CNN-exercise1-solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H7Z7pl_YN9KK"
   },
   "source": [
    "# CNN Exercise 1: Rebuilding LeNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dahpSd3BN9KN"
   },
   "source": [
    "Created by Cedric Ewen in 2023\n",
    "\n",
    "Credits to Bogdan Wiederspan who inspired this exercise with his exercice from the last years deep learning school and to [this blog post](https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118) that gave a good baseline for the exercise.\n",
    "Thanks to Github Copilot for writing the description of this exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xYmn0uZxN9KP"
   },
   "source": [
    "# History\n",
    "Machine learning is a topic that is researched for quite a long time. Biggest problem in the past was the lack of computational ressources. Especially image recognition was terrible in this department, since the analyzation of pictures needed huge feed-forward-networks (bad scaling of parameter numbers).\n",
    "\n",
    "Feed-forward-networks are a bad choice to analyze images, since the learned mapping is not translation-invariant. This means, that the network could not recognize for example a deer, when the deer was not centered.\n",
    "\n",
    "LeNet-5 was one of the earliest convolutional neural networks and promoted the development of deep learning. The main benefit of convolutional networks is their invariance in translation. The other benefit of this approach is the reduction of learnable parameters by introduction of convolutions.\n",
    "\n",
    "We will recreate the LeNet-5 network to train a model on MNIST dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "4qXmdFQ2N9KQ"
   },
   "outputs": [],
   "source": [
    "# basic imports\n",
    "import torch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9L0nv-kdN9KS",
    "outputId": "e8fe5dcf-d858-47d8-f384-30eecfdc0b81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "56UNjo3oN9KU"
   },
   "source": [
    "# The dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3xT4DgdN9KU"
   },
   "source": [
    "The MNIST (Modified National Institute of Standards and Technology database) dataset is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the \"Hello World\" of Machine Learning. Whenever people come up with a new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns Machine Learning tackles this dataset sooner or later.\n",
    "\n",
    "PyTorch provides a number of datasets in torchvison.datasets. We will download the MNIST dataset from there. The following code downloads the MNIST dataset, and creates a training and test dataset. The training dataset contains 60,000 images and the test dataset contains 10,000 images. Each image is a 28x28 grayscale image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sdeRlO-IN9KV",
    "outputId": "64838f49-70c4-43f5-dcd8-59be5235280d"
   },
   "outputs": [],
   "source": [
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "train_data = datasets.MNIST(\n",
    "    root = 'data',\n",
    "    train = True,\n",
    "    transform = ToTensor(),\n",
    "    download = True,\n",
    ")\n",
    "test_data = datasets.MNIST(\n",
    "    root = 'data',\n",
    "    train = False,\n",
    "    transform = ToTensor()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-3P0K0GbN9KW"
   },
   "source": [
    "It is important to first get a feeling for the data. Therefore, we will first plot some of the images and their corresponding labels.\n",
    "\n",
    "We also have a look on the shape of the data. The shape of the data is important for the design of the network. In the following cell, one can see that the shape is (60000, 28, 28). This means that we have 60000 images of size 28x28 pixels. The images are grayscale, meaning that they have only one color channel. If the images were RGB images, the shape would be (60000, 28, 28, 3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4gnfmVI9N9KY",
    "outputId": "1b0e4e43-95d3-4b3b-da8b-60f395a278f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: torch.Size([60000, 28, 28])\n",
      "Shape of test data: torch.Size([10000, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape of training data: {train_data.data.size()}\")\n",
    "print(f\"Shape of test data: {test_data.data.size()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 675
    },
    "id": "-sFrzD_-N9KZ",
    "outputId": "2a6528ea-7583-4fa1-e36b-bc11dfb9323b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(10, 8))\n",
    "cols, rows = 5, 5\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(train_data), size=(1,)).item()\n",
    "    img, label = train_data[sample_idx]\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(label)\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QsB24rTwN9Ka"
   },
   "source": [
    "During training, we usually want to pass samples in 'minibatches', so small subsets of the training dataset, instead of passing all the training data at once. This is because passing all the training data through the network at once is computationally expensive, and it is also more difficult for the network to learn from all the training data at once. The number of samples in a minibatch is called the 'batch size'. The batch size is a hyperparameter that we can tune to make the network learn better. The batch size is usually a power of 2, e.g. 32, 64, 128, 256, 512, etc. to optimally utilize the hardware. In this exercise, we will use a batch size of 64.\n",
    "\n",
    "In pytorch, a dataloader is an object that can be used to pass minibatches of data to the network. We can create a dataloader from a dataset object. The dataloader will automatically split the dataset into minibatches of the specified batch size. We can then iterate over the dataloader to get minibatches of data. The dataloader will automatically shuffle the data at the start of each epoch (an epoch is a full pass through the training data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "UpfGqQ-9N9Kb"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "loaders = {\n",
    "    'train' : torch.utils.data.DataLoader(train_data,\n",
    "                                          batch_size=64,\n",
    "                                          shuffle=True,\n",
    "                                          num_workers=1),\n",
    "\n",
    "    'test'  : torch.utils.data.DataLoader(test_data,\n",
    "                                          batch_size=64,\n",
    "                                          shuffle=True,\n",
    "                                          num_workers=1),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vEkKQmyKN9Kc"
   },
   "source": [
    "# The Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A6lMIAS_N9Kc"
   },
   "source": [
    "We will now recreate one of the first convolutional neural networks, LeNet-5, which was introduced by Yann LeCun et al. in 1998. This network was used to classify handwritten digits of the MNIST dataset. The network consists of two convolutional layers, two pooling layers and three fully connected layers. The network architecture is shown below.\n",
    "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/c/cc/Comparison_image_neural_networks.svg/2560px-Comparison_image_neural_networks.svg.png)\n",
    "On the right, we see a much more advanced version of the LeNet-5, called AlexNet. Its quite interesting how similar, but yet so different they are.\n",
    "AlexNet is much more advanced than the plain old LeNet.\n",
    "\n",
    "Could you think of some reasons why:\n",
    "- they changed all sigmoid functions to ReLu\n",
    "- they changed the Pooling from average to max\n",
    "- they use 3 conv-layers with small window size insted of one with bigger kernel size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TEptsf8MN9Kd"
   },
   "source": [
    "In general Conv-networks can be separated into 2 blocks:\n",
    "- conv-part (filters)\n",
    "- fully connected network\n",
    "\n",
    "In the convolutional-part of the network filters are trained to detect different propeties. They workout the different features of the image. These processed information are then used as input of a fully connected network to classify the input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5MfG0fOHN9Kd"
   },
   "source": [
    "## Build LeNet-5 with PyTorch:\n",
    "Lets build the model. We start with a Python class that inherits from nn.Module. In the init function we define the layers of the network. In the forward function we define the forward pass of the network.\n",
    "\n",
    "A list of possible layers can be found [here](https://pytorch.org/docs/stable/nn.html).\n",
    "\n",
    "By looking at the Picture of LeNet we need a ***Linear Layer, a Pooling Layer*** and atleast some kind of ***ConvND*** layer (which Dimension? Remember your input data shape!).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFdGLACcN9Ke"
   },
   "source": [
    "To help you a little in building the network the start and end is already given. Compare the code of the layer with the Image of LeNet to get a better understand what they do!\n",
    "\n",
    "Try to answer the following questions while learning how to use PyTorch. PyTorch error messanges help you to understand whats happening, but also the ```print(model)``` and the ```torchsummary.summary``` commands.\n",
    "- Which properties do the layer types have and what do they do?\n",
    "- Why do we flatten the output?\n",
    "- What is the shape after flatten?\n",
    "- What work is done by the dense layers?\n",
    "- Why do we use a softmax activation function at the end?\n",
    "- What is the sum of the softmax function outputs?\n",
    "- Where do the weights come from? How can one calculate the number of weights in a layer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "HDBTw6fuN9Ke"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        # define the layers\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=1,\n",
    "                out_channels=6,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=2,\n",
    "            ),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(\n",
    "                in_channels=6,\n",
    "                out_channels=16,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=0,\n",
    "            ),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)\n",
    "        # fully connected network, output 10 classes\n",
    "        self.out = nn.Sequential(\n",
    "            nn.Linear(400, 120),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(120, 84),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(84, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.pool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.pool2(x)\n",
    "        # flatten the output of conv2 to (batch_size, 28x28)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        output = self.out(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d3NxxtwJN9Kf",
    "outputId": "d1e88926-fdf8-4a5e-dbd9-c7219bbd31eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (1): Sigmoid()\n",
      "  )\n",
      "  (pool1): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (1): Sigmoid()\n",
      "  )\n",
      "  (pool2): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "  (out): Sequential(\n",
      "    (0): Linear(in_features=400, out_features=120, bias=True)\n",
      "    (1): Sigmoid()\n",
      "    (2): Linear(in_features=120, out_features=84, bias=True)\n",
      "    (3): Sigmoid()\n",
      "    (4): Linear(in_features=84, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn = CNN()\n",
    "print(cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "QB3cxcqON9Kg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [64, 6, 28, 28]             156\n",
      "           Sigmoid-2            [64, 6, 28, 28]               0\n",
      "         AvgPool2d-3            [64, 6, 14, 14]               0\n",
      "            Conv2d-4           [64, 16, 10, 10]           2,416\n",
      "           Sigmoid-5           [64, 16, 10, 10]               0\n",
      "         AvgPool2d-6             [64, 16, 5, 5]               0\n",
      "            Linear-7                  [64, 120]          48,120\n",
      "           Sigmoid-8                  [64, 120]               0\n",
      "            Linear-9                   [64, 84]          10,164\n",
      "          Sigmoid-10                   [64, 84]               0\n",
      "           Linear-11                   [64, 10]             850\n",
      "================================================================\n",
      "Total params: 61,706\n",
      "Trainable params: 61,706\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.19\n",
      "Forward/backward pass size (MB): 7.13\n",
      "Params size (MB): 0.24\n",
      "Estimated Total Size (MB): 7.56\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "summary(cnn, (1, 28, 28), batch_size=64, device='cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAVMhZrwN9Kh"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SYHWK040N9Ki"
   },
   "source": [
    "Now that we have the model, we can train it. We will use the Adam optimizer, which is a popular optimizer along with SGD (Stochastic Gradient Descent). We will also use the cross entropy loss function, which is the most common loss function for classification problems. Note that the CrossEntropyLoss function in PyTorch applies a softmax function to the output layer before calculating the loss. This means that we do not need to add a softmax layer to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "-3Ec_kuvN9Ki",
    "outputId": "a649c880-2968-49a5-94c2-d5ba819fa7c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CrossEntropyLoss()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_func = nn.CrossEntropyLoss()\n",
    "loss_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "vT4bc6k9Oi1D"
   },
   "outputs": [],
   "source": [
    "# move model to GPU\n",
    "cnn = cnn.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "FZNJ04uVN9Kj",
    "outputId": "9dd91432-41d4-4758-9d65-f33cf0f23fb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adam (\n",
       "Parameter Group 0\n",
       "    amsgrad: False\n",
       "    betas: (0.9, 0.999)\n",
       "    capturable: False\n",
       "    differentiable: False\n",
       "    eps: 1e-08\n",
       "    foreach: None\n",
       "    fused: False\n",
       "    lr: 0.001\n",
       "    maximize: False\n",
       "    weight_decay: 0\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import optim\n",
    "optimizer = optim.Adam(cnn.parameters(), lr = 0.001)\n",
    "optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ayeBbCCUN9Kk"
   },
   "source": [
    "Now we can implement a train method that will train the model. It takes as input the number of epochs, the model and the dataloader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "ZzvDe5EtN9Kk",
    "outputId": "1f15185a-d1fa-48b9-d95b-21f6ff3949ce"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:54<00:00,  5.44s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def train(num_epochs, cnn, loaders):\n",
    "\n",
    "    # Train the model\n",
    "    cnn.train()\n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "        for i, (images, labels) in enumerate(loaders['train']):\n",
    "            # move data to GPU\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            # run model\n",
    "            output = cnn(images)\n",
    "            # compute loss function\n",
    "            loss = loss_func(output, labels)\n",
    "            # clear gradients for this training step\n",
    "            optimizer.zero_grad()\n",
    "            # backpropagation, compute gradients\n",
    "            loss.backward()\n",
    "            # apply update\n",
    "            optimizer.step()\n",
    "\n",
    "num_epochs = 10\n",
    "train(num_epochs, cnn, loaders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E1JWHUXEN9Kk"
   },
   "source": [
    "To check the performance of our network, we can run a prediction on our test data and check if the events are categorised correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "lSPZE9xDN9Kl"
   },
   "outputs": [],
   "source": [
    "def test(cnn, loaders):\n",
    "    test_prediction_list = []\n",
    "    test_label_list = []\n",
    "    # Test the model\n",
    "    cnn.eval()\n",
    "    with torch.no_grad():\n",
    "        for images, labels in loaders['test']:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            test_output = cnn(images)\n",
    "            pred_labels = torch.max(test_output, dim=1)[1]\n",
    "            test_prediction_list.append(pred_labels)\n",
    "            test_label_list.append(labels)\n",
    "    test_label = torch.cat(test_label_list)\n",
    "    test_prediction = torch.cat(test_prediction_list)\n",
    "    return test_label.cpu(), test_prediction.cpu()\n",
    "\n",
    "test_label, test_prediction = test(cnn, loaders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "YeDhqDV5N9Kl",
    "outputId": "1bf29664-ad55-4eb7-d7c4-ff0846d1e340"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted label: 6, True label: 6; Prediction is correct\n",
      "Predicted label: 4, True label: 4; Prediction is correct\n",
      "Predicted label: 8, True label: 8; Prediction is correct\n",
      "Predicted label: 5, True label: 5; Prediction is correct\n",
      "Predicted label: 9, True label: 9; Prediction is correct\n",
      "Predicted label: 1, True label: 1; Prediction is correct\n",
      "Predicted label: 0, True label: 0; Prediction is correct\n",
      "Predicted label: 0, True label: 0; Prediction is correct\n",
      "Predicted label: 8, True label: 8; Prediction is correct\n",
      "Predicted label: 2, True label: 2; Prediction is correct\n"
     ]
    }
   ],
   "source": [
    "for i, label in enumerate(test_label[:10]):\n",
    "    print(f\"Predicted label: {test_prediction[i]}, True label: {label}; Prediction is {'correct' if test_prediction[i] == label else 'incorrect'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "apO4nKlFN9Km"
   },
   "source": [
    "We can use [sklearn.metrics] to take a more detailed look into the performance, e.g. via creating a confusion matrix.\n",
    "\n",
    "[sklearn.metrics]: https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "p60b7PwPN9Km",
    "outputId": "ce0bb3b9-404f-4744-d27b-24da8d21373b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4Gg787G1Y39TTV40eTxTi9BF3iKU69HkxHz2eLMTkZ1sY6r5vmzcGT8zGO4sz8O0nQfD0VWPE+1nYs9nX6lMtmmIfBOwnrq1rZJi0OB0XT1erh4sWe76/2S+vIve6GF/NDwEAbF8rw8Itl/T98g9P9BxQoO+X74bf2OONfjk+G5mpUsgzJIiffF3fL3fr+2WbuDK07lSGU4c9UF4qRJu4MrzxURb+3OqD0iLjR05Ey0qIJFp4eGvg6q5FdDv9l73Uc/Ub6NjiHAQAWagSHt4aBIQqIXTSfyEAgKw0icl65fYaQ0ynMsTEVuDsUTeUFjohOKoK8e/KkZUmMRlI69tLBw8fDVzdNIY4U62QVb8Tezm/miotBNBYeZqN1kGm8dj82sTbb7+N+Ph4dOnSBffddx+WLFmCsrIyDB8+3KrHfXBAAYryRfjukxAUKMSIbleBWd9dMtygosiSQCg0fotWVgnx7YIQyNOlcHHVIu7hIryz7ArcvYzZt9c/zsB3C0Lw+XsRKMrT/5jIY6/k4qWJ160aS30d+MUHXn4aDJ0sh49MjdRzLpg+uNktN/o4ElvFdKfjykKVJlcUkhLcMH9sJOKnyDFsqhxZaVLMfDUKVy8YP3B+WCGDs6sWExZcg7unBueOuWH64Gioqq1W8Z9xERg7JxPzf7h848dEvPD5+6Y3R/V+vgAjP7wOgUCfeZr8XHOTRfcry50w7aVojPk4E8t3XURJgQh//eKN9QtuvZrT0JpiHwTsJ64Dv/jAy1eNoZOuG+vxSrSxX4ZY6JfjohD/7nUMm3Jd3y9fa2baLz8PuNEvM4z98hVjv1RVCdBzQCFeeVsOsUQHeYYEW9fIsHW16Xrxs7+9bPKDIyv3XAQA9A2NrX+sNjgHh06So8+LBcY4ftfHMfnZ5jh9xPJqSfYUQ1WFEN0eK8KQd+RwdtUiP0eMhH0emLM00GT++OzvUk3b60acfUM61inGhmAv5xc1fQKdzvY/oP3ZZ59h4cKFkMvliI2NxbJly9C1a9c7Pq+4uBheXl7IuRAJT4+mdTPIk6Fxtq4CETkae/1pz7tl+48p+hdT61TYj59RVFQET0/POz/BQd0cUyWcC4S7lcdUpSVadGmXbfevqc0zyQAwbtw4i9MriIiIiIhswS4GyURERERke5pGmJNs7f03lKY1R4GIiIiIqAFwkExEREREZIbTLYiIiIgIAKdbVMdMMhERERGRGWaSiYiIiAgAoNUJoNVZ+cdErLz/hsJMMhERERGRGWaSiYiIiAgA5yRXx0wyEREREZEZZpKJiIiICACggRAaK+dQNVbde8NhJpmIiIiIyAwzyUREREQEANA1wuoWOq5uQURERETkmJhJJiIiIiIAXN2iOmaSiYiIiIjMMJNMRERERAAAjU4Ijc7Kq1vorLr7BsNMMhERERGRGWaSiYiIiAgAoIUAWivnULVwjFQyM8lERERERGaYSSYiIiIiAFzdojpmkomIiIiIzDSJTPILMbEQCcS2rkaD+jUz0dZVsIonQ+NsXQWipkvnGPP8iMh+Nc7qFo7xXsVMMhERERGRGQ6SiYiIiIjMNInpFkRERER09/RLwFn3xjpr77+hMJNMRERERGSGmWQiIiIiAgBoIYSGPyYCgJlkIiIiIqJbMJNMRERERAC4BFx1zCQTEREREZlhJpmIiIiIAOjnJGs5JxkAM8lERERERLdgJpmIiIiIAAAanQAanXXXMbb2/hsKM8lERERERGaYSSYiIiIiAICmEdZJ1nBOMhERERGRY2ImmYiIiIgAAFqdEForr5Os5TrJRERERESOiZlkIiIiIgLAOcnVMZNMRERERGSGmWQiIiIiAgBoYf11jLVW3XvDYSaZiIiIiMgMM8m11H9YLp4bnQNfmRqpSS74/P1QXDjpautq3eLX9TJsXRmIAoUYzdpW4PXZ6YjpVG6xrFoF/PhZMPb+6Ic8uRih0ZUYPj0Tcb2KDWU0GmDjpyHYv9UXBQoxfANVeOT5XLz0lhyCen7RrOtr2ePJQsS/K0dgmBKZaVKsnROMY396Viuhw9DJ2ej3ch7cPTVISnDDsqlhyEqTGkp4eKsx5uNMdH20GDotcGiHN1Z+EILKcidDmQf7F+Kl8dkIja5CUZ4Iv3zljy0rA0zq0uvpArwwJgch0VUoK3ZCwj4PrJkdgpICkUPHJZZoMXhiNh5+tgA+MjXyc0TYsDgQezb71Vj/hmIv55Yt2u+j9Wlo3q4C3n5qlBQ54cRBD6ydE4z8bDEAoMP9pXhmlAKtYsvh5qFFZpoEP34egH3bfBw6LrFUi/Hzr6FlhwpEtKzE//7wxMxXm9U6Jmv4t/bDwDAlXp6YjdhupfCRqZCXLcafW32waWkA1Cp9Ho3tRf9WNs0k//XXX+jfvz9CQkIgEAiwfft2W1anRj2fKsCoGVnYsCgIY/u2QmqSM+ZsTIWXn8rWVTPx188++HJmGAa9fR1LdyWjWdtyfDi4JQpzLX8X+nZBKHZ+54/XZ6dj5b5zeHyIAnNGNMflsy6GMj+tCMLOb2R44+N0rNx/DsPeu4atK4Pw33WyetWxrq9l2y5lmPb5Veza5IsxfVrh8C5PzFh3BZExFYYyL4xVYMCrCiyfGoYJT7ZEZbkQczemQiw1XtCZ8lk6ImMqMe2laHwY3wztu5birYXXDH/v0qsYUz67it++8cPrvWLw2bQwPDNSgaeG5xrrcm8ZJi9Lx67Nvhj1UAzmvB6JmNgKvLXwmkPHBQDTv7iK2O6lWPxOOEb0aI35YyJx7bLznZrzrtnLuWWr9jv1tzvmvB6J13q0xscjoxASVYUP1lwxOU5qkjM+HhmFNx5phT2bfTF5WTq69i5GbdhrXEKhDspKIX5e648TBz1qFYs1/Zv7YXiLSgiFOiydEoZRvWLwxUcheGJIHoZPkxv2wfb6d9FC2CibI7BpLcvKytCxY0esWLHCltW4o2dG5WLXRl/s+d4X6ZecsWxKGKoqBOg7KN/WVTOxfU0g+r6ci0dfzENEq0qMnZ8OqYsWv9eQDdz3ky9eeFOOex8pRlCkEo/H56LLw0XY9kWgoUxyghu69i3Evb2LERiuRPcnC9GpZzEunnSrVx3r+loOHKFAwj4PbFkZgIwUZ3yzMBgpZ1wwYHjejRI6DByhwKalgTiy2wtpyS5YMD4CfoEqPNCvCID+Q+Deh0uw+J1wXDjhhnNH3fH5+6HoOaAQvoH6N9XezxXg8C4v/PatP+TpUhzd64nNnwXghbE5wI27cNvGlSE7Q4Kf18qQnSHFuaPu+O07X8TEljt0XF0eKkb7/yvFB0Oa4cRBD2RfkyA50Q1Jx+rXxnVhL+eWLdoPALatkeH8cTfkZEqQlOCG7z8LQOvO5XAS6dtm8/JAfLMwGEkJbrh+VYrta2VI2OeBbo8XOnRcVRVOWD4tDDs3+iE/x/YXNP/N/TBhvyc+nRiB4wc8IE+X4p89XtiySoZujxnbk+1F/1Y2HSQ/9thj+Pjjj/H000/bshq3JRJr0bJDOY5X+/as0wlw4qAH2sZZnsZgCyqlACmnXRHbw5hhEgqB2O4lOJ/obvk5VUJIpKbT5yXOWiQdNZZv06UMpw55IPOy/tJc6jkXJB11N5mSURd1fS3bxJXfkrlIPOCBNnFlAICgCCX8AtUm+ywvccL5E65oc2OfbbqUoaTQCZdOGy/FHT/oAZ0WaH1jKopYooOyyvR0UFYKIQtRITBMP+BMSnSDLESFex8uBqCDt78KPZ4oQsJ+d4eO6//6FOPSaVc8PyYHGxLPYe3BZIz8MAsSZ+veWmEv51Z96tEQ7WfOw1uNh58pQFKCKzTqmucyuXlqUFJ454GKo8VlK+yHt3Lz0KCk0KnGv9uSvbRXU6bRCRtlcwSOUUsb8vTVwEkEFCpMP5QKckXwkaltVKtbFeeLoNUI4O1vWidvmQoFCrHF53R+qBjbVwciM1UKrRY48ZcHjuzwQX6Osfxz4+R4cEAB3ujZDgMiO2NC3zZ4akQOej1Tv2/sdX0tfWRqFJhNFylQiOAToC/ve+Nf830WKkTwDdAPAn1lahTmmf5dqxGgpNBYJmG/B7o/XoTY7iUQCHQIja7Cs68r9M+/kZVNOuaG/4yLwHurruK3q6fx/ekklJU4YcPiIIeOKzhSiXb3liEqphKzXmuGVTNC0f2JQrw57xqsyV7OrfrUoyHa76bXpmfh55Qz2JJ0DrIQFT4aXvNczwf7F6JVxwrs2ezbpOKyJfZDUyFRVRjwai52fGv9+xHqw17ai/4dbH/dpA6qqqpQVVVl+P/i4vplM0lv1KwMLJ8cidE92wECIDiyCr1fzMXv3/sbyhz8rw/2b/XFpBVpiGxVgdRzrlgzIxx+gUo88kLTubS1c4MvQqKqMOvrNIjEOpSXOGHbWn8MnZQN7Y2EakTLSoyelYkNiwORuN8DvgEqjPjgOl6bft22lb+N2sQlEOqg0wHzx0WivESfPVr9UQjeX3MVy6eFQVnJ79LW9OPKAOza5IfAMCUGvy3H5KXp+HBoMwCmWdeOD5TincUZWDo5DFcvWn+++N2qbVxkP/yCVJizIRV//eqNnRvtc5BM1qeFAForn6fW3n9DcahB8rx58zBz5sxGPWZxvhM0asDb7Buqj78aBQr7efk8fdUQOuluuUmvUCGGj8xyxsDLT433112GslKA4gIR/IJUWD83FEERxi8iX80Ow3Pj5Og5oAAAENWmEjnXJPjxs+B6DZLr+loWKETwMcuO+8jUKLgxL+7m/DhvmdokA+4tU+PyOf0NiPkKEbz9TPchdNLBw7v6cwRYOycEX80Lhk+AGkV5TojtXgoAkF/VTzV58c0cnDvmZlgZIi3ZBZUVQizaftmh48rPFiNPLjYMkAEg/ZIzhELAP1hlsmpBQ7KXc6s+9WiI9jMeX4TifBEyU6VIvyTFhsRktIkrR3KicU54+/8rxcyv07BqRgj+2HLnLLKjxGUP2A/1fANVWPBjCpIS3LB0cthdx2Mt9tJe9O/gUCmiadOmoaioyLBlZGRY/ZhqlRCXTruiU/cSw2MCgQ6x3UuRlGg/y82IJTq06FCOU4eMS/9otcCpQx5oHVd62+dKnHXwD1ZBowYO7/BG1z6Fhr9VVQghFJj+fKTQCYYsZF3V9bVMTnRFbA/T+nd+sMTwQStPlyAvW2SyT1d3DVp3KkfyjX0mJ7jBw1uDFu2N89Viu5dCIATOnzA9rlYrQJ5cDLVKiF4DC5GU4IqifP0br7OLFjqzX9LUavTfhtOSnR02rnPH3OAbpIKzq8ZQPqx5FTQaIPe65ak6DcFezq361KMh2s8SwY13ZLHE2NE63F+K2d+mYe2cYOzcUPvsnr3HZS/YD/UZ5IVbUnDpjCs+nRgOnZV/SOJu2Et7NWWck2zkUF+7pFIppFLrZLVuZ+tqf0xakoGLp1xx4YQrnh6pgLOrtlbzAhvTwJHZWDwxCi07lKFVp3L8vCYAlRVC9H5Rf6fzp+Oj4BesxLBpWQCAC8ddkSeXILpdOXLlEmz8NBharQDPjsk27PO+Rwvx/bJgyEKViIipxOWzrti+OgCPvpRnsQ53cqfXcvLSdOTKxfhqXjAAYPuXMiz8KQXPvp6Do3s90XNAIVp2qMASQ6ZDgO1fyjBoQg4y06SQp0sQ/64cedliHN7lBQDISHHGsT898NYn17B8ShicxDqM/fgaDvzsbVi31dNXjR5PFOL0EXeIpTr0eTEfPZ4sxORnWxjq/s/vnnhrYQaeHJqLhP0e8A1U442ZmTh/3NWh49q3zRuDJ2bjncUZ+PaTIHj6qjHi/Szs2exr9akW9nJu2aL9YjqVISa2AmePuqG00AnBUVWIf1eOrDSJYQDT8YFSzPomDdu/9Meh37wMV4XUKkGtbt6z17gA/fQlkUQHDx8NXN00iG6nX7Ys1SzD2Rj+zf3w5gA5J1OCNbNC4FXt6lT1+1nYXvRvZNNBcmlpKVJSUgz/n5aWhpMnT8LX1xcRERE2rJmpA7/4wMtPg6GT5fCRqZF6zgXTBzdDYa71smz18eCAAhTli/DdJyEoUIgR3a4Cs767ZLiZQZElgVBozOQoq4T4dkEI5OlSuLhqEfdwEd5ZdgXuXsaM4usfZ+C7BSH4/L0IFOXpf0zksVdy8dLE+s3DvdNrKQtVmmSpkxLcMH9sJOKnyDFsqhxZaVLMfDUKVy8Y35h/WCGDs6sWExZcg7unBueOuWH64Gioqq3q8J9xERg7JxPzf7h840c3vPD5+6Emdev9fAFGfngdAoE+QzP5ueYmi9P//oMvXNw1eGp4LkbOyEJZkRNO/u2OtXNCkCcXO2xcleVOmPZSNMZ8nInluy6ipECEv37xxvoFQXVp2nqxl3PLFv2yqkKIbo8VYcg7cji7apGfI0bCPg/MWRoIlVJfpvfz+XB21eKl8Tl4aXyOYd+nDrvh3eeMX3QcLS4AmP1dKoLCjVPBVv5+EQDQN6TjHeNqaP/mftj5wRKERisRGq3ExuNJJvWp3hZsr38PDYTQWHmigbX331AEOp35BeTGs3//fvTq1euWx+Pj47F+/fo7Pr+4uBheXl54CAMgEjStk+PXzERbV8EqngyNs3UViIiIak2tU2E/fkZRURE8PT3v/AQHdXNM9UlCd7i4WzeHWlGqxqQuh+z+NbVpJvmhhx6CDcfoRERERFSNVieA1srz0q29/4biGPluIiIiIqJG5FA37hERERGR9WgbYU6y1kFytI5RSyIiIiKiRsRMMhEREREBALQ6IbRWXsfY2vtvKI5RSyIiIiKiRsRMMhEREREBADQQQAPrrj5h7f03FGaSiYiIiIjMMJNMRERERAA4J7k6x6glEREREVEj4iCZiIiIiMgMp1sQEREREQBAA+vfWKex6t4bDjPJRERERERmmEkmIiIiIgC8ca86x6glEREREf2rrVixAlFRUXB2dkbXrl1x9OjR25ZfsmQJYmJi4OLigvDwcEycOBGVlZW1Ph4zyUREREQEANDohNBYOdNbn/1///33ePvtt7Fq1Sp07doVS5YsQd++fXHhwgUEBATcUn7jxo2YOnUq1q1bhwceeAAXL17EsGHDIBAIsGjRolodk5lkIiIiIrJrixYtwsiRIzF8+HC0bdsWq1atgqurK9atW2ex/OHDh9GtWze8/PLLiIqKQp8+fTBo0KA7Zp+r4yCZiIiIiAAAOgigtfKmu7F6RnFxsclWVVVlsU5KpRKJiYno3bu34TGhUIjevXvjyJEjFp/zwAMPIDEx0TAoTk1NxY4dO/D444/X+rXgIJmIiIiIGl14eDi8vLwM27x58yyWy83NhUajQWBgoMnjgYGBkMvlFp/z8ssvY9asWejevTvEYjGaN2+Ohx56CO+9916t68c5yUREREQEoHHnJGdkZMDT09PwuFQqbbBj7N+/H3PnzsXnn3+Orl27IiUlBRMmTMDs2bPxwQcf1GofHCQTERERUaPz9PQ0GSTXxN/fH05OTsjOzjZ5PDs7G0FBQRaf88EHH2DIkCEYMWIEAKB9+/YoKyvDqFGjMH36dAiFd/4iwOkWRERERAQA0OoEjbLVhUQiQVxcHPbu3Wusp1aLvXv34v7777f4nPLy8lsGwk5OTgAAnU5Xq+M2jUyyQKDfmpAnQ+NsXQWr2JF53NZVsIrHw5pme6GWbyREVA9N7HPLgO8bZAVvv/024uPj0aVLF9x3331YsmQJysrKMHz4cADA0KFDERoaapjX3L9/fyxatAidOnUyTLf44IMP0L9/f8Ng+U6axiCZiIiIiO6aBkJorDzRoD77f/HFF6FQKPDhhx9CLpcjNjYWu3btMtzMl56ebpI5fv/99yEQCPD+++8jMzMTMpkM/fv3x5w5c2p9TIGutjlnO1RcXAwvLy88JBgIkUBs6+o0LMdtlttiJtnBNNF+SGQXmEl2CGqdCvvxM4qKimo1f9ZR3RxTvfX3U5C6W3dMVVWqwpJuv9j9a8pMMhEREREBQL3mDNfnGI6AN+4REREREZlhJpmIiIiIAABaCKG1cg7V2vtvKI5RSyIiIiKiRsRBMhERERGRGU63ICIiIiIAgEYngMbKN9ZZe/8NhZlkIiIiIiIzzCQTEREREQAuAVcdM8lERERERGaYSSYiIiIiAIBOJ4RWZ90cqs7K+28ojlFLIiIiIqJGxEwyEREREQEANBBAAyuvbmHl/TcUZpKJiIiIiMwwk0xEREREAACtzvqrT2h1Vt19g2EmmYiIiIjIDDPJRERERAQA0DbC6hbW3n9DcYxaEhERERE1ImaSiYiIiAgAoIUAWiuvPmHt/TeUJjtI7h+vwHOjc+ArUyM1yQWffxCKCyfdaizf48lCxE++jsAwJTLTpFg7NwTH/vSsVkKHoZPk6PdyHtw9NUhKcMOyaeHISpPesi+xRIulv15E83aVGN2nFVLPueofl2oxfn4GWravQETLSvzvD0/MfC26bnENyzWN6/1QXDjpevu43pUb45oTfGtck7NN45oaZogrMEyJlydmI7ZbKXxkKuRli/HnVh9sWhoAtUposp/n3lDgscF5CAhToTjfCb9+7Y9NywLrFF9N/rveHz+tDESBQoxmbSswenYGYjqVWyyrVgE/fBaEP370Q55cjLDoSgyfnoUuvYoNZcpLhfh2QQgO7/JCUZ4YzduV4/VZ19Aq1vI+66ux+2GH+0uwcMtli/t+8/FWuHjKFa+8fR1D3sm+5e+V5UIMaNmhfnHabb+0nbq+Jo2psdsLAAaNz8Z9vYsR3a4CaqUAz7Zpb8UI685e2sseP7s63F+CZ0Yq0Cq2HG4eWmSmSfDjygDs2+bb0OHXmr20FzVtNv00mTdvHu699154eHggICAAAwcOxIULF+56vz2fKsCoGVnYsCgIY/vFIDXJBXM2pMLLT2WxfNsuZZi24gp2bfLDmL4xOLzbCzPWpiEypsJQ5oUxORjwqgLLp4ZjQv9WqCwXYu6GyxBLtbfs77XpWciTi295XCjUQVkpxM/rZDhx0OPu4urbCqlJzpiz8Q5xfX4Vuzb5YkyfVji8yxMz1l0xjWus4kZcYZjwZEt9XBtTDXGFt6iEUKjD0ilhGNUrBl98FIInhuRh+DS5ybFGz85Cv5fzsWZ2CEY8GIMZw5o12BvWgZ99sGZmGF5++zqW7zqP6LYV+GBwCxTmWv6O982CEOz8zh+jZ2dg1b4kPD4kFx+PiMblsy6GMksnReLEQQ9MWnYVn/+RjE49S/DeSy2Re/3WdqsvW/TDpAQ3vBTbzmTbucEX169KcPGUPv4tqwJuKXP1gjP++tXr7uO0s35pK3V9Tey5bg3RXgAgkujw13+98dvX/laPsa7spb3s9bOrbZdypCa74ONRzfBG7xjs+d4Pk5emo2vvooYLvg7spb2aKo1O0CibI7DpIPnAgQMYO3Ys/vnnH/z+++9QqVTo06cPysrK7mq/z4xUYNdGP+z5wQ/pl5yxbGoYqiqE6PtSvsXyA19TIGG/J7asCkBGijO+WRiMlLMuGDA890YJHQaOUGDT0iAc2eOFtGQXLJgQCb9AFR7oa/om0aVXMeJ6lmDN7NBbjlNV4YTl08Kxc6Mf8hV1T+I/MyoXuzb6Ys/3vvq4poShqkKAvoNqiGuEAgn7PLBlZbW4zrhgwPA8s7gCcWT3jbjGR+jj6qePK2G/Jz6dGIHjBzwgT5finz1e2LJKhm6PGeMOb1GJJ4fm4qPhUfhnjxeyM6RIOeOK43/V/YuAJdvWBKDfy7no82I+IlpVYtz8dEhdtNiz2c9i+T9/8sULb8px7yPFCI5U4on4XHR5uAhbv9BntasqBPh7hzdenZ6J9v9XipBmVXjlnesIiarCb9803Ae4LfqhWiVEgUJs2IoLRLi/bzH2/OAL3Li8VVnuZFLGR6ZGZEwldm+y/HreMU477Ze2VNfXxJ7r1hDtBQDffhKEbWtkSDvv3AhR1o29tJe9fnZtXh6IbxYGIynBDdevSrF9rQwJ+z1tdr7ZS3tR02fTQfKuXbswbNgwtGvXDh07dsT69euRnp6OxMTEeu9TJNaiZYdyHD/obnhMpxPgxCF3tI2zPPhuE1eGE9XKA0Difg+0uVE+KEIJv0A1jh8ylikvccL5E66GMgDg7a/CWwszsGB8JKoqGvZbkjEu48BTpxPgxEEPtI2zPEWgTVz5LRnrxAMW4qpWxhhXzdMO3Dw0KCl0Mvz///UpxvV0Kbr2LsbX/yTj6/8l4a1PMuDhra5XrNWplAKknHZFbI8Sw2NCIRDbvQTnEy1fglRVCSGRmi7CKHXW4dxRfXmNRgCtRnBLGYmzFknHTPtBfdmyH1Z3f58iePiosef7mi+L9huUh4zLUpw9WvfY7blf2kp9XpPGYk/tZS/spb0c7bPLVuebvbRXU3ZzdQtrb47ArmpZVKT/Vurra/kDvaqqCsXFxSabOU9fDZxEQGGu6SWjmxkzS3xkahQozMrnGsv7Buj/LTQrU5grNvwN0GHS4nT89q0fLp1u+HlRhrjMMtAFuaLbx2U2JaFAIYJPgHlcpmUKFSL4Bli+bBUSVYUBr+Zix7fGrGNwhBKBoUr0eLIIC8eH49O3ItCyQzneX321bkFaUJwvglYjgI+/aYzeMjXyFZanRnR+qBjbVgcgM1UKrRY4/pcHDu/wRn6OvryruxZt4kqxaWkQ8uRiaDT67PP5RDfkZzfMdAvb9UNTfV/KQ+J+D+Rel1j8u1iqxcNPF2D3pvrNLbTnfmkr9XlNGou9tJc9sZf2cqTPrgf7F6BVx/Lbfvm2FntpL/p3sJtBslarxVtvvYVu3brhnnvusVhm3rx58PLyMmzh4eGNXMuaDXg1Fy7uWny/vGFuVLNHfkEqzNmQir9+9cbOjcbBiECog8RZh4UTInD2qDtOH3HH4nfCEdu9FGHNKxu9nm/MuoaQZlV4vWdbPBXVCSunh6P3i3kQVuvtk5ZdgU4HDIlrjwHNOuGXdTL0HFhgUsbR+QcrEfdQCXbXMC0FALr1K4KLuwa//2i7G3DuVk39kojurK6fXR0fKME7izKw9N1wXL3ocucnkMPRQgCtzsobV7eom7Fjx+Ls2bM4dOhQjWWmTZuGt99+2/D/xcXFtwyUi/OdoFHrLx9V5yNToaCGecAFChF8ZGbl/Y3l83P0/3rLVIZsJKA/xuVz+jeJ2G4laBNXhl/TTpns57MdF/HnNh988lZkjXHVhiEus2/KPv7q28dlloH1kalRkGMel9o0LpnaENdNvoEqLPgxBUkJblg6Oczkb/k5YqhVQGaq8W7p9Ev6eYcBoSpcu1z/OYievmoInXS3ZLIKFSL4yixnqbz81PhwXSqUlQIUF4jgF6TCV3NDEBRRZSgTHKXEgp8uobJciPISIXwD1Zj3RjOTMnfDVv2wuj4v5qOkQIQje2q+Ia/fy3n43x9et2Svasue+6Wt1Oc1aSy2bi97ZC/t5QifXe3/rxQz16dh1Uch+GOLbb5Y20t70b+DXeTNxo0bh19//RX79u1DWFjNH3RSqRSenp4mmzm1SohLp13RqXup4TGBQIfY7qVIqmEOa3KiG2KrlQeAzg+WIPlGeXm6BHnZIpN9urpr0LpTuaHM5x+EYfSjMRjdR7+9P0S/tNvc0VFY/5/gWr4SNTPGZZyba4zL8iWy5ERXxPaoTVzGfRrjMu7TL0iFhVtScOmMKz6dGA6d2V2p5465QSQGgiONA8ywaP1/Z1+zfJm/tsQSHVp0KMepQ8b5Z1otcPKQB1rXME/vJomzDv7BKmjUwN87vPF/fW69ycTZVQvfQDVKCp1w/IAH/q9v4V3V9yZb9UMjHfq8kI8/tvhAo7b8jT0wvAodHyjF7s31/7Cz535pK/V5TRqLLdvLXtlLe9n7Z1eH+0sw+5tUrJ0TjJ0bbLdCib20F/072PRrl06nw5tvvolt27Zh//79aNasWYPsd+saGSYtTsfF0664cMIVT49UwNlFa5g/NXnpVeReF+Or+SEAgO1rZVi45RKefT0HR//wRM8BBWjZoQJL3r2ZpRZg+5cyDBqfjcxUKeQZEsRPvo68bDEO79Zn6RRZpoPByjL994+sqxKT+aARLSshkmjh4a2Bq7sW0e30NxrcXI/ytnGt9sekJRm4eKpaXK5a7Nl8M6505MrF+Gqe/o1t+5cyLPwpRR/XXk/0HFCoj8uQcbsR14QcZKZJIU+XIP5duT6uXfq4bg5EcjIlWDMrBF5+xm/vN+fCnfjLHZdOu+DtRRlYNSMEAgEwbm4mEg+4m2SX6+vpkTlYNDESLTuUo1Wncvy8RoaqCiEefVF/d/0n4yPhF6zC8GlZAIDzx12RJ5cgul058uRibPg0GDqtAM+NMa4NnLjfAzqdAGHNK5F1RYp1s0MR1rzKsM+GYIt+eFNs91IERyqx6zbTD/q+lI/8bLHZmqr1iNNO+6Ut3ek1see6WaO9AEAWqoSHtwYBoUoInYDodvplyrLSJKgst+0Nl/bSXvb62dXxgRLM+joN29f649AOb0P2Wq0SoKSw8YcR9tJeTZWuEX5MRMfpFnc2duxYbNy4ET///DM8PDwgl+vXOPXy8oKLS/0v0x34xQdevmoMnXQdPjI1Us+5YPor0YZLyrIQJbTVlohMSnDD/HFRiH/3OoZNuY6sNClmvtYMVy8Y6/DD5wFwdtViwoIMuHtqcO6YG6a/Eg1VVd2S8bO/vYygcOPlsZV7LgIA+obG1i4uPw2GTpYb4xrczBhXqIW4xkYifoocw6bK9XG9GmUa1wrZjbiuGeMabIyr84MlCI1WIjRaiY3Hk0zq0zekIwD9ncUfxjfD2I8z8cnWy6gsFyJhnwdWzwyp02tTk54DClCcL8K3nwSjQCFGdLsKzPouxXCThiJLYjKXWFUlxDcLgiFPl8LFVYsuDxdh0rKrcPfSGMqUFTth/fxQ5F4Xw8Nbg26PFyB+ShZEDTi+smU/7PdSHs4dc0NGDVNdBAJ9pvn3H32h1d7dm5W99ktbutNrYs91s0Z7AcDQSXL0ebHA8P8rf9e/901+tjlOH2mYVWXqy17ay14/u3o/nw9nVy1eejMHL72ZY3j81GE3vPt8y7sPvI7spb2o6RPodDrdnYtZ6eACyx/OX331FYYNG3bH5xcXF8PLywsPCQZCJGhiJ4ftmsWqdmQet3UVrOLxsDhbV8E6mmg/JLILNXwGOrwm9r6h1qmwHz+jqKjI4jTPpuLmmOrZP+Ihdru7qZJ3oipT4qfeX9v9a2rz6RZERERERPaGt4ISEREREQA0yo998MdEiIiIiIgcFDPJRERERAQAhh/8sPYxHAEzyUREREREZphJJiIiIiIAN36W2srrGDvKz1Izk0xEREREZIaZZCIiIiICwDnJ1TGTTERERERkhplkIiIiIgLATHJ1zCQTEREREZlhJpmIiIiIADCTXB0zyUREREREZphJJiIiIiIAzCRXx0wyEREREZEZDpKJiIiIiMxwugURERERAQB0sP7PRuusuveGw0wyEREREZEZZpKJiIiICABv3KuOmWQiIiIiIjPMJBMRERERAGaSq2MmmYiIiIjITNPIJOt0cJx7Jf/dHg/tbOsqWMX69IO2roJVDIvobusqWIVA1DTe+szp1GpbV4HqQsfPLbI/zCQbMZNMRERERGSmaaZTiIiIiKjOmEk2YiaZiIiIiMgMM8lEREREBADQ6QTQWTnTa+39NxRmkomIiIiIzDCTTEREREQAAC0E0MLKc5KtvP+GwkwyEREREZEZZpKJiIiICABXt6iOmWQiIiIiIjPMJBMRERERAK5uUR0zyUREREREZphJJiIiIiIAnJNcHTPJRERERERmOEgmIiIiIjLD6RZEREREBIA37lXHTDIRERERkRlmkomIiIgIgD7La+0b65hJJiIiIiJyUMwkExEREREAQAdAp7P+MRwBM8lERERERGaYSa6l/sNy8dzoHPjK1EhNcsHn74fiwklXW1frrjXFuBwppj++DsbOL0JRpJAgok0ZXpl1GdGxpRbLqlUC/LYiDIe2BKAgW4rg6Ao8Py0NHR4qNJTZtigCPy+JMHleUPNyzN933Jph3BV7bq/+Q3Pw3OvZ8JGpkJrsgs8/jMDFU241lu/xRAGGvpOJwDAlMq9IsW5eGI7t8zL8vVu/Ajz+igIt25fD00eDMf3aIDXJGKu7lxpD3s5C3IPFkIUqUZQnwpE93vj6k1CUlzhZNdbasuv2qmPdejxZiPh35fr2SpNi7ZxgHPvTs1oJHYZOzka/l/Pg7qlBUoIblk0NQ1aaFAAQGKbEyxOzEdutFD4yFfKyxfhzqw82LQ2AWlW/HJQ9xiCWajF+/jW07FCBiJaV+N8fnpj5arN6xWerOAFg0Phs3Ne7GNHtKqBWCvBsm/Z3FUNTpYUAAlj5x0SsvP+GYtNM8sqVK9GhQwd4enrC09MT999/P3bu3GnLKlnU86kCjJqRhQ2LgjC2byukJjljzsZUePmpbF21u9IU43KkmP73iz82z26GgW+lY+ZvJxDepgyfvHIPinPFFstvXRiJfRuC8MqsVMz9IxG9XrmO5SPb4OpZ00FbaKsyLEn4n2Gb/tPpxginXuy5vR7sn4+RH1zDd0uCMe6JNkhNdsWc7y7VWLc2caWYujwVu7/3x9jH2+DIbm98uOYyIltVGMo4u2px7pg71s0Ls7gPv0AV/AJVWDMnDG882g6fvhOFuJ7FmLjwijVCrDN7bq+61q1tlzJM+/wqdm3yxZg+rXB4lydmrLuCyBhje70wVoEBryqwfGoYJjzZEpXlQszdmAqxVAsACG9RCaFQh6VTwjCqVwy++CgETwzJw/Bp8iYVg1Cog7JSiJ/X+uPEQY96xWbrOAFAJNHhr/9647ev/e86Bvp3sOkgOSwsDPPnz0diYiISEhLw8MMPY8CAATh37pwtq3WLZ0blYtdGX+z53hfpl5yxbEoYqioE6Dso39ZVuytNMS5Himn3l6HoOUiOHi/kILRVBeLnpUDiosFf3wdaLH94qwxPjruGjg8XICCyCg8PkaPDwwXYtSbUpJxQpIN3gMqwefiqGyOcerHn9npmRDZ2bfLH7z/6I/2SC5ZPi0BVhRB9X8yzWH7gqzlIOOCFLV8EISPFBd98GoqUs654aliOoczerX7YuDQEJw5ZHmhcveiCj99ojv/94Y3rV6U4ddgTXy8MRddHiiB0sv0sPrturzrWbeAIBRL2eWDLygBkpDjjm4XBSDnjggHDb7avDgNHKLBpaSCO7PZCWrILFoyPgF+gCg/0KwIAJOz3xKcTI3D8gAfk6VL8s8cLW1bJ0O2xoiYVQ1WFE5ZPC8POjX7Iz7n7C9C2iBMAvv0kCNvWyJB23vmuY2jKbq6TbO3NEdh0kNy/f388/vjjaNmyJVq1aoU5c+bA3d0d//zzjy2rZUIk1qJlh3Icr/btWacT4MRBD7SNK7dhze5OU4zLkWJSKwW4csYdbbsXGh4TCoF23Qtx+bjlAZRKKTTJigCAxFmLi8c8TR7LTnPBW13uxeRuXbBqfCvkZUphj+y5vURiLVq2L8eJQ8bXVqcT4MQhD7TpbHk6TJvOpbcMfhP/8kSbzmV3VRc3Dw3KS52g1dj2Q8Xu26uOdWsTV35LVjTxgAfaxOnbKyhCCb9Atck+y0uccP6EK9rcJl43Dw1KCus+NaYpxFAb9hQn0Z3YzY17Go0GmzdvRllZGe6//36LZaqqqlBcXGyyWZunrwZOIqBQYfrtuSBXBB+Z/Wbo7qQpxuVIMZXki6HVCODlb3p50dNfhSKFxOJz2vcswO41IZCnOUOrBc7+5Y3EnX4oyjGWb96pBCM+vYh3vj2HoXNTkJvhjLnPtUdFqX3MZ63OntvL01etr1uuad0Kc8XwkVm+JOwjU6NQYTpVplAhqrF8rerho8ag8dexc6PtLw/bd3vVvW4+MjUKzNq3QCGCT4C+vO+Nf833WagQwTfAcpuGRFVhwKu52PGt378yhtqwlzipZtob6yRbe3MENr9x78yZM7j//vtRWVkJd3d3bNu2DW3btrVYdt68eZg5c2Yj15DIPrz8USq+mtIS03rFQSAAAiIr0P2FbBysNj2jQ68Cw3+HtylHdGwJJj1wL47+6o+eL2XbotpUT67uGsxafwnpl5zx3eIQW1eH7sAvSIU5G1Lx16/e2LnROgNMa2sKMRA1JJtnkmNiYnDy5En873//w+jRoxEfH4+kpCSLZadNm4aioiLDlpGRYfX6Fec7QaMGvM2+4fr4q1GgsPl3jHprinE5UkwevioInXQoMrtJrzhXDC+Z0uJzPP3UmPBlMr44fxifHjmGefuOw9lVC1lEZY3HcfPSIKhZBXKu2N8cPHtur+J8kb5u/qZ18/ZXoUBh+cbKAoUI3mZZY2+Zusbyt+PipsHH31xCRZkTZo1qDo3a9lkX+26vutetQCGCj1n7+sjUKLgx5/bm3FvzfXrL1MjPMW1T30AVFvyYgqQENyydbPmmzH9DDLVh6zjpznS6xtkcgc0HyRKJBC1atEBcXBzmzZuHjh07YunSpRbLSqVSw0oYNzdrU6uEuHTaFZ26lxgeEwh0iO1eiqRE+1j2qD6aYlyOFJNIokNU+1Ik/e1teEyrBZL+9kbzziU1PxGAxFkHnyAlNGoBEnb6oXOfmm+aqiwTIueqM7wDLA+8bcme20utEuLSGVfEdjNO6RIIdIjtVoLk4+4Wn5N83B2x3UzbrnP3YiQfr3nJOEtc3TWY+90lqFUCfPRqC6iqbP42DcAB2quOdUtOdEVsD9P55Z0fLEFyor695OkS5GWLTPbp6q5B607lSK62T78gFRZuScGlM674dGJ4vW9Iagox1IYt4ySqK/tKrwHQarWoqqqydTVMbF3tj0lLMnDxlCsunHDF0yMVcHbVYs9mX1tX7a40xbgcKaa+IzKx5p1WaNa+FNGxJdizNgRV5U7o8YJ+WsTqt1rBJ6gKz0+9CgC4fMIdBXIpItqWokAuxfbFEdBpBXjsjWuGfW7+OAqxvfPhF1qFwmwJti+KgNAJ6DpAYZMY78Se22vrl4GY9OkVXDrjhgsnXfH0azn6uv2gvww9aXEa8uQSfPUf/eoi29cFYOEPF/DMyGwc/dMLDz2Vj5YdyrF0aqRhn+5eagSEKuEXqM84hzXXXwUoUIhRoBDD1V2DOd9dgrOLFgveag5XDw1cPTQAgKI8EbRa22aU7bq97lC3yUvTkSsX46t5wQCA7V/KsPCnFDz7eg6O7vVEzwGFaNmhAksMWVQBtn8pw6AJOchMk0KeLkH8u3LkZYtxeJd+7eubg8ucTAnWzAqBl58xk1mfKwj2HENEy0qIJDp4+Gjg6qZBdDv98mup51wcIk4AkIUq4eGtQUCoEkInGGLISpOgstz+7tuwlcZYfcJRVrew6SB52rRpeOyxxxAREYGSkhJs3LgR+/fvx+7du21ZrVsc+MUHXn4aDJ0sh49MjdRzLpg+uBkKa1jP1lE0xbgcKaauT+WiJF+MbYsi9D8m0rYM73x7Fl43LtnnZUkhEBqvSamqhNi6MBI5Gc5wdtWgQ68CjFpyEW5eGkOZ/OtSrBoXg9JCMTx8VWh5bzE+2H4Knn7qW45vD+y5vf76ry+8fPU/7uEjUyE1yQXvD2lpqFtAiBK6aoPW5ER3/Gd8NOInZWLYu5nIuiLFrJHNcfWicRBx/6OFeGfRVcP/v7ciDQDw3eJgfLc4BC3uKTeshvHVwbMm9Yl/4B5kX7PtSiX23F53qpssVAlttcVhkhLcMH9sJOKnyDFsqhxZaVLMfDUKVy8Y2+uHFTI4u2oxYcE1uHtqcO6YG6YPjjZk9zs/WILQaCVCo5XYeNx0mmDfkI5NKobZ36UiKNw4nWjl7xcdKk4AGDpJjj4vGu/buBnD5Geb4/QRy1eI6N9NoNPZbmbIa6+9hr179+L69evw8vJChw4dMGXKFDz66KO1en5xcTG8vLzwEAZAJLD9mzT9e61PP2TrKljFsIjutq6CVQhEdncRrUHo1Pb5ZYjIkal1KuzHzygqKmqUaZ62cnNM1WbTFDi5WvcLuaa8CsmD/mP3r6lNPynWrl1ry8MTEREREVlkH3eEEBERERHZkaZ5zZGIiIiI6kyrE0Bg5RvrHOXHRJhJJiIiIiIyw0wyEREREQFonB/74I+JEBERERE5KGaSiYiIiAjAzUyytX9MxKq7bzDMJBMRERERmWEmmYiIiIgA8Gepq2MmmYiIiIjIDDPJRERERAQA0N3YrH0MR8BMMhERERGRGWaSiYiIiAgA5yRXx0wyEREREZEZDpKJiIiISE/XSFs9rFixAlFRUXB2dkbXrl1x9OjR25YvLCzE2LFjERwcDKlUilatWmHHjh21Ph6nWxARERGRXfv+++/x9ttvY9WqVejatSuWLFmCvn374sKFCwgICLilvFKpxKOPPoqAgABs2bIFoaGhuHr1Kry9vWt9TA6SiYiIiEivEeYkox77X7RoEUaOHInhw4cDAFatWoXffvsN69atw9SpU28pv27dOuTn5+Pw4cMQi8UAgKioqDodk9MtiIiIiMhuKZVKJCYmonfv3obHhEIhevfujSNHjlh8zi+//IL7778fY8eORWBgIO655x7MnTsXGo2m1sdlJpmIiIiIAAA6nX6z9jEAoLi42ORxqVQKqVR6S/nc3FxoNBoEBgaaPB4YGIjz589bPEZqair+/PNPDB48GDt27EBKSgrGjBkDlUqFGTNm1KqezCQTERERUaMLDw+Hl5eXYZs3b16D7Vur1SIgIACrV69GXFwcXnzxRUyfPh2rVq2q9T6YSSYiIiIiAI27TnJGRgY8PT0Nj1vKIgOAv78/nJyckJ2dbfJ4dnY2goKCLD4nODgYYrEYTk5OhsfatGkDuVwOpVIJiURyx3pykEzUAIZFdLd1Fazil8xjtq6CVTwVeq+tq2AdQqc7l3FE2trPISQix+Hp6WkySK6JRCJBXFwc9u7di4EDBwLQZ4r37t2LcePGWXxOt27dsHHjRmi1WgiF+okTFy9eRHBwcK0GyACnWxARERGRnXv77bexZs0afP3110hOTsbo0aNRVlZmWO1i6NChmDZtmqH86NGjkZ+fjwkTJuDixYv47bffMHfuXIwdO7bWx2QmmYiIiIj0dIJ6LdFW52PU0YsvvgiFQoEPP/wQcrkcsbGx2LVrl+FmvvT0dEPGGNDPd969ezcmTpyIDh06IDQ0FBMmTMCUKVNqfUwOkomIiIjI7o0bN67G6RX79++/5bH7778f//zzT72Px0EyEREREQFo3CXg7B3nJBMRERERmWEmmYiIiIj0dDc2ax/DATCTTERERERkhplkIiIiIgLQuD8mYu+YSSYiIiIiMsNMMhEREREZOcicYWtjJpmIiIiIyAwzyUREREQEgHOSq2MmmYiIiIjIDDPJRERERKTHdZINmEkmIiIiIjLDTDIRERER3SC4sVn7GPaPmWQiIiIiIjPMJBMRERGRHuckGzCTTERERERkhoNkIiIiIiIznG5BRERERHqcbmFQq0HyL7/8UusdPvXUU/WujD3rPywXz43Oga9MjdQkF3z+figunHS1+3r0eLIQ8e/KERimRGaaFGvnBOPYn57VSugwdHI2+r2cB3dPDZIS3LBsahiy0qSGEoPGZ+O+3sWIblcBtVKAZ9u0NznGoy/kY9KSDIvHf6F9WxTlie0+zsAwJV6emI3YbqXwkamQly3Gn1t9sGlpANQq+7jgYi99sDZ+Wx+AbSuDUKAQo1nbcoyanY5WncosllWrBNjyWTD+/NEPeXIJQqMrET89A3G9ig1lNBpg06eh2L/VD4UKMXwDlXj4+Vy8+NZ1COz0Jml7aa/+8Qo890Y2fGUqpCa74PMPwnHhpFuN5Xs8UYD4yVn6c+mKFGvnhuLYn17VSugwdNJ19BuUC3cvDZKOuWPZe+HISnM2lGhxTzleey8TrTqWQ6sFDu3wxhczw1BZ7gQA8PBWY+pnV9CsdQU8fNQoyhPhyB5vfDU/BOWlTtZ6KW7LXtrrbuvVGO/5jIv+LWr16T9w4MBabU8//XS9KzJ//nwIBAK89dZb9d6HtfR8qgCjZmRhw6IgjO3bCqlJzpizMRVefiq7rkfbLmWY9vlV7NrkizF9WuHwLk/MWHcFkTEVhjIvjFVgwKsKLJ8ahglPtkRluRBzN6ZCLNUayogkOvz1X2/89rW/xeMc+MUbL3Vsa7Il7PPAqcNu9Rog2yLO8BaVEAp1WDolDKN6xeCLj0LwxJA8DJ8mr3P9rcFe+mBtHPzZF2tnhuOlt7OweNc5RLUtx4zBrVCYa/k7+XcLQrHrOxlGzU7Hin1n0W9IDuaNaInLZ40fmD+tCMbOb2R4/eOrWLH/DOLfu4ZtK4Px67qAxgqrTuylvXr2z8eoD69hw+JgjH2sNVKTXDDnu5Saz6W4UkxbkYZdm/0xpl9rHN7ljRlfppqeS2OyMWC4AsunRWBC/xj9ufRdiuFc8g1UYv7mS8i6IsWE/jGY/koLRLaqxKTFVw370OmAI7u9MOPVaLz2YFt8MjESnboXY/z8dOu+IDWwl/a623o11ns+42ridILG2RxArQbJWq22VptGo6lXJY4dO4YvvvgCHTp0qNfzre2ZUbnYtdEXe773RfolZyybEoaqCgH6Dsq363oMHKFAwj4PbFkZgIwUZ3yzMBgpZ1wwYHjejRI6DByhwKalgTiy2wtpyS5YMD4CfoEqPNCvyLCfbz8JwrY1MqSdd7Z4HGWlEAUKsWHTagTo2K0Uuzf5OkycCfs98enECBw/4AF5uhT/7PHCllUydHusyOIxG5u99MHa+HlNIPq8rEDvF3MR0aoSY+ZfhdRFiz82W/5g2v+TH55/8zq6PFKEoMgqPB6vQNzDhdj+RZChzPkEd3TtW4h7exchMFyJbk8WILZnES6edG+ssOrEXtrrmVE52LXJH3t+8EP6JRcsmxqBqkoh+r6UZ7H8wNdykLDfE1tWBSIjxQXffBKClLMuGDBMcaOEDgNfy8GmZUE4sscbacmuWPBWlP5c6lsIAOjauxhqlQCfTQ/HtVRnXDzlhmXTItDjiUKERFUCAEqLRPj1WxkunXZDTqYUJ//2xH+/keGe+0ob4VW5lb20193Wq7He8xkX/Vvc1XXkysrKu65AaWkpBg8ejDVr1sDHx+eu99fQRGItWnYox/GDHobHdDoBThz0QNu4cruuR5u4cpyoVh4AEg94oE2c/rJ3UIQSfoFqk32Wlzjh/AlXtLmL2Ho/n4+qCgEO/uZd5+faU5xuHhqUFNrm0m919tIHa0OlFCDltBtiexinSgiFQMfuxTifaHlAq6oSmmR7AEDirEPyUWP51l1KcfqQJzIv6y+dpp1zQdJRD8T1Kmz4IO6SvbSXSKxFy/Y11KOz5akvbeLKcOKgp8ljiQc873wunXQzlBFLtFCrBNBVyxQpK/X/3e5ey8f1DVSi22OFOP1P43/psZf2aoh62eo9vy6aalxNiU7XOJsjqPMgWaPRYPbs2QgNDYW7uztSU1MBAB988AHWrl1b5wqMHTsWTzzxBHr37n3HslVVVSguLjbZrM3TVwMnEVCoML1UXJArgo9MbfXj3009fGRqFJhd4i5QiOAToC/ve+Nf830WKkTwDaj/Zca+g/Kxb5sPlJV1/w5mL3GGRFVhwKu52PGtX51jaGj20gdrozhfBK1GAG9/09fVW6ZCocLy1JtODxXh59VByEqVQqsFTvzliSM7vJGfYyz/3Ljr6DEgH2N6tsfTkXF4q287PDUiGw89Y3+ZdHtpL09fdc31qKHfWz6XxPCR6cv73vi3MNe0LQsVIsPfTv3tAR+ZCs+9kQ2RWAt3LzVenZalf77Zcad+loafL53ApsSzKC9xwuLJkfWMtv7spb3M2ct7YUNrqnFR01TnUcycOXOwfv16LFiwABKJxPD4Pffcgy+//LJO+9q8eTOOHz+OefPm1ar8vHnz4OXlZdjCw8PrdDyyvjZxZYhsVYVd9ZxqYQ/8glSYsyEVf/3qjZ0bbT9IbupGzkpHSLNKjOnZHs9EdcHq6RHo/WIuhNXenQ791xcHtvrhnRWpWLwrCW8tScP2VUHY+wPbx95cveiCTyZG4dlR2fjl0klsOn4G8gwJ8nNEt2SPvpgZhnH92mDG8GiERFbh9Q+v2abSRGSka6TNAdR5CbhvvvkGq1evxiOPPII33njD8HjHjh1x/vz5Wu8nIyMDEyZMwO+//w5n59rND5o2bRrefvttw/8XFxdbfaBcnO8EjRrwNvuG6+OvRoGizi9fo9ajQCGCj79ZeZkaBTn68vk3/vWWqU2ydt4yNS6fc6lXPfu9nI+Us85IOVO/u8JtHadvoAoLfkxBUoIblk4Oq1cMDc1e+mBtePqqIXTSWcg0iuEts5zR8fJTY/q6FCgrBSgpEME3SIWv54YhMKLKUGb97HA8O+46HhygzxxHtalAzjUJtnwWjEdesDy/1lbspb2K80U11yPHclbf8rmkQsGNqwD5N/719lfd9lzat90X+7b7wttfhcpyIXQ64JmRObh+VWqy75v3MWRcdkZJoQiLtl3ExqXBJvu2NntpL3O2fi+0lqYaFzVNdc4kZ2ZmokWLFrc8rtVqoVLV/rJGYmIicnJy0LlzZ4hEIohEIhw4cADLli2DSCSyeBOgVCqFp6enyWZtapUQl067olP3EsNjAoEOsd1LkZTYeMsD1aceyYmuiO1heiNM5wdLkJyoX/5Jni5BXrbIZJ+u7hq07lSO5HrE5uyqwYP9C7F7U/2ze7aM0y9IhYVbUnDpjCs+nRhuMqfSluylD9aGWKJDiw5lOHXIeG5qtcDpQ55oHXf7m7Ikzjr4BaugUQtweIcPuvYpMPytqkIIgcA09SB0AnRa+2ij6uylvdQqIS6dsVSPEiQdt7wEXHKiG2K7m05j69yjFudSbJmhTHWFuWJUljuh51MFUFUJTeaMmhMI9e0rlmhrLGMN9tJeDVGvxn7Pr4+mGleTwtUtDOr8Nblt27Y4ePAgIiNN545t2bIFnTp1qvV+HnnkEZw5c8bkseHDh6N169aYMmUKnJxsf8PUTVtX+2PSkgxcPOWKCydc8fRIBZxdtdizuXGnFNypHpOXpiNXLsZX84IBANu/lGHhTyl49vUcHN3riZ4DCtGyQwWWGDKkAmz/UoZBE3KQmSaFPF2C+HflyMsW4/Au47qoslAlPLw1CAhVQugERLfTL7uTlSYxrHsKAD0HFMLJSYe9P93dDZi2iPPmADknU4I1s0Lg5WfMWhTUMJe2MdlLH6yNASOzsWRiM7ToUIZWncrwy5pAVFYI8ciLuQCAxeObwTdYhfhp+kvrF467IU8uQXS7cuTJxdj0aSh0WuCZMcbl9+59tBA/LguBLFSJiJgKpJ51xc+rA9H7pVybxHgn9tJeW1cHYNLiq/p6nHTF0yMUcHbRYs/3+i+yk5dc0Z9L80MBANvXBmDhlot4dlQ2ju71Qs8B+WjZoRxLpkTc2KMA29cGYNB4uf5cypAiflKW/lza7W047lPDcpCU4I6KMiE6P1iCEe9fw7p5oSgr1n/k3PtwEXz81bhwyhWVZUJEtqrEiPczcfaoG7KvmWabG4O9tFdd62Xr93zGRU1dnQfJH374IeLj45GZmQmtVoutW7fiwoUL+Oabb/Drr7/Wej8eHh645557TB5zc3ODn5/fLY/b2oFffODlp8HQyXL4yNRIPeeC6YOb3XJJ2db1kIUqoa2WhElKcMP8sZGInyLHsKlyZKVJMfPVKFy9YLz89MMKGZxdtZiw4BrcPTU4d8wN0wdHQ1VlvMgwdJIcfV40ZvVW/n4RADD52eY4fcR4N3q/Qfn4e6cXyorv7s3GFnF2frAEodFKhEYrsfF4kkl9+oZ0vKt4GoK99MHa6DEgH0X5Imz8JBQFCjGi25Xjo+8uGm7KUWRJIKh2DUtVJcSGBaGQp0vh7KpBl4eLMHFZKty9jFeTRn18FRsWhGLVe5EoytP/mEi/VxR4cWJWY4dXK/bSXgf+6wsvPzWGTroOH5kKqUkumD6kRc3nUqI75o9rhvh3szBsSpb+XBoRbXoufR6oP5f+k37jXHLH9FdamLxnxMSWY8g71+HsqsW1y85YNjUCe38yXmFSVgrx2Mu5eH1GJcRSLRRZEvy90xvfrwi0/otigb20V13rZev3fMbVNAl0+s3ax3AEAp2u7gtxHDx4ELNmzcKpU6dQWlqKzp0748MPP0SfPn3uqjIPPfQQYmNjsWTJklqVLy4uhpeXFx7CAIgE9jdYIHJ0v2Qes3UVrOKp0HttXQXrEDbRbJi2fmvwEzUEtU6F/fgZRUVFjTLN01ZujqnCl86C0MW6a0lrKyqRMeFDu39N63VXQo8ePfD77783dF2wf//+Bt8nEREREdVSY6w+4SCZ5HrfupuQkIDk5GQA+nnKcXFxDVYpIiIiIiJbqvMg+dq1axg0aBD+/vtveHt7AwAKCwvxwAMPYPPmzQgLs49ls4iIiIiojhpj9QkHWd2izkvAjRgxAiqVCsnJycjPz0d+fj6Sk5Oh1WoxYsQIa9SRiIiIiKhR1TmTfODAARw+fBgxMTGGx2JiYrB8+XL06NGjQStHRERERI2Ic5IN6pxJDg8Pt/ijIRqNBiEhIQ1SKSIiIiIiW6rzIHnhwoV48803kZCQYHgsISEBEyZMwCeffNKglSMiIiKiRqRrpM0B1Gq6hY+PDwQC4yTrsrIydO3aFSKR/ulqtRoikQivvvoqBg4caJWKEhERERE1lloNkmv74x5ERERERE1BrQbJ8fHx1q4HEREREdkab9wzqPePiQBAZWUllEqlyWP2/POCRERERES1Uecb98rKyjBu3DgEBATAzc0NPj4+JhsREREROaibPyZi7c0B1HmQ/O677+LPP//EypUrIZVK8eWXX2LmzJkICQnBN998Y406EhERERE1qjpPt/jvf/+Lb775Bg899BCGDx+OHj16oEWLFoiMjMSGDRswePBga9STiIiIiKxMoNNv1j6GI6hzJjk/Px/R0dEA9POP8/PzAQDdu3fHX3/91bC1IyIiIiKygToPkqOjo5GWlgYAaN26NX744QcA+gyzt7d3g1aOiIiIiBoRf0zEoM6D5OHDh+PUqVMAgKlTp2LFihVwdnbGxIkTMXny5AavIBERERFRY6vznOSJEyca/rt37944f/48EhMT0aJFC3To0KFBK0dEREREZAt3tU4yAERGRiIyMrIh6kJEREREZBdqNUhetmxZrXc4fvz4eleGiIiIiGxHgEZY3cK6u28wtRokL168uFY7EwgEHCQTERERkcOr1SD55moWRPTv8lTovbauglWsTz9k6ypYxbCI7rauAhE5usb4Rbym+ot7RERERERN3V3fuEdERERETURjrGPcVNdJJiIiIiJq6phJJiIiIiI9ZpINmEkmIiIiIjJTr0HywYMH8corr+D+++9HZmYmAODbb7/FoUNN845xIiIiIvp3qfMg+aeffkLfvn3h4uKCEydOoKqqCgBQVFSEuXPnNngFiYiIiKhxCHSNszmCOg+SP/74Y6xatQpr1qyBWCw2PN6tWzccP368QStHRERERGQLdb5x78KFC3jwwQdvedzLywuFhYUNUSciIiIisgXeuGdQ50xyUFAQUlJSbnn80KFDiI6ObpBKERERERHZUp0HySNHjsSECRPwv//9DwKBAFlZWdiwYQMmTZqE0aNHW6OORERERNQYdI20OYA6T7eYOnUqtFotHnnkEZSXl+PBBx+EVCrFpEmT8Oabb1qjjkREREREjarOg2SBQIDp06dj8uTJSElJQWlpKdq2bQt3d3dr1I+IiIiIGkljrD7hKKtb1PsX9yQSCdq2bduQdSEiIiIisgt1HiT36tULAoGgxr//+eefd1UhIiIiIrIRnUC/WfsYDqDOg+TY2FiT/1epVDh58iTOnj2L+Pj4hqoXEREREZHN1HmQvHjxYouPf/TRRygtLb3rChERERGRjXCdZIM6LwFXk1deeQXr1q1rqN0REREREdlMvW/cM3fkyBE4Ozs31O6IiIiIqJFxdQujOg+Sn3nmGZP/1+l0uH79OhISEvDBBx80WMWIiIiIiGylzoNkLy8vk/8XCoWIiYnBrFmz0KdPnwarWEPrPywXz43Oga9MjdQkF3z+figunHStsXyPJwsR/64cgWFKZKZJsXZOMI796VmthA5DJ2ej38t5cPfUICnBDcumhiErTWooMWh8Nu7rXYzodhVQKwV4tk17K0ZYP3V9XRyBPcVki3730fo0NG9XAW8/NUqKnHDioAfWzglGfrbYUCauZzGGTMpGZEwllFUCnP3HHatnhiD7msQaL8Nt2VN73ckfXwdj5xehKFJIENGmDK/MuozoWMv3YqhVAvy2IgyHtgSgIFuK4OgKPD8tDR0eKjSU2bYoAj8viTB5XlDzcszfd7zB6tzYfTAwTImXJ2YjtlspfGQq5GWL8edWH2xaGgC1Sj/DTyzVYvz8a2jZoQIRLSvxvz88MfPVZg0Wc304Uj+sC8ZFdcY5yQZ1mpOs0WgwfPhwLFq0CF999RW++uorrF27FvPnz7frAXLPpwowakYWNiwKwti+rZCa5Iw5G1Ph5aeyWL5tlzJM+/wqdm3yxZg+rXB4lydmrLuCyJgKQ5kXxiow4FUFlk8Nw4QnW6KyXIi5G1MhlmoNZUQSHf76rzd++9rf6jHWR11fF0dgTzHZqt+d+tsdc16PxGs9WuPjkVEIiarCB2uuGP4eGF6Fj766glN/u2PMo60w/eVoePqq8cHaK2hs9tRed/K/X/yxeXYzDHwrHTN/O4HwNmX45JV7UJwrtlh+68JI7NsQhFdmpWLuH4no9cp1LB/ZBlfPupmUC21VhiUJ/zNs03863WB1tkUfDG9RCaFQh6VTwjCqVwy++CgETwzJw/BpcsM+hEIdlJVC/LzWHycOejRYvPXlSP2wLhgX0d2p0yDZyckJffr0QWFhYYMc/KOPPoJAIDDZWrdu3SD7ru6ZUbnYtdEXe773RfolZyybEoaqCgH6Dsq3WH7gCAUS9nlgy8oAZKQ445uFwUg544IBw/NulNBh4AgFNi0NxJHdXkhLdsGC8RHwC1ThgX5Fhv18+0kQtq2RIe28fc7Vruvr4gjsKSZb9btta2Q4f9wNOZkSJCW44fvPAtC6czmcRPqv7i07VEDopMP6/wTh+lUpUs64YssqGZq3qzCUaSz21F53svvLUPQcJEePF3IQ2qoC8fNSIHHR4K/vAy2WP7xVhifHXUPHhwsQEFmFh4fI0eHhAuxaE2pSTijSwTtAZdg8fNUNVmdb9MGE/Z74dGIEjh/wgDxdin/2eGHLKhm6PWbso1UVTlg+LQw7N/ohP6fBbo2pN0fqh3XBuKhedMZ5ydbammQmGQDuuecepKamNlgF2rVrh+vXrxu2Q4cONdi+AUAk1qJlh3Icr5at0OkEOHHQA23jyi0+p01c+S3ZjcQDHmgTVwYACIpQwi9QbbLP8hInnD/hijY17NPe1Od1sXf2FJO99DsPbzUefqYASQmu0Kj1i7dfOu0CrVaAPi/lQyjUwdVDg97PFuDEQXdDmcZgT+11J2qlAFfOuKNt90LDY0Ih0K57IS4ft5wJVSmFJhl+AJA4a3HxmKfJY9lpLniry72Y3K0LVo1vhbxMKRqCvfRBAHDz0KCk0OluwrEaR+qHdcG4iO5enQfJH3/8MSZNmoRff/0V169fR3FxsclWVyKRCEFBQYbN379hpyZ4+mrgJAIKFabZioJcEXxkljM2PjI1CnLNyitE8AnQl/e98a/5PgsVIvgGOMblnvq8LvbOnmKydb97bXoWfk45gy1J5yALUeGj4cb5ntkZUrw3KBrDp8rx65XT2HbhLPxDVJjzelS9Yq0ve2qvOynJF0OrEcDL3/R19vRXoUhheR53+54F2L0mBPI0Z2i1wNm/vJG40w9FOcbyzTuVYMSnF/HOt+cwdG4KcjOcMfe59qgovfsBpa374E0hUVUY8GoudnzrV684rM2R+mFdMC6qN10jbQ6g1oPkWbNmoaysDI8//jhOnTqFp556CmFhYfDx8YGPjw+8vb3h4+NT5wpcunQJISEhiI6OxuDBg5Genl5j2aqqqrselBP9G/y4MgBj+rTCtJeiodUCk5em4+a7ko9MhbcWZuD3H33w5uMt8c7TzaFSCm7MW3aQdy4H8PJHqQhsVolpveIwonk3fPdhNLq/kA1BtbWPOvQqwH1P5iG8TTna9yzExPXnUF4swtFf7fM+hrryC1JhzoZU/PWrN3ZutM9BMhFRTWo9GWzmzJl44403sG/fvgY7eNeuXbF+/XrExMTg+vXrmDlzJnr06IGzZ8/Cw+PWS5jz5s3DzJkz63SM4nwnaNSAt9k3TB9/NQoUlsMvUIjg429WXqZGwY25czfn0HnL1MjPMd604y1T4/I5lzrVz1bq87rYO3uKydb9rjhfhOJ8ETJTpUi/JMWGxGS0iStHcqIb+g/LRVmJE9Z+HGIov+DNCGxITEbrzuU4f9z0xjJrsaf2uhMPXxWETjoUmd2kV5wrhpdMafE5nn5qTPgyGcpKAcoKxfAOVOLHeVGQRVTWeBw3Lw2CmlUg58rd38dg6z7oG6jCgh9TkJTghqWTw+46HmtxpH5YF4yL6O7VOpOs0+mzHz179rztVhePPfYYnn/+eXTo0AF9+/bFjh07UFhYiB9++MFi+WnTpqGoqMiwZWRk3PEYapUQl067olP3EsNjAoEOsd1LkZRoebmY5ERXxPYwXdap84MlSE7UDx7k6RLkZYtM9unqrkHrTuVIrmGf9qY+r4u9s6eY7KnfCW6c5WKJ/hx2dtFBpzWde6zV6P9f2GC/wXln9tRedyKS6BDVvhRJf3sbHtNqgaS/vdG8c0nNTwQgcdbBJ0gJjVqAhJ1+6Nyn5puLKsuEyLnqDO8AywPvurBlH/QLUmHhlhRcOuOKTyeGQ6drvLnudeVI/bAuGBfVG6dbGNTpa5dAYN03Om9vb7Rq1QopKSkW/y6VSiGV1v2mlq2r/TFpSQYunnLFhROueHqkAs6uWuzZ7AtAfyk6Vy7GV/OCAQDbv5Rh4U8pePb1HBzd64meAwrRskMFlhiyIQJs/1KGQRNykJkmhTxdgvh35cjLFuPwLuM60rJQJTy8NQgIVULoBES30y+jlJUmQWW57W9iudPr4ojsKSZb9LuYTmWIia3A2aNuKC10QnBUFeLflSMrTWIYxPxvrweeHqXA4Ily7NvuA1d3DYZPlUOeIUbK2ca9EmJP7XUnfUdkYs07rdCsfSmiY0uwZ20Iqsqd0OOFbADA6rdawSeoCs9PvQoAuHzCHQVyKSLalqJALsX2xRHQaQV47I1rhn1u/jgKsb3z4RdahcJsCbYvioDQCeg6QNEgdbZFH7w5QM7JlGDNrBB4+RkzfgUKY/Y5omUlRBIdPHw0cHXTGN4fU21wNc6R+mFdMC6iu1OnQXKrVq3uOFDOz6//EiylpaW4fPkyhgwZUu99WHLgFx94+WkwdLIcPjI1Us+5YPrgZii8celUFqqEttpN6EkJbpg/NhLxU+QYNlWOrDQpZr4ahasXjG/eP6yQwdlViwkLrsHdU4Nzx9wwfXA0VFXGVNzQSXL0ebHA8P8rf78IAJj8bHOcPuLeoDHWx51eF0dkTzHZot9VVQjR7bEiDHlHDmdXLfJzxEjY54E5SwOhUurLnPrbA/PHRuD5MQo8P0aBqgohkhNd8f7gaCgrGzGVDPtqrzvp+lQuSvLF2LYoQv9jIm3L8M63Z+El09+wlpclhUBoTI+oqoTYujASORnOcHbVoEOvAoxachFuXhpDmfzrUqwaF4PSQjE8fFVoeW8xPth+Cp5+DXMDki36YOcHSxAarURotBIbjyeZ1KdvSEfDf8/+LhVB4cab/W6+P1Yv01gcqR/WBeOi+uDPUhsJdDfnUdyBUCjEkiVLbvnFPXPx8fG1PvikSZPQv39/REZGIisrCzNmzMDJkyeRlJQEmUx2x+cXFxfDy8sLD2EARAKeHERUO+vTG3apSXsxLKK7ratA1OSodSrsx88oKiqCp6fnnZ/goG6OqZq/NxdOztb9fQdNZSUuz33P7l/TOmWSX3rpJQQEBDTYwa9du4ZBgwYhLy8PMpkM3bt3xz///FOrATIRERERkbXUepBsjfnImzdvbvB9EhERERHdrVoPkms5K4OIiIiIHFVjrD7hIEPKWg+StVrtnQsRERERETUBXHmbiIiIiABwdYvqGne9JyIiIiIiB8BMMhEREREZOUim19qYSSYiIiIiMsNMMhERERHpcXULA2aSiYiIiIjMMJNMRERERAC4ukV1zCQTEREREZlhJpmIiIiI9Dgn2YCZZCIiIiIiMxwkExERERGZ4XQLIiIiIgLAG/eqYyaZiIiIiOzeihUrEBUVBWdnZ3Tt2hVHjx6t1fM2b94MgUCAgQMH1ul4HCQTERERkZ6ukbY6+v777/H2229jxowZOH78ODp27Ii+ffsiJyfnts+7cuUKJk2ahB49etT5mBwkExEREZFdW7RoEUaOHInhw4ejbdu2WLVqFVxdXbFu3boan6PRaDB48GDMnDkT0dHRdT4mB8lEREREpNeImeTi4mKTraqqymKVlEolEhMT0bt3b8NjQqEQvXv3xpEjR2oMZdasWQgICMBrr71Wn1eCg2QiIiIianzh4eHw8vIybPPmzbNYLjc3FxqNBoGBgSaPBwYGQi6XW3zOoUOHsHbtWqxZs6be9ePqFkREREQEoHFXt8jIyICnp6fhcalU2iD7LykpwZAhQ7BmzRr4+/vXez8cJBPRv86wyLrfwOEItl37n62rYBVPh91n6yoQkRV4enqaDJJr4u/vDycnJ2RnZ5s8np2djaCgoFvKX758GVeuXEH//v0Nj2m1WgCASCTChQsX0Lx58zsel9MtiIiIiEjPDle3kEgkiIuLw969ew2PabVa7N27F/fff/8t5Vu3bo0zZ87g5MmThu2pp55Cr169cPLkSYSHh9fquMwkExEREZFde/vttxEfH48uXbrgvvvuw5IlS1BWVobhw4cDAIYOHYrQ0FDMmzcPzs7OuOeee0ye7+3tDQC3PH47HCQTERERkV491zGu8zHq6MUXX4RCocCHH34IuVyO2NhY7Nq1y3AzX3p6OoTChp0gwUEyEREREdm9cePGYdy4cRb/tn///ts+d/369XU+HgfJRERERASgcVe3sHe8cY+IiIiIyAwzyURERESkZ6dzkm2BmWQiIiIiIjPMJBMRERERAM5Jro6ZZCIiIiIiM8wkExEREZEe5yQbMJNMRERERGSGg2QiIiIiIjOcbkFEREREepxuYcBMMhERERGRGWaSiYiIiAgAILixWfsYjoCZZCIiIiIiM8wkExEREZEe5yQbMJNMRERERGSGmWQiIiIiAsCfpa7uXzNI7j8sF8+NzoGvTI3UJBd8/n4oLpx0rbF8jycLEf+uHIFhSmSmSbF2TjCO/elZrYQOQydno9/LeXD31CApwQ3LpoYhK00KAAgMU+LlidmI7VYKH5kKedli/LnVB5uWBkCtqn8Cv7HjAIBB47NxX+9iRLergFopwLNt2psc49EX8jFpSYbF47/Qvi2K8sT1jrc+6voaOQp7jsse+2WdY4hXmMbwQSgunHS7fQyTrxtjmBtyawyT5KYxTAs3ieEmsUSLpb9eRPN2lRjdpxVSz+lfu7DmlRg//xoiWlbCzUODvGwx9m33wXeLgqBRN9ytLzvWB2D7qmAUKsSIalOOEbOvolWnMotl1SoBfvosGPu2+CNfLkFodCWGvJeBzr2KDGU0GuD7RaE4sNUfhTli+AQp8fDzuXh+QhYEdnrHjr2eX03h3LIGe20valr+FdMtej5VgFEzsrBhURDG9m2F1CRnzNmYCi8/lcXybbuUYdrnV7Frky/G9GmFw7s8MWPdFUTGVBjKvDBWgQGvKrB8ahgmPNkSleVCzN2YCrFUCwAIb1EJoVCHpVPCMKpXDL74KARPDMnD8Glyh4oDAEQSHf76rzd++9rf4nEO/OKNlzq2NdkS9nng1GG3Rh8g1/U1chT2HJe99st6x9AvBqlJLpiz4Q4xrLiCXZv8MKZvDA7v9sKMtWmmMYzJuRFDOCb0b6WPYcNlkxhuem16FvLkt54rapUAf/zog/debo7XHmyDVTNC8djLeRg66fpdx3zToV988dWsCLw4MROf7jyLqLblmPVKDApzLedQNi4IxZ7vAjBy1lUs+/MM+g7JwX9GtETqWeMAZdvnwdj1TQBGfnwFy/efxtBpGdi2Mhi/rQtssHo3JHs9v5rCuWUN9tpeTYaukTYHYPNBcmZmJl555RX4+fnBxcUF7du3R0JCQoMe45lRudi10Rd7vvdF+iVnLJsShqoKAfoOyrdYfuAIBRL2eWDLygBkpDjjm4XBSDnjggHD826U0GHgCAU2LQ3Ekd1eSEt2wYLxEfALVOGBfvpsSsJ+T3w6MQLHD3hAni7FP3u8sGWVDN0eK7J4THuNAwC+/SQI29bIkHbe2eJxlJVCFCjEhk2rEaBjt1Ls3uRb71jrq66vkaOw57jstV/WKYaRCuza6Ic9P/jpY5gahqoKIfq+VEMMrymQsN8TW1ZVi+GsCwYMzzWLIQhH9tyIYUKkPoa+pu8BXXoVI65nCdbMDr3lOPJ0Kfb84IfUJBfkZErwz+9e+HObD+65z3KWtz5+WR2ERwcp8MiLuQhvVYk35l+B1FmLvZtlFsvv3+qPZ9/MQtwjRQiKrEK/oTno/HAhfv4iyFDmfIIH7utTiC6PFCEgXIkHnixA7INFuHSbzLwt2ev51RTOLWuw1/aipsemg+SCggJ069YNYrEYO3fuRFJSEj799FP4+Pg02DFEYi1adijH8YMehsd0OgFOHPRA27hyi89pE1eOE9XKA0DiAQ+0idN/MAVFKOEXqDbZZ3mJE86fcEWbGvYJAG4eGpQUOjl8HHfS+/l8VFUIcPA373rvoz7q8xo5AnuOy5H65Z1jcDeN4ZA72sZZHoy2iSvDiWrlASBxv4UYDhnLGGMw7tPbX4W3FmZgwfhIVFXceR5CSFQVujxUjNP/uN+xbG2olAJcPuOGjj2MAyShEOjQoxgXjls+hqpKCIlZNlzirEXyMWN7te5SgtN/eyIzVT/ISktyQfIxD5MpGfbCXs+vpnBuWYO9tleTwywyABvPSf7Pf/6D8PBwfPXVV4bHmjVr1qDH8PTVwEkEFCpMQy3IFSG8RZXF5/jI1Cgwu9RYoBDBJ0ANAPC98a/5PgsVIvgGWL7cExJVhQGv5mLNrBCHjqM2+g7Kx75tPlBWNu53sPq8Ro7AnuNypH5ZE0MMuabTHQoUYoQ3v00MCrPyuWL4yMxjMC1TmCs2/A3QYdLidPz2rR8unXZFYFjNbbn454tocU8FJM46/PadH75ZGFRj2booyRdBqxHAS6Y2edzbX4XMFMtZxE49i/DLmiC07VqCoKgqnD7kiX92+kCrNQ7ynxl7HeUlTnizZ3sInXTQagQYPOUaej6TZ3GftmSv51dTOLeswV7bi5omm2aSf/nlF3Tp0gXPP/88AgIC0KlTJ6xZs6bG8lVVVSguLjbZHIFfkApzNqTir1+9sXOjn62rY1Vt4soQ2aoKu2ww1YLIkQx4NRcu7lp8v/zO83Tnjo7C2H4xmDcmEvc9Uozn3shphBpa9tqsqwhuVoU3H+qA55vdizXvR+LhF3MhrJYI//u/vvhrmx8mfnYZn+48h/GLU7F9VTD+/NE+57gSkdHN1S2svTkCmw6SU1NTsXLlSrRs2RK7d+/G6NGjMX78eHz99dcWy8+bNw9eXl6GLTw8/I7HKM53gkYNeJtlSnz81ShQWE6kFyhE8PE3Ky9ToyBHXz7/xr/m+/SWqZGfY5o58g1UYcGPKUhKcMPSyWF3rK+9xlFb/V7OR8pZZ6Scafy7jOvzGjkCe47LUfrl7Rhi8DfNpPnIVLePQWZW3t9Y3hiDaRlvf5Xhb7HdStAmrgy/pp3Cjqsn8dXfyQCAz3ZcxKQlV02ep8iSIP2SM/b/7IN1c4PxyjtyCIV3/ynj4auG0EmHIvPMYq4Y3jVkFr381Ji29hI2XUzA6n9O4rMDZ+DiqkFgZKWhzNcfh+OZsdfRY0A+IttU4KHn8vDUSDm2fhZ813VuaPZ6fjWFc8sa7LW9qGmy6SBZq9Wic+fOmDt3Ljp16oRRo0Zh5MiRWLVqlcXy06ZNQ1FRkWHLyLC87Fh1apUQl067olP3EsNjAoEOsd1LkZRoeSCXnOiK2B6lJo91frAEyYn6m07k6RLkZYtM9unqrkHrTuVIrrZPvyAVFm5JwaUzrvh0Yjh0uvqvfWTLOGrL2VWDB/sXYvcm22TL6/MaOQJ7jssR+mXtYzDWyRiD5RvNkhPdENu9NjEYyxhj0Jf5/IMwjH40BqP76Lf3h0QD0GeN1/+n5sGkUAiIRDoIGuDdWyzRoXn7Mpw+5GV4TKsFzhzyREzn0ts8E5A46+AXrIJGLcCRHb64r0+h4W9VFU4QmtVP6KQzmZJhL+z1/GoK55Y12Gt7NSlc3cLApl+7goOD0bZtW5PH2rRpg59++sliealUCqn01jVG72Tran9MWpKBi6dcceGEK54eqYCzqxZ7NuunBExemo5cuRhfzdN/MG3/UoaFP6Xg2ddzcHSvJ3oOKETLDhVYYsgEC7D9SxkGTchBZpoU8nQJ4t+VIy9bjMO79B82NwfIOZkSrJkVAi8/47de87mM9hwHAMhClfDw1iAgVAmhExDdTr+cUFaaBJXlxhsRew4ohJOTDnt/argbL+vqTq+Ro7LnuOy9X9YqhjUyTFqcjounq8XgosWe72/GcBW518X4ar7+noLta2VYuOWSPoY/PNFzQIE+hndvXt26EcP4bGSmSiHPkCB+8nV9DLv1MSiyJCZ1qCzTjyqzrkqQe13/t15P50OjFiAt2QUqpQCtOpZj+NTrOPCLT4Otk/zUKDmWTYxG845laBlbil+/DEJlhRCPvKgAACydEA3fICWGTLsGALh43A15cgmatStHvlyMzYtCodMBT482Lkt376MF2LIsBP6hVYhoVYHUs274ZXWQYZ/2xl7Pr6ZwblmDvbYXNT02HSR369YNFy5cMHns4sWLiIyMbNDjHPjFB15+GgydLIePTI3Ucy6YPriZ4UYdWagS2mo3aycluGH+2EjET5Fj2FQ5stKkmPlqFK5ecDGU+WGFDM6uWkxYcA3unhqcO+aG6YOjoarSf9B1frAEodFKhEYrsfF4kkl9+oZ0dJg4AGDoJDn6vFhg+P+Vv18EAEx+tjlOHzHeAd9vUD7+3umFsmLbvYne6TVyVPYcl733y1rH4KvG0EnXjTG8Em2MIcRCDOOiEP/udQybcl0fw2vNTGP4POBGDBnGGF4xjeFOtBoBXhiTg9DoKggEQM41CX5Z74+taywvz1Yf3Z/KR3GeCJs/CUWBQoxmbcvx4bcXDJezFZkSCKpN7VBWCbFxYRiy06VwdtUg7uEivLU0FW5eGkOZkbOvYuPCMKx+LwpFufofE+nzSg5eeCurwerdkOz1/GoK55Y12Gt7NRX8xT0jgU6ns1lVjx07hgceeAAzZ87ECy+8gKNHj2LkyJFYvXo1Bg8efMfnFxcXw8vLCw9hAEQCnhxEVEv2+rNvd2lbxv9sXQWreDrsPltXgf7F1DoV9uNnFBUVwdPT885PcFA3x1TtR8yFk8S6a2RrlJU48+V7dv+a2nRO8r333ott27Zh06ZNuOeeezB79mwsWbKkVgNkIiIiIiJrsfmtoE8++SSefPJJW1eDiIiIiBrjxjoHmW5h85+lJiIiIiKyNzbPJBMRERGRfeCNe0bMJBMRERERmWEmmYiIiIj0OCfZgJlkIiIiIiIzzCQTERERkR4zyQbMJBMRERERmWEmmYiIiIgAcHWL6phJJiIiIiIyw0wyEREREelxTrIBM8lERERERGaYSSYiIiIiAIBAp4NAZ91Ur7X331CYSSYiIiIiMsNMMhERERHpcU6yATPJRERERERmmEkmIiIiIgBcJ7k6ZpKJiIiIiMwwk0xEREREepyTbMBMMhERERGRGQ6SiYiIiIjMcLqFvRIIbF0D6xA0ze9lAicnW1fBKnQqpa2rYB0OspB9XT0ddp+tq2AVu7NO2roKVtE3JNbWVbCOJvf5JXCY6QENgTfuGTXNEQsRERER0V1gJpmIiIiI9HjjngEzyUREREREZphJJiIiIiIAnJNcHTPJRERERERmmEkmIiIiIj3OSTZgJpmIiIiIyAwzyURERERk4Chzhq2NmWQiIiIiIjPMJBMRERGRnk5n/V8ldZBfPWUmmYiIiIjIDDPJRERERASA6yRXx0wyEREREZEZZpKJiIiISI/rJBswk0xEREREZIaZZCIiIiICAAi0+s3ax3AEzCQTEREREZnhIJmIiIiIyAynWxARERGRHm/cM2AmmYiIiIjIzL8mk9x/WC6eG50DX5kaqUku+Pz9UFw46Vpj+R5PFiL+XTkCw5TITJNi7ZxgHPvTs1oJHYZOzka/l/Pg7qlBUoIblk0NQ1aaFAAQGKbEyxOzEdutFD4yFfKyxfhzqw82LQ2AWlX/7yb94xWmcXwQigsn3W4fx+TrxjjmhtwaxyS5aRzTwg1xAMDX/5xDULjKZL9r5wbjhxWBAIAO95fgmZEKtIoth5uHFplpEvy4MgD7tvnWLa43suErUyE12QWffxB++7ieKED85Cx9XFekWDs3FMf+9DKL6zr6DcqFu5cGScfcsey9cGSlORtKtLinHK+9l4lWHcuh1QKHdnjji5lhqCx3MpRp1bEMr07LQsv25dDpgAsnXbF2TihSk2vuO7eNc2g2nhslh49MhdRkV3w+IwIXT7nXHOfj+Rj6TiYCw6qQecUZ6+aH4dg+b8Pfu/XLx+ODFWjZvgyePhqMeawdUpNM6+YjU2HEexno1L0Iru5aXEt1xqbPgvH3ztq3jzXV9dy0l3rc7XsEAAwan437ehcjul0F1EoBnm3T/pbjjJ6diXb3liEyphIZKVKMeTSmIcOuM3tpr9r45St/bFkZgHyFCNFtKzDm40y07lRusaxaBWxeHog/fvRFrlyMsOZVeG16Fu7tVWIoU14qxNcLgnF4pxcK80Ro3q4Co2dfQ0xsRWOFVGf20l62+Oy6SSzRYumvF9G8XSVG92mF1HP6+Bvis6sp4o+JGP0rMsk9nyrAqBlZ2LAoCGP7tkJqkjPmbEyFl5/KYvm2Xcow7fOr2LXJF2P6tMLhXZ6Yse4KImOMb4QvjFVgwKsKLJ8ahglPtkRluRBzN6ZCLNXfshneohJCoQ5Lp4RhVK8YfPFRCJ4Ykofh0+QNE0e/GKQmuWDOhjvEseIKdm3yw5i+MTi82wsz1qaZxjEm50Yc4ZjQv5U+jg2XDXHc9PXCILwU286w/bzOv9pxypGa7IKPRzXDG71jsOd7P0xemo6uvYtqF1f/fIz68Bo2LA7G2Mda6+P6LqXmuOJKMW1FGnZt9seYfq1xeJc3ZnyZahZXNgYMV2D5tAhM6B+jj+u7FENcvoFKzN98CVlXpJjQPwbTX2mByFaVmLT4qmEfzq4azPkuBYpMMSb0j8E7z7RCRZkT5mxIgZOo7mf4g0/mYeT7GfhuaQjGPdkOqcmumPPtxRrjbBNXgqnLL2P3D/4Y+0Q7HNnjjQ9XpyCylfFD3tlFi3PH3LFufniNx520KBVh0ZX4aERLvNGnHf7e5YP3VlxG83ZldY6hodX13LSXejTEewQAiCQ6/PVfb/z2tb+lwxjs3uyLv37xbpBY74a9tFdt7P/ZG6tnhmDw23Ks2H0B0W0rMP3laBTmWs4Nrf9PMHZ854cxH1/Dmv3n8cSQXMx6rRlSzrgYyix+JxzH/3LHu8uvYtXe84jrWYKpL7ZA7nVxY4VVJ/bSXrb87AKA16ZnIU9+axvd7WcXNX02HSRHRUVBIBDcso0dO7ZBj/PMqFzs2uiLPd/7Iv2SM5ZNCUNVhQB9B+VbLD9whAIJ+zywZWUAMlKc8c3CYKScccGA4Xk3SugwcIQCm5YG4shuL6Qlu2DB+Aj4BarwQD/9yZWw3xOfTozA8QMekKdL8c8eL2xZJUO3x+p/8j0zUoFdG/2w5wc/fRxTw1BVIUTfl2qI4zUFEvZ7YsuqanGcdcGA4blmcQThyJ4bcUyI1MfR17SeFaVCFCjEhq2qwpht3bw8EN8sDEZSghuuX5Vi+1oZEvZ71jrWZ0blYNcm/xtxuWDZ1AhUVQrR96U8i+UHvpZzI65AZKS44JtPQvRxDVMY43otB5uWBeHIHm+kJbtiwVtRN+IqBAB07V0MtUqAz6aH41qqMy6ecsOyaRHo8UQhQqIqAei/6Hj6aPDNJyG4luqMqxdd8N3iYPgGqBEYVlWr2EziHJGNXZtl+P1HGdIvuWD5e5H69nsh12L5gcOzkXDAC1u+CNbH+WkYUs664qn4HEOZvdv8sXFZKE4c8rS4D0D/peKX9QG4eMod8gxnbFoegrJiJ7Rsbzmj1pjqem7aSz0a4j0CAL79JAjb1siQdt7Z4nEAYOUHofjven9cT5c0ZMj1Yi/tVRtbV8vQ7+U89H0pH5GtqjD+P9cgddFi9ybLWcK9P/nipTdzcN8jJQiOVKJ/fB7ufbgYP30hAwBUVQhwaIc3Rrx/He3/rwyhzZQYMkmOkKgq/PqNX2OGVmv20l62/Ozq0qsYcT1LsGZ26C3HudvPriZLp2uczQHYdJB87NgxXL9+3bD9/vvvAIDnn3++wY4hEmvRskM5jh/0MDym0wlw4qAH2sZZHiS0iSvHiWrlASDxgAfaxOkzb0ERSvgFqk32WV7ihPMnXNGmhn0CgJuHBiWFTjX+vXZxGC/N63QCnDjkjrZxljOCbeLKcOKg6aX8xP0W4jhkLGOMw3SfL4zNwY9nz2DF7gt47o0cCJ1u38FrG6tIrEXL9jW0T+fbxWU6KEw84Hnn9jnpZigjlmihVgmg0wkMZZSV+v9ud6++zLXLzijKd0LfQbkQibWQOGvR76VcXL3oDHnGrZf07hxnmclgVt9+nmjTudRynJ3Lbhn8Jv7lVWP5miQluuPB/vlw91JDINChZ/88SKQ6nDricecnW1F9zk17qYe13iPsmb20V22olAJcOu2Kzj2M54pQCHTqUYqkRMuX+FVKASRmWUipsxbnjurfHzUaAbSa25exJ/bSXrb87PL2V+GthRlYMD4SVRUC1MbdfE5T02PTQbJMJkNQUJBh+/XXX9G8eXP07NmzwY7h6auBkwgoVJheYivIFcFHprb4HB+ZGgVml+QKFCL4BOjL+97413yfhQoRfAMsXz4KiarCgFdzsePb+mUcDHHkml4yKlCIbx+Hwqx8rrG8MQ7TMoW5YsPfAODndTLMGxOJd59vgR3f+eGlN7Mx4v2sGuv6YP8CtOpYjj3f33lel6evuub2qeG1tNw+YvjI9OV9b/xr/loVKkSGv5362wM+MhWeeyMbIrEW7l5qvDpNH9PNNqwoc8Lk51vhkWcK8EvKSWy/cBJdHirG+0OaQ6up3RuuIU4ftcX2K8w11vvWOFV1Kl+TuWObQyTSYcvpE/jvpUSMn3sVs0a1wPWrNWcvG0N9zk17qYc13iPsnb20V20U5ztBqxHA2+xc8fFXoUBhebpFXM8S/LRahsxUCbRaIPGAO/7e4Y38HH15V3ct2sSVYeOSIOTJRdBogL0/+SA50Q352fZ3e4+9tJftPrt0mLQ4Hb9964dLp2s3B7sun11N2c05ydbeHIHdzElWKpX47rvv8Oqrr0IgsDwAqaqqQnFxscnmCPyCVJizIRV//eqNnRvt87Lc7WxdHYDTRzyQluyC3771x+pZIRgwXAGx5Na5Xx0fKME7izKw9N1wXL3oYmFv9uHqRRd8MjEKz47Kxi+XTmLT8TOQZ0iQnyMyXAWSOGvx9idXce6YG956KgZvPx2DKxdcMPvry5A4O8jPBQEY+k4m3Dw1mPpyDN7s3xZbvwzEeysuIyrGvrJ/RLY0evY1hDZTYsSDbfBEZEd8Pj0MfV7Mg6Dap+S7y69CpwNe7nwPnozqiO1r/fHQwAKTMmQfBryaCxd3Lb5fHlir8o7y2UWNy26+/m7fvh2FhYUYNmxYjWXmzZuHmTNn1mm/xflO0KgBb7NvrD7+6hozCgUKEXz8zcrL1Ci4kVG4mVnwlqmRn2P8JustU+PyOdOTyzdQhQU/piApwQ1LJ4fVqe4W4/A3y4zIas6MFChEt2Qdq2dSjHGoTOPwV90SR3UXTrhCJAYCw5W4dtmYjWz/f6WYuT4Nqz4KwR9bavdNvDhfVHP75Fi+GcZy+6gMmYf8G/96+6tu2z77tvti33ZfePurUFkuhE4HPDMyB9ev6qdS9BqYj8AwJd56KsYwLWP+uCj8dO407u9TiAO/1D7bUFwgsth+3v6qWzImxjjFdSpvSXBEJQYMy8Hrve/B1Uv62NOSXXHPfaXoPzQHy6dH1XpfDa0+56a91KMh3yMchb20V214+mogdNLdkmmsno005+2nwUdfpUFZKUBxgQh+QSqsnROMoAjj/QchUUp8sjUFleVClJUI4ReoxpzXIxEcWfd7FKzNXtrLVp9dsd1K0CauDL+mnTLZz2c7LuLPbT745K1Iw2P1+exq0rhOsoHdfP9du3YtHnvsMYSEhNRYZtq0aSgqKjJsGRkZd9yvWiXEpdOu6NTduIyPQKBDbPdSJCVavgSTnOiK2Gpz2QCg84MlSL4xl02eLkFetshkn67uGrTuVI7kavv0C1Jh4ZYUXDrjik8nhpvMf60rYxzGehnjsDzHLjnRDbHdaxOHsYwxjpqX5oluVwGNBiZ3iXe4vwSzv0nF2jnB2Lnh9nfq3xLXGUvtU4Kk47eLy/QqQucetWif2DKLcRXmilFZ7oSeTxVAVSU0zOGTumih1ZreX6DVCqDT6ec31oU+TjfEdjPWWyDQIbZbMZKPW57PmHzctLw+zqIay1siddFnvLVmb0haDWye/arPuWkv9Wio9whHYi/tVRtiiQ4tO5TjRLU5q1otcPI282Bvkjjr4B+sgkatXxby/r63XrF0dtXCL1CNkkInJB7wtFjG1uylvWz12fX5B2EY/WgMRvfRb+8PiQYAzB0dhfX/CTY8r76fXfTvYBdf/69evYo//vgDW7duvW05qVQKqbRuN0wBwNbV/pi0JAMXT7niwglXPD1SAWdXLfZs1n9jnLw0HblyMb6apz9xtn8pw8KfUvDs6zk4utcTPQcUomWHCiwxZIIF2P6lDIMm5CAzTQp5ugTx78qRly3G4V36tXpvDpBzMiVYMysEXn7Gb/N1yQSaxLFGhkmL0/+/vTuPj+ls/wf+mZlkMtn3fRUhwmON1g9VXRStR6m21KPE3hYPqmKp2qoaS9VWpRRVuyraolJV60O/iJ0IkUgie8i+znJ+f0wyYyYTspok/bxfr3m1Jvecc11zlrnnOve5B3euPZaHuUozfip0ZRwykk2xeZH6i8aBjc5YuveuOo8/bdC9X6Y6j2ll04WV5jExFYkxZkhJkCIkNFmdR7g6j6DgfLRon4+rZ61RkCdGUHA+PpyXhL/22SMvW737tO2Si8+3xOLARiecOWynqQAo5CLkZj19F9u33gVTl8ept88VC7w1uiwv9dCU0BX31dtnkWdpXi5YuvcO3h6bivPHbNG93yM0a1OAFdN9tHltdMHgiSnq7ZNghpCpSaV52WnW++bwNNy6aIXCfDE6vJiL0Z89wKYwT+TnqGO+fMoGY2YlYsLCBPyy2RliMTBwfCqUChGunq36TW/7vnfF1GWxuHvNElFXLfHWyFT1fviT+sQ89esYPEwxxeYl6u1zYLMrlu6OwoAxKTj/ly1e6vsIzVoXYOUMP80yrWwVcPEsgaNrCQDAy189RVLZLCQJ92RIjDXDxC/vY8NCb+RmmqBzryy075aDuSObVTmH2va0Y7O+xFEX5wgAcPYsgbWdEi6eJRBL1F9AASApVqqZr9vDrxgySxUcnBWQygRNm/g7ZjWac7066sv2qowBY9Px1WQfNG9bgMD2Bdi/wRlFBWL0LJ1RYclEHzi5yTHy02QAwO1LFshIMUXTVoXISDHFtmVuEFTqqcbKXDxhDUEAvJsWIzFWiu8XeMI7oAg9BxmeicfY6sv2MsZnV3qS7mwwRfnqYyUpToqMZPXfavrZ1VhxnmSterEXbN68GS4uLujTp0+dLP/kr/awdVRiWGgK7J0ViLlpjllDmmhuJHD2LIHqsSGmty5aYtF4X4RMT8HwGSlIijXD/JF+iIvSXibds8YZMgsVJi15ACsbJW5esMSsIf6QF6sPxA4v5sLTvwSe/iXYcemWTjy9PNpWPw8HBYZNTdbm8b6/Ng8PA3lM8EPItGQMn56szmNUE908vnUpzSNBm8f72jzkxSJ075eF96ekwFQqICVBin0bnLFvvbNmGT3efQSZhQrv/TcN7/1X+4Fy9awlpr379I7Yyd8cYOtYlpccMbfMMWtoQMXbJ8IKiyY0Qci0JAyfnqTOa7S/Xl6u6rwWx5fmZYVZ7wdo8gKAwHYFGPpJMmQWKjy4J8OqGT449rN2zHjCPRnmjmiKIR8nY8UvdyAIQPQNC8waGqBzia+yTh10hK2jAkOnJJbmaYHPhjXX5OniUQLhsTwjI6yxeKI/QqYmYnjoAyTdl+HzsQGIu6OtAnV+LQufLIvV/PvTNTEAgG3LPbBthSeUCjFmD2+OkTMeYP7GuzC3VCHpvhmWTWmi86MkxvK0Y7O+xFEX5wgAGDY1BT0HZWr+vfboHQBA6NtNce2cugo6+asEtO2SX67NsOeDkPrg2U4LV1+2V2W81C8L2Q9N8ONSd2Smm8C/VSEWbo/RDLdIT5TqXBEqKRZhy2J3JMdLYW6hwnOv5mDaqjhY2So1bfJzJNgc5o6MZFNY2ynR9Y0sjJiRDJP6lz6A+rO9jPHZVRk1/eyixk8kCMadrE6lUqFJkyYYPHgwFi1aVKXX5uTkwNbWFi+hH0xE9fQsVV0V3LzY4Bn7Gn8dEUka55RBgrzE2CEQITzpirFDqBO9PNoZO4S60cg+vxSCHCeEA8jOzoaNTcVz0jd0ZX2q//fG5zAxrdvZjxTyIvx9eE69f0+N3mP5888/ER8fj5EjRxo7FCIiIiIiAPVguEXPnj1h5GI2EREREYFjkh9n9EoyEREREVF9Y/RKMhERERHVE5wnWYOVZCIiIiIiPawkExEREREAjkl+HCvJRERERER62EkmIiIiItLD4RZEREREpKYS1I+6XkcDwEoyEREREZEeVpKJiIiISI1TwGmwkkxEREREpIeVZCIiIiICAIjwDKaAq9vF1xpWkomIiIiI9LCSTERERERqgqB+1PU6GgBWkomIiIiI9LCSTEREREQA+LPUj2MlmYiIiIjqvTVr1sDPzw8ymQydOnXC+fPnK2y7YcMGdOvWDfb29rC3t0ePHj2e2N4QdpKJiIiISE14Ro8q2r17N6ZMmYK5c+fi0qVLaNu2LXr16oW0tDSD7U+cOIHBgwfj+PHjOHfuHLy9vdGzZ08kJiZWep3sJBMRERFRvfb1119jzJgxGDFiBFq2bIl169bBwsICmzZtMth++/btGDduHNq1a4cWLVrg+++/h0qlwrFjxyq9To5JJiIiIiIAgEgQIKrj2SfKlp+Tk6PzvJmZGczMzMq1LykpQUREBGbOnKl5TiwWo0ePHjh37lyl1llQUAC5XA4HB4dKx8lKMhERERE9c97e3rC1tdU8wsLCDLbLyMiAUqmEq6urzvOurq5ISUmp1LqmT58ODw8P9OjRo9LxsZJcXzWQOQSrTFAaO4I6IQgqY4dAVSAyaZynPkGhMHYIdaKXRztjh1Anfk28YOwQ6sSbns8ZO4Ta1Vg/jyuiKn3U9ToAJCQkwMbGRvO0oSpybVi0aBF27dqFEydOQCaTVfp1jfOTgoiIiIjqNRsbG51OckWcnJwgkUiQmpqq83xqairc3Nye+NqvvvoKixYtwp9//ok2bdpUKT4OtyAiIiIiANoxyXX9qAqpVIrg4GCdm+7KbsLr3Llzha9bsmQJFixYgCNHjqBjx45Vfi9YSSYiIiKiem3KlCkICQlBx44d8fzzz2PFihXIz8/HiBEjAADDhg2Dp6enZlzz4sWLMWfOHOzYsQN+fn6asctWVlawsrKq1DrZSSYiIiKiem3QoEFIT0/HnDlzkJKSgnbt2uHIkSOam/ni4+MhFmsHSKxduxYlJSV45513dJYzd+5czJs3r1LrZCeZiIiIiNSq+WMfVV5HNUyYMAETJkww+LcTJ07o/Pv+/fvVW8ljOCaZiIiIiEgPK8lEREREpCYIdT/tXQOZVo+VZCIiIiIiPawkExEREREAQCSoH3W9joaAlWQiIiIiIj2sJBMRERGRGscka7CSTERERESkh5VkIiIiIgIAiFTqR12voyFgJZmIiIiISA8ryURERESkxjHJGqwkExERERHpYSWZiIiIiNSE0kddr6MBYCWZiIiIiEgPK8lEREREBAAQCQJEdTxmuK6XX1tYSSYiIiIi0vOPqST3HZ6Bdz5Kg4OzAjG3zPHtZ56IumJRYftu/85CyLQUuHqVIDHWDBsXuuPCXzaPtRAwLDQVvf/zEFY2Sty6aIlVM7yQFGumaTHvh1g0bVUIO0cFcrMluHzaGhsXuuNRqmkdZlo1VX1fjLHO2tgW1nYKjPsiEZ1ey4GgAs4ctsPa2R4oKpBo2rzYNwvvTUyFp38xsh+a4NfNTti71kUnlpffysTAcWnw8C9Gfo4EF49bY8MCD+RmGd6mfUPSdXOd7YmoK5ZPzjU0WZvrlx7lc52aopvrTG+dXAHg+VezMWRyKpoEFaKkWIzrf1ti/ih/AMBrAx9i6vIEg+sf2KYVsh9Wff9srMdX32FpeOeDVNg7yxETaY5v5/jgztUnbL8+mRj2SaI6r/tm2BTmhQvHbTV/79o7E2+8n45mrQtgY6/EuN5BiLmlfZ+sbBUYOiUJwS/mwNmzBNkPTXDuDzts+coTBbkSQ6t8eg7PeNu4epXgPx+nol3XPNg7y/Ew1RR/7bPHzpUuUMjVdRlTMxUmLnqAZm0K4dOsCP/3pw3mj2xSrfyMmSsADJ6Yiud75MC/VSEUJSK8HdS6xnk8zaEfXLB/rRsy003RpGUBxi6IR/P2+QbbKuQi7P3GHX/95IiHKVJ4+hchZFYCgl/O0bRRKoGdyzxxYp8jstJN4eBaglfezcCgyckQiWon5n/SftjgcXYLjX9EJbn7m5kYOzcJ2792w/hezRFzS4aFO2Jg6yg32L5lx3zM/DYOR3Y6YFzP5jh7xAZzN92Hb2Chps3A8enoNzIdq2d4YdK/m6GoQIwvd8TA1Ew7Q/bV/1lh4Qe+GNWtBb4Y4wcPv2LM3nC/rtOttKq+L8ZYZ21ti+nfxMM3sAgz3/PHnJAmaN0pD5OXPtD8vePLOZj+TRwO/eiID14OxDczvTBgTDreHJGhjeW5fISuiseRXQ4Y+1IgFn7gi8B2hTrLqTDX3oGIuWWOhdufkuua+ziy0xHjegXibLgt5m6M1c11XFpprt6Y1Le5Otft93RyfeGNLExbGY8/9jjgo56BmNK/GY4fsNf8/eSv9nivXSudx8Xj1rh61rJaHeTGeny92PcRxsx+gG0r3DGhTxBiIi2wcNvdCvMKCs7DjNUxCN/thPFvBOFcuB3mbLgH3+bavGQWKty8YIVNYV4Gl+HoKoejqxwbFnrhw9daYdknfgjunoOPl1YvL2NsG++AIojFAlZO98LYlwPx3TwP9Bn6ECNmpmiWIRYLKCkS45eNTrh82rpaudWHXAHARCrg1G92OLTFqVbyeJrTvzhg43xvvDclCcuP3IRfywLMHdIcWRmGa17blnjiyDZnjF0QjzXHb6D30DSEjW6Geze0HdSf17jj9x+d8cEXcVhz4jpCPn2A/WvdcXCTi8FlVtU/aT+kxsWonWSlUonZs2ejSZMmMDc3R9OmTbFgwQIItfwNY8DYDBzZ4YA/djsg/q4Mq6Z7obhQhF6DHxls3390Oi4et8betS5IiJbhx6XuiL5ujn4jHpa2ENB/dDp2rnTFuXBbxEaaY8lEHzi6ytGld7ZmOfs3OOP2JUukJUpx66Ildn/jghYdCiAxqR/foKr6vhhjnbWxLbwDivDcK7lY/ok3oi5b4uZ5K3z7mSe698uCg6v6JN3jnUycPWKLQ1udkBJvhvPHbLDrGxcMHJ+GsttwWwbnIzVBil82OiM1wQw3z1vh0DYHBLYrMJzrmHQc2eGIP/Y4qnOd4YXiQjF6vVdBrqPScfGEDfaueyzXG+bop+mol+XqhnN/lOY6yVeday91rmKJgA8/T8SGLzxwaKsTEmNkiL8rw6nftJ3kkiIxMtNNNQ+VUoS2XfMQvsuxClvysTwb6fE1YHQqjux0wtGfnBB/1xyrZ/qot9+ghwbb9x+ZhosnbbH3OzckRJvjx2WeiL5hgTeHp2naHNvniB0rPXD5jOEP5Lg75vjiw6b4vz/tkBxnhqtnbbBlqSc6vZoNsaTqeRlj21w8YYNlH/vg0klrpMSb4e8/bLF3nTO6vq7ddsWFEqye6YXfdzjiUVrtXNA01n649Ss37N/gjNjbslrJ42l+2eCKnv9JR49BGfBpXoRxi+JgZq7Cn7sMd9JP/OyId/+bjI6vZsPNtxhvhKQj+JUsHPjOTdPm9kUrdOqVhed6ZMPVuwRd/52Jdt2zceeKVa3E/E/aD6lxMWonefHixVi7di2++eYbREZGYvHixViyZAlWr15da+swMVWhWZsCXHrsW6IgiHD5tDVaBhvu3AQFF5T7Vhlx0hpBwerLWW4+JXB0VegssyBXgtuXLRBUwTKt7RR4ZUAmbl20gFJRS9evaqA674sx1lkb2yKoYz5ysyS4e01bObl02hqCCmjRXt3GVCqgpFj3cCgpEsPZQw5XL3VH+laEJZw95HjulRwAAuyc5OjWJxsX/irf4dHmqv2QEQQRLp+xQstgw5dFg4Lzcfm07odSxAkDuZ7RttHmqm7TrHUBnN3lEFTAmvAo7Lh0A19svadTgdHX491HKC4U4fQhuwrbVKSxHl8mpio0a12Ay2e0l3fV288aQR3yDL4mqENeuc5vxCkbBHUwvL0ry9JaiYI8CVTKquVVX7ZNWQ65WdUbLlIZ9SnXuiQvESH6miXaddMOlRCLgbYv5OB2hOEOrbxYrFP5BgCpTEDkeW37Fh3zcO2MDRLvqYcqxN40x63z1gh+OavGMdenbVPX+2GjIQBQ1fGjftQKn8qoneSzZ8+iX79+6NOnD/z8/PDOO++gZ8+eOH/+fK2tw8ZBCYkJkJWu+y0xM8ME9s4Kg6+xd1YgU+/SVWa6Cexd1O0dSv+rv8ysdBM4uOhePho1Kwm/RF/H3ls34ewhx7wR9WO8U3XeF2Ossza2hYOzAlkPdf+uUoqQm6Vtc/GENV54IxvtXsiFSCTA078Yb3+Qrn59abX51gVLLJ7gg0/XxeFQ3DXsvnYL+bkSfPNp+UvnmlwzdIcvZKabPjnXdL32Gdr22lx122RlmGr+5uZTAgB4/5MU7Fzpijkh/sjLlmDp3mhY2xleb6/3HuL4AXuUFFX9dNBYjy8bB0Xp9tOLIcMU9s6GLxHbOyvKb5t0kwrbVyoOewUGT0zG7zuqfinf2NumjIdfMfqNzMDhrdW7UlEZ9SXXupbzyAQqpQh2Trrrt3OWl9v3yrR/KRu/rHdDUowZVCrg8ikbnDtsh0dp2vbvTEhGt36PMK57a7zlG4zJvVrhzdGpeGlAza8q1pdt8yz2Q2p8jNpJ7tKlC44dO4Y7d+4AAK5evYozZ87g9ddfN9i+uLgYOTk5Oo/67qe1LhjXszlmvucPlQoIXRmPBvMV6h/k9+0O+HWzIz7fEotDcdew8re7OPGLHQBAVVqE8WlWhI8+T8T25a6Y0Ls5Ph3cBK5eJZi42PCYZGMQlx7RO1e54sxhO0Rft8CyKT4QBPWNMPqCgvPh27wYR3Y2zA+Oxnx8WVgp8fkPdxF/V4Ztyz2MHU61OLrJsXB7DE4dtMPvOxrmPtbQjfk8Hh5NijCue2sM8OuI9bN80GNQhuZcAQBnfnPAyX2O+GRNDJYfuYXJK2JxYJ0bju1pHNuM+2HVlE0BV9ePhsCog3BmzJiBnJwctGjRAhKJBEqlEgsXLsSQIUMMtg8LC8P8+fOrtI6cRxIoFYCd3jdWeycFMtMNp5+ZbgJ7J732zgpklo5ZKhu7ZOes0Pk2bueswL2b5nrrN0HOIxMkxpgh/q4ZtkdEIii4AJERFd8h/yxU530xxjprY1s8SjeBnaPuMsQSAdZ2j79GhI0LPbA5zB32LgpkP5Sg3Qvqy+opcepLkIP+m4abFyw1M17ERpqjqFCMrw/cw5Yl7jrr1+SqV/Gxd5Y/OVe9qqO9k7a9Nle5bq5Ocm2upW3i72jHR8pLxEiJM4OLZ/kKS+/BDxF9wxzR16s3o0ljPb5yHpmUbj/dOO2c5OWq/Y/nZeesX+Erf3WgMswtlfjix7sozJfg87FNqzWExNjbxsFVjiU/RePWRUusDDV8o2JtMXauz4qNgwJiiVDuClVWumm5fa+MraMCszZFo6RIhNxMEzi4ybHlSy+4+hRr2vywwBtvT0jGi/3UlWO/oEKkPZBi7zfueHWg4TH4lWXsbfMs90NqfIxaSd6zZw+2b9+OHTt24NKlS9iyZQu++uorbNmyxWD7mTNnIjs7W/NISDA8jdXjFHIx7l6zQPsXcjXPiUQC2r2Qh1sRhjsGkREWaNdNd9xhhxdzNR+8KfFSPEw10VmmhZUSLdoXILKCZQKAqPTdNpUa/xtUdd4XY6yzNrZF5EVLWNspEdBaO1at3Qt5EImB25d116tSifAwxRQKuRgv98/CrYsWyH6kPiHLzFXlZq3RjBPV68Noc9XGrs3VcAcuMsJS0zF/cq7aNtpc1W3uXrNASZEIXk21H4ASEwGu3iVIfaD7wSqzUOLFvlkI3+lgMJ7KaKzHl0Iuxt3rFmjXVXu1SiQS0K5rLiIvGR77GXnJCu265uo81+GFHEReqlqH3cJKiS+33YVCLsK8kQGQF1fvNG3MbePoJsfSvdG4e90Cyz72hiDU7X0Y9Wk/rEumUgEBbfJx9bGx8ioVcO2MDVoEGx4rX0YqE+DoLodSIcLZw/bo1DNT87fiQjFEIt3jRiwBBFXNt9s/aT9sNARop4Grs4exk6wco1aSQ0NDMWPGDLz33nsAgNatWyMuLg5hYWEICQkp197MzAxmZmblnn+afeudMHVFAu5ctUDUZQu8NSYdMgsV/til7hyEroxHRoopNoe5AwAOfO+MpT9H4+0P0nD+mA2698tCszaFWKH5FirCge+dMXhSGhJjzZASL0XItBQ8TDXF2SPqOVED2+cjsF0hbpy3RF6WBO5+xQiZloKkWKnRTrD6nva+GGOddbEtEqJluPCXNSZ/9QCrp3tBYipg/BcPcPIXO82cujYOCnTrk4Vr56xgaiag56BH6PbvLIS+HaCJ/e+jNpi8NAH/HpaBiyes4eCqwIfzE3H7koXBuXn3bXDG1OXxuHPtsVzNVfhjd1mucchINsXmRepL6Qc2OmPp3rvqXP+0Qfd+mepcp3nr5joxFYkxZkhJkCIkNFmda7g614I8CQ5tc8TQqSlITzJFWqIU73yonl3h9EE7nfi6v5kFiUTAsX32qInGenzt+94VU5fdx93rloi6YoG3RqWp8yq9BD11eSwepkixebGnOq9NLli6JwoDxqTi/F+2eOnNR2jWpgArZ/hqlmllq4CLZwkcS8e5ezUtAgDNTCMWVkos3HYXMnMVlkxuCgtrJSyslQCA7IcmUFWx02KMbVPWMUlLlGLD5x6wfewqzuNVdZ9mRTCRCrC2V8LCUgn/VuqbS2OqWaU1Rq4A4OxZAms7JVw8SyCWQJNHUqxUZx722tJvTCpWfNwEAW3y0bx9Pn7d4IqiQjFeHaSeBWf5xCZwcJcjZKZ6GFjUJUs8TJHCv1UBHqaYYucyTwgqYMA47VRoz72WhZ9WecDZswQ+gYWIuWGBX9a7osd7GQZjqKp/0n5IjYtRO8kFBQUQi3WrJBKJBKqyQaC15OSv9rB1VGJYaArsnRWIuWmOWUOaaC5ZOXuW4PFV3rpoiUXjfREyPQXDZ6QgKdYM80f6IS5Ke9DsWeMMmYUKk5Y8gJWNEjcvWGLWEH9N1ae4UIyur2dj6CcpkFmo8CjNFBePW2PhSlfIS+rH9NRPe1+Msc662BYAsHiCD8YvTMSiPfdKf0zEFt9+5qkTW493MzFmjnry/MgIC4S+01RnsvujexxgbqXEmyMyMGZuEvKzJbjyPytsXGh4vOjJX+1h66DAsKnJ2lzf99fm6mEg1wl+CJmWjOHTk9W5jmqim+u3LqW5JmhzfV831w0LPKFUiDBtVTykMhWiLltg+sCmyMvWPdx7D36I//1uh/ycmp0GGuvxdeo3B9g6qH/cw95Zjphb5vhsaDNNXi4eJTqVtsgIKyye6I+QqYkYPi0RSffN8PmYpoi7o82r82tZ+OTrOM2/P10TCwDYttwd25Z7IOBfBZrZMDafvqETT0iXfyH1QdWKBMbYNh1ezIWnfwk8/Uuw49ItnXh6ebTV/P+CbTFw89YOEVh79E65NvU9VwAYNjUFPQdpq7JleYS+3RTXztXOFGqP69bvEbIfmWDHV57ITDeFf6sCzNt2R3MTXHqSVHNVBVDPbrF9iSdS4s0gs1Ci4yvZ+HhVDKxslZo2Y7+Iw/Ylnlj3qS+yH6p/TKT3++kY9HFSrcT8T9oPGwX+mIiGSKjtSYmrYPjw4fjzzz/x3XffoVWrVrh8+TLGjh2LkSNHYvHixU99fU5ODmxtbfES+sFEVH9+xY7+gWrrZ6nqmwZyIqsqkUnjnBNVUNTNzDRUN35NvGDsEOrEm57PGTuEWqUQ5DiBX5CdnQ0bG5unv6CBKutTvdJ2OkwkVb9qXxUKZTH+urq43r+nRv2kWL16NWbPno1x48YhLS0NHh4e+OCDDzBnzhxjhkVERET0z6RCufts6mQdDYBRO8nW1tZYsWIFVqxYYcwwiIiIiIh0NM5rjkRERERUZc9iHuOGMk9y/biDjIiIiIioHmElmYiIiIjUOLuFBivJRERERER6WEkmIiIiIjVWkjVYSSYiIiIi0sNKMhERERGpsZKswUoyEREREZEeVpKJiIiISI2/uKfBSjIRERERkR52komIiIiI9HC4BREREREB4M9SP46VZCIiIiIiPawkExEREZEap4DTYCWZiIiIiEgPK8lEREREpKYSAFEdV3pVrCQTERERETVIrCQTERERkRrHJGuwkkxEREREpIeVZCIiIiIq9QwqyWgYleTG0UkWidQPqv8ayCWWKhM1zosyIkkjPa4a6fZqtBrp+f1Nr+eNHUKdCE+6bOwQalVOrgr2zY0dBRlD4+gkExEREVHNcUyyBsspRERERER6WEkmIiIiIjWVgDofM8x5komIiIiIGiZWkomIiIhITVCpH3W9jgaAlWQiIiIiIj2sJBMRERGRGme30GAlmYiIiIhIDzvJRERERER6ONyCiIiIiNQ4BZwGK8lERERERHpYSSYiIiIiNd64p8FKMhERERGRHlaSiYiIiEhNwDOoJNft4msLK8lERERERHpYSSYiIiIiNY5J1mAlmYiIiIhIDyvJRERERKSmUgFQPYN11H+sJBMRERER6WElmYiIiIjUOCZZo9F2kvuGpOOdj9Lg4KxAzC1zfDvbE1FXLCts3+3fWQgJTYarVwkSY82w8UsPXPjL5rEWAoZNTUHv/zyElY0Sty5aYtVMbyTFmmlabPn7Jty85TrL3filO/ascQUAmJqpMHFRApq1LoRPsyL83582mD/Kv97nBQDPv5qNIZNT0SSoECXFYlz/21In9o8+f4BWz+XDN7AICdFmGNezRZXyqk19h2fovkefeSLqisWzjyMkHe98mAoHZzliIs3x7WzvJ2+rPpkICU1Sb6v7Ztj4pScu/GX7WAsBw6Ymo/fgDFjZKnHrghVWfeqNpFiZpkXAvwow6tNENG9bAJUKOHPYDt/N90JRgURnXa+9+xADxqbCq0kxCvIkOHXQDms+86l+rsPS8M4HqbAvy3WOD+5cfXKuwz5J1OS6KcwLF45rc+3aOxNvvJ+OZq0LYGOvxLjeQYi5VdE2FLBgSzSeezkH80c3xbk/7KqdR/m8UvHO2JTSvCzw7Vwf3LlqVXFebzwqzasYifdl2LTICxeOa+Pp2vsR3hiSjmat89V5vd7KYF5BHfIQEvoALdrlQ6kEYm5ZYNbQQJQU187Fv6oeI93+nYWQaSna88hC9/LnkdBU3fPIDC+d88jgial4vkcO/FsVQlEiwttBrWslF528jHR+BABTqQorD95B01ZF+Khnc8TcrN45pz6e4/1bFmLg+FT86/l82NgrkPpAikNbnXBgo3O1cqzIr5udsHetCx6lm8C/ZSHGfZGIFu0LDLZVyIFdq13x508OyEgxhVfTYoyalYTnXs7VtCnIE2PLEnec/d0WWQ9N0LRVIT5a8ACB7QprNW5qvBrlcIvub2Zi7NwkbP/aDeN7ByLmljkWbo+BraPcYPuWHfMxc819HNnpiHG9AnE23BZzN8bCN1B7IA0cl4Z+I9OxeoY3JvVtjqICMb7cfg+mZrrjarYsdcN77VppHr9sctL8TSwWUFIkxi+bnHH5tHWDyeuFN7IwbWU8/tjjgI96BmJK/2Y4fsC+3PrCdzng1G92Vc6rNum8R72aI+aWDAt3VPwe1VkcfR9h7JwH2L7cHeNfb6HeVtuiK95WwXmYuSYWR3Y5YVzvFjh7xA5zv4/R21ap6DciHatn+mBS30D1ttoWrdlWDq4lWLTrLpLum2FS30DMej8Avs2LMHV5nM66BoxJxfDpSdizxg1jX22JGYMDEHHSBtX1Yt9HGDP7AbatcMeEPkGIibTAwm13K8w1KDgPM1bHIHy3E8a/EYRz4XaYs+EefJtrc5VZqHDzghU2hXk9df1vjUqrk6LEi/9+iDGfJWDbSg9M+HcrdV5b7zwhr1zMWH0P4XucML5PK5z7ww5z1kfDt7n2Q15mXprXIu8K1xvUIQ9fbLmDS6dsMenNlpj0Zkv8usW11nKs6jHSsmM+Zn4bhyM7HTCuZ3OcPWKDuZvu6+6b49NLzyNemPTvZup9c0eMznnERCrg1G92OLTFydBqajevZ3zeB4BRs5LwMMW0QebwtHN8QOsCZGWYYPF/fTH2lRbYucoVI2Ym4c3h6TXK93EnfrHD+vkeGDIlBWvCo+DfshCz/uOPrAzDtbwfFrvj8DZHjPviATacuI0+QzPw+agmiL5urmmz/BNvXDplhWmr47Du2G0Ed8/FjEEByEiu2XZq9MoqyXX9aACM2knOzc3F5MmT4evrC3Nzc3Tp0gUXLlyo8XIHjEnHkR2O+GOPI+LvyrBqhheKC8Xo9d4jg+37j0rHxRM22LvOBQnRMvy41B3RN8zRb0RGaQsB/UenY+dKN5z7wxaxkeZYMskXjq5ydOmVrbOswjwxMtNNNY/iQm0Fr7hQgtUzvfH7Dkc8Sq96Ed8YeYklAj78PBEbvvDAoa1OSIyRIf6uDKd+0+0kr53jhd+2OCM5TlrlvGrTgLEZOLLDAX/sdlC/R9O9UFwoQq/Bht+juosjDUd2OpVuK3OsmuGD4iIxer330GD7/qPSSreVKxKizfHjVx7qbaX5EBLQf1Qadq5yw7k/7BAbaYElk/1Kt1UWAKBTjxwo5CJ8M8sbD2JkuHPVEqtm+qBbnyx4+BUBAKxsFQiZloSlk3xx/IADkuPMEBtpgb+P2lU/19GpOLLTCUd/ckL8XXOsnumj3i8HVZDryDRcPGmLvd+5qXNd5onoGxZ4c3iaps2xfY7YsdIDl888+cukf8sCDBibiuWhftWOvyIDRqfiyC5nHP3JWZ3Xp77qvAZmGGzff0RqaV7upXl5qfMKeSyv/U7YscoTl89U/KVk7Ox4/PKDC/asdUfcXXM8iDHH6UMOkJfUzum6qsdI/9HpuHjcGnvXPnYeuW6OfiPKtm/ZecQV58JLzyMTfdT7Zm/t+XHrV27Yv8EZsbdlBtdT47yMeN7v+HIOgrvnYsMCzwaXQ2XO8X/sdsS6uV64/rcVUuLN8Nc+B/yx2xFd38g2EFX17FvvjN7/eYhe7z2Cb/NiTFz8AGbmKoTvdDDY/tjPDnjvv2l4/tVcuPuWoG/IQzz3Sg5+/k5d3S4uFOHMYTuM/iwZrf9fPjyblGDo1BR4+BXj4I+OtRY3NW5G7SSPHj0aR48exdatW3H9+nX07NkTPXr0QGJiYrWXaWKqQrM2Bbh0WntJVBBEuHzGCi2D8w2+Jig4H5dP615CjThhjaDS9m4+JXB0VeDSGW2bglwJbl+20LQpM3B8Gn66cR1rwqPwzodpEEtq59uSsfJq1roAzu5yCCpgTXgUdly6gS+23tOpVNQX2vdI27ESBBEun7ZGy2DDl+zqLI7WFcTR4UnbSrfjFHHSpvy2emyZBbkS3L5iqWljKlVBIRdBEESaNiVF6v9v9Zy6TYduuRCLACc3OTYcv4ltF65j1toYOLuX1CjXxzt96v3SGkEd8gzn2iGvXOc34pQNgip4bypiJlNh+upYrPnMB5nptVsZUueVbyAvmyfklV+u8xtxyrbC9obYOsoR1CEfWQ9N8fW+W9h58TKW7L6NVh1zn/7iSqjOMRIUXFDuylfESQPnEf1987IFgp7RcWfM876dkxyTlyZgyURfFBeKUF0N7Rxvaa1EbpbkiW0qS14iwt1rFujQTXusiMVA+255uBVheKiJvEQEqV5F30ymws3z6lyVShFUyie3oQqohGfzaACM1kkuLCzEzz//jCVLluDFF19EQEAA5s2bh4CAAKxdu7bay7VxUEJiAmRl6H5oZqabwt5ZYfA19s6Kch+ymRna9g4u6v9m6bXJyjDV/A0AftnkjLBxvpj2bgAOb3PEe/9NxejPkqqdy+OMlZebj7rz9P4nKdi50hVzQvyRly3B0r3RsLYzvF5j0bxHelX6zAyTCt+juolDUXEcLoYvm9o7K5Cpd1lRvW3V7R1K/6u//bPSTTR/u/o/a9g7y/HOh6kwMVXBylaBkTPV+59D6XrdfIshEgPv/TcF6+Z544sP/GFtp0TYzrswMa36lDyaXPViz8rQxm4o13L7XLpJhe0r8sHcBERetKxRFbwiNvYKg8fbk/OSV6m9Ie4+xQCA9ycn4vedzvgspDmib1ggbEeU5mpATVTnGDG8b5rA3kX/PKK3D6SbaPa7uma8876AqcvjcWirI+5eq9l9Dw3pHN+yYz66v5mJw9tqpyKb80gClVIEO71jxd5JjswKrroGd8/Fz+udkRgjhUoFRJy0wv8O2+FRmrq9hZUKQcH52LHCDQ9TTKBUAsd+tkdkhCUepTba27Golhmtk6xQKKBUKiGT6V56Mzc3x5kzZwy+pri4GDk5OTqP+mTfehdcO2eN2EhzHNrqhPWfe6DfiHSYShvGfICGiEv3kJ2rXHHmsB2ir1tg2RQfCIL6hhGqP+LumOOrj/3w9thU/Hr3CnZeuo6UBCkepZlohn+JRYCpVMC3c7wRcdIGty9ZImy8HzyaFKNtl8pXPI3t/72WhbZdcrFufsVjexsikVi9oQ5vd8HRn5xx76Yl1i/wQWKMDL0G1t74T6od/UZmwNxKhd2rXY0dSrVV9RzvG1iIuZtisG25Gy6dqv69DDX10YIH8GxSgtEvBqGPb1t8O8sLPQc9hOixXs201XEQBOA/Hf6Ff/u1xYGNTnipf6ZOGypPEFTP5NEQGO3rlLW1NTp37owFCxYgKCgIrq6u2LlzJ86dO4eAgACDrwkLC8P8+fOfuNycRxIoFepLYI+zd674G2mmgSrW499gy76Z2jnL8ShN+43czkmOezfNUZGoyxYwMQVcvUvw4F7NxuEZK6+yNvF3tPHLS8RIiTODi+ezvRnuaTTvkV7Vxd5JUeF7VDdxmFQcR5rhYQGZ6Sawd9Jr7yzXVIkelf7XzklvWzkrdPbB4wcccPyAA+yc5CgqEEMQgAFj0pAcp76Tvey18Xe12zP7kSlyHpnAxbPqQy40uerFbuckr3AIRGa6SbmKkZ2BitiTtO2SC3ffYvx844rO8599dw83z1th2qDASi/LkJxME4PH25PzMq1Se0MepanH9MdH654v4qNlcK7G9tFXnWPE8L6pQGaa/nlE8cR9sy4Z6/zYrmsugoLzcTD2qs5yvjl8B3/tt8dXk33rfQ5VOcf7NCvC4t338Pt2J+xc6Vbp3J7GxkEJsUQoV/F+vCquz85RiXmbY1FSJEJOpgkc3eTYuNAdbqVXYwDAw68EX+2LRlGBGPm5Yji6KrDwA1+4+xYbXCaRPqN+n9q6dSsEQYCnpyfMzMywatUqDB48GGKx4bBmzpyJ7OxszSMhIaFcG4VcjLvXLND+BW1VTCQS0O6Fisc2RUZYot0LulW0Di/mIrK0fUq8FA9TTXSWaWGlRIv2BZo2hvi3KoRSWf5SdHUYK6+71yxQUiSCV1PtSUViIsDVuwSpD+rXHcLa90g7flP7Hj27KeAUcjHuXjcURy5uXXrSttK9MtKhm6FtpV2mhZUSLdrlG9wHszJMUVQgQfc3MyEvFmvGi968oG7r5a+9dG9tp4CNg3pap+rm2q6rNnaRSEC7rrmIvGR43F/kJSu066o7xrbDCzmIrOC9MWTPt274qGdLjOutfQDA+s+9sWyqX5Xz0KfOy9JAXjlPyEu3PQB06JZdYXtDUhOk6ums/HWHVnj6FyPtQflpx6qqOsdIZIQF2nWrzHlEb99sX4DIZ3TcGev8+O1sL3z0WiA+6ql+fDZUPWXalx/54YfF7g0ih8qe432bF2LJT9E4+pNDlXN7GlOpgGZtCnD5sbHTKhVw5QnjsctIZQKc3OVQKtRTXnbuVf4Ks8xCBUdXBXKzJIg4aWOwDT1GeAbjkRvI7BZGHZjTtGlTnDx5Evn5+cjJyYG7uzsGDRoEf3/DcwebmZnBzOzpHxT7Njhj6vJ43LlmgajLFnhrTDpk5ir8sVt9l2zoyjhkJJti8yIPAMCBjc5Yuvcu3v4gDef/tEH3fplo1qYQK6aVXcoV4cD3zhg8MRWJMWZISZAiJDQZD1NNcTZcPbdrUHA+WrTPx9Wz1ijIEyMoOB8fzkvCX/vskZetfZt9mhXBRKqCtZ0SFlYq+LdS39hSmTk1jZFXQZ4Eh7Y5YujUFKQnmSItUYp3PlTfrX/6oJ0mNg+/YsgslXBwUUAqEzR5xd+RQSF/dt/F9q13wtQVCbhz9bH3yEKFP3YZvkO67uJwwdTlceo4rljgrdFl20o9hi90xX1kpJhi8yL13fAHNrpg6d47eHtsKs4fs0X3fo/QrE0BVkwvm7tYhAMbXTB4YgoSY82QkmCGkKlJpdvKTrPeN4en4dZFKxTmi9HhxVyM/uwBNoV5Ij9HvQ8mxspw9ogtPpr/ACun+yA/T4KRMxLxIFqGq2erPi0hAOz73hVTl93H3euW6lxHpanf8z3qXKcuj8XDFCk2Ly7NdZMLlu6JwoAxqTj/ly1eelOd68oZ2qqbla0CLp4lcHRVV7K8mqo7jY/PHGOoQpuWKEVqQs07k9q8YnH3miWirlrirZGp6rx+Uk9hNvXrGDxMMcXmJerj6cBmVyzdHYUBY1LUefV9hGatC7Byhp+BvNRVYS//Qp28ABH2fueGoR8nISbSAvduWuC1dzLg3bQQCz9sWjt5PeUYCV0Zr943w9QdoQPfO2Ppz9Hq88gxG3Tvl6U+j4SWTc9Xeh6ZlKbeN+OlCJmWot43j2jnvnb2LIG1nRIuniUQS9RFBABIipWWm8e7WnkZ4fyYnqT7xbIoX32uS4qTIiO56l866+s53jewEEv23MPFk9bYt95ZU71WKUXIflQ73YgBY9Px1WQfNG9bgMD2Bdi/wRlFBWL0LJ3ZY8lEHzi5yTHy02QAwO1LFshIMUXTVoXISDHFtmVuEFTqKe/KXDxhDUEAvJsWIzFWiu8XeMI7oAg9K5h5h0hfvRi9bmlpCUtLS2RmZiI8PBxLliyp0fJO/moPWwcFhk1Nhr2zAjE3zTHrfX/NDRHOHiU6Pxt+66IlFk3wQ8i0ZAyfnoykWDPMH9UEcVHaS4V7vnWBzEKFSUsSYGWjxM0Llpj1vj/kpZP7y4tF6N4vC+9PSYGpVEBKghT7Njhj33rdydYXbL2n84Mja/+4AwDo5dmuXuYFABsWeEKpEGHaqnhIZSpEXbbA9IFNdTr/k5fGo20X7Tf+sryGdQpCai1UwCrr5K/2sHVUYlhoivY9GtKk3M0wdR7Hbw6wdSzbVnLE3DLHrKEB2m3lqbetIqywaEIThExLwvDpSeptNdpfb1u5qrfV4vjSbWWFWe8H6GyrwHYFGPpJMmQWKjy4J8OqGT449rPuzTVLJ/vhg3kP8PmWexAE4Nrf6uUoFdW7M//Ubw6wdVBg6JQkTa6fDW2mydXFowSCSrvsyAgrLJ7oj5CpiRg+LRFJ983w+ZimiLujzbXza1n45Gvt/M6frokFAGxb7o5tyz2qFWeV8zroCFtHBYZOSSzNywKfDWuul5e2fWSEtTav0AdIui/D52MDEHdH+wW482tZ+GRZ7GN5xZTm5YFtK8q+RLhBaibgg9nxsLZTIibSHJ8OCURyfO1Mnfa0Y6TcvnnREovG+yJkegqGz0hR75sj/XT3zTXOpeeRB9rzyBDd88iwqSnoOShT8++1R9XniNC3m+LauZrPNmCs82Ntqq/n+G59smDnpECPtzPR423tNkxJMEXI/2tVK7m/1C8L2Q9N8ONSd2Smm8C/VSEWbo/RDLdIT5Ti8YvMJcUibFnsjuR4KcwtVHju1RxMWxUHK1ulpk1+jgSbw9yRkWwKazslur6RhREzkmFSvy6CUj0mEgTj1bzDw8MhCAICAwMRHR2N0NBQyGQynD59GqamT9+Lc3JyYGtri5dE/WEi4l7fIDSQSyxVJq6dqZDqG5G4+lNa1WuN9M4dQV7zccv1kqiR7oeNVHjiZWOHUKtyclWwbx6D7Oxs2NgY72bFulbWp3rVdihMRHX7mwcKoQTHsrfW+/fUqJ8U2dnZGD9+PFq0aIFhw4bhhRdeQHh4eKU6yEREREREdcWowy0GDhyIgQMHGjMEIiIiIiqjUgGiOp6irYFMAdc4rzkSEREREdVAvbhxj4iIiIjqAUEAUMf3DzWQ+5NYSSYiIiIi0sNKMhEREREBAASVCkIdj0luKD9LzUoyEREREZEeVpKJiIiISI1jkjVYSSYiIiIi0sNKMhERERGpqQRAxEoywEoyEREREVE5rCQTERERkZogAKjrX9xjJZmIiIiIqEFiJZmIiIiIAACCSoBQx2OSBVaSiYiIiIgaJlaSiYiIiEhNUKHuxyTzF/eIiIiIiBokdpKJiIiIqN5bs2YN/Pz8IJPJ0KlTJ5w/f/6J7X/66Se0aNECMpkMrVu3xuHDh6u0PnaSiYiIiAhA6Y17z+BRVbt378aUKVMwd+5cXLp0CW3btkWvXr2QlpZmsP3Zs2cxePBgjBo1CpcvX0b//v3Rv39/3Lhxo9LrZCeZiIiIiOq1r7/+GmPGjMGIESPQsmVLrFu3DhYWFti0aZPB9itXrkTv3r0RGhqKoKAgLFiwAB06dMA333xT6XWyk0xEREREaoLq2TyqoKSkBBEREejRo4fmObFYjB49euDcuXMGX3Pu3Dmd9gDQq1evCtsb0qBntyibZ08hyI0cCVVaA5kbscoayJ26VSUSRMYOoY40zvqA0GjPhY11P2yccnIb1/kwJ0+dT0OZ27emFJADdZyqAupzVU5Ojs7zZmZmMDMzK9c+IyMDSqUSrq6uOs+7urri9u3bBteRkpJisH1KSkql42zQneTc3FwAwBkcqvMNSvREjeszQaux5kUNC8/vDYp9c2NHUDdyc3Nha2tr7DDqjFQqhZubG86kVO3mtuqysrKCt7e3znNz587FvHnznsn6K6NBd5I9PDyQkJAAa2triER1W2nIycmBt7c3EhISYGNjU6frepaYV8PCvBoW5tVwNMacAOZVGwRBQG5uLjw8POp0PcYmk8kQGxuLkpKSZ7I+QRDK9d0MVZEBwMnJCRKJBKmpqTrPp6amws3NzeBr3NzcqtTekAbdSRaLxfDy8nqm67SxsWlUJ5oyzKthYV4NC/NqOBpjTgDzqqnGXEF+nEwmg0wmM3YY5UilUgQHB+PYsWPo378/AEClUuHYsWOYMGGCwdd07twZx44dw+TJkzXPHT16FJ07d670eht0J5mIiIiIGr8pU6YgJCQEHTt2xPPPP48VK1YgPz8fI0aMAAAMGzYMnp6eCAsLAwBMmjQJ3bt3x7Jly9CnTx/s2rULFy9exPr16yu9TnaSiYiIiKheGzRoENLT0zFnzhykpKSgXbt2OHLkiObmvPj4eIjF2puyu3Tpgh07duCzzz7Dp59+imbNmuHAgQP417/+Vel1spNcSWZmZpg7d26F42UaKubVsDCvhoV5NRyNMSeAeVHjMmHChAqHV5w4caLcc++++y7efffdaq9PJPxT5jQhIiIiIqqkxjlZKBERERFRDbCTTERERESkh51kIiIiIiI97CQTEREREelhJ7mS1qxZAz8/P8hkMnTq1Annz583dkg1curUKfTt2xceHh4QiUQ4cOCAsUOqFWFhYXjuuedgbW0NFxcX9O/fH1FRUcYOq0bWrl2LNm3aaCbN79y5M37//Xdjh1XrFi1aBJFIpDPxe0M0b948iEQinUeLFi2MHVatSExMxPvvvw9HR0eYm5ujdevWuHjxorHDqhE/P79y20skEmH8+PHGDq1GlEolZs+ejSZNmsDc3BxNmzbFggUL0Bju1c/NzcXkyZPh6+sLc3NzdOnSBRcuXDB2WNQIsZNcCbt378aUKVMwd+5cXLp0CW3btkWvXr2QlpZm7NCqLT8/H23btsWaNWuMHUqtOnnyJMaPH4+///4bR48ehVwuR8+ePZGfn2/s0KrNy8sLixYtQkREBC5evIhXXnkF/fr1w82bN40dWq25cOECvvvuO7Rp08bYodSKVq1aITk5WfM4c+aMsUOqsczMTHTt2hWmpqb4/fffcevWLSxbtgz29vbGDq1GLly4oLOtjh49CgA1mjaqPli8eDHWrl2Lb775BpGRkVi8eDGWLFmC1atXGzu0Ghs9ejSOHj2KrVu34vr16+jZsyd69OiBxMREY4dGjY1AT/X8888L48eP1/xbqVQKHh4eQlhYmBGjqj0AhP379xs7jDqRlpYmABBOnjxp7FBqlb29vfD9998bO4xakZubKzRr1kw4evSo0L17d2HSpEnGDqlG5s6dK7Rt29bYYdS66dOnCy+88IKxw6hzkyZNEpo2bSqoVCpjh1Ijffr0EUaOHKnz3IABA4QhQ4YYKaLaUVBQIEgkEuHgwYM6z3fo0EGYNWuWkaKixoqV5KcoKSlBREQEevTooXlOLBajR48eOHfunBEjo8rIzs4GADg4OBg5ktqhVCqxa9cu5OfnV+n35+uz8ePHo0+fPjrHWEN39+5deHh4wN/fH0OGDEF8fLyxQ6qxX3/9FR07dsS7774LFxcXtG/fHhs2bDB2WLWqpKQE27Ztw8iRIyESiYwdTo106dIFx44dw507dwAAV69exZkzZ/D6668bObKaUSgUUCqVkMlkOs+bm5s3iis2VL/wF/eeIiMjA0qlUvOzh2VcXV1x+/ZtI0VFlaFSqTB58mR07dq1Sj9DWR9dv34dnTt3RlFREaysrLB//360bNnS2GHV2K5du3Dp0qVGNZ6wU6dO+OGHHxAYGIjk5GTMnz8f3bp1w40bN2BtbW3s8KotJiYGa9euxZQpU/Dpp5/iwoULmDhxIqRSKUJCQowdXq04cOAAsrKyMHz4cGOHUmMzZsxATk4OWrRoAYlEAqVSiYULF2LIkCHGDq1GrK2t0blzZyxYsABBQUFwdXXFzp07ce7cOQQEBBg7PGpk2EmmRmv8+PG4ceNGo6guBAYG4sqVK8jOzsbevXsREhKCkydPNuiOckJCAiZNmoSjR4+Wqwo1ZI9X6tq0aYNOnTrB19cXe/bswahRo4wYWc2oVCp07NgRX375JQCgffv2uHHjBtatW9doOskbN27E66+/Dg8PD2OHUmN79uzB9u3bsWPHDrRq1QpXrlzB5MmT4eHh0eC319atWzFy5Eh4enpCIpGgQ4cOGDx4MCIiIowdGjUy7CQ/hZOTEyQSCVJTU3WeT01NhZubm5GioqeZMGECDh48iFOnTsHLy8vY4dSYVCrVVEmCg4Nx4cIFrFy5Et99952RI6u+iIgIpKWloUOHDprnlEolTp06hW+++QbFxcWQSCRGjLB22NnZoXnz5oiOjjZ2KDXi7u5e7ktZUFAQfv75ZyNFVLvi4uLw559/Yt++fcYOpVaEhoZixowZeO+99wAArVu3RlxcHMLCwhp8J7lp06Y4efIk8vPzkZOTA3d3dwwaNAj+/v7GDo0aGY5JfgqpVIrg4GAcO3ZM85xKpcKxY8cazZjQxkQQBEyYMAH79+/HX3/9hSZNmhg7pDqhUqlQXFxs7DBq5NVXX8X169dx5coVzaNjx44YMmQIrly50ig6yACQl5eHe/fuwd3d3dih1EjXrl3LTad4584d+Pr6Gimi2rV582a4uLigT58+xg6lVhQUFEAs1v2Il0gkUKlURoqo9llaWsLd3R2ZmZkIDw9Hv379jB0SNTKsJFfClClTEBISgo4dO+L555/HihUrkJ+fjxEjRhg7tGrLy8vTqWzFxsbiypUrcHBwgI+PjxEjq5nx48djx44d+OWXX2BtbY2UlBQAgK2tLczNzY0cXfXMnDkTr7/+Onx8fJCbm4sdO3bgxIkTCA8PN3ZoNWJtbV1urLilpSUcHR0b9BjyqVOnom/fvvD19UVSUhLmzp0LiUSCwYMHGzu0Gvn444/RpUsXfPnllxg4cCDOnz+P9evXY/369cYOrcZUKhU2b96MkJAQmJg0jo/Fvn37YuHChfDx8UGrVq1w+fJlfP311xg5cqSxQ6ux8PBwCIKAwMBAREdHIzQ0FC1atGjQn8lUTxl7eo2GYvXq1YKPj48glUqF559/Xvj777+NHVKNHD9+XABQ7hESEmLs0GrEUE4AhM2bNxs7tGobOXKk4OvrK0ilUsHZ2Vl49dVXhT/++MPYYdWJxjAF3KBBgwR3d3dBKpUKnp6ewqBBg4To6Ghjh1UrfvvtN+Ff//qXYGZmJrRo0UJYv369sUOqFeHh4QIAISoqytih1JqcnBxh0qRJgo+PjyCTyQR/f39h1qxZQnFxsbFDq7Hdu3cL/v7+glQqFdzc3ITx48cLWVlZxg6LGiGRIDSCn98hIiIiIqpFHJNMRERERKSHnWQiIiIiIj3sJBMRERER6WEnmYiIiIhIDzvJRERERER62EkmIiIiItLDTjIRERERkR52komo3hk+fDj69++v+fdLL72EyZMnP/M4Tpw4AZFIhKysrArbiEQiHDhwoNLLnDdvHtq1a1ejuO7fvw+RSIQrV67UaDlERFQxdpKJqFKGDx8OkUgEkUgEqVSKgIAAfP7551AoFHW+7n379mHBggWValuZji0REdHTNI4fqSeiZ6J3797YvHkziouLcfjwYYwfPx6mpqaYOXNmubYlJSWQSqW1sl4HB4daWQ4REVFlsZJMRJVmZmYGNzc3+Pr64qOPPkKPHj3w66+/AtAOkVi4cCE8PDwQGBgIAEhISMDAgQNhZ2cHBwcH9OvXD/fv39csU6lUYsqUKbCzs4OjoyOmTZsGQRB01qs/3KK4uBjTp0+Ht7c3zMzMEBAQgI0bN+L+/ft4+eWXAQD29vYQiUQYPnw4AEClUiEsLAxNmjSBubk52rZti7179+qs5/Dhw2jevDnMzc3x8ssv68RZWdOnT0fz5s1hYWEBf39/zJ49G3K5vFy77777Dt7e3rCwsMDAgQORnZ2t8/fvv/8eQUFBkMlkaNGiBb799tsqx0JERNXHTjIRVZu5uTlKSko0/z527BiioqJw9OhRHDx4EHK5HL169YK1tTVOnz6N//3vf7CyskLv3r01r1u2bBl++OEHbNq0CWfOnMGjR4+wf//+J6532LBh2LlzJ1atWoXIyEh89913sLKygre3N37++WcAQFRUFJKTk7Fy5UoAQFhYGH788UesW7cON2/exMcff4z3338fJ0+eBKDuzA8YMAB9+/bFlStXMHr0aMyYMaPK74m1tTV++OEH3Lp1CytXrsSGDRuwfPlynTbR0dHYs2cPfvvtNxw5cgSXL1/GuHHjNH/fvn075syZg4ULFyIyMhJffvklZs+ejS1btlQ5HiIiqiaBiKgSQkJChH79+gmCIAgqlUo4evSoYGZmJkydOlXzd1dXV6G4uFjzmq1btwqBgYGCSqXSPFdcXCyYm5sL4eHhgiAIgru7u7BkyRLN3+VyueDl5aVZlyAIQvfu3YVJkyYJgiAIUVFRAgDh6NGjBuM8fvy4AEDIzMzUPFdUVCRYWFgIZ8+e1Wk7atQoYfDgwYIgCMLMmTOFli1b6vx9+vTp5ZalD4Cwf//+Cv++dOlSITg4WPPvuXPnChKJRHjw4IHmud9//10Qi8VCcnKyIAiC0LRpU2HHjh06y1mwYIHQuXNnQRAEITY2VgAgXL58ucL1EhFRzXBMMhFV2sGDB2FlZQW5XA6VSoX//Oc/mDdvnubvrVu31hmHfPXqVURHR8Pa2lpnOUVFRbh37x6ys7ORnJyMTp06af5mYmKCjh07lhtyUebKlSuQSCTo3r17peOOjo5GQUEBXnvtNZ3nS0pK0L59ewBAZGSkThwA0Llz50qvo8zu3buxatUq3Lt3D3l5eVAoFLCxsdFp4+PjA09PT531qFQqREVFwdraGvfu3cOoUaMwZswYTRuFQgFbW9sqx0NERNXDTjIRVdrLL7+MtWvXQiqVwsPDAyYmuqcQS0tLnX/n5eUhODgY27dvL7csZ2fnasVgbm5e5dfk5eUBAA4dOqTTOQXU46xry7lz5zBkyBDMnz8fvXr1gq2tLXbt2oVly5ZVOdYNGzaU67RLJJJai5WIiJ6MnWQiqjRLS0sEBARUun2HDh2we/duuLi4lKumlnF3d8f//d//4cUXXwSgrphGRESgQ4cOBtu3bt0aKpUKJ0+eRI8ePcr9vaySrVQqNc+1bNkSZmZmiI+Pr7ACHRQUpLkJsczff//99CQfc/bsWfj6+mLWrFma5+Li4sq1i4+PR1JSEjw8PDTrEYvFCAwMhKurKzw8PBATE4MhQ4ZUaf1ERFR7eOMeEdWZIUOGwMnJCf369cPp06cRGxuLEydOYOLEiXjw4AEAYNKkSVi0aBEOHDiA27dvY9y4cU+c49jPzw8hISEYOXIkDhw4oFnmnj17AAC+vr4QiUQ4ePAg0tPTkZeXB2tra0ydOhUff/wxtmzZgnv37uHSpUtYvXq15ma4Dz/8EHfv3kVoaCiioqKwY8cO/PDDD1XKt1mzZoiPj8euXbtw7949rFq1yuBNiDKZDCEhIbh69SpOnz6NiRMnYuDAgXBzcwMAzJ8/H2FhYVi1ahXu3LmD69evY/Pmzfj666+rFA8REVUfO8lEVGcsLCxw6tQp+Pj4YMCAAQgKCsKoUaNQVFSkqSx/8sknGDp0KEJCQtC5c2dYW1vjrbfeeuJy165di3feeQfjxo1DixYtMGbMGOTn5wMAPD09MX/+fMyYMQOurq6YMGECAGDBggWYPXs2wsLCEBQUhN69e+PQoUNo0qQJAPU44Z9//hkHDhxA27ZtsW7dOnz55ZdVyvfNN9/Exx9/jAkTJqBdu3Y4e/YsZs+eXa5dQEAABgwYgDfeeAM9e/ZEmzZtdKZ4Gz16NL7//nts3rwZrVu3Rvfu3fHDDz9oYiUioronEiq6O4aIiIiI6B+KlWQiIiIiIj3sJBMRERER6WEnmYiIiIhIDzvJRERERER62EkmIiIiItLDTjIRERERkR52komIiIiI9LCTTERERESkh51kIiIiIiI97CQTEREREelhJ5mIiIiISA87yUREREREev4/Wt+Y5bf+BnEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# Create confusion matrix and normalizes it over predicted (columns)\n",
    "confusion = confusion_matrix(test_label.numpy(), test_prediction.numpy(), normalize='pred')\n",
    "\n",
    "# Create a plot of the confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "ConfusionMatrixDisplay(confusion).plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_2p5J5vwN9Kn"
   },
   "source": [
    "How can one improve the networks scores? We can add more Conv oder Dense layers to strenghten one part of your network! More Dense layers will increase your classification power, while more Conv layers will increase your propety filter power.\n",
    "We might also want to prevent overtraining, e.g. via including some Dropout layers."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}