Chest_XRay_Image_Synthesis.ipynb

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      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mdanyalmalik/chest-xray-synthesis/blob/main/Chest_XRay_Image_Synthesis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CS 437 Final Project\n",
        "## Evaluating the feasibility of using Generative Models to generate Chest XRay Data\n",
        "### M. Danyal Malik, Danish Humair\n"
      ],
      "metadata": {
        "id": "qFRen8DpS-A_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: Please do not change any code in collapsed cells"
      ],
      "metadata": {
        "id": "tDw4wmUcYhLN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Method 1: Using a PGGAN to Generate Chest XRay Images"
      ],
      "metadata": {
        "id": "oZceZOEsTB5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This method was used in the paper mentioned below:\n",
        "\n",
        "Segal, Bradley, et al. “Evaluating the Clinical Realism of Synthetic Chest x-Rays Generated Using Progressively Growing Gans.” SN Computer Science, vol. 2, no. 4, 2021, https://doi.org/10.1007/s42979-021-00720-7. "
      ],
      "metadata": {
        "id": "Guu0p_PKT19J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Imports\n",
        "\n",
        "!pip install pytorch_lightning\n",
        "\n",
        "from argparse import Namespace\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch import autograd\n",
        "\n",
        "import torchvision.datasets as datasets\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.utils as vutils \n",
        "import torchvision.transforms.functional as transform\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import copy\n",
        "from collections import OrderedDict\n",
        "import zipfile\n",
        "import gdown\n",
        "import PIL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "dLsiURKrTKcX",
        "outputId": "3055c429-3e4b-474f-fd9b-f4ea1df49741"
      },
      "execution_count": null,
      "outputs": [
        {
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          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Util\n",
        "\n",
        "def calc_padding(dims, kernel, stride):\n",
        "    \"\"\"\n",
        "    For constant output: W = (W - F + 2P)/S + 1\n",
        "    W → Dimensions, F → Kernel, P → Padding, S → Stride\n",
        "    (S(W - 1) - W + F)/2\n",
        "    :param dims:\n",
        "    :param kernel:\n",
        "    :param stride:\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    padding = (stride * (dims - 1) - dims + kernel) / 2\n",
        "    assert padding.is_integer(), \"A non-integer result indicates an invalid pairing of dimensions and stride values\"\n",
        "    return int(padding)\n",
        "\n",
        "\n",
        "def exp_avg_update(target, source, beta):\n",
        "    \"\"\"\n",
        "\n",
        "    :param target:\n",
        "    :param source:\n",
        "    :param beta:\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    with torch.no_grad():\n",
        "        source_parameters = dict(source.named_parameters())\n",
        "\n",
        "        for p_name, p_target in target.named_parameters():\n",
        "            p_source = source_parameters[p_name]\n",
        "            assert p_source is not p_target\n",
        "            p_target.copy_(beta * p_target + (1 - beta) * p_source)\n",
        "\n",
        "\n",
        "def compute_gradient_penalty(discriminator, real_samples, fake_samples):\n",
        "    \"\"\"\n",
        "    Calculates the gradient penalty loss for WGAN-GP (https://arxiv.org/abs/1704.00028)\n",
        "    :param discriminator:\n",
        "    :param real_samples:\n",
        "    :param fake_samples:\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    # Random weight term for interpolation between real and fake samples\n",
        "    alpha = torch.rand(real_samples.size(0), 1, 1, 1)\n",
        "    alpha = alpha.expand_as(real_samples)  # Broadcast to correct shape\n",
        "    alpha = alpha.type_as(real_samples)\n",
        "\n",
        "    # Get random interpolation between real and fake samples\n",
        "    interpolates = (alpha * real_samples.detach() + (1 - alpha) * fake_samples.detach())\n",
        "    interpolates = interpolates.requires_grad_(True)\n",
        "    interpolates = interpolates.type_as(real_samples)\n",
        "\n",
        "    d_interpolates = discriminator(interpolates)\n",
        "    grad_output = torch.ones_like(d_interpolates)\n",
        "    grad_output = grad_output.type_as(real_samples)\n",
        "\n",
        "    # Get gradient w.r.t. interpolates\n",
        "    gradients = autograd.grad(outputs=d_interpolates, inputs=interpolates,\n",
        "                              grad_outputs=grad_output, create_graph=True,\n",
        "                              retain_graph=True, only_inputs=True)[0]\n",
        "\n",
        "    gradients = gradients.view(real_samples.shape[0], -1)\n",
        "    gradient_penalty = ((gradients.norm(p=2, dim=1) - 1) ** 2).mean()\n",
        "\n",
        "    return gradient_penalty\n",
        "\n",
        "\n",
        "def calc_epoch_num(scale_list, batch_sizes, total_images):\n",
        "    \"\"\"\n",
        "\n",
        "    :param scale_list:\n",
        "    :param batch_sizes:\n",
        "    :param total_images:\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    return [np.ceil(scale / total_images) for scale, batch_size in zip(scale_list, batch_sizes)]\n",
        "\n",
        "\n",
        "class MinibatchSTD(nn.Module):\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Calculates the standard deviation across a batch of feature maps\n",
        "        \"\"\"\n",
        "        super(MinibatchSTD, self).__init__()\n",
        "\n",
        "    def forward(self, feature_maps, dim=0, epsilon=1e-8):\n",
        "        # Epsilon → Small value for numerical stability when dividing\n",
        "        batch, channels, height, width = feature_maps.shape\n",
        "        std = feature_maps - feature_maps.mean(dim=dim, keepdim=True)  # [B x C x H x W] Subtract mean over batch\n",
        "        std = torch.sqrt(std.pow(2).mean(dim=dim, keepdim=False) + epsilon)  # [1 x C x H x W]  Calc std over batch\n",
        "        std = std.mean().view(1, 1, 1, 1)  # Take average over feature_maps and pixels\n",
        "        std = std.repeat(batch, 1, height, width)  # [B x 1 x H x W]  Replicate over group and pixels\n",
        "        std = torch.cat([feature_maps, std], 1)  # [B x (C + 1) x H x W]  Append as new feature_map\n",
        "        return std\n",
        "\n",
        "\n",
        "class Checkpoint(pl.Callback):\n",
        "    def __init__(self, filepath, save_rate=100):\n",
        "        self.filepath = filepath\n",
        "        self.save_rate = save_rate\n",
        "\n",
        "    def on_batch_end(self, trainer, pl_module):\n",
        "        pl_module.batch_count += 1\n",
        "        if pl_module.batch_count % self.save_rate == 0:\n",
        "            name = f\"{pl_module.name}-Scale({pl_module.scale})-Img({pl_module.img_size})-Iter({pl_module.total_iter}).pth\"\n",
        "\n",
        "            print(\"Saving Model: \", name)\n",
        "            torch.save({\n",
        "                'state_dict': pl_module.state_dict(),\n",
        "                'settings': pl_module.get_settings()\n",
        "            }, self.filepath + name)"
      ],
      "metadata": {
        "id": "664vrSgBb9DH",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Custom Layers\n",
        "\n",
        "def he_initializer(module):\n",
        "    \"\"\"\n",
        "    Returns He's Initialization Constant for Conv2D or linear modules. It is inversely proportional to the root\n",
        "    of the product of the neurons/weights for a given module. Scales the gradient relative to the number of weights\n",
        "    to remove the correlation between the number of connections and the gradient.\n",
        "    Formulation only valid for convolutional & linear layers due to weight arrangement\n",
        "    https://arxiv.org/abs/1502.01852\n",
        "    \"\"\"\n",
        "    assert isinstance(module, (nn.Conv2d, nn.ConvTranspose2d, nn.Linear)), \\\n",
        "        \"Formulation only valid for Conv2d & linear layers\"\n",
        "    weight_shape = module.weight.size()  # due to arrangement of weights\n",
        "    # Conv2d weights as out_channel x in_channel x kernel[0] x kernel[1]\n",
        "\n",
        "    # Removes the out_channel weights and multiplies the rest together\n",
        "    fan_in = np.prod(weight_shape[1:])\n",
        "    he_const = np.sqrt(2.0 / fan_in)\n",
        "\n",
        "    return he_const\n",
        "\n",
        "\n",
        "class PixelNormalizationLayer(nn.Module):\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Normalizes a minibatch of images by dividing each pixel by the average squared pixel across all channels\n",
        "        Norm = Root(Pixel / Sum(Pixel**2)/(Num Channels))\n",
        "        \"\"\"\n",
        "        super(PixelNormalizationLayer, self).__init__()\n",
        "\n",
        "    def forward(self, x, epsilon=1e-8):\n",
        "        # Epsilon → Small value for numerical stability when dividing\n",
        "        # rsqrt → Reciprocal Square Root\n",
        "        norm = x * (x.pow(2).mean(dim=1, keepdim=True) + epsilon).rsqrt()\n",
        "        return norm\n",
        "\n",
        "\n",
        "class EqualizedLayer(nn.Module):\n",
        "    def __init__(self, module, equalize=True, bias_init=True, lrmult=1.0):\n",
        "        \"\"\"\n",
        "        Wrapper layer that enables a linear or convolutional layer to execute He Initialization at runtime as well\n",
        "        as set initial biases of a module to 0.\n",
        "        The initialization is performed during the forward pass of the network to enable adaptive gradient descent methods\n",
        "        (eg. Adam) to better compensate for the equalization of learning rates. Equalization first sets all weights to random\n",
        "        numbers between -1 & 1 / N(0, 1), and then multiplies by the He constant at runtime.\n",
        "        :param module: Torch module to be equalized based on the number of connections\n",
        "        :param equalize: Flag to disable He Initialization\n",
        "        :param bias_init: Flag to disable initializing bias values to 0\n",
        "        :param lrmult: Custom layer-specific learning rate multiplier\n",
        "        \"\"\"\n",
        "        super(EqualizedLayer, self).__init__()\n",
        "\n",
        "        self.module = module\n",
        "        self.equalize = equalize\n",
        "        self.init_bias = bias_init\n",
        "\n",
        "        if self.equalize:\n",
        "            # Normal distribution mean of 0, SD of 1\n",
        "            self.module.weight.data.normal_(0, 1)\n",
        "            # Scale weights by a layer specific learning rate multiplier\n",
        "            self.module.weight.data /= lrmult\n",
        "            # Divides by multiplier as the He Value is the reciprocal of multiple of the output weights\n",
        "            self.he_val = he_initializer(self.module)\n",
        "        if self.init_bias:\n",
        "            self.module.bias.data.fill_(0)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.module(x)  # Forward pass through the module\n",
        "        if self.equalize:\n",
        "            x *= self.he_val  # Scale by the He Constant\n",
        "        return x\n",
        "\n",
        "\n",
        "class EqualizedConv2D(EqualizedLayer):\n",
        "    def __init__(self, prev_channels, channels, kernel=3, stride=1, padding=0, bias=True, transpose=False, **kwargs):\n",
        "        \"\"\"\n",
        "        Modified 2D convolution that is able to employ He Initialization at runtime as well as to initialize biases to 0\n",
        "        :param prev_channels:\n",
        "        :param channels:\n",
        "        :param kernel:\n",
        "        :param stride:\n",
        "        :param padding:\n",
        "        :param bias:\n",
        "        :param transpose:\n",
        "        :param kwargs:\n",
        "        \"\"\"\n",
        "        if not transpose:\n",
        "            conv = nn.Conv2d(in_channels=prev_channels,\n",
        "                             out_channels=channels,\n",
        "                             kernel_size=kernel,\n",
        "                             stride=stride,\n",
        "                             padding=padding,\n",
        "                             bias=bias)\n",
        "        else:\n",
        "            conv = nn.ConvTranspose2d(in_channels=prev_channels,\n",
        "                                      out_channels=channels,\n",
        "                                      kernel_size=kernel,\n",
        "                                      stride=stride,\n",
        "                                      padding=padding,\n",
        "                                      bias=bias)\n",
        "\n",
        "        EqualizedLayer.__init__(self, conv, **kwargs)\n",
        "\n",
        "\n",
        "class EqualizedLinear(EqualizedLayer):\n",
        "    def __init__(self, in_features, out_features, bias=True, **kwargs):\n",
        "        \"\"\"\n",
        "        Modified Fully Connected Layer to employ He Initialization at runtime and initialize biases to 0\n",
        "        :param in_features:\n",
        "        :param out_features:\n",
        "        :param bias:\n",
        "        :param kwargs:\n",
        "        \"\"\"\n",
        "        self.in_features = in_features\n",
        "        self.out_features = out_features\n",
        "        self.bias = bias\n",
        "        linear = nn.Linear(in_features, out_features, bias=bias)\n",
        "        EqualizedLayer.__init__(self, linear, **kwargs)\n",
        "\n",
        "\n",
        "def Conv_Block(prev_channel, channels, kernel, stride, padding, bias=True,\n",
        "               equalize=True, leakiness=0.2, normalize=True, activation=True, transpose=False):\n",
        "    \"\"\"\n",
        "    Convenience method to simplify the logic of creating a convolution block by specifying the layers to return.\n",
        "    Allows specification of equalized learning rate convolutions and normalization layers after convolutions.\n",
        "    :param prev_channel:\n",
        "    :param channels:\n",
        "    :param kernel:\n",
        "    :param stride:\n",
        "    :param padding:\n",
        "    :param bias:\n",
        "    :param equalize:\n",
        "    :param leakiness:\n",
        "    :param normalize:\n",
        "    :param activation:\n",
        "    :param transpose:\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    block = nn.ModuleList()\n",
        "    if equalize:\n",
        "        block.append(EqualizedConv2D(prev_channels=prev_channel, channels=channels, kernel=kernel,\n",
        "                                     stride=stride, padding=padding, bias=bias, transpose=transpose))\n",
        "    else:\n",
        "        block.append(nn.Conv2d(in_channels=prev_channel, out_channels=channels, kernel_size=kernel,\n",
        "                               stride=stride, padding=padding, bias=bias))\n",
        "    if activation:\n",
        "        block.append(nn.LeakyReLU(negative_slope=leakiness, inplace=True))\n",
        "\n",
        "    if normalize:\n",
        "        block.append(PixelNormalizationLayer())\n",
        "\n",
        "    return block\n",
        "\n",
        "\n",
        "class ScaleBlock(nn.Module):\n",
        "    def __init__(self, dims, prev_channel, channels, scale=1, equalize=True, normalize=True,\n",
        "                 leakiness=0.2, kernel=3, stride=1, padding=None, bias=True, mode=\"bilinear\"):\n",
        "        \"\"\"\n",
        "        Standard convolutional block that combines two identical convolutions and an interpolation operation.\n",
        "        If the block upscales an image, the upscaling is done prior to the convolutions\n",
        "        If the block downscales, the upscaling is done after the convolutions\n",
        "        :param dims:\n",
        "        :param prev_channel:\n",
        "        :param channels:\n",
        "        :param scale:\n",
        "        :param equalize:\n",
        "        :param normalize:\n",
        "        :param leakiness:\n",
        "        :param kernel:\n",
        "        :param stride:\n",
        "        :param padding:\n",
        "        :param bias:\n",
        "        :param mode:\n",
        "        \"\"\"\n",
        "        super(ScaleBlock, self).__init__()\n",
        "\n",
        "        assert scale in [\n",
        "            0.5, 1, 2], \"Scale can only half, double or maintain spatial resolution\"\n",
        "        self.scale = scale\n",
        "        self.equalize = equalize\n",
        "\n",
        "        assert mode in [\n",
        "            'nearest', 'bilinear'], f\"Only configured for 'nearest' & 'bilinear', but {mode} was selected\"\n",
        "        self.mode = mode\n",
        "\n",
        "        if padding is None:\n",
        "            padding = calc_padding(dims, kernel, stride)\n",
        "\n",
        "        self.convolv = nn.Sequential(\n",
        "            *Conv_Block(prev_channel=prev_channel, channels=channels, kernel=kernel, stride=stride, padding=padding,\n",
        "                        bias=bias, equalize=equalize, leakiness=leakiness, normalize=normalize),\n",
        "            *Conv_Block(prev_channel=channels, channels=channels, kernel=kernel, stride=stride, padding=padding,\n",
        "                        bias=bias, equalize=equalize, leakiness=leakiness, normalize=normalize)\n",
        "        )\n",
        "\n",
        "    def forward(self, feat_map):\n",
        "        if self.scale > 1:\n",
        "            feat_map = F.interpolate(\n",
        "                input=feat_map, scale_factor=self.scale, mode=self.mode)\n",
        "\n",
        "        feat_map = self.convolv(feat_map)\n",
        "\n",
        "        if self.scale < 1:\n",
        "            feat_map = F.avg_pool2d(input=feat_map, kernel_size=(2, 2))\n",
        "\n",
        "        return feat_map"
      ],
      "metadata": {
        "cellView": "form",
        "id": "pxcQ2PAgbyvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Model Implementation\n",
        "\n",
        "class ProGenerator(nn.Module):\n",
        "    def __init__(self, z_dim=512, channel_depth=512, init_bias=True, norm_layers=True,\n",
        "                 out_channels=3, equalize_layers=True, leakiness=0.2, mode=\"nearest\"):\n",
        "        super(ProGenerator, self).__init__()\n",
        "        # Store Model Parameters\n",
        "        self.z_dim = z_dim  # Dimensions of latent space vector\n",
        "\n",
        "        # Initial Number of channels to produce from latent space\n",
        "        self.channel_depth = [channel_depth]\n",
        "        # Model begins by producing 4x4 images, incremented when alpha reaches 1\n",
        "        self.register_buffer('current_size', torch.tensor(4))\n",
        "        # Current number of completed blocks, incremented when alpha reaches 1 for a given layer\n",
        "        self.register_buffer('current_depth', torch.tensor(0))\n",
        "        # Mixing co-efficient for use when upscaling the network\n",
        "        self.register_buffer('alpha', torch.tensor(0))\n",
        "\n",
        "        self.init_bias = init_bias  # Initialize bias to 0\n",
        "        self.norm_layers = norm_layers  # Whether to apply minibatch normalization layer\n",
        "        # The final number of colour channels used in the generated image\n",
        "        self.out_channels = out_channels\n",
        "        # Whether to use the He Constant to equalize layer outputs at runtime\n",
        "        self.equalize = equalize_layers\n",
        "        # The co-efficient of the negative slope of the Leaky ReLU activation\n",
        "        self.leakiness = leakiness\n",
        "        self.mode = mode  # Interpolation mode for upscaling, Paper utilizes nearest neighbour mode\n",
        "\n",
        "        # Define Layer Architectures\n",
        "        self.latent_linear = nn.Sequential(EqualizedLinear(in_features=z_dim, out_features=16 * channel_depth,\n",
        "                                                           equalize=equalize_layers, bias_init=init_bias),\n",
        "                                           # Initial latent space processing\n",
        "                                           nn.LeakyReLU(negative_slope=leakiness, inplace=True))\n",
        "\n",
        "        self.init_conv = nn.Sequential(  # Initial convolution on latent space after initial linear processing\n",
        "            *Conv_Block(prev_channel=channel_depth, channels=channel_depth,\n",
        "                        # Convolutions maintain number of channels\n",
        "                        kernel=3, stride=1, padding=calc_padding(dims=4, kernel=3, stride=1), bias=True,\n",
        "                        equalize=equalize_layers, leakiness=leakiness, normalize=norm_layers))\n",
        "\n",
        "        # Stores list of scaling blocks to double spatial resolutions\n",
        "        self.ScaleBlocks = nn.ModuleList()\n",
        "        # Stores the feature map to RGB convolutions A new one is needed for each network expansion\n",
        "        self.toRGB = nn.ModuleList()\n",
        "        # The RGB layers are stored to enable extracting smaller intermediary images from scaling blocks\n",
        "        self.toRGB.append(*Conv_Block(prev_channel=channel_depth, channels=out_channels,\n",
        "                                      kernel=1, stride=1, padding=0, bias=True, equalize=equalize_layers,\n",
        "                                      activation=False,\n",
        "                                      normalize=False))  # The final convolution acts as an activation function\n",
        "\n",
        "        if self.norm_layers:\n",
        "            self.norm = PixelNormalizationLayer()\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.norm_layers:\n",
        "            x = self.norm(x)\n",
        "\n",
        "        # Multiple of all dimensions except batch dimension = Total Feature Number\n",
        "        features = np.prod(x.size()[1:])\n",
        "        x = x.view(-1, features)  # Batch dimension x Features\n",
        "\n",
        "        batch_size = x.size()[0]\n",
        "        x = self.latent_linear(x)  # Initial Latent Processing & Formatting\n",
        "        x = x.view(batch_size, -1, 4, 4)  # Reshape to Batch x Depth x 4 x 4\n",
        "\n",
        "        # Perform initial 3x3 convolution without upscaling\n",
        "        x = self.init_conv(x)\n",
        "\n",
        "        if self.alpha > 0 and self.current_depth == 1:  # Apply mixing for when the network begins to expand\n",
        "            # Expansion determined when alpha is incremented with the completed depth layers still at 0\n",
        "            expansion = self.toRGB[-2](x)\n",
        "            expansion = F.interpolate(\n",
        "                input=expansion, scale_factor=2, mode=self.mode)\n",
        "\n",
        "        for scale_num, scale_block in enumerate(self.ScaleBlocks, 1):\n",
        "            # Start at 1 due to the first image dimension not requiring scaling\n",
        "            # Process the input through the expansion block of upscale, conv, conv\n",
        "            x = scale_block(x)\n",
        "\n",
        "            if self.alpha > 0 and (scale_num == self.current_depth - 1):\n",
        "                expansion = self.toRGB[-2](x)\n",
        "                expansion = F.interpolate(\n",
        "                    input=expansion, scale_factor=2, mode=self.mode)\n",
        "\n",
        "        x = self.toRGB[-1](x)  # Final layer to RGB\n",
        "\n",
        "        if self.alpha > 0:\n",
        "            # Mix the inputs at the final scale\n",
        "            x = self.alpha * expansion + (1.0 - self.alpha) * x\n",
        "\n",
        "        return x\n",
        "\n",
        "    def incrementdepth(self, new_depth):\n",
        "        \"\"\"\n",
        "        Adds scaling block to the model, doubles the spatial resolution of the final image\n",
        "\n",
        "        \"\"\"\n",
        "        device = next(self.parameters()).device\n",
        "        self.current_depth += 1\n",
        "        self.current_size *= 2\n",
        "\n",
        "        prev_depth = self.channel_depth[-1]\n",
        "        self.channel_depth.append(new_depth)\n",
        "        # Adds scaling block, padding is calculated from the spatial dimensions and filter properties\n",
        "        size = self.current_size.cpu().numpy()\n",
        "        self.ScaleBlocks.append(ScaleBlock(dims=size, prev_channel=prev_depth,\n",
        "                                           channels=new_depth, scale=2, equalize=self.equalize,\n",
        "                                           normalize=self.norm_layers, leakiness=self.leakiness, kernel=3,\n",
        "                                           stride=1, padding=None, bias=True, mode=self.mode).to(device))\n",
        "\n",
        "        self.toRGB.append(*Conv_Block(prev_channel=new_depth, channels=self.out_channels,\n",
        "                                      kernel=1, stride=1, padding=0, bias=True, equalize=self.equalize,\n",
        "                                      activation=False, normalize=False).to(device))\n",
        "\n",
        "    def set_alpha(self, new_alpha):\n",
        "        \"\"\"\n",
        "        Sets the mixing factor used when upscaling the network. Alters the functioning of the forward function\n",
        "        to include the second last layer and interpolate between it and the final output of the added scaling block.\n",
        "        \"\"\"\n",
        "        if new_alpha < 0 or new_alpha > 1:\n",
        "            raise ValueError(\"Alpha must be in the range [0,1]\")\n",
        "\n",
        "        self.alpha = new_alpha\n",
        "\n",
        "    def load(self, checkpoint):\n",
        "        \"\"\"\n",
        "        Automatically scales the network to the required size and loads the weights\n",
        "        :param checkpoint: Saved network state\n",
        "        :return:\n",
        "        \"\"\"\n",
        "        for depth in checkpoint['settings']['channel_depth'][1:]:\n",
        "            self.incrementdepth(depth)\n",
        "        self.load_state_dict(checkpoint['state_dict'])\n",
        "        print(\"Generator Weights Loaded\")\n",
        "\n",
        "\n",
        "class ProDiscriminator(nn.Module):\n",
        "    def __init__(self, channel_depth=512, init_bias=True, norm_layers=False, input_channels=3,\n",
        "                 decision_layer_dim=1, equalize_layers=True, leakiness=0.2, minibatch_std=True):\n",
        "        super(ProDiscriminator, self).__init__()\n",
        "        # Store Model Parameters\n",
        "        self.input_channels = input_channels\n",
        "        # Can be augmented to allow for classification of an image\n",
        "        self.decision_layer_dim = decision_layer_dim\n",
        "\n",
        "        # Initial Number of channels to produce from image\n",
        "        self.channel_depth = [channel_depth]\n",
        "        # Current size of images for descrimination\n",
        "        self.register_buffer('current_size', torch.tensor(4))\n",
        "        self.register_buffer('current_depth', torch.tensor(\n",
        "            0))  # Current number of completed blocks, incremented when the generator completes mixing\n",
        "        # Mixing co-efficient for use when upscaling the network\n",
        "        self.register_buffer('alpha', torch.tensor(0))\n",
        "\n",
        "        self.init_bias = init_bias  # Initialize bias to 0\n",
        "        self.norm_layers = norm_layers  # Whether to apply minibatch normalization layer\n",
        "        # Whether to calculate the std of all layers prior to the final convolution\n",
        "        self.minibatch = minibatch_std\n",
        "        # Whether to use the He Constant to equalize layer outputs at runtime\n",
        "        self.equalize = equalize_layers\n",
        "        # The co-efficient of the negative slope of the Leaky ReLU activation\n",
        "        self.leakiness = leakiness\n",
        "\n",
        "        if self.minibatch:\n",
        "            self.miniSTD = MinibatchSTD()\n",
        "\n",
        "        self.fromRGB = nn.ModuleList()\n",
        "\n",
        "        self.fromRGB.append(*Conv_Block(prev_channel=input_channels, channels=channel_depth,\n",
        "                                        kernel=1, stride=1, padding=0, bias=True, equalize=equalize_layers,\n",
        "                                        activation=False, normalize=False))  # Initial RGB to Feature Map Processing\n",
        "\n",
        "        self.ScaleBlocks = nn.ModuleList()  # Will be added to as the network grows\n",
        "        # Blocks will be added to the front of the list as the descriminator grows at its input end\n",
        "\n",
        "        self.DecisionBlock = nn.Sequential(\n",
        "            *Conv_Block(prev_channel=channel_depth + minibatch_std, channels=channel_depth,  # Add channel for std\n",
        "                        kernel=3, stride=1, padding=calc_padding(4, 3, 1), bias=True, equalize=equalize_layers,\n",
        "                        activation=True, normalize=norm_layers),  # eg: 513x4x4 → 512x4x4\n",
        "            *Conv_Block(prev_channel=channel_depth, channels=channel_depth,\n",
        "                        kernel=4, stride=1, padding=0, bias=True, equalize=equalize_layers,\n",
        "                        activation=True, normalize=norm_layers))  # eg: 512x4x4 → 512x1x1\n",
        "\n",
        "        self.DecisionLinear = EqualizedLinear(in_features=channel_depth, out_features=decision_layer_dim,\n",
        "                                              equalize=self.equalize, bias_init=self.init_bias)  # eg: 512x1x1 → 1x1x1\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        if self.alpha > 0 and len(\n",
        "                self.fromRGB) > 1:  # Check if the layers are mixed and multiple resolutions are being used\n",
        "            pooled = F.avg_pool2d(input=x,\n",
        "                                  kernel_size=(2, 2))  # Downsample the larger image to mix with the output of the\n",
        "            pooled = self.fromRGB[-2](pooled)  # new processing layer\n",
        "\n",
        "        # Convert from RGB to Feature Maps:\n",
        "        x = self.fromRGB[-1](x)\n",
        "\n",
        "        for scale_num, scale_block in enumerate(self.ScaleBlocks, 1):\n",
        "            # Start at 1 due to the first image dimension not requiring scaling\n",
        "            # Process the input through the contraction block of conv, conv, downsample\n",
        "            x = scale_block(x)\n",
        "\n",
        "            # Processing blocks are added to the front of the array, so mixing always occurs at the top layer\n",
        "            if self.alpha > 0 and scale_num == 1:\n",
        "                # Interpolate the downsampled input and the new input\n",
        "                x = self.alpha * pooled + (1.0 - self.alpha) * x\n",
        "\n",
        "        if self.minibatch:\n",
        "            x = self.miniSTD(x)\n",
        "\n",
        "        x = self.DecisionBlock(x)  # Final 3x3, 4x4 conv\n",
        "\n",
        "        features = np.prod(x.size()[1:])\n",
        "        x = x.view(-1, features)\n",
        "\n",
        "        x = self.DecisionLinear(x)  # Returns logits\n",
        "\n",
        "        return x  # Final score for whether the image is fake\n",
        "\n",
        "    def incrementdepth(self, new_depth):\n",
        "        \"\"\"\n",
        "        Adds scaling block to the model, doubles the spatial resolution of the inputted image\n",
        "        Includes two channel depth parameters as the descriminator often has different channels for each convolution\n",
        "        \"\"\"\n",
        "        device = next(self.parameters()).device\n",
        "        self.current_depth += 1\n",
        "        self.current_size *= 2\n",
        "\n",
        "        prev_depth = self.channel_depth[-1]\n",
        "        self.channel_depth.append(new_depth)\n",
        "        size = self.current_size.cpu().numpy()\n",
        "\n",
        "        self.ScaleBlocks.insert(0, ScaleBlock(dims=size, prev_channel=new_depth,  # Add block to the beginning of list\n",
        "                                              channels=prev_depth, scale=0.5, equalize=self.equalize,\n",
        "                                              normalize=self.norm_layers, leakiness=self.leakiness, kernel=3,\n",
        "                                              stride=1, padding=None, bias=True).to(device))\n",
        "\n",
        "        self.fromRGB.append(*Conv_Block(prev_channel=self.input_channels, channels=new_depth,\n",
        "                                        kernel=1, stride=1, padding=0, bias=True, equalize=self.equalize,\n",
        "                                        activation=False, normalize=False).to(device))\n",
        "\n",
        "    def set_alpha(self, new_alpha):\n",
        "        \"\"\"\n",
        "        Sets the mixing factor used when incrementing the network. Alters the functioning of the forward function\n",
        "        to include the second layer and interpolate between it and the initial output of the added scaling block.\n",
        "        \"\"\"\n",
        "        if new_alpha < 0 or new_alpha > 1:\n",
        "            raise ValueError(\"Alpha must be in the range [0,1]\")\n",
        "        self.alpha = new_alpha\n",
        "\n",
        "    def save(self, filename):\n",
        "        settings = {\n",
        "            'input_channels': self.input_channels,\n",
        "            'decision_layer_dim': self.decision_layer_dim,\n",
        "            'channel_depth': self.channel_depth,\n",
        "            'init_bias': self.init_bias,\n",
        "            'norm_layers': self.norm_layers,\n",
        "            'minibatch': self.minibatch,\n",
        "            'equalize': self.equalize,\n",
        "            'leakiness': self.leakiness\n",
        "        }\n",
        "        torch.save({'state_dict': self.state_dict(),\n",
        "                    'settings': settings}, filename)\n",
        "\n",
        "    def load(self, checkpoint):\n",
        "        \"\"\"\n",
        "        Automatically scales the network to the required size and loads the weights\n",
        "        :param checkpoint: Saved network state\n",
        "        :return:\n",
        "        \"\"\"\n",
        "        for depth in checkpoint['settings']['channel_depth'][1:]:\n",
        "            self.incrementdepth(depth)\n",
        "        self.load_state_dict(checkpoint['state_dict'])\n",
        "        print(\"Discriminator Weights Loaded\")\n",
        "\n",
        "\n",
        "class ProGAN(pl.LightningModule):\n",
        "    def __init__(self, hparams):\n",
        "        super(ProGAN, self).__init__()\n",
        "        self.hparams.update(vars(hparams))\n",
        "\n",
        "        self.name = hparams.name\n",
        "        self.directory = hparams.directory\n",
        "        self.batch_sizes = [int(batch) for batch in hparams.batch_sizes]\n",
        "\n",
        "        self.worker_num = hparams.worker_num\n",
        "        # initial spatial dimensions\n",
        "        self.register_buffer('img_size', torch.tensor(4))\n",
        "        self.z_dim = hparams.z_dim\n",
        "\n",
        "        self.generator = ProGenerator(z_dim=hparams.z_dim, channel_depth=hparams.initial_channel_depth,\n",
        "                                      init_bias=hparams.init_bias, norm_layers=hparams.g_norm_layers,\n",
        "                                      out_channels=hparams.img_channels, equalize_layers=hparams.equalize,\n",
        "                                      leakiness=hparams.leakiness, mode=hparams.mode)\n",
        "\n",
        "        self.discriminator = ProDiscriminator(channel_depth=hparams.initial_channel_depth, init_bias=hparams.init_bias,\n",
        "                                              norm_layers=hparams.d_norm_layers, input_channels=hparams.img_channels,\n",
        "                                              decision_layer_dim=hparams.decision_dim, equalize_layers=hparams.equalize,\n",
        "                                              leakiness=hparams.leakiness, minibatch_std=hparams.minibatch_std)\n",
        "\n",
        "        self.register_buffer('scale', torch.tensor(0))\n",
        "        self.lr = hparams.lr\n",
        "        self.b1 = hparams.b1\n",
        "        self.b2 = hparams.b2\n",
        "        self.eps = hparams.eps\n",
        "        self.drift = hparams.drift\n",
        "        self.lambda_val = hparams.lambda_val\n",
        "\n",
        "        self.imgs = None\n",
        "\n",
        "        self.channel_depths = [int(depths) for depths in hparams.depths]\n",
        "\n",
        "        self.register_buffer('alpha', torch.tensor(0))\n",
        "        self.register_buffer('alpha_jumps', hparams.alpha_jumps)\n",
        "        iters = int(self.alpha_jumps[self.scale] /\n",
        "                    self.batch_sizes[self.scale])\n",
        "        self.alpha_iters = iters\n",
        "        self.alpha_vals = np.linspace(start=1, stop=0, num=iters)\n",
        "\n",
        "        self.sample_rate = hparams.sample\n",
        "        self.sample_num = hparams.sample_num\n",
        "\n",
        "        self.iteration_sets = [int(iteration)\n",
        "                               for iteration in hparams.iterations]\n",
        "        self.register_buffer('total_iter', torch.tensor(0))\n",
        "        self.register_buffer('current_iter', torch.tensor(0))\n",
        "        self.register_buffer('num_images_seen', torch.tensor(0))\n",
        "        self.register_buffer('iter_images_seen', torch.tensor(0))\n",
        "        self.register_buffer('g_current_iter', torch.tensor(0))\n",
        "        self.register_buffer('d_current_iter', torch.tensor(0))\n",
        "\n",
        "        self.register_buffer('fixed_z', torch.randn(\n",
        "            self.sample_num, self.z_dim, requires_grad=False))\n",
        "\n",
        "        self.register_buffer('epoch_counter', torch.tensor(0))\n",
        "        self.register_buffer('batch_count', torch.tensor(0))\n",
        "\n",
        "        self.ema = hparams.ema\n",
        "        if self.ema:\n",
        "            self.ema_decay = hparams.ema_decay\n",
        "            self.gen_shadow = copy.deepcopy(self.generator)\n",
        "            exp_avg_update(target=self.gen_shadow,\n",
        "                           source=self.generator, beta=0)\n",
        "\n",
        "        self.set_fp16 = hparams.set_fp16\n",
        "\n",
        "    def forward(self, latent):\n",
        "        return self.generator(latent)\n",
        "\n",
        "    def training_step(self, img_list, batch_idx, optimizer_idx):\n",
        "        device = next(self.parameters()).device\n",
        "        img_batch, _ = img_list\n",
        "\n",
        "        self.check_network_scale()\n",
        "\n",
        "        # Random Noise Vector:\n",
        "        latent = torch.randn(img_batch.shape[0], self.z_dim)\n",
        "        latent = latent.type_as(img_batch)\n",
        "        # Training the discriminator first appears to promote more stable training\n",
        "        if optimizer_idx == 0:  # Discriminator Training:\n",
        "            # No labels are needed as the higher the average real score the better the discriminator\n",
        "            # and the lower the average fake score the better\n",
        "            real_imgs = img_batch.detach()\n",
        "            real_validity = self.discriminator(real_imgs)\n",
        "\n",
        "            fake_imgs = self.forward(latent).detach()\n",
        "            fake_validity = self.discriminator(fake_imgs)\n",
        "\n",
        "            gradient_penalty = compute_gradient_penalty(\n",
        "                real_samples=real_imgs, fake_samples=fake_imgs)\n",
        "\n",
        "            d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + self.lambda_val * gradient_penalty \\\n",
        "                + self.drift * torch.mean(real_validity ** 2)\n",
        "            d_loss = d_loss.unsqueeze(0)\n",
        "\n",
        "            tqdm_dict = {'d_loss': d_loss}\n",
        "            output = OrderedDict({\n",
        "                'loss': d_loss,\n",
        "                'progress_bar': tqdm_dict,\n",
        "                'log': tqdm_dict,\n",
        "                'opt_step': torch.tensor([optimizer_idx]).to(device)\n",
        "            })\n",
        "\n",
        "            return output\n",
        "\n",
        "        if optimizer_idx == 1:  # Generator Training:\n",
        "            fake_imgs = self.forward(latent)  # Generate Images from Noise\n",
        "            validity_score = self.discriminator(fake_imgs)\n",
        "\n",
        "            # Calculate Generator Loss\n",
        "            g_loss = -torch.mean(validity_score)\n",
        "            g_loss = g_loss.unsqueeze(0)\n",
        "\n",
        "            tqdm_dict = {'g_loss': g_loss}\n",
        "            output = OrderedDict({\n",
        "                'loss': g_loss,\n",
        "                'progress_bar': tqdm_dict,\n",
        "                'log': tqdm_dict,\n",
        "                'opt_step': torch.tensor([optimizer_idx]).to(device)\n",
        "            })\n",
        "\n",
        "            return output\n",
        "\n",
        "    def training_step_end(self, outputs):\n",
        "        \"\"\"\n",
        "        Method collating all results on a single GPU. Can be used to increment values that will be distributed\n",
        "        to all GPUs on future training steps. Image logs can be produced without redundancy as well.\n",
        "        \"\"\"\n",
        "        if int(outputs['opt_step'][-1]) == 1:  # Only called after the generator is updated\n",
        "            device = next(self.parameters()).device\n",
        "\n",
        "            self.total_iter += 1\n",
        "            self.current_iter += 1\n",
        "            self.num_images_seen += self.batch_sizes[self.scale]\n",
        "            self.iter_images_seen += self.batch_sizes[self.scale]\n",
        "            self.g_current_iter += 1\n",
        "\n",
        "            self.check_network_scale()\n",
        "\n",
        "            if self.ema:\n",
        "                exp_avg_update(target=self.gen_shadow,\n",
        "                               source=self.generator, beta=self.ema_decay)\n",
        "\n",
        "            if self.alpha_iters >= self.current_iter:\n",
        "                if len(self.alpha_vals) > 0:  # Check alpha values exist for current scale\n",
        "                    alpha = self.alpha_vals[self.current_iter - 1]\n",
        "                    self.update_alpha(torch.tensor(alpha).to(device))\n",
        "                else:  # Correct if no mixing is specified for particular scale\n",
        "                    self.update_alpha(torch.tensor(0).to(device))\n",
        "            else:\n",
        "                if self.alpha > 0:  # Correct if alpha not 0\n",
        "                    self.update_alpha(torch.tensor(0).to(device))\n",
        "\n",
        "            self.logger.experiment.add_scalar(\n",
        "                'Alpha_Mixing_Coefficient', self.alpha, self.total_iter)\n",
        "\n",
        "            # Generate Fixed Samples & Store\n",
        "            if self.current_iter % self.sample_rate == 0:\n",
        "                with torch.no_grad():\n",
        "                    samples = self.forward(self.fixed_z).detach()\n",
        "                    grid = vutils.make_grid(samples, normalize=True)\n",
        "                    self.logger.experiment.add_image(\n",
        "                        'generated_images', grid, self.total_iter)\n",
        "\n",
        "                    if self.ema:\n",
        "                        samples = self.gen_shadow.forward(\n",
        "                            self.fixed_z).detach()\n",
        "                        grid = vutils.make_grid(samples, normalize=True)\n",
        "                        self.logger.experiment.add_image(\n",
        "                            'shadow_generated_images', grid, self.total_iter)\n",
        "        else:\n",
        "            self.d_current_iter += 1\n",
        "\n",
        "        outputs['loss'] = outputs['loss'].squeeze(0)\n",
        "        del outputs['opt_step']  # Remove tracking value from output\n",
        "        return outputs\n",
        "\n",
        "    def check_network_scale(self):\n",
        "        device = next(self.parameters()).device\n",
        "        if self.generator.current_depth != self.scale:\n",
        "            self.generator.current_depth = torch.tensor(self.scale).to(device)\n",
        "            if self.ema:\n",
        "                self.gen_shadow.current_depth = torch.tensor(\n",
        "                    self.scale).to(device)\n",
        "\n",
        "        if self.discriminator.current_depth != self.scale:\n",
        "            self.discriminator.current_depth = torch.tensor(\n",
        "                self.scale).to(device)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        lr = self.lr\n",
        "        b1 = self.b1\n",
        "        b2 = self.b2\n",
        "        epsilon = self.eps\n",
        "\n",
        "        opt_d = torch.optim.Adam(\n",
        "            self.discriminator.parameters(), lr=lr, betas=(b1, b2), eps=epsilon)\n",
        "        opt_g = torch.optim.Adam(\n",
        "            self.generator.parameters(), lr=lr, betas=(b1, b2), eps=epsilon)\n",
        "\n",
        "        return [opt_d, opt_g], []\n",
        "\n",
        "    def configure_apex(self, amp, model, optimizers, amp_level):\n",
        "        [model.generator, model.discriminator], optimizers = amp.initialize(\n",
        "            [model.generator, model.discriminator], optimizers, opt_level=amp_level,\n",
        "        )\n",
        "\n",
        "        return model, optimizers\n",
        "\n",
        "    def add_depth(self):\n",
        "        device = next(self.parameters()).device\n",
        "        scale = self.scale\n",
        "\n",
        "        print(\"Adding Depth, Scale:\", scale +\n",
        "              1, \"Img Size:\", self.img_size * 2)\n",
        "\n",
        "        self.generator.incrementdepth(self.channel_depths[scale])\n",
        "\n",
        "        if self.ema:\n",
        "            self.gen_shadow.incrementdepth(self.channel_depths[scale])\n",
        "\n",
        "        self.discriminator.incrementdepth(self.channel_depths[scale])\n",
        "\n",
        "        self.img_size *= 2  # Double Spatial Dimensions\n",
        "        self.scale += 1\n",
        "        self.current_iter = torch.tensor(0).to(device)\n",
        "        self.iter_images_seen = torch.tensor(0).to(device)\n",
        "        # Generate a smooth linear array of numbers from 1 to 0 for fading in layers\n",
        "        iters = int(self.alpha_jumps[self.scale] /\n",
        "                    self.batch_sizes[self.scale])\n",
        "        self.alpha_iters = iters\n",
        "        alpha_range = np.linspace(start=1, stop=0, num=iters)\n",
        "        self.alpha_vals = alpha_range\n",
        "        self.update_alpha(torch.tensor(1).to(device))\n",
        "\n",
        "    def update_alpha(self, new_alpha):\n",
        "        if type(new_alpha).__name__ != 'Tensor':\n",
        "            device = next(self.parameters()).device\n",
        "            new_alpha = torch.tensor(new_alpha).to(device)\n",
        "\n",
        "        self.alpha = new_alpha\n",
        "        self.generator.set_alpha(new_alpha)\n",
        "        if self.ema:\n",
        "            self.gen_shadow.set_alpha(new_alpha)\n",
        "        self.discriminator.set_alpha(new_alpha)\n",
        "\n",
        "    def prepare_data(self):\n",
        "        self.imgs = VariableImageFolder(self.directory)\n",
        "\n",
        "    def train_dataloader(self):  # Dataloader is reset at the end of each epoch\n",
        "        # Using the variable length dataset enables finer control over the number of images shown\n",
        "        batch_size = self.batch_sizes[self.scale]\n",
        "        img_size = self.img_size\n",
        "        worker_num = self.worker_num\n",
        "        # For loading in the middle of epochs\n",
        "        imgs_needed = self.iteration_sets[self.scale] - self.iter_images_seen\n",
        "        if imgs_needed <= 0:\n",
        "            imgs_needed = 1  # Correct for potential image number error\n",
        "\n",
        "        self.imgs.update_img_size(img_size)\n",
        "        self.imgs.update_img_len(imgs_needed)\n",
        "\n",
        "        dataloader = torch.utils.data.DataLoader(self.imgs, batch_size=batch_size, drop_last=True,\n",
        "                                                 shuffle=True, num_workers=worker_num)\n",
        "        return dataloader\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        self.epoch_counter += 1\n",
        "        # Number of total images needed\n",
        "        imgs_needed = self.iteration_sets[self.scale] - self.iter_images_seen\n",
        "        if imgs_needed <= 0:\n",
        "            self.add_depth()\n",
        "\n",
        "    def get_settings(self):\n",
        "        settings_dict = {\n",
        "            'alpha': self.alpha,\n",
        "\n",
        "            'g_size': self.generator.current_size,\n",
        "            'g_depth': self.generator.current_depth,\n",
        "            'g_alpha': self.generator.alpha,\n",
        "            'd_size': self.discriminator.current_size,\n",
        "            'd_depth': self.discriminator.current_depth,\n",
        "            'd_alpha': self.discriminator.alpha,\n",
        "\n",
        "            'total_iter': self.total_iter,\n",
        "            'current_iter': self.current_iter,\n",
        "            'num_images_seen': self.num_images_seen,\n",
        "            'iter_images_seen': self.iter_images_seen,\n",
        "            'g_current_iter': self.g_current_iter,\n",
        "            'd_current_iter': self.d_current_iter,\n",
        "\n",
        "            'fixed_z': self.fixed_z\n",
        "        }\n",
        "        return settings_dict\n",
        "\n",
        "    def set_settings(self, settings):\n",
        "\n",
        "        self.alpha = settings['alpha']\n",
        "        self.update_alpha(settings['alpha'])  # Update alpha to previous value\n",
        "        iters = int(self.alpha_jumps[self.scale] /\n",
        "                    self.batch_sizes[self.scale])\n",
        "        self.alpha_iters = iters\n",
        "        self.alpha_vals = np.linspace(start=1, stop=0, num=iters)\n",
        "\n",
        "        self.generator.current_size = settings['g_size']\n",
        "        self.generator.current_depth = settings['g_depth']\n",
        "\n",
        "        if self.ema:\n",
        "            self.gen_shadow.current_size = settings['g_size']\n",
        "            self.gen_shadow.current_depth = settings['g_depth']\n",
        "\n",
        "        self.discriminator.current_size = settings['d_size']\n",
        "        self.discriminator.current_depth = settings['d_depth']\n",
        "\n",
        "        self.total_iter = settings['total_iter']\n",
        "        self.current_iter = settings['current_iter']\n",
        "        self.num_images_seen = settings['num_images_seen']\n",
        "        self.iter_images_seen = settings['iter_images_seen']\n",
        "        self.g_current_iter = settings['g_current_iter']\n",
        "        self.d_current_iter = settings['d_current_iter']\n",
        "\n",
        "        self.fixed_z = settings['fixed_z']\n",
        "\n",
        "    def load_model(self, path):\n",
        "        checkpoint = torch.load(path)\n",
        "        for _ in range(checkpoint['settings']['g_depth']):\n",
        "            self.add_depth()\n",
        "        self.load_state_dict(checkpoint['state_dict'])\n",
        "        self.set_settings(checkpoint['settings'])\n",
        "        print(\"Model & Settings Successfully Loaded\")\n",
        "        return self"
      ],
      "metadata": {
        "cellView": "form",
        "id": "oIhk4FccYTKR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Loading Model Weights\n",
        "\n",
        "url = 'https://docs.google.com/uc?export=download&id=12KQhv3DOL6BtNayxHLKOO18GOCx_pwB4'\n",
        "output = 'model.pth'\n",
        "gdown.download(url, output, quiet=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "cellView": "form",
        "id": "-h80XnPkWwnG",
        "outputId": "eeda4c81-7ecf-4231-d8ff-c6c77b5c62cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://docs.google.com/uc?export=download&id=12KQhv3DOL6BtNayxHLKOO18GOCx_pwB4\n",
            "To: /content/model.pth\n",
            "100%|██████████| 287M/287M [00:02<00:00, 126MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'model.pth'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Model Config\n",
        "\n",
        "directory = \"./Images/\"\n",
        "# Number of images per batch at each scale\n",
        "batch_sizes = [16, 16, 16, 16, 16, 16, 14, 8, 3]\n",
        "ngpu = 4\n",
        "batch_sizes = [batch*ngpu for batch in batch_sizes]\n",
        "depthScales = [512, 512, 512, 256, 128, 64, 32, 16]\n",
        "# Number of images to be shown at each scale\n",
        "scale_iters = [8e5, 16e5, 16e5, 16e5, 16e5, 16e5, 16e5, 16e5, 2e6]\n",
        "# Allows for alpha mixing for 800k images\n",
        "alpha_img_jumps = [0, 8e5, 8e5, 8e5, 8e5, 8e5, 8e5, 8e5, 8e5]\n",
        "\n",
        "\n",
        "args = {\n",
        "    'name': \"ProGAN\",\n",
        "    'batch_sizes': batch_sizes,\n",
        "    'lr': 0.001,\n",
        "    'b1': 0,\n",
        "    'b2': 0.99,\n",
        "    'eps': 10e-8,\n",
        "    'lambda_val': 10,\n",
        "    'drift': 0.001,\n",
        "    'z_dim': 512,\n",
        "    'directory': directory,\n",
        "    'worker_num': 0,\n",
        "    'g_norm_layers': True,\n",
        "    'd_norm_layers': False,\n",
        "    'init_bias': True,\n",
        "    'equalize': True,\n",
        "    'leakiness': 0.2,\n",
        "    'mode': 'nearest',\n",
        "    'img_channels': 3,\n",
        "    'initial_channel_depth': 512,\n",
        "    'minibatch_std': True,\n",
        "    'decision_dim': 1,\n",
        "    'iterations': scale_iters,\n",
        "    'depths': depthScales,\n",
        "    'alpha_jumps': alpha_img_jumps,\n",
        "    'sample': 300,  # Draw a sample once every 100 iterations\n",
        "    'sample_num': 16,\n",
        "    'ema': True,\n",
        "    'ema_decay': 0.999,\n",
        "    'set_fp16': False\n",
        "}\n",
        "\n",
        "for key, value in args.items():\n",
        "    if type(value).__name__ == 'list':\n",
        "        args[key] = torch.Tensor(value)\n",
        "\n",
        "hparams = Namespace(**args)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "WZNxIbMgbl94"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Sample Output Options (General XRay Images)\n",
        "\n",
        "NUM_SAMPLES = 0 #@param {type:\"integer\"}"
      ],
      "metadata": {
        "id": "BmxsdRUvXRuv",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Produce General Sample Outputs\n",
        "\n",
        "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "model = ProGAN(hparams)\n",
        "model.to(device)\n",
        "model.load_model('model.pth')\n",
        "model.eval()\n",
        "\n",
        "\n",
        "def generate_samples(num):\n",
        "    for _ in range(num):\n",
        "      with torch.no_grad():\n",
        "        torch.cuda.empty_cache()\n",
        "        latent = torch.randn(1, model.z_dim).to(device)\n",
        "        out = model.generator(latent)\n",
        "        out = out.cpu().detach().numpy().reshape(1024, 1024, 3)\n",
        "        out = out.clip(0, 1)\n",
        "        plt.axis('off')\n",
        "        plt.imshow(out)\n",
        "        plt.show()\n",
        "\n",
        "generate_samples(NUM_SAMPLES)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "cellView": "form",
        "id": "AkW1obLMXjZD",
        "outputId": "9922e748-37e6-4789-c8ff-59de804d4bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding Depth, Scale: tensor(1, device='cuda:0') Img Size: tensor(8, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(2, device='cuda:0') Img Size: tensor(16, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(3, device='cuda:0') Img Size: tensor(32, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(4, device='cuda:0') Img Size: tensor(64, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(5, device='cuda:0') Img Size: tensor(128, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(6, device='cuda:0') Img Size: tensor(256, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(7, device='cuda:0') Img Size: tensor(512, device='cuda:0')\n",
            "Adding Depth, Scale: tensor(8, device='cuda:0') Img Size: tensor(1024, device='cuda:0')\n",
            "Model & Settings Successfully Loaded\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Feature Extractor\n",
        "\n",
        "class FeatureExtractor(ProDiscriminator):\n",
        "    def __init__(self, checkpoint):\n",
        "        state_dict, settings = checkpoint['state_dict'], checkpoint['settings']\n",
        "        super(FeatureExtractor, self).__init__(channel_depth=settings['channel_depth'][0],\n",
        "                                               init_bias=settings['init_bias'],\n",
        "                                               norm_layers=settings['norm_layers'],\n",
        "                                               input_channels=settings['input_channels'],\n",
        "                                               decision_layer_dim=settings['decision_layer_dim'],\n",
        "                                               equalize_layers=settings['equalize'],\n",
        "                                               leakiness=settings['leakiness'],\n",
        "                                               minibatch_std=settings['minibatch'])\n",
        "        self.load(checkpoint)\n",
        "        del self.DecisionLinear  # Remove duplicate final scoring layer\n",
        "\n",
        "    def forward(self, x):  # , map_extracts=1, cat=True):\n",
        "        batch, channels, height, width = x.shape\n",
        "        extract = []  # Multi-Scale Feature Extraction\n",
        "        # Convert from RGB to Feature Maps:\n",
        "        x = self.fromRGB[-1](x)\n",
        "\n",
        "        for scale_num, scale_block in enumerate(self.ScaleBlocks):\n",
        "            x = scale_block(x)\n",
        "\n",
        "\n",
        "        if self.minibatch:\n",
        "            # Calculate the std across the feature maps and not minibatchs\n",
        "            x = self.miniSTD(x, dim=1)\n",
        "\n",
        "        x = self.DecisionBlock(x)  # Final 3x3, 4x4 conv\n",
        "\n",
        "        x = x.view(batch, -1)\n",
        "\n",
        "        return x  # extract  # Final set of extracted feature vectors for an image\n",
        "\n",
        "model.discriminator.save('disc.pth')\n",
        "\n",
        "model2 = FeatureExtractor(torch.load('disc.pth'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "1jeS6ZDt2Q8P",
        "outputId": "157ac5ce-680e-40ce-c82c-ce4de5b5c051"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discriminator Weights Loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Generate Emphysema Sample\n",
        "\n",
        "model2 = model2.to(device)\n",
        "\n",
        "url = 'https://docs.google.com/uc?export=download&id=1I0TVgGXUi-Qvp04cnwbgoEI2YyNLF_HC'\n",
        "output = 'emph1.png'\n",
        "gdown.download(url, output, quiet=False)\n",
        "\n",
        "PIL_image = PIL.Image.open(output).convert(\"RGB\")\n",
        "image = transform.to_tensor(PIL_image).unsqueeze(0).to(device)\n",
        "latent = model2(image)\n",
        "\n",
        "out = model.generator(latent)\n",
        "out = out.cpu().detach().numpy().reshape(1024, 1024, 3)\n",
        "out = out.clip(0, 1)\n",
        "plt.axis('off')\n",
        "plt.imshow(out)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "cellView": "form",
        "id": "f_Rj6yxKbq9X",
        "outputId": "61575121-05e6-4ef0-a67d-830d2f631f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://docs.google.com/uc?export=download&id=1I0TVgGXUi-Qvp04cnwbgoEI2YyNLF_HC\n",
            "To: /content/emph1.png\n",
            "100%|██████████| 417k/417k [00:00<00:00, 90.8MB/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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eCCGStjv3284Pf/iPLN7yLz99obZGShmc4+3tjX29UXPg+z/+e9r9z/zjf/mJx7ZLBYnBWY/B4pxAedMkCcKxw+XT9/wff/8Df/6nf+Kf/vlfqO238Yp++6VQitx4rRHmV/7444+8vFy5LLpxgmCMU5wGTPSrhYOeKozmcC1Fb3d5G8ZYgZU0Y7EK8bgQMCVxzxlrHZNzcrnopTBfrtpUk8AXrJVM18jB7E3OWgqlNazCUrKpHdZUaikY4/HGslykMd20FN8Vx1sfD9gPCXbG4CzUIlmvt4ai5W6wjlKzZIzW0JqlKbQEErgMEgjmaeLIdTQ2p3mCktirwVr4eH+n2cDnl5klOH78Pfz1p5+pLvL69ongnTxb53j97kf+xz8s/PzTT6QQyaZI1WXPJjbGMs8zLSVyKSzzC5/eXvnjj7/j93/+Z/7Tf/pntv+3HRWCdYEff5TLf7ksLMsijeSnSqH3Q2gN/7TcsqiO4CyVQslS0RhjpJqwloZALiNIWEdwcL8/aMZqhSkXjsHg48Tr9XISC4Ifl71xevC0kVo08+qN/56p5VIo1hCMI4ZZYKda2PaNddupFY59ZU91QE1TkIq3tYZznpIPnHd4E+R31aZEBLkHai2kjotrpjjPC7f7CQWEOGFN5f5YsdawPW4cpfL7v/sOnOWH3/+I/eUn1qPy8joRndO+lCHEhT/+3R+5/fwvvK/yM5sGY6OQUWkwTfI7jppw08wP332H++Mf+PEPf+C//Jd/5H/7x59GQxkMn3/4HZ/ezrWenhrJ0zRppaAYv/O6ltrjwuCCk75YTlgnxAyjz3CcuX7JG0OIgf1+lwo5CrrQYSHnI68vV0EdfGCKAlfV1nCAC14vEcnqW+sId5NEMASpknPGBGm0z1OEVtm2nW1bKbVR0sF9XTkuFacV7khmlZyA9UQniWXKSS47a8Fbcq6kQ3tA/dx4uYy3JH1NuRAs9/sN5x352DhS5rsf/kA0hnJ95XfG8fFxx79OAlsW6U8GH7h++oR3G3/6ywdTO5MvZwylSRIncLkjH3J2fv/j75ij5fOnz/z+97/nT3/++W97KfRbF2P5/N1nYvAEbSTHEIjTxBQnpnkieDdKKvnes/Gn96ZUEd5roOyd/KZMFIctWRvQkrmnZohxwjlLiHHAR9ZHXq9XoAq7yEsfIdiIdcJOogmunvJByln7GzPzFKRJ7T256MUQAiUX8ibsj3Vb2Y+DY9+43x9g/QhU1jZSEtZAKxbrPa0UjnTopvZ4F7BOAk4tRX52SpQqB+Xl82cqVgKj94q5F46c5funiU/ffabmg53AvFz5D//hE+9f31m3Q75P4ba0vfP1Lk33qUJQfNJpQ9oYIxnjfhCdYb688Pb2ijeQj8rnH/5AmK789P/4v49G6eX1E5flXOd+IUyTrLWUq5rN6xrW1kYTHsXzg5twvlCLNIt7puWsw9SqTDKLdZacMz7G0bPyzkoSbCyv+n47IyYELxm56QysJjBBTsosaTgXuLxchPniHTZrht+kCbzVRDoSx77xWFfSkXg87hxZkhPnvTR2j0OqSd/7NZWck8JdjhACULV/g653JuVMMxDnK99/33ho068z62rJlOaJcebt5YXJwHrf8T7wd//uf2C9f/Bxf5BrHWtNK/zlp68YF5iXWfo3mmx1lpspiY/7AzMFluWFeVoI3lJSxseZP/7x37Hd/p+jmgnzlbfrVUgCutYhhtFLmGJfa2mcDmi0Nc1cFQqynuCcYOoaPIRRY7DGydpbKzGgFAmgQXoG3ntsk57lcn0lBAl0YYpMPmA6I8zIZZPSQcmF49gppWGcZb5cWUKQqsEJS1Gq3sKRdnLKHPvO+ngI0WJd2fad7cjyu7wlpcSR8mBCOYOwEJHEJoSAM4Z9b1TbyJIJkPNBKWCM47sffsftfqeWhvWOGAU6yjkzzTOX18jLxbFuD+IUePn0HW+vr3z55StH1vinCMrj55+YrMBSLkiTfTCSGnhnef/6hVQsYbrywzUQvZP4Wi0vb9/x78LfuFKgNdnYceH1upz0wxieLoaomavBOitsBGWd1CKZ82j+dQy6NlJOgsU2c14QJkipr5lAA6Z5HpifU3ri5foirJcQtKktTd9j38hV+iC7NlDTcWCcZJf7nqhlIU4TTktO02The+OrlsT99sHtvo4GrbHw2FZe2ye8MgasdbSaOargoYN+ZwzHvkq5V6qU9CFI4NNNZ43nh9//TjbmkYmTlK6XyxXvhWbpLaQsvQsbIrXAPEeOXHCK5QpUV3jsmWW54ONETknpbgdgmS8XJjL3L3emeOUlNHKWw1CVYWScJzrptxjneHt7lctf4bQQvMKEkei9ZIHGjIvSWzsyJvpqGgmAFkhFWDGtnv0iYyTAYAzOWHKT/sMUg9LvBKKYlguzXgTeB61OKvu2saeMMbBtmzQTj4OGwH7eyee7XBbFuj3WopTEQ4K+gW198P71XbLHUjFwNoYvjl2rgdoqaU/aq7BIVd7Ytgfr40HKwn4JMRKnGRcy6TjIpbFc3liWwuOxYn3A0vBB4FcfAlPwHEeSDNw4cslM85WUi/QjDPgoDL5tWwnTzLUaUjrYto11SxLg48wyLdT3D2oJzHGilsKadkqWvZxrwwcvmT6Gl9c3IQ7o8x2sIi/r3skZRqtPqeA6PVob1dqv89YJ2y0lweQ1je+XRNXg2vuNsk5SKVoD3k9c5lngPa1OTGvs68a2S2WUk1wEOcmznZeFELxAuMuVyyTsN2sNRftBLTUhC+TEx8c7eypaEUs/LOcs/5wOnHU4hZmsUWqtJrj7eud+e3CkJPtbL8/FO449kVLCusB333/HsW6kUiUBIPB2nXHOM01Bmv5IZWNtBed5eblwX3dJmrwkP60WqnNcr1ehtq4Ptj1pNSOwWvSWRyrEMEv/Y98pJZGT7B3r3N/4UpBQz8vrmwSFIPhY5wg73SDAoHd1Gpk1lnm+/IpRJJtOFqyzY0opWqKDJJ2Cm9WclGs9MQVpahkjB35Z5pFdp5TI+eBxv7Nuu0BDVGH3HMIHX64XYGFxjnXbaBiC0mUxTfsEDRcccV5YlpU9ZaKdcUCulWWWrMnkndY6b71gjKPUg+2xse87276x9Q3hnHC9lwvXy8I0zTiXySVTSmV5eWVRZpExjlmbRvnYOVJTSKAACWIgLq987wNbqsxa5peceHy883g8WLeNlERfMSCFUpjnC3PYSWkHY4lTwFvPVla2x8q+J1IpGCNEgsscR0Uo9MNzrRt6CRp7NtMU7ps7JIgZmoHODd+2laQN/GdetWSbDWe9BMh5xmpCEOPM9TITQtQ+kVD01vuddd3ItRG8UwZYYtt2XIhU4LIsHGnH7Ia5yeeRTMuOgBiU934kYRQ5bxXHn1iWWWHApntS2S4lD63Jvu3s28ahEKcPgXmeWZYLl8vMvFjSIXvU+cDn72ZlLzXiJGvdSmLfDoxVrUNqmBYI0fPp82e2bSNMM/M0UXJifchaPx4bOZ9UzE5oqARpUh6FNRfiFKWPVjIp7WzbwbpLVevDzMvLMmjj/Uz0hnG/tM+zLRCZc55pnoWCilS/QkGXpn9KB/t+DGrwSaWWc16LxVuvDDbpA/gQeLlcCSFgjGDsR8k87jcej5WUKyEGhZ8L+7aRlYFdWZit5dg2hZgC0Ti0ZS3xKHgu1yufUua+HwQfKCVRm+FyveKdY9dLpOcutRXuH/L7BTnYOVIGpG8zTRPzPHO9XuV5OKuJHMzXF67WUnLB6bOlFfZtF8oSQnpI6aBVz3R5xYUJ47zEGSqP252/fnlIJZtUd2MUCaGRcibOF/b0YN9WfNAKGwtH4ljvpHr8jS8FYwjTzMvr88YJAo+YTu/p4NBJNUTpaa01vGZ5pgHWSnbkHfZieHt7Jef8Kz6utVY4uAZcsNIkmqI0qnzger1IMG+FfU/cbu98+frBkQQi8iFKuaxYcCmZfdswtZGOHesc93Xjh+9/IPh6CtSMlP7Ggp9mvv8uYl3AtszH/cGyXHAiZiA4gQZqyazrjfcvv0ipr9lYz5qd90I1y4l1M7Q2YYwIiTrWGqeZ17dPOGspaWfVi9JYJ01EtIF9AMYy+YmXCI/7ndv7V9IhB73UOrjVEriVjlkSHx8r1Qgd0YdAOoRm6a2ySVrFIfDMp7c3zRb94KRL0xdda56goAYo9bQ3tqM041trqlsIOOu4XK+S4e8HWdk5kmxW9mNXirFWfkYqwutVRFrWNI4t8bjd+OXLL+xHHr0I63o/Sj5HS4ltfVBSwlnL7f2D1+9+4PtPQXpBNKIL1FoQCqTh5eUVjCUEx+N+oxpp9jeMXLwN0rGrQOkr7+83jpSUqKCiNmuZlxemKIyXbbd4KzBajFErYMfL5UoMXi6Xx6rQpj01DopfNxotBqY4cxwrf/n6RWmHu+pgRNQkPHp0XRrrQyiJJUvjsqREtUayeRdobRd830qyN49zHUezVi7skybM8+WgWiFrnSAHzg6aqSR9luVyGbTcPSXSfnAkqWZyOmRvKktRBKkwLxemKWKtlctrvfPlly88NImzVqBaixAhJGhXtm2VS2IVUkJcXvjDH36Uc4gkfM4Z1VpVbIi8aMKTD6myYgyUUpimCWOFKbjtGx8fH7y/f9XsvJ6NZyCUmWmaJTgfB7XKZRFCUOKDFZr+64S1TbL87VByRKM1o0ltpSgDzgWHK5lffvrKkZKymrJeUj05sZrIWvK+sx8HrRVqs5QiELU1qr1yDlP/xpWCcY7r9UUUwzEKVBQ6e6iM7N3HyDwvzPM0skMQil7PMjq9r2m5KX2EwDxfJAtUBeq+7RzpIE4LrRUNFpKlXvRANSrrY+Xnn37i47GSVaHovR8qYWH+WGEuWEdpmbJXWtuAlVzg3/3xdwoZVXAG54Q+mY+Dbc+EUPBeoKxShA0izcLAuq7c3r/yy5evrKpOTFnYIL0RJ4wkuRzkfQgMYXzg4sPIYmouGI9UObZz5BFV7L5RigTPomKtT29vXJaZdHyQs1xC17fPXOfA+9dfuN0kKJoQySmNTRq0JM25CFsFw+XlRRumjnm5MnUqYpyISkEsuQi8Zq0EynlhVljPqTq9Bw3XqaTPrWelHzofmeZlVIelJNZ1I0ySxdfaVPAmFeE8BQxCw/3ly898fb+TcxoNtuj8SECMMUzTPOCOlBJJ+efbn/+FEAOXKeK8BAjvHME59pp5rA+MdeQahP6qkEI1YL0nbSuPxwe//PyFx7qKviMnaXRrX6WVQi4nNXjqVF2tzEyP+SWTNIAPGKcJB35fV46cwaCaE8fL6yfeXl+YWiPfhAU1X1/5/fff8bh94aefv7CnDHhMlu9D2WaotsI2WZ84LVxzwTtLnGYuyyyKaG0qO2XW5WQEbjOWaZqZtUq2nV0IQ5EOjD7iuETo/Z+JqRbqtYmWYt849mmcJ7RPEbw4IThnSfvGx9cv/PLlXQKiQrDWeqUUVw2+ER+V1loztUIrjZS+8FcX+fH7V6wXQkCrDh89+7Gzbyu5QgwqVtTkLGVBN1rNbI87P//8C7f7ynHsSvktA+9voFVrxlqB1b2TZ9Hh7M7sSseBddJXCTEMht+xrazbpgmBNP2nywufP3/i5Xrl/eODBITpwo8//oHAwZ/++U+831dyQs9L0cZ9pNVGqkX2mRFo7HpdKLfyt70UJg30Vm91p0rhabkoC0nk53GaT3GRfQoItdEVHiflTG70RhtZhzGy0ZxaXFxGBtYDmGwwa8EaeDxW3r++s6U6MM6OxXnl/ErmF0aG27njuTasqWyPGx/3F757uw64CxrHKk3ZfT/Yt5VCEwZGk7LYWsuxrdxvN768vw+WUq1VArBCJ8JUMtL8zJm5NSlrh5q39UKAlA9ycTgvhzXtKx8fN7bjoGQJbFJdiE1CadCM5eX6IpuiWP7Df/wP+LrxzxZaFbpfKaLgDlHYQiUnahVGjS4QX3/5ma8fD5oNLItQM0Pw2mxzXK4vXC8XlsuFZZ5Gsw2lmFrD0J8JqcCeCtZeQ7aKceG0u9D/3VpLiLOwtKo2cNGLXJls+/bg410a7L0P4D0jQFmLaGNiVHGksJL2Y+fYq/DAK3x83Jin73GIQtgZw36srPsu2H9t5K9JguDlMta6KDT59ctX1u3M0ktO5NKk2WwdLmj/JAk02IwlBA0ORRTSzllRQA/WiCcGeP/6lce2a0BXNpG1mCZ9JUNjnhfpyznH7/7+H/j924V/+ZPsuy8fD1FnV9lji/eqpWmjYm5Ubl+/8PMvX9n2Kj2ofqlrVrlcX/j06TMv1+XsLVirdFoNiHoRVmUmOic2EjQlE1Rp/FrbrSuMqM+tU1W8XFy1ZHJtTHHGedkrNR3cbh983FepsI3BOWl0S49JKpw4RaYQVOQn1ce6rqMBvu93tmPhJXhJNIIlHzuPdeVIiZwS9/eMMZ7LdaGkRGpCn709bty+vvNYtwF9CXRZsK6CV4Gu90Ph3powCbsiOWXpGzoLKSdMtXhriTFwf3/n/fY4K3rbhhtDLVkSKO95eXmVzzx/4u//7kduP/+V777/jtKQP6XgnFTiYstTcLXIpdlge3zlz3/9mdKekrO/xaUQlGbqlQr4+uk7/v7v/o6X63L6hnTRUZWM1Ruv9M+ON56is9aaXBrGqiSecQiiNaT9oLSKtR7v1c5hskyTYMqGxv124/b+hY/7qhlVl51XYQrkIk0vHyQ462fph3A/NvbjwDnDvq2ki6g1ay0cW2JdBcNb9c+eKy+vbxgT8E4Uydu6sj5WclYqmnE4V4V/rpWSEHCkRLx9fNBKZdtXogsju0wls1xeRBhTCseehdpXkmQnWmUIHCPPxWgje/eWeYpcX1+o73fSAXESf5hpilQsnz4vkv3ops6qIu9CmJ/++hfe7ys0KTcDdfQQlpdX/uHf/3s+v72oRcUZGIQiKLRcq0wPgXyeREj6Em+bLnpqOCt4rDGGvWPb3uMaRKU2G8Xv1/XO7eODL++3YccgfHFVF+ei1WFXO8tes3rxBhWIVeMpWWA2N0esFyhx31e2dRV/onVl2w/ivPBdRQ5brWz7xq49IrS57jxMrRGNI3Z6sDFQC4/7jdZ0rZW544wj54QPkevlIof42EnSbpN9q4I4b0UAZ7CiAaiZ+/3BvMzM80UYUduG+XzVS1YSoen6xjxp4zQd5GJIGSKN49j58vPP/PL1Q4OOG73BEITd9vf//h/4/tOLeEcZOdMiOjNCtUaC1Vkxos9aRakdXtKGs6yR/K7ZOfZdegDWWoKxtBC4hIA3wjpK28bH/cbXr+/C2aeNgCusaiukB2Ua5h5AlbxysVdlDxUssG4r8xwEXiyF/TjYt43H7ca6razrDtbzVj8R5pmpWWEnKQNR9BKCPGAa3kfNyLv/WOHxuEncSDshRKJXODMXShPWXPBWYsYuiYD0LARl8arzaQ280rbX2402z8TgmecLez1IjbPv6T3GeL57uWCt4djlkjMmE0yk5oOvv/zMTz+/03DEvzX7qBugLcuVt8/f88N3n6WEU1zNW682AYJlOXtuFGctWLD04K/6g37rK7RS8oOkzBofIzZniuKZLghP2RpLOg72fZfGcoEQPbVUrI20dkIizvkRuLbHXTUJRiTkIYKpWBdZLhOtZB6PTWws1LCvB+VDg3JQHNPY06wLrQYul6soSVvmODzH4chFBDClszMwhFj1IFWyTbSGsKFSomExS6TkzHEU6YkEVYXXRMftezbRLTP2fVVq3oGfXzBG4IxSNLAXuL684Sjc7w/1JxKWxvG48f71K7laLssLrVaC80QL87Lw+vaZ3//wvTT8csaHGeusUA5zJhXVWFiBoLwycjAMuwGjFNIuZsy1EWh8vD9w/TPOE60WzT4tMUScVym/Ns1rgxijqDgbIiZT9kevyJqudUNYMt5P+OgIepnG+Yq1sG13YvRQsyYepyVFrWLRMM1hMGm6lYJxhvkibKGcC8e+k63hyJmkcBag8JdeUgaxnGjyc0s6OHIZe/NIx8j04hSFSQWUJuvXMX0Qodm2rrQqmpvr5yCBC3S/HCyXC9fZ8/Fxk2alDzhfKMfGz+9f2VLhcrnSamXbpbKap4Xryxu//933eIWsYpRGpvNd5yIB0uqzskoh7t5FvUo3vZoFVRB7tseDaixBISrbPaIww6zPaE/pSIlaG3GaMM5JxjsZjn0f1jiyVkW9nqq8DyxhisTotYd5YZlkn25bYl4MOUsA9kMP0VliF+ZpJsSAcwan/Y4QBVZLWUgChsbe1K5EeyOtNaF+O487PNDISZJP26BUeNwd3ol+AyMaFR8i3u6UdtpbuCf4rdaqFNqN48i8vn1POQ6pnOaZuO/gZl5fX3l8fAUQSUDxlJL46csHjy0xX5ZR5fxNL4V5XrhcLuK+OEW2fcM5N6iTOR1yi+tF0BWmvRtpn3QL8tIysVaM9UzRc+wF0wq3jxvW+WGX0SErkJu2y88bQlMM06RNFREdbdsui3is2qhDzdekuX2kzLRciD4i8dUoFbQIDFAatSaMdVxfXnFhEa1E2gUL1waoMZBKAefxZFKWSzKnRFLIq1sHVN24vYnYG0WihBZo4/b1F9bHxPWyCNdf+d3TPFP0Z/Q+TIdjDAbTzFBKrx/v/BIn2uuF3/3+D9y//MRcIDhLPoSdYtSnKH184cv7DR8nXkKUCuXIxGlmMYXLsrDMkZwOtk2apMYYWsmsKeG6eeFTr8iYJ5M7YxQelBSo233MMVLyodnNyr7pRe0FBgohCixVxYwwa2PQh8jLqyhljXPUnNj3nf2QA7qlQ5xFB34fcSkTamSZJqZpxjqh8AXvyPkguOVkAc0Lb58Mn6yllkQqTXyN9DOKH44leMN2ZMV/C3v/nQ2xaqgKB2pVfAbL7tJa2e/vrOvK9XrFW4FQaxWtzRSDZsjy8yzSVxqnp7vapoOf/voXWs28ff6B5XIDF5mjI2fh2LfaJGm63/jr13eMC1yvXixfciKGyLIsWn1MtCKOrkbFdc5U9j2JWFTX1XLCv9acZpOdZdBFrq1CnCcMlWwFEnrsG8ehWb6qpL3vHl2HnG3F7OflwqR7BhpHnDjSIZqSYxdWk9qxhDgzBU/KmWWZ8SHinKM0mKdJzmKpNJNJ1WCs53J9Zbm8YWxhfRyEGDQqNbWuAR88+yZCz1Yr6ch6fiXJ68LF4Ixm/JJMOkUAakmsWoEuLy9cZ+mZKf7Hssys2yb7RlOPHluM9uOMnqOvX37BWsN3333PH35cxD7GBbZtFRuQWsQGwzS+/PJOaeKyWgfE+TfuKVyu8xALGQPUyrpKs+9yfeH15aoU0950Etl7p8g5zR57UJTz0iRLC37QVZWNKptz39Ua2YKxeGPYayUV4QEfR1IbW0MIjpozzkfePi0YGsch3O0OCaRc8HHm5XLh2FYpW68X8XTCaMWR2I/GNAVp7mOI3kKzmBgp7bSpLqWKGMcUtj0PS93uh+KdoZoKWZqOIUTmZeFyWQTiSokjZ8m4UxYWUCx4H3i5LmPTGeuZp0kotP3GbyLoKrXimiiCxRq58fH1K99//wOfPl3heHC83/h4v2FdEMV3EEVubYbX1zdtlBVayXivvjjlUMhQKMCtZD6+fmXfN16ur2JXbc8MvV8MViuFzjpq/YDo13QDu9aeA6ZAKCU77CLN5zBFbSRLhrYfOymrwZkXSl8uVSiay4VSsqz3uioleeU4Di7Xi8CDTYJ+VB2FteKGu64ryzIrPVXsVKDiTKS2pApsbbBbg29GWT/aL0AFZXiqlYowN1nDeVl4uVwUT5ZgVmrmSApJOOHof/r0JnvMQGnCt2/tQS6Ci2tRNPpVtontvPOeknaq87y9vnCslXjbuN8+yE2ajsvksKaxAa9vn8jpGJl4Tl6smedVrUu8ahAMad/48vPPzMvM9eVliNY6K6lrFTq0RBXtQW86Dzt0K5n+gI/R6q8UZuvJ5sD7RYVfatSYDqkMNXu2BoF4jCHO0uyu+cK2r9wfD9bHyrreqXXm6hzHvqvOJUogbI2g/72in9MxdBK1ipmi0/dcq1SizkmyWxXmaQg05pzFKgEkp6r0Y0mag3fCmEpJDRCzwl2efb0zBc91WSQJKBXrA9NU2LYkvbemV0M7HWuNlbWmFB4F/uH1QrSNeZr4uN1Y9yTV/zThrWUrB5eXN+IsMfIomZb8cH79m10KwsNdmGMcLIXueUMrbOuqpZcEBR8CwYbBUOiNJqFxmWGhDQIjpbRLMEXK0lILrTT8NClOJiIhka2XoSK1zguEsj54rLt28AvQEK8syfav1+vwLPcxEpRx05lQtWRKVVpcrXKJYfBOWC9iv6ylq2lDISx8ZCmRBdZKlNrE9kItxpvCEfM0EZwjqUI6l8yxHwO3tNazOCsY9CT+PsYZKFVl8gKX2X4gTW/jCx3YOqeCukYpB6Ve8NFr88tog66R0879sTJdrsytsm8767HRqsekLIyZYlnmWYSJ2mi0zqrBYWI/BP8vqjyf5mWIjNALSnKH/t6lrLZO7EN60xNV59ZameIkTUdgN42qNtbiV+WZJi2p15XHuj1xyJuw04zFelGGvrwWKtL7CNFDRS5wBL5yRfpNOgRjsDQCwr/Px+k82hMZ8fMROrQvhWPblf7sRqOxlKrCJFnrnKWCOY4s9OqUBmslTrNSlHf8dcEbC1bWb5omyraP89ThQjodWJl4hcqWEtuexAxS3ihBnYNbLdzud7CR62UiJ4ddrbhtGsOsvZu+1t3ivs9FEB1RwuBxysTyPrBcZnXEVZM7Z8Z6isodYRbS2HdRluuVIL1CL0aYJYs4jFb1IlD4cBLH0mPfhOI9Km1oyjACmOeLQmHS9HZOeP3G9YatNHvFnoPRexDI0aI+5RQU7uzZunO4WmneE5Tuuu+H9uFOR+TOagxBzsUjbWybJA1pT8ON1znPcr2Km4CKfp0VunyIEymp5Y7TBKp1YobUXvKsDWY7uN8f+NeLuLVitP8mdvKP+41UmlycNWDNhsuWogrzv+ml4NRWIKp/fselzyEqqoJUZoyIlHYuF6E2dqogDh1+Ih/eK396+NCrCMUaC17EK2AkQLSm2ZYyPYxhX1futzvrcdBtNILzmsUiGV0WHnrwEy4GTMtsKTHFSS4RKik1nJfhHPMScd5Rcm+kQphmOEQQ1RtqABjptxilyHWIJGXxOW9NLyEfqDXz8b5StLnb1CnHhYBR8Z3Q3mQz2bdPLD6CinqmONGOonzudmK5AuATQmCaZvK+8+XrB8s8sz2kqXe9voqmoyZ++uUXqWSsVGDTDM0aWk4cqlmIlkFP7Mp1gXfC8JIaOoImzVpALUea9hMEF+7e+s4FahVxXOuCBt0X0nMQUzFUkZxVpGOUrbFvK4/7g3XdRqPZqUOr9VHtqdPYa05tT45jxxhP1OZzyTv5kKA5RVHwlnxgDar89MQIpRoRRio0JLcMTCGwrw+FiIS6mI4slYuaMhoqt493cimkYyeXJlRF67AqjBO7BZmH4Kzl7eWqVXbF2kDIWfYWKBSnAdiKQ2xYZrFffzy43T4IVpqoIU7M04Rz8MtPP1OwRK9eQ2YGDEfKJCt6DB+CDJiapmF8JwQNr64EXodhiVCq1cq2blxfX4ne9cciBAWloHeh4nFkhdY6fAjGOKGTK9mkKKUzFfEUCj5wHDvr/c5jW8lFEgRTtEJygeDF76jUzJHFQWH2kZIyuVamWfdmzeQsSZ0PnqWro62hIr2fMMWhm7KaZIAkIN4F0r4pvdxiTSEdSftrUeGixv32rjoBgdtCDHpm4zCvzDrPpBr45N7G3rLWME2RXEVHQTtheGukvzlNk+p4Evf7HUPmsW+Y7gYdPY/3X9hzUX2QVHDz3LBJ9ltpf+NLwT9NVOtqx9BVzd0HyffmjYEmNKt0HIM2Jg0WoesJzz6Q941UxfgJoyUaAr9EtR8WmqqIPvYj4UKAmnmsq/gINTGyEqdEadJi5OA4L8yRdT+othAszDEw0ShZvjeEQN4VK1TmlFUITCi0ktXGacL5oBOOZJDJcRyakZRRetdcx/CTeVkk2Bwbt9sHu4rGUEGXtYZyiIjLGIf3hpwOPr5+pZaG//F3zN4NXUJsuwaI0z3UWotTB8laEsU68nbj//O/3bgsF9ngyOCgfRMiQIwO0x1rvWGxlmMz1CZZVTT2nOgVgkzQC16Gq2hG6Wy3O9MBQjSOnHm5LGCk8em0aSk2AidMZixqPV5GeS3pgFCX93Vn2zbpf9C4q7grq3eVaCtEvZya6mCcJwbPtm3kJlVCsZYQonpfJaY4nww4ze6N8tLMYHZUaXRjpDmoIrLjOHRCGYN2KVWFWqHHWanHBx/vNxEoOacVce8fJR57GdlfbZIg/PKzJDqfXi9YFQHO0yQ9rqcejfgbSd/FKIRGq/zzP/4nwnJhclZhF4FBci7KUpHMuBUDccaYQ9cojMs/9qE5ajTowznRzbmTl99UpJr3g2V6Izi91FV7IyQSw/1xgOuKd4FxaGZUP6XKmcvHzu2xymyK4Ljf7xyH0KhLPieNFXUkzoiLsYuecgi0Y5ul5IyznkZSvZKYFzaMwFvd+E+pymLLKJfUFM+LKufMvu1CPdXKrV+G6RB7cpmZIr9zvd94rJugEEqLl85L4XG/U5qSDqoolz++fKFWQ/jd94PtGKcJdiEYGNfPtfwd1CJmTwe1VH75y5/46S+et9dFyBkI/beo7sQaTpEpynA0YMrfmJK6xEkdUM/LQJwTl9MGAEY20WXY3SlVZO/SKzhaI8YZUzPrJl7z1jqcKVRboAh9TbL1diob941SoZSDr1/fOZKU5Fmzgz7sQ4XjpMzAtUPsDUMjE4qMI0ZtHnqlLqL9gNbUyCxx7PtgV8UYwDgqbYzTbMapvcJOUTyjZvGuiVOUy2A/aK0NmKnVbqgmGUFKB3vKYr9gwFVxdN030WCE77+XUrPJQJfOTukW1F14I89CNm0uZQz+WWIgtZ3VANWocZu8LKItkBGXXnDbZWKiaAJwqtfjLP0YsT1QrNMaYYHIhxnwhUDzlaNIgzg4y/v7g4oMQRKjQ1WQW9n4rQoU1Uph3zdSybha+PL+znokak7CLc8ZGTwjNE+spaaGr41qRd3sValrqRxHZV5kKlttKjKzMvsCjwafxHZs5FJwPmB7hg7KTLuTclOVata1Npgqc0GiNs/fVxmvWjTLb63btaP3nfS6jIowTcmU4kjp4OP9nRgCl0Wm3oU4wbFTnjNHexqhCe1aKq9aCsfHDfMiUMr6WIlBlMa1qbPoUx/HOk9EPJLE7O5frfU0cblemYKn281476lqq97fT22NycnnTFkEpssUuX98pbSm6niHqQVjqiZr4vnUrNPen6ypsZYvv3zlse7aIxJ3g6zivRDDYLeVmql7VrtsVLzlSWXHhZngn6ysjUHGKUkAzg3SvqtfluhjvBU9fq0Chfd+pjFdXGcHZTSGSKmZ949N2Ga10qwlKGRsx2IL2yg3IdpkU/AlU4rl2B+8f0S+/+4T4tlpiBEdwdob+TrbwYhATqYCFoUqN26rIyidnhgxzuNbJ6MoaU0rHKejaf+ml8K8zDpi89w0Ly+vXC7zGHKD0hCdMjWGt74xekurV0qcCNbw9f2hmPJpi2GNBddOBopypbeUqBVy2vj4uKlpmeB6vSwr5c6tGc2k1D8nBlozOINOLBMM0FqDm2ecsQQXaE2gHmuNUAaNCGGOQ0ynMOKk2nsOOQuMJWIbRvbaiiPGiW0TwyyxXxAPptYkk805SwNNlY/Oe1w5GSh9sletgqneH6tACxSM9cTadMIX43fL5Vt1L1qsFRgmHwfFB4JVRbUVG+hcZBpYc735L06OwYpydWr5ySkzctW17nzqkbkaS4xtUDEFTpFZBsY6ncLnub1/pWKGmrc3p11D2WWWkoULvm+PMdjkl/d3jiRMrqrqZ7EQkUDdOCvCZZ4l+7PSV0q5EEPHWxHHTeeIWrUaa2hU1mOTWQpqftcQw0Lnw5iXkLNgvsg214y94GOktY2v7zfBd4PHeziaZOFieWC1YY8yv6Sf1QVXfUhKLZnb/c40SfCrrRFjFDhRI1IXbXVIpq8BttFKY902qdRa04slyPOqMlFQkiQJpLb7cU3xVwnA5XLlpRNHnlhlIAw0EeC1wdQRH6OG1xnn23rjyK2Tr8b3O+eUcBFotYjFvToXt1b5+CI2EtQ+zEjsO7o9zf0u2Lx3Moc9Bgd062wZVztNAXxlMmE4JYwL3Ij+qTrDkRP5SBjnyfkAp9RbTQj71wMiwMtG3QgqX798JZVKjJNU9sc+hlxhxOdMelGyB1BLFfRy7i7C+7aybjPXyyR9wxCpdec82frskEqfp3PdGuzbA//6CojjgfUBT784NOHTy8lZmZb3W16/+VIYFUIQBsvb2ydiEMpbs9Bn8DaFXqx1zMEPl8GUkmabC940Pj4+EKETJ+vImDHOL6hgKHorboi1SKB9rNrQy8Nb6JnHXXMitYOUxOxsirOIZiYZg5c1mMTlQjh2YpjAKK5tlU5pLbnmMQnKCIokDbcuh1dZuTGDiSfQgoLPJbfRrKOXrQgPu9ZGsKK3gK7CZUxcO7eEXHzr404Inusy05rAbjWl8fvRv0+IQWY1oJt0Ow6W5U0qhyaN0sk7tk0YSGgzy2jzPsRIqCdk+Pb6xjzHEYj6mFO5RaVQDsskjc8iUArOs0wzU/Tc3t/JFYUIGZlMn63QsxgbJZMruZD2nffbTUVLRTUg7WSnKQsrlapZ5s72WInRq2lcJDrIWZp+nUUkZnjyXoweFkqHCIRE0A3lWra4oGMejRW8V9elVglYTS+MEIKItvQg1lbZ1S7dhYlS8rnWIWCsXIT6KJSyW8jHzsfHg8+fXofw03vdPz1Ag1IVpVLss1MMlVISzbxgTFPFtWGeJ9bH+nxMxvMPISo0KJYm15dXrnqp1CoNYdvFpcg+c+roapoEtpQL83Lhssw87u/se1IDQRSmkUu0Z7/eWZoSNo69UFISrYy62uWaNcloA2kAaRqXvFNsZl1X4jSJtfc8EZRSvT42mnF4C9HZMZDnxOhRNpT0Iborq3gZ+UE17Yp5jNqsF6mQchb9kPEyWKnpPiidLWWdqPJb0z6ZJ7pOea9j/UrNlJK53z4IXij5aNzLo1ro7DwzLmeLVUa6hSx2O5froheBEHOc2TlyoXUIXBOlbr/xb71+u6JZHRZ9CFyvVxkwU6vipXpUesaPKB67tYSvhZqrqAWD5euXr4Py1YdS6EnFGac3m/hYOGs51sTH16+C29XGSWvVg2rEIiMdUs62UuhmfLU1TM5sbNCEzlaSeKh7K5tiP4w2vWQTueBxNIo2HMEOvNFpyevjxIzVDSu3cavi25SbCJx8yhxJefQ5neUsiO+KDgKZbB8iItjuYLzowZSs+CF+UlY8h2IQu92R0TSp0gZ/HHTAjmScWAe96W5hmSfccdBFXjQpgYVhFPBVhFPL5UVK8VKGoK/z0J8zwOCDChE92coc2csycfv6hawVyjmBrmfbZyPNKl5+lIPb7Ssf9zt9xm5nlnjVRRR9nzI3S03kaBoA4Ng2aKqzCJ77Y2PdN2lixgNqwwlDQHpX0XMcRczQqtioOM3qhbDguSyzWkBDaRZDFeV+reLm6jLHIcwTGdNZx2Vd8i4Gj84zTUat3uX39FGfYEZVcmwr2zzxcp2ppaq2JvVtJimgJirWGaoGDazDVgsVvDdsR6ICzYiga1PMWewPlGLqOlFELGqWKSpjTIO49nm6B1effBeDU3sFR7Oey2UhrTf2Iw+L5qZU775XrPFqeyHoAbWw3j/4+v5Bn2ZWlQ0WggMvAlDnPcexyc9ERbEo80+FpdM0s0wTra1s66bC2MBhjIhRlb0T1E3Yh4izHusDIbjBOGqtEecZo/OcW60yZa5JgubDhDGG4xA7jVKq2mef44LXTWzbQ0QIAyFIMjD5MXrWohqHIr3RafokCYkRseJzddNjo7WWZhutSOJng1Gii9itl9owuTDHiT2tWMnZxGHImF/F6r/JpeCUKzsvF+boxxvtae1gDBmZvxqU+ZF1JF+Mkes8CYxQJXPonkeSaZ/4n8wHsLqZGh9fv7Aeif7LeglZWlH73A7leGxK4EQpXUoZ/jolZ76sX5iOhZe3V0ySYfUxRrZN1cO6kQ1GFdEVqz5PwfvRSColY5HDJ1mrQBhNGVTRBaHY5sT99hjClM7Jrwo/NbWUAKM9Gc2EjcVZydT7c2i1yHzY6yIZnGblmK4mlXUQdkrFVDO0H5rkKfvTYIqUl8syc18P+f6mghlrMd7himOaZi7LNC4aWZ/+WbojZsMYGSAiLC9539fLhbzeOdJz/8SO51BMHRveR49pFWMN2/2D230bGZuxnujNqAxb71c9ZVLey7CmXuWZVnncbux7UuNEsS2Yp5lt27HWsXhxsGxGmtS+iO9WNKcVRx8XW+jFqKyfHcpsWQehSGZutxu1PX2tQgVZPZCiNiEFXrDj8w/2lZd+HFpdc72I66mRCYJdj9pDtfwMK84CvVkuiApCzkjiwXQkLstCedwH7ZtBUnDqcRV5ebnShyUpp03PtlGlrWT/MUZM65P5PHG5YFrivu2nUZwmWSCWDE0vbO/DELwdx43395uMTcXoND2vkFEhZdHyZDW+a8ilZJTEgrF4Dzkf3G7C9rteLuR9FdZXOqjGEp3MFyitr13F1YYNQfpirleNlmafK3VUsc2YxQ1Sga2PG9tRR9MdhGzSLcx9iGOtO/tOMYVhx2F74lqEuTZHr81xp0iEJgGSugkCYCUB0p04nrFcSuLr1qaJyxzYjnTGB13v3/L67fBRCEzzwuvLy8gQeykkO6dp9iKMFKt9BJpyzOdFpoepIZYdm+zM/JsGD6cVhjFW5piuu5b35hQq7ZqRHYcsRC3UpsKbI1GrcLQxQueadWyjM4b0eCj2XVgfqzBfnNdgqxvXnkG8VVEtOmf0b48PFa/wQaORH9vpenispJRkGEhT3LXpJK7aXUHrcK9MOUmPw8eBJbcB6zCCR+dF9zn31lqZeWv6tjFjcxsDHfm1ph8pMWLr08CWOTJF6TV02wTTwAZHcBOvb29qh3zCVDA4QiO7F7i7yoVhDVOQxt2XVShzstY982kDYgToosSCUDvvdzUzwxLnIM9HmVy1D20p3UjOQxLRmjGIRba60FrbKM3J9zTZj9v6IJQgM5lDlIBoZSB8tlaN5CpQsNFrZqhsnCJq99YqOUlDuqtcjySEhH4DV51HkXIe8JJ1fii0u5bDutPjv0NzPRj3PTepnXQfqWpMhwQ4s1MM6IXZI0kz0tMREZSoc6dpJunYylKSVhxic/Hy9iaTCOk9gH6uGWvXxWpNMXGDKNGnKbB9/YLpwk7D0CRVrWC7UUjwTkaUGiPMHBUcxDhhjFKRVX2bdGRsrUX3vZGBXBpbnPPEWUgv+3bgdHiVNY58HKy1CnMnaDVklbWW5bKUsbhy0dhaadqLrDXSjGiaSlYaaRFTwUOn+kmFDS2LwWHJWZlL8l5BTDON6fNZhMot3lACV1pNaqSPUbBzlDnWNIwtDAcIjYW2iV0QvTekyVof+eqcG/syxsikVULJT6NKf8Prv+NSkJnAU3C/vhSe/6ioytAHpkhAcWpcVXZ50MbJbNGmkEspRfBYY6k0Fd00jJNZAcZ55lmEMJ2p0MbBkQygN+uc9/ha9WuMsiUy3keZVpYyzk3MMejBFb96a2DD8DrNUulof6Gd52wcDKfDgUrJpAxJxzjmlNj2Qw61QdgAXnzS+3sVTNVgjUxiyiVDMhzIZ6i1cLle5fB08Jy+eQR+CSri6YFaMn2FZazFSluD1p1G9IQ7Y2hOHCBrLXpgDcFFQnDs2wbGEEPgGi5clziy8fPPs9mcjPrsPP7WpIpzVvjhpchF6jTDak0YP6VJVdnnTDQqzni29YOCYVFfnvSkvkUbmh3L7w31EML4GmOVj+5ltOWxb6TceLm+iI9SFm2ANZbb+uCT/8SJySirx/SsymCDGEAapzO2y0GthftdBp2kDr8ZS7dQ2fYdidHCmLG9KlBtizTKT0/+y/UqUJ5mfCOwo3PRY7enlp6P0aAgwaxPAis0a6D2YN7090rVk/p0MQDnuMSJXFQtHgPTfOHt5QWr2Sic6yw9NFUoO8lSJZjq8CbrcMBRyzBFHPvWNlAWovj+O0l0rKHmjfUoYjejl2WfkCh7qVNIezYuyVprjZSE/i24vMX5SIgiwkR7G0ahKPHCWsE45llzV+uwrknE5EmJb6261cpgn1wy6/0ug6cOcXLtLEnnAq5Uqoove8XlrKihDefwrZLg/vFBmmZertcn6v6Z9FWllp5nrF/+5uwnWem3mp5A6NlH+zXWetVKFcDTiiFME5cZtv1QZujf8FKYp5nrMgv+LO91wD0nc0g3VRVcv1nxmTEGjA63sMYol1YFYNYObUAzonoEoSoW9bLxIVLbxrYf5HSQch2NO5ktUEbAyOV05jTOELyj1syRhHPuvaPmg+RliMzVIJu5iSo2pzIyMu+sSMRbD6zdlRPFfwv7sfO439lT0hm6wqluLVObmH2JA6iOeLRWp6kJHGatGQe4pIOtSgl6uVy5Xi9DNdqrqVwKQaly1oq3i7HyGehlIuoHbxq1PlUSTpqqcZ5EVIT0EvZU8cEzL9KwCjEqzdiOADWgDvkt9CYn+t5yKQIxamJwWv+qWV6rNNOx0tZvK6koG9iW2XdRrhsk+0/5tDvo9sJFrS5SLhwp4ZwZ69WfSzoOiF6slp1l2w/epjdeL1dKY1R8wiCTw+gUaxdjMjkWrVSq6aMkGyn3MYi7ehNJQ9IZoeXG6yzumU1nMMgbZmPXORVKxVV2XmqN+4cI7l6uV5Ylng18GBYuxnBCCj1JoRdmRpgzCGxrTG859O87JyJKNXVwWMc8BXKShGBZFoIme32tjX7D89nu9V6fQeHUFVd4/6oCVtxaPKDqEwTFoLA6Y1i1igYz5iHnlKitDTZSqSfTrNZjPDfMmXyUlDm0EpzmCVMzpUy8vb0KTKT6BtNkAmOvMEwpom8wUkm3WsWmRJlCpRT2dZP5KEl6M9Y5ZR8WZh+lP1mrzlyvGNPYVrFM7y7CUh1Ucm7AxkctzJcL18uFKUiTe8SV0gjeqhW90TPd4ywKIXXhbGcoyMtqXJ6XRZ4RQpY49h1/uTIFTTh+w+u/y+bCezc22zMLomcSPaW2Ax8XhkEpTWx7lQPu1G6gg9094xHLYGnqOCubr7QqzBOFZpoxtCYj+PrAi06PE9Vet6xoOO9oUTjiKUkj0TrH5XLBYFRwZZinqTNjZULUvuPtrNxgFeM4WQhvvZTkSsHMyqza1AO/NuFmg1xAMcrm2bdtUAOL7Yvb9RHQyq8XOR0bN91AL9fraRtRCo0wLuA+HVLgyTbk8WjG4WyHJyTbrCVzgCqNjXQr28Hj/hC4KICLovcYiul+4RgzdAVnBSGZS9+YpRaciTJ2cuDkgB7cZoXyWRXaQ7FnOfh1sCgkDksVcuwbj03guT77ufPlZXZFxlrLESIxiNdOLhFrLMv1yuSdJg0OF2SWraAilWPf2WMAVZ1WILRKLQK/tCZslQrULPDVruNEm/YMnFbD3ouJYzpEYFhyoVS5jA31hPV0VsK4ZltlfdzJufDy+sKkljC1yv4P9qzO6RW2JLlgLKb2iYFtwLggGONxHAS13OjV0LGtYK68zBP75oc4qsOEvQLt88Yl4BgwIqszCNbeSqbZoIwnO3omXfxZFcOvVWxptGMyKt4+TrNXgoBYrqyPsQ961ZHVvVaqLLnE53mhlqz9GGncL8sMVOkBYJgmT8DTjPTxtn0nOkfSaXnG2LEe9um9tVLEjPHY2VMC5OIDHX5lLdFL05rWMLp/x7wHhQarVko9JmIMad+51UpV6u+ztit4h7FSBWruLT+/MapSa7QKa9BMn0cjl2utMsKzv49UEvf7jbfPn2g5/6ZY/9snrymOh2FkiDxBRp2b2//7OWGNwasW+pc2SkwvNxqmWZpODfPYjo4Ls0eDGUgFst5vPB7bULf2wC6oZ9Nmr/x9bAnRKHTe86QZT8P5iDUPsZUFKE0GolvJBI50aNB2nB9LhwL1TawYTlGYwzlP0BrfW8TYK2eqVjzdH2eU40YGw6B4oPRAzKi+G41923SugSyVHKQ2zAaN0YEm9NJYYTgZIEcVLO/ETo0KtYoYannvsUfCALfbnbdP1wHt1IZUdRrBBq1PLX4Nmqmap0ZaryQ5vZb6PhkNagSLN9p87AFESFIW0Tg1GapzuwujK2VtOKJwnzkDimmknIRua4L0mFqVfoi17NZwWQRyuIYoh1OdeY1Rbx+rXlp0fr0EyA5ByGcTdonoBk5hmPMeklQPmSoeWwqLYoQpg8ILkix4hUW0su77KMv0uRiCqPa1ugwq2DK9Oa9VdXcrLVbZZhrQrAZ3r75c+7ZSo040s2KkmI6VPQS6309pDW/Mrz67UfaaWC/oFdYZbkijU2xOhK3UCcNWz73ttFS9qJrpZ6ioiNCOTD1tG++3m5zrlM+hNkb2TNfvyMesHDofGq0A6H24WgjTjDGWl+sFaOQGy7TI+ymFQhtW21ahLfkd3Zm4aZUFx35QSyPGSdfTjj2TWmXfNmWlVYWNlMXoBCrrs99LLr8StZnW2PdNPOScJ04iohwJn+kg4fmerNrdGCxW19rouljnMKWS006tQhH2QbRVxjbujwev8/SbYv1vvhRaraRauWo51OmE1thBMxulZt88T4HAIEGqmUrDjRKYJje0oBxW4Q+LM9LUESWhiJEejzvHkeld9I47ygVSR3HUs3hp4IgthlfjPONkJm5rBpaJfdsxxrDMExjtF+jnUQLMWUZr1m203DRGbZ61uea9E8uKlmiuSQaFjNtLpQ0DLGnAG6wz5FKYl1mmWrWTSQC/VoenlJSN5MlZZjs4Z6lqBDfavx1asGAQ+mxBcVml8JYq/ZDHVrhcX4ghkIqIbvKRqa1n306Deu8j2KeLwUhVor/MmicWhFYqCIIgF5+RquYZmrBPhohbOkavgVp5PO5s+4ECIXppdJedRioyW7rVs+fQaiFlceY0DWozalVtaHhKStxvd66vL0SLPg+d+NcqVYNiF1w2veOaBhzvPXG4hDpoOpYVqVIrVVT6+yFBQTUXvY9Ws4jdvNe17okTcjE62Vijyorei2d/jCOAjgyFcZfoOljQc9Lfr6xfERX1tkEVRXzbRMTZSm/ai8CuwxTWnD2F7ngqx0CRgC7OMkKJbaXn+ZqdKIQpTrhaZWqvwXpH2XcZgKMY97qtbMcxYNreR+jnWS5t9SloSvmr0sgPuh6lymzxXAu2NpyBx+NBwXDVYCj0Zy8qfKpCcT1uye/qjWHjPD7Mgo5UUTsf2qfsrsatFtZ1w1iPs9LvlJ8he7iZk9nknR8aJ6yRmRRGvNlSThwpKH0cvCaJuZ7MJgx0Z2FrUB1SG8+pK92NETTgcb9zfbkSYyFnqWZT/m3eR7+No4RaNFvBT621J7aph3x4vhvGTenG5zkbm7QnzH9AXBoEnWQ8o3RWN9S03Xn/uOmN3IbbnzF2mN/1S6lPOoN+KFRNrRzwkmWwtUEzLapaQmfN0IViGbwXvYJeEkPo9FwNOZlW9fbdZ65TlICP3PI5JRXZaTarzyulY9j5iud9EDxYhS8hznJJ+NOIzCATuZLOfi6drtabuk+w07AObb2KMyNzbGi18JShrY87blkIwQNZ1qnIpe9998tnQAsDYsAMCqgbWLKWtPVJjzDeW3v6GWCMZqCaOAg8UClpk4lbWedQKAvH0J11pXHrVGPSBTl9D3QWj2RuAiPI75Hff2x3vrx/jIzbIAaKQstU9blRe2RtBvaLMIbA5eWN7z+/aS9Lh6ZnqWJaz2Y1uJWcObQCrqXQrJOeVn++zuvM46iMNj/cAKTJLqyr3oAMwY9L/1z0c12s7cyznozpxat7NaWdXC3Xy4TRMy0QmNjG994BRpMKDfyCZSsiYE+6uGy3qjMfNA94Oh9P/zggMGvQtZbG/cdXGWNbniboYYwkcl5M7bpmIsTYN8+gitbWs3Rh+zgvavWqCeHXX35hL33IlVR1ndLuXB8brH0vfb9WKbrzvPC73/+eyTsZq6koQr/oa+tVpVQC3YKiao80hqAiPDknIchcj6C6Ht8rnXG2u2BWBZ09GaWrRTQl0qRkWNL38+46RVnU1etjY56vIqwsbfSq/q3Xb74UXPBcpqjli9EMvkNETbIGPZhWJ56lkgeO3ulTjV6J9g/6BE10CMpIJttaY33ceKz74C6PUYxNmm/GdhilZ/Nu4OH9wDTNXmgKvzQJZMexqwf/xpd3EdS1yungiDk/kwYiJdyORpc4GC788MN32NbZEkXn8nY1c5Nxj5vQFo0eJpkjLdXCYCPUc27vNIsjbQ+4RWGUUuuAmaTP83QIe/nfOfvGDo5yK3Wsn+2Zf2us95X5soj/UBNywOXlMrZh//oeSHn+GRqYc8rko2jP44QZRr4nIL7iooaz0pRh66VW8v7gdl8p2sQf4rVWlSPev/dcW3El7Xg55140BtOqwi+BVjL7vrFtG4/bB/c90VrFBY8xMkdZgl4PfqfFg/UOH5RJ5zyvnz7zcgnkIo3RUsWOojXpAXRrcmGrGFDWiogAK1g3Kp8+QjbEiWWaT5hQGXSlP0/MuLCeA+44yD2713+GpslDb3BLcN3XB9U4lmVStpHl+nKVUZj6swarBZ6q5TPpKlknj+Wi1dSv4V6JmN18TpK3rn8YZzAnPj7uw86iFa32YHwO0AZq6xezmNadv68qDOXOz+tEWLjtmw5w2rh9/ZCfrZ5YvZfQZ6d387geg3piKftr5vtPgseXUnRmi+hqchIo9n6/C3xsBRbqpAdaUQajJIElF5L2VZdF5pr08brDoaGe0LqzXd+DJjBytrsorZ8HQ1dk96TcKNSUuK0H87Tg/dm4/rdevxk+evn0iRjMsBroy3b2B3oBKSV5Pg7qyA7tELaN227cWpJxtSYBbkjaa2NfH9wemyg1ncEkOZRNceRWi3rgN6pIz0R5O824II2iVpv4t9eugfBAwccF2xrrvnPkjNl37uvOD5/fBJap0sHvvZB+W8sFpsGsFD0vljhd+Pz5lX/+08/yvRb2+4PcoBWhtBUrnvBSSmqA0DGVrcpFYgBTxZ/J66WHUbnKGLzRzsNoHMaI62ZHersrzWCH1d53aCNoGg0wBqMbe+Xl9YWy74TlBTvP4qVkGuDOYsT03yFEgv6saqvjIjgdGuXVPXpoTRvKSpnUHUNtlLTrEPMmdiRZDqhro1tEw6gzZcHOjlYzuTReXrxWYIrj10rR+QjeANYSvGN/rDL/oMLjduPTy4L3QWwPjLB3hKLIyM6bmpk1I1h9sWIB/vb5e263O49DIK68r9y3g+CswAjGUI2Y9hmjl30Tfx3UadUAlEoME8E7fBCoQuZf8LTW2vRGVNu51XHmxrPtFVyfOdBP1xAxapi3hY/bnbfXV5YZcgq8XcPAosb57MvN+S/mSbzVLbxH75DzMpIlE/y+B+BO5GgIEeT940YqGRs8LiWyNTp6FLCGWsR8MKWDaVKlekoslxfmVtj3RB+GU4vAOiF6Sm5crpGSd9Yk+2PdN7a98v0l0leZJrDzKKG1t/NMtzamiEXO9crb2wt/+fkrDUMrifePGz5E0iEV7p6qqvoF5cjZChsKBKVA2I1BnYdlb5qh2RgU3K5kRogy5ah6IXcUo1fXVjgiT7B5rw47lRgD+Vh5WMvr9UpK+78d6PnvcUl9uY4xiFYP+LjVe4raRPhkNAOyTlTNvTQzPXS1vtP6gxD3JqVaC1xyHNzXTeEIKcXEBjjQponWROgisTILrdJ5bJBGo7VCtcs5caSKd4Z926U0P4Tq6Kcok5KOnZwy2+NO/fxJM57T0EvolMK76HOA+1xgVz0tCYywLC9M/q/cjorNldv9g2bDgEmst1grvY2UCnGWMYGJJnBFE63CrAOMhBOtbc7Wm7RNfFaafA9New96ycr4CcUi9TP0P/qDUA4oGKcNwsq+PXDOc10i8/zCox5jAPvzhdAzFFCY0KqdQhWmhzTwzAgsVqNMtwzorrXdmsIYaCVzuz1UDdwwNuBcYnZ+fE/OMh7TIBBDaw3nJ4yVbPUSZ5q67sYwkdPKUSSD2g/13dJm4Z4yx76xp8pb38fyiZ5BGd3XfQaAVXdZCXbOOa6XhffHLwTveTzurHuhii+KqKKd9Nf24yCEWbPLNOyVxcs/DDqomOYpaw+FoNQEEHUZdt6L7xVaZetR6plu/7+RsdMU3lGIr4lPz8ftg+8+fWaOkc2rKNP3zPyJRDBeMvskq3eXD5LMWKEJnntEykhaVc+onjT2/hON+/1+kgaawGLC9FJ4OGd2PdvWoHYafkxSczYS48S+rhgfcKbKgK3alLIeiGFiqpX8WMk5ydRC3tQrzZwaJK2W+t2APveuiWhJ9C/L5UL9y88Y69m3h1hTFHFc6Gp0Z8WB2RpZ+5SSxAjJCoT5pihGV0ebAVEyIOXOJuo9nWfoSA74aWPez2TRpK82JaUZeT/ViPrah8Difxsw9JvhI2OEoTDe17n+CqgozmXEF6SXnb10pD0/df321k4vo6Y/xRishW1bFbMzakUQh/Bj/PImnOYjiaf8oS6XtVS1j1bFcWMYdk3LRYRriBgmqJ+895JtSl+gDdZFhzq6P89gFmjUG9RQJEu7XC5YzDAKKzq/12AIXuYLZOV5W826xkG2jnlZhjitH8ruUdOFLd02uWdpzv86kze93H6C0HqQabqAXUjVRUkWUZjmJlVYt5KgSW51Hpg+j9nqhZoopY2eTmeXnRvnVO1WveBpTeEryciOfSdrdtlFRJfrdVh8twa1NJpqExraY1FsWnjuScV/Yvi3LFemOHFZFrpBY5yizP2IEWnoKo2wUy/1srLP0JFzJ6GKppoQeQ7L5cLkPGnfNHOViX+g1hnWChbtnFhPde2GPqtpnlVFLM+hH3Lv3DALNAZyKgNi7VBQh/VonPh/P4/m7PHRDLYZrVRkz/dKft0TYFWhq8+500A1izYq4gzBi9lkKieppNOp+6fW7zt7Ok0dho3aajgoojMCnRToZM9Pk9hvC41X1lVGYUoykXQ0a8lPqna1u5ABQVEhUKmup2liTMDzUr0VVRt3JfkJY8oCj74U4gYwkoQmMNLLdaZWmZRXa3edleTIO0/Noitxzg8Ytbs3hDixzLPu+U43la/t8yV6k/i03+5+RWdPsGmMrE+BtJ3vdMTVkoWq273aHo/HaDf+W6/ffinowOqRUXVWgtWmj0rIS05qwMYoZXr539/3rz+A/lRNZYVRUjh0Aadl4Xq9SJaVizBQlKGR8jmdq5Q6ZiBUxd9609hQ2bZNsOqSFT/uDWTx6rGWYdomC63jNAHM02My56CRviIGOzDvaZ4px8FjFXFOUX+cEHqFcCC9yKb2Ej0AM5qE1nnZQL2RZHtT+8Q6JaN48ivq0UDfoyZn+q8dXpCs4tkqodSGdzI0x9DY1o1sVXlummZr8vPGGNBuF9CqWjqcWP/IuAx6OekzbA1a4Vz6Di02FfeJFuZ6vQidV2mdrelkrpI48vHU0Cs6na2MLMsYmae9rw/2lJSNVEbTuLM2gpfmojiJVrqRWU96wDCIM80Mu2MQbUnHeq0L0DK39axyGmrZAGOOM603V+Ww5ipYs9BdZe7waJgrXdmHMBqJVdXhHZc/G+BPAfx8qronnjJQ/SOcfis9Eu+EFZeM2MP0BEQv636+pRmrc6qVz9+htd4C7ZXh89+j99ThJbWJ2bZNK6SZ6+UV78yZWIhNnPQhdZ1Tln5Tn8muZac8txjJx862SpDet10bsHYEl+AjVBWtKlNREIo2LgY4WT6jTwGAzGC3TjUXpvFYH08zHKpOoJSBRimLaWEuScd1JnVMUPeDksWhWL3j+uXqvMeHTmJpY65zJ7fInXX2iwRtOc/mc0N6DJDSCzkoWaWWxKauzP/W6zfDRzZ4RZabQCH2nAzkNBPrbcVuZtaDUy90rbHnbaUl5vj5potdLNttpTXDsiykQzD/qhkI1gy+vfOBUg/NbrThkrMOnLe0KnMgDI09Z5mbXCpxEqYJjcEeMdYT4qS9D81ulRFk4Fc+NeNPhz+UfVFq4fbxzmPbsM7JnNSms3+9x1vDI8s4vqrU1Fbb8NDZ2dn2XYfI2NF0NGhA1pGVAqmJE2f0T3TBXmOa56MqgaezWoxx2D7hrsrMCDvPMmDdVrXzliE5rV94yle3prM1JAmo/dLUjGkkIq1fPEWhavOrQtE66R0Zb0n7xpGLjBHVAUFP9y3QqxBHcI09J91TQu+1PowJZtZHZu+431f9TsFjp9D3gzqbIiyUyfvRaKxZ861xuZnxM2SueH9TstdkJOWNj/tK0x5QpAlkGjw+Bo7tIfMZWh1TAZNqMpLZRdiVRcH++fNnopUqqw9Oci7QzeVSzkxxGoGgPMEI/UCNxMC60Wsw1g6yg/g06TjXUilVdBVdYNgz59GYdicFuT+Dphf986tTWYcJXt8Mev6NsBegSC/AxwnywbrtdMV2z6zRqs2HQDuOkcwM6qUTO35jLTGIHXXRLMRaEYt61XhYa9i2wjR7pinKhejs0+/irIZAm//tjF/0mReNknfev36ImjtOOhDL6qAnj23CIBKBaBm9VzFETDjnSKVSUqHxiderGz2C7hTsdN1KkV6JtA7OZvMzqwzVh/Rqsmksa1RxlKhylt084UqhUgZl9t96/fZLoVQZcm3kPu/slXEr089MU4hDYAJR+bkze9HS0XT82JwZOkg/4kgJ74OW92XMs22tjX8uRT5k70mUJo3nnJXTr/4itSHmZ06GeS/a2HXqgJmSqKW9kwHvMndY3rswkPqNPGB7Oh4jTcFOm6yYJnjpsxrRO88yTbLxWlc3ipai0X9u5chJGAuIEOvj9pVqGm8v15F69zZgtxIouWCDTIJz1o4M5qwM5JA5XYuBPVuDrUYsHGolbQchqD8PjaaXhjW6bp3dNeiOyMV03kKAQoGm0b2DpNzVrGYcXNnMWKXapqTU2syh65vU7qCoMVpJmePYB9wolZV44BzHLjRD69i3g+UijC3rHJfriyQsOqLwUDuCitGRoqL8rE2yYOP67Ouu+9DnbsA0Q24ddpNsZn/cyVV6O/2Az/NEHKNF5dm1WqiI91SnquI9rokC/Ng3vvzyhU+fPjHPgaerdtChpZm6gGmK43dr7nOtjV4IknDJhWiNWKKLlqCSjx1jpvG5SusK5HNv9xnGPeNvPZNuDKhUKmqpnNB9LCe7Ow+00XiuNB3LKg1h7w1HarQi1vJFR4oeh6jW92MT80E1kKShmXbB2V1neVfmywXnHN4I3TMqfVzYcIfoCppW6jo/vquVm+mnyYzqp2/nQdhQWnNtlZIEbhtW9FiczrZ2xpB2JRdooneuW9F9jxouZj7ev1CrnG2ZK2/HJd59wqSCV0pwhweeY49eaLYxLIJAUJuil3Atme0weI3VZ9b23379dyiae0nGqEmf4SFjevba6A3OHvSB4apojOye/iG6WEVgHqPf+6+DUIMqENC2bYrDQ0mZohBGqapq1AAt2Xalsqrs2ylO3aEYR8vCYJDYqXMEnGgwunujoX8ezWiq/DepTuzTfxdoaN/TmaX1g+0jy+xJ24pzlmPbqU0siK2W5cacYiaZfVx53D6oOfPpu8/iMtljMFJZtValcipdFXwija1/zdj6ssll2tn4CkAcM3MRBkqHHWSpK4yL4cyeZVHVR34U3v2b2rg9u8VG0+dglIJyXoaMPUCHtKzUm/u2snePId0judQx7KTp52m1kfadZA0ln46z03SRQTDWagKAYtACKTofRp+o1aw9HMbBEargWf7U8QxQrnpm34VhZxFqY9EKaZkXasvktLNvOykXQpjElE9ZbR2isTrkJR07X7/8Qv30idfr5Vy1DsH0cwgjm34uzZ5pnMYYET7p75DETKr6nGXwvOvnFuiDZIw7z3PHuDEGi0J8ppMszPi9DaWijt7RWTG0KtWoi2qE13uEdDhSvkfW+hjJQGuMc1xV81I1fuScKEUMCmWynse4wMuLo6m2x1lLrmIfDwrPeHcG3CbVWP8MVu1W5PRItj16cpqEHIf4VzXTq26JbdM04a2BWvBuZ10Txkk/SS4W1Y84O5IlamW9v1NK4fvvPgsL6Vex9oyJtUnSXJ9iSj8zVS/pUgrSPLdiK26bJmJCgzXejovqt7x+86UgMwSc2itoZtJ7CtZQs2BWYv98ilvaU0DqcwUqaMNR/OxLa0RvaFRtRtYnewHY15WP+2NkaDSxrcBCU28iKcWlmjBqbiZWErIJJVPcR9NRxHhi6NXhhL5xDI1UTm68KEXPiHFCIvLvtZyccukJWN1oElR74LcuEEPkOBJGfX0qRspPezaVurlcCIZj33j/+s6nz594WeKoF+oT/o1m50KU6RdBh/LkvY6h5TplzmhVJodTSlZj5EJsRSidoTe3B6xi1eCr8LjvoJoQ6EpjBItvjVqe05I2AmyrDauzKfq+6AwjYyDvGx8fH2oLItlmUnO5Tr/r9glFcVWDNEhDEDGQc+LHc2wSOGMIVOswKiyzzolgTd1uU59i18OqJixt/Hyjh47RDM2qFbFWRpgaa6CIF3432Qs+EGKgGIOxbQRLp+6i3WkXnPLaK7ePD8Dw9vamNiHnxSrVhrwfa3oWCadyuDN9OutH9vJonqreo9U2IFfxBBJIqZve9YDYz8K2iuYixvivLiOgar9F8BfNuuUyMJpkqE2Pajr6UC1DTge3jw9lr8l5rb3n1s6vc647BKMVscw5sdbIfHe9VA1I0O66HmRGh/enUK2kpOfAPCW4PUHp1G3ZC0M82fUYXiBU46TK9j7QaiXrfBghriiEZnoPzw3cvzVlUnl5L+lY+fpu+PT2iet1GazOqpdpvwSMNWr90i/gX711uotsrx5cE9TAqV1yUSv3XxFA/huv33wpVC2v+6bps5hjCIoFn0NwDCjbAH33/+rtPGHykuz2TctQ9zYkI3vcpbEzHlavDAa808Ufar3RA3GfFhYCMQTVAlRKTjxuSe0JBLZxNkiAUqFXTgelyWa0Q9naBlTEWAy5GFG+ME0ogwHOxpaVEl4GfCszZp5lroRxMtHOOoyVbKvVOqYyNT0QtSSxEMfw9vqiQbCIVxI9s+hNpvNJG85KTayltVnV10eHRxrjxNysFgnYXQUDg31irVIya2bfdq0zFCAyv1ZVSzDt2gqBD2goZx3lVzf1w2va2Kus9xtHrpq9KyyovZdustYhws6aKrWJ1kDtLOZplhnIrVFqYt8Fs4/BklKiGUd0UlU5I/MWcs6ESfpJVk0fz94RZ7XKs2pa9lkMfsB+zoP3wjhC99M8z5S2AsJACV77Qhi1KOl4dsXbiAGZy323OPvGMvszSWrS25JelxlLrWGNfkOemXsbuI6x4illVUyVa8ZaP86ibIxevQimbw3SxH2qDjTFO9e6JyCqNeqVonDzJViKbkehyaY29euDdT2kb3bIDL1eBfY9Za0OrK8oeiD7s4sSuw2F95ZWGscho1dzThr8G3FeaGpjLhDVob1DZXmZX7Ol+v49obsOz1i8C+JyYIxojryyjDReTfM8EgbnAvMU5PJqGqMaoz8jFZiYLK7rA2Mtb68v8hz0+UmSdo7w7Ge9r7UZiaqud0MqbqOGogjNXZIu8ysU4L/1+s3so6pB2ShrxzkxBDv2TT1qdJOM7n3PME9EodVzO52lplYLJY/NbZ1X7vpdWDbaQOqQVOsZms6M7g9KeNEdgpIgmZW+Zn1guSy6pQ05H0MN6XSge7fNrroQAi8Im+FZ4do/Z2fHyCzmTiEUFfFzdmJ9oJWkTdST5WKVN96Nx4KOMJ2myBQjvistNRAlVcqe2fFpqvbsv97/PKEfTwHEjPWAXzcWTRMss9WmVsB2VAchRmo+eKyb1AXmzKSEWWZO3EqDwylQlABRq1RGfeJZQ0gDjsbtdmNP0qALcWKMbEQCVe8BdIZWzziNBkEDyloTE8TL9UIMUT62HkijQ1Scl8uganDpamarlu4nNfHJY6tULentyPRk/ZRWaKVRaFU1u63iiptz0d6bHz74VSmHzjpCnJjnWfnrDIpkLeL1lfSS7PYJz89lLG0/Y/1f+kuz4Z5xD2WxVkVF2Xx9nZw2POVCqNx1CI7p51pdgBnnoCcFssLGKmtHAxG1St2qv98ieP/jdpMBVEYSzalPIUOsbrwPeO+0erF06WJPCnpNJ0y+Sggzl2XWSl8uzRAsMSocbCqpZFV4K2NP52Rgjfzdn6npcxAaqH9Up4wKRV3OtnVWnVkjSUfuHod4SvUKNPfZzcgZDjESJ3FNdub82bRGPnYe66YXgSj6pRLt8GqHDs/lbU9mmP3YN8pgzjltrGO74+5v8z767xjHaUeQ65u55YNtPwan1hodaF4FE8cNCgvj8ygu1z3B5UYUPFMBBayp3G734TFCbXgfiVH0Bw4oRR6cscpFDoHSpKF1HAkXJiyZlJvguaoCXC5X8Z7xnhg8+74T48QUI7UkimYmU+xmcLKgrZ04dsf0xs1GL+ktr6+vuP3QBZcBINE73tebjFg0hhAm5gmh3SLZkHXSwBLjPpmJHb344aAViTOWkg/uj5XXlxfk0TSCsWLGNqIy46CffYA2dpUmhcpLMmJUWEWBW5UdZb1k4s46mWdrGvfHJvYm2vc5PY4atj0FhFGetxGwTF/v3lPQy9taw+NxZz/yCSkZwzRF2HZwTSBCGrWq5beqkK13ZHWznUJgO3acD8wxUovQBX0THcC8zJRjpznPPM3YMdi+4OM8lrJDLV1P0XsNQpKQR9kr2/ly5a00xX5lqtG8BLb7bUyJC3Hi5WUetNuuFwC1S5CfyDSFoSFoDYFpgcfjjjGGa/C/7svRK8SmRM7+audfTRZamH1VA6/F2Ep1Vhr5ual3VU/2PME71sedk6ipfbNWsW3UmlrNj5Mt/ba+5nTIqKqxoyRax7ZKEtkkKait4UKUgTVabRgrlasP4gmVS5bqv1Vlqk3kY6NZGRlrjRAXYhTYOk4zzjZSqszzIvBhLmpp3uckKzT11IsbyaRVphqMhrsLkbdPnzQ5QGfQT9QiBIh87Dgfuby8UtLOmDldG0bjlDOSGMVJEhyxwWk6C9yQ9o27NVxfXuk1WK/++1r3ZKwzj7qsZKBhWv03YwZ0aI2lmkr6W7OPuunZsE42TaiXpk/z0RAjkYHO9FWoToLUEz3uOaDKw5etbTCDddQ9kkCaQ857FmfHeLmcBeYxTQ6FawjVFMEonI0YJ0O9X66L3JYq1EHLtGWZlXba8N6R8kEpEGOgKUACkk2Ypr6jPUPu2ZluJOOkgTn3xlWV8XjbnjEuECL4MHFZIodmBYcKdLpPkdXDKR9dho8PLyYNBvnYud0M4ftIN9Ibw484n1l/iautk/Gcpl8LUtJ6gwQp80T9sxZvxYxMAoVlX+8ngwZGxqine/zqX8GEI4058UxDxRiB64yV8Ymd2to/X60y8nS+OOH3l6L22ZIljkvSOowPQyAVgtARl+uVYKUv1GmCpRTCvDxVo0F0DCVjfYV2TqXqY06fKxL6pTbWW7JHGSNpBvb8eKxyeYVAs57rywstb8BE0malmIMKfCeqYG0oOxkdC22coNYq6/pQv5ymHmPn9f9MRR4W1U3YLp35NfICI0Fc7FSK7g39LPaskEoWgZlXpXU/pkMg97zYrZ/204qk3wqtn2vFDMuxK5un71E0EMtsBO8SuYqHT1ISgZhfqr7GCpOu1iK9BCv+YMsUZU6IDq6qTVTPi05rrE0GK+WcMd5RK8Iw7NWJsSMpRXVQnZ4KAiGPpFhZizll9vWhMFMgRM/L6wuWxtoqrmS2I40ejDGMC7dXPV4ZUW30gGRA1GN9sCyzKMfdE9lGE6nxd5MKTYz6VFjZ/zcjArfaG099f/yG139HpdCbjWY0bFKpItEfnfZnZgLnZfD8hkxvPPfKoEr5RsNYT007neEgVgYyGKdkKc8ahkK31xV6X6113PAYKRu70tmr+6Tg8+KwiS30YeUxelop6jvuFFcWPYP3RoP7adnc31c/Eb2vMQRS1lCOIgrpfR32z+vjzpELl6tlbZl8iJCoNR1Xqg1ApxCS17/PS9gMRpTAGZltO3h50Y3bM/NxWrUU1oBd6Yyl/lHUssLIbOSuJpdMTTbSMNRrlf3IeiD+6/Df+Fdr3M4Rih32tHpYqwmS7TodcVnVNE57BtM044tMjOpCQmAEnZSkorBOBuDEKYrDZDrER8qLMta4gG0F7wrGe3G9jJFGVe2ArKXzfgQB1+mHtR++9tQXO7PxAQtqRlmNpeXEuu7kWqlpZ912cFF2eMlUOgPEqYGfeNuIXYRQob0TAVPw/le+R7UqXbO+aeVix8V8hgszKr/Wmtqq67oYaVQ21JyNZ5aZHWdaYC7D4779qqrEtJH5PleiPbaLjTwnxv0EJZ7a68ZeyugVGCsXQSlJGFoKQ1pjKIjIsajDrFV1dwhBqMtHogBzCPRYjtGs3ximOImRpDVjdjKmN87VfsN7oQpXTRNaz2H6hS9W6t0Z1mgP4MjlFLEplfbxeHBk8QkzNIH8JHMZccGo0Z6ca1lro32NZqw07DvbKmeZjNig6xhyywOKf879WtXq1jVdTWTWfRM4Lxdt9hvEreA3vH7zpeCHslE436vaUOieOSuCpw0zmnUV3bL9k5yluSCNcGoUZNZBnGZwgZYz67qSmuQhtYGfInk/5IDRyEV5uDRlUnhidNhciPOMMzIib45e5jVrgJAExg3v8n5pyPuVAKn8D6yxw3OnZ4peG6U9oEmDLrJgOY4dYz2vbxPpyATv+Ph4l2CeMtsuTCgxh/MyjnGWuQrdPbSXhUY3WAjCdkg6vKWWrMPkTxpv6wvxvNFVMWiciJY6NZSmTcN+jo1QK6u38CRcOvaNVCrRnxn/+PkNvF6UpTalmhoBs6yh25g0UOxe6YgGNq2SnJ+4viDEgm0d87VBtCNV3UhjDByHBBxnrQwpz0iT2DpMELVoKZlcxWTQhYk4S4Bo1kiz0FZCiMoIqYOzLim8GRi+BGQwVvylmq2jAeislapwWSgpsSbD29tnjmOj1Rnv7mxJLLC3dZPLV0v+eb4wLzN9MFXvE3TLlKp70RrJbpvG2OM4gIsUws5Q8rjhx6o8P+tncVNRLrxRwVyntdrmByRlrFU32YQL8bz+u+J0lMZyETRtdp82NUIq6MNwho+St+R9FSq59SyXC7FU9nWVMZValYldNOz7TogRQ6I0qQhrkcsgxsiyOHKDyzKL1uHIeGfAOpZ5JjiprK312Ogw1itFVF79znruEVYdzjVouM5ia6VaCbrOe+K84DQevb5+hiqJ8RQD7+93aEKbT0UxBmMoPjIvs8x814tLzqXsIZlT7rDBDmq9MWfPsw8josdO2rgc2oijrYMweua6FsxIomMArUp/U6z/TV/VN4fRW6kV1u3A+qBB7Tlr7l/MyHKedizdyKk8+RKJ4tbI7OJDuO/eefZNrK1zyZLZYiitULOK3rz6E6mwrUvXSy1EH1D7ec0GG8Z5XqdZvJSU2iYR0Ulz18tsBvE+kvxGuNS6GE9Brn/IcSSNbJzgPSknjHUslwlvwXDQYhABTio8NilzjXUEJ89j21ZSziyXKy8hEK02xDAjmzbGYLxj0oFB0IRR0yZ9r0YHDD0Fi65ExWhAU38bms5wlj5GyZkidBGt5BgZxmMVhXlXeEM7E4J+6SgDq44N0OFE2bhFG4+dHl5LYes8fyfVynYk8pF0HrP0b1LKJ6ffOHwRSKHoeNbuJ+Rc10AYtSS2zK9vsuecVS63JhUhMs9xNIqf7URkm2rPpOXnSp2nHE3ZWNKTKrUxzQtTFLtkgR4KlZ1tfdAfVPDi0lly4nEvShV8FXGlPsuai+grNDj1mc/yzHpVLBUcCgfKVujwVl/3olVgG1mbqdJU96EzpGR/V/X8d9ay7zdyqTjf5yR0wK2dPWyj8GGH4jU2YAzkJ2UwIHzUxr7vUo0bgbS2bRVn25TPJnYpFKUbC3RpqLlqn09+2bZXoUE3SEcW7yhjmeYLwatDbLcRyQUXPMuyDBi0Q27Gndbu/VJ8divolXbn/ztVv29Zhl3FKVJSo1Kw08w0Z/Zd7dKNeExJ8K4y+a5WOilhCspqqgXjwthW1nmc14SuijmefK1atNBONKC1wQqTZPmEiJoRJKaWigsR8jHi1295/Xbxmt5u1lmO7S48WPqmUJxZw88pxlA+NP0yka+qGqhqL3ybMH6SXgBarQMSAHKRP6Wo703HmHXEXSvdKK0HQ0ghYGodrqkhOLAiSzfaLPbOk9OOmTwv84x1yjiyEhR8cGM6WH+dEnhAfWo6G8Ig3kfbJqXlNAuFkyYlqzWWnDeMtUTlyZt2Mn2cZmr3jw/q5cI8TTKbop5U2J7diAWGDm/RgGCtxdSnja3PUOoxM977ifBZnKkcRRTkvklzDnrV4GglsR5SXY1yEMbFcT4Y6EwyEVIJoFGVzdSZTd6iqlINCLp7BBfddTCRDtzRkaunBkSq1G0X/5yuPDZJKKe5iMfQNEVsEkuPOXpKrvjgqTlTcuV6mQnKVOnac+8F1jmtoM9qtz+v5+mCAmdMzNPBum0EL/tHFMvSt6pqhx6moMwkxhAZr4K67XGj1sI8zTRk+HxtFRQWsE10HUYDca5VoNyO1Z/Je9+gshtbozd3O7iEvjesJ/hIzdvA92WWSWNVq/oBFPUP/6vkTs+mPpth5tZUAKZ7wboowswmw4ZknGXn+0sAr0pdba2QchKzu8HqsySdww5G52TIig1TvCqzSBpgLhdaLfgQsaZxpEycrkxTOAdBGegznb21TxGDXz3Ibvff19pZuVz2bSM3oeLvOdOaDEKS28gyRT9U9N3uolsC5bRx+6jkPDHFWcwzXUWlKvq7ZB8aunLeDAitdDr8uBhQseeJJsjn6Hu4qrtDIIRCOv7G7KNueOcM3DZhoYzYqFlEz5q7PUTTTNVgRGVnoeYzc6WhrJuKq0ZnraqbpGKORzoEKiBTShYH1lKHQ+IQBAV/Wkr7QPRehUHCBW7ek4/Eo92F2VJkpKWxYnnRsfyOeVqnE5q8l/K2l5btORM62Qqd1tkaLMsLLhaWeSLtO61WPu7C7fchEicjWZxy8L3350WpU5vW+wf7vnGkxOvLVYbB6Oe1zgwRFMj7EWxV/Ia6IrJ16g+oXPdp8+jvM9ZhSmLbHlJme0fJwsH33rLd73R/mo6rnz+pKQp14s2tGazTrKSMXSDZn7LRWndhNeIAWlrDx8iUktqaiwneno4xXAn97FUvxKKCQeukyrPKrjIoh91Z9n0FI/S/lhreCoZrnFUKqFJxQd1ZtfLsVRVQjA76eUpuuglkOyreR95e35QPL0Pcy02sVrCOZYmjWspFAlbTNbatkouBbSMlUUhfS+PlukipLx1SpGflh37DmN4sPWWDo/pr3Yqipyp9wYwGRCfVN7LvbC6YBsE7Stq1wezP71aIAj2vrTaqqcrI070kjt9QC93crJ8jud+Kfm/XEwWWeWLfxd8/Hwf7fujAoZ5iNrWfkfciZnMWF4OO4LSDQDBNi8wd33ZiENfdED3WBpzuF6eQEE2Sne6I66QZIpc5hUYVR149212j0wWNl5cXjAvDc6zR2OqGsYY4Tfrs29ibpnV6aw/yB/d7ln1essDGy6I/C/pwLKt91qrRf4hTO7TZY19tQ7PSu14SJ2S8biuZ7f7Q+P03VjQ7J6rLtG8cSeiAmkrIGxywyqm4Ne2EV5riXX1znfbOz3RPkWXXylD0eefYt411P+RAYGm2SieiotxjYXI4K8wkGuA8UwhgHBZRH1o1aCupCKSk2XNOSQ6XNr+HaKUUSscYn+AkqpSNRpOnni0JBdXRmlfKmqhUS6v8fvkdX3/+iZ/2A9OZLzmd+LSVgyQlotB6basc68p7KWzbzsvbK0ucJKBrW6C7bXor6yFlaX/ObSggrd4NQ7UMWj2Ad4GaK+v2wPqgzAgPtbCu+6/k8f1iahqEUCpmbXU0jKsGLvRrO4jbf07/GVUpoU33ilOK6baukoX2TFSJDLVKoOhwkd4VtGbwMeCbsMZKFXfII1dqThgfzolbpeilh0BLtl8CVXHl09p4xFQNHB3G6f0j5wW7DmgTUxkur5++47qs7P/8p0EN7KrsznrpNG4Q2mYwUGpifdzJaWderry9XrUiEyhtBOlemRo7BIxa12jz2SmJQJ99OyEe6xzOFdJ+sB1ZeiwKjdzeH9KL0ArTjKz0TILUtEw3H2fyYYz8Aj0U3XZjKNyrTmCsTWm5grtv26qzjxU20bgAp4jVOoerRWcWo7Cr0/kYWWdMZD1TME+zPgu5aNtrh4nkogIhFJjKYJmNBln/LHXgGHSrlBb0OWsf0nnLtFy4Xi7kvPHYK9ai0ya1UrPdz8iMy9vrXIO079yqkCquL68si0CF3eqjn5O+F3tV3V9NIaSncVWj2JHv0QrQynyK39pT+M3iNR8DzsLjfpeGYX+YBlXzNprpIhvdoCLhkw9QnycEQY9gZtguGNks6nlSSuJ+v7Frk7bftFWDSNF5qNKEKqpZcINDbprglNY5rtcXgreEaRafEmMI08wUhBdeS58N2wa+KZvh9LSXRTjf/fDBaSfryg12gRw0Y5z+c8D6yNvnz/zud7/j+tRk7IuL6RvgXFpr/bigSk58fP3Cx/0h5Xd/hsO+Qr/vCQ7VNzoO1/ga0/+7Hnx7esyndNBUSLc97qSurOzvrJ2wUetVE8ro6AymgXHLFuvvxegb6xVXtzyXAegyitOFINlWFepirW18XQ/WVUdYuqfg3Q0GZaxlVOV4xJo+CzkSvY7M1O+nCTEBTq+f3uDv7/fZ5kMeb1NoQyEIVcw7vUi7en6+XvnxD3/gu7eXIarCKEPInK6jZzhH2UcSaPf9wS9fvpLbmeF1osWvz1F/r/pDmvbWjGaWnEHPcnqKSf8pU6thioGcdrY9DbprrwpPO5Uu4JNLrNIZiX3/90tDz7Q2mbvuo0O/pVVRbN8eFKywF/k1np/VxVQ0GwInnnMk9DlUIQhcLhecsSyXRQVl4hIQQiQ4o41vcTHOJSsLr5/fM4bxtM6dfdcrr+6L5pTd1g01pxh1lnvkd7/7Az98/5kYIn0c8bBOafWstowZVfMQEZbMx8dX7o9DWUn9aEt/cNBaR8SXRKP3Jeh3MqeuQaA2WYduOJpz/t/ZOf/167ezj0KQmadJbKZHJjGIcB1Q0AtABTOuudEfkIuh6ObpGxa62Z70DUSjsD5WnZmQKI2TYleh6lCXWopUAspEKa0OiwPrI9EhOGUR+tkUonreBEzNpOaY58i2HnpDA62MJtTJvNYwYQ2mqTCvWYxpWNuEkkiVTeNkYwfb+e6B4OXCcmbhWuFr2tSewdO1CZ390OEw67wE/n4j6d/HsXG7e95eX0SUok1XwgkV9ErgOXMQmEqz0w719OysdrxemnPWBlpOMs2KX//+X/VUOKsG03+HE8uO2rPJplBhv/z1j7xvudi3x4PjyDIP40iqu6jy+SUCU9VsMTghCEjFoHbtzbDMM8dxcByZYMX2Y1kmaU47w7YeLJcrQb08S1XIjf8anzf94jIqBWoSVJpyynuWHrzM4Mb36qLpf5Nm4zLPlHRI9YphCsJzR6mvRnF1/2R90Y3TBCoqPO43vH9jDkEvsUxrcSQUz6KmnmVL9u4Eb25NdYtPODS6N7t1hmm8f9yfwAe9wGvvq+jPBVVjC2xjbZDKvTWpThUikpkTvcIWho1cxMI4WveDqiZzVa03auvTFOS9llxwMZzv2ai7cK3EaaIVsbIozpBzxYcL0xQJIZD3DR9npjBxlEatmWZkxoVY4/+6irWclWwfh9mspTWLdZVWzLBn788cpGKxVuBc6z3ROz6ywGBdeGdNGwlxt/Rx3tGqEGUG7Ruk5/BwYmVj0bnN3aOtn8F2HmF9NLWhe1n9wNA+RDMjMZcexf9eOvFfv37zpdB9iGpDG8l6DQju8rThmjpHanNRp4yJ2Z1i0Br8rOsjLoXR1KeK3W+3s7+Q82i4oJfIyF2ajORsept22wdDY19Xprc3fOjc6Max78SXF/WJd+rP5NQTXXa/1WHa3XriyWLmzCCfYIUBJVixYA7B62eVEjTXPG72IyVtzknDz4WJeYrita7Cm07ZrNpwtQRsdEPE4qyhpJ3HFvj8GrSCytQWRzDrTem+cToEwVPWiAaKCgJMq4iJlqitsq0PUql6cXVWmdgl9+gpswYMWK0SS6F19pI2l8VK+YSTjGaSPfg+HjeZnIcc3mc+fK9MesZUcpKkC1nPzsoq+eDIk5AIrMEHr5PYJuZZKGiT98oKiWp0iGZRmkVbYbuc4tZfa27MWG+H842WwRlh31H6RSNqZescubNt1Da+IZVq8J6WDwoSVJx1+rmrskWklwWqxaiV+/1BjJOeQ8Hlg7ejUn06peO9n+9Zz5tW9EZdB4TNZbQqWdlTGu/1OQE4kwtd824WqO+3Q4bPsEcvRkEuHzFns9xvN7Zt12z5hKSqWmnUVoe9QymFutXxM/p7pla2dePleqHrmEAmLi7zAsC0LBiEsRMHe9CM4Nqpofohx3/rSUGPM/1sC8PSEJo/kyxlvznvqfkYflwhePZUdJqjDnJK8pyc89BkjxR1Mei/o8f7fIiPXAxBoO5SIDzpgzrKpSNTTe93aSVqtJrtEPM5WVC0Xr/l9ZsvhX3bSKUMIUl/DepZx8DEd1qEH8YMdoB87Sllr5XhAumcpaQMrfC439lzolVppMZpprVVOdrtV3a6DYP1VoeE9PJPFbE+CNbuZfKa9YE5RC6XK7SqzcqM8xZrJ3zwA1uVYCRBr3LeCk0/6PM5lACs2WaRxq138ruPJMI1U2XACg3mywukncd0qLmXHLLgnOCs3aTLnqKd3mg0xuDUylpM+xqBnp11/3rT9865Rj171A/x60a0QHKlMqqUmg+Zc6BN1X5Y5ELvhoDmfEbNdnH9yQZ5wqLrUPEylOvQ2B53dh2T6LwXbQoyZMigYrdW6ROzvPc689aOCrOosZtq2gUCK4V5uXB9uUpz2QdazTStKr1CPN38jh44tUnaBX88XaZ9U3QL566T8F7U5IdeAlUN+1qruDjx6uF2D1Ix63lxPkj1oJe40BfNCFbWWjFJVDZTq5XjSEwxjOfZkyRBJ87GaOeu19a0j/Ic8OSSliTr9Anb9h0JmmfCQGt6QTaxDVePxFrFbA29rK1x41vE26lqZaiXKHK9H/vKtm+S/DiHC4HJCBSYrRVouNtcgMCuzg+6sAQ8gRSFDCEme+lIhGnicrkyz5HuDFuq2Hq4jv93YWpf13ORzzj2qwsWVVPL5SBz30WbVPLBsR8yNCmJ+aExluV64f7xwTzb0T9zTi6N/pPHkKBJKNYyZc9LgqJsukN7nKAXbUdUGH388eoVulHxa8+aDV0MV8ZZ/ZWw+L/x+s2XQtZM4tmJU7jy50CZ/ovlS07M/SzV9fWUJfTSqaqDoTSjIKedlLvlbBMcvTW88WIu1iEpOj0wiDDLosZzhZygFnGnxNThmOqcAbVbaMgN36W3zkf6+MmmtdkTlDdWQrBGA1mbe/pchEqZ2FVl2+cA55x43G6kZjEtgWLdwcvgj+78Iz9FcWTTpfCanRstI9WPP+2JSXns4/3QzsB/frVYXGhm3RXaz1ljTQe5lvH+xdLbjg/eA5ocznMPDMGcwhG19KqgX57PXyuYZyuVYz909oZs6rRt2jxWdaw1WDyT9b+yKJdsLDL0u0lmX+Qih9OHiDeWZoRhNEXxPtJJieNiBOnZdKX+c5Ztnhb7hGXEWO0ZTuwuruu6kUsmHbrWJbM9brzfV6YQyUfDTaK0N03GNcoFrxtJ4Surqtde7XWuvvS90nALlnPDU1Dr+6YNyEZcPwWq6tDDOXhHBhRJQlMwpo1mKIqp9yRiMO5ompBoFWcEz65P2o6ROGjSYJShmI5dLkqE8ZOOQxIVbTj3r52meVzQHXKz3svM8tokSUhl9IG8UuRba2okKZYjRRMa6TN1CwnzK9v/ZvQSe1pn0/eGJrSSdcs5oUnCs28rR8pitKlN83178NPPX/FTpOwbxXjmEPBe9TG2DyPru0fMFp13YyKibZaqA4JMa6QjMalYtb+/M+IhZ1f3zmicG4nJ3RCvtSZEnZQGq/G3vH67eK1JY09uobPxNv7nUd2MI8OJN9YzQ9RD18vxzkhKxzaCq7MWvFNxkzRErTE6TQ3xRkmCHw8PnqZKvyysFoPYYSyXVy7LJN43QVwKO14LhZTUZkEvqD5/YeDfzgymVH+NG9toJtBk6hLeaaNcHC5zaYQYCM6Q8877xzvVeoKDbds58sF1uYjHeggEa5TK1vnTbVDcvAoFzVOmW2oilUIccEv3WenY7AgVwvKq7VfiNGutqFuteB39f2n70yVJkiRpEGPRw8zcI/Koo6ure3p2CXj/hwCI9h0WSwsCQAA+zDdH15EZEe5mesj+YBFV86yer7KIBj5TXVmRHu5mpqpysLCwVClwldsY4qAh+KfI8IYzoDylEGYoONzEoYmzQ8DATg+U7jLaZJj12nDUw95Lh4lkhcZacZRjzNNQ9eIbjYchmNi2K949P1kn84JlWanfY9fmLLPRrGYHnxf32IPg9tpHT7rTjRZ9h8F2Y+H07bZj2TZ2YN8rbm8vOA5CoEc9UPcdl+vFhheSGVdqAQRIKoMFo71TriPSQKecB9Hi2A+s62oG2PY8BCp9RNjjD7Y47mD8nugXXOGVxiTnOKFGkQlSP0SWdi7UJFNgXeytDyhmPEqrETIAYSfywM4NttoNLo0xoMc46h4QdjS3ZoOPeoemZONu7bwrgLDg6fkZSxIKWK4rJcwN9inHPmQqBvU4eEDDsxG8oGwOYSRgXek8hwJARIyMulstePn8GWlhs+JxdJvF3CCRM0D24xVHKbgsCV2Bbdu4jxuzNwVrVEGI/a/rAu2k3y4LZ3PXeqD2CxZfU3u+HiTPiKDP826SNH7gnAobQxhn82teXz+O0+aHzlZwj7y6KV76rjB2BTjkAjCPjHljyg8E1Ao/6CjlPJFK4CqvrXcsxvc/tI6oxRvpZjHW+Ou1IigLtSIw2ueGlBesht+nIGgqaL2g1gNQQVovVvuYm4W3KVZ78GiZRyIEl622YTyK8WxiZFt+acA1X9GOOxTAuw/fAKDue4wFx37gDTRIWelUAHauqlIyw7OAUk1nCoTLgjnlchzQywWzWY3Ga8hTG5QQjGroKLFiPu+ufawVfz9gTNwWZ1A4u8QJa2Yawqkg75vX3zsw6RmNAUBpDNvJrGJvhV97cvbR7l3Trj9julV2PSkmwEY/Rjv8+37Hsi5Yc7aOZTalae+I2vF67BZEuNQ5ITp+h1+f6UiJ71YzGMKDLKGzSVcE0ipFEAMHAW0xIQgNw/XpI9YnxbHfoT2j3e44joMyJpHPJliHfNX2yMJpDQgLgw6TqocKm+/aOEHzufrvjWwHCD7U55RRx+Ddyye/IeFhQl6wgEjN2YsIuhMrBqWczB8PBhwm9OcVTlfXqutb2V6T+VRjSjSPChRvUFNzMgapqRm1GNlrEoQ4fj12vL6+QZ42bE9PnLWdpnO5lwNHaZCU8fzuNCwqElLy7t7BQAJGcOj04y6BxXpRhB4hjeQCVweOtn5pueCHP6049jt2AUplT1VttG/3/W7DqMh2SzmMYr1Eqjv47JbpYLvVGdnUGGxLDoVp8/COzvgoXPpMNTZdH5nQyTz87uvrnUIMw/BhfAHDhh50cPwjhF4Wj1zbMTFIp3sYTJRWJ40OVlyBmGIki80xZmwmxxBF0EMdBahaK3HxFiyKjNDWsB87JPF3v//T9xSVqwUaIqAN99udUYzNTDgbglkAmpHE2OojsgSAyGgG1suRM1otWNcV+ysZVNqBlDd8+LDi159/Qu/AkjekxAyq1cpW+GXBuq4I9lksTh82xAUQk+OIFiWLGEOh6zjAEEDJrp2OWzsahLOaYY5BCdm5A3b2jEccIU5IZfxLAZxqCuORuZbSeITctMNQnIytgCJd/DVGkl0Vy7ogK0eDdhEs68KUtwtU43B01TpeazMWmKXM99tOw5cyLt8/MbruDcdOo3K/3UamsF7McNl+DjFMBhJvDbCAwJNhn3QWmhU9DdrrvbPxLieqoAogIeLp3TNeP/+KYk1M1+sTILy/o7BucrlcWENICRIUpR5spLQWoHW1TGdkK5T98GzQ9yuz7rk+TAjj2LOAZcbBpVO4xv45rtKqrOIPCE0VaKIDMoJlSzrQgkeH0AfgbUFbEOxGg6SBJ1MuLQtSXtixbsO7Fh/GAzamtcY+HrcFOmwPexta69Bwx/X5iTPQRbDf7oiZDmMv1eQ6FgRDLFRBOyU+kArjfLux8jX34E5BGCtZ/1FJCZfrhvte8bZ3dFCqu+5v+FwOdGWh29eiHCQVQAXrmkfNsNqkw2WIIy5YPCBSAJ2Zrc/AHiZ9xgRQhUl5iF27nfnGOhB7Jnz06ITBf+/1B7SPJsTihFnHrdVgjUHVckOggFgXK1N1HYtD7ICLcxQbeq0wzHhFEOA4dhylDKqqO8jWfEbuTg0Xq180G4QyZh4Etpwvy4qcyOBptaBKh5hEbwwBGky50vHNU3u74+rj0I3HIeOpDChNnOdAo/50vWC/vXLKWjQabK0otWBbL4Cy87VW1hgEZB8s62JRG+CTz5x5JBBjrShq7cQfbY7xXKXzK0CtYBvD1H5RIbYvzoFX60IfEefpvv0P3UrvJ5htwIWd+KwfNgBz7CosIwwyxO34fZw3kRdBOXaUgywSOrpuxoGG4X6/j3kUIozMozWkuZGKKSIk8sd98l1tFUuMlMi2Br8l54F6eQT7uNYejWE4uBHfiq/wjMqhwLJt6LXg82fOIViWxC7dUhHSgm1L3M+1QsD9VsqOnjesyoYzHgky8si04ecvJtWiIPSmwDDo6v9hL4VpJAVv1JrkCxhfn7OGLSiYRQD4MKexkUaG4IQH32ZG8ggRTi8f43UhNh+Ae6q0DgGzvhgzrk8LajlwlDooya0S7u298XwcB/Z9Rze5hloKJwIayYSOjEOstnUj1KmKWgtUrKYIGvV1WZjcisJH3jqkRH8wwtt52/hH/0EqPLW6Mi6b4tPnX1BKR15WlJ3ijr0D2/WCVg7s9wNdhGNt0XEcFesSIDkMKXCBNZiBPSFLWoDO7Eq1mfjluWlt7ks65W6ZRsS53tBUkWAKAmrQ8X91TWFKV3i0PLOAYfTHNTs+WtGbNaFgyiBDybJx/1VM3rbVimXdkBI7EakD0i2LSBygve+GOSqbUWyT+LNydg657BV5veD53RMW0zHvraG0wmKnSxGsKx+wRUIpUfJgUMXEk2E3EDMSFyRq0qCNAmwHaw2hMwL+/PkFiBXoNuvV8L5tdS1+c3a9oiq5xtu2IsAidjWGVu8I2pF6pxx0Cugw2q4zNyRAYCqj3vwi9h6ZomC+wVpvxs9u1hR2mnchHghYRidkpESJA0UaB8vE9WI6QWmYEIVnFbUa7dQGj1wvxH+rYbih8d5C60Nh1I2HnwkyoIzXH+Kgc3YF3r37gMuW4dBVqxX3SqhOIVgtOnc83lVyYwxDrHFACmqFSGXaLsqZG1C1SW6EUmJKyLWjiSDKC96OaiJufB5NGzoWFlIBY9oQY679BmDDAhMm7B05MvBqvSPDRPwsKxHT9LleTpnhOZMz41FrNUOBgUG31iEpIuWI4yhWq5u0U1vRse6+Vx3m0nEWArR1aDD21nAkzD48+NBWrWAMdAgu1w1lP9Asa4xBoIjISVBrGUGe9wrw7Cl0kEASgpEheu9Yr894erogRmua1Y6233HY3Iq8bJxF0hn8sAHOJ+SdMgXbyHIymiIwGXaTpxhOJGLJC/ZScNk23G+foUqZEjYlVtRSsWT2kshxmLhdg+rUQUuZPS4xZTsnjC5abWRCChl3tZQhOGlx2SmzMVntVtF7skBtRM7cjymjFY4G+JJd9Z+9vn5Gs9GdZtpqF+fX53xlOUVamPCLh1QiGGqrqmBrukECycZ7HsdhEXRFdXXLxvf5Q5EARDU54BNF0TVsQiM0sl6uyCmaZHBFNY9ZbSjHdl2xrgs3VPfDaiyNL56BG4oxgMNspzMV/NqCRCzrakJWCR8/Nry8sZ2fm5Id27edoWAMTCdr60gxI0XBcd9RU0MMQF42hJTwdOWEsN4bymG0PSZcqKWi52iG7eS87JALWIuZ2Qyjid4akIy/rYz4eu/oQecs5xE8zlRWBkjp625ZJADgxFm3tYY9s8OEzCRmxH7g9kYnX2uzTlb2thRTUJWRUXLy1nB8EiAxkMrbKmRZIGHBZeNwpCiKfS88H7WRPZc3Tm6zoCHqKfIMzGQe0iyLgBUYVcjxDHz9jQ2lXXEUwYdvvgF++YT7fh/RKpoyE9IOBBO2qxy8tCwJvRbcW0W22SEcxpIgkYXnWipnL1gXbKuFHd7jakzGQbxIHgZpYxSYFVCl1LhvXIXTux1uOp3tGQZNSMX2x1k+w8+6qxgMeQu1xlGT91hSwu12w31n9q+djtUlsceMdYnQqEhgM6uTUVjsTdaOTOfzdN1YI8kJrd4INRojrDTg+rwixzgccG8dPXSICsKQ8xxLbWJ+ajCajMDBDBVIH07DsOend+it4/V24zm2OmbvDcdutga0capUvI0Ais+MgaBIQJTIGplRSEtRdkYHIZpQG0K2+pu6DphddRCgnXotQKjQA6IoTgCfkua/9/pD0tnj6Q08zjbPqOLrMJoAYCX2kW27guJgdxj0VG1jeoRPnfw6NtphEAu97fx97yJUmx1LnQ8eVMoNkAPszVgymles0ajPYeiuFeOGDPYdvE55eADuF13iAXAJ4gZRE6oTwdqVcxrkCff9QBExUbTVsoWG/ahYlw2pV4TSDLtkoTCIbSQhvnh7C7hcfY5Dn52TIRi8tgxhwvPyjyYcy+r8xkIgc6MZu8sHxI/JOH7fvl6jKxmW4Sg0mESz+yFPx1szeIkBA1EORbdJagIahGbqty5jXguZHPNnk6WR88L3mDoumRiCnDc2DQoNcL3vqLYdJbKgp9TMgA9iEstmnN4p5yX+Yq09jvPsYzxLc3gxJWTAosmM29sNRzkgYMf9usYR/HA8q+JIEWKwh1rBPWVqNLVWcLsJLsJu5wCYQYtYLLJ0xUxy6NuINgWUn9BmdbzobsPWrDXEROZR6x0xepox19ksG+9nkC76UCigM7RzGAireLYQQhjX0XszWqgMZeOznEUpbPrqvZNZZLIr2pVT6KwfQUVIy9ZOyrGNMWVdqOLtrTITDUKRydYAg0wmKWbWOF3S5MvImW8VDkQa9yOjAc9hqwygYwEQcFnvnI2SAnojNVZAAb8kAeuWUQ+jX5t5DkgIMWFdOHq31gP7AYR4QQTvv7aGLa+IMXL/JrGgi7Ps/Zz7THE27gYjSViPWGvoVlty5t7XvL7aKRBmiJOeORyDTyViTQC9o8GmdFnrugzva8/FmEeAUu20U6OjF76HBy2g3O7YjzIKeyLd9PPZ0crIOnLBQjCVU6Z9OUXU0pCEXZBVqQTJdLANaYbqk5lsMbrzs3FKLX3HuD2FwQnOIQ6CoBwViBTZqNQAXTJQKoJmLDnidudUKDXsFICxZBKFyQ7ikpw9AOOs8xmnAAp83W9AX7Fuy4hgKEVsUuQGNfXmd2HRW4iQbkVIs/nzmTayLCCDD65qIykxs0MxpsqoGwidXo4uH+AZCR2pCIAuM+PS2dPRLZrx5q1S3rDvx+Bbe3bpdSbY0PrW2ojOY2R9ZV0yWilApPGqnYOLYuDYxGbRfrdsTFZG1k5vVNvHvsyzRgR4nOz/51kFi/YJmhQKE2HM3FvbtuLl5ZWzhwHsB7t487KyRrCuSCnhsManbk2h0dYg27yAVgvub4p12bCsecglxMgu2xhhuLTp31jkGC0b6TqzCQkB0jtEyPQTgxc1uPPwbW5rGATSxWqFIL4PTqXzCXHezTxhNuVYSzvb6pFe7+MZxxgh5cD9vqPp2VDRRog4o03gpIaUErKxkfK6QHrFYUbuKKRwR0lojZmJO79qMuW8zD6DVQ/67M9nFhdEEfopqBKyxRTMzBVAE2BRRekNed0Qw2dz/hxI1dXqmeY9122D7Pvs+ofRsHu3Dnuu536/o7WObdsAdYZTpINWN51zLjMD2og4ApxH+JOjVQubOwWDefZ7ry8Rkv/0NboNT4ffewqo745x4e6VYpSB3Zm7NnokmQseifKCCR24gFNr3eYMuH2w4uSgQPLz9WTEIcIxfIaRH/VAqQdu950/kw5ow+EDrNWRPApe5ZzYAWk8bhcOCzKnYlECYz42fvecbxuTjdoLYQxER4i4Xq6I0nHf7zZsBGMBKRdtgn6Rw8i3Cznvg+0ks3Fw33e8vNzgk5m8GU07LMKLAx91oz7GAmIKgnmkGaw/wmE4bT6uEiPycK53MG0ej7zQO+qQTQhuD4ZmkB9AdeMbApoyO3R4xSdOeabu/GoYg4VNd140mx3ZhFrYNb8Xdka/3e7Wi0HHVGoZ+8Z59snGc6aUbDxmGhHhIBnY/bjsiEfC4+9MoiAamyTEMMgKy7rhslFReLdGKk9BtdcxmCcljoRdLxfCWsE7yI2/34FWKrv892qWwGdG0/W6oZirzX9H01IKVnsZEXKIhJG6zfbVKUM/oOHAYC5EEwoEACteUtZ+ngGFDiVULvcMpAh3sG+H8ZLpIHUdHb/ejQ+IFYz7uF4dz2w6Q21twE37/ca4WzhHpNdmlF1z8IFGM+WMZVmQczTVAltXJ27IiVyCyTjzLDcMmxAty+A5FwUkJTw9PQH1wNvbjV3ztj8hwHHf6bhStj6EFdu2Wd3SjSZrbL033O9veHl7pZ6aKqoJ3ilg5yCMep1Z1THHY4g52vnmnubsCRcK/ZrX189T8A3jX24HXezQMF5hdNA9a/Bsws04c8MRifmidJORdhtyv93Qm5o8AY1OsQHv/hml2tyA3niIEqUOBCZBUBo184HRJSoKzlpYdES72WAmGr9p9JzG5QqjMiiovhy8lgC1xiOTve40VsGyiFYLjsLxhs/vntH7C+5Hw7raEKAYWUzsHWlhg1peskXZHcFmCNdaUAGkhdPcWit4e9vx8ZtsqTn58fG0WwQyon6BM4M8kpuH1rWbXu5kzji27hiz65wOnvqgIhIrjTablofK2EtBIE05stH/zqLJdpBBEwDOjKjFdGHUBuuUEVgIcGpQ82DAjHiMnJtRC+dZR5MDsWuNIWLJK6fBRUFRzkT2tR9KlsL11I4TFn/CadXTdQ9ixHYAmR0hJYRagRg4FKoDT8/vsJeK19c3xLwiZjqZnBaU0mzAUodEzrDQZlFx5FzhUgCVgMvGzPJ+f8O6cZh7rc30j9I8Tx0gy8dOnPWl4BQcONiZU8b+wpnhSxZYPj/2iEDGnvboRU+OWhWUvTAqa0RAgwkFYgY7YhBGazRix75jv+/cMyGgitXHSh2/48GgQ4bRBCxjpCItekOPCVE4DjWAzymkhDUAkI4YAdwP5LTMTu1h6S04NThx4oauAjDtnVuvLt069nUotwqYedWjkMzy/IyXlxcctWLNGSmwF6aUfdBQOyjrrWoKz3GjekBl1hzihhgUWir2+x1bzujVoPTVJrRZXw87wnX+zFVq/R7NPoeYkdodt9vNBnP9/usPdDQ/4skemQd7SCo6MHYBLOUJVty0TSfWiUovYhuOGyRFDrp/eXtDhzLlhlCzyLx0NyVUbTNSCbJyalaKLFbFU7osTMVdLvvp6Ql1v6OEgJQC9kOQFxuYjqmjP4S5EB8iH3zx59E4F6yI1ee0pCCOrdIYkJGwYrsU/PLzv2AvVzw9PxucFHDfm8kcBCzrClGmv8G0U9hkRIOxXK+IoIDc7V6Rs8lrtIaU44DGYOvVTQUVwWs5vJNuPP851Q049oJ02cahduPCSGTyvZn1AZwP4CgyBt6sHWOtxY0UWL+JOQPlwMsbJ/hxulpjn8fCaXOkHRsdb/SLkD2VUoa2yeRgtBxYpM/EQZbtgoSOUnbkvOB+7Cb/Hme0H1hbcP0qZ0gNCOm07AKvYdggpNYsutOZPTZ3ImTmrOsFby+f8MsvNzw/vcPzu42MMb0jmEZWTBkpBBQckEjxwV776MvoKljyAlVgv79hWairxczamSvTtg2jBg9UMCy1QzKsJVAgrYY+bno6fTBr9WPsomvisI5Cg8/NCKPfxZ2nz7PjemWIVHz69QV7IWmklAMAz7ysxOmpeBxGsdan6rnSaGsGQWM6/GTvTWnBukS8vu5sYD1ujO5tEI7IaUaK8plGwBq75GGt/f0QGRMhu2U7Dl0GCZResWfWakNeV2zlwE//+u84zEmsJsdSbd5HkIglJ5SqEIlW2wnkCmpHrY3F897QK8fTEnKczXLn7nQm6mrZzLRUIrNnhH0e7HmiTtPvv/5AR/N8wDyFp9QSQgvRdPzcd6goOwIRhFKu6OB/WoetAsuyYr9/wu2+o9uQnZwSSqmmkmpKqQKkuCAvEZ4nqfF5W/cJXWKRNrVFXCxvXciPJm84mOJrGPIPzpoKIRo92yKjgBlB+aGwSNidBHVFPA22sZg+77mxCBYSgJawrRd89+03+PeffsHnTw113/Hu/XtctgQXx3K+d4yRsE6I2FJErYfBK90YKgGt7tiPBdu6GoabiXEPY26RhTMpYKko6JBFxg/g07TG4JFTpufptadzrlwq1hGsXRE4l4ewoZwIzCYvoGD2l+SGz7cbSQW12eQ5k1c2ooGC3c05T5aIUxPZx9CApghhocFOAcu6cIrYsiIoOfJLzni7MSp+upwm1cHx8gl7eDQ9HaI9L/o9mIc9FUvN8VmMzGFOcQjjrduKD998j1L/DS+vn9HqgcvzMy6XDQEyDKBqG2ut4nOZO2rFgMtiYsHwfr9h27Yh4+zr6ecyiI93dS9hlvoEioyOWOUzDeJZsI4941mxCh1CA8fsstXDYdduMxr4/W6IXIOLhI+In376FdVIBRrIGCuNUuitVjQl3XRZSCQYlwqQMdjI25c2A8i8rIjBMv8k2Pcd67LidnvDXiryeiXzRrvJ3VsPiCkSjNPgQe4pY8BAJBRoNhiIkYcde0FoHO17WE0y5QXb9Rnffdzx06+f8fKZIobv3r/DFgVUIgijaU21clZMzFiT0FFaphJzRpSAdtxR2wZFJi05zP0Ag3cJPXkQM4vpD5vD9of3DP3e6w91NI9mmTA9rECt3uCCS17kcHjJfgZzDsrNBKeTghDQfd8nZCAWUXZBqWqYnhnNxOHoRykc17esWNJCmAaEoFqpWC8XXJ+uiAIsK7np++0OMfYGwPrDkmc7PfnzfcgvuESCeuQwNo9ApsQInL7mMbiIQKyusKwrnnrH29sbNNOLP3/4iHIUfN4LFB33+x3v3z3R2AfB/b7bISMrK4dsGHrGspGG6sqPEoRaQlXBBnDHvv0AOAmAP/e+EhhEMui1oKSCO36nGGPeFW0iXH3Rhya5LAignRuzn7DPPpLaxtxKFfdjn8X+wN+LMUK1AiEiO49cAlIEMeTagBixLZSfPmRHbQptDXExHZxIZ7yapv6hHVteIKDx4KAUk4SwbLBJRALxak+hHokUAgytTxmyAZ6FeZ0tWVR6vT4BeIXLQevlim+/+Yj/+OlXSIyoR4GuqwVKLkTIvUM+fRpGe9u2wWTjUCZ2ne/Hjsu6ug0fBqEP++/y9dYvYF6tW7HVM6X4BVdfFQgqo1mymwNsrdpeCMAoXXvX8+yYhTHX1GAmgDIszaDNkW0awUGEw7tWU1qNYLNisaAgpwXb5YJyHDi8MzpEPD09YV0SegdnWveKw868Z5RU1E1mbxQ+rCnEZENwYAHAJPbOrLbbn63gLFOYUxCQIvfB5XIFFHh5fSErTBUfv/0GR2moXRBEcRwH4pppDxQoVs/UU0rduyBlUuNh0FmwnorjTs2sYeiFhfPepgKEB18sNXGWDR2+BbuBGXb1UYW/8/r6PgVVBDcomJz0DkvJDBeLwkhBhXoxYqMzRvlIxEb+8TN6KaRtxn9AEQsB65bsMwAIhgIpYCMZ64EQGFlJYHoWLeIn3KSIqeF+o1JglmVQZb2YnFKw5qURNA/GArPpE4g0DKofCxoFavSzsuKBRhABYsa6sduydEWyw/vNt98Bnz+hVjbZffr8icYlrwgB2I9q8IsttGU9dJgRENNeiowq9nLHU7ja9ZwkJsZ1c7Mo9KRFZfel3WS7kzFKeJ/nxkQ+D6sZdRtoZBOmgqV8EiyktsiUyJrLJ5MFdhzl1D3ukAadyLpdJkwJUjPJSKJR52CeynheKQMRI+tX2hv2VrFKxH57o/qmuMy7iSlKQI4J6SSjHCxjcAacO7+HfehP0vYCoSpFs7XxwxlTRNaMdVuxHwUhZLTe8fz+A7oCb/sBqOLt7QUCwaaC6xJxf30zrS4ZTsmzCMAEIo2ZE4Kg1zIEBdU61v0q+Ssy1k59y5rzgjaKIMJlHGwv+J7wg2Z7XbsRPiwAES+Ougi1Gx7bdxIB9FlDYHeza9paF3VM2JLVzeyreuujAB9tFCzl562HKS8sVDdmkrg3pGU1bSkjRth+z0tGTNQpSpaxn/95CN5O1zCx7wmPu0ElCHM682a0l3XFcuzY0ZB6BOSK77//Dp8+v5BaXQ68lB0SMr799iMp2eIEFn6Hz4FXo0yLkwOsIH7cD7x7eh7X5aSNcbq7AjbcS33dxhJy5nRMEVL+i53C3GQeTnEDaevWJWxDMMRkA1ShTYHs4loWPDgnXulJd8sQ8rJCwMMchHrr2yXYuEZ+QKmUSPDCsRJLQavVEngyEEqryCsQU8CSV1PJVLx8foG+3XHZNmzrhrxsc1ympYY05sZCGWI+mBGkRcbewwPA5B0CEC397jqMsj+vZdnY2i8Za0i42vjAX379FXslf38/DsS0AuUNb0fD09MTlkyGzFCoNYMZYkLOnPIlASx61Q5ZHBLyDnId8I0tJKNFu254dmfOipnb3GAOTXiPQzO+uf/OjDI5LTYoswm1gp5DUiEE7Pfd1CRJGX67swiXow1iRx/RTKvFYDKbL2FRWu+kHCrUCrINIdH4LilDhPMKXn/5Gbej4Ha5Yls3pJywrNkKl8E2IxfcmUczk3VRvFkcDyNr8kjbR6LKwLqdDcZRjRn3vWLbNoSQsKSEv//8E15e74ByPsneFLhkfH55xbJd8XTd6GStTjfGsYaAvCxm9Hi95SgzYFGMng6/9jZqP2Yo7PkCIP249JH5jX6NgTDxZ61TTqYrDcXMSvrIqDGeiu1NZTTfijWpKbCuGS+vnP+8LHGwm2o1FeRaDSZ1BldHU2MuNTGadoF2QVdB6h1hWYilp4R6e8HPv3zC55cN16crlpyxrStiJDuMhC4/u1Piwj39AwIgwMmFwetqMfIzSKGfNlGEaEBtN4RlwxIE1ws12z59+ozalZIXdce6Zry+vqBLxLt376yzep4Pr3MlYzB630ZtFU053CwIUAa1lL/XTppVzvjzHGLmO19EOv+D1x9yCieEBD7YYwzkBgZ1E7YJ/emJRZmDc26ZRjMNlG4wx7at1joPi1CogFlhQmitmaxBw3EcxriZOE5MCWtK2LbLoI9BlRgzDrTeRsPMt998pEBVoIdm4dsi32jKlBArXD+m2V5jGLRMWORgUW6XZoUxQyEsHVzXFUepw6Gt24bLUaBvN9zud/QOHPuNLeq14vZ2gzxdObc4GkfE00ErtOdMVoL3X0CAIBEN1daNVwyZXcU0FJOPMgAejz5P7wNkNrR1Fry6NReNsYZmOL0e4Sk7gNFRCXSTViD0tKwbrp17olstwSP11tlPwOapbpTdah2uwSLpiJAF23bhOtnBJrvMn0nH2xv7Ba7rimRyK60LGJxNZUrWlMIoSMo/OEQjtzH4g5lSR6mTKst+AA6VVz017eWMbaNQ2svLCyUslHo4UMWx3yACPF2fxnVAZ91CVRFSQrKsxqHFFAM0CKoxfNzWBV+TmeKeAhVfYxk/H706CjSlXpFnhQNiMYN53lcyHkw3nTO2Id1tNkrrDRIy3j092zQ89sooqPVfq681M8paC46jDK0nMSgo54y8LAjQudYhYVtW3O5viCGhlR37PeF6pSCiAGNOu9PMOdfbCCBp9qY8GE0Lxz1xYFAVgaBA9YHCDpFT3nxdu0nNdJTasa4rrk8dLy+fcTPZ/nIcgAC9V7y+vkKvT7gueYpxqsKlxQE6U3fsrVZIWo1G7DaE9sCHWhG6w8jqAbNtehYr/P3XH3QKrNxPvSGKWTVrEhneVwGIYarqkSoY1dpmF1DvxHVrWutYtgtqe0O1xibtDTapl9X1Ts6ta7DUWm0WwYLn65UpdmCSd5SD9M3Vhl0UIOVEepfNL+BQd+dLA4gmtBUCfCCJds+KZpHZnYGevbObDHF8nEbJtZGgCYs5w945mEUC9dNfX144W3h7wrYu6E2sMFdxv9+5AXJmWiwzeyHcPe+h1TKa0xwT9ismO8bTbI8o51noloW0roieTahSWqJzrSkwR+XGYNkXHx4YfXZAEiwVNgchhBp6LWgGCbTWEHLC1jPudx6UqopSFR7dCGD7w2APmCRHFLx7ekKyaBhWoA6moptTsvnB5IuTuhyRTf4kWhYLZV0m2WhOeAHSmEgurTz2Pw8Bs4ITzCkxEhLsNoTWGsF0WQCwh4INTcC6bri9vaLWArFsMVoGfNSOWg8ce0DvhSKOa4Yr9Y4eE4dxLKuKnuUMa4BxFn1KnMtP90EA6eY0OISGeldhnHGARIJSq3NH3HKaAuss0tL7ws3sgKNKZc2oN8KWy7baECcbluO0UwAqilbq7NI2meymwPVpw2XNJnzZUHpDUMG2BUqEtGpyMkL5fQlYlzx6SSRYfSVYzcxaRtQGTMHgT49lR/CLGcieM4qYEkDdUziMmSKgi82YrwVH70h5RQycGNm7YN0uWDJhsXrf6TxKwdsru6AZyAbr6xLLhq2/KAars6hlfF5QnkFD87+HQhGcucr7sJjwv1zmojcKNEk4RRUWCaiIDXLw6JnGcNov9b3DfUvSOSN9WwSxG0s5otzZ6RkkDtG7chwUsIM10kGQDVrZLlfynmtDK3ccqkhpgYBNM7Uc2K14m3Ky2cgrUgrWvBTs2rh5SIP0Yudk7TykYUI82pN2f3kaGtMJVhFBCDar2RrzVBW3fcf1+oRPv/wCYouU4YgxYV03dgvnjN6ojVNbw/PT84AVvAgoQuZG8AITjBVT+wgdHa9WeFTR4ZRbKpiqdSsDrkY52BoST9pQGGstcCbUzGJOj8KkMziKs7c2aLshROtMp+FuILFAjX9fakE5WEvwzCLEhLwGLMuGHKwfoFYOSYoBS3ShOUqmO8wYUsa2sTgdUxoicbOYLAOrH9HwF0UFj7xVALR5i/4+iieyThIWIFRCkQEukSyA3hFCwLaSXirROlUjJ45JKIiZZ6aYNpCEiGW1pi3H91tFjqvBLPNs+bOf8MHMAgCTboJlQ0E5SzuSRstpdx2cFcEMw9drDKnx6FNPxdkZC/E5Kow/342ua5ByrYMGW0yiRMxxtMYpZs7YogZSRFoWbClbQblDW8Pd1IC3K9ECVXYBH7Ujpog1XHG5OCScjH7sbDLfs8GmGgLQmWHOHGueFWlznfX0/Pxs+7Op1kUdWkWzRsVdKq7XK1L8GfdSITajPi0LriLoEE5dbA1FGTjF5wu8VsM1se79OGdnewNb7d3WoE+q+ckue9OlhGBaTycU53deXy9z0ayjsrnehoz0MIRokTQ3Z9POQxEmNdWvH0IWkqewQ9O/em8AH8JhB8C7XR1XJYxEymVKGeuWkVPAvu/8PCuKpZSpM9Q6qgAI7BRe1ot5e8VQQjVjEMSM5ElFcUwrc6OByeuf9+y0vPm8BtoqETmxNR4ikAJoLzYilMyh6/WKz293bjpTRt0uaYymVACibHlX7Zwq1SLT6ZFGyihM++Y1c8bQaET13YAjjKwBBp1E69L1DFBEII333bvazwJpunaQXD7gPH3LU2F/DajaIKJeXZGVxW1pFP2i2quNWswLeqvY7cCJNXItS8b9fh+9GB0BOa1UVUVFb3kUrbMV7ujoLQrz0o87Noc8+ZBHSs7nYrUMe0gC7n2fH/LoAxnFBUTjwGM+n8iCZOgV2+WCbVlwqDHcYkKM2Zov+XkpwjqmOZCovjXkdcG6nIrRticBMHMTQTsZ6OEQDJaZXbA06tFH6wrFIT2TdNipW1Mou6DF9lFDbzJgF8CNkc9l1rnWM9ymgyF2jBgE+15M28y0x0LCEhTH0aEWLaeYkfOCVg9Uc07onZ3JgfLYy7ohGSybbewqGYM22cxYVHA6tf3MDqZXQvhzC5h8v7IHiXUmM1u/fYVgqIBnZB1d2ITK0HzFdbugtDvXOrNBM6dkkuIw8ohw+l1r2OuNM0FM/BLg7GbPcNV7vdpUABjjLlTHuWfwB7NnYhnm13U1f7VTYGpmUXBgYaP1Bu1OYXPUyAtXp4MGQj8Wl9EkWfQqSulsl9GApeKlHKilDc51965mhXGUGW3EGHHsd0rl5gVZZMw+vd92LMt1pOAikWyfzmaY1juSAsEidE4LiDOCBEYx7Uxbc/1zgA4i+KGxF8/iKcUTLkqCEO4IzQqSEYIV7z68x20/cC8NaVmQbcCNiwKKuJy3D3gBjp2aPvF6AWwK2pB+NoMwJoidjLTOc2BO2TnYgOvma62osM7uENGlm0ZUH+/x7s8ZQfl6Aw8HbcCJYvvoYB/IeH4WLT4MVOkoRxn8dU+rGZXRSCyZDJMQIlIInGa3RUZNiMgpotWDDtywcQ7rYTfsCfAzh+57ZEaKvC+XzODNaiDVc3oDJb/OMGingSa7/9YbtWmSIjXg+d0HfPPNjl9fXgFjr6gCrQUTaNTBmFELcLSb6JsZoATHs2dwJSFAGh3YuYlUT3/wyVxeKI8hAI3KqUGaQZsyIBk3ImJng49g1g+ZRcrITAaHHvx3tdpYt2yGPUc2L8M0gFhMNrFLgA19w9BybntMC3LOWFZOTeytogwdb9aRYq8otY1mM6fHpxgGLk9nKiMQHE1qXEbD502ZgG8/MS5xeqYzIocVhnvoBo8qtAIxCdbrM7777jto+BlHo30KoMOVYBRkCdZw6YgJcNikxuu2QhLtx9ivDm96hmPr/XCBfo2+D2TOp/6a11c7hWoTpFgS6BwY0gMua4aXLYdJslRXlmQRnWnP9Ek7VcP5auNict3aiAJTXiAo0FopfVxnkSoGGelTNiO0rqsNV+Ec533fkTKjLXZ2MkqNkVX86IdKSZNjcTma9hEzBbGNFQz2UZsnMJrh1ROGWW3wUzTjSDF9eWNVdba/+2Zs0vHhm+9QSsXn1xtijFiy4HYvSCs1Uuq+T6fpWYuSsvn5peH9x2/olG2MoASjEJvQFiyl9xeZLXHcgyqZJzGIDX+paOKKrZRuvt0PbNbpbG4HokZL7jZc5eSMVE/vg9dSOrSxIAco7vc7hfskYVnEJNNZS+kWYYYl05BUxXrJkCbY1g3ruiBl0ktLuVtncByBBiUJEiQSjlNjY2lX9EBjEUybyg2Bm9LZqMcui34qyDKXIz+Fz+/kQADLFojrBoHVrfh3RYHt8oRvP35EV84QjynhMBbWsmwQbThqsZqMG+WAgI63T5+xPb3H01XmukFMRDBApPoCD2egvU/yhkNJFsiEGCGmSKtJR1R/u90R84oln8I4JgKn54SRLTsDzp4e/IQ3q/lBBMe+24REO9tiGlDWmOYy81DKTse8QCFYV+pC5WWxbKFiP4DFAw+jqUpKWAJ1xsY4zd7QGmuJPjfDIdZu8iBirB+y7yYTy22UpULjuY40UjHQAx8y5hE5gQha+Hcf3uN+FOylsd5VdigCljUiSjD5HtN4y9Pm1LLjpSu+2y58ov28pjgFgDJYSn7drpE29gLE9uGjXPh/9voD8FGzYSERrSnebnek5TIiRm/ucMqeN764p5puFqe01669K4CO++0+jMGcWUCHklNGchgVCh2NKAHX6xObllJC7RUQYvJyHJaeCoIkpJzw+vJqQzl8fCdHPtpcEz7Q3mBqFdZsIyPghmIwOvz6XOLbfyanjTRE4VTHAfGXSzBrV2zrgtJ06Lt4I0stFetmw0ksC+tV4QNeurJI1ze/lgaYRDi/2NJMv/YTrGNPcqwF14ppem/dsGHKjncxKZDxizqiRzoD176xd/jDgm1cy3O7OfP7/Y5aGcW37pLdGHS8aqwJEQzJ9BgT0sIO5mVNFEDrpDMDpo0F9m6knHG8vQBC/Lk2QbLCp4B9H1yJPlJ5hzvJuJmRL/2DRV5jX8Kc/7zP87Nt9hmcxcv7iCYIl9eV3dchI4giLwuvo+xY1g2LiBVWeb0IQBQ2H6lS52qBDqE4UYc4JvY9Ax61fY0ZbSpO96YopaNnrkHdDxyl45r59x6Qu3EREfSmCIkRuH+uP4Vg6z6YVyFQWqYw06w+HbFzwEzKCbVUYFkgIaDXCgl8JhSEBJ9P72iVGmLLqjiOSkkNCUjbAq07Xo+KvIhJpnh/hZjIA1/V1pr+lpl794uXWRdjEKWP58XPricPCkCNVOH7RzkJMgQ2vkpayIYLicKhicq5tVYgRjISD86Jp+S3SfZU9imV0rCs9vyNzg+DiYY0uA8ws2Ccrc/u1GYD3tfCR1/3LnCTd+sZOO5vOKo37Ti1sVsBmB6SHswJvY7fjafL3gFgdFbuOzF1uOFrzTB1fn5tHC2fMhUun56uuF42LHll8TiQ6lVbBUXydMBaISZs24r99TNu9wMpL3D9WadyNvUiMjFW98IeDXkm4E7v/PoyK+uqQ3bDcVPHcYJxsQVe4AyACJZ1swLYLBopmNWICNUV14wcYJIavN51WSBCuQjAjJ5MPXjxvhGLJHi+5xQ8N2pzhKlho5YK13rg7b5bHcFScEwtGn/GGIeIn+efLKfvBgJiTrjvN6sdcMZt10YoEhyI40N3QogIkSqUT09PRg6gZow3MXWlhLoKEHO0esKKftzw6eUNISTk5FfTLeCw52eQhvYTmUB+u562hUdBbxYdfN/7WjfrGMakTdpUt5Hyg3pei8m8y0npN5k0c8psgCNO3kcknReSI2otHFAzsHzAp9HNourJWNvnYxgOHdGtr323fqO3l7exqT246TYpTpQCEWoQjbO4zsECjRUDMeoQ3bGb4F3vndlXt2CmFY5ZLdUyacHl+oTn5yc8XS9YMvWNvI9BQWiTsuELonAGRWgVv/z8CV2BbVkmxu4U6taY0NjsDs/4fAzA6M84hVJy+j97iMPo9rHWLvViUHOI7KEKDkXxE7dto7O0miCJE3RVAs4jz8tCujGYXXGsbLL/ZoDUdAaDEDo2761y1thkQ8r478Gy+q+Gj7ptTO0Nr287JC8jyE8S0KtVweGDOQa5Aj67chR7hGPrEDgH4Ha/oRxzAP3ooJSOVg8chZSPkCLycsG2JA58ASmGpR64Hwcr7IEpWYhkmywrC5affvmMX375BZKfkPMKwVRinJvb1BNFIb1DQ8DQB4IMA+fbx2cSBz+gOjfVOJD+OXbfdFYROcOae/jdMXH4+/1olPDu1hmNjmI/Iz02IOeEVg8Wuiyy5gHw6DaYJPXZUNO59rG/O8akNLt+uIbKKUu7v91GN7ozjVq3vgVYkTsYTAUWdX1PnhuCunKexefPv9pUPUZstTcEsJBWeyPllQ8Ly7JgXRlBorMGo73h7X6zyCxCW0FD4AzunKG94eXzJ7x8+oS3veHjdwk5s3fZZTcSTlmeAOJRvXpGK/MQCUagQpZWgJgxYMzjB03ncwWsac/mWWcf69g8rMB22fDp02cgLEaj7ZBAQkeTZD6n0+h1Rtg5sDAdRCwI4PfHFNEKJgSCM5yr8HGOmFfKKBYegAAqyoH3reNp4U5xB9+0cwQrQEtnw7SIHnp04YV4oHaOKC3HL7gfZBd1uw7P3KWpkQ086NlYMI7RnBjnGBz3O/bSeN/oOEpHXrNdj+Lt5QX32yteXt/w4fIey7JQAsKjeb9nz5KNDajO3ea2NScehqHn/g0Q5eyJkTHY3oA5Nv89Nd2bECMSOg5vulWrgd7vKLVhWRY2aIqp4Z7GFUuIWGKw0Z6cFhmdbip0xiGuCFaThHgWSg2vbg5blf8NuJPwfrH/Yqfgi14OdqVeVjMyvUGtMOQFOOnKvqaRY4kdEBvOEQNKNaGtyuh+dGRaFKdGUesGLUlMuFw2LIkYaqsNAR3dDa4CCNTXZ5d1RS2UtvXN7129KSWI2hxji7YY0dOwjiKeYjqMEzVVRKxgxKwIE00YzmDkFoIhhcDpVVN+NwZOU/YoOcUEQUVtysJQB9BJ0QSovy/G/IlpYTSCyZCptUCE3ZTR+gZ0PPsTFAAd1D8zG+YYZrosYEHvft+R1utEg3hDvD8zGvZ4RpKuOp9LiCyU9u6UQzNSoKw3VFG1wedXuym7bBc6Qu3oB2nPtVUkq/u03jnTuB3sBq+MOmOw8MmIWOw7kVHHgEVO6hCBM1TUZENOkSH9QxjrDQHXxCNN8T+fnalH7AyKQoxQ0+8Sqcy2VS2qZP0m5wTpHTFEjqY9OqXALeMOIWIJcewbNkI0c0o0JkG8es/AbPApFaB8ujUa2v+h6ymQoZG8328QMeZd9+zDlh1GZ1D/TNfePHF4ZNaY+mna3TlUUu1opaMprGbSCJktlMkvtXLAjwBodOYxqM2oplbYfr+jV8U9CRTWa2LrHo2lpJ5td0q4JKUTH+yjIY1u28LWctTDTsEfDYzjMA6r8cE0Qzxgez4GoDciANXqBRDWjmrdUTUzQ5CIDirGClbLLvkMk8n5+/ZiU1/Htk20gZmbml1W92OE37uO2e78K0NfvoCO/7PXV8NHfEJcEDsS/JFFJq25eTHhLfjCYBrYkSlw4zizIIX5AJy9AMCiooB1veD5+QlLzuit47jvgAbEKDgOL9wsWHLAsR8oZcfL6ys7JQHc9xv2g5TaZd0s5XLuNewAnDeuG4O5azwCA04FR7GoyRymR8cDapJ5z7PAbp/tCx54Ha1V22iERcpRcN93YrLeHWpqsd50N7R/3FmBU8e4KeygYEZMjjsCGDUfjJ/NiMI+DOXYcVQ1Po07bIv4/C67DhI8P8Y+1w6Vj/ksx46OgNVoeWp9EL6vYsoIIsjriud373DdVjqMg9pYKaUhpxKtw7XuO46j4vZ6w2HRZCsF+72gFjWxRBqWYFnfgE78Ns0yPGSBvdshY4pDaIN87wGrnB+ULazz9/25iq07M6U+qNzdZJgBq9WVA/t+5+ChZt28/h5hA1NemEm65IuIcNqc1QZmcVEtyp+hslO/3fkDmIVy0IFyZjiVOgnpOSKoNm4TeBjX6jCQ7wWLmAHO/thLs/6LR5irq0LSgpwicsp49+4d3j1dEQC0XlGsn0GUPTgxJSw5ox079v3AfrvhdtuRVkqalGNHKTtEIi4ri9PelT7lGC0rsKBIT4tvT8uCAKsZeTaA8a8Z9VmWxb19XmPLPmSywsQ0vmqxrvVSGWjtO273A4Cye9t6OprtoZwy1pVoBuMwBmjNDLucz7WqBX/TxkzI2myWPu7533v9oXkKrVGfR9PKFExdLZMt4B2EUpyjLebmfBjE3LdUDzyM5ppSQtd93JirbMaFre3RlCNLKRb9RQAdR+FmPY4Dog2vr29Iy2ZdrlRQjUERYzZGUseqHIW5LMl2PTAthJxgD7XI0e7Fhd58l5w3C7xiYjf48HcyNhMsM2nqm6ePSMLPaC0F9wZEbThKMyhCUI8dsl6QAzOlJS8WBfNZdEtnazlQlwUpplMh0uAhDPt/wh51PPfRzamGv5bDIA+cJC76gDZmpuXcaB/yAkyVXGse3AkhRBv0odrRPBqFQELCes1j+lYtJAkgBARRm7KlaMcdQQLut1d0RKwpoPWKFC9IOaK3iDVG3G5vSEukntS2zmDDHflYIq6xIyzega/jb8+LzcPmEZeYAfVozH8G9RI8rPeG0RzxbAscRMDhOTf0nHC731G6IgdODdyPim1bpxhcSka4iHCcmI16xWYWMxJ2I9BPu9KjREqO6HTsvv6toTZG6Xlk90QBYvTcyfcvLGt2w9RH/0wIkVnbvtt8BBfzU0BdkoafnZcVlzEetxr1nJ24Zd8n4aQ1Qki14bKtNjo2E3KrHeuWUPc7eqd8dl0TsjFSZk1gZrUCGTUHd+QeCHoSYT3Dk67t62oZrm37EQB6psGhUwyEuhl6yKTAtrKj9Y6y36GhAOuKKKynXi9PVphONlaYvRpEMRoUVEzlAB9jT9ltUMvMz7HvvHgKBkd++FWvPzSjWQqHj6dAzX9SoRpaTxYRNiCw0GRh8Mkh+MDvgFp2OhdlMZm4eMBxNEA6lm2zIpJLTjRqxPhLKEFbyjG47L0eKKUAMaGXAxDiz13I8S/7DW9vN2xP71H2A8E2LL09DylTSx2Gf3h8APDirUWWwTeHG1PPn0RGBuANWpYADHpuM6zfowvvRQCAlCP2l08Dj3+73ZFM8dEhuBg5VjAIi7Q5LcTyO4X1jlKwLGmkgQ06SQCY9Q+/vxklcYf31lFcb8r0VLy7uDW1QTF0mmLRXxQ/gHwODi2gd9zvOw9ZpQwBcVUyzchBT4jCe+qdw4VcdgNgMbgcNn83cvbtsRcgKqQbdVs7sVgbHrPf7tCQqaVTApacHhvWjJ43ptGZMSU90ecsiDFcuOkEAdLbMDDcB4AG1iO8P2DkhOIBke+pmcI7ZVZbwYvLKe8HZFnIuzcNHP5j85uN+skZzoz+j/0w0T1G/Mx6LUtQPGR33sd4fnFuQ4VYN7HvcVJiO1RtJbs5S52GkR8g8PkJIQhud/YMOYNwWRbs+4FicOW2XhC9g1xgfQu0GwKSRUph8CkQpBxxlB21KmqkbMm6railMLCqFce+oyCanSiEV4MPZmItz9eZ8ZftXXf5Ht1bfYO357i8Iwew+xd2hHt4DnfEsDXhD8MImBxSJA3+08sL2ZCqKJWkk5xInfdxvy4jwp6sRHZZVw5Fqg1xicNxwc+vZw229l8GfX59X/P6Q30KKsrZsiBe7Qept4qUE9qwmWdjCktvbZxdCKhHNUkCNU66IKeEWjtiMtaFMkrzpik1YxqDHfrDec88tPcbB2PXWqCtI9sYw9oZ9R61UTsoBJRasOjCDRF8HoBvEB8wgmH0LRCGDyNRNd6/zE0VQNx6wJFWO+kWHc4XI0n/Dmo6sa7gmu+tVagGXJ4u1OkTk+MwKWCPEsdEsuB0Sj5PCo+tkEiKnxskjzC94OcMkrFpzNBp4wxfCtAxAGCUTN2mbiLCnoWIZ0T2LNTvMZDyW5tDDSZjEJ3TTXkOAYvc1WiYjgUjcCbv/b5b3amRrnq/kybcO0qnUJwEOg9AGT1niybLgZ5treGOXyxAOcNB50iKGYUXmvmc+HM1mQg3oN4NTSG8k7bUMBj+PyzIB+kolsk6RbPWinW7WhbQIZIQUmKfhZpqQONnDOcrPlWrDDkKjKzE60N91Im6FSDhRgMOARLuGVnziJBtlnJPbBgz2IjzoN0xcJ1hOLyA1+QqBN3w/JgSuijWzOClGQ1UzQYAsGiYUC/l5KkSW487jkZp/VILVNhjUmtBF3B/x4Rsz6rWhnW1cxwcAqZxHOtt98mA7bzWYsGu39qkfHq/j/jeCJYFnh2DBX/w8x8iJT4a79clWyQuRoDp0JjM5kQbwtMgtSJYMCsKSGQjY7WBQ7n7XHWdDq179qgnKSKF180VM8j6vddX1xSKFYS9mk1P6Z2iTj2dfxa7qBE9ykybu/HfW+e8g1oPICQ8vXvGui3wjC/Y6fLARwCU44bb2yujwhCQc4Z2NtalAPRaoIGDMO63V+ylACCeuywru4WdSiZu6GWkjoIvmj/OPxvppBpE5pGEv1eHwbe4BC5g5i333lHp2KBYahmNdRFzgtutZF2tbq6aT21qFft+x34QkvHOTOLVLMKfRQshM5pjvceNxCmA8OBSQadg1D9Y1qxdZ1QHTMMNR279dMiomZBlZvx3a5KqtaCr4PruHa6X1QrUAg1ObfZnKGjlwNvthqMUYq3LiihKraQgRmUlTfnYb7jd2MshACVQ1gUzpIXVWKwWZEbxoQbgYb6w/jB7ZTy61NMe8BX3CFSsPmTNhcbEiycBSA8qSFMVdI3I1nEbnHVjjCbpNMoSOJfgbj03AsteYhiQlBtUiwtmXcvW6HEo0oQLeS8UtPQxsn6+HSo8QxDDupzOxfl8qOn7uHIyRR0Vy3LB8/VqFE19uD6nV2pveHt9xbHzPlNKyEtCbYolEZsvpSEnaoG9vt6gdfaXbMsGl0D3k+kB1TSGp+cCWA3Pa0l2Lbaefdz34xZypzI6o0MYiglsevUtxM8NIZrEfUfONvfFzgfNog7WmYSAVgpF9Cw78fGmrMM261XxIVZ8du6ox3q3Ps7yb23U77++2in4HF1/QqqMdmqbxc3efMN57dGxOX9IPPjVGojqcaBXysrmJY4FjraIxfoHqo28a63g9e2GaqwO2AGrJvzW+5xs1ErB/X5QbMuiyZwzUiYn2DeLqlpzlAyn5RvofPrpaWdKOqJuu09Gv46zmnE71SgGA0pIIx0t9sxLTR44ARqwJE6HUnBwixf0RKJ1KmdzJMHwS4cK7FrVlbxcO57/DAOnXCvPGmA89lGospkJZ6iDswLabDR7kALU8cggj01P1MlnBldaQ8g27U5JpWOLv4zN7OulveP17Q2ltaFVJZbFAIbZWkSqvWG/7zjqgVIrp6zljGSU5Bgnw8gHBDlc6Jxynw/iqy2ndH4aEh0GOUbvetZx/2IH1Nfb92d0qRTbC9GyviBqTWyczcA9rVNmwvZLzpEQUorziZ/Yeh6c+v7zbnWHs2DwwwgEdLhwy+S88/i81h5cMIPlz8+3a5lhOJ0Zk67ovdm8h4htyfDpi9HlO9S6nUsd81LutzuO2hACJwsCsMwLozeAfH3Ooii1GqTMInzOJgp5GqDk9+DPxvW53Cl55YhGnpIp4ufeYSDrK3o42+bk3bj7/YtRhn2dGYwFm5IYqKhrAYFCEKNJgZi8d+/cD9TDigNNGCwx96Eiwxa5Q5rMQcsYBnT1R6oJfP0B+KhBIjc0LEKJkZu5lYqeaKi0saNOAGsyGaVacDdZo5UEQj8h0HhpdxwCruRYS7X5AwHRRtsxBY+IAtQGVC00sq3hVovJaSh8QAsXN2DdNixWuzg7/sEK0jnZyhf4YcEBg4aM6REUrbqntujQpBz6kLGFbTQdPGZPzUm3BDQ065fwSJPf3UtBKZQBruVA74r3Hz7AJSPW7WKGYRYxfTOqRYtwuK6ZPpOlmQ6F1NqQk20ox6Kh0NpRDaoJYBTXe0CUOBqAXNdf7cB7wViBWVyEmEPHiJABQh1sXuOShxhIYigHm7RSwl4OqAQy04Kglg40QmF5idhvh9X9FK6iSkok9e290W0kdL6GfqjD1K33+eOEeDzoOcENLJxYNG7/HQKCQySWPYkESASCBjQ0GjUokNinAKG8MrqxmSxzEXTc9x3bZYP2htu9AmnBJQZ0KJZ15ff3DqRoEJ5ntIMbdoJLxDI8v3Hi0WqU1u4RpUXqtfpaixnsjiTMKFvtXGvfO5EOY+g/mTqsSBzidV6zIdHECh3iRisgSsOtFIM6IrN7ADlyAllvDQjA3qkXVMo+5kVQkiYhiWX6KeNy2RBDGvvQAxwKyFmNaDiEMDIqrq2hEKonHomOMzSdPCN01zfyAPJL9QIVwQIb8Wsw4dl4RyFDUhCQguLYd/Tuoz0VEjNSYH22a4RY065/hjskwMkcJGJon2oJDtOiuw5Uf8j6fu/19U6hN6RgzAfr1O2tW0dmsEq7IoCbhOJyHDzDh0/GQrOIsSuwLIma7bXbqD3DFhv1XxipEE90NdYkAdCO+1GQ0oLWKJLn/QQ+dF5iQlSbzZvZ4OVibrUc3FhR0Lsg5mSFW6eWimXKzsef+HmH48vUZ1fuVNL2bGO40ihM11xU0AUuy4LeuRmzaZ10VRTrp4g505jtxXRhCCcNcTzbXIyaaChVO0f6KRv6UraNflJUdGoohbi4Hk0bQo+j2O2Y5JAZsfc0KzBzz3GjB8dWLTXsXTl+NZw6J5WFv2ZDRjg8hQwLAXHq2prpHZHXPor9EKRETvex7zQenRS+MYITQqchkRIoQuXYlBe4Dg20sYs9Jai20dToneQOHxHWiBh049Gr4v/t8wYwIC4vIIqwKUKC96ww6GFaT4jNWVc5ZUCBVmk4l7zwWVjWFkKA1moNc9xj3F/G9toPu35BVSAtrv0iI9MRy7ygHhGLMfKsm9vWWy37671BJaAHCwZ6Q+3Wa6ENTQ3iggkBGK23Bz5/gP74frAQKiEhJ66VwhSVDXpslYQBzk5g9E44kA6ylgM2sBHH/Y46mgCdamoNkmJyIaYU7DTUfb8jLZyJrCrIlqmJR3XA2MtsOtWBTAxiiYg5jilhMwyqeDF4Cud1mzrpvUtdw8gGuyqKNkiMyEtm/0CxrmqZysZdOXRIxKa7QSCtojXBkpMFmnFASu7UfMUHT85+1mozyElHDfGBcv4/eP0x7SOZ0XCtDSl1iqIZpcz68oCUZtongpwXDmyJAfvrPoyOSDBxMVMYrAX3fQ76ViiWhU08ZT9GUSogIOcF6A1vtzvExK5SypStVQDWVbmtG7Z1MeVUwf2+I0dyhi/bhhCFQmp2rrg7zvIBNI7onCw1CXr+sN0ZwOAvTz9l4MmllrEJnKqnnfRTFSDHiIKDmZcyOrosDffKDuIoC6Qq+yqsMSVFwbFby78prjHibUYXtlM8LAUxdHVlM4AwhYhBMCfcsRsuLAJpluqnhKjWbl8bO0qlQ8eQGv5+kASnLGo7qFxpBs2LsU7X2+93HM1qF+oiiADUsVODanJGgOL19Q0U86B44ZpXE5/rCCkixoztsiJHpvz77Q1itE4FkJOP/eQKwp/TyCROKbgqBxpZztftZ2P1FQM+oMNnVnVYtsNH0scJbqVyFnmKbFiyLEhF8O75Ca9vN0JFKVLOJYZxLRISpO04joMdu9bw5vuOmIJLpzi+bLGNbUwF5xcEgxd8/zLr71RJ7YFNnz0hIiEqfyebijDJ52pPnNuLYo8MQPbjmP0QIRgkwvrhse9j8ppCESXaNEFlZz+Y+cSYECG439+wF/YppMA+pJwCmxCF53812Y8YIooNMhILYtI6G8Im5HXa4143dMcQJoIwnw08PoHXEoIKZ1EIzw8HQzF4rEbX1t4GwkHDbM+pA0/v3kNeX9CFASsUFiQZ9A5FCoGMvcQmtlIINXktVJXKvEHEoFYd6+6YTKmF9SoL9HDKMH7v9QcoqWye4nxYdor2rggeTaoXNs0rdYWTHbywmoLgKAenCNn7QkoINn6POus27E4AgSkgBsXbbR8YXesK7RW3+w3MqI1eGjiUoxwHD5IYNtobUBWfXz4jrk94uqxYLF0Mdm+jgu9wkQe76n0TJ44+bKNAxsETgN2xDpOJzYPo+mAkSmucgqU+yazBDU9ICU/v3iMtGy5XUmw1sOiYTD8GoJEOhrOXsgNhQ7sXNmvlNK45OpwA7qcgAV2mRIVY1qetMzpRa3xpHa1zrWsBSghIBiGJUocqpxPGas4/jCxJEVLEsRdziLx3n23AYto+1HE5wMY6r0PA28ur4eTBRMLo/EnHc7xVSDJoBa3zM1xvSlTx9vlXHJ3ReEzLQFUHHbebs7fF9jK3rzVhFssWxbH6U6e7Z6SYvPTa+jiWqqTgEr6gIW0Gk0CIr1+enpHygqMceH7HQTMxCHLJZO0EdnFrCUgWUBylIkWg3Rou19UOfB9wl4E1AEie8NqeFyqbnuQ2tIO08GrFd9I8c+rQxuCi94K+mvHqHdpt73SXb2Fwh9YM5nQSCkaGcBz7kHTxblw+a6CU24BYEChid5iAnoSIFKh6HDNrDd1IJa20AZnubze83u54/1GwLZmG267BI/2u3k3jkI8FrSGQS6def8HYEwMWwqwJUiKDTZQ64nODWC1jIAeH6q8K7tMYE9a84H4ceHp6JuML7PZ3IoW2hlI74oWNm2U/ELaM4zjITpNu8S73hhgCo7YG0uu8pt7RoCMDH82qX/H6evioNZjANXrMyHYAg2P4jXinHzxCCxY+d8sglMNUiKG4YB5lHPadRjAG0xlWpq6lMmJ1jXVVJfOmFAQvwnnhLrBOEVMGAA6K72zoaq3gOCrWxsVdr08UQ5OAmEZwz9TQO20Now9GCRWrKQhIW4M1OXk2B9twxMuNGaKg4zRM2NvuHddOdm/rtqHViuM4EGKw0aF84jFGMo6OghCEs47vByE3rTj2g45mP9gFHoSQjTUHBbHh3mF29Z7XtasCHXABQ+8VoKOj/k5vHT0pBB3aKkQo3uaFyeCRtAha7cgLo2aYYXX8ulVy0I+jMpqEwx0N9TgA0GCqQZRH2XHfzVitVmBPcTS5+cjFeuxoXdBrAZSzNyRmfA4RH79Z6ZBrGY2QEzM+rTXoREOYpAHCoX0Y85FlMGAd2Lxa8OHQjYKaQWqaRhCxSXAJF7mg94Z9v9s4Te6B1tXWDyaprOit4LjfIesCgaLc78BC/Hw/Ep6eIymPnhgGARphuNZlNDY5JZLX2YcRJGGkQVDQGouhzTBpiQFNWTx2Vg0dvFqQx/pGzAl1v1kzIkxfi/NAjvthmYDj6vwQ9qLQGfl6H/uB275DwSFLQYR6ZzGw+GzPsJQdx16MjYdxzl5ePkPevccz2LMSYxzzXQYt3lG2E84/a0vT6Qe4AzhlFmJBFDAzIvB+SqvoHQjKZx1TRg5hfGbZD+y7IGPW1zj1MaFrQz3Y7Vz2HfvOi2xa8XZnQ+O+35GX90ZR7gO2g625BLGpgBPJICmkAUq7+HV5wh/JFGpF1YBeBVidEWOU1G5TuSxKVZOXIOZIbyiRwnS9K8Q9ObgRdtsIy7Iafl6w7zt7C1I28TvOXtj3HU0573ZJcWwIKCM2UcWx33HbD6bCr4oUEqLJyR7Hbik8i+MhW8qtDZ4T+0YYRUdgNOEpvAA5YQe1tn9PSb3JC559ACwyVmr9dHOWKSd2Hislsh3aYNoZIMKGnyUnxHXF9YkbbH97wS+fX9Fjgjabm7ysiNpw3yuuT2RsYV3GAWB85xnLFFMToxZXx5vB9WPPQGenulMau0VfsOFK9Nw8HJbWi9+vwpRU7ea14zgKjjuLkSllxMhRhsdxx/3O3gLyv3VADrUpxdK2Zejz+yEXYTf729sNUJgQounmR0HQhuPYuWfqBkkRKVoQox2M1BxHBgZzCqes0KQrHHI7485OW23NFWq97kKxMzee0IAQ0mDY1cLsKZgceRxZtyBZL826Xog1twM///1n3O477X1v0EOw5Ij7/YanpyfUUqEri8Dna3VCVVcTbTT4UW2UJesKxu6zXqy8LgNO7ELn0WpDXExQUDunzmlHDJS3yQtnCMNhGlVmg/uB0qpNkaMmUa0FtzeDfJOJvbWG4yBszOmIJhvfKhy/8Vrg29sb5R5qxU05pSwnyuC2WkwpgbLT0dhc7gS7T3eyQrw3Aw4IuBv4YijCTB0sSzBYVcW7jJ2E4FLziiYMKPLCvUxtNxuPac4fShQjpoSYIkQWyMbLev30C359uUGCGLW0Iy0Ln2dr9nkNa84PNYWpQjUDvlE7MomMr5um8IfYRwVHs27dZbHmMwrIQWFy0ysE1v0cBKHrgGeWJRjVzthHChx7sQapwAOvjG59atKyLtBW8Xbbrekj4/nd++FhtVNZtRltUdDx6y+/otY2qIxBuLnicsESI8TYR/fbG66XDcF5dmqZSztpyZhBk9GhJTSw9isQhwr6hCAaed+k5tE5xBiZXlua3lrDsqxIOSMGQTl2+OCclPKIiLJhhfEUFYRARhJU8dOvnwCJcPZWq4qOnUOJYh24r7/cYEGdntjhs5eb9mEsai0opaAUxbJeh1NorkUPQjVstulIBkVFb+hJGexZmQXsWg4Oj+HpsgazxaAPkJqpire3N8s4Iq7P78j9NplhfhbVdWMQvPz6C257QasVEtIQmAuR8urewFTLgf2oc295JlqbQZI08OpjNtULdo/wAdMDOf2rW98FAKPGdglIQmPQDRII1p2fc0IthYQHJRffM8SUyJ4agm1mcMKyIH4v+Pd/+w/stSOANNpiUtIU1CsMQpLY+tr1GwLCzMUgk8boURvModmMZBQL3HTAidIZRtTWsWhkURoGQ6p1tQ+YalKWtVerL9BI7UdBDImzujuNnIhiv73hqIQ5L0/PyEbVjlFMS60BIFS43z7h8+sbg1GAhXtrbOXsbc/uWKu6bKtlSTNQU4B1LbX6UXgs0I4Rtg4vCR0638Vz4cKK2gmbx5RsjOyUmsgpGrRJlCNEElIWk58pNdl3BgvfbYeFgPfffIve/45fPr9wVK3I6A+rVshPtY49bFc/fAHPiP1ZdcxF1945EvgrXn9gHGexBVgwulN7R+p8kM5IEulophKog41BlkE5mF8qZuetT1QrhfLQx36gdNI0j9sbMdRlxfv3Tyw6G2+Y+Gkg1JLYdXt7fUGvFW+3O2CbMoRgjSHC+oXxkWOMqKViSQlzOQGv0M2ClDITGBtHDEPu4/cGbVVgnbcKp7ChN1SDTQBGL8QYo1E0yVRJKaLBpttVRl7ZPI83E024I+D5w0eoAD//+hmCYEyljjUlvN3v3KytsQjt8IdF7B06oLjJOurQpmboKB0gMSKvxhE3/fhgMGDtHcEiEYgiWJ9H104ZjO46NzRC+0F4sPWOXmm4e2UhWgCUcsd9P5DyiuenJ1y21VglxvE2njkdPZkpEYr4+kZN/qOa6qZa1hgQE3nh3B/MKK08y4MtwyRwHZ1g4AVeixD9V0btyEHE3tCqjswCMJkRJXafU0DMm0XKcewTFkeB2vjMitUY/GS3xtPuMxmW9YJvv/sOv/z0E+6lkqWnHTEuuN3frMmrgqLgfexVF3FslhF4I2E/s48sSidtOw9WobYOFbKOWq3oLQG9QQPQY+SI0U4pZ0E79TAQ3i1mwLo2iEQIOvbbYRE9z2jKC96/f8J1u1gkHaxexPPgU/FaKXhbM1La8PLGBjcGkzKaFV11No8M1AvkhgufcV7XMVO1dZ1OVHB2CMy6vEZUrRbTTcan9Y5+HPyu05n2c9patUFgACr3ZEW1Tn9eTmuV+8maXFUy3n34BhIifvn1E22POa9+HLjdd2YOlpWOPen30jvBwgFX08mfWZO/9/pDToEFMu+itMEcybjyndTCaNhj60q2CihgFazo63TN49hNKO2ACkcsqjaURsracd+BmPDx2+9xWTihyav5IaVhILxRqJYdebvgQ4i4PvNzj/0gPlormQFCjf5tW23ANwtDvTuc0uESFmQR2SYyCIW66zZK06v9hpV0VUhnL4Ua5MCu7T6GZmzrimyYsfke9C4maMZXqwUsDMaZ2p42sjNMYkp4fvcBMSb89LPNgDUK5/32hhQznp8aYLWYgYkOipptejNovXl3OpkypVQsgYX+Nv7eRM86o6YokQgTuX1jVkMMEd1SeCjIHKsFx76jdWC7XCC946iFKpcHNZY+fPMdni4cNuTNOyEkZMNfR/Rrh+35+T3W7YpjN4jI8H1X8k0pY9s2SlObsxB1ZU94HfGccZvdsIKzwBhGE5d2JkdTRdl3g8IY+btkhYSIzaS/Ha/2nMOpqR2NkI7V5UTiYAM5p5/RI9d83S749vs/4ddffsKnlxtSoiR4PQ68vLxgu1xHhj0ZcdNZ+TZy58C9yeCslIKYlkHKcKWB2GcAV2phxHyi6qrqGHjkqgDVRN+OY0et1DFLViOp9SBFuQPP7z/iw7tnSBBKVIhAojeKkZkEM3hVqG78LkZslwuOcljDqeI47uxqTwnbtmFdSEd2xQAWsSdEBHjXsq+9jvMfZdYz/e+YVRPGO47KWemiqMpifIdg2zYsOZMWq17BIRlnsL1CRO3Fgtk091TIBmFyvWMQaE54fv8eMQb8/PMvqEpGYteK2+0Vy7pZzVBsX/qe7WNf+2je5mf6TJL5nddXO4XjODgsZEAJDb1X9B6NKkmIoAdhN27v6J01hpzyCcesKMeBVqlWikCxt3rsKK1aS3/B87sP+O67b3FZMnsUInE4EWLsHrkxlONCL4mZxJIXLEtB2RqWdcUSA15ePgFxwdP1Yk1iMjC5ZIVFsiMmq8Q3huu60BPboXUjYwGHw0hM62f3ZMoZeeH4Rf9e6iZZ+ohGHL01HjqJWHJgZ25k5FdbN/YJdaKIMERoVFyf3yGljE+//oy3o6FrAGozxsdOOq7JX8yZB2q1AMqSsOHHWFCqhBNa4/hKW+vaGrJFjzB8F0qKpxdJl3EQOGSktYajFJO3YBS+5IxeduxHRasHbvuBy9M7/Pjdd3i+XuCSDcmcmStGzkOtaArj6kekhVHasiwIy4rLumC/vaFUxXa92CF2uE9MUyiOsz+aFe2zfU850nYONOcbieE2a+TzA821Zp9Eit5F7hpJxrc3rHhkDcsCqYwWg0Gs7P3w7BAGl0RAVnzz3fdYtxf88utn1kUa1+s4DtR24ZCa4kEOxjkZGkgjS+BfU4CuQEJiANCIgfdW0UOyIKDhKAeJHQZ/5kUBy9RbOdgFXwtK72NKGmsDBW93FoXfbjuW7Yofvv8e756fDBLsQ+KFxeXA4igs22kMLkJehmR6LgmKiOv1QhbizcaY+og9xZChcGkRgI1yA0H1QE6mrM0cPKWWNWCc8dr5TIoFttEgSh9y5evZe0eQZGeZtcquAUA1tWYGWawtdOvbYa8DzHlFo3k/vXuPvKz49ddfcN8LukRopYM9SsWSV7M/Y/cOJKbbuvPPnrV/3esPZAoHN0RqhqE1tNrRc5vYVy3WYMYonE82EHsudzIqlHMSdiXnutaC/fY2mphSXvGXv/4ZHz68x5qjHb9Z/HGa5eDeWrRNUbuANdERxRKQFsG7pydAG/K6ku3jg57VBL9iQkyzc9GzgFO4QBz9hCN0D7tsg42I29Lx2huwLJwkZVkNnYAZZxfrgmcLjDhSSgBYuE/GDQ8hYwns2xAkshssIkg5wyWLl3XFr7/8hM+vHJQCBV5fb7hen9i4dSdd7czLhymgjkYnsxS9VsqLd5O7MOPXWkeMxIB762ioQMxwWYQIMeXPOqarpRRB0dqERaZEQTkOIC344ce/4uPHD1hzprML64jQvFFPe2ctAoDTVrUr8hIBZKTAAvPl6cmmk0W4dIhHiYzITYZgdLd6xudLaTUzTGd/VsN82BXG5CmlIIuwQJrzaEaaM8vlFAyYPlA1Qwj2CDgDRRCQ02LiiEY3dKcS4mBFfTAn+NNPP6NYTWd/e8NxfUK+rKQS125cCGNaOWT2sP6Mkj1TYHOZw4V90DihVBeIiQq1zKSNuaOKe6k4Dk47zKHiQMC6LNjvd+yFUjMNAd/96Ud8+81HbBsb9vzMsSnT6jbaIRpHJq1KFqOkYBPzCAGnZcO2ZpTjQF62ISWhdk8KQo+ubTZE7754KTCCQ9VTF7hXmsUCQSWbSmLCZVmw5GWOWQXZWTwfwXaK2Cxm7rucswXScXwfkQ4TyQP7tbSRuZmzaaFFrvUvP/+El9c74pLQ6o7XtzdctwUpRdzvO2B798EsgQGtutP7ut61P1ZoriGhVuKXrTa0bLod0o0OykUlMq0W6TIau9dKcTpJAHaEGHDcC8rBAmQtFZenj/jLX37E83UbhV1faNWpA0QKnS+mICUzHhDTjGdB6ZIyi4uNmB07KK1O3wMUdHCybEN2YEoeTKriCFJ9I6kfMHv4Og9vyhkBgRCVYaIOBfhn+1jTjpNeTQyABqREGCLqbJwKaYFUit8Fa9cSBBPgEnNEEd9+/wNy+hm/vNwAdNytJhNX4vBqpgGnLOm8hYwuwWE2paC2jOiMH68rdJc9tnXxCUUhIgQlpbEdHH4k1jwkgPaKUg7sx4HaKvJ2xY8//gUf3z0hGCPItX3YK8DicjWVV4d/oqlKqqo9M06Zy9uc3rWYEJ5na10dQuAzXyIjXJdXFkxA4eGpPKIMcy9YgT6mjG2jUJ/r1XjDmsMf7EwmZ50d0xiOI4FBAJQNmWxMZGZTVAEJpDkG1+4RaDI20DtGzf/xH3+HCFDKjrf7DZctm7YORrQbmDw+bGRnb7ljS7khxmpaPHa+Iye6kQRxdpCmYyTsGD+OnQFWMAOPjtu+s9+gNUjI+MuPf8F333ygw1bSXR3Xj8YAbLUOSQeA0X6WjB51wMQKN5SZ67asOMuWOMzH7LThumWL7Kcw3jm79/jP5yxDPVt0jN4i7UDoKqbEuoVJqKhyz/v8EFUdfReU3ibkjWijA6QjII21SDmhloqujQoJXREFAz5ttaGngO++/wE5/YSXe0FvFbfbDbW/nx3ltlcpmQNg/KPDVjqs9Xuvr3cKpaJIMYlriyCtM9VTU0aawR0sUg724DvKcTBthg7F1ft+w+3thtI6vv3+z/jLn3/A9boNmxVcS0iDdXj2UTwe8A3oALJHleDDSAYTABgLxrqAICIAQUeQT3pkhOvX86Od5uXFRrMKpz+O9NIPSmQHtXc2Ov4/FDSDaRwBKJ1jFB3bDRIgURCCp5jNomTYYp741EL5jFnUAkIL6CHiwzffYlnf8PL6BlXF7bZjyybDjGLPTMa9u7GTkwUcjJRSERPZVLV3ZK8txA5x7RsIMzjrDYkxYL8XdjIrbCB8x1EOvL6+4b4fePfxO/zTX/+Kd89XpOAP0p87WU0BlEp2po7OxUZKM9KHKtRrDgCQIqIaTdRVSSlsboPPIzubg0d54zZgdnissTrteDwa2xNCiDSLAFKtIGy+NrgUQ7AajJCNpoCIDphgBCBmSIEZbHAC13Tg0ZzY2E/Wh7Jdn/DDnyNeXl5Qasft7QZ9/w5B4mgi9MBpvjwjxuhNKbUi1UKyg0s99/lP6AHqNTaATWUikMShOuUgK8cp6sz+b3h9u2F9eo+//e1v+PD+mcNvwHqZwuENsTnEdP5DjdifqfeyjNoMpW6iwX7smuZo0qlHBDv3weo689n9owYuz6Y82PITouh2Tf48TTHYn6Rd26h/GJQ9Bg5ZRh/E1JIlWN3OvlV1rP0IdMLUURMAKRm6gIAP336L9e2Gl7cbWtkZ8C2Er8px+PI+LLeMqOxLCf///PUHnEJBQUDNeejwdOPt9h7hnaBuTocaaCAdc99ZRzjKwcp8rbjfOCbzux//Qodw2WYF32CS2VzCFG40GxluFsYm8EiAKRMjQRmRWW8VFVN6QlXRg5IqF87FuZOxOeNysIIqr8TkrWeKOSQPgNHN7QffF314bItsHHIaqfi4N4EPflez3k0M7zzBKlOsLyJGjzwWXJ8Jk/366wvu9zv0/XVipecdc4JFZrqpxE5LwVEKYs4cF+iGQslNj0FnMiWCHJIdILDA3xqOsqOB9ZL9TnbR08fv8Ld/+iuerxekGCzy53s4xYzX1+AzpH1d/ICGcc3+LEMgVKQ2q8HX0iE6z+SiSVl75knjYxmZOsykQGDw4AJx3tRmNzueV7cNw+loeWQuXr8gNGiRthng4AKRIZyc/Oy0FvjvkJDhVMkJfViWFmiElvWCDynj0y8/o5bDprdZwXI+qbGfxRywP9eubdQkUkqoo9DsIyJ1wmnm+UKyJr8Y0e5vOFplPeFgY+p+HHh9u2N9/oC//dNfzSEkysIH1gmUno8yN97sGn39Z9Tl6+z9Awgyam1Ul1WUQl0xn9nSOLPHeoB0nOEJSbkT6Jy5bQEC4I4B87vhEJcxj1SRRFhHAOZe899WOrneSbWPARhQGUgoAGajqKChBxk9JOfGSe4TrmMUgYQF8ZlElU8vd9z3gssSqfMEmDyD34U+pr9f6RCAP1JTOAqCBixLxdKmBju7mRt6SP44AcHQFne2UbM0sxTSyV4/f8JRFX/+69/www/fG+vEHIIZfFjUHgL53nDDaQudYhhDYtxrjz9bRBXE3x+RxJp67feDKmKOZqQxPnc4BdsijsN6FiLes+DX6A/f09ho/w4zEq21jMxF1XB5C9Mfi52e2QipaF1HYYqpIdUPJ03WV8ggi64AIpb1gqengr2RSjzgq2EjzjmmAGIcfYBMoePAUhbk2sZ8Bpcw9qjJ0wwRsVqNGFRRWeg2Mbz72yte3nZ8+O7P+PHHP+Hd05X0wZgB0cEUkdbI6e7dECkz2F0hMY/rng7U2vvhnP4MCPsPUkyPAbIqVOKIOsUOsHhTmvPYxwHSE1HFS8kwQ6/TWEePfmVkYV0rSuH+5BpR9ZdOwVhvMgMWVUUUJ2dwkJQgGOzV4NLoI+gXSjMMtU4onp/f4dPLG+mRy8TSp1O0xzAyXzNjylnpkhYstaKaWJ16ENAa1NbWM+OYFutLCWyoKt40VlDud3z+9QXX99/iL3/5Ad98+ICco9VyaPgEASHMLuymrtvl+9Kzaodd3a7IIIgEyfB51ymliRyMBZSJ+Z+zD9gR8SDQvLgaFizW8jxNvQ8CklEb9DXzs1+OYzIhBezU1/m9HgBCDPwVGnkPGGK0WqF0Y0KZdIjbg5GJ8L+X7YprJc197kuh8Kb2cbbVfv618hb++kPsI0Gw8YZlKC62zuldyYdMwxYtLAhKDDiGjuN2w30n5PT29oa9KH7469/ww3ff4LKtJ7jHNNAHrkhjqDEwufO0GjoiA5wNnuCUZtMhsW2egy5a9zTvVHiU+f4zjOLb4uFln2dIFNwN01AEpJztOqyXwfj1Aiqdxsh0T2Et8crU0CM3L9559NQtWkwpondB1Ijaig1F4fc7HBCUGHAXFme3yxXtfsBqV3ApaLWNObaKwDRt+CypZEk9qnKwl6A5vbZ3hO6CJxgpfoqEZlKkBsvb7Q6o4n6/4e1+xzd/+hF//fEHXC8rx6x6t6mPgATsIMzay4AMZHYwM0v7IlPyYiLcmSSEaFDNiJpskpXMZzbIBcNg0DnC9tiZ8TQPNyxgwICQhr5Op6JvCOTJVzXCgH1OMH56CCa3wIdnzDwxuiesJwOU4G4CaB1Qy0hQQ0BUY5kEigZe1oVaUaCjEpwMgll0zwh9Z3fT3omlcL53KaMHiWoF1PIxJjbXOkd0ocBgjYKXlxdAAuqx4+X1Fdv7b/DPf/snPF835BTHGVObzeCP1Pswop4ysbGnwUY9u+GpYMz3unMOEhAS62p9qAy4FMqZUBDGZ5yzPujcR9AJJfl7ncUTYrQxsm1kGz4gKwQa6JgSVVD9c7paxq9wWf7udRnRMaU1Ru6vHjhNzVEYnwgpticwIKbIkaSnGs85U5k5znnNf2PJ/tPX18tclIoqbEBKpQ7+PTWPzJta23gQQV55M2ldoPVGTY+joe53/PLpM77781/x1x9/wLYmeFMXu589cqPn9vmqammXneERIfrhGg/Qo0dhd6PbTgGoc2Sj6poGppwn1pE7GLFGGmfjqD92nZsSGKoYMz0UGc1VPog8Ogxk11AL5RzYteny2m6c7DqDQDq/JDhZShXm40y/pw454iH7Lf4MOmJMaL2z0N7V+3Dm1jCowrOgc4bp7KHjKEi5WIHZsgU3RGrXLgJJ2abdsUO514r7viOg49dffsb67lv889/+gqfLZQyTEVtjz9rcEHjBbxQWHyBD2w9hHuSJIwu7cXlT9uwMorMIPUZjfQU3iDx4IQQEO2ADW6b1Hs7Im7NGsAExza2A47DeEovo2PnNKPaowJoTDaJDhQgmxmfI9YgKA5hMEu6CYswEcRYYQhzDlVSU9NWYyGIyNprDjm48fWX93gCxR0TcvFZKnJeFhWavA1HMkb/jAoVBAhblAKhkkvbHfgAiePn0Kw5Z8X/6n/6G98/PVjA1tteAcXx9DGeHBSz+kAE+d/j5PykYe6ZAqg+8vgN3pIDdPxtUPar359kN2rFPt7M9u719vQ1LHFmG2xMyAg+4hD1UbUwxn1EtB7JL9ZhSg3+/n7xgNssNuTu33huCGAysDVUrShXkHAczKwDQGBB7RI8J6MH0tjAWe0rZ+Lny9f/6bOEPZAociH0cB5a14WEWa1No7CMi85qCQpGiYH+jgN1xP3DsN2xPH/GXP/8J60IeNCEenGrjM8WV08H0++P9T7EpsYWGRTI+MIOaMzPi80hTgMFVF3MwE80nHHZOu0Q8gpiGcDxrW1n+y/sdghXeTDkSMJiL0SqFBDNiiFRJlWisqdkc1G0oD3yIBqhhgwBEYy+w1Z4HOOY0I1gRdNNZyTmh1oIU6HxPT3hQ7c64K0DdGmaEB3JZBx3VmxZZIMMwLHZsEVNErRV3Y5W1VoC44Z/++ldcbbh8GlLb00CJO3Y5DUMZF2pO3j3iSQrCM4bedTx/d+Tu5ejHA5KzS3ByKIFGsXUZjsQhgZEpjPRTHzJRWNYazJGpdhNp8yFC1HcS5WyCHCPYFwI2fHm9ACdWiDJjC5GZKEyhVWGCgx4ExMDO4ubPgE1xqSucaj2eAw8FMEZl+nrzC1s/TBrkwHEsozPeAwG12gIcXgkKRBgBoON+uxEuLBWlAX/5n/+K909PVi/ynaFjrb0WNwK5kQ1gwDtnkULqQ3mk7dAPgGBm0q5r3m0Yk/YGYuAGU60fAAa9BaH0/8gUde4ZYNgdxgcz8BOBScF4gyYDiBQj5xiYplmz2tCAuIXEEZf/8L0YhNke5zGzB6ubgkDvzpg0+XkQFtOUOKO8NeRoc1vmyYazIVV/O23v915f7RReb3fEKAYd1ZFitt7QMac5KazYmpJFNRGvr6+IKeJ2e4NKxj//019wWfJQN4VFZtX4ukGsYcfqCwA3v3sGhxhVxcTP5uZwXykAEECM0J2VYswa8AKX2BB5CXG+z4zjfMhimjO+2cYOnA5rBgPTQ6uiFJ+3SqNXLRVVWIF0+BcP4y11dlhC5IHW2j3ijJEdpy5b0OY4Rc+iyDiCSZUnjAsdhu9xo/j4xvJ6o37UUdA2Y6N4r0JXNrWN7EcRlBr4S0w43j6jSUArd9x2xY///D/h3dNm8y7iiI58JvA8U4xCGbCdMie7ViiM+meT7IDhuBlom5Hpah3lMBkLGfcmwgMl7ojcZJ0MqIwVP621wQnD+QfBXOKZabn4GcBsiH07rCcgMjKmPdDTt8y1HtCSrbsGGRlnihGaOmBYv1UmqT5s0eyy5CFRPZ7dqAX4nvaiq2XMzRSHIx36w1q3k8z4KWtOgdLo2g7c7hUpAL+8vuHj9z/iuw/vKS1iBAJmJd7AZ70bMgv+0znA7sPPkgwaqXoma9ktDQQFI33VHpli9izjhBnPsLC6QzgFJCMjwPx+2pMwA4zzc+j+jBp8wM5hgUCC72f+qmeIw15Yti1BEDSgiyJ2WCMkrztoRASfl6vvdu0Ga7FRUo9iKICOwNrVUh0fdrHO4Ye+4vUHnMINz5fVaglziLsPxIFHH8qCZYY1TdSCz69vCCGh944//fVHfHy+QFz7R10+oI/Uirg6HhZqYP6naxLxhy/D4HmK768YA3o/RYYng80o3PnNsyFnvMVwP49EHAP0wpG7DfVI0j7Xo2nVNqCW1tQkqCukd+RltXui4aYkrwyDZ0G4GbcAcu787ywyt65fEZemhmULRoUL7H0ILnUgMI3+x7U9Gwmootw46yDb2nZjpFAJso33D5hmEQ/i8fL5M2diC/D+2+/w/cf3SC5bYOvYeztF3L62j5GdQ0n2aE3WPMz3+Rqe1hrC+d7uADxzAgB8wWSb7uL06/6/vtgeyRm91I2OO15+7CzszSicnx8EFtlT5NChr24zHyAW8jjEKfO7RTgghc9EDZ6bzY2sn6hp2thzMXzcs4AQw4CRPATWsfd5Vno7cN93rMsyaKVtwEd93BO8KTYsY2/d3j7jvtPxXZ4/4Mcf/4QlefF9Om3eqhMDeKYdCprwDJ899zsmSUQw1nKsEj0p0QGDRo0nwrX1ANEzMd8PY6F9D/j+x9gPEuysjUemI7PxAAwW1DjEx6yQE/KqBGz2/hwdzpNx2fw8/7ZHKz0DoIgUFUCzgWRz3/XhPycMClXakZlyzU82B/TbEPA/f309fHSnZDWj0j7gI8cfvQPSH3pQQinl/oL7Tjnb73/8G3747uOgXVHuQU5p+owIguGmvpgPNK1RbBo73v5iooXzI4WCVKeUza9RbPF903jLvQgmdxxuHxzCUTMw/GxnL/xm48CjYd/qPgvXKZHdGpPioFHKuLqZpYwRevYdrhHflV2+o2ej+ZKfIiY5bTTA6hER8FkJ7kQcOlJY9EhIAVZsbCNL+AJGsstKwTdbxee3G47jwPX9d/jTn36w4q4X/GcUTAN2ui8KS3G9TtfMDM1ZJG7UzGE8HCxPJyyggE7ykO8fJdMpusMQ1hS0ndbaz5Xti+4G2Fd7njYAOmZNz2mCYkFABVK0CE8pMBctCHGW2MAmLIvtHa7CDlhmZPAgYDCpKprWU6biNynjGYkwqo2BjXBtPB8MR+n/27ti3w/g3XT+rdVTwGfOwU1nZLAUY8Tnv79Q6ysu+Ns//wnXNcPZUNQCE6Nyz3qdwnn7Mh74yNo8IPzNuZ4O8+F2IUB0x6PjTI77PIXHPl3tHMzwe+2/u2VdOs/fw1Inl+A2+XgjlvDssQPd51Zr72gwwkQIth/NVkiwvhvbnSOrM3hsBBnWuMtmn7nY7iH8mQyLwYCPCTjPc8dca8Wkwf7e66udglp0R02Wdto00zkMWl8AEICQIl5ffsF+u6PFFX/94XvK4/IDAXHZBzFGjsECxsSITn08OQc5bag5ZB1jMyrm+8WMxEx9PZtwDPr037bpxlQjTHze91aADHzwbJA8kuFAi7lJvZDJWw1wddWYKOnAgpWJltl9jaEeJ6c5rJVBDGNj22aKp00wvh8wayCz1R+U9RCUxyjZD4JHI1BUW4OhffQPmprcmQsSIBF1v+Pt7Ybb0fCXv/4V122xxXGHQ2npEOKM7JxJFHxuAXCO6sZw+5FZnKK+8TP/uReWx1+frlE5NHFa9emg+GgNljxHtrC1nJGliAcCzP7Ir9BRS/C9FA0A18CO+64doQtUXLYizEdvES/8+sTX2IfhTGfkmUtr3sNC/PlxML2JPAo7bVufhmPYSdfLUQZ5Saw5tJ7OdT/92TxVtkg0acfnFwZ8H/70Az68u5pDmn0iVek8Jq6uxigKw/HPtTaasWVoYTxneVj3ITA4fOo8g5NWfHYMhjZ16naNWuHpc+YW0qGcO35uAcaYia4Y5zUK4T0SJ1whOM0gUzvQMRAOR7ga3En4HvZ9pl8kD2dFh277VIfa6WRNAmhKCm6z4EB1PHPeA6Zt+Z3XVzsF0YbSiQs25zO3R+fgwywksNErxYRPn15w3xt++KdvcdnWEY0KuIgIlm5aqieqQHSYaOL4/gruHW0D4pQqDuNx2mj+UtsBbsqdrz2ZL5PjOysZp8XqMmicLoNsboMOwa6tmeaTwoStzGFE++7WO7XWT9lJV2eZ8JIDAiBtHOKH6U8WEfn1dW/qSpGqjWMWsw5e+MBWgcHugQBjahhG8DTE+speBg/ch9s000JKLZlT4AdpDAgpouy/4r7fcX3/Pd49XXCOuM7U4dFwaBHUGfcdzt/vFzB2Cg3LAH14M6cVPv3Jv3QYV6uz6JygBuuOt84Fi6D9UNozDOOLxs/G4Qq2f21dnVHljl9Ehjqu35G6AFo4Ydd2PZyWNx3ArG4AYl1JnokHo6RVyyDGoXcI1PezP1v7O4+cZ12B2WorOyoEWTkRjYoFFGJsBif5MKKAzHMnDa9vdyBt+PbD+9mZrr4vPVNxh27yLH7vYvDP2AjjxE8kAPIwKc3va2YF8+WQ0QgiPTAS3+t6XkH/f/6sC8S7l8elOJh7klP3/pTAiYkiMgyodo5eXZJXfDk2F8L3mi/lPVjA505s7lsZTmmgFSImBW6d8L6v7Tdn866RDuD714+AaSrpVIP9vdfXOwUo3l7e8P6ymXCW66RM/vr02jxqUYD9KFivz/j4/nnIVXtnLMJJf2ZEfzIw+zPqe4ZBAJwghdO7/KF7BgBYswzGhvHoSnUqVQ4cWBVkabiRABw/F1vV0YFJkRYMxplBE34dHP33pbGbndZeh6FiZhgG7BwZjgOA+dl0CAI2ALVxrTPTAK96BEu+y+fzHQ5PcLo3l9XmbNdjf8FRPiCnPgKAXj0rnHUkAWVdQgwoLwdCyvju22/mQBuLZFXpuM4O++zAPfWfBmD8EMEZXzKONDyanD9QT55o9ASM0nx9fY8MJ+vXNmhUFgh6iOhMMz+c3M/jY/o8dl+KKUbYYQ1k4Li0AamiNBIOQ9g3zyxkHPr5PDjkyXF3/l4IwUZNnmC1072OQr3d88TlPZO1qWod6K3i9XbDmhMlTny9TQfJSQVQIOQAqKAXyp1/8923WE3Jlvc5pWim4jCfTRhrfT7XM1T3/fCwL0774THYm0Xfx5eenqufKZhRdCVS3y80+EEIHTnDresJrlaMYEFtXaP1m8zrFYSwssY3aNv8bJfj8F4WD5D8d2f0Pp+NmmMbWlKW/XHC33SerXsz9jxDzCS8uG/2yxw6e1h+//UHnALlC7wNvg68sc1NZEZNEulYaAVHbfjw7Z+wZBOEihaxdR3sgrHwCoifPJ03f34QjxtGhr07R4yz4KLwWoBHEDwoI7Qai+pRFCPzeQiGkQbgqxC6QAMzgt7PG9RgJ49qzkXR4RDC2FwWdtJQ935qoNUhlzFW2xzPKbgExMS2hPr+YlIBrfb5ubA08pzSjrRAT59lTsUEDVvlTIW29uH8B5R06llQsecBTtjarh/xfFlHnCuiqF1HE+J5jUYhkA+az1FmJDnXHw+OgtAT19CDBR0LhlPEiLGX3DlOmeTphP39Pnwo2N+z45YCYzKM6SNW/eUZsScO2BqrMUd4wE/r2/q4R157HxmCeq3LwhLeowvOMfIDrGMXOrI2v23KN8/swAMbtYh8FHtRSSdXzjTm+poAojELWzVo2PZkCnw2pRSEtODj+2d+F8iIK43RcbB9Ps72KXh72MPDGMKt7GPG+OBA5nv11Hsz138GeTwewd3H6Yz7e/q4EAV7G6BTWmc4kXNAAdB5zDvgp1vDkp+JYB3Nfh1jkI45gbEW/jzUo1XAnWOAoA/H0Fl0D7B5F6yLiShh/WB09BHrqAUCgMON3o/yNa+vrylA0eoBp6h5HcEpbIOloErpbOV7Ylo4+EKAani9wuGSkzXXc8fo+B+7s/OVWMRtP/9y87inHdGiR1JMG843dPo8HZsqyExfnSJ2zljcRXCuwYxmvMLvzqZDGcmN63KslyvndUb/Tt9QX7JmZjT0mGoOp+k1ixEhWm9Gm2qjJALoSKEnh5mfM9J61TEXWXvHYaylM/3YGxZHRBrtuVrhe904N6K3hmATsKI1Enn2w+dkka+H93YvI2E0T//gXOX083N2OZ7JeKin5ybAoC4+PlM3jl7gg8Gjv3mdjMlk1MzaxnQU87rU4ED68v7F/jyxxR5mffuDwIjuzhbUo2V3UIQyBKL+vCNEK3rrCMGzyzlL4fyMYhT2DZUKACj3wl1mmfwgFTjE6NlU4H7hiN3VqKUdGhLQ65CYUX18lrNG4EFZeDy7liE8dLOfHMl5rWedCDjPVB+vL997uoZgSra0019mIA8fAoeItc+1PiMCrHEAEtIwxl6jARgIoTv7pw878oAG4IuAxi/f7EvHDH58rUWZvXn2EDRizFfXidgM5lcQ9ML59l/zCr//lvEs0OuB+85h2o4xEwrqwyH4grWuKGY8YhIbXk1VRR2h/TzEHiHMZ2PRjL31DMOEEE7dpjIa3IbypfiCz6zBo6jx1P0fnKI+jwTdw5qnHtpEgpMz8kt3eGluPtcJ8lTR3jVqL/ZlY+Fam0NF3Bh4pDIN0Fncza55bBZPI+0aA3/WravSYmBMNs2ERMb1i6C3gtu+s30eHW8vr4w8z12uZwOhHYI4skWmQJGRkXWl9n5aY+BhbYY/EIz78+fgPwsxDGdxhmicPnyKr07ZB2YWNHOW8bxPy33K8M71jrlnZvSK+fsPUV4fkbofdI/ea2vDn/guoNEFvCg+M1Qdn+EGyYOD4ZNkXqM/TCcR0FDrYPpM43QiHtj1u2O73e6oNpDnfn8Z53U4ft/HvuYKm1znQSAgJnkShTLP4jc7AoBHsoYHXw8wov33Wc0UEMsC5vk+r/+ApebDHXvnvFbz/M0+HLct89mZf/a/tf2nOh2rb5hus8fnPxhZVT99F0DYtDFis+95zNzGavs1aD99J+Z921qfobXWbK399wxpmJmIrQHIijrud+yupPo7r692CvZU8Ppmw7N10tX89vz/XCK41oa8ZgR7eEFYyHpIyNybCW+OS4Zx887HPTsEN9jjIWE6jfDFwxvR5zDi0+CfI0dGy3K6Jh2bxc3GOpnYUwABAABJREFULDpPgw5ws/nmediI52hV3Ys7vuiHZ24uuzguzDl6ceP1EAFhfLbYJpfTATMTaA0v1rWJPiGMU1giIhDt2Pcb7vcd1a5vv99w1GadsG1ATw8G0NLbXiskJWSfjRACR7iqWwIDitQKeCOQmI/oLCsxorCT4ZvOWP7BP6eIzzcQfvv+aYgx19/3gD6ECPxz7+Mnauv10I8w9oqORknV83r3uc7zU+FQDm/KqdQ61tohD9ifx6xTmLMc9zdMI3y8rP0anLEy6kZjbwhKOR6cgraK++0YwYhHnv6Q+M1hGkAErGs2LD6i1WNcogIMSkZ2gnkfcrrmEeDZeQwyz3oQUnXPZ/kc8I31xBfvwW/OgMLlaMYi8mxg7gOP2B8yKugD448BkZwP32nv6HCg04ng4d8PWa7/lQd9vlmhD3/nwQpwZl8pvPYZXULkRFUdQZA9j9YKbrcb9vL/F6cAHPtu3nEeaDk9Rz6TOet1yRx4EmJAOXb0k9qiHSGMDEFPD9ge4zB1MzT/4pCfo4aTg4BtkCEQZYZz/OdpU9m36IhuLHK0h/qAXyvmzO/zgvp/n/obdPy8nzaWG8PzofPFBIZqp84ogv7DHM84pDq+YHyXRWQ8mOxp0NaBEB+omITpwnhmUNYQ7vc7xe9sY/dWUFsdjsC+ZKy1/9t7FxAiJYVDoKieRapuzM9H6RzBjWh8rO0sLONhjadzmDcjD+91g+gZ5LnYz++yph+R2Rpvz2Tw6VXx8DVqUab2h+8+O3Pfy9MZPDqNebPz/eIGwJlc5y/0vSV8H7WSZuBxNnoizoiz2egxjD3x4FDtAlqr2O93HIU1Bf/Om0WSivM1zz0CM3zesJpTtjPWTf/Jt/NcbJGzcucXhvHszP3MjqxhBm5fOjT/jPl9eNgzj9G16Ww9BApzmNbD2bbXaNobX3AO8HTEc2cH3h2aHedy/q4HI34n/tkPcd+XjsY+QG0NZIQNdMIxRGuMTAOK5paWh/vpvXGG+XF8daH5DzkFBdAPHnYFBiuBFz5vqsM6dFuHhISUEkLvKNUjF8xDasepe1RlPwsnA/6QBYQvFn+8CY/G/jfG1t8fRmcpxBtMHqNOj+ScO3+2QfOBz4hx3AbcRM+o5JS4D8NKA++fZ+yV05KcD8n8Wouc3UH6d40Nh9P1Az6j1h3P6PKVyazww6Pase87jtJOOvwAtOM46oiG+vl+3HE3hfZqGlgwSeyAvRxcY3dyfihkwhlnHN7XYxz+k3P4TRCAsTFOjt0N/2mvwBuleL/D0JyCCwynf4pEMa9xPOuBrZ83w7x+//O4W5337W5bHowXv+dRyPB0ryNretSrsi2Dwc2zP5N27La7j3KdzyX2yBHgaM37XiYN2l6lHA+QpXoA5HcrbGIjZbUgLxkxJRzHAXhfB/xE89X1C2hNAJmDOIYj/0dO4sFYi55/xe7e1y2cPsPthgwY1Z/dkNnQiQqIXfR5L7pjPz8bT4HEPYJOB3D2F36+fe/MgHXem2fAgt/aMoGMIHsYDDkHKTo+A6f32W6fCIg9J+0d9/0Okcgm3q94fT37yNama2ch0tJoj6p8KwgUrXQENNScEBPpavuxD6qpP8mZHs9Ny4382Mj08M/peh6M5tnt8iczevtiE/kl/CNP/eXnjGdviz2uwQ/9iPD880+H15fR7tWNv5yep/h3mmEaPkaM7vpwHXMnnMf/TeaCGx5Aupw0UazmEQK0tsH6crnx3hunnMWMnPM0VKpoRx2QwnR40zH0XqCIKLVhCZFdlWW3u5+Hho9IT3+eBtIP2uRcu4Gcz2rsB38ap2WiM/BDO9fDP0OtgDwM1ilqHR+jp8/+zUtPB94uyKAEX8cvCNQPhl8waYZjvQGo6Onn51P+KPTm/Tgq6jV9TFYdr4G9pSauZnvirPTqtYjSO46yQ0SQc6bUua1ob2VomI1oVs0eK6Cto1VFrQVLWhETi8ulcXaEdoWOdhv3JnMRaczOUOCjI8TpH7Hn8JtjfXpOY93tVJ3nYmOsU5+fj/njx086L7U/V98nfDrGQOd3ngrPCmDEM1/sHzl92UBTgKGwyjskM0jtWv05OSzvnzT+LAJYT4XXDamjBGizQr2EsWfKcQCdWknp+Dpz/wcyBd4cnYIXW76Mnrj5j3IA0lFVEAXIESi1ccM6RmePaMIjdsNmqLxhCacN4w/5y830W4fgr2mkuS+N2fBFZnCOHOfnugPz73w08nr+jpODce//AHOM73g0Zgo8LLb653hE4U5EZuQ4i6En9Vg7teOIiEDMgNB+sZagxl9+uDfAaHhhZBejnqE6oAIoBmToh05BaqJqQDkqUgqIKeO+F4yOYEdchJHMl1HxKBiHCevIP8gazvtgeNZ/sN7DyciJIDCw6tP+8TWVE0w4l8I+n891Ov4TXIhpJOY6TCc/oY75+vKSz3v4LAc+CQ6kHDvZYTjr4L879wpE2VA3spRJrXwwnuq8d7H19vOno0bo9zkjVXs2nQ1t2gpCSlBEaDlOFnGGzTzDbvx9P3tEj3EvowbozwPnZ/iP1vi83PMZj4l857OBR/gM4lnj7Ac5LTY/cmwCnJ7B3DfcT1PJ93EPPJ7vxz+fbMzpbCOcz3acGaIzR754MuHhZ4Cj+N5JLmGSoodTI0XqQSrof/T6Q5RUTwMxLunB7o2ftXIgXK5QZVv7cd9Jz7IbCO4VHmhpwdJ7T4G+MPx+cw8/sys47cmxmPZz6XxEMxqf0hlBJuY7pomNRaYu1ojUTg5i7ic3jjrubUQTDkn4+4bhGCf5N7ZtOpazE/kHeiUm2iUCaA+Dy6wIkKCjU1yUYmmtd+sP0YdVc6ckZohpgKa8w0NGJfM+4JuWb0QZWj2C3g40BZJM5pGtAOw82v1N5zlmYci8urOhPDvtf7wzYVGf/e9g9pzslc/ls8TqcV3OtYT5HecVGtfxEBDYu2R+6D8MVE5n4x/ew9gnU4tNxl40MT4IoJ10Y5P6Dn6fAog4g17nnA85UUNPxm7o859lHx4i40fYzsh+QOCUseuazSB33EtDWuYgIbcT7hB8iM04y8MxeD/KPG90FuHhmTys9EM2r6P71438eLQCa+D6YqV8D49Adjqe3wSH8zDys9X3C+YzlWmwH/bug3v74gLGd57P9tw7A7ZUa4AFi/4InPmhPbDZzjLrph0xzqbVoSnlX+fOX4QDgL7i9YcLzQKxgSWR6pcmbpdi4vAP17WRBNGGuGTOZQZmR97pAJ2f14MTODMRMP/8ZZfkbzzz+YGMP/iG9deErMTeI2fnghPTyKMl4HHhw+PBORt5hwTONZAwNtCXhuGLP3+xl4a+k0fBbsCHJpB88XPfBH64HGs2FoZdawjsrPaiWwgBEcHmHUynEWxyVoy+1jLWPgVKIPfWseYIyMLW/hDY9HSC2MZzmbKd1qnM+35QzMQpaxgH6R8Z0/n89PTQ5mqfahdu/IdjnvvBo+sHE/KwN2cUeDZgwzjpozM4R6YzeJnXNPfT/Lnvf3xxvfPzmAnQucrELMzIuyKHRIOQTnvH62YheNAV2EwYI8LpO0NMnIpnk/Ek+FlP3B8xsVt5WW2AE40Tm94m4cH320NAccqEvnQ6vA2DPPwZ/3a1T+dieMyHLG5mOPNXzpnd+WdOCnj8+W+dw9h7MveJ255xrof/mHZk1FBOXzF33bQZD0wzuFSFryuMABHH2UCY9aPgUyk90wfgWWC09Yohcq3DnET3e68/0NHMKCSmjMuakZc8xiqmlBBSQkwRIQXElNA6sKYACLVSmonFxQToUNU0KQFPlQIfmkev42H/gx3i0ecwAfrlG+YBlPF+PWF1p8LX2SAooOjj89yo+He5Exk1gPOX+2HgHycrwIq3k2Y6KYLeai/zyyFisx68N8EZH8rnH4Ocon5+fQwJaqNMg2nApxhQO5BiBvRAaR0pJRP9C9CQkHNHbxkxFkiKkPrI2NmuG3LOyJnrG2NCTgkpMzDIMQC9Yl0ugDaETqpcl8mIGX5ZMLLA4dBHUkdLNnyZnn/PEmK1Z+TP/2GpHyyBHbjJehmYpZzea/8eirh+QH21T07Hs4hRuzivta+Xj9TE8AnwjTGiwJGN8F0KbpQu0zD0RsUACRFLstncnddEbbvAqWfSbK6Csqm1VqSY0NFwv+8sBGtFTxGxMprP64JSG2Is6BpMoI4OY1mvWHPGkkkOSTHZmFz+OUTWjLzY5RLXzeZ2W44xloGQJB5+KiNSPj3b0zOG2Rn58mydXl5LG6ff3jJGfdpa+vecz7ar2o6AoDucet5Wv20i9c+0k82bD5YlujvwgE7EVAROgpZ+n8Ia3pgI1ztqo+ptzospFMAQYbe5EaGBPQ+dz7SZokIOCft+R0e2sy02mVGQVLHkglICWguc6vYVr692CsHGZa7XK56ertjWDdvlgnXdkJcFKUWklE0ES9HbgWX9SD0UzEPMZzbTK29FHzj5iAr5MJ39MM/yhAu+NMx8/jKM/AgabPfo48pj5OiYbxo/sU5Tdwbn6A32fWN4hr9GWjoP/Dm7OEdBnsJP7fNxlzjHsE5DFYsIailAivOa7JZgUeBZvx2IiOiGrUeU0hDXDJSKGKPRFyNyoqEvR4HmjOaOPi549/wOl+sF27bhsl2wrQv/LiQWF80I9rAg+vMXwiAhysnKn56FZSjzwXh0DBBuMAjk9Cx/Y/TPL8Uw1v6ZDxS/8zJ/GR2e/ix2gGdWFcY+U+ChPjIgF5jWDDCaicRhSpyy3pNjmN/tomsWsvi2DILQxQgAGGm/PyKAa93Rh45WDB2Igd+rCo6vCCam5pLdEaEF5BRxhIAWEnJLNugq4MP7d3h6esK2XXC1NV9yZuAX02Dw6tGxvVvQe0UILhE/I9yxKYUOYY7KhR9rHr2AeR4t1TlTlc+R9ezTOe8DTGfrjuRUAxh009Oyn+I5nM/bOQMc2caJsux7gPTg03txVlc41flCeFjr82tA2bCsXiObHWslpRt4YH2NGoMKRKhNlmIc95RSQml0Hv1ols0H9M4APYWAGgTL8l8sc5EtG/jw4SOen9/h3ft3eH5+xvVywbqu/PvAzdd6p9pgzMhB8abKUXjoCKYt0kNHgA2J8U5kXzU3vB5ZntPo02tEZJgbZnzGiMjMs//mxV3phlvce4yvprt2yEN8ePwp0pHffObpGuDxA07O4FFGw3/FN63HHH4L58/lDFpjnwxY5gQp2RfFmNB7hSudA2q6RTDIgRi1hMisrXe0QGMSQsSyBOgSkfOC9foO79894927d3h+esbz8xO2dUVaMnV3ImsbzTpfl8uGt/3OiKxVQICUFBwkr4B0WFcTQujTYQxn7Id0Pp8HYz9jw7nO52cI9/PDAzyujhnKc71n/uU0KAMqGn8lp3/7s/e1nk5/Borym337JYiE4VTsvtUUb3GCowBwNsPUQ+J1cMbzMIgmSdFbh9pgFkAGawjg7HBUQGPEDjqIHBKWkJDzgpRXfPz4Ac/PXPPn52ds20qGUooQm1vQeoNGYN021P0Vu+mfqQIhz9Gs41l55MsHMxJE+eJsz8fzJXTE/zpj+D78StVcqgKiLjH921N5/qQZes26gqqpAAxHPnzaY5Dvnz0CHBm/d65/DfjnlB2e78X3u2cenDvN66D8SR8oQxCOK+5idq7ZkKXOveEqy6PPBjbDHQ3SKr8pRqQsSGH5T5/N+fX1TiEkSErQVvD68hlaD3z+9OuDymlMGcuSkfOC6/WKp+uVE4l864dwSkuJcwXDpR3XHsUonBgY5zZ32IE5eeVHfDIQ5gjRPCa/i9Gaz/c9iYidjEFMCVHCydhGRq19wklutDAckf1U1fT5zS0oD0rvj85sFHItMqi1WjYgoEY/JSMUwHG/4dOvv+LzyytKrcTpu9PXyAzyInlKedJUTasIwogy5oyn6xWX6xXbwmlQtVZod7mSbtleRl4SvrlGKDqO+yv+H//7/4b/9/8rjXUe9xETYaWUkJcF16cnbOtC6DsFe/5h1J+CzLXg78dZ3xjrlowiyc8nrBHG4KPRdBQEyb4/poxtXZBt310uF+REWDOmZJH0b3WlQkxYlwWO7bJmImPu+FwrGdGjGwJn6cw9KeitopQy9pP/PcyQHvuO4yhjoLs3eJZ9x/0oOG6v+Nd//e/497//hNvtjtvthmICZv55np36GeDf8TpTTkgpY9su+Obbb/H+/XvOs4ACErFuK5YU0Br7DKCAhgV/+T+/B/6vQKsH/uX/8//Ef/zL/3dQlv27JUQsOWNZFqzbhsvlgsu2EttGQMwRMaQJM1rNKSarWwjPOETGWNtotQqvQzos5ed2ypiIDdwizLysC2JgJrtuG9acxx5zHaZW2wgMW+8IIWG7rAO+9RpoN+imq8JFsL3r/nzeHZYa9QJnadnnNZvpDtAGlHLgfr/jft9H5qzaUY5CqYne8cvP/4F/+7d/x6eXV+sRKidUU0/ZKW1PiJHIjPWG5JgQU8J2ueL56Yrr0xNyzgB01IBgTLJtvQCr4Bref5WtF/3PcpwvXt999y2NiEtbqFPYKIuclwXbtmJdN2zrimVZsCwLUs62Wbipck5IMbKolSJ8ElUc/NpH1snZITj7iJtmGhsAA2sHgLxsWNeMECLevX+P5+vF5kHnMfmslAOqAp8ZAAQs24ZtWyHKIfQ+HY5NPYpswn5nfSI+Pp3TtezAqqkZ8ln1qUliaUEtB37++9/xy6fPaCrIiVlLrx1HOXB9fockirfXN+wHDcph1yGYEgK9d2zXZ3x4/zw2dC3T0YxozGC6WgpKJYZZa0GrlEH//PIrPr9wbOpP/7f/Ff/3T78OAznXmn9OecG2cZ3XdcWyrliXBSkz6lxO6+0OOlkmKWaAnRHxWCgPD47Cw/Vo0+miZRYhRqRMZcgYM9ZtRQwB2/UJ79+/Q47BHFCCQFFqMXHAMNYsxoTr9QnJZif7/qrHgWIjNFOKcOr1OKT2+y4FIXAD38f0Ne/hcR/aW8PLp5/xb//xd9yPhpyTZQeKVgvCuuF5W3G7vdn0r47b6yuORi0hOiR+j6QF33zzAUEVtVYc5RgT/gZhwCScSzlwHMUi/IqyHziOite3z/jll1/QGlDav+J//9/+w861axrNcZwhZVwuhJLWdcW68lznlM0R5/lnW+OU8nQC4VEefwycOf37QcvJ78HqUYBgWRf7a8G6boSq84qPHz6Mc55G7YXzIDwbd7r1erkwIAKQzIG0Wk0QULAseZwnP1+jP2c4ZW5JP9PNegTOWVzvDcf9hn/7t3/jGOKYxj7X3lBV8PHjBzRTdyC8d6BW+55GOZlutuP5/UesmcSNZvf2ZabbG/uMynFQ+FI7jp1rf397wU8//YS3vSClFf/L//J/+V1b/9WZQkzJ5H49uraZtzHi6fk93r17Hs5gNSORU0K0aDLEgJx40FiwOmUYwWEcK/gMDNb3iqVkzjXXWbR1+ImYJ7HcyA4eRoAhmEEAD3b3saHm9S2HV1HUUtBz5sOuHS1EE/qquN1uSPWK65qJX4ZoOjA2cav5cJKKkDJ/rzW0WtAcC1aFRFNO7Ir99obPnz8T27eC3rpkev/rFa+ffgJixp//8ido3VE45gulHugdyAtx/OX6Dt99fMbb6yuqXes5ymxmPEqpqKUwQ2kNR9lxvL3h18+v+OXnn/D55QVNgeM4LOsIiGIT4uzArpcnfPzwAZfLZmu9YFlWLNaomJMZ8MiCZRiMNAGCFZ9xgmTgzt5hG4vmAMJd6kHCxODdYQuAmOOA6NihS2quQxbeJD+hRI+BFLU1pubKT15yQusNt7dXKBKenq+IwUAii/gluMpmJZSCgMWcRzdJ+cEZH5TPjt4rHXyp2M2I5XVFXjc8v3+PUG/Yj4ZvvvsOS+x4WS9IQVCOA7UrlnW1CW4RP/74A9p+w/0o2G837KUMQ9FbxbHvKLWi2hQ1rn/Bsd/x+fMnfPr0Gb/++iuO1tDa7SHL5myBgBAFy+UJ33zzEU/XK5Zl4fleF6szJORskHGIDACiEJaUCWOc7D1Xe0AscAyQ/xpd/VPfR9Wyd87THdmlACN7JExsNsnWdsxaN0MulgF266wr2qGa0VvDfnvDbT/w/O491iWz0dGK537drXb0VlFbR14X/qxZRjJujgbeh1Tdb2/Y7zsUO1KMSHZGPjw9Y1szfvr0C9LlCc/vnvH66RNyDqitohVBSBnbuqG3hm9/+BFLaHh7u6OWA2+3mwkfBss+DpRielSdQUYpFUfZ8fr5Mz6/vOLl7ZUDkb4A5v6z11c7hTmGcaZXMWV8+OZbfPvNR1w8O9hWrMs6NkzOmQsXwtBKcVZKV0bfYmmXL6wY1MSza9CTYBgPIjdn6lYYeHE0ByQhIOfFNs9kovih9aix+WLnBSkIaimGhwes24pqOjGvLy9YKrDm92Mzzk7fPiiYvTa0DsSVMI1HEs7ogX0nGSA0bkNVNfDu07Iywk4L1sxD1oOgHYWGyFoYe2ssOpkz9XkHXRlRxSCWXdgYQYOb8rIgqQ1Ben2B9o5126AKNFW8fPps8thzvSVEvP/wEd9/+y0ul43OYF1pKHK2551tvhYGnDWa0poxp6LM1NsxVYfUMBk47iwe6iZ2AP3PpMsSEsx5xbJY2mzOv9VqM4JnxF9rQQhk0vTecN+ZfaVlBbThOA7cb68oPWO5rIxY7XpUldPtRjRHuYcQL4ArYJpz8DGKvfEzRegIqnWGO4GTUEBEaxGX6wUhAhISAnaU1tAAhBjQe8V+U4R8gURi6a0VlNaQlxUxsI5zb5WRbusovSEtK7btgqfe8dPPf4eIIOcFT8/vsNSKVg/8/d9/O2L1+f1H/On77/F8vZgjWAhBLQviyAasyG0wkGPrUMrbcNDSI/RKSNZsKTAyaz8Hg+Zpa+3GzxlQIkBKvJYx5a51NNQBpTqsXGrhWTAbdNzvqK1xHO7CTLnUA/vtjrxebR9POLq3jqYz46+1okOwxDDtoXazZ4FZ+LGjVsKx4V5GwEFnQRsI7cjbBU+XK7Q1pByxH1SmFdOiuN3fIGDAkqKgtYrj2BHSgmxOa78fY65Nax0hJTxdLhAVHPsr6n7glnZcLk8IkbDTf6lTcGlk3zjL5Ql//vOf8fHDe1wvF6aUS8a6rFiWzI0TE+lTRoFkREAWU4eS2hgCP3tw66cRUIPvGVkGS+v6TEMtQoj252SRakwZ1+sV22pzHGoxmtYB7d2gKxqGVna02pBiQq2K0Nn+MzeXFeVyRm0Vb29v2K5XLFajaK3i2FnQ6b2jds65ZfWfmk6O/aeUoSDMUGvFerkip09QnZFt7x05ZQTwOi+XK+4vn7C8+xbfpBv+49cXhBABdBzHHSEuCKZ+GkLGsgD3o9CwWaRI59SJ2Rv2HkOAtg3PT1e8//UT/tu//P/QWkcWwV3nkPLeFTGv+NMPf8Z333zE09OVkJHBQ47xkq4abJKcR+YBIRrubQa8NR6OIGCDnWOu7ggAy/ZgDnyOdITrVgmNUjbq5Hq5Yl2yfX6F2sFqtUEFfJ4pQjqVe9NCee/e1ZIRIfUycHRpXlb0UnF7e4NcLtjMAIVGCK91QLSP4m4tBUG8SGhS6JoRgqnL9g6JGZdtQandiC1hGIGcI8oOPD0/QfqBlz3iz3/+Af/6r/+dg1YAgwaAD9d3uN9ukK54fnoHLQ0v9zeUUmy/MZuMKeGSM3LMWHKEds5b+PD+Pf793/8F//KvDI52waBEsukx4ts//YDvv/0Wz09PlhFmLJlrnS2j5f5mQZsSIjb0RxWQjJRNjFFnN7Y7gAdKhRC+a7X+BkL0TD/njJT53cu2Ear0uQhNINKgZrxTMhqtwcIdEb1VqIplLgEpZwaOPZBFlxPKccNrUFwvVwZrSomI2togTvTeoFU4O0KZ/lNOHsjJteCoa/Z0ueL17Y6mxsuy/buuC+r+ZkGb4tOvn/HjP/8zbr/8O37+zFnb3cQkn9+9R91vuGnE9u491gX4+88veL3tRkrgAKgYI7ZAiHxZFgQoQlD8+E//jA8ffsF/+2//Da11xPhfXGhurcECZMS04ocf/kwK2/VK6poZm9VqCdEgotYbkkxdktmMYQUVGwTDTCBSCcSLgWYMQkwI6LhZZ3RO1oghdBZ5YWHbi55LztwkjbLRwYwWXJbDi8TKIpwIoz43QJftipxXxKB4vd+IO0pA22/4bBTb8PyEiFmoTSGid27sZWE04B3dKoLm8IMdiSBATwnbuqD2YxyIvKyUBSlUQNxvL7iXinfbgu+eP6Dpf8cvn17Qese6PeHp6YqoHbf7/f9g7d+WJUmSI0GQRW9m7n5ORGRWFYDqnu6evdHS/v937MMM7b7shWZopgndAAqZEXHc3cz0ug8souaBnW0EaMtBiayMOBd3U1VRERZmFtx++Vv8pz+t+B/+x/87PjIPoGH5xlCB91iXFegNj2NHGx5//g//Pf723/8Z/+V//p/wn//hL3iMc8YDxOEPf/wTfv3yGdfbdcJGKSZEhY94CBWv9688fDYYRZteNhjE1LbO/HJe/GmceITgsD2f6AMIyc1qD+DFf7vdkGJEiBFrijyMOswkqGjHQZ+3eK4XMDFcZnke3gFxXbFopn3sG/btoFlhq/j4/n1mnyn6MyPzfk5OCzFOOEGcAEosoL++PkLh4V0vF9y3DMsAYkxYYkA+Djgf0PKBx7bh/de/wfXtDX87/oR/+sd/xlEK4nLB5+sF0ZM6vC43/J//L/9H/L/+b/8DtpKpWndAFCYMHKMJrMuKGIDH9x19dLz/+kf83Z//Dn/+x3/A//Q//c/4x+//qKwlvt4//4o//vILbrcbbtfL7A3aP1ETPiZoflbr0Ia3ObQa1ExigV0YeGnQMiaEFJH3jUErnn1GiwfX2w1rSnDeY1kWnpve0B05+paMtFZf9pAwww4BuTSl7AqcD7iuHAxU84HtuTHoY+Dx/QO1djjxkAvJGr0zSMsYaE0QNJni8C1jHTH5IznkhCbjsiLFgFy7NtYjliXh2DcmdT3j67cH0voZS1xw+ePfoOOf8HHfEZcV75cVMQQdYBbw7/7jfw/39e/x7WNHjHQydupxxGl5HUEv7317olbgcvuEv/3bv8Wf/+7P+M//63/GP/12/+teCpz9CUAE75+/4HblYUrWfIrEGy2LNBw4jKCPCbNZJ6LK2dEoqHrJFp14OHBSmJOzcVbLoVg1WQ5uNp093t/flFGg9Dlh1uy1uWxlbqtq/Ka8at2vSCGgKl7sVNG7Hxt6Ldj3DUeuaOXA9/sDcHFeMsFBR9xpGesDooeyirTkjQHiHUbOLD9VmEQmCXB7f0Npd2VesOTO2oSyBuaXP/4NLtFhPzIu77/i7f0TjpzRqo5FhcJh9UBpC2JMWNdzSLz3Zp7XUUule1rvaOLxx7/7Iy5LQN4T/sP/4f+ET7/8Af/X/8f/E8Y/uNze8P52RUoRS2Rg4LqfvSOrqs7XeJkspk17aHZu9YBK95XViiFsSHpt7hszLcXEi0WD6/XtHSl6vTzC3EveB1YjTpRVVXHkioGCECuABaMTOmi1orU6M8JanniWSibQwXV63D+Qq5X8Ab0FtFJQW0cA+wwhOLRSkSsvjugDQvI4jgOl2pjaitYHZAws6xvergW59dl8H6Nh36vG1Y63z3/Al7crtueO7gP+u//0H7E9HjhyRq0MVCGyCvl+f8LHiHXhfGBewrwUxsDElzs8Ojw+//oFn94uqCXjlz/8DZb1ivT//q/4H8ffswcQV3z+/K4kgcgE62Wt+XvcZAdiMnWULcNIjug9umOyNFk5gzYLHFrPisx5r9CyR0h+stlsL6X1hsuaJuRFgoo7Ez1PsLKUs8kqzmFdL6wk4BG8isU6q9NSCp6PjNbYe8slY982PLcDqVSIV51VJ5vMeTagfQgYrSHXTDZVYBwqGWSV6Uhig5gIt37G/bHxDJpZZKsohfbml+s7fvnlE/L+gPcev/7p3+GXXw/c75uSIwjDOe/w/PoNyxAsaYGIYwIU1DpoACIDz31D1b11+/yOL58+AaNirFf8+//0H/H2y7e/7qVg+K8PCz69X8+N88I+MHaCMUzsn6FNEFMTGywTY0LqHaW0SfWi7opUUCbyjv8bootNKpbTZvJ6fUOKhIyWZZmXQlfoJLeKnJlJGYvI+wD/PPD2fkVMEeI6vGODudaiPeiBnA88ng88HhtKzsilAJIRQsB6vUJaw3FklZGDFPzGsDcvBAzUnKd/jQ8REEHOO3obSOsb/uZPFzyeT7Q+sF5WKNwMHxzicmXQPgpiTPq8V/T+DaVUNt80gPZ24L4XLJcLfEwTB60amNZ0QZY7vj8O3C4X/HG9QEbB9tg5WGcA1/dPWEKYEMf7p09cp3TSPmOKSIm9I6dWCGZzImDZ3oYNph8K9wSILDw8vQPDZviqEEvLYe88cm2IM7lwhH4GENKK67rABY8USaFlQ7+ilAwU4CMf6L2jlIJauypyPerbDWtM8EGUssfvy9VPqGF7bvj+8YFcCuGYAcj9jvVyxZoC9lpQB0D9ikBqVaiLNGgRzjEvVaEppWOiFl7GIvjlj39C3p/Y94y4rGxkBzZlfYh4u11R8sEeRArooCYg56LvWyZt9+P7A0H7A7031FKQcyGMEhPWJeEv//QXuBDx6ddPwBDc7w+MTqjDhYjbZYHd3te3d1ysIjDWYIqTPGIXgl3CPjg6p/Zzrfk8vOLgypbBa18BGKIQmnMYtZIyqskF9wn7Km9vDO4GHXknJEhkznSpJStEynniIUYET5Hm9XpBikETD4c++HyGJ/5vTff9yHyfrQHHjn3b8en9hpYr2DNmr7AB0z6GY0+Bfdtw5EOnoGlPzXG4VO0Dl+s71vWC5/OJIQFrok2IV9uYy+0GtII2BkjF6HAxIsVMYawIxAc4ccjbhrh63G5vWGpFqQX5OFiheo91XZG3O44OXK5XyOisGFpVQszA2+3yV74U9HW5vVGsFuPE74Lxk92LU+HL98RITrNuCYUOqKIVDJRcsOeDEMAP9C6W6kNnv1IkFyf0wN7BiqR/1ntHyRXHtuG5bRzoMxpK7ag148iVXP11hYAH5Ha7ai+C5a3AodeGEANSSkBrGnwDVh8ADXAxBDi6UsF5oedPF3gnKLmgtg3HceD5eODIBc55xBRxuRCnvVxubEq1DriAz1++sPFVCiAO6+2KdVkg6koLkAVFeX/E7dMXeP9gozEuuN6uADqO7QkAOljjQC51lrktApfrDY/nwZ9ZK7HfoU25I2NX7FoAhOWC25Wq1rnWISCG+KIdkDNrBOC9Q0pJG//KJFKFNEvvirzvKO0UPQFK9YQhC6LVzvJDX+d6vWHRZnLvDSUfeB47Hs8ncm0I3qHkwgbyfsDFBW+3K1bvsD+fwDqQRtLDy2y/9wpBQPAJKQbNYonHjz5o5xKsMgWb/krXDCGglIySyezZ9g3bdkxYicrgK5lD3lNr0hou13dcrqQNdgyktOJ2vcKh48ico9taRc8DkgQxLPjlV4/nc0dQLQbfx4FcKyfkZaWfKg7fWse6XHG9LMi9Ie8NPiZwKmLDfuwoOWPPBYDA+Yj3tzfCglGtTHSfm8r2ZAa9sINiwhqmX/akhwZtqHIfFnSFc21XGGuvtAqvmoOg4krnHC63K6FJIVxTcsazcK2Po9BKpxT00XHsO9oQvL294XJZIeXAcxOMQUp8cEJxG2g9EzWIxnBHbgEpLYQwndcY41E1Vg2FUr33qPnAvmfkfGDfN2w7LyfvA9Ky4Hq94rKuWFYPXwr7JD7iyy+/otWCXCpSWnC9XBC8w3HsbEIPpU2PjpQSPn/5gvB4As7jcrkieodSMr5/PLBtT1aimrTQ/4iX7vX2hvKxIe+7xscAqYKjFuT9QKnlp2L8z18KIvA+4dP7DTHEyUU3odAPZlZyWjAPbVoSE0xKGxxwISCFhBB4WRhVMuvDbJ0ZNxkCA8sSEAMxbF0JvL+9MTgLaZT52PHt2zdsOxsx3qeZsZSSceyZ8vE+MJYO3ykSe7vdEHxkdhdYxreuZdinL0jrTRvkDffnjsvthhQcRmMpa1BiKwXfPig2y4XZYVW2kA8Rl35hWd8qrtcLZ004ZTX0gcv1DZ9TnPBKLgW50IbamFm9N+RMi9zr9Y2N/NZw7E/s20Y6aiHltCv26T3XopWMvXSEwCl4PkZmwxBE7/BRGVgciOe+f/qEFMKZOYZTCKgUMGUXad9Am+3NdaSgrCQN8imSkSZaEVb9bLWq6MtzfnfJhUE0JoSUOPRHN/wSI3zwqCUjHwe+ffuKx3MjZdY5LCkpPKeVQx8QN1BbRQwR+Sh4//wZIXi0BmaREIzRMBAQ0oo//OmPaGMgxYDnxx0IC66XxIDnHRON3lB7w7ffv+Hrt284cplZMUt+h2W9wDzt18tl2oqLkG8eQsQvf3jXXgy1KXsuZEu9MNVqzsAA1suCT18WlP3A9ryj5KzU4zp/b+9DK2Xun+15x4BDrweGeHjXIE7hvDyQa0FzTNIutzdcljiJCJZ1TxbgC/vP2nGs7DsGItY1qXjMgiQTh9vbG0bvyDkT5ilFzzaH+gDLJHLEQJp3Wq+4rHRJaLWglozv37/h+/1BiAhAWlZgmJK7obSB7flEaw0pBMi+odZ3fPnySVlEA4sDeqto+iFu75+x3gZSWpD3B/bMc4lBH6KBSo1Kb7g/7vj69Xds24FqzDa1lImJn2Hfd4wBJbh4uOh4DgdwvX3ClyUSgdh37Puu+1bjq7ID7byv1ytGq3jeP6g3OZjg1VbnWk9vKREmWiVjjIZageQius5KD+Kwt4be/sqUVHGOBzOxwRc1UFjD+HWAOTG3qM1Aijd84Cbzgc0R8yVxnpjZslzgvKAWYuVZs9zW4hRsmBkbBFjWC5YlwYmgHDs+Pr7h29dv2HPRjenhA5tgnW+KTcHBeQ+lVYxOBkb9w9/gT7++88CXqti0h6ARR64NLmjTWw9GU2rrukTknHFsD3z9+hX3x3OWpKcbI1DbwLJeECKfC42wGlyIiAvFV3xWxLBLzaw+RKmZraM1lqUygOfzjhBJN0x+4P54KD+dvZH360WxaAYsiYmHs2Q2ybwHOieuee2j3K43tPIB7xzScsF1ZXA2+MhZk7Y1dM1GQwhkp6guxVTMQWEtVhKn1D+4ABcE1+tt8vpbKbzUS0WKPGwQNwf++JgIGzmHVjMeH9/x+++/Yz/KbPwGrVyMfhtiBITwxrHvyGPDGAPPI+O/+/d/hheB9wNNhPvUO+RWcewcBlV0z5uFRxuCtCzIR8F+bPj996/4/nGf/QODPo0y5yOH0KRlITxVG4Y4LMsKkcGGpv4ON0St0KkLweBFRsgJaPc75JvD5XbD2/WKatlirYB4vL8Tcnpq4zQIJx1mVbaL80ycSibnXwbSesW1NUI9Man7QJiMsuCdMucq11Qr0mVdqKo1caDghZJ6qvctDjgncIvH5XrVXg5x93zsyDVO2xb/sneu1yv7Er1ifz7wl3/+DdvOz2vUdiccXo/BhGvxhFtqzmiV/YXHY4f4gF9uFzr3tgqv1MyRM468ozXtl2otZJCruADvOvZ9x9evv+Pb9w/sqko/Fcz6qjw/pGhHQF0QYlwQkzIntUKbPRk5raxbrcjHhmGEBREsywVv728IXnDsDaU2dAg+f/kVHg3fvn1nlVcBlwS18ney36m6DIW1CEte8Hj+3Izmn74U0rLiclmZBeiFYBkNIYMVt9uV5XLSLEMzybMfoVxmERWiWcoxOCXMeSzLihY7lvVCDUEtc6hPq2TkmHpSnKDsG75/+4rv3+9zkDXdREn1E91w0UVg4a3aSkVpBa0DwQ18/f03XC6JHF85hXEWrLYjT4Ums25WNs45Ckoed/z2++94brtCBBV5P/gZFX900nDkA5d2gXiPtC7wWlEplYO2B61RFOOJDZa8Yz8y9m2nRB6iUNyA5Iq0rHAh4e068ADgguA//Mf/iEsc+Pu//3v89vtX5NJQywGBI2znyQrrtUOG2WEIjkyoDD7ger1QYBVpfXGKlBZcLldcLgvSsuK6rmzYehMcmhHZqx+MQXPnBDKDiLxr6CEiqVintjYtQMS5eYmKc2gl43H/wPfvH1zr4Cel0XQxPgwE57Bag7N3rUgaxAny9sDvXz/wh18+vexBDjc/8nGW9Dv58DESGuwiwGjYn3f8/vvXaT1CyPJA1V4VEZChe6fgoolA8B4mrBQNPkUrJecD0nKBbwXPxxP7seM4MumGL018HxeMdeB2u8Jg2D/8+T/gj59v+Od//K/453/+DR/PjbRcfe7LskLQZzVhRI9SDmzbjlKBVfF3g4GD9wg+ThPE29ttMgydM3t1nCUD/2NWtAKopfMAgsUIgw/ZKF+WFb1TI0Tl/ECIQRupAEbH83HHt2/feU59wNDzYkQTCQrnejcV/vXIOEpGh2D0gm9fv+GaEi4rTf+cCsz2PWv/p5Piq30qKsHZ+N+2Jz6+f8P3jztyKaw4VAXt1E7HRJT29xCPJQVeis6BU9soKmx6CfPiu+Bxv+P53FSVPublLQIccuANb0z8LjxP4foZ/7v/8Hd4fvsN3jn88+/ftJfUAGEiJ0Kxand9kj16K/j4/h2P/a9siLcoLmoZQkoL/vi3f4s//PILLmuaGbEPYdIxjc41S1DItOqdY/L0kHhlGTlxcI0KXABIacUipzArLaeHSc07no8HPu7PmVF78fDg5ik6iNzpBjdrgZE6cj5w7AdKq0hxwfO54bquswIoY8fxfGDbdxwHMcSjVLx9+owvEsmaaQ3HtuP5vE820HTPtGDlCXkETxFa2Q88nUMrRRvRUCEUcLmsnDvRK0TN+lqreNzvqM0YOYbT0yoh54zLEpHWC3zw+HjsiOmCFApu7+84jgyfu9pokDNudr09nFYl92+/4x//8jvL0uAR22mLHmPCL3/8E/72j3/AZV1P5aZzp+1HF7KcwAb5+TqnfolmRjYPwsR7rZLV43zAGuMpAIsJ0XtNCDLu2wPfvn1nH0Z/tleIo5WMVplwWKZrezIo7XM/MoY45GNDqVd4L2iDASBvG7bN1nrHc9uxrG/48stnLOks+7ftie04pv0HNFAFJ9r89up3z2bm40FREy9WgRdBLQ0+rViXoErUjq7rsm9UKjvvEE2346j3Ga0i14p1XfD2/hlOvgOtE8pdVlyvV+TaEZZFab/8/lYKXCD9uykk8Y//9E84Kplr5oTqA/2TPv/yB/z57/4G18s6M2g7q0NpubQZgSYoL1PhFDk47WrUy8oYOALsei5DTAhxzKb8kgjh0hbkju/fv2HLhdqP0ef5rQoxO+cQEy8zu5y89/AlIOeDM+EHqewp0VreNeDYCrZt49nW/iNcwKfPXxAiBaP1OPDcuSdaO+OZ9w5hBNXnuHkmWznw8fHBUaUxTo0WK0iH6/UCB1JX0ZiwlJL1WQhC9LqjzetJsO875HLB5UIPua0VwHONjPk3nMLyqtouraE6j6EJ0f584C//9E947Pmvr1MISsFalgXXt0/485//HT6930j3VGFWiF5vfVHRip+DXBjHZQY1kaDsDGaZtVX0vWEMKomXZdWgzgdIr5o3OAfU1lEOLubH/Yk2XkysBjniQyGEqCXusTEDpN1GQFBqV2gNy5LQasG2ZyxLRNAMqHdCD3elBA447NuOcmOfoJaKXIkDx7TA+8jLK1XETO2B+QflVtH7gaPQHqGmlcFTBMGzOd36wPv1itYytq2QYqu+ULWNyd2G9lucCHoreDyfSvHtWFJCawXDd+wKhYh03N4/IwXHplxrCN2jdWC0jH/6p9/w7f7EsiwYgJrNda0Kbvjbv/07fPn8rhfYgB9AWhaMNlBb0UCv+pNXt0ixtWbJ7OUcylNHxvZsSOuCFCJWJ6jK3IIIM+zggVZx5IxjP7A/NzQbljsGvPOorU6xlkGVvWY8jwHvqakJPkAWgYiHT+S6Px9PRH8DYgLnGRPjZqZ+oPWB2mgNMki8p1oc6nvvHHonWy2lpBAneyWjGEkiTFVzqQEymA3XUiGlwPtPEHTs2w7xTtllAUe2huB5XpwGxGPf0Sux7gHSMmtrqLmokIkZ4/t1xXFsKLUzIekdMhp++8tXfP3+HSEtCHEgVwbVlBZcrlf88U9/g18+v1PT0zl7OaYI0WaosYbIjHHzMrAKYYgo28zPrLx34Pn4YBa9JKR0QW8VXZvVS1r4O9CRW8Zx7GQMNlpO9E6XZl6gmBYbGMDoFY/7oSy8QFHlwqQkrV51IBtyXnQmCPfKGB3l2PWCH1Rfd9WaKBWcg4cirjeK62otqLY+QwM8qooQnepS2Jh2elYdup5dal3KcaAB04DSu52uBwOqpTptvJuRCEAh5/XtC2ou9FDqnd5cLuLTp0+ox44DgkUNR4GBj6+/4evXb4APuN0CMPxf91JggLjg7Y3ma6Mxu7tcyNwZvSHvFVBaWghesWRiZ2cmwc0zPWwwSM1MCa1mlHIg5w0hJKQl2tHgJgDtMGrNqnYV3N5uSGUhPu8cju2J/SCWn48d+z7OTSwOrgtqrVgvCy02SlO2jEPNO2LyyOVAUIfV29sVEMHaiFUvlxWXlXjx0IEdzgVcV4d9z6ijI/cyM/DeKDQxPrYNzDErjSECgFj9/vhA3jekxMvFicPwAcuyoONA7+Rcv5bvlj213pgtj4zbsSG5hE+ffkFwgrAMXK8XtLyhtEYWkUTkY8Nffv+O1gW32xsvtFJobhhIf3v/9AYZpN5eVDncW8VxHDNweW9+Nzb/170kAoYWEVpoaimCUWHGb5sI1mWZegeDJtE7Si3K/QbisuKTjxNaqiWTyttY+e3bczbWASYyUoiFr0vQRMGw8I5cGi6uoQxSni/XG5uC/Ybe6FN1u1214UqdB4bDuq44th1lMDjzQu+zT0QmlsC5MbNMU3q3RtVrzTt+++eCdV0wekcQZskxLUilaWLD/sSPz5H9u9EbjlzwfG5Y14Db+yekJUI8xX0YDFbQACQl4/fffsNeun7Ojl4r4qYsqdsVt9sbE4cjI6VVA63Dvh2gSFxdd3WtSUgQnaAnSuVWh3ZhH8yFSLJHLWgl4+PYEeKKJQbAca2Dp8cBG9GE5FwIeH//jNIK6b+jY9s3HEcmkyfvqo63WBIRhHvlcr0gpgg40lh9CBQARj8dYsOy4P39E9LlyqZ37bhcbgqdCYowWKdlRc07GlHGaUrZtdkM0BvM696GkkHMbqWD1dnX339DWlYSVCQgtgEEsp2OnLVxP36w+MBE5+hp9PHxHdfrgjVd8Ic/Rrgh8JcLPd/ACo2QrODbb7/hsR9Y1iui2qnX+sIL/m+8fvpSuF7ogcKsgWZq7evvKEfG+6c3LCmxWaT49JDTHtkaTvbGh37o0RuaOKS0IHgBGVN8qKUcaK3Ah0SbXhfgvSDnQz1GNIsD1L6B8u4QAm7vmIHr/vGB7fnAcWRSPS8XLOvCwxIC1suFFgCB2UevBU0AJ4FH2wVcr1c4H1HKzmlVxqpS6w8fPMpeVOkIjEb3TFo5AEMvBu9Yqq+XFUEIb/RaceSGbWcWnNKK90+fcElxNk5Dop4j50K8uw/YIFVuu6H2xB69VPz2l78g/vnP+OOf/ohvrkGeB573D8B5rJdV/Xw6vn/d4UPC7S2gVW1kB086bM6kYzq6SW5Pmu29vb3h/f1GA0J3VgMWQJyczrZW0pu4po+B6Ilb54OX+lCc+9h3iPO4XC4QADF6tEIX11KUk93PIGe/+5f1AoxBjPzxxPP5xP3jwYZrXHC7kU0yRkVarwjOAR2ISyLDKxckdgP1Zy9Y6FlBGMcpWaGwcUchJQVubQxV3jGjbiLwChE4HxA1+07Rk+teK2rWTLg2MlyE7DdvPRA4XC4rtm1jIxXnfAVzX/XTJbjhcf+KsCz4D//ub9GPiD1XHNsdRU30nKMm4/t2h/iItzevjJiGkivW/YbLeicVNXitgA98+/Y79mPD+/s7kyBtAjNWuel1ZO7GRt+E2NlmXy+EAHQSJuzc18qzvSwrvO9wPkGUwFC0jzcvBxVv1Zxxud6oyWhNBWdPwnn7AYAuuRdx2PYNMSTcFk9fM+cRvWA/jsmAlE4INplF+3FA1Il3jIY6uNb0KaMYFZCpirfKqFRqhbjWnC1jDrWtD9RckCsp6bUPxPd3pORBbsFAWlb2vdRcr48BpxdE7x1Q65UQAmor+O3bA//7//QrLhHIjyeex4HHkUlLv7CJfmwfgAt4u71zz42uhJm/8qVwuZDpwslbzLKDcxigq2RvnTQ0tRmIKU7MWMTNB0mbWTebl9MMq5ndM5uhQ5kpPmJy9wGZ1NWmzSkhORroFfcHrRGgmelwDu+fP2O9Xokp7wd9bjSgzU0M9XRSKmg5KtI1QSQgBG6QWg49nE6N2s7hFr1osAbLvNYFLjiMrjOBe59e9+u6zCZWrVWxzwqKlRJ513nHui7K2z43T+s02zP2gmUSxuxx3sN3BpHLus5LaDQ2RUOk/70DsO0HLm+fsHZensee0UdAKYU+9c7hsq6E4JS77bxHawWP+0NdO72yptTWxA6LCoao2h4nvVEMYz6FjMZoqo3+U63xoocT9FJnM5dQig57aRXP+wO1mbkcH0RICz7FhMutTuhExMPHMJ+VCMcattYRfOBzWSKCJjFBZ030NmZ1OSexBdJmqcfwyOpG64LXn90mG2dRa3GHgeOgk2k5CoVXUAFd6MhHwREK3q4XaCuefPkUsR0nHXkSEvTqEEcLk1Y7JPHZmn38ozZAvNKNO7bnDviEL18W5djzgvVSyatfVga0pGK1aA1lBuN9DFZvnhlwSotWW0o/96dttSiE41Sn4r3DvjVM11g0tbogrFlKg3MFQNe1VndSRwZgB5v7R1bVdx9zf6yXK1JacLsVsu6EtGTB0GrF7F3oRSVoaG0gBu7REALQugpWbYLaOEfhilYVgSMveU65f02pTSPIRc+rQzkOHMdBc8LjgDkMx7QitYb90DGpnuys1jvSuqI+ntP/zTnT/ogOLOZ+GwBctz4tn/PH8wkzIhwAhZG54f3TO9/DtrNK8g6l/lys//megvYTUloQEwVMNlBjWmJr81AwkI8DfQzcrqRVsRsvswEb7MJwjuo7FWZhkMM8tCcRHC0qei04GlBKnTS7dfWoNeNxf+D53HQx+XMBXjKt0S/dpwWflhUSPKcXQWefikMbGbWwypAY4WOA84LR7IbxWJaA4zjmARjaZDNfHucF2/5QuT0bXFX7B+LPZtBx7NiejZlgZ1Uk3sONhn3f4ENCH4OCvOsVKQUAdFldUsJR2hzFJ44bSxgplNbH7OYvv/2Oy2XVngYogkkJMhoeHx/IlbAZXNKujUzIKoaIBKilRZqeUsFEaEoBtcu1t4p971ivV1ySueLSsCxqkmDKZyrFi7JFhsodHJwbSCHoBdeR911xXNqIxBThasHz+cRDLwRjtDnHpnzRITbO0ergpmygkhtEPXIA9oIsy00xIfoIDJrajQE1xCtoQ/RZ8+pxqlxf14C8b1RuK4TRa0VpFSL2rMjwOfYnK4NSSSIAZ2fs2xP7viFfGPhDCHqRQ4ceLSiVJobWj1P0lQmA2mR0X/H8/Rset3esjhn2sl4mLPO4k7qYdKDQsrDfV9XJNcUwZ19YsDdhKgkSdBCgtTsDYlG44+3tygpHKZfilHbuHLNuvLr0qscVzM0gUknmBmo+0EZHLmpcp3qO7fnE/XFn1TDYjA5WmYyGrMI7xgKKD0uhVX10hN1GayhSEGpAXJiszjnh2jxeloScCxlsQ9+WUJ2flgW9FVqnlAaRQR1QLYB4vURpw7NvD7RSsO8ZotY6KaXJYjyOHe8qhHt7vxE2GwNwZHR2iM6E0bVWEg5puAmxd5S84ffvH3Byw3YcEBdwu9Dv7Xg+8DgOusA6gXMkbYjOjpC/9oxmTlU7vY1SinoZsMEXtPnjDe9WPO04dqT3d9jnN8/93ht8SBi94DhOj5QOs2oWxMSGEMSBk8AOVQcTpjiOncpMxZhRaWVsB94n6hpyyTg2GlHFhSZkNWfkXiZrpxykQvbWERIdXpsUfV8dA2Rc+RhVUFWnKG5AMNSb39xRs05TistKBk3v+P71Nzy2AzYy0wXOQ3AAsjpVAgWPj+8zmP/6y2dCEwMIcUEfGWY1bbOOnRM4hcLQSSvtZcf/+r/8L4iBFVFQj5yaabBHaEFtjCVhFY9SDkI3PiCJUN05PXDiVDNzyElSuwXNrjrQckZLtFdAH3AhaaAnVc+5gX07JqMFWtVAoIFAh0UBaCVj2wnXheCpjFWeuAjN7EptaDqT1oWojTvBc9uQhQ3h9bLCB/L+W+tYIy8AAHOClvcUSTmlS9NKO8G9DD9qrdNWoXUGFQt8zpM2nAtciPTkHx2Pj2/4eDwBkIbaIXQrHYJ8VDYKPX2uvn//yvPwhz/ieklwFqxSRHth9hijxwmxaO8EGYDDwD/8/f86oYvhKB6T3nHseSqyWW0IZBXkmlldBT+tLKZIUVXc0c52TIjhnJ/OTH/gyBWf3m7MnGfzd6hrqMexb5MaO9Qugowlqp57a7woW8Nz21B7RwwBtWTsxzGtQXz0GIVU714B8VQpp2XBse84jsKEzgmhSZ0dsq4rnBJhhlaH9gzhAPEOKB0SPdLi2MtT6nspGVkZToYIiONAnJxJeb1cWJXszwc+7nfUNghB94Hk2A/NB9X73ntIrXg+H+wJOYdfP39Sx4aBdWHC98NYT41jMSTEFFCPAy54fPvLP+DjG1lSrFqJ0LSSST7QRAyjIyX2/Pb9mL2Hv9qlYO6nKVGQRr8jbqJ1XefkIzFurlpEijArWdcLzBq7K/3Qy8B92zCgRlfNQRSXt0E8LEkJGz2fTxo+eYf7x3ccR9FpRAW9y6xWeu9cUFREF+HAjFC8lsStQHxA0A6/Dw7eM3NtmtUwC6yoNU8q9rJEQDv7tTCTaaoipb8Qm4r0VK8MdMouKaVgPzLx0aaLr+XnsROGoOBKEBDZlPv+DSlFfPn8Bj8GveFTQGlnxvU6I7Y39loOrVZG74jLFUsQeqVkMn98DKd40BEeiQEAlpkdpV5fPHDOiWrretHqRWDT00wwxuZig/NXhCjaWKaYbokBj/t31IYJOw2F/SxTNs/50RkkjoOB9vHxFffHjtY5vrTV9mKTAjUGHBja0+KMDlaavZKxElUw2IfMy5AiORMsUU3amhr1xYSg+7e3gm1/4siVkI1OwEIHatWLwgd4EWwPQnWHss+Azj6UcwAcess8oPrenSchYdue+Pr9G2L6I5L3aL0hrSvykSccOsdUas8j1xf8fXTcnzverheMVrDvflZpc7SwQqbOO8QRgDSmh5Ule3NKYkxzWtnUSWhy018ZMkOwLpGkiFZ1lseCfDxwFDLobIh8l44uWtFiwIhk+7FjP8iUy9sD37UnZFDxEFEbHc/zWQ+M6BGGZ5WQIpynRinnghDVTdXOidPfNkjTrCAkfeQMgUeUgBBIFx4q6Nz2nbRv7QWR3twmySE5h+P50D1TaZHtZJoAknQxsG07zS1VxEZdS8b9+3ekGPH57YY+KsQnJORZAU+yhhqLVlWDZ13rtu/ot5vCkzswIobzauE94GHEBCErKw20+r81q/7/j0thva7qecRRfCFGXC5X3N6uSIp3Ga3Ki0wTNlolW4BQAzEfcFkjPr5/00N6ZkO0eeaYxeAdqpZU21501NzA969fValbp3lZzuYq6NW3KCKMgOEHVZN9YOSKEhMzsD7w9nYFIOrOqQwhmMEa/UaOQ+mBzmMIWQZ9MGvM6okECBkSjrNqvRrK7fuBvbWZYTmddVsVm3Rqg+C942JrpdJqQ3PEt5+PO5Zlwe2yYLQKFxPGOP2JGPzUVK7b3Gk1ERy0g/bXKxwY9Lz3WGIidj/7EUOfg0BiRFouWEeZlwL7IStub29YdWwhLaXVqbI3VVLbkBXNwhVvvqwrjucdpZ2ZivMC6br5fUBKAU0N/mreGXRF8Pj+OzZViLdaceQyfe5tvy0xwQX1LlXrAOs5rZcrZAy45cps17nJCQeAPhr2fJD6qr/HB/oemfCSCnuyYGDsOaezwGNAhOD5fGDTTM+a7j5EYFSMYYwsa8if9i9eIagQ2Lf6uN/x65dPiiHThp1Ytr5hOyPocwykOAdpA70VHCVwglytcCFgWRNKOceEKhEO4j0CCBFe1vVlrQPSsuLtjZMU3STDnNY1Vce5inNU0wxWYt57rOsC1Iz9IGxrjDTzNKK1fAIGk6DRGpXZAPbHXW1LyNCjVQwvhruOzDV4K2GZiv/RB0qtqrz3tLRYFtJ8XUQdhKMHqOsJzqMcmY7J0tV1ls+jd55tUl9PJlDwHi0EpDHwvN/xNINCTRIdHJyDzi0gLD20WT3aOcSIvbSBMRq2+4NjbUOYc9L7cWg8eWHt6fsaohC8uo/v24bb7arOAOzt9cHxrBzEpSQF6GCz+FceshN1NqsNV3l7f6e4RYFO7zzMotp5j0Ubk1Bcny6PjllJDHjcP5jxGiA+zqaVOCCpYChGZmqtNfSa8f1DMUbl6vJg6CfX2aT7zgUVUTaL0hwFA+X5QM4Bl/WGUsrknDs3fuhxlMwA0Qe1E7F3HEdBWtXPsHMjMwaejacBVg7b464DXRK9/HuFD8TZAWscdS6wjjOccA7GvGRNvBYjD/sYmvWWOr1ogHPa1CyP4SABQCN18dP7jSV0hc5YEM6FxSQJcfNbBdhlZo63tze83a6TdWVzl20CXAjkxnOMIad5IXjESFfTY3tgP4pywF8a4+KAQAzZQSApzf5Sbw0fHx/q+mmzac+1FmEgys8Dh9sBEZ1F4VW8RpuE+/1D7QYU7xcKJcfMwkiX5ID0U5DWSkHX7J8wk45StYc1MG0Fan0ywEeKGodWYBg2fyGcvHahkptBUh+7/sDRO/K+4fGMeL/dIKNDNGDoh9YEA5oBi9pknOtXjh2XyxeIsqCGOKSFdt6jj5ezwgsqRZ2NobDRer3h86d3hUjZOPaexpUdhFxSuCDqfJLjII00pgXX6wW9HPjY9vk+tW3EPamq7hg8WncqZDswesfz/oHtyIRNdZqYJWnkfwyUenD6mOpe1mVRd+TEeRjHjlIcbu8BuVQs6jAq0ORUNQgcjDOADsAzgSl56Px2hZmczRDk6nC+iDkAj6mVGIPkqqHGm7SAV+dnvcScxwmrz70zUGvB/eMD4fNnBM/eTIyR5IEXyBB6rucQInD9a2/IbeAt0sSTUxUXuHLMpBHgfhSVCfzM69+gU0jT/fByvWFNLPc5Pk6VstagEWbNSQd4H8dGF8t4xe2y4v7tG7JaVgwd18dSkt7/GKKKVA5N2bWJ8+3jobRPHcTTzII3ILiOrIdm9IYuDhgF+0Y9xIDDuiRcLgk5U5nqvAOG44IMK7vMI2nMZh6GUMEJHRWqGOpiTEbB5KK3VlELbTp86ByYXiua2vwypjB4EgoLdINU6X/Ueb+kIDKLrDXj/njgly9f4GCjDgfZV8qaMEbEK/YMcAhK1+8ZddcNz99V6x1jqH+9skOcd0hhQexd3StpTkasWgdQwOZeC8QBIl4hxYBaHPZ2QFzE2/WK/Lxj2zMz+W4jKwFA4RCDEzTDruXAsW/4/v0DWckHFoe7VkFRVc9OOiGJQT+okjMkBrQ+OAxoZY9l2w9se8A6BmIoXEdHBbZ3UQ0CaVInjkwbU11DeGlf1ouy46BVFtem5IoYV4gE2joftO7ONjcZgOsde6kwF07vPQcUmaUD+TQMhq3h+eBkrjWRUJBSQlX69XSl1Y3nIIThwLM3XMNogI/CATOD08R6LSjdRHB8nmZVHZMSRuJC+Kk3dPHwGn6AwYsONscg6Bo0tQBxuN5ukHbg/tyYJKoYjLude8sHM08UNoxHR8lqYpnLbOgbo8x5BwhnotP+mXYW4litt9HRMl1p02XFbXF4Ph7YnhsExNoZ4WysK9l4tNoIrLq8GvGBlQ+6o/W8xpZRVXcyOBtdfMD1ckWpFaUchB1z1uSOcOEzc6YCIVetbF5s/TE00dDP/9gSfvn0jjG6ustSECvuXGeZcJIGexFEZX/6eEXZdl76Smq5P7NC+QrtetFZ13/FS8FrEzktC66X5aW84d9bILMsxDKP1tTEKgbcrlcczw/s2sARseEvgIyBLrrNlcXEQwA879/x8aCh2dDmJE24IkS6Nh/JnHiODS4EBCeK90cKfbYn9u2JtxuD3PNJp8IUA/aD6thFmR5DHKK6qy5q7BZ90CogAJ30yeLPubwxMuixSRnRCtDbjrualBEvN17/QD4IhyFR9JJeZlkHlc6zr6IDghRPvK2JIqcY0dUugHiGTGhBhlP4gwFNtArzIqhjQEbFkAVLjDhq1WAyFIoiH92L5yzf20UzjlOTgFkZQTccs3O0DgFVu5frDaMeeOy7bk5m6F0MosP0HgohwAk/ezmenCynsJbRP/20u6Cr6rqudEGFzElo4tnob7Xg+7cdabngdn1HcsCuosCsQWRZvIU7+up40oJZcfBYeEf20miVz3JgOt5aw9WHwGu3Nxz3J45CAWcfFsAND64IUfdkdBoqT3sXGxZkA2v2fcfl8hnSdESjZbeAPnv1+XIO0rvSKfVIC4dVjcH3UmrFermgPjZ+vyOcIlbhO3pcXW43pUFbRcJsf0AvE62K07JQj9HG9G2KQfD1++OEOAbgQXEm569rFRsi5sSwfODr12+EHoFz1oCjrqM3QS7sK0VwrG9wHjIaoUmlsu/PO7Z9U+vqpOZ5Calm9CYIKfFzjDFhszaiTlLz09csaM/k3NusGIL2ASHUOxy10VHhyBqPhvZLqY/icJ7ApXDmIEw/KDLwZM5aGYOkitoHUqALbwgeRfH/+V4MNhzjnPeul63TRLULfbrkesUSKrKK4QbMnPDVfub/9+vfZnORFry9v/0wN8FqRFJOTxM051heYjCTX5YVDhUPtV4w5R554LoAQ31SbIgOBK3s+HiwwTwGx9w5IbuntIaaeVvPObMYxKO9n5jjkhb4QY5wKeqiGhyx4iPDBYeULhijo9SOqw/w0k61pmZmNtxkuHNI+Rh8FqUW1E4cvFdaN28bqYin+w/pkDbUg41oXXN/XgJ0nHXw4dUihDYMcl1BFyNuxPF6M1t/Qd9T18E5TjMYQFQDQrrser2gPzfUNs6MBFSAhkHYyC7KeeEDsNvaaebilDrXNVNY1gvW5PDt9+eJJdvVMtSU7YWeGLRUdxi4f79P87YQOSazZs7C6K0gV202q+1HbRUyhorIHGK4YFk8sB8Yo2M77lhChIyOfOwYnfYCWAZqa1h9QAgOtZL/DeHniCFMuqxRcFsfGI6CQ+pqCHEcOWPfd9ofiM4KEEE1mFOda43LPwaZVoQUhl4ury6jQ8cwDjLXoJDXi+KVCaf1F7i6r5maQQYC+lwtyxXXJeEohXm4XVp2IVxvuF3STFxe3U4tM2VPwemFSHjEpwXXy4rj8X0aUooGouG4xl1oYQ5QlCide+b+vGvjlBTm65LQalUdBTPwpsOyoHBcKW2SG4ILiMuCUTn9rteCXROWUjI2YZ/Mx8g57FEQQ0QdY0KhvVNn4oYlm56T9piqq4MC4UwMYN+O6eg7FEIUcUp26VN9v6jmpjW6rRp1vbemfQg7Q3wftVYdk6tXsKhORtdaIBjiSFUcOqseDiKmkXE6cIqan+WyYui8B4xmv+7nYv3PfRmQYsLb+2dcl8S3KHoLWkahQisL+EO9xiGqHIwB7XgQxrNLQV4wPtHRjDjHzMEL9u2udMzIDKJX5MxSeujBMYihDyBF2gW0pgG6M2tbFk6YGnpLY1Dgsm0bfAwQHWl5uy08UJ4+PqOxdDajOyi3OzgV07WGVgv2Y0crlaZr1lDSi8RgAVPkTsMwgTadFatMA85xuAhbJAPddXgwMPdOwRx/99BylfOHhx5yOIEf+vwc+yEznjvAS0AbRYMOCQM+8r3kXOalcIlvpBu+4pnywpU3fHM2lvXic1xrjIpS+7RGJy+8o3dRVa5CUCEAMiDDkbFS20xAxmgoR9Y1Vj+aPuaa+xAQA5vcAAA9lGFdsV4Eey7wLtFTBgHl4EjLZR2QjUNZiFvbPlZhlFWvdkFqBm/khu3g3O68H2pj0PWM0E5h9IHS61wTcxIm7DRw6LoPCAeuKNbbBy9Gm03RaseyEj7pTmYg5//ArOxEBHXuK0WcjakETHEgKwL21zjyFVr9r/j8+ZPOPZcf1ttIIKdSHbScH8x8vTp7ml8TR2SKzsRWew8ncBpkHRyG6xi94LkXzhkJEcBQ0Zcqe22tR9dTK9pLK/Mc1dbgBxvjHTsbzJH9KaDhODbElLA9n3Bv75g+UlrN9I6pWRDn4HS6mwjQx4bWO/b9gf3IHKZUuQ8xSPFu/UAHHXa7kWqcn9qhWitc0bM2Bt2FdXDUKywqQnNNVn+i7K424aNhcRXqJg0NlLAcwOke8xg9097Ee1JVI23Uj1qZqP/E69/QU1jZbHQy1YqMDZrNQn1RLHNpXTNqOiNCaHhmRnkmSoM4DBkQVbiKLukgeIttoxzeuUJOcs6Ks5/smTabhBwmHrxHbvSvd46NxOJo9FZywePxwPv7Z1xWvSgYcVHygbauemE4BBfQwJ9N/NwwQRsfSvbT/rj/sKFDTBiDwrV1XbAfHOHZFR4arSCX05O9AwgClLzjeytY1gvebm+4rBGiEd8yttYahT8CmCkbXpJ4EQ8njTu/65/qYaB18cASA5lAQnVwGw7L9TIDdVwiblcOQDKo4jS6c5o18iDp7YXaKTq0Qe29Nb1c3awsAYeuEZdsEGZDdO0Y2LcNzidcViquc9HRlnqoANoK8Ps7SiGpQRpppyFSI2BCpOA9iiqIP396R/SOSuXglN1ScbVyPHhI0YpKrJJlFi/6nm2Q0/N56JhJNWcLgq13rJcrIBuGfn7iNBww0wdmY9kp7XZ7cFjO9XrD7XZFDMrs6tZjqBgg9EE6Zv2hohPNHNsYcEP99DW7NDgjphX52LXqocDPhYTLetWZHQnrZdAJdmamcu4nrZItARhK3w2agfN7hu7t8EL3BUQb24JT/Dd6h3gOhWkdiCnO6Wwlc/pYqZUVhlKde+8YLc+KU4bABZ15XAoZitrM358b3j59wvXyRvsa79nULYRr5/uUhqFjfvmsujaZAYBN4Hxk3O9P1DnljFbuZR/KNKRR5eg2XEnmOFY4WweDiynGzHnF29sNl3QhKgBjhHFkZtDZ1U5Ft2oOwn/rxcyM+AW/10sOjswunhVBbQVdgOu6IjpCij/z+nmbi2VBSgFz2+gbYsVgNrn6z8SD2czrrcOBYqOgmRcUW5YBNN10ozeFZMjqyJmZWC2Vi6pUq670xFyy4rVsBpUXVXQbUO67Q5OOfnCalksJlxTRWgHcBTFQwctyHDrnNmPxK602ChkcIfCQeolondDMAFAzN/RxZNRJERRId4hpxZIibp3QBal3gmPv3JSa6VHcppmeevE/H3e0WnB9e0NazpnUrTYMVWV6hV0m00M3jqjyVCtNBlGWcejsQuISmZGQ212x7Qfe366oOSOuZJhNE0ODfmytjUwAsaoeYwxCLK1Ti1HKFCkRxBhMCITJwuhNSQrc8r1zJi69ZnSiltpH1lp1qExBLSQaNBWjMXg2OBcmLtzawWDk6IfjBah9YE1+NlV5sTSUXFCWyipDM77oHIODkKrclOrb1bE1HxyDaSGPlyGddy/X69wTrdPyW8hVZIKkoiLi5wySOe8Yo+FyueF6u+i8Z1aPfXA+hDgN+AZ3a5YgopXiZDMp6UCTK7MtMShJABz7E+72jhgT9rxgWXRuA0gcsPNtTeHX9RbFtcUFoDV0F9BbxVCmjRePjs73MtgEtz6JN/FUH7q+euH3NlmErTHDr6Xp8PpzDxAaVl1Li4Cq52vviOo/tES+H4jA+4hlibP6K/nAnhOW4Di7vHd1NtYLXoNz72xsH/lQe+uilbCeA+0xXq9XtEYFc1Pn2urM/8sSKPOE0n7AoL6p1sbxoSno9D0+k+itWpsdZf4MPSecRUOK21Cng6EnlNqohEUb2t7RNHKD4O3tglb+yjoFLzYt7SX4Qzel0yYZ49bEOeEcVavQhw2Zplq83niB+DEm75yNEbFWBd0qX8Cw1jIe9yeOnHU03TgrC4UYeiVmf+wHyrrCgbfwYk6cb2/wMaAcBV4CRlyoLFZzrFYKDk8hin3e+dlhpZ+pJBtqpVe/ZdAA4QIOqR9Ab3PsZdVpb6TemmXGa6NQg/CgVfHjfoeI4KZeM13pel7/2wLFbNhDsxMnwCDttYPQg/MBTjj1qqldsg8BrpBG+vHw+HxNs0cwhvUL7GJwU7Vt/GnLJCnNovbbCZA72DC3i2romqq6tfYxv09E0BTfxZn8oNeC+/2u5bvqUMSYJAYxEBrpPSOXiDUF5FoRDh3F2ocO6cnIwSG6gOi49UMQYLCvxIAp8NpQxqCWwtZ5jnFtlUpryKQZ+uCBooaIMpRlNtRFlxALy/swD/Lc51rF9d6x75tebGSsQPcXp3WdFfnMFMfLpeCB0amrsHvcO6CoBQnV6eodhYH98YH3z18mJNQ7RY2vLp0Mbl7xcIOs3IQpm57X3uvUX3SxPaHwslq5DG5MiHhgtOlnpWwIdDQ8HmSq1arPUj+nXUQGH9ZW1BzyglZoN5FShM8NfU0IS8K2Z1zWpNUaqPAWzjEpw8OU5nM/2eWlVdoAgA7kfUdVuxGMAecCvKO2woHVY1cm0VA2IEkwJ1uRqugxG70D1JQ8n4/TNBDQzxwmZMeDDUx9jC60oFP0OF4ICyGg5oJyHOidItMQWCX1krHtCbf0V9YpAECrHW61DX1CR8ZYsZAGkOnCZGEgaAAJjm8SbnCDdMt2aJTFvajNZicopb7QHoFje+L+eKKaEESbKgJr5YJCkWEWtnQHdDHCuzGDijWno/PYsyDEiiYOPjqk4M9sSBycx8yWGcB50Ic2mEIk62aMjpRWuqy2BvFavmGgHDuhM+ElZ21nM+yK64roPP1UHAMF2xduNoU5gS4idDP14t+3l0Bp2TEGD+/Z61c9iBN4cejS5ma+Xq/wnuUxRsOh+Dyb9Mpr0PL0lURgwUTs0hCnl7lmrFrO9iHwYs9w/ABrOa10vHfIe0aHNTIdSuYci6J2xGOYiR6DIeOFZfH29w2tezVrGxjiNOPbEVOkJ414FC86MCq+OH8KxOtzcy8HUsjPH4OssrDrAQ8B3g2UXNGcm+MPZVQ89/zSV1PPHy2pnItYFzLipqGjwpVOK6BSKIxKOnthSQki/sSZ9fMbltzRCQ2IOgkoXEtojZX19twBuWFJEUch+4/QmjW1MS8EOhP8eLZpkSATwlCtp/r3nMmSzD1ook3Mcw3wouglqxEen3UtGY+PB0pp2rMYk20lIujawJ+9OtBWJeeCGJR2PTqCogriA1AyDnR4FxBVTObNl0mTN7uUDQKzM8Q5EhTAmvlg9I7VqqMKv5RG5f1z41l2FtQV1lFYKC5JrTz67L9Z8jjUfJF6qchmNXQio1d7DUMA1O4HGPrePUSTTT5XxtwOkm+eY+C26pwPOIxRUeqZXP+3Xj/HUQJQetNy2s3bb47fE2vU0Q/GOaeYH+mRvCmZrUMb0m5oiYTzjXqnU9NUBVhy4cFuFfePOxuKgw/TsmP/Mo3MFIS8H2SqCrt50/eu1YSWs96j1wPffv8OE9sOmKgnTiqklYPOmWzEghqZCrf3N50QRmoiBk2+LJibEI6MlIMjLwFeMKL+9GMgJjMdpJdJCFE/D0cElkx1J+lxzPy9Ow8y+c/jzN7FHCyVoaD9HKd9nl4Ltm3Xi0GY1feGXrihQ/AwKwv5F5CCaOCxwGEcbNLmlJUBzIBrr4lVu9M515g67Ml03D90/izAw6Arw+ld1tweuja6kfWSagpFWABpTd01ReDcwPP+Hc/t0MSGgS6ar5P/0bnX4Fvj2QfvsV7f8H5dMYbORhjgMB5lRdVa1ATRKdxUJoTXx0BMNsyFoyfXdUUKYRrQcfZwpS1J0bXmA5sZpRUL9v+tYnNaeU4kUQWloonVvj0gftHpdtDqjDh+SGFCwtBg/iMi4GbDmT5D0F4V9UKQs0IZ/OV61Pl/QZX/JnDkHh543j/w3Ha96BiQRU5/pNcLyhrydh4tgHd0jEboacxg79QC/LvOLOEFb8QBp8aO3iAya/homeoDmU2//PJF14TJoIlBySBk39B6R8crKaI1xJQwOmFi0yGtat0dNL6MRpuerMpt08IEhbTlXGZ9e6LnTHTCn/25o7Gm08qrFuy54Xq7EDToJ6X5X3v9dKUwRLAuiYfEGbam5e8YM7M1u2Hj9o7egRF+YKqA7MGXT6of1gnguOjmN1KODff7Hbk2hWPMNpvaCVEszgkbhiFwCEypbLZOR1NtbI9aEdNV8bYKkYF9f2J4j+tlVXm7DSR/UbAKzc/G6PPiiGOgpwSRz/hja/jHf/6NtLHR0Ia+P/DyKCWjD7UbgM5JcBEhRQ70jhFQFozzDuIpfmGpqBVDLaglaHVAqIC+S6b0tQtBoQXNTlnts8wfg7+lOxqrjVpw5IDb5TLnAmMI0mWBoZUWIJzexEN/tv1ZDB61a6bsBG74s/pjKwEG8c2LQjzMC4s26cSYP+4fSiZgX6Gp7YdXGl9vTTFrB9FRhG0/qFlwdv3opdU7SmvU1bSKMhzqvuNoDdfbDXF00hFF3Xs94Q4RIASnF+nAcB4SgNYikgj+8Kc/ov6Xf8Ajd2UVcf1G7xhNgN6w74deaDpu0jEQnIJP2EOBuEDDyegIFQy6rtZSUJP2njRQ9VJmtjlwziiwrFwMbx59VrMiOrAJHdtzw/und4gcqOpIHNIFUft8ZklzWqVohqp/5kPQ+SAMkk0c2DVys/eJWbcb7KGiVC1xOAel4/5BgkbvnVPVWleoSdQvifMERM9gjGnSlW2tZ0WtfYdrpEHfkTN6L8jlQFwvWNcE81gbnbokSwqcdz8y+CTSGDNGvL1/QSkFf/ntO6vhVhmw5WQT7tvGpMqqQ41DrD5kKpD76PCgAHhddPSvY8+1loIawqQdDyhzUeFxC/6w9VZo3jRAhC65jw0Wq3lHTm+4Xq863/3nXj99Kby9v2NdA9AYfGaRMSZwA72jqQFoBa1xEzhnmZyqFcWmsvE7rdEzJ4qBnjQ1H/i4P2kohjFxSqcQyFDapw8e0rWs7p3Dyl3RxnNHb3phcQtggKyHVitqqzhKAZ53bPkXvF0jYnSoUxB0Zt2MOCc/v6h5nzjBenvD+/OBv3x9EK6BQz125DpIv3vuOi7ypSTXC5UT2RqqgdlFsCz0o5HRrPD5AVcdVnQ5D51RqTCa7hv3QgYYbmaXAzQlc6LNTAiOfYfzV6yXlQrdyxXvt+vMkgfOyxX2XqAIkbo8NrW4ZjOZa9TMpGWcYaI3qLeV10Y4D0vvDR/fP1CrNr+h+Kxz6OJmFulDhFOmGQWBEdfVKcNn6LQxrW4cVSbORXgBykGOubSB+8cTt+ULRYDKcpliOEt5yQFFEA+dtqH7e8GXX77g+ff/BbnoJd8qntsO7zhbd2hmbfbsltlapkcfsICBisUvakJHyMCe86Tgjo6gzBHvBK2ZXkGD2ktlCIyZcJnY02lPA+Dv/Xg88OntDa5l1LbgyyfOfegD82KYCy309dJNBRHuex4FrRw18bAwPQZ3onn9QIS9jA5NWDoe9zuOTGNKZLW39zS8c/q9IbIJbDOYW2ts5JfMoTSd+8Z1rowPfH6XJaCVzFklHdied+Qvn/C+xmnEZw/Z4E++f4WMZaBqw915h7dPn/F43PH9cej3Ddzvd4gPyLu6oDZemEa3J4NMq+BaCeWC+36x0bHu1HtNCFSZQwD0eZSZRJyUY33W9uyh7sKj8VKfZ99jvz/g3t5wuayouf1vxvZ/+fr5S+Htjdg5NMgYtUVklvAAWMIISLl0FAaxIaclHISe5RZyRps4qOkCICBk9LizRIdTC2yaXnnvsQyWoc4HjFZQGj15yCLpWC839JZRWlV2EplFhmH2sdJ/vRad+NTwfNzx6f0NYuwiFdw4PQCwTOIFTogKV3nf8f75V3z79oGtDnhUfHzcaRcgxP17G5DA7y/5wNDPou1ZCBj0UyCe6b2bzB1rIts4T6oz9TA4mY1sfcgztaDiUYOzpu1noPdwrqOOhuf9gfDpM9YlYr9cSNcbOJtj+gaM+84fflpHm6rbSlqxS1zMtIC89abltHcMiALakjwfH7zE+0CICa4UltghYFkZzGieOLDvmT7/TlBKgfgF19sVtTTkfIBioqyZWMVRK2Ja4V0FBCg14zieyO0zPs3PZuuqMr+uz9cqHe9p3TDoMrpcrnh/W/GPv90RgsPz8YHHXnVAvO4VZe6UkuFcxOKDstYIhfXW4H3iEHidrmWmj6KXSDMyhbgZVLpSom2xhwVkzWGGQUggr70PnLAsgFIO3J8On95uiG3Bmuj86lyY2TezU4NKmQTS7ppjPoMOrvfWIxoA3Hkh2CVual+Z5WLnLImDtha0knHoQ834Vptc1mgNng/6CXmv1E/gcrkQYstZAzw9mEYXlIP27VFNAY98oOSM7bnj8/v7TEStkuHZZjJgTB4nguAjuh+og1Y0nz9/xtdvfw+EgLqzt5nSAtMcmQtDVSv9dfGa7L4gDMPP+EU/qR+DNEd8tvkebLb5vKK1CrNkzw32++ycaSGm6ATjdEfD436H//JFRxr/669/wzwFrypcb/Fxwj6WpQKEjUrmgJ0gMge/6Mdi5iGAUaqms6flYYqXbc/nVK32VuBjwluIpKBqBXDsZpttUIN6moyOo1Ab4HtH84K3txv254NQAKijcMuC9bKq0A30cG+dWaE7ZeWmarXjAbEGmmb8ip8777EuEc9yIOcd+35AQoREGoGtKqArhTMBgvdopWEEcBARnDrRqt2BPg8vZ0YhTmBT6WJI7KuEoB4tmBeC9QDsfhB95rCMcAy00RCjhxue7KP7HfHLJ/gQ0NrBdRMrq18DjyjE4mnX3RorD+Vua0E1qxYINAMeGKCoUdwghigOdSc8qJgPhni83d5QqnHXSeVrmZc3xkDOxxxAU3ImS0Yvx9vtBhkJ+16poB28lpZ15X4rDV0x+9bZ6Dbn0YnNz2xLM7mOWeFJLRx6dLlC8IFaC/aNe8cYH8tKNbY17H3k8COAlta9daR1Iayh2TQUw/fe3EVl7pfLSjhPfIBTXx67GQzGO/fmS6bPqIlGNx4KRKXh2B441gVOKAiFUlZZRr6IUud78vQqOjKhNph6+Vxngzmsf8dA2AGFYzmDkm60Jn6zBC7Gipx3hRE7BB0lM2HsnRh+UAuTVmmb01uDTyuua4KXJ0KKMNpoShwwVVsHxXVZ8f94Vv3jpI5a74SVdad2yjugEv+IacGyeByFzeVaG1xocBhwISKFSIqt+qT10YGO2WsMMXG+w7/o4k6Wku631ui4YAlf9E4dB3DGAFFMQCxG6F4ZAwLaw48GLCmg94aaM56PHZ9v60/F+p9uNEsxzrCWMbCrQG0MHK2EzeXQfN/lJVs8Nw5LcxNunPe2ZUgcqs6Al3C73dSieaC3Ol1XS6lKSzWOs1ogGEtBmys0s9JBPNCB7ipEcQKkVc2qhv78rl4mWs5Nyf9LQCTiMCYOadnC9e0T3Bi4P55oynm2YRkAM16zFG+qyBa9XM7pV9QJBO+0gnJz4pnlgObr7gRTMHQGsZeswjY6ZLJq7DMAHaOzJxACK649ZzSpypbSjNeubDFoiIFTwClUGArVGOwyl5SJAgwn72MqMe2NBsdB8Qy6AbfbO5bIzNUslDmPu6BoE3dSUZV6bPCUiQxzzjOhgMJ1ZlUhLlKvYuQH3avsB+ghe933Glhn4BPACwkRS7rgEh22+4NQosJPQW287T1aNTS6ubCCMyp0SNWyLjobgpe+85xmaDoRqxa8Eh0m4cESMmN+zQrxDHRQijQG1KI7qL+QYN921OZUEc7gR0xyzH3ivOjzc7SIsWpRe0rnzW9gI1/d1uTcEWqLfijhxGG9EtZoL+N1W6WBZNYpesYuq6XOmewDYzLAWi10WpUX22pd7zHAKsw5yGA/akJcKqybCZ698TEUKhSFvhX6c0EdfzeSB4ZWYCEqUYQmiPbhzcGgdShU7OFcQIyLsslk/mxqt1S027vOfVa/JMcY4F4zcbw8W+EZm1CxfsI+BjocvAsIPqJWWu78zOvnvY+cWjsM4oP2gYzq5RxZNOYRcma6GqigDeRhmxecNQrj8no1GwvIzw/NIK40szs2DfCsVMKgwEtEMKpuHAgbYK2rYyez/PVywXg+sG8bb11tWobwYn6lPjxpWWaAd8Jeh4lIJjODYDb/pRDJAEgh2zuO/Yn744EhtAmvo3NegJq+lUKmRq1lQjNKsuM0uJxx6zeOhJz+JjKrFWNadT1EQZ+vKYNf7SiY9eglQQoCY4Q77T9qK7jEBd7zEs7HgQFaNkzM2p8EAqdZtamfrVOBCRfpdzn54c/RO5MDMXICDcZa5YQ6HxOuYeAoeQqWbG/Rk4qfqRbTfAzUkhF8nJVVSMzEntsBxIDWK3p1uN7iZBINhV7EBSR1x3ROaMMuRrM9Fc2sbHBivdrMMYfL7/c7Su9IaSGxAqJzzD29lqB+R5U3S9U+VBUOkq+14vb2hpQWTUCM/WQzrxmoSq0cFGV7UZ+tBWZyOPj+nD8Du81u4LatqM1gsIDeCio6UkwzzT/ZV8ZmcvNsaoF5Nj30NYVWwgRn7ohx7sU+wPkg2wH4gNvV48h5Mgmh8cF7j5ILfPDIR+YZEK2OMbCkBINvr5cLHvc7SukzQblc3xSG60prZ1N6WRmIxXsMtRuxi838xcYATLwGvFxmAFop+P79A/uREVOC99SupBSpb+oN7OWawI6kFiayHgD3eW1XXsphgnRM+ryfSSvdGiyhpi7B9qmtv3Zt5tmvQ9dBkQRWWoUwdGf/5Th+rtn88+wjy8S800DliElbNmtfA6jiTgPmGMQTnZbhg/CAXRxjZrAdg+bm2PMBHxNc27HnrApXyxxJZzMjOBPKjQEMLyiloTUz5iMgcL3dkHVYTkiL2tgGhbrINQ+Jzb6oIx0HgNatlD83TsfA6PwcdiOY3/4YxO86MLn8S4hYE1lbITjk49Csub3YgAOj0bo5QHDs9F358uUXXJaoGYucwcI4zqUhrkEPpkIJZ505S2LTfjQlPsuMkB29NexHVWqe+SvpABgfVK6vSYA/ISmunOqptRS3Ztm0w/gxdihriT+PvjtA3jK6eKTIkYE2VKUpvmrrbtYPdnr59jvKyBigs2bOBeuy4iqOUFxclHrIQ1dz5nMfDiGdM8YHoBWdZl9uNsymmMkCV+/siwDA9nwi16Ew24CPZJUE5yDBoRYzA+yoVZuxgw3vprTnnDPw8YExgM+f3ixfn0mICeQIQy1MrjydUadOSIsBroMa0k3WCqm15puV88FZJSJk7vWBNlRLpDAEEz43E5D5LHQfsri3PtgEfjGnzI/zUjc4iVPw1A00Rc4oL6wAa21oLxBwH9TiAKI/SjUotaI3Mu4EDeIuWC8XtaDwau1vjrpqPFl5RjhAyKuYju8dM4mCEvYYszjFcChJRc9D2fHYD23+DoToGUtS1P3cYdYiFKgqoUBndcREVmTeN3wbA5+/fMaS4plwQun1Qg1UGwNRLGXWkz0rAY2nw1hTJ7miGwOxAb0XlHomtWZl/q+9fvpScJCT724bUPnPdmpexUSiWYvuDzPDB9TWmdGUGxHQrMLZZpNZOovIybjppNSV1jCUL0yGRZ/+R5TJ5ykOEf/AkiK84wzjdeUwmxgjZ+6Wog+NcBIrCGDIKZQCLNMes0Rn4+ysFIZSJ7dt18fBm7L3DvER10tC2enz3pSPTPiKizUMUlPIo9eCr7//jvb+jve320lD1ezQHDfFJYymBnMKmbxsI37t6PMZGz9/CLOt7gbdV5OZBAqGG1q+jglpQKEjTSjhBMo4GtqgG+cdoAkBZSjG2zkDyuicYCeaiRuFbrIwwMvq2DcaDAqdUg0WtGZcVyjpOFjZ1TY4LSw4LMsF18uK4MP0RCq1aDZOHyxadiv7bYBIF6yy1f3r3MnusgOs8MCxPRVqBOCcCgUdLrcrWi3IjjTg1oZqTkQhJNXQwDzuydfvreHz588UWrqOyZdz5s3DPeKcgzSZ72m8BAzTCzTQTsTy9gEHN/jsjlIRlF0DrYqHnfKXc/cajGzPwo4xoL2aPi/7M9HTKgV083Sehnem6pWBSX21BOnYaWXftaLJpf1AqjDVcCmssFutCNum88IX3G5M6Mw2/9jrFAd6x1kfwTt07WNaVs4qS/elnu8+zy93aO2N/Y5C5p44XqhjdCzriiCC7alWO6XSnlsV0BxMZPuflXYpB77+/jvePn3G+9tVWZRn39Bp3HDBa4JpvU2LtfaeO0R7QnaenV1uXiCVfm5RDUbdDNb/Wqz/yZfd4q84JS0QTGrdsekhEd1ss9Ov1DgZJ1NpDiABb7pguN+8SDQgC2Ghx+OOj/uDnimVEILXikS/bJbJTtWFIkLLi8p5r0c+kHPVkkztCSyT8VQ+UtDCgPdyL/OAyHkhTiobSwXURuGS3X0+2ABtr8FHEFScBpyfcTqmOpa2tBPpmqB23O/f8f3+gGHjmJm6xV49us5YPw7TV0rrN+M0T/tsZQ0RClLBVFH1LcCgpzjztO8V6xHx9+3bhj3T4XVKEMe5tl33Aan6KjAaUNdMrxcmP0NXii0EQGe1dX88kKtWUo2aE0swjAE1tF8DfV7eifYYaD9A3YU+X+3H8PdoP8l7nb9ArHp+1gH7REwEXrqDp7VJRVNowykc6byK+GCkg4VeNlplGwRjoj3OymgYauVS846v374hT2xc6+8XxoyI0Watof9CQ35Zh9H7dFV14idV0jkHqJp66J4wSwln1gEGIym8Uo4d237oL9HfMjAv+67WzPY+DF06LwcN/prAKfCFgY7n84GP+0Ntx0Uvft0KY0wtlPXvTEjmzWBS6D57ZMIzpFlzL1hsICuOuD51LpbVThBOn5/Myth0QL039HIywDiMKMKpuJR7isLTpL0C0Xg5wOxfcMKgTUePCgYeH99wvz8x4NTJ90RbjM3NyuW0ObFjwq91WsyOeVZPBTp1PHxenZc3fu7105VCbw1QDxbjwjpxOuaShmq9D4gbc2zhDKS6sZsGQWhVPtTrZgz+3N476armeyJA3jdsO62K54YaQ8vMMSsFYm5kdZDZww2W0qJNRgaPUrI2tt7g0XhJOAZtUYVjqwV1DEQBxTOKy1rj2cJt16x+qqahrpnw0+yMC6OjGPXyuawrqvqkBPPtdxTsnEFAD5QI8rHh47vg/dNn3CKVjsTd+wts8KOWgm/z3EInmwXc+CKckQwHH2jTPAbgQV58H9B+hUIUzjxdyMOn8R2pxmbh/Qo2nAkBpq7Cagmn2fZwY0JvAkE5Dmz7oV5GChnZWs/+Baa1h/Psa4QQVOgUsMSIoD2G1hv27YFSC1Z14xygEyVAlW3vTQfgaPPP8HEYzDDjxwkhqqGdOD/V0AIgBEKExSoocLbEkKxrHWHK+Ilj4wwEFrDvHx/AGPj06X3CCX2MmQjNgVbzxTKH71UrSJjaVc30hlMYh1VubXUGVmMKWgZqgSWFgGPfUVqdP987mWfz/N36EuhZ7FOZ61zAAKmZwKB2BfQw2577CyFE/904dtX2a6tN1ejMiH0gft86sK4L1cmOMePIO3ItbF6XglJVVQyaNYoMDu1xZENZr4zn2uabn5/JMvSumoMQAyQEWsV4z5nItaILe3QpLUxaB+a4gNkw1jhhl4it/7FvuDsHce+4Xha9iDT7EdUhOAG6/RyZYlAn82RTVK2L4sWhuw7OBh9zNCjkZa3+G69/Q0/BECCnE648QkoYvWDTBoaV/wYf/HgbA9OyYNiHgYprMG9wIhUUkDwfD2xH5uSlWLAfTVkKqnrU7Ifumcxa55QjHwDh14dA/I+N3orhArbnHYYMpBThg+MgH7AJPNQGmJUDb96Os4luNgrcPHozD2bsHsL5Ec1olkOFcmNmgK1wg8M5pEimkROg1s4BL7BHKBMP3J4PeO/xdtXS1KAIp26p3bJGmQHHssmu+IiV+cYYYqOPm6fWiuGiHhJ+n1fmSQgRwQ1sz+0cFTiYOQs8zLHELoN5BcjQasp8eQjrcTwuv8l7h/v3J577jjGANS1cpzFmxmWMnlIoimxK05u4ulOLixEA8biuURlLHUDBruvllPHTGy/pbofYndXTrEANzlJxHXFjPw3uDH+PMVJINQDVi2Pbs1p/nEEAQrZKVNy7tcr3I0L4QOxyJaT3eDzx+fMnraoIj3pnivZzfY2OaNXU3AVa9lg7xmmvxBKb1qgYtn7DnLrn2YR/Ph+ofbB61bNqU7+sCtWlnfYUA0K21ksvpttlBPZItscdjwcnEoaYEHJBL30qhJ0Pmm1X9GbzkXnOGCs9nDoYO+dwWW9Ar8ilwovDsR2s1n1A8Op6oFBUbwM+OHgfNNHhmRh1EDLuUGV1173p6Tosp21McAItXhG8w/P5VKr7GXTtmHnnp35qiFM/JSvIiBD0TudU7wPerkFhs05qcBsnImGvPuxGOJEOKGVeE0oHasm4LlSMw/2VdQq2+Zy3EirBo+NhGLpio701NFWSGmXOMsQhxpBR9o4ZhkGxMxEM6QjB4bff7thzxhiEINKyoPeBYwAhqIPpIC0talZBujczSzPxYnOVixqXhGVZYMPUKYjLiInlfy2F2CkcorfALC+Vgm32E0aBvm/vGGSv1wv6RoMrLwLxEWuK+Pj4Sn42BOI8bpdIq+1OFk1v/vRacpzha1i7TWRzTlg5ec9ZuqBFePJO2TPzqML6OfYHgklZn39GJpmHF8VyROacitlw9JTlp+jxvD8m3ioi2hBkM15wqtFNrKYS1gkPsSHmTgwatAI5fnvQedT6Ud5jXRKOPQPBAxWkC0OQIsc5WrUwrZRDpEJW1wLO43Kh9wycIMWEmjMkRDJYuvnvcC7ubNYqZENK39Dzd+Lf3Ou8dJfLFZejQUKciuDrZUXeHlTdZgba6+1KOFJILx2dLpfeq+tsjGSxTOaR+oW1isfjiff3N0ShHYIkmZXLqTJXgeDQi8VslbUHIYzTGBAqa5UBOHpDrXrReaejKTkz/NieqFVxHAdArShEjCZpfSSrnMc8f8PqH51FbNAmxKOXA4/HE63xYut2tq0KV8in904UQhx8UxbgaCitcWZ1zoB4CrLGQIgLYuKoWiaAHbVydGjwglIbai5M2hSOMlX/aPp7uwnRzhuXqnjAxxXv728oHYjOofqGtFwhKGgfFfk4WF1drqxk9LOUmtG7kR24vpwPo3OqxSBcGn5653C9Bgx1MnB6AcnLWsO24nkv6J+r3YiykkT95VynZUg2P7F/5fXz4jWFYCyjCt5RDDb4xjHMvU+zq4klDBjRm8070q3sgI0ucxiLQR/FGsD2gYdlMp4D63XQzOhaXaiwrnfOB85H1kC6oNaBmBbcrhSpDYWmTJEskuBj0kYUq45WB8LLk3FC5k1r4xx1qYF2zGufi5uWC97jQrdW53lb16bzfjn4+3a9opcD0vq0wuYG1aCkZaHzEeu6IjjARh0ODBz7EwDw5fNncq91ZrA9bntfEzIypordEtw/GM101IyIFBOxHA7aE3GqPynHPscY6haE6kEVLtJ4NHtB7nw/VkESS5xZF8CKqdRi3Q6IALWxeknLotnkoO1JHQhOIK7Bq99QqwV9iJbqASFwjChG00M5Jlae1gurHy39W23ovSBdwvkZQKgCo0McG7YYmOwUI0E4cQg+4nq7AXI23WstaEMQQ0AdwPV6g3cN+15UA9MxvGj572YfAwPac4oMTloF15pxv9/h/Bdc1FfHDChnYBjzXxy2Iy9aBqsSxgDUQ8vefwPx+1djOJq0FeylakWECVedCcfcopjphu6vV4CChAaZkBOdOonPT02L2osT5g1qaKgCT+8RobMeFIZ1pZIl6DyGOFxuNyqya0cpnL8tAIKP8B7qhusRZHCyoI9IVt5o0BdaLABNq/qG2fMTYF7SaVn1cm5ww6FXQuYxJXSpuFxuSNFj2zaIjmFFd4Sg5YQNR+/wYcFyWacxhFGH9+0JcQ7ruhJFeTW9w3l+LeMbg8kFq4VBSHuYaHXAjfOsdTNX/FdeP88+8g7mOySaxRylkrZoG2YQz//hzWuJeTr08RM6MbbfoGWxDH5v43CSs+xWVbQ1DJ2gVDfFIbX1l54C1aRmppVLJxMgBogPWAJLM9eU+ywOl+uCobjy5RpAARQtrQGWq1QnGlxA+TqzSVVtQgUqemmi0relHLvipsBxbGht4HpZ4BxQIYgxYK/mRc9SL/mAlJK6pGqJGzm4vGumjd5R84E9F9zsGTlyleexFMw1AaBePP2lxATMAbWUrni2zD8n5mnUY8FzPwDnJxbKjG7e/FxurcqGBmS94+fFyQtDKZMQHX5D/PvVi975gVJA+qC52nb6Cx21z+y9d4qxam3sF4hM6GFZFlWzOq0UHZaVmG3Ted6Mm14ZQXahqX+SAABhTOshTHtyq+h0mBHEI/iO52NDrh2tZuRc4BPnaRPmoEWEJVVAP3sSyoRikuKRVo+uDCuDUfZ9x/vbm+5zdz5vO20KIQ0hC412GOaJxSqdimL8+PU4Vb02OTHvTxjb5fy6V5hKISFd+9nCtL/XPTYG+1Kc5cFs3TpPbVCTJM5zMmIFpKuTsE5Bq1kvEEfmlPiA4FlZtw6kJajNdARQIBLRBz9DSAlB2JQuzSBS5iqtDXg/plfWLPqdehZp0GVFe/qUWcISvMPjsSnvv5NeO+hGcBwFNuPldRbFEDqlRnXEDWqWyerGLLehGf1BkoUmV06oo+FjVoNCO19gb2t0MqNoM4LpGtG1Yz0T2J94/XyloLelKM6c98dkfLxmEDYlSGy7DMMeh84a5YYxoRVmaHHwMrDXMm/6ZQGwkFu977viumzOVgz4EdBHQeuKMyp1MsZEPcNgaRq16euCQ24MQrST0Clw8Kj1vM2t0mEiZDAMN5C2BOGcIIZhKfn5bIKHa52c5FoRE50dQ/M48obn44FjA47jmI1SHxLi9YKYqGx1LxeCWGbqHLyEKVrjlLjM5j0Am/IEyDzQ4yWTM1vx3tkgtlmzXWEAGUYDBuCdWj/obOHCTZqS2hXMMm9gDHdm0mMAg4why7r51QOilBKndD4fnA4EGfAxYFHhYC479u2YDWjvIxwqSh+IIaDUCi/cj2SfeBoYAnCBUN0M8o32DSEGLCkx8/UCXxsblBPDB2zaX1cmnPHTeS65HkZRdsqIMWit94Y9F4REI8bmnTY+D9wH6YwDOoHQeVxuV6zLiiVxrb13p95Hs+oQSeMspZK90zk+9DbGvMxVsnkeUoFCB0Mzdy1i9YxxmFNHBSZMIsPUttqPGw37ngEff6gGxhiEWYLqT3qfPQx0Vj1tGCIAGPFhCBOmVrPCdR7LuiIuK0rO2PcNVS9j58IM1jEmoGQGVz2/1AdE9gvgcVlX2EwCD6AP9ndSWrTqCfC+AWYprhejWH9t7tHTAWHCtQaODRXpKqSLWlGOHSEuSCGg6rTI+31DPg6UfKBUCjUBh5AW3K4XLGmZ6zzdpDWYm11L07ndIrRumdWrd0BtsGhpvplMDDSQC80tKSrtkM5Er/UKB/udP3cr/Jt0ClwgbaRtO4YqHeEVUzRM8CVbILbWFdTUzGWoGMVKID0MrWYOPocgrQvaGDjyQfwRpnpm89Y7B0SV33ulxHUVmwhZHyGQVeKDVyaJR1oWuBA5kq9Zo9hhWQPWhbqBNksuBgSDPMywb2i6NeaB4d8Te08YQ5CPA5fbmw7nyFjXBQL2VPZ9/2GqW28V2/ZU2IQXmcmCOGyDnHVxDtE5+G5C/DEbslBIpuntPMYpMrJX10PQFT/FAEbr9LXKdVY8Q/F7KOtoe2wq2uonnjxsZ86OEX2EFFPuY2i3jb/LZjNrqosxOjUIo7P5GjPykVFL18DrYWaJonTdpPYkltHbxZFS1Ml/NE60zH5ZL+wlxURfGnDQU0he9SrCCvBFJ0GcXs6sd4yXT3k28r0LSCGhpIac+btSSsj7TjaPF7g909ZBGXEmRCvHwX3aOt58xKtbJoF5w5qZDFiDvpQyKxrnBMp70Esd88zZs2HfR7UnitkHF+BHUTGdmlXyllA18YZcG6J49OF1CV/7U5rRDvrfW0N5aPVWMdDbmHuQnws6TrXPC3bbDp19bDRp6DhbimJ91EBpI1e1PzEUxrEKwmaCh7RgcYDzEaJEBkBZQOuqHmzaKHdOky0+O4ttA6dYbr6G/Z6I6D2JK2nBGhN6OeBqRwwerQG9UoiHAR3eJBi94vl4qO/VO2JKk5TBa93PxM+nhQEd/Ky1NaRgYmGDjU5q6Wj27FWNP4aKa5mltUFmHmME5v77117/hslrliHTv6TUjpD8+QAtQwBePsDAaO3lz86qYnQoY0MQNS3PqiOAcvXZW6go5UCrpgVoEwIojXBILQVVIQEB0PeBIyaOI6wNvS8I3mGIw+12pcwdzCRqKfBpwdvlog1jQjnGlCATRS8A2zAaL6yygNQZVNKycAbrGFjjAofzho8x4rk90RT2EGUy2Lzn3jkR7X6/4+3tDUlk0jmNJQKF76yiaTqXWrRasUNkG1prYIazKdrhWsJxfXoXLGvEvjW8fLd+9obtOJS5oCFxQhdW7Vml2NCULycKD9ohM/dPMoOpTM8ln72I3pG1v1DV0rwWCqZIGy3cF/qMzp7OwHPzWBaPMegntSwJaAPLmhC9BVGvMCEN82LwOhKVl5xz5lw5Pw0Y2Iz+aZgwJiwWUkLIG+6lEhN3ZJA17QnIULqrjxBzA1VGmoCMu+fjjjFuuF4uMN0KoGs9eGGRw85AX/tpPijC1eoaBOydW7N8vCY3wiBYKiHfqDRkmUkZ4ZXHc8cJTOjiQCsQ6PuwNVULbav4SRppoNFlo6XEoJ8Y/ai6Vr8DrfHMlqy+R6r9oImdTRSjm3AzHy79gMu6zq9Py6IMOVK9S1WIDoSZ1usNl4UJgA9eYUMHH9xcz7na8+YDpneUrX0IWNcFj+cTQzhcq7Q86dvOCY5K12bzMxqdl5PzJFccxxN9dNyuVza8h1dmVNPYyt6J1/WrtQI6Y5qVoUFILy1+fSzdEkN0Ur3BXmYH+1utV/RuEfq//fp59pEwaw3O4b5tynQ5m2RcanPgxLReHuYfZBYJyj/XRw/nidG77lBLA0ZXY6uAdV0nu8RojbVwyhXHWmJSytBEsX6jojltQupDTAlj34ExdOQmmQtdBMEFpBgpSlHuvYlUzG7Wmoz9NUMep17jdXTh5XbF2DKu1xX12BGCw+O5qZLS43qNEwKgyO4l2A7aP398NIQt4np7Y2MaQ4/kmXV6/zJnQADzSQGcZnK2h8Ysny1zhHSY134rBftGZo4bHcOxSgghIO8P1AZEL/PwAydNlnck58WyX9dfKLoUeA2D6FWj2zVQDJyW2GnhWueSZ9CslY64rRHqEm3YeZ3zWxttV9JCxXLXKVYihPaez4ElLaQOxgAzJowxkN1lWaPSSoNCd6xku67xeU0K8MNaA0BaVtwaEJcVDg2tJRyPB50twUYo37OttWUU3FO1FmzbA8exY10veH9/B/kJekEMssNs8FNvHaIW3hPitwJDlEEzh/icgdwiiBNg3w+l0gYaB3ZqNjhwqcCFyDXGGTHFbEzg5uVlAdZowX1UozkBwsE6NoHP8HuuWWQAL0XPLtf6yLSxH3oYBKy+ARpdQskHVO+3yahZlsgzg4HoHUYQBM+19T7AzymKpNa6EBF8RAjWsD8nOVriZ4GYJBMlgXiPt7cbhkSsyeM5aHi45Qz0gWW5TESh94bhHPwYE+brraHkAx8lY98WvL2943q7wIuf5zhE7avJi2eVVoa9iSZbTBhmRmUvfe4AhYveB2p/GueQiPsrN5q9M7FGxp6L6gC4ePMCAEB3oI7RHeSlsektjReqW7v2F6ja5Pc02GAOHboTgmZXplisUyvhA2aJ5U34NbTxDJCqmgID9xgIIcGDQSN4h9E4JtGLMCiLechoQ0q4sK2dfHTxggCvfOo+VbrTHFAPSAgJb+8L1iWh6M9MacH3339ncz54oIO+N7pxaWKl8JR05MH3d//4QC0F1+sVt9t12mgPfQ40wusIyrARaL946EWmjBWnKKnIaUlhKEkIgv3oyI87g6eWy046Pp4b5kQzzZpZvbDsp2aDwMrQDGHMtbasWvlN7ux39Na1ShmAaiXI92/IBx0dDSIYCp72pmWyXsSwKhSEC3vrtJIQhxQ9q8TAYFJrR3C8lgiH8zDZrI/eG3pjJeKcn1REfsaO1vTz6aGf/kAh4nJ1xJsHmTMuJqzbHf+4b+CEOVBfgdNK2aLQGDRACwFq/NiwrBfc3t5wWdPMZAWYgYIbQCY1l3/bZmFI/AK6AHy+3BsDEhxCE+z7BoiHBOqOQvDYnt+nVsjW7gTPXqmmJzRk+gTMPWeV4gnKmDtsG22+fxc8OPie6EBXaHJo0mIWJlON3ljFjkGPKR88xXetIy0LWs7o3ZNA1BqaZvqtV9iEPzsvMpReW5XlpY1274ZCq1AWjBoDzoQvIKaVkJR3qK1C5BOutyv+4e//K47K/UdNEm0qREQZiwrhdZJfWqu43z9Qasa6XvH2duPetWpR18vOkjg3URdbY1L6zwSFZ3t+wRTE1pbx3AqSjz8V63/eJTXQefR5/8Zm7w9110SL5m3LBu04J3N1VZJazdOZeZ+wEtMdU6+21lQ5ywdZCpWOXSlrxmbyIvQ21+DaWj6zseEQ04o1elI6PX2Naq1YLzcsgU6Nx5FxYTqMoRANdKPYy7KHydyxKonLo/hsQAh8GE0XM4QIXxvispAbrwelHHl+P4aW1XJaKpClEpSVVLE9Hyil4O39E64XHYbSX8SAtml4x768GMR7IIGUF8T5bcRYtSGHjJIzUkqIKSLvG47CGQEa1WGYpujIxz5ULdsbJAQ6SvYOJwEOgoaXIKZaFruYKCZik+842GAWzahNLEY/fbVRV0sKa6jN4UJC/r/3EbfblZRB77F4xdZjwu26cMa1zrq2Ne29sZktp+8P/04rBauMHIi9K6Ri9iWMgdwLc3iKFyzhE+c1tIHj2NEq97f57jBmn1mejbCFALVmfHz/hlJu+Pzp7ewJ6LM0hskJ13KdMQbMTz/otD2r0AZ0SiEcnKsQpwOKJOG2JkCnA4oL585mRndeBn2guwFREZ9zVi1qQqVVSxuY6nqB0+qfhne9c6zs40F7B5m9Ib30O83gbBxoU42C9+ZWwB6cCM92cHwAcV1JuhDB5XrFGFznUsyqnkw3jh012NfWesB6R0SMZFZyRF/dKSjVi49wYMQYhAd//fVXbLkgHzuOMYDaIaaRmifx1Dv5wPMxGocO1Vrw6csXXEOYcWZoYsdLckxEZh5eAKO9+Djp+x8zPhlBxWOMgp+UKfy891FcElo5sO1neW+HyJqWfMZKIRyYoxH5kPUWnqWsZRNDfWmoXGy1obeGrAZZpZRpr8AP7WDJkHkWeeems6YpX80zvtXCAI9BPUIKLPV10S+XFdMmWUT9+k/zKbvj8PKQzbF0TkVivTtphd5oZ54wRUoR3nm8vX/Br7/8ghQ8YAwjheWYrY4J+/DQDyuuuElbxeNxx1Gq8pe1mpnUwxOGso1twWKYocxrE62rTmGcjJbWGge5jI7n8zmrQUsFm25qY2cZbjrX9gcrCMIa+vj0PfH5jdZ18HlHPsguy7WgDah1gVFitZwRotqG1ZZSVYvC97fokPRsw4bEYUkJMS5I0aHUirhcECLdYGsfk9FiEXZYZmbrrTRNcvTt3/zgdOWkO2sIdOH0PiKpqWJaLvjy5QuWhdmZ15kV038fmOfG656aRcDguh/7hvvjyWeqrK1azx6dVXsdZ7Y+oOpn+3ucf8afrZWj0q7HEEQveD7u5zxgWCFzNjWNXWY9D3PcdZYojAHA2HmAWZ+YirrpbOVWMp7bhlrZP5p0TwhO40VeNtbjadoDDJ4zjJdlJQU7ZwCEpWJMSDHiuibUckAk4LIkrUobs25N+Mza4ofTPdmVDm6u+5mB2zwTHwJiinCBdOIYI3yIuL2/0aZCr+6QAs35gnvRHilNVXtEolWw04Tueb9rtcEKo9WqRd5Jz7f3Y2fKLg+93vj8tacgmqTL7OP8leEjwcDj8dBJVWeRQAoiN8b8ypdsklmbNWs1+9LGgzhh9qI4mB3IfXtizwVjdOSckUtVyulZvnYMDMWc0U+6JSlbA6V2rDfilq1DYYmMy/VCX/re0AYQg0fs5sQ4pgWBcbZnRTBjrEC6A1yHGwL4SV4DhDjtNJYbQxvgzDSLKm6toUwfJPq3lJoxa3AAovx5uMCsQs4DczyfCN7j7Ur6ZasVPSUYDtvn89cqBpgZyrzE5YQFoEPYp/e+APv2wFEUSoFlKLwN7GfqR9SfB7Ta4eNp3TzXW1MwmReMZkBw2Pc7NjWuqzppzQRiw4lCU/y/UiphR01KhjKTai7A5YKokKKLDjkfCOE2A0NTyCiqiRl/MC9ryxwn82hYUMMPB9E5B3iH3gUVNNXrQ+m20AFKmuXWmhlkx+C0wCFYV46AHTrSUoUSXAXlmUdNFgDo0KqC+/2JL7980t+hM0MsSdLm6FnF6uUyrOrUZrlzSv7Ti0gz2DGAkndN9rTxqnudy2pnDrr+vAyG0sNtHvFwQwmGY1b+ljgyUxe0/cDjuc/qL5cym8jiaOlg2WXvFaWo35N3s0ndO2eqX5bI9+c8ej1wlEJ4VZlthH0Cgn1Ire4E1Ew47/TZnRHOKlhossvBXjY2NiC+JES9mZ7FqTKcam8iEQ3relGVeuc50ovGLtBaGyTRTkQAiL63/fFADPRNMrptCDpHA5oA6PmZf0IMm7Fl9hvI1GqTzaZ/9xOvn74UynHgUGaQ1RfijPlg2C5mFo/BkrX2hqRfb4F3NlVsY2vjyIlg3x545h2cPs5h2K5WNo272mjoJCpjNgTtb3hxpusA5faCdVkgAvhlAURwWS9wGPBpAQYbfyElnSVtOgXNkOSlYpg2EuM8fPZ5NaskhEAIqYN2z8wMhh4i4qHX6xXbzsEyok1do4s5a5Iri4jNbUyxi2hpcuwHrtcrt4U277Xfri8GfrJLeAhFjeIIu9iF8OJ9I+rF0grKi5/V6+IabCdiuDK1KLzEGlp/EbcpPktbB241507//5x3bFtG7Y0N4BAxGpWpzaAEFSkaNik+qO02Zq1sNFo60AIQj8vqeDCjA8RjkaFDdCKblT7M0bLnQsp54LqVvmclMY+i8/CjoTuZzdrWeKkH73XiHhuqb2833B9PuA4ExVjozkslVVS6tFXTQ6sQ2qZA4ZKCnBtSMAvqDkg4q8FhO0X/p65nmCW5VgqaJFRjDTk2rHe9lJ2dR/BmmHt+2IWjzCoxdssA9HvQx4R1B1S0pvtpgJDY87mzwnM810tSQ7l+2t633mAsRRGHlNSiJlpgceoT5uE8tR/rekVMScfdQvU/fGteZyyYGzDjkO7P+R/AmZRajxFTxOgcNRg+cP42lek6M2G0yQq83t7wfDxRB5RVyL6CSTu8WrXbbzSdTEj+rJTAyvl6WYEBEjUgZzL1UsmakpymkKL9GKdz0KGVuvachDqwn3n99KVQ88soPoiWlxYggNG5qSHyIxY2uFFmtjozD/tebpzWOnLRZnJtKLVML6AQAmJaILWgaFnpvUyYZM7OZbKOIxd4x6BTxalTorB5tyyAE9JVCx8umTssF83ETMQuPMz3KS/v28q5PuEmLpKxk4hd8/OUQh//VgsayHX2cSEHWUtWDEFtxD6HWg8Yj9yHcwKWU7gEGJysFCPMTXQ2hG3bKUOB/Hd+Hu902A7s/fNra63oVRuCpip3vGTtRxLWGFq2yvwZfBZ2SWuz19bbxq7aVlbVaCtkm7ROW/MCnfnrHEJIzLhaQwAtGmy8oo1adHrp18yGfC1F+wOkW4Z0YxWmAcEGqg/vTktpT+99+wymjBc7hIbr2jMc46T8iqDXDih1mtbsmTBm62gtY98rUuCBXBYdpyrQBnsjiULFhE7UPE35+Wx8asYt1PD0sTDwvsKbP6IgGuytKveT/aO7k8+gd4VzGoZ0lKqZ7Gyt2/cDTu8V65vpzaJ2SG3SX+3unJWEXjIC8vdzLkoiqchF1xNDrUlIL6VzQZiOBKLMn6AiTxkduVa00nAMzhBPKYIzsS+87EWDtt3nel5oS+LPKtMu0pe9PCshqxr0/BuU1ruyH0tFzsekyLdKjyofAmotmuknPqTRUYYKRs0GUgDxNMK0sb/iDapiMl1qQ4qGqli6MjM+XaTzvBv6csZa7lujynZNSn/m9dOXQutdS237E3desuPlBtb3PoaotH5QGq6YaR8ndip6g2EIypHRKn8AMyV64jvnCBtoNmWCJFsc6wEsqnCFmlvV2uGDw3JZsKgzofMBIaloaTR0DTzTm0cDLxfHeggmTnuFEngJDnQNVuqe6hx6r6glY3vSBRIgpLTvT/zTP/4D6vBIUbBtBX1dcL1c6NgpHkvi5djBw2iNZ1LjgD4qZAREx3nDrRYdRTrboXhddmOt2Wg/es7QYmHK4TUo+dom+4uluptrbRcezskJsKzNMlF7LhxhqjOK51ca5k2jxNYyjqLOl5587Xxkes70OoV9YVmQwOlVWSd0CQRYFvZRRDMprSIuKeF6vRAicA4hJizRBDuBWSlkQiizN6RJABW9Y/6ZfSh5sVHxrs8n4ZxDyWXO7YVziDFh9APfvn7Ft2fGuizY9w3iA66XK7x06kKWZc4xsNMwlLHjSK1D70BMTFQ4A7hiSXYDvFQx/+LlxCq4F+8i5yCqmI3BI+vwmtZ06t58G6L9Ig0u8/aBPjfR4y76HrvCblZdMwDMfuNo2NUIkhBOQO8ZRz6UlMBGcowJS+R/l1x07oDQs0n59WSIDUA6nKy43t7A+TGmZE68hJxAVO/wo/gUMGDrdY1ftTYOApvB4b3XKWq0Jum9IOcDj8emPcGIMID98R2/f/2KtKwYNaPUjrI0rGtCrx1LWsFhS4xJVFPbEydM1Qd0YBCh4pIP9HX5IfGGnnF7f68sPxtgZE080erQOafzLBTu+onXz4vXnE5xGvyNswk6S2zNDoZTuFqPzhgsZ8WMksdcJODkzFOcdFLADIIoOZO5g4FSmJFaNm5ugMBAaV3tqit/rtk0hIDr9YpSKy7rStGSE9CeoaOU07vIv+DIYsFOqaozSOhriCkx6SkE52apJs7hOHaU7vDpfUXLB8bQUaCemLJgJ920VgyQ4wzhxeZG5yxrd3LTu4+E20Tg1R5hjIHjKDTP0nVhdnRuDj4d+0z8L9FDzAxKqx1bC73ZvQ/6NdyKAPH9cd4VkK4ZjqEtuiqG6Z5NMDcvVV7oZdJNrZksQiPEEBcsSbAfBlcY5VI9f8D3G6NSQIUKdq++TCEt8OhI64XDVQJne0QB7jbz1wX46H9w8v3/EqjZoeNtoJoO0R4Lr+Gq+2P0hm0/8PbpM4P+6PAx4f19AXpD8w7PnXPGnQBRMWLo82q1Ad4rBNDRayW0MobCkYSNWinnTBJbFV2zrgvNfpbMZvYExHRfDKt2RGYqYUaLGPbzMBO7mYlCtbcCTLt7hSdE+f92LkUlu0445KopQ8bNM8L40MVjXVbsx65zCDT7UssNq87hWNGxfUg2HCuVgbAkOG3wcw8MiESU40ApDRKA6y3o5eDOKvBlP2Luez5T1kMaP8D9MxyDeXAOOe+I6ztSDNjyAXERnz99hshAPgZyfmDbKdB0zmGImvqBYlOn1T+G2a37iVYEhVmHCh+XVSFFABBFAwxatj6hs0JRYS+tqKEX/zzCP/n6eUqqc2iwmGHwBNBH48MeA0PGpHK+wktT7AKocM2yS+JoZm43YZuhA18CzeF6a4DiwAYp8EByE7Q2iY8Q0LPeCZCPHV+1yvjll18JUw36/IzesW+Hju70uEKDPwBrMjl/4pCv2QS5xzxstPdg1RRC0CYSxXDH84DZC7iQ8Ke/+RMeHx/YDw7kcL6xlzA68rFh9EUhBfr8cOBPnWpcBDKZggswqK61cjYW7bkqBC+gCpLZu/kdWfNRZ+yKULwkHgcIsYh38C+V4KwNhkZ/+0MJ4OEHMWU5v47WSmfpe6prh/K7LcCw0vPeYw1X0pLhsa6rGtcZTdjyi5Pa6ABmbFo5fj8O9NHx5cuvSGZt0vl5874hHxlDBkK6wMk5lIYByyadWTZpn5FBkhxywHWdDKbiRs7fTXCyKX2yw7mAz7/8Ecd2x/f7AyFFXEXAPgAN8/bR5xQ+UeO03ir6cEiOlOYl0b0XNjq1E1awZqi9NyIhZ/CfAsV5/gh/WCImzkG6emQoRDPP6gwslni97H+RCRc1E7nIJKzqGfwXCYAyh7gHODNBnMeyBq3oG62zm2oH9GIKcagYsWoTntk0m+8V27apEO4Lfv3lDR1kszlHr649HyhtwIuHjTOlit/mT7uXpPZc55kQQGgnM3R2i/dIcaCo9XquBeSGOFyub1hSxu9fvwHOY1kvfA6On2/fdqSVU/ic6Fzw3lHGQIw878uyICZahYsuKp2iX96fpSMzwTsRHOuh/kCvdUIygPdzL/zM6+crBaGh0pibEBMesjJlYo5ybtJzSDz/yBpR0Ddt1Cv9kbO0S8uKXisnpfWmc3m11NIsE6jIB7FK8vTZsDIPpj6GNpsYTKN36DnjcB4y2sSHe+ewDIt5br6XHzfNyyCzuXHk5Q/lZcOFtODSB573u/oAsXzPB9lUMS2IKqI59jqbVeISUvQQN6a60xm3Wjcnn7UO2emCXKq+qR8TggnfDEoDndiADz5jU1iz5upadI35+VhM688RK0l5chz4Hqbr2hisCOw320XxgokSp6UAkVkz90xaKN7LB03TrLlsBm3QUrjkMid3vVIsrUqhut1jXRf12W/Y94oYHGpr0OSJM2tfwpi8PJPzs+sj1Qps7k19yAJMlbhzDpfrFbUeuJcKDCBGj/046JYaAtZLVDfXrO6tAl8L4Uw5P49Rr82VWJw2r1vDEFbOvCG1CpjZuT7lMTBemE32Xsdk5kEhGDcvgjNHEyu8uXydVszGwpv9CzsD7gxO0J7MfEp6wXLq11mVxsTqqZTM/oCutwhLjzZoQ34cB73JRCDCngeTS3tOdGemxxnJB/nICDHQI0pVMkbQsIz5Fan44Xy/frBhX2dJAjDpqQBiXCC94f64o/eOkBYc+0bb/QGsl4sy6fKEsHqrKIhY4+sZO/tbIjxtPnilChPaq6qsn9qrlzPNy7sBzWZ9Y6IArXMv9dY0CX7t7P23Xz8/eW2Yl/tZXjITFa2wjZIkUPAO5slzikH0nzGmYZQAKJU0xFobXEy4xshN04VUrTEgjgybWirNtVpFzWVWGADQNLO38YUDtNe9XC4qPqGqsuWG0av6pHjEtMAcVkVZMK9uhufDHhMLHMLPNhypq62fVLCu05JSXIBa8dv373Ahwc0AxuwwxYi878jOqR3DmNzr1aUpdxfwMmBl0BECEENkJi3UYszzKux1zN1tR6E1dB90qCK3dx9cPTNXgypOh1V9wMtXi2YgZDic3GtWhhz409V19ty/XAXOoxiAwgknZ/x6varFQXsxLlR2i1pGl1rRdNa2fhwI5PxdKn7yacG6XqkNEKieoaA30XnNQFjSOQIVhBf9y2X+w1rPaGm0Wp21+9ILIBPIaUMbuH98R2ngRaSiygHRPZbRqocPfH/WlJbLBTFqo13Yu3Dzcw1I9LSBVvW3TQica2AUxMFKfdSO0qpatmCu2+h9ut+WpiMwtXo8w/vLZ9a1dt4B/bwE+cybCt2gQ+y1IsTQACWEzpQbX3vHeuWwoSPTXsX7E+IkTfuYNNVayo8hLCjbShlOfQhuywW362WiEr0V5N7URJPWDknH15JY4BF8mGf79VIwpb1lrwNGbqCa3YItLbAD0Bw8nvh47kilKquKlucQh3VZ+dyV2t5bQ6k7ZADLkjgRDtxTBqu1MRAAnQyoNuLzOcys7DxfA9qjq+ie893PIMueUvABrbQzPv3E699wKeDHhussF6A43lm62BfaDW34N8bQYYXcFPbmizITvPdIAPZ9QytV6adqHeA8esuotUxGBQ+cLezJXnE+KF93IKYLbpcVIejQ7srDUAvH96WFts1GfwzhR2bU68vKb/PWZ0bkJluCIiIGwGVdcOw7lusNt5KxZ4p15kWYd/QREULEmgZyLSja4G21YO8NPjQ4AHFdkV4CeO8NpYo6gvK5llJZYYgDhummuZlselNrHUkhLgDq9V+m8V9pVn0wIFgGo1cClIEKqyAso5z7QH+fTOjBsiCzKaa1B0cDeoTQecGXQt/7Uqdr5lBfGZuPawPcjflmnkXeO4i66IqP+PR+gxcPDyBX9pdqVUM9F3FNCxucrcHpJTQHvthef1n70wOJe995R8sFbfY5Y7eNgVwcvnz6hG8fT9RKKM45Qa0Fz+egqC1FHKWiHAVmzJaPHbVwmJHzFMEN0cFJo6s1S0D06iZaMsa4/PCeRUSdaK0KtPUj3ED2WQUQFD7StZahlTdeLkGebzf0/ArQfvBrVpQA+vPHOfLT4CM+d5IDxuDEvHwcyMrUsgvRVOX8On3/zjzR+ryonfD5jBeR1O3tnbMTvEdRdllvVQ0yBct6pa16b5DOyrjrvGqZB0T3lwX9DtAjWCaSYcQE2utwMt042EOqfeDI+Wz0CoW3Tf3VHJpa3LOXNHrDtj1p6QLB1TmMscD6bq1WDG8KalGDwE5thRgIJujyIgy2KlDfqwwmgbVb4qAkn5c+43/r9dOXwhzwAgsU0MtrzDckKqUHyLAQOeXuGJgHnocJ54ZQQY7z2qBVtokNdB96+5fCzEucQLpDTB5Sy7w1RdTlVACHgBBowd1707J7oGoZ2TSLcd4j+hf3S4OdXhpPk4GBl4ChB4Z/JvMAD830YoQqZoHPnz+hff2GVnlxpvWiNrcc0GFmbBgNMdImoWs27SKhlWM/IE7ge9DMo6IKaOLnOJayjzQTPfrE6CUOxf1/uOzsgDcV952Q26vQbYZ6IZPkVdnbNUMcTZMA+3rhaEZ9WISotPQnha8pU0Kpu6UQ3hGOWc250BgPxv4Sqqyd9QnUE0dnJcdolhORPzNnNM2+xDml2zZIYpUgBq0My7gsT379/7qyGiTpM8Pvobr43AshxLn31+UTci549IpRaL2RUsTo7HGJDnTHGFqleKCfw+mdA0rJEAH85YoB4s8VVVXT+t+tw+NHWrGdTTermZNJJSoQNVO+8UKfnhvb9oYiFbRA4dPguFFV248zBgiEnkZ6IVGdr72NWtF61R/dUdrpgEt0mWtda3nRTtAryEWn54cQktfGs/cO3kcIdFBOzTgmHd7EYQ30BGL2PFnVdl7t4nuFjGZAszvAEgF6uHU+HFVZm72Kw60cZBlqRn9ZVwDUZdSuw56qo/WI0lIH2PS2Oe77/sRYr/AxwOvcliYdy8KRvLVWJEdDx6Fw6lwv58iOG2anrwFAeKGY2V7r/afbCv+GSmHMZrKI1+fHm8jDFHMMeq1znOVghISFKQ3xXDzF7Ljwai9tE4igkEwrOPZjUjudY4bPZlGnyVlQr/whih9yXmtQ3DhowM85MwODQjx6a9ZaZ1XVx0AbHVEfqpOXp2/b5jwN02PIicNQBoZ3Y0I9MQ5msSMheo9DPGKyeQUAGghtxQUiPCAsV7sGm0BBl/7Omit2HLBRomarPYPSMPETLxWr3px38PCqJFW4CMIRo13QalP1pgmQGDDkh6DCYOCcTGtf4p5tqjEF6tg5KykNFHLChkYtHmp4BlCT0VpjQlDqhCPr/N+cn+0UQmDWFWFWwykxSHBfQAVQSpVsXW0SAKj+gvtZB6vYurpXcoSt8Ww5Q9mq3BfOzUSEl1EDkJAGg21aEh7PDTHFuceAQXsVnQuREgOkWVKI0ozNbK+1hv3YQWbaQthPe0DBO1UJD/3aquHAzg57TTbHQWBVjaB2WqULtKek682vO5OIIaITEQktdR4Qvfz/xfkYgMABYqwzxoYxLBANtdAgDNpqxb4fHMNqFQ3Y63BKrnhtgLsQaCQI0WFMTDqcwrajsYfTe0ctTUkO/aQwQ0Wzw87rSy/kX5xt+0hTouQc3DAYjjYbnHDGGetLWuDlPm11is44h4tYAhDTgrUnzokZnPoGiCYnLzYgLWPfAVkTPZUwZmN9tAZ6JNtZ4mmjoYLGk9GBoV5ccg7bqZVjVS02/Mzrp72P+FBnq21i3TKn+gDQLLy3ht6Urmlph/6bnOmzBNTibb5pywZG72SLDAtimGISm6zlX7IS0Vt8WVZtPAHHnjnK8LnDUgDp3JDcq12pcDJ9bMzYgpPMjHL64oppNMofSneoQZibvkdenSeD99Mq2C4jMpQ49MU50bkCwgvMecSY1FPFz0NjPknSO7bnA7sqjuewe1Fbbw0c/2K3w7vwQ+avf6GQm+o1tOk8TEEsmhVrUBdPuAPO9BDnsCTn5Yc529BSd0wI6kfhXx+ENXwgtlpymdRF/aJ5qTEweIVU9WucO32lvGNTr7LyqjPQ0IWzaTCnSnoQitEKy4kKMm2tDYL8Yb3lzA10L3inwqpg09PosS9DcLmsCJEEgD4hEg1ihdYsTiuDlBb9J871cY7wgUBQjgOP+wMccqTPcGLLfIbm40UmlUGYxgI696gFi2EGgwqZTFRBaZfQCsHcYrkePL9s/vrzDGjwNMbZSX0W/qxBkZ9ZegsoNGw6m1ksCABzfc1fbGiVIAAHYy1Jn2FFLoX9uMI+iUC1S42YPhvreomGOP20Xh2NX//x/mWtFZgfcsYjW2PvTWBJdbJPC9YLSTFZB15ZJQu16WlDhYkhYF0WXBY6KFg1HkOkB1kveDweOnzqVTjJd8Rz5mdywqpAXWQNrbD0TM6qrVrS95O3ws+zjwx/EyupbB9ZJsk3YyMrO6BD1rViGPQsfxEJz42AMc6B3t6hPtkUTClO76NW28u0soFamgZCtXhQy9pgrCQTrvQGCX5eNGwWKt89dHLZHWXz4lht+BC0wvixa2+lmdg99/Ih2EjlAnbv0Dp7G1LZKE2XK66XO77dnxjwWFKkb5M4yOAcgRQTopD7z2RMDboUn+WCJTgA+/6E8wGfYiQU1tnE9vZOLTgPffea2c1AoZRCVjcBo2bs5aDCUnNOs+4wx8oBTIO4LhYI9BAgwi6Dk8g4fohKFmBqqTrGkB5BuVSESGZUzlmthxm4nDj2kYApdnKqDejaOK21aJXAxl7QBuFwOpnLlNxDD3+QmUQ4F1QxLnNqlWXC+gRtkfW+00rZ8pzuIdL4u71D2yq6C3h/+4Qj/46c86RcDq0sauXnDYY72wF3UN1IRW4VIgFLCmi14Pl44Msvv8zsL8UOKF13TmdTTNwuuNe9ar0Y7n/B47lz4JqVvFopwAGqSJr892HQ7xiKIAfFqlUzJA4i54CmoWtFunhG1Z7gaDv244D4gAhOY5tw0rC+YZ2JUGvWqMdsuM9mb2DlQCYZq9MUI98LSf2kdSv5RPQi957QzdC4AY1d41xhmK2EcAPCDZmyK+c8XB8QZp2opeF6u+H53PHYNkAIW1NPkVgNDa/jXIXzHuwZKZqRcwYGsFxWjNGw7xtSSlgSNSwtKLFhjEkJngnSeKlwrJ+AE+HgTJQdx16m7f6/9vo3DNkZqqy1wKIBct72HTafVyCQ0YHuZqAccvYXZP7zevAEXgYezw2lZByZXHYfIhKAo1toHsru4cs5j5CoCwCAJZH7XaXAgdQ14qEDtyunrrXWKA5qDSlGpGktoFiqctFPQia3jH02+zO7DA0v50INLX21jPa8xErpuFxvKK3jL3/5Z7TrDZd1xZqSZjgm/e9Y1v8Pa3/aLEmSJAliLHqYmfs7IjLyqKOrZ3aGANDSAkT4/38DIMJiMYvdwSzNTB+VmXG8525megg+sIiavajurkii8qKojHiHu5mpqhwsLCwXBO0otZsYWAH6oWsyXRagK8q+YqszsnG8e+1IKRy9IH6l9u9gm4KRhtIWQNC1Df73XhrSJTuOcAoArKnQ0tbeTy31hk8LABg0oWZhvP7gMYzP7Y1R8PrlFbUck9RCCOxKtefqkbGEYB3egmWakPMEEc5vjlGI3ghlq3OO6ApcrldMKeDlyxdM04S63RDChJSpUupOkrLuXuv6Cir86h8iAlGjRtpzVVDVtTjxQAK0bEjThOv1iu32gk9bxdPDA5bLBUE61nVHdr2uxK7rUgscE2+tGwWTxnqaSCi431ZM77J1nTdgOmk3cYXQ3NSfjJ0n65S4oIBlDAFl25HmGUCCNxEGc+zeQOrOhsGF15KA0AWIGPs/0Jvwd0dQEsCBUIr19QWbOfxSKll+mXOzSy1s4APQLZIXCKZptgyecFYMAu3WqBjZxazaEeKCx8uC19cvSGlCQEVNGTllcwIulW2GFA16VmeQwyHwnwdE5nVPTpPrI7MYA5PQ0XrAZVlQy4Z/+vlXPD494eHhAXPOkA1kIEU60mWe2bwJWCPcPuo2rXUsC0kv275inifkzj6j1pWQEFy2xAFDH1V8nPcRh+nRx6C9oPRvM/e/qU+BKUs4IQSGwza17kbrpjTv7g91FGpFrNtZzFgK0AXTPKH2is+f7ywIWxfxXkxVtHXWLoTp7TTNVmQ0qAp8YKVU7LVa9MB0L+Vohd8JiorSGqacsG03S+sYZTk+F6LzoTEc2jk1cwN5dmwuDdGNQshxgBGCgF6LLRoASbhcrrjMn/H582dCJtdHPFxnQOrIUIIIGpj+MSrKCK0bE6vbwWDaWLY76jJjVpcMyHBeuG94eLYj4c3WVz2GIDldsXVjzcipluBGNBwpEn9Xhr4N6YuAs81YO7CdGcx4QBGnjLkkfP78mdIVjalt2wuqied5RhhMcE5kGek46xVkYNVeEAKlDXySmIggZXanl511iFpWrFvB9WEBbDCS01B9WpmGjmMYC8Yh85dDC6odrfpa95G+J4B1DINdWm1Y5hlPz8+4//PP+PiJ7LOn5yfMUwZCIkQkhP2iF9GFkt+tNZTqjLjEelhvuK878rxgKAJzteFwWcBXMjL+d0/ahM/YJT26Hvd6EAXCiEgh1EzqahmsZfYIPJchBE7qO9eg7HMlRMzzhC+fP5FxZbRNEaOqKqGNbpByTBHTQil7NWDfx7G2SlgsBgyHwDOQMOeEvawIMaGVgtf7HXFahhMIBvERkgL3I9yAHrRzDwyolGDPpx/imxA5hCtFUIz8oFqRphnXhyc83e74/Pkzai1Yro94WGYAvic5EVFi4HnuHWmaiJTsBhmB9xRFsN5XzPNizajMEpQLNkwye050PHPaLEtjjRmmZq/q35p9NOaZyhExOF2swwfgWFpl/P4xTcmbzQRAZzOLqA9uoQFab3fbILzRmCKSmgSARawxRRaatA+4KZuW0b5t5kAaChQ5ZSyXhaMX84QpJ+z7hhASarujVP5MSufh5AfsoaAT8qlyQ+pC3hap3FuPWcp2uFLM0NChfcayVKzrjp65gN99/yNa+0esteC+vgIieH66joLvbpIfao4g5WyRxlG/iSmPesC2cpQjzAF7jWXUciwi9kHhGDAcxnyEER1yta0jFgMB8iiyq2Pk3Q5YhEsCjxKVuszxqbjlTW8i2LfVhtAfNaaUdFB2aViTYcFhUFZDjLjkjFoL9sLfraUgpozr5YJ5orb9Mk/QsqMqpai3UpFStrkWVlAPftC53pRVNhDBsxuDIZx55HAixrgbGpFjepdiuVw4R1A7NAMPj+/woRX8/OsLail4fXnF87t3EG2U4faRsepTBBNCCgiJ9xpDAEK0EbIBvRVKhc954PkM1Y66QjdxNGfQwJRCGPQfAL4bQ1/3IxN2GEu5pxyqC9Fw6sP4NDVnGgCtOrYBmcvsoXHhQ5/BkVKCWrQcYkAUGu08ZdYbKokB0zybMsEOgBR1IOJyuXAOSgiYF5I01q0hTxm6c1DQlCebcqej34N1HR0zHKysgSPIE7NnHvgK+qkz3892tCLxYoN97rc7UlYILvju+w8o7c/sqdruKCnjsiT0BqTQsRsCQjmegJx5PqdlGRIXXpMUAOu2YbnMhgFytYmKd1v1ZrRqrwe6DegnVhprGgV/Y6cAe34mvjhSLPeirXYzOXJMlZLAeoKwcYJ2RuwPuKnNwCtM/dHYTNUG9MSUkCQY+4MNKozKecN7KbgsC6ZpAiwSSSlhmmbMc7KGJzaB1FKQcmLq3jcgiw3aPgZv9MZxee7EHPIYrCNbnAF8CQ4WwZiGAzO6bIy7ghkNu3E7lssVHz58wMfPL+Rjt4Lb7YZg3bhimkxiGjDOLkg22YvZTLd6QEaAYt82PJjcrhjdx7n05rYAuN6NGWP1WzLHl1zWgzUh0bcZhqrpMMEniDm9MwBdoFaU7mr5pPpTImQUwH4KDMPFqA82QpVF54A5koaq2sgB75S5qLWaA4kIFqXGxGcyT2ShBU2oZTecOmBapnGYnGfuBXGviUh2Q/fWONhq+s7n34T1J/Zz0dDQgQTkqMBCXLiUajIYinfvvkOtJjsNxe31FTEILnFCCgHbulEO3FQAOKWQhfBkM8KbdiSQeVT2Hb0/2qFkRIixVgBgUujqkf4gK3Jtu8/5pnQNz62OYoCTRpyhVaoetQ/bTwLWb1zeZNTbwuGcStmHJAOPDVURKkBxSmE2waNHyfRqzDtRRSnNuvsFKRidWIRZf+M8lN4K1p0suCUsB/wVZBhXQkjMAIKLNdo5kVNGOIIpj4RsvWPk5EanGwqEM2WmCRelRL4HXpeHZ3z40PB622if9jtai0hpwrxcoW1DGzAds8RkEiueZXbtEBWrhzVse8XD1doBcKpnur3pCqRTYRqeORoUrmQxyf4WHv3XXr+JkspCsB8OOyae3rXqISW8WUm1meKjX6xJTY2GFA4Rbx2Y8oxXcACHdkWME+bFC2i8htYbyrax9T6ZsWukcgYc3ZdlX41RoZaecZzntu2oXZASsFyvjESz/QkHROKYpj358WfUQETIwBlFHjG+MOBCcGOXgZHkPM/Ya4PkCUDFuw/fI8SEz1++oEOxbyszhTRBy4b73rDMC0Ii+yHFbLuWzyPlI5oI1rfQlGb/7Kx5KYwoYO3usJoChyPxHrtN4XKYB+rjPGBfc/iGhU5VMAAQi1y0E1d2bw+3xUzDQwio1rEaU2YEbz0HMQRMD480MCbCR92bg5rr0VVvDdWeewh09sWGAbGwyGxxv99ROiGOPM2cq5szJpuIF2wNOT1PPGk9XuKBj4zo2/e8c+k9xBkOxoIdqrPu2Cqj12mekULCrx9/xX2rrN1sBXsH3l0ybq+vmE3Az5ktA+oxxsmUJq6Z3fe2Fzw9Xbmm6lpFBtuYfo46pGtrct7R3rTHTNFhBh0QkprzqI0NhT6MyJaTTmf4fV9vL8YLtFVsO1l105Twer+P+tCSJwRjvMGcfzXpjZSMAmv09FLr8M6tFNSugEQsMwfUdFX0uuL1tpHQkSMulwXzPCNZ/8rhzMw2nQuu/NhhpIGTOoM9jxCOTmGHlbVb9h0CpnlGvbE43CGYp4T4y6/4/OUF6Iq9rni9rchTxOvrCxAzLpcFUYQ9F4btORLBfhQGRiFGtFIGbV5w0qVSsI9BLUM7DSob9SQIVOtfIBz/1us3NK+pD+sakV4Ai3StEfbxEZHuHLqybZsZAyxKteKYsPtwc6mKlJBTZEFlnkbKFwSojdPFSq1jkVwzhXWMNozYJBOhh8xUN6jy3wH4+Osr9i8vmC8XPD8+Ylpm06yHNZ25gY82r+HkCEY3rTk+lTcLQFw2wHVOnJftDi0OIa2KnNiU4kXv11fKbK/bjvj6Amkb7hsntj0+XeGRarDhIq5/lALTWBpsG9XoEJA2jMMajihUrWgopl2kNu8AlokNxpXDSrB/iwKdc3R764OtpbBngc5SxuhhMIx77APFtm2j3T7PE1KpCJNLCbB5sdbOeRrWo8BCe0MdrfoOiwjHIaZkcABZNVDBclnw+vkTXu8c8/n8/A4Pl4mRl8Iwcacas6nIGSq8YzHc2d2Fnsjbh6EIIuihjyLpObOc5hldN8Am+UVRrPuOri/YduriNA14Vft7Vzw+PprDCpDoDpeyzRKYSXFNOJeAsGZAkIYGGHx4hDHqVgxvGSnscPa6kwcPOp5Nt73TDb4EqHUVRrBsGZY9b6hH3swcQo5Y1xu7lHtHSJOpvrLQ3fpQ2mJj6VDNpRx8MU0kR/dUgZh5fuYcbL1IzVyWK9Z9xb5v2PaC6/UBz88XI56Q1qopDbpx8HkpOODvwd45PSN/eX+HCGHSVv1sH7+Xc8Y8kZrNCXcBl8uC1jtut7sVlgW3241sq8IZ0w/Xq1GiXYzRVXr5eRxCZvBX49AmWH+WHfJDeghq/ad0MK4RpbafVfUv7u1fe31zn8I4jGZY1MTcmk0hUtuJjKY86hh2BU5pPChVAbVsVrBjR/Pl4RGL6db4A1cxvDbK0CJh4ali3Vas2x2lUFhstsaPPOWhnBonYsmur58SZQdCzjZtzaAYT29tqIdHTp49jHnBAM79BsOAWhTict3OtIo+9SlGTPOMeZqRYsC2bYxeZyqjbtuOab4gp8B6QQwo+4aXLzfUenQj+ki9EEwuxBg3Dq94tGdoGDy8eaOZf9oeAiFTzH72EJoz+MD+eJNh9WEdViISM0SQQIhq9DU0dHsGgLXdm5GorSKkyaZLubMlU4K6SqZDpI4Fd5R9x7bx4PN3LzYbOY0AQkLCZB2lMdNhtFrRhAVrCgvqCHBCoDYMRo1Ej07l8bQOo/kGp7XAxh2SH2hy+yOzhWVGzpkd1grMUzZJhg0qEdfLzKzJmF+32yuz2Uq2WXfG3VjLYMwr7tnafC8Y3XosrMN3vJOujIQHbNtNbsb3sGLco+shdWv6G1PhvAdV3VkalOIBtjkoOqWDYu7zla8PV0bk0ckp3Yyt18lYH2KSSs2qfS9ATHh4fMCUKXcOUPZDojHVLBOfcga0ofbOGcrBCC/uVYKdxZHVeVbqkJut9Wm9XYfLb9ShJO9l8SFgMWWu9TRB0LGXhjxRpr+1itIUl+sDJrNNAlKvX9c7BzN17xU5yB0ArCGODmO3jmnOe7Hsz4o07tzONc0Rzvj5hI8c+Ouv3yZz0cU44iwyOTWudxxToqywIzHBCQyeupI+Fi1SNeZBO00uUzJt6sqIUjvQbdxdLZWbBEdUQiqgYL5cME8T9VRqoZSFzxaWgK4N+7rBo94wJSzLghSiPXRvVz138HrEbW3p6hzh48FDAS8y+6bybCKZAN/RXHb8Xm+M9LZdcVlmfPqVUR87mBO6AClRTTTGgLqtqPuGaVnwcL0QH3Qwrjd0iZw7rUqmgvHdD0RBj/4CvwHtgDe5eaFYjPM9WFT6xmiMLMEFz2DHSE91lxEFwDJE6z3Zy+iottiFxXMpRj0FMxHTpdo2Nhi60005oakiTwsu88yIszXcrdcjzRb8qg5IUoIgpMSxnJZRRIPhusigpkLEJl8J+smh8nYcQjuemwdd7jg4MQyOLdjeKRCQZigL0O8r5ssV+fULiRQ5jw7mnCd0VEKEaLjdbkh5wsPDA5I5Lgb3PG/Z4E612dpHw5iOdZEgQPOAhfi/2Dr1QOjMG8SoCSQIZwfRj876AWVZgd4Ly2Mr2QMyljPPrDHl2IdjDaIxYN8dyuBch1Y5xax1y0Tt7KaYIDnicpkN7mrY7hyStEwZapTQfb1jszpVCAnzsmCeJiRrtBQcGZ+YszzqhWJ1NcuQ7R5cJlkHXHT8jMSAZPBci4DUI6iR2tCMIbQDuCyErkWC9U+Ba90BCIPPfVvZdzJf8Dgl7gkEuCRJSom6bbYHu9k+n7SmwIk9xX/3zqFmzfdBdzr5t72+ffKapZFNGjnKIaCpyRyEcGrZpgOJI9q3dAbuyWSkqi6J4e33hIcsevfNVU0OQUkjrKaLBHDy0eOFSoilkuHgw7QnL9C1bqk6rEs4I02cjcxhFzZtTTmVzOWcwyk1JqQkYzNbM6kVdKk94g/cs9BggGtINMbVZtMKBBV1DNXJQXC9LvjycodCUSob6h6uDyiN4yUNMERvFh3uBTJlIHgBmJsyxsAhOKpm8A2PVRwCiaY/5c/fI94YOf4xBufKGw9bGNH1xkOs4nQ40mW1g120MVlhmg9ARifdQZFs3iwEQoJdWRvRvUKFKqsURgNCSjaWdR+Uzev1AfM8Dzn10RA2JSQxmmrZTUk2QSKdfgoCBIfdAO0W1Rvc6esCPoqj8UvPWa+Zl6A0niNrNOchAFTIhjMdpyCCIhVAxuUClNpwuT5gTp+xK9czxwnzsiBlykczGuRnUihuhwpM3fM4jxz2Ewy6O7q/BUctyF8C7w72vRmA6HudImxBQBp0oEHSVk3k7TTCEh2tCbtx7d79xaxaj/2m3UlPxrNn3SeGhl5ZH6ymz8QGs46tsmYQhGyceZqA3tgpbE6ENUBKobdaAKEa6iIRlyupqgKQLIIABD6b6PcRDm/mtRIJ7vV0ZAZBBBooHDgicXuWYjNKo2QAJNN4wKhqsiQhAv2KZZmw122cmRSoX9V9x8Voa8IaS+8bcs7IckDDdCrH+8cgFMmjpz4iFg9cVa0wbvBXOLq5v+X17U6hWiG50TgDsFqCIgXD3czI+Gxjy1oOGMkxQjn+IYClT/2UnsK0yOuR4sLhp4hpPjZ0TAmt7CiVXjVAEHPGNEXcX28IMWPObFqapolGB7DZC764zhzCgECGCqqcm9a4KYKxe8wiW3OVjoxAzzdrBzY64yU1tE7vnVJCF6bWDw8rugDzQgntIAIYd1lMxTRli3Jbxe11x3R5wDXpiMjODSy+IZym65bZI2B/1iOTOxlJbQ0NbTjD3sW6po8AwEMpQm/BgQk4EKzgLAtnQ/j/t9b47OE9IBy3uZUy5kcobJaEKlKeaGCjAIjQTkw2xIgpRzrWnFG2O8K0QGRBSmx67K1Zhsv3Q0zD4ZOkYLj42Kee/XlHrq249S8AehgLPxh61E9UAGlmUGJARBrOlYVwwfX6gIfHR8i6IyQ2TrLEUcfPkm5MmKAr0NY79pDx9PTA+5gmnsEBCzAI8WmLvq8dX/alFjV5ERCz9gl91TqHIR2xswm1GftPYFGmwRX6Bm4xP6NAt7rhCYlhZqkuzBdG1r/v21BI5c9RTj2EhDkFk/zmjlltzGmKmZLxOQO9YC2CywODqpATcrL+FrDOmcxxJw9WBrbCPyJOOecXHZsP/vwEkGYNjnLcsMJYXXZuVMgK7Na30QI7zWNivePd8zu0/hldKZmtCkjvAw6Kxn6MZjhUC263HdeHJ4TA0bacbug2ies3MvMu44z10/Mf9hIkgniN9Fte3+wUaqXcsbfUt8rJRmNgiaU2gbsSvSm7Sx1eMA/gG4fpEMW8yFnnN/d9Q60sOop3x5oxqaWSmmfTqDx12luzKW0z+dyRA91hRo1SEdy99PQnCQu1lBhWD5BA6eUUzMm5Ls5B7BwmQcG0G4CP4oQZuyBhqMIG47ubFvEQZQsiaBLxw4+/R++Czy83sjOmiH0vmC9XRAH2siPEbAtvPGso9vUG7TMeHh6GM6LsgZhgWjgOpDeT2aYPQeD4KezQBOOhN3MK0QYP1VqwrjvmeTLneKwLDCIbxsfScrf4qh5FMlrlJC3e/75z0ApCRIodxQrJrTYGBLUj5kwNpc4pVa0VSIxjUlUKkdljF0hII+rgvbMfJnjBTg/o8ax3kyxaY5R+ambS8X9H9C2U0zgrk4bgsykwePz+9Vo5KVCEU8ge332H3/WGP//6CV0D8jyjbBtSmrAsCc1E+zztR7RmuN7w+uUFy/URF+9M7wokMa2dCIFNOTsFKN2CFtg6iMIaqFijk1ZQW2W0rkqG3/0OSRMNCTxC5VPwBmdHXAdbzR2qGdkgGPcSALSy0xBaxg4tNkypDSJJMAPeG/8OcA9Py4Ip28wTdOybIsc4utzdjXv/jt+3qrJ+ks4aR9H2LvdpDB74HffoPT5kqIVT34cn1sc9B+twDxLRakDSiGZbJcaEH3/6EU2BdSuIIQG9oorg+vAAgbJvIXhhmAX0gIb1/orWZrx/t8A347EdDfoze+uzY9QWYMRscOcQEUJCDPXfMvHj9ZsyhWBa/NQE5zDyaUo4UDsML6xepLHmwHGsCECbaVHr0AWAjm27o3cYHY0HtJXCoSLKaFuUxaK6VxuXGbAsF7ITcubvxQjRAJFyRIAQpDQBKLyGrkcXcu1DgdO51M2ocEwVCc14L8N42G4Y/oUCzihQwZ6HNkeBuOHcYCsgmrAsMzusEwtmMfrEKMFlWbBvBQ2KKS126Lm1923DclngDonUUIu0AkzW+ogbjkYc/oxd4UCVfEB5M3hHe8dWVtQGTPAeFJx+158FAGHn0sDkbWcYIdZ+jtTXfd9MUZJDTBS0K6USpswpU/s+BrTS0AU8MHPALNGiLh2FOC+8QwiDSYhIUBRrPmqtQbMySxExfrxAAijvrLDD6fWGAK+XiGU+hOe/kiCWt2vt6W9r3nRpXw2BBqQrpnnBvGwQScanJ92z1spu/bpjr42Bkfcc2PeppdOt01fhct5HiH7ADLD1g2XVzidwFqFnkq22UWCue8F9r7ikPPS8TqAgjb1a49SYunc8o4N9BnjBttZiAo465qPAssgQA7Jkk5U+2EkpUUDukhLytMCT8ygZMsN0w2TUN1IA7luHD/aJgTPGm9qcZfvZXj1LZV+NNoFYndODTNWjAN9PkNKx309nXOQIisazEMP9uwn5zQiJDbS1KFJkdiwpY54ztn2HD/ZpZr9as8FSrWHywM3OmDu0bsG42yE6QQBJDhRA/Vwe8P5fe317obm1UYAsjbIBD9cJI8SyjdCbFfDAbIFwnRVqR24jR2Rm0Mq63llw7M564GKERAnaWnaoTJgT5wvMEwZ0QDydRq7WhgggpWit9IDEiGVa0LY7Xl9ueHx6ROuNctKmXR6C4YtQa8eXU7GaVs6r9xKsD8PbyG3Rjh3jTA4/TFxEVcNVY0QvdWzo1huWZcG61VH07p00R1GYJr8njjSi0YaNMJ1WE0nL6K0j5oRWTYZCOB1PxKZsnQ7s24DYYQU69IZuVLiK2+2OtFwxbsadi2JkYb23ASu+aeIDPCoAQLhvW1e23CsG7dHlUPI0odXC3pVlgUCR8wwFMGWbmgXWUGpp6FFNJt0iJMOj0Qt++fSC+fqIDJdhIYNqpNwCyk5LgOs6eXDzdgi9GqW3/2VPqPokMH8e9pAsOPJ9KCDs2pTjG3NK0JFzimUpjEzzNCEkUnerqX7GAOLpkfUpnWfSUmWiYKLJJ0inU+O8DqtNaDeIlKvdLWPwKLsZ80q74n67oYnLtdv1dWtmDGaEHT5RQK1u5PchCGPvxpDQ6orN6MXanNXkc0eCMQ/boUkVSJhIKZOCK+yH6NasFhJPUwgJCiBPE2IEPv3yK1rgwKxmAWm3OmiPFMzkICz2OJG6zbqYHQiroZl9GnjGeaktIDRIm46Az4UNYgmhWZBp51gRsUwTdGcHs6fPISSIBsQcMYs5pd5t7EAwtuJE9pwRBlpXTvTrbQRbalmO7z8SX05qr+qzbRwG/euv39SnQHy/43a7QSVCgwzH1Jsb8UCmA5imqgRE97bi1L3DQIYYsd9uZJtYamsEFWYNtdhELkXME+GV5ULZCcPiis1yZuwsaL2gd3ZsznNCBLDdX/DrL79Cw4LJpDK8e5fRc4cK4S7pnViexwiCwzhYet6tqBZCMHVN2CY4oiD/4xxkdyYhUsKjWDu/qgmjTRlb7abQ6g6Ig+5jStBOpk0UpvksqJO+WjspoEx1ifszMnRIywTWcERC5y7tYdSsUOV01G31LAGWigu6WvOayXE7/VH903xkKTwapVHOKWG93bBuG2duq2n54/jMzeooEBYKl8vMQrCywNlqo2ZS5drE3rFvbIDL82Kzeu/48vEjXreCh+fvjLJr7KLeEdUjaWU7R1BotOxLrIfDrZHdCO+FWWSnGM0bip9DNt32x3mMa84JpVRUHSE6rpcFX15uyPOM0IxplgJa2UmXFhI5ppxRGw2DU64ZcXMSn4p1q1a7HzcGbtw8YvcL1WO9h/aT/bvWHetWMF/JmoGoMQBtBjuOrJ+/fzwba5eDz8GIKUM717rshaq61rkuxnoqpdjwFwZg0zIhJ8KkXrfb9w2bTdaLMWDbNzadpoQpsbv7y+0Lfv30Bc/f/4RpnuDlya561AgADOp5j4hWzA+CtwZzRNce8JBY4pRfB6sAGBNvhHxQYFCch4wLOFRr3T+hNXbgi90zsyI5CscpQYzGm9JE7bbIvie/uBgSQujjuZPoESCWmR5IgN+z3bcconl/7fWbpLMFNNLbXjFdMjF3k4Wo3emmjJbEC09mVYm72cMOAb3QsDjW2Fs3rN0wats43oErErDMM5Zp4lSm2jnHNCbCE7UhpIwcI+UQIg3itm7Y15WbcG+QSS26bpZSd9PBscPSOiMTo+CyIcquG0evgpwOmrzZKjAShm0U1aGx4uSMECKQCDFUS9tF2Mm6bjf0kJFzsu5uNm455CSgUB4sQggG1wTlM8DE64shMs0PPIy8Tt8sarpUvvxHJHK6KWjvuK8bJE624VhUNl4c4PwSo7TSnA6wGfB71WoMso7NdIx8zGXvPtHsGHyk4DCV5TKTfdUJb5TWOeBHG5oKhePqDnd023pDKXdAbe5374jWFNntBCnicIpDm0udUTQWevxx/NkFAJ3qidM+AIb9xZgBEGQ0ZHXQedUqZgTV5jCIyWFMaI0MoFIaSjFOfaIjyzGhhoMeKgJ0HxwE9qBUsQ57mwDHpEAtuLKOZDck7igMIuHPMgDoIsblB3oDghxcfces6XdM7+YkmM36HfWQFEqWWHNoCtDu9SdYUZifk/KEZZ5t5jabAatQ9pxBho4BWgrCfaXuKLb2BqQAdoaak0n0GBjF8xyOPoWRKQsXzya3uaP18+K5/tHp/9YodgsMmQ0GhKicHFfZaewCmSkmrHtBjJOxywLvY9+hPjDKCBUirlYgw7GwQVItKBR0UaJCthXVjjYhs4A0SBO+zke38197/SbtI9WObdtNTiHYNjFGg1eZfQEGsq1HpOKGAkCxSCFYo5dHlQOJ6X1EZzELom0c7RXrfUcM5O+u24qcM6Z5gkQ6m1Z33FYyKnLOaLUghEyFRetuhPRhWN1AHNvAdUYO69D1UAH1VnlKSnh8DJwjb99Vo8g7nqGpiA7YQlD7aexgtIHvQhbXlCPu9xUaZkSQ6hhtkHvIGSHAUk4Beh3PO4ZoEuNHauxFfwDGgTYRLbMUo2HPbGMtnHVwyTNEZaxz74QqiAGb55eI2AGNtoCeFUZBK8x2Oqgnv47063gmjm/HlDDFjMtyAWsmKwBgmieUfUMLGTFPpgZaUVrBvvFZ5ilDt2pU14AQ8ygiHx/W3+DE2hWBleCxRl0VQelpHVLziNA7Rcc7ii+3hwb+PI+9EtBQlPi5VA77aZ0QYNkKAjqdw7xQ66YpFglD4l0CMAV2ZPeugDFJSum4dIUmo6da/YFMFbMYCtO/oQKBn9kBgZhV0d6wrhvitBh6wiE1wwqOoKDZ+WBQ0A1qIfRi0Get1C2aJs4YGA/sQAxCABAJrczLghwCSiWdeJkXaKvYN9Kzl0Bnsa47da2UYy7RuT9znhBMYpzZjCCI0nHZc+DRZC/OgLGHIqqddZNYoQLAcAdwx+frO7J4MaMtx34asy2MpadFCQFaMbm0QuXkMFHmZ99xjTyrSbjGISZMU4b3SFEyn1IXbFgNb5h6RyCnI7BVE8jzffiN/gDAb5LOZuq93jdoyIAN/NbeodEuQIwj3QGJFknYpnShLTVjUAvVE2FjCF0KOMApVgHLlMhNniakSO0b7d2iYzHskpioqOL+5QVNAoJSo//x+T1yUJQCiDaUrniaqUcfvbhkD8s9LX2Cx9Tq9tE2idO6dLiObr84ooqRqupoyhOXkLCN538/F9S1sABe9h1FA6JW7LUDcuFm2nbIPFOIq6vR8OjoRuQHoOwVbabKrMMCXsA7rhzDmTvs42mmLTZUFWXfqEmjjk8yMhLuTr5LwOnheVONp//8e68V+301Y50B3KwgfhS+U5oBdCyJ0Em3gqNH570RR+2tQaJgvd9RWseUIuq+Y7o8YpkmrHeypupeEOcLJRtkQhgywt7QZOs+ouqjeQ8AutAxiPj9nwIWe5I0F2cI0mEUDB569zTRDqsLyvHfBdu2ARpxv2+oagS1XlErjRwbPtnUmIzZRGycss61cK538IE39ulvjIBHigNeONbcv1/Ljn2vuEyWBbeOHk4Tu0aWcQRBbnCCjXtkM1zlWFXL4mMQ7BZwKehwswRG/uK9F4rdJOZjTKOTmrRYRS0b7lthg2or6MiWJW6kHtuQJe8CH0q4RjA53ebBzPEDazbfZk7Be366P7fhFMUPO7MoC2T8mQTx+g/ZhvtuVFxnWXTFtq6I04yyrlCpWGY6sW3bMS+cA9Nhw4FSHNI0Tsvftx2TTQzs9ZCxUGMWHvvzMGpjzfq4k7/6+g3Na2wwKrUgTBEeDvmwlXDSzhlPPeCIDsZMMMr/rtuGbpIJ0cTdam2oytGLMQRIjLheLsDQRzEvblFsrcUodwXaKrXmlwVl30eqreBgi1Y3rOuGeds4wGKaQZVvwi+CQ/sI4kmNR1LnzMhyCGGkIE5rtefkCqU+TIb7SWyD8T3biVDsAzRGA2AUrF9eEQWoxspJgfzwTgARGllLCJalJMNh2SldsBcKg/Hu5YAKfKNYUXGkk+MbvoE6Wq82NvFwIH5QcxQqpSIZJEaDyvWn4Y0wTjs67vcVTTnKUUy4rtbNtGISciDWmqcFNALFMicMh7BvxWQdWFO5rytUIqS30e3pAYT2htf7HUkS9m1HEBmzC9wZhDBm1GEYfIgFzk5H9kyBMF03Y/umyK79TRc0xv7nGndT6fWaTjM2EERM//8LXiunjNXbKwezGPQHCCfJmYS2FzNTYC+JRtZgKB8tbyGsESFal3BXa8j82niQade1WfPUASv12tlcBYdBjKXmU/tGUmhOUgTbtrIXyHqYpjxR36zxWc7zghiDzdgm1Z3KuRgkh83qiFCYBMyKvbIfai8NeZ5MgDMiReC+vmJtAc+1YN13zNM0mgcdyg4QaHAv4HvedkDw4ORUMD/BTLDsxmdQiKgxuLyGBnigcO7YpzG3oDKyAe315cWCrwYOLguHBI2y3phTsgycM0JgAWbZd7RyQcyEvPwa1DNDO7t+UYqx0G/+/tdev4mS2jpQa6cGuJKLIYYFpxjgw96pg0MVTWcaBbDGEJJwoEozPZ12FExbYwQ/TTM4XtQYA1aV76qISjhmL4VwggLTNGFbN5TWIdYEFfNMYToIWisceQeMQliOCSEfNFSe/CMV9kOjlr/QMZyijrGnrLmlH3kFU1g2tPlweIU39TFDGE7Fctnu+idKyERBGW2oQiVR+yUlw+htwwq52jDnBlWTDdjRlol4dmvky3eMA9+1jwhiOCuLKsSghFLYJIaQxs81KNA7abNWI8BoWnJHZ5izWKbY+2hCdKHAlDJiIAw5zbNFwEfDmrOEQohWC9qxFc5gztOEWneUUhETsKuS2CCwLmdmiTAJE0adnNAlIY0GPYEzMsZ5PmULXCdHuOQUVYs9o7MujsB6CVobh++Irv3BmrORhjFISEnX3GvDNF+MlgnLCqJRq/Wg0Xodw7Jc9vjsxvILgE1dA9g34cbE/6vB/b/vSMsISwEsa/XAZ6iodsrhHxEz93mEjDMhzpkXjF6iZl3xsD6Q3putdQJs/kpratkynb2C8tn7XkzdIEPB2ccQ6v90o6Z781stO3zGulhH/JS5fi5iOeS0rf7mJ1gBwkw4ZCCYzYx+Y/i0w64WjcM7nHn9/kz6uXHQAwYRVLWMR9nvs20bYs64zCQMSLC57jExsG4NPQULBkmbDoLR41MaR7mytOX9LBWKjjb23UEcGQcbx/75a6/f5BTU5xC7F+wK1YYugRiz00iD5y0wJcU+DmIAK+3dmla60SlVAuZlOSQuOsXRem/jjKLzOtZ1RakNEkyEqxNHzjGiV6qLppRQ9x3V2DpAwDQxneXn2fVIGMZZhy2QUxHKHoCxj4LjiF4UH9/H+BrGe7EhzusVDCL9s2wzhoggzSL9Zowq6tzTUdbhWHrvSPOCWgr2bWeX8zSxqGSRUTPJac47EGjtx+aAiZ31bhRNBwJ0CIexMF4P/f9gjDAA0knf9cCoqSICI91mduBMDmKhqmVkGG4EWu+YL1dzQBblhANao3+h89y3DWvZGVFFqoXurVMLyeil00Ia674rsViBzdTgzzQ9GYngSr4YX+P6mXcYX9Ox5lw+O2zDPKqtsYzgk7MxGrOLoGzykjCaFSWEsd94cK33xmU5hIHBKAxCkIJyjGWnbtMl0fmHGMbZab2xzgDWeLwvx8eZtq5I1mk7vJsHFlbYJQ0LY2+rKCoakuHjLBBHhG6ZBpywquPMwPTQXPeom7ptyhdMM0bvEudt4PRceEa2becMZwmDul1LQYzJJD8aUiRl9Xa/YZpmszERc55Pncu0NN6QOqy9n+njH4CG0QPB7xqUilONzbPo4Sp4tmMwcUUEiMvBMJ+Ey/6Ekw0Y2arE0dgYI+1pbQ3Xaca2kX0XU0SO2Z4P1603zjbXTpHNWncLASwD7IcyhNfNPLN9w0D7K69vI66CaZ7r6Pu7a1OTwfWdYsfF0przu/viMKIz5kktJiMrmGY2crBXwDa4uhHzwdUNL68vnEsQqGcyTRm91tGhW8pufO2K19dXaiIpqbJ5op5+Nk/LHaUHdg2P3jHSxjdje4cxdxuh8EDzzSAe+5qY/Af/hJHKZpNwPp4JIYJso0OT9Uh4Sn0uwDtUV2zgTHRJDkvKAMexMe6JOKdnGP14rp5ynv4AdE6uo+/rqSZz0Vs34+3fO2ok8Efq/PYgNjlPUFsxPBhI02zZQkSwOb/Egwm3tN4hori/vmDddh5wEcScSOezSJiHhM7ufntl9lh8EA8lJKY8GcXXDHE9BswPuOB8gMxmBJFDzsCMhAcArpLpLzUn5qqfTDpZaI8pMesdgQYhoClniIg14dkcD6dyexDh+9EKyCkdXfKwQUbalREjDvgzilMkdQRu7iB03Kuf1WaUV39fdww+cvTomP2XrIoHVxCDtCDUrNoLeieLLOcIVyAOBuFRebcZNRnY1ztutxUK16Pic/OMQFU5njdQBG9dd+x7gQYyjvKUkadpiAfSMJpWmJ8j7+xW72gGfK4I7yEMSYvhXIQaaTKeKcbXEcLICMgKTEZscBiaTMExOjSzp2T01Ziz8PpEV5vT3jszKqcrWhauCiOPWOBi9+SZhJ+/zkV7k7HSpX3b65udQjHtfzeMPsuAHtylX9U2F/9Op+Xht204MMULYPcqJa0T+dDdPTAPVq3FNJA4H2rfNsM2QbjKoAmnadXC9Nk50BAaGVVgmhcsy8xJXeGAD2BRGembh35QcI0fCyOcjTB0+0/eGJYCcshLHEYEAMZ0p3BsFAnxOCjGj3YJYYB0RCjvIdqkrWIRE3qHxIzHh8uAvrwA69x/Hamu10xc9sFhEGtQaz7z+K1T0HYM0hExh9A6C3K1jKZAHc6Hm9LKbqP/w7tw+Z7cOzFGNlwa08NnGminJk6xbGnfOIUrxGCsm25aRpRW6Ta6FVDjcZMW23tDyhMuJq0dh9jdUVD2gu8x1D0iujGH+QHLAsY+sAYiAYORnFjoPxMP+D2Tz5bD0EejHLooG/eF7zXCA2XfoRKgnfBXUxYsmwLL5QFL5iwFEvyswfPknLk32e2PEMa6WZclejtmDb+tMXGtHYroILTVDA2oY4ymi7zQNPaRDbupDBZM+PtYB21wSQjwDESW4kth7aurQI2tFEzjiwFLQ6kNeZoAqA1cCiNAjN4xHBKW5Yo58xmPxknBaU9bhmjn0dUJjiDusFFH71IfzzRGSrRTIsNsrO0Nl9L2feKOIfogJ3OYDMroYMq+o1ayGfd9x75Tt613RZpIxVbLVJkNElXxwMUe5ikIsTnzFtRw3frbtf7WNAG/RfvIWsejH6zOhQunwRlMJY9IhIsb38Ax2rjYXVkLaL2hVh04oWv3O7bcWwdCsIckSDnbqL5Cz17qSD/V8XtlF3AGTC75iBijBPTaUBM57zx8tpAnZ3EUzA1aES8eHZ3AHv17gc8jhGgYquPAMRwFSZ9xLJKRIYOZxPnEgc84BuhOZkmKwWi4Dcv1OrR1/ID7DAX2MJACGW0TBJhsdW9meI8DA5DNFW1DeWNi74peTTo7YuDflFdmFtBaH1LTI8FWYyZZ6uwGZ7ciY8oTtFK/KqWI4NCGdrRK49MtoNDGpsiYJwjIVKMB4mYXiwI5y5vyFynTMLGxic1N7JtpNu0tAo2HfHozbvVUJMaRiQ26oUdqUchGMgiO7k85hN3u3w1P0I4WAOkUnBOVES127VRaaRwdmzOZfJxLwuNY9h15rhBZeK/WEe1S2cmQLVVTmfW418keMUD3Y5CNCIwGe8CHRybALMGDhl47NDaOVgXMmYSROWtg/462Do3mABkX0Zm1CgkJU2TAprCAzc90qXRCFlSmFNFUR4Rda0VtjZ27dafBs/pjMKMccwIaZ6xn60OJloXs224NcIy+c8rje3xZtm9OQ/R4br4JPBiwLQwM0FCsK92l6D2TVBItPRhMxkzqmWe7MAiZpoQoYvI6zWQ2iMBAYTI+Vv+BoJeC7npNXaHWGOeeyfeAB6hmpgC4yjFREIy67BFA/Fuvb3cKvVunr/GsDSMWCBpschb0NI0J1uTTEfJkvSEBZb2jV58VEI1mquyObm1MWKOaqSJYtNfKPlQgeYgFKQjWdUUzrXZ3AMGcCA/dzAwhmJduFUE7tGTMUx5GXCwVHBCYp/Cmokka66lQJY6YnTB7HS1rdGBWEHRWB4aXF0inBoyAIwNrpZGgGuqEtFVik8LCe2k6KLCMdCgj3izqTyb3IaI2w9nthAzNeB4CUBcKnHDWvEnQ7kNEgWZRuZizbRW9R8P/TQbZoRB1ai9hHer5WOWpFZTW0NTUVaVCLJvslgkVa2ajMWMHJ/FTQlvuBFOO2O937I0GRITd6oQNu3XHJ1yuixmJgF4LmiTU0rEsyzHwyLI255ONedESxtp3E8Zz+FBsrbkFfSN6pIwBjbTKQqtDdSO26JXQWUocohMC7zUEzMuEfTXZ+Ilr58XZZINcWtvRm6BZ1J5gPSbwZkJekEe/w5k5qmf7EN0L+n3cWO8NvcmIxGtr1jjHwU05R8sFyNiiHSSPMwZehwTYFMXmKSrQKrxGwZpPGTpLCFS5FQF2swfmp9nMVzas625nOnBSn8OACIiJc1Sm/Nb5UyLlinmKw7g7hOprMoyIrb92ylF7N7h0+Yree6AEzaYOhhiY4Ri5wGXWccqsQ0yIsRkpgmq48+WCdr9ZJ3rG1BWSk0G+Dkd1bHtFhQybK4D9/fALQQJ6M7tjGZsYfbrWOtAUx57/9oVmY7GIeXOmVMaDr4Y/G7aV4PIFAUGNh68dMSRCIc2xTioJqg3lYEq5HwVQEYiyELOuGzdOFDs0Adt6x145nIQ1hoxlmVB35zyzByIY2H5/+QyNM67LBLhMsaW3HjmJANqseGYpagcGF5tHz8+aI5M6cOVBSe1iDsEKeXZoKfscDEYrY7RfVxaGp2XCo7xDzjPWbWcBEgGx2WdpR68KLDNUG8pWMS0z9n1DzlS29DnXnIx+YKSkLfqzJVtHqs0hsEYf7z1pJqfcWkWpESnpaExrVZGM9qQWKbHuAITEMYkhBrR9Rdk5RUzVOqgjn0k19hgx3yOiVQDbeuNzMaeUYsS2rlj3nTNtDZa5XC7obUft1LoScyCqim29Yd0b3j0/w7WXDgjNstjuWk2GixuWDjExvHNztuj4GXJlwsCuvdfBVT8JzymZJNb53bsa9fLI2NI84wlsxFrvKzoiYsTQ4Aoig5ARIDZbYkKKjPqWaTL6KQ2X06s96wnBB2LR2bdamenY/mB22CyqpNMuxWTHjWTgAnYKsPPfYCWfp8HtHiDWG9S7w5fsOSg292ArO1ohG0lchkUVrXKWdRCju8aIWjhGVcUYWEGQpxk5AvteDV5VeB9O2Ve8vt7x+PwOKbgisD/3A3VwBy3m7Hvnc+1GcvCgLajV6Gy9+fysJmhMq17LQEegavUL0ma70gkS1qcFz3nC0/Mz5mXBw/ZAfbVEGLJZFtR6R9eCeaK+27YrppywritJOAZXd1XCnUEA678JQUhhF88MG5oX/n8jhPSbnALPLjdUytl02All8MYCN5J2UNxZx6ANa9eibo01Unkhs7eGbV+xbzvUInp4VtLJmfdimiplsfeNPPdo1D0fjQfQMAUJ2PdtwD71vuF+XzFfHhGD4PlyhQ+rd8a6QwDB4SKLBA2mHXCDZxHBvPMwHHoqkBtuCmUBsHnKrh3a6DyDsF09hITHxycbVr9jW3fsy4LHZrOvBShlR6kEBEotwGYzgrViLwVBFbUrHi6XsSliOjo8e7eilHYMJp0QFqQmzQm2a9TY5/NvCIEb1zuka6tYwOjRGWfcnKwRdJsOdtssu1N3tIQd9o3OorkhAw1rN1ZYd6cMNr5t647S6sgCk/0XqqzPBIG2Op7P2m9Y1xUhTkjxhsfnd4C60OHRjEUqqcB7EQ7811krMqAqOdEZR/YgB4usVpPyEOrqC3QIs4nTHq1A/fD4BB8xuueMfa9Y5osZYtYVto0SDpw1sGGeEtCB0guCRjStSCFiTtObaNBppEEEEWJy5PRevXeIzTQYzrH3cbbV9LR6q+iNs0matnGGoR3ahU7T2IGtV4Q4o5XNaONHZN20oZQNZd3HzIZhVFtDsxoQWWqKpg3rfUWpddQXQogcm2sZWbaxm6vVDMsGq0UBCBGPj0+IQaiWEAO70kf9KsLY5TgYaFbrGZbOskExSjkAw2LpMzphvOpzuZUwXbfgT6MFRYY8TPOMy+VqtYMNMSbaThsPEEWwl91E79qohUKAXndszAWxbzuu1yvEel3YVCgjMBFzzEwQxYK3NjLuQRb4htdvnLzGTaQ5njBoK1rWjsmbLoJlAtIhmcM1RCjo1roiiQzlv14Ktv2O2jpiYut36x21V2x7NRnkZA1PFft6Mw3yiHnOpkEk5hTMTWrHbV1R9h2v9zs3lzUPddwQQsD18ZEUN2MCsHeADkHU+xEsgnSMfMAMceDKA+Azx+BGxOfneiagoqjd0mmj/8WUkI3Z0GpFM7qsFymbURqnacLl+mDPj0ybdVspU9EV+/2ONE0IumMvM64iKLUZPVP9LsDJXkKHa9dMx22jFy2CVZuBDe32nHl4WR9SoFdAFhoJdBsaf3QKe3NerQ0+e0AFJrjmdOKIKfEw1cJmRk5DY8QvrWHf7th2kgeW6xWA62JZxsjIAvf7Da1xQtdrpNNQdIgRGablamqoCcHWrrsjN0OgpsdvJ31kQGIwkss9M7wxiBHBnlUZWTIswlTvuVBGrRIC5jyxjlULWhD0anRoy8AZEWekdMH1wWpG+4pPn1+w7h0Czh24dxY+123F5Xq15rPJrp/RI8Sv8igPu+N2tWMv1LdaRyYZ8zyi/d7NgHfKPStgtSbqVYXABreUhVpThrWTpVZRtg37xkbSeVnGvlrXO7oGpBSGY95tBreq4HK9Dnq1Z+yM7jvWO9GBUnbc5NUKugQCtzUgpcnqnw4dHQ1lGHueL1fG1d4twrbC7YD/jujJs8Rmci1ev2QS6b0ICm1c72laiAh01lApg45RlI5RbdQmRwN7Bvbl00ds68Yaa+/o24Z5mplVXy4WJDQg5QOyGA5OmW1Z1OIZcdM+oOpvef2GITsF2gOKKi4x28ayKWKqVlTKEGVRCyII1gXZGnVQ1BpVquGOrXC0ZrdhOq03SOShrcVUVAOw7duY5RxixuPT1cbuqSktmsZMiFhvr1i3DVthi35vDU2C0dXiuJ77/W6zfocdIP0NjHY5rELRIwb8dBy0A6sdNQihAemdB1c9yutqwzV0sK5aU+QpI04JKQSUstOYKJ1bnqkGG3MezBguesIcAq6XC37+8z/j9b5Z/QQjnd62Db1W1FiguEDcKamOwliHGwQBrOFrRBS9oxUOPqoViNNsWQIbygSCUC1VjoFyCBCEYH0RFm2KRYzVGFGtFpS9jIPUGhvjulLxNYaIkCLKtg1V0JgmPM5Xk3Gg46xGxU0xYV9fcbuv2PfCAS1QwlWZVE9Xtt33O/Z6McG1k+PjEFvCojAOjRygoK+v14jGRoEZcVhjnkXpzWCLIIrdmFKcVx6Q84SckzmQwEJzikg9Qyz7gYjNFTY4LRAiiyL4518/4ui4xlF/a9VG0QLZLvwNjRoui3Hs8dYUXXl9rbGuE2zY0bI0qzFQ4E2Va5UjR2NCFF2pWiuGBEQRbF2h/ZjNzN4KPQIeJW7funW1h4heNtwLfz6ljKeneUC+rTXWG5W03l43fP7yShUDy7qcBZeXy6CF9l6JCOTHNw1cvfNMOjkEGnhm5by2OGHv/OOZIGCQTOPvMEhy5V4r1qOjdmCeJuSJY3RL45nXEJDTRBq56Ya9GQMbyFCacsLP//zPuK277UsGrJxYV1Bn1lvlokA/KPGjrVatxgFY/Y9OgdzibzD0+C1OoRTOdQoJi6V9ni1AyVJwhgL1xIWYC88LZYE3x/fowbbTyM1t39msoh27jVvsreH2uqKqYrlc8LAsnLhkFfkB94SIgI71fsPtxVJ1W+icIrbaMMeAaZohQYb6Ym2dWLlYkYgWk3OOhYlk0AAVpuDOShqNbrBGnGFIhPIQXQfOq62jaeWm7GSGxMyNn2KCmPAKJXcrQliQekGJaURv3NSwFJjyHx8+fID+/Ge83gsdXSXlcFoqsXfHKgMGe4q7z1lhTjLshi8rWQrwLG2DQHBJEw1/N8Vagwf53nQKUQSq0SLJhrRMxwhQKzSWjVPWemPXqoQIqRVlJ2QEUdxeX9GaYloWPD5ekLLPVQ4j02GxWFD3Db9US8eTQVchUrU3JKTIITExJjYD1gq1cYgeK/rf1WYlEBu34rIXowMzXd8eACFSAeGWWllA7o0/UFtFACnbIdGpJ6MzxhCobxQicgZaZ2ZU94qeB4eIwZP45wuuz8/4AOCXX34FAqHVvTfEyE7+KWUyWIy2yiwojHt07N3XcODsFuF6t/80XwZLhZP7+Fxr7dCkhH07MInaZbDkDeuj6OBnl7Ib66yh94ragISOvRx6VvfbKyVTpgUPz1dMOTEQOhMBzEH0VvHp46/MGsWzEpOSAWmuKabhUAjdKRU6xCn7hr9D4c0oZ7qmBM8UvLygjiaa2VaT1enWYW6BTaV0v8SIEDKVEizj7aUB0QgFtSFcAoIx4Qg5y2DcwdY6TDO++/AB+svPuG0VIhi1qL7v2EpFqnU4XNrcw8721sd6+3waNaae6Ld5hW92CuRRC6Y5WdTejiYJWMNTLYgCdA2I3dVSyTSgMiqxzgDFfduIk1uknzMlZe/3FQqwk3UruDw84f3jg3UjRzhbiM1MGSkEi7yKNbFMuL3ecN9WbCuHvmfn8ALIMVmKHqGtQsJMaekMXitc4mKgBUwxzcCNVNJSNf8ZANSPMc0WsbSbk93I+Z+W2ZwBGRvs2Ka8szeMcIYxh6p40bBr9Vrp4LanPOPD9z8iffwVH7+8MFIOApGO+/2GlDL5+yEPTNUPAk+wjn97wdGddSsVZS/I0zSMhHciJ9BJ1lIQhdxqaQGarOtajVVW2D9CNc8Ve6vWe8A+ghypadOaotYN940zpz+8f8Q8T6OQFiyyCimx7Z9PCSsEH77/EQ+PK+73lZo7JpNSO7tKc8yYpoycp+HAmcFwcZs6o6vjFLPBGTPOPAtWH/PAwQuvThQIVvBrlZx/CNdnnvLo0PdcxOtevQsAZmgShM9VuXGqddm6CkAMCQ+PT4gx4peff8FaGqbEMaPbuiLGgMvDFUNQxYPGfgQAsJpcUx+W1cxREIqJkfukdetf6aaAqqzrlMbi7hCs7ITL4hS9wQhBGByWUkxgryCmCcvMzuRSC1qrWNcNaVrw4fsPmCYq2TpElDPnaOfESWwiin1TPL17h3m5YtvuuK8ramFRf9/3AfPlaRqCcbV3LFFs7oQQGrVCuRMaDnPfIWqeHqag7OdaYfVAwpPRVHe7MfRaJYtonidjxbk9UCAEZJNqQaQsOpv4XDZRLBMF7Yr9meYF33//I9KnX/Dp86sFF9SMu72+Ik+zzXJwBMBgYN/beqADzc4360nfZuu/2SlsZUdKeTQ9jT+RTRgsnFX0QO5964rYeYEhJkC9k1SxmfZR2Qv21imDK4p1L6il4H7fEKcJP/zuD3h+uLColRIPikEH54ERIoqqSkbUtOApJizlyoKVdQh/+vwJMS94fHxAStGgBDADCFwU7yw8kCE+6KDmKKSbcuYZbwR82Ipq59hBoxuOglOghEdO8cSA6Vbsipz90DtnUgszCa3MaNiwZb0JysI4U+WIrhnvvvseKWd8/PgZGiJ6B8pWcEt3PNcnSi/DIiUlNt7tmjnow52XDrij7oXGW12WoqH2juRYPNihLIVwSdQ4Ih5i8Ird4CqyzSpaoZ5NyhOSANu2odaK2+sNiBnf/fA7fPfuCWIRe06ElnL+aq0hKIUyAEBGTBHTPKPsVyAETFPG/fULtgo8Pj5intKgoQJqQ+EPSRN/1zME52tPdd8jqjxdBKAMlErVod3kUu15mkmVDCaTYYVqVYWko1MfAAXPhEVQETX9L4yD7PTqqBGX6wN+jBEfP37E7b6TjdIrtnXFthVcLy7LboVjcVVbHbo4vqe77ddWCR9JSAwADEqk06CwYe0VZSfskrJwH1rtJKWI2opBvtWCPNLKQ0pISbBtd9Tacb/d0BHw/N0P+PD+PXI02fecxxnPFumT4WWQsriAHrOueV44T8Pks7+83LFcrljm2Qwu6cZjop7td45tskVXIxCYINRRhv1qrcHaSbH+im7FfAkR00SSyzxPx+f4VLkBQdmzh7KJUThBLsYAteL7AT3SScQYoXnC+w8/YpoWfPz4CQCdUy+s05TScFkyoPu4Uu+pIFBxEAkG++hv3adQSgEQkXMzbK2NYSkSozFk2GyRJrGiLrm/KTFSqJV0VE+RQ05YkqKUdfQm1Nrw9P47fP/993i4LqPD1BdSrMnMU2CX0BZhGrmkCO0TUtqRGvD09IiAjoenJ2KZPrgDJrObElLE6HylAJXgvC0G3c8wXccZid0ZnAQ1mIWdxT0lTNM0upw9JfZ0NVg7vitDFtsoXakhJCBveZrTKAR2m/HrfPmUEjRGPL/7DstywS8//4ytVqTEGsbtdsfVOnvrtmGEPr7pHS9XPWorYPdrqRW5/eVa9xCQBp5LR9wjWVURghCzwSoNe6EmD3aFhoh5nlHrjtetohQ2MF2e3uHD99/j+eFqKp46gl0xmHCwa2wtxLKlPM+sYdWCGCLmh0dccsR2vUKVMt1u911gLyYXxcOQIRnDdWyTiTgw71pC/m0ZxAa1Jks21iVM5giSzQc59I3U6kIyDmcpze6Lz58OkFTUGI3J14XrrWrDpLxz9gE/TQs+f/oVn7688hkpabylPmCZIkKpKCc8/axrdXT5HoEMefRt4Pj8k5B6hMaE0DtK5QyTmFhf4jCYgBAU21ZMmTgYd541nVoq7rcbM4TaMV0e8eH77/Hu6cEc6ds6zeFEYfRLQDwIihlJBDk37DEipBkP1wV133B9eDZmGt+vm8gm6dwy1uMcBBxFI4wccUTtwDirYpkhaaYG0U4Ts1n/TAsk1ai6cTDj2EsUQjQasRIqNWZfmiZIM+UAs2fNuvNTEgQNeH7/HZZ5xp9//mUwuFrdcFvvWGY27nH+tT1HG1jF/zi8/40pgr2+3SnUApGEVtPRkdiOaIa2pYG0aRquGNLodqxbxb7uLKrliLBTu2XbXRWxQCXhD3/8e7x/98T0Ucl390yEcscsTHIQBpeUyotg0dAOowTBHNmg1lvFPDFijymODj8xHnPIkzGUZHC9Xcvc7eibjeOcZk9D/bDBGkqiYFkWql2mODw0VT/biD7Rna5qkgsIUFQaATMUUNjc4gq0Bo2kbELE4DSgWwSVc8Yvv/wZr3fy0V9fXvH8/Iw5HwqQx/3YLRxoAxxZb7VRvnoiVt2qGYzaONA+BNvEOKbWWYFVQkQvOyVJlHDdHiICWBTdVtaRWgd++OkP+OHDe0w5jbX2tNfFBMnG4tWpRVGEQThIiBG6IE0LlpkzGXLm7HCf2z0iP1IzBgw5xBBxPAg3l2LZg3dqAw7nGZBk1xJjx7xcMGVCku5og0SK4pGaNTBg3weOVZdaoZFqt6qMJqU1VK02pKqhIwxjQ4mLiO++/wHTlPHzLx+HHtDttmFOVxtb6Vo3xjxzAKsrTruZchKlIE0N0eQUmjVfsVFL0SNJI93YS2rBmYjVN/aC0jx7o7OtpWNdV9TG93z33ff43U8/4jJPXA+/H2P+sLO+ojW7TsfYJY3iNPeGD6FhQyryZJ9pE8msYbB3ZX1qcoz/vNaH9pGOZySjI384Bwvgmu3HlBNimrHMeUC+vVNXLQZBA5vYvHPYdZ4QBSlkwMYKh+Bd1YIpZ5Tq85sV2ikNIiGwWtMbYnzG73PGz3/+M7ZO6vbr7Y53T4/WP3WsqIqYQzhOtR2frzKgf/31mwrNIgm1phFR1M6IIgSBWkVzSD4okCbGvFEU97KxK1r5Xl1JJSzF58I+4Y9//APePz+ycClWLLNdGFNGAEwHySmW1k0aAnII49ABpHsyXePixOTGSyApIvRA3aTe2ddgHdGHNs0ID038zLbR+AjCLzKoqbze5Xolhc+oZ2ILFUy8rllhuhYWWIPJg/vnpZTGrOUox+YJwm7WAOq7pCgDFtFIRklYAn748SdMnz5hK6QQ39cdU1oGFurFeXjjjdUaxEXbOrWE9lIwWQTpgmnd+izIpz+ttQAhsqM6RkHdykl7qloxc8W67rivK2Je8Me//yN+eP/+MCRyGC3XBaq1Dg47iH4xwsrHuEE6sjSer0IOHBeMZmG/xz6CMDSRQjiMI+m6GLgucCy14AQRmvNgpDfj4nLcDkkLIGIMEyUX3RuZorBL2T1CTAkIYYgGihpsYhlMF3C4e+L1xRgAjRBpUI14evcdUsr48nqz8/SK98+Xo8/CHtC5y5nZKS9UwYy9lIJUylAYaOYYujFXRI9uX947e0NgPRUUvzM4yprV7rcb7usdKhk//eGP+N3332OZk13DAcFq5H1x3KQ4N+WQu47pEE20U5jFgihl34LPXkYIiKA8uVNxp5wtsDwaS4eEjRic9BUD6ewwfE+knHFxgslpj4Qog9UWJNKJWw+C9uZYAm1BtkE8w37Y2RuFCKIdrqIqAoTOLvMQHvDjTxFfXr5gKw26rdhbx5LExrHyHk63wX+T+kSWIb7t9RsoqRWQgpIzciVlsDcvYjDlTeJtYNzEpNkZh3groxAnxgYppeDl5Ybr8/f405/+iOfHK3LMFjXjEOCzYRT9FOnRqx7KkJ6ueapE6hcx8zAd0rtDoz4ogkZjOKRxoP0GuF5eeO7ojkdaVhCCD+4AI35fjW4HKgREoUMJIITgm+8MTmk/TiolA/h3Ro6nRiDxTINRVghixWk6B0FHE2CSBe8/JNxfvuDltuF+v+P5YbafEWYp3khm0bcPlVEbItMtUyi1IhV3CM0cAv8ew8lwCmcA8B4CbisLyrUWOGe/7gW31xfI9IA//ulP+PD+iUwyg9Y6OozNSgMOw1gDDaNqHwJgo7sYTJWDUCpEtSGEDIASGmKjFxUK7dbhnqzXwhdbjjV1GE0ho1lpQIeBGkfdUWmhWa0o6I0sJwnB5FwY6WlT68h3h9fgNazgBx8VIYvFPnz3WhkkBDV2m9dEzCBFqzUBCdfHJ+RpwsePn7Dvq3XVmmCc3aWeDcVX0IlqN+HJghQTWmmD/k2Jk87M8PCeiMlUa2Nk13otllF2QBR1L1jXO2qP+OOf/g4/ff/eajvU9SJJ5YjRyYOQE+JgwGyQ0TviY20pMGisPUMGaqH6bhyNiQp1luE5Qvb/qAeIPKehW1ALm/5oC+zspoO15LU3ILvuma2JWtrtirfwPgbbsy6R47CU2zJ1mrfR2aPttfG43agHRZ4XvE8ZXz5/xG2vnNgWJrKZNjMvvrMNPhSVv7j/v/b6TTWF3gOmzOYQx5hb74hNodHYsRY6SggIkSm6dnbI7gYraO/Y7jfcXm94/u4H/OEPfzCc8Sgit+68dx5Kj/ycuQMFJAFjh4djMYkXm0xv5L+rKlKyCOqErYboSlYmTeA4sx5Fuk4b8falVIoUJ/YIDRTEsgwxIStLIVurlnLD1D99gDwPGew9BDa/FuA12QYKNqdWRa1Bxp2hBTlmtBSCJILr4zNq+xW17EeKLGKwGAa+fd4rHjf3yqFE014443oMyWno0RsXj98kI4za7yEQEmn2HrV27PsdL6+vyNdn/PEPf8SH98/IOSGFhBAZwQe1TgGxhiODk/ioFcLFBneZZUgWtbrshauTtqZD+oS/34Go0FNXskNAYpmSmjESOHXPuLzmVKA65Cz8sKt4Fnkob0KY8jPahe1j645tnQcYOoII/x6DD9vvEkBJI5Ow8LXxbA7eMWvxxLzg+ani8wujR1eePe9VPWe0tk/4LUb6Ie7IUzZo7xDMa70hwujfZnKi9YSEGFDXilbrIA7UuuPl5Qu6TPjTn/6IH374DsuckXKyQVtqjt2lNYBWm62dZYpiTCaDRcQyOgriecc59a+0N2Mj2pPx5Fc60tkxWv3RoSVmn8Yq85my/sgGffWoGzKbZoB3NvAAKfQ5HbMq3F5xDgKZWQ6xSuCTD8qhZF25J8RqV90xYxlPG4iw7/P6Hx6f0D59QtkrcJ3M3sDs5QFxnh7GNzsE4Dc4hX3fERMLJ6UUE3mz6Wm9IvbTgxUghswBLDEhho6ykyHRW8W+b/jy8ornDz/h97//CU+PD0dBVoz/a0aMwa2gn6IwP8ReuXcIaaT98nVPARkOfr0w+EQCoQTAorvzOereuerhwIE1HodKR57JGgXpaQzmWUDuho2jNxSl0FkaUSg3hiUbhiowMux2nWpNMtGEwSg1cTRMne85xsgu8g50CK4Pj7itbKFPhCqPyOnkEAIoauivVklJLbUYQcDrRzRsPR47zLn8OSkAG6OqDbfbHdQg2nC/vSIvz/i7P/0Bz0+PmHIe9zPE8OAGwHSy5Cz3rf/iWotFvb7OlP1g8TvGk4y7BIg1EQYcP3vkOida4Ak7cKjIF8f8x+maEpIHQ2C2HO3aXA025jSYID7hbwyFF7JYWu+mtGqKtk7RVEUIDaV4Q9Ih7ibBx5/S6aVpwWXpHAqfJ/gMCu5lv8+/+A+6MlgLeWJ2WKplCn38oSYRfz6EgDBlNFVcU0IPgtvtTipr2dlrIhl//yfWBp2qyeDQOtJxsHMEAEzgUMSeuT3rYPc6CsV+76dzLUZ8GDNXaM+ZgcZD8+poSDs9e19P6AjQPLP0Pg8oJw6GEAz/N4keq6UGETTlUKoYwyiQw4YgnWFJD5DHfOd2DNVi53+wrEdHBsJrDZCoh4ZYjLg+XNG0DufutVQ/18MZ+CE/3ftfe307fLQXqAbspSAXH6xN7npUHZirP/A0RcrbXjNEd+zrim1v6HXDp08vuD7/iD/9/R/xeJ0JNaUEx5RpML04y80SR7rmm8YzhPDGAYzfMYPR2jFwJgQboqGKCgwO/LmDkf8RUxpllKCnZzscguHcXvcQ+3uyaKZbJOKc5BAjZTq0c8CKGxYL985cjON9YTipNyMeC66mtdO7c991cL0VnMnQlWyrPtJlOT7F78miivN20UaHUEoZTUg+YKcnPehuXGxIyFYIZQ2H87I3CDpePn9GCxf8+7//d3j3fLX0P1qB7KTBA0bNEmSocfpe8uzP/z3EyWDPzlkn5pi5TkfG5ey0mKxm5M/B9pEbYB2H58iaRgHvLSAPb66abbY4WS58/la6IFbvOv9OBxwZnhrhQBDHwuuY5qVK6EnFi6bNsOV4yjIYgcaQ0AN7fbR7VjPM3ykC1sMumAXt2kkf3QvKxACgjd6UoxHK949IwCIJgCKngBWKdb0BEnF//YIv94Y//bv/gB8+PCOnwIzdHPpZF2k8h6/2/jnIOTuEETkHlx1paNZVzWPi/R9WNzK4dDhGi/udMXVkiW9XXIRZdFBjudn1hRCQQgJASNQhvW5igt2UhKeJ8tYuR+5LPfZwEARvRAXl3P38SQhAipxI2RqaBXriATJPNvtDYgK61clsa5J1bguvnuEee+tbX9+ufVQaOog91pmMFG9g86iVXGAuRIp02Skn1NdX7CadW9cbqiT8D3//RzxeOX7TFxQQ6x4+bQ6DBtzzUV9UifDboRh1BZFRoOIiqykg+nPxn+MYyXHAAw0ID6JFAZ7yjUTsiPaOQRz03h61EjtMEFXstZKaeX5GoHRGKdWabIyXHjhmUj3bcyMpYMMVDM9uNnFOIp9CMQqbHF3aCjKgEANaj8hTosZTPhzlcG922I99w7/1WseAo2JwYT8VH3WsNa/RdYhiSuitYtuodotWcS8Vf/8f/wPePV1HVy+N4pGZHIaAuPD4OpxN4gOIDDwyY+pwzQA25Mjkjh1gbB8fQCMHPCjKSNkLkN48plbD4Mk1ONL2gtgeY4YUEYRSHqMhrAPiWUOKaBa5x8jZ4DB82d9fALTT/auynuRDU0KIzMJUj1kDwSXDrV4RCF/FRGkFn8tsizr+nP/nO7sr53aE6AGAMwsdCVDgRAn2nh4JEVEV+7oavXjH/b7hw09/Zw7hoGIr1CTbz2dOTmvtETpfY06J4hTti8FPHU5pDFYDC+YaXHuJ+/E422JnHzBJdsuuYA1rCkMGTFZiXKcLztnZDjEiVCoW1FIGaaA3n/XQUUo7HENnn42axogEAUV03V13QwXagXwA0F6hQhmTYJ+rvdl+D1YbS5CuKK2RdAG/dsse+xG84bwdvuH1zU7htm6YAezFpSmctmbzWC21sWVFlwSVgBQEn2+vUADb/YbSO/7wx3+P5+uCkGz+gW0HL764Bz9DQv7AoN5TYIsdrbLvGYTISbxMSHXDcQDdbLhm0BGJuBSybZ7uB8fTVnvscnjgY+avGfRTwuGFo21bGVnW8SoAAQAASURBVFkIi1YcMEMnEd0Z2md6tgHV8TOMdsDCNRjFsKiWoIFOTzvVK8fmNgfpOH+vFZomqKfEIkcE45H6SD0V5b6i1opt3zHNdYhwDQZS95Gedj57RFRgSgn79gWlUchtLw3vf/gDvn/3aOJfjHI7AFdkDYaDepZAQ2AGAJYh4jic3hHsKTnM4Piaer2Dj+Bgbw3Bt3DKNDyKVH9HX2MZe8n3jO8ROpUja3RHAVB3qVq9x98xGowQY0DM6WSwPbulUfOIj/dAiQZq7CgQImKySNtnc4gezZZmpOeJTVZNlZL1AjNsAVAfaWsfP5QIVmylQEJEmSeusZ3r1jo0GeNsRPi8tpAj0Ctu9w0BDS+3O/L1Pf7w4w/DIThhxJvnXIsKInaGYc8zDEPpkbkKz673DA12GlzyRM3w295QgWt7qaX2TnE+owhuJD2DGv0Ldr7PtQdXD3Ak4Bystdaw7wWe1fM8H5k6iSWeHZ7WOwag8fyQGNONUcgzMYgE2o8Z0PUYmOOTFlM2imvrQDIZbYPTUNvYz6pHOHCES//265udwpfb3SRfPWrsB/aojsn5hShSEkI7ULy8viCnjH3f8PD9H/D9dw9D10RELf1qbzIE4OQIgMOL4zjsKsfAGX8CR3OT/144hKC8yGz/9CJ29G5XOSIXAGMDDC9s3wtiekin1PxshFx/BKZO2JVy1TknlLITi84TN0BMAGREHXLas34BQYS9FUIH4M8pmkzvmJ1sEbiaqHqIAlGKGPpdu3wC3/uIGYEDJthvlIyo1qwzZC6aS2EccKECkMRnHKPgy8sXMnR6RZwf8fvff8+Jdw7TnbIE8Zw36Mj2hmKonFVLzcCGY2Sp+nqfHMZZ78cPicME3VheEg5RPPeM5loAOdVo7Pq8Q9uVQkYW4ntwOG9253omFWPiEKNG6XEBcfOYZDjE4bRsd6kZphBcodSuHxjNbrz3PtAodkt7JGuUbSawVBKQ6k/reG4AnIHT2oZt2zClNLJB71Pg2T6iTMIT2QxmwLa94r5VZkES8ae/+x2W2ejdp8UbWky2p8c6D8d4rB2zY4v4R+3IM3XbM+YEQnAVgsOn8FybsOXISo+jOn5sXJ5HEACctTU4/oejEntmgzVkwnPamLmkGFj7kYAJ3H9ZOBWvtdO+0sNAM7gQElaU7D4Fjn4qW18X9mP90SVFSL31ADWcmJ8OHbod8QFff/M+hXVb0fRpRIqt+uZpNi5PbTEUkIDUid/W9Yb7fUepDY/vf8Aff/8jsnWVOgNg0LfOTsAhmcG4kEHLg8gwDuG02oOSqkd0dxgiLoQPmWEDzbE7BNaIEr2gqW++NyATVVNXtBS/H9GDR/nNDHNr9UjVR+doGF9npOx0w+4eyFb0fBj8ahhhufFmUw6Xv9ngAjVjP6LOEADnRquN/KseXQFiFsSjSKii7YUa/5NP5WqnuoJtXk9NAeTk9YWOLy837GXHdH3GH376Ay6m9Co4sNpxN5Z1hZENHBH8OPu+7nK4Zd8RsOjxwN1kUDH9MfqBFunHPghHt+pwGP47wmfS1TD93gf9dhhpe08EnPTq+yiiAhRPS+a0D7aLrR8UMpZbMLSpHM4zZzcwaaNBcp9EUxk+BTfoEInwaNS/9rZeZs9dbb09EDDxw8eH62CYca2rwYUOjVlGFilEMoWMl1//jFIqNGT86d/9hOfrhTUjf76nZzsMOIJ1Q791CvAzFNi0NQIGM9oW88OTOn5bTJUKdk+2N+yM+Bd4344oNI+Hxp4hlChHPfNkD0bcZ9kBlCIcrR41ot4aKnAKbu08RcKL3fe0XTiLz300pwJm1G1SWrczoUGh7Virw6gbfCkCr6KphGEbRuAyfkeHU/2W12+Yp1D4gPWgqtWTsWimi+MHUUUgMWJ9/YJ127B3wR/++Edc59lWyhaxcexjihFnTybh1PyFI2vwjXPg43iLVY4fOkzI0MO35faDbRcymAT+4GMgrXH8/HjKpmNzgmjOaVrXDjRXj/QIwYiidja7FYk8BcVgVXlU40VCbggcH22+4oiAFVY/CNYvAv8MNqP5LAcvztU30aMbCo+6+SVVk882bIjyym3gzLU3ZHcM7qARAAlo+4bb/Yb7WvD9j7/Hu6cr5FQi184CP2dYmGOzB+PSE1y+o+mPRt5htAMOEvHIxx2/34P9Rc93iBN80AHl53frwmUzmK2VHlx1P1BjQhtOGYIZvWZ4cDfpAhpCi/y5QmxgM2kIv+Zg83hha9i0jUCALs+c/Ol/YtlfUA5noqPtZjz7cKghsNgNYR2lv8m4xhPhvfZuQ+SPWd2tttHF7qoFfjaTPd8sHX9+ecHtfsfl6T1+/O49fJiQG+TadKieQp1UwfWNXwWAgJ9rDwr8+Z2CAbG+X/FzjSOYcadqdoEG0RlFcvq7d1HbGg1Y69j7I7mwv6gHXSMTbMf3oeO6VTtCmuGT3wIYNB6d+m6fdDgli8PsvyfHDV/LeKAowNBIkh7eBMcAa1jFiTWeGehxFnyo2V97fbNTQO9oIqPg2WrlgPchjtdOuCgQIjVdPv/5M9Z1x/P3v8Pz4xUD8PVNaRGRwySjWegUOY5M1BtDLEocNWT1yBHDYJiFo4OyrIB2Tk8L+tawuMaOY5LDJLv39eE7vjkHfGFe2AyzR3cS2HnrToeOwJzCoJx2VOjRXOYbHocDODMKj4vioekegcZIvBlCY96BaM000bmoogPL9U2qYw9y85DP3ik6Z7LFtdmwdY8kO9d8OPHIOblle8HttiItT/ju/bNF+8eBc1iFyxSG5pAbMs8ehmm0NQqQAQn6Mxp1BvFComUAUFCNfHg5y6psTCnOht2KjeOlx7W66VRf7NNPWVDRukF15viiMeg4Ttam1sV02r/Uz2Gx0O43ADAnAq8h2DV2i2DH/SngMzegLujmjtIyXevVcEfpz5VQ1xE5unvoyr4UjjLtVsz2tbazfXIKEQFifLgvLzc0JHz/4QNyCkcEbs/IR2l6wyH0YMjB9saIzSBjTc/X745e3qz9+XXm5dlnWJbhAaqIqZqGiGgBl4wt71kyr39sB89GzD7gDeTHGQ+sX9KGefdyctaiuI4RO9lHIGP7Xf133baIenvEKQzg+kUIWu3mdP35Gow0uvJZgxlPxQNHvwfFIdP9V17f7hRUcb+teFrmYSRqq8ZMOXjsjF6Z1qUQsK4bJM348N075BjhA7HVxKHGIHWcN4NvAl/UI1IYQYOcDMUpFfQA3qPY8dD9Nsbt6Fdf44byyXF+z2foxjcseoBGN1zjR+GZBiBGh+RmOITZFDDaquUgaLWZUfQoFmP+AZ2LOSq7f1GY1r4ZDPd9IRKaMRkQb2905zQO0yibvI14vQDqEOC2vqC2J+RO9cuRFRpdznFbQCBREFJEfdnQIfjx+w+mZ+TRmjejHcKADgk4DOSH0CEFe2e7N8EgFhx8A5B2ekAK3oHuxsTx1nGwT2s/YBr/OwAfLcoov9sw92AT6TCCFP+dI6Ijnu+sJG+2opE4ahg+SSwmo2iaA/P9TOj1RO+WI0t1dhpvgJlGrw47Weet7257lp6xnG4cHp762rTGOcmv953TDT0AeAMZ9uMchAwgQLWg9I6n99/jeskj1uvmTMjhd7q4rWY4ZYM4ntM4u2DUfq430N/Z17/KzEdDnQdl8DOIw6HokQ27kKS/gduiQUOx8wI/53ZNg7atsLMaxlojHT/r1HB/vgdd3Kiyp2L4W8cmx9cEB1RmTiCGAA3s+D8y36NWEKKYSsHxbP0n5bTfD3jz3359s1MQVWy3O+q7R5udUEcBcnRBWtipKfPvvWIrFc/vP+BiwlSsvDdrCDH9cTkXnATnkMMj2nNE8TVFlcV7Of2QPXw3vcOoDmthr7efQePS4fNhDgPAf/h4TKeSjk2tx6b0hZAQkYamCjdcDKavhGNEoIh1brtD8uzARfeCQNpbPNCdZTBqnqf3IbhgWh83oDbEh4dLxgQu34XH1mHDGw+OopUd+14x5f7GGbRWUduZRUOVThXFfdsxX5/x/HCFF2nRgb1wqtdoWJLA9ZEDMjtu7HSX1qX+xiHbvXt8MLJCj/88Eh5Rph9tjMPuKbrDBceq6XAMAUBDQAyuUc9D2rs7mcPAMhq2aBBeywpmEARu+enk+DttpPJKXzYoqjKKkSMwtKiaz8KwaBjbqh3FRmYP7uhP92n73IuyvlF9H3bt2PYNrS0HbNQ719r6YfwcZNMQq+sOlYwP3z0fjYUq0F4hOAlL4jCahtCejp/tgXFmj/U66gzmXk+G+nAMdmejX0HG10cm6Y/WzpdDaU6jp6lRa2YVaqGJsAPeyTO21QVqA7jCuDa1PRis8dbZUYOMIUeHu4AwdofDgW537H5G1EP4lzMrGO2zr6exN0GVwpTu3C14Ul9/d4zjlJgTGbpL//brm52CwkZu2kFyvNGho+bjB5UZArX5KxAzHh6uFG0yal4rDUGsuQTeAm5OwT32+UC4A7BFADDqCkdtwDeVHEbeIgUJYUjTDsujzFTYoKLjoPl7B6Uaa4cMfvvxJNyZyHAII5KAnU9fAPvdoIw61Z6TYwpDUrs2FpjC0R8xolwBXO5gHCmh0SSdU4Yap6smslMYgFFyx2Yx6vAwHhaRiFFeVxt61G2GwhA5c4E0dxB2YJzlA9BgXi4LphzRa4NE69j2TEVkPD8Z6znCX9C7H5v4WNe3e8PX2IJEPh45ePuA15Bweu/DuXiWODIWk0vo5+IvzFioWGeqv48Mv+NW+/xZjIC7UW0V1JOydYUMGuLQz8dxPXrCfM9BxnBrYu8hPjY2WFZrcjNCw9FaQwp4Y8w9Y1bb3zEG7Lc7FTpVUfdm99JPtFQ75+rje2SsYWsd83zBPE3cmxpwRPp481LAxAr9XzrO+/i73adnF9wvJyz+jBjYeTuK6h7Ff203ZDxb91CjS/5YbEMaTgGk4IQ+2Lq47eludI/+pB5ZP+iKQSuF2tkG+xXgjvpI9441NsdzVFFg+1LQg32mBVnBrx00Ia1XRDVRQA8C+mGjIECMCa0VbNsxe+HfeoW//iN+4UCrK0plZOVZwsFbP6IvEdLnamO0yyHUhEm0c86Cb4g+0svD86pHeqeNcMZIxyDvc5Zh2GU4/fw5lR/G4G3w7utjUcMR0flnxJG9qK8WYDHJEbO4GfEvngyvwzJQqM3FhcjRFaw0wM6KOj9LN7zq9+9Riy2+ADah7YDN/J5JOrJJUV0PKqkbKttYetpk6A33lWq2UMXtvoK1Fg8AnHnU3xxM7R29WnoVkunZM5Ph2ZM3h5vrE8Z6+IMLkNFz4E7j0MKXsdbBD4j1DYzD7Q7ktDLuJIaz8GDhvHYSIAPqCGMZfT+e9yEs2j7FpQMbHv/rGGSMEQWO9bYZJHLAd75f3rCbbL+O+sbJgY1WbTsLsPfqAMl8pyxIx/tgXIffx7puKIXOYL3d0NQokB4EnIKHw7haUGgqpLxZ6nfVVse5PZM5jvU4DPTxbP1Lnl24Q5CxBqS4hiPw9e+fHcvpk8Jprfys+sJ7/Wp0z5/3ju8Td1LDgB8v9gT1Y01Bp9FrYyOr7zEhvNNbR1C+fx8/f9gFD3rcsY0z4Xsdx/X5vhdQNDG6QKY7Aj32mWcr7izrtmIrf2OnAADoHV9eqWkzhq70djrcFgUGOo1aO/VfhFzlGCiHfKTfw4aOQzIohqfFPYyeGf4zx9m+TwNy/P34Pbf+Mg7zm++NTe9bCqdD4G6DRmNQ+fQwRFBXisX4d7MGqjeHSWEzUw/M2O+9U/fsK28lY6+7IXMdFX9E/HsYBwomGDCmPtlmCcNR2b9P7z2gjiAmb71xxrQA6+srW+77Wx0chW9iBcSmw9UKsQa1Zh2etXB4+3Hy8eY5w3eBuHHDUYh0Jx/s2Z8cg0dRfu3y5u+wNHxYm2E43zy4k6M6DPr4lbEPhlGxa+22vr7bezv6dM5G5IAeTobQMzX7bd+a574awPfHESz5dfuynrWADieIN9mHT9F7Gy3zOnym9/3OsZYA+xXWdYe+WWt3WP6ghIQT5VS2nNlBDcuAh0E6nWdYljOkMnAY4CCnc+zBjAV2AwU4OYC3P384jjcvpy97gGlBlT/MYXiPaGQcNRcNFGDsh2N9MXpS/HGcNwbF7U67yA5qBwZRxB2W+OeNfeK2TsevOmQ9HNd4Dn69nOPtel+q7GMCdOz/YRd6x3q/Y9u3r5/Wv/j69kKzPa59v49Nciy0jAPPO2L6WVtDnjgsPYSI1nYbqM2nMvoKrHlsvJd7z/MBOB1+Nwp262Oz+7+9CQhjkTy6lwHPjAMvw1KNVT4+99hE4gbLllq/Yje4kTzvlWHbT06gn+ho47qG48LpOR7XJJYaOib8ZkMaU9kCHNuL9iyCy/FG+9Ip6/DHZu/SasO23rGXQjqqkoZMipuOwzBqNXaNKmaMaoXEhORv3BvFw0KyjMLuyezzGSrxixmHfYSDJ+d9/lH/txna8+n0x3ZAM4Dj/AMawPh1+3M2wvYe4av1B/8b/CjgMHJ+Ye6wbVkPJpHxKM0tfOUUD8PruxTucMeAeb6fnNdWTpkGGDAQxomI4QhIAt4aUAGhxW1bsW47Wj0iD4cX3spk+FWawWodHQ094hiOFIBt3U/G+HSH4vdoD0WO8w1b6zM8yG8fmD3fQsazkWOpjwAPJyjSnv9RhJbDQY33Y03rONvH+/Jsnz5/xBIehb19KmPtAgbF2PeWn9NxzeLrgvH+bocGgvAmsLD9ZHXF7m8sdJ6tN8SU4ASVr4MEAOC8i43BXi34ltdvyBS46du6DzricbBPEbH9vTWbIBY5RzegY9vKYfSMPuhn6EhTcXR2ymnDjAN8OIbzf2U8sJNxORkUOUWMI4X0Nz2zIoIfXZzYEkd0xydhruFE1Tu5/rfPZAQlB1RwRF/HhveF9GvzdZXz/497sc8xJzqcnPgqEdtNLs8tLED77w9us/Oz0VHKhnUrFvXYDXUO29EOa3A7rntERF0pTtY56N2nzW3bhsGjMG814kZPdX1nvVlnGXpDLhboRmQYDnsIZ+OP0+ccP3+CG21tB2YNPe5faVR9MM0wEJ4RikfeRzYxltWu5U22PIz1Kco2dxBGYfW83073JG/XOoi8HbguDg+8/TkXTnvTvCQ4am+2jxSkxa7rOtbP72jdy+m6T/t6BAEMTkgsaUiZkth13xlI0AOMMzPWfRTn8Tbih2W5Y72O8/mXmdCxpmPP2D25A+eeCX+RgcDXdxgfnPTWfK35XkM2/wTD+IN4k4HaMyG0p8NxuP3yeuWwMZA3dNzzmX6T7Y6Dz98bn+nfUxvvanR2wrtHo+7oJPegqDds6zr6jr7l9RsyBTPevaE0pllDk39EOXwirSmCVrQcITHDB2H4SL0jHnqzIvYgdGDFx2Y4Gw6MiOcwCEdK5m9zzpiHyZW3lHPF4RVpGAyzPW3g8Z7AEM3yDxie/HTK30QzLiNBL/gVTIARtTumePoku7fDCfTOQl73ew9H9HK4KoWentkoDJouv4QARTORscPpau/Ydo5KzTmNz1YoRdyAo74xNjXfv7cKVSo7RnV6HIeTh2ASB3oc4iNa90N/sFTG5h/G8fCMb4z/+YHL8a+Rcvtj7E4H1dGsNKJIPQyPfr2IY0PYZ42I0t0a7JkYRo1Tg5hfwzBgCrhef/BrGOEsf0UOLS4AB6Ei+OfJCDiO67D9qRjstaH2AoX6CCaf6WEYOipQ9h29c8xrPO3J1mjcVV3LzLcjP6dD0QDUvSGKIKcZ6iqrp+bTwX46Z7vjv4ej9XN9OPhhP09O77Qmdi38j4738DMnkIOXH44GRD2993mlzzboXAM5F+X9Io5Z3Sea+9iex/0BOAYrCfc3/Hr1tL5fheMCGaDJoJQ4JHmKQMR4cbD3PveGtcZeBdYUaQ9qKdhbQ84Tcqr4lte3OwV7AF1ZQNbe2XF7Cih435zklKOidMUcAnIU3G8VybTlh/DZmxXngwlW9HuTJfj/3OOe/nc+y18b+CM68W9wZ7Cg3958vv+OhzqHkzkbeRk/fN5M/JYX174yWv4e4TBsfwEX4evf8dPhRiAgBEJW7O8lbc4zFYGDSH79PCg+rUyV3bvBR0S+EYTzJhvOB+BMC/GPpzruicVyTpGhilYLFAv2reLpISPmjG2/QySZ0XOWx+mPAiJ6aNt8/Wc4DJzW+auneo4C3nzZLvxEIREIupzhKncSh1PmV+3ZH6vwZo2PjMwcN04R7WFB3ji1c2QoIwo59tmIrD2SUR0yDSLRMjzXAcUw0J6FDMfWuU9ca8vv+2wQAQZ1XZ1aHayZ0fLLfl7rPnjyfoPaGlrt6HVHvD7R0dSCBm9n6whmUvykYnz+uW9BTgSQs6M4O4jTOoyz8GbrwbyHPVeBw3t9yCm+PdMQeWtzTnvq7BB8L/jZcnt/hi3d4H29zqc41a/0+Of4v+N+OGteCVmZAwjqDXDcEyqn9xP/bYF40V9JUaXi7+FtVMl8CvbsQ3ybHf1rr9+UKagdci/IjPLc6WYVwL7tuD5e2DwTwBm1vnnHaDsYvnwYyiGGNrqWcRJI8w1zSsvwdvP4GtgjxwjGxs/yCl2Vc6h9iQxCB//u0dkxUu/0Nhhm3Dfc6f89JT598Y1xe7Mn8TZqGdfrz0No2NUOcYezfRi5uzDfALfUNz2/7hikC//JWKoDNuHHCji68thU56hvYKfHLcHNqPaKpoLeK0KYgF5RSse0yKD98fMPQwCPXMUZFW/XdTwvOZzpARecXqfHLG+e3VctyMCJ6gt4e/sbyGpE4Z49vFnwcV1nzPfYAzjZncNIDDjr7BNxHOpjE8j4dj/emT9lsgwYHHoXj8f4uzsN+P42gxGCoBrv3rgDEDGhOI8mxc6xft3cJOMsicA6qxXbXjAHsfGV/HfK05C2GDUTW1c/PyJ4G+h95ZRHMPAvOIXjWcOMpD0dN5wCV92z1RVTRtZTavA2uDiG/YRhKw6bf9oT/8o18CJPe/hYzJGxnZEM/ocXE+A9L4d+EsA92lof+5ni4ADeQL8C7QwwKF2jJ/FBhcQjkxGBZYjBMqe/OEH/4us3OYXxaIzn7LNrXWtcYhiTt5i2doQUsW0rYpoHa8NvYMT2Jy/sGKPfxJvs4HyIT4b2zdJ5YACMDekb5szCcCPkHbxuVI44Vd8YgHEV4zrfyi8P6ObrCOds4IYBOEUccGMj8F63cd2APaND+tuf/2h4MpE06c3+zp6M4GqPIqCIO2UlmDm4Abbr8cws+AjRY4lE2Kl57kamdn8cxqW0zqKjJLSyAzFAW6XE9ynNl9Pa+MEP4V9YVxG8JRgcizAM9ilDPZ63G213PudM7tC2+kuDc8AM57U7It1z9H88m7OXGoGEnH/XnNobryBf/+rYAxCh/IlHoZYXxmDwHVzXiJlQCHo0cYdDPRdneMyzgnH91jEcBaFzIprfEDPSaHMQfE9E6hdF1+GpmC8PkK7IAbg3IEeXgAgeY41n+sb42zMKHgyeHGnwTmb/+tfr8xcLZnthnPVzVGCO4RT0nIOZc4F+oAH2M18P9Xnz2XIEC3Jas7Exx7+P9/Z9+OY6TjbBnehAGkZPla+rAC3Ae1MEARL6yAwFeiTFADwjayfpoCDUEHMp87/2+s1OIYSEOWfkNCGnjGibKCdKYYcQkUNE64JZgJgu6FuBNJtbG20YjHh6Zjg+aADO3n4crLFIp6+ZOz2Ss7dW4s0mkpGZwQdeDLgTcvxdjrSR0ZMAcH1+sff9ii10+pARycBv4zD6fl9nu0KqnrtGrwfw1btHANZ1atLa0eXGVaBD/AkUutMGzhAJFliaJk8OqIXNbDFGMsBCQE8BWRNajoh7QpKGFsJxHUEwLTNyyjZTmWucUkaMVD9NJhG9XBeupQLN0laR6HEjL7MfzXp+mI5DOVI1eET35iHbm/yFcRhZ0gEpvMly1NZETtTSN9g33mgCOTWYe/QtU+jNR7rh63pc5ghUjnU+GwXX+jpZCb8gy/Is5bcRpSFG6tk4jCz8cCcK9O5DVAUJnAAXrWN+K4RrgwmyhRiRNCHlhlQSYojo4eDQK4A8XTDljJwzqeQxcQ5GzogpISTuPeSM0jvyFCEF1oMUhrFXW1mXrOByHgGgG04ncrw9T3o613/50P18juc/NgVhVUDeiF/iFDIqfK3P+0BxZJZvbYG/jks7rmpkQF9lIn73ctobXzuXrjqeR7dmUJGAlKMNDLLPVaBrQExA6wIxmW7RSGgvCLIA+74BMSPFQM0zkxPKaULLDcUaUkP8y6f6L72+2SlwIwryvOB6XTBNGXniBso52QaKI4NotWJ+eOQcUgnEOSUguoH3aNUKty6PzKdx9ux6Wt7zirxdpL/81on9A//ZAwqB/8snMuHwtp5OO/TknHMxzRO+mKCGrwhcgwVk7xvMyAp0dDEfGQMv+AhwDmujUFO6tH9pRysNLUbklEDVt7G7OdNAIwJshqwKQkjovSHGDFFg3QrmZQb2fejza1TOusg7UANiyqieMYSEh4cr5mXGPM2Y5hl54szdZNfBfdGANCGIolqU2pUzNU5u/E307yn328xM/BGNKJMOWt7WMr5ec9U3e2GYAncI/pzHDsHIDMcjPP0yZxSEN+k2nfTph07R5imH4++DQc1bfrn4Uh0ZjR5G1AMVvyCXsNaUR/boUaaCc4l9T0ZpaABCxDDEoRSIRMTU0VpEjA2qXLM2RWx7QAwRqVlmAODh8YrL5YJpmrnm82RnO4wBSUEEtSoeLhNUiznVjpBsbT38t2DogFf07bMTMTkPYaQ2VIr9PfA2G1PgzPJ5s9Z6CgBOUaHiTP/2bx0BoeDg+HOiIX+QasdnVOB4f4eMuTcjjrqV/fdNlnEimhzp07GfT0FHrZy7PtSiPVMAjk7mIPAMMaZEtYAo1hwM5Jyx74W1osixnSknxBQQmiDnv7HMBYehJzy+f4fHx8fx5+F6wbIs5hCYdnJcZkeYZkTt6KsHByyCxRDN8PqDhKU6byNp98DnlM5X9xy94+tfsWUaQZnqQGG88EqbLSOiH1nfm7TWPi2E8XnnAy5ff6gbdyfD+wbxP3hbQOfn2kZ3oazh/WQcDMIsEYjWJDOalHTcp1g9QSLQ0BEaRnEaemxYgRiVjdFj7BxoH2NCiAVZJtSUkKcJ0/KAd8/v8PT4hIfHRzw9PmBZFkx5GqM1oWrkJkFeMtq2IoCQUnO5B1fyDHJch7w9dAJ503j05qUHY+lfeokIjYxFZi5cOgLA44wdzxyn/aP2s3Bq6cldvTEC7tc9G5SvruO8R05/9/vCyagN2OGUwZzE8YIdas5lPqvhjccEiRFRO1qjoWiWBXdVcJjQYaxiCECM0EjIKKUASMLUJmZ+OeO79+/x9MRz/fT0hMuyYJ5npJgQJQ1Jbu0Vy/yMsneomDpys2FE4ZTFq9hZGGEBDnP81dk4PUu3/8Mb+jk+HYvzz3lGJ2OvnPbWV5G/AojumODal1/ZAd8pJ2d8XJ4f8pPDDwfkfYY7PfB4s//ODsPe2GXEGQj2Yw1Pi03nb/I1Y9NSdRkSIMElctgwTBiuo9SAFBNaJAz5La/fkClEpJwx5TyE0fZ9A2cO77i9vuL15QumaULKGZfrA2qtCAG4fX4BLLLx9+CAGMMwB43qKzZKENNjty5mE8ITyMBq5bQCtMNy8NLNKA+8dywwH9jw8eaZHSM/mQ/z9DZkxHHWw1ofEc3JUBxBgbJmMZr0lGmgX4t696iOTcrfoRxGLTvWdeNs63YIlcnYQAc1ToaB7ujS2ZTk7wkY7DPhYV7QtIK1S0IICECeZjynCSkqnq8BKSUs08SDE4R8521FKTteJSB+/ohl5ozt5fKA27ri4TKj7CuaCXmFlEbfgnOrxdbyWGvHrvn3GA6VTIyl/UvDcf7a12Nbz8YeI8o7orURYYcwvv5mzZy9c9pL/pkegZ7xa99DR8ai6GrNl0FsxsbbMabcVn3MwdDG5sH7uh1qAeq/Y1CRgeTBHDL3iqK2MmZnAIKYE+Y8YV5mRBEqGas3IFL2eblcEWLGz98/Dqz5y8ef8V/KitmyQa8vhRBtnGpCTAnX6yOenx8A0wbzmkBMccxP4GRFzg2hQTs7fKtLnb/vZzbGkaF5N3sMgpgzUkyEMfOElCKmaUbOiRmMZa9jEt4pgARIteYALa5jHE63H/pcALwJ8DjbNkkROPaCeh+WwLNZBhYMuKg0ezC5BDC5+Yp921Hrjk8ff8Uvv3zE6+2Ofd9RShlKBy5P7/cy9rwHwyGO53d9sAD94YqUI1pT5JwxTRmtVpRtAyQghowPP/1f8S2vb3YK7F7s+Pjnf8Cv//zfR9TvRi1ELtI8+5+JDiLlgVP6n5i4YXyOawjHfw8DIW+/PhzD11/n9cWYx9yAaVowTwkhJjw8POJ6vYxh1zFFLqqla17xFxHMywVTTuMghBgB5TCh3hVRZCzS285D53VbRAmFD+Q4LzQDWR7OWne8fP6Ml5dXlH7MOShlx7aukDQDbcXPP/+CLy+v2Hc6iPP7qf1XYkIMfC7zPGOaJ6R4HOJ5WXBZZs6E/kqMzRIm1H3nTOZpQRL2ltxvn/Gf/uf/J/6TGRQ3TBICpmnCPC9YFq73NE3cjNn+O03IUx7GJaVszsAzSnMIo9Ac3qzxUcgPwwiev5etmzalhOWyIIaI+XLF09Mj61vmkEQEtRZKb1jDT1cg5QnXyzLWmgU8Ps/aOAchCg33mSChMGPuXaQWDlLni1pDPpjGX71VvL58xsdPn7FuZUipt1pQ9h1NEh6WhM+fPuLjpy+43+9Ytw3VZNWP4rkipAnv3j2PfeojUo9zEZGnCfM0IQbObHbJC0qONLRS8fJ6g4rg/ukFOWe03vHl06/49OsvIIXZMgAJmOxMLzNhpWmax5mepkwO/DTZbOaAmJKN2pVTMODOQw5yymkPADj9PI28R+055xEAzMuF65snPD+/wzJnOoSUUAOnGrZ6sOh6Z3/O5XpBNiguWr9Ua5WT40T4PSi6kp59dtyqJzlzUAxwSF5Y5j4CkN6xrTf8+c8/4/V25z6yCL7Wir00PD0/Ybu/YCvV9mTFtpdRQwQCckroCjw+P2POhIFr5b3B4KGcJ8J9OaGUFV++bMMeV2se7rXi8+dPWPeG+Tp9k63/ZqcwL/OQT+4eOXd6/fmy4OnxEcuyYJ6nN0YiJ9YeYoynLOHYwAI7lB6tiXwFsRwRv1h6NxapN/hkpZSOTKBrQ20Bwapzg7IJHYvcurWIeMrdFfu+I+dsFEoWTANoKPZSkCUgp0CRqxjQazucRKesR+sVISSIOc3iHcGnTEKVTWEvnz/hn37+SF0og99iDIBEvHt8RN0EP/z+7/AfHh+hrbBpsLMPpNvwHIEiLY/48O4R+7ailIJ1205d54xqaimoZbc5GByzWPeC+3rD68sX/PLzL/j8coNCsX650VC0BhFGt07lSFPG8/MzrpeLOYYZszkAFqQPp59zHgbdR0P6QKU3rJSxvl9nBZ4t6DioqkDOp85QkTeH0rH+Y4/0MQ9gZGRqBrllTNa70VSRQmRz5r6ho+JymU9wQh/Bg2pHs5kDAO+Nsi6FXzsZcmLwFWVb8cvPv2AtlfW3GJBShgTB4+MDohbEfMG//4+/w5wE960gAGNuSYiZ8hVI+PGnH6Blw7oX7PvGKNPurfVua12xt4JSudatVNy3FfeXL/j4yy/45dNnlNpR+ytSSoBNjgtyRNpRAp6f3+Hx8QHzxCBv8rOd0lhrN1DUOQs2Re9k8EXGxDRXPj2f7QGlwvA5CQNyCTHaHHMdza+Ef8No8nPEwDM1N98jV1CgloYpJXBMLuucqhybuu0FDw+PLNQqg7/WD/y99YZaqe0UbU5I6w2lHBMMYddKIcyO1y+f8eW2MkpPDIpyinh8fofrkrG+vuDD9z/h8XHBersDItyTtSKGhJQTWmt4/+FHXKaA+/2Osu9Yt93GsTJ7L7VgXVcOwyoFtXNvbtuG2+sLvnz6jF9//RWv6wb5umPuX3l9u3S2pdV68qKQgOvjE3744QOu1yvmabKi5ILJ5ifknIdcwZQnUtsMLlJVslhO0KEbTi/SASDTRpyCJbZBbJcBGKM7BYZ1MxJNKR94naV4PilpbEZr2PFhP8wg6NVjThDt2NYVn1++IC8V754e4W7Go8gQArG+zol0TYB5SujlME6O50sI0FqObnBYVGZ6SjlnzMsV85RRV6b7IU1odUWtnTpIBnmkPCFIx3S54rJMqGVHnjI7TK0RTXvDvu/MLLRZloDhLLZ1xbpXxJSRpwSTOvpK+ZZHbL484PsffsDz48MpI5wxTYQDc8qINnkq52zwAyFANtYk5BQtij5w27EWB5gMwLp81RsCbS9YNOmQRTb8NMaEySJBx+3Vg4YgYO9aN6PNg9pbw27zsiUm9B6wbxu+fP6EFhIkvseS4tibrRHf9yjai8Hhws5eNcFDXigOB3LSsRd77oKImDOmPGPOE3ptg/ZLefmK7hiyjicCCQnzlLGWnZkVgJAyFtPAKfsOtIaKTrVbc8RdQDhy2wHLrmSvCOVONVeDtnxf5mnBd99/j/fPTwz2Js8SDFqybKBrR0wJOSbDxyMg3MtTzgOKO24iDDLBccBxKAmIB4H8RgwR7hBySjzrMWKeJuvMf6sKS7iJb1sradHTlCGqKAbDBonQGaiFhvN1LSRVXC+EZQ3b57ozM+idndtZgsmSO0PxoN2id4OBMBxjV5fMAELMvG6zTaT9xqPXp3b3nBQa7YqcJwQh3Ot26jLPrNuVDQp2LbdKuzblhBYFfTOtoyBYLheyEL5SVPjXXt/sFGqto1uScFHCd9/9gB9//IDHh6vhe3kYiRiiYYwBPo7S8V5G4Uzjco6WjhHKcbpWCOe02TeIoOnRIObFwRCMgx+iyTQE5HnBw/WKmMIYLqGGs0tg8UWCWKRQcVkSeusoKOi9QhFxzfHAnFVRSkGtFdM0jchXy2bwBA9Wqw1NG2KUgSN7Q0pOCb1ycMm275iXxehjYjAGYYhrTghBIWnC++sDtN2Qr++g+IL7ukEkIQZgu9/QIUiXjjQvSLc7bvcNTQOCNmwrscrWGuENKLKl/ilHRChePj3hn/7pH/FPteIyX4AQUP/8z5YR6oAQnt9/wE+/+xHPjw9Y5nlARISq4htMF3KG+mjYoxDOcb2sA74SS/V1mAk1zNkL6udsgnUfDAgphIhpWnB9uGCeEtcAAJqicZq6saQiVKnllKaJFF9VNIPzlpgNDgpIOaGsFXXfUYX3GqxXoJQ6KKOtMYKU3efx9qPDNESEYINq9gKJE+YpmcAg76fXBiwBOUesVfD49IQ5Ku4F+PDdI3759Qv3fQqmh99xfZzQVbBM0SACyl7cb6+21jY+UwQpkT2UY4IkwdP7Z3z65Rf84z/8A+mracK+v9jedal24Pr4Dr/73e/w7h2LzVPOhKTmCckYZzm5hv+hpcWCOACxLAjspGezWRgG/4z2CyiNzWDNMgurFwY74xLCgH5TnvFwZZZK4U3um7IRnk3Ju/KBbdugCLYnFFoY4ft6SiAMFbaCUgq2PWJZ5gHlbvuOUhkl0XE2lLJDLFsgDN3MnsQxhbK2juv1gtf7TskLg49675imCb1umK8PeHi44sunF3z48CM+//oL7mBg0DvPbl4uDChiQJwytO+oreD2+nLMvLCMJqWMOUXkmcFYvT7g8f0T/vEf/hG1WM9QzH9jp2DDo2kkAz788Dv8+P17PDpsZBDCNE1kp1haqTbY3KUtos1tTVbY1d5pGIzKOhRgHK+1NDQFwV52MxhxpKUCRonLfMU8JT6ceeGiA2/YCAKDhCzFPM8rHhhxD4aRLpimiO1+R2s8QLUXfHn5guvlAdfrBcGiiGLYoEcXx+GwwhXo9Byb9lJzmmYsU0bXMjKJGCPmnDnLOSVo3fHlyyuef3iHP/3pD/gv/+W/YtsNC4Xg8ekZkzT88vOvWB6+w//lT3/A/+d/+V/x+XUzZgKfTwgBmhIQApZ5QQDw8vIZGiP+/j/+n/Dhx5/xX/7z/4HPtw3Sj7m8qsDz+w/4/e9/xKPVZ7jWE6ac6RTsIIpaW71ho4zarLVeCA+0bphukJH+wwqgbvxTTCiF+Gi0bM+fT4iBtYx5RsoZs0GTasY9DMPDaAuBkEAKx57ykZEagGmesaSEZZ5QDIrhHGTBly9f0HrDY3hEFgYXZS8Yg3IUg2vvE7JEQHnpLo56UYKhE9e+783TYUa882w9KGSXfP78grA84fuf/g6i/xU/f/zC7FOBh8cnPF4nvHz5jHle8D/+T/8T/vP/+/+Ff/z4CbVWcyCJUI3txWmaMaWE9f6CVhve//h7/O53v8d/+6//P/wf//WfUDreQI3zwzN+/4ff4enxEdfLZUDBXGsWdqPV8pxf75udgZIpCYtJq1iE6h31Q4LD4OKYEnot2Go91jrYwJ7Atb5eLkgp2V6LJutNoUcvRHsN59zl7BnxQAdyxsNyQZ4nSK+47RuVnHPC7fULzwsEl8s0MHxKgog53zBs2jjHIkcgYvscCszzFSm9oDaD4gJrMz45L0XBl4+/ovSI5eEZD0vCf/vv/4D7XtA7cH14wsPDFWW7I2jGH//D/xny+b/if/n//jeTNIdB8tHqSkDKCcuyoOx3rK0i5Cv+x//b/x1//PM/4n//z/87vrz+jbWPzkOfr0/v8f1373C5XLEsLDZOZijmyTMFx6/8Iw6sz4u4WitaEMSQLFvkQYFFqS6FkFJGKxuLvSkh5jQMOiTg4fEJs7ELpmmyLMmoYjFZU52M2bOiAo2KbkPUUwqotSKnjJAjsi3e/bZhvd+xl4raCl5fXoAwmY6IIIVwUMKEWOg0cfDFXnY4tTBBiQ1XYr90QIKugsv1gr12SIi8/pyxr3frMahY7wWXh3f48LSgNcUPP/4Or18+oUHsgKg1qiTM8wXPj9eRsVWLYpw6WhvhlBwjn2freP/9T3j3eMX+/h2+//Aj/vG//wP+H//b/zYOWZqv+P7773BZFq61GwnDl+dT1gQAmSdzGHsIi2bUxGpc6wAaXmtsaq0PgkEMCUE61k0hkkhISAYbimC+XHGdZ4QgFvFFgyGjZZ5pOITa2+ju7sK5FdOUbT075sRCeABwv90pJ71u6Npxe3nFXmisU8zQKQ46YBTOxphyZo2nFAyjCNKXa2vYLHt0Qsa8PGDKKzpoJFLO0FZwuxUEUeylQqYLfvrxA/Z9w3x9wu/mBfetMnNuhIRYv0mIiU55nmYyyeBqqWQrjeI6OupecX38Dh++ewe0guVyxYcPP+I//a8/4392qCQkfPjwYaz16E+xLGGa51G8B4Bkgee5WYw1JEFtFU7BNvInYhLLsmztQ0CKAVvZ4Y2QOScTDgRinvH0eAUgI/CAMipOgXaAiAJrJiyoY2SnIWagVcI+Bl0jAOvtFbUW3O8rWmtY7ze83FYsVRGzNeh1Rv0SIqfaGZRcy44Ko+jnDAiw70YYGGymjpASHq8XvK4FwepHMQCvr69ERGpHQ8TvfvodpG0oLeJ3f/gT7rfXMfmQ9P3MfVIbciI8jyDoFjR6LaW3ykmJEPRaMU1XfP/994iiyOH3uD4+4M+//PpNtv43jeNkqp/w/t0T2R2DUcTsYJpnXOYFIZxkMMRTp0Prw2sG6WIzYQ1jVjVZtxghFlGEmBDQUSFYlgXBilrBiop5ueK6TJb2T1jmaVDsHI/e941MA8MURQLSNOHx4YooMqaT8TqZIqI17GXH/faK233Ftm1Y1xUqkYZumtBNDlxFkEwKgDNtMSCqlAJqgTkjXnOvjEKgwOPzd0gx474zW5jmGVECRQWnGfNyxfPTE9Z1RZ5m5HnGjw9/wJePv+C+7YbR8vDV/Qs+vTIiuYqMIjxHNCquOWO/v+B237HMM95/WJBzxG4bOuQZH376HS7pGMT+9PwOc85IxirKBiVM84xlXpDSIY1AmmQfdDqvRdCxz8jJxfcwshDST2m4grDQXkvDNC+jSY6MMfCgPVwRRCxTmcm3h7FMRLGud7TKueG7PdMYIx4en4bDSMZsYQE+QQXYy4bb7Y7b6wu2fTdqKCPAnGekkLGuK2pX9B4RYhr1J7EIPaeE3gpqsyjYspRamhmWCT/99ANurytK64TyEhkzEjMepgueHp+gZUfpieyShwfM91d8+nJjEdSnkGnHL798Rkykfy+W6bIe1hDjBQENv/z6EdNyweOH75BSxl42EiQEuD69w3dPF3gT53J9xMNlZqZ8XmtjmpH+GQYKIDiotq59JMHqO9M04JQRHwjhHZ9PnlIEekNIGdeFEB8bM2kjHp4e2UU/WG2sL3imsK83lNrsbBNFCCFiuT7gukxjX6ph9K2wsbPVhnW94+XlBfd1xb7tKLWiNMqBX5YZtezY9ooU1ZyUohrLK1rjpmpns6YQ6UhBUHZnngneffgBy/2O+7ax7jPlgSbMU8JyfUSOHVvpmPKMmCKen5/x6dNH0zRi0R4C3D7/CkyCZblgmhfClwZFqwQ8XK/49OufUYpgeXiHBwlo+4a9cUZeSjM+fPf0N3YKDP2wXB5wvczWzcwWeG4eMotk9KfIgQUaZnzMuj24z6odpeys7o+iJgjxKCMdMaXNaVmQUxwKjyFEXB8eeB0pGb+5Y1s3RgG9A9qw7aRn7XtBNhpl144XBS7LbBxsUlV776ilIsVgEeQrXl7XkcVIIMPA030EQYoBrewoVaym0Tna8rZitU2hCqScsSwXzMuMXJMZ44bl8QkX7bivm9VVgOvjEztKE+loABkzhqjh8d074MuLUUSJhYYQsG0Fy2WBivCz1238TMwT5suML6+rCQJ0lAr0SpZKM2aST4EKacbT48OgGyZfb+Osh3hmCB2OcHb+v71EohkUUMrX9PfHLNmIodMUrFEwTxnLPI0iMgBcro+Y5oRspACFktt/53CgEAK2dYOqYts2SMpkSWXF65cvNLA5E04Thjm9FWhg4VTbjtfbjcwuY704zTmZscopAb1j21bDzenw933Hx/sNr/e7ZbQZi+215RpRyo5SGiRkvHu/YN83TrVTRZ6vuF7YAKrWt9B6hRYax7w84hmCdSN0NS9XzDmh1Q0hT8ipYNsq7ve7yYsI0iR4XBakKGi9I4H1M+0dbW/Y+zakT9wIPz09mfFNg9I7WEbRWUOHTYgxYppnjOqAZYbRJG/I5Nqxl4YxiY5mhFFwjCR0hIh5WZDCMRZ1Xi5YFs9M+PPbVmxPr5AQUfcdXTv2bUNTwcP1inlZsN1v0N4JMY5rUXOYPo9AjK3HQTXOcnKopxkszaByZ2FYWFvdthUftw2vLy8ojdnvNC94eLhimmeklrCXHb13LA8PuDxcsRmd3GnyU07QXlEbs7p935DB633//j1eXm+sh00LLhb01lY5637fyEaqbdjBcFlwvV7w6XU34kojOtIr9m0jStK/bcjOb5LOFqEnm4xamnIeBtWxY7HCr8svd6sZ5JyxLIdWfzJ5jJQieq2kUdaKvdQDH4SNO2x1ZCTLlLmrhA5hnmakaMamVdxfb3h5fUUpnAQWhTh32Tdse8HSOwQKXBZgXyECLAAmK8J4AwpSQp4XPH/3HWK+s6lGO9ZScLk+YJ4yULeBJ3rr0OvLJ3z5wr6Cfd/tftQw0RnbtuFyueJ6uWBZ2Fi0l4qUMt69vxDjMGcqUOzbxgYhYEQHABDnGe/evUOtjVildqzrHf/8T5+w3ldOUDOZZG9CqnVHjhHLlLBvKyQmzBPT8lZJWVz3MvoYHp6eMU/JCo2HMziL5oUgo4fAaZ8hCBunRgEyIU+EBliw37HvFaUWNKP1ijFtukVr2TDsYMSVy5WMp5y5X/ayY729soejlMG86I3MsXW9I6TZVmUBANzvdwiAKbCWEaMMwlOMEdfHZ/wQEkrtyFPCersjJO9nIFbcjJ4VAKy3F3z5/Bn3lVRgrjUhh3le7GsFDw9X5DxBpKFVTrZbro+YcjR8OQ7mEKWOZTDA9n1HVGB5eMR81VGvqvuGT59+YXOjkQm8X4i1uoatsO/kvu/YmljvQgASoLeG9bZia5Xn8/KAh+tySNekZBpXPgMFGIJ5Bt12VaB161s4zvZk70GmVrVzUEbQAQmQ3lBqhRoEOOUJORF6TfOMh8sFOWdA+QzW+ys+f/6CbSdzL0+zFZobtm0jdg9q/8xzRu88J5ewIDZhFiEkqgQRTPMFP3z/I+57QZ4mlO2OUpX0YKNNa+/ogTWFbbvjy6ePeL3xbHnvC0Rol6yh93K5YpmZxfZGmFJSwrv338GbNrWzT2nfOduEeYii1R1ARFoe8P49f78D2LcNt9sr1m1D2bnWzZhW3uOxrkZ/DSywT/MMiYIkCW3f8bpuXwmS/g2cgohguT7gcpkMSmDxKQbnFHtHp51iHIVT1+KIFnX4QR9NTdOM6+MT0Bu2fTecb4UC1jm9Iw9HQuOd8jyi/F4LtvWOXz/+itfXdXQoJgmIKUC1jSJmLQV3YMwrDSHhh9/9HilH9KaIYMGGLBlFbyxGhpiRhNBIjMQXJWUsMyOQ+23Fly9f8PnTJzKA+jG/2tlacWJKC8uOeme0NE0ZAB3BNM3IKWLf73h9XR2Wh8/LrZ0Ux1Ybo6Cc8eXjr1jLjrqX0awEObpstZuKbe+4bSuqN3BBsW2rMT4CpmXCVgplEPKCx4eH0Wfi0hZca67fWWkynLSQYrLu02kyA+eMMkb5y3JlFFMKSi3YNg6Q78pCbsp9wDFkG0VcLmxaUm3YtxUfP/6Kz19uzGyCd8dyrWEwArTRqdaKm8Cagb7D73/3PXV3Gil/Etmu0lu1Q25QUM6oKgjCTvTlsqDUhn2948uXz/j46RNut7t1rzr9uFstKeN6TdbDULFXtfrVZIwoBkU5Z6A1vLx8QWtqbDTLDEtBE0D3grJvDMIC8Pp6N8dKB04xREJzg/0lwLreCVN2auTs24YgdOh5XpC2nQSPGJklpEQYeGK2ABgNXcmgi8EIGsNhhMH4S4mCiZAj2/e9fX18NOZOHZlibQ25ctyrIFiASWmO6/VKGikU233Fx19/xecvL6y5yBHBu8KxE1RaL1jXjn1n4TnlV/z+7/6ONSBlAJOsA7ppw17LyHJTjCiVcCMZhjMEBXtZ8fryBR8/fcTtdqdTchKGHYauguv1gRlVMKqoDTGaMuxMJCvSC+63G2rrCMmdK7P1KoBuivt9RZomXJcF+/0V9203x9cGUadvu0F3gpgZFLMnigKIrRS0xs8OecZ1Kbit+9/WKcQ84eH6YIaLVERi96TmhRYxTRHTMrNnYZ6saelgn7ichReVYSbF/5pNdK21hoeHR7RCbPeyXKGjah8QYsZlocRCawWvL5/x8y+/jgXzNC0l080H2+QvkTxn7WVQumIo+Od/+ifM+fcsVgdTWUyMZmrZcd92bnpj8YgVjsVglu1+w8dff8FnzxDMQI/CuTEgaimkphotFOCBzokOaTRONR6Oy2XBbotf6o77/c56BaiuGvOE9+/f4//P2p+uyXEl14Losj34EJEJkKxBOn36vv+T3b4tqapIIDPChz1Y/1hm2wNSX4n1fcQ5pWKBQGak+x7Mlq3h9vaG8Hzi0ch6+eu//ivel4S//ce/4e//+A21VUCcW83KJmU1toVZMseIHGbk7UCIEev9himngSmn6AuY86GcM9Z15Qa2De02IM7ACs7ukitx70rGSljWhNxnrLcbWmULfdYL93V3z8Wou+gNz+cD//jHP7DtLyHkIkhTtu9FfH81jYSCwTDaGxAiPr79A8u64Mt9RZ6MGYaEJEAtFc/HExIT4Sgh26m2Cu181+ex49tvv+G379+NslgNvqBtiAQaAtK6oPIym2Y+v+B2DsSnnS4cJCAvK0It4+B8hQfsh8Tbl6/4+n7H29sdn588qH/6y7/iX/78M779/d/xb//+N2z7gYJKmw0fyiZaHvRgM7sgiBoxJUZ4zusNy8TOn8K0eFGGJXC2NS8UsZlaXgRmG3GxzFy1rEbDdJoxlArynDLaPOOmtHA5z2IVMkPoqcYmW0pEsT+f+O3XX/H53H5gp8WUEM0eOiBgXhJme/cU7hVG0J47/v63vyP/9U+8/AFDAUAK6rGjNLAQseIVvaPUgigRpR74/PiOX3/9Ddtx2Lth9++uCrAD/ThPLOuCkBKmnBBAu5EgHK63VnG0asXxhDVE7C423TacZx3Pj2chabfr/Q0dn+jasS6/4P/zv/8V9fmB//vf/g2/fv/gszPYPaaEPOVhseEVXDQov/U/mH20LBSkRWMJhBCw3N7wp59/xrrMAy7wS8Czcd0e2w8f0rgCMMRorIJTDkad9K6DA6j3rzO0qakKK1ImhMBytWJ7PvH94xOqYk6OcbB+ABtwxYR1WYgjq0L7jH3fcLZOQVgs+HxsWH7+ilYrYp6hrY6W7TxOPB4fKEVxe7vjS5pxnieAjmPb8Xh84vOxodRqFSOrebWLMMWIaeKirIWdTzBXxtoLuc+gEMtbQ5pa8QD5+PhufOnLjycZFbJZ+t2y3hBCwHZ2/PUv/4KMHfvxE8pZsZU2RGheKdVS0LNL9hvKseE//vY3PPcCjWmwihwumm93/PlPf8LXL+/EeQFjP1wKA4e4SNNTINg7Fg4yg1hWdMzm3RIhraJUtuHzcsMq5hNTKxAilnmCi/ye24aP799xVuvWQke0blVg7CoAy7oimVIZYPu97wdqK5CQsT+fuN9WJIP+Yux4PjZs+4ZSC87nA9t+YF7f8P7+hvM4EQUo5cDj8cDH49IEQDGsM2J0cgF1IL0T5opBoDkTpzIRUppnRFGUWuyQ5fN5Pj4pMBP3dLqEmc2GijklvL1/QQxPfPnyFV/e7tifN/z801fg2ydCJDtFYkKOZNb1nAZU12rBP/793/D9seEsimWebYZCVmCeVvzlX/+Kn798wbLMuDx3zF8LFFERKjQ6MADP9VI4FdtYZmZDIarQwvlViBn3t8kgUXooLcsCdwU8tic+Pz7wPM4BmYU8De+s0iqgipTJjPL2tU0TjmPDcZxoXdBKwfMo+JomlFYxhQnHtlnVf+I4ThzbBg0J71++YFoWaI/Y6xPP5xOfHw+cpQ6oyH9ACc6IehGJHjYTLFTy4zx4jknEPE9MrTMNi0BRjx2fj42uujFel56t51Y7kDPe374gpYASMu5vd2z1wJcvX7CfZE96KkNO7DJrcdcJ7qNvf/8b/v7tOyT8vuP+d18KySCBaZowLQv++i//B/76lz9ZdR1Micll0U2F6eEOr86Br5UPh9Fkl6h2lFYQlQKVIAXnWQAVhEi64nK7Y0qkiPVa8PH9ie+//YbnfnC/GYauna6NbuKVYrp45c4PX1ak3HAeJ/IUUY4d+7liSgkRilLIXtm3Jz4fz8E8KX4xpcAuwiirYsPIIMHChkhZo90FoNqxb89BTT12wkSQgBiBUhrm9Y77OqP3iufjQLDFYzqZYYPRB54YUOuB59NUmykj1QPHUTHNHIpJjAi14+3rT5hyIqWuNA7TVIFOz5t//OM31A4zQ0vI6IQIpxm//Pmv+F//8hcsk+coGB0UbMO1dUgiqwfiCU/uYcX9Gq2L4MCWNh+tVkx5wjJFMsScvYKA+c52XBvhi8/PD3x+/47H8xhePj5zqgBiyhdmrg3ncVkI5zQh3ALb65CgvWF7bsj3FWGKhv1WHPuBz+/fWQgUzrdijLivC3pvNjuorJ7zhJhIBUwxGs1UjPbbcewbjQxrRVlnfgYzeKz1hBwHvn55RwyC/bmZ2MyqVfAyTdEs0sW1Lx2Pz0+jfAvSnNEKh629VtRGmGi+veG+LtB2mlo9otaGeRI8Pr7h13/8HXvrmJcF+3FlJszzjK8//4L//b/+F27rNNTpMWckod0DyUQvnQFsb4/3DiS70FJyfyZarERjM4UYzCKCBcM083u74vzYnqPQau1yDVXtUMuMyNM0oOnzOCyUi/tjWlbEENE6MEXBsT1xmp6GXTYH4M9PMgvPciLEjJAy3t7eEYWZ5ad19mnKCJGEj5wT5tIgQWiK1zknIrxDC5oyzVYURwgaalO8f/mKJWec54anmdZJjDwGTXVPqJFdlgTg2J/QXvkeumDKwuKwd4owU0RHxNevXxCgOPaDwtlAq5Xj+Yl//PYPfB4Fy7KOLuSPuxSmCcuy4HZ/w8+//Iw581B06MAPglppG+BZv4CrUN3TCKMVJDOJ1hIpTdBS8Fk2pBSRU8Y8J7RmFa6Zq4kA2grO44QCmNYFb0J1p4gY26EOi4VWyTw6ereAHzWMfLYgmgX32wJVYH9uyO931KNYaFC6FJzTjCVm3Fb6O7E64M+VYsLb2xtho1LJdY+N7IjOEHsYYwkS2CmIoDVWWVNK1gk80etp8FQnkyFHzFM2jyVi1inFgdvCLsPj6GjlhMQFogWtC6Zpxm09gTjh/f0LtDyxb9VsESJqK/j+G9vzPC2IE8ZQf04Bt9sdX376CW/rhFoqxGiJMDy3qFc+fEbdDv/L0M79rGBhTGEMYlMAtp1UXxdxJaXFQJzSwI2bdrswOmKe8fZma0wE2gq27TBvfg7aHbZShQ0MK/168oSuHfN6Q4SgtgNHnSFSoDlBAqHP6ZihIpgWwbSuuN9W/sxdEI4wZmv1PHiRHKQY9l5Ra0cx+IuUy0COfE2AnlbZzqRp9gPfvnWkyO4wRpq+5XlCLBXuz3UNet33q6OZx83ZGubbT4zIXFa8g/qJ+/0NSwZ+++0JkYh5zoixYvv8jo/PB59jpMbi+cyYlwW32w1v71/wdltQzh3dtEdBBNoaTig81jUOBOBH5To/pgsQzQfIKtiz8ELdZMM8LTYH6WZ9ws6z2WFXG/VIb+/vKJViLqBjez5RKumo+7ZdRaY4uYVq9GmZIJ0cfx+AH8eJGIPNzAjh3m43SIxY+g0xJqy3O6Ypk7FVLqZXqieOs6AWRTkbeq9olaQPNigc8LdaUFtGcAuS2I22q/j8/h1bMtabcFifYsacM8orBBSDc7lsXfH7nceJOSQc+04zwJ9+IsttuuF+m/D4+I6q1M7EGHE8H/j2/RMSMu73hK6cQ/6hl8K6rphNvBQFOI7doIKIFG+0nzBerc8MuFjk5ULw9kvtB+6GexLHPBpf2LEfKLEgFbbidFtlFd4rsdpS2dKlOCHcEnonm6Qad9fZP/tGSKA1CsTWZSHmNgumKWG9JRtKEfctpWOaFbWT3zstK76mCTkm3twSkYwXSquDjpAy6rkbdq2jUxoWDcNwzSqonA0vF0Dpm3SaMd20rHh7fxs0Xu3ANC2ozQ4cNXaURAdsAFjMZggo5xP//h//gV9++Ql//df/he9/E+SiqMcGRbCKLCBFwefffsVeGm63O2GFVlHOgGldsfRqYqXAVrxSUv/lyxfMidTcEMwS2S756JoJx0adYmzeVa1xhjKbejhIoErd3B9jjKTrRmelNRug8Z26YyerTVb4y3pHqxxePp8PPD6fKLVAFXS9nQhR9CljmldCHEIfrt4rao9I1SJZQ8Tt7Q3vMUFrwXbWIYRCpyWyhITQK1qvwy2z6WXHEUOg9UHkPCKPoimYyKhgN5Fcyrysl9kFfZyFrfOE/TjZIXqU04DpMOje0ip+/fu/Q/VP+Nd/+Rfcjw/0HgBUPJ4d87yY9UNEOR54bAeW2w25kQRRS8U8r7SymCZMUzbB5BOt8ed9f7sjG+1RogEV4XIVGFblthS105W0Nc6E5mkCOkWUvRXUquj9aUPoGVMyHQoUrVS0UlDKYbBuQA6zVcod87Swyi/s4J/PJx7HgVo6Qs64325IvaFpx5xnzFNC6x1LXgZUF3Iyu29eDHlZEQKwPZ6DVaVKozkACKI43MZGFcXyLYJ2aAyXoNFQlMW8jeiFRVuV53ODhIT1/oYv73dS2UFAfVlX9G0blPxuF4wdlHBRZEoR5+MT//5vAf/n//4/8Zf3N7RCNtf3j0/ElHFP7MLqseEf245pWZAy138pBS3+wZTU++o2yZa8Za3Y8/mBWs9hiKfG7x2YsWOjPoRUoo/RBm8huhXCpXh2r/NycmAUa+UhlBTVhEm1FouZ46aJMeDYN071YzSxDYehvoD2/cS273i78yA5oLjdM4I61h2hxmBYl2XAYskw+JASugYEW8TaFSmmQU9jV8DfjyFCQ0DoAVoK2T0zK7JlygYhFQrqaqGHUUwQG67/9OV9YIsdwLIu6M/NDgojsQ1RIGmgIUZEVdSj4O3tC27LjLIu+Ny/47k9aXqXCKeUc8c0v+GXvOA4mNnQQYhwva1Yyjl44jnxYGu14vPjO8q84rYuPKyisyrskBDPyXA/G4z1QCNE33h82ylExnda1ZVbw6kGs7Q64LZqVGWvpuqxYT/LoOXlPOPL1wnLcmA37UItBTlNxJorO6RJxOA8wj3HsSHeVlaZIaLFPoSFydcwMCiAIo2MGQXU3jlV2JbFpwUa2VmutxszKVrDdmw4Cwuapoop8ff3bUeevmJNtALvrSNNM6becZQGt1JgFWXajsDPTv59R14XrPOEohPtEz43dI3IU0KaErRVhDjh5z/9gnIcOM4drSlqqrgtNyzLJwfiOSHFPOCpeuz46B3LspiC+rIkjyHSo0giUrx0SO4gwHwUXh6llpeBdB9FU87KeU0IgF1SFLwpXCwboqCVE8/9gFOFAgSTibiO48CxcS3UWkkV7ySCLPNk5nYNIglaCk6JWHIEU+kSZ3ylmQYpjLkX/bsU9TyNnELfIWdvRfNy6qZsX9YFy0w/pu15oFbOK2rj/CtNkfqCPeLL2xtMzI8uAcs849nolOrvW8QhM7W5TIKioYeMZWXuyZwzns8n9S9CYTFax1k6vv78CwV4O5l9KUXU9gfPFFzVOBlLwQ+KZPQ0+vQ38uH7iVoDlnWlrkAAD1DxNole+1S6hRBw7hs8RMT3gEI4KGsNqCe0Cml4NpicJuKQx37g4/sHN6sEoJxwsywVUgHfvnzF27uOQymIAJGLu9UdsHYwWLsZo3GaQwBSQIgNx1kMHgGGh30UiEZM84TjpL1t1Y5gvHu2hhiBINobtu2JfdtQe0c9iyfL4lBgbitSonp2XW9kW2kHlJ5Ip+fwiuC/ion4OrtWfPv4Des60+JbHYrKCKo4Dhrn5XmC6GSD/4imHQFUa0+BVV42LUo2PQNnS2YxDFZVx94QrVLL1v7GyEsRUG6KTIgghDB8jRRulsZ/zlOm4lkEx7GjtcKBIugImzJQjh2fn99xnNXWDneXD1HRFXlasaw3mGEz4PMMG4qW1iC1sHAJ0WY3zbodQZwy6nnyUhfP7SXrBRVY5gWPSjotWSOEidhN2aE+TYS0tifOc8d5NvRWURrhv499Z3UHQd4npHgbiuHeO21K+v7yrgXjJEEHAucAvXV8fPvE4+0L5kCmS84zA45ixHnu+HwQMppiRDL2X20dKQQTq03DzsJpsiNUZxR2MuDYvTXM6w1rnm2/WkB8hKW62eDc6LhXWtjVPceUzLUg4jh2oCtOC5qRFDHHwECnz09s+2GMNnZcTvfmeROx3t9xexcz32xQyDXPME8oXmoWK+o6G2OtxR4GRg+DMKnHUIRpBnrDo5MuTYSDXU3t3bzWJupW9o3PZyfc6dY55XxA9w1lWSEhYEoT1nUmhbc3IGXkzAL4YuwZk8sOxGiuqm1/4Ndff8Nf/vTVlPAJ97zQ4bic+Px8QBFNO8avFWNFbRGh/cHW2cGoqPO0/CBmii/D3MsumZVgOQ9IiHi7r1YpyGjdFe4kqTiPwmNB3WaWf3bKk1FKKcY5K1XJlPsnNC14PvdB3+Pia2a5kDAtZmJWCo79YFU7JcQInPuJmI0+KQHH2ZAm2jsvi9P4GoI01F7YHmYgpGmEZjR1WmeAoNotzwOxnAdKrUBIFvgTcGwP/LqRSllOKm4DSNejV42itYfBYw2QjC9vKw25oJjmCR11qEsdootWoc/LgiCg7/q24e9//xv03NAbMM8rlnlGr0/89o2WGaQIg38vEMqpAHJImCa5cjGmbFirB/dEHhwulLKKZt935Pd35BQMf46mVVBQhxFxHge086DV7vbhASETimqWbV0Lh3fNeNcBah3fjloaoJ3CoMAukeIwZ3cdKFWQYsayzIQjVBBNa9H3Ay0xQTBm2qbUslvbz4IkTxOCypihdFVoM8W3/TkJ1M7UcqKcBd3guRACyrHht8eDEZm9oalgyiQWtFZtSKn4/KCZnWrHTz/9ZLYdHJpOmYZ+NANmMRICoTvXs1AM1/DbP/6GbPBpSBnruiAJ8P3bbwhxQgoXrDcvglBPFBHkHE0oyIvByQWj4IuuYvchOPd2KydKyhzCa7PhOwuG1unFVe3yF5jVjVmC8GBM5u1DyOk4DrN8Z3j9flCNf1Y+71IKXWVjRAqRRWUP2PcDx3maW8CCIDIoo858a72TjqnZokVhBAzOgVJOmCB0XhUATU1XURCskw3C51Fsnllbx7ysiEJL8u+PTxwn92atHdOU4TqrWhsg3B9uCyLxF+R7hk8QpnlmwWDP2JMIabM90YWhFZyl4/H5DbVsl2hzWTDliM/t08giXugIMM+IIeE4D4Tfdyf8M8lr08AdX8Nzxn9n47L7Dee7SxtaV6wzhVvJQmrIl4/YHg80FUSJCIFuf70rxHxvoI3c9tbweDzYCk0Z5+cHnvaQa6mjOu2dlLuiJ2K1iypEdDFfflX0zk6lGU48x4gQbZBrRlTurtg89xfAPM+QaAE6lRS0s1RWd63DiBI8lKwDWHJg9bA98XiYf42zlMCO5NifqJWCLWYFKc79iV9/BVL6K97vMxk+MWPKysG1V2+wIb4N+2s5aCOxb/j47Rtu97u5uVqwTmlXNRLMe154CAKsiPM0YUK7gpJMTZ7NqZJCNoeCeIi7xXgpjbqC4BqNjm45EeXYcNSCHCMkkhPutuoppivdqiueT4p2QkzQ7cTH40Gcu9YxgI3RMhFqRQkdKUekKEg5QrtpDCqrqWDvWpGHkZhrWtzW2CmtEGpAkjGKWmMU6X7Sa6q3Puio7qxae8c0Z84MdsPluztrWsxroB3KVgqYD8DLpfeK79++QWLCn37+id+zdyzLjKNcHdFIp7PZTS0UNZXW8fnxHZImvK3zMH60yuGF8BGZmZxdWoqhHP9hb1u3MJnBpWf+XgWA0TM7B/7zZDoSgzNTphHddpD/L5EwKksbjCresyfOY8fj8YQiIMmBb9+/oxQWeaXxgPdA+1oLWq+oOQ5TwBANATD2IYkBvAyY+fBahHBPj2cEgUjCPJO+CwvG2jYaF0KpMVD77/PgfCGlDLSCbQQd+fO2CFYRBO14Ph4ctucM6QEK018IVdzrzDCdPM1GYSWqEhx2l0DUop44jwNHKXg+CKO+vb0Nr7YgyZwTLP/DzoYIrnXmtv/B7KN1ZYj35YOTyW6533GbZ7h1sc8a3MrXuc0+NyA7icZOdX/gLFT2hSgIzTC9YJBDDGQiBEE5itlFA4+Pb3g8nlw0hdTVUh1mSDbzoH8Jp19KpWWtaCEiKtAQiIurDXMkQDqgQY3fS+HauXPglxdW+2KeO6Wc2LadFUdkspN7v4u1r7VVPB60LBYJZLVYopk6E0thz6sYzY0tb42cpXx8fsc8/Ylsr97NKuMcB/jA8UWsAm1jztBbw+P5wLosEHNtjUInWcI37HLA83zAT/OyYNY6DgkXLK63O+63mZVfJw8+RV56P8juTYdSa0OAIi8rpBfsO/FZWHfTRcYB55myPOgLqysAZX/g4/EkFm/2FsWVvGaglqYJEQ2tkK00fHbM/6aVguV2DfhcgOie+NQcMK2q9m5hKG7mSEbWtm2oCrPSKACMiRMDWk+IWrE/H/D8Ce4FwqqqdlAYHNkbK2aKPuk5FUMkFXOe8fX9jggFQsKkFvVpXagYcYOHVSOt336Oeuw4YjBbhmMc+D4kHSpcyOhypmnBaomJAyrMfNdv9xvfk3aIdXoeKwlcWqBgsBJAlmKOAR+PHQAh4BgimjS7VAQpzxA0BDsraFcOaDvxj8/PYdtxnqfN3Ljvk+Uz5xghDTgNKvKwK52pRQpxxjpn0tlDJNHB9nk5OfMpdsC6OM8vydaZlX3WZt3nhemHwNQ8dFKOmd7IvxecSdn7YFkSQTDTwNbQhSSEHiNaK/j+/Tvyn35hQdQV8zThOMtg7LnZpYBOwl3dTAfQ3vDYdqxTRj1J0EnTjIxzaJfUzj5BQI4ZPfzBl8K0TBfGPE1Ybze8v71ZwMrrIJmVsENEXBigqhawsI4Zvex47ieGHbs5ekokjDDNk+HAAaINz0qxyvb4xGPb4ZnBrbnZVh8VuRolLmZSSLVzaNh7Rd135GnCstyszWWgvSoP5QCj4J27OSfSXz+0hFIEeQqDqUOrCPPPkWBDqIp5mvDx8Q3bcSLmCdOUoFqhkkbFE1OygwJmFZCHJQTFe1eb/ng8kL9+MYploN5g5Pe+4I9W5kqwXIrkObQNt3U2dgNN3Vo3oZ30sWEQBCnwsp8QR97yvKx4/2L25GID5clCaQJtgfkVePC0WoEoiDFjWSaINnx/PPHav7pHVgT/XAwCsQr+PDY0bTi2Bz6f5Kp7iFFXfzYd+37i2dWk/5xhpWDOugIUG6BP04SpVURjfUU1aCFwoHecB9CrkRg6skSU86S/v8joGCWw0nOhGUDmXDk/se0bRHigAoreyKBqTQd/no+ZMAtGUXBdTr03bI8H5mnCOvPizjmj6zmYe4Lre3O4KAhwqxkOrr9+eSdVuVKjwDCp9nIpqP29K/9kZGvPCzn1M+1oglEn+W5ZdOU8ja6hGSsujucPfHx8R+u4ukjwMgsaDRaO6I0FTTmYV1LOnXnlpsalE6p1jkKK6HEcENk4wE4zWVvmCNBqweMsnG2lbKxAkjfQ9Dpca4X2YCrjDma0F6harKqyq+YlYZ/dkAIJETFUPI6DGohpAiCo5STso50usjFaV0ITTLGO0U9DX7+lHHg8N3x5uyMEhYSElLq5Fljj8QL5uPcUCF6g1gNtnkmhbRVQQk3QkywmvmkesDEgyx+c0ey0UBrb3fB2u5mH0UtgjQkxGKUZMU08BM/9ibM2zAttEdq54bEdTFwaE3Yu+RgiVEAmDhRToPNnLQUfH99xWLeglpva7e/513CKYO8dMBgnp4R5XbBMM1JSnOeBTcXi/AQ5N/saYCh2olEZxOyP4UlghJb8Yefsthc8OGqr8Ni+kDIW0jPYeZjNxXnYoN38ZLxK9vDzFDx31jjpjVXJR874+v7GhZeyPfdL+k94AFZ9W7Ue+Ptk3ryhVw7BJWQsOeHx3AATH/kcJwYLGVJWZtO04P39/kPFKIZXCif2EBUa3qWM5tqBOGG9rYgo+PbtAUhkOhksLUsMzggOpwliDsTIK7MrntsxnFS9GmSFKij1Fe5jpvbnR6VZ4zSTKbcsaK1g3zeDN1i98yK1z2BOqLXDsi/kUmr7BQ2DJITtfNc6Dq5ST8C8mbr74JtVwnmenJ2ljL7XoXdJKdGx1qyU/cBotaGlis+PT0z5J6RII7ec8mVm5lWoXFnXPncQC75pGpADB+qqwG1ZsLXnGJirAKI88HNOZBtZh/D2/obsimjzIhI7kEYQlF8M2mi9oh3zeseUA75/+46mYmSEDj/hJAQkMbFqEITA/b23xizhxzYuATV/L4UpfVvlz6xqSWMBZX9AO5XY4bZimhYEKJ7bju35hKyrmVgGdhOqpLhaSA+7TEUItMwYBRXsIA/WPlvXyYE581CWZaXZpJl1lnLC0xUlRHofqVt7BMs6mTmLhCFvjX9/f3wip4T3+2pFwMTOCXjZ2xjdx9CEBEHqvNDyyg5DW0deFizS8Twr1DtiAFEYRvSHXgrk1pPOeL+v3JCqw1oYfGcDv/TFpj4fgGK93SC94OPBw4g+Q208qFfR05XNKijbhm/fvlsV4WpWzznlECqmhFYLdhHE3mka1Ylfkib2RDsbVksO244Tx06sshykhE4TY/wkRuQg6DVjmllVThMP4pwy0Lvx3DEw6WSe/60a/CCCIifDW0pFq+aFZC+qGBacEp+rmwvmeaJa1BhbPPg6zn1Hvd0w50DtQs4MgbGaxnwgDY4h6ypCEBKdI2W0oYD0gjDfMNlQMIiMhKlglX/sDSlm3CxzwsVK4i9aXg6XRBuPAKA3QmfzumBOgl9/fRqrAkCXcdlqu4ZpVPJyQ9Xjgd9++4bjII3RffpjopleqwWtsbPanxtCsNzkrgiJB89xbCjlwHy7YV0WtPLgxZAS4nlCxLNvfa1maG+YJvvf5qPECwPQHo0ZZSQIE9Y51XGeeEluzwdt0rsON05qa3hBhETvrcm+t4RoVt4RPjfQzoNm2098/XKH9oYwZehxYnhcetfguhB0KwYYUAQQWz43DiJLo3Pocy+cTahXvsFgWu6Deb1hitHgLqtWrUp2IjSE9N+c2H3QY2zCbZnx8e1XVHd5FUFQDHjHB8zB/McCArQVfP/tN2zbyb1jRnPR4Gk1J9WUEsq580OpAOhIyAy3rxWPzw/saaJ19hypAUkZOVccjWr3lDML1mQ6E83IShVzCgEqoF6isnDoIJlBEYb1tirI2BNB2wgfe2fhEK525rB47kqKHu9pVP1EyI3vnOv7OHZGB8fA75dovwO7GLwQ8O6sQxBUICkMzYjogQ46BdzWFbF8AiC7D/pymfyhl0KkI+ft7U5Mzw9xeFXrnh48NIK13kWdhTJjTgG//UqVpZvJsSXHYCeJbZYUga4BohXfPr6R2gkLGZkStHHBMLT6milwaAhW3MrKfkoJaBXP5ydqK1jvb/SyOSvaXLD3ipwXABw2rkLFbY3upWTiHRPrKK7hsyeuibFKIOz4zlpxHjue2w7fViIYuH+tJoQyBe4IJbJnCZEfLMkBxXGeWJe7ff+AYNXJ1WvaQRs5ZHLPIDcu9EOQOHrHel/xeGyO3tnG5dA69YTldsM609XTN/r1jS5Bog9Tu/kzTfOM+7Lg+fltXDbiD8YWqPZuayAM64sggs+PDxxHseo/2uHQzXqZanUOG7mhems2NK7QZgZ+OeA4dhz7jt465imbrUbBKYoYM6ZMG5B5luH2GeEMH7GLOVzrXK9KDb1DW0Ojy7k5fx54PJ8ArpxxhzY9OnU2oVc0yPWyG/fY0TgO4VpPdGXIDA+RQLdTq2YBOyRCQDB4wum3Ery65O/VemK+3TB32rfwcCfi7TYz07Li7b4aeeHqPtWgiiAuUjO1ssXsxpyx3m4o+wNHaRgpfKpQZSduIbUACF0GW2dPg4IbqVyYlwzRbk7JtJkZe8Wra3Ot7aqImcSHVjjsf25Psznn7PI8DkgIWPNknmbZIm4VIfTxc4ZIA74QBD36gNcEotqhSlgxhGiZ7swzqBYcFkRGh0giBK07xG5Vki7IhOqtWbERbV+wMyq1YVrzINkwA8VLvpdObVgG6SgcdRz6gt7ooHC7LbT/Aeek/u9/11n/u/4UyFK43d7wdluvNmYMVPyo0KF0ZYVLBEMDM3DbuaH0Dg/E4ZllODFohaCwahxAiIL98zkq4pxnxCgjQW2wUZopia2Sb+0gvKCWxZs42BZVSGK4hQKI6Hg8NuQcoYgcZE3GY45q3QbnFSIB2SysU2Yl2fVKnno8GIDuvvGt0fMkBIGnzvVqKVWdB0qOpLNxYTTMSxizFIDfq4eOKKY/aJXzjWgX6jiU7bD2A9qqvGZGbS6GgYCZrpWUSM0z5nkmw8riMp2PP4eAL+bNg7EYxV60jHkC2TDsmDr4e9M8Q6ThedInaKjcARrCWQHh6k3CkGRYbEexanSiTcTpjraWmdutW2wdyYb27lhL+EWxmPfMWZUMNps7HPuGWjKmhb+/ruuF0wor0K5ghKd5TM3TPPQ1pZA6um87Sqt0tLWks1orhYydXlBNmQ2hBnGFmG1WZXYXbaJaP3ukosU4unq5E6+f5vjD3EjcTQIYnWcUoPHBjv2o8sL+6t0C6ycIOBM6i3eupLd+/foVOb3MKvxdWzcIGB1WzB5ceSDlzOr38XG8dPiEsjwLm/ObS2TqEOzzsVnxQ81TtbCnbvCR7xXPhaddhjnAdrePT1iWG/pzNw81niO9HvhstMCOYUe83Q22ErPmBC8u605gVvjzFI0CzHCq89yxH4UXT+Nl05sNyRu7We71bt0jnRMAspjE4NsVAWldRopgNzTDrWBaqxCZEQIFiq9zK6MGsLMTgdRXMaWXCfRQU5BhOE+RavIQ0YfI9w++FKZpwZcvX36gneLlUHK6nA/UGIweDOYRE/gY3GG0OlYTrxCSuWlyLUMAPB878rQgpIxyOnf9BUbSboffWLnIOeE4XdJtQ+FElfN+nDi64v39jcyZRoySNt2CPM28BPxz8sjmcDXE0fqKDzN7x7k98dx3Hg6lcpgpitCuz0JWDoff6F55BMNJO2LvRk09cbu/4WbW4F51+QttvXN4aFWOsx+8AvfOS1SpDxDizapkHnWwA6n2NVmxRyzrimp+UnnOuM8Zy3Tl8eI/FQJuceAqdc97iIZD90Y8OWS3GHanTYG0DpMjkG2kHSlEPD8+gRBxv99Ryon9OIaS2d+vD5wVvFxyzlBLMYspUZdSKw+rfuD5fOL2RoM4HgJAAH2SYs642ZbzCFev8oPQ1gFWjQWjptZykA1VK/FtGxy3VpHnCW3fOZOwYCURSzyz5ckiBai14Pv3b5jnFfe3O5acRyX3ijtDTAgWPGfBHpx3MFEgjZW3Ot4j/HlOkHp9HqftJ0XVjttMs8HSKlqZmXu9LgZF4Yd9HWxNeVpZV84tougQtkVVKyxsjwi73SCAmjOxKK221Qb25XzirIr1fkOxvICzlFFkQa0IsnXVTRkcGzsvv/BqKQjThCknlH1HS8DXr+/IMaBrQwiReoPzxLKuVsxExOgD/hfGnB+/IhYIVPD4+DTnBBau0zShngcUEyt8g1ZThmUpGBPP9paCz2J7PnCeB273O+04TMw5MknMIy0KyTZix9cgGAQgwTLhXS8iPjYUkgJSNqIKL4xaK2KOuN9X7Of5gjr8979+PyV1WbDM+eWQ8Db2pVodwy8YjCMDQ5MgqL2ZEZQtnA6Iyg8+355MxIVzoJnatdZ6pTdZm6b6YwVZ/dLxeUd0tk9nFGcIyPPMdKLzxPL1J6zRFrLhb7WQDrnkGQEnSvepPg8k3svNnDop0Nu2nY6YrV5snC5ok+B+u5tPEzOiEQLKsaN30ko9nAbAoGE+H59oteJ2u+F2W+1ycGZPg2T+edIhAUf7fZAYJACmu4BecIPEAC2dHdfLLOcshbkF04xSC/I8WbynHYoDTvAq8opNZBnJjioaOUBCQKsnB7cGwQGdB0SzNWNrCAYt0NLC6MCN73q8W6NBtkpbEOYe8/dDcJ+dq0Mt5YQiIQRebtooHntfF9qmRIF2Hjq1VPMuusLuZZrH3CrYhSTKam5k+pp+IIaIGOginKcJt9t9UGc9PKW3hqqdTrFWTDAykTqHx2dHW1bc7m9YhpGkH4ozQuCBKr0PSAJj75ESDWBcnKKv7z1gva2cKwWBHg37tuH+fkcqQIwZ67KOPQn17v+l4AtXN8p924HkUDEAJazjMz7+bVo4AKbuB4b/U1TQ9tnWM6vvblqfZnuQ8JF3DT5o751W1GLdcK8VJ2iXsq53mzFW3G7MI55ShlpoUbG8ihQFaiiDpIxoxSXdDa7O4dh2ZnycpzkYhCvjY5pxv93QzDLbBWqt9ZFcGGK2tUatjvRGSLN33Fbf2yzwnMmVcgTtY8LYJ+M5y49zgUFIsSKtt2bW317YWOJkTFjn2ajN//Ov3w8fmee741gXniyjQ4BgLGjAcDC1bGbtqI3VUxzFLxdNDEwFq6B61RFIshAoiCHflpXHeezDsrobnQ/gJmpdh52AiGCeFwR0NBUs84wQI97ebhwetYYKwTrPiJDx9M/jwBFpU6vWkmtXy0fgZ+gWUMO0sxP7TsbBYIKL2K0fMQlw9IaYIg8d4VBLwT/TzTDsOniD2Ts/0XrH+9s7pjmOF9+7ji6C78OAXwOAJQikGzbqBnpq8xstOI6GeV7GIXXWA8e+ISTCfCHGkagnjjHj6kSiHQi+FuxtG1+9IU20GOdB7Ti5DMoyO0mzJFGDGlq/RINePYHY+rbt2C1syP+Fms1ysWhEVbfPzrQXmKgsvlkeLu2xI0IiY0WihyWdOI8MaIUHysPWbDA4xfMSaiW9d9+YXuebeUCmCltXFFxppWVHiJHsDw+lskPDkTgRqtAf+gG93/E+KJ99JKu9duReYYoY7qzcZ1F9nmHrLwgLq5RtoI2hZ3l8PPH16x3blhBise74CsphjedZ6L7OvEAAZDCUqFNx6+fRyIhAAq28L2YOf98MTtC0WwfJ/12NoskshG6jOM7XOE/oponhe7rdVlQTAk45I02M8UzRuokQIHOAqEOYdNzVREsXNcsPtVkM7Ll2y7yojUrrozC3nPO7MKw2YkqjeIBQdBus6HUNBxmG9ky9u28N+/5kN/XlHVNwCKlBc/xhfsPBMll/8DPK9iE/OYvBECOh1q1jWiZAIkJIQGvYtk/E/BOm39co/HNxnBykTtfAwqoI5+Y6nPF6YXAAbQK2SIdCVYc+ripUx5nG/+ftui/uBtIVPy2ntdU6RFPjM0Jt8ShEzfUzRPrx9IZSAkKr2DeBBKoel9t98N/zslhlywPAIR+vkGAdELq/Dlr19kpFa84ZaYiVAlo7oeVEs/jBblCRV3qO0cZENTiHz0YRBb9GLSeO86RZWc6Dgume9d0G9GAnPIbVCAIF+dJtVHw87GvlwTbNzLwO5wkYyyu/3eyQbvYuf8SZg2HwUcR8pRxisA3skJcEBKFAThCt02G12NENXjBoLka006NH2YVoq/j8+MB+nLQ/t06Q0NgLZm1Yae8N2hpyjwbZVLqDlgO1nLgJ8BTB252WIiG4LcjrOjPGm3gTY7Mg7UZyYOZ3sWFhijCWiLFOmiKg0eEU3Px20hBuE4GGiDknm43YMFh5yKKzoy3FbVU4r5hyHp2ZaeCsABEWM6Ddh0MtvlhDEEinXqO3htv9hpwiCyZt2PZiHebr+/DDy1W1bnjH3xuwFXyNMiI1xnRdLFZcieUliMXhch0F04SwyxNj4z0fpCD7LIE2INzT6hd16zaHJBLg+gg+Yu79Y9ugWLHjieV2Q69cx54UeV2onMWMoTwwbFfcrXaaogn1SC+t9bSCpdNqpZOS6xCREztY0XOWOJugrtSGkUZnxbObepZoViu1QHWCu0orbF8p0Q+iKAAMIvZ5iABDJ1Zbxb416MKs7NoUEoHz2JDm+Xed9b/TDYO3WFPiZl5B+o3mG5kdg0/0X6qOyAGyPzTjzBjmLdchKWFExwmUQzmbtp/HZpxofblEdBxYLjByeqJfOL1Vs/WO6J3hE36YxCnhPDZ8//6JPtrjMCAdDsislRuXHGwOoqZpSPbi+Tlqo613KScdPkslE+YsxElLYdIYuMGbOao67ZWOo6TeeYgIqXllDNiLwUyeOTBgHL0ODMA2XAyG5QID8w30Xj32A3uhCybsPbVa0atl8qZ4HQLj8LfKMQBR4nhfjle6CRde3g1T9hyCsK4mCGLm12Sq2WnPIuA8Njy3nQNNv4x7t7/Gr1c9qlLdKwvEmG0Tu8ukEwXIJGn4/PhGV0y/eSUgT5437CLMy83Vl6eCFtTuM0NrE6a9VWO/uWX7URgz2bViP3lBiAgN1CKfaUoZs2WMB7mchV3hW8ppX9M466MbZyHGopHU49G9DwYbf284fwbrDp5PTAu7ZDcrrL0YI+bar77WB2PF3719H2alXIJVjMPpBe5wiJAvAyE6eSSaoLJa0XPQncA1Cnbd+PeW4K6lRtm1Q5eVdR0kFWqWOjoUrfNd7dsD3z+fZlHPTxRTHkZ/w7lXXliFYMEXIsO+3m4rfxKfXbZ2hSc5pN35t46DMBNE4Jb6YgmT82TiXzPkc8O63l6+Tq0DAkwpj8vdt1M3COkqzsycE5e63ovCsm2oGrAuFJkGyB+fp9C0Y0oznJUgdotd8IEtBPvQOUYwV5lMkR/waUKekLEAbGBih1AIAa0e6E3NrvkDn3ZIUInY7MURiuqNRllqLV1rTKHy9heq44W3WtHzjEmIKwLA8/ODYRvzYlU+N0iUhGiQEFWpMi4idkcJ0zTjy88/o9aKb48nqBXQMR8g7EWYzDFytUqrNSDPM0bfEQJi5MXgiVdiVWav9DjxZCnHeHOMqEPYxMXpp5jjigCHfrALNYC87BAF5/5Eev+K2yzYz4LeI7QDcUnMShiQgVWK8MP+ghCDzRK60c16jwNCgxJ2C7DOwjvBMZeAHeb0Zno+Pk3F3AxvduVpREzBKkkgRaEduSi2/RxFCR+57yAm2r3dZqg29B5Rzh1n7Zj/dQW0IwTqQzq/nBUsBoXYcFjEOsLOOMi//qXh3/7j7yhVzQCN75pLje+2Vn5+d9MVpa1EMDKFF1Upk7odYsKUrDO1y62UgpSv6i6nNFL+LoDN3zXfjzcKDsXFADQRY1A1PI8Tb7cb9oPYtqgCZvbmh/HVDVyHqSvAx0zEurbWGqK/a7k+k+3sAZHRPZd7vdqleWwbPj4eKGb14XRtmHbFi7dgHdw0zwO7h8TxHGyZoZwFq7EjS63QXvB9K8gzLXq6F6tCLcQoXGyOM4S4Y8634Oc//wWl/t/4fO5kNZlrb4wREDXI1Aw9e4dKQBda6ifXz0SuIYnBioJoVjOwc6Da3o7GFuRnit2ANquj5OXZSiDiAiU5gAjEBTsJmDj39uUdqwjDiXxB/w+/fnenMN/uuK3ZNolX9zLaMT8EBBQvsbptNBmzwbHDBV5CuLyf7+Zi9wg4H+i94/HxwR/oheHwAjUapmuHi1XyVF9fA5zenBPvL56qzHqeOPYdx3Hi8+MDxdrTnNILLUyGlsBxf4lhpMYJuFHef/oJczT2QWOWbhB2O7UWbI8HtmPHYJoLB9KiMKWwGg2xGowULb8ijxbZKX7dISjhbOJHairGpvYqz/pVqLG2+O3dbIu5wBInrMsMEXqovL+9j00z3rm/67FKCUkRbuO7Hpbixjzryr/XjflDjNRweFtH3Py0MHHvl/4SXDNw5d5NQ2AVM6gWniYfcFLxqtbJsnMyDyLzNjr2E/ux4fN5jH+nvdMoTgSCMMRG3oXRWfOanUzLDV/fb9b++6UVsO87Tgti37Z92CiIWNcnl0iw1TZyx7u6Nf08GEsj59vmGSEIEF5mPeKHAmwt+GBS4H5WpELLENEliejHju2sWJfV+PMZX798NSPGq0qXccDbTx28SxDTibQBhfJn1B/+PN+yhRdJGEwu7vfODOYnLSscl3dYSkSG/iRGmhzGENBVsCymYrZisHcrvqxjgghi4L7b9wNnOfD5+YHmxBOBzY7sQggvGhwhMcY7ZICdzU8//4QonS7C1aIu9w37fuIwLdJxFkBMBOcdPng2ESpsaIWsQ3qJzcMRAfCZmhob0C1UwviMfrv68xlBVnYGcW7YTUtlMy4BPj8eSHnGPKfX3u2//fW7O4X3L2+gsaVeGKj9Gp49eh3+Z2v2gswbaTSVGJsV4cICFUq/e+OidlU8Pmlr4RzlWui9AgCamBIVY4Z2cpdjDGiloGPCvKwoFoc54u56Nw/6Zn47iv1pge4l4fPziftfbwgBFCaZxUJ4qZCtHDQqIFvzoAEpT/j5l1/w+fy/UAuHgB/fv6Mh0r2xK/Ssw+e+tYaiwLzSwlq7Db4agCSmcI4D81S7Cd0TRlWtkg8QqaMqU8XglfPjCrTbv7P7Qe3HkCAQjdBa8PH4xNcvXzGHgrzeEczj6Rr789cYkNphFKNZUxg0MpK4xp+/YC2Bq1bZ3oMZWIAq9u2B5041cM4J51loda6e/a2guhQ4zoIlJIhwoDhNM6ANRyFcB7CSRYhI6KiquE0MbK9Gc9y2T9Sv70jpClUZB6w9q6CXn31wV9ZOjcPb+094//jAr58bQog4Hh947AU5m/oV9NOZ7FBo2jFH5hl0Iy/wkqMfGK2qI8TMDKmO1WFmxw5C7XBsVhOY1clL9RhUhvL5gm8u22yF4ng+EOI77rcVrUy437IZDIbr5x/zHa/gjElo5I+U6FflyXvOj1QFO0KVMd9zR2Beihz2fj43UkZjRooFNdCVNyZqgGrr7IyOHRJng5oKIBNud89VMaqozepijihnxfI+Q3rFedDY8jwObNuJ97d3vmubS6ijFi/zFMAZZREtMEApzyt+/vkr/r//v/8AJKKVHR8fn0jTTMKJKlqjAl61oZwNMi08XG3m5Z9xSfSYcqdee9Dc2y8sK17MEQJbm/5O7FFfl6gXgl4si0GH/DutVnz/eOKnn97R5fx/Odn/669/whBv4WBnDJR1LMSLkkpbid7pOx/TNPA7X6I+qfeTYnDQDaemAkpw7ptdCKShxZww68If1JgI53FYm0xesSp1AFp5ay+3G1o5IAlYlwnb4wEVql9LOZFWOkTSh6Rh302l6BWyXA9cjG77wmbmEM58iBoEMU2Yk+AoHegnHs8NEjJyDrzY5gy3PegdyFNELw0l0JaAXG62lv5Mo0STquuAW3zAnnKCqjmyVq+2/Gx7gXtErN28fKbskZuEn3nS3z8+8fOXO+a84LPtQ809bBHkVdlsmDXINIJwvuLDvOtekLEJGy7birGWIWjlwLYf7AYM/1/nBX0mZbFpRzkttawSjiytjLXVjMO+zhHPDqz3G/p54CjsNlppaBmY1xWqiv2sqM6LV6VlQBC4T4xbFjtI411mShym9sb/vawL+rcHBIrn88n3LgsUyrwK785a55CvN5RTDaoBMz+mCWFc1KwuQ8DLO6OX1jKZQjZFhCFE0vGsx/BXAFfXADoOwd6Fil4FGhoeHx+Y8s/I2ejYA4e56n2BaR8gnJ8Fwb6XcWHEREbcIF6o7e0OeCqgqgLJPw83+PbcRxNaWx+xkdXgo9Ya+nHY4FUHw4hxuRW9M/vgaBVxXbGmiMfzOdhArdIdd1k62mOHasN+MNsBL4VLxwUxX0w+PtcQqervjRYc7q1UO/Ohj/MkGgC6N0wpAegolWyxADX4j5kJIpmdoO1D7mWmRF4oC/NAVG12KwExvmiw/K2IjMJvfG7bY1zPMP+4AO2C83zisU14m38fMPS74SOpdVACuQCuw9EvBvLUu1nSXr/Pc+GCHcYCdihCX35YEYRASwdvv6ZpRo6kGwI+V3C7iGbsEQqCWn2Rmxuc4R0M831nOkCGYFm/4cqndetpxRBm8Yd3R02MlpbPAaOVB1gpvL1/QQjA8/kcdrf+8qIlLdXeuICtMgiW/pVSxnpbRwxkNJZBEOYN+GEFqEXwmS3DsB3hBxLYjAYO6/H5ulOks9vIlRcLU6F+4qgNPRjTRxwaVO9eXyA1Vr2nDVLHxhrYoC9aXCWrDSRHQWFr5thPP36Z7md+/qSM1sFb9xkDL/Z2HSLmbureOb2bf06MNCKLvLAm89+fpgkhgPBmbWZBYNQ+cRjJ23Tj8Q12ibf0AcvtDbcpY38+cRRjvTn8YpBKrY3Dfpj7ZjAH35hoUT5lhhalZO8q2Pu4BIqtlsFF94GyPV245QpExzMc8xxXu4qLzlhcEU9vNC7saWRaww4Th2F8oCmBa6SUE55/MTpG/HiJcJ0YLAyjQtsipE7G6KZQTNOC2zqbfoEHYjkL0ws9mtMqmGbv/4qg5UyDGhS1wXGyQW4gYwsy4oO7h3CRwwsKvC6mJN+pw2b2LOxsC4H5Gu/vN7R6YDN6tL8vv5jdyC6YJsv/vXuDzfN8ucy+nCU5pfFM1YStEl/sTX54tu5AptfvikFMELgGundeODGxw923J2r7g2cKQS0TQf0iuFgajmHFENBrt1vL8PZwwQ2D8uafTTEGggL6zIcQ0U7PFZ3wtt4xZ6tmbTAsQS4rCQt2L5XDquOkrS3bc2U0oTY8HxsPj/OkI2ag/QG7HcITyQa7sCraFaSjGvP6S9sL/Q8DRtHeAOk4ng/rqvh7ITCyzw85gO6G1ewbynnY4VaYzqRuy3vhuxLIkjEI0oZbflH9eCD7P/vlPS5cAxui/zlwgJ0MGolRsO87mvDS8sHWYOUEGSIxtytQf5HqVYu7zfpn43cCC0oWnfZZgADtBcdJp9H72w3TzHlOaRVqObq+yJuxsGop6CYaKpXMGZIMaFOwb0+ctaL1iv0sL/GL/AwhCRASUjQoJ4SB3fth6IepOmRn6x7Kyx8mnNz3J2NaU2IdrIJlXpCD2TN3zrQ4a2o4D7KTWqt4Ph/Yj4IQsm30q5BiFrIVH50USB/O/1DZIhi88J+qXfHD4vLKIfuO7zqlhFYOnLWz4LDDx+dnzvxxXygBnwHvjlc2GT+Dv2bvDAdqLwJwqyEIsB07vXnub7RhULXukJBiTBEey+usrtY7Sjk5bwr+LiPW9YZWTmP9NFprByazDb1GoGBumiaoWVsDFuRleyV4h60gNGsT4PHvRdA6RX+PbYNYjkdvDXmesdjedj2VX2ilWEpbaXg+Hng8njhrH4QVag34nCe3zoei1DLe5RCr2fode2rUq05ftQ53wM0dpfXxrkU79uMPho+C0Up9ob0OYAd1zFWrAKsFcWro1eI6G8U++ag+eTHy72zbDsZYKvZ9GwNgF26pDZxjytYe8xbo0EEFo+0wEKeMNaw4jmJwjP4w5JEQzGWVlssxXsMfh5rZyl9Uy67yciBy0wKsXj++feAw98g5JEDIkRYR5BTYEncYhbaiqNkzWMcQSzOMsuOnn74OzNYTt2JyryBWoXO+gk76Cw6sflnYRRAC42CJf9FMr6OjtYJQqTxuqugWgjQqkVFN2eVvMBrsM1xlor1b/q1B+XQ2ThuXrQASh8NuOU60TgHhvu8oZ+U6UveUMf8k8ZAXJyYouinhPWUKCFjXOyAbP0USZhPnjCln9HICvaJpwC1lzNkqS8BUzcE+ox+kALoaRMnvyc9GSGZ/PvA8GNQyZXoIpSkjh0A7k62hBjK/3BxPpEHRUSQgp4w9PNF6wdevP+NumRf+DoPRVF3Rj2UabLTWvDjjc/bOZDBQtI91g67s+joh15yZ16u90zHA2FoSf7S1CHZocb4BuL26rfqBcUP8UNVxQPWXjglm7qa14DgbZn/XZjntnX23+QlZTkxrG51vuEgj0Sz30zThfr9ZBcxu223A/fOVUrHcZv5+ZLQqmr5cWn6Qml7DumuuXQurUqCfBz4fG6G5JBZLmzFNCSIR0hn2BaHuBWN0oaitMEPj2PlkFPjp6zu7ebm6bAaUwWYLOvb+jwUABnvRtVKve1Be9mY9TwQjj4RAgsDv+fX7xWudFEY/EPwHuZz3+AG7qTUv/NovCVsg1TIr1cANCbZpFDm/DrIiSjkBMF6zGsuglMI2sxUce8Fl+NXtoGXF4MPJsJ2WkWxOrVO2A5oJWudxGl5PR06a49klBGUCFmCL3elhxq6xcB81aKBrx/PxNIin2UsKI+KwFd7UzHemkpuQG9k2QWjMpkofpH/8qvjp61cs0czj7Hk6P7nVhjAztSmOAaaMTcpD46pw1XQc6EAPxIBDA85zR56SryuomAoTvukvvyt7/bBTxup9scsSxLttk/HPWZutXpkFU3ATHmqtIaQI2HrtBgmqguwwrwL3w6p2Nb61Mr+i6YB/ugK3lU6fKWeslj0dUwI6zeg8XjWZPTPPLB3UQcBb8asT9IxsPyxc0HaMyE3uAQ98X9YVqrRf4OekuCxF63oaPaH80NDW8O3XX9H6F7y/3eDOm0NkZcNlv6z5/ttLZ+iFpB3OwiEv7wtfqw0aaDooFrYiIaD18NJdANch9AKlCADxeZBVol2B4INufk6yzV6ADbk0BhAZQUXj8FIMiKi3iudmgUq1GOOQIjYvCH0IGwK1DvOyEDKyfOYp2z6PdMl1qjBhpWwYu8IzLHwi70es/ze7LuOL2vlSyoHjqBgDIDuIp3lBBLBvpNm2F1NJ23QQIwn4ntqeH+i94evXLwixGUPMoOLIPp4BYC4GvpyZHQ+4Zl4y3oujMk074crecGwnphR8l/6uX7/7UlBwM8QQEV5w1YGni5hr5Atk8AJhsZqmwtFvYf/f7pvjFY23QBfnFqjHjud2DFdUp6A1dyD1A9q+Nh0KgX4WaJvH10oxYrJwdRi2CagFcHBRBYjxkV8fpKsN1C3drxba8NpaCkrjgeXcakiwQJWEKsochPNhXjEUsJEmajzwQSMFajnw26+/4uvPv+DtlsZB75esW2z7uwi2AcdetiEg31EfdEiPhhQIemzoRanQ9EXXAUWDRhPw2aFx+doIg9sbW+HrCfm0gF2IhOvfqX2ebqK/EOmqOhwq5eow6nni8XyaMMw0AFGGNxIvZ6veA4zbTlretjP3OndSA5dlYWh9tLAV+/xpyi+MIhe8AY5tCoLpKuzd6EsYjgvWjOlESwEAXZFCYleiYUSnltaYIAi+M278MHxv/LZ5fnyg945ffvppXPwDmrPD9rW6VIPsDPViNd65poqqDYc5ZFY1Npl21FZojqh8Z107pEczrnyFfXlR9EoYLuYMt1Txp8S1r6NTGBcdNz07r3CFacGKA65PMFnwybhVP/j9ounqA/VLHOcXhQA49gOc31viWYiY5oRXp2BVvIjVkhVkPsi9UAy/8AeOZH/Zu5hSiuWHcA7ggr+cqNuY+ozpPPA4CzUG48bhexwMOltHrRz47dff8PMvv+Atx7G3fO7q+SVdYDGfsE72sjFRY4uoxQoEE6yy8esIynjQ0q1z/KMN8VqtCDkhv2gCghBjTkFwHjvOUkmvsxzTcfHaf7WxWFh1uBCsI5gVRTfjMbcvALQ1PDyCszue49Vk/3HgI0BTKiVpByEWfRhoI1EqjnBYjqqLzBikERNxVQ8gL00pkPJDdvSUsEpHzWOfIrhm9hMxRsTuzqB8EQHdMmxd8DbZhqTNr3Pvo7F5OvltyIkV3ufHNwAdX758MezXITSvwpzNwEtCXiA6d1LsVYePPSx1rvcO6QExOVXUNmNvaACis0ZeIIVsA0fHd6m8xKgyfWErT3y4RYH268BIMaE3gxR6M1ohZzjP7YmHDeltzeMsdWxQH6fyHfMz01KAdOMYZAypz7PQgVcV88QQJgat8ABJ0SyOjSrMXz5ms4une/fkj7qPYaebH0abWXRVpBxQWxlrcppnMl5sr0AJy7g3jud/OJOn7Du+ffuOL1+/4u3GYWm34fyPl8BVOPk+gv97da8cQF8yIugCyCGzp/51gyqzWXizMWR1mnOyC4E55RHmTTYOdocPL/sJf24wlS8CPYdU3fTNKLZQ7NuObdvN3JIb4irwnCVnaEKgejzGaJ5JQj8nz4RvFYdF6N5vK3orKK0jpHjBrqByHtaRe/2lAnQPUBr7iXv7Gmqbrsj2a49EFlpt6HbLpTxhWdipXdA691Aw0oh3pBTVNnx+fAeg+PL+hWw12yuw9SXukyU/HKd2Odjego7uS2EXnqkxRWD2ROH1b/+3v373peBOkT5kjoFDwBSChVgbNKGvH9hXKttNscpBrTV3bHbw9FnGjcV9HjsFLh6ZWNWm876wWPnXWs32wIVNAdnUr8R72WL2TgM7rQ0fvSOoQVXTbENCVk7F6WbBOMuJ6sVu1Dtn0HSvzG2jQNn+ZQTkTJtbt+wolVF+acqYesW+n0CMSJaRS4Ga2zdcNEE3UTvPA4/PT4QvX5GS2GDUKjERahx8dY5nf/2jVxXe8TTf/KS1QE1VjEz7Xf/7TiBwlsR57MNskF9bx2FyLRarQIHxc3j1wr9DrYUHyAQR9Frw+HygKTUaz7pzwFrbOPRSimhVUXoz5jJZPdGod62T+hljRgpiSlGB9gfKEei3EyPmlFBbgwfOl6ZYckAMaYjM/H36Wu3GbAoeJGUbMSZe8nQK5f6oxohyjJvUV2psJoMSIWQVSQwDmrwOoobt+UAIAV/ebgZhGN0wXn5XvsVHt2EvQU2cZTpyYtAiCF5ZdoGiwYNlByMsuEV2RJ4n892qVk2bBmFAlPa9rDv0JeOfqdtziyIjoMa/B3rD58cnjlIxzRPOUqDogzkIEV7SjZ5XzfLIfQaYEAYLTYRGl24/kZLi8WQIEiQMqqiCRcdZGqY5jQF6MPHlYNr162ISP0+A8f3jNCGaG2oUFlHlOKGBMPFwVI28RKZpMmi3QfWai73OZsp54PH4RPz6FQkvnl4O47kQU673AHEqiXeQfsPp6CQB1uYByiI3/LBL///++t2Xgt9IIVqVZeyI7fm4LgQYlNONqeKCGR1fwhY/jHVESCNGt14mdpZyxMfHhudztxcEhsE0RbFq3Ad/rC7a8HqHVUpqMvjaCmKLyHPEbZlNSUpRVs4TJDTkFEcn0XtDF/3BC8cXREfjsNa+txU8GM6IVqWerRmm2CCBcNX++I7jaDY/YWgRgoefV/TOaoZpUhyKuTXEOKh6x/Z8IKWvSBHQxmeTgvms//jC7ECzId3LqeHVNsRhJ0XvxFGdkqvqTJRo3U3GuW84Ky/aIKbCDLRSGEZ44yCVl8ByHUM7n2n4oRZSRisf+Ph8EDPundm25gdDGM4vIAESqy33rO8Oi9lhUyuFidO8AOCzDcIApSCWehUTtBarltXon7wkfZ4QxIJrXp+n4TfBvH+oRTgGDNS6Yl5maD3w7XEMiI/iOoJXzVS4KZMWnVLClLKtfdjz5rMs547nnvB+v3FvtT5SBzlav97jODUwEMULpjFiBkyRzK/lYe8uljLvMqNP9nJgt8AjZ2e11iApDqbUyxaECqt3UUVjScy9bcUbWT4RgoaPz8eAWRWkjepxQGNE8iKmkw3IM44QojPEUiLbbFwg3n13j+lNkEysnvCODvrxEFjimp3o641mHa/vbbGiKITMoDCw4BRpyPOCHJmVUI0xFfOENC7fTkjdBLwx2dwyUXPi3WMQshSfjwfily+YrfN1NIYsQx2fzwsVRyy0KzTaAQtc6yCwaOoGH5f2Bw+aKTd3OiIza8uxWeKQbXBrR5nMFEaF6iOca9As9u9Ys/auVJZCaPtQC7Z9R7NqDNZCJmPpAGp3igIhYr3dxmGWU8C+H4hpQgwdZ+nUJkyZ8E5KCErByTJlOpBOTCATMLSDvuZhPOPLw/yqkFiFOQPJqYQB9/s7Uq601w4RISUkuxhbq+gmqgvarUIyTDQCvXPBaVdIoi/OPJu4ybsGpT8Q5A3v0wS3eO79grr8c1sNQVguhFHJSgDnBlQ6UTQn3ai+hThmsioqBsScoK3gMBMz2y2sVDov4HFCwBbtS4dCHPUFWoCxmDqA3kmDdWqymElgjEi9exnElr0xNCYlU6XnjFpOqNIR8jgOpJhwu9/hTqiqESJkBmkkzdWN6JjDUTEt6w+D3WAeN76e+bmshfWBoQimecHXr65WbcgSkJLgLPxZW1PM64o5C/bthM9C/JCFsU0UgMSEZZ4xJXpZpcRC4Ng3CIAvX7/Ah7ajC/8BOro0Kb4mXUuG4GsAGL8ZAqSzS/LcEd/bQRSf+3lVs3r9/KOrGFiGu0XJ9azGSuB/OywC6ZZN3i4s3w43t/dojYynVisP0RCHH1IU4CgF07ygF3bz87oiO3uI/S/Pp5j4/KeZxV8n9RsxIY/ve7GafO5hdyfnm/7eDX66v72TidQqz5FANX+IAb1SWb+sC87jyc6uGfnCn5C/L6GVzDJPVspfa+7x+Qm8fcE8szNMgcMR8W7SCkWuScuNcalAALihjeSi/LMSOkJnVszv+fVPUVJp9WxtnHbse4G4945VUg4hjUtXAYz0JcecfeEYboxXzjPZGo45OFTt+GxK0Qy0mjlrdlS1IBQbnvlQhz55rHyW1dvwilKtEuyKdb0Blum63O7Dsre1jhR0JIR5PjPgHkIvik3YrWyulDElDlVDw3HsOLs7N2YstzvWOWF/bpgXUvPUf0bwcEiJrBin/U45c0N2P+Qb9ucT87zgph0ieUA4F6Rgz9ipsxIgoJmYWqUeDApo2riWqniLYBWMtcwi2LcdTi/94TCyyuUVsbxoo7YAgli4Dj+/WBJbjMGytQlP2gTNOq4JMSaG1ViVWntBP3G11q3BXXHPk4dYSBkxZdzyYnkLFtXZGuZlJaxVC5Bng7isu+UyhR98IUQ7QDu08T8e3u5Ouh7WjhiRlN5Hn3uFWtjSNM14v99Qyo5pWXDuG4ecfkwYM829ckIICClhjcHyOsSUsQxyuq036ixi+AG6c6jDYTpV/pkROzkWhEC1vVSgBjK9MAlDoGlg64rs9jTjsH+BqV6KAP/NV0DL8SS3Bo8x4Dyv2YN/Re+CY0gUKQp1CRUdpTCOExCEyIo5hIB6HlbgReQYsSyreaV5IaeQOGGZSCa5NEO80HvrSHLNdHzNDU2KuSw4zEMxqWdqA1OasO8bHo8NAn79kCJubzfAoleDVGaoOBlHiA5kc0dN9mynZYVHjkrgOt33J+73G/dp5hprej1rUZ8r8jmHGKCtX6/FByV2vg6479qU/+2v398pWGsdjEPMVDRFdiwLViF2BZJVjMqDHxb04piYt43OxpBI6lUOAcd5jDY6hIh5ptXteR44zVrWW78YAnqvgxbIRZ2Q54jTLKqzKxp7R4qC1rh4c6YuISVXGVsBFY237IdpZ3XXVaDtWvQU9tDwTYIHvRvM1AsCArbtObx+zvPAvh+ovePYmDMdjGqaYkIMaQxySZdMI9hoxF5GHZdRUIYBOdPD3WCvX/+pXlNaWhDqsyreBtPO7BgNUbiUrGxtK46zIqUJowXAy7ng/9un5956D4jhsmOACOGjFNmV2OXgKt+cM879wG6GiAOqEk9ZM397s07JOY2ksxAS5olCIthhCbCgWZaFWD4wguB5uJuXVmdgTO9KzFjb9dkDuyXrURF7HHx+CIz/v6NaVnhr1APU/USv5xAaqrPsgrHg7EKIOSPHxPUrF67fTSAHpViRxZJ13FyicB3KQJKExWck7eil2GLlKF0tBc0P+Wsg6uSL50YR2A/sMfUCiO/4YgYBNGLTMUymXsnKElFIME+gViFmGy2mNt/3DcdOsZebySm4Jzx7OwQOk12lXqzinWemA3K2p1YYRszLQi1GiNBWbeDK2Z8EI7kErteuAtFunapRgE1dXlWhZtIJpwY3t1who6zbJbdvT87qasVZG6IoVALytCCniDxNvABjtIQ6dydgYp0LW9XU4Md5Cc1ecxi6dao+f1Xv/qM4oGJsM7tk6mWP8/uuhH/qUnCrZCKK276Nm0qsJaAPVgfUhNja0NUCl+2kCLZoCSP0QfbwA57iJWBZV6aCnTv9buwgFgXyFICjQ1PGJIIaiHOrUhWMRN5yShXTvCIGqiNjYEW93m6Yp2RUNnrCBBXkKV0XlV1ebvkdJKCJiefgSmji8GMIFRPZJiHi2HfM6x1TDtiPgmVZ8PHtN2jghbqfJy8rBarwAFqWdbSywt4W0UwC1S6NFEFBl50KZ+m4Lf7i5dq4sEvaVo53C4YFcOhviyxGoDT+zD6vcTgsxIBze6D2TlU77Et2DjBtcmEzA6tWXmAB73BYZbt3Elvqh/nPp2nB7aYoZ8G20++Gmg2FmONuV8UyB5xF+HUCnVlLbZj9cg8J05To2Fp50KWF9gI+v0opYgo8kBxG8w5FxELbbWjvWcFilyqPOR6EDqGuupjx4ozb+4xj36GqyCnieVRob9zgtm+cLYd5RkoNEPNIgl3swplJihE5RfQQSOQTsxRRPscYAjOix3r98eAeiYHWqbNeoy8PShmXC2dfhvmHSIXwWZCyoEcbfjtzDFcHqF2HUpnb+6KXvzrpOr31fJ5GJWdlTzfZK1gpRhOeWm56nhjm1ay69+F9njLWnFBbx2xivlYri6gpYjbPKUKQAcgTQrIDOVhnZBeW+12JddHdqxy93jPPgIicE6Z5QagFu3a8f/0JqCdK76Z9ekDECCWNhS60o5YTOxwiy5jjZF/XuhNtEEmci4QwUvl6Y+xw1mDK53GEDvjIQ3Y4jxVzSjX2oSqg7HCaBWal9PuO+39i0GwPNEZoO7EflY6jqkO30A27BjAGNa82tVCnSl4Vb7cWSQHU88BxnqxSoTiOnVLxswyvo1Yr0OzQsYWnqij1HC1rqRU9c1Gd5cQ6L0gxIKSMt3ca4AFq4p0GIGJeV0wxGb1QRyXo1VIfmJdvD9hmuh5RCNQ6bNsGSMA8T4joiIGeK/O8EE4qjGuUEDEF930p2BR2w0fcb9ysxKtd8Q1ABDlNY4PSNGyx7y/o7eVCsMv3VUsRrLrh5g60KA4RKShOdaYKXi4nxbbvAHi5qxgzCv5FrrQyd7g1Qvd4Np7spUOPwPnKcRzjkKml4DgOhsuYt1WtdbBY3N+qt06nU/VD8EQpxboA/qxTTkAIuN9v9sq8iu0oFViXFfNk5n1GhmDQjZtCORwCeIjL0M80jEsk54x932xuxQCVFgOhl2lCqhXPxzGG5TnTsA29oxzngOJCeCPUoRRppUyxk5odS7ShkppdtdvL8MNcewAO4wg7GnnRBfmfaU3JLNoPqJKB5cEwIQq2z53mjrbnR4Vps6BxHNjX8jX52qR265hgNEx0GtIVW3/aO459Ry3n2NuepwFlJ6fWWZVShqcVVLHvO+Z5HuufEbtAzhOWeTIbGbW9XzDNM9ZlhluzuGLcYavrsMK4XPkjcWrorzuYr9Lz8Qnx4gMVUroF8kRsz6cdvrwAvBjrrbGT3biqbrfV1lYDkPizdDKsUk52+FvnkHkOOHTlsw7/2ENB7sLAsaYIRYVIG/Bq0bC/59fvVzTjoifuj20civx0MFtmY7kMiEihHjEZzB6hX14yXiVob3TzK9WUyTwYp5wpGtE+vIaqpVF5dnNwXYFVUWLdTA0NehSIe5TkhNZNbRiAEOleep4nJM1Y5oXGaYEVhYSIyVKaRs2gQk6yXk/l8qFhZTrNM5Zjx+d2Yp4m9HpCxM2ymAkxzxNCMAuD3lmFu9GZdmzPT/RWME0zbrc7lpl02gB+f188bNn7oNKGSOtlx20xPrcfcjIYQKKMxXT5uwRBnjL6cTKMxJgovRw4S0PKL2ogQwz9Dri+B2G2ALbGivbSJQJu69t6h7ZzcMBFBPM8o9QCyoANK7cqu1Zaf7iPT2/k2SswGBatFrSmxkrJiIVUSpoo0kuqNwqQ5nkya/LAgkZAewq3MwYvsu5dBEB7CfcdsvceYsa63nCeDWmekQKhqtMIGL0r8myqahMzNjvwPM3vPHZ0VRwpYbndDEvu6DEg2GML7jSsfHaTCfF8SHpV8nw/AwbkqWEVPPdv6wXnUZFSRu4dR+VBmCKT6fZ9R7CO2jtNcYsUL8K6QkM3J42AGGEzOuLxvvtzZNHRTESlVgimPGGeGUVJaDBA9aRPkNmBwApQdj3GfLJ5nSovC1hhkBO729bIQkx5QhB2p3mixUUyHYhYxZyzKZwBwPQv7hPmZ5jEQHt8s1nJ84RlWVA1YE4Tz7ZeGHykipQmZDGLcahpbfS6YFrDtj1Ry4FpWvD25R1zMBps4PeP4cXuwrVatvaazT+uQo8aK9qi8v2H8d6oVTv3HRIj8pRQ/3DvIx+gahtJV4YaOnQIpyL6h/XiRbtxeoXDD/gCFhg84pTFblUhIaQ8TUjHDpGA1rloWm3GonM1cEM0H5naCofLidoExACVYH4xGVBWKMsyo/cTqmlAJGPjBhrGuSJZ7EFLCIwwFIMAugWP+wFhmKx2IC8rvs43LPOE8xhWZ6hlp0ulbXBGLQbDxVn1td4gNrSsjXYetb3hfrthMq53DD5TSXbQd1ryGrb8ajPi3dnFhefz8IUfIAiRop5eFHEKCEqaXwqCx2PjMzQbAy5Sx47JPZEOeIg8DK/1oqQ1VukAWVJ+sPBQJ7WUDCpy+J/YaGx4HFQM66WIHTOGSLovh8hqNgbJ/j3smUac50n2lAj246DPUYp2kEXAAt2d3sWu1+ZZIkCSIbYyOH/MrqJ1USFEvL+/Y1oWaK+2pgPC9sCxH4jZhG2tojUT7hmEpL2hQYDjhDYaPtZacb/dcbutdvk6xGGXgIlCFRiVLA/qbj8DBobsf7DrQJAQUkY9TjyeD4hBkgog5YRz31A7AxBf24RRmeJiH1kjyiNUvVOQl64qjCKReiR/1zzY8jQhhidaazj23ezXjSYKzlOGPiHFEZ7EDm22Qz6idzCRrVTUmCA50N/JrG16b0MoGMyenp0qg63cOtt9huivZINfL/qCk2kC5vWGW8qYTLBIkd+Ech7sGCKFsx4z4BCY720ocPj50RtutxtutzvWZcIArfz5+fGusK9jB65dhl0V0f6Cz2udPMCOPCHGhvPcUVXM7uJ//vVPUVJzTji2J2pXpDT6LLvZrxaWC6Sb5N6m+toRzQPEMXm2OfASBx4q4qZ3zeiu57ENe2XV8aj4EFVROx0kU2DGMKscN62akaKCVgT8bKXQNyXHiFIqL5u76ydkpESxLesWJzm++fVmbKsAhBZCZGyjQi0XmqldJWX89PMNKTSUXz+pyiwFwyeqY7B9BvURdsm0RtvbcmJZ73h/exvPSv0QVB723Md6YaPWrdFG95VdPlYWVCjaatJQ2oF6cBPmnGkTvJdRsXkR4HbGvjj9Egp6aVVELL3qBZMWCRdNUi0atdO3qpVig1p6V7XugiMdF5y3xLDZB4uHi2+e84RlmVgxxghIR68VkidqVEpBPU6cuQA35h6QlcsKkmvTdC7+vuG2AjD2l61bS9/znwvASGhbbnfMU8K5H2igjQbnYsyUcAPJK+/CLyJmBzwenzjLibe3d9yWOJ7zteJeLn+rzK+V6Awp8R1i0BGsixAkEVSQ1RTChHnOEHQ8ns+hy3EYqg9Y6mVeYa5gHprjqvLWr4uEa/mC7ZjUZjnKBh12pSiz2CHsedq9Y5A9XAMVY2RWgVXIIUQstzvZPkJKMtDRGnB/ewd6RakF52mMRJilenCFtz+0ayam4/XaSveL1PQk3hcrhMVjEKRpxrreqCk5G9AbzoMuyl10oCsQsdqDn4EIB8kirVaUcsP7l7dxjvql1JVBTGoQxavoDUKfo2SwnlzVuaEwgpAjUCP6eeDsv+9S+H1/CkCeJ4g2PJ7bwFSHEZh9CIgMczEFzABLBubowye8VjIwCEAEXS1go3NA83x8Yj8JowxbA1v0zVSHXgUw1tBaN70Gb10b8rQgpYDJvPpT9vDuhHmhRTWl7LTIbbap5D/9nKOSEdclyNiwAHHEmFzYl4elxWS5DV++/oJ/+etfsc50Y/UFytd5We9eh5FbYnPxnvsD375/h8JD3DGGTa8y9rGo/RrwA0UugR8kEHUePxM/S60ViBkpBDwfj/Es/GfX8dnEnpuO58fuzUkGOiie3jY49ZjY7pW37WZo+3GQnRIv7BrAGF56fCiralaAY0grGDGo05QJ7cwLUiQNMARmW0cbLnZ+YZtV2KUwjld73UHGO6A77HjE7KRS4kUwGGOZtOkYsaxv+Mtf/4r3+zoU4WQX2X4w2qN/W6eXijPDWsHn92/YjjriIdmx6Kgc9fWzvlxsLuyzf8HVpZaDwFOO+6kx73rKCef+xFmuvye+se37QV1pexEHRMSiE/rAu71oIhvu6qhGZnVr2LYN+3agdcWUJz7b0YVi/LdbPjNxzdXAQMzMV642YIYELMuCGDlohpoOKTNTmwwuM1LU61L94V2Li21972MwgTxPwguBbOfHNE/IOSHmhJ//9Gf8/PXNZpfxgqsCQHzI2FHh6gbskAJEcexPfH48aItje74ZXZt5Ew4L+/Phs+/NCxiMDg7jgmM7FwPpuNW+3v/065/qFLbnJ0rvCCGNBfefJds8JztUIyhMiwjmH94N//ZK7KpITOhmsZW1HHhsG2rnrOAslTBAvwz4RDww22Tqjq+60itEzFNi1kJrQK9AiFimCXma0VvBURX323wxJsbGlOG86eeAeEtm1E/TnAAxwr1wQoxImqH9Gsk5Pk8IrLG6bAwTQaB9c0xxxEiKXBcMoauIZNRUnsMdn5+f+PrlCwV2Zvsx+861hTbQA4VBTG3AIn5JOIzUrdqSINBCskA5N2xHgfvP8yjVwefv4xt0dLWN36sFnl/v2NvcUQjYM/Vn3mrB083vzH/e6X8shhRA5M9ZG4j+BMOB7f10hrp7OEuIVCOndcW8ZMQoOI6C23pDnqiS5UA328Cf3aF4CwMZa5OvWyFBERFHR8RDK8MN/sR+Zm/9tRMDF9CDqatimkmL9cPYdRKEHwE1SJCqY0KY5/HEIwZ8eb9DzEOLcZ1jC9o57IwfvhNfy96NIlw2I90vFe+MesHjsY8L6lo7fgL5b7IIgRE0ohpdU/vg2jtqMLjxcpFNtDc8Hk8UI40cx4H6YoXtXY6CmQRNEvT1HQjXMSQiT24dkVDOEzEZFTVF1FIQ4kIiTL0scdxhIcUf6bZ+cQZjekG4ByJIZdfKLPCsQ4bLs6x7h65GABAbnlez588IgcmEIjKU/K4p8cuGlzwHNx8fH/j69afxs7bakIaD8Qsz0rtZ6+AI5ztqY10FqG+QyMuplf/sevD//uv3G+KVE9t2jCrFsTl0HRkKA2sURTDaVG0NWS5bCjd7UjXOtrVY2umb38qBz88HB2Tju7s1NQ8wJoZxsfXjgFNCrV0BAm2X1ar0GAJimizHlQrhNK0Q4YOjUIovO4bowVFjiDxwVDj08SoKEpPC899nj2xU0H8FF4vnqBUdHHQ+njvClMyZlZUPZe8JMQK1cqN1+5oxx5fiouG5b5hXXgUjLc4q3sGYefmc+rKg5RUKUECb4elWFfdWsW8bKLq10tUuG7NbAmyuIGqeOnLZXmCsBYO5tJOrDiC8XA5+SNTmrA9umG74OpQQIqmMnc9EL9uRAad0UnOXZQHEBnWtQSJZIgrB22QaC8lm/hYh4aWrsYvAnxus+bpAG3Dt29BRRdnV5MRsik73zSARkhTnfhrevWDOnyhqQSohIAJo0Q4oS+BTKA+w1BHCZB0cv+15Hij1Ritlg2Jijo5wXCvzxYHYhYZDqWtFP2GUProF7TSmK9Z5vXbwF8NPxlriXhfrsowKidfv65/G9lMMqCcvwOfzwaFsuOA3T9Wjfbalo8GKhlLZXQV3hOVXbrVgud/sLIpYlwgJVIT33nG7v/HyBjAv2VxyjcXl71vkh73x+t94+XchBGhQQJlLrVZpaVODKEFVvZEKlmVB6TtjTu2zp8Svl2bONZtee4Prlew0BYDecRwn1jmP83NGHn2snXAWWOUFliJmjLOZudEsmrTRMj3ImED9j79+96VQjsMyAKwStKrqoib6E2YpokHHAda7WSKMv8FKOyaviAS1KVopdE1sFa3wMJxmYr/0Ui9jCA0FYkrIMQ6nUb5EQSkVMUbTJnh3kbCuN9xXDvA87zXGMCyUY4zmDCoDQx6bwMdqfuDapqUb62v1asPzVnGcJ9O2zmseMk0z+pGQ5xkxJwqKTMxUG33QFXRIdZ0C5f4Jnn4mYKVbSkO2oKFRQYxq1369VHhcUP1HR5/O0VRrOtgOrZ3otcEdHn052tnCdDpx2ZTNiyzJrnW9bA2sVXfOvMMKqqwat21DrRWnva88kcNdKw+GUopV3oIQMlJyPyoLjJeAVorZOFQcJwsN7YrldsPb2x3JuPgMGCEzyokFlyo5jE52XIAsxi67AzsMfKYRghjGTRZXreT311Log2PPm/GQGUtMA5qIKaGcBSmy83CtgK/Fy0vLulUl/q/LZNCA2oHgL/fljQ9It9ug9Xr2Xl221gDlRaRmt0KRYBzvGKqXgYUXRT8USFf36ew3v1BVxNx5XRHNXGNmbBc0hfmNcXbVzYyRlNVo1O4fld4KNZGpQkRx7CSgZE1ImcrxeZrsomtoJoGSFOApkaQgv9Dn/XFpGJ2WdxUQD0bi3u6WF6+wc2rf0VrFWSugPHOmmQXIsqxw37QQgHYQ1qSegP5OwWBSMWq6+10BQG8FtSmydV4+NwNeLy6MdeCfeUQH2LvoVpC1erlO/55fv79TsOHd6ArFGQd9YLJsT8M4iLzKbtqQxCt5b0mtojB4orWCs1arJmm9sO07hSjungj6zfAnv8zbyE3PtmFtUm+JXTDpfhBS+1KemMpmD9uxcOt6h7nZQMRsbjB+jSqCpeQIYBEvpul6uB3MfvAquJwHfv3733F2IEfBWRuWlDDnPNSzaUAvhCTIDIq2KYwiCQ6zY4h0fF1mq8ZNaPfDfSDj91XNdNDafYdIBq33bCObAqZd8Iqe+j0bYMJf3wtvXce1YZ5B6Tq8vJa15xOsEzleKMQxMItDTDzVLN82GkbvEJnTFVOkGE0AIAhar1BcMZOsCFmFL5kVF4z50hWmQ7ABooTx2Rwie3nV5PU5JBKujevW5K1VPLcHSmmDNola8PHb3/Hb54ZlWbFvO+I84bbeGP7e6g/WJSoukhQzXxN4vjdtMHjQldowTdcQ8odfCnj8g5snYtgh2MWgChWzui9mO68NEmUY5TlM+eoc4J3Aaxfh0FTvYzf88EvATgvQoUcRY7FJq9j2iiAkfZBjn7DezMl40M7peRRG52vdaOuQKWKZqQ1xQse0zGa9L5DOiybAOwNjnNmB/9op+GVty2mcKRQI8ieLgZYsx7bhPE/zfAucvX1+x7/97VcWfPVE04B1XehjVQs1C76voRBwtuNDaNFOZlpKZEgJ9VXLMo1627tYX6W+Vj0/obU+grYI1bJQTDmjnYfBXX8wfERsL2J8NK8a8SK5HsvDs29twbSObmEsfvj64eoV/nmc8INKQkQQ8prP2imuSglMQ7L0reKHhPGjoaSDtYvK1pWGZG+3FaUUzMvMdKbgrI2K0y8SFzLh+rl4wQw7q6t78E0Ctq/S7SWFaC2aYn8+UDTgvtLbHZ05vcFw6FZ2fDt21PWGrh3zsiKZSjeKGf9FMkXoqknfnhDcHoEK7tPsh0eF6wtI+/Bt4gZ33NY3tO9yZ0pdxmVdgRyYQCf2rrrKoO39uCquLsKrl2aeLxd8JeNAERE7+HVUUz60P43WN6WEQ6tBTPzMzbDhrh04T7NCb/AoVFU6VL69vUFbQ5oXzGYtACgkTIx5tY0UrXr0i4+fjZYasIPRe1v2iTLoi1aXoVn9084Tj+eB+9sboigqeGik7DnbiufnJ1rhQRjzhGUyozeDcLooLeRtXjVPXCcxTYQiwMKir8tLY34N/P3A8FmO1wdetHhH7nO9GKM5muqgOvvBDccoAECt45dxK+BV5NW6dYl6HTg+OA4SWPW6Vsno370RIuwSqDY/QLqlM6l8cA13uaUXGLVyl0fTvKxYp4iqYmFK7MZCCDj3jWu/m8uwh0HJtZedPPDDBWu3j2tSfA30ZucVOh6PB/Jyw23K2Lc6EICcIqpGHM8N32vBbCLKdV0woFfhvnQL9tYr8rzAGVVzpoNqbxWldkyqr1sbBkiMz6qq1FOMThyDBNB7B3rDj24H//Ovf2rQDKOfORsF3QZlZjYFVbZrxkC5cDNcB4R6lXYdYM4CYVUqdgvyBdIcjoO1AorbUgTQFdnfMpc/ZxRaEa31bLXg4/sHBMDtdsdkPiYxBDQA7WAbGCRiWu6IEvh1jDLoQ1ZnjLAyvTog73yaBu+50QwrjSFgPxodTCvx5z/99a+o5oHUcgaK+8cD576ZChuovSIF8qy10ze+tcrBlQUBic1sSjmhev+hraTxoNiiMXNBuSpJNlp90GC7vmDPACSaz0u/LkeBDSq9T7KTZ+Tb9ovx5d93DLRtaOxzn2JU0uG4ah89Z3rPNwggAbXRqK938uj9Xfuid++oHCKgDZ/fvwNQvN3fsNhA2WNTe604jQYpMSOmYILLMOzgvXocMJz6QBYWXAT0ZnkGgQSAHjtpyP1pFyd37Jef/ox7O/F4PNGnCS7+DSGg14JdKbqiQ64wW0I7WhOE0NA6odGc4rWfWidt18KVXlDx0am9bvzRDds7CSFA7PvV4iCQe3j54eM/uwkRbX34bwer/n1IfQ039YfLwtA91HJ18yaxZxdoPkatMZgrtobWA2jilhFiwtAu2RpSE6EKFNvjgzPFL2/4yRxk3bYC3YwoqyKYsaLY1xAYTCcXNHoRZQDPulYAoduzBiE+t/OOIaDVDp25r5bbG/66rPj4+IBqxjQbfBVZWG3bTgYiyMhMWTiALuwiamuYpsxwKCd2qF6uplaAenECfXEd7m6GqLxwur8HNz0MCJ7D/Tt//X5Fs1DgYWffOLQp9uhWbL1g0EHh0YDdhWywqtU/n7WTvR4/VOOEqhJuMY2YS1UgpozQ28UtF6pxGbhxPSQFcJ78/ZAoPJqX2YZeB5ptwOMscA/2KG7PQeZUgLWYXkW+Pgv7P4IAhD6k5s5nbpUZCmfbsD2eABQhZkxS8fw4UGpDyjNS5gs8j9OEPYJlnZEinRl7reig0tjalhHa4ZWUi56cbcKf4fqMKgJtXDDBePU8c00G76ZhwEDKnInhlFvPOxNgiI+C9wgKxBd2CH9PYQZIPMAxvimce+5wBBd1xLJEVLe2MOVmTskqUV4qpTKfe6xv5YC8mS0GL5KOaV7sEmnY9srM3vOAh6eEVyVrHC3uBY/IVZnpD7CJD2cJmfocKqUJt3VBOTdU77RyxL4dOM8TiAH3243un+Vk5dw6RBZME8WMXRmLGtMEgM8+WkfojLGOTtba60K0zz72pXKuk4WUUAShel2bscQuS4RxUASqwvn//X0ZqwgObYXxDgVqgkhbg4My6T2JjgO4VreU8dsiYl4TUikGHRHWoDK9Q6xLbj6A7mR+tcazgylt3LOtN+RpNiFcwP7cEHNCPU80BaEx6wYUBqMFf7/y8l5fHqiOJYBBubXZXo/N1uqKs1QSYgyiOQ/CSl0F83pjJ1QL7fGVReac55FmSLO/ZJ+FHXNK0QpsQoelnMby1R8urpcDiIWadgy1jVC7QOIJrTQEDt3/vl//BHz0YxsDXDAFfb/j1XIbrbKb/0rwwtUq6w4FxWRcWLvdiK13SGQ+AgUupw1y9VI0Km/YWoupfvvwMGmRVcRralGKcbR2PmvorUN6Q28c/IQ0ISarWoNF94Uw1LA/ts0vtDDALrZXqpgOaGZZVhzbA98fT6REnUczgd680KjvPA70jsGAOo8d0zIjDMgMA/OklUCjnD6L+ZnADorr8nIoYIz21bD+GK7DHBiHAisjZSfYfeCGcTgGD+0AUQJJAqfA+f81bfPlJ+NfX3mARBPP0Zqcg2ZFwLLe4S6jojIONs/NoCNlxXmeKLWMSgpVkM391ivhPCdM04x5ysbD71TKtmCsl4AknON4imAyvyuHJCCGyY+5i1+HapWyXO07XKWqSNOEUE/8+ut3NARMmRi0QhGQMM8TSjlQDsJKKSW0duIswBQCppgGLdYTv9wWYl4mUlGVsIIxPDkgRx3vZviW9m6GcN6h6+jah7W9D1ZH64gfoBRbyjzQ4BeCH6EsNJD4+27ApvbuYrZ8De3Di6i1jnlZMKtiP46xP72Q4CyhEK+vlbY1oz6u1ChAzVIckEC9wLKsLOjse3XtOE9qIGLKmJfJhKfUML0Or/37/xf2Eez5WsodvEvs3X4/Yg4RH7/9A4+jYJonFHM1VWV4F2LDsxbqWSx//jh2TLJgto7fRZMxyoghzpnD8mpeX8XsWsbz15duwS4xpjWml89vMyWbj5L38GMX+d/9+t3itVGZWqn5OqgBALEhjuBFEKWXYOPli4wH7pVJeTE4myaa140KzU7FHPMYSB4Hq22IQRASRn/ri8VpbDFN+Pr1HdlYKapAM/O1ozA7YTbsdxRONmSK/nVhVYMvJPNQ8Wox2XBoPBeRIbhbbiuigOyU47TPLKwCIGbYNdnQjcO1YztIQzxP+z0dpmgCMT0HLMFJAG3mtdPhgUDysoEpdOojnc3OFACsvCRGpEBPdod9MA7DHytn/nP0IEeMZegYpn3j68LB1RKPn5GVdsqZ/jg2WPaDJUb6QjVLX7sYP3G0xTHQriJl0npjol3Dl69fMM+TFQecOZznSQ1Da8jzDBEZlub+MwUfrI+fJwxe+6gaQxjhKmp/RoQzgpwSxDoGwLs/XiK1FhxnpaDudkMQOp62xs92HAe2fTfqaQVrbe+8yHMnVZmXRjnL6KZemgV+7sDKuBlp4/p5rCBSNcO9Pozm1A49+0EJR9hCEeA/cfr9Dxrt1fjb6hCRrW/A7L5rA9w2JcZR/XfPYXfsvjeUsxCWUYy95lb9ELK2XDgWAnB7/4p1ZjfVDep5vVhCTJjM6qK7f5q/S/GfQfF6OfkGGZ3gy75zyHKeJ4gAb1/eaYJoanwRyzoxOveyLITIakGpBa03lOPAtj1x7Af243wxpyT0Vo22nhMtbForzDC309UhOlUBjLnYm+/ZsZztkmXSoGeY/+GD5m7YlR+Iaivm4vH7ovAfkqyeUVHbBeEvZdAAW0Up9FNPOePYNh7YxzksDqD0Fy9W7YUYgNYRkhnmvRzGpPIppPOFzrcbVCsUGV0b84Wh1soX5GWlRF51LBza15JOJmMjvHTrXklfRyJfxJiXMKqRHU7CLz//hN++f+KwDZ/zxIFfrTjOAg/4UAXyTF41B6ukzgIB+3lijSvhHOXiR75C42sp6HOyz6rjPfyoRsZ4HwQB2Hmgqg1sTfth1XeIfrPwa7x2EBBxGys0dXqf3R6KobEArACwK6ScxWYFEaIFRzlxnIcdHgCEB+p5XiI2VSDbz9kMjhpcdyMXTClBQkYWdlApCMpJTnzrFH2FKVPtLPa8rasMPRiF+hWnx7g9WfRezxTXkkaQaPAegZfpp5/Q+m/Y9g0ATdJS4nPbtyfcRwcgGy6nDDUbjCAZCibRSQjmk6SQ1lAU/OwhmI8S6csiNs/xdao6KN7eYWJ0PmqZ1+4MwIO0a7C84auQEFE0IZzj9gywv+PvOsjFgqEyCxa5yWfLDAgecikKHvs5Oj+/hNrJYql1ziaDdohGy2S3TAsr/jj34XxOAEw5oNUTgkQyh4kctTf0JpfpnZ0htOQg1DL27Y+VgL1XP3de1z4vYboUmKX3MuNLLfj4fAKFc6ZlTQZpHji75UPY5TflBIBIAn2L2AnzslmQeodKR60KyROLTdD1OYrDuXoVfHaOBptjiVdv5vLsBqD8fv+pE/pvfv1+62w/AGnUY8CBHfIhjJa7d07oJagdRr5W7UoUBSEXQx9NBKOKH/4ZhgFWs87mxdNRy0WnbL1jmmcq/7qLSThIXGYurBQjFIJtf+JSYpsYDpS+O1zq3YpXBfzPK0nRD9VrQOqXoTMVuvHfGcE5oTZFREd+PlECsWOHMWqtSBMtf3unwVsQGCTGTTDNlO33SgWoqOBmVTFnL6ycVNtYyLygbLsazMDQFs9Jtq7GjsDmqlJ3qHR4AaTy+XylC+2VPbDcoToO1EB3VFsr3R+a2gXk79cgEdjFrGbJwMVPrr9XyTQM5CVba0FMGUlZTUWrgBTR5kXsHFQErRQ0IaYqIihGXUZr6KbOFbv8/NBxBpS+nvqv1WO43n8MkV2nXUwREXDqKxRTTtgPQlO2QwBtwDQzUCfQnbeD71UkmBAuj/jFehYc9rNTLEoq5jzFkRcgQYc+Bng5KEJEVDrJyqCR256pjWwVe/70A7O1/ALpsIAItlbIdNP2EtpDnMtgKTPpI2LBPWTv2/eTGwt6B1Aru/Vhl4HGw1CoOVEETBMpnQjsxqEUA3qWu6uJ9+OwA1SH8WBXCkB9bV17w6DPF9jodW9zHiIDfvUMcyjPl564weZ5girdfR+PJ1KKBuuZR5cmLIm02VoKoUTVoSb3jHsIKdrncQAQrCECHabczsxcqQ3IL7kKdmmrMjOiK59vGiwkv9+U508IQ3H+e379bvhoRBH6gWnICv3er7mBoNsNFX9o0/ACNSB4pu8FY4xWLsjYCOe5s4L0q9Eui24tH3FBwzEDYzfnmSrmKTFsRQC0g4rDIGLMBLOQVTWs9lIdD5xVrkHUj5eEjM/on4s3fRhzgGQq5WSVSkiZNtraGegDDEFbzpbJO02YciKrJZN5FHMGOisQsi7Ykn8+HvzZxBePDfTVFwppmF44+Dsabjlq+LiJuOhDRBM77xReoZXhiDueudeCpMsStvLH4aNKK7MlDHW7XxjcyzzIPKi+lBOn2QFc1adbJABu0dF7I+ZuvlLzlDHPGb2SNFDLYaHwfDfncVgyH0YmRwykKL4yv//LOx7vOgwvGgCj+uaQmdGpl/8R2SPrbWF1LYIUAofLjcK6XitUaOAmEBIHbGAtYqrjlEwp3LHtG7b9sG6FqmkJAe4oLDaXuw4DGe/VIQkvDHxfcV9wUsCDu2OYhvzwddJQfXPZKA9ag+5oTseNP2o+O7i884WYaR6EVPNIg8DzdAo6X5V3GzyIo+H5LAAZcjNhWWZ2ZcoCqrdiViz8aWpl2qLYIdhcuR0jLp7/9Q4dHnx1OfZZmbeEDiWHyKyUZKIzvmsgzwvp0c2iPntHLSb8VM60LkJCxmQQmKe4BYO7BbTI+Hw+0Gz+4wN4XgC8+4NB69zAwoH/i6aIm4UXNudHbdC/h6Hj//Dr918KhkcPKMU3TPAFKAMiouTfeehmHWC9hRUVP/wSEMZwa2THO6N5/vihNSpXMZqdyPDVB9xVlX+/lMOYSyeq3/LeLhv7IggdQUPgQZ4nWiCkl9xcVpD44aAIdtE5zudh797iMsDjSqtrTbHe7phzxLnvKK0bO4Y/fa8Vx34AwkuFmdLUVMQUTZRCB9GmhEIej0+c5oVPuMfhHbaq3GvXBhW7GGDvDaMa5ACu1Yqm3QRww4DXfj6MSno8E/YjHIa2l+AX4e/714efG8Cw9221odVuG5BzDacoslusFyssRgtFMuGSQZWOj0N4wbrTZqndjNPs0jK8PNqBRAt4QjPZZgEpu3r4ZU2+vG8eWtdqdRKCB7cMeCMS9ot5xv22Uvl6nObvz+cRAAbMdBemMa1vnmakmAh3NOLBtdDS5dg2PJ87s62dvq3XpRnGu8SohqPdFINl4wWOsXyOkyZzAOARjyIYWRjih6EXXsBYgwJYkSHsKP/Tc8PLuumtvBi7GSki0PKDlT0HxNphqvh+rfVK9b2LF9X2SjW1fTkL7fxFbI2IQTzR9qFFm0Y3qbT5k2kE1Cr7Vzo39wuugm+862j/4YGbIrvz3gXvb2/IEThMgJmzK9Xpw3TY+o4Wy7lMJBrkeYKgM1iqUMzJou8Txhm62HhgITUue7uIg18K8MLFz2f+c4yR88thJfM///r9lNSA62W/fIT4oqJ1rcI4EAwtUouIQ4B571+trh9qPCQEtZ7Y9p0ViW02NAz3VF8AtbFiRFfLfc1IOTNmUYByssXyKlmVbpCQiK6CbH5Dk2F3bvsGWLkW1IRqQ/rzw0MHCItclRUAFQazh4ButMcQAO0VHQHvb29oHfj4+ERYV0yZn7kHS+tKcVAqGRxD+iTl/d0eaMRkCV779sA8T+YZU9DajGyXMDeymGLW3p2L86ysc5gAYBTpx28bajP2jfj7uQ7G4LqEcUjw4I3i3COCSAraBjCqlNRIfglCI703IAjqUbAfh0FGGAerDyMVMCaNCbIA23QMTcnThBSD8bkvd0qHFeeUgN6gkdbBDValDZoiD8SuiuCw2SjDrIsYl9uFuasIYPGlMKUsNyg/ZymVNhtHxbdvv+JQUH07kYF2ntVmCZyLzXNCK2YDHhSlXHOvPE2QqIx13Isp9+1iyBniojL3PTLYAFbMDFW5/z4UMWfI/sRxnPz6r53SGCrbwNioxZ7XoGImawbnXlcA6BZhWyQEwo18rOandZBAUQ2Gcu5/6z7Ytk7d2HvBvKVyIltnShNU2ziQcp4g2hEjPYfO4wFIRA4AcI697et4GFf2zirbzioiBPLDofnasbqPFED1f+yKptU6gwqNGff7G1r7hu+PB+73G5Z5MUjrAGIcLMZ1XdFrteqdWe2u/O6qmMy2Zts2hmvZu256CRX986F1M6ZkNz8+u443AlXBFAXHsQ/d1f/065+YKVzMm4FJy6WSHCwGu73E5ge+oQLExDB2OAQThSkYMqPA8/M79v2w6vokHhadmmmOoJ1BJbMdcDB4I6WI86xs14yDHSKHgMkEX/M84dg3+/f0YIk2zIHI8FDiYdGtxRb/6V/uAzsehpCNKupqEnzCOQExAM2eEzoN2tZ1wfdf/47fvp24v33Be6Kf/X5QdJdn8+l3q4eYIJ2CJ1WySmSezNMp4Ng2LNM0DMVCSsYyskNM3H7E39lY9dfw1OITUwyox4ns79A6BNimELO8RteBUYrZI/Dc4TAv2CUBEwL5ulHtiIm00f35iX3faA4GRbUKEkJWUohxzDqmZbFDyz2zEnplBRpsCXsUqqfn5ZyhraLbTOQ4K+bbjCFMGpAaIZQuMoKgRnU9HpWMuQpUR4HiHUwMARoV6FZth4BaBfMyY9kS/uPX7zjPFe9vwO2+IFt1n5aF3QuAHgRBEqG2yOqwNUIgswn3aj1wlBl5dkrvZP2aHwrKS9AqyfBywMMGpfz8HZKCdWWG4/vXEFs59hxGngUUcOjQCommxKvRr6+tQs2PAmBm9ox9f2LfPIY2oJdifkm272JCEHMuwGwdre/BAAE7pxbauASjeScRsrVhfWOR+tye0JAHdCaQ4VigXgQg/BcE4HrfbmdigTu9wT3O3DPL14WEAJSGOE243W74/v0D334ruL+/4X7/gmnOqI0kgWjvWkNAtH0xTZl2/WYrLtME0YBoorc5L7bfFCm8uN6Kq5vtYhvnvVXi9osdN+H91v5gQzwPsIfVg35ICGxV6otfileTPBcIJYnz7PnBybHmwZtzxsf3D5wmZqndHAm1Dsy69YYgEes60WXSpPAhsrJn+8XFI4l4aDacHhIxzRNzUmNGaDuezxMi0dg9V0UVxCX718JwY6PhhQ6YZJ17oTV/+Do2U0oZQftQYQLUUUzzjD//9a/4t3//D2zPB5qFqdzXhYyEAZdwO5zngRgScqYHkG/SkLNFPHZs24F5mQ0quYaEPigTu6xGzqvaBod1dnoNlb3TG0wcolEXRGiwmTq8g3B57Rj8I2Y0Nyir4nYYQAoJvR3Y9sOoic2yCegJ5BGdQMC6rqz8G0NLEAgxlJPKZLSGZhtz5FZIwLwsQK2oCkwp4fH4ZMufEjyT2JXYDiMM1bot7tfLQfs1x1Jt0HopREUIQbpN9jzzgtbeME0T3r/+glIbvj8OPB4MhPr65R3QPuCgavumlh1u6RKhCNETC4PRnpkq1pcVOrldLWyt2HDfPq/vv/EOBlTif+WChUYHiPHDGx5tjDSl6C9KGH/ONRHdlMTwYbVvF5t3CDqe206c3GYL2WJ2ByNGBHmeMc0Tq+jGTPMlz1SinxTA1VYHs29ZZqQUaCwZFcdZMU0Zj8cnuvIQJsnEYCV/f7bOr+Lox4th+BPZ4zD3jQsqDgE5B/TAzPVaO85u2oQV+POf/4T/+Ns/cOwHevuG969fME8RvgNKIbGFcK1a1zshxAb3MXKWGXrDXg5MSx7wILfu1eWwG7hYYCOqE8D1sp0a/QdTUkc75e31+L78UNy4znbwMBGrYvwH8L8DHfAShA+oGo4MEMusvqh6sNQ3agkgAjUFa++KqGmwT3x4KxKwrCtutwXaO6ZMd8nzOJnEZhmoMXNA6NuBVDIOhiSw5et4qZpfnoU6nCDCKse6g+6LMAZEZbDPHcDnxweaGWOttzf88vXEt+eOnOngWRtpjbTH2C8nU9vR2rsN3DKcKUp4kxYP51mxLOMRAbCDemB7l7iI9Aa+ANvLVrFdeguID3uvg8J1J54r7elqwyhuQId26L7AMd2qSqqW3YzNrACGICkgCTDlycx2WQxUY25oA8KcsCwL5DyHr02eZtxvd0AbJEZEKM5WUDtwW8gpd2NEtzsGaE3eApAlDX2HilfM4/Qcy50/o7lfRhuLWiccAyB5wnrjI2u1ISWBzsDPP/+Cqr9CQoKoYt8PpBiw5AUd1NxwuMk11Rphw5xnzDmRp66ARLK/9vPAelvh2LIf7sB1+V70YYXNa+1d+4Df6L6wx6E/Ekn8sHFoF2reRXb8yHjHl2hTCRyOosOtsGEspIAIkTaKlihAXle7mBW9+DkAABUVgnma6BLbDQpOCffbDSnK0IGcJ/NJ0v2GFCPO0mymkJjBbXu7tw5J8TrLgJHR7R/bGYfiSIAjIxp+8JASAYsPcL4FENK9f/mK8yzYK9lA57FDekbMM0IgUcTjKYJ366FznpTzUK8DMKue09iIL2eP7Ss//MkotEt5KBivM01AFmaRP7hT8A+iDhV4662WvVpdl0AxGatJDDiG3jguxbb2KwrqcaI2IOeMx3O/Og5hSI4Xr16lnueBYtYIOZK3Xs9iNyQDqrXxIDn2g1Vor2g7F2hIXOgxT1QPxoSUqTocSVv20r06xMtB+XoxYPwzB1HclP4iwAPS6GfLPKFY16AK/PTLnyDxG5klvePz88MOyRmCjvPkwDRYpRjsn2Fbz+EjAS/gWk6oKYIdMuIj85kAfasuWhq3hIQObTq0A2FUfQ4b4Pr5YWaEZkQYfdPYs/JkL7W2nxXX+Cf0RmuDnNNl3dA7RIF5XfED5x6KUguO4xxkhlrrpYhWYJomqFIA5lbTS87Yj93U3hGlNJrKhYgciU+7n5APi71CFHu/bCJk3As8dC9IJTijKkSI0zGB8e+meUI+TlQbmq5vX/AXEXz/+ETtinLsOLTjbMDXW8ax76ZkJf5NcRa/V+vdhGvR3jVZLWepeBPfW965mIZDrxnP8KnTa9BIHr9d0nDY17vFa3bSu44YzUFtDZfodPSNaliQXgesaMdxHIANXrfjMJgZSNOM6eXgUnRo69j3g11RDoMVdZ4n/44Vb+U8sAdmcM/zinKepnVqiMcBtQ5zmhLylMxR2PaQkVR+oNXahxido7zube6tECMI3vfxU/t0PSaq1bf9ZIXfEv7817/g27dveD4PaGt4lhOqO/JffsZxbEw2THHEcr6yKBUBKUeD4HiulOMEvlx78FJge7Gu1++/vBsvKP39XWXtf//r94vXtJsNwdXHeNXYSrVvnMaCAgipxHgxkzybgBRFts77caD2RhO7ABTQcZDtUSBEoOTPl1KgHcbL5/emPJz/vvVOD6SutA0IMr5/TgG//eNXVP2O9XbH/bZgnmfkFBBCelksalWMc7R1/B67havNdBWhmrKQ6yoS1+3+4jjsSXnGlEmZu60TZxkC4Lff8NxPQIkhxjQB7cB2FNzf33FblnGwOiOErBAOWrXTaZPtdUMIE3onfn9VixetTaHDvE6CVSpQhACU7qwGHRAEgCty0XD0VhvcK9+l+uTtX3Mib1CiBIPFBNunidTAYflzPzDlmc/JLNhrJ55eSuH8gMT3wcBobpvQze0zACFNhOwybS9utxX/+PyOj8eOeVnx9vaGlCdMU4JbVPtlT3rpC4POIUCI0a79omUJ1v1i8KJIKBLyXahQhMiDoh8nlnVFSglzCuhd8fH5ia7MAzgbEFrG8/FAmhu+vH8ZkBXAgspZcCmn0bmKWGyquMHIJZzk6xaIihUnBnyICbBUWLT5Pu4+aMc4RLwAbN09pdRorwFXzWCdInzPcLsSFss4jydK5T7gMPuJPJGF1c1ivFYqs2uhGI/MKBNP2syqKezPJ+SJ1T+7AOYwLPMN5+dv+P79Gz4/M+7vb7itK7MVBD/sWdgBHyxQyxpqrmK/DASju4CqJSdexaIXRC/VIfI0c+ahEXmJCDqPhDxmbJw4K/DxkbHvOxoOvL9/Qc5haGm88PGZmJ9xYudo64psRn5NLVZzdDZWRtmeGF0tP+DLVXDBSf/dr3+iU9ALn4Spk5ua77ubylklYYtE4XfIdUB6uysiZnrVhsfL7f6GEHcTNZmHkAttSuFLDaSvnodZQBisknImvTCZdYJ97hAClnkB+s5Do1Qcx463tzvNyITCnK4RScIVfmE3t0Mqr53Bjw8G8EsDoN+MCFC1WhcdECJVo/O8QAJfaCkFKWcsyw29dXx8fiLmCdDL+nh/PoGuuL/dB0TjYjFK15tVmGIQnuVRxACpGAIyGa2mVxkKgKE4/qJ4iLjS9foPO7cOdMJ2xPxtliOB9agtzquruOBBidyMtEEnRNhaQ5pXvCtwFBPMdcInMUTUyoJAAtdBqYzprGa8GPOEOU+0HJjyyB4AAj2lTEOQJ2LXtQNfZvpJjUFjcBpphA9UR8j6y4Fhq9261RfHWRFWX6M4smLCNDg5T3BKbC0nFOwg1rbg+7dvhpkzlCWliF4LPj8/cLu/YcoyeOU9AMnEckgJc+bcxofEKfHQqkYu8ENuOGPKNQPkv7ycBbpekanjP93RAM57Ou0+jbmiJIvAIQxeSs2KDxdIRuGMr3XPPAl4f3/Htu3wOZcHzqBeh2LvnMGdZu+iShPMdZkhEpDnySirDSIZ0zRjShEtMpu5FNqJ/PR1Hp10b4qQeFHG5PvGKNQxvFwY136+1Nu2f6xLjMK95M86hIDYFT1GJq61zpjfsyJnaqaO48BZO+1NwIK2l4rn44HeO97uNyukzIHa1h4jZif4RqqlAjm9zHouRRDJPj47IoriF4qqWv39+6Aj4J+9FDqrfU9zIsWroTWzz/ZKC2K42Sv2zkUYIw8IScxP1n7hyxQCVfTOw75p4z609tipqBBi0WJYc8wT7veVfi/KTFwFMM8rQqJ9bascXqqxg/I0GyyTBlVL1Ti/wbfQRekUwY9sDr8eXzaSATImHsNQvf6gGREKa+p5IoSEec74/p0qzGmasCwTWqVVg4JD12PbUM6EZZmxOPMIAVHs5UsYykfT6xBuaXVczkHolHm1nsStTSEPt4PWbj+PdqhegeFuYnd54/OAYW7DZUUs8GG3jgIBkCEu62agGAIHjrVWlMFmgsFWnQldPmuAIKVA+C9OuN9uCOiojTYBjCOcOKQVDue7cgP3rsgTh5I0RDMVrr0r97VnoX/ZcuNFtPVf3rcVAjCWSkrRRHZxrIOUM+BdhOVLz8uC5+cHZwgpY1lmCBSpRJwutKwHPj8L0jTjdluHqC2YmK13t2ZXtF6R1LyYdNzHA8oahZwqJNCuokGGGWAAUF+ylRVhdBfaOrUBdhheB4F1BDLQSQS9NBgNsAurjS6E3VhEjHLNk3off7Ya0uBKd9fNdDEoKNFcsJwHWXJpBhQUBp6HKZe533J2Q0RjJxlVNzj0hpfOwWnP4cd62rvDK8ns+v0QE5JtGufeeL0ooaEH0Ml3mjCdVGSLcDjO2Wi2jp7f8fl4IKWM5XbDEpn86Dqj3oigpBRRe8PoYuUq+NgZElFxbY7qRZwgsYJsuz/c+6h1Btc4rUuAUQmMRSLEG7upNwUC0U5et3IFCWTIwbtx0B3Lo/FXBGoZ+DQN1Copn1ZpVmPnxJSQF4apMAydfH3izTOtAbqinjvOgwPkeV0R82witXhBB3boezt3pWw5zioDNvMSQkKAGJ1RXMFjJWYIcbhhQAJqkaGYbLVCboJ93xHDDcuU8aFPSARttWPEut5QW0dOHEDTNZaVk0QaVLZeEUNC64ZDmsunqv4Xt9KLEfYCEdnnlUDvmxhgh6YrnPvwgdKuJiYy6qZj8Z2XgO8QLwz8oHUU0C91dk+ML+xqqmrTmlTzwBriNaXNCX2YIpb1Rh2K6vAzKqqWnyE2S6KDLhlJM+aFoUQpXnRVfz4eTjIKATtZfejs7/uaN1CsxUd7cb5ZdWJs2BgaSiD8WKrXfoLSE263G3777RuaClqp7BbXG2KplutAmKnVglIy+e0pwr3HLg6/50vYepMLQvKPDmBAha4MF4gVcMHWr1moi0DsvwkxEvrxOFtemoapSxwXhShGRvtY/qrDXDEEMSJJQ4qJQ9MQKMIs1SpY60hbRTFh2jQv1KEIFdi9tiGIjabTqa1SxwIgTxPyPGOesxEKEtylgJ1IMgakd4O+Ty8mkndNY29DeYG+7CWHG/ne+b243rimqwjWG3AeFbgVzHPCUU7TywSkPOEeaMfiFvi9d8tWMFeEaP7SGu0ZZpuLwAKxruJWvYh5ee9eqHFvC6Kw8PVi9X/69fsvBWdIiNETg6CbO6dT/ZxD3HtDQh4H02jFHYwTlzV7FYPh9Kew6LtymJTdgmgCq69Wy2AEBfOLCejYKymmhBcS8pyxHwduKQPLhDQBaUpDzax6+aIEH4XaUPzqHLxGdIaFwyQOg5kfkFWW/ucJONgqC4qgF7NHtaM3HoZ5mqAVWG83zI8dAayeg1y0PXYv/Dwp0/2zngdKObGud+TAyjeIY+D8vmrVxmv5OA5pr/L8EHGYy8zaXEHqRUA3HJheOq545UvV3tAjkAY9wjusbhPbq9Tyg4MOmYa/hwDtJ/MvrIJUu1SDMWeYaeuqUhnqbz7TgHma0c4Dmjw3I2Nd82iZaeJuvYHh7cHBeH9j/aJWOhwm1vGOSwL2+y+UxoHN+2EiARIuq5HWOrqQYg0NWJYVy23F2Rg3mlMae6obpzQloadV4KD5+XggTwtut3xZP1iBASMPBPEq3xX+hCSuUlZfPq+/az6EWqvtVUEIZKz11i/mHeQ6MJWWJjo2iIyv+XJHDei49z5+LhUy2Mp5oPq+Vv++pleIVJ4LmKdxHhtaFzoA9I6cZwQ0PI+CvK6YUsQUZkj+f9p71+04jmNrcEdmVnU3AJKSKMmSj8838837P9kcWzcS6K7KS8yPHRFZDck2tZbnzwxKiyIINLqrMiPjumMHdYdkRmcFs2nNocRAnk5tpDnnCGFPbXu+zc/4zBBMeC8dKu61pISMwkYzk91lFUAveLg8oDa+9nRa2ZBYStCq50xqlKWQ82jfrtiQ8PTuyaI3o/r29KzttSrhyj1SwuYAeHTuG6De9fwvUuCvri82CtV5aUSgmYWP1hnykuPORYdS4QfL/hW+o2B6qmK55mBNBEL5+CxWpzIYzYqPUORESgSAIwz37QaRwgYRYZpAR8fW2lT8iayndbci8BCMrkBhaO5RThSkbK5zKACL2fh1mh65KWX3KP35M6hoXGFIAcZgD4TPQYYqejrj47ffovWOz88bUik4LwUcis737kMNp85DVgoH47y8PEPwwIH3Qg+e+dKZHpr5/omcmKkGjYI4gOCIGW2gCz8/DUUXQv6S1XemgR8g5LFDhAyQapEFACRhDlr8AFlLv6cJtHdjufU0nHWs+4zeRvACIcL03kavNjSpcLRqtoFJW8ey2KEWIAlloHtu1dAsigXFC43pUGi2fXcloCNMvxVQec/s2PaICzGr+Ug2RnHh+0TDkAjQgfdff43eO376+VcAgtP5jNYqkTFJgia7JHLjFDeEG1lXz+cz712MoypL9AqIKgSJs0LsXqbNsjXHlNuSEzaLzrKxC48x0PadXPxmECKdYu9w1/1rqagwDv49gc1ZHhF57EYVPR0sS6UaxbbY2eVnstbX2sBio1U5g4DTBsX6YwBBSQWqDXUMWCuq5djVomcaUY8CfaaEU3LfOUgW9bP2wXhwqPeEuCxbMd6q814UHr2jLAppAmhDenjEDz/8AEl/x7Y3ouCUDtdlvbBHxRp0EffLfX95fsb5fMaynmxtMRU9jMbEhG3Kom8492mEP0Zajv94R3NvHSNboW4MWnBkPJxP00MYgBpLImcMJ6YIxA+rd0Oa4Jgn1IcCMowRtUHBYuoiJDSrbY6tFONw3+tuuTpC3EpZaRRSRkrAtu1Yz9y4lJiPFkkoq21umiyZo3dk+13mPpMpbwDJabRpVAiCmUyuUeCxAzfjCP/blNkgd4ukaXQ0KxQD6/kBj49PUGHfRBLiskteSOHRq82PGEh5scPJ1Nynz5/x4ZuPDL9NEHxCniThKFAhR1SE9lCD0Nolxt4oYLPYvrMYXApUB+pWcds7LpeLj1uZew4c8uwO53yVTjBDwi7gYfxYpA1XY8UtC2mjOVjJwxnnE6roqnh4WJBSwcMjm5dSYmG61x2wOQlEavH5T+uK5vUvq1lwNnCxUN1YdFXRdI4aTdl4olTj2V4XH+2pDl6nPz+ndPvEORqVhKzKGg0SHh8fcKtkAnXIdi7cj8vlglp3tM4Od0/9MEK6QvKCy+PEqsMUrBtdl41ZO1LAx2oO1yy2NykjpWZdu17LGbjeNqyWyz8+q70hhipZgGEOhx4k3j1aY/psQyHacdu8WZGF0Jw5dIZ7nZGyIiXSktTGucUiBZfHR6xWEyJjqc047kQe5sQIaT2vEKMKCYjuGOgWdUua6RP2FUwEVs5eRFcaBN93aHjbvpZy0GFu/D269R4f73oeo2Ndz7icL8jLwFoIkaYjAwOJNOy1M3OQfHY6U2nX64bzw0NEaA5191aEqOM4N9JwUMBswqOR8Of8T6ePekMfBVkVve243naczg+I9noYindY+mW452yu27HTzlaUwsRD23vHvjN/3HozhAUgueBUmDZSEGZG5IK1xS8Fq3Cma07kwdFScFoXbLtaoZ4eWZKBX341vqA2MJJ3SgtkaDSFjdZik7M6fYBiCL0khtRqxUzzSvSgJ0OO+PkKvo51mRxNbkkSNDHsu5xP2NugcuwNq2G1e20sSEq1Lm5yxWdLgZVUoK2iDxbfRu9AXuDYNE/zRAJBvSieQ2FT7jWeWcdAF/OsR8fLywtkOfF9bDv5bLNWMbSbMZpU2/YqZEmksxAaKvSBfatB5eD1A0+vjN5Q+8D5xHtshdxCy1pswl+ygS0VYyjWdcHYrKNcBWVdUErCbz//DFlOyEqkjo6GMQSjC5gUJ2NnRAgWLUCt8A3M2gxFAD6LwL/pXriPojTPAIoRCqc7k60V+9bTGSU/s5lNiEgB1BhcV5zWFdVyxq23qPlQ4SgpxZcFx6FG1K0paggud2HUYt/spIa3T+bTsXAPbrcr2hCcYOlUhcm6+ZzJAQtWvzKvOcV6TOMkkpGlY9tIeDf6sLNNuVjWM2D59Lw+0HkZHasCeV1siJQhdnpFq4PzRvJAHVSOuSw4l4yXz7/i2oGHEzvgi/XTAMKZ7o7QaR3J06xARN+xPJ4/EsxC9OHyiPaPvp9zRmoNDYgZIjBdpJUTIjlfgeelK4dxSWqAwsAwdFQGBovStUINok+qCzdOxgghAs9VK6hLvLnO9ZAbmrthSf/i+nL0kUFEVRXXlxf0jjsliKGW2snhDVNm3TuZOS2na2Y1PwP7Trja4GESexAOh+gsQA52/clSsK4XTqFK2QqMhoIalm4yrzxZro6hE/DLTz9jHwnv3r9HMu8GikBDwKKc1tkwFIITTzpDNpcbWCOTd7eqhXhz0pNaUZa9Ao7EIAbbLf9AWU9YCukbkggaeNJcCDjcnHOIR29GCJhwNuy3jz10oQBoiByuyiLlcPscCt2F2/O7yboF1SO4bcNt73hYJIpuOhQcpmmhvsIG0GcTzqmBousSYuyUO27Xq9UPZsptWHqn3nwym+G11xXnQjw8oZtkiq1WpMwlYdsbBAllXazJD/jtl5/x28sN33x8h6U43bexzWaLqNTnC1COSG7CDZ7Rn2+2hBwIDj0gQBzYOKw661IpZ5ShhCj7WZGEh8sZL7cdOS+AOSElsw7CKDmbM0EyxAYqkJIztB3k3CLD7vtnaz3zPRNJFh4vaQbgU9q8x6f3hufrDafTwyEKgHWvp0lzod7YmQxgMeNCAil6wD+fn6+kr7G+GRHBAHmEet3mIJ3EKYSn5YIEkvapcQLVnQyikjPqtkES+bFW4y17+fwb/vHzb3h4/xXpXoZiToRTc1gyfE4Da4/usLpd9cjH8WNUommwuBv5G/XUjBlDdyTAjEAuCxQ2cY7Lj9Plgq1+gsoCQYOKcl766OiaDJFE0MgwdodcyNicIAasId1GWgqk+6hS9npJRIHmtNyl95RjkSXhCD75V9eXJZngHgdRMNfbFlZnWAt2t7BuwD1UU4yW3/K8ly8eFzkBg+/XekM1ZVdrIyTQvFYSWHH85MPlEQ+XcxBMlbIgZYmJXiklohoUBjcFbi/P+PWXX/D5+Rm1k29kWTjSL4ZP6PQCoEZsZwuq8UxGrBXPZALluVdfLHGDManEKXxWFMxsRppeJtM5p3UxYriMJWck4TCj1tiNrWbEiLZJkS7x8JdIrhFpF4dVeq5+isT0/Hxv3Tj6i6iQBq63F3jn9FDzBEa3GqYZ+5RiTm98ikUoTvVLimfgdr2R6qKS48b5jjzkdcWRlwWXy4UjDZfFqKAtvWUTvXIp0N7CSxMA++0Fv/72K6nF685I0ozFvZcP0LQdDPjB/ROZdSjxNcIsvL4+Xh4XRXHb0ypClJyTNHpEtq5k+GxjGE9PMgenGU00lVDKBUsm374I5zMwX9/u5ClF7QuHCGcaXAeJhFLr99xJgGK/3aKGx2ex11pIHDMXYhUV5qbSDUhiZ4qoo/12RY0udDY+snYgkYaCCFImAut8PmNdbJb6IHkiMFB7s2lrGugbgOmnXz/9ik/Pz6itQoVzR5ZCX9dTu55qG/H3iL2wG4f3SsQCAphjZF3xx8GGs8eGcbSzkG0gEM8d1y5gpZU06j5uVlWN4qZHM2RZS3Tax+gB9T4cPrvvNUcIwJzOOMx3EHieqX533v/d9SdYUikEdbuhdeCUHKGjGGDY6paIOsa1CxdwWLUc4MPUPhVdWGZx6JxGRyDsCKZlxfnhDIFir5zOlVPG7VY59MKI78Sw89t2g46Bq1FkE7MsEcaT+lcsSqRCLZhcIcnDNGd7iYyBAolRimCG72qyYq6XeZYJItND8mOac8Hou8Fvm+XZGW6XklBbB0dYJ5Ss2F5uGDbPWFMKmKDPkGU6zWbwWuouWd+COl/5vEEaAFJKxr4CGkrZH7a3ittWcTL+IBYMPcqY6QlRPyP2PsSack+TQI0BkgNiHITg62tNVIMKm1FkwsPlYsCAHQrFUgraxkJ8LguSihVmK+q+Y1MwRw7uTyoJqcFGONJoAXZQ+4AWM4QAcpyX6T26oZxooxFKeEYOFksorB9HZhrJlpoNc824l4i3d46vUhbcakUqRK6UdYEOnq+0WmNbTpA8cCrEufcxSMAHTug7w547kdvfx0bioATGsCE6ZhAierCvGT0O3G5XIJ8mBYl24vJU2Fxoxe3s0aQVZNWn2Qlh5GN01GZKXf0Mmwiq2mQ072AGThcag14r9jFQygqg4VobllywFmYU6rax7rgZHUYWc/JWTNLAabAprzaDQyfS6JjG1gE2Sdq58R6VQ1iHgzabMZFQt90V10HQSmswCniNRsOcC7DfiLrqFZJX5AS83HZczif0wT4tIg9zUPurKqOFWiGXB6qfJDyHSu3p22gmHD6r3fXqfP4vMwxfHCnAYFC324086abM3YINjKh+9/DIzDPS6U0wxARa5wS0nG3k3JiNQ/RCE5tQcsbD0xPevX+KMJm9EdmKxURa5Mxw7HrbUHvDy/WKVOiBRRQCDtNOQuua0vQm7CHjQE/vIKJwWNdPKAxE0engQbjwmCcSuXVVOL1AKMMYuykxAa43tvzv247b9QXdMOu9DyNGM48kZ5TlhFysadA+ulUbTZqyPY9aA6CHl35/I55DXwuN1Uz2bTNmxfnzDncE/NB1eMCtiplv9RSWkPJ8224YKljXEpFYcDmNgfW0ouSM8/kR7z+8x1KKpWQM4WaGt3c1IEDDy/MLWu24Xfne55UzCnobqFtHWc/kZxLWYMIwHJyQKZh+8E3p+z7a83r+/f6a7+f7ImEt+ROnLfcaRc6sFZEXqaLuzWaIXLHtNj2u+SB4V2Ts1Pdpba7IuzUERhAw5j7NE8iNcQiww74j/WW/21vFtlcc0WWBEsMAZHY/H59f9biQzHPvO8EhZVngnff+W+w5KTgvK06nCz58eIeH8xIABM6B97efA7SeP39GHd0oIxrOl1PsSe87aTBKhiojZ67HvFORmf6MzQ/58/N5kA3f34OMeKQehyhUBO/bU0qAzXk3D75b0yZnkVfcbhuuL1c4nUc1yhJ+NgviZbXeG3haeqC2Gp337rupzEKzbzhTwZjGQl/v27++/gT3UUdv9NKleEpgWJeqFS39g8chNPUQy1MZdEuwBWnZJLsinYL9XmL65+l8pjLqg2MWxSCDreJmzKytdahV69fzBaNVQGkosgyc1hOgDbdrQ7NB4ad1gRdho5VFYJQQDAcjksbBWLlyMM/wdeohwbxGgU07cpz0iPXoDvWBz4rGxCIr0R8ZA3u1Yme2AeXphBNDCBvpRyWUbc7B6AQB1E6yPx2e13TlPQ2ADw9/rdhUAQzy3rSdA9VdsagqtJkX4pGW51P9me3QLKa8BIotEGREmOSU0cAmxKES1BKXhyeid6yJyVNTOhTX69XueUdKgtvLC1ToRXo3tueaRQSfPv2GIoWpzsuZE81sLxzyyN13+KPA2fs9veFslnH6I+3g8pJCFkKduNKx6LDbOFnCL2e9JFmdad9uGCNju3Gt14V06LU2LOVkhXQ2Wiq47xBlFJgLbttuxsLuyT1GVwS+b0MBm9KKVz/rrWOgz/y+OWipdyCT2VUHIcaHtkQA9JRzlnjeut2iv0ghWErGtplhGEz/kf56xTkTFVhbmxh7HdhvN3ZaD5vRvt1IFXE5YW8N2fiyUi44Z8Ht5TO2Bmy3DfuSsS5ryKM7eB48+d66bN7RaMe+iuk3uEBb9sCiIpVIyU6FK3bGJNLjXuhO3vGvHS8vN6LlpEKV9UAy6nLgUF7I8QRlDaVEYb+g7hv66cz6lzuWYxqio5Pu3zs6dF96/QlIKlEDtTUseQ2hY4E3o1hBU135qxoOGGZwrXCWElq9odZhAskC5boUXDfSyp4vF/IPJUI4e5tCRsXJusO+E+a2rCvqvjP32CpGbUBeOPQ7Ew66bxv2WgEQ55yyBLNm5OZN2UXRyb16cTzz9P49MOCQbvdKZk49SYZafhW2P959qJ1dnBFFDC+I0Ths1xfAMNutNRvRWWxQSLLuYcd7AJCMkr0xjiiu08o5sCwS0lCp3cuR4whuCA4yM0ZH7Z3dxMjxOUPnmFTC/txDHTaxPW6LvRIQYAxsG1lse+tQFayn1XLNZDpdrL4iSgNA4j0lv5JS9vZ95wClskBGR90bpAj2Yb0QOmhIlLnm1juyEtrH5qwCn/PM+/M9nZ73TBfyAbxbO/lme+5Y3WFwxWMDlQ5UHXB+GsvHUz+ZsQEjJZEE7RWftw2l2JStxIliPkDGLzdOPp2L3minougPfK2PNQxldnACdCBpChdg/t/HYTr7LKOMNBRDCMsWjyZDofJeooaUBNoVOQHXnZxHw6YF5rIglx2jkrLhfFosOnGHyhiWLZLrvWGv5LrilL2Ebd8xlHXDMRRrYp1NVG2UZQekQDCMEYDFes+9x/hYk825JrBhN4foLjm01s62RdnHkaNOlTJJCF0JW1e5RU9ia+SRko6Bl+crB00ZeeNSnDGVUPiBAVWTU1Vr5GO6t1UCSpbssFOE8+lRqcNXna/r9dn+kuuLjQILWx5mmYJRjxYaJHN4NWRAxRUJD1myBYb6CDobnmML5kiaUmysZuZ4SsBYMc3Cc+BOQt03bLXG3GhtDXttxKy3ClWNUYx1dNJdgLj1nAS1VpycfdQLOiYszCm6w+ehJr/yuc5RiLYzQuPA1IZH0wrvhPVCnSBGJmLmKFNOkA74MCGPKIYqLusa3hxARVaWBcMoP1QEqRhKSoyczOipHV3kk+DgaQPvHLe9u0uZuJB3Ng0xBKYBGGMQiYEGhc2gsFGXnn4Ji2CeFom5bEhS53t0p08oC5YTD4MbK1XnbwEAQpJb3W1wvXfOciYzG/OYKivLCmBg3zbklNg1uixYl8LU2zjBm8m8s5P5dLEucFO5xygAAslWc3KggMu0pcSi8Gpyk6zxKCIGJQrGg1+f96uNCh2RnlDCaMOJMOcjJ2RlOglgyvRUzqYsvPErWX1IfNX4+wJLOVpNayiQpkFzJab+/h7JDkYFSIrRFdqpBIMmwxFrdiYF5BWyQQrWdErKGadkz3lhsdWaPscQe/8RTtYYijYqrlcvdtPDrtsOCGtFrTWLNBPqbTMEjxqzAcnyeh3Amdxg7gQ4ytCZfQ/HGt4VjogmlGvgPQNGPR/1TVPGKZEN2ae4DRsYBp0jSnNK6Hb+PV3KgUaF0yb7HNakyiiqtso1TUSbAYjIvHebN9EJUa7a4QBgdTTfIPlZEBtCyRztFuQLri+uKTiXvXtCGukjcrNTp/AguyIT62Zlq4AvPDuJh5LjxllSy3LC09MjlpLj9V5ECQsI4Hp9xu22YQw2/JxOJwybyASQTVVyRhbF8+dn7M6dY6RUS8mBVAFgh5YKzRygMBDHw8NfGOEh47i+7jwdE5ARttpwEitwSTLyOk+p2WvYyAKDwuqMilxhiuHdqdmxbxu69wnAaS6Moyg6wZNNEpOgcHABclqRSC8AscZOPNjNy3XvY5gHP/+bez2f2dMqho4wOB37XChDyCQA40Q6z9c7D4xGRLVfr3i5MhqUJITlYmCIM8YS651Lxu36gtvmufjEZsbTYkiPSAJaCnBGepEqAshrZGkRAT3u2GuXk5CbgwCYbLuDkXIKec/FUjumdARMl67GkLos2dBXKTxMH7YzugJJ6A0bZ1AcbvM8uAaUER4BJ36zaEWplLqGuXi158P4ppjqYF7cIKqjYxj+zjuE3WyqzDWYypXOV+8DrY2I0C6HjmSAvgNTrsM6noFeb/j8/GLpNbGhMxzJ6rWnvVYOot+ueL7e2A0vLEKzwY2/EzelYyJxDvs14cQSeznrQmpRtf1iQkT/8ZdHH+YkioFucikoOcEJHxlckvQyJ0FZT1AboOSKI5xsiywxFLVWiEeJ4RCKOVdunNm749kUT9uqran/rmd0/kyw8CciBTZ9hNcZ3qYbh1desHrZRM2r8PB5BAEXh1aTuKwaCielggRFbTu9VfM6RBLqfmNxOnGM5hgDmtSUPu+xj4E0NLpCpSYkWbEuK6GBFq55sxKta4dmKunsoSb3zPMg9gwTudC1ebqRqScpSJiemT90kgQkro+Y1kwZKGNwWJBJmXdTA4LTskA7C1SlFOytodeMcjoRyVBWLGKeJ5IpU/f66XH7GUgmMCQha2HQCS8ePKBuHPzAN4MO2kp4VMEPsP0AwqNy5SKgoDrhmILNV7AUIMcPrlFMT1mQpPDfFv346NbeKm67cRxZQ5+mjK6TZG30AbWvayMxY+vkQlqWYmNMCwJRYo5M6x1n64Pw1KFttq9gKHkIh873QOuykKkpQfocj8ptNONrRs3pX4qhi8IxCO4hMaK+Bh0dUgpghH45nwwBx2Hvfj88fjZbecYuttcZSegpSseUQ1X28mTzJu188uK6Jwhy4bP3w+jW1juJ20D6d1Y8DGais3kRsHVSvmdtnfUPkAVVeYNE0jRGsw4Y0cGOZzUD70SW6B1SMuCMrWYE1SjiW+s4nU5YT4sRXJZoTBNLp8BYRnKeUYL3kHhkoOJOlTlhw4kgbc2TUYeMuX4eeQ6l2+uoupyLFZadewxRQwLYa8OZzxbxtEowzcOK1jvW89mQh4Yygg3J8ZjesxmwqM31mBlktfPfLJMzC9X3fuy/ur7cKPSGIsbECXYgl0ROmt46xuLpFBNE0BvQdAzNAETud7ZdO67Z8/I+urHWPYpfJQ1LC5AdUkfnvOdKrhgMEzKARaCyxAQrsi6eOG8hyx1Kw6WDin26AiE4JjxwvWEt4+wkNIhbsi1LGTLMgx387ZQJTU2DfEJqhSFZF4Mx8hBS2SULLxPGtqMmQV5XjFZx7R2nyzm6FUspGAOWKzav0m7UUTZQInVG9UNu4TpY9GqtQ6xzmhGE9VUY71TOrANxgLyP6iQ3VfFis95HVmyKMvy8aAxQKZkw4NY670kVTq3A+7ZuV1WIsWnmvITH19ntRHbJnNB2KiA2IQJLKaTwTuxoPq0r8mLRY+dBJ3OkIqUlGFK53Y4zv08RQD2XLPCZIN4v4K8puAcLsNBovEt9wpGXxVI+ehgAL852qtj2jR3JOnC9vgBlwSUdcPIwUspDLpmKKU+j5K8/7I2rAvYKTJqa+ccnEnKvPfUzRrJ8dccwsjYfRcdUFM+qCGtJKQn6XjlzWwqWooRephINlO5AQj1yNFK3SAuxztBaRy6CVhlBtUGngetL6CYGZbuUYlEIjX9rHesCKOgwkPbG+g/EHTumhfTg6CkUkyBR4OSa9pQGZy/QNMJJTWZgGOnxDOtQrMtCtgIDRjQzGtlqAbU1tFRQkqLtO1QTHh8e2Yuj1kI5GGkVOHEhUVuzYOzMuAZ4GZjoqt4xWqNhNOSTmlH7kutPGIVhCmlg9IYxCgtJIpDBVIPgMFdBmSKScaCaTQnN2U+VRFJ7JUlWNo+ThcEakQPE+GmcjiAlYHRstVsb+A3N0kM+TjMVuguEdnHYRSkpwrZeN2wWbSiAJduwlWR0ufAmJLGZBaBF8547C/NdSB3Z42kkRzvENDdvPhHYsHZAOqFlI2cb0bnTcywLliVDbpZKMQPQAh5IQYyaQBfsnWvBnoiEU5r8+rPHdeZFTe4t9SeG6hmzqad7PYG5c45v7BiJqQ3PBat72GopqdSRJEcaA4NFwz46cinotdqBKpbWoJfadnIbuSfFt53Fvtu2IZclBpjQmUg2YMaiPmNMPZ8vER0kBXQ07HWw8C5qXfD5zgEY2k2xca2Ylp2znAEYn9J0Jqbzw/XMHkXaWkA15MDTYUkEixeUAZSFE7ZKEttfQ8wNJepkKPsUZMI6e9uhebH+FEAKZ5dHdiTcwekaSgK02chVNwrxKqaJck8Q6ZE2SprpbAyfd6IQMLc/dEBGgqZ5BkSA27bbBDXzujvPBileBtpeud/Od1VY42u73askaK2QXADtHM9pjlKxATP0nhltn84XNgGKWKGfn7GuJ1L3S4rJdceZ4RNizKhrjBQ1TzUvUDxtaiwLsbYK6wuhrMy6wKxHqtUWcykoZnSzETuWZUHd2KGdcsGyDIgRYOaymJEfbNIbglqBZXE2AmZEVJjanEg6T+yJ7zx6H8jpYBTuheNfXn9qnsLoNArdZiCXXABJ6OjGIQRGAJZG8nxWMhbAlBO2bbMBOsPSJolt+wY/2/bbzInBGlIguO07IWKgATidVtT9htu2I6cCFKAUhpLDWsxLKVjLirUsSFKI9Kgd5yUZB0p+VXjUiGjUZcAVgAKqr+Fr9kJ4EXTCGQUG3Rycp+uph25cNgoNhlCnvUjmSSynR7xHxnWrRqGcIJ4uUApyyifU+oK+0yDUvaKsC5ynRocax7+lTQwvL/YsNBINrUuk/zzMhHWfDknoqQcCyvPArVP5z4PlipKoK4DKdzTCE9WIeHJK0JyIYuqNo1i7d5QzdQAhOq21TiUB1hJGa6RCkRT0yqfzGVCDumYq2GXJNhms49PnK54eH+GTAUvJ8IL1UJsjHTh/c2SGD0FnZ7hAyO9zaBMNpWI1G68VsdBZSTNhBkmtVtA76x3wxi8hx39ZVnz19Vd4fn5BTmQutSzjlD/eBbZt40jLUTFKwWJGe6ZqU0QSLr8zp2g5flgkCE/5MipsQgfO97qo5/obdCmHlNOsMalRX1jVC5shj1SNhbVktE59sW83bLXFjTklyhgDWyV1t0IhZUHuHS9XcjAtxmC8nE5YMmcW+P77SNskCZ8/fcLp8QkZhHzPxrn7rMCB3QJO7652tgNN5Y8al0Rk5lUnGgRLKUFtIptGVFRrdQ1i60GD8OGrr7FcSRO0+qxymfXAho7TkrFtN/Q2sK4F20anxnuAxlCbaSLQZv1dJn8A77O3GnM4Zgf3l11/ghCvo5mHuYNWsI8OH29Hz9IX2KyvWTDf8JyAvdZo46dzmADlCL5t2yynnCLsZjv+IL+NLUTvaggkjjkU6/INdsvEXOWwan3rnM/w+fNnnB6ekNMFl5NRDmuiEVLrpk48gI5fVlOo2QZeHKK3EJa7nDImpK3W3RSlw52pDJNmQ06pzangkJBcFqzLivO2cb5AZ91CoDHAXDDQ6oj7I6V5gqCh6cDD6WSCM6DWxBMpBxOcQ1YhEEFjKH9mRWhHhOnOcadLV+SsIISxYzWSOraWeB+HpY4GveCX3catmsfpuO5adxbKOxWz479H70iZDLcuwqN31Lrhtlcq9jRHaPrISklAwkBtDaU2NFS8XJ+hIOvs5fIIwKLdcAAs+BMrLZsHGqlPyFQq3Fzusc4IadYixFBOjvTiO/QxKVLUIzUBp3CVBafTGefzGdu+4/HpvfEwcUztVhskScjAurCGtu8NSxbstSE9PJDV0/uCBIZG8TSXIZOMDc8Rf91QblQWBHwEfh+CsS5Wb+LMbF8RNwZwBS7kMYIs0LYbbH0QV28y1tqOutvzWB6e4zs5CXHfb9xnVSTpfO22WX4+BVSzWKLdJ7jt+850S874/Nuv2GrDO0m4XB5Iu2M0IIhU8azpeLDsB3nSWdj5EN9uCesghxwyJxCO+DkzNpxACRU4Q+5Q1v8eHwta67icT5zx8nCxkcMAMLBtG29FWX9J6YwEa1otQG/ci9XG9Y4xzCBaWlOYafGeIrtLA4sYlcsB2fjvrj9nFGB54MW9S2fSZOFr9VGHtglDBUkWfm11gNYMRmX58NE79rpjryR5KrmYt9ptfgI7OleDcLXajMlRsRTjxEnOgZTM8+YQi23bgb3i+mwFm9bR9BkAG6V6a9CcsRgnPcQhi0Zqlmb+mLUBUwwGPfQQXY7CA4eXanieE5NtDTnWpZtSJjLBeFC6dTv2fjQ+Ccu64OmJBbrRB1p7wW3bqJiH4nq9sYFp7KjLiksyPLM1+fDASxwIk0DQU/MZ2RYGCywf3iK6W8dquXBSe1CJnMz74H8DHSx7UzYkcWoeP89TcgNb3bDvDV0HG7IsQql1p0PQOspS2JXcqjW+NayLoVdUTTF4Ljth1IrrvqE2xe3lZRYMpeE5JZTljNEaumRILlEsdIig10ViQPuhocmNhjcLcVDM9MjUPGGeaXtOwEgaDxGakPCNNTmu93a7YvRk9SEFFqZUlodHqNXe2qa4bhugjAJbvaIPwra3bcP5fDFKmMXmZiNSV0mADk/NKNQaasiCqyEH3QxHFyCVBd1y/0mSIQWNjl3VamWE2pJnrGM5r2jbbqgwfv7o7C3ZN6YPF4NXj9FtXsYIeCbZXiquLxuh5cuCxbILknJQTUsSaO142W7s+L9d2bSoRIZ9fn6GpIx3Ojj8y+pY3mskTl8RKaIUhXI3ckzvjujJUAd8gc5ZtYK3F6h9z5udnz5oUcqyWmRKaK+kG41ELnTElDDkdVnw+PgOfXT0VvH582ds24aUBG1U1BdyY23bhuV0tkh9BL/TNFbukLtPYogm00GvJ7T9q+vP9SloQkcDjINljOkNkXenRPoCypyupGw55RPGaLC2CqjVJvZ9Z1OZFQkpII10GmABcfQWKavaOufXnk4ogoCcqkUYrZGF81YbZ9D2QW+jeMMH5zZs28auZoMpO3lVF8DZTD0sI//LIfhSUhD45QbFLbSnY+iFG5Ut1ObwMmcvOXPc4LGr03KlKWdujCEf2MjEpi8RwePlhP/5xz9w3fiM/BxOKrvddjw9cp0uh7TWvMTqBxJR2hgDoxlVibJbttWKIQ1SVlzGHHokohB1Kg1SdbiyRDH8vtVmvGt6GH5623drYPNUR4KCDVi1dZSycPDO6IZIIg/O5fEJa840HH3m63U0XK9XXG8bUspsfALTUKt1NvfesG831H6h0sxUaqN3Ipd0NjapYflFNIzyxHT5YbPiuqWBunluAkY1hBkTU+9kiJIzSjElMQZqGwa1zEipYCms/aiAjL4JABJOJ0F6esSvP/0Dv77cbIiSsnYkYqnYjtoqTnphYkM8LLV7jpwg4NxF3XostHf00ZjiS3Tc1tNkHE7gW7XWUIKexNEugiTkMcopYR/D3o/r0SqjRJ/RPFSRVM0JbOQCst6SZjxnuax4d3nEuhRGiK2ac8asw/PzM7bbzSpkhKimUnBaT+yVsBTZ9babQs5xPlng9gFQlt5LA+wwNWfI0ow8JRHwWVLgMH1QjJUWlqoxNlcOPqKRL9Y/1GqFWrrV+4xEBEXVRoZSoS9pRU6POK0F//P3n9DUdQ7PTh/MlrTaUJY+A1JMZxSiQeTpX3uKawzFQWX9y+vLjUKt0MbFWU4XKolBEi6ohmcJKFqvWLCwEKW8ufVEFAyVL4Vlv20xxJojFhOAgl475yMIUPeNdYNSsJ4veFpJGpVtiMX58gCAyrvvG377jXxB2gc0c3h3bZ3zW5cCSWQ6rTs90xIhlUU3w6BgRj+bdDbrEHFhltmcMmdJdE+jt2qNZUxHEadfolibc+bnWtEpC6I+0zEgOCHlglZbGKVZPmKUImXBt1D83//zd/ThMMiOOgaKVNTakXNFV8SMCBdgwNJ7DMdCaR8Lza037HsFoDg5xM69XgUkmdJIpPGGFHphxoyb0mL9FRrNcnXfGfmpotUNA4ICQBvpjJclodcd1+vGGRrrGe/enYIxUlRxfnjg5+cE7Q0///R37K2FMc5WxEtpZe5dGHlCFbU25mUPB4MTtbh+YoRvyQyCT2VjpODKgzlo4Zmz3HEL9NZQBQy63Tu7dCEwNt+CUjL2fYt8+xBwNnWrKAaqkINSl8TP/+qbb9D17/j0+RpjPmsl389WK5aF6YnTMrvPJ4LKUol2v5Oaxs5i4zS8pkBeTpSNbmlEy5/3NoBF4AyweVi3vioRd3DSOWtQ7Dt2ry+MxjkhpRBZ1Dsbt2rl4B0knM8nnM8XK6qnyMtzjgQjq99+/WkikIZ3+qplF+j05UQ+MzawXqaL7yltFasXgZGTGh+WODDE6VvsvPg62td7rZYBsKmJdgYcIZkkcWqeGYXRq0UNPENnSVZbKjQ2Fn3x/VlXOD+8w7dfK/7+0z/QDeFUjYmh7ztaP1O/uLGy9zmwUrI9xZB9zpD6/4pRqHU3xMTJUCvH4ShqVrTHrPouRFHABtNkEdTZSYK237DXGnl3GIfPZhGC9o7r9QqkjKd37/HwYNQXIjadzGe6WjPY6Li9vCCXgufnF1yv5HF39I/nDoulHspC7nJZ1zg8bPgRBLG+fd+LUTL4dTocOB+yAw+vOyGHCC+Zh2T0ToK+knE6l0gfYRBSS9VvdMiVO6PexKQcDkMlRU9nPT/g22+/w08//YTb3ixtNSAYeHl5RinE/5fFUhOe/zaPSUN4XEFYLlgZyu87ET9q0WDXjqEleju8b2UMJV7fUGdjKBajt3b7yZ4D5parYa6XpaBuO1pnUf7l+oKhgqd3H/BwOQeCKFszWEoleHMEJOsrS8Hz8zOuL1fcrJkvZfZlqAKpZOOl9wI8wtPX8LQQxUffbs+fi6cZYkoXECpDYCg8I66zVGpvhNUiFaPEnr0QAkazqQA6BE0E2mbh1AEN3ozmhGq5LPjq648o5Rf8/MtnqFJZiAi26xVLLrg89Hg+v7zhLFgygd8ZhT4a6s755rnowbMcGOZEsJO22DlSjFyMQkSRl2zyBItSBrYbHYDdiq3LshAxuO+AKm63DbUPPD29w+PjA/meDMtPepuMxSJ7pIS278g54XJ5xMvLC67bDdV4wao1c9HQLgY7T8T8r8WcH6t3Cea+IgoHiANvhmF42oWiwATp8DShpROHdRibL3w6n4yJ2d4ncZhT8YJ3IL/I7urQ8NYqosk1UdbOj0/4NiX89NM/cNsrSskAMkbb8fz8grKcrD6GoNqAMonrVCXqkXq3bE6fkwX/3fXFRmGvlY02eqAs8JRCYjhVa0MRwUgcP8g00kBKpCHo4GK1VrHbzOVtbyjrinXJaLWi986aQVdcnt7hm68+0OMrFlZrQl7IR+4eJITjJvOy4vIgWNczHh+ZluJZHvj8fEXKGefLA04nG1Yy6P7r6DH3QUJEHIJIb1Ktk9aQ6FOBiO8Ji2e32w0wzDXGzOkt5zNhkdabQfZL0EsW4tOT2t9WG+Gkro6ENbwwr3tACk7nC7777jv88tNP+PRyZau/EqXy/HLFu3dPgHUNHxXF8JyoKT/thyZEVfSd6SfmP8k75ZGhGLqot4omxu8j6aCMxNhsbZh8IxeTWo1HcuYA80Z46nZ9Qe2K0+UR33z9lSkIO0wQmzPgEEBESkhSwrqckN8VXM4X8uKAee6Xz58wJONie70uxQy+wpuC2Xjneznz6+5Zc1uNO8d/5rUjU7Db7YbawaHrEe4L5fB8is754c4GBjH24NAdkWE0H2pGgYc6G2swZSRDckIpinfvv0HOBT/9/AuGZmQBRq14fv6Mh8cnjIun1Ubk9tUZBgD2F3i04LWFRuW9rtngqMOKpiOaEElQWDm0aSkT4qicHT6MqFJHtwjBOu414XxhSrDVzqH0W8NyesAP332Dy3nFUOL6YVHHUgocygsgEDelLDg/kJXgodKZID38hueXDefLAx4uF5RSot7EwvEBUWgONXfTEGUGZc2QmE53lyq2rW91x75X5KVEaomKPONyOUUPgtNL0HllM2rvDdKZFs6qJF4UytmSTnBoq5gDgqw4XR7w7fcZv/70Mz5fb1jWBUMHbtsVp9sj+lNHXhNUfa6GAWVgTXvmIIzBHh0OZfoPF5r3usOx7qN3NMPfDi02f4spgpETUuEEqQFC2ZZlsUiiWf9Bt5GXRN0oBq7P7EDetw2SF3z33bf4+sM761zugDBnLlJiqIyHmMNSUCkVZMvbLsuCtXU8PD1hSYLrjVHHaV3hfK3ejl7KLPZGCA8AB+V/tLLHXB5fBfMc3aOoGKbMTpcLO7DN86VH3wEbxagWYbRG4SXlBTHjnEHNVJJIjv4A6hgxz/mMj999h/PnT/j519/Qu7ChsDdcbzecz2fkwsFDGk+jU7mpWupj0hR7CqwvS+QlW29YemEXsqU81Atn2bpgFTw0Six6rZ3RxxjoCizrAgC4vbywgaduGJLxzXcf8fHrD8wlm0B7AW0pJTw1h3Fx3zLWMxXqshTspWK9PODhtOJ2+wp9AKfTydYUBjXNFl3meH+fkew7q+7NyzQArgWoaD2daDWoxm7kUoqN2cymIOgx6hhIMKSUEt7ajZZFugSsciiwLCebOzGoPIY135mhkCR4ev8V1tMZv/zMCDHnBWkM7NsNrV1wWjJTWmHKXIFbqlANhWIejRriJWfbp9ajfqTZjNUYlpbLVgMbwIlp05wE7dY52MpYWHozIjwA2/VGyufWUYfgq4/f45tvvsblREcH0KjP5LLMmcNC+HQHYc55ySgLoKNh2xOQVrx7eqCs1o68LDZnxA1JMsi8RFTle4K7/U4BJvBo0BP2d7UlZXNl12HQ5wW5HIg77fc1CadCmgyPTkdKUocoWXzd4MpCiK1IRkoj0nuS2Dme0gXffr/g8ttv+PnX36B5gQozD7dtx7o8oCRBbcdjLfDtMw2yAAAy8klEQVQis1N56910vH9//amaQpaMUZoNijCjYApZgCD5EiM5cyRHzoK2VQtTmU9NQmOxG5KgtYbaO95/+AbffvwGD5cz83wpWwEHdsAtDxfPaJwzmgAbhA472OuJw1p0NCA9IgmZR+lBjSgQF5tclcQpjafk+GZ7tKn2uxP8helxe7pIBcuJs3ZJC25mxXDlR7oMQmc7uX1sXB/pLbI9c8K6rlabMIx95+nLuSAnKruvvl6xrit++vlnVOvs/vz5BU9P77CWDGg95kf8TIRhAGxtBztna2tYzRh0SxeSIXVGOr0zxeEGJSVAUkHvFXu9kayuLEiZdCZJOe+iVTa1Pbz7gO+++xZPDw92cJM1QzECSVaghPVcDAXZbTXb1/RkS07IZcX5gaMc19M50oyRVhN6/ylnm1pnBUzr/o3Nhh6KMAfUke2hmkEd6lBE8nZNtldLDSRP/U0aDecHQhfkwnv3CXJiXl5ZFqbjRrfcpPH35IxF2ORE7qQVP//8D3x6vmFNJ2zXZ1y3JyzljJxJn3CfRJYwbvGl1fdqrSiLM9faH2M/Dhro0RHWS5WDfoSd8XvdUWtDXgtyc0oHxfXlSgh6qyjnR/ztL9/j/dNjUKU7mowMBkxDOQ9U1BQAOBkgzx+98/V0xpITGtQawzhlUVXRI1cvUacICpJ5bE2HMFsAP+dD74NCeMRmXcWp4HQ6Yynegc1ok0OubE65/Z73KaUsWGQN8EGx+xPJOK02StfWmVkLOoNQAkg+fPMRy7riHz/9BFWmkT4/P+Pp4cxoadsjtUlfx/SjZQN8+/Xu8P/z60/UFCpyKvQgG8NLZ2FMXTHEGzdswYHIp6UEbJUF0Og9gGC7XTk1a2e/wXd/+Su++/YjTkuGw8hyIj47FcM49x4wT0+/OKV08NioIqdi3oNgWM1vRgEKZTXQLPlqRaoDHDG8hSOUU2apIWTH/O9BITxdLliRyL2Ts4WJ1kmbElEfalTkdjA87SEQHgKnCc+GqjIoXY+v1dJITKtkQ4s8vf8KpWT89PNvGKOj7Ru2vWLJK7JYDhsz6jFTEJGOf3+0hloZLZRWDPlFL7LbOsG9D9vvlFdGa5lRyb5XWxfuV68btp2oMlXBh49/wQ9/+RYPpzVOqR8ygM/F0NdzwFQQkgSyUKlEiiFnrMIxiKo9oIfeN5IsmmQxHViNlnqmpJxTiHlez/YkXxFzQsT2ziOqspzwlBZ2rlq9wxugstFPpAT00QzAYM5SThA1riGTj2zU554yq8ocsCQgZUQvDpA5A/mS8bF8j1J+xq2SwPDl5QXvHk6T1oE5jglNjUvsLLAQWWtFaewU7+bsHWsLjpCJqAnsF3FFRjgx4OifMRquLzfCiXvHw7tv8Ne//gXvHi6huFPOkQ4tmWkeJ/Tl3ppMlGJjOadNWxbTLQCKR3SHCD+NERj9c+F6cGSpBJGfqs7ngjucvj4HpWqpt5QyHh8fWXsJ2bE0kZ1zFVhjZrczQjRTAoDMfEof7c5zZ59OIp1+Yu1MAKtFFaTWoEnx7sNXyCXjt0/P4BCuG9oYbO5L/ux2Jty58fOdCJjpXxgs/ClIak0VrSzWEGb0156fVFOXtrAiHLBB8KF1O7qgKREubCh7hpQTfvzb3/DtNx9wXhdAmJf2AllQDuucXgUoxhDjj5nkZe7Z55ImvUbO5BIxj9z7CpzQqyyLFTOnonHrpgqDu00BCSHyRXfoqiGFqhcbgYiUUs6HATyKZukIteEjfI4Ep3dm2OdePKcJx9xdIX+/K7VSMqQLhgguj+/wfVnx+dMn7K3j5XrDw5mKC/seEU90ccMPlHmxMPqJWtFqRS0FrXaMtc+C9GD+M8d7uANAr/35eTdWXRs43jtabXh+/ozagb/8+Df88JePOJ/WGC3q68tzepjJkWaqMIr8Jl8kGjAlXEjxnTNTVK1WHjLxkHrep1NHcPfcG7Rt95jA9tplxPPcDvtMUpAWwRi7yX82JS4R8RD2yjQRIwyDBspsuCQNtzGYDt6TM70mDKg6f49rrYSycDDLspzw9Tcfcb2+4PPzC7bbFb1/hZyY1rEHooxiroEDJRSMolqr7IjPmVxVB0g1DWlCCe9ZyEKcBKlktEoa+x7cOt5v84LnW8W33/2Av/74Ax4eTuzMzzmQWyxSUEN0rdQV2dI/Mju0Id4kSqPptRqVhFJsAp0a9xSPBzIYSfteUzPIlCVMMIFanU3slxNcztiDIKJASvaMnNUAgSEDUxgAJIH0GY772FX4XqcOGcka6kYAavhezmqQKP/mmOZc4PxgD0/vsKwn/Pbrr6i1Mn14JtKp7h4tzH4q18MOYPjSFNKfSh8JEtZlwRJzha1TbrCxKR0gHSIJYsVgtRC1toZ92wEAtW54eX5BWk/48a9/w7cfv8Lq3nW2TlVTKtGMZAKBULhTwSHd19azYftVPcJgEdw9bR9HmYrPBhgY1pMg8RpTyIOohHxwkGcorjOSOGQe1Aj/KKgjcoWAFcWdFE7F0AgS3Ese1nqaTNW9NQtHI+pxQ2xQOwhECuQseJ8Tfv75F+zbDcATZgP/oXN3Rspx8wqLFPYd+7pyfoMzRBrT5hgdxaCRlF1BSYXyL/QcR+uo+w11UEm8XD+jIeGHv/6Iv3z/Lc4nIrEIGXUK5fCrrF7B9Yr0HWY0c+xMTcZxo9btPHqHGkINITtcAZ8/HPh9X8NDGg1I0ISIKqF6PGbhEIyDHCSTW8AhoBROtdRAVkKzsxlPcYMlAIwIT01ZJ1OKCmCIgwvmzF1BIquvKgQLHp/eAaPjeauog6iUbOchooQZ4vhjmNHrhvffCW21QrMPVEpWWxhQ9h15LcuioXZjvWCvO/ad9cLryxXXveO773/Ejz/8BQ8PJKOM1OPwrvoUBVdJLJybW36ne1L8mwqebLD2ukHoeZJkfRWGqgO97UnBMqAggWeMGVUFZETTpa2ImYKZdklmuDlsizDUHOmsgT5snIVFBjl7RJjjfIgZOQhfrCrsWla11Lc5iLSSU7Fb1GtHHrKe8f6D4pdffsN+q3g6lUOEyvsdr/f6TjP+++vLC817hWpCXZuRWvVQFqRBVqi9G6GE7DOQZJTSrcUIzl4bPv32G2S94Mcff8Q3X32FdVms4MsuUQCWg+cm9N6nd2qeRioprCAx3SnWwPHOrgSQC0Sc2Ersd8iRonCrbzIJRJQS0QcMrSCOPXKlJPNncITBiKlh3rzTK7unc5qGiZ2YkwbDlXayovdsTHP4LT/HPSOnAqFzTVifz1/IacG7d4942YbD6+H87/3OM54y44LYa8W+V6y1WcNgi1RCHx1ibJu8Q3rHy0KjxRTEwPV2s8lgFS/Pn7D1jB//+ld8+83XuJzXKMjCUjrkZRI7BHa4ks+ipdKQ8O7cO0NEDxBYXtqa/3zfbW2INJkDiSgCFhcco0CPIGHIM5cXWyYRKhUVjwKJMBFXbgz/sNfBRrSDYvJUZHiogDlAwzzFEZMIxdOmg/QJHin58c6J3bKEMA+cH57Qxme01nDKiykwU3MjnvQuQmI0RgcgF+a8W63RgBad7i4idv9pWSMFlATYthv21tHqhtvtis/XDd/98CO+//YjHh/PnI1gtbyYKW3pm6NjxyY3G4oqB/lOjgITU9ISKUJNCUtaYs4HIaWMMDlHw2oJrug1fEo46V3UBCMy8M+y82ZUKkd48TGt1muFlhLd6gCjGKqP6XZRbqdO7Y0ouoKCkRSqif1grl58r8wBZE8YifOeHi5MRdl7eZYC5gAdT7SGt/plxuHLawr7DsfV1rpb8fGeglcwW69ZPAPKuSAlQg+3vUNHw6ffPkHyBf/9t78ZwohDtxO1YDRRARL50ZLz3eH0h/ZFjqlovoiWJnKmVac0cPqJrswTFltMDy+d2fRuQyzVA/GwF9CuUccAPM2QUYofxhlSiPD+W2+AlkA6CWA01zasNATBuoAB8yYVTrKuMrhtNvOYc4Tp7bJmwWvkhFJOWPo2u6rFURZzX2VGu3H4R6uo9mffq9WPrNDsjYFwJSmkKkiwuokgjYqX6w0iiuvzZ2xN8bf//l/47uPXNuSoWKF1tuArwA5uEYiYJ+jrjhSKXHDYnzCI3Gv3QN2b7UZFop67duy737cBIZjPOOC4Xx2gaDDy19rPsxn5HhDWA3OujQF1+naPTLinFhnKXHeTdtjAAkulMSWo2qKvAJIY3IBdvgJgJKJXVhviMhdOcKwDYPq/IQNDCSXPe0VdrAs5pqfZ/IAxrEmNZ2g1SvV1KWgJeH5+BhLHyP72+QXf/fg3/PUv3+O0sgBMUktr5gwvNk1j7nNxBEhHlST355Gi4OkRDYeL3F+T/l0s7ZJcZtyYRe6dMHNYWmkaSonUluT5OSIw5JqN+HUxESYwU0qsEZYSlPJ+BZDBPssDVEAjrR3AaNXoXvYsQU4p9IKY0eqDbAjV4a9HaXW1M12eP339ifRRgwix/0tzNMqsEUAd9scHWFZa2nJa0Lc9FEzbrvj0suH/+N//F7795r21gztq6OCh+zOGVzC5O1TnRnseLtkhCE/QLL1YvkfNJfYDSsiXCWKanok3nh0RSL60flDlmHLw0DCx+SRnoo2qMbUKEENlIGKjFZfAd6tqpEmOV2SkkjC49VSdh6eqUCX1B0oJTLmH2sS6N6YEekeRFMJjq4hIUU1Vwf8bLr3uFXVpd0XH8Drd+02cCTCSYC0rdBCTvu0bBB2//fYZH3/8X/j+229IKyIGDLB9DBig/ZXTNNFIM9JKMmVALI8nFma7YeiucAx9EfUICIrLmMxZ3LA0jViRPxBGg4ZYDkpFx8Fzte+lbCyh285icVK0VtkBTGlGrbDnNhZNQ7rZJ0UaRUBZ8v51KoaJbddGplUkxSKOcvJoJWFojh6IEVh5ClKkIx1Zc9AUw2Rot2YzR5t5L9KwvR5haBIKMpAERYC21+hU//T5M9aHb/BfP3yP8+nEFJanjORokiiEMdNCcVh/HKhlEHPevS7gEYv3Yrj3DzGqdDcaiSo00sUYbACNvR6Y4sK6SfKwRQ8GJ03DkQv7hkhxwelyPvAopYTRKrqPE+4N06lVdlMLoGqU5yA8OSWby2x1yVIK1Go0vSvEoLZqmQ2Yvhg5YxFhE5tvqe+1TOMTGYE/YR2+2ChsO1u2933H2ZScw1JL4USsycInUFgTUhJ8vj1jbw3bdsW+7fj4/Y/4+PV7SyGkSPvEzAFLtrp1h4eQLk1AFP+8GBkeZHIjYF66dyOb8IVl9dGApn0DuSDAZEOdnr7YyvpgHJjSdmMEs/dOmUBF0nG93SicwqLoYj0cqmzNhzX+OC+TCGkDAOewF0ASowV4ZKGAWt7SOo7zwai6wcx5waKsEWheKSy2hBPaO9MnbjybQYT3fcd6OgX02KOFYkVIzazHZAWS2ozZ/YZtr2iV0NPz+6/x418+YrXGveyetHlCjlH38YbhFZqTm10B270fIcPiBb6DTPjyHMN2JNKthGKJaMGjlT73GkzTzMuUlXubIsaSOqM6H3W67zebO8KaGDvvB1EiJSMXp28Qu0eXizH7iswY5ZQwLL3hTsAYjEpGVhu3THpxovQy1pUdyZFSBeJzoIj99siHdpEkdJJ21HWNhtQ+fK+dYpv3hjQgxs8lSorr0TtebhuQzviv//qRBiFnRoP2Qd5VnyxqCeQXElOtDINjnQGhUbmL7CTStEgcXOWpR98rgF4+X8L382xDdJ7H0dYZidg7uJPixWZGW0DAyE0eR2/YbHKkAOjDqEvGADINdG8G41VEKptB67D7sxSho8WgGG4YlN3mQxVpTEeZ2Q5yKLXWjLE48Zk9usQ814B3qsyU8b+7vtgofHp5QSmJY/SGT2zyotQUNq6sIC0FEIbXnz9/xrosuL1cUR4+4Me/fLRDQngXw6pJdGYBHvO1drjdm/cNcr+jHLpduZ+mbCAhhM5UafsbZpMGxei2xYXAJcaEzL0HNw7xb7c0gEcgMEXrE45U7ZBC0cAU0r7vBrPjwO+U/fndANGbcM+J0FQnQqO3N9Q9xIKhNAQ+3IQd5kaPYPwwtY14L0fPHD3GSCVYAW5/vqK1bgR0PnhlhMLxphi4l5YKRAVLLnj59IyeCrTv2HvB//njjzgtS1CScLX0/nmByE+7oZAkMc4y0i6WnvLIItYnjERGHoOcQuE/ce88hcQuU1cytsemkKYC8i03I6/e7WpKy6VEYSSC/LfDpX2oSm8VXVgYdZity5ebIPdyIUxpeApwqMa4T4hYNFAgxkvU/X2U7LSSEooU9LFH5B6IvMN2+x+nwOjthtu+Tzjq6NGX4nO8D/oFMKrzXDJaveF6a1gKcN0q/vq//jeeLqfZC2Jnpit7bCzbclDUR2PrXAEII0zE3qHoqxp1FeqIhORRvSD0j99zSv77mDIS/6ODh9hzyhwTUce6gqkBmEExo+RR8xgNkISSFXXvZBUw0junt/Za06wBirE/e1Mj99HnyKRMOXbnycdxem8M06PFqFXMcZBDDu618j84fV9yfbFReLne8OHp8VBc7uFNODPfMe2zmNUfdcPnZxKgLVZYPp9PMT6Ph0Mt7+2EVBLoCVh36VTI7hnwTwzIPigY19mQ6Vm6evD8Hj1ETM8kFI9DIOezH70IV1S0+tMdG0p6YV8HNw4z1TLQzVtpteF0khCKJIJW20FAjRohjIRFCUmA4UpJkLNiaAZGt3yk7cGBUI1RC0IgShK0fsxn20Eyb1BV0bYdtTWcvV4UuPVuhsGnOXFdy+rKfODT50+kO8kn/PDdD3j/eGHRj4sPn4cBcbU9I4NjkxH/tvW3L8SjRtsU32bf/yyASp7jSO35RIFxqPD5mh5ThLbBZtjt52b0JKUg9gt3wRyO3pg+leggpfJo2lg7GmTKpchausmbPD0cMgOZjnvtn+Hpj5zJJQYEUywT4PQFuS4zNQW1BjMcIhoALln+uWOQmO7hfLa9HocaUjdSO3IbAwDyggHFmgqeP/2d/Eap4Lvvf8S3X5OBIB+i86HuFVsUd4B9pzQNtO9rpAr93Llyto0T0w9cG1uDkG7AU0N2tM16KLuzj9ExEPU0pqBsLWWaoRF1JjHl3oNryNFyqsr6EbjWHA5lUW4xZlk9wOWj0O6OIN8jiQCGSIQybZZVY/29XTb8MFgaW+g8IRGBNTCcF+9OF7lMf8n1J2oKHJs5rAuyd2+J79OLtIIhuciZ+91fPmPfNuy144f/+m989e6B/EGC8N4a3KtxARiRo2ekPUM8DcUBW5qDOIT2FnhBUECK32OEMF8/mKvXg8CAELQeqbD7y/lp1Cx+YIDFvDtTpACCQIthL/s6+FwA+U7MwqcU0+kglgsVo7Iervz4IWzwMkoNTI/Lm+HE7tEFbxbiaMRSWQDPebthcOG0NSIdCZVHOADGh+NOgJqwAuBxEIGOHZ9frrjdNrz75nt8++1XMABIHKbR1GoK/kQIz9EjBS9AwqMANxrH3Ts4FP7vaURHHAq3HtF3YHvnhj8bdbnGUUO4017Id4UoGqt0kIVhjVcdannjYQ6Dinm2IoaeA3L5fYosGdTY0W2BhPGo2ZwLsfqSz9J2OgM1LSSGW/QUAumafeiORVaxLrzGGKh7owKPs91mFsCixGEGicGeoCTgl0/PuF5vkPUBP377XfBWmXeB0QDJs6nPsmaYneRu+MSChhmtBQTXnDAqOJkILHVDY+cPGso45MD2gOeP0FU+pzkGsdn8LCfNM/sde0FZccp5DWXrHcs9zfX07ykESa0h9YBsRBLI0Hh2j5jV9ZzpI4CR0lByJXnEodDYU3eQ1c5XzoI2JIzoNP6+1/9ho4DRrYhjPN3WnOTwNRcer9wjs1Ho08//g+ttw/LwAR+/+TBTNK4khgY9somteRAyi6cHj+6YAxSZ8u0v8bw+cMjfxudNZeBhGM/IPIyzn8Dfb0YN/n3//fA4/ByMgW6C71C+bJ2bKTnMsCOnJSZ4qbIQDUn23BpRi91ApMO8EH6E0bnHmVKKyV+qgwCL5PQTKdbMG3+Aw/tgetz+vd6tw3g499FMK/Ths2ltxaVAUsZ+e8HLyxVdFvzXd9+iFJnjKgEzijY1TeSwdzKLi55H1kPTGKbSPypLr7f8/prpRf8Qrz8clbrDCoUu7V30xOYpO1AOVjCz6/t+HIbkuX8Y5UfJ88CnPD3n3km+mH2vIYYoc/F0j94hlOFyxya5kmWEYjDOxGK4R558qaXhJju8u5AhZ3QwutUtrBO4+V5P1Jk7TBnsxhYd+O3TJ+xt4IcfvsPD+QSv1/mZ8uH2ngp0I+/f1zACvtdMd87oHROzf5clsNfM+GU+HzTgvP467g8bbO9/3+/TDKxMPaLzkNOpcd0GiyBsnRlLeH8K02oehQ7rs8re7OaHDK4zzDGycyeKu+dx/QEY9bW9jo12gDdBEoVGI4LabF89ZTyjKHfi/t315UZBFbet4el8jj6F7gLUGrpPjwLrdFk4M+D6+QWtC374+DVWIzcLg6A0AFEv8IhA5u54ISfye3ZNmNrx+56GcEHwE4tQePeG4YBld28ivCgTcbqXkX8UuIKR+D3Aow1EUc7zfv5MvJ+BMRas62JQUhaBIWJTs6aS4WE2Btf45r1H7LoCkowyukExqChc4biRi0Nlb3SMcvw5oXGYttsVivfsbg5OnHsKBPfOUBJSKei3X7G3jvff/QXn03LHG+SEdG4UPBXoHp8tZkQJRxmAh8qIIxURRqytK/DYubnPB6mJ5z0WXKf/r7OQGRGKaabwv6Zcxe8LmA5VIOUFq4nZ6INUK3JMP8y0gj+JG26v2ejxSWWmTOIpEik/KDuY1N+qB/mQA++Se9rTJKpSuY0B9LbhVjuWhRFn870O2Hk3paVIssDHv972itPje3x4ukxjYJBvR2fJ4cy4d82V9jQhEBGap8EsioIZE7V1ODqH/gyv9/mYfnMb6MpUPaKAWoObR07gPHMjO5TkO3PobTDgB0QIPfeMxTK9eB8HTMdhoHUz0oB1MbvesobWSOtM/SauV/wBYT0po8ZxCylX6xQy4xURtsyfmxUDm+w6vuT6YqMgUFw/P6M9XcDW+GPBeQ6wpkIkFW7GwFYrHt9/hafLmUU0VUDnDGAn0zuiDGBewtH7DyPBF78yHjwVR4MQSxeKyU7PQXjuUQd2/0MxQjkbGRkUbHEdAY2MRiXE3s3wTDwvqpDD304G6MrXvfJkbe1e6AXscLlXrG7zEc/C6XA+B5vfyzlFD0E8PzR40eTwDm5LySOl4XlCB4Yo6n7FtlWU4iiHMT3HA7gAmlCEB+l227GcHvD1hycWlg1qx6lUyYzTFGx2HCOMaMhacBwf9hmuQKZxxmH/prF3BtDpAISBj5cd6z49OgcjLQMB4Fw07nU64mYaBFvgaByiobf/UkI+ZSv+97gnhzB7rn0yWppjQkGI3Ys1SAQmwCDgfnZGV9f59JzVntajrTAGFjmHQRwhs6N3XK83PJxWGoU2o4TWG5ZjpGAs123foSj4+PFr9vpIsi5ozpT2QvORmbRYf0NsjT3/PLcHg3B0EI7etW/i7wy6/8Qj0PiAULzHSP4oK77cpKz3aYkzavdiv5/H2STLe06J5J5utLpHezpTVy4zs6ZBJd6Vbv8xZcjoyGG5Fu1n8no5f1EyedXMZw1pD9ixyY5/Lpxz7t9fXx4pQIlHjsJjmzBFyzd7oTMVKv/RG7oK3n/1IeBakgTavKHsyEhKYWCh7CDobvlCmfg/7hUG7xCvFIWLjMzio0wlHoZBZhRxr3zpIVBwfeMGZAg0CYB8hzSa0j6FVsTx1Dbz1sPQRE6o8OiNNuRY9EZ4Nwfv1FdLkr23PZCnKiQDqd8ZB/dK3Ahw0Lva84P4eMNj772h2941m9/skFT/w/qReU7Wjq1QtD5weXgiyd3w2cSkPxYx9gIXVpkcT948Zolc294U+5jgKUFXDAcHIqTHwmp4wXaaAl9Lsc89Roc+eU0N3njQPLasFrrbm47DXvh2e8Tl3p4KDXFSU9o6oawCm29uzJu8bzc0JkuQV/vNs9LhvzOs058d3N0GuAjoWY9mo1K9xucPY0vn6cTWdhtAT5pzIl0OtYRDVMjIQqyILGi1Yzmd8fBwgfjEPfH64FTu0+k5nMqDfebg+eO+u2xPQ2Fu0NFN9lNjG3AwDTJlzBmT3UMPJy2M4tFpcCdhIoWGnZMQBtt7FXd+eX8xH8GQaExLz9Snc4BN2Zu1HVfaHgXM8z3f39W712F6G0BO4XBoKgeDAxwNpQinD3LsasWXXF9sFFSB0bbwEKO5JYRoWrsM0j6PTsHlEBLztqBoRnHtWVov8sb0KV8c8xJcbyBNQxCNJuFB3tcepnqejWbD3lRE4+cIz8KMkGObxYqDmkLPH10cN1gubL4hYq+LiUgA1MP45IqL3tkUdH+9KSBMpRaGS73QxNdTf0l4smrPRLAKDYbnimu1ecE6P8c9EKh5F0gQHdhum6FbBm57w/tHTMSZRYaz49zWXNWMBFCWhWsxgLII6j5mc6BxSHHZ0zwUtqbHArPE33KHOjpGlBHeH/bDd4mGD4ioExpfe8+Hv0/2xrcZT2D6p/eXG2EfwwrMlJNJs8UKmDBiVfSDt+tKIIrE7nCEwfT9t3flSw7oMw0Dk0QxEtM+RPQx7eNQbEdDhYMw5ufs24ZaCYbYrrdYt5jjbF8fETsQOkbd2GiTKbiUM3rdw7lgquiwmgdnDOYUzZWeZylgu+70uaU4qsy4F2Jcw5FzR9Le1Jzwg4PpinVw7ogAwDEtefzbHAiXJXGv27qhY78FUNYKJSJ+p+mRyB7MdOX05F3H+RAvuG/nci88y4h+FJDvShBoTY7mzHH+IuLHdGgAQW87NuOd+3fXH1Xp/uk1esPnlw1Ezkz8+l3BUhXIhmpofR66QS7+bpzifgBD6aej1HCtknlSEotkdBWez3PlEd7FTCm5YKTfRQ6x5QhBC6/+4AX6+zgf++H3nPfGvcQx7nPXnoPVg8KAGYI7BIBp/+7oErsfysUhJxj3Nw3S0aWQu/VItkZuRCbUcQrJMdXF90hZ0NrGeQf2MC+fPqHDgAUBKNBQWAyr2Znda2OO2PY4GTGdw3Z1PvA8YHC8PGZ7eRxGjyZSHJA7g8CwEl6biJ8f9n4qC1cs3JNpsA/GUeb7umF3DztsFYBjBz+N+KSC0IjYdObix3xyKKKL/c59MSVhCzI/05WxoamYljoW2jXy8iI2M9jvXc3xkN+zBHgq4nq7Red93Z9x25s5fH0izHTen3ABaDCMvRWD5H+ig3BM/b0pZSEf7pTfnVVHxjmfladnwsGLcz9BJmEwwljMpfT39INyH/nPdbjXH9OzdrmLNRyvIm53fg9Kfb7Oz6yEcRivzq5rIz/CfsbvHBzXBlbMFofdJ4l0O/c2xX1NFNoRAOLAA8V2vWLbt9/tzR9df8IoUFCv1xf4qMDfhVcRBvFnnJO6mt6hxWvj4KV7OKVe1T8sNnwhZigZxuAQns7vz0Od3IjgkPs7eB5uZOK0H5Ws75h/g7wXdyrb8/3HZ5/h27TYAAwzfP/z6a3bk/rbASHjYcrU1JPGqhxu86BADp4X1xXhrTLN4J/pHvUrL250bNuVFOemyGrdzZM8KL3jIqlCk1hLfoOUBUuah3jf5/APfuJUbl7D8WPtDgI7m9nzkQ7Kw7+O1ISb9djv6aG7obxHqsVPj09++Nd0EOAK2LZ/el5uo/lETicRcEJMNJvaWg9/S5cpN+wug3q8F4QBdXgqwHSN77dHnRNhNB0ctRScpxkcAeTP74ZPdaDuG0kLazfRYJTonvFdUdr/U3YUcza14LQuZPnMCfu+R6e335cOl8VDV21s9uvzfDDKh4zA0dhD7l93FOCjsYCXMY6H5c459K8sEvezbQbYz3OcUftzlzbE4dz62T46BqFTvKb3B8bS3+voOMRZ1xC4146tPQ6Gjuh2j7Ao1kZNJhR1r5xZ3/7D6SO//7btGOOg9O5+PnNzzEeyHdst3LbdwPF3h3cUADJJpKJYl2Y64T4iMMFw9zAWSaYkHDiPgIM1pvsVhz6I2IzRlG8xO2ED+udGID7Pi3V8BhEElmyiHyQez9eGb6GWSROjZZieC4BIVw1LfzjUkO89iehm0RBhMDw6cI+plMyCfinQA/LgaERdqGutuFrqyF+po2G32RB33lE8O1iYt6KkIsWwmbpv9CbhCtYiQrHfnzs3D/nRe3On4O5n8RtzP+cu07jp8TX3Cn86I4g1sseJtFasscsb3LgOM1aOnlHMzbDvxL8pEO5IAAe5NjlyheAyEKkjN6Hi8NtphPyiIbKIyntFciFCTI0Y0TZxzhnxCEzRa8Pteos6oN/jdathrI8OwNSFdGpa78iSOZIyZ9afLFUcy8tFtd/39zoaaMz9xev9nw7R0Yy7rtP7hb83EOARH+ISN0XA00GCmXY9ajC/N19vsSL4sDOmhw+IVTvowbssA6yP4rBrxwjEX6YmbD4zwT/nTm+8uq+UCHFtvWM1uovjZ86zQSDFvjEinDRE//r6E0aBCmS0impV7Jlb1rtFGUr65VESVAqyJGi3QRimFOBekkg0Bc0Q6yg4U1DiTwiOGYDf+QB2tARmHKYQzldpHFR+y8RlRp6xEfH4quHJzNDUn2P+3vwIE56Dvbq7/P3k/h5iLRyjHfQAKbwH9WEe8uozjwVoH0NjngQnPLUD3YQ9Sx/Y9huGzolWvL+BalS+MwUx90kBSx80tFEgXVFWdi/fep88R/ZUvgDHkJl7Pfd7fn0PQQx5uNtnk49wKA7Kw/LvYczHwTFQU9nmYHiR25VG3CNw6E0xb9E5qPz5TXGoy7AiprZRNOz16vt5jOimXEQn8uHZvJcliqPmPd6J0XHtXA2q8nns/ud54lnrrWJvA2VZD3UcoDenyHAPee4RN5IGs7WBDIPb5oT9diXUOBSay4cG4ABITPcl+9v/k+P9+ZocrPFxr2O/EcV8/z2TzLneYJQUEZPrjIPs+fV63aPmk+b37+o9/lsh0smMuBsudyBnRBd3praf4vdk6CLY/suIOg0/wxBp0kMXagKky51+4uhajhbwsyMi0N6x7TtKWVDyf7im4Dp4mKcwq/NzOd07HpUU2W0MLAuHQGxbg89e9ehqWkb7hgrEeFzc04vCXSgFiT9UIMefx46ZAEh8PQ2LN8rNf08DE0fgcDIPX/tlwm8OxEHZuNE4pq2AcHmOSkvm0umh+9rxo54qiUN/DKkhs/fADcMrryhsVfKmNwqqqtqAkBT32VolYiVnLD6cxh661zZD43vxBgDUXjEA7FtDWQryUrDvmylYTy/q4Z48Y2phcZoUykwdvfIcZe790TGIKOBwAN1gRTSZ5jAUsQ75u7pE7PWULBye3RfxPkJ65YQc7zHex//DQb4OHqlM0VCPHA4plKijpNcps7jJgzC6oR2Rb+7jULfwezOATx8de2OReCk55iUDsLSQTV0LGZ+eMM/+QNtvSNmG5vTGLloNAo2IcCTSXvid/L5+Zl/3SHPhuNdzuw+PbM82F5QZCed78jrbwRk47FekLu+cjUPvjit3HPb1lQH2vTjK6N3eHOUqJAI4zn7xZsNjqixkxgyDQNlcCVeZcxyA7xczCnp4fkaRtZLINOUUvF//7vpThWY/FEe+7/AEBXBkwm3fIUnQlMuQUkcdh/DpoHfjoLKS9Cqf6If71b9f/U1DPQXIFXGgjA5Kx5qdOZP59fvbaznFyTbQvKV0EI7Yc5iSOzY8Qe6EbXpDKXKDatZgvt88PHdCdii4efHN50j710nYoewZ9fmZEooXanlNT6m9UlKAK6Cj4uRej064IRSRbnPwjqpiNA5farVy7KgIHQC2rCIyb7aKAU+MGoF97l2x/A+Mwp3iwGGfX4mor6c4DBR37w0zhlPZzP6JGapPmfHv+kd5RBCvS/P103BhFsHv1vkPlJt9THKF5jJoXnUYhMPZcO0Qa5Pcyhz2V4wgUXHfPW4ykeL8uFFRa2bT+Pe9D83U0V4boB2wnoNWN6ZJBOGVh0zFHr4ybiJ3Z9qNQDAi+xricI+He71bT5/GFgp4kmdyf9KcWvbqvcPhujPzc0+OadO73z+88KiL/niT7x021y/TyEuc5Xxo7Lzb76OxdN0UukRN/6XoX5mpA3v+g874kuvLIan+9yEUcyUncfD4sKNXpHKCUZVgv96Q8xJ4ZwlrfFBkh8W9G6zxTxTE7zbisOge6vHfNF6TbA/wvG5KKdIi95vKe3Mo3wwBp0DcHSgujH3f7itCf1dG8Yt3v4vje9r/PX0kYgyLhxvjvQyMYSkFwaS4AEIokpI7iWRuHWL53vu+4HkHyU4OWWvnXkOsBmFKioY8m1Hlv+tQLAsnv9W6QVOKLkqxvhOXhVi7V2vh++5G/I898Pm/WBM5GipBUGbbN+UQLXq5E/F+JkdAODn3++IG6Og0gCG9e/ghZ/G/O8UxjdndXUOEPmCkQEyhTyXgj5ow+xfIUDqcZ8ny4nRihuXRh9VIBGocXkeFiVDIyTrMpwMATF4ierKmTIyGJktC2xveP5yBASQd2KpiOXP+g7jxsc+IM+PPl1LIUDS1vTrj6W69Xl2HLEBEbvDt5pq5wzWH29iZ8GeXYzf7dDDk+LUc9h5HJ84+/67h9n7vuG8HhJjJS4iVrQ/7Gjwd7JJB/qIxDM6cmArufXDoj0VjIwlkKBF7vZE5OEgXD04DzAGw7IvPrP9315dHCpYr8UEQpcx5yjllm7dLKuwsgoEFMjrK6Yw2BuDUsDrfjn8r5mpTWLnQR4/u6GGk6Rn59c8MYGiRWQB3u616REL8/j2cytsrB68/Z4aUv1defwR7mwKDEIAwKcei3tGzDKd+8tHPz8Thj9xFPkm8gciEIhvaR2BDS9j9TO8k2SxpU/TedWr7UZYFy7JgWTgxLmfutc9GKAsHipzXDMiC1IhWat1HBTIv6mmYOHQiEY3x4JpSOKSPfvfnd9v0SpjscHvq5DXqy76Ju7y8ztcBM9t1fOOj4xKfc9jrSIce5OEoWP7/u+g6/id3vw94X8i9xy2Y0WBKOSgkImI0CvZkE8d6d+JB68ROGanY3iXOOpCc7hRAKSuWxc52If119rOeElLhWLi0rmi9YzlRxkab5Ii+sNN59O+Z4hsacuouoZ+TID/EP7kOSjoULuaCOsXGXWE+ztbhZxO5MdNFsLOnRyfu/oxHJBSvd2N23KfDCVc9RF447P2c1+J9XrNmMaNFmpdktPIZ00gb+20Wg/l3QGjg/bU8p9zHVMwByP90Ze+uL6e5sHRAWU54uJyoKExgWMTgjGVyqRe01vH+YYV2QDWFUjvizumFJ3gM7hZXY1PsNXNbD8UsTEj0HxTV7w66HUifuepyMg/mjCzm702FQwpdwCBR/GynWsTdr8+cs2B2nYpA7p5keibDPBZ2NyN+13HI9Iqsw3gfyDbZSfyxDRfnozDRFdAEJKCgoDVFKQKgYa8d67KitoaSM8bg0B9dB/a6Iwm9CTVvEZLw8PiAdV1sj6czwJGaNmd5dORlBbRHJDYGsPh6hc2/jwKPa+FrSMfJDIkc0xFzqf9gs+8VATS29Q5FM+aewhWKf9+jSx2HfXB2SzWyuT+QMQBeCKam48l3KPX02uyd3GABB7kARtfw6NXg3IBgWZe45SmVCUsWNJAZd6hyOtsYkESj0NoVqZw5EMmMAWRA+4JSKlLduYcjRbrl/PCI8+mEdV04M30pNj41sV5kTkQfgtOajYARUGtko2fqe2qtJzBFPwOD2B9xxWtaPs7j0eE7brovnPqsgfvX3O318VAe3sapLUI3GAABvo9wpJmjEOfqeyTpxiOlWVi/b9Rzd88Rgt49LeGszGcxVuKqKIcpa8dnK6VgtH5wJgSa7bML0V/dznJrDbkkaCvIZWBZOvaNTYUl/+Hp+d31xUbBZ54+vHuHp6dHPD484OHxAZfLBetpRS60YCT/GhijopzfWVgj4XUDM1XjB8KF6Rh+zSSoL7aGUzCtwv09hqGIF93pa6je/5LY+/vLaah846az8PuPcsE4vtd8lbpSuhNKiUMwi6uCdDBexw8SUzDuQaSUkZTGgrQR9rMIYQFBwhAbTKTEM6Q0TBFnCDq5jxuAlCFJkbIiJSp7SRVlWdFyQVkWlOWMD+/f4fHxEQ8Pj3h8fMD5fAooYjaaBQAYumAtwK7k6GmDE8IyXh8WmQ7AdCNjryJ9cFi611C/310H5eKIMu8tEDmK0gEhcpAMgTgsDh7VRGLIFPfUV3L/pji81yFlIPZ5MU3O5Ub/SGhd9ih0KSVk0MvsvRuj6v19SxIkzVAZEGsgS4bsAYxh2Ki8YeuaU7bZ3RwyPxKwDnPmUsZXH97j6ekJj4+PeHp8xPlywWldmQEwym/VgdEr1vWMMTZIFvTWUAxd5OlhBzFEWtlX+uBN36eBJZ7teByOazS3TKyzVybtiBiC5xXRIOSg3AWcY5DEbDfJI9X3Kj4rvJPXQhZfzyyAeffu8KSQHJMMPXiv8dvxM0mCrAUA55W8fuUcMywG/wVGGkhqPHLWh0SnhXKfckKBQDUjNUYMex9YT/9hSOo5Z2BZ8e79Bzw+PuH9+w94eveEhwcKzlIWlDLDmVyAlBaU3PHyYlA9bRjJ6HTz3Iip9xUTrTEXcKYP4niG8j5eVO4SZ9ZTL/c25KjiZxgbkFR7o+ghuMv5HYTXf+/gAf7RNeWYnmd0Zfo7etSkbvQ0vo94Uhg/kaO33FNyKocUz5AMNz76iCbS3noIoCABauRkCotm+IzLsiCvK5aSsS4rLk/v8O7dO7z/8AHvnt7h8fGBSqJkLEu2tJRFKSXh4XRG315QB++tj8Gh7Yf98yePPweb/0epuKPC/f2iHqTEN85ygknM4/c1ldmB4r8Tf/ugFlf6OKCYbO+mMpvOwH1Nyz7bHuoOvBAfOT1FL15yz+29D8aPdRwqrqGTPdUn7tFnUOhI8OEvCoG2zlkgKQFJmSISIk9GZcTbjYzydF6Qzycs6wmn0xnv3r3HNx+/xfunRzw+PGJZC4pFhEl8YAwgFjH2vcMbItogJUzC7EwHrNAfe3jYPsHs2JXDXsRSvTrdh39GRkGBZGf0SNDo5/dfXdyDSdb3u/rVq8+P/Yp7mHW2nNLds/h5tbsMCgpPQfFzFGqDdpIosit5ey7PViQbnoPCd2vHhmFPPYEpYu/6LrmgjsrPMbkpZYHWp3+5JvFs+s+02dv1dr1db9fb9f+7689BUt+ut+vterverv9PX29G4e16u96ut+vtiuvNKLxdb9fb9Xa9XXG9GYW36+16u96utyuuN6Pwdr1db9fb9XbF9WYU3q636+16u96uuN6Mwtv1dr1db9fbFdebUXi73q636+16u+J6Mwpv19v1dr1db1dc/w8VLqpqpN4jZwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Method 2: Fine-tuning Stable Diffusion"
      ],
      "metadata": {
        "id": "LbYarpSX3SZM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Full process to process and save images properly for fine-tuning a stable diffusion model, along with the required cells from dreambooth."
      ],
      "metadata": {
        "id": "w4HWS-bxOEAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Imports\n",
        "\n",
        "!pip install deeplake\n",
        "\n",
        "import deeplake\n",
        "from google.colab import drive\n",
        "from IPython.utils import capture\n",
        "import time\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "print('\u001b[1;32mInstalling dependencies...')\n",
        "with capture.capture_output() as cap:\n",
        "    %cd /content/\n",
        "    !pip install -qq --no-deps accelerate==0.12.0\n",
        "    !wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/dbdeps.txt\n",
        "    !dpkg -i *.deb\n",
        "    !tar -C / --zstd -xf gcolabdeps.tar.zst\n",
        "    !rm *.deb | rm *.zst | rm *.txt\n",
        "    !git clone -q --depth 1 --branch main https://github.com/TheLastBen/diffusers\n",
        "    !wget -q -O /usr/local/lib/python3.9/dist-packages/flax/core/frozen_dict.py  https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/frozen_dict.py\n",
        "    !pip install gradio==3.16.2 --no-deps -qq  \n",
        "    %env LD_PRELOAD=libtcmalloc.so\n",
        "    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "    os.environ['PYTHONWARNINGS'] = 'ignore'\n",
        "\n",
        "print('\u001b[1;32mDone, proceed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "lY2SC9vt44bn",
        "outputId": "d4c7deb8-5207-49ed-f00a-e38c272debde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: deeplake in /usr/local/lib/python3.9/dist-packages (3.3.0)\n",
            "Requirement already satisfied: aioboto3>=10.4.0 in /usr/local/lib/python3.9/dist-packages (from deeplake) (11.1.0)\n",
            "Requirement already satisfied: boto3 in /usr/local/lib/python3.9/dist-packages (from deeplake) (1.26.76)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.9/dist-packages (from deeplake) (8.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from deeplake) (1.22.4)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.9/dist-packages (from deeplake) (1.5.6)\n",
            "Requirement already satisfied: humbug>=0.3.1 in /usr/local/lib/python3.9/dist-packages (from deeplake) (0.3.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from deeplake) (8.1.3)\n",
            "Requirement already satisfied: pyjwt in /usr/local/lib/python3.9/dist-packages (from deeplake) (2.6.0)\n",
            "Requirement already satisfied: pathos in /usr/local/lib/python3.9/dist-packages (from deeplake) (0.3.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from deeplake) (4.65.0)\n",
            "Requirement already satisfied: numcodecs in /usr/local/lib/python3.9/dist-packages (from deeplake) (0.11.0)\n",
            "Requirement already satisfied: aiobotocore[boto3]==2.5.0 in /usr/local/lib/python3.9/dist-packages (from aioboto3>=10.4.0->deeplake) (2.5.0)\n",
            "Requirement already satisfied: aiohttp>=3.3.1 in /usr/local/lib/python3.9/dist-packages (from aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (3.8.4)\n",
            "Requirement already satisfied: aioitertools>=0.5.1 in /usr/local/lib/python3.9/dist-packages (from aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (0.11.0)\n",
            "Requirement already satisfied: wrapt>=1.10.10 in /usr/local/lib/python3.9/dist-packages (from aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.14.1)\n",
            "Requirement already satisfied: botocore<1.29.77,>=1.29.76 in /usr/local/lib/python3.9/dist-packages (from aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.29.76)\n",
            "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /usr/local/lib/python3.9/dist-packages (from boto3->deeplake) (0.6.0)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.9/dist-packages (from boto3->deeplake) (1.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from humbug>=0.3.1->deeplake) (2.28.2)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.9/dist-packages (from numcodecs->deeplake) (0.4)\n",
            "Requirement already satisfied: ppft>=1.7.6.6 in /usr/local/lib/python3.9/dist-packages (from pathos->deeplake) (1.7.6.6)\n",
            "Requirement already satisfied: dill>=0.3.6 in /usr/local/lib/python3.9/dist-packages (from pathos->deeplake) (0.3.6)\n",
            "Requirement already satisfied: multiprocess>=0.70.14 in /usr/local/lib/python3.9/dist-packages (from pathos->deeplake) (0.70.14)\n",
            "Requirement already satisfied: pox>=0.3.2 in /usr/local/lib/python3.9/dist-packages (from pathos->deeplake) (0.3.2)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.9/dist-packages (from botocore<1.29.77,>=1.29.76->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (2.8.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.9/dist-packages (from botocore<1.29.77,>=1.29.76->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->humbug>=0.3.1->deeplake) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->humbug>=0.3.1->deeplake) (3.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/dist-packages (from requests->humbug>=0.3.1->deeplake) (3.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.3.3)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (6.0.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (23.1.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (4.0.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp>=3.3.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.3.1)\n",
            "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.9/dist-packages (from aioitertools>=0.5.1->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (4.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.29.77,>=1.29.76->aiobotocore[boto3]==2.5.0->aioboto3>=10.4.0->deeplake) (1.16.0)\n",
            "\u001b[1;32mInstalling dependencies...\n",
            "\u001b[1;32mDone, proceed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Mounting Drive (Optional)\n",
        "#@markdown Run this if you want to store the processed images to your drive. This is useful in case the session gets disconnected, so none of the files you save will be deleted.\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "TBPG4Uu8OM_7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load ChestXRay Dataset\n",
        "\n",
        "ds = deeplake.load('hub://activeloop/nih-chest-xray-train', access_method='stream')\n",
        "\n",
        "idx = random.randint(0, len(ds))\n",
        "img = ds.images[idx].numpy()\n",
        "labels = ds.findings[idx].numpy()\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "info = {\n",
        " \"class_names\": [\n",
        "  \"No Finding\",\n",
        "  \"Hernia\",\n",
        "  \"Emphysema\",\n",
        "  \"Nodule\",\n",
        "  \"Pneumonia\",\n",
        "  \"Consolidation\",\n",
        "  \"Cardiomegaly\",\n",
        "  \"Effusion\",\n",
        "  \"Mass\",\n",
        "  \"Pleural_Thickening\",\n",
        "  \"Atelectasis\",\n",
        "  \"Pneumothorax\",\n",
        "  \"Fibrosis\",\n",
        "  \"Infiltration\",\n",
        "  \"Edema\"\n",
        " ]\n",
        "}\n",
        "\n",
        "label_names = [info[\"class_names\"][label] for label in labels]\n",
        "\n",
        "print(\"Example Datapoint\")\n",
        "print(f\"Labels : {labels} ({label_names})\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "cellView": "form",
        "id": "1d5WUlPmOwp4",
        "outputId": "f81001cc-ab20-441e-c521-b3ca3529b32f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "|"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/activeloop/nih-chest-xray-train\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hub://activeloop/nih-chest-xray-train loaded successfully.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r \r\r \r"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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8HASZ5AdgApjj/jU9F3suAIXQmVF2mRJlYenbyVNBdAUWMgM3PAK/xpxY10BRhQcxoxEDFUUEaia8MqyEPhTHb3J8kFIISyoGQZdh2dYrgX9QDugRWQAUX9SR/COA3DrTgAQqbAJ8A2rRgzfBVdBvxS1kW20ZQ3U3lvpYrDm+nohTxYCSulSl3IiKtgwLNNAXgeSIRVYc8irVNlXfyBLS4TGIeTNl7VjWhqWaYuhKKKTmIXRGKQJmxXpqECFsAHqpaAzgLaP4fQ3BhKO145vtOWIoviPVUGHtMqwcWe1vPfcxD900DHUCP3lCU2HInWLfNeQL0KdVo+EF7ISy+f1Ma49o/B0/LeYL0D5ZvR7rN+9jJLdDybEahBhKoGIAhxwDMi+u3HzOmimGgLUGNJPYBd9OhoLzpJL4Ot6kHgQtuaAEYElinRTuJHQMFWVrulRbWM0Fs3iYCp5gh0NiZ4SUTklCBRKuO5YnmQfHLuQVeQMJcGjsKK9hHJEbRJHzgIz1HgtETgo52XnKk4E3ZLVzlG7fZwW4qXtxBCHC7RMTzqc3irKFhMdkJeMgJMeC8T3oeaEIF8XfIM1QE6lb0AIEYMLWt+WvRCkT4HGOQ5L+WfjqvaB6ng8eYRjXAzC8B3+mDIvlf5RGFkJxKEZegglgsjRW07ykYhisqSAiWU3kqKHY988UBCiVRHqD/l56EZNFmDkHJtDe8HWO37JSoClJNBssc7KWQRA4SmSyQmlzSeGx0oz3Eixx61Y12OKwaX0rLEG4m9VFNUJLsAcnyuQdhDKuSg1+Dh2x1rCq3KuIBGfch5xMifGOgZSZhG08a7kR4F6S3Aj9xBamOHWUU7dQUe04LQ1L6SikeHs9oTWGdLMsS+2oVVCroVeuMMPBsy8oT774Ilwwe4GzhzCvEfb1uUbtgC14PSnkrjvM0oVVnKoTylsTDMMq91ht1RSqvFs9RX3SvBb3WdGOe5OYe4rx8mXgaKJIBs9CkKfQZ6KxJiszwlLw54s6F3alUB/tRPtLX3teNxLPRGKAgNjc0mmE/3yN5piEUHCj5Lngic9oI2ABShHU0tGK5RSEeIQ96xQvZ3WrMOLHLtg93xDWPe1sITv3hABbzzHe6SkyDkIQ0xqVSrYGyqRk4juBMFsU7WU3VN8NgFhiP/aK6U9XQGxjvn1iUObTl4x6VWQoC5NioHEfhlIaIA8bh+E9pSChENI0UIGiKevi2F6SeyqThxrzFQgkP3+M0XNghvq6tNfHuJArnow+qD/TlFwfkx/XNhlBriDSK1zMWiX3eEjcyHUEk61NAnUBbx2KYvJPXb7NygcAHYqn/Dxd0osgVaDwIQRlaKdp0f4GxwdDUvPewvpOzeqajOCFWzbgHK4t+4RVWFLuZLUBSjBUT1EwTGlII6gy0Am6FaDa+wD8p02MIQgi98AWRiJg1q7UQ0iou89DEWRcNhRtAYTi3LYxI3wwBsGePSzU8khod4q2iqFaWHBaGl6ebth7wdNume1aBddWTNi0AhHC6WT3wqTQtUNYsXOBUrHY/pQ3CSmam07HphuFeEC70xRmcmmW22C1cEbg8ItZK/xoCiFginJSkBdb1QcrGlzfWrKx3CyG0ldORNIc64/7KtsYp8C+2/xbstCsef+5+OuhRNzbS4FEbr3B4cZkcwKYRavnsQ7rFQAR9leaa5EyUYyRcPV1pPEeYIbFpCxHInX8DGMoCuPie/teUIuAyOaY2UODDlPNiYo11DGsfPdMyAcwciX1IRA2OkIrcW9lKF7zyOk9o8GUNaGfYd62e4AUxZ1s49/do5CzYl8UAGN5gMHbIxzkY8c3U8j7PUEWwulLxfIouQ+fK6lQfM9rmL4qjp+va3zP4bgeGgph3c9AOxl01gwQco8mks00rh/np+OeSe8k7iHmOL7ontl7yiEUx5xAJ39Nbf60UMoVIQZvYqFxIhf6lGvb8ihsoTt2dESgucJbiBwDs3sXY+BoVg6eu3wvKf3jyCmY5aNfaa2SAny1N2YUSf49Wet8s3BFPxPkIv68Pd1xLkDnMjaWh2bACqKRoA5hp4tr6Z1G+GPStplfqAphTJWxGOErHK0LS2D6/bqVFgnICKGQwMIIDKBbTPm+dpxrw94L3t1WdGFbP6SotVsSEmYV7bsV6hGroZnUMO3tE0BW8xgC7//8iHuA60MlUwhmBQpw6eAqGVNOobRa8JiuFWDF/qmHGkhR3lScPiecf12xvhXUmwkmGxtFO/MRkDFt9LSwfVwjWR+KjaLgxzdFhCb6YpXWgYhSNypAowKdO6ANmWCfa1T6yT/vXkm/EfZXYnmnyQPJOH7E3cOF93Vt56VE5cTGtzyK5hoKbzc2aN8Lbl64pmqKQbqAInTWLWeBzonsiiPraxydVZ+ActMMy31VZW8I+LRKN01Fm2saI5ksK3khoOWAoqiyNIOehlfRL4rtGwItjOUtMnYfSpdEM8QoC/D0bUL/knF6a/M0wn/vJ45zreIrfuaDYShuNeHaYwzLuI/tU0LZNdfAnMuwMXFLGcjcQFjMsTbtw6N4LqHcQNYtMI3Cz4hK5Hl4mptnSMpIuNt+YPDu67gjvSRSU7wcg8MFKgpisdCY31+cP7yYRJMBdmPdvQR1GZMGryebfyM2hOn4gDoFSpRKPAuFGzx7DATzBqaKy6hqnrwukJpVRK2gv3DLji3XUKogKnJUvaIYyKpRwNx2VYKyWPEcAaoMPZT5U1op1GAhpsXglwDcncewCAps07vmNwy4ZK5jzjsA5oa3e48Zk6IUAZHih2/vvTjKi5u8OI/ZFIOI/a5qFdyxOrkoRBS6CoQVujL4OsX757HPZzRFJydYQn8V8LmPKn+BeQmA1VuwQrcCXQX9hQCdUN4WnH6dcf6h4vRGUDYT4LNAN4oOSoFvAzBZ1DrdlysE7uElINeC3bLaYvckP6mgL4R+Iux3piASzx/f8b9TUWzI/JUVrtlnyhVo97B8F0eowa2/Rha63H2DNXisd6L3YBv/oMTIyntPzmosZPXv+tE9R9N8Trh0iDB0L8DGKI+chXlGJ0EoTxb6skriySPQsBbH38+PjIP3UABmGM1UIdQJ7R6QnQwxFpZ4GefkDpQ3hH6lNCq2bwD1YSDOTAG7sGy2rtoFuH7bPP/lwcZ3UF18hTAK+ZAGmI7X4etnNiTVFTnMeIhc0c3vbXk0FFIkyeNL5mkQMgQ5wZ0zOjB/JjwFmi4MQIjMqYh9UFwxuCEZ+ZPwIGLNp+Igh9GSplCPBLoKQEwQCIrDqOH5CHRx+Opwb5TY9p5oIp4UDOpTMplinSvAHhEJy+Q3OT7MU3AvQatZ6poVezbgEcNMGB1NVq5PSGjrSaahPDK6AnIHYAFYBaUMqJUU9oE0C4BYUN0KFiFIZyiLbUwvjFJ4otXx9OnGOvdNFjhFeCgQRHGv04ZXUqAajFUrZUy7nwW6KlAFy4sN5/OOL7+4hz5WoCrauVlYzHMmtXbUaiGm3hmt2S7QTg5RsyI5gm0ErYbNl6diuYw+jZ0jYiKc0F518P2OUgVcZNCFhKdAAJ8MTit+TXpXcfke4/S5Yn0nqNdhIee/GAO3ujJB75ZqILvsxTHP1I+W+px3OPz018uuyRe13xH6lHfS6bPBmRReSHBiqc8xu0cHcZQVqydmfZ4DLBH3U30j+abuJ3gB4kShMSnkqEDW8FwZkBgTpVT0Gh7CzijvSsbrqVnsvj4C9VFHrcwcopzub1ayORZ6RABlCJTs+SXCGOIW5mmar4jTP9uP5ebPTQ5MuFP0kxW3WX5jjEFQtrQLcP3MhN/6ZvKuypiz99B8k0U+cnV6+IxShI78bx65KCiwvaIMU7YTZX4DqUSG8snnnPJced74DM+GFuW4EGnmOQ5h5/hYjuFQEMAwXACgM2VOh1Rt/VHYFARRHgluBpRLegxxoRmeG1BX6goUz2mAkRXRQb8BwY+kDXl2fHD4yNzHUWzF+TA6adExCZnQDS09H+GOE8A3Ntl8BwgzuIhZ3tUtard21a1scsvcBKsVheHs/oVbbXPSLNcMwWPIdj9aTeCbO+mCN2LmkfwL0jIi9EUAh53Goy2nhmXpePflnRUrKUEbrDK2KPoqqKcOLWKFTsJQJZQikM7mVnaYRe+hJBQrV9diHlo7secAnsHg2BRCfbWBHdnUuyvLTNAL1suOWgVPjyvwrmL9gnH5AWF5qyaQ3VKdERjAsIJiQwGm8BObEW44T14XcIQYTrFeu++xSZ5blfUq4EbYL4x+xoHEMOYyK3Z9IyeC0tfY8kDQwhCShHTSjYfCykIqtfqZqh6KgI37ZrUOssI8xCvhcAkl9MhR0fBeQQZBlqsXwyGgvkgalHKDeQjXEQKJgw/hLmTYJv6WQmCBecZhgIX1+cwozKJS/xeeU3hFygCCxLG7MnXPrz4C/WSIru2V1WbUR/PCZsVfr4YK2z41I2Z9/awmJoRm7DsCMCfLNeb2uHZybU9KIarrSQxdt98R6pOmUfScKiTDwpPc0RCqzw2UeT9hvBZrOsblUIz3zLiZw7nKo2DOwkHxvDQ5ACbXxBFZ1DVDyWCGdvPWrVKcxj5TXxMMRNV01kBQsaR01EP8PYek+gWoqnHEOGzUvAaMStQcmPgDY4G8l/fQw6+0M/QKiNNJMIu7S6YEwrImAmoRdDGIZ3XF0RwGJrshQJRcKE5CLq0uGbOp5w46d3BV9KdiSiUJyGIWAb1rWC47zucdhRSnpYFIcd0WvP7yDnot5maaGWCPV50oTQjbbQFoeDurw1e3rVpeQO2+BDCvqHj+hBVaBVIZ+lRQntiegYH2UrB8ekOpAhFC2y2JrWK1H+XUcTpbgPjxzRnl+yvWLwjrG6BcNeGb3P1+XRgdhPgMOYxkMA+vIeddMSChMoT+CM2MtWEW2xGPnrFTuHLo5GElTetXiwnFsHhj00aRmxJADVjeEXpjNAX0vptHpxiQUwDU2SyqtBhtrMvOntdwqOY14vJIq3KEQ1zBeIhQFUBj4zbqFvqLAslAFZEA3WGgnMRpAAVb6ewZRBKXLG+QYxghIMVkiY71Tc2WYHVYoxEY2r6dgQkhCKNIkdxbW96ZImsXy7FsL4FazEKPIkao53FgzL9aCKfPpz3tFnEqBAw5kVb67JlO+/N5rYLSIHFUAvZXvs7UFFi96lGRTLULhCmvkLDYkfuYPdH53mcvR4nA0ON96lDqz5UYELLQ58+FeUYsxENi8HsJT6X7PbKOUGXcUFdQi81CJof7tFVZU1HY3z+W8BFy4wQiQIEMH4Wbpvk5WAy/qiU4PUkMIIWyNrJN5Ggl2gj9oYJeKBqpIzoEzPB4vEH/Wmev+rbwDDzEI12ABRAwNCoL5wTxs+dR52YiVpwvG/he8O71BfpkISAVBZ076iJY1obTYv/OteFcd9x6xeNtxeXFDf2ym6UoNskqgNwK9KlA1Wo4UAUoCl4o0aG1dkhndDU+JagptqjR4GJhpc4KKYpWC2hj6CKon25Jn6EyMPjktRCRw3h8fcH6awvWLwj16nFst0QPxXLT4k4rK6yfyeBPAZSbZJhbmVT278YmxPxdmBKYidoApPKBDmK2UDL7PRlM9vlmnCy9UDi8uQJQRnPFD5jHMBMSolDmluYQhfIQfiGgI0GIxZPN3eLBKoahJ9hepU4oV1Pc5TYEbSzT4bHo2DMLrNpbrS4kY+BhUCHGElmBPAui555F2WH3NKGnuozQp8a8sIXM2mnMWT8b+qw6tTt1o8Zv90auWK4TygwjpLe/sElY34znmvdajO08f5lglsmgTMXhYSRXxhmFiJDpHaFcFbrQAeX2PCIhhVIYZ9xf8Z6XOhNiJNxThocidAwl5dIPgY4xJxlKi7EGBtIqPO5EVmnmalAGhNcQajIZDiHsp2f0Qt+DYhBHJ/09TzQDiZLpxesNiDIjTjEgbNZIvwhwFlC1/ICdYBp0j61RBYAo0ybotYB2RnusoHs1GgLfFcWhfyO3oqjpTdixrEZQBrgR4uelWaPCECW6KnCKpKyFqC7rDvrkCQ+LBWJP5w21GNz0vO44lY6ldDAUnz/d4e3jGftW0bcJKeVWfOQq0qK+kQmSYsnnpgAxwKWD/Tl7un6aygEEFIflMgngiuJ82UCk2PdqxIJ+vlI7lsWE4L4X3N6dsHx/welzcmGpw6KcLJ8UrgePjtJ1BwG9PFtoOrvYOsIQgdzwk1JYrBWHQsPZEkxBoQOKGRum3hTcGNtLGrUOswcSX61IUkESS0ryTti+YfMdRV26wH+K1QbsBPFQk5wVfHPBUQDdjkork7meU8iRIptXRJ2BI4viPMFmmrHn4PbxUE8CMsJ6D6swvIdAwflcsUxGWI4xsqdJUEJU6IgvHwSm7cFyMy9EFkU/q8GayZSDPYcn9kG+tynzN4EYKps52NsnAMjCkoe5mQ2MUIo6jU0oh2fWukzrb5zLpOZ+P8JyFk4aMmg+xro6Cu338h76/hgFtHb2erS44TMbRqm431dKh9D5pDziklJG3mDQ3ZunoGSCJNaCJZz9XGIhbXgOxTx8G0gFj9qy3+T4MKXgVnsWNkdMFmOwpSrkZQef29gwLnkOtxjhJU+wZrXe2tGvFdgY7VZBrM59b2GXtbbkDjFEj2DxUBJgwlsdBaIC6OrWXOdheVW1kNHqVjipnxMoLPiJl08or97g3XZCE8a5NtwtG5gUooQvrhd88fbOwllKphA8fp9HxIcJA/3iC98ghQQRg4XqCYAjl0qVsX48AQ0gPQAuimVteHV3xVo6rq1ChNGdT2I92fhsW0VvBf1WUD6vWF7TyBuEjnYvISzY54gQdSKyvtqmy0mMBegbJuPIvnO4Gc+YBqxx2uxzE5j5elEvoUzg+T7SqgZOrwUkbPTtXvPwlZQgfl9BnUEdKE+MroYsUoekEpPnEwR9o8E3xBMePSRGWNwxfp2gNSBA9hlRgmwF9crpIcw4/bDK06MJyzVkkXsnLFMC2sdaGVmYF813EhnjY/zc89MGYLXB4U0zl5NKP8ZsN0NLmVCfDIUUzyUnQKt/j9UbZrmCd3hr5EJ4t3vcPrH368NkvQMjohDz5Anmg+cQcx5KwpWIel0LMAlzsZxGfbT1oOEtTB5U/P38GjOj8JyvSu/WPYn3vLQY38mIif2UjMKTUoiQUI53KhcypmLY+AXde+QdModB8Jqtbl7tlIeiGC+Hn+a4sC/UH0eimQiTBPHBngZUVoXeN9BiOG2iUSRCAMiTrJxC2EY/CaL8s+XUrZhrZ+y3CiLFujYwKWoRFJYU0EyKQgrx13ZhLLXjule0KtiKomsdDKRFUe4buPSR9PEw1WnZ8RP37/DbLq8hyng4rWhq9/vYVnz/4QU+f31vVMudbdMGJYdiCPxDAxRXfEoj+WUmgNFILGI5kG45gKAXB+BjaFJDPKm5nna8urvixbph7wVMisu64+60oQthaxW3mykEuRXwm4rlLaNeYwNrwhhHgU4UfNGUPwD6Yr/LapbkLMyfJ38P4Q6P//PkCoOQHkJSAHiyH/D3FPlC3EcKCdj31neCciPsd0C7pxQUUXkbmz8UUfT1yFxSEPIxLEEnVt0sZ7I6gpsrhsWNhBvlBo1waPTEQGNIswvLzuaV3iyxHP0p5nDJ7C2EIA067EPR6uRpZbiCLC91FEwxdkjLdU7wg6IC2K1bNRLIoKCOUFKODQDagKWP3IVVovskutTsc/V/I+g2EtW8WZHZQTFMQjoFtmIo3h51BTgeJebQ12E0bILNX/aVcAbeSD7H8VVW/qEQMNblND+xlqPQNqMfed+UYw5C5jlmryH2FTfNGpqYbyU888Sdy2sPDy82xFQwJw4yACYU1ORZh6ESsqMDwcb6dY4PVAqKUhS7h4hQjNaCBBDn1qHoOUDqIRH/cmhAIFWpKQpNAq6cTkd0lFOHOpJmKR1rNdoIAKgsaF4YBgCnaubqUjpECXfLjiaMx21Bu+NEL5VikE9RynqB89Lw6nzFT17e4lundzixEZxdyoa//fgpvvPuE3zx9g7702Iww7iokOUtoiDKE34HlygSkeEt7Dz4bJprVW+qoqIZGqIMQPspGThfNry8GG/ErVXswlhLR1l2qBJuvUCEsbNivxaU1xXlceDN0zPoyBishWIoW4XOLn4u9GYJsdxQIUDGdB2s4rluJfo2ZPzeNxaJ3UO9ymj1GfFe4JhvmDpokVgbSXqnIGWra1iRKCSr9tWsqM9q9mk+1Cku+ImtYNAbKgkEFCRlng8o1yFA+52kwo/51M5mEzT275lnlYV7GBZuGARzlfLIBfjPmSsoDxPoAQaI6u7p7aPyBaAgHws6KgvPRwBmGKQgrEihJpjup5oVHtdh55HqF4GsBL7ad/VG+bxGO6+4fcNuKqhODhZ5CGjy+Y1uapPgVgLE278GTYqN1cgRQAyNVB9hXo3XEoV9cYDyItYHsvYB7KGpOt1fic8ck9eH/M68AeJafj9jXmlQzft4z02+4OOsRVGYgE0TFpx8R7BxldUQSUxq1N1TLQMBXvxmGe3wJr/u8UHFa+TxeyrOq1EMbih3Arq0dGeinqB3BvM0SgC4jLtVqMfB9cCmCSATd2XtOJ12558ysqlzNa8BAK6tpncWrwGmHABYjgDI3EN4GKqmaE6l4dvnd/hseUhF89RXfL7d4cvtgl978wqvX9955ywaOPfMLMGsz1UwuEtggn8iEDRL2RVEjcl3y+dGDo21ce1KlmAO5UmKu/sNL843C431gq0VFFaca0Nlwd4LujC6Kzs0qz7mHs1q9CCo2onS0pnd8YAn5rQ5LE4my3S2jGKjcFq3OgRft5inFAxX1z8f9AzK7JBYzYXMXcemKBNIICw4FxzLg5nvTWD1I4586mcc0UJhxCCsQhNA3KyiGhsfQkc4KbATdDO6eGWgv+wGEgBGmLAq6tmMka6AEkNrRyMFSbEGQDFeGef3cZ08gfH3RGsRxsWzMZ89uMxz+DznUpsSznH01foQzEI552PKS8T3w0CoT4B0oAeFORkDrTKh3wu0EvRmgptvBl8lRzL1iymG9fX7iiET0Rp2wrgBM0jcaAlU2WStUw9F7/QXq1H/AOYtrG/8pJ7/6as1D8oku68JUwA68kSI9TLqrGxApoGM+3Z4ftS/lI0sDBetesNzci61EX6aTjUZ8mGccYtiuaHMDaVk5xDy60BGeI1hE9MlEU/RAOjrHB/kKTBbeIMCWVTUuHVOlsXNlnZqlXwAIMJjbF0hsFcNR4KcSNHBCesL4R21CoDLCGHU0i1/4FKrsKD454kUew/YqmIpHc0Dd5GMPpWGz06PuJQdJ26obJ7Fk6zYW8FTX3DtFe/2Ez5/usN1W0Z4CBgUCYDvUnve6PlgGLlJeaSnNH0nMlOxQQSgGxuCSAm0dn9mU4x3dzd8crkCAB62BSKM09KwFqPjDq+hdca+V/TdAqa6mqvfT2bBRivJ4CxKDLwgETKx6TIc0S1PNBMiHuoZYAI+wj9hwXGf3WG37AJFQQTsI2TVzgScwrpSlJuCd0+udbN8CGS8QQRQxG6b4f21EDpZe9gu8IpoE0rZHpVdkCgMbeZgCC2moNlbhZJX7lOjhG/qqoNd1scAVVBOBikOL7SrVd/zjac8y/QdIBO0B14rnZSm/5sJB2MugjsqlEuEycoegjTe07RZ5tAXSC10FAJPh4CUdQjKUGKxTPmGFL5RbW4wXYaeuxVo7WxjtSAp2Gm3v/eXviY2jDUf18fkpcwpmig6TcseI1ZPto6lWK2EVDMEypNVs28+2LIilUEUImaYKLwAF8yzB0jTfk9maBm/EwC6TmwJYkqQt3nOMHJ43UOmOubsuaIB7HmkEOrNQ35AzpUZABHaZeN92t1r8OQtdQbf2rBXfxw5BfbagYjNR2cxAF5xjIGacqGtwodQCOXPcV6ZErQcCVdXCL1bodfeC+7W3RE6hMjShEIAkNb/3q2aV5RQWSBuBq3ccS477usNl7LjqS+4tYqbFIgyrr3isa14agu2XnDbLZ+RrR0npRetGu2mNYuVUGVYYfCvRc/eKSmqpJboxDg1nN8+QhJcFOvZPAQAeHdbQWT1EZVDWRKureK6V2xbxb5VyB6Afcvz7C8NvhcokuKbfBY+SUnM7yMrZgvHFMqE856eNSx4YAib2AyZgFI9oGrinFLJKLoXg+mVG6CbKYc8PKTEuwlI7ppwyLhvkBG2SXWhdIIJg6m5U7bB9H7PqiH9bI7YW2dyT4cFAKe3Aa8fIVhfBhGC3AroWkw43CLeq8M7dAFXIjcRXQEnTyDrO8LKdDhmXwcJW73aM5dNUiBRD+9MB7Jr8gQAoJ8Y7WJgC4nQCSHJCftK6BcX/CtGVzZ2plsdYSdZzCKuD0Ajth7ULOgLrCHOQtnsCN0Mk+2V1cZkSNBtogwD0Vg/wyscXkJ6mEXNK6KRT+Bu/UMMFqu4fhNZQZ4V76yjxzemsXGjLNYib4xsFhV01p5QT4WZCndwScV+mUNEmJ8v9tIUTrJzjPEIz7ZdCPVRcwptPGh4UB6m1gr0WiCBhuoKoIK6rQ3+e55TIBoCnQ03X6oJcMmMM7LaOH4H96zeBUwBGBfQ8DwA+7u4e74sLfdKJF57ZzxtC16eb+huQc/J5vAIKosDGwgnEggRRBgQBpWGF8sNr+oVT32FKOOpL9ikYOsVD23F29sJt71icZ78UmSaGQxhDxw2dB47J8GabdSv+B6AKDgaFqSORHQjgAnLZcfLi7GtPrZiiebasbCk8nvYFgsnbdXane7spfRe7XmK+wCkARXDvc3CtR0Zrx6tAO2HlCE4YsMOErJB8PV8LKxCOAq0POKiQ9kgFjhgyd5uvarhQ90uDL0DlkdCfRKgi8VVA3Meioo0Q2T9jLS0LaFt1lsPAjvSif+JByiAdSQxaXwf8Pi9GJMrAcn/xQ4rRli1nUyY7MPCpW0aFyB7TmNSnLP1Ox/KgPiL9WaMpLyrU0y4IRY01ACCn9/QZBbWZadXtiJCoDgVNO9RKTsmTBz5pQVoZ7Kua3fIiuiR83ClfDblWR4ZbVHrK9IYWgS9Wrc09n4NgBFM7mLMu/mM09YILzPHQcLCnxLivj/UhXi/2JxJBbZPJRFb/YUBB8g5riJcCKHRnCuKWjEE9QyWmO8rebfcoznQuoTXGm14PcyViCwPB2X/EDkaT7PHF/Mwe1LzMbMLgC2U1C7GuGssxhZeJ2FwlxFT+02OD0cfwYRxPe8Wxor+B/kZU4nVBbzlFYaiKGXAScMjUKWsSi4sz8cirx0x80LjE5EHiNfDK1i8nqApo7LgXHd86/yAT5cn/17HU1/M0u4L3tzOeNhWbM0QPYUd5aRklc4bDpb+e0nlzPT55+a/Q1B5jcIBuhpCMtAwBOdvUty5h3DdrUvY/WnD4vcFALswtlZxfVrRd4Z2K3jL0emx0Mf1cpPFg5AXztB4LLPKKMNMMrnwZln7Iu552oFO8o0TSiYFypxrUE2FEZuk7F7qH8KJLCy1vTALl7oJsrLrEGCTUC27F1RGkleBZgXkKFcXXM6DpMqAW4TRTAfh5frzU8eox6s62paSgk4d62nH9WEdHQIj5OCwTb5NayGGWoZV+VUJywg3cDPlWTajLDfFPXtM07oDsihq8Pq7co0ipwzLqNfJ+PdCt6cVq1Zj0YDl0Xm3vEhsfzGFmPK6jiZ7YgjrCK2S1YP0qtCrMcMqGZ9ScD/lc/vYIzyE2A+ujFIZxFiq5Yv6nULuO/ZXHrpygkd6cD6x+w48FtBGKBsfQBIZUiMcqruN2BJHYex7NRRUCHcFjN0BSLJNSxqP81kI0Qye+RnT+9bpmef5ZNt/RgFE+ZpUtnDYajmcdjbFWJ4IL/424/xlBxGDu7cM/XGhjyoL6tIHDbRS5geYHXmEkTcIhaDTLihFDiGjuQjN8glA8yS1CKMJYykdXQnXbcH9acvPx8/e7NEKCwqAhTtWbuBFsXLDy+WGT1whPPQTdil4vZ+xScXDvuLaql2TLEQW8NeohegRQppczZzRnFgdkxu4Y8KIS06WhT24KYKMVaqRDNKl4+6FewjXk1d0x9jYWF57wXVbsO8FvbHTWgTyizIxTvvEzjkJPUQc2ZkmgziQd7XQzURTnfcaa8w3bjSyOXgJ4Y6D8lkzXMTOIRWwvhlcMG3E3DjdQmztTGgXs/rXd6ZwhJF00bHJeB8Y8nw9kn4t0FWE94gPveAwkCDhyg+WzvDkTPAtnlwOWHLmk5RNsNiitvEq47wxUJkwbSH4gfWdmkcEeMGeHoR2WpcHpe0CpyvgFCSH8QylVgjZGSzGney5M6fwDD4cob/lUXF6Y97D/iL+YbR1ZVvf/FggL5vNeXNtXQXywlqg0s16Ve8vAZLRH2PObZBOivgQS7ewkawGhdWTAKvldMR7YUMJ5dzRhQz44R5b8SLEPKevYVlDKUzw6vAqYPeo7kmRwOnlfb+G7OreCY7sIbjZ+pbFw5/hGRQLB2W+zpdCdjT055RileOR91MOD92VwMWqyvuLDvU8brD8Xr9dcf+rFfe/JljfCpgEurzHK/SVx4c12VHCWq2QrPWSQjysf5DBS5e1OdVD8Be5wsCkFBmpLCInUHxCRI3bKCIZET+39xydo2RhFO5YPER16wW91UTkVBawKj5ZrvjW6R2aMH6wvQBgrRjfbmfcesWtF+yuhIpTWu+9ZOe013QeQuurlO9XuHpp4UTVZ4xhCKTZOphd3Cq4e3XFUjrePZzNe6o96zSaK8kujK0VSGdDKi1qFCA7ZwiJdhp030Ej4u5q8wY1GSoJub0Pfpuy6SHOm6EfT5bKvOamjT2ghvFvMoO8gCpi4gAG0mTi9onCnhkZFU1eypTQC9QIpwU+BG98l70wTU8yvLRi1ixVhW7+YYIJ8W2qQicc4MQqRnoX/TyoiOdwMJTNxLcViUvqdFCC1lBHsTwo1gdBfddtnKsLI5lyToAJdQzywWgwkzFup6xIDisf1+gDkJBj+Bg9W7PcNOtLdHrPlENUlBv0c3kL3D4jbJ8a95YUsVBNY9ClmSyI7nOswKmbst0JuhhFTX03rwkkZoOm61o+xb2Cs0HeY4x5cfqbomio0KsVagJGQggyyz9grJa30pHEdo8OwTHknjoFSGYKEWU7VRvUCbBA6GcTynPekDc3YjwsVZ5MgdteHIMe0NjMJxCw3+HgQUU4Sius38U3nK62m7dLjnDTVfH2P9Lw+FMF99+puPxAsLxb8XWOD0w0i6N6JJO5AA5eAGiEh8pkaS+OlDEqAKsvKKToAFaP3weyyAwbmyCmgT5iv1b3KmOZksl3ywbRE7oSVvcS7uuGlRte1StECT/YXuCxrf49RlPGtVVs7vFEYVzrBa2bd8KkkD5jKjGkTZ2EzPOjh0Uawtg3JYfZB9/QNBRCUZxe3bDWhnePJ4jEGI7n3jvn2AfqJbiTgocH2YSeRpx8ssisRwAQ7UkDzhhdzWIjzaGUZTPYKKlZNNzUE6GDLE/e84IMGROupExhC+rGYxSb5NAkxgWd5TKGMoMPnVQnJ6PpWlNCMtz4fAY1ZSfREzyVsVv/CusCGC/7Bj8UUxEQgbl+K+hkypiKgtRBFYu311RTmGWuZJ08Lt6B5Z1Z4cuDgDcZ8e0o4nMZQ8GWGcVM/rtBjEMpRJjBk+1xrbCKl0kJTAIoxnmEDDHQUK7YM1Krdm1uQH0i8PcU9YFw+2bB9SeahWsEFr6c4dhiXhSt3RTezuYttGJFftNayQkgC4u0CyAXgV5MGcR4x5ACMAUdzbiuBKySYU+o1UskGkqRFdnW/3oyxsJj7O4h+vcB2P1HzqjxGDevgVEnrYTnBft5ukEFaGPUN4z1DQ3PZXrW+HnYP4yk9pAV2F8J8O0bahHsr0+er5kMDbHv9FXx7ncqtk8Y69sfg1IopHixbnjcFxTSbCgyYueUHD7VPYoI74TQBYDCHbJX0/TAgbsooKUAoIEwYvHQCqHWhq0VbL1kvUL1hPKlWrbnfrnhxXLDy3q1Kmct+Hy7w9VzCJtUC0mx3WNhBZGgiwleS+ga1PXzx4s3Y/cbJEw7BaMQbeY8CbO6qPHwODdJkO/B49VpXfixvLzhtDZcHXaaORy3jt48niFCqFWw7+6psdM1d7JOddHYfR+kb+Koi3BOyFFIyYoZyebb2Pj2HP6DjRdeObwOUwhWjZtmqQlwX9xJudCR+YDMSUwWcF+jWpU8UWj8SRlnjwS4DrbQSHY/VyrWcMU6hGlAXPfY4GO8NeYtBEHMa1FDgIWwmKc6FCvpoGb33hdBTZIhRmD0dQhwht8D79brul4DejuFilyhxaEEw6uLQQ/Fg9ksc9wN9nwFllx8phBMYUal+ggXzYonCAhjzOflHUda8p7zABGWh4DzVlx/QiAvrEc5nxqub08WwhECYOSNRK58i6LtBG48FSgi4/qyWLJYFzEl6vxkiBykGAOuNdUajKIR8pG7bhDvqsBJ0E5s+TwP/x0UUKxz96wjlHsQuI/FgRkDbZRj4la8Eiy8VWGthhkDxqzA/llHe0Goj+x8XMe5DiMkemgbik6tt8W9gL6x4XK34fHN2bzequjsXrDXFfFmIVZZ3MM6/ZhyCgt3EEy4zsI8BBQwPIr4XYQzTp/w0NpxcqEuSod6gkg4x+9bsz64tXScPKQTnwcsaQwAdek4l4ZTaVhdsjUpeOgr3u0nbFJRye7jxeKVwb1iI80ktQKpGF4/nfH4eErM9HvafWq4/l4IiTCUA8JrePY+kDBHvt9xf9ksPLSXhPQyj45uvReoAttWsv6jN2dWnZOdQELwoN5Gsbv7eiMXTCacZi4iAEnKZhf3PZ1eRlj7g26h7Cb0OSzaLLoJYajgbax+UkAWHt5DtbasgBVIGcHZ5GITgb1/rYUBXIl4NaoyOZrGoHzKpvAiZhxJ9LTOXDlosar5GDtqUek8cdpE8rPAvEJHMMX3pFnQmAjoG4MfCwLSWt+GUKFE7izvFMuDV3LfBHwbYwaEbaUHOyvzBOJ7TH0sYp4JSOvBrUVx1Mng4w+vK5ajKYFwWOcmN/Ga/TE8hhHGmyrbYYn/5YFA32VsnxA2Be6++Yj7T5/w+PZkbMNZTKkJ5+2fNux9sdBKNSI+WXTQ8i+SXnXxSIIIo21lhOvS4xNgcRK4RXD3jRuuT6vvHwdlXOthf0QhKnkVeiago5p9I0MUeYI4w6K5LievyqlKgpwviEKffnbD7/vZ7+Jv/OJvT5iyFGC/10Rx8U6HUGg/u7cBUwj9XoD7hvXUwN6ZEkUzNJyQ1mpGSdkMKpwT+DWOD+inMCz9vKQadYXRNdKoY4ANUoSFwOKIuUHwVks/IIcADFRRKBSyLmG1aMb3CwtOpaEp41Qsd8CkWLmDyQvZoA43tYXx0Fa0iTdg5Ya7uuFF3XBXN3y53eHtdoL0AlHC7rUR19sy+kTHQxGsolvomF/I2AOQ3UNiwkht5PP57PdkhyyKuxempPZ9VOmEQoixXpaO63UxwsA2KRp3aXOjFI97dgJ5X+D6QN4D2ARNQFJHUte+qy4EEvmhI7QT3sGQGkbpwI1SQWQzkIRCDq8gxol3SWFH1aWumrcwC7tQLhZrd8FW3G0v9nl9AS8c0kzGRbewEjHZfJbJIp7j/21SqkUtnr8fvTgwQB4uDJRSFF/KzqDHaq1T3UOIROqoDVEs3nqzbNb7OimYpxqLuCgpQE3BTVxgKbJ5SiiKZ0Rzlo8gyOrdvLrVJ0T4SKNT14+QFVGBfkj8xjwoEGRspHBvzgZCyZTP8pYAqnjkC86f3HC+37Atgv5UER0Ro0/IcunAiw23L84YD+zzQrAwETnvjxtDsrurGKFZspBSXbs33GKoMNbasLzoeHhaXakI5NTRH6vlPcIraG4geEKY3Nout2EQWOOloQDis9ef2fHH//C/g3/7134Xnv7fnx3BGQ3YXyr+2T/yF/HH7v86/qnv/DdA3znnOQM6qwVoyzDMIgRKcAVxEehdQ/Vi1n2v4MWS63S1sYi9O+f8cl/3HzHRz44PCx9FQRpGmCcte2dQBQxdVNjCMYUNTRTfjRUZCiCNbw81NSfMi1xBEzavAUgvAwC+cXo0hQDFpewQEHYpuDpJ3MoNJ4edAkAl65fcpOCxrfj16z2qJ6p3sXDU1qqhFwDs3ZK4IWiToynCAb54o6nKAYUTRySaGSN+HWMwNQFZXmxYSsfjdbV8jHC2HF1rM44nJTRnPcXuqKIIaxEsPDRz/DSrrK2PhOUNOS++vxeCfpoDxmwNjgWWFmSF8cnzHNMOCwUAGZVAuPDVG/gwA+yFfdTcYMhCG5Nrult4sMsQYIB5DlLtvOkF7MNKjk263wN0pvRy2sUTzw3Wp8CrVQOfnkgSjxNjOl/E/3mnRKdoSsXpIKvVUQV040zQ8+6cQ2npD4EzJ4CjD28uadEh5BUDkx635h6BVoIuFj6bEUUzkkfWkSjvJ2O6leIKvDvvlQsUBRICCyCr0JNORKf7mara4UVU3BT1yS6sTJAbob+tuBKw3G84nXboVPUNWA6sFsFpaXhcOp4eVrP+4yaEPDdmgImeDxiL1RRLWS1cVYrlLEthXJ9WPF5X3F9uWJaO3tnrScxbKe84Gy3F0IdHYKExHIrmAk0WxWwxHuW+4R98+Sv4r33rL+NfuPwX8b3/+0+nZ7W/Uvzn/vH/J/5LL38B/5ennwV995z5ujQsBZa/iPbAgYzyW5VFoWfxvvROzlgE9y+vuNYVezuBb3w0TtMInRT51zg+OKcgavUAhixSg1ASEjYZn4tD1FBFUWQGIGsRQsh3VwQ3p6iYn6V4RTKT8ykJY1dDBn1WH/DZ+ohNKr7cLnhsK6694q5uqCS4lCc89QWVBA3siKMTXj+dsTmE9eRkchGOimfcpw5mMcLk4QOzEtUb4tDIKySDox4VRNBfxPcjkUUKOnecLxtue83mPIHoWmvLDnOtM24PK3AtbuVYVy+CJ6RoWDokRoIX8cao+s0kbODXUyPjQENNiuxJYO1f1WXAeKgIsSTsUoelZCEhs8J4B5aFPFxiGGp0QBcXcG4lq1vDUgntzJAyWFCjKrU+htCyf0TH5HhfkWyTGSrBJKDd2gXImjtVzY0YEE3ArcfZm3DLlcjDLN42NYfDaTIyoRkeYHxdjmMrhcBMo3nKfDjKSKoloqwd65RA5jEPM/V41iq49yELZVLehAySRyi62AU8koQylxQGwUzFkB7fvKa94QzI1kfZCPpolCpJAy0M5obzsieIZG/F0INuTL68XMEseHw8DY8i9pxPXOQTopsgrx3sRlOEqcOIPJ13PD2seCuEde1oe0V/rOkxL+846TekWIiVFMDuBs2Uc5EyoZd8PWyfCtafekT/4oz/6Z/7J/GTf+h7+G/97r+IP/2L/1WsXzL6WfGH/+i/j3/2W38JAPA//+X/LE5fELJt6ATgsBCUjr00v+6930FA2wuoMaQKerEe7Ms3ruitoF0LsLF1Y4zQq1IWDX6d48Mgqf5zF0ZhRRcFeb3CTEMdiqNMNQpEmrH6uIlTbbi1mrTXMblbq6gTWmmJ+PqkVG694toXLNSxoUJAuDrwt2nBwpZkDk8AAL68XvDF48WTtKZ5gytphsburWTzdToIB83mN3ZDmkbTc08h8itakAmnOJdBIO31873VXLRWUqlyVby43HBZdjxsC7a9Yrsa7I42divYLAxhE2LLG0KNCtKQFeV4T7awx+8R6w40RVqaCw7l+EHJUKLRR4yBu9RSCJiEUXAPAQCvYXUx2K2u4m0nI94diei+EtrJFIpUo11QF17suQudxpnUBDnvI72jBabQKHIUZr2DLTEHYAoXKbKR+qUbxC+S0XV6xmg9G3CcWA5kVc2h/FLwe9HgTB8CMqVl8Xy30jc98FCZsib/rI9fKo6hGCjyDmEhKhKJBJg3pT2S9zax4sYBMO5zXhN9mQr/+ggHJm1G7IG5EpqtJ0BfyQqpzg4mKEC5tOSF2qiglo67ZQeWHQ/bitbZIwkuK4qM/urwca+SHjZ5xMFodsIgpSyODY+gd4Y8LODXZzy+EGNuvprQVLL6imUjqzZfg+XZ5yNI5Eh9rm0dBd9TOyv+5D/6b+KP3f91fL+/xJ/+d/9J/Oqvfobz79nRXwj6jfC7f/4/xJ/66T+P77SX2LTge//uT4BeKb71h76Pn7x7h7/2l38uGYVzDrLgzffQSa1XRxilMBkjzWCoULKci8sQnAR9tdgUXRn1gY2kMBB9v8nxwZ5C/s6Cwobnj0QoMLlkU16gp8bX7IUwM5pmTwRXJuxw0/JMGcRrwWdUSbBrwS4FlSTrGVZuuBRTlRZSqvi1h1d4/XBJmo3oBhduLbPd09aKxSaVXLDrsPoIB6pvghfvuGWjwXwaoaWIo8agPCvko1VwXnfsWfNh8N3T0nBZdogSbvuC2+PiHelGUqpfjNO/PBHqI6E8wdEg9r5B2ZzGOXon8BRmcCvSGnpMgs7rKrTCrPewGnUIkyR128PDcCuW/D0aLrgAwGpCM+PUhYYiAqwRkguXrASNxiFT0jfuF37PSpSFPtnpLt7vABZ4SMvei0SgRBWzw3aTQTUQKisSkqgnAa1ieSSlVBQxp9oJfDWoYYaCfNkEistI/zxs4+MQNRn7hZ1LCN5+dArdeF4gEtKHcIBoMmnCk9VJXRFKwmsU0gCInEDkKKb75WaKQYqNmzJBrqbMc648rGRjTkmQ173CNsjr6EYW5lybhWy71dQAxnBsNPcVT9uC216zMLNeGhqXVNRBw19qR63Ggyae71OB7eVgLw5Fj7EGli8Y22cKvetmECjQq6LdMZYEApgwjqENVoFoVZrnY4B+6or/2OVv4U/+4j+Nf+infxl/5g/+K/i8v8CmBdQJ+2+/4b/9O/8C/sx3/zH88c/+Hfz719+Gy8+9wT/3+/9N/NG7v4lf2j/Df/PT34Xly3KEO+e1w4AaHmByqrn3ThsZ8KEXUFTNsz9DUegq2BcFP7ExIX+N48PqFFyYn7zjFzCEPzCURtQyzEiiyoKulLw9NesPKP+FZ/D8yGY6oVCguCx7ktrZ+4KVG0QZ59Jw8bhCU8avX+/x5vF8OGfkQwDysI3dX2vFEluz++woCA1LMAQChmKAVxTbGyN2QM+S8xKTvCgu97cco0Bo3Z12XBZTaI/bguvTCr2WtKB0sQXM21AI1REtWS0JGI3xTVFWpECXCsDRO2YN58Sat6MAeytM6AjDhJA7VDRjnDefrbgXsDu982QFzygWqc51FAk2Hy4rxrLNygx0sZ7AQfimxUIukhBLZOxXye438gmBCukv3KDI6lFP8HsiHo2AOzlYqEoKCq8AgDaCSjFrde0pexSA7ozyRMkAOmg9pmeL0F2zMZLV4/6NUpAabQGhbNZvmHdNSnEL7QSrZ9xnJBn1MCXP81oHepJD+AdZxQyM9yIZarkIYxyNsBOAQdY3xd6DHyni4iQAbgXbuuB82Qy0sS1J725sAR1L4aSVqdXCQXXpuD0uAOFAHy9C1knQwyW29mjoteAOIwWqov9kQ3+zgG8M+mTH8uqG29NiNB+rYC/F8gvTPh8KdVpXxc+pdq0HOeHt336Fv/jX/zB+4Q/+DP6Bb/4a/tJ/8HuwfMHojyf88/+fP4F9L/jv/rY/j99/+jX8U//xfxt31PGoBf+zX/njKG+L929ww3GCrR7uA3Do8zxnZggkCwLbmgpEneV4AoyhY83/JscHE+LN7JwHSCqGgjAKa84K5aV020Au/LswblJxWXacarPFopQeRRx7L+ktRMywK1mnsWpCf5NqyoYEa+lgNLxarjhxww+2l/juwyt88XjJ+zJrfBTY5et+jd4sQB70zMBAQR0qTKcHn1lgzUP4ivf9yNqLc8N53dE8fFVrx3lpWGuznghKuO2j9zN5gpSchKw+eQP1ayxich8Tw7JxwVi8klIW6x8gq1vlYVhGRMAtrOQ8etZrYc5BhGKJ9yJckr97aGfEqjXfCzqKQ99ZwHINJw+ZNICU0EjBe6ChgkJAk6gvh3lWTt5XIcNbiylTe9N+53MzmoqNRqx+Dg2qfRZT+A8wpa43T6IUBV2tb0IWzymGt+ahrIDHBnlbjG99DII7/7wrt3YicDWvhaspBukhqGiC+yK9goS1+rrtC6GfjCKkr5MHkXmIkaQGuaKqSOUWzXcSTHAaVmxUZ8/AhVwXZAKKNkJ/KrjSglo7VDkZj89LQ2HBEuwIvidUgXVpoHvFdjOASLQ4BcG85ZuFgjJEWkPJA1okQR117Vh/5oZ9LwnxfvHqCY+PJzvfi4ZOFfw4aiXiGRwMl8VpES6TH57wnf0b+C/8/P8L/4e/8Qfwvb/9DXz5V77t3rZ1fdt/8VP87B/9FZyp469vP4m3csYfOf8y/sXv/nH80i/+jEUaAnbrSmGm6o6GT897sUROUqsZMHFfclarwJ+gtcUBFe/Rt/+I44M9hUp9kMWRNXxRDHRRFIN18WKz0kEANkcFzbUKTRgrd6AA+77kNQBk1a4q2Z7170VntTjHLgYjjVh55Y5KHR2M711f4tcf7gylxJrUG6GKg38pQkfX2wIcBDhlnN+E5kQ2R55o1AFRtC/Za4OHyF4b/aoBVMH57I2DfOxq6aheAksAWi+43ZYR8y7mEpbrCBeFRXoogomN6ecmFxqGjXc8/4msWtRbCcb3DhXENPDoB4UweQek4/VQLJEfJEex5JgQMjQSLUEzKQr3HkJxuNVfbtPAqp1ORdHOI8mZMD5P4JE61vuJ0lIihVEceHGSLBaOkKLoL3yg+lhD1Ah8Ze/D4FW0isGG2skI8sSERrtYojWKmmaBaoWDlhNJMEADyNkzg6FWyTwEKaPTGOCULstQqKnFMeYhkqP2u73XT/Ck77inbBeb50HmJsIAEO8hHUn5UFTxbJBImno+RIFy9SLIYAqtgG4EKcVk29mobwCDXM8Nr05LA7WCHbC8GinO6265iHcr5sZW1MkTquO+o1jTlLihALUS9o2xYwXfN5TacX1acXd/w/3dDQ9qUQN5sUOwoDzwgFqHfE5QSKx/g3X/mV/4R/Av/Cf+VfyJ/9RfwZ/5zj+GX/jB70vQiCWlFf/eL/02/DO3fxq3veJ//Af+d/izX/4R/KW/8gcMue8KQSdD49DDYVIAB68vQkphrEYoOPbBqsAG70kTtz+7GT/6+GClMP8UJZyWhi6ExVEyEQ5JLiQlbBIx+mMoxfiKrGdBIatxiAKypXRsrdrfDkstQTvhm0JAh0Tyyg2LF6fdpOK7715aIiqT3fAWnHL0Ejzvoe6OpuWf2XNNpUB+f8QCFSvIIcKUEIodhZxUYmQRHxTg1fpBA8HmKu+FzZ42r0fwzV7eFaszeKJBwRwOigv+jP+P27Zx8sKliFkv3bhsIrEbbn96BXNLSD/nIbGJ8XvkCuzF8X62faRnr8f9epgqqqAz1+DjFqgPbsbWyW0KGXejEtBiPPpp3fr7skZ4Q9N76feuWJ/VH/Bq1bL9VgBxLqRrgdwJ1BtIBWiAWKGdoVWw3BsHjZwY7d6sTaIB83zelzc8o1Tk/l5a+7AvskNVE1GmwyOK+ZBgL4jtROFJxdxQWv6hMHWiv54BBxEWfD6Hcc+gMf/wNWeeIqXCk2pCKXNr7CdoADZGZwUzW7UzWyMsZsGy2CCstRsJZjOSx3hvI2TeBx2DAYARiNixqCKEQkD2zRCCNka7bzjdb9i2inVtuLu/4nZboFIg547egfrO9uasECFWNX9YL798wf/wzZ/A6bMnbN+/w1RmY+O2AOv3Fnx3+yb+9D/65/A3t5/An/0rf8SgyosOz2MW+OoewlzgWkcxMFUZRglg0Yag24jCWpiBImTAiq+nDvxSfweffe9gaNYGMBlvEZNiWYzj/+beQBdOKu/urqHBsC0vMHMl7V6Zy6TY/bMn75hWvWBtfsDmXceiFiH6IawM3NcbTtxw4oZfefwMV2+So0B6CXOiN5hcLaY/lMQQ6u8PrQkGnxzAvAJPTj1XzJGLCPZY2e1768mspupKrrvSFCUsHj7a94Jy6ujvKuqXBctDwEynTZoXmqx2QSZj/fY8EQUXnJT8OoExl8UT0jNzJSEpqoNILC3BgOmFMAlhHQpp8iYyRutChGdXHfNnTNo8L/yGmgKz+7K8jpJ5fRbSsO+FlWtN5XXkQnx8rEpWoTfTfu1WzRNcTOiXc3OgAICop/L1Qu5JGshAoKvRuWxPC3AtphgrHM0UY0GJ4CK1eWNvcH8oloqcwDSGwQ4b957THBaxTr9jCPRI1Oe/UK4xT44iSyXv78/rKIas+P3GEYooq7zdm9PwJipB1qkyOe5dAeyMxpaTWZaO6JAY9UuGTLQH3TcTUetpx3LZsd9OllwNK74a0COpUKa1HvdGYSCwcxJdC67tBD6bUrqcNpxfPOHhuuLGa6aWIiylDlog907nvUQKrL9egO+/wBL3FEZVrjWb/7/4xe/Hv/XLPwt6qNDV72WRMdnAZLkRgq0XRNbRskoUn4/5mVoVBMz2uTwIo+QQQfgNjg9SCkDE182iDvK4DCcJJ0JorksQtVmak8knbxITD3FtNQviooBNMRRJd/rs+Sikhx4KhRQLmUL59eu93a/nD4Kcb1b72f6SjKQPQI5u1AscusLxUCgabUb9PlPZZF2Dn86VjLrVQsUXpTcSmvMokUjvfr/buxX1y4rVi8/Sap+StjQv2ukR1C2YsCYlElRTEhQAuIt38iKLQ68WhxYvHDM+IZcWd9M9uLttVNjIeQQmxQIksoa63cPy5B5CMVK76CQlbBNCGInwOAc3C6tk4VrOo0mAFHBsXb4syTYKjWKTY1H0+26UCAqgEYQYGpXCpLbpZk9v3r9kUEVVwvZoAIDyGPEVpKWJ+WsxT10zOTsL92PYx42UMvpNwxXHnIOpV2Tr1Ejcq+dEstjvzi358A6CTXYfiiBDUhiKIkkLPaR0KLqL3+OeY6l3mDJUMy6sot4XHiNzMt1h1+ti6MLH25KsywkyIUW/FVz7iro20F2Dbuu4X8JUSGrPHHmMpMFm81yyhaZLfdkY16VCPwG++eoB33zxiHdLw0M9oS0FrSwoD2VKNKvDQy1uzzuNcJUv+EMSP+6HAdoI/7df+H0WrjuJwWuLDtCKAj/723+Izx/u8Ob7L3xcdYASFruIqikCjXXfvHD1Zoi3+uhtQH09JQCAcezB8RscH6QUBAYDPZWGJyfFg1vzASuNo7hwI1KvlnXBzYJKVmfQhA9CHUAqA2BsrEQwTaqPYednGMVFQFBvsuCpL3j0HMUMlVXShKDOjX5UPb8w3X/mEijOofk6Uc8qSVUaERJFKgSiEWazBkF2/nre8ep8w7nuuLbF46C+2b0Z0PV6wfVxBb+pWN66hxDY5kkehmV3gBqGtxBjGJacx97ZG9kAHpZwgRaChQRYnhRNPRE91Tp0b7JCOpSCFT65VzFV8s6l/EHfHQppEQCqEDpW5KYyAxCFbFpsbuLZ43nLZmGLxS3vKJaLBJ6sOv52Sw5e/UlVLMnMakk7JVAVS0DGOvCYsnYyds7VQhrXZsg09Wpy6x/tQqrAvQM6JC9JJw+JgHZCtm2ck7V9oZyz4tQYxekweB9V0PP4AEZlMbSQot8sxJhJ7e6J5IUSnhssnCnwdbKy2VFUxT4TYcmZGiXXQRodcM8HSL6+RTO3FoWfvTFurqF6Mw6vWnsCWCJvo7uFnU53O65CoDfeGYpN0QXKiRq5J+QG6GpGATkZX0KxuxsJnXDbL/jeXvHbv/0FfvrVG7w7n/CwrXi8W3B9XI0OAwBdOkoVtGuFqhm8DDWiPze4hjIY8w3CqJpfxQR8wNE9tPXzv+c/wH//p/+P+H88/Rz+J6//CehWMNdkkIfE5toE8lav7Apqhp9n5zeyNchqrAJf5/gwpTAJ78ti+Hp1q76QeQt7sy5hMclzWKiQ4lTawZuIY5mSrM9fm5FCoRxCCTUA93XDq2oNdN71E962k2H/YWs+FJQIO/uoDmUxWyl+9aEQrLL4eUMgIPDy6gVuI49CRUenOb+GehEKL4IXdzecSpD18QGmG/mRL97egT5fsX7Jo58ycFAGGUbwhG0KnVAM4Qm48JZiLS4BSWEb/EQzK2ooluXBWDwjHxEu8ZGMbvwDTCm0i51PpvivCWw7t6wEfA4vjIpBRSbNIiZv8FOglykhTnbP1lnKyNg6bHPICYj4t6xu3bVhTVofbduQucgCHswKTf8fB28vBNrpZMnPvg06hmznGYYBwXaYF4yloPXaj6+CCMbzRA6r3AwQAAXqk4xQ3+5xZY4cEGXRHzdLvgOTtU/IWpJ4nb1YblbYGT7yv8M7BJCFaJmwFy/Aw1AGw/qYjIKglUjqDjO8IIROVmBWirEa7FtF20uS3kFheZ3dUEe9CM4vNlxvBfTE5pnCvd7ID7kVrdUYWNkb+syCOpLFgBkP8sMTfuX2LfzkT32Jn/vk17FdCt5uZ9A3bC//4OEeT7cV261OyVyBMJm9sA9IdOZRAMiLjk9/8i1ef3FvCKClm0wI45eAv+9nvod/7qf/PD4rOz4tjwZkWLvNWZWRO+gE7AxqDH4yCDrvlN5QEFZGeDDzVa6gD6HB3+D4u1CnIDiXHQ+79SWgw3u2WdYpzNNdATRvWgOYRbxLeS9xHVXPczI5BHZNxJPiXHcswYzK5qmcuOEmFaKMx7aiywhfAbZ2mSVhcUEpMa6Bg6JI+u7pvQPFdy443x0tlA1GQl2dK8i/tqwNn1yuPi7sTYAMURWK4vOHO2y/fsb5S064qZ3Lf58XusIStu4dHNATISC9jWC7hEfAhzDGgcLXPQnXpAb5vinoyfZTf3JUyzrw9bMXgg4r4uLJQ6Hxe1ItEDlax5RZdEcrm1pLyImGPEJNkTpLREUokYBjKhzFAWsP+dIauyR1hWPb9W5yqQXIpipZEj3GkQjGsVM7TrXj9dNqDYyKWogkOZVsbGdrO5PJ4d1Fwre4sI4NPD3P+k5RNrGK16jxUCC7TY3NlkVpNkZqRWbk8njqY52hPIWhXybrHn0o5hAgDEsYB4S53OAFaq40TppJ7WDdDQEkPiYkMApyZWj3e4tWpq6Uuyth2a0Gp58Y63k3ASeaIae2F5zOO8qrDbKfkElZmMfAjrZJtuG59Sam/eJjkBBQAPSu4Hv/4Wd4+qkFv/ebP8DvfPEFLmVD04L75Ya/8fm3sN2qKakEXyiErTqanRo9IaYM/Pzf/zfw3/vpfx3/8uf/EP7cv/cH7TpuIDKAn/nJL/DP/65/DT9VbvhOu+Bf+tv/cBboxYQqm1GlO1s19iNbNbpSJtmB8Nh8wS6+dCePXL8KQv8VxwcnmpsMaOnznOFs+c801JFMiqQq3NrvPmNzDoIdcppzOQliUWugw9AMJRXS7JtwkwUt3DwaoT8AGasPFNBI4FAykQLTgKf38KOPSF5n4tyVhwpbXwm4ImFgKQ2v7q9YuUMwCvZCITApfvh4jy8/v8fyZRlJvllIyYilZnL2K27RqktpuLbT2shY9fS9eN9TP0P2eGUxmfdr/ZGvg2QtBH0/T/DPQCwlrt6anJAA/WKv9xPS2k1lprBCvKt6+ETxfIHNTehDMJFaBC4TfM5QKmdKimEokqgu3fjAdQPOXXUcKHIWTiJgWbrlkDubIlnUhcFQOvNUhQLPUIuHYXgK82W1s1ODl5v9IzVhbEJfE7Ybnw1Fm+G28PjEeYdCWbjHEAWCce3nNzvnFfJvP7dBgn1s2cJPVvsAtBeKdq/AnR7CZdQtwa59CCWtajF+AFExrEuElczb1o3Rq3lhXAGZdu/tVnE+73i4q6B3hiCIplUiNqdaB7Is+LjiOdVDVyPOO/6mRnjzvRf4a63g9337B/gDr34NJ264Lzc87Cf88m0x0JUXp1JR6254B8jNWAbiGVEUL5crPuUN/8w3/6/41d/1KX7hOz9j7yvwrVfv8D/43f8afq6+w2sp+Bd/9Z/AL33vW8kIIQJHKJL1W38qqA6XzUrnyeg7xK3VjQ02iLBGePBrHB+caG6+gqKhTSiCUBRhnZs3wIk2WqcqZiZbpUUlhWPwFKkSGvhQmwCYQorrP3/9jjeju1DGLiURT3F/UUNRAKBIQlMBcjYFPSSb2ektwquYIazzMZoBUQoQyvaj4WmYUFlKx1p63n8Xxqm2HMfPn+7wwy9fgN7Wryw6MQHvQjHj8JSKIamkJ6RRhpJgf7O3sTxY1zQ+k58NhRNfjZxhYOu9hiAa5+wN2O8o47wR7qFmllwccf3xUEiBo2xKQ5wSI8jt8r5kflZKpfBVii82uwLJiAk1QcSRAE0J7j8zbuXWXzeTX0tPQ6EuHe1+AA66CNCL4c6JLKfQAfbevbwp2t1kMKhZcrx75Xdc3vtRaDVj5EA86DBdmdlQ43anYjZulmCe5ytj/tO8AjiQH6anN+UXym2EIyNcGMn/coO3arU+GO1eB7100aGUQeAIBzJBbrEIvLUmAKwCXgRaBfJU0beSbTaFDOfJxTofigiW+80I4MKonvqgczb0Me8rH9m9uTHPbvjNinBnPH1xwXfvXuKubvhsfcSJd/xHP/0uTrXhb335KR4eT7nHJWqeVslEMBSgovir3/8d+OVvfoLfu7zGn/rpfx3/o/afx9/68lO8PN/wp37u38A/sH6BqwL/y8//M/hr3/splNoneQTr07EX0KOBGJQAuagjvKZnyQUyFkVnM4K0GHQd9PzDX318cJOdEOCVjHa6u5IIZRDkdT7e71nccQ7LOTTsUhK3HOeqJNi0JOKIoQdqjIV7Wv739YYz77i1iiYFm/8DLFyECe45h6ks/DP+jp9zSClYYEdNAw5KBDDNHsnm588JAKUoTsuOwqMaPJ7n3quyX29nfP+Ll+hvVgureDz+UJg2zS91xWg24wghHV5AcB1FD99IJCc0FSNMFIgWADjy52g+MBFG/wOxcSsREhJgfYusOjYeeBdQERoK72sSPBHbziro6flCULWVMkQEeBvIrpnkTIWmk0KLMYp8QhlWJaqapTfB+pQU2SI1FIULQHIDonfGbV+yv8Wytqy4jZariXyJuZtCRIEKsVCdE80lAd5AY+X4TkJMmTK5GrUoGvMj5I2UhnuX6CabpuPf8xxMCnlWECE4+xoCHekVDuPAfq8PwPmHToh3D2yf2HiLmmIoV0qPsXs3MlnI8xxm5gkrShXQXUO/lVTApgyGBmx7wbI2tBc78DasDlhCV2ywtI6CS8wxdYq/5zU8xijQPg+3Fb/0+pt4c3fGXd3wyXLF77z7At88PeAH1xd4czvj2ioebytaMyK+WnsCbLpa/+l/+Qc/j//OT/2f8DvKDX/yZ/7P+F+Vfxh/4tt/Ff/J03exA/hX3/39+Mvf/904rzu2VtGacTp1r5anK4N27/9cjwvbvEcaPSVmqw6AnhT7opCFM//zmx0fXtHM4tXJ4jHxI3oIQOKQA32kavj7yClEZTQYKfgLjc5sXa3S+RBWIrVqZa+TWDyMdFc2MEl6CtHUPno5IAUxnNDMaXunsE8orrh+3PMINWkqA2C8Lx7PjGe2n0j4K5Hisu5YneBvVj73ZQeT4GE/4QdvX2B/sxqSIigEJgs/20lOFcLihVsjlOQx3mA3DWG058MjQiyRnMxuXGFZjlQIstyayJJZNBRJKI7wXiy+amifsrmicYH1vIFINJ+R6uENADOmehZo+b1A2agJilAisey4aSa2yQXybFXpYvNNq1uhUiwn8EyRzKRG2QuYzCp8elxBBNTF6MyftjJQIY2yopmbQQRDCZSrCUODDdo9RaczmnMFIcAV1mLUPaLDvBNhrtoxVBKSJjs8jAgdzNxS6Q1GApaG0FQNb8DmlWh4khFSmr3FVHIehlxVsD8RlnceXrpD1ixEzoF3q2MgMQWHyDtsjA6grg26hh3ie69bm1n24rDWiqGRNqO7iHBK9PDIUFGsI4n1qRgINBzycgFEYCfce/1wwdYKvn3/kOHqS9nx2ekBlQTXXrGUjqdt8bGj0SXRT/mLP/xp/G8u/yD+65/+W/hD6w/xp3/7/x7fdvf/Lz39Lvxvv/OHvQ7LDGOgorUC3Rh0Y0sgX+ToFczywL1eyofGMGZ83/ezoJ3nB/3RxwdxH9mDK5oWvFquQDPuoeh6xqSQZyZbJI4BQ9sEw6mAUalbWb/v7pU7ntqCMoWZDrUOgPdJ2C3BDMFCHY/9hF0LRBkyBU67hLB3RdU4S+xFGM8NhjiKh7cMUXR4fP9dXSFMRW8eLqq1J6XGed3x6nzFtdWE7TKpcTSR4NoXfP50h8c3Z5eUQ6jlfQ0jML2C3NT+qGXyDmIzJAumKxXG+Ly4dRkwwhBGEsnQAOW4AA8Kie6QxkNlc9xHLEyYMFzf6gGZBHfx7fkU/eyYdsEhVJFVu3Co6zoJr2JjFNQQcww8Qggq1oEtazSEkhOnnhrWtRsteiSMvfIVQZscilEBrprhQMCSf50KnsSEWXgXHAphN4VQH7315pOd04r2dHhJ2UxnWnXx0jOPyRTs5G1xhEjG5/qJ0RdMivroQYkL1fAms0K6jvUSaLKkTKdxv9ZNDmmsBFmfncSUEu8RUrSc0f6SsH2iaE5IyLt5b+wLpVeHDDd7pXtxG7OguMJmpSk0qx6Kbdjvd/S2poB8zo56qCNgjPmd1ijUn/mZvGIW3PaK7797gdul4sVqca/KHXd1Q1PGwoKNNXtEpAHqx94K/sKv/X34fefv4o9dfgU/VTq6An91+xb+7Hf+CJ6eUfoYfQtZy9JVzDvYny2IuG92s8DHw0KWlLka8mR0rJ+vc/xdKV4DLIRTuaCpWfAz0ylgOYUKYKdRsbt6KCgUiFn/G0QJ125JYqPVteN5/ULloRBECYtPxLtu/ZcBEyb98L3By7TUPnIDbJp21Cr4RvHrEgB4mGn+zJyoti8YfUfAUCOXUIvg1flmkNxeMqdS2NBbW694fTvj8y/voTdvEegJu7DsYhNqBVq4837dsgHLgwnNwdvv90Tju7FRxHMOpnCiGGoSUHLcHM/j9PG5uU3nfC34eIV3EWGQcGGLs7ZGXqKvDlUVDzk5aduMZlIaiimrTNlhqq5E0xqORHcjS2q22TyGNyzRbAYlXi3Ka4dsBVTEvQ32hKKgFGPubI0dOw6QW7DoU6J6tlAV2XIzuBhGD157RuN+mlx+muYB5sGxC10lQ16l4o258b+1ELrTV8d8z3IkDIioJZEIBWViWDPEE+soPa5m41uvmvcz5lxzXaRX4mNRNoC/VJQbYXtpSel+Vvek4NQoPLD8ZPDTcuoQYZxO1mPkSjBIqBr/mXbGvitOpx2PZUH2XJ7Df5NCIADR+VARHoWmh5FEhwRXSMPYu+2E1zgb8pHEGne5Yth7wV55yley73POng9bL/hXfu0/jZ/6HV/i9y6v8Uv7K/wvfvWP4YeP9/lZwJuGbdXW4KVZePNaMoSZrh78OWdDpojVdISXADhE2p71eUj1Rx2/daWgxx4IgFntKxtypnkcP8Iwh1CMW+dBireyyaDquYHm/RDiSK9jUjBxPSZBgaAQ8KLccJUFD/1k8TwPH9lk6UDyhUAnYztdUsMz2sxGiNF5TYHDd55DUqMvA8hqGWaFAAAvzrf0fOI8S+lO70348nbB52/vITcvmAp4YCgDt4DlBIdy6mjm4h5BdlcKwezWISJ26z0HnvdRyJACRtLxOf2BFEKJxG7E2MNy3KfK0fBkiOAF6tbDwL2L6IAW2HcpBFZTDrharUEqieDnP3sIwvd9CBMAicTo3hCGn/x1jxkbH4+mMkX04T1h4uQ3mHG0c0yKpqrQ0hHtHsP7m7O7mVfKVp5mQEQvZt6QjY4sCTys9rIZskgKUMSquKPvQczDDDKYocLzEQl+wENHxbiOEjkkY/wTNjxZ0XYdtUYzvt60D4WmZPNidOw62Gz9uppuTZyfMkcy12LURwVvQH8kbK8I7c6VgxdekZJx3QWJHzHKanN0WXeHkFt9UWD3rRcKwHfNhKf3U0hDalacx62d1rRN2CRg+8gJMgPdZcJtr/j84S4Nxru6YeWOl6vBygXWsXHrJbtRqlICn374eI9/6Xt/FD//6d/EX/jh78d33r2y53QjWZWw7RW9MdjXonSGLpLJ64Pim2tsJoWWv6fywEGX/GbHh4ePyLmPMHojixK2CfVz6yUF+T4hgVQJp9KMDtmVwer4/MgXZA7BzaJAJcXfi++UqEt40y6mEKRkPsFuV8E4xvEBD7tl4li93eWICwYCiabvxN+KSCwjQ0ulqOcQzEIgGMHXi2VLRQiYIjp71fW7/YQvHi/G776zafUJpQMahG9Zxi80hKOMTZ7hHhc8WTPAU6ihRVI3Nrd/Toc1bjFid0FndtjuvP5OpoeJAlsnPn6phLIekTP2fcoEdH4WlAoDICxNUa+C6jUO+x0AtQ5s/WzxaVnNC5jrKqRa/4RAxPBmpGOReA80TLY+DXe9M3RnSBFjMCVHmgX3TDGOnlp7kinWaR31zpBFQI1Bt2HAW//jYYVn/sDrCkZvZlcGFtcEVFF21zbTGtDifSm8KCat8xKc+eRelsGEU6lHGEjH58PSDyUM9bCQzyd1ZP8GWzOTAHJr3k42hcLceMjruBLK0JXPU7kqzpsphv0l0C92br4F0aFJcGVAF/PMmisGuWy4boslYX3fcrTd3NlD8poKOhPo073HfZMSsoENwRR/FSgTis+1ALmfVQl7K3hzPeFUGtbScOYdLxcz+N61E57akiHyhQUP2+J96c24/Btffgt/8/U30XpJFuTukYa9F2zXCmLgfNnQvJiyLAJdxKCvMh6GimRL0nydpmecvDXziPC1jg9ONEfMPgrZqlj/413K0SJXC+NEnYKoadWlUHYaqyyoSjhP8NbITwBAc1psI+KLIrUdlQU3qXjXTxBlMIlB4EjRlEcBHJCQ2MpyuK/CgoLo2eBjKvYeHZ7Xi+eKubaRoDYloENwTN/59PKEpXS82wwjWFgSevrYVrzdTrht1eLa4fKGUK9Au9d056nR6J0rR6sZQBauZVXxREuRIY34b/IoIi5vHb+Asgl4m5SgRLjJlEG5dWCiWZCVga4ozq/CO0G9+xhJQVTeRriphdAKizLooc0l88iT38/Nmp9tTJlI3V8o9GQ7PkIbWoG2DKSMfdcSmko2dlFkZj2WjbpZJwXcW0FvZm0eQg9TPiuERPUqXKidF7sllik8h2lOgvguk7eRXFYTpBZyGQrDYLwj7iNlnrwx/+GBZDFZtYS+LOOzAIATnI4c3oaTPLc0wYrTE7D7i7qEucp59lISgRSC1xUAdSQcOgv2wsN0a4q64vSlrZPtEw8p3UmGOhRmsWsnoA4izbN7DFuruN1qyphaO3gR62+BcU1E86HJUs4GPBj3HhPFbFQcl5NdZ/doxmwANmE8tQWn2nBfN9yXDZey49ormpxSxljv6TlXMELZs0JQtZYDt6s1Ezqdt5G7dPZlJjWOrk6j06N7r6pGvSK7J4uidWz+e6YsfpPjg3MKC3esxZhI9xDIIbxpUFpEo5gQxnF/QYIX8bintuDbl3e4lD0psBcX8nBF8HK55vWZFE99wZf7BaI8OI+cSymUUFgVM5FewFiXiaLb7pkyGR3wsvAc6JkPZsglyoU5Cw9RSy5fqkFkb/6s59oMZisVj/uKt9cT2l6N3CoalcMt4sUs3oiVR7UvMCz8pDAGTCGsllvIUIBvznKz1yKOH8JgtgqTztlDBNkTWBR1B2RhyEKA8Ki98Ipj7joEabhSBJSrQJawbu1+lui0BkCKooGzsKufrEEJvGl9eDjs2HYLkw2qZiVAF+RGl6roJ4OsKsHCEwz0qhm3Jo8XB/cVqhUiZYvNOJ7Nd+uDGkXUewDvDDQ2ZE2E3YKDaQXqlNCP8E2Z8h9ERkvRg7YcSE8i+ZBCGXRMobqgJ7H3ZRlhpPjMXAwYkN9yVdSnsS4OSe9CaO6xRZtS7uYZkgznhaA41gGMQjnLfymITBgpxv0/P+qTe6KdsL8Qw+AXNStXYB7cYvtYfK/yolg9HxjzV0hRl47tFjFLGo2U3mtQA/ce4mFs3aqYDFiWPva7f624URnH074YowIU9Sz4bH3Au2XFY1sRLQCAwfqazbNKR+uDvUFJcWsF19uC3hnny5bGSvZodyOksALFDJdAiNvc+jytHUKT8UA+/9NcfZ3jg5VCV8LZk72sYtTVvR6K2PaI60/Cchac2Q/BG/S82c54eXdziJaFqEzgWwz+zst7LVx0xiYV177gXHavlTjWIMSRr033X8hYVyFzoiiUh6DTV+RcvyJjUzwROZMArrXjk9MVopRu5Vo6ztXG6/V2wZvrCU+PJ+uopiO0IWcdE74VRxONkEBaijRCMRHDB6YQEpkVXW5eFeyWYgmYY1p3g900BASJpnKYB62vDL5JfjYpGKZkqSEfjNIgzq8RWlDKcEbEqwEBt1FrETmCDFM2oD5F0Y4nJyvh9gkgJwVvNLwrJ+rbX4iNWci8yWsi1iHQJ6mlLohoFb+2JhwV43YgQqiOGddOoBt7tytT4oBPp7fVrNfBWCqV0GUoaVmA/cXA/pcrkgk16zqmoRUvLgsPUbyHMoAMBZU2LPwQ/NHTol6j97PdS9amRA9s9yDFq+BFCLSYEud9rInn852hSv/+6J+NzC0kWk0nr6kD62uAb4z9paLfKeTs66tb32U9WaOpYEImAC8vVzzc1jT41rVhL0uSzMFJCQ9Q46gCJp2St35sbO2dWXDjBWttRtfP1le6sOTHuxCuraLLHSp3XMqGF3VDOz1ik4LSVrzbTiilZ8gYMG+hsAz+NfWOis3oO6IOhjlAEBaBaG2q2Zh54pQyIc5sMGvAkHEQWL7H3aTsT/KbHB9Mc1HIcgkLBV0DH6p0d2HsznP0vNNanueZgnhqCx7aipfeRnOZMJnRMGfXgrftjIe2YmVH8pDi1qt5FcABjjrzKM3XjKMEisQ/G32kQ8tnyJclud5rkWxAzqyH5BJgJIF3dcPDbonvtfREUz22Fe+2FVuzxFL0mdVFR6JIzHPgyfoM637OGchpGsvs0eqK4GrWZtlG7B+YhDg5tJTCrzdhTQqAyHoHiyIHQBXlKoewgdFT6PuLTjE0qiNt5iN7AntCtewCKU6bcKaMcWcRXiix5qfaFSsRtk+8aEvcQGoW5uoXACdBeWT0s1XKQgmoVp8Q9OXY2fjq45GqFVC1Tsl1pEq4bWO7EFleaN+r54HGuJsH5zQL3n5zeznaYEYNQFR6q4fU6hUZvsnrRNhjTM/w/lxuGv215vxn9zYe6ybPxTjAR9nXk8X/g6JiKImwOENRswMWrBhNPKFv//aLFUiVDQmBjvHIz5F7lpMnSRY1wvLOvLvtFeH2LYXedRPgYv1EcDqyJpxKB59vWSOw1oanxfdn9DhhNaqV8Jj8OY/r1AUnAG3WW6NtFe3M+OarB7fNCIU72oQSar1klOGHtxf4dH3CqTRHREoW4gZPW0RFLstueQSY57ltFVx6QtsDEQcgQ1elSNLozKgoIk0KcnHOLaPpYWvGA6DvnMClr3N8kFKoXshx4W0StsZjFAI+YnIhD+aQzEw/EcqiuFB9bCvuq5FRRZ5goY6nvuJNM8hp9HIVT1gzFA+OeeRpBGZXLSuLg+rCcxlxruc9GZjU+iR7KEo9ThhHnCeeLc5fi+DT8xOYFJsYQ+vJw0b2fAsebyu2pGeGxQKdKAxTGAlwFzssw6kyMxhKybHj3ID6oN6NTY+FbzoJEi86iqRgxvdDObh/Sm08X4a4J/hhJEututZj4k6/Mce/gWG5hnWasFJ/lghVlM2rYs8Wcz4UsPlnQ9AtDwpSgzr2MwC3hrmZPlIG+gtL1Jk7rRM3/aSoGqHv1t8CscGmo3sjdLu+Icy2Zoye8CReNK0nd+f7qqAFIKX0WGA1mu4xuLcQyn7X0abTPQgowDJQYZkLcu8rKMOjfSc3zdyEUaFHnMGNgGktWL7nKClIdFIiMuWCLLSSbUEJ6GvJ/tqyuJLjoZBSmYUC0HnN+PqFhfbM4FGgAetrAlCwfUqQT5szhfIAh0wh2rV00Dra+5bazesm+EAT5vaWR5oLPeRIAsqqnlO6NcabpePV5YrVjblTjUrlglNt+PT8hLV0fLlZ3/cgsmRYmPuRVlzbkvLlft2M/ZgKHvcFD08nTxabMK8sqEXQ+giZARGGGoog1yQsvCTdnpk52Fc1Q5wggBb58SSaLRHcUNkE9js5HYvFlAbdtf/N00NFwxzAFESgjYiMCvmhrajcsZDgvt6wa8GX+wXv9lNWUEciepeCa6/veQJBqd2nfAaAHNw6CfXwZgzzPhQOgMPn5u5xgIWJwpCOArn7dcO57HiznTOPUck8j1u3/ELrPLyTU3ehRcbNHxC0sBRnt/vQatEQNeVq8dn6qEYi55suhH02mQFG2GA3JZAIEwGi1Rk3TXhjhJYUw3KN0E/wLZGqC3jbaEHWNucFQlkcSN0KZay2n9nubUIxzWESG3TAILZIgjnArNMILUUzGN4I/XlSkWFN3NeOzSGYdl4a5IJCGGaMJRiZZWw+JZTS01qL5vCyArogQ30RutNpHmwjjLoNYBDNkXt5sdTKbeR0uNFABklY9q7c4db7HqGRqGj2MBLpWAu+pjjYZ4G0/NOin7yQ7IvgCisQRlLNw+zLIEKEWojP1ux4xiyKJPdS3GsgT3QLURop9kzA+gYgYdyoQj8T1PNYBLv3Xak8ZMBSCq6totaOvVTvZzGts2kJzSgqOI7/gP2fXr8+rcawwIpTbfavNJzLjrN7BW+bueqPLq9uUvHY3DglzV4pW58Qma3i7dMZvVmR3v3lhlfnmyE3XSYUZ02OsDZHmF1N4/WOA9UOu8gIhByxmkHhz/Vjo85+UUxYF5LkGorwURxhKAA4xOOjF0IwmV7qnr/P3dOireZTN3ruc90zEb1JwdZPGTK6q1u2B426h5nSO9BEQbud3E2T0J9zCzzfy7PPaCYcn48L8Ml6RRPGbVJUEVp62FbsrZhLDMPHqxKkUVYyRpeo5Ihn9xDiXA5JLTtheQDKkx5oDNgtyb5QtnJM6w2+iRECfUyQFctRWpHRAYx6NEcCUCaoaQohMtrC2U0P7qTFKS4kYtmGtolK2hJJWU+UBk/TfA0pDp9Ui8vPGPzktXEhTGoopICg6hpxF/PE6mKUKcsC6KWhF0MgZWvVm9Fe0NrBExxVlSA8PE2FxXf13CDsGPkbO9TRk/gu6A4d6yZPBzruWQlOgoec88i5hEKJmgIplBQlUmAU03NYBl4AiJFITuJDGl7b7BkMMj3Nc5kiMQUSysI65JkF3leHCjuaTBYkTHhW5tFjelbkuV+aFbLNLSxIzMjB54ybrtgXwd2yG6WEVwAHDxoA3C8WrbjWBVdWIPoQhMCfGWx9DvJijGHhERLqGULjtlecloanveLWKk61WR8QANsEqslnBeHtfkqQC5P1jQnU5a1VPG6LJZNLx/1lw0+9fIvHfcXDtmTNQhyBVAKQHkccgXoUu3C6mfFtUUKpYiHqZ3LqRx0fpBRW7hnvZ1isP5LMsyCOY+FjEVq4NjGooRDiPSZNuFcTxj6ji6TizX7OVp1RyPb8iPMEuoiAtDCe5zaeH5GD4AgLhYfgO7jwCBcNkjzzEtbS3HKwe2qeZH67ndA6O+mfxxMAaD9CI03oO41EVQuN+KSWG4FvVsFcHyMM5JsuEtBxHsWB9yZDD7E5Q6g4SiEEgiyMchPo4sJ9ziH4wV3ss5EvyLCDgjaBcoEsnFBJK0qz2LoU681gX8BAGIXFWN6n0LCB8hi6h2tMUHn9wQojDeMhhMCw0JHfez2ZgG/OeFuX4ckGiggwz61UwXracVpazq+wGQxrNb6b/L6QQYpZR+LbByuUrilUf4bwCtwLa2dKmokIyWRh2Y5MBse5ooH8oV1mpVSUQS8hlVADZEApM0ypsBk9KAPhlcaBhneph3kPWG1cxwS6hT+sCQ+hO8qKd03K9zmnkFPJQ1Gkkg/l6M9XbsDpc8amZ3xxd8Hv+eyHxnjQXM5gUM5fqs1VKHciAFVt7RKOF6ex9mzh+bMrBuSTbF/ve8kcUlPCda94/XS24tPa8fJ0w8IdQpZTXblZiKgXQIzL6W7ZcGGDre7daqiiP/Q37x8t1C6Mwoouk+x5FqGI242kcySYS/HWAwJvBIagN7Ppc2/x6xwfnFMAgDPv2LXgJsWRQnbaqBQGcBDAQWIXtNth1TcdIaQujBM3XMqGAsGTWrtPIYOgvtnPGUe0c7hlDz+XlKyqDuhpQNpoUkxROT17N7MHEQnqQCnt/eiDBZJg68WTUYoX6214CbCcwqXuYCiubeRCarXkUduN6Cuhc5ldxNTS0jbf8kBY3nkCOaCoNKzrmd4iBEy48rHpyOGgoQyGNejCOazUOhHlLeFtjHscRVgYIQOo1S/4Y5BaE5IDrFKQXkcUXEkNrP5Aw3BXY9D0c9s5zapUJvMGXBiSIkNH3IclrKyWq+kEMFCcxbJNSpyLVc2CTBlAjRZ7PbVUAFsryWfThXKNNu/hrO7dJQnZfExKoJ/9M04UGAoBPEABgfLhPqF2nnujbSSkSRR95amwEQntDQistVf1OZyABIAmh9UQ3JS5hFEL4caBr6GEKquNfdY0VFM4YO+z4b0yygZQQKEVGQ6djZjZk51zH9yA5TXjzQ/v8fDyLT5Zn8B0PiIcHczy6fkJb9YzbrJAI4Rqu8eoTab+IzMSTZ/9nftPABQTvsLe98Qbcm2t4rZXbK3gG3dPuKuPOPmknEvD280AJsVlCJcdL5YbdilQPTsXmuGsb70aMKcd5cvsMQS0vnmx27J0bFvxULikx5C5Bla0AF+UH0fxGhFObGymC3W8bpcDAV2dBGjEw5qHWyK2Xn33zCEkIGofOip3nLjhXT9lF7XX+zkL2EJ4myJhrKUlAioUVt4uRoJqFvrtK0Yq3otCk1AGc06iY4SDMqTjXsJd3bD1iuJJ5QhPXbstIIIjV3oZcelwc2OvBatnWIadsb4h1AfvteoblYBMUo6CJjtJJHTNS1BEDFmZUjA8R6gAyOSzVEKZYIeRZ8g+BjRgbgldjfOGVdqtjaSG4UzmBfWVHdFC6QWRIttF5nOHNeoCK0JOwcLaznQgySNxeoyTaUGdqYabWVW1CB6vPA+JGQBVRsFlMcSIzT3nxissqAWpECInlNxHkS9wz/T9UI0X6q32ZuRj1i/Vi8pwJLPD/F1T6KGg429T9CFZJyu/M0g010vcS47hrp7jIS/2iwSx5QrSO/B5M+t/Wl+FkpE1vdNuhpmqrbOYm3axeS83v24YLzo8G/h4pec7Ht/QdF9WfOfNK/yez34dn6xP2KTiYV+9URewoONcGl5cbkZlzmN/EgNBOR55ndS0Tm0BYFCZ+Oszc/Lx5/jYba94ezvhftlSZj31BQtfDH0pjBMID/sJ98sN98uGF+fb+H4bEP4wQuOYedbCmJ1ZGZYFhyiF9W6ZHoFtH0SHu69zfHCdwol3iDJ2X/kCOtwkPD4292AWkMGIQRkTnK30c2l4sdywkOR5AeCpWyyxSVB1W1LGFMOQapGIqQ5VFaKEl0aoSciuPd9DcJcEGmkODc2UG1kBTaNyObT4/RKEfjU/W8k8o13M2lSVZFQV597JRIBbtMF+yDdGeSKUqyWTeUdSCQTiiASZKJQ5MasBSxzVs3G/Uly407Q55iMTupwIJEtm+u+xK3p4IEdSN1DATY9ueuYZ1OLbwYEUYRJyfL46bJNc2bHaXMFzExGzLptCTqZYxHsYIJLLqsBJxrgWxeKgAKOnKJDG2WjH0CuSFAoKB0B4TLew4PwMiQYAMjXusdAcDUI4cuu5WqimbJ4XcWLA5S2wPrih4aE6C4c5jFh1KP02mdRwi32XDM2lQUBmyXK3qmTexI0G8+76iYdHQybE55zDXG+Q/bs9ZJR5hewUh0PSPMch1kUbNRNSnd5drTKdt8GkerDNQiHGv1hCnfDuh/f43vmGb989OJGkwT7JIwENjPt1w7vTjtt1cRnkQjzCs85ldWARiYQzAVysb0oaaMLggJH6ugGQHRp7Z7x7OuHtuuEnzxbhuK8bTqXh1k+JtiRSXPuCyoJX6w2bmJWfBqIL/AxXT0c09oq8ZvSEMagqjz7XOpqARevghNv+OJRCwEQfpWKTmhZ8UEY0T7TEkbUKsIcuGC0543trMebB0LZMgqYFT32ZksJ9UF6QHhSKEcx5ZfU0yLuwydpnEFIoZeho9j5ygFhwa2OYZpf1+WcLS3oJqmQV3hgIq2BFve3VBI5boBo8O5PLDAC0E+oDoTxShkTS6pyVAUUSGu5BTJbfHJFiTcoFywNQKoyZymBOgioBNDWFT1qKQoPdMwR9GYonk8QOy4qQUE92VcpkadRUBLbdCuo0r2+IHH8vKcENdiuFsLxRQAj7S6dHkCFJyqVZ9yoh8NpxWfe0/KWxvafGetpdQZrnZsVtqzeQZ1cIM1VB9No1bRxemHrB1CArVLKwlhYYxLYB1Z9h9Gc2i11Ukz3WqohdoLiyjCPCbuQcVrJaGMsaMrliDQTYdJAaZDV5cQLRFHxFDmXlaETP5O1WkfmpIPGbadtJcLD+A5pqTARxn64c1onLyxVamShV0lPQ8XuGRK+MX399jxeO7nux3Cykopzy4FJ3vLjc0PaSYUEuxh0Ecu+QPRkbe42sKjvHKZx3IUi3CuNlsa5oo6EWJRuyKuHLxwve3p3w2fqAS9lwqbtR/7Nk1OAmRkGweF1C9KYPcEvrNaHtgWbMYXF5llRBnneg6V+wKogwWnPvIqr2fxxKoZDiTDte693B2gYGxHTOls8PD+AQjgnh+qLecF8Ny/vUF3z/9tISNx6IDmUQ3EaRYI5zRx4BOF53Vk5x6KTEuhfd9clDAJAsq6EE4nnmjnLx/hqQVrhn5PeysoXCCpe0Mud7mHMIwZ3OjwX1HWU/Y/XFnOEUFyaBX0/8t8uoiGtniMg9iSgIO6BAKDyHKVaM6fsR9okNFBZl5BD8NQAjJxFWJhPKZjcT3d7imiQT9UZg23mEp0qEKggIGoW4b252jhKEeGT31C+EVgS0E+RkVebKNs7nuw2FBY+bUQoQ6fF+gg470Cc+t9UFwep5r8d9cegxkC7YtOTmEFg2hVfknM30FSSWUygOj10eBeVJ3nPe1OP0SrAWpc2E2P6yoj55Il1jvEaux9BGfo9zOKoPRNOYa/cYXFoS4AqdUG62vvrKaBdDikm1vEHUyQQVht3vEOThhSD6XwR8mF05END3MQaHtedzk5X3O2F/WPHli4sJ/3rD/XLD2/2MrowKMwbvlh3Xy4bHh7MLfBy4gp6DJgDzEPLCQKRcAHJqCTEairDIVWO47fNNGG+2M+RCOHEzOL17mUHZ04XRyZIps+yLIrW1jp4MXYciOKwF/xnRj0qKRsYGEZ0fwwuKojfmHxMhXqWODj6EeGY4aRzP4/gxGCnQ2WL757Ljvm64lA1PfcX3nl5il4Jvnh+wlO2Qr4AAza87U2nH9eJnZSORmtFG4RnM4aFETLnWn1FQ2QgocgzTJEUldBTQzQV12TaUO1bueC1emxACp3MiI9KdbQx+KFhcIQRiI2mx43gmNLIgCMjFPmLBE6cO5a5PAZVQRoQ3QkaGFwpGo/BKU+gneZubVNR0Cg35PQRZnvMeRaN3K6pyRE0H9svgdjIlR55HCK9E00qd90cgVMI65d1puhUj6QtLuNW1Ya0d171aFfJh8Pw8wECA0aA+J9K07Bby1q+BLIrxLGpjKy6QPUmsHMl/wvLOFZxX2AYlxYDcxnPRGHsAvAmIyQvEPAnslrmuNq7U1JSFjue2XJH+6LCBIutLAv6a6LRQ8hrrz3MXraPcCO3C4EZozajN+8nuIzzNOXEd9Rdz+GnOJehqOYd+wmhTGmODMUb5HBvjzcMZr85XVLI8QlOzypswupol/fJ8w/W6pEWN4n3XZRgDM4nvHHYJaygpTjzc21pBKZLx+0g6hyx5t53w1Nck6owjDMEgAYUwBA7Dd8Oz9zKF2cnpNSIyYd5Ol1HgyyzIhmHdOtHNdP3F28cuy9Q35mscH6QUFure9tJaX6b1PiWZQ6Ba9zR+L4cws6x+slxxceH/67c7vL6dQQDayoYAeBbWCWs4YaegVAzzZyNUFdd/XjQX3s0cDkrPQsb9ZdUzy0GDV0+kn0vLSuuk9SW1Dk1S8LQb/1GSsAEo1RXOzsCtoLwrqA8GRw3hEaR14OEZ4NlmswfFJMjdSvRisb5Q4uZnRaAwC/DgOgdRm47zS7Gkc8Z6wwL2KuiDQgBGbNzPGT2HoRZHjj6/fSVrZs8Gry03TcUR1mS2Vwx9o8PSJO9NHIKEd7MmLWRBRgHg1loTzgWfRj5rCgLFUAQ8JSkBq5w9e7XquTYQFTy4lUjsCqHYSQ3t4zQXcG9BMHoxO5Ed90CGaVKZKwdlx0go28O4Ul0Z3GyMItGsDEukko27zruaLESj2XyFPGTkob9IwPI0f7PideUe1OqRPyAxSHH1udzvyPibCiyHwhihQdh6ScsfSGRVrDGQhZXkZJ5WsPUO2hAMhdUJ+82Su4tT7K/cIM64HLnCc224v2x493D2sXBDcKYkQCyoUS2cxmK8TVEHQOiNsSwtY/hElPQTgEFlH0IpkOBUrO88l45ghp6htKdiawswclAFDnkDANkLJhRAvBd/39zQ6Z1QyoCxDh4lycf8OsdvWSkEvG3X4g/KaR0/R/QEtcN8zMUeTIIXdcfL5YomBW/aCZ9f7xG0F6KEm4wisJit96qXoQlNFT0WlczJ7zkUJBgK67mHM5eZx3UOdRQYk3Sqza7t4xDXebVcsZDgnTf+6U6/CzXOdgDoW4VuBeVtQXmaTCLFIWEJT8DSFUeLU5G0yFG9jALwo7hCGdZiJhxdyOSzNR39mneZiphcgE5wRHVBk8RoYklQSigPIC7I4iAC0IDqlbf1EdhfFLQzJSY/6ZsFqRQSeRQYViDDXNqsyxg7hXfPUBIgi0Kr0VWoFlS3lmYAQXo0ioQhSmejK362xiJufZcJxOLxZIezRkiiqLVQ3Bi0eQ7BuacCZhrc9lEbQuE1Td4BBYon4KEFWUkMeNLXCxDLrundJWNpMNfuAzqM4K6K3tp1rLMBNR77y65Lmdcxj1DQTwUgZBEddaMbCc6jdjYF0ardo1QT8LxNHsQkIqi7rgKcy8vqcmSjrHOQ6JHAMOqKnfG0LXisK1Ynyly5paewd4OBvzzf8HRbRptcGvQP06OO94L5V6flS16sR8ikLjCI6+DgmlJsjT20FffF8qKxfgIyGzD9Oc9Y2UjzFv9+wJ2fh7xFkdX1QRba+pA3oaCiOySg+fdXkXj+qOOD0UfyrHo5vIUo8ArPgEiDQSHrFOJYS8enyxMKBG/7GZ/f7g8C3cJE5ltzmMl5fTq4aU2K8Y9wB0vJxbCU0SL0kBx+5lnMoaJDkZ0fB/oMv+4ujFNpnkMZ4bJXTugnMC6n5hBUclcwG7RcC/htSXZNmkIiaaxOG1MKZTFSvB6VpNEcRzlCDVbMGXUJwAgTgCwObHUOEf5xKCtgzU4ARJxaTl657AqEAQtjCEAldnXcLFypCfrCLvTGtQKplP2B3WsIJRCJaSnkwnGyKv0ZIjkbCKt6BcQt6LyFWwHWjnKWg5VFLCAtnrgzyF7xXIKKJV94stjaZCAspeNUOkqRrEofC5DAT2z9mW+GGLPn88S8aNJYHMJf8Tchw2vmaVlYsC188J7MEwSymxsp9ETZUnUocEplQ11RbpIegjBDKz2D+Y5xj3zETJ8eVna07WQMuHAYMDGH5eZd885AOwM4B1vvNI+hHJ2yIXsxLIp+0kRpaXgVfVSe324VD3XB3bJlx8dzaXhqS1rjl7pjXRtut8VDw/D7d0HqG06fVQpHAVgsf3stUIPmJRhQZMgxVUIXsgJekIeNG26ePF58gKIie6b7iSOK2AAk2ebhs5Oss8Q0j8Q0K3qf6MSL94cwW+DHgz4aoaMyUDmTpxAoIwAHSGcUmEWi+LP1ESfecZMFr/czHvb1UINQqSPoswHTrjKZoVHVHMgkwOL+gUZiKu95AQAOGODnCe8cIB5KY86J7MKH0NKczwh3Nsixdim4NktOWixQvbUgY39awG/rUAhiluVziJ+9aT/khMHFnxYcUmmUzazRPMIIjFiuhoU3zKF+5qw01guPZKgCUE9Mh8IIYjXjAEO5qWPikV6G1gj5sAmXrilcjJjNBNT6roO0WAFboUMhFQDvmuaoJzi0szutwpQUT3RKN+u8z0nUaU5H2I8yAelwKdtUHhJi9nkM9JHP+7UvaRCoWoyZopXjxuAro2ymEIIrKDyS8L4oKa+nkN3UkQ1QoI0QXIbs/PkCeBB0JAe4r68BFh0hO1hyujxpjj2EgCg08/FLZU2AMo/CRBm1J/M9j94NjqOPe4WT8+3WF6OuQLsj7PfROc+8hqRlKTisdwMRuIGyqnVmA8DX6UNiocHWCx73FefScK47NqkDcdjNQLw/bdic4TaFaxneoKGRJN+PHEHg/U2gepcz0mwBOgS1Hs597RXv2oqFDE15koaFrcXAnKM0GWUJ55BBc3XVXCwJwKj8XeZkyDPDXla0FlXYgUCKZPjfyfFbVgokRmvRXVCHhzBb8nOoJmoDgLD27fh0fcKlbNjVwkavt0syqzJFotZdJeUkhYq4YQjgTep7BWtZjxAYdxwtffFrCJB1E3FEbUIojYDZAkfFEWiq+dpGlrVn/iL4UQDT9ERAa4ztaQHe1swXRChobpQSWHLFcL0z5OHDOFNYHDZXjwpSzVDAfj/HdJBQ1kxG+3mExt/DUzEPoTmtRQonpmzQo8nKZUJLFnhV9vgMtYGuoQ7URzEuGdVR3VxpQjSN+ztMsfMCKQHNaZuVXOA0j6N7L9uIAQOWHAzeqiAOG0gSH7uJwiQSzfO6u7WK7VYhwayKMVcxeZmUn1E43tXMTdRUFlpG1XgIaFkpxzbmodw0BakIpp4KYe1HUnhSwDHZ9gCQYgsslM7sPWTlePdCqNl7OLF5aB2Zt5hBCsDw/ILOW0CoVyu4XN6actg+BfZXpjTKE8b55jDVnIyu6v+MCyxhr+rU09040M4YDbYAZFjl1XrD23pOC9oEJjzMkkvpEOLOwlTEmvDxceLEGXUU64YAr0MyFGQpZgCfy45rX9KonaMWcZ8AMmTUlaAul4Ittfp37PNDYdAkw8pEAAggDU8RRq0dz5yhH3l8IEsqv+clACP2njUIvqEqdTQt2KTirm64r1s2rn9oJ3y53WXlcBSRxeCVycMAgM1zDE0Y7OK6Oea3UMfzhPbz37+KxOrwGZaEjyZaakqgB3opPJLZ07irownQwh1brzbRasUmrRVsjyvwpjqufBQfWS3BVIyEEUJK4QwTAvVprkNQSPLXaIaglIFejX20OUokOnCFYB9W4vjOjB4Z13BLleCJaw224SGUgIF0iobyagKor94A3mGoGeIqTvWA8Xo8I/x83fssR6+AEIzVidSiR4H1jgD6DnRBsl2Ko76SY89x3kl85hs7EChzFenixI0xx1svRrFSBFw5G5rk3BQAKwwC2Qh0tSrr/Z5Rtu41BvbswTUVCpi7Zh4gakEiyUz+XkB9Y46MjE4zTKQVoA3pkWTITgGp7rl5opmAg8cFIJPhWfHc3fOb4MJhySeKR595Ha7osvaErNFQda9yf0lod4C+APg21uScU0nvKJoWVYV6L2wQss/CrVY81hWv1qtHFUwThxV9qTvuThvePZ08xMKTNzBXLSOTxvNaAEJp+DSnseD3RwOxKGrGa1NGVQshCQgsJru0LSknZlTmHNospKCQgSWKbkd4qpaxFuP91os134mcJSL/8HfoJuDvAnW2/ePk+AltuIY29IduYKw0JVdIcCk7CgQ3WfDFdsGb25HPJBJCTUoWDEVhWlBjzMJYQDjzsNCZbAEzCRj0niI41FB42GvmQiLSwbqsA7I6J5xVCRdnaBQlvFhuTuBXIGTFKRlOY8G+L7g9LcC7aiRuQBYwRRhjtpYylh734X9LRaI71OUa95FAVgIaD0hrCD6elAGAQ21CvFZumh4A1CpSAbtWcbTLiFN7/H9WDHPS1K1bLYZSMYHGIwHqtAv2XuRETKj0MjYkCUax1GbCRoLxk5CV0XmfT2RQSbfuovXmcK99PAM5QkgmVNVAqM3WoqZRE6/VKlDtAMb6VMJgWy2AkpHjyQLsd8DySChXHZ4XhyKmHMesZQhB6RIpPcOm2bDHFKVmCCbCSkYt7rUIiR4ycAB73Qg1sdqBhCvTQKbB5oDUFIIs/B71xHuZ2ukIxafkHuvErVWvlv9pd4TtFdDu1DivnPkhahLid94IQmYw4aTQ2hEJShFLKkd3x6wVcg8+9t7L0w3XCYqc9DJxv/57eJOx7uYHNMEdIRnyc4zPRnUxAGy9ZhLZal0Mln6qDbdmBm2El4Og0wAMNTtBEo1cxwg9y4HhOSy2LpwKYU4FAuTKgv7eo48AeE/mYZGvbsWL0CG8EzmAQChFc56VGzoYX+4XfHG7w+4sgAokFpco2FMtmRsEU4U6IEADH6CogFv9YfDH/WGEkMILea5QAtb6VZXNcyjseTFbnOdcdpy4WyiLxLrA+TkWFlwB3K4L5N1iich9uN4h2LVoFjzZhsII6TiKI6C4ym4NEpJQTj38EqR3oWCic1nC8N2ay+dzDyHYM2cupEhSzrkJ9rqEvhjPmHH5D2VA+ZoLl/Q8DB7bzjyUmJP+zZ7KgZo76bQxMaNiFBOXIcTUezEMxJKPz85o7A2fIlwkw0J7blHNyI8x7wPpEQVCCWGsYv1Qq79Hjiq6+cS60txeMM57HwV4MAVgipMgPJBkEW4zDqup54CzkBavJWEFukOCpQbU0/ZAvQn45sinTcCbuXrq85KemTrufhmGUoAP+srDVA5AAcbYRo5KKsb8h2GDZx6AWNiIXMnxDtw+JeyvvMPeNgoU1c8XDKa0szXJWQXlNIS/CCXz6Lk0A4/49Yobjy+XGz6nO59QgeqxSDX2uP0c1n88dizHcshFaKKBRthRUo6EzFs9fwogqTkirBRHRExINMPnS3gqwti6hZDOS4ttkUVuA8BizZ9CaR1rMPRH6e/3jg+uaN61HGLxoRWzsMwL1MQ/dy67h45uGTb6/HaHx31Jau05K/9ViKHduY+AEbICjLU1wkz2fasf4AjOTzi4rG3QYTHM9Q7pUdAgqEqajUAsIf42jqUXdTOL0d3H+TNNGE+3Fe2pWly02aaNkMycTNbidAUYgv85Dz2pt6wswOi1gENYKM4Xn0dcy4/wUIJmuq8TudkEB5VKIHK+Ho2+Bsg8B+DegX9P59UIZNiBPMlKHWiXQboWP4MiOqzg4HM6cPF4OMpCK+N5gk67RYtLGt+JgdPu3FarWW+arLQAwrv0kKW56kAtihG3pcxTRejBKIqHxaaswAKjkWhjDqU4lfUVSQseoRXeLXTXz7AmVJ4DAnmoyOlAerE1IxOUNBBJwDifMtC8DkQLY4EYjBnAQVJEonp6PTmt4sezFqvUNKGsNvaxTyZjwJXd3PYzQpES0GZfe7wBpy8V5UbYX5rXIN1yQrKEVomLu1Xj7VPr6gKSFHsreGyWcF5Lx63RYR+vpeG87rhuxj30VQR3A11kF51rFkIIR2hpDOHIOwIAk8kkfiaCIzLy4M13Cg36fguLC1btuFE9hKMjn1qe1VZEvjVeb547AOL+3g9xyfEUP/L4uwBJpcPPyoLNEz1zj4MYpJU7Tq45N6n4Yrvg9e2c5wsrPpvckKZV3yNURYKFBE9Sxkb1MJOQQb+ifzS4Z1hrHug5uRNEeIGMitoFBo0wkisDAR0WQaAJLuVYctyVwGRcJgDwcFstbLRxCnxgWPoZ1+44SH8LRyArnxWAnId+C3hfxNvtpial4MpA3eKOuoVQEtAB6yw324Rli9CMmUlBjZHcR3m/g747LP9sq+gWnhRnam0KiHP0KJIzSaoxaZK4snF+nLBCs79DcvRj8DQ5/DLCJjEOWfkcXkHzz3T2uLFXkru1z1VAvuaIFM8zcjMCDYTDpk10h18PcC+Bhrsui4KijeUy+lfEvffFPttOKf0AArYXNYsVo7NZ8gjplIeBFZKRIMNwMR5SzSuL2gjenDalDoUQOYXiyDH43PSTJ2YVoCa+hoZCyjXq+Y161aOS0VBUQ/DGupXJo6NmhYu8EyCEdq/Y70deLJWKutmugDj08nSyxjutl+zPvnLDDUZbPxt/59oS5jlT8BjMc0QpAmQQzAUhK2YlAZhRHIWqoVQKy+QlWNg8ZEpTxqfrE96100FpXPsQw6fS0ighGq2CI48gSoe+NHPYK0JXB1SV//019YGNzd/BZ987GIq7suEmFTsVi58JeQwfCCrtyh3VccSXsuPEDU0KvtwueLufsXt5d0D95k0Xh03u9Pcz7+T55+P+go7g0C9hCiUBSHdTdJDjqYeUAF+MsSD0/euey54YZFEeHOr+/sO+4vG6QtxLCAFCU5jLrHu3IKckc0JBT/YP8Fug4U3UK8YxJ6eBFJTZHzmSs0BSWmTeIv4BGfLxT7pA1vRCLIY9WcdE6A5zLZvHuSNkFE3qPamb3Es88h+kbmG6osmQUAfY7yUa0dg1R+OYfSXM/DtBpz0q9uy5LKmsg9WUxzqboXxL7bjtFeuU0AtvL+Y9R0YNyppWmMIKnYpb+aTWSrPbOO2NcG2MekXWYkT+RhyBk/2LF08iB/25e0osLrzcUyqbot7EQ2yE/Y5H4V/APsPTWl1wRJOdSGqvRrMQtBc6heSSWJHHnGgNDy5eC4VOhx7ioCBTROaogGm9xdoTM0pOrwmkhOtPiNGb7Ma7BDjlhBsw2q3QsHfGqRrRITDg6URqyEglVFhXyHWCroWhFwAQnoxQv63s0ji/Ho+lfo4aaCEdaMUyNo7dk4cdn7Bg5YZvn97hwbtIHpCMILvHCjzua+YxL8uebNCHfOZ0DYIpBmZJuos5BNb60dD5jY4P9hS+Kv4eQn3rx7DLpexpUX++3eHNfsbjPjKEMVFx3vkaR2psxkItX5tzFTIliyMJDuC9nMPzrH8spoWPPCFzcjkI9AICBjXNfnH46a7HCuiFBF/2BW9vJ2vw7pz7ZgH5xEajm4kvBn5ukHHCmPDWjPVbyIkSXUJthHpm9JC9iSEYnnEHhcKInARg4QPRUQOQCWkXLCYcRsjHXmMLFSig3te5sBxi4FI5+ZmiN0DJPISPhQsIEmTyF0Bay/WqaHeeOynewCW8lsmiBAF9dYG7+WZgDz3Ak4OsoEVALsCq1yMU1uyjMNO9A8NjyCZJLNjgiJXogVtceDTOFpBSzXNARdZRgBTl6giu3QY4nr27ogxBGeEnq/i2Z65XNdoMr/8w78EMBd41va7okcCbfU6mxHL04Ihwjo31ERoLENpkNARE1fJR5F7nxJ9FCvK+3CTHWhhgIMci6XxIVhNAzUJJ6+eM/aWiv+gAs9UoxOcUmRwTYbTOONV2EPq2XycZQuKsA5a0jQ5nSQHh35kVwXwEAhJ4jkpSwz6SAROeIxzn+9l6ARbg5XJF9KCP9wCgwuTXXd3QhbFJyQY90RwMQOYcQlHEMXIcRwN5jmx8nePDw0fTEAZDaRadeeY9Qi8WV9vw5X6Hx7bi3XY6QDznY0YCbd5FzQrerIjNQj6DdtYsuXF9wBLhSYudioLfs/Ti+vWZhg8FACCpb+OzcY7o+8AkKCBssMVzKbt1iLud8XBdIbdysMSVR0KZPJSRCbkQtgUZVw3FUW6Eg4UFF/Y35OY7JPZc5pamhq9mjLABkIqBu2YlrZJZo6EwjvdNiQKKquKM+bviMYglo15l8ChFjDMKpCqARqhdEE3u02MRt9w9YdlWyx8YnTSgZwBdrVnNpOjIQ0t9BWTVoQhIrZ9Cah2Yxdn5/8vbvzZJcuTIoqDCzNwjMqvI7uk5u/fD/v+fdkVWdvfONMmqygx3M8N+ABQGj0x2cw77touQWRkZ4eEPczwUCgVk61EfYJHwUmPiOk+vMYLrie0h+wisfR5sNOH91Bj2M0bBuJnY4ebjTK1RUFN6xywqZQNncvYF2N4oVyIRFNBBlq6o7xrZnnp2hqnRqS5jsZBAFldJYoUe+U93/qZlJOgv9nc6nXrMWI/AWivF6w3RY8I1N9fbgOXUmX1awANs3yzwOaRgvE7rUTgk5mLAM7sxTMqc7K+ftkfMRaeLMCfeIoOIojAhGCTD7FR0xVIhBXBxAs8bnUQfBV/2GYoNuVGN2/d+w9f6wP/av6HgC37rNzSZONyWdbdlt9Yxu1yILLvTW8cs0WF/CWDnor3zmLk+Y47zH9j+tFPgdqsdpxb86Ndu5KnidYSOL81HzzlNk+3cWx0OUbvyH1b1HrBWuOIYH7F6YI0D3Qpfd2ltrzPk/Iq1COAKBQCeBqZLliPEhhmDg3ImA8C0Vdoj5jtw/CaLz//9+At+ebsvKQTBJYpXIBqbQvirqUMfFxvhWP2y5kFhFdh0q8faB5k5ucDXmwCTDJ7VIWzRqQa1NbjlWM6DgmlIRciLVAZF2ZjxQDErcL6WEHoLxlKap1BOctkXFBFOo1IV1L8ysXLamxVtgVWM7i8C/eL//mpzkrWqGxB3DuonVdcNUNgDY4HDYh11/2J2NNsasgf2zOuhKErpzjjyh3Qb0KLQo0DCO8NwfZ9DwBoBrx3nXPebQV/Tr8cUDalpUQ7eQTK+q1GNkE/N/QVB2/WeCGL8KQOUrphiCrZ2Tdcat/oQswvFcHYS7/fcaqwf1p1yNjBriUzn0g0v6/vjUZzA9GMQz4r2XwrOIeg/Deir931Mb0xUQF2L6OgVj2rP30s98a3fkDfWC/c6cKDG/YNnBp/VC4DlDChK95lzMN0m9fc49d0DjCyPAgA/+ob/U/+K/9f97/jr9gOA9TPZmqvR9cyf3Ub0reNzB/h2bKuHAU8wVJrzsGiqH53Z723/Eqdgeh2rR+EiZS02XvOn7R2bDLyNPZREBfYQcjYBDzxPQ4vX0knfIk0sAdvQYTQvMj889WL/xPPxZi7zfDL4z1TVmpxJdgqv7QiHcLIXQdRqJlrx98cL3o9tWfcLdJM6M+1UMO5qmUHOIPJ7BBGVF4q/qTNb7oL2Q1NEn3B5FoThnwdgoxE9ak5KnRrdtnQuxKX9frggzHSYKG80/pzWNauNy6wPmxMQdQ1xWHFbxpDQBOBZC7OastgsPBcoUN9X8Zk1A8Pggf7V19MQ6IsNRtGTIThW1lAU41FR7oq9DeytQ+AzNKYNXK9evX8mVNQycWsIVsqcAmnqejjNoIvijomZ3bTvNKfgBribkabMR2RKxaGUpBRanaJavEvd2DseFZYE+3hXM2ZmCmk4fWM7rUxDC1yZNhX7g9GESDdD0TVJbcwKKKFEKaiHLjXY3CXP3ZQVdPhjEE6OE+pYUOe5bN8EohXnXwf0PtYscwWKO/hzVDxGxfex43/t3/HX/Qf+S7+gz2K9C7g+2yKKWgCRkRAAxL3lewCgj4paRkBO9p5FmX+GZqw2WdGw4O2uBXvpeB8bjtnwa3vxYTxrfXFtMSP4eXtP52bqEX2aEOA56sXQtzIxRD4cE/We/m3wEb3vJgNvuD44hGymX4xb6XhMk5XlkHtKTvPgV3XdNeu9sv+c9tBYs5BLhzC1RMF3c6ZQKwNNC6bS0K9j543ghRyzxHQ4fk+VGQyo3Fi314EvzWS+rUHPVjGHa/x/3n/Cb489hmhLWZG5LXozBroZvR1wA+JwQzkl5C/4ABIyusA6vCbNmoEyA4kOpK5xsJE9VFfslKERwbducE/Mdia33WEOABGVzloCS9YmgMslkMs+q4mtjb0E35zie2YIfD9tyWTXRypuC1xBVb2ojDBiwIqcx+ZZjNdbUBCUXhkeuZe5MgZi/cPvgb+nlgWEDueeM2C51aV2ybGqd6/Wt1pxuAbPVEHvS3/IuqnVHPsUZwDZsWozplB7ENZxo3yYcx673f/tbcb9YURuBWzxhjDP4Hx9JNY1tC2ml8lYLC0ju4YSTWbcxiaokXnYeWgRY0YJorEw1qBnHdOH7hBiYg+FTGYelvUwkAkGGaEjd0wBn6VMQtWaERUV/W8KublwIe0NZxV4p/mpBZtMfN0eNvSGQSA+ElIWAmCXJctTc7PA0VC2rS47kIvVzDIZODYZYRO4svj7OSr+v+9f8VIP13xrTlhZ0Q/rqF+3B37VO+5yxvmSGpt/V7jemzOxjMiCGByksKX/R7Y/PY4z1xSem7sI2Xx1q0SI5fB0aasjhlbf6krTsyMIWpYW3JCMPcFV37JDYKbRZGJo/QAXPS8MLgJmDTljeHZ03L62h0v1WjcrMxMK+/3X4xVHb1C/YdImvCS1DNo9OYLhTsCLxaublRCPxN+ABRFFxNasKG1jLRHNXiw+sweAeG99aGQB0lksBMo5UTwDCA2doUCTBVPkaE9gRo2Rp4db9TGgpwSvXYWfA0ofkYWUU9FfC97/Uq2A+nBD7YYTXaLJ7WIYvRhbDy8qK53FghigWIPYxQ1oY6jnhtSd9TnK6sL3P7W6qNHAajC61Y6tDM+OWVhXvJ9taeOw+NymZX7OOmP2w+xhUOqiwJlbBr2RVVQfzMxwVbE9NSRCSl+Zhxl6m5AmqdBKxdNglQmsKY3QoNh+66EoB2Ut+OEF010m8yXCvgxFUWaIgN69Q/3EZXhQlr0gNRfqEiz7ejYYBCmDH3X69d8b+s8Dch82QnUUzAHckgoum0df6omjVryPzRQVXKTy4UHpvJ5COISChRyco/iwGx/xm+wCaxS0G7XM0GpjbxRrnbSTDC6PWfH38wW3MvBSKYtjtdP30fDa7KK91BNzE/x63HH4TBbAHADlLaLOBUB7tXUri30UNQ/8se1Pax+tC2rF1iaCKavD+Gs74kI8ZoumLqva+yhML5ywQENHcPLm/YPUhxnDVob9fHJU6/g0PpFnLbD28byxSenwSv86Z4kFx4L39MiEbetvY8O347aG6fD4PYqFCOY2bfSmG/tyCio1XvKBpIeCzCOVFMV5FzMfonED2g9zDuXUJaftBoeGSVSjQBnQEGSl8qSRQlDhjoOOwHWGAFwcgT3AHhXuJd4fyptNMJqgSAljVx8DmIqbrKlikmoUvCBaAFDywSEZUmS1iCFCPuTGlGZNfpkHJi6Mx8lqHNQu1TvTR8U5DUrIMMIq8vXLOjlc1+p0Bd7hMhrRG+HFc6frLyfk14fnR1otB9hYYVqCrgspcY3ZtU74CEBQWTnzOmiuzaPgYfAOqaYC+w6b6ezXbrdrbwyvVW9Zx2PHzSBj0Ut9MZb1ugwr2RDaGzcTK+RWD2dQkVAB2MwFnwHNwIcJUFGEpIsKUA6g/VLRi2J/PdB93jngDJ/ZTBCvdmso3R7RSJvvKUPKUldzan7PUk/2wDQ0iFbAeIGe/TI9N64RMWnwIFoXGeZH39GjLjqwlYHHMGHPLUHWuwcg772FaJ4VtpdwKINeEUTxXFIg+2+vKQAWwXeZhhcrooHspRyYKvg+bngbG47RwgHY1nFKDShqenhQdHGNK+DDMwQDEhfseaueIu4OQpONFPOjVUJIj9tz1vDMQNpTBsPj26tBRMdkYdnG4u2lh47Tb++3NQcYjukVtcEodTp8BNPxGbQcCDyZRh6AwSBGoDDYQzzLYDo/bBgJ8eftN0V7XxEZcWhmHSzJaAV0EI7iQ27ZwrhVZ4KoP7m68F9gHa8A4HhIl8h2/WlMhy+ioNr8XgjQX61AWQ5TTG1vE/NcfQizFjcQGteB++Gxh4MsZBwZW4sd4P1uxV4IIDc7afEO5boNlKLYNht8ctu6FRf9BF/2idftjIyTUgV5gNQ5TXNnJCjDDmitn4tssXq2xwzOswH2FJwvJaTDtXppSbEg1mkChGz4MgkMXDIC8eAhruPu0fkw4009KdanbNays7vONe2N0+Ey0231VSTHrXYsM1kSzrmoD19jRS/w0rj5PrtDZT7Gs5yIXo3IEMD1CVO+LUBRRflecdy3NTypX4fSsKH1VgZe24H3JERnS9budZYteR5qwywgC94BqeaABT/VMu1SsQ4xK3Yfz5unS0LNpvRZcMyGvYwg3rzUE60M3Jxrbiyqpd80wvCvYCdnLGvNJUg0258/uP1p+ChOFssrbsXmGLBR7cfczSGQGZTUT4E1H4GwD2lX4o5izIJZShSOs7YRJS9yw1hO1zZvTGOx+dkh0MvydVJhFw64jpHCetQsCZaUFh8jaufw2+k4ZrEBLmE/byNkludRgS6BfZfuBmP4qdHGFNhoxyGYOyl+Zvzqw3T7998Mr8/dvnxYIeszUEaoGnIKYQdVAY5pVP+ZBuFUatw75TTXFpRQMPNxVZfHFhuw45h2OScmSljKuQu0Vpvg5vAJG6C2HxOm6Q/Hpb0IOhGSHqRP0uCMm7pOUlLwHOaMxf+bfY1K3TbTkmnV5FFuXicoHoFxcNK9nZeaAmBBSnTEirFPQle/AMVhKoXYMQyTNikPu9fE2uubqaZqE8gNMS+ivZkeUsyqJozo96Q8RrCLtJX1b5fCKF4RHRSyq5yrrHFcrCnQIRj1lDCUwz26El3/YPSs2O/eHyP+7JJNJg45qd2vIgwUFnNM5nI8l051X7+ZH5Ipq1CgPATjv3eMvz3w8uWIYVrd+f0/yURXC9p+ctbje98+RQWi7wjXiPr59c8geWYPVEs26Mghv6itGhmFsHnu7aL9qDLwNlbP1vBMAgB+9B1bGXjdTjxGdc2j5ZyyZAedAe0rz3dvA+//jpoCANyk4122gI82kWDgvBabk/C9rywhF2vZwk3453xiCQHrxGn4aSxZVzD+rbGfqgzvGdBQPc1CfMAy5HkLjjJvJtZ7c48DAC+ar+xhqIB1OiuOm9Y+ANMz96VU28D2MnCeFYMUVabL3J1Hb0u/yAwaCjCaXh6g7deC9ua1gnN1vNLIj03QKHgmCzYwFouAA+WZAUC8aUcUCgm2SvECZWQSUwyO4DxhMmYsdwZCQoPwjBuXosGXl6GL3Vpsolu/FWzfJ8ppGEd7M8dHzJ0FWFEF9mLRZxOMF+D8ybMEX82lA/1FcRk0BHik7k5CEHIqLBY+z/FgwHCMipf9DBmFzGk/nAVS4Jo54WU9unfxPXHxQzr0YObEyEvB/m3YevDDbm8dck67h41QpDuFPr0yanN9oYr6PqDupOvDM6NWIGMCRVDfqt3vZlPcRAtUitedFj2YaxFOX+XxzGqQotWodFGf4T+HRfEsHscaZ3LSPRhxKRM2ZgK4TA6MgrlnyzlrUG8SLAPAm2D8uuPYBn764kyd6FXyIq+uxtnuLDGe3lYHzlHh/IRVW5RcQNYIULk/STYsv7+6+Gf+W65LhhJzGUEjfe8b7u3EhhFzYb73HT/6jsdoa4xnGfi6P3AbFd/PHYcNBLepgaLALObA0rFkKPR/sv1Lmtc2GehS0KWiw6Cjm0M4p1a8jS08eKZ1hsdMD+/h7ehRXU/v7w6oPmuSEMIhvHTzSW1F5eIAolfBG9qyIiq3IhN70m/i58wJ2fB2sqqsg7mgyKKV/XberXORmyi2NrG7uuF5NMzusIrDRgEVJfpl6dYJy8KdqOOp3wXbd4TGkDicQLYRqariuC6lqBkh8oE11g8ALVe8WQQCvT7MHoUyiicsFcJ34XzEOoS9oA2oqXICYVymXPdLuGI24PxqU8uo3kktHUJeLJS3PlE2wdvfBMfP3tlcnW3x7tHtT5apqIe6ejHYHuG5ZPG6zxIDUNh8xPs/vDjI6IuFxCqKQyU0dcQuov27APMsi0LJyFgWfCTDI9A5Ud+HGefNp57FdYTJXMuK3gGYA2ikBhfoVMiY4UCYRcCdsS9HyDEgXVDPifIo4dhZt8gdytGo+GRdrBaxIvd4hARBrc0QKLMNFrr7thyQzWX2nxTKc4iN3y9ObKODkGkBhxyC8/uOfj9xf3m/IBCh1CwDu0zcveicm9KKKGOzD5sAzmo0z0TblZmLAMJ5PEPPgGeUeoWxu147kfsseMMWqAd7FLIk+Lab02CjG2udKoS/7OJkOm1Ng6LmE6z5j7Y/rZJ6K6cNyi4VTQdKtdeso7As2EgNEkJZnvTDNDQs+e2M+cWNYAXf8cJngw+YQ5rJQ7LTkfOTgcVOuqipil46pPMwHx5bEZPBqGEQCmgYALvZ3/oN51gRyf1+Wq3Dz98wTEC7RRzCIS98ipSpswa+Wjqw/WbzfpkZAMuBGMXV/klmicEQS8I5FwI5u5lD4BmBK6N9leC0WwF0ZQXQZWAgWDUHbmP97WLA+DCPpbAKrKzIGDT+QUjAD/ysehYFhSupFpuXsMOL934ZDqu3oAK6zbi2pSrmMP2cYCT5pZtu1Llmbq1/6I+hBPK7Z7tF1B/iFEEKghcOgQ3fcUXccq6Hkr0X7V1RHiPkqGPYzQQkXVQhOb6IOYwJaKvA9NoL97sVc8jV5h9EJjcX7TScbPfxkjURAmR12ZM+WxRBbTY2ER0IwgFkGiwUXudYgUkYdYce5w3RazNuvAkG/8EfhXIuGRdes0sdS8Xgzi6Q94q39w3jtbhTXxg+n/FNJr60B76dtyC31DIv2aTwM+mekuaZ38N9c6uypFGKJPXTlCXYbZVL7ZRzpfm+Qyvexz1qrlsd2J388Na3cDwvzeipdBgA3AFkmQ+/biponvVe7tM/2P6UU2gyjEHkHvJWOk6tNjxHJr71G773WxR4eTF/D9fnzXpuyuDGAnKOBADPVpx9xH1SFXXOJGOdrkpukAvczRUK86JgFsL9rmNwJ5Sig8doRrGdNszltp3Y6sTRa3QWWlq6rqEW49RrhRXsOEhGrFeh/SbYftjforBHu5vZOQtZu2yMTEN4burSvW+CeszUaGRGsxwzICOZc7GLfGJXeVxDDo5yjO90OArVCtAAoomKcxjsg4L2NsxgfbFRj7MtpdTeJOSXtZrgHvsSONDdKI0GZzUf1agN0H2ivHToFJQ2sW0G3QkMymttXh4cZqUcgLKl4MQeZuOr3Gu3YMSfv+dGJxGEc+ieJZRT1hhJMamS7Zui/RiWSRWJyFwmNXvEu4N9/wprLARi5oGIxkxsvj6+OLV3WMYR98NO9Lo4MqU0M8V8m7tBEjHSNaAeOhnBrDzWBd+pxUrRnJihpn5fNSFRZyJVwHoiBfNm4oJzcxmNNwnxxAVDMevzCzOA/r7hl7c7/vbFuoQZ2B2zoQGYYpD2Xjtw3sABXrVYRjnHYgV9oKzz8vlPQkv825WduHqi+N5CyE/WnPnXdnw6a77Pgl8fhjZsdQTZ4TGMVVWajR4oovj1cceQVSyvgkvBnFTV6N7+g9ufbl4DzDjfy4n3ueFVDlSZGFrwSOOwmkyUli4WFnyTcVprnpWUj9pGx2JUUY19AsA5iw/eWZHBVAmBOv5Ho09W0vNM50/PTTItcQTllQ4jv//hdLhWJ25bD8x5abTDolRq/VRXSvVoed7Uo2+g/hDc/8ua0VSYUnu6mLPUFMl96GGgzHBjxzIffDcCsqAgAEFLtOK3BowT5+isJKVuDoeuAME4IgyhTTBuhmEv+Glp8ZRzNcTxPHJT1JratZratAB6L9HPQIfwvgEoAjkB4tHlpeN2Py8Pg3ifCNUkaRgAfJA54D29RJu+XpghPHrDrVmh+ujeMOSwVHSUxk+H8R42s5i1AxkTmAIRZmclrqd4ZkWjrs1hJScCgE1pnpWpD9pRAca9esfz+HAf+f65l+g9sIxvOZKArdK60Wn7bj4SdG5rYh7pwNZPYSSCsbPp0HdZPBv09Qx3ksyOKcGim/elNEC/qg3e8V4HMpBSAO6WWfHjfcdt6/hpW92az8/ovZ42tRElInTWNx+EfZ14kme0A0sIkfaEtQTxCN4+k5CO+N4ZQSowA34kGymCULH9DjUdpcfZcPSGr7eHyYPPcjmPvhUMvaFiOYBYg6LALFc7+lnU+Mn2v+0UGD1uMvCOLRyDyU7UVUtww0wsH0B0GpMllBvE2KOQ4aa4iClqHyoo7kwYrWfGUBHrg+i43qDnLWcF/P33KKvZKPBvGX8mw+ll+yiNwDotALs5aeKFbgp1SKO8F+x/L9i+YT0I50qrqSskjoywGHf5b66foghIhyMfa8xEcLYIDal3MTNrsO+cy6A4bDBuNaikCkS9gaJr4vimMFJtJQw+cW71FMLmKZQoJAPe2aom4hdOTRHDgGpS96ynYv9FADcU1BSajwrcT6Od1oH3YzOj7w95Tfc5O4Otjigy5878Pitu7Yg1wAc0M9fCERS1e3AUyCEoh8k/l2PVfAA3bD49PjD3YRkJvA/A3mO9P1FnKLCfTtede02yFfZxbQX1e7fr3mfgCcpURj8JMAgPqrpm0arvWPRvQYCKOasia00RsuzuGMZtQU64rWbEoLnmzTNdTTaMWZVWG7wj06RNygHojmXg/HxFFKNXfH/s+H7b8Z+37xbASYl641TBS7X5CzPNNGFdc0sRNo19pnJe14QdKIkKuYeBwWBWQchQOWEgUtm5TW347bjh6NVlNKxp7tf3G77sJ17aiUdvUdt8bYcF305sGbqa7mb6d77Of2T704XmgaUNlCP/X/v9ogfEGsAzlj+1XuYtP+NwuahzoQWmi9lKonPJig6Gfyc1R9Y+l6cOjE8kxKvyMeQBK8+DdJii8vv7LLi30/jro4bOei0TfRScZ4XOAqkKPeEPg8IDWNRvBfsvJWQpDHddd5IZA51B4KyKpVeTIjzRlDUQHuBPIAqLfBI57Sui8mrCY5E1yHpPFB6HQnejRC6H4OfzowMiGC+JbQVE9mBOzD5bp3fopmlgeXh73Nu+okhmVfuvdq5zM2G88yuAs+D9x462d5ylGprVbJQiFVH50AhMvqCWiXvrUS+oXruCGAGizRow5UhwJ5uFTLWzYPSK8duG9mtFOSzzCgE6RshY5xIMn4gaJlTMKINSFarWHV4NhlSvL0DgE+2mNe3tZWVgQQ126ClmH0woZUqK9ZM4d+WyTur7iDnOOr2mROqrBw5BmfUMpb0jemPGbrDouAH9dd3T+vgABKw1XjSkxaPBjSKRgHdwC3kMXrw3k6sKPB4N344dj5eGv+3fg5kIGLRD+QuqMy+WjrHV8pClZ25/dVme66S1J8PrG2HGHDh2LyTTIZBCz5rkt/OGww38VOuoH1MwZ8G3h81feNlOQ0w8WP5pe0fBDd/P3SbK+fdHwINlQ/9vzxQAoGKiwrSPNhkYMPrnmAWP2X6XJfQZRTQXYMIBPMnOToZBKbzha8/ZQt5yAZnbpaDkzooOgNQxevT1bwpcXb+DkFQR00QazlbhZvBRMdxSFFJd8qIpUBQ4Crb/rqjvErUD9ai/v1gU3t5olBGMj7k5FESCjzeihSyEG89yIurYs0o0TBGy4QNdYAVcPuiBVTdZ35GH7xSY8WrO7uGX2kVxw6/WxOS9DdaxjIveDqN+cT2iwj4DLFhMkRwi2HRl59Aehj+PKegvvs9ToI+KcxgdtjQrGNZqmcPeRkirWAOakwB8bWx1oMrSrt/F6ILfPJu5tY5+FhzMaLkWRsH43lDey4JAqosKiixygMMmEcFPhxLz2CyjMkVPiHq2ENRfssHWQjRj3D4+A6IGT6AYeWDcasiPhPzJ6bx3/+rIPIYatVgdHqzAvK1CcIwM9XVrQ3+A2RT9Jjh/lli7URcbCNjocpyEVv1aSAcEznxrin4DyiORLERthoUaa05nwffHjm/nDbivOuSZepe+1AN/l5dwBhOuhpzgzGuDLVYE7r/nyZD82WSsmRu5fqlLoTn3MWQb8b3v+O24fagNDHdoqoI3795msELH8NLOmAefncyzvfu3OAX7sukjaQUbFL/Nu0fNFilfZSJ4Ya7G9fd6B64OgVnHvGQkZCLF51KOdJnXnL4j0rzEYsqF51zjuDavKd7GFn/rHnnktLGnSBywxUR+tIiL4hVA7nYN9K1i/6+K+kOiiMyOXBketCRnQIeQpQY40B5zpeHPkI8SShITW4sRmd6NOjeXSVDSRF3aWhRsWoPDDeWcF2NEB4YqUMeng9aYNzaxoaC/VlB104rD1FNaTovOh+0rzHpMhtm+tx26nOFpRXvAjIsON4K9YGKgVKsn3LaOzdkdvMfs/uSDeg7LCp4H7XAd9uRIgJQlHNWjV4dd1LOEaoZ/VkFLENJqOiOMlIrDnnWxCM37TWNsELLDSd58aK8LROclQ9Qi0L1YVtBnyJDUxwxn4CdycU58dM0pMzApCy701c4iNOUoTIjH1nF9N5bQ3GwO9ayAOuuIAUsUkRkgCIw1tXk3fvXLURTjq2J0MZG8CnBuhmwKKTZ57Jfjjrex46UeeFNDCR4cE1wMfsljMIEEFZeJPJL1M3o8309ZnhxkZlsTLEktpoSQMgTAiCqP2fDtvF2L3KKejfoD78dx9IZf/H1/3d/MMXjg+tY3lCe0gz+3xKD6Z9ufYx+ViU0GTljwwlrCSRG6xNfPcA0fpBiKkxzC8yS3z1IzNrGxrpCdgxV8axjFXLPgMRMmevdhHM9Na/ze1XFoolIVM4b99Fkj84nCdSpCc+tjddCWOjFHQd06ulTM/75h/y/j5kcNoFtxlbBQ8eE52RkI7D06U7cy/MGZZohK4rjz8zIRU8sAxBAc6Yr6Dox7ucA/sxmGG9ckOQOyVCi0Zg4FCColZSWp3Z/7EpVZjUemTXz2gqK9Ta95GH9esbKcXC8RhXf8rr/TMZRukILdAEIplsWwfvDSTtQyw1DwnjNTANzQq6l7HtLQp+K1HZd7PHzyFwCMXkJbSTdnlYnZGOsdSc6F8hTTw5FmjsHqBvNSsA9KKhCMrst98Aws2ETi0J/A7s1WoFtBv3vUWSv6S8H2W1/3MT4q4Xy4idp1121lcBLOH4sZpOLsNolmttmA+g5Uh3zHzWTe+4s1GI47or+GA6RW8LKyUnUnYdmRQm8TnXRWOq74KXjvDW9jw0s9YuAVdYduxfD43DfVZ7GxnY4KPDc+ZkbP73VF539fCtxQHxCmIe1PmPsxG95Hw7fjdslE+J0sIGdF1qNX/PK4415P3GvHl2YPKetbDFhY0+DxPgEcv7v96Uxhl0UTfeiGH3O/CN89S1pwYwHPjrYg07XYoPHco9C1hC4IsOigLPQWuWrTcF/MBC6NLfJ5swmdy7Pcxa0MnL6oqigOLNE/nodNiFt1lGPY770XcOJTayd6rxjfN9QfbDIC5AQyGWoFDOqpdELN+AAPXLIEccOpQshHw5msYt4SY7O6gGHVmYpIfSE2ltkxWhYAMlmcdijq+kVzwRxmlByz9u82yECj+a0+RkBXQyogwPnFumvrY6IMtdnLQHDcZ/Nsq9gsiFxwhtdAykkYigdu3oJSy3Ne1+FWRmjTC67Tti7ZY6otPE/4A4BJQby4R6aAK+cSGeTIVWZzYy9opYTDkzHDGYb8eHHo0OE8BayWoIjrzWKy1oK5Wbdy8feNtJby1n6YUu26j+ty5fVHaJFSHHS+2ZlYQVli7ZB23O98DUg20ujD3w0aPf7iQ5F2g4fktJoBFN5ns65n7INBxm2gbNMywliotqaPXqPOuMnALEv+pogP4im3a/ApNseDkXWuZQZc81Q/JTGmyYgeq67lQne3Or/9HgQHJSIx8T62gC75HhaNt6dMld3zR2/478cr/o/X31DEagvHrPjtuJvN9MxAHN6sny2C39n+dE2BP09sOLWGAFTG8PJFzWyN9ZMNZwbFsKchY/wAXApbLhpI2SPnRqNcu3guVt+dmsqZzvmz3J6L2pzxwCKkvWdG5/Z33D4Uqlkk4og8EZP6PX65Qd68vqCyIvMKVK8RMIWm4UV+WPnAem+D/cFfE0AbfHyjuOgY037vh+A+PRpldF/Oedl/IWNIFGglQUZMp+1787wFg5o80pz2u6hlMCgSFFZxfr24jGh7E5wvFcdPBVu12gdxdzoznn95rEH1jEojgvUoU4uuFKJZHUe8qEinP+eal5EjPUZmU2XpIfmaMw2bGpgvSQS9V4erEHAG5ursFbts0a1rSqHrnOCzIsQL92Mrwdoa92qyFj6RTouEIw45cbVrZveXBtsNzVQfVtMXQYCzGODOqPg69HvHx4FOfNblEOCyGVxP7IImKWLsBecL+ywSccHvNZ3jNqx+1N8E/YsLGN6AuSvaD4lAJ6A4OnpfF1IVtQ2gYUnHwAznOSp+Pe/4f94rNhkRWFpnejPxORnoqBfHkOuJF0jok+if2UMOLjPNNNNdgz2Jtb9zlhgn8DyvJW/8fUwqMcBrDBu+nTe81BNf6oH/2N/MwQyJ8+Dn/61DdjaxwurUgve5YWD1BgCL3sW0hhudRIaSONMZuDatRZSf4J1Lj0O6WSO+t1w+u3SMrh3LjPK7tg/fkZkDmT3ASHFzp/CYDY/ZLnhzVP9F0ZpN/zrPiuPvN8h7XTiqW4xoMmspIxiwFz0y49+yhDFgjkHrchIZaiKLCA4ztHd8YCmRTcJIFGQAeRRpUSKgpXjjkaC9TaNFch/JwMm0pip29sboTgBzM0NnUOmqO9CgTFfuZPdrPf0hc1VOFbO5wLpmBl+kBipScoGYn1C3idYm7rt127PHoMrHORqZg36FMm1fewHeZsE5bLhOyBZX9QYzCh2u6wyszK5wuhkjb58RQYhNhwIbM4Dl0AW4djALAjPRKhivbVFE1Zx18TZ+nW7892qOOqnP0lgHTAksCMkdAlzS3EgMsoYjNfs8z2luPscZSwFW0v2iSB6zo3Iq9sODgq+C4y82fXDcECyk3BVtjUwC7DDHq4K2DWtOPFrUdrY28NY3h34VVWdI6w8VvFSX9B8OuYBqA64d5MFCNtSnQ8YfaggXNiPTL7M9fZZQ2GVgCcCp+ja+mN9bxSaucNiXqsSESmYOWI8lpgr+/v6CvXTs946XeuD/uP+G//PHXzCSY+Oa/qPbn5S5uMIvz3g6gA8e8IK9uaGmSJQ5k2IePOmD8OcxqntlNoPYzy3hLpeCdM4UUvGY71tqhgUfvLRyHzVudmiYeDTwUg+bOueFrOyZD7UsYasDqMD7seH4vkPOsoy2WFRUDoHwTgtWRBbQi3eEBpURqyGJGcAE0Pl+RBSvRTAqQgOJQnJUGGXUFbNzkaJvsddjTCR8GMpmxqm9awzJoWGm4SgPWc7Cj4WUUzqAjF1TEmPswOxrDWUWUknMLM5rZsF91hUplwHIo9joRgGkWZbGInM2+GSgVGdy9FkuapgFal2wyPUoxPvfFDE3o/j4TR2kN6YAY4hLlCBE3+bmCqePHteOOkT1nCY9jrUOAMvm5l68MA+jqYpg3mvMLZBuEBz/dpHWHmrd5SIOV/H+rP4DL6xg1hpBBSf0AYjsICbqObHAHIIdQz0R2chiHXGNrcJ0zBUf6lIuguOvYvWGqtGvALLlXC9s1gkMcwClTuz7QH05cJ4tIEKyIH9u7+gouJURxhiANYA5ssH5KX2WS+S/p6axKvPS/b4C24K9nhcIHLjWH0xh2T5Dqj7rmgA+9EMAuPQcXH5ioaN9FvzX+xfsdeB/7d/xpT3w19sb/v4wdtVnNvmfbX8uU4Bik453h47e5wYKxeX3ZOpnfv05yrcHdfiBDRzaPjiS9d4rnj+SAyEs1NPF58+8P3YnHw77PBeQ2IUY0JFHTn2KD8KwWarMjpgqPkbD+2mXthbF27Hh8bbZ9C03whGlJTngwP7LelgsTDANo8IIC4iH3U6fBb+0f9h7+xfbByNX01vCcgQwZ9ObxDAewjFaYMqXqauWPHTrTpWAKuCTtIzHiTgAGm5KI5hxQUAPgER37PRO2Nn1Ms4xb+YcPdq6SRTQyYfnaE9ZPs4+V1Yaz7XQZ8FLO7GJAmiXtZjXGYMVYGlhkbFEI1GKQtWm1o2qQHPnMBEQYelYNQfYsY57W6KBtmNAFHIqxCEaTkSTY8bvWgrau9Ou9lVElgG094HyPsK4yzGjqC86P4i/sR4h3i1tcuhsOCuQbjUeju7sLqVRk9yJyrqHZCaxfhXmwNcofy8dUZPgcKXtsJrC+38Kzq+WNZTToKSwhEVtaBEAHVYnonPftx6ssD4Lvvcb/rq9xT0jrDKwmIPTA0DoKs7yWbY1cDXI3DgULLOKZlpCz3XLnHkAwLvPXQZwEejjsYZkxSeMImYOqoLHqPj/vn1FgeJv+w/8r/07pgp+ebzEPjNK88+2/32nQGkEv8DmEJYXJC6XRewyzgXgdx7CxAYQjt5c2D+AKJrk+kIRxcnvTAVrFqjzvrMzYkq45yovcKk3ZKoqFRFZS7Bh2jX2+RgtJGtrG3icDe9vO+abcygZ/bPGXjWMJ6EQUQD96ixMxM4VJv3BqydhIY+WN7Ja7DNa7XNW+PMTI+VvOgQ3UjTm61WGNR7N0OZfkFM9FI1ZiFgUWQ8NMTs+4GP3ZiafsBajOFOPAuEuLWKDVoobmoC2/BoleuqFjaJOV6XQmp9/nKs6Ll0NwitJCO15Hjcx4uHCY4QQqJALLLYc1xKAkMewB9SVSh1G0s2MhEXgK7sZxYUKh+D8uaIcDaXPS7EfAOScUO8nmMM6xNVrNnMX9FLjZFnvqe/DZy24WioZTlEYFpRpTiAkNbDu/WzGVJpbARwGZAYz9oLpxX/OerDPuOTFJjH3O9aHMCMEUCwTZE2BXyvTaKtl2P2Urrj//6wT/PGfirm78B0z2ZuuD/uXzCko1e7h3kYwwjJhhc8ox+jeagdOBJuQ6yAb5s8gmOdaZ/7b+ow1urHoXAQXePqYLaAj7jPXPWOf6Rgo51PEqLfZMbz3hv/r/YvVF9oD/7H/wPvYjKYKd3CfBNefbf+SPoVTzShaodku7k44KBngkK3GNS16dg75wljKZVE/DzYXegHg1OJemhH/CHioe7bx2U07HXoaKRLMKSD/zaygw1gym9gAIU6XyzUPynpQD+ftfcP40WygzpRY3FoQ/567or47fMVmNDqOmaKuamkAZyeYKNwysIRPqnP3LTpV66pNhtYgAUTvAXzYzthWMxMxbssqTD1T5sKIx4bUOXuFeeh0Agtnf0I0XME1c2y28diMqcIGOW3W91IKluQ36AgIYUjQa2ddDpASzBHSFXMytU7nfFsPwpiCe5NLBHVmVlFikTEizI2VbBa6b90Gxp8WDEiZKM3YSNrVIRaHPKj7M+xeWdJRAeyob3NpU3H85lbQX20aW7kvZpY53YLzdWWV9TQjLYqQ3tZWTIWVfSYuqcH7De9snntdGlbTZzcDaN6/MFsJh6BFUN+naV8BIc3NdRFd2r6JAvD+DAYWJWcSsmpkUBtKaIeh2L4ZbvT4mzsGBjvNA4dhFIkL5bxa/8mNk+fS32zG9rrfbEYFlmMwldPFVHxuYst2i59jD9O42JiP7812qM8SM775N26RDTwdPx1Cn8tikjgxR8V37Pi/Hl8iaP3aHtEnJvmm/JPtf98p+MN5aovehDDIU8B87yppUS40zqkfI6+8EcfP3puvc+NM06xHxPdUWR3Jz98TkULa51QK643QNs9TlJgp7LVjY3dzyh7yIuuj4v3Y0B9tGagc+Y712ops7YcMe3DW+2XNV66IZi578BEOpD5SRuCRtBoaEd9XusakMpnwYekS+2A9wXBtXdFZBZqn+3CIYdwQtNUy2AVNfvxyQMwgJiQ6ZSGrjhG31p3LrJGIAkg1iAqrm/gELzpB9Wa2VVzX4NbzWgAILFjTwxt6RWKKuuPpIQ2+txcNcxR3jmprrA5X9/X9qc8IUTG4bhgkQiIB/NrPjfWFip0sop6DgAXj9bt4IMDMwOAzdrXrO7BN4PxaId6nI1NR3TmQRRQOJ888QHK+za4vh/TMWlwKxZ3Pe8qoyyo4X/aRIFIKG/J+0CEAK4PK86YZUPARbz/seB5/U8wXq9nERkgp3UfAjOdLO9HVRl6eWvFaDmwyccLrjm7I2cRWREMws8Ggt6lrBkxeEzmwvNCSEyLBoLRhjSZmUNyd1sxsNW/P38WC82fOpYrimKsPyuoLr9hrx/+6fcdf9zd0Lfj1uAdM9Ue2P6995FG2eb3Fh30u3uaiL/9uP+WSOTAa45CbS3dgRGrwi7Bor7GvdEOGp2+MBnPEFw+9HyOdwfr7VZtk0MEJAjpa32vO7kCNiOHRK95/7NBHTU8eDDbwrCEbRDb5hCMQ2JAWj9bHTSIii+hqKMpjwTiZbaQFKD4Uftz8/Z3F3hXJS3ZKz6wmk6x1GYN1LBBBe9hTT8bPZWJWgnXKyKJrC3JI/hNazAGNzQwV50lrAYY/9JTIOL7KxWiwYF+OddxLftz+M7x/0Uyn2nhCwDWNysQkgyjdV0IOdAbPPTdbHTiGiZcZPdFOfnJGhivisjgaU/XCeQHnT+x+NlHABq9T1NXtXY90D9qCigCuAyvsltP1kfy9hfe7+LmpO9Jmsyiu+P4yzOUw8gNiHVimV1PmYBnaWpNcx4SLtFg2CNjxA2udkRhgwYfGCE/eAGayAFwlF7j9V8HjPyfmf5ocOgdUAYiGzamCs1fUMnGD4Mt2YC8db2PDaznsmTYOV9iNrYyL4Qe83uQHkLOECPr4OHvk3tLgLzZD0n7kIlJPgesx7TifJXWIrHQ1h8FRAtWDmVxb6O4QOCN8TOtJ+H9//xlNptUXbt9xDJvm9keThT/tFADgfW7R2EWMPmcCx6yRYgUF9Mmg58Lz0q/nxVyzGAxGuiqskoHE+czFL2Ls+ykLyWnk5X1YjoKvb14LIXz0pT6swKxiNdVPRoh+e7/h/X3DTNzpOFWmwAKU9CCMmz2IWp2E5EaD0eSK9FeqrcXSZ2oVEa6ZFQALwb4urdfAUJFy2OdnW3r2mMtRhA/jQ1cQmH0wn2COIeP9UavwVcVjDgOQKJrl1GCxAC6xPDQUUss0TSOjIwr6bTlR8SjUIKNklASh1U+nIMVotr1XvMGMx956FCaDfgjvjM9QAYuSs0QzEB9IOoijNzz6UsR85oPbACBdQ494vVLFUovYYCBIzG82Bylret5QYPh7eRHdmO7frBAshI8UyRF/3AyqlliLMrHIA81qA9rg9GY2CzpcxFpilWAE5Xv8XFzOjKX4HLBqD/DP1XTMvt4YsFBEb/ut4P21AvcBuU17BlwxYAxBTc6SgeCe6gXA9dmeag2oz0oEQSoBLjaKG1lofC/rmplVNLW68uq4qC2Q1Qgs1WVS3nPw+7xl1tHU0A2IYKQPOgbg7dzwX49XvNQTt9rxdXvgrW9/tKTw55yCwUbtgqnndGoVXPRi5Ku4pDU+6heFOF7C354ZI7OsAnCO+KtY2j51CUPlYvNUibRwyemWlN65Toh/5jITWs1RETYaKChqN30rA999yPa3Y8f7w2EjN1QAFjSUJq1RHI74vW5mK/QOj/Q/PtxhVDwKVoEbC/uO+tA1BN0fUmLNUIQcNyNyqGcoQESmfEDVo/QyAHjxGVDUg+9HZApI1ypWrAB9l+iArYcbGTcsUXx2w1HngknIeConYqAO3I5Nwiqu2UTdfgDeT4Fgp0hzvLeX6NcapWDqurBkeYgKUBeOSyn0vAa4FRhvnQ2Kw/cTKqxVMZoCJ4z5pXCiwGJH0cGXM0GDTQKKY0YwNqA9HK9PTB2OWaVcSdYrUtI+1TWsysooM4xUH7rgHv9sGPrNJS8AaLW6QvQ3MNi4rbXgF3NlwaKxLFZzIy5Dd9Ss3GI7Fdtv6Rq0aet/sOPffqk4AdS/2A2PhDNlgrynACKSB7yzWRZxgNj7d+zX2qBeFZ3j32n/ex14hrVpf2KNJKh8KxoNvdNZThzbu5URoopVZtQa9mKTLNkUyzWY6xcAnDixzntM4Ntxw3+3F/w/7t/wt/27CQT+we1PO4WAVXxbQlB2khccL70LQBSZox8AH2Uqphems8PJN22J6xl/PENIORzjdz0fH7AyBH42H8NwZzBQorg8UkTAukkrA+/Hi9URjgrtrLz6jpIRF7hx974CQiXnTxPlIQ71OJOjr4eH0XDsV23mgAwAVENlsbaY0Rk3ZhDL0UQpxb+/HuyIvcJKq5/A3l4Pe8/Yk4qpsD6xJCeIVavA5AsKrEPVuew0agH3OFRSkmyHimC8OARCB+f1g3CW7pTGzb5nVuD8qhhfhynQtonarLFppol+CsRDVETX6yoxzlVV0FPHQmQIrDF5DaC6DDe7m8csOFVQ6jAn/7hGaEGxhF+ftu7VrCb9UB8LQgMQ8idBUwYAWc6eekksxNS3GXRS69uYwKHQVjCd6TU38eFFdk/KoYDXuiK6TwGrUX6twG1wH+IeMrgQVcB7ScYu4Txkpiy8rpoV1wjXmDBroWPnM+MZs0xATqD9WtHLhvKlY7t36xNxeIUMM2k9jGk8tyg4dcZdrVjKqK0MYCIG3czxsSBtQ75G2ImWhm6tdcIa5NWORa0K2WkspQX+LWewubOaTsOg9cWo4uutslvfamPHqPjvxyte24nb3vGft+94OqTf3f40fETYKAw8SA1dw+3ztiRks8TEKuBFlJJWZMZ1ifM+6xZtZVxm5hJOig5DRXyONLHP9gOsGxc0NjH46LWsqXKEkABrYvs+dvw4dxu92UtMV7sY8fRV4pIIc1M3dh7l+UxfRr6ERAJy8IdJGK0JVqqfWGfiYmQBqQAhN0x4h8VrFp0jcuN7VFbfgiAmv/X7KnCWYd8/pnUfsyjKyzo2wdzNaZxqtZSQzdaVOdQDQHd1VZUY0hJMopQhzZ3nJ8HomQ3oXxTjP06U+4AUa1ZrbaD6A2NT0dTn35oR34pp1gR12uGjY1Q0v2F0Gnefp9G9DvHSVnc0hcoUHqXqOsfnbTY1OrFf53Gz60yWGCFE3se4Vt16VdhVHkbXMf/p6rcxVc9nLDwX3PN+NeP5YPa33siOakBiuM1sEsy3Mlbk3949QAGFChfpIMOGURMqHts8sdgIIa7rhVVj8C7n+s3nlfxl4nY7cZ4VY5jjL3NNviNCcJOOE2SXFdw8Zc41zis6MZ/s07wY9CYTm8ywY88NYoSG2L9wzhrB5ho8ppfxw6SXRubwhCJR4p3HLaIRiBT3tMIgdhpN9e/HC17qiZd6Ig3C/Ifbn3IK1pewVFGLXyRMFoWvFykPu7CUakSqFBSvxBaym2Sf370gdMyGImdKAe3vI91MbiwU5+IMo73nxrZb7QEnXahkahHFJuPSwf0Mk33vO957w8k6QrHIC30VGgkbYYgNDtk0brwcEg8KEiarAn/w/H1ORyW7hDj+B7yQ0ZguZ8Bomym5aeNoNHwVzxwoZTB2QHOU6offHoouLCYmQ+ZGgL0SNAiEmPrNDaHPWbaIdGHZpNUy6odDY1JWhiXTMwtxZ+rXgj9ZR2ht4OV2rtGYDeEY+DBxPbBj+UBbjDexDCB45K6Vb8GJpSj3etozINWit1kwZ7FolbpCuorx8mycczb1hoDZoiaCdF8J/cDx+DkjCue9Lse0YvDk9XT46aWubILjNTnbgkygymbINEBJ13cq5y/LOg+yx8hA01ISrdbPhTGhN1aWE8Ewi/PK671wXUtkMRfigB+X1d8Kzn1DbdbRDCzYiFlCnyWKxrfS8W3cIvOvSLB1itaX/bkqJ1DpNP6GRWXP2cItyfVMlWXjPGB+71v8LUuzDy0hr5Hlu7lvfgNtE9dz834bAWKwF2bB0St+fdzxdXvg5XYYlfcPbH86U5geNfPf+SToLYdKRO25KJxxsd/bikwcY13ER2/2MD7BQHQyvPCsKeQ6Qy4QZiiJx/jx3CT6L76Wh+/bbrB1RFr/wsMbUd6OzSarMVD0BxaA4cmePcz7NMxb3WEwcnI657hrWEE5YTWDBO1EpuBRGxu++HDRwDMi43/5QeQsXUxB/yJON1xYL2TBA5d5yYeivZm8xdglmuNszKRBQNsP4HwtTnm1fZaDIn32ve3dBwFVk1IeN+BUidfY4CTuxMh/l6cO6zCgZZ2PTuswnuqUUNE1O8E16gVGNz3FLkqfjjXrKlA+S5uwCahAoylKVUzYbFxnK4gAsxennpqypRKaS86Zmdolmnbq79wE3R3s2ASnDxAqQ7H9WLTVAkCH2FyECZOx8G5oiH22nJ5lPA3gIeRnF8fuoV3Lxfhi5H5hPQ2NbIMMorELzleJc6jnmnUBcD3JJeChI79AZCM5oYqgXUNhho1B0SmQt4pH3fD1L282x9ids/KeycLy+bxmdGPDGr36eYGXXfAL8mb0T9mKVgYacMkELjXNFJTaZb7WXvXpu/ssv0shZe2LDoEqqgrEREAAwUh6Ozf8/fGCn9rj0/19tv3pmgKAFLUb5YsFmIfTVHnhLifnN+siIKVZ4rpElsCiDOEhRvvHrGh1YXjVV2kergOYc3obW+qR0KChsVchUsSIDjwieCom5ZtHZ/g2djxGiwYm+6ACp4W4Kv6gFcO5UQCc7hCA+MnwOlJuYBk94q0JX59NbL8dZnDCmF8/k+EB3obhuK8ZZYRYGwZCipjU1uGwgYmqAaUgZKun1zzKaQNb2ptJYveXBq3mHEzS2prX2NVbHzOi2f5dcPwsePxVwkCavj4s6t0APQ33Fvj5pQHwYkEzxusE2vR+PAlDvdURTU3vp3HStzqWTAEvyuTasIxhrwNlXsXPQlrdPWxX0gItte+9RMFZXAvJoEtcs7cGYEoYTWtmU5QumFjGczbH54s57bGLO/gazrEJVp2Cncebj0hNa0A921jDjda6DrqsrzlqGbEPgSwwOugcoDA7DEcnPoLzxeGlg7RUX++TGQFiPXAOwyWDcMZbXDcnPmhdGXL5UTBqw/ttw5e/HNibNY0CxgzMzKMqE1/bA9/6LRpuW1mIBSHm3Mt0nZj2UW4/C2u+lGFNjfg4/Yy27ZjtafCYwUZQCW2li2Q7r9mlgH0d46m+f03OglBUHwXfjht+2e9IJvEfbn8aPgKudQUW8B5+Qxbtz06EWcLax4qwnovMjNR4wvlmUXmQF5uy2hBOSZtBWx0qFxpqFIo+wEHl8jvxwlvpeC1HeG+DlSaqTKfjFvw4txjyIXWuQrM4vtsmZHca3Vk8K7CeBUb50t1IAJheYygqoSWkHmUGbFAQ8wpypJkZSmwOYrNSpP5U61Rgvnl2QbE2XZ+Bmt5RPZIgWgHQM/9clkGAPczSJ8a9YuyC/ftEeXht4sU6YutjWoNtK6jvgMyKuQmOnxAFdiuUuwNUMy5G7UxGzjOj/qLQ1wGpGsq0n61Xm0VjB0pNKxaXi19zb9a9wJrsbOXDmouA8d4yIVIwp0k561FNADFBPA6im3Eri4nVvwCAaxzRKTfPIhw+I2tobqYLVB/uIIZDOGpCeeNW3GgK6mHCeHMT6L1GH0Ko43pRfzkEu+fjVoKxxEI/7zezzgX5ODTEmlUlfGTHPnfLBk2XScMZZDhTHcJkxmfTANd32xd/uKUWkPwo6C8NPx47/uP1zTj9vOdY9HayB5n1DRRgJt20FNB2lNTl/DGbGLroyzTOmZ7+rNpgFwrBOMqb6pJwH0/OgWsrU50Fq/Z1phrDzPsrLuI5Ct5Om+z2RxsV/rR0dnnynJtMk4MALt6QWUF2CM+qgvHgiYZq6jPMA8A7FWtU6GncY06zGk84bxSrythhkZmyFZsOlSW2r+fqDmDWgI4Ac4jf+80HtcBHbgqfLUAF8qWjtmm0yJ4AcjsQKNjQtgzH3BcEBCA6YWuiqWrBJT0H1sNo+9IwRkX1kv5T4TTWq9CwrP0Vl06obkRY8F2USAVQcL4KtAn6F1OAlT5NouFe0F8F7eG0SEVEp3MzDIxa/IDRaXc4EwYIOYhyrClr/S7eLbyugTaYHk56fW8mP0HstQ/WinDpaI4M4En/Zt33pZWT10QRG6K0lRGRGQDvjK7Wo5L0esJpesowdy4DEg0oSJgi9mnZEuVF6Oz3XzUySEIw0anclvCgvcfqRhnqMyHDZGQSc2zcJGpMFlhIOOFMeUWVUE/Nm0yXqlAz6mAjIunD3oQZrDdvemTWYRcLAXFGLwTfY+P9LvpeosD80fBju+G2dfzl/o5HbwEZs9eItuZWOk43zgYHl7A1mX1I+5ZrkWxU4+znaz+UsShbskPPKMP7WNpaM8E9fD8j/Oe+iVyOyp9nlsD3csaDLx8U//3Xx32RV/7J9i9pXsuUTqZfFVfFQPv7x0wg00DpELIeTdcrR9eaxoxaFyfBAey6OqqzM9lkBgsqe/wsn/3hnFKRyQpS9vmtDBRV3MsZmUMsIo8UAYMPUBXlpaNuAzotO/g0hSMWXW31R7TeJR6acAQC6O43/Ek0TxSBudM5UP7B6gSrQ/Y5q6hdI9K7FB+nj130FF/JBFJY78HbMAjKqabjLqjvxpxglGcU1roM16bR+WpKrakz+rSZDyx4EzJrb+nheupY1qqY+7TsqxfIrWOrEyZCxvtp0dU5Cs5RcG+yumBD0uR6cw6XUNnTsiXc2WTgocZP4ucEiAluAEKZ1jSmdBX5e8qsHOobu12H9sPGpNp9sJ2O3Wik9UHarl5qRGUYDbU+hmUK25JnH7cCMrdIMb7c3ydRu7lZo6BMDflr6l5l2Icii6bDtI6D0hy5TsJCNIX1CAnNJti+u2ou3z4tWWFhnu0kQSoosMWfnKIFR4L+qPj2dsOX/cCt9TDk+RkvsGc3BoJhQdjZuK77rZef3AdwtSu/N9ksIw6cBJezS/nkcyJe/8EiyVigvJrmyMYUXNefpn9b0blieoYiXT5812fbn4aPrvrh5UPBdrV7r/SKBegsQMaTzTfmeQLbTA0aNO77JwOpo7jjjiGzBJ41l/IQ7ZUlzJC9oK4RkAroPlioig3dPkZdOut+fFIV5ebKnN3wSh2yHIPjiID9WxtcNM8edpONUHDGMSGIrG0UVGo1g8GHJKLNiK5c377rZeQncVnAI/JOZ6Lru9I+gjoIywbEmSxlqImoienpj9TQVHxIvWksFXcyFjGOm83lLScZUAsDE9WAG46fzVi1dwUcXpkbcH4hdg3rS0iL/u3YrCgnGjIUscbITFEbGUrocD2A7L4fAX1u9cTD1+NWBlpZuHX1GsXRG8awiW5m8E3p0xybdS2r2DmLDzYvp8FI9V3QvqtBbefq4eB9UjHWF+FAa1ZzNVqHlNqPifI+UL250+7vBlHFBphe1YtBJsVlvbXYbG5N97m/2ALTN8tMWbsI4x6w0jLw1RsmSSUG7N4H5NWSZLtHrHMHjp9lkSI8MJm7RKE51tt05+/d+mHnRWONYgj6WfHtccNfX97w0/YIg/xc/7yXE7/Ml7i/AIOI8mQ3HN7zYTkxThOrhnpFPzJ6QgNefQjXel8mJeRZzDUFxg0Thx831X1FNCioRdb0NwY4ZFvGdRPFVhbs90e2f0mmkAvN8Mh+UU1X1M7o+9lxfNbFnE8sxlw6XayjRBEoOybCUwVp1KZei8OUQi6wbsZcFIpzwOqQfqlnOIFc/FlF5g0/+n7x+LVNtG1AFRi9xjB3ABE92ZNu6XA5SzzkKvbQhbHegfqGFe0TqwWWON5unzWlTHzIRuoJ1GONzAyD406BtYooZMMVUT0S5LEE22R6IxSsEUi6TbbSKmhwnjmwRPVoDLrVKeYN6Hd7+MUzBWLUPJ7al+Havttx9NuCNXhM48UkqtHFnsFtolbDlIezUFQn7q0H/tqq47ez4Mt2xHpjPwx/58N/zhqd6wCiOC2yCA3MRLDZPZ9nMeM/PGPgfVHEsCDAakfth2D7vmA7dYguS54Xl7gAFjxpWZLNwqiPgtlclK8KSp+Yt4pxr6jvw2YizGnMJMCG9VTBvFvAkpclIb1Fo5VgjUWHc0nGv6ffafTT2iIEFh3K0+RMQhrDnQkgKCcCWovnxc+TfTNc3oS3MNZ16mfFj8eGL/uBsk3ca49nOuwUrO+IUxOtviDRaGv3v15sEHuWuG3JwtJ+MJAETAMp1o/D3T/6frFxhIVyzWDMEl3RnP+cbSID4jX4J9mdRGMNJC5Bm/+3F5rlGRvCNcXiRkXCLCmx+gEIUl8vei7Q5J+Eac5R0ZtJaU9dtLAoJIteWAfAtU8iD9+mg1r1CCssR1roPQqMHAgZ3aRHPYGj70TURgM2q4c8Hs5GmhKjKWNzGOHyNCIZ5wJjojQF7oL67iyON7/+SVkSwFIcnfwvywT430DYaMEBwDICUaPw+gFlFYIZ4pg0mOS0glFd351Vdl1SDqHmGoYmrYtN0F/tj+1HGuwjxi4JtlJdWdDYrYdheMMXm3GkA1It4pZtRkb5stsjHjNyRaPQ/EwTzH0sxj7SkGcporih208PJMhcOXv1pqIr68NkTAiN2TVp3yUMKAvIdu/s57gZTNRCxwrLifjc5IDdeP8dXuovgvZWg2FktYMSa2XEEB4NMbu5WaYUgnvuMLbvuibxiR0Xs0qZpJ8i4Mpw1APYvZBskNeCwKQDdRI2RDQ9MkOxdQj0VxgL61wSMFrt8zar0p0TsJR01Ryvdst4zrPiMayn6as8wrZUwJ/VEY7hgfYhaHyGlHONkbVIkmAIPWbEIWcT04PLz2CpnC1wE9GwMTXVM9ikBuDD+NjcU1PqQP1kv1my559t/9tOIXc9Figqni+k6X0MlUsmQaVRK/qyMaRemEeLkrq0SJ6Fxh6jobUjLsxiOs1L1zIvPm8e38eoj9TYKRr4Y84eKJucecNVjHn0bdzw63mPAnl12mMR4P1sVkcAQLXM6FtQL/QttATU/TcLCrDwxoeDonh5bKbhxrqGlggNjlyiuLgnpxl7ox8SbEdoJQV05K/HZwCUqRh3d1zEpIugTBhzhEVyatjUNXXNPmPU1353h+HMEhYT5Uy9CxFF2zUSP+XSFXgXlGrnDVgfQn8FugqmTEgxw8zu1i3LT/gcBRFjbWxlpgDjGpEVUbyPhr0ONDXs+KWcaY74top9uuoT7287xo+G8lZR3ySyhbhffi0o5zE3h1Ychul3QXtHOG0WmSfEdJnSaNRVB2BfSPPireLx14p6WM1ntoJ5K2tU5sPowzaT265xYefyAWxv0/WGLJofN4TyquIqbx1EB65jBgyKkL9g17ndxPVvoWNwllXmhoy7HU99k4Ch6AnE51TkbELFHcNm8ODjbHjbNmz3ecn02XfEngVb6pYpXJlK6znj9kxoARBBQnYIzxDOYzQcCT7KkT4ppHnjcWbJiwIN+r190RoidtGQy5Y/ZRxllv/7M4U4AcItUaj5mNaE/LRKXMCsERJT2pITsH0vb2qsDtOwz3NPpwqmLF6v7XP9TufA73oWujpnjRv73FRng7RN1oIXu8qiJQ6YLDc193++P6AAjt5S/QOrlsB+BPW0Dis7YFSmAh9qvyKxVXjGxdDywcprSsUhp1OCOUTDzu8QzXgBIoJgprB25ifgx1jfpz/UH0OOuRVrnvLNjI6ELHI9FY9aoF/MOWRsmE6tHHbswIKg+Gzl4C0bpfYdXrg1yeh5mv5/KWJzscsarrNE0vzeusMg262o4kfqNuXD+w57bZOJnh7EJqvDdaig94rRC3BaLSGi3ed7FsEBgCZRgzE83c+XEbgpRwakpB4t07jOZnRPrSadwSwB8P4Gra43db3X9n0OEQ0FnLlEcb2AMSlBoro0r5JRZ/c7awdRTHepjen00ot0BllxXnOae8qcAhuyY+hfrTGyuHOlwxG1jCLWaVqzOk299rOGND67VCeoWJ3JNS1+Cy5N/+pDVO6fpSbS1HrpjRpICgpPtYnnjCHPYeb+J4wJmTOGHBQTbs/9DPkniTtFFJRiLnVcnO4/2v40fDS94Aoguj+f9Y4y5vacbj0zi56L19xyj0CfBbfawQa3wmKTAkDBQ5Pyaapn9CTrDeBCR+V7IVfxqnJxbiWyBABRPKqug3POir+/vZgGDlM46iDxpgZMJJEZqACFjWxhJA2HvhgVPhATVlugI8FyLLVrNBkB6XOwyC/gIeBiIC7OAFjZitNYhRpIyThzxjOmGh1XYfOApwS9NQ/+2b5P9HsxOILsFFgkKtMKqUMlCuZEF6OewmNNmRThtnIIZAdAyi/sPooKHmd9CjCG+zvxBqeCgor30aIvJmcBxHXfhvWkvHhHPTNODuhhIIBiBkvr6mLO2V3ALQprPHSjH2KDpHqK78sl0NV1osSvwdzMIZxfgO27oN+ZBVp0zbnKthA0mgjHbo2T7ZwRhU9B6BKpeGbg0/7GBldvxZIlVSQI0+9HSVRWxSIcqK2RLJ8BOou6HAKb97geBeYw+qtC9lW4zms3MtEU2OsQHEc12ZlpMjyvcqyZ6l54pv5RiF568JrZaA0zgs48dCseEz+Aq6LygsZPF6d7ZjZlJ0MmEd8TsDnWe7LkClwvKQ+IAnD5vWFG9vA/3f78kB2/yPmEeWHXNuP1+Fwy/s8XJm/Pr9FgH7Ni06VVDhA7XsXs5xrHpe7gmUE2/DkNZC3kXs6YP50LjRw92srEXk0CIysZhmcXi5jCgj8VgsOwC5Ik9YIclgIlQiWU2QGjv3asYm5ASkp4hk1r/lCKGvMFCOfBh/uqwomLExLCAqDBNqqqGZDieLdR5GY1FlJ9aBSSbX+mAFoPw44vYxgfipZ6EUQtkpVzRZmZhmrXCaGoGRLk7lwpfAcAfRTUajFjqwN7MxYJC8WtXKfz5YHt5Hkz6qN0CtfJXseCqKS4GN66uZSqhjtywnKE60LTqjP6NcdQ3ycUSTwQNLpWo5jNJCWOn6zYXoZcYMP2Buy/IpzL2Iz5RUHD/iLYfhSbk6DOLErOi1Ak8X+r76z1uBaw3ad+Z9eznVt/tWBn+65LONG7u0mQILU47l/KQLNGlxRAm6LfjHlWDrkEO3Rk4Xg9Mzx6XTOaGcV71k/r9FJP3MrAw+3Vc4NtrnOGbI6WcBqflFbDcdiAr3qRSvk9CGmmv0dmkuzXZ71T/Dv3dSG7UAoeCaJ6/vzvbP+SmkKu6pcIaxHRObDmF2T+Pz/7nFrlTr7cszCchZSnEJGytT+N1qwpvXqewfzMKf6sUDTVnAi1jk618Xn3cmIrHY+54W1sIYrWtT4NcfcIvqpFaZ2ZgRl9rboaiJiKn/YeLvhcD7BCKwCIcfZprFOEz2wh+hRmNqjUFZL4rBWz17VgdP78wOV5uoBnCCwa1vU6JRTGS7ECsS6F1bG7uqqL3RESM1rqcjrlVBRnvYR8tA9CyAV0HuNk168gRpjWNkNCOT9ICninp0diuibzxUOnK2t9pjuzaz6vpyYDL+3EW9tw9rpmCHgWGBlbXcfA60rq8FIAXY6ZDpvXl8Xpua2a0eOvLmT4MIMcT4p6Zjc9OChrTfDasxgMlGhEnM0yCWoUlaLAe1qDIpg+TW1sEuuH+7N7IThf13ocd3HSgp+8rPUVEJkmUoI3p+WMwIgLTmDY1cgN/qwInoIFgcNeBhMac3EFgssm2Gv2PC+nEDCQwCYx6ry8nmHqPCgsw0dFlv7aVdwzyWJn1We3Z0NXfSG/n+zLqCEIyTXXcaDP2cfz9hRv/+72J/sUSnQBc1sj51LB5vL69ffr/iRYH3QWufs0WsT9pMndje/WgpdExGdKV0XRp1xmMgMf4aHfa0AZWEwknjdgyqo/4YFfjvuFLqYqNrg99yQwWk7Yrla1+QnngoowbVFDPYpMhVoAq1lH0n44Fc1FyghZXNJ18DsF9EWBHedwhzQpN0j2j7SgCqxxcF5XGLnsKmaQnNnqLCHB+VpwfoE3rAF4d3hBGOWv7moeLyWieYycLWwDV3gS8G5ZtWt3rvGFHHBOLSQ7PYnAAsBFDytnnDlQ4dTAvHGMIxuQ9tbxVja7d4UO06SM1Yv7czN4RCgHsSH+zWhXhHLjS8GUM5JZU+gv4vOxcRGSa2+mjwRByHDLUNy+z9gP1WhlanSss2+hPGY4Xgh8doIJ7QVT7TTWkha7lhzDynOoPqaVmcXM+lqspcSi4fqFZa+bBz4aS9sdpK+Nbvddt2no5rBnB1UxK9leaxNRfD9veNxsTvM94CJm9KveaFm/1wgSrpoHcmUkglsOJG0dGSuJ9i1nBtnQXyb8yepYfp7qBiCCkxwslyfnwaE/bPZlzwOluf8tM5plukyES2fnok4ei0m6aIpjLidFL3ipLeRMIZ1k/iyLKUuhdeFnuW4RNFRPCT/LEoBVqGFXNDuZqYRo79WUDVUrNMIYKmy0A+DMF/u3jQv0P0TU6JpHPr834BlS9VgvcHicc2oBi8b63bVNThibiGwdEdSR4CrHkRmN2Us2C5njENUNLzdVCaOw6hISUVgWMMsQVqbGyoMGTzG+FJwvBWNHGKRweMBlulxo5njUWSCrflBX1/OkZk+F4d3bqlugGvNoNiv87lsP/JUBBMdr9llwb0v5lOtgzcwdmJ7lRgOlQwJWaDa8uRVbay/7iTEKxqOGuucUJXpqsOBTwVzdCFbPGGax5rH2Lth/MyuqxYwzVUs5cKZ9RyiI7r9SqdYMKyP4+jBjv8ZiLkiqPiZEFf1uVNaSHIJ9H+BFO3AR22Cf6dG7hNO3e+rr5oFonhwOEY27eFMcAvYbN6xrowgdr0vjZcoUol7RxWoSm+FxXCMruzDtK9qILIVdtERZhIZyLz3kK55ldXKj7VaOT+udz1uGoDN0lcX2MmX1s9/7LJdM9TlT2Eu/1L+4f2BRsAErMNt7BtJp/cPtX8I+qjIjms4YHH9mTfLcL3DMGlIClvLUaCB6ZiBl75mL0jboZD553o+FbKBcsMLMJY6O6wst1v79mBZlMEvYniqyj9lWw5NnN5ElAIj8PW1yCupbgTgtcRl2jewignsXhwvmkBuPsa9eg8giir/+YLSfYCanrpZzFXZNPx9hJKLXgJ3BAijsAdRksLPTyNo3HPAyN/Nmc6cWzzp2Zgc2c9c6rOdmGYXVVHLWkv7pBkIpEFdw6YPABHQDtBl0l4eXEJ9lR+jmdQBgZQ6M3uwhlmAWFUkaXOkBjHOWpZW1t477XtDvFf29YB7VoAyHicIhsBbujl/8GrAmJMMgmPaQuH8q5hz6i8R9hFgAweE248bP22v14UOLqgBDIedEOSyL01osaGjlWi8qq18BU428kowJ4SxzMpb59Jdi2cm5el3KWPMzygDkh6I+LMs5v8IHLyEy50yhDgc/zVHlLn4tfv2oPvs6LLjStWaL2IAl3v8R9mKxCAklbTLwtT7wm9zXPcVSeSb99MUHLIXD8APO9FRgkW3s38sZ+W3/QGSg8Q+IqFyz0qwHl9dcZDnp3lzqEem1T+sR/2D70/CRFXU/es/NZWQBXA4oOwQOLPmsuGz7XyJV3CSdYG424oVbBW6njYXS4fxwEbsW7P561kIaTxeXU9YMdrguglVwMrpsLROtTRCosmszoSimFPZWzSHQwKYsYkloe7QUmUWKolJWkWmOFk37ed1MbbOEBDYM7gnHYNeB0AVF0qyHQYCmacD6UvIMB8DjTjUDcCynSvQXnK9mxFQQIzvbu2PRZ4psv5hs9v6r6R59FoxpMYPS7xKGRNL1mD4SGxMhTFi9sa5VU4sErAC310UlFdEQOpta0KV405qGIdlrj9+prMp1WrCKfOJrsm0D/TYxz2LnyCZF4bVa9yx+8pz8nvQXwWPUy8jMfl8QDaNpZmpzN8favrszyBmJw24FBWANqfiacLgPdQkTzlKsA/opuyin9UAgFb9lKtrbXGKKcy3IMoDpTkX83GLNK65S23frVOYsiVz3upIfDCaSYXU5VIXcpisPIzKPTBZ4H5t1LssEh49NrdG7QOXULK45VcLg5yxiFZHZCDsuQW/eLlE8g9wnG5QhpWzU+TlmsZna+kxH5X6tPiGX72uJKPFHtz/nFCAX+OiaNuHJY13ho+uFWB68p+IgvTVga2d6uEgmAAC89Q0/7Q+L1gsWFcu/i0b7A2yULuhnFNr55HzyZp2QW7yXC4nNUrftxBg3g2I8A4Aq9K2ifq/BNAHMYLCI5hc1mpv44MehuxHIReTZNGoQgEfTmxnnoAzSENW1MzMSGg1tEoU+YDa7HuyEZmNTQAtNrNBcyjVbcArm3MWLmHZc8+YFRzW4qHDoPBlWp+L4aUXAZZi9YvRMWuT5Ze3zamAFKIrxosC+7sVky78obluPWsLz+M1nLjuA9GCuh6p49mkzbEyH//20gKR5I1wtVuSu94EBYH5rZigHMKuiBABPaCStBRpLAeYdOIpg+0ZD6Sqqvymaz1Wop8uGNFJZ4WwsX1PqRpzyJiJQLxBrs76SCLD7Sk9tWJKvHe9fWGM+LXtUGnq+zwkMxWeGW7f5uq4qWPO6v9t9npvRTeeefKSvw8v0vtiJ7YiOEy59AZkoLx3zLMY+kyTtALMpj7mhuqcpGXb2bCETZJZhXkXmvC42sZ6Wz+jz1rtQceBjVnlxDPi8RmD7mOEAuFby93Mflz4FEniwxEU/FKH/HfARvWwYds2NYtcDYq0gF0P+UUqTK/TnqDjTYIpLEZAOplxb0fn3qRW32uM1UlhZVMpifbZfi+xDUuOTjo88vQlANMGx7lGLNdqRidRHhT4KynvxSMeicosOEzc7SWVH5K98H+Kmyrl+pyplORHsFcAeKjjN0OoRFvFnrrdNVJMwUCXksNVZKP5QFjcExfoLrKuWCqpIUJJLNTjXHmIGbcCgjXGDqYB+Fy8M2rGV04qkKlZAFWdXsYBOPv7lVrjTmF5TmBXQl+EihNfMMhrU/LVj1ksERQIDs05CRbmAx0wiG4jT53pYBlHQa0GrFedwFlpV6MvAKAX1zQ1O8XoSLd5T9iCCNXUsQSqzWXTcguaLiOTHzUdpDs8I+oxsgqM3OWpz1gIU1hXKojW/j8R0SuuiIJhMUVuyC2uBDo8/P8ru7OgkQuIkBTcygKJAg+AsGrWRLJuhXrSXIRc5jOjw5H8qkDKx3TvmLNj3EQ2LtE2P2VzfaGBKwTkZ1BEWnui0EYms8pwh5JongPR6iSIzgAt0nW3hmtkwUXBlF32g0Mu8vB+wDIW2zGbEfB7QPH/+f7L9SwTxNhk4i42lzJgd8NErZj54VOjTRWb13TKPNWd1TIMNeMJkHuVC9fSHbDWa2HewZ+GZacTMJnOSSVV8bvrg5zdPF4eu2a/HMCjsTLWDWs2xzFmg7zZsJYphZBQNoFBeuSSclRk+Xy8UFVtOIAy2mEHgAHUgQ0weIVZ4tJdOyB/m6bo2jDotS3HIyIfOEBqwnSHeQ947jYBWoN/LaigaMCjmXA81aZWlS8xNAGi8kBq4XFnVj33cELIIRAjZ2DQ3F8VrilI1mshKghC4woroB6pqTtHZpcrXcx/M87ZmjK96Vy2rvqUKoCm0DfSmKO8mfCiVCY5nTBejgZhWRqhv+zbRXwtKUZ8roeh3SpKbo9+/KWYzA1wPdUE7gZbVi4ChKJiYUoJkYLObF1w4nXEk07JBPzFTaKkSzgBAOIm5l2iSi4Q315J8bYgidKwgXkd6B8ppRejxsorJMizjnYIoMrOOYMeE9TwRCiyK++3Ay37as0rF0VRsBgzS3WTgx9wxIXgtB27lDGnrTJlfMtnXQNA6oev6WxyW0VEv0xslkVUI0aWshO/JP5H2xyzg2bYygFnSK46M+P6zQ5gQ/M4y/rD9afiIYnDm6SZGomIR+4IiMgQWluOkxbWPvKDy8YIoTjEhs9gvEBhuwEpYFXoWYuI4tcRgFitq2+trZF6Juap2TNapuolp5g8UFAxXVjzXpDnoxeEILFVVFbzcDrxhx9svO+RRrF6gADX2o/DoEX9mXHzoPeBlUS8wJriB7KG5WUQVxoDNUZ7OCwCOYGSxWotgFDa/rRGQbJBTMAJd308MOpyCX2Ytrt3P6DJlPITDAhoILRyDv0KSedq+TSVTYo50RJm+b60pQ9g1ol0AKNU0qPQp8uK9zWvs5rWCtRbXg1lkjekk/FhlYC89dPSbTDy0eQBSYnYvM8TSJiYn8N2mzXw4HXLzyXvlKKgu+CYVKIcZYKmATjP8pStu/3Vi3GtAgiZVLahvM+4H52Jn6YlxM4NdHxP1nFZs9nXHWQuAwUnSzXmIKkB2GTMBN/qWObozaOI9Nwg4qfvEtrEzU9RFCpgeE3UPDBxCkmkZJMQmymnzrLfDVV8N+iiHYN40focopCjKZmayFCv2f9kPnHMFlLeKcN5VuhFjnmCfW+kBGeU5CbZmsr3SINVsMjBQzD6ISeKcAPrYLlBRVoHmPo7RLkwnFpnzFpmHG/gm45IpXFma1W1sUm1ItY6CRPn+J9u/Rjr7d6CgMNb0bF54ti6/ZU1aYg+x/4Cf6wkX5qjPUvVykVVZJE701LiRHsWlgnV3AbwL3xeLfdK805kj8qqyLjEDJstpZHdpZQ6Bb3VAVfDL0QAWxNwhIN33QKsURgc8l0FlQxBpqoSK5OSDJjGHgHDCuNlO67mMcnwHVuZApU6ZQHt4VOu4c/Q6sKP0NGcSRhmLPw+ykADMraz5uljHThppRIwNNgeC8E8wn+CzfGV1uvp7eCs/FNuDlaKWRW4GHdUkHLa3EdAjM0t7gM1B7KWvGc16LVBmbJkRGZsi2eD4HOHx81Ks1lO2ua7dFMg+MM9qzYxFMZkhDoF0QfGOb62CslmGqBXYfukow+ijJjkOtLe5VGu9/8OEBgXtmEFj5b3XViCP4TWDCRwwmNMiK7uM50zSGH7flJASoM3v/1z3mEV/6hxl2jKzw1w7me5EgBUM6QbUdwBTcP6kFjikwIKF6dXcxijAAzKHi6oLIu51REbYJ4O+Ao7kPAPmKRHY3jyVtjoD14H9pOHnVmF0/PJkR2x2w4rSP2gvxXq5zoIH1qhOMuFsf1lHacHzzAj4nZlSvWzfmgsydXW7/7PtT9cUuPXwxNeHhSfGTGJ5Mo0B1h1XiYtLkQUW5Y2nZim+vnmh8Ih+hOvoPWApGUItyHmu+N9qTzpIJTKLocWoqGVJaJ9aMdKN7tNa6YdnGmyc+uX9ZkJ4/mCB1NMkaHZhiFQzBpyUFq+T0ujRXUAzk8PLrw9eyCZMvc5W4HNeBd3vuk3xQkA7pL+GNEUywsSNBRrHVRySmLdistbeZEUH1u8msfz4D3vQ67FSWEb/URMJ1VCHjfw86SAutRbBxWDo7gahmkMo4kwWj9hJP2ahOe4/1sOXh7cXWYq5AMIRAAiq6lTBSP0dBV4/8kyxFHVdfIlrD1EnHyhwm0Y/nhaOa9MQ9dNNUHa/H0Pw+EtFOTfvz7h2Ha96jgvHVUSGQHhvvAiaTgv2nXI6q/29fR+Ye/XDU1jn+FqAtu6sYD33AswlsNh9dGd9uFdoxWZ3+N8ps0IY9LmwDrEpgJaNCODPAH4T9K9GHJAByGmEjfEyvRlQsWhEEghBrTN6Ub5sR4zc5PZj7vhJ3w3R0KXZVmVGY1uGXFoZQUV/tmc5O5jweqSUyDBiJrx/jEFwwNzwTMBl2j+T+ZkeMDPTyMKh9wQZxRqU1J9ARxIB8zUL+Ufbn3IKOcV6ppb+MwpUga5OPT94PlC2P49+Z+7QM4cT83DT/jKElJkDx2woqUD4zILKqWE+l4wtA8BrPUBBrHP6nFWPBCjLLAC2MvHtseP9bQfrB8KWfArcARc6ZURCZRlACoXlmb1RT1BZzKTB2bz5AUMwQwgBAL6vSd2cEil8CYaKDbUJ0Tyl8TZvGoJ6ql47MA0dYwfJqgFs3nn76lHhhtW1/ZxQugNRBUYFhq/doOzOVGsRxMQy9ipoVfNOVaOoTMNAjSOFGXaBBQ2kNQcWKwteirWUFteHzndvO+8zc84/4sF2ItZ7YpmDOQEtlu0C1olrY0TFxfxgkJhDeI+/2RGcXza0H+qFZnMMoYM1ltYV15VF7GssZ7CSmix22bmyA/YsBJkhNzB68KEdgFpmOpvdT2LkAFafgmJBmMOdnth9XAwnBD2W86lV4XO8rdZ2KGz2tt9nOQV6Y7oBYAOksX4j2Oq49KZ82R740fc4PgZ09ZPC6zPkwm2gJCUDidfWOrH5K8v+lE/ls4O+jHJxAs8B6qWbHleb9tx9n+U7sr0iBZ8B+F77hzX9j7Z/CXw0sAorj9Geisnp3yEMtXAuKvkd8+rVV++AOQRrRrJ6ADtSGQmSNRSOSVa9IHc5b2WAdQXLCK6SuWyue4wWE9dOVEcNNRbTBwfoV/vWOr4fO36876axP5h72/uCTcT6QQUAK8TmaDww2rkcQhhIDpahiiawxmi64Vz/CbL0NWctoyJgB1Eeoka0X4s+GQWJgT1wiQktguNrwflFMG/mXFRcFnvDkkKeQHks56aEwciSgkX6447ITPi5+u7OdJhxEO9klQmMGw8eQFFIm6htXkZwMgtgdsAicIHipZ3r365fdQL40uz+b8XqB3n+BjPQEwinAh/E/tkmnIRVNJoZ6SDs3zBn0QyymQW2ZtivAjOsx1/MCN//aznV8bMZ8HJSSNA61U0WREMu+wL/qTmKDDnaWiLWSJFDuZAUCFdaV7ksKW7gwj6rh52HDQuKU4j1a47Bqcf5+yeZcQodgnG397bvgg6g/+S1ji7WWLnZfbfZGf4MlCVeCHhHcOuXxjAa91OXZhHho630a8HY7cpzsyq3XGiOUZ9qCgznky17hpCWQf8YvT/PYI7/UsE590QsdlJ2MLbOmCGbJwABAABJREFUOSPknzE9n7d/iVOomB8M5QUOegKzWNglsyO3f4eHFbt4tUxMxztMTVBZ87p810r5Zuwn5CpEsUm/fD9ghcbrfNXliYkZnlrxWg68UmfCtwHDDY9h3pi0x7djs3GM3buaq8ECbEwTL7ahABzazq7miIapScQov8GtJ4JBpP5gm5SyOv8f4VTGZk9qPdwx6JJlpqE1/ftFQ5xVoC5YJ9M1inQVoOu55I/HLji/ugaP6+GTMRTdtsUcnunx23fWsaChsSMcWLBOpjFOxLF2DgCaDTaHWYH6INce0KaQpijNjAIHkWx14NZ6BBxjFmdliDuHRTWNdezr6KWeH9LwCz6sBZuc2Dxd536igzUZJ/GbqPBrUJdD0KdBS2W3oEVHgUqxovRhNGBTFpXFvGIPh3Afdm9nxVJmBYC7/e3+y/RMj7CTQKP3wDIIFfu7+MCnuRX014L6NkENKt4rOzkEkyiy2WLHaQwmnpi/J9kl1ifggYpu9nwHzKRebP5NgFLQv0xjmImtTYW6TJd6/LPuFzcjC3Q0uTaiPjwg5bAdbq1MC2KIKqQCcZ8lsoaKGfARHcdAidGei6bqNHpdxzOny1R49N5gUir9yUYCizjDY4nZ8hmh8Z1Tu4k2t0c9ZQXif3T7U07ho45QdgTX9AlY3X/ZCfC9UVhJF3GqdQmTSUINm3yC3I+IVd7v6B8edEZ812PljbNGu+e/kX56ao0CM/9m+jcrIqB++X+/v+Ds6Xu4ujeDCKIm4DUFTDjeqsHuoEQAcLn3UbAV30c5YYNcGPG706BjIU47NjWjLGYsyGYBEKwfdYEzZhzUGQJc0E1wYTTJdGiImbm60YZhwhRjm4QFklEoPq7R9k+qKeGwNLCewWsyQOPLRHkXzGnOYO4T2KbN4n05sNVhUtZe6KduzV4GesocmaI/R3AUUFx9LglqTDUFZsXN5zYffcFIt9aNSdcGVCvmtPUhWin2avtLCrlwJ2iZA8ACirrmD3Q5wbkB2w9rYLPJaL6uamKOMesUhCzG8bVEV3IM4oks0oML1SAlAHRCAn2taG8zjjeTGBbU6bg/GxdlrVUo1yN8zKoHQ75m6ezEAxrACuyz2uG038z79J8ntPnJeZagU1C3gdvWUWTZEhryvS41AkrWrFrRalzkJLaZHEiOsjlEzLtRPwTAZ6p5rpkMV0dVxGZGU5k3b00mDr32YuU6QtwTQk2p9lHSms7HNJOt/LdmChNyqcyHZxRjGxGHt+xg0alIr2oyY7oRP5+LK61MvKlfDH6nWvNUTRdwe1JIvD0VYp4dVlhlLEO/GlUWhnwvJ27ljPdcZL99f6/twH8/XvHjsaOf7iiqWkEMANoEHk7XbPbghaQF1r+1AcKZCinqx8CqQcz1NyvCGi8dSa2ShUAtEpCQuOGms+CWje6EdcmOujISwjnGIDIDc34RjJd0fMm2sgGPEhYyVr8B3BmIdzPPZtRKrteQSmBhnVCTR43z9CzlVTHvE7gNbC8nbreOn1/eUUXx8+09HDaNw1SLWqkrn1Pt+rxusSiIIY/tUBJgWfFIESKF8UpVNJ2xfkKdchR0l9RGXevQnEAaxShWc6DjkG1idNOPKg8JgzmbOe/6ULvlviYGvM/Er537EtSqq7nMn8vZEnX0xTS4yjnR3u288/qQ7JwVIZtuBr+swKK4Y6rXQjXXFteJTGaiCHmVICKSdECJF2YiXDNvgvFXtZMTh+SmmPgdda0cEqSkPQDc62n9SljP73PfAYO/jPHfSo/6AdcIP8cMY6C4okO5SGmTuJBrm58ZW7Mzq+ntQrCZBZycRpsYI4MTFP+cHV2L2R8zqH+2/XlBvIiqFsOIfQf5QHs20Ik+FSkaUnVdFEWZMllNQYF4qLNc9lTBnvRo3kfDHeYUXupqSGEz2/pcCe45APQndlOwDqAfcEXWFh7TFFLf+4Zf3u44zxrpbFhKZYag3gWcIufCNNp1ijR9LENKsJ/1wdd89KTDCv3VnvpyuDFgI5ELzQFwLrs9XBnnjaIyXLKZaqTbigJVDEoaN/uu/rIiOo7V5LFm51W+e1bwF8H5YhAaM5UpXkD2gexRc/Brw8iYlxB0MC/TpCwKUPeJ+/3ET/cHXrfT6gQyMHW7DESa+Kg932fFXnv0HbyNDZsYrEQ64rxguXzYFU06HrMBOqPXIWce76Nhb930luqECDDrRO/WzMjehefZvHN4HYonLYBuCnmzX8cOlAehmYnSBeduTqQMQN7XyMxLb4k79X4X7N/V5nHADPLx1emjw8gSpbuk9mnQUn0k2KHAtHV4bGWtZ64Vy0IlSBMhd+LByLwBxYdCjdtS8IUCoyEYcFF/M5tpkuPfBVoa9G8npE7oIFzHKWTGaHxppxlhvwi0Bc/Pb944AoD3mc8/YWQ6A4pjMnOg3cmNu5d7qmsKJEcDPxeUeZ+yIR/TEBWhXa1ZFSLLBNnD9+wEroOAPqI2/2j7U05hPF1Aw8yWM6AYHbCkiQFPxT/B0LJXMxrqeg+HoxNCYoG5YjkPXqRjVrQ0Ui9v62LND4qpuTtwwChrmwxUKN7dQt2lO4vBFyQUvxx3HL0ZDXFatFeKAmxeEhgeDsDYG+mh9QhsVnMMfJhUgNrhEgcp/Y4eAUTkxXoBJ1+hcCAPQv4CrpNUpvser22wmY31i3FzR8BjTZdPOjBfsR74O1UxvTO1L4cg3WArdbzZBMyAeQO6Q1Im8ezd2qlgCRg8pX7p5uYwxxC7jnVBhtUfoLeTsgU+Dc8xfkqqPCtNmkMY4QhukBBwvLLqPmpg5QJk8wZHdrFeWHBeaJYyARRs28B5iNWZFI7d23rR6Q7hXDRGO0kYVdU1juYNOF8E9WENaedrxdhN/qK60GG/W6E3AmEFxotAD8V8F+w/hulTbWK0Uqe/zs3Oe/uu2H+bMa9ZpmLsBZ89tjmTIMxIR6B1HYOKZwZYcujBnPP3lANBS9YnyyQdwGZ9LP2XDfofB8pmOlMXZlgWgCNUp4JbmbiVHga+yrxkClR7zlB22KNgitiPgJKQ+xXmU2e6RD1zpDWRh+TEe/VjsbiSpAArhvd5LTrHuaWbcoXVPUPJzcJ/MFn4U05h8wt5XlKZifexaGDPk9aABVtmBpL9bunRXgbeniau5aHrTPuHCsos2MvwbGHGheuzAvW8fO/zcRD7u5XDtZNmsAqyVLZFBx5NQzCwsqJv5wt+fb+FMwCwZqUWe/C1L+jIjKZcDGDMVBjX9DrXCcJZtCVGRty9HhYh9i/JoXhxlzAOO5/Hi32unhatQ8UeNtIJ/TutiAvDuaf9m0ZJC3B+VfSv5lm2XwrKkBDxk85IkPAWMA6EIFo0M02HBTwy5GeLd+sSI+c85/HFhM/UI6faxpp0B+AcFQ9teN0O3LcTfRh2+9433Mk2EhYfF3QIXCmpXFu5P4GMkRzlZepgkCec4VRFsdWJIcCYK9OtbUJPiehW6oBy5u6wfgVMIM/1njfF7JZp9TtQfhLUs6C9rfkKZxHs31ewwdoCBJj+HsqYyLS+knGTCEq4HstQk8mYJlki01hF/PszGWducs0GSvrPba7wc4KgRAOe8TJrThk0bwXXcmyeOdQ3wagb8LcHvrwaAYQT9QQWLOzsUE64e4X1Jww32PdyRr/CggpnOHx2Pm9iEj5cB58FmpkNBCAMN6f0FVlzk/M+VoQ/sbvJ61g1B9qS02d43FqP/X+WmfAcjOJfP/37P9v+ZdpHD7QPB/FHGicYmRfRMHIAnHXk8BGuqTYvaDQiQVzkzKr5gKW5x2z4Uo+kRTIvDzyA6GK8lXNF/15YYvFpQD4wFez8Cn47b5izeBdtMalgOBSgzkByaEh3BU4B2/0JiRC/z1ARMfeiiGLtbBY8yICzTBhZLScTJCldEAwNOyaWPHIDdJeoP3CWcwRFBUFd1CKRfYwbMHbFeFXofYJiN9Kx7p8bBxvAsmAhmd616kag/bBosb0vR8h1vn2z7+1f07ns9hiIY8q1GtuIM5KpVMriIpuXttQP02RGf8G9duROeAon0sgD19GtnPtBqvJjtqiTbWUYzCETRXYobEZw7r0RMYaUsLO8UDhvGCtls8L57AV6lIBOzCErcLeAor8C7yhoP1w7qXsUnp7mCARghl7eEY6gv6Rs/s0yOtNCWgFHRPgV6C8FYzOH0t5nUFRngjBnWwKJfinWOFWuiTg2DeoqnDSBCbNGkjIcrM+aNIqG/lV5CMZ7A74AWxsYU7CTcuyIQUsdyt2hn610VCgmNDqP+XV0IFXmh2AVWP0pwLJBuebA4u/pTiDXFOIzesX3nzMHspA0wZH5vXmuTGZJ2fFnGH9RWPPx/pHtT2sfcYuDVGN75L6DzK3NrJ/MEmIjWY8LaTLEw9lHef7xmAXNH/QYpEFpDBHcHT98uL4Ij4+F5AGT+c4DMrLRr47RXRlKZo25oDYZ+Hbe8MNhC3vgdRmAqkY51WJwxxSgTMy7oLrchcwFC4kuw5nvHxu7okhY7K5dDAADNXYk+0OtxT5LvF44OBoeiVdgeFdsf3HjcnKkIneO6I1Qp8ZqhRV6twk8qqlz7q5fo2Yg5gb0L742fLpa+24TwqwQbQatHqbHrwU2lMeNxfRRoNoEcorJYg/BPCogQNmHOYTqg5Ocgre5Q+ioUVSWJ/iolZEgghnBC+tLfJijtvAEk/LfjCAJN7G4+NY3/HjseJzt4hCy/Ea+d6MX9MObU5wOCoGx1LwmNb1re3pPy/GT1wh+09DDUjHnTqkJ0xBSl8RANLmJZwMAfKrfdOdjPQhjWyq3wDWKH7ficir2edKVOfApWGyRsSyaKXIR2RvgMFeNYXqdoXD9+mes8c6n6zF7rACG4LdvL/jLz9+x1XnpYF5ElWGMs3i22aRmhWVoCaSDcDE//8wgyusgN7CdbtcaCC3Oy3qgbbuI2nmAkmsBbIC81r5WUFE5BCoVw7k9K6E+w1P/k+1fkilc6U8VZA0ZV/aq4Lc+s2hjxkLKDmI1bgiWl6MDyIVmM/bX3y11KhcjkKer1fQ6L671WqyUzuoVawHxuMJwaMX3bg/+SA/+Gr1JCElX8dB1kOamKIesbuYCNwBYmkNzGfeRCnmMzFiIbu/2/pkwZF6O8kA8RFBzGJS/VsEFt9UC9DtQi0Wj5dCgrtIggNpMCpS3gtkmpK8aiVZ+B0IKuR6WHdSHor2ZXk891Z2X39c+gVZQTkV9V9c/siLbcMz7/ImLw42kFDyOhp/uD7t/JfUMeJ0gU6Dv9Qx8dS/D6giXgTkz3s/CINdIXltcHxRCAwxGfcyGKiOGR+UgBkDUxCiBoSqmpNsL+qNBHwkr8etbup0r6z1z0yV8aHYcj79aPaexWUzNuTLY4OeZRZrRRtyDiOsc2innyhKHO5j2YOezGgS5AVpL0EgB76Yvtr7E52+ov5fHfyFSeIZwyVxdyZc1NK55GUCB0asHBfGKPU/jveL7dsP/+vl73CMzuJlB5vdVRsBIeeAOXcmzTH70NnjqM0VQMGJ+DPefqcsMEPLMZmDVm1g7eA6oMzHnMyMuYiMEpposz17HB8eQz5//zvb3d9CmD9uf1j5igYYQEnC9iJcehItXXHr1O0ZcFBrqOY2uOviwlyt7aJ308uR7Gdhrv3QDPma7wAJUQ8z7YHTA42KTSs5ubvT+Xmh+G3t4cTouSmeHISBZm7htvxYRueBVYA+lrEifD5I1h1kTGD+jYpGTyCrSkTJIATmm2YRu5g2RUsxkQACEIuUyvGt/MoD6rmacmaFM6zaW2bD/veD2fy2DEg/1m0MKamMYS1+RKgAszSaBSo0OWxV7X4FFt+UnxfEXwfg6Y4AOzDahny246K1MvLQzgpNWJo5hFEHiy9khbDJDCRdAaCY9vFpP3RsWFQkzcl0NZUZpToejXo2G7dPe5kT3vhXOeGht4DgaxijQKSatrrBs8hR3smJsm5T5yRTIN3uNxh2wDnJlwxqumP+FVgxEdqDFmGZVOTeDDYri9wMREACpaY3NcUV85vYKQADSYTWcCof/ZB0uBhUqWF3NYvvMTkN0OQdCaMpzKbDaCwCcBcfbhl/3G/72+pYkTGbYp2f4OtOPAYRiao62DVHwr3A6K+uL2SFwiyxC9Eky5SOhJjPV6BCCmjrbgpKeupsBCy7W3HnWDnyes0OhuU8jZxb/lkKz4XFXeGaTazHlinF9nvbkoTsfiziypmiVGf0InK1rRefrcV2q9LBiz14TzxzlyphKD/3bvFlUUccH6hp/58I4k6phKRNVBUNs1Suphx7ZBvZO1VR2ogrccVxv2vmFD7te8GEa+8xKiboEHyrPNGb1r2HWQaqi/zux9KzH7jc6JMeX4bDYiSg0GyQhqN9NhuD2d4Miso7S2AXnqwDulIyV5Pj3eR3ME8bH7+HcrzdTxcTRlA5B9BLxPM4Wg3CeGRytTBTNBoKRvkupf7Kth7PGaEMAoW/D7KBiuo6NedChgnenPzexGkcVxSwamj6l2DNi2cJck+GqZ5PF1oGqXU8RsWWhBsHV0xxC+6GRAYzNoLr2plGwvzDRRDBcTkWSsQqociTI0ser1tOue4H1OBw/u84R61gOKZrB9+O4rX3xNnFGAy91ZLiy6g3TjzU69WVlxMFXKQ6fEWb1zIfrRkfBjx833NrA315+LCKAiPcr2ckyCLCuZMUE1tQ1bnOPAHIhECad/+wMimeIeWNh+VIg5a6RGs78Z6ZLd10oC5V9xyyOtslFK27oGhsb9FZZnc2X7/13ZgqAs3ESFQ9Yc5hzxB6SFllZMqVRfGApr80TpfKoRQBruE5ua88QQP5s7HO2oB7asXgBKjmskSr+lxF9QOCO1dvZ3+e2vrvMgAVqMaXGOQ3PFmrecI0InIFk9NDmQnmEDcauwM73ISL12LxYbAPQ/eHZFdtvC/+Fs4fM4C5IJ3oAsKI1Ogc+kIVyGHe/Jw4vWHHS/t2+WwRf3+whLV1RHB9+Zp5cxoE6t37JIaTIULF6ElKxk7WJ2RQoGgVmylKTfZRZQJxGlUdu8m9cf00sqj9nxUNaNCkx+u+6xb1/ziRpHMI5iF4YJrt3rG5l4vBAIXptVKzOkIYAmWPwNdLTwyvAvE3g8OyBayFncSdw/29jLXAYz7wJHj9VFyO0/Q6f7RyMNr/+Q+FBhUbxGDAHLlDMUozeejPIy2ikAHJmMrH6Zm528MEu8xoVawOZcRcMt7Qm+fNZO2tWjc/EmuGtrbYm5lnw7e2Gn+/v+MkVDEhL3oupHMRMBUrheFDL+/tI09gqLINgAFgu9mY148akPq9TUiW6z2t3MovEeT/5J3ANkEU0shUBlaLLBSbNG1+z71l0/Mv3/DsyhQLFOVvQUukMTKGvXQ6KjRsRzZUZbKXQjBGNkzncA2fn1kexiNzfq3CvyTSrVGeAOC3VvxMT+FVv+Lk9PAOYkQ4OsZvwmIs9FWwSWFRAqWyDjhp+zN1qEw5pDUUUg5JopEWAHcE+MrZRgoHY2t/Uteg1msYgQDmyh1/Nb+kG+MB2RK8BKag5FScbiDBW6WYogOV06mmLZhJyorz2ad+tb65/9G5soeLTuvrLkswWZYfqGp35TImNtJ8/isQMaD702kwc7nyVOE7bmdosjTJR6kRrM4TvaPQJGwFLvphOIzc5xi5F48Ffxv+p6UcQhoFSCHl6W8jFw85jL93WWWlQnZY1OlTUzxoBTZ4hrX5+ykKzzxuW6RPzJpasujBCN6imvftEtqEoPxScnEYDm8e80mCTHVYOxDhYLcDmo1AhEtlEOUh9VcAzQtNIWtmtxrHZ72MHZLM1V04JjSPRtb61SGSu4ueXKaroKytmZnRRh5AcJCj6WfHb44b/uP3AvaVmtSeDDtizzOf6Um9ID9glqEhBr9kWCyqGkqJeAkIEVi3qM0ZmzlyfhUD5HiIh/D4F1mTH4jUsnUvKpa6+mQxZ5Rrrvw8+khkPCBVGeWKfcnr58Kbfc2SfO/3y9CxVwdZGGF96TQCBIz43rORCUailurW6FZvAhAk81PLUXHh+pp+uYRt2gXmz7QFPN7DkAiMhIl2hkADS2bylwB0m9AUAXaDbNGNwLofAbDQgIOWDZmqh46bYvssq8jI52RCCeiE70WGsOi9Q12OxV5ie14ei+xxp0mHbdw3miGHC1jClArz/rRi3fXgEekNIXTRnMo1ijVJAuUo9T0ISftCwY378bA5nsHtasIq3DiGKG2OA+GvDaztwTxPVomEIS/wus+BixriXGymFgCfiQY5ONhn4oftlAl+sbxoFh5BUBX3CGxsBKTOaHEUNoqvbQG0T/ajQswC9mCKoZ4aMjGnklwP1uQuT2VkxgsChkJs523oC23dEfwA7nlc9xxRqCT+ODuuuh92f0r3w35YuFR1LfTBDlJjjwYZEbiQwcOxs8QL3uGU5jJV1xE/CSDFlzc+xPVk2/5u4zfv+vuOX+wv+j5ffwmAOXQqpFRPFmWPvugWMtAI/fuajYeXnK4yUYPM0ViA8dPUGhHgd9wGNzCUHGlwz4bA845z+mUnoHFiOgsGEroZervP9qYZyscP/Dvgo83rjIJ6++R9Rp54r5c+vLYgImPwuh440PXyXY/LPxBQ1sTm7W1lw0CMVc7iRffTQFv0Jtj+7UVRZZav7hLEAjlpNzgBXRxyFHcJDohAtlv4LoLdpDsCjwujWFaxCoxvxiMZSd6gMw5lVyGgCqmcIcbnTvy9r1A32itbX+3g52cCUIQfKdc9mBcd62DqzISv+Hh8gn0VlZQBbV2OtuH0lUwVu+7Lf1GoQVn9FzF+WZjCJqmCOghIjN+1ab852s3vPTvqlZMv6Qt6YIXLdGFlAoshMo591tYyquMWaaWIT97IeDTdVQR/l0o0vWVdI1CWfFWNg9bT4GhE3jArLIgvE5kkII327B0ONrXW+2n3bXLzufDXH3d4U2/eBcSs4X4sZ2y6AahSCee37izUbytQ1s/tca47MNcR60VDeJbyU1XrZpzJuXAsS/SnF+20ylMRsgf+V0zMU8bpChWVTdd2TCBL8mn87dvzHrUadsDo8TDvFmWUVGs6gQk0SHQnS9hQmjxy+rp/EKvKgGL72npvUAiHBykApzhiOAlc4CVG3WgoPl34tNaluOFJxa/1S7/gMRvoj279mnkL2qtB4mFj0yBBSSMEmo55TqMD19VqryDOabdqaLYIJcZ2k5AjKDKG9rQzcylJOrbAJVHnOqsk1LvzYGErzCh2BDqvgzStgezWq694GHqcVnotHgkEvtIOEKV3aokYbEMfJ9SzGLPEUXtQE0ABEWg3AuqDtAsd+VeyB7RujKleXTNFkPKRA0FDFC79WjJYFXwBRa8g9C3xAw/Ab8xj9LvFdlLkYN4M72kPDsLDAXb0QHUNa3KBMV+0k7HR8NdhovKhJadwmanOoAIg5zHMK3s92GazTZwmOYV5X+aGwNbQeFAYAHK/42bQtzunO+yPk9AZcXud2up7QTMJ3l4c69S3odFKC+D2uGsGEilhvCrwe5buYDRgPc9A2l9m0qbR6kNKAs64GxXJMbOr3iFla8eJ+13DM/QUxma0evg5Odqhn+MfXypnoqAxM/FKJpCBFDLbsr0B7E9S35WjyxD32I8yqQduO9dfF5lcLLo6B17Y1G8P5o+/40g7vXh+eIajfR6/DyMSgRIVYgEaHwG7m3KvAdWBrqPg6MnRk6mIF5cmRuaD8rAcH4INiataD+4ya2kcF6pr+B2cYoQLV59w/1xvWBfr85eftX9K8RkrqgQUdAVgTf7BU/g4vAMU+nv5uVCov7uDjgwQgPKbCh2k8YcURMcp0bfwZmQC/j7ABgItxmCJxPlm+g9updbGl3PtHp6pTVGs1TZbe65WEsE/INmOgu/YCNisFNRUXdGAJ4xGvnctwAzTeYlBUuus0/tEtihV5ykQIiDFKzJkDgGCmAIg5QZzTG7jxzaGswzMLJeRgkaBMoOgqQsPtnPoEN77G7ANiCqznTwZBjRswvg7UL6erYC54jo1gTJ0X7Xk9YHlN5QflVtnF3kMLH8BljVyCBt9svaTXy1prm0xMWYObTNwRPluhRK2JPQrFpZ+Z9SrgyrruCLw5C2VBbBBgTlsygEXO/Yv1KUxdHP/HX63PpD5owAX9tYTIncxV86kPu3e5M3nuvIduqCUHFmK1nc71KQgvJde1mddTOVcW0L8o+uv6DgZBi2ygsc4ouDjv3p/g3wNRyDZjcc4pqM2Chj4KHqPhe9/xcju9XqBxj2e63ydSDxL0kikACAgKAB5z/1BbgLLGUD4Y8Vzb5Hqk6OdHIVFC4Gutqa9rTnXcW8fRl8ke6bNjlpgRT/tKuxUNwX8wWfiX9Cmcs67pQ5DLPFL+7FjF5KVN/3H0XN6yljjTb+6P4xVplGkUAA5kH5E+/u7xf5JSETqqKUyvWDULnmeTCdRuxeZpBiAecEZ4jPx2cwSlGpZMlUwaRN0n5qiXJiUWpCeAMg0+MMewIATuoz7sgep3RRUJA58jNDaXAXzIdTWdpWIegGC6lLHgBf6dA1/GJt6NvAqI58vqbmXhmTY1HIP/W2Tp+utEcOCPn4zp1F8V42Wi/nRi33s4gnCYvh5ayhDYg5DT93yfczrPjJX0ZDajNVmBDLuW85ap1pzfzWiRUCOwApcqCnWPbmwj3jiJrLJ3e+il+dxmnmVTKGywFKN4xTKeLDwTZiQMZz0Exirav5vSqRVyfT0azuIQpYJF33GXgHk0Oe3oaBeHlZwGa53I/nz65LSxW9TKQCKOWxDzN4KRBNa9rA+nHHxNYqASBBibz85gBiUJPtI14ZABQlHBozccreGcFV+qPb9V/D7rcuxn6uB8nsjGjufn7ubnPoOtjNDZ+hCcpsI4pa9pIz9jIAVLTn3A2KzY6rCem7SeaQMV5hyKrOY2ALjXj0HSH93+dKZgEEuJ9IkdpXQMfVbco6noim199tDa7xPFoy6esGFqNiry02N5ql3cSrfOv99xDM/fmTOHaHV3VgK3gRKMA+5jKwM/dEMtE1s1UbbzrGbki0UztZrVnKMGHRFTLDJUmGrmNiFHWen3pAFQzGJFZAxAoCAzRIMJAmMqNQ3Yh4VD9gREVO77vshoz1U8tCzCPscGJJkLFiCLio131Ttg+90gn/q+6hE0FGTlTC+8B/1RHb7yY+q3NUxm3E1bybTyzZn2Xo3rX+ZF5oSREjxjfI7gmqyiMbuVY9AKrj+55dGNeQ0Yw6TEejBn1I3uGLo0M6iDIia98ayBpCoYo6AUF/UrEwUFWqdF41MIJCwpFBfM46xk5VS/WVDf4QN6EIye8ydZhebhHcyO2xeItc6k4q6MFYwwe+PgpXFPMtcI3xefRVk1iFDZBSLij5kaapnB3LxL+mFrQqvVT4VsNbX3zBtnUVtRfDWDylKalUU+KGKNg0MNlfjMwZu2Ee/3ze/lx6D0dF2rEdG90ZwfuiasVVH0UdzOmDllgMI5Mc+ZKp8HQtzxuii6O9k1Stac4TnqRTWaDiEHKQBMBWAWzOKBOdL85n9XpkB9cW4TH4WcgOXdcuHlWXCK78sD0fl3i7rWe9gAxH/nBg0agXwjMg89H8tnM1hJTxvaUCC4y+nHwX4FxZEihSpGUfO6KaYXnveX07RtkuIlN1UYhszmtqaYmKhvxjwJLvqUkMT5kNgUmD79w6EiSMgDMAsISGcgOlfzfN7QmMF60J+hTPbnlNP2Q9377c1x0ftiHBGuUNFohMMQkKgxNs8O3FhY8dq7X11A7/yipq3Ulkx6rUuW3BIm61sh8YBrJI8vbGJzap+JDqwNlRQwBFSo1+jt8lm5ricAkd5vZeBtbOAc5yLqtY5iKqk0TiyQlxHsugGHlcToFLP7ym2Apmlo6kwcYZDgxnTcbX41sfYM4ZxfgO274P5fHZyzbNdnORE6aNKPrUjskA7pxb6/GKHK9yisNkb2GJ0GC8ZebDYRO794Yv8LSKzporTefF2QWg13ch5QKIplEUVNX8zpqKVYJmmG0ozoW9/wNjbD/qs906dWN/KsG80FHclE0aVPRPuRoeQMKVfIhWbfYRTTDBmRDsou49VLUKLLnRlGMOX4b10ZSivObhr1IvrIy5lhpANGJNhCPdqfj3+HU/goiDcxnop7zy3m2XgD/7iwEvjuFGz12pAm6cLZVSloPqj7SzuiWAysqC9LHj9DR4E5ypK3OGfzxWR/I/OIjKapEvid7dfPuRqPftuGw0kFozuc0BSjkzfqx9DjYmDe1MoMBUvqgA9alsXw1HpWhXDuMqmJjkPzc/XQyAy0ulUt6cFlAKLrczKcOaLp77IcBGEjkz3wP0djGp0DYpqahgFCTILjrYMq+q1YsfFFMb4OoHrznyJgw1YVjaM267qnm8tc5I7Pz7SLrvfbiQTyUffqufnxsy1P4OpSYnIWv7N6FGkiZgkGzcfAaNAbHu24jGk1veN9iq0P2acFEuxf8NuoN6O69q8mPQKBdVEX6zgfd0G/2/xlpQPg93dFv9sEtvZQzA50eIfxqS6quKRUZNqamJsTGpoFCVNWFsGMIK8ZEg0Km+8qzOG5k2PPDkePWuRvxAzLEBCLWabYRMO8ZmUV7QWwWg6DRVzrQ6whVS84c2bzqRWPuQVsxEJz/sn7E9G/Xm1X84bJw7uR838ALkEqe2tMimJpxDUZ6FhDyrK9U7cxWzHkhA2zGU0JmwizZd0Zj/+T7V8miAcYVMQo/xn/Aj5CPFnBdD6dUCsTj9ECow8VVCD436WQamoXfi8dL/UM+imZJLn3gbLHz3pH6+/eoKQF95IaYJyiyPcyMmzFZwID6GMz9dXNpJANHvBolzUPenc3eDTQvHG6T+hZAwLgFqJ2DgetGoP1KlA8jYZ73FLncd7c8FukdzXk7Q2hOUNWkZxAHcnh1LQPXKNSg6U0TrMe6tBBklkusmoNgsgS+qvg+Is31e3Tn271TKug1YnbRv2h1dHOtbK5AF6oUappIN2cG57l0nOgQtgwZ7uM7GKdpoCBP4f6EPcETb3UE9/7vmYqeEcztznLBXrkpRPRy5oZvdq1F6DsA/VlQorifG/QogYpsgmyKOYdSyeI/6k52DKs36N/qVFotkY0E7xjcCYT2A6jiZ4vLprI++o/2Wm8HD4i2oevm7EZrAlFqONS/mJWLPU5sonU1kPMItkM5ppNoTfLnsqxDJtBSFipswpEZlB7zQnjIunB+8yfu6wMcZduBVqHiiij/kxLpapBrAedQay5lY63saEyU61rHa0C8vV4LqhJqq0S+oz151AaM8+pgnMU3Ld+oanSPjIwOp2GPUZBd+rrH93+51WIp4200XxSueDC1zPtitF11/XaZ8UWgE1qV2/Zk+ZQpq3uT2P3nje+L4vi5R6LLJCWMcYoROoa2J4r/yzytDrxup/OR7fPqlokuAa2M6xmPi624Kc3rE0xeYOUDQBIBVvEJDJmDOIQFKPvGIjjWkWk+AHLmDNDCA0bPrikB95l4f0DAflQ9sDmQ/u+mPqrq29ORA9CFJc3g46Ij4faawHe/6Pg+BmOH2tkTqVN1w2SoJ2u++ZaW3VlAndnFW1l4Fb7UsMVk8EmrMjPPmvd5zXIJqVsTAgV8Pespsr1QPHFFg5Evd5kjDTzg+t7uMq4ZlbdwZ3F3r0ng/g5rAZ1H9bnUtSG2dNZ7FagH68T/VVxfjGH+/hLQX8pAR3OumCh3BdQH1acNgKC1xRcLkXdIbQ3rM5538bdHEJASIqgmV5o1JUZqsaatUUEq2f455RrYVPMv5yYPw1rXPNentASa+xbMkNq43o14JUffQ91hApHHrSkmgLw2cwXFp2flVPzmmjJgTRfC7fLAKerYnOeBPgxcLZsMzuP3/v3JUiO9aKXdQUgtNlUfargvwM+ApZRzU0Tz2n3lTIoFwG8/J7PLoKIFXJqSfhy3pcPuPl5e8dP23sUgJiSDZTAlZ+LidxYPBxaAFm0tYdWbFiRoQkAzouxOabhyVsd2MrEb4/dntsyo1FJFeheHAT84VYsXaThBtSxUkAwXiakFa8NiAmIwYwF1KEl93+ExkOUzl+fzZqRKEvAzuaAcLyoRYcwdkZ/C84auxmBeFhryiaaGxexfdSDGjoWFfaXEr0KQHYQq/YxbsD51WojSicyiE8bdbO1EZO1EAbdC86yYKRb7c4VNyoyjcFzVJazBtJPW4oes4Oozq6D5LXuXfGylFM3mQFpERJ9Hw2jWK9C9Ye2Fg2mSlBRRWPNaANONajsvp/os+A8K/phD7W0aVRmRg2bO9HyBNsoMF8Upw9/kl4wq2K2FkN5RCWax6Z3lRexPpJyCuq7Jtzf3rN9t+52qxOYWiqF6whpRRaqcSgXynWGLOfmE/9ghWbW1/SJUCK3YcX3xzWOLel9DBrLk1GmysKW7vFE8SzR1gQbEu1vgpv0eO5t5zP6WKIe6ZLcj9nC7hFFmGPVVrNt+yxgZlYLdSjJ1098RpcaM7fH2WK4ELBqWMPfp/5drKfEjfgD259yCkyFo6EDC/r5rFEjXwT+l98bmP+UTz67sMK8FVG8tgNf2vGkcbIiPGDVFegA7AEvFwzR/t4Dn3uW3c1NepuzAn70HXsd+LodF5oaMb9IwQWQMkMkb4xi/xYFCiCuAmq6+gD2Cd0U2gXlvUDHEw7Poi6wGEgdlzm3kAUVicIH2Njr6tBNPRSYGngwHPYxw77gBQAxeIUF635bDz6hIxV3Nq2g31amoWL9B7PZMJ76sOJi/0IhPfURoBYNym1g2zv2rUcxuaYHsroTyOvKrr1lBTNYR6ZPRfVeRndUpOQayYNTOMOXD+dW1u/8e4GG5pc96JQrXpRlWwce8Reur4l3tFD+jbGLPP6iaG1iawP3/cT7seFw2mT0MdAQVvgA+2rZwiwhmSKHAAPQXdG/TPTXAohg3Ar2b7qkKBxuYkGZjCMTOrRGwtCxGkA51OtRgvOrS3fLWoNkOIVDKAYFYbvCJ+V06QzCT76GSFtlRmQ7Eeiw8y9fOrSvvg9Cs8yySrkyv1i8/SBbQ3vgdYYHNnK9Lu/lPaadKFCDn2FSJ62MjzO9oRjF4MDMDmLB+GMA/BGweQ6QpwcQn9W5rHYCjN4uYqEbRUUpIvrvcAp5chmwZGOz8ef2TD+lM3guqFixZeJIaVuWzoafYC3LyH/dHh+6ljN7hLjx8wXd5KqkWaC4h4Jq8X3ZZzKeuBpPSnRsTxV8P3cUMdxzKxOHR7oc1zmnSTSEguoQW/ikpiY6IE77fhmCcgjauxXeYv2QeeROgKJphHrqI8FCLpxGZ0HRvfpmn0ExRtHcjELKyL/5v02qwP/muvtQh428/0Ee5jSmY17TH/bRBPAM5PirFcbLKdh+2HeO3fYzdmB8mdCbFZlrm9jcITBLpFxFzhQZ1d+fYMMM7WR8eIvscVzWScipS4l18Sx1zN/z3N8xN8NuZ3E5bRYZ52WtFxi1eMT6Bcb0WQZ67XYlRvx+bAEBVIdJBjxoCNVYh2IKoJgxc0JnRXkUKCyS4L0v3ai/e1dLYdWK0CQM2CGbPAYH5JjcuVGOtUjIjlvjmR8ziQ2KNTuhOjHhvhyAqK9T/7sfnh1/0eXwfF8gMUIA7QWyD7Rbt3kU0Z/gxXq1TnXx53T3CXtnKuLv0i/QEWFC2gPe1wrF+9wuDbrsWYg15mSDNbJ1jb9kfYG01M8GicW/PVghWaHJQBGL/pnlcL0z2zx6CZmXOBcSJHwNZbTlf7L9afbRZy3VrCsQq8vFFj7I2RnMp4fiOe1aCqRs5DExsa1M/PX2hltiABV/cAgH5Awhb8MpMhdqLKh46AshnZuxE6iTY1EqU7xb7fjR9wuPmN2I07MFOgTA6grqxbqyTRvYftRwCnJadgA4LPOQiLa16Pq9rMJfYPRi0FJ9t8YyANFslAuFKpQtcOjBoz0VgTgtUQaQWUoWXVrzEgqMIbMhNJH6TUKOg8fTX7xD9oTRDGX1PHTXNxp3l7NoE9KMfSOOv7OeRH0pGswsnU422DnrJ0OU7GF+ZiFdeg/SvzO5gNnAJUMIaHLinG3pIyXa4lYGHj4kavr3nt7J/1DBrVpvwjlqpPZjFhzdRs9S72aqZ5SiEQ1XGZiFBtGOk9RMVLHMQcUIC+qBhDecyQT0fa2X9r4CBlGJdEWLF4yrrIxUFfXNpUm8Ma79IB7kx+FFbA7PGTeHCTuCLjsLrsVmN/izOQ2Zr2X4KMmm2/sV95cD51kDYlz322zN5teYisdLApuU5QKoSd6QcbjFPqYb++UI8tCd/F1Vk6yEGKLBtcP3ZJbksolrQBOwnMQHlVNYdhH05ZDPNqpzqxNjCgok0rPpttEvbRSd/+j2r6spYGmJr6zgyvzh9vz7et0uRp8lDGzmeANmUKtH51/3R8hYnFqwAdjkyhjiltO/Is+3N53Pk5MrYo0uPLapkm7gxL6945gtpntVlzWYDht0b0tXFdQ2MYZg+jSumNCW4DJ5FNR3uUzN0mawgXU6u6N4X9GWiqfdmhzEi4QwGeW1ozOVUdhuVEZMxOyF0gFwpCKvOVVWhz+zxSWy2b+ggtPnMXO+M+U0YvMIkiym/gqcPyeHcJvApijbRG0jIj82LFIR1SZP2byCoQW7DNzbeYnO+eB/aY94ILOzyOsvZxQALk7AJvWdYH8KYSny3Ak7kpp6ag0ntAV0tZrnGPVLMlzcBjMWdkJzmBRgHc8Cp+cCc1jjI2sscEcwpxhnH4reSxSipxpMBxXIF7tv03WH2vtEqc4AEoOPtALds8y4fZn27ESC0lfwQImNTIKwOpPBTn0a0k1yg0xbdwqvMbCIrDCnQXJJm5C6iBt5+/Jy4HF63WgKNoebbMDRdBaYkU8y9Es7BTBogPdbmUAe6wtrRvvqdN5kBOEEapnArZx4m3tAztf6ag5+U+CrnFvvOnFawoasY7M18hgtsgB4VgpgqUgXs5NEUbLywwUd+R27+7z9+SE7LBrDcHQKPC0Dv06QBwrgUnjm78e4pmchGZF+53ZvHa/tMIliUU+5rgySz6hgwJVxZDsu8TqLUbkWcWoLNlIrE4+JiErfxuaCWHyY/eapsaSoo2+UOSs+V4eLzkfDfHiG0AVyFpvdzMOjkd+Ndmq4/eoaFR+jycgrmtUogwAE2yNktGXt1yAFRXkYrh+NSTBDr04dJSxEgz5eJFhKgQPPJcYXTCl2PT/Wd2vx83kR9C/GltF9Am1CvPObjUi851MVL/tpnZoeblbRRUOVGUaf/SmclMb7z+7Uz8gG+YHPa8QYlIoqT9O5sB7qm/SYyUG6MxyWIhuqiIYoHjPMtZ/VwAaYw9ib9VxwwNTDbhmmF50hyyFwXc0pUJfLMJzdocYCoCnGi7OIvDjcXPKiDEE9FUMExetCx0+emXuwwTqDxoyFNQ2Q0hTsTeGUvhjY41BSPWCzo4suQT2es8L6D1wpWPYQWopZCdG/AISD6LNE3YUNrOcoePHghxh+FuncHT4sThgxvSMWppkZ4NLgBv99+gJesHNJaswDQA3HABRsmJgyMYuhBCsrqB8Qlgx3A0u36zMonuskho/B7jNh1ec2ACoI/1tqCsDC2il5sXDeEVE1cffPhlNHYa6Mi+cUsZnHI8FMvAh7HfiP248lMKUTLV2zzDR6ho3IKHiuJ1AED3A5C4/+CEflTKHIxF+3H846WFea3a3AcmCkFgJIfQvAHAU6yOlEqKRGdygb1Ur+aeJn4npFc1/ROB2CChY3XO2hZCIiSf+IG5lHIXBWkvOA/ZSuPibT4IHjp+VQtm8S8xviUvD71KCl0dIgeYe8zr8o+uu0CNHrKvzoKs6viXufbeesuNfT10DBze/pOZsNIYkHbDhTRPCmW9yzPIOZA5fyeijwKCw1LwFGRrDvb+BMkdP3u2Hgoe0S7DBoyin81KueTi0T7Smy2xxC2+rAOSoeZ1tMLs8aLmQGpN9FF0xTgblPjJcCPOydXdxwS8H+qwdsT2sDMGoyFChvtk+3zTGMiYEC54GLrvtH/EI8CDEHYk4eAHRHyGJDATkMzFWt5hySdIXUGVLjeavFWFrMGODXYK8WMOyheJyeda8dDRWficB7siJ8/j6cDVTE7iuwbF5GSaZY3WCx2hBBCuCU/ARFFZk2tU00fv8siDUK9fRsoEThmExM/ixAzLUhPXqrphhLRtIf3f40+6jKGpvJdGm1bV+dANkZGd8NbC15NnKJWXFnFNDKRKsTf72/Rfu24b6fy1XEd3xS97gUEd2YRHFQr6yjHF0yIq2YeGA9/KwzPJwBMKadryaVTNWC8zDceBxlFZPPVIuoAKw8CBEXCcvRfaGxRfQK0AlgWi1eG8xKj/U5UlVZG2AUqNFQYTpGMq14KNSTcJh3FFMwnTtw/sUjklMw7uaJyjd4agLvb1hS2OPmheQXF88TU3XVfTmEshkzq7aBbRux4G1NpCKb/7fWjzphYUd3qOB0+IbSngXFtPJTbWvpW13lVNaDWbCVc/HUGRRAUJKTYOH5mTDRysANgt73EOhbtTaJz4osbR12z49ZPtAJMw2XdNYxONFtiUVeJv/FOFiDZubNDLAxfpK0SDM4ko9lZIDejQ69rjtHLJZTcLqqpBoBEu2UjyYdg3RCj75GmvVYgBLyrvukmwLDqLQVxspa5+lBpTc2vt7OqDudfu0mCm7ee/QcHPKaZkIJx3Ny8E6RRUnNDKaoN/iaGUjidiQkuF1jl/NeC370GrUF+D1n8TmjHDa50rIJ0lE/yxTmLEBSC9ZZorm3+LExe9D43z/f/jXzFILKN3Egz0i+0vQAREqUL4I99Fda1gz4pYQcgIjiy37g5+09OlfzhW1lMQkuPRCfUNKetxgYhImJa8iUG9cAK0aGSJZoKB5y2pa1uSNcCWW0dQrmEHtQz7JC+EQ31eqiZP43NoaV0yerNTPQ8QA3S821wDpJ08aHlJBOOVY0L0Mh31cRmnTCQuE0Gg9/uNklDQHKYTBCedgDbTN+LV0YuxfUqkQNY27AeNWY9zBeFfPFKSiiKA4XZGhgTqcdPkWvzxDh8+unlnAaNa2NPE3tGWLMLCSrEyD6VpgxMMPMAcZWeoyjvZWOH3NHwfDojrTmNVGrpzVjx2QnO9ThAllU7bCtcqUz1n0pxZ6j4v1sXnNQ64QGog8mLirX1qYmteW23upIgsdfBfuvwP4rWWpLe4uT9+bmmlk1wUS5+a3K+sq5vp+sN6jDiF5TGK7MW7oZ+FEQ2aLy/kwBqpqqsGdAQdlNgohED25tNck+RsN9fwtn8D43nD5Ay665RdBbmSZ5ITZbIUZrct0wOExFYT7/pCtjwjIunw291puvMQ8c7vXE+1gPaXuySZ9B3QyGGpbM9/NGcbzcwwUg1Jr/p9ufYx/5Q3IrHcdsONzg88Z0rSiqMeYyc3RD4zvvD4ttVEQX4wT2cLzsJ/6yv8dDdytnpGcDBUWXaB6ASzSXIYAMBdDrF48Q7LXm+0yppcMEwCo22TWQRT3DVcPJnEGLqG6MgnlWIJ+2wiAjhUXc796wlt5CeGdFecbiYNFZBWY8aUk0QUX+ANuDbMyRWYHqWvaozCJWk5n4gx60QD8G6vPLWFo3ekhQD1WsgKx1GQ02J/GndrGIldOzqoaKbIvuVAl4ZMyCl/30+5aglida6hreVFBlRGdxGAFZLBCuGcKDzwFD7mI19kr3/a4M8pzXRydDBRtMv8ZmLEx0sUbKoisC5Fo5xooIM5Q0OS0unVv1Rripgnvr0fvQvfg4t4HztLoCDWzBEhIE4F3CrDeJD+ARHLB7Vg/E+hQ16e6AL8lEEg9WmJX6OonxmgmGpBNIpK6L4i73VzpsJojY8wB2b4uiuFy2RcIfa0JcD7fanRa+DCF7qQwqlHimh2cS73OL2sJMi53OIb4jQdv8ndIXRBdISbb1kAvOVksiPHiM6hTVq0gocHUMXcuHnq91zrpguKfPFdG4Spm9+W/LFCjqdM564QMfPvIyZwrH+Hhy3Efeapk4+hZeUmEG9i+398tA7ty5yPbzHPEBH6GjTB3k+xgJAu5cPGr4wESC4l5O/PAOGytCL2dQYDf/UZvdSGeSzGnZgU5PH55UU7UqyqMs8Ts3+iWl3VoQnqL4PAXaJab1OToUBTDcIbhDken9COxS9tpBOTUUMjXtdzYvFsALhQ5X1cOmcymPLWUR46bxeznFpZgtcmQRXbpAeoG2gdIcNvJpatkhqALN5QoiGsKCDqhISitGiZOXaoKIfGCLKG7oUT/gfO48dpWRH9dCpp/mYjNhhikrk4D/vDgThxPPZOxZW8jQ5e6y2phwaMSzboeSbrUHXET46Dg3NF9bewg2OpwajVyWbRm8VFeUUbxhzIf5DHao74LjL4LbfyuqT1KzTEFj2BPhIqj/LggF3tLXkCWuQfafXJCbWHMWXMzdHZXLvBgLbcnCAHD6tmWgrDnRtuxt9QlN2EjKMVkHsPXx8F6Sx9yCIGDrJ9WJpIcj4D3Ohp3rLUNHwKpRMpM7lRiv0YfDJoKMswNTb+ijJgTlWk8gnJ57ux6p/pTntky1efc2chQuIX9VgiZh4Y9ufzJTMOw/4KMyQsP8Aw01FVjWiX+EjGKqmsqlyPzT7cBrO4J3vPRFlmOIogwWtgcsB/Gc/nPj32/ldPhIcKKmY7X9fOiK9EV3peD6wjw3MHAaw9/Dry6AD4S1aOksbtRTg1oBdBqsNDeDBMzILqwWgOndiESqz7oBnQB7DcAIr9qDO26C9qYukoYVtREuIqw0jWY6dkG/A/NmTqZ/NZip/ZAwADQaU2Ari9r4xWoj9d1otWaYVjRIfaPzrCFslouK8TD4v/MC71rwtZ2hjGv3seO1Hhf4h2vwonjpGWSsAUZ2vk7pDADPGp2ZQp45GSpFjP1zoq7IEStKfR45m2GDAoNEurhmGOya5AgyqKdzyRnkLTpYmznQ29Zx9oo+dfXH3LvBMGfxL7UsAKLm/Kdi3A1Kuv2ipm/UdQkZMrovCxKaLqinMWgHF80rTlpjgyNvIk1DZiuJWBAxtVrfzqbGSPNjJGFDxKFFx9LHk3jhVsZFJLP7gbS5gsEc7FW5MtICvYhawjXqZx+L3XvbZ7YKpJTmbniDERekxEbLLJnNbU3t07CH3O8APmVkZqiR1wQpk/63Nq9ZmkLvlmaUuqHcXaEya9zTnuXaQi4yL4nYdeJf7w/87f4d99pdI3wN5AauHcxDl7op12GetESq4dASmHDeOJ1pS7eabKRX59/dpOOBdhn6zhRvLx1HqSjSTMag12hasy9QW2nVnIHdUQ310Khren3BXl+pO/FZ+2Km6e4w6AQEQGaGFKsvTFhdojhbhMwmwKI5nd7QtolTES2jmE0wXqxQPDdA2Mw2lqOjrIwN5HEj4UwqUaAe4pCSLIeglj2NswI68fJyrMvkkAhnJmxlur7UCCNAGvJeOm6l4zFbUFG/9ZvdR1mFxudZ4rzfzxvhIlITCRuxD2GDDXl/1v3airGcLINxNl4ZUSQ+XJfpWdalFYMWutQwCI/e1nwIMefHSDCC/qf6CLzbu3vDm0WurDPAGiWF663G+hsv0++LRfYyBbdhXfHFCxLMEuOxZEZbAb1LrAFRW9/CugQQI1pjLc7r2ozahVpWqVqgQ6Evah3aXqIo1YYuWYG9YNvOQBK2atd9r+PDYC0Osc/3n5uRZXAZyxr9JgCKF5/n76yVvIYW48wVFLg2hO+zzGETa2o8RovAkdmu9UV9DKjzVDVK/vDfcRzsrP+Emvo/2f4lhWYeQB5C3UqaR+p4HBkBzw7hOSoaKq4dBOxt4D/ubyEJy63KGm7BzTi5YxWBhFO2PmMezAtObGnjTKqv3qDkBZ7XcvjCkVRP8EgxnYuxqyhxW3EezecnWORvwLtYcbkbnBQFXcWaQ0sjPhXysPfNzR7QMPzTu537StHpVBhcAsvwA562M4O5W7agxZupxWAEipxpAzoQsscyxPX61IrW3rREXDial3Q5sAnrwAa8LvKwJAlq2Y84ZrztHbfNdadmuSx2GkJCKlnSosnqDH2uUTFYONWmVl2KjE/r4bNehbhmT/sdENyl44fuC47Qetm/zd6wqC/DqrlonM/h1kzM791lDUojvNSDzsgu3RB4S538qoY5troMQtARXR4GAqBZc6DcO/pRMY8KTMHYBfpufTLHX404UB+K9lDoDbbuPOigYR8F0N27n73GVN/sOoamEddVW2uTm/XQ6JVBF410Apw260H2sQbS9OtOtroqfyw+2zjeVfRl8GhR/VUhNW/sSbJrulhJXAMZeoq+l6daA7dLbTFBhlsxx/ClPfA+Wog2NlmBBO8pswNuQUJQwdBFQQVWnPicRQILbv2jCcOfho8ugyNk4kgVcuLs1PSYKjiS0iC353RnQnB4gfavr2+41zMalBojL7/QfPA72795E59gooviIa4RYJ7HvI591RgAg5asVb4EDGH7VXQ/VzbgPUbD0W0s5+jGMgpto2F4OumiKE9R1JkfKmcikcExAIGHCYSL0lhNFUDZPCa27/ogJsyFoZDTHIKxg+xh7z+v41GHnuD7ie5kb5QrZ2IsjVWDIEtq3BVyijWmeZRIbX07T4XcTcMGKiheT/j+vhtLS4FtM0Nw9IoiBbMZ9v5WNjy2FlOlDgD3dvqM5HUf+bD0WSLzy4KFmWN+kcN2x08Zi610VOilXpDXSIZAKZx2ajXixbTh8cyaubGhqmvBMRp+aMF9nuEobC2NVWQuQBFTfbX39ItzpOSy9cjsoQJLavS+D6gOu7YFmKOacJx3CxexwU9zq9AfFVoFxyHYfhNsbyaMN/z+xj2sCC2jgJP8v3rCsgVCTmKw42y2ngvLTISX4OtVPWN1tdYpNqDK1osNdlbYFD5gGTtepxBAhNpsC4eLrUdk1Y9o6Kc7CHanv+sWtNQzBQnW4EZdrzWilQy1TBxgzTJDynawHiy7LID12PRwBPETq+7EnidSS08PuElHzQhLhlhHsq0Msv8nGcOf1j7KF2Ad3ERP0EoumOSMgJ4vBm6nB7mPgi/3A3/Z3wM2etavyRxfpn+5WMhjfIYKnh3CYFaRXGme3WviaMZ1j0WgSzCN52JOEfhxeoelp/EqgCsK4bnAbENGlk/iQ2cF4hI4P+DY62EOIYp+8woFBU0wCoMSDyYUIVsdA1GYhjZcBM2YgYfRF6ttYCynM9NDPe7Jr3rGQ+cicxkPk1xe9YKpgnk2jO7d30dFvQ2oS4IMIDIISgO/nRvKpkF/BoDHbCF5khlFJ2rQRLlWuAbgUFCuJ1zmaKS6FVN68tgHrg896xOPpIdUYKJsB/sUnuoFfRa8Dxsu/xgt1umt9Rj0w659iB37MVpkA5wZUPT6/JCBY8bE+h/sOUN0RI9ejNWTok+pJjcyquAcguM3wfYmUUyOpZt+knigstbhrB6c0IE0cwizweYj+Drj+mKmSc6AeD2tnJ4FvQPDM5zWZnRxE2beij2jx6zYC0I990vj/M+PGWCWuljReYkAIBzHUx3puQ76LKdexPSQkBohSc9nHZWOgdu38xb/ZrDwDKVHRqCrwEy5FM4bIZW5PNlTwDKIP+oX/iXaR4SOnsWd+PrKGK7wEuBdel6QidTIexP+8/V7KKCuovHy/NE44lTUpUvCpiW9ZAiM/u7lXNS0bARSWkk8OvRtRgtOOsCoUAK2aADeR8N/vb/ix8N1UHzMolTFPAqi94CzdjMmfwqw6SrYDkF9XJVRA65xg60C9J9WJG4vIuYlF0tMHOs11hG7oDkhi2cszw+qf0atzmeKmdwPB+z4A2/n4lCAwwX91c6x/ShL5sL33X6r6ALM7cqK4YGQscXhRCyU5WLaMSt2LFyeA9AZeORg5Ll56aqD8ww56aV+YEZhGYjqmQR/XwJr4hLM/3/e/rVJkh3JEsQOADN3j8jMe6t6ZnZmuSIrK/z/f4VCIXdJDkd2RPiYYc90VXdV3cyMcDczAPygOKoKGDxvVmX1hUhIRPjDHjBAH0ePqgoXvq+WGjvjAegbTPHaFLo8Vjxazayv6YKXZcfH9dG6eh2dEcYhtEiBUF6w43Es0rz+sH7nGnDk91utpHJE6dFQ2nO4VhwReCsJcZdS2wAQsxAOWphBDxWahZ8ZO2jvkSWXA0TSNKFPuAiA9Qh3Aov7A4RAc0B5WxA+VlzXrcvk5fB4PpXrS9qV+m45IUHPxf4pWT3FQ3MZ6DEA6Oip3kDQtr21j1eIvHPySQ0SK9G+IDcPUK7t7bic1gUAFfyWzRw0uMzBUuGEjqRnuGM0/RXF8IAfDTTrZgnd356iuRFjR99t6OFqf1MeCP4rOOin64afLndlEvhZ8DEJwkeEBLyXwEEIIIMlCZLBRjymWoqxLYxDjo2CS8j4Gq6dQtBFGIS+CojG38meGrjFIRVUNNiIFS7pIai5HoAo0Eto2D0A5xlIbCFUw2hDFWGd7mgxgea+N++AljoL4tGDUOaTagVTAminzhcT/qHRWwkTsBgeu7uFQ2BroCmPFFSJ8TxxBy6HQExAQn5tFMSFXcdkLkoJUkGoRCxLxpLKVDFwHWjCUkz4qQVyfbDt2aB1v7p+3soyckJABUSg91g1tsTvc7ymDXtJKHFXIsLi1qMX5GzbyX2RmsChYMg1YN9FQTzygg/L1mHbMw8cMEMtF9mVrLdPQbqsh9bk6oxH/89SkD8FvP+HiPCPNEaCCmpWzA2HlNQ+brI20ibPuVxsXSzvYuQUrp+rKwGPtuaYcOfN2RpaRQAAR0D+uuItFfz88f1kDftuZ+IpCty8hoz3fMGSdiOghNLJE0AMh6+4uviieYP+2Y+xhtK+i2hyIQVXCqMpAZbvZ5l15ngtMeNIu8YXfKx1VHzjGmJyGhs4cS6uy2FzU2wtDfHrp+OH4SOf6UtrKNZGBRsuwm8IHxAZm0u/rjt+f33Dz+tdX2MyUhw2GD2EcYzKgYrAP/RnQwSBPLQVwNZiCbQg0AJW3gX9Zb/hbb9YaYZm6eYjoRxO0kYgbNEWZZDXKWTDIXVKfJ0iEexBm58z/hB35zlEw3Qpo+jKl5vBSnBuPl12BvnSZsrmeOUkt2OibfYWUKy09BaLZWjsoUqiW3pIraa4t5hCrVJOuSkkOX4LhLY6+KhS86YUKYnM/gnC8Ta4kM+YQmCJRVk+XkDe4q5rQRKVysl48OvHZ7OOeQcshraGA7muXTJbbIIjyT/ajQtwBkTMuCWpeTPrIwJAs1eXpeD9WMHGQntO+FyvKvx86fCPq8AkS8i4lxWXlFFrwP1YsKaMppeFtZOjNLmvbZ1FqZKKlFELJLkS9nykpWfE+rUKrBNM8Kt3uDS2WktijMXgpBCkRHck5Mit0OJgZQGk3pftBVmXVWnaksoMIAfs7yu+LhmfXgwaoiG6BoHrCNfw2b67+S2gULfGWh4qZgzRj5Ny6LyCcmIw0jvwnirjEHruaJ3cPiwPPMqCbZPj+GS1vaRO+UtvkXbswPaicu9LKF3ZFMYiUM739K3xd2MfeWoqx9h6jtaLfF4G8bHislU+rXe1ijioEHyPhgTLbPXdsvR6apxu/DS4h4A83LXVkZBYieA4LFfgx72snXf0Zb/il8dNC/gdJbbkmyplhksA6J4HoIbaqJti+aNKuQhUsfhDixOogcJ4Q8N1ldkh4RuFfhQe4v8LUFuPXAZ6WV4ATXhTfsbmXbC1Igk+eq72d25luEM7N0o7HOvqNO8k3QPWr8DaoAdRNkGVTrm0ejetOqpaRZzqGpBSbvXiI2JySUoODiEt9aWREfwgLMPih1q6oHmMPnnNJyxJDsKi3qTPWbD6WOjWp89wlfVkdZpIRJBraZUzQ9C8hIK+A6Evre0t4qNEfGmBa/Zk4PeUpkvmTYytQKMcU7ntfODBKo7GKIuI5V1YM6kmEfjHB7TGSCKcc4Mgeah0F2ujNCiorLCktFaNtXs0zXuNWwBeGm3ZGShllf2BOMjgKJ0I379eEQLw6eWuhgDnXJ8PjH32kjZNRhRGogl/MpG8MpBEtogxzuShZj/SoCioXLzsY8yyY1rCrvvD8sDbseKeVzMsmyecu6AytKujf45+3VQa5ejl8feOv1s/BZ8sxixnvXBXAxzovYS9Ru08FUPFp/WBj2vrk+AUgR+axSrkewmkIVhRyOEhFQYVvUfhHnifzML6+Id2ZcpImiLvG6sA4uV83m9W2iNHzcplD4Wao0IjIk3QTDeHEC1Swlq8gYCcnPXUmEh1DcLnbEI/VFEeuVlo+SZZpcereBYlCbWQ9FGEZu0vhv+H2moqtfhAXSw+EQ4ph5FvYgXi5jZ7DmbYZWj5bOntC6xfINYlpzwA24cmMK5VkpMCZOOnqt4H2TBpKbpWUuxz3snkYRmICxOE3Kd8KYvx2RI20sCuKgKX6e7XhCoGQgjkrp8DzntNjYsufHnSqxlkLMGaStHAWWAlDfacXDtFiZF5aCQ1IUBFUGrAPa+4CdFXa/IDAiMwNsHBLmVAlDpcFVqNVKurhioBqSiNb/ZPkPLZ/00ozLLWghosoUoC5PomCkMz7XM4e7yHrCPxWKuWWuk+19a30rVpADVPFwDe3y64rjteX3Y1FMb8BIFtdnxMjxN7jFRzfs4bfve66jMHYPkGbRT0CkK9EuSujLo07TKPkNVZbQ25vs6h4PeXd/zhkZr1b9+rzTDdjkXrwcmzrGYbVlJV/XW1mMhfqRj+rnkKo1byzR685rq4z3g3eo1SEvtD6r0EBpVZdbC7gVg6xcThSxV4160Mrh//k00u//lSGOSg8zMPBE2S2mvC+yYFrnKVAn687xCCFvLLOaJupBA1PLC1UFQwnxUiI6C9C1aYt+BjEHAQUAXyS7O8G2xIRoc262nKpUYXMyhB3fcop7ceu23UJAqHliGphwCUMVWTfCfuQWGj9TOQ9tptYgYYRamIwKiLKJdamqVYguyZUJFzQAgBj33B7bLjthwKDWl5hwYfkfu9pB1kHnmv0a8Nbbk4wIceUuygQoigvzrBLJPTjtVgSV9OmR7KFdJvI6lbxZo7Bid1cQFUxKWqgbTEgvejrcnUeKHJBACT2/YWtxOM+tDvlprxslr10BorShE6o/QMr7peS+sZztcAIFyKFHG8iQu4f4TCgUCrZRUlx4HxqhoFKiqUVsGgpNiICqE1dBI6dkAt4hmUUt2ab31DMpCT7KfKrP8AIBV8/nrDbT26tSFzKYbkUSJyiLhGS4r0+zpWkzE+EVaMBxONFsOJylTMdbGSJxoDMlnGIDP/9ySWUU5mRK2q+3F54J+PD2pU04AO7rtd2fQgzZv4vidasFnZX+st/HVg02TsRWp4GKvC3GdAJko7BLXXvd3nA230EKzvcbOudOOUbnIBmaS9WJExq0ViRa0kyNMHj051Tdr3WLZDYgYWjPaJSRzvecX9WN3kO6yXeRcNGw8Xwc0BQNsKolnd0ZgaDNyiBWYDA3z8nxBUq2FT1pZpTHYHIB7HboKcWC+55RJ/qHo8X+0yFBeTaMcHoDRWdtbyTKjQYK/lLnhz2qtCXvEwb8Hu0QSrBhhDK5/dLFWiiblaqv8ac2cAXNKhQlGggT6G9GhQoB/krQPQOMMpdoAeRzaBIGuRnyfcxM9xMGlSYl5W3p2DEILFQ1qzIJb6DsZUYnntoxEYmJ9B6IjHI65+z6sohzbp1+XAugi/JgQrOri0EuVSUkTmf1myljAPDdYLsWpp6+NDxfEalMEmiYuSxxCKrV02V1KkqohXsH+q6s2ixbTEEGnxlBY3Y0vXfKmasKkGRoUkf9aAfCT8+esLAODDsrW56FlAfM6f0l2ho4JziZ2xdhqRAv7tny0A3MKhsCJgckFjVqHCxxBiqLp+/LXxc/R0PiwbPqyPLsh8FDNhlKbacp9ylrac4z2fmGl/xfi7eArC4283C8GzSDXtPuci62P3qZeY8W+uX/HStLpPNR+HVwyebngO6PSBaWp5jrFnAhUKXbuba+3Jzz+KUNYeZcEf7h/xfkhzdV+PhHEIlrgIUQKOYM4AgjXVARTGsRcEQgKt/GY5UXAfLxXlKgqFMQAKclUcDftFi18ATRi3zZUerexENmXhFZN6JAatKuS0vAXp71uA+mKWIF38koBlpzIKVoN/d8ekMrxmybANplCpEGILPh+5b7YU3WaJSYQu21/Oxl4lT+AajtN7t7hPSQc+kzWrpzg/Pj8XGwkhwjJRl5CxB0JWSUsnx2CN3fk/wIC1fJeBQi/8yaxaYAqjlNB5F/eWyMd43c+3O4CbNKIJFWtqVQPY/zlUxIhWOiJLJvuRhKpK/b1WHB+A7RFw/VNAKrUveQHGA+SzcbP1GnKLH6BqPa1woBVLhN5vjWJMxFZ8kd6tGVOw31wDR8Kf32/4dy9fupjSuBaubfGVJ3YwrXqWzvfIh1n8zSgNpnz43H1xRG+cclCmpGCeRAlBISXKpZe04feXgK/7FY+8YG8KgUYn8xTEWzCYOtcgpVLYaGdIbvtrIKQfVgp0e/Xm25MLoSpWwddIjeP3AOHUVgA/X9/xMT0gtNRkyWi1T8KglXYd8EOPF/txtgiaZtfAuPRYlfuwABShJzm2ZSrGtgn/ZfugKerykIEUK3IRYUXcj0lstIhDDsbDBmxDJJHWtKLKKvWMGEiusW2UKJ6BsH5cXSSX1VwuVaEeQkm1nb+s4t6rCw+0ssUN2mkCOxTnbSxA2AUiilGC0ISqljc55n4DWOJ1SUEA6iDHJZW1tvuqayuI1zyC1Mpn5xxbpVToGkqtsRI3kpaTbjWQlgbXvCQpo+6V+y3uzkDoR1ckMRSFh7rcg9pblPe6npOg2nryCWtc71aptTSPVWreyLFbuYuWHDmWR/ZBSD9u6cBWrFInFUGtAlcuISu1MYQqjKNQ8bruyKShhopQg7Y8BcSjBaBVVlMqeECqitYk9YVKkI556REQf2nrNrbWrK2VK8tgHK9V+javVvoCDe6oi3kasi5M6IcczBtVYgb3R1tjrZCi9KuOuG8rvuxX/PvbZ+2nQavfM4y4xzWLucUS1nBMPQwhFHijMnYKwhMLxmfnKalewfjnyfWRIMQDlW3pwKfLHdv9g/SkDxVlWAfSwVEUxJ4TUhRvdGkJnmxI5WO5T2ym0/ghpUC6lFBTg7q7zFPYS5+NJ+9DM5j52sd1w7+9fu0mCUAXmPER/VkjHUAeBnMSAAsiexd/TDtHYIJSMUENdNARr5MP7VFW/LLfdOMBVAgSV7BWnGb5xqXgIHjfNlkowYLNEagQ3ndzJlDWinRnBnJV3LZcKtI9KouDTBDt30FLu226THpfC19w4wLQoF5NQGbwFwBqRaoB8QHEh0BDXtEAxlSqsXHRiwiI41VgJJ271aAEDZ5zBGuzKq6xzBdpqBwMrDKYeGkZ7qlZ3L5aLr09ANNNK6yO3qNUL0Ct9NhTEQGFIeU84kF4RlyuyUgOzUg5SsJ7ezDSA6QV7ktHs96ieIaBsQSzZWOwJlIsgQBAKwlTQfJeGWQ+ipX1Ds5qvCwZ2wGsqaDUorBDKbF5LEXnPjXl8F4u0vQmip7PrwXb76LWRlJIko8zAyE1r4CZ8Fe3Tzm/i6wxKg4ArXlT7crEawyMMShA9lAJCEtpsBjwx7cP+Gm941Ojse9VernwuVFuNJXQSlkws5mG4NHFEih/OL8jZdX3Z/A1kHzvFZ/pLP/3DcYMomrxpSDr4dP6wNtxweYK4RE28hCSvt5AAO4Tknc485UQ3HeMH6ySWtzN9paOLwJmn7esSs84+ncvX/ASN03+8IESPzhxfiOk0OPEpUobRQDO7TcowJLUWDDPrr2gp7D2mYytEUcN+PP+onEEOSeLVzlIjG0SY0Etwg2PrwfK26KKAaX1G2jWUOiEZbP6V1EGYo1Vo/I1RRCqBOsEdagaTFb6apM7cRcWEgPUdRU3HVUopjwnArB8Der2p7t8hiWOC/+OQHmV33ED0rt4IMerHG//EIR91LwcxUQLDB8mLNUsHt9/twJYUsHiipzJerGmNKUIDnuJw8aC0VF94bIuux194yUvDPosd28YRMWXmZAk5yWV2Zqb+ARHH08AoBn6zHOht233ePYSyFLy9ZvG7wHAVha1HktslmSJuKYM8tX3xpBjDR3GK7SmUDvny2XHcURsOUjGc6yoIUps4assFmYkh2ICXJ8zLBYVDyA32rLCocHWqlHZoK9PhRhFArH/NWNNGduR8N/fP+HDsukzooHA58nkNXu2Z9M5I+rrPlCsxm2d5zLoMYMQe+9lPVXnLTXoujSP0pQOjeISApaQ8HF94Mt2xd0xyCyeEJTRmHNEXeSZsz8Ht1jAX88++uFA8zjIrQUallWsYQQFqddiv7u94x8uXzueNTd3r5UH9zpUfd3HNLJz6caYgh3LaKh9E42igsK7lKSmAsCXfMU9L8glYitS6phJVXtOgn8rLTVoZvOxNw+C1k5ziQWzr1bBkMZ6g4H2jxKcQxCvIW4B8Qhq4YdWWI8sXZalpkcQswhtxhoC6xq1TVyuDaraRNnEFmtY3oVFtLxX9ThCYeKS7VbCUxzLm7yWb0BegyXHcZW2e5S+wQU1B+TMVotFrdTkBCM5+WqAdGvM4L5nm7XLRPeK2/09UwSjoRMHVooXHhqgrlahl9/RYm2w6xelFPQzdg6b26UFrOkRMIuZdW4AKfexZ1bmjXp8AEpvHZXSkZPuw9wgOS82uHYvy4Hr5UBamxctwS+Ul4Lt9xXHK7qaWd4b1FLuLcgMEDpFVwuprmLUxEdQraLHSQB7b9SloidqyDWmJPBiilJu/E/biz3fQRjmhuvr/w4KehaPYixgbSUq1ONo0CIL5gG9cUpKKtAHn0dGnG/u5ZXQGjM+LQ/8dL3rmuj7KMhEl05JBIb17HPte7nEuZKdjB9SCikYdtbVFxmyNXOJGjsAWuXKKi0F/931y8kDkLLDqRPG/E2lodeA0gKNwkzxgcOxlIUdyzY9GSQaaAriYM7GW7ngPV+wlUVduMexYG+bjMPw2YrQNlzNUmcmXtu1VwBLAbQnM7SJTkmSc1DYnrDdQjyEZhpargLXcWHj84bJ0kMIjeaa7qEpmeYpEAIKUI8DtPgfYvWnd1gdmybQWzuAdjHQ0heRDYMAxK1qz+hyhdAVF7MIiRMjVcSlIKaqGLZvTu9LW3g8dmn8/9dla3CJbSpvSGgJCkdBnVXQpUCgATGDGnuh3a9HCggVHE45rK0yZ2xxD2UbNQGRQm+0LKE0WKyPb4xsJc6Fz4L2SoMBaF/qgx5IK7CrwoJiJgYJ6l+XjNsqZRLuu5SiSUvDOAuaxK84fso4PpzLVdAYQRNa9FzJSIqtjHq9VuQPWUgTyRlFaGuSsTAff6vQa6gKowCPI4FJXZ+3G97zqnPFEvmewchBOGivy+Ap9vGF0digETDCiePw6yw7hT0aB1Q0VDpW8l+y1ZdW7TY7BaAKFBJXERlkRjjLpptRfrq8p+OHlAItHYktNIvYQSp6kmCtFJnMkxpsRAxwdJmJA/rXvXegkxeqc+OzwkIde6QJ/r0uMCHSXMrGMOqTnGwGmfWaQsFfjhd8PqyiIaP6mokKNBYNA3YNN1yyWFs8bIOOSMsj1g8A+bUif8ool2KCdg/KIKLVzfyDchFrq0bxAqhc0kNyFFKrBpCvFflaW6/k2jwVEejpHtSDuPwi3gFhHwQgZofzRkhDFlU84j1QmZXVkuWOW2OjtFaKAkMFVTLLJSM24Z9CVUWQosGMAVAGzhIKLilrSQvB6LM+L2/N+WxloGfI+WfrjQaWRvexBEKI000fWGeHkFLLiIbV4XpJuyZiKnRAJQHW6MlKxe68IPR7CBBBc2m01I7W6gRUCPYZX1460/pfstbL4WCzmq0JWCqQC6mrqxgwNGSwFOwfq7bcZFygtGQzX9Y9be0+2uuBBIoSUNeC42NViCnujjyRgzWiAqA1Mlo+hRScFFJHal7EIy/4y35rz0+eAYkkAHA5EQWKehClQUdUFt4opaHK7/QeKiHLA76sPp+X9wg8Hdp7FV6+Aa2wX5AY2u9u73L7zbtj0y7mq5Rs8YXc0Bh6gqIgWl7LdyqGH2YfWX4CMzQlUl5K6DD34D6fS8RPt3f8vL7jGo+W3RmUXTKrZeTP5bm9KZTmrlvhsjFLmRqdlDIGmoAeQ+ToXUw7FnFin3HIyae1dtD1rZaKXrIVJqt7NOx0iwrj1LUCl5bpG5vUdMXk5KAwjj9aoDdKcJoWeKgw1lAAkITv7dtl0iPp2nfC4f2QzU0ItiQHuaxQbyE6KArtthhcpNKpEQi1SutGWpHtcLUt5LQWVaJLsgQ1P9aYNS9ha/3AKWQv8cBr3DqDgu77iY0G1skxK96eu5vb9tz7hLaj27ia4YxWCwuuI1uVar0KJVHBO3aSJK9ZjAOhNQ2CVH3toCNYXgKbxywhY6uLFta7Z4v4GgGiSP+EULUMy2U5EIJYlhViqIW2XpeUxZto8Y5L63i3rBl7C/ACQN0j6i3jeJG+C4QJARfPoiFRoGUsEEzg12YrlFuReXsPokCa58o1VhbH2FtFIYRmQPDnyFFhtbdDvPmP6dHlCsg8y2/f9+XCdeCWiocGx4AzWUyskMr1Qe9B11C1Uv60/gFTCPzkrETQGrJ04gsFt7TjsmTcW6Ks9mjms8gRxxGRl4gQMqI7HhWCRzJ+bfztnkK1qD0vgFaOteU0q6dCvIjtSLgtB35/e8NL2k+UrZEbDJgmBcbgm1lPoxvn3fwRRyRMMCYl+YxmjgQrpvae1y4+QnzXD3oJWuqiKY3KYF1uJnYT7nWRGvaVtYAImzZICY3xgwDFYOvaYgEXNtCpQwlr0lYtqU2CwRKTCE0ZaHmLaHEHCWgHzXuQi4Gz1uR4ZBQJj9wCiur9NAUQqngWotzk/sIegBykARFMga4pa+XcNUoLzg/rhk+Xh5aOJkavlrZCNNZUnZ6dh3N0zaDqJlUL7wnN1K8jn8B0cYlNXIeeRTIezzNPmKjmlRXLzjMJTxvstF7kXQmZagolhtoC7WRkHadzEqoCgCvnt32mDvEEg63sfcYl1jUjLuKm1lbNF5B1pkZAWwteuDIXgQqhLOKFxkcwqjRkHxwfi5Zc5+jCApeCdM1AtDIovG4fc3rkBf+yvbbArszJyEJ7RlVXQsIknumfY4LJkHFQAYxrwZMdyFDyxRlnHu4SM16XDT9f742GCo0jyAS1+1HInvcHZZfVbhJ/ffww+8gLfq/pfM3vUq0ExJoKfrre8Wl5aLS9Yx0hq3b2D0HOVx0OmJRN4OseeVdfGmYs0GJ3mAWse6VBQbKXVd5rD9FjjuqyswJhYz9IElDBcSTkLJxwwT9FmzPAGpbSNhF3X2jKAg03Dfqwa6yy8VrTERxCzaO7HtznQEZHs9riFqxj2xHU8qeXUBb5We5QaqC4/FWK112bC180tqexhWq3Y+UvQoOwquQFeOsRVWAkWom0OGMqPdQWpBDcNVl3MSMh5A4uogXt+eiz6qdeAXi68i24+kiOuSTHcWvQxbw4OmVTxVsgd30UEzQg1MNt2ntpxshRk8KqgKzhJQh3fYlZegLA7pnwzhhInZUzCKHi2nIbYqhYCd+2ORXv4FBvNxepsVOKUFiBBiG1PJItS7mJkKp4vQCOD7XRSZshAGjZ7NDWhmbNJ8j3GTc4DFoUxZAR71HWXfOSq/MuAWC5HC4buzaCQiv3kDL2nPB2XPBWLvg9vopMiFbGRuZR4GQKd3nmRT9zCUffd+NER/XvyYX6fitaWaG9R8U+QkuaHe8YSpSHS8wq/F+XDZflwOO+tra+Mie1BPuBKQK26gwNNvxtKKnB6Jq7v6G2+Fj4i24LNfltkSDhS9r0OwkFa+w9Ab/o15i1uxTgXLvGFFK3DFbRcvQcnsFEGphUL+Gc9Srvy/3Sdec9AQYhsSPWgSSso+Yt6IgVIVVcX3bR+Dni2JNASACwBxPsS9XWnHKxzWVsNZMEtglWQoJWeXsvbi5jOZrlrwqD2cWtpIbg/WLN1xgU+kE7psJCFVrmm/2ZtadCCzCGCilup4Fw2IJ0txQiEGPFpQU316YcrunALR3Nah4s/VDwIR7YWzKYt9RYJtuXTabSp1InLLTC1oYP5Po11gWOEZ3i8cez9ePXnH62Vq2hL1i0Uy5VsmelJEXCS5KWmylkay8KGiHi1WpToeIFnEFcHGzgTg8+hmpN3ZlYGSSXqOoeNUszRglOL1XiNxuT3iKsPtIq7LGjiiAJGUCUaqndo6Zwd6XVgwtaUyFQzpabxNPCI2qcoQSIt11wykQsLb9jDKyLZ28UdarrS8jYq6QNSjVUIIVzGRuuA5/tLDcWgXquj2TP41zEU87vseA2hY6lRIoqC+uhQONSpUb8dHvg8xdjVzGYjwKUXWIr9DE4F5RNf4238LcrhWpFn3wde59osR1JhSUX7uu6aZCso53WPlkJMK65H+NnqIEpIMaYQt8s5cxBntVQ77W59J3ea1I3n5YfI/yxYXwHWQDZvl+JwwYpdrasGa+3B46c8P5+UTc47xG1RmP3HGZNK9Yva0Y7s8UNkqPAtVqB0JLMyAwizJPusinz1QlvKoNDcH9RAHZOn4msnkUrU6CQUm3B7iB7ulzkXFRQ5LFHMlIgcBcu4n6kVLAuWQrcOQjgqBE3BlM1IEeGUmmVTXuSAQCDjdRAkN67gAWS/aa9xl2sw2qbmdYbeyeYEOcaNGsQ1ePOXD9Je3oz+M0M/I6ujajJbIwpvKS98xokoC5wxKP2iuAoydUJY5C7JY7W2JdOgQmKmDKQE0qoWFPRoo4pFhRYpy+5TugaT8TxF1oxEXVt3tEWkLIQGvi8a22xqz2ghNrIEO3+F1mwNVbNWZCF2lzSCtRrkTIpJCe0PUAyBwBtNMOYAmDG2tYqGr9iE+UYzoIagCoG/n0hquCMXQzflZwFWw/XUPBo7nupljzoq/H68ure01S4qFlsTHhbYkFG1vLnH9YNt5cNX355kfmhbKkBeETse0K6nhsscfwmgWbtgIY+jftxLNpkmtZzqQGXlLXoncfNrtE6BSkXOJRuYoFeSXha1+oCgnThKRBmdLJnNWwiXIGrprEBSWz6km9SvrZlLDOyz3tj0TINOqfSAkERSBVpybhcjkb3a9YpPYt9EXgJ0CcXjqiYDZVATVVrwoQqCiE0a12hmmbxU0AFbib0RgqNnPWLvafxBeYhJOmqxfc5/fEQ4Q+I0PdJcWieg8Fadn7CS3ELKE2Grinjthx9CRSHn3srfmklstGsa88b59irKOU1HkjOTfOJiNy0/BvNmPC9eXVNeO8hUIA0L2EQ0iyJUBDVMGG6lN8fPhHKxwjYOnIJogD3GrWUCiDrv2TZRzwmleZYZNITIEQAiXBie1w2gk+x4LqKEKRhE0NFbLkhgOQ1LEmSxPLlwLE3wydWIFYtzhhyQCrQMhU1VivF0ijZiP2aYMwhtFwV0pVlsqRqar1C4NUk8YySI0qSRkRkUaVoJcnZh2IvCW/5gp/TuyMfRAAmR7rqpROF4OML3qA0JQw1JHx2ux3zTHYYY04eZipVCBRaBLIFnGMLOP90e+Dt7Yp8X6AU4bbvSWjh6Osk4bfr0RxDVeVwlKi9YXlRuZhL+mHdcEv7CToCTDHo68Gi9DFk7YM6lkTW6qUI+oDQfqdvKAYRBix/3OcpAECqFodgjXze7+6EgdC/mtfQBHuMtSVkBcSUcblktWLoTfBB7duCsrcAdKOKgp3ZCNe0+jChAswCY+cr7477/rhdHgID1hF95zYHKdESE+UQTq8TRuJ55CTNCzgAsI5NsNcZbEZgcT6DragoAbFQWfmT5YI93TIGKQ/h+3Mbe+h5BzUKe25eWtp+097Litf06JLXuJ6uwYQ2x2UCL66Q8ghswzjGFry3M1K1PZzkhYqvx0TI8hKl++B7XvvAc7A41yUcOEqSjmuup0mEKIKE0q1lYs8AtGYOhWwMFXuOGsO4LFkzavMRUR8CfQrTTBRDepfcGDESWuIZ12Ftzi+dqsJ+C7XFFgJqaG8uBUZBBeLtaLWOrLRMrQHLQgUJoAb1vJdQcNSIL/mKt3LBp/TeKKoO89c5bMwhZJVdhAtzjV18wbORIjLeyuUEHUkZ/jMUNQ56tezTwuf0KIvKLSqfa8x4Q5Ohtx1fHywm1ubxkGdSSpSyJG7Uthl/E08BaG5wDTiKCU67kBYVjwW39cDLsivURPeZ1FLfkGJMR/e1RPxrXSG+2hqshzOldZbF6AetNnKYt6FCa65R++165hHds8PVJyH7qJSAZc3WU6FExFasCkBjErR6PzmYab1LkA2pwSylAnvUGIJmHjfYKFDoN8EP9PBRxwQJQE6Ek6oKaQ0SVwBFgsyAZCWL52CQUWkBQ4WmgA4z5jGt0Qq00B4A7dEbWhG8JRVtuSnzUpRlFENVOIUN2akQ+NySe94eFvKv+a5qsh4EkvH9Em7h0Fo2FOqzQaFyrxew+B7XVc9IEqGeEYByMZadW+e8B42XBaGxSgwkNcOmAo262nVxq8YW4v/cV5d0YKkBd6x4ZCmOd01W+iG1ILYcu2iV3+1IGpysNWDLlvUcA9Sr2HPCvqHFA8SYqdeKozSG2yHLtrSmTJqI1gwG9UqbR0B6KmJ1fweEtUgfklg1qLysRS1gBsZZriM6D4ny4VGWvlpp8xTkORqcSK9uVkXVK4RzALopgEY99j0XfF+WUUGs8cA+XJvvxwBAPMkQsCKjRImzfQkVL5cdb/Eqjin7sJSAukWtmqr362K63zt+DD6qwpw4ipUPYJIMIGn2PMkai/LMAVpUDnaKwjwCzgFkDmaoduUJqmwkOXc5uf/6uY4tUDolM5bU0M81S+FeV/UuGD8wr8Y2I121GFuGbrJyDYBAaaUCpUFNLIdBSh8KDD9t7jitbeK06SGwkn6NCgFomdCy2dK7Hbckh/EHIBUgvzjBHwz/D9X+V2op4w8A6sXiCowzhObN+NyG2JLZKASY9UzBsKxZY02EH2cVUAHB2VPoPQQ+H5+lzA1qAWZTBtkVwJPnFpU14gvj2bM3eAoQDyHCnucNG/a6aJVVT0n1AvoWDrw1Sg4DibP6OTRMNNcCWaCntuZ2SF5CjkHpq5eQW35QUMVQasAlZslzCOadMMhM1lyu8v8arJNbiqGVjCh47IsqhJwjNgDXBgulWLCs0vu5PKQkfF0K8mtA+RKxfG3xpRIQcivG2NZTDQ19a2u4tvXBddeNCsn4bxVBxYsBliULUaNEPHLUa44BSmsGJC5Fr0o9yyHYnCi4Q2mxnNjJBv69NciQv2nJr82YQFtP3nT1hghgJXgY81Jo0eUy+HPey+pYZwUvacfrsuF+WfGXS8b+viiTL2QAj4TjSCJLHUFDUdTfJKbQnuLmPIQuCNvglCUWfLw8cHPljQtSZ/F5t9oKhrl4guN2j/ARG6iTaaA1i4JZUrmeE1L8eW9hP3kogMQTfJo8vSHf/AWA1mEhhey6Hqqd1e2uASUnHEWCQiXH1oKyCL3viLJ5jtBMreZ9FFEQZBhJwxzJTTianCGOy9/7p9AUQW380HadZDUVQlDB9/yxIDLQCuIF3ay+xr23/Pj4FVaaxDDKtQW522vLmvFy2XFp2PXrsulzYxcxaShzOBJB0SJjfPZcD4QDvEIQa71PXqJy5zphYVkfgxq9jTUcRn9WJRC1zMtoXfJ7pKne4m4F0kLGvayqGHzcxPdNoNGkBk+V9X2NB67xaEUZE0jJHeMvEZLlfE0Ba+xfj6EiLrvGx5Z1k3LczqsF0Dq71Y44EUPFtZXBqCWgri3/pq2F41XaykpJFkjl3LW20uxVhHzzcutarS1so1pb03DxZNOlCKW7AoaP11bpVWIgtV0ra2ZxfWx5kSZMjfb+IT5avKc3HpWF5hhmIzLReYXV6hT5+kkegvQNvrgZvFG71yQZ0A3LpWLwfWm8p7EG8RboVd9eNuyfL6pJQwHq0Z5Je2a+WnMpEctvoRQ8E8gL9dyS1I5D+MGv1w0f1wfWIIk5vrm5T1ajgPeeAKsO+jo24wTSWottAfhgsWz8ofdCUwi+TLcf2o6xafa9JoWPxqA6IlCrYZwxuszTLBvtvq3mJVSrXxJiQWUFRNc9SbuRAdq7tlwKMkSoY22bpyV/YSkWcOJxUpHNdhWhW44oyUetoiKytFFk+Qz2NyDzIxzC+pC+zQHlLkK9rFJPPz3MA1ne2nUztpGZVGexidKEQlmB8pKxLFk9A4BQiHkGgODCXDM0Jq7xOGUr0yjg+niNmz5XxYWbdSz0Q6ETSq6CGQIxVKzYtXcvYBRlpaDWgESBUINuXE9XpbLIiNjLOdN1DRkltOflWC19fOG8t9iZLaHgp+WBr/miCZWcQ/HeJc/hCPI7xuI6Iharqtpu8ygRC8TI2Rv11CxziSVYGfiKSyx4hAUxFaTaCBB7RK0V+bUg/EtS7zBmIDO3pq0RFsgTg6X9zSmvgmOGAOlv3lzi0OqISb8N6R1xTUXkjAuqs9rAlg1ee8uXFsyVPITMeFMo2NqJ+Xx9roL3GrxCGBvokKSgz7kmV1PNmJDW24HPtcU/mb8wBKhZy20vCTlE7FWCzWvM+HDd8OWWEd6SsRGbMggtya0WafBFufSbBJrJPNIYQSvD6xPXUir4uG5SKdK1GxyF8Sz7dHrONoGxeQ6dqwfjqHvKIV02r7gsK5GNs3vWwRoOFAh09DnfcJSErSRdeEwdZwYuBxciK1dqJcr2/pFbMIgNT8jTbslnNUg/WlL0yrVavRlIXCAsRZJx6ElEEfqkqYVUlVPO7OqYcouBVIS1AitQswSlpI4MXQlIxy2IkitbxLEHxHtU/vjeaIZMWMpXY0HFA1jQaiMlCTDnF7Egc/s+Irrid6oY2rO7uKAyLXjmILymh25CKTpnCWhGQw5g9yx6dmSRjElHTG4kju8VQhQJ1tadW5uMPzWBPqar5Roak63lUTRFdC+rxkC8Bfioy8lbHgkSmvhWSWXdu/MxGM1e6AeiKl3mNBxFPNFY2erWzsdANGNmhB9Y8gIAHq1A3pKKrg/p7xwQkhgmdanIt+YlBLHwEwB8aXDrKoaF5iVUQBlHLrAcWtvQWoMQEijY2jn3nFqcowIoWvuIz5pzBgDv+WIW/RPmocx36D8TcIKTZL7j5PkUJTf4wdc8FbpjJ01iCmO8dI0ZKLJWFEI6VlxuO7a4Im5RHKwjIG8JdclWSaERCUrBbxhoJi+6JDxatVDW20AVjFIoYmL1bWWRZJ0QsbgH9K2CUX1yhyV8jLVriO2t4VAhUGroFILHWTmEtSF4o6+J4q2BvW2ivi9uo4FVKdFAb4ATG2MROp9TFMeRVCGIWyxQDrJdT10LwksrK4AG7bfNkJasCiFBcNuUKsLlwHFEtQ5iC85xEI/dtqRBucLSyotZsce+iMveFEwNFRUJec0ASyg3L6Msshj3BgFIkLn1bWgxCu205vsyZ7H0YqhaGZRBZfYZYI/hwqg5TPCTseHjBhKItbwCwincuFtdNHjrB9dMwZzGzOCityBTqEPOS+piRwlmgfIeWNrAN/cRRlXv0RA2AoAI6/Znweeg3lJGxEtDph95aRTW1LXm9B6HX7vMafCxnBQLrunA5+2KvoRLUpjmaIafFGeDsJByRD2CeK4FrW+CM0sLrEJqBVCCKFkmSMbm/RIyjY16GoMqp2WVOVuWrCkyzN5lbCqXiOu6d4Kcta68N7fXBRdJ89c9vqOtAU8WaDSpsf/yr7GKZMYMQ2Xsyr9HD0L4Ifa+z2vooKRIlmXELR3yrC4HHrcCvEfNJMcjot5i6+Visi40BOB7xg97CntJ2h0ol4iDFfxawtan20NxYSkAdvZhxvogevymMUlLnX3HswfO1ycP+toggtyCfPzbjtHiBc0q1FIXFdrYnZbt0txvds1i8szLumnBsRiq0KqjWTBHjng81j7DuY3aGu2Ea8ZyPZCWIos/VOmVW4IG2HyS4JKyeizMlQgUoIlB++Bqw0DLcIRQgbbRUqs2WSuQ0iaNV/aE2koD1LWI5ebaIOateRMsr9EsvroElEsQrwKNWeJgAiKDJCGEFkD+kDZc0yF1gTqTRpKC8hEFFnLxo7HM9RoOtcK9YB9hwhlLifXxWWqZnqOnMXqBwfeVIQSLW8jxV/jBZj+Psqg3co1HX97brXF+JtXSKTk5Z5CMV5gRdU0HWL/XZ91vpe8LfZSIEoPWTCJJxM/56vo1rJHnX5BbDEJpq6ni2Fx+ASCF9VIjIzSKNFlp0i9cIKV6NK+YqAIdnwgEeg1VCrqJEdS3bGV+AinM15Q1N2rPCWkpKjtEYfZp0B7Wkb/FUxiho9FT4P8+4MzvjHWQvLHiSQ+Emujt+uFZU1QEho5UvMYNv7u845f9htfrhq8fDuDPC5RSnqVqqjwnqHvwm7GP9EYclcpTNdc146frHZdWy8YnqMn3glLkOPKwWSkE9rJ0mnMczFPwQ5gB8aSIJOBmG/AWJcg80sMknrC0h9cCxWg8+sYHXhos47sbsV3idpjgY/COlnzJUiQPkE2wvO54edlwWbLS7ACz7lhOOjU4SF4rzTOr6qGwB+9lEQW8NI8mNdf72iwtUg/pyd33Rb//2MVlDwHIsaAsURQSLY8SgJD0nkMS975WSKJRsEWoWZexSvlliFK5LpIMxQqfe424wmASQNx+tnpFAXKMwsgIcxqqrYXevR+z2z1tkJ+jlxBDVSOC9ENvQabA2ve1UVgtvkA4ib0cfNCaPcQBaHzKSqsYdOoDkYD1It+RunjWwbIZgXsCOGrCtdWHOqpk27Pmke8USIXMfhRHsZIaNC64jgHzrlOoOKpUPBalUBCXgpyTJpfhEOgwX4G1tWQtrbS7JGJWoAAxtNIVvgXsUoEcUI+AeM0CebrMXK7740gIS5Y+06038TUduCYjvfCaCadRAF9CRgaFvckADy8x4OzlwRg78hVyucb2FkvQGAJqT3wZqNFcF0p5dfAR/9cCes3rTSj4kB54XTZ8SVdcrjsel2srfy8/5ZEQWgwncFmG+lt5ClEnnZ6Cf4AvVylpoZUea2wCqm1EZPU2vCvNIHJ0m5k9UJVp4iL2p5K1J/Ug41kmMzexBYsODUL5BBfGUAAT/Pxbz+EYHKwwCQD3+4paxK1DTgoZxVvGzz+94boeanFaRy5RJhSc5/mXYLV5LkCtwk6gtae9s9ux95xwWawaaQxSkTSsByqsWxdx6JQiSpFSFFRCFVDP5TgSAoDLtXljOSLTuyGPHeKgpVQUBgCo8NvmLQmPLNDie0uX5ub4mB7KunkWdyo1IgdxvWfP2dgiQfMS/Ou3sHcW4PQc9DRbH11u7r32dNdcTajzfOoRRDnnoywNF69KH31N1pL213JrAGhZBfWcGszg84VeFykpfs8rHsdi9ZDamjhK0lgOAjrYKUP6K6ypaGzhshyd95vXKEFmnjBIIDlfgBVBcxQkV4WZzw5NIfOIfZxbfCFEabUZQsZxxFbaohkalcXfIpKzplODIpm1zRgm52qvCy6BWc0ys2s4kOvajIOeSeYbbjFOpIFq9GuKz4H5Ld6L9dBRBJyhYIomQeKfj7KqV1AaGtGReNpa+f3lDV/2K16uG95/OlA/r0itN0p5DW2ZCgwnjK3vjDLj7+QpAJbZW5rgi7Hgp9sD5NfqCbWeTVY8U9B8wXa9xZSHCY5NESTA6FxtkHW0DrRSH0z21hA9FnKNU5AaORESRNwa1bDv2iSWEtlVl9aRSssgROOxb4XlbYXnXZp1VvaoDIvl9cCnj+94vewK93h4yF8r6YRUOtz41lHLymysSYLwR41I1aizVBb8XnZW3+LKHqyt0mQuUrMmLFUFQYpZ8OUWh3i9iTm4JOkOdeSEIxXse2oJR0DOAYhBYbGcrfE8ANwPsf4/LBuO2pRDazxzjYcGmT3UAzUgoK64wIX2vK2AWb9WPSGB64NWmKeuWr6MQQReMfSepSMxwOUIFIsn7C3Q/BplzrzHkCvwl+NFLdI1ZPUScjXPeUfqoJDSPOQYhGGkRgvhn1BQXF/gozaPIVSN73G+jtKLA7LrGD+jt8pGR29Ysa4H9mVBbqVR6lJQozCg9i1g/QJNsIxHQG5Uaok5ScZz2IJwqxeIQmhMuBCA67ojxoR9X4SRxOquoCFqlRRelr0V/LN7WILEScZ6QzIfBSkEU/A1dc9an6V6Bb0yGNcUAA0qA4Sv0Z5DVqNT14xTJsxo5lrh/HsPwSvzj0nKyX+5XPH5dUNdV2ECfg3YfxLPnDXXOF+/WaB5b32KffLWcUR8fH1otcs1GoXQJwQBPQ3Pl8DWDemCiZ6e6muZd3WP9LjnblleuHtLTJgurQFPgxYKIrZGWyTN7yhJA3Afr1vHnKntOITPYhDLeNsW6ZQUyBAKCEvBy4cHPtw2vK6ykGmtsW6LXwB7o7YypnE0q1DKBhu2ek1H51GkKpb91u6Z3+UgM8HPD5XaJWVsjWe/pIy1eSMSvwBurTjap+tDvL2ckGNz56PhvlI6QRREikXgMZfBXBrE8XGV47znFXtJ+NBKq/vy6KQH3+KO2NbH7p6/f7ZkHwFQA6QwjuRyF+KwUzQYWRIusYAU0+RKICS3fvIgNGKLQdha66ECK+AotFRPyQbEuNnKgqMJ/4/p4SCu2pLaJEaSQ89Yym4vXZsieGg9pYBl2XDPi85TqYLXexrsI7u2lGQtZTOMuA7XlPG6Skezx2OVJkoAak2ag7D9ZA2gQm4xhsZIqxHQiDGat9BqI+l6LAH3bVUWU63QygCs78Q1lNseTSh4WfYuqS+iCk21po6CrMQBWAnrFCqkem08KYhunTj4iEYG41J+WJVmQxw85On/PvUQd0SJUREtUeipL8uOl9uOz58Krv+SpOvhEaQ7IiBMw+8PJ8ix/7qP98Pjd8TVJYmr4uN1a7CHJZ2NgwwKscm5gV1tfDehPjA4q4aqDwWtPWKbWwaX/ebvFAKK0k/HiRdoa2kb3+CXl2VXN50bBIDST7djESJFicpOKkcESkC6Znz4cMfH28O1lmwMi/agPY3QgtsuG7cEPadXqgBwSzuYaQ5AKmLCvAFkaKKSLzW854SrK4JGF73UoJt/zwmXlPGyQmsVkfN+iVLuucSCnCz4vTRvkJ7GkjKWJOf8ul+0bwIhi1JDVzCRQtRzy+lFGgZ7Th4avUO/RnxpbQ6P7+fhezrvTVAwiS7zumDChH+bt8I+wX2gU/NeggQVvecaIfW1Sgn4pQrbaFy/SyyIteJRFxxVCgSuwXJ8GP86SpISF63g3s0ZDpo016DSLZunscaCPRgFFAwIgy07pTBfigWXy4GSI/IeG9MoCMFgqcgvkOz6FowOR5BCqEsFYpAq3oF5MgD2iLpHlJtkdEu/DYlfAAJPlhIQBsnlA/ExFLwmmzPuHYHYrKaZH14pAz2pgJDRGE/wiWsjcUG+NxBhhviXp1b7eKn3FKREd7929pqAKvk8zOCurwfymrC8MxFQYjz1iAiLFOccuDpPx9+l9tHWqKjsIXppdY4kaEiBZ8wjtTpCb62NCSKAufqlKQG+RoXgNTQFQ/S4oIMTgEmXrXaerQWTaZluLchcYDDEEjNeFvn9Vi94HItCNwA7yy1Kya214XotoJwuGb/76Q0v647XFoWrDh66tIxFdsviAmGGL8t239LuemGb5cHyEKUFZrfGf5dnYbDRJeYu9gFAvZVOqNSA2yKlSUKouF4Ovc4+52PFkizmUCCeg1dsmg+SMu7HInBjkHLiF2c4MEeBz4oCdSwBUHQtwDYdbMOyaBn/vsSCr+U6uO/9OnlGNWRPcXoNMZCa2CBBRCRk9Yx4PAYhPRQqxxutyV4B7Y3RJHBP0p68APCSNrnuAmvVWc3T9jBWhARgtROiFzZ8LiD0mDoCw4qMeCwNHm0QZSslkUvAfV8kptD2/LJm6aSXKmquCJAcluNFktgk6uninQXaBKquVYLU/NkljyfvLW4XrbYRqwbQM2fhvl75y5oChONPOi8hYQC4l4ujrtucEebxHsJsXXjqMMumdzkHLqDsA89cfXzv6qBP5l9JwNpyWHhPRxHDldV011Dw03rHP6WPCKkiv1asX6Wlab0GZTVWiWScYNRn4+8SU/A9QEOo+Pnl3oSMBYFEyBjnnDjuLGlNN35jCKh154LBgHHMx8kH2Jf5bPH5olZGO2wCqVHHmNRES/BRVg1eXRyLihYX2UVHo+TmKrEVSTWX8xIuSq3kx8f1gbfjojGBW9qVrw/0FgMLxJUalW1yawXOeF1yZ7bZSwgKSTGOIwrl0IzXGAq2sOAoUfFrb5OsMeOWDr1n3vfFNZhXnjstzAZ/XZMUTiNc5SGxPSf93BILbsuujcq9Vczs1NHTJL1zjccQH6j6jMdgs0844m/f74Deom+gI2vReWGNSZJQW+yJ8Q10nxclYg1cZkLF3+NYkpnn5PNnVd6tLHiURfIUnKco68BKXjzamiADaWRoeQjymo5OkXTJhLHgcSyaQcxqxzQAtyaohcrcmEh7UwxLbTASUJaWAb9KsFn7cjQkry5A2CNqLAjXrFVVa044akBMG5alaImLZSlKsy5V1izha1unRQ3SjIgrDtzrpXkAvdFpvbXprTXDSL3H4wQjEeWIwXVPeyJ1x57xI4RELyKGgrd81bXWxY6csZGi9W5YYsbvb+/48/qK40WKWS5fA44XieNpE6McMWyJp+OHlcLWeM5HFiG4LBkfLw/EUPG6iNW9l9TKP/iCZE/oqKGoYPK0QwyveeUwix+MjBAfR6AnYslIfbKRP4b8FoHvq1LGUHFbdsVgDW8nba4xjWrA5brj08tD3W0t/BZYJTQr1EYIgBACLca93ePFrc0YChYwEco8sZ1CYWAsyWYvuAQrdSCK7qxASw34uD7waXmgIAg0BFM0hAOOYkKbXkqpQfsFJ9cz4GiMmzVJU51L61dM74CwCwAV2rlErRk0lsjWgHMHCR1gGYoVh8aG9LmBsFQ2byiy9LV4AgWlw2FNmUh1Vb92PCXVFIJRhMdBlgxZRj6Q6L0LQArgaYyrKfKjRpS8YokZqcFVntNue4JryPqlL8HKYGgNJAhss8SM62J0VzUAlkO931zt0bMWUQhBlUZKxXIw+dkoWfmpJa7V5i0Ekc194cX3lmG/VIU+6j1hiyvSh4eWuMhZeiasrcxFLhFLstLrsa0llkfhszflG7pseDZLAtB5E4wpnBWCKBg/EkqL9dRvKgf/jGIQ4X6NO1CFbs3r8RVTV5xzb7iW1iAxlA+vD/z59YoaI9ZfgP1jQG4dFPlAJojodPxQ5zUKQS0lXSFFzmJfyZQ34KEiPiRvBXoF4SduHOSY++FjC3sLKI15Eb4+DXFCXwQvo7XCY1CzWBbsEoXu5jfVEgse2Vt93DB277eXDR+um8AwLWN0CRlbTrgtuyYQ+YWsJTqC0OuurRTyURKKe7K+UTsAHNV6WYjC6IPrXFMUDojAApY2ECVLhTfGMdgERjMrm+e3LCZoPDwoSm7DUSLueVWYAQA+rpv2ULik3DKYS8PFcyfINBu9TSmDhgB0U8tnI27x0XjoBuOIRX9o3oA/7o4V1niF2aSePtjHnkZDAxABIQqFEGg9f7cpetJSZy0fFV4L9vz5m+uD98/XEM0gogJ5OAbRaQ9S8VfD2X3sRang7XmUKAUcS1MAzC4OgDaRB6CCOqWCeMkoDFa3QoxlaS1eI0QRBNXlQlcNYu4LCykCSxbYlU18HgnbsuJy3TVXhj3fRTEZFHoMku+lQa2lhq6MiSWm2fNQyLH2AWYvL2gQcD3wOZGpSFbcGPPUY7l4KP+3ILeDbx2M69eJntNBhTEUfLhu+PzThv3jgo//pWL9JUjwPzj75l9dKcCClwxKplTw8+2uUM/YTnOvCStyx5BRF94xM2QCavc7htK1I+RQraz1jaLCP2NAaaS4ilXpoCrnSdC65EP5sGx6nOO46D3S4g4Qb22JBVsrcne9Hvh0ewjDqFnnl3ho56ylWaapQUEvSaiXVFNrKJqlmhE6hcH3FWJgc3e6+DW4YHjDwmtRwf5oFST5fSqoNDEnJM9ECATqKUA2FIPtt7irpeyZFXesiEU479d04LZIRVRfGhsQqIpWnYf+RmaaT2YEenopIUkOb/F5pguCSywLuRP03spnrGBWSsXDD8KA6oOPKdSWjVw0KO5LWQAmpH2QlPNmf1csZL7BCkE+WjLnTkZckNIgK6Dw0SgECCnx+pjDknWuZca2uqjVDQgV9chSKoGvMWbGQDT/T6mgrgV1Vy0uxRAddHQKeAZICZWcGtQRUGuEuh0FON7lmtbL0fpwZO0DEUtBDKGbT2GPtf0d6XkumsBmvRP6izEqb9bcBK4JT08mhV3rWwFdxV0tzQ5Lop316PBlWmS6jJ3m2wt0sLIaX1Fjjh/WDa+vD3z9+YbyjwHrVyC/BqmivJiH9j3jh5RCbrAK+fFLEq75VhZ8XB9meQyr08oBVNWOK3pLSN63lHAPEXRT6193cFJ3vmDC3rv+e126TGYGEAts48YgXb9KXuE7rvngKXs1q6udIy6XAx+uQjmlQrkteycIxcIrKrTJGY+1dBbfErOWToaDl7iJS1PAe0lqMbKl5dqCuHQ1U7LgIiEFBHRYPgXHEoq0BqzSZMWfh8/ppbGdAHNnY0sgeuRFSyxcl6N5Vxmvy6Z4L++F8SeP1ZZqzZW0w97gJTCTPTW6cx9oLVrwjuugb6HJdRFOHgKppmI4VD0X2UR+Pa3oc2YIN3kF5fuAqPcTB0q2u3auMfl8wYal20+k7xKueuQFSH2+BJ8/nx8AvDSDgLkgQL9ffF6OVCFYcdFyKq3VLESU9qWZTVmEVFAvQYR6bSyka5XS6gG6gdmVLWxREttYq6vFJeSCmjRr3d7Q6M2+0Y6szdAZm0e18iNaaRShUwSxxQ9SkBwbBGY8R51H5qWMuQqAJDzOymew/tqzpEMxFFIXeO6ScR3sp8+wQb2pFhx17dd5EAbkbT3w+dOB7ecLLn+pSG8CH2XO+W8VUyjVlMKnl7sd2FmhXJjLk+j3qDyYg+A7ZHFDerzO071GHjBdcm/9K/VUrSUnLJQSVrS0hQZ6Y0Ysi5ynShOTe8u+XUIRWmDLJSgVuKwHfv/6Lqn3i5QSECpg7izxS8vStRaMtWMWcNCtlYY+BUsw+qLMV0sEbB7IoWU5XF9eGDRVmhdB5SDz5gPJ1sMAEEEiWcbizfF5UknI/JJRJsURmb+ytEpdDIxf06EeEWMV13SArTUBE5op9Kn+XhjzczI/VZkko9v/a6PUvqF7aYFhgRJlPMOIaTkmdybPfrqEnqXFwmdv2fDoMY5DD8bYYgm7IwYcA5OIZAwA6hWSjtrfJ6EoBqYjFmQ8ytJBfyQuMB/ndd0UCdiORXMWWPuosLhjlVr+0p8cwC1LA55dyrPXtQKtgZRv+gTATNhYmyJBix6jNZoSxSKtQCMejwV0oQOgmfnsz3zPq5IfCAnTCx1zFeS5WE6NJCUGlRMj2kBPIbr9Z8/SjFfSSWeDpflZFgOA5mABtr61WJ9LVB3PmZthd4kHbsuB9XXH9vOKlz9AgvtXmaVyAPH4Plfhh5XCUSP2LJmwP13v2EpqLJp+QtZAS9dyE4A+L4GD+Qa+NomHk8aAjVYcHBTCmMXoccEZI0TYPbGrprkO8ILed3tQa8qoexMmrdbR71/ftakQ2TreYqeApnXOFoxLtGvvkpLaQrjGvZs7j793lLwqMJIowYBYiwWs26xfW79fq7NirBd6Iby+o8SmAOX4S5BsWG4Y8Vii4py89iUUROeZrC1LGbAYxUvaNdDu78MCuO2eGvYbQ+68BHk+Tamgf9YAOkVBYT9WQpV77je7hxX1M6FMvc6R1sjv9dcT9HuiVCPWIF4q8W6yjyhUEgoOJFXkxT/raqwkWv0rSrsTgU9G+NZyFyxWcU0HSraYzSUe2FwtJJbJOGpUWJTGQwVaoFkEcw3QPsqVwh0NNroAcQ/WhpOBZk5bq48FQK6lwN6s7f9G3DjuK0qJuN12IFQcOeLCYpGoCqnNCCoM3HONsPghy1zoPqiNZoq+7pV2lfQMNbdmyDoby/X79+T7VjOJge8T3frk2cp1lSDeiMSgJIHzddlF5rxs+PwPVxyvC5Y3IN+k8GAsAckQ8G+OH2/H2ehpL5dd8fVbYx3ldtFjMlBpGtkXpRsF75gJ6PMS4B7s+B1PRRwhB46tLloO+TIJ/N3rinurQeIT6WILeG7Fkk32zCxnwV1fLzv+4fYVt5Y8MyYevcTdWEWhdguHbCC5X+sOtziLcwk+uBocldRBTpGWv3kIsQnyUiM+LI92PoOB+Jlnz2MN2ZqzAAp1qSfWvByBB4yKrAIrWCLj0rjja8pd8HZ8dmNgd6yM618zr6+CzW9GKIlzKvVuLvr9UZn07I8ztdUPT2HldV1CxgbJ39DnyeCxEwCML/C5HWrpRxw14KWVw2AMaK9RM5Xl2iXOxiQ1RGhPXjnnE9cc6BQ30tGeU8HWejqzVwggVFh6vbkxZCrsXNo3oXkOAZBCiOw9nhoBA7Be3oCVUy+Qhk4XGDbM2EWoCA37qkeQPiAVKHvEnhKur+duieuwlrnGb4FGlRhHfA7WU+P8bP3wzDK/Px+ONQS4bGcX46CReqrq3ORZccaGeDjPhy9Aerh1+rpsuK0Hvn7Y8fh5wYd/rDgeoqnzpY6tHp6OH4spOAomaY2AsWKE3mcWqGGYpiR83wSOs5fRY5+jYhgTl/h3l5zUXH0Eoyp6KMkLGi4WNkaZDVpU3k3/eN3wb1++4OfLHeSA6z054bvA+v8qtj/QR1f0QjcxbufgnhR8VqUoIMl2LPLeAMe8xA0v0eUMBPcc3NxpZVYubve5MfDFzUHPSYPdDapaWjyFkBibw/igst9gJ8LBk5W8l8V5DFEt8HGMWDCFgNF3k60N9PAhy2jb9fUBZ7r7KTgqDRo+rOsqdOwjf0/8fbT4QxdgbtDHEjPeM4WOCzK6uBLbOUotI6MA+yxqzU0hFTUQTmzeHQK2vGiVAuYrvB0Xfe4vqxh+X7cLHq4CMGDspGWRkvMhtGS4q1TQDa3VbMhAPALK0tZJhnT/qwAOWKkLQkkRqKiSIX1E5E2q7dYSkI+Ex77i08vd9kV7LlIYLwGQHh1SJoW1piSGtCEp/g9Y1V+RW46kMBmUL2McSI7f58mwqgLgSq7DqMb6OWc4pFCwT2QP68T5YqS85kvM+HDZ8Oc1Y/t9wet/D0j3poRrQDy+Tyv8kFIgd39dMl7XTUolLEfHimHgBGhUwJaByAJPHGPdogJrwu6H4e8Fqz4YmeArA78w7rwqhqbdJbBs8QP5TOi1N3rFAMikb80t94FavrcuB/6H18/ibjeF4GMkHB4m0kJXwVxMMg60P3CDGmaQF5P/eB3s0LRDjrEMc0zoRssyu4Y0cp2mICigcxVMVue/HTuit8Zy+8zRgt2XSFaNXfM17p3y07LQjbPvx2gtcWMDIuy07EjDiFNwcxosX6BLzGr3w0C29tZoCiU5Prjc4xlm4nFGJglf06qosPLZz6w/z0WnImcsK0I8r0cxBeY97lzDEF+zPAQmFgLoiA2MFyihwNFaOcekowLAPS/KLqvVCjK+rDtKBR5DguZxRFwuWWjKa0HOweoWtUBy3FpwOgElVlEI7RLD0fCnpUq3N97eUlCDKJZ6RGAVWBKhYtsS8jXi9frQ+/R91B9lcQUVj05Y+3EJGburndHFGWAeqP1fJD4Cn0zbF1XUtQGLMYzQ8Boy7uzNQPjIVYPm1Tyywdr59GyjPqcP64Z1zXj7WLB/WHD5RRLa4Pqj/9r4QfZRAErU2jgFwE/r3d5DsywRumqMAIDgONvBKIa3uJ+unrifZRja5k0gb9gUgnynd93GwUzW6X2hb6xBa4r3xMAcE4Re1h2/v77hw7JpQBAQzHd1+DCFDRdJborq6spGeGVgis2ClLSiU4CWN7DnYfPDe95hEEwHyQQ4pQZ7VsN8yTy4IG/xHccGdk3zPmaD8JSHe7yi8/cLmLXE18ZickCfwT5ej11/VXaRFUAzvjdjEiPUMnqdo/fa5dSoVIN5DNWoiGs88CjrCToahxo41XtmBUddmlArGovROAP60ug+ZrDE0go5OuXTfi/D+mbC4VGSngMwwbOmLMepjUkWKtZU8PUhmE+t6LKbL9cdW6goW8OOUhWPoenTGiCvR/SxBQA4gqhFrxhqMAyqQruKoQZ8vV9wWw58vDyc0Sbea2mxMw8Dr8hKUfa5Sx5dGKnZ3sigN5ndepqtPU+c8Gwik1ER7MxWYMZjrBEjq0nzSOqZXemf4+siPVne1oLtd8DrHyrKO4DQell8x/jh5DXEgp/WTQUKrT8yU0aB3NHv2gMfA4g+gUe+U05/+65pY0EqZdGEAxv6hiYGBx24DXCDb2jBbEPWNhfX3dxFBuNiyvjd5R0/rfdOqEn2cV8VdmkBUdITPQtoFKYiBLJayEyy4/XLws+mIGDdmvx1MAdkDGR5uMUnVKVovSq6QHYTyKTw+kQ1DyVZFnorZNcUAXMZrBx17a6J12UCv58LPh/WOhrXCGMI3ktQwR7MuvLFD2lEJMTuXhU2Q8SlheZ9pV67rrNRoQlLHjz396HWoBkt4/B7iJnMOkc1gq1hfSVQzh9gsQmWJGfBPH+uA3Zcr0iie+1UOM+RAVKQcgu35cDnx0VKpmdTPmHJKCVgOyzfoFyL1nGKhzTTASS2QDpqqKHxXeXv2hrvAAxQS3FJaTlbpD95ifjL+w2vq3TmEyg7aqLnXhNu2FXp+j7cYpTKumBOibzuPcyqsKzMjSkDYQUGNVw7eHdQFowrcJ14ucW/x+J4Uy/afY8GK4+/oODT7YG/LK/YP1Yc14DL54qSAoZc16fjx+CjHHFpNWweecHvr28dqwRAR32cMWsAsxr9GGEjETjcLBa4sZwH1poZfg/DWC0X3LB3rKXc3P8uKO5hsGa1HSViaxVDPy4P/G59x0trkAKgUwQUiLxmCukEdD2UFWJr0IFZMFXr76sQrSbEvMBUYdKEa0f1VTyZSTGWMMjKoz6z0ueN8LPohHgjCgRo7OXqAuKMcXjF5yuc8hr5I8/S5sHPhcZtCOmpB2Xd9rwiuMU+SE3LsKMto6CE2K0zEg/Ypzs1RUYvY0W/Tj0TzldtJVbMzNcEaymrLKTAuYxaDnsvJigIkcr1imHwkvbGrCF7yFhEAJTgAUDjC6yJRAyaiuCSDlEMsHHUpMUOl5ClDaZ7nkdJiKl2SiIGKfeyl4Rf7jfkErQibikRZc1S4ntLqLeCeo/Sw3uBZVSVpgwKUYSqzCVWW5V/ILGGKk1kapD5yjmgLAFftit+d3tv8T5p3HW0QP4tJhcfcs/NxYaeDZ/HMpa8MPZfr0Dl2M14CkYg8QaHb8/JY/i4EzPgd0bqQTKN0LNGj4HU89/d3vGP6884PmTc/82C3/3ngstSfztK6stqaeTM0D1gga0Z86jzKNpDIaY/uulW7KxXKF6xnBhHDZaJ6Ove87Oee+wFxSXuyPW8REbe99Fw/9dF+qUKuyarkqJF5TO1+XUm6vlA6igIVcDovaOzIDThj+fUc7TzDoJ+XGhe4QDolIs/ZufyqgXVH8vPO2NGfP/EKNMYy7kMimeKeWx1pBX7HBaE3NZMVau+r5Hkgno+oDczFuhluJiE3m8YP8skp7Nxkx2d+RZ23FspjT0knWe1Fqt5K2N8zQwJURw+bsTEQD9o7fM7C6DQkc8ep6Ld8qKKQWtgBVKkrXT7JZlRx1paVqG3EUtW+d6n9YH3Y200dRfkTlEyiO+LJKwtAeVSm/BvJZ1jCzJzHmoLQGcIJs6mMbFqRzGk2uohSbfAt20VWuZFij5aQcGEa9wxshPHZ3paC5O4lCn+PpjsDUHACBRjeXYas0xgGweNr9JiU9yHZKiRUSi3RqNCWGh8Tpd44HLdcaQV+08VNQLrW/kN4CNYRuP9WHFbdi3T67MuV2cN52Zderd0VXdLhGBuFp4lk9mYudxjoJSBWk93HR8ghcQHbMp197XxeT0UzkssWKq4pFtb7D9d7vh5fRdGT6v/z809K96meLjDxU/ekD7YvmUlIBm8e1k6YTzzrsQzadAXggpRz67iUhxxS1rxKrzdtVBZ8drFq3IeXSgKY3XX3T7rvRMAGqj1GLuvZSQU4Nh10iNs5Hsur3EfvMUAVtMllGMuu0EDLGjGBCVhplhyY0bEBQ3OxNGsSyuRxgKKnsfu55H3tLnCjZxHX9eGAUi25ATQYgZ8/lUTziicLlH2GQsQMnmRc7LEjM3HFUqUfJG2VwoCbsmy633wn8zBEg1epLB5lCRYe8uc9ol0SyhSPPEa8PW44Mt+1Z4d25HwSAX3AOQjAG9RIKNVaJI1tCS1ANSlSGOe3PoN39zcLkWURwXSKs2ljiPp7xJFWTI+8sgSi2G2POGcrSatiOxzFgTbF0jo2bD1S4+6h+U4KAtIzPA0VT6nMRmXsQVhvSVdQ2OZfK8gOIhioIocfL3ueEsV+QZsHyNe/jn/NkoBAB4toeWSco99toVimbRWy4bKwgu1XI2/y4w/4BzE8e5ZbNROtIQzxfwGF46/WRhtc9Q0wCquKkTiNjm19KMYXe+n9Y7fre/KpqE2f41bZ9l661vuRR7eXqz0QkezfTI0juJiLr5K7Ohl+b8Ve1fr2s47SxxMTolQEXH4Ug2EZ0YYi5/p4hS8rkGh8bo9A8MP33LVKzyuiRUYnpUco69FZJDMmBOxV+mupt9rlVJHT8IXOkPzHCKhpLbPvDdCD4VexxqyWKq1dJx2n4zGTmJjjSNdA5A8k1wDrjHjw/JAzCs2zXGgEjj3LaHlz9LsMZRTVd5ZuRX/fc6XZyoBUI+F2foRFR/Shlva8eftVYpGpgUfrxu+Xnb8GR+Q06JQUQ1VpFAE0NhKCC1+QBlWAZSAcM0IqXkJaEZpgbxWA1IrB/Evj1f8j6+/oLS4FmMzHKQg3+KGr+WqSoLDJ3JyzTDL2eeleE+vq58FI5Xwfz4PjWu4/Usjx3uQY9Ka7K9yVgRtvSik2H5/vD7wz2tBWSu2nyJe/1CRtu+jH/3tSqFYQa3rcuDj8tC6OFy4MThqaltYktnnMDPXbtHfLH97DjuHVxTqBYTze+MwTJ5BS3PlOjoqLPq/htxcbNH4H9cHPi6bZiSzmfxIYR3LMnQCboj4zBKlxprr3ec9hdbBNJodCau26YUqAKVN8prGglyA5QAkVKS46/u+sBuP5+Err1CesXX8vfs598/Cv8esdrKECHPkGnFHz+MeA8xdpcpQcGOpwRDx1p7nieY7/E/mkU9yHD2DrnQKjMpreTQs2XJoSY3eKw26N6Twt83LErK0OW1xO7LG1ihspA2iMNhUhsHnJRjVucuRaUUZr239UomM+SKj5xtDxVLpmQipIaF0SYweov3dKgrmT3hFCqUJ7YIjJ2zXBfu2IG9R6hwBqLGAtZIYY6hB2nmiQUW1BIQk8JH0cK5Y1qyxuSWJ8P6yXfGn9IqfLu9gDkYPD1ldJEDoqGs4sNWr+wznzsUAJuvaU5s5N5QxXZZzdTToACUhjI13qDB4nQw0J0jNI8AYdSlUIOaWlNqjJz9f7khrQU4VxwegxoD4+L7Waz8MH8Ugnb18FdE1CBXu0oSFBJ77HOuRmkchbeyV0qyTpVlVplysHEDfaxmwBzFSU3mOU0aqx6shlQ/9BJP6yvozP7X+AmL9HSoQX+Omgp/xAFq2AIVFdoJCLLYdZy+Bn5Vrzp2QH4cv/7GGo0Xw2hgELI+HmpDiUP/FKUZety8NzCCYPZ++wJv3THb0HtCo4MYWql4Bj+WG/bFHqiwZJbbpigrxMgjpvawnmJAJgJ6WCABoyWuE3i5BgKMu3ySQpWXBZW9t8tzFQVsJBdcoQs937uJvn7fR49NFS1r4elWAlUEnwSOG2qrwZoU6GVNYWpmRNRS8pK0T5qUGXMOm15NCwWvYFDKMTtmtIWuRuSt8fC5qvPAFLPFy4E/bqxRGPKS0PgCUEpCWjHwkKbNRAVwgeQhHQD1aCY0IgMXyWpJbTLU12AktT0JiCylaP/O/bDcpytme2WPwRCUHxTyEvS6dgPaeAmrby+j7aHSQtEcnnPHJ/z2Ffg3SV4UeB/de7po7Wc8Nno8KXDoRltbG1cVtq8WMLunAejmQwxVlBY7XiJf//q/tKUCyGRMbSKdN+wIDaIvIauh0bJDYQwuW3BMQm1DghHeuOHrlwMj+iNclk+mdC/csSDQOBqnv7aGQTUWeeKkBr2kT69V5AB6eGdtEyr2IQPaKgm1GPeTj67N4oZ8azMLXrq0T2IyyC0Bxe69geR3eO5g1BiGE56GTW9y7RiCeHtp1lnIejC/Y5q3+sX6V3quLBbxGS0hS2HGMt1TO7dEJZR/XoSDzlrs3FICz9X+Lu+LOVvtm8EDavN7LxRQdemXhiRL87hqyMLVCwL2s3Zq+xgOxmqXOjb8CSK1y6F6SwjYdu88pi9xwda5ZFjikItDyKKForSz+fw323hoy7k2hAlA65zUcUjepBUSpNHww/RpJwS74mi/4S7jh634RZXGR/iLvj7WVrgjSzzxV4FaR3xbpL5yDvAYARYru1dhqLRXmJRWkZDHOXKRN6D0v+DfXrzpnHia8hb2xvpbeq0fvRfDZ+/ibX3eEr8fBY25DjSTAYCRvvIzHn+WxWEJi33NDrrfgni/wuUIfbhvel1fkS8X28Rz3eDZ+SCmkIJm7Ugo5471dFNAs+iqVUen+cIxcdQCGi5GF8kRweyvJewqSxJZUoI/DxxSSe9B8MClIs5V77SEJZpj+tNwVP7+mA9dwaO7A6oVzO9Yo6Lmprs5y9hZyR7Nsx+HoOfRVlY2HzfSZwGq5MHnKe05jTgeVg+93bccildXmghCR/0wPvZEOyzLTfeLOzCviXGjzJGWLGOdb52mA58aYCC17wDa0wJgRxuKC1r4Cendc3P1qFl0w998Lflax1TiEh4MYfA7oaLy5vb4CiKnFFyJ6RRtgGeQFXWIjxx4TfLb4DHZcg9WqorJhgPJEcghGFfbMNh6X69xDh96AGfNi/CgI0EpsF+DtEKWwu6ZLuVn8JUrtpBgrwiuQj4hKERWrZDaXgLwFxFuBT+15uexifeeI1GTOl/2KT+sDn5Z7e1ZGuWaXNF8HS6Fl9EQC3jtlCD/rPc4xfgCQ4txkTkDnLVgODU7lLJj97o+7l9B53MxV8UNqVyX93ofLhj+miroAxy2grM8D6N1xvutTsxGlf4IsOMMfu5osMPqm3KznhPdQgF9oHM8Ug6eZeqHn8T1+ju/P2tn1uLxlTHuNTibDa9rEXW7Wz61R3ChQvTVMIddBGN01B7CeugaA2/WwFssda2fBz4LIZFn5TG6AHoRZxfo7oFMIjDnMIK6xKi3v01+vZzfJfDnvQp9hX0Jk9IB8PwQqsWuzwLkBN7dM6cL37KagXinxYVr45u6XE73UQ1vawrPBBF4BcF7OBdJcjCE4JQQLQsdQ8BofrcDixRR5myvCciNFl893DbuWMJA57pMKCdf491+bEI6oeESbu9e0WezHBbKZHxJHge4UjG9E759xGhRMZ+jUhgJUo9c+rnI9tUry3SUJzPW+rVL2ot3HssgxH+uK/X0FDlJVBUIqNSCmjFqifkd6DkD7Vz/ygr9sN/y8vmOvCfeynuSJ74+hz9XtpW69VZy9iiHemQcZInMkHoOXT8oEm8DZIyuQn4cas9G91tPz/bXclh1oNabKFSjXX8vIkPFDnsKSMm5pF2wdnlvdGEQuu5aWF4fvn+AVwl4TEOyxeNiAQprwEIX+WD56rK45sgMA2+y+ccr4gIjt/ry8y3W2fARynoVp41k/ruxyQPeeH+IZsJFLP8aN5xUDv6dz6Lwyr3D5WR97sc96AX6oYPTKJzsPyr/uWWEANMGN966bKwiZZG+Kz5/TeyRrOHQGCG34+9jqosqhz6+wujQkLvD4GRWsv5uC9eBQK9hBiAwGetYQFeAOub6xJBktRMWDUZBC6FhIALr/zTgQbzY1QXONO97KBa/pgVJjV3yRXpkGKNswA6sxgmDW77X1lPCDcQF+ZvTyxj4kHGP859xo6By09PsuI4hH1JQPYbFrOvBxfaBUKeC3xow1pWblW4vNI0fEWLB+yNiuB95+uaE+IrBWMIEtBCAuGeuasR2tZtgidxSDMJLujaF1XQTqMlqywZqMJY4xIS2lPoFyPOLAZ9uhEc6jtTntGyrFULHC1pzGI2HEG64ZwGShltsIrr/G5Dy3dCBeMoAVZQHy5Xwfs/FDSoFBLZ/N62mpDIB4beoXtYcDOgHt8elJToIPIgNnoT8mrI2DdEb9fLCWiXpeyLX/nN7xGjfdsNe449aEKf/nOZlJ7VkGZ8vEoJweFjKF4qEeAFr4zyuT8Tu8r3GherzT+PytdDDjNBNoaYSRZn2x/WtecHEOZs9h7Eg1+0wHNSJixdFZ4vyMrxLrjY5uM3fr0YLCmscQqgozXyNfym+3tYGgkE5HFaQVCSt2p14CjFrYwtQ6T5aU5vM8xDB6y1f9Pus/mdJyjC9w/R1a8M17r74p1fjc5DoYVLfMWp2/ahRmDYLSMPFeLWwN0mCgpStIfzMAilgJucTWEEbOs8RinfmuApO97QJBL0n2X4BAQzEWfP18Q9kSQqooewRCxfW6S0+TajTVLSesrdLCXhL+vL3gd8ubMNbKig+tlzfXgtxHH4/hYFtfzzDjM/X7b5QhPg41Uwxelo1lUbSUTdtTXHuMoaIK5OkL/+n7biwx43I9cEQ5dH3W5WwYf7tSCAFrLJq49XDce21AE6tZgB2bwhKixn63vzZmOLOHoig8R5zPPzxOqh8pFNzdpK4h41O6q0Kgy30jpugEFGGOFYcErsAqm+fEFioM5X4jYVQc/vg6b7UXnh4W8gvP457yPfllfZ0MguDfscUNeCxa796yp2Li63Ju8/R4LK+YPGwoFk5/zbxeeYbyGd8eVWAg22R8ZtmdxwgJzB5n3siiiuGUi9EMEt+Pl8f164KW28UpG46o5z3a9SZVRjo3QcpI8/NrzG0tkePeC3sKVp/g56/dP2fSRj2UycGYDiBC3CuGBPYWNsrvCijcqXCg8xyoYLTAXzNOzkXbhn3c1tUOF8uI2QW+zUD0kOgjL1JXDdCeDulFWk7+5ZcPyO9S3ZX5CoCV8OYxtmNBWCQYfs8L3soFPy/v6hlanEmeE1lisx4ULH7Xrdv2HJQeXdZOxsncVrTWSUBdUEDjYw7jMK7jP+NhQr0/9DksftADW0LBJR14vT3w5+UDgID6pFjlOH44eW0JY3LZiJH1Ajsid9ARIBNMi1shC4dnkprl4anxAXCMluu3WUY9F5l/ryHjNW5mbcWeeuktLwpgpUSq0pFjblg6BcVr64RZ22B67U6ZeFzd03A7D6FGFaDC5KKl2QuVKZU3RK0T5QWt723M7/JZnBk1PJ91mxrv2T8TWroeLrqFDSXItXPDeiFbmheWBpaNHNdKlxibrXTfnz33sTTyOFRQOW/Kj+KewSVkbMMhFHJxG9rTOnegO2oMRZQiAnIzRHyGOdAH2yMqXtPD4h+huDWxGnRXDfvWjHvnoY3QkYeHmO0dw9kr9bDk6AHS0ChVytWzh3NE1UZBMRTNrGZ+xeuydRUP5HMVWxSIaU0F/xI/YL8vWgxvOxaEUHFZDlMisaBC8ja2suDrcUVeo8Jynkos8zUhCrhnltqKHJ+7/57uBx6/ntegh7pH5dBlNw8ehtFd6dFzH5IAUQAkjeGy7MltPVCXipJ6tvq3xg8phTVJZiUnQ8vtul7EpQqbwMMeQJ8dO+L53mPo2EAUXvV56WsK2JnSII7sz2MPkP9XpNbDlfGK1+BzMLJaB3pcZW30NFsAWqmV35lCPEAn7AGoxUxBfHnSCJwCgm4ug61btc7BZMiIsiLtbl67yDwTuz6vRHYn+GdCfwbrAOiowz7R0Neh8ha7jxtInaGhNwJ6Ica56PJBqj0fYEwy63svyLPK+swjZE2WYMpJIaD2t1dEMrcWoI/weL/V4qfSS7BsWL0fxqFgVXF5/T5h0FOBn8GmPhjMdqUj1Ea4k96hNwa62AucsRfQBUu7OFDzgMdeaP46r3FXo7Ag4OOyaftMNhOiUrgfUm58CRnLImyqazoQQsW//PJBrqsEhBCkh0MRBlOKrf2sU1rvecVf8gt+bjDSaGiv4ZAcJSfEp7RQxNPrVJAKKVXr6MbfnIdnXgLnuAxKivPGPAvKWPtO0NdJitFktgC8rjvqRRhINf4GnkIKpVnU0TSURtTN3U/NdY61Zz6YEPBJUcU05mDB6Hnpuk7omMpCgsPgJiO3SbNqNr3GZzOevb5Mce+RnnaySBtEEJvl7YURv0+hyIXm+c8UHjN3lpYLB6EJpuLf69nS4W+BQ46TFTIKc+u/PLDEYKVF/P3wb+9eyyIlFILuc1RM3X05QW3Hw+kzkRvuiReQHNUZgAnwivZcUicg2ZtXFVfgvMhzbaHL07nk+4a338J+EhhiaSdkB9+xBzChzz5u1lZiO0ysRWteaZ6P2ysjVDfClaWtKw2woi8hc4nnRkoAlKlGQgA9Ch7Pj5HY4b0SKsYYpJZVDFaLaGnXrJVZW3YuIHkFl3So8SHJdxKD+Pl6x/X3GX/4/EGorEUorSEV5BKwJusuJ+cRT+TrcQWuOA2jTreaQn4/wseizkYf79fvGx8IprfuYT8mxFEJ8znpM4MxzMaYqiYpoipd3t4rmrDIY0YIlRcBKN9HPvqxmAL7EOeKbnGb211Vg9r+lSDW2m7Kl0fgjQA933l0b2fW9iz4nGBp+R5Hl/eM514oRANalqwEFj/Xm56DsQBqYG8ReAFM/PnuF41bFOPf/h4AqDLyFgut5hkU4q3QApu7Dhapcy9mnDsqG7GkzNPpztcE/1Qgu0AcFYfPJ/D3vcJatso1Hy2GMRP0UT0GC9AeOt+dx4N82oikqHJFMdN49Lw8HNYZANWYKuP98nX1Nr3AbLGrN74PeZa+t0NCVGhwJCHoPbX+AIQvPXxI4eENIlruBS5Jz32Hc8pnPfVAYbEZeqFc9zMv/FRvrHkNN+zY64KCqn2I6aXQ4+R1r6FgTZtSLlk2hwqjFPEaLqswmFIs+KfPH/F4LAghAknKadz3Bbe1bwl8P1a8p1ULRXrjIUPW0Ri/8d4h/9/r0q0rPnu+dq8XpGoe8JgFzbn1/1tukhPoCN1nlHkJKV/CGO6ic19wuMq5LIK4pox4yyip/jaewiUdDkow66DnvosgZQE8KgG6kMwI9owJwMrd+mP5iL/GJxxFlYsroXSWaU+VE0vZeynjYB2ekXamEE+1tp5MdKLg2moyAR6cVegs+RGjVEjBxx0GT2sOHfWQChegD5iONVwAKJylr7tAms2BCIWt9p9V5Q2nAKgYAzrBP3oztJz8tWs8YsLoID9eLHwLbntF7OMmI1GhK3I24LVekPJ65Bm3HAVHKSVVlQqHCrtnzlFZGgW2IGoSFDNb+/s71JNUj7b2SnFtvSHgrHtJ8IMaXLP4mYdQbY57lgpjQP669Hk5lpSn1FLg9wZWf3zvKcS2FyJCy+Y38oHEVcRqXqJkWR8lYnHeIiBeQ2rewmWVLG1A+jAvoeC//PlnqZJaA9CS9TwtdctSUfZRJOBMhiPpxN6Y8YNWvDKLBqQgISpt2o/U9vI+PHt9FuGceKaxhm8wL+k5MIuZCtN7CwCwIOMIUe97WbOwj+bO7mn8cKCZae7GXJDXqaEzIhYVKq2VpLt5wy57F1qqk664utLIe106mueMYUTIyC/WKe5arXGGTzYirOGT3brvO4HEhUtF4BcylQGHt+R1ITma5Vigi0rmdEx3vHGQ7TAbXVC1Ex5Lp6QoZHldzPwdA60z74WJX+Pgd/n7FpgJbp8lTMHfZhwsOg8KLba1JvPkBbPBPF5ByDXFRrOUshQ6L2glv4MlIgItCKq9n/tjmWLv81wAdAoho3l8QXIttrrgrVxFsQYgNeXia1Yx8zUGK/x3iRaPomVNo8hDcbRk+Sw8ZORjQ7pP3Bzo/bn9xJwKhLN33rG/glmzKjhrryj8GnkwkcudT/eYIM/4kB7Ya5LyFA2CYpIe85+uDal45AV//PJBaKlBEuAqhJFEWirPcy+rxox0nbR1tU7Wuo9DeuXgPWjvAViML2H0yGfDl5sxOmtrjtVQmHEefaCZlXZLtcC/TxguNWBdMx4JJ7j92fhO3fHky+FcZRLohRcvjBqNXF5ysPU71N5uc1I5kBK2Dg+ic1dxDoryGv3w2p6whv+fC6PbSMPnVFCBVV8dBo4ztKJWaBO84qkcKlhp0bKxuNHf6kn4W3Cz6m9/jL4hSP9dUdRRz83jfUuwC9vFChWOyoHn99czfsZj4bewY8yCHj/jvUV/D96j3OuiJAQvnDnH44goipsTrvQ/QPMSxjXhYgb6XZgC8MNDC/49b2n77oFUHjzu2tbFazTyBoPEUtvJPFwqVj/8sajo/XrUGBhYQaBqvMHThS8tCc57ETYfc6VyCppz3hmvYs5DsD1KWu1IVyfNfQ1CZGEXR8DXepIKrR+WB/7Dh1/wu9d3VQIsjFertbyVLnFynV/LVefL5ynYHvLUYkMmtiaHxpicwo4Km/f/+7/Pc+eSX93niaiMcJ8I/iH4jz7g7EcMFdd1R7lWTOzI6fgx+KhVCVW30eHbANTNMzpVX68m13gqI+1r/Fip6X34zKIPmBYRISJuvD6QGDuYidfKa+D5PASiWr/2VjGFKrt06fGmgnVRSp9XHIBZcd46lrkyj8Xz9BlgHf/u6t9UaNCUgnr0Nny8YaTijnMj8xP612sZYBmzIOkxjcfw1vgpF8VZ2IwPeOuJXhOfY2cY1AhmL/tr8nEiH18gL32s4srBjGiuTxGyrqkP3LrAGa6y79h7rIIbXXtYxf9Rm0XeJyaOhgaD7+S8+57ShP/82ixubfmEvBH+o3folYXfm97w6kvUZLfnexKFh430OTmPew1ZEtkcoyrVXhiy7zhKMxYW4OtxbZ5Cv88QgZ/Xd5SPAe/7IlnROWJdCC1XTWzjtbyVKwgBkgDgm+2Id2C93T011sdhxgC0RwDGe/eeXvc6vBE3j/vxXmlcc54AdMF5TYqs0no1l4gSKmIAaqyo6ft8gB8uiMdgcgwFj2xu+RotKDYbWlWx1e4H+gli0xpmnzLj1HPS5TuOe6+u29K9Dxj+yo0wWlqEHlg5EfBJdlFhGdlEWbHirkG8O5a4pedEN527zpo6v09l4YdPLhrZN4rJDwJ99NqewU7j0CxJt6GBXviO16nvOcrpmf/eB5xZpA6AKuW9rDq/F0c9psXJ7/L6POb/bFDBC6X2wBbM+NBrGp5DrhF5oJnyO2P5D77PQmvAGMeo2Kth9aZIJWZDNh3nzENBNn9UrhJrI6xCIyVBFD4VvzzXQwUg5yuiT+qbwYaeTOGV8ikZk4aUU3iEncbP+zIpHjtnVVYiCdcgJSlibLTVIJ6Tryd2jXtXUXa9Fhw/R/y///J7lFZs734s+HAROvlRbG/sNWGrCR9Y6t55C2vI0xiAXL9jqKFPArXnbEqVn6HsoTclz8eSQzVbvDW1Uor7kLg2xhVZ5oKKlPGGgiAKocmhEKp0uvtXjykECo3mttSkwdRxePfKN6hPgCoEXymSN2yJHkNyV7AU8YjcaXAVFuqt5NNkjvAGgC6IyO8ZhdG8Hy4srcTpBLRZ9+dS1H5jzVz/8fO8Bu38hHFzHaokPB+d78sxbXOOgUQftwDkGWlsBEYFzfAeziAEw/y1uUDrFc9o0XLuvbL18/BsjvyxPc7PTGPCL94D3WrqviOfGRORojvG3imzvmAgmVWreii+XEtulv0OU0ye+muC4hhgGjMq/NRxLUiWeZ4qfsCS/YC5Rziya/Te3RRT4JNVlWtfhwmwbF/eCwWoN8ZY8iVC6iFRAc8S865x7wSjxI8y1iXjrVxchznxSq7BaKv/4eUz7nnFH98+4MgRIQBbTtZxrjEkhcrp2GBI+jfhszk8NDCIfAkZpww8287HcTwF2WDQdFIsPoeL98gKtyL7RH76Ln1jgDoESeyLLTaB9BvBR77xu0xIzzwCoHicPIZ6+i4Hcd6ebURXqRcqvhIjMLfElSftMgjHz7EUgrCheneuf8g9k6e7t0qsr2gCnC8yJud2Fpjn6/tzDBYr68+MXoSm5dd4Uhbjtc0gro4v3xa2Lfqq92KlwDmfdh1sX2gVRqU5CYNy9OY8Rgv43IezsPKf6+YLJpBGL8Vj+2uDeXyJBl/h1Y+RsqoxifadrVpLVcPnLcvXewk+nrE5xeNHCgWpli4R05dU5vrbKj2j2h3HN2fyge2EihyCKmCuT0+i8NYxYVD/vs5j6JOrPAnDe35yDT0c6y1ZGmn8/Jw154T90HI1I2CNh5SMKUsrlWFxvXtY8WjJWcUJ1RgqXuKGf//yGaUG/OGrBJ4DJOB8SzuYu8CyFJ/ie2cg7kina+2QCMyRjzHIPxqnKdhciExaunXJWBmP5ZlILKHNoffdFALzrRiYHkeBKAik38JTQB/E1WSKSgZIwRIsCLVGD2vIgsiDAvE1dcY2mSyxLO+Pwr3FEwYszkfgZTJdo59mnfI73Hg99BQ6ITkmUvFa9DpD6YS0D5pzEYxWARcQA/C8f1+imptIUvQtU3U8lx8e1vGKmR6Cp3vOhLS/Jx/4JetnrHPDOArvld3KeH6veKmIfaDVW4bj8PAS4Y8uqAoGt7Na7lqmo/bHn8UasqsJMypZscZZdbU4DyC0M5qC8DxzDkKk+hyHZySv9yUPTowmvy7cnHCezRDptzM93ItTKgkFayTfvlcOo7HFY3DulVLcKMJeYMpnxoKJZ/jIQyUzpdftj4hu/68AcogooXlXLSFWvrcjh4h/G75gLwmftyveHpc2B31AVqjWSQPKEoeT9XCLuxbp43zNEmDN6/MxjgMnzwsz5lY4/Z7lLhESGqnC3LOs0yvPsWp/5lKDxlH4fMJSv5tW9ENKobgJLwiabLLGvr66L+k7RuRZdMxbDOPG1EDfMHG+WuTp+G3/MFjmWQQrDkvIwRknB+hKmrLwVrlnk3TMgfa6h71Io2SpBn/v3qLwr8v1272SDbUOm2fsteC9LM/79xabBaTNqxst29Eq5WDpjdFi9vfPOEF/P0MCnFM4qK4Mx5PB+fPYuM8T4N8+n4XxLDJMPGWU35ltYH+Oca12FFQvoJ2C8vEFeS/1z6xZirZeTakaVt37UAJjrXpuJS/A2H+XzljwsEbtYEGgp/nqcHV6OsYXoSE3VyR2AL2B5teZ4u0u/2K25qJb76PnlVqsiO+nkLGmjFtMeCsXsBKC9XDJSKngd+s7fn97x3ZYAFbvp0q3u3u9gPEohZMqOuNsJAaMa8LHNv3wOQ0jQ3L0nBhT9a18aWCLXMyS+Nuqo5JdlBqMxL95JSJzWoC5QVVrLED8jeAjoE+62AfL33ORPVe4t0TMheJQgQGoUC01ajKb17w+ngCYsB21M2CUPF/1cgo9obgF4xNYTNiPo8uqHZgwswzIcYxMEX/ck+IZPBI/Z14wdIu29tb/6TwdTl7VmhrvwZdknlmzQC+wzWiwhC//fVrgPsA5PssZy2ccZlH2CY5+Hvm53Xks4nr3ZUF672WeSW7z1q9fH9DV4zkjAoGrq09W7HMzrEKmp6D69eHLo+QWn7vFXS1gmT9jgrGMh4eRbG6YhCmKaXOYu78nwBoeeahJi+wFy4gvODe1GteeVwIjIYHvq+fo3v/YqsJ+yTdVCLwmAPhpece/v33GUSLe9ouW8/fNiC5KTiB8WOw5hNIlG+o8UXHAIOPRc5X5ljng8xuD87zHggp4Y7jGQV6cYw0pSsc+33mNJbRjqKo0/H48WiOi3wQ+Goe3VJc2GewFOy1Q5zb/Ne7KLvAWBQcLfJFuOhP6HP6hUGmYa2y0NlIa2VXrXvvCKJ5eSBx9qx6Tn7FM7NhMtLrFTRKmgstFqFH/nykNb5EQg4QTbF4ob9X6GnPzaP1//Zx5FloaYlA2XuBnpxC6uR0UB2DCdRSeFPr+3hjQB6BzOX6v64LnFQZ6i30cI4ZNT0GhvRq7c/pWmn5N+NemOQ8TpaoCzHmqBRH3cukC8R3tE/bcZ4qPBpFPcjvniZhnJsw4dNTec4+IfJ4nR13eBghs5lF58gKJHBSYvDe+Pipof5+MAUWg29scPia3Oi8nQWqulRrxVlwrTVCWHPiHi/Rm/q9vPwOQwDThGN4LezUDVqqiyzJHD/2Mv/2z8F6/F+qzWlRyHzbP9Po71KUpXcYK9hJcsFnWGmMKjDOcMtYdlbaWgIkIno7v1B3Ph01Qa7vYzizBINNmHvLxhZz0t1sA/jcHm6KMCTI+w9Nzh4HeJY/OatKgboMVuNm8lcUkKF1sMNbRDG6awR9ecM6Cyr7KplcEfH3kKnN4heC7dXlr39Ps/N8AOron70UhHfSC/xmU4pPe+D+Dgd31hD6vwc/zTMB578ZThz2jw0M4/lkKht1jufJ679UAmHplQA8JeMU0GwwQzir8jjEPXxbC/54pBA8BlqbIxrmlhUpl4WE1JsHJ/BhM1eVyUOgxCXPIL+Gxx5jPKBjH4Q2cnkpcur3NH8+c8wKYCXsjrV2Ni5DxMd3xMd11Xm6uI+LPyzv+4fIV/3B9wy25NRWEvvlWrl1cgc8K6OsgeYOSz02hocFY2YY15eMt97qeAsxjbIHzl2vEoy6dF6QVaYMV+pMAvP3tlYMqv0JPC78dfKT8W+fKeOUAWOzBWxFixVsnKcYV5POO1eInsEaUhqly+GJ33vViWYE+IUjw0hR74XALe/dglPs+COWeX99bPOOghctr4vE64eqYLuekrGKsj2B4vYdxck1dUxq7tv6aPbNpFszl9XXQzSBEgSHY6F7j558zoc7KqkCoubx/XqexgJwC8NcOek4Oiqo9M4nKoWfB2Rpk1VG9/gFCiijYkLA6o0BdeacEvFDj/8zA93M7pxuflUEHl3kF0KxaCmo/7+PQQDCcghvIBF28yV2aWcc9ZbPLYSBVuZ7pqTwfi+h5Q4gBUu7j0Xvi7xEBkL0ZVTj7+GBCwS0cKA0Wo8DlHviYHvi31y/4c3xp12HCcpx3xhO8x+i9If85PqcROppVH/aD1zfGJnk+mTvbvYTcfU2pRzMCWdZiL31fC/87u/dCqN9d5uKHlAJxLRZosrZwEaXiKXRET0G5uGDyhbldtPD2suAWd7W2Rg+iq87osDmg9yZIvRw7a0X2TnDCc0bXG9kBnUU8PFwK+VGJdNTCKrDVs0AzFQMVFO1CxjZmXsiYH0BBPx6781qCUXIZ0B4tUk8VHa1nSfYbqJhOaY6MIUJK5ok1OmlgiQoZO5yyCD0tdGxm4xXCKKj980mh4K1cp7EdXt+oFHso4Oy5+fNZoNDgBf+8GFj3x/fX1z+b1M0nBdZs3Y1e18jy4nc2V6NsRca9rgobMebg50OupwK1aN0xryS8MN/q0s3fWI4FlffcJ6qOMcgVe6ewvCDuDaqCDQuubf/e69IdL0HYNv/D5TNiqNqrQe7N2oz6+92QBKrjuVz84Nk+HZXnOIhkkFHZeQqDoefREwaZGS9IKDjQf87mt+Cohsz4WG6pQe7yOxUC8HfIaPZjtmnkdbo1rbl23MFsPVnsfbMQrVAaCtZozeX3mqS3cjgHYeR65tYTB9kKgGyQcZN5LLlTAhVdfwT/8GXxWzDaC5cTlAKjMxLyAM5WNjc5aaWjx+KVFwUuN6231mZjFFhjYNQfl+MWtwl8YDAJYSYfg5mdk4MUScX7h+uKKBZ7oDcwxG16i6x0MA6D9lrgzgnf0QP09+Qtan8fKbCqaj0pnURPw60XL6TJYuP1W/Mkl2n8jQ3bx3lW3OCK9DmlOdt7YwA9wWIeTPBjGZVbq8aqgXNYLxAGx3W+3brjuIxGULC+AbxHHzekBzDdx+jjJ9448Oe+hQ17XaRrYzHFwOd2jTtesSGi4k/hFe/5AlY+UIsbERcX59Ln5QzLPPzfPR96nyws6YW8o5PSyO3uszO++v3P3746Kll1pDgf1ZJPASl5cdQoZbObF8FSHwAwbMun48c8hUkXIJa8YLadT2jxQR7PGNhrbxUCfcCqSwKjhnaTYRhbb5XOjpdooaq1c3bhx2vx782gEwqak4U2eQojzXNUDJ5F5Jk7/py6SJk8hF+/VoWI3PDwls5R7d/jd/k5zeQePQD33VFg/NocdNfsvS4EFzBP3Wse+x6puAkVOdjrDGTmNs8+GOwV2ki1FcFpxdw41ArWZ+YEezuPwYUBe+0VOQI6IcKgdKew6Uk4uEHzMSrEy2xelxciHSwGdPuASsBDWrwvKgTl5dex/lJvnbKkhl/z3pPgKGBdp6xwbs/M6tvs+u/5wVIeMsfDWgfrnQX3eYsn/ry8YY0H/tvjZ/ginrTcde6cEcAYwez6ZgwzrzjGshSz2BPPH91zlu/0nnwZIK+j0YtLJePTZLAvhjdCSiHgt4GPfD4CL0QuorWnG/jBrIpI9wiANNwZM1WdQuD/c9e9ryMDUJhVTa0fN0yHMzehwTZ8/P4MDuqwY1iCVOfa8u9B6Hs8/aSE0HPfiX2P1pj/e2ShjIvU47kegvKf6QWbu56JQvSWqT+fPz8x79n7/rzy3jlQx7nu4jAweK3DYuGCzQOkQA9sxvIxxRVtnQz34b0dMR5MMHaKW8/ZkvWQsblojoeh/Nw/Y1WNc9EZMxMDRRWFU3AI0BIePobgjQjGhVY4VhvQM60q4HuUrOHABfb/OPye8yXj/T1p74zQVyDlmHkMs+HLzQibqlFzYd3jxDAoGn+IqPgU73isK958fTZl4bk59Tkk7XpFkRkbb1SE/l68kE5tX5ByG33c0hnG99aPmzA6RxdfaH8vIbfS2AIZybF65hHZSloOqAbUit/OU/BMIo5Sg/Zp9oGaUoP2+J0VH7Pv94EoJo8RlztlBVPQ8br8g3FaFzhDGykIg2cmCPVvVOBJHgGP7XHvCCuM5hk8vENldAzKwM7VW+++TSBHp5jca4DAPeOcjp9R6x9MrDrXbFILE/QOPE4+NpoxZTLGX8Z5m1uTPeMqBesqZji9h216wkFpm9vKCQxxBQg9UwPbSE6RyXq5tfnIlbm5vSfMilV5WLtyfDvPeF7eI++d6+QZldnPk0+y/LURIZTUrfZd2sXqTcg14RLss4x3+OGVP4Wr3UsPm6XQdxgUL+bMZPKd5tBgTv3Ok8DvzBgCrGYXjy/xDIPxmHUvSoJQmyjCn9PiymKY0tXkRgdRUcbsJYkic8l/fEYbzkSWkakU2xz79eiJDl52jnLUmJ0JDLSXATKS+4g4cgTpq0dpFVI9A6kZDd8zfjij2VPbqAzMEmzlKmrVSS2tVgcAhY388JZ5aTetLKTBCvcunWavPnGhRxiJWbIJFfe6alkGw1IN+5fjS4LLlHnjlIFa4s7+Hjc8oQWvJNRL6FzgA95rmA0vzLWWk1cyoUwFth8+EDl7ndfIe/ZQmY8xEH5JbdOZUu+hJgaZ9Ry1V6w83qjcx03pB9lYo8K194y3z+Qxvz7WcFjAPEgjHk8ZHuty+eGNmJNidIH1Z2PcA+N19cHIdu+OBePX9hjb8PktnDu/P5JbVzPcu6ORoi98qPW3XCkMuefhXn1lUcSh/td8Ts7rhM+yLwYp54sALD7iKx142PRTekdGwFu+qpd59hQoyIP2T2AzI7tm86g6inHtYZuxdAkAjQfIPfasTMLqvhge4wmW0GiQVIQwig56BSXgaBVi2WgoNQprrWGWszodf7fkNaZaG64llSFfYoZvpMEbpCXoh6WrfxuPjjBWwMg+GhUC/x7ry1CIe0zRu9A+mWTsz1swT5x7JrhHITb+Pwr9ESsWHL9PcBnvFzAlcoIWhvOevCCYknw24rApfBY5v++PR7iHCsQs5Pmz9deUqwVEPWuLFN7x/sdz+79pqfnz+Pk2uHDpXlfPz1nNfRDaKV9a36EgDc/IY90jI2uk6fK8yvZynhiHv0Z/Hi0+2LwFPw8j5LXVhFswKNLm05oWybXYvHuY6RxoH72yfn/4RDAPucywebkmi1dw7/rjnWNI4rX4MjJ2rAO3KNngF+z4XXrDa9xOvcfpMXp4qIsV6Ryd95Bch9RWY5lsP48kKdzLioJgbYYhpTm8PJRid/IcHk6BGPW+dAUBZf7k9xIKSgiqOAKv63sTFNr4uymF3C7S2mxKETxNLvPPqzYoIAhS5783E1h9PZ+gmOlY+dSqsU7c+5bL4IU+4aY0nLuDshA7CuQoHJ+Nb73nF9ZIJ/TffyY0TscbNu4oiMfv+eNqYtTo6TS47FnA+eYaH/nn1OOgPbd+jK3wei+D8Jnd4+j9zBhZhBk5yFgDnlMH6UXOvDJRsBaLGnn0PoZwul53jT6Le742B9JDOM+XH0/zO1Bb1v2QeOdgWMKV4/rINZ5KaRBeIo1yTCy1ukXtC8EymGeDge6t8xRqV3epwMpD8HXfYtYXmeRcdaVvgsFu8lrsvJsbdg1Cn4gWTmmnUPHmykf0c+K8Az5D9eB8PKIPZrNfQk8iccxM5yV4Y5eez1YtHsJBss+Yp1AbFTU3zwHl+xXD364Uqrlzj7zoBTJxbdGFIxjXElu11JC7ALVRugzr4wLca1KtKscqzUsILu3dEloup2Cq1ZufN5fpoSR/PRwjnvvMUvdWz8yTGGmQfK3HTG3z8rcKhkFwzBLCiAfL63n6mWdzwGsnu8fTN2dwjX7feXa+IiwFLOeVFv7s3nSOvPKbwGm83o76OHgHneAOcAKxgKJgFMxcL2MwnxBDfw4vsPvjdO043W9CZCP0yPnuE+/OxASfx+EhLr5+Cn5TcHrva0ZyCGeYlE159Nqd1zDOP487Dp+9DJy9LACndUFBXPpL7FhEOnehdPfjPXz5vWBvx8k1IkYyDsXr9AQG3xVRPx8YhzADVxVePVdOsBpQfVzL3z+vk+fgXJZ6Niw8/KWWfjBoSdCW2L1fqnRbYwtS0lFDqMglSJmL3wI+6upttAj7Gopqu+xuCEXqj3D4Zjtrc6NS6HHbkY56Or+HjpyXYIt6pNH57EOnBAbBO+LbegwXhPPHkPuwRikUpDPPw59H5uHM+R5dfo/Xn/IdYBU7YxN8/JuBYb12N8bA7bPXRg+DAmTG21chNoGhtJwGrPd290ydFzjCLaOyWBvffTy/fHaoVeNyCIARLuljPN5bkLyPA7EKVZUb/oK+h0VfnLHg4q73mTIdrVO/BlhV1T9XDg850dq36/D5DAN7zSlHf/6IiJmkGGG0Keyoa9tK3KthVC1Za1QOwNg61VUwrubp+WxrX9qGcQy7tnMtsntl4LgvIe+TRRlLGGMKJ8UJYx7ZffT0WVVa7np9DbcZg7G0+MVsjVBmeuhnKwvYr0balfaGLushpVhQivSRyEX+D6ECOfw27CNe8BIz3vMq7jrQNH5ADLQSXJ5CiFID3Qt++BofTnE4JTEqi3F4hdBDPBaIBpyFViPWifHcBwj7iqB8f7QeZ5DAFAJxC3JkmYyBWLj7mMFLT7n+AwxEy59sqDHWMs4XrfuZgB6tszHo6AX+7Fp9kpmSB9ArujEGMJ77W8fvX2tYMyECWLCOx/P36RPB5DOlu1cv+Pk0ItBlvY4xKyry6I4J2BpTuKQ9Gwr9MeYxe1YeevRGQQRczoQF8ikAu6B0E16+zAvX9lbbc50ZDhR+A+wywl+jITeNFVbOVx+b8IrAex5noX2GdgihZVhezawys7+nFIRWPEJDIxTGUvwjTs8Oc/7zZAuNhACl5NPwbIKer0+Pj9rmvSA2T4EKQj4fNcjsC+GlIB3YvlchAH+HmIJucuf6LaGoS1OGlGt/kyMFi4ObdsQxO6pqsAXD4fn5xiJqJaCDMTm4kfk5sg7IbJh6CPxbH3DuNihwhpZYVfVsbffH8ptdcd4BbugEp3tnJtT9IJ7dWXIwC9mzVcZ79YOWyDg0QSxY1vls3p4Nf1wK/BmsZPRUE+yjMvKCHOihNu3xrfOM0+8uIc55lBf0wt0PaTxvQnFmSPC9aWG7On82M0Oj+0y1184GBfRztFR9batTjGi4Zu8JUQnwuNxjfD354wywklr1wTGVMApT8y5kngz+I6VzNp7F7JKTF5faMwC5tyKsltTWzElTBi1XQb0e67rmBb51dlzaPgpThcC/Aajwj80I8clnUdfqeHxPrOg9haPFCY5iymGvEXtu4Ga1KqooAd+ZCvL3LZ0NoGFbbcXWJvzbhc3KZwM2Ifw+szXH4+rnneXWWRSh31gcI3sBeF6QDGCAEe34rZZ9s+ZWCJtqR+rOwjyKHlrorezxPHb//Yb2OLS5+uiawvM4WneHSpDHdrc2lnbw5xnnwUNA3lV/NnwA1mcPj3MAnFkj/rfvJzF+Ru8Xtfuc/+xo2avSrFZvaVbk0A8tjzHxzMbr8AKUyow8fCqhEUu/OAgQmMeZRlYX8O1nO4uR2DWacusCzzgLe35HIcTOkLHkRC9cfcC0uxfnMXjWkicWbIPyLzj3FCFc9MxLAIa+EqPnyXIq1QtrK0njz+VhVjbekes2eJrXxCKgPP/oTVgmfVCW0a1BnntZLQ8ExiAyiKlnC1E5sDbc2ENhbCBEOUtoqdYg/aqP3yqm4CLevgIhg7sseeErmXICfWkLxhM0ENNZKu33ANlwWK9bH2hq1wcJSwMS0PrQgky0mCiMfemGmZfgXXQ7bj9o4Y6B6hMtdFgEfqN3bKRqFqHCVcPtew52GTantx75vZGBowljQ/yCx/MCl0LfC3OvEEal7ZliXnCOMzfChaxC2peVsBaqozWmkJOoow6W8uvpVGKgKVjCa3K8BaMl6+dnvC5+dqa8xqFsLgePKbTjz+WORXjrAuvjcSYclM6I6OIODjoar2WmHPtgbezWRXaKgNcRG6wzhTdr7OJ2/ljsTTK7Z5ZKnxkT417ySsTHipJb55IsNzcWtbf8REmPxuY2eA98lvZaf4+lxi5JjXRUTeTV2E+PmLCaLK+PiMtIlPGylz8eOgowhVaKxHS/Vyl82yT6tS87oZ9CxRIZO2h9Q4PHnc8bxQeb/Wf9xub/3tomBS2Gqgkm85tjDOLQGumluY6zhezdSJ53NvwCHLn9FOxPg4xNcZy9GYOieHwqKb/Jn1FMx34G/t69MuAxrd6+C1Q7xezhoFlDo2d48fgcffKhZbabRWd/x+71Z8Nj+zy+n4dZYPPXjklcn8cY56Q//xzTnlmL4zX70iIzSHJsS8qg8cmQQM/M8pj1zGvwwysEbwT1cbizZT7WvBqZVOzp4NeYP65Y7vK6NgFyc7Q6dGCWd8L7n+0t3SdP7mU8jl6nW+/+82QPjpCR76Xg+0UAvQGye6XvjjuDzH3JH1aIAETY+2oQdh7xAgqC9mTm5wtMMRw+o/lonkKdy7Nx/JBSoPVPT+EoCUeNqt1Gy8rnFTDALALKLAqOUYAA5rb3guecEJWCNZ+wc/dJQeMYqad0pUd8lQ/6FnbFY/31yvlNmI/QEVsdegExnjehdo0+ABMQ3FzeIxm7wXHjXZyFrdfkrovzMm4OVUrt2Xg2j+9cR+X8rTFa0ePzBHzD+EPPaec/umfsBf8Mb+b7fpOydLG66E75zxQ/BW6u8XSPowVrAuz5Ndl3I0Yvxiv5W9hN8E/en11v5xW0MQpiL6z98Z+RIdQLwFnJMPPf74WZocLrKLC4xYzK6r3d6X01KNMrFX+Ps+ufKUd/br2z0cMLY1xkBo2JETxSTn3uBKEyvxZ87KCgjxWM5/N/m4IRtIWKwHsIR03IRQLNfM+PY0+Ivwl8VPtOQKWVaxUXNmBpG/uoCdISRdrJxVAR2/9jolEaJpGb2zNPPLyggoICYFBCKfSURQsOp27RkJ43Y+bw7zGGwO91m7Fd3zPW0oU1n9y+7mIQNXalPGYWod0bWSLpxHPn728xNfSzg5BiEP8MwUzghidW+UgT9TCTf1+Fu15Pj9vOxtN8i/bsZptNP+Oul8LNY8s+mG81ksL5uQ9wlCRFBuWXy/dczKSRIi7I2CaXP3oN3bk8XOT2whho18Hv60tRsfWeXXWmu47ECY3xEG5ENiE/8YQ76qpjPo0Qrcx//92uVPZ4rOEc+tnaG2re00huPXtF5+93vO4xzjE2zfFUUT8/es4B4fAxFzknv5cUJcnNG/eeSQwVqRosqzkJsGZmPocioiKEiiUWvO/WNwIA9hxR94jwW8FH99a44iVteoGiEIpqtjwEmGeBSwpz/x7hgQ7Lr8Z0mQmOFKwYF9Bbb1rgCiY0xuCbf83YSz3W7q9JXfmmqH4NrjgpAL3O/nh+aCVH9FayKp0JvDFzw8cGRR5+8bEDzi2tdDlOz/0fLSwe7xQf6O693yyj4vDvzZSD3O/580o7fWLx+78JPdADfOYxRBSti6UCw8FL/v4olC/uGnqWil3f6FXwufj5G+Gr8V5m3nJnOPD7TzwBepv+XhWWRQ/PeI+gY8ehnmBTpcd677Yas270OmYCmp+hZzv1IJynwPP5fuTj8N6RH/SuZ/cynm9GS+UYy6iz3fBelm6Pe+iLrCMqCd+qdQw0k2lkhm097ZsxC1qZRyVgOxZgjwjHb6EUasX7sSpUdI0HXtKudFSrjCj/X+MBNriQ/qvfZrWMAlCO1eN4s2Fc9LkVyw0DjIE16N/aS3XwHi4drGH3MBPMHFOLPZRzLGIiCPx5vWVoCT7z73QYdDePpVOq/flL95s0TDtmX8LgHFwtJ8U48vPH13mc3uo+uu/47/KaaHV3NXW8MEKfCU2LbYwdjM/fv+aP0bNXUveah6ko1HltojDsfV67JlpOYDDP0lHh7gySEV7j67PBWNO3hg9Mc817T2BGmuhiI+59z2SjUhop3rO8h9FD4es8plcWPn7gS5J3xtpguM1Kl9Mwmc1PdoZJx9pr8PcIFY7rDZCSFuqtzIw3NSpECXhGk1cKy6/ISR/494SfFAtSrDiOJPGEHLQKxa+Nv10plIq3fcV7uWCJGdd0tEBzaZVSzcXxrKNIqxX2HtBb4vLdsyXJQI9PQX8WXBuFlw8A8Xt+jAvSCwg/nuHY/bl7q81b9J2L675HPH3E+H3tlJllzWsa2Tp+w45Bs3FRi4tqAXuZ56Bz7a1xxhVGrF8pqYMimP3vA69eSc1iFN8DI/HavoXnc53o+kI5PXODNnJncY9C+wQRTBR/f+/PvRzP5jJ6btZ5GffHOMY4jB3b4Alayx7fHyviPvNiZ397T4JW+ki39dcwrvOZBzN6bWUilP35/c/mm+UgmIfjn6EzAArOpfln9ODxb/ls7GJDWta+PT8fCx0hthkURvgd6Mk4ZB4dNWFvsdq9JC0pBABb+3vLqXkGUVlHWjdpT0AOiH+Fp/C3xxRKwR//8hH/9OEj/sPtMy7xkAqpacdR2HYzY4kZS5RNv0YLnooFBP37dPjBeny2qYARD62doOqtlzNTKEGa7CgWPHE1kxNkY/AceI6tyzU8T9BjDILXSj74+DlaIryPZ8rh+TWYcOf/4/V7q9a8hL6ev/Sw7YvCcYOMwv+ZcCfG3l+XQSIeWuE1dJj74IUB327QQgUnR+LfJijJmdc8lHBIq1bCE+18LG8R3Xx5S/Fb50fzbGy4e3RWrJ/DEYLSb6riOFRAzT7nGTAWlzjHAnyMwq7OzsHhEzF9cJjeqI9ZjPAlx2x/pWHd8Htx2I+eaq3377wZn6TJ5/xMkRLu5fFs76fuXHqNsNyEEQb196gU10kcga+z8qkvdue7uzG+MO5hz9T0ENPRFAuPSeUAAPuRkB8JaZdlFvP3eQp/s1KojwfC//MD/q/lf0L5n/5/+J8//AkxFFw766viGg9VCMo3j5ZJ6zdWh3V2cED/IEbcEsCpbzMVAo8NDDVXGl58CblL8+d7gHN165l6ymuZeTQ9fnz2DM5xg+cbfGQ18DUmSH1reOt5hH/kfRPIqfucZZ5+y/rmMRiYjqGHl2bfHQUq52YczwTt9DnVODWDqLAYOPb8e7j14QOjMh8We2KZaZ7X50b4+zwJQEF2T/c9rvOZQpwZQGPeh0+Sm33Wz88O9/90ns5JljOBOmMZce4y0slb6O6TysLNMQ25WSOhqXBu55rdi3oWYN7JJN7ojC4+v7G5k/1u69/tGZay4PHude28DX5fyAt9XolShkPthLqWy27KoYNo0ed/6b1qkpoYlWyoQ/hIiuBFPPYFeAh8FDO+O6P5b4aP6mPDv/nfKq7/pw/4P//H/wX/tz//j/h6XAEA13jgGgVOGjOZLRGpp5z+WpyBlu5MWYgCIK01dIJJvmuutN14jweywuWMVqd/N5d+pNsSm2TtfL95R2ExozSOVMUZe8czXohdzzB8D8lwwV9gpRqId3vLjAFPwkMjNDN7FuPoWF8wSOhZDKi3DF3pY3dNgEEps2Aq59PDa5wbO3ZQpTdCSIQe/PABaK90fMCbr/nz8XnwGON1zK5tHM/iNRwjWcCMjBlEZ/MTnfJnPgTfG+ezmzt6AaN34aAR7hnfVIrDwzp8v1M84cwK8mMWzOb+3FpHPn8ufx6ghwT9a9xLY2Xk0UDi8Ph/n6tgMN84Iqpa96MHQRSF72X05WHYk4Y0f6D3OHgt30IMjhyxbYsohAOIOxD+tT0FlILXf3zg9seItF3xn/f/A/7yv9zwf/zdP+Pn9R3XSK57PVn2vgZKT4X0mPS8AiaPKZ9pwvjJJc42kLm5bYIqF2qPMatbHc549WwR+IXSXXO1sh2KXbpjMrt1Rtv0g8f1DCwOryxmiWbPLMGMgA0JF+STK6wbBD1rZvQEnsUrOCejwGLswSv0mTIYKX68f690Pfw2zpn3ErwVZxCSWLfMoCXFcW+vecgJsKC2X58+I5bXQ09CjQYPJTzZxKNX8QwuteDonMr9rZwREYaps7bNy+6VlfY6hpDH/XVS4cy69HkjLMMKDopH07+fwlBtdmKQna3vxUE9vWXPgPKMKuvXgEKCqpDasRusw8Q6ztk5obWoYtAqBvT+Bg9K82Ic08jfL9AHlbXTGvrXPHHnWc04+a4krwHAdiwoW/MSjoC4AenxfUGFvz3Q3Mbln9/wb//XOz797wl//M//gP/0z/8Of9pe8Z4vquX2Yppyrwn3smIvS2fRzITcbBMZtt8s7PbjG3r7MRN2nn2k1qPb7LrJwTIc5h14Zsl4jd5y90rFC7JnTKLvuffRGn+WMOWt2m/RNfk+MG8Yk1CxIXUCnNc2ntMHo2eKqb+u/j7MU7C18AxK9JmvXknwf752Ypzx2O0aV/SUU89AUtZWW12jdwdYHo0f38qRGO9lhM/orX3re88MgWeju24PGwYLPPN13++Cr3e5PAPZwUNJnuo7Wubf8tLV6Apz+iyPT8OsK2UxQlhPzuGHp676c/vz+Hv1LUA9ucXfm1JQB29A5jeoEezru4miCGBOQgysNdZDRZpnBfEYrAaSxRP4Wm55YpeUkUvEvifUPSJuAWEHlveK+Pg+/OjHCuJFACFg/cMbfv8fE8q64k+Xn/G/A/iff/4z/s31q8QZ4gEUYI0yOY+6YA1ZuhDRU3CCkhaXQEqGtxvkNGSRosDXZed7FLozfHK2aEbhuSNZ03q3YGjtEk9XazhYwsl4rBOk0e7PW9xn2KsMTc59cPMcLPZz4q/Vd6761nV1Vg+4eex4hKDmjesZs+hx0Zm3wLoxdv7+eF7gjRCZf90fn5YaoZ6nAf5mKfty1YwnjMHM2Tw98z75/9ZmO7WjzNhK4z3CKZrpNcPWld8jo3Lt5hA8pkFF4/35PZBCOVX0BQbGD1rSn/Nw/ffV2q9mbe5oME+AKtr+3PPAN4d6JN9Sfg4KmpJFKn292HkTYzzBJ6xyRBo79LBCPJXIBoY16RQCYMlqERWjWO6K2HHPVFPAR2XJoKpKwI+9pC6TuQLYjoR8JIQtIm7A8g6sXyvi9lsohTZCrbj9tze8/PufcP+HhD8tnwAA8XdNy6UdL1ES3DxNlYPaVN63DQ70rJyilrFLEqpoTUQKEGQCtQSysyDUoiEsgXgSenLMs1cxE8aeqTNa2bNgoh/nJD0Tml6xPBP8/OwYNJuNkc0zQkIclgNwfs9DN/2x5wlZfl5nMJRnhI3HHK1hroeZwIyh4DI4QTy3z0fQ153g7/B+riMe13mOKhQJd8LgD1ECERdYWYPkNu1eE27hMPE3uTc/ns3zuA5mxAQPz/k17QWdCsqK82uToQmck8+M8+r3rPdERqjIKyQef3xWPK6/Ps8UOwetW1kSJMuk9tfaBPp4z/zumLAKJLCJjafGEv8HzEuNoWhP5TgYnzbfESMLSed4yE04GQ2oXa9mfvZgYDoUINr378cirKN7QtwC0j3g8rkibRVhbGv3ZPwwfFRD2yyPHddfCtavAeE94ZcvL/gvn3+Hz/tVCz3xh9QrQkkAehfMY8bomUTjmHkBfnhcsuu65QS3DzKtyOod+BpAM5onj+Pf21sAbMRc6f4/o6/6Tf4sOOu/M+YFzK5tFLwej/aQSjdPo1JsAnlklPjfs+OMn+nwY8Sn/Y3pGXyL8snj+esag6aEY7xHQvju0jxQnxg1Wog+mDoLwnJcJrBCdxxYDRy/4blunvWf8PCgz+vg/PX5I73iZ/KlV2gzGMf/r/GWQQnyvfE5e8bVs7Xgx7hGxvn2xxj3DoDOE5gFtcd7Gn9GReEzmvkdei+lmnHZJ8tlrQU2PtOx9hkHYwA+8OwHPQTfgnMNJJu0vvffWF9jn5r3bcW2SRZzugesX4F0l3hCOP61Ywq1Aj6aXSvSo0iUew/Ij4TPbzf81y8/40/bC74eVzzKgqPElgZuAZXiNs7YnMLz5/3QAKd74GmAkUbOv7dSvCvLv/sAW+gEA4/P66ElT6yzc+W/gZECfeyB5x+Hz+ztLL9BqSQUZRedg7p9605+Zxxewfn/BQqxY8yE/yh0OSfPFEV/3jNtl8d8xtryxoPNVcZlmKMZMWDsE+1xZR9f8mP2HM1oqVgHOKkTDqgnwe+DxWPQmNc6zvcIxX0rXuPbhaZguRZGsHDrx1nBDNSO97s32GUWV+liNRPrfDTk/H7jPPo5HV8bz8H/u/05M0KexApY2yii7x3uvaGCs4c+jhEi9sbHGi2ZULtJqrImZBw645hDs+WdoTmnpBJ6rN3v+7Hg/liR7wvS14j1l4Dla8XyqIhHBf7VlQKAUAU6qiEAzWNIDyBuATgi8hHx9rjgn94+4U/bCz4fN7yXi07GvaxdrRAfpJ15C10jbcw3rN8wY9o9cLaU/OiCce17l8BmM4da58A5xuDZCH4h+3sYg4P+GM+8hvG+vHU4ej59gLlXBhRQ/vP22TE4aNAYYMphFs8Yg72MK/jXOLTByUSw+TnzAXgqUH8vXnCOxxiZOT747GEDnTOn+J+tjVEgKuxQ2RZxRpWNeKb0ZlRirwhHCq6fMz/3/m8qA2H6zC1pT/Ecy0s88xq9QvHXyvfHtd154M7w8uedfc7DjJeQu6BwRui+p98ZrhuAejvj+3tdOm+DBAIqTn4XgHoJalzR40CP6fv793MwQoNyDvneo/Sl/v0+KA1mIjFHvmtso1kLUNJbc4l421YcW0J4S1h/ieIlPIC0FcS9IOTfOKaAEJCvETUItBg2UQpHjthywl+2F7Ga1gewSk2Pazwk4NcmLWEMpkoG6Zxm2b/GHgkMOucakNDooM1q8fXqpxZhW19+s3CTzaCdsYm7F2TjZgGGYHCwAKJXNhz+GF4gzpLPLFg5bMBgam68ppm1Nr5mG9eOS8z+GUzAeZhh489KJI/BWypJrzz9Nftey1ZMLHTfnSnA6f1+h7Eg35OAPQW2fyZq7TnsPA1CrD9We20IHI/zMsa8RkOhaAB3sO4dh3/MzPZYvL8WUla7uZn8Pa7LMfg9tmxlTI57WXH9Yf2M8+Q9HO8dUNGMBpxCP03dbS62kFCxD3M7UmL5/XtdG5X2DAHLdfoSF0ZJRT0bjF3veVTEkPGoltHcd01rBJvCshmEvYNCUEdjGC0xK7MTAXjkBV+3C97frqi/XLB8jljegXSvSHuVbOatAMf3KYUfiynUqj81BJQlQOexAuWIyDliOxL2nLCVhLdjxXte8SgLHmXpNa8LLHqtSzrYqCn9Rr7FvTGNRMNfiP+5TU+KKV9TZkQbXcG0iTXqg3n2mpyPgsgXTHtGW/2eMVIW/WIE0PUNHgXwzNUf/x4tUeL8M7iIf1Mwzyx0f161zGG89pmQmfXM8ArB/x49FW+J8142xC7OMEJbMw/udC8D7DBTfp2nMYG8nmHsM+IAr2PGsBrnbPSwPGwpJUhq127Sz6n/mXlKXPeMqXlPgnDLSHRgoqTH2JnA2d0L5nGAZ5DRjBk4Qr6cE01Gg+Wg6HHdvjACrvPw6xliBoz8QcJKGu67Z871hgyF+l4WFeYdVdZ5IGMhPDmXQUJd8zL1ouWzS8y4JOkN/flxxS9fbzjeBDZa3iQvIR7tZy+IW0bYv10ckePHPIUKUwprQlkgTKDQ3ssBJUfUlnxBt+iWWV6gYq0Ze0lY0xlb5W9hGfH/s3J4NqjBNfGFbuOgCICs1gStkkuzCn1MY6RXesiG4xnGyu8gnIXDM2twVEI8JhVC1wd4IlRmv/Xvk3J6jveOgutZsFyhBid4nr2fEbXhvbf2Zx7CGA8ZYRR/LTz2qFRGwZsRsJW1UwK8Rt9/AMO8jYLawzXPYic2r+U8NypMbf37XiLn5+RjC3IcBu2j83Z5T7nGTiCPdYEug8U80lW9EF3b+i3V1zjq6bKSYNauy1Fc9Vp8C030jDfWL6LH772B8TXGAXzmuc2z3f9Mqcs9xVNvE2UptXvZXH7VSGjpvAw+pwEd8AohhSItOV3FhSUWLCgSa60iqzx0dOh3K0rrl+DHUSK+7hf88nbD9uWC8JYQH0DcIUphB+JREfeK8NiBx3aai9n4YfYRAKBUYInIl4CSYIoh8O2Gv2YplOcr/j2K2CXk9nohMOLgs2GeAGGN3pKgIphBF1yUTMcfrYnTuZwworXqmTIzhpH/7rPfvWU5w9qtVDSVlcxrPC/OYczmbwZp8VzfEmz8vMfqORhsG63o0ZpXYeQ8Hb4/3rcPaI6fsZyH1MUN9mqxKd+AnWNWR/+ZNzB6i3aOdLreX5s374HNPksoYjZ6r3Hu7Y0MJX9fI+XSwzL8nGfd+e+k4HpLNE/MP7PZmtXzc/5ozAyxDnonqvR1bxk7aTZfY37F6NWMY9MAbqPJhn6P+5wOEkjEiHReXu3hahpKz9avXIuDN9WgMo+PbTP5WU9bXWPGMhgkl5hxaS2PAeBf7h/wT798xP3LFeGeEB8RcQ8CFx0SXI5bRbofCG8P1PvjdI2z8UOeQqhV4eZ8TchXWLJVhWphTm0YikGxx3Op0pVtdW7SDiB1gdSoE+ldMMVMh+xD+TvAiwSPO+rmmVhWaXgY/hqA55YqcE68mn2/DOeQY9KK7hcfy1CYV9Jbj2Nt/fE6xAKbs49m1vO3sGR6XrPPj4Hy5ISPPx6tqhlk4q+pYK4o/FzNrmOcb6PgipV7cecBgNwE/Kk8yaBsS43IwRLSZsJq9toI18xe97ENnm9+rGclQdraqv11MJBKlt5sjM8c6PM0xnlVZd08hjzAR51HCpwUO4+v709iDHpOFE0gHfMbxpIWM+8CcEbAxCsgHf6chW3lYrQ0CKDBZn+do9Ex7kMauxHWRndGFmCcQNcaYaLA7OeANQqqsuWEP75/xB8/f8DjfUV9SOZyat5BUuioIm0F6euG8OUNZfstPIVSEYpMwvFhEU9haXNf5eghCi4WW+W+PrjSY9pjjwV5vW/ufroEnAtqzah+wIgNzt3MM3/6vGDG4QPI3nogNmn01bOiGAWG9xx8lvCzcz5jsowxiWff5f++fARwLkc9QiceDx8Lg43ejx+/5vmN7/tYwIhB+/f6IPo529f3mtamKy5G4jFyHntMMuTrpL+eYwZB5471vc5xgX5eZgF5f6zeKv8+mNEfn4PtR0f4KKFq33CgF5BGmz26tTsKPs8q8+PZvQFmtfvgMX9GQ2JkB+l7wZSXv7eMoPFC/5nZ9/laV/Mo2PqRoDWzwuPwDMJJWY7oBgPLDP6zEupYlQEQxbC6dewD1qWhK7/sN/zj15/wz19esW2tvtEuHkLcrRpq3IHlXrF82RE/v6O+vf8GTXYglFTUCsSA/BJR1nbECPUYJJ3BUrnlhltNcLQmEp6fC+PuzlxqBmY02Fijso1GC/X0XWc9+/85WFbDoJC5BcHN1yce9YLWX6+3in0CEsfJupgoCBFGueNEe4Fs13EW3J6508dqvsGMmfw/Yv3PMf/nwVm/ifj3zNqiZzQqgllA3QegNQal8yf/3+uqSV0eYrJjGRzI83uigPe8ZtdAgT9Tes8CquP8bN+wmn3LxmfGQsdGc9/lfftjekiVyXzAEFtxymakIc/iNH54ssE4Dx6+eeZxcYxwLo1ABonH0h2jov61MVJdbU33SpcCfWaYUlGuIavwj4EF987BasowH2jeyoKtLNaPub121Ij3vOLLfsU/vv2E//rLT/hyvyLniHJEIAcgC2QUMhQ6SrvkjaVfHqifv6J+p0IA/h6U1FJR1wXHLZ6hoxxQiwjtOiRhzCLqADDL+pPP9PVevJIhr1h+y+dHT6BPcrMSuaNi4GbY63Ki+9lmbFY/+vIXtDb95+SYVr6C//M7gNWRyYhWX+gbAsRb+Nx4vyZ4iAX7zTwmWiGcA5veoj3FMobXeP5RMfnrZSDXHzuF8z0ziOkVfC+MI7wiHoPUoxDzxz2fp/8MNzZnxzLeW3B88JrsmsJTRTc7px98Ps8EKdCKAVYLLnvF0BtDApURHhnjaaLYkja+1+tyz8dyfIrWqjJWDpNDUzd/aVgfrBAM9DCOr5FE6unM4/eW/TNG0vj/mGjn971PWlMigbumzsioDi4enunI6BuNozX2hfuK8xJ2t9dZ/nqv0YqHtr83FsCrEfe84G2/4Ou24rGvyDniOBLqLkohsFdCabDRBiz3guXzjviXL6j7BpR53Gc2fqh0NkoR5tE1IV8CagRqrCZXQkUIFUeJuALw6dr3vGJdzoGPbjOFArQsQC5Izz66nBpzTDZTjXabGtwoWm7bFqlh87Q4n3kKHM/caLIwdBP5zepGl32KChauMwHfF7N7dl5+3x+Lx/kWds9BFsyMXeTH99Bpic3TaqKy8Ti33a89rw1W0nh2HgrBWTyBAirXCNSkjJoxTsDP69/ViryN73c4clsfxNDRYh4+/tHN05Pzdcd3npMvtz2236SS8IykZx7ZeK6Lg7DG4C3HDFIdnz/X8egxnK9xUbgpTqAazqd/TQ0wfz4XG2BcYVwRXomM8QT1kJC0Gu6s2N94PA6W6AZkPVO5+vkQYZ702fi5G9lHhlCYYuBnSMunh3DPK44inthWEt6PFfdjwWOXn+NI4iXsESgBoQQNR6UdiFvFcq9YP2csf/yM+vYmRKAYge9MXvvBmIL8yi8r8gUST1CFAI0pBCesSasiD1eSOmjxS1CGf8vnRsGb9LvedST7iMOs0EGwU0ANi9PDBTOMdrTyPQRBVpC/XuKwhEDysGBnbrOcx+O9tjBHLHeGtftjcIxWzfjatz7H++mChwM08Sxw/T2QCZXE3BPoLesLrOS4hzbk/Tn8NmMJAegsxOTuT6+jbWQvrLxCm83HPCjcxw98nGLmOYxxIH8fwOjxWNbzfG2csX+P2/tgso8neUPmfD+9IeHXQj/nc7GSYLWn7DotngDYM2FdKj8/em6cYxVjvIDfJcPJ7sMZJm5+tgGq7ujnoUzvyX/eP6NcY1fTTbwFg4uUYIOgJBvCR0eJuOcVX48L3o8V7/sqyqBElBIlAyAH8RKOID+1xRM2YLkDly8Flz98Bf78WRTCXzl+MKZQgVqRXxeJJygVtXYxBT6+5Hi2vuco0ENIu9PqXpB5BhJHzx02PDgjTFtocvgsz2mAqcZuc3ic8Rlu7JvZzxLcRljAW8vc3M+UhR1nHnTV+3JCeQxq+yD0uNFm3sEzj8G/N4szPBvj/c+wZC/4Z+ebxQK0ebpX4i6OcjoP4YL2Ge+danxk2Bq9tdd3XPPX6+Ec/7q/H99lcOad+e/w+N5L8OfmMWbeAwPs471TLfBaeH6fDJcRnhYt9GMWm+L1nODJbn7qSTizRMVYc2xUGBGlb46Es8Lj/uZ5mczqFaBAZfOezP7v8X2LD/bP8JkHZt8TJXCUJLHUkgQuatDSUSK2skg/hCwJv7lEHCVq85ySE2oWLwEV2lUt7kC6V1w+F7z84xviP/0JKJkP6XQt3xo/5imwvMUloqwCHwkCIBeMApTiNmoxHA1wyRmwCoS+Bnlxf+sxJta7BZ36gOe4sL7lPo6b1wcsgd7yn41nCWn2uw8e9se21nzciF4gPLO+vZLgffvgch/gPsMS/N8Ldi98/A/f/9bczRSFf5/XekHRn/GaRgx9tILHuIJ+r/aY9PisR+hqdjx6CYQjOmqmw5VnSntUtqOy4Ri9tRmbx49Z/IHf02ue3JfcW//cmG+whgO3sLdCgtaedR4nGWmwRkHtAvA1aovMGWz5zAjgbwp7nx9xeoaDshhL1vjv+rXgy1X4Eik8pp9HU/JFv2vEEoOVvNc4e4Y+wEwaKr0E33uZsQQqhEeW4PLRlMGRxUMoJQh5KFTUEoASELeI+AhY3gPWL8DLv1R8/P+8If3jv6AePbQewvcrhh/MaK5AisjXgBrQFALk7wDRZmj6oTWUpuvkJ26vCVcY+0Eeas9OicGCoBYwLt1iSqFnf4yL41uDgdMcmnVVz4J+HH4h+OJxVB7PgsYj/OEtjTXsunC9cPWY/LP4Af/3f/Mss++Nf1OQrSErtn+KPeAs9L/l2fjj83O+B4EXRKOgnMFY+YlQ1mPAPDx+3itXnUOvaFrMQOY5aACULJdveZy8vzGpy9//6RqdwuY1ecPANzHa3BoU4Wmfs300V0I92UGuha1GuT79uXksm/dve656vwNe78tVS7XQmYcXuufCe8soJ+EPzGMSXblrF2PQMuDtum8uJjnSYFMIuJdLC3yP5WLMaDUigFUQoDc+rm+bS7+2DRFh7+W9MmE3KHR0NC+hAtiz/J1zlNjskVC2BOwBYQuIj4D0CFg/Ax/+e8FP/+kXxD/8WRRCiJDIM4DvCyW46/6RUYB6XVEWueFQW7Z38xI46PqQgcSYAAC17oGB4ukW2bXVNdIa+MwKDNbMY/y+H88yVxXCGVx3HyuYtaPsXX0TBH2AuG/D+UxAAD2mO16nF/oz6Mj/eFd+FNz8/JjXMA4GPjlG13gU1ie8t8bT/c6olvxfYIpzXGIWq/i1QDeTmmgNe/jEUyRPnov7DNcDy0iP1moM5QSLyPn6JlGzPI/xfgCoUB4V7kj1/ZZw5nH897ukveFzhMC8UvZe4UypXb4hWWbrdYR/uTZZPeCZFwycoTugjwP54aEwvj/SU7eGOszqHPljzpAET17xczl6BjPoiNA4y2R7lIS5B1u2zmm5NiVQA+77giMnlBJQSsCxJ5Q9oj4iwiMivUUsXwNufwB+/592/Px/+SPiH/48ZxmFv07M/zAltdwW1GQKAUCjpDKoYB6Cz2a28tf9BsoNi+QiWOMuDxSyAK5RsERP+1xhtZSAQWi7B06YQdkwzUIkU2aG63rt5umoCGTt0KXuPz9b9N576GEqc7et5vp5s8ziEGdY6uwJPBNInnU0w4afwzZBrVPvgZi1N2TrDmOmHACXMDfxTvz9+mu5l1WtROaYnOAOQoETZTbGKrxHOvNW5smLVTp+gSyf/nijMhuV6swj66A392y4Psa16hUIvV57LqVr63pBbpnIZ7KAn5tx9OuLxlAPv9AL82s08vPhPO/+2Dr3LVvZ4/0z7J/jW2iAKF2j00puU9Lr9L2VjXrc36MfBtGWUxCbz0qL4rlqtVTEVBRaPTXUzkuoAI6csB2LUE/3JLXjjoh6Twj3hOU9YPkc8PpPFb//f7zj8l//BOyHti8IIaB+wwj9tfHjSmFNUh01NK4s0JhHEmEOrdKfQUeiHJaYO026oABVKKcU3Gswup6ejyVlBzbQmKcA0LpYJKMDXLjQtHlTFM8Dar1lbtiibdqJdcHvnASN4c7PcGluZjihMApkfx4/ZgqBg4E/KiAAp/+765jcE5Vp6ubzecB4do/j6wnzpK1xzI5l8ZJDhcrseczuy1MhKbDYWtXnmnil7+M9o0Lmuk6oCv9472a0zL217OfCs1imRkoonQEzU/4erjHlPC/z7q+Nr3uDZGaAeOMGTun6fUQDLFEptnlUWGcQ8J1nW4UqPOuYRsHNOIEPQs+qntr9W+E+/55XOp6BJB7z0iEZMVSskLWhhf+eGDAauwgFmsUcgvaN8X04BFJqhUNLRHbeQc5R2UbhnrC8Bay/BLz+94p/+L9/xfLf/gzkIgqhVqOextDDRn9FsPnH4KMI7aGgg/uksZAYT8hFcLHcOLiHViCULx8uID263h5ekezMiFvYwWQx7+IB/WYcRwqtnspQOOyZQBoVAtAvYN8YfSbwMyxhxePG47USIhgtdu8djNDHePzxmvtYRT4J1mdwyveO8dqeKaVnnsHsGsZ58QrMXyepvopbh6IZ37zf83mt6q2HmPgehT6FB2nHe120BtUI39n1UjBbGQxe91gskPcQJ8pj9AJmCtcrBD93owLyY2Qsje/N5suf39/3GCs7kyyM8dMf66z8x/gRFZ6H7nzTHJ53FpCekQvkmIsp+eF9Mp7Ge9R7Gbqb+ev2g3O+l+XkDfhs+eTyrDzzUimrzWMojZRTS0A9onoI6y8BL/9U8fv/+CYKoVWUmI6/EjbSe/ubvgVI2nSMqEsUpR8EQopHEASlAMgWaK41aGwhhuIi8HIJ13hgjVldujgIm2d4akF0Wtm0vGK8w0LQY9Yz9Q0wjHccFCj827MweFwvhP3Cf0Y/pNDzm3IU0LM5GP+fQRT+PCNj5tcE/yhoRkE4Xsv5vL4m0XMYaaak/GefdRDzAT4aB4zhdHzxDsayICGFPMdeF6O1NgjPM1Y4j+w34ZlxY0tEFi/zCv5Z0TQPHY35C6OQfrb+/eszz2IKjw3jmaLge/55eGPkmTKQe146NtIsTqTB32qB95PRU/ugscLKrnWo38esnWRF93yMxrD7bq9NBLxcQ3GMRlOAowfovzcea2+ZyYApAf+9w79fA0qlrJTP1RyAPSLem0L4Y8XP/68Nyz/98t21jP7a8UOeQl0iyhIkLaF0npTGFDgNTGALg8ZdWynYR1m0lviM/tcH8vrsylyZ0dov1DHo5EfHaXcWkN8A/SZwC8Wduy+DcS4X/WxzzhbV+F0OzfYd7mO0uPzrXqg/swJHaGDmoXTY9jdGgljIIujsPsb2hd9S8P4zPuDthedZOcXheZgw9rWTOLwAypM1oBb1RHjy+H4ufCtOb016xcHB6/L38oyxNM7T+NzH6/CvzRhj3/r/2fMYKcFjnGuEZLxxM9ZSGo0Yft+u/3ye2fCF68bhLf5Z+QyfjAZYf+aRfqzHwzhXZ+9rNFyeXRsg3SbZm555CYf7vDeeSwla2ygckpyW7sCH/5Zx/f/+2RrmeKop/+bvGPr3v3P8kFIolwRCeDqfXnYM1+NdbABYQ1F6VgwNEqjWW2HU6B4zJk4HQCGErq8qjLue0Hds8sXAAJw20tgcvrfYfp0Fw/P733puBz35DefPNdvwADoapxf6nvHjoQue55nAmSmB0Rt5dq+8FsYDxvu1ZzZXYrO58YpvhEK8JenXCbPJfcYzj2EwhykAriH+5nfGiqgUiDIvc1hidn/rIOR5vJEfzzFi/+Pr4/w8UxrPxlRwTbyLWbDZQ5vj9+x6zhBYdy6FhQSDJxOIsFxGxKw2kx4/2F49GS5cA6EX6BTyZCsClm/gYTx+j2qBtZFUUdcGLw2GB6vpeoU6m2d53RsQ9GaqKgYAGktgslpuuQn1iMAREfagJbGXt4zAZjmMIzBr2XkOXV7Cb5W8FkIQpRAlyMw8BaBRUiH/lxL0MUZXGC+itmALN//ZsixOeI0LrsP4m5tHV087OzUXMyN0DT06HNFZLbONPFp3s/IBfsysN39MuUcTeB4e8t/3r61DrGKkcD6DWcZrmima2Xn9Z+ZQWlXvZbxHuSazmkcK8uzzM4t4pjQ9HAbgVH5ghODsfL3lPPMQ+J5/Nl4JMRYxzp1n1I1z5I/zbB6flb0YYxDmQZ2f08wA8XO3D1CYvzY/RsPo15QOvbHoBPdMydE4m/U8lnMtnbF1iqsMMK+PIcwC0QCe7nEK8hGO4nc2Bz+mYMYk6c2zagV+vtZ4qNAfjUsGl9lfYYxT6HWUILARvYSWkxBybTlgw3N5JvSpLP7KUhd/u6cQBTrinBI+4jMKFeop5NKfplRxm3yAmLkHfqJmsIdnjGRYIaw+df/Qz454I9AHqlhW2Neq6ZgQei0HRotRjnVmaoxjtvmesUtG6MnDMd+yDkfB8Mw7uOBsyX5rjNflX+9w2c7K7xsp/dp1jhYwgJNwVqwfPcToBYm/rrMH1gu8UTlwjHAOBxlJ/EyvrM7Cexxe4YxWZn+dc5jPhM/AnsGc/utfm8FJ31IIhvePgfqzYvfUVK/IfbDYC+pZ3SK5r+TWrO0/egs07mbz0nkM3MMODjIyiDMGZvHE2ssAXsdFFZ6tTY9IdKyjsnRGi11rORFiGE84dF00hmYU4xk5GCM+ADUFHB8S6vqENPo3QEWz8WPw0SrMoxqBoC5MUwyl/f3kOlkMT2IKRV8DzoEfea+AbKPYchv4sMxdzFN4J4XS9YNlY3J+V17vLdPZ5hyrRAJ9Us+MOTSyK4Bv0A0n45ng8J7DKOxHhTAeb9zcjAU8+zwX/rPP+DEqgZkVPSrQZ3/PRheDcdAS58Nb0/05z4QCO+esIm7t3p+uK4XYerbJ6B2OP3z9mec2U7b+czwvvbXxO2P5kNl18DU/RrjIz9UYyxmPP1M2psgW7EiauHavq1rkHlb7Xmhtdl3juAQfHyT85JQjYhdznN0b1wyb7PQxo0U/5+83hYJb3DFmMxudPWopC//+UaKWAVIUqNWQq6miJqAsELbn9YLvHn9DMPpvVwoh6Ldjbl5CZuOddmTFzCyruSg+LOViJTpvdDT+ZpkLbzl2weVvXPq42E8FrZoa6Whv6Bc1N5r9PwqEuRAhJGBW/pkb/YweOEIC3xLCo2AlpEQBz9/Pjj97n8edjVNhtQl8QdbROLzn8Awu+7XYi3zmLCTGvzNkXY1eXx/UpPC03sOGNxtE6M/rYQe/Xjv4ycF934oVzebu2bP2yu5ZnOrX5nN23JER5+dqVFhcz/71USDPFM3M29AKtOhp2vx8DJYg+ow8cXeXuZQAAEPsSURBVMpFoCdRo3aRywhP4SoAXayRhuEl5NP8FfT5BM8834TayS9CRHJf9F4s5uTh1eoMp4oGufvz1KCKIV+A/Ok6t7b/TmykH/IUdDRFEIr7ewJzH0U6ER2t/DXQb3pvcRVYogdAmps9ZN9LgXWPZhYfhT+HXwQzpgCt+rNVOI8j+I36LZ54f6xff3jPNt8zZXKZCL3xfS9ctMzIxAIEoIvXH3sm1MbzeQXBMYNXnsFPgHXc42uz+RxLq3N9eOE98/rG17wimNWj4vVk9FRlzsnMexjPNc6N/39UkuMYlcbsc6OBMHqrfh7Hz4xxLq7/Wf2t2XOgMOwNH08DNXqo0McX7YJ3r9anYPTcfF7BCNf5wDMAR05hvaWB+eQ8jhjOmdF7XQwxmKIU5jWIYnjO7vKw1NiZkUaD9GO274WWzyVJaxExtmepwVjpUVMTcNwC9p9WIMZeCQxxBe209luzj7xsoIcQKmsfBQk0Z7lRLXXRNCgbSXiry7fpBOQBna2F1sjaLQQOnyRikf5Gk+RDdUwDf1z+nvG5TYDNYQb+PIOExsDZ+N3xtfH/cZP7DODRspkpET/oIezfwLT5/kx5+aDxbIzWFL2Hb7GQxnM/6w/d5YU8metxw46wnR/e0OjLNRi3fjYEAllcsps1SSGc5D2x8fezY4/PcLT+n3lVv+YdUDF8S5g9e/3X1ue9rqdje8/Kf8/j/LmyReqclaUlbCYB7JO32Y45Kgv9fChdfSt+h99fw6Hxx9kckOnY1WzrDKi+lwoLevo1SvTDH7+g9xIAh6oEALEC0SoGlQSUFTheE+q1b/wzDSbXYorjrwg2/5hSaBfbKYQM7QYUWpXUMLCORPNLPEGutxfssQkGz6wRYXF0n7ObMGFti6kduzEKfHNyAAN0FED20WhJeotqBlv4zTuzxAALbH4Lq11Dv+hmkID//QwyeAYJmWDorfPRw+Bx/fHH4/ogck+5E4Uw1rjyKf0zJTd6Ev46OG++MKFeu6tzRGUyzrFlkS8nYcxyBTMocpxfKiWOGMops3ec82/BRc9yR/z5xuvg7xEifAY7fcuDfMagG+MO/hnMPC+/9seYhN5Ds96lXPdhLTHrrCzFuTy9xRLPsPCzvAC/hvKwL3cMpXPASrVSc0m9ooEsMcKgBne5ALQG1eW8IzvNr/WF89DiCSFUNexrse/wtmsEyhKQrwHlw/V8095z+Bua6+g1/s3fBIDSQ0ZwEBKfQ80tEaPKR44acWgRqqBCYw0ZS2R523ASpMQa/fClsX2sYaSX+s+O0JEvWAX8Om+cwsELDG8p9AL4OX31fHybixmkMhPW4/EuOAe6R+vTL2q55m9DEvRKRsHtr5vv+1ap/jszy7dXmmZl+3OM/HjP+pDj0+t5Tp/0534GFxp8xHl/BhVGTWoc40Tj/I7vzQT9qDhGAe+/y//9fIzzOr43Xsez/2fe2ThHpGj35zjHbvzneezx3ukxJLdfPNtnHOPe5DUwUOz3sp/b/hiTigDDM6ThYMrMlEHv6Ro8Nq7RcY2N8+rhJ0/HLzVYZ7XaknyjtTcmfFQTkNeA4+NFIKRSTQGoRhkUQp3P67PxwzGFUIB4VPUWusJ4AMDsPIehzbjdHY1xsET6yY8gfZUjBdvMI54JWABpVpL3okHhHjMdrXC+5oOPfM8vVm8BeoU2WhXj+DVhPgoQnzQ2g4zG/2eejYc9ZhBERuiylH2cYVQAHF4REKIaBYMvgzF6PeM1cq45j723UJ5+xz9LPY+zfsd1xbEhdcyS3lO0tTWjro7z4a+J8zIjAjyD5EbPcHxvXJvfUhbjd2ZrYjzHLFDsr8sPjR8Mwr1TEMECumy3qTBwnVcJeDbGchcz2qqHl1bnnczuQY/RnrnEHoL7/PmaxLvIIDNvZgB6cozVOrL/CyyPJzfFgHFfBSgqU5YGIb2knppKbTIrnc3xrffc+HGlUKuWuYi5Ih78G8qxDcG8Au+KsSgeYDEFIYua1h0nWx4ClQetfss6HXsekLY4tR5+JSB83miW0MQGJd8aPmj9zNrjoKD1/88268zq/BaE4M81XsMMBhCr8SywvEfh6/18S6A9uy6rI3O2asdrs8qkPQEgDhuYgnbGlKFgetYekhDS6J14hWDX3mexjoJ1pqC/9cz9fHCOt2FbzubpW57GbO79dY0e5Uwxj/fK3sv+OGMiWD+/cwbW6KmnQbn267oPOPsMZUtIq3q8WbtNfy6VDUNQeVZ628+dDzTz/9EoyAhaMjuiKnQk82TraokmvxhspkEc0DyEjnk0XFhsiuEaUG+rKYNxMJ7wm/doLlViCLkiHtVuoPY/DJ5U7w14AeiEjHRqMixyZDb0Gc8WaPZt84BhMzahzFwFP2ZW9Yw1MquyyDF+3ucm9Oyqwf11gtff20xo++ub/f4WXDD7+5knxOuQZ2ENx/2YeQjjGK9nJuhmguhbYxTOVBhlUPwzoe09QX/emXXcXecEJvKeoPf+ZlaqH7PnKec8P//Z3Dx7xs8gwNkY18Izq39UrDSEvNClorXPmmE2Qr2cV2145SxzAOot8Jn2CtczwXqIx3sDzCAmNERKagpWeI+DxgAhKMYdeTzPjJI5MYPWQ9sdrBxYNiO0mKh409co8ZAI6Z0wGlMVQC5BxGUrDxEi+l73ocFH0SCkel2t1IUbdaIkZq89G3+7UlAPoUqeAn+al8CAMypQW+NpL0wOFoZCOAd0/v/tfdmOJDmW3SHN3SOyqrurl4E0GElvetKL/v99oP8QBEgzhZmurqxcItyMpB7IQx5eu+YRmZFdJUB+gYB7mNtC0si7nLtQJrhnplvJTaEyWwwmjlrC4sZ1o5bQ2hNUfMbv7TylzOGlUhN6b7voVVDesgrsGPAedstGe71lWlazt9aBtsnvx2Bi+mfbdotoGdm+7RnfHEQwtP65Xo691os6ioYJ7ZlunATHguHM5rNtXL191hGthiEpcfy88dC54jmNq3Xj57R4guIIatQ+U9BxLGy46mjXDBXdyh0is2YROippR6Gg4z3o+3fKf9yAg+Y+xilvYUHpVVxZ88iOoedTmCPr9ryGxTkzRll3rZKaTAbzuFcVCLQUSkbD4pt/tvkXSGUJSN+dRigqoaNvkKvwNktha5ZCLqPMxYaWpxDa99CHOeXmTE2jUmDdGGfemW2yFoTxEndkiNhwMM1CRCdOD2kTq8LLU4j9Bc5wj/oOvEQbT2jpObvJKovZmqiWcSjTV+ZQtZyxGB6dSBbLZHXCk4ns21umyU8/go0w8qwHMjRlcPobGdpROKsHdXhM+zWMyDJpTWDjO7s0qIHPUy2TDEOv5X09AVAjkeyuXXsL7Mg6qffwLTsLYVryFAYLH71kXV6Qd8mMNqFUj412DVxfyRuj/mwdTwkT74lnZexfoWuPz9TchX7cEQy6QU90ntnbFUZWs8Ji+/YPq5nRUBpqvmBs2NP3USgj3yOXmqi7GIUqogzYCA1RaUo0gB7jz39LAEoMyAuQHpbqV8i5C4TiQUa/Wu2jnBGvaUBItBhK6dnN2hNCSGvSSUbpysibZbfIuShsJA/TyZXqBhbjBS+YncsJYYqVVvNVLQx+MtplMOI5Q1JD9Y6gHl2cWori0uKeqTHzfAtBeMzGWghHWv1rNEdP69d76nO1uJ0VIPb+av3Yonh6jrWGgMqInsp5giEUvlNFoT7jdQ40ardujoA5totrF8hk5E3sGT+/32LSHJcvpSO4R+esJ0y1/RY+8qLKqJHzu65BnnfuFhRDX1O3pucKxA7zDnMkoBUqc84DlaM6F+ZdD6sld5ENpKzwqfN2Dl/v9zWCRd9xVTrHHNa1z3bw/l1pjNvkD9UcmHF8n+nMN7CbE/w3oG1WI78FIC8B8OogqT/hCyOPgDcIhVIKlqcNcc3DUhBHc8jNX5JCa2MdlIIqFa02OWkhZoHqRCZWV6MWiCcW1L2eR6ILMb76+zyJ9BgwtB5OrBqBskyL32rqXkiikuLcqpmpBaDM3FoJt7RDj5F6v7Nt+t0m4Fjmb53ItS/HNYUsvZTYxnvxmbbUtFUMNCpllDBWn8EM4ZCh3WLcjHmfI9hmgWMtQR7zkrtsufJc4qR5e74GC7fZ0N4j7X700a886z2T99VPtRqVjoQK+9WVrQOH/NiDfA/7aRkJL3PYK5Z35Js4sthIul4t7fZNME7vjkhINNTRxlsahp5LnPyhNqKyt93MgSWMPK6O/pT21yD4iZq1gAiUyyt3VM751X6FN8FH8fOKeM3dlxAyau7CNucv0MnMgk9TuVo6bDA2niAm5/ZNBrpXP2ww1JFfQPFjOqF4HPCcbzOG2a87MP+tBcE2DAbjWxH2fz3vKDHJsxis1q738spNKH7NaxUm0tBT2y5e7wUM2OfYa2xfF+wzq/fMTBf6cZSIQjgeQ9axPAoa0PdNK/FWeKSdN0oaMnzrvPqsOenvKNeh9nO27PgM+xySFez8tMzf+mNskl/CnECo48qtXnXvaNtfG42k8E4yCqFNhLti2b2PHXKAsRHTWBdjHqxlmQS/PrOX2G9tYjiqjUoaAnXUaGJJlmwES71v8teHrCnyvZq4NlsO1UJo30uY88ACapXq8wJEYeN167bp/y9xMtf2vYXWDcvHK+I1I24VPopSFE8FQ9qqDLWT3y6CU8zC9OdkGas1kmxJWl7T09ZpdopTixPgSDO6pZHo+WfBpj0tkrBLXTz7GvlH3z34Qb97OL/1S3hk4SHeU3/XZLIjyMnLXLbC4iWrwVpDT0528S3/ARfnLQx7tNlGlEmcuwgWfmrJBBUWFirUTHQ912qpFqbZW5Z7Z+aRMDl6LyocPEuA80bPsfdRS6tClHufgmep7hUrbW/sgrhbcLIG+adbbGqf5v+H9n4E+Wp7KRw0ezqGuToqMBRHu7+Ctn+KfCxDIPCevE9W3lNG6H3NeN6/F/LFJYyM5i4QSgvYocCIIzEYAcjnZY5AIlz0ldAR8FahACB8vlYIKZVaPrs0GKlHJAWUFCdroba5DhY98dyvmbG7t7a14/W1AyPUbZp0hHpadAEXuabG253d5vsPvFrvqSF4MeTJOXkUB1/v4+PJ1N4oPPRavd4e57mq4VsHsmUyFrJQUmHjwRfz2OydyXpf/r72MdxbO5axzP3UjdzH/hle3SmFjVQDtgJHY+55P17HEgxeYbzR5330kMJvKuQ8hsvzjpIOdZ7Y92QFLBUMy+B53KMRTjln/R6953rOwW5q3Wrwrfn5HhyTuX7VAoFyOV5lOJytVe6Vv2ZlWx2/w7YcCB3bXqDtEhfmWk2ENceaGsetr9Or3dXb3KKPtrz0AqHAbPl1ylUgFBUU7bMsQAkBZQnAElGKWARuHaTXWwtvEwohIFxXLAIjxVQGjGQgJAC9zkcpo/DdKgwYGI6Z3SYYBy/+qLpldxYiTlYDNZOLvDwvbNH7TcP0NLPVa5/V5l/S4u1CsP4I3tODc+x9+DwvSuiWg9gyNI9xeG3wBA//twLEEwRqBQKDCYwxH9om72EhBEvKMFWws2S21tfi/5oAxet43rg+4xxyZ8BWmFcNe0TvqACwY+kJejum/M2zDi15Tn0VNHy3VoDsNfK56q8NT2ViGWEjkgelzmHiM/xEupalKnYH7/Mo3FXLk3h9sRY2+6PvYi2nSWDUGkhz++hY17aoBanfkyhDp9iiGhGmPRSAiopwszFGZpYy72JcllKjj8Q6KPyLAJYwV0K1FgOAnsn8yiikN1sKKAXx43OFj7bqaEZhcbyAkAKw1c6mFJFy9RtYnwEn3lRuNows1imCQJ2gGAyDmgMnqrePgoaj7k34/XBoCWFbBkMnBLD3A6jmrtq3LS1xi6ygsMzAQklWy/ccx95CsfQa5rWHaObIJLU89BwAE5Rmx12jO+Y2jYmusAYttnrtGBtvv2r95HM1E1r7deRY9iJ2POJ8sft9e2PuWXhz2/3fb1ly+r+21Y6LtVaAvQZsEwH181bf9v+bqCYy6GBKVMha053yRm2z8TknullreR/JdC1zQTwvLHUJZVImAd3db+St0FqlwOS7HpByvd8pJJxC3X3tFHLPbD6Rh8R5XpUUqpMZrcBoQd/ymKeVWK2F7lNwQlG/1J8AvCkktQCxNig8XRGfU4OQJJEtAXGlWBvWwrhFkBLa1GybhpX3GocXO83/rWTXqABgH3Ewbd1nNM4jyIb/8081KA8KsPCB5xBUJuPhsvq7BzXMfT5mLEfMn+2Z8G8VKuY3bY/dGlL7qnCK9p/X8P8r5lIJo/2DIXMrVKstara7CgZtj46jhf/43cI+alnQieq9G+vo5fe9o3bW0vUeo79lZ3lYf40e82ClW8LkCKay1ouO6VSqXCFYUcJ0rHUdxXBc5wsYvpzHuA6GXuJN2JjOZpLOh1t+ElXmRh/mIokadFLLaFNgjfnKUjz6bDt3tWT23vKrDmyNPtrEmufbK2jKfwCQh2VAGnBSQIkBZYnDh3BkDeT8ah/D2ywFerpzxun9Uy2MlwuWFYirwEcASopIW/UZbDnieTthyxHXfKqTXgZ2y23StUliNTwmkgCz74FY9JEmfOSc0ygHXucJCQ1T9MLf9Lses45I+3zF3v1JbfHVvFvseh7plqNXYQRtS8J4F/p9Hq+9vyDJRNeIJj5L/1chSg2wjs2M5+te2/U++0lNTZwwDbW1q8Mw9NkekydZ3F/Ps+3XZ5Ds3PDutR+H8cnf1OpTi+8l6Mge03bwvR8d177MTHdY4vX8GTbRYAs7LnY9UMBPpfCd6B0tVWGTUrnWPbhqup+ZM2s5ISPijKoQPIZ18h3Ya4ZVwPe437NFFYn6jBo9yXdFQbvJLpNH0Xs1eS10cyCU0FGXegCVa8cmHAKGpaDQEa0ESWorvwp8FAeeFT4/Y/m0SYZzGTDSFloJ7YhtW7CmmmNAa2ArWrtc/AOGQc0MbEjxiNxhJJ7Lz4R9aBmFjZqvwN43oRuA2DA5+hOsRrQ3X/2N7JU5kWmuJe5qBOmkUU3ylq/AIwtD8bt3XPvhMT4NYWVbqDlbR6e1GKxGXZ8zlyJX7FbD/7Td1gehTIf3viK2iCa/guoRk+Y53hiqlWSFm747qzjoXJosWMdioDCg8PWcz9pvPW7hLR1zTxFQsol62hfLKL3reK5WDlUrwrt3nwPBMGaBdFQQaFisCiMy5f6cMOaNl7NC4ragUwDKDbbInAX7brtyyfndLIBTpKAYYffs9wQXteN9TwWiK6F0xVqV7H5dQOXDL9EXZDW/MvPhmEoMNWQqZ5x+ecbyhzO2d8tUKTVsVfoxq5lltFMelkOOsYduxaUgNqmnZbSfyqkWtQuz46ee1+CDUL/rRh7ZxKXr5ON5nEBaNG8SUMYZfgQFeFq+54Rlsbl6zxG2eoSNApgYhEJOVms/go5umfM9yxJ1dzxPSFjS9pNhUgu1TlHbFkZzaE2pmVkOQTCcmuN8TZSymDIMkzpq/0vMUQMJeK5q8jZCjddM0Sch9/bU+bnfB9gbp3rfY6vAWmj6P8ffu86SL7DjTgjUNs/QzVEOx4KCpcFuOpbT70iTX6jft0h5CnFQn3d+kPm+R6HJNozWKolqoTALeSTF7rfu9PxJ55Ami6VCrnz+8FFwjV2lqsOW69HswOudeJjCIZnq2iHM0JH6Erq18Prw1Lc7mtkoAOHTM84fN8S1IDYIKa6hlrxIAWWLSKkmmjGRrU7e2adA4bDmpZteeyx/RDIwD+EcUk9is5t2r+V0iF2qSXzBvMD5PMBfQPW6fYjfkRmvC1YhlyOGrb4Ij+Eqru85F5XoI7jVF91wnNfod2WOR9aJ1aYtDa15D9HNMJx1ZIowB0sqjPpFyoT0u5Iyqamq5g0NWu+fEHbQlAZJ8BzVHm9Rt2YPrDyF3eZ++GVJ9HfvObaPKqStJV7vo1DabJ0x+sizlnlvatUWAlZIdoKWzP82V2Du47yHgyoVVmmsbRl10GghaI5EPzeM8jjT/u5GubOwETD7FIZFU9ddjUaqm4nNVkP7bI7lKau5USjiYCa6pFbCjaijXzd5LZcRDhUCQspY3l+xXFu5iw3tM1SHSRkVU1MeEJLCRDWxRAYMAU/5vAvtI2kSEovrcTJQMDBXoWLkcQp9UwFQC3H5WqbCCjZBhs7SGb7wMcNbZGEFCxMcLWg+x2LQRxppnPDdJmi1j00wWEat7fOsH2VU1vLR63WsPG3R659ufWkZkMfg9bd9dJPvW9pDf3OUkucv6trnATxi+30rEunI0juyPr255Tm/vWftn5nlvDnqiD4frW80cjo4D/ZQXmXEfqE99SF5YcWElOwezfrdho9bv4WOXQ1EOI28JbVQxVrQbGZNdp3HZG8tRGNVqLVIn+nWoWcy/8oH67tkDlqYctFCg5GQ0c2DIEZb9StQMDhhqLUBu/G9Rd/OUmgNi5+ecf6Q+h4LvWpqDkCuu7ClFMXSGeUtakLHHPPcBYKZdF7FTKVuXYQ84ZMAdlbEkTZ35MTyQvWOsFyN01dGqoyLfdr3YTAqe71ea5/J+/HaaVE2Adwns2H+/F99Obe0aBv9ok5wq+WSKVprYBaEe0E8/reaamhO5YEzc27U32fmwfsr9McIpHkntP12q+cDbZhjcytWX5kkLQ3LsD2YyBOsryHLtI6UA1Ve9grH3odwC36s7d1H4w0H+suCeUHZwTUz09+vd+vvAPZbYNpnkR94MBLQEmFDkZL8cxTS/j3trQpGKnHt20J4EbWsxRJZI4l72WOUzQaaMBiWQvcrBPRE4WkznTdCR8BbhUJ7eGkmTKlFwXH++Rmnp9KS2VpdpDUAW5V6KcVeLZV+BQ404RBqfr3gmWjqqh3ecgolhF4zXZ3LgIYVRrCsAcMaORHViaWL2tM+ubB1EXqWg30+j3nCQQWCDUUkecLCPp/3igIfnadFPzME/mbv42nK3nN00RBCukKrRg7tSu+ruLDHCG1pAzLsCvvN4aUjjt0wBBEM2hbvPI2kUccyhYinqfKYhshaTdiL0FFSH9ERvHSkbOinJc2CtoJex8GWkrBrzCb3sY/qgFWrzfrjLO3qIpm2DXjW+vVma4XC3CaZ7fpYRuSTJrXaNgGADVKxbbbzqV5Tpszm3BAMtpHvSy0FVnzooaihdGczMGREnwb8VCezYxH8unkK9Yn1s8XL9pt+uuLy84bl6vgVUmwCLOK6nWQix57xt+YFm5SdnasXztBRLhVe6tjjwYLQnZ5YjVEhobkS5yyApqgGzBE5tyAd75gXTmoFBDC07HNfdEbjFwagDOJIqzxb4YrZd9DvaywJO9mtT8IThLcioewe1rqgbabpLIxtbX0P865tIsyhdanGdTJPjdXQx6DMO+bdFth5xxDZFwut2CiqIytsXHfbR3T0m7UOVGtXP8/taLV5z2QPWrNM3htHPUZBYWkqDyNjOITBWI+2jaOfe0uR72C3P7NAPTYElve6GofzgoxL30NhnzvENvd+Grgrif90y5XXMWx1VHkgxB5ECog/oTWVFsMQEKVbAxqGWvdZ+DWFwi2TpBSc319x+lwkka1GKTE0NeeA67Z0v8K11QKpsbzzYljzGGRPS4+h4Nrij5cwaq9Pi9hYCsCxNjruu8eJrVZwBOEohOJpch60o/grP7VUxRG2bK/tbRAmDgxBwL+6O9Q4h8cT9tEe+3Ha4+r6m/ZToRrPaU9S5jphtaLFDrhn+BjsOJJhqdWheP4tOEa1X2sZ2ed4wsJuH8v2kGy0Vf3dt/zsvNGgAp5nrYUjP4IneLyyKwrLZmGS3jjp/T3M31oJHhRGi0zzHnhs3zbt23Aus4qqpdlfuEzWACGkM1Lf34FUS160fqkQl8gofc82qEB9ovXdsDLDbO17TuxesYIwUfsLUmgUpUV2llLho+Sv1SLC4kvom1kKE4WA+Lzh4W8bTp9LtRg21I7mGp6adRe2ZkZtpmQ2BcSIZtmXruBxSwodJIS2hys31h6agtV2GKW0D0UcsJWaxR6TG9qvv9h9hjiHECrDOAov9Bxe2gZ14Nd+zwKi9jdO1oNea++bEbBi7i/hCO23Ysn6m2qOqmUrWUbO74QJ9B4KKQCjYi39A9EIWYU91GKx/fSgJf2z46xt571t1E1/B8Z5XYXVPkrL89UwjFlDVY+0fe+djLEs7rn1dwvH7C1oS6qNax/1Pvqp7RjjOgJGbDSTvrP99eO6C4a1plBTfz6knIUDG+maphAhryADv0r5FRV0Hf50FDelKYNZ/BOhQUg5xcb4g1RKDUNAYHwOIUE46u1WQh2nN1DQ2t3ibG4txOXnK84fCpZnIF4D4rX6FcoWkVO1FrbmXwih1AzntGAtsUclJUQ859MkEKj1jWxYNQEHpjwx7YYjMm+BEBKhAo2E0MmhWi5JJ6MeA+bFrBqzDSH0yIYe6gJWaMgyg1uwEbX/ofmEyWLQc1Ro2O1QbZ88Wrvg3Ee+EIbj4nnNntaHUJgUqLO5CtzRjtd792CEmadFcyx5L72Pnmd/H9fPkJPOQf1On4P1odi5MudFKFRxDNEpw1WyQu3Iihi/D4thcQQtr1U/i2r5fOf0D+r46Bjp2NnSGbOiMdfEUv/BrMjZHItRldXb2xuowkGFWN0SuN5Hy3vofffjNdbROaqQ3AsMRiTFUB3OLBJagEkI1PpxAhn1oqNjx8vJ0az0m5TOznkWDEohIGwZjz8lnD61vIUtIKwBZasQEh3OW454TqduLVzzaVdREGiaqjHbrNk4S/69uejt7mTPZTEwmxAzL4bZEuGnp+17ceZ1Evv4u5eHoOa+l5xmmbx+Li36gccIHVE4uKUsDrRC7ZeShcaUASmD9JLJLKPR845yDY4iw7QttwRYfe5g9LZIHN9P1VCHX8fuWc3f1PGsUMJxG9XPcFw362huMDLJ+037oJ/2+EvnDg19VKzV89hX3WZ2tEX3SU9dWDyVs+nnXDbiFvQ22rXPXxmWUO73I+0qHTh+KCXWPiKRp1ykfPdaTjurVTcHY8RSDGUk5eoaFWsvMYGNeQotUnMqbdEEhW5oFhp8FFQgqNXwlfQ2oWCxLKchpw8rHn4uiFcgXlvOwhaRt2oq9ZyFVjmVGc5A1foJIdFSuLUJx4gyCpPFAKBHIUXkQ2ahpvI8qRgB4sdUe/ezWh4gWKJohbze0+x4jYVSXnr2rl9lZvI9J0QZ8Q34pLfloI3aVkvWvH4Nec846jffyYCM9pAd/S2EYB5biCmPqWDW86xAsH3V32IoU5Xcl5ywzM+wAs72oZ7jQ0VHTuojmMvOMRXe3n04tgq3WWGlys0Ym9nhbhn9HEI8xukq2Lz2wZs3HhQ1xmsWCBrAMJXQb/8zf0GjGccu0rNP4RI2sG6TV5m3K1zdD9TysBSmLaFnNVehEMZ2xTl0h3IVBmgh/cbh3I+XXttolMjOQHrZEj+irxYKhRYC/1jCVWGk9vnw01YFw3OFkLjxTi41Z+F5G85lLoDnVM1EQkj8jS9i59wp4zwWvbKmo9ZX0RT62RrwMWAPirBO0Zc0N7aTv9kCcfWe8yZBFtpIMrF4LcdJNf++eccLwoD/0zqwkUe3hACJjIHwEd+R1aSslundh59qLVhhPPk5xHqyzF/vacnLIlefD/88Z6wKzQVl55Q/EoRqJdnQWo3MeU3U0S3/1NH4qlav/VhQcEWc5rLO+7HfRBaIyEAhpp36fGs5aqjrS+Uy6v3HxkCEmDyLguHk3h7cwMhPoq9gylMwbYvyVwvz1V0BkzjF1UfFtqqA75ZAmdfgvu2tEB6HLKBy/yxWQR45CoSS4lYQUh7CoAmGt1gJwJsthVQ94AcQUgmoTuet4PGvGedP1eEcrgG4RpQU6jadTXI+b6f6vcFHn9MZqQR8Tue+d3MymhUzFZVsNMFaFlzb39oKpGmtdKvReJPUS0hS+Mo6JdU8VIGn5v9Lm+70YXaETgyVGXWNGWXH7Hnc/r6EsoO0loPr9R62JpKFOvidDEUXje2PFRJWM+QiV/+Ohn/yPDsu3lgdMTCL43sQCt/jsXU5nO/nMDP6cY9ZMHgQjI6Ljqe1ZGy7LfFczkE97iktti1HMJLW6tK+a7v1GvoR9rk+wxFs8yAuZk8LO26MXrOQrhbgm5UHc26oNdE0U5l+BrWMSLbUhc1/0HPtPgpa4mJpcyi1eXaKI6y1oLoDSsGUl9AdzcBsMbTfQ25CY2sOZc1gTunVFVE9env00ZFUCgGqKJ8+ZTz+W8HyHGrewhaQry3rLwes24K1xe8yma3G9I58BVIqEU+5YpO7JDJ5UVcnuU23/KMJaePS6/NC/02fobWRdBJazd5bsNQ8j0IOeY736Zn31mGsloBmZNZxEcbpMRMRHiog+Bz+ZjVTCjcKuAvylB2s7eWnZTiWZgE9NGIufLUimG1s6cgK0P/ZH2WkqY2pZ6F5fXoMybUUbH9sdI4H0SmU5P/u+648oVeVlrgTDPpphaO1EjxITq/3hBvHyQo/K+w8i9yGH1snvT2Pz1Fo9yg0dVIS6FuUIJQxfk5UUjm2Znh/ogYaENNhPzOGhMQJk2shvP6dMFEOsx8htRJCpTqckWaB8CUlsg/785aLS7od9lQCUJa2jyiAh/cJD/8uvoU1Im/V4ZxKwJYWrKkO6tZyFxSH6xo3WohprhbCWhY8lXPF6ho+yBfvvUwbhqYTUCEoq7nyfI+UaRwxKWWInrMSwI4hAyPx7Miy8CAiy/hZF+qICB0RPvIczWopEELwNH77/UijVEbxkmPWmudWWHtZ2N5YT3kjDsM9EmbaDys0+HyFrY7mic3uHW0cUJlaDEdRawo/2uPqS7GwLL/reXwn3IHOCnUdx1sJb0dWob2PfrflajwlYfg2viyahvClWvl9fwaw3P4BPNfX3GlX4I7fbXIeiVn2PZxcrGegzjv1pfbII1oKdDKXMG1pTOEANBQmFwT6D+hTaL6EYMtph9ez+jdaCnm2Fm5hWa2N3/1bxuXn5lvYAso1YltP2LalhqfSSkCVpKnDSdUcfW6CIDXHjYV16FugtFZrgZqYbqpxtNMTtQ5rPbwGY7eLjgv0NQ5UAFglPI6aOjVau/BshvLRxu21r0bLN7CPtUqsEIqGQXbnnQMj6X0sNGEFJLCHWKx2d4vOyHhsDFnDa3s4rmO9eXDZrXBh7Y+OAYDJKuM7UDxcI6leClTwwlRf05ZbZC0EFTZ8x/rubeKlZ614wsHOT+sMtu2ugmi/T/p8z7xT2EY7x7ha69JWSE0IPVDFEwgJYZTbJ/QjfGKcF5slJkxehEONcgwtgnG0gxvs0DrwKIQyoKJgspYx+xVYPqg7mTGshJ1AAH6l0tkqEI5gJG1cQOtowbsfC86/hOpbaJFIai2klq8AANfMrfHqvT6nCz6lS2WUjDZC6JNGJ87sVzj1ScRoA/oWgDlsUkPYLISkE/cI01bzXjU9G86o5+v/te37khLqPwCwY4IWLupVY2nuE9/EGE9aB2ejHaqAUWc2ITD2TyvEWgvAY1xWiBzBSjrGRw7n7rMIpb9p29/5XuIANkxKGeCK2CGxl5QAK2gvFE5hOEa1L2rpKPF/wky24ivbqBAS59lLAQ32nISwq+yrEI9NkNNx0vt7UKJaHkeCX0uCHM2To53cjori2b+jPKJdYc2GKlxC6mGk+k7tzo63CnASuWD7uYPk3NYZxmU4KtCgoxymTGZkIKYwhEAem5jFlFtGc3o54uhXsRSOHmIFRGgwUgh1k2kAp+eCd/9acPpYBUNZI7Z1wbYtSDnguUFI13zqezhvrR4SN7xmchs1K9UoaC2Q4etC7NqFWBPAzKw07V4XNrFhxUy9ye/hsCRPG7UwEa2DFXEHVRDa6cxZoJ5UxjGGoU7huWUWBkchqfb/yTIp+x3ZNKJFo6eUjmAEHud1noVgx1txZUY9vYbY7rXEyvhhGfPMoCysZxnYGXnKAdFzdQx6hmwZMfl2TIB96CUFg8I2Khi8iB+PeXs+AJJ1bh/N3SMr11oTa9lvh8sMb35XS8IKCKsgJYTJH2NzDl6Tx9KVFM9Ka8cYDlvXzYCfbbIbw1aT4S36XXMRxm+zn0EzmwNQKzykthVnRA9DBSpshC4QgGWt2xJ0f0LKKCm/2ZdAetvOa7lJqXO7DcNRWRC8SbTQzCGEWkm1ROD8qeDdjxH5HLGdC3IEtiVjXTKWWHoV1Y7LyQK4xBYjnNEzB2sccNMaEIGSp5c3T6yABbNT2k7GMxreWVp6fci7xcxFYWGJ2p4DrNI4+W4t2J2gMQveCzvVYzt4xDmmIaz8XBHhaaBc8Nr/IxjDc5Dba/V4LlV75T0pjD1/hT5brRPb125FOA7avkgNXAIMLdiOlbWkjnw0EbNTPoYMNIXkUtTHEgEMYVifPRScVBcM1gmSGe21Zcn1U98d36W1fFSj1/difUVHZP1iCwq4y5z3Xuw7JHk1qbxoonrOnCB4BM31a+2aDaPEDSGlJPDUJSRcC5pASLPVwGgkzJnXQ7FJeJ7KYJSKfNCqbhDSbMXV9zM5mFO1GEIewTp0LBMy6lseb6lGGx35EurB/bEb9Eb4qMXEaljqlLdQrYNeQbXQaqiWw+WXgnc/BiyfItDCU1OKuG4Lnlumc030OCEj4Dmf8DmdZZHXchhP+YynfN5BQHPEUi2j/ZTPyC36SJ3JSoMRzSauRj1QO7aRQ7oo9bvV8HRxTxCBMhOzsKz2rlZCr1sk2LYlG556y/Fsx+IoZNM73yY28bvnk7HnsL82S9ZaZbxOGZ1l2prFTWZnx922xzJHPZ7MGFsrjETWpBaUQmA3Bb9YTFNEiwPVWMHlMfIj57AVqBdhti/Rl1oT9hzbTrUcgKG1j76OUjTec46CFLjOuz+B89cKmg4Dy/tCdixA61/wrWvCUM+58q0RUj0rhLx62oaTQiCP/7uDuQBxKxVKSgVhywgpo2x+6Y6vpbc5moE5+sjWPyKVUgW2/tSEw+WXgscfI+LHBfm6YL2esKWIp+sZT9upFsxDmLKcP6fLHE3RmGMuEc9NOAytfh9ipjuypVJT7+32nCpQjkpd1HPnrFlL1pK4BR/ZAnY8RivB+hu8WkVHpcN5LwDdr9DLZhhroZr5I+LJq87paa7A0MatFnsEH9kxOdL8j3HqpumWgIh5LC0dhWIq6fNfYpCXkCf/RSozFBPDyHK2dYHqs44THy1ZiIf9yE47bbSSOpSPYL2jvqp1RWHAY/aPfT66nwqDPUwkfjyzllQgeNCcDUxQi4HO44VRXWXUT9JsZkUSLvKdMPPRdqC7PopvSHMTtC/12vobk9ZKKxQKoDuakdGikIZgoECIW1PE1w1lrULBtRK+gt4OH3HD6AXDQrAkx0LBsBzax/kXoMSIz3FBOmWsoTbrfEp4blZCXgIuMQGIeM7A53zBJW6VKZW57g83SH/O56ppYaklcpF3GCFJJyInWg2XG1nLM7RRpsXCY7rmLBbfGZizOKzTj6S1iSgcLFw0bWeqzxRNkt9tHST+ZulIA2dfgL3Ao+Z6BKnpdztW+puFMzS0clhtwuBDhS/ICkYd/MGw1UqzmiqFmCcIrIBT6vcJ45lnFKwIPYKsliaX5DgynKbBWojEzgELo7G/fQzlOhVGk+ALqFqmWDiWrJXQ+1wwKQFW6Koj+4xc/WDybvW7ZyXqMRtJaCMAdUw8ATC3f+vBJRHoeU3ACEdV4jgTZVjC2E1vCXnM1TD8Fd0XIr6EGio/9pbXEOSca1234ox/JoTSthjoRfC4UdnWvm8FcS2IW0ZYE8r1+tWF747obUIBQK9/5EQhlRC6RTCOzZ9ALex0eR9Q4oKnAGw/AAgFT8tp4L0oeIonXJaESxv4HkESM3KY/QT0MTwEVnoMWEKV/I9hrdeFtmk3RoZsdSaz8NWxicwFr8eUvLjyXOZcAU8g3CKveN1Lx3fnlFkIWGHiafRHyUvAnMWrXfAS83hchcqCudAffzsLHrwLJTX3ADDlYWgGKf+3ZMeciV4aVcPf+RuFcr+HMOhhaY12riW6meMAkMTPoL/dmgv93RQZq+DPU45Lb0O4bSFZzX1SLsxzdKzU92Qd93ZO8NgRTKX7bdTnagXjsmPCtZ1j3BgIsjZomPcgWX+CHl9FaPQxZOhqDxCgYpen++eGONRw9/28r0jHKOWzxIy0nXt+QvbCVCkYKBBKS1prgiFsBeFpBZ6f99cqfaE/AfgWQkGllMBHxVgH9Rh6WGqJQQREYwQfAPxrFQzph4JrLO22BUvMuOZamTAvw8mUs8QMt/4/iGb/nM94iGudWMHUOykRsWkFKIDV2kg2I/UWWcaik0RNb8ssPAexZfTUPnXBUvM/KlHhPcOzNuhcZh9uJSmxP/35mJmDXfRqIfF/pXPIuwqw6sNICNNeAjzvVsJZOrCKlHF5lpDHlMkklVkutk/dKgCAglWu9SDDCzJSKLs27ARx2TNsPk+tG7ViPSvUs5BiaJansfbU/0JShYbvM7f34kV/8d689misgRZaXAquWHaOY5vHYH0yLJWejJXhvscyV0muOQUZNldpl/i5E3YmLL3XVBrzo0Y/hu7s3srYQOw56Y6ToZe4KInWgtQ3miyGMgTCmhA+PSE3pfxbQUfAtxAKve5GeynNUgivLMpUKCRODQb4CIT/veBzuWD9U5UxT8KkHpatYYgFH7cHvFtW1P2sIh6woQV01JcTt87MWR99juWnGTl28eLrJnQE7EMFdw5Ni/XKhPQYQoU6DExhtHcvcsjV8Mr+OvZx8gUIM7NCxLZFmd1RHHx/vmH0Cm/o8aeydL+L5y+wAsVaDXYcvXGNAJYArIQrGiPr4bkCPZFRTW1p13G/BTqKj+AjzpwO0aA6me19PavpyCJQpkwN/1ri5Ajm74uMGa/V+yhkiSBzQM7fvd8yt/fWu7fBFJZUEHHc1bexh5OGL8+uOQsZMTqJz/EiA7uDuSECe+Y/ZygvyD0ARZ3RY5/4oe1Pfkvxzdl941mmpz8nD4uDm+xMkUel+REyqqAItA6a1ZBqfsLy8VqhI+A4Ye0r6c1CoZRSX8OBELAWA/u/m0MtBrcEYHkCHv9lwRMuuP5pnJJyBC4t1KssuMS9f8Duy1snZMMkw5zpCLQoAzFDc9MmprIWJUo4gPEVIHQs1WOQXvIQ72t9BGpav1ScztKUnSzasQ03tWW0LSPxtHz22YPOtF0Zs0DYXeNo9DpOFufX53sQli7QDt3IaTYsVZn3Lf9PZygO4yYEx4S5zmBvQFVem318vOzgFgBTJJZel8qCx5CqJYW8e39HPgc9Zts7/R8GDKYCwFokCqPurM2y7zfvc0HGFaa0dW/bsSKmY3HFbXikhpguo0JyARIkh6E/W3hEWHu+whyAsreqchM6XmUFoM6XtVkIuu7WHLG1jcZyihVwKWE4lvNwLoetCoW4VUshft4QPnyqYajZV1jeQt/AUsiYSrcyR0F9Cy3yqEcgqWAY1lddEEs9b7k2wZAfcP1z/TmVgNBezMNSBcIpZmwlIpZ5N69z3Ham+FM+o++/WtCdfnaBvpRtqaQRO0qq7drFqed49wP2uQe2nMVRO5ReOtdqiuduuu8Tnvjdg560vd54KTPm/wptWCfvpHlLW+z36f78HkZAXPd3lP1mQkfMWRnerb6rD6MzuR7hUpl0DnMBRI8uGH1XSKiPDTLWEIdQM/TUykWvYWjKds5N2rqjhNjztM/Wx+TBQDoG2v6dEMYQfPq+CbFMzNncPxorswqEBZcynzPuN4/TWk44hw3cl7nCSPQzDNjqmpcm8Ef04sjz8Dd2WgU+0kgs3X9ehUTKNeyeVkIBUFINy6dyTKGwPAPLSudyQVwzlg/PwLUBlOWGlfAV/gTgWwgFYBYCmsCGGUZiRyfLP6C+QGl/KE0wrMDjjxHX7QFPf1qQvw99ip5ixpZHaOY5zEIhl4hzXIXB1w0yrGZUJ0cGMLKYbfQRsF8g1mfgYdaKG5KW4N9HoSc9fkaetP3Rbn/XNP3dPscTQh5ZHwD7Z6Oc+L8XGmsjpuxv7Nctp6cnEPr5ClEJY6+RaJVB6/3oA+jO6wNryY4DmbEyvfFc0l7jznK+tbzmEMUZ1z9iupaOfqf270F0ngD0/rfXeNbPkd9LhZK1LGw753EZgQX6bC29bS2hGk2YdhUL7D4etb25WwUJy/AvSIRQf7bjU4io0Ys2t6n6JVk6JvaS4fU9C4xUli4wGIqaS60Q3cNRG3QUtoC4hQEZbdW3ENeC04cV4ePnwW9zfjV89FqI6e15CkDb9cfsq+Dtr9CeFoidZfm/nzT+SqyDcflrxOnHM64fLki5RYKEjMelSssYCk5x7wi2lTSBlqZeQt+JDRgmZJLJpKUugL0WxUVM2GhFxLPZYINtI9mFYnFxq6XRmQcchI2a13eUzcyFqkJqivIxbTij1lSyMJkVhLe+fyl5TKk/2wgEq71SOA04g+1p51CjF1iNFU15X91Fjf/rcQo3v+x4pXPADp7TdvbzwygPocc8K5IBAK8R6odWmpAqHZ5y8tr7TeVVjFDRyCTL/K0Veku42fIXmsSnYapWGdwJoLAPQdWaZ1QAGYqq51LwjPU850uw1E4qsfd5a6GnNnR7TUMwlCYQcoq9xlHIYVgJGoLaPpenjPj+c81iBsBieN/SnwB8I6GwIzqbU64+hdKQmjIEQn9vmrnH3zBMqBIqvBQTED4ueH6qoVxk6HXDiuqIXJs0ZlIa4Ed/8MVXk30wRVtSm5+ckPqbhYS8sEmLh7OQ3BG2DuwtDyWruZNRRWehHjm7uSgtE9J2WIugZ0Dz8wYccqsftn1Wm2MbOEZ2/HYQk9yfGnkGkMrxxNZ2Weahz9vBF13gWGE5aC0zJq/jxjbrn0feO3kpEmw8P7pjpNCR96yXrBJPGfAsEWCea1671cnPonz1unlPEy0MqJ+2IurUvoYW9ES4EvvfFF5qwl5rSOmAfHjttZxky81Rt4qCjpsIdcGAARX2OmRlbDGcUQUDnc0MRS0pAm0bztAshLhVCL37Eq4F5/fPCM/NudwUcRzVO/pK6Aj4VvARG2bKaNewqvqZY4OF2iUhl57HANRwqxxa/fAI5AVID8D2riA9FpRzxSe3D2f8NXwP/FAjkZ5SdncSW8uCMzYkbtGHDVecuvav9dJtQtswU/fZp9WxxHpLw0yetCMHA1en2/BdlF5UbdTimZmmt2CP8gzsNcSbj0jj74lp23v2/vO8EmDDPIEZvyfdsiI0U9vSrfLfOubs274GUIWQvLtcQsa1RGNZzDDKuI/1V8yKgBKft6gJjOHPsH0/uo89RqXGi8ACZjjK82Fpn7wkyCN/g/6u7T7yt1jLI5ox9eAjEpmrtQL6c00iZfcdAIfX8D0sKH1DLYA+hFkwWGJewoKMFWeg5A4drc3/QKE0hBVLkS9T1B9DUQFgbaV7Uh7QUUqxhqLm+ld5ZlOkt+FkPn/YqpWgCMw3TlojvV0oxAio+dIlWP03sHhTCmBlvEk4JNTEmlP7uwDpEUgPBekByJeCcs51BoQqGNZPZ/xb+R1yCfgP33/AKWScqGVEIJaxRR8ApBDAInkI9WXnJvURMs7QKJJWc12yKZk8AwA9u7k7n1i4S0LuJGEImLVzpQ4PHfBtVk21cIT1JdgFd1TyeRf9Yk4h7GSFQe0TIzOE4YgA5P96r93zsfeBWCbkaaMW01cL4zgay3Eeh/HJ/nC8ANRInjJDUWoheMTlb5enLVuuz5kYHMq0f4Yl66x1AxdknqU2n7xAgd42Y6F5Dufp/s71ivEr8f3cggI9osJV77GHghdUx3KNTNnnK3i72+EG419C3u3MyIij0aYBITNZjXyBkNFAJAZ8xKJ3/NxKTVLb2oY6W1pG1BGL36WAuKJZC8NCWNaC88eE80+fEVanGoMnGN5gJQDfylIAgBhQlohdmQsRDqUEhFQ6pARUIbA9BqR3QLoA+aEgLxQGBeVUgNgm/lIQTvWGJQf87f13Nazr93Xf03NcqqOxlB4mxtjhXCIe4gpWT+WkuABY2zAsNFOK1daqHphU8w5z6QU6C7vGVDgs+xBTu2CUKRzhxp5j2woGm0SkGO94TTOzY9tu+QMsZKRMVv8HhHma23nWh/cct/03YAsVqgrtMImM11hH5SXkod33sM762znkIRycfntMTxm9FbgLCp4kC5fXMtv4JfjmFu20c66r1v7XWF16HwtH3gpmYLAAybMIvDbukg7b+NsIo33ugUC9YrHX+5J5L12JuwYtZz37FUaNom1s7WuQARttRMFF/yPhUIac6gZJGolUQ+iZnd9cCDl2JzO2+hdSwHKtwmF5HhDS5acrwufrPKi51J0v/w709UIhNAshFQQVBK066gTq5gLkgLjVTGYEID0EXH8/hEF6LCgLkM/1E7FBRssQCmHJiKcChNKn2cfPD/hfacGH7x7wj9+/xz88fGxm34IUqvaQc6gCAUPiMxrpWtAmY3P4hIyEpU/ALgjCPCktvGA1oCtqwlFPQjJkQxWtduZhw4AfNaMa6BFObDW7oxBSb38Annct8aaloBv72GMULLvKk3JMvx/BZ+zHBN11Ky9IVvEcHaR9HWOgTuJ6jDkB1jKYtftj2mHtJfTEMVogCh96sIr1wzAPBhhC3Tps+1gFGGVlfs/digx5d71HuSkb6nDX37x+WaLw8OAlm7+gytHR+9e9NVhunb9pJnlvr5O4Vp89itx1x7NAR8P/Mfsvem5CroU0VRiPnSG5Z8LYc57CIKWIlFoG8xYR8vAjxCsQ1xpptFwLHv66YnlvSlkwBeAoR6EhIhPFALyuKMPbLYVA60ArpBI+YlntyseRzgHX30Vcfwi4/h7I52oR5BNQzvUTp4ISmzA4FYRYavpDrN9jlJDAJSMEIOeAv31+xDUteP/4iH94/Ig/Xz7iIW7IIeAcE86lFsgj0cfwGKoEvpYFlwpHd9xxEc3F7kxmF5LH1Ema2Qlg0uCOFuPRIlOHnn2+ZXou05BzX8qatnWaLtLmDo8YDfxQqMhxjeefd7kaAkH74ZVT4K5z3CyH/aFPAcAkHLJ8B4ZA0EilLJ/eeEGuSWXcR8d4yl8wzktLGm6s/SZ5QQO1DTUv4ci6VG28b3oTJADiwDLxnMcqvNRy83xfr4GqlvbOrOXM3zUZza43JTunFd69Fil+1/IQpvuwvAWY8Vz3Nej1i0LpcJBuijT1q1kKwPAnEG6um4MtHTrqNY4k8qjkOOcmbDUUldnLcQUuHzIuPz3VsH5bgZo7X3r0m8JHtBYuZ5TTMvwLMQJL3UehnCK2dwuuf4h4/kPA+rtmHTw0IRAbVHRqUFEodeWdMuI591y40OogURAAwOmU6r6mAM5Lwpoi/vb0Dk/bGe8fHvGXh4/44fwZD9jqJIx1kkQEoJxwxtY0q5aXIJ+dt4h5r4tGNRRdPEzCseero9Ca7EoTFCP3V0tEn22vtXHw02+GsfTfHMjnVoSRttHV5pofwpbW6N9FiHiwkmWSFY6LO2bWcwGwLxtiBYH+rxYC5BgZPZm7fT0qRHiNHQNrJQHDgsll1FDSvnq+nCPIxrMeZ2x9+K+SRLpl6Y+ez536PGvWU0w8OIlzy1p33jrQ5y/mXvY6W86Cx275OQDgMaw9Goj+w6VtnlMVvjMiKjpQhcOctdzHS+49RTuJT4ERj2thif8Fa16wlbpz5Jpq8tpGX0KpzuWcBGK+DkshJCBeCy4fCh5/fK7bbXpUyrGl8Eb6eqHQNtlBvAApI4TUo5DKEhHyghiqUDh9Cgi54PQUkX4KSJeA6w8B6QKUU7UgygnIp8q4C/+W0rOda2hqwfOpjMUaG7S0VCsCAV1IhAgsp4TzOeHhvOJySlhCwT+8+4h/+u5nRNTcBpbFYO5DrZHEBT42OrETs24rmKHOqLkK44xPPsYV/+n0U53oeSTZeAX4eI23daNdEHr9EzAz9uaE+n289gXoOZKBwbh28A9hHzH/z9iHpWr7COlMtXmCo91pxAEcQVTqsbO0j8fJyGI7zsxgkjL+JYgFUPbaPuQ8HvP8JUANO7XWxICUZstqYOb14yj73eu/nmMjurzAg9pnvzw724My+5eAucrtGf5c9KLSrIXjwX2elauWLgJ2Soz1J/S2Nf8BC+AtKM3hPJzvqq3XZwuqEDKWPn9TVQYxIhEXZFyxYEENRe2wUon9nmtZ+j7Mu3cQWgn/Nr7c/yWEUp3LjDqiHyGgO5hpIYTmQzh/Ah7/fUV83qr/dQkIG1BigPrgi2cpvNFKAN4kFArS335+8bTQHjI9KASE07lHLQXrnCbFV3Tw6NqD+6zff4f/+e4/Hl5XXpsI8prnKp0WrH/5flxnw1Y81fVrSHGQRh/+6QHbu1ZcS+aRGhRHx7+EQsH0jPV3AU9/OTjh6CG3fnvlaWUpWP/zFfGcX3eBvZ68PIz/Q5jvUStbYrImQgD+/McP+Mff/eJCNK+NyOJx7xqbB0CG+LvTM/7b7//P9NxdPR4yxcZZdo79A+z9Jfqvj/+Cx7BOReRIhGH5fbc3uvwG+NazMnqeH5HxfXzGH+PnLvB4fRIrgRnIqYwsZj5Pk1VZ7ZTt0PLYAMb+CA1yUuWRwrjfB/U9XZuS/JwyLhyPHFHOabbm9WsCHt4nxOeE/HBqW25W6CissYEXBVhXhBDGG/SEwVcmtX276KMvoVJQ1uFNPwYqvj3lX375FZ82098nU/Bl+sNv9NzflKJfp+bvTWFZcP3GGaavoZ+WBf/j+//yqz8XMeCf//LfUc7LsaKkUvaV1ZNfpBCQvjvj+sfGbm/c9hAJNW2ZzpOksOl4AfI54OM/niuKUaRCA7vZQox4nMUWTqngD7mF6beQ07BtvaZRWBPipxXxl4/o+9SkVPllykCuNyq5oFyvKNuG0ts5e5GD41guR4luhn4boXCnO/29Kb8y1OIbU/mtngsAnz79Js/Gv/74mzw2AHj4TZ6Mrvl/a8r4epBA6S15bb+V8nqnO93pTnf6f5DuQuFOd7rTne7U6S4U7nSnO93pTp3uQuFOd7rTne7U6S4U7nSnO93pTp3uQuFOd7rTne7U6S4U7nSnO93pTp3uQuFOd7rTne7UKRS3gMad7nSnO93p/0e6Wwp3utOd7nSnTnehcKc73elOd+p0Fwp3utOd7nSnTnehcKc73elOd+p0Fwp3utOd7nSnTnehcKc73elOd+p0Fwp3utOd7nSnTnehcKc73elOd+p0Fwp3utOd7nSnTv8X/7MewMLIj+4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example Datapoint\n",
            "Label : 13 (Infiltration)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Enter filepath to store processed images\n",
        "\n",
        "filepath = \"/content/drive/MyDrive/DLPROJ/images/\" #@param {type:\"string\"}\n",
        "\n",
        "if not os.path.exists(filepath):\n",
        "  os.mkdir(filepath)"
      ],
      "metadata": {
        "id": "dn8sVMNVPogv",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Process Images and Save\n",
        "\n",
        "filepath += \"ChestXRay\"\n",
        "\n",
        "filenums = {}\n",
        "\n",
        "for x, y in tqdm(zip(ds.images, ds.findings)):\n",
        "  try:\n",
        "    name = \"\"\n",
        "    for l in y.numpy():\n",
        "      name += \" \" + info[\"class_names\"][l]\n",
        "    \n",
        "    path = filepath + name\n",
        "    img = x.numpy()\n",
        "    img = cv2.resize(img, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)\n",
        "    img = Image.fromarray(img, \"L\")\n",
        "\n",
        "    # Handling multiple images with same filename\n",
        "    if path in filenums.keys():\n",
        "      filenums[path] = filenums[path] + 1\n",
        "      i = filenums[path]\n",
        "      img.save(f'{path}({i}).png')\n",
        "    else:\n",
        "      if(os.path.exists(f'{path}.png')):\n",
        "        i = 1\n",
        "        exists = True\n",
        "        while exists:\n",
        "            if(os.path.exists(f'{path}({i}).png')):\n",
        "                i += 1\n",
        "            else:\n",
        "                exists = False\n",
        "        img.save(f'{path}({i}).png')\n",
        "        filenums[path] = i\n",
        "      else:\n",
        "        img.save(f'{path}.png')\n",
        "        filenums[path] = 0\n",
        "        \n",
        "  except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "id": "EAqEiZjHRRPl",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Process Bounding Box Images and Save (Optional)"
      ],
      "metadata": {
        "id": "8vtEoMUcyJdK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: Do not run these cells if you plan on training a classifier as it will expose the stable diffusion model to the test set. (Only run after above cells)"
      ],
      "metadata": {
        "id": "2jHyT5H80z4M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load ChestXRay Dataset (Test set as it has bounding boxes)\n",
        "\n",
        "ds = deeplake.load('hub://activeloop/nih-chest-xray-train', access_method='stream')\n",
        "\n",
        "idx = random.randint(0, len(ds))\n",
        "img = ds.images[idx].numpy()\n",
        "labels = ds.findings[idx].numpy()\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "info = {\n",
        " \"class_names\": [\n",
        "  \"No Finding\",\n",
        "  \"Hernia\",\n",
        "  \"Emphysema\",\n",
        "  \"Nodule\",\n",
        "  \"Pneumonia\",\n",
        "  \"Consolidation\",\n",
        "  \"Cardiomegaly\",\n",
        "  \"Effusion\",\n",
        "  \"Mass\",\n",
        "  \"Pleural_Thickening\",\n",
        "  \"Atelectasis\",\n",
        "  \"Pneumothorax\",\n",
        "  \"Fibrosis\",\n",
        "  \"Infiltration\",\n",
        "  \"Edema\"\n",
        " ],\n",
        " \"boxesFinding\": [\n",
        "  \"Infiltrate\",\n",
        "  \"Nodule\",\n",
        "  \"Pneumonia\",\n",
        "  \"Cardiomegaly\",\n",
        "  \"Effusion\",\n",
        "  \"Mass\",\n",
        "  \"Atelectasis\",\n",
        "  \"Pneumothorax\"\n",
        " ]\n",
        "}\n",
        "\n",
        "label_names = [info[\"class_names\"][label] for label in labels]\n",
        "\n",
        "print(\"Example Datapoint\")\n",
        "print(f\"Labels : {labels} ({label_names})\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "R6waStf3x_hj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Process Images and Save\n",
        "\n",
        "for x, y, z, w in zip(ds.images, ds.findings, ds.boxes.bbox, ds.boxes.finding):\n",
        "  name = \"\"\n",
        "  try:\n",
        "    if len(w.numpy())>0:\n",
        "      name = \"\"\n",
        "      for l in y.numpy():\n",
        "        name += \" \" + info[\"findings\"][l]\n",
        "      count -= 1\n",
        "      name += \" with\"\n",
        "      for i in range(len(w.numpy())):\n",
        "        name += \" \" + info[\"boxesFinding\"][w.numpy()[i]]\n",
        "        name += \" in the \" + pos(z.numpy()[i])\n",
        "      path = filepath + name\n",
        "      img = x.numpy()\n",
        "      img = cv2.resize(img, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)\n",
        "      img = Image.fromarray(img, \"L\")\n",
        "      if(os.path.exists(f'{path}.png')):\n",
        "        i = 1\n",
        "        exists = True\n",
        "        while exists:\n",
        "            if(os.path.exists(f'{path}({i}).png')):\n",
        "                i += 1\n",
        "            else:\n",
        "                exists = False\n",
        "        img.save(f'{path}({i}).png')\n",
        "      else:\n",
        "        img.save(f'{path}.png')\n",
        "  except:\n",
        "    pass"
      ],
      "metadata": {
        "cellView": "form",
        "id": "At7eH34507iJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Dreambooth\n",
        "Original dreambooth notebook: https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb"
      ],
      "metadata": {
        "id": "VNls9-cjnei9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import time\n",
        "from IPython.utils import capture\n",
        "from IPython.display import clear_output\n",
        "import wget\n",
        "\n",
        "#@markdown #Model Download\n",
        "#@markdown - Skip this cell if you are loading a previous session that contains a trained model.\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "Model_Version = \"V2.1-512px\" #@param [ \"1.5\", \"V2.1-512px\", \"V2.1-768px\"]\n",
        "\n",
        "#@markdown - Choose which version to finetune.\n",
        "\n",
        "with capture.capture_output() as cap: \n",
        "  %cd /content/\n",
        "\n",
        "#@markdown ---\n",
        "Custom_Model_Version=\"1.5\" #@param [ \"1.5\", \"V2.1-512px\", \"V2.1-768px\"]\n",
        "\n",
        "Path_to_HuggingFace= \"\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown - Load and finetune a model from Hugging Face, must specify if v2, use the format \"profile/model\" like : runwayml/stable-diffusion-v1-5\n",
        "#@markdown - If the custom model is private or requires a token, create token.txt containing the token in \"Fast-Dreambooth\" folder in your gdrive.\n",
        "\n",
        "MODEL_PATH = \"\" #@param {type:\"string\"}\n",
        "\n",
        "MODEL_LINK = \"\" #@param {type:\"string\"}\n",
        "\n",
        "safetensors = False #@param {type:\"boolean\"}\n",
        "\n",
        "sftnsr=\"\"\n",
        "if not safetensors:\n",
        "  modelnm=\"model.ckpt\"\n",
        "else:\n",
        "  modelnm=\"model.safetensors\"\n",
        "  sftnsr=\"--from_safetensors\"\n",
        "\n",
        "if os.path.exists('/content/gdrive/MyDrive/Fast-Dreambooth/token.txt'):\n",
        "  with open(\"/content/gdrive/MyDrive/Fast-Dreambooth/token.txt\") as f:\n",
        "     token = f.read()\n",
        "  authe=f'https://USER:{token}@'\n",
        "else:\n",
        "  authe=\"https://\"\n",
        "\n",
        "def downloadmodel():\n",
        "\n",
        "  if os.path.exists('/content/stable-diffusion-v1-5'):\n",
        "    !rm -r /content/stable-diffusion-v1-5\n",
        "  clear_output()\n",
        "\n",
        "  %cd /content/\n",
        "  clear_output()\n",
        "  !mkdir /content/stable-diffusion-v1-5\n",
        "  %cd /content/stable-diffusion-v1-5\n",
        "  !git init\n",
        "  !git lfs install --system --skip-repo\n",
        "  !git remote add -f origin  \"https://huggingface.co/runwayml/stable-diffusion-v1-5\"\n",
        "  !git config core.sparsecheckout true\n",
        "  !echo -e \"scheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nmodel_index.json\\n!vae/diffusion_pytorch_model.bin\\n!*.safetensors\" > .git/info/sparse-checkout\n",
        "  !git pull origin main\n",
        "  if os.path.exists('/content/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin'):\n",
        "    !wget -q -O vae/diffusion_pytorch_model.bin https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.bin\n",
        "    !rm -r .git\n",
        "    !rm model_index.json\n",
        "    time.sleep(1)    \n",
        "    wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json')\n",
        "    %cd /content/\n",
        "    clear_output()\n",
        "    print('\u001b[1;32mDONE !')\n",
        "  else:\n",
        "    while not os.path.exists('/content/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin'):\n",
        "         print('\u001b[1;31mSomething went wrong')\n",
        "         time.sleep(5)\n",
        "\n",
        "def newdownloadmodel():\n",
        "\n",
        "  %cd /content/\n",
        "  clear_output()\n",
        "  !mkdir /content/stable-diffusion-v2-768\n",
        "  %cd /content/stable-diffusion-v2-768\n",
        "  !git init\n",
        "  !git lfs install --system --skip-repo\n",
        "  !git remote add -f origin  \"https://huggingface.co/stabilityai/stable-diffusion-2-1\"\n",
        "  !git config core.sparsecheckout true\n",
        "  !echo -e \"scheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nfeature_extractor\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout\n",
        "  !git pull origin main\n",
        "  !rm -r /content/stable-diffusion-v2-768/.git\n",
        "  %cd /content/\n",
        "  clear_output()\n",
        "  print('\u001b[1;32mDONE !')\n",
        "\n",
        "\n",
        "def newdownloadmodelb():\n",
        "\n",
        "  %cd /content/\n",
        "  clear_output()\n",
        "  !mkdir /content/stable-diffusion-v2-512\n",
        "  %cd /content/stable-diffusion-v2-512\n",
        "  !git init\n",
        "  !git lfs install --system --skip-repo\n",
        "  !git remote add -f origin  \"https://huggingface.co/stabilityai/stable-diffusion-2-1-base\"\n",
        "  !git config core.sparsecheckout true\n",
        "  !echo -e \"scheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nfeature_extractor\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout\n",
        "  !git pull origin main\n",
        "  !rm -r /content/stable-diffusion-v2-512/.git\n",
        "  %cd /content/\n",
        "  clear_output()\n",
        "  print('\u001b[1;32mDONE !')\n",
        "\n",
        "\n",
        "if Path_to_HuggingFace != \"\":\n",
        "  if Custom_Model_Version=='V2.1-512px' or Custom_Model_Version=='V2.1-768px':\n",
        "    if os.path.exists('/content/stable-diffusion-custom'):\n",
        "      !rm -r /content/stable-diffusion-custom\n",
        "    clear_output()\n",
        "    %cd /content/\n",
        "    clear_output()\n",
        "    !mkdir /content/stable-diffusion-custom\n",
        "    %cd /content/stable-diffusion-custom\n",
        "    !git init\n",
        "    !git lfs install --system --skip-repo\n",
        "    !git remote add -f origin  \"{authe}huggingface.co/{Path_to_HuggingFace}\"\n",
        "    !git config core.sparsecheckout true\n",
        "    !echo -e \"scheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nfeature_extractor\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout\n",
        "    !git pull origin main\n",
        "    if os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "      !rm -r /content/stable-diffusion-custom/.git\n",
        "      %cd /content/ \n",
        "      MODEL_NAME=\"/content/stable-diffusion-custom\"\n",
        "      clear_output()\n",
        "      print('\u001b[1;32mDONE !')\n",
        "    else:\n",
        "      while not os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "            print('\u001b[1;31mCheck the link you provided')\n",
        "            time.sleep(5)\n",
        "  else:\n",
        "    if os.path.exists('/content/stable-diffusion-custom'):\n",
        "      !rm -r /content/stable-diffusion-custom\n",
        "    clear_output()\n",
        "    %cd /content/\n",
        "    clear_output()\n",
        "    !mkdir /content/stable-diffusion-custom\n",
        "    %cd /content/stable-diffusion-custom\n",
        "    !git init\n",
        "    !git lfs install --system --skip-repo\n",
        "    !git remote add -f origin  \"{authe}huggingface.co/{Path_to_HuggingFace}\"\n",
        "    !git config core.sparsecheckout true\n",
        "    !echo -e \"scheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout\n",
        "    !git pull origin main\n",
        "    if os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "      !rm -r /content/stable-diffusion-custom/.git\n",
        "      !rm model_index.json\n",
        "      time.sleep(1)\n",
        "      wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json')\n",
        "      %cd /content/ \n",
        "      MODEL_NAME=\"/content/stable-diffusion-custom\"\n",
        "      clear_output()\n",
        "      print('\u001b[1;32mDONE !')\n",
        "    else:\n",
        "      while not os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "            print('\u001b[1;31mCheck the link you provided')\n",
        "            time.sleep(5)\n",
        "\n",
        "elif MODEL_PATH !=\"\":\n",
        "  %cd /content\n",
        "  clear_output() \n",
        "  if os.path.exists(str(MODEL_PATH)):\n",
        "    if Custom_Model_Version=='1.5':\n",
        "      !wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml\n",
        "      !python /content/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path $MODEL_PATH --dump_path stable-diffusion-custom --original_config_file config.yaml $sftnsr\n",
        "      !rm /content/config.yaml\n",
        "\n",
        "    elif Custom_Model_Version=='V2.1-512px':\n",
        "      !wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py\n",
        "      !python /content/convertodiff.py \"$MODEL_PATH\" /content/stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1-base $sftnsr\n",
        "      !rm /content/convertodiff.py\n",
        "\n",
        "    elif Custom_Model_Version=='V2.1-768px':\n",
        "      !wget -q -O convertodiff.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertodiffv2-768.py\n",
        "      !python /content/convertodiff.py \"$MODEL_PATH\" /content/stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1 $sftnsr\n",
        "      !rm /content/convertodiff.py\n",
        "\n",
        "\n",
        "    if os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "      clear_output()\n",
        "      MODEL_NAME=\"/content/stable-diffusion-custom\"\n",
        "      print('\u001b[1;32mDONE !')\n",
        "    else:\n",
        "      !rm -r /content/stable-diffusion-custom\n",
        "      while not os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "        print('\u001b[1;31mConversion error')\n",
        "        time.sleep(5)\n",
        "  else:\n",
        "    while not os.path.exists(str(MODEL_PATH)):\n",
        "       print('\u001b[1;31mWrong path, use the colab file explorer to copy the path')\n",
        "       time.sleep(5)\n",
        "\n",
        "elif MODEL_LINK !=\"\":\n",
        "    %cd /content\n",
        "    clear_output()\n",
        "    !gdown --fuzzy -O $modelnm \"$MODEL_LINK\"\n",
        "    clear_output() \n",
        "    if os.path.exists(modelnm):\n",
        "      if os.path.getsize(modelnm) > 1810671599:\n",
        "        if Custom_Model_Version=='1.5':\n",
        "          !wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml\n",
        "          !python /content/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path $modelnm --dump_path stable-diffusion-custom --original_config_file config.yaml $sftnsr\n",
        "          !rm config.yaml\n",
        "\n",
        "        elif Custom_Model_Version=='V2.1-512px':\n",
        "          !wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py\n",
        "          !python /content/convertodiff.py $modelnm /content/stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1-base $sftnsr\n",
        "          !rm convertodiff.py\n",
        "\n",
        "        elif Custom_Model_Version=='V2.1-768px':\n",
        "          !wget -q -O convertodiff.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertodiffv2-768.py\n",
        "          !python /content/convertodiff.py $modelnm /content/stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1 $sftnsr\n",
        "          !rm convertodiff.py\n",
        "\n",
        "\n",
        "        if os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "          clear_output()\n",
        "          MODEL_NAME=\"/content/stable-diffusion-custom\"\n",
        "          print('\u001b[1;32mDONE !')\n",
        "        else:\n",
        "          !rm -r stable-diffusion-custom\n",
        "          !rm $modelnm\n",
        "          while not os.path.exists('/content/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n",
        "            print('\u001b[1;31mConversion error')\n",
        "            time.sleep(5)\n",
        "      else:\n",
        "        while os.path.getsize(modelnm) < 1810671599:\n",
        "           print('\u001b[1;31mWrong link, check that the link is valid')\n",
        "           time.sleep(5)\n",
        "\n",
        "else:\n",
        "  if Model_Version==\"1.5\":\n",
        "    if not os.path.exists('/content/stable-diffusion-v1-5'):\n",
        "      downloadmodel()\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v1-5\"\n",
        "    else:\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v1-5\"\n",
        "      print(\"\u001b[1;32mThe v1.5 model already exists, using this model.\")\n",
        "  elif Model_Version==\"V2.1-512px\":\n",
        "    if not os.path.exists('/content/stable-diffusion-v2-512'):\n",
        "      newdownloadmodelb()\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v2-512\"\n",
        "    else:\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v2-512\"\n",
        "      print(\"\u001b[1;32mThe v2-512px model already exists, using this model.\")\n",
        "  elif Model_Version==\"V2.1-768px\":\n",
        "    if not os.path.exists('/content/stable-diffusion-v2-768'):\n",
        "      newdownloadmodel()\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v2-768\"\n",
        "    else:\n",
        "      MODEL_NAME=\"/content/stable-diffusion-v2-768\"\n",
        "      print(\"\u001b[1;32mThe v2-768px model already exists, using this model.\")"
      ],
      "metadata": {
        "id": "HhVRaeuRnhUg",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from IPython.display import clear_output\n",
        "from IPython.utils import capture\n",
        "from os import listdir\n",
        "from os.path import isfile\n",
        "import wget\n",
        "import time\n",
        "\n",
        "#@markdown #Create/Load a Session\n",
        "\n",
        "try:\n",
        "  MODEL_NAME\n",
        "  pass\n",
        "except:\n",
        "  MODEL_NAME=\"\"\n",
        "  \n",
        "PT=\"\"\n",
        "\n",
        "Session_Name = \"CXR-Synthesis\" #@param{type: 'string'}\n",
        "while Session_Name==\"\":\n",
        "  print('\u001b[1;31mInput the Session Name:') \n",
        "  Session_Name=input('')\n",
        "Session_Name=Session_Name.replace(\" \",\"_\")\n",
        "\n",
        "#@markdown - Enter the session name, it if it exists, it will load it, otherwise it'll create an new session.\n",
        "\n",
        "Session_Link_optional = \"\" #@param{type: 'string'}\n",
        "\n",
        "#@markdown - Import a session from another gdrive, the shared gdrive link must point to the specific session's folder that contains the trained CKPT, remove any intermediary CKPT if any.\n",
        "\n",
        "WORKSPACE='/content/gdrive/MyDrive/Fast-Dreambooth'\n",
        "\n",
        "if Session_Link_optional !=\"\":\n",
        "  print('\u001b[1;32mDownloading session...')\n",
        "  with capture.capture_output() as cap:\n",
        "    %cd /content\n",
        "    if not os.path.exists(str(WORKSPACE+'/Sessions')):\n",
        "      %mkdir -p $WORKSPACE'/Sessions'\n",
        "      time.sleep(1)\n",
        "    %cd $WORKSPACE'/Sessions'\n",
        "    !gdown --folder --remaining-ok -O $Session_Name  $Session_Link_optional\n",
        "    %cd $Session_Name\n",
        "    !rm -r instance_images\n",
        "    !unzip instance_images.zip\n",
        "    !rm -r concept_images\n",
        "    !unzip concept_images.zip\n",
        "    !rm -r captions\n",
        "    !unzip captions.zip\n",
        "    %cd /content\n",
        "\n",
        "\n",
        "INSTANCE_NAME=Session_Name\n",
        "OUTPUT_DIR=\"/content/models/\"+Session_Name\n",
        "SESSION_DIR=WORKSPACE+'/Sessions/'+Session_Name\n",
        "INSTANCE_DIR=SESSION_DIR+'/instance_images'\n",
        "CONCEPT_DIR=SESSION_DIR+'/concept_images'\n",
        "CAPTIONS_DIR=SESSION_DIR+'/captions'\n",
        "MDLPTH=str(SESSION_DIR+\"/\"+Session_Name+'.ckpt')\n",
        "\n",
        "Model_Version = \"V2.1-512px\" #@param [ \"1.5\", \"V2.1-512px\", \"V2.1-768px\"]\n",
        "#@markdown - Ignore this if you're not loading a previous session that contains a trained model\n",
        "\n",
        "\n",
        "if os.path.exists(str(SESSION_DIR)):\n",
        "  mdls=[ckpt for ckpt in listdir(SESSION_DIR) if ckpt.split(\".\")[-1]==\"ckpt\"]\n",
        "  if not os.path.exists(MDLPTH) and '.ckpt' in str(mdls):  \n",
        "    \n",
        "    def f(n):\n",
        "      k=0\n",
        "      for i in mdls:\n",
        "        if k==n:\n",
        "          !mv \"$SESSION_DIR/$i\" $MDLPTH\n",
        "        k=k+1\n",
        "\n",
        "    k=0\n",
        "    print('\u001b[1;33mNo final checkpoint model found, select which intermediary checkpoint to use, enter only the number, (000 to skip):\\n\u001b[1;34m')\n",
        "\n",
        "    for i in mdls:\n",
        "      print(str(k)+'- '+i)\n",
        "      k=k+1\n",
        "    n=input()\n",
        "    while int(n)>k-1:\n",
        "      n=input()\n",
        "    if n!=\"000\":\n",
        "      f(int(n))\n",
        "      print('\u001b[1;32mUsing the model '+ mdls[int(n)]+\" ...\")\n",
        "      time.sleep(2)\n",
        "    else:\n",
        "      print('\u001b[1;32mSkipping the intermediary checkpoints.')\n",
        "    del n\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  %cd /content\n",
        "  resume=False\n",
        "\n",
        "if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):\n",
        "  print('\u001b[1;32mLoading session with no previous model, using the original model or the custom downloaded model')\n",
        "  if MODEL_NAME==\"\":\n",
        "    print('\u001b[1;31mNo model found, use the \"Model Download\" cell to download a model.')\n",
        "  else:\n",
        "    print('\u001b[1;32mSession Loaded, proceed to uploading instance images')\n",
        "\n",
        "elif os.path.exists(MDLPTH):\n",
        "  print('\u001b[1;32mSession found, loading the trained model ...')\n",
        "  if Model_Version=='1.5':\n",
        "    !wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml\n",
        "    clear_output()\n",
        "    print('\u001b[1;32mSession found, loading the trained model ...')\n",
        "    !python /content/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path $MDLPTH --dump_path \"$OUTPUT_DIR\" --original_config_file config.yaml\n",
        "    !rm /content/config.yaml\n",
        "\n",
        "  elif Model_Version=='V2.1-512px':\n",
        "    !wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py\n",
        "    print('\u001b[1;32mSession found, loading the trained model ...')\n",
        "    !python /content/convertodiff.py \"$MDLPTH\" \"$OUTPUT_DIR\" --v2 --reference_model stabilityai/stable-diffusion-2-1-base\n",
        "    !rm /content/convertodiff.py\n",
        "\n",
        "  elif Model_Version=='V2.1-768px':\n",
        "    !wget -q -O convertodiff.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertodiffv2-768.py\n",
        "    print('\u001b[1;32mSession found, loading the trained model ...')\n",
        "    !python /content/convertodiff.py \"$MDLPTH\" \"$OUTPUT_DIR\" --v2 --reference_model stabilityai/stable-diffusion-2-1\n",
        "    !rm /content/convertodiff.py\n",
        "  \n",
        "  \n",
        "  if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):\n",
        "    resume=True\n",
        "    clear_output()\n",
        "    print('\u001b[1;32mSession loaded.')\n",
        "  else:     \n",
        "    if not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):\n",
        "      print('\u001b[1;31mConversion error, if the error persists, remove the CKPT file from the current session folder')\n",
        "\n",
        "elif not os.path.exists(str(SESSION_DIR)):\n",
        "    %mkdir -p \"$INSTANCE_DIR\"\n",
        "    print('\u001b[1;32mCreating session...')\n",
        "    if MODEL_NAME==\"\":\n",
        "      print('\u001b[1;31mNo model found, use the \"Model Download\" cell to download a model.')\n",
        "    else:\n",
        "      print('\u001b[1;32mSession created, proceed to uploading instance images')\n",
        "\n",
        "    #@markdown\n",
        "\n",
        "    #@markdown # The most important step is to rename the instance pictures of each subject to a unique unknown identifier, example :\n",
        "    #@markdown - If you have 10 pictures of yourself, simply select them all and rename only one to the chosen identifier for example : phtmejhn, the files would be : phtmejhn (1).jpg, phtmejhn (2).png ....etc then upload them, do the same for other people or objects with a different identifier, and that's it.\n",
        "    #@markdown - Checkout this example : https://i.imgur.com/d2lD3rz.jpeg"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PfKhdPF1pHtC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "from google.colab import files\n",
        "import time\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "import ipywidgets as widgets\n",
        "from io import BytesIO\n",
        "import wget\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  %cd /content\n",
        "  if not os.path.exists(\"/content/smart_crop.py\"):\n",
        "    wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/smart_crop.py')\n",
        "  from smart_crop import *\n",
        "\n",
        "#@markdown #Instance Images\n",
        "#@markdown ----\n",
        "\n",
        "#@markdown\n",
        "#@markdown - Run the cell to upload the instance pictures.\n",
        "#@markdown - You can add `external captions` in txt files by simply giving each txt file the same name as the instance image, for example dikgur (1).jpg and dikgur (1).txt, and upload them here, to use the external captions, check the box \"external_captions\" in the training cell. `All the images must have one same extension` jpg or png or....etc\n",
        "\n",
        "Remove_existing_instance_images= True #@param{type: 'boolean'}\n",
        "#@markdown - Uncheck the box to keep the existing instance images.\n",
        "\n",
        "if Remove_existing_instance_images:\n",
        "  if os.path.exists(str(INSTANCE_DIR)):\n",
        "    !rm -r \"$INSTANCE_DIR\"\n",
        "  if os.path.exists(str(CAPTIONS_DIR)):\n",
        "    !rm -r \"$CAPTIONS_DIR\"\n",
        "\n",
        "if not os.path.exists(str(INSTANCE_DIR)):\n",
        "  %mkdir -p \"$INSTANCE_DIR\"\n",
        "if not os.path.exists(str(CAPTIONS_DIR)):\n",
        "  %mkdir -p \"$CAPTIONS_DIR\"\n",
        "\n",
        "if os.path.exists(INSTANCE_DIR+\"/.ipynb_checkpoints\"):\n",
        "  %rm -r $INSTANCE_DIR\"/.ipynb_checkpoints\"\n",
        "\n",
        "\n",
        "IMAGES_FOLDER_OPTIONAL=\"/content/drive/MyDrive/DLProj/images\" #@param{type: 'string'}\n",
        "\n",
        "#@markdown - If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) instance images. Leave EMPTY to upload.\n",
        "\n",
        "Smart_Crop_images= False #@param{type: 'boolean'}\n",
        "Crop_size = 512 #@param [\"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"] {type:\"raw\"}\n",
        "\n",
        "#@markdown - Smart crop the images without manual intervention.\n",
        "\n",
        "while IMAGES_FOLDER_OPTIONAL !=\"\" and not os.path.exists(str(IMAGES_FOLDER_OPTIONAL)):\n",
        "  print('\u001b[1;31mThe image folder specified does not exist, use the colab file explorer to copy the path :')\n",
        "  IMAGES_FOLDER_OPTIONAL=input('')\n",
        "\n",
        "if IMAGES_FOLDER_OPTIONAL!=\"\":\n",
        "  if os.path.exists(IMAGES_FOLDER_OPTIONAL+\"/.ipynb_checkpoints\"):\n",
        "    %rm -r \"$IMAGES_FOLDER_OPTIONAL\"\"/.ipynb_checkpoints\"\n",
        "\n",
        "  with capture.capture_output() as cap:\n",
        "    !mv $IMAGES_FOLDER_OPTIONAL/*.txt $CAPTIONS_DIR\n",
        "  if Smart_Crop_images:\n",
        "    for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):\n",
        "      extension = filename.split(\".\")[-1]\n",
        "      identifier=filename.split(\".\")[0]\n",
        "      new_path_with_file = os.path.join(INSTANCE_DIR, filename)\n",
        "      file = Image.open(IMAGES_FOLDER_OPTIONAL+\"/\"+filename)\n",
        "      width, height = file.size\n",
        "      if file.size !=(Crop_size, Crop_size):\n",
        "        image=crop_image(file, Crop_size)\n",
        "        if extension.upper()==\"JPG\" or extension.upper()==\"jpg\":\n",
        "            image[0] = image[0].convert(\"RGB\")\n",
        "            image[0].save(new_path_with_file, format=\"JPEG\", quality = 100)\n",
        "        else:\n",
        "            image[0].save(new_path_with_file, format=extension.upper())\n",
        "      else:\n",
        "        !cp \"$IMAGES_FOLDER_OPTIONAL/$filename\" \"$INSTANCE_DIR\"\n",
        "\n",
        "  else:\n",
        "    for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):\n",
        "      %cp -r \"$IMAGES_FOLDER_OPTIONAL/$filename\" \"$INSTANCE_DIR\"\n",
        "\n",
        "  print('\\n\u001b[1;32mDone, proceed to the next cell')\n",
        "\n",
        "\n",
        "elif IMAGES_FOLDER_OPTIONAL ==\"\":\n",
        "  up=\"\"\n",
        "  uploaded = files.upload()\n",
        "  for filename in uploaded.keys():\n",
        "    if filename.split(\".\")[-1]==\"txt\":\n",
        "      shutil.move(filename, CAPTIONS_DIR)\n",
        "    up=[filename for filename in uploaded.keys() if filename.split(\".\")[-1]!=\"txt\"]\n",
        "  if Smart_Crop_images:\n",
        "    for filename in tqdm(up, bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):\n",
        "      shutil.move(filename, INSTANCE_DIR)\n",
        "      extension = filename.split(\".\")[-1]\n",
        "      identifier=filename.split(\".\")[0]\n",
        "      new_path_with_file = os.path.join(INSTANCE_DIR, filename)\n",
        "      file = Image.open(new_path_with_file)\n",
        "      width, height = file.size\n",
        "      if file.size !=(Crop_size, Crop_size):\n",
        "        image=crop_image(file, Crop_size)\n",
        "        if extension.upper()==\"JPG\" or extension.upper()==\"jpg\":\n",
        "            image[0] = image[0].convert(\"RGB\")\n",
        "            image[0].save(new_path_with_file, format=\"JPEG\", quality = 100)\n",
        "        else:\n",
        "            image[0].save(new_path_with_file, format=extension.upper())\n",
        "      clear_output()\n",
        "  else:\n",
        "    for filename in tqdm(uploaded.keys(), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):\n",
        "      shutil.move(filename, INSTANCE_DIR)\n",
        "      clear_output()\n",
        "  print('\\n\u001b[1;32mDone, proceed to the next cell')\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  %cd \"$INSTANCE_DIR\"\n",
        "  !find . -name \"* *\" -type f | rename 's/ /-/g'\n",
        "  %cd \"$CAPTIONS_DIR\"\n",
        "  !find . -name \"* *\" -type f | rename 's/ /-/g'\n",
        "  \n",
        "  %cd $SESSION_DIR\n",
        "  !rm instance_images.zip captions.zip\n",
        "  !zip -r instance_images instance_images\n",
        "  !zip -r captions captions\n",
        "  %cd /content"
      ],
      "metadata": {
        "cellView": "form",
        "id": "ykHEPyFFpLXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ---\n",
        "#@markdown #Start DreamBooth\n",
        "#@markdown ---\n",
        "import os\n",
        "from IPython.display import clear_output\n",
        "from google.colab import runtime\n",
        "import time\n",
        "import random\n",
        "\n",
        "if os.path.exists(INSTANCE_DIR+\"/.ipynb_checkpoints\"):\n",
        "  %rm -r $INSTANCE_DIR\"/.ipynb_checkpoints\"\n",
        "\n",
        "if os.path.exists(CONCEPT_DIR+\"/.ipynb_checkpoints\"):\n",
        "  %rm -r $CONCEPT_DIR\"/.ipynb_checkpoints\"\n",
        "\n",
        "if os.path.exists(CAPTIONS_DIR+\"/.ipynb_checkpoints\"):\n",
        "  %rm -r $CAPTIONS_DIR\"/.ipynb_checkpoints\"\n",
        "\n",
        "Resume_Training = False #@param {type:\"boolean\"}\n",
        "\n",
        "if resume and not Resume_Training:\n",
        "  print('\u001b[1;31mOverwrite your previously trained model ? answering \"yes\" will train a new model, answering \"no\" will resume the training of the previous model?  yes or no ?\u001b[0m')\n",
        "  while True:\n",
        "    ansres=input('')\n",
        "    if ansres=='no':\n",
        "      Resume_Training = True\n",
        "      break\n",
        "    elif ansres=='yes':\n",
        "      Resume_Training = False\n",
        "      resume= False\n",
        "      break\n",
        "\n",
        "while not Resume_Training and MODEL_NAME==\"\":\n",
        "  print('\u001b[1;31mNo model found, use the \"Model Download\" cell to download a model.')\n",
        "  time.sleep(5)\n",
        "\n",
        "#@markdown  - If you're not satisfied with the result, check this box, run again the cell and it will continue training the current model.\n",
        "\n",
        "MODELT_NAME=MODEL_NAME\n",
        "\n",
        "UNet_Training_Steps=1500 #@param{type: 'number'}\n",
        "UNet_Learning_Rate = 2e-6 #@param [\"2e-5\",\"1e-5\",\"9e-6\",\"8e-6\",\"7e-6\",\"6e-6\",\"5e-6\", \"4e-6\", \"3e-6\", \"2e-6\"] {type:\"raw\"}\n",
        "untlr=UNet_Learning_Rate\n",
        "\n",
        "#@markdown - These default settings are for a dataset of 10 pictures which is enough for training a face, start with 1500 or lower, test the model, if not enough, resume training for 200 steps, keep testing until you get the desired output, `set it to 0 to train only the text_encoder`.\n",
        "\n",
        "Text_Encoder_Training_Steps=350 #@param{type: 'number'}\n",
        "\n",
        "#@markdown - 200-450 steps is enough for a small dataset, keep this number small to avoid overfitting, set to 0 to disable, `set it to 0 before resuming training if it is already trained`.\n",
        "\n",
        "Text_Encoder_Concept_Training_Steps=0 #@param{type: 'number'}\n",
        "\n",
        "#@markdown - Suitable for training a style/concept as it acts as heavy regularization, set it to 1500 steps for 200 concept images (you can go higher), set to 0 to disable, set both the settings above to 0 to fintune only the text_encoder on the concept, `set it to 0 before resuming training if it is already trained`.\n",
        "\n",
        "Text_Encoder_Learning_Rate = 1e-6 #@param [\"2e-6\", \"1e-6\",\"8e-7\",\"6e-7\",\"5e-7\",\"4e-7\"] {type:\"raw\"}\n",
        "txlr=Text_Encoder_Learning_Rate\n",
        "\n",
        "#@markdown - Learning rate for both text_encoder and concept_text_encoder, keep it low to avoid overfitting (1e-6 is higher than 4e-7)\n",
        "\n",
        "trnonltxt=\"\"\n",
        "if UNet_Training_Steps==0:\n",
        "   trnonltxt=\"--train_only_text_encoder\"\n",
        "\n",
        "Seed=''\n",
        "\n",
        "ofstnse=\"\"\n",
        "Offset_Noise = False #@param {type:\"boolean\"}\n",
        "#@markdown - Always use it for style training.\n",
        "\n",
        "if Offset_Noise:\n",
        "  ofstnse=\"--offset_noise\"\n",
        "\n",
        "External_Captions = False #@param {type:\"boolean\"}\n",
        "#@markdown - Get the captions from a text file for each instance image.\n",
        "extrnlcptn=\"\"\n",
        "if External_Captions:\n",
        "  extrnlcptn=\"--external_captions\"\n",
        "\n",
        "Resolution = \"512\" #@param [\"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"]\n",
        "Res=int(Resolution)\n",
        "\n",
        "#@markdown - Higher resolution = Higher quality, make sure the instance images are cropped to this selected size (or larger).\n",
        "\n",
        "fp16 = True\n",
        "\n",
        "if Seed =='' or Seed=='0':\n",
        "  Seed=random.randint(1, 999999)\n",
        "else:\n",
        "  Seed=int(Seed)\n",
        "\n",
        "if fp16:\n",
        "  prec=\"fp16\"\n",
        "else:\n",
        "  prec=\"no\"\n",
        "\n",
        "precision=prec\n",
        "\n",
        "resuming=\"\"\n",
        "if Resume_Training and os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):\n",
        "  MODELT_NAME=OUTPUT_DIR\n",
        "  print('\u001b[1;32mResuming Training...\u001b[0m')\n",
        "  resuming=\"Yes\"\n",
        "elif Resume_Training and not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):\n",
        "  print('\u001b[1;31mPrevious model not found, training a new model...\u001b[0m')\n",
        "  MODELT_NAME=MODEL_NAME\n",
        "  while MODEL_NAME==\"\":\n",
        "    print('\u001b[1;31mNo model found, use the \"Model Download\" cell to download a model.')\n",
        "    time.sleep(5)\n",
        "\n",
        "Enable_text_encoder_training= True\n",
        "Enable_Text_Encoder_Concept_Training= True\n",
        "\n",
        "if Text_Encoder_Training_Steps==0 :\n",
        "   Enable_text_encoder_training= False\n",
        "else:\n",
        "  stptxt=Text_Encoder_Training_Steps\n",
        "\n",
        "if Text_Encoder_Concept_Training_Steps==0:\n",
        "   Enable_Text_Encoder_Concept_Training= False\n",
        "else:\n",
        "  stptxtc=Text_Encoder_Concept_Training_Steps\n",
        "\n",
        "#@markdown ---------------------------\n",
        "Save_Checkpoint_Every_n_Steps = False #@param {type:\"boolean\"}\n",
        "Save_Checkpoint_Every=500 #@param{type: 'number'}\n",
        "if Save_Checkpoint_Every==None:\n",
        "  Save_Checkpoint_Every=1\n",
        "#@markdown - Minimum 200 steps between each save.\n",
        "stp=0\n",
        "Start_saving_from_the_step=500 #@param{type: 'number'}\n",
        "if Start_saving_from_the_step==None:\n",
        "  Start_saving_from_the_step=0\n",
        "if (Start_saving_from_the_step < 200):\n",
        "  Start_saving_from_the_step=Save_Checkpoint_Every\n",
        "stpsv=Start_saving_from_the_step\n",
        "if Save_Checkpoint_Every_n_Steps:\n",
        "  stp=Save_Checkpoint_Every\n",
        "#@markdown - Start saving intermediary checkpoints from this step.\n",
        "\n",
        "Disconnect_after_training=False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown - Auto-disconnect from google colab after the training to avoid wasting compute units.\n",
        "\n",
        "def dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps):\n",
        "    \n",
        "    !accelerate launch /content/diffusers/examples/dreambooth/train_dreambooth.py \\\n",
        "    $trnonltxt \\\n",
        "    $extrnlcptn \\\n",
        "    $ofstnse \\\n",
        "    --image_captions_filename \\\n",
        "    --train_text_encoder \\\n",
        "    --dump_only_text_encoder \\\n",
        "    --pretrained_model_name_or_path=\"$MODELT_NAME\" \\\n",
        "    --instance_data_dir=\"$INSTANCE_DIR\" \\\n",
        "    --output_dir=\"$OUTPUT_DIR\" \\\n",
        "    --captions_dir=\"$CAPTIONS_DIR\" \\\n",
        "    --instance_prompt=\"$PT\" \\\n",
        "    --seed=$Seed \\\n",
        "    --resolution=$Res \\\n",
        "    --mixed_precision=$precision \\\n",
        "    --train_batch_size=1 \\\n",
        "    --gradient_accumulation_steps=1 --gradient_checkpointing \\\n",
        "    --use_8bit_adam \\\n",
        "    --learning_rate=$txlr \\\n",
        "    --lr_scheduler=\"linear\" \\\n",
        "    --lr_warmup_steps=0 \\\n",
        "    --max_train_steps=$Training_Steps\n",
        "\n",
        "def train_only_unet(stpsv, stp, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, Res, precision, Training_Steps):\n",
        "    clear_output()\n",
        "    if resuming==\"Yes\":\n",
        "      print('\u001b[1;32mResuming Training...\u001b[0m')\n",
        "    print('\u001b[1;33mTraining the UNet...\u001b[0m')\n",
        "    !accelerate launch /content/diffusers/examples/dreambooth/train_dreambooth.py \\\n",
        "    $extrnlcptn \\\n",
        "    $ofstnse \\\n",
        "    --image_captions_filename \\\n",
        "    --train_only_unet \\\n",
        "    --save_starting_step=$stpsv \\\n",
        "    --save_n_steps=$stp \\\n",
        "    --Session_dir=$SESSION_DIR \\\n",
        "    --pretrained_model_name_or_path=\"$MODELT_NAME\" \\\n",
        "    --instance_data_dir=\"$INSTANCE_DIR\" \\\n",
        "    --output_dir=\"$OUTPUT_DIR\" \\\n",
        "    --captions_dir=\"$CAPTIONS_DIR\" \\\n",
        "    --instance_prompt=\"$PT\" \\\n",
        "    --seed=$Seed \\\n",
        "    --resolution=$Res \\\n",
        "    --mixed_precision=$precision \\\n",
        "    --train_batch_size=1 \\\n",
        "    --gradient_accumulation_steps=1 \\\n",
        "    --use_8bit_adam \\\n",
        "    --learning_rate=$untlr \\\n",
        "    --lr_scheduler=\"linear\" \\\n",
        "    --lr_warmup_steps=0 \\\n",
        "    --max_train_steps=$Training_Steps\n",
        "\n",
        "\n",
        "if Enable_text_encoder_training :\n",
        "  print('\u001b[1;33mTraining the text encoder...\u001b[0m')\n",
        "  if os.path.exists(OUTPUT_DIR+'/'+'text_encoder_trained'):\n",
        "    %rm -r $OUTPUT_DIR\"/text_encoder_trained\"\n",
        "  dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxt)\n",
        "\n",
        "if Enable_Text_Encoder_Concept_Training:\n",
        "  if os.path.exists(CONCEPT_DIR):\n",
        "    if os.listdir(CONCEPT_DIR)!=[]:\n",
        "      clear_output()\n",
        "      if resuming==\"Yes\":\n",
        "        print('\u001b[1;32mResuming Training...\u001b[0m')\n",
        "      print('\u001b[1;33mTraining the text encoder on the concept...\u001b[0m')\n",
        "      dump_only_textenc(trnonltxt, MODELT_NAME, CONCEPT_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxtc)\n",
        "    else:\n",
        "      clear_output()\n",
        "      if resuming==\"Yes\":\n",
        "        print('\u001b[1;32mResuming Training...\u001b[0m')\n",
        "      print('\u001b[1;31mNo concept images found, skipping concept training...')\n",
        "      Text_Encoder_Concept_Training_Steps=0\n",
        "      time.sleep(8)\n",
        "  else:\n",
        "      clear_output()\n",
        "      if resuming==\"Yes\":\n",
        "        print('\u001b[1;32mResuming Training...\u001b[0m')\n",
        "      print('\u001b[1;31mNo concept images found, skipping concept training...')\n",
        "      Text_Encoder_Concept_Training_Steps=0\n",
        "      time.sleep(8)\n",
        "\n",
        "if UNet_Training_Steps!=0:\n",
        "  train_only_unet(stpsv, stp, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, Res, precision, Training_Steps=UNet_Training_Steps)\n",
        "\n",
        "if UNet_Training_Steps==0 and Text_Encoder_Concept_Training_Steps==0 and Text_Encoder_Training_Steps==0 :\n",
        "  print('\u001b[1;32mNothing to do')\n",
        "else:\n",
        "  if os.path.exists('/content/models/'+INSTANCE_NAME+'/unet/diffusion_pytorch_model.bin'):\n",
        "    prc=\"--fp16\" if precision==\"fp16\" else \"\"\n",
        "    !python /content/diffusers/scripts/convertosdv2.py $prc $OUTPUT_DIR $SESSION_DIR/$Session_Name\".ckpt\"\n",
        "    clear_output()\n",
        "    if os.path.exists(SESSION_DIR+\"/\"+INSTANCE_NAME+'.ckpt'):\n",
        "      clear_output()\n",
        "      print(\"\u001b[1;32mDONE, the CKPT model is in your Gdrive in the sessions folder\")\n",
        "      if Disconnect_after_training :\n",
        "        time.sleep(20)\n",
        "        runtime.unassign()\n",
        "    else:\n",
        "      print(\"\u001b[1;31mSomething went wrong\")\n",
        "  else:\n",
        "    print(\"\u001b[1;31mSomething went wrong\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "r-pKBvU9pRrj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import time\n",
        "import sys\n",
        "import fileinput\n",
        "from IPython.display import clear_output\n",
        "from subprocess import getoutput\n",
        "from IPython.utils import capture\n",
        "from pyngrok import ngrok, conf\n",
        "\n",
        "#@markdown #Test\n",
        "\n",
        "Previous_Session=\"\" #@param{type: 'string'}\n",
        "\n",
        "#@markdown - Leave empty if you want to use the current trained model.\n",
        "\n",
        "Use_Custom_Path = False #@param {type:\"boolean\"}\n",
        "\n",
        "try:\n",
        "  INSTANCE_NAME\n",
        "  INSTANCET=INSTANCE_NAME\n",
        "except:\n",
        "  pass\n",
        "#@markdown - if checked, an input box will ask the full path to a desired model.\n",
        "\n",
        "if Previous_Session!=\"\":\n",
        "  INSTANCET=Previous_Session\n",
        "  INSTANCET=INSTANCET.replace(\" \",\"_\")\n",
        "\n",
        "if Use_Custom_Path:\n",
        "  try:\n",
        "    INSTANCET\n",
        "    del INSTANCET\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "try:\n",
        "  INSTANCET\n",
        "  if Previous_Session!=\"\":\n",
        "    path_to_trained_model='/content/gdrive/MyDrive/Fast-Dreambooth/Sessions/'+Previous_Session+\"/\"+Previous_Session+'.ckpt'\n",
        "  else:\n",
        "    path_to_trained_model=SESSION_DIR+\"/\"+INSTANCET+'.ckpt'\n",
        "except:\n",
        "  print('\u001b[1;31mIt seems that you did not perform training during this session \u001b[1;32mor you chose to use a custom path,\\nprovide the full path to the model (including the name of the model):\\n')\n",
        "  path_to_trained_model=input()\n",
        "     \n",
        "while not os.path.exists(path_to_trained_model):\n",
        "   print(\"\u001b[1;31mThe model doesn't exist on you Gdrive, use the file explorer to get the path : \")\n",
        "   path_to_trained_model=input()\n",
        "   \n",
        "fgitclone = \"git clone --depth 1\"\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "    if not os.path.exists('/content/gdrive/MyDrive'):\n",
        "      !mkdir -p /content/gdrive/MyDrive\n",
        "\n",
        "if not os.path.exists('/content/gdrive/MyDrive/sd/stablediffusion'):\n",
        "    !wget -q -O /content/sd_rep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_rep.tar.zst\n",
        "    !tar -C  /content/gdrive/MyDrive --zstd -xf /content/sd_rep.tar.zst\n",
        "    !rm /content/sd_rep.tar.zst\n",
        "    clear_output()\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  %cd /content/gdrive/MyDrive/sd\n",
        "  !git clone -q --branch master https://github.com/AUTOMATIC1111/stable-diffusion-webui\n",
        "  %cd stable-diffusion-webui\n",
        "  !mkdir cache\n",
        "  !sed -i 's@~/.cache@/content/gdrive/MyDrive/sd/stable-diffusion-webui/cache@' /usr/local/lib/python3.9/dist-packages/transformers/utils/hub.py\n",
        "\n",
        "  clear_output()\n",
        "  !git reset --hard\n",
        "  time.sleep(1)\n",
        "  !rm webui.sh\n",
        "  !git pull\n",
        "  !git fetch --unshallow\n",
        "  !git checkout a9eab236d7e8afa4d6205127904a385b2c43bb24\n",
        "  \n",
        "with capture.capture_output() as cap:\n",
        "  if not os.path.exists('/tools/node/bin/lt'):\n",
        "    !npm install -g localtunnel\n",
        "\n",
        "Ngrok_token = \"\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown - Input your ngrok token if you want to use ngrok server.\n",
        "\n",
        "Use_localtunnel = False #@param {type:\"boolean\"}\n",
        "\n",
        "User = \"\" #@param {type:\"string\"}\n",
        "Password= \"\" #@param {type:\"string\"}\n",
        "#@markdown - Add credentials to your Gradio interface (optional).\n",
        "\n",
        "auth=f\"--gradio-auth {User}:{Password}\"\n",
        "if User ==\"\" or Password==\"\":\n",
        "  auth=\"\"\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  %cd modules\n",
        "  !wget -q -O paths.py https://github.com/TheLastBen/fast-stable-diffusion/raw/5632d2ef7fffd940976538d270854ec4faf26855/AUTOMATIC1111_files/paths.py\n",
        "  !wget -q -O extras.py https://github.com/AUTOMATIC1111/stable-diffusion-webui/raw/a9eab236d7e8afa4d6205127904a385b2c43bb24/modules/extras.py\n",
        "  !wget -q -O sd_models.py https://github.com/AUTOMATIC1111/stable-diffusion-webui/raw/a9eab236d7e8afa4d6205127904a385b2c43bb24/modules/sd_models.py\n",
        "  !wget -q -O /usr/local/lib/python3.9/dist-packages/gradio/blocks.py https://github.com/TheLastBen/fast-stable-diffusion/raw/7ff88eaa1fb4997bacd9845bd487f9a14335d625/AUTOMATIC1111_files/blocks.py\n",
        "  %cd /content/gdrive/MyDrive/sd/stable-diffusion-webui/\n",
        "\n",
        "  !sed -i \"s@os.path.splitext(checkpoint_file)@os.path.splitext(checkpoint_file); map_location='cuda'@\" /content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_models.py\n",
        "  !sed -i 's@ui.create_ui().*@ui.create_ui();shared.demo.queue(concurrency_count=999999,status_update_rate=0.1)@' /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py\n",
        "  !sed -i \"s@map_location='cpu'@map_location='cuda'@\" /content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/extras.py  \n",
        "\n",
        "share=''\n",
        "if Ngrok_token!=\"\":\n",
        "  ngrok.kill()\n",
        "  srv=ngrok.connect(7860, pyngrok_config=conf.PyngrokConfig(auth_token=Ngrok_token) , bind_tls=True).public_url\n",
        "\n",
        "  for line in fileinput.input('/usr/local/lib/python3.9/dist-packages/gradio/blocks.py', inplace=True):\n",
        "    if line.strip().startswith('self.server_name ='):\n",
        "        line = f'            self.server_name = \"{srv[8:]}\"\\n'\n",
        "    if line.strip().startswith('self.protocol = \"https\"'):\n",
        "        line = '            self.protocol = \"https\"\\n'\n",
        "    if line.strip().startswith('if self.local_url.startswith(\"https\") or self.is_colab'):\n",
        "        line = ''\n",
        "    if line.strip().startswith('else \"http\"'):\n",
        "        line = ''\n",
        "    sys.stdout.write(line)\n",
        "\n",
        "elif Use_localtunnel:\n",
        "  with capture.capture_output() as cap:\n",
        "    share=''\n",
        "    %cd /content\n",
        "    !nohup lt --port 7860 > srv.txt 2>&1 &\n",
        "    time.sleep(2)\n",
        "    !grep -o 'https[^ ]*' /content/srv.txt >srvr.txt\n",
        "    time.sleep(2)\n",
        "    srv= getoutput('cat /content/srvr.txt')\n",
        "\n",
        "    for line in fileinput.input('/usr/local/lib/python3.9/dist-packages/gradio/blocks.py', inplace=True):\n",
        "      if line.strip().startswith('self.server_name ='):\n",
        "          line = f'            self.server_name = \"{srv[8:]}\"\\n'\n",
        "      if line.strip().startswith('self.protocol = \"https\"'):\n",
        "          line = '            self.protocol = \"https\"\\n'\n",
        "      if line.strip().startswith('if self.local_url.startswith(\"https\") or self.is_colab'):\n",
        "          line = ''\n",
        "      if line.strip().startswith('else \"http\"'):\n",
        "          line = ''\n",
        "      sys.stdout.write(line)\n",
        "            \n",
        "    !rm /content/srv.txt /content/srvr.txt\n",
        "    %cd /content/gdrive/MyDrive/sd/stable-diffusion-webui\n",
        "\n",
        "else:\n",
        "  share='--share'\n",
        "\n",
        "configf=\"--api --disable-safe-unpickle --enable-insecure-extension-access --no-half-vae --xformers --no-download-sd-model --disable-console-progressbars\"\n",
        "\n",
        "clear_output()\n",
        "\n",
        "if os.path.isfile(path_to_trained_model):\n",
        "  !python /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py $share --ckpt \"$path_to_trained_model\" $auth $configf\n",
        "else:\n",
        "  !python /content/gdrive/MyDrive/sd/stable-diffusion-webui/webui.py $share --ckpt-dir \"$path_to_trained_model\" $auth $configf"
      ],
      "metadata": {
        "cellView": "form",
        "id": "9WgdBkRiphhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from slugify import slugify\n",
        "from huggingface_hub import HfApi, HfFolder, CommitOperationAdd\n",
        "from huggingface_hub import create_repo\n",
        "from IPython.display import display_markdown\n",
        "from IPython.display import clear_output\n",
        "from IPython.utils import capture\n",
        "from google.colab import files\n",
        "import shutil\n",
        "import time\n",
        "import os\n",
        "\n",
        "#@markdown #Upload Model to HuggingFace\n",
        "\n",
        "Upload_sample_images = True #@param {type:\"boolean\"}\n",
        "#@markdown - Upload showcase images of your trained model\n",
        "\n",
        "Name_of_your_concept = \"Chest Xray Fine Tuning - trained on chest xray14 dataset (only 1k images as of now)\" #@param {type:\"string\"}\n",
        "if(Name_of_your_concept == \"\"):\n",
        "  Name_of_your_concept = Session_Name\n",
        "Name_of_your_concept=Name_of_your_concept.replace(\" \",\"-\")  \n",
        "  \n",
        "#@markdown - [Create a write access token](https://huggingface.co/settings/tokens) , go to \"New token\" -> Role : Write. A regular read token won't work here.\n",
        "hf_token_write = \"\" #@param {type:\"string\"}\n",
        "if hf_token_write ==\"\":\n",
        "  print('\u001b[1;32mYour Hugging Face write access token : ')\n",
        "  hf_token_write=input()\n",
        "\n",
        "hf_token = hf_token_write\n",
        "\n",
        "api = HfApi()\n",
        "your_username = api.whoami(token=hf_token)[\"name\"]\n",
        "\n",
        "repo_id = f\"{your_username}/{slugify(Name_of_your_concept)}\"\n",
        "output_dir = f'/content/models/'+INSTANCE_NAME\n",
        "\n",
        "def bar(prg):\n",
        "    br=\"\u001b[1;33mUploading to HuggingFace : \" '\u001b[0m|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ \"%\"\n",
        "    return br\n",
        "\n",
        "print(\"\u001b[1;32mLoading...\")\n",
        "\n",
        "NM=\"False\"\n",
        "if os.path.getsize(OUTPUT_DIR+\"/text_encoder/pytorch_model.bin\") > 670901463:\n",
        "  NM=\"True\"\n",
        "\n",
        "with capture.capture_output() as cap:\n",
        "  if NM==\"False\":\n",
        "    %cd $OUTPUT_DIR\n",
        "    !rm -r safety_checker feature_extractor .git\n",
        "    !rm model_index.json\n",
        "    !git init\n",
        "    !git lfs install --system --skip-repo\n",
        "    !git remote add -f origin  \"https://USER:{hf_token}@huggingface.co/runwayml/stable-diffusion-v1-5\"\n",
        "    !git config core.sparsecheckout true\n",
        "    !echo -e \"feature_extractor\\nsafety_checker\\nmodel_index.json\" > .git/info/sparse-checkout\n",
        "    !git pull origin main\n",
        "    !rm -r .git\n",
        "    %cd /content\n",
        "  else:\n",
        "    %cd $OUTPUT_DIR\n",
        "    !rm -r feature_extractor .git\n",
        "    !git init\n",
        "    !git lfs install --system --skip-repo\n",
        "    !git remote add -f origin  \"https://USER:{hf_token}@huggingface.co/stabilityai/stable-diffusion-2-1\"\n",
        "    !git config core.sparsecheckout true\n",
        "    !echo -e \"feature_extractor\" > .git/info/sparse-checkout\n",
        "    !git pull origin main\n",
        "    !rm -r .git\n",
        "    %cd /content\n",
        "\n",
        "\n",
        "image_string = \"\"\n",
        "\n",
        "if os.path.exists('/content/sample_images'):\n",
        "  !rm -r /content/sample_images\n",
        "Samples=\"/content/sample_images\"\n",
        "!mkdir $Samples\n",
        "clear_output()\n",
        "\n",
        "if Upload_sample_images:\n",
        "\n",
        "  print(\"\u001b[1;32mUpload Sample images of the model\")\n",
        "  uploaded = files.upload()\n",
        "  for filename in uploaded.keys():\n",
        "    shutil.move(filename, Samples)\n",
        "  %cd $Samples\n",
        "  !find . -name \"* *\" -type f | rename 's/ /_/g'\n",
        "  %cd /content\n",
        "  clear_output()\n",
        "\n",
        "  print(bar(1))\n",
        "\n",
        "  images_upload = os.listdir(Samples)\n",
        "  instance_prompt_list = []\n",
        "  for i, image in enumerate(images_upload):\n",
        "      image_string = f'''\n",
        "  {image_string}![{i}](https://huggingface.co/{repo_id}/resolve/main/sample_images/{image})\n",
        "      '''\n",
        "    \n",
        "readme_text = f'''---\n",
        "license: creativeml-openrail-m\n",
        "tags:\n",
        "- text-to-image\n",
        "- stable-diffusion\n",
        "---\n",
        "### {Name_of_your_concept} Dreambooth model trained by {api.whoami(token=hf_token)[\"name\"]} with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook\n",
        "\n",
        "\n",
        "Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)\n",
        "\n",
        "Sample pictures of this concept:\n",
        "{image_string}\n",
        "'''\n",
        "#Save the readme to a file\n",
        "readme_file = open(\"README.md\", \"w\")\n",
        "readme_file.write(readme_text)\n",
        "readme_file.close()\n",
        "\n",
        "operations = [\n",
        "  CommitOperationAdd(path_in_repo=\"README.md\", path_or_fileobj=\"README.md\"),\n",
        "  CommitOperationAdd(path_in_repo=f\"{Session_Name}.ckpt\",path_or_fileobj=MDLPTH)\n",
        "\n",
        "]\n",
        "create_repo(repo_id,private=True, token=hf_token)\n",
        "\n",
        "api.create_commit(\n",
        "  repo_id=repo_id,\n",
        "  operations=operations,\n",
        "  commit_message=f\"Upload the concept {Name_of_your_concept} embeds and token\",\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/feature_extractor\",\n",
        "  path_in_repo=\"feature_extractor\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(4))\n",
        "\n",
        "if NM==\"False\":\n",
        "  api.upload_folder(\n",
        "    folder_path=OUTPUT_DIR+\"/safety_checker\",\n",
        "    path_in_repo=\"safety_checker\",\n",
        "    repo_id=repo_id,\n",
        "    token=hf_token\n",
        "  )\n",
        "\n",
        "clear_output()\n",
        "print(bar(8))\n",
        "\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/scheduler\",\n",
        "  path_in_repo=\"scheduler\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(9))\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/text_encoder\",\n",
        "  path_in_repo=\"text_encoder\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(12))\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/tokenizer\",\n",
        "  path_in_repo=\"tokenizer\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(13))\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/unet\",\n",
        "  path_in_repo=\"unet\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(21))\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=OUTPUT_DIR+\"/vae\",\n",
        "  path_in_repo=\"vae\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(23))\n",
        "\n",
        "api.upload_file(\n",
        "  path_or_fileobj=OUTPUT_DIR+\"/model_index.json\",\n",
        "  path_in_repo=\"model_index.json\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(24))\n",
        "\n",
        "api.upload_folder(\n",
        "  folder_path=Samples,\n",
        "  path_in_repo=\"sample_images\",\n",
        "  repo_id=repo_id,\n",
        "  token=hf_token\n",
        ")\n",
        "\n",
        "clear_output()\n",
        "print(bar(25))\n",
        "\n",
        "display_markdown(f'''## Your concept was saved successfully. [Click here to access it](https://huggingface.co/{repo_id})\n",
        "''', raw=True)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "-kvM1un4p8s8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}