc3U67NhsBzRtlqhRk2Hckxx3jyl29cRCNsLf48abQglMUALA+rArFNDRw1YSoEU8gtaI3jo2cquKDlELgxyybVX3Oj0odn5VXxNZcq5uc6Fl4mquGYKSIBd/PnFcy8gYDMwA2YiKGVBrR7RF3ZMbduOVoF1aRDXnwkHPXR4Bi4i2uHZFuHYhL7hcSNJWV17JcBJLdQaebxu2HqnqN1CpcoOH8cwonOgoFhxkUHgfF7w5MkdagXefrjgC28/4rp1vPzSHT7wja/CFPjCm2/j2bNHjOFlIpOZPZBDiRhR3nBXFNmTm6d9uz7Mo/mIMUJ+nKn6cIbBe6polQMqQ2ROqsHsAao7um4wHTDtUOsOnkNfIorNTp4tXBfm62tAp5BciqxNsa4qwK1qrE/flXmd4ZXpBCVQBaZUvds2cQ6ThLN1mxl7JRaF3+t0UeIOWEjiVBWXRmGXGAMtpIMNZ3wEJ7TbE8jEnn0xG3PkovA5ETQHAGDQ6CyzCAz9+cV+n9AhrncGKAG8kDDlBaMg8yjpa8xyb1t+yvyd6F5iA0ac4wGIxIFt2pYvWZXBg5Pb34kyJfGmzPs670J+7+YLk0+DuI6sQBxO30IyIe7Yl7ejf9Tr/+dKsj/zMz+DUgo+9rGP4Xq94qMf/Sh+9md/Nn9ea8Uv/dIv4eMf/zhef/11PHnyBN///d+PH//xH/8Tf/aX3oJYFfMr+tiJFDxrAiCyJobi6T9mIAYE3YRKmxZpry/9ir0c7WSRPpyLHF779QfuC2Moa8ytuuy8O06Ou6aDiYVZpDiJbsoUm1iWWoaTICEjjyMlrttFo0p8VkFZCk6LR/3wbpreoYPKngZvYS2CKgVLbSgtBOHIVVFViFEevCwcyrf3ges+cNkHHjbFk/t7tOWEioK+bU6GZUcNH0XB6APPH6+4P5/QSvEIkfdpqKKqz2nxdmx2JwyYMtMyurow3cKM0uJDBa/MukgRFIVrdoCt1kC2Q8dwMZi5/L/gdpt/+Q0WNsde2J1xT5unt0M/gxubz2bvHde9ow/zDpZ5zDBjQaCMexACblKm7g2tvVAbxA1rDGwMMMZBi9ElxHbiKuYS+ASvw9eJuTrg0hqkMOs0Roc1SnbDBrRTnRamqLW5qjEzRwXetj48C1CWHPgWIlt9GB63jq0PLGuDpNKpQIxk5ncedzx7uEIMlKRfq2dmPBOZe4XZjFqBu7vVwUnF28+e4/NvPsfeDa+88hLe/40vQWzg859/B8+eX5y0Ww4P8nDf43lnQBGPN96Ybo4uNPbuobV8FnpcKdoVeNl+XZwDxEyUABBTwK7o24YxnmHoDrWN33fOCI+rvoY6KD3eHQR7dkMU8NIdJByKAxAxZk68UzjWoyAi4+grCk/npTfvakyCZQJ4/x1hAMdbJvn3sLUCO+inFI/yw3FODgrADJmCYJHdj9OhTycabvJggL9kZ06QEs0LMSVLjfYn2nHjfTkVOp6tb8ZZGnNuGYLIGvfYsx0Hg3DM7ACWGfK4JzmTyLMSyYmMMwg/pM6BVFBbyN8RJNnDTThYqwAsFg85zyN8X4ATLl0H0DqPGEH1BIHzXkXWReP89PazY71pEG2zKyjuyVf3+hMDlH/zb/7Nzb/P5zM+9alP4VOf+tQf+jsf/OAH8cu//Mt/0o/+sq8ovcfi4HqZD/u2JVBujA43lviGkMymDKPhHZktmRmSQPss44Qhk0Pr5Vx08XmhbBqfbAXOzqfw1vA0sfoUU4MhBvTFShkpqDQ7GTId61mXuCai7+HtwvChfHRYy1JwOjUfNGfYdupO7K5tYUqiqg1wXFQprhobM3e8w0cVUig5PjyoUwWulyset46nT+5wWhpqXTD2mcXonSqsnBOk2LYOLHVeN4SE4D5wPodKbBB+K0Y31Kro3dBHZ9Q/OkyYYVqWhm3f2f0BlidqEXRlFui0VIgqbAwvq03Vz5Ts9vs8I6TDFnPP9aXwRRIgVjNXGZb8RQVF2x6uHY/XDjOWoWYLrbcbesRrhw82BL8l7A9Bi3k0WopkFoYzUPjbVB6mYerblsB4rfN6A1jlihVXbrVocXUi9+gwVZzPK9aFmjR9+BRlKa6roxAT9AY3sl7yAjD2jodL51oxlqDGALQKhgCbGt565wHbNnB/WnG3LmhVsAsg+oLmiHN37s8LXnpyRmkr3np2wRfefI597/iG97yC177xPRAxvPGZL+DZs4sD+ShchFNw6qUIJpchSgI3bhvhoPO5O8mdpPjokgGS69UKWl2cU+JTqgUJ5EYffk8fobrDZOPvinlHWCzCeEKWzhQl9opnVNJhB0HU8v2zfHAAJ15Wjqdv7pjFZjkrSKl5PJvZaS5YnQmWXD3eaRh8PzmAIJHp7PzAxZ9B6PsIzO32AWjccC38VPy+sMQQm+TwPszfjfLR0Gm3o1Q4W6IdvMlB8E3k8O9oMIi1kwW7vItHAusx1EnZe4mBk5KZ71yHB59kDnqj+zDtSC7GWLOxPOL4+bByKR9zOuLH5vtmlixAYmScgqAfJTU93FCRUOm2rCpEN5TAspsLN596/PMrv75+ZvEcELncbBQ5OBrJ/yJS5p7WLB4lcOBRhbh0sVnIGE9dFB5rAtOcMZoRQSyAuTBJwtXDxvToTYBI41eLko6gq0IG5tRkjd9x4qE7Hqkco02+QWRODAPiToUlG25WIqjqfI7rRtLp3hvu1oZYpK02LzN5ecWdYh87th0oO8FNlIQC3ETLQhGqw5ZikMrI/p13nuPaGu5OLBOok/nUF3qtFaVWqrB62ynbfskFumwbhhqWhe2uQxWtFux792nKQFfFw3XHaanYe0dbFpxPK4YS+ORwOsBF6AStGpYWJDv1CcMVggkAc1HFg5+PGTjYi/kG/jfktdVYUhk2f0fBtuTLNvBw7ah1weIck1YEa1tw6eqgijN2jmWHcJXVRZ5yIqx/OomUzkuApHx5FePkZCyorWWGTC1m+Ki3FZNT0DvvQ0xfXRaOVtiuVyxLwf3dCWaKvrHLayhXER0vheyGd5d1JyMbqFz78LiTl2HC59iNGS2peH7pePawoQoH+q1NIKC2jMQ+KAViiloM93cNLz89odaGt5494vNfeAd7H3j/N74H73/vqxh94I0/+CLefnadQSXCTpg7V/c8x6DCmDEMo3KgIUyzo56l859Ei3+rwvEPpwWn9YwqqztKzrfa9h175/wmAm5DKcMHvNHpi2udsGzUQasWZOWQyp9/xnvjexp2CfBofUCtolh1oAuCEymIoXaxuoMkay7OxesthysHAAop5vsREu8DM6bn34JYeQRIUcIJIiygiEwQuyqRzjk6HWPmC02+zV1nCePTxs4v3hcGa5IRf9rmAGOYJZ3odKRmVAirEehHByQcHITCrR6NPuI8D23JznWcqseRyQglVwkk4wDHcz4WfuIIEg79gTJtSyD39HrCbkJECTPuE3HcLLeZc1LAnzPLhAlO/DnF/QWOJbLJvgl8LyAJ+/82Dsr/1i+BO/wZ/2WEGTY9DY9jX18gQShN9Jk3NPr+S3JVclNY1FuR9eWE9CJu9P1zIXBhwfnziFaNO06koDV3NsFvUdaQo5UyxN5qq66UylrwddthsNxAuWgcFMGXTimCc6tYWnMlUTr1oYbdO0OuO9sTWy04rw1LEW9npVNnd4+5zL4iZpVEt4aquUPkpqhG1dfhDvNyGe4ABaVSlXcMzlVZlgWAeYcUHRtr7sze2FAOJRQnm5YVKJIovg/FZdMpdW+CujBq3bbhff0zYoPRsbS7O/S+8+9VcFoaStk9FTTN7O1f4p+W3zs6vSrAeSHhc9eIVG8PMcywD3aImSqkFay14P68oCtw7VcIyO+wwZbzUgv2farKBhOfS00ZmZtkRiImC0f0RwLzgt2fM/x8dRg2b+teCwcgRtYtAU5cs2cAa2sslY3BdZSGr1CvZ7ha7XXkflsrYItg62yxPp8olT/6cF4O1+QX37pg7IbzPSdZt0pUVxwAV5GUcbg/r3j56R2kLvjiOxd87ovPoKr4P97/DfjG976Ky+WKP/jcF/HOswemnD0VJgLyfjztLs5lSuB5CAoiWg3OEiCufqyZaucoioJlbVhPK07r6hkroHfFw5U6KxTU0yTQZtlAyIPh3+NeE3SoA5AIvljiGUi3GrLjrsZsxt+ZDjzs01ypYu5IigKoCJ4H7WaUdmZgw6ME70cB8xK5gRkUROtuBSAH4D7ZKgcNbz92OXSgRLYgZkYNdAXGiIwHn90RnITfPcJGxS0wUScrh4NVo40NMm2UfWIvlCre+ssSbZZGi4ex+ax4R8xPgrO//HsHUAHAAbADk8ISfkg+JGTz6zAYAYXBAV8AVPqd6AwNcBSAe4KiEUs0bVKcb2SEIpwJTmUEm/F7sc8je8bZUzKvyYFX+LDgTrJEi3msXARHWH8T0f2Rr68fgAK84EUknh8fxAFgAEhwEYtC4MpPsYBdEyVqr4JymK5+IBDFpyVLOl5ZBUSmjePzDmm0PrzcAYXsg2l5B1NSgjfitNJNXep7JOEx5h1E/R+D2ZgvjfOI3VvhPBIIcLkC4zp8YNyAVJZEuocr+75Di+C6734uNOJ1Eed8AOZzXIqTegUecWLyJ2oRLGv14VPDjQZLUbXymL13lmLAjI8WABosdl4faQcKKx6vyY6lsVOgq6IroNvIyFpHx9lVG3HMiAjIbDfFtg0MPXPjKQHgujR/XEdwYrePd64yr+G6owHByf2p4O7E0ogOGobQzYFMkMT5SOpGihHHaSk4rRXtAmx75z1v5A0BgqU2DNszXQzvshGwk4lrS11O3hPnZiRiNrY0By9HUMGhgN3LbW3+e3C4YykxCbVAh3JAZlc0NuBDTHOQYvAc9uEDBgcHh6lyoF+5W9FaxVDn6DTBpKWzs2fvHc8fr1jbkq3PRQCpQB2uSefAobUVT5/cQ2TB5956xOfefAaB4v9436t433tfxfPHCz73+TcJToY7W0Q51LkPbg9mN4Ol7EC8L517IUGX7bwD7LyqOK0rzicf5tkaTNip9fDgIyB8dITadIahGzMBChAqsSI+mA87OA06yLCAwdvEMTzC7ocMS0d22jhHKS1BCIlhAj1Kkhe3X9GNFKt7uINht5fHxH4/ituttLAvWJoJaDMzY0HqDXBiUCsuAlkciFgCEv7dXDMqul4s40CEY0eAdJv2wuLvfhwvgcZZCiyBDMxyDRQRNA+6WuEYhVoFzTvY5ODoeU2Sl380D37YgD0ObJmpmhOHuf/CkUtweI4ZvHQysQbt0GET9xBZzZufaRPMYpaqooblrm4C3INNksNnB9UhMkHBmdHD9anFO/1QUrysVFDUKxN/6gFKIoYAA3G3A/lHeYZPJ5/58ddDhyGJSZF+5HGKbzpqf/rDgiPKoOwjouUQRZtINE4vHmikxmLR7WO2t06FTI/shEJNJELyGPRNwXWxTMcBkqn/AAmRQelK57MsDefTgiohBc+IbCkFrTRyT2zWHjGoK5JS6X7vhqdmYwghWdvRDRRRRqh/8rnsLvoGBbREW3aBuoQyzFx3Y+7NaQYZqRUBdB+uvUBw1p04WXdxxUp2KUlxrktjyW52PAm2fcfj5eqdJnSGtXyZJKS9+I142PwPS32eOVkLnpyb83EUWoHRSnKZZqTjqVOdhOPVB/YtPq14d8Nea3PwZ2hLRTU6j/j91vg7p7WR6Kxw3oNk7T06ynp3AbUmDhAIarkSzcnGA6osvU3j6Po43no+MCBlASDYB9uLS2W5aOs+gNKzbn0oZGE02IckqF2XCrEQBywYytIjwHZ5KtgacoZPk+QHrGhYz3dAWfD5Nx/xubfeQamC1977Cr7h1Zfw/OGCP/j8F/Hs2cW7zoAZK0yyefWxCEjH5tyIzJbAgQFnOY0xUKC4O1Xc3624O5+wLCuzsWrY+47rtuO6Dey7+loOgDP5B1IkBbqKKGoJCfsB5gy7l3gcdCCCmX7IWjiQcLIs/3RpepmLdmZeSP03JyUfwcV09DKdIWu9nnQSTNcYvxWTJwPQATGvJbM2GZt51ifsICJTYglGRpQF1TkjQcCMTIQ5WElwELad3x9ZHnKuiYWdjXIV8rol1ck8cyLkfLUoz5WYkO2TkIObFleTPufQXYkDV0f8Z+A10BQLZuvtvI9uNg/ZfhzOdb4zOTKQ6YMi05HgzAGD+yFyx8Szjl5KjAWBybuKc5/sKr4073kIsonb/Rh8K4dnEXuM15mk4f9/kWT/d3pZIMFAtHK7/SCxgCZQmSlDmTVAzIUfC4LPzOIgsfYQKT5NIx6GIOrkM1MTaHeCFD5I8w0EZ2vDDEUtOyPYtQGEAJQ60gmCYXGjB1En6s60YUYGYIbDIHjcOvauWBaOuw/uw+W65SwUOMlP4oIAz3xwMVNCo8BEc2pvH0HE468MBwHVozySOTFrlp4ZAaagT6kFYiQkByFO/XgFwojKvIVWFQrqXgzzcoIBbUT9u0CIWnDdOwDBqs3l4On0IRXPH684r4s7cI6Yr7lWPBV6s5BuFl1GVK0A56Xi7lSxLAJTToNeF3al7H2gHw7C6IOlr947zOfT8FkRYNR9GjxmzxTinSACsDQCy+F7rfEewSyjplIrOryEsncarNqwOmFzDN6bEDTsYzhB2+vqQ1F5Q7Ilc5gA3aBLybU4TCiu57L0AUy6xkwf1z/pim3XSeR1sAIwlRzZnsUBVoB8hbfIVheFKwusLPjiOw/43JvvoFbB+9/7Mr7x1Zdwve747OfexMMDBzyKC+FYbm9m72qbWjEsL0Rdn9dL2+BAwJj2P58qnpzv8PTuzKGXpXp5ccfluuG6Uc2VJTTy10puJmQUnrwGEc+ahI0h6CA44frlqIzh67EfoQQMA1IimzJgonC8fRNtxwURJEy7U8NZhlP0IwfdE3EezheaVjXmLtEgThPJe6hZUlUPyJxnYuxGiRIZhQ0jW1J8HU1cHDYzNg3t6bGt9hBw5vujVDRtrd3ci7CNBUH+FiEQaU0YqFV4JmU+p0AShkN24eaI87kcHbFBfHKwE9kVsDJJ2pZOK3yCAytY/i9sRuqpvHBP+PTDturMAnuwrAKfkUNOTIxIKaANm+oxt8Yu/CNA8BiAL+iw8QyO9jFwSezrW1DypxGgOIhL+Olgw/KHvhjT7DINCamesnIyrFRv+2sY1qDaULR6yQfIjp1IiUjU90u2z04FSj6IIgKTIBA6UvbIgpoQll0YIpFE9cVizDgAATwi6h6ITEKqUxYKq6kaugGh+CiA/5wRwejA8+sGPFyxrg3355V8E23Y9j67ZLz2munIQkVWM2YrCphR6UoZ67j7xwjGzDBEPGqX7NgBLAHQGCzRpOy2INUW1YLoh5xrw4FVRP67DgdeLJcJGJ2bUU6boIkRvojisg0St8ywwrCcznh8fICIODeH2y6G9h0SYy+gXf6zFsFSgFMTnJaK89qYtbBOB9RifQ1cNic+Y0ZBZpGCBrVeCksutRSc1gV7jySs4DI6LvuOqpzRRKIyABOcTiv1bw7dYwWCtTW6O28Lx4j5Hz5bx0tM0QLMMkS0q0ZkKF4mEzd2TjSOTh0/vxBpG14yVKXTN1XfbcyQMLNiOK0lZ+usrZIQ3Z00XebgTOqI1Ix4RQRVFmxW8NazB3zuC2+jVuDPvO9VvOeVp7heN/zB597G5XGHSEGrhuBHqK/ZWosPlCwAfJ5RRJ7g/j12JsyBgyc8vb/D+XSCgCW4x4cLLtfN944vVqkc+BhOOqNVyTlNs0yL3FdsWe4gSbsD2HkOFlkVQ5F+ADMAQH0UyHDZgg51QK8g6KbNCp2aMon9KO7WgHBR3FkxtfbgHi3sXrQdx7VGABNuuzqPju9F2E3jcVWBMWirhkbGxOe8aLQASw5YtEMQCV/vcZuP2fDoxgki7ewwmV0pvjVy/0Eik+ZClYWdbux09HKPCyciA17xANWgElBEM341BxlhEyeIcvqAOn/KpiNHZO082OFKcFuPaQP8cfpnzu6++dxm63QAF4Mfs4CcZiMQF5sBdPiVNG82G0KC4mDgLLpxA07Cn9oEq5j3SrLN+AhS/jSSZHPz8DV9iYsCRfopN+YBjYYanomr4gWAifRrQUWlAZGoASINWrxmWcbThe7cg+AkblBqofOMFtBj2jCO02pxfQoagiDjUdgHSSQzNezaSeRqjDr3PtwQHUpQzhtYmnNahFojDxcSYzkPZkHxeShDh89rqT7XY9aO4dkFEcFSuZF3VUC9Fu73FEAq4sajIR1PEkNG1AIXgNt7z1JE6sWk8ebnxpTbIMZHtjKA37YrRhHAyLMIjRHDAK7X7OTpuuPpshDA9JEHan5NZOVLbrZYWCLeslsEpya4XyvuTnR4plQbXheChrWRd6Nj4HmAPUzDFM+nNQ5R5HpgqebO09tFKq594HHz52qKChI6FxBsLMuK7fLgZFbxTqCCu9NCFd+uKLWgLY1gOsjRPiU7gOzh7BJgSiHoa2D6G+FUjaCwVV7IGAN9sFunq49/KCSEC4QCezuwDUNBwXmJrhHe1FoKrBGcmHOryDNpqRxsCrTWsA/g7Xce8bk3H1Cr4P/12it49aV7PDw84vNffIbHbfjYBnoBNYMNYwq/lVTZZVaQ2aXIPCbIdjdxOlW8/PQer7z0FKfTmZo9zx/x/PER29Z9jRRAmnMTdNojOa7RyKT4OjhE+oKR/BPeE5JjM4tRAo/Y/B4iCOHvRUccW5PJ5Tg6MRwIqtzLATLUn3c4Rb/XiOYBt04emUdpI0CLhGIsJHkNsXbouHz6rYbkQXWeiUJH8ag8sgbiXTUly+uZpQgejNm8hjgXm1y1m+yJRqusZLkjACjg0gPO86s0i2ileKlbMlCJDNHRxxyF626jl4N9CwIwmFG+iXDMgEJCLg7BNbPU/PkxS5QvOfTrxfaJ7D2CXxOgze/P8dfFPZw4XSD38zEae4FbItHVY06YjbMNGoF3aAWGPKazbsDJC9fyFV5fPwAlkia5+UDIKAJK1hcYmqO8CoDlDrPFf7YwspA7mJ2gdoLaCrXFkSuBRDESysoBnY5YULF5zDDEp9AmCp0bJ5+df694ChEIw8iXqnd3iKSKbGyQ1hgB964YvcPAUkFKaweyNjcEXidhq6dhXRqWtSFk8Ldu6GMnmAvjHIQuxD3kZQ4lD0EMroRZ0Zbmehi8rr0PdooUuQFLOjqT1l6KqnU6u+qdT8NLXUNn5HCsZ3OOCK9ymLnEt3+CAdZJbgMsZ9NkfVUs1VZF4F0vhph43VrFGQul/z2aKYX16GgfXSpwtxTcnxpOjYYNNjB0x3Bexf2TM87rgiagRssuqBHxRqjk97N4Ork1OKmVPzsvBdorFILrznNuPtWYKX8OkSwQ6riYYFmoFWNjJy9lqdgH29CjbMlSzw40qpgST3p6e/FZON4lZMaSTy3UQzHz9mUbEM8aDic6qmcC+/AuH593RMfBOLD7811awXllO3ePzwKPI0W8tVNSTIylN4qxQQRvPlzx+bcvWJeG1973Hjy9W/D2O8/x1juPuGwDIjXXBJVXabhbK34PS65jPo5JLjajLPvdacWrrzzBK0/v0VrBtnd84YtvJfFVYZPwWOLRToMubpTI/0Km1NUss1ap5yI7gB1mF4gMiOwQdEB2L5FEAn9AsM8MihDcEAWRf2LCUpAVg0ZyRCKLMED5hOZBADjUzp0g95l3jnigNnkOkQVhSZn+NgiwDp0EEOn+e8xYmVYHKMHvUwwtXtZxMHJoZ+X2dFd7DAYzaEHa+LC3LCG5XQ0OSzrSCbtnxoo5pFK80624sm6WdIpn77g3AoylDY6tjLhv8/mnHY9zdkK4P3zcAkUFpCQICpxS5gEnIIusRN5vB6QIIHjI7OMA7PyYMZGeLdLgLCbxWsKBL3h8cX0gM1LxbOZ9LfSzh06vCC4hYFkrsygHAPdVvr5+AIojuXkLJG/YDeg9tBAf5wSwXa5ys/gXVWXJH7AEPxYfkJ9TioMUbweLUgYNvc62N8wFHYt3Gij+kJtMsSNIn/DheVMOWQCm5o2pey5KRjF0wgGKMFGxp326Aao8cCiIRhtllYgYxAG8YfR+MLKJAjP6695iChxa2Qol5wXkTEAP1+9/Kq0XzLyT5zYNksYuUHsYl3pIJ8fl5fN1ouMwGuZA/yIAvA12mKB2nm8rgst1o4CclxSWymd6PjXUC7M0L50bnp4FrWgarrW5wJmZX7+hVYM0welc8fR+xVorKgxbB549jHjyM34VuEy8d6rACITcOlVR1EpnptrZJtxIgo2LDyE3HcPBg/o1k9fzeOUU4Opk2W3fATX0vlPUzqL8wd9tS51REAgWYsJq8ZZL2MDuxtFU0c2JoJD8Xgi8hb2ksyiwTiO3NrbVbxu7W4qrB4+uaJUAghwsdvWIBZit+OI7V3zx7QtOa8P73vMS7k8rnj17wNvPL9i6QeqS98csyl6WE8FF4JOSDw7XswO1Gu7uVrz89CmePrlHLYJ93/DWO494vOzejQMPLMRJr77bvMQbNf1sKxXPgibnQpN7Jc4HiC4eiSyKBJGVar2MiTtYAqKS7FSWZWJfjARYSARCt1yJcBymw2eueDZWpxBZiFRmC647fNMABN5RiLnxpl3wAC1VoOf+DLLr8AaEMbzrTuM95WAjImsaRmw6PfWfqY38vNkOy9/Nrh0Le4jEAyyhyNQ68QxGdeLo/Jq/4/Fp2t+jzYnMUfiCvG7As4B++maRAIJ4Q0MwjCDKkpxwPRxisQPQMdw6+emNBAEtI4hmJmy6q2NTCIFcaKg79ZFMqUN7O2IN5+8RkGieAW9mgkcA0SY1nwVQv6RGfvzzK7++jgDKXCAZCUgFkl9SAKwQaQAWAI3oT04g2l/9/U8hWFHwFIY7FHniWijh/1y1E4ESLZdkEOzykbiTyEyAA5186LF+FYwMi2T5ZngOjVkyzRRdPFqVmY1h2j8c9m0JC7HhPHsTKcGhCuwGS56JZdtvTFA1M1yu1AeByMFI+bYsPtnZd5vUkhmZ1irO64IqHX2I62LMqc+1BKueNeiYfwNIloXmKHpP02aZbsYsYYABeaH0NjudqogrAStJgcUzCWKopaMtLYnCYopWBE/OC84PO/auuFsF77mvaFVRKzMXsXl1uONrFXUR1CZY14a7k2AVZSXfa8ZqSI0Agi1kKa84QqXDoqMpMLRKzoY5oAHE243DYJuL+7FT6eHxgrtTw2ltUBM8XDaYKlqp6NAUXotJyLtPpd5jVACYeVAv5Jt3nJhOflUpJOPuO89ruAovuVslOT5HB7YPn6w91FvLgd67dwz5enWQVWuF9uhScmcmDQMFjw873n7ccV4bXnm64rwAzx+e4dnDhjEsieDRzk4VXScRL5R41+5dQ268RQytCO7PJ7z09A73d2dUn9j91rMLnj9cqDVkkVWceinBEYjs2pFbYoiyhWanVmRYgthey0ApHSKbZ002ADtML2C3zuaGQj0zYZ5hiZJxoMkgskaXzNTOMI3EP9eiykBRhUmFBodAgMk7UAIGBC8kgATLQgkaApCaAVZdyiHanQM8eKBzOA55J8WddEzkdsDxAuhg+63b3ST2wm3wEZgEB2Wqm4atiRb8iIFqmZlh7sUZyBy4zG5fBMfpw5FFOdJLZtA1l/1sxEAa/LR+xjJJ8KHDDzDLxqgs25hx3Erha/xnAkRmmOvCA0lhlpwnaQceXWTxHUv497O8c6gDxV6PNc4aYwEzZtTtgoQUR4RcQn8Lz5racL50JALiRP7UAhTLCP8Im8VLPDNjwhJPTCv25C8EC4qsIJBZIViZVYHASoF5uQLwhxYP26Mv7nF+/ogMR7wHB1R8QMGqPG8tzA5w/p875tudgtkhw2gnnJqaeM1fJqT2X4rMB6Mj3yLh6OmD3MkZtR+jEwDRAULQwfNhClwdMFSpaMEs8w1iyg2/VkbQp1bQx+BgvG1gG36eMpnqNFDw6NsSAIog68DRKZT38biBHJHEOcMMqV8R0QOYPQp9C/KSDNc+sOydegfC1GUtBXdrxf3a8M7YOJRQKpYqEBkugNVgPnF5bQXnE8XzailYmuAkA2vhSnu0gd6HlwIDiET0Jl6yoSPm5q+owmh/QIAruzbEuTpLK9kqHaA4DHDvnZ01IhmtFykpiiei/nkOTjztu4/hn999kbKzxtRg3uWgalAXQizSUMpw57vBQAViRokFprxmOmuf02MDagQn++h4/sgSZmvVO498ajJIfuawOAKT3oFtH3h42HB/1/DKSycsBXj+/DkedgIrHkc5N8jBjpmX0JzPNUbPsk4tLJGt64L7uxUvPbnDeT2h94G3336GZ88ecd2djF7YNh6wOHxPmJooE8ReU2P301B1sIQMFtJBSpkZE7BsRue+A7ZPcTZaCgSAjeyMy4MilVcRjpwZlxB8pNih25TC98zUAC8qnFw4XDUH9MoAS3WSVYmTK4L/ojF2w0ENgRGPr84nYQmQ4EStODckAMcxSyAzyArbcHT8FgGcOhAgV0VvjjP5E5kp8axIdYBZD9kKx/3pReJOmM3yc6Y13J4EETXO2wx+HpIw4kUOTWQqxZslBrEgFB78KmageQhIj7wXc0QTWRMNbZsIfMJwwo2cB0cBxiOnpubZsgA5Lu4J9w8RGEYmGw7UQg0YFsAtAFL8veQ6iQzefOXN/KpfX1cA5UAd8i+ivILVQcgKYIFhpYNBA3CCoQJ2BmSByVOIrCh4AmBFsQVNFFZKCm0FUIBIQmm2KkZ2hQuxD34da82Rvje1rEsyyzKjq+igyEVXYgQ9CZDBVo9dbECmNOFRgWEuGcDTh94rW4Tci4gImNEg6r32gctGEbBjdB97pLWGrnTSfQxUcaVTMwywRHDy9lERALVgqQVrK9hrxXWnPka3IK+RCshr4vyfeIQFDlBqnUbfAUyUSIqDF/WwIGupQoXQuE3im4wRvx+/8ZgPj1fADO3JCaeF3SStKO7XiucXweNueNwFd+viBo2dELUWrKeCZQGaO5+lGtaqWES9BCSAst3WbhBnrFkSTfd9oFeuodpYv1+lYevXvB/iDqrWijH4jEKgvLSC1s7eEkxHzTbngnVdvKtmuBMtbIUdHdaoY0KugngXTnel34bIWtXq3BTXz+jjoJHSueBU4cdVz6hMDkEtxXUvyC26bAYMwdnl9tUGSyOVxrJ4O1dXYBuKx+uOx0vHq09O+IaXzygy8OzhiuuuAAjA1M99dOdgGOiMKss6w8XopJCbs64tgclpXbHvHZ/7wpt49uwR2z4AIUcnOuUS1adTCx2V4qCDjyq6m7orEQsI9EuWfA5OwHZmSXAFsAG4QGwHsEGE7fmAQczF2iTAefT6EdTQwYy5j5QZpHCugnBGwegSj4KngmzcM7XqYnQ2uUUuXHmM1OcaHgAWwMJB+VgQi24cgk3yQkqC4swM5FdkaKJgEVL3kl/m4CT5JaYIfVrThFgOpKYzLBKdOXwOsyqVhjQ/C/EF4WR1CaAgDgBtdhuZpUMmoTjKhlFeTGgJRKYFzGSLt/JEE0cSG3EITm36mhQDdVAR+RPx4FH8MLSN/hmgH8lA0H8rZ0vFR0bAiGgqifP2f1hI9DOLMtXWOWrFAIhV+AhW3HbuTF7d/PdX9/q6AiixPOfD443KiYrmmRNUwLMjkBVAhckJZhXqwMXQvMHQsl5LAme0YcaCYJcPELXPgajzhWPMuSG50GcrGB+sx0kyUWe0IsYjhmcZAHOxrMAnjlL9+cemEAGjZkfcBkMoFap6KcFrjrW6wVH/fa+Zd6UeS3V9DfFzLD7YTsizwxgDtRXK9w+vV5lh23ZuYji/Y6FUe9kGrlunCFlco/km9rJYPTDoC4BTa9lTT0c3nBxcM0LrymgzujMAeKuwoWaUyHsYBo5t5uRfXDdqqtyt7Mg5r8yOPGwD7zwOPL1bcbcY1qWildUB0g6xHU0KTkVwWgynNlBMUdCwm2Drhn0PoMrPj82rKtg2xV4H2pMV67p6KbFDECUzcbsUpTFmGmqtHEVQC/U4hEZ8H1QBNh1ANVyuVxfjAxbvWgrANqMaklpFhGUdLxupi/PFsM14XpRZdw5FKdh3w753bDtLRlJ5jBiEdjqt2Pse8Ru2XSFasTZJ8h5KdGIoxyuUgscrVWX3rnj56T1effkEHYqH64ZrH1BUiImDE58bVHlfSyGILCLOOelUfT2fcDqdcX93xtoq9n3Hm29+Hs8fLt4OXyGl+fTxmW43E5TiukPFj1+c02LqpM/ghAlabQkoEHtVQ20iyjSbl25cZM0GggzLdJTzjSQ6emL9AFnWQXegMrytm/eYdddDWVQm1yTAllh0coRtAtQl81UBHbQDzKLSsZRSIUWdZkq7wuw1UWqUOLKl9qZMREcfgVysq8iM0FbRXqcsvR2UZSNzCBw0TibvI25N2OYopx7n3kTgdqvY6hjJgTYATj3370mCt+Dk8M+egASZeZpE1WNZKrq1JrdOXM7BxBDE0zhvk0BvAMum8xy5df0zEoBGcBoyFbSjJSor6s3kAbxy/xtmaD/PNcphuSgQZNdKCxz2Gl6hEBBQKgFq+tpEWvFZ7+71NQ5QbhcYgPkE0+EdSjpY/Gv1rwVmZ48i7vkeu4OCXTwDysWDIBE6AdMOtTzMOjPFqSyFxYBJOkrmtQWMmoEDEImYWVOtmUuNzQ6PZCgjrp5+Kd7RUgI+O1LnZ8+OAi48l74W39TqhknDKB1TrfCSFgmQRdiWGecZn6HgHB+FeWcRa49q7BrpgzNXICTlttJIkCzrDfAzJeF2H5FSnzofNjrgBlOEsu+tVQwxF8Ni2rgMli3EhbEivdtV0cq8jwEcAfJn1qX67BnFtg+WV1rF3WnBk/OO697xeOl4fml4+f6E+xWoUVEWQatnrFVwbsygBJ25loKHXvDsKni8OkA5eCySjBXbUCgaJKYPC1BbwehBbuSx1lZS9KwuNS9ECsmoo+9AWbBtHEJXayFpdOwEbSBfJPkReuRMAOJZQFFJgAcHQlxPNO4xidsCjA9Okx7OKRim7ImTIDQahw8q236jVRr+PLa9Z5eLmmWWZ6jhcevYumFpDadFsO9XPO4UGjQIpETXD8swQ3wUBAisRXieEKMU/d0Jp/MJtXAe0TvvPMPl8cLZUoCrNgefhIbaDqUZAp65fiwDFkaOtbRUfmamaTjPxo1+RLWFMvaGDbAdgh1A92zIBCwiHP8Q3AjiCvXOGwIRZk7Uv0LMba7/TLU78Xg2ExiCL+K5EQelbmsOwEKdHFvydyMXw/JdiLkF18XMMxpHkBJgSIXBYbzPzJVJAdjh942fOzw46weA4j53ZmJkZgMyRo97Ft5WJmgJA5w/S0scmR83c9FFJjy3GIrn8M87k9wfIPyC5bXNTiIer4ijwWIMCAWuV2MZhET34DxHZrECpEypAstAOZ5nZooD3MR7CzwBZ3N2kcQfM2CCX8PxPnItF49IqaKs0hD6OpNfUlCkQqRlJi7U0G/99As++494fY0DlBdfviERKUhBqtOgARIA5QTBGQFQTBsgd2Dv/T0MLQfWmVAeWL0kE5szBMQs0n1qntWI6CZY+XJT08sIFF53RB4Uc9347/j3WAt2Z22WBjSwb4lF7ZYzyLCxyHMzO7cjgVJuuiiR8P1B8ouyUmyM4el5oHinRextnkmBoVQHF61gXRtOZug+X8XcolQwypXqGjOFKfreBx4vFximvD+fYnUnODBImkFdKtblxC6SfbjjBlBZ5jktLE0NHZDu/BN/dnYUsBPBaV1wPjU0yUYgbH1gWRpeuV+xbTvevgx8/u0NL981vHIquFs4p0aE6q9LUyxlsNRTGkoVbKPhi887PvfWjncuA8PvrxwMYoqbGbkKQwfvLYDed3b6FMPiYgkB6KqDRzXFtVM/phWgFc2oEeDU51LoSNQHyRV3bpHVi9bm4fdYCtPsqnYQ6uPCGV62WBsd1HUfc+ikueS9Usl0qRWtOai1KYYFnYaVHTzAWhtW7yorhV1JUAdmy4pWyYfZdmry6NyJTuwmEX67DqbkFw7uMyFJtBa2YLfW0PeBh+2K7bpj28lJkXDeJaJurtHkjJQgkxfnQASgHygS07gbYtqvOvckwMm0TsDs3+iAbTDwawIUBywyANsYY2UbtMFkpLGIyJzlnCgmkDckscoL738Qa6MEyi4hyTOCBW/JS8YaGQeJeNzfKYg0LeOoKDcpsgyi5mJsdOIpja7RvhzBkNufaCtGrL3i39dD9sRlBTKQixIiN/dNw0hx25slnbmOE1xilt4D6KSrNyA6ChLQJSCKbHqci+W+zLIPpl3NgNV/VgSozv8wMU9MTZgQUvQz0eV3TOf5ix3oAHEfbPqE4ysgwjGZEXSCuBczsxs30TNDrB/5uQjUfamgYU4Xa2Cn2kIerT9H0eJCiF8mkfBVvr6OAApvQMoAx2ORACkTqAgazDt5gBWGCrWFzHI0qFQClMg0xMKL47tjRy5MX4TFF55HSyHyNjMY81wrjFUnu10WBMCOjw2wJLYqIllbBOlJI/0fBpTRh6KKUDgMUbO1G7l2U6ogTlQe6qzeYVJLghPxlRwzM8wGluoCYc07Iwbr4CHvH7OIWqk41YY+WnZWAL6xQlumGKoollNFKydc9+7fL85Iry4CVnJqsahiXRtKW2BLxd4HnWVnSYITiRl5tSLYRKmu64YO4PEFJDfenVYO2vN7tfcBMcWTtWC7X3EdG55fBt74whV3y4rzqwX3K4GgQtG8E4SaCQWPO/CZt3f83mev+OxbOx52Uo/FOOF5rgmWprZ90FmOBdaYUrfRvTXWB9R5vlnEUCrQpOZspQLD6e4MAYUAJYYGetYp5poEABORnJszlByR6KYoacC4DiIyo/bJnO66a+wxhmchijXGwChAEXKRQhsl9ibBPw34PgZOPj05+E4xbLIU8psAS8Xbuja01jCUfKVSKBa4VJcDaJhgwTObqJNHdbls2PfdgU7wr1rajphIHGCqOXcEDgKGn4c6QGBWhcA8zjPmKkW5J5WYufAxeQ89v8w2cAjgDkOHYQeUGRQ+AQIwRs3hGLw1V8fMkhxcVGRSwjvmOcTvhU1we2AexKgG5ywc9CH0C2G45BFkzgQZfZs4+V59po4i1J9vu3RuHTvXBAGKqbgsAIX/hkaZJMoLQIKcOBPfV+EOJQJEB70RUB3h4jFAnH91wOM2WGxSPaN8r/k9wZy5Y1kCivdE9yE/J5oNuG0CxPhOzbMK4MLAIs7I+SZgJiQyG3G1U8ZC8s+4P7m2/bqOVLg8vkX2JW7NRHJxH7lyqBIsxiYTlvY8eJYKln5dVb0UVKmYTwTHD/iqXl9HAIVEKJPJIBfv0iExNsDICtgJkLNnVJ6AwkVP0A3szS8GiDp65KKNiC0yIJEViY2QTVSCdAThYBOAHBH+4XcYvXDl5N/DcNjtAmE6FAiZ65nW9CVsRnTtDsgM7pB9cZeIifzcPfsheaL8dzDlhwYZNSYVO2AT7+gwr8O7jkE4w23ruaGWxrQ3S0AUdjK4EzZQ62IMlMpOjG3bAbMkHwoonLVUHqePgW27oor5zJoF53XFuu+4XNhRwp8JpDasDVia4bJ31EGAo2pUwfVopArVW83LPOsCsJMFeHq/YlPB0Cs+//aVbbQ447WXK86V4mGlrBhgp8k7j8ya/H8+3/EHb+94tgF7EJQjMgFB7TAOibxsHZcr5fCLiHMV+DxTlwGMkmstaKWy3OBkpCIU79NBrYylzZECWTe3cFkRFWqSZ4PwStDmGi0u9Da0J5hqC58RDNl+XmpBUXFxdGZqWj3wP8IhF28ZHtFdREt9WjAzkfAyVJmZJvNshUjFsiyoVdCHYMgggdyzHlUM9VRgIhgq2HZf0qoI8bneO++BlBTz8+3I+9qqK82W3OfmnUEjgICx1ZpDBgEUEsZTV8U3++SnxcsgMhCEWLOr//0KswsIYS8OUi6AUZQNxuncHosg1Chm1BzgJATb7PD9SXCnUmwFKawKswpz8klw1/h7Je2PeEspO8f9XgV7HwdbiAkcCE4E5IYx+2u+NsLERHkmOCbULyFAsfyZeVlnlkqC7DmzBNMJk9OCrChEkDSd+bSXAbqT9+FfR/CiZpBhmSmKT00z6f+SG5AS1+5clAAyB1SgNv1Kgfjajo6j+bOY3RNPgvaaz8QE3uo+gdYEUTzXma0PwiwSwGUmSMO/wLt05n1IvJJYRZAcFGkocPXtFBCsgCxQaw5YBpXJ/x+Acnw56oPXR90YmWuhmGdT+PA5f8ekQY3D4zuQBuZ4SJurEpHWCyPkjyzRcpZ2ECzvYERbAo6MTGJt29Q14fvt8PFRjiAy5dye6bhE/P2q3vlSWe4xy66YKpX1T+GmQCByCRIZgBJEMo96XLdEIeB8EJ7DaV2YUlfKpEsna3xpIRvfOfnYIwDd3TyVkBgnxyHOojaqmfa9Y3e+BMA6uI0dRVxBtRhaA86nBf1UKCvfFft1w7I03J9WwDMsMU+GZNvgxlBjpLrBaS4F38Qw+o7R6birGBaP3LsZTsXwnqfA0I4vPhv4/FtX9GvH26+ueO8rK97ztGFBxcNl4A/e2vGZL17xhWcDDxtnzgzEeiAHYzrfMMJUin3nYaDWHdvO8+Qogg3DgD4IAIu3bwMkhgoEp2Xhue4kJde6OKcmMmxTG4IA2vyz2RWiBujO7Mi6tNSk4T0XnzBr3kLP/RMtyoCDI+f8jCKoZcUpBNFgLsTHzqFofR7KNtbqm4DZCQFHMIDG1QyjDy8ZkWBtmOn+cGwVxTMfnHt07R3XbcNQ7/roA3NGUWjOuMP1/bi0Skn9VtKZMes0siMKApTS0ErB1JFRmHgrsAFAyXR+WPpwbsjd5DwTCTKsi7I52TX+HaMxeOho5fVjGQHmsQX18Iaj+wZBakTA3nos4jFOdNzcRuB+g9JOhGvOMlFCBM2/TWARGbvgMxzcp9u40LgxnQTR5KscwEiUd6ICHWY4HagcrzLceNjMEF2bJNAAOIEpiiDXQ+CbhHwByBIAxTM9NC8kGDxwYiQ4IPYCN0YCYUygJcHkge+lIKEf76yDSMgs7QAoJpntjCee5a8Ico9r0AiG8jz9uXp/zi0ogYHaO4cSjd/w4hId1BQzAtpUkq0oWLza0DOb/v8AlPkoEVM3Lcs6K0jw8cyJ3QG4B+Xs7zG0MntiruwXaUqPRmI8On8UjxWA+eImEuHni8DqXBkRYQgmCAmjeCxHxaJKcGIzuZefK3pwbpIIOQl0xrRgtDJHqjw/xnyB+4YtRXxqLEmWRQxt4bXufTgZ1zJrwvui6DvTz5xwK7kBKW9O/Y5laTCwCwR2XPrg/TTxrA3PpZWCdlrZmunHHKN7Gr/g7nziJtUBHZ08hpDxhmHfdzQzL+2EhDkHeJVCM1xEUeDXKAVmA9XBzxgDl8uG82nF2hqWJmjriuvesW07npwWlFdZ1nrz7QveeRy49As+/+yKJyuzFQ9XxTuPHc93xaYAS4uEyUurbPfdd6rz3qwLcjfeetiwD8XdqWFdaLKu254dJaVUH15WyMfpwzMBzH7sI0TYKsw4XXffh0shuCMCNXN0DM7m8WfPDAAzAae2Qvx5jqFQL88sS3OtEwLApdVUoKzFsO0EAm2tnK+UgJz8h1oLFo1InTObpJAnEARdWknxTFnH7lmVWmj4r9eewMfAqG9XwdgGZB8ocNLioHDdcBB3zEBGN0URoC0F67IwM+OdSywnDi9Jcv+3ZXXQD89KHsCBeYYg/mNya47D4+ACytk/zuyJPALYYHiEokNwAa3QzvUr3eeE+bnjGI+6zXEHZxK5lJlBif0R560+xZakR3eih4ifgJb2Mz0lBKFQK2juvLimIngLLSh45oSct2ywTYcfmQeWN1yx2wRiC6hN5aVG6A1QmSWVCSKOwWjY5MyU+I2fdjuA3bSz0y4is+DumjPLE2sm96vNZXoERlk/j4Az0FMAq/z8yQGKvR8le2Ifz5pEaT+u0uZ9jHOOyfIGS59wc28ye4M825mBmvdlciv5jwicqzpIKvNe8hwaM+pSYWgsgUmD2c7ztgWZWYl18qcboBwWqogL0ByITfEeC4HfBkiDWANTVp6GOjw8RcyCMedIlAkEpLDxR7LKmwDBP5Ipel+omaUoEalMclYaDp1pyyITjQP8M4DIkvNQeAxGc3Q9QebLXZNtm3QctbA0kDV+AMV8qqUIWhWcGlPXY2FX0t47ep/8AjNkRwiBlTi/wCAmaAGkwWwMU+GsSXsuJiOS6uWBJOw5z89AUNJKwTCScVUdfNWKYoYhPFaJ87HoWDI0UZ8oXDLqWZYGFEEpRyVVZkWsmztkZqXaeXEWf8dpKRBr6H3g6amgvCJoArz17ILLNvDmc8PbDwQKfZhrvES05M4RwHmpeHJ/wrPnPjspHAC8E0oB7QO7Kh63jmWp6TCpYDuyBBIv8j06qrJFWP1z+T7FvnfsIyIqn1fUCroS3KwOCmLUwNABSCFIdMfMrAVn8SzRFWMTcMGiRDWjxlLg65LAQJVgoZUCYeetDwTk8MGhA72De6pIrp+9K647SzK1stuHvCmeC0rBtg1cbGCpgvN5JVgL5WObdPkYCMiSFEH5utTMnMAKVDt631NY7Wbqs1mCtXA4M/WN3KxpetNWRDRL2XpK17OVmB00zKKoRRfMAJViFVPEze2KHj4gjg+4PZk/Mj9qOLaj7ggSKExhLu5jy59Pm0pvS6ccB9DpkAUOwn3/klkHpFW0tEUp8JWfFxwNhDdHvDnAzLEDZgIBHHaAuTyBf1PiTz9345lE6Ud9fWYWBXCxxIJ6cO4JKBJc3D7beba8jwbMa/OT4+2SacQxIdXxNk/g7BxH03wO3AZH586TSpBqlv5lfkqc4c2NymuaZzDPZgLMAGL+vOK4IuwqDV8n0cBQ87nzjfUAlmZL93GlvtvX1zhAmbEE/ynTcjgTyaKDx8hBiQ4ekXsAZ8CWqddglmt8biDntHgbpGRXg6fXfCEUb/WM0kK20OrMuFSpDtMDwUY0h9m+52i1xIKZq+TQNUOsXCBz4mZBMqbJnOdCURss/QgomLY0RCakuoZGATuFAA5Ek94Rk1dbY0YlRJtiETPtqjlQS72DQCMScFAlJer5kxCrzg9pNUi6bhxLxbKWnIuiA0C0nDLvj6VUF70CrHKTjGHU2DCKlIWOi6lODQcheXIxZLqf2QR4a7L4VM+d7alFMMaOpTWsC7kErQjOK3B/usfT+wVvPtvw7LHjuu+UcY/rtQBvc0uOPrBdriwVIAh1kilmA7CroqtgG4rahxM0Ba0aqhBYRVcNu0wqhpfZ4BOKa2uM/nUAbnyHGsbeIapQoSR9W6ig3LuitVg75vwNGsLQYSvOJwHISwoeDIercU0sRXBeFrTqnJjCexZdL/E71sid6cOdE7jGAxS1xuvrPWT5eZyhnFKtS0U1YOzqQQDLceu6YPUBj7AJ1IMIKgVYW+UYB+exBLhgNmoneIBOkToI1/1gRw5n6AAxRluyPotc3zRDMv8UgMqwHeYZFMgFsCsgFxgeYbbB7BFAh8gVJM5eQZdFgKaHrIzcoB+PmyMbJwDUCzXpCWcZYjoV76rJ6NaQfJXMPgdL7tapMbODw89YCpjApU7T7GAzNkI04Wvon6AhyoZpv5Fo6oW/f6mLs+N3/HSO7jCARrTxHoGRP8bMZhy7CLLkZbNtWPzep/XWCaSCZOy/nMA4zjEBVn7f5j6W2WEUJt+faooFR3CssNmNCUEpJCwfCnMzMy+TZJvLIdYmbm4rok0aALPbOFyP/3IN31SiV21xvwpAXAEZO2Cr/735tWYB64U/v/LraxygvPhyKKCxnWJJFYpPmUveC3VRBEFiPWz+WCT5dA4lDLPZSy5wljV/KVJs4qx1GopYbFFScWRq0SI6P5T2xjM2TLnQwAM3KVjo5FZEfXVp3sFjToi1aXwZrTKabdUF0xp5BmurWBsnGj9erz4vRb27YbhTytuQSH1OTXYSZxEEw86kepYjWi35JW5/inAujDQnX7rWx/CNuFRgXSpMDbuJl4LY3hj3gMx8ICK6mLXRx0gFUpiRmFuA3hXbGHnvIuWeegXwDJYAZQCXndmGKgWtsO22VXOgUnF/bq6RsuDthx3PHjdcrjsuOwXIdudHHAOZfe8Yg4MXg6O01ILT4nNz1PBwuaIr18o+FC10SoahCcEkQABkh3XJ2+BARQdbMc0gpfpwRYX4PSUwZFvgvu8YiIzH1C2ZD1v9+UYq3/w5KMS7PWg42cGEVrF6FAWLNLq7xfOStf51qdR/6QO9hyIthzkWNt04cDdXJPasocEJqwTwRQynteL+ruHsAxe5naaBNjEXsms4rUvyd/roLLcNEr1FmKWh6mzJzODeO0b3co4dok6/xulskJwGyzqAOkihaoaA3BLm0V1gxAXW3PVgdsSof099n8W9jkidH3io9vr3WQqmXbKbvcu1GCMxgrjqxweAsEl+P+I8+L6SzjlKZnJwgAbzDPLkSUQkDkMKntGEOTixGxSTDjG/6wt8umP30lF+f9Epx+fGN92OqyIbHtT/TW8fQBLzfoVN8M8NvJSIJxon4ti+zjPjbW7j07JMG3AEUMEfZBv7LEHG2/R4D3H7Ol65WRRvJYGqo9YJhdLOOVyIrI1fb5ZLceDO+AcMnWsEYMY+kwCIstPMoMBHYYQM/oRDh/vxLl5fRwBFQgbgELkSjFiItAlF2kIp1rAkufWYBo16Z5CsYoswspy8jnpIY4elIAFwpJS9AalTksY+UHDulLnJ4Q60yhwLb+qtwkr9hegQaqWiCjxSjQ4JfqLfkew2CKKY9gErglopMR4S/UUqqijawn/3QaXWBCLH+wwkg9wM2e5ZW4M0J8ICqZoZqXW4MyPYU+jOLBAkRgNs2ItA7k9YamQxaMgT3MG7PYyZDx0dtTZACjtiLnsqeLZaM/LfdcN13yEQ7wwSd7q8puGgUiHYdpZAoIuDEzoLBdDKAhuGVYBX7xteultw3c949rjhrWcb3nnc8bB1XHY9zN6ZRiOiobVV3K0N9+cT1mXB1vuUn/e87VB2SxV/pATFQGe/NCfrOkA079IZofwKIU/HU1m1VlQHsqWwnKNKHkqp7PgqhmyLZ+ZP3SkOiBpQXOcm2gvh/Iwayr3qWiLVW4uZ5YDAgQEjzXC0ZizvwSINT/fX2gLvbSNBFYCBWbS+cf3fnRqenFec1oIizLbUUlEqCcNmHPrYKrMrd+cVa6veAbZj2zeM3iHC4Y7rsjK7Z7yv2zZyPMGxLZYXESbdd4QEydEjc5jT4FwYEdeMMCnItgG2AWUHdIcIRdoozNa9xDMgoZfi5RTCl5KORiCZ0Y3z4jMJoGYJIrjMYz0W2KFVeF7fbWCGtEyBgsoBnODwOcjI/1Bdyd+04ARm1iS4gTELrR4+C3lM5Hdn1uIIWI62E2GzX/DmzD7xznl85xk9OZy/ehCEeawXEAWvG3kuAZAyQDieu9/r6Jw5WID577yHoNCm/5RdTTbbjw/gSGINxJ9EMrQNdgtmwidxnzn4lcjrzGyfOXhEBFXgGuBz49kOczwtHtSpOP0oANBK4Gurr5HVz7EiPOC8ehz+/Mqvr3GAcoOXE1naYVlYNJ3HxgxiFiLV562NNpFrbtJA5RIkVs1/ixSEMon4KlXTbGmM5Usj4oDdVTx5ygQp+TFphiNyr2jekhvSRpAA/TNNSOcKQIrzbgYjdX+/gKJppTLd1nvHw2XH43XnADUpjBDBTEzzjVOkQGtDiTSmR+gGJ5gG6hYanOFnP7ph7xvNqEU9Eii14bQsKMWwbxs7VcoU0Lo/cSbM2Dm3YwOj1+tGUarTSuIo20W7z99xlE7ERCXR0flczNBHn10Bajw/bzdtzqEQVWx7TwNHAFpx3RQ6NkAKnt6xawlGWX1zJlkFsLSC+1PDk/OCV57e4fml4+3nV7z17BHPr1RB1cNzDmPH0lzB6B3Pt827YsbEMwEEBYjOqojUrnvP8mMtFbU26oeAbbfDqDwJGAGuAcMGpPLfVKVkN1atBctSXOWXAnyAoo8OtpDz+VE4raPJivP5hDwhfzGwim6uSqMmHQBBsPq+6Nqx7R37YFmq1YbrRjJrARxQ8iYkZwqCbe/kUdXiWbaC1iSdiYLE1aJc+60GcIILv3U8duqfbDvJ0uu64nRa0dqC0Q2XyxVb39h6CWZuAuz4WFZEi+sky3MdWHrGGduLz9GB7Py7gw/4XB2zAS30DDyWTqd1jKgjchLh/i+CoO1LlGgyKJsxazjVABM32YC0j0e7OX8/kkXzNA6zWCJb7HZVXOtCvKSXQduNQ3KJAdfPIBmpUgwuInovTfNabp1Y4oUXXhH1H0O9vJZwwv6euEdH8BG31tSc0+7nInbzeXbIGgBh925P7Bhq8hueOQ9wE+DVg5RjoSOy0/A9rmpIrSvl+ioBSiSetyVIKTLLSpFNKYf7LxLdmzbXBIKs7M0h4bf8XKMMCJAdVWwq6XoDICB2uPqF8C2eM9rMJuZ7jn9+5dfXOEA5vhyIJJMsJmvGLiNit0TtIUjuCFLA6BAC0eNNj4VgKX8cM2Hi4YahFpCLUorlBg8lylYrJ80a67VLrSiYiq1RduA+CB6Ha17Ulq2ZzSglHqqsXMwD+z4SNNQ26/a8J4B26pLoMEhlxmBsO6NO5wn064Y6qnMbOFulgCl4A1xuuk9NDYlz5WbVbQeEzve8NG9Fdmc3yBE5nxec7s7MUAAQV1DdNt67ENCqLYh03CgP1x2PV3bpLK14ZM3n0gVYlhXLsnjGZaA03mMR8htqERQFuin5DWawUAXtmpodofy5tAUmwPMLHePTuxUryEOpraE7sKlmOK+A2EB1Ybe79Q7vebrg+bXj8ap4eNxx2fbUlTHAu4b4uRR3c2GmiJ4CxBpo6Ep1rhQmsEWIiLlirn+3SgEKs23dNLVuUlwQimKRAYSvw4LI3u1+D1qtXKcCoHH9HrUaeu9obUGpFWfXjbHR3bgXWGFoV4vAnL/1+DggnceurUHVVUH24aJ/VBTWsWNdGpalpaopeSWK+oLjjHXJLJt3CzV2G+2dbc1bHxhKwHR3PuF8WtAKCcOPlyuu193b6umIwllEyvvoegKYhx9KorxMZ0TjPSBUxwEFDHZmSIIgKwNizJZE2Ydkc7sBf+YaIzAS9mFBVhaCZeepvZjKD1rqbTYi9m6UqZCZk8gm+sZ0Zxo9QreZ4jykZ1WiBDQd5LQLFkjHDiCFoRhy7MLBzg4H35yDhgRWEdkHvpKbq4rvyc3P5uv23Yk3/O/qJP95qybcyA84+Fqeg2SWysMWzxUc74+fkwe+cjy5vJ+RFZulwwAf5tpY0fWW4CSeGSZAF2NZW/Biycfh5CHFEnzGWGbq35v3MrJDB86N22OJf8SN8MOKNAemzY/o0hbvApC8+Po6ACgvoDKJNCK/l2SvkLyPYYAOXEQExTRrflWIJvWAUBOlB3jwtGqWGzzL0ETQlopaahqLUqKkwKhupGAWnXOosS4OUtSY1lafuQIFpJKwavAOBou5NdGKFtoLTO9mL723g9alsitEgEV9ER9TdCCQGWOuNnMCrXqrZdwrLlJLYwGJKjXviymnBW9iiLYcEnYxiY6O+kWQsu5j0GkbLGcLBV+nj44+mOputWKViqUxBBlquFx2bPuFgnASJD5xmWtBEwEnnnKQ3lD1gYvmtVLyHnznMZNRWBqw0bGNgcu+IzRoqjvrPhQllFYd8paiaK3i6WnBk1PB1oHrk4bHbcFlH7hsg6TeMbD3ga4DxYJ0GbVpSeG8ggDEiq68J62Uqc1g5oMAySUxOPAI1yDMSiylumiS+tiADlkWYBg2LyOIfwbVeA1m1G2pS0wEVl/zUfpBihFC2XFSqq9Tj75U6XSb/8LdeQEAbN0l+Qsj8wCiEGDvO41sCZl5OmW2vR8k52mVKd9fJKXqayGZeCiSFyJLw1qXORBRBx63nc/Au9UgE3jMScCHCr3MZPXR6EbGY5IdGV6KbYDsgF0IVOQKOGCBz96RBDHeuVOilc08Na9OiiSY0Bt75zmc9DSeFYiMb5xg/gwIiGU2hxpOPOROWcKOYGZEjhd7gCHTxvqE28hYI4JCAhOLiBoLOEk+lLzd+YYTd9sbsvbHbsabEsbhaubrAE4knksQYGcJ6MuVj7wI9ALd9Mt8wvFe2NxrytuWnBRfMXk8pwz59fp1glmJ43oSv/+xBs1KflgCY0TZ8XA8A/2SRdaGRemCKTgYJarALfFPWIBZv3+pv8Nziu7AyX9SNhQgfhcelFSvRhKgiFQUOea2Xnx6X/n1dQBQ5otJWEfksRrjcd0QsjwiSIBJBFwsSiziREPv4JHc1shNIIHoI9IAiZvxd5molZNO/ey8O4YkxoPSIX81meHHWq8qS1BDDS3AiH9IpCsnCzxAFdt4qwBLdfluV3kF6LRqpVEYo7OjpIbB8P+UCjksrlrYCSTizv2w2+MzU93QnBybqJxaH33s2S7dgtxbSpbHYv5PcwDVKluNhylG52cO56TYoIT6vptHx8C6rtDO7AYzJyVJaK0yq6KLT531dPJ6Wgh4MKfR7tuWJSgFZ84wUgfKzoKWgK2wizBjta7+OW5TxNherHcFey+4bIrHi3o78sDDtuPxOpw3JM5xAdO5DkLYrcPshmbJTDLaPBrqMQxFFUOm/kSt1ecmlZz3ox4KqbFba+/mk5yBWg3DBk5LQa0BKjvOK7lM/EzeV1RJA2T+/KSEU/ABjVKnQzBDLRVrM8A6Sz9SUS67C/iVnBbeovvIwUetxbuVxFt/Xa4dzk9ybg2HBnr0aPy94q3EBs/W7Rv2rbOTSGemLngKx5bbOPcAIZG5cKuckXhEp+EUaY2cb4LgmJBnYuiAKMRLPVkwlUMuIdqL1fJZA/B2/Yi3ld09R08Bj3pNvGSCDKKcvggzcoWy0z2jbHgg4lZUJmieVuoY/OVjRZyVHT/fIvtSD38ye2L5FaBklsZSD0qDOhwZj4NjO6Y/ZGawbi28/93/EsdBcX7y4Xi8xzJLOy9kCG6dqtycgtMKuYY0dqMvEQd+yZuO5xjg0M1rPIvwG3FO6pwtyHzPzOrF4ry1+8XvJwSQogmeDygTCUQDfMjxiR6ygJigbH42bt4HBCIL+O6lwBtA8tWDkuPraxygHC98pgp512bHjrjuSSB4wXpAg3xIOXAPcM0Trw1HyUjC8RLSiwAtyCXVO3oEnn63LO0AwD4MuytoLpWlD8qD8+eq/PlQdjIQ4VLm3cDa+uYTjPtQ7xQIUDDLQwhSo/D3l+rdGYVE1m7mRpk6IUsjWIJSmbOI61PE3QkAZx7Ze10/JjcPnSnYbubR8mx7Pr7myHam/OnJDGoDtXhc6JkPK5apRTUSegXAUJZKHjeKdY0+0tAsraE2Du7rAPoeUSmdfW3FuQWuvLoYemfq/LQ0tCWG6fEz+zZgesWyLlhawe4AoUdrtXdwbXuHdsNpbWgtrl0Q3U0iNMttqVhrxVoISIY13O8Nzx53PFzY3dMVXopwAyfueD0yr/5NdQG9cFQEqpbtwbEO1kaAOYZhM58/YyzZiQm0D5YjwWMWEbSFjqlWGs+uzC6ICE6nhiKKKB3UYoBnAVFi0nVEkR21NNTKDqV977T7Smsc97wbsC6CobOtFyCfKtZ7E7Y5Ny3YfB5OZIZKZXdadHfVItyXaTM5YdjMcN13bNvGcqhNZztCGiC/3ElIbn0kr+IQ1KTTCJzOMBbwco7hASTFPicgkQ1MiW75HjFmUcy1UWDR8RMB0SGFAN+U4ZxsOu+pATKdVTj5UN4Np1gKVZjNO96O/jeulX8/FG0OgUaeAjxv6MRXCq8JVBs47G+FaYHgBEiD4Q5sQV0QpYt4BhOYWF6lm+cvcYrx8wRUNgGBRO5CZvtuCFdmK60dP2Fek8ntZ823hSOeH3z8t38i74ZESWZyQia5egKPOB9kqW0qB0d7umnAUPonPYAfZht50vP+sS05YvEsE4mXoiBfupSABKLx7ywRuhOIjHxcazyTVOC1OH5MWHffm58nhy8c/vzKr69xgBKvuWizRiazdjrTjrNeWg5Gxp8io5HMivDBwQggUv/ELPVSFieZ5hRhVXSjpkZ8D2CHRAWg+459cKx7LRP0mMPvSGkCgA1FF+8GMMuOgswegGnutVEJU0eHeVuqlOKkX3XCJA3V3jn51ozlpqXFQECCKfOQvRbO3miq2HdNQLMurhnjNf3u5RI1QzO4bsXs2RdEW5+6gZzPZeuHYWqFGZ4CoHSgDbL6xxhJ4DXQWYUwrSE6qcSHBjICp6MlgTLmvtjoaD7jgwJ35Bu1hSMPhnbYxhXEaL1ClgrVkdfUKmcAlWHUJSlzunTfO65dsS4F60Lgt9YKKdRoCVIqs0PU02itYVmbd2MJpFTswziTZ+u49uFZM6DDsxulQqqPnzfztmMXEMvUMo18lYKuAtv9GUUU7lm3ruTeRGgXkdoAib97V1yvlKq2wUyFwdBK8GPUMy7MqjELQRC7bdfYjQ7mGSiQaN1RC0uOEIXowP0dBztyrQEiDUsDWuXuaEVwPi2oBehjx+W6w1ojyKlCirYDxgDkex/e3t5wvV6h2r2MW1I40Mz1f17wWeIlJDcjmYgNuzJfx0zn5FGQZ8aSTcjZR1aJkvXeahxthxP1hGtIkES13cO5JYAIV+xnMpmN6ZgoEw/vADyctZoHATyK5WfP/80Lj+PPaw4PbelwPcurhqFy+Fw4eHFyeTYo1FynAaQOt/8P/TM/+nDdAszsg1/DLLWFVEOgL/P1GDouAVziuJLPNLTjMpFi4BqTWSrJ7BoO3EFBLPub84rzn+3qE6QEQfcWMoVv4i9Ghg9WZgkfuPkN33EAHKiA9rfkSrH5Xnvh8/xxZ/bcv0lvmRcaVzv3jJ9ngirEuqDvre8CjHy519c4QJEv+cpkVN4kz6CgwlJOOZB6xgeYuDHE2TQ/AcBhlocbh0hDwlKkh7wQikvJbrmgp+GrHrFxhgy/51G2S1BXJ2uNGT85aKhO0vU6aSGxqqsAvROBA2EtWMe1Sa6rpWBdG853TiQFchOrKvbrDjXDujbc31WcT9Sm6EtF30eqz9bCtuclRty7szRV4LwgarmmmnMf+gCKR3EKb6PrA6gERaMbLlu0/1ne9CDoBZKPenvwZ0otFA8yw7516Bh0aJWqqvvg9beloVRGJHSk3QnLAFAoz95IZBYR1AaclgWqDY+XDdu+gzVzgsxahATOxtS1FXiZRskvqcBwMi/gZSwxHx/AklQFRwWcF+rziLCEcV4XXLaKZ5cdl23AINgHu1yGGooyg8VSB7fvfJ6HFSu3XKECgRxm2fTuLek+yoEv9ecD2ODI+6HAtnVcdw40PC8V55Xp+SJKzodnGWNOUN8FpSzufHyqaSGgH8NnSBUeFyg4tQpYB5tyuK7vT42dCWJYl4pW3Qn73rXcsRHdR2mV0aiqiw26OQhJAHWQSgduEFeG9faYDCwsuWVIx6hx/NhnmCUgC4RjniGRC0SunjW5uPvYqBQKZlDMha3Ey0Hs4lFf68zeJgw57An+JSMx3L4mTyz5cdkZE4Akfi+uRd1DBcQCyCfx9x7C7smBiM8RxKC/MfjVtaCPiqELTHxIqzWY3UONQoDqoInm6QCEgJtMSi7meXXZCj9B2yzxxNpKLtcLzyxvnyBtOH9W5v1MsHJ8Wd6PaZP8toHZd8U8Hm/n8ToOzw0Abq45ODC81xbgx2bm6gig47duHuMLYCifpB2zJhPWJQ6R+X4poJxAftPy3s6Pl+OP59nIsfTXwDkkBWLHYtsRVn51r68TgMJX3sB4+HL73miJjZ+HQQ80OSxllKaq3uEYka4aRj2R7pFqpBC7qitkznbkUuZGiXRbImmYr1fBEEOrJIGG/PvQmaEQsPW3OI9k9MF2Wl8YrUpqpqhH1GqWnSo6FLVVnJeGEqMSvFyw7+adKYbLdeC6PeK80sEyG0JDbNEtpAMiMVytwQzYnahJshtgwftYGjMM+8C2M4PTPXPSxQ46NNMw5ZYWH/SVG4KRT/Mpx9mo58idkgByyOywM0hAnY4xOgQVd+cVUtiu2zuzCb17NwQEW9/R9+5kZxJ4e98AiWF61F3ZXedDQNA0ULGEg9eOoXSsrZUEsALBsi5+rZxnc1oWL5t1tE5DuO+AFsNAAaxihMEthRkNY/dWifEGqnOuDZDlyeDehPok8as6nwOYJEhDawWnteYAv+uu6KoY3ctwEZG6HkotBddNMZTdYFLE26XhuhLRrcMpy2srgHd2CYC6LuhasKuhSoMtNdfB2rgXmhN6hg5mLWvB7kMMwwxXz+qpGrYO1Co5lBJwvlWZMv0sewh6Z/aO2c+DgNe0JDha/2P54/iKiBJR/hKjPpF30PJozJiw6+aQRYGDRLjb8M8XwKs2cuOMCYrizOQFR+5n49eYNsBBR5QDxOZe09jfmMeMOTAcDRKzjOjd49NyqnkCoTnYrw+WtenrCPwChEQVMvVlgLyGKB3YzdVIPotjNuvwgF6AL6CNv8lIIe/zTcomn27cobifkjbF8l0vPO+wc/7j23MOoHVcG8f3TJcfmZbInMc6SDHKw+ccj3mTAcLxd+NezTOP37u5d5jnfoRjEiDVbs8pz9/m3pjPbg54DJn7CP31hvv5pw6gxOtAjM2bSjASs3c45GoBhdv8wfrmHQbsRhXVYQQqs+0L3JgRTRxQMnkOmu2alH+facsizlUo7iAPkDX+GwJbnP1DCfDks9g8nojPySkV+862XTVGE8Sq3JQF4u2Yll0nqhzkbtcdqlN5tlV2dtRmkK44nU44qWLbqLBZpKA0OuV1aSmHrqbQziF7y0IAdN2Abd/RewcQMucsiYWDZhZnwdYVW+/Y954S8Ydw6BCVBKF4DmYTidkuTKE379hYnFOztDhP9bY7RlRBFI2PCdAXveM1pwQjuTQBMEWYjYn7XUHNEvUOCyhQWoF0Ot61Cu5ODde+o5SOs4uEiZCnogC26wYdzgdwUEV+EDMMFBUzyCgorSCGp3FduTCaeY6wuWaHkJdjBs/MeYuwWWo8iAGjzBJkiHXVUrA0J5lKwdYN275jmKIVOpi9G1Q7+uDeYGaDfJ1WKQo4nJgM5+PQunYsUQpCaJkyC2XSsPeOffhegCHqL3dnguAxOi6bi76LHcYjDD6PQocHY4auSkUtYIeWEEhFK390oo1h2IvCtuqZu5EOwSCw4mUA33syLXg6nHRoEWxggFonG0rZUOQKyBWCKzh7ZwOF1zaYd+5AYnJx92v3ycYWpc1DbSZshnsWOZRYju5vAgfN7G5kUiXrRX5sr0MQxAdQYMZPLGi14pkdQWQA8jMMXiYtGKOid8E+KroWTL7f2e1wyROMczqWCxKchC/LHuO48jib+Zp4YjrB4M4cyZ23d8jXC2ZDg/fUzGNbrLHDh8e5y/G8JLNMIQEQQAs3wC+uaXZcRrIiCdqOgkWCPnzwFTb//eK1JIBGrF4XXESgkpt3Ips7ZH53YmDJ4DymTIc9jkwljxzPzd/jaCayVxLds18CTt7d62scoBwu/ACOs4ZqXsPlIwN1UAKcRHcMAUo3Y13epgJslissIosD0kbUWKk3AQTilDRsgYzFgcvwlHqRA3HrAK+HGvbBTw3l1JTQh2QZgQ7VZ9aAEU/v1PqI2TVMZfuUWl/NY3Rc906DLZJaFEUA0Q6pFevS0Cr5JEEEDQEmO9zv1iiWVaoAQ13Rlgs0ollVw7YZReKMQKLFbB4xtjBjGiYD0onA3KT5pmA2hPctjs/fFWZAVKBWmUL2kUdRGoJodiSNYRj7mPfRwcuysNslprkGv2Pr3rlDmVkIIj1NB1JLBbzTZ+wDOgZ0qVhPC2wAug8M69gXX4HFSc9d/dzJjeg+Qdd0ar8UJ0pTF2N2OjUPzaNbheTWAvO+WgXvdYwqCNCYu0AEsjTnDUQ7LTOLOgwDA9s+sHdmWcIZDeuoYlgHHVYfC9bGVkJ1zsjeBwEDFEvlHqsFPp3Z3GYyE0h9HYO4TD2J2gXFpzGvCzWFAPJiOIKBmYjpqAgaT0vFPgC4xpAcW81LzKoS1EYiH1uz3aACyG5OBwAWQ/DsEK1amprcu5PfwPVWiqKUAUHHUSE2BgGadVATJTIo8X31tTUcnIzMsuWHSjjWcBhuH260JiJbEtkZpDN1dkVeoxtLiB3AiR9hdgvNziIcBxcatWi5NgKgHL6UbcUiDKogNWe85O+HPXWAcpxYPF9Hq/NiBD4fSPA9ikR5Z0b5wfOzsM+In8m8tsO75xM+AAIn7UeHTEHYZnDfZBYY01+8eKo3Pvr2GuK+H/3ZTUvzlzlE6qrkVc0gbvqO21/iUnYw4WBL5HBnTSBKikF8ZgiF5poLcCZxX+JcgCR5B+H35qxf/PMrv77GAUq8AnmW3KzMqDA9LtHJA04tDSIbsyWe4rcJTuIVGZnUQPGPmmlILoqpHCv53+LAQn3oHQGRn1n1s/Zojv44NrtrIKhvpDSchn3ffKosnXx1A8GykuW5wjtBKFLlC/+ApMfu0eGuUNtRCrAUwWUbWNeaxzfjfcG+Y+8dOpCCYPfnBZAG+Fj73mm8llYzwxH6KNEhAg0DDFQo1srMhyK0X9xjQLLjJ0TJQhukwLU3FswIRDkU0LpiGzuqDHJkqpNl1aD7jtEVYoWlqUoORcuMy2zHZAdUgVpHK8BayUpXUw6bK8WBn6I1Dp4zAB0dJpycLAKgNoyx4+Gy43IdGV3E4EPAYLu36cIyg8IBdZwsXKtA/d7G9JTFBzhS8p+gpnQuWPJopnFj144L2+lc0+w28CjbHDgT2WDfR2q8tMLuou4gG5Vu9doV+9hxlYGlCe7OAgOzQL1TbG/37FetgrvRUKt6GdOf8WBHU2sUhKvOh2EmyxzsUmCQAAdYLgWtsFzZagGMf1+XFc15RbUKNDIyFpFdwbI0tBYaRZwD1ATQWrybCdmaG6WVKKOIOwzuxFt7ICDKoXbIjiI7RDZQ4n6D4QqWdaiDItLzHMy6g5AOA7kzZhO0xNThdEMSfwrESmaTxO+d2TGKDscdvAZzIODdfkZQSr/ttuwQ6AVAYV4qQEpAFpJeDTW7doZWdCX3RLUCWJ2LtPAeOVjKcMSCazMBys3n2yyySICO8LkCRI9uWPzZiWlezfHxFfG+RD8OTA4ZBH+wLyifBgCSG6VascPDh/uDBCezLBRrJ64vgH74jlkuPAKhmSeRfN6HrEV+JvI7x1OdZO35gxhKKoKpywLkNUmWGAOskJd34wv9swrk5r4d1wpAe62RlhKSdV+8xnfz+hoHKJJfAqaUh4SCLDMm8BIPRdqi9ZgPIVqvSLxTV1d04/TiAsAstZQycXgQEUNuGcZIP7pjuCcdxzeP+6IWffiEQ6aYRjLIVBpGxQ1kEbgGbaqNxkLZ1SDa856wDn+YQhyb3EWtgGDfD2yq2AewDaUOiU/xPXvaf1kayqlidIXagNTinTzdVWGD7Mu24OW8ZoZ233aCiQB6KQrAzcKMD1Vks4eq1rzzwzuYaqGuCwyZ+aLKqL/PlIqllRkNgr4C7byxQ+HlLUbTp2XB+bTAlCCPGbSBfXSUVlBrQ7ORIHJYtNh6q7R6Ka27E16bczTg2aOObXM9llqxrg297+j7oIP1qcniz1fBTqVORTZX4mWGgauhJiH5hnzn92Ft3M7UfeH7RFjyKMJOquFZuD44x0ciQ+OH8i7gbClffM3A1/1SY5YP79fVdpyU5aXdu690UPANoN7LGMyGtFbw5G7Bk1PDqQyIKdqysAurwNu+CUyoyFywb4Oqt60B1nF3bixL1oXXiY7z+Q61OJhVPqPia2DoQEXNMk8tSBCzrpzbU/eBrTOzNTCTDhalXcxBfXSoXNiRrZUkrBqKdAcpG0iQ9bk7UAg6SjkMBzSXvfe/E4z4uIz4dxBj5BDBhj8thkj8i1CkDkpgwswtrZ/J0Q6lNePvqh5k+sOhRidGxL/O3fNuHOacGkwqVFeMUTHGgqEr1CrMTqBiN1uK8/wDOMvhDMJp+/qfw/eO5e1wpMcCz6HsZtFNdYQW/F1m5Q57Jf5I4OYmNKHQzFQEqEhwIwegAd5bRFCY6wMJSgJw5Tb1poVJgo0PCx8wrymzIIk85l6foEQws2d+owLA5H9uYlPvduP6DdATwAXCeVyaa2oCrDyJPPEXvSPPp4Olx3jOIVtxc//fBVgpX/kt8/WjP/qjN+kjEcGHPvSh/PnlcsEnPvEJvPe978XTp0/xsY99DJ/5zGdujvF7v/d7+J7v+R7c39/j/e9/P/7hP/yHnoL+47zmAwHkRpQr50KgcoqxNYgsKNIczohHHZZl5WPnSzwCAYP65q2wi0uoN4/+z0vBeak4LRWnVnBaC05LaJAYqkQZAVgbf7YunHvCmTs+JdSNK8salnoaiZWkAKViV+DSFd0AFBIBSy3prIfOTU7QVr2kwusJZy4II11wOjWsjdNe12XB0haIVGz7wDW+NnJGIMCyLBARKr8qUGqj5kXovmzUm7AxsyAj2j6F05TXteHu/oSXn97hpSdnPLlb8fTujLVwWu9SgPNScLc2vHR/wstPTrg/sYPktAhaMTRhN9Haak7jBQRb73jcNjxeNzxer3i87tgGM2Tb3nHd+PX84Yp3nj/g8XLBdduw60BXxcN1wzvPr7huBG29d4yxM1ovMek1wKrPexHqcUAM+7bj4eGKy3Vzo0tC7uN152d33k9ydnaoduruOO8gRMcA8neWpRAYCLINvngmbW0uv+/7kdogxSNdzzYJ+TVsbXcSpx8vxPfWWqj7qQNrEaxVUNwyndaG1dvA933g4hmhq3OIrn3g+aXj+aXjsg10NexqeNwVzx53vPn8is+/s+Hzb2/4gzev+NxbF3zx+Y6HbeC6b+idmYVlEZbamuB8PnGPFILlWgXrUvHykxPe8/IdXrpfcXdquDut0OHDHQ1gB89IBxPEcsDAVCY1hVutOJ0WnO8WnE6+pmrcP0GpIUwQrcvFLcI0OyQlR9092ldDGbbD7AqzDWYdMC/32K1oW0wzNnNeU+gh+RRwZtT4fXXFa54DuQ9WaBpSF8vtX5Q4wqdEBM1zvi0BBLfAKesHtyOY8gwVkAKzCirBroCuMOMX7ATYyi+sEJwgOKHI6jyuw55J58uXGbJ1PrMNkCy131j7w+9mLiJLXW7jHBjcEnIPQSAMU8rdMpugiMCHeke0pwfpB4ssCFIAb4qmHd3RLD/b4Tw0r+1IYIaDHORzjc7OvEKZXUmRwTrAq4DN82YZbkjxeU/998M3RpBIkDR9abbN5wOQ+d787Lgf8x4cAeewyATb4Rjv/vWuMyh//s//efzar/3aPECbh/j7f//v41/9q3+Ff/kv/yVeeeUV/N2/+3fxt/7W38K/+3f/DgB1Lb7ne74HH/jAB/Dv//2/x+///u/jb//tv41lWfBP/sk/+WOc/hGgcLBYqUDRhmFMPeYEY5sTM6OUEs5jZkFiY1i25pFXEQ7+BqLTybu8thkNiZl5pMaFpBppdHWQW/LMM/7JeQ5xKWEQmaEZwUswxneq4IC1PlDgGyk1RcDMhFHCmxL5UTrydRhRRSxc5yG0Sv2OWooLGgkdk4DpePXZMQeWNu9L8eiI2alwloHcSxEs7iTCqGufSKkIwR4B2+IOH97NxINEl0iUj07a01BQ56FBjUJnex+MJuGaDMIupqHqNWQAaKgrxd3MBvre0bX7eHG+onTWnW+y1CWjJAjv27ZtTK2jYNvVJfnhtV03CKXA+uDsmlrRZBqBUERVpd6HtEpuEwI8e6lCLb/UC+6rC+2pZ5FMFaU2Rmpq7thLdnWtS/Vyz4D4+5TtHinEN7KVxecUKdVXi4hnx2raM4IplkyvO0cvrAvb00vxOEwqSgGu28CDdjx/3PDsVPHSueHpueFJp9BbkYrTmTyhPgxUIvaBll5ubKg4rQ17B/Z+VAPW3EvqghJU0EXqzwgkjaeIT3eWAikDIUVAoMBNwg64A8nRQn4g9ixvAv8Il0qQQuIrQUhML7ZUltX8HEbUBE121EQ5RqwAUvo8I+8MvbmGDgDg1mlPcKJAOjhI8XKIO6JMyYTzj4xJwVR/bQz2ZAU8O6KoMGULsdoKtYUABie+P4PEsHcRFJJzJAXAmIAo7O8xq5KZDQfcEpmKcIz+zbwESH5Faczi2nEEPEeIMz8fCBIyMuMkN78xgZ/aZH7EupjB7eGcwrccMYQ7d3LsImCe/JijQ0+y7NH9eEon1uvNS26WCKFnZMoCyCYoCcDk5xvrxj9J1ZhRBVhhyHvkvi1AlgbhmryqIHvLix1rLzyFr/R61wCltYYPfOADX/L9t956Cz/3cz+HX/iFX8B3fud3AgB+/ud/Ht/yLd+C3/iN38BHPvIR/Mqv/Ap+53d+B7/2a7+G1157Dd/2bd+Gn/iJn8AP/dAP4Ud/9Eexruu7PR1/cUu1IjBvQe1a0KVgqJd6nAjGttaZzs20YhzKZtMZnWfJLh0gDFU8KBqthuYzRfjz5oPnptywgxIRdzCTNBuoWgoj1ICtsXSiJAPQWdW2IDQzNNL0EEq4q2JklMVV2g8eN+Ihy0XrIMlm/TYdD8LIc8GGg2c6m5ot0uETg7sPvQNgNtH+8X4UzIF8PsullHaoK/u2Pi2eldDU/oAZaiEpT31O0do4SA7CAX/bPvB47Uh2ukdloTAqhcZSnaBKLgBHfBVh1iwE7dZlnVwFI0eCyu4RwQASHCGBzyzSnCUU11lLQR9ecpCZZaGhLi7ZLoz4lfyWmEOkIHHWDMjxBCXE5tihxCyIZ98cMGVC1bu0RARSvdgp0YXFzwbI3zHnCNWlYd+7lwTFwRmJ1tWzNFEHr64obEJOjZUJlobyZgp8BIC3ovfrjms3VzR2NeLRUGRgaYrWeL19WA4EbK2iNp53KQ1wETS4IKLUilqZzRvDM4QO/FqNmVPwawVEWY6LzGNmrE2gyqzG5iVB8xZ/C1J0puxnG6hJNOJalhJmodjJruiIUQDRoTNj6Szyzn1/cBCOdvkeixIp9zb3sxwIm+LDJmdaXhxMFbd1t+42N9/BjnrG5BCpT69GsEIQEpO9XYBNCQ5jGCsBitx+FAi2+c8XhtolEJyty5FdmKd8OF7eGgdZ/vf4lQCi4mR78/tjfj+BGZDGo1AHAOonMn0C/5jE23kCLAPK4Wne/AfHQxwbLQKgxGylm8dwuOZ8jr4wCB5mcBl3M8BLiQWEyGz4vSgzEJ9ra36c2swY5geK21xv5ZEyUQ+rDcjSfRyoBF3CxzjkoNJ5d77k3vxRr3cNUP7Lf/kv+DN/5s/gfD7j9ddfx0/91E/hm7/5m/Fbv/Vb2Pcd3/Vd35Xv/dCHPoRv/uZvxqc//Wl85CMfwac//Wl867d+K1577bV8z0c/+lF8/OMfx2//9m/j27/927/sZ16vV1yv1/z322+/7X+7bWWqpXqw0VDLgjIWbGgY2iDSXILYNwDmMKqcA2G3mFowI/joumEnwSGqUs4+aTWGBCoCBxNcAOaRCSNSu3lMlv84IGEzR6KHdl1xGe+Fuh5DBb0AFMwSDqrSkKB3eqp/5tFgwb8/F7DMDEbhBs6Ju76gg7QICSNA2fh9aE4fLurdOTAvW1ka0jkLhyUpGeCU3EYJe9gka66rT7Adg4qh+8gMVe87tn14hkWgPotsKKfhXrf90LU0DXv1qHBZGp2QO/xrZ6lnXYrP4uEza20hd2cM1GI4n0iMvWxBaGT9ubaKpVGifd97ds5ERSGHtpn41GhvkRXXhTgYYnG9mohii4OjGNbIGTMlI6LoaOIj5JqMEmc5Zum83MFhf8HvaGmozAq0hsYKHZQaM1xmlMsf5qRVuOYFzDt1+PmlCEpj99joAyY+KdwVbFtj5kNhPrKBIPHBOvlNdeB8Hp7x4JocQ5JsXIqhLQ2Cgn1XlGJYVsGCBhjvA1vjNxB3cQeWEm7Zo03fv9FyTAmA6j9zd1kKpCi2MaC7wYZi5AiEyUUoEGA4YI0hfzCU7MjxKTIWoCSiymmsg7QqmAFPBCUa0XHYfrdNxeDZ2HobDB8Npr3AgUhHbP4L/s2DPL4EtLVYOQ4dJACKB3kxCd4agYqtMDQoGswWUNBwBUeL3OYDeOgvjfZnZ4ilXU33mw73gEriO3L7vdhz8zrdziFssDt2OwDDvMXxbwcNBgRXLjgZR7caVxW38wg1Dw8CGTD4Mc2vLxstuPnn9yGHQcqTjG3A5IL5eyZc8P2L6Dib9j4+R2xWCyTk9eOsFcm/1LySeZ/h35c8YR/uqQeQ5yBJzcAxDztMNxSLcqZnVgD/91f3elcA5Tu+4zvwz//5P8ef+3N/Dr//+7+PH/uxH8Nf/at/Ff/5P/9nvPHGG1jXFa+++urN77z22mt44403AABvvPHGDTiJn8fP/rDXT/3UT+HHfuzHvsxPBMfFO7MN4iRSpoyvJq6PIIASKQPITXF4VCx3RCoUc/Hmo8q0riBS6aqKDh8s5wa8ORdAjGnyruPgjJwzAMBqcQM4WyymIBa5BOJIRs2wXTdfrMJhf0CWAlplZwPLPZpcBBIvx+xOABjJZCvzvFJGzd5+qoZdgFo7loXdFcOAh8tGXom/T73W0tTVZaVwk6uilZCtV1zNXFCOt3fsW4Kv0Gwowjbn6r9Xqo9kN5IZS6U+yL4pHt65YCjbc4fPGlJ/RjOL485IXKbdyZXsvKHce63ugMUdPzhAEUYy63LIiPXe/d5Tc6XViqHD5/DMqH0Mw9Y1+RPRkcEuruoy/p0ONgbljeFri4allYrenY8k5mCjsBW8OXkYBB8wigaujU6d3VIjSwIx8bdKTan3LC951DPGjtBgseg0KzXnKAmYfeleMgrO0TJ3B0qrkOHlU/EUsbKbYmls8+/gOQ2jGNxlVzxeOs4LcHemhkkRhQ4asmVd0FpB31mKA4DzHTNtfVek6qlxmjL3GdeThAMscogGQFAnzCrJIrmvWfbo0E1BapwdHEuAhOkYqepriInEJlSDFZirxk5uR4CS/G13FmG7jrlO8XVrDh7kcO7xM5aLADGf7OwAMrJumc2aHm96WfEytrinSxGzmSXznH9+rh3+EhoZ6n9a/omb3znazvhumNf4KuI2OXDTC0fI92JCJ///RAfgZZpaePJ5BgnqDh0u8z95luFwZ5Fznn9kVeBlkuIXJBnc5o3ND41zjZGI5lmMFMLLe+l/SsZO+YO8e34DIsNBgDJzT7F2CU5mFjnOJyaLq8otaTh8oFFFuoRScz7LuM/8T3a3HTNB7kPCV8bseMLsMR9sXvWLQO4Pf70rgPI3/sbfyL//xb/4F/Ed3/Ed+OAHP4h/8S/+Be7u7t7Nod7V64d/+IfxyU9+Mv/99ttv45u+6Ztwm0ExiLC/BT4csNQFImdIucPeKzMDhZud2gcCzZ0BhDOLxW9Asugt0R8SPQZ4GKrA8OjYfGCUKYpyafaYIOtLiQ6aD1vEW0NbKF06l0TYsUH+AHKB0DnSoY6dDqh6CSV0SCoEpVSczycYQAKoZ1t2b2GN9CpMUwFy7zuCbBZOGCjoOtCvA60bYnw3MwFsQR3dpxb7ot539lXXIrhbWS6J8emlVC+feC1eXfhtUHeFhGlNSX2YoVRmKlpTyHVHf7hy8FvwWEB9iwLJrE9xiz+UnBApBihLAgKg+X3s4UTdkXCj+1dbUaTgulOgzFAc4LDcMlSxbY+QAgcrHCdQayWIg2a6GvBzMpbLBHCpfBqZve/wEMRLQUQIQoV9mJJECRHyvhwcAyzhiACtuPKvKKTScLMbp0LHgQOldMzZwk3/TqC9DdTTwoGWIklerI28AeqaUCEyhiIC0Q2Ws2edM1RQvDyy7x0mguYTs7d9x7VzoOA+DNeuuGyGpSnqwq6dKZTH0l7vlM1vTqQVA1CZxWmtQGTBtgv2rc/MUgQt5ryzzllWTQ/BQKUIgbm6rBnLXil1Hw5Wki2Q2UHaBnNHFbV2fmWkng7BV2vEOphOxny9amADTM5I+MXkKUwf6IGLO18TDxw0wUnaN5uOxT0rracrxJIbd+x+FM86+PfhOkh2bDEuN+4IPmYguHIiMR09UMe8j7U40TSB2dEbTnt3BBoicU+QSRK+y5+DeZlGLe2TYGaF4x7eghQ/goPPOaccCTIML5yjCwCSzxYO3fdiOG9/aEegFV48nttN5sS8BOnAw7vHHdh4xsQi2OKxJiCcJOib7NELmDyBq0wAc/x8iAcrwOHY5o/ltuRlx5voe0SCf4INkA2CHRAOxbzNoOz4al9/ojbjV199FX/2z/5Z/Nf/+l/x1//6X8e2bXjzzTdvsiif+cxnkrPygQ98AL/5m795c4zo8vlyvJZ4nU4nnE6nL/MTOXxNFB8zXICCKhVrZftnH3SoDHGPA7T8afkCpuEK4+FlkjEXHSBe6gnMGIuDC2jACXzOIbgF1zRYIcRmjqbF5uImb4S/NMw8EpR0GKWo1+brbbfPMQXt/xYBakQoAKQC6kZvqKVqbmxQU3ekUbsXzZ9fd/I1Wq1oLlXdh2L3bh11sm7Jzy94vG4ouyTx87Fcs823OOE2SiOlSg437Ffqh2zdh/8VCmwNYwmrtoJTLSmaRoltpdS6O6ziG6g1qpaelsaskvF929ah4GTbaCVFZfR+Xsn12fbhU4D9PDxzApBDVJuXToQZkG1TmBQ8PFwhhdyUPtRVd1eIGbadCqis2DCN2yOj4RF2daPU90GSahEsa4VF2dE4pTczTyVq8YdoJSI81VzPzKJxfYVibmvFS2sLtrqzU0j4vHrv6IrsBiuO4CIDKKUmmVYHMEsa02kMNYT4zxgjS5Bm7AB6tu3AM9arWz2jtYJ966i1oaw1j1OKwKQk3ysAu8JQFwYDpbtqcG0k8Dqwi2yfwKCj5L70MMH5BceU/FEjJqLTkqMpItIOFxrqnBGNWnQIAjiKLWbrqJj7ED04p+CSBBfFhQkNKMr1cCR7huOUA7GbpcMpHJYO1Lk5CbikoMAnUQdwADKjE34nApZwU+mc/C+zddZje5nRc5SpCXwQZ8uMaOx/M0gxckXk9j76nfY/JUNR5E+P1x1ggu9Qt5kB9uTF37HbIyW/SALwuD30t0a5La2+31dFgL550wxTl2omoYLYG+8UTNwaEGuuvuGZFoGkn4rJ46UEl+Z4RbGuZmlOLYDzcS9q2pe4bofqHrAE6J73Yd65SSDO7Jdfq4C6UiVBquUemKv1xeN95defCKA8e/YM/+2//Td83/d9H/7yX/7LWJYFv/7rv46PfexjAIDf/d3fxe/93u/h9ddfBwC8/vrr+Mmf/El89rOfxfvf/34AwK/+6q/i5Zdfxoc//OE/xhkcMygA66IGwYKCBYYTVBcYVr+BrO+2wuhgmG9It1ZhFHw9pWFJHZFczPFlOLzZIxEvV6QBRCKY4JNkajYMUiyiWCwBMkrBcoy2gmDrzqC6NHvvIweA5YwLM0jnQDoekOWeKnQ2YfiiU2V4tmYYBcOGCapbviTMhW4JDD02uFS0peSiJx+GAKw1lkZgCikciEjJeTpJdUS/K1AUqApIZ83/8bLhcqUuSq0VxRgVc1Is5d1nROYlnWIohXyNaAdfl4p1KWiwLL8ATijWASmsqY8RwmHdy3FI0TnyfxrMFfeidDDHuE9i33XvyVVYK8m+1Xx+kBQMG3n/AQK7Wguqy8enEypAaGNAeC7ryk6l67bDNLIL7pi9m2x4qzoJ2WxNXRdBK5ENUV+nUXLx9O4Q1NawLktm8QAXeXNVVpQ6p3kLs11Vjs6ee4UquXRGs52SYLJ7y6zBeUQGXLaOYsBpBe6uhiIDp0VwWg3LUtw1WXbUFec6cVkLxNtzi8Bb5B30O6lPjJ9lEo6yIB2GUiczHBwnTofmjkBrEIoM0WYZ+zDCmdjk5sYZrnaLYoj5PGk74r8HexJWX4Dcl2JHBwEgszfTbogDBj04SFVxsOhZlLQHAS5mUKdC3Yuwc0B1G1UBJ7saKgQLQhU2g5k8/+5ZF/C64YDHKszODkxXAsFD18vcs4KiTvI8RPZJBJYjKPH7H6gjDGzc//Sjkvc1IJJIAILJrsjj+n3RA8CIqxGxw/V6A4UdVKwScBwAlYW/8Kd6zJTEdVgAVOTPIlcfYIMgmMepOsFiwMm4zIBSmleVP/AgxY8vDISPgWz4GzuoOBJgxYqcIFsOxwzul2rAKkOVK0rZAduhtgHYUcsAMyaGmUGZ1Yiv9HpXAOUf/IN/gL/5N/8mPvjBD+J//s//iX/0j/4Raq343u/9Xrzyyiv4gR/4AXzyk5/EN3zDN+Dll1/GD/7gD+L111/HRz7yEQDAd3/3d+PDH/4wvu/7vg8//dM/jTfeeAM/8iM/gk984hN/SIbkK73k8OUo0ARiFSZkmo/D9Ff4u4BwOkA1oJphBFCBHcAJX4zS8mP4RwlgcVhgvhsmypTkT4QTk0DViAjWN7exUh0LJ363tkJxMljWlc1LEnpI5ULEZ/7E9/k7VUmqgllmWmgMqJZa3al172oyG9m23H3DBghjZ4rAdqMTcENSnJzJbJPnriSGvbHtdAx2zSQnRbn5hrFteBgNbqkCHR3bxvNghoJgRMeMWMYeIlauEVCoCbIuLE1U8RlCa0Njn7TrWRDclCIYo8MA7ENwdUXWWkhW7UN9YnPxFuniui9sYwYMUijI11xpNjpTShGcTievrQtKYwZvc8G6ZWEGxobrnngXTSmFKoxubtTXYnPRMhJh4V0JDq7TUZnraBjE27OZ5YtWW74/QA3Az+BiQuQAAQAASURBVBumGL1nliYAthqf3yIFFlkmQ4JKGIfCEWhq8hGGAnsfPgbiGDNpZjGixMUMILNMFxt49tixVKGK8pMVVDAIZ4PsZiPZlPtd1XyApWVnTlF2lwWXq1RAiwCeBZXCkQbma1Civ8U79mqI1C2VYyqU4N0O+/3gWnKL3GavJljxXgi6EBNMhY94X9woSQMS9iL2+k3gFI46HKcDEAWfTddZtj1qi6RHytOPIO0AAtw20Bk6OIxST5SR/KqDV0WfPGa2Bt0j5w2zf0ggYDt5cie8zHTj/MJBH1q8D+42CaxxMzLLEb95+Bn8mdx4iaMbSLjolOYjVyed9yxjWh57gos4WLYkxxKdH5L/nm3Lkwgb78tOsBc+Pye8F4F4ljWAXh5+fkxe5xGrxaWag68AwPG76gDlFrAh11q4QjmuQc8o8hz5JIp0VBlIEq6Zq0MfS584/PmVX+8KoPyP//E/8L3f+734/Oc/j/e97334K3/lr+A3fuM38L73vQ8A8DM/8zMopeBjH/sYrtcrPvrRj+Jnf/Zn8/drrfilX/olfPzjH8frr7+OJ0+e4Pu///vx4z/+4+/mNA6veByB4KvfMLa5mS3em3+LKtWOzhUoJijO8p5ZLj6M2HQA5lMHcgMnUdboUqpHBTmEyze8GgmZhslxMEySXBg4nj/JSgKl/LiXW1jOIXcgaoIsnZS4MMAXoOMTFJArYWZoHjmbAt00FXHjf1UEVn0w3Qjj6AALbEU2I+2JSqXI66lVUlhrCSlzJ2WJeAVy77hYn5yMWpK7oMrOlta42Gso8R4zTH4vwxmbhWoqa+mU6K/pmAUG6x3d712kuOO8oSST6gBbbQHUZWEZQ41aGZXPUtXvJZxTpMP1J0hajc4wKSwBrUtD792dHqci9717N4pHLP4aqijFSciDjn8MLz4Isy9QpTR7qznxmhkJLhCeM1tZSSrhqkpBJswMF+C8G1PnvJRpycRcUC9UmBXNI6Xhk7qHr3lzUMYy5QQwYSLDeEXpkC3+h0jXcBhEOPDOwyOqKFpZcX9GOkDqtUQb80DoBsEKRue+Kr7mDMhsYo3yhQGiHkSYsbNOWbYtIfLg94ocKcOyGs6ubbN3HxOAcav9AMu9kWAlCLLufXwHMUg6ABOeok2ugTurCEJC20aPji+xGjf4MYo3s1T3Habe9suMyoRQ4VgiF1OAA5eEJaxCYCIcJGooEKuZsSEIgj8Td+CI58RWYy6xBpHQ5WUHlgjFMp0lh9BEuTWux8xAfD928wHIhNM//GZmJVJhdTrlKKHHEQK4xP2c5NQ81PzT4olRvXqusQNp1Y8zCdSHZ+WfdwTsecWJegzBl5nQ9wBs1KAePAiOhNibQ8yAJT8ZmM/e90DYR4lyFbxVPU7q8AwO90wQ/ixALddKQDygo8iGIjvEhQhLCe7JEaD8L8qg/OIv/uIf+fPz+YxPfepT+NSnPvWHvueDH/wgfvmXf/ndfOwf8QpwEjojrithC1QbDA1wgDIVA8N8zAc4lWcBuLQ5smYbg+H4SsSJ2JxhRGwyu707IjYx2zdnW2i0F4ZhmSJL83uD/pgzWaI+i3meamFwo53QcuGYp3ZL+Bz/neJ6HmMwQtZhHlWTl1GEbaWlFlSf58OBiwRNt2QwRqbhQMbgcdZaUFuBCXDdBx73zvuo8IF4DtLgJaQQqQteit8reCTYe5/tkpFVEmYu7p7cY1kqdPTsUhld6bBKQTeFCjU1SIguXq7zVnBhSWhZFpzWNXkyTLNTon44ONGuMB0ojRT+yBZtuyFnqxjLJFKKE28p+y9SUGC4vz+hFMF127hya0lQwPLI4Z4btT1qqxRHI8HDQa079lqh1lmOWleIKa7X6yR2OyjoOyv4dJRjhpLJvak580mE2Z8xDPveCfw8KzUzABUDnH/EzBgPl3ottTrNywiaTKAyCcgqlP4qPrRSYsqxkZOyD3h2hvtv9O5Os6BWng8g2HeundP5lNo5vQ9vd5/OOO5bCUl6X69hQ2K2SpCOW2u4g0CkI/lsBrYe60F+PvYsAnCqf67ln0dLNXkD8/uzGyJKnq4gG3X8AHzelh4Ow6Ih1A83jIKOc2SDTTI/plOhpDmBSWQmJYaoJlih+i7tZwMshqyWzKCpl0EJWMTBCXMAvPYdIs0/OBwZS0bmWRm+Cm6cXkTpUU5LMhzyfgd6sPTOYXjjPb6HDsiP3K45rTiAAGw+g5sPip/l08O03TZd7S2uERx7LiRP1fxa7AaoHFYBYt5PiUuPU08CP9cGKwOWl5sdOw7iymH9md9H8wUQZTR6zFluOw5XhBxuaV7VIYuCWXYTiOsMRSsxibHiAEUcoPDn4VSB/2UA5X+/l9x8uVg3FMcaamOzU2Y5PE0Nprd7sN39cOJ1cXIPHMQI0T4cjR8Fm1yEdW6wEuRBblhGQgNTOTQMTKRIkd+HuRGJabX+TT0AoWgdZcq/+DkDwaQSAcQdnyoNdtypYU5iIlxy3YyC1lxt08EOVIEGlBGb2w3g8BjQO3+KTGw9zKA7I8y6SaYqR573AemDxN2lOuBz8mKrFXU4vyM2zdIIKvpIo7LUgvN5wdIAGzugA+yHYV19WZjCH31g186ySKGRrKV4ZMgItTTOFBndsHcO9Gu1QIQdOBg01uspFHapVrvtitEN8PZiAK6IyusLpUU6TE6JFiHpFAaCmNCsCeEU50ywxZqk4HVtnJDshFbrbEXuo6O4qiu1aBR35xWnuzWVY2NuUVdFA8H78I4yln1qRnzhHKt5FmrsHE5ZOSkZ5nXmxj6uoYbrdoWIYGnN16emdZMIBySAQghzkcNSIS7Rz+sqYtkxtA/Dw7Xj7ixYu/iUB+7utizpJGro6nh5rA9FbQIpgs35RJQaMN9bMWRR52ZLMOEWxcdP1Bp8FEMtLO319IEyI/CMNGf1f6q4ToA0gUU4RABmmc0NgBZZnkztYzqqcA5Mq04HnER+m3yyHiKQFmfMMqJVd9NCUAE58Piy/EJJe1VmOkighBcd3eaFPTVgGJsP1MIYmR/Tu3nAMi0F67gWCIiAzEFYBJk++iPn/sABkibRl07UstQYDy/hhdnNPeb3AClKDsbhefNHhyw54rnhiHkCY+QzRgCcF34eh4lnHQDoRe+fAMLX4DxI4lAEZTbCiwLaaDmU0Wt00kXGHhEoB1kWCQbDXmfHj78yxxrXeyx1xTnEL4S/SugmCVBUOOJBYrSD7P45AUj+F5d4/vd7lZsvpgsrDCTGqq0wbRjQFI8aY6ZBu9nkXoTpkYkW00j4Q84oRKaRKRF5ghmAECMDuJDgMzX4CZIrkxkVkvaaz1OJxSzuCGMWRw5PE7icfkRskW51ACAAgnCGUB49dFQoZ0wEUXcoBbG2jRfQXLac94bXHB02XRWw7qWwGhDLo3R4FAfYmJ0QZlQNVTvcIyGBtbWKUgV7J/+FGXp+NrMrzJLEfQiACHNdmT6nvQoAKwVjB6777u2VnpkRwa7kdNDhKIqa2wRF3wzjcYcZsBTOSrLBTiUTATt7ec4iDJ14DYKlAad1Re/MoNig3kcpgkvfnfNjnjnqzFAtjQ5035hxWhfAFW5p0Ebq8yyuZttH5xoSPtN1aZ5p8njXw58xdgS5jhkuIsDonOE4FYF2Q98VK1gjHq6YGuXI0O+oraEWyanYJG1LzvQwzDKKaUSHfFacRq0eiQv/7hwRhaKYi70d9CnUgH03PDxu+P+y9zchu27LWSh81Rjjft53zrX22juJ+T0ETxo2FASDkaDxNMSAYkdBlECEcBADQhBNQ4ioDZGIYiPEhiE2REF7otgxIPo1TiPEaE8UDWjnCyYeTfZee6053+e+xxj1Na66aoxnrsRkrXwfnLXzPXvPNef789zPfY9Ro+qqqquqjga8unONDcBx4xBIOCL1wff6NFhlB9ypM9GjrNknavVM7xFYyHDMjPiUgqxcAmizGc1rBKpz4DqAsxfUuaIdaQQhu0lQLYW/G4HsdupyghQhefSodwPHcKhHszRPo64U6XbSNjIyxL3O9BqkwuCRAowSWUTaNm9ymaUJoDh9ej6v0hvx7LPEZ1pOhWc0rsCiQzJMKUalkqROCXj47CVAVhCYPbovOkE6U40H7wO3+H3W3i8AswFM5LfyuRM8OiDeFiKlrz4mko+1b/uVAkTshj7XKs6bIr/5Dm2kJUgkPqFzJEc5HmSLuiHS+9FwsWxyZTGMtZBjl6MLMtLGT9b9JVbU85uAje4LsQa5Yp9cs1wXGa68aHzuhOEFsA7zFzhOuN+BIMnyxtSYTcDk/0eN2v6f97KHP44SdLSC6fxDpSSUDBodo1dJoBIdZSFOx4qaFIvARBiMKacrhASQ4vEt1CYUvBRDpnBCkFJYrGJ5cJYVN6n8XL0YhPrjgETUUzN0Uv14lJHZ6gyqnwLM99Z6hGKlFlPtfbFg/7s60TLU3USsxdxKyiwjRV4ISnRide/wlZlfQh2ecGGouEfEhetJoi2cVTstKpRmRICOmPk0Y89eTsJKtXvvs+Psqx2/uB/DPZt73W5M19RC790AzCC2ktfBZ7g68HKNDJG3YvBJUDCm4xqUj1odc14se07CLEPWPtmRtk/H/c4Ot60am+85n7k1DqwzsxgeqP4xYEt8q7jfX2I4I2f5mGY0iRRqVFYKrluxGNI1MMfM4YEqmVU6c4yOUcbDvKMxHdUR5F0CEgfnPk3nwEsRVcmvsZiPNGOGDzLVghnnIT05nVEiDAF9vq9k6vWaE2/Pjqez4uO3F1pZ4x5qAa5eAgABSgP0fg8QSwDkTp9zC3QjI6N5BiMy4B78kojc1a1qIc7k0QqOo+G4HL0rsrpxCsJ+yaalwXffvvL9qCKjqPGTR3BC+dpJt77euhyONKIL3EgPFQQtNy7hQPJx4Q4vQci0CZjI+vtDqFJJLf5LRqD1OTN1VdyErVQiAQH33VEiRbx+d5VAr0Zv/HlMnI+qIQ4pdP4d+6oITMZvN3C4YIX0luV68Hc9I2nIK6yXbdd73EtdVWBBP9fevPP777zSBsWnJE1A7/I1DoMDLtVsct2TCMWl7ODh0b7AJbtLoDLa4TGGRHK/Pa86W2sN0nYJKEuIHp+Ke20qmogp3NGwUCnPT0ZOfrVV+uTrcw5QFD2hUE8cmF4x/Qnuz4A/w+xANWM3zDgIC14CiXddqFYeqMdUWe2bRT6d/1TDoKJunE5DOiPn/sD8TmEMDgHwcJDVHIrN2iw9WsTH0otmw7FVhbHAQq2s9FGFz+4BkDuwQEONKIn6YZix0dutVUZYhgAd0tiOPuK6mm0S/zNbTagiVaYVVXdBDSv0SAnVSsV5BTgh6TMOXTHASxhGIwADsn+Ju0d7ff5xn2gHPa/rirRGhLBLJQga0ejuOJiiMVcqTJ6GoR6MfLHjK725e+fzcKS6wfpgVEUh5VzvmDlT2EX3ELCacyuxZvrRjKRcAyNiIh4q9cIGepOjAgIszel4FR1rT4thfRFFsiE1S8/YCpvFAY4xLozZYdZQW8UcJKqxQqXBq+fzWwBxF+k2ysJZSsy+LX06WoszYdEAD3Ef6cFFEidKjBlJDGMScmdRnpjkwuD4RBUs+pw4h+HlHPi4ALdmeHpqODwI4QFMxQ3xaPIHBDgyArLaGhCRtzT+7jkSglGrVcVTCmJEwxot4c7zfRwHboN8o/OKKi5/vO60APBQjQoHpRn4x6MfhECHjAnSeZrreyZjoco+ARKd6+1POkVrL4GRkZwEZdC5pY6oAVIQ98r+FYQ1vKgilMEvweocq/vZDZr0Ib37GoXh+r1FoNYjahLwaotgUZ2iXj+c+WNR5kx+4Y0kXtwiAkC9Tx2/AI3F+/m88beVLbJlqSM334kyu/OG4odZOPEOQFlRlJD9LaKW/7AFKvPnu07Gsv9yFA3BzRI4CbSgCPwjqLI4D0h5sLi2bb8hz1gt8fMm5fxCfy9J23GEInHxpFwJu8PsjmIfwaxj4i0K7nC/w3EBdg9geOHxgr9JIyiPA6sYHqxqswmGnYc2qtB5eBAkgQpQWct4aNgfQ5UrZ5xbZazwEKcECOACCaU/CCRJ5g52tlpA6YpIgpQ9pyh7GKTt/dPDeVykr1oiTQQAzjy0pFXt+PneESRGgztBEr1/8jc8/ihc3ocqRbgONQ67Jf4XV0ZcF4u5L0vXzkh9aB96DAFk1YSld9ujcmiMnhiS06JLVpBcg3NSxqDhnzHS20AQwsM5Y4YNvepXT0+43Voo5zVQsBRjG/vYX+fGAXAcqHgqBS0UPoAYnEjhuQZ7h7TjFvvNyIVSTmc/wws1WOFUYZ9MtbTKah2ViHuf7L4bew4rMShRHXqZ9mpFVWpcvzkGciSCE2T0EOhWS/57Bh8hMtDsMvwUfBkj4RHRb0WRlqK9i689KrJGVBO4c0QDRscR5cKjM1o1lM6JCh1xSi2AI9uHjqBNhTzRatGQFcf9HHhuBfcLOLvhachT6KjNotNtpDs2jzeNfxCwSTamrGZRj2uwoyyM4+lWgRhpIOdFXWYPFNwOw3FM1NphdqV9VjTSfMLVqCo7ag6YDZQYmrY4I4xe+hQEUd8Y3oueZGXqZfz4odkPw5YxIRYMIGAFVhxl0pjOdIvkNMV9kNBAMBFOGU+0IrMDjEoHMNF97AadCxXRMvYgqluJsOefQCiwFcVxGsgCR7PCjqnuUGUIjayA9Ay9IxApPRlOWkQTFWNRifmCdZ4cjDT8ie9ixdNpXT8HLLlBuo5t11m/ve8c8t72nynqJlCRUSBVcgo0+ePV1nL7dm3d4wZbt0iK5gipzQVyTcJmpUPheZu5Hvsr9hWSX33bHLABeIcH58TRoSZ9rsqdBwT47t+/9utzDlAW/8RRMUbB9Ab3G9xuSQYrBTwAiM6e4QFJykwyMtZGCrDQEy6PMBcUL5FYydWgYZkRas6Qqc0EGdp9gRUJlTgEidT13gjJIQ69x/j4UlaLdI+GY3BOw2UzMKfCDMErCJ5LzLVRXrFEzwcqes6GGRcrSdwMV/REUa5+kXoVWYiW0ogZRHOilopWW4T+Y9BdgC4Pb6Qm8CKRFaFcrq5OtOxuWkqB94H50nP+iwIYTG0wdXFrDbeDvJjr5Hyf2lr0Z+EsHRrYkYd3TkT6iuXEpRYMN1zXwBWckiMiSCzVZCrwKIbRr0hrMarTGol9ozvOq2M4y5A5M4cD7eZk1852hHcU3jr7eHRYqWjRXdY9Ikzu2UxtRMdegV4A25RsRgxGHxi2Ri4QrEXUzS3LoinGMdjQOOxSFWIOlqX3iLiYGdrRUI8AT87GgCwvNijNIl7TDIBWY5Ky7pW7E2m1MJAWZ0beKIE1n2fC0GfB2/tE+ejCGORE3I6K995/Qq10Mkpl11kzRrA4fqDHPCrLDtDtaOzBET1Ceo9qHKs4qhyagiRpbsCnFBFmmcKrpWKU6DfkUULsHWYdbh1mIwFSQURHoD4qnsZZXaQh/RDAOR32dL7i33JsXZE3pBOVqt9You9DoXeqk9VtlmdoYiKGwwAlTKVp0Kk83BqfpxuylKsFCZU2DIcivXSL50TwV2ZwQMRmiOGTBra9V5+VKHRY0ZAKQwt9egToOGKftvJlNJT4t+aw0fEg2TyjKtHjhdGURQHQGmcUYYuiTZ/bz5QS3YjSD2Zdzs4jkNv/rGiKCasgS+eBLMdf8FNrL1thuR+pyxNXxz1Pzz3RcyyAklB1A/bxKbbt7savdKyydrayv8PsDRxvMeeHITNvUKzD8QIHG7aRf9O3qwC/aat4yGZfUZS1CQrurTknI4wQbMuNGkhs3a7vOlzYECrC2IKTe0v04lBeFo5oS7zCegQlyztbemcNN2NuniWpEq5SCp4OGuA+RoCr1XfCzHA7DpjPmMUSAgqmbmqJVFJwJACVhjKcScInK57Yznzg5bxIMozrTd2jR38GjAWs4zGCgQC44ewDSQSMKID4JQiPnNyRkr6dMsq2HSB1Zx1DvV02z7EwBfR8NLx+dUMpwHleIKtSDbwudHTM6F7LctkKTfgFWJYMTBQ3nFfHmzcn3IH3Xh+AkZ9yngN9AFYcT0fBq6cntINRDjVVUdM8Ki3DcdxwNHr94osownN1lnkXDPJNDhqU2QcwGU26Aqjdbg3uBAzunEtEWUNEPggYlZKxAHc+RRT1rAAyRMpreA62ZGoNUdmEmNU0kxtE3lKJ9z0CVoIczsxRr53bceTQw6KInIskThkpIVeABzmVPXoYVHQMsBvthx/fcfWGczg+fnPHczN8yzd+HY7bE0ohsGMUyaL5HdAv9ouZkymcETwcmHrMBMkT5PhUY1v8UmtOon4IvcuTj75BHMhZGVWMiAPXg7LgNqO0klI8UwcsUqws14y9k7NkWIBOOsI2YLL+uTxl2EoTFji8ElyVCFgkXyIAIJ9lOWC+ff4OOnjpuVJxWNd71JDBmwgVK3YIQLDk4WRVgLxXgbQSz+KAUkDJjYP4F9ErJbkMI75mJJvRjJZpi0ybm6GgBV+KN2ZeAetQFJF7G2Aioxor3rKAVHzX6sPvyZ6Ej8vrWoFBTQQ97Uru2ZZOsVCHq9qIis3M8kwgdGzKgX6OkvuViApyFrQnnr8vh4TPzHYSu23C5kjIpkQuP22NGcecsLv1HbA7VFYMG3BcMGOKmgRwclFYnZXw+Z2/f+3X5xygvFPFgwq2WK5gs7YQAjDnrKNDkmTBJQ9oCrEigQYDGUTiI3LcubwpVJFzjrCZz5XKmWMkQOL3FgH3Xc9DYdNWCBZaLfDZY0rvwIldHm0RzBDM7qryWSqLhqZgUAoggoxWorncBDXYnB3ndWGhcbbCn23lr9k+QsBrqbEEcFL6hhgHH8+d3mWAM6mH4CBoarGDhskcqCW6rPJN3NlKr14ZOe4hcGsHjlI58wjs0NrccI2Jq1/oYeQBHtg5OYn41kjCnT4x+mCn2MEozXGreLoduB0V9+vC23uUTheLpnAF1wDmNeCz49YMvfOzn54bfDrO84p+JuRNcJ5QRHU8PHxH3AfLjzE7bo0Rhbdnx3UN3I4S5beMwtVKsqYHiXkaoxkzn5OpmDHCQIYxKdFfht1zZ1QdlQTXSq9dfYCTqEFlmxGX5Q3TKDIqZpPP7e7oZ0efnCxcK9d2To9rKHVpCW7kBdKTpjxUA7oh+s8wRTXQcY6OpwYcH7xGqUA/T8wiwxh7ObkWY8xIjRaomR4CQBks5gVFT5XY01ItI1cijWe4250gFIMVU/Ks4zzuZzjdlvh9ps3IT5tz9TaBe1bmZJTDPT3cpTXWZwg07L6vYxVvx3GOt1hqxYmtagMejdhogPS/9ZyRZrQSlaY9zk58ru28hk2+zJI0n1pSOsCkM/k9GWXLewqnMHSE0h7FKoq1MOY1uCci0Mao7Yis8PcCIampnCnKHBESdcU1dQAJeOFKWe+kYwtHNQy+OTy4LKsKLH4uYGcrsqLlohR4yPnaJ9+WKPFoROkJnDcKsNJlikoleNL9bg6w3uVYv4uSBPVV0m3b/vH7CahS6LSH6r90BzBgeAvYCfMXmN2B0gF/C/cTsBe4d8BOwHrIT0rhko3fPABlX/idMFsZRXHHBEO6ZlzeGOoagmAxtXRm1AEp0EKty2PQBkrQFKLOEdrTFxARiAm0PGcQw3gl/sSW8pD3K/a2l4IGKtYxJl7mRaJolEvTo6HCu59sy47GYXNjcvjcGEGWixI4656t7dkef4R6Wp7OimAE4p5RRWHlQawWX8c4iTXADLAqcwBEWoC/y5JlTmL2qTJP/mzk6VD4OxS2XDOGDGAgF4RdYwuuSDccToB2RVXO9MlpwXyINCRHq/S2nZUotTHiwpTKxHFrcDg+fnPi45cTYzpeP9/whfduKGDX0vvZ4XAcbYE6+GS2vlpEHHoCkWt6GG2kkVfU4X6erELqE+2pRVO8KYgWXBvG6D1I0AQYsSRDjcNIOFZVDddb6cBN1qphjKj24ofBp+PqUQ5fYg9M/YAYBfOoftBeK0WkqF5XSbxN1Nowh7gG2kPfIg0rkiCFyshTAKZrZtRyXjMGKVZMGM7zRAGHP5bjxjWaE+MamyGK6B1GGsFVmrtAroBuLTTOxNIWqbMCn4zS7emXjGzaihChlNA3yAAp3xOe5PQccjmlJ7bryNSQ86KIxzIYuqZUx3KQwN9LJyj+jsCewAPCMQmlhfDhM4rKqITHlHeZeluAxBB7pT+PP1OE1rDrPSyvSvemByq6BkGJhQPFuVN7CgZICJYgjedZFpTAYC5AEelvDafMGUS7jbBlyHkTqvja4Zfelyyf/Fv8Df0JSKVVy+vI9GchKLZIkUBDfOTiOWrfuA47f0TUcgvbJUGWY6yu0ZYbEwAsP0cRI99kSiBNAJawV31pEPV17i9g5PwOsxNwRVA6YErnXAFQCM5NjRE3CJVC/Ot8fY0AFIGUilIaymS+eYZhmxPoPnA5AQqMlSRDlT1AKjEddmFUfoyEdfv9MBAGLHo6nD0aohSWrwdIsoAp1vV2YEDPXx5vibkh7BViPrOpmk/AJgWeRNaOPipKiT4hgyCgRDUSBTa8SPcgCSLz6rqOpF7ggOcoWqnHARtzGUWYqmFIdAOiwmMYSq3B32EXz1pZKqqmeSQv0mvT+miipqJcofaipLYyynSwuVwfjjGZ3/Q+MUfHNflcT0fD841t5fsgB0eRE5+O87rAQXpsmNYHjdnVJ95cF66LZehHKznoDyD4Gb3juHE6siHIe8bmae62unn2wV4TpXDGTJTfHrVhwLnXfeLWbuiDpdMWqUF2HTacnes2gzcxZo8oCUGKhgSqeqwURhNIxmXQmxU35GHRnpLke11bKgcWgA7sjxNgG+58v9G2EbQAqk56ecvOtex9of3fjJQU8ly9OkoYO5KYaxovL+HxYZUt67xd3fDxS8eHby60euDpRiMJU1SGMjsjVXddrBwopSFZoKnReU9MzQaxWvdswOpzggy3kyzP99SYvzQDgBkqbBbMLZWs841NXwyP6eEBUoAFUBCAp7jF3KTEBcjV3K6lVLLnxGDkfskrjztjjyEYzAU/gJ1Lwb+iKMCjt06mV2TQ8oQGt2/XYsu0J7Ba9nb7PU+7uUdh9TcxhkNSkLoojCgwwrBHusWiYucBVqkoYoEOgjnBwNijjCoY1KdkRVAWPFo9TGqktOWUrFLlHAKbzdIs91b7ouSOZEx9YnZAkS6tWXDULL/7uF4CV3jnb4Jl7f2CBAKkSDu2A0dFf8xFagXU9yvY6wFIBgRK3E5GTUDOiUEDAicYiRsI0gV+EwOUd1I8Ro5Bqw1mLbwatjeffeLqA9dE1Oazs+rD8TIkqY4jUJHdVunAj+WlOHD1Tm4CwhOLUKd+QWkj5fXEoA4tmNBKTbpcTCdz+PDwbuQ5BK43outpwaOIPCu92L68Ouf03mL0yFXFYYj0kBRieNlwYGCVT3JQ2UiSrrxDcj+WiElPsYS4Uo90UyNu9DFD5tkqfsQBkKETB4VPxwvXGp1t4VDn3KdWUVtj9UbwT+YcOBq5OVeP6EpEEfTsJQ5tjTV/efuSFTPHUZmOuQY5IbVExUnB6/eeuD8+4WPgo4/fwGqJYYAEBSr9rsVxu91Qi+E8L9zPC1efnOVjfJ5WEBEgA4qhnzSgz09PqLXi6necV0dpVAw1yqrv14BdXQEk9lypBYjUlhV2cp1zRPM6ApmrD1xl4Ol2ZEWWOAQj0hwqCz9aQTuiOV5ruF8d59VDGVegsFvtuEhGVfm2o+ccIaVy+Pk9FbBmJrmDU6LHRDOVlZc0uhYyC3c0BGmyEnidYwB3wMqJ46h4frrhdjhqnTiebgAc09ij5OV+4e3LHXM6jhtJlfAgOoY3R2CnMmg2b7RCrhXj8pMkUgRBdotumiHnbbGRljhuwNZIF/6OEpahkKGccEw5BLYiMuSOWMqudA1tUoASR57nB+OX+p+pthUd4X2X+HuF/3WnAkvLKMLGGmwqpGRp0rO9wOLMrOddg/5C2+k/toilmrit/UDw0eICW/OwPU0ReiIXxImavYIsYHHa4hMcWwTl0ZlVRGMBGH4tkJ0/j0pJ8T48V2+L8ZgitQtA8t/U5Qk2Q85zpTJtk08GAe2H/QxAJVCrr1eE3/PsIcjw2t9VfbRHUnR/AkSKvMgWel6Ldz3AKMkAG7ENmF3bv08wajIAG3DXvwVsV8TrXVn5tV6fc4CyhI7HquR3MirhbPTT3NCceI/zYNTIaEVF2GJ8JuGPoW2lJcSuVihZKJ8bHTPbmGAKxWfBFZFQODwVxEqBLNa1cvYlPF0oimJLEV194H5eAVToMYpLU2BU7EbB1HRkDeRDlN0WpaENm7fh6QFUI1jIlJR7EkCFsM1WyoqphCBSBgivFTBM2ESWmzo0j4WExBL3rferEsgsBg0iODZmcB8xGK4/HDqYo7WG5yc2Uptz4rouzD5xjokapcwA0yijO0ppnJljvN6cHtEew3SmxVphUHh09gS53ThHp7UZ4KjjfnHN2J69BxGYBus49hAt98mcMnI/TzgMT8cR4IJAudTCqiYDjuMGhLFXZZiqhtjplkTTMSZGPwnMgmdVag3Zmyj1YIXUdeLljJkYM1Qs838BbqPLrSO4TyKTEgTVWsHyZ49QPNef5FRxkVjaXYI4qw67tR681uS1HRSQhccjkO6U6eej4VbYrv4+ebZepmO8IVeqlgKfN3wwCp4Gn2n0CdiBWgtevXqG+6okYppqMx6hG2DRE8UdHo0dM2JSHKUGmBCJcwbHZyNEO9b1KH9hiFxgJIy5iQ8R14J6iyznowBohWXW1QhS0p/ZNB6gNcMCHHkmaHCUKhY4kSleJbLiFG1edF4P8AfGF9ITUWZBmi9tnos3Jt0ssqreG9GYMKglfybANbHADqOuvj20MFLedN6bw6MKLdFb/nt/cyq8dIQyYiKQ/JjgyutQ16rCyQKJyqgb1DtGcCpTbrIxgXtlqtdjLdC17+0CVWs/l4EnKFtDFklAVoWNQFlGRRRhy2d3LKdQsrVFWDZZQsgBzzuH/7mfcHSYX4B1mNFR8YiaeOQXN+iXV/pNClAiX5zzd6JMLWjiqqWvxXFUi9kwiFRAugehNBiur2Ly+8zGWoBlJz7I+OddWN6JlEU1kjsdWOTZRKwMoQrIeAAnNo4SZySEM8Lec/Yg60q4NAdjzfMYcI6UR4iiYykkRAhV9wA+43V1XC6uComnt9sBM+C8LnrKyt9LOUjxpbjzf+qZcbSDfSXgUUmCmDejCg6FRAGLqA29STDKUGsACASZlumH6Zz0q33tvQfREUT3A4BzEpMVHVCuoYP7YVsIFPqexVwaRbjMgK0k2czg8wqOBRVeawXWCkZnczENXmPPGnoPz89PAIDzGhh9wKxinCxBPo4DvTv6eU9lnFHxDDlrYB+NZJ8RsYBST1U7jT4mbreDJNFquB0FZ594e3+LWklkRdl2bQ7UdkM9KnqA3rf3C2Yv3KNaUAP0cKbTlXprDnJEauGemMlTiwnQhdEZpffSI9PjuYy7rJu8P53IierAAXJihpUoDR745Q/fovcLo7+G2WtYKbgdNbrthmccZFmtOS9NXsoYHgachG5bN5UySFAsOYu0pnv2BBrxJycb+wiDHtFC14kYJMiCJeMECL7sDPH15nHPBLITBL8WZnN3vGRvRF6XXVGqDngnYuK+DBV8uxqyQZ6IjIqIWhjWtUCx92n4PT9HsgqZ5lzXdYGHfh3UBCttbJ7N/jyi2uoyu4P8JJrmMw6IU+PRSt+shnEX4NI1Frduv2+hgGLSkgKW0hIl1j0cndA9ipYt0LlMshw38rgE9WRjcuU2TRz6KBczmnCmF6m01AJgCSqwnN0R1XJzIltR7HCIn+8rAhX2QPee6xHrQxPYF2HaJwCSXz3+ZuTqCvm/CNZsouQlJ/aOPtuH/LpeXwMAZftDi86SMuUi3dNLyXckIl9CA1gAiWg4ZvL849Ds51IHf8kThJ/zuIcCqsWy1JK5xYXOa7HMzRcg0i7BpgliIUlLAxpZLQ9Qno+ifirjzUFjcQ8P0RIs9vxC7x6scd4Ap+ZWHO1AMZbuDrCHB6ujZn5uKQrLRiO4XVlFbxhvEQWIkzmGPB7Pnhl8j7waRERDYG9CLZ/H5PtHkC21nu6TfR9KYe+SGtfZzsGYk/NCiJbQKvucTAeGhfcfKT02UpvRJ4ZEQkZVLA2elSBhjwst1jhLjanFcmifz4lSK25HxaiG3rl/l5NXZEDOvSlG4u95P7MNPTk7IwZbBq9oGOD7EErySuC8T44uIKdmTPEx4nmqYfgaa9Cn5kWRP3S7HTSy0dl2jMmUz2SUp9RCXVUM3kVEtTSYjGJF2/5W8qzt5c6K8BVf5GuFvy1AWANTdm4FfYLl733gw49PtFrx/HzD042N8m4H5W3E3CPNBqJaiGZjUcJtiLQO1OBLMrjOiwwPZoxCG47zIm/n6mObiWUPEQAPyzXniNQqy6YF4JiRDWcnHHFm4KKSqSgWvPgpD9EQIFO/5kvfLGCyPHnLddUpeECJUBVLXmPnswhw+HYBAdHNUOqSq9Hcu5/kj9+Q7sUDE0IOfs7oUZMvkUH5dhlqEX5lSBkV4fPI+OqZAt4VAQl9PwBFAAGL8KYq5PhMFpEBg3gv+5p6gCi2QEACqIyaRwRtfnIJstRbloO/oIiMSKzr2S1Loz3194KjIhJrzwSaDWuRwwj4zHt5xJ4rpcTPKbFLI3ZqgH1y+Pfq7cOeP+uedTmPPfkEGelTvT7nAKW884f9PNgK+QBgTFEOi3HkG1hhw4M8Jgpd0k4GYazUTBto7o2FsZwPOyxW+hJI9/V9dhENoqcGnnmg9mrootcFqFE8mj/eUHQY+GycpmNrlvyXGXgmW+Wngoncr0kJlk9eo6i1fcclpB6HTf1VfD6GzFeofAUOx5h4wYXGKWwbGZkKZGDxAegBSGkgPaY51MCNazWiKmlsgM70w/h3qyUG7EUqYSM7zoj8MMoz4V5xRPqDlU8cPKeqOHcAlWtazcghsQWQrKwQe62sIOrDY9DcAaYKPWbEAM0omUcrOH3i7b1jgO8VEJV3xfcNtAAe0zt6rK8VNhubDvQAaqWwZNgDoNTOCE+pDSW61/bBrqY3O8jjiVLgcTGFVAoHOIrhr1SGDDDiyMirE8joY+BovOYYbNkPn4xGmOW05L3/hyJb6uypFOsOXmzy7CYHygQoJ67h+MqbE8+//BZPreGD1w29O2qbUVXUo2pFnEF6vCT+8lpdhEZVoM0ZDe74fd53RGejZLpPjlq4xrY2AFaPk0j/bWXFnJTukZKZSSivUl+qYkFU0MihABKU6DnCpG5O1h5E38FKHvz83vIEMo673itwsDl6vhHNlmGO6zlyL9f3HiGJDOyCIesKU/fh8XTBfXDIyGPjnJQwkjLSAYZtAx/YAZkipytSykheyciHAUlCJTiydPqUFl+zgfjJmaLdFlOzjhTJDgi6I0Jkcz7dp9nqbZJrrvUUyFyrFa38pCnzfYr05ILLdhi0wrmWVLWyI567IkBrnoYnddACKwQaCcECjDg6psCJRVhbf/vgOUAHq6nejaC8+/Wv/vqcAxTb/iyEuhb48ZBSoMKrfefQOMIIl4WKBSrgcQDKdoDEGXmAjbH0thjcQJBd1SYdwD4g0EKJ1+2+Z1TJqJ+DQvwzFKLmfdQg+skrNEMqvunb9bfPqtWiJT7D8GOue6mN/T5HH0zNgM11Ldd3pXEidZ/kyBmxYh46pjnkVY3g/CAPHvIz9ZXIiPLaUv1ZDBqLZm2tIkm9GZIvJLgCNEJ9KDKg++Whr+pOOxcQaNVwa5xNo0N7xfRfEhkdXgusVVyj4352AhGLYX8HZ91krwljGsnnYEO0ILuOPnGfZwI9g7OaKCo6Ths4ilrcMxrUB6MwbI6GJDY7AhAqajYn3DuOo6GPjpdz4IYj5gvFKQjAcfWBFoRjNl+j18xUGcPYV+/x/AaNdG/N8p7Oq2Om4o77Cp5Wj55BO2k2Z0tZoWdnQKmNaRQgxhLsCno944iIxHABDMrYeQ38z6+8jSnT7+P55sAdvL5zZKiZRa+d2IOxnIdSDVYO1EirzuGwGf1uqsFqAYIbhbm8Q6Z6HD1rRyOdE47P9FWlNqfnDKQ88/CsLykl0hAu4GFgQyzRYyMlZzQUiqwszZLMizxHvn2lnywjpd/Ru3PJIS3oywqv30uM4wkgfVmwzXBvn+5Ykdm8h/hsX5BD3CPJyoJYZXunHm/mmgjoPTo5lnpwXwc6E9HlOIy7nCE5hWZBzUJ0SEY4s1P6SPe+9P3cTHem/M2ykkbgZItlLZARj6nP36R/wzd7Ms6iikhgbkEzcSmV7nHbHfFYRaK+WPEAzFYyDQhb402yD1g8jkXjNck6oyOcMZVAK6I2kpNMJbkgz2d7fc4ByrvRk8c/e+fGbIiEOBaRCkk0jqhc2bq09k4RrFgeTH5yREsQqQ0CB1WzBMIOY8727ciqAYkWG66tKoesEAIVXe+DE27jJc/E4lqtse02eQ9CDMARKKVURoAQJZut1SScugs0MSw+58B5jnSQYJZdQ7Mksqj6AllyutaYz1SNKZblIfP76pOxP3+Nks0Sc4SKrbB8gk4nDwNATJaNqNaYmW6ohWHVe4+c/4zx7851MB3IwX4xx9MRwIEzWdqNE4U1q4lcgKEoM1NA0wGrsGjR2edEv/foVkrOkYWHPzTvB1QC8r4xqEhbrSjtAK4elTc83BcsyaklCJ6j95xHRGPP1IlVbAoHsUeswmHn2RlrXHCUgtoO3O8dYziuc8ZgRAJANjMLcDG4V1fMr2kis0alzjgHe7Y0Eo0Bg8+J87wjwUUopYd+QCZ+jm9l74AGQhKbLqOn8zSjXn86coaTDuzLNfA/vvwGT8eBr/vCM5utRXWXR8+c42i43RpqpOY0MqE6I2PDVkn/nCM5KqjhCoROmCNGQfTFQVlBg/BKfRmG6NEWJbsK/tuWuon25DrTSH9+OTcOqAV6/p4BFqkDGQak4UKembRyW2QjuRXpKD2aDtdCh27Lt4alW5VW+3UfHTQ83Afe+R17+DURZgmeSvJbOFwzol2rO5WgSUZPUpOY7o1k1tyRAMX6Wv/NihvfIYAlQNSrAOGwChRgi7Lw+uLwmFJS8Wz7/J79uZNXk0CJ9mcBjKX+uP0LkHF75nZMpGUitQiDyM41SdArfZb9TkLHMWLMkSGx+bGGyGfhOXKQALvr543B6Ur5jBCt6IGSoEWn+ZMy8mu9PucAxR7+cOOltBcIAXjI5R0Vd2CM3Ki1se/mPPkaERpM4k9IIUFLCSGVJxB3ZtGTwhW1AWyOqApib40SqHiMmSF+hQuXp7OetBaVBbOsuZitXPsUEKMhZzRiRpktwpskEPBIufRIOZcw1upOC6yD1FoJoY2pyYWwn4ZU/JCSh9iMQ+TU7pyAoqCUGSAlWrYXgTQK7wAPLSaBxy0aselQLMW/IiRm4T1E3nqGYuCopYiaFA5CJGk6PFcZg9g3piU800DcP5aVFxFRXcrDorqDAPLNy4V+cEYPTHyOESRprgejYsurVkiZqRem946jBZCJNdnWSeWfCLBr8JijE23ECyNf/WJ5L9OIJBFbY8XUET1pBHh9sjJnTHI2amWKZ3TlnJGg4romB88p3Rn34TGzhcZ9BvG2xj5PNhW06MMSPVpKzLChfJSH1KlcSfeN/A3k+vXBqI7SisMdH987/seXP0Yxw+tngjMfg8/cGmoliLq2yFQNEOBgq4BczzT6wVcZ1A69D7zcL7zcCSgZGVmRPpkJ3bw5mJ5DgdWKoklE6vEhDkdYmj3aKgMlT9WCaLxrOmwgJW3fjkPSBcB2nt/5pfUpu0qD+FPb7eX7aJs8PzcNabz50f1ApA7WNfhroS+00bbKvW16Gm71d1wAF2mk+YHihlBuGOE2WKTnBR6Ur/VPgCreuMf7V8RmGVJFYnZNLMqOB8hAupTrJd7ZDpx28K3vC6CsSNwGuOI+1/MTkJW9MV8AGoIGVj1Wr7meGjXgD/dGvZZpLURvMMlisYzoAAY3jXmR/hoETVq2AEdTUUt9dqTSH1vdf/rX5xyg7BGUSIFMhYex8oMT6K4iV0M2XtrCiitsaSHz9Hy4W0LjSznqcAHYBF9htSVYK9SFVMA6bGpRn0HCaMZm4cWwz8Lmf8RhFpFOZY/FSgw8433Xajgi5PNIFmT5ou1GBGo+dSO5sc8tJJ+qKxqn8evRB3xM1GK4tYJbayg1ym37xJwVODhyfYYR9qk8PDIKqvCuRynpjGoaD0PeanRHlTG/BiMjoVRIweK/a6EwOwxHA1ptUaYZXJTp9BhQcA6H9whRhjekMDWbyUXjuhnGyBzdR5JKRfgFmGbqNlAONbvjvpeoPHFn7w8P8DZhuE6mz8R/kOJ2n9F3BxFuXeuREZFacDtaNLzrQFSelVKYeukDddbsA9MDfHnx6JlSMHrwJPpAqZbck3H1AD4VZiviRbzE+S7AKru/rk7AF5G1Ug8g5EnRSKsFtbZoFufpncnDfEhSpEHi/vcEnTPSPeFORMrFAXQHPnq50D78GG9eKo5meDoanmqE+aI0+opIFRsd1gCtPAMCDy1KqQGWC49OY3NdnMd0XQP94rrNsfNzBqqRPFjDeGdaNStxkGdQ+iKNNGxTJSuKKgOwTD4yOiWowIZtG3/DpXPyHY8qcwMMAiSLXLq/Ze2M6W59u0QaUce+hQRZMrQBV2TU466trJQYx3PoOnz2as4IgCG7IrPlAvOc6hslJ67A1mfGGiDVs8iej+AswVV+LUP6Tnm1diOBSaR1FI0Kx26/rkX4bBHYl53Yv17/FcF16YLsxwKR1ylHtQpY+QPugQrm4guBuSHH09d+wyyjmHsqRpVMsKTdwuDhMCPsphz6FSlVbxsNgBVoET9ugTjd4G8aDopeXFhOnQ3lEumJCaC74RwT3VnGNyIcbqUQtcfhmyrRNV1VOUNf/Q3kORTbRDi8mexWiC16E2IbGiqViDOffFQSHC/rMU1ZVQ3Lk4eUVtyDGs3BgVboKfLXZoAfldN6zr6AMy3SnSWqtRT2N4qSsKMVRh6mb+8PIzCIhscQKKKBaLXiKDHcMJ7nVoByVEZOjFwBu3fcfaDDUYLEViLKw2DWil64GatVovlaqSMPQ+8zAYqIdqWw42yTojPQcERHVmR/BVvICLGY4OyfnBFjiNA8O/Eu3UiQAne0dqCJ1DeBq408oBSOWCen0ilAHFKuZbMCBEDkI3OfcxhgWaqhNhrM+3lFL5RkSlKZhwFnLwK2nXdVOAGYJfLWPmFR5WSG6LVj7BfjhlIbhOqP6A/jHkMiPcB+pDLVRyfHFOzRsxLDCCeb1KW+tGVmqxlKi+nfu83YwIkCgtcgj0jnZQrfp6Lnk5594OOYeP18UDkfrcInOG4gIjZMObKkfqKmo8LIzupbMzTVHBbcF8Nw7jcjJItbIU+4hkOzOqDGv6UvZPDfAQ6PMGAtyGpi5ukLJayREdgcLOkch0DM+hwLa6zCkbyWL+O0bdWv/LdASxrd9RgWF9U1l3e9UryKPKzSWWTVoqII4h4JtGQay6BRNZRl2x5axzruJ1MgrmcV+F2oJcuxH67jubfA4zoqOq7vac2TEuAh3Wth8wzv6ZkVr9r2hgo7GvSJdgAE4TEBhFayoIaozLQL+gydnX2mkZgyQ4UXiBLkDTR5RmXo5BctOMKm+IruKC25tw/QfDatzwxGTsno2A5OPt3rcw5QpK35byFbpkn4ZzpwTcM1qfCITR7nyihCkt5HXG8FECJi4VhzNLCHuRBlYxnoTdRKmxhThkESZYneHYaILMAiVw24DPe0TTg9UzVApInjPTUGncFXM7WsKgCg8Ka7OroSlJUgAJoZu50WAjsrQA3+ipnoK6G0nRGYemtoaWQIJmanh35rFbcG1BrPdXBK8u3onJszJ6IBK9SPApjw+N6YnLarUfYp2JtyW2mwmIJbCzzST4u0G5UWEc400Guvk/wLa4XMpcoptzMUlruqaKKpXS1Rykzg1lqjBxgk1Vob+2P0K6J2Ftdz7nHwbACL9u3OiJNxLaQwPSZM0xqp1X14Ih4yFJGzOTpD/AEesozVeb8s/+alipHY6s7yZKYXeU/nxeZ1iFLhVkrOcGLn1bLOgVH2ZxpNRhxSv8vDJCaMaIRFk8BHHkWrJXlE6veQats3gzTftYIyEiviVQy43PHSZ95ruyZq4eRazXwqxTiHaA7ON6szOvwyDVRM07fDwDpVu0BtNYL6VgpmrQHiBCZLnNFIB/iiTm7uS2qWBBRyfkK/rP1fYCXBBpEZBGQ80EaecV2fB2tdZvt06ckdnCiGZaHl9DsZ7I3/8JYso2XhCRBkYEUFdD3Yut6KlOkMyzQv3Yft+8WWI6gWHWbSzbHGuj8sgKKIlMsS7+tqM4mhmhg/t3WRns0SaoHi+BQ5nAIQCZq38moLHl4Coy2qI0CkSMYO1AziHQmgO4sOtHcW9xcyaikfAlQ8MPbwPPpKFi2exCzsgyVBdo8vUQe/E/XJxRbY8tCHJjGI/Y4r7byy3+Drcw5QHl8OzUFReSmNwuUxPC82LVXdFvoislwRDgCL4Q0qKW6zFDXD13AaK2xocS4Zh/KM6hBbakUrBRUOHx0+llAiPLNqhlkQkR5euzU2OJNHW7Ba2fsUF8AyQqQIgwTI3YN8VkhXDxIhbaeemfc6pmd1h9bnqJr+ynk2tSrtMiO6ZGEQSC7ufWA6ApwcePX0hD4dL2fHm5cLL+egsVCjJXhMnAUSeMq7AtIrLrYGAsKAoxS8Oiput8qqlRL8nsoBgPfzit4hfGafAxMRcSmI5nsOa2smC6ahVKA2fibAyEANJTAHJxJf3WGVTe2uHt4+CLyUt54O3PtSGnB6+U0pOCBy8ksJlSA+X4N5dlbRRCO5OdBiAOAMTSl5PY4WhNqlGQxMQ40xYEG8ZcQIEC+kD/6OtYrzGrh6x5wDtVTU2mDuaMUIxCIyWQxozZJEV6xmRFLBkfQqLYz1DEBtCODAkD11NTk1lG1+/XSwX830GQ6Hh9PhohZA6fOXswcvhmBkjBP3k1GTo1bcWoGpM6wFgbtUVoQFkBKQKVsFUogh9UPsVauGIvLkPAE/Ab8QHVP4vNE0a5WZ7iAhvpNh/ZCbdCoCuPpS/vy+IhdhuOYCBDKaun5a7kcFGVY/fzEdrbIBA+GcsEnLGXMC7qIZWeFdE8MtAKloiFTQQxpP+mhfC9dnPzqOfNT1zPuVBOZcj2uCWOuzcwn0XLqWgJ5LoxtUvZMpRiiiwXvh10H1yz17/Czdh216a19jcyRH0uIbmhav76+EyAboBOCA4HpkbD/Hmohsvsi0lIEZdkXdyRcAYkS1BKlW/xEIY+ozbJYZZkZZIqInsAfxAD05OWtnDQ8b+hleX1sAJRBt7wPdOURO3vZe6gh5Kr5Q+hIaIUcsD9aQXABzejwr72aZvtE9AETQuiY5FWSYuzG11FpFOWp0A3Xcrwt2XQEujL1M4CloDCPTIBQw4nG0htsRVRYIUmF81py+yzWm05O9n9HavRQ8HRXmhnsnWGCEB0E6HVHOGz1aVHJpYGpg6GB6NhSDGc4500gZ6LEeraKWgyXOteD5duDN/cLL/cQYpPIexVg4AYf5Hm0o5BTcyDC5OlM/4+AxPqrh1a3h+RYdTAfb0JsHIbg1DB+hVFsCIJIzR5b9ssEXUyS1HGgHo0RApDccwdc4MOdAr4CPgZeXk5+XlSlMdYiIXFAwsKqYqgGtDkZ7MpyLlEkZ8LFFIYBIE0WfjdLYGbYGELNoJMff4xpKPosZbreG2Up0mkQOcTwOqZOBK0htPYrGam0x04qAEwHSi7HyrI8BKzMiDxy+CKh6KsL15tm/plg0wSviGKkaIa0mdyY0tLlHdRe9ye50PGzSoPnOWA8ZBdhQDu6414KCjqdW8Xzj/bRDTUbYI6bVFmtBsKBKO7XIR5ypOTkgsl8XrmvEdHTuBzxmlMQcElX+pBlTCsBmPuOKBgmgrK9Svbs0ivhImyGkotlMwfbaMUnqIn3TH667G30RrtV3Zu8Ym3ougJ3612SYBWrpsn5XAG/mPa5PJajUvW0dn+P7+VTprScsyd/TnckRkBP2oJf10w0wAStFs27LIxq5CgX2HidKL4r7kqDEDJhbug/2sPzbRqQTmOml/Z6B1cHXEX1bFsdF67kqeFYUc6/MSewZ7/W5wHEWM9iyawT6rFLVFGmtV3zKkr08Dy6/N8xe2M2p6ql3Xr/ygvy6X59zgPKOVyIBqBUYZCcPkeskvBkm1Yngu7P8yv3x+nEG+xj5y0LEQuHZtyGUkQahyauRoE0UeB94mRPXZeGFRpMtnzELCBidBiBr2V3oPUKiwSFgYyhDu7ExmHeKY6sFdtAYcLgfq0t6H/AzWtsXwDFx9tUZVuFKNf6qxbBITwQHnEI88Xw0HLca3urE2UdwgJgGahGtunrH/eq4tYZbq3i6NTwdbJBWzfD23nFeI6t7AB7kW0wjfr4VPN9okN++nDB3Ap45UM3wfGt4CoPNyExnF9NiuVYtoj3CAeyoWgE/4K7+IIy61VpRaomZRzTaZpwX5HFAR2cfkFYKyjMblC0+gOd6MxjjqO3A062EV8J1bqXiVht5NWNGr4t1DQ1fVMXSjEGQRzvID5rKfUsx8XMVjZAhYlM2i/fErKLzjDL1RmM0g7+V3VBJJkVE8Xq4kWMOCMPPkIfn5xualSjBpXwoHcV0K4nGtUYEcM5I7eg+VypSyltKVXwAARZzTkIem6danCD2+amhNfXHIQDq7sQPcNRmePJKhTcdsw90avEgK4fsJz0QgICfKZJApT/GREZJ/AXAHYYTOek124GzkdXytyceTVha5aXY01qt2MjiOMl2LD2UGA2/si2w/P4ORuKjUtfJmVoeuEVDu/3d+fP89rp/Gc9VjSV5Wr+uLtqU1cVpUORm17/rzhB7I3ASac4NZXBlRUTfgdyu9/Uh4czl+9bv7mmcPWKiKJ2cCVsfnPuUPBOtqYDH5nxQf5etw8sG6OK9K22y/8zpvJjl2A51xtaeWHDBVE2H0HPq6RWHKLlEqauAiN54Aib9ZAb48mhFoFTP9EnHyx3wkWss8LZAmGeLis/6+pwDlMeXKnb2WQgyKqkinAPstBlJSooN28OfEiGXklzuyzpoPJVY5WRIVKsriN+CKI0toMGY7hj3gcss8/LaWnkyyluOSeWsDpmKMAzjKPrp9B6vzntmeW3NdvqaceEh6NVCDxrD/EeUkAI0xjU8pa7mVojw+qQxPYdjnJNt5edE7wSCHJZIoZ7uuF8sQTsrQc3ojrMxujWmh4cw0QqgzpK1Njw/HbgdBcUnzvPEeQ2cUT5cbKCY49YqjtYwHHi5d7y9XwQOx5HPbuH59H5lnxrE3ikiIRDgXFiMcc9qHN4P+RlMIa5BcaXMqLQqKXeAAKxAK4AxUFzv4cE+rx6pGt4PG/Pp39wvc0CDHnkf7Psi0ieABKtwFl4yt0cBb4VyMXuU7QaogTvJdkF8rWa4HRxaOKKyy7CUKicW17TS1Qw4ApQM3XfheILJ0G+pFRjOioY8mzygGWHxpZy5JzqIS+FN95XamUpnWShs4NVTxRffe4VXT42pUd3zmLiujjGUflA0xHKshBemJKeLYO2RAhwET6HAkc3nuP4Y0VkajMIBk+/JKApHBKSnm104sYdAQlJkZ5fXuoCGb1/L0q+fpsrx5dk/AKAHVaZBnap84zo6lv7StGa9TyDQw9Pa+QcCDHl5D62YHUtX9MGxwIlZdCaK9+58k3wmRxrDfAwBWIEUz8QY5UTcoXdQGm99rbVAnmPNUkpQb8HpmoBOQUYcQmZMpyPArWR2bvuxr+EOoiTbtukgrv8CdrIp4u1ULQry7Xk+pTPVvFH8qVIYld8CXPH3KsGG+5IVW7ZwJhhcbQWmexBnl0AlhQBLX/FyK5UUi4/fyOtrBqA42PqbA706RhSh7mskhUflIqWlP0hEL/lSvk15Rw3xgy8losOj0Hrej0cOU2E21yFgqI39RErWpJeosFCHp9XBVqosXoY0GupY2eMz5O12GTTrOGqUnBYqhluzLHHmfdJoZhdX+KbMKOzpRTuCl1BxdXYlRQ8uRyloTm6MbWtxtIJajyDPkjtx9Wu1v/eJVmngpEbMDHOOnOfCtM4ASgOjIh4D4irGnHjz0nG/XwAcz08Nr56fIufKg352YM6SC0gcFiH3ufopaI3HfMw/e/hcJQ4916Zk/xW3aG7WVwQtDemmgKl8mes++8CJ6Ooaey2AA1DZ+OiRJ15e1FCX3MFoRsPqNuwwnJ3g5KgagFhW6s/5JDV6u/QxAqAqWkYJyIGO2LxFo1cNp0zURnIqux7LwHHsvfsaCcA/8dyTgEWl4ZKRDaKkcs7QurPcmNwTKWo+33vPDV96/xlfeP2EW2tQyafKt+/nifs9JioHUJ9jYqbjQv5UbTU6ESvCGvs6I2LYCQhXUzkDcIFpnTvgJ4A73O9gFOUCwEmvcjDUKFIRhwQkvjlFWXGiz/BtnXay6wYOfAcxvFRSkGT45UgBKSsLLIXJjYim9ItD5NcdU727X1gfJI85gVQ+St7rDh7WLW7pnUdEReCUF9F7Vlon/5X3BUSLt9TdcN2M5w3IUZt5JoKkKpsab1sRpHV9ONZaPuzJ2lau3Xq2dYs7uFwRiW27dKU4b8tJeojwB3gwWEYoZvQiyeIHWTBDFPxwPUotqO7QFLUHvk6ufER6yoqsrE0N52IyI6CUn2gPIuDmQ5d3ZeXTvb5GAMpCchmWcwdDJfErOpRBPHWh+00Z6krJNHdVBnFzloe8DE4Jompu7tQdCQWHxKZSllBbTDOu2YDtAeBsylpXn0CGxxVBmV4wfGRX1toMbXrgHBry59sRVTpBpk0Px+kc+uql4T4xVk1gRHxm9OYlWbe1hpez436fEZEh/wOBnGuUCVYAr55veLodWwRiYHgBPBq5RSWI+h0oZXRdE93IsZgIXgAIhm6N3UHNgLf3Ey93DiN8PiqebxUFE/1kxAQlDlwQoMfwHIzHIX+szhiY6aFPZ3lpHz28uRW+NgHMWpZHieCwzK0KJJRINVLfGsu20HsPWVDX2Zllf30MjMloGkrB7JzHU7PLaSfQKkopbaXsQYIbs5Pn0izInIB50RFYaRSPMmp3SpYMU4COUiO6MCZaLQGABwCuVa2M6GRbd49qoojSzN4xHLgGnQYCZuAo8tKXMc70VhzP4Y5raC8iIuIEisWA51bwpfef8Fu+9Brvv7olR2oMggkzpq9aoQc6BvvLlOQ7rOccAJpXWNE55G1xxACikR2v28fIEnI2T2MExaF29owGysxJR+ymJ1DgprekUzxkTX+WQfCMMtl6rwNKBe0RkQy8pqYSANojEFggJW4pow/vGHW24ecPBYXWc+3/QOrUlLN87I23sq2BQNInTdhaC1UM5R25etfEcjw824r4KIq+QMmSuT0lkY8bTh7jYp4yGau63ZoAp3TBekb+8tpfC/2ibyjSqT3m2A39fhDkwTjH6jr+GHERgVufO009hdYtCLDmWIw4b3pWdfsmSNd7Y18jNQd4EsmTj5S6JjhKUckonSiwnSL6bkTvM7y+RgAKgBQqoFiFyo0lbOugrPCUQICMSeoQ8HcRihy2oicZbTELwiMjFAjlPGyrw7eVmwSChDXATSVEDr4CME1lwCt9lG3myZyiJzo9n8OiZHkdFhnF1WeRTd0Yx/bRARScroZqDPnVYvBW0dp2qK0wGtM7ps9IGUU55mTvlNtRskxaTZbIq6k4gux5HOQFnNeFl/uFawySakF+RG2cHNwjxVEKcNQGN6ZC+hzRL6ayO2vc29VHkH4nWuOQwBYdXs+r47x6dA0lmXFO4BwDV1/zh2614DgaDORGnBcnBp89Jmsk6tzyqg4AA9577A1CHtTDBEGcc7gXTOM+9lgzTUGuteCWpdxhMFMuY5p1jdApNTG7zhbg1m6oFmtjnL5sZigTsQ4ET+fJRm61lGiYFqoxbBY7ySoaw/2FcYK1wt9jTNyOlkp1Ipo/9ckurbaMuSO68iofDs3QCf5PMWiY2qPBQIajz+l4iVb7j/l8Gp9bK/jGr3+N/+0b38fXv//M57wGK7sAzD7hw1FvB1q7oRg7wQKshGpVvIe1n72zYqk9sR+KznjO09E5DEPCu7rD7AIHAK2oidI7mhOzgxDqec9AiYicsb3IaFW+4zGm8KjqieySh5IaCxtgQTq+1BMLVO+/t/DS7rFvKYC4uMlIyXDb9sGuD5N5XNHGHYxksAiq+xNeWVpXTpx+R03IHqbSC5BpW4z8jPA2U/6UEoc+xx8Ns74WgTza9j2suBxbs8d9yXuNCNkqv8Z60A0sCJCsNRDoc8JMiz3aQJcMv0jlRTxvrMKHPj2LGCxBguXARYHPMRkV9OhGbgHw2JV7q+jRViIIuwiOUa5jON4WZc+hHzXLS0AV7ijtNwYxPucA5fHw08OZy6NzekhzYoEVGYKHw6KyQ0vh9hTgEBbEIXdbfANjN805xDmJQ1ZibPf0LMuTep/Ba+9zwi8aLZGjakRR+hjZJCyVAWOOKLXi6Qh+hgQsRr+P4VFx4VlYNLqjgxUtaahQcNwiGhPGQ3035BEDSm2F4BdWUry8nFFhws6yrVUURKRpzvx+LRUOkmB7pwHpvaMWw9PthqNpoB4rMJ6OA/WIrrHRpG3ERGI1/TqjI+0IXoaDTeqebxVHcaiTXWsshQZ4rbMz0tWmox7RXXRynfv9IkAa6Qdn9wqkQgxvOL7WwZbcKWNUwXVhX5OZRD4v7PkBZzVHqwXmA+5UCqWwp22JS4orYpBiUVdXyz9WAO+Oa6hVu0VpN72bHrnpYvVhMmxWV/iEx6RlRKM6GaUafBNxl8aYuD3dQiHxOsDcSITLu55pxMQ7mSnfi/+D1W7Cwhw5wcnbPnB/UOI8m80MT0fFN37xGf/bN34B3/jBDc+HoRTyjd68vKAaidXiI7hPDnoMAK9RDSpm5TBFDo8cvaNXB1wGjcCQKayxuGoJPsQ1WURYxCokAtSqxNY5AhTot8Lt5T5HZOuhO8ejplPbg/WNhUNSMuJZFwHWciL36m2h+y353gUKKHSrUkWf549f43GP1su27yxdapNl3sBKS+iy4oOYQNS7z+/ra1UbIeTYSuzUA9jzBE37OVXwSsCELcwU0Y41iD1x+Iq27+8XiPPFzXMPfesWIFzX0J4EKAbQU0Ys2beK/KgkGIiI8hap0R2OiGhagAwPfV3hjIxs0bbt7rlmiory4anzDQkVRTUQQYjAKAaDZoTKYoTBok8IpDBCVBe5Xb/7G3h9zgHKejnYb2rMCbeJGSWOEyUE8bFsjARay6iLQv00SqF8ZzRPi52wOOw8B5tBDxcwCV9bfwADgnzoQUZXBQ7Jf9ZKdkMt4V0XJwFQHAcJLStSCmrxMHCe3ncOzzJGRI4MEZK02lpltKCS3EhmYMVEzaFuPRqpqclPtjo3yx4TPJQEacUKmjEKYHOyD0cg9ZfritB4h/DZUTn99ym6lQ4uFq87Hdd9BKF1sllQYUrJp+M+B/t1+DqUVFYTPgsQLdrl7aY5cUCTDY9KQvA1JqMp0cG2j1VGy54EI66B5VXuiuudMxeqG9Md1wjV4CBJ1IAW3p4MM6Jd9pg9epw03I4Ds4yQleX1H0fjtF8AVjXwjnyIEUqGMkgvqNWaoALRkt7Dq5x9AV93sGQ5IofUWZzDY2L/zx736TjPKxSlQSWKI7w6prYQRphpNM05Uj7a5C9vSh5S8OCsnJcxcc5wIEBjXgvTQu/dKr703hO+5ete4Qs34LkBTweBZi1MH1VNDNfyTzYYdGzevgNWS3T4DaXKY4/rPNGTCBg8ooiczCn9wpJiwx2GC+x/cgIZQWEURVCX0RQJCg1nAsZdkWMFInJ9Qs9kF1et4/7L8U9VWSRgCcu4DKQMiD98n+H6uI14o6ITiYLy7/1leS8CBOrnseGTMGA8E+a2Igib4RVY0IwsfeQWfI7v+/p33nCQbrVuvlI32PY8Hc61pfD92bb19GigOX17lNDnenIBoTmjqKBGb5hBgCnZqrmeSDnUXuh+i87KFMAMhohJbuUgMwUvoMBfWSR5AXl3RFk+9Yac34IY3xH7tBxwLGAHBPXAMor6sPuuyJsFkXiTMwCpCIDQE+/Kzad7fY0AFI//WtbYMz9PLsEMtJ8h2zASO18+YbXMjWmj1ph3onyABCIqX33fnfm4Gh6AlSWYLHvkNVbKR4dtEVZHRF0QhiyrDfLw8AdzDlyBrhUyD4BMIY1uogCy+2efDu8DHla+BWG3z4n7NSJUKGb7TOGtZrkOoQnhAO7XQO+O3j1z98Un7nDcr47zoufciuH92w1PR42BdTRg1+gkP44Rs2GkmBnuLKWhNsZ2e7/4eZp9EiemIKo19P6p6IeiD54VU3Aa9HsfOPvEPRqrjan0gQEpN0vRaatkcN8FJ/vJncFI271ZdxJoH1J9oeyKIebuUGlYklmRpLPqS6EKLI+YBSMyqxRptSWvBuR8JAhMbJ+fBmCKFGkoUdVjAXimO2qJUvJIJ805ccVsoRmyJU4PpzljVXstoXnwWtfSsRT58kjrzFX2WQ1o5nh1q/jg9Q1fjD/f9KXXeO+ZM0msVLTacL9O2X7amXAmjtaiQouRuGItOGOWFV4wEr/NEPJ8RtQkzvzg968hsu8JeAdwB9AJUnDBE6ywkofMKT3nZgRCt2CTL/081yt+vtvOSIakE7SjAAETYYNMNeg3XGmGJZOZOkFUNfkyQ2Yl9EYAC1sGOSO6WPeRT5JnSABgS6OYnjCiBUistEGveAa93xewXBGhWEcdxfDSdz6WSOG5pq7PwMNKpxuTeDH0j4yyZRIqH+9h3W1FpxhB8VxrQzS23O5fPDuLVAgvS2CjM2u55mttaYs0l2sH+gKsAexcQOcxCwADK9biOUQLAAQzw05s2FLgNYGnnqsYMhHkzAkk4NSaBcBi1/X/P0CJ1wZ3eRxT2LVxEiBPTSHvmD9Pw7Q3p7GlYMcI0lhZCt7Moh58vxMKdZ4pW4Qoj03VwV8BEv6jmKG2kjnwOHIEN6WQJxDNoDzaNrPaQGOx1RVz5jW0IgCiJwVz8XMSTFxjbFU16/AagFnE0F7PkpGbAsD4OcUNs9PIMM3EXiavnxpePR0xaJAA4e3LhXufoaRmcjlq4VyYGuW3wx0z0j1dKY7YK4Wsh/OaiOiAR862FESqCTFbZ+K82K9F3UgFWPGOon0AJ9rTBzdivXbPwrF8sv13x2TliNTwDFmtpknHTDHthsr1u7GPDsQEXU/P5rCIssXMHx8M9U43RqhcXBjnsL/CVvi85kS1SOVkOsqSY+TO++MxYBSSTdc4awhleb+SjZmVO2DkJBp3IZTfnB6k5eUpD3ecY+KaPBXFqJSej4L3niq++N6B3/LF1/jS+884CvD+qxuen2u0v+NFmBakLB3yZA0oqIwsRrQRAqzC2+t482y1Ch+Vqb8+oiMyS+3H0PgIgZMTjJZExU6SZUec7c39UYlmehr6owMVu77JiERodbNexkSRCxmxZVQ8U4aJcXxFpFJmBVI0nCe/t8AODDChRUWl9PM06IB0kYxZpgewgMZ+GCyv5ynje1PL6NW8nQBFLQNOmFKU+jl1qrpuSyaXL6Hfw+q6ao/35fEmtaNg/w8BWDk7Dw/9aJCBTGWImJocm9iIBbg875nvXxyZYCQvoxV3uAPqBI/iRgUIyU6+7gBmRFMKNDOLpegWjs5js73i2Dg4zvSPCWxI9/u2knw+DRdkN1xG8RPQPZrEePk7f//ar885QNkPOqBsuJaxmMHZ0woa/c0cGgCZA0kK4jBvzaoYBhOuZb4Sk557ldKI0IXAx2q6tQydZt7AsNIPcUjGdPgV4+Erh+xR4UaY3gCBrdZovMeYGAG0ktCn0fDm24FQuFMoO4RtDDg6oxeKKMXvF6gRHKtMunOaq46LPEPxPDRZmQYsGseVguOoeH468HQ0XGPi7f1CH4PltV3zeFgyrBknJYicIqzK658h/NVKRhGU2BfI4mFieqPPCXQnsTa8//s1cI1PAjYBrl2d/WrHKHXT/nNHKux3JRPvfNZDzw9EZG+S+FYdmVYS0GnRGXcIHQWpWmk2pllYDZWB1Uj1TWfKTsYmgXakGucIoxNkJXmAMlAlXHWmSVh5JG5U0Zwdd9jgTBv2axnBz0Io8E25uT0oP4RBvKZnWqcYcCuGVwfTOd/wxWd83fsHvv4LT3jv1RNGv1CLxwyqhtEnrn6PWS6csm2IDrwouObMwX9wYIyOyzgLqWOiQWMFWDKfsMAJeM7LcT+ZWqTz4Cj2AvhbAG/g6DB7AVM6EUGxDsNAiaoeAaNHB2rJ3m6o9b3wf0KHPcrXQ6rKsHnii+wfsVMoJRc7QEci1h0pyyXAx0yjIo6HlZm3qcik4KX0W7ZRkHybVCr1XQFHC6xneOcQxftUlvvuqRNgWG9a3cGZSl3VbDvvJsFL/FvnKkF1LJtAnCIEAitYy5pgROn21YwMYZjTmqTc83m3ddZ7ci3WjKoiRwCP60ReZPQVES8tSNwAss8XBA50fTBVXREt6kPGOG+M55YgJT7JAEMNh22tE+WvACU4cNsau2TVKkrYHKUQVxTeAftNTZLdXwsBO7ZDA+X4Vi5X/9VB5eYB8jYlI/JUgSjZ9DCW4MYJ6VcDiZB7/wxd1wHMsfKOtg4ivRtufh8DvRiuwaPKtIejxHyZAkcfF3yeULlZifb7CINFnktQPG2F8UjipAJSZ91HdbDQvyYdz8lqiDEtAUwtEeUI4b56x6WoT4y35yBE/s55v/DmzZ09TEKZSrmslst83lIK6tEwxmTZ8JyrlHfOyLF7GB/D1Xu05w+g4iBD3ZfS0X1z+N0OX7f0nrw/LK8mvp2vNPJaqlg4fX//+SdskEKpFimauYXMwXD0GIj00JJlsygljyZwovPRCPH+iikKt3LhYyhaAEY54vlaq3A4m5dNpGK/ekernFcznX1Cyqb44IBvixH4Jzy4EWXSJFEXN95T0X74A4hbwFInEziVSgJ5U+8/NXz9B0/4LR+8xtd/8RU+eNXw6mY4mmOWCnhJPpR7lKT3E0cF4AW9d5y9o7UD6q5JY8vnuK4r9qJEhR+V9nld5A8Eb6XPVSLNzJdA+oCBIGSlciJ6YqrmWJEref5bsGIJygM4ybhSyJRBXuoDQJEw+apykVFC6Djxs/bqxOVJb0Y4hFd740lAXYZXH6cJzjLGWzVt3pkAFfLvqG70zcGRgRYYN4nqHglaQCfBQxwyRk9mypa+L/2/1uhhxVaaHwI9y0lIYC6gFCUuBNh8UOkurhfTSkX3iqVHNWkbsgGGNNwIIL1+vmSDvUQCKEJ9saK/li8CbcEiGRss+2mJN5kcrJAPtrePVO8UCNv6v0jujFHpUpn2HwE8S9AWdoDmhqw4LEbnMjTosp8RLbRdeD/D62sEoASyt6DDRrjVAxXuCL8UyxCZDpi8PMtoCf+qRhSq93q6Ep4plcyRTsQcnXVyfbvWOhwb6tYhiEeYwyKVsRTANUIR2vJsajEcwT0QQbIAqOZg1D46eSLmqLjTshTALYgau0WNk67yyz1fqlAf/717APyhBQemO+DRnl/G2FxlprFOSnFJkRjinoB7n3h73km4hCdh2cagwRuTXVSd4fbzHGks9QiaPyTj36O6SR+mxke53/H8qT/0q+8gEEoFQY2eP9R9LqE4P9sHbqFwKYGQv/ieS3JDODKEv92Y2m4njwCIWTH8vapwfvEg0QE+JJcF7n2TNRpmq4jeK8iOucOVkjRccy5Fa4wiqmqgD54vPV9TxC/LzkKYt2d8jButNSFXhevXDPjCU8M3fukVvukb3sM3fPAaDZOguNYo4aZhKqXkXKneJ5jY6lTuseh9EDyoSs1if+ZkifpRHI6C2ScV/ASjJecVzQ6N5f8pKxcKLhjeoNhbAC8wGyi4AxiAsb09LObyBIVwYvEh5IlrKWSz9m9aSsE6R1q1PH+wmP20VUcJT8o7kl5KhyqM1HaeU3jzEKXAJkC1iP6Wsn4vy6QhMBHlpZuBlrwj0n0WcrBHGzxAgw6UJ+DaIh4C2yGH6rCcejevhNSle58wHeWxyaNebBevHj0l9xp6rgAoBGdlcfFCP5eHtVw6Y60ub4COiMBdARCt492SFB/bCc+ts+zQqtXjDwTeVgbAXOd3wr2kg0jQtqLQ0kOpf4zgZgekWkcTsBwWeiYGA8o2GKM2rSwOkwWBl3qSV6pm21p8+lf5tX/l8fXzP//z+FN/6k/hG77hG/Dq1Sv8zt/5O/Fv/+2/XVvijr/6V/8qvvVbvxWvXr3C937v9+Lnfu7nHq7xS7/0S/j+7/9+fPDBB/jSl76EP/2n/zQ++uijz/QA+8sSE6/OmezbsPgcZmJY634RghEbbmyEVSzKaKvIiWGwiwXpMhQCeHAGVCGU4DkiJMsoq8JEobTkmYRhkNWpteK4HTiOhnawWRmskJkdpND72fFyv3BeVMxHBd57bvjg/Sd84b1nPD0d2Z2WOUytj7yrqH4oLPZgc7Wo0JjRRt0UDeGhE1eiD/YNmEEyvfrA2Xukb/jnGpOgJZ5pNe0SSZmKbbjj3gdezgv3qwNOg6TqGnaSp9D3CdyDR0KvuAKa3hwM+NYqamsPE33lUTyQX4UEApAtqLFAgoCJAuXNEIQ4ysatFjy1ilstaMaft8qeLEcruLXKA2zIqpeUgzyvm3EJ6VUZO+VjReUUfeoqi46uslKwFpEPLwtAST6TpF2YRqtV93qgWAm5Gjjj2n0ahnPNe7SN97mGjqXHuz1POGkLmD+cTaQnqGiWsi/VDO89NXzpvRu+/gs3fP37B7743oHnJ5Zjn+eJl5c7e9sY91u9bs5r4uWaOK+QS/WL2FCnGlvVwtEImn11nRfevpx48/aONy8db186vvr2wlffnHj7ciV4CwgHYMAsKnMQU7C3P/QqxFXZdJDkLv5kdcT2J0F9LJTOrb4WGKllkS+rrU6e2vv5SUl+kLPVr0IDEbGiLToPoOOxp5O0jgIQ+x9xEaTnHJ4zWQWi08BlVDofiynHuXTiCB8qo615C55PlmdGDo+ADNY1cgI2lOLQz7bqx1invQJoC9/EyI+SspTpMbMggAo0rIi1lRJrHOnWSbkUaJziNO33E63iSdgu6yypB1NRS3uPZyT4dkMUZPAZ1GG8uwcvZ9OBkoQNvM5dRrGWceepiBZhcVa1n+UdeVhSp27pdTlun/H1qSIov/zLv4zv+Z7vwR/4A38A/+Jf/At84zd+I37u534OX/d1X5e/87f+1t/Cj//4j+Mf/IN/gO/4ju/AX/krfwV/6A/9IfyH//Af8Pz8DAD4/u//fvy3//bf8C//5b/EdV34P//P/xM/+IM/iH/8j//xp7z9hAJYzOcJx8D0HukJy/4nANKL2RvKyKPWix4K0jCjOBusyRPGO4cMG8ESm0ooS/j50bahVAFiW1KhdtjwqIJY950M/Ckm/1Jiz7caE30PTBhezh4kv5HGWAZSllC6p+Q9K12wvLfl8SCbbhE9LwPl8Kg8mg/PSMAVXmscltwCaM2AGQPsEM3g1I9levB8wlveDxtBJquJRkTCpKSZztrz1stT24Ib29+e4EBAxGwhdykG+WsZXjWm/Y7WYNirZOxhz7xWXD0iD/GZ9GYnFJFRebOlJLM8z+aSKd06+xjM5FdRbKJKyA3dZ8rpwPLYr6tnS3skEI1Ktym+054uCjM8qUy1F5r0O6ZIzssordCa5bOK3+NYRGuV+Ct9elTgC69v+OC9G14/Fby6VRyVEc2Jit47Xs4rOE8FvQ+83C+W/XrBy72nJ879GTkJOopog8g32dSuVnaGnexYfHZyYfrF8QpsXMceOwUIMumJYh+j2FuYvwUicuJBlmUVT0Rx0kkSWAm94SvtmGdkF8eMCgRE2PgH0llpNFIyloxvJgjZbtzkREk66RjsulNGcDNn8Zsl7z/fLQW2f/LmAOwPJN2B1BU78X8BnpRx171ITvgf/WzpWjl/8S3t/fZUyZkJsfT98+N81KhIhFlWAq0PzwsvsfaVIklicKSes1Y7n433KgBC+VSqJPq+2Iqo5mfRm4mrJJsoZw+lnAcYlZzUaDbp8ZmM+jASvY3QySocTWif66Mf9zXWmtO9o6+XSpxDlqX/9z2Fr73SeJVPuiu//tenAih/82/+TXz7t387/v7f//v5ve/4ju9Yz+SOH/uxH8Nf/st/GX/0j/5RAMA//If/EN/8zd+Mf/bP/hm+7/u+D//xP/5H/NRP/RR+9md/Ft/1Xd8FAPg7f+fv4I/8kT+Cv/23/za+7du+7ROfe7/fcb/f8+sPP/zwnd9YByMiW/D8n4AIT4tvSl9GOOdlYCkLd8uyK4YwSbibCYSwCGLaXH22I7kY3EuLDdPXiRMekHmBh0BrCBVvsqgaJYxLKVF2FpyQozbUeqC74X4xsjKmnjGeH74tk1DuYyQpcUWsggCD7snMMT28tgSGq2W/DNKuNhUNSCyW2xUHEoHWY2QAMDDHqt2fk11v1RxuGVjPpnxSrqz6mWE0HxtebcuZsiLlCqriTHHJ61MEqcYgxxEkUcnOUQxHiw6RvrylLh6IPmebeySeDpKwa1sYfzMwQEQYHkl5zJ87UIwkOEfOXkIMaFRFAyBPJ1R2pEcYXnZgao0Euphvcyd52qLfCS9dUsEbLFvP01hMiE6ZUbrtSCDOIDt7Wl7H3XEU4PWt4Quvb3j/1YHnW8MRDfvIp2JIXN1u7+eFMQ0j0qHTHdc14LDsoAsH5SBBEQ+d8vS1RvUbHGcf+PjlyiaA6vZcbKJ4B0r0KLIJmCp1oteJM6oCKKWszhB0BGTUc67Rtp/71FrN3kq+iPsCI1HGs/zTpaOWGHvqkYxmyDgLVKhS5h1LRPwoGdNlVvpTXCXe996VdU+XArsT9oiVBMyWbOva2c0XK/0taZReyPO7FOYCItJroI5dAMVSjwlcyRHd/NRcjYykBhEVjtyzfK6yogUsQ58p12shEbppkdblXJVwetgczaDSKvW+WhnSeIICKBvvkcJnSlvg34DNGUlnGIqAycG2mClEsKCeMTWiO2sNF19tyYijxAgItuzwPNQ+aDCyI+9axkz3mpelbz/j61PFX/75P//n+K7v+i78iT/xJ/BN3/RN+M7v/E78vb/39/Ln//W//lf8wi/8Ar73e783v/fFL34R3/3d342f/umfBgD89E//NL70pS8lOAGA7/3e70UpBT/zMz/zK37u3/gbfwNf/OIX88+3f/u3x0/WSSCSk5dKY8o+EWEAy3YYJssuhVRlrIUMNe/h6iTLOZBt2IWMBRbmnGG4xNkQlwPRgI1/BE4A6RMaGs6G8Qj7bRUQcc+tVdxuB1preUBXlILkyrfXwJc/vuP//uWP8JUP32CMiadbw9PtiGGBVLLspKkQP+fDtFZi8q84FPz8Wi3a+DNdodTF89Fwa6uKYzobUalLZyvkwhQgh7MVMxy14FD32braItfiOCqbuLVA+63wj09/3IMADuas2ICrvTSCLDk2bpFCzpvyW05lAklGQkrcX8GtFTzd2JH09a3h9a3i/aeG958PfOHVDR+8fsIXXt3whVc3vPd84KkaDvOYA8SS6veeb3h1O/B0FFRztMK2+nw+8juUOpP8LvC2AImIvu6SUc2TkTLgP4YD3QlomKe3SMVFlGBwNPs1J97cL3z89sKb+8BLZ2M0MiY42uAcE90NZ3e8nEzVoVQMIzfl7b3jzcsZYwGWAlYJ9AoL7wZVZptfKY9dC/D+U8U3fOEJH7wmSPnCe69waxw/wEoalvuW2mCl4OwDHGfUABiu68KcwLgGZmevllpJtr4iksioY/SMmZQdD8GYA7guRlL2ijY4o4XyHN1PTP8q5vwY09/C8QJ2/TnhfsG9E7yY+CczvMwFSFRGq15FGkuwp21WGmeVCxfsJFJBys3SQv8UGNnxgRqGFRSr+WySocfXHr1YDg3lcgGPB3Di27tt+cvbHebvypkopaCVup7R8JCm8D0aq/95Sb2pw6y0Asea8M8C+oIqoQfmFv2EjLNH8cDi6QjFFAHLLdo3wzq36L8jcOFAcLS4vpnGfWd/tFglmoIqXWpb51b4So8ho3CIaDllOGF/7KkH6V2pVzmuOR8t9FzR9PTJrrOt1JWmSWdDJsCSf+ShfDnRfH2+HH85HKlr9cwLh33m16eKoPyX//Jf8Hf/7t/FD//wD+Mv/aW/hJ/92Z/Fn/tzfw632w0/8AM/gF/4hV8AAHzzN3/zw/u++Zu/OX/2C7/wC/imb/qmx5toDV//9V+fv/Pu60d+5Efwwz/8w/n1hx9+GCDFtz/YPJiVEhn8wfKm5szDJ4HXYU0Ska8DJmCs69GMxwZU5sO1q1TMtg5/zICRoVyzEVa+8l3v2UG0zUFvymNu3kl4zXNIiEe2L6azzju8LrZAL9FB0M1RqqGgrmjRFl5chF2FxDnTRt68YXF3puuwb91zDdnS22Ds+xFVHQJqZpZcCs8rx+GDwayiFcfwkcMQSyk4jNUp2hVFexgNKEkm2ztKPoQdt9fugZrxvo6jkmDsnh1Jj1ajlJxM+gSddWF6ebkjq4foNx2NHXP7GDgv9mgBVnXXGLl5AJbHsZQuUunBxVtgRY+BeeBWOYFaaQRLZbpKhNXB2AzZJ0RlhNUKZgGRDWYYNnaQHeEdFgNKjHY4+wiFvsLbUtB7mi0BSISkM/IT8t/BL4oZ3nu+4Ru+8ISvC87JB+8/4fXrGwycAQVHNvErpYWmrTivC2bRKyemOjuYZut9ea4n2/rCDjUvVMqKDQJ7J0kyS7KN4L1U4zyU6QQefgJgWsdxwf2C+UVHCD12a8bXvhwRAPAV4jZ4jL4ITkZ6+eIQhTdr4n1x/aYIqpvHbpsg75wf6ayHslMhxgQXmz7xx1OiiMq6lqLLspqeBindZqzUtNARM8GbnqV0I9PGknNFKKQ/pd/A0RG23UPeuYtfKNbh43rvSan1yUt/ZurctyqTBCV1WzOEfonIypiw4gGWI6LqTsfDLFu8q/Eh9XHo1Twnqxw3Cbih//S9pbMXD2jPBmhBCXrLGh0QB23OFanc5cYMyQlU+XIJwKLiDpGSBRav0F1q2MhhsjPXsgjADU4NxyxZ3YTJTuJKnX+W16cCKHNOfNd3fRd+9Ed/FADwnd/5nfj3//7f4yd+4ifwAz/wA5/xFn7t19PTE56env4XvxGa33goShwKGAmmNZTqFMoEg7KLwAbErsT7+LWaOqnLng5nHEkAn/RC0ntcWimNqoDSg2tiKVcUwUKSrCI/qSw2QCU+hhmCG4D0ADLtE95ZnxyQp88bJNMgsE2GIpmtX88HD8Chpj+VBEMClInboSnEbLte4jAvBbDAWI2oiYElbGrkJq/Airw7lg9jWqYUHIAPR2tBFgZzm7WS3Dld5dj0sERElXqSHpeyVr8Fjg1YE2xLgM8K4DgYRdkjYe4AxxzNVDg2De04cLQak4gnuT/XSGKvPkc54znZblpKe5HTHPL4VmWYp3FxrKF7VYbWQjWr94lSjtJXxbKyiR4R+TLubByHueSfQGxxuYqtzrTXxZb3tZaYcrwilD2UtLvzmthTAss461l5/8BTK3jvqeCD9w588N6BV0/spzA7u76O4GDNyUZ9tTYcjaRoi462fcwEMDIMc1DpEoB0KuMAW0flPo4xSLDtkz15pjhSA9MZRaulcln9hPuHcP8IAimwE44LhomJDra+v6IfS5Qbh5zpfDNiJghtUgb5ewbetwoJ5RAYbKWesKp2RJjMM4KlHxSB1drP2A/pEJ2L/K/lf/KaGaWdmyJD3nbuKfwdcIIgtk5H1Wwa3RcUhV6cmB3ASF9AgFwGMAEJf0sR0aGO1xvQk64VcTajV6mX5nYvoahjzcTXEAzTDCwztnQoFRnlypllljsd0aAa/aD4XZbulkj9zujIrLRxpE032sFydgsyVagozWZrlCKUE4cHUnY8g610DZ8nKnXmhMXQVm2tiMqchRTyaZb352OgivMQ9rKEkzKz0ghb1Q6WnfkNvD4VQPnWb/1W/I7f8Tsevvfbf/tvxz/5J/8EAPAt3/ItAIBf/MVfxLd+67fm7/ziL/4iftfv+l35O//9v//3h2v03vFLv/RL+f5f/0tHEzBzTtetFe41cnoRlnKNTacz20sJYQnGuC8PUFilKuQYygEeuUMh5DSC785b8JR5KWlFQeaGfoHNebEFWmgIeRhXudgCRCrjXYafnuEcq0nVoSm5Y8BjGjBKCcJSEO5WHBEzDAKGiKr0znmdFZlZ4diW3rl7AZxrvDPBsd2fCdn7RCvAcasoee8EFldUJwEe06F1Xw5UoNVGou5QhZH2lI3MFJlh+/VY1jjmZlL2YP+CgkxbmbPB2AylriiXT49OuQFqq6HmaIOJEeOMeh+pgCRPV6SfEKWLCS4jUuLYRzKs9fqkdAd42IEqnIPXFHozREn6Cotju7bALFMIYShBcLnPqbGUQcdR6sPeKHLyaMJ45rS27jH23TagFb8tbzeOFg4zvL4VfPD6wAevD7z/+glHpbf1sqXuai2oFTgvNVKrQEyHnkHeHZ3TmGv0x1H55BgEiyT6Gs9v43maY+J+TbycA/fh6JIdn/Ae3XgP4Kgg8LAPAfsI7m8Ae4FZ5x+uIkGrT1jwVMw0iydSB0CkPvkdRWrLr7TnodQ1GXuv+kpjm0Z/c362l+RcBlt6ZROsvN4eQVi8lfV764qewDOVpC5skjvbdCP3nF1K+b3FH1v3k2dA58K3KidTmlYxEUtAIs2fnv/+tPHDjPKUKXsLVSrtFn8BrhWdXdo95NZVagsC/Ji3ls/AAxE6hw3SdGXtGcJBmw4sFJGqIoCBbeusSLUKGNbn5bl3Rr1XJoA9s8RrYRp5pc4sHEGCIoJAtYKAi6KgVvUzZK4GUNI6CQ5iASpbMiFNtYDvZ399KoDyPd/zPfhP/+k/PXzvP//n/4zf+lt/KwASZr/lW74F/+pf/asEJB9++CF+5md+Bn/2z/5ZAMDv/b2/F1/+8pfx7/7dv8Pv/t2/GwDwr//1v8acE9/93d/9GR+Dm3hrBniBgSDFvaKUFgTHiis8pquTI3ElQt2gToTqpEC8gEY4PmdKYbPTWAq90Kimt6ahdqweAtsgpfiouOzSHiJ/eih+pAKi8FZbQAEmbkxJK+hO4l8frIxplZUXGiJlhYeFE2sR3mo8T5AM1bjr+XakYPN+I4+uzwUPBJxVKhVy/4Sug7blHqk2R60FT7cbAE8EzrJZVj9kXw9fa01eQYGPnvcx3TPEOn1XbLGHWMTcuKvM4zM/S4MtL3FXdlefGSZly/yJ29GCXMmGfFeAI/cZfTmC6yHPylmGKoMpnbSTynZwQhAiAOP5nApvU6FG4NhI4KaO9Ae1sEjPG58ifp7PHrLaCxWTuFqD7X0jUrJGRdxuDVVy7eSOSA6OVtBc+zG351tEXz1VNcOtFLxqBe89keNzFHYIRgDFHp1xDVil8qXgPAd6P9GyVX9wtyZ7/RiiT4uT4Erh5s9GH9Gmn8TyOYGzO+7dcQ7OqZLCBzy4UwPTJlq7UMpbGF4Av4Ot7YMcG/JT4vzSMPDcmc2MgtZSmK4w5NoY1ISNHugqP56ZpnXnnoxIEy2H3x/kQtVgFkpFfJfH3Vd0VM5UGMhwwjKisd6QnAt/0FFbqiF1AqJgz6Bq6/jpiq6BQC2r694BWDPufY8CIM7kOi/xBGbZ6yQjBMJNui+sqIo+U+Mxljuoa/LN2UfJVwpc4Dv1Cd4FkYu/wQiFZCGATESD1areUbJR5tR1LaIlFs/jsYhEmvyE4DB6yL5FJNEMmeKGLU6J+u8sWgGRHZ9rr4BUYYanQ6cokyKee9o5/yFwhQB/WmsBXSha/Rt7fSqA8hf+wl/A7/t9vw8/+qM/ij/5J/8k/s2/+Tf4yZ/8SfzkT/5kLILhz//5P4+//tf/On7bb/ttWWb8bd/2bfhjf+yPAWDE5Q//4T+MP/Nn/gx+4id+Atd14Yd+6Ifwfd/3fb9iBc//+rVQKleLnkwpM4xUdLgMLofZzD8EG9ycEpBcB2AXah6+8A4SfKQ2WHfhntbQY8N04NRuOw912Y/IUibZrtlk+C3UAWCBdq0qLxkHO+rEdM/nGBGBsRjOV/JgehBap4NdM7PnyYSVggPKS+7CFYMGp+dBmwF8mjh3c5KIW2P2SXhUjDRc2RTMnYTcp1vDdZ6AewxSJEv9qBWzkA+gen62VgdygFt6GeEtGHLMgO5d54dpiiCXBSgxeJIQawFaaXCLpk2TlRzXcNx75FkdKDbQ6mSKASSbqtcL8UXJahJVYJjRa1YUTnKySiMX2XkpAP0dXkmgDdv+Y4bowrs8xIxkSA6pi9LjBhYp96iGpxv5G31wlEGJSp/zjBLyUun1jkjdGcnUEHAvlM0xR4R415yqPiZ6PKfC8gbuw60UvDoKvvDqwHvPTzhqRe8TLy8Xno6GGZ5dMcN1ctr0cTTAaqTO7gTNRuDB1vsO88JUT5GxjjNfG7xfuHpHn45rUAbcgXM4e/X0VQlWakH1MGR+Yfb/CbePAPslmL1FsRcAd7ARW4BltgBDifb2mseTsqfUToJ8HXvpEM/ur9OXEyAN8VjWu5EvfckR4vdYnbX2XZwk37x9UyRRpkNyhDVbZTltS0fpOgv48JSVUpAzlywMHAkUkPQSxMW7ElQJoGBFIKiJkNE4hTPMkmcHi0iD5Y+gBm5Z+utypIiC9s/wTc9DRjjOis7Ziiau/UCuK/UYHDl/Bw/n1jN1pDEVrHivOQAWQKZftJqUe6WJIs4dZ0/3l59mSikhwc2c274aUKxm2bHldbSmSochwaPgq77WsM9HOLc+f6v5WN9H0MPlNJVPAuVP+/pUAOX3/J7fg3/6T/8pfuRHfgR/7a/9NXzHd3wHfuzHfgzf//3fn7/zF//iX8THH3+MH/zBH8SXv/xl/P7f//vxUz/1U9kDBQD+0T/6R/ihH/oh/ME/+AdRSsEf/+N/HD/+4z/+G3gMIVIqKsOAlQ4L8h8nD/PQlWK4sS92eC4x2npahiKVCB4bIEnBx/J4JNBsCrTl2+LkrPAaMqeq6hx+XwxtvrNgKZ/pk30sgr9R4iAKLEGhSlgabzZQM6CUqNphlKSr/X0c/mv01SoajtZWiaZYJAZfDdsAXJGHRGVjN3k+xUCSabTIvy41IxtJBq1HY4je+DmqeiqNERorMYOoT3R1Iwy9bOFB96Hhh9j2siQxDfDU+TpY1Ug2vrW4vq+UxCIpW7a2dgc6ZnQS9ZWGgQMn10JREBkUreJ+jm1y/U6b6TUvsBC/I8NlloBEikM9cJbxWeV6juVp78pDM5T2UK8HSCy2RY0CsBsAm+yUrHvMjq2lAD0GHBqSiPp8O5h+C9Dt0QiFzp5DoH/4SrM1Ywv7oxiej4L3Xx14//nAe68O3J4Ozkk6e1QIGCt0wCjn25c7breJUg/uzRjogyTv+9VZXuwshXRnxZgaexWbjBy2hj47zjFwirIDnlm2sY/qu8LQfE1QCxS7w/0tfL7AyoliAxyOOeCzB/dhoJoDNpMLJp1Dx4Chcoe4AlTjcYqXfG0AVM0gDTJkq7NvEpW1b9DaWxon+Ha9OPnpRGEZuCSTp+cU/CYX+X+3QpvDFoDBtZihywQGsC63vHz37edy5raS3Th/y3CuCE6Ccv1sygHddGXoKg9ZfEhbbmA/y53nAieaCs5IsW/VKx7tFRbwc6zlrUaOn0ijZpbOoJahzxiCWZZcuUf6HSuaRRkoCyhpr5gXTJ6VQfZj7YuiPIg9TYd3siJIvBnYXmEUe1AKbM7URVYFKwnCshePK3W3yfj2OezzLPpArMXWrO6zvsw/Edv7f/7rww8/xBe/+EX8H//Hn0VrTwAq5qj4xf/y87jOBvffArNXMHwDoKhAHAilFdwn7ieHyN07vbUenqAiIMyjPuZ/gSXkMggZeoz7ExHMwKqPVsn/6DN6qbiIZTFcUCBlM3ICUk8Hh/IZDM4xsZigcj0jNeJBdLDkDayohMANsDxzXn+VFyssWQ14imZYcM6/uS6ys2tlh1bAcN5PmE08v3rC7XYAzkFsow9cncCEJdINe1vyUgy3SqBQWwVKwf1+cZ6LFeZCB1Mll9rmh4c+Y91GVA81jinGdY0MVcIsIk+eHuytVT5T3Vjv7jQysc/Tg7zrBCZnnwmIPFZu/RfYlnG9Yu9s+xK2/14YHwtQIJCA2Hv3DBFLr65Q94q0rKmlu1cYlV9hZOXB9cmeLCqjbhlFsjTMmpsz3PPnjO4UylknmLwdB4nDIFdEnvwV52XMSC1Oz2GZ6rZ7yxJu4IP3nvHqqfGzKiMxs3e8fvWM29EwRkfvHX04zvsdtRbcnm4Yw3F/+4KjNTgK3ryceHs/UWvF08Fp2ZdKzcMRqQF0rzFx7zNkc2s9D8v/VsT0azM0M9TyBrX8J5h9CMPPo5aOVkakjybYWt8BDA4vLGDFWvxhxIqh9zHHQ6QMWKmfldJYeqfVklHWOT2dH8t9X7eviETZ9JNjDZqTvtL1S3DRsomcAZoQUEO+CJIJgkNCkem9yjTCPqeM9xGA2R7TP8UWj0Tl5ZS7jdQ+xSBJVyMjOPT4Nz6ODLUMORRtmQm6LB3Bsq6BlXol0Nj2vhABySFTagfhWMoRa61Fs+AZETqOTbg2sFELInLNdeys/cet1QAnW7Qo1i+jFQLGXDzug3SNI6skETYJwpBa4+lrM+O6ZhbFDau8/9L08uAhlrinbDVpKxrTaoUh+F5x7RKAS/w+NdOL1qJpzwyAtYbyv//vsBi9ApBz+n/9X/8vfOUrX8EHH3yA/9Xra2QWD8KrutCvRVRbvTQXWfWoCv8VHFVhaGBiMPpivkKMYfSyu+l85JDoRJnVhWoFPuK63kXQWuSnEoIo10CoHHi8/BgTL+6wk9EHRTfY2yJSL9j6gwDwMfJ72bQqnkVroAZs1QA1+wEcpQWLxAArrDyxAjy1Gz3RMXDe72it4vXr1zhaw3VdeLnft1I3VSExPyvAQcNQ0COq1fvE/bpwXcE5KNFzAlIyXN4ZVRpdQK6o1Hmm8lauVSFTC3CidvNspqazTaWsjqh9sNfKOQhMrgAqitL8+gXw4a8HT3L/pekGK44qLzL2NDL7jGQUejLeZxBgIxUZv63qJRqDGe+1UFie4EXlv2oUVWuJ0QSdPRCCunQFoXSUkorUirzXUFUGzADIO8gtY+AKhf3q+QiDyY6tz7cDRzUUkPh8Owqej4oj+C3sR8IIzUcffYxXzzccR8v5Ugkm7OKzueHlzsblL9fE2YEGRykTZny2M4BlQ+Ek88GIGG87uuPq3GIzCgKPBpQyUYqjlI4S04ktmrMxKssLuK1IqkW0TFwDyW/+XEd7c0IU8TBECsSUikUO/XRHELtLvlnD9wzyjAVMkMaBwY8lC6pGMVh+PX1muSmgiIokNUC5b1GT6DLtAUSWlYwPXxX4APDQyPIRUAM2Zv6bhjgikqknkUZaZ1ZnBbDoT0OCKEwVUlwiRTvMyIHSKavGH+qaAjLrTVGEEJE2OrMRnXM6LZku15k1ZJoz13DOjJKnno8lW2qSzsCcscC5Ph4tGtZaBU+Wez1nPBDBl6IkS7AiRRg9Ufi1NEekc92j/cUkl6XYg2zs+kqtE/RzkZcBywZwm8mCEO9KRQ78r+pvf63X5xyg6GRQosbs6O4wP2FGb28JhaOGp9604GWmp9XMMIuUvHSIZUkYQ5GbAZLHk9pocQqAJYzuwNnVqnqBiRah9GFiUc+8hkDFGB5NqRSG5OZ3KV0hWRFnofsJYfU9l7yUH2V8krjKy4f3XOEwXJ0k3TEGvUAzXH2gXx21Frz33iuYMfpxXRdBe3hr2QLfSTa9Xx19jGxQdU3gGp2k0muuUOBQN1Tf2upLwfKCIt7Ss5wLBCTi57Fl1IQGUVyTEtM6rVRyYgrX+M1Lx5gX7n3gHAF4pHT/v/YK7yo4Myq5zsWPv2pUA033zNmbtG58PYeD488Xqe4IeYIqq0zVI5SFOR3DPIjTYRSk68ryzMcY6CHviAZ7bSNgz9Gp+ArLZWdEbo7K4ZR8Lio99oJhMzJ5jrcWoCi80tF7VBIZho8wqHtFVux9jowvlKcZDeSMbCkNxOyZ5qTMMpfu5KtMj0Z23PeCFZKvRXOWNLH7LVr9GLX8Esy/CsPHYBkx77HYKusUI9QUNYm044qsenYhDfUSoXALuTXyUzb04hJ6nePtlWkaLEMMLBBL9WHYRVjgAFgR35lpX+T+TADJL877z09L/ZHASVb3V7jHBPhm5PjxChAXLvmoWBo8MobbhX6lY2jptWuECXVrWcBPztqMahus6pTlPK7n2wGR5EIfXovBrcCH1kgRac8mbxl5ygiNjvVGMpXDCkYnHYCPmW0eVrTIQr58AYKITlG3i2W31oJv8+wOq3ST9kGVj7IjtVQs+UVU7QhHRe+lpADYgyzIxiXQpFSlPktgnuv6G1OkXyMAJb4ybTR5KBKQrJ6Ao5SBKrIcNiUShjIDVftpcc2F4RXdBYmiQsdXJKPkgWUrcipfbfYMYiY7tQJgm4E4rTKyqVgIm1PgNORqD/sn8nbfcn4aQOVB9uWJGYNeejXAo1+KqSKjsPzsHBNX71GRQWJonyd80Bi99/oZwx1v37zJfh+1tSAudhraUjDdcL8m3t6Zo701liJf18D9vCJSsLHb45lyqKBIyrEiHHJXInoCeClZqqfqC6Z06KW/99xwa2U9HxapzMHQ63UN3K+Otyd7Ygyt/ac9U5/U0Y8/A9JTr8XwdLTYowAa2nuiigCRnu+Tpy3+wZDMgvunLqTTmJjUG6s8s+BBXJdneTcjLIMdPWvB4Rpgthmv8AJreHTKwQt4s/8HcERXzWKeQPeozEpjAqXWmEI94L64Xj4iyhMdMq8J+DkSVF1BLq8iPJjhmmwe5ViluGNO9OjpM5z9aiibCCWPLO9UpAlw5EDQYgQoxchdKW/4x07ALxTjkEC2tl8RUmkBReweR0csgjAgsqyI9mGUtQ7pVnueZ4Fu6J6BByMRpgQANmMb5iLkw1E2sLEBEZHKIxrJ+S/ICMcDSvBc+jTci5wtwxz3sQGTGaRWBSiUciru2/DWkOs05+8+4+N95D/j3m2uCIruyQNA2nY/GSXRwhvTWdLh4popWl5KYTpdACP2b2qNbHE6EuRFmshd1YuUM0VQtO4AYKRAcv6V+HC+ZEWaTw6ZqnYk7+4z17ZYCf7N/nOCBiXOFGnB0g68fwG5tfgrDV4iOpPwKWHqttcETnKOUxm7QQMoo4T1M78+5wBFr1AWrm6jHRzg5YmWAQKYPiZwXmhNs0VW6WcNhSzBHq4DTWAhkE6BWYBCjoQ6RObk4yAeHRKXudI7yunOMTNqZwFW4m7jcPOrERULmoWifVdPCINKoheI4aBELRGfqYSiqZsHx9UzvPSRU2J9ArXRc7hfF4rRsL49Oz56+4LeBzkmMNzvJ5tdjc7fuz0BZni5ZpRwGjoM5zWyHf2tVtzaAcfIhktjxNC26ansI8C4nldelRXIJwG49kcrnOUSfU6ebzVK6iybzl39wtuz435OvPQRLd+jTbw0xGcRv3g9XMKkDz3zx+IFmRlwLWUvtFmp7dJDmWOGHFBBzhC8jMCIo1DUgbJgZ+AXzGjYhpBL8huYXiH7v0VTP3hUIKQX6Oyjo46WAZLH6KGU9fgbyK8Ft0aJh2vy9kCtBRxWrXb4AgqGK5pGTWi+Ds/FeUoBKpweJZqGTHfqTPTJvkbTglPTmQpSx1Y5c7WxUSPm5PwleHTmVat5R7GfR8FHwPwKqt1RCtM7HjpF5ztlLwxWMXm6IUu+zqsy8Bay6HD00TNtrGm/qVCwjHWJ/U7qsUlflBVt2YQvo7vVgfkYtt/JjiIzKgosMCeidX5QSJOaconzxuu9y1MJJy6kQnw9EU0zOuZRKuw605shzHyOpd5CRqcFRJiaKL54PHqpioQOaWppciik5xNwLVCvK2RkQWbXHBUFsJGBrexTkzOO4hmjYvGxWWQY9RlVlHMB7mKKPEfKxgEfMu4IkKOICgnh9B8Gtk8m6IxnMqxZbrpPYK6hoPHOYuVB5ynFtPia5MQpCraAL3LNRSMAIvWTei7W9oHQ++lfXwMAZQmmvFQXYDGSIdNzig3pc8IHN5NnjYhP5D7q6si7+4YhBVhCscrTkyjOOdExNy9qhsfKa+TEXV9CaqXgKcpXx5zwwjbkEqYZCchVNfJY4rWj1xnhfZ/74dgURBwCHXC4GoaR5NpndE/0IFNGr5Qc9W3RUO2K5+2aGh0VNtPxdGtAJxA4Y4Lu9Il+9SQgH7WgHQ09DNFwEpRZzkz+Qs65CKVyTU8OhoOh1vSQwDHlt6MF4W2QoHk0TJ9488IIUKsk4vYBvHTH28txTna18E35fdbXvi8pkxBwtSTtFolteEaZA44ycPaqiecvaj1PwvWw4A6EMhD5EA72KoFD02mBBUi9UJaLlewaKVBIoxzKMiIevMeZrbHVc0ZpFd7rYvjPSJ+q7F0EPYdxZMLw8HRpxNXa3aygD8AH0zbVWP5sAOaMkzfYDK2UGtEcGpUdjF/TcY0RHxMAbzrGuPLcFQcwS1R0Gaqz7PooiC6wH6HYiVY+huENYCf5KNLBKMt7hUiCcW2RzWWQY4/nDIDoAYzgaRiyGtAW14J/bToNCL6Rp3h6GoQlr8kh2GRPA1IzGmMOy0L8de8mUOFIWUoAjEDuwgzxKezts7gtwkm7kZehR+iOOWdUtSDT6DLkAqxZJl1WaoGeujg4/NxVvYcVZQTWDWBVs8CXjkZBRtr1e3nv7smRq+H9s3Q7ZMAslyIjMxG9Kb7p98FqxIyCYKW7FHhJiOu+nndKp6W/kvf1bkRE3yflw6PbuTMyb7sMBdiI9M+7BR28Te1BEJcTLob+CmdwpgTwvgrUCoO/SBpEjIwoIY2/AX0KfE0AFECrIDTP5jHBtvcJn4WzaCKsqM2dmFu5XPAEgvldEWG1uYe0t39jKysGsrPnZChmI2Q55mSPBXqgaww7oEmpluH+MXlKPYdfLeSaQuVb+BSWBlAMfIEUPhafi8qUXmhWMgERet/aP9dKoUgvuoYBmLiCg7DIbhy6thqROfyaKP3KviJQr5C491orSmvRMK+nUlMIvhrg4lJs0q38r0Ne6VqPrMsP43drBY0MYLag7yPyruQrnJfjfk3cNYgwFD7kbP26D9WSAEXGDFSyJKVSQR2torUSHhDlZAw25Cq3iPf6BDT901S2CfYfify3hsz1gVS+YzpGNNsboZRqXSFu7XWBR+dJNbijIlZFWN2E2TzkORSWpgbTGDGNo9JsKyXJzXNOnGcYOHe0WhndcEe/Ru4nh8qNaOUflQThFFxxP2n4QwHPOXkWo6+PeADTkeC2T0VDR8pFLZFonVGJAcetcjx98RllxQL9H6LYW5h9BYYXGDpKkIGW4WR35QxcO43lnBPTYp6LRMgQQ+QETXZAQS/TYsCcuQzPKnPV9eUYyd2Q8VF0EaGrljiGIUxQwf+oyicjEVi6ZK/0ydvMjmseYCv0kO2pgS0KGCDDEUA7rLFDkbeZKT2HCO68H5le2tNILSjCETrLAuDyExDdjVc5K51H5vrUKXv4SBBgkR/uwcmopaSHn9EqASPoMId+8BiAGg7AnHQeUgv5AoyK5ojXg7jXUozjOlLV2KIlZKgbuXdaD60918UDePPMtdbQSsVwnulB85NjE5TqZyQy5DAcnrX2lmAHIWVqHZDE6A28sq+YQcZTgG3MmE0UcrMk9rO/PucAxR/+9NFj6BdTPPArfvZEZDjnmk/gYbym+CeLWASQI1I8WhaPIJDFwVRjpUUkwtptSDA9N2eE4PCHE0v3OLp3zKLYAIGEyvsiwrk2OXXQTsRS34RNoWHlLnW4XGTa+LUZwsT27HoPD9IAIxl0h0cqJgBR3bFykPDUIyAeGam89qoAg8AfcA12YVUvgLxevI9czlAYsSeaE+RA9jNRaR+NC0mKz08NtxqVRLXCIqqgzrNvzo6Pz46Xrnkvurt8xE/9IrCNzrThNR2Rcrq1gtvBwYNiwF89QJMB7ThgMFzXuTwpcLhfnwTQrZbca96tprdO8iw8WoRFVGROz/CwB/gpkf6TLakhCIpEDBng9PKin8mc2RguLFKuGcPFaTYxw0i+OTurqGJPhyONEdxhGFn11ntHaxVAJcjoOpPc+1sTOTw63RZk12alZzW5WemeVMghlzVKrJsBrRQcFsRekNBa7AUFF1hS/DHgL4CdKDYTCKX8m0jpfBY1EBuZp8WKiEAOgiJaJMiGDxKhdE932mXkw4kyLC89HWJfBkxRtD1SAkUkNhCxOzaAjPA26HGLMAhA537LWG4GPK/lKRJYUEnrpCgX9d/aG0arV/8VpSl4/3ToONBVTeB2uVt8vMeeG0qf1ExpvJPaAtifyBhtXW0jQlfHLRSEQgH12xwWVVTBIcI2DDPSrxlJWwd0A4zBj1kSEfyZBfCGa1Ah12bddOhlM3iNSqHoAaMSPM1kquEAQ7InkBB7XFCSnD/tcW0e1N8DqF1yG0cXe6n2KpsXgIndZNg2RHLXXJ/+9TkHKOtFpT4whrFJm0U7atT0VpDkIgvAstoTOxSuVnO3+HozzojryNvrmKkMFanQwXw03NpUla4Fao1GcHMMlOhJUiw8YUeERf0hn6lDWjYPSQKywqoJwTPM6poj4x5E0y1UbZG3D29cmmnNm7AFjuR5ZxSIgCGVqLyHtWrra/fMgZI3E97D9rvMEPiaiRMHwpEqCvLUEIBAwxGfbg1Ha2yHDsPLnYCVnpTj5er4+H7h7TVwJZlFL96NlnQn6eksfwK/BBGMSQ/trbFbayt4bg1PreDpoNK894HuBi8FdqNnXyujCM1ucEwO44NjDODmN/KfKsnbIwiWhujeKn5BMXhhymK+Y3AmWPlTIvXiULRnJ695RpEcWy7cN8BjS9mwCqYQSHqU5BrCAyvhCAAsvV1GJwvlQ6bIe5nJyep94AyAwl4gjjmARBp4BEOS2T5GkLolFgtkFUTJeQ3QWGv0hInBfROoGKilA7hj+h1mI8FJiYobx5J5j4ctQEYPHyIcITMrivmOPIX8e5ApM0WCUPJT1R+elVg7eVSGNSMPrrSeLzSynbmZDSFLqgaYBklGEzmB042vtHRbHLlwOJI/tFk0nc8MpmCRT3eeH2wBy4VuLA28quh8e0+u3KZbDEApNR0YDxkt6rTtjILk+sV7ajwjDWlEJkIn6mnETeHk3qjSUVPFcMq4tsG/eccIVytQvFA6MakY0mVy3sywyumMZyTWWhrS4vNHNBbkNGUWCShKmtEq53lHALzFSQmirCkSmluXJ0tGZ9kx3bL0QwA63yPYtkPMVM0LoDyI5Kd+fY0AFB4PC1KP+wVHheHO7+N5C1nPVb4awhXnFDpa1IdBPM3vOuZY1T6MUFiG5izQsozcGrbmUNSktbpQuzPUmY2LUgmEoCynYnlKQIqCDpxKMHeSGJ0yHnSBIgPDrNWQZCah5GoW/Siip4ZthL0NKIjvsV4KhyJRtW5fdyNvMg1d3NOmr5fhT1ylzwyTGpgrD6LLFCDTHk9HQ8tmQAX3Fzb8KgWoteHlmvj4pePNSXCS6/fO3zpQWd0gRQlkmblvvy9wou9WM9wq8Pqp4r2I5nCKLqNwoxNwtLZaT48xcRw3ADT27o5pwHHcAqxMlpvHvlpEa6rbpuhjtEAYAD3VqvySkkGGlck/2MAH9RPGWC3NFRY3rAiYu2NsDbLnRPK3CHZCDqIkuTrnXsn7lhzUWlBLYylwTIPeeUVMJ1LhKpQvYqwZOTVZWhxkbJW5FwtgUgyHkaTajLOADvX+8I9h9lUU+zJKuQP+SwBeYHailoFqE8UEiDzTC5iLYGilkBOTwHDxSZZhRXaT9TBKDNOTM5DRDmdk06GSZ8v39Cj75u9zd9eeLiCd4Xon8V0+xkoTW0bEQkNkaa14LNrX/UwAUX01/HHGyqYfVLmTDcgkH6lrkb+rDzDduECYjLatXlRlvTHlo0SKUedxlQYvgKPvRywnwQcjsI5qFcnNM0+5ZIshWfCQq/JO1CFuPnUZYkxDVApagKaHbrW5x3MN8WzRvRk6f5uzOQlSpaf30mE9k1KC01kx96BnyxrtMGc0WouGdEpXLl6U6AsOEen1WdnSIdd5yU/qTFP1ZkyADgNjOsv4bK/POUDZjDIQ49hjiJgPMFkx4RuKYP6QBD/fDuLe8tkLMPuKtJAAyk2RkJGYlKA+Sz/hDlSLfhtRAhYaRWAjmfe+DpDKiPvYiHNyOSIqU2CZMhEIEGeGjrQUnScAUaQlIxG2QEUqrvi5RamJzqbJ8G3REuj6WGrM9/88SGKirvxyCfQObvjjEgc9Ffk7VxLhkx4F94udGw21aJMqrqvjune0o+Lp6YbuwMvHbx7AiT7W4xNl0EoAoRb5YhmCPhwnmBJ7fLoYZmj8dzXH81HxwauGr/vgGa0wFXL2jhsqJgauETIY3t41Os7rwuvnJxgGhne0IPu+fvWEN29fSLoLuWutca9KlK1OysE95htRITIi1/uIVI1nPtpdw8HWumc1oMXUp817zsq0WBsCng41skIoQCk6dwTPQOHmQkUcgMNHENeLoaBijL6RJZFARA3KJNNSyix8sdwXRQJVRRTzWnEUdrA9ilI8TPOwemfCSkfBGY7MW8BOFPSInujul4wrqifC0S7qGR0svIdVdork7wBBLC4ldcs6MB6yFERiVU5BzoGKltcfBxIMyAnJSguBRqwzo7O1U0tKYdpLoCd1E5CRBjoiC4RsWCMPAp9OAGV9vSIB/MVsgBmytkDRozanWlrfdxcvAqHHlU4WIFWztpD1eIZSygMnL+8mAA0Ntb6OTt8xY4qgPYDDft9Y61oDhO/8HfZdYuRCn7icrgVSZS9Y9h/Rz7lAHYnnBtRCAAyPflN8n1JLU7KHVTk6NgBcbE1PR7EVnY51KGFfFJGRk5Frr6fQfKWFTqHYlPQEJqPzfYzkv60esp/+9TkHKMASa0ctjlnYA2XMjjHvweNZZVc6f2Yrv26b8CCEKTd+rtx+htGSUf8YudDLJnD2DnFd3BdnxDA2b2WFPQVWJEx0QHlf6V25p4Co1t7JQ4UQM1nnnu9TlY+OV1Y0lCXE2fVvU0YiQE2BLv1UIGK7l7wnVQltmGQn+CqMvHs4gY+4PvIgtI762ywOvX5XysfCVpC4ebSYCDEnbk8FT08HJoCvfvSCL7+94z48pkUA73qJxZA9MY7GaxU4SqWhvV8DuA+cTr4GDzYBa42ctYHE2Odbwxde3fCl954Ad7ycF6YX3BoN7nkBL0EmLaXiaI7zPAEMmDlaKyhWc20k5ZSZQpByVHp9JEGg1oLXr55wRm+XYogurhEhREmiooYuUslHpVhW/swVKVFDsTg0g6VvKb+eq+j8XSIjGojR4SgpZ6xaG2F4iYaui40AYexXg2gJX4zk4PTsN0DsrrTViNJiT+BUbLWsb6VGA7v4U9l2v5qj4g1a+e8wewPgqwA+gvsJx1dRrMefAfhYbebNokV88FuwOB4ZPt+kVud7pWi3UHwAE3FrNMSzVfIDsvtp6gwkEbSEgRJ/zKfOdIz0CECQJFJDGiCl2zINHbovUyJ5GAVU7GEcT3wLKkWI/6cKnu7ZS0hpwhG/IHO3gNmSa0VJxTXT9dKB0XtB0jOJ3T3aNmgyu29R2XAsEBVvWGmLEvsIsHsy9TpSrrH1SgK26PPGHdRdF6sJCADxizx6lkQ3463CMV+2ZEfr/RC5slXE4CBRwQKk1kzbLHskcip0XoIkO31GandF6Dw7UGu/FZEzlMYS5pUmJlBGiTVLvC4OCrC68cQPLfhxc0Qq9JP28dO8vgYAil4K7YlDMeHOMlgeALm+yyPTyeNWLIHxiDRI4h88nRTGZdSFyhfBEDHBOMo8s4cHhaiaNjjaDvsCUGZruNNOQtJsnVLqdp/AHNGjBfKCPEK4az6LqjWYypKJXsLMoqcF9BZC1uElIUuCvHwiPqy+D9hqRgQZVcRo8CDYmbFfxdbhkcp4CXI680AeRs6Z0RG2BCdHA1493fB0q6jRmEts9asPfHwO/NJHd5JisaHB/DhLUGRGYPJ0VNyqwWdnf4xGY0clOrbImcGKCHYWz1OSD0PPaKCVglu7ATYx73eUAg6sDHkpMDy1G243zili+LfArCaJ9OaejfHGmOiDER52/40ooRe0Vtmltw/YYFRxjolSCyf6Apg2g2hqbMrXw2uDlJOmGEsfEaDs0M5c4wIKgIEjZjiNyahWn5yNg6iCG13NpFhGfwTpT0MkVUnLniSFrfjDcGboOc6luCdza+JgKYeS+xLXiaGRRq4JybAvAN7A/Q3M38DsBOzEnCTXs8M0MGekA3SOAVxO0mf1EuHzbb3KqjZZ0UU5KLaqcxzZF4d6yNPQmU1UW9UWQxY0n48Pu7zt/ed0UMhv43UT/GMRdz2Nfeiyd7pYyychmEEaVJ3lT6R7A6zSSxcUQUY98jCnKDGNXOIbij7oWSgPdJBqKcHzUeGAobijRdSCn8W29yM9Hq51Aw0ySeBxv4UNrQg6hIT4n1pIJF3kYWQPLfZmoe5QlDVX0xWhpG4dNjMCoyGEakeR0axEKMjIC51QBOAWN5DfzLSci1uitQvolvpJlAGBzti3wtYAvA/1VuK/p680424TswhB3WW3/1Ws92TDSThcIweAmDL/mxqgJKSDsKZ7x/QzlvFMhO5TB9QTSOhtK5hLwzWFHrGM7GKc6310JygoQuiAUhfTV0oiKsuCgO3QuO6EOAn99xz6Boh0E1Ceb3X91GdIgQIhjEXX0JCoCOv7UlQZTuK7sIOTPeKUAUrHO70X9E9++GP307Vabiv6IcWaz/gOXjCohFSfwa9bLVkJAwC3WglOniueov9JwUpHXHPi45cLv/zxhTdn35TRJw+MR9tsNmvTWjKf3KpFxQcw0eAwjPvgELCUKBJAaxpFGpjeB47DUJvB1GYebExmVmJYngMFHOil3idTTQIJPGplj5cjUkIv9wv3s8MRgyT1PiDIqJZrXetKwSi3fjTOjqqVs6nuxufh+7eBaeHtrgy0AO8CwKUYfJDHdGuV83CuDpSCKwZZaiAhzyFgdYWx+5CBVzSsIOeq+BIMR5TiY72XynpFspimo5y0YjgKxxyQiwIcpaOU/zfcP8b0XwT8LQregHO7JgwvgEuHEIwWi3SQdttnVHJwtlQ2SgwZzXsW+IwUrCIwedLL4hQg9I9SqqmaVkgg9jQAha9KP50hRZcYLaDskpajiBeWkXR9fsiNWgGE06bqrbBsMIgwv8596gzptgAaSfCPz8b2jNxSPc/6ns5/8mHi3E/1BbLg00QUrxhTKw1Lh+5qLPWPSbcbgBmNCUkDn4rWgERtOaxHLdAsNpaOAzAOOUACuRWZVpRMUTCLZ7wi5fJgNTYH1p0GvEffJ/GtxnZ+RfJ2rVEAc9iMLtG++d1GwLFF+8MiBurxdH4lF7KDj+n/TfnGfRaT8y94stlK0MFWilaOzSJyS4bfQaq/ztfnHKA8vjL/qmXMhY0+Db7CUepWqNwsQ2gy0vbQ7EyTKYG1MXwvtnIyT0my8BSlNIVmzeT1UpGxQmNTSPFehV2FSoHlOVEoZ9ayqwdKq3uoTdGKwsm8fUUIEsi456E2/T4ix795FgJdtt1H1vbHr81QethAmUCGFA/iIAvE1KLfsQjrRlQkHlP6i2vJKcvFkASv56Pg9XPDq+cWB97yucwMfQAfvXS8uV/veEqfkBreg7OXxjUmZjNo2iyXlVGq1zd6/ffOJnorNUjgWUuJHiw0bn1worOVgjEvvJwXYJXPUNiXh2tGBXVeM9amokZzu+N242TnMTnd+tbQmqHWjvOcEUFbpM3emd65tca9DDlWDwqNdK9B2iu1wm4FPRqx9U6jL6OmmP7cPLMaaUSj1QMn9lJeW63oo2TlmhSx3mtYxmNG/xZ2ajZYyDAreqKMNAzMcMcZzQut1AcAU0tBKxF9iTLiWym4VcpTK8BR3+AoF2AvGPMtpr9FwR1mF5h2nbAyGJoePbheG2CPvwmAwzClntEziZAY/ACtowB+KHkEEAk3FqHp1/C1bZ0YvbQA7uHJ7h1R4zZJrF6fo1tfU45jP3SwNnwA2DI+/g751pmm4TRx5JwnoRSBEka0AJE7oedfdxTftP1jc22ZjtoPPnsyJVnbEffPNy4juO59/zzE70NrEKkh7pMGaQbvLvT93NpQZBEElHZywGf2OFrt3Pk5ikiViP70IHyXnF0lXbpAjGRhFoQDFLc893XObchHMpFxpdYigkPiMDYgsUW5ZH/MchiibM3eOn9tk+UzO8rmKKzIktI7rvPhGwCNB96dzW1j3v3mr/r6GgEoAiYzFQmNeAe8AOgwazDUDKOJHZ2KAIhukra+sRlwiFEPLE9l83YoRBQCztmRseUhSINX5V3znpVT3hv1JOAR1I4Du3LIYDlq3C7n1MRYbJUuT8fExHmxUdFR2c48PYN4PCnDhwOyAzCzBC/ysBARmbw1X0ZHXrUOChwRLtfnrXLcmEnHrp6VY8oNW7g57k2Gv8Dhhd7/q6eK917dGGaf8mrJfYBVvLl3vD0HrkRTv5YMMUR89olxFFipaJXawoOsVlvFK1R8/NJxxn6ooqGCXvrzwRb7jboC5zXRnY3uGFkgGCHBc6JaRa0VKrM1KzhuJHZ2c7Sj4e39xOgdz7eKoxVMr8CrijlPnJprVNmIDha8mMrBj/fzIqHTCmpdINFaxRwEe7eD1z2v/hAFBBCjAlbkiiRuy4qpPsbmGHQ8haz2GAgpgq5ZkDHdg8QaUR14Xl9VEPLgi9NTnnBcPrMZm6YjK0XQDHjKdA7JsLdacAQ44CDA/4lid8B+GW4fA/4hil0o5c4IiTlgg03XorkXK6qAEREUM5YpK2Wjs0pdrrLdlcMvEHlSrA0pfR5cE1gJrofWIvtqGFDi3KxGZXw/idklvddHxJFmnPsSgCp/FPhE11rGZTkUSs+wgop8H2KWvUkeeTCaFi2g9s5jxsfyF7I6ZgNL1KnrgDIyXBYgCweo2OqJg0lApvfukWfxJ2hIo7QYi3wt3ab10pwgVaAI8NeKdG55T5aRPoGp3YYkhzBKYtwXsPXt97Q/2WreLdvwq828UunuAgWqDtOnstKmRHXODMOUFVau/U/0TAk02qStm8QmNwtNyAwSdG4UBDCylRIda7GmLwvSyS39jb0+5wBlP5hCi+rUSmRYglw5AWBoUBnfzbOyQlYK3WX0JNFoABJ5NuEpIdp5sx8C37BC1UHaC6ibLetDKcNAAqYrP41UEqv0OKokwO/1Eax1lVzo8PjqjqgSNjHpzSI9AsDHSIFZbPIomZ5r1o/eKCUpsp10gxpMJeDYDv7qUsvwZUaP3GMGhlHZlAA+7qg17lEoyQylalx4TH+ONs61VBxHxetnpjzGcIxpNJRzolQCiI/eXrj3beLxryVHESXrE3jpwPNgR8TWIm1tvM8RqRz22QnPyYCjGV7dKt5/PvCF1094dTOYVXz89sSbe8c5HG4HXl4uXGOgtgpGMxyo3I/np4iWzImn5xtePzW8nB3uE+1oeLrd4A68vJx4uRylVjw/3zDGxYoGi0iHOxpI4JvO/kC3Vtlxcg5oEvKAw0cMfCw1PcQVO7Ss/EEAQB8THcBVJtNLG0jvA5h+YTpwDk7dnmEcZVz2sLKAPqtqLBVinEYMBHCB+mNY8rkMDMffCvDcOH+p7lGU0lHtguFjmA2Y/U/ATpAU+xbud7hdAC44wjMurnYU1CbmMStrZEoMpgqTMBw6aLFWLAVenXznXFV5PEM68/Gcvs69G6LMdFl2laerikp6wWDZJ0m9m6ijdL43/eU7OZVg1UxR3KXr1lnddd8CPkzXAdlTCatSR5G5/bCJoPp40lIDEXy4jPBmHj3k+KGjbIxICIM7PeYvQVFnAaIAKFOTyZGN1rRm/L2ovMu93EDbzBBD8kAs3iPDndGL0IljMp1ZqxxRdhzO5wmnMLldoV/zWdMmEVTsjC8u1QbgPPrRGEJ/A0Vk4HznchLzTMl5iWGAWZX1zuthxxJ4PP5cOqRFZJsRf6X5Y0d8J49/ttfnHKA8vko1+BmRE9Ar38OlM0MEfDmA4cuAanqrZZVOLLwExCVAfHeJuQdiyCuP6HNiGrkDmR7yYLRnGBd5qLIM2i2FIRGoSF5JOHKV7SRiH86GVoGZKLy+ABecQ/ikqEoQP3kWfZWzbQAhV8gFlLa0jTtPmuuw8plaK3h6esJ0x3l2FAuFNgUUdfiUHnDcWkwpBlNXpUXvjLJ4CA4aw6fbgaej4vVTw9Ot4Lxf6BMxHdnRWkWfwMcvHS/XJ9vlf+K169N4hgkOOby3gsMmG3zVmC1RK5o11HrmWhRzHLXg1a3hvecDr58a//2qwa3i7TlxjisAClM9Vx8kk/nAAGL2EOcY9Tcd1/0Cnm94ej7Q54wI2QGg4DxPnN3x1Tcn3nu+4UtfeIU5D3z1o4/RWgPccF4X+gzGf60YvQNgFAooMQySFsacs0MsvNDLZzR6Ukmv1nFVsJgRMNggObaVNTvp6oPpInnViAhfEUeEcsPxClx3gX4SbgMsp2FeKcdWalQmOW7V8NwKXlVV6ygM7dFDpKPgjlK+DOAO91+GowdguaPYGcBlwOPzDAtATY8w/RxpkMfIgD+9bAELD8KlO0vHK+GWGoC5UgEpdoufUqrBB1JWZ6D+qpLZADmARf8bES9laHnFB/AncJXcA1sWWIrPpYdWaTJ/tNLBMEApXw/9WMIQrbMVgCcvvN63eqdbHjXpu7J79tKFYdyS32ECrTMdxWnicq0jbKbSWct1EXHUI9Vi07LHCAQYsQZXshDS0pgzCJzQZ4sMIMupSzGokwjlVk6ao0UkxT1ATzzrKjmP96uPTuyjOxJYAZvdgWEPx7B6b9k2zcVRgAquVIzWPYCbeZK1pxOAS0aUthVfCgZ4pDSTLBzrhYHUzwbk/iCyBQI/PrbN+gyvrxGA4hAjWVU8rXEBx+hw77FGRxwGHWQdGE9ULCW8D+eKT8hNz2jC9l6m/1JjZR6f6NqThKir6VDGZaHDve6LCmCxxZE3k/egKAQinz9oSFoxznBJQafnScWKQNbhQW8GIEOlmxemQ/Tgbek6dGVQzPD8dMPrVwfOa+Ll3mGYLJv0baKoA/DJUHxbM4EYtqXHVEvBcRxBvhwR9q94frrhaAXvP9/w+rnC/cJVHOe1pTgMOM+Ol6vjivEE/+vDsfY5l9g5TfntNfBUDa9g0Ya6wErDeTquseSgWMFRK55uFa+eG55vBBt9DMyIYJRS4L3j6hc7XfY1sO+aAzDHe6+OLANWmPfeO17uEwjlc14DI9IyrbJqic3OognU5MA+evMTzvIstMIulGPMJHJqlsdxtCTi1dJgOClPKo8MA5y8pJBblr3qTHjK2kiPbVUitJhNZU6PawyOpWD0adVmiUibs382749VAxO3AFpP1fDcKp5biUjJgPtHmPMF8Ldwf4GVE4aPAD8B+wpYMP4mvr7DisMxMJ3jGYqXjZjKtdv5VNl9OYijth9gX1UmWj/26SHBYI7BNQqdkGkcN81dzyiCeFjGkEr+1H1GlUREEaY8aUUvRCQV6VPREl1choMylemWPJq6nm39nvizEiB95+YRCHF8hio2HjkKART2cxj6NQGG9LFInvGskmOmXcQNWfwYGcoqQCPgm4YyornRY8R3pRbg26cDZaXP2KRgRQxEvhbIGOE2ao94f/o8WxsXKkUBlJV20p5rKCuvXU3AU1GxNRqgYKWgBK60hy5AvVVjObBVjU4kq7GoVNgz+jENHOXitF01o3zaMIvswyqSoK1j/yJF2yfkjGySoWvt1Ruf4fU5ByjbsXbA50A1w9EarNasEHAf0HivvXxXvQIkUJPQNJE7PyJNfAIOKotNOEORV/OYAiyg4aGMNatjzcJxkeJ852fs7994AHHPFmW0iq7sIIhC5zFvQrOFFolJDQ2KsUyyjyCRmJ6H1241hNIFmFbofT9kMj7HUfHq+QmvXj1zYnG/YPDku/B5S3pppRQcTVwc7lsBoy/mrGZpRwsDD9yOhttx4DgqPZNW0GrFy3niGo6zD5hxnMGIMGoqk18Tuf/KvzDdmaKYlXNk5iSVaU589OYirydkifyYiudbw6unGycoz4k3LwPDORBR6aw5BoAWHiW9thFM/jHB6xonPU+feHl74RqMnswxcV09WnkP3I4oKY5Kmdraw2AwytjAHD0bBr69X7gdLZT/iBVYU6Frrai1ZMh5hd9DFi1AeH6CLVAipTrVjVjk1ZKl7iJCWoJCpTejMi2iNX3OjCLCsHglRSMECFCeKrv2EpxccLxBqW8BfJXVObgAfwvE32wHf6IWAmhEHxndLw2MLzkvNIAC8eRdkSdTIo3jSvNGBEnAwecGWHwjK/K0hfFXisPzvTWiJDJu6bG+I6u6nlqdZ5TC10/NNu7K1hjBHcn30f1o/enoE9QoUgKT3nlstLYcKWzO0Pa1vLiUlhWxyaoPCMjyc6faobp0u8ivugzXQiC4RrSpGB0uRZp3Pe+QPouZN2XNKRMAVuDBQroVQcH2fUX1akSMtN6rb4jWRM8tZ9aiQgYRmdJUcofNmbOB6C/RUulMKhWk9FkCO9m87f6QACoRaabrDMup5sDGvLn1XkszgSWxaw3Ek4JArAFKIenZ91cpZdERPuPrawSgEF4c5igHPYM+J8Y4wyO4GAIzg1cdEmzM8aWM6VsEfI6PEDgx25X2J19mBgteyXDH6DMPgU/luGmYtdGLcR2CZQYL70YdEKUUa7QmVkVFqqkQGPFdLFCvvDSeDHp+CG8nbhgKd7J7KstkGdBYbcf3ds3KfR6l4NXTgfdeP+Hp6YarO17eXgAINjwevNaWFR2qNtIobs1PKWHIjtJwe7phurMxmgHPzzc81Uov11jweQ7H/QTevB2Y0Y+CBhLQFOhfBXt8Unx+lR9MNwwU9EmCa50khH709sLVVeLJOTuvnxqej4pbrTE12XH1jpfzxNkHUBtqPeC42GDKJ6bJoBlgFec1WIocRa3n1ck5Kg1WJsbVMQfwVA/ycApVyP1+0djUAxMd4+IekJi8pu6aqbzZ8Pr5hgPA/eocwAdEK/Yl56VEs6+xjLhkTuAU4GewHf6MEmekUlTJdcEqt52DlUdSfwnwEROJN56UGfk9T7XguRY8t4qnVnErJ44y0Ox/RF+T/4HpJ0r5CLAL8BcYIoWDDmDC7AojFalHlTJgGRHdjxo0ErSwfwTmql5RjwvN0rIwkuKOKd2jNOVq5igeCOLsL+Ahh0bmRkZeEQb4er/4bnHwE8TIiMR2QL/F/d3EPT5Lv8NBk4QwJSJais5kFHKs1MRujByLZ2BCeQKzihxEZ9yF0gwiXq69pt6pKAkmVEaeKgzBCTGNYwjdnKA5rqsF3QTMPWQzy4NXpEB7Ypshx6bvLeRX0QsHU3mIaMgCn2Eb9mqquE4t257YY2UoYt/W9ZGRFu4POPR0d1jzPgXkVa2lRnQCQyuttaBM7JmtggY4MlLlG9dGe6w9kAOSZ0AyELIqUV1r8ht7fc4BynqZAU+3htEdZ3dcV2epYhAcKRQRJtujE0KA2YAIWWq2UPvicgA6VL7rCKZ4IgQ6xnzoiAggyXdSSek1xbWF4ceMqoEH4OSoDigA6XM73PF7SvcIPY8x6SP7wtUcTGeZCvIASCWiTrVYlsRNAJicuNL4cDRGzjTC+6/Jf7gdDW/fnvjqV94AZrjdSgxtdNxag1nBmGw7T+/d4JO9DMydQ+4KDdGtsZrkJVq2t9Zwa5UGwidaPTDd8fHLHS8vF+7XjFJEat/pBS/nwKn1X/r/U73Wea5prOCGlz5xdmRYtpWCV08V779qeHWwS6kVdmT0y/Fy3dGHR++B6EUToGz0Dnf1Gpl4e86MYvgcKM42/fc+cT/vOK8LzQqO3nC7cYhg752gbBpqg+ofqVDBXjh2tGD4r/12BNgMHkGtrNS5nyfEo5IxMHNyUiShYaBrreRpjAl1gJX0GaLqAmvCs8zIGD3OBw/XDCA8BORDpqtxhs5RDK+a4dVR8XQUNBs47EIrA9XewHAH/KsA7jC8gWEAdgfASeYiMyMSKTPG0hdblR/yXqfPKDdVJIpPneWjcU6rnJA+cv3YuyWcDx8JLiCAgZKgIyyQUAlKcflYrP4ocpzWwM4kxjoejEFVZcsGIcX/MYsIgXSNOxT/p+Mx06iFlgod5AotrKZx6ajYBiweCwTsQcEt54iP61yDSIXBVH2HB5nRXchI69lK2RxFRzqQhiXTgeGkufnz0DP7vCE8fM4y+o/jApBBnFXlw66tAk8Ch6UAdarqZkXfFml4B6HISAo7GKxIitZ5/51cewgsKR3EB1jk2ZCJ2GszVZOWjG46Yt3cgs6ALIBwCMTOWG+LEQi23ZPShwA2ELbs2Vpz3veyY5/19TkHKO9ieYeXiek9pxS35jDvqIXeqYY0ZZgr4B71w6bEI/UPxPFNQdjQoa/3A5FmETKFAUVeV3iDihwou2JIJMqBaTMadMV74jMIcllWp8Mir2oKIelwSJgmtqjPQs0URg5Zy54kKn0ek02b0gMESmFKQpyAUiq++IVX+C1ffEY1w8cfveCjj9+gVqC2BlUoHLeGUhuuq6NFeWwNI6DeLQbPRmPFHaXVKJslYVRG0GcQjgvBTe/c31orUOipj+F4ebnw1bcnmyQ9Ogyf6iVmP3kdDh+At4aXk6WuAGf/HJX9WZ4Pw6ungtutZKXHNTru14CVA+fLiTdv7+jTUVoDronzOqOxG9M3wwmqbwejU+4WnJc3eHm588YaJwU3Z++XGmmF8+o4+8Vo0+3GcmaQG3P1ASjtAu7jdV2QwVRI2WqlEjMpqqVUS/CNBCRXZC7OTh7DnSxOY++RsnQAZx9R2bMqU6YvhQwgmqxhRUxqwVMFjuJo5ZdR8RX+Xe4o9ksAOty/AuCC+wnDBKcoTyiNo14tpVhMsA1PMc57GvD0QcJAGZAVgRtAm76mDKfR1H4gohBVXudS3ErFZO4+IzCIyg8RLUXaXA7MAii+qxyea/0ebPEo/z/tvXuwZVdxH/zrtdbe+9w70ox4WJKxERDiz5hAEgIxyHbiqliBEFVeplwVFyFy7ErKRCRgUgQ7jvNyYSinKqkkhZ1HOeQPP6hQhUlCyIOHY4dEPAPYggQ7H7ZFOUh8MRaamXvP2Wut7u+P7l5rn6uZ0bw0M1c6rRrde8/ZZ5+9115r9a+7f90tnS/h8HJJhvWwBEUDSmQN7paGk21AfUzcYnac1UmVjUhrG5cX22NX0mIgRdwD3Pl1miXDbTy3LHIb3x5yWfDuFtmOgp5Jo+Um1BOgW5vAG4iIVYSlI8+783gMePheAFoYln3sqkFsT2EORpo+2vrEx6kZxsvnSgFMBpile9v8DGTzRdqY+Tm5NXxlVySL+SBiq1sW1cVhHma7sxDJSlto9k8gQg269nXPNU8qehFI99To81HD37+zPQ8CAmKboJe49T5KniAARQetFu2ISjQYOxqYUgBzs1+swqJqfQJQCcaTpIZmFawIgiwsLP8mnzTM6Nk7jspV4XrzJ3dvVrOCmgfH0p9jiK3bpCNgMvTaNnxbkEMKreeLgLQyrm84PqmpKw5VCguuii2xHkv3IRTUWlEK2zSm5oJkAUL1UA+wmgacunkfJ6aEWjLOrmesN1o1VIt3qRs1DZpCCxHLyvGS65oKHK38+jRoSIcAFNa+J7lWDClhGpO1fmdIMMUMsUqhXi+lag0VSihScZgL1kWrVghdwdIwYMdSTJkGcAEODjMqa12NFIFx0AyfFAPGMZlrXbM+5rko4OOqlV9zQUwa7trMBXOpGKJgCBExEoo1p/S4bWXBej1bRgxjGBLGMUKq4PBwNm9VAhEjR1V0JRdM0wgIDMAFcM2NIBejevg2ucKbzyVPiWV1w7NZSUsJpnAcVIp5b1xRucJVhdSt/974UpXDJpeWVVAXitCmLhIRhgCMgbCfCPsDYZUqBmKAMoCzCHQWgQ4BWUNkDaEC5gyiihCMw+WWpJ23GwiW8i/mYVwADL1PAswLCvbAq7Tr8xYSUrnF1lX5GDCwcfPosOsNz5JzwmBceGt13AHt69J3tKU7v/Niatvxq5VRqHBvSmgKt2VuGA9mCQD1WYulpRtIYwslN3Bj192ej/NP0Aw8kTa6Tfy6afG3ftp9SICE4Ai48doABbJomXv9ufmeq/wm7VUVg1NUzbAUWfRU8xC87uLE1DhDSwOO0Pk5W6+Te4moPUP9Nm72TuuyzUCI+r0aiufFufxovcfSOIgOJl1zbTetbWJ7sBCMm8WWUIB2TqB7Zh1Eud5Q7MEthOMeUyzmlbYwouZB6VFPWszB7SydrWu1YnU+np2jifYd2+Dp0uWYAxQXHXhNm9JupHtDgkRCCqJokyuAAgkRLFHTJ0mQWRd77XC6LzsHpw6soZPTefWehQIoqnUwAULjcCz8odupl35akeZmG2KAddppKXcQ2urlo3F+79sBOMvfJ4OnsTWnZ7tGsZohjtLdshHr27E4py0MzVbREMXJE3u4+cQexkQAFxxuZqznrG3gQwCxloVPWt1IEb/1XFkNmlKVyfkK6mIcR80gKVUXYGEFMOOQsBpHlKqAM8SEed5gEGhqZ5WWcaVgRUNjc9VU7oVKeeyp86jD1PU6JFUMqlQIh3PF4ayu+xC0UuveqEBqmiaEQBpyEYARsZ4rNrMSfXNRK5wZyCW3EJhmdmSspgHdmtfZVwpbw0uzbsnCbFKRc8He3tQ2+GkcAADzZm4hCifyeu8lgYbqGKRkZudLEEFCrwTqljUWc53QezrpbtSL/RVTbm3zDj0uXwGwl9AXr95JXdFDrf4AwUCkXJNEmGLUDKokmOLvINIhBA9DcBqCM/YvA3IWmvq5gTmujGzp9Xx6kf5m+4vGzz0E65/xbs8tO6Rttt0z4B6oVpo/hDbe0s7dJ9RCvxt42FbeHklZTkEPeRGozT0YoFloBvu+fi4hNC+nLHqrbH+uT/iuUDrJ0cejK6JuBjv5nNvcoIUC0r3Ki3W5xmoA1H56G4nuC7FS7ObV8DCEG0ig7iWsFtbuBH5pxFPxQYSn7R7hFyFYNopBN0F7ztqc1QjePjrsIUjzBJHyzbTtARqgY+h+Ew0ZeHVvHz+yW3NwB/TsFj9Qw4Vsx9izt8zGBvgW4cg+A2jxc6lXGhSEq58GEMlAITnwWKbV+/U56bpB5a4QXW/Y/t7Kctjq6s6yPv+pLYLLk2MOUHwQdXBWUTtqpBAQQoRwgrBNDqvDgBhQJaEWLZ4FWNzZNg/PlGnxdH84C9TSWOLBaxJoJVOQpneW6oDFXH9GiGwL0M7EwlqHwja8GAOSLdTqDHubZJW18yaMbBujNMuqXRl7+przaqBllL3Uv8emoZt4G7gGTnzS6+erqOfj1IkVbt5baYO3MqPWgs2sHJ/u5saizbxfkXoYdPPhVtCtsvWfACGXqmEIMuJVUM+AiGDezCiVMcXB0mQFwnY89DkFqKsyV7b01IufPf5sF1xFAx8RU9IGc0NUYLDO2q+GYKnQKWBK2lyMQmj1Z3KumGvFXLQJl8CsF6hHIRdufVBqrcjMSOy1HTytUh+SFA39BAqN2wQrGDcMAYfrGbUKpnEERFDdcwetJZKzehS4Si805kvG7jnXqjUibENrCujIv4V5BhhEb60L2pyireZwbs1XkUVtBz1eK7IQUgBGCpiiYJUYq1QxpYCIGZEKAj2CGDYQOUDFGiIbJcRSAcgy9FqRKlfIujY8gdmLnPVOFtTecyuaSOvBsD3fGCJaCErcu2BrhG0dRiVs+rp25RyaF6dnw8lizbn1XZxcCs9s6Yq7Ken2uDqwW8xgQKQBRwWkruQN4HA/g4ZEdCxa40L0PcZDMERoVvkRG8sPbIabu/1btkgbKw/dOcDzWWOgd7GxcjtOIUKkzqcLNn9ggM15frrd6iCzoT3l+Rm4XAyTzlUfaVfQqladmwIbKo0I9vkatIKftRLRAVFOiI2VzQP30LlHi3y9iBsey6emHt6eh0ENJC6WpgF/q2Ab20rs82rpgTwCYOzFDmQWWEPXpUEb8pXTr83r3WwN4vL5+0UsrtWv4egzP+c5LkGeOAAFwM0jIchgZd8DSiHMmVG5IqUBKYm5zAIyRQQy0uliEwqxuw/9K9xDoUre3LiLVGAnTImgpe82kpLxRZzx3xAm2XIhtNS37hq2zdVyvnyBtRivXkmbWG5NLReC2HZQBKDqFVUFKWgZcO/dowrEY9s9BRAiGIeIW27aw837K5AI5s0GUitq1W65wpr2OwzRUqzt2mMCQdpYnTncYFoNWmegelqmdrvdWNGwadT0YnfHbtYzDucZqbVSJ/U8wMCkdfH1zshzqdagy5b4Egku5dGr2MZcAeyQAvaniCEwhiAYE2HmgHXR8MRAhNFTXQftAbNeb5Cs8N2ctUgciwKCEAcFJpuMwurB8jR0JZuqa75a8T1NS69IMaCUarFenT8lq7clDslqT3g6sIaSYuyWDAXLaKBoGxUa+RPQ4nACJcC21GC4dU1tc9EN0hVfH9uW0mzn6yXAfYMi8xAIKjypWYFJhMbuh0CYIoxrkjHFiiGcxRAZkR4B8wYiZwGpAM5CcAhgDcgaFASRGLUWMCpIgik1gUgBkYavmCus7lxTimzgqmVokNegsLnQClnp/BDVCHAiJZt12TkQHZz4GC2LNra1bkDeDY5q3kefrm49R/RrPRoGg8/whhDsHy2e3eKz3qm7ZQt2bWL6dkuTw265g3f4cw9NkTeCpu2d7vVr1+TfI31v7eTaBemXuv3dvXbt0mxc9fVoxgsAMPHWcW25e0G85S1J37Njq7mExUEeWnXwgcXN9/Rr9/w4AHZA54agG5gO4PRo0r3LDFqHRX2sux9Er0O91YDNG9Zz9SJ81J8j+zP1gerf6/e2BJU9ROOefbJ5uQ0wmj4lM/4Wc2Y5dVoaNzlI1ve8cOg2kN76NC5FjjlA6UIQrJIgivbQ0OZoGRDlKwyJMCSBtlQ34mcgiPX0CFWQSdW+cy4AbD0gYHvj0PoqWIAC+79bL7CMGW98IErG0s9jkXKLHn/mxTQ2uEswQ4i6V4dsQUjoFlwQAKRda6N5RmDn9JS9yaqejoOOwaZqjxgNVwedXFUQU8Spm09gf5Ww3mwQRItlzbnC6MSI0eqm6E4MCpqpw6BGTDwzV1QW7IVgtTwY0xTV3coBOTNiIsSYUHNBGJN5FipAAcOYkFLAOOnrBCCmqOXtLY5eWMzD0RnmFyNiOxuRWvJTIkxDwBAYAYwpqVU0Z+VtBKg7dzUk7K0G7E8DplGzmEph5FqxnotWJw5aoyRG9Zxs5oxctdKqpwDHGBUosFi6r8bnQwa4Ks+DQsAwjcrfqRUxjBhSRM5F085JM4LYiMTdBY8WstF6KRU1Z9vz1MOo/Adue5t6QExxsQEfeHZBAUuBZqhUMIoeZHMB8DQ1aa5q/bQ013pARSJgDIIpEKZE2B9gJNiCiArCIRIYkQ5RKUN4jSIVFGaIrAEo30Qr09bGBQvwsIVt6sGyY9wnQW01o/M/BFi4z7UVQ2h1WdSNHZT015Sqa+eeqomFsdkyAXlBrnUAIZ0E2f+J7V5oSsZTfVuTRaC7/xfKahHZ0L1BCCAGtbCI1z61vatlbqHVJ+TK0DCy3ohzG5qyaohFf3UvJ5pH7AjIaaCp5zW3lFRT9i2DZaGr9Ny9iSXE9kIzBJfF2DRlvVeX3VaYet5em4Qa4VYjSd6WQ9p/bU9dKHy9xT4GfY1Yp/gG2BuO0HFbHNybP/b3t/YfJ6Xb9UQ70IduqdIJ2PJOKqHauVwaTu/V0G0e2oUJ0Iup2eUp2zC0UJauGe/txODaPYv9SXcMpAaWf9fy6Wsl5+qvH3Uf4ejfF5ZjDlAWN0uCYYiQPNvGGlABZNZjKldEZgDRctoZgyndISYMVbCpWqAr+4MHFpPQY3r9Oz204S5NgMz60ksqi9z1ZQqbW8/BjxVPZTU71i097lkOXnHVtpFmGYiIgSC01E0HPL5QIay8ihiwmhJSSiiVMRcjckIXI4WKIMoNOXFiD8Mw4JEzB9hLmsGxmQtYGPsDAUha24W0gVpKqjgrM2pVJUwUcDhnnLp5H6VU1MIIMTSCXqkFgBZnI9KaHhriiRaWCNjbW4GC9p3ZzBlDiogp4vBggyKMFIDNRgmyraT0+WTbwFBvT7DKpIkwBtEqJCwYhoC91YQqQTsiV7GGgIQhEUZrvhhDwDiNmAtjzhnruSKzQEuoC+ayxqZUZAvXaKgPHZSybQiWOSMkQBEMSRVlz9jq8fY5V2w2XkG3N6EUIfNwVasUa+5wb/2+sMC8qFo1L0yzvMlSoZ0M2YZMIKgQmaFF0TZQ53xWsA8NC3gvGa3QygpcURFJMFDBKgbsJcFqIKwGYJUEgTMiVURiEGkmjsgG2o9wBrgAmBFChUhBiuZ6F+tCTIQl72TpLVnY9LY5+333GLxr+WD3UY37JLCS5VY/xhNeglnKnkFBRCDn7gjglSh0RzCPbKs3sQh5+PMwUOCeBqAbFd39Ti0MHSxEGILzhryujM8RTWX3/Se2cI4r3B4KbYXesOCYSGOo6T0Ez3jixovxvZDsdfcFdKO6mVXtObjyb5wFvwYLqVDso+bX4kXRtpY2AeQNFj3E6MacP49ghiajKWmf+22s0EP7j94mFlyRtnY6IVwxm2cPKTF+mV0kRM349LXufeAaP6hdE1oxR20cKRAizYBy74eF3BoiJX0uZLYvQazUhF5X20+wACd2zQp/uwfEn3nwcuOifB0S6xVmx0YsyO1t7tqaWsxpQmPBXHg/vgh54gAUaFyeRa3ynq/OtrGqG5yg4YfBrGNFoZYuZt4HJmmks8ZMdkRukL1NsCXz2R8298Xgi8cNEf8RbEHx8rx2T/146q5BQ0KtyBoE1QqdUAAiaeaMZih4OqceGUk7Be+NCdMwYFMq1rMeo54Q5TgQC/amESdO7CHGgLMHB5iSuuJLKQBX410Ea3sPpJQQrGrsJlfMeVZLrQasM2M1jWAB5lmMRCvIc8U4joAUDEnvMeesadQWniNBKxw35w2ENawQGECp2MxFK6wyae2T7FvPBdC5oC2iRKR9dgbNogmeaRUIwxCwmrS645nDijPronMmKngaYrRMAo3rx2DPEoAGwsUq0BLmkpulTaKhlVxrqz/h3CStF8NmYUYMViwpELW06XEYgBAwz/r8tNdRwpAiKEQcHB5C4KRoalU12XhLrtgqC4jrUl/ZBqmKt8Cz2QogBSxrgAoEa7DMAGYAa2gRtNmG3Ma9eV0YJHqNKVSMgbBKgr0UsIqMMQBjBKKBnGQZOJourARhVEZMjDgAzOq9UY4YG/m2dkVpzSWdwxMkmbucO8mca/M69BYSGo6CuesVnHHLaGHjbwVbj84ta4rWrXg0E6UpSFJyhM0T7+/SwwGa6Na5CL5BuAIgc1sIHDy4p9U5FsZvAzcF4QaRe8/aeQVbytm1b0D3bni4wquJ+lP169ExFGBRQFKvtx2JQNGUlDTuiHM2WojGEwr8e9mMJHaFCgMDBKlW8dZAtN+HtG90EKoX3a/Fx68/r+X4dq6aPRff5+016r+2n56k4KC03T/QOClhsWeLeTSCPQ8vra/F4gJA7lt0RY+2Htu1ENrY+3Pvd2/X7Z6a9s+q0fpR9j8S2rpXx+aq3/rc6be+PK9zmsiIzUeuu88sgAygGLH/SuSYA5SliKZVAaosvLttGGyj9j49mo6onVjVDV+sKiG8GmTVFVFsURBbmSexqdsmUI/B6jOShrCPqkoGlFBlE8TT5tAetm0Y9oRbqqBPLjJkzr1ra6uP4BkerpBsAbp1FILyRFJKFobImEsP+0AUGd80Jty8PyJFwuF6BknFGAa9TmFMUXv8BHOBkyGtXKEl3Ul/MkUUW0OrMeLMwUaVOjTVFM2y0bHJWT0zGraKqMVixsZw14ZWuqi5etEhtVPnXLHO3AiHzoRshjF8Q9Cx9XFLgTBEIKVuxUYijNYpeRoImwKcXqv3YwiEaVCQtxoiImn4zkuGOzmaLYOCmTHXanNPryNYwTspfUMuzYL2ujSEELQ2TM7ZAG7QNG0hHKwz5o0p2qhUU21CCGzmat4od4cvNlD2VeIhR0KAZwu4ojFl0eaR8jtEZg3vYAawATADtAAokKYICIxIrICDjMcTGXtJ65mkACQqGIKWqYcUINSWHiwoIBIQF1QuiL7pB+/SzWbddRe76x4KAFXj2lRBQa8z4t4FgtcrMYVhGoG5K/QlL6SVo19a6D6mrirtubWmm8vrQj+P931xRRDdYl6EQ3zD7wRKacpOn6OgEiubW9BDOM2qATRjbNGrxeeCK/nlhZGOe3VjyTcsU4zNwyPcr0fQ9hnYyvTzsYVwurLzfRMILeOwe5RJFpwnoIWw2Cat3QXcq7JwYDTQ7/evt9nvw2xFOFhZ9jVrKt4UbVi8vgQ8inE6GHMvo6AbsER9DKQ9i5755YWtm0/e8F6rarswJMiOc9JsIAe9Cjy0BkvHLe4t7IkehCUkWAJNggCtsePiGCJYVHEBaHRuBfSy/uSLzMfPERWRkY3bgDaPHdHRb7s0OeYApSNJQOtAuCWsaYMFRAnqbtYYutYesfgZWddQAsiZdIigrPCALCukBvWSt3Vuk5pgyNgX4/LpNkTpqB0tbucbTQPHRPBKtgAsM95SZhuc7lwBWZSNdjJiFbW+Wyozefl67VxMUS309Wwl1EkVH6CWdooRN5+YMKSAw/UGm7lib5VQi1qUKSg5NITYxoBCQK7Q0A+LekcY2JSKEAmrMeFwM+Ngk3HyREQujDxX7E1BuQMhohRGsZDQakwAMzalNPDkqalcuZMwCa3i6iY7OXZhQZCXY+8WTTBQl6Ivdht7KwoTDYCsxoi9MQKBcOZMxuFGa0+kSFgNETftjTixGpACtaJjMRKKkN5f4VbHpHJVPkBVizNG4/jYduEWmStRFqvxYOBnLtoscUwJA3T8tDkiIaUET92trNyWXAWg2kJdbQMkAOIVhM3NbSBqGWf2TKnKM1gqqpwGo0BwFoKMRlLFBrBeN4QNNO4sVmRNMAUYGBEMQbBKWq4+WOE0gnFIoMCDyMNOAMifMyMGJemCfdvuxNzgmyHctW6/k2bytTAOLbJqSN3uyQqqaVaMue4bkVLPJ6Trjz08Jj3kAEJr9tZ2IvFGbW49u2JZZJRQX/vq2YkLBSLd+GFuRRnbjkJopEV1++t+0HrlLBW1YOs7l2GbpgIdm6Hvacvdq4edsD1P2nEONrpXwp9CC6XQAkyge1I85ERtT3TgqGMfSb1PTkbW8/Q0eK9XE9rcdj3Qx6sVPKM2DH4VfbwEbczdEU4+htSVK3u2JKFnZAq3cRmsa72u4a4bFMkQGpOr7dnS7se9DT3ZysfdjBj0/cpnieufYPM9EllvH8BdhEuQpUulA90GfhsIMu+IoJUI8Pd8Xvo8VWAZUKWi7WTkHsb+uQ6NrkyeIABFB6MwIxFsxndryzcXIUaIhJh63DTEYOERBlUBwRWwAKSdVcHc6iK0c2H5MNCUTHc1OqO+HxOt0I7n9FPcTvXz81SfnItiQQp+e7aP1zvxEuGOuLslYo20bHMuZs3XqnH1EIAKwlx1cp06MWJvHLE+XONwPSMNIwgRm5x1I/By9+iKMxBZuKhgTAmFGdlqSQ2WJfTI2TWCNdCbcwVXYI+0CV+giCKMXBljSAgUtSpqzlhNCUMKqKWCrfCZCLSMPAiaYirI5lHRBenWmfeBMXe6hS+WWSYan9ZnFQkYx4hpDBiTHntmFjxymJFLRSRNJ16NAfvToEBKFACXWsGIqBKRGcilIg0w8uZig7eNsZRqFjm12dvqL9icrrWCrZOzECCBLM6t4EOJirAx0fBQqRqymUt1VY0Yev0IOBgCbDPSc0bjQun9KE+m2n9aBqxCqEAb7s3QbscZJDMCZSQqSCQYgj7zMQKrKEowhha1m5KCFeFqHBUrcgix0JLOTzaulCoCsXoQAKC8pqoXb4qSm2Ji8ag6Gk+EqynD2BWwfl/UUTbgC1N+cDBrvDAve68bcAdwbiQU81Aku/5qnClQd8N7ewv3vGwBDv9f83B0w6dqOU+I8YscZPo53Evs80UELcQTg1fxlMV+AvUuh76HiRF3Wygai3pJBpqFqq0qm8/OhwKs2rKHUXz9LUGMA0a07CgRaVWafcNTdaeLtmXZ2H1FENwzwov5CwTrbr1Q29JGo22ASxXptVMceATq6873a4cWhG50bp3FNlhCB4Rk/3qYrntMnftRG8FZQSZBGjWAKKhnzUC480KW4UPfL8jmIi08SWqIOE/HMnSwAG/2ffatNtdDKxTX+FB2rkBQsjXLgofZ9VOr43L0b/Q9zOfxch5erjxBAIpu8xS5LdBYBAMLwAVMGUCEdjIdEOOMUiNEEoSrVtk0BMksGAKhRp+POhli7JUUG7q0GaJzqi+QZj1Z3Q8I9yqcbt06OmdVYNVmQKRepMgO1QUQvMqoh4ccKFGbqL7puecg2mKqpaBClXaIhMk20lzU67K3GrC/GrHZZByus1WuJbA1tvMdslbBwaxKe281YS5ajEwAVKkt1joNCTEAh+sZmYE90vTbklUhurUgVb0ns5EQS4oouYIhGMcRMUWcPbtGqYScFVyCBZkFpVStVQEl2jaEDw/laEq1esvQrBBmb4rmaeA6TokYA5SrUxHw26fXOJjV9Z6i1oMZo9V9FLXyQ4TFZAmzeU8qw7gH+jyzhbRi9J4Y3BSKF1VqYwxVs072rAZGSq3gYt8ZYiOkVgFQGYNVm2XRsvfeK4eA5rnxdn9edKrUapuTfpe6iCuqFBQ5UM8JHQAoAM4CmBFwFkRrBMyIdBZjqNgbKqYoGEj7trjXZIhqjUUCBkuflKBVlZlq22xhT09HUeBFu4KlrNsIAcnSrlnvL3k/nFpbjSCR2pqBuichhggKFvKFZ7yJVU5WBeGl13XvIADczg3SkJtAAR3Xqi0W4B7PYIo1Nu+l107xiUnklqcbMuTGNTxbRoGPGyTOh6AWRvG9iNxkXSiJbiSRcZiWasGVuIFlP9Piu5ZuexgfZ+vjoso8iDTPhYMN11wO7Ny464TfThQVeM0TGG8iNODVNjxDDYECDEu29eAGmN6+j5C+2oig7ZWFQnaQZdPLzxF8utl9Gr3GxsE9MB7u1GsO6OGiIcYGeJbeiuZNAwGtrpGGqrwFQIeXelxcZN+0Cr4LQNv4LDZfvEeTZuosm28aICUPjNmab+Ooe0Kw7FXnxrRecb4mW/jR7t/PhUXtoDb3+rX7n+7Nd4PgcuUJAlBUhpQQzFMyRAAgjUmLAJJBYFA0y7fo7KSoG37lot6SEC3eLxBUEAjMyqiuwZpWNfebPnBb6u1aAhZFy8wKc0a3RyZZpKfFLuaju067R0X3jxajN0uits/1+1+S8AKZ220xobSpGQAh5ALUqg0W9/cnHGwy1oczgghWY0IkQa5FXa4pac2SzYzCgjSMYFYLvhRGSBaWKF6OmVBK1dLm5kkqVkiNgqbjphBRq3ogCguChTTABavVhGh9fNbrDAoDBLoJVibkotY1glaQbR6KxSZB1hBLU4O98BNAKbWFHkiT7RJV7I+Em/cHIAz4P7+TcWadtaBcUN7EGLWhIrNa0jRGkKiiz7XikYMZ67kgRrfQgnoMxDkQrhypgxFZLO/FRKgsjQxdq1rUwecQaxo9s44bCK2kvJJtDciCzCshLW1T54aCl2rhqVq1r4haX55KnAFUgAoIxcBDVRAXgCFoyGZvIOwNhIEAeIM8AANZg0lR7pIrbZhb2JW2K2YRzzazjZPcI9DHLkCrFFeweX38nixlWtRIidHc8Ja94Rs/Be3rVLk2Q0Sgi6R7WDyU4esxtH9OmiKbh16nolZuBg6RAWB2N7rNhUbS7amxAi15X0VaBVLPfAtBQ75kmlSv7wjZcwEEsAj59qJxS8e834sZLKxWPYEaQd4zkpwv4fwK316WXBZG/27luWDhBfJy71iMo609ccUsTaHDARfc8narvCu9JWfHryM4oIEDlp650l5TrIkWFoMqYwmkWUDLfdbGaFscEm17BgJhkZa9ONqUvYNND8+3sIy5jwge6lPDINq4YwFuG4AhwgLumGFEYA7We4ibNxbwUJHxESxlu6V1g1rGlD9TNMMa7TvETuHzpoVWAYskLKIDNn/9uQF+Dw5+uHlpLkcuiWL77Gc/uyPGxb97770XALBer3HvvffiaU97Gm666Sa86lWvwkMPPbR1jgceeAB333039vf3ceutt+JNb3qTZohclsjinxNkGVIrhDUDQTfYDJYNNuUsDtancebwERzOh9jkGfNsVq8739g3DG7KLZnSHULAGC0lNepGHa0hYCBYfJsw2vuJxLIYPLVLH5q2lXf+wPZdLBHplqVEvYy9x2LrckKLnquyky37xHQeBlhd35tacVgLQozYHyfUXHH2cEYVRho91KIkT4FyTM6scy9AFpQMO1v4RRCskquO/SZnrBs4MY5E7eTduTDmIlrGHQHChLkyZgNsKUZUBg7WxeqKKBKPMaGwYFO0RooIWppssI3Sw1opqiJOIWCIWnNjNUashqD/Rv138yS49eaAZz79BE7dfBPOzITfPr1BydUqnRLGIWBlJOPKsGwlvf9cGJtcsc6lkWKduFyN3BujEldLdeXV3dmq8DrIdV6JWmxeodZTCJWcrN+rxOJa9VqKEUiDhbWY2UISFjJaWvTom2tmQeaCLLN5Ts5C+SWHSDjESGtMtMaJmHFyZJwaGbdMgltWhFtWhJuSaNpw0lDOahBMA7SWjM1/oq6wFVRIs+4gaMX7vCGkAhfdlLXEOABllmgmWAoKOqFASME/N9ezpr8H80Z66MbDN6GB9Rj1cyAnrzadByLzjMTOuYoLzxRJzwRi8Woxbj3LFrjRTR5egLZ5WL1BaKnudYWBvEU4yMNMcD+EGj1cpYGs7pkTH2JoDRYfEFMQptM15GDWNjt3TdPdG8m0VUBdhAB8INyqNgt+O7uEDGT13jew6yd/HkBPV/brdeAaenijMqOIZlRFCkj+TM2MayNiaITha6ejDrHrXYIeLJ4z/JnY72T8RDJvtYPEtngMXffwFqyCd9/f2l7eTkotK7TyAnyYJ6QacPaGgLQYL6ewN0AoXd+ENiv8i9pILgCkoCeIuBFspGho6AhE7ZMsHZzYhG7v9bRi/S4f/+3r82un5i1q+uwy5JI8KB//+MetoJTK/fffjz/6R/8ovuu7vgsA8AM/8AP49//+3+Nd73oXTp06hde97nX4zu/8Tvy3//bfAGh9gbvvvhu33347/vt//+/40pe+hD//5/88hmHAj/3Yj13mLfSHU4yQoZNFJ6w2oVPFkecZ67l/MpCS+8aaMA2T9XURwEAFC5ArABCIKyIUvaYY2wJlt76ou3F9YrmS8atsbHDbpNvc7/uIThJsGRYN2Cz2By2PZdZxjy3r5IghYEzeiNCJs9IafWWLJw9jgkCbz7GwNegL2JSq/IZAiKLEyWIbEZGOqZZ0102FrQVzCgGFC9RI7RZELhaDtWsvVVBLwTQktVrIemF4bQNBCzepd4Ubn0JDKVppFe41gMWAyUI7ZCXUo3JKNETTwwIg5SGNCTgxJTz95hVCHPHF3yn4P19Z42CtmSSRNBV5TAHToES4wtp1mBEBA1KbXDsYMKUDG3Mti69F5jRVdrlce8zeFXLLAnOLFJ1w5xtBZYYYFwmimw01y03rMpTqKZE6McjmMzNDA36aoVO5c0wIawQURNLu30NkpMCIgFbONbdwJMGUDKB3NWiePq2dwFbELQQNjwaQlSnXY53cp5ak1RkJ7j3pa1tj7qSGh4gBE19DPvelgx0HQBZiasWwxEGZ1XsgaVVeg1073ANl11UN7Tg/gUIPBRM5adXCddyzhezy7BakAZP+zM3CZgFXBTTBuG+uEABXaJqKGkNsnXPdyKkd1ahSaGECz8BAGycdZzFA3wFQq3ezsIXa2GPpyUC3hMn3uNCU9VLhtzRWOO9iaWeF/lnf1+CgbLugm99fpA7ml0q/XwwauVMHn9BCTHZ+B7qu1B02+c2J/bksfe9AiRzgoKeKk1+MkVOpeUvQ9/J2rPJGNNRjBRmp73XV5mjjjVD3fYlfJfkYd8+Pn3u7yaMDfLTj/Jra/GuQo/NaOrjw7w8tU45sNjigap418jNuk3zJ1jbZhtTw3WXIJQGUr/mar9n6+21vexue+9zn4tu//dvx1a9+FT/1Uz+Fn/3Zn8Uf+SN/BADwjne8A9/0Td+Ej3zkI3jZy16G//yf/zM+97nP4QMf+ABuu+02/P7f//vxoz/6o3jzm9+Mv/N3/o7Wxrgk8aWvA3ywns2tPqByBEu1RnQzqiSwbAD0gjSVCooIKg+onDCmiBDErCcAhc0zoA9hMIQv6JPCpoP93t2gvXiRxX7FrKXAxmXxDBV9+Ay3Sjoe9kXHzX2K7c0VyqMYLDukik5y5ZrYYrfMjdbPwtzB0zgiBMJ6MyPnooXBKGimyJyRCyON2oHYi8+xXV+uwKYoaCFyixHQSrQe+zWla7VZ4JuyQENmtbbsCt/YfWOrXJFrQS4FSgBVz8xcBHOGpdRmNIVuizqQmPfEyqhbZdgGUJIumhAI0xixtyLcvEpIwwq/9ZUNfv3BM/jKVzdKsrXQzpQCVinpmELXYGFG4IgYA3IVDWV5OWwR5FztngSDdbf2bBBXfsxiFpp57ZYaYqHA3AVbvTKpc6Acwdp8dLdqrVq5ddlAbblJFBaQhXIqChgzIBmBMiKtMVDFKs4a0oqMZPySMSowAatnZIiA80bsCpoHS+d6ASDmfRQoWOmxcC2SZ/OBOgRzyyuKk87FdP62gmgWYdBnql437lYoqTL0bAtfV8Ji6dkdCDWvjSte27C9HoqnUC7Ljbvr2xWYmp2miskNAm67VDWAvozfQx9fMzaKGzBw5QjzjElTGo2fRrBicx1gLQmqDaQ0Cwh9j7B558Xo4PuUftRc97YHuXVt8wumqImCAaau3Bx8+b7YwAh1UNf4GgYmPSTl4RvYa8U8DQGhEdybNe/eIZsfTryNFIzCtLyOzqPY0pQGQNoe7pqcPLy4DYBC/1j/P7VPt/HqoMD22+Cja4DQ5kr/bi9Kpw8y+nkbyPUHYkYu9RCKvWvPzV/oiKR12V5cO7X5iV6XhRYf82dEUC6d9M+KPTPnW9LizH7P3VNG1izxCtAJroCDMs8zfvqnfxpvfOMbQUT45Cc/iZwz7rrrrnbM8573PNxxxx2477778LKXvQz33XcfXvjCF+K2225rx7ziFa/Aa1/7Wnz2s5/Fi170onN+12azwWazaX8/8sgj9pu0fwLG6cMNhD3uHWDqGiIRkIJIVv0zFoA3YGiIgnnG4cw4nANCGBAjQBSxKRqjJ1KPRLL9x6scEqFlQOj878WHtCSyYXoLu9SkgaMqQC4a9ijimUJLK0sXTKv+sSj8Rgk+00AEjINeW3Evh6iHpG648S5ak0ADBEPSQmOlaMbM0lNTS20ZIZ7qS9QXMFm9FU9vDYERoK555zXoJmIFrqJOXldOCrSkARFvShWgiiZGdYfnUltoR61Ftp4u6mKvpcDLOys/w/q7xNDDOINWiR0TYRwJQ7ReREPEahowDQGlAv/vg2fwhYcO8TtnZlWCUGs+BeXjTKP1FoJZ0UnbBJSi4ZWctfeLz4XWGJHI0qGVMxIc3KJveD5e3XOiqawhEDL3YjGsgwqgKyLPxqgwS1y6uuloR4+rnsUlWQEr1mApAM2IqBhDwRQz9mLFKhUNUQbtDJ6IEakAkoHISFHHW4zt7x6LaDsrm0u5d/bWNRpi79LqoqEJqx9ivCFQz1Zb1tTwEv1YGHAORGBjIQugCwM7XN3d3UNowdjTrWu4gSMtttizPhw4Rws9MPe+Sc7FcO+dx+pdqblCbympbrHavRM0nMnQrCHnErWy7tQ9GFwFlSqkEXl7iB3w0G/V0Aq6ogSwKJAGeCqbG0RLToxbwD647vKHz72maxbhEtOc/Z6oKamlZ4uAxfU60b8/+96t18wbopZG20AOAeQhcdkOd7hS9TR9m/haLoYdcGIBOPt5qdsBi/2JG5Akm0sdZvjKMi+BP2+ox84Ju8ppMZAmDrY72Ojzq88H/0MzUNG/za7PG4b2PaOv+H4OYAuZYAGYHJiJE+TduOngr9fX6oqBDMADbnj25+1f2eviLI32K5PLBijvec978PDDD+N7vud7AAAPPvggxnHELbfcsnXcbbfdhgcffLAdswQn/r6/dz5561vfir/7d//uOd5pSwuAOAvF+mfYdkBkHYMFJFXd15S1NohzKbiCkSAUUSopPzBo+qIrmsE25bYZwC1gX1BuHehs13i6TXYCKhgDEShoiu0mMEKuoKoPP5FODkfTFX0heolkECCltkkRbANWA1Wra2azwrQgVmysb53kDEjAMA7KUSilxedjUuKfd0P2TAm2yprLWgjVAAy5kQHRCrzMDd0vlZCoOdcmracrV5bWmE2YUUpGChG5FOTK5mFiZBakmAy9a4ExLoA3yyNoqGFKCkr2nGMyROxNCSdWCfuTkmFb92EEnNkwfvPLZ/Ebvz3jzCEv3KYa/lsNCashIUDDk0wAKFqvFsuCspDnMAwITvwtxYhj1kKeK5j1uwsbm9+Yk8tiYa4/AinAYwO6nfBMvhM00FhNebS4OzxEwW1DJnN5V2EDKAxgRkLFEAqmWLE/aAuDMRAS1DMWqCIFAUErwrqV5FWZtdkmG+gnA2eLnjagtnE5EHWyp+99vjn6fIN5HphFgaDPH5vvbACSolUFcSBh7o9uqTLAxkuytGwFHqw1HGrPS3Ag46GBAtOCZLViAkBsXhfWMvgxxgaC/DQOGmEQ1Cv6NqtzYY22zBV0RSGAZT0swAUcGBng59pqrfS7bQvRQsFdaXmIudWFCUrMhXhtDuqEYd9S/b587BfX0u8AzTBbwmJ/V/x9qHZ273JTetbYL1BQHtEC8BBpWXUAW2nWXcF2gLKcTD0DhhoBlKDz0dWle5OdX+gK3QPxzQPloKBtcg21tTEnuw9uY+CAZwnm7HrsM15CwsHIErgFO3+VXspiOerteaCDYX9yjY/SwHmfYz392EJd6CXxAWwBFECsxP0yFGbPSjrwX645HxT9fmm37fvZlchlA5Sf+qmfwitf+Uo84xnPuLIruAj5oR/6IbzxjW9sfz/yyCN45jOfiR7iAeCbJhhDiPagqnEUYkPGpc6odYOUMhImECaEADCyKfwBcxGgBlBIxnJnpBAxmHVfDUx4brq0xbPNOe4FIkn7ihC1qToEQQ1a7TZZr5uADlDU06IKbS6Mmb0gW68UqE3mdMMu1Yi37tmhHvIInsFQLQU5EHIuVmnXlhsFFGYr4hZM4RJyrlha7iANcTSwYGNQvW7H8v6hgEp3MWo1LmCKorVFt8WjikmLv81WLC8EYM4VFGyqEoEigTig5AJv9haDe0+S9XkJWnRtAvZH9QgQAEbEnAW/fWbGF39ng4cennEwcyv+FaBl8KdIODGNGFMCnFRYNA03UAIL4WCTsSnVPESmMM2t77F+Z9oHUs8LGfCqRkB28XWekoLKXDRE4ltrVzz6M0Vrj+eKVPp52EB0EYaIZuCIqMcQWCMRYwwzVlGwnwqmgTHFgjFURDBYNtCmmhsLM2pWj/AGgqxeM2IIFxC8kJk+cS2HzYhuUYq0jRPiXV1VtVbzEDVCKnVrrqkrMxJdD2k9FPW0xaBE6WX5cQgsvVE9F8uNGvAMJxsnFiuKpi8IL+pVUFeoOn0Xm7nPQ/OWsSlaL8jVVS3gHrUG0qhbvq5MvDx/e9rSAVwbBOrhWQBtvvrycW8N2fEdBHXuSRUB1WZSGGhw1Xnua2tjjx7eEkhPd3ZNtKWUYZH3BWhpBo2rVAsFYDtDaIvYS0qIbbVRZGEELr6ugQpxHoU/hw6M/BIbmBBTxK6CXa82oO/ztz9L9dYuQ3Rq/DqRFfbsfR43wNPmkrTsIR9nL1rX9vTF89QhVLDrng/lcXVw0sAoLaGMtGtpz7bdZ59LgXzr7SCL+ohgW/w7HCBufdVCD9o92C989DSXKJcFUH7zN38TH/jAB/Dud7+7vXb77bdjnmc8/PDDW16Uhx56CLfffns75mMf+9jWuTzLx485l0zThGmaLnxRFtOmqharrju2CpHFKkAKYCmVVQrK7N4J9SIMMTVLb67QTCCzaLTJoA+XKQSrD8wGWT3eGYKl7x3ZOASEykVdztxrQ6QUrXOu8jMqazXMCk2pnYNgrlpUrVgdDRGACa3uQXbSnCxcnj7bg7td1bMjtQJcm/XgjP1qdTyUXCsomRuxVicg2T1I20SXm+CSawKggUIdl+4S97x9ZvUsuWhYS3kzuTqxNQEomtoLQCN33ncIkMXyq0KWFaULUImhZHwZXSxnZsZvny74/x6Z8ZWDgk3RrSwtNsshkvYtSgFBvOKjW7Z6xZu54mA9I1dp1ku1YmMtLEFoHoMQlbPibRfYyI8E6sQ726XqAogm29g1O8vtMVmc30mF/pwETD6WAIsSYiEZkRhjrNhLhL0I7Fu2WSSbbZLt2WhqcRBPk9bgmntStFkfUIu0sKFXNtWHzs3wdO+fcrs8W8a4NIpykUI0D4bZ4vbh7h1xAqqdHwbOXTma6LzXJnrBNKIDkSq1baraqFOBVLXzV+OphOBgqkGaNr4+z1p4ANTXom/y9h2arSWth1QVtrTPbSXioQMHlULd6qS+8iACFKunFLsboSldTwP1cFMzZO27nSjfT2ljHWVL47hnzotEyuIyu1ISoDUg7Mppy8NCC7wiwNZtL95r0FGkXbR7hjxFthF14dcGAx1d8S7HavvvHjpykNTujRwMbGtR9ej6XiWAczkaOEXz8Ci5Go0lSoAVmEPzSjhhN0Z14+lcaU0K2v6xlE5N7ajA54WPNblHQwSyMBYZsOwqWYyzQcOlxw8LviAWfjBxvCltnrM/9+WjNFAjHkXw73ZgJtjisVyOXBZAecc73oFbb70Vd999d3vtxS9+MYZhwAc/+EG86lWvAgB8/vOfxwMPPIA777wTAHDnnXfiLW95C7785S/j1ltvBQC8//3vx8mTJ/H85z//Mq6ke1AIgiEa+c5SDjU7ARBZg2CdGWmwvikZuQJEIyJFRFqDRDkrIQCx1RpRqyUzgWq0FE4d80Bulou/oA9VCIFiq2cASz2srK7aUnUzixQQE7Q7btIFX9u0ISQEpMSYAiAIyAJsMmFdgE0dUCWhygq1DCgSG+LWGKOVyocuMrGVFYOFaGwHZlaPiruAWfR+JBc7Jix2JrQmhFvbrPTFvh1lX1ijcLBiVoYGh+E0CwKAELZSsEdKVk1Ur3nw4lxmMXsVXYFAKpQcKgWVA+bKGOaeGSLQMvyPrCsePig42HihMgKCW5xGjB0CJgvtMFewW6+i2RSauVOwmTXso4RDy5qxIfOslGhmihubHnvXp7HcpsWUKbcWBzGQcT3sqLb/dlKsxc76ZggBDJQIZggyCAVD3GAvCvZTxv4ATHGDMQpI1hDOEGTlpAQByYwUgEiazRSCekqElJMSg7JYYnTSqn+3knNpWaadXKmjvV5BdlznJ4EW1u3CouweFTUikqX9esq9h4dEpHXAdi5QW5MWXulESJ2wRP7d6m2hoJyhiAXghnsVKpzg6Zt2MFS5rPvA0kECe8+jQBALEWGhXF15tdh9e8CE7k2wj5lSI4RWZIucQ+NgFdLWoPMhWggghJalBHLuD5oF3ebh4h7E5msHZPYsOg5oYMFBxVIh+nU7CCMv9tHdFa0AnGyNbQ91tr3Djve06+W6Ef88WahmAWh8PJ3oLKBeul76NPFp0fS3z0ffX9wjArIQSFfmkA7+fHy1hePiswa0otvJEgww2HcursOvm4ia0dJCMAtl7+Ptj9DBU3uUi2vqYFHHw//vHjE/kbT5vhhLUgApDaUuEYec489OTCYHwrg8uWSAwsx4xzvegXvuuQcp9Y+fOnUK3/d934c3vvGNeOpTn4qTJ0/ir/yVv4I777wTL3vZywAAL3/5y/H85z8fr3nNa/DjP/7jePDBB/E3/+bfxL333vvYHpJzynKwBFNiJFg8GdapOJLW4GC2jdvQOTGmFDAmaWW4cy3ItQDQTJAgyrCvpjSZvSaDutqis/ZtEQe02QdwAFmPBqFFnQKYJ4PIyrFTn6DG4wCb6y/4+2RgiJA54GAWnN4A66zppEUKGINuugBAuolXVnTeYsCmGDyerVway7xwRj9piEC4ux/hJEb0omCNeNc4FG1feBR4iYbjWul+6L0zo2+SAIQC5sJGBHOlZUuJgTRFFGhaL4vybboC0783peLsDMQDL5ClILGwIDOj2Pc5hVoaoNS02b0x4MSUMFiHZsCtLkYtmt47Z8amVBT3DhEWtXz0WgOAcTByrZE0i1V8XY5DGyfbqbzQl5JufQNup25jWxsy7LsDC1v/EvWIBFTrJCzYS8B+0jL0Q2AkKiBhQDJACmICVWg2jvbKGSJ6QTQCuALuhQwQ62Jd+nM3oOrhGq+4SzHAMUMgi2U7hmjrom/mTdWZt4VFEOEdx6kBac/YCZGslgirEqRFZokBSC1S2MvVuweK7Hu85H/a2uCpgYz2HAgNdYVowJW7Nexrw40EBb0aSmDxLCPNmGP0WjUO5B0kyGJAxcID0bN4BBZycmDrYMD1jzSlqZa1hqYpKqfJ98zqbSJ8HKSP23JdtlBJMK+sA1LudjctFn7baxbXplY+HNHAF5aY5yksPhsIkLCEFwbYXIn7PfrvDsPIC4kZsG2GJMwzFXyhbOlVIX++PcCh+7Z6raqgeXjEnrGPjYbdu/fYieJbpFsHCwby/FmrodfBaDt3M7phgHGbt6Uhor5vkK+fhYfHxxboxpcDe4iS+MW+wNsW+PfqdZAaXwuYp/fNbT4skOPWHCD3UC3mxOXKJQOUD3zgA3jggQfwvd/7vY967x/+w3+IEAJe9apXYbPZ4BWveAV+4id+or0fY8R73/tevPa1r8Wdd96JEydO4J577sHf+3t/7zIvf+FBIcFqDAisGy2zIItYo7YIkaBVJEEYhoRpHBBDQqRqG9+AUoFNNpKqAIWd1NhLlFuRbhDr5hnAVhwsqAtvoUR8EhdWsKBxaGBKogWnYmrHaZM2BglrIaoUsSTS6RwgpDTpZwPhbBAczgMOS0KRAUJ7gEQwTqKK9ocJDBCxbpKWETPXTmb1mK64+xg+sbXJYCv65hkooqTQ5Ybs5geRE+6w5ZZVQGiAw1K2Y1Rnd1n4xVtWgUhn9dtOyUIIMSEB4M0acy6N0Cv9YvRauS8sv4au462Ohv0VzNoZY8D+lLA/JazG2PhAFDUrIOeCzZzBIMxVewCBTIGTgg8vwNaAg/RaOVqUS7PGXEk0rwdLJ80uxsu9TL6JsysKn/0dG6qblTK0+vEGiSpWYcYqVewlxiodYgiCgENAKkTWQGDEsAYRg6iApKq3gYsne5hizaaMKkiqpVR7bxZHlzqHNEzWr1v1qQID5xzJwhL2kAK7S93mDovXCMG2m982WW854SEPDT+6ASEtY8olEEGshIDqCgeXZk2SlSGnDuLd0+Vl3NGunTXUiGhgxCrLivSie9J5ILyYB/BxDD53/dlTCw+Sp1+zhwJ0HwjRjII2t2wewLlnnd/T01EVUHqYAVgaaebhbRa0qkkP8QZb92Tz3PlDzVPjni6fAqCmRFVZuwmg4+bhaAeFQdxA6Gu4eSYIfa7YHHIDimzud1hoBON2T7YV+PryWiV+Xt9WllbVERCqBgIQqvtCAE/zdvCp4+7X4rhQbK9cghib134NDRy577HzWJbgJIhznsT4qGH73sW9LKHtJRr60XvzjLm+E6JdNxrw7kBi6QPz83lIkoXbtRC8zgna8+3GqR2/2KeuBKRcMkB5+ctf3jaSo7JarfD2t78db3/728/7+Wc961l43/ved6lfexFiFr/4Y9dFm7k2a0ssHVZ7rwgiecEqIKJgSBFDiBBS3sLBrNVQ0xBRa7Zy7QZSRJvRjSlqaAm+QUWbMNwmk6YAu4taMBjAEMxqQbEu7EGzV7WwlZkUtYVUCEod2QAImEICjYRISmrd1IgiFSLmPUECMACIqKz8mRCATbaOu0SNXOqbOlmKIltnZy1MZtwPeCVbYMvKg09RE4frRG2DqCzQaFi3nLZqTUAtD0/fdOBA4GZ1Vy7IpSJE5Sv453QD2poGtslQf6GdlbZ+I9LsnyGq12R/GjBYNgoCtQ6fhRlzqZhZN6651FYl1kM5fu0+rrUy8lwQY4Rm/PT01mBeAL8SgViWlA1gsFoiRWuOsN0F29z2rra+eZvPC0BpKcMnBsFNA2EgQbLMtUQMkgyB/R0EMSq3RD0p6jkR42kJCxAFpIjPvB1qQQlprRNVXh0O+tytFW2TFvfOhdBi+05sTVGLI1buIRRviyBm6SmYVfALAx69FgM1/UqmZFXxBEvtRRvzprAFLXvMScw+39WzUbfAA1Fv9MksYOKWzsvVm+ux1RWh3uTQFVeKoBgRYZkaXDVNHp7tFxvYjkujxOZTCwNxaGPtafbBiMJNVdMiJIIOeBUI99TZ5bnb+jDF3Ats2cq3eelGWss48XOTrvE0JAxp0PvLMzoB170IfbEGsv+Jhk59jBWYcD+GuuECQp/7ME+c3+DW+m7LHHAwBIJnEipAUo9rCAHTOAAi2Kw3gANW23M11OUhEWrrW4End4Nsod7Vax0MGIknT8JrHTkUcWdSK+gmnc/GBDARhnEEIJjnGdqjbEICkOcZtdb2PDsfqHsgG1GXevgM6CDfJ8cSTG15uMSyVbEALClhHEfk9QbE0s+72NMb7Gnr7dx44WLkmPfiWWTxgFvaaWXBXArmomldFCxLA8FQfQC1GpjG7A+jlSwGhMh6dlR7AAEx6nepMnGrGKgcNRxk3XXVdGl4XKuwVlXs3rMjUgQzWqZGiMGyeNx6RuevkJiiDjb5EoQJARFjiJCk3qFACUVWKBxR+REIIkAnQBgA2YfwCMQ9szwJgIaM3Nrzde6TXSebKSTbXKrF/LurcfFZt0jgi7AzuJnFcrSXW6e5YpnVerXeRaUU25DUKiAxLwW0EV6y5ecW+Pmnvj1dX2/NQhN4r6JIwBgjTkwJqyEikHJi3BXs4GquWpo/V+VXFJY263oK7JY9AgpBlTRbdVnuO+lWWl+zHuXo3mCFwozALAsrUG8EIM8bmS17bcYUgRNDxYlUMYUMwgagDKJDxCCI5kEBDtULImso5C5KgA0BVbSMfidLGpBkK6UfVImwiDXRtKwkYQCp4dOwGBFB39ibGexPkICA0LwNbtk60dnXkpiSdm9AB9ihp2+2Zy3oqdoAhBooEnsPoPYconmwKlvLBuOkODCDe7TIELOt7SV/pGX8iKCSAk1KGpYUChCKYFLPaQwRebPunhIjzjO015IsACzZNWQurceUcticIN3vk43/xFuvbxNafT0vU4+1C3RoK1TXLzePgZjRrR6PbaNAAiGbJxnqPGvfF8gsdnbvqO1vUc/rQYWtDEi7ALH11c0KXdDuJWGHKoJ2j2R7DzyNlgIqARwjhhgRK6NuZjUcmZFS0jXG6hlEIOMiLr4TTkJWHeIcCwYhxIBauvesLV6Rzk2E7QPw2lS2Lha1ThiCIIRoH6kQbLiCEDHGCM2yrBgDKaXA9FIHm2jj24Zwsa/7g3XvuI8f9Q/b56SHmG0wGVYdOxjVAO6ZWYRjIYsS+R2k9Fl3eXLMAcpCRYkAojUKNkUwZw8L9A0pBiWkpiDQksPq8QhQz8ScK2rtii8FfRQsfePUTVyasp6LAokU9dgoxl8hbeaUSwWJYExanTaQKq5isfdxiPa6od+WSRCs+qQ2IwsxWAO/bBZVUk4MIsaQIDEishajq6GAJSrpUQaIzApUcNCmE2SAErUGeBaPiFpzMQawaI8cgte10EXXLGbZXhAde/ju1aHDsg+MK/TmMbCNPaUIImDOC2SvqwzBCpe5xeCpvPoVi+9aoJVGeKO+PHSDUWDiacnTELEak3q0Wrpq6OCkMNa5Yq6MIgLwoq8NFpbTIqtBoGNUqnIMvDmXKpqjkMqXOpql4q8qJRLNYuxOYLNOUABURMqYgmAvVawStNhaKEhhBpABFBBps78Yqm3GWYFK8GaAClBiiEo2CVZAy79fGCGINd8zVy+h85lckZBljQgjkJOE7VlaXaBWQ8jO3jdsHyer7YPWIUvB2+I4Qff4lUXjxICFJ8ABA4tyc9ru7ZlZdj2sXiIt3Y4Ouu15MhZkXfIQkD8HC8dZOX/lkYlWx42hgYlcCyQAMY3aQiIqaPFwjRCQ0oAQI2qtKKyhVO8GG6KT7jX0FVJETANqya0mTKCgtWOYwaWAohlkgu49ceBH0EwcaHFG77jr2U/ecBLOEWpAX4FXiEmVrZUmELtGAKi1IIXeWiIAGhIT0eQBG2MAiGSf4aKhTr0w7RxMoXUB7x139ZmmqF5IN3vg3gLjDYndg/ek4srgARhjAo0jZLNBFe1e7gp7GAdt/bB4/kIABQWVDpK9si+DME0rpIFR5hkQfS7u5YSI9oUTQbC+YK1vErqHyondIgIx73UioJSswM4YtCykzW3L3J7nctOL5l0GrNCld+M2kKu1v6J2ty/WssZAyHLvCdal2XtkUYjgmiEImGLqXn0LZztR271rvnlbIBFXIsccoGwLizbCWxfN2IiBMISIXKxGSlDuh7qx0SoIci0KTGzTIgCDdVWtrOXu1XrWGihqhVvJ+irYFEFmtUKGqHVHiLw5XEGKpNVIrS6F8jBUKafIxmPpaLa5dSvbscEmHiNCNzPmaJ6UhEgBI0XEOCEhoEpWDgrPqJwgskGgAQGjOjKEwJwgCBDZg4QIoghCBNEIQDv1ssTGhfAMC0fWbGCqWVLN8vcN3/9GAxvNVSxaWVWgm3uMRhrkCs+u0OMsrEGwsAirhwXdqtIvweIieqZQA2M6rPZ8tOGjFhdTEnLL9AnRrOZg9WcE61yxya4AF25u+z7n3UTbnJtlDistzp5+q59RnkHo3iu/Rld+6JZPHzy/R4cqCkyIMhIJppCxPxD2U8VAjIFmBBQEZBBlEGl/HSXBzgrOSUM9kAyyYmxK4BOEUJtCUoNNLHUXDajCQIDyNMwrZT751tE3ihE0u7fC3wNZc0Bjhy+5S14TowEIMXBpVWTbPDNQ5BwPCGmzt4Xbu9f50TUni3nps4WlgouABjJyOllxOwOKdssNf4s3+fQ1q2vK51v1uQEl0EYQUIrWpUEAWVfskAZwVmWDYO0p0oA0DCjrNWrO5sEkVRqWlMBFwX0aBmxq6UXuoKAI7KURAApJyfvC4JLb2nSlGGNCTBWcM7hq2rWHLUMcdMxKRs2zeg7cpRkDQkpamj0XzHNGSgkkarwNQ8QwTdisNwoWUmyLOaYEztotnKJy48ABPOfG+5GgypjsnPCq0yDMecY0DLqfstg9AlIySqnKsomhzaORlHvHuSCHgGEcQbUCbvAQNAySEoYwQuZsNXE01EIhQGJADAngCqlFU8chGAkYxlH1RJ51H6CurEMIqLXoc0lJO1jnDK7FZo+OJw2DgpBYEQAMQetSNa8jkTUMjVtgeWvfCwEhJggFpKjk3srVehp5M84EhKR8tZIbUNTsOh2HaDWwysEaLMD+akIW9eqEmCDzBsyMYRoBYdTZgJT0jLx2XZfvPAFw7AFK96AIBOuiFvhcbFNrCsXIWZY7J80XFVBqRTVOxjgksyK8h4Si1liAMQWzqIHZwIwAyESLxmxaHC0XQiZYB19LORMGsZd2DgZitGleiGaBmXVQSm29Vwhq2c9cLWVSPTteSp4FSEIIpGhZKKBUQa6ETBmFggKUMCDSgNT2Lq+ZvwdIQKDBELG2BlCviv3EqK5+CdC6EIPZtqkraqAtzB7TdIYFwVtMtpAFA57lEQgQruYhsbMxtIZEcJY/oQiDa9XNJJBaG0svCkE9UAuQAuhzTESYUsD+NGBMDibQLE+1ZHSRZ2ZsMlvXZQWoIpqNBCzXn4VuRK3hbul3UqQDjnbv6B6AptS9voltlCzuaREIsqZPo0JjhxWQghQYY6hYRWB/YKwSEGmDCEGiGZEqAilACVSUqwQNW4IYRAradV4qMFE91tBX89h4Bo439XNXerA1RaDe+VWwVazMO9563Qev2yIiRlo1AGBhCTJL2UnXyx3OAYETYlv1Vat94t62aJtk41LYLYXg9Rx4AXJ1zbQ9weeteUC10GNXdjq/3aPq1aXJDXiFpiGCgzVWjBGJGRQJMhdgXoNoQq2MYRxRckGRYvO5Yq4ZeydOYNgLYDmr5OsYkIgwZ+1APgxDm2NzLhiGAcMQUarW5hmGASEmzDmjlIxhXGF//wTK2TPY1NmyPrxYIGF/bw+IA9aHByhckShquwyu2NvbxzgM2HDFXBhMQTkQuSKzYG+1wrg34rCcXhRvMy9N1NIIIoLREm83c0ER7QwvIljPGRIEeyf2ASHkw0MwCUJIOh7MSMOAFBWElCrYFEYadW9f5wpKAfurFWIgHOZDUIpqVOWq3J9hxCAEBiNDkGvFNE0I8wzJylGca0UVYP/ETRjjgPnwEHMptqcDc85I04Bp2gPKjPngEAWCzWYGRm2gOjMjitZI0fuKGFJCJELOGTMLbtq/CWMIODysyMwYknrKSy6gEDX8R+qZzCItu8xXo4/xMgOnWnsMBqHkCg6CYVphFRLOnj2jPEIilKrG1rh3AikNqOsD1M26ectEBGwxXLLFkCsDMSEMVUGVGCeOFEAKEwQZZh/onm4eG2pXfflyzAFKF4HgYJMhtcBbravHwpR61MZ67pauyuIDYOmFMSLGZU0Qr2Gg4QXvaKloG8hW6TRS0IJptjErWCkKHuwx11yQ54xpGDCNAwIBpRYIa20PEiV8EVkn2lIV7UYlX2opfrLKh6YtSEtwJw9HVFVAFLRKbYxALIyMag3CKkJUgKTZ0Iq0tTNsAGiETqkEQImdIlGVjxQEBCQaoAmfeg2RYlsosHGpAHoExl431yisIqJbv2wu3QBYFVt3J4tamqxN+WAWH1k2AsyLs3RvKljvJDN39beOxJalc2IaEQjIudgGEE2xSrPw13PB2jbSyvocfeG5FlbL3e5dtC+PW+tsgEYDmTBGAAA4jklEQVTrVXhJcXTFDEMuZvQ2Qwho4Aq2ERRUHT8qABgBBUOo2B8IJwbCXiKMgRGpQjgrATxUBNKianr16rkTqVDCq2tTH0P1jkTLPtBsIwMIYVFDwkMwoXOXnM/jaecQVtKwAxnog4ghGC8C7XWysbPh6RuaFb1yCywsnreHbCpruFV7N+l88ky6VlcF6ODIQBAa0PAGgv0amc2bY8fHELXoY3OT6PVV9krKBqbJcY2GdgNFbZop0NBAPgSJYG9MCtbz2oBaQmYGx4S9aYWa11gfHGLNM/b39kApIZeKvZSAyuCccXC4xjhNOHliH/N6jcqMwedWqai5Ys4VN5/YwxATNps1Kq8xrUZVKBYGSiEgHx5ivd6gMmNvGCFRK04Pw4h53mCeD8HMiCf2Me3t4ezDpzHuTbpHzgWbww1yrjh18mZQHOCcsRh1TR0eHKCUCkqDKVcBl4rT6w1O3rSvIY+8wWGeISFgFSNmA+2rYUDNM/I843Czwc0nTmBICWV9aPtLQBBCqIy5rHGmVpzcX6GQes1FBBmCgQLOHJyFhIQTqwmBGevNBqubTmjYxSo3o2o4/qv1NJ568maUQChkpQKYAWacPX0auPlm7I0jUppRcsEwDpDKyPNsYUPdK1AZ63UGTxP2pwFgxmaTQYjYnxIoBnCtGIcRXCrWmxmFGatpwnTTPjZnzurcDFE9W8WLhtLWtldZkEkwjKNRFQrmzYzDPOPUiZvAxu9LMQJF9dA6M06dOoU0juCc2/yvou0lAgVwzrqXiZfoMO6RaGXwcUjmdXNva89DsqejutYz4C5TjjlA6QMC0UyAKSmvQKrVPHAMZxtGqapAAgWkpA9OLTVPGw0Qqj1dElACnnlhUiDAXNeaOqpZDZk09kxRQz3FeubohhnBpaCUGSEk5ZwASGPEGAMCWMNMRa3McRisoFpBDIRpSMhm5QZooTc3zwms52+p0AGCDDBhCCMiEQpnS/tKSCQWV40AJYSw0bh5jYgpYgjqHUEIiGGwjASlphLtAZQwlzWUmDuCKEGQUHhE5oBSeiE79zJ5wR6uWs1TXeSWkhxI+6IYYIFZpsxsfYo0M0QzKkiJibUamdVT5tCtZFJyZjBQqiEdwhgj9oaEAPUYJGUiq6Vca/ME5FLNa6KW1oLnpnVX6sJ9S4s0QBFlKNrvFELL7nEV3FOidd5qZgY1wEDmNlIQpqEsRrb3NAtnCoITA7A3AKsgmIJm6QgqEGetT4JZCbBUILIBUCEya2n6kDWbhAQs2lizgyRqyl5JmGj34/fca3z0eg4CKHGveV5UcWvIAC0NX0g0fu5KrK1j6qGMkPT7jHTr5FU/dxWtDqzP2uP4+uyZRQGdkFW37NwR2DV5czqi6BAIBGr1SFrVVahrXxunGbDG4lxBM/HU+2ecmKiNCZ2o6jCsVEulhZJWtfGlcyWAnDNSTNhf7WM1rHB2fYh8oIo4xoQhJeR8gIG06UAxt33rspyi9R8DhqDBuhAjSARDIM0gMaMrpGShGCW1rpLuTZuiJfvTOKoxwIK9SCi1IOeMMIygmDBNE6TMIAL2UwITkNeHqKVilorVOCKlASXPAHVidYza2DMRkEGgmJoBoe1EBJUEHKLtywTMjFVKOJuL1v0h846S8V0qYTAPnKf6IqrXlwmQGJEoILFgXSvq4PtQRmUrsmiF+RIRBiJsjDOWWWsexZTA6zUGCihSMM8zhhQwxoh1LsphFN9zSA2eavW4bJ6EGM3fDABshG5CTANiTJBNwWhuymqhb/dA+4pqYd+F6hNRg7Ba2Aw5I0KwSgEbYawPDlBrwbRaGfcxY4wJh6Ug5xljVLBac9H9V8QyXNUQz+YJg4itkYJ5I1iNowKmUhFECc7VzXHpF+hjciVyzAHKQgha+CwqzwDEmAVaTEvUS1CLez20FkpK7qJFIy+xkdKIBMK9PLYjY7KHKKRM9EiK4kUEhahZ7IMxrFMyr0yKuqlYpsQQLANB1KItpgRSjBhSNHc64MWTggimQRUEbxhUBWPw9usCxIBZ1FoEVVXAKCBEDFEr1Ioo0WkMAYUBWFEub50+DqrMEzyTyHwFUq1oG2OuBZsgACWAKthqxRRERAFqjOpgAbWF5oW1FFctKyya5WfxfFdsXhCowKxU8qqL1C3d5QIwR4Bb4co9Ii2VH5xnouctVS1tTXlVT5r3H1KXecUs0psVLgCKpsO6hYDmzhZNW4AXQQLQuBX+tx7pOQudu6+ns9RdSAs/KAGWQcgIAKbAWA2EEwlYJcEUNDU+AQjQsI36nSx8gwIRPcfSe0K2s0WguXZ1rmuWgdckWaINNwPI8z6x3XHXQ0X+AecstVRqU06AeTykc4ha7yiQzbUFIPJxow521XvR0gXglTvFQYB4jx+dxOJYXgSMqp4iq0br353Mjd7qqwBGwNXrF1ag5WNC1nCtD5E0zx8gyimJAaCImYEQBpSiIZMQE9I4QkqFkJYcqJmxPjxEKRXjOGKa9lDDjMPNrMUaY8RsPnT14lraq3uizMPE1AGX83AiabqqKznv4szWYyuZQq+lAikgeBNEaPp9rRoK4qRzfEgRXCzrxOLFUis2Yqn3NCENA3KxQoD2AL1migISasReIjXo4uL9Zcghhk789MICzRNmPzSk1zldIE9FJwxxwGw9pZbuShYjzqprR/d1tAQtm396jlorEgKGoOEYrlXBu4NV6orYPUU+gXslZfcGhramvBGtr0eysRJfcDbpfZ9050lfhR20UIhg0QSKFCPABC4VYLHSDIRsXlEC1PNhHly2PGiGe0mX6cO2e5kBXkmAEJGS9gvzui+0uLgOq/rvlyvHHKB0DwoBmFIAsRHBALWixPrymAIJAIYxYppUGSyLoYlUlOKK0wfXKsnq0zMlxcisjzGRgo5AiwI7REjJ6p0kUwASbMNWJZEI+hlhMLuHwCwH1l45gFpLwsAUA4ZogAuC1aAZScIwMpXG5IsolyIRwFLUJReSLXhD6ebGJtKMpiFEjCkgJUFA0d4oISHngspAChEpRu0jVBiVEsaUEOMK62w9WWSFRAmRBlBIIFqBJQIYUatWcC2ivX6KWGEpwEJnZnGjc0dEHDQSOKAx54XIFpKDl54O58/Hs3QSEVIwsGAeDOWyaCZGrpqBUFit21qlNUJUMqGVUBf1P3llSg+/0CLVWfknDC0t76RN9VI0fsMCIOjksjTQVulEr0N/K5Y6zJgisJcYeylgioKBqpGqi3pMLNwHzCAwyAq2iWSIFAMmWUMixCCyrBVXsA0wqHJ1UNDArwOGxuGCeZ9so194hghof4ugbdzVzhvJa11YdgwzBitKqE3tNCTp1ZadcycGDktlq1brlYL9+j1eb9doG6qTmttWaRPMn7Wex0COo1zTKgIFbmoZhn4Kso1c2rSD1yQPerPagDApH2xKAwBWhn2MQIyIQqCqimk1TSgCbOYZuajFuxpGRBbMpUIoIsYBc82oEET0EJupUXhGxxK8+Tgvs3C8TUTw7uAsSOOAkKAFHFmQpgHFAJsWsFMQKsaLG4bBMjl0zxqGEfNmVmKlAdPV3j7K+gCoyuHxzD5fs7QYTOWBWaq3EpIQYlLL3A0CCiAhtKpMC+Cj5Gjr6WReIhHScFci0JgQctVLoKjeJdZn2zgVsljLDlKgnraYRkjJSiA1L05ta78D6iUgWT4EDZMvyfu9dEBMCTVGDfn70bIAWyYCBy42HlHPVIqRzmHPtFRUYeUqjRPWhwd2fERMqVW8Dp5R565o23Td9OtgXJ+Xrv8IignrXLE3jYgjoxhh1gFYL6i4PQqXK08YgKKIn6ymgBFGW9iG4DVKgoGGGBYWvG+uZFa2VS2noOBkPRdkNkvLJme1h6l96zQjR0ish4k2VBsG/S5ALTzYZinQ/Hdvfc3MiKQpvlzN0grB0g2BISZMo6ao5ayho3FUz0gpWnZcSZqEUtE6vTLDvDasNQBC1E7BRcmO0xAxeQGpyIaSba8NEblmMAMpjM3K9hRftXqyIeeKiIhIbKz3ClBAsCJMMUGru7KCB2LAtNAWH8EVUYBGPgBVYKV6lU2vAWublC165520mSB6nUzGA4oKVjR9kTQrKyu3g0Wr2XpTO4bWzlnsJm1D2UqrbqRPJ3pWI2VqbRvNUhIDJ0bKdOvEioyx1fFxq0W1iCrbFCqmSFaeHhgjMARGtCZ+kYAQ2Db3ahVM1YMivUKEpvua4g0WloC4B8Sre3YirNeP0I3JAAdg3sNW6EHDIUBL/e4bWrefYgOHbCmh6skis/arhfa2vSWkqfXBqlU6cIDFuMmacTpAYvMcOWixAlkCJfUqz0k9JMHGwO5goUmkGRbqSeNGeo9GGAyhAyv/rBBaeCeFqADUPALaPbxCAqGQZrG4F6+yenKoKt+MxgmrcUJKa6w3GevNGkPcAwHIJWOdC4Zh0jAacyv5zz7sPuSK+Btqcr6TuNKngPVmg2henDROKJZhMsSI+XCDnDNobx/DOGIzz+rRjUGzV7hiPc+4eRqRJmjxsEgI0wjkinEaUSpjfbjG6uRNiNNKs2Xg06PXz5C2dmFzUUOyEM3UY6wQhwlzzgtLXEs6wJU9OT9K138KAUnU801xBBHjIM9IkUCVWhfsIQ0a698CcwswDtXZpRSUWjHsrZAPleqfooKNuWgoRvdXNO9GPxfMY+KOig4io4XjSi6aATiOqLPuFUtunfj+4xWO7TuqCBACUhogVLHZbJBzwZAGBGGUnIFAiKsRNK+Rc8YQE9I0KSgn51oWFK4IKSpfydqM1EE9+RSCVgvzTDjL7JrnDUDAahwRIcjrdeNo+urykaUjcO1S5ZgDlC4E5SpERHiBJi3BoE+ZzY0fo2bqFPYUSPWcAJZFA01FLgzMlmKaq8aXqygpYoiEvRhBwZUKYaVqzK5GWkErmLKsRSuDOrkOMaBIAKoRGwd1TR/mAhFgHKIWUaKAGDULJhcNObmVOGftDzMNmh5bjb8QKahbnzXUkSJjGgRDCjhYFxQUDGnE/giMpphKPQRLROGACqAIYT2b5UJrU9Dq2hOZkJlwmAlzAYgSYthAmBDjAKEIkcHudQTRAJYAogRQRKQIEqDIBA/1AN5HgxqvRBUsIMYFocDNVQygLQivFUP2vAXSvCIshISoBcUqg6uOYy7VCvRpOp8XJ5LmPUHzsBF6FdK2WVoHYCCDiAHZ6HO3cIsWPyNocp56SLwsU3AviXhlTk9M1QrHQ1RQsjcE7CcginYYjsZBEvGy+tYXQ4rVfVFwpKE8vYZAFSKaYhyJQBY6CooY4aGlZmk72RWyvbkYeNd0X/VsZK9/YRlMYLFMmb7Jet2NatVZvUdJYQVHyrXSEIp3Ade0Z2pj7x6rGEMrsWMGH9i8h0EfF9iq3nqJePeqBBtvMBm3xAsB6nV4Cquft4qGOIaodZI0Zdxc6uJWZc8WVGUUgKChV1TGJm+Qpgm1FuRalesQtEM2WX2RWhnrgw0GBvamEZMQRlFSJJcCEsHpM2dx8/4e4jBgMHAp7IXezXAIOrOIenjEwZ+nVgcigCsO14cAEYbVBMwAuGrNi6phmtOHB9gfRyQxbyQEOVekEHB4cIAhBIwpIQyamVjmGbXOgCSMRNiUgofPnMFTTp5EnGe02i0MeFXWXgtExyzYdQ8x4DBnPHJwiNU4qneE1dvl8w2wZ8Zao6rPB31mh5uMsBow7e1hfXAaNRdIZdRQEMOgKbybja0VS/2Gcy0UdEfLqjo4e4ATN51AGAYNqUOJvsJqkGzmgmE1Ig4JmLNSGc27A5B5aTy025/DQNqW5fTZM9hf7SEMCi4Eyn2r6N4vTwzwZTWbFzHFARNFHM4bnD44ixP7JwykqF4q6zUiFEgegDBNI8KQMLAAtaKUglwZMQ7YH/eQcsVhzggxYn8aEcYRIRfbp3z/Y6RAOHPmLMKpmzFOExIz6jx3rxh6s9ArlScAQGnLFC1mbDPBN5GaleQ6jsEUrCBnLTrV++yYfy6o+qi1Wn2LgJisZ0sQDFZzQPfxnnmi9TxgseyAkitqyVrFELGVOh9C1NAMgDkXQIATU1S1zIwUgGEYwEKYzTVYS0HN1rQqRkBU0bqLfV0CiDSnfUwBQLXeJwFTShiHgCEKgNKzEqSi1g0qMSgOWpBsU8ESUaViLhXAiCFZ6rWRfxECyrzBwUb5JyEkpFAslAEQBgRECA2G0JT3EhARwgCiCKoBlAI2dYOZ1WeiRMqkAExCI77BvBdkm4gX99L0ZAWFibS2yRgDKLgy0caGEN0UN1m5JsAiBGEzpxjxtXmYXen4ZBKYtVVMaTO0GmsFIYOsmiuM+6GxdW+q1cmREA+reGaOz1tGgmCKhBNDwP4YMUYluw5k5dqpWIaPAMFCQn4+KzpIFtKEFL1e0msUKaDgPV/8G8UscQOz0tsOCCx8EjoZ1RWeu/zJNyF/D72fELvVHMgasanyZq+cKQ5e1MPG9kyIrQ+VkSbds+lVUfVDTrq1dGwr5kVE+lhEFQLDn2loVmmAZ/hQ33TbPmq7/8KbxczK36De7btl7TSlo/ODjQCiHZbNyylA3Wy0R5cpVS7KKYowQEiarbY5OECd9VpTiDbfGGMMyFJx+uzp1ryTK+NwzUiAZcJkZK7tWUUI8mbTrNpgXimuFWNSwv364ACzeXsCqec0QsspzJtD8GatjUqDrrkogr0YMDPj7MFZHEbCFAgrBKBkDAyUww2GGLGKAXPOmA/OIkHDcrMRR8nur84ZmdQYEVKOXc0ZAwVIjMh5xtk8t7EredZ9g4CBCOVQrfZZGJUIIxHmrOHVkYB53qDWiv1pQsSIs4cHyDkj29wE6/xXMq8XugOiqEfA+3OVWnH2q48ocddALcwIHCOhbDaYpYKEMRChzhsQtCeVAtWKvN6o8WieL6kKbqakqeBnctGMMWhIeM0HGloU0ZL2FoqKFMAlA8woXHEoBxhJuTHCjIOzZ9SDZxw8EkGSAFBA3myQNxudH+Rhcp3DJWcc8FlAGIkIeb3GGTsWAOb1WkO/XFE2a0QQxkCYDw+BQWvhuK/EPV3NwrwyB8oTAaB0ybkANbfYehVoaAXAMESMo6YSC7O2ZheCUCcGKtjQGB5BO+7qfiSWSqn9coLVL6n2gKO5KJlhvA0v5OQu3YxAwN6YMETLca96znHQwj5qCQLDoFyTw1KNM+MZAMrULrUiCGMYtXQzQbAuymXZGwKmUR39uagVNg2WKkcVpWQLHwApspIFITjcbHB2I9hsjAVCakumREixNOIakDCXioM1Yy5ACsZV14C7WkTQ0vtAMg9VtESRgICeIQQm1KBplhBNbYZMCBIgSCBE682RjHBpJf0xqMIQJV6SABRF65xY1k6Kqrw0E0ctjnXWQlkOeAANvGgIyTwjC+Di3AP1dCgosRwKeHVW8d9FoAXQzHshAljjPaKl0mP7XlegCjjGAKwi4cQYsJ90s42wSsfGGYlkrlkLe1SxSrCAARO2DAD14riHRMBaDJBYs3ag5Eg2ArgXWlNQ5Qoexrch43Jo7RnYPGQjFRKRFoOqlvWWkq4Tspom6FYgiFpTM8A8nh5uEUDMKmTDX+7FEljY1p5ZCoRo1rYDQJdlg7yWxu6kd/uf2D1WZiO6967jejxbYTP3qKpn0zlTISjfQfhoM0HSrC/jAziAi0TWxbpueTSYvPihZt4kA0LuEQQ8VV5J6555bV5/UOPiaMYNpBqHS1NrYT3IYGT9Mq/hDQ0TOqhUbg8QRAHjQLDx9ZC2Zc4EG2CQ9XYJSIbuPMWbwRBWxZwogor2akqERjQWOy+xQ2EgCsC59PVAWmGJHYRDry9Y6YchRISqwMYBspRi7UnUe53A4DqjsBqdEdaZumj2TiTCmJJlrNVGeh9JvV9EuqcEyyAkAYg9I6/PxURBr0vMcyYMb6iaNMtAC8LZZ2rJ2uJEFCBNFtZvXmA4gNFsGlRpnErUisIbBOi1gXUniaRZio3gbevXuS+JyEi00gAWzDhpAKBqg9CRzAPqThuy96BgQfcYrWMTRIBcUG0/I5ujbb0Jmsf0cuWYA5QlRFOyKnuBJyKgenyaurekavVBMEFIc7idvT9n7y+i7eEpcLMwAJjlpB0sq2jsPZg3JRfBeqNhHIJAPG4n5rZP1MDJXNS1P6WgZfOt+JiHmCoXTSWGlsKfkqa/VtFusuMQMQ1a+r5WoFg9iNVAGIOgSgFFtZKHwEhB3S1MghT1nlPwlOWCw03FPFtTvxCtsiohYG2bZESpEZu8wSMHGetMAAJEtFCPjn60zTCZAiTAK9SK8UQomStXuSCJIhIqQAmRIhKNCEFL+DOCfpYGS3mLRpgbzBLWard6nREQDSsFCRgwaCfmmLCpBMwasmN71uJxNu4dZRWIoD1o9zMAAoam6gb7KZjVS4EK0IwlORbwzJnSFm13t7sK0Q1QQzkKSqZIGCMwkVqyBMuxhjYM9Equ0kifVT03gmZ5sRTLSJHm4VNQInrdAr0u1s9q11y11DW5QzV463PEloLcRkSfNIsrfDKgo54AqyIF3wP9k8z6Xa0CJtAqtprOgaGjZni5u9gzPZxaAbPCyb04np4pol5QmFeDsN1+wJ4xsWWS2cMWMS5M6Dwjsu93LpNrlyVRkSiYx03Pm51PQqGNGaCeAVQt4OXg1ysGuw9GjR9qINBHrlmiWKRzm/HgvjBNr5eeCUcKWFurAL92K7kOoZ6BRmbEmcIK1D+jK5zac/bNjIiMb9azmHrrBwUVEZrGX21+BmhorpqHThWnkfQ9PGcePDKA612qvcdQaINn3wlpYCuaIvYyBbYbgWBEaelZOkt9qZXEja+0mG/wqt5QINO8hCLodwoLN4sZKNSea7WicHDdY+M4gECVUdCP9w7oDkD7DzWgHZW2lgWoDbgCPTzuKfdivZoakDKEEInU+HOQYs/agbTveAQLVZLNJ/FLkO4pETXSI3T/cD5mW7u0mMNLRHcZcswBSheCuieZgllJALGCEl2LGhYRghU8s83IoF61OCfIemq4Sz54kzCb7FU3rBiVGBsCIWfGJless6LgMVnZYBbL6NHCRZusXVqJ0Hrz5FJQWDAk9aSUypiFzMNCVmBOCbPCFeMYsBq1QSFX5Rjsj1FTrJMuVGsli3GI1iPIsmF0UKzGCwBhlAyUYhZbBLQ1hnl0LFVyU5SHs5kF61mzCnSMdLx1LMnIXLG55rVLc2y2LqyfK2AF8FjJyiTKS4lhQAwJDEJh/e5AA0zbgEiDIYEiCElrylBCsC2AeUQl9TLp+4KIgCFErFIEFfWmqBg5symk7blkRyiYgDfU059AgVAGpECQ4aEer1+i27R6M9A2Nt8GNXNqigF7ibCfAqakViYJt1RH8l1GagvtsGgRNYggJdtMAQMnarkpCHTvg4CrcU4sCUWkKqEXrsQMFMhi04OYElsABKLWkbUDOg9v9o7DnprttoPbCk6M9O/xarQNjpjCJAdEwRdxV/ZaNyc0xYzFca6cdOP39B5dg65InavRro+0pop6Q/SGmwdNupLyrtYiWhMpiBd9VGWQmW0NUQ+jiYE1sy47adAUkHSytOshWTwTB8wiroz10xrisyfPfqyNweI79FFaTxj4yTqJ0YFbsNP0Hj1+7egPsF2jZaQ0s9jCbLBz23Ou7qm0ceueSb1Or1bMBsYjkZVjRwt7svSii7BV561JlATt68n6DVHPxvG4XQgenuzgw+BeDzVKB1gOOKgNIJpC18vrZ3DQxA422oTz8Q9tvHwdJfQKxALzWrGH4Xx96rjoWDXM34zXo1kyPnfaawY27ZHACe++J7e1ADQg5nOquSkdCTqob6PX/mjzsoP5bXDi3tf+BC9PjjlA8UcNwKxMn0ROkgSkpSPCJpmum15dsohlMwR1+YKLno+c+Kqu4gBYjxHCMGhTvZKL8k2yNlObEiGSQCq1kuxFCDlrmfEUCftjwpgi1rni7KyExyEpcCiipfRZgEiMWjJmAxcpEfanhBR9oajrdYoCrWoWdeG5Cz5opde5FG1hX8nCGVqoS8iK0BHMlcmoNVt4Q9PWMkccbmZsshaBCwQjW8IsC/1OsWw1tsI+0hZqgq8WLaBkKWyG5lUhW88fJJBo3YMUyNSkdvJMQe0ftsofkAjCoJ9FsnjpgCoJcx0hiKA6oDIBHJEoApFAQiik5DqBbgZBgFYUADDrDmCZLdx3FkIVhI25bzMIRau7SoaSYpWYSpLtNGWrxglBS2OMUau/7o8Rq6AERBIlnhEEwcKLRO65Uw+I2GYoBjCjxc3VG1Bato6Id+l27kYxZeXKrVoDP/NmBFooAk/LZaBxN/w5qtfAa4qoetB5msz74nulc7/EMq9CCI3g2Hrt2JxpMX1TZjBPyDaAkYWiWIwFbCPsri8DOR5atSy+GNqYNtVK1J65GEFSYNYjFNS751V80zXF694WwLsGG4k3RAMyBhztO+tCaVNw/pqf05THlqLpP6Uf2pRBVyRAh6lt8GHxGGz5BQyZiY2Pgy61xs0TGRRwMdC4Q01DmWUeXDHaBfocUGDZOU5+X73frp9IQZuAGhgLMOCB7inzZ9w76va7dVJ2awsBbl4KoV5nhxaD1+rs2LxxQny7VgOHC2zb5ikcrNo9+vNhmIeMtM4UOUFWg4QwjAGnMxu66iDYRgTUTThtBRL6sX4MuhfNkwNkMaZ+Ue5Bc9i0JK42/w/1c7a1COjNL6fW4vrazfh9SX/uDgDRxtKBCi1qJ12eHHOAcm5R54dukLCNVSe8cUWam4swz+r9EBFMKWIaLMQDAGwErqCggyKBkgITihG1MDbrDKnAaoxtsZTCGvuEEgOd5DREwt5AmJLGtdezhnLGKDbx9XWt8hiV09KmlWUdMSMM5oJjQpSgMVRSpV0tlEGBMFfGvBYIF60bFSIYglwFVSwdEt3yqsX74QSklFBKxWGdjVimoCZIr22hdSzKwrqwDd+Vn1XghLBtXbYMxfucwM6b7HPFsn2CuXc11KNuae36DFYCMSGBaIZwAMKoY80zmCICJpSqlW61xkwAMCBSwhQDkliAhN1hCkDUgeleJr2yGeqonm1TnSHGPdFGe8pBEWH1qOjo2kIudg4N8U0xYC8S9oaAVQJSUNKr1h1Rd6lWGO47hI6zEWqhwCGSKvMYYIUE2QjKYlEW3WUFsGJ/mrKrqesKJgCrgGzpjsHTifkIEHALiGA1aYptZB5isY3V15vt4m5Fk4dcREORSjTXnyJWdI0XQADao8lT45unD+hhES9PT/qdpVZLlTaXuY06t/oZy01a//Yy/n4NDpBUSduB7JuwKVL2OQ0QRftOCxuyP2mx7Bpphd7Y0pApBFNEOv76iQUIOGoRH1EUcLBivzjQ8G+2x2LHSv+GoPevVYvRjxdX3B2wxKastpDcAiA5qHJvRVfkHnLz+xIA1PKg9Ye2fXBgiB6KCEtF27FaqxK9+C5PE99ukUD9J8ELOh/V3To/llhB0LgaPuReK4kWz8R7dXnNLPfykI+TAaEG6pbf65Ya/HlL+x6CgMnCXQug6TWBGq/DZ8oWEvUxcODtY7AQS7eH6YTGLyQP7aJ5NENDLT1s1IGuj64g9G9feBu5nae7a8wQade8BKoXL8ccoPSVLGKNpyxM04iqovFhtUjVpQvo76Uy1plRmTSdMGjl1/aQbMOt5pbUGgeaYVDyBnkuAALikFCrIOfSUlaZtIQ9pMe7o5Hx5lKxzqJcFGgb7BADSlWwNETf5BeGFQVU0XLryBUh6nXNuWi4iQjzvNEeQaag5qoZKikCqxSUKAlrfgfNpKmVsbHUZiuIipgABMZmPcOreOhIEMSuQ8dIrxHim1S0JodsBFlSD4YpO5BaH8ymnOy+1I8A89D4YtFNTNhdhdoDSBAMuScQBiN0GWihAHAEaNQwEA0QCagSocETjWERLB5LYvwVsmvr2kG3DwUdhEMbhY2CFykACrRxnx4DcShppempWtloYDVE7KeEKQoSNK1c787BB5o3T6wOiipPgjQPCndLDARigrA2HKsWz1cCq22iDLPaVZtV20SChWiKAWHv+sqid1gsW0VNaQLMk9AsfSeEG8jxsIGwZygRMuv4VUBTxG2DDIZOfIOui03TPTrV1osSTHsopzCso7QgsDT+R2VRqxmAVEG0ppueoo/gGQvORaBGiPewQgCZtSoNpJWq81NTbbURGwMKVGptxalaaNiAmVrhnoXGvjiaRlzoKzTXuPic6y78Ho7p9Yc7SjFvhoFNSAeSzUCw7ctDZqrw7fBF+nwwheKKW9eneZOaQg393CSazt03yAaMFvgIcDDXXvM1Zc/c5nKFLSlYyrADMFfkipzbTh/a3tpO0TwGR5WfO5qcmiI+39r1wPZKac/Ba9sEB+f2TPxZOcBoPxevKVj3Z92/oxNFu7ethcbYwWa/boHO9cYNsjFbBAL6OLZpIe1NsTFpz8S8Ng3w+RRrICn0EBN8PvZzqcrkbVzkc8PGVNrc7NNd9wUckUfBqAvKMQcoXVgEX3h41h4BZm0dLcLW7QOdEb7BAYqSYxCkM9bezTYC2A9afE7XjPW2oAAvk16sSVzLW2+iSiJFaqX1axUUe4hprRlD1WLZrW29oKXT+qVokZ0OVGtls/yUp1K5bxxefCwEMi4KWf0Jb/JmIKcwmkVCaOmipVQ3anwY0EdR/+9KnQwxb2+OPhnbK/asbCMU+0IPr7hrHw4Y+oYCAygs0QAKI3C1zxa7rmjPo+hypgRYVpB4aMiBjphSczqdtCReX+FopeJxrp9aPr6FYAzKeUjHsw8YAi6CmZUlT8KdYyINnuhzhipg09c2wqbkFtuiKx4Hv6rY0DYZD3P2uY9ONrTHweKcieUz7RZ/aN6vhVHka4EWCt//2+IlSJtLfi8inu1i9yV9g+9LpWfrAAtyepszdg+hW5Rb+590voxnEfUS5L5zbyvhdp1tfzDl6EqavA+PrevFOXxclhwGGyI45PAxUW4HtmRLpT56MBZHAY/+8KOj+74Su8Lsx/npxc9F3evgY+Df5fwZvydafEE7j++PbWtajt/WlS9ee/Qry6uXI/fYIwdy5Mhl+A9b19f37f6ZvlcdOf/ip4ivLWljsbzaC115f3X5/M9zlCv9xbV370UXXlxrv9+l9Bfa41o8t0dNDnn0c2nXsOVllK1jz3kP7f99nGXr5NLOl4aE/0cuH2g8gQAK8H/XFXmuR9650FBfzPuPJUe/70aSx7q38yFZv6fL/fzFHk+Ln3SOv5fHuf3g3ZSDveb/CGjcfTum/QzneW35meVrHot1YDDbT/OWwIEJQ0M5svhp5FgoUVkbj1lp4qZOZfHvQn/jAj8f671z/X2xxzzecj2+88kqj7VGL/T++d671Ncv9fyXOj+uxh7/eMzJx3OeX+pYX0vp9z04z+wy5QkDUADs9r1HybYl8ej3ruTzlyPnsq2Ovrf8e3mc56Me9RkePefSjhN0UOK/+3kdmPjf4chPWpzDU3X5HP/kHD/PBzYu9u+d7ORGlqNr87Fev5Tz7MDJ8Zdz+dIuT5c8sQDKo+Tx9p4cB7lSpH3UuXk1kPtSeR+duOfzsoTFz6OAop7jtXN5R87nOTmfJ8Wvq3tHHg1Q6uLnUTDj93ghT8n5jjnXWF3Maxf79/WQG+EadrItF1rTl7Pejz7jx/r85cyJqwU6Hq/5+ESZ51dqoF7IY35x8gQHKDs5fnIx1tn5lD6d4+e5osfLhbI83qtCBGx/1xJ8nMuLctSbcq5rPHqt53r96GvLn0fv4UKvXeymcjU20qNjeq5z3sju6J1cWC60HnGe98513MUce7HnuVHlWl7j1TIWzydH98jrI09igHIcJvyTQc7n5j2Xm/AoEAGWFRK2wz9HF9hR4HIuT8zREI9/v4OPc3lJzhXmWV7XxYCPCx2Dc/x9vrl7Ka9f6fy/FFB0Ldba47VZH/d94mIU2WMd81heFlzEdyyPvZjjL3fcr4fn5LjPkceSiwUp5zJSrmxdPokByk4uLJeqBK/UOjpfLPropD/X9zMe/fmj3pGjwOZcgGT5+7kAyvLn0decWHwugHL0ui8XmJzvPBeSJ8pmfL28MFfKkTgucjEgBRc45lKAyvL4qyXX0mt4Nc9zI8q5nuGlgI/zcVCOfv6xZQdQdnJEHk/L5XKttIv9LgcqrvgXVbce5UE5+r3ugbmQB+VcZNijxNgLhXGOXv+VeE2uxKNyuXKcPSFXSy5vo72+crFr62qEDa6e9Xzp33c1j308z3G15PEO85xLLuX7zgd0dhyUnVyyXIuFdzlW2NHrOpe3ZAkylp/hxetLLwnO8/Oo92T5necDIUffu1jPybnu7bFAx8WGVa5muOVabcg3OjA5Ktc3Nv/4yZV6Us51rMvVBD+P52cez/Ps5FJkB1B2ghtv8V2KxQc8Wllc6H7Ol67sci6QciGA8ljA5HzX9XiAk8d6jjtwcnXlyQpS/BhcxHFX+pnl566HPBGf77WUHQdlJ1ck12MBXsxGdfSYcynooyGbc8U+/efR3x/r76PfdTRsc/T38713oXs733091mcu5b2Lef9Sj7taclyByZNBLsdIuJRzP95yJd9xnEDJ5YK+xzrn5ZzvQjxCusBx55cdQNnJDS7nC/scJcGe7+f5znk0JHSpAAUX8dr57uVcfz9eHpEbEZzsgMkTT64HH+Jc8mQBJsdBrnw+HHOAcmmEm+sv51sA1/oebpSFuLyOi02FPJ+SPx9Cv5jfl39fjFfnQmDiUoDG5YCSC8n1jNE/lhyndXqxcqOso8dLLtU6v177240876+VXG2+z6XI4/ddxxygLOV8jOEbZSJejIv+RlzI10ouNu59vjDO+d47H08Fi9eXnznXdVzNUM353ttZfsdLnkxjfqXekcfLu3K5z+DJ8OyuVKfcGB6xJxBAAS4NpDze4GWH6i9dLsaj8ljclPMde/Tcj/VdF8okutD3XklY5lo+/2s9126MDe/JK1dDYV2Nz1/OOa7mXH2y7bFXOu7Xd82Gxz5kJzu5HnK1NpKrQfa6WvJk2xx3spOrITtg+2SVHUC56nIhkuROrr/sNrvrNz+fKOviiXIfx0l26/bK5fjN2x1A2ckNKtdqQ7pRNr4nQxbNjTLWVyrH9T6u93VfaVLD1UiKuN5jcD3letz70UKZl/YMn2AclJ3sZClP5s3oRpLdc7j+cjWewe45PrnkYp734zsnjrkH5UZcMDfiNe1kJ9dLduthJ1dbroYnZifHQY6lB0VE3eGlFLhrvJQCEQbkamZcXK4cv1jfTi4kV2tO3SgZPNfj+55oshu/S5PHI2tyl5p/o4sIo5QCWmBC1dtdj19ISC7mqBtMvvCFL+C5z33u9b6MnexkJzvZyU52chnyxS9+EV//9V9/wWOOpQflqU99KgDggQcewKlTp67z1Tzx5ZFHHsEzn/lMfPGLX8TJkyev9+U8oWU31tdOdmN97WQ31tdObvSxFhGcPn0az3jGMx7z2GMJUEJQ6sypU6duyAfwRJWTJ0/uxvsayW6sr53sxvrayW6sr53cyGN9sY6FY06S3clOdrKTnexkJ09E2QGUnexkJzvZyU52csPJsQQo0zThb//tv41pmq73pTwpZDfe1052Y33tZDfW1052Y33t5Ik01scyi2cnO9nJTnayk508seVYelB2spOd7GQnO9nJE1t2AGUnO9nJTnayk53ccLIDKDvZyU52spOd7OSGkx1A2clOdrKTnexkJzec7ADKTnayk53sZCc7ueHkWAKUt7/97Xj2s5+N1WqFl770pfjYxz52vS/pWMlb3/pW/ME/+Adx880349Zbb8Wf/tN/Gp///Oe3jlmv17j33nvxtKc9DTfddBNe9apX4aGHHto65oEHHsDdd9+N/f193HrrrXjTm97UGkHt5Nzytre9DUSEN7zhDe213VhfXfmt3/ot/Lk/9+fwtKc9DXt7e3jhC1+IT3ziE+19EcHf+lt/C1/7tV+Lvb093HXXXfi1X/u1rXN85Stfwatf/WqcPHkSt9xyC77v+74PZ86cuda3ckNLrRU/8iM/guc85znY29vDc5/7XPzoj/7oVhO43VhfnvzSL/0S/sSf+BN4xjOeASLCe97znq33r9a4/vIv/zL+0B/6Q1itVnjmM5+JH//xH3+8b+3SRI6ZvPOd75RxHOVf/st/KZ/97GflL/7Fvyi33HKLPPTQQ9f70o6NvOIVr5B3vOMdcv/998unP/1p+eN//I/LHXfcIWfOnGnHfP/3f78885nPlA9+8IPyiU98Ql72spfJt3zLt7T3Synyghe8QO666y751Kc+Je973/vk6U9/uvzQD/3Q9bilYyEf+9jH5NnPfrb83t/7e+X1r399e3031ldPvvKVr8iznvUs+Z7v+R756Ec/Kl/4whfkP/2n/yT/+3//73bM2972Njl16pS85z3vkc985jPyJ//kn5TnPOc5cnh42I75Y3/sj8nv+32/Tz7ykY/If/2v/1V+9+/+3fLd3/3d1+OWblh5y1veIk972tPkve99r/z6r/+6vOtd75KbbrpJ/tE/+kftmN1YX568733vkx/+4R+Wd7/73QJAfv7nf37r/asxrl/96lfltttuk1e/+tVy//33y8/93M/J3t6e/LN/9s+u1W0+phw7gPLN3/zNcu+997a/a63yjGc8Q9761rdex6s63vLlL39ZAMgv/uIviojIww8/LMMwyLve9a52zP/8n/9TAMh9990nIrqAQgjy4IMPtmN+8id/Uk6ePCmbzeba3sAxkNOnT8s3fMM3yPvf/3759m//9gZQdmN9deXNb36zfNu3fdt532dmuf322+Xv//2/3157+OGHZZom+bmf+zkREfnc5z4nAOTjH/94O+Y//If/IEQkv/Vbv/X4Xfwxk7vvvlu+93u/d+u17/zO75RXv/rVIrIb66slRwHK1RrXn/iJn5CnPOUpW3vIm9/8ZvnGb/zGx/mOLl6OVYhnnmd88pOfxF133dVeCyHgrrvuwn333Xcdr+x4y1e/+lUAvUv0Jz/5SeSct8b5ec97Hu644442zvfddx9e+MIX4rbbbmvHvOIVr8AjjzyCz372s9fw6o+H3Hvvvbj77ru3xhTYjfXVln/7b/8tXvKSl+C7vuu7cOutt+JFL3oR/sW/+Bft/V//9V/Hgw8+uDXep06dwktf+tKt8b7lllvwkpe8pB1z1113IYSAj370o9fuZm5w+ZZv+RZ88IMfxK/+6q8CAD7zmc/gwx/+MF75ylcC2I314yVXa1zvu+8+/OE//IcxjmM75hWveAU+//nP43d+53eu0d1cWI5VN+P/+3//L2qtWxs1ANx22234X//rf12nqzrewsx4wxvegG/91m/FC17wAgDAgw8+iHEcccstt2wde9ttt+HBBx9sx5zrOfh7O+nyzne+E//jf/wPfPzjH3/Ue7uxvrryhS98AT/5kz+JN77xjfgbf+Nv4OMf/zj+6l/9qxjHEffcc08br3ON53K8b7311q33U0p46lOfuhvvhfzgD/4gHnnkETzvec9DjBG1VrzlLW/Bq1/9agDYjfXjJFdrXB988EE85znPedQ5/L2nPOUpj8v1X4ocK4Cyk6sv9957L+6//358+MMfvt6X8oSUL37xi3j961+P97///VitVtf7cp7wwsx4yUtegh/7sR8DALzoRS/C/fffj3/6T/8p7rnnnut8dU8s+df/+l/jZ37mZ/CzP/uz+D2/5/fg05/+NN7whjfgGc94xm6sd3JV5FiFeJ7+9KcjxvioDIeHHnoIt99++3W6quMrr3vd6/De974Xv/ALv4Cv//qvb6/ffvvtmOcZDz/88Nbxy3G+/fbbz/kc/L2dqHzyk5/El7/8ZfyBP/AHkFJCSgm/+Iu/iH/8j/8xUkq47bbbdmN9FeVrv/Zr8fznP3/rtW/6pm/CAw88AKCP14X2kNtvvx1f/vKXt94vpeArX/nKbrwX8qY3vQk/+IM/iD/7Z/8sXvjCF+I1r3kNfuAHfgBvfetbAezG+vGSqzWux2FfOVYAZRxHvPjFL8YHP/jB9hoz44Mf/CDuvPPO63hlx0tEBK973evw8z//8/jQhz70KDffi1/8YgzDsDXOn//85/HAAw+0cb7zzjvxK7/yK1uL4P3vfz9Onjz5KAXxZJbv+I7vwK/8yq/g05/+dPv3kpe8BK9+9avb77uxvnryrd/6rY9Kmf/VX/1VPOtZzwIAPOc5z8Htt9++Nd6PPPIIPvrRj26N98MPP4xPfvKT7ZgPfehDYGa89KUvvQZ3cTzk4OAAIWyrkBgjmBnAbqwfL7la43rnnXfil37pl5Bzbse8//3vxzd+4zfeEOEdAMczzXiaJvlX/+pfyec+9zn5S3/pL8ktt9yyleGwkwvLa1/7Wjl16pT8l//yX+RLX/pS+3dwcNCO+f7v/36544475EMf+pB84hOfkDvvvFPuvPPO9r6nvr785S+XT3/60/If/+N/lK/5mq/Zpb5ehCyzeER2Y3015WMf+5iklOQtb3mL/Nqv/Zr8zM/8jOzv78tP//RPt2Pe9ra3yS233CL/5t/8G/nlX/5l+VN/6k+dM0XzRS96kXz0ox+VD3/4w/IN3/ANT/rU16Nyzz33yNd93de1NON3v/vd8vSnP13++l//6+2Y3Vhfnpw+fVo+9alPyac+9SkBIP/gH/wD+dSnPiW/+Zu/KSJXZ1wffvhhue222+Q1r3mN3H///fLOd75T9vf3d2nGVyr/5J/8E7njjjtkHEf55m/+ZvnIRz5yvS/pWAmAc/57xzve0Y45PDyUv/yX/7I85SlPkf39ffkzf+bPyJe+9KWt8/zGb/yGvPKVr5S9vT15+tOfLn/tr/01yTlf47s5fnIUoOzG+urKv/t3/05e8IIXyDRN8rznPU/++T//51vvM7P8yI/8iNx2220yTZN8x3d8h3z+85/fOua3f/u35bu/+7vlpptukpMnT8pf+At/QU6fPn0tb+OGl0ceeURe//rXyx133CGr1Up+1+/6XfLDP/zDW2mru7G+PPmFX/iFc+7R99xzj4hcvXH9zGc+I9/2bd8m0zTJ133d18nb3va2a3WLFyUksij7t5Od7GQnO9nJTnZyA8ix4qDsZCc72clOdrKTJ4fsAMpOdrKTnexkJzu54WQHUHayk53sZCc72ckNJzuAspOd7GQnO9nJTm442QGUnexkJzvZyU52csPJDqDsZCc72clOdrKTG052AGUnO9nJTnayk53ccLIDKDvZyU52spOd7OSGkx1A2clOdrKTnexkJzec7ADKTnayk53sZCc7ueFkB1B2spOd7GQnO9nJDSf/P5n+v7Cs3GUmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}