self-supervised.ipynb

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      "source": [
        "# Tutorial 17: Self-Supervised Contrastive Learning with SimCLR\n",
        "\n",
        "![Status](https://img.shields.io/static/v1.svg?label=Status&message=Finished&color=green)\n",
        "\n",
        "**Filled notebook:**\n",
        "[![View on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/SimCLR.ipynb)\n",
        "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/SimCLR.ipynb)  \n",
        "**Pre-trained models:**\n",
        "[![View files on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/phlippe/saved_models/tree/main/tutorial17)\n",
        "[![GoogleDrive](https://img.shields.io/static/v1.svg?logo=google-drive&logoColor=yellow&label=GDrive&message=Download&color=yellow)](https://drive.google.com/drive/folders/1BmisSPs5BXQKpolyHp5X4klSAhpDx479?usp=sharing)     \n",
        "**Recordings:**\n",
        "[![YouTube - Part 1](https://img.shields.io/static/v1.svg?logo=youtube&label=YouTube&message=Part%201&color=red)](https://youtu.be/waVZDFR-06U)\n",
        "[![YouTube - Part 2](https://img.shields.io/static/v1.svg?logo=youtube&label=YouTube&message=Part%202&color=red)](https://youtu.be/o3FktysLLd4)    \n",
        "**JAX+Flax version:**\n",
        "[![View on RTD](https://img.shields.io/static/v1.svg?logo=readthedocs&label=RTD&message=View%20On%20RTD&color=8CA1AF)](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial17/SimCLR.html)   \n",
        "**Author:** Phillip Lippe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_lO5uAWEW6bG"
      },
      "source": [
        "<div class=\"alert alert-info\">\n",
        "\n",
        "**Note:** Interested in JAX? Check out our [JAX+Flax version](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial17/SimCLR.html) of this tutorial!\n",
        "    \n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8NyNvoXW6bH"
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      "source": [
        "In this tutorial, we will take a closer look at self-supervised contrastive learning. Self-supervised learning, or also sometimes called unsupervised learning, describes the scenario where we have given input data, but no accompanying labels to train in a classical supervised way. However, this data still contains a lot of information from which we can learn: how are the images different from each other? What patterns are descriptive for certain images? Can we cluster the images? And so on. Methods for self-supervised learning try to learn as much as possible from the data alone, so it can quickly be finetuned for a specific classification task.\n",
        "The benefit of self-supervised learning is that a large dataset can often easily be obtained. For instance, if we want to train a vision model on semantic segmentation for autonomous driving, we can collect large amounts of data by simply installing a camera in a car, and driving through a city for an hour. In contrast, if we would want to do supervised learning, we would have to manually label all those images before training a model. This is extremely expensive, and would likely take a couple of months to manually label the same amount of data. Further, self-supervised learning can provide an alternative to transfer learning from models pretrained on ImageNet since we could pretrain a model on a specific dataset/situation, e.g. traffic scenarios for autonomous driving.\n",
        "\n",
        "Within the last two years, a lot of new approaches have been proposed for self-supervised learning, in particular for images, that have resulted in great improvements over supervised models when few labels are available. The subfield that we will focus on in this tutorial is contrastive learning. Contrastive learning is motivated by the question mentioned above: how are images different from each other? Specifically, contrastive learning methods train a model to cluster an image and its slightly augmented version in latent space, while the distance to other images should be maximized. A very recent and simple method for this is [SimCLR](https://arxiv.org/abs/2006.10029), which is visualized below (figure credit - [Ting Chen et al.](https://simclr.github.io/)).\n",
        "\n",
        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/simclr_contrastive_learning.png?raw=1\" width=\"500px\"></center>\n",
        "\n",
        "The general setup is that we are given a dataset of images without any labels, and want to train a model on this data such that it can quickly adapt to any image recognition task afterward. During each training iteration, we sample a batch of images as usual. For each image, we create two versions by applying data augmentation techniques like cropping, Gaussian noise, blurring, etc. An example of such is shown on the left with the image of the dog. We will go into the details and effects of the chosen augmentation techniques later. On those images, we apply a CNN like ResNet and obtain as output a 1D feature vector on which we apply a small MLP. The output features of the two augmented images are then trained to be close to each other, while all other images in that batch should be as different as possible. This way, the model has to learn to recognize the content of the image that remains unchanged under the data augmentations, such as objects which we usually care about in supervised tasks.\n",
        "\n",
        "We will now implement this framework ourselves and discuss further details along the way. Let's first start with importing our standard libraries below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "tUcnuBAmW6bH",
        "outputId": "c63c36d3-fced-43f5-c18e-4fae28f91527"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-3c365f0e9091>:10: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
            "  set_matplotlib_formats('svg', 'pdf') # For export\n",
            "INFO:lightning_fabric.utilities.seed:Global seed set to 42\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda:0\n",
            "Number of workers: 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Standard libraries\n",
        "import os\n",
        "from copy import deepcopy\n",
        "\n",
        "## Imports for plotting\n",
        "import matplotlib.pyplot as plt\n",
        "plt.set_cmap('cividis')\n",
        "%matplotlib inline\n",
        "from IPython.display import set_matplotlib_formats\n",
        "set_matplotlib_formats('svg', 'pdf') # For export\n",
        "import matplotlib\n",
        "matplotlib.rcParams['lines.linewidth'] = 2.0\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "## tqdm for loading bars\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "## PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "import torch.optim as optim\n",
        "\n",
        "## Torchvision\n",
        "import torchvision\n",
        "from torchvision.datasets import STL10\n",
        "from torchvision import transforms\n",
        "\n",
        "# PyTorch Lightning\n",
        "try:\n",
        "    import pytorch_lightning as pl\n",
        "except ModuleNotFoundError: # Google Colab does not have PyTorch Lightning installed by default. Hence, we do it here if necessary\n",
        "    !pip install --quiet pytorch-lightning>=1.4\n",
        "    import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint\n",
        "\n",
        "# Import tensorboard\n",
        "%load_ext tensorboard\n",
        "\n",
        "# Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)\n",
        "DATASET_PATH = \"../data\"\n",
        "# Path to the folder where the pretrained models are saved\n",
        "CHECKPOINT_PATH = \"../saved_models/tutorial17\"\n",
        "# In this notebook, we use data loaders with heavier computational processing. It is recommended to use as many\n",
        "# workers as possible in a data loader, which corresponds to the number of CPU cores\n",
        "NUM_WORKERS = os.cpu_count()\n",
        "\n",
        "# Setting the seed\n",
        "pl.seed_everything(42)\n",
        "\n",
        "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "\n",
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "print(\"Device:\", device)\n",
        "print(\"Number of workers:\", NUM_WORKERS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggmSNHISW6bJ"
      },
      "source": [
        "As in many tutorials before, we provide pre-trained models. Note that those models are slightly larger as normal (~100MB overall) since we use the default ResNet-18 architecture. If you are running this notebook locally, make sure to have sufficient disk space available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cN12oxhqW6bK",
        "outputId": "c24e33e3-aa5e-458d-f8c2-386954bb3e4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/SimCLR.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/ResNet.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/tensorboards/SimCLR/events.out.tfevents.SimCLR...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/tensorboards/classification/ResNet/events.out.tfevents.ResNet...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_10.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_20.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_50.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_100.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_200.ckpt...\n",
            "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/LogisticRegression_500.ckpt...\n"
          ]
        }
      ],
      "source": [
        "import urllib.request\n",
        "from urllib.error import HTTPError\n",
        "# Github URL where saved models are stored for this tutorial\n",
        "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial17/\"\n",
        "# Files to download\n",
        "pretrained_files = [\"SimCLR.ckpt\", \"ResNet.ckpt\",\n",
        "                    \"tensorboards/SimCLR/events.out.tfevents.SimCLR\",\n",
        "                    \"tensorboards/classification/ResNet/events.out.tfevents.ResNet\"]\n",
        "pretrained_files += [f\"LogisticRegression_{size}.ckpt\" for size in [10, 20, 50, 100, 200, 500]]\n",
        "# Create checkpoint path if it doesn't exist yet\n",
        "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n",
        "\n",
        "# For each file, check whether it already exists. If not, try downloading it.\n",
        "for file_name in pretrained_files:\n",
        "    file_path = os.path.join(CHECKPOINT_PATH, file_name)\n",
        "    if \"/\" in file_name:\n",
        "        os.makedirs(file_path.rsplit(\"/\",1)[0], exist_ok=True)\n",
        "    if not os.path.isfile(file_path):\n",
        "        file_url = base_url + file_name\n",
        "        print(f\"Downloading {file_url}...\")\n",
        "        try:\n",
        "            urllib.request.urlretrieve(file_url, file_path)\n",
        "        except HTTPError as e:\n",
        "            print(\"Something went wrong. Please try to download the file from the GDrive folder, or contact the author with the full output including the following error:\\n\", e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9OugWjfW6bK"
      },
      "source": [
        "## SimCLR\n",
        "\n",
        "We will start our exploration of contrastive learning by discussing the effect of different data augmentation techniques, and how we can implement an efficient data loader for such. Next, we implement SimCLR with PyTorch Lightning, and finally train it on a large, unlabeled dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hXx6Uj_W6bK"
      },
      "source": [
        "### Data Augmentation for Contrastive Learning\n",
        "\n",
        "To allow efficient training, we need to prepare the data loading such that we sample two different, random augmentations for each image in the batch. The easiest way to do this is by creating a transformation that, when being called, applies a set of data augmentations to an image twice. This is implemented in the class `ContrastiveTransformations` below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ajhX0AsPW6bK"
      },
      "outputs": [],
      "source": [
        "class ContrastiveTransformations(object):\n",
        "\n",
        "    def __init__(self, base_transforms, n_views=2):\n",
        "        self.base_transforms = base_transforms\n",
        "        self.n_views = n_views\n",
        "\n",
        "    def __call__(self, x):\n",
        "        return [self.base_transforms(x) for i in range(self.n_views)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwfOHZPOW6bL"
      },
      "source": [
        "The contrastive learning framework can easily be extended to have more _positive_ examples by sampling more than two augmentations of the same image. However, the most efficient training is usually obtained by using only two.\n",
        "\n",
        "Next, we can look at the specific augmentations we want to apply. The choice of the data augmentation to use is the most crucial hyperparameter in SimCLR since it directly affects how the latent space is structured, and what patterns might be learned from the data. Let's first take a look at some of the most popular data augmentations (figure credit - [Ting Chen and Geoffrey Hinton](https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html)):\n",
        "\n",
        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/simclr_data_augmentations.png?raw=1\" width=\"800px\" style=\"padding-top: 10px; padding-bottom: 10px\"></center>\n",
        "\n",
        "All of them can be used, but it turns out that two augmentations stand out in their importance: crop-and-resize, and color distortion. Interestingly, however, they only lead to strong performance if they have been used together as discussed by [Ting Chen et al.](https://arxiv.org/abs/2006.10029) in their SimCLR paper. When performing randomly cropping and resizing, we can distinguish between two situations: (a) cropped image A provides a local view of cropped image B, or (b) cropped images C and D show neighboring views of the same image (figure credit - [Ting Chen and Geoffrey Hinton](https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html)).\n",
        "\n",
        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/crop_views.svg?raw=1\" width=\"400px\" style=\"padding-top: 20px; padding-bottom: 0px\"></center>\n",
        "\n",
        "While situation (a) requires the model to learn some sort of scale invariance to make crops A and B similar in latent space, situation (b) is more challenging since the model needs to recognize an object beyond its limited view. However, without color distortion, there is a loophole that the model can exploit, namely that different crops of the same image usually look very similar in color space. Consider the picture of the dog above. Simply from the color of the fur and the green color tone of the background, you can reason that two patches belong to the same image without actually recognizing the dog in the picture. In this case, the model might end up focusing only on the color histograms of the images, and ignore other more generalizable features. If, however, we distort the colors in the two patches randomly and independently of each other, the model cannot rely on this simple feature anymore. Hence, by combining random cropping and color distortions, the model can only match two patches by learning generalizable representations.\n",
        "\n",
        "Overall, for our experiments, we apply a set of 5 transformations following the original SimCLR setup: random horizontal flip, crop-and-resize, color distortion, random grayscale, and gaussian blur. In comparison to the [original implementation](https://github.com/google-research/simclr), we reduce the effect of the color jitter slightly (0.5 instead of 0.8 for brightness, contrast, and saturation, and 0.1 instead of 0.2 for hue). In our experiments, this setting obtained better performance and was faster and more stable to train. If, for instance, the brightness scale highly varies in a dataset, the original settings can be more beneficial since the model can't rely on this information anymore to distinguish between images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O7OlHHohW6bL"
      },
      "outputs": [],
      "source": [
        "# START CODES\n",
        "# Apply random 1. horizontal flip 2. random resize clip to 96x96 3. random apply ColorJitter where the brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1\n",
        "# 4. random gray scale where p=0.2 5. guassian blur with a kernel size of 9 6. to tensor 7. normalization of (0.5,), (0.5,)\n",
        "# 5 pts each\n",
        "contrast_transforms = transforms.Compose([transforms.RandomHorizontalFlip(),\n",
        "                                          transforms.RandomResizedCrop(size=96),\n",
        "                                          transforms.RandomApply([\n",
        "                                              transforms.ColorJitter(brightness=0.5,\n",
        "                                                                     contrast=0.5,\n",
        "                                                                     saturation=0.5,\n",
        "                                                                     hue=0.1)\n",
        "                                          ], p=0.8),\n",
        "                                          transforms.RandomGrayscale(p=0.2),\n",
        "                                          transforms.GaussianBlur(kernel_size=9),\n",
        "                                          transforms.ToTensor(),\n",
        "                                          transforms.Normalize((0.5,), (0.5,))\n",
        "                                         ])\n",
        "#END CODES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0m1_FyQW6bL"
      },
      "source": [
        "After discussing the data augmentation techniques, we can now focus on the dataset. In this tutorial, we will use the [STL10 dataset](https://cs.stanford.edu/~acoates/stl10/), which, similarly to CIFAR10, contains images of 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. However, the images have a higher resolution, namely $96\\times 96$ pixels, and we are only provided with 500 labeled images per class. Additionally, we have a much larger set of $100,000$ unlabeled images which are similar to the training images but are sampled from a wider range of animals and vehicles. This makes the dataset ideal to showcase the benefits that self-supervised learning offers.\n",
        "\n",
        "Luckily, the STL10 dataset is provided through torchvision. Keep in mind, however, that since this dataset is relatively large and has a considerably higher resolution than CIFAR10, it requires more disk space (~3GB) and takes a bit of time to download. For our initial discussion of self-supervised learning and SimCLR, we will create two data loaders with our contrastive transformations above: the `unlabeled_data` will be used to train our model via contrastive learning, and `train_data_contrast` will be used as a validation set in contrastive learning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4B9mpAhhW6bM",
        "outputId": "e6e99956-5455-4e52-f674-70d094423bee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz to ../data/stl10_binary.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2640397119/2640397119 [01:08<00:00, 38549379.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/stl10_binary.tar.gz to ../data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "unlabeled_data = STL10(root=DATASET_PATH, split='unlabeled', download=True,\n",
        "                       transform=ContrastiveTransformations(contrast_transforms, n_views=2))\n",
        "train_data_contrast = STL10(root=DATASET_PATH, split='train', download=True,\n",
        "                            transform=ContrastiveTransformations(contrast_transforms, n_views=2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IibEU8owW6bM"
      },
      "source": [
        "Finally, before starting with our implementation of SimCLR, let's look at some example image pairs sampled with our augmentations:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "qXkZT5d_W6bM",
        "outputId": "b5dea5c7-18fe-4b48-940d-c8f5eae8da5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:lightning_fabric.utilities.seed:Global seed set to 42\n"
          ]
        },
        {
          "output_type": "display_data",
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LcgQgCET3noIjgPzkPJNKZTG5/zYNzmQ2dpeo/YRKI6YSLOcUeTB9yfLNZLbpdzlWOxsFFEUomMlV6LECqztTxJlriWrrY2XkuNM7BsUbzl05qWRxo4x1VHUqcEzPlfVR3fl2WZR9Rw5lCtiscxxs4MptwxgnRput7g73iSBPJ1NHxe0g2fAHJ419lasrcJ1s9tFLMA4E/UITmOSLQOsMgcbNU/TkEuzj43bngWBveRFI2RDIkSEYHYJ2nVz/4tb5vf9xhjvPtRmuHO/id5jWdsdfYpIVcwGL3Cmo52suWtcZOt6TM8fkpvuGzrlgl7uDTO/5P9bP+v4DHilm+gplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9MZW5ndGggMTYzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQOxIDIQxDe06hI/gjAz7PZjIpNvdvY9hsUsDTWCCDuxOC1NqCieiCh7Yl3QXvrQRnY/zpNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1mDnqUoxmgEDoV3u2i5HKm7s75Q3D1X/W/Yt05m4mBycodCM3qU9z5NjuiurrJ/qTH3KzXfivsVWFpWUvLCbedu2ZACdxTOdqrPT8fCjr2CmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAyMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVC5jQQxDMtdhRpYwHrtqWcWi0um//RI+fYi0RZFUio1mZIpL3WUJVlT3jp8lsQOeYblbmQ2JSpFL5OwJffQCvF9ieYU993VlrNDNJdoOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/65yj59eixs+w/FDFJGSDuY1/1j98nMNr1OPJ5Fub77iXpypDgMRHJKavCNdWLEuEhFpNUFNz8BaLYC7t17+G7QjugxA9onEcZpSjqG/a3Clzy/lJ1PYCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCA4MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JvY+UZTC3r8NECVuuCfdPVwdCZkpbjPDQwaeDCyGXXGB9JYwC1xHUI6d7KNh1b7qBI31plLz7w+Unuys4obrAQJCGmYKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvTGVuZ3RoIDIzOSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNUMltBDEM+7sKNTDA6By7HgeLPLL9f0PKCZKXaEviofKUW5bKZfcjOW/JuuVDh06VafJu0M2vsf6jDAJ2/1BUEK0lsUrMXNJusTRJL9nDOI2Xa7WO56l7hFmjePDj2NMpgek9MsFms705MKs9zg6QTrjGr+rTO5UkA4m6kPNCpQrrHtQloo8r25hSnU4t5RiXn+h7fI4APcXejdzRx8sXjEa1LajRapU4DzATU9GVcauRgZQTBkNnR1c0C6XIynpCNcKNOaGZvcNwYAPLs4Skpa1SvA9lAegCXdo64zRKgo4Awt8ojPX6Bqr8XjcKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDMzNCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUktyxSAM23MKXaAz+AfkPOl0uni9/7aSk0VGDmD0MeWGiUp8WSC3o9bEt43MQIXhr6vMhc9I28g6iMuQi7iSLYV7RCzkMcQ8xILvq/EeHvmszMmzB8Yv2XcPK/bUhGUh48UZ2mEVx2EV5FiwdSGqe3hTpMOpJNjji/8+xXMtBC18RtCAX+Sfr47g+ZIWafeYbdOuerBMO6qksBxsT3NeJl9aZ7k6Hs8Hyfau2BFSuwIUhbkzznPhKNNWRrQWdjZIalxsb479WErQhW5cRoojkJ+pIjygpMnMJgrij5wecioDYeqarnRyG1Vxp57MNZuLtzNJZuu+SLGZwnldOLP+DFNmtXknz3Ki1KkI77FnS9DQOa6evZZZaHSbE7ykhM/GTk9Ovlcz6yE5FQmpYlpXwWkUmWIJ2xJfU1FTmnoZ/vvy7vE7fv4BLHN8cwplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggMTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMza0UDCAwxRDrjQAHeYDUgplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9MZW5ndGggMTMzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWPSw4EIQhE95yijsDHH+dxMumFc//tgJ1uE2M9hVSBuYKhPS5rA50VHyEZtvG3qZaORVk+VHpSVg/J4Iesxssh3KAs8IJJKoYhUIuYGpEtZW63gNs2DbKylVOljrCLozCP9rRsFR5folsidZI/g8QqL9zjuh3Ipda73qKLvn+kATEJCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0xlbmd0aCAxNzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjM5IDAgb2JqCjw8IC9MZW5ndGggODkgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU25EYAwDOs9hUfAj0i8D8dRhP1b7IQ0lk6fEcoHa+QBguGNLyH4oi8ZhLULDyr7SHTYRA1nFSQTw68s8KqcFW1zJRPZWUyjs0HL9K3tb4Meuj/djhwKCmVuZHN0cmVhbQplbmRvYmoKNDAgMCBvYmoKPDwgL0xlbmd0aCAyMTUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVE5DgMhDOz3Ff5AJIwveE+iKM3+v82M0VYewVyGtJQhmfJSk6gh5VM+epkunLrc18xqNOeWtC1zgLi2vC+tksCJZoiDwWmYuAGaPAFD19GoUUMXHtDUpVMosNwEPoq3bg/dY7WBl7Yh54kgYigZLEHNqUUTFm3PJ6Q1v16LG96X7d3IU6XGlhiBBgFWOBzX6NfwlT1PJtF0FTLUqzXLGAkTRSI8+Y6m1RPrWjTSMhLUxhGsagO8O/0wTgAAE3HLAmSfSpSz5MRvsfSzBlf6/gGfR1SWCmVuZHN0cmVhbQplbmRvYmoKMTYgMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL0Jhc2VGb250IC9CTVFRRFYrRGVqYVZ1U2FucyAvRmlyc3RDaGFyIDAgL0xhc3RDaGFyIDI1NQovRm9udERlc2NyaXB0b3IgMTUgMCBSIC9TdWJ0eXBlIC9UeXBlMyAvTmFtZSAvQk1RUURWK0RlamFWdVNhbnMKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXQovQ2hhclByb2NzIDE3IDAgUgovRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDMyIC9zcGFjZSA0OCAvemVybyAvb25lIDY1IC9BIDc2IC9MIDgzIC9TIC9UIDk3IC9hIDEwMCAvZCAvZSAvZiAvZyAvaCAvaQoxMDggL2wgL20gL24gL28gL3AgMTE1IC9zIC90IC91IDEyMCAveCBdCj4+Ci9XaWR0aHMgMTQgMCBSID4+CmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL0ZvbnROYW1lIC9CTVFRRFYrRGVqYVZ1U2FucyAvRmxhZ3MgMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvQXNjZW50IDkyOSAvRGVzY2VudCAtMjM2IC9DYXBIZWlnaHQgMAovWEhlaWdodCAwIC9JdGFsaWNBbmdsZSAwIC9TdGVtViAwIC9NYXhXaWR0aCAxMzQyID4+CmVuZG9iagoxNCAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNyAwIG9iago8PCAvQSAxOCAwIFIgL0wgMTkgMCBSIC9TIDIwIDAgUiAvVCAyMSAwIFIgL2EgMjIgMCBSIC9kIDIzIDAgUiAvZSAyNCAwIFIKL2YgMjUgMCBSIC9nIDI2IDAgUiAvaCAyNyAwIFIgL2kgMjggMCBSIC9sIDI5IDAgUiAvbSAzMCAwIFIgL24gMzEgMCBSCi9vIDMyIDAgUiAvb25lIDMzIDAgUiAvcCAzNCAwIFIgL3MgMzUgMCBSIC9zcGFjZSAzNiAwIFIgL3QgMzcgMCBSIC91IDM4IDAgUgoveCAzOSAwIFIgL3plcm8gNDAgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNiAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4KZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgL0kxIDEzIDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL1R5cGUgL1hPYmplY3QgL1N1YnR5cGUgL0ltYWdlIC9XaWR0aCA3NzUgL0hlaWdodCAyNjEKL0NvbG9yU3BhY2UgL0RldmljZVJHQiAvQml0c1BlckNvbXBvbmVudCA4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9EZWNvZGVQYXJtcyA8PCAvUHJlZGljdG9yIDEwIC9Db2xvcnMgMyAvQ29sdW1ucyA3NzUgPj4gL0xlbmd0aCA0MSAwIFIgPj4Kc3RyZWFtCnic7P3LsixLkiWG6cPM3D1i733OfWRmFbrRaArYDeGQQiEpFA6JT+CAFH4BB/wJDPgh/Rk9oxDksEUoaBBCNlsIsLsqM+vmPWc/4uFuZqrKgZpZ+I59blYBoAi7K9Mz5dzYEf4wdzdTW7Z0qSr+1V/9Ffx5+/P25+3P25+3P29/3v68/clv9P/vBvx5+/P25+3P25+3P29/3v68/TuxBf/P7373+3/5L/9LMzBTMzMDACTfmAKHwMSEiIhopmKmaqoqom0TVRERbcf7KfqGCID+CRAREMHPRdg+EhL7xYiQCBENwFTVVETMpJ/SAOB2bgN4f6VxxfYl9t3A+od3u9326WfcnfybJ75debeTX8Dan7uv9te8PYH+TADh+++//5//L/6XAPAv/sW/+O1vf/uNK/57viHiH/kpxvib3/wmhpBL+emnn0rO+8fV+gYRAPrjRUBmZmZAQAA0IMSQUkxpmqdcynVda62IwABgrb+1pw2AgIC3V+O97z/8x//oL/7yLwHgb37/N6qj/3qPs3YUeI8F/8dbaIDeX9p+rdXtHSPc3nT/sx1rBmqmqrWqqIioiBpou+vdRoSESETURsut/xmAf+PDFJH8ezVrZ/QhWUutRaXUWrUWMEUwBGBq59u9oNEx2zBt9/zuLSLsunsfL/tRae92u513vIRvbP/R//Cfpmkqpfz1X/2VmyAAY6IYIhGrQSnydj6v69W0MuE8pZiS94QQIhGLaq1SawUzZoqBUgwcQ7sLIiIuVaRqreINIUQkCjGagYjWWnPOYIroP90aDmBqYGYGSIiEu18ACYkDxxCIWdUfvpoZs/dBMLVmV0ERgMhfGSMRIquZqaoZIaiqW1YD66/AL9VNCQIAqkGtknPJpbQGE6YYQ0wGKGpSFRGY/PkEZvKzuNE2VR9KIYTWswg/f/4MAP7nrfu397breDsT2YcW7g9pk0frvTT2uT2v/rl1nfvJ4hv7fPOb/VF+Id/nriV/6+aN/+bV9438eNSdZfN93ICMZozb/9jCjxdSVQB4fnkupSBAM3QAAKbaOoDPeFUEEYgoMBNRGzK90/iJVW200cyqaBvPRNQsEgICUe/oiEzcZykfAGatc+owpK0x/Ym8ews+txmYPwawbiVgPCxVVdP+Pd5OYeCjywzMzPfxXfwefUdRhQ5SEJFDCMRI6M9ImtFT73fNllKzc33qxW68TLvh9qd6PD7GlGCgov/iv/iX/9l/9n90C+3gBoBiTDGlaZqOh+XhME8xxICMKvVa6lZr3mre8rblnEtZc76u25pLEalVvG8YGgISIAKQISEyADMyExEycej/jxynaZrSHFOKMSGxmtZaS8nrdik1q4qqmml/G2BqpgbWO5nf3g5y2G4KaU9d2zRn3U7DOwPXno61LmgfYZTtTtlhkBmoghmIgSm0FpqZaQdNvbcSIEF7ZT5r/k/+p/8zR0X/7J/9s3/+z/85/K3bracBInZDuTMfbUL6CAB3ELB3uF8yHGM8f/z+b2/haNu3bMf+VyJ6fHz8X/2n/+nTw8Pry/P/6f/8n78+v+xuBBgpcogxAVK1CmCEtEzLfJiQiAxYLYXw8Pnz0/ff/fibX315fv7tT39zOp8jYTJjA4KOsls/RCQS0AaYKCDD//p/97/5i7/8SzP7r/7L//u2ZTMVkSoi0rA4IjCNNQL7KDNDAFQ1UVA1UTNAa7MBMCMREAKjEQIzECI37IKqUKuuWz5ft3Xdtm27rpto9SfDTIE5BGamEDiFkGKIoU1vCGgA3kORKYQQY4wphZDAQNVyqdu65m3LedvW6+X0cjm/rpfT9fy2XV6hbmQWCKaAKXIgYqIO5QgAgQiQgRiRiBmxPT9sSxs0Q2s2xU259SHoY6F3sTFU4Da595PcOpT/53/7v/8/pGm6XC7/1//Lf15rUamIMMf49PAY41wFvr6u/6//93/909/8tdTrYaJf/fj50+fvluU4z/Px+BjjvOV6uqxvbyfQejzEx+P83afj4eEIREDMIcU0vZ3W83m7XlZCYEJmiikdHp5E8bptp9Pl+csX0xwZAxMHgjFJmJYiVf2RcwpIZL5yA6AY4mFZHh4epnnOpa7rtq1Z1eZpAkA1q7WKVJFiJgg6TSGlFOMU4oRhybnUWmutkbGWXMsmtRgoUEAkRGAEMO2wEwE5V7tc1i9fX15eXkQKE0wpfP709PDpu2q4bnK5bIF4maZlXh6Ox2VOQCCgVWvOq5QMJoHo4fiQUiKiFONARTFGEam1jpm7w+53M/rdT9ZuU/xX3m37w/0Sw5hIX1YP2zIsw/5aY5HQAaQNDOE7Bx8tRACgqrVWXxL8kuW5+/MjBNyDMG+km8TxvV9uHO7XHZf2q/shzW4whxB8f9/h7kIiknMGgH/zb//N6+srAizTvBwmRFCxKtKQj0FVvVwvzJRiOixzSkGkqogZqCL5QgqpiBIiExJCFTlfMyI2c8EO7pGIUop9+UPTNOOAEhwATKTWWkot3GEdmJUqpg3pq/qUbIyYgt+MitRSi7T3ZAZATIyIAFvOpUqfrtFBliqomBgSoxnUKlVETQGAEA+HJcYIiKq6bRuY1lpVlIgOx4dlmkIIYFBLXtc157xtW4jM5GOcQ0jE3FaV5LOuWyyRKv7i1UDU/tE//o8/px9goKLW2wD0Nu85YhOTaipmAkiIjIQcgoIqGKswcwisJlGpBA7eyxH13axsN4DWerYhgKGpmiGYQ1Zi8skE0aCt3vz51Kpm0geP/9+gNdc6TG6NBsRuQPYQxhqPc1vnYlvtQwcWfnhnf3bww3aLxtsfNtoCZuAQyAYM2p2pfYMA2ttIDhoM+tD+xoh9t40V+Fg9vsco7461/V67wz9sbmje7/oNRPXfc/u44AOnIwy3NasYIQUOTIz+Unp7FbRYBQUgVlNCAiTNkuXq72wKcTpOgWKgCEZbKW4KCdEQEIkxRIi+GvIpHQDZEbMvmkBx1+P368zxBnsXGSxRsxw2etF4bmYGCIa+UHN2q1GI+/czNhVT79i3iyFQQ93thXeof3t07aoN/3o3UzXDRttqXzJYs3Ed3JAhAbRfBpz52PHwHf7/5obf/OmXe/C3t/2DQaSUDoGraSW0eUocpq3a5bJ9+fq1rKclQTwsT4+H777/fj4c0zTFOBEHICSmlMLxMKGFZQ7zlDgkgFjFpJism0F5e7tcruu25cMyHeaJmENgM81FtnVd1+u2ZYCKxkxEzDElIkIEEQGoUBWAUoxTosBAhEhMFHzCA4RSSym1T4eAiGqgpgaKBGSoBqZQRaMhIBlyLuVyXXMpKjLFAGBIgRMHJjUQVTAlgilGZgZiNRABLCIKyyHXWmrZmGCewjynENgEEMXASs6JGUCZAFByLmvO122rZSPQQEQpjG4GOxCge5O0+3Lgkv6ycPw0gA70we7D0HasyX6fPZi4O633Rr/cH1mDDWw0oBIR+VGwA1XjXu7O/+2u+K3LjQvdHe5nxvfP7Q7e3Z1wNG80DBsR/m5nQmJmtx6lVH9+hEwxAKCZWa3gCy5kAHQY5iS6aNmcZyIOKbW2ASJyjIGIAlNkX/0AIaQUYwxuGZCYmN1ygIpqbYYLEAxqKQBtWcnEFNjfcqnZagUDDoGZyGc6I0MGrX6uWnVbxVd646E5LDMAIAIDZajagLWoImHi6PDO52UCCEgaotZCIWLCmOI8TYRgVk3BEEJkpBQCOUwlROIQIjMHB6og4MwGIiA3SFZVq2hVFWm9Jew7BbTB7BDOzMQUVbHWTTWosSFTCGBohEAIPqWgERqiU8fsT9UE9F2vMDPsXQY7qlFwXwgxhRjTFGIiDmpYStnyVkouJW9lk1qGa25PuDkq8rOOS9kNEOD+1vy3b46z9up3cOgdJ/POPYLj7xsS6zRUO7LDL7P9Fc0nMwVAtDbrINT+Jv4YJHrX0G/PR3tDAwA7oPONsW5g3ZXz/sdvz3S/eJWPP8G37M79n4AEBACENE/TlJL3gm+01txw8DTPbinAgNXETFUQgGNclrTMaZ5iYAYDBVQAAUMVQSBUZSN/IA1KA1MAAANQANoh3ps/WG1vDTt3jGPl58ZcDdC0U3VOGgMAIIExgiEQogEFcjzWu6q6n6XWKrWKVJWqUk3FjQ6CEhL5sBqMJ/YWIBoAGozR6mMbQFRBxKpIqbXW5sfp9i4wB6IgWBseU1A1BUQwJAQEtB3gh3aNO5xjt85/32fQFzo3IvNGG7WR860Ovv8OEWM6mAqjBsZljluB09v5568vv//p9yWfnh6mT0/H73/84fD4GSggMhEZBTEAwpRiIESUKYZpiiFMamHL5bqV65ZzKW9v5y1nFeWA05yA0AC3LV/W7Xy5Xi7XdcuRwQJT4GVZpmUiYlPLuZSyMSkRz/N8XFIIFphCCBwSAKpqFc1briIiVU2JCJBUpVatIkxu9kDUrMpkoIAEeL5urydvlRzmKYXg1OCyTEUk501qJYZlOUzzjMRb0S1XoOosFKPVEp0rOhwPFIMVC0HNLOecIqsmZgGzvF3Op/PL60lE5hSWKSZGE1E1xEaOw44auRu2A140MvQ9deS/tpEzAL8qEYmI++mGC8m/Hyf3sw08sYcg43KjbeODt/M2NhFFZH8VIrJO4Xw87TdPvh/vd9+/GwI7qLd/CH7Lg6AaT2mPosbDHI2/I5wA4MYqIZTaRiuxgWgb7QZIhESGUEVQAUwREdBEnUEAIgNumhdCIoQpxhA4xhCZnRwmwhAixwiA1gykuAO3rZWRiAgYTUnBrFHszd643WNEIAIzBBMV66s0X2+YAhgQApEhEiD5dYfFIGy2EdFECyEoAgGIqoAxMDOlGCMzAJoqmpqBe8VMcF03pxoIUQ3dKRNjDCHmLTcaC5GYAH2N0ed4AwATQOvsNe7I7TBsEzXOBglIUcEAyQgVQQDErJoFAwZkImaM/iIjVDBBUACtIQirsqmYkvlYGyta71AK6HyUEQAYEQASMYcYQ5ooRGeJ1pxXd83VrZQiUptbq/fR1oX0RunY3lLfLVnvUU4/gd323a337z/dznF/1ttBQ1TUO/ceXN3a5JfaazlE318GPs5EH9u++2knPdh/edsN3zUV98caoCPUD/Dv/7cb3v13fI84zbNz+OouUQAAQ2ysDCEyhRhjSjG4SsMMqtZaDQzQiBG5KTSIiUNAIgO0McF/fDbuSkdUMzTplwJwa6XSYMtYjLaDcAdLulqiXcBsrBXdqhqAITIhIGFzOXmrzEzVRFSkQSKRqlpNxU0bgTmmYkRGYHcB3sRoN3uNt3drpioGoiBVSymlSBXnjADQ5QeBQ2QOKtx1LqBqioYGZM7d+p1+C0X3jjP+Hf+HNjOZ82E3HrXvvp//3p10jNvdN0UsEHMMMRByuJ7Oz2+nl9eXbT3PkT89Pn7+/Pnp0w9heSjVRLWI5Fr7K0EkQgNAVuCqpCrrVq9rvqxrraWULFK9d4nItpVSzQDXrZYqZkZEMdI8T8fD8vjwEFIUtVKqSK6iqsC9OzI7KmLiAIBWBcTMQA2de/UXpaaiUkqxgGZqoiIK1vRDKlJrzblsW5FaydCSgQIhOeNojoqRiAMim0EptZQKACGEZZ5MllqI0FIKaZqRo4JFscCcTVWqSBbJiKRaVKtpRVM0cidCLRWpqBoRjyGwRwb7CftGZuzojbE53Ln7ZkAK18fc4ZXRJQZu2J9zbyU+Ypf9PgORjOb5efxCDrn2Jxy3M65+d13bres+Yqm789y14a7ldztYJ9L2uPMeFQWOMXiTVNRMAUzMTKU/DnL1LQCIukzQkAi7M9vd+lxqYGZ2yIKRKQaOIcTASDj0dk2EAm6hxLluh5PWVu/YxroZAhC6VURzZR5zu6S6Gs4QDA1csICNDOtcNWHzPDcmXl1RgACKSoRmyMZgYNqwDjOFGJjYzGTXbRDJzGqp7oUJzD6Rkh/DhEQg5j4mUreG4itJb6ApNJ3P7W2284dxmchOVaGZmqKBIQAxMBk1VFTN2CwgM2MERENTK2hCYGhaY6hFVczIjEwaUvRrdhuqJgCITeHDhti9/mGaEEhEsshlXa/rNeet1CJSVWUQRMOSdkJmnP6Po6Kx1zvcY3dz5key4uOnb3wzDrK7A+6OGlPO+PARFQ0iZ//Nh4v3E/4SG7ybs+4O8GZ+vMrf5bTwy2Zib2Xsni66uYBcU+xXR8J5mecpEZEpqAo48gAgJEAgoMAhxuSyAR8sKuD6MkZAQgX1BQ4xppRCCMQEJk1TiI0HaboWMCJCZgNEU1J1NbO3sgEJd9a2hY5Bt8kdEPWgA2j41d1QLeSg8YYA7DM1GZIpGrI33sBUmi641iK1qFSTilYRgAAZkQkCQ3C/OCETout/d/ACwfo9OSYSBauitUou1dV9KqoGziKHmGJNJSWVoqYmJqpChqqIpKYEDvXMGfD3sAf8QfSu9a3u2GBi7x07X2P/s8PLby08hmG6bHmZY6QEHIrA8+vpy5efX1+ewcrT46fvvv/x6dN3y/GzcSqaa97WVYqUQBQDh9ZFAJzJRi1Sz9frum0lZwAJAUMITCHGUIpsW1U1oGBAqkAcljkdl/j0ePj09PD5u09ieL6sVcp1y9tWiDgiMjGF6EtTMVBVMFRzuB1MqiH52ttQzaRKKaWY+tJZVZUjgaqJGFZzfWiVUmSzbCJaBUynFEzVqlPkJIp5q6XK29u5qkzz5IoQlaUwIVqKHOKEHAxNBKYUN0YzqSVveTULABICzDOD4BQ5BibEnLMacri5wAag2dMYe8fQ6IG95+lQyYzD96eCHRvU6N5uK/bA6+6ce8ZlnA06HTV8VWPbU037o37Jjv0S+PvY+I9tuPtmf8j+tLAziaPx45Bx1P5GfOcYQkwRAdVMUKAJiLUJrhHZBciMQ2dF7025y31qqeyGw4wIA2MIHAIxEzEDkgGKainikwUCoAkzAxJCvxdE6Jdpy+jGTCAiNrGQajU3foq+ggQkbKQOEzKjdrNgzid7rEnRZsywSTKZGdEQ0WpFgsCUYogxIqCKAjrBqs7YizRhEBIysWojvpstavFaUoovcVVEwBk0AAAUlwCbOYW0N2sNFcWAx2VojMB7O4BLFSlEZBKAooqiREZEFCkSAmolFVJFkUqhskAwUACDqqRmzfOF4FJp6YwKARIBcQjTPC3LfDzGaSqlllwv1+vpcr5cr6WUKrVFZNg34cr7/3zcvvWDvf/nb9v9b9l+gcP54zvf7sU1ZbcdxmLlFxmWf5+2D1ZpgEIAACI6LEucEgAYGvlA99HHAABICGAqAspABiamUj0SwcwUVSxveV3zVASIHh8fPl0uVrVczyBAxITETqv4+giDM8DeFgTa+3Uc2dys234a2N3O/qb0NjGIOTwDQwQDaooiQuhuX49BqLWWIrVWkeKOM6fHmBr9kFKIiQOHJlbt0Qk+c3WMtoMSqmJQpJYqpdR1KyVXdUkKKDHHGAkUTbRmFCkAamqqIk5HAAEDmTNGjrR8wbdnUf8OW3PB+2Mbz+iXATben9wF30QCcM3l9Hr+/e9/9/rys8r2+dPxN//BX37/w1/MhycKc1UFMNGyZV23EhhTDClCZAagaoRCYnK9XrZtK1IBdFniY1yYmCiC0enkEqM6zUuMiYgi8+G4fPfp8Onzw9PTw3J8vFyyQRYBMwyBmUOMHGIgZgCsqloqQCUMiATIgI7RQRQMIKiIiuu7qggCEvEUw/GQlnkKIRjiFCgibKZWS1EBZVSJaFYnAzAVUxO1y3VTkbxtb2+nkMI0BWICZGDWymCWhbACI/rcGRhTYkSoUtbrFXEiwmVJIaDkGoiYCdCkFiJiahK5/eYCYewyHYcpd96ogWDueBd4j0t8c8fQcCqNM4/doHdn94V9XH351YefDnbgo0/b8PHAO/5mXOWOW8JvyZjuMNa48b0YfDyK+wlq9/0+sm9cdH9Oh5UhhPbkkcyNH6OBSHcXUo9Ddb2028YmZjQPLCXXkSla5JBi9PUkcxOk+3JOrBETVfS6rtqORSZECu41ErNailqL4zUzRGAEA1C1Fnsy7IXdmF+f38V54qY3IO6rqRvOrhWpKSQQ0JfBJiJoZhBjJEKOzMEV9B5VKwDAgQkRQE2q1oLMRBxiIjV1PYNprYIghFVq2YoWao2MMbjrEgABo7Z1iomo2c2H1pBQYHo8pP4+/VWpgfoqlZiI3edQqxTmwERMHIhIK0oFEYHCgAFZyCKbRSCpoqqGZiaiiCDetnYRQKQY4zzP82GZ54WZ3Gt2Xa/Xdd22rUp1j8bfDXrsf/mGC+q/7fbfASH9d9juRuI7GGHdSfNB5PhL2/5X2x14F5i2j0fDb+PDv/0Sdybjl/bZ7dAOGHuY2bquz1+fU5piiP/kn/6T1+fX8+l0uVzO57PUYqpVhWoJlUTNTF16r4J9qJkJSNGaq4oeD4enx4eSy8u6sQEagZEadLTlix1CbZ8RUW+WCjyowhqVquMhIUJXH7ulUwBUc25J1GMRQD3o3VddhIB4W8OZqjPTTU7U/zUwIgiMTBwYQwgxhJRCSomZPVmFk8833ZVpn3maTVVQUSxVcpFcJW+5lApmhECMgQkxKkFAM8kgFUGLt9xMFBANUQMSqBFhu9MbY/Rus3cO67FDu+vhbuydC/oMibc+hvd9fmzM/Ksfv1PR7bqe3k6/++1vX59/DizHx4e/+IvffP/r38yHJw6TASFACJxiXKaEJogaCJkgJU4phRCRqNYiJs6uh5geHg/TNDGzKr68nHORWgVBAxmDEkBAPC7x8fFwOBxCnHKxy7VI1cD83adHMwNADiFNMQRWQxHNRUQ0RWQGRC5ia64511prcKRhxkwpBVAIMaSUDof58+MxRDaDUmUTCwGJFKyaIRHFiNPM8xxEQU1zqaUWkay1Si2R7bCkKQUg2nI+ndfLdTNVIlymMqWoqqUUrTkyEQITqZophMCMgZCv5aKAaMCIHOiwpGmalsM8Ru4gimA3hw044rDDZ/FvAo6PDBC/38Y+4/x7+7anVcaf49ceei174/PO6JlZd97tvxnfj2/2siRm3t/IOO2dZvyOoLpr250lHPsPPDSQ0EdIpDvFuqhUqb4oExEXH5pqDMwdXblkyAyMkFCZnJJhAAjMZgCIx2VhZg/BDcGVzghgVcxEQa15rkyxmRQOxGbuvGoOscbc+LcIiiAIImrWjK+K1uYQvBl2QDQbd9S+J0QANGRr0hr3dQESUiP0oYKBSV80o6nVUs22JsIW9e4EZipSS6kiLawXMU2pxYdpBRMX74KpSQUga7iGrMf1hxgByRAMFbAlHvJXcENFhyk0C95eVwtUcc2RkesnnPnVAIBEgQg1Wc1aSoHhsHClh6qPK1XpXggcDE3zaXKMcZrSlKaYoqs6ci25lOyxHOJCjb8jKnrXJ/9Oe/07tt2rMnajbD/kdpPOPdzbn+HOZLxzme06MNzQ1zcfm337678Nn308i/OkMGZJAzO9ruvXr8/H5QBgnz89Haf58vR4Pl+eX15en7+WUsA8uq8nyFIBhSbkRSLmaVnmZZ6mBABo4BhCG5pB7EJCUKdw3j1TcGbjPfXj7b3N3T6howEoGELzL4Oaqai7d1XFHBKhuZ+cCRiRELrQGjoDK6KiIi4XIGr/IEJgCjHEwCnFlCLziGf2W/AWtTtrT7XHmolYqVKKJycqIuIaagTnyxnYGJOWyUoGrVZLEfbDRY1cwULeTezWIdxyvvM0NwrcV4V9PH+LV4P2yu+kXTs66Z4tIsR5ms+n8/l8+fr16/Pzl1q3h8f5+Pjw8Ph5mo/IQcGxpaJZYJpTDKQISoTMPE/xeDwQswFuq8UYAhMihhhTWoiDGqy5vJ7W61ZMNRDFEEIITBQDHw/zlCZCrtW2ul2va87FVGPkyIREyBwiE6O6/RattSKQg8vS3JciogJGnqABMcZgCimlaU7zMsd5RkQRz2zQFRfc82ih62AjqkERQHFzjGghEIbkodSllst1PV8u1zWbKBGCVDQlNDAN5A5mxx9AzMQuVQHPCRcCxxRj4GlOU0pTimNQ76XQPq+3bC0dG0GHNR85m3GS3SBDp532LNEeM+33H2e7Led2Z777196Hyt/G8Ht/357O+cj63H2+a9Ld1cduH12K+wi7/bXurjIaPMDf3dV9V2cvVBqVAWCBKQZ2VUB3WXamhTgweS4J7GsOQlrmGTssca4IAFTNg0oaD2xK2NxeHsreCBEzMOsZCrHWoYVpO6gBiIqAZ1obN+lT+1Av9JmGGlOP6HlM2pL0NpNZNycdlLdJAtVXBiKeJMk7Ur9owzJuWRnMWSdDskoGrs5hAGCmhiZEgWgoVxGRDO3WRd+jIib8JVSkYIrogmqDoSlFJA4xBJi0ZOHcJKG+GHd45JNU9/Xte4i7/YkoxpjSlKYUY9y2XEVLraWWHkLzjbXI39cNPzA6+5/++5/9F1VEiLfQ9HceLp+yHIG8J4f/6KXsXlQ0Tvi+FQgGUEp5e3m1KimlOU6Py+Hp6WnN+eHp8beIL6+vtRQc661+BURwY5Dm+fHz08PT0+F4RMOSS62iZkAICogIBDB43sb6KIzpYginb3dmbhEcHiE4AaLN7W6oKtazS4iHfKh2rbR7owB96dbEQN7xrR/ikEjAFAHY4yMYEdG1kCE6VxR9OeiYSoetcqgDjnlt5PyrYjVXTy1SazWRvkjBwByJETAQQJ21ZmeepWZ1EafLrslcRmnWiLGm4vROceONsHcTR0QGO8boPR6CYRbHJNica9j0BWho73sKAq3X/PL8+uXnn09vz8vC8zIfjo/T8kg8GaCqePNBhcl4CjQRUTOo8zw9PB4QSdTA9FAn6NEcSFyrrbm8na5fnk+mJQaeUkxTiiGGwFOMx+MxxqQGeSuXa75crrUWRIshheC5I5hDMICWENG0VjErKApIpYonHxJRByhTChQ5MJpCjDHGxDEqkInVqlvWWgUMW34TUTUTAzFUCmZgKApCqExGIZIn7otBRNc1n8/n8/mSczVVQmCTOQIxB0T0CCDnC4k8OhkAEJVDiCHEGNMUU4rTNKUYQ7wJTO/kRLrLwQM7F5XTLXv7vFcQD6oJe9jmPkDsDhXtP9zhFfwFmlx3CYQGgsGdl+oj7tmDkjsuav9hf6GPwXH2gYja3+zdqfC91+yuMfvtboe2eKri46uJjqmpAAAo1+o2EBvGDSF4qAl6VBURpRSa1wcMALn9YoiuUVIAIDMm9yoAu6uqZWA0AnOfGhFZm/LbGklb6hFXdErLcwZgZkQGvWXYkQ3Q7TWJlGbAEVtqyJa75AaxmfmmYlSrUjvmACLsEAqIA3muVQSVKkKNTKOgZKBGBDFSTIZmBbL7nQjZwRNBM3AEYL4sueeKCA8Tv391rlw0NShmBaAqiLnjwAyQkFNIwKg1S8nM3Ekqda2Mvw5nAkdMj3ZxcZvVUpynNKUUQ1jXzbOZFddHfYue/Xu8jUnm70Z03fid99/+MQ31L247IqD9uTcR3wJqf+xk71eKv7gbUUrpL371m+PxGAKDitYiGqZ5eTgcf/XjD48Px7/+t799fn6+nM6MhMxqLFLNTECnZX58evqLf/gf/vpXv07TREi11pfXt8vprLUe5ikKaBUwaOOm5e9q9+ZwCYY2DwAAmjbIIdHgKBFNqxGoGrQYePTQBhHta+gGodwYMKMnCXRU1vxc3YMNKqCKpkxALXcSsi/fQwghpBRCDL7eMs9D1FIGNDAyZhUVFbUqmovkMgLyK4CRIQIxcmRMkRkZIjMoaG3BLbUU1xaZuClEBFQgojZorfu9bu/wrn84o2a7nwb6xL5YbADpdhrsS8pdr/KfRPTnL6+/+/1Pv/vdb5+//gS2LscfDsfH6fAINJViAKVKzbmKCKNFpinyMh+mGN2Yx5QOy2IGpVRJkemBiBHZgF7P+fn19Px6/vL15evz88MyPT3GNB/SNKeUppSWOT08PoDZdduu1+3tcr1cL6rivgrmMHFg9PcpUotIARM1K1sxKGpYq2w5N3AAkIsyo/NQSMgxEgcReDutJddaas7lel1FlZjTNG1brmprEcpyLWCGWVkgBLJ5xhCIEFShFL2u6+l8vZzOZVtbomEzEGCbpkAxhKpsALUKInKIi7NTqqo6z4eUYpriNE2+RAak4RraQxafsPdE0Z2exjp15J9rHWlIbxKiQRT1lBbfthh3pn4Y/7vvHZPd/bRnXz4aHNuJoO192qSxg4jsDxynwh4xZ91p+BHH7KHPHcgbj2v/JXQ/4N1djA9Z6lqqKjBQSgQAVWVd1xXUM7mHELLqnFKMERE94ywTRk4jN7QBFG2gCsGAQU18IVdrkVp9VYYhTJHVrIpsW6miAC5CYmw0L7o8E9GlQaCqVVqQigEYOPXSx4UYGbpcOITg0SkAzQyqVjIFQ09ZuJFH1REAqBiYeZp4/2ast7jmEeOy5fx2PjdKZXJDiwAgZuv1QkS+1GHm0EUIAJbXKyApUEpTmiZEVNWctzVnEVUxAKy5aG0a/9B7G05tudBmZxdo+9oFzER1xHA7yYvEHGMw1DTluJLHf1gj/myIiLvYVDskMmjYkYk902aMkTlQp8LM7JZA409p22V7uQcqsPsTd7983O1vpZr+yJPtcpX9DOb/of82iO3u6vjNz8z88PDwT/+Tf/p0fMiXS15XM6g5b1ulwMsy/+r771OMf/jD8fe//b1qZSZTFQzF6uEwfffD9z/++le/+c1vEHBbN1/OWq2HEOcYQgyQ5fR6KtsmWoHYYZF5KsjxKHTwFwAAKlVq7SimPV5ERGCpYMRG5geIx1DIwO5GLS68BdJTlyJZ8/8Z9EReBJ1QbVfw+FbqQqKG3sazGiEo7ncbMM7MUZFW0VK1ev5AUVBBBALPWmDMGJkCEYKhTVoXM1MVB0UVswkqiBh4iL7d6J/2/w5jzOAmy3WnmvfGmxPS2aG+MnSKuu/vGTThBqJ8bb/rUznnf/X//H+8vX65Xl5CqA/Hx8fHp5gmMCwlo7rkQnKu1YQJA3OpCQg4xMAY2Az09HrOpZRSVCREVlSzkouez9vldN6uFyvbcQqfHg+fnh4fHh5iip4QHAHLLGZQqtVefMW9A8XgvNVrESIKTAjmmRVEfGLTKlarXK7r9XolohiDa5gASQyLQCQ086AU27aSt63knLd8uV7dOlIPBGATzdvb8zNQUFEwrQA5Y8kmqttWalWnAL///OlwnLdcahFQnUMIMQFHJQYETmxUzQwphBinaQKiXOR6uSKiGG1ZTasTnDHG+2HrS/ZOjWDPrHhH2Azts4OnAYOg0077nD32gfuxD4GrA4t8XBXvYYr/u8dDH/f8Jarmbuc9OsEP29jHObMBBHtnvz0K7GKsu5OP8+9v/+7u9qe6XPL1mpmYY/A1LhMt81xqBfD0HXic5zlNMSYivF0aSVQ90hXRqGVJNgAT0VJrKwehvkIkcN2NmqdS5oBIoREx6ukjiYENQHu2QAATNWfYCZGabskRMImomHopDnATgdberUfeShEpHghiBoSsNxLdxPlpT8MG5KcGolWl50kBVajiOkjw4gVFFHoYGjE5T6wi1QxrNVMwJTMCTBxcoe5JUhrHD8CBnD91JyPcUBFiaG+0ZRtTID8azESRFKBPmO4JJw6e0tXzwyGR9afuk0BzpCkRA6o2Mq1l62kDJjAzB+Ymj/du+KfEEN02BKBeimS3sn6fYeibBNG77+2X9rqXWn9rcyr1zs0xroBDEbZfAI01v+3PY/sL3f7j4QxgCEjIIcbHp8dguG6lnM5aKiSuQJpxva6fv/u0zPP3332upb48PyOoKyxSSt//+MP3P/74+fNnMDifz3krIkIEDDSHGEJIh9kXRmBarsXQ2o2YFwdy8bIC6F5VJCIibnrMrKdAdRMAnoMUAFpeAFVTLwkyHiwCADenEfhocLaleb+GHNujPMbCElry6ZsNVlV/2K5e6nokR0X9Ibe8R062q4pYjztDQEJjBAJwR54HjLjDWkRqKdu0ihSPZPcqNbYXC7VbsO5ExA6DcOdO3d94tw3YWKI+mrEdYIgIfVnU+4SNQxEAqtQ//PTXplvgmub09PQ4zwtTBABTqZZrlVLqlktRISbmUIMxxxjFJbOqUjZPAFvRDGBCBFHLpdZSTCujzomWeX48zsucYggI6PF4QlSrqIHr1ouoOlAEKAL1mlUFESJzais466hUPHh2Wy9l3UJgRrPIqtAyCyIRgFQBIFWrnnSo5Fo20wINTBMEBjAmJNBtvSgweJIBo81AVGup1+uGSId5mqYYp7DIdDpfSy5aJRCZRw+oiaH/38zEAAGZI8eIbKV4WSeXxKmr9kZ+kEGKDK/QkFfvtcbjG3ei7RXQ46hR7uOOYsFfcIoNYDQufUeo7CERj+xlHXAM194dmoH32zcpqD3AuiOK9sBrD7buAKJvd3Lsj8Du441//GDSLFcVBDJGZKQYo6uKiXBKKcQUYwocAG8X7cAFEQGpzwRtHdIWZj4g/Qu3PADN3oH50s595s1owXDuQAvRd5jA71nApngmgpY71nLOVJHJA4uhoWYPuW3rLqfhoaunbjSKNxs7v2S9RqRzVW4y1EAUgBs3hmJEaArSlmpknvKiVpWaAruNMfTwW2wwESkQEnEIIYRGa8EuiyN6tu7+FH0eMTQwRcJRZ84dgtQrNQVGcHd7q9DmmQFMG/YxIARXFCIhaltMUq8LE0JomGhoS7/R2f4kto4ZEExHhPTujex3hPbTu29+2c01HugfhZtt9d7P1wBsVxfdHf3OnDml1PATfsuL9+4WRkIJD0/BqppzOZ3rtvJh0WnKCufLhYg+f/50PBx//NG2da15NRUjTNPikGia5uv58vr8JlIRLDAxxTRNUwjzNGvg9Xqt25YvPT20NWkctMT0BqYtBwAAAOgo5GQAvWgDYYvEaOIiaKK7ZjKsLxfcZ29NkQ3W5RddVeQfXbaDjYHB8Tya6VUzNEVFQWsZkawBn5aV6SZtdJWSVK2NnxXo5XOYgKln7RnUU8uWGqukNE1pmmopZlbNzBTQC/5075k14rJjoPFOzeDdu99DcGwokm4TyI1FulFKHR7dbyr1/PblsPBymB8eDg8PT3FamBnBV3gmnopxy0UECJmDRA0cpykihAhkVre8bluWWhHQc6araRVREyKbIgdKKcXDMk0xMDYWCxHQTM1ylXUr65a3UkU96RuW5p0saDZFtin5slLMm1RzztuWy7ZqLYYBFE1YULuwAhEASmUxIhJtqczBKoEhdRmGhZY0HaHktaijGWIIIp6/sazXPKVEyzKldDguRauqbURaKrj+TBQUBMiLq5lZkqEICgEhxOD0TvPjqIxwS9jBAugoxD8Pp48NOn+XrGgvu/Zpcl8EbZxWe6TVnYkfx+4vOgiqO2AxkAoijnSIeyZmf/L9veyRme14qY/zzR0wgh1YHJf+paP2H/aNHy2Eb6UPePdMTMEUFEQVBYiZvEwyBwQk5nlOISQOsQPWXiFU+ytAaNMxtgzzCOZzcvtffwotn6F53IYSIqEPma71UTAzarVM2/zM4FU/yCt0taSMzZVkZm5HKyESYWBCQpNWPx6GZhEBEEVBxJl3G2sqMMPue/Q1H2KfxVpaTjMDaXG9ni1AvNCyp7IMzKYmVUrOtRawVu8RnfdSz+tWYkrsFXtcr9BN6xDZAdNY2JmBEqB4VD0YGTAQe2UR4BSie+JTSgTYzklNyOol0gAA/DtDAyQhImNmJPCA/GmalmmapymlFKMnIndk5FlZ4E93w64oA+iEC8LNI9mYCA+s+ngwvFuMv//h3Q6d0oD2H7zf8z2WgYGQ9l/hftcOixpBAg3qvT9Zv7ZKzXm9XC6//v77vKSMsJ3f8vX69A//ASX+6fnyh5/V1B4fHj5/+nR+O728qKoC8Q+//vV33/+AgG+vp7/569+BwZxiIITT2yqChyN/esLjQl5fFYkMIjTHNQKgKZJ5RBjcZn0AgFKk5AKdIGvkPw/xjEei9ZmjFR4f78nNmQMZkiHn1BHU1p6WjzPPSNWoJqegbvb9VtEJoJNSXup8tzhWtZb7Qj2izStYACC26qfUvIa3zuSldaKmSeelaBV/QQIAWvxZGIBzXPaNnnDDuwhgdzND8/1TJ79wuM9wgKk+MVGPuNr3I0RIoX56evjhh+8fn544HKr2espu5UFMi9atbMUAOAY0yREuZ9UaU2IiNFBmBGBT2EoOISBTTDwDMaO/jpQCAoFpyVsInGJIMaQUkeC6bS9vp9P5WkVSiiEEM9i2dV0vpoUJUVPy8EJEVS2lXK/XbV23dQOpU6QYIIDIdskrxJRsmhitmtScAZCQYiRUYTSMlGJCZkQ2JInRO4mp1stVy2YGSkQwAcC2ZcdpIYZpnuZ5nmKCiiGwalAEqTWXYgLutrtcV3dpRcZ1KxSzGCJRIBSCJkdlVKRRcbv12B1uAABEjDEysxNC+2A07VXKfbeBhELfHCFZ9xnt3V5/BFVgd95Zz0w9cBh0SDF8c57kAroPC3deswGDxpeDk3CsM5RP+53HnnvCaWBB+KAW+ibwsvdh/B8FRsNZsz/zGCzYvDcQmacUp5hSSuAhVRymFAHZ05c1hngIW3qGIETota1bqnTPI2vWqCh3/mMXUIOpC1gq9JHv/LCpAlAgr0HZ3k0LZESXFnkMSak151JqERGtFcxCIGZWZurEnoFVVVBDUw+Hse6eA4AmTgIzQ621IXIDbpbGRK2WjNCwipnVWhyOcY+fATOVWptuoa0kS6lISszckoZLrWW9XpkDsueiBJU63kKvgzZQsA9LQDNjB7YI3IvRGZBRnOO0pMkrWKHY5gOBQyPLEJ2WR/RgH0AEZgJAJkDilNI8zcuyPD0+fHp6Oi6HKaYGQxsE+NPERHvhb5+I0BmOxid++JHef+fbH2WEfnHbIaW/8+a837sF5jd2eX8Rx2KqUsv1fPlv/uv/z48//hgPSzoe0mnKyIfHhxo4/BReXl8f54OkBIE/f/4kUlOakPnXv/41Gl7Pl7eXl/X89v2vfn1Y5qB2PV/nGB4eD8unx+nx4bSul8u6bRt5XlQ3W4CeKl7BFCAbLMzQiVMpRUpTjBKRGZAX70F0jtT3Y0REr7L6DhM1SkNNWtJUa6agL+Bot/i+Acy2wDJ/LIhoOiooAUB3vL+3mx1gtv9jy5DUtDxM1JYYbcHnqzQ0c542xJimaZZeKyMDaAV3UaoBmqtNmm30azYCsfe8Rvzc9vLLkz+7vpi+oWKfMug2c7Rb2HebwPzrX3333Xc/fPr0/bIcq6EWUUBV23IJgWLiwPOUQslZAfxGQoxmUgVZkDnENDFDrbqtZd22CSBRSHMiFuaiUsEspVCrlwCDZZmnFAjJDJ5fzj99eX49nbZtjSFMETyflZaVQGLiFMOcErfZnTiElFLeinCwqGEKx8MUAzOhquYixMgoJmsVRmAiphBSDAwEwKZBzBRJAcVAxWMYEYnmeYo+cSIxc86FKXhiqzhNgFjFclEAWubDNAmAlS2fzudSq6oFwk9PR++EROG8laLnFHKMPCUOHBCJSdRgXa+eCP3WuxCdpPE/XdswFA72Prx8dO/BDO1Zoo/Q5+++7VmZjyJr68kea62I6LLC8eto3t059/hjLwaHXRE32K1P9gprTzAGHf188/y+7bHR+GawRP5heAAHuPRQMgAIgaYYQozzPE2TfwxEiMjYUacZNEWOiWgFsBsX69YNQVSpPQqflD2zjqp6hqHWQE+rRi201XIuomYAzBxdc42IyByiT9HuBJdas1oW3ba8lbyu2+l8fXk95byZVjZ7OMSH47ws87IsaZq93rWCVbFa1UTMxM2VtbwfSBysMUrqemo32QQSQgRANVB1W4oAIOoRoAoAGAK23I5WayFrKbgRgUNAJF9eunUjM/LQ4lqFCM0IW2EDf1+3OmgdFfkSHw1BwNz4EyKjBUI0Bg4pxKZNYk8k74LpFGIKIXIVQlXQPgM6jQaBEYFjSofDYZmXw7I8Pj48PhyXeeIQRMQXtX/CsKiPosYR7P7d7QMd3H8bbrTj/47A6HaNfs2OjbC/BHPf2IcTGiAhEnlE8a3w3q0ROE60a5hbuva51vr73//N9Xo1JErJAENKIUUK4bgszz9/dS8GYTjMB/jO1MzLdmr1IKoKQPNyWA4Lq9p8mkBjTBSiIV6v27ZuUsVDWt2Z7QWDAAHVPABL4m2l7GoL6GmwfYWAPpv1uR4AVRUIQdGwMXhj8h+mcCQBhr7yU0IkMlNojWmOKhuYaAxDa0SMPyzrZA/2d2sDcbRG4siXNmaRtnDy+aPlWJMuefYyOzGl5E5AFyUZogdE9ASOPU9SP+8e47aJp13V+V0vwuv1dqkdNbjN5kMb5qXb5F2fYebvv//x4eHzPD+GMJtUDq1sYqmVMMQphBTpkLROakDEzNGAVXWkKkFkQ1CQonXLRmwhIlGIzMpQDU0FgMw8I7PFyCGwiK2lfH25PL+8retFtc4RAymhgBmTMfOUYoohcDQFqUqMnj54mhICpMAx4ONx9hK1VYRzgcagmWj1G2fGxMRECKbGWdQDfXWs3QGQMMWJZ2zJc9XMSI3JzHUQuQpuGRBCijElFy5tIZYqAFsVCSEejgcDTzVZS6kqWrksmlJciBiJkUItVQVKFugvY4949rKePb7ZkyjvTMJ7hsa+5dL6uH0TWwxINEDYHcgAeN/Vd960oWS6a/MdzHKctxeDj7smIqfZxjRpH9IQ7NuDffvY+D2G29/Ovm2OVMY38zSbaghhmqboGbdakHt7M6rSSgupqVaXuwGAJ2Ns5LOPzBswNWxFNWCYtdba2zNBUxv8DVJfQzFxCBxCm1hEainXrVxzOV3z2+lyuV4v1+vb2/X1dKllA5WA9t3j9Onx8PBwfHgsh4O0Q1U3z31aW+VHAupTPhFzyVlqEREmphAcjyyJl3kKMRAxhzB8/OCJLMxGAhQ1aP7EPnmhUzVtEFmt4n4CJpyniYjBQFUM3r3fMN7N8I/gUNUaYFcUEWHw1HQcY+QYQmQOTAAhxpjiNE3zNM0prV637IaJAIEwRCYKIcR5WZ4eHg+H5bAsx+Px8eGQUgJAc2ljX+z6m/olMP73c3OPin9sc0eP/vm47+67906u9u6gH2kfDzN7Z6Z6UdBfatR7ndBeWt2H9zAH75LP3IAbjn/Gn77sEP365cvLy+tDjGGeEIlTdPHb4+EQiL04JgcvbsaAQJE5pmrVvcAhxnmZ0zSRVIkxgnKMyFxFz6fztm2i4gHyCrIva9AASffJ+5fDPJlHgnmKfQ9CpY6KDK0r5KgBhW6jAQZBpLuwDUIEIvJ6WUZwM+7OKe0sLIIbt6H03nlSb+QM2O2Kbs76+/BvqWcL8aBOU5E+FlEdFlErjuYLKK0VVLSCgrgC/QaMugz8DhWNzza4ImgOPLglKkNfXw1NUksW9QuonZk/f/fDPD3EuBBFZ6xqCydWAGTmFAOHtmIlDERBjUsVR45VDMFqhZxty5KLxKiqnqkNmciIvBaB9WQhnoKtVFnXfDqdr5eLSGbSwBbIiMwQNBEhxhSYAgDmUqtqCIGZAtOUIiOqcgp8OMwhEADkUtTQ7SeASa5OwTXnJhMAmJJJFVNRazQEACASEhNO8+Sh16XUWk2BPIZfzdYtiwggHEKIgUNgIhIx4kAsbJBSPCwHAChVzNZLKSYCqsJoOlFgRDIHzGK1apPctaF5Q0X+YbirfgnB7FGFtYF1m+bvtER7MPHHLfwderg7A77fHMpYL6+x3w06sNtLyMfn/ba/8bvt45ffbIaDqo/uvL0T8O4kY/NvlmUmAiJqodnkU6hnG/LlVq0inVyu7VqAYEZoBmgK5stW6ksSa9Ed1g0EmFrLw7Z7hswefiBq3LS/zIG5ab0bM51Lfj1fnt+uX14vL6+n8/l8vayny7atWbWgaSTQmmuVXDRX27L52qyqXkvZtlJr1ep5trrKlBiIt3WtJatICNEvioBPh/nxcTkclmWZp2nSnEcvA48MQGT2BNwtdlibfWiBYQaAoCZWa0X10rE0z7M/FLSehfEjKrot49uDbCYNPdUqEHEAjBynOcUUiAk9beSUZjnUUvLTda1qQAxIWLbxxBlD4DSlaZ6Xh4eHp8en42GZ52lZpilNSCyiWksg8jRpbQzK3z5s/j5tDBSIGnvTMT14HNhtFsFGKQ3eANwdOxbkTqI6m9oG7XteSXdH3jimPaXTeIybMOgGiWwHAsBMq+Se44E6Y9I0OGPStL1da5MkeLIg1evb5d/8N//2n/zH//jx++/e5lk4VJUA9vnT4z/6B//BfDzGecIUKLJuWVXJIE4Yp5iWMB/TNPO8RDTRvEHN8XgIywyRz9fzH37+actXMo2KWqs240IkIkRi4FnrPQlja6veBKEwkg8RkWf+cjOHQIqt7FCvcjaMtz86FY+QbUsDbWiKrCc/c9zrXdt25rJF5ROZqgd13qiW90RNe8WtelBzPQ+uCKAzil5CCNE86T0geMCZY82UWpNVwLQiQi1m0tJcQu9XPSFku3YPz2+NGrLwTk05LmqojLxj7EnChtcRenD/GAIcvv/x14SRMaBhMhKJ1YO8qkdzsRcQR4DIzEyBOaZZAdetXNft9bTWorVorbJt2UCqsEittRAiIUSm4M9eSs1ZRV9JQ4hVYN2KrOcINQaLgaYAkSFGJuJpSqJWRbZctq1eLpurjqQuDw9LihwDqcYUwjTPAFBFnKDxzEDMrHD1TlFrKSW6LraqbUW2Uj1HXQohcgta8cJMLtFB5BhFFKCKGGzbVkuOgQEsRFbjWpmQr2te1yoKzCmEqRXIEkGRAMqEkSEytES8YFUs5+xeIW7lIEb/uuGDVoGrwwj41kS+/15Va61D/QO9pNrYB7+1jcP70Lvne74Jjwa+YWb3cN0Csnqaxz12GSkDQgj7E97d11BK7S83II7t3GGjqQMDuQDrm/elvUzKXl8FXSk1/nx8ephLcgPcyVVPX0j+QmrNpdROBztxZAgYkAAZ+620f1vGM4uxJW4DYkZ0wwAq0IrWu+IthNTqySO0vPgUGImbSlkVVLdt+8Pz21//9Pq7P7yeTud8XaUUMI0xTGliAkIThdNFsmxvG4bXWkrJJW95O18u27p6zqRaK48HhSTA67pKrWgWUkppdi/U03H54fvHH394+lXgp09P67Y6/K0iZuaVoZOn5XC5JlGt1QlRhxO1VlWrIlqzevGAmOYpgrPjorVUlTJmuh0q+rg14gIZmUJCThymEJdpmaaIEQWgIkAMPM+z2qcihhTSNKd0Ol1OIhUAiSiFtMxODy0PD48PD4c5xRQ5Rg4BwCMxhJcpeUbH5Mh0t1D+k9h+ga3x3xAMbkmDxtffzooGY5Jsx+43chHdGIS3Ce4bF/3YxB1j8ceb3E9+RxS1w7G7gdj+9b/6Vybyl7/61fSrH1++vuVrBa4U49Onx8PTU5xnYNRaEIVJEUzyBkzzNNHnzyK1ipbLtZwvWiXOc1HNr6fnLy/1tEUmIlQV08I8IQXopQhNjADFlJnww2PEVnbdQydvZq/dVEtAod36fPPGbyTPzT76TzqqEA67Zf1pta9vILefDQC6XrkJCvx4aKSRdTanvZ5O7NzsvJNENsJFXNwQoxP8aurkTq0FTD0i3dC9Y4g9COX2PnugngEo4MhDr4bWVraGvs5pgioXwXn2CW3tNbsVgQEg5ofH79EMxOdu0uA5fokR1cAUxAAQUuQQYgyepDkp0DXLZS0/f3ndttVEPTN/iCgVayWpHGLyoksGVKswgoCplO1yrimpedGrEkgQLRB6GgXqNVOyZ4OS4sprVTOtgXGe07JMiGhqiCSGnr5IBMwohDQvh2WZQ5y2LXu24k0UahGDWnUrRUwRMYZwWKYUo4qUXC7rqgBmkKYEBqKaS9lyKSI5Z9DKGD2jXt6Khwuva7leNyLiiQHQi2epVFAlaFkKVeu25Wq5im2l1rwhwjTHeZpuLpWdpwl6uY+BKvbxU3fAgvoGu9JpA0zssRHu4rrHeQZ62EOZbw6u/RWth6oNXfY+5+TARuOmbiN0pw2iXapGFyrtD9kN/He4bWyj/dCpNW+PH7tXi2uvoAI7UHWnwUJi5NBdqW2dWUstdVOnoFVEBHpOTZHajDiigedINFVVwGlOgA3PuLzG8xsBEKHPAYgU/M6qGjcnEVJo60ADFDHU6lYgcKiAYpZzXq/XvK4M8HiYAs1ETZgIvp7kQHGugJervP3u9y8vzyWvKhXN1KqnEnH+phMBZOjp4AAAqBStthxompbLVvl8jSkuy/WHzwXBmNEMawWpGkNkCgiuAA2NMWq6Hc8mylWk1rptq4googkAQxLgAOClst/7W+9RkbldHVMlAgIyMYUU0hzTnKYlphgYiQTc+WBKiDGm4+EoqsRepZdqKQBASNM0HZeHZVmWeV6WeY4hMgYyBmFQT3+pAacpuuf+v79S79/Hze7FO+9myvfYBd/tsp+bDXZ58/a77rfhYRu/j1UR2C3d3sej7xAA3n1p6AEE767uCx4EGrNqo498XiS8Xs+//e1f15L/4V/+xfz5ExCXraoW4yAGWEUFpHisNZBBvWaOQUREoarZllUNYuSnx4vZ5fnlum7n86lqjhh6PWYe6wAFNfMYMJNSEObbXVKPnUJE7DGZ4xGbAaK6060FcYyaUO/2dDvqFg08FQ26R9pN53sGtL/E+yfeXrl1awud1xlON2ejrDvOOiC6uSj7mRyuWE/I6LoDBPeksQMMSZ4AjsBUBZoTDT0EzOexxgWaP0bQnp9SRKuamKmCOORuUlYW5sDERKHBQxhPFd8zYX73gROaeS4S0RYVgn3di2ZEFAIv8zTPE1EAJDHail7WfL6s58ulbOsoVoBGphW0glngsY4mUE0hohojciQOQdvMDYAOdsF6LigD8QfGjClyjaSJRZsMWRVcfw+kplZKccUHM8/zNM/LPC/LcmAOMW2tIo1iEY8NrqUWRIiBA2OKIYRQARTKlnMpxVRqnYh43dbrum45VxECSDHMU5qmyIyeTUKqllJNBdHQ2PlDZ++q2VYFAYikqoWKuUguZdsyox2Oyzylh+My3sFwfo1pfhAke6wwZv09SBrMzUcwYV2ag92TNTDN3dJiP0V9XHXcnRk7bYO7bV/5dWTcxh7nb10ytT/VXaRbswcfqpvdNeObvkXryZPuvoROv308yf6WPcO+40ZE7IrqrLVWkSq1lGIKI5WNmQcko6cJwV0RjJzHWENHe/5UtCVQ8+SHbRGNBDqWPgjggmcPtvRoMAMRXYtcs6phCDylUEC5TxyGyBxdhFRFX87Xy3W9Xq6X02lbrx7oEBgRtKW0BVcSGyKir8q8fYiB4zzP87Kkeb5cTterXeZ4vVwvl+v1urk3PKU4xcjkRQSQ2JkUUFERIQRCQ1CXEPjbZWYArCK2bSoyYyIEZkZADjxed48P7K9tV2+qeUv8vaUY0zSleZ7mOQYCEABxs+wxfUw0TdOhZQBDRMx5AwMiWtJ8PBynaZpSijEyIuEtHxJhAKTAEJlCaNELI5/Sn9Q2xKcDlzYzDt3Z8IH0HR86CkGzLjXrP+Fu767xGDzcjZDoLMAfI+h2jbgHTIMa+fZhMEYbAAwJGQCASH19fUGA48PxuBy0AAkiGoV03TbKJXAY2RrUg0Vz8Xp5opWKgOdlDnzZ8svpdF3XmnNyF6IhIhBxf6huEOgm59m5J4mwmRpqodft1tyWIbqWr8e1DsMKcPOhdS894RCHtTSQ/Rnt0e842p/8gJljx4YHekaQ9p9u2/tJGgy5o+Twho38DC7RvPUnQPQc9RxiCNUl16I67gQGRdQ/DDeZ4yE185IjVayqilpVsK5VYAoxmijFwAYEREydyBzA6H2P6RaeAL3Siae1ddoGGSkwT9M0L0uKyYCq2JbluubrmrecpRZQQQJGiowxYCAgNAJjBPa01KpKFJgtBESgQCEGVxiFSLUSuH4TEMDU1O1qq0QZ2ObEaFXR8/tr93G4ds2LbCBCCIEJ53mapjlNM1IToRtkUCADVAU0c6msIbpUVMXXtaVkAvBSeMyxTYhSTTWmuMxpmSevbCBeA10FTEKrgGBmAuYOFytV162YGSHWajFC3rJv0xQI5kiU4s3DtUdFd/N6nyPuk1aPbr9nPsZPd4fDB6xz9+fdgWOHj2AId/rlPT3jP+210nc3MhRIjkikRbGPFcg3ip3t27b/95stv4Nr8F5XNJxo42G+a56aqnVYJVJLLUVqS75Za83bhoBKXT5IROQD00SM2IEFmFkpxV8LE0kVNwdE5LjBs1SQx3Ah+IIAXXqhAKAuWXRGTxRyretWXl4vz2/rmgUAQyApXgQDzIAYgQNQUOR1y19fXk+n0+XkdQsEvC60Enmh+vFOERCRvaabV39Eiukwz0uIyQDythLwdYrny/VyWXPOHJhijDEwMdp+evGoNkLPM+mPRE1VwICJQoj+rLzKnCq39RsTAtxXh23j3+uVoLX5pL9nIgwhOCg6HGcmzx8nRbKKqbIqgkIM4TAvgTnGFEPc8gamhLRM8zIvjnfAFa5iCmYoIICoBIGQb36MPzGWqG19FsYBVaz92WcS324TuXcIG6vvPa+0m22siX7GfOynvOVAwo7BOhGL7ybOXROHXKlfZv+528d3UAu66rb9RO3XW+Q5E0mtb69v//pf/etpSr/64Yfvv/v8+btPwPzbv/mplDrPy29+9avA7ImeY+DL9Xq+XtZtRTAWAAFVELSXer3WrCITUIC+dgAApM6PGKgKNwYGQ6Sd9fZRSdhKmGFjtkD6POHOaWkFONQPAXSln99nj72/pQgn2NlK65zP7bKEO3LOH3KLrQUXwY5VpvUlXv/DZeBmyO0p309FPd4WoOWdd7lB+6rJmJgYQtDkjRPPWQJNUdghEfWqaI13qmqiJqpVLBcpVapoEa3VpOdjY+KUUowhCU+RIQSDtqLDBtAHvwUAYKrXyyWlSESKWAXyVs0EwUJg5MAc05SWwzIviwFtWS5rfjuv1+v1er2I1CkGCsCEgSlFnmJMU5pCIDSANo8QYi3SbDIxk1feNEA4HKZAZqoIGGNw2hoQTMBMESmGMMWgx2MRy1W2IgJqrdolgWmRlhg6hhhjnJdjmheOSdTUsiiY4jzFebJa63qFl7KZiBYrAOe3CxDVUrdtlbJSYISIoAaCBCF6Chs4zNNhOczzPE+zmSEWV4KkQJS49VutKiCiWy6X6/Xt7WwATJRSnVJ1VYdpJUA0Aa1Qi7+CPZMxgALuXFpjFreuobmDSh48Du9Bw0f8dAcsBizA3Qbvt9ES/MDNjFONYx3eMXMpRfsG3SHoBNLIijTufY+EHC3tW7WnjgZ83LccekHZ8dxGs/e1z+B9hZA7UNjrYaio5py3dc3bFpjAeQgRdxGZirpPEwytFSmraO70R0QDrB7Z7WH2naotpeXqUFVEKwXaGOAA2DT/CghgzBpjZCbicMn5+XT9+fn029/9/POXl1KLF+IuirV6Z6CJkyLVKtv5+oe/+cPz68v1es05myq3dRpu6vUfXaVAiBYYW5KLeZmmxRNUckhAWEWuL5fz26vmOBEtU3p9OqUY4kgEzeGWikqUmgbytgBXlZJFqxDhFKYpTQgtTo2ZPIuKT65h93JHLin3K/iM1RarzZXWHP9A5GX/aIqgFaUaqoK1lRQYAWCKMcY4z8syL7Vkh4cphMDsnayKlyYxgJYzwEzFpFbdti1nL2FUPG04/CltBE16sbMl9ksPYQzE/he+t0Lv1zcdqXh+vd2xPbDp5kHDPRT7eG0cl36/ysdv7WYd6d2w/N5b0ikRnxtFyvWq1+tFq65rXos8PR1+/OF7r+s9TxMiilitwoGPKWBgesPz63M+XyVLFV21Xkk1EjIJQIXiOaYZGABayix3e4coXlg+cFCiTo4EDhoCOlXU6jSAczLeIaVVv2rZPgidg0IEpEFnOTaCLoPuT9LJleEI86fdFifYJMjN22goA7nAILXMQaGLK80MwTwy1Iko7qnh/fXfUHbvD56PW3qoBSIYeJoCQKIQo9+pqAF4+DogoiHZiKQCEAX3IVbRhoSq5iIeVlJEctUWHwOd5U5xirGkeJhTChyYjCnSvqpN+6Cml8vJbA4xmuGa6/l0JdCUwrIs0zwDhRBTSgtAuFzz2+n6/Hp6fX0tZSO0xHD8/BDAABQBAvPhsDATEzIZQiV0kpxEbKQjdAtIiCmGT49HPcwenoYIISYOwRBLkVr9m7DMC8e0lnq6rNvbSUwM1CP/iUCh5QsIaYopcZyQgiispV7WvF43k/rwsCxLMJNtIcnX9aIimiVvuQJ64HVFsDnSnChGEqSUYgxEACnyFFMIiTkQsqio+loFpikcDwuAVamlVgORmmve1sv5dDqZATOnKZUaEIzRYqQ5BTKp23qlWz9xKDDgwh3aGMqhMdM3BLzbBnoYh3yERAMxjOuOX8dFb133W360/YH71mJXVVuPh/ei40N1tFdK3V1l356xz2iSewZhR/zsfOi3PanXSBntHOjnDkLZbhvN9sVnrZLLlrdNahMhqUfMijZHuvqKxjxsBaDFWwGgGFiredqsgajKtrnlEVWpYgCEOKVgYLXWWqFg9XIV5G/MDNQIMAO9Xcu/+d3Pv/vp+aefn3/+8pK3zR1gauCp4LjFGdjr2+n09vby9fn89laa2woAQHxJ7M1RY/LGhmmK0xTjNM3znKZkhiKylZwvp+IOgVJVRIogMsf0ww/bbw5LSlMMAQEQlAgBCJjcJSdqKlpERBv3owZFDRCCB5yAIWLw+DriRv2LKNNYEgwPmiNf6GG40G2rk+8q7tWUFi5BhEAYAop6zUxUQzBCDkiMiHOaRIqjIkYCs1prwaIqSEQBmK0n59Qidl3tcrler9d13UqpTR36JwaMxrq5j3i8pQravRWApkxxd1hfxN9oAYB98L31g3Hkm+5n2RmFHTIaO31YrPk8ORDPfpf9h50uqbt1Wus7ozLOB2DYw+C9u23b+voCIjXGvzg8PByPh2k5GKgW9YRfIsocmMhEyzVjBTIgM1QxVK+/RwgM1NYkCAY+CXeVjuduNhOTdV299hkAcONTcV9+ZrcUUalSdqEu5KmPEBFRWpQp7N5XeyDD5dUXi11DMNLxwzvLDu/6/fgJRsY3F10iQAis+/oaY/rZDx2/unNFqtYy3jY1knP8bY0fQjCTWlVFnUpEBCBXmnkROi/7JaK5Sq5SRWu9fS5Vc5VSRaUZ+lRKKbGkKJIQTS0kC2YEXjEA4N0zAwjMCKAitdTL+W29XmPkGD1hfxCDWuSim+h6ulxP58vb2+nt7c20HOY0TfMyTya1SnVmYOA/oiYDshbepcjIrXaBuyCREDkmiv60VUSQ2N2fgZHIiDmEwNF1JKgKIg0xujgLkasiIhNHpKAGpVYDQ0KP9lIzU1jXDKBMCMDL4YDItUj1kgfMCGYapGCIXs3eBAQAQggx8HFOzCyCVbXkXEpdvda3GRLFlEJgMysiriMx1SXxFHCrUsVkg6pGhIFwSggcjVgR5YOl3c/r33SWAYB3Gd8thDCqYu05lTuZ0f4k7/v8/U+jGXd45SOuGiPrDoTh+3SLA+jcnefu5IMJG2Bl3NRARdAV07YrY3J3CwM7+snHPtZD80arbsakHWtqzY3qsetIBEQm3eT7VZrtIW7OWXTXZXPEqpkatyoXbSXjT0pEpIohAlHz6auZmYCqgRowG3nBRtBr0ar5Dy/nv/7dz1+e315ez+fztdbi8QxI6KnhQwhmdjqdvn758vb2dnp9M62etIxaxjFAbLiNGQNTCHGal6MnImICxHVdGw4qW865Vh8wEGISA2dnq5oLHaCPQSDwdNbb9bLlNeec1+2yVWse/EgcqgoihibNMUQIgad5nqcJrD0v10T4K+geNDOXavkStU+cQ8ngrGPJueRSQiBnSAmRGNhIwaunMHPwuiLOU/miDQ3Uc1iBqQqSBQYiBXKXhG1FLtdyPl8v13XbtlqK7rMH/HkD2OGWNolg/xJvxWRvM+GALHeJ8mB/ih2wwb/rw+7m4z1p1DvMjfq4XenuwvenunEGaAAEUusGq6sM3ewys6qHKxiCmQIyerRI2UpsKMd2WMAIEF0cYmg9fdkoyIoKTUooaiLWBwPd0jIP2bU/m8HTSO210syA2bP5uDZZaPilbzl6GhDz7EXeqX1Yeik2a+l7GmqB+04/8hvc9NVd6m2IQIqqaF5zrbf61gma2XUmeLjQ2jrXUREigN2S/HqsCtUKI9LeiWQAAPNkslWkVtlqLVWqWBUrolWtKoiBqNWqVURFDcz3qlLNhAjMkqlCDIjBdnR3e2yIc0ocWFXMRKSYiRM8ns/QeRQtUoqs1+t2vdZtlbwCCALHSMucakXMUKXiMGO+TqWW0aSKACiz50g0r9NE/QkwdeQEeciwzBFAbEWs26tUQwMmip7HNkRAxaLgEN0AvfSAVSKstboardZ6ulyrxBCYiWJKqoBUwJfvISIgmFb02wWrqohEFJhTjF75IdeaS81FnFw3U0ZYFJCIQ2QiVs158x5TjvN1XWHNW9GqimLk8lZgToljZEIKo/TTPSz4qDgefXSoi6Cjn/twqh2kgPdw5+M2UMLYzT5s+zPsdxuoaCiX9yTNnjry+dt2SZiwq7PHObFrkr6Jxsbn0aQ9RIMdEtpDyV+65W/dmtd8r57Z2KGE9QrOCMhtjI+Hy40ockgtzRIiWO/aUAF6gvwmPW4FQ8yTQRq0GmoG0ugdU6iqW9HTWv7691//8OXlcl6v61ZKkdrYEQqcYiRiAFvX9fn5+eXl5XK+5JyZGg89CnkiIjF7qu4YQ0ppXg5Tik6Pbtu2Xc6lZKlVaqltmUBEkTmYR8MZiIKz1GYg4oU+TEW29Xp+ez2f3i6X8/l8OW9VgZijq/oAzbOreClsIooxHI6Hh4djZGYEQiCCodTcq60VmiPsttI1M/PEBqUAbgZMIRDGyEIALQSXyb0/SMTBC1BGZgbofI9ZyQW8igACoDIaoZpBFVm3crnm19f15fXtdLpc1zXXIip/opCouVga89ldTP7TXRoxG/vfDgHb/evbmCb9zO9pCMQm9cEdnX079E577ZVQd4lE7o/Yc1GdM8Hbb+8Yo3GMqgf/jFY4F5pSJGJVyFsm7y49M48H+1zXdVs3jkxmPSgKzHtwewzoCK2nT/W695VNEQlimgFBdSj7h8baPd/cNUc9Bt/Tt9RaPQuqPwY3WCBqbV3WJoMbJaZmLVyr2W6X+5B4XcG+jzmLZT0vLTYucMf69Xy2Q9VEyNpz2++gqsEYeY4EYEzkg/w3MGQy9QgQQkTrhFF1vUVzj98k37VKqbXUmmvdcini0iIQQ0UyMjM0MrHiZeRFJBPkWkoJIgXcOyTJ4+YD3cIqrK+nH44HQCiliNQUiJY0T9M8J2LyF1CrlCK1iNZMVgNpYjWzFHCOfJijCGXGUsi7VcvsBARNU2K1CBK0yk4dBxMSI8UQkUhNAUWL58ozfztp8nUuAZhIVakEGgPNUzouy7IszKxrMcsePA1QjM2MWNGYvOvkUq7rerpoinGa4jxPhzmFZAYu6h6h7yAipRZURYIQOQZOrUp6KCJbKafLer5c1zWrCBOkyPMUa9UYEd1LqTpBk4yIAb9dz9e8ZvFgm8AcU5qWwzLHwJTiDRVxVztARxIDPYzvBzhwJA0Ae6JoLyUe3+8hwkAb+3/HT7rLAGl93O0x02jk2OfutGO78+VZTxDgdXPvmrRvA+wkVqMB+30G9Nlve8rqDg993PluGzuoFK1ZarH+5AGglJZu1lXBatCqswZ2Asa5LFVBFWyVwpzc8nkakKi620sNEZmYiEUN3ECAOdWtaiLNFb6u9eW8/vx8+qvf/rStuZVRc6xmAAiROaaIiNu2PX/58tNPP13PVxFBwMboAFpLqAjMFEOc53k+zNPswVfJpG7Xy3q9nt9eL6c3dXiKiIyIFJhjjGmeqwoyAoIAnC9rKZU9Z7WpSZGyXc+nl69fXl++vr2+Pb+8vq1FIRDHmKZlOXD0um3kaRscFT08Pnz+7tPjYTku82GZUgojne9Nbb2zqLt5DFoYRt22UmErmqtueZoiBjamZjIGQdZyg3PgQJ7l0cxUzAAV0ZCQSLWa1SomWS+X7e28vp2uX15OP399fj29Xa5r7skr/3g3+nu3fSCxbx/dA6bvd8GPh+x++ubnWzxar4h+2+cGmPbjeQ+MRoDmbn2/O8kNpo1v6P2eAxgN1gvuFmEAAMaBH54eD4djjBEASikEWHJZt3zdtqqaIr+9va3XC1glQzQjU2elxRRUK5l5YWLXUzVheqvuClKQA1OYkDhgHKthNAczhNhCu9sta0veAR4M1rhW76MkiiStCKAnOmqe+XZrar0ASH/GrUqPEnhgbZ8d/An4sxiQqCM6AId1g8ABRy3U1nZg1rOQm4FayyB002cPPGS2e1fts880YFbJC5x4q0Gd/wFT0dzZ4i2XrdSqpkAuSgIKSMhkbIRctVRPsaNaOeMaOOcoUkuZt3nKJZU5RQ4jB7d2VPT4eFQDqTJN05SimjB7e1BNkYRJjDRMsCyTSiglXh+4lDJP02EOhzmkeKhVXJ5ooqq+Tq45l15jVzlS4IAdD3k9bSYKTAYgxYroupWSiz8kh2IGaIbMiGCBZJlwjvPT09P3P3wOIVbRdSuqpVUiCzxPgSkyRSYUwlLz+Xx6fn4V0RTjNE+H4wHoCVSraM6Sc6EgxAGRt2x5KwgWE38+LMfj5OSSoW25nK7r8+vp+fn1fD4jWIrhuExTCPN8EbVUEhCqSghpmZfD0+cwPx5eT+fzdV03n0e9oMSnp6fjcUohpBTbgCVy7mQ/qe+6DQwt0R3g8G0PgMbSafwE39p+CRJ9k2i5I2bGVb4JQbDTRbijrFxa5KhooJ8yvBM7joeI9qVw/ZABUz7OULqrlbvzSNvdae+aOp7M+L7mXHM2d4KJEQMBSS0u9RvmwFl0FyQgB2IiIvHMscwBEInARKrUKmrKRKJaqqpB4BBCJKZbmQtsktOt1OtWTtd8ueaX8/rl6+sf/vB8Pl96lQskxBRCSIFDSNMECG9vr89fv/7N736/rhu5io8IQL3MuXO0MfA0zfOyPDw+HY4LBzaQ7XJ+/sPfXM5ndxBBqxWJPQxtStOc5oVCLJuI1pK39bL+dN3MvCS2aN0kb1pyLXldL9fL5XpZ307Xl7e19CwhAEgc/aQmKiaIwETzND8+LZ+eHj5/evj1j999//2n/8G2+SsYHjSwVijbbC+zteZ3q1ZNAKptVbdSUqQYMLKvpwMSAwYKaDowkrvwoIX6kyJ5GURStVL8Dq8vb+fXt/Pb+fL8en49na7XNecs72th/ols7r4BuFX5sM4AOW1gLZIM+j7f8GH1v9vqvuEP3PEIDRc56/OuAS5Fsnfn8av14nh+Wmw+u96Uxg15dgZPDdD6Enq0JSKCVGmCZLvxSd1IOBJxuOWMd3x4fCRmF7wgkVRRsFzL+XJdcz5MabtuUiqIeiYcA2qYy6xlWUQvh+NaVPSgLuohBaYKJjyQmL8C38dXNl4Wmlq8vGoLhe3grRM7ouqqOrzpLr2kg0/5Q+5lHRcBgLbg6Z4ax9qr9j7AY7rGll+7vx8TJSI09WRC4/3f8gz4CdWNhg2upwOhnj/SBvzqrkJPvKhE7Llpe8BdS75hKqJbztu25VzWUnKuAgjIxDFQZM+QiIBsQGyAYlak1pIJoTKKVOeKSi11mqrUGAJ3f+VAmeuWzdCT0MWQzGWavuA18HVVCM2jBGAi0zSFWkoMPKXkrAqCICAoVC2G7PWVhwsTwJhaOipVEFMzQUQijCGoWSk152wqgcEPVDBQsSoQhAMzIyU2IwSaU0AwlapV0YTJawOLiaiACtRiqkFqBakMOjEaMRKgiZSScyGDUiSXcj6fY6opTSEkNO35rTw9QYs0dRWCqXouuxSYUFOkKVIMgKZSSwFDRmop79kxTEqRwB4P07xM2jNRIVMV8DxTvSvdz+I3a7ALqhp/jhl97zjbH7uHAmMH/FDXbP/nLx0Cu0B334afa7/PQEvjG+wqKOzpp0f0mfYUGwMAfWzJ2HOcHHvqIwAYBc78JOPP/UO4a+HdDTaDOe6UGIhc/9OWSOwjtymJzKAXR2tcxO0TBwMalsarybhiDZECI4A7jsmQmmzM06oaFLH1sr2d13XLa67ny/r8enp5PV8u18CsJj7leCyL98RtW8/n08vXr6e3U82FWl4GJkIVMHOXLoUQl2k6HA7L4bgcjyrl8vp8Ob+dXp7X9erOdvOMsZ4ohEOap+VwSNMUY3p7e9suZwnMAG+vp0ghb9u6XUvd0AzN6+RaKVYqVYxhOhws5CIiWlUVSCmCoxRWbmk+zEwvly2v+eXL888//fz9D9/9J/+j//E/AIB9FkeDbj1vMpU2lZqLDw0EzIpuRWKkyJQCTIGYmDgQx2BEHNmsW/gmpERSRAaXX5iVUtfrtq7X09v5+eX0cjqdztfX8+V6XXMptaeOgD+17fa8HBtBqyvm46vvATs2pv9D9wkg21HW3+KOmukDtuteBi84qJx3bWq/3TQaePuhOcLccigiERMjSVf2QQMWLUrAg0s7PmhtxHGK3iREijEdHx59sjQwYqoiDtbVXIhIrvk1z3oFvfBoL8RlZgLiixUbUN/AwPi21BMi2uNA7FiBEBiRO1A1ICEiuq043ZUFDYO4jKQfS+j5+cFTtTYYdUOx/fbVOoK0ll0BgAltTDPIvQRb7yAW1IRvtvtdmFnnj8yafHJUYrOO4aCj5F6oDDsw8vq1RL2mkrXwe/XgHVGtUrdtW9etc0ViSMiBjSAYNmza8Lg2XXYttSCYVFQVQgC0pmw0jSPaBdDTgojq6+uru0CxofEm4WpvuN9Qr8WCrnI2EUJkYjBSAVU0JQPSUYQe8da30fuFlxC3UrRKNTBCTCmCV5mSQqAUCD10XbxgjGHLz4bchGlMCGXLAFhFVCqTMQ2PKdVqpopURQRUAsGcAkJzB4MUKcULvIjoer2aKiMEQiaITAZGCF4bIQZmbUoNAotMyxQDKoGkwMscp+hckqgYGnGMoKZVq1qtlRHCFKYYj4/HIua6+CqqmhEw1neoaB9aBZ3CGQDo1m12GGVPotxWD7vp/2a/7gzVL0TavwMKHz6MfT4Cjrtz7sHHXnU07lRERiqBAZI+thPeO8jGU6Jef21grHFFdz7etfPjc7jDf2pdAdiegCECu7O7PWv3V+4erRm4a54YgMwMzEotTXbd7feYJjwUAREDEyIVkVLlsuaX18vluuVac5XX19Pzy+t6zWCS0qRKLsn3mpQGtuW8rpfX5+fz6VS2TIRMjO6qQnBtJxKFEOZlOh4Oh8NhmmdCu1xOby9fz6+v18up6pgEqCmPQpqX5XA8xJQIUUrZrpe8rhZDDuF6XSEtl3U7nc7X9UojHwSGIlArqIAhc5gDCoiCSFGAFkhLITIA9DSStQhsuV5MT9d82vRyveOK/NkOtmC8wU60i2oRqyrFBLbKhIFxCrhEjIFDCCFOEzBzUjYzaInwEAHQUEgNKxmAiK7rejqdT6fz6+vrl+eXt/PlfF3P1/W6bqWngvlmp/z7vnVGxwyQmv+nbYNfeU9utD+azH/P33RnynCRDGDUvSfvFSsNFfUZ8XZugE4WvXONjc0HAwETYWSOIeVaq9QiggghBHeSMPAUm1K1xwi1AdGb4qVIgEOY5/nx4REARSoJE7O1AJ2QYqg1uGzfk6UCKDhbY4i99J4BiAlYT5nfCR40a3pkAwU1pA4P/T5br90rJACQnX+m5spn1T77AzQ99e1NmXZuA9wZB4CestTQsJE3dwofMPCqQ4Qt+Q0NwsnJthHIBqpuHVFFRsbJTge2gh5NYG0tCVkb0beaww2HemqTkeCEAFyDCogOa0qtWy6ejKCUsm3bddtyLjnXLALExMZKECqwIIGnIq+NtveA6GoqhCBCTnRI/6FRRUTYF/1S6u//+ncjF2AIbNYQdmPPemeJSc0oxBAjT2Eh9NcJVWAr1aXxVaCKZ6kkDoSEouZrs47qTUS3ktctqyogzDV5nLOZBLYUiZCUfZYF9oM6aUBIwFCK5Hy2Jqh3TAOOTVSrFan+UlRQa2IMhwhGuRRRNS1aNo7Jgce6Xk1rJJgipRBjCgoGZpLzuoZATEiBGRskCokX0whWA9GU4mGKkRHBa9EQWrBaq+lWyna9RMY0xcfH5fHT03UrlzWXet3Wq6fJjF1tPWDBwD2220IIe4fUx7xE7y3Du5yKe2RjO8aFei3Vj2cYsGZ/+PhpbPt9BioaF7o7J3bOCXbKIS+L5vfunrI9PBrn3COw8eceRe31T3c3tT/bHrF9bGEVD0nUIRhCpBix1gZxqAnbW1Esz93sy0om5BbGoTlLX58YATCZitZSPbYLAEPgOC+GvFY7r9uXr+eff/5qYGqQRb6+vLw8vyLANE3zHNWCqBlYDIFDOJ/Pp9Pp688/nV5fVY2JpjQBMXTIwMxAFEJIUzocHo+PD/M8MeP55cvzH35/enld19W61QLHC8wc4rQsT999fjg+Si3Xy+nt5fl6fqtVEKyWUnI+TEcRva759XQlChxC4BAT5Qwlq4qCaoBgFMEl2lVUK1gFUo5TIDZFESiVAaOgqIqu9VTO69aCkTsqwsFH7GY+N/q+zlQQsSywVcmaAYzRJrJjwjlyjCGlxYw4pBCTGXniqLZwNjWEqhUL5JLf3k5fvzy/vr5+fX55eX07r9tWyppL6XlgvjXE/hS2tswGgObhaiikldn10QbdhbPb9AZj30Ejz9PYWZ7x/R3mbf6gP/7Yb0dhB1nOlgYOy7y4CocAlzQXKVvOl/XqCa58Nj4eH6aUCNG8hs62rbVkERVDaN5BNyLL4fDw9Dgvi5mt65pLYQ7X09VjwlEkBbper+vlkrd1QZwoMYKolroxoiIaoCIJQGi5qhlaDiEggB5PYGCo6rLE23qXmImIyZOs88hJ3YPXlYnMI407erceptTQDGIIFJhCi51qiiSPmO16Bl8PqjSA5eSSocdKMIXAIVB4XyNJG5GLgBVrdY+e17Sm24xw89Z9eyjh4PeAPTyeGImYqAXQEiMxAIlCrnLdtpxzLsXdZ+uWSynZDSsFDhYVFEMRQCQzKKVcPC3/upW8lbKBKiKoEoCpSC55y3HNW/cxIiCIKgBs2/pf/d/+RavUzRyYrSfrV/P+FogImdNyOD5+Pjwcj4/HT58fpymJ2Zbr+bJd16wqXsIsEE5T5BAhRgOrxYqoilDAwIRgHieC3VuIYMkL42HLeWFIQIgMQME4FMDzWi6XtZYamOdpSiFILQhKCMlLTZkSmJjVWqx5H6vUAp0OFi2lrlLFzCTSPEUiLjHmvJX1DJIJ5NPnz4fjARGryNvb23oWBgVbNE2EME9hisQEaI58tAkoWjIPADMpRbV4IkcpWzrMIXBMqVbb1ryeTpfXl7ytbuRpmrr9uC9JsaeObBep3gfIDTHstzv+4xfNSt9tTzvdIZ6P28cdbEdQjW/2bf6lq49bcJ+ao7RSWk7LvbTcdl62fcu5l4O19xwbdnH6vp37Ro6d7x+UAQFxYE+a4bfrgWFIxIghhBAT9eAOtyROKzORx4PknNd1cy88h54nyBMom0otqlorA+Na8evr+eXt9Pp6qlKQeNvy8/Prz3/4udY6pYmIapXqC+AQYkxvl8uXr88vX79e3t48w60BVtVGQQMQAAdO05SmeTkcHh8epylu1/Pp9fn5Dz+dTi+1FDMgDqPMIhMvh4fHx8fjw8PD40PN29vL19Pr6/l88hVLYJyn8HCcjsd5266RicAYNBKGyIGhAKhYKVrdt48BAM0IwNjQTMGqSayGIipipUpXPARPe0G9Tl8PPfBERdjdOG15iWYGhrfSS2qelURVCdRIggIKm0QwC3Eq+RDjLCJqYODONWQAj9FFIlVd1/V8Ob+9vZ3eTufzZS0lV6kjocIfH0Z/rzena4b/EgC6b6VzjB2RtFhpMGgsxE2ACwA3bIvUZMYObR2bvy+U5qgFbz8NSqhf6gaiejF1ACI+LPNhXqY0xRBdIwJSESwaxxRjTE49uGPtMC/jgiFEDgHXDXIuqOTZ2QlDCKD49PT4+PSADCBoYqXmVa6nt1PjjAGmQM+X6/VyyWue5rl4dVKEiiA9wYCCB0iDqmmrS9oEPNY11Y5DTIr1aQDR/WYtgY9HpI1wfx8dLfgFCVGV+i116Tr5HMoUQ4tS9jgrA1T10jiwmzVubB2RhwuxwzGPWfCS1bu5x992m0VEhAjY56cQqNcp88QDjTEEgJ4AvUm3cXgJmxe/qwHI84oQMzIDkSGK2Vbqddu2LW+5uc9qrVWcu0MDARTDrVQFAFXLOa/rJW/XkrdSi0g1U3f7+E2ItjQfLY0NIKKnoQOV+vb1D44LmZGJzFTF3OWGHJgDIhkRx2k6fJ2Px+Pjw8vr5+VwVKNc7bqWrRRTAVNCmLxeWIohRlG9Xq+1FDB9Ejkuc4qRQzwyE3MuudbKxMEtIBqQCZAoikCuykHRDBRK1TVLrZVFAVBEwDR4m53VUxSkBmK945nVWpzj8zGoii6k8P+roRpsRSRfwdRLhRyWhQMpYs0ZADxBboiRGacUXMYnRWquUsVUG/PGjNBE+YBGBCkS8UQhiNHpWu16Wq+X7XqVWhg0BGYO85JuVug9HLkDH/uf7rgZ+ACqYEer3FE+d9TLnlX6JVTU1ga/sHLeg5X9l/jelfaxbY5UxhUHSVxrHcm7tW/70rOdS3633QGp0do9/Wxm7pgeKHPfvMhcmVolGU+S3PbscDSETrIgEWNoLjM1AxVtGBmJXVEHPnO429rMDI0Do7EZrlv9ctpe3s6Xy1qqANL1ul7Ol9PpJKX6cq6K1OtKMRBjlXJ5ff3Dly+X02lbN1MlIERyXh/UTRNjCMuyHA4P8zxP80xgL1/+cD69nF9frpdTrVUBAdEcEhFxjMfD4fsff7UsMyFc315en7++vZ22basiiBiZ53l6fDh89+nJ1EwrgEwRp3mKMTEHRChgZJ6DBKBxnO2d+bNBxK0IUpsJEHlZpofj4eHh8OnpcZ6m7z4/+Su4cUUOPbtl9T41gBHc9JxmoqoiCkJaK6kgV1BCLHmrOZeUSym1SgiqHrRPyB4hSgQAVaSUknPxVNZ1ZGv6k0ZE4K4gBTODlt0Ou1MLAABG/uMdI4T+5a1gKBi3YOcGi0ZSxpFCZ88KOQgTtbvMMft22e5s/hURpSkdDsfHw3GeZgQMTF5jqpSMBhQsxglw+IA4hujFPr2ujJjOABxCUWPE6ORMCEjh+x++f3h6HHOTZ6dHE6ti7AiATVo2QuqLfdBbKDwCMEDyuR17pgm0jg+awqjBEr15Km/eqOGQAnCn1bDR2MyYp3y5ORUbKnLk1Khv/4BIbP25EykRmZp5QiVf4SMwN9ankSTtk2fQaTVDTI0Dh5uECTsqajR7b4afc4jB9g5UB0eDLqLbMh1bsiPi4FgMid0jlktdc163vG6bIyJTA2QicGsAtUJVVROVnLe8rSVvtWZPCAlm0CJ+98yCsnsAHRXduM5qCgq35veEAoYmYBWQ1GDd1ut6Cef57e3w+vY2LUfAKMZV0Wu5mQmApUBzSjEGDqGUuq6r1Ipk1UTNjgc8hjilCZCYQ63Fab0mXiIwZDWopkXUsLIS3PLfGLV3Yg78QwwxRe9ZYkBqYj0vFpMCbqVUUTOMMZlxD8piLyHnBRRyrggQAs/ny3J4SJCkSi4FzESKZ9wmYmrJNGiTsm5bzRnRYgqutwUvquYzd4AAoSqIYa52LWuVKiVrLQCYYlrmFGNclkN//veI5I4d2dmFb4Onb+7soGePIcaYGpBoz6N8k2r6Jay2//Xu6r/EFd1BpTtsB52+GrV9XEZ967s7mmecf4Cn/c2Oe4RdeqdxkptJ2bXQoc/Yx2V4AOBV7N2kULsCka8TwDzFUe3pCLWT0Nh1peKoyElra8lEcilv5+t1zaWKGRpYzqXkggYpRgVAxFoFCGMgqVprOb+8vj0/l1JUPfc3A7QFPRAQUQhhmubDYVnmKUQGlTWvby9fzqe39XqRWpq+EckAPav+PM/Hx4eHxyOY5nU9v768vbysW3ZXHzGllA7L8vDwcDgc3l7OKgWtpoDLHGL0FR3kADUSYjALaVlEPD94zblIdUNlIspNEBhSjD9+/+mH7z5//vz06dNjIDoss7+CwRW1CWRMxh0HNRJi0PL9NQmCKFUVERJGlYolr9u6UphDzDFlDsFlC02PQY2wh65x84R4/rZ0zEz9P3cd+W9HTH+bE+jf8U3BxLSqmFkgZiDca15uKzCDwVR6YXM0NXA87PWHfZaz21P0hXojY7H74XyqFjMxzxjqBUea7AYMhyPPpcHQcjEDEx+Px8fHp8fj0zIlKRXa9G5FFQADUQwhtGK/zK3UvIhqrapGrHKMEZEEPc1uiiEa8zQv3/343TTPKvU29RNNMdSc0YCJYwgjxQz1FMmG5oXLvQdFoAUDuJ3teAl7ckXr6AwI8F19WPQFvEHLQgRjwTeMeLOY4OKv/SKi49CW6KhHnfesRwh9uafG5LjM7ObMiv7IwkBDHs/BQ27dypeEZlXazRFyz7rbAXObZpo5bp0A4Ta4sIOjhgKtfQA0JA4cIoWIzAZU1UqV5kHLWcSp/J62ABHAaq0q5nqh4mUKpHhSHzNpHdXznsnoW2rMpC3LnHUZ1jRFBE8Y7S+0gX1RA2IkUgMTK3kt17OdCEPkL19CWihMxDPFmSj4LKYqgTBFT82I21a2ks2UA1atqqhGIaR5XmIEQGIKRKjdJEFAQwJRaGsVAVBCDEzzlMACgPmUQ8TBC67NUVWNRIBYQaQ2BhZBgdZc1y1LteORQw8iUuNcNNe6blnUiohtCmAUpmk5LqKmum6Z0GoVM2WikKIZi2gtdr7ml9c3lZoCH45H4sAcFRFNAZW9dlzgIni+luu1nC7XrawEFhnnyNOSDsfDlNI8z6MbD3hxB4/uvuxT7+2n8afvMKiUgTz2mMBxxoAFzKPH050g6ZtNumOkBroaHfyPAKxvQqVxoRgjjcRdtbrSCHHUnL9vzN3hgyvay65hxxXtH1GXqL0/FRESucVU6WAGkXaKQ+dTkQNRQETzqllSt3Udp1JVhLbWVYMqogru6qrVqkAVPedyvlxzqe4Iy0XyllV1mlKMYStFREWEiTy6cb1enr9+WS8Xd+dR8+I2AQYRxzRN83w8HB6OR2ZSqVvOb6/PL19/ztvWiqgQtWWpARPN83x8OD4+PaUUz68vp9eX169fTudzyytJzESH4+Hx6fHTp6d5nr/89LPkFbUkxjmS+xJVdJoIIZqFEMPjp0+l2LbV87qdzpftCjlblYJoMVCMMU3p6fHhP/qHf/mbX/3w3eenw7Js6zr1/BQjeZdrCQelb27IGgXhnR5MzaSVUlICMRPwROFiArCtF+STAQMwEptrSwFw+A45hBBDTIFj45sBxv9792wv8j2A7rBst9cNMlgztL2p8O/jJqJFXGgCZlJRW2Q3ALyDfNjA6l4J2HcwxArKMNZk1pCNAWIjWu2GddA8yAd8EsVi4uGfQ3LvO9+QhQEgzsvyF3/xDz5//nw8HLz68XbdLtfL88tL1RpDCDFM8/TwcJzS3HL4xgDIIppz/vrl67pOrt4oalOKAKgGBfSH3/xqThMCFBNilIquXS5q4H4VjoGj500lQDVby8aIplZ9eRS0ub1M0AyQwAjMGlxC9FJVnuYdRJnYuktRzHqHtSoeGg1umeS2AhswA90yvDevhr3aSsNVnpceUW9vrNlGIkRoYfhMGGNkTyQSQmhsDQ2/mMFNv03s0W10y43UhDA+ZKETr2NigN0Iu41za4Fw2PN4IjCgGofEsXIsFCKgAyPNuXpKWfSSFjHGmLxeRhWrtZVhz2WrJatHpTef8OiLHnYHHVs2E38b/kgwHxCAyBgxcC8ZbSBigATEalZFIWYqxatCSc2lFEMGDMgRMTQpOwAheoVXQvLxZWb/X+r+tEuSZMkOxGRRtcU9IjOr6vXb0BgMMDwDHh7+/79DcjAA0Y3aMiPc3cxURYQfRETdIuuBh/g0Q+/qfJGR7ua26HLlypUr7p/+uO2//fr1l9eXz58/J9mGTisRFWQkhDoVZkIAkQuj96Ky7RCiAT2htdZbbx1aJcXKE09FsUrHx/7+5lYxorZ3eH+097f3x+Oxvt+vl3We53meunbYHk1k2/d5rm0rx7Ft2350MCzX65WZ7ttemPajiWit0/XlenS53bffb9/+y7/869vvv0y1/PTDl2W91mk1YhV16+GuRtKhaRO4Pfbbff/96/vb29tUaJ0rvKzXdT5EtDX5iCf+CHrO2/kgOeS/Uy880MCZB6JUZ5tZ717PuLXWHEbUWqd8zfM8oMn5gAOOaOLrf/ivY46dj/D/42ucsOfOvJ9J7721Nq7Uk2v/Xw4yTs9jlXEm48YGHZCgsLXmICzusMcE8KRVQ7X4DLDAKxIQ0UxUxFSOdjzu97fbA1T9/AGhlmhZ46t8V2hij90eez+a7K3dHvd97wDuiytfv37b7jsTXa8XRMPbfT9aFyGE/XF/3G7397fb2xs4dAM17eLVQUilTpeX18vLdV2XZa6EsN3fHrfb/f39dns/2u5PhpkRWC16SL+8vHz58uX6cp2X+euvv/z683+7vb3t266+dCMS4rqsf/nLn//y5z/99OMPoLLd3tp2g74Rge4gWowLEb+sDGth5mWpP/74xZRbt8fRf/92+/33t/v9fhz75bq+XK/rOq/r8qcfP//z3//25dPrsiym9rUfw873yRXhKe8ScCjz05q0m1giJRhrm3mkZwq9bfv2MKxAE9fZDfVDxUEO56nWuszzsizLMs/T9Nj24RMcG07kIIifAQOYQRYrghPy4IGq11kZWJIl4dUZhT7/QzPi/+BXJZ6pdAQF8y0/5sBoIRq5rNhEghkCBEQxFYjapAIYSbTYIiNBFOQQkgvQhtoIPJw3MzAyALdF+p4rAgTkUupUL9fLv/mf/vlvf/v7Mi9M1KX/9suvX7/+/rjdeu8vl3VZ12VZ1mVdlqVw9f1GFQwUEOd1/uvf//L169uvv/72y6+/v7/f/vbXv6zXy3JZfny9cDT585FACiaq0hUAai1ew1a5rpf18XhIOwhxrpUBRbWAoB5kgIpA1kDRgNXHHqkPwlikEJUQCKydH0EXaSKknn7gTJGodF96oqsxwtPtAAaAT7AKEUIAiCKiqGE4z4CNMexwyulAAvRuhezSlJHCAwOQp9+3qZlkthnAxwcNPWBy49HhdexWntQ7xx0uTjIMTASqIMQQ5wnIHoP6f+7i5B/x0+bCtU7LspZSo1GXNtUu/ejtaO3Q3tydHLPCMJEYDhhXSplqKez2c5jgHjtWTxMgkxVG5vgH84GJBEhqOMmkDji8Z5p0kS7SWzeNEAGRsDBYQWNCRkYAFAVRu9+2tvdvX99+mebryy+uTqi1TvNyXS/zNNVSiPSK6MqkUq4evR9NFHaxBkDe8LGLqUlXa6qHYGVGKly01K7mHa9bEz2ad6pv/Xg8+o62m60IK8Hsy1YF/fHTlWR/e2uPe7vfbr9PX1vvdaoi3aAeTZoolTqt177te7//8tvXf/n5Fzse9fPrerm+fPphXtYu1qWptWTvpKsoQOvqvUHG5o5Ianh7HGrbGT3gxxTPwP3++1Gi1bMh4Fkf8x3HA6dOIM6CIqKIHMdxu92+fo2e6r33Usq6ruu6Xq9XT5TMc9gfD13d+cTOuapx8oN0oVPp3Hec0H9vV/iHbBMRuf6as5VbKaW1Vkpx2mPcnHEfvoOJXt02WKKRjDsLtwdpNE0TACRFZOAtnwG8vw96HYmCale3Qkf/rKhqO459e7S2m0bmBxEJZiwARGLSxB67PA7dDjuaHa3v+/F47E5etNZv99svP/9qZlOdXOFoyKUCEh7H9u233+/v74/7o7WDmcEtJc0QC9dap3q5vnz+8mVZJiLobXt7f3u8ve3bY9/33rxfIBAil8JUYqVE+ulPP728vBSm7X77l//yv99vt966qvekZCKqpf7pn378+9//+uOXL8s8ff3lv6kcTFoZ1Xo/HtaRmMq8LPNSaim1TLUw6nKZiCsg//Tl5bcvr8e+m8qnT6/X6zrVOk31el1er2thkH6oaK08PFCelfnPwfNx2FiW2Gj8mbQFghd6kLtAGWpv0Hbknet2HFs9KjO6apIZVQXAmMscqMhNvzcDRPE9BxCBCLzGuxQ6jbnwYHO446dKkVoIiCA6/tPuvOMHdun/pK9x1wmxEJKRhnUDEtBospHPJzgcjP7DqaRBJCC3J4pKK8iBh5Re1GlvQ88eIA7zK1GdanWBL5NphCZg3jCYiHgq87wuy2W9Xi9ffvyxlLJt23G0Y98ft5u05uLWeZqWaZ7rVGtFQM89wRA1EYWDxWV96U1MuZTXL59m/0QpkGsKZqikIt4tmNjBHqLZOk/LPLWtRA4oORR8XhrEJcQdsqE1Nue/TMHlKwPLAHjFOCGaKRSjzCXnXhKSJD891xQ9ee8BM/MMxC0TMX83UtKWDyRqHMA3kzNlamZihmronXvM3YfUO3/7uMYxIiI5Gm09RKPtWuioyAXakFMpZ4WBIUQDJNF44OCcVmYSceDz0UKWvW39NM2lVAMQ0aM1C6+BbmHvq0kXQJRwpOUBE9VCUynzVGsphQudPJlUQCmLKsk9sAjdfSHuExJh5VKCvdYuwr23LtQ6tu69wgEAKVz2malw8bQGqlpXUenN/0dEZNr2UgrXWuv0WLZ5nmopha21i/arXHRdllJZDdUQol4dTc3QENDhHVNRheNo5LYaiADQe9u3R2vdACcCmGuxBU3ngjPBxDAzGgIwAhLzRLKTCap2UZPW2+6ANryjQjPKIrbtx9evX+/328tS1nW9vLysl0udZmhSzIqWDqbNjtaOo3FhBChMyzKBvaxTvSzz68vKtW7bY9sbQLOPHRPHhn0GFmbmmObwgsRtG73oMZt7nFGIA4LifeJqdQbIK6Te399//fXX2+3mjBERDVT0+fPnz58/v7y8XK/XM/I4k0/nr4AETP94gf34fvjvAyNIbHQ+8ndSaId3wwt7/JOdXt/99fw6g6Hzl56xlPQePe3jwsUMkBgAEw1aLaYdMLcBkS4qAFiYBdQ0at+YycCY6Ohye7Tb1rdDu1kXaV1al9Y6Ebfet3273W6Px52QQI0QyzSNYGa73++32+PxOI5dwTCLtwCx1Dqvl2W9XF9e5qmA9sO3hG/ftsejt9Z7V8Po/oEIiBz8LXOpDol6O96/fb29feu9ex2HB2DTNL1cLn/5yz/98OXLssygfT+2WuGyTlNF0Y6mAOahmhvJe/uaqZRlnkqdiHiqZZqqO4FdLus0zx5+MpNX73oV3znJNFARxho+IM94Zuf/PGrMd5w0G2aqAl3bAbxRm1vbW5u4uFEnuPUImDHzPC/rul4ul8tl3fcd8GDpTdTAkIAJmdHrmnhAbIs6FK/dx+zdxIi+7RiYN6rsok0Em3QRfO4g/3/wIoTiCg9AAO/7jgaS7c9C8GWmCCcnDAMAl+26KizIDIAs8uYS2hGRgCWIAmDmglwgIKp1va7LMk+lllK6CgAyF1WJEqdarpeXy/Wyruu8zKrw28+/fv369f3tfd+3uRQmXOeZEOdpqjW2Ok+Mg7mIPIgCIBazUvn186dlXY99//zlMxioqYo7DMUi6ChYurSjmSrOgISGICrTXOd52qaKQIBouUAB0kiVZe+NQDYIjC49DA8zDcLsNDpa7601IjRjMwu2EtzCw1Kp7J9KvSc+FwiDVOE9F97MWX4chP4JSkwL6a05QmEAN8Y0A5dS6pAZ5NcCusw8sqVoERioiPYglMxrPHypH2eQ52OIqIZkYCAGXptIniJXg7RTJsQwGS8FmLlO0zQt07xEOR50zKDVkxt5bzHyZEPE5F01CtdS5rmu81xrrVyeUvGooQFBEgJDTUE7RvmjP2MiJjQABlAArsZdqXfiTtTUnxU6LGL2PzwBhApuRituCAm9q9neWieHZsy36VZrqYVLwcfjZXvs1+v15foyrzMSAZqoIaSNkhkBzHMtXEsppnrszQVvpkqIoKq99X2farlWvpbVLkVEvMHTPNXLPJmFHedUK0pDFVS93zcGBRXVjkRujNJF/REfrd1v969ffz+Off3h+vrp5fr6Oi0Ll8nQ29NVRDuOw7NNUyFiroVqqddlvSzLuszrMneVt/t2311KHGjyvNkDgJ56wpvZvu/3+93tat7f353pceLE3Yy+S1qxt7LKl+eknCv65Zdf3t/ft207vMhumpZluV6vt9tt3/fjOFTVsRR+fDn1YsljJRv99J7OKYYDsny32J5/c/75O+D13ff65XiL2ZE9NLPvEmrnj48ZjemHNLqCnLHmGRW5PZgXXYwJy8wWjV0tWs/4JlgZibxjNRHXAoRdRHoHEWmtm0En2g95v++3re9NgcMywG3FoFBrx75t2+3e2l6oHABmNqmUaUJCVb2/37bH4/DDobv/IxFzrevlen39vFzWZZnR+v543x6329vb/f0h0kLgjUhASJFeKrXMyzrPy7JelmXZH7f3t2+///rLcWy+8fktrKVcL+uf/vTjv/23f//8+dWkP257b/s8lVoultuDV0YzF/LufqXMdVqXdVlWLgwIRDDPU/GanlqjHCFZfEIEQjZoNNLIp+6w//0XxoRBjGxMpBBGCGgYeasOcCAf0vbe9y61NUMUACm1EJKZMfPlevm0f1JV6d3M5n3zojWPxd2sZZmcuC6lMHj3TbdF886cZuidvoOCQE/wHV1a16P1+3Zs+9GC5M3yrP9zv7wdKRgDACEDkKZ+FzK297/i2XtzwNfs2AWA0zzP63r99OnzTz/N05SblrhO2QDu9wdShMfLsrhrjZke+65i5GQ38bHvnosgpOuX1/bYH99uX3/+7diP7f6Q3ipoXablshIQiLZ2qIKKdeyWqU/fLAGAC6MhqABgb16EovNUe2utdenChZelxOKMOeAU2tGYoZQKiE30sR9ihoXKXEDcHVFzzyc0dM215wFVjVx6ip4KSsyIUZhOJpmihHYcx7ETkjCXQoX5KXyJWilX70SG1h2kfRHUDyDAr93M0bzXP+QQ9GWciBjQu6KAJ9K6GoGSkTKiIhFEuxxva6o+BUyfyolaCkddPeRK55Uozr0CU2j0vGgWAIJJUo3CcQQiKmaihqQAJGpH6631FpMH0C2kSy2llFqnaZrnZZpnROoiKL4bOSTSKAbOVDgEp+s5cZ5KmWtd5um6LpdlnabJ+YOIyMHc9VlB1Lv+KXZ+YrogLv3BDbU5ABIzFzMBKCktTGQUFXzhu+30XGE2b2Bq5sSRWUDrGzlhSqXQt2/vl8u3y3q5XF4vL9d5qbVyLYSg1juYEcIyT5fJ11/a9nZsdyK3m8LrPMFlKdoPgmWqUymFERFEBRGZuJSplOpP1b0OKs1LvVwWNQEjBFPrHah07cfRtu24b/t0397f3t/f3m7vbwz6+fXl8+dPl8uqANKaqCDRvMzTVH2Yz1O5LBNSMcNDrIlNtRIXA2xdt6097vt3Mh37mE4am7qZ3e/3X3755bfffvvtt99+//33x+PRWnNYwyeXh7HellKGWsjzA36ofd9vQT8cTr3c73dm/vr169vb2+Px2PddVT2Vhpkm83Hij96FOGdqZ3zpQEtwwknnwGNcbAYhz2sfk+vMUQ2FUK11IJuRDvOme+NbLI0WIUVUnomzj2otv8N/3Jhi/HAhYgVDKsRUCrV+eHVl4VIKmamYtr23bu4JighMPuKhgDMR0ET60bZDH0333o/eoRsA9Nakd0/xWFftol1BjSozI7ESKYJI0/3x2LbN924qBYimwrVOU12W9eXl0+t6WRF0u3379Zd/vd9vx34cR+8t/WOdbkYg4lJKobrM6+cvP37+4YcffvrpX/73/+1f/+v/+9dffn57++odaz2XwQCfXi7//M9/+1//1//wH//j/2W73b7+9uvj9oZ2lIoIdex8BuYroWPHaZoul5cffvyBvXtPP7q0bdu0N1Bh7ws0TaVMUOd1xTJVZjaDqjqkXR86fiCEMjzJoTQ8yf03todUOue8wUgPqAG5jkoAu9mhCr0rophNA82t60U/q8sJSi2Px30/jqMdrupixlp4XeZlmWuttRQkMgBREdHepInbwqqZeiSJ4B1CsSu0LvvR5/v9/b5tx7EfrfmQGUKj85Q4D8ZAHv/HvSz1EM5EQBoweqlTUPbmOBdTbeIfdQSCSLUu19eX+bIs63q9vry+fg77HcRpmrCQSJfW52kGYCTiwvMyHVuT1kzEJeueTyFmAEMVP3R7bA58e9t727kAEqmCibhgWA0AbXjtoCeoHBeZiUhXQWJiNuyeFHU6Qg16byqeaDNRUSBKc0WnLeZlRS6iejQxk317bI+j71IMhJ2p1KaiqFAKIAqYmHGYdQON5ACCmrqrowGa6plb9xoqRFQtCIxg4d3tSyQAeKG4gZkAmrmZYaaHmDnnBz4fqV97DlmAZ9G+KRkTeQGYGZFhxOWKkftDAxM1Ee0R3AlY5DcLcy9amJmF2c2ZVEV7FxUJytB3N1BvwGPgpS3OIKh5cEOqqszejxtF4dj33UvOWhdRP+daK4BN01xjWamikRMYtaRjco09NYpQCZloqmWe6jLPl3W5Xq7XdQlUxJHRR8SJUdTEA5ku3VBZAdDcwTwPXrhwKehG/kDi4ijLWR54x9RMM0epw1FPzS3HXV4lURyZWUcUJRLEHWzb9ve3W61TnX+/vLxeLsu6Tpd5WgpU0rnwvEzXChMqWldVlINB0JRNJ5ouL+tLpXZd27FXLj5nne9mLq6mb03aDnvv+yH3e7vdtm2XfSfDyUy1mfSm0JELcZnu92/fvgHAt6+/P25v1ttcyzovc5kQ8PF4bFsDROYyL3OpdEWapknlWOcihseh7Xa8f31TvRNCLQioj9u3tt3GFBjUyyAwfBf3P3vv7+/vv//++2+//fbLL798/fr1fr87ryPZ4P0Mp/CUQXN4tK7rkAr58R09DOrFXUDf3t6cN9r33bkZSNwzgNEZ5Qx+64+v83vO2StM7fPgcsZNoHwNORGcAJmZOeM1gNEoWPPk4BmHOdw8N0fzPwf99t188SkDAKJ69L17Wb6BKLvnqxka6NEe6XiMiMQIQOBduZzcNoR9b6Y+ExAAa63Np6qqqNPBQFTe32/32227P45jL4yl8DTP8zwt86Rd3m63b7/9aqqlTgUdc1EpdV6WeVmWeQGTb7/9sm/3++1tu99a6x6/2XgcoSOsU52WZf706fOPf/5pvV7LVH/75Zf/8p/+t99//flxfzdRomrgakt+eXn529//+j//+3/37//9/9yO7bef/+Xb77/e376CdmAmLqVU5tCSIyIz1lLnhN/zuoh082UXijECFDQxRqMJqFKZpmlRhcfewBoClPKk00+sgw3/QEjxRKQcEgxButr4EISxASA+SQtCiFRJNlMSiZnmtNs8z2bq6SBEuN+X/dj2Y++tY6CiclmXZZ2dPmUmS51aa+J+ayLexUeStSJEVsOudhydSyGuddsK7ztz6z0nRCrFz2hkzC74gJn+4ev8YYPvDwXn1OP/4Muiku5Eq4ICPFvC6kn7nJ9IOQsAIdVpul5fPn/5XKfqkdlU2Eui3AWHCoOBooNi8oTFvh1g4DcTkdzwyowMFJEMxc+sHXuZprjTMYcNwNTZDkB0a8RMf0FsTHlfAVQUDcFQSJAgtfFArINPyjnt+3U6x5g7xEAX2Y+GACIhn1EzePLMgasQkmGGof989tMVVRqO1abANJ6gcyhEaCaqaEYQS5gXosdQVwUlxSDs1QU6SJ6oImcjIHKbprE8Rg/7EUwmZwUQgrBYRxw5jaHkUqEBiZwq9QVHmFVN3NuaCbyPqeogipzbVfWiPEAM5i6ScQ5iIo9qAAiKaVDU3M+6hSwg+j35DpdkAKho7727raOImxeMwCkC7uyVW5inwnOty1SXeV59fNap1DLktABQmYiMFdTDgjAvcmvaJ90W1JGLFYAifrMM/e2pFfPbJSEjy7xDSgIUsirOtxILIlABzNRv+HE0PtrRZd/nbZvbuuLLXNdaSpnnqZaKSKrgC3BKpBANCLmW6nYSkeIUETUBUEMU0K73x35/7Nvetr3fbvu2HWF9qeYnbGBigKTM/Lg93r++mdr72/u2bardAB2SepmJqKihOIBEdP8aJkKuqGDYvWWvSkdwwYJaO6Af52Ul9jKizJA+E7v7vj8ej8fjcb/fXQ/U8+V+0N+xMphZp6G5drpoJNR8X/ABMEgUTd8WVy+5FvsMUOyk/jk9ZRgn8N0vB0lzxk/n6zq/ExLM+ZrDH7uaDIJn3KXz7RoXYqdCuTM7NX4+n8Z3L2JiZstFzPtTq0qPXnUIbgkbYhJkNwgxVfU+IZSbNQFFFawoIgIxlVLkEPPCSFVQbcfRWxPpgEDEft5+eqriIqdSS2U2YEBEoGle5nmqtZr17X7fHvd9e+zb5pDInSKd10VCZp5qXdfLuq7Xy/XLj1/mZemt3W/vb7///vX3X71BrPvHmxFzWeb5T3/609/++pc//fTDMk+///qvt7ff98e7auPCVGopE0epRnrDEbmGupZapopIIecmnmamYl6pRwSFqxvjGWA6BThz/9y7y3eP5COR4uDI4AyMHAPl+4Imjwybf4F3Y/F6fI9gRVVM2diIqVJFBM4BNc/Tvm/7sbfj8DEx1bKuy7Is01RrLY6KXEHWevc+TE7b9t5TUsyFq/cBbrNQqUS11qmWx33bjtZO4WAM0ROiMUvrtgw2P9yJ0+36fngHtfMRS/0PASM8/WSp0syMmTpjN1IAflaOXi0/7GCkTvN6uXz64cvrp1dGKoXnWhnBVN2K5nlxBgQABL1r79I3mZfZI+bC6MXpaqLqnnYul7HeWpmmECpRy13UeRKHQhZflDc3+lnE15pZVlOIMJCFGWuk2gIQ4omiPGXfEMkApEs7DmJKdyRQO1nsu1twZlc9+T3wEJzO5IT9LfvJPh/qOF1/J2Lar8VqY0Tm+30iUgNDAwWg9NpJSQEgjR5s44HaoDyAEBWUiNQAnwErxuWbmYG3OgkZgO+9AIjky6KHrSQZ34cwWxH84GaIiopKTuc95dhROhSML6IAohr2rj7D9uPwKabZBZPL8GJF75LWWj9a69GtRwE0cw7eZC3k1YVoKjzVOk91nqd1npZlXua51poxXyDOwsRgquDFYpYNZykVGj7nosddAEgbsbaniseW891MpBCHAXpLNjMzQ9RowxzKADX1f1VTERWSTl1613bsxz5r62vl18ta6lzrjFzF3PHfWtfgeBWaDFBH6m6QIr1ba+q9EdSgi7693273fduPbW/eHdtUDJTAEMSnq6oh6k5cyv3t67fe++1+ezweIl2B29Gd0vPdW1V70/1oXU1EwGyZeFnQjQla72JiJpypG1RBlfOuPyQ7/ptRRu7l9Pf7fUCioY8ZIOOcJ/oOG/nbaq1eXHa5XF5eXpZlGTh7VKoDeG+c7q339n3/DhX98XVGb5Apv/HLwetYPPHnCY9zPr8wq89i2HzkwM5/nuHRYMvOVWbn0zi/NAvo/nhR4VeEaGBsXkcBGQ5FbjrTA88NmMDA1LN10YupEJovs9AMDIQQuTA29Mfpcms5DpHuiB4QRIRiY+Cj9S5qiFOduVZPyJiiFx8g2rE/7u9ft8ejtaM1cR1LCJQRkdlFGpd1fX19vVyul+v15dNrb8ft7du333/77Zefb7c3ddcNYu9xUKf59dPnv/3tL3/9y58/vb6Y9tvX37bbW287onKda52ZK3GJHmjRgpxrKR75I8VVAyBxmQtPACIq0r0TGSEjkpi5aRojVSZ+Uvx/QEV+r+2JiWz8l7xRLPNDOuChod9xrqXWWqo31o1QLqXSCmaESMX7ufn/8Lou+77t29ba4UOwBCqa09UOfaCbamvhi7Ifx7Y99v3wjnlIXNgr8UgNpvmyLJfHtj0e23177MfRezvXQ34EN7E+upRVozfx96RRYKUg5tOJPVaB58yMkf0P5+4/eo13emjtX/Kccs9vt6dsFSk/iACEjK+fPn/58cfXT5+u6+pYlBCLC9UhmFxVYamOiax7jk5VpG07F9YMj8FcagEdEIDi8vz5iRDiVCc5epMW5C0jM6PrQmhY1obWxO+bxmZm7t0DTQEKRKbD3xaA2iLnNSAgOh2GpmAgIvuxF+bLsk5HaYTazMAACYm5cAUBNRQrjBOhgVvkgMLwOfC20n6TCamGNBgAIDbysVI9w0FCKqkvCkYGJYUH45FhlJ1j8RFr3vYBC7Oa8cjtxM5NyQ9GVDHumhnAgOmR7XJdUegZnC0S6i7O68xEaWluMUMp7iFKWDBZkk/OP2kOfCBEi0gd1aC33tqx7z4pvcWHOLk7Ho2IiPZt8zqkx75vPr9Moxo/soRMhV1ezUutl2W+XNbLul4vl8u6rstSa+VQW8dKP00EBqqgZr275ZF5IGDsVQL+/xp1aeAuC+gkq4ZBAUCGaWMHdDoTk3xVQNQw0I7vTpSbfXhjcDqn1rscx77vu7R+XZfX15dDaBdqm7Tej0OOJr0LkvdKwXgEvrCYikI7eovdQz0rerT+9n67O0EUif5IfdYweYstFEBhO9DwN/zt/f22te1+fxfpSri3tu9tP1pZgIi0ybbt7/dtP5qaMdHrZZ2n2kUe+35/3FUaoRJBYUKFyiSljHzOdxs/ZT35UAJ9+/bt7e3Na8fMzJUcTiI6aTQ0N2cscuZpXHx9uVx676+vr55T+25V1FRk//777/M8+2fXdf0u8XSGNeev+w7YjVTXADF/REXnjwOAEz/+hiFjGrnF870CgFKK3wp/DfIscUywR+ebAx85ubMSS8REnZ+uPEHkBPCA3IMgGia6bT7XUgBMtLuhIAAWolLcZRRErXV1z2tfflpv27a143Aurh2b9sO0g4k7nhuaSXk87LHtR+9cyrpe6jRZVqBPE6v2Y99vb19v799aayIGwIiIHPE3Ii7Luq7r5Xr99PnTy+tLLYUI77fb199+/v3XX759/e3+/g5obvDMXJBoXtYff/zh3/+7f/sf/+P/8tOXT0z2/vsvj/ffte1kSlymaap1MnRmBwtRrcyxcRiAqUgzk9ycCnl+Vomp0JSRLJqpidXClbnWWgv7khAPFP7hK2iUTDnFFmVnrIDP/5w1JoYaYKcU95jK7Q1U1EiNDcBcUZVLoRVmTwb21lw5xczzPDtLRESIBmZetJ7pXvKSOjDoXoWfo53cZaXUeZ5ertfW+75v+3H03rv03rqaDMLIMh6P4hkNqGMxN5LkjEjfww5TFVHxce8Eo29XI7j/H3rR6YdT8QYaAhKrC00iDB6VC+j4stS5zsv68vrDP/34+fPnZZ5Boe8boQGi65Iwkh3dRFSivr1LQ2ARL/vSdjTPcHoXeoXoOMhc0Htqq5poP3auk9vqSO+ZpUj8QASAXiQzml6hl9IB6XA1NjUxRSeWEABEvSuOwzlxhmEMRKcVa/FMS5jfIhmQgUuME3sZMIJ4zTQgSQnsaN6OHjNNoz2GsxoQPIXaAIVZwl8Enza7qbt0XbSYhlu7+NtQ4y4gMxIjj7cbaPbqAKCOgAhd4iHmIX1tewqfnHqyTA86qHRJTVRqAUQySVF6MyX1FHViC5c2O3BAdcFBWEAFvLCslRtcy/hqMzVrTSKJduyttS4C5olFi7AC4Dj64/HYt23btn3fpTcz8eleUnRbmKZSp8pTLesyv75cXy5OpF+ul8s0zcyFKbqh+W0pRAZABGbARF3VzbTdMgCZo5WTjzY/5dydnM9mItSs2wzQ47yqnZYsT3tGwaKr39CZJIo8AUYykADI7ZpEVPdDVPFf+dHkevm6LlMhki7t6EdrohImbQCAEDWyLidS66LiDkIhr9Yusu+RdUjHE0IEIgh/UAAwU89iA/Te397e7M2OfnQ5COz15bJeLkB8e+zN3gFh24/H/b5vDzWjsGni3rbHtr/fbu+/fduOvTCXUrSWgkh1WkuZ53D1HeuXD37fzp0W6r3f7/dv3745MHo8HpjV+F5cNqTHrq2xpG1G9b5nlJz7cTF1a+16vV4uFzfXHlTK4/GApKl674/H4/Pnz1++fHFg9EdMM77ijDn8EsY7JV2n8VS2Zic+aUCWAX3O6FBVz5TV+Oz5mHx6USq+/bDnmjW/tHGfp2kavweAvdl9N0JF3zc9UAdlKp5K3tvxuD96UzUgosrViy18cBkAkzKLqRIXBWqKh5IAifZ2HNtj2+4PV4gf+0PEuwuLaTNp8zLXAiqtHfuxNZEnj1jCG0Wlb/v22LfH9nhvx+68hMddMfVrWZfl5foycj4qx217b9v2/vbt29ff9vvj2HefyIiMVJB4uSx/+ac//f3vf/0P/+Hf/fTDJ5P2uN8et6/aGzG7tK6Wibl4wTUz11rmyowkZsQxKuRorfVSC3PhAgjWWkdEb23pc9MzIMPsDZAKF0o73z9k0AZBFJrFsWA+QRGawR82f0RAb9Eb2zbYMxVihlG2+6ygAWK3Gag1Nj8m87DfG+ARA4CqIWQeBgwMfMcqpdRaei0AIIL6XN8VIVpKTROoqaxL681nV+9dVQYksoFk1AYcyohBB9mOA9tl0UHvvbXDEXdr3QUYGcfn+H7+cbpL8P1r+Gl+SOQkpe8YyMNvQ8j8j6EBl7Isy3K5Xl5eXq6v8zwzsUo7ZYNAoyY7KrYtkj3k8a+Ieioaeue5EqI0jdWZ0BC5kBGBmhgAgIhQUZcocQON/u0wFg5mDmkLeUvWCMoU4txhsHCZxvL6rKzGt9N/boxuiEDM3u2mMk2ljM7GHzbzABOg4KB252DSoCAZgbkLFCASe/U/ASoA6LDJhMJFfNU7YaHnwwnLzAECEzOZwSh7yfoyRDQwVId6PiSe2TFIdXb0jyVEV/0kJAUYmWlPRxGlyj4L0WzsCuAdgkNSEADTiDCW+w8eTgG/nnjWabpA25ZPpPeYL126Rtk2iapaNwBV3fe2bdsRStuu2bbdg5akiMrsWbOpXpb55XK5Xi5uSzPPSy2V0rVyrLyUffti5AI4RPdVV330RyX7ADdqwMEG5UWRlyV4e+TguW3Ao1jEHBhZRCaa5Xr+dALVe+UiEgSjLL3Z7XYzsNt9nqfCxNY9HdFUNSoLPfdKueie3KJTeGGqJqYiGmyYW3oiIMHwuiR0PIhgfnYOFHrvDVBK5WVepmkGpO04mmIpRXtHsBq97KgUmiYS6cex7du274/euhC3JsfRmWD2HOY0xwJ6Su7Aab+H5G/OuiL873fqsASrQ1tzZmL8B3c4HJmmIcH2f3VjJAcT5yIvL1r0n8c7vyOoLKPks2RtrIvjT7/AcyA7Vm87cUjjPQPojHeefz+ufdy3sTCe1daYmvGRMRxtaP2z3963r9/ewEmjiPkVEZe5EpGCbsd2e3s/9t7FEKGW6jQWmIoFgPZnMC0X5KrIW7ejaTvasR/9OPx+hbEfoJh1sd4EDJgICURabx28PRTi0XqpBZGJUNWOfTu2x7FvvTVzBWfWg3It0zwvl/XTp89zLUSIIPt9247t2Ldj27bHbd/23pvfC6JCpdY6TfP6008//P3vf/3bX//8+dPVtO+P27Hdjn0DJDfZ9w6RRO7A5z0is5m38ya52LauCkKKrOBFw0GvnNdVV7Y+HY/KiMZPNWg2frA/wp5cWQ3HeMq3j+U+czo+pOAplPTyqTNi+LCVktvDwGgLRV6UY+LnEUrJJ6QgxOimWYr6Z0LoGXsqYkj8vH7Neza4MENTjGkGqk+uyPIENQI3HZqSnOcheZPeW2/7vt3vdd+24zj2jTfcI0cXMlYbmd8zMvoHqOgPv8mPBQ9nsZXhWY0MgKWUaZ4Xt5KepoIcW6Ob0SCAkQteQM00SoQUjJhVzTSyMmaiHWGuno71LhWghAyFWcGMTKP62qEucPFxqKBjqhOT2+RLbNUUkNI3yydWew4YyLuqnhrGj9cfIhJ0LVOhaBbGSBxcpm8jFrkPPzs1FVBRZUM3O1AiQFNSwsi0GQaxA9rtxNOUwlIKICSwieqCJFYMYKyhqSdGchW8Uz5nasnvIoABkwsV/ZQ1ZTCp0vEPeG5LUUP1AhmqEgMZgS+lpCKYHUgsBzKAijkr6GUO0ewOAvXnrR5rN36Yg4EDJa7PtZjaVXps45Fk7D3Aqohum1epRW461mJCJizMnkX3Ivxlrss0Xdbluq7Xy8XbaE/TVHiYcD4HCOaihYhoOCokDNGjLMuHgSPusFhkAv1EMi0RDTg8MvwwIzWWMgvGyJJq8hjEEUq6x3vnGIwRqXLsm5nu28alMBJouL+oGcaW6ZFoSnFP3EaGZM/n4hB9vJUCGGGYfoORgluheiQnImbKiBPXaVqoFAXoR8du67wAQGEvQC5cogKwi7TWW3MFiVkXA1VrhGbXZSkEkVZ2k5tGWX4FJwDhrI8roD11Culq6KZEQ57s5Id/dkCWsQCMXeB+v/sbHCHN8+xH0JOoaNBUrtohonVdBzCy5HhO0ggbX+cEzz8UJA3YdP7l+Td2eo2b8KRjT8BoHA2CMOYxyb7DTONLx5EH8IKs8gOAX3/79q+//KZqvYtnQlSNkdZ1JkYD247tPVCRAsJU6zT5QIToAaFqagh4eYUyzcD1UDv2vXnnZlcBejdTRTHoYq1L7+Jdv/yrpXcI2SG4oTkSMpN2ObbHvm/HEX3NYulDJuJpqss6v3x6+eHHL9Zbb0fbt/vtdrvfvGm09CY9bEEwCJAyz/Prp09/++tf/v63v/z045dlrnI89u12bI/WGxAzMiI7YxS0cnjLJhc8gElsj6gGpqoG6AYlgBA2gK74VDUxI4DiqO4MoD9U5o8f7PTX8Y9j6XqGz5ZTNTW54DGQaOuduDBHdi2zUiNvBQm9smqFkIDHAi7eatt9VdTtB88hpQvisBQ2M0QVHbk+M1AAIeJSyNN5ADBCtcR8HrI4InrezTzB2HOJkIrPhJiBXnbhtlfut/F4bPf743Yr+747b+SbiY0F5Y/OmGeEd77to1gIcvXO9J2BV8hDFFMTlmmp0zxNtTqvGe453r3D68XNzLkT8SsHVVAFEgOwVMiapgmzk22epyPw5LQAmJqiaGhozEwRjAoBkAJ06QDeHgNL4e7qCBB2u3pnAiSCIf1YvA1IYCIihQsRAYasKuXOCu7q6AYaoIAYzmZeXcbO+yB5YypVNeuqokLaVdCYDFERrHVHK+6oSpVlgoLYewcGScg/1WoaNHusfUgGqGIJ7oaWF2K7IzeOfBbcu54GAQCIzBQVkUfEgfpcK4fOlIgAQdRA0VUQSOgoODJsbulppq5OCIiftHwc27NliuSeAk8M91TSebgXFkJPZMfMvu2DoTfTdjfIIP9V/CAgamAi2nt3873wrlVBMEZkxOKq6lqnqazzfFnndZnXeb6s6+vLy3q5zPMyTXOtE0ZOc4w9gIy+fNgYIDMafggyAvABmI7eJ/FbX5GSFvJsmDpADX9iXw4DloTWjUOHBWFlENMMiMico3FCl4AUc/Xo/VBpDuXRYn0bNvSDcbdBKwIMGBeI+jSC3NA/wLGH3ZxYiQCMENRFIypqpTAoMPNcZ6ZJFXtXBSMyrTLVWuZpqjTPlQgMofe+vd+bSAfDykxwHOJqegMFMkNc1u4D5jiOt7c3RzZD73IiyFvWJjYncjKy5WHS6NTRuq4ppfjQQG1MLlXdts1BmGuEP3365F/q3+UYwu2zvfbNv/HTp09uYjQg1OCHBtQYEiLf6obb0MAxsfYmuBm/0bSsPCNCP5Tfh4F+xkVBirvP6wZmko6I/Oo8ZXZm1EZybVyFH/P/8f/6T//P//RfXHymNlL8WKeCiAbQez/2w9dZKjzPZZlrCegtIuJ2rszl9cBpXrCW4zjut7tKMxXw2kpPnAA2tb314+i99Ur02DsRqT1NnBBAtRfmqRRievS+bY/tsbXWDIC9dU/hwgWR52VZ18vL9fp6Xd9+347tfnt7uz/u27ardBVFJCRAVUNAMyJa5uXL589//zd//7//3/6vP3z5NBXqx2N7vB/H1vrRRbhOhThmA5KH3WhKiFIUARRRJeJwhxTLMhuSp4DQcApTNCjMAKZiqtJ6mwp7RzKCD6DnH9SgZTAWxbHPBT0z8V4PgpEW8Td7IamZKHbprfcmxB2w8BPLnNC3nf/6TAho7jnZ4MPLfZWCtkEmwui7ZohITGzs2plBRWFaAzBBYSxMFK6aZMY2VlEItXQEigFgYo0mQmIkIi6M0ePXTKW3YIu2bb9e1m17bNt+vz3e3m63++avfdtb7yqSFjXPm/hHrmjsB4Gc0J0NvOkeEQAiD/7OF1Qinpf5cr1er5eXy+VyvSzLDIDWu2tQc0qrSsfwiZdnBaoiM0lUZysYcGVCNDXpHT3rRFxrRSKvHvQVvXBhYs9QAaR5sZ81IhGXOlkLWlhEaMRPpaCaqKJINhPzplakpJnBCIrRo9aBKZmpEJtZb601ETVKPjK0GOSZkRFqMzJPgBeeY/UBY1PtXh6hHQ41Ra7E1UCRqe/RDW2epywTG/gc/WLVxBcz80TqQNOZYkP3XI6iq9CEgIH3mE3hppmJ5ZrocISJMEIZdSTjiChzW1hL4cz6Pav0e2+99U6S5WT5FJwhC6QVwCjxPmaSiAg4F25fqg3I1BSCRsy6Uff8yXA2vJektX4ce29tDCEkj+WolrJM0zJP6zxdLsvL5eJOypfL5Xq9LstS61TKRFwg16KMub6bFAgIBFAQNYvUfRkKEhyZzMQHIzxjG4pSQEBLlxCMEeaV96hmFs0ZEVDB2PwLwiIMn3V5aPxEOeTH9QQnWBpDOvlnFDYOFoRTLCcezMFTyQ2ZWUMEACYaXqFIkDk3wGfrnniuAOH5qQgqhIC92e394HqfVk3aeEZEFb3fW+9aKxOTqB2HANI0LYZ0bAcCVabrzMQ2zVMpmAb68Pb29p//83/2rmRuMoSIruzxrNkAHL7TO3wBgG3b3JfILar9hyyXYRef+VeMdcjMHOg4YlBVHyEOX0Z2zN82at9+/PHHz58/f/r0aV3Xf6jyOW8tA5MN/gZOxM/gfuRkYnkWZcMJJ1kG1gPx+GEHrPkOMGE6T+ZkDyWTiPg9GeL0IXjyD/7626//8q//mubylMOxUPVghlBNoiJAofejHcfuskvatod0RXQfUdp++0r0DdH2x+Pt6xszzVMpBN2jGRER6yLaG5giogI3dc7J419vNKjMOFWepoKIqrJt+3E0UytTvVwuyzzXWojY1/Z9P+T3r8dxWO+muKwvdV7X3dcKOfb79ri3o4GJIRXmz6+vf/3LP/0v//5/+rf/5q/Htm2398ft6/321fsIIVIlLsXLD0A9tvcFw1SkIVVGJiYzrwIxVZjLclln5mDoCxe/nGyx1DtQ27tgaQJ29NbvtbQvP4bYa3hbIyDldhCNzT6EVM+NWdH/s9zfDCKfhx/Eyx+i6jPPA5DtpM5EpeN9ff4tHFAUwAyRCT0/QjjSGeAknGf+MZERZF+LFLRmpsWeI88+pAijrg4yaUXsdoNExQsEcodTYqKiIhI1gPNUl/lYpqWWeZ7v9/vjXus7Iu976+jufarPJuonS6jvX2MXJGA18BDZVRdmaCGTdaBWlnW5Xi/X62Vdl1oKwli6XZkRvILXW0Y+U9MWMkf8wGpewwRqvfdCTByJIAPzrTHO0DlGb8sQYBKJmBBM1BNDVIrbXWA+3AyM4tI96gr5GJExNREzEEn/XOW4XWoI7uBAZn3bj+M4pAsQSe9tb3J07AbDaMI8vceIXmcUKS1GYyN2jBAkMwxFt6lyDoVai0r1Wxe9NYKDU3T3akSMrmSD+fzw6DAwEY/WsgBAkReLATZe0dUloic0S6sDwLDVcUtojp4VRKgKxOQlwE45cEbKDoZDlufdwxDA/ZkAvR0CDcRPWCgXWUQgEvNydBAz8QU3E1KQsUvEmNKdE1WJjsyEkDwclVKmqS7ztCzzZV2vl/W6rss8r+u6Lmud5lIqc0HmDEWcHs2bYicUYc9iS3gCZYtzNvOOw77NeisiF1TFVMaoYKDEUc8hn6Sse72Y+qXaYAnQ2ztS0k8jaAkyM8lVRAMUNTKw4PA9dIGYyk9E5HcR4YSKMPFrsgeQi9W45pSSY+yNCCgAYGIKXeyx7fx+81ozV9m7HOfY99aqO1wrmKp5wxZD7IdwRZ7KVKhW9pI55kAP9/v9v/7X/+qW9w5rHKO4osjdg4YMaBDAI+flBcIi4qDKOSRIZBBhQRIk/lfHVe/v717JBQBuZj0QjB/QOSoAGJm1T58+zfM8FNAf19Inl3N+nXmgMyqyZxgEcEpmaZp6jwOO35w5sAGz9KMbpF/1KF6DtHMcNNLARucDuieFGgiEhBQRFJokVgcDyFAZzUThMJPeEO3xeCBQKRNz8U5nhICobb9vj3cikl4LkdsQuaGjmJkagSETAErrHcwwKu/EvVQVejv6QQa2b7uoAiIXmqdpnVd3FkSidni9qnfwaz6Xies0r58+1VqYEN7fvv3y83+7vb/vx07Mnz9//ulPP/7pTz9+/vwCICqHtL3tzguqGSB5HtwQDZAKUsF0oCYqtXppF5i1o0VDMCRiIEJ3cstIihBJ1bOBSoQvl8tUJzM9jtag2WxZpHHOoCX3mxhotKlP4iYev6bQZ4Al32wwQFIsXxEAUmyKz/AIxpue3/0BTVm60VkIzQDAu3Zj/BDHH1E3mAEGgDIEiwU/IFFG/Sm2sszT+C+f08HZGHT3P1doJKiCZPqRSJ3fBkQopUx1muoc3X7qVJjMhBDogKMZ9EzXga/4Pl3OE/g5hfCproBhgHCi3hERCvM0T+u6Xi/rMs+1VIyMm6NWG2uC9/k0l6ZhOiY/A3NnEQARkBGRDEVFzJfySNtn4vMpc3Htmo7HGNoHFXPnBSbLNFmuF+Ywn4kcWDhOjBDKCHxiqiGadGV2Z2d/lF7ozqp6HHs7Du2dS2kR6uiw+4vRiuCPLUYwjtJEzg3KwND5qMz/6khillKkiAUkcjMI0+FMiOZ57DCdPMnkIheFobVmzvohJ32eBtrxifH3/BwOIid/H3/GXRqmHAwkQJlXRQSVREUAueF6heZzkIXaPcrT/D9/D6FnCRGzsO35wMfk9RPPnS902CJi6lE15BLviiKe3JRo8W7C67qui3s2TlPxUnwu0aDm4xVjhiWQyMLnPDkGGiDUjLw02QzQFDGaREKwmjnOR2NbSILZhdAWK0E8AFAC/62rEJxszjRXnJuNs3W6NR+cF2VYJtcAUpRPFHW4g+XK8eIIGmOBIqYn5ElUZBlvxuXEWY0xouDc73HsjwcjYa0sqZB3+QiYlkLCBESRFY+EM5ZS5sKXZZqXyZvOcil+P+73+88//1xKcSchf2H26PD+HppyZkdFGcPKSBVZ5pUcVw1gpFmGhh/rv0TEy/4xaRX/0hyQ7t/bXFpkWU1mZtfrdQ7Xq5JA80nY2Mlj+ox78A+v8a/jhzNuG/PITuLr744JSUENRDWuBTOFCgnFBjaStDhy/VMcB1C9+6GBgXmOVkGtjwIERAr1vakJGJh3/ez73goVQvalS0UIjdBAo4wEguRENVSDribaQX1zRfBnZKAAXALnqUhXfdxvKg0Ajn3zC4mehrVULszeLwAJoybXR7oZkiFRWdf15XpZ5+nz6wsCEDHfb3We/vTnf/rTP/305cvnZZl62/vx6G3r7bD0oQWHge7eQuAgeOxc6YZGagYkxIwGzJhzyrcCMI2moD5OVQUBruuKTK11MVFV7l3D0+NjBs1rnxyeSIIU8Rgx/HBFpJsKmgK5sDczbhhLjJ8LUSahmJjZY6ehDUAc1EYQP2MCWGqJfM31+uK84fgcqDkicya4ls0Pa15/4QE2ICSSg48cp6ebhiAqaPoIA8lQGQliB38Oe2e5CbAQYi1VJl0WnefF7RmmqQIYIz5SY5mI7xQQ/2H/AwAvNjcAgT7mlQGAyjMBR1inerlcPr++vr6+sKeWxD3ZzJsJYLQddQSjrsyhwhimSm61R4BoIVLF0ZAVAcCEqCKimrqNnFnWORsG05LK9pikZtqbQfiP+SgGMFFjJYeo5IkGcySPPepcyCGtamxn1jo7i4CRGvGy/Mdx7NvW9h3VkCn8EMyQsIOZJ29BDcRzOYgkEsVZ5H0h0IMGNWSoFQFAFOVDaF5Kker+PpK4B1wLFTTMaQVMdvPUhyKviBOVuEVlth1M3OqrhqrRcwqMPzNciAyOjz3KyBIxCXXfRBGVkyo6rcW5N4ABoKIKmK8b3p81qpN8IyFDFCC/3WLqIqHxzkKsFOpu6b45OSrqUcqOyIQlRNY8T3Wdp+u6XNf15bJ6Kb5bb9Rp5uL9PVxkPYb/E8JRxg0+RYiecM3t0MfjGvsfJYcE6P1ix7JgmMDImWJJlUbCXEQAI1DF0RbNB0xM8cCGEMowc2GSIXA+HSebXPWTY8NnKj8fBJz4LEjeh56QyMdN4sukA5IQj5EQtKKZ1/4jQpOu2h6Pm7sc1WkyM0TrvSEIkxKqV0giIHjxtSgYcil1npfLdbksTaT1Tlnr8Hg8fv75Z8clQyTk/VlV1W2KnLwBAP+9b/lDewQAHrQsy7Isy+Vy8ac/VEruAOlHGBjCG334z8y8LMtACUNzMxCJf9wJJG80voT3FY+VEz6+BpIbMCUnVFSEDVTkf55/c4ZBfnCvGvsOgQ1UNN7md8+f77hXDiVH7sxvyxkVOV7JlcJEQQCi3mh0ZjeJsE4MwPOMKipgoECtN93AgFQaE9ZCyzz/uCzIjMhm0Lpwa8fRdH8ct3s7NuuNQGutTq0a0tGAQNA6at/3/ZefeymFibo0QsQscjSwrqJNRcQUAZATLjli6L2baiV8Wdfr9fq3v/15XZfL5fLt69frp+s//0///OMPn1+ua0Hbb++P27ftcettZ4BSOJSm6IpLIsKpllqrhMmFIKpPfACc5sWjUVfhIwCoim/v+T8+ehGh1rKsc664BiIh1PKNYMxkp5eHZ48OraVoF+1dWte0QhQCpbQlTl4510jK6JafdZuA5H1/kkrKXSK/3gYyMsue25rcl2X9Xa47qZFxAOYbh5m64AkyW8+MOdmdIHkuqeYVctZzpyJT3/9cwECGqsBqRJnQyZ0IMSN7YiLGamyK87wsy7KuyzxXQiuMtRIzuDa5qzv1xno/Xh4dnhZTSL4NBpdxntt1mi6Xy6fX18+fXsHsaIeaEXNV9Qa5cfsMED3plot0rOIeUSMY1MI+twCMQ5mNXCohgvdO6cKV8mZFFSOB97NAKgUBHS3LCI9MySsnLSwY1JQyGQQSc9ufoJmEuAi9TtB3ZaNGJUrTBwnB0apUBD08kg5qFelyuVxqIVFp0tXIjBQQjIlmLhhVm4QAZDL2Gl9fDaEiwKlJmmeBAFTJA52M1yyVcIO20CSMHDTTQAhBs0VKQiPr6ZTMHxdrM82aa/BFBJJkTQAEkcMJ3gKIKcIPMC+UDQA3skOIzy+ysPr2UZ4rNfl2hxS9MkQ8FlJfTZh5rtWWGd1UENEs3HXNbV286AyNEBiRCWvhuZZ1mq7L/HK5vFwvL5fL68vLy8vLPM+1TqXWUqaYk3iiR5KEjZPPBw/JaGKW1ic4h0CO5tGCgTs5xgMCOrGhDEZoTrHICKXUIGMs/zpjl1ODmETxl+uznIjHlFQbWJqxO7AjJDW3L8+GJBhTDSKPH9opzHXP35LcOQSwymfNUUCHEqDIMjWaYAoRvMkNehAhvbfHZmrqLgfrutQ6TZd1qcylIljrTQe/p1a5THUqZUJmt0D3CgS/s9u2/frrrwPr+A+OaxFxtHTtvSOitxVS9d00qBRnlZz+cQD0+vo6tNgAUEoZMqMz4HBg5JuODxv3h6QsNwMAF4O37EVzv9+v1+vLy8unT5+u16t/BZ6ah8BHvkejQjtWle9UR+OvIzNoH1uL2ImmMrPvgJF/JDouADgSGqJ1RHSU6W/zHwbBdv4i95s+pW1jbcIxPyDa3ofzbgb0maQRcTXxcSDYNE9TXdx5oYt5N6DerYs1/6uRGYrA0fq+baUWJAZkMVfjGqhI7xsYqxATmZo2QDTk1gm3RxdxzcU8zV4Iq6rtOJyaB9Xe+5scpoe0R+W/1ULrZeq6Xi5zJQRpfbO3/b3tN+nNRMjDLE+5gnpDcSRm90NyoaEXhXQB7MWAGAuFdzQSSBd/JIYASFFxJc6+ekDCrgStBcBwl/1Ir3Z4oqLUtcV6ny2BNYgbDxXjT1PVSL/kunRe7eNmPJmZWAd9ocHMfsFgfMaghWS+EWJfB/QuVIxJ5kcEH+xOKIjQQaWC4bNnU2T2Y7HEwbkFPU1k4gXnAFmPLonJHK+rGVG4+gJAylSSxsag2b0dMOUEtnZsIs31PL11AMPe5RQY5fAeO53/zICMaMH7hEDhuW8AgMde62VVAzmaqhhCQW8xloZLOBTeBgYEASiZcYSfYsZcxnqURCUwP8ukkztLXiTcNXzpE3cRjmQHepW+qiFxBErMjBA9IsDFKaAI4SpZoDAxARHhgRh0BwJ4oUeMGqiFa61Uqjfjct0IYFQbCNqhckAhAEXrphbJIhDQw6wAA1r0oIIRpzsTiIYGqqPB2fOp5IwYFw9umwS5jw5yU32rJv0wimP/ArfxMMrAlFSd2fZ+acl8anjOpuWKZbYmx9lzmmgcNq0aEZHQFEBTIx7TMSaSn3jmYyDqvb19PXGEYuLwFSKsYaK5VlhmRzwQZQrYRWnf/cwVIWaTE0WF5srLVNdluqzL9RI+jZd1XZal1rm4+JM5y82HsOaUp/I7l91cxqYwriQygb6EwBMGEoZK2k53DAEcD6XZYwzlrMb3mkJkT5UBu0bbXU+IiYiRw6Y6iGMDNI0WtbGHMiJ7TxwEJDsRhwPxASBSNILJs3AYfQa+7kXlqnDMFSuBEUTO1fLikNDNJcFIDUxM5Nj37fFox3VeljLVZV0mJjDVrv1oIu6qbIi4rss0lVpIRe794d2XJwsXRxdWn0UzA7i4e/UoRvNd33f60S3V0vLR/3QM4bu+00XLstgpVxWbTvIrI/s28IeTRuOXDjv8/d6+3k/Jx5IlSXNOY52nph9hAKPxKTuRQzCSAkkafVgTkrJK4EvPzJeZZ/payITjfAbrNsb8OY92PoekxES9sBefa1I8cASPlsWLqX3RRPJhR+C5HQ0mEqGUUksh5i52NDcyVlPznI9Ib4f38yEgNqTeVOzw0wPgp9sFYanFiU05Wu8HAIq4gZlV1VrqPM+EaKoiehwNzDVzTMyIINLv728gx2WtSMQE61ynQgQix/ZoZtqk7zGvQgWqiMZohFhKbCUG2Luq2wQURioclbxe7qCimjViiIjEHhugN7mshc2ifEZF0TWyIqB63ovLeOAqkrpz9xqSoAhMo6I7NgoIjOHrqGUkZGkAMvBS8C6BRXIRBAi2ORXZKbAeq8nYu/w3zB9GcEZcBkPUiISI3qbU0S2mrui54RmmygQiFI9AXJ+xpa+2zsGrr3ZsadoQhyLflHNRyzDDszaIACbHy6X33bRLb8exe0oCwQRDKP7UJ5yoI0eObvwX+uxhHJgnvCzLsi51mkT1aAeYIZGxeezupVFpkxsPwS3YEakW7k0wBaNB6JGqqFfjgCsi08qPzGDYXzhENvdmhPGUn5uwoUV30hiPhLH1EjFgFLYDAqEf2CuW2ADwlCUxBHXdNSEjFy7MBQB6677lIIyMiHcx0cQPPrjQdzLLvdR/B2O3AkT2rvQQ2xnxeAbjqp6aIQvrv5DHIQBmqiufobmJ4vOOjBAvyQ5CJlJi5SBkYDA6iVP12T8m0dsJOQSZG9xs3mEiNPCkty/pHyZSngIFjgoQE3LwpK/kw0kjIZVS5mkiRF9vgrM1aL3XUjqTu0UaPPNxThQt80BFy3VdL+uyrus8z6VU4oLOrFLeSMLTBP2Ai+J6Bwbwctd8RPlI7Tl9xmI20CTGEU+QKAk/G46OkdInDIsAAiQGX0mJCJmV0DDTaLHynXFnwLexReJpnOU70L/CMPPKgYrymp/AKMrl8hmbPAfSIJswbwshgRvhxgTtduzb9tin+ZjnWWdQ7wysYr7+IBBhKXy9rKUQAEiXx3aIdAORPjn17jiDTlVXiNha87YbeLIIAoDeu+etiGieZ0cDmoXxDnEej8eQG7tWyd2MRu3VyI5pSpQsm2wQUY2a6mfjEWeJHGZ9hzx8PA0U5fDoDGLs9MpH9CSK7OQnBIkIx3v0ZCsw7kwsWh8lUMNWYJz5OLHx83cvO4vQRUzEJSgQZJAhMFEkRrwOUqNVESmY+JZtZiLOLSLRNC91qqVUQGpdt7054x6CI+1+rprmQd4sS0VJlQkJJGqwCF0/hISmekjvR/OFmMMPCIcX7fMuNFHO4rFC1rXt7Sb727fL9eWlFmaamZFApR+iIrKbaqnV4yJnmQgsKvKT53AXIt/OvUFs4cKMCKpukBjLNYUdLxERD0xCjKwkqgbWRcnrbUXB7BweD65ITZoDDjAF9Sozbxrui0PsNY4mIqMAz7XKIHgKz71pRCeiKmbFV/mARBnXqsQ/n/YJGEtWri3utulDx3eysTEAJN7yvTK6MOWnTwwAWvgI2MBDqs9ALbC4wz1QhSdX7vVH+EExF8ceQ9q9lCjydXZtF+mHA/fjOEyFfOuKci4bNw1O3tbkrE+mIS1NiwKEIZbKl3Wd14Wn+rjdj2NnogIFAax3MxXpYh28XXDuvr7XMVJhlvYwAHRCx4wQmVhFIMkiZo6PKoBS8Vq28K5xW3BGTFdDCx/kCqFfltZMhBDct4cirU5j68glhgjUZWcGGNlpgNabmnYiJChAtcyejD+OY9s2L3L3XggKKE11b0x1BmJEIRNERurEHt/NvvMjARCQoUqyiwVAnX5GZqOh/AXvThULdDA4gIBEwKmNDW0Unob+B4Ip6VYawAwoI0rf1jQrSyO9YhC18GZDnOIcjK8GQd2ZmYXrAgIaAjH5Zu8sREjWR64vgLsLvZ7gPcIIIiBvqI3hZgrmPplTrUw0z3Pvvc0LuRUgUhfZ9t0rOLw+kAhKoVqcJZpf1vnlsr5eL6/X68vL1XXW3tYDiWHUZKFDsxwQyZpAQoQxdZNrOf04gEYuYL7ejeqPBEOIAGSJiSxCLwjtNiAaYXieMTEBKKBmSaCn0MDtcEa4BOBUU66dT3IOIEmqmEeBdmDENZ55pVgwEcFhSh4HHaLRoAeTJMN4kM9QdNwHcscJNvNKB5Vj32/vbwCgotL7Uos7mTHCXIgIDWma5h++fDKzo7X39rjdH/v2UGnLXL8bygMcjL862hgpKs+ROeLxSv4BFAYyGNVVA0Z4XgxObFCPjdkGJPL0nH/pOQMFAIOF6r0PRTac2pYBgB/f99EP9HyiokFNjRdlfdwZ4nwHns6k0dg+vzvyOP5Ak+d3nveRcyJPTv7dAOAtyZi41gJg0q33DmrzVKapllKQ8GjQWpeuXUGkH/uhvfuAQYpM0uXlwlwMSBT2o+3HIb1HKCkS7huiKt1SCgBUTJqCoqhBdyiHVOZ5nabqfE479mM/fB/lqgDMXM2QuGBh7OYltV1VTBHBCrtGoqsce7/f3q/XdV0nolnaQaAgqtJUeogAIweuCEKjaU7sHegmwR4Fh3couSGiNgejAEwEPNAoAWDvotJNhSgYADNrImyk4r20rZZsJXlSW5svLDSIZgMk13mhkjFRMsqGjlezEaOBr61goNI7Uuvl6G1q5fAVuNaakZCPbDG3ievSW5doyj3K5wbsoSfJRInQ+wlW5BIPuUwhnmb2adxaDFznLJ5r14gpc2UCBDQZH/Kkz5CF2hj+/nHzh+AGcGiAyEgTlPUyi14BAdBUO6J6+9Rj9zwUPJOP8EFXpJabWvBp/hY1ICOq88WQ9r133aT3tu/GxLOV8sK1AgBp5To7wQ3hmYsu/FVVI+ZS06pYve4TEbq0Y3uMpccdlkG6SEfGLl2e+nd1u8zeO9dAsGraWkNvkuWRNzECqBknHecPw0tyHMTacIMl9+OhLiQiCLDM87rMc+FSJwO4P7bfvr499kdlQkMROW7vfd8Uuhbb7bjtN0ZQ1Yf13VTUEEmRDzA2ZMOCqGDFYv9AIWA2ADRkgm4y4GM72nE0C5AS9Y8+M/P2oJkZmZl5E1CiAfhibIgaqYJYqu1PbnJEkp28UjYCkkVf6YDga1E2fCM8q/bjpg9BvfMGzuq7hM0zcv4+RMpscshoKKCJf1EEM5myI+KCXEp1ytD3njrNXGcuMyCLKBHuGx8H934wQS08T/yyLp8/XV+v19fr9Ycvn758/ny5XOZlnaeFS4UB/p6s3sBpf9hexhaSFK5niwxRMWIbn7WMMDTOOW/8bpxuzTML6lSEQqjcfAUO1RA7SjIS8UWIkUgRGQHTRc+nvY7lxLvvxRAehBskwjuBu6gXobGXYi4+sW5iXONz0UpiKO9DfnMuV3EHmczIKJXy7bh9++bTX3rvl2Wdp2WuUy3Ipaga0DRN67I2kdati+6ed2uH9MigDXrG6+R9g3cmxkXT1+vVDRXdQ8iSE5rn+XK5DO2wa4/8464Wcon0p0+faq3Dg/F2u0FaBA26CAC89eygkRwbQeq4/Z+chcITJeNV7q+vr0N5fQY3sczmaBjZvTEMxz+NvJgrhAY2wg+lDM+fxwHPzkPn7OGgwYaAffzTwEnPkzQtjNfL8unzCyLs27ZvO3P9009fPr1eL+s8VTqO7bHt923/9vb41//22/v96/3+MLXL5eX68ros63K9lHnqTaNrTxfrHaSbiPXmTmMulunhr2EGgEQEk8NqswNRGZkNKiGI9N73bd+P1rqAuT8bcJnqfJnWa50vdZlMlbsUbsSPrB837Q2kgyqCmXZEmQoy06Ppvt0rMzMxz12NEF1AjahMRIBq0Pbe9SgV60RcuXK4sYJKb82bL3aRfduYsNZSavUnrdIPM4XeukjvKn0KJ4cCAKJSayVEJRSBUtOCZ6AijGZQUbkMSDaSs4ZKSOS1TIoEYaPkvMaI0FzFGJ01eu8Ht0LEhbuIFIvZb2ae0hOR3nprPbYfAA7HXct1I9cvREQ08GiDABURjYJ/eOIVMBi+Q74gQiZ5dISU/2gdTrEsOV9kFPyQx50IT20oPON2BSBw4wpQUwTDKBMA9q51yySyHvultwZm4CaJkUJ5LuPjbGJr9LPMf8dUawICMZlZb03USVNVRFHzilzfCHP2gpGrMkPgYiqizGHz7Uf3AvjUnbmdQ56DmQGYSPdKRAMDJIsiTVM1UrUTwOXIeYPvEggQboWxSZj/nhIn+X4PCJJrASHWWk1hqtNUKxcGhMdje3u7vb+9+f4WLlZIpooGjFyplsirI4JrbEJ2U6F6eO1PR5kRCiEYMjzbqpNIHzYVrXVP1fl6EVuyey0yjvIG13MQutzSMiR5Jh18PbTniPHowAd4JoyTIjJD79UTWB3DKystiiLbkpkVG5knCnxjnpQxU3RohABRMO9EV6z87sDt120AYiCpElQxBCAmzmZDQORZMwM2jNIV6Z0QHswbU+9ECFPheS4v1/XTy8vry8vry/X19fXl5WVZ12maS538gxbCvcHmxHQ9UULxU05PG/8ytOYxSXyJ+jiLMQc/5WUbggbtMjKLloe3tInyfe5ZXIjxOJ3TojDV9jyy96l1YEqAzx5Ezks7NRjXhAjkNmq+2VlapSVYw/MP40nh8yrtw2qmCJSt0JAoMvjgOnpnUAmkS2+HykQAtZSpVteCUOGJCnRpoq33+7b3Lvu+S+9TJZhqJRhd691haNQ/+2afwjiutV4ul3Vdh5IaEtM4kPLjDOwyjuD9Yv34w8ponueRRJPshmap3TmOAxJ8uNx7cD+YxJWDrX3fp2nybrWI6HjOBbZ0KoPHj3kuzOqzERDiiXnyd+LJQWDENt9hI78Do6bMMvvmpzqUUrnwBkQ7H+F8KAC4rPOXT9cfvnz68YdPpVLCqfr58+vLZVnnulQi6Mdx3Lft59++Vepy3Mh6b/pyvV5eXuo8M5d9b72li3Xr4jBIVFoP4t+Fw+K1Tc6yABGDkTJKl8wI9GN7CGAXaV2QCrN6EFXny3r9tF5fp2UVoOMQc0f8rhpUhUhTAytongFU6be3b/3YuLCprctswCFmsC79AIVSCBkByZABqMmmIEDKZnOhWgpYXIpIt5yeTqqH8kx0cl9LYoiAxAiAESoTIQOgmtZCpqg0mlj8oQ/aeCqJMtBFgkiGz2QTOSkC4MUSTg87UoicIiZqdj1X7z0rayxkFN42qMf/ZwCXbD/iCBNzViC4qiJliXmuuYAO+8hxLSEjsByj7mBuaZSDz/ASTzgMQwGA2WNl+B6dRBDPr1EP0y3avLkW2I9K3h1zqssy78ssvUlv/SiqAhJSprzt4yeIKNdyQcy1ExJ4mmmXjqoUWbLAl+l7EW+L+GOwv37XRbgm+YCJCwCYycGRi2FRBcPu5ykScsoOQrJ2Jp8xbu+Jd8PcGZy9HHmMfHAeX7udL2hv+eCQuRhGrsqXvPf3+9v7+/1+nyrZU3qDNuCbp90xXIWe98BPCy3NpyBchmPEAEAkxE8PAHprvTfIrSz2zrhnvuFiIGskImPjqAvA2KvO1MSTQxpQ1XAszWDuEgIBixCzSzu6tUaaacYpj+ngh/IxmePW0ECNENXM26GGEIdG0uw5X3wKom/zkv1KISRHXGst0+S16NTFPeXMQETasZuJn1VrSABToWWu7lt9vV6u1+vlcl28+Wt193BUdStYs0R3ZiEdjmQfPufDCexYPsbnpIbzv/ofyagmBEdMFZLFJHe/DW9e+3xrAPZTcvd03HStjp+SaTJ7fi9CkEBOm0cM5q4qNB5SJvAgQ5uRNfMTGX4mEXkBOktsqjraxuK5TgBoLBVoYahGihbqbAEVAGWC4YMEYMzo9hBdPQ2qrQsAzFMl1c401ZqrQfT6GLBjjDoimqbper16GVopxRkjRw8OmPw9y7K4JnqkxsZxPCduZm7AOE3T0OJo1nyNj+z7Pj4CAA50MBNPDpKGe6Tn8vz8B77x91gyQP4wBjXl4G9cYD6UuNiB0jS7qp3fMN4/IJ0f9rQLwUilyck721/Ocp2PNg7404+fTPcfv3z64cvrNHEptZQCRPNU58pToYmtoPSDHzOS9bev18f7y8TUhK+ffuRpUcAuEnWHvYu35TFz9WI6jUUZlXk9LkLSyb6kkVmBlIwf+97SUBsAuUxIXEqdl2udVuJqBu1oh6kXuWmXLg20oymB1kJcKxMXRiaQdhygRWutNfPqPtYFTD2Bzcze0YmQvXzVrc7MULxDqc8ZiQrc3nthAkRV2/fDvDt2LtSFUAGBuDLVaD1AZuQbCpgRYWEeYUmiotjDYNDqiKSn8itAi/R2/JAxmSeKAGL5Q0XXoUgXadK5c3HHggQMGU+r9N414gy3GHO1g09kGlsphhOTM3zm4g5fyC03MAc+/gOGFtzGgNasox4bikMMSDrad34wMUIwMsu21RkWUKYecteLAB2IUNVLgxHH+ikA5trGaZ6XZfYeKMe+d2kAanLCnk9UFCs6ePMmQEDKkNHBjap2AAPEShWREl92lY5YkoYHjj7uIzoHMOi9l8JeE/XkLGD0/fGOHVM7mmn3rQvMnJJgbz3ixgpmlAIMIK8pGwuBS1ssEhPe3PhJimXM5KdYCiJA6+AKKMTCbKQIJtJbs6O3337//e3tfd/26cs1OoyLKvuOHkOouXrazACYWSLZCk07IHC02PSZF8MliS3UiP/ivrXWemv+QLzyOmy3UvkYdxRc108FwDLz4ag0hrcoGA79fA66WPhi+KhTHoaoUaMZ/GdIIrxaaxx1cCG5aFMu0y50stHk6yl/+IBEE0dGYGJdtKv1aBNpPv1KKXWa6jQjsQJQiYnr4096AzDvxH7sjKa10jrVl+v1en15cWR0uSzLWqeJuQB5ulA1cfY4q8w5DcDw4ZUrxfPvTyDxDN+CtDXNbOyTCc6cl6OiuN06EGyUaDyXsIj4iLycMEy5PAgNLJ6mEuOLHW772NGoRlQPAXwmnwH3x4s0x4IRWXp9EQbfpRn7urnFWJxg1NPG4wZfJb3ayNkkcMmXdFMBMANVRVVkk/CqVN223a+JmNZlZsDWmucUwE0Z5jmS6aeupY5Xpml6eXlxk2tvwSEpxHMg5VhnXdczKvIjO8qhtGF0uDNK+r9TF0GiDTgBCK9Hi1Faqx/KG9Yex3F2BoL0DfL6OJ8slizUAEwuTrIUd383CP1oZ6DzHeiBAZgTGGV65QmzxhX9ERvltoKjis1f/+7f/tNPn6fPr9eX61xrvV6v63ppvR3HDipgUkCgC0CvKNdKP35ej/bl9fMn5SvPr4/H8X67b7e7mXo0Lr2bhq+Rqap2tSxzioAo2uOwowUDMSSqAIVAEbojaQU0XyWmpU5zneZlvZYym9ghh4hoPyCLp3o7rB+EVgtP07qsyzzXqSBBJzACZKJpmqMTZPZ4ZMbCOE3V3WaIiJle67VMC3FFLF3s6L0wFvbMo/m+KN2myn6Tj72ViZ305cJu4g8AaFYZC0b9FgC93x8iighTLeXkfHt2ccQRBAVERMRniwqEwUmfOHCHOk+uxkeIi7h6R2pIxd0lkBjDdMjcHzgz/eC0MBN5M0FHAOOc8tQCFTGBGzBKWJT42vdhjaXQdKtvHPK0yY4KtRyxkEFm7PqgQUd5toOIGEPODkj2/MKMAc2Q2NWHiL7ZdCfJvKcmM08uXG1tq4/eeIzGIKlyMpKB96z3+YJJsVhcEULK74l5WlarkxwHqmg7dKqURDoiAjthkFVLUfxi27ZzNjBJ4AgArp3sHmeZKXRFBUJ2BOa1NbXORKQqkZrA3MifYAAsxddE7G22BmoFf6ToItinabIBIBUHucTk+HV/tN/f3o/efv/6+/bYzZFuyMetWz/63nvTbujZYCQ0EO3muxchYmG3eHbvP5OsFABAxFJDLhK/j0fgOV30eg+kQRkCgGZi03JFC94dxhD6EHKaTwx1Of1zykA8cc+4WIKWILMAEu9kqRjmyIdMuzy5y3EsxAyy6aNV+/MHy8I6CX8j7Wq9q6iJGYB3X/T2SYVrgSiR12meIaxsEcBK4WWZ7rdpe0wIWgrNU72+XK7X1/VyXdZLnVeuE3EBcpraNGLUlG8ZGCjELjWeyvn13caT+QiLjhzBUEa/1CRWh2Y+L9jAWyYpDGwUUwkg672SjA2vBD8XUXVg0kWyubiX5OPz+M/MVxwO0D0ODKIpGwWPlyAvSvPRYXnsQ/Hzk0aKwaOmIt0vjZjdrwmBDNPeNII7HxLeXxYATPuxP27fvk2GMM91qgW0MigDGYKg7e3ojg8JiQuwajeA2Jg5e3ToR53y+NdPnz45AtiyPbanaHzsq6qn2JybIaLH46H5EhEHUt6+43q9em3/5XKxVNi419HgXb5DSN50dqTD9n33Q/kY8m8ZHNLwDYIUX1uWevlhHV1ZluUPNijj3ieI/zggP2wB5xSbH2HgM/gDsQTwIV04BEx0ii3/+tOn26xTZWZAlHZ4f9xHbw3BpRry9vvv9/u2bcd9P942KfW6ltJg/Xp73N5v+7apNFe9ezmTT7soVEsGE00RdanRqhQRS6FSGABFbNu7iIBqF73f7607SCl1vlBdeF55nqnOQsWClKxEZNJAukn31U0RBYjq/Kd/+vOPP36+XubH7ev+uKObvk48TeyxtPSjt6ZGBojUL+s8z5NHjVwXwigwrIUZYLAnoXVmArKpEAEaYy1OtrMidtGwpwUDMyGqpqwFSQ3w6L13BTAFpYL/oOPHoLfHcv1xfbJh4uuEri8xJ0sbi3VPUbWLdhKmzg2Pfd9LmXwFdyCPGPG3V01y9mHlwpxiCV9MRkkNqiEqc/EFygy9GG8s95bNhQHNkAHV0oYlpBOqCAgcFfqnS46nSoqKiBRvyCRa6DIA0c1BnvcDPggcRgV7l9Z7c/rSIFzbuHCYTClZFjjhmSsKoit+chAM2S5CAVqT2oVLQe/OxtTSVFRFBN1h8dkX+pSv8hSCB3/mHoWxz6h59q21htEFItLnqobMZkKAQFRKAVM3cvJcEeQNoCwvxOCAuJTq0CtGUZyQgQEqItFY+Lposlp6HN0Psm3bz7/8goTSG6Iyoolo66CKJt2gO/J25AeZD0YOnJq/T4j7TJdA9NrNyvuPC16UMXvCLSnKyJEm0TI8yjH6z49jYBJJucMlCEimJv5mkfkblhRDqvscDLlOpUplUCUfyP7xcE/XMDZuiz9tKI29ws6chbAm0UnRAJCAYaSN/Yjh74AIpbBZBVtFOpi6P2opDNqZaaplXlaeFqoTcAUsaiTqIMC6iHQNN9hwpkYDKpjLM1oywTkHAAzSwyITkk++yJzqeiYiYybh4EWfCSYBsMFkZw4rH0OC0SeRh4jY4z6lUuTZ6egjdnM31OdTc1CVvk9mpjK6uKURpVPBYzAiJaB2a4p8/DFS3OTLoQ4YgqKhARl+PBGMPC9RIbei2baHfMVdZF3mdZnt5TpPMzEzASkebbvd9/1oIm2eyBHjNM1+NEdFjirGUHTGxdkgRzyOir59++Ycj4hs28bZoP56va7rCqmecWPD8TKzcJRUdU9qIjpX/g/gMtJSvoYM4seflKSVtp9Yct44RFH2sbTeDzIOPn7PzD0b2mMW8w+ia+CVAcXOHx94aGT0BiwbNXGQtkbjJAdc87OF9I30NzexbZejKaBXgjxMwcGiZzQQbN9679i1KFGZtCHqrrfH7f39bXtsvXVVARVU9QiREVCt+fWLMXrJGJhJje02p5OvPKoI3eQQ6Sq9926aPTinmYjAVFvbRXHbidkXS+kHqEN5QyQqExiI2X07fn97Xy/relmxTFiOSDKpWu+uBPHCCa/GUzXmSqWWUgtVLpNotEZgUvUMhoMFRDOoBsu8FAIaDwgsN3AcJDICoNnRxI7mTL0Zg4sRDUbRMXzX8cNnQbIuOBYiOP8vflhbIDcN1bRiUSQV1a5aujRA2veduY5RSJSCJXIKEYaaz+NvQshn5Ey4p3XBTUOIKGQsaJAUwhm/o+Ni9Wejjp5SZQ/qditjAY3X04IWn/93AvoRoY7NaaT38JnhiNhGzi9Hqpi0g0MszV6hf7j3pxv9vMfxaD1pROxtPBULE5MKijPS3uPIInsY15ZcPqC5VaKIr7bgsZgjOUTU3oWZqY7rJu/5SWoamZ20G8z42rcrNeCkzcBKdOxCRnTVsf9bFH4iKhABaK5NotpFAUBU9n0vXE112/f3+32dJyZCZu+NDCJkhhhshz1vmW87AQNobIrg2ag0BoAT6HjW9yFGVyw/VkpP/JCZeRprRqT6EDCTiKE3ggBj3/NCAGaW6Y6AP2Ok6sgZ2HOmJXMUH4YxvOA882K0ZtTg79WEW/4tjqtNRhbI+/2ompmo9ZAPOwAcKXWPIcQjCn/iiFCYrdZ5mXtvGjGGqXZPE1OZkIohG5CLuA3AbQsl+RbRSGCRG64Yop6LI3IS5U+Wk0qzPgFGcvyEozKif94JHJMXwDySgXF/McK5523L+T+mcs5il6JmoPXEbR+QVWgqA9ohIsE4nwSBY7U4BSq+4sRUe449HzxhH2AJr/2sBk56Im6DgFmYV0xmJq2bbA8ldlhwWRZA4sLkZYSq+7E/7o/92PdCXBhTuwkALgzyrV1ERjX+8CXynI6q3u/3l5eXbdsG3Nn33RXZnupalgWTxYRMXZ0Bh/9yeFi7K6MDsjEzNMXaTiO11hwYAYAT8A5c/CAOm1xmNNbuqOY81faf4c7AXphdYMcvB66CsW2dkNDAQ+eBM4DRd5c53jnWBD1plfDUIQ4A9kMfe/eosquqgYodxyHhNGgAJl3N0KAAKZCaaRfd963tu7TDq81BBX1TLQUZt21Xke6rNHGphRkBBJO7AjNC54octkadWswChBBZzMs0VSQGMGkNoVsprqPQ3gCUCZ1ydtmiiuxHe3u/XS+XeZ4924wqoIaCpm4kYxqz3632pfU+qTEQcoHMRyAAEqAO/SVxSlcJqbDjuzjjmOzmozqawKmId3MyMzWvgAmnwYi8AOAPGbSYb6e9z8OqDHjx9Mt8wL7QayymBoqiXUSQmmVXV4xuRYoItVZIm2lXOHnFTRCKrjA189bdqgZIuR1B5BYsMgIR0YaaZUST6D3KUZWianYYwZi6PyEM0BDFy9+Hg74FDbLl9HuM8v4TYkIcMibV7h2JXUsuovnlmK0ffW94LuJjfQbMDTpIAAJQzBI4My8ZYgOT3upUo4S4dREhkqh6PwMujI3bfBeCoA0QUEU0twBDVBHt3c6NhHB8XAHAVMARe9SHj7yGgjFk8Zd3CuSRKYaUW6q6kt0tFMV0FG50NSQUlfv9vs6rED62fdv3l2WealUil/uQBJJWEc3dAGJWBI/pRBkCEhpTEK08LgYgkcSQQiOcZgMiIBklkUlpAujrW9w6VwNa1AXQB51LHOY8kswshBnJO41V9exfihhlaE9wYmaIY0NP495clINgOL9bcyYOFAFqFjkg+bAhqVkPcImI5AZWAxEYdI05FfQlEZTC0zT1ZdH0ORHp6Gp9qobsFFEXw65MGff3jA08Sep6PW/pSuRLyxluPJkvxxVPQs0+rE44bskASKcJijERLDZjVICMRBCC7c53Ezwh0VhVVUR7wp6Y5c8zGA8XfLw9wenAQAAJ1MOiP+FOHM6ekwwAokYhBjWFe3ssPYiJ1z0MNIWRSxw43j82Un8NsB21lN6rmLnDLyAVbYDeg+HYt62h1Vq4cM2OH94XdsR2ljTJ6MDqVfpEdBzHzz//fL/f3WhnYBfXFXn/DU8PfYc8LA2NBgYSkWVZHJDBSfpz/qzXmrXWhqLIv9cB3L7vbp7U8jVm5UBFgwoaWAeSPXIG68wGnYbc01soJ9oHoHPWJJ2R3Bne4ckZEk7IzH+ABEZ+kP1o98fhD8TnsqgeRxvLhrnN/7NuTpvI0Vo7Du3NcqqjKk91XpZ5WbmQ2bd+7A5BaqF5cj8rbZ5uUzHVynWaJlHRx6P3A9QgdHtEiFxqmaZ5WeZlUYvaZ1OA0JuqmXrbymWZ5qk+jm5HE9HW+7e32zRNxHRZGK2jNFRRBBodgLLmx6l6z/wZIGCBsPtCcAmNaRTVksvqEBGYSyFKwKmAoZt1K7boS2kAJi6LNQMx9apbQrc6fk7tgYrw4w8eHGVb8cBGp7dEoAaqoKhgJuHxooQGHVpnQFDRTr0d0poc+7EtexdZ14WzgwynUDyjtWgvmt+mfnWAowJaw/TPwOuBxur/YXEFM3XtAo61BTLIesKRIJ8xR7g8/UjyjX5gPAUHgOgFXnHiOSOylqKrdJUumvuRiKiYCQAgGRIQPUM+Pt/VAWmIEL3XkTxDDDCRVpTdahwRmdiQRffeGiIiAbs7b1zz2AUgWSpS69I7AJlmuxMAJnRxPgahkRPfoQyQN7Y50SvP/RfAvFUhYvQHLUQI4Gpoy3FqpgLgTbzQF6LWe2tHP8TApf1d+rZvInLfNgKYlmUi6q1Jl/3o1TlTJoti1yhZ9IekZqKCZt7XVcAaWfE+VkRAtYR4+Vl+GRukJ019MhRSYSJvE0ZpeQIGJtqzMBshyA6A6Of7nD6naAEERlYn9nXNEMKSPEjWCxFg8BUR8ZgL+XKmxfCP2ZAU1cBJCXrMxG3IHYyKiWbRTche8mScMjvBaHPcLWDdWSUn1Im9jyGBW9z2aaq9tb5Aa2AKhAboeOjogtTVLMPqU9CsCl4HQF4hCzjw6rO8LHDhmf4N8PYB+DwH9vi1I8eARJRTAMM33Rscxv5FSaZjqNwcPvlfPrBEaQogfpZZb5tPOMOyXHH+cIJZYpZrKj4XoQGe4gZ8+IhLBEfkFJEUpEhJnDaDQFca3vAQ9reFqU719dNluVzW9bKuc6nF43sjWtfpk1xrpXkp2/1hItpUjubfNE3T6+vrGUAPYDTSUsuyuKr6/f1933ff/h+Ph+fRzMyRk/eF9QwRpAHjyJ1Bkj3TNG3bdr1e3cRoTh2bAx1EHFX0LqweYGtAEFUdqKjW6lwR5Io9UleU7WDhVBhvWew2rI/GXIDcFQZmPqMfOOXFxqHGvw5MqSfFul/IeOf5/Mf3AsDjsd3vW2WqtRiAGyJ7u0TfqTSiITUDFT16v+/HfhxgVpm0o6i2vZnpTMUDVzRE64VgqmQEU8XKUAiJ+QDovbXjAFVaZwLrIu3YpXcEElEPfmqp0zzP87LM8+Vy7b0fdDQ1AORSkAhMoZZa6zTVZaqE1sSEgyQWg7f3m6n++MPrOgGpgLSmnVzIWLggF4JaGbkg8bSspc7ARcBE1EXZDnyyCAXHfCIAjuh4TBgiNPUyD0yrZARErKX44tnBEMAbZwynW/94+hWNWNoy1vUfcu2NhSvSComRksqOyisRQSA0E2BhJylA1EyotaO1/Ti69N4jjGBi9IULINqbqiGa11dbKjnAMMNDB63KTGbArJTDLBfGEzIawidDRGSm0UoB866mUwUhomnq+wxCGIwGql7qZa5PSc4n47YRkvro7p6FFZHk3Z1DCl0ooIZdHAJQXNOAPF4kgoDqPShiv8gGhGaW+n5Kd59mBu1Q6YBjT+UyTflhnzpKQB7tMREgiGprAhAl9ozhzO191Jvn2Cw2xdjTVSxlIBihtkGIijjbAVMpxe3VRbW31pvLn/1mqJtui6ghuMuqJHEl0lUVgfb92Pf9aK1wQYgeU4gootNUqTAWOllURIII1Hu/BXwX1b51BeLsOTJXUBb/C7qZZCnEBdUQGJMhm2oFFSQXUkRtmivXABCeqAbMY3Z3rbInegaAsF/PHFBkfPItY+mEzIw6vCF8snRpAu+DcaSMxhptiC55fz5nn4JOy3TR7jhkaIlc8WfP3dWntSvGAjeig5iuzc0e1FkQ4gJWiMhCsuZjNSXCSACoAKLWRaijp/Y5Q7OMlRRCq0gIrAKWHgeQO8mYBRZIzwB0rAHPeW0BKix37OE95UBhxNCJAEfuzG85hYnCyZPBUkqfdr+a0n6A05qS5/vEcGYCRt/JkgffA4k5E3nmswJM94CxhATAi0kCht6aE/NOPBHVOClz+3TNVdJ3ajeye319/emnn6Z5LXUqZemColSYy0TzshzNex7AzMUXk/Wy+nHnef706dPY1we1M3Z3xzG11peXlz//+c9vb2+eO3PVs7//27dvXk7vwOh6vVp6/8jJsHFwRaN1mh+5lOJ1cE5KuSsSnZp+WPoMDZGQI6Faq3sX+cmPfJacerfFFIqYQRJ7R+n+GQPFZDx1yR3YKDeR798f8zp/P756gKQzsYQnAklTqAQArcvRuip5V06vsWemyu6Ca0wk2aHUhfdMvmL1hqjSWzuOYwcAX++t7ZUJRZZaKq/HvqtK7w1BCUna0Y7Df4nfYNv2LrI/7iYCaCZN2g6gtdZ5WtZlWeapMKkYgXFBxLSERizMpRIShPu4CiBwYQIqhUXhvh3l7WGXOjEWLETg3tA+spm4EnOdyrxM68s0rcTFAF1ThCHzRZPnlPJyFESIxITreMJrAAGBiNX0aN1LpwgJVMwZrsKEUDg6F3k6zR/BR78i8/AW8blMWc5td7UefO8QcuSyHRIVt9/X6PURYa+4kkREfI2a5z7VWkplHhAeeu9pYY2IkWzwP55sdLR3RWbPjzkmRH2WB+VilJEk5nKSY/rJfCY1ipCpjZRqhRURgCfjEqKcylzgucxBEEXaVbv37cvFEXP11GRWxlqJ8Dw9AAecAf4obyxgminFjpvZEQQotaoKEDo/gK27qJtUkNl75sR2GJQYEZGXt/e+gRFi2HS21rk0IiYuebvQTE1iW/K1FxFSHxreDH7+7I+QmJlBTUxEtDWvCNXT8zADCeiqYu7qhMV3bzFVkXa0dhwi4jM/GjkBIKJr1SFrvB0M2UjQxrZBOWbNDE3BE60dDpWYVeDFvabFzAyYyyAiuHDR4hi6lBL7XyyXXsD43CojZ+otiZNSCsCUjxxSmnPaWU/4xrvjwnMwwAAtOdEShz0PYMETnFCRzzKRtMTQ7rJqR0UnUc5pJSf3bvPfcVTTJavXHdmb89cEoMaAlu15LCMCdMrTMovtNAv1oWA0i/LP4MYCuPpEsziDDzRQPt4AhQbPmf/xHliYBvgtR0cicW2+YAVqisebzZRczWi5lyWeMDCIwoUsG3k+r5iK+YAA4LT6pYYBECC785xXm8RG52s8rUXpVQsI0Vogvsbzp3G/RpiT2b/ESPZMrhl4LW8t02W9vr5+KnUGLAZ4NC1VgBiY2CvVkQhpnud5qrXweln9G0ZF2CB1xr4+CB4vaJ+m6fPnzz/88MP9fn9/f7/dbr739969PM1jzsvlUmtd19WPOdwdR/vYgQlG4mmwQY4bXImMKUzOx/qBuRnoakCQQXSdIYud9NdnIIInCsfHyPmXmk1LzkcbxxnIxv9MMcYHIGWZOIM/dA75sDaMX4KJiTXJAgmbSmUMg1YwNe1RKGGl8LTMrErS+k3kOI5j3/px1FIYjEEZhImnaQGELr0f+9Ebg1HBqTBoV0+GtSa9F94AQFQQETOSNwRkLlOt81ymKRJ/TrMzZaQYPsUiitgTrAehrWagCKLv98PM1omWinNl3/T8eEhUaqnTxHVmrpjSTa6Ta1VzimUA5LMgRZuqptm/3kxKcXNsal32ozHRVEsh9JwgIBaj0W5hMO/+OmXQkJ5LQHDZw1Y2FrAkbG1w3U+6yPOpCCgGgqJCKq6bFok4rEusN733Ps3TJLUWH/RmwUKpplQ/YYqjoufwdWAEmNJsVAIyPy2LQXW6qhGTJTaKIU3hzRe55AGJNBtmPbGUL7kUC68fLpiAGA0WoE/ExVW5OMfK5hker6vHiLExFZpjyjoDjulq5/c1d4MQFUDiImNmKgWZxAy7t2YBcmtiih5az4UAvE4eSqlSjY9mpugMlMpjaw7ZkKPLuD/QKMFJEo4QATkUDVlP5F9amN0DMOBwl96bimRLBkJmwCDNEJGifpWKcfcFt1tvx3DFRabWuvreB1aI3KpUIdY7VUVFU8W0AEXzflaIRgjoqRofwtp7eK0SGBExiYjUbkClBsvtW4Kp+FRn5mDoAImUiDSsgGywBWqjNsE0JFSgoVP2k3yi2DMo91xeLBiUerDzuugjYiiL4q9J7Cf9kUrvxN1uzBj6ZjM7QyIfHj5r2AfD06kn7RosNByHOSpCJHJPZQNkTR4FwsY9OA8EzMUaTSIzi+OMg8LLKCHzfc+B/wE3umYsN/snWox3+v9o8KZxNgiZOsty0WcgEZiKEIiRmZCJmAwTsjyRhw36MsRzlotbPhXMkf98xSZtsVBAUNqIQ07kdUPPeT62VUCnrMAbHTisjiUMCOPJAShkPOpLR5QU+MqgBNHum4AMkbnUOq/L5Xq5ItWuuB19PzpxN/S2iRRhjti0Tpd1mae6XlY/Lzdj1JT7OMohovHXAZLmeX55efnxxx+3bXt/f//69euoR9v3/Xa7+SV7Usw7ufphB4BwB0gHRtu2DfYIP7Y/G/VZQ/czXuMRyPBwPgGjgU6+e42nllPyadU4YMp3OMnOgyA/FeM1/Zy+m76IA4Y/S9u+++x3oM1frgtQ9dRmNwNELMgcGQnp0qOQBZmoTmVBBsLSjr13OfatbZu0Yy5rZZwLTZWnuS7XF0A49v396+8mjQpNBV8u8y+oYF5zeRxH51JKLbVWLqSqbGCMB4CbpJdaiMuxPURCiUVe0GDmSw5EtIzIhbOKV03F0BgMqPe9996WInNBLGZGqMQeXtc6L/OyYJnc/hDRClOtNZuCqpvV5s5jo0ZB1aQHxy2ivbfZplIKErQmt/tWGE0nq0wR06VBR5LG56jro9o6FhNf2z3qNUufg2hwD5KtgTQGw4nNFlMwNJWuwiqRf/HuIaqKamoqeuzHPu1eOFBqKcy11i5FpET5ZVz4WFJjXwkFbMiutRRSRbOs6op7h6cF31ywfIZEES4/mXYnYSQqoqSDgUY3nDAywbCgoLF9WYbJvpN06a1FASqEhDVpt2yIngt1RANJBEXooEjiemxTGDGEWXoOGniO2VMkXRCAiYmrAnpNghkQHaUUJmYyevZdBUBIQbp3VnHfTFDV7dHf3m9bO7Z27Ed7uV4V0EsKzbRn31IEZGJCIVTfQnPKMwQ7oh68t9bU561lRTUq9GGcAFMp8zyRN4dThaOpSjuwP7bWmykQEgO1x64FiYCJ68RzYQDtXUANAoJo1960Dzc9zBtfvHoTkYkmf/zMuR+pidl2tMdDsRx4b9vu/8BctAgiMjGm3tVACzvnhQIuDnPWMOoW/aqHD5Ca9Ziccq7aS5cHZNdyoz8K36RpFAH5apkLd5TwaG69kMRRBCSRYEwOJieijaPkZu54qJYyTROXQpxPDTKXA+CBVm+HtN0fHxEpgWJau4mZuj+ClSBZvVoCC+FwwzSVwEpP0hQGT4cjDlH9iId8nJp5+omyf/PzSpKXAjOATHQBQJZrRcxnANG3WNXruSJXyUSMyJRVnWiEaWM97AtSAuXzLknR5IL8PJ40LhimF0qUyPmCG97dHloOyhfHQZ5pNW/hMlTYkAkxv5OoBt3VjhgBso88ekrGjcAYARiAcKo0TVwmNrPW237IY9uJSOVoRy2Vj6O1fevH3tpxs9b7XpiWffWR5rVjmmpr3yp9gx9NwbwQTFWv1+uf//xnAGitvb+/j3BFRN7f3/2zZvblyxc3vPY4bfS39w+OFNgANz4kzlVv32Gg3vtZx23JFY0kmn928FKYiHnUxp9x1dlE8bv3jx/O+AY+QqIRU8UkEnluKyc91ne/h+fj/p4rUmnaNtXDZFPtAEjI0EBBWzv2dmz7TkTLsk7LdVoXLC+iVoj2/U4o1g85HtoOK3idP81r5VK5ltfX18JwbPT1F7RWf/j8+qc/ffnzX3/69fff7vc771AQYSqXy2W9XpbrBUy326037AVhFyIEk3ZsqrZvGxFGcsBjR5XWukvcMCp0jbH61FHV1oXENwvYt/3tHZhgmWgusM71elm+/LCW9cfLpx/neWoi7eiqgghqZFDdM8VMC8E6T650fK4ZgGAgal1BBHq3beu1TkRUajl6N9MmQL2pSSGqpdRKhSnSz4jATz0ufMcVZQrAzKtXTMVE1CQQg4qqnxyOiZsRlRpIJBQII5z0aC/jWzAz7e3w9MqxH8dxLPNcp6lOpUtXm1SlFJbePacWHJXTSGgpPCfEiL58rTdDM+o26q0DGPlW7eFdhHHmzP7p2LkDZecM0YiS6bRfwCiQy1aXbqvmuUwTkd4OR0UiAhaYIwQ4sQOkgNOZQKV8oPEdTFSYFRGjlYiZCSogB5/gyySjUVjSGREV5sLFi9vNLKhuEWVWVWZi39HMHCH67kolFGb+iVJommotrNb3/cjcRoTZZiBNAEBJ0/Lbk57km5NpNMdgZrVI39iIopJK8QQiMbI36LYk2Uy3/XjcN2nKhgxIyBMURaiEaWJliNBUm/Yw1Bta1FiqLAsSfDgraI/+nkhUKvh+aYbAYyskYgSoJZdFIkDOiJ9y4BAgjR7MZglEAEOsm2yHiHbvzSO9dZHIxYQ9naOfWjhZiCAxYky7r8KAQ163Ah5peTvaqMmCXErzHPIwEZ0CEqMpkhui+egNH6l5nqZpKlFPxNELwwMCEehNxcC6SlMRBAMgUPZ0pyGBGJkyamXESlaKj18mKhypKXIRNcYddCoKMbD4cJ8094Z4Ft2fV7iAEYRGiZsQKARb6ABNdXiuxeo1umaAPYkkC4ohQqqwaaCR2IrbGJm4sVWeuP8gaDIYGvfbXBGpZoBPppAZEMLeCoPaDn0e2gCI8d3J8YS3tZ+b5emognryEoMCxTx5ylYhY091eSQXnqYy1QpEW+9d9Wit92Mq3A8x4X6QqFk/VFrvRz9k3wDRLscjjxMoxCkiTG5mEC0DvrTWlmV5fX31v769vYmIj819311/PYCUi6mJ6Hq9Ula0MbMX9o882vANGl3MarYisZPUyU0jh2zIz3BgOHdO8od0RnhnBGMnKc9wZnrOO7NhrnimguxELw1Mc0ZsY3CMP12LPTDl+bPjzP04T8lRf0C/oXXTBmrEPFWaq5M1xgyEakBTqYXYVLU1QDA5ZL/NDOtctJVNmlnb7jeVTlypzutcXi7TpcoPn5bP1/mHL18+f3phAACqZXq94Pyl/vjTDy+vL0h4HMft99/LxJ2KVAQsorLd3o9tL3WiUmmaPHI0NZNoPStmEXkQMsCxHxGKm6vF48kSousdt10RtBaepv1fv+2/fGv/5u/bjz98eX25MpG3vFbV1g7pXVUQDKZihP5IPOj1WWnmjrxu3xidYRTMQC9z4c8Xrx/2BDIjIGo/Hl3JW05yQxX58U/xjAIVmVfxQyaEnABRcTTkYUMWZzhPZg4NcttzYBQ9hzDVB5ZVppboQ9RMmvoGogKxJCp6cwaKI2t0l/PRpkGXeeqBmZAxitIsi6iRCPXZoisDazAvkEoAYimfI0BFI4uvk7xGdxjyhiNPvH/iRTJwzXVUVEWCue29q6axIUaxy5g5o1xl0HIfJBUO7sybkipCWgIjGkH4+uYmlit+AjxXHEfdaYbgFjEt5fIftUkaQaFfwWN7iGnrAohsBLoPHIuZHSGCaLiBhgoASJ3ItbWQHJpHV+z4iBxWR6w8GAsmjxQdTRrIvj3E8Hjs7bEbPBNMnh6KZ0uEAHtrTdrhDX3yWRq5JbgPPjFCL+ozpMjyQJCCua6DxzGW3HhufLFkedihOIATWLaJCAuZ0NL4qudbngXyc3cK0d6jm42GyhiB2VF2PrWBZywHKw1uMMDoaZFN0u0ZZEAOaMzHBOrKQwN8kov+NmIk4lK41jpNU52mUipxUQBTT2331g7V7lg/2OEoFPf4j9CckzAjRKYCBbK5aXQWwhwJAJhtlaISHzFJosi2ScLkZDAHoxa587g6DdHMOak80nJxgZEKdxvT0RNRvWOG3x0KXivzWQ4yIOPDjKUiSkpqavz/6M9rCXFoTA5fKLxeDyDNN0a+K+Ar2jNnN9ASBmqMh5j/mgMDFUwNDdCCSwIgstEexOGfmppnQb2zfZ1KrUiRmEbTyljZe9WKiWYRNLgUDcyYDUDGYPOE0ajz8jIryHVM09THcYbrkH744Yd/+qd/cn7onMkys9vt5sRSa83zaI66/OuY2ZXaZ2W3S6c9aeD4CRJDDHnTWR4ApwL+wRgN76KxMo+WbeNo57V9rNJE5KVzZ8dI/JgOw1OnWDtl3DImg/PNHEeQkyNlLvk2Pj7ObS64TmwAR9Pj6J5dMYPwC4rlwk8ApPdD7r232+39/vatPe7Sjkyk47Y9em/gDcWs9dflskzXdVmW5XpZifDXX38DtS+fPk11+vT68vrpQkzt2G9y7Ki1AAGK0lxh66raFcyYsjN9LVw8/hvX65NEzdA7iatLINxA2Zc5q7V6eCK9b8e+N21yHOJCKbo/9j/9+MOPP7wu81QLUziSdxdLmJKqAZmvIwrheiJi3Qt5kJCwidphXdWbWIt4ybYXmRoxAAATeb2ygoEbXGdXnzIej0Y4KhbC6cBDSRJ5LYmnSXxDx1zQM8vmB8FwCIwbNQKtXM6kmyKIkKkQRV6PmWotqoQIHYyMnogr2rG5HgVZ1XUeiJHjj8UOo4tCJvV8zkQYmr4jSsZmFnjE3ETXVHpvrfewF3hy5RHHc5hJ51T0Rg4aftYylgIRUYWs1XQyJWDXMwyNPdUAMo821kHVuBwPlmNxd4CZ2kBfmySctTIUtRHqZnbeTsAocnYWOQLHSmTaVfq2PQxZXeNcqKN3hwVDI8InUx36XTOwrNlGjEbw43qh1FLnuTBzYe09Q2xUIETmUmotRLRvhxqI6uN+J6rHfWvbDoUh7T0NjDD9ggwMbWvH0Q9xT6zcsdTRiwJYWA0AcZoIpSo5fKLieSITWqjzMbwaE6F4kA+IapgoNBEUZlPzgLZPN70cSpY41meO5x7B+xRaVB6eOYd4wC4Yt2H1OLiKGIPjMQaGgHB2h9MwjT/Q92ULDWMuuLmkM0ebs2mqE5eqEEwndTL13ug+oNQ91b1k0u10AYERjJCYBC3K6rNd4ODPnlDAtTyOhxApGSQ/MbSTCcZ5I/FLfqKKpCyfdxpyHo0p6l8ET4d1OD+2WBzCkD1OL+dFDpJYJU4H/v/Q9WZbkuQ4liAWkiK62OJbRGRldT3MmYf5/x+ac7p6ujIjI9zdFlUVERLAPACkintU24n0NDdXU5WFAl5cXFz4lY74hTtGyfvfMeBoJ5IIUHF0gsbz0CER3G96UGg7SEQd3frH+isRwbzBxSI/DAA6CNEgWzsb6Q7UuaRcUs4AKE09n8yJcqKxQLBfCjUVlcRIhCntWiB3MhpHCQMnjV3cY0JrLefsbWufPn16fX11QDOmdrjy2oVBjk7meQaAMfB1VLWWZemrP4BRT5/ugzjGC/oZ/8DWjE90AOR+kvvBsQDg7Wx3YeL9XsGAZe7Y5DPa9h7Z+zqav7l/D7sRH+NlA+XsP2LE5z02GtdzIKp5SsdDNmMAbbWp+ugZC9YAsfjtRxKz2ur1ti232+X97fr+tt6urW4qzZdd8HBqYiZ1qcuhPZ4+fvzw6cMDcVpu67dvLyWlh+fHh8eHD8+PRLbcrte2kFbGlhkIiBSLdxLFxE8oOU9TmaaZmTcEFRVxc4E4I1VDAm/ojHjmbL+ZAXBK8zQxJamDzpSmm6mK2HJb61ZzTiUXxMRETZ2DdBcYUFGEmPnpzpZmoPFhkRWttW2bJcacOTGCKSfudkZmXv8jYnNV+G4uKQDsUVFtFQC8DubF9c46ducdT321hynEEbG0u3Y3iwqamCkGcUBA6l1HBiDi+jpENGnEwITMmJXNxIydaRLRnhKqRSuemyMTiY8T9+QwNpa7w5r5sLBw8BiP8QhxA990BtWZ21q31X17Eb2w0p2oyRu6w8HGHyow814zT09qrdu21rqJaPRy7hM/i4fwftmxUwG71EIBlZg4mWee2kd0xccDIYNPu3OG0MANszv+de0Itrb6M8jEFGfuTJ62Vi02VKt1W7f1crlutw0AhKj2LBcJ1XST1qM4AVKUK2LCXTUARkpIhRkAqpqYqikyffz0+cPz86enp4LeC8khwekbxeXy/u31/XK5but6LMxA6+UiW0WcEYzAiGLyuwcwt69f17VZc3MsAjJUQRVrzaQL5oj6vBpASDn5zq5mBEacEZDQAEBUnHxDS8PbBgDEoA26UQZ3Ax109ptnMJYbDALD+ouiegIAQDu4QnfbjFgYztP4u0tXH99XbAde/hmxhCDenACts1b9/qL/1wRqt7FG95FApBAZEjHnnPM0pVwQUdWaNNpIpbZGUQJzXgshMSYil70CIGVkAkuhY4ktlnAIisbVgoArTm8EFo1A4cVk7Ot/nxf0RwI7Ouhxdk+h9KsbJU1EAO46r+EfZf0bGGRMpGghBwzgMj4PnS8fBoJxN4LX6Vi2V21D5olR32YECxyNd2AEdzLvfnqE4NMxAx4hAoUtVhMhMDcgxWi2BcIYUUYEiMhMAeNUm1lVqdKQOVF2kz1OGYi9HkWIjJQQwM2texsLLIuo1Lq1JtM056nMh2NcrX6s1EeDDVQ0brB29x2f9kpEx+Px119/XdfV/9VR0cA31+vV4dG6ru7u6FiqlDJN0+12c2m2W0H6R9Ra/Z1LKe58Tb0z39GGdO8f7Y1sA5QMROWtcEQkIm505IcdOofdyfpx+vu4C+X43H3dzXGSQyInsf765WfnB7wHXnt10U/w6KevnHPOpWkLLyQzAjVtYIzMjMycwNpadatt2dr1Wt9eX6+X61Y3USHCeSrzPHl2qo4U1/W23MwEzD5+eCqZkajmNB/Pn7/89nA6HqaSGK6XV1ne5PYK7ZpZEVCZpbntGZOiIiWmp6eH8+PjPM+EdL3ekC6G0NzmINQXGMpJMHUtpnZQD5hzPp6O8zS32japerVt1db0prW1y7Jsl9utNVmX+unThw8fHpnJsiqRqRpgU/MJRT3whsw0JRY1VW211W2tW03Mx+M0PZy8Acefppwo2t+Rirdp6yjjxHoYFTQFawN3xxQ5T5y8a8ob8+3uS9bTb5+OZWLWvJUbFZSccQLwqmo4H4lDRhVUNTBVkg1awpRQGquUzsX5bq6BilS7PQsQASELCnWTg0FstSZ9ElmXK3SmJbylIZA8B953RD9410XVlbY0TdwteJgpdU1I168Q+jS0GJdUt7qtdVtrrWbQG0j98BwP9RHcI+pHYDcMX9D4F/MNG6w3tBMxA5CKqQCBgDVlsSxAfFnX1uT2frm9X7dt8Qym1HyYJ5fw1roRlV7v7P5+rbUmTZQTvb1fXr6/qCg7VAADawbkVSIyFaRNWrTVAZip70CJmBEZlUGhNkBMSAkJORsC1GatERFPs3W2UNWqtCqybNt//f77n39+I4SSUtVUNwGwlJOSJUpIbC7lydjFT4BKqrq1bZPW1KeiiRkUEgLoUiBsnqd5adtb8RmJGROrCQCoYUrMwP4MECe3mvQvVROvKtn+lu0Ihv7V92p31DGX+wT6IUyJEYx9BzLzKuAPkwd8DzbTJg01Ns7B9kFUW/sGj33lGLpYHpEIE7IDQFQzwCpaRbcmS23LWqWJC28So6ZkJRGhRCBG6mYwqkZEplZTzilpYkkMmkAFERIn7xJ1mbIZWuqgp+/TTj0E/RJtGr17PIiWse7DRmFswISAFEaKP24NcUNdohN3x5MrCFBK7rMFwAghBxuQ6IfGhv5eHRhFLNjRUf0TPMaGCXbQQE5EDf5qHB9YgDWnIim2ut7J0UHRHXIPWAbsw1ydg2IWwCa6VbktK1pLjDnjlJMXehEg+cS9KCaTAYqqmG2trduqZoyABJw5pUSJRntNjDIwEIGSmNJUplLrCti7qQFSmsp0nI4PA2QMJc0upYwrxDEhMdI8F2I7RHh6evrtt998ebtaaLzDXgk0AJYDCMR7q+wodbU+K809kGzX7t5zg/vh+Sv3FTEnigb4cBskBytm5kPZnDQaFE70X7UmIt405yeFvezlqMsfGa/NDWslf3PvszscDs/Pz478/MW0s47c80b7T8cfFz+mjLmQMGedZzAzRpqmkhJHkolUtwWwOcZblutyXUT0cDofTydKnHKZpnk+n72pttW6LFfZNkKYMn/+9Hg8zWoEmD59xEMpiQ3kti3rcnm9vH+/Xi/LuhhASgkMlEwNgGBTbEaYaJrSPKXDnBM79Q9MIK3dlkUD1yIhSlfbwHjWVLVJ3eqyLGBm0S1CbgKpAOFb9SYi+v5++/Tn919++fS3v30+TDnnZL3N3dcB+rPQt3kgYEJSRITjIV+0AoiaImJi9nYKIko555yYfYSwhuMTMjD1tHW4ON6ZakAMT13v+DKXYkRYCcENYWAliGAO0Z9m0ZccBQRSM0XgINbRPBmyPshaG0nlxtQS15wBwBKrMgXZfu/HGAtHTcFMRM2gV/ecPhNR8fx7aPB2grZQmNzt33e9yK212jbfwFLOo/LdY+ouVA+F4+iSbK212mptrYFF3gjqKakfiI64MvLTe9I5gmY8MHYP0zFhzOtpiuFpC0ZYW/vX7//S1mRdpW7u+WBm1ZQJkZOlHPl+9PvERmLETFZbu16W5XKVdSURjNILACYv6PihkSkaaG9WcktJn8gSRZ94tn1NAHPiueRSfIQEIIEO/+RoOxLRutYEQAasovU+xJVVvT8odgAD87qh1zBTYVACSyQG4P2koSVApDD7ol6sNQMc4298dr0X+QEQKIa+NOulWF9aqiqO16237EVP+9jSYKzFUTy2/koEJsdFwN5yYOAaNS/zDFAEcHffu4OfvolIny7yYwkUCTExBkgBQvSYAgCoBg6JtirbVtd1bS1UTTmRZQFQJvKEuGOjeFMKT7ygQ1NyLyckQI6RoIGKBjiMVUy9ihSn5I9nvzTxo1jO1vOcTsZ5Zg8/4Y0xmcfFE1EKNbDYyHHHAflN6Rpqf3ws4oVBr63F/4UfBHakNliisX321rO41/0CuZ0JhZfYwMa9bu0JxNj0PK6gF8bCBHf4edxxs0uhAFHBbut2Xbb36+397WLWUsKppNNhPpTs3aSZcywdn/8lutZt2+qyrbVWSt4CCMzEmTnl5LPIOfcBT34tyQDEQL2EMU8P55OJnY+HwzxNuYwoN5RDA4j8FeL58nSsA11F+/DwUGtdluXr16+uMfJIPDCEcy0DDVivTDl15F1jA6DYziF6fPoeDEEHSV7TICJXiHv6MdCYt6TlHtUdeznY+isq8nMfYvBxpv5xwybb6aJBDnmHnWvP/fXH43GeZ2+1053E+6c/9zhpXFsDBGJCLGVyvp+JSy5I5g+LK0uJqGSc1UrClJE4HU6n0/E4Hw/z4TifTsfzAzOZqbbmXYcmFU0OExLBVgVMEmlb3xUEQUybSBUfy6CKSDklMDQyI0YBFqhKiklbk7qJN48nmkqqJU+F1xWaqQt1KqB0xNGzDDMDVVnWxUy3dUOibdtUFRCxa3ZFFSq8X2+mtqzrbbnlzL98/jBPmZm9KOwXwTtfzJyLAXLWgzElJ2tUVJkoDghGmukxB1vTWpsz6ClhSj2f+NHFEfqxu9OOGVhn/om858IpI+ggJxY3+MTb2EsQQK3FqCRVVEAeiMplCgamoKCmUqVSI9yYOCUzE4muI3ea7BnePeeDrt5oTd14KryCuhQ89krrRcROjiF6Gdt3pjBvdGwjrTVpgBA8ZyndrO7+1Texe7eKF7KHqkhaG4DYaScN7bPa2Exg5Kljc7iDUUIA2+tsgfq+6aIsV7yCaa317c+vII1U2NljUzBoprW2MglA93XwJD1KTEBGSmimt+ulrSuIcAednnACKpp26CvsId+P3BumEcEUrMUDfK9CIFOa5mMuhZh9Wxpb5ACpKmK1FSI0IzWz1gXSgBb6Dxfm9kBiBIDEnBJbSs7mgImKkyrREY+kg8SAUcSB2FMtJDBABKIW5ScDMN6ZHHuV2KI3zhOdQH4UpRAcF2MQDP6nC/9jyhdRjGB23ISdffA36jfU7E5qxuVRv0LaRmNDlH6jBbLkZKaWUt/OA6mJQm1Sm9Tq3OVWW1MnvoTAlNBS7DqBi3oABugbP/c82DT58mG+bwSOirCfMQ1ZTad1/EZT3DR/VVyBoZAe8rr+UP+QJtsOT/XbFz3tParZgIn+713DfQdntsN8w3wD+8Q6hPt9jKA3KhoBbe+xpmOqXmU1giH+GrAo6Cz/pfCV7Y+d/kQQ3GEcBajdRN5ut5fX9++vby8vr2otM09TejyfHo7HeS5zKVAYkQ0j87lt9XZb1m1b1xXAJpoCEvkAipwpFUoFyaMuEIH3GqhBa6IKKaXDYSYkUDgcpmkqOd2lFOPPfhdi+emPJoQDQ/i/Hg6Hw+Hw+Pi4ruvz8/PXr19dQy0iPiHE0YOjDQoJ7V06PWa7/kQyjd6xgWBGEe3Hx0ZFxIGRV9/8/cdoNhduQ3eh9MdgrD5/5TieQevuPxG6NHsAo6EHd1R0PB6XZfGfDIpoHPb+rQbMgp0R9rjgY2pmLqWkHLeVklm7t4KrRX2NcDllqUUNT+fj84cP58fHw+l8OJ3m0wlMQRW0aRPZllYXqYvKuq63Vte2bdaWershqjdGDxwTAIMTAKrZPCE3SAJVYNPUWt22lX1YBxEz5kRT5sRYq5+nSWvDnclDn0XWpNu6ShPmFZmlin+km1D7Y6Wm26Yqsq7rsixPj+fjPDGdmDMFOAYFQ3UJKCj0OObZHTIRqqk0AYuA4CnRYHBATfxhgNEOck+P71xRXwS++ZoRWBjhY5czK5GheRPHPb3z3U4UZLi2AGypbakxIAMyU+rz3Xoa7cIf1bY1d/I0UZV1nTjllHIuuRQH4x6VgUJLCSrehKnLsi63W93W1po2rwr3OlE4xUQ3/Mj5kJB5JJeudr4rqPz5sWlydT0zhzWjKvpjiYDdfUii06LWutXN2/IFAFUh1YrIiCjSRJqJgErM+iBwgU53G7G764IqivR2eLyH9Whhju45IgTTVrfrVhGUAQrg0GAasIi6BwoRq7qVIRKTo8YqbZPWtG3rZmacEjCrqKlgTLRwSwVHjQmg9Z0PguPqrNeQnnYRFxGn+XDkwpxzgGgPT365vNy7LLquaFFiDEmUJ0FEQmRExgg+tU8BcWirB9F216cmSplzpkSITUHAncgRDRKSP5+WwsrFDFSctAX3LKX9OgYI+/VudaPhuonkjwf17fKHLw8j0XjoGpAOzO4Z7b2A2v9Fne/svGYPdyLu0tukhrOwAqBDk5yTagGbemXP/SPUACSIorZtbV3WbV27eMK0EZp4RW/b5lprqwHiMQdtBRACxJSSpOKCa4CwAWdOToLE5f/JxnBcwTvUwA6EAPqYGokeItf7K3RVTe/e7zRc55I6XemUdE8L4L4S/B24y7gBEQax3POo0QAUwqPYw+8H6/8b9gD3t+4v8B46HujJrSpHumrgWavHht5gT770omh499wG8KqWu2sDNtW36+WPb1///Pb9z+/fX19eDZSISk6n4/Hp4eF8PJ6Ph4fjcZ4KEYFZU73cluvtVmtVkcNUDoc5ESUmTkypEBfkIsYikAkyYc6MSGLYVLYmSECpHDgfJgXAnHxXu3v23C+OX7puLT2UPdC3eS+i+U+cjDmdTq21z58/f//+3X9LRE6nk3MnpZQ9Mmjd9dH66DTtNo/j5wPlIOJAMwP17p8y7aZH0Omf4Yo0EIz/05AW2a4lbTBb8KP99PgI+4vmel9Nm+f5er36vBHnvUYL2/6K+fv8dAo/gSefEJmZM6c0+06UwKBJtVpF19Y2v2Kl5DPjVPh0KLVpSvPT4+F8PkyHkguDbOv1TVtD00SEsmG9ybrcltvlerldr8tyW2+L1cop+QQkEWNOpRQE85PzJ3g+FFGozbYGS6Wtbesafa05JZGGYFNJpaTWxAwUzaBTs/co6ZyL+6LVLWSrTMhuQ0iIan5lVA22Lk/5n//zf+VE0j5+/PAwzVMfHuB5WHTfeJxAJA9iTISATcI5GUwRgRhjZTVQRJHmjSDMxOmHW965IkRENuvkgg7ilBAFwHzcCam6XhUB0F0coXs7qjWxKqZohMJVNqpsyEAZkznDRMAchRcBEzEwlSomW6vrti7ImTlzzvPheDge5nkmnChzTsmRt4E11CamVmutl8tlC2C0qVTtz5yoSR8HcGfivfmfByEBvc8rzoKIUsrSmideHkE5MDkAWILgVKW1JrW1rdZ13ZZ1W3wfMkAmAeNWGyKqyrrcpFVTQZ+OCn2q2y4E+f8p3P1XYltVzS6YRlQCoCTukwS2brW1lhJh14Bbf8DyVPI0ccoGJuIMYXBiy7a5CeS21dqqEgJlVQFGbd7wH9NqEVGJFQxwciqw78Fxye4F1+DvgAhT4cNh9tmqarB5WbWTcb4fa3OY1a30REHFlfSmCrWNbc2AwDQQeALTZirePBk3x4uUMVQCnTCNq0fQRHw7iydTxzKIG6+A5IYU/VRUxD1bfQlZ37yBcWxvA0jZEJb0QtEPbbjYm/bd9bVr3YKACsCu7mkUkEiau4huta7eUCwqPXlNKamWRCTMTKiCGhDdvI+sVmm1Sm3WNmgVpIEIgBkmbaTctDVptQWOX2tOZgYurVNRnzVEzCn76d2hHtLOMy1i3CgdDhQCwYaNi3PP46N7VVzqFKCK6U7zBJzq0MQ3EAAAt6gIlggxQm28mtzFfHdUEAxSEK7R7dEDGe2ScryHbBuH/NMzGaXcHX7ytNc6DuoFPQMbpO+dqLc7l2odUkdMUcCtyWW5/e8//vWP33///vb6/v5ea/XTvKz47f39j2/fD/N8PMyP59M8TURgZk3asm5SWyI8TvNpLt6tk5PrrFMzrhWqVKnbofB5Lod5yjnftia1LZtQ4pyIiSgRIiAZOqEEAJ0OGd9Dr6CNu7mniKCDDG/18qzSrR0vl4vbZNdan56ezuezt+VfLpd1Xb2MNZQOcVm6PNkBzfi42+3mI8x8BOz+V/ZrUlWHyZDTObqb1OFsEOw6/P0A7kt6R5IN46Xx57gOe2DkkLH1cSh+8K+vr+fz2bN5RHQBkx/D+C3rHXC0Gy0yvgiZgNGQiUpKnDJyMgMfG4ZB36acMzOpqZows4gB4HHGBKsstV6+r+vy9v2PVldTZSb1HMmsipMQoqaXZdFap8OciFDb928vaEZMp9MJENd1q1ttrZVSACyBEVmZ8NqMkzGabBu0yghTIdNpPoghp+oT35JIDQdAuz9gPU6424XHwgYaugyfUmHm6MINBtp//n//W3W7Xn+p7W//8R//3u+ZB93o+0CAtjVpIszTZDln9glRpkxjC7RtDaVa4ugBAQAFWNdtg03aj35FAAjAOFxBPEFDBB+VhRoeYgRkZqYYNYkIXr6ReAVN0Uygqbr42iMidKMfjzI+ItWiw1xFAaS2WoEYOXPKquqsUs4MkInQhzUZgKoQCgKZmTat4Qi5aKt99FMInDvvFQsPCcUHYdIPnbrQU3kiNlEwWJecUvHms5yzQ09EFEQECw2T7BRF3cLRFBuJKm5b89dudW3NjduFQHUnG7Jd/PVwbl2m0X/s5kfuaxQUfN9QLZG33uCgl4zQELnkVDIyNREgTBS8YW2tNvEumtu6NQAvhymqh20vGYw5Bd5bCeg4bgQhgyjHUZd4xKohwlTKfDz4eUkT0daNI/sAgdakNtOebRuwa6WHzQ3u2YOOaYPACq0tYmeKdpmWk1tjikSn2hAIFYxMDcnArV+wb7kAyGZ3tqNb8fne7CNoBsEJHINwBxMEd0AVB9DDXm/A7+EPBBS9oy5kafe6m92nCnqXg0TTtEZdOB5683mh1s9+QPpwHFJppgImaJpQfSc2Gy3rnbER0W4p40jDwIdetd5Hi4jcbab8IpMNxY9zOfGY9XE81m/F8B7oqKhzQ9HU2qk14I4uf9rc6O7FGjWquF2x1DCMo3c3vuegP5JWvdDfDRsRegzuR9pPbvdE3hHT3e8DMLqAoUc7J8MAEJComyL1JxgDJDtI6tUD2BFXJADrWi/L+u3t7c/v39+u7+u6qDZGA0QBENF1a9uy3Zblci3X220qCSmSmyaNgQ5TOUwlpeSMOqVkQE1MTQSgiaGCARkmw1SNtmbLJrdlTTmBZUgAiCrqE0OYbVy6vZ4mHqO+lw9OXX50anb4MiTVp9Ppw4cPDlNaa4+Pj8fj0d2DbrfbwMoDVA+ABR18DP3d6O03M8deP7Es4xcdAOFustiAHeNJHLrs8f19wfSv8Q7/J1S0p472l8iF3t5S57XCcVT+smG/tD/UPdjyw8gplZwTYfIClppZ0yaIQGAlMfA8TTOn8CsCsMzVdyFUUdlUtdW63JbWqoazg1u4ISCgCUplsML0/HA0Q04ZEFsTZqxbMwRGSomzKGiMNXUuNDETJcrGOaXMSMGEEeP5PGFKt7W6o9w0T+/v77fbstxWq82Fef3puSdREUxMsAl21ZSqoglEwoOryOv7bf7+Nh0O54fHo0uvkw8giKzFq29SVarUTShV7vppBEPyQeNGHBuNKjRRcjWOqaq4PNzv9R0VGfYKGowCv3oLMAD5dMye2OF43uH+egjeCAwNdWhqYgJprDePgkOg5EYDXtNXqAYMVN2JOOeUc5Ipm049BHa5ZPdsMwNTlSZSq9R1qDSkE/idanOJJYT7CkHIo+5JqQGgsqoZIG7rmvPKKaWUa2mpZSJ1UISj6S1q2c3RUW1NalM1A2oNiKq/WGTVVn0+Lo4RckNV9ANp1DNUHBvLD0LUIa4wAEYoSITIAKQW0AV8JllIGkUVAVXVKYXW3EyJDHDbqgb0BTADHUPN+p6zywU9g+9Cn/0O0lUtHpUAmCiXHNeltlbXoS2W/tVEdmGmS4ioS2/i3H9Innyd7bvA+q4IgSD7bjmOr7OeHTzAXZIyDBICUv78UbETk/U3dpKXeyMiI/bk7/6mDiU4NLHcZ5yZDwEMZNj1LDsTx76v+0PhtUvv3Ha/4+iKcDTGhFEr6dYU2pc4mKIpmRGAN84jkKJZjH27+w9oDKprrVXoREcs4DZMW8ZG4dHkXvCN81YLGZ9G2RU7+zK2ys4umPb/RcUV4352GuWH1vyexIx8xcaS69GnVzExHurdAxLXFHpS2h+3wMEOnPydO53UvyKTjXXlAY46ctoFik4N9rPsGBt/PK6RigHe3zSYmSr6fltf3t+/fn95eXtf1lVUmJCRkVkNKuqybNvmlh/btq0p86D4EWHOZUpMSCEySImIDUjUREUMFDATIyWg1IC14bLZsrVl3YpqCsrTahOK5f2DXxF0EDAu+8ANDn3GRj6ggwuW/Xe9lOYIptY6iKJRHfurvAY6cBn/On7inW7Wh8uO44Qfv/yjnSLaA6OBXfZlMtupmu5L6y/47K9HOF5pu864cd3cWduNKx077k2epmnyMuKwaxrH5m/lQMoF84RARODTftSkNfdeIUKmVEphdhkjqal3OLnHnyfstS6tVmJmr+MBWxjrKCUEUDJNBHNhgGRAqrZineapNrc2NMxUSmbC1BjJ1zwREScmM2IgUsDWwBCRUyqHaT5wFTcJpjLlP/6gl7A2EFfcIaGEztYMPCTq6NP26+4lNn9sPWIq4OW6fvv+zpxSmp7Ox8NxnufJswJCNLOtbtfbWqs3XAGldD5NhylNhY2JGS0Ingi5TmZ3MbKqCu4Q8OjMR7WfxsF4oFCDcFqMgyRDQxhYB0dEue86eFci7naynpYRkam6CyKCRXIsogZiZMjYKiJuJW+Z65RbbVrUlHqKSIi+T6XETCOHcq4uonRP4e9xEBG8MogWo2TvFEEP0bG/r9vG6xpuwKWmlKM2AoIIZiP/1Z57Owioog5CGiD3faICNDAZDo0QezaY/Tw0Eu/f9IfNCTsDA2NRiIsGTDj5kwiAqgQgiEJkyDln5gSIoo2QVcUMVG2rbTjW1aUhI5qSGjWJiVMUVxJ6qosipuJzr4i4C8E7QDGH+dqT8G6IjGCq27Ku281F84gozbsTtImA3wnfdJCJnZyJWxalqdgsPfoiRDXjDsl7shGzDzqcUTBz/weA+7U2RKc3Y38f+6kr//s9oG4S02F/XBVvTnefBu+3d04So2Drv3vvcOx7jIc8sf5AaZTxIjfwkawE4ONXBQiYEQyNVRXBmDEpIVJKqeQ8T8VlqzmlxAkBWxM/QcLeHwjEmBhBel9syCYT81CPusnWtpnGsVX3Ina6WxRsEBvABHsxlfWiaJPW6wa+BpybQ+hO0AEF9/ufmdO0QHQPDgg/RAl/q2GROSCRLxdC7LU8i/vfb/IPUeYOobErv+8wy18VDfUwfn9gGBpCoDuHGXGifyxY9Jr8vD3j+A97ojHgEqAaqsL7df368vr1+8sf3769XS6tCQHmlGeiMk2GuDZZ17outbVt2+C23LoaEolpyqkQI2LOqXSbcj8dUWumYsApT6XkMkMqm/DW5LK2621bbyuKCCOjmtJtXan7jvTrF846++oSdLhwJ0N7/WhAEJ9oNlriSynH49FRkc/68HawtSveBigZF9B/6EBwSIigK5mg67v3QAQ6dkFEVxE5Fml9XAnA8NQF+Asw2qOi8XoYa/dHPDS+QcRR/8I+9tVfwMzX6/X19dVP/P39fbwtER2PR9dXHQ6H1OXtfhmpT0EBgJQSc0JQF7yo+cQdiMcMEIm3KlM4xwBhAMFSMnKq7qUiQJzm4+yj45uAXLe6rmLqVksgDa0xiBmJgqgxk9pjrbJutbU6WTocpsQHAFjXZavVgIA45dxqs1alVr9wmctc0tPDlKdzzlMqU55K3eqUKCVSUBFFolJyLrm2tl7XWquKajPX9XojeJ/LbuZOPt66AASIr+/LsrVvL5f/+ue38+FwPh8eH09fPj4/Ppx8rOrb6+vr++W21dpU1Mo0/f23T58+PHx4Pp2Oc8rJwGktyyl5rzAzSIv2cAsftR9REYDrXwNDeRqs4JPR7pt1f8oN7qnWXatx/8/3O4LBAYzE3vrLIQhqCwGjD0JUBVAjk7rWddlyWnPe5imlZGrMhsRNwAwJKadSylRLaVtm4mbgv76LmYNVgJ4y9mg7/n/kyxg0gonWra1pI0rEOZc5pTZIdAyLI79I3ZS4j7L3fUKkaufpEyt5acs/zft1XdXitMHYks3CPK9D2fAINgAwMvMCIvWQm3raqoTgNlWIibmk3DcXQEBVUJ+IrJLmDGpSK6iAv0h1FJBivLx1xIMEgP5XQnSjzkj3va5l4JDIV5W7um23GyLWbbtd36u0nJO5Wj4yHvFCO/WFowACnfCh1L8b+wjcc210I/TAhdRnSgQbZN6LEORG3+zUkALDd5YbVPrBG/JQfQMAJCZhH6zjCRwiog8vi04sQuzj5FS1iSCg2pjMRtSxm++3bpFNZm7IAeqnQgBGbD5+xlRMURGYQAmVSRKnlFrPqIEoc8o5l6mcDsepTIkTE6lBotYaNRddk7NFniCkPtzFXKjAzLlkZjfBEZHaqouTTEVqa+tWa3NUZOjojjklzt2oy5er9qkmXogbgnHrD9W+o2aPh/x6moJFht2Li/EkjmdxF1YAYspKIOQ7M2SBjO8U4wA+6NKE/jKN5dkHgVgXbjsUYCIM/TjiPTI5WsLu1uiP2tAX9K9unR9I3fFWOF87grmvZqLaZNnWt9vyr6/fv37//na5XG5XUfdTSYecvzycP33+SJyu67Ztcr0uq4qq3lGmZ0iQOHEueZrnMk2UEhADMnM6lYMYiaEhp5wMrbUqKlvTrS61LU03SokYiAys1WVBxMRcmQcRMsAE9Js4/oSu2hmQYtAwDmhg95VSmqYJuq/jsiyvr6/LsuzH2vvHcZ8o0vogMwdP2jtghv5m4Gzoz6B/1t5d2vVDLk7yolW3r4S9rGcveR4ipEHh+Df7cxxXY48Xx/H7uxHR7XbLObfWXFk1QGQp5Xa7LctyOBx8Jsl4ZxchHQ4HN/5OuZTpQOSmkUnNmqrpQaT5c7dtltmAsSCp2nVt10VMbCqZODMlg6xQoGkq/exFcxMR9CqJ9q3SU99a61bbsm6XdUPEkjMhHuf5/HBOKRmAfP+uQENlu0mL/Y0ZkBDU6lovr7Yu89OHp8f50+fnpnI+zr9+/vCvP77+73/+vqxyPJ1Op+O6rr//4/dvX7+/XW/rsqnHbevxoz/QagAC6CUsQpVaN7xe6O3ljZnnqTycj9d///XLpw/vb9fX17eX1+/XZelBD5jo7fXbpw9Pv3x5/vvfPn/48Oy9R56DCRlhNHZ4TFA1aaL/HVfUm2pdfxHmImgAGiaOEGGp0yyBLHpy5EgIwdWZNtJ6wDsgGb85AhwGn+7bLQC4E703GdeYiZOyGjCbK/q9e4BcbR6S87653WWPMHj/HT7qf79LKx0xwRjqpgZNtNW2pZa2um21lLbX0TpBogZmiOi9UsOny6Abk/uiNwTjwc0Plh72l7GH2F68MehH4ygFoXOL1mktQmgjVCMpooCzGikRqYiaiXrJD1VVTEyFIFeRbdss9mewqPWEotSAgSCOEmNMrbNACOQMLKj5UAkLX6vYsgxJzUSEAKS2rVYFSz2i+BpxKAGAQdmFS5/jAeclqLdCx8Ry7Atq5N17biFqI76+1WLyuF9V1T5WNYAU7hRaUcNWxV3zARPdexHcx4GQicM3hqh7XLloGkNjF47K/k9BPt3R+J3b8ppMNMnRWCKEpqLuSEWkZmaac3RqmYUZjLe6HOa55OJubqIm42IRmJL5NJsxoMd7DgBjDhpzdJ/6a6SphlP+Vuu6rFuV2lTE3EKdExfNUDL7qDF/OgB7d6d3vKpz1gFJY1HH4oq9yyLguTIRx/bSYdZ4CAyg9ZYDb6MEAwDFnnvFI+bvguNnfbAOjAY2HOhFoec94+54ZHOj884kIXTp6/6/UQu7r7jx1h1H7WToPjLWSR1mYmLvgRCA2uT9trxfb19f337/49vb9X3d1toqICSmOeeH4/zbr1++/PKLIX5/v5bpf7uErVfeoF/X2MUj7qXkd5eYU85lKmrUFESBEFSaCgCCKpj4SHNQE0TzqtlcsqgimMp9QurY9akP+ujmuuM0cbAy44f7upgjD1+3g/7ZYx3rXkQDEg1EsqeLbGeu7Xdn6KB/+rjB2fivj/eXPqyte0zwgHHjHfwYHHul3vbsJ+WU1XjZnmTaU1bW7bb9AEbpkPtoWGYe/Xf+njnncfzOEpnZx48fwVHRfODEJWdCat6bDKqizuY2MUZESmIoaoaZs0Iy4pRyIQBiRW7cJKVoX6VkR21MuK0beBOcusQFCVs8fAhlKmWe/KlOOSOyKNRat1oBIDG5kRN5eYUQOXk+jGC1btu6MmFOeJzLfJg/nqdDpufz9PH5eLnVXEou03K9Ql2kLnW9tU1ayOdjXQcZ3HMgA/ABn+bzkQFVGgK2urVWc6Z1XW/Lcr3eLpd39/U1M1MxA0RVbZzw8fHEeTaFZalrrUTe5Z4PE59O80iE9pzi3a9oT1qr4yTA+HPkQUMTsd/dexq3T/R/+g96yESMPWPHGY0szcKFDSNqt1pbrdtWU6pmSKxOkXh91Fez5wHY09nByu/3pf4j6Ef513+I8GMAoKBqTZSr1P4fhVOyh87IOfuW5N3B0eDi6jrYmb71YNZhV4cJtjvO+JHvWNGmhniP7WjeN9/3WXU1dOd5vDMQvdZDKH1fBBSmUdg264KSO8CIsZV36IIQgWN8Vt/eMEAakEWg7S9BjD3MD3XIA2AUoxDd1KFD7M7XWdz8Me0eccitfVWGsCX2LgxaAMcqBPf13MVshz9+qa3f2Y6EnbDDWOoKtu9IdrInvid0cytmTo6KkIAQDNQUQf2Kw65arOFp4aQadjC7W3w7HmUkCn6/CX04m47M3Dq1y74DJi45T9OUU/KJNKxW+yUhgsE/q1pHReFf5iyWn8Md1nkLqNfTXL2yta1KawrEKeWcszM8GZGBLGT7znV23/sgFs12yyh+EN/aQEo/AY5R9xoMjJmtrbnDoesju3/ozuwUnGPGGKDoj62Nj7wfwk/P9/jbT4RP/4fAHnGDepd+v177F46VO25jRDa8g9Qw0DdE9wq5Luvr++X72/uf319e3l6XbW3SAJSZMtNhKo/n8y9fvnz49HFtcmtGzCPSjmA9zoFC4sHEjC6AdTYxJTUk0QoG4G0hqj5gxJQBldBE0SwxzVNRBUcqYzqs7+vjxKj3hd1pvx+v2fgn6AJtf732FndmdisgdzHZoxn776RL1hvEHI44XhnwYlA4+7pVX9E/HPlATuP+DYBifUD9vvLlWYdLf0YP/5jpNk5qHNi+/Ge7L38NOFboTNJwNxhwzfFir3wV/3Q/F+acypxzKikbmDXxbZNQiZKwsboyNth64pwLghkTcyoEgKgATCyIYayPBKVMpoaA4g4ynrGLIBowoypnO6aSEotKXauTK6KybJuI5uyXKKeUPAK7Qb6p1YouDm51u13fE1MpGeGxlPk882E6nU/TbWvuI3opdHk9Lddj2xaEKnXZRNyHkZDNCNGiJSriOIbGwfNbBQD0Pq6v32lr1UezL+smqmDuwNtULd14msrlurxelqavddPLZbksKxFPUzkd5w/Pp3BKRXQZ5wj+w7xrTH0O2tkhUcz5uAOjHR4aG1VnfjDyeRi73C5w9Sd7QCIAjI6VyMi8TAMAYPfJ40EWpSxqSAmhQVRO0EyRkLjPn+yJ+f8hGO7xD8LuiHrMHhHbuSpt7I3Mbav3KnXiGPnknZCIzF3/qhQqbk/747MIsPcPux4aBuPzY7hFYiQGdZXPCLJjnEIPAQiAoMwS/cdGAApGiCnxIadEYU0kIgZAmaAXm6RJq5vUjUOdj1Gm61PjxoY9NizuE0lQzaFYfKzXBA0cDKnbuhAzs8N6BAIQjMeHE7FVRUUQMhNBYgA0cy+qAZ2HLBi7OiRE+Z3uCUNl3SWvqjEkvm+4jlLC1Mj1PiN4gaEzmRixHLugCQCIverhzEHE0JQSp8xhuwd6PxYHiOr0YDxCYAbq6AFht6P2qD/sU7EflOddQGi6X7PjhOKqMnNOXEpJiaPVIEbPAnmjvjqcVbPwUA1kGh8X22g3pFC/hGLdNWHb1iWAkRGnnIsUAUNmQLQE3Nu5NEyhqD/YYRYyKLwu6ukMDgCCdbiASDEqBP7yZQaXWjNzSZB3P4YIGxFviJB7GmTRR+n88cDh96f9h2tKaGYkoKg/RKVxayJ/6RHKz+oOo3o1dxdDcHfi3QkztGVIJAaiumzbt7e3P759+/b6+v3l9bIsHTQgIx5yejwdvnx8+ttvv03n87f3i+Bru3eA9WQKDYJgDYRAXcTGOeVcppxLIu1KiGpeOG/ahDkRQGYGzSaCADml8/k0H05vb2+32/VOrXV18z51pm7huGdKBknz4x28wyPoW+dPzM1AUffj76BncEIevvZMz2Dr/dd/oq+02zyOw9gDr/3BDAbLC20DSE3TdD6fHx4ezuezAxRVvV6vl8vFHeERcUijxqePizBOxLod0QB8/k/jAnr10Jk2P8FpmlJKx2OMouNUUp5SZmJ2ShPVzBoiMSMxZsTw2BNBoJIL5GxeFU7OzjcDYEwmbey/RIk5K5sqcAIgZCHdNkQmg0REuRzOZ2ZstV7wVpu2Vrda120lgmkqh3kq0+RFvThnYtfxqneeoy3rqvbSpLVteTifj8fj4XR6eno2wHWrt2Uh2T4+n1Sep0KvD4e2bW9v72sTMBAwNEb00dFMOxMOz6KgC07MrLb68vq+rEv2XjyRqOZLcOhuFbRW+frt/fffL9fberncLreFmE+nw4fnBy74xR4Ik4uU/dn1W7CroI0d29AFtBpJmBe3ehFt/+V/jeo5Yp935kFyV3OCe+7fXUcwLiya2wKjqZNT/hoLKX2r1cueIoYknsJRWMiamdx7VHbswX/3tc9Uu7BusCH9/3tMjcyjuqNWrd1D0lT8d+NDmSllziWnnFUrAKMTN3xvyOyWKn2rwPt1gK6/BgBGysQGCNHsBl7wIQJDUmAzJTBAUKSKiGlITxXNpjKd5vl0OjJCYooNE4g5mdM2BlJFtmYiXtUG96huBmqoLkcWaNL3m+CJfLcBMPZ5sdZvJACMAViqqJqYCrMi5JRKybW5y2IqzIzYsKGiiSmooSEyuz8iGjEBOk3qhZoebiiQUCeKyJAA9O6hg0hcfOKmmptrdRUK4n1OxcDsAOCm8gDBQOx0RWO9ItzDXCcjO6nk3kiRRYB/493kZipeikYj6lIVADD3PnX7g/GYjzwBAclcxQeDi+jPD7gjFTFzYk5MzOzoREBTSm5KreIt4lGb8CKqNwX0vRXdiJLuaMwM1ES8E6NtddvWZdlua1XAlHMpU1MFRFHN4tS5i658ERNzIiRTsqCp9hZhUa+yYWM9pgnSvVb101dT+f3763ku58MEJSdOnS/DuCTkswGJexdqqMni+Rpo7If3xk7L+V+UABTYhZzB1vWXdf50FMlg91d/54h5HZZ5CEHvJ0f04VfIbEhN7bau78vy7fXtH//61/fX1/fb7bYuooYOiQgPmT89Pvz6+ePffv3y8PRwafp6uX39/rosq2em40xM/TkgAC80M2FKqUzTfDwcj6fT4XBgTgAkE+Qm13VzrlBBQWvhxCWn85QSn4/z8XgoJc8pAUhKd1Qh3Q/ad+4h6PF9fXz9JMoZFNGAIONl0i0TpQ8U29M8I10Z6GHUsJxxcRgxanl7UDUeWP9+T0FB54oAwAtq41ccgeWc53nGXr97eHj45ZdfPn/+/Pnz548fP3q1y+XSb29vY0Sa95ddLpfX11e3GPB3KP1rjE4blT4/wiE8GufuV3gcMzMPVRYigoE2EbXQ6VHaNp+d4ACBXMQBYIxIKUOMAQEi1ghCQAhqCiJNtdX6fr15DDAAQGICBFNA4ORTUhHxcDghKCPbwZZlaU0Sw2Eux9OHeS7uUNOaqnk6AgYOTy0GYEyTGBjo++W9btvL95fDYT6ezx8+fDg/nk0V2gq2nU8554+fPz2utUnd/vEPen19u15XAQuNkQCqGLP7MwHGpIR+r2ls2rHwAFSkbdVbi5CIp5nyLMZv79v7+z9fXq+rM14Tn88noJIKHI/pMKU5MwI6gdCRwK6C5uHTAmWgj/Lq2TDuQ849YHj0G2ADBjyKepI3GHtwx55k9RZ/YEJR75sxMwMJoswFAt0wW8L1iBQV3JvRG5Nd8jICcQgjf863PWpRfI9d/xgH1V8TsTB+1Ksfw4CwpUTSqCEC+0KP60GEzJxzSjmL5N4B58+DeyXZ6CqPzcgLSB499gesDbT5Y3G/KapxeC7i7SAVwCh6qgAMiPgwH07H4zzNJsKEhmTqiQb09A6GrdadmwHMCU0AANCVtHdyD0NtiK7Ls+in9M9ERI/ZGlOSvQU8JQYjNZu2hghTTsUFjya3XuggImbr6mXwBRbaCWtm5KgcIftdCpAUdJuD9rFXmIEaMiCGUNzXGcXQYjAlNeRuIYHgNMG473vbEiJk3nM5HdWEaBfVAkWouh/4CMTj0qhTEqQuf+8egMFYkWInhHewyItvY3fu6zaeo96H39mRDqhHkhqrgMwMewshoEMk78COgmkcxA8klsVaVDON1rRN1LxJ30aunySl3BXnMM4MnRY2j5A7VDT+vPMoSN5b3K/+Plfoq93ebit0lckcNx/i1pOXAh0bRdAJcb11zVI/KQxiFnpWFqEFwMZOj71BdxxMX26wD3X7khvel0ZfIQAQJdCwZgBEBWvSrsv6erm+vF3++P7tj69fb+tta81nARFRJpoSP5+Pn54fP314eno6A8L75fLy+vLy8rIsi5n2AGZRUL/HOF8elFP2B/90Ok7TjEBAbERZ/JlSRhUC8oa1nA9zORznnFJKjIjMWKYMMA/QMIDLuCl7DfJPKp/9b43X74GR9Fn0AMDMOecBkgbo8a+BulyYXErRnd2iv3LAr3FUtLMjgh0SGo/W/gUDtDn8mufZJ6MR0cePH3/99dcvX778+uuvnz598p+LyPV6vV6vtX9dr9eXl5fX19ec89vbm/XeNy+B+dc4yFFl88u1Z7NwJzAfT/H4VwnQGUsNEInJwtpbTI2IY14kOSZLAChmrbkVKzATQAKUutZWN2l1Wdb3t8vhcDyez/NcpG11W6oIQLiKe2ikxGZIUAoiMG9rJREFOBwfUkJ3nW21MadOS4CCBrVKlJlTbOBgSFuT+n55v1zfXl8/fvqYSzZEMy2ZiaAwMdCvv3xUFWY0e73ctvtuHAyNioDvLZGoR6qI7oBiBtIETLU1A/NqMqeSyqQKt9u6rpup1SqINhX+9Onxt799+fL5w5dPH/7+t89Pjw/atG6tbhVIx7LfV9BwfB/7XJT1cRisBBs+osTYvvujil31EcEXx5fDjYjXHpbUART+8KJOxBiqz3BxDmN0g/sO5IWB3giHA0l0or3/5L7/DCQEiH2o9F3s0KHSIL6sdzaJSOQ8LCRIHg2j3RuYMCXOOeeSRLLedXjak+bOu3n0HId3z13jEiqYmAJRhHSIgmqXonuiioagiAJdH24ApnMph3k+Hg4l59otMxHJCE3crUABbK11rdvmtGrf0ZjZSxqgBNFxHlfMpBn4+BczA6OdeDrkNGpda8xEmDiXZGrZbJoyM86lZJcFNI11DkiAjMghCPEFR4BG4/447sS4ETCKjne42DF2aGYo8PYoAvaXxUUKRiAAhUUYNTLqhjsw4teIXNAdotQswF/MIurlKRuZ6w4Y+SehWWgHCdF7qbyOZGbDhzweOPAefutvAGPRAu5MkccOGQ9oFN/Q8z8zNSWLFjojjmIDdjrjzuLi/lL2N/f+MtHWWhVliyEzKTEASJYkkjjHDCjXoQc8IHLlfVim3rGRdVTU72ovqFu/pz9zu6iYNsWlClNjwjTMyu+E9SBovFwW9PKAsH6uUQLGcba4ozhtRDME+/EYrAe5vn76w/oDO3HP+3YLFhGZDUBMa9Pbur28Xb6/v7++Xb69vFyulypVfBAmYmacMz8c5k/Pjx+fHx/Ox6lk0Xa7XS/v75f391o3iyrsLs/DCKLdPhRTSvM0H+Z5nqdSshoAsk9TVs0JtSaU1gAoJc4pHebpeJh75DMwS0yW83017C7jT1/9FIOwwd6gDn8pYO2/H4SKzwtzRkT7ZA/qpj77vjbu88VGwW70o1H3PxwQzT9lTxQNYIeIXvlyWOacEwA4dimlYGhj0vPz89PT09PT0+Pj4+Pjo1M+qno8HodNwLZt7+/vPgXW+SF/cx91Mgp8DuPGYcPOzmD4BeDOR2AU9fZNfx5RRyzx83EbXMfHvgBjvEXOAC6ScncYMfNY6g5oYiYOldy9qORUobUK4E2wXlYHMDNRRSJMlIhVUZQwGRIyc2x6aERcSnYQ5x0XCmB9alB/WhA5e5q1trptN0Ar08QpAWdEzpwYSJp++fxBVAFwqyr6FpMG7N4xOmKwd8qYKoIQJyRGAIuZAWpgUV/kxJyIHMOpNDMzIpyn8vBw/Pe/f/kff//t44enD08Pzw/nkvPaNhGpUhnSSH5GBQ30PpLLzKy3pHVlZRda75KC3TedYcHIU3Eoi2AEY0TwsV8IijEXzQgjyUVDMlRFcMLKQA3NyMx9xpDQnYBDttLN+Il2EQrxDpVgxP8AACO7jif87hrgP6CO/8KfqWtRW2uNmYkaE6oh2R7JMaKa5a1O3qcs4vKGKLAgIIK4Kw38FHF7eup/rWYbGPrsr94g7xcSOmPhpHO49iqE/xLo8zSdj/PxMCdOZkBgbCBmIrZZVRMDQbTrtizrrdYKiKDgPeczJ2BEYgQEBYJecwEwSz7WBaHXYgycalSx4X8FaAikOVPJeZ60iZodpgkP85Sys1UitZdjEYEyAvk+TMnZIgSjKRERJsbIDPpOFnXDIbRwjOHfISMzMu48BYZCB+O2Avrzi4BE4EmBqYEy5L58wcOx9i6VWNNmIjo2euvmbxGv7adYDF0uDYTGFlNNOIwqCQCoIx/sO14Ihe/vsE86+hPUF4mq9yAq9BfZuCpICgrq3m42oB4OYnAwnP18++AK7QsexEy6m6M3chCRiGanQ1Nm91JmLjkje/sTEALBLnuxkRLYDnVEWTY2LRvHvjtfoun4bLYtTQFqIqQpEfQtHBS7s1g8ql1n7QGGMERiHQtaRy0EBnpnFEacu1tVxVWx3j94j10Q/KxBX33E2PvA48qDZ3eGWFWXbbsu6/e39z+/fn99v7xfr++327ZtvnMgYyI8Fno8zp8eH//+65cPz4/H48yEddtul8v1/f12vbRWRxkPO4GFnSx0At0Acs7zYT4cDvM0YeLhgQlMjEUyqZamKkBggIhuiBwEV1QfMBHhLhngH7v097TQ+Nchixkd6Xa/rHdWacAOtyxCxFqrT+1w5TX9+OVrY0h//GvIeqh36Y/vB4YbT2Vf2AGMvA6IO65rnIWjNFdYPz8/Pzw8HI/HMcU22Mp5tk78OCo6nU6n02maJh9m4qjor84C+yKjdp21G2D6u/lvOboainI/+DC0cWsPjW5iqVWkGhgRlsKJCdC19plSIQQQgdpqqyICBoTsww+MkdALkzlxItpRnICcspcuXbOybnWaD5SSGkJDNsoEnFjrhgaElPKUUukDJE1FGKlKczdjIOSo/XOeSjiima5re3l5YU6c0nQ4HY7nPt8UfvnlC3IhLltVA1uu11abiEp3yvGDJd+OTLVuppBKZsqAHG55CJwYOXvbEyGgtZ5LIiKUKX38cP7tty//z//9H3/79cvpcJhLmaZiYk2qu1eknMbWvEdFPVb02OnN5z5A/V6q2sVrD1e0Qxz9guO4v/37SHUNfL4pmktLiBwVmZkPBjU3LTIgBVIgg0Q4ZaaSu1kR3B860C1nzplTJs7hkulGcH3K+YgmLkS4t6tBx0sYmM67q6xXqdTMB0dtdXNZdzF2iOBozB0BzDgV1shOSQRrQ7FmpgBIYF7hDbEu/mQrB8N/7l3an+vim2YbO3PfSPy1/eJiWBurAUDO+X+cjufT6TgfRLQcDoO/vbSl9RlXgPz6flm2yEFNdC7TVI5ffvkbuqEdEjHmlPwUAbHXEgMMqQn2W68GqqomZopMosZED+dTyrPBxoBzzjknUnPN3hKpvQIapzyfZkQDE0C0Wn1b8SQfRNGQE4tVQ3YIYLEVDgiLAKBmW6tr3Ux9OqzUDs57xmJqotWHmkBI0XSz3vELA2sCAAAnVmXzRgYf6DH+p+Nbsx29ZHDni5xo83/ZqZzR/49+4LoGVRiPnOe6/c/OLEKHRePB7CwXwP6tHDR7V4QvG3ZiEv2s+3gN7RAsto/4gLDRBmAwFNVaG7r/vRmCtVqTD8hOJeWScs45q1lJOScGZJ+ei+hzo4eHZkdF/eBVu/9pv7Tw41fO+T/+4z/evn+9vb8s65XBGCEnNxRQ6Nbw4/VxjyKA3SteGAgQe2L2F0qqnzd2ThI6Pz5WeNwtjBwlMLgnXv3b3rSCYlBFltvydru9325vl+u37y9fv32/retWW5MGKj7ZfiJ+Ph++PD99en785dOnX778UnIiQlAQqdtyW27XZbl5U8+I7NFJhH2MuTptoCmnqeScGe5r2RCBiDNnKElVa2tLtWWrtbXrbd1W9omwRGRbnGJOoW4fPeTwI3U6+CHuPo372pY/AXuxEXdPZ+t6IBEppbg0R3+cpzYg1ED/tFNhW58ji4iu3fE9dfSmaW8Qs79U0KAjJP9+9IVRNyjKObtRECL6zLX393cf7xr14p2+kJkdD53P5w8fPgy6C7uWaNs2F2inlNZ1HYByQDd/vTftOzKzXoYbqCglRsC6Nd+dRH3c5uqPW06cGNXMbdKkwmZQpsxI8zSJ2W1dt22ry3u9vSEogPjw+W1r83ycpsOyXNf11loDs8wpMYb5fc7H0ymXWQzWrRJAfjglJiLcbheVqi5ANQKEklJiNGnLslQRMyCinPxZURU1RAJmKiVz9VACVlut72/X6zW5UUiZUobDYfr44bHVjVC//ksXWDQZEaoXE7UKKAMDAKluPnDdNhVOlICQUyJOCEimDETuNoMACN6Jcj4f//a3X/7+b7/+j3//229fPh3nOXNi5oT0vt1qUwGklAV47MX3ClrTQWR44NUe/+2e+0F/SS8+oXU81LciT9BDwQO4T8f8Za5LVMcQhmTk1BCpgyRnqYAQ2EehJSol5blQygakNuITroTbVretpjxRnghGx7hTQeMwR3HGqy1xjJ1cj/MwV84quENFoImtUqrEnFuS0TmSKGfOOQazFk0R9BW3ZriKgg8r81tzj65u8QMj9zTo8y9gnufz+QFM3aPCwz0iMJFZOLGknKPDBZER3AJuLuXD8ZiZHTAyUSJUwc1bP1SRqCSubn7qmlBmBMwpzfOEKIkcUNYmUTPs5nxg1uexeK8VBmi3sa2CEYEZpFSI6PnjE+fETKpQSobavAlkW0ISQURzydj8inCmDoeJxeMPMTICAVgKnbW5p73vZWQIiOaN5oXycZoLMwJUFTQR35OBEjEiq4E4pwOAGmoo6N1kKg3x3pvv1lNOjomqm3pI9DfbmKzeObtd5AUfKAvB93hDpRkaetV9kKc46jd9045CGIGKIuhozR9bOkZCgT3E9ye0H8PY1QEGG2kYHWPdXNyXf4ASG/S8ARkaIEPI0gkgHLmgmRBKJTQzFmm1UeVUU861FFOTSUVzyQackm/ghBS+Zob3lCjCiTteuGmFI8if4ErO+f/6H//++8T/Qnl7qcvWMrcjEGbgjnLJFG2YizkC7bjUK8XBrsYF6EXNu14oHvq+F3dVo2O4iB49fQIANDSynkJFpIshtYhoBlXktm2X2/rt7f3r69v79Xq5Xi/X6+22uP83mDJYZp5Lejwd/v7ly7/9+uXjh6fn56dpPsSsAzNTE5/h68N5ItAagjF79SN0XV64GGtCRGzdtHM0xmCMQtEzsm31/X25XJd1rSLt4ZAPh+JUhYER8r33BiCldDgcBpIYAMjPfPwQnFjd9VU5o2NdN72HR9gdfaj3o+1JHfhxigh0sRF0bITdd9GBBXc2d9+8No5t/Gm9+wx7b/xPeG7btn2dbmAaAHD+xl/v7BTtOvl96u0QUPsx99GC1fGN4z/vVpPuYwSdn9MuIVfVgUFHPOlbqTURpsScjznlAQSJELWuS4QnUANspmZWt9q0EWBJiWEGWaVtrbVa27o1FWsiSSuhpcQuwssEE4OCqKIYIrKZEWAh1Jy6NaZNh1kliZgoiFGrKyAiMpKZitRqBjxPzMlLLEpqYFqr71+cst4dTEhEpVWpG25bbg3UDoV//fLheJhOh+nl5fV6uS7LWltzFY2pCAR14rZbpqpVG4oRSmtIVIkSh9sue7U455R4nqdPn57//vdf/+3XL58/Ph8PR1TYtmZac6J1XVttngM3iXII/MgV7SgMuz+o1veCEcBwMLo9bceRfo0crf/MAccPWMoHOHiUIzJTMjTXToV1sSkCR30EE1MpaZozlwmRzRj6UFQzKGvN65byzHkzIHJ/PRySyL77eIqAUSrCgYqCJu/bpGoThdqHCJpik9xiaUflo08bKcXTCVBLqlab1KZ52ZDYQDQMbEjAMOY69AEDdwceaL0fe8r5OE9gikgOicwA0Nhb380QdgRy+OoaIEwpgYjURkCuxGNjMQCDpmoATJxLNpXDNLliltH76Kmk7FaOXvEzMInBD104o9E5ImrQfX4dJ3UazjuQEACaNgOI2G3IOZuZhpmkIQIyAmNVAYs5wQqQMCuAuIBpD1J3RkVx2Xa8SDgZOb2MgABNmo4uNl8doT02ZAYz7D43fc2CAXgzRo/R7hsGaiBqTXw2hjRp3tAF/QB3UuwAuGqB3fxwNW7ZPVHtRGGwKAPOeKkncAwQqiJ1XqzvwePD7lvJEMhgbx7vzy50G9OBqO6abwg+Lh7xuLw7fmRgL/9Dw49foYGSompvLHIzB18kUADQ5/xCSG5gUHbxjoCGblMNCjtAuf9i5k8fP7S6busitd7e36pYFY0JUGaDtY6TtkES9X/qZxuKrk799iPBvv0YRWt7VzA4JHJudyAgN0TGLlu6T+uLm6BmW22XZXm/3l4v1z9fXr69vl1vy7K6Y6FAFwAcczodpvPp+PHp8e+//PLl86fHx4fT+QRI0U2iBtq0X/K+U/ZgGnRR3EgPIeQHaKYiaticIqdI/TVmKUjd2rYudV3quklrG5ZEipARUoRBBetWF06H2M6ocFBB9xXfYcr4Zt91j715fvwidDTgWGT0N+yRlgOvsb73qMiD7E89XHGje90N7onE/dOdDRqszIBTjoHcYtul3+MsfH6tczkeafdE0fhd3LE7+OMAkL0ldyll27Zh1LQ/YP3RSIm7+3Y/r96dTV35U1x7REhkKkIVUb2OKtJQUUTXZQU0TkxIlHNNWaS5X0TORRPknJNnncxqCqZswjQeHVftABFSYtEgUM0sp6KUiA3FTAxbBQD1aYjiq6/LAogQkIjU1P/PmJgZmDxqmVpDFPAb1Op6M0ACOBROjydtz1NJ78f57fX9crku6yqr9PoMASEja8xpAhcsqBmId7EDgTlCzjkdjtPheHg4H3/58vHj8+P5dJxSYiJvoJLWRB3XOiOOozsXdlyRSXib7FGRwp3Z/6mCdt+q8F6J6t/cU7W+vcWrDJHQTIEAjIyMuhrBwaACMKKpm3YzITOmRNOU5rmUeaZUAJIhmqGIqUFZa1lrmta8VsDkh0p30HM/LMSuzP0BJWHPoiGmdNVWm580eC7dRJta7wpAIuJEOadw1IvAilvTrUopK6WEtZkEqgQnOwBdZgTBDkRErxoPw5TSsRQ1dQUV+saPO+cjZPTRGd6WORxvVG/LLVECwFRKIiQYTq8KiJxymSYQOc+zqnTLHHIZnb9JNBIpttZUfGpbr5765bkPpDcLEdoIY2DgWUvvufAlwAzudmMKZsTIhXCi67YdUk6QXBDMNBkMr3ZQMDIDQlNQG7Zs1ueY+YYY5bEN5FZXUUbArVZjJEgEBKCCEL1JjCklMXWQbeCbpSkoIkTfEAAEGAIzEIUmWpu2JrXFhDu/ZV3KFitn3NAw0e/QChEUDQ00RocZ+RTzThZh74ccDC0iWTRo0hCIUI+2iKP02h9S6EU1hAHybHdbAEZ9dvyk5yfQt6v+XtaPw7q2yX/kw5TJghVQEcDWao1GvCZSxMwMigLmkYt0ZNTZFr8E90S4V6h+wEdM9Pj4uNW61U1Ea22trVWEGyZC5vHU+PXGOOfuN43o3tL+kDP1cK8GEUiDnwWEcLfrDcp+i/Be+QQI6WJHlkQxCzI+Xk1Ua5P3y/L19eX1/f372/vXl5f3623bamtVRNAsEWWmQ8kfHw4fnx8/PD398unjr7/+9vD4OM1TKlnUjA0URA3lZkgKaC4v2KV09xvazyE27MjWxQSrqogiAJKBaZOmsRGZtoa6kVXTpg2koSWgRC408aJn3IKOikZmhJ0jsa4f2nMbA09Y/xr7/R4tOR663W63281NHXHXh7X/9QFcPJL49Iy9hmkPjPYgA3tvP3a7xQFfvO7GzHtnbad2xvgRP1ovirlgaIAY7n4B45RHLWzclHFN/OP8yKdpWtfVe/hrrbRTVTt1NGaSjKkmAH1wIQCi9kWI/ipiQgAFIGZrTQGaGUgDgFbbui6IOMFEyac2JgNGSinTNM2mEKYH5D0fAtasLgBARr1TQgktJwJMTn6ZGgBSmnxPwaaKTaWFdUCrHrid1FezyLcJRRpx8mDIKVFK4NV7USYVJhGpda1tNTNEYkplKvh8Ps3ltp6/Hec///z+8vrWWlOTzMzJ1zuG44hPY2zqFA8iEBkFWigPD6fnDw9Pj+fn58cvnz89P5znnLCjGtVaWwWJqaROLaec+C8ujr0bs7NEABEsINxqfqa798EZerLZp3RRhP2QMIOF8NgDIlNnp5ATWsi8EyOYEpiYKgBHETWlzNOUj8dpPh3zdAAsBqgK2yaiuKyyrlLmWqpQquqipEEQ0Z4scpsjB0bUd8QI446Kamu0bWKksDYRv9+Aca/Dpi0RJy4lzXPJOTF76olNpDW5LVu5rlVMoQJSeOs54jPt2XlP9gEUwtI05TxNkz8t0YIcPVpRidztIQyQfTdAF4dTSilPuUzTRIyqKqpVxAByTqXknBIgnI5H74JxGiAyZgBTGazC5XLxoaH+pSONhj1vg9orgP1lmIDa1kzVp4ioazEIEAxESk5lzrPNkqE0Td1hXZst1R2zlRM1MGMe/I0JuEuzmLTWxAS69btrCCeepumQCQEMvBUrXBiRCDklRRQwQGMDU62qrRmn7ImBqbVNVILPX2vbtqZmTaRW2WqtMTe1uekhATCjW9K4Umj/HJh5fdSgV2N9lkdz59ZemvE76sFDfXGarwTow3UNgpgYeqT/nlwZGCf+6vDIDHYN/JFWwY4i6lHci6KeDQ1ybrwzISRCfwodSbemTZpYa9WZ+bquU5km31UmKVKSlWwAicAo5uB4ISuuhztOIREZOpPzw/kg5unh6YMYEOfW5O3bH6J1ay0nSvfMLMLJ6OkMARwyAEaHnIsLDMyAzNwiyAamAOiaEW8GiNxv5IQ2pAC9z67jIVC1qrbVtmzb9Xb749u33//88+Xt9e39/XpbpLn2DjLjIaW55OM8fXg8/dsvn3778vnjxw8fP348nB+BkmeMzvwaQBWFTdTYjCwm4VL4SxECRiMk9mYQRkqUAKjVZrAptLW2dd3ABE1B27ptbouUcmJERj1kUCZERRMwIbTDVHytjrklvmc72zEAzZ6PGbhk3LGBHoayZ1828ndwcPDy8uKN7g4pvAtMd2bQ8TR12Z+DjJ8+cXzEIJysryLqIzu80OZvO1TVtOuZhz6MtvWvPfCC+5aB2OmiYXhtOyLN/zoMr8ebQC/w+b86JHKTJG/jNzNEzDmfz+fz+ewK7v4UkI8MZGQANjAxY4DWKgkCmIjWZmpkyIACYD6l3A+n1U0FEXW5XlutTDhN0zRNjkn8CXLQZALLak2NmIlTIkKDueRcCjJvlXH1tZHyfEbn86g2Qz6AStVWrWHKGRMDYSrZEwdn5wwwT0YpeEHy9BsNqjB7/ilIALhKa2YCqiA6Jyjn8nCezqfpdJq/fz9+/z6v63acp3kuZSrIrBpsrilsW7ut69aaKCAl9+86nY5///tvnz8/PZ5Pp9PpOB+nkpML3VRAxVRMqhjkXCKBSmma57u0a8R1n58dgC7u9733BqFXMCK0QhjuxD+GaiRo685h9/feFUS8XdC3LgNQIwZFImAATWDiuimBlBNPJc05TzlPXOY8zWU6HJCKKjUxwJZyJU6U+n8W+pdOAIWpM94rCRw9jZ4odl85x+miwk3cswGZREQNiHk+TNM8lWnKpXCI55mYOLHP3kMEUZ3mPG/lcJyOx9kMU86teZuktw+Cl6mGosi/KdPBr1Ip0+F4dJUA9nAIo1Syo+mijNUlJAjAnD10+sZfa9u2KtoSk7cMkVtY5AytQXh5WbTxEGkMwXBT7zoSt/HZ/vzf92a7W6mQY99QPpmp1nVbW9u2ev7w5N2ZdV3fLzdFLIcZp0laA/eYBjRVa6qaDJQIRRCJ/Y2C1fczNAAiVJ86b9BL76Kyibgtj6iqnw8jUgLA6q0MSK0GDQsIzub6HqnSFNUZKQC4LZv7WzRRTyODZG/VVzl5kQvBjEzVcdGgb0LL7LIeJFVBIB9Xp9rpPwjMxkRM5skfAgwtdzA0FIK9iJIw7kO/B11yNJiX/qCBmXdu7w5NnSPGUAmaRXHMQNT78U16OcxRhz86TFRymnJJTGBYqa4VarRt+PCYrTquNBUVsckArKACJQDt6ia/Lt3dCSJDuTvbx2mo2m1ZahXifDo9/vrr3zLCcnnVtlVBMgJPUCDaEP0Ug9IKLyMGJCT20b6g4XmAZKKjvHnfy729uT9c/cHs4AlHtVGhiVWVKrJs2/vtdr3drsvtcrl8fXl9fXtb1mXbVtA2EZXEc04Px/nD08PT4+nx4fzxw9PnT5/O5/M0zamUaoqmnskwZTVsoia1L19KzIm5CQT4ps5LmhkAEyaixIxEq8j7stBWxWxbt7ptBMAI7A1CZmJ2W9CnnjERI5oC1OZkUy6zYwWSH2D3uD6260vweyQ7f8JRJxpQY8CLPc7w0bDX6/V2uzkZA7v611AFYS9+jTd3JGGduBqU0lAyUZ/g4d/jruN9nMWgYQZF5Kfg7wwdZg1ItyfnBgAaWGePycb3w+ZxvGyQTKNiODineZ4Ph4P385dSjsfj8/NzAJe4+GBqrTUz4BT3volYEx/sLhIliE6VaVOjGAIvrVZTkbq9vn0Hg1wK2lRyzolVVLSZGlAGAwBSYENCLs4iAVjY1aqBKiIhp5RnzpOpEiAbzAAASSWr1JY4Tcn1OERMaNEDBkjEpUwi3KQ6YgNERAZGc37JiDjPM5mT6aIioiZqhkzHQyZ6OEx8PuQm7XTwHXiilJtImaZpmksutcr1tizbtjVTIyBKzPM8/9tvvzw9PUylMCcmKrm4oLzV6vySb8PSmvcdcEol50HXDRdHs85O70DwiMK4r82PhGtsnd7AD/cQ1x/jvhPck1H/OXQLF/a0mM2NrQmAzf2JOac0TVwK5USJO/hhpKyKBkZkDr+gF/FcsRQeI6OD3TcMBOytb65xiJJMEK+EhGRETECoapi8kg2ceJ6neZ6nacqlJJ9CdfcFiOFSKVFOXHKapjzPkxqmKq2pdZXAABrQEZF/P82z34CS8zzPngpQuNR0ZNlhai9UAtid60eADIREXtFo1rat+gB0JEzZFwZxVxFazH2MScUWhDCoyrIsXruFrgvr+y+EJmQsl16+QABAElGiRMSIaNJ8wKOqKmhrsq7rum1wPGRmBlRpg71wMY7XzrCPuQEwNVVp/gI1JSEQsQZx92C/zu7YoKM1d9jofCiiiDdX+dYtRM25BBUhGB1ZsKzbumwaM+Vbq+E3J9IAjBABvObbz9x+qEdZFxRafxIgJC/glhPxe4SI5EJK6rrhsC0FAzOKuWPeTT3Mz2Hs3B30dzTRH7Yw93BDGzAfy9ZlTDZEvRZxKBrrpGuHxkIdWyMTuU15Zva/+gl7cVZajcoT3t0vKayrGWycnl8aFR0i/TsREm7bvTaxrKs0VQPO+fzw2JYbmq7LRaQ1AU5EQBCyC+z5gi8HMiQD0vDpJAYv5Xr9TuPl9wjlvgloAL1ICYAE2qOaY0dTFa1N1m1b6rZs23VZ3q6X9+vldrtdr7f3y2VbN9NWCPNhOubpOJXTYf7wePr8+cPT4+PD4/nx6fF8fuCUAbGprVvlBDlzQkJKYTZqYDAgUSImCl0J9gzTwADBiMmHUhGzqm21IomTBNIqIBCToeuatapsoojojVuJ2dRag9ZE1dxngRBTdyzaL4ABLPalogGVrLfQ92L9vTd+wIVOKK7Lsrj6eE8y/bTYqM8VGQyN3xYHEI57HHMMDRD0EbYiMjrk98fs9Sl/qz2Ogd1YkgH79pjs/rz16SL7Uxun7Oc4pt4OtskxkB/waKAbP8w5n04ntz46HA6n08nPMZ4gRABvuwPizEyAZF49dEQoisjeWhpyGvVuW6zatDWTJm2t26Jqqo3QDvPMJRMaIpkip+R90anMZN4PFhVnNRNRRHdlc5mOj/UwBHR7fQBrrbZGgMCJvP3eY8zgIJiZInZFtdsDD+ySP+JElBAUTFtruhoxoAExAbEZSCtymAin42GapjJNhTg3kcPxeDydDvPRDHyv25qqkZv2MPPj+TRPU0o52IWQ1FirTdU9wxkBpTUDryuAC9n9Ftx70PreYB0Z7dj0AWUC5sR2aYAuQLW+sv1/nrgFHHHaOdxEKMAKEJj7EYIPizBTF32ghi1gyinNJc0zeT01IighkQES7ddu4DWvJ3XrQ9+wuzAXHJ/FOXolnfyjvCvO/eLcugMhteSbBKc0TWWepnmapjJxdggVshrqQ53Yx8zkNE3lcJyBUsj4dPj/gYXG3Xr9zABgjL/JJU/zDN2ArnMA3APiPYVyHkIAIHTs5o15atpaaypOckgTZObka54BcZpmx/VOh/ojXkVUqoHW1i7XKyfyOII/yK5oR1UjUt+QAlJgbYpEOWV25YGG8slURWTdtk2lMFMuRgjCnQ0HMEG3nzH0DuhR+22tdcWHkIhtVUxdeGOgwUcOO0EwJdyF3IhAHReLGkiUlGtkosygRp0PAIBlWW+3xT+99uRPpalqkEQIXu/dkeydOd2RrBpdbq7/u/N81jMmIiJ19VQsSB0VAQAiTJASgA/u6mA0Nmsccf9eH47pcBBX0hS7tVlfZ9YXYPTSqapGzqYxStbGT8wsJg0QuTVRyYnR2/o8l71XSVQVEaLSh5ASpRCrobnzimdZaiohbHTg1EnXe0u2mS7rom6RRTQfDg9PT6oNAK7XS1XLFpDI5+PGc+SqRAADbArNDMiILTMlwphNCMDde8QXB/hANzMVIwZAjLICRp7np1bFam3X2/J+uVyW221d3m/X98v79XZdl3Vd11YrGUyJppyeTsfnh4fH4/HxfPrw4enj54/nh/PheJqPB065iWy1rXW9LWspvt33YzDw0UXMnHMqJTGzqHVIxABOfCmCJaaS81Ry5oQAIuqqL581FEIUIlVtZluTtTYwkKwppUxRKyTCbauGOM9z4jTBHfeM7f++xjvOGBmz/+ltaN7GNZqwBmqR3urlcqJlWbZt050ie+CSn+pxAyQN+meMAdnX0VzCvD9UL3KNN+E+i4N2HWSDc/qJ1KEutv0JGI3jHP8Ku3kmfoKulBrlQttxXT99EHX1t4Ohw+FwPB7neXaoFBeBwFlwb0P0d2rxkBsAKZofLhMwR4UXwFRpNVGpps1AiUBaFbHWkqkwIQATgiExZ0BA5awmIUkAQyBiA1QNEhY7KjeL/hjGxImcfRUD9kYibWAu+epdigBEbKgIjKCCBmZNTEQjFrgZfcpMQKBmTkAYeLcnoQEQaWIumeeS53kqUymlcMrS2vEwn47H4/HInDzCNzU1UovmVjIif2oQE4cgw7xs6j41lBhpq+IyAkbL97l/O64obAZ3ks4OORDvcAPM1FmX/mvYuSIKKyLqJBp0CgcRmIAYiMG7/TzTTkowRWuPjXdz2glyTmVK5ZCpFJeMBMZUM4UI5NBrUq5+Z99lFADGh+9OxABBQQzIJb0m5oJYYqKeMzBTzkmilw2JeepTbkpOTMAU86x8H7HuNMWJc87zfDifKZcmcnev6ahmbFEwQOfT42PciVKmeQ6XOjcDMkVEQoZRN3EPe0S0RN1+Bs1QxcRTW0MEYsY+HxTxxM4Wce6JDqipUTNRAwURNduabLVuaklc8YnMLGatVUTKKaf71YxtWsOwAQCMCTileZ4Ss3fxcmLKCasBkhHDRFQyJgYkTAm7MZRCg0DJYL7AfD2oJebWmomImqIsbdukilkmQmBEAcRm2pqkjEyYAJBZAVRaE61ErtCj6Bac/PBH1wkAqEhJeZi1vF2ut+u1gzrVsAUXAEguR0fs4p+4UUHS9GgqQbkAABATIwLRyDbGYBEPjqLj91RbU1MAIICUuKhaSmaMGUGxS3DuNVOHRBTYZSAl9NVvURozl8b5ygtmyHlqUfE4pdqa+rw/92ILLG9hQuSOOHMpiZNNmlPKnHxmsK1bVdW2basN7B5dLn7CKXkK6afoaIN8IBtTDj9Ivh+52bIu1lmwhHZ4esBEPOXbP2prVRGBGDljrzyqL3/DJrZVuW2yVEFiTmme7DRNib3xEZHYc1kiAmIxVLGtya2uzJi8kzdj1BZVW61brcu6Xm/L95fXr9++vl1eb+ttXW9126Q1nwZwYD7P0/k4P5+Pv37++G+//eb80OnpaTqejZMi1iaXdVmXxVmT2sSMEDOntgmYogczZpqnfDhMh8OUM3tHp3WytpODNOVynOfjYT5MpTAlRk5EhFC4VXHM2RS2qmvT2lTFCGEYBIGGWPgKsCzb+Xye5vls4ethvccKeskMe3e944BBw9huMJkX0Tzf8IqSY6Bt23xwmP/VO9UHP+QAYnyN3Wash/G4jAGx40+nhcb4euy1MyeHpPfMz/N8PB4dhC3LQrvRs8NtyDkbl/tAJ4f87MZff9Ibxd63w1jQp4vsC3Pj+P23pmlyYbXXzhwSeTXNTypOWICASp4AoOSMxICYUxIk0ARs2fth0ZAwZ87M04R1KiljaysTIJSc+cPHp8vtqgrMeTocgRhE0JCJOWUFEGvrWq+Xi6lyosNx/vTlC4JvN04EZsAEQKLa5W1RshZhIW4SA9AQjNC2TRXcQighojS3KkHABGZNtWkDE0bI2ffaJCLr1qRupprKPOdsBuu63m5rrQ0JD6fDlPPhcChTKcXdAlLOhSm1qgY6H445Z1MQBdFoH66bqBmBpcRTzipiAECJpwRNWhMTpUTTNJdEpfBpzplk9Bzc56AN6DD0CIOix3smAWBkSkDR+wyivkX0WOYT2veFLQJi4ISciBk5MfFu5Y9M+r7puliFiVKmXMitNn3+VK2C1FRQRC0avCl1h3gkMFPXG8AOEg2pwF6LA44ETUW1iRKbq0B79QocKTNzLiX7ck7hoQ4gYtqkSfN83Tt4OaVSCh6OKRcb3bX9Y4de4Y6KDOz0cPKDXNf1crv26x4l8Gma7qO8AbolMAQojnBi2eBAXIizGygjIZEZbFK1NcvZzJrU2gJseSnJIy4xl2nirHnK02HqnIgXuNqyVVVrrT6cTzFS0Ck4U6SY9QGABJhyKXNBCMDqBT5mTjmneYZtvdUK4jivq8g9hUCvp/qvijMqBhbFKxHVBmBrrbVWM0hhwEMAWGtd8soZOOWcSqEkZiLaRNAAtIm2Wi1meKXk9eN8PHlyCIiJ+HAIadf75XK9XDuT7nagFhckMSH0ClAo6falHxiqBYfrCCykhEoUjlO9CEpE5rO11RW0HqOrqTi2z3lMRTWMBA8iVMF9SDP+/OXrFtFAzDx5xJ2WSM27KaOZton6vLMm4ix0jWYcU40OCEJkpJRSybnkgjGNgYkolA21VlHTpnXzIR3blqechJHCZIDuuCw0zL7RZUqZU+IU99Mv47pufq08kzpOZT4ezOxyW26Xd+Bsbq1Erp7XprpWXdZ6W+v1tr5d19vWzJCYDtP88fnpfJyPU54LO2Pgl02Rlq1el/X9dnu9vIGprw5O2XNlEamt+kZ+W5a39/fXt9d1uUnbTGtGO5U0T+V0OHx8enh+fHw4HR/Pxw8fnj98+DgfTnk6YC6L2OV6vd7W6+2mbTW/9iJmyFyIG60VUIAcs1EueZ6nwzwd5imlBGuFaM24CyWZoOR0OEynwzzPZZoKh6GOo172y2wgiN7kSRPjYfK+kEREptZVP+o9rVJb3dax08vOD3ps9mOPH+Ie2JXb/MVOnFyv1+/fv18ul3VdHR4ty+LdXj/Bi3jMeu2Wdl/Y8dmAPtgVQqNvy4tizlE5MpumaZ7nh4eH0fx/Pp/dn9pfNs5rYCnnk1wgMaidcV57YLSf1zEOb9BdA9yPaiP8WG5jZq+XHQ6H8/l8PB5L6DHul9q/lnVprSa3NwFFJCASgPfLzS/IfJg4o9RWmwEoJwZv4kplKhP1K6oG83xG9HoGoSK7jk2ltbqJ1dpE4Hg8qDZyrwFioOQVn8wppQyAaoASpTBVVW0p8ZSJsLSSTBtaIxBGy1mae64DJsKEqMyiVlurW0VCzmTSSuacmAjWrfrUAQ/TTQBum6q11gDxdDqXqeTM2+2SCDNTYZrKRJxrldvlogiH05lSAWAAH1vrLnSwbVWtSWICTIR12wCJOU9TSYUJ2trW12/fEe10PqZyJC5q9LOLIxKmTAPjBnro2ggPwta1Od50C0bB3mgfH+u1EIhyWyd+yIiAGB0YpUSc+vLqwgj/MBgJSmTA3O+RGoqBa7HIVNWrwkpuHZRTLiW3SkpuAR55vB91EC/eQdgzEkAECtDWNargQk33B+yPZ0opp9znHKBpi5Z9GyIVAHeSc8KKU0qMwwG/A4w9HhohBQBKiR60VrdtXaETAr523XYFOryCnvGHj4wFL6FAqRROmBl9Nws+QXrx3vwNm6rXVaKWB7FDZVJNxnES/npTMQU00aamhsqckLo5Z9hqwbjQyQudI9fusc8vpyE2b/jvkAtiwll0B6GfdOjSwQCj+UzFzLxS0MInMeG9ouso1QQtGinMmJUl6mLiw33QTEWruYzfSz2I6YdUHGBZlttyQ7+iozZDmIiM3KzAOQ/PQwbmjYDh+mPHcUgIzGBE8YK+zP2+e8zWLr8QabWqCqGTIJaY/CGh4f3T68RI5HZM9+AbT1wUVLt2DVxDNyREIt6q4v+JN7WK9zB3nigCal8b6G3CRF7Z6U13aAqbtOa9e9hEDUFBxaRZayrNNIGKO6THrbZ7VdhhDXICTugUcpTbzSO9uW8vw5RzSXmaD8fTQ6sCaGrhQS+iVWSr7bpsl9u2rPW61suyXZe61aaqhVNtdXs619MR4EAlMQCimdqm+na9vV6uL+9vb++vJg3RbVqSAbmWqLVa61Zrrdu2LEtbF9SWCDKn05QejoeH4/H58eHz548fnp9Op8PxcDiezuVwMsoVaVm2l/fr99e39/fL7XrLbImx4/lURFXF3ekIowskcrucE6fotPDYFE+Y15VwKuV0OByPh3maOIexr8Mnt1vyzQu9zy5xIjwfD9NUhhTJUVGtrVYxsCZt2zbPb6xX0AZ1sSdv9tTI4Hi0d5ldr9e3t7eXlxdHRd68OQx7Ro1pvO0eN/yVWfE3l24s5ORNv0LZR3NYr4l7Aumsz+l0GivZlZSjZPZXaso/bqAx29X1xrnHU2amqnsF0jidQWJRtwYYvzWOxKtmTg557cypoz2K8i8H4mHUJIZsSD6AR0Pl4uEn4Jpu2260nEdONc9apzIxx26ESCmXlLnWVRYf7iZmQEycMhGVnNHrcl6eSwzEqtZE6laJGQGC4yBmjw1qqgzKPocYUEK+qebMdyhTEpqCkRGbWSq5i5NEKSUkyiVLa2Y+oBxyKQCWcp6mUqbiYdRJQlVDRgMTEx1yCzOVQUOAAVBik2j8BUPmHEsO0QtNrcm2rnnKHo4MqMk9qKa+ODDlNAo9kaLc425fJX00WXw4gheQnLS3ztF0nAE2RNTYy2ecnADvGqiRBfXn/k7cAhEgmwGIkSqqG8eZV6a8sRxSopRTmUrVKhI2tR0VmQEoqIii/siChpKZhiFE54YocE3sBcSJHRWFYSNokPYqoijijUgxytZVE0QQuqlAfjvWKmTWTi4oAHCKW6AiWisAqhkS+M4lrcUDZuodvAGGfAxZn5pAyMLJuFcSzZD6JLpxJ81r1XpvX7N48r0Vz8cnGAU4QQXxHcrUsaY38A1U1E/Ji0ng1So/VFfVGXSJGZGFC1UQj2E9dJeGeN/74Jh8+xo8oiGSGchwj96FawUQMIltd/AmAAAEpGbN7ebETNWaNXXfnZyzAhpkn8vs8WhZl8Uvt2MRhymUkil14yEPmmrjCvQKUZdSiER7mhEkr0TBvQbaQZgX56TV5s1cjopAiRBRUmos0YkIgJ1MQ6TxhJqZdTARSYwzMoFLd3jIuUEHRgMSdRwkXQOy25+6Yz0N9Va46flDamZbq03E18LWmo8tBhWQ5u2vYOoDD/2BVremdJSPBMTeom/kttoRUSzITDEzEtCDIuU8lcPheLveWt3E7eAV1trWbbuu69vldl1aE6sGxklAbltdbjdQFWtVttoqohIeEyMCiNj7Wv98fXt5e3t9e7tcXk0qmKLTU0BmIfZXqdFaJ62gUeHM+VD4+Xz48PDw9HB+fn7+/OXj09PjfJhLmThNFehW9bKs316vv//557dvL5e3921dzodymEvJKeecC3oZGwEohi66TIw87NAQf8eKiQTVvRKOh/nxfH44nefDTJzMJ/25GAup239EWoREJfM8TdM8pZQpSEqR5iYUy1abyDZa8wcqol3f2f6f9iWwAQ7WdX1/f3c89O3bt+/fv99uN+mNY7JrSdOdnhq7umgAEdy5YPvPx+86meS1p2manHQZre/rujoZ43wMdADnZ/FXa6KfTmF8uv+554T2EOenS+FfsHObHJW+QRoNeoyZTycXwxwdFZXuAvzT+zvBRpS8S8bNYDhjZDoeBru5uogu6+pqiiriCYOaqUjxUl0uTCRNRI1STqWoGUD1DcgXXmIviWSI4nZAPFFoTbetLesWg1aIQlTq/bhsiOR95Yxq0AjIIDwaBwXhiB8DKFHO7P7sKSslmqyYjsqAGiASqSiA5ZxKzqgzRfXOmgztkg+Lc+EidjdBAAMkyJxJPAchAEqJO9GDBpEKmllMCyFWQLS/oCJmnucCXZmvrnjsbSu9RAA9DLNTR6YCYNrUMFSjsWUaug+Kszui3pbv2ChFJHAHoLv8B8fTMt6pZ6CoRjGBBWiANURIiUvJk2gVUXTjsm46gkHoqCls1VrQO9iFb4mT54fsdEa0aHiFIIXROxNz4pSZKSVCNLc2DGwTzTsmYKI+tMWagHQYGUiP7hEOAyx67EKz+96amEpKEZ5MFUmIEYCH6aJnVwxmZhw91Q6LEnhKQECUcwEVkoaEuWTsQK+YJgLtdQy/hBZqF3PduZfWVJoTH5fltlVpYgDQpCpk6qRVF/vGVFFETMyZmX0QQQ8t4OR/SLZ6/S9E4sEZKTgzZQLuXx9fKTEiiDSIawUi2lorORdkMCBE5oQIqtaaNlSTDYg8n/FyT+mmsV1ZbCqw1bo2beuGoCtz3baIR+u6rbe+CQETesOrEngy0LtAPGlUMAw35L5h+Bbg7hBGZKyALi7yhe2+2wMLqu8YUmurm5l7TpqzCNKk9Z2DMeTlfdAZAGIDpR7EBQDdiSqGaYCqdsSjQ0vURFtr7tk9UFFr0mpVERN3CrPuW+FyaOZE/kjwqG4AHFXUTduIeNvUjGNGrKA2NEFThhhtbHDPiCMpB1IgAfJxGnaPHfFABP4VQTAmOswTMy23Vs0txPB6vb5dLq9vby+Xm2I+Pj4/Pn2cDqfX17ff//H7Vv/1/fuft+36dnl7eTxdrh+2z59yYjNbt/rny/uf318ul8uyXNkaQXCqcLc3swQ2EeSMaUqJp2gvncrpMH98fnp6fD4dz4fT+XA65jlKIU3h7br+8f3t92/f//O/fv/HP/95fb/UdU0IH58eT6fj4TDN83wysqMmxuMhT/PUDJtAbaoCIuGYELtJQHMIzpLpUMqX549/++23X3755fjwKEi+T7BpAkwEyAhItaEYqCEBAaYGiGLVRJt4aDSFanDdttttqbWVrfrdGbgnHtmOGwZG2eeWANBaW5bl5eXlH//4x3/91385JHp7e3NEMt5Be9eYv9tAHgNk3PfPPgLWAYeTTL4rud+Pc0LH4/Hx8fFwOLgYyIt0RDTKYWbmRtWXy+V6vbovgMubBmnU+jDXZVlGt9qQbPv61x+HtXEfG/KT0+P4XdqJn4jIJeFmVkp5eHhwPHQ+n/3I90TR+F7MtiaiTUUx/BvL6Tyb07qqCHTMx5SSqSzLdV22UmY3miQm3+fApEwlpYAyTLRum0sTOOUyz0ZEhMrCya2VEqcMQMTJB8A0sXXbtnVb123d2uEYgz/N7LZswe5z1HURGRmIEmE1FIMmpoDcM7AGCHFtkYkTu2t8yhjZXEwREE+lkVRMVJgocYLEnafx3imiVApnJi55Zi5M2V3/vOUeASgX8lYVt2PzqfNh9NDcwWM+TsfTYZqnlDPeU9w9KiKcJ3eGcAYCVZvCLvuMgwIDgrvGF6ypIri5HtwxDoKCNCP0/j6TkMK4DEkjdruwv5fM7rDc/88A0TjUrWQUTiUY7T1IjJwpT3kCqyaCSq1GhQhHN7yqVCMUhAYqqhy2JsSZMyfvU0nEiTP7qKHIigl76/C9exjHIBECMOe9wfl8syZWmzbRWm3wFhwZ/o4i6K2JPpVcu9d+LtN8OFKwfFGF8v44F7pF7IgsEiPDUjMwJspe+yOilNgog80iUiuBgTuGlSmvq4gP5UAR83Y3BSEmp6AQQGTzYtu2rVttLp4xs9Z0W9fE7OFC1RDNO4nUACmG1o3uLGZkgj7pHpABDUPGJeKgyAFNeIB73ULU55HE1JCRyKFR7ybIXHLvV+SEhAjefYeorKiEiChICGCoBimjRecBMqecOTH7w5MZDVPi3okZ7RtAgOgzWoCAAI0BDLrSyFEtGAI4qwyEQATs3tlEaMJMicN4JufwLPA714GL9z6AMyrs7SXd+dDGToIAxorB/QGRGTCZqvUeOwQyDLtU8+pcCAvuwCi+r007PRTfOICKijgakwuosORUcsr+p8dWjjZszzeaTD6Gz/kkkYZE3tzK4JO4lMAS+fTvTh7HQ+5DbEDUDBT7iJLYF3sZEUyjjwwpp4SIS63Lur3cbqr6/v72+vb+8vr2dl3S4eEDH/LReKatytZqbduy3G7W6rYst4u3hjOTiCzr9vr2vq6LqSayx2OZU+k9IOS96pmxMM05l5JySrmkaSpTKcNjZp7PqcycZ0xcAWpDM1i37Y/vr//1zz/+v3/88//9X//rn//6V11XVJ1yEpHruh6Oh/OxMRfvsHt8fCzz/H5dNtm22urWLAYt3oc/dOhghFASP55Ov3z+8tuvvz1//KSc39dNWksAmTEn0iBq1QtkpkqIotZU27r6s8YppNPbVl/fL+u6qnN+PTy5eGVgI+gqnD2t4q90MfW3b9/++c9//ud//ufvv//+9vbmtTNHCV4hcpH1/s39+5+KR9TdjxwxeGlMVb1zDRG9cOZfzhiNUpSZuRkSAHihyszcOtKBkeMh2NlUDkj3/v7+9vaGiP5W3rM2CmpjX3LI5Wc0UKMjNu5fg2Qa7zAMGx3JecfZvnA2AOL4Ceecy8RqogoKIYDsHK022XSdDzMARsG6NWFBn97MzGgb0bLgWjVnYeLEiTObEzaMOWXr0ySqtpwL54lTZs6UshpKDxMtbAWEE6USaZE2MfN5ZEau3CCKflgiJBIRIRJE73sxpOY6S86cCwLEuEskU5K6AQBQYkoILWGMaLVkOephIbFERAp96AQABpCIc5mJM1AqOXlq6spPJE45h2WZxoAEYp6mKTETGBOKTtM055QItMnqs3N+QEWA0OUrgOrJOYYaxkfueL+Wa6w901WECGdjIfAd3zg2sn23GkQbsI9s6ONj7qjIp/zEpu90BCCCGoejHXV44scEyEYpc1LOJaWWDE19sCnAvc6mMRUrcJgHAEJiSpnZfT0pOV/pwIK7j0oI1aIMhIhoikpEXhfhRB0/QX/anecM6vuufvFrHMDSK+qjUhN3glPOGcOMHchcUGTjMbuX5AHBnZHVjMw8ue91UyQkSMlgmmQD6PcodLJRnB5ZMQBGwXjUZJGIc0IzKwrC0RyPhlLVBMj9KdymnZmxmBkYGnOXTxkidPOH4ZhshCBmptLaFsMgEEVUYJCSIRxDItVYDNCv3mGeSy4EOKVMgA22CmiEzKN1HWKwCgGqKZg4ChOTWs2V+dy8qZsgbnDOZUgofHC8L1/22hlhTJ7BuzMpghH2LrB+kIk5p2RmhKhMxO6MllIIisdvd0GAKhirKpgiMIFFy2GYZ92hEfY7233uwNS8S9rZazTXr6MOLqvPsdNwR3NUZO2uHQqNAoKPYQ6jv5xIcgK00h2Bc84pO5PK7l5BxMzq5ys58FXQ2ewH3+eEoBJGLdJ8cGEwpl4rE3CDexzNH7E7RuqBlnPyAMopIaemdlnX27os63q5Xi6X6/v7Zd10gonerkrfvr9eXr7/+fL96/v7q7TNQLbNfGigiCCCqLRaW62FcZ7yw3H+8nw+5JS8B555mkopKScuiadS3KLMlYuR3HuyQ7lhEkWr0MwU1BRuS315v31/e395fX19eble3lutaCCSiN+r6ioiBufTgwGlVMp8BOZma2261XAX9HqlM60dgfgDyyXxw+n4/PR0Oj2UMt9EW22hafcQYSbmTn61iaBZdzBw/lFFFC0BQhP1aW3SGgx+fYSwe06p+79CL3I5Yn9/f//69euff/75r3/96+vXr6+vr9fr1UGMl4e8ouS0yv59PPjDrkJHoV3LPpTeu/3XdXW7I//d0r/2M8i8cOaiaddNehHN39/rPq7d9D9HXW/QQrXWZVmmaYqWnc4S+ZkOfIPdOWkQYIjo7zY4pD0kGnDHz87B3BjuMVDmYNTulwJTSiX+rp6u9HFsKaa3qpk1f4R9cIqZKYIxoXHSlBOXpYp2HwxmNmSI/CSeQwRgIgxhdCJKSOwdd87qIWJiIixA7I+I7xm5JBMAU0+E2L1ozZyodOULGiIqcUJOgMnAEjOn7J2zFNUTBgv/NnDdCaVuRWvgMaI1SiX2OE65ZISYp5s4pVQMY5oB2L0Fyb1aiIgpETEomGucOAFgKe7uWlJiADCV1tqISHdU5MExyiMUO7vFrmbh+eNElI36ppdBuvcLG2hIaWLHI7cjib2mK/RUdZgJ+WLwtjVEI91DKozs2KKkEdsYIcWaBHRQ4/tOymygEt366OKDKOh52WZAlJ6Pshd9vHg2JNbh0eCBBmMyZaigwBwzASNQ/BITIAL5vFOvPvggB+h8EwyBkZePDLxHyEsL/WHo7aYiip0yc54kzhujKAhRwALC0JD3ghQ6cYG+c3FibtB9D1wlpSSiUcMKuROSmaJhNBwxh1Yc0YB7q7nlxGCGhqCopiKCRGCgSQEUkLtYKPT2LreCrvwFd35D7xu3UYkDgO6uYDzuNUQNcgQaJDodT4TIyKRq1aoBIwJDSinU3XFjEcZHqvrMzVYFQ6NmIpo5jUG93SkCACAxeUOUA4XQ8jO5WMC3qoBH4JfLjQ0BASylnBUBhEijr9URUUgN6I6KYmNAf37B/ONUFTEem3EaAYx65tA0CFMiUzNiRreVDM2ZuqRARLtAxwsfri5SCb8GM42J635tE1NOLJlVs5oiQpmKG4TknDokYiICAyI3qevzoURzVj9AZuL+pBCaY6PhYqAdFPZroKKAphgOZb4YB1GIiOh8PiJxznmaMZWq9vJ+eXl96TNYN8CSvYYi31prb69f19tlW2+qzTffrYJd3WPCY53OiR+Ox+eH08enx18+PR26kNyQ3Rkl51QSlzKlnCkn4pRLIWYwqK2tW2sGamjNmrYtegltWev7bb3ebtfbbXV7nrAVM7jdBKAZIKXbujVRMVSg1mytslapXtaJijvdn45ejGfCqeTH8/nh4bHMM3KSbdUmjOaFbAUTc1v25pPAPW64XYuvJFNBIxFrra3b1przSaP5Bsb2PBZej0IwnkenfJZl+f79u0OiP/74wxXWY8YZ7FiQnyCRdbH2eO78NY6KHNOUUrwGtyzLyBXvMD2lAc4cWDjgcFTkfJ5/ikubU0reQUZdruSFOV/AZjZUR9ob0wYq8u+9337YJgGAv1K6BcBfuZ+BkLB3uoU0p7/zf3spAhcBU0zy6zlY6JYYwNAIu7QJETlxNEChgyD0MfWkRMRR3iKisXWrhcTLgLkAcN9ACBE1+AT3WMrEDP4ZbrehigglZ0sEpk78+HubGgH68HUAYAUkIzDiDJRVHaNQcsDiqxF6wkdMnIgBMVmXAZi5PTswTnBHRQUhmxkYMCdOaSghfbS7hzhmNlUg8nBqGJJfM0I0Sqn4SFZQ9+ltTbz55UdUhEAxftOvXOiWrJMaaKSqQOhwyvqOysSJFROQoTl+d74CCJA4ceoqrdDpgqnKjjKPZ0KRyO7rKXx9gcZ161qVAJYeXRmQE7JSypQqqSEIGvTkByEcGj29VUNAJvStKsWYNU7JS2nsiW4H+PF5AbnYvekAjACYFIkg5WD0wIwVE6MmUlPkODuK+ZK9rIihFjez0cOVeWcvDqHzhN4cAZ1q9ipPkxbCEvL92MxIxCty42PYQbOokpu4A4IicUqcVM2AVYTuKA3MgIkTg2UrFsPQM6WS/BjNPNMS31mltaqyDesFr32XUhInBK9/kBHBXTptBpaIOHOilEpmnyEL6LoXFQGD/5+9f2mWJMfWQ7G1FuAeEfudmfXox6GRNE4045BmHIi/gD/i3qGkOcc045y/RwOZJNNYA0k0O5Nzj53T3VVdVVm5M3M/IsIdwFoafMAKhEfsXVnnQV7pNrp6Z+zY7nAADix8+NYrhlBKcYVL3RiATWOMMTIxqaZpVp6DrEIJFKkaVxadcyKGEbqpmcDCglhY4modgkgUFsk5DXGQqv0s036XczWqWI1DWY1ck2RxAFFUPV+qQBx8PoggDiEzmyHzFpUYSlElhHsOLDWweKdBqwbSwioUmSgKqwaN2k7PdeZVdz/EoCLkt+BCJFwppaISzOqVrIScJ6olN1eytm0UmFprs0SzGm5A2oYF60dgWdAm42rYbFbrzXpcr4ZhCEMMMTBRZcyaohmDo6pUz8RcR4QgOIytIEYzVTK6Jv8hMyvYy8X3fmYehwEixJhDDCZSwOquh5u3X91O6XE/zz99uP/4ME2TmYUw3N3dXd3ezUmfnj7ff/w47R5VM5GGatKkhQop0VzWw7BejVcXl1+/vfv2qzdvbm/f3t68vbtj4lzyNKfnXeJxpHGwIWZmGQYbhhAiSZSwkiGaadEp0ZSr9Xre7efdnDFNS8kzrNALZkGd/sVsmmeYmccQH553nx62F1dPcfWQlZ53u5RmrdHPq0ON4VzqkIh5PYzXl5dv3727vL42kSnneZ7MVEIwkbnoXEpRmuc0z0lLCcyrcVyvx81mTcyqGSlxWaKWOeWSUkbiSm32DwtItNBw+TYPcuXjx4/ff//9999//+HDh/v7+8fHx/1+j/kWYNNdcAQ/sg1CVQ4peugASHRxcXF1dQUcQ0SwIsJn2Az5MQOUBqIirVar0E6VyKrBzHC2v7y87HkjtGq/3z8/P3v2j9M+SgsgBPEL41w8uv/e+TNqsK+vxK9Hk3pghBXqd/m9RJRzmecEimgch3EIwxApBIkh2sgcmDjEUIohRUPojD5K0ZKLGoUw3FxdjDXlKWs2tYINPZe8m6Z5zmS8WV/gzXApFgpTCAJLyDqS4GiNJc9ZtZBZEB4GxKRGkN3GILKQISakMJPADJeIGVkoCIzqMAQWLgoj+swIPx1CiCNLEBiqipRCKc1EOTJHrhhXWGIY4FBGRBICEVsupqY5W85pmuaUZ9VxvUq5YCMgClbgJi9GVTnPEkKIZIWKFrVUClHTn3WoCJ75piY1+7niSK9GYqYtei7iFZuIEFtkiRQGGWyoB9Jq+EPNeAfmyuMQaqreHhM3k8KaQQ2RNK3Z7pAEDsC44pykELMIiAYANo1RBpOxhHkICBFHFMxskKDGxhIoCMkYtMSiajEI8NBqNWCSBglIKOJPOVAULEOIoPGDCJGSDcJspiIygMoVNrJoeTAhKRKtIHw0zCSbWUD9p03/+r7IxqEZtWj1l8esokrJUckZ4ik0w2EiUrP2Rqy5OJlQi3fMjNM/UqMwkeZEtBnGVTEqOo/jWIPgMBHLvN/D9n8cB1VF9EEy4wCTNXAuNg6rEIIRb7dbraZwIkFKUQkSQxzCYCkbCfEA9SLlQsQsQwybIIGIRFRK5EaRSYzBco2owTDPb04upjXdsDFR02ebkrBcjKPRWE8QGGZec82sCnMr7MrUJnsLjy6lrIY4SGBh06KpEKwLiehqswlNjRUFwY7ZSRGpHzAbQmwyD69YgwUJjYupdFA9BxiVUqpys74f6Lll4BhjID0WjU1A+tGhAn0yMiv1mKDBRM2qI22X+dI1aKivaClNrwzVJ9Z7EB5iCMK4Nw5xSOOc0pQSMY3jAE+fYRxDHEQq0lf3C6QqzGLQHEIEL98TA6pasjIWODguaWctVmKuS6Cx+kTYGqHoMTIlplSUchwkDKv15c3VzXzxeRviCumM1uv17e3db3//VxeXlx/vPz49fNw9fyp5rsyuHHB7CHSxHu+urm6vr9+9ufv97377zVfvbq5vrq+u1uvNlNLzbv/8uP389LnsSGYLUQPZaqBhIAlKlC7WtF6rEKW5pARNZZlT3u32+ykFicMw4Iwvjhwlui5AzeqptOT9fvfw+WG92jANFIJqIYIegBA7akpJtfkBMxnRMAyXFxc3N7e3b96E1bjPWTXNecK8UDVDilDVnGrEhCHG1bgax7WEMeWcVQr01iJgn4PEGK0U+B72XEWVVNryVPT7fSllu93e399///33f//3fw9boqenpz5CI9gXM4MpD5zzF/jD6wQkijG6zRDIobrzNd2TNAc0XI/b4VyGsFJu1gMmCYq26+trPxXD1ElVc87b7fbh4eHp6Wm32+Fe+MxDj+a34KHNi6L00xuKMGsWDr58uePJHAYNnT7aLZP8ei/o8jTtHh8/pTmHINfX13R5GcYgRKWUnFLOmUmEqWhJOaV5JqbNejNQCFSsFCEa4rAahYyULOWckrEVNovjSoahatsJmygjoXgIMsQ4DmOarQQkymxquxCUmSMc2UqeNTDXtMpkzDSEIMKmWrLVXO8SwhBa3l0jshiCZtWiMYYYhhjHYSiBaxpNmEYScYGRi4Z5TmkqRISAkzUysxERxSFy8wIjsiDCpFoKMY/r9bDmS5HN5nKeU055nrZzlPVqZAlGNqd9EIJ6YD8lsFFKQjLUkItE1Hvmwyqi1D1QVVlrXBZVEzWYSUvtdzWeJAtqVh2WCekNG9omDuQcbvUwbhST+2jBiqxKsWp9Ae4cRrdNB+GApbLzsO4JRDGSmqUYx2Egwy6ZpTowE/zfy1C01HMzFGcxyDAEZKho5kSNtu8JUJaI7Q/aXxjEciQyEY7VX5/MNASKMMASC9WRqnax3+CwEtSMTJq5T/PM9wQ9Rbl11Vp8MGoWgpVHqLiKIb58xz20XKtMoUhkpiVbySHIEIMpkr5Xs7BSioSQFYmsOWdj9kAQKcQIB/JcVHhgjiyIHRCYa3JvM0UwZGap88I5Rqt68SHGNudEuIbEIarqTmj5MBdEAiJL56JEWgVUHQz32q0KouaZCY9uF+1VD1d33aoPF2PBXk5Vh1yNsP2Utlmv2JSRvyVWEtu1RfX8CGVSQCwY4FTmGiKKgwkseCplhWyHVqxyj9yM1ipiAsxlGHNTDdqgbZJUAA0qsDKJRi1eKhL5IYw0k5v7GZKnuAaOqgK5VsZtCoUgcYAGOZBIzENMechpmJMxEkjFYRxDiC2yfLXk1U6aSztYa9MaU90hqqE3liuIImYFMCJiqYxRXSLdniyqJWXNpbDkXMowaBw0DLad8m4/56IhDOv1OsZweXX19t1Xt7dvzMiMYUcO11zn54QpimyG4c3N9dvbu5ubm9vb29XmiocLldVsQ5ltP5WnXX7czZ+f9nOpu8EQeLMahoiM8bTbz6txCMymxcgEMaJzJrL1EIchrsaB2Ha7cYgDrBlERJusJHP+04TZCOrMNEa2pi5TolJKSnlOqWh96+Dlx2HYrDeb9SbGoaillBAcvqiWOQGfEoeipsQsEiPFqo4vZT9VZZlZEIoyBOFhGDZGRW02bTaOS1TkcqlKLVUozh4eHj5+/Pjzzz9/+PChtyXyW4BXAKrwecEVcfPA7yHLQj/l2rFxHLnp11x3Rp06D65kjooAjHC94xIUacbjiCi9Wq3gm+aoyCNNozj00c7/Tls8J0dF/qU0AyO0BHgOjenb0ANNalSTl/UqXl6uy7oECav1wIFLSzLOzEECGeWc1cxIJI5ayn6/n/Z7YhaW1RDHcQxDDMJouGatRAZsaOthW1UpRR3G2lrVsp/2pRQjazxENRYw06Il5VJyMdVxLU1s6gDZzMwiKhaIzFhYOAZVleoUp5rzNM2I/3V1xevVuBrGKFwd1YsVtVQ0zTMxD4Ox1AxrqkWbqQ0xFdU0T1zFGOwpNOcyzXPgEMcB4QWDUAhciuWSS9EhQv9HpsWMldmMtvu5SiPoljX7ttRxRTGYkagqgjMV7uSzqgqkYjU36o+xELuKc4vb5ASoLyo+aBihsmqtk+yGSMw1UIf4INfICfAUC9Wig4WR452UTYSDcQw0DDrmgQw2tCG0+AVtYbfYXEaYGyLVXkQaG8WOuqQtLtiPOijD7K9UFcUIuyLoVEUCBSWLzMUstK35MFDWMIAaUlW19oWepq7bn5JVq07qVPta07bXFGwt2iEqtqa6DCxuIYbMl6QFdHaKIUYRi6EixmbXJiGEUtp7qSxOjTlQWM1yLrlYLsqcmVmLhgAAZB7UkNvyrlyIwAKvSpCViLJbY5uWuqyUSo0F2qByGwXKBXY2sDgsocZxIJbA1XH7cBCHExtZc9ljsRqJkbgGB+c2TFQnNgG7uKKD1usVmcK20aG4SLXAh6wDMmpkhIjbFRkxHANFqaghsDYGvjgqkhC0UlBSk4oJYxEQU4v2CyHrLobsfa36LyJiISvWTJVa1tbDyjJGMLfeXoQMzQCNAp8y7BjYc8KQYx5iTJ1d1MBIGWaENHLWqH/f07iNUiU/ibUGTCq5qkNDxW3M7UDmRwbu1wlmVCmWc0k5w0YKAYMk29Pz/PT0vNtPRgRO4fr6+ubmZlitn56ekRGZkPGmmQIyUxAeQ7xYrd/c3Ly5vb26vr64vOI4Zgv7TDoVoryf9s/b/fN+2k3zPhVjCSGshqimQ8hBxNTmedpjT2MKgeFaoCUL07iKsMIy0vU4rqrNQKhosTraH96E1ACNhUmHUKkyI0roeEozmBUzHJH5gIouYhyKKucCKK5KuaQQAuZjVlM1mGNFhBJRzVqm/ZRLJrMhSolSMXGMMQQNgtxqh7XXQSL/7OY4YFk+fvzo5tVgifQ47avDGmrubP4rd2wKiieQ71GIK+McOTms6VER6u8zsDKzK8uYD4QHdws2NJd72GKLCIii1WqFOl3z5foy8FKlZYqVpj5DA7xHhySSjeXyevAnr9CHqx9nIopDXK9HmCFD9hCTliJEHAPHwMQ5Jy11vw2RS5pUizHFgCBsRFZtnZjha2wSBuSHF6uGa1ajmFX0rUpF51KMRKIIIcd0NZlrdLNBaFd/V0gA7PI14pdA+rALKmZhspK1lJJyUdOLUo3ERSTnoqZGJRed55Tm7PQ8C6uRFs1GVS8v8KOpLZZA0pzZsxqJBaosTMkJ9gK5FNKSS8FwmBbV2rycU4yRpXlD2wHVOFckwzCQNYNNVQuxDoLHTWwGUp2c5YP9ETXjAAJlEejgf+Qbj1VqiF06S2+xLW1a+09oMeuZtnrnsJAomGAzIiETjWQjBw45lFJNO3zh1LyAaEOL/cSHk0VVSWCPqiC5WlA0JYFUtqbmKGaiGtyompVoAD7AGbiNjisbrDFXbNirDsPI7pApEoKoKqgJDGLJqk2n007epAT7dcHeFDhUWEBGMVAI8EXEq4FCxVTzPMmqWrPO85RSqhwZEUsYNmsiKmoX64GIiyrlEggkhgnxZjUIaUmTmYoVRiAeYyokwoMgiDsXQgzfIWw2Rqam8CSKq3WddaaC1B6qSNuS5slY1Gia54SwLQB/BuKBSs6zzsMwwPiLQmNOiBCzFrMuhqAHcUmlWKjvM+BYhTfBTCXlum8Sd+lXab1aC9JcVEEmDoyoIXVujpJuEgsZL7VyZhOzXNOQ5TynNOeMJyAqFuKKWDCSmki1IvE2VYopFyZmhVNeNS6qRl1V2CibwXw5YFZTYwoRQVS5OnsKszJ8KVgEfhWVDBtiGJDMZhyLUcplyHkYU0a6qyAxRmJRIypaiKwAAjfp6H5nQUqXm1dVcyGY9GUmIqMgRJBr1XKcIEGJe5rIjKY5p6wp65xKKUmLYIuklD99evz5w4dPnz5P87Rer1ereHV9s95czHP6+PHjp8+f9/ud1DVcF7YwjSFcrldvrq++fvfu9uZ2vdnEca2F9lNKhWXCK5qmadpPk1nJZVbjnIVtDGwWwHJTTpRCCMIxhjHCvd3IbBziZr1ar1bjEM3sApTOZr1ajXEbSkvyCM6nstdkRMqswjoODIY1q805pZwn5AvLySxTy9B5sbm4ubm9ub4Zx7UhNDoLkRAh2xQjxfh+mphoiLJardYxErMqUv2kkhMRCUVTVRLQmcwE+mLdIuxby4PGTVXkX8Kc6NOnT+/fv//pp59+/vln2BI5D+TnN1vIvQ7HuFB2lBNCgDc+vOKdK0KFrs+SPlNYq8Qh1CIdxzRN/QWOipyzAWTBnwBc3O7HR8DRDDdntB7K+BA5x09N3+d3gY5yrggsFDfPtdMxQSVZSSmMQ4QqfDXSeh232xxjHIYwDnEchmme7j89TmlvRpebNUdhoTgECZGMWbmkwmQhCqwfh5HgSEPEXGgcBl1pzoXIsNhDY3FTUVIiDqWYlcyUhJkHpuqYEtQ4Z4PVAnZsCL9iRNzSQpttdzNOg7CKAck/jDI01YsZp0I4/alaznmeJmFGYhozy9BQqKWsq1WMMcZxiIEDIf2XGTOMZEjCOI5ABkXV5nm/fVbiXHROhcxy0pohVa202LPjGMZxgA4ilyKxZoinAypiaNCoMe4O7QGGSmf5gM3euR/Hug4GmDuLCkBN4CE6DCVXe6FwOHOHgxar0jUgcWLk9RhHqKuY3IgLeioSpshEbMqBQxEtAXOuTd+Kaw1K0LalkTRSxFu+cB3wVoXQaHA9qPxYJERwRYgHAOiKBGXdyb0upCpi6maKDAjQ6zgqijGOq1JyDLEevESGkcyqaimEAPmqZlSqJVaFfblUQ+tQCYgQh9FoTsmYCHkzUrZhJGEmm9PM1fTdVAuL5SqHhChyjZ0TQI0xqxEyxWtN7c5QXOHMgKMFk1mZU8mlIuhpHok4F1YzpZRmCbHyiGYgXYYhqlkqZTWOEoR3Yb2GastY5Ob6GhnNUs5zmitVyIRYGsIcB3cWxXumnBAGjEOQcagmCNRcW4nZyFJKw7iqzuFFQYpUVLReY4L5wU7afOx1c3XKuOj3t80coB4UWGPBEiBN86T11YdxHExNgw4aOCJ1CRO0KC3dmWi1J2GVFvGxcvgIH4KGDBZhOSNMFCQEblw2qYqqCoKsmiJpIdLWSVOfIUFKzZazWhmHWEosReY5pUQEf1Ix46JUKolZSFvEpn5zqOfSA3XHVdEPDyxSIjUKwtyUvSJI6caNWyRsMI/PO2YGmC6lCBmNymRF7enh8f1PP3/4+GG7fbzYxMvLq2Fc7fbTh/sfv/vTH5+eHub9jsmGyNUkkWkdw83V5s3t9W++/frbb75ej4iGzEpW0pRSUqNpmswymw2sd1erGGhORY1WUQaRAOjBFIKEQCFQEIpsMOVmkWEch/VahoFCYNWLi4vb6+s3tzf311dPz0+lqKllqzEIimnOeT/tpxlPL0EkDIMRczHbp3med/vdfr8rJZkVYjOSEOTi8vLu7u3t23ebq2uWWGOPMKJjRCOb5nk3zU9Pz6sh3lxebMbxcrNm5qJG+32Y9srKRoj1OqU0zWm3TxLCOIwxyOXlBSZzKaW30aFmJDTP8263+/Tp03fffff999//+OOP9/f32+3WE9f3QOG09H8KnQc++KEY42azQSAfwKDQOd730KF/kOMtbY5jTj5hLiGKYw+YAE2cHwJMgSUTno6IAB7msZvgbGYweBKBQkUcFbn6DCGU3PipEa61uDOdm/9R51Xj5WIcaD2GEFJKphSDxKa3NRXoQ8Y4Xl1exBDKnAUqqyAxRgmRlAzBdRDCI1T7ECXOpeSS9rspz3NgikMgJrICPTyzqJVxHESChAhzaMYZPI6l5Or0LjGy1WAQRKZWciFmZHhkpNTJJU1bClECPJlkiMNmvSYzBFmbU9nPuagNgSXQKEHiKoTqhYUzfggislLjrHsJMca4GoYYgsGKmoxAKZUiIYwGpb3lNOdS9vsphODbbVHKpXqEcAjwgoJFKlUNcl3RKK5BkxBi1fM0YNSKqgW3YD2cBLgdk/19NuzLB0hkVRxXDQK5YYVAZeZ+XyKh01nUcy8Im0BxQFyEqjGwQxwkRBcmIQ4sNYqRUYFF2WEnw/nMOoVdVd+5KEdPuG5TTmiRCwetvk3qsKmRWQzlCbPAN/Kwb6LS+pGhQarigZtzfIvYVs/gTHVGI4nLQTdiTftURx/aESwusxoyGNFOjAoVuF/mXLLmbFqy2mAw8RCpQf8Qx4nYSoO72hxrQcNSxcaVF1GXbsBGDO60aKHCxYypTFNRJQkxhqr6UWRAJaKCqJ1Q5IImzDkNwxBiYJbVMEiQnGYtxczGMZaiUSUOQQKxVT+MoiaI7CKVqnE5yFiuwoHFSUq8LsBkEVmt1gxbKCssBl9Tl9ehxaUN1cI+9GIL87mtg0aOcn1TVS0kIjBexQutxsNGzGJUsgCOF7goljrfkG9isZNQjTveKFuFh0mpK5apFA7ChuhZcIyHiQDC5RUqqsLgN5VJGiryRdc4WhgPMRtzac5rUHkBkVFzj3TpcOABQPWy2uF8BCNEUiUVqBPr5iXVJtCCmQRpPozccKbt9rthGKo5l3AMIcYhxEFNJtXdtN9P+1Ky2TAnnct2nsvPHz48PT3N02wGXwpZjavVOKzH8Wo9vr27efvm7uuvv7m5vhWJTIhjEDJRUispq+YoFIcwhNGMxHgKWYkuNherYUDeGTKrKRyDBJGxnoaYmcfVar1ahaqusvVqfXN9826///Tp08ePn0oqWlStZsBRtVzKNM3TNM1pVlUgUzMumnPRuRXVYs0IjDmEOMqw4mFtYVQiQ0IVNiES4VRszmm73e73O7GVrsc6J9o65iDBAtfAiYB5EoPEYRiHGIPE5vMB57IFsbHb7bbb7fPz84cPH3744Yf37997/tc+1LULB+kC8FR53qEiQKKrq6s3b97Ajie0eI8OGhxqn/URQ1VwTOPm9w6eiTsWijoiR6rpjOIuPwvhJ2JCAjDZiSGUNd/7EELfWW8wABNoJ0A9b6Tr7wC50CT3sPOa+/GJQpGNDakcDTF7ahqJwjnRTDbnUlJmNcFeY/U/YeLAJoLNmpo1CoaraMktgwrERRyihBhbEKY0K7EYY7crxIpQ0sjEwMKBGYH9mkafipUCviMEhnLQVzv0CN2xE3hnmlLKJWsJgZgE1sdRhFYrmP+qFoEWjwUhl8YxjgNcm3hqIXmRE6/mE9SSZxjxVPjLHIyYgxlSYRZiJgTUhUQXYcTCNmuuOa7E9NkGVHQACk3uQY9Xs6s2Uw47IKCmRCAnXeru0OEqbKDM1e7FlWQwW3UTotAFdhEYuDCRiMUKkwixDBr1o/Xf5sLqmEcOXi3tdbjFhV90ZGLY+KLGzDQbDPjdwRBF3T2qdan2quV6coasU1ESHRrC7dcq7YiI5NBEc3tb/11EjA7R3Pqmke/NZI6wiGDYChN4LSXnlGEjhpQXAvwb45xr1lWvuuq3tLCbwZOb8B6Mb7iFEqz9MSpaSNVYAltJSVV5OABqayrZfsJgeSA3moggzBQTxSCkAXl0WFgQSyEEnECgReWiLmgxqetbcjTLLExVsd3klyoS0FIIgQleljjhHGZJm5WH/yrWZK60aQtL3ZaFHfSv5lbQOGL6oqiRDkDuYc0rc2EWqESh3hKxGoOyDo+1hdZNda3QhIyIVcg0IEY2M3kcAZGKbMRj0Ck010pNweQDWC3/4DFKzEYiQTgT82EWNvW5qnneXGphUa2jgdt8pDZQ4I3q2BlyRApXhxNG8lw/RJCZzmmSwDEMQdhCgKUsh6iFkycKVVO13TTnXLbb3efPn6uPusCVJmzWq6uLzc3F5vZq8+7t2zd3d2/evltvLptXIsVhmFU55VJUhIYhroa4HgYytqJDCEZ0cbFeDYPBn6/kEEIcQowSQxhjCKHGZWlx+QIRsdAwrq6uruY0v3vz5s8XP85Tygl8aWXNS9GUcsvJpTA1ViMpplpSxp+Sqh4M9CSwRJNoHJSEKjxVJm17f5nnebffp3kuIjgFYd4U1aLFqmTnOk+JQ5DBkC00hhrmg4hou92+f/8enuTAFimlx8fHx8fHh4eH+/v7n3766ePHj9Cd9Z5l3BWnUlBcaYVfh2G4uLi4u7v75ptvrq6uegYlt+JGQgAifnANzfXEjg2cQf8s7Ie4s8uWZgdNTSHYc0s9o9M/zmkwalDvIP9dXHTdRByBhQ+ddPkSejnjkKinuCAxTDNMaoi45nNjq7Z62UzLhFgHyKVVw9QQm6E1ZqQM+da2JLWSS/M4wvmQJYRhGEMMIUZmrmCD1JSNm9xnZmKtbkyIihiqxqKm4TS4+wTiwoojb3EjM+TfqCaUTEbznOasU0qqumKBUQoxiUgky6bWeCcclWtqtihRhFtuELWWhw3ub8yaDYoxYQoSKGrzBAIOQS4jCaiWCMkStB1d245WS2dXFIe67zZOopV6dmwQpLufD+PedtW2eR8EOlmdHEBFPpFEWJpbcT2ygg4Sj3toBGOx6mNLZlYaSFH4lZmxKmkhbPt+luXDVn7YzrGVH5gQasiWHMcwGSkr16z3QOvkWQ7NjCVQ3QTbvmKEE5hDSYwg+TC1x5LTdHwQJXQoTCCMuFJiRStQQtMRrQseDErq/BFef412Lkyljn7Jtc3CjBSLgTiEsBpWU3qmA3VlQCBaUw8TUbWrwp6oqvCdqQMpUsEdJG8u2UyMIpPAzEFFrR5xWopIo6CAGlhpsCQWETWbp6mUMgwDYnxxCAWOZmTEHESG4aJOXjUiW49jCAgRm5HZgMhijDT4+zxYCwEIOmFJ1emZsC9ziG7a1eZFh0FBMEJE1rN+Zb8BVBCqFTErEfHc36y43wDXEObIHWiqyoWZSqEgrEyFCfY2eGLNjg38VA1TDDG44OVeoYdK5S8bYdZCLR6OsIEliJgg4hEWVA0eXXXU9ajhwPKgpKBO6FXWv5SmPjMYszeJYuwCAPCn6pIJ4hF1tJjgFIK0hxlCP9TtwCjnRDoEGYcQAg3DOIQ4qsR5LnPOuSTSwkQp6X7e7vb756en5+dHIhNhJP9ejcP1xebt7c1Xb27f3Fy/e/v25ub28vpWLWSFv4Ws1qsh5xBmM9MybFbjZj1uxhX8f+eUjOjycjPEUJ3CZmWhIDyEMA7D2CxqzQymhW3W8DCMV1fXwvz8/Hz33Q/Tfp7nNOfDuT9noKKcUimqLCHEQYwSct+luSK/psEE1iUWJcnGqZCQkRprETIzUbWc52m32223llIWyXNKOI0TzTnPKeVcsEkiHR4TRwlxFeIwtDCkdZJ//vz57/7u74ZhgCYohDDP84cPHz59+vTw8ICwje7Q7nnNfM44gHDOVUSQmMzNkEEUffXVV7///e8RmsjMkKRsmiaEoXJA4zsRNYJHWsJXALLease13i7wHQ/hr3WuHp+CemTDzTmur8HrcYty139xZzwEZIZA2+Rn1AaMepTmj6YONvn1uaR5nlVNjXyHHKKkDIibE/GcFPlTQ5AogXHYEPbDYfWMYiEWVSolF80NEA+wYR1iHFcrmOqbWSmmBpHuGWCYjJSYjGML2EZEec5Wj9CgSwoLk4RS5vrGmioAE6tu/XAYVJ1TmlMmsrVEBIjGwRTwja0IsqQyNDFcDQXIFNhOS1Fi0OGqQcSE1JBmi1h4ZKFIuagZs0QjTmmCgAst6kyV6CALimkp1M50tLS29sYd0EI7JapjZLNeO1VB4GEQqTFJFSCBGanop188zExCbUY28yJeNsEwlwyGw8WMCDC5lKylqFJRKoWw8O3QRIdufv6uVL21ratBvqZsaTeICAwgEJOg1IBkOaVkRiJaFwZ4iqoCw35nwBWOsZi7fw6FTz/X/RhARZiaIw+mVp05LZ0QNsw29HXj4iCloSSY+qaUrVVcStrv9qo0jAMLW8lG8BRjI8qVbKszADSemuU+RfZh5KiuCbNSshGXnK1oMLu52HAQE5nnWUqJLcyClZxKRnhjlgg4SU0LVmk2o2lOLZ4PEZH4/CFhIaTJiNXowZgKM8UgJg3KCDn7Vjk26P+qeqsScTiHC1M6IFeimuBaRaSoiiIZOYmaESNRRs20msuBJWEOgS0YMxV/NrcMBjGsxgHJ33zr8HOuEkFbCTdG6A2sqt0q+gIKBBXEQUiFqdL4nnXkMILssbZw1rAadN5IWQCw/G98PCv9DOS8DTXCuIm+GiSyoaJGmjVfOXXXPjJT+K5bASpCVIUapoGqlRXXIJ/c1J3CtBnCegjrIazGQXiwMGSS/Zwenp7n3fOKNUcuhXf76fF5v59207QjyzGGGDgGGYd4dXHx9u7m23dvf/ft17c3t5vLq3G15jCQ0oBgHyEO4xCHGIc4xni5Gsaxen0Xo8soq5StqAhDDhfLc5nzlOMcVuNom3VoXmSqZqlwKjiwDMMwXmyGIcZh+HZOv/3d/Vx0KrqdJy01YzhnzWqp6JwLTMsvJDDLUNTMEiK8wPebJUgYRCILGSE3YZqmIXBo09XSPKe83++n7dbmPRPhkDKlFOekZnNK291u97w1rQEyciyI4R4kkFpWJaPQAs/e39//9V//9TAM19fXMEOe5/n9+/efP39+enoCHkIAa7e/oZPCzTIP28rd3d3t7a3DrM1mc319/dVXX3377bcIQr3b7ZwoijG2E3hdMf1nNMlaPCQzhFkPbW1x20EOiAeoxS2pnWFynR01lFNPOw059bQTPqORSGCSc3bI5RZRDtp8HPzXesqpZg15nmc8BcSSA0oIrupHxkQMx5d8sR6MKOdS8YiIqWKbH4agGIc4EMHs0nLB0lMRBW8+joMRm/E4roiUkU+CA3MwtVxsTtoSMYiwFDNiKWrFbIzc2C5WtaKZGzYoRTlwxVW5nuJYKMZQ7wlCUAIYFatJTWNgljCuNkIGHCtEpoWpeAx8MyS6kZIzMSF/airFiFSzFqNmMMpmJeeWWIOESdQkGPJ6SRhSYmunerdoMavxBrDkuAsY03vm11zB7MRPxUSVLPN90QGE7/x1K2jfqi+VOkGpee4ARdWjSSfNq+ZJ1Ty1WmPwzNiCkhYuoFIQZqZGRCuqpoWKUjF3J3IVT1UstWZWiq1+6mynGl1UG9sv9ZZhE2s2WbVollKKFtNQdXWITIPgeUVLNzRVEdgg2QFAMjf9VPumYjU/klSzd6v2vswOVOG/JcxCUh3xD2sRfgHIfqQsXPkeK7Tf5VJiGjdjjDFO81xSEpaiWY2ZZTUg0KKwEGxTtKYxZmay0kYK+USFufa+hrgSFomDBkaQlMRytdnUbBGZc0pkRjVWByEuYSnUxUIE98dVC9VgGJuRZXTbVFnEcFDCm2JmEhClSK7q71ebu0Gzl685gNgyXkAkgqUzSiol5RxEcIuq4M+GwDBZS9GcgcXVaqAERoqcCsUwUETU1PaDqVKLK+3vyCr/piqmDONoRkeNEKDWzVGo5gsNxizuwEIkNUtRNcGjponC4rUDTKpqlqp8rdKQ/YSM9rRsddAzwpG+6WgrbVVax7UtmcreKYKhwXkX7sJCRKEwIpxXE0CyAloS5uDGYmTwk8WoiMjN5cV6tV6vahrOZDwlnVNO0/5qswnffrO7vnrc7uaf73Oa5mkuuQRBigQaI19vxq/e3Hzz7s3XX7198+btxcVlGFYSIrPEAQZVEgOyF7CFQOMQhBBEhpi05txFVjp184shRq6Q11Q1F4A9UqNc1FLmZu0Lbp8khHFzeX1zcXW9fnwMMVJJzVRLUi67aX7e7Z+en5+329VqHUKY07zf79OcSikQtkE4CkPzJKxshTVTSTglgWcwsiAyxnixGiNTCHGzWl9cXIjElEtRnXNRtcBiYpAJc57NoqqEUpJWva/yQYP2008/xRifnp4AYqZpgsrM+SE3RnbLm7ZVHIgQzKsYI2ihr776CsBoGAZkb725ubm6ulJVeLE59yPNO8yr6vEKdVDDUdECi1BHwMQWVBpm3d5IoCKHRI69/K+OsVzRFloESHyTUqKG/3oFWZ8DpG9VPcqWAm8+3B5a0CPvGhEN42pzccEAASnnVErKm3HESyRipSBWEJBeggiyWxCrCVuuIVyL5WxsFmpSrLhajTC1KaWUnLCvsZIgdi4CA7WDW5UDMDMxFvFAdBwCR2HFxqtKIghzzCyBO/nARCRkrMW0JjI1NhqirFdDjkEkrFej5UymVsyYRDjEwIXNlMnY1IrmTFRsWI0UgjHbnKzMTWdIGV73ZoFtHA+ZbYhlkIFFiMVIhyFUydgEn5GlXBn4hkEPu2hvVyQH6FA1aU0JZrJ0yneEZE1l0HZ1Q8bStkU3LRs11uSoCp8xrUbt4FxFRSSGyDVUYypYzRJRNdAGughpWND1horaM2uIH8Av3zW9uLa17qi1OWqqSlzNlvGTYLIqoarv1JQBHCtRVEBoeUoUqQCoQaLKGVSUxK3vWDatKWBHqEpeuCQFriab5pxdDf3MYrDLd1VvG1lVFa6WcdAF0JTCMMvlBqverBQrOZdSjEWisJmGOBipQINbSY7ATJW8UNWCqBJVYKjVgHxqNpdixllLKiVrXo+jhBiGGMjSPGOyiItRT4gHRbu66wcRtsrm1NlOJlDJMIm14KxcMxIaI+/d4cxVkUp94VUtSG6ChGMFrHlqwVs2M0YWDq2GPgqkUmVaBQcNz3JdU3gKA6TCa71S90YEkOPvt6lY8c7FZyGmLgSWVedkYmYhIRx96kBB7nBnDX6Y0J2um7z/3Oi3WiG2Wm72zt2IsVOGgJGVMtJKUNZ+ND2amZlVEqkdDBjmWqxiomrwNcO2A3GGcMwwOaqmXWD6hK8vN8MwDsMYYiTmOVspueTMpm/ubt7dXu12u/cfPz087YJ8htOvCAfhMcpmDLdXm2/f3n397u27N2+urm9iHD2WbGj56IJUD3liY6EwHKKk5gxzlsBcUz4x10AeJUdMDGIuptZyYyO1eI2ramNRK6apaDEaVqvVaj2uVsMQ5yTQjBNRLjrN83a/e9o+b7fbzXodY9zP87SfUppLyWQ1oVVzlqEaH5yUrZCJtMAiJHA/kiGEstkMwzCOq3FcDcNYVDPSwTKvxrFNCSu5ZMqqnJj3U7I6v6ql/zzPj4+PoIiAG1JKHqoRHmc9gHDp3bMsmJIAQDc3N2/evPnqq6/evHlzc3MDbRqsm0Vkt9sh/6v7soWWZtVXpYOJ3obJkRDAh9stUcM0vY2zEzl9C+HsRg0V+RN7tsnhjjQLJ8c60gyD/AJuGeKoUw5gcOoyaWWeZ/eM88H0W1RJlWFJlFPJKZtRziWXVLSYsQyDiBEFZpIIEgubx2yW4KqiRLA9aVuwmBKJGZmWkueslomIAlGhnDXlMqdsahREiFnYFT5mNqekZhYtRmxMVcIQVyUQs4AiwjZWihKzasIWqGbV3UuL1RhUUoWymRY102Imgeo2WzI1FV0pFkIk2HhzCBI0BGx0dUvKSmo8SmSBriiXEpoxKDAisI0ZlVwdyc0ol0qqwI+MO0RyyIMWqvaOGrfWK9HIWixmwmHUzW8Pe4MLeq4Qyo42KQaH3uRvteSph3c2mCY0oOV6OmYjQ+5YNiUjhX8A/t9QkcFSnTigC00vdthxq8qus54Az2+V5tADKmIWM6wUgvuU5nZYgbqUpVTEgJ1M1cAglKIIoN8G1dgOiVKrlqwCooqKnFfTojnlkjMRBRpYrKK4ehBwgg73VhUIvMCMLBclLilnBXmqys1TDHttKmW725VCIQTRfHl1GWMwojTPZGYlm3IZopaMCN1kWm1HqHJ9uVT5pFqisNTE80JqwkGFctHH5y0FBA4zKzqlHOIQRUKMaqSmInVdURNCLv4c+oDSq7QfMZlVVIOxggKsYicWNWqWw3U+tboqEPYg14DwYJcaWdcddwl0oIQgTCXG5lhuqlQKFLal5IwZU7vQDtlUjDgEVpWGKGrEYa4TBjOpgGhoRKYTgqpof1XUaUtViNSOTWxr0OBRMprfWdMPOs7XlgitaSKMKqtXoWDTVzO7hRGR4yFunC5DDUnNyht8EYBXyxpU8RySMuaKiiRUWlYD/GSqPq7q54xZa7xB45r71ohIWG6urhABiViKUdGUsqrqaoh3795eXFxt99Pw5z9//PTw+PhgmnZ7FbYhyGYcbi7Xv3l781e//earr7+5vb1brS8REIWYRcIQB8RcpWaDDCUlzAIRPWuecohhGBihag7RkswQJKJqNMkU3BtLMU0phQq2LOeSNO+n+Xk/EYdhGNer9Xq1zkjiQSxBVHU/T9vt8+PT49Pz03q1AnG73W6naS5wg2hoDLQvswkbxtPzFg9BgrC20xTj1B4HCbEUS/sdPKlCkHEcjaxomVNKOcGpsKg97SYzEhFukVpyzrvdDlwItn9EboTWrDQN/mK/t443shYE6PLy8u7u7u3bt+CK3r17d3d3t9lsAEdUFbZE2+3W6yciEDw9KjIzEPX+IGlRjkIXz7q3npbOlsjNqKXzjMNT8Cua7VX5ZY6uXBPnqIiZ0R6/khobpJ2Bdg8f2zIpvTk5Hee7xIfHx+3j42MQTilP08zVQcT2+2dVlRAvrhg7u7TEZ9N+ql4IeTYzYpYQNqtYoCTQWLJubY8tbdpNaZ5JOAQZVwNNlFKeU57mbEbjEJHqkZiwQHLRnOZxiOM4IktWmvcKASgSmAD3TQux5VRyLnPKIpLmCfvjarPZT5cwHcCRCUIpz0nIcjtnSqCcYKkyV2OFXHIu48XlTdH1ZjUOQ9Fm/Kqac7ak8z6Z2mYdyyrkojj8r9ex7sulkJVxHIhZlazUncEIan0ItWKmIbAraRoqImh6XDtLTWojqrKCQTkcK/3sDvecThF02PAWX/PhV3OFVfvJiInWWXNbjetKZAxOjchKyfOM9IvVyqMGjmWkhYC+BTK8wjUREdZqXCpENWxBPd9oZxvh19fToCreSoP/FGKIcWDWElTVci5ECTqNeZrTDL8SD6sNmyPWFiep9bz+qWqHGgdXiuaiMJ23Fq8IshEjVMMVIq0pEkmZwQShQIGhprbNuYxB2CwOw7Aacy7ULKift9Nuv1fVtN/+Xn43rsYhiAqHYRSPRspu6F+GiEjpQsRGhlyzNV0pByhdsJtxYM223U3TvB+GgCxaZLbdbXPJq4JEDAT4ZjFWZMciTdOnTbRyoy+4aWKMmilLm1VaMDbIrkxS0Y6RanHaxqhpeKwmbIH8aZ53OCe4OCaiUlLOKagW5lJylbewaWt6VFCCTUNa1Z3AHZatiIhyM5YiCaGmnQpaVEsuBq7BwV93QlGoykEaVYFLyAToHk9sEqDeMaUa9kkEXhyGc5Ie5lJTeak5s9W4olDdMVxDQAjW0EJJgYqsdv8VcjV9mSPCKgPUVHNlU7UYVRQaKAirW79ZW+pmpKTKVQ1lcmDymDkMxFyIVW2f9Wk3zSkPcXh7d/fm3ddhXG+mmYZNznZ1ef3z+x8/3L/f759uLldfv7393dfv/vW//Kt/8Vf/4vLqehg3xVi3k2VAQy5qxlrItJQ55XmeS1EyWq3HpBX0JKOUSio2DnZ9sb5cb4islDLPU5kmU0A9p6NNNZeUUpqLCJENc8yqz9vp4Wn78PQ07fdMcrm5+Prt23fXlxKEiIvxbpqGIQqRlqQlFU2ULaU0pxn7ZSXzRVSCshSjXLJqZrMYw2a9QqahmtCkBmLQXAomUDA1Ii31dASuds4Z1tyhmWlaOzhlLXOa2hIo8zyLCOxmrAUr8v2+LwtIZI07Wa/Xb9++/eabb7755pu/+qu/+vrrr0EUXV1drVYrPALRjx4fH5FzQ5vTmWMR8kNqzY5XQghuzePWQqElNqFjxZmrtKxFZXQLHml5W3tayIv/etiqGsrxe4nIneOs2+McS+HXZopq1FzY3BIL9Thu61Hg/+3/+n//m//lb9QU/FngmnG5aAF5OI4rPwUyM6Q1Tuls5ha6rYNCxKasphASOScrWvNGxGiwp9R63BtaCMqaXo+ICE6FcB8RESlaqPbb+wu0w80tQ5klpQrR4jCGEMBDSDumYhaKME6AZjaM0aqVhGou+F8pheOw3qyHYUCKdCYoVY3IStE0JdUSY1htVizBiLSo5llb+2KMItxMuoq7CFdr1IMFF/1P//O3X3/zO3JU1IEX6rVi7XRX5SlRZXSaV3t1Ezu4fPksOcwWa1i4CkU6RUWNQjfnY03NtFkbkTKTwaspT/Nck1LDNZGBiEJkEVY20NrFmv6BjDhI3QsbO9XYf/c5NtekYP2XXHJ1FclWJ3EYiSREbcYfCc4dxCXrPM25pFKzcta9BzSN2OEc0A8xuu2KSQkhxsGcUHOOLcQ6MAga0RiwQ+trbBJW091ut9tt16txvVpt1uvr6+un521KiVRDiJcXGxba76fH5+3D48NqXsUYQ4zEFGIMSKFKwSqBF+I4clP0wdlPKQRhbUSWmuZCRFXBlEqac0LyF6Mq26eU93OKQxyHSBLUjA0gFQGXKZfcJh0ifNZdWKTi9JaNueCFwrToWBXb4DQGFbqf6lStaibDYG16FSJuOAzrzuk692F0BGCsLFV9ZKaN6aCmiGvEI5OCPjAzYa3KrjoLJIhxMWILFBFnpmrHmp8aetAoZRAE1RhSQgsgy0YIwm/EJDXwWs00CGpN1SAxEmgtxSnIKhap6WWqbIPvDv6LMYIRJDLVqKUQBfBEqiYsyuoTmKt3N0wOWrp1Pymrgq43JlM2E7I2Z/2g5dcTUa87MJpSAZrNartctruJjDeb9d3d3ebqasrGYbi8vvkX/+JfXqxXtzdXFxerh88fbi7X33z19je/+eabb393fft2WK2IQ8moSqE2CCY180TJUAaZmhBTIS0l5TLnksxyyYE5pbgahmEgM0pZn/Zpt69p7UPgMSKbDfLoJLjmqUrOuRTb77f77fO02wnRejXQ1dUYaBAdxoFZUtb7T5/NdL1arccx1BzejlybmGYhGFvHiKQrZMzCMcbVeoVM6FbqfC1qUy67/b6oxhhX4xhE8P5NsxZTDYjAyURxiGSkxYx4CABGhbuV5GZDLg/ppPR4yL/ETrxarW5vb7/66qtvvvnmN7/5zVdffQVzIjPDsE/TtN/vd7vd09MT3PuJKLS41QuA4uBGVaHXo2YV5DGKqOnvcPvC0AdAE6ChjyS0AGHW9IDdCZkcS0kLbO3gT1pYbf/eIREfFLLZ0aSZORcFXOWQyP0ZceXf/O3f/r//P/9NoVoi9Rqh/MBuR20jh+R0VtgxHVXA3NZc080QDlRacwVWt576Ks1qLPemkW9qjXZIq385nFHNY89CJDZTRDMmqZ4ZzAyVNDXxXIcRI3zADBKk4QPt/mfEHGtyLekJdrQc0eAEMYrEg96CyasgmJq9kdsBca+xqGCHPn76jL+28EpkBh6skii1uQ51DguCqfFDTDCVqZCoQZtXi58L6fhyq1DMuEExdJqIClvRamyRU4KjKVARppoE4pYO1i11jhms/qWY1eSnfZfq1Ri7nPM8p5RSmnPOhasSWuAPRASFpc5zImJTK7kR/VW0QW9GjWmjxl0cASPHkfhF2nSv1mpNresjzL4bE9Vf/bDCzAzzl7Kf9jlnJt6s1+NqHfZTKaUYhSgXFxtY5z19fnre7nIpq9W4CVcs7OlWcDIwa6QUngVtYA0sLhTa+enAdZkwwQ9IqkJXMb0RKEsCmwWqWy8dPKCalIGkClJzjHQvEZochx9EAapS8qBSdVpVXpEaHmsSlo4UOOAKXQzUF1FfgT+lPqvBeWt4yKSGJnfU20gcbPRk4CUr6nepRMJMIhYsMDFJxezuE4yR4CqsyTgwVXuYan9AFTr5sqZ6AfsFmAwFerrccqpUM7WqLMBD26n64MxvLMasZFyKSMDkYzEWYdFqhATPPwRKo6omJdgCHjTi5kbakE9kTcHpIUzbmaQmVHMjBrPtfjayoppVd7mklIdhjEMcxqFonqZ5TirMN7c3wkqspaQhysV6vLy+HTfXFDfZxLIR5znlhAzyaiEEs+gacVDPUpWJRqZacsppn0vJWZhtiNO0ghJhmtLD83ZOiZmCcAyAFsGUkBxQEZ6yLV0yCsLrIcYQg9AQZD2GzSjjaiSWOWU1KyWPw7DZrFuw0wPzxy2TTAhxGIchDnAIhQFHCAIkizx7ORcjnnOZpnm72+VSEJ15NQzVAM4Ks5lGYRpiEF6FIWixXEqhHHKBCcpSMEEl1/l/nf3Z/8otmqLbEr19+9YtrKnp5nLO+/1+nmf44fe2yU1Ss2vBsDTca72ndjxK9QIV9TW44xiaKiKAVtS4H68KqMj1Bv0tKH1n/UH9Bz72uj8Inw6EeTe1yy/rPXV8+fHTp5/e/9zqxw7QvR+30+3fV9vv6sVcpVl/VYMx/r6aBGuWqge+9lBrmxr+NyLyHbwag/R4h7tmVzWZN/hwlW+3x207AKyD+PfJxVz9lhzkHVgY7KzSTIDaCDejVWm3mJMxi/Grv0/7yph60Emqan5pAtrhhZNlzO3dOxHgfaz7fjPpXAwrt3+tQRB2QurQqmYySo2KMqPKqJXm0gg5B6JIlYwkcGTTajuBN8PcT/u6uqT3martMaQtUxFRbdbEpcxzmqZpnnNKWdViTcxCREI1rx7loiVP1cK6+iRZm47M0tyo6tPqD249Pbydw2vyCYnMpg7y6/SqO1+bUgJzA2apOKz+fb+fSspB+PJyM6zWIoE5MFOIsolxGMdhGKbd/DztixUjGtYaQhxAyDOLSM5mxuxZDLkh3tbUau5A1W0HvOgQ48V6FaU6FIHtgCsmDqMKrI3E9e3wc9izRQS8tJbGglk9/LR5hEdyCNZM0fvhg2apjS9ePTNMpLAJGylyitfZZeQ7NhERBQk1M5PU7Bk+l5krIWc1cV+Td9zgwOFNAxco1ahO/fKvIThgs38IJNoEJzX4UkmmerZskWQbXm4riurE5pp/3sigSk8pN07drMV2A15lbpGNImiigOTLRoK5JCGwBDFSqNHFWEyCiQYphYyMtTXezJSVS7P3prp4tY4UKRO8Sg3Ljxpx1EytcRSrBoWq+vi8IwYqKruUhcM6rGMMSjbtnp4en0uhVVxt1usQbtVKzgmuK8PmovDqeTbZTjFkJssl7/dTzoXMhhh5NXDb1YSqZh0oJyVTKzmlab/XojGIgHad5t2cd1N6eN4Z0RBliDwEZgBGo1QoFTOlaBQq8KMYw2azWo9DjMN+GqdpTGl1uRnG1cgi05xSLiXnIYary8txiAyOkiyGGEN0jDsO42a1HseRjELAApVqIU9mZkURpJXmlHbTtN3tci7jkGtuEUWq9SKkZDpK5DhKEAphzonnVLQGuUFgdjou/fbff3P6JTUgAkh0fX0NWyJozTabjYiUUvb7PRGBKEJMpipMOqNjaugqtvRn1NlfgwGylh+txc882D4vgJEDJgCOnLOHLOqbjccBBeLK3rJCmveZUz6On/qq/KdjIGrk2eKzAzXveC8DiGi/n56fn+mFsgSwhxfRLqB+Yz1ztaOiBbLqkQ++6WroP9O5C5YPoUYhv9hiZ0L8Y//ow6bZenK2HgeDxxdYo23wh/7LlwoT5WZKcdCgVVxWrIFbNK4DdB4H77Q+ogYbxSPccmWelliyQVRr1A7G7rg2qtkOzEyLppybdXqphwJY+xKHUFNuAnKjBqmbYcOYwTUVjeVDjxvuUFUi1ZIRZi2nklMpue6hqD/EiErMAIPmkkrOpXmctfgWgZUoUPWyJqOaPY3PQEGrPmlERLkUxLaiZjGMDkFgAJ6oEY7mzMyhuqFnr4sYGvVU0vN2O34Kq/UmzzNZETYyy8Vwbn737t1+9xhjDHHIuRBRHAfy0QvBXKXsBzRh0nb6J8ImK0wUiHiob/biAm8VLa6ue5WfrcSO62Bh8IJgjAbKgKjCGkYML1ZDguFucjQr83qoMaW2KqrpWYO8HASAsob+gkGLKbfDosAWpEW7J6L1apSaLn6Afr0yW1jldcqRn/G4UuWmZhkOdPWMQm09KdwUDGE8IPQlMJKXkV93EEncePg2n5mIitZsgi0gF1LcUGWq0Ixm9Qy1L8Le4GK4qmFgnR+KIdTIbDEAaBqzGHEuHELFN2aIvG+Bg5JFU8oVkFe5pSBxB2ETgo8ZM0WiULO/VvMpPj4wYdwqSG4nOFV9fnqMQ2DmopqnabW+GAaRwLvd8+PDw9Pjk4jI9c1mEzkEGUYZNjJcmNmUiPcpPO/MNIgw4vvs9xWRj+NmFbnGPy0iQogUhxBqsLIKIixxjFBspKy7eXrez7v9vJsTEcXAQ+AxkJRkWpg45WylIOMQFbNSSGg1xNUYhxBCGJ632+ddmOd4ebkeVyOJDCnllLWUEGSzXq1WY4xBlYYYkCE1hkFYOPDVxeXNzfU4rvb7WY2mKe22u+1qGISGYWAmzQVLCsr31WqEXdlumnMxEYpiQ4ybcbhYr4dhFeLAIRqZ7PdajGnSknNKWlSHoxDV7QXVvb90EJu6LaCfqwj8CEj029/+9uuvv767u7u4uAAkgj9/KWWaJuiVpEV89md5nZ4yzBGGWwghvhE1c2nXPXkl2nTT/e2+wLQGipb+yt7AyMkbajZAuKUfFn9cj8N8lKjL8iEtyoC32bki1OkYyzV0ff1ny6t/hDzp0cSLV5ud/+vJ0+2Fz7/4iPb96y3u/vhLHf+Fnp9e4AP6+o1+sdfQadDaPmhmh6Gt4QObzvCoEm4xTphbyJwm+GC42dzK/GBLDR9Yo7/U/N4elVr7eehRNTwOHA5ZXRlqp4b3uVmb1LApuA9+g1y1QNTsnMhjzBCUoLn5Kku1IxpEgoWAQ0mIIbKwquZkOWdTTXNukEjiEKnqp6tOZzHc3IyNjKhxe0czOBdNpXhEUDQSmzEbjhcKH31sJQeT3CDVdoSMmC+vrqb9PqX8w8/3lxcXa8RsNMo5wUhlCKv1KtxcbUQCuwsYi7Ig5jCxmdRIAxyCVsmCl8bURtmMSh2/OpKwwPHOCzMPY4BNAHML3GRmlgv8WkmGYEWRWawwCfQIIQ4hSAiIwtCMeOtZvLSNjYVUMwxfMAVrlE8ikBONZoIGXYyITOI4sFXLp8GkFFxJRLReraU7PjZZTLlkZOQwgzg+SD2rqexVipRSlPlo1iLdfLVtqWNSN2CpeKBKxrZ+mOuUblgN2XVLjYRolV3Dy65vvbFE1W8jNz5V1cyCSIU4cOcOSCQZatrBECRECaFAfyhGIVAOVBEpWkxCHLQdhdUZICUlZdMoJYuqUAslEEQQRqi6/nOjuJus8XWB0LmHtWJpFYc4DICSq9UwjpHInp+ePn+8n/bbcRhsHdO82c72vN0+7/a73SykphqE0irkQWC5ldOc54lMQxAtPM8Ti5hx3QzJWDmLRWE1JgkxDps1j+MqjoNImOep7CYrhamsxFSLqAlR4CDKltiYqFgUijIEmL6lFAeVECXG1XotEqeUZJ6ImeLAYWThSHx5ealahHkchhZdkJjkYrMZx3GIMcZhvYp3Nzc3t3fDMBA9mtk07Z+3T0PkwLRZr0KMLCwSiXkIxnEwica7OaWUSi77zXqIw3ixGq8264v1JUkwFmUmteraq4q4HLBNoOPiPraqB2NeagZA3BUYymw2m9vb27u7u3fv3r1588YVZzAZ7uuhhhs82jW3oESoGQY3joT8cT106NGMozdcBjRTaj7HozjUoQshHRoX3pM9fg10bb0a0SvsQaF/QBvQQXdw897ZsZawfy5c9H9xy/9L+e9ZGlfkiAfyqv3C5lqfCm/a3/yLVtoMITsoGw/4qgKrKiLb7XWXBFAiasqIk1ZiRTBVyyluutN6UKnWFw0TVVdy8gVFFYwdtH7VP7uhI2MVFmVhDiwaMKfVzHAOizUbCZNqqVt4LjllrTHUYzetm79ZswXpxokXhGLfWW5pISQc0vdy1Ts6tKRmgUEVWzVzjfYGa1wvLqUknadJyGIMAkJNIleDNUFUQCLQG+TvKyPUhWHTBUXRhvvwEo1a0pSK3qBZh96HqyoUeSzUTKrSTQ4MZOtXAdJr8wLfqJEYW67uCKaFjIVFCQmCiJnVlAqp5iYNpVkI9jgUkUhCiGI1VriWUsgK8EjLTlxfyTiOFT2E4AJU1VBNZegCsiaFEGIQUTNRFSmty2TIxVLfr5m2OBFYF4xJxpiwDRJZi2MFxzwo8aSuEm22zk2FelizVkE9WYsoVPN2KQIL4dUQN8++amntgXBEWlqSxsDV80ONPcI4eRgbcTDR0E5DxqxMUqecCHJpWYXQ1YrcoyHxMQlB3SGsIUgjIhG5utxcX1+P6zWRhGEVxmFcjUY67bfT7rmkvYlamfO0m3YlT3uxcrEKgcMwxnEIQohyy2wmRkOQICHGMA4DMeeM8AFadalEwjIOK5AZ85xKLhYDaaCKblVIB7YYkI6Pxhg2q/FyPYYhMHExU0VKJa5REtppxqy6qxlYb6px/UXCMA6amaulPIcQyNgGBAALIcRxHK6uLm9ubq+vbyTEOeVAPA4xBDEzuLkKEXNQtTmVrJpKmaZ5nmsvhshDWK9iXA3jGEcSyaWkkrIaDLGZaAhhvRoDcyllPY6H99IIksVPxw2utOJmlIPUZtfX1zc3N70tEVCCdUWa33vvKYYxc+DVO9L3D3ViCd/7CgUS8tnVU1zUrK3xJ1Tr6ra+s9aMndEp69zsFy3xn33pmSQnovohpS5cpNNU/dMXC+Qv5X9gca6I2451jE2ajcaBGnL01KDMoYJ6+cEc61gxiNldQYkd3eUziKhOI3yu9wj8kNiIqU8b0maSx1ypmEFIOvTWKj6kg7UDWmIiMq5WOiJilSdo5tsh1I2T+TDjtWjOpeTSOFdrrW3Sv6EaA34whw60XFNtiBg2S8QhDNxIBEG03AZIm/5P1Q6UGROxsFrL3GYWYzAbyIzJtGQVDjGuhkHCUPFTTdhppsbV/hgZ16Ro24Fb9gnMBuxz1GyzrBkvV0CDftUttlmQVD9CJTYR4RCBnNQKs6gWs1K0cM1dwSKiWnJStcxS4KFORCIUORKTqSG8eD11mVnNfOlwgolrtjEihaWySIjCORcYg1Fq/mVGFm0Yh8NiGKJVH1S3fDczFRZrOeHBFYE8DBLUTEo7UBZl+DsLtHXAry2tcl1jNXUasCsuY1UP083SYzJucPiQOLYtxm76NObVlWi11Oggh9N2O0Nw53/DNVwgcQN/LTJZU4g3RIUxMVJjUoEdF3C8YMDgcYZnUWiWRIezEFd1BjWbgwMgApEm8ubN7fXt3XpzyRJXF8/Z1NimaT/P+zTvWBOpkOY07abtrPN+Lba6XA1DS8AZRHOCYicGiXE1ruIwxBBjUptTmlKeUkbmcDMSkYuVqpY0p5TmUqBr1hijFWUqgRXeOkPgcQzr1Xh9cXFxsYIcU6OUNacCVxXYeBUjREOCv6rWmFh1CghTjCF3sRKQ8MBa1K0Qwziurq9urm/uLq+uWWSep4Ho8mK9Wa1CjNasw4hD1rTdp2mepzTvpxnBvk1t2IyrGNfjiKEparv9tJ/mOec1oD/ROAxysSnjUIqu1us6nRDRvuOHnDTqd24HNyCKxnF0VHR9fb3ZbGAA1Af14c5Fi9vBw4ERKneDm96vnppRjsOgHmB5s/0bKMgcfPSoCEROj1fqManVYF2cRmpAzXvdQ7Qe3DTZfoL7O+DlaJI6/3xvA3W25H8p/8NLPP91h2r4+JsjeXz+JZ6DREbVk6mv5BDV9sVaKhgQJhUYg/sZi6uiqqv2UB8f6vjCmXbYNiSGWEC0Y5+qwWJqSCyYy+SUq/VwRXaH5rQG9K16abS63puB6TE4upeiRYU5DsgaJiRqarkqRyjG0JThIUoYK+YoRtY/20OdkggzXDph4cNCBvMWWLQTGwdhplIKdIIxRK2BoUyLDUMLZWEkEnKeU5pTKuO4ChWAcI1qiE2ALQgJIUIojasVMytRyomqAVowK9Q4PyDBoqZqgXQcojUVaSCac66+iEalFG60ExHBFFq5qFKNu6OmqitZwX+HmeNAHCTECFnbaCPd77Y5J4wVAqdyDRDAhOBWbIdpy236tUA/MFKTJvWsuikUPtI4H1AREXHNX+Zo0ohZW+A+btgSej2tGGI5d/zkAvbRWgb7FqxB7WRhNWjSSe/ucyNy67Wds9sB62Pbr2ZFoK7qoqwck7QkRg0md+0+LPljVTw13pEoBLm9u72+u11f3EgY4mq9n/b7eT9P+5LnnKfIxqZmJU9T3u+45HXk1Wa1Wo0iYszznOZ55hCiDGMMqyFsNqthHCWGx+2kllLO+3maUp6m2cwCBytsWtI8pXkyK0yFNNs4GjGbChmiRK7GuFmvLi82d7c3F5sVBk2JdtO8e94jmC+TFKvxeTnnMAza3gleFTdKjN3YxQwuAYHrWIjIMIyXV9eXl9ebi0sj2qzXa6ari4vNesMhsPh/UpR2++lpu93t9/M055TJLDDzelzFOMYhhkBEKefdfv+83U7TbJcXwzAI8yoOqxgR1Wpcraq4aNRFj4p856bj/BjUAaPVaoVUHhcXF9Af9eyLi1b/VTpsjoWzUJChOGKQ46T3/vQFHFkgmJ65WdTvfy3NxtZLD1BeUm95Cxewr8dq/gjnivrG9y30Z50+6C/lv3/h7777johSmve7LXXntsMV9MXA4ui605nER5ecPumg/6gXWFcPZuYRmF44LJ5c3F96pgfHjT38zw+xB3y1OJm3n9Yxqm01Nv1gh45eezCFGC8vr4jo4eFhnqbaq8oAkNfovTpeooenHa5aPOBwAzu712OxQ+UHErjVy32PD6RQX3E7Lb16zrHKEyxbdPJavYuLZzWCyrp7j974ogGtWu726MXQHaiKy8urzcUFEW2fHtuddBhFa7wYHQaB+YCHMRMad2ZHA3w0n7xJGK9DF06WCh/91StZvHsi121S9zpc6B7VxXxo+aFIey8Vl5iPcu1HW1CHntjh6d65Sowt304PkMjf8ileI7q6uREJqjrttuD3iNldfVQV0VSRUgVRl0oX65LriaRSrBX7EVegSQzcWSmEroNuwOcENbtGsfYHXCT+E6muG4ephhqprTtzIcbMLE0FbMj+3b2qZmfW9lEzqsHYclbTGOMQB5j9aSnM1EwEGjjFW4PC1HvVWisiQ2xsXYuUAdLS/U6IDhp5Zr66uSWih4eHjx8/LmbU+dl5DJd74qc331nc9VIlPXfSo5zTi0/Lop0vLPM62osFcLbaUyn3Ui9On9WDpLOtPS2H18FMRN999x389f5S/juXb7/99urqihwV/aX8pfyl/KX8pfyl/KX8pfxvvLi1taoticTjcjiad4djU1WDk42qVRUswdhZjskcWjAf1OrrDsxHxPoxb+TP9t9gotpsKuAgTd3J7UzrX+qVeMqhBW9R+1JNMCQI8s7kUsNecyWQ/Zx0qKH5JHeqhP4I0j6IhDhe0LFV4F/KK8WsqhHB7ctxWRy8vqS4h/BPP/10li0/WxYHzVMmzLriFqCvHz3p5bPp2ced/vzC8/3rD6WXT94vlS8f7bMd/Oqrr2KM8zz/6U9/IiL4ZldP9S7x53+H8uUTAIU7HcrZ239Vy88+/ZSfsE4vI8eefae1WQuN83pLoPba77bb5+fKQTYy6KTSJv0P7OhBQNerju85/er4z9ZX4D6c3qFzlXacZbVmXW43rzy0coz184vM0IsNfumyg3rBiy/Y7qtzL2Kz2bx584aIfvzxx/1+/4XT5lT+nAqiRbMXUuiEPz6Qfwu5uuDY6IU5f1Z0nP18RG+fiMqFzPS7/jGi4HVa8d27dxcXF+SoaC675/2H/u7DZ+AcqmmhcysllznN28+ft4+Pu+fn7fN2fnocx7iKYTWE9SqshuruIhVcMBNx50dA1vlnFTXQ0eRpOIBxtMAxWc3MEHcGjiTTbLtsKeucdD/r45Se57ydym5SO0Z48Lbn9qjm7WVEJEaD8GYdry5X63VcDWEMCFZRV0uIcXN9tbrYrC8uLq8vHz4//fjjhx9+eP+nP33393/7x3Ecr2+uv/7NV7///e/eff3uYrMZA42sA5dIOojGIQwhINJJCMiVwXaw+6b11bvf/e/+90T0448/Pj4+/qp3/E+7VZwup1cW2OnTX7rmdUHTbypfKJJU9fvvv//xxx8/fPgwzzNsPK+urm5vbzebjeeMPNtO6/JL+OO+/fbbt2/fmtl//a//9fn5+XTxU+fYgl+lee+7V0tfrZnBrSnnPE0TMj3N84yMB308X7/XmuWBdXYGfo1LqP6D+za7JzCe7hYhvUOyKzhOtRt9v3rZ1JuYnMomavJUlsbbZ+ak377IJuHX/+f//J9/85vf/PGPf/yP//E/hhC+/vrrf/kv/+W///f//t/+23/79ddfI38WHYv+RUv6Z5024KWVciofT2XxaZf7t7N4yun1i6efftM/jk5G2KvtJ1ifKwPJ5xcvDleWalRu6/UaSwO3n56+ROS3v/0tEf23/9f/8//xf/k/i4iE2Fzihjq9UX0pOSckGchpLjkhH2UV2VbjmZlZgzd+kF7uhd53eCMgOUBRzUpZrRghGW9RUuR2UNfqGpExUWAKTFFoEIqhuj22p5xgKyKCwRxzITGSwsGYC4sRmR1ybqhnADu88+oR1FZoIWqBzI/wnN/kOtxmKojgc503Si9jmOjf/bt/93/4P/6fiOi//Jf/8t/+23/j7ho6zruCb6TlKul/9oZNdpK8BZIBcskapB5aQZCIcRxXq9XFxcXd3d3t7e3FxcXl5eXV1dXl5eXYih9UFkLPn9jcPfSlz15qJJGUkBoPEc+RMxgx0KdpmufZcwNTW3f+9NPF9eI0a090n8qFt+B/+k//6T/8h/9AL1pbt5od6h4+/f9T+XXHwv/NFZ+F/9yP+FXX45YY43q9JqKc89PTE3IIYAGv12uHC4vGv96XxYZtnYNJv02ePSEtauhX46KcfXT/uC8BiKf1n27Ji1apIoa7LsTZ6U+XWXQMB19v1S/Oll8chP7KnPPj4+Pf/u3fPj09vX379uuvv764uOjtbc8+69dOJy9elbt/9+3sq/W9047xq78176NfdtpUh5KnE+wUWVrbj31knFf21nrkQOucvVFKKXATQxCg05lDHeGEjdztshYUBzZ+o0bZW/ud6ESYGnV/+4Wxr7x8NRGrBv1WU2wKt4ZJcz4wIiJhEz7ExOorfOG5fHQFbLNYpEIlgkUTlkkbdj6wZVihwmQkJGZK4jGI7XgidpCmeiFUHUJ3Msd/1nNWR8N37vx2Wnzh99Pv10psTLkQwjiO6/V6vV4DDMF2frPZQKIucu7SOZqnhzt2AomsAyJ+i7UjYmhBxksp4zj6kQwhnXqb9B4YeaFulfGxpRp1Yq3Pi9fDo/7211GRv0TXn5mqpjSnlHJK8zRP+ynNSUthLVLXkzEpmZhyDS/HRCJsykx8rKZzdxRu0Y/NDma17UcNFonknNXXRknVSi45W86Wi5WMwMJHa+LQAYxRNwExTYWJA4lwYArA9EFUCR7NR/U0eSBmgSwyD0JD4CEwQuINMQ4xRLZAhUmJoFUkFTITNRFrYb+pW0fnJvzr++I/GKZ8yXb75c9aiHt++fj+JS358oaZ2TiOV1dXzDxNE7Z59wSepsnMUkrr9Zpb8m2/8fShi/WDskAniy3EW9uTvafHpl5GLMRHPw7cUQ5+cDk9zZ+O0uJBjop6GWTNdtWXvR1bs3oH6UR8WLe/Lh69OJNZt7Mu5Et/Td+wvp5T5IpAyd98883FxUVK6dOnTznn3/3ud6vVyiPjnR2fs+VLdpfTTr10Bl00u58t+NWZm37+LJ7uE8bfWi+a+w72eKvbbNmpAt8e3JnLH+QbWF+bf9lP5gMqYhGMcAtpVYOeNLLEDvIZVVgnJhte6lRm9SltCA4XtrGkigxqTgVhDjXWPRclYmtEEQmiejVgJEzCFioh06v6+rd88sa59lIkkIhxaBHYahIBrUoKb3Or18AOCQbOE0nXIem1hPU1UQ1kUtHXAWRKC3/PRHwUL6Z9egES9e+x71M/hU6B72k9Pj+pg0TQWTs55EEW1uv1arXq86v0p4JeBmoXx2Eh/RZIqJsR5o0BKuplha8OEXFB1JPTi9PFAh4turzgitwMw0+Avln0qKibQEdYiIgMkXeMrGhJ8zxNU5rmab+f9vs8J81ZTKNQYLxymPnUPJLMB2eHQ4bd2u6DKrZNsxpXENX4O1VgMjKlSqjCZTxlm7PNWYtaHbduKCowPyzbio2QpYmrvwYH/MccBP7VxKxtqnMdADJWQ0iWSDQQDUyj8BhkCGEAKhpCNJWCB2l1kVYkLDOVdrwyb9qLm82iLETwKxe8XtuXbCen1yw2jC+/8Quf8iXbVS+KgYpWqxXIVW2et1g88zzjfOxMb78CX3mQnRwaFo/ur1zsZP1mY8eQaIGNFl3zhW1dyJYvGUnr0FiPihYyiDqM5cq7RT39z9MunLa5F7UL1OW7+CnwWoxDL8gWj4gxXlxcfPXVV1dXV9M0gVS/vb1lZmhIrTsi0wurwxqSeP2lny1nYZBX6/GdT5ebj/niw+n8eekVLwbHjgPzOABCG1xz2o+8P6sHWP7hrB3SYcILSwjscXEBuTC3DzjnJAsYHU/sQ9+7Jx191fYDPJSIuAaYZ0FOR1MlYRNj6NGMSbXlRzIjY2eJgkMih0Vteh6NbL2CK8gTCRJMkGq6huIzoubR1yQ1tr1m7uR9rgk1j66jw2bmgS4cFXlEUzZy0sga8UXEtNSRHbX9WCItJuRi7S/2gv5DPw99kgCOeBo7ZG7Bh9VqBZbRadqzQc+ti/zZwyM7OQudzjpvPLfDrR2fnayhIu3CNCzK2aneDxG1M6ezRNUQqAs/4UvjNa7oEImfibmYcCmK4/h+u5u2u+enZ8vJ0mQ5cUNFAWHgzNQKlzojuCDCCSlrBdKNyKkTCXEOO2zEFTL54NSYfmZUjLJSLjQXmpLus+6zZsuq1nJf9L1oXKXVAw9CBHNgJg5U4+kFkRg4CkViJsoiUJOrV2cmpqIciAfhNdmGeRRZSVhLGOOwGodxHFizmHAhViXNxEwsxqRsUoxq2OhOOOiBPftF8f2rYMc/d/nCxvzj29zvLpjZoHZPl6I1Q+yzt7/UNm8hFrlv84t93Yu0GG49GnDkgXaiKl94fSi8fsfyVe26Dz4mGE5Hz46LtsyXfqWeUFO4DBkMehOoRe9Ox3nRYOrYpkULuWOhzkoll5XWHQRPr/T3AmD0m9/85unp6eHh4f7+/v7+HteH4zTp/UNP3/LpMH75hDw7+K9X9ZJEPtuS/ppX1v5iCyQiJE/lFoXZL7N2mJYWeNrb0/vMn7awQ0US40BtcmKCUmNL2qnVTItpFx6rYaMGkHwCt34d78bds4mp0jHSeiBEYmSCWE+EAGZqpmJm3M69BgSFWGVcuZkzw95BFXSQiYVDoBAoRJZAUtMdOu3T2A8zIkeARGQ185KRUCWNrB7cPXRFexeH4B61bYy3fxRMjrtGsh3Rw/0ef/b7/nO/6/dqWfqCqQXTImjNbm5uYKN5dXV1cXEBigjaKxixUROSB/PiTrj1zJAe80nHb+QQIqEXR95UNAl9gdoOB11fQT1R6qdK6kSB966fDz5KLotg5elyCQHZ6Zc0aMcjSCakWlLa77ePT89Pz88Pj9EsWBFSJg2BIluN4pepkHFLo8ZNOSzcTRzqZ1FT51Pldgo5k0lKpCRmpoQXYKVYUp1y3mWdks5zmVRho0dt2jU8dOgAtQmKMQzEIVAMLEhZgMSZkdSID2kwiJREKSiRUTQdyCLbILwWGUU2gS8G2oxxvRpW40DFLIsVUi1aEsvApfhp6ICBzqGi/z7FvoyK+B9e+rVNRDlnWCuHEKBJ6U+9LgJ8s1yst77Osx+oLZizf+pbpZ01CeDIQjyZGUwI8dNTY/Ygo1/hPTXti19fUKLR8Qr3XxfCxQt1p8lePd/Xdlq5dcDIO+tiejGw/fdu73nanh4aLu46201mXq1W+Ot+v//06RPIfADiU9Hf7yL/TDP8F6s9i5P8c99xOl6Jp1vX4hvvr3V5M2KMffzo/mJpJvn+18We8UL7paZPFkGayar7sep31rLJlAqMqoU1ePzDlHMERa3RZ8yPTjZrAAirP1mMVU2IlMmM1FjVKr0C8p4PN54vy0cwkbAIiXCMDFRUI6rWVptbUWMJdCyMGUtN+11DtHe9PMrXcNQwx0bU9A71Z4fbjBwV9a9p8U7P/qxVtCXQv+7Tpd3/iqXqETjdqhpWRDj7AfdYs++BTANGcWDUg6FeuJ19bi8lekBDnVjzSe5JgmFvtCCkXxqos7/6N65hVNVxHHspN7a8Ny+iIiGiGm5YS1Y0Nec0T1Oa55Ky5UylsJiIBSZhjhICIbOPVDO2w4Q4TgBWh6Eft+LfWc/KqoE4hXsC3B1KtlysWhRlTVlz0apdMxOq8wuzUBaP8pEiEaGqOwNyq5mLSJhFKBAz0yEkmSIxaSFVUQtkcH8IwlFCEASoFSBBdaXdYT82Qm6Ng4B4oWVfXE7n/Rfe9U9y+z9healOb56q7vf7h4eHp6en6+trP2osaui/fOnDosuL6PsHsf5Cq1wl52vp9DJrXNFZLfuiSSiulKEOnNkLEe28I96Svs5eMPmf+n6dHeHFl97BBSpajOFirLgjw/sB77nrvsHex5eaAamN8vT0BHz50qtZdH/Rhv6J/WC+VM+Xl9clct+wxdI7ncBnLz59nKMin2lmBssP1w70NNKXVNtqR0vqYRaHSCKugszskFMGGg2r/7larcKEbn8jnye9reYZSFQZFZ/HgBQsCOGKbH6tKrxbwuWndVr7cfwYbjdyp3NpGytuxG+1A8xOTlWkVJMxIUUo1WGpmSorOGqvqTWTrAdGrXkeFrXtEe1l9eeKszPkrMDxD/2yWlyzEG7cWVjDoujUisglknuHwfmxZ4x6wr6v/LT9KNL50i6o67OimJuTXb94z8Kvl4bl9b8uxC85KurGr84QJmJhVdK68LSUnFKa93NOWUsmVSELTKFiCxmEBIcNESIWqWkDxBo2Ie7nsJtwExFSKPrkI3zw/AVqRQ0tyYVy0Vw0FS3FcnXmNMepRNpzRdRmqs/E1k1opmsaKG66XkYY2e59iNS8GLUdplgdUGwHrokfPcum1qi6bSpQjQXApMShNsPtirr39A+Q0V8okfvS75S/9nEvNaBfb4tN+teW0x2XiFR1u91++vQJgXfdHanf+fr2LL7vEUPf4JQSPD/9r/1Dz26ffk1PupwKoFMw5Dcu1jMfJwqwk+gyfFz6p3glPcV1diRdup294LRYV/qGnd7eAw5/xAKR9Ly619lv7a80wNeg0939Zacj/1LvvDEvdflL6vyS8tL1i++/pNp+kpy9RVV9Z7Jml4ZvqGnNOnxiduwZcK7wIRt0+4baYdXMtKhDosYVNSWTZ1e0ZoOzWM6voaJlJhhAEzRDiLQe1M+dIvsq7dwflg/CLse+TdSLrf0E9UUmSA9ZuSrrPx9541GXIAfajn7sWkM6E6J25iff8KxvrS+fs0jo9PNZiGDHwKifBg6JQBR5tpbNZoMgYfDADy0XJDAQgow8Pz/3iidXorlfpFfeIx7HQw6Jev1XL139enu1LDp12n06nnuLpdS3ijurUHx/yA7LzqpUlKCYqWaWU5qnPKc5pWne7ed50pLJSmCLTDFwFI4icRBwM1IznWqHl1tpTA4s24iEWL05BY2HrZtyTayppbT0WLnknGnONucy5TIXy1lzsYQIE1Zx+WJSYvZJdSMz7y2aWiNO1tRPTCQcOJiwsJiEGESQZ95KzloKacECkVoJM5MwB+JApEY1dSkzyFokZccTC7WF3iTAl0jc0039H1++vKrTfeulpWgtH2S/hf/aNrvs7udxKeXh4eH9+/fv37/fbDZ3d3engYL84l98HOqf5/nh4eHDhw93d3cI1uKLp+9U3wvuDImkJaFcXOn1LBbw6WW97PNleRYALXrnxToAenacz1ZyKlZ6ELOQR4sbT3u6+EBE2qyXrMFH60Kn9E/8xUb2KMpJEa9hMZJ2Qjud7anfeDpzFre8Ao9OYcrZz6dv/xfL2cXu09I/ODqEoSe0G9TWIJahdB5D/VH+9ImHnrY9mhtj1J6oZkjOmP0/gyrNrLqV9JCowqDa/+WyOg9tFgfFvoKKJMQDzR0uOtelV0a9o4UMeaQPVRyZVeM/bqdzeMsAArKf3w//1Rp4+aF7rHnTjA9SYjk4C7H2Ejbqv3FM7AO3ECCnj2BmQKKLiwsYEiHwm1tYj+MISO1sekppv98DFfmids0alGvUqAQPjIJmuLLMTYXkOOPv2X5Rw/0L3n0hoM6KWf/+Jb0en8Rac4PUpQaNa+ZmYgqBOcEVP+ft/nna7udpztOUp9nSLJoHsSFIjBJDGKIMLWRjmxHVVIjd+pWJiEyatb1JP30LUY1ObaaF1YqBcSfTbEU1m6VMc9GUy5w1Z51ynrLOSXMhoWoYfW5LPHMwYSaKLIGCcGRmDsQMq28hEQnGgnTgMUjAVqFFS9GsyI4emIOIBJHAAVo3FhFRlsBszFp9Wisry0RMyq+u2dfLPxUk+ucopZTdbvf+/Xtmdh8xbdFxeprhlUV+Wnz7hz57nmdfVP029iVgyMs8z9vt9vHx8Ycffvj06dO/+lf/6pWLT9eSngv802OUxfbvXHFv5PGrSHIfwwVmer3Xpzv3L5ZFtX0NfFz6nvaN9Ha+VPOiUy+1/3SIXClp57QDr0+qHqb4xYtpeVpcNQC5jLBYpRTYosIx56Un0mHbO8JS3OG2L3l9p9/0SJG6LdCaY7Ybjfplvv30m0TPUB7aDEzA7QCJsbJKC2hOOaWSUslZSy4lt9rMRfdhuA9NPKKIXpyOh8v9Xnzstj2zf4T49EFR1mKFyZTUD9EHyycHdmCIuNE5bOpJfQGYOtB2LCVOG3kATkZUg1K6sToZ+Za80KCdLpx+PfotdnKSPGpPc271yT8Mw2q1ury8fPPmzbt37/Dz7du3Hq0Rpvo4PaaUuKMhU0o+tz0GI9yBqaGiYRjcc4W65e+oqE+c99KaRad600wP52jdwWCB9nw5WOeOc4qcTsXaNE34k6Miay+yxjAlomxUDaymOe2m/XY/7SYricpspswUo4QoIUgIjETirfNHs4ElWIX31mCLnYDp5ZgcfTAyomJa1FKxuVgqlooWo2JWyJTgvHkgMHuW6Eht59UKBSL2CGDgxmowr2pbxBwk1FzwbKa5aCkIi8QMTCRSs7eD2NI6eof41UY1KBpUdlWxVgGhdbEKTsorEvMfU35ttS9tq/2WaWallMfHx++++y6EcH19Dc207wE+BRefT5vEna1JvyP6isJpBlzRYrej4x39bE+nafrcysPDgxur9pWc9t2XGartIdFLj3OIIM2P2rvvf/Jb7ITn6Edg0anFl690th/wvkd9dxbVLq7ULrJO32Y+1vGfbZ7/SY8jlOCunjx/aU72f+3Pi9w2eICkl8RrPz50zLV4DacXe+92u93j4+Pj4yPmycPDA5Stv/3tb7/55hvgfulMyxej/Ur9/bidvqPTyxZvxNkgalSQHPuXWbPigjcA7I2Y2ZVruGWz2Sy90pqKjQXvVxUkUc4FeAhgqP4snZd+HV9qsv6o6Ue/nb7rHjc1KNT+cIStgEiO6nth3F48ICspGxdmIuOmWjjipw6qNEK8JI9LU7Eit4a0TbM1qmO7jmH7cfVm2rQfpW7YVMrBKMcD55yuSu/26WrlTtgu/rQQLNL8zhCX6Pb29vb2Ft5ngPtA/NbxjqCC3O+sxxw9l9MLtMV65w7TO+Wjqot56+3UqqotgGWLR5+OBjWVscNKb+QpUept6xu55IqYSNiomrUZ1ejqlnPOc8rzPO/303Y37yejEiyLqTCFwPBpr2nEzh68uAcobbofz+Zz+J8JNG5z1rRCRgxglIulYhnOod2i7G62GhSiZy0P9GiFblxNgppNXKsK8h/uGA71IGmsFIN+mRDojJlriDPwyWylf2l421wzwwnUdMZQRB/7UZyU/n3/r6r0O6t/mXN+fn7+8OHDOI7M/O7du35Tp5dh0NlvFrdYO90ipHUPuRZrr9/s+92a2tH/+fn5/v7+8+fPT09POedFepD+ca9803fNjomKXgpwMy20Y34bHxao6HScz0KNRemf9dKQLlrlg9Y3flFhjzj9xgVZdXrBoh4+ByUX43Paa2ovEaKWGh7CeRR2YCEE5+1VFVOij6ryev2LB700XE9PTz/99NP79+9//PFHVcWEwYNwpIZF8+JBXmd/QuVmabGYCXwMLul4JviVPQ3gx3cwQ06hwbADt+Dgzp3OAo/GaRgNxli5m09tA7UFhV0ETiKllJwO/1UNmqvPzGXnESSiM8tmOf7+7xEqOqKW+uGpbet4nRfqPvPmK9hiIlJSRy7c//2wMXl1VrVdbQ+wtlmc3OL/MvUNW3QVlaiaKmLsVX1I0YNYWDDr/oL6HeGlWXc6i07HxY2sXX2GSNaeeRATxk8dvtYcnfjcdhZnsa5PX8CpoPY/9Qp3/+mQy4mo/kF8vEH0Esmf3i83Pjlz9uO2wEyNK2KigNetpqalNivNKc/ztN/vnnb77TZNE7NRNCYSoSFIDAJT64PtJPrpMwHNqnOKjtii6iumRKR1hiJuEbMwK+y0iVUpECmbclZKWet/Css/q8cUOkxHR0GHI1BbX+0IAHczYB/YgGN4zNgYXREJIhSiVO2yWc6UM6syGZEEDiKBJRAxPORUM2tWOzyEqtk6GANRJXbLvBq64rAA7Bjjn86exdtdrAr/5iU49c8EszBVcs7b7fbh4QHRUa3FcV/oyF+p5JU/OXeKI85L4Rl7FXK/bPAzpbTb7X7++ecffvhhu93mnEE7ucbhtBln4VEvpPrPC3TV70nUgSRfgad6QD9C9cO1QBtez2kHF5LipcH00jd1IcvcUsE7xedUeIty9k/a4u0uevdKU62FKd/tdmYGG8/n52fMLkRScaeYUsrl5SUMRYHI3VygXw5nn9jvJQuxXkq5v7//05/+9Ic//OEPf/iDiECPgBRRX3/9NYDRAtouDqZeLfCTH4u5aTR6cbwYor79C1S02+0Qw30YBmwb+/3eY1ZZ43sWuphSyn6/h+rEFWpARSLy7bffohGkxYhVWgQdMy05p7mkOaWppKSpASNtxkQtSpx10rf9OPN6F79ZpxA4u2+22tvNi3r743B9g/0/tS4+NFGJjIrSYvr1i71nIzqU5o47HTbqru8IAOs++7+YDwUeQgUn/KJqZFS6jBZ9RH564Ty5WGj9qJ393GMFzIGLi4vrVq6urgCJFjGsfXYhmKonc/TZuyCKnKp5SURQF0+Eug1rIcytM1qapglorIdEpzKwN1SAyO157n5Z+S3alX7zPVhb1+2ZC1pW8pSzzfO8n6bdbrffPm8fn9J+vxp4WEVZITllCPBHF6iJ4CbAh9lJ1k2vDkLbCytGiKx6Z4qIqSmTkAUiJlbjojRl2heaCqVCcyb4oBU70kQ1SWNyoEcrIvMGMVwyA0kgEeJCFkiZTIyIJLCEIMMqipgmLko5E0J0FGOlIMRxpDhQiERSjHMpoYioEnR69VQhIsISiIMQmxCrCVkxIj7rUPH/kwWHeFUdxxGhUS8vL7+EBz57dOg/Q698f3//8ePH/X4PhfRCV+KV9wijPwSY2W63e3h4+Pnnn3/88Uck1sHm+ouEzWnpgZcDI+ksjbxCF3B+8OqNjvnYU2OBV7xHi22+f8TpZx+Q19u/EJd0TFxRO2Z5O3u50w/1opz90+m7WGCyxS0QiNM0ffr0abPZgN776aeffvrpJyDvp6eny8tLp+6JaJomGEOALooxIs9ln8bydJTwLDgbQ9z7G8SE+Zu/+Zs//vGPP/74488//wwAhAAn4JCmabq4uFjQ9a7YAqmJ9+uGq64voOPjjQ+IB9ulTh1ADejgYgwO4KCZPT8/Pz4+fvr06fPnz0QEL2sPQjMMA2yxXRnhViPDMOz3e3CuMcZ/82/+DRGZqpZMBDPP+t5KqixRTnOep1KyerCidtakFzHQl5ZjSHSsUOtmyhc955Bfo60IMzPtWB+io72pwZrD/vRLLf1VxTFRsyUq1UIVFhlqRloOqOgs3Dnq36vEMHXIuK8BczuEADzkujMozvy0Sd0axHJ7fHx8enpClmtgIzfW6c8AfGxn/dp4tOZh/VrnGeCyonQRknrdmbzg2C/HObO5s7TrNe/9ycSfheeeyfiB2kumUkpOaZ5TntO038+7XdnNlIuYInR1YI7MUbiaEzHX+OZExy6dANUewPP07TKRkTWQSA5dgIFqgDAlLmTZKJvlrKmUlEsqWvFqZWWofzCAvH/H7elCVsDekJEZwNyhsWrKWoqEgYmYJXAQYiFlM8tmNqecU9F6SMKGZfUdl1JUs7IpFYNZEYOSqljR6KDD9hb9ajrnJfTwyl9fn6AvPeIX77JGz2AD+/TpExLO92Z01BaAHFvO2gkrtkA51DQF2+32/v7+D3/4w/v374no5uaGO71Mf4sd80PWhcnBtvrx48dPnz5hYzhVuNDx0ep0GBdf9k/sR8yOVSroOFAR1u2CDVrs09yyf/ivixgEX/L59PX5r3ZMjZzefnY0/IKeOnr99S0G0Lry0lB7wf79+Ph4cXEBK/7n5+ftdovgckQEgxin4pBt29vWuxz3LefjghmCWQGJX0qBZMSc+fOf//zx48fHx0eYE8GAdJ7nT58+MfPHjx8xkRz9LBQfQB6r1erm5gYtWYzeApFbF57R3zsdp53KOcPaabvdTtME+ufp6enz58/39/ellDdv3qxWK7S/lDKOo7tMY+I9Pj5iA1iv10BFwG21Gaolza09ajDtSLnkpCWXnHLOqtltF451X6jC/63HzxPJz/3FJ+qwI1jUGBhuNTVx30WCPsJPDNEtLTYjhL2ZGZNQcx9j6x53TFUdV3mGmloiQDv3t0On2zRvRlGHgYVaRhvjtrxhWV5ZL3S86n1p97CbmmxBZg9P6+EUkZ/QrLnoYt09PDw8Pj4+Pz87JPJI0z0e4nNE0WKZL6SQYyA7PjI5JFpozbwL0vmveZ3cqSb6A7k100Y7Poj2LXwRFdWIQU1bMU/zvN/P+2nez5oym0VhiTJEdsWZcNU/9bjcutwup0ioi0pRGUaH0dTmhtlRVLBiCEoE1tFyhlHRYXPhGn2xBtqQE1TUmmJMJGYejZ0xlO3xqsZkpddOSpOeZlqKZvB4uZiatZiPdDD8LCXAT8FnKAb/0BJrfba24F6e6qfLYLHf0Dl48UrhE6ehL7zxlRbCyBpH1f1+T136bjmOlfJSk85Wa2bTND08PHz8+PGHH374wx/+8Pz8fHl5eXd31192+tnnOj6AgP348eOHDx8eHx+nafL84Y7b+rWqL0dzeV0qeU9P4YI09bbLjrMX95KrRy39jv5KA/ruLz6ffrkQVV75QmydRU6LK19vgx0Dx1PV0ukt1AHi7XZrZpDCfnaE8mi9XiOkimeUhAwFCBARZP/u8Yc1Ch3vHRAHVNDT09N+v1dVcO8gpT5//rzdbvF0fxEppe12G0LwueTOzDBvohb17unpCeDDeaPF2+83LTtOiuKDBiTkJ+Z5nkEO7Xa73kFauwwGaCow0zRN0In4OPu5OYSw2+3woNVq1TaPUnJCa5o6MMPjTKtbPgLm1i3e3zOqPzcdXl01x6DoUN0BXjARd+FWjCrHU4+X3RgSERkTMxvirXANuAJz2aa4MCbrg1L7g4/hzjE4e+FEf9zJo9+PwJK56RW1B9uhUPPx767vV00vLhY/a1vO8aALaU+NTemzwIIiAnfo/ApmFJzw4Wrw8PCw3W5BqfYqM+uU7E7hLADHWbHZy5n+mgVOchan7+kCfp1W3j+iZ+utQ0WnBxI7kweNzWocxVxKnqc87/bb5+08T3maNecgthrEQhgGGQagIrhf1X2fGQk+kFb1lZXAQqbVbYuIxKy0/MP1P9UavxEcbTYtmXLRlBVaszlbgvuZeQM8cqM1VISJAhTmy0gRWhLBipgtEAXE8G4RN7iGla8G0hSavWHJOc2pTotsRixSB06tFNOsORQiC2TSLJZEwJ9V9ggLogWspMVioFc3G/qljbm/4JWd6RfLF6IlTNxpmj58+HB/f4/Qw9xIVN9B+0PAYmc9bad/SCkBD/3000/ff//9d999x8xYuovaFvf2n7G2P3369P333z8+PpZSIBFeSr3uLOvrg7AYxradHLzSFm3DZ5cgvsjPttl5NetAUj9oL73E/q+nn88OeN+8/sqXBNkvVvVKq/p7T7vfFwjllBKOp2DX6DgrJBGBpAEQsWaFAMwEQNB7F7oG01l0TN2np6e///u/f//+/fPz8zzP3KyP8dfemqE/FsM6x8yQcANfrtdrbDNmBoUyPNew5azXa7R5waH2+wfQT288gW/2+z2YIRzTd7sdNiczg6IQNiK73W632+Fx4M/A3b558wZUELo/jqM1NYR7FeE8Q0SmJc+TIUhcBWmlmhBpi1+M1Xf8npcv8eifxSXnPc6O54Q1JARYIy0+tBHkycFS9bDbWL0CkAiZX+tGg3/Z/zMjKzXVGfMh62xnDbSAaCfT9PgHnTnh+l+BwWpdDoVawjX02u9d4IDWhoNg7yHR2TW4WMK9JMFUhJE1puupLRFOC8/Pz58/f/748eP9/f2nT58eHx97VNQrzrzyfnlaV0570bOq/qceD5Xj8GaL3jk2WlS7uKx/lrUT0SJ6pMsiO8MVtWpLLmXOab/fPm8fPj2UNFPJwTSIDasgLEFoiCEGDgL7bP71pANXe2OrOVsdKmO+mxoRlWJFrRQrRKloypaTzVn3pcxZM4ITaYU4RMI1XtEBwx+RNP43JTESo0AUa2RqrDUzViWh3GyvhHkQKNtUS5rTPM3zbp+mVHJLEshiTJmsqE45c2BhDWzwbRMOzC0kK1sLlNqahFRrXwRU/tdYIP3v7+//9m//trRcOfBuwIkZ0wI7UI+NFgzNok4os+/v7//u7/7uxx9/RFrQT58+XV5eMjPMab+kbcBDHz9+ROxHyAJYWiywlP/q7ewRwEuPs+Pjmn/DHR/Q37tYhNQtZh8WPx4tpMYCjvTCokefPXjyz6dgdAHXzn4+HZ9fu8jPlsWA9L3w74GJn5+ff/7556urq/V67f11m6EYI7QAwLhOpbgNgYhcX1//5je/gdKNmWElCjuGEALqf3h4+POf//zhwweEQASn4qY2fqBMKcmx8ZBvXYApKaVhGO7u7qydcYHgobHFRnJzcwN78H776dcFUJ2nz0ODYesK5wC8YvBSl5eXgERYXzA2zzkjxyfk++fPn1NKMCTC7cBP0lJc7XY7QCJ3188pTbstQaegCocW4KHXsew/sBzDj3N/RwrvdtSl5kR2cq3VvzETGwdiMT4ckI05YPNmEjIymJEWvC4Sayq5f7L+HaGmbus9bPYVCzZIeA7HLOts39sLKoIFYOqFgDRv/MvLS/jhw/qzR0VYeoBE79+//+mnn2DQeX9/v91u+5DWvZyUZuC88Od9CdZQJyoXMs1Rkf/sbz/t8kJSnT7U4Zq31sP/LtrTD2lFRUV12mciylNJU0rTPO+nMk2aMlshsRgs1AdwCORgy2pI7JcP1gA/vzjVjKvpXps/xQx0bSo05zLnMqvOqqXAel+JSAwRgBwJMRKu4RxRzxr4g7ERq1mDSsxCTDCMOh4gIiJhCSIhhkBM2aiolVLyNM3t8EjMPFZtIoiHQCRGzBxaYjgRCiwd+2tdY4+Tnxxvdb+qvL6E6OXTP53IgF+182Hr+vHHH3/44QdIfDfZwb7iV2Ku+CwvpZyqhPA90nrc39//+c9//vOf/wza1o4t7BYOGqc9wkEfhtWfP39+fHxEVEnPeqidE0RfQDD4Un9lNHya8gk51CO//vtelvmVfYX+HuXEK76/4KywOIuHvLa+R96v0xYSLdHJ2e9fEscvjdVp6Qft7AXYsO/v79+8eQOo7eLY3xFiKkJL1avYACmYGTs9MAERDcMAVIHgPRDxUEW55c08z5hg6/X63bt3UJah9IZo0K/Bakeax/vz8/PHjx9hnORuzGghYM3d3Z2n3qQ24UFQcTv4omHgmeB/R11ACneZpk4bApS/2Wyur6/NDPsTwB+2t960CA/yscKXoNww8lpKmibsTTBicHLI37HPCep/xTToP54wJ8fsR/3Omras510qdUNMJOb4ph6cxU+4jqkqAU9Vg8Y14glbtWplNqo5wKv8D6TZmNkKM6sWU6nIihS3nEVppz2woya3Tz1/1K7DIC6Evl/0ysNeARYvfc8nxyH3xr9sxYki152BXIcQBiqCacTj46OzmL2k4s68uodEC0zzErLxth12/c7AqL++38IWDVj0fQGJ6JiXOmum7TcuUZEWnfcTEeVpTnC9S9lysVKIlKU6sQd2d7OaOLCyRcfvzxocMaqzvsHhepFfr5iCxoQZY7UKs5pzLKumTHMqcy4pq5/RtHpaYv62GBIYi6qPNtCvyCmC5jCzsTFTYA4APiISmBsXWIkveNIHkRAAs8ysqKZUcikFpyaApiHGAZE6JQaOgQMTwj7iv5M3x36cAYe0mNgvTaBfW75kl/rCnWwxyajNoWmaHh8foX24ubnBZiPNDo5O1mcPR7SLec0taup+v3///v2HDx8+ffr09PTkYAiC3kNo9JDidIhgS/H58+cffvjh/v4esW1AKrgCjl84bPkZyDeeXzt6PU5aoIrFMJ5tQE9ILB7UC5qzHPsrqMiBEXgF34MXeOiV+dA3/qWOv3TvL9Z59k94jw8PD0TUp7Z2VIQslQ5TrDtigjQCKFmv1867SIvc8/z8DP+Ax8fH3EXLtcZ3Xl1d/f73v9/tdjCsxizqj5jWed9w07g9PT3BE9Pbg6mbUvr8+TPMxi8uLoDSHBX1y4SIQA7d39+rKtggn7p+mtfmv8Mt0rpPqtJCvDw/Pz89PT09PTEzYKKdIGDfyQ79Us054QNkcgtd170sPhw2j3zR/X+0RDntvS7+PSZNu1tAthOzcbV3sOaa09dmbUMx6Bz8wNlOvkakzciTiY0Ex2L8xs1om81IKpFP5OKFD406g+iojcpy4/c+eDPJzs92Xh6NiU4OOafAwi87emKnnPLp1C9b2MDBM9GTnfW6M0wtN96HF8LDw8Pz8zNU0ou9iTvHmpd8aReN7wXU4mC8AEYL6NP3qC8Laqr/bCd01NlxW/QInxsqymXabokopzxPU5omzYlKZitMbYaiM+1dtsoaOYP2qfp5wtq8bd2lCn3QbiCgxva1ywkL0cyQ+ywXS7nMSdOsiBdVA6UTMZGSdrwBBhEfDIuAqR0tqH7HbLD4CRIQlTsg+SuZWkV7IsBMIURhI2Eho1JKyjlnhJdgDjGQDMMQhyEOcQw8RB4GjkyRg5AKK0sTQyxGrC3rMzeq14he23h/TXlpj/GJ+Mo1/4ACXufjx48//vhjSgl6DVdP+OFjgQbkOG6Ntw2r8f7+/o9//COgzDAMNzc32+2WiEBBeQT6U2HRr0b4Lv3www/fffcd1Ae3t7fX19fu0dPf0g8RNaXGos2n66ovfXsWguzsXcwvGgTw8XnIv7d2furlgo/h4nGLnz0kckzZn5b6n/wCWOzLS1PoHzm1ThEe5N3nz5+hHnIkgXdELdy5jzm3c6F/g/BUmDZgcXxL+PHHH//u7/7uhx9++PjxI3Rn1AUvgS7sX//rf/358+cQQkrp8fExdB71/TbgTQUt5BMAU321WhERUkfh5zzP+NKap48fFUBuodkfPnzYbDYeRQZw1g2ugQvHcUQCnMW2AYiGXe35+RkOB85FQZOCDVJEcpcxjYjUtFRtGrDGQWKzIyGqx8eeAyc6wCL/uHzL51649Xf3gANGRQwDB+6O1lZ/8X3FSBsJA1caaf42ShWQ4K0EQnJLQpoBPAdW2NVaSYysRbC0ri1nukDd4jsPXI6Gxv81osMeit3haKz6VUkd/n4FGLmk7Re+S0tMRbxxNyRYr9cI2OgTG+dS92eE3cLz8zOOH6cisVecucx3QLMARnQicr2D3VgdSt9fF1zeTR8TNw/tUZRXde511O9PrRT6UlFRzvn5aUdEac55ntM0WZrIcjNeDkLwya8aXgDul/VmxwAbNBAWkbK9dihttymc3S0l3c26nXUGe5WpnHfbWiB6rssKHQ8Iv4GGi0jgGCiGOIQhhiCsiEKEO0CJRQlDGOJAVurkVEPYjkKsxMQiwxDG1TAOqzGOK0Rx4igUKAbKQiY9H8RsLH0Da4v/qWDRP3NZbJZPT0/v37//85///OnTJ0AWItrv95vNhjrcvdhl+60L2xsi9T08PHz+/Pn9+/dQiNze3l5cXED0Y+fIOYMu6iMuLoqqPj09/fnPfwbhVFq+qqurqz4zFL3Ax1pzYuqXq8uUs9hoAUr6A0pPhvWnlpdgx+JPC3nHzWidjnHYwhD4tM5+516UnkxeYKP+Db5eXlnJ/2CchG17s9m8efMGJvyr1Qo5ZDyPt/M01IVlk+aU695eUL9O0wQfxru7u5ubm5ubm7/+67/+67/+a+BvvLXSoh9hJo/j+PbtWzP7/Plzb5t/Oph4NTBE3e121N6dbxXoS2kxghH6xRWCQCS4Bd4AzAz7Dxw8wCcREay/sRbwDbLf+LYElOPLCrlKoBME4eSrALAJ6mno0S4uLtpro8Mef+YVOjBySHT+5f/DXn2rvN3etGAKeGGG3E5m1lJw4BRdWR5raEkPGI6sxpg0Ii5m8EwjJgqRWpZWICUzU1My4ToA+ksd6SHRrxuGxjJUSqD2lYhOvAp6KOC/+uP7z4fKj100cBnwEAI2ukGeW7lB74xs2Yhz+9NPPyHFjR8bTvEQFLi+ELyFvWHQUa+b/O+XDx0L4YVA8yc6MLIW6sUFb19J/7kHiws8JE1x0Qs6b21DRSk/PT4SkeVScipp1pTYjMmCkCDPabubq0+XQTNVUY4pIdJifQBmRZ2o3KaIUY38bDgBdGEnMJtxfSbKnvIs6T4XxLMuWgzT3xDgsZ9OZ6be8TQkVqa2oGvASRHCq1GthBgRiTDyu8XALQ1OKaXMybKaCAmHGCTGcYzDOMQh4nLMjergBl6qMxO0qhRvoSXroesX9p6zE+vLi3WETY+mf/FZpzu0XwMHsQ8fPnz8+LGUcn19jUOnx7F4fU/1iQv/NejgIKBheAFbEBhzgPj1nc9VYF4JM8OS4+np6Y9//ONPP/0E7RsWv5uj0vFZxFdXvwj9e2+q6xd+cfvvFyR3uEqPXf2Zz5wJHIicHStc7yISjTkVE4uW++0uVlyc+YcFMFrAo1ca9ouI5x8MiagNIIx7RAQ44OHh4ZtvvsHcWEjS3m0eZs7oHaQ8ZiYRYRtAOsy/+qu/QlDQT58+WWfn3hsnqSoSiV9cXISWs6WfeBgZmCs5tOptOV0hS0SuU3aDHtTQa3U3mw3CcUEZB02ZKy9Q7eXlJREhFhHgo29RII1wr3Z22cj9iUXk3vugiBCaL6V0dXXVZkwjsfH5DC5qljuHF+av/IASDiqB/h//fEwU+Wyxw9+ZSKBHw+/1dauSKZsSWSNOCf45TgtB3wZ2CedrXFSIRVVUWYKQBDJmMVZiISpV8JOoKYvYcXaaoy40Hqw13ht+nlI6+ktHtDGTMAdhMqTVPEMec3fW8ieWk6yr3s4eDLnUks71DAWWZH7GACQC9//zzz///PPP9/f3cDpz4zPpdO7u8eAUPp5euiRlC4nU7yY95jstiynR96XfyKDpdvUxdzz6YTq1D72c507p/NIwNmvrknfbLRFxUdNCJZGZkAmbVK7DMPlE2cD4adXJ+rZujBQdh3nhpKg1KkcdMHl8ImvXUYsMZGRGRQkatLlYztXCGicGnz7sTzo/wEQG77RmgIRQisxMxAK7H2JIAZ+Uri0UZsTaxnipmhbTwsQciANTjHGIMYYgwkJMysbU0bs9LjN4h9a/mPR48aXmf8FB/EtA0mk9v+r2xY2qCnuLz58/73Y7EUGEOhxkfRt4HXt51voPHz58+PDBDWM986svnt61QY7Tznt7cPBFvipsCZ7/wXcjOkFFrjE5kn3nrrRjgvp0VM+OsB3Do5fKQvieQij/Rjqv/v7Xfij6LviC7wWlk2EY1d6GvQdMpyLjV5VXpu6XFG2pBmAF3IdydgnoD+oFMf4UWqhMP1PCkR7dn6ZpvV7f3d3d3d19+vQJ2gH0sSfkYJzk2FGbzRwmZOyKDz42DLTQ55WPp7+UHoRh10E9iGnksAz8KN4OTGLdtAjbmPNDWCagglzchxbJaRGKxsEfVHWAX7IkR4kQko2O5eshNl33rtv7PoNwji5aYCM7+XDYCJyKquoybD4VEuF1N6IIkAg7C1Njj4yMmBtUqyLZYH2haibknv/othm2BmwHhm4iD2zffJ/X/UrsOr0U+6f9JYdEgq6xVSdq9rnn02mx9BY4wy84u9xcTkrzPoNUXOjOVLU3J4Jgh4WcB8TqBZFPNnC3flpbyEP/phfXi5a/1LvF9nR6vc/kfqD6Rr7SHh9keVkJ4Bq0snveElEwY1MhDQj9DJNhr5lgfWNkbKeWws6AdCQo9aio8pzMdTa36W2mRABDpqRqVqgYZbVcNBVNSbNpaWKf7JBCrINkdQBqA+qDayxTfyHUQXVmEpwU+qa2C7kdmImI0SzNVAqzCJMI8xCGGGIN3KTN8b7Su1iTqoy0u3wIx6povNZFefTyXtqB7Pi40H/vn8/u2acVLqbLK088+yW0AIj5i9i40Jr1qMj3rcW+jqkJXufjx484mjw/P2NLALvja5WbNgQ7jX/2TuERMCT6+PHj999/f39/D5QGirhnSv1664rHIlsMiJ2opXqK+OwooUJfnE4RaWfdbB3fcHZ4++8Xn70Z/aP7ol384kXlfFwwLCAbfOPHZikt+rac6NR+bTnbwS+52FoStKenJ+Slh53ydrtFog9E3OnFn3cZM8dxjIt+xy77/f7jx49mdnNz89VXX8G/bG7FWswSgCdnGRej58pc1x17WcyTnpzDN9riLsKMA+AGBf2CJs7TuA7DgIB7oble4k0BJ11eXjq0xTLpuS5r0B96EGv23YBE8DZCWAGPbQ1UbEsba2AV9vi3VPNvtyNuu9TBUS1tI3D40P3/8H2Hj/ycyO287WtWSQvC+/YxGAF7VGumKGII4DaT/AMRgg0UVRFRNWmWRa3JzboHE96AknxH6Bf64Z/DvD2a7cci+vQ7rqBLGPa4aseoCEcX6gRdv8x9qr8ELFB8dYsIvM8cFfVhGzHVEZ3IQxN58NJFt33+e94YF3elFDopfpf/XLDRi5b3H7zvC2zk44CyOCsuHtcXv9ef2O9T/ZWNK5rT/vMjEQWhIByERXhEpA/hUEGGkhEcGMGyLBLgEVHxGEaHKbU8NBiZQgtXk8+3KW5kWqNaF6OSNWWdsk257EsuSmrg6GrlDecT0RE2qkvJ39GJ0QUz4BAz+KLq6SnEaggnxEwkTByYTSSQihYqSA2bmUJgijGEVRyHMIQQgwgV06JabYdA71ZgZFrRGwuZeXuEqm2gv6RlQ4/bvPj1V+06/1QFrps//fQTwgGXUoBmzMwP9D3OsGPUBaH8+fPnP/3pTz/++OOnT59CCHd3d25VKl02nNAcjz1zAla14w8iQsAkaMF/+OGH6+tr2CR5VdQhM25EgjO92sXeWBS/oF9Lpyv57I3ecT6OXXT6EhdD9NJnv95ZnN5uySGXDzJ1wqV/Fh1z7L5hu9Zp8RllIX/7Vr0+Cf9hU9Q6k3loVBEa8f379w56ettnIDxqTA+IEGbOOTupg+44uiKiYRh+//vfv3v37unpCfoCZDYAGQl3yFLKhw8f4D7m9WNAAImgznMSC7uFWz4RkZM0izGBj9g8z4gZ48HW+/n27t27m5sbRJTwjQQVvnnzhohijBcXF74zoX7PXYWIAMBYDw8PHinAQ1OCK0Lj3XYEA0pmgi6wNptQomOPWT50qDmDfcnrxnnxDMF/euAgnL3JjKyYKmkhUrKqK6OmHdN2GiWnj+pA163A9yD1gN0hqJEYkVHLt3E2WocvxqNWLi/79fO8poQQYoJOnEJYoqLegtC6I1zPx7tii7pd3+EChCfOnL1DvgMaN0H7/Pnzzz///P79+48fP3qcd39K/zjQTlDOYrEs3NNO76JOEdZT0W3wjpAKnYhZOg4j4gL2FD6etsGhmHUn1f6w5w3rK4mtNSXNOyIiCRKFo3CIMbDUJGd4hi2pwSOoj5LbUYD9j2b1N6u/iiH8YuWHjMxIDdnytFhRS9nmYilbygUfinWxHvgAyfoWnXzDtQX1D9Xfk4KxWKhODsTaXd96WzVrHJgy6Fu4/Gop0UyYwhCGYRXjEGKQQNLekxCZFCM3GxJSUjGjImadzxl37Wt9Op4WPlf4hDb4J4REZ59CL8wzOL3//PPP2+0W+xMWCYK+vIIScO/j4yMczX7++eec8zAMEP2eyJM6toOZV6sVaAM/6yA1gbWQdx8+fPjjH/8ID6O7uzuYnnhVpwNlHaeCXQG7xaLX/fLDWtLOZ6EfmdPzx2Iw/XbuCAM7JqV/sTiko4aNekVJb1jj/lkuO/qGcXPnps6zw9VDziG5ssY/cFd+ce79I6GSS09m9oyn19fXyFIJUfjVV1+FLmyVD2Y/eVxFhVkau0yx1Gy6ASzu7u4QJQjhi0DbgA1FsBZXZgGdw/4afXElmuPL0AXPxTV4Kf0YeuPv7u6AbLxtCLL31VdfuY1dX2HPReHLfvqJCAynSos+rC1hCPCQG1njA5yuU0owV2q9CKCKmKGFYqtsd5OM1IBGb/+wmHD1Zffv/ewMqLedXN4eYjXFwQESOR8FBGBmBA0aGsaBaqv6tdXtpE1vLjBxrbXUe6jV2w7ri0afrvSXINFL4+BDKIIBbm/zGEZYK3Q8tKcC7Sws8BkSYwR37sGsnSiidsp9enpCWg8gddgzuLoWFUqLjwVfNrdzcIrIW7uYpf2X/UGrv8sO7/PoAOnfu3zTzt7L6W00YwEQFyLLTjin08t89Fp+HKpYNTAH4Rh4CCzCIQhxYzU6VMSH1VGDNfsc6U2JMKSq3cmCSKttHFW0AWCkLY1wqd74KWsqmtWKIgTpCRrtnvfC3nJoFJglaX6bPuyMF2YtpBG+lxbqkaBZbnFeERaAjIVFwoBgRdFD+KuZqJqQIX4qERGpkYAfKlQOqr1WzhIVy258AQZarIov3G5/1VM8OdRut3MNF4gcfIPtpAcl1OY6wlW7jbaZAeVcXFzA78Y6jyov1JYTTChwpDYzGJ3ADw7ZLjetLJ7e7+L9kkPj8ejQpRJcjEa/ovpvTiXRS9Jq8aWv8LMjzMfwt39ifw0+LM43/tkv1s7GvG+Vg4ZeZLjAqtuEKg6C/Z96aXLa+C+ZpV84kzGRNpsNFD0igkjWALLY+0EH8jEq9Y5zU19CkuIaJ5l6meizC8oF6NGgdMAeAHcwZgZz6bGA/SDu1j+9bZafiX1i++OYGZMfptwwzuhRlKre3Nw4klug0sXg9wLE/+qBIq3lQnFbImQjQVQkz2qCYAGHWqqHMVs7HXr1REQtulyT89XMur2Fc0ej5ec+nFGPPdq/YHnA3ZvzQ3qEwvDszg0NOjc7nZxNI2ZGiE0J4cLWUJc53qrtMQdMS3DyEh6y87vQoZutZ9xfC8tVIiIPFcAneYFcDtBx/gq/+HRicKfqwmQDweNeY9TwuquP3cAOkefcOM+f7orjeC6Mdf9c/3IBdBaTuV+2vYZhIccW9ftffXr7LahTXnCO+fItsqIiER5CICJkfo1B4FUlQWor5Igo4vZalaim7SDnKv1lHp0nat+YVO0YFRk+wKQ6F51LDds4F00txmq1/bPD7DlMtr5hLTdtm+A+lsQ1/qTvi8RMwiaIFQYYdCR76kpXsg4VVTslFolDiEPEWZqqOCvCdEhZWJddRV+NQDrK8qF2ZPVy+vkXX+Qrb/fXltPp2LcHJtJw1/TZj0UCUQ6q1k3wqMF5kDoIq/jw8ACfeQ8mhoe63gd4aKGJwzaJczP0IE9PT4iCDasIUMSLfCA9Glh00/dCP+uf9pe7404vmF4pZ4euHw2vwV5Vlr1U+ssW3NXZOaOdseRigvWS9FT8OyTqP/vOfbo3vzRjf+013jU4tGPz5mZeA9Pg5+dn14KFLi/eQshSB2pRAKkXAt2FNSR+P+tQCWYITHmAkxbME3dGb466pEuWiadzd1z2Ji0YLDpmK/tvqBP9ffPOjnB/pStGzQxGWm7N7WvN7YoOo0LNTvTwol2DZt3//fclwvGb2hWdNUX3aYEx7HCBEakRAxVBlLY/VMlsFZdZy6LkERrbvlsnQXPBR0G3mdmsU8JZ26sWkKg/Vxz+9SE/7mc/IN7RDqy0G9jHuJ68j047aHxvQkTHhLF/75Ot/ys+u+HBupVFVmznEYGMtYv8Li1zTuXVGlpyk4YFG+Qz3EsvTKw7Esgxi99f0xO9R4N50mX/kx/tnLLijgh3eLQQDmdLf0HdD4LwZgxEFERi4CFKGCTCrigg6A7e6LEjuZkQWSgETA/HSa5Tt2J5pYbTW0oPw7VmZlQQVd4oW1abc0lFp0xzLvs5z1lTLnM2BXawmu0M4d/71dr+ZUccXO/Ar8B2LEQSKEg10w6YmUAqYkzcwsI3OSVmRGqqWjSXlAs107gYQ1yt4hBDYOHCTKTZjDQgmxqZVFLXtIFZhXIN56Am2uzodS4KHwPqsxcs3uhLVX1h8aoWdZZSHh4eEHXaiQRmxkqDqIU2DQsPt4Oevb+//9Of/gQ8JCI3Nzd3d3dAJP3R57QX3jtpJq5EhEzOj4+PCIvXByU7rer0g29j3Kk8zo6A82Ha+YPYq5T16Wc+iV3kHM+pCDiVCIsnLmruZVMvkhY1LCDC4uncLGN8vwgtdZ0X6SyNvCP0a8rr1y9Gz9sJOIIX7e7lqvrp0yci8uwZi0r6Gpx1B1kCghAgph9SHzS3WCKi29tb2Hdj5/C5CkPU3i0OkKjXLFCnpHMg1VtEnXb5bC/+YWN49nvrwjJhbH2X8sMJOSZoTim9IuoYEhztiC+2oOECO/6m/8KhkLUEmXDqwbGSyLjpFLrnHoZIm4wnZoQZFhGkcgLnr6TsCgvQQ6alEGshLVYKqZmV7vjuHVM79Lo9ve9JX3jxxSlyahdWPYsBt2H/8WH29RU6/9mzMscXe8+A+vWQxnDF93xnPX8JKwLPD8jMEKGQ5z5VSsup7LZ0DvcdM/lDpTl2tTd6MA+XTgXsf6XO567XDxxe7omH/wIV9Vcu5OHRy2hV9UJyITP9mHHgilZARSwhSIwM0eFdECZiOSUnVQtbICITI1WSyrAwucsVIBNwUMVK3LCQKXy7TI2SlpRtzjpl3c9lymWuDvlUlImMpUKiX13YiEgIqQFFhCRQaFAP/yHxLQI9Ch9TY/b/Ze7tuuTIcTPhB2RkVan00a3umfXx2mfP8c3+/5+yN3u5x/baXnumZ7rVkqpUlRkE3ouHRIBkZFZK3d59OT2lzEgGg2QA4EMABEzNVtN1XSFyMAME1Vkh50QNaDEefpAEMdO0xQ9wBq5QrSvzla3X14Gbr12fvqEpVX18fPzzn/9Mz1NfVHLOPE5MkxY5J7W8BEzAyYhEnz598lNmb9++5fIW2djFq5Oph2lxgvagGh8+fCDAIs+7zgBX4EjrS9QN7N4YbUl23v61+1znT+f/FI7TezvSnykdpJtfjKJhqOBIy9c5F0D0ErDgOT73EJNWKbV4gHHv6OuoXUzxuzsJ1xdrTmNfvnz54YcftJ3SZ8TznPPxePzpp59UlVE6qSAcpssladxElpZSzfPGx/7HdcWFJjfHd3d3PnulFNqR6dHM0EFoJoa4F5cGu908x1XKw094TSfFyxMYZ3KY1Qvk55+tWdN8rrz4YrYhjtYu9l5f0+tc9WrPkMgIIrxt1LM47VHqK8fUBwtt1HHkvCwpL6kmuBRJKqpJrTROV6XZxcQKdEUpKCs8ZqNhA26bbfCKgV5H5lFdZGFyZsTjXDav5U5gQ/HbU0qMjU6PIo9ESiTkxMDDB8/Pzx7i3AO8kQepU6SwjQcX0LJGuvP+rubS2bC9mxx/RU+W3rF5XYi3zDLHGsiLqCtK1wi8zs2bhEi5DRVJWhbqiiQn2s6khbGOqpgRf4nwGDqqbK7IXaBJkoFbDhWxFq5HgCJ86QlWRAAxgYpSc3RSPak+r3oqthZb1Qw67FasJy4xqMeIjqRpG+XVc2VVEZSStFxlHjqCYSWRMjO9McsJwLzKVVtkRSwXM6ssKFVz6WkUBZoUhEYi1SNLKvPKxsUSFFxn15VdMTe/gt0br0RU1xQykofKpfR3VETXHHdr5WrKQ78MIPTx48enp6e7u7t37955TNUBEjnVRrziQ/Bl0nOMPD8/06TiBjuZjmxEnoys5fub2U43rEwyqYh3J3YALvMaFu/SPgyJBJ2NTcBot8jeZijqpXOLXxBH5Iv60Ml40XsYh+zSOYeYNywpHK87Sz2/AbVTRosIxSU9inILw/Pw8MDGI7jh3HoPh7cZ96PWTGkuGX28PhuxZR++iDBWNeecYSaoPQLG4y0EkdFHlQ5SVG3yawRSuzM2f51pbC671OhzMry4bblyfyG4aJL6Axww1aYcNYSuxP50ZoX+06Yd2p5ZHyybUojAxwC3QMTbOqzENSWJHxHIXMCQLKmoikJES92e8wN0FSsoRVRHVFS7FUZ9dqqvkrQiW/N1Qnu2ifAo9c5DFlARi6u6z6Ei7ldZqCWSlm5SWt5AbjwYBoJO1lEYIqCHQc3j8pN7FTfFxhu95/wQu+rVnEm1xdDyRiL1DvJwpvlYzUJQVhe2UebHccUSmSX4FR0ywFDPKafMhGfdi9vYZXyNqKBHRCwxUrppkhqZSA2J0MdErGbrS0iAqjTUIGKGoljVjqsdVz2ttqoVbX2VvhN+xMDQjrt1vTND+N1NzYl5YZMkpLQhkyQgKpKUUmqRihh4qJiqVlykIomuRYkBjVINTWradhWakRkHSUSSmDZna0H1q2Nan7aMyf5pqd+lWI8tLq9eu7ejHaf/9OkTM1/6uaS4AJOmqYBlFJbHx0fmAyFa4pl5z1of27e2VyCvxg5HnuERIVX95ZdfpCGz6DkYUcWwtPtn/3XAYfGJcQa8QWAUUsNcSQArA5d+1ZzvtnllZQm694gMXKrOvdrtpAb/p9InynbbqN97DVF91ST4QOhMxg+0WPnXZVmY9QLAnJHDH6qTZ4bPBisMvvlxXAj0IAF1EasxAwmJX1sIIsahjjPja5ifS7i5uWHkCPrAvnv3juqu3WMK52ZvINTd6R2GcK5Nflhr7jNeGhsBAmxxYOQgZXvE0I0BCTmusPB5Q0b8s3Us2qqGpac+1O+PcXdTSinlnHJOksAFJ6mo2rrWEHlmjOYILWJFVMX9ika7noErTzfD1xEzSc7XoeZ8sq1KY/WOxobrkZzSmcAZkcFTStGdyMM3uAK+tJhVhEQ0okWjeeR66V2XWNPPNp6DU975YauAnvwsRBaN7BmrxckZqH0m/jiKiHjmTp57dQ0VZbm9W/i6SFp8M8KXKwmmEjSmVjEQRJIVNWFiDMCDt1a6MDNL2RSmynw2WWEqACwZMrRQpVJgJkVRCtZVn0s5FStqRRmIgvRU2akdDTMQnTSOUuYFbHa+SOaJx+sEKWXJSTJ5SNqsQUSyQETykhbJGZJq3kAzqxNd1pIOYlbzlDCuk9QcJkb/IZXMLLPkTbNkNeYkY1+ina+oSqPeUjcSwVyGpfdczUHqOXFcaH+XBEn9j4+PTB5O7mLMUzTPCecTrlU//fTT09PTX/7yl19++eW77777/vvvmZ+VCGZ4tLVzEM/Pz0Rd0UzDY/ksIrKuKzOBv3nzJrU41zMD2xlU5H8jGIrLxrAKxgYtaFkuvB3Z26CfK3HtnFld+qP7cakePsSnz/I09jnqCYZnxa/84JVTi5Rj4Wyaa48GT4JzI73w61xS84X3Q/VmxqihIkK9I1N2AKBxKmrOU3Mmnbew0k7s03vaeo1RrDZ0W/qtObfgb968effu3fv37z98+MCQpJ8/f3Zwbz1q9GmMQSBpUGa2vnfv3tEefc6F/NyUDhT+ovSY33gpMZBtGzAAa+IqxEWxPiqQWQ8mxob8Kdu1CIni5R1XnFBTQqUNZlGMAlQUOSZKieoiMbWiKqJm9CdSNdNSYEV0FStJVz/dtrmltse6teHMsPan2i0ZFRvJWF/QXwl2GBLGDCzQYjHkVhwYYSJaWtDi0TNnCmsJHwf7l4N7RyfR2OoP8goUzoOuSEJxDZMzzu4ORFsEjRldbXPUG8Xm63Hx4iYq7t+8S/waAeWM2NDpim6aZ4YJTBhnaveV75YEz8lnRhEANYMa1UOWUlV4JgiQmo3Xapwu4AQ7mR2LPhd9OkKZY4NvJTUpYNjCbaV6ofXSagKcbtY5KUncbrYQFCGljbtEkC0BqW43KhkrTFHUyknLupbVSilJVKkXknqkjdxqhgKDSdaaSFcWSYtYMbOW87mIiiGZCVCuVL3ulq9dZlCH+dWqCx6A//XXX5m4Xppl2sloWRbGQWFUoX/5l38hm5nZDz/88Mc//pHHzajC3X1EagdkpEXTcfZ2jz8GcQHAbLKvXr1yZDPbpAYBMWCjiG8c2/FeDb41AxyZARZeshwNJWIsv/2CCPDnXq48dMOlTww76wLuwio7DBZtrlI4zeGTRvBqISnbN0zIi8VfBF2q7+/vqTEC8PbtW56ipxbznNbQv8bWtEWQonB3A5b0Bf0p6NRiQTlkR4sPdHd3xwhD33///Z/+9CcPDRyfjqaB41Lkcplh2WlK4+aBVmbCo2EI8R3553jdv8br80uZCThgR+k+iEc+iUqUcFbr9y/9s6ayD8EAEWZioGdDdQERESTL9W1mScUU9UxzWakrMrOaWA3mywfPhE2Q6FsG0yGgnlM3SBSkEHO4IgBxtMXeyWYIixUXdad2jyLhnv6RElzf7yCehU6i7hHhjfvtZH+CGJ5C2HVWcxjnA5m3xGiHlN1WEC1ocaYui75B/qOdsaDo855Is6/FMvM7HBWBYGh4edK0fsaQXoA1P37+aUhFuKlospSuRkZ8z7ky1/i04BIGNStmpWhRW4usBXQkYga0yuQbAzdf/UBTaAok3zy0LYn5OU5TmKgSn/lyyBoKVStiVhMp0/tIqm6oaFnLelpLKVoKw6uKgqY0LaYFKlaPjFo9a2ECdzeqQWKtyhaDWDLRJAKzPG1LtnJZqJ2rOZQL0vCaYs0d7+Hh4eHhgayInldJWNwxcO388uUL1wNG3ovOsLvS3K+7o5JfdDZLKd3c3Lx584Zet2k6D+XiwB+xi4p8ydc9E3isPwOjuZEr53CAOHYG68QODz8NTc3txxbQCyb3whlmdXgL8yPiXEnwQ4x35T6O4jBp5ybkwq9DcblGEf/69Wu6NjOkEE/RM0j0/f29y9/YH9dvRbyLiTWs6cOk3ztGOOXNRoukT7LreA6HAzNJffjwgUG8dieczCXNIc+jB3358oUBiBnSeulTmJ2bw90r88VhXHFQJaZraKaf6tsQcUg9Odxkv7sV+aPCDjVe7w5uhd/i/3szW9Oo+11Dm52BrR9m4wBKBzVD3ThtnVFVU2MeJzUVU2mdlEoetkGaXiGL8duor936v1PRpRM75JokkeZKQZd8mYov5H6O0vU3/mhnQzS3PNakbjLSNpGNH7B1/ZN7WLufdVQCYRKG2wsKwjMFj8OIP4Y3tdvUIAOHyuO7Dr3yz96IrwUDO8+oKFZDQEUS7J9Ng+I4Obz70DMLRB0c5tqP7YNEPjAnfTMzUzWmgKVvdSlWeChNAx6C4zNDiAEalHHWet06g551a3KyytC6xe5SVaE/kWvGBGioqOi6lnUtzEyrqmqiLQhYMS1WRJO0WBp0X6oT3CBW4JlkArEiISU1uu2Dz/DlpeUbyletRl5/XVceUnh6eiLP+LrOaqQkbhcA3Nzc8HNqgeH9SJqz1vwgZyS0FWion1KipHh+fo7R85yyzw1zXqfndW4XGdgZABc7PN+4O4fxiXFVjmvSIAh2X9a567tlFqO+8KeWGG6WaOegAIsDo91upEk3/mK5PBxrqnVrsVJev37tObxOpxMP7dP2yq8SAnKi1/RY71XgZOYv2kVkRDxR1LoWcxCj1naftPf5UsTG/XTPMJORurQ/78PMOcxjz8ilQzytC4R3+XPs+bDGbKi3HRE3bBTQhGlwoaiNWvuZt4xApe9oD3L2aBzBn2CvhoMs8xVn93ffOaMtDJsM2FZh3VJvtoFuXZFtGRz6KMPFXsi3NztqvAZUJJKazqF209c1xi8diC1uAv2oY+7zyUjICMSLJB6WGCQdIf9x6QOvL8tCMETTmFvTovTTEGrEPwwUNYigyFPTa+02cs6VA5dhkqj+PuNXhH27f0i90jdNpr3+3QVdUcQuIu5APXSf9CVQbfoP5kRFBSFkExE05VAjXuaq8XbMzKxYUawFx2LHtTyXcqznzpTTjy1cRoPeGwn69oJRilofZPNDajFRNyVWdapTmEJNVxWoZJGUUVCYKVZAWEad3mldnwNoVhSzYqpWUNZSRKgAKtWZiQq3CrNEeBRNi0Fgkt2zCCYiNcTYFGpA+mmPRD++jjM3fi0G2i1m9uXLFw8LxDyUFlx2vCbl/uFwePfuHTOBiIgffGDlyHvDUwZ2ilfcwkXvVDZLK4MHRpKwlqPHlJGT58bjh/h0n8nhswTdCXrI9SKOmWvGTkqPluanz50cbh8u+o0OiRCQgeyBs3P9HFBCLD6HucX+iS3PE3KhzN1w+astlOKbN2+Ynunp6enLly9EITlnqlhS7yWQQqZ6NEJyqO2T4GTmWpkoRiVoIn2AQ9GQSXdZlu+//z4GFP31118fHx9LyIowfCgtnrvbETwXB5OgeeyJy1R9biYxkVzkQZ/nXW/r+G9zJdoe3aOS6aFjH8LlMeKPTZmkAFRHzPn6fuE2e9Nk0QA2QSH6ZChXAq3rgdUBClpsgtaBDcpEb5LtMwFSpI92EfA1y1VQ3qo0XVF4F9tLoTnVf3JGRlvRCXGi/6UznTtWo8UB8dCjHq+IIJv2L2nn0ZxlHJ3f3NzEeEWRUDGJr0Gj7Op8P2ewqyvyh5qZn5KL5OqTQDkgPZqPEz9gncHvKtJ8lBI+hKFjDRV98zpqkXhV2mF8Q43riAqXoF6tJb4vsKJ6LPa82tPRjidbV1tX0xXMASvtv20WBz4ZEbn5dyvtwVCFOzXBXXO1kA+4WUDO25NURVctp1UM5bmsp7WspaxlVRPTUrSselqxqtE3SQwCWqaDKU6a+mk7ZSZJzBLMWlCxr5l1+0/QIV14FtO4fvz48fn5Oe6kyRv0jbB2nIH5pN6/f+/uLNT/X9nhYRW0dhSf+xXyDM/z07Lw9u3bGBc7tsMPErYX8cPu56/qofSbswsvZXd9uv72F/uzC4ywt21yu2QUOmUvx/WFx/ldUTBFl8ZB1gw9+YbC7azr8A+Hw5s3b9ACir5//56KmaenJ57S5xoQRWFq/lWOruISMuA8beErHRWRtgdsFNWT0hsLpFl7GaT09vb2p59++vnnn+mWd47SYpd4jsGPTH/8+JFeJkOuQH8jMx5yKp3b9w57b31aooW065qbtbwRVxzh2lXjd9iivfwEq6HvarYEJvFIqDsZDxJYqpLfeJBHxTT5cAQwiHTrTgpUTJASP9ff+kVXuo7xvdj2Y8U6pChvUqQlbSCIwcTI7TkiweDlWhBeHE6cSctr5EFMSEJuhvYGU7O2SzsQMECi0mfW4xXqaP2rq0X9uZchkbQgwBriCdmeWtefXqbASBJ2Ml5iaKUBEmGPa4brwa+o73SvwYgvmipH9BoDKoKMr7hpjBo2oo6SCh1tNGKA4lRwWvV40ueTHlc9rVp0c0HqOiDo4lm7QxHBvVBrIwASn0P3b4jVXGfICVmQc0UpBhSYFDOoiEKy+1CZWilWTkUM67rq2vKNiKSU05Lzkm9yPiyHw5KzaDKTokk0J+SUEiMhtXlpui0RAYN0s+PaIONcZE/T+OIac+UCD+wsn/Grma3ryrDUDBXjaZVIlzzJ6Q4rdPintp/JN0SEnhbOzDbF/XNCtLZf9wXG999oDO9bf7rcEhJxe7E7V/7QmQ38oZEP5wkcYEe8y3r1zGV05VPtlWc0Mzx0l1Hnt3+ut8Mk+G6Sc+j68Hne5otDH6ztC+e7+MHF0/WkeKFYA8d89QTBlHT0s6YQp78/aVJDEl8fpjQfUgR68/UA4Vy6k6uGiJfDW4uwSULaYMcZnITb29v3799T7ovIhw8f3NA8DzO+O194rJ3BZBBtnufn3sBvjI0Mo44/7ZKK9Nqy9nPEQU1HtDWOFtew1RxIaBxbUCYNmp9JbTSX67aNzF5r1P1YUS2l4iBRtJjmXLW1lA0YqQoVRzCE9GSbGocJXB3yuIpoUyER2Pgd7XI3yEpE1cuj1xW1UXZWGcf03SDDSyRfRLMUb/G0fR4zwvETj+j7dbp+ajv85Zhjm/k9uO8CM7cwj54Mxx20o7oouoSPb7aVCFyiTHYx5YuO9qf30SjZuyc9MIqq4lkIx4uYBN0Su9n1my91d+WufOOqoKoMqh2A+IYCxEYavvJYvpoZVLEWW4ueVj2u5bRW753dZzb87lwm4RcAlhq+FwigSWAVGKXUTuGnhJzFT9epaqF/tKqmTFZXQI3MJQIrpeI05j6TnFNe8rIsSz4seVmWLJqsJGQRzYknHSrZNyoW32VZm7eNyM+sH7uL3DeXXeiwi43QwjYy5qmGQFi+StHvwVmX+2nSN92JRISHpYfhxKXI++AXIwJwMo0UnHNmVGK3ke8Oc1gDIncNnwdUtFsiRw39mWf1XFO7KASBGyMD7776C50cWHr+VXoNc5yB3V7NDx2qDQpzDY4yvxck8paJDPwrO+Ox1Cn+qFOkaHZ9T2zHRXN8g5EUo07Ifxo+oKdM/+o84l/5uFevXpmZY7iHhwfr98E+t/Grvx32ypONrOt6f38P4NxmwN/mwALoVUHDwOOc+Nf2twmqBoPiirCFKzoHXXrYVHfCw0fAUcRLRGPTh63HaHs20aKlaCkUwRBoD4mIh4x733owyIm8QSImlarTtM3Wtn7yXz9O32oOCIdYbUN3DQX55O9yukwn2KMQk4bznAAceeSW054KGOd3blk9nLprB2ev6ghHvDPeHwsnUrUFruMx+IiKnDWi1hYTmUnTJ3kmHH+JDoP8s+Mh7hbibMTij/OCXjrtUNWeDKyoSAZ/SR4lV+3wTSDn1PPCYGNWADDZOIgXDIxrTfChVszWUo5reV7L86qnU1lVV7Nis6F5oyqRPshPm2SIVQ2XEQ61WImKJCmlnHLKOeeUScBmVopZ0iR50VxDAACFscyzSAEMp3VVLTxgJpkRLvOy5OWQb27z4ZAzJGkWLCiaxEihTBmioe9B6jg+CgY/juElJDQL0Gvu8prXFGuKIkaydiJzlSkNFqfTyd1LudXmcsgEsSmlqCtC46hBHO92Mop1Z1HX1voeaBbr1hRXFs6KD025RIgsF0XA3CVn6TgWTCqEF6f6QuVr3uCFsrs6xisuMtxfXsNh+wv3xpEODy19Jo35ibs9+YZCUeua89LCJJI4uQDQd4cuES5eXUqyHX52ue8di/qbQYhH2Yo+1LUG9zKvE0UzK9/f31OlykOakQjRv/eARSoPoiVVoI+5B/R68+ZN5Ky45Pjnodnh68yGodpGEmZ+lnfUGXUAZ1enY512yHogZGNN7P+0V/zZMpgwVCHt2PCSTSyZGhiCoZ4jL+uq9VBPgdXYu1xOHP8wfVpwUvNXvHlai2t62o3DZarUaig9JqcddvE+w1OZhRv/+skDF2KurZ9RUXQ8IptwJykiZCj6FXkIR8dG0X/I9jK2Oh9R2tOCxtRpVNYOOqeZJv26d9iVWC7tIxgqISMbExDNAGvgQZ8630fFCgisF4Wbf3BUJCm8rhn+2wb6eb/7PBtQ+h/h8IluzazmWWIJiVRR1NaC02rHkz09r6e1FEXpp7WbStSw0FsZlAVaHZ3NUmoWqjUhJUkCelUfcrDxgVEiFVKQU8YiEDVZDWktyVSSrOu6FisKhmbMecmHJd/cMBnx4ZAyFCcgmZRVYHkRj409ywtr8ewNGNLE/v+kmBnT0f/6669m5inGyEvH45H56umpRxMGo8j7QScypGMmBGojaV7ADS4CXDXFTQkfJE015etTCnF0YvsaLCn+6PhX2mmsWVl9YWaGpeubl/zfBS5c335crSMw2hVY1zTOD7s+RtLrTn4L7JO21XPp5nogP+RF9SEzFvM8Gq292tu54gfHVf4UR8becx9ppKsSgmn5vYNC1Iv7buec379/T7jGFH60Ssf5tJDxyq/wiBAL9yFfvnyhNe3Vq1dzVuNh3mL/EaT/7nISFoatV5tCO8CjvXIl/eyBp68s/dLqoIwDK1agQEkZQFmWlLNB1JfY9VROJy3FdIUWaHGIJVuoOuZDr/wihEfdiouAgdDpj6Trm9TgBglmJnH29jTB59lQgmnJ65CEnCkcYbhvNTFQVCOldvSMhPTQypcvXxxzOCqyoEefO+ZMYc0u71J0wP0zycVxiSPPIJZ9wybBbOf8S+E/yHxvMD7UUV2UftIb0fyuQRJu3tZWIhapx9n7F4eBlf16uNxUI25Pa22hwkBo0aJYi55OOK3lVIy5z1TRDIbABngGRmiUGGaX1cTvsc3RGyIJJmLM9JFzWqoosQrYRCTL4ZBvDjfLzU1eliUvCyyLCani+RmnVbSImGSGTa36oLqlMEFmUo+J7S2GLKgja6oi06iS7ssF6PANZW7twnLIsI3EPR7lwnmSaiRGmk7NT4VbWAB0AUl9PCH0uhznAV65DJK0nT9y9a81JYGrAQZmGBZj/6ztOJvzW2Q2n5Z5/R7WlYEVv+E1xVH/Rtxw+Snxq4sGt+/4ax1ewQUhOP/q+hvvP21YcwvfNsBIQv6UlBK3jJTmROGlxeqNaC++a5+EebzSPDqJ79GrhXzUA8T3R/jXiIoclrGd169fv3//HgBTCg6AcpjbaNx0xYAFr45SimcAjE+/MJOxcYR3mlrA4jim6d8All5CNps+yeLX+EOo3Dw1QnEpOumWpguyHT7mv8UK1vVogGiRlIAt+V1ZT7rSglZgpWZYQAM4DfzQMTQ5MEJ8r64skrAOhWnfVAWBuzkglxjdTInP1DCts4SJ0CG+axfF7mIcvXmcx2l7KqVwW8skAYy64vapQT/kJDpwvV+04IqAID+dniMKGfqMxtROgS5gLbhXR3A2SM5dgrcQLJsfRIRqs4jehikdGmlZJGFm0fbMi9recTSftVW+p91GnEF1av4HLqWaL5GtRU9FCYnWUp2s1Vcddhq2uanxIVIvbwMLX6TSX6xmW1CvlLKknJJI7aKImUjK+bDkG8YAZXQsXaUUMTViN11hKobEHB+p5QpJDDqxdcU/e8ykNpvtZGsDjWEEZ6XMwA9+5WsXmOtXbm3Hnj0L7GA1oD6f5zZJZNTtu+xOk6+P3xvXjKF7kf3iZ98MuUJ4Vz0bS5TyAwNEDp/xjVc7Nzl+47CGzcvn5RaG+rG13x0bDcXnP/XW92soZECH8TollwQt0Tky+NreRvQWXxmRgbZzwkTn3AfP+r9da9cwKBZCOpm2kjj/amaAFdcGnxDGW2LyHNrCSu95HUFS5IL4lVc+fvyYmvMWDyud6+FAqMOD4kK1be69fpPebgizC6Jqax3w2h0kGj/sA6H+lx4/hX6EO6r9yhqMk6JFDJK0ICWDmB9AK6uVwvRngEpbmfy8sNTAjyN3BOJxI5qricLItgnaVsB+PvzHeM+MH0f7ztAZV0OmkEMmoiLvs79rggMK8Kgl4hZ3A45NjXoOEs0idCg4U3wOh+vskrOJt+NgKP4dOjMUa7tfbfZBb9nMPOjXsCjEK96464q6oO/tyTR+YctuGkGtg6Z2y5Yxx/VnPZepohRdVddVj6s+r+VU9LTqqVQQX+FZexo2eNSgNoWtOl6ywQQlgJgw94g1I5pISkgZsiTJS5YWM4h9zznf5nx7e7i9vVkOh5xTOalZsZPa6VTW1XQV1WRYkHKSnCUzwaxAEpKZFeVBty3XaxAoIX9cxEIvL59Xlq9aUGeaiO2s6/r582fqVP1QZaRUPy3vee9LKYzxKP3pJN8rO4n7T1GXYL2qNhbfu3h8Dn+E9KrXYUQ2yZfIVJHNBk6+sAT6JPtdMilj56m+/B7t97ajvUg2EWQQYka5cCXVDaMe3q+/FFfLxbu+dkSphRXQ5lok7bx99NRxUpxDJs4IIMrfQSDyEecg3S6jxXmIw4/PYmHeNEZXikdp/N7h0f5rVBdZ01q57nMYVxzssDQOJSqNNvYM3kNt3aufEXVGPsBxvQ9r/iDzLMYf6vUru1ApXuyBhaA66vj8SQMWBrF1hZlKgjRUxO12KaYKKzCTtqRVB2v4RrdCjfY3gulwx37H4jSb2bZebjO6qQu2Me7qirbpadzEr1ET70hoPog+AAsHPZ7BiXL+6emJfOQi14exi0KizJw/DzXj7QPEjLRt/c5wXgjmx80d8wraYqIygGq0uMWexLk9i4rMTDsLWke3/v7QGo/xsnjS3WK1Fq8xEIQyNlBRPZ3K86rHkx5P6/NaTquuq5WyCYmalbbhncuiNPUVKjirB994lr8kkbSkdMh5yUvmETRjJAERLEu+uz3c3d3c3TMwYHp+sHI8raWc1qMej3ZaUYqYLhmHJR+WnJdEQy3NzJIECTafuq2okqS2dZFvmUJC9CsA0IXyG5dYM2Mk6z/96U+uKIrcpc2p6PHx0cx8g0KoxNjTnr3cgm+gBXyTWmDWyDO+Qmg4euBOdmgWE5dac89n5vQOpOB27WIiqhmwx2AXZik+VCbN8G5llgFJ+JXfFxhdKC4CmvDfdOzla2IXefEFNTVnHfRC59uAkb/Q0lIOs6uukRIRj/kWcYw2m1puUSVnAvAe7r50C3qv3OczsenoFvYoxwkjVvAbb29vf/zxR+46zIwHpHenNLbsGC7a3dhCSmmwo/ktL84ziwYfdnZh68umHvqaXdrXlotNO7LareX+PCKg7seYxUAVTDUAab4bDNiom+GMdzG/uDTFJLe6HsnT/afbMnq2+03ubH2e1u95KgdMtTsDjoq8Vw6J/HCZZ/ZgZZeiMVvZsZXHx8fPnz8TD60tZ2UUyOdYw6vpnr/RvLOKn92zYneMtqcTGjpzGQ95I25JZ8h7lwZskHJjdy8RW15wrlijAcaoaHqh9mrdcwdmxVr9mrmVJCpmNeOYwrAWO2k5lfK8lueTHU/6vNpayqparB3nA6ShmuhDVK9v5AwRgyHXb3OXvabAclV4Vrm28VIbGwDJIlmYODYllGJF15OejuvxGauKIUlaDodcZTQSipgmQFDMVtPqUK62uYDvSSaedwg6sLRDRpeF2vWr+Ll75/YJiX7++efHx0dCIoRNqjYfPfJSSsnzMJPaGKXeFUiphYuQpqqRcGLZF2PrfX6HQmDkaxjp27dHg5VkmBYJqZ2iuovtkGHosZhSOp1OXzWB1pu3r3wdkavjnv7cSz/3pq58yrmSQvgiRy3Xj2LoWxRhfJXDebQrkd+upHMVVAoBgaRFSvTYSwgSeQ2JLdHDMpezsTO2py5yEvUhnOv2DPh82RgWCecFxqKkM4eHdtylh+EnXuSEeFQChvyO0R1j32Zxj4lN9ua/rUbmW2B0dp+havvnwrHkwWB2qandb+Pju1dC8uW5Y2m9tpaI05qU54ikWeDSFomo02RIQ0nhAdv23AfVYyBzFUE0ncVq3WRsr3i77G/AJdu8cjv9UxIOuY9IPJRmZsbkesRDNNpS6PFzPHHmXdoFIgOb2xRdwtnTK8TKCEfkfFHwgTibsz++XsTb5y7N/fSOlb745sFnzw+F+KzOwsfjFbUIzF2IouaA4+AhGkx9+OGlmtb37LSilSR54szW1Y7FGLbxedVV0ZBq4NUeErFBCWCiHngMJrZYnNJECM6k1ep4qfF6y1+LmnI5Jeo3ihVGCzipKgySJOVMHVESSUAyFTVBjSFfozAhYTOZtQE1MOnwLrUauyL3G1bEr6ochTWnn3uIjx8/0n3VeSyqXmmELn20X/IVdy0eScXXFWfmOKJh1cSeVtP7GXkSTYEcWWsYUWwtrqmxTWeegfeG+Yk8M3RjvmXo9u6Hc0OLzxouDhVeLHIFvpG9Mjw6lnOD9VssJJGVEAJxmLrrSyQ8ishoHXC/Cn8XkVzdfzOS1tCH+c36lVi8/jmbWmxwfum7cygid3d3r169YmaPLtXG+XvjZHIJ4Tw8Pj7yzF3uM6Kj57XY7C51aTBw14thcWs/nB2+DZ8iJNrutpf0/l2LI1io2cf7RaHJeb5hMYg0Q50Z6um0umgxNFHbeIsEF+tNmGyLRXjQiGralwEVIa6Prcrwf8Ta3WcfsYuvmeWjkHSoMSh9S4vrQw9Rnr3nX1cdeWChuSfnYAfOcEcEHLGHMxP50oAmxrn39t0vq2l//mPuybl+RjtgNB2i+Qvy74u8jC62NWP9qMRsrvxfzRZTX7hWZGwwVWtp0HjJas4PEiM9akwhZlbUSrFTseeTPa36vJbnUk41qlbsUq8lCnu2+GlLJGgb6JAWYBuGhBR9jlrogTb5BoA5Y9vMCiSBztQCQ2FwyRPNe4BJklzNtylzR2ErNNV0a1as6mbrGwx+eAKY1NAVJmICS22W/5PsJ/NyG78O1egb9PDw8OnTp9TCpTuQLyFMEVGRK+3duMaNCy/6ohWf7tHDdruBwDMaglyjp3uvkyanorgMe/uDQ4YXCQfoBqSyu2agIYBY5xx8efGlRAgShUj8KT7UJrxyDiFd6FKcwDiNOp2iv34g/iBtXkpo8trf9TUy6MJTXMChuaNJMCL4T47RIypKLSPNuWVmd3rjQ+e3E2+//NIHAo4fbm5uPNVx3HbrGZemYYHxzjw9PdHljm36Jjj2cO7nLoU4OOtWsnBbsKZN6Mi6D44PwkIPk61aIJ12y25r3Qy0+uFWQQ3lS0FD71G6krYd/AZOpMp4NK/qdhRfUuqD/w3IzczBXIdsop5n711vdc171AYYIF8Ybfs4C0/sEZtrX5xmtHkuM/Dpw8PD4+OjQyKak6JD20De/mGmt+F6lIEIkMh/cg6aZVcUv1wy/OxnbNPOuH4OHRg6OYh6/4liYVe7PL1rYItXJAJkAAyvwGQYda5RMRHbM6OXNcODGgAtZmbKk+YFBlR0AM99JmY1hvXTSY/P5cuxPJ30uJZjsVK2FB/No2hnT9FAcg3NiHPVhsAQFVpJi8cFEQONzWpaDGaSzKqWL6ecDgkZxcqq62k9Hu3pmSnVRJIsN+mwLDkvSZKo6CqSBApdRem1YCaCLFMkJe+RMheIAiIwIKWRYnZv/M8r1rLAfvr0aV1XJrZ00MM1z63RdBT1PYq18HTS9u5xQ+AlrruRVWadTbzdP/vBCjTqj9YxNNwT23HPIdc0+OPQcBsjj2mfIyJOy7l34Qy5i2++dvJ9igZRNXfp21oeyoAs+da+za9oeFycfH9WRF24jsJFhKcOX79+Lc2Hmi34yeHBQSFul3dBcKSrKE/jq5Sm++RnhsiLnR8+z92OszF8iD9xaDyP9uuvv9KSG+cn3jUDd/7lVHz58sXTSDEjyvzEefGbP58lAJthy8VyrqZhR1qfh5QXasVW2tAsJaEySCBo9orq1Skwq7BMquEMLdWBJEFOXBq2l1ufu8GUbk53pzEin/aOdu6IR7THn2zbSERC3V2/HRK54tzCaRiPKufOQ7SdRQg+a9BnPOHXY4V4caDMaMDS4LgpLeOeb3el5UJwXwhnVefxfjLHp9s422PHfO80vIZBYu9KpKArYiWSnWVrkZkTUDiNRELCTGNiAlMxU0kCQ9KkMGQDA0ozdQbMFEVhpqdi60nXdX1ey/Najms5rWU1Bl4fIRsVVgJpvYDRec4AQgpADSkckiNV1TEAykBFZ/HJUBLIJEmQRE2Y9GNd19NpLSYKQcrLAUteIFLM1nU9ntZcANNSjlhPZV0hyAoDTEWz5lSsaoOqVCjStjAJtbdx73TesyQun0MFXNy2zm99uMvPah6PRzrueVZXJ6BSCpWxXDNoAqC9zOOZcvVyI1rs7a4IplWOTOsrjVM8ORwtWJFLfG2O2ywuPuzMWTZXZcU+OB8OU+rc6IJmnm3ptQvz16H+/F5sD0jZtBzizHp2ZbULJaIEOx/n+soS5YtjIA2eZOfqv9g9F5GUsykl1/yLCKmRBBzFqIYDxrFBR+3WIyd/116ztGPMCLth/xXTbt6fEh83zOcwt8uyvHr16rvvvqMR7dzkx7mN7fhg6e0nwZNvnsndSR6eqL26HmHxthkY2fCt/dr5Es2OSBZV9/uQqN4vTDketrdGlQ21/bJVNxGBJVVFElPQAaKQRsySmdq2pebLawEbxR2s26459AIR5VSNfoRA7ev4uYdHiG++UxJ1v4RGa0cvbY0cUriKNJK0m8zck3qe5gn/bdJyoLT41a9EyBLFbMwcEllV2h4jti8ibl6QvrjiTqfTlxrcsef5cS5OzbObw/QwTrvyfJ4fR0UtbLkZvZoZrVwcJwGGVA80+j3J1JKYmeuEtLKTaPNzMzMzOhWdiq1qqzIDva5V3xTeRO1K26K0s5wi0iiq/SMATKtm1gwt5qTVPUE7u09DMUFJG5dtbNweGCZBxLZUaLqq1WP31NBmQRKDrMWOp5LFYKWU1cqq6yowzWYGzZZzXnKGNIYTJJHm9efBC3bIbpvei1q+yxfn17/7ma7HnvJsNym3q2Tdhejm5oZhFdHOwmg4OB1ZLn6gstTaXp+30AfQmcGfSAUVmYoWPe1dH+J0xbXKf/IT3aw2bBqcmWfpEOdw+Io9+CI9Qtp9Lxeand/abmvez5lILqy+Fx40FJyht8tlmA3HHNInWP3a4l1ywF1KYahGbZHZ3Fm+hPhvCDGH5tbmJcSJPE6vtK3tvDjFduaBz6AEZ+bTlWE3NzfPz8/syQUEs3vRWiac4/HocY13p/FCs+fXmD5UUbfcBwAUuhZAwvYh/C591fmjf2vowTbgwXFId1aminRjqktljg+eehEzY2gWCaDOE3ukuvQ2UEQJfUZoSvN26DBfD5K+BhXZeE+ojT3IEidI+hyokaSlKVZpLLMWeJrCMCKkSMBzGZ7rFBL5xX9ybiotcKIX1xV5PH0v3tUopWeujD/FPswcMYjKFI4tswPRMTHWvIyKGvQRAfnTMoRop/ntQ4DEE+2e+gVG+qPyqKZWBUykaNleGP2sT6sdVVfTlZEbi1q/FDn9dLisbRWcVVqP1YAKNPyEu3FTgQwVpLY9gI9djI7OJNDtNATXZSAhwcDQSgRwpoAlInRgSZZEDaXY83ElKtJSylpQjgKj7VIXXXLRnFPKqdquRauyq3a02hnPpDfBxA+xXBB2199LwuVhe1fM+O0WdgDcfGg7sUzljR/LR9hAnKOz6AgS8T55GH1kGvIYUVEK4eojqcQVnRfj7RaOZOvkc81xocV9iSuiXXEuzM6gFr8+V54bObem2qQemJ81P+X64o2f8yv6BmyEMFgNrtbeeGzwSqjkXXLNovUmV7rCDFpAr4A2kz63/lzr1YqRYn0GtDki+PU4ioiN4qzOg53Ha82v4nA48Njmly9fdvfoc1NDHW0+f7lle+A73aXegbrQ0+f2lGHJtnGNh1+IC3t8n2cQD7HNiIJiiSPduyh+dDckZm0Mm+g6ISaidcEQc7Ac1HhBd9jgUY1kHWfAsC0ZNoxoFxH5ZPSvcfx5+2Vc8vzjLELjC0195oBIJNqiF5YWFSUSsItHTHvmKO2HJ3qFWDNKMAuoqIQYK85iu0TotgU/Nh+zjliPkGYLAKYyXHTVaWqurhFHDmWedj+DJmjLBrd7TbEiogaxqhAywGogRfYiCawdpS9AEoNZUYVmJDOFQooaYzY+r/p80tPJTic9rbrWjYht2c0ENcCE7yxs86qutcHg1WbNA9BQjXz+NRmQLcHSzgROpeK7RH0QLKliNVuLnU622lrTzGYsy5JTEklmWFd7ej4lGLRoOWo5Yj0JbFlKWUpZljVnBtZaliVLgiQ5iNHpSjZpc24B/r9QCIl+/vnn6GSNAIlKy0n59PT09PREgwjlb27J0bhP9ZSETuXDswbkgWZEkGaDm9e2h4cHapWcsvkTwx1Fjei8Yvnao+0kczTtDYuWf44Lkk179+GK9Thm/vWaVxDvjbLm/wIx+NRFSXFlt88VH4gL3wErXzOubcWawq9FKRnhbMTNgwUtLoFxjBGaR/noj/Oz/d5ypOF5dfEOY3r7A5rxFev29vbVq1c0ovlKJgG9XZ4uC3pcd78bPK7mmfd3FJe6zdu6e8CFJ9v08xlktFNnxEabtO+f0Q2k1UndYeRqRoDwWK+papJcRM2avYIRYtoEtPjV0rT4gxPkbl+GEexDog0l7mKh+HUHEoXNfnhlk2Vz0xUNRGtNcTgc20wtwOkucUb44vqYbrg9nQxfJRi53HZm4QCps55HVEEzL1gLIsDb6Q/uAGtu8BwemovDQX+zLk/m2RuAET+EPGghvKlsSMSqGcukAWgDPLRo1cSYuXN2o3sRtcJg66fVjmvhf6dSVmXc9WYdc1gCJLGmGg1nuAKhui+11PBAFV6YmHlUADXL4lg/hdFeoPca3wKmpqvitJbn9fR8Wo/PKw4HyXQESiJiQDE7qqZ1TWZQ1aJaVi1FDKYCM7FC612igTHBmlI/NegWlcC4eh3dOnz+KPW8PO+u/U9PT58+ffr48ePpdLq5uZHgDmLNs4f2NXodAeCu1F2tqdFx7f3g5eN86MubNq8g7hKIsfg1rlLWdh7OPOu6DmE5WGZnujhYX2Nof5lRzu6s+r2jXO7XleEtDODmQsux8d368eKwVO9ewd77PVfic2WvfAMdxj5H6am9Ee3Kxq1p4xnUR5svv0twf8p8r+t4fF2Jr8YlYFwGEPSOsf/xSpyZGbhEFovEiZ5m4gC9Mj35uCR4hXk5HEgXgYt998JQfmSK+PToRTcToa9qfcPdMn/mNW3/dOFchn+ASe5OBH/mt9Db6trjERdd5eMN9Ot0MrMkfkKo1upv3V66bDirPtatZnuj9v/vQaLu97MDGn6KGFMaUIvsE2nP6zhVuFKforKEMG9Ove7B47xpbXugvemqH+/I1HPN2GDkff6aUrq5uWHaPm5N6UuqvR+SB5CLrQ1t7ryPvSLNauZfJUCi4W8kA19NPA/a5jEUXXiqecocfTSFTXi7VueFgCRwi9HV2lbV46rHVU+rFeafHzhG/FjZxv40N1kL3hieDTCIpG0/1IsNIiUYDWibJVlEUt1kGGibEzJbi/elPF6fDGsp61rWtRzpUp1KSgk5iySTZNWLXEsp2lCRrVaKJViRtBTlyftqgURFeSllNyw7DybZ8Qa/sH5I2Of9lsKwjczFPZiore1BiYqoKOL+O+pvXQvqJ/lnf8/dYg0zSb+W+4JHSER/I5fpXjkuPDbtp6VHJ76gDjMWuT1enBscbkH/doa+XRjy8HUYOya4M3zdvXLu4rnnDiUK2Wte3IXiI7osQGUCfLuFUvLp6cn75nvHoU1rCSbjbtJpOI7Lf43Pjaos77805KTBvBjJL/6No9MQvNF7sjtX/tl555oZxkQPFkI0cV1BbweXttUZHj0st10/t9W9ivLtT/gL/238Hv8FtgMwX1G6BaZtcPkSakwhnjqr70UAhomBcg/GeDGGRO9WF8IVU0Gki2hVf4S1DJqTNqefmx4MdZe334aZ2S7ufQklcqX1jxoqsPhugeGIXOk4tzkIQztjnBqoevh1+LxbwR8hzcWHrqjce7M4gGNh53dbnqfIB+U1B/YcqjkAioqiiIpSf3jC/YpseJ/hYsUdVv8lrqjdVjc9qzV40cCywcyK2kntVOy42qlYKVB1C2o9ieXaUU8F66Re43RVNO0gje8y8JvBFP62xWBbcApp6W4ad9XHtRimItQ6qVkxqBlR0elUjqfTaS22lJwXmIgkQ1L6W6mWogIzVVO1YqWYiRWtWXdgBuWhCSfK3Exnmz/TLiq6QArXVJul//wr8zHRLhaPviO4uDJyo2cQ5BiiEOfq5Vocd2uw3l3DmXzAXgMwQuMo53Ca5Nxvbljh/EG7c2KTeiDtHUa9PIHzIjRP+yyA5s4Mn3db3n3ouVsuXHyxzHLkHKl8Q7G9MtSJ4ix2yT+7YcghuBvFBl26BU//CzvLQSZanz0j9sEpyv2TBmtFBFv84PM5kOjlKeKNMaPnhRt3CW+YAW5jfB8cK3jHhhZ2155+HTYXVdb9uA+GBiDQ56hE2Az2A9nDIFWENteKKkjTpupJPttSY5zUjahqEubllLpsuamgAiC/MWqINjS0O7BxZup0RJ/0CRW1X4Y2voplZ0EXKY19cYE55wGMt7hHARuM5rOBp/xv7MCLn+NF32+kluXJUZEFo4FnHYkn5qI4GhaRYVDYk6XDLPnYpYdHQwXMuiL3c+neKfVHWtGR1WNeW1avmny16X4UVvFKU9oUs1XteCxPp/Jl1VPR01pcV8QuJECcbuGABalqglr3mraqgg4jEBJTcU1X01SZFGSfKzpdQTxjmgAJUgBJaXukiaoVVQAnLcfTejyeTk+n0/NJD6eDLmKWsqiImqiiGKQoTKGlrCuFaAIyZE2aLfg8VuLIOYtJ7rMkIqX91DBfW84txrugoZTy6dOnz58/Pz8/k15zSBUubWvOuBc8k996uymEXBC/evXKjccSdCfS9qkDEudTUoujmEJ+HN/Xks/fvHlzf3/Pozoecyja6SyoauMYt8lvxW12CLsrFwrD/MxLiOzphOZ5vn5dHK7IpES5cPuVdS6UeSEfFvW58cvjisSzKy69w/ISknOhWfoM3jMesmbDJTpn/ajjQcDi9G6mjcmDSmvv9hSHIHvZS+avceC703huDnlXbjmY/frwZl8kJ58cbr5jWL947wXS7T4jLAQDRLD4J6z+ttfQCHyuLTOO4DgYd1GCjwgAQiMAEKgZzEQVzOnKr+L6etlam5+5/f/MT9uFHvrEWwNpDi10n2JEy90ZCLwTDUnA+NasgXsSv/vlRHqOhBRZ8hoT1cDCM19f/uzPdWDEiKO+5+FfVxrxxgHBeCPxQ2QxmVCRQ5y49EjIuDC0PD9lzINW4Q1Mmn0K1alZmsrGL6NiJFMzYwRDqFFXUtROq66rnlY9rXg+6Wktp1VPBWWP8Fu36t80p8oxaVMe2FC9H4A2VRJADyURk2wZkrqQlGoqzQWJTw3PJ+5aUYquazmt63pakwJIkvJyc3tYDikLElRtXQusmBZdi62llJJFlpSrz5SIIAvVtW1ExtFZjHDwu23Txwk7Y9lR1aenp59//pkWiphWUHqdLS1obgaOmRasbejXdb27uwNwOp38uL4/faA5J0du7hHOpsXK1jZA7sY0LIqzaUMalooMHz/4UR0LTkslRHq9PIG25zk0TO8whGF5O/eIC7+eW1xx/uXOMuLFR1x4ym8p1vsVxQm58onaQmZ7U7HbTgm+JPhpNQlmI6dYaXG2SE6kavS4zeUy72XNQfsyk/Rshjs3q7welxDSP72kvT/DNH7tnHMeIrlG7o49jAzlTVxq//quvFguYANriiIA3M1KxT+9q2zaEI752uFjFL5b8d214SuFrc/FtuT0wQrinj1U3h+TD3xa//qpsLbTG85kDfo/f2VuOPPT+N4d3/j5XcN1C2Ua/Ih18BI1Dq1ZUEfF/afby2IOVyfaWYsTOWtXxxP7Vimj1wnVaQ4rxdDt4cNmQave1o2IdHvltNJuMb1s60ebCn5W5SJTqEApeip2WnUtuhZdVy3FVI0ZQEgJ/p59smUjwW16aw2TkPDY2hCldlIgJn6K301mkquayIBiKFW1ac2HPCUgwWAmDDGggKdwVy0KQFJOeck3h+VwWESkBmYqNV+JlVZVhDPQNEQm0rgT1vyzAJFNOfaVS9KFJdw/O604ofhPqno8Hj98+MAo1UNsq0jHdOHk4uFLRdQVaTsYzBudrGM3IiHGvzMWsb6wDx5kYrge18joTDprg2M3fDYoOEqfMAs9d2HS39gEjGKzscQlZ1gF55rxEZeffu7GCxcvS7Ghw7sL+dc25ZUjEcZJmIe8e/sgXofuxTeu4dwKGllaD8oj5TjIdrw195wlnvCPb9z/DuNCUMLPIniQ4JEao2U5fjg35wM9+/WBO+J6EAkyXvFRxxkIC3W43v5fK1u4an1VBPXMOfRjO5/aFnzzTm1uQPN6WSNS1wZc7IPjinipeTbJ/tOmq/3FsH+O47Tx5h45jU2+gAK7b0HWRdPwIAalHYtx85P2hxsGcbr7eeCy2If41+tcX/xZPKd8PB79vMtzK56arbQcA/52L5yln72khx7u1tnt4e6o0euK+P6lZRme8a+ZBZIxc2NavV4dbKwUK0XX1dZV16KnwuhEVoopqp6k+T3vsI4AMPEMa/X5RjNZqglFGPxahEgIgCWD1riNNb+suFsPPMCkajsXb0RFVjVJBjFPi2YNGFkxQFLKaTksNzeH5bCYQc10VS2mpVbSUrQokqipSe2apO6ImRlDcSc4P6M5k5+nrctr1TV3xRdPrPPrr78ej8dIdk7E2owXpGYCeSfHQYKXPoUNgrze7YCTaVT8xAqxG9a0qRa0DpFR5cxa68UXgDhMP728y+d2fjXy6xaWLvRrj48xNhX7fLnZcwM5d8uLFy+UrxVzL7bm82C9BI8zIBeh3tzmUGG+18IpKuudi4f9Im1tdFGKB5hnVOT3ukkuhbhW/n53Za4F2OFdRU8VcWgD46DnhcsCfWAx29sz+FTMvJb2zm/2FzZsM6z8/Uow46Fz3/d+HIGJtYgr7D+k2cmG1Y5ja9gstiyy7Y1FhpVv6gxfw16vbazVw50dYLRThuHJJXhUx99pu93bJnJTpHZHFRbO5LKag6q4B5gZEz3VzX8HWr3Q83kg5DsGjyCv0SuD7qoxVW1kWI+7OFjTBpwUB6vhSOmw3T3XvXMjWkIl/q3RqdW98qNu1aGKZ2FtehAGa67BD4ueVjue9HjS51WftBwLXa23g25yhZYkUGK7yQw1oYgAyEg87WVmAkmNWcUgCSKSTBZIcG42P8Df1DYVG0mqJkOBoZ6dK1oKYAmScl5ulru7u7zktei6lgJdVyurqqrpasWsGDJWJoRLhmRJjA7lTVEEM4Uwmr2gKeYulG+ARMO98cUTEn3+/PnXX3+VFjN68EHW4Gf95csXT3M2YIu4QWGkopjR5tyy7e04u87ddn6OLO1H663f7EaeibwhTSOlLfKkx1dFWyS09ymJHbgMjHxNmhena97X7szYpCSfu3Tu64WLX0s/nI0Xxd+FcqFvAwK4RsjqXhxIBFi8e5e/5dvb2xhI4vPnz4+Pjykl5odyV4bYWlwA3PSGQL27XxHwx3BxKD6uC0L58mSeG6/TfzyF5IwwtDO85XOfgSqB5eXXxbp9kTMH3GcstA1lO/cj9ZwK17lx5v3m+rc5lQ6P2Hb2Fv7tsE5fr++jhZqtk12dM7NiF5Rk8+X4bhzBxIPrAKSFXKel1d+s286cedlFF6HxgzfuJYxkA0aRoq6UBrFa7AlzaKaU6MNqZk+t0GNVQ5Rd90A6HA7xEE9cOzDQQCP7CP1jl3bHNf+KYEd2L5CNwsy3Bbr5FrUoibwbflhf6cujTVFUbC12WvX5qE/H8nQqx6OuK9aia3vmVyXRro8svn2Q1oYZuvwjBujAgyKSRQSLSBYkAQzFtGaKNYGklCBZkCXT6MXYX6upaVHTUgFYZpDDw7IsC2QtqhCsWsyKWYGZQJAhGSmJ5JQkp5QlL21KTYsBK9IiEhy//++W5+fnjx8/fvjwYV3XwcPaGcB1ng8PD1++fCGQ5+30DPVoiuSuw+Hw+vVrtobes2dwuRgYTPvwwSw2xUUdioX4jTGIu/8qDWHE29HCWMcnzuvNBf6PK7o/Ja6Rl28cGPJcsxr8payHXPPX3f6/uIi+ONLfpURJNFyXAG0vt7ArsuPkeBmkJICU0v39/ffff39/f888GDx0SYLkcRje+/DwwGw2Op2rj6gIk0EKbVfqnXF3Tv/JO8ZgLYOHnKr6OYbfUixoF0rLfxy3y7tUGteYbT736GLYNARVzm4JcOCryMwwtMxZ3rLZ1yF0G+pBfHnverS1q9OxrsZ/RnlJLdTVbCXCF1cCSfP9H0guKpNIfqXl2fAWdiGRP26APgMkuqbvg3CLL8sfygAu7tLHKBLcmTinuEc2/w5GNHzlHm8YyO7n+RZ+8HhFptrSJvtxw0phJmaMJl3vbP+HVUdrUzPazlTXosdVn0/ledXnVY9F1+bSagYIc7taPz4D4jl7FqnkrbUnKtagmxn9u1t2WNaqFY0udjXZzZIZ0T0lqcY6KmOREkAFSG77O4aqVFiBGkxhpWEC4TEypIykkpKl2lkRIMuSE+SwZLm5yYcl55z4WABmULr2qqQskps2WBCP6H9biYQSV465DhNJPj4+Erm7U1486aPNVYhhiniowdvJrbgwTS1CFxU52qzdw3bEV7KoyC0tIpE/Ov5EbvGIqNEf3Lludzac6KVXTqTJb3eXN+KyHX+SSdURO3O5V8MLutDsLqOea9aHeU39y327sv+XW5ifHqdxfikvlitrDvpCv3hzc3N/f//mzZvb21sAT09PzKSGcJTS9wBoljKn2AGaY3rv/KztqLO0bLixmn/1zCSU/rzobnnDkOc5fHGKnIPiMjMTKvrVbudDZyYK8XwtVgtr6qSb2XavVT6G6jNKCM/ql2GrkCioi3xH0Poj7ugR3QmnVbDxnQ/j4oo4Fb/Pdi6eYc0Xvs83hBraFwubpfh+2WGdsgL4W46QaGitn+od3Umcw6mrnRCL9b2ONIMAf+LW2leBIbmHH+JxaU+B78aHQTTt9tNCuLvYH5vKPLqNsAZUxFns3lJERY6USBymyhdJemyoSEF3Ij2tyhQfx1Vbwjg4dbInfEzj2gruA9nXTtDYZDxtttG3Q7PW31qh8UXjJgKjliG5M/KzSsSi1BW18RhM4TTHG1JGTtBUY6wCaGbvJSEJDoscDsthWZaccqqgaFOniWQkQUnchsrOSbtzJS7kOLPIxYvzYsmwjU9PTw7bB3CgIeqJZ12OT8yh+ETOUa13aVR6VGQNjfFwfuRYF+5kDFel+kD8r/Ymp2H42pvnnCe1PxgV2WPo7eXFaYZEM3fhJRxgPfz6KtDwYpGgcBr6//s+CP38DJ/tIsS80KDfOzc7f4iNk6cZO46FP6WUKJF5GpFE4hrTSLdoJMTlJ4VzPcNkSg/Fhnl2VES2Yk3yS8Qou3QYp2KeH5lgutM2/3qKG+t1rnEdjbuX7XGdEqU3HTm62LUDdZ2ePoTPFr443LIw2NR8qR0RIbxri93Y8NRFSDRsSCw+fevfGaqcJsdP1w/AaDpfNrV3FknFTgw4xhnBZyCiIkzCbW5k3qkOYx7Ib6gzV47Vhpr+stxRwY+YOYuVcA7fIdFQZrk6P3Qg4N3Pwy0X+uwXg1+RBsi5IZ9Ar5IUMNOaU97MGFCUWhouanTULeXppM/Hclr1tJZ1BSMdNlKW+gdoaT0stcd3lA+DgQYq9sMdvF1RxHrF0ZKZAXS5FhMRWdKyJOSUUpaULQEiBuZKTnmRZUlLSlkEQq1UUVNAGW7SgAJhe0nSAslIimrVViQIJOd8WGxJOOR8uyzLkpcl5yVLTlQ2mYHC1iCJp/YzYNmAlleuKzM0nq9fvhh/JSE+Pj5++vSJkfF4Phk96WgLu+52X8+ORNJ0RdHNzY3DEQIsV3ViIlynfoc7vksoLSavx6vwOuu6eno1WkC8k9YQ1TA/uwtVnAeWElICzdumF0vk1ajgjQbvuWMD1808GSv7jHnloc1d8rhQZ/5sewhjLnPjF4pdVGsNEO2aNuN0vdiTWCe1pGDStEEAcs53d3cxZLaTqwOFAcr7kjOfr0SQ+37FTzX6AH2VIjeRVNzbCYATv/fhwuTEGdbe48qC+YwOVW/evHEej2tPPKznjoMdQe5xgwUU0iCFbT98TYm3NSTk211ObJ1eEZEkHm23u73OB/0T2mrU/vqcWMNcdVno77/0ZbfP25eIKtjpC3fPjUn3rf8Qd4aOZtqzqlvCIBxcp+KY3unhnJZolnu77BapMdLnrsycBZ23tjbtSGQ3X1OWUKIZen7iILeH4o+LLLx7fe5/5Dj3K1LVFQBMHI9o+0R9EQ1phCaqagqYrFaJWlXLaseT0aPo+VSej+tpxalYUVUapJwkmnKodqha0DatqwFWoDUyJFEYlKSnG1l6ZG1uEzwSEqc85ZRzXjLyklPlL+MEUEuUU5acsNQY22ZmalaK6smqOa7pGxJsESySl6RIWfNyyrc5pYScZFny3SEfshyy3Cz57m453KS8SMpJAdE2kVa9xK1+LiLi3uzDUnfNOoTrliumlPrll18+fvwozcXByTf6nJKv6IHhfnBotBjNAWjcy/13btn4fKPggjiuLvyVG3QX6zQouBNSaaHzPN3sLs6YGc/NH15mWQCAZjtpulyO4pqpjnPukGjwbRqmy3nYp2XmRhcQcXTYA0PoxVO8EtvZnat5RbfpuPvvWHbF2TU4zG/fFbsXir9TUoJnpJfmkEGBa03x42pRmokHYBRJ11uep8tpdZj8OBBepEmaF51sADBQqpuqL4x6nsb5uRrO393e3g5OTtS8xrn1MxPbNuPCrNuZCvLSjS+UuhaYbcr/VNX8riKS7jHN28IXugaJaht+GWgb5fP960HK/8PSvRQNbtT+6qOxmJUd3LvLGgICmHVFUdY5GUSS2GW93fVo9xbnEd8x5hCeXlt2Gq/pYIirUtxaa39EwC4W9FvceD1+nns7tM/PDRUpyurAvVFogLFFAChMaCkzonTV1agrsmK2Fn1ey3PR42rHU3kuuqoVc3tbffHMeI9RXYRG55H6Sdm+Fdg2LMa0G2006izikCmZMBxqtaAhtTxosm1EUpIkkgGooaxFgXJaaYwtZg5YNqtbynlZDlbS7ZLtJokuWW6WfHtYDjkvWZYl3RzSYVma3EsHg6qpqamkJUvOqfp2u0DoyryAnfvpmkINEN2J0Dymo3yX3nzgkRsj//hsoUlebSEczYx6Ha8Qb/EHWRPfjiec02LjMUARA0LucmlcBtCY1oPguT7JAiSa5YL1WWm/qnifIyryX50bEc4/6xRNJM5//BDbGd747pU4IfGrhYV8V64N5YJkPCcWL7c5VxgI78K9M7xDIKq5q/4h1ok7RXdik3aIkkcyeUo/tsOWNURRH+YwkvTQW92z23r3/OlOtzF3VWzwAk2e+yn2/MuXL7/88otPhSvPPMgqidYDtEZZXz9X4bwZurqD6C/plna6t3+D+dbbi2yJmrqwRO2GepevUNaViIrCcM4DI9kWHB/eVHWPVO3sl0t3dbX37iIqshBjdpAPEVX45xnu2B4qwgQvot4FFwlv/nUg2gG0+e6XAj/iPASk7roil/y+M5lH9GIZqg23D6OwvT0G4hk0bRY096aOjNCynwGKUnnEoFbUrIDBrNdSTkVPqx5LOa7lWLSoqoJhgCIwGuBAA0ZSYU7rUoU6vouwcL3Re2CGWlMAD+GYajjUBolEOBQBROiFnUUSwHcmYlhP9bh9m6MNEpkkyTlDxfLhZllwyGJLlptDvjkcbpacU8pZcubxqKr4NZPUdLySM8joVv2lIpkNSOUcae4uGOcKRf/j4yODtfgqPte0hoo8TJE/yzmQHXP/jNIScw6wIHbV+kUlLhIaDMyRoDUcFovbi5nWIyryi9ZrieL+KTYSn/7iNMZ5iNgumg7jTFrjfA3Hys5N+wVg9NtLbHkX2USRMYuSoZ3dxnHRfudlwEMXUNFuhXOIKvbc34uIrOvKnYAFKCwika7oPHdOXzgPORLzPK55BpzmI+l6B2IIuwvv/cKU7haqe6OGcjgngbaVd9/B7aU3uTxZhIJTURTkLxbZAxm80o4MV20P4EdhJBwtRR0FNsKsq0Bdnmwr8L9t3vwBoUMh/dmGhcQ2b6G5RMB6YZzTMC/dYvvfHBUNeAWNBvT8SdXW2Q0VWV/QS9EL/B6fODfun6Osk3AuOPWnW0SEIn1GRSlEJ/Jmd/eQdr6cG9fQ252XsHdxO4NWgwkJUB2EATMdKMqAojxHpmYwMGAj3YaOazkxz/y6Pq3r88p3IiiuAG38ZCZAii1nALQmtai1Ffdog//sidj20zYTpUf69SUl5IRDhhthcoVEkiBAFmRJueZ8VS2rQK1w6+jkJPSsTpCELCmlhCxYIAfksiRZlnR7yAywkFISQRKj+1IyWOrGrSD2M4O1NHLdW9mV+/7+7GsONvMDIdHj42MpxaMKxQYjC7lH9vF4tN5JMx4KoLaW0twtwfGMfezeTNy+IA3HobUdTKNpQ4JGyj0kdqGMz0zqc9NK0A2g8SHXS1fn2hTH71wZgBGCljj1zrNxFRz0DcO0+/xcD4x2375Nmx4f/qDz8M7HPlx+4vXFJtDgE+KDjX24fqU/1z76+ZR2vldEGCYung02s5wz89JIM6F6RMf4guZHeG+HqR7Ie2hkfpu07okIjXfR82N46LzUnbsyrwclhP5DiH1nfdCKXU0DzMI+ddMVxbd6vb2sgxM9zK6IpclyGGPftphPTQfLubAGccJuud3cBu+oqCGiAN+6qZPuE3MxNcnarkZA+CIk8sZm+Hfunr6ybd8iKkLg04Hq/IVGjm793SghKooGIpkfMdDqIFK6cYXilR3c5D7jpFvQ4gZAer9MXz6G8Q6S5FwZRr3b53NjmcuWHdYp0wBjYvtSfwqKUwAoBtOaE6SYnkphmKJjKV/W9amUp9XWgtOq2tKnZWDUJ/bwfafrUPPISFZ3Kops3iOzEuYEXXtWFUUZdP2pD+F0V81sspyRF6l+BiJarJT1eFpPZV1Vy/bCbRPryEkyePD+sCRZDunmZrm5vb1ZsiQx00JG85cbBqlmHFWLK3A2ytlvL2a2ruuHDx8+fvz48PAQr2sIB2zh9NnxeKRHtvsZ+BpGaR718GTI169f393d+TE09NRsYXn2pwNgXkAqpdzJidd5/I3XWT96b2jvJITGXfzs8fq0xSP24hIk50z74JVgaC7DijIs85GrrY9DeE2xqyHv5VUzTpH2nrlebdarX9nJry1RdMb3dXmku+tB7KRXcIB7e3v7/v37N2/eHA6HGPqB57+42Nzc3PiJGEcPLFT4e7NRQ4kJhaDR3lA5Di1Sgg+f7MNFwmHZ7vB358d67DVMjogcDoe3b9++evXKs03RbwPA4+Pj8/Ozk6X3c+MFBxJfh1froIHz0QvPaVPCNRFq0hsiqnJbhuqc/YaBDN3Xazp5rtuyk6TsN5brp6IVwmW8xJIy5V+aNdZRZT4AiN1y7Zim+imcq3dHIu8DyT5uRJ1oo+HC+xYfNDxxdyBz5SsHcq40VLSdNCflJcA0ZimrWdJgPR2rWlE7qa2lPJ+Y3MPUrNQYkMPuoD6iOVy37+5oXQNBA8qI1AkSzt8P0X2mwXN2qSJKxCM1AUjVwLKCUUubJCepbtgwNayqtpbjuq6lKGN8c4wtPuW6rqVkgUKLqIlpdW9qOlsBmuAEAKn99amluNlnvCvXTpvOOccWBpJ6eHj4+PHj58+fj8ej5ytGT3/WfCx4PMd9P61XYAy4ntd5rscfZz0GGv5GCqbu1Mx40Cwu2M5XxFuOwFLw9WYH4obJG5emxdHg4h0nNqq1cs6uiLqmeDtxRTlX8xxzDgLF51l6pdHlRr72id7h2GcXmhEbXfm4c4+4vlyoH6WeV7Y9RdHw9kmQ7ljtOiEO8/b29u7u7rvvvnMT8PF4/Pz5szVY8+rVKzcfl1JevXrloMqB1NBPJ7PY4RmwWtjTc2Gg5e6yBW1+3IuzSs56/fr169evGaE+58yDCxwIgzO93Cw3yUFZtNUK1XsZb8Ovl58RYQ36DZjjolAXsAiJbLs9fA0dsYZ/mqGidnUTxqFUPZFAUKPzSXuo7RsBd0e582m3WpzB8Q5i5TbsS6jIic2ms2nokUQkMOv3loNkvtTtCaOQ+F0/5O5BEauZGTcbmNQ/Q+Pz9fmJQ7d3W/taIRafwg/byXwC81YRGfDI1gBRRQBFUgmJuWDXYqdix9XWFaVAtYbFtgAIiBMgxCQQqYk46kfvocMmE7clBwBVu7fZpfsZYFNkqASEMVlshiKKp/MliakVU1PVoit12tXVWhgUQFW1aClFSylWYCqmycxSv1/xB4htoCj0XDavpsboXed/k1tJvNfMSimfP39mfGq34Kbg/OuU7TGKYiYE9MLdIRH/agvTTo1ONCFHnnGQFNU23loOmW7Qr6wi4vGyS8sFHZd2H2zcvtukmhrqoNctEXINe6wXy7yc73LmwNJDzdh/+3pINPy6O+SBHoZJjuIyypcrReRcbMLrTgwRNER8822ND9g0fna9vTU9kPs3pBZxlFiHGSsRkA15hNsDF+J+WNJ6DU0cYOznsDLFSfBn0TxN9dWLTkUXZuNyhQHsRrXBbv3+s6fa3gxd/HFEPpc6bv2f8AS25AYKAEGP6DJhmG/bPtlZSOSvpv2JXd1wnU3oqC5wfrOZtZ20ib2kQ7Lun5eqXaxsfeSR+dfW3y5Qlgvnuf6gX7e9cu5Z8xZ3uGt4a9F8Frk1Qt7hobtiZ1dgzpJqqHNNeZH+4ajIJ5RPo1k51zSqUAFSQmnpWnk9AQVqWBWr4rja80lPq63FSsd69UC8mPBfMI6QOGNLAPMerHrTMqYE1Z6GDMIUrM50FefDo2bXPGgtv4iY8Sybty0iyAk5Q8RUTbUU1VLWsha1wrCQIhArBlUUNV2LrmsyVSsqPFiWYLAaOolsJXVrIfAolc6RAohVXIhkZh10Qi/oh1d1/ULiwvfDhw+n0ym1iHaOGKyHKbRbPT090SlbpyMAXB6c+qXFghORm5ubWeBGcc92ov+gL0LDohK7ZO0MGm9kz31TElVZkVXol315oprA7YwaV04sphdkzVo3vKxdIbIrgGwPTOAiq78Iieb244cBFc0K9stP/AbsvivLrrnL55YP3dXqpT52orsPe/AeMyMXuIFVWrJYBuyhH+hACfOEaPCBG96R7WGmSKIIE+5Di7G7hrnyMjR7mVadsJ+enkqf54FQb/St7peZOPfgaRAgQokr3lyQePEfbHjF2t92hrgOS8QP5G+KooafzHGPv5bha/+IbjfsH3wAFn7gJly2BYf/1r9NT9TpxGYFz/6Q4/dxYs6W2bg/k0eU5GYWUdEAjHbVQi/y+/C44XY0Ao6QaNjixhbOPWWXFOfxXlPn8ih2B3XhyqYr2rCzSNWTpBpMJwEqAkkwq5cMpiiAFluVh87W56LHglPBaUVTKPVLhVQdkHmme4DBgtpEijCdWbJkUEhD6f7fmdLIOoEp6auiSDpSFsDo9JPocp2S1JxkokpD0npcT0dqtc1Kkpom1xRaUFRLUdFkBaaGogbVqjML3K7hsdZQpHupV71SY+RtsTk7uuuKi+ZSypcvXz58+PDlyxeHRJ5ngAuGLzAuNJ+enh4eHgYZ7Rjfj7FErwgypJuKfR8wdwwB9ETu0hbD3prrK72tARwOB2I7eodQPRsdmGY4Qm+kuHYOEiFOtYVYTd8857MiYXh01Mnp5Nv421/6uWLTOh3RzPxG5g3l0Nru52u6Yb12areH5+59sf0Bx5xOp4eHB5I6LREuqc3sy5cvvHhzc0Pb2a+//krNjTfi8xBnL767ARid63NcTobGvc0SUix/AzEMu3AAKSWaCN+9e3d7e0sKZ6o1Gp1Pp9PHjx+pP7ZwziAsw9/OC9cX48y4gocK/qglSplzYTG4MGBG+RnXaZwjE15u9SsO4yGhrXgCKBCWNTeMK1/FuMRdHPN1bc5suMuVDoy0uTPHCUzBJS6KnUh+8crl/sSORXaQEIYxqoh4S8TlJQTptWkLPQ/8wth/i7i+8vaAiurGgD0zpce1x+wpjKNoxWomDDVb1U4r1tVOqz0VOa5YV6suOVsKHUTA5TnqA4lodbmpZ9VhMKhoZRwUqHdOastoRL5BJSIhaR5S1sIcGVCMXttMalKQ6Bikud1c6FuzruvpVI6rldXMIJJTQpK8LHmp/vVLzgskwQSylJQhIjxUVqysCoY+Umx4rFRTmkLFnKsLSvWiunrT/2Lx3cPz8/PDw8Ovv/4KgEgixkJEkKQs2g7ku8/BsNh446zPfTZNAL6EkCtc95t6z564sbCwuXH2cHDGluNXZrCiX5R7FGlwkPISQ4QNmicL6jE0DuSqeW4+40I1zJhX0D2LSWzBesAxy7sZVA0ksduxWGGYBG9wl5b8uk1lbvw3FuuxxdwluwIYeW/T5ExqzSXO3zKT/aXgzZ2C16cFxzUCbh5Nj50cZoMt8ynx6Y5IvmFO0BC5RyqKP13fzkCcDv54ktRxTwnB3CMXxM53ygnXbVj3uLECWrVLndy7vf3gUV8AejMwflxKOaegKKqVQyAi66+ENmXbmtb8BzyRUxcsLmHbotHWpSSCqqliiDuG/G3YSLbTab1B0Lyd8dXtvkm79GOYm05WzOThAllCUhpVzSHX3tDCwODfBomGpiRk6oheEF6cMcsUYvvcuHZ5Ye7n3O2v5Z3LFULGD/g81m5siL5iiorNtVipHkW2FluLrqutq51WLQr+xDbF225Y2f/CHI9bNdxufQATkjUjdhiFbc7KYra1HQzC1WWOwAhmCpXWcy7BySRbRvcOiIrKaS1Edsa9Q4JQOZgJhpeUMpAsZWzZ1QCYmppBS+2/bezJAUhDcWaEa4R6YiGo4MAGF16kk+Bck5GsHx8fn56epIX0dSG+S0+0oDGkG3qjUhS+TtkeZ8Wb9Q1K3FLHkkKg57iniWN3GMRucN1i7hGqiOj2ESdqliAOv2ZFSLwSR/dVa1tcDv2h0huYgLPg43LjLm6GKxc6EysMvbpQE4Hezt0yjOj3LT7ScwMcJu0cOtQ+5JWGrBckGPe8ZsR2V3B65ag3nZ9rYYMbJ2T+PLDkQCfDdeejF7WVL/7kMxPZimpXRz9uWPFE5XxoZMDYcP+nu7yVQU3iYr2vu7U8PMPfLKq2RhJDyIW4jea4yNBA0bYmuJaoTa1/4wlpNajW0z9keicUv4njb0hIkkhOkkwyQyYlJJG+bry7/+eKdXkSvnt1zjDmfNEFHQIZzDu3+fZdiTSz2PD0gR8jJErh1AsCY7rIjUy6S9Lx4rnxXrhlt8KFcrkPS7hi2weiIHN7TyVBK2ZAUZ4ys6I4FTutdlr1dNJ1tYKWbD4IjTQ9WqsfNOkrAZDm1ldpPh49M+usss5KBsCkISKplwmMqL+xQqAilaVoF0OCKJZ69B/C5LZlrajotHL/ZhDkLEJEtOSUlyXllDMsmyyWJRcJOaVVtYaiN0HQb4HPr5igKtIcOdpeSq/r3998kaaBh4eH4/HIfGe5TwQ73K4tnjUP5EemGqS5724Jobg7GRgjKir8ijsD+dfISN4lP67vqIgde3p6UlWetY5LVOQx/xy9Sbzo5DoThci5mRzqRJA3TOMwUXFc86NfLJerzZjpzNrW3WJ7rjDe82/o5PUlzt6Vt3iv0pRvdajmMMi1HRKMrTx7RYPax48fSeR+wMcf5NQS50Ena9cwoguzPXTYm3LC0GY+u+YNXpi3SKKp5fRgrDwPUBmt3ox5MXNB1BWdQanTL34YRzqEFBa3fUjkZNe+BZlQwzemULF+apgIvhi1Z3TdMkC5M1crZqXoieH02pWN7DlvAOrpHB68kSWnJSVLkpJkCNJZddgwJdeyzTxRu7X2ZIt3PlaL+FuaIfXcBsn6Mj9x2KjEmr63lOBL5Id4Im1bUBTFvag3dVlSzb06d9dlYXXh18s3brGtt1W8Rk6E1EQadVWnNqQUrIZSj6rradXnFc8FJ8XTOjSedlG0wFKXE1UB0UAsDZUN1I6KhMgaqEEQN/DUiLeyusFMrIZVAnd8q5mtKktJpkVtLSpLEWa0Xtf1dFqPz8fnJy3FTCHC4IUpLw0YLTmnxXIyFSuLSkpIgmRUoEFTf1bBTLQ6kBM+WSnKIJlN66pa/D35kL95d15K+fjx41//+lc/kesn29GTuIv+UgoP5NP/AEGsS3Nx5TLjxM2DynRiuL+/92xlsf0ZV8Xi8MKXH0Kih4eHL1++mNn9/b1n9hkYzDONWH9EIvIkRy1NfRWDQAKgpU9E6IR7zWxHPDTUn9n43K7rQuMvduA/o8z0cLmfX1vmGfsqbOS9irdI07XErrpQTind3t6+fv3a41BoczEmICBQOPcghHmIV9gO0OGhcwORcBRoUDJ5g6Xluoqb7N1mLxMbAmWmlA6Hg6MixrxZluXu7i6l5LGLYiiyoWPfXua7L7QXqEw85Rn1RDlLzm2jOWsp0P7b2xO6ikhxKlrUStFVda0pFkxraN5O29NMZM3XO6VF05JSTrLkdFhyNlTt1Yujloujfqns3jqzZPzqAlD7GKROV3P9QVq+0KVJPmhwJ1qWxQOixgqDfihq6+MQXuzAjJD+88rwoMGviJoMM7MC2mBh7ktkKMBqdip6Klhrco/1tGph+gcUqXRmcJTS9sxO62gUnAAi8XZiq4IhrV5AZga16vrdlDEwVONTtRM36l4yIJKS5JzyIkvKi6RFsCSBmEnSJNlKjXW1luenp6KrfDlATb88nb58Ll+eyvPT6enp+fH59Hwsp5OWAslrOZ2Oz8enp+fHh2y3lm1JmhKWJSd6bC9LXiQlV8mgokuhOS84X0tOVCVZzTyb0n6wHF8/rl+u6GTNMIzWkIELQada63erngs27qHRa+YpWw+Hw/39PeOgcAWK7s/OojP1zyuNF9+FU46zJ3wQE3wSe93d3ZEDOSjGOjKzIU68WwDRVkptGWeHcaUW3mM+ATQoY4YleV6lhhFduervVpv1QHOZ185zNaOgjOqi2PNz1HVhTr6hSF9w9SzFDqRw3NfRRoTvaKEiqCVCy95aSnl8fPz8+XMMT4pAn1FX5NDfq/nFYUTD59gTbVFSY1N+C/mOBc3yBSB6OA2dlJBeauZTJ3UGG/P4AmzcQ1nuQrTh80AHE2EIqCMS2V3GOw2O7T8lwpqW5kxSTik3VZGILz7WjF9BmOx02ICiNfHUaa1IaFVbSylqERLxzlHF5KiI4VlSXpJwtpacstHnKRgs4s5359MwI/z/SwTvSPH8nuoynojq0gs86zQwU+b8q8uHSMk550Mr3LsO7BO3svF2m9Sx/lyZyrlRD8M/NxuXf8I+ZdYro7d184RBo8pKm9VMa1aAk4L5zk6rHonH1VarGeEN1pQ1aCAJ4u3X056VWGrELMC1Q01IG9sz5an3jR82n+UtwnZ1JPKg1QkpM8OZJKZCM5iZqJklasFUmRnRjlZUn57Klwd9eirH5/X5+fh8XE9rWYuWIrko3bCPz6enp1O2tEg6iOSUlpzrObbqIcjBaqRGRULeDIKJUiUY/lpIJQpHXwD8r1Pb7uq1PadpfdydyDNBYgLy/Mq9I2+Zlfksvg0l/iAnEHURFc0kNWMjvWigsT64NnPNUsv16tUrnvy/ublJfQwMH1HcFWkI8DrMWPwau0TuPTerF8oul34zerggwnbrnKOE3Trzrsv2yoXuXfO4cxUcLnwVKopdijfGzg+PQEsudjqd3LDLV+zZP6y3JO5K2GFOrpmfedqtGXPjjfw8+Fk7SQ+Mc+FZF8ZOA7e3I8HvZODK2GbPAhb+7wjAmrsm5XZfXeJt9WJFHtZf3iAR/1LQNTS0HcgPJoPWb7/R+85/mZaqqBW1oowkrEUrMGp+1q2R1o3tH27IjYeA2MNiljbcZIKcZHottnVhhEQXEON5fdLI6TPl+IdzRKLtSEFUge8SZ/fgycNsJn6/kkLARiqK9HzhXQNUGpyvvYfuWZGnZJ2x27s8e3ZOr8NMQ7W4cALNAcYApvswehcVKCowKlodq09reT7p8aRrsdWsmEHNEpxnXTkZ/YqqizRagrVCXCAwUyh/j5DIzIyu1+q80g5bKrQdtEz8I80LrMasliyJAawBMVhBhmEtTN92ei66rlbWYscn+/Koxyc9Hsvz6VROp6LMGpmUO7vj8fR8fHq6WTRjWdKCg+RlWYiKJCPntBnxWqYTyg8Rafyo1VQo0oJgR1REK5UEq62jIj9r45XjG7UWoMg3izHfnhPTQJ2uKCIqilsNv8tRUTQka4tYmlqIPD/8FW+PnDwAo1jYJT+Wz9WCKqj7+3u2tiyLmwCGNqNVm+vQkLeBZXDTdp6c1QBXlplFo2SZt19Rsnxb2X3WhTqXm5q79A3dsyvUSI5p3M/swi27s7oLiZw+Uzh1SHUpiYFIWpvPPr2Mvc9x1AjwIpJlpJZhZuYheIWhHb/oVyIkGmhmt28Xpjp2+xwlUwW1O/Majnq0x/SKkNgBnFV37N/Smbrqx/bXqoqoSuwtEFp7yPYe0L30/oENdxESrdVkVk7FuPAWrZvnurqhLnEQgTFY3NaimJmYqdQVpynFgATRmoyke3o3OWem5twPZ3/xbcPMjFGCzXUsnHGZP/tdA6H6vcNT4rMi/ad2iIGrDBoVDSqiQSdEmo9mXG0mNu9JDP7i56YHxp97O3+95qfLLUx50Ig3HOAboGgWK5SqJSrHtTyfypdjeV5RtSqrKpAU9Yi8t9t3xsH69rMRMtTnMyJAi4rIE1rtGD6stCasJkHDWumdwaQhgiwiC1J1B0KWtLQwFEvluLKe1ufn509fnp+f1nJccXrC0xc7HXE62XEtwAqGGLA159PxdHp6PuX8dLMcljXLjabbjOWQcz5ISgJhnreWjxo14jUEyGi7HGaGzcK4BGbIMNssaKQGa3h8QEWkjxLy2CPIX1WlU87nz58BMGGZ64q8hfgmuKd8enr6/Pmz64ri42bG8yse4sXMvnz5gha+iHGu/Ymx29ZSHPDGyGa81+Nr8y4mKHAlLQCPRIy99dvCIWR+dfOENE8Un4So3R0saJdLfBfpTORGn7S4EA5C/f9Via/yP7VXwxrsn1+EULuNpJCkxV+cBLDOn/jSHx4enp6eUgvmDoC6IteGeuPzE4dVRydDmLODTgY1x3zOtk4AEjS+rihCWP/ieHdfx+77Gm6fb4w14+Y77ql0NDH/PsRgm14mXuQDvFfwV1g9i9ztolnNfMTWw7Xa+boq2VrstBZ6EZ2KrUW1emKEh/Hf+se4ffdTPt5DP56nFibWkiwZYlsaEoO3dH4Wrp+w3qAXrPy4DgQ43UbWc5q8fG8s2uvUY5Hgu8biu2KdvD8HgqdXH6OucH8SUREa8fs5idvbW+Y2oDpqXoauGdGFiZrnLf5UF041XfUEwLSeidc6KqiZqaxqxbAWW1ccV3s66dOqz6ueVj0VFDX9GhJoca7RRTUKv7PDYXPSKndPIYQyNKOVQHKSJWOh3dvUNJ2s2Fqb4NCeTuvT8+n4fDw9n9bjqRxPOJ2krLKesK6q6woUQzEoLVNaVi2nsp5Op9N6KB4ILNPanDZ1WAtsYaTsBi4pvgAIT+e1jUuUzBHaIyzAUQ6mPmSLi0WCm48fPzK0j68H6AFEvCgiPMP/9PTkKQ5YNGg+HajFX+mERJeFjx8/mtn9/T0TKfjuIS4e3ppvZwfmieHs0LYjDMnoxkQ32GlLviMthhgfQY71NSnOT+RzmTZY18CCuCBpSz2LaXG1sOOPA49PueZxQ02b9ATXNHJuvYwEFqXe13bvmjIIZUwzttvnASj410FkI9id0Wbb/XW8nXmMfnts3OtE+o/QFv0biY+Imn9/RIRT/tUhESawstvP2OF43fHiXG2ob2YeZM+CSml4bv18iQC4z9sCrGAQx4MGx9rHYTfcbmZoE+bZlv5APl02VNVsBFhmtRvqKiK106mc3JejtHO+FQLJQHNNbWJR1IrAWlIHM2FIF4GJaO24ALkGHN6WpdCv8wvgpFa8uFY60Hc54+DjMkcjyDqSh2/hZqbbxd92vqDZzuje4JtV39M6MGL90gqdNIiKHBJ5TQ0aWQkxIVmZD/KsDLtj371yZc2Bqb1sZ9BKsTYxIDWqGlGRVpMtSsFayqnoqQUoOpWqU9kUOjDxeKSyaWRdzqESllg9yN5uxcA9NeYQLzDm0DYeeDyuxmhGLZXbppvGopS1tVOKAnY6reu6ntbTaT2t66plRSlS1lQKSjGl7U7oXZ61mqiLllVXCrp6pF9ExKMj0mRWR0TnoRZcif3eIF0QFxtfubRCI5GIVGa57EVVP3/+/OnTJ4ZjoeFggAKxEZf4PPc+xPZFnzQ0hXwLbMEP/dJQ9fT0BMAzirg5A9N+eijzYiMt72xqJ/k9d3QO6WDRzGHoVzXfps8PiuBs98O5Hvqy6vOpIVrahRvjZPrqePlZ6G2X87u78ESWK5HTLO+uafzKsouBht3qZWA0tzO/waEM21OvOUjS4d55Dz1MS7xyrikL4SVnfBO/+maaF91mFDFfHOMuJfuDLg/NGnyPt8QIqDaV6SnS7UGn3Wt43na3x1dp0jwggIAFJODdARqiHaMJbwRRztflwKpvNQ1nJ7VVbdUaaY7Tt208pe++mXkaWE511PsGo5qqlkLJnpJolpZDatitv1DOw8wz1yXoGh12xxe9q0PyKzPiGZgutoyehFzKDbTh+1LfdcvkUeT3EiE5DCIq8hMGsbK377tlYqbYDRHxk24vTfQ+WLz8df6wGVkqKoJ785gq87w2R7aCFrNR11VPzHqmSksWXF3Z/IgbMNj6IWByWH60cIzB91P8VvcSFfEDxjUptM1/KQgBWErUeIogVfuvGUMrFS2gjdtWVVM7nsrpxMhE62lddV0TIdGq0OpApZaIipIq2yiqxUpRnmmozNRszfQzlzaUyDQCKL2rm3os6HVD0T4mym6Z3yt3nx8/fnx4eHh+fpYWYxo9lfsHp1q6NsejZ3Fj7VgebUeLxks0vfmpS4Kqu7s7Zx4JEasj47kQnHUn1jYKzPLBrS35yprKKuKtYUR+Jf4aW553/5cneV7FrVf/XPOOvGNRBODMajf3/JpnsVzYRZ3r4UwVu/cOc/Xi1J3rWHz09Y2cQy3AiD+iViZuLXYfFFuI73QucTMQb9m1ug5IZVBr+f44bt9nkp77OT8lXj83k77GeAUHH34ub6ZMIAomvyT+y6zD7yBRh88sttH9I1t/6FAkUj2MeEf/ErZ+2baTNKPhjMfv+Z9a0eZ90QGiaTUlSHLwJ2bb/i3uY8GwfKImUsMdiW2b4HHCri77L22CmQPrzSBGekvCcHv8OnPiTEgXijQrtrsTSTAKD8DIIZFbzWiOiHAnbmVdusbbIy+ncPboirk9yx3nvs6zvemK1rVlZq1HGLGamsIUJ0VRlGJrqY43x3V9XsuXo/oCP73os9Qi1e9IgMzzbU5iEXm3Xg6+/4FLFIqay4NgKwEZSCKLSA2xZHJcaQczVRRbteB4LM+n9en5dDqejqdV11Nen9NpPZRVyqpaqAoqgEKOWm7sVEo+rcta1qIn1YMZQwVStolZfimncoxFvz83hCndLXtEMCwVpRQm92DoHVcUIbzsmXxPp9Pj4yPjXxN5XOp5I1x/qGecTSkxytHMeBq8fDg6f7rnIuBntI3s7e3tq1evSik0KnNj4RsUt44NBmzvlcNKDnk+cu9ffdPsX4ch+ypoARINK6XLKenVcvFBw/VzL3GYut2mLrydF+vMo4uCYPg69+TcV5whUUyGs3Ptv1giMfv8++5ZmkpyFoU2bYWHCgOP6HSgwSvHt+BterVID8MtXtzEoM10lVpcYPS0cbmrEckN3Zj77DPjXqu80Q/4+JZjILP2eRsQgJbDkr90GqDYbbMm3Yaz7/E0PogIRaoDs0jaTqpYTaHQB/3zRquWCEXtVHgI305FqSgyg4bltj4Ns8VKAIPQtGZmkJxti/UAa7hIzUStiInqqpKLFRiSLNKPP/y78zrO/rJfoRt4+OyaRb7TQQDutuDg27/iJdvZgOb90X4an9HpEGK7DG6a8Wgz4/36GRo0Ue+N55DqW8L2lVaCiIrcuds7/NK87iCkPTrfEYDbGbRqdQKQakZ6amsKbAVWxaqMVKSngmKi1uEVsowkZ1kThGR74b9gCbcWPNsAGsmcRJQ2LH4twbq8eeYkLCYlCUQOIkuWnCVnWbJBmCaWt1d38ar9Mqip6UpZaFqsFNPzaCUMUJS6LqmsXbW0lccq90liIliYCy/Pt8Zz+TH0dfcKI4kP68pu4Ymbv/zlL3R5dh+LQZnp9MrPBPKMf83c2ggiMNqGLewSpGmheD21YEWq6p5xREu0ghPk0THIQh7WKK8HXmXxXIOfP3/mvtad74jDNKRKGPbZPmn8KbJiNNX7RemNbhc4zab9zbxE+cyjX6pjs9dsd3A1JPqGMgvc/4ynXHj6cOUc9HcpjCnIAiffEYz02sfd4QwXB+aK1Oj84kTrHBEXFZm0aL5iDTPsTBeBlB+0sXYKwZ97zfzMgxpAXiTFebyRTUYUeO5hV5X9u3vB2uQmYxS5R1EbUZuxFgyGg2pNEA+p2VpwXMtp1VJtZ9AwGc396aoiImaQlPjy1LTCPzOIVGAErGpZVQQJSUVk1yH264vNH0LxlxiF5GBNu9R4k0UDFQ1SaxbLCIJCeifrGRL5duLUCk9DU1c0UL73KiI8M6Ngj1DPjRi+rrHytRO7pwo693coIypKBpMKd8QY/9DMlHEgjqqnAlWUik1cUWkbRqCCFC0ZveMIMwESc2xUELUBbavbiZa/rCWyIWrXNrp6sL89mxroBByS5Cw50fPZwiF5U/dParGXDKbbKQfyXz2/6V0ywHiqHgnaYJBxaM3hTioDCl3yDJB64JOOTrI1tu11hD8YNsDYv3tgFIUzQpKWovXh4eHTp0/rutLyatZpxbVPVqzhRMCuoijKcV8J3I00utcRvBOpvH79mkEXo+UOgQd2Kc+Zyu9i96TZ6TxzJ6EMn+7e3/NqIcH51GdMw+l9Cy4gURbEYr2OwZfACIni30GgxzlEYLwBQu0+d/fKZcEn07bvMsKLkuLcDLxYrgR2Q9FmOYpPnPsfHxFlqAOggagIkWeQPZSBZuZZiotQZB8LK9NA2NJriYYpjX+dg6xZil1XND90t+fzxWFEcSDoo1zuOjANXe2e0qR/OJo+/NJf6SZn60jb4g70aS0JrCPJKk1rN/bm0VxLpFDD2vJvluZLpFb7JVfhoWFK28SIwDRpUgRgRNFkUNVikmmAMGQ09dnXA8nLd0TpcYFDh3c3k3288QJfxHYGqcU60pygPXmUBd1q3Gx7Pk2m1nFI9OIQEKQ3SUKDmxGVTzMZX5jAeYDDYOevURYtbeTuDQ2rXtBE9TCBUVGkdipE5WYGbafOSIcS+Ie23A09wAiJmjaKyhUwFgV/Rw2iZWYwEdWqQdINFaFxRwU5ScTEEiRBlkRIJC3vcQNrVEZ5I7VN0+qsbdriU8LMqK2qTwrmPEu9cGgjbMO0wIhWB8fPUmEc58i0nrkzkU0Hu0lqqhOHtzuvECzMjP3w8PD4+Bjfa3RV20VFDCH95csXdw+KYj3qmRBYYkBF3DTc3t6mlN6+fXt3d8dfvSnpD8hEmvMtta868XSM65aoeiUnMJZjFAGxeLMzoIlcMa/BZjYz7VB/F34Nj473xjmcW/PPw13xyiCq5qfvdnW4cvnrBWk7PCtWuyyPLvwanzv0dibsuZ8O0AcM6rMUpflur2Lf4rsbHhSnfX6VkbbnN6i9y5o0Mx+/ppCYz0FSagHW46gvjGKotkvPw1iG8cbentFObaimbYvZSL2CYVG3CRhZV8fiPyKuKJIKjnbfapPytrVVZTg9rIvR0WGt4ruKeuxQYANrYWTx13AD5XINTdRWqfZf3aXX/6g7eMFrQtrknC+7v0YUHt/j/NnlJ6a3PFQ+x5sz2aOnQAnnf91jdYBE/pdaIqIiz64zk+ggDZy5pFeDsbBZLjd+Ou9yiU8cBrXL5j5S/9x0RdLCCdbw1DARZBhEFathLTgVnAqO68ozWdTfoCKEejhdxMQkNcdjaXwkQDI2vamXar/VrAIiqJqiZrZXQMHoETAeszRGBCJukCzUEyEJliVnyCFhSYyWSnQnCiQe3TejnUwBU7ECK6pFTU3V8moKyaMPU5ua/osgCbKjvi1egPjtQg/rhpf8RWYBrMW+Tn3rcUMZZVaMH+0fVPXp6enDhw8///zz6XSiAWvXfBaNvvTUeX5+/vz5M+M9lpb3nsXZbLA9cYvA9hl+mkxye3t7f3//7t27aCrWFojS83KgufLs0qsvNo4IVdUTedKSMvTERxTP4Q/NxkFpcD+KLLHrSnKuuNCZF1oEHhvK0MLcrO0FC8AkPs7hHpzXuOxWHiTCLBciXBhut/Pi9dwAh1HMlS/Ia48sOgx/6Pmu77PfklqJz50h0e7fQWm62zjavLlW0qvFqHTWgje6wVrC3uAyNB+6jb23E8fiyyTarsbnLfKa75e6pqixBxUnXAcorG0TcN1636Lu9t1oX9Ek3BaDRSB0sgY18a1CnQzTLT9HO25DN4titvLEWbHjdvwZbp7pHt0G459tiuwCbPJX2sAkpVRjP6oIto20QdWKWBKzkJShJrjEVGz34/yTDR+0eVsivLX4lz+RlmJOArcuRQZxSnPYNHNx5KbhDUZIFJcYf7p/8BTjnuF7ULKee4r33yk2tYPGPhuMVs/OxGHOsx6vz0+flXC7TLRFcWy0A7NK6kWFHv6nos+rPq3rcdUVWI1H8RVAEiBZU4YAiCDCnIkIccSQxOpxNSpFA4EkICWhyU2RADPZsmfQT0hqAGsIkFWw0CqHhQGgEpYkS05IqHouU0haaSMrWgxrKS1dIKRa6SrPqdZjbx4rUkUSJKW85OVwONzdHm5ulsMhLZmYps5a209sgqIpmXbf2n7xdxa9iSPFSFB+nE6nn3/++aeffvr06dP9/f3d3Z2Dp2hQQNitlhZKkfEePdQvGiHOon/QWKaUaNiKPUTwe8Wei4Zfj4N1x3A3IsQKqkpPKVqyb29vh2xr3m2c2RmgJ/1B9MfuxXk4x2bzmxomyp/iby32xFu+DCmGR1xZ86vKIJ6Gp/xeDx0Wb222s6HOhTmx5rYZFZCR/q/sqjPO4D2W2klJR1QDbSBAB1K7t2BNuzljlGH1yjnftGJmXC2knQLDRK5XTun81uZGBiwVGTkSf9wtdG0Oe8HhUqDrzVD2UqlbQQm2s5QknCtqiEhh1fW5StfaYWMMYa5HzDF1cTcTJsrnZxxS/UVELAAjSBK45q8io7ppT1bMspokmSwIX13GSQcQdnEWNooDsvFq1gL6S/PRwd6q77zj68g4U3vk5yjEbWdoS4yHqPacBM/Pzx79bliDhv7EjsXnDsuN91ObHY22lHl7EAcSR7T79EEV58+KtwdU1BiEnxUo9egZ1lLNZ8fC4D5Vv5MaHfmB+6TedlUXdX0FbbFWlSwxXKkgZRFLEEHUAiUIwNT0IpKQckU8sgBILWa8UTtbD+cjsLBW/2pThmAqzn2m1FEpj17C1FQBhSYxtExlKaUl5SUfDsvNzeHmUHFzytsWwcw6gNQTmOdxA2DbudERRD4+PjIiYqQ/m9T1JEpWpiXLc4TFtx7/uhx0RP/4+HjuiJaF4iykqqR1Hi7gIuGHWeLSEs3M0ajs/YnieNi5+im8lNLj4+P3339/e3vrEcMiF3kjjqswsVkk+rgODX7fqT/waVNgj3NTpH2IvzjVsf3YjgUJFSnEzuzehuFgKt7sjC2Gl7s7hBfbv1DiKIbPuz0Z2r8AbRFmOLXIWz7VCHyx2yuHULEpCxDfwuoizWvNMffAL3GJ8sZJThFn7E6pF/dpm/X/A6nsTuMMvy58nWfDHTK82rBidY2EjiG+2S2+nNeID90Qhv860ZMk8XO7g+qrYpggTpuah9drgBj3ra5aotazuVP9nEwzM/duBEZcT5DMlI6i3NirwsQ0WdoMC7bf6Ab3tn9mHrOxlc65ONKGs0NENkOFXcQQSdf6XeUFrnfKiXTrOMytZiWkJaCKaDivMw8E6FgmXo/8GxfBuHBcRkUzM86fZ1YdFtkpOyy1o4TGmxHXVtVTsdNqWowxHtGywCIs8FLtakGp2izP9QtpODXgFIKsCrcRKUmuKcwkSUpIInlBYk5jSUuuzkMZECZlBUD1ppbG8c10Rw4zM/WZbRuRulADZmpIvKsyqNDDiD7kktOy5MOSbw7Lzc1yWPKSU0qb4hdO3OaoKMqPoLjd1MlOB/XDhw8f/v3f/11VmZ3eCdFPQqIt6uu6fv78me5EQzTnSEORAiLs4J5Ve7uAV7NeaMbnMhjX4+Nj9NEeGFinwmpDfwZadCBFpso5H4/Hd+/evX79mlPhgMlbcMtXtBtGRoojioZCn5xGci+EwZhXvjiEed78Is7jlRmH2SQyYjtzf3abmm8fLg7iYBAN15RrRrT79Bn2ncND81fpd7oyWQEiNeaQxNt/jdqdCGhSC4UyhKV2GoszZg1aWb9rH95X7KGzgK8u8UbsvWIf8nBlfq3zdR9gbPBcV/Wc4dgXAtancLZ2RmSoNryyaY33TsSetP5IZz4L7MUW/Ju2gI1M+Fr8xJlFQcs1ZVx9gmSO3dyheXFfz6YcF+jmG+ondZhlvPLteUVZG0V/cbyy9at94EZX+wgmCNTlRDgQORpy8srnuhZvmSZh1DI643hnSl8cFQ3u1daX3afE69pH4h5YYEBFuzKzo5/p6fH6ueGzhOywtWfV2qVmpqZF12JrKaea3KNGe1TipopmKlxP2HzyrXrVOIt5UhlA3BRMqzwASEKWnDOT3eclpyVJzmnJsqScD1hEchJZ5CApS8oJWSBpySlRxfl0WtfjiTnarPC4mqlWFjFVqJYC4ibloQIrhsKzl/S/Tm3PokmKJJV8k0VyTjkfluX29nB3e7i9WQ5LysgCVE1QPSPng99g0AYJI6PuvYl//Md//J//83+WUt68efPq1SueKUspvX37lke9NDjk050tJqNxwvIXH/U0GnLBPj4+xnjW0jv8R6UOQgTeUspf//rXx8dHArL7+3tiowjLNKSs5xISRbz3J9Ixx+V0T90sgdG7d+/evHlDrenj42Mka2dONFTEmYlmxFg55kTjGOOiOK/o85oU5YKPzufc9gDlzHvnoINN8HSPQHb682IZJnyQC0OFC41cBj3nbnE5vlv/miHE+Yky2uUm34JHvTocDnd3d9p8P0uLX6Wq7rk/bH+j20Ts1bzlJUgaYPQuOPOvDsFj590byatdJpLdluPF+MFn26comj/mIkEJVxtpSpDmciz14u4Lp3gLaGJ6zfwj4ok3RESi7qrNgWo7lL8hGWN0ImvJX5n2tXlYjxasBlz6KevNfI6dthlgXalL2dZvGiBo1BCitObxVPNiWezAGYY4C4Pir6FD9/f3b9++LS1PZYwHjWBKAzqOnq9ECT8L3nM07FdkMrxqc/CIB/JnRVHsgAXw3QPi2o0ZRc098adrO6S8u2cYnut37UrjODky6cwW9MXa0cdjsWOx48mOa3kq+lzKsZTTWgRua2Or/f2Z3sWJLkQE3yLIuZ3PF0kZ7RRCtX9VjUxOzZ8q5SUvkJyRkuSUc0JKSMly1SEh1UP4WegVX6wURUomapAjtb1tdspa6lbDSlnBGKgG5Jq4o4Z/bCAZkpBhliBJFsmHJR8O+XDIt4d8yGnJkhOZpZg2b0HTQO8NGfAlwYneNzibtkjbXf/4j//4P/7H/yil3N3dEQaJyLIs9/f3tCJZS01/c3Pz9u3b7777zlPAWliP4+kAp0vqWnhm7eHhQYMBq3ZjsnBpc5om7Z5Op7/85S8PDw8MRHF/f08TsgfXmgV3akds/EERNqEBstTONZD/yeFc3g6Hg0uBFDIROmNbv6HXM6Gr/aL0yiEL9srLxdv0RU57C5rtQaIr29z9aVdGXF/m13F9x37fYj3m44f4Ri7cqOE8f8Q0fHEeadetum/fvv3xxx/pOcccON7UIKBTi9VrIWIQIT69+903Iu4u2I7rbqXX0Q60N6woDqoiLUXqlRBw79um2ifTQf9uhNKhfWfz/7xSIZFgy3fWpgayv633L0WVh85oO9OmKBrn6BwkmsqA74YXNxJk9ctgr/hf9cdg6JXrtidjb2IPrO/y27dvv//+e1WlxB4yqg67r7jqu7ccegHiHORAGT1XDiXiIa8cN8yeu6O0GNZRURSf7k/xU8zOvwhhNeLQWHZtzREY+UXrJfPAqtgj+DhS682LvN5iWzuY4nGtoqdiz6sdVz2edF0ZH8KKAtXjhp88VX2F2SJ0AEJGSlL9gXJCTpIyRJBTXhIkJ0kpQ3K2ajej7x13UykdcuWdLEiSpXojmRiyCD2TGpqC1eg/9TuL2iDLdDtYqQozMUtmi0FhkiDFYweQedOSktKpKKeFWqsl5ZRzPfxvjaINAIoGindAiuARaEANS9ZtGFqFh4eHDx8+qOrDw4MnmnFPN1JJSun169dUohwOByInP0vC1z+jomg7o5LJwoLkpDDQkMtK8idtZ4REzhIWbAqXyS4+KMruYdkgSGJWNTTVFznHeTuy+tBO/OuDKqXEKEfRDyny7YvFK0d5EdvR3nc1TnK8vjtd5yZwvh4ncx7v7r1RQGB643MPd1u4fpbQjzF+PjfMc00N90Y65683Nze3t7fujMnw6Gb29PQ0EMzQQ1egOiwWEQ/UTga0gGbi7a5r1OByNPffiYSq2bjj3y0XJiry0aBzxUQA1qvWzuH+qGOeoECTYlZdEdr2tntO9++mH5+G1f4ZOadhDcpJf1HmtjTU8/DFlNtapTuRiQ0DskDkrevW/dyBoTiBPrfbxaBGQT2DXxvkMxweXUPNPHodp8qX2ta17ScWClWi88Ph4B7Nrp4ZaCmudC4fBlEZ5ZUPHL00jmAo9b6qPlEaLGjsmC8HEaLFvSt3Gr6NcehfQiyi0uK/ewvevdjzuJ+PYu1CGV73MOQ4P15t87Z2xKo15Z4dT+V51WMpDKxOjyIw0mPDKB390J+Op7JFcpKcJWUcshxSShkpy5LyIae0pJRkkZSzBSwEV5Aviepbq+fOiHzMzDTV/GrR+8/ATGOOlESshimyhq7NagKTZhQ2Y/BrASCWWtRpDiUFP6acaUNLObUEz0RpmwgjV3ekD+f5jezrHMPMZ87fBEE3mmJmeFX8zLNmNiXBHjRDw1dfABzRY0+S+oNcmxIjU/PewZlubmcuc4W4rjeSU2d1xvwlKirt3Nz8UGdaZ5u4zx7OYqQQBiNWs8kgcq5EvrK92DnDiF5scGh5xlgX6l8JULzxc39xxev7jR3ANBsWVqBdgOjVKp+GRStKZ15kKFEaWHNIGuPpJOfJ9CsENw5xLNi5XEU6rD2zpB5KpG1/SsytsVv/ygmcifDCXb62eZ+9V5GPhqURiKpsx0MAPHRR/xT/NdYNwUrEFUX+lLb7dAW69yj2zb/yXFpRKzwxAwrPaLqq97YpaqAjBlqJCWKNqQgmXqhJ0Dr/bY5EZJuM1jExwz7tjmUDQRsA2gbvUKnWJj7g59ySClAqRosVcQmmyO8+qIhL0nSANxJALA6MopXZwpZvOE8TUdEsCb3zPEfMUTj/ckkSEc/vEUkUPWpB0612r/2lEjtjvbYsDh+9LNosaBy1mhZlvrPyvDZUVJUPhahIAKnhvKwFcuYYUkqSRZaUlkWqsWmR5ZDucmZGjsOyHJaUl5SzHFJ1HiImymJM2IMkqTn7bTsJZRyjVdcachEm2qhLjF5NAohJajxRrcBaSilkrULqEwMKc5IYHcCbBc1MzDIsAVksSwNDKae8SMoLskgyEYF4YIEC3VKXyCA9rAaSdJYwQEz9VXXv2N+QizPXlBBxv3r16s2bN/f396mFSHc84VJ4ICk2Tl5yE5V3T/ptPU1yfBxBmIi4l4brSCMjDXTmLbvNK1K2V9PgC+Wh4s2MLHR/f48Wk8OjX8RHOKsPzoCRqRCYf7g+LwZz/xE4arhyrtosFGILcfXdvXFoc77oP30tmpnnZB7F3OH5+rnnDl061/P4qwT9x26FuWVtvm4IAvfm5oZUenNz8/r16++++44KVxe+Q2vDlaHbjook+EpH6o3aFwtALb7TSAaDu9KATqSZhi/0EHvEsMtxQxkWPG9nXi+DwSLG+KlIoV3HhkUcYMh8Fgt1sxlTHVRg1HMN0Ha72jRF2+iJP4paMaxmdC0yoMrrODetc2GWapqzKcDARk8W1CroaTIOsAIii/sWLnhmO/HtOhw584C5IqvNaNxZ8xP9HByXRDp3DY3vUf0Y/GwzGr7u2qTQB/RyI9dAObGpEk6fudz2dSG+Be/53d0d/UCcVdnhuPlH76DpnRzYavZbulzim40N+t+Bc/nBs8NWXcep2KnYqehJcVItxawIBDnnG+CQNQGS0tLS1yzJpEWUXlJeMpaccso3OeUsOWNZZFnyTQLhxZKwHJZqXgSWA1JKWVKVHC2DjUiCVZduM0gRTVYEWFEEYBRqDkFRQzLWGJAmaPcptJhVDKVmCkWhNpbpXd1i3Gu1RZASkGBZUsKShf9JxmIQozO62FpPISgUZtpM2jyiJy3TCBovV+KvVRo3lHWmVDSBFd/uzc3Njz/++Mc//vHHH3/kIQU03h78naPI5u0ez9p3IQiinFpNhlfhYXjqoqKCNKpJRYTA5dWrV5iiATmpRRNylEHDChFNe1zbiPyYZM1HlENK1yjlncRtz+AyVMBUIldcX3ab2r34tY38LmWWib+9wd+jX9cW6fOn+kV/U5S2qsrENc/Pz4MDjQUt6UASsU5sOS4AGhzUpN83e/H2o5zV/hwNwoHq6PbhPfTGh1VtnpOBvK+fQ+8M/7oJZl1XZ88oFvqnxhb7r1d1gj2p/4UzZxEOuArIrOnfmZGpmBXDWpR+1gzYOHRh0xIFYBGfjkFPxG18ry4Kw2U4b+sRlXc6PtQ3wb2abSqOMW34PBzpa18YdQUBo5Da3R5KHwMNh28iUonGgZmoLOycraGi6KJHjY4TvLNDZKhoPnMDwixt2Nrd3d3r16/fvHnjYee8hdSC5PniFRuxaaOl7UDxAH10cqWKf2MjAyQa/nrPKyoqZsdSAKylRoMopVgxwCThIDlJsUMGchZmYE05pbzIQSRnZEkZkhdZUkpJckpMTJYS3aUl0z+abkbC3BzVxxlqhqKuZt1oUCvxuI+bqsUMHmpGdFI5iy5D9f9OumqqJoWxGbdCDjUTM2EQbMYvNavKsJRSAsGQNCWUmIcbslLK0qheUZyzFdQnV4DGL1ZHpW0f5i/ADW/DMjaoPehJ+vr1aybZ0P5c/SD44gvWdvqMxxmsJRxwfabDIC++AHhYo6jUlWa2YMiAudt+xdekuNhEnpyPMEjLBcsgTLt4a3eYsUiv/SKictXrzHu7jczlmmqjkP1KXQh6sXLuodfDuEFMoJcUQztXzsPQyHD9XN+GxuW8omhoasDBEW1wPXDQTN+FuDzsCmv0szG0P5QIaPziLIW9zaFla4758YrXcZzkfUjtgIL27kcXYP25ahKsgb7wRJsIwumEgNW2pXrCAvX/AsA6nDH0gn8kfBs+bj451SJGr88mN9yjWa2o0nDGzJhjvxq6Gt5vRy31Q3t0G8VMEoYafqC+uKHrAdnE/9QsjGt6SuhpG+/W6+56u5k6GH8pHIujluhm6gjJesii7QzmHD0IvUqGxQUmNVJ+tDlK6eFUb3QqGjyK/BWwNe5yeXTGzwZxUCmEiMx9DpzIttIrfW0q8W0O3cCkppVe7T3IXn5wXRHWomDkxqI1p4eaAZKwAEtOECRJS06HQ7rJKS9pWdKN8ExWWoiWGg8eMhLhBAMwAsLoi1YTx7supQaWN8MWUF5MSqMlal7gWVyJdyoVozpPdy++/u0YpuKqxoRWEQt1RVbPXW57CGsO5JKkOZATRsFbULVmPGROtabcQrMWMzB3NKj5QTWr6GtDRU4u7G7cU6aUDocDdTO0yw5qxnOEEkG9mREGcf0g8hiUQznk0yghb+tgOPMVKNrI/NH+LtwAMVBtrDmoYaUpXR1vxe3+vIpHJvTP82zMPw23v1hmZvuGEns7NHthaF/7iPnGofPnNklfpYq40IEXf30RErFon+vbJviIZmO9vb31hTCiouF17/bQgm/Z7Ky2O4To3HCuzYGRrQ9xhF5YSwgu76O4PDmY2GG4LsEEM7zrOCE7hN2kcMQgexMBP7cfH95AUf0oAZEA9WgwmgdPk/3s0/aB29OaI9wR03aMZWuudt7i80WkTmvaQ0VtsCpCOQ4zWgLNeyXiPkZhVMPwJ78iix9s+zBazcKvgSjrR0ctxBzsLdEPC2Wvw4gI2SM2orI/HvKycFBg5lNpaZro1plb1gH3XvI9bfS2LuH4mA/FEY8DI/YZDVQ5ETrx+0AGYOTj2uapl/D+dXsLPSS6Xrh5hc2CVgqlDBgsS824qCdBpt9PSktOt4d0c1huD3JY0mHJB1kOC6MK8Qi9ZLopZw/l0GxHlQrUrCjAnGQGgLlZy2aHNUHhhqRphVCREEyp4WnAiAGWWoTGerVOkydqq5xgMIUVg9FTDiqNgYmPOHl1MoGNsZooM4NBFUl5pK20iDX1gFvYB7CfiVqljaUUWoFYbU+irshfZHypBAq0WPmJ/ZlE4g4gKmMIOLSdWybF393d3d3dRRWRi2wNjkoIOwNCltQORbt1o+ydxrTmQF1aeIlIxLFoi7jtYYqi8lZDiSaMXWaI1+OKOMyJ13RuGVo4xz8vMtX1ZeDhF5848PmVjxjanOlq+Gl3Mq8v83xe08LucPzdlT5Pn0tP2ztro+FIwZx2YLdLDhekN5PNu9ULg4r4JpqM0R9Sc6GPECjLGcQfXdrRTl8JJJR50nYJ2NvMfaqTXQ2BhT0YtnUamIxmVZJPuhHpa2yQSDC9XvNQdm0H2Xa8qJtgine6V5cWU07NrC4KnRLLopplI+yaV4R/3JDnyAt1UaCbmpgyZrU0+GJtBIgtR8BoMIUk2BbfyYfkTbRLEXjaVslRWLwTLi3dpbK0wGxu5CLOoOgmgnF/IJfYy7LEY2uOjaiSl7b7jdTlUCYecHZzQWmHnXcVRZHsvbfsqmdfsHB0xmHW0AHtTdLoIdGwCffr2CsDJHKJMbDzLILc27rSb1qwaLaUsNihwASS5ZCwLClnWbLcLMvtkg8HOSxyyPmQlqWez8JS4w9xnKUe72S3rIIfVdGSoSrVvVtqKHXiJNmUqxuoiX2uFxrFOl8ZtxZGLyM0GN6ABmBAEZ5CYzuMptR8f8gHCUazXQ2IRN6qgZcaRnM5XFAEAhVTgrNuZi8sCBYrdCy15SVw7Ex1zuvXrxna0Y1ckRbLXnHXgZTSmzdv3r17d3t7y2BIRFfApk9yvB9JLW4LosImHn9z6MO7oqJoQB4pRGqx4LrhPKaqZEUz42Fmh2gIzGN9QY8enN9ihUoF/UbE+3Ylzvi9yi4fxl9/30ZmxPN/f8i75ZpuRPoZbpSgjKH0p2vap0+fHh4ePn/+7Pvs4d7di6mF9pEehGF6I7FLEsxPEvKwsqZf9xsjmwxABwHhuQTwW0TEj51GlkTQbw3UHt1QItfMO/t5huPc7OOj8+XyGzWzoaHIxT4CNVMCI0VRsw75AC5C++thcc2M8yIifnQnPk7pfsFkTDCkJKrbeTB/Rj8YoU/S9mizHjuZLyV70MeCe/VYByj9QPghqtIt6GAcGLmyP57wYiHBuOrILWsRl0S8xXvDa9pIOvqtxiAvMQtsFMUS1E7RuKEhJ/GufI7FG4ycPt91uezy+4uloqKU5eZ2AQBhvE5RSQZBQkrISClLSshJlpwOqXlSk/Fq/J6WwJWYRKp1SVRUqp6mKlTqpqB1IQlgKEjtmkCaOa0pU/rNiMCqX5AouINIUm/egA6fQRokYELh7rMo12dp8RYLAKTArm3L0+l/DauhqEkywASyVL+9YlJM1IoZUmoZ3ASSIArL0sx+SMjuWnRONxslFFECoUzuS9SjuOObJy7mBxH5wx/+8O7du7dv397d3cWtNvYCn0Sh7F9ZotUgtThGLqDd5oUGTVI425J6t3FpMZbmBdtNy58+fXIutXac3lFUXHji7dYXdIIysVcDZprX5gtc5D99M6qwPc3Q7lu40Blv5ALPD4txrOk/DULw+v5frnD54ldNnW8S/DX5QDS4XrqU//z5M4OLchWhNPcdMKVzzAfsT5mNaBYsa9ZrU3bHFaluV+LzgwazoA8ktukrSqzpeoKUEnPN+iIxmLn9udEBJapLhyH441qXAs6oqhn+EIcdBXL8shmrJPodhSnaJX6LnAvq/pmiiWEba8ozv63iigYxrA2EJefFjUvcXbJHhhYUyei5BfTKPG+9/k9MxvNuWwf4cJoEQp/O2s6axgjNzhHr1HnwCYnI1ZrK3z07B2taPPRO9X80SDlVc+bJCA5NtPl9Rr8FKpOctKKc57LCqHVcZVzlg3758A7Y5B8tIWjcbIzeme3eK8MveoVBpAzyLUp4v+Jf473+uaGihJsbATWPKaWcU1poD0sJAqoiTURyQpKUxarPdHXAsfqfNlaqL7/TrzSW5f+kG5YYKg4RVJNv7VqjMwMsBSWqeGTRil02+BIVpYAp/zMzWFFLDNjemg0IqPaqqrwao9WWmxmucSMJXNRQDMV1RcrJYgBuExOFIRk0BMOIgeX33or33ncGdH9zMa0hwGhE7vSq9nObr1+/BsDz/Hd3d/6ggZTj0wdBHzuDJkOjhcvaYeZh0UJQDnnLEpQ0TuK+qfXF+3Q6PTw8xFEjnPN3Tp47L81NdZcN0hRre5iB3XJ5md9d488t/LYHKWzS6Pr8XOjVuQZ9wnef9WIPd597ualzd+1WmNu53HIEtRFbu6ZEWxYCacZf5viLynkiidevXy/Lsq7rp0+fYsabSEsDM6bgsbGzlk+6RuvLcOMgx+cZG1DL0Bnrz6zNzBVtHFS7coH063Fc/qwU7HpdPwW9z1DorVTfoPAT1w7UD21KpO1iQ+MQ6ebCzCWrRXykWgOraL3WAxRX7PT+8kFDtrndNNluluhsUYcpssXfjw3XHB/V+Un6IQTwJPB5qCzbRoSAivxiAEM2oyKPAjwr7y1oWUiljD7Kcjwe3eZFI8DgwuxM5G8fDfR7g349qnN8rfH42r7WDIqijnIamflF50cLpmSX/5FZ/EYJuiJvZ7iyu3QOjQy/nkNIw+2OiuRwKwByypmocznc3i5S47NXEvAdQA14SMKpAdidAASgL9CWO6bBdHAKTcSty2SSinHqzfA9h0FqJlmoMZIRoFAGeFTXEYkIEl172tyhuUWjOmWjRgNjfr/kKtMavlFq1G6pQA/CzCIiVe9lRhaFGhKt0ioogCpMrbAjSXRLSwIoRAoZraad3iRGrY++RPQg4fSBU+0aQq0TuX/+/Jn4naeUXRNzf38ffaidAaKeyVnFOcGJzwJIj7LYt91E+rkPnustp+BjEXkAweQHwONYsB2aQj59+nR3d0fvcjT1UuSlyOrO+RaUKM6ul8FBZMiBP68pFoTy197is3HuoefWyAvV5vrDkj/PySx3Yse+9rlf273dn+aX60pEBDwdG5cAuIeHppYk59WrVx7I9NxINcS2iF2NS8g8Ip9b78Ms1h3T7M6JtT36jMOc8fnVPT9yCJuE3s1cVd3zj+sZWdhjcPhYokYBTVSi7k6nTraewX+VemwmtYWhttZjGJFtx8u53lRSdQvbME49TVwzwpKJdybLVTqyAbEKiJb6f4IjEc+/ZKrFWve4EGnIONSGhq57YQjtU68TckJyWgI2dUBr1EcRiWK3HQ3nyLQ3TsXdrDPF8/Oz+187Koq+Fo6QIjYa5ENqOSKtOfMxsiKfzsAuT09PT09PjPASs54NFDvIFm1pKL3PA5FHronzMJe5/d3PF3DPPPB40a9sqOhmyQCWJR8Oy81hub3N969uRBLQXIAqKvDXbwxfyDetlmgbbVIKxSz4+5MIGn1UUg+EBmwmtMZv/pF1kraEjkgCM7r1EHq8fGIjSn12UwUd6zZUJhBBSpJyzcQGgj8u+FYacMqwLZp7Ik4DMpAgPLiWgIyEZKpulJu4rf8W4igO5HU8HukzAYBk6voh5rGPyD3nzNhZbG3WrwzkGD9rMMk56eecySeOPyKL+nJ1PB49hAwfR4a0Pj6eNQTGaI1rS9PmHCgiTP0mLe1JCp6wCMwzLKKR1gcGi7y3W+Grirykj3mx7D79t3QplqFj3zzYc5jmt/dzoPD5iVGGDsJ0poFzLUs7Mukun1HRgp4dhqFFzeJsfooPjYgnlojefCCDcI+sFM8xMNI9IR3PE3GZZMqdIYiGNzUMxP0L0QBfSskXUe/GYEETT5jav59Nw7FZ1kRgKYnWHJjuWNoURRW0nKGWBolQFxHU02eMDsNDx/t8Et8ERLZMW/RmaThgs4GaahJRURE1AJnb883hvY7GxzmVuF6oMbf5hnVcuDTtV+3mhnsaJPL/x8f4M/2wi0MipxxWKCHnMQAROR6PfLNfvnxxEopKo+HY2sx60mtGEWiJ+IwincnFGfcuauUj10QMV/qQLtJC67H+MMxvls8zvhm+xgUCk3yYcVJDRYLDIgCWhEUswwQoq1JRY8bIh2YhJwb/UyJ2+u2UCI3rX5hBazXZ1LJEGgDawXXqaxI2ZjSz5p4ENLOTNqzE/QxQNa2NTUy28BF8uDnBCURkSciKDCBJNhERI3xhnCFJliBpySmlLDkJvde4U6Bi0aRYEtVsJgaFSEoCxZIrSMv1LL8ABgEKhOEe63cOYHt39RU0ek0hninDJJ5Op8+fPx+PRwoytxS4rZdeFM5CuUXB5ollBKGMRu4kxxLCf7kAjf5JJSQ7S83/gH/XdX16evr8+TOdnPiIp6cnZuX88uWLD4SGsIEt/RbfcKTm8eqswi7JnkZn4B/nNB9a3GZJgHGRCdHH15+ZcHjKuZ9iuYCT4kJ4oZ35yoU2Z2QWBcFQc5Bc31wu3P61LV8YWiwOL+KD/OUOtB3pAc0Pwx/kZGxtD+BN+V+/8ZrRWb/fGEbnF3dltDOd6009WNeHDx9E5Obm5s2bNymlp1YsOJiT9eaZtLZHNzOaFMlfpRSaWmxKg7PdC7Mmq8NFNtskKrgjlCQmAqjlLFbRUDuqgura4LqVaPZygNBpU+rpM9SsZ2Yt3lTXFfMutZDT7viRqy1pAwHb6g6YGg/rZ0umKSUrFfh2q/vAN01EO8arujKdRuN7frQpil21rcVZ/RV+nY6qpBaUyAGcAyOXeLzOnGLBjLgTB9IlMIKsQC8xIkzhAnFshYoih0Q5nDDwDrORiIqc5imBpUF2V22O07FXpN+IzoBm/oue3XZ5cL4loCLqa5IlMUZsNqXpB6qopmlDJqELwxGZmW4REwOV60YjMCcPBXWX0WJNcF6NVkZ4JBsZ0iFnq20ATIzOOn763syabzUNfo0QufngmCECy0BNRmvSssyi290wT614og+mta3kasVUTIsmVdMai0kEKUG4eUiWPESTwbZTCvTmNpFqfrRqc9/Btjkk92D0RaqFEHaWbmaOpiLnB3f+dxKP1od4EqGEWLdUtg+pbRyvMNi0iLBlssrj4yM9Q9kH9pOIzVklMk9ERbxrbSlN3GfQ1zByVGlRUOfVa6Z7DWFY4yIXUVH8iaO4wIcXvs7XX6zwYhmAy+UbLVgMr3/u7ip+DTi7XC5Dpd2evDgt8c1eQCSDgBvke7yem/91nAQ5Y30bRPDlYc79nEc3S+RIqznEd0nN045yQJrXlPQOGWiaA+nVadYnObdwuNXrRKN57GrbuYkEBYbL9xZEt2mUAPp6JnOLWpLESL2Uq2gpI7kSbOmPou6kPaQqj3TTG1U+wFYxgAqhWK97tkwQ0LvUxDcmgo1eEh1ltykxM74cq7CHS1l/yqwRgQbQ2Fa/Cig7hIStTteCf3pJMESUg0BUUQ3J4gIQYS04V5wU0WMFBOnq4GzYLfu6kEO+gdgH/+rydqA61nGPN+e+2I2hDJz44t9BIKDnu+Hz8IiKigRyaFi4pj5VNS0GMUNROZWqLTIhQhRyQ423ZXQkUoOYmcGUIMACBRvA8/CoEbK0jV2aUxEZCM3zOlrSahsNFRlUE6Dt8SRCunxX9VONzFgqmYrAEswnJKP5PYvUvUKCmSClRAOatIywSBwMp4TqKFM1VaFbkJgwSCVEBFlUqAOrzttohja6Fkm3+Qi6ItcuUkv09u3b77777uPHjzSQeWT3CIPQALs07JJa7o6bm5soeaMnZgl5zRwAke4JwqJKPz6I4tUP9hOuRU0v7+KvbrBwbX8VR+GocIzGwZABr169iulyOHC0wEiRhQYecKHgDOnMWULIjdJHYqUb08yEA5/sfv1a3BAbuYxIvracg1BRSPlz5fewf+124PKVoWO7sm8ozhQWzh5eFppzBb83LgmD+EYj7wEeRdSlkxENPQWmyTAXOzPI67j2sIK7g7gvne9GpMdzripAbxx3TyNp6gSErchgIol9iMDBHXFqnVpRmpi3ptQxMIs2AIGaJIiJgAfjM2cZyRGNM6U1fVNFDg6PmKWgOVmHV+HqGu9PRRlV5NYE4yFScn3JaBtzb9JhySYyIEACGF7EoYqFx03fA8rZxhMMZ3VWQ83zO4banbmChJOzuR3air9G1IvzmZckIOY2VxsqishAekOYy1JfKbhwuOrxHI8PkIhKTWuWAa82K/WHnlwQaFHmx4Fc+Clej5MTm2VpqKj1KRmSqkjRBC0ZMDVbi66GUgCDCHhm32DVkFwpWxtWruGHCGGsvfNZbCZz5OOD7j8nwCy5QijezHhfDRJVnoPWcIrYrkKBUvuUASQkQ4MwYCZZ4Th8gpoyLDFOVzLGeNQaNLKjfc5oFkFqDuOSqMyCz3VtPMNK21pURrPmEsU4jamdl3nz5s379+/fv3//+vXrz58/Pzw88Mgx3dycNF0t5EVaDgHakt35IKKEqDVB82aIh1Z87XTMRMDE7r179+7NmzeszIMPac8mjabmiQGWSIjU/K/rSlsbr9/d3X333XeMILCu6/39vbVTqa5/ckEwMICEMxSdlO8DRToWtFCenp68NZwpA/PEdfTcLb+lRNl0oVcXSpQLCF39z+jz1zZ4/YhciKOBG7RtAPrlfOiDv1ySCve4AByFo03RoL2f0UwERuSOoUIKx7ikh7w+5/GJ/pOzMK9EVyENThs+lrnxuKlwj1p/VgMuSUR8W+KP9nhO3nkAkISUm76oIqImv3nWRCI2rCenDFpP3yZIRsop8/Ro88JmNkotolm1mGopTGjZqU6ILbSGbWxZtePbaXom/q3h8eoOdnFERHfYuqJr0Rbnd6MMoIaZFJHmCmXVdrbDIMarE6ZseM7tFXXCpm5fU3YAt5OHqvoG1b3E0HzFwrBqx4YrlyECepb0u+IutOzFsB4eFx/q3Bf5xcksit+hY3H4c/9j5TiQc9cv1IkMK02PxToVFSmwcoFUWIZJSgoV8qGklBe1lM2AzBhG9Uw+oQcVIbQQBRHjHMPeODXzZJYb0RpcL4OodCJJXasmbbdSoCu0qJZipZTVrJgWUyW21VK0KIoWNI0S4QuVp3xEiyfPPpC+EtIikpEEGYBAa6hGK9ClKFK11SWGwjBsMw4YUKrqDNLZCusTiedqsjboWonmv//3/+4YAkDO+fb29v3793/zN3/z9PT08PDw8PDg9il+oE6IecI9v/2XL18+fvzITKv/9b/+1z/84Q9v3ry5v7+/v7+/u7tzW3tUFDmJrOv68eNHN2k5Inl8fPzpp5/+9Kc/MUPtP/zDP7x//94lOG1ezrQStEE0hHs8sTgTlMuPj4//9E//hOav+vd///fv3r1LKR2Pxx9//JFHqb98+fLp0yf6TqHtg7U5RfkTuR1xbVCd78acfqb06emphEhiADxmwTkxMUgQb1lDQEhfb4ZqX1t2JPJLB8F+4xPPFbkOP8U6XwWPdmf1Qon0EwUxWjzoOBX84Oolt5M6ypdw7nLALudGN3ceTdZfHoUFI90Abpw7APhWwbcrpGf6EnE7xAbj6oJeHeuP88/aZ4weLO+D5iyltBwODX1YW760ab7N1HjWhQJU6tHdaglQxmuTLClJzonhW6TtXrVoWYXC3pgYOy7GSkciRrX2MEX1101L015BfQ9MvOA+NFkk+epcSk1e1RbgEbDM62V4Qf1LbHCprRyNCNvu1lVCG3Db+n6GPMLo5x8jJkjNhQBNf+9FQzDoXaSCMwlk4th3xQ56+DKAmP0RhXtdURQxh9PhgKJeLDPEGQTIBTx07vMAjLypdvRArXB5TgliKCbtoDpSzSZDx2sGpWgGY24q2iF7k2a9cneiOkkbKVOfEpccVumjKFr8DYBUhyLj8YHmw83sgVpUV2PytgqLzKoKVpUmNDHLBoh0r8Dt3TUxW1UateOlAnofoYahVPpQ1d61iQjuQVa3DCr+EdX2XR+lrkpmDzd6/du//VtCGTQiPhwO33333eFwoGQk1iGUeXx8fHh4sJYijWGvJeShRDuGxqjWVD4ROXkYU6da346XUpgR1jciPBH266+/ruv6888/39/f//DDD//tv/23v/mbv3G/pRg4AOGcvA/Eodg28WZmRsTz8PDw66+/0jv7u++++/777znkH374gQHr6OIX6ViDn7hfd7QX9zTOzNQW0BlcQ1BgEfnDH/4QeS9+GFhuECIuQ5235fz+BmF1jFeGD/Hz9Yghtj/08MVbhqdf0/KuML1SurG82LH4iKjmdLqKPbce3Pib8rfvpO7udK71cW9Q9K/DnyV76DBO7zDV/sQ4Rl8nZske7yV8l2AcIY8AcF+9YXSp+dIO+rOBkuNCNeDLfrVLOR9Aodj8etQ0qSl9B8S29dmaEryuFq1NBiQx0ZYwqVkUIGZVFIqZpOqB0UxcZqaG0oIV8SUAYSGoCwuqXh8A/USrVSicODNQVaGlun4g4JYeH4l01Oj79/i+0ZwgUO8f17jRlNGb3CwiIH9M9+/FIi8Vr3NO9zl8vv7r9rYDU1wokaIiL0T1Z2x2eGL8O3CHTwV6Drr8+fJPfjE+aMuDpm7wkyTFIIZkaN43mXgBsGRAtRYLo1/DrCl0zJJ7V2/Yvm0lADRHmwFLA5UY60cx3UPcZtIcmRQ8v0l7iBaiIipLSwt5wfCNFRWJtrZlexBamCJBC5e9/ScCqZGsicO0Pn0bDqmdrMsQTGqAmjRGFKQW7UsFqszbZlpAiOVE/MMPP3CN55AA5JzfvHnz6tUrSvAPHz64aHt8fPz1119j5BWP6MiDKqXFtfP3HU3L9OBxgsghLUCM4G5mPP72008//du//RuNZd99993f/u3f/t3f/Z0fdZZ+uzybEmQyQju3fPny5f/8n//z5s2bT58+iQjR2+3trZkxGy7HLiKMXZRbzkIX7pHQfXWcUZH7hv/1r38F4BFfUkp/8zd/s8t1Q+M+XWEJ6b4On8+V4a4Lssl+swrqmr5d0+cXb4lX5GuMdOdEHvqJconsF6MyP5pN03T0HWHbquGYggUNv9PPMCjZc8R2wnOuiY8b+hYHYuEYEYLjnVco4dA167udyy2/gxx323cOCZVjnTgQH75M5ozaYEqJuiIzpMpKSZOKpro1bDC07nXpKCp0rGyi0lSN38TE/G0KLJlVHJRMq+w1tGh2jGTt0X4HIrLtfzXqdOPK6DAGh1ltdxTbCm3UEU8ECetUO4Zhydqmy8J+3/q7fIHZw1itwhDUabda69X+ck5qd/cjJ4/4Trf+XdySXWbqucHY7PzVWQyNnlMfRDdWGB4x8E5sfyD+ucTrmET37nWZRGtDRZu9S8VKsQRYaofWq0tx1YsI6vn3hlssQ82stJbmeeUfAWCyF4BCmHPDZwUorpVRQAViRaygQBWiRbmLKs+2rroWXVdVKw0amVb/IiaiRYVEtvGBBaNcCzZQpWmq5+rrv4DQ3Md42Fag2UpTRGnN7gFgTYV5bQ0G0wKz5mCVkWrUy0JWLaantSZua1pxuleLSMwez2O67mjp57OOx+PHjx+5fUwpffny5Zdffvnw4cPHjx/5oZTC5DifP3+mBc29mG9ubt6/f//jjz/y6L4HjKeLkpuTKJEZpsKa883Nzc39/f27d+++++477xjOIIlIygPpO4ve3t5698zMtUrH4/HPf/4zU9je3Nx8//33P/7449u3bwmYGAyJE+Ik7iuWw8qI1bgLf3h4+N//+3+LyNu3b9+9e3d3dyfBseMcX6H3HXGBK5PKeqB9CSvlOWmFSeIMq+xuiXXsWzHTi2UY4O5TLgtTb8c/zBLq3KNziFIYgYg/JU6ChNMG/oLQoAZtwQg2NWuaGOqKnFQiRvFR++dza0N80VG4Rwr0ixGH7TrkxTl3G7dzDYvrO93BeJh87cNR+vWBsIeScl4ON+B2jY6AqmYq/GyWm1InNXiEpi6iw6kChd6X9eyZJAMP6gMJkiEGyQYFksK9QVGKbonJzVyPD1R3i+ZUZK554RTWTLCSeIET1fZNBfWsr3hTLQns+A4dyQStEOIH9FfMOlLfutXX3KDPOW3RcLVPT+RvPIIeP5dTWnQGCbFOrDfu2wTu0RO2dHbDs9ubc9y6KwGGfWk01w5NxQpRzY8zIAZ7YuTy58FM7PM538IKnh3WX3VilMSlJa6okAhoShLW3RiXAXkg9XCmda21c2kOSlYrHsOdudEYVItZxLQqbqvO08zUihjUXeeUkKSorcXWE+1nWgrWUoq5k54lUxOTJFLoQ06wVbshIkgCTUhZRNJiWkoqyQDJIvmAlBkDg5JZDUWlFFvXNYmJGYphXSQlQYKkBLOcAFgxw4lD0GKy1OnjjJdj0WKqWtZTUQVw87q6+lLNI+GoC0mEGvUo+oldyDPUJz0/P//0008//fRTSunjx48///zzw8MDQz7+67/+K72wCYmo73n//r37G7169YpqGNbhMWASK2NMf/jw4T/+4z/+5V/+5eHh4f7+/tOnT//xH/+hqkzN5l6c3Kx5ZFUPTZTamVK0JYHxJ9kZLlciQvz3/v37V69ePT8//+Uvf/nnf/7nm5sbGgH/4R/+gbGJb29vn5+fP3z4sCwLPaViUsPSskz/+uuvZvb69eu7u7u7uztOI//yXjb1/v17zrwHqIx/I1NFfo4Lz65QGOSFz2cEFrMM+r2K9ZvC3/Kg4a7fCL/ivS+2E+ffr0RTrzfCK4T7pGdG4OVpymEIIsLQL9YUJ9HvYXjcAIziujL0be6534uermKJdYZ7pRm4vQORciJGdJUnu+cLwEyx0gKi+lgcOQVva0GiN2XzZVFVU96TzEyp7gF/6iYkJUlVY6SqBiRFspJyEksuwxRJYYpUIH4Cfy26qq7FTkVXwqNd6BIuubKo6cCp5ql6tbq+eOf4x1o6j8vsIOOzundmDZx9Vbmab6K0iZIzruKucZQQ9D+uHSmkD3e6lR4nzSNDL9/QE89M/ENTEQY5xNlI60zxmqWPXSShRGKOq+G5z3P9C7f4lTR4W4OpxwAPvoUMoUt1c8VpQ9+ULgAM2qAQc3JUCFSBE9GBNKuamVXztBmqRkdRMVD0CKqm4PZi+Y/S3kz7mGpRK6XOKWPDM9lZ861Gopeb1F4DqElBBBCkJJbEjCfHWkoOAFX4Zmm26toCo4oVW6FMfiaq9UB+YgBsIQg0PVVdlZpkOmWJJIim9bRqoSw+ajEAz0/1WLgf8vL3ZCEnq/bBcwkprJ25vbm5+e6771jh4eHhw4cPjLXFhcGhiZ81+/Dhw08//eS6Ivc3Igrxpzw/PzP9+IcPH/7617+eTqdff/313/7t325vb//0pz/xfL5HTXVVExU8HoyO+QtJhUQtP//886+//np3d/f69eucMz2pl2X54Ycf/vZv//bm5ubDhw///M///NNPPwG4vb199+7du3fv/u7v/u7t27fLshD/8cAa40NK2x6xwx8+fPi3f/u3Usof/vCH77777u3bt+u6fv78+fPnz7/88su//uu/8tDc69ev3759G12zoyyIf4cSN3BRHOAM+LBJaXSu5tDULLleLL6ED9Lt3BPnIVzT/tf2Kj4oXokSavcWf7msmUPWAu8z61Dp+P3339/f39MNjk7KLtNdvLoHXnwX1huehl4NkxOv257DMvZ8w3fnIc6nayIRlqjY+bjq+C1+uwQ7tcNH9Jq2+YoM+qq63zUTj5+bhOm4zWDGXlCAmyQ3RUmNGiSQ6nVQ1wZVExGpwRLRAhYpmFG7njhbi67FVkp1rQY1j/XmrsxRUeTeDnEyHUfWNcM7JjwZTKzU5TWa6G4o/fsKL7Tprl66Z4+0w2UZ6lBWR/oZaGaQQj5wJxIHKxoc8oaCOJB+87Y7CwPjRwmzC4w682W/i4jtNJXeBom885E4d1HRZag0X5k/x9U2dizoiir9tajM0jJXgLGj66j9T+MZoiKryqQw3Vsoo2Y3rjhng0FFlcBJzVRX0PaFYsVMSzMxF2OcIEYIKo44DEVbiAs226B8PRiPeoYMDuRoqhOiP6F1HSaApuzorwuAJRvn8UFWoFBoMVuLpBrLNaeKijyFNyEdUhapZ/ZSSad11VJKWct6JAGcnk+cMWpNXOPCV+V7O21BhiRYCngj9RxEDznnv/71r//6r/9K/+W45Ptwcs6EDjFqogMmGpVIH55XhD7RaDuGdV3fvXvnqMhz7vBAHDU0VEFRNcUz9iLC+Eb//u///tNPP71+/fr7779//fr1x48faRF7/fr1+/fvc86MKM+17XA4PD09/fGPf/z8+fPbt28B/PnPf/73f//3169fl1Levn1L7EWHdGq2fvrpp//1v/4XkdAPP/zw7t274/H4yy+/fPz48cOHD//yL//y5s0bxhd49+7d8/MzoeGuLBh4LG7XhppRmlyJMH7HEuWaX5EJGKHv226Fbygui+dp2a28O4FDHfR7Pm3ZKuKLiODg9vaWhzEPh8Pj46N3ydv0rx4nws8Z+E/WR3nGNKXzdR+1/x2WHIT15sKoo2ieJ3NufJhGC/ok8rhPkQd8soCY3UYztFPtXwDED84ASCJmkGqGIrGZxSTb3Uok4ofY1bh7FGnKHJ6+rXio5Ttb1Va1UmwLJjYVt6D1WdaqfG4vsa4yIKwCj6glEZEk3IpLzXrpL6iDN4JmczsDeixaTIZ3GDo1v+G+xn4l9IHr0DaT7QEi/a4sXrHmYGRNnyABJM2oKIqIgbQiDQ9EG3/a5XqHRF7OCRlf17x4ZQmQZVDV7xb0HHHl55n+WRwVCYK3f6r4wIB6Dl5U0EIBAWB4af4LKEwJ/ttv1Nnw1opZGLGiFDMUHjwoZdWCtZ7ILNSjqhpWXaG2mjbvQDXuIWArihWreV1FG6FtEN4AuoBLjfi4ZDETJnCFiKgJU5ZZyjkzqkVBAixrlQBM9pFyEkk5pdS8iwpMSjGtIZAKtvMPIpCcIXRiaCehzNLC5CIpSxJUnyEta1nXoiuAObCytkCI1OIworQHFXW1pMeAf3p6cj7hGbHXr18zhKOzh/MbScHz5sTiJOhU4o8rLfNGKeX5+fnz58/sWA6FOiFatZiCjblpGaHb2zkej//xH//x5z//+fXr1z/88MMf//jHP//5zx8/fkwp8eR8zpmZ3Z6enh4fHwE8Pj5+//33f//3f384HN69e/dP//RPf/rTn6gb+y//5b+8e/eOk/D/tXctPW7dOvijZE8GDlpcdJO56KLARXfdt/8fyE/orosMigRF5xF3PDPJeHxE3gVFmkc6x3GC4D5QC+3k+Dz0pKhPJEXe3t5eXl6u1+urq6s3b97sdjvFXufn5x8/fry5udEYuh8+fLi4uFitVt98801K6e7u7vvvv7+4uJictOj2636nefMw/pDxdoq6Be/Lksxv4GhGYjSXjv9QpraGRzakf2fuK4f+yUySfYFHkO6w2Sucn5+r2Vl07sV2lFIJT0R8A8DMZ2dnqmIrY89yaWyvTWMhjVe4GVCvGGwH4uoA71L/6pM95nnGNcxHRHdNURrUjBeZ32HnDP3GPXexGgAwl109lFeP4nqWcCMZ0sBMe/c9ekFEdo4XcPSgzDdSKYtIYeZ6CL9IKTIUGYYy1EPFttGmGVHMPvkQ6DojRKo70z6xWhl/E5IqaRpJ8g4XMZsmINHUeI4x3Kja9ek4uRtKMXmP82cn+OYiuhqJf32aYHwiIV7HC4yJfJKnNUTbsz79qVuOhnk2m5kSwioMIYinvxwXpphD5MZxwWq4dP/ThmqUYcxKX1h4UyqhDMLgIXHaoQhYpacF9UyYMFUfiUpMXCBUFKMwFLiICKSISNFrBkQY6vNiqONEIsIoRciOYHIxn6bMmlURthPszOIQneohfiJkjUgIgDQOGxTAZxIUBWsQEQLlJEIkBVhASgYTUQEl9cMhggF1whDR2dlysUx5kXKmM0JOaUHIkIxqbQ6IgAYg2YHUVA+FEhEhlwxKSVgkJ0J2v9cA8iITM/ESzATgxQtzpGlMEIBCEJXErFYr1RyrE8LtdqvunnPO6kxou92mYNDDFi47pRRJzcmRxhi5SV4Zp/jITMVik6kJVAretHMI1OwuJdNYgyYiu91us9k8Pj56SJPb21sFQ+poUZHK77///vj4OAxDzrmU8vj4eHV1BeDFixeXl5cquPr22283m83Lly+J6MOHD3/88cevv/56e3u7Xq+vr6+Z+f3798r6P378qIHkdBVUVqLH396/f79YLBQVHQYQNLWG9Uzh/zfNscKvXkrPpyZrAgM0zqnF1FVxYSCi8/NztXJ7enq6ubm5u7tT9RkCtWeLkQnATYuWy6WCpwa1eDWcUfo8ihhIU5wj8YgZhZAIsVc9n3gmgEwk5gU5auGxMo5MhEzzyLXRC/e4yhuoN90TEhfe7Z4BItP8x+Y0pcS8GgBTxSwWTTzetJWay8DqVW4oZTcUtbaOzhvH5bUtHeMvEcVe6hyptlelW13STpDQMweK+U8lh0fKSCVYTLtQsxHA6N9SirL9yacOjFIImhTZO4+1bBSAOAVRUz/6MRN05CF2fNJpz1EOgjzJAzY4xfqy4meMDqxWDTPpf/bX8W+Ts1feZUUCLgCEEooAwkJscJsLu72zK8OgR63UcggVF5lvCCkiKO4fUdVokL3UVDcbFkKt9pKYv+pgcS0SRFSyl19qOxIs1AZKkqzlqR/qJCSUxOAciAiUCTUoiDpOSqh2UZLURxchJVouU91SprTIyOq2kqgKXk3ECgKDksmnJNUd1oKzW/QlAlQLp4gqiySCJOEFS4bIchmUmEZt2aJ8q3qIzahIVUVKbQp6FBjBPLmppx8Kqgen0eban84Rjb/p38Z51TgK0pTH8QjjBYVgBbqPTyk9PDy4ps+bRkR6pC6GFXx+fr69vdWDdZvN5unp6ezs7Obm5u3bt8/Pz4vFYrvdXl1d/fnnn3oQ7+HhYbfb6Wl/CfpHnYTr9ZqIhmG4v79Xm/RmPjcz//g0t0rFRzR2Zt8vVP5O8/lkZWRKXOQXkwyrLw5dqw+XfqCZR6ZjOlbGye94DpqJ2s7rwuC2dBzsZiiwb10qPDqs56bXKWjT9HMOZ3kmm++Zu9V2zBMBWnGwzY/v6P3mc18/eGw+4oX2I9vUkMe2RGRaM0dLMjbN1pssPAwF0LM21QkjEcWMAIMUe60TVUDSWOFUHGQIxH6Jmk+UMpRSClcL62Iui0bKpj3ymZfq6LLECWYIvh8dX0Iq6Ky2F9IOqf+pF4127OB2Kfzf3Ip3Cf1VFBXtBW+j42Z+oRwy4h4JyMkRUi9PkoB14ic9/TezzGnMf/acIb7m9W86F2NUFGviPkt5bOjWKzE8fVI4dPja7yBsSOJT9LIiiyMmLImFdQ/AzMKk5saoBycZYOx9S6jvHSjad3sghzBcAT2EqZD68wEMwah6jdiVboYpeE++dr5NB0C7P6WkmKP6TJKhnjEQJlFVOENV4VW+Wb1qqFsBNQrUaLVI5tKdUlos0iKnrP8lyh7t1cK6Qs+KCgC13DN7LK2L8ggDS/oD1VIrVWNvs2pcLtojtRRQUWTfZIEyFGK7tbU6gPYjx71wKF5o4uBizmkiEk1D8Z6aNSASsdOr/20uKGxM47YggjxFeLvdzs2YtMTtdrter3e7nVqNqHhM08PDw3K5HIbh+vr67u7u/v5eLc1jgDaVYCE41NZNvMreohJTxmDCp3fsk75zWhY7xb6bHA5k2w8cjRfFAx/GT+RTlt2TlZx7+XCFj8z8yDcb7txwt2Q+WvRlDxymbt/dM7t/5RToi0ezlniK3UWmvDim7f0a0C8VsW97ypGAO73OEsxOm9KbqRq5h9MthTW1uYmxRbbnwyyFiyIcoiS0P/o0AkYygg12TKVhHQhgRtHKflhN9SODxiZgi7MpIpZPA7JmiNhXCBGwC61kT8yiRqdUNVDiH+xrZQvVyGjI/z1Etg1481vUvjaRyayg1KEDGS6HjWwjEPKbvnn2pz7ojTQoXjd0GCmkYSMH2MIkk/Fy/eckKmqgmzcz4pXUKdEahnDk9dyjfnI5KqpSEBoYiTir3Z22tjoYYrV+LgIMFnCMGZChiB6yB3gwITCj2LZHKa2KhThJAouelTegQQQ3FBLAlG68H9Hqz2hPT4ScQUSUoVFdcwVzBoFYiFCS2UgpwRMR1B+9xZmFR1kjPfJAKeVFzou0WCBnLDIyCdUwh0yA+tlmIcCCggAFyCDSM3zZvFxCUD+pcyulTBB1igRiEM7OWuvOKCgCsN1uXRHm7nnU7FrPjqWU7u/vFQToqt8cccTUktyvAZNE33xO400qGeKOlDpH0J5b3K1Gbq6KORh2yRZChJk15Mhms1kul+rqWpVi6/X65cuX2i0aGMS7S3XVmr+r87RnFBiJrY7RrAQBGM0xAn+taQI61nBMl35ZiqMgM2IkHDe4sT5zHPAAW/ysdGSrnWO62SmN7QwkyHXUyMaD5XGIpulrg7uQ8JwxpsNmLxHr2bDRvjnxBc8hXvga0JBKnIYIK1AU9Da8O40tM2jsqbVh+j76FMQPJcSS81qFmjO7O6W0X5WJaL/aq7SowgiFH1G6E8cRDTCKvcFcPe+qLVFdHAkQ8bImDsDrjaas/TQMa4Tut5lTQhxqWyZaxdkeEXWFHiJ9mrw8nA692EAHHwKeSmQiQDFFW9yvRopyFZVurXsM5FsFTMEmdEzAn0ZqjA3ptxxO2PE+B6FmnOO9Bq3BQ6mzMWr+fvJRc+2pVd+c0n8rJdM96QRQg+hiwe2VdFShpmKVnLMe+FqtVldXV/f396o7UL+LHjXsyHRgZW2eStiD+l+X/0c2msbOTKlbM/pS4uLhvjqYWcU/ccev02a9XmufkB1w07MMOWeVtGkN1eFk7EBlDXpYSQ/xndKB9FUg0RcU6sw0Im8Jog6Vleo0cRzsnyBEPs7BwaMiA4dHDhpi0TTeMfdiFYzXA6fzSegj462zi6wc+vf5YGrKiMmD/QUe6wo95mBsnSNCJXWxjTsHh8hWYy2CAIFa6qSkkiESIj1cJhHvwOAEBTBjD8Jrjm/2HSIe5UNE7NjwJ2QzIdH+zTkxEuCDKFTPC41WenTw6H8qNZw20qEnFyb5gOp9CdIXMdmqM8++LKcExzo9z5+spM+Ruadihk1etIyTNxZTkGXy5uR1n0ms1dwnE93+7t07AE8Pd5vrt7CtABGq2AMw3ZWptaosRyU/MLmoTQ4LS1xfDwRuGwXVLbkzDHtEFken3V7IXvZan5n6WtsWxZWVj2oFxSSkYYT0Mx+G0fbAZLak6vTYs64j3veieFfpJsotj8Sb5UWE7t73BLTBZ+erf/zzXwD++usvDevt1K98PG4xfQ+hVsl+qF5hkPJBDVv29PSkh26+1nrWUFjXH6M7PVuP13EWxet4J1a7J2Kfsb74UXDvJEGnru+7aWoUO3u2P/zww8XFhYi8fv3atWmxxLhHQWTr4ShHU+e5Pjz8wme99rUybFZxdA1pxuLIus1Vo+F3mn7++efVavXw8PD69Wt/6puE/v0mHwr73XgRs2oOqCNE0+uniRMJOpLGFCU31ZukhL6IWArGs6bZVcdqHCCzWJP4od+MUCnWarlc/vLLLwDevX375vINjME2FdNf3oK28KkqTftiFMDAkKjJhXH6WHQjgJrjCX3/xxo1g7cXhYx+BAx31HQePZxq+WdPk1evXv30008AfvvtN7d07CsmM3WevDjweZ9/07DJm23LY4PHveCvzc3fPiuaSugm4IGLvjKTTGPuqx9//PG7776Do6JTOqVTOqVTOqVTOqW/efqEN+5TOqVTOqVTOqVTOqW/Sfo3wln+kwplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjE4NDI2OQplbmRvYmoKMiAwIG9iago8PCAvVHlwZSAvUGFnZXMgL0tpZHMgWyAxMSAwIFIgXSAvQ291bnQgMSA+PgplbmRvYmoKNDIgMCBvYmoKPDwgL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuNy4xLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuNy4xKSAvQ3JlYXRpb25EYXRlIChEOjIwMjMwNDI2MTg1MDM4WikKPj4KZW5kb2JqCnhyZWYKMCA0MwowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDE5MzA0MSAwMDAwMCBuIAowMDAwMDA4MzU0IDAwMDAwIG4gCjAwMDAwMDgzODYgMDAwMDAgbiAKMDAwMDAwODQ0NiAwMDAwMCBuIAowMDAwMDA4NDY3IDAwMDAwIG4gCjAwMDAwMDg0ODggMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzQzIDAwMDAwIG4gCjAwMDAwMDA2OTMgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAwNjczIDAwMDAwIG4gCjAwMDAwMDg1MjAgMDAwMDAgbiAKMDAwMDAwNzA0MCAwMDAwMCBuIAowMDAwMDA2ODMzIDAwMDAwIG4gCjAwMDAwMDY0MTQgMDAwMDAgbiAKMDAwMDAwODA5MyAwMDAwMCBuIAowMDAwMDAwNzEzIDAwMDAwIG4gCjAwMDAwMDA4NzYgMDAwMDAgbiAKMDAwMDAwMTAwOSAwMDAwMCBuIAowMDAwMDAxNDIzIDAwMDAwIG4gCjAwMDAwMDE1NjEgMDAwMDAgbiAKMDAwMDAwMTk0MSAwMDAwMCBuIAowMDAwMDAyMjQ1IDAwMDAwIG4gCjAwMDAwMDI1NjcgMDAwMDAgbiAKMDAwMDAwMjc3NiAwMDAwMCBuIAowMDAwMDAzMTkwIDAwMDAwIG4gCjAwMDAwMDM0MjcgMDAwMDAgbiAKMDAwMDAwMzU3MSAwMDAwMCBuIAowMDAwMDAzNjkwIDAwMDAwIG4gCjAwMDAwMDQwMjEgMDAwMDAgbiAKMDAwMDAwNDI1NyAwMDAwMCBuIAowMDAwMDA0NTQ4IDAwMDAwIG4gCjAwMDAwMDQ3MDMgMDAwMDAgbiAKMDAwMDAwNTAxNSAwMDAwMCBuIAowMDAwMDA1NDIyIDAwMDAwIG4gCjAwMDAwMDU1MTIgMDAwMDAgbiAKMDAwMDAwNTcxOCAwMDAwMCBuIAowMDAwMDA1OTY1IDAwMDAwIG4gCjAwMDAwMDYxMjYgMDAwMDAgbiAKMDAwMDE5MzAxOCAwMDAwMCBuIAowMDAwMTkzMTAxIDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgNDMgL1Jvb3QgMSAwIFIgL0luZm8gNDIgMCBSID4+CnN0YXJ0eHJlZgoxOTMyNTIKJSVFT0YK\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualize some examples\n",
        "pl.seed_everything(42)\n",
        "NUM_IMAGES = 6\n",
        "imgs = torch.stack([img for idx in range(NUM_IMAGES) for img in unlabeled_data[idx][0]], dim=0)\n",
        "img_grid = torchvision.utils.make_grid(imgs, nrow=6, normalize=True, pad_value=0.9)\n",
        "img_grid = img_grid.permute(1, 2, 0)\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.title('Augmented image examples of the STL10 dataset')\n",
        "plt.imshow(img_grid)\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "plt.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zYyV21FW6bM"
      },
      "source": [
        "We see the wide variety of our data augmentation, including randomly cropping, grayscaling, gaussian blur, and color distortion. Thus, it remains a challenging task for the model to match two, independently augmented patches of the same image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1McZPGqW6bM"
      },
      "source": [
        "### SimCLR implementation\n",
        "\n",
        "Using the data loader pipeline above, we can now implement SimCLR. At each iteration, we get for every image $x$ two differently augmented versions, which we refer to as $\\tilde{x}_i$ and $\\tilde{x}_j$. Both of these images are encoded into a one-dimensional feature vector, between which we want to maximize similarity which minimizes it to all other images in the batch. The encoder network is split into two parts: a base encoder network $f(\\cdot)$, and a projection head $g(\\cdot)$. The base network is usually a deep CNN as we have seen in e.g. [Tutorial 5](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial5/Inception_ResNet_DenseNet.html) before, and is responsible for extracting a representation vector from the augmented data examples. In our experiments, we will use the common ResNet-18 architecture as $f(\\cdot)$, and refer to the output as $f(\\tilde{x}_i)=h_i$. The projection head $g(\\cdot)$ maps the representation $h$ into a space where we apply the contrastive loss, i.e., compare similarities between vectors. It is often chosen to be a small MLP with non-linearities, and for simplicity, we follow the original SimCLR paper setup by defining it as a two-layer MLP with ReLU activation in the hidden layer. Note that in the follow-up paper, [SimCLRv2](https://arxiv.org/abs/2006.10029), the authors mention that larger/wider MLPs can boost the performance considerably. This is why we apply an MLP with four times larger hidden dimensions, but deeper MLPs showed to overfit on the given dataset. The general setup is visualized below (figure credit - [Ting Chen et al.](https://arxiv.org/abs/2006.10029)):\n",
        "\n",
        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/simclr_network_setup.svg?raw=1\" width=\"350px\"></center>\n",
        "\n",
        "After finishing the training with contrastive learning, we will remove the projection head $g(\\cdot)$, and use $f(\\cdot)$ as a pretrained feature extractor. The representations $z$ that come out of the projection head $g(\\cdot)$ have been shown to perform worse than those of the base network $f(\\cdot)$ when finetuning the network for a new task. This is likely because the representations $z$ are trained to become invariant to many features like the color that can be important for downstream tasks. Thus, $g(\\cdot)$ is only needed for the contrastive learning stage.\n",
        "\n",
        "Now that the architecture is described, let's take a closer look at how we train the model. As mentioned before, we want to maximize the similarity between the representations of the two augmented versions of the same image, i.e., $z_i$ and $z_j$ in the figure above, while minimizing it to all other examples in the batch. SimCLR thereby applies the InfoNCE loss, originally proposed by [Aaron van den Oord et al.](https://arxiv.org/abs/1807.03748) for contrastive learning. In short, the InfoNCE loss compares the similarity of $z_i$ and $z_j$ to the similarity of $z_i$ to any other representation in the batch by performing a softmax over the similarity values. The loss can be formally written as:\n",
        "\n",
        "$$\n",
        "\\ell_{i,j}=-\\log \\frac{\\exp(\\text{sim}(z_i,z_j)/\\tau)}{\\sum_{k=1}^{2N}\\mathbb{1}_{[k\\neq i]}\\exp(\\text{sim}(z_i,z_k)/\\tau)}=-\\text{sim}(z_i,z_j)/\\tau+\\log\\left[\\sum_{k=1}^{2N}\\mathbb{1}_{[k\\neq i]}\\exp(\\text{sim}(z_i,z_k)/\\tau)\\right]\n",
        "$$\n",
        "\n",
        "The function $\\text{sim}$ is a similarity metric, and the hyperparameter $\\tau$ is called temperature determining how peaked the distribution is. Since many similarity metrics are bounded, the temperature parameter allows us to balance the influence of many dissimilar image patches versus one similar patch. The similarity metric that is used in SimCLR is cosine similarity, as defined below:\n",
        "\n",
        "$$\n",
        "\\text{sim}(z_i,z_j) = \\frac{z_i^\\top \\cdot z_j}{||z_i||\\cdot||z_j||}\n",
        "$$\n",
        "\n",
        "The maximum cosine similarity possible is $1$, while the minimum is $-1$. In general, we will see that the features of two different images will converge to a cosine similarity around zero since the minimum, $-1$, would require $z_i$ and $z_j$ to be in the exact opposite direction in all feature dimensions, which does not allow for great flexibility.\n",
        "\n",
        "Finally, now that we have discussed all details, let's implement SimCLR below as a PyTorch Lightning module:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "THXXMYahW6bM"
      },
      "outputs": [],
      "source": [
        "class SimCLR(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, hidden_dim, lr, temperature, weight_decay, max_epochs=500):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        assert self.hparams.temperature > 0.0, 'The temperature must be a positive float!'\n",
        "        # Base model f(.)\n",
        "        self.convnet = torchvision.models.resnet18(num_classes=4*hidden_dim)  # Output of last linear layer\n",
        "        # The MLP for g(.) consists of Linear->ReLU->Linear\n",
        "        self.convnet.fc = nn.Sequential(\n",
        "            self.convnet.fc,  # Linear(ResNet output, 4*hidden_dim)\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(4*hidden_dim, hidden_dim)\n",
        "        )\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = optim.AdamW(self.parameters(),\n",
        "                                lr=self.hparams.lr,\n",
        "                                weight_decay=self.hparams.weight_decay)\n",
        "        lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,\n",
        "                                                            T_max=self.hparams.max_epochs,\n",
        "                                                            eta_min=self.hparams.lr/50)\n",
        "        return [optimizer], [lr_scheduler]\n",
        "\n",
        "    def info_nce_loss(self, batch, mode='train'):\n",
        "        imgs, _ = batch\n",
        "        imgs = torch.cat(imgs, dim=0)\n",
        "\n",
        "        # START CODES\n",
        "        # Encode all images (5 pts)\n",
        "        feats = self.convnet(imgs)\n",
        "        # Calculate cosine similarity (15 pts)\n",
        "        cos_sim = F.cosine_similarity(feats[:,None,:], feats[None,:,:], dim=-1)\n",
        "        # Mask out cosine similarity to itself (20 pts)\n",
        "        self_mask = torch.eye(cos_sim.shape[0], dtype=torch.bool, device=cos_sim.device)\n",
        "        cos_sim.masked_fill_(self_mask, -9e15)\n",
        "        # Find positive example -> batch_size//2 away from the original example (20 pts)\n",
        "        pos_mask = self_mask.roll(shifts=cos_sim.shape[0]//2, dims=0)\n",
        "        # InfoNCE loss\n",
        "        cos_sim = cos_sim / self.hparams.temperature # divide by the temperature (5 pts)\n",
        "        # calculate the mean nll (5 pts)\n",
        "        nll = -cos_sim[pos_mask] + torch.logsumexp(cos_sim, dim=-1).mean()\n",
        "        # END CODES\n",
        "\n",
        "        # Logging loss\n",
        "        self.log(mode+'_loss', nll)\n",
        "        # Get ranking position of positive example\n",
        "        comb_sim = torch.cat([cos_sim[pos_mask][:,None],  # First position positive example\n",
        "                              cos_sim.masked_fill(pos_mask, -9e15)],\n",
        "                             dim=-1)\n",
        "        sim_argsort = comb_sim.argsort(dim=-1, descending=True).argmin(dim=-1)\n",
        "        # Logging ranking metrics\n",
        "        self.log(mode+'_acc_top1', (sim_argsort == 0).float().mean())\n",
        "        self.log(mode+'_acc_top5', (sim_argsort < 5).float().mean())\n",
        "        self.log(mode+'_acc_mean_pos', 1+sim_argsort.float().mean())\n",
        "\n",
        "        return nll\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        return self.info_nce_loss(batch, mode='train')\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        self.info_nce_loss(batch, mode='val')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjOEqoQHW6bN"
      },
      "source": [
        "Alternatively to performing the validation on the contrastive learning loss as well, we could also take a simple, small downstream task, and track the performance of the base network $f(\\cdot)$ on that. However, in this tutorial, we will restrict ourselves to the STL10 dataset where we use the task of image classification on STL10 as our test task."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9huWrr6W6bN"
      },
      "source": [
        "### Training\n",
        "\n",
        "Now that we have implemented SimCLR and the data loading pipeline, we are ready to train the model. We will use the same training function setup as usual. For saving the best model checkpoint, we track the metric `val_acc_top5`, which describes how often the correct image patch is within the top-5 most similar examples in the batch. This is usually less noisy than the top-1 metric, making it a better metric to choose the best model from."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iDgY8ZMfW6bN"
      },
      "outputs": [],
      "source": [
        "def train_simclr(batch_size, max_epochs=500, **kwargs):\n",
        "    trainer = pl.Trainer(default_root_dir=os.path.join(CHECKPOINT_PATH, 'SimCLR'),\n",
        "                         accelerator=\"gpu\" if str(device).startswith(\"cuda\") else \"cpu\",\n",
        "                         devices=1,\n",
        "                         max_epochs=max_epochs,\n",
        "                         callbacks=[ModelCheckpoint(save_weights_only=True, mode='max', monitor='val_acc_top5'),\n",
        "                                    LearningRateMonitor('epoch')])\n",
        "    trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n",
        "\n",
        "    # Check whether pretrained model exists. If yes, load it and skip training\n",
        "    pretrained_filename = os.path.join(CHECKPOINT_PATH, 'SimCLR.ckpt')\n",
        "    if os.path.isfile(pretrained_filename):\n",
        "        print(f'Found pretrained model at {pretrained_filename}, loading...')\n",
        "        model = SimCLR.load_from_checkpoint(pretrained_filename) # Automatically loads the model with the saved hyperparameters\n",
        "    else:\n",
        "        train_loader = data.DataLoader(unlabeled_data, batch_size=batch_size, shuffle=True,\n",
        "                                       drop_last=True, pin_memory=True, num_workers=NUM_WORKERS)\n",
        "        val_loader = data.DataLoader(train_data_contrast, batch_size=batch_size, shuffle=False,\n",
        "                                     drop_last=False, pin_memory=True, num_workers=NUM_WORKERS)\n",
        "        pl.seed_everything(42) # To be reproducable\n",
        "        model = SimCLR(max_epochs=max_epochs, **kwargs)\n",
        "        trainer.fit(model, train_loader, val_loader)\n",
        "        model = SimCLR.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) # Load best checkpoint after training\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5O5FKGDW6bN"
      },
      "source": [
        "A common observation in contrastive learning is that the larger the batch size, the better the models perform. A larger batch size allows us to compare each image to more negative examples, leading to overall smoother loss gradients. However, in our case, we experienced that a batch size of 256 was sufficient to get good results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iMhxEJ9W6bN",
        "outputId": "ed51e0d4-544d-471c-d783-6d8d20b48767"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/SimCLR.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/SimCLR.ckpt`\n"
          ]
        }
      ],
      "source": [
        "simclr_model = train_simclr(batch_size=256,\n",
        "                            hidden_dim=128,\n",
        "                            lr=5e-4,\n",
        "                            temperature=0.07,\n",
        "                            weight_decay=1e-4,\n",
        "                            max_epochs=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XnjoasTW6bN"
      },
      "source": [
        "To get an intuition of how training with contrastive learning behaves, we can take a look at the TensorBoard below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FwhzHZ8uW6bN"
      },
      "outputs": [],
      "source": [
        "# %tensorboard --logdir ../saved_models/tutorial17/tensorboards/SimCLR/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyQMAmtwW6bN"
      },
      "source": [
        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial17/tensorboard_simclr.png?raw=1\" width=\"1200px\"></center>\n",
        "\n",
        "One thing to note is that contrastive learning benefits a lot from long training. The shown plot above is from a training that took approx. 1 day on a NVIDIA TitanRTX. Training the model for even longer might reduce its loss further, but we did not experience any gains from it for the downstream task on image classification. In general, contrastive learning can also benefit from using larger models, if sufficient unlabeled data is available."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gFTxYWUW6bO"
      },
      "source": [
        "## Logistic Regression\n",
        "\n",
        "After we have trained our model via contrastive learning, we can deploy it on downstream tasks and see how well it performs with little data. A common setup, which also verifies whether the model has learned generalized representations, is to perform Logistic Regression on the features. In other words, we learn a single, linear layer that maps the representations to a class prediction. Since the base network $f(\\cdot)$ is not changed during the training process, the model can only perform well if the representations of $h$ describe all features that might be necessary for the task. Further, we do not have to worry too much about overfitting since we have very few parameters that are trained. Hence, we might expect that the model can perform well even with very little data.\n",
        "\n",
        "First, let's implement a simple Logistic Regression setup for which we assume that the images already have been encoded in their feature vectors. If very little data is available, it might be beneficial to dynamically encode the images during training so that we can also apply data augmentations. However, the way we implement it here is much more efficient and can be trained within a few seconds. Further, using data augmentations did not show any significant gain in this simple setup."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vtqh17F3W6bO"
      },
      "outputs": [],
      "source": [
        "class LogisticRegression(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, feature_dim, num_classes, lr, weight_decay, max_epochs=100):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        # Mapping from representation h to classes\n",
        "        self.model = nn.Linear(feature_dim, num_classes)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = optim.AdamW(self.parameters(),\n",
        "                                lr=self.hparams.lr,\n",
        "                                weight_decay=self.hparams.weight_decay)\n",
        "        lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,\n",
        "                                                      milestones=[int(self.hparams.max_epochs*0.6),\n",
        "                                                                  int(self.hparams.max_epochs*0.8)],\n",
        "                                                      gamma=0.1)\n",
        "        return [optimizer], [lr_scheduler]\n",
        "\n",
        "    def _calculate_loss(self, batch, mode='train'):\n",
        "        feats, labels = batch\n",
        "        preds = self.model(feats)\n",
        "        loss = F.cross_entropy(preds, labels)\n",
        "        acc = (preds.argmax(dim=-1) == labels).float().mean()\n",
        "\n",
        "        self.log(mode + '_loss', loss)\n",
        "        self.log(mode + '_acc', acc)\n",
        "        return loss\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        return self._calculate_loss(batch, mode='train')\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        self._calculate_loss(batch, mode='val')\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        self._calculate_loss(batch, mode='test')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h-W4-v_W6bO"
      },
      "source": [
        "The data we use is the training and test set of STL10. The training contains 500 images per class, while the test set has 800 images per class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5EGo022W6bO",
        "outputId": "ebb32115-8281-4194-d8c4-8aa5db5be527"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Number of training examples: 5000\n",
            "Number of test examples: 8000\n"
          ]
        }
      ],
      "source": [
        "img_transforms = transforms.Compose([transforms.ToTensor(),\n",
        "                                     transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "train_img_data = STL10(root=DATASET_PATH, split='train', download=True,\n",
        "                       transform=img_transforms)\n",
        "test_img_data = STL10(root=DATASET_PATH, split='test', download=True,\n",
        "                      transform=img_transforms)\n",
        "\n",
        "print(\"Number of training examples:\", len(train_img_data))\n",
        "print(\"Number of test examples:\", len(test_img_data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-z3sk8t9W6bO"
      },
      "source": [
        "Next, we implement a small function to encode all images in our datasets. The output representations are then used as inputs to the Logistic Regression model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GvJltMQ4W6bO"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def prepare_data_features(model, dataset):\n",
        "    # Prepare model\n",
        "    network = deepcopy(model.convnet)\n",
        "    network.fc = nn.Identity()  # Removing projection head g(.)\n",
        "    network.eval()\n",
        "    network.to(device)\n",
        "\n",
        "    # Encode all images\n",
        "    data_loader = data.DataLoader(dataset, batch_size=64, num_workers=NUM_WORKERS, shuffle=False, drop_last=False)\n",
        "    feats, labels = [], []\n",
        "    for batch_imgs, batch_labels in tqdm(data_loader):\n",
        "        batch_imgs = batch_imgs.to(device)\n",
        "        batch_feats = network(batch_imgs)\n",
        "        feats.append(batch_feats.detach().cpu())\n",
        "        labels.append(batch_labels)\n",
        "\n",
        "    feats = torch.cat(feats, dim=0)\n",
        "    labels = torch.cat(labels, dim=0)\n",
        "\n",
        "    # Sort images by labels\n",
        "    labels, idxs = labels.sort()\n",
        "    feats = feats[idxs]\n",
        "\n",
        "    return data.TensorDataset(feats, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhswNsiZW6bO"
      },
      "source": [
        "Let's apply the function to both training and test set below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "4cf31352756743b9b812e9dc60096aa4",
            "d8a96fabcc044856b2b8cd23156a95b5",
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            "49cad0dbfa764bdfbd6bbb63f8e824ca",
            "9dac987159564d129b9de54c855ca96b",
            "d3ba1a038fba476f9dbc872505bdd348",
            "c73ca91468fa468294433955e6ecdff0",
            "079079191c63498193692820fa2a28ba",
            "9cffa43baf7f4a3e81352448cd65debf",
            "bc3f944ee7eb41b5b75f500cf07135f2",
            "fe7e1b58f1034eb1a13265a035f764e5",
            "efb95c1f7c604e7e83316b291af11d44",
            "761eb6451a3f4a93badbb9113ae44994",
            "a54325c2d17047caa11fdf9a75ccd446",
            "ca7385c90e7147328bee8059d470ed7f",
            "e81a26e39b0e461f8c8aaee8220ccb5c",
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        },
        "id": "IpQmyF9rW6bO",
        "outputId": "45507745-234e-4641-dfc3-cba4d447f938"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/79 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cf31352756743b9b812e9dc60096aa4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/125 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "efb95c1f7c604e7e83316b291af11d44"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_feats_simclr = prepare_data_features(simclr_model, train_img_data)\n",
        "test_feats_simclr = prepare_data_features(simclr_model, test_img_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6IAy35yW6bO"
      },
      "source": [
        "Finally, we can write a training function as usual. We evaluate the model on the test set every 10 epochs to allow early stopping, but the low frequency of the validation ensures that we do not overfit too much on the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oQ1XP4rzW6bO"
      },
      "outputs": [],
      "source": [
        "def train_logreg(batch_size, train_feats_data, test_feats_data, model_suffix, max_epochs=100, **kwargs):\n",
        "    trainer = pl.Trainer(default_root_dir=os.path.join(CHECKPOINT_PATH, \"LogisticRegression\"),\n",
        "                         accelerator=\"gpu\" if str(device).startswith(\"cuda\") else \"cpu\",\n",
        "                         devices=1,\n",
        "                         max_epochs=max_epochs,\n",
        "                         callbacks=[ModelCheckpoint(save_weights_only=True, mode='max', monitor='val_acc'),\n",
        "                                    LearningRateMonitor(\"epoch\")],\n",
        "                         enable_progress_bar=False,\n",
        "                         check_val_every_n_epoch=10)\n",
        "    trainer.logger._default_hp_metric = None\n",
        "\n",
        "    # Data loaders\n",
        "    train_loader = data.DataLoader(train_feats_data, batch_size=batch_size, shuffle=True,\n",
        "                                   drop_last=False, pin_memory=True, num_workers=0)\n",
        "    test_loader = data.DataLoader(test_feats_data, batch_size=batch_size, shuffle=False,\n",
        "                                  drop_last=False, pin_memory=True, num_workers=0)\n",
        "\n",
        "    # Check whether pretrained model exists. If yes, load it and skip training\n",
        "    pretrained_filename = os.path.join(CHECKPOINT_PATH, f\"LogisticRegression_{model_suffix}.ckpt\")\n",
        "    if os.path.isfile(pretrained_filename):\n",
        "        print(f\"Found pretrained model at {pretrained_filename}, loading...\")\n",
        "        model = LogisticRegression.load_from_checkpoint(pretrained_filename)\n",
        "    else:\n",
        "        pl.seed_everything(42)  # To be reproducable\n",
        "        model = LogisticRegression(**kwargs)\n",
        "        trainer.fit(model, train_loader, test_loader)\n",
        "        model = LogisticRegression.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n",
        "\n",
        "    # Test best model on train and validation set\n",
        "    train_result = trainer.test(model, train_loader, verbose=False)\n",
        "    test_result = trainer.test(model, test_loader, verbose=False)\n",
        "    result = {\"train\": train_result[0][\"test_acc\"], \"test\": test_result[0][\"test_acc\"]}\n",
        "\n",
        "    return model, result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7M__NytuW6bP"
      },
      "source": [
        "Despite the training dataset of STL10 already only having 500 labeled images per class, we will perform experiments with even smaller datasets. Specifically, we train a Logistic Regression model for datasets with only 10, 20, 50, 100, 200, and all 500 examples per class. This gives us an intuition on how well the representations learned by contrastive learning can be transfered to a image recognition task like this classification. First, let's define a function to create the intended sub-datasets from the full training set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kUVX-Y4uW6bP"
      },
      "outputs": [],
      "source": [
        "def get_smaller_dataset(original_dataset, num_imgs_per_label):\n",
        "    new_dataset = data.TensorDataset(\n",
        "        *[t.unflatten(0, (10, -1))[:,:num_imgs_per_label].flatten(0, 1) for t in original_dataset.tensors]\n",
        "    )\n",
        "    return new_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2RZ1jn2W6bP"
      },
      "source": [
        "Next, let's run all models. Despite us training 6 models, this cell could be run within a minute or two without the pretrained models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NQBhD3KW6bP",
        "outputId": "a0681a61-faf0-45c6-cfaa-9fe169ee3932",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_10.ckpt`\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_10.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: ../saved_models/tutorial17/LogisticRegression/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:478: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n",
            "  rank_zero_warn(\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_20.ckpt`\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_20.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_50.ckpt`\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_50.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_100.ckpt`\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_100.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_200.ckpt`\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_200.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/LogisticRegression_500.ckpt`\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/LogisticRegression_500.ckpt, loading...\n"
          ]
        }
      ],
      "source": [
        "results = {}\n",
        "for num_imgs_per_label in [10, 20, 50, 100, 200, 500]:\n",
        "    sub_train_set = get_smaller_dataset(train_feats_simclr, num_imgs_per_label)\n",
        "    _, small_set_results = train_logreg(batch_size=64,\n",
        "                                        train_feats_data=sub_train_set,\n",
        "                                        test_feats_data=test_feats_simclr,\n",
        "                                        model_suffix=num_imgs_per_label,\n",
        "                                        feature_dim=train_feats_simclr.tensors[0].shape[1],\n",
        "                                        num_classes=10,\n",
        "                                        lr=1e-3,\n",
        "                                        weight_decay=1e-3)\n",
        "    results[num_imgs_per_label] = small_set_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UEUPeex3W6bP"
      },
      "source": [
        "Finally, let's plot the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8AusOZSRW6bP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "228c1694-cd99-49d2-9c61-298a1a064859"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy for  10 images per label: 62.79%\n",
            "Test accuracy for  20 images per label: 68.60%\n",
            "Test accuracy for  50 images per label: 74.44%\n",
            "Test accuracy for 100 images per label: 77.20%\n",
            "Test accuracy for 200 images per label: 79.06%\n",
            "Test accuracy for 500 images per label: 81.33%\n"
          ]
        }
      ],
      "source": [
        "dataset_sizes = sorted([k for k in results])\n",
        "test_scores = [results[k][\"test\"] for k in dataset_sizes]\n",
        "\n",
        "fig = plt.figure(figsize=(6,4))\n",
        "plt.plot(dataset_sizes, test_scores, '--', color=\"#000\", marker=\"*\", markeredgecolor=\"#000\", markerfacecolor=\"y\", markersize=16)\n",
        "plt.xscale(\"log\")\n",
        "plt.xticks(dataset_sizes, labels=dataset_sizes)\n",
        "plt.title(\"STL10 classification over dataset size\", fontsize=14)\n",
        "plt.xlabel(\"Number of images per class\")\n",
        "plt.ylabel(\"Test accuracy\")\n",
        "plt.minorticks_off()\n",
        "plt.show()\n",
        "\n",
        "for k, score in zip(dataset_sizes, test_scores):\n",
        "    print(f'Test accuracy for {k:3d} images per label: {100*score:4.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXOEAFjnW6bP"
      },
      "source": [
        "As one would expect, the classification performance improves the more data we have. However, with only 10 images per class, we can already classify more than 60% of the images correctly. This is quite impressive, considering that the images are also higher dimensional than e.g. CIFAR10. With the full dataset, we achieve an accuracy of 81%. The increase between 50 to 500 images per class might suggest a linear increase in performance with an exponentially larger dataset. However, with even more data, we could also finetune $f(\\cdot)$ in the training process, allowing for the representations to adapt more to the specific classification task given.\n",
        "\n",
        "To set the results above into perspective, we will train the base network, a ResNet-18, on the classification task from scratch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Twd1YwcKW6bP"
      },
      "source": [
        "## Baseline\n",
        "\n",
        "As a baseline to our results above, we will train a standard ResNet-18 with random initialization on the labeled training set of STL10. The results will give us an indication of the advantages that contrastive learning on unlabeled data has compared to using only supervised training. The implementation of the model is straightforward since the ResNet architecture is provided in the torchvision library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CIErP8j4W6bP"
      },
      "outputs": [],
      "source": [
        "class ResNet(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, num_classes, lr, weight_decay, max_epochs=100):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        self.model = torchvision.models.resnet18(num_classes=num_classes)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = optim.AdamW(self.parameters(),\n",
        "                                lr=self.hparams.lr,\n",
        "                                weight_decay=self.hparams.weight_decay)\n",
        "        lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer,\n",
        "                                                      milestones=[int(self.hparams.max_epochs*0.7),\n",
        "                                                                  int(self.hparams.max_epochs*0.9)],\n",
        "                                                      gamma=0.1)\n",
        "        return [optimizer], [lr_scheduler]\n",
        "\n",
        "    def _calculate_loss(self, batch, mode='train'):\n",
        "        imgs, labels = batch\n",
        "        preds = self.model(imgs)\n",
        "        loss = F.cross_entropy(preds, labels)\n",
        "        acc = (preds.argmax(dim=-1) == labels).float().mean()\n",
        "\n",
        "        self.log(mode + '_loss', loss)\n",
        "        self.log(mode + '_acc', acc)\n",
        "        return loss\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        return self._calculate_loss(batch, mode='train')\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        self._calculate_loss(batch, mode='val')\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        self._calculate_loss(batch, mode='test')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iz8QqPvRW6bQ"
      },
      "source": [
        "It is clear that the ResNet easily overfits on the training data since its parameter count is more than 1000 times larger than the dataset size. To make the comparison to the contrastive learning models fair, we apply data augmentations similar to the ones we used before: horizontal flip, crop-and-resize, grayscale, and gaussian blur. Color distortions as before are not used because the color distribution of an image showed to be an important feature for the classification. Hence, we observed no noticeable performance gains when adding color distortions to the set of augmentations. Similarly, we restrict the resizing operation before cropping to the max. 125% of its original resolution, instead of 1250% as done in SimCLR. This is because, for classification, the model needs to recognize the full object, while in contrastive learning, we only want to check whether two patches belong to the same image/object. Hence, the chosen augmentations below are overall weaker than in the contrastive learning case."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v48tLNq8W6bQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33c4abef-ac03-44e8-f92c-3ff618f59874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "train_transforms = transforms.Compose([transforms.RandomHorizontalFlip(),\n",
        "                                       transforms.RandomResizedCrop(size=96, scale=(0.8, 1.0)),\n",
        "                                       transforms.RandomGrayscale(p=0.2),\n",
        "                                       transforms.GaussianBlur(kernel_size=9, sigma=(0.1, 0.5)),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       transforms.Normalize((0.5,), (0.5,))\n",
        "                                       ])\n",
        "\n",
        "train_img_aug_data = STL10(root=DATASET_PATH, split='train', download=True,\n",
        "                           transform=train_transforms)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GmCcJ-NW6bQ"
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      "source": [
        "The training function for the ResNet is almost identical to the Logistic Regression setup. Note that we allow the ResNet to perform validation every 2 epochs to also check whether the model overfits strongly in the first iterations or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F-PwZci-W6bQ"
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      "source": [
        "def train_resnet(batch_size, max_epochs=100, **kwargs):\n",
        "    trainer = pl.Trainer(default_root_dir=os.path.join(CHECKPOINT_PATH, \"ResNet\"),\n",
        "                         accelerator=\"gpu\" if str(device).startswith(\"cuda\") else \"cpu\",\n",
        "                         devices=1,\n",
        "                         max_epochs=max_epochs,\n",
        "                         callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\"),\n",
        "                                    LearningRateMonitor(\"epoch\")],\n",
        "                         check_val_every_n_epoch=2)\n",
        "    trainer.logger._default_hp_metric = None\n",
        "\n",
        "    # Data loaders\n",
        "    train_loader = data.DataLoader(train_img_aug_data, batch_size=batch_size, shuffle=True,\n",
        "                                   drop_last=True, pin_memory=True, num_workers=NUM_WORKERS)\n",
        "    test_loader = data.DataLoader(test_img_data, batch_size=batch_size, shuffle=False,\n",
        "                                  drop_last=False, pin_memory=True, num_workers=NUM_WORKERS)\n",
        "\n",
        "    # Check whether pretrained model exists. If yes, load it and skip training\n",
        "    pretrained_filename = os.path.join(CHECKPOINT_PATH, \"ResNet.ckpt\")\n",
        "    if os.path.isfile(pretrained_filename):\n",
        "        print(\"Found pretrained model at %s, loading...\" % pretrained_filename)\n",
        "        model = ResNet.load_from_checkpoint(pretrained_filename)\n",
        "    else:\n",
        "        pl.seed_everything(42) # To be reproducable\n",
        "        model = ResNet(**kwargs)\n",
        "        trainer.fit(model, train_loader, test_loader)\n",
        "        model = ResNet.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n",
        "\n",
        "    # Test best model on validation set\n",
        "    train_result = trainer.test(model, train_loader, verbose=False)\n",
        "    val_result = trainer.test(model, test_loader, verbose=False)\n",
        "    result = {\"train\": train_result[0][\"test_acc\"], \"test\": val_result[0][\"test_acc\"]}\n",
        "\n",
        "    return model, result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImmoA1CHW6bQ"
      },
      "source": [
        "Finally, let's train the model and check its results:"
      ]
    },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found pretrained model at ../saved_models/tutorial17/ResNet.ckpt, loading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.4 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../saved_models/tutorial17/ResNet.ckpt`\n",
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: ../saved_models/tutorial17/ResNet/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.9/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:478: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n",
            "  rank_zero_warn(\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ],
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        },
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
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        {
          "output_type": "stream",
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          "text": [
            "Accuracy on training set: 99.72%\n",
            "Accuracy on test set: 73.31%\n"
          ]
        }
      ],
      "source": [
        "resnet_model, resnet_result = train_resnet(batch_size=64,\n",
        "                                           num_classes=10,\n",
        "                                           lr=1e-3,\n",
        "                                           weight_decay=2e-4,\n",
        "                                           max_epochs=100)\n",
        "print(f\"Accuracy on training set: {100*resnet_result['train']:4.2f}%\")\n",
        "print(f\"Accuracy on test set: {100*resnet_result['test']:4.2f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TISFTRHwW6bQ"
      },
      "source": [
        "The ResNet trained from scratch achieves 73.31% on the test set. This is almost 8% less than the contrastive learning model, and even slightly less than SimCLR achieves with 1/10 of the data. This shows that self-supervised, contrastive learning provides considerable performance gains by leveraging large amounts of unlabeled data when little labeled data is available."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQYBE8RYW6bQ"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "In this tutorial, we have discussed self-supervised contrastive learning and implemented SimCLR as an example method. We have applied it to the STL10 dataset and showed that it can learn generalizable representations that we can use to train simple classification models. With 500 images per label, it achieved an 8% higher accuracy than a similar model solely trained from supervision and performs on par with it when only using a tenth of the labeled data. Our experimental results are limited to a single dataset, but recent works such as [Ting Chen et al.](https://arxiv.org/abs/2006.10029) showed similar trends for larger datasets like ImageNet. Besides the discussed hyperparameters, the size of the model seems to be important in contrastive learning as well. If a lot of unlabeled data is available, larger models can achieve much stronger results and come close to their supervised baselines. Further, there are also approaches for combining contrastive and supervised learning, leading to performance gains beyond supervision (see [Khosla et al.](https://arxiv.org/abs/2004.11362)). Moreover, contrastive learning is not the only approach to self-supervised learning that has come up in the last two years and showed great results. Other methods include distillation-based methods like [BYOL](https://arxiv.org/abs/2006.07733) and redundancy reduction techniques like [Barlow Twins](https://arxiv.org/abs/2103.03230). There is a lot more to explore in the self-supervised domain, and more, impressive steps ahead are to be expected.\n",
        "\n",
        "### References\n",
        "\n",
        "[1] Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning (ICML 2020). PMLR. ([link](https://arxiv.org/abs/2002.05709))\n",
        "\n",
        "[2] Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. (2020). Big self-supervised models are strong semi-supervised learners. NeurIPS 2021 ([link](https://arxiv.org/abs/2006.10029)).\n",
        "\n",
        "[3] Oord, A. V. D., Li, Y., and Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748. ([link](https://arxiv.org/abs/1807.03748))\n",
        "\n",
        "[4] Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A., Guo, Z.D., Azar, M.G. and Piot, B. (2020). Bootstrap your own latent: A new approach to self-supervised learning. NeurIPS 2020 ([link](https://arxiv.org/abs/2006.07733))\n",
        "\n",
        "[5] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C. and Krishnan, D. (2020). Supervised contrastive learning. NeurIPS 2020 ([link](https://arxiv.org/abs/2004.11362))\n",
        "\n",
        "[6] Zbontar, J., Jing, L., Misra, I., LeCun, Y. and Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning (ICML 2021). ([link](https://arxiv.org/abs/2103.03230))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LR0sEavSW6bQ"
      },
      "source": [
        "---\n",
        "\n",
        "[![Star our repository](https://img.shields.io/static/v1.svg?logo=star&label=⭐&message=Star%20Our%20Repository&color=yellow)](https://github.com/phlippe/uvadlc_notebooks/)  If you found this tutorial helpful, consider ⭐-ing our repository.    \n",
        "[![Ask questions](https://img.shields.io/static/v1.svg?logo=star&label=❔&message=Ask%20Questions&color=9cf)](https://github.com/phlippe/uvadlc_notebooks/issues)  For any questions, typos, or bugs that you found, please raise an issue on GitHub.\n",
        "\n",
        "---"
      ]
    }
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