![\"img2\"](\"https://i.ibb.co/hdQfRtm/img2.png\")
Figure 2. A three-layer neural network example.
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xy8Est9bDcPi" - }, - "source": [ - "The neural network described above is a relatively simple mathematical equation with three inputs ($X_1$ to $X_3$), five intermediate values ($Y_1$ to $Y_5$), and one output ($Y_F$). It also has two distinct sets of parameters shown with the letters $w$ and $b$. As a neural network is usually an extension of linear equations, these parameters are called weights (shown by $w$), and those which do not are called biases (shown by $b$). \n", - "\n", - "You have probably seen more complicated neural network architectures than the one above. But, one point that this figure can help you better understand is what some of the frequently used terms in deep learning really mean in a mathematical context. For example, a \"node\" is a fancy name for intermediary outputs of each of the calculations happening within the equation ($Y_1$ to $Y_5$, and $Y_F$ are nodes), or arrows between the nodes show how some outputs of previous calculations are regarded as inputs for subsequent calculations. Likewise, a layer is a collection of nodes at the same level (i.e., they do not receive inputs or send outputs to each other).\n", - "\n", - "However, deep neural networks are much bigger and more complicated than the three-layer network we presented above. Instead of three inputs, they usually receive hundreds to thousands or even millions of inputs. For example, a neural network that receives an image as its input actually regards every pixel value of that image as an input variable to its equation. Likewise, neural networks that work on text data regard each word in a given text as an input variable. The general principle, though, is still the same. A neural network is an equation that receives multiple inputs and generates an output that we desire to be meaningful (e.g., a correct prediction). \n", - "\n", - "Before proceeding, let's use some real numbers for the parameters and input variables and see how it looks. Lets suppose that our model receives three clinical input variables for patients with pneumonia and predicts their chance of living as the output. We can assume the input variables as follows:\n", - "\n", - "* $X_1$: patient's sex (1 for females, 2 for males)\n", - "* $X_2$: patient's age divided by 100\n", - "* $X_3$: patient's white blood cell (WBC) count divided by 10,000\n", - "\n", - "And we can assume that the output variable should always be between 0 and 1, where values closer to 1 mean a higher chance of survival. Using the sigmoid function as our activation function for the last layer, we can ensure that our model always generates a value between 0 and 1.\n", - "\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - ">\n", - "**Note 1**: all values and parameters in the following figure are imaginary and chosen randomly. It is also very unlikely that such a small model can really predict a pneumonia patient's prognosis. We just made up an example for you, so don't take it seriously!.\n", - "\n", - "---\n", - "---\n", - "\n", - "\n", - ">\n", - "**Note 2**: we used the sigmoid function in all layers of this example network. However, many more alternative activation functions could be used instead of the sigmoid (at least for layers 1 and 2 that do not necessarily need to result in values between 0 and 1).\n", - "\n", - "\n", - "---\n", - "\n", - "
![\"img3\"](\"https://i.ibb.co/rtyCmCN/img3.png\")
Figure 3. A three-layer neural network example filled with random numbers.
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EStOXNAyQG7m" - }, - "source": [ - "Now that we described how neural network equations can be useful in helping us solve real clinical problems, let's go back to our own example in this notebook and use it to explain the big picture of neural networks a little better.\n", - "\n", - "Here, we are looking to build a neural network that will receive pixel values of a CXR as its inputs and then rely on its parameters to calculate a meaningful output for that specific CXR. For example, we may expect the output number `1` (or values close to 1) for CXRs with pneumonia and `0` (or values close to 0) for patients without pneumonia. This is similar to how equations work in the world of math. We provide inputs to them and they generate outputs for us. However, there are two apparent questions ahead of us if we want to achieve such a desired neural network:\n", - "\n", - "1. What type of neural network (or equation) should we build? How many parameters should it have? How should those parameters be organized in different layers and nodes? How should we connect those components together? These questions can all be summarized in one technical domain of deep learning: \"*architecture design*\" for neural networks.\n", - "\n", - "\n", - "2. Even if we know the exact architecture needed for our neural network, how should we know the appropriate values for its parameters? In other words, we need to know the values of an equation's parameters before we can feed any input values to it and expect meaningful outputs. What we do to find the appropriate values for a neural network's parameters is called \"*training*\" of that neural network or deep learning model.\n", - "\n", - "The following two sections will address each of the above questions." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_DZLvNqSdIb8" - }, - "source": [ - "### Neural network architecture designing\n", - "\n", - "We previously tried to mathematically describe a simple example of deep learning models for you. Having such an equation-oriented model of neural networks in mind will help you better understand the many different architectures introduced across the literature for neural networks every day. Look at the following fantastic figure, for example.\n", - "\n", - "
![\"img4\"](\"https://i.ibb.co/JK0kDxP/img3.png\")
Figure 4. Variations of neural network architectures.
Source: https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464\n", - "
\n", - "\n", - "Each node in this colorful diagram represents a different architecture. Putting their details aside, each has a different number of layers, nodes, connections, and activation functions. Imagine these components of deep learning models as Lego(TM) pieces that data scientists combine in unlimited ways to build their new toys, aka models, at the end. You might ask: how can I know which architecture works best for the problem I am working on?\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - ">\n", - "**Note**: The words \"model\" and \"network\" are used interchangeably in this chapter.\n", - "\n", - "\n", - "---\n", - "\n", - "Deep learning is not only a science but an art, and the above should help to explain why. You can look into the literature of deep learning (and you should) to see what others have used for problems that are similar to yours. Take note of scientific comparisons made to illustrate different architectures' strengths and weaknesses. But at the end of the day, there is rarely a clear rule for the correct architecture for a novel problem. It is your innovation, intuition, and art in combining the pieces available to you that determines how good or bad your final model will perform to address your problem of interest! On the same note, data scientists usually evaluate many different architectures for a specific problem to finally find the one that works best for them." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FyoYaFami-bt" - }, - "source": [ - "### Neural network training\n", - "\n", - "Once we have selected the architecture for our neural network model, we still need to figure out what values to use for the model's parameters. In deep learning, this task is accomplished by looking at the available data (the training set) and \"learning\" the appropriate values for the parameters from that data.\n", - "\n", - "Let us go back to our example once again. You saw before that all the CXRs we downloaded are already labeled; i.e., one or more radiologists have already labeled each as either 'normal' or 'pneumonia'. In other words, we have access to thousands of CXR examples (or input data to the model), in addition to their actual outcomes (or the ground truth *labels*), which a deep learning model should predict. The training task is to find the best set of parameters that will make our neural network predict (the mathematical output value) the closest outputs to the ground truth labels. This process happens iteratively and is called \"model training .\"\n", - "\n", - "Recall again that we generally do not train on the entire available data set.\n", - "Instead, we first split it into training and validation sets (or sometimes, training, validation, and test sets). We then use the training set for training our model. We already did this splitting when making our data loaders (look above and remember how we built separate data loaders for different sets). Next, we build our initial deep learning model with random parameter values (later chapters will describe this 'initialization' in greater detail).\n", - "\n", - "Of course, this initial model will not make any meaningful predictions for us, but as the training iterations go on, the model will gradually change its parameter values to make better and better predictions. Such gradual improvement becomes possible by providing feedback to the model regarding its current parameters and performance, and more importantly, how their values should be changed to improve the predictions. This feedback mechanism is the heart of model training, and we will discuss this further in Part 4.\n", - "\n", - "In summary, we need to train our neural network on a portion of labeled data that we call the training set. The model will use this labeled data multiple times (each time is called an 'epoch') during the training and gradually change parameters to find their optimal values in order to minimize the predictions accross the training set labels. A model trained like this can then be used to make predictions on unseen (unlabeled) input data. Applying a trained model to unseen data is usually called \"inference.\"\n", - "\n", - "---\n", - "\n", - ">**Note**: Most neural networks are trained on labeled training data, and this form of learning is referred to as \"supervised learning.\" However, supervised learning is not the only approach to training models. Sometimes training is performed in unsupervised, semi-supervised, or self-supervised manners. In these variants, human-labeled training data is either unavailable to the model or is available differently compared to supervised learning. More on this in later chapters.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-QVLqFULw2wt" - }, - "source": [ - "## Part 3: PyTorch models playground\n", - "\n", - "With that as background, we are ready to start coding again. An excellent way to understand the training process is to compare a model's performance before and after training. This is what we will do in the current section." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FoJ21m46oMK7" - }, - "source": [ - "### Creating a model\n", - "\n", - "The following cell calls the `build_model` function, which we will use throughout this chapter for creating PyTorch models from scratch. Please note that this function will not train the model; we need to code for that separately.\n", - "\n", - "The `build_model` function does two important tasks:\n", - "\n", - "1. It uses the torchvision library to create a model using the architecture the user requested: e.g., to build a ResNet50 model, we can code:\n", - "```python\n", - "from torchvision import models\n", - "model = models.resnet50()\n", - "```\n", - "or if you want to create a VGG16 model, you could type:\n", - "```python\n", - "model = models.vgg16()\n", - "```\n", - "Our code in the following cell does exactly the same thing in this line:\n", - "```python\n", - "arch = 'resnet' ## Is this correct??? \n", - "pretrained = True\n", - "model = eval(f'models.{arch}(pretrained={pretrained})')\n", - "```\n", - "The only difference is that our code uses the Python \"eval\" command to build a model based on the value of the \"arch\" variable. This means that the function can build a ResNet or VGG-based model depending on the user's interest.
\n", - "\n", - " ---\n", - "\n", - " > **Note**: You may also note that we can pass a `pretrained` argument to the above command. We will introduce this argument and what it does in the last section of this notebook.\n", - "\n", - " ---\n", - " Isn't it interesting that we loaded a relatively complicated model like ResNet50 or VGG16 in just one line of code? This is the magic of deep learning frameworks. Many of the complicated architectures are now available to developers in just one or two lines of code (1 line to 'import' them, and the other line to actually build the one you want). However, there are still occasions in real practice when developers need to develop their models from scratch or change a pre-loaded model.\n", - "
\n", - "\n", - "2. When we build our models using PyTorch, they are almost always designed to throw out 1000 values (or, more accurately, a linear vector of size 1000) as their outputs. This is the case as many PyTorch default models were built to classify the images from the ImageNet dataset (which had 1000 classes). However, we only have two classes in our example for classifying CXRs. So, we should change the final layer of the model (also called the final fully connected layer, as it is not a convolutional layer) to have two output nodes instead of 1000. This will cause the model to create two values (or a vector of size 2) instead of 1000 values.\n", - "
\n", - "\n", - " ---\n", - "\n", - " > **Note**: Here we have specified 2 output classes, but above we said we would have a single output ranging from 0 to 1. A binary output (2 classes) is a special case where there is usually no effective difference and it is usually better to have fewer outputs. Why might we want to have 2 outputs rather than just 1?\n", - " ---\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "84lx39aLa3Ov" - }, - "outputs": [], - "source": [ - "# Building a resnet model\n", - "\n", - "import torchvision.models as models\n", - "import torch.nn as nn\n", - "import shutil\n", - "\n", - "@make_determinate\n", - "def build_model(arch: str = 'vgg16', pretrained: bool = False)-> Callable:\n", - " \"\"\"\n", - " Build a resnet model using Pytorch.\n", - " Parameters:\n", - " - arch (str): baseline architecture of the model that could be called using\n", - " torchvision.models.arch command.\n", - " - pretrained (bool): whether or not to use pretrained weights.\n", - " - model (Callable): built pytorch module.\n", - " \"\"\"\n", - "\n", - " # Loading a model with the user-specified architecture from torch\n", - " if 'vgg' not in arch and 'alexnet' not in arch and 'resnet' not in arch:\n", - " raise ValueError ('Only resnet, vgg or alexnet models can be loaded!')\n", - " else:\n", - " try:\n", - " model = eval(f'models.{arch}(pretrained={pretrained})')\n", - " except:\n", - " raise ValueError ('The name of the architecture is not valid!')\n", - "\n", - " # Replacing the final fully conntected layer of the model\n", - " # Please note that we have two classes, and therefore, the final FC layer\n", - " # will have two final nodes\n", - " # The VGG network has no FC layer, so we directly change its final layer\n", - " if 'vgg' in arch or 'alexnet' in arch:\n", - " model.classifier._modules['6'] = nn.Linear(4096, 2)\n", - " else:\n", - " num_in_features = model.fc.in_features\n", - " model.fc = nn.Linear(num_in_features, 2)\n", - "\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jNqLmboSzwvf" - }, - "source": [ - "Now that we have a model, lets see what its architecture looks like. For PyTorch models, we can use a library called `torchsummary` to see the layers of our model, the shape of each layer's output, and the number of parameters in each layer. Such a library is extremely useful if you are building your own model. For instance, it might help you detect that your model does not fit into your GPU's memory and you need to check what layer(s) are oversized.\n", - "\n", - "It is very easy to use the torchvision library. You only need to use the \"summary\" command, and pass a PyTorch model and an input size to it. Please note that your PyTorch model should already be on GPU to work with this command hence we use the \"to(device)\" command to move our model to the GPU device that Google colab has assigned to us." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Xq5Ej3IB8HW-", - "outputId": "e8a41d6d-9ddc-4850-e735-b2e7240fe654" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------------------------------------------------------------\n", - " Layer (type) Output Shape Param #\n", - "================================================================\n", - " Conv2d-1 [-1, 64, 224, 224] 1,792\n", - " ReLU-2 [-1, 64, 224, 224] 0\n", - " Conv2d-3 [-1, 64, 224, 224] 36,928\n", - " ReLU-4 [-1, 64, 224, 224] 0\n", - " MaxPool2d-5 [-1, 64, 112, 112] 0\n", - " Conv2d-6 [-1, 128, 112, 112] 73,856\n", - " ReLU-7 [-1, 128, 112, 112] 0\n", - " Conv2d-8 [-1, 128, 112, 112] 147,584\n", - " ReLU-9 [-1, 128, 112, 112] 0\n", - " MaxPool2d-10 [-1, 128, 56, 56] 0\n", - " Conv2d-11 [-1, 256, 56, 56] 295,168\n", - " ReLU-12 [-1, 256, 56, 56] 0\n", - " Conv2d-13 [-1, 256, 56, 56] 590,080\n", - " ReLU-14 [-1, 256, 56, 56] 0\n", - " Conv2d-15 [-1, 256, 56, 56] 590,080\n", - " ReLU-16 [-1, 256, 56, 56] 0\n", - " MaxPool2d-17 [-1, 256, 28, 28] 0\n", - " Conv2d-18 [-1, 512, 28, 28] 1,180,160\n", - " ReLU-19 [-1, 512, 28, 28] 0\n", - " Conv2d-20 [-1, 512, 28, 28] 2,359,808\n", - " ReLU-21 [-1, 512, 28, 28] 0\n", - " Conv2d-22 [-1, 512, 28, 28] 2,359,808\n", - " ReLU-23 [-1, 512, 28, 28] 0\n", - " MaxPool2d-24 [-1, 512, 14, 14] 0\n", - " Conv2d-25 [-1, 512, 14, 14] 2,359,808\n", - " ReLU-26 [-1, 512, 14, 14] 0\n", - " Conv2d-27 [-1, 512, 14, 14] 2,359,808\n", - " ReLU-28 [-1, 512, 14, 14] 0\n", - " Conv2d-29 [-1, 512, 14, 14] 2,359,808\n", - " ReLU-30 [-1, 512, 14, 14] 0\n", - " MaxPool2d-31 [-1, 512, 7, 7] 0\n", - "AdaptiveAvgPool2d-32 [-1, 512, 7, 7] 0\n", - " Linear-33 [-1, 4096] 102,764,544\n", - " ReLU-34 [-1, 4096] 0\n", - " Dropout-35 [-1, 4096] 0\n", - " Linear-36 [-1, 4096] 16,781,312\n", - " ReLU-37 [-1, 4096] 0\n", - " Dropout-38 [-1, 4096] 0\n", - " Linear-39 [-1, 2] 8,194\n", - "================================================================\n", - "Total params: 134,268,738\n", - "Trainable params: 134,268,738\n", - "Non-trainable params: 0\n", - "----------------------------------------------------------------\n", - "Input size (MB): 0.57\n", - "Forward/backward pass size (MB): 218.77\n", - "Params size (MB): 512.19\n", - "Estimated Total Size (MB): 731.54\n", - "----------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# Plotting the structure for a Vgg16 model\n", - "\n", - "from torchsummary import summary\n", - "non_trained_vgg16 = build_model(arch='vgg16', pretrained=False).to(device)\n", - "summary(non_trained_vgg16, input_size=(3, 224, 224))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "erU9ZdcN2t-f" - }, - "source": [ - "### Using a model for inference\n", - "\n", - "Now you can do what you probably expected to do when you started this section. First, let's test our untrained model to see its baseline performance on differentiating between the normal and pneumonia CXRs. This is a good checkpoint for us to learn how to apply a PyTorch model for inference.\n", - "\n", - "----\n", - "> **Note**: The code to use a Pytorch model for inference is the same for both trained and untrained models. So we can build an `evaluate_model` function that receives a model and a test data loader and then shows the model's performance on that test data loader. We can then use this function to evaluate the different models we will develop throughout this chapter.\n", - "---\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "CCy0IFUbJoNg" - }, - "outputs": [], - "source": [ - "# A function to evaluate a model's performance on the test_loader\n", - "\n", - "from tqdm.notebook import tqdm\n", - "from sklearn.metrics import confusion_matrix\n", - "import seaborn as sns\n", - "\n", - "@make_determinate\n", - "def evaluate_model(model: Callable,\n", - " test_loader: Iterable = test_loader,\n", - " plot_cm: bool = True) -> float:\n", - " \"\"\"\n", - " Evaluate a given model's performance on the test set.\n", - " Parameters:\n", - " model (Callable): the pytorch model to be evaluated.\n", - " test_loader (Iterable): test dataloader to be used as the test data.\n", - " plot_cm (bool): whether or not to plot a confusion matrix.\n", - " accuracy (float): accuracy of the model.\n", - " \"\"\"\n", - " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " model.to(device)\n", - " model.eval()\n", - " labels_list = list()\n", - " preds_list = list()\n", - " with torch.no_grad():\n", - " for batch in tqdm(test_loader):\n", - " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", - " outputs = model(inputs)\n", - " preds = torch.argmax(torch.softmax(outputs, dim=1), dim=1)\n", - " labels_list.append(labels)\n", - " preds_list.append(preds)\n", - " all_labels = torch.cat(labels_list).cpu()\n", - " all_preds = torch.cat(preds_list).cpu()\n", - " accuracy = (all_preds == all_labels).sum().item()/len(all_labels)\n", - "\n", - " if plot_cm:\n", - " cm = confusion_matrix(all_labels.numpy(), all_preds.numpy())\n", - " ax = plt.subplot()\n", - " sns.heatmap(cm, annot=True, fmt='g', ax=ax)\n", - " ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels')\n", - " ax.set_title(f'Confusion Matrix - accuracy: {accuracy:.2f}')\n", - " ax.xaxis.set_ticklabels(['Pneumonia', 'Normal']);\n", - " ax.yaxis.set_ticklabels(['Pneumonia', 'Normal']);\n", - "\n", - " return accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DR-ankIwD7Ui" - }, - "source": [ - "Please note a few things in the above function:\n", - "\n", - "1. Whenever we want to use a PyTorch model for inference, we first use the `model.eval()` command to put it in the evaluation mode. Conversely, when we want to train a PyTorch model, we put it in the training mode using the command `model.train()`. You will learn the difference between these two in the following section.\n", - "\n", - "2. When we iterate through the data from a PyTorch data loader, it returns a batch each time we call it (typically once in each loop of an iteration). Depending on how we specified the batch should be made, it may include different samples for a given batch, though all training samples should be returned over the course of a complete iteration (a.k.a. 'epoch'). Because we built our dataloaders using MONAI dictionary-type dataloaders, we can access the imaging data and labels of each batch using the `batch['image']` and `batch['label']` commands.\n", - "\n", - "3. Like the training, inference also runs faster on a GPU, so we send our images and labels data to GPU using the `to(device)` command. You don't need to change anything else in your code for running the commands on a GPU instead of a CPU, and that's a big benefit of PyTorch!\n", - "\n", - "4. Look at the following line of code and try to understand what it does:\n", - "```python\n", - "preds = torch.argmax(torch.softmax(outputs, dim=1), dim=1)\n", - "```\n", - "If you remember, we assigned two nodes to the final layer of our model in one of the above cells. This means that the output of our model will naturally be a one-dimensional array (or a vector) with two values. These values look independent from each other at first, but we will use a trick during training and inference to make them meaningful. The trick is to use a mathematical function called `softmax` that receives an array of 2 or more input values and change them so that they sum up to 1. You can look at the following image and check its source page to better understand how softmax works:\n", - "\n", - "
![\"img5\"](\"https://i.ibb.co/JCPxvzJ/img5.jpg\")
Figure 5. Softmax Activation Function Explained.
Source: https://towardsdatascience.com/softmax-activation-function-explained-a7e1bc3ad60\n", - "
\n", - "\n", - " Now, the two values of the final node will no longer be meaningless. The first value (the one in index 0 of the output array) denotes the probability that the input CXR is for a normal patient, and the second value (index 1 in the output array) denotes the probability that the CXR is for a patient with pneumonia. Well then, how should we say what the model's prediction is for that input CXR? The predicted class will be whichever class has a higher probability value. Therefore, we can use the `argmax` command to get that array index. The result of the \"argmax\" command and, therefore, the result of the previous code line is an array of zeros and ones, and the length of that array is our batch size. In other words, we now have the model's prediction for each input example in our batch.\n", - "\n", - "5. Finally, please note that there are 2 outputs of our `evaluate_model`: First, the accuracy of the model, which we calculate by dividing the number of true predictions by the total number of predictions our model has made. The second is a performance table, also called a confusion matrix. It is easier to introduce this matrix when you are looking at it, so let's proceed and use this function to evaluate the performance of an untrained VGG16 function on our test data loader." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 504, - "referenced_widgets": [ - "d6b9487396c74c9ebbb4710825738c8a", - "97adb5724faf4ed29ab150d8ac3ae6f8", - "a1d0339b8c214afe9d489a97b6f01695", - "04a41bbdd14440cabbef6e603d6734bb", - "eedd4f505bd74f188b1f2d2f81ebc574", - "f3a95a66a4f2421a96a8533d79eb9826", - "2712b5d9230f488eac997e5f83d41a03", - "3503548ea4224ea59ce76aa5d43c8172", - "667485a1eda04009a4939cf0a024bf5d", - "d1cea112d2af4516908f4cb4aeff735a", - "c207aca462bf4e918bf581c4d5717104" - ] - }, - "id": "rzwivQLRIrux", - "outputId": "9228c58a-abb7-43ea-efea-853d5b9f164b" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d6b9487396c74c9ebbb4710825738c8a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/39 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Evaluating the performance of a non-trained model\n", - "\n", - "_ = evaluate_model(non_trained_vgg16)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X7eKE7a7rT29" - }, - "source": [ - "The figure above is a confusion matrix. It gets its name because it shows how many times the model confused one class of inputs with another (pneumonia versus normal in our case).\n", - "When we ran the above cell, our model had very poor performance, confusing many pneumonia and normal cases with each other. But don't be discouraged. Remember that we haven't trained it yet! In many cases, an untrained model will even classify all cases as one of the classes. In our scenario, for example, the model may predict 0 pnueomonia or 0 normal cases.\n", - "This might be a surprising result, but it can happen rather often when you use an untrained model for inference and if a class is very rare or very common. If accuracy is used as a metric, and some classes are rare, then the model can be very accurate by always saying it is not the rare class. However, we know that it does not mean the model is good.\n", - "\n", - "---\n", - "\n", - "> **Note**: Please note that your confusion matrix may be slightly different because the model's initial weights are random and they may be different from the initial weights for our model. However, the performance of this untrained model will almost always be very poor.\n", - "\n", - "---\n", - "\n", - "In this section, we learned how to create a PyTorch model using the already available architectures in torchvision library and use it for inference. We also observed that, as expected, an untrained model with random weights will perform very poorly in doing a task, including our task of classifying CXRs. This means we are ready to move to the next section, where we will train our models.\n", - "\n", - "
**Coding Practice**: In the above code, we used a model for predicting the class of an entire batch. However (and in most real-world applications), we will need to use our model to infer from a single image. It should look trivial to you how to do that, but it is not a bad idea to practice it before we proceed. Look at the following code cell, where we have loaded a single CXR from our dataset as a NumPy array with shape (224 * 224 * 3). See if you can feed it to your model and get the predicted class for it by yourself. Of course, the predicted class may not be right as the model is untrained, but the goal is for you to practice inference with PyTorch models.\n", - "\n", - "---\n", - "\n", - ">**Hint**: This is what you need to do in order:\n", - "1. Resize your NumPy array to the size (224 * 224).\n", - "2. Convert your NumPy array to a PyTorch tensor.\n", - "3. Add a channel dimension to your input tensor in dim=0 (i.e., your tensor should have the shape of (1 * 224 * 224) instead of (224 * 224)).\n", - "4. Add a batch dimension to your input tensor in dim=0 (i.e., your tensor should have the shape of (1 * 1 * 224 * 224) instead of (1 * 224 * 224). This is necessary as PyTorch models expect their input data to have the batch format.\n", - "5. Use the \"model(img_tensor)\" command to get the model's output for the image and then apply \"softmax\" and \"argmax\" commands on the output to get the final prediction as we did above.\n", - "\n", - "---\n", - "\n", - "You may need to look at the previous chapters or search online a little to find out how to do the above steps, but that would be a fun and easy coding practice for you, so enjoy!\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CN1-Vj_GN8pc", - "outputId": "9334347b-c9dd-49a5-d567-1be644633c62" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(712, 1088)\n" - ] - } - ], - "source": [ - "from skimage.io import imread\n", - "\n", - "img_array = imread(train_imageinfo_list[0][0])\n", - "print(img_array.shape)\n", - "\n", - "#### Type your code here:\n", - "\n", - "\n", - "####" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_K9JBLqgZBlw" - }, - "source": [ - "## Part 4: Training implementation\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j-AvF3-3gWUf" - }, - "source": [ - "We said before that training a neural network consists of letting the model make some predictions, then checking the gap between its predictions and the known values. The gap is the penalty, and that penalty drives changes in parameters so that the gap descreases as the training goes on. This cycle repeats many times, and the model will see each sample in the training data multiple times. Now, it is time to expand this initial explanation. Before we jump into PyTorch and work with our own scenario, it is not a bad idea to play with this concept with a much simpler dummy example." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KClFRYW37nt8" - }, - "source": [ - "### Training a simple model\n", - "\n", - "Imagine we have numbers 0.01, 0.02, ..., up to 1 as our inputs, and a label for each input is defined based on the following formula: $y = x*0.2 + 0.06$ (e.g., if x is 0.3, then y will be 0.12):" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WRnlQ9uDiXxe", - "outputId": "4796352a-0d8f-4a99-b67b-d8b1ecf38f19" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The first ten input values: tensor([0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700, 0.0800,\n", - " 0.0900])\n", - "The first ten label values: tensor([0.0600, 0.0620, 0.0640, 0.0660, 0.0680, 0.0700, 0.0720, 0.0740, 0.0760,\n", - " 0.0780])\n" - ] - } - ], - "source": [ - "input_tensor = torch.tensor(range(100)).float()/100\n", - "label_tensor = torch.tensor(list(map(lambda x:x*0.2+0.06, input_tensor)))\n", - "\n", - "print(f'The first ten input values: {input_tensor[:10]}')\n", - "print(f'The first ten label values: {label_tensor[:10]}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Et7EFLQaXb55" - }, - "source": [ - "Now, let us say we want to train a very simple model (actually, a linear model with the general form y = ax+b) to learn to reproduce our data. In other words, we want our model to learn parameters a and b as 0.2 and 0.06, respectively. You perhaps remember that PyTorch models always start with random parameter values, and a model we create will not necessarily have our desired parameters. It could be any model like the following dummy_model we created using vanilla Python:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "A5FN5xVRkDCd", - "outputId": "f50629e1-379e-41de-b562-bdb5eaae79ed" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The first ten predicted values: tensor([0.0400, 0.0401, 0.0402, 0.0403, 0.0404, 0.0405, 0.0406, 0.0407, 0.0408,\n", - " 0.0409])\n" - ] - } - ], - "source": [ - "dummy_model = lambda x: x*0.01 + 0.04\n", - "output_tensor = torch.tensor(list(map(dummy_model, input_tensor)))\n", - "print(f'The first ten predicted values: {output_tensor[:10]}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qZPZVW2WYsZT" - }, - "source": [ - "As you see above, the outputs the dummy model predicted are different from the actual labels. But how different are they? If we ultimately want to optimize the parameters of our model during training, the first step is to know how different the model's performance is versus what we expected. In the world of machine learning, the difference between the observed and expected performance of a model is called its \"**loss**,\" and the function used to calculate that loss is called the \"**loss function**.\" As you can imagine, we do not have a single loss or loss function for all machine learning problems in the world. As deep learning models are diverse, the loss functions to train them are diverse. Even for a single model, more than one loss function may be used.\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - ">**Note**: The terms cost (cost function), loss (loss function), and criterion (criterion function) are often used interchangeably in machine learning. Also, the letter \"*J*\" is often used to describe the overall loss of a model.\n", - "\n", - "\n", - "---\n", - "\n", - "Now, for the sake of our dummy example here, we are going to use a relatively simple but widely used loss function that is called the \"**L2 loss**.\" This function is calculated given two input vectors y_true and y_predicted using the following formula:\n", - "\n", - "
![\"img6\"](\"https://i.ibb.co/hX9QVW8/img6.png\")
Figure 6. L2 Loss Function.\n", - "
\n", - "\n", - "It is called the L2 loss because the difference is squared (power of 2). Can you guess what the name might be for a loss function where the difference is used (without squaring--power of 1)? Yes--it is the **L1 loss**. If you try to code this yourself beware of one thing: an error should always increase the loss, so you need to take the absolute value of the difference.\n", - "\n", - "\n", - "---\n", - "\n", - "> **Note**: All loss functions in deep learning act on two input vectors: y_true and y_predicted. The reason is that regardless of what mathematical form those functions have, they all reflect the difference between the expected and observed outputs of the model.\n", - "\n", - "---\n", - "\n", - "The L2 loss is very easy to implement in PyTorch (remember **2 means power of 2 in Python):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "itfpfPbMmZFJ", - "outputId": "5a051546-7cd7-45e7-b18a-7ce02120d35e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The current loss is: 1.6015435457229614\n" - ] - } - ], - "source": [ - "dummy_loss_function = lambda labels, outputs: torch.sum((outputs-labels)**2)\n", - "loss = dummy_loss_function(label_tensor, output_tensor)\n", - "print(f'The current loss is: {loss}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ScoW_N9MdUb3" - }, - "source": [ - "Now you might wonder what this value of loss means! Well, that is not an easy question to answer. Most of the time, loss values don't have a 'real world' meaning. What we care more about, though, is how the loss value changes when the model updates its parameters. The general assumption is that the closer the predicted and observed labels are, the lower the loss will be and vice versa. Feel free to change the code cell above and see how the loss will increase or decrease as you make your dummy model more or less different than the actual formula we used to create our data. You can also see that the L2Loss function will penalize outliers much more than an L1Loss function because of the squaring of the difference. If you REALLY wanted to penalize outliers, you could use higher powers.\n", - "\n", - "
We can also use the matplotlib library to plot our expected and current models on a graph. As you see below, the orange line is our current model, and the blue line is what it should be, with the yellow area denoting their difference. Please note that this yellow area is not the L2 loss but is only a visual measure of the difference between the two lines." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 599 - }, - "id": "jEZlUaDx4twV", - "outputId": "f600a7f2-fefa-4649-8633-4a81f91a4228" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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The answer to this question is the cornerstone of deep learning and is called: \"**gradient descent**\". Gradient descent consists of two main steps:\n", - "\n", - "1. In the first step, we calculate the gradients of the loss function with respect to each parameter (weight or bias) of the model. These gradients have valuable information. They tell us how the loss will change if we increase or decrease either of our parameters. For example, say d_w and d_b are the gradients ('derivatives' if you remember calculus, and hence the 'd_') of L2 loss with respect to the parameters of our dummy model. Suppose d_w is a positive number and d_b is a negative one. In that case, decreasing the w and increasing the b will both decrease the loss and therefore move our model one step closer to its desirable form. In more complicated models, where later layers of parameters are built upon earlier layers, the gradient of loss with respect to parameters in earlier layers is obtained by applying the chain rule of calculus to gradients obtained for the later layers. We recommend this blog post to become more familiar with what the chain rule is and how it helps in calculating the gradients in deep learning: [Deep learning and chain rule of calculus](https://medium.com/machine-learning-and-math/deep-learning-and-chain-rule-of-calculus-80896a1e91f9>)\n", - "\n", - "2. Knowing the gradients of loss with respect to parameters, we can update the parameters using the following formulas:
\n", - "new_parameter = old_parameter - d_parameter * learning rate
\n", - "where learning rate is a number denoting the pace of updating the model's parameters. More about learning rates later, but for now, you can just use the value '1' as the learning rate though in the real world it is much less than 1. This second step is called \"**optimization**.\"\n", - "\n", - "The above two steps happen in each **step** of model training, i.e., for each time the model goes through a batch of data. For each batch of data, the model predicts the label for each member of the batch, the loss is calculated by comparing the known and predicted labels, the gradients of the loss with respect to all model parameters are calculated, and finally, all parameters are updated using the gradient descent approach. The following figure shows how we change the values of a single weight parameter in a stepwise manner to redu e loss as much as possible:\n", - "\n", - "
![\"img7\"](\"https://i.ibb.co/xXH6pDs/img7.png\")
Figure 7. Illustration of gradient descent for a single parameter.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", - "
\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - ">**Note**: A \"**step**\" in model training denotes each checkpoint when the model has seen a single *batch* of data. On the other hand, an \"**epoch**\" denotes when the model has seen the entire training data set once. Therefore, an epoch consists of (number of data points / batch size) steps. Actual training sessions often have 10's to 100's of epochs.\n", - "\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vNNzyPjZuM2s" - }, - "source": [ - "It is worth pausing here to reflect on the process we described above.\n", - "\n", - "First, note how the gradient descent process is simple in concept but complex in practice. Unlike our dummy model, which only has two parameters, actual deep learning models have millions of parameters, and updating each of those in each step is not something any human mind can or even regular CPUs can do efficiently. We almost always rely on the parallel computation capacity of powerful GPUs to train the model. \n", - "\n", - "Second, note that selecting an appropriate learning rate is critical for training a deep learning model. Like the [story of Goldilocks and the 3 Bears](https://americanliterature.com/childrens-stories/goldilocks-and-the-three-bears), it is critical that it be 'just right'. A learning rate that is too small will hinder the pace of parameter updates, but if too large, it will cause the loss to oscillate and prevent the weight from finding its optimal value or converging to a global minimum as described below:\n", - "\n", - "
![\"img8\"](\"https://i.ibb.co/MGxgs57/img8.png\")
Figure 8. Small and large learning rates.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dS2fLuafyecG" - }, - "source": [ - "Furthermore, the loss landscape for a single parameter is not always as straightforward as in the above plots. The gradients for a single parameter may become zero (so that the loss curve forms a saddle point\") or change direction during the training and form what is called a \"local minimum.\" If the gradient is 0, the parameters will not be altered. Even more insidious are local minima where the loss will falsely look like it has the least (best) value. It is important that we not stop our search in such a local minimum, and that we continue on to look for the \"global minimum\", where the loss really has its lowest value. Things are more complicated when you think that we have millions of parameters, and the loss landscape is many dimensions versus the 2D curves we show below:\n", - "\n", - "
![\"Fig9\"](\"https://i.ibb.co/fFL9P9C/Fig9.png\")
Figure 9. Local minimum and saddle points.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G8S_lG5U0lk5" - }, - "source": [ - "Considering these challenges, do we have any tools to control and optimize the gradient descent during training? The answer is 'yes'. Here are a few tools available to data scientists:\n", - "\n", - "
**Tool 1 - Learning Rate Schedules**:\n", - "As we said above, selecting a reasonable learning rate is core to smooth training. In simple deep learning training sessions, like in this chapter, the learning rate is constant during the entire training. However, there are ways to alter the learning rate during the training or even give it a 'schedule' to change constantly in a way you desire. Data scientists usually use learning rate schedulers to overcome problems like local minima or the problem of vanishing or exploding gradients.\n", - "\n", - "---\n", - "\n", - "> **Note 1**: **Vanishing Gradients** is the term used to describe when the loss gradient with respect to a parameter gets very close to zero. Conversely, **Exploding gradients**, occur when the gradients become very large. Either way, this gradient can be multiplied by other gradients during the chain rule calculations making the resulting updates either zero (no update) or very large, in which case they dominate all the other parameters. Both cases can lead to poor training. Therefore, we always prefer to prevent our gradients from getting too small or too large during the training. We will describe how to do this later on.\n", - "\n", - "---\n", - "---\n", - "\n", - ">**Note 2**: When training a deep learning model, there are many value choices we need to make that are different from parameters. Unlike model parameters that are learned, there are no simple ways to learn these values. Examples include the learning rate (and any scheduling), the number of epochs over which you train your model, and the batch size. Such values are called \"hyperparameters,\" and the process of finding the best hyperparameters for each training is called \"hyperparameter tuning.\" Like selecting the best architecture for your model, finding the best set of hyperparameters is a matter of experimenting and using general intuition, science, and the art of data science!\n", - "\n", - "---\n", - "\n", - "\n", - "
\n", - "\n", - "**Tool 2 - Loss Curves**:\n", - "Another helpful tool to understand the course of optimization is a plot of the loss value for each training epoch, known as a 'loss curve'. Unlike the above figures, which illustrate the change in loss landscape with respect to a single parameter, a loss curve plots the overall loss of the model for each epoch. Put another way, the loss curve shows how the loss is changing during the training. If the model is learning efficiently, the loss curve will tend to descend during a training session (although oscillations could be normal). As shown in the figure below, we almost always plot two loss curves for the model. One (shown in blue) is the loss of the model computed on the training data and the other line (shown in orange) is the loss computed for the validation data. The comparison of these two curves during training gives valuable information to the data scientists regarding their models and how well it is learning the task versus learning the training data set (overfitting).\n", - "\n", - "
![\"img10\"](\"https://i.ibb.co/LhjjxZs/img10.png\")
Figure 10. Example of training and validation loss curves
Source: https://bit.ly/3tYfWXF\n", - "
\n", - "\n", - "**Tool 3 - Optimizer Algorithms**:\n", - "Gradient descent has been very influential in the history of deep learning but is nowadays one of the simplest algorithms we can use to optimize our model's parameters. Many more algorithms are available that change the parameters more effectively. These new optimizers are more resistant to problems like vanishing and exploding gradients. As discussed later in this chapter, they help avoid overfitting or underfitting.\n", - "\n", - "Optimization is a complicated process, and deep learning frameworks like PyTorch have made the work of developers much easier by creating optimizer classes. These classes will take care of the entire optimization calculations for us! For example, below we show how to create a simple stochastic gradient descent optimizer in PyTorch that we will apply to our dummy training. We only need to pass a learning rate and model parameters to the torch.optim.SGD class and PyTorch will create an optimizer instance from that class for us in a single line of code:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "u-HAup-dNd38" - }, - "outputs": [], - "source": [ - "dummy_optimizer = torch.optim.SGD(dummy_model.parameters(), lr=0.005)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "221gb5EtJorp" - }, - "source": [ - "Perfect! Now we have our data, loss, and optimizer. With all these in hand, we are ready to train a model in PyTorch. We only need to run the few lines of code per step of training (i.e., per each batch of data that the model sees):\n", - "\n", - "\n", - "```python\n", - "# Get the training inputs and labels from the training dataloader.\n", - "# Prepare your loss function and optimizer.\n", - "\n", - "model.train() #Line 1 \n", - "optimizer.zero_grad() #Line 2\n", - "train_outputs = model(train_inputs) #Line 3\n", - "loss = loss_function(train_labels, train_outputs) #Line 4\n", - "loss.backward() #Line 5\n", - "optimizer.step() #Line 6\n", - "\n", - "\n", - "# Log the training loss at the end of each step, if needed.\n", - "# Using loss.item() will return its raw value as a standard Python number and with no gradients attached to it.\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e9LH3NlKLHU4" - }, - "source": [ - "Let us go through the above six lines:\n", - "\n", - "* Line 1: As mentioned before, each PyTorch model can be either in training or evaluation mode. When in the training mode, PyTorch will keep track of the gradients of the model's parameters. This is required for training.
\n", - "* Line 2: This line makes sure our optimizer has no memory of the gradients that happened in the last step. It resets the optimizer, making it ready for the new optimization.
\n", - "* Line 3: You already know this line: it feeds the inputs to the model to obtain its predicted outputs. From a training prespective, this step is usually called the \"**forward propagation**\" or the \"**forward pass**\"
\n", - "* Line 4: You also know this line. The loss is calculated using the loss function we defined before.
\n", - "* Line 5: Calling the backward method on loss will cause PyTorch to collect the gradients of loss with respect to every single parameter in the model (unless the requires_grad=False for a parameter, which is a way of freezing some parameters--we will describe this later). These gradients are then stored in the x.grad attribute of each parameter x. So, the model itself preserves the last gradients of loss with respect to its parameters. This step is usually called the \"**backpropagation**\" or the \"**backward pass**\".
\n", - "* Line 6: And finally, the 'learning' happens here. Calling the step method of an optimizer will do the optimization operation and update all the parameters based on their gradients, the learning rate, and the optimizer.\n", - "\n", - "---\n", - "\n", - "\n", - ">**A note on training vs. validation loops:**
\n", - "As we mentioned before, the above lines of code are executed for each training step. This means these lines are executed within two inner \"for\" loops! The first loop iterates through the number of epochs we use for training. The second loop iterates through our training data loader, so we loop once for each batch produced. This inner loop is called the training loop. When the model sees all batches, the inner loop ends, and the outer loop goes on to the next epoch. This will restart a new training loop, and this cycle goes on until the last epoch.
\n", - "In almost all standard deep learning training sessions, there is a validation loop following the training loop. The validation loop also runs within the epoch loop (i.e., it is run every epoch). However, it is distinct from the training loop. In the validation loop, the model iterates through the validation data loader (not the training data loader), and the model is in the evaluation mode. Although the loss is still calculated in each step, no backward pass and optimization happen based on the validation data. The reason is we want to use the validation data to evaluate our model, not for training it. This is an important point: models should never 'see' (i.e., be trained on) validation data during their training session. Failing to do this will result in models that look to be working very well but that may perform very poorly on a fair and real-world evaluation!\n", - "\n", - "\n", - "---\n", - "\n", - "
\n", - "\n", - "The below lines of code are the validation loop of a model. Please note the differences between this code block and the one above for the training loop:\n", - "\n", - "```python\n", - "# Get the validation inputs and labels from the validation dataloader.\n", - "\n", - "model.eval() \n", - "valid_outputs = model(valid_inputs) \n", - "loss = loss_function(valid_labels, valid outputs) \n", - "\n", - "# Log the validation loss at the end of each epoch, if needed.\n", - "```\n", - "\n", - "In summary, the following schematic figure demonstrates how training and validation loops are organized within an epoch loop:\n", - "\n", - "
![\"img11\"](\"https://i.ibb.co/w4RJgBD/img11.png\")
Figure 11. Schematic shape of training and validation loops
\n", - "\n", - "\n", - "It is important that you now reflect on what you have learned as it is the basis for all things going forward. If you feel ready, it is time to show you how this process works in action. The following code will train our dummy model to learn the data we have. Please note that:\n", - "* As this model has only two parameters, we train it on a CPU.\n", - "* We log the epoch number, the raw value of the loss, and the output tensors of the model at the end of each epoch. Before logging, we put the model in the evaluation mode to not accidentally change each parameter's gradients anymore.\n", - "* For simplicity, we do not use any validation loop in this training. We will do that in our main training for the CXR classifer.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49, - "referenced_widgets": [ - "8ca2d16eb39c4bdfa69573a26a47599c", - "7cf5259a599d4b9eaafb1daa7d5a3b86", - "dfc2b08cdcde41428a7d6f1f3a1e4286", - "7742bfd105074ddbb3887b36847ea7f8", - "cb25585a197e492196b2914aae4f8d8c", - "39f3c7a1908e431d85e08675bef6ff30", - "888038374c0342b499265dd8d9b1e830", - "2989c32e4fae4719b8d52b39ffcf5b31", - "696130216db349588b93356709096633", - "2214303bad7e40829ab8fe9d52507ba0", - "353f42dd9ae645979ab410f6a7f04b5f" - ] - }, - "id": "15pVKTgr8S6F", - "outputId": "42e1c367-a142-4f26-b4ed-92dfc2f14a1b" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8ca2d16eb39c4bdfa69573a26a47599c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(3, 3, figsize=(15, 15))\n", - "\n", - "for i in range(9):\n", - " index = i*max_epochs//9\n", - " ax = axes[i//3, i%3]\n", - " epoch, loss, output_tensor = epoch_log_list[index]\n", - " ax.plot(input_tensor, label_tensor, label='ground truth labels')\n", - " ax.plot(input_tensor, output_tensor, label='predicted outputs')\n", - " ax.fill_between(input_tensor, label_tensor, output_tensor, color='yellow')\n", - " ax.set_title(f\"Epoch: {epoch}\\nLoss: {loss:.2}\")\n", - " ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EXI2CLKwP0uD" - }, - "source": [ - "As you see above, our model gradually learned to simulate the ground truth model we used to generate our data. If you print the parameters of the final model, they should be close to the orignal *w* (0.2) and *b* (0.06) we chose for our ground truth model:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Pl-22wwGQZSm", - "outputId": "5f2f8504-cc4e-4aaa-e66e-94c0cfe20e8f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The final model's weight is: 0.198\n", - "The final model's bias is: 0.0616\n" - ] - } - ], - "source": [ - "print(f\"The final model's weight is: {dummy_model.weight.item():.3}\")\n", - "print(f\"The final model's bias is: {dummy_model.bias.item():.3}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VMAPo_7LQZ1y" - }, - "source": [ - "Wow, these numbers are very close to the target values. Of course, our dummy problem was not difficult, but anyways, seeing a model training well is always exciting! We can also plot the loss curve for the model to see how it decreased throughout the training:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 622 - }, - "id": "bk0SIi6hQgy3", - "outputId": "e9a47cf2-8960-4d7c-ae7a-e3f4ffd7202d" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
![\"img12\"](\"https://i.ibb.co/thMdMzP/img12.png\")
Figure 12. Binary cross-entropy loss function
\n", - "where $N$ is the total number of samples, *$y_{i}$* is the true label for the \"$i$\"th sample, and *p(*$y_{i}$*)* is the model's prediction for that sample. Cross-entropy measures the relative entropy between two probability distributions over the same set of events. So, $H_{p}(q)$ means to calculate cross-entropy between the real data distribution $q$ and the predicted label distribution $p$. The distribution $q$ is simply calculated using probability weights from the distribution $p$. For example, if the true label for a given CXR is normal, and the value of the model's final prediction (after applying the softmax function) for the CXR is 0.3 probability for normal and 0.7 probability for pneumonia, then *p($y_{i}$)*=0.3 (i.e., the predicted probability for the true label is 0.3). The BCE (and its more generalized form, the categorical cross-entropy) are widely used loss functions for classification problems. Although we cannot discuss the math behind these formulas in detail, you can check the following blog post to learn more about them: [Understanding binary cross-entropy / log loss: a visual explanation](https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a)\n", - "
\n", - "2. As opposed to our dummy example, we will have both training and validation loops here. For each of these loops, we will log the loss value and the value of our accuracy metric.\n", - "\n", - " ----\n", - "\n", - " >**Note on the difference between performance metrics and loss functions**:
When using both the BCE loss and the accuracy metric, two questions may come to mind: First, why do we not use the BCE value as our metric? And second, why do we not use accuracy as our loss function? The answer to the first question is that the output of the accuracy formula is usually more understandable (or tangible) to data scientists compared to BCE loss values. For the second question, not all metrics can be used as loss functions. Although accuracy is a very interpretable metric, its formula is not differentiable. This means we cannot calculate the gradients of that formula with respect to the model's parameters. In summary, not all metrics are appropriate as loss functions, and not all loss functions are appropriate as performance metrics. However, depending on the problem, sometimes these two may be similar.\n", - "\n", - " ---\n", - "
\n", - "\n", - "3. Another new thing we do in this training is to save our best performing model during the training. If we do not save our model during training, it will be lost. The process of saving a model during the training is a simple form of \"model selection.\" Model selection can be made using many criteria. Here, we follow the most common approach, which is to save the model with the lowest (best) **validation** loss.
\n", - "\n", - "4. Finally, we will run this training on our GPU, since working with images take much more memory and is not efficient on CPUs (it will take 10s-1000s of times longer depending on CPU and GPU).\n", - "\n", - "\n", - "With the above points in mind, please take a look at the following code cell which provides a function to train our CXR classifier. Please note that this function receives our model, data loaders, loss function (here called criterion), and optimizer as inputs. We should also give it the desired name for saving the model and the maximum number of epochs we want to use to train the model. This function will be used many times but with different parameters in next sections of this chapter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "0pMHGSlFEykp" - }, - "outputs": [], - "source": [ - "def train_classifier(model: torch.nn.Module,\n", - " model_name: str,\n", - " train_loader: Iterable,\n", - " valid_loader: Iterable,\n", - " criterion: Callable,\n", - " optimizer: torch.optim,\n", - " num_epochs: int,\n", - " plot_curves: bool):\n", - " \"\"\"\n", - " Train a classifier model using pytorch and the given parameters.\n", - " Parameters:\n", - " model (Callable): any pytorch module,\n", - " model_name (str): name of the model to be saved.\n", - " train_loader (Iterable): dataloader for training data,\n", - " valid_loader (Iterable): dataloader for validation data,\n", - " criterion (Callable): any loss function,\n", - " optimizer (torch.optim): a pytorch optimizer,\n", - " num_epochs (int): number of epochs to train the model,\n", - " plot_charts (bool): whether or not to plot the training and validation loss\n", - " and accuracy curves.\n", - " \"\"\"\n", - "\n", - " # Sending the model to device (preferably GPU)\n", - " model.to(device)\n", - "\n", - " # Releaseing the GPU memory. This is not necessary, but a good practice to do\n", - " # before starting new training sessions.\n", - " with torch.no_grad():\n", - " torch.cuda.empty_cache()\n", - "\n", - " # Building a saving directory for models\n", - " model_save_dir = os.path.join('Best_Models', model_name)\n", - " if os.path.exists(model_save_dir):\n", - " shutil.rmtree(model_save_dir)\n", - " os.makedirs(model_save_dir, exist_ok=True)\n", - "\n", - " # lists to log the epoch values\n", - " epoch_train_loss_list = list()\n", - " epoch_train_accuracy_list = list()\n", - " epoch_valid_loss_list = list()\n", - " epoch_valid_accuracy_list = list()\n", - "\n", - " # Starting the training loop\n", - " for epoch in range(1, num_epochs+1):\n", - " print(\"-\" * 20, f'\\nStarting epoch: {epoch}/{num_epochs}')\n", - "\n", - " ## training\n", - " model.train()\n", - " steps_train_loss = 0.0\n", - " steps_correct_predictions = 0\n", - "\n", - " for batch in tqdm(train_loader, unit=\"batch\"):\n", - " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", - " ### Zero the parameter gradients\n", - " optimizer.zero_grad()\n", - "\n", - " ### Forward + backward + optimize\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " ### Accumulating the loss and number of correct predictions in step\n", - " steps_train_loss += loss.item()\n", - " _, predicted = torch.max(outputs.data, 1)\n", - " steps_correct_predictions += (predicted == labels).sum().item()\n", - "\n", - " ## Measuring the epoch training loss and accuracy\n", - " epoch_train_loss = steps_train_loss/ len(train_loader)\n", - " epoch_train_loss_list.append(epoch_train_loss)\n", - " epoch_train_accuracy = steps_correct_predictions / len(train_loader.dataset)\n", - " epoch_train_accuracy_list.append(epoch_train_accuracy)\n", - "\n", - " ## validation\n", - " model.eval()\n", - " steps_valid_loss = 0.0\n", - " steps_correct_predictions = 0\n", - "\n", - " for batch in tqdm(valid_loader, unit=\"batch\"):\n", - " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " ### Accumulating the loss and number of correct predictions in step\n", - " steps_valid_loss += loss.item()\n", - " _, predicted = torch.max(outputs.data, 1)\n", - " steps_correct_predictions += (predicted == labels).sum().item()\n", - "\n", - " ## Measuring the epoch validation loss and accuracy\n", - " epoch_valid_loss = steps_valid_loss/ len(valid_loader)\n", - " epoch_valid_loss_list.append(epoch_valid_loss)\n", - " epoch_valid_accuracy = steps_correct_predictions / len(valid_loader.dataset)\n", - " epoch_valid_accuracy_list.append(epoch_valid_accuracy)\n", - "\n", - " ## Printing the logs\n", - " print(f'train loss: {epoch_train_loss:.2f} | \\\n", - " train accuracy: {epoch_train_accuracy:.2f}')\n", - " print(f'valid loss: {epoch_valid_loss:.2f} | \\\n", - " valid accuracy: {epoch_valid_accuracy:.2f}')\n", - "\n", - " ## Saving the best model\n", - " if epoch==1:\n", - " best_valid_loss = epoch_valid_loss\n", - " weight_name = f'{model_name}_Epoch{epoch}_ACC={epoch_valid_accuracy}.pth'\n", - " torch.save(model.state_dict(), os.path.join(model_save_dir, weight_name))\n", - " elif epoch_valid_loss < best_valid_loss:\n", - " weight_name = f'{model_name}_Epoch{epoch}_ACC={epoch_valid_accuracy}.pth'\n", - " torch.save(model.state_dict(), os.path.join(model_save_dir, weight_name))\n", - " best_valid_loss = epoch_valid_loss\n", - "\n", - " # Printing the best model\n", - " print(f'\\nTrainng was over. The best model was: {weight_name}')\n", - "\n", - " # Plotting the loss and accuracy curves\n", - " if plot_curves:\n", - " epoch_train_loss_list.insert(0, 0)\n", - " epoch_train_accuracy_list.insert(0, 0)\n", - " epoch_valid_loss_list.insert(0, 0)\n", - " epoch_valid_accuracy_list.insert(0, 0)\n", - " fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", - " axes[0].plot(epoch_train_loss_list, color='blue', label='training')\n", - " axes[0].plot(epoch_valid_loss_list, color='orange', label='validation')\n", - " axes[0].set_xlim(1, len(epoch_valid_loss_list)+1)\n", - " axes[0].set_title('Loss curves')\n", - " axes[0].legend()\n", - " axes[1].plot(epoch_train_accuracy_list, color='blue', label='training')\n", - " axes[1].plot(epoch_valid_accuracy_list, color='orange', label='validation')\n", - " axes[1].set_xlim(1, len(epoch_train_accuracy_list)+1)\n", - " axes[1].set_title('Accuracy curves')\n", - " axes[1].legend();\n", - "\n", - " # Loading the best weights and returning the model\n", - " model.load_state_dict(torch.load(os.path.join(model_save_dir, weight_name)))\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jrAhXo59vMiQ" - }, - "source": [ - "Alright, now let's proceed to our first training and see the performance of our trained model in action. For this initial training, we will use a VGG16 architecture for the model and let it train for 10 epochs. Please note that each epoch will take a few minutes to run (depending on your GPU), so grab your cup of coffee and give yourself a little break!" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "357dcdd72abb41e898df659daefd5d10", - "759a8fe7f71c4257ba423bfd93e1e376", - "cf479fdccb9a4078b463ee54fe37e279", - "bff20abf942d40178a72637de02e312e", - "1f107310246647e0a6f3ca512e8d55bf", - "5a8816a5bebf4212bcd91d80eec80b61", - "0ad6acd499904cf6bf9ebbe07ddb5f3d", - "de2a41970f04422baa02676120876232", - "a718673190c14f01a01a20aac04e36a3", - "06d79c695cf3402ea13230cc8d8e9e8c", - "04afade849d84d6d95efc99301837dd0", - "e3e30cc3b7a54c0eb67c543a0cef2b67", - "4be6277b68de45909f965b0deedf3f95", - "fd41ecc70bff4704a199de99714c3cb2", - "34f5cc4031f04d72838a4ffc93d6a983", - "c8b7a4e5b480474985127c8efb8d66a6", - "82b30678c2954080aa6b0a4914c958e4", - "02dff62bce0d4d359fbb3a3137f0d8f1", - 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"# Doing a base-line training with a non-trained and not-pretrained model\n", - "# You may need to vertically scroll within the code output window\n", - "# in order to see all of the output.\n", - "\n", - "vgg16_model_1 = build_model(arch='vgg16', pretrained=False)\n", - "criterion = torch.nn.CrossEntropyLoss()\n", - "learning_rate = 0.01\n", - "optimizer = torch.optim.SGD(vgg16_model_1.parameters(), lr=learning_rate)\n", - "num_epochs = 10\n", - "vgg16_model_1 = train_classifier(model=vgg16_model_1,\n", - " model_name='vgg16_model_1',\n", - " train_loader=train_loader,\n", - " valid_loader=valid_loader,\n", - " criterion=criterion,\n", - " optimizer=optimizer,\n", - " num_epochs=num_epochs,\n", - " plot_curves=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 504, - "referenced_widgets": [ - "c31ed7b98add492d954928ed54c7be3f", - "918f22ec37d4492d92bbad4128ab55e9", - "336a4b5fb1a440ae916e2139b3185912", - "0784eda61e3344fc97cdf20f79418900", - "31232d276056491ab150f4af05d226a1", - "3735bf51e36b4a27a6ed1be76f67d3c0", - "f56b7a0733f144928519d60d04ce8813", - "0241b8d8a4d24223b92a81b248a6af74", - "c6cfac4b80a64d7f89c870e2a073bf91", - "3461f19d5ee946c6b0e47a10622a3be7", - "e4d750ae0933409693dd64921c61c396" - ] - }, - "id": "s96uQASUCm6c", - "outputId": "44163c7a-2070-426b-e4e0-cdfa783f5ef8" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c31ed7b98add492d954928ed54c7be3f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/39 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Evaluating the performance of vgg16_model_1 on the test set\n", - "\n", - "_ = evaluate_model(vgg16_model_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "48MQfr8byKTh" - }, - "source": [ - "First look at the loss and accuracy curves. Can you see where overfitting has started? What do you think is the reason for those oscillations in the loss curves?\n", - "\n", - "Now look at the confusion matrix. How do you interpret that? Does our model have a more challenging job detecting the pneumonia cases or the normal cases?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8R_t42m8O1la" - }, - "source": [ - "## Part 5: The concept of fit\n", - "\n", - "You have previously learned the definition of fit, overfitting, and underfitting. As a quick review, whenever we train a regression model, we are trying to find a curve in a multidimensional space where most of our data points fall on or very close to this curve. Speaking more simply, a fit model is nothing different from a well-trained model. Unlike a well-fit (or well-trained) model, an underfit model has not learned enough from the training data. The curve from such a model will not cross as many data points as a well-trained model in the same poly-dimensional space.\n", - "On the other hand, an overfit model is a model that has learned more than necessary (e.g. the specific examples, not the overall idea) from the training data and will therefore not generalize well to the unseen data (e.g., the validation data). The mathematical representation for such a model is a curve that crosses almost all of the data points in the poly-dimensional space, even the outlier data points, which a well-trained model should ignore naturally. Please take a look at the following figure to remind yourself of these concepts, and feel free to review our previous chapters if needed. The first row of the figure shows under-fitting, appropriate-fitting, and over-fitting for a regression problem, and the second row denotes the same concepts for a classification problem. Looking at how we defined an appropriate fit for a regression problem above, can you now describe what an appropriate fit means for a classification problem?\n", - "\n", - "
![\"img13\"](\"https://i.ibb.co/JQfKTDQ/img13.png\")
Figure 13. Comparison of appropriate fit, underfitting, and overfitting
Source: https://towardsdatascience.com/techniques-for-handling-underfitting-and-overfitting-in-machine-learning-348daa2380b9\n", - "
\n", - "\n", - "For the sake of this chapter, we will focus on the most common reasons for underfitting or overfitting and propose a few solutions to combat them. But before that, let us first specify the exact situation when overfitting and underfitting are occurring!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BkIPlpHs2y1d" - }, - "source": [ - "### Standard training strategy\n", - "\n", - "Although not always true, we generally train a neural network by pursuing the following three steps:\n", - "\n", - "1. Train a model to the point where it clearly is overfitting. As a general rule of thumb, every deep learning model will overfit the data if the training is continued long enough. In other words, if the model is not overfitting after an 'infinite' number of epochs, then something is probably not right in the training setup (data, model architecture, etc.). We will discuss a few of these scenarios below. On the other hand, it is also undesirable if a model overfits after just a few epochs. This warns us that the model might not have learned enough from the data. Again, this is something that we should be able to deal with.\n", - "\n", - "2. When overfitting starts, we can use different techniques to postpone it. For example, suppose the initial overfitting happens at epoch=10 for training. In that case, we do our best to postpone it so that it now happens a few epochs later, hoping that will improve its generalizability and ability to learn more meaningful features.\n", - "\n", - "3. Finally, one should save the version of our model's parameters that were in place around the time the model started to overfit. Practically, we do not usually pick the model the exact moment the overfitting started but choose our model from around that point and based on a desirable metric (e.g., accuracy). For example, if the model started to overfit at epoch 10, we may check epochs 9 to 11 and pick the model from either of these epochs which have the highest accuracy. This will ensure that we have a model that is not overfitting to our data and meets our expectations about metrics we care about.\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - "> **Note**: Remember that the usual way to detect overfitting is from the loss curves. Overfitting starts when the training loss is still decreasing, but the validation loss is clearly increasing (some jitter is normal due to noise). If you look at the following image as an example, the red dashed line denotes where the overfitting starts, and the yellow area may be where we prefer to pick our final model from. Some data scientists prefer to end their training when they hit a checkpoint like the red dashed line below. That approach is called \"early stopping\" and may help consume less training resources (e.g., GPU).\n", - "\n", - "\n", - "---\n", - "\n", - "
![\"img14\"](\"https://i.ibb.co/DM24nwD/img14.png\")
Figure 14. Loss curves showing overfitting
Source:https://www.baeldung.com/cs/training-validation-loss-deep-learning\n", - "
\n", - "\n", - "We provide code snippets for saving and loading weights for PyTorch models.\n", - "\n", - "In order to save some weights:\n", - "\n", - "\n", - "```python\n", - "save_path = 'path_to_save_the_weights.pth'\n", - "torch.save(dummy_model.state_dict(), save_path)\n", - "```\n", - "\n", - "And for loading a model from disk, we have:\n", - "\n", - "```python\n", - "dummy_model = Build_model(arch='vgg16', pretrained=False)\n", - "weights_path = 'path_to_save_the_model.pth'\n", - "dummy_model.load_state_dict(torch.load(weights_path))\n", - "```\n", - "\n", - "---\n", - "\n", - "> **Note 1**: Weights (or, more generally, the parameters) are saved in PyTorch, but not the model's architecture. Therefore, to use these saved weights, it is necessary to build the empty architecture for our model first and then load the trained weights into it.\n", - "\n", - "---\n", - "---\n", - "\n", - "> **Note 2**: PyTorch weights are traditionally saved as files with a \".pth\" extension.\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-il_3aZ3QAss" - }, - "source": [ - "### Under-fitting\n", - "\n", - "If the training process never displays overfitting, the model is probably underfit. Even if the model's performance looks good, you should still find out why it is underfitting and address it since there may still be room for improving the model's performance. In the case of underfitting, there are three aspects we should check first in our training session: the training length, learning rate, and the architecture of the model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G9Yae2asV7ND" - }, - "source": [ - "#### Number of epochs\n", - "\n", - "As mentioned before, one of the most common reasons for underfitting is that the model was not trained long enough. Look at the training session where we trained vgg16_model_1. You see that the model's performance at the end of the first epoch was not as good as the final model we saved in the later epochs. The reason is apparent. At the end of the first epoch, the model had not had the chance to learn enough signals from the data.\n", - "Addressing this form of underfitting is easy. Just let your model train longer. Do not forget our general rule of thumb that a model should be trained until it overfits the data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fc5V45PdWFPe" - }, - "source": [ - "#### Learning rate adjustment\n", - "\n", - "A second reason for underfitting is using learning rates that are too small, which results in slower learning. The following code snippet shows this phenomenon by training our model for three epochs and using a minimal learning rate. You should see that the model is not learning significantly during this training and, therefore, will not appropriately fit the training data." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 463, - "referenced_widgets": [ - "659da3c7f3264128a1b1f58a5ace9a50", - "e9a3bb30beae479fbb0557cd6188d2cb", - "217d3931f5db4f438b71ceb4eb0071d7", - "1905a03f4aaa4b85894787e52152c4d2", - "f060ebeab1924388ab575c6ba126c4be", - "54e2c4a62ae54c5baa95f7f79dab9b10", - "536854a4e52a4f59b7c3764cc72eb56a", - "87e4d60158284bfb975c9cb8ef6eb183", - "da61a126ac2948ff8b92192bcc39af53", - "418f4b8f32c64d08a12a23eedaa7e00e", - "4edf01ed3c9848cd94b74c1ffea26ecc", - "1b9b80fba1a641c8bdfb9d897fceffb9", - "dcfc0aa0c9874904b228e1ae792c3086", - "350fec89290441449c93cc6daa24f920", - "5e2543e514774d61ac9885cb568264cc", - "aa44ff0034ce4fb690dbfaed1a2a5865", - "600047e6d4d24b73b995ee06716004cf", - "b19d6d6bc60a4ff7b6c0082a860878c3", - "94544211e32e44a29cf91fde57fe2470", - "7dfc2a31e5df4e6dbf1f0ea2e871fd3a", - "e35eadbb74764b158bc55ef75a19fca9", - "80dd6ff925884a9281aae126c883ce02", - "b0ad5654e72a4c7bbd17194a9a616ae4", - "98a18ec77c694859a4970a97739a8811", - "03410778d0f3440fa41f39c88dcf1379", - "9705a8ed883b4a188fbe363ca8557ca0", - "9d4d0d9d4e6147aea56e03b7f713a707", - "3ed49b83d81c4011bf5fdf412651b9c4", - "4fd5965d686c4632a77e698098d6f897", - "17e958585182475495b3009798a55b0a", - "23ee93d280b74e1a83f8a4a8c11ec9d9", - "391e00a28b234ac3bb1aa4be302054b5", - "f4a37190bbfc492f94b569a05791988e", - "ebd7e699c61a426da814258bb88a28ca", - "610365b950c9480d93dae44161ff0949", - "01c1fe7360d34b6280d8d2c9ecbbed73", - "55f57e24af4e4c26af4abf5052714ad8", - "e735587b93f743a8a3af580a41c5a180", - "e05fcf46cc9b4035a7cc564dee67f618", - "83b7cc610b9b432f9d5f89f2d49c1134", - "dc6106e86bb540bca688a76c237cf43c", - "4e9514807b8041fb8a2b568ebfed4337", - "97ebbf2e50914ac0a91a6c8d730a630e", - "7adb09d9617f4f3a8491dedf9c1eb8be", - "d5ee8aade2a84829b458baf37eec2434", - "c767d1d134ea4b9f986b057994d57d62", - "b2c3366f6d6b4c3c9b9823553348647e", - "aa9bb9d0827f43208113561919589186", - "7c9165cadc4548089df21e0bbcf573fe", - "73e2787c445c405e9eb320412e14b012", - "c45a3682f2a644dabb42f44f1db6e101", - "e260f56a676b4ceb81288aa800445e2c", - "f5c0d2b1f28b4e7ead0bc5bc168006d0", - "c5d4cadc93eb448ab0a3e352d600a56a", - "4e9674e4d2684062a7152f166daf310d", - "17e398e452b94e78961e9f9155d9dc84", - "1aa724f2644d42fa98b10bf7b24dbcb2", - "a5f009c1b0324aaa90a1007911395f73", - "7a9feadc34964fefaf795ee4b30a9b3a", - "477fd96f99524d2a903be6490197ace4", - "234cac5e95e5471fa73d38307b2e0a93", - "d97caf463e504cfa88f14b9ab81e65aa", - "c2c7829c0c7f410bb1bd578cc855573a", - "7d46f45037e04f6cbb6ea1882070acbf", - "072ae8119723480a97de638743d3cb6e", - "f308688fa38644cfbe29586bc86733c3" - ] - }, - "id": "FesBMb27Vody", - "outputId": "6432c0fa-3386-40f9-8b8a-b58e27295111" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------- \n", - "Starting epoch: 1/3\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "659da3c7f3264128a1b1f58a5ace9a50", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/654 [00:00**Practice**: If interested, try to down-sample your CXR images to 8 by 8, and create a linear PyTorch model that receives 64 inputs values in its first layer and generates two output numbers in its second layer (do not forget to apply the softmax as well). Try to train this model and see how it will underfit the data yourself. Do not forget that we have about 5000 training samples, so a linear model with more parameters than this number will easily overfit our training data. Can you say why?\n", - "\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "JPut_xzHhpGd" - }, - "outputs": [], - "source": [ - "##### Use this code cell for the coding practice described above:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NYTAI4pFRQh_" - }, - "source": [ - "### Overfitting\n", - "\n", - "Although underfitting may happen for the reasons mentioned above, the problem that is much more commonly seen during the training sessions is overfitting. As mentioned above, we always want to postpone overfitting as much as possible to give our model more time to learn from the data and improve its generalizability. Here, we will review four aspects of the training setup. Investigating and improving these may help with combatting overfitting. At the end of this section, we will deploy several of these strategies to run another round of training, with hopefully, more resistance to overfitting." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "x_ns_kk6lfV8" - }, - "source": [ - "#### Training data size" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VZnLmmqEiYHk" - }, - "source": [ - "It may look trivial, but we should mention that the most potent way for addressing overfitting is to add more data to your training. When your model sees more data during the training, it will have a more difficult job to memorize all data. Instead, it will have a higher chance of learning the patterns present in the data. As you can imagine, the problem with this solution is that finding more data is often not very feasible.\n", - "\n", - "---\n", - "\n", - ">**Note**: If interested, build a data loader with only ten training samples from our pool of CXRs, and train a model on it. See for yourself how overfitting will show itself in the few starting steps of your training!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_W03U5alDje0" - }, - "source": [ - "#### Data augmentation\n", - "\n", - "Data augmentation is a well-known strategy to combat overfitting. Although this technique is never as effective as adding more unique training data, it will still help the training. The fundamental advantage of data augmentation is that the model will see variations of data points in each epoch. For example, a single photo may be used in a slightly rotated, horizontally or vertically flipped, zoomed in or out, or brighter or darker variations when the data loader calls it in each epoch.\n", - "\n", - "Although the list of possible augmentations for imaging data is very long (and newer techniques are introduced every day), there are two challenges that data scientists should consider when augmenting medical images:\n", - "\n", - "1. The first issue is to decide about which augmentations to use. Not all augmentations may make sense for medical images, and some may make the model's job harder for no reason. For example, in our scenario for classifying CXRs, it does not make sense to augment the CXRs by vertical flipping. The reason is that CXRs would never be acquired upside-down!\n", - "\n", - "2. The second challenge is how much to augment. For example, it may make sense to rotate the CXRs for a few degrees to make the model think these CXRs are new data. However, if we rotate them as much as 90 degrees, we will be in the same situation as described above, where the augmentation has no value. Even if such limitations do not exist, estimating the benefit of augmentation is difficult. In fact, the types and amount of augmentation is another hyperparameter one needs to tune to achieve a good fit.\n", - "\n", - "---\n", - "\n", - "\n", - ">**Note**: As an exception to the above scenarios, if our CXR classifier is expected to see non-standard CXRs during the inference time (e.g., flipped or highly rotated ones), it may make sense to add such augmentations to our pipeline. Please note that augmenting the training data adds complexity to the model's data and makes the model fit more computationally demanding, so this complexity must have some returns (e.g., delayed overfitting).\n", - "\n", - "\n", - "---\n", - "
\n", - "\n", - "Recall that adding augmentation is often what we do when building our data loaders. So, let us build another data loader using augmentations and visualize their outputs below to see how much they differ from the outputs of the previous data loaders we used." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 930 - }, - "id": "JOtAEq7EMr3g", - "outputId": "acb8894a-76e7-4ae0-eeea-0509df207b43" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
![\"img15\"](\"https://i.ibb.co/CVCsFJn/img15.png\")
Figure 14. Cross-entropy loss with L2 regularization (weight decay)\n", - "
\n", - "\n", - "As shown above, a new term is added to the loss function, called L2. The higher the model's weights are, the higher the L2 will be, and thus, the overall loss. Here, lambda() is a hyperparameter to tune and determine how much penalty a model should receive from weight decay.\n", - "\n", - "Fortunately, implementing weight decay in PyTorch is very simple. You only need to pass the lambda value to the optimizer class when building your optimizer. Then PyTorch will automatically implement that during the training:\n", - "\n", - "```python\n", - "optimizer = torch.optim.SGD(vgg16_model_3.parameters(), lr=learning_rate, weight_decay=0.001)\n", - "```\n", - "\n", - "In most cases, start with a value between 0.01 or 0.001 for lambda and gradually try larger or smaller values to find the optimal contribution of L2." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aG2MpE2jD6Ao" - }, - "source": [ - "#### Architecture selection\n", - "\n", - "From the discussion above, you can see that the more parameters a model has, the greater the tendency for overfitting. Imagine such a model as a smart but lazy student. It will learn the easiest way to solve the riddle of training data. If that is possible by simply memorizing all the data points you have, it might perform even better without having to learn the underlying principles.\n", - "\n", - "Albert Einstein famously said that things should be made as simple as possible but no simpler (https://www.brainyquote.com/quotes/albert_einstein_103652). This is particularly true for deep learning models. The challenge is knowing what that right size is. The question of how big a model should be is, again, a matter of experimenting. A common strategy is to start with a model that worked well on a 'similar' problem, and if it overfits quickly, then reduce complexity and if it underfits, increase complexity. Repeat until satisfactory results are found. There are some computer-based strategies to automate this that will be discussed at a later time.\n", - "\n", - "Another important point to mention is that smaller models are not 'dumb' nor more challenging to train. Yes, they have lower capacity, but they may perform better than a more complex model! In fact, tons of research in deep learning has been done to build small but smart models. ResNet models (which you should be familiar with from the previous chapters, otherwise, check [here](https://towardsdatascience.com/review-resnet-winner-of-ilsvrc-2015-image-classification-localization-detection-e39402bfa5d8)), inception model (check [here](https://sheng-fang.github.io/2020-05-05-review-googlenet-v1-v4/)), and EfficientNet models (check [here](https://towardsdatascience.com/efficientnet-scaling-of-convolutional-neural-networks-done-right-3fde32aef8ff)) are among the well-known examples of such novel architectural designs. These models will always beat a model like VGG16 in terms of overfitting. You may also design your own architectures to achieve even smaller but smarter models than what is already known.\n", - "\n", - "Fortunately, working with ResNet models in PyTorch is as easy as working with VGG models. Lets' try a ResNet18 model and compare the number of parameters it has with our VGG16:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R9ZNSurnm0Xj", - "outputId": "82d1d417-6519-4781-a26c-82423703ae1c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------------------------------------------------------------\n", - " Layer (type) Output Shape Param #\n", - "================================================================\n", - " Conv2d-1 [-1, 64, 112, 112] 9,408\n", - " BatchNorm2d-2 [-1, 64, 112, 112] 128\n", - " ReLU-3 [-1, 64, 112, 112] 0\n", - " MaxPool2d-4 [-1, 64, 56, 56] 0\n", - " Conv2d-5 [-1, 64, 56, 56] 36,864\n", - " BatchNorm2d-6 [-1, 64, 56, 56] 128\n", - " ReLU-7 [-1, 64, 56, 56] 0\n", - " Conv2d-8 [-1, 64, 56, 56] 36,864\n", - " BatchNorm2d-9 [-1, 64, 56, 56] 128\n", - " ReLU-10 [-1, 64, 56, 56] 0\n", - " BasicBlock-11 [-1, 64, 56, 56] 0\n", - " Conv2d-12 [-1, 64, 56, 56] 36,864\n", - " BatchNorm2d-13 [-1, 64, 56, 56] 128\n", - " ReLU-14 [-1, 64, 56, 56] 0\n", - " Conv2d-15 [-1, 64, 56, 56] 36,864\n", - " BatchNorm2d-16 [-1, 64, 56, 56] 128\n", - " ReLU-17 [-1, 64, 56, 56] 0\n", - " BasicBlock-18 [-1, 64, 56, 56] 0\n", - " Conv2d-19 [-1, 128, 28, 28] 73,728\n", - " BatchNorm2d-20 [-1, 128, 28, 28] 256\n", - " ReLU-21 [-1, 128, 28, 28] 0\n", - " Conv2d-22 [-1, 128, 28, 28] 147,456\n", - " BatchNorm2d-23 [-1, 128, 28, 28] 256\n", - " Conv2d-24 [-1, 128, 28, 28] 8,192\n", - " BatchNorm2d-25 [-1, 128, 28, 28] 256\n", - " ReLU-26 [-1, 128, 28, 28] 0\n", - " BasicBlock-27 [-1, 128, 28, 28] 0\n", - " Conv2d-28 [-1, 128, 28, 28] 147,456\n", - " BatchNorm2d-29 [-1, 128, 28, 28] 256\n", - " ReLU-30 [-1, 128, 28, 28] 0\n", - " Conv2d-31 [-1, 128, 28, 28] 147,456\n", - " BatchNorm2d-32 [-1, 128, 28, 28] 256\n", - " ReLU-33 [-1, 128, 28, 28] 0\n", - " BasicBlock-34 [-1, 128, 28, 28] 0\n", - " Conv2d-35 [-1, 256, 14, 14] 294,912\n", - " BatchNorm2d-36 [-1, 256, 14, 14] 512\n", - " ReLU-37 [-1, 256, 14, 14] 0\n", - " Conv2d-38 [-1, 256, 14, 14] 589,824\n", - " BatchNorm2d-39 [-1, 256, 14, 14] 512\n", - " Conv2d-40 [-1, 256, 14, 14] 32,768\n", - " BatchNorm2d-41 [-1, 256, 14, 14] 512\n", - " ReLU-42 [-1, 256, 14, 14] 0\n", - " BasicBlock-43 [-1, 256, 14, 14] 0\n", - " Conv2d-44 [-1, 256, 14, 14] 589,824\n", - " BatchNorm2d-45 [-1, 256, 14, 14] 512\n", - " ReLU-46 [-1, 256, 14, 14] 0\n", - " Conv2d-47 [-1, 256, 14, 14] 589,824\n", - " BatchNorm2d-48 [-1, 256, 14, 14] 512\n", - " ReLU-49 [-1, 256, 14, 14] 0\n", - " BasicBlock-50 [-1, 256, 14, 14] 0\n", - " Conv2d-51 [-1, 512, 7, 7] 1,179,648\n", - " BatchNorm2d-52 [-1, 512, 7, 7] 1,024\n", - " ReLU-53 [-1, 512, 7, 7] 0\n", - " Conv2d-54 [-1, 512, 7, 7] 2,359,296\n", - " BatchNorm2d-55 [-1, 512, 7, 7] 1,024\n", - " Conv2d-56 [-1, 512, 7, 7] 131,072\n", - " BatchNorm2d-57 [-1, 512, 7, 7] 1,024\n", - " ReLU-58 [-1, 512, 7, 7] 0\n", - " BasicBlock-59 [-1, 512, 7, 7] 0\n", - " Conv2d-60 [-1, 512, 7, 7] 2,359,296\n", - " BatchNorm2d-61 [-1, 512, 7, 7] 1,024\n", - " ReLU-62 [-1, 512, 7, 7] 0\n", - " Conv2d-63 [-1, 512, 7, 7] 2,359,296\n", - " BatchNorm2d-64 [-1, 512, 7, 7] 1,024\n", - " ReLU-65 [-1, 512, 7, 7] 0\n", - " BasicBlock-66 [-1, 512, 7, 7] 0\n", - "AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0\n", - " Linear-68 [-1, 2] 1,026\n", - "================================================================\n", - "Total params: 11,177,538\n", - "Trainable params: 11,177,538\n", - "Non-trainable params: 0\n", - "----------------------------------------------------------------\n", - "Input size (MB): 0.57\n", - "Forward/backward pass size (MB): 62.79\n", - "Params size (MB): 42.64\n", - "Estimated Total Size (MB): 106.00\n", - "----------------------------------------------------------------\n" - ] - } - ], - "source": [ - "resnet18_model_1 = build_model(arch='resnet18', pretrained=False).cuda()\n", - "summary(resnet18_model_1, input_size=(3, 224, 224))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mjAWLvRtz1PY" - }, - "source": [ - "As you can see, our ResNet 18 has about 11 million parameters, while our VGG16 had about 134 million! This is a huge difference! You will shortly be even more surprised when you see the performance of ResNet18 is also much better than the VGG." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "paSvtepMXId-" - }, - "source": [ - "#### Transfer learning and fine-tuning\n", - "\n", - "Last but not least, **transfer learning** is one of the best techniques to reduce overfitting. Not only does it help with a better fit, but it also reduces the dependency of training on training data. This means, with appropriate deployment of transfer learning, the model may learn the same or even better with a smaller fraction of data at hand. This is very useful in fields like medicine when adding more data is not always feasible.\n", - "\n", - "There are many ways to define transfer learning, but consider the analogy introduced at the beginning of this chapter. Remember that each deep learning model is a huge mathematical equation with many parameters. We told you that these parameters often begin with random values when the model is created. In transfer learning, these parameters are not randomly initiated anymore. Instead, they are imported from another model already trained to do a task similar to what we pursue. This source model is called a \"pre-trained\" model.\n", - "\n", - "Imagine our VGG16 model to classify the CXRs, and suppose we already have access to another VGG16 model that others have trained to differentiate viral pneumonia from COVID19 pneumonia. Although this second model is not exactly doing what we want to do, it is doing something similar, and perhaps, it will use many features that are similar to the features that our model should learn. In mathematical terms, many parameters of our model will probably end up having values close to values of the second model's parameters. Therefore, if one starts with this second model and starts to train that for the new purpose, training will probably be much faster and smoother than when training a randomly-initiated model from scratch. This process of just updating weights rather than training from random values is called **\"fine-tuning.\"**\n", - "\n", - "
![\"img16\"](\"https://i.ibb.co/ck0tQt6/img16.png\")
Figure 14. Transfer learning vs. learning from scratch
Source: https://medium.datadriveninvestor.com/introducing-transfer-learning-as-your-next-engine-to-drive-future-innovations-5e81a15bb567\n", - "
\n", - "\n", - "It is not always possible to find a model that does a 'similar task' but it is often the case that transfer learning from a model that is fairly different can still be better than starting from random values. The good news is that there are a number of pre-trained models (e.g. the ImageNet database) for a variety of purposes that can be very useful for transfer learning, and if you have limited data, it is valuable to start with one of those models.\n", - "\n", - "According to Wikipedia, The ImageNet project is a large visual database designed for use in visual object recognition software research. It consists of more than 14 million images of natural (not medical) objects that have been hand-annotated by the project to indicate what objects are pictured. As a tradition, whenever well-known deep learning models are introduced, developers pre-train them on ImageNet and release their weights. This means in the worst-case scenario, you can find access to the weights of a standard model that has been pre-trained on ImageNet to use in your project (unless you aim to use a custom architecture of your own or others). Although ImageNet is not a medical database, transfer learning from it to medical tasks is better than no transfer learning in many cases. The reason is that the ImageNet database is huge. Therefore, models pre-trained on that will have a memory of many features, at least some of which may be useful for medical training sessions as well.\n", - "\n", - "If you check the script for the \"Build-model\" function above, you can see that it loaded pre-trained weights from a model pre-trained on ImageNet to our VGG16 model. Do this now and see how an ImageNet pre-trained VGG16 will perform in evaluating our CXRs:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 540, - "referenced_widgets": [ - "934314848e4046a3a354914f55933036", - "ef88e819f9eb4afa8253a44aabd88063", - "2968d7e7534f4bf7a37b8dbb65f677e0", - "35514fe35fbd48db84a8764b4810039a", - "f35a628ac81d434da3a2450ab51c9f5e", - "a42b50a06c434339882d0622fde17e10", - "e3db85ad5025451486b9f81c7c051b51", - "a3b058c4f92f49709e691ae4f9d1ea9c", - "97d389e26b4a419f864055b4592640aa", - "1d12bf40a0404f97ae78615416341650", - "ce21b2a432424b43bb705893a0bdc4e5" - ] - }, - "id": "u0kLJjOvrc_O", - "outputId": "0847371a-d575-4059-a79d-4b3532aa05a1" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n", - "100%|██████████| 528M/528M [00:02<00:00, 192MB/s]\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "934314848e4046a3a354914f55933036", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/39 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Evaluating the performance of an ImageNet-pretrained model\n", - "\n", - "Imagenet_pretrained_vgg16 = build_model(arch='vgg16', pretrained=True)\n", - "_ = evaluate_model(Imagenet_pretrained_vgg16)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pdZ6EbLJv0Mv" - }, - "source": [ - "This shows that the pre-trained model was not that successful in differentiating our CXRs from each other, but that should not be a surprise since that was not the ImageNet task. As said before, models pre-trained on ImageNet are trained to identify natural objects like animals, cars, etc. A model pre-trained on ImageNet will probably not do better on a medical imaging task than a randomly-initialized model. If we want to see the value of transfer learning, we must perform fine-tuning.\n", - "\n", - "To emphasize again, fine-tuning means to start with a pre-trained model, freeze 'most' initial layers, and train the remaining layers (by 'freeze' we mean that the weights are not changed). The intuition behind this technique is that the initial layers of the models often learn low-level features (e.g., lines, edges, curves, circles, etc.). Subsequent layers recognize combinations of these (e.g. 2 lines at a 90 degree angle are a corner and 2 circles might represent eyes). As a result, in an ImageNet pre-trained model, we should mostly rely on the first two or three layers. Anything after those layers are likely specific to natural images and, therefore, not helpful in understanding medical images. Of course, if you happen to find a good medical imaging source model for your task, then feel free to use more later layers.\n", - "\n", - "\n", - "---\n", - "\n", - "\n", - "> **Note:** This is the most basic form of fine-tuning. More advanced forms also exist, which are beyond the scope of this chapter.\n", - "\n", - "\n", - "---\n", - "\n", - "To see this in practice, load a ResNet18 model pre-trained on ImageNet, freeze its initial layers, and train it on our data. We will also use L2 regularization and data augmentation to focus resources to avoid overfitting and to improve performance. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "id": "6uq1lhFobWQ7" - }, - "outputs": [], - "source": [ - "# Defining a function to freeze the resnet models for fine-tunning\n", - "\n", - "def freeze_resnet18(resnet18_model: Callable,\n", - " children_num_to_freeze: int = 5,\n", - " print_children: bool = False):\n", - " \"\"\"\n", - " Freeze a resnet18 model up to a user-specified children module.\n", - " Parameters:\n", - " resnet18_model: The resnet18 model to be frozen and returned.\n", - " children_num_to_freeze: The number of children modules to be frozen.\n", - " print_children (bool): Whether or not to print the children modules.\n", - " \"\"\"\n", - " for i, child in enumerate(resnet18_model.children()):\n", - " if i < children_num_to_freeze:\n", - " status = 'Frozen'\n", - " for param in child.parameters():\n", - " param.requires_grad = False\n", - " else:\n", - " status = 'Unfrozen'\n", - " for param in child.parameters():\n", - " param.requires_grad = True\n", - " if print_children:\n", - " print(f'******************* child module number: {i} - {status}')\n", - " print(child)\n", - " return resnet18_model" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IBCgfYvgbcrg", - "outputId": "ff190878-4b28-4497-f4d5-7d2d08023733" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", - "100%|██████████| 44.7M/44.7M [00:00<00:00, 118MB/s]\n" - ] - } - ], - "source": [ - "# Creating a resnet18 model and freezing its inital layers.\n", - "\n", - "resnet18_model = build_model(arch='resnet18', pretrained=True)\n", - "resnet18_model = freeze_resnet18(resnet18_model,\n", - " children_num_to_freeze = 5,\n", - " print_children=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - 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" weight_decay=0.001)\n", - "num_epochs = 10\n", - "resnet18_model_2 = train_classifier(model=resnet18_model,\n", - " model_name='resnet18_model_2',\n", - " train_loader=train_loader2,\n", - " valid_loader=valid_loader2,\n", - " criterion=criterion,\n", - " optimizer=optimizer,\n", - " num_epochs=num_epochs,\n", - " plot_curves=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 504, - "referenced_widgets": [ - "2a07be74eafa439aa8ca0355f71e0f06", - "6359b0b788a94cc2b4187289a2c51530", - "7fdd2832f7794057b7284ced3bc9dd82", - "7820b094320445aabe11759d0ccc7820", - "6a070d1404fb42ebbfd1c5447202b418", - "f666ad7a0623413391123f3396ba38b9", - "ab86a17d7d34443d9d3392c6dc021417", - "511be0ea26b3461392959f8d58598ff2", - "598bd896988e484bb0fc64360923415f", - "22423193233443aa9596ee759cf412c8", - "b6c717dbc0254372856c57b8142a95a2" - ] - }, - "id": "83Up5jCrSPQD", - "outputId": "d4b2fb7e-b0e2-4088-d677-0028444c82c6" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2a07be74eafa439aa8ca0355f71e0f06", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/39 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Evaluating the performance of resnet18_model_2 on the test set\n", - "\n", - "_ = evaluate_model(resnet18_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qAPxE3pt1quC" - }, - "source": [ - "You should see a dramatic jump in performance. This is why you should always be familiar with as many deep learning techniques and tricks as possible. Now that we have reviewed the most common techniques to battle overfitting, our discussion in this chapter is almost over. Feel free to review the above concepts and redo the training in different variations. Try to include or exclude different techniques we introduced to see how much each will affect the model's performance. Do not forget: Deep learning is, of course, a matter of science and art, but it also is a matter of experimenting and having perseverance!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dTUNSbHvO566" - }, - "source": [ - "## Part 6: Conclusions and further reading\n", - "\n", - "This chapter described how to train a deep learning model. This included a discussion of how a neural network resembles a mathematical equation. That was followed by a definition of training and how to implement that in PyTorch. Next, the concepts of fit and how to combat under-fitting and over-fitting were covered. The discussion was enriched with many practical examples and Python code.\n", - "Nevertheless, this is still only a tiny fraction of the world of deep learning. Other resources that may be useful to understand the concepts include:\n", - "\n", - "* [A Beginner Intro to Neural Networks](https://purnasaigudikandula.medium.com/a-beginner-intro-to-neural-networks-543267bda3c8)\n", - "* [How to train neural networks for image classification — Part 1](https://medium.com/nerd-for-tech/how-to-train-neural-networks-for-image-classification-part-1-21327fe1cc1)\n", - "* [FastAI course - SGD from scratch](https://course.fast.ai/videos/?lesson=4)\n", - "* [Anrew Ng's deep learning specialization on Coursera, courses 1 and 2](https://www.coursera.org/specializations/deep-learning)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LwT17BWFKzl4" - }, - "source": [ - "---\n", - "\n", - "##***Feedback***\n", - "\n", - "*Now that you have completed this chapter, we would be very grateful if you spend a few minutes of your time to answer a short survey about this chapter. We highly value your feedback and will do our best to leverage this to improve our educational content and/or strategies.*\n", - "\n", - "[Click here to begin the survey!](https://docs.google.com/forms/d/e/1FAIpQLSddhdaAmeHmrKKRNXCLIQH6_mnIC3KR7XlDIVWGt3FSQhPDhQ/viewform)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "A100", - "include_colab_link": true, - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.8.12" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "00db50bbbb074131bc4992ec5c3906f6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - 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We described what medical imaging data looks like and how we can build datasets and dataloaders using PyTorch and MONAI. We are now ready to train our first deep learning model!\n", + "We will do this by building a simple deep learning model and discussing the steps needed for classical PyTorch training. We will also introduce an easy way to apply deep learning models to unseen data (also known as inference) and validate their performance. Hopefully, you will be able to train a deep learning model of your own by the end of this chapter!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Du_XolBHxV3G" + }, + "source": [ + "### Preparing the notebook\n", + "\n", + "\n", + "OK, let's begin by setting the environment for this notebook by installing MONAI." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XNE2VBoadB4J", + "outputId": "7d76127e-16f4-4940-b02c-974fc55fd2dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting monai\n", + " Downloading monai-1.3.0-202310121228-py3-none-any.whl (1.3 MB)\n", + "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from monai) (1.23.5)\n", + "Requirement already satisfied: torch>=1.9 in /usr/local/lib/python3.10/dist-packages (from monai) (2.1.0+cu118)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (3.13.1)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (4.5.0)\n", + "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (1.12)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (3.2.1)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (3.1.2)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (2023.6.0)\n", + "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.9->monai) (2.1.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.9->monai) (2.1.3)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9->monai) (1.3.0)\n", + "Installing collected packages: monai\n", + "Successfully installed monai-1.3.0\n" + ] + } + ], + "source": [ + "# Installing required libraries\n", + "!pip install monai==1.3.0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "mETSBV2-EGSz" + }, + "outputs": [], + "source": [ + "# Import the required python types for type hinting\n", + "# Turn off the notebook warnings\n", + "\n", + "from typing import List, Tuple, Dict, Union, Callable, Iterable\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BnUTpIPsTtiF" + }, + "source": [ + "Next, we write a basic wrapper to make our code as deterministic as possible. As you probably know, deterministic programming will make most of our code generate the same outputs if it is run multiple times. In other words, random generators in a deterministic algorithm will create the same values every time they are called (which is why they are technically 'pseudorandom').\n", + "\n", + "* For more information about deterministic programming, read [here](https://www.geeksforgeeks.org/difference-between-deterministic-and-non-deterministic-algorithms/). \n", + "* For more information about wrappers in Python, read [here](https://www.geeksforgeeks.org/function-wrappers-in-python).\n", + "\n", + "If you look at the next cell, you will find steps that set up Python, PyTorch, and MONAI to work in a deterministic way. However, making an algorithm deterministic is more complicated than it seems, especially when coding in Google Colab. Google Colab is set up to assign you an actual graphic processing unit (GPU) every time you start a new session. Unfortunately, there is currently no way to make this assignment deterministic to the best of our knowledge. If hardware like the GPU is changed (particularly to a different type of GPU), results may be different, even if you use wrappers like what have here.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "1KPZtgXmG8A7" + }, + "outputs": [], + "source": [ + "# Set random seeds for deterministic programming\n", + "\n", + "import torch\n", + "import os\n", + "import numpy as np\n", + "import monai\n", + "import random\n", + "from functools import wraps\n", + "\n", + "def make_determinate(func):\n", + " \"\"\"\n", + " Makes a wrapper (decorator) for other functions to determine a specific\n", + " seed for Pytorch, Moani, and Numpy and enable deterministic programming.\n", + " \"\"\"\n", + " @wraps(func)\n", + " def wrapper(*args, **kwargs):\n", + " if 'random_seed' in kwargs:\n", + " random_seed = kwargs['random_seed']\n", + " else:\n", + " random_seed = 1000\n", + " np.random.seed(random_seed)\n", + " os.environ['PYTHONHASHSEED'] = str(random_seed)\n", + " random.seed(random_seed)\n", + " torch.manual_seed(random_seed)\n", + " torch.cuda.manual_seed(random_seed)\n", + " torch.backends.cudnn.deterministic = True\n", + " torch.backends.cudnn.benchmark = False\n", + " monai.utils.misc.set_determinism(seed=random_seed)\n", + " return func(*args, **kwargs)\n", + " return wrapper" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y4j-evwaWarU" + }, + "source": [ + "Next, let's check if a GPU is assigned to our session. Before running the following cell, make sure your Colab runtime is set to GPU. To do so, click on Runtime -> Change runtime type, and select GPU for the hardware accelerator. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "64N1yDtSygXq" + }, + "source": [ + "---\n", + "\n", + ">\n", + "**Question**: Why do we need to connect to a GPU? The answer is that training deep learning models often involves running processes that are highly computational-dependent. Running these processes on central processing units (CPUs) one after the other will take a lot of time. However, GPUs can run the deep learning computations in parallel, significantly reducing the time needed for training.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0fZ2UHJTHzTn", + "outputId": "af666018-633b-4d16-f68b-5400c068b33b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name, driver_version, memory.total [MiB]\n", + "NVIDIA A100-SXM4-40GB, 525.105.17, 40960 MiB\n" + ] + } + ], + "source": [ + "# Selecting the processor device. Make sure your colab runtype is set to GPU.\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Checking the GPU device\n", + "!nvidia-smi --query-gpu=gpu_name,driver_version,memory.total --format=csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GLI8lbr3zDxJ" + }, + "source": [ + "### Data collection\n", + "\n", + "Now that the environment is ready, we need to download some data to work on. For this chapter and the next one, we will work with a public Chest X-Ray (CXR) dataset that contains images for normal and pneumonia patients. The original dataset is found [here](https://data.mendeley.com/public-files/datasets/rscbjbr9sj/files/f12eaf6d-6023-432f-acc9-80c9d7393433/file_downloaded), but we already moved that data to a Google Drive location to speed up the download process. That is the source from where you will download the data in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q58zl4V4oOSE", + "outputId": "ebd24624-bda3-4cfd-ff72-ba164fcf1ed6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?export=download&confirm=pbef&id=1L8ox5fIwb_PijLcPEofQyhe3oGiYESO2\n", + "To: /content/chest_xray.zip\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.24G/1.24G [00:17<00:00, 71.4MB/s]\n" + ] + } + ], + "source": [ + "# Downloading the original data from a shortened version of the following link:\n", + "# https://data.mendeley.com/public-files/datasets/rscbjbr9sj/files/\n", + "# f12eaf6d-6023-432f-acc9-80c9d7393433/file_downloaded.\n", + "\n", + "# Remove \"sample data\" folder that colab always includes but we don't need it!\n", + "# Please be careful with the \"rm -rf\" command. If you accidentally run it or change the\n", + "# location it is pointing towards, you may remove important files from your\n", + "# notebook.\n", + "!rm -rf ./sample_data/\n", + "import gdown\n", + "\n", + "if not os.path.isdir('chest_xray'):\n", + " gdown.download(\n", + " \"https://drive.google.com/uc?export=download&confirm=pbef&id=1L8ox5fIwb_PijLcPEofQyhe3oGiYESO2\",\n", + " \"chest_xray.zip\",\n", + " quiet=False\n", + " )\n", + " !unzip -q chest_xray.zip\n", + "\n", + " os.remove('chest_xray.zip')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "msfHz9k50zpA" + }, + "source": [ + "The above cell will download the data and put it in the disk space Google Colab provided for our notebook. Feel free to click on the \"Files\" icon on the left side of the screen and take a look at the downloaded files. These files were downloaded as a zipped folder, unzipped, and put in a folder called \"chest_xray\" (image below).\n", + "\n", + "
![\"img1\"](\"https://i.ibb.co/qMRQfzs/img1.png\")
Figure 1. Accessing the downloaded dataset using the File viewer in Google Colab.
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mBVHrpq14ioy" + }, + "source": [ + "Now we can collect the paths to images available in this downloaded dataset. Fortunately, the original folder has the data split into training and test sets and also labeled based on their classes (i.e., normal vs. pneumonia). We will use the same data organization for the sake of this notebook. The following cell will save the paths to images into two different Python lists (one for the training set and one for the test set), along with their labels, either normal or pneumonia." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2ibWSg1yufyj", + "outputId": "409a83b3-6d1e-4aba-a86a-4798adc23411" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of images in the training set: 5232 --> Pneumonia: 3883 - Normal: 1349\n", + "Number of images in the test set: 624 --> Pneumonia: 390 - Normal: 234\n" + ] + } + ], + "source": [ + "# Collecting all image paths, along with their associated labels and sets\n", + "\n", + "def collect_imageinfo_list(images_dirpath: str) -> List:\n", + " \"\"\"\n", + " Return a list of information tuples for all images, where each information\n", + " tuple includes the associated path, label, and set for each image.\n", + " Parameters:\n", + " - images_dirpath (str): Path to the directory including all images.\n", + " - imageinfo_list (List): a list of (file_path, file_label, file_set)\n", + " for each image file.\n", + " \"\"\"\n", + " imageinfo_list = list()\n", + " for root, dirs, files in os.walk(images_dirpath):\n", + " for file in files:\n", + " if file.lower().endswith('.jpeg') or file.lower().endswith('.jpg'):\n", + " file_path = os.path.join(root, file)\n", + " dir_path = os.path.dirname(file_path)\n", + " file_label = dir_path.split('/')[-1]\n", + " file_set = os.path.dirname(dir_path).split('/')[-1]\n", + " imageinfo_list.append((file_path, file_label, file_set))\n", + " return imageinfo_list\n", + "\n", + "imageinfo_list = collect_imageinfo_list('chest_xray')\n", + "\n", + "train_imageinfo_list = [imageinfo for imageinfo in imageinfo_list\n", + " if imageinfo[2]=='train']\n", + "train_pneumonia_count = len([imageinfo for imageinfo in train_imageinfo_list\n", + " if imageinfo[1]=='PNEUMONIA'])\n", + "train_normal_count = len([imageinfo for imageinfo in train_imageinfo_list\n", + " if imageinfo[1]=='NORMAL'])\n", + "test_imageinfo_list = [imageinfo for imageinfo in imageinfo_list\n", + " if imageinfo[2]=='test']\n", + "test_pneumonia_count = len([imageinfo for imageinfo in test_imageinfo_list\n", + " if imageinfo[1]=='PNEUMONIA'])\n", + "test_normal_count = len([imageinfo for imageinfo in test_imageinfo_list\n", + " if imageinfo[1]=='NORMAL'])\n", + "\n", + "print(f'Number of images in the training set: {len(train_imageinfo_list)} --> \\\n", + "Pneumonia: {train_pneumonia_count} - Normal: {train_normal_count}')\n", + "print(f'Number of images in the test set: {len(test_imageinfo_list)} --> \\\n", + "Pneumonia: {test_pneumonia_count} - Normal: {test_normal_count}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jk6tvx6NVg1f" + }, + "source": [ + "### Data investigation\n", + "\n", + "It is always a good idea to look at your data before applying training, as there can often be 'surprises'. First, we can visualize some random images from the data to see how they look. The following cell will visualize nine random images and their associated labels." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675 + }, + "id": "gaDiAlTZzDWD", + "outputId": "9ee09453-d426-43bc-9a41-c79068f6fe19" + }, + "outputs": [ + { + "data": { + "image/png": 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znqfPfOYz+su//Et95Stf0SMe8Yi93nvddddpbGxMl19+uV7/+tfrpS99qd71rneF+ZKWgsRPf/rT+tznPqdLLrlE5513nt7znvfo//7f/6uf/exnYV3wM5/5TPX09GjdunWh781K6P6r6L9NoHHwwQfrrLPOWjGr8ZCHPETS0mLllYjvHvrQh6Y+Hxwc1JOe9CQ9+clP1ute9zpddtll+trXvrZsoXFMZ555pn75y1/qwgsv1MMf/vBlz3VCyE466SQddthh4edHP/qRfvKTn+wV0d8XetSjHqUNGzaEH98h4g8hz15Az33uc/WpT31Kf/mXf6l/+Zd/0be//e3g8O9L/eNK9Y7JXtZ3rF27Vr/85S/1jW98Q09/+tP1/e9/X6effrrOPvvscE29Xtepp56qa665punPs5/97L22r0X/Pam1TfYDR61tsq/9YzelRftIT3rSk7RmzRpt3rxZz3/+81UqlfTVr35VBxxwwLJre3p6dMEFF+gb3/iGfvGLX+zxuVu3bm1qcy644IL7pd1HHXWUjjnmmLAu9IorrtAznvEMFQqFZdfel+oOiKzGH7JW4/LLL9e6dev0+Mc/XtLSWofnPe95uvLKK7W4uLjfz7svdPbZZy/zYzo7O5XNLrnGi4uLGhkZUalU0hFHHLHPgVVc7fHYxz52n6oypKUsUjOwSlqqyujp6dGpp56ayowdd9xxKpVK+v73v79P7fuvpuZ5s1VKb33rW3XZZZfpve9977LvTj/9dOVyOV166aUrLgj//Oc/r7a2Nj3lKU/Z43v+9E//VCeffLLe/e5367zzzlOxWGx63WMe8xht2bJF1157bdM2QSyMetWrXqWTTz459V29XteLXvQiXXHFFXrrW9+6x3btjS6//HLVarXw/8EHH7zH6/d369mxsTF997vf1UUXXaS//du/DZ/fcsst+9fQP5A6Ojr0tKc9TU972tNUr9f1ile8Qp/85Cf1tre9TYceeqgOOeQQVSqVkMFYiVpb7v7PI7bJftOb3qSNGzfu8VrfJtt3sHv5y1+uj3/843rVq16l17/+9WFTAYhtsiF2Z7v44ovDZgpOZ5xxhr7xjW9oeHhYg4OD4XO2yT7ssMNWzPSxTfbZZ5+tN7/5zZqenl5RTz2YaH/bmcvlWosxW3S/0cc//nEdfvjhamtr07p163TEEUcEp7MZnX/++br44ot14YUX7nGHyGKxuFe7sy+0J9t05pln6gMf+IBe85rX6LrrrtOb3/zmptfdl+oOiCDr7W9/u37xi1+suDFOTIuLi7ryyiv1+Mc/XnfccUf4/IQTTtAHPvABffe739Vpp5224v0TExMpH6ajo0P9/f379O5m1Kwqo16v68Mf/rA+8YlP6I477kgFP3EJXTPq6uoKO1dC+1KVcfLJJ+vZz362LrroIl188cU65ZRT9MxnPlNnnnmmOjs7JS35UhMTE1q7dm3TZzxYqzL+22Q0JOmQQw7RWWedpU9+8pPauXNn6rvNmzfr3HPP1Xe+851lDoAk/dM//ZO+973v6S/+4i+0adOmvb7rb/7mbzQyMrIiGiktKYWPfOQjevvb377HLdBAHP/6r/9af/7nf576ee5zn6uTTz75fkElTzrpJD3pSU8KP3sLNIrFosbHx/f5+Rj8OPvwX7GPdbx9bjab1THHHCNpCa2WlrItP/nJT/Stb31r2f3j4+NaWFiQpIAC7U/fW7S6qbVN9gNDrW2yW9tkryY6/vjj9aQnPUmnnHKKHvKQh+wxyJD2L6uxNyJD6Y60U7Va3WMW8wUveIGGh4f1v//3/9bAwMCKDvt9qe5wOv/889Xb27tfWQ0yrVdeeWWqcuO5z32uJO3Vzzn//PNTVRn3dTfIZlUZ7373u/Xa175Wj3vc43TZZZfpW9/6lq655hodddRR96kqY2/EmSM/+clP9KpXvUr33nuvXvKSl+i4444LZe71el1r165dsSrjHe94xx/07gea/ltlNKSlBUCXXnqpbrrpJh111FGp7y6++GLdeOONesUrXqGrr746ZC6+9a1v6etf/3pYtLMvdPrpp+voo4/WBz/4Qb3yla9Ue3t70+ue8Yxn6BnPeMYen3X55Zfr2GOP1ebNm5t+//SnP12vfvWrdf311+uRj3xk+PyDH/zgstRoNptdEcnYXzruuOP0ne98Rx/84Ae1ceNGHXTQQXs8XKdcLutxj3uc3ve+92l+fl4HHHCAvv3tb6eQiweKXvrSl2p0dFRPeMITtGnTJt1111366Ec/qmOPPTYo1je84Q36xje+oac+9ak655xzdNxxx2l6elq/+c1v9JWvfEV33nmnBgcHlc/n9dCHPlRf/OIXdfjhh6u/v19HH330ivXwLVr95Ntkv/GNb1wxq7Gv22R///vf19VXX62XvvSlK163P9tkv/zlL5fU2Cb705/+9IqOwkrbZO9pjdgDRZdeeqle+tKX6vjjj9fLXvYySUuAkNNznvMcHXbYYXr3u98dQArfJnv9+vX63e9+p0suuUS/+93v9O///u97zTqyTfZf/MVf6Oyzz9b/+T//J8h8bBec2Cb7iU98YshC33DDDfrxj3+s888/X1Jjm+x7771X5513nrZs2aLrrrtOb3rTm7Rjxw596EMfCttkv/zlL9eznvWs4BABfrTovw9dcMEF+tCHPqSLLroolL/8IUT530033bTMF6hWq7rnnnv2iPZv2bJFJ510kq699lq9/OUvX3Gh92Me8xj19vbqiiuu0Fve8pamjjFBebO1KRBB1oUXXpgqUd4TXX755Vq7dq0+/vGPL/vuX/7lX/TVr35V//RP/9Q0AJCWwFjPCu8tk/KHVCd85Stf0eMf//hlayrGx8dTmeUHik488USdeOKJ+ru/+ztdccUVeuELX6grr7xSL33pS3XIIYfoO9/5jk466aQVxwh6UFVm/NH2u7qPtNJ2dEmytMWYpKZbys3OziYXX3xxctxxxyXFYjEpFArJIx/5yORDH/pQMjc3t+z6Aw88MPnTP/3Tpm347Gc/m9rebE/buTn5dnc///nPE0nJ2972thWvv/POOxNJyWte85okSRrb4DX7yeVye3z3/tCNN96YPO5xj0vy+XwiKWx1u6dt+LZt25Y861nPSnp7e5Oenp7kOc95TrJ9+/Zl28WutL1ts7E++eST97o121e+8pXktNNOS9auXZt0dHQkW7ZsSc4777xkx44dqeumpqaSN73pTcmhhx6adHR0JIODg8mjH/3o5P3vf39q/q+77rrkuOOOSzo6Olpb3f43ptY22a1tsp1a22T/z6Q9+RNOK9n4Cy+8MJGUPPzhD/+Dt7fdtWtX0tHRkfzZn/1Zsri4mPru4osvTiQlX/va18JnzbaUvfbaa5O3v/3tye9///vwWbMtd9/1rnclkpK/+Zu/WdaOq666Kslms8mTn/zkvfZjfHw86e3tTY499ti9bm9brVaT7u7u5CUveUnT/v/4xz9OJCVXXnll0+//EHre856X9Pb2Lvt8T77aIx/5yOSUU05JffalL31p2Raxe9reNiZ03Z5odHQ0qdfrqc9+97vfJZKSj33sY0mSLM2vpORNb3rTsvvn5+eTsbGx8P8JJ5yQPPzhD9/jO/+raNUGGi1qUYtadF8pdjDOPffcpKurK9m+fXuSJMuN61/8xV8kkpJbb711xWfecsstiaTkpS99afis2b7mkpJzzz13mVPtRnBubi4ZGBhI3ve+9yX1ej3ZvHlz8pa3vKVp25IkSYaHh5P29vZgmJIkSUZGRpZ9liQPnkDjBz/4wR7vr9VqydDQUDDsH/rQh8J3KwUaD33oQ5c955hjjkme9axn7fFdb3/725NcLpd885vfXPGaY445JnnKU54SgkZ+vvOd7ySSkssuuyxJklagsZrovgYaY2NjSU9PTyKpaaCxadOm5NJLL23640QAcNJJJyXvfe97k49+9KPJC17wgkRSctppp6UCkGaBRjNq5vwuLCwkz372sxNJyeMe97jkwx/+cHLJJZckL37xi5NsNpscddRRyc6dO5f1o1nA5MDnngKNK6+8clmw5LS4uJisWbNm2dkk94Xe9773BZD2iiuuSL7xjW80bZvT3/7t3yaSknPOOSe55JJLkle/+tVJf39/cvDBBz+ggcbFF1+cHHbYYclf//VfJ5/85CeT97///ckRRxyRlMvl5Pbbbw/XnXfeeYmk5PTTT08uvvji5GMf+1hy/vnnJxs3bkz15xWveEWSyWSSd77znckXvvCF5Lvf/e6+Dtv9Tv/tSqda1KIWtegPpbe+9a269NJL9Z73vKfprnL3dZvsd73rXVpcXNRvf/tbvetd79qvbbKPP/74/d4m2999+eWX65WvfOWK9+4rDQ0NpRZIlkollUqlP/h5K22TfdFFF+nKK69ctsDxgd4m+0tf+pJOP/10HXDAATrttNP03Oc+N7VByC233KJf//rXyxZ8Qg/WBZkteuCot7dXF1xwwYrrFbZt27biOk0vBXrLW96irVu36mMf+5je8Y53aGFhQQcddJAuuugi/c3f/M1e14zsK+VyOX3pS1/S5z//eX3605/W2972Ns3NzemQQw7R29/+dr3uda/b500ZKB3bm1xefvnl6urqWnGb1Ww2qz/90z/V5ZdfrpGRkX1aeL03esUrXqFf/vKX+ud//mddfPHFOvDAA/d6GjubZ1xxxRX64he/qEc+8pH6t3/7N73xjW+8z+3ZE5188sn66U9/qiuvvFK7du1ST0+Pjj/+eF1++eUpHflP//RPOu644/TJT35Sb37zm9XW1qatW7fqrLPO0kknnRSu+9u//Vvdddddet/73qepqSmdfPLJesITnvCA9mFF+qOFOC1qUYta9EemZkjmOeecE7IaMYr3yU9+MpGUfO5zn1vxmZRUfupTnwqfNSsL5NThD3zgA6nPY7Tthz/8YSIpOeWUU1Kp8GYI46Mf/egVyyolJbfddlu49g/NaMTZmb0h9nvLaDR7/xOf+MQkn88nf/u3f5v8y7/8S/Ltb387ufrqqx/wEswkWSqv/cY3vpG8/OUvT7Zu3ZpISl784heH7zs7O5NTTz01ueaaa5r+3HXXXUmStDIaLWpRi1qUJK2MRota1KIWpai1TfaeqbVNdmub7Ba1qEUt2ldqBRotalGLWmTk22QfeOCBqd1b2Cb705/+dNhVyIltss8777x93ib7jDPO0Kc+9akVD+9im+xf/OIX+7xNdrMd7D796U/r8ssvv8+Bhqfn94VW2zbZXrKx0jbZF154ob71rW/pyU9+cur+8fFxlUoltbW1tbbJblGLWtQitQKNFrWoRS1aRq1tslvbZLe2yW5Ri1rUovtOrUCjRS1qUYsiOvTQQ3XWWWfpc5/73LLvSqWSvvvd7+oTn/iELrvsMr3hDW9QkiQ68sgj9aEPfUiveMUrVgwYmtHrX/96nXPOObr88svDYXb7S9dff71uvPFGve1tb1vxmqc97Wl69atfHU4Nh/7+7/9+2bW5XO5+CzQ++MEP6mUve5ne+ta3qlar6eyzz95joCEtHUr46le/Wh//+MeVJIlOO+00ffOb39zrqe33lc466yxdcskl+sQnPqHx8XGtX79ez3ve83ThhReGhbiFQkE/+MEP9O53v1tf/vKX9fnPf17lclmHH364LrroIvX09ITnffrTn9arX/1qveY1r9Hc3Jze/va3twKNFrWoRf+jKJPE+ekWtahFLWpRi1rUoha1qEUtuo+0zxmN5z3vecpkMuEI9vg3RNySyWTC3/V6XUmSqF6vp65v9ix+FhcXtbCwoIWFBSVJosXFRc3Pz2t2dlbz8/Oan5/XwsKC5ufnNTc3p7m5uXDtg40ymYyy2aw6OztVLBbV09OjYrGozs5OdXV1KZ/Pq62tTb29vdq4caP6+vpUKpXU2dmpzs5OdXR0qL29XblcLiwwzGazSpIkPDubzSqTyaizs1OlUkl9fX1qb29XW1tbGP+5uTnV6/XwnIWFhTCns7OzYc4WFhaUy+U0NzcXtrHkHblcTqVSKbyvXq+rWq1qdnY2zJckLS4uKpvNhnf7wsiFhYVw/eLioqanp7W4uBjePTs7q4mJCU1MTIS6aH/W4uJiaHOtVgvP4u9araapqSnNzMxobGxM09PTy/j0wUxtbW3K5XLKZrOpv7PZrDo6OtTW1qa77rrrj93MFrWoRS1qUYta1KI90j4HGrlcTkmShN84pfG+zu5Q8j3OIc4iTh8Oowck/HAP1ywsLKitrU3t7e2pQMMDkNnZ2VTA8ccOOjKZjDo6OsJPoVAITmMulwuft7W1KZvNqlqtaufOnWHc4iCC/yWlnHfGKpPJhDGYnZ1Vd3e3isWiMplMWGCZy+XU2dmptrY2zc7OqlqtSlrar5/7eBZBhrexra1Ni4uLmpub0/z8vGZmZsI8MFfMNe2mrQQHSZJofn4+zDPtn5ubU7Va1fj4uGZmZkJ7GUv6CsELc3NzoX183tHRoXq9Hj5/sAca8AR8EQcabW1tamtrCzzToha1qEUtalGLWvRgp30ONDo6OoIzmcvlUgGCZzFW2tIPp1NaCihwPHFM+dyDDNB4/gYxx/Hi//b2drW3t6uzszOV4Zifnw9I+X81tbe3q7u7W/l8PmQzyCS4E9ne3h7+lpbQ/pGRkVSbPdDgOgI+nHjGlixFrVZTkiSanZ0NmROyIgSM+Xw+zGmSJOro6AiZD+ZkcXFR1Wo1NddkHvjN5/Stvb09tNV5gyDGM1w8o1KpaHJyUtVqNQQq3i+CL9pKAELWZmFhQR0dHaE9nZ2dSpJE+Xw+BEMPNqJ/8DN9gT/43wOMVqDRoha16H8yffGLX9T09LSmpqZ011136brrrtPU1JQWFxeD3QO0AZxr5pdgmzo6OrR582YdfPDBuvPOO7Vr1y5VKpVwjduqxcVFlUqlUJEwMDCgQqGQAubwSwC3ANscSEXP0y63AQB73g8HovL5vDZt2qSuri5JUldXlxYXF4MvgX2o1Wqq1Wopuw/Nzc2po6MjPJf2eZ/dL3HADyCM/nI/topqlHq9rrm5ufBMQEa/l6qN6enp1DwBPFLBwnu4hjVwDlp6BQz84AAjvgNzwLX4osyJt9cBUe6nf/5MnjEzM6M77rhDMzMzqaoc3jk/P69araZqtRp8NR9rwNKOjg51d3erq6tLj3/841Wr1XTooYfqq1/9qkZGRkIVCe/1A1SdAPrxuZtRs8/xTTo6OvQnf/Inyufzuvnmm7V9+/YU4Fyv1zU5Odn0uU77HGi0tbUFoa3X68t+Qz5RoNpx6YwzvA+SI+IIGpNF58hskNEATUdImUwCDS/RiUu3HgjKZrMqlUrq6elJBREwkf/vY4tzjoM5Nzen4eHhIJye1ens7Ez1wxnJS5rI9uRyOdVqtRCUdXR0BEbnZ2ZmRnNzc6pUKiHQQJh4Nn3o7OwMjNjZ2Rna4RkXaWn3GAIVMi0IFgHO9PS0RkdHNT4+HjIpKBQPpPgb/nLBalaWR3tLpVLoIwrrj030kR/4AUXHT3t7u7q6utTV1RX+J/hoUYta1KL/iYQvMj8/r0qlokKhEOw7zrvbL2glR6uzszOVBQfMjO0qz6jVauE9Q0NDqVJlfCJ8lbm5uWVArFcl8Dm2D3DVA5CY5ubmNDU1FeyBg4C0PZfLqVgsqqurKwXqOrhFkMG9BACMG/3A4azX66HvVEX4u2Og2AMX+ukBj5fXx9UZBBoEALSHQCqbzaYChXiOPZiRtKw9zRzvuDrHA0wHOv35Pj9zc3Patm2bZmdnlcvlUvzHuPt84YN536SlIKNUKimfz+sJT3iChoaGtHXrVn3nO9/R6Oho8G99zGIwnfn18Y8D3/hvHz9pSc4e9ahHqbu7W9dff73GxsYkSfPz88ueuTfaZ48lFjgEigbRoRg98AFs1ikmi3tRFlKjzp8J5n8cMpzoOOOB0pifn1dXV1cIOtyx9ozM/UUdHR0aGBgIB285Ks1vz25wjUfqXk6VJElw/CmD6urqSgVjPvbMEX+7UHgJEUEFAZgHdIwVkXzczpg8GIBw8IvFoiqVSgj4/F3ValVTU1OampoKh3/FvOPZmj0pK3jE15E48lEqlZQkiUZGRv7oJVTNshcgTnEwQSbKv+PvFrWoRS36n0huw/nd1dWVAsa8xBq7UK/X9YhHPEJHH320arWabrrpJt10003q6upSsVhMlWmjY7EX3I+fgTOZzWZVqVQ0MzMT1la6TaL6wysHmmVYPEAi2HBnkTZwX6VSUU9PT7AP7uTjFwHQkuHwLIv7XO6wxvbUMy9uezwA8HWY9JMfnis1/MT5+fmUXXffjwCDuXCw2v2auJzcfZAYuN7beuIYFJcUgjP8FcjHyv3UxcVF7dy5U9VqdVm5N33j/7a2NnV1dSmbzQZfjLHo7OxUoVBQd3e3zjjjDA0PD2twcFC//vWvdffdd2t+fl7VanWZn+f990yUBxc+3vEYxPzY1tamP/mTP1FfX5/+3//7f5qYmEglG7gvDs5Wov1ao+GN85p8ZwhfwyEpFcE1K5/ykp9YYHge3/mCYRcIkH6UAM8D+fdF4ygJX+NxX53PTCajUqmk/v7+kMb0hbswFiiAIwqxg+5IA+NTr9c1MTGhWq2mQqEQFoq3tbUpn8+nsiEol0wmExQLwpvL5UL7QFCkdCaJlCSBDgIVl0bFwZ/PP0HexMRESmHMzMwEFGp6ejpkWUi7Oi85f3g74+DQsy0ER/wPX3R1dQVlOzw8/EcppfNSOVLPHmjAJ46u+XWeWt1X4W5Ri1rUov+OxCYklNpiw93BQrdiB3O5nE444QTNzs7qt7/9rU477TQNDg7q5ptvVrlcVq1WC8CnO20Qfzvyj71KkkTj4+OqVquh5MWvp/Q8Lr3xZzsC7fbPn+O2vVKpBNvm5ezz8/PBRsdZDoIBR+d5Z5xpj4Fl97mSJAkAoYOdXCulnXX339yXdBvvfqUHE/5sSanyffdx+D/2E1b6m3e6j+H+aBxkxn6sZ4p27NihSqWi9vb2VBVOPH4+zpSNzc7OamZmJvgqg4ODeuYzn6mJiQkVi0Xt3LlTN998c/CdvMojztS47xj7Oe57x5/FwO2xxx6r3t5eff/73w+BDX33YOV+DzSciRhoOhSnl9yR9mjTGxsLsguKp8R4pgsoz/fMRixMXhcHI3oJDcEG6znigGNfndFcLqeenh6Vy+XgnHvEms1mw85SHR0dqbGJxzdWNp5FIOBCmeXzeRUKBc3Oziqfz6eCGIIFHG3G1cuvnOIaQldAKEkUBXOB4iQa9yCRhfmg8+3t7arVapqZmQk1tHEw4H2GPGpfCZWAhxhn+IRxY9eqxcVFDQwMaH5+/r/0pF76SZDhGYtmwUWz7+mfK/4WtahFLfqfSvX6Uv0/6+/Q+wAycVm3Zzd27typO++8U7fffrs2b96su+++W6VSSbt37w7ZAEdt3QfhM+zhzMyMurq6gp5eWFgIoGA+nw+bsTgYK2lZjX+cqcZR9fe5z5XL5VStVoNziw31dSKsU3Sb6b5aMxCPAASQ1DMT9DGTyaTWcZLdwAdwB5y2c537hG7TkiQJQCR2n/mK75ekmZmZkA1wm+/lTu5suz/XLLPhfpf7oFzv4LfPy8LCgnbs2KGJiYnAb3EfnV/cH3bgsaenR7lcTuvXr9fpp58uaWntTbVa1fXXX6/5+fmw7sT7Ee+06qBv/F2cYYmzGfDDEUccod7eXl177bWamZkJbWY+mN/9oX0ONAqFQtihyCNWBMSdWR/kOHqF4jp8Z5QYafd6OBxuUoxMJALiyLpnXEinIoAsHGbBESVDce1enJ5yyuVyKpfLYbvXbDabchTJIOTz+RQyTY2hMw0IRLPx8uwGQdTExIQqlUrYMhelViqVljG1O6eeDfJI2IXLo28UjbfL5wF+IEDhmYuLi6pUKimEAKcaFIp3+2/6vVJWw3mN5/ozyO4gDI4i5XI5DQ4Oql7ftwVM94WYSw8uMILxwm5KouJr3fg0y4C1qEUtatH/NCLzICksNoY8K44zB1iGDXrc4x6nAw88UJlMRl/96lc1ODiorq6ukGGP0e0Y2ZcatoZSZAc5sdFTU1Oanp5WV1eXCoVCsPtuz7yCIF6MjK1woIp3YCNYXFwsFlPb2dNm9zE80IntqKPjsVMJOQCMXfX7KVvH1/GsDcGI3+vPIGhwpx4fxn1Lxg5w2f07B5Xx5WJaqXzK/8aBh3xOvFyqVqtpx44dmpycDOMaO/NxJZDPDf4gc3fIIYfohBNOUG9vbyg1v+aaa9TR0RE29XHQ3INGJ3wf94Niikup+H/Lli0aHBzUD3/4wxDEeeDEtfy/efPmZc9uRvscaBxzzDHasWOHRkZGwrkHTo66ehQsaZkQSsvXfMRBB5+RqfBokQDHOx3XbOKoOdM6Eu+BRldXV0Dh42DDGRtnGgVG6sujSM7E8GjVhc8XflNWhtLieXFGiD7HEaikgAIQ4VcqFXV3d6u9vV2FQkGSwoJtRz1gIMYMhiSIi8ulAsP8/31AsTCXlKWRMZqZmdHs7GwKwaEPKBRnXJ9bafl6HxcoL/Vy3qB9zlfMGZmvjo4O9ff3q16vh51F7k+CN+IAotnuUZ7lwBi6AkJuPFD0tP5qJF9zIqWNuY+TgxiMKWMjNfQNBgx+zGQyoUzOecwXZsI/bW1t4dwVSTrooIN05JFHqlwuK5fLhWxYuVzW4OBgan7IhnqpBfMoNdA/yPWflydg1PP5vNasWaNCoRDkSFLQJ2zhzAYBudzSBg/0cXp6OrzfZY6a4e7u7gAIsKBzcnJSIyMj6u3tVX9/fyqbCY+Wy+WAHOMM4GB5eQG6tVwuK5vNamxsLKwv80w1m3Mwd7SJMaPdrONyp8SdiSRJQskB22KPjo7qrrvu0sTERFgDBvjjpSB8ls1mU9lEnDRvC3OHPiJzHDtwrp9pY4wIu/PC37R/cXExZIGxoVzHvDNO6AbmHZvkNhDeLRQKWrduncrlckonFQoFHXLIIfrYxz6278L7IKFSqRQcMcbEy1JiJF1Kr5e87bbbtLi4qJ6enrC7D0i6y7ODnl5dAbnvUavVlMlklM/nJaWdVnZ/otKBHSDRc47U+25KcQmXl1ZzHRUEXoYLreRo+/tiAM8rByjncadU0jLfzx1z9ADyNTs7G+Q1Bn8JFgqFQqpywecR/s9ms8uCyjgrw/W+2+jeKlO8XzzTfyD3U9Gr27dvV61WW+an+G/a7zrPry8UCioWi3rIQx6irVu3qru7W+VyWTt27NB3vvOd8BwAa8bJd1X19T+Mlbc/DjZ8zNwHW7t2rdavX6//+I//CL6bB8Ye9PH/3XffvcfxhfY50Ni8ebMGBwc1OjqqoaEh7dq1S1NTUykEgEFEWfIZgxMvLHJCcTaLMJ3xPCUK8+J0xguJIFbJx1EvwQLOLw6yL4bmPV7ziDMT/xQKhVQ5jKMQjiTEThT9cMeUiXSn0hnEFR7IBmVM4+PjgTEJOIiacYj8uY5i+FZx3n+Pxjs6OlLRNUqJNkDMB8YAJeiRdrPInz55wOcOjSM1cdDpY8VYci8OHI4XqMR9JeYqDihc+Xuma6UghHmJA4zYSVrN5MbBjR2GlDIEqbFmiTn3/3kGQYU7iYwhfIyS90WWUiOLKC05fPfcc4+6urp06KGHhk0XMHAAElLDEZYacoiD4ps+4Pz5eifXccwxgcSuXbu0bt06FYvFZQ5NsVgMWVmc8Fjpe1a2ra0tbMFJ9s4PO+3o6AhOKsbZZZyx8x12HAiKkTAWMWYyGY2OjmpsbCyMn9ek+9zEa/x8XJJkaWEkAaKk1D2xjq7VahoaGtLc3Fy4j3F3nUpQARFoOEDhvMJcYi/IirsTGOsgxtHnDx5l/AC8fP69XMYNuztDbhPiHejQdYwnPOHAEm3NZDKampraX/F9UBABBoFojOIzPoyvgxrZbFbDw8P62c9+phe/+MU644wzdMcdd6TWC8ZOuNQofYlBHtdjlAXn8/llyDbtAoDjPgBIfIhsNhvO2+K9DuDCH65/CObn5+dVLpeDT0N/6FNcLeFZGucL9BuBggMj3icPQPzZ8/PzKWDIN3txMJI+eODi48b1rDmJZSxuB7yQyWQCoOHzGQcdbi+8HbFuc5qbm9PExISGh4dDmZ0/y517vkP3xeNHJcqxxx6rgYEBdXd3a+3atdq1a5euvvpqVSqVwJf0i+c5kOVz7GXuMfjrGSmIZ6xZs0aHHXaYfvaznwWQgnegdyHkIAZB90T7HGiMj48rn89r/fr16u/v14YNGzQ0NKTdu3drcnIyNM6zCR4FesDhAx6TGzopvfUbQubP8mfU6/WQqQDxkBSMswcZsdFk+7ZisRgQPI8aJaWUvZe9FAqFVPbAHU2EzY2SGwoIJeNBgI+jozTurLiBpo0YYd8Cr6OjI6Ap/I0w00fKyBzVwJjH62NQZAi3KyNH/Fz5O8pDX9wBicnHLOYhJxwXHx/4yMcYZwqnEPT4D932FqTdnRXGwgOOuJzOr/OSKuYe58HlxA3Oag42PMB0xIRxLBQKIdOHruB7V3woTM9wuMMRGxWvoYaHcfyQO4KN7u5urVu3LjwXPQBi5A49bfTsX4yGu8MNkV3x4GlhYUHDw8NaWFhQuVwOKLYbkZmZmRBkeNYTPemO9+LiYtj6s1KppOSGffSr1Wpqu+q4HNXb5iCD1AiuOH9nfn5eY2NjKZTdHR7Xu45Eo8PcqXcAyzO/mUwm3EsWoFaraXh4ONSr+3c4TK6PHQ2OkWIo5s848xijvK6jeac/ywMQAgKXZ3Sml7byQ99jm8rY0BZ0KXwtKQSL3d3dKTmQtGoDDbLTk5OTQQ7cX4htic/RPffco6GhIc3Pz+sLX/iCzjnnHE1NTenuu+/WzMzMMnvlOghZaNYeruX8BDKRyIHPj9TwSfBVOEcim11aa9jb27sMeHL9wrXwYy63tGajVqupt7c3+CPuWKIf3ClnXNyXQo/6tVzH+/17vuM57jMBeLiPQfDrMuSBkDvOvMv7Eesp9An+CM+MAwcPTnhOs/lET3n7FheX1uOMjIyENZ4umz6GABQAEnF2I5vNhlL3hz3sYSqXy+ru7tbWrVs1Njama665RiMjI8HGNMvQ+3jQDtdR0pIvms/nw1wB2vih1rlcTuvWrdMjHvEI/fCHPwx+vMuNX+tj0my970q0z4HG0NBQWHTc1dWlUqmkcrmszZs3a2RkRLt379bo6GiI6p05mQgE2IMJRyAcvY6RSz7nO4+qGBQmGFQBJ9KjzTiN7Q4zhLBQTjUzM7MMLUUhgMD6lqSONLlDIzVQqDiY8GyPpyu5zh1Nd7bpvwdj9MH77II4Ozur8fHxFDJH2VWcnoR4jjvErlgw4I7m0D8nMiX004WaMYoFid8eYDlv0dc4bYxwIKwoOOdFjNa+Eu3G4fLMhSPUOG84yB5ceGDhwZmjXO5EOsLWLCBbTeRzG4+H1xlL6TS/jwdyHstGM0fR/3cHkHd4RpKg4vbbb1cmk1Fvb28wDGQDQMsoIaKd8L8/P9Yx3M81GH7ng9nZWY2OjmpxcWnzAhyUXC6nubm5YIC8zTjijKOvtaJ+3B15d1JzuaX99nt7e4OOoI0AEpRQcD28jc5ZXFzU1NRU2KjC5dhROAdFfI4JYLAJILSMjyOTHmgg25OTkxodHVW9Xk+VNpE1gNcoOXPnhn6hKxwBdZTWM9PMO4Erf9Nn+sbYeIDkSDVAg2+9Tt/dKeW5DjzwQ9t9PBxlTZIkLJiOwaDVSpw/gDxJWuZLNPMrkiTRD37wg6BXZmdn9a1vfUsHHnhgKA30cYnvh3yu3UdhvvE52GkydkjRYX6fB6lkAPr6+kJ2w58RA4tOc3NzGh8fV71eD4cFx+10kNX9MQ/6XU68jXFgEY+R9w9Zdj/N++L993ejL10Xeb/RQYyXlxC5b7ESkIktoE8xmOsZwCRJwvligCgO7PDsuJ2MoZehw1uspT366KNDqelBBx2kyclJffe739WuXbtC39FZ3sfYL3ZKkiSATDHQ7bwCv69Zs0aPfvSj9f3vfz8AD54BjHnGx6qZnlqJ9lnboKxqtVo4B6KtbWl71Q0bNmjt2rWanZ3VyMiIhoeHQ1mVR6P8OBMzOP4bZmDi3dFy58ENAYYVJnUj4saKn7a2trBwmn2wnSnr9Xqoa6YfTDLXuPGlX0SV3lefdEcnYAA3ADBx/NNsHOJ0Me0kAnbDns1mQ6qO9rHvs7QkXNPT05qenk45KswdTlq5XE6dE+LRLUbPd5ZyoXREk2tjp9Kdk7gPccARI4huJDzQ4pnstT4zM5Pi7ZWQDadcLhf2Wy8UCqmT3t3hZU7jLA9z686K80TMMzzbHcNYwa1G8r57lsLLyeIAwUuRpHT5oZTOVuBkOsIUOw/cz/UELhhmgo2tW7dq/fr14VrfStONJXogRrXc4XPAA+PDtV7uhHHksE7ADNrusk6/KVUg44EsEfB2dnamEEQPaGdmZgJAkM02DuPke9aGuG5CTrPZrGq1mqanpzU+Pr6s/Aq94I6LlA5AYqfNa47ps1/nWRlQRg6SIlubzWZDHT9lF/CX8wL6J9aXzmfo7manKMO3zlvujMU6JUYjeT7vbrZJiCPHbuDRCzFq7s6so7sg7W4fVivB82QgpPQZEE6xzXA94vzi4+o8FgcucZmPj7sDpPV6PWQeCTZcdtwH8iAXAv1H/rE3tNP75PwgKeys6JkVdIbU8Js8+OR5PMcdfuSS9jlAGftuyIjUWDNB+RXAXJIkwc+QGiWyMzMzoT/xfMDrPAfZYewAcx0Q5h6XoVg/eWBEP5gj9IfrN/fPYvn193gQ4+/JZpcA6mOOOUa9vb1aWFjQ5s2bNTk5qf/4j//Qzp07U7qbeYvLzNz3df53/vT5pb1kv7q6urR+/Xodf/zx+slPfqLR0dEwbg50u1z52HGdy8qeaJ8DDRgORw2nAMSM7Vs3b96szZs3hxQ/W7DVarWA3MQRKhPFQMYpGmcCyJncMx2OSGEkvI6Tn3w+r1KppHq9rsHBQS0uLmpyclLT09OplLsreDcScVscVfT73Onkt5Q+RM8VpDNo7HAjcFwXByGUfxFUuDKgjII+5PN5lcvlEGQ5WsCYOUrkhhWFx/u9PIsaaZz6eG4kBUPHOMfRuY+xBxlxn50nXBj4PDbSKKl4LQl98C39UDJJslQr3t3drd7e3hCc+rh7AOlt4XP/HyH2MzI84GAOIH9XM0O62sjXq/gYeQkR8+hZPjcGvtYHBxij44qWsfLxdAfW+Ywx5vuZmRnddddd6ujo0Pr164Mh8yDcA0dkBH5zFItnuoFz/QI5Ok6WgFpjaUluyDwQLMeZL87LyWSW1k3Ar15u5M49O+K5UcfRpwST8aIEAl2IEZ6dnU3pQ3eofWyZ21gufd6wD27AydLguGB8a7WaxsbGNDc3F0pFkGGeVa1WVSgUUge6MW6uOwhC0Ht87kGWbxca6yHXlfSJfjhg5jrNgRr4moCZufK6ap7jQXrseMLrnuGu15c2vuDcBdc1q5H87AGcvXgupHQ2Pbad2EjOz/ATvCHX2e6su+1wfuA9/jeBv+s8KX1SNb6D63nsiQdUOOr4XnEGLPYj+L5YLKZ2vIoBU+dZskQOmKEP3IFu5qPwbHQHbZOU+rutrU3d3d2qVqspX8szEowt17tf5f6EB2nuD7pOcvsQU8wj/DDulUolBIyxj9bsGXEAwmfopLa2Nj3iEY9Qd3e3ZmZmtHnz5nCuy7333pvaGCMGxDwwix1899/cJsZZKJ67efNmPfzhD9fdd9+toaEhrV27VjMzM0Gfo+N5pgdgHnzsqx7Zr0AjRlYymUxY2ES6Dyezs7NTg4ODoRYRJmbySO/4QNRqtVBLHKPYcWe9Da7AXfGGTlqJDr/dGa7VaiqXyyqXy4GJpqenU5kOf6a3zyfXES13LuMfJ5+oOIjw/rvjHQdZMGM8L6CZONHOKHNzc8sQTA6DcaQ2Nqoe8MC4GGkcl8HBQXV2dmpubk6Tk5OhnAIBjBVdrFw8WIiDOsjRIa5FQRAgMD4xn3kZU6lUWoZyOtKdzWbV09Oj3t7eMFbOV+6IeNtQio4m4NCALDhPNAtS3BGJg87VSqBRjqLF4yWlt310HYCRAfVx/kEZxwbX75UaPAy6hoEEuWZeFxYWwh77AwMDqQwkz3Jj64ECbeJ37ETG8gwv45Qgc21tS2tCxsbGQsBbLBbDScY+howd5Qq+gYWPqdQAdFyvtrUtLdzESLuecNRQUnAkPDCMS1A9gMAxwmHxxdDUDXvbfFyy2aUsGEEXDtTExIQmJiZSAQFgAk4ZW4C3tbWFcgHmz8eD8XInIg4mmgWHEIbdHSHnj7jMwm0Y1+Tz+VC+4yVwjtzHWTz+9jI9Alzewzu8jMVBr9VGznMxxXZaSpdkuz0lQz0yMhJKId1R8xI47ncgM9bL0vIqAmQIAA790sye8jznZ3f+ceDh57a2ttQ6Q2SI+5MkUaVS0cLCQsjCe/aXchwPpJBZt1nOR5DrMew/AY9Xsvh8YYvJMpZKpWUZQR9PB2N9PJ3Xfa1cXDXhvkQz/ohBEJ49OzsbtiZ2oMV/S2nQxnkt7ru0JKN9fX167GMfGw4y3rp1q+bm5sI6ZwJC+sFaYcai2ZoI93udPDhzfyFJEm3cuFFHHHGExsbG9Lvf/S4Er6VSKQBZtVpNExMTIYtDHzOZTOqzZrqwGe1XoaYzoqfHJIVAY25uTtPT06lyAhxddmZi+0iur1armpmZ0eTkZGqXomZpGUc2XUhdwbvj4FG0Iz75fD7UH4M8SA3EdcOGDcrllraQnJqaCovDfZF5GERTCjC6tyWebEeyY6eI2mLa6fchaPTJkSwXqHq9UeeMgvMgw7/3yBR0E8fcgzacY3bjoT1+P2t3urq6whoXlDdRMkY+joy9r80QqVhpOTrpSswDQ59v0EjQsGKxGBwP340Hvunt7dXg4KDy+XzqcCIUMHztjocj6P55bLw8QInvRdlyjzstzeRhtZEbDwcIKGPA4fXgLc5mMK+ODuI0xhmQGGXm3fA2vOSoE9fBDzt27FBfX5/6+/vV3t4etpWVFJwAD76RL3cYPGD2lL+3ATnyYMnnnyzH/Py8isViCgjA6EqNrIA7q7TVA4gkSVStVpXNNjKVOPMASG5g/fBLPyyNH9cbPtf0xwN9z2DEIBBzwHsdLKCfbM0rLZXAuAONwZSk3bt3p5wAjCTjE+tXNhNxHcS1cRABubF1RwMnjDn27Wk928v3cUbT+dEzVXyGY+c2wcGMOABmzSG7mq1WwGJubi4AlTiusS1ZyaF0W9fb26ve3l5NTU2FLeGbIbd+rwNwziPoDPiYv13mPYCPs4jNApxmzhyONbzOVrnY5mq1mtqRjawYWT8vF5PS52rxTteJziPIoAN3zpuMnzv7Pk70yzN1+IkODnJNs0yFA5/4bZ4t4TrPGDngEesX5xX0Cgcuxn5WzAux/MTvzOVyYT1GoVDQ2rVrNTQ0pHXr1mn9+vX67W9/q6OOOkrXX3990IPoJNb+sn0u7Y9BUdrl491MBmjv2rVrtXXrVs3OzupXv/pVAH0caJmfn1dfX596e3tVqVTCZk/9/f164hOfqKuuuipUwTQL9pvRPgcanmpmUvicCXCD6Z2enZ0NnUHQYye8s7NTPT09IavhFEeePoh7ygh4OxxdzGazqZpkVwa0r1arBeFlxw5QADeqIFEIMW1DWRGcIBCVSmWZUEmNQCgOSGJE250uKEYf+N8NDvPC9Qg6zpTXbzo66OMPWujGEWcbJUm5gtfho5wZX8YlVtR836yPPuc4o7TZr8Xh9DS1B30YZ1/ozxZyksJ8k71gjnG84kABhRIbI66lP56x8PIOnzPuj2WKOfD5WK0UI0g+Vs4j8I0H9XEAJ6U3dYhRSM8mOcXOPM/B8Hk7adPu3bu1Zs0abdiwQfl8PuzwwhoGnydKSx25dMeTv/23B03xOPl4cQ0GiGDeiX74mRXuAOD0IjeAPaDejuIxHu601Gq1VJDRzLB5nx3tx5FHB/l22q5TaTPP9vZOT0+Hxd9egpjP59Xf3x+cxtnZWZVKpWWBg6RgjwhSfce3GMhAF7pzCDmC7WMFOf/xPndW/X6exziQYXOnjmcjJ7Hz5TuIeTuxR5VKJWTuV+taDTZjcP3vTprrZy/VdHCiVCqpr68v6BPfBnglAExq2BcCVh97aTnC6/qLAH1hYSEFJvKdg7PueMbZJ886sPaGYKO3t1czMzMBkHAAFLtXLpdDqTuAQ8yzoPuuD91+80wPGljrRRvjoERKAwuSQlUBIDQZGHSCB9sOxKKH/P3uHzgw4eROuY/9/Py8KpVK0Cu+K1jsk/mcxkAgPhHnYXDkAcDrwx72MG3cuFHf+ta3lM/nVavVNDk5mcqG45PGQV7MAzE1001OAwMDOvzww5UkiX71q19peno6tYOeU5IkYX0PuvRlL3tZANo9aNwX2udAw1FjNyw4RvV6PSxKhNx5YqAc8aEswOtQicrpvHcKZnK0wKNVd8xdASNsHtSgYHzrRJ7N4BLdYdBKpVKqXygusiNdXV0pAXQnASGfnp5WtVoNO7RAbmBjdMsjew+amBNH1l35uTDHf3t5gl8bGz0YUVI4MMaNMAqTZ7mSoE0YP97LYvEYQYoDRUcheH4cpbvChj/d8efzGMlmfY4/n/9BgHCISGky5jzL3+MOBd83+86DM75DebtzzLU+Hz5Pq5VipBonEWIMmo1tbNDdgXQnzxWszwV6iDl0wxnzB89CrjOZpc0TkiTRwMCACoWCenp6UtkwFt/FwYvLGroLnsdhiA0EzgalSzgb7HZG/0F3KYvgvTzXgxPe404Dz0BOmxkQ5godz5k5zYCBOLBzp80RX88g8r4YpHKnGp0yNTUVtgBmR5Z8Pq+BgYGQKXene2FhIWyD6iUe6ASfL9rv69D4jD5wfYyIOg83c3B8LPgOPUBAiO3xc0PcefG5pF2OrMbgCoRjJilUD7DRyWokAoj169dLamTqsNvuB/iYA6zBCzjaZNsZO7dnbkdiZ9bLlbkn1j9ObgMp4QOclBolj4AXHL6L3MOXLnv0P5vNhvIvNn1hjRd9QUfRBz+zKAZjXTfB82Q83Rd0p93H1n09xgR9GAPHgHgdHR3q6+sLIM7MzMwyO4r80jb37+JgHHJfiXfib+CX1Wq14I95gInOioEft9H83d7ersHBQQ0MDASbUa8vleEXCoWwy9Rvf/tbjYyM6DGPeYympqZSmSwARXxI14P+Tu8b5GBX7Dd0d3frmGOOkSTdcMMNmpiYCDyD3uHdgLj0rV6va9OmTTrmmGN0880369BDD9W2bdvCIZT7QvscaLiyjTuO0EnL9yB2h8odZRxWlCbKOUaAYub3QXXnmc88wPCJiI2FK2me7d/7MzOZTEAOC4VCcHDdiQEd8kNSpIYSxLiDmK9duzYgcxhub5ekgGh5W7xMwY2TB1VxJsKDLKi9vX3Z2hPe6YzqCsczCV7O4c/gfvpD1gDHKC4ZiA1BrNidd1wYMSxSw6FD0XG9I5DMkwdujs7EAVzshLBzD/fFWbnY6MNv/r0HIs0UlTtorlydYiWy2gh+hdxQxOgQ5IGilK6n5Zl87vcyH8xzHKxISskAc4rh5h5qq2dnZ7Vjxw7V63Vt2LBBnZ2dIQhNkqWdVMhywN8obOdBn1vXp3xGX+BjyrPQMW6EHAH14AW9GvO+Ox2827OZ8fbUyADPxClwPeM610Ef7ovHHHTPwaI42GIu0RlsLjI6OqqFhYWw9zwHXSVJEspffUe4UqmkSqWSsh+OentfcGDj+YkzYrFdgv8c7YyDivg+9Azvdz3lPEz2zR1arud99Id2uxMKD/PMhYUFVSoV9fX1abVSoVAIawHpDzLnJX+uK318CdzK5bKmp6dDMB87kbFzyVhiA1xvu81xR70ZwkxVABk9D2CRTdrLOlEydmxFzTM8IwFvUS7d19cX1kj6mMzMzGh0dDQ8h3a6zaJfXtbo60BiMI3xQV8B+DUrX4/nhHbXarUwL17CyDpT123IAXLBGLje880sPMhwneel1rEv5L6hByb87wBuV1eXNm3apJ6engAesMajXq+rUCjo9ttv1yGHHKLbb79dfX19Ovjgg/W9730vVNfQZtqLPouD12ayHftODhR1dXXpuOOOUyaT0R133KGhoaEUmBWDUTzPn7FlyxYVi0Udfvjh+qu/+ivt3LlTP/7xj/WDH/xA+0L7ldGInQF3QJlwb6hHlz44mUwmOLqOXsE8GFSUrKNpPD92WPw53EPb4lQdg+jvc8QPQ8X1jgZMT0+HuluMRFxqRBaAQMCRTR+37u5u9fX1aXZ2VmNjYyHdCSJA2g2DDRPSXo8+XRhwljD8rmRcmTAPzI8bSr7zmmbGnTF1XoiVM+0CpfHSiDgg4D5/jwcKzj8eIHk/PPBwJR//eHDjCCrEnHpZAWPrY97MaYyDAngnDiDpjwchzcgdcCl9gvtqJs86YfSRVdct7ojFc+gG2p1aKY1IOX+RwnZHgfsYY55LNgv5QSbr9bpGR0dDnXNXV1eoUWbzi5mZGU1MTGh8fFyzs7Op+n7XS16u08w5ol1u5N0hxsGESGv7ziWMbwweABbQNw9qnE/RCW6IaW8sP87jvA/94o6VZ3wdnXQUFJ2GA8l6qkKhoPXr14dttuv1eqhLp+TAnX/KZqi7dn2Hro9lGb6KF4s70W/PjHm5hjtQ7sCgR2KwwN/pSG4MesH/Xu7l8+VgkH8uKTguOK9k6Fcb1Wo1jY+Ph0W1lUolBXBKjUDXwQXGnjMYurq6AtAXB4bMr/s1sf/TzBHlcw8EkTFfG+FgATLhgaKksCaKzwEypqamQqDFphDuG0kK5VlkrnyhOPI/OTkZypacR+mftwtdyHXwfFy26X3ytRsOeHiWwO2+Ax/5fF5r167V1NRUqvzKeRr5YO49Eyg19BRj7XoUG+RBD3Pm+sznhbl1/qjXl7LeBx54oEqlUgBj/NDSTCaj0dFRbd68WcPDwyqXy3rIQx4SdHB/f38AXzKZTFivTDDUzLdwvuBv939cLx133HHK5/O64447dO+994Y5cxubzTY25fDvadfatWvDs+fn57VlyxatWbMmrIXbG+1zoOE7oXhEGBsZdwDjsidX5HEGhMAhm82GlIwzPAqUNGdcsuMOGfV1LlwwmiOMGDMMDqUDBBZMBClOD2RAOpkUFsv09vYqk8moUqmEiLbZAncieARx3bp16u/vD4cRYUjihW6OhlGqJaWd0ra2tlA2EEfwOEwoM5x2F2bmxe+PnWvfYtR/3FFkbL1EhXfGCsP74J/HKWlXOPAkJRGesm2GLFL24RmnuB0xukq/qCGFNySF+XWD44qTz5oFJXEfGbNmTrIHSHEGabURfXc0y+uA6TNyGAcbLufuTMTZIHQKznicyncD5bqJAMN1SLVaDWU5zC3I+vr161UqlZTJZDQ9PR22ze7p6dHatWs1NjamsbGxVL2068yV/nb5hMdwPNBHfuqr9wEZ8YDUM0LOizja/I1sOk8yTg7KODDBePIcX+PhjhXyx98eGLpdYZ75vq2tTf39/err60uVd6FXM5lMKEGhrZQIUYYxOTkpSSH7hN51vvGgn7nybEMMKrgT66hje3t7KJ1A76Bj2TTFgTh0JLKAvDOXXM//tB09H4Nh3l4CQvoCT/u5UauNWKPhOw653fBAXWpkPHFwu7q6VC6Xw1i4wyWlddNKWWbARim9NjSWZ/QF2wp7W5FZD7bRPzzXd32jTfDRjh071Nvbq56ensD/rvew26VSKZxm7f0gSGPXIfpBKZ/7Fr5uIF6Xha6Gx5BxqeFf0H+C4NjGOgFKZrNLOz6ig12f+ZkzDlr42Pr/+FvIiI9RnFF0G+v9pK0xALply5ZwRAKbCxF0OIC+ZcsW3XjjjZqbm1Nvb69uuukm3XvvvSm7TgUI/MpammalVFA22zjPx0HWXC6nI444QgcccIBuueUW3XnnnctAT9ftbI7E2HlJ8PDwsKSlg7s/+clP6ilPeYqOOuoonXTSSdoX2udAo1QqqVarqVarLXMAvcMwDVEzn3tKke9gBDdSKFtHnkANcRiIRnlPM7SJd7ojDQP7Nn+g/0RucRmS1FA8bjyYJFcC9AW0bWpqKtTCuTBSF+iIXr2+dLbHmjVrlMvlwkFYzjw83x0wKX06uCsH6n1RBqRZXXk6CkdfGUPf+pbaT69npe/MqQuvz7MrHX8/fUfJxhmXuD3OD5xST+1qs9004CfvH2iDb6npSLMb+3gxPcEj841yd96NlWeMRsQy4gFJnKGjbT6OjpytRnLEV0ojMS7XjvjDa65vnF88GHZ00csOGXMMnf/t7+ecFcAEnN/x8fFlGwR0dHRo+/btOvDAA3XAAQeoWCwGpJAtaHt6erR+/Xrt3r1bQ0NDqYyBZxViwACZ9/VALkeghRghwAmpsSaO7Cjj6byHc+HbCJP5YCcqiLV3nql1FN8DA59bPsMAeymDG3Dmz9FSAJaBgQH19fWFTRtALnGUmY+pqamwL7/LFX2LgyIHNeAbR1bpk+sjd2Zdbp2/WBDr6yLg4SRZWmAJf9FvL3/xgNjf486N6xN0kQfmOKeuO+gnz5ienl61gMXu3buDQ+SbinhVgvOG1Oh7kiTq6OhQT0+Ppqeng1PnPMv8ux/gc80zvEzL1zJ6VjGeU+dz1z/oK3jBN6mhr+w0B9XrdU1MTGhhYSHsiIdD72AFdjLOhNMO1plJad7y5ziy77bfg23/3wNsZJUMWuwjxHyIzsVXoLSoWq1qbm5u2e6X8HgcoOOrEEzSR7JLHnzzuc9x7L/G+qJer2tgYEDr1q2T1Nh5tVgspsaR95RKpZBR6Ovr09DQUMr/Qq8SxGIf3K7BP35OiO/s6bRu3Tode+yxuu2223TLLbekeAe7SIWI+7JeIod9vvPOOzU3NxfOP/nJT36ihzzkIcsyWivRPgcaHPySzWZTdciO4rpQOUoVO6N0gElgMmOH1Y0RJ1q7I8D9vENavjqftCCKh7Z4Os0Xwzha6ui5pJCehyE9BQcaODY2psnJyYCceOqQyWMRFoLgazH4v7u7W4VCISBynEXh6KEbo3ghII6Fp9GZE8aERVDNDiwCNSHAyGazwTjhpDBf8ABMx2c+T+7Io7x8fUmsBH3cUcKFQiHsB+4Mzrh4iRprajwTl8ks1WyDBHt9PsYpTg8zDgR9CCZKNXZS4qAXXqJPnjVqFjA0Q2Q8/e7oxWol77eXKcZZNQ+2GFt4nb99u2kPXhytjuXe3++LIaVGqZDzV4wyIytTU1MaGhrSrl27tH79em3cuFGbNm0Kp1Lz/u7u7hAIb9u2LcWnOAGQB704/5R6NEPpHKxxo9WsfIrAwPWw16e74+H6g+9okzu03m4PrnkWoIqvLcNGcK33y9u+bt26cPgrwY7rKgKYsbGxsGsWfOC7e7EuDpCMwMNBLpd59PBKQaA7/u58cK/rYnfI4FPnyWw2G7I79Nuzru44xaBaHKD5fKEzaJMHWZQA+2Ykq40Ydw/m+Nxl2fkLfQ1QNTo6mlpPQBAtpYEO/nc9Ii3POvOb52ATmBP/XkqXrqADvOSY6+EtHElHryWF0uve3t7gc8DbyDo7B3m7HfiNA1va53rTS0AJkBwIwo7HZUk4xdhQB04YA+TAfRDnY7YjZlxyufS5MPSXfqAneI4Dfg4M00/3U2PZdx0Q265Nmzaps7MzbDjR2dmpfD6f8i9nZ2d17LHHqlgs6glPeILK5bIWFhZ0yy23hHVCUiMAclDR5wQeRDcT2Dh4xlh2dnbq5JNP1vDwsH7zm9+kAkIHiOgzwJVniBx4BcjBZ9u9e7eq1ar6+/tXkNA07XOgcf311+vggw/WmjVrJDX2JG+WZood0GaokBs8D0gchScKh3FxvHO5XFjgxOC7AWAwEVDe42gbTOllTY4s4KjEQQjRvwcfXDM1NaV7771Xw8PDmpmZUW9vb0A6aRPCBLrFonJ3Xj27AjK6sLAQTlr3tQ8+3jFywnc45l6S1dHRoampqRAcTU9PL0NzGKN6fWkxEygAC95dWNmqLZ5f5iZ2whlPVzruZDOXbBPHGR84Io66en8944VAEdTRDncqvC2uZDD8vvUtfO9lfd6vOKXqzpQrqHgcHEHlh3HhO5wl9uRereQKP1biUvNdo7yk0WXdSy15hst+M8MCTzgYEaONZKpwALq6usKYe5aA+2ZmZnTvvfeqWq1qenpaa9eu1bp160LAs7i4tCXjxo0blc1mdfvttwe9hRzFoAwOkfOQpJTz4/zNs9xRJzCPt/akfhj96KUMXOPZE9of63qQN7budp2InPkOVT5mzL+DS+5Y9/b26pBDDllW9jY9Pa2FhYWwhScHvHrA6hkEsj6xQ+7XwkvucHmpGW31e92W8D284u+OsyMeZGFf0Bu+EJ8sPo5KtVpdpq+YHw+EeIdnlwDBcCKYo3gb+dVCviGBpOArSMu324wzRGzg0NbWFs5QIih1nnYdFetyxp/SZsgRcXf+XOc50CalswOAjNhXAmp2mqNMkOu9jfPz85qamgoH0Doi7YEM18f+mdte/9z5DH1DoMH4eqm26wj/nmc6uOrl2M7TTrQBfwi54DnwNCWmnp3xrBQyzrwhI8gw38FL9COWOXfQDzjggLBGIZfLhTNNGK+enh6VSiVt2LBBfX19YT3Q3NycbrrpJo2MjIQtlmkz8+4HizooQft9m2X65mN+/PHHK0kS/fSnPw08zNx6lYnrPsaBz93vATBi++Lp6WmNj49rcHBw2Zw1o30ONCqVim644QYNDQ1p69atIVWYJMmyY+Q9RRsrYy9VcCZ0x62zszPsE8+1DERXV1dgOk/bu7MSv5dB8wH162AiUvtuhAg6cLSlhiElSEiSRKOjo5qYmEilpIn6NmzYoP7+/mWK0feAh7ndYYoFkG1041IEnGjP2tA2FlHCICg23/Oa+cOINwtU3KFxZvRMgwdSMVrPHDgK6P1kPh2B7OnpCQobZerj4Q6XGwPmxgU1n88vQwj57QEywoxgubLx9vLOmM+cd2JedIPjvOdz7YFuR0dHCDRRyo58rkZiLOElR4YZBxBHZCRW/B5oNCuLkdKbC/h7PABxcmUOoZfYBQW+8LpuN7qjo6PBSExPT2v9+vXhJG94e+3atRoeHtbIyEiYf0fp3AlFZgjwvYyKLKrX/3vm2A1fvNgeNBDd5ZlW53XXKcgh48yBVq7n42wg9cbu2PjcxCg/78tmszrssMNC3XO5XA5t3LFjR8jyggSSRfAgNJPJhJ15qFNny0yQ0mYllzGaRx8dEHOwgr+xVR6EeiAhKewexHtip4e+FAoFzc/PpxyX6enp1NoEd/Q8KKTttMHBOOYbZ3pqamov0vrgJEAxB5ckLZPtGLhytBc96oCZ86WPo9sWB6MABd13cd3k4EUMbDhwwhy5PiMLKDXWPLkjDn9D6IBKpZJau+my4Eh2DFLGNjvOULptpL3oBl9X4oCd22n3ZQB4FxYWQnVHs8BdalQVLC421tPgu1SrVVUqlXA2mdt35tL7iewREJLd4/qVUH9k1UHqdevWacOGDero6NDk5KRyuVzY/h/ga/Pmzdq0aZOSZOnciv/8z//U3NycSqVSAH8568cBbwcovDSQz+HZQqGQKv+mr1u2bNHWrVt19dVXB76KAWDncddJyBH87TofXz9JktQawX2h/dredn5+Xjt37tT09LS2bt0aDkNyJxlGghljI8Pke5TLQLnj5r8xGM4MHR0dYdtUF55YKGIHAybzSJ1UdcxQIHKTk5MaHx9XsVgMTjXOPogqO1+4QsEI7NixQ9PT0+rr6wuItJf0xILqDIXzT0BB+0jPw7Cgho5AZjKZcHgVhpDPUda5XC6cKcEaDDe2koLD60aVOWTXChzDeLE1Y+TPcz7xMgPaQptxVnAMnBe9/MGVNoYCBxXDmiRJWFDmJxx7PT5t9JrFmF+8DZ7B4DN+0xY3dnzX7DN4PZvNhoNycIBAbeGPZo7yaiHXDcwhiBhz5qWE7jw6+uS8SXkbBH86cu0os5Q+8AzyQJDAmtIlvnNkCTQJ/uRMi127doUAYHBwUBs2bAgyVyqVNDAwoJGRkWBwaVNcbkFfGSv4AYeEgLRSqaitrS04Pfw4oYPhd5A/0E83unH5CHLE55Rbek1w7GD4OgSfFyfkxwNFSVq/fr02bdoUbA469+6779b8/HzYhIF2+TzTFg/KeFdXV1cqAw3PuWF3ZNDXgXhg5P1wPvHSUQ+gHS1nHCito51+IChOFPNPm4rFYpgnxtpPRib76k6Fk8sQ87gaiWx+nHlAPh2t9T4nSaJyuayurq6wg5PbXvc53A9wcjDA1+FA2G1/DvbEN17gWbSL9kqNrCXrRyQt86/gLTJo8CdZeHwmz+4D4EK81+XFv3MAyMcGn4trkFPGBlBFUsoXcL2E7aUcHZ2OnnE+dr1If4rFYhhPHz/suGcy+Bv5Zl48GHL7Hz+PvxcXl44nWLNmTVgTgxzzTvQqAdHo6Khuvvlm3X777cG+wVvd3d0hwGBNr/vNDmq4zwCPsyaHsUqSpSqZE088UT/5yU80MzMTxsg3N/LqGedzB1acz6XGGj7OMCqVSqmzyPZG+xxoOMNNT0/rlltu0fr16zUwMJBqpC+YjCfOB86RRr73IMIPmnH004MK7vUdIFKds2AG9CpJlmoFYUImnwXC9FVqOCeUHbFOgkNrpPQ2dbQ/RtclhYABxNq/8xQiTsDU1FSqTtMjddrAd3EGw3domJiYCAZZauyqw/zwPwuuKDNCOJMkCcaY/uHk+FkiIHOO6PG+uJ/uqKEAy+VyyoH0ReygqYy7O4+8E6Xa2dkZ1rcUCoWA3o6Pj6eUBv2Gj2gLziNzxM5g3IfCoYQGFMXb5Q5rjCx5MBxfG/OujwO86MpxNZIH0oVCQQMDAyqVSoEv+IGfUKDuNMR6gyDYd6GBVhp/eMaR/TjwwDllfrwMI+Y7+JT/x8fHVSgUQlkipzEjaziM9MmNYTa7tPCRcgTPHru8s2AUsAQe4Ye2+DggdyCmnqX1BZXMgSOXBFSOqMcOB3KKs+FGLAZU3LngPSyen5ycDA43mZapqanUjiw+rzGS7EQb2OrTtxp15z8uP5AUUFT/zIMHn4842OH7GEiQGqgq8+JOaLFYDDvkYC9c53vA2dfXF5w8bBH16ZVKJbU+xm0Ac7caiXbHDhk8F2fKIGxNoVAI2cc4e+U80EzPxnPp/EYZFt/7PcwfWSrmgPf5mgf0E5/H/gj2OUbvHUyglBCeIFPmZTOQZ3JjXRRnSl1HenbHfQnXE56F8OCCucM+c637Jl7u7EEKNritbWk3OsrWY/0PiILOcH0OMORAiAOFzYIOthTu7u4Oz3a9yqnm/F+v1/XTn/5Uk5OTwVF3/4HdCtmNj3Vk7vc4gMx36Dnm3Pnm+OOP12233aaJiYkApBSLxVAqCdBCyRfbB8PPcfYMmwdI/p//+Z+BV/3Ax73RPgcaMBTMuLCwoHvvvVczMzNav379siyCl0c5Ys2AuKHgmV4eArFNmDMrznB7e/syZ9wdWd6LIqbOmqjTUXa2LvUdQDy4oh9uyDGyrqwkpSYeQ8OEV6tV1etL25+5c80ks0jPlSnMFjsNKBKEFAPKzgCOQqJkmCcXEF+kScDhDOy7PLjQ+7h4toXUtO/JjDKCh+r1xqnLoDJsiwf/eNmc75yBsLqh8ZrP+fl5jY+Pa9euXeFZfoIzY0m5li8oRsDoB/xDyYWXlrliAx1wh9B5A153pzVGU+J30z/aGWdCViOVy+VwPgxbM3rJgAcbcdmk1Bg3Lz3hWvYej8sUXF7cOHK/yy1zyvV+pg9OBTLtRnlhYSEYd3TS/Py8JiYmdO+996qtrS0Yio6ODpXLZY2OjgbHAJ6JEUGeB3rk+gh5xnnh+Y54E0jxPHQFxp00fybTKMUAQPByRtolNUoTHUljnpgXR5v5zuUhSZKUo5fL5dTT06OBgQHV63WNj49rfHw8BPSVSkVTU1MB+OFdjJEHLHHg7iVDMeLstsjRw2Ygj/OUv4P2u9NIEEOfPTCVFPSpr9Fx8rIExhsd4LrJAQ4WArN1KwEGSCtrEgDVVuticMYFOXbQQVJK/0qN3aiQk7a2trD9sTvQrqfxHWLgweeJOfZS1th5xUbDn1RhuP2UGjzK+3m+o+8x31EqxRwDALoc0A/4rKurK8w7AY37aM2CK2TGd9ZyXYot5VoHE+IAxe2YX0u5qWcpsLcE1u4/otuSJAmlrWNjY+Gd6HG3J5R3+1y6vsQPQl4Zd8rDsV3x3KKLWFfBPN96660BMPJ55bkLCwsqFAohAOjs7FSpVAr+n+sr/GL65X40gcKWLVvU3t6ue+65J9i1o48+OmxbS5+7urp0+OGHq1AoaHJyUiMjI9q5c6dGRkb029/+dtnuVZK0adMmtbW16aabbkrxzQMSaMTMuLi4GLZs3LBhwzLk3TMRjji54fN0s6MAKOZyuRyUY7wASWrUL/KOZui/7yBDedDU1NQyAQY5a9YHr4X29rvxxeFxwyctGQBHbev1eshY9PX1hUXulF95CRT7MrtBdzQwzobgWEgN5cJ9vliN57gx82CG8SeVHyOdzZwIz14gnJ65gAfYQYxgBKXhTgJzwry6I4oBdYcMZ210dFTj4+OBXwqFgjZs2BDK3bw0jDn1rf0ckanVaqkSHj99ljlyp9X5wJ1BNywuP7EjzZizGJk2xeOymjMaa9euTSk8nCPGy3eLQuH74j7QQNCvGKjwwE5K194yH54Z9fIt9IdnGnhn7IhAsYFFCRPEzM/Pa2RkJGUk29uXzs1hy1YPlHHo4S8HLQhmqI91JwE9h0x4IIxskJWl1t+3SMRhI7XPM9yZo10OsPgYM7a0IwZsmFN3gBgPtqvO5/MhO8OzarWahoaGUoihgzjxfPtvMq/+nvHx8cBbsa5k/F2nQR5QuczDB7GT5d95gOwouCO1Hsh2d3enADefZ6iZcwhQAzjk7ZYUgg3W7q1GYiybZS7ch4CcFzo7OwPKDZ/B725fHExwW+4AAzzabMfEONsgNeZpbm4unMvjcsF1sWxxr9s7rqO8mjWbBBNxiR+84+druU2lzbH/xFg7EOaAQQw8+vde3dBMN8Or6DbsKTaQtrj+db3hY8BulKOjo2F9r2ea0S9xQAoA67LlGSM2AsFOeabWg3l0NeBNNpvVzp07l+16yb0enJDB5G/8QfeRmDP+dlA4m82qv79fxx57rK699toUv/f29urXv/512HEvk8moUCho9+7d6u3tDTsi5nI5HXvsscrn8/r3f//3lC+YzWZ11FFHaceOHdqxY4e6urrCIvZ91SH7HGg4ehWXMnFC58aNGwNjutGBwZj4mEnjzABKhIGEAZ0x2X0AY067GBiEhmwGqLtvk4ah9YwEC3ow0h4IeSQaO6dx1A0z5XK5EGRg8OhjW1ubpqenNTIyEu4BPQe5j4WMcZAUSiCoD+adXmLE+ygr4j5HJHzcJYUAA8UUlyk4kYqTGggG98OIvpMHfaQ0wIMXhNKNqgccRPm+fgPFMDs7G865cCRjZmZGO3fuDKc2x8La1dUVlC81u46Ygy6gFByFdccCQxGXUfj8cS1OcoykgZ7H29cyN/E8rUaifDGbzQZnmTGKUT70gDt8HjQ7UpjJZFQsFsPW0pJSBtADEkf/mCvmGDTfHYmY7/m+GRoJOao4Nzene++9N6zNor5469at2r17tyYmJsL7YsCF4LZeX6rHx1manp5WoVBI7dnu9zE2ZDDQWZ7R43qcEw9w0YGMoWcR4uCXZzGXbhi5xjPcnhnHKWL8MFzu/I2Pj4cSNKe4TGqlAND1UWdnZ8huekDrKGqz8fHfHnzF5bDoZpw4/9tLx7zNDk5gbwi4GJtMJhMyxT7PXntNG3zdT5x97uvrU71eT23/udqIxfIECvFaIHSl1OAJdH2hUEiV2Xj2E/9CWr6JjTu5nr32IJz3OeAZO+28h5PZHYT1bCnv5Xt0YAw6eVZhcnIyBQSSwaFfyHA2mw3OMD8uy3H/PUNKO9HHzvfocb532fe+u1NPsO8AB2PtwKj7iy6HAI5kQ/r6+iQplMcnSZICtOJAUVLQi4x3X19fCNQdIHC58uDLQS5kluBJapxzQX+53+2DBxv5fD4s9IbP/SiFmLq6unTiiSfqxhtvDNkq2rR9+3YdeeSR2rZtW1iXKy1tXDI0NBT+n5+f19jYmE499VTNz8/rpz/9aRjr3t5eHXbYYbrqqquCHO3YsUNf//rXVavV9PSnP31Zm2LarzUaDA4C6khWpVLRzp07tX79+lSUCqNR1uNRfGwgESoGr7u7O2UUvA0e2brBQXC8XhPKZDKanJxUZ2enent7Q2qeex3JYF2AO7uuWPjcx8H/pm/d3d0pZwXBwvDt3r07LCgC5cABQiBdWTFOjhzSXtBeLw2D4bif4CVGKGBinwOCDC9pcwcMpeHPdUeRYILgh8+97tADMpRLPp8PC6JdKXmEzztHR0ebHj7lvFWr1TQ8PKxSqZRSttlsVlNTUyG7RBARozqOJnlA4fPDvMVIGOSKG7SGsffgxBcY8xx3st0pW43kCL3zOXLhSLGPsRtUvnO+BSSAFzxT4qgg402QExt3d6IdkXOHzxFAKb3rmQclGAkvbSIYLhaL6urq0rp169TT0xMOo4oXgKJfkRf67IutfUcZSSGYdtAhznxS+gTvsqkEhm9ubi51ZhCy6Rk6FjDTf4AUr0dmzr0Mk3khswtg4A4/9qJarWrXrl3avXt3GGuXdf53fYRs+a5SbugJSD1T5rXVjDdBJzLp+h3yPsE3ccaCcWcu/d7YIaY9lIriHHhZKGMFL9N3eMedQncmmWcHulYjMT6szYrPHYmzCZ69LBaL2rlzZ+qwPYgxdd/Cv48dYSgOHuO2enAgpcup4ClknespgXKfJ9Zj/n6upQTJwUXsJvYcQCCulvBqDteLLnNxVsJ1YuwruSMe2yz3h+BPB2yw8fA94+2+CM9AHy4uLoZ1FNh2SUHX+3t9bt2XcttOn5EZP8ON9+En5nK5sG24lzg6OONgMP31AK9YLKpSqYTMxtzcXOqMDXg9DjYe/vCHa35+XnfeeWeYT2Sbwy3XrFmjrVu3BqBlZGREQ0NDIdPhC72f8IQnaHx8XLfddptyuZxOOukk3X777brxxhuDDioUCrrxxhtT+mxPtF+7TsE4cY0r0Xa1WtXQ0JAGBwdTSpmo11EAJoDn8MPk9vT0BENNHRpKthmS5Y4bhoeMhJc78d5sNhu2d/VngdDDPNzjkXSMkKLsYzSkUCiEnbnYvcaNPse6w8hEtl5v7e11gXPHx0twcNI5cyNGepzRXZG4YGA8/V4cB77nN9F6HIjBJzg9vggsVhoYvXq9HhYv1mo1FYvFoCSYk5mZmeCsYSzoO2PbrNwBR875LEmScBiiz2s8Lq4MY753Zc78+73OXz5uzDkotcuEK2p3Ht3RXq3EWHo6m899vRIGxMdDSqN8cUAH3+Jkx45DnHGTGroH5exAimfP4OHY0XC5gXjm/PzSeS/wMW1kvQHrtEqlUtB3ZOdAsBwE8eyspICekSGBX3CUY+fSZTSTyQTUzIMDxod+OJoa6x8vdfAA0oEQ+oxseLbUz+7BUXQEfnx8XDt37tTo6OiyceV/lxUHJjx4IqB059yBJHQVCL8HZfCCBwsug2xk4ddCBFiO8Lpc0w53bLE/ONO+kDu2OciGlznAv/TVx43nO0+tRuJwNHiHoIxxhi8doKEcx+2MlF576jpBSpfSSssPXvUAFfJxd3mInW7XTS4f3OO87CU6PMMDKrdLksIco0MIuH1nqLa2tlBG5uCV81lcptgMBEKW44AIPYzv1iwwct+F9sfgJyeBYytYu+jPIZsiKchwf39/WNPpoC3tbBZk+/dub7y8Ff0EqAIfTk1NhR29XCYZI7/Xn12vN45VwIGfmZnRrl27Qn+pWvH55dlbtmzR5s2b9Z3vfCflH3uASEaYjRB6enpULpe1bt06VSoVbdu2LZyp8/Of/1wnn3yyXvjCF+r2228PO5h99atfDXPU1tamZz3rWerq6tJXvvKVZePYjPYL0mjmALnhqNeXDlTq6OhQT09PGByQIXfAHIXCWePZnFkxNjaWeocbBATThdoRUAQDg+KRKoRy8l0JpPRhgrGz54ziCsodHklhYQ9twFHv6OhQqVQKuyA5s9VqNU1OToZyAtrhTlicLXJEzyN9xhNnBQPmSoR7HFXhHY7+eqmIK1YMmJdG0SffQYK/PVjycY0VMW1k0SK7xVSr1ZD25j7a7kGVKzJXxj6mOE++7scDRXckUSwgQrGzH6eb4T8PqjxAQ+kkSZIKMnhGrPR9bJij1Ur0F8OPoxovmJMayDz3OSAQI2Q818+CiNE1yPUAfIisw8seaHhJgD87RvF4NnxEYIyjzGFamUxGtVpNExMTqd2f0CPUBHuZDM4GAU9bW2N3OK8RJ4PomQqpUUKUJI0D/VxX8E76iLOCfKBP0AUgYfAk+s0NK7LDuNAu+uAoa5IkYSxmZmY0PDwcMs4eRDgA5GPuspbNZkMWyYNS9AElSCwGJrCLM+Ue+MQyzfexs8S7PGCiTQ6o+Lu4zx0tr58nq9LW1paqFfcx9jZ51sODEZ9z+HM10p133hnmi7IY1i55YE3/QbpZFItNjMEzbAPzFAd3UhrBd13QjJxHnSd4D3X9yCbXNQNRAA/8bCfsscsBgTh99Hdms+lNWwAKCUDjksE4IKJdvr6H7DCbF3hwEa9PiPUydjZG6Hk3us19Oh9LB5AYH/qdz+c1MDAQthyPAUYHC/ntgQY6ygPHOCgiO0qpO/PkZ/W4DMf9ZkzIFHtGkqqUnp6e8Fx/7+zsrHp7e3X00Ufr97//vbZv354CjOAV10c8Z3h4WDt27FAms7Sb1GGHHaYdO3Zo9+7duuWWW1Sr1XTkkUfqiCOO0LZt2/SlL30pNYZHHHGEDjvsMGWzWR1//PH7ILF/wPa2dNYDBj5HSXJoDMKDcvN0oCNAKHJW3nd0dIS0lyNhjtzwPhjbI0Zq/iSFiBbl4WgAjh4IhxvKmMFiZzJmmBgFYWtVBBEjUSwWlSSJtm/fniotIntDqQVlVLQZZxflEqMvjKkbGdAAxtedWX67w+RBS2CQtsb2soyNv8+Fx6NobwvkY8Sc0i5HbxF8njk8PKz5+flQcuQ19D6v7OjlCp62NjPurow9uPD+4RD7bly0zzMtfrBObJycL0DycQoYTw/skI1YOSNH7nCvNoKfIEpkfM59HJFrnyupUVbkdas4D3Gw4Q4XzwescHTJeZm2xoEHzq7znoMuHhSi/2q1mnbu3Knu7m719vYqn8+rVCoFZ35sbCxcj9PkDqQj+K6b2GwBh5pgBATOMxmxzNAHsqwe0MKLXhOOXuJzqRHUYCx5Dvegtxh7nAH6wlaTvBf0cHh4OJR10HZfyBkDQOhJ/vYsqzsP/O1BJIesMn8eTDhfNZNp+APCxnm2GYodI/QNDog7kegW1mmARLe3twck3uXID/vyAB45gTxQ5j2rkZg3dml0hzWTaRz46Qejlctl9fX1hYwhu5lJaX0dZy3cweVzPqOMxsfYbU8ze+pAEkERPCMpdS5CzD88m8w82QoHy3gnZyjgh7lsejbH197iX9EW/LU4g+hlerlcLsV7Dny47qHdDvSh83in6xtAQObWD4rz6hQ/6wb583Hget8ERkrre587BybJjDt4JCml/1lXAWCD/HkQn8vlwmY0nhFy2wPQNDExoUwmo/7+fk1PT2t6elrd3d3hHbS7q6tLxx57rObn53XrrbeGMZMa69zg0Uxm6Tw1xokxTpIkrH074ogjQvZj27ZtYW3tV7/61bAFbpIs7fB16qmnBn184oknriSmKdqvQIPf/uNC5kYMRveUZqy8XYDr9XooG2JxJA4yE4SAMEF8zuJDSpW8FMkDDpxfjDB9YAF4jLy7c+KBUYwgOLNiPDhMq1AohEAjn8+HvZJZf0IJEAxIiQUBkjO7H/IUI2Zu3FAOCIKjeyg6R2/c0YDpmUu/D2b2IMCNswuj8wUOmBtHlIufdMv7MeAIbKVSCcgEKVHfmWZxcTE4QZVKZVngS5/9xxd/xUiDO0O0A/73Z+IMOMrkjq+PL3zr9fQe5PmP83js4KCMVyuhC0AfMfaxo8+4+cL42NGEDx1A4JlechX/+FoHnudoUGwMcMjjtTJSY3EjhFz43GKcKZlqb29XX1+fBgcHw3fIwezsbNgml4Xe8CUIexz484MeZJ0WKXcvNyBQAHVDTyGjOE7wM33ztVac0eAyT9Dlf3uQyBaOsc7AGHJGxvz80v7y0tImD64zVnL4nQ8cpa7X68vALXcuMplGJtSNuIMOrv+RbUeIYyfSg5+YH2iT6weyWfAh8+Qyzhx6u/1v51lHbp1vmEMvK/TF/6uJHMBy+8vYs+5udHQ06G8cucnJyZBJa6ZvXV/HAJ07/wTkPu5+f+wjQB6AOxDg22i7A+pAIgEnJUHO483sBO9wIM39JT8jxt8JX/pmEJD3G5kAIPDAlT7GvgJtc/nyDIwHYi47UqMsyqto+N+zB66zJIUxGBsbW1YOlsmk19QipwDDZFR83vEVHSh2f8d1NLJLtj0Opvz9gOYDAwPatWuXenp6NDU1pWq1qnK5HHRuW1ubDj30UHV3d+uWW26RtJQt97VKbucAw/jB1jlQMTIyorVr14Y1zIcccohuuukmVavVwHMLCwvavHmzNmzYEHTV+Pi49oX2a3tbZ+bYucYwMRC+oIWOOTPhxJICxqhOTk6mJhzhdgbxAARyY026KXYOcfSYAP+O9BUGxJU174wDjdjBQEjYzpaDuTC4ksLCYxakk8plq0t2GJCUcsYQIp6HcqI+2+uqXRE7coDD3NHREXYdWVxc2rHBUY8kSVLCQb+a7brg/OAOvCtU2u8KC6PnBtWfWa/XNTk5GRwvUJ/x8XENDAyEbemYo4WFBZXL5eBkxYgFf4OcMC7NHAKup1zKedYNA6Uw8LijqLxLStf+egkQPANCBRqEAUFufJzc8K1GYpwcdXbjwrg56uI8wvh7thTC0WNeY+NPYImucqPqihcZceIan2N35uL5cb0hpXcbwfFub28PPIRRkxonNtfr9XDmiJe8kAXCsXZ03AMVMgFergMAQU0w5EgmwaBnm0G4WNOEE+KgjZQO2hYXl3bC8TMLMFqMqQM9vtaDww79bJ2Vxh5jGgcH6D3eRbtyuVzYNABb5c6/PzeeU57v18bZkxi8wMbFmU/sCf+7XXL0lbKpeNG5y4i3Mw6kYxCI79x+rSba225Z7ptgD6rVqqanp7Vt27ZQtuygD2PiTljs9LofESPwzv/eBg8UoGw2G2y7lN6ZUGqcY4NMOhgT7ygWV1rAu8ViMTjjceC8uLiYstMxcMUYYI/x1QAjuMbtInotBtl8LrCbyILzJ/2GeHcMAiZJEsrzsSHwNzrQ3wfY2tbWFnYcc9lCR3v5O31AV/gBxoA0zoPcEwdtjD8AEm0sFotBJzhv4eOOj4+HwKNcLoczbzgfbe3atRocHAxlufAPeo22YScdOIZvGGPmo1araWBgIIzzli1bNDU1leKrDRs26LGPfaykJZ107bXXhtLcvdF+ZzSglYysZy5gJk8jIUCugEHumVA3jBg6BgihJip3xJFgoVKpqLe3V+VyOZUJcYPiSh3jQEkLCLs7Fv5+yCfBHaC+vr6QRfDrPXWGgzk9Pa0dO3YsEwCY0E9f5D0oCUqtcFh6enqCMDAHKE/mhgVGnZ2dGh8fV3d3d2BOhIL3k3p1JJDx9kCxmaHlGTiV3Cc1kAZ3pj0dSrA0PT0dHCMUc5IkGh8f1+DgYMgmwEf1ej3sP0/g6SgUY+jz4qlyvudZK62fIFPlh+04HznvurGKlaA7v+3t7U13lPDgxu9breS15z4P/I4zNjGgIKXHBOOHUfFSP4ydG1uXP76Ly7IwYHFZQpxJYJ2CO7oeCDoySh8o6cB5mJ6eDql1glDajcEgcOBZvJ8A3I096KbzCIYO3YB+84yvj0W89sDRS6nhBMeZgBgkmJycTJU2IBtkcclwcBiW7wuPvLNrioMXzCd2xLOkzAOOURyY5vP5MB4cnIhudD3RTG94gOH20HWjgyX+P/ND+x0ZjjNDjjzOzMyEklDmDv0PuV2hbS4jAGzxu1crxf2Xmu9GBrH+CHs7MjISwATKrDxQdfAhDtKkRiDuPoSj9o74u02D8Hk8GEaPEVB7SRG23B17H4fYV8nlcuH0as+cI/fS8lI+l2MH5dzhR1f4dbGP5PPiY8Cz+OEZbW2N068dTEEXxQEy3wOSeimV20nGCZ8SoLW7uzv4WjyT93vwQvt9HS8+qldfNOs3mQzaEQNqAIteNomvzPxls9ng36HnZ2dntWbNGm3ZskWSdM899wQw2NcswRPu2wBYOcgW++aM38DAgPr6+nTCCSdo8+bN+s1vfqOFhQU9+tGPVn9/f9Ah09PTuuGGG7QvtF8ZDY/qiNZi4fbolnMumAxXcF5eBCLMM+g0wuv3IXC+fStGmvtRzJTZMIkekSPI8WIj7o9T4M0QoDijwaIoN5goD3cyJycnNTQ0pF27doUFj7zL6xJhGEflGE8fZ/pRrVY1MDAQhJ/Awhe0EqiQTq7X6xocHAzbTPp76ZfPCePBgXyMJalTFyxXKNznSoX2o0DdgeHME3bGcYR1ZmZGU1NTWrt27bJnZrNLh11NTEyknFB3ghgH3keb6WPsqPkYENT5wU/IBUFJjLzyDEchXdHgwOLs4Li6kfDnxI73aiL67MiXI0IYcYIGd/Y84GbeHDXzMSYTJC2f09jRgg+ZE9ZJ4KD5PLjhwVl3o+3OBQYuk8mE9VmcqcN7MXRTU1PB8UHeea4HCvAq8s076JPXnTt4g57DQPtYoz8cpSXwdbSs2TvcgLKhhaNqUmMho7fHA0wO1yyXy2E78FxuqXSsr69Pw8PDKRlmPhwB9WDGNxxwuaYf7LCCDltYWFh2Rkes92KwK7ZpjI9nPyj9cvCHa1zeAUucr5izXbt2aWJiIuUQeA08uoM2O1852u3OmM/7aiWfA6n5idZ8z/kZlOBCBG1TU1Nhu1wQYGl50IbtIEPiAaVnD7mHWnnXT/AnPIWMe/mc92lxcTHwEXzGNYVCIYyDO/t+Xhcy7xUl9NF5EZ5opm/doUa3cvCxtzfO6Ht2jfHzuaGsyPW0+zUeAHiQ5XrAwSXX8QTlMXBDwD05OZla28HzCABpD34mZ2y53WFsmgFKXsnj5ZE8E53Jeqq4jI155rv29nZ1d3dr06ZNam9v144dOwIwGfvHZCmkJZCqu7s7FRC5nmDsCoVC0Blk/tasWaMDDjhAGzduTM0h4/+kJz0pbDu+N9rnQMNLimCiOKrzaxcWlhYlwnw4+qBOLJb2dRc+AHEKzieSdCjM6ucSMBAYPIwJW5jGysnTkvxPassnxdGO+H/egWHD0ec7jPXExISGhobCXteewXDEDOWDU+lONmPhqAafIwjlcjnllFFi4e1zVIjyK2qkeTa1rB744DRR8sH2azHy7GloVyBuFD2adwNI7SPRuiO6oKA4J9QZO3JSKpUC8uC8E6PXnvr2wAJH3899wUhQDucOkzsi3O+84wioO8LMN2MeOzE+Jl46GMvbaqI4xUwfY5RcSo8bhsYzB83mLR4fd5LhDzdYLnNk0QhwkWNHCmPUyU+r9nfiXEqNE1opD8UZcXQLJxw9CcoKz/o6Jn8HDlJ8VgTtdCPmQQY6tF6vB9lnjN2gMq6OLDrP++5ynDiNrDNP9MtRTcqkpEbwWalUlM1m1dPTEwIFSgempqbC/DH+np3GFtB/HAYysqCj9A0AikWaOHQEBDFvEdi4887YAui4YwW5c4XzA0DBeFWrVdVqtTDPUqOchmuct7wiwO2kI8LwGd/FYE58ZstqI3cgJaXGwfkWPV4qlUJ2LNb5jnxT1eDjKDWyeL6+ie9jHSE11kjE+sttCnzq/gvP5D7nVfSklF5o7oEMgAbPoN3MdVym5SAKOs3f4cAX3+HDoZ+kxuYEDvag/2Jd7f3nPt/xjrlFdnmuz6v7NbQVfcP4O184GELZ+tTU1LKSfNfl7pt0dXWF7bgdXEG3uMyjfx2s9vlkvACDHGSLgXb+X1xcVG9vbziscmJiIjwn9gnxRfGPXO+5bSW7niSJisViyIjMzMzoe9/7ngqFgg499FAdfPDBIVOCjhobG9PNN9+sX/ziFzrzzDO1N9rvjAaMyeC6UMaIDYsRYSaPpt3IeibEjTAD7M8FKUAwisViCk1icuv1pdq8crkcnFWUCYzuTEZfvGTJ01mQMxjGzQ/lI5pEibBTyLZt28IhUYxNvH2aCyPKxR1kR9U8qqbPZIcqlUpKCKvV6rJTf3mfo/6034XUU7y8g7n0z11YXPk76hyjATCtR8sYQPpEzTnOn2/9W6vVUtksd1ypx3RFHDvoIMGx0fAA0RWtr5VhHBkjdxzdAYgVbJI0zimg78iEKyfGCJ50eXOjtNqIXTW8VKxZVkJavpheavB67PzCV3HGx5E9qZHJ5Due4/qjWq2qt7c3JTPwEfLrchMfGEn7CcQHBgZUKpWWGQ8WVvvOc75g14NoHBTfdhZdODMzo+7u7tCmGAxyeaWPOD3xtrXoeMaIaxhzvsMI43gx7jwLR9rHmPlCrryMzkswWHsG0MC8+84xscziQPG5L6DmHRh2d5xAC1kn4v10Ge7o6EhtbCI1bCKnEse6zbPN6MZqtRrOAYoPmnNdBU+78+Z8hb71e6g0oA/Oj8hYM6dmtRF6wXnAie88W9DV1ZUqc4VcNwBQEjS6zDDnzJnLGc/BfnFvPJfxNv9UFbju88AEvvOgMgYRCIy5D/uITHhZupTe3tjvI3hANnxcPUuInnKAyHUsoCB2kH763Hhgww9tYey8j+6H1Ov1ZfZeUsgSuD2JAz2pUZrL+TdkX/HJ0HscTcAc5/P51KG+Ma/xtwNYsa/AbwcUud4DZObPQe7u7m6tW7dO8/Pzuuuuu9Td3R2ACIJpzpyjoobsOfPl8oP+Zc7z+bwWFhZUKpXCGsFdu3bp97//vQYGBrRmzRpJS77k2NiYxsfHl62x3RPtc6DhTpGklGFyY82AY5Sp02UwqB2EwTwl6AJKMCOlF9uwdaPUOPGxGVMR0UnpVCKG3Pvg6acYKYjRD2cImJVrvD4YgR8dHdXIyEgo58IA8+O15E7OrB5tu3PFfS6sEH13pYEjjIMdLxp0J5sx9NIJfkvp0gIcAnfEeB/9IoXIugvSz9S5ex8ICnmWlxJQU+u1tShbV3qkdp0nXQG5M7uS48KzcBjpMz/wqJcD4by4Mne+cSPpY8Q4g2wyF3FQG6e3Vxv96le/Ui6XU39/v9asWRPGkjll/lGCUtqRwIi54+oZQMbTt2R0vuV6nD83ph4IT09PB2RIaiCB7e3ty2qdFxcXA1LqzkBHR4cGBwdDxkFqLOhkgTTZQAw4fMgW4eVyORXgwgO+/shP/8ZwxHqFYNgzip6ZdN3BMxijGAjwMyeQA3eYcK7hb/S/Oxa8M3YGOVBqYGAgZGGYh5GRkdBvzxw4ogz6SGCAzWIsKJFy9BY0lbaiK5hP13XOZ/AK5TelUikEYLSRuUL3Ub4Tb5rB3CL7jBd2MwZr3Al1MIRxgKcAy/w6f99qJAfdXI/Do1KjxBpdAt/GgYY7jTzPS77d8cV3gM/8x4NwnkPbcI69LId5bWtrC+uFuBZdhf6L7anbawdh40CGe329Ce9wfmYc+N7HEr7xbCFj28wnchCD58HXzrvOvzzbg3TPlmCP0f34bx4UcS3vZmcuBz/dB1xYWAhl7uz+GQMlBBu0q1gspio+fKx8vmPecrAFclAHefRSLi8FbWtr09atW5XNZnX33XeHs8R6enrCWUwAxG4H2P6cd/i7JaUWcufzeQ0PD4eAg/Hp6urS+Ph4KPH3bJeXve2N9qt0yh1+jATOLx3xCB/hwzAxyZKCMMcMz3WOSmL0fCtCR+x9sl3RsmgXZ9QXXcUGBsc7l8uldhqCAZy5iBqZSCYXFK2jo0PVajVMEPV9BEkePcO4rjBix9X/x3i40YizDmSGmAcXWhRAjGx4JoK5dWcPIYmNI8oc5UA/Mdi0kV0/UNb8zdh4jSLzxqJ1+prJZMIC9WKxqHK5HAIA+kp5AgrRFZDzkxsoxozPcVAYO18QjwFHAaHA4Tfn+3hc3VmIUQwMBU6KG0Tm3pXTaqWJiYnAS4yfO6EgYmTOKLEEWceAxHqD8V5YWAhn8MRoEgYP+fHgVFLqvfCxnzNBgO5IM06+G1PmuaenJ5QlYrgnJiZUqVTCtcxzvAsUAStZOxaQoxtxeAjOMKjoKkpuHACpVquhXhgU33eoImCSGtkWDAnBWTzuOAC+HgKDBPrLHPsPYANOmGddWByeyy1tSZkkiXp7e4OeQN8wDgQfADwEGi6HvFNqbEjigYQjnm78pUZdtTti/hyyYJRQefnH7OxsKAVlty6+Z17cCXK97c4Lts7b5jo4dibcMXV0HvvtjudqJPoHrZShIRtONgPb0Ow+/sZGeXbZZdVtYax/aJuTgyfYdy+v9vmLecwDe3ck3Q67bYyz7vCtgwAONqDbfFw9UIkzM/CoP8950n0K14meieY5rrfiueO+OJsB2OG2nefybnw92t+s+gbQFz04OzurSqUS7Ozs7GzQJdxPhghwiH66/Hog5e1zfw4wwUtaHTiJ/YyNGzeqWCxq586dGhkZkaRQ9cFxCZIC0IEv6nYtrpTI5XLhKAVsW7VaVVtbm3p7e4OtKpfLqlarwd7AgzHP7432OdCIa8Q9qvYXMtDz8/OB6fkeheyLLXlGjDh6VE15AO9gkFzIUp36/4WYxVI49l4Ph5Fm8D31BmPDUDgeDHJ3d3dqS0ac066uLs3Ozuq2224LWxJOT0+HbIan0wkyIBdElAFBU5w+d0F3JMuVj1/bzHg5wzEGrii4z9EFR0nisaf9CA4BBPOOU8F3rAfhh/sJPjhvoFgshh3EQDaJxAlWOIUdo07furq6VK1WU22MkRv6jIFhTFCcvrDKUUEQWxQR93qWxffZjsfXjYrX4lNqh+Jx5BeDt5oDDT/UKM7CIYdeskjGS1JYTB0v0Hae9v3cPSvkOsgNADwupQNsNjjI5/Nhq0gUMgGNAwxkpfxMH/QEn09NTYUgw7N06APnE/52XmWhOGPk+/C7o+KZMK5BjgmceT6liQ6uIK9S4wwc19mAJIwBwQrvIfAgq8O1BB4up+i5YrGoYrGo7u5ujY+Pa2xsLKxr8XM1PJuFnp6eng7GFpmmTTgTgGLwGYYfg88cdnd3S2oAaz62jlbHGQay7QR3zs/uLDqAEQdtkAcRIN2MqxNBo+twt8UEM66nnT9WK8Vtd7DIP8M+5/P5gPzGgQW/3UbW6/XUms4488N9DoA5xei2g6LNMg7Iu/MDgKLzTWw/IOTNN5Jw3nWfwvWOBz3uKKNH0Idx373/Hlz5+g/PnMXBE/che55x8OvQr3Eb0IHMsftHMUjnvMA4Mg5JspRdLpVK6u/vV1tbm6amplJBnm9DnMlkQlbDbbD7EOgVD+QZc59nBwIcDPAKCcCqDRs2aGJiQvfcc09oW5IkISNOMIA+AEBx0MuBHH+HpOAHOwDKXGNr0bv0T0rvdrc32q/tbRkURwRplCtmhC9uNM7Z5ORkQKwQAhxzV8Y4pnFnPFpFQdBGmA4BYA/leFtAUtmSQpDA55lMJjiMjhImSZIq8eB9ZEqGhoa0bdu21KnmoFrOdBhaHAwMvqSU0IDosgYERzR2oHxhpS/6QUj9ebEyc2ZhzF0Y4h9/vv8g/L4NHDXa3IezwRkePra8OzbmHN7I/PX19YVsFMKWJEnIgMAfXq4Br3hgGxtcVyYoCt9a2BWDl3kRaPrGByj++fn5gKQxprFDhgOEEkCx5XK5gCT4gZL7gyI8GAnn0A2ctPwAJakRGPI5pwDX6/XUWh2eR+AppRcBQnGWFBlw5IyNHThvB2CEdnNfoVBIlUt528lO+bqB2dlZTU5OpviI7FtcogT/Qc7/ON3wkPNWrAvJJqA7eG4+nw/y4GcJYIBAutB3kkK9r+tj17MxYsl9ONljY2PhDCGegRNNBtgDEk7ELRaLYdGiH7zmclQul9XR0aHJyclUcO9nCSwuLoYMjp8m7XMP39Tr9bDuxkEH11Oxc+hz6fzHu31uned9Lh0o4t3+LHdefez9HvglBpAIjt2xWa2ARRxo0A+3b66nOzo6UkFg7Dc0IweBYn3bDIH3uYgDj3j3M9cXzgOAFZ51iK+h//4O9BZ+id/jAT4Ajts+r/F3/kQu8G3oN89h3Bkn/BD3K9A57lh7X/AL6JPrRSiXaxxU7GAM5xC5rvJgwgEJAhqCEHwt+kTZY09Pj7LZrKamplLBj7edcQYAc98JP8j7DK/4s+Kg330qt0ksxq7Varr55puXAdM8Z3JyUn19fUGHwttxgOe+H74LOt7PHnObii9MaZnrHX/m3mi/S6c8oKAzMCcGyyNdGgfzgPTCjJRGuJNJejN2QqR0eUOzGm6P+pNkaXGXo+/+LBwFhIdUmQuZL1jv7e1NZTdIy9ZqNd11111hRykPHtzQgFD4AVb0lXe0tbWlttnzftMmGJixd4eYOWEhvjstsYKMEYxYgfv3HlS6U8iYO2ri8xY7VTgCLqDOY7TTDQJlByCtpPEYJ9BMeElScKbgNZ//GFWA3Dn1c1DiwBmF7EGxr+HI5XLLztdAdhzRYsxiRwPnaGFhQRMTE6F8hrGMEZvVRDH/+WfNxpjvceABK/hBD5Ahc1lyZ9CRIEd8+Bx583I99JnLDfqsVCqFYMMdOYxNb29vKJeAFwcGBlLZvFqtFngXfUfGx4GCTCYTtpKOsx+OzjugEI+rj3k2m02VhDHeGB4cDwy8pLAbD/eQdYvROt5L28bGxkLGgcDbg3IcGQw4TgJ73aMDAC2q1WoIBgnS0SfoYq/hdh0qNRboe7BE0MpYsBOVbzriOiAmL62NbV+SJMtq1N2ZQwegTyD0veuamByxdCCF71x2HGRxh2w1kiPAbutdTpMkCcATfONr59zpbRYkuN1tNi9c70CqjzPviMc/BvD8XVJjUbPU4CvXebFORO79BHDeDVACqOkZvVhWQcPdfrtz7yXM9J1nQciL61X3LVxG4EMPqCQ11bfuG1IV4yXnXibEu7EVrpvRvQ5kMx+AOIC6vparra0ttYkEh6s66OcZb+bBbVls9/jcM1pxMHLQQQcpm83qhhtuCOBIXPbGvZwd59vwejmWz4XUyFRLCv2hXehU1mvga7Hznweq++qL7HfpVDxYsbC7MXKUK45kqdN31MgRaJ8cBiSXy6UWsMQOY9wOmMEFECccAULRg2LVarWU4SMVRZDBntRkMoaGhnT33XdrYmIiMLELFWkn+usIPo4NSACBjx/E51u7FgqFZf2lLR6BunFDiXiAwvg6OcLlC8D5jrF2pMcddxwLxjiO5mHguHQlRiLcUfKUKdmlWq2msbExzc/Pa3BwMKQ8qadHqaM4i8WiqtVqMBi02QXWDXU2m96C0BWXB3uxc4UCotazWq2GtSYebHrAA/nYsWOY1NhsYHh4OKRxHVFajeQ8SNmMK3y+c8fZkRgcUYIANzSOvGHIpHQtNW3gt8shyCDfxcgN//tar4WFhbBgmyxeW1ubenp6wtaz3d3dYdvAqampAEbQbndG3KHgt++KRMAdO6KAJF46wRh6qh+HAx4GRfX1Au7AsxibzDBoYK1WS53/wLbPyFa1WtXw8HDqTCJ0IeMJdXZ2ht3jfFtagBzas7i4qJGRkVA/jWMA+JPL5VQulzU2NhayPQ6IIIO+sx73us6UFIINz2YzVzHq6rojRsq5jmfHhtrt5570s1McIHh5jMsK+suRSJxH+r4ayXW362f4Gd0AX/kOX14G5fYsdsh8TmKK/QzX6fH1bkdjmXVZpxTcyxJ9Y5VmvhC6ym2/lC7ZA8F3xJp+89vvY8ctxhkd4tUJrlf9bwca3Q+M7b0Hz3F1jPsHLg+8y0vYHaiLfU6yi57VRc7d3vJ+AOD+/v6wBlRqLExHTwBUkB1zn8VtEX1zUCj2Vx3E8OAqk8lo06ZN6uvr069+9avgJ3s2kuvdH5mcnFRvb28oqfL5hd/oN/wmLQUaY2NjKXmgwsiBNedJZGxPesppvwMNGsMaDG8UER9MKynlSHCfo+jO6PE7GHTId26JHbWYsV35YGzjSNMjfNoKc3jtMFt7LS4undEA2nfXXXdp+/btYT9mR+55Dtu10mayD+7Y8z7QSqJE/veyIBwqNyA4C+5kuEHkOWRhYjTLx5u5cWUQoxEIkysIxt0RoHgeHbWH+NxTqDH64ylQxmV2dlZDQ0OqVCrasGGDOjs7A/LvBrWtrU2lUkmVSiWFjDja7QgOTpcbJM/iudHAWeSH8hgCG5Cmer0eaiilBjrsCpXn1euNckEcroGBAW3fvl3VajV14NtqJBxNT7+vtAAW8qCOOaM0LkaS4nKQZorQedb52RdpQj6P3kZfLO0ZAkCKUqkUSnWq1aomJyeDLHAt+sYDLd7tJZTIO8iit593OzCCY+C8GSOpjsjlco2F3vAkvOjXSwqLzFljARIGAMPcjI6Oanx8PIwh224nSRK2rqUvLvtSeuEzGRHaQBDR1tamSqWiqakpDQwMhP3lOzs71dfXFxZNAhygYwgoeJ8jg4w/tg29QWDnOtt5k/c4YIGuA5xiHLifa1zuY7DMeZSxQYfFgJwj6u6MevDBmLo+X+3kOt1BL2lJhljzgwPvY+TOHRTfvxI10yuO4scOKJ/HQYZXLsQOMOSyC/9S6uTOppQ+vBaZjMfFgcBmFOsKDwDwi+BfH3sHLvzeGOjBrvkYeRs9ePFxBkH3UiPucV/Kd45zmffMh88X4zAzM6Ph4eFQrunl9B5sAA67jJL5wDn3IMxl2209z3P9kslk1N/fry1btuiGG24IRyI0qyTix/2iiYkJ9ff3p/xe7qvX6yk/3X02B4Ca+TuSwuGpbrv2Faz4g9ZoxIi1O28e5bnCY8J4VibTqNtHMD1ic+dPUsoJ8OidQfHFN96uWICdAWiDK2ln0HK5rFKpFHa7GhwcVKlU0tDQkHbs2KHR0dEUaoAQePkUyIrvg++1krGixDAxyQiXbxHMODfLADEevAOnmefEwuXKIlYGseL0+YDc8Yoj7kwmExw0kF5KoWKEgrnxcgkWaUqNs0BABkmd0s81a9YE5NXLPGhzd3e3JicnU8iM9xfHB/7yDBjOJsobhJnghz22p6amwqE+rlyZA8pNCMxd6Tl6ioJg3LLZrNasWaM777wzbHu6mol+x+uRpMZuJS73rlwdnfU5dD0kLUd94/e70o7LML0tfOdKnTQ6h/UhpzinfX19od6XgARjgnPtKBvoI7znRsyDcxQ8zjkOBnzrGQF22pmbm0udCRMvPqe//j5KPMlUsF6FfiC/PMezIWxbjYPOZ4AxvgmAy3m8cYKDR75rl6P3vvAf9BrjV6/XgyzSRj/jwDNezDtjiP4lI8pOXOg2bEWzsoHYyecz18/+Xr8mdqwYP5cZeMCdRMYSHosdaHde0bnYiNVIBx54oHbs2JEqkXN/AP1ApnL79u2pOfRSpjhQjEEwp5UAnvge94Fih819GF8b4YFPHGR4Hz2TAU8h8+gR5ph7yODFh8zGgIvUOHWe9sVOv4MLfO4OvI+F6+YYQEQW3Idxfy3uO3Plf3v5FW3zxfP8Bph0ICa+xrPr+HKeOXZQJpvNhkOd6bMDobGv7OOCznffgM8ymaUS2UMOOUQ333xzOJSPMXc75TJPdoV5npiY0KZNmzQ3N6ddu3al7KLPiQNc8I0DTD4/gKrFYjGUp7a1tamvr6+pTMS032s0cO5gHj4nSiJzgLKPhRVF644CkT0THZe5IJBeTsEEcD3M7+1y4eB6V+xS45A7V7y5XE49PT3q7+8Pbe7t7VVvb6+2b9+uXbt2hbpHZ1QYVFIqc+F1xAQitD2OlGk3KKSXK9AvMioIK2kwnCAfB5jexw9h8kwPygIl7KVRHjT5okmI4MlThSh5+IDPJIXFeVxP3/wUXFdeHnDQNkfmGFfOZvCFX52dnaGPfX194VyXOJvgKAx9d8RRajjGCJ8vRpufnw91ne4Ae7/hg2q1GlBdD7jgV0dEqOXv6OjQxo0btX37dg0NDe2r2D7oyNcBMJ7wEgYIfvGAz3UFSt3RaDfUMboc6yDXCRh6z444H7DA0p1rwAXfWpVzXVisDD954O9OQK1WC/XTyBb9LpfLkrSsTQ6SoGuRBbba9q3EQfkxtFIazYSvfbtYxp2xdF3hY+dGen5+PnUOgGeF6/Wlcke2jox1OAYLY+noIbwBSOHAAY4+bRgZGdGmTZvU3r50zkhPT08I7FdCbik58DnH4XAjm8lkwi5E9NHBFD7zbL6PjwcGHjzwvwcejHtsw/iOOfDr4TH+93IM2sC84pzHMrGa6I477kiNN2MFUd7C92y+4uMc/+3j5oFBHPw1c6Q9sPNxj4OXWG+hH1weuD9+B8/x7dz5nuAEGXd77FtUOxDJOKFXXb48+GCc8Pni7GMcfHlfaLfzoH9HOVdcdcFc+NlcDhgzP24rmAcnl2nGhIqUWCbdVwL0YX1vJpMJRyFAvk6DufX2uPx7aaePHX5wvV4Pu4YdeOCBuvPOOzUxMZHiJebA7SDzQf/wo+fm5jQyMqIjjjhCmUxGu3btCuODrHgW1H0Pb5+D5LwHvcp88ey90T4HGjMzM6nyAC9zwjDFStQdQibDU9Cgy67Qndk9W4GjGdeauUPN4CEgPIuBRLBcMXjais97enrU3d0d+kgtMKchzs3NhQNMQBRBFjC0CIxv68qYeDRJMIJQzM/Pa3x8PPQP5mQHFg61cgeLdxWLxWWZHH8GAuqnpPs8INwIFUzotY0IFsKIw4WSyOVyIdqHcTH4CAbb/bpy81IOFBBORpIkqUOv2PLSI/x6va7h4WGNj49rYGAgdZijO539/f0hswE/en2lIxOeJXOFDD8y9/C9O7wu0CgAgp56vR4yND5P8DH84I4ljk65XN7nQ3IejOSoK3McG2R3mnBEkVFHZGJkMkaV3KA7GMH4EsxLSukffsdZTknBkQcsoNSOrML8/LyGhoZCcLu4uLR4cWpqShMTEyHQ9QAAgMB5AyS/u7s7LHx2PuNeTqWu1Wrq6ekJY+DBEvziJXmMoetPD77d0Y0DLoIcd7Qp1fKgOZvNhkxfrVYLxtl1HzqP692uIMMLC42zP5gr5ph3TUxMaGBgQD09PYGv+vr6NDo6GrIfcbmDO+LOJ+5kkbGMd6vh2tj2xI4GvOR20evY0amAS/QNXvWdrDyTy9zRXpw1R11pv2dKIe5fjeRjHPsc2F/sAzsgurMqLV+DJTXPKjm5fnI9IqVRe28T88u8xkGJt9sdPX8+n/vaMORSSi+Y5rdXVkDwiusIKX2or2/EAx/h8Lv+cTvovIY9ZUwcCERO6IPPhTv+DiDRbp8blykff/QdsuCgHeNSqVTCuq729vawsJw5QL+5PqfsGh1I//P5fCoDAHmw5Xok9lU9qC0UCjrggAO0fft2VSqV1Lw7n9C+ODNB23nnxMSEbr755hBsDA0NBX1MW6QGyOT6DFvkgIfPGetePRDdG+1XRiOOqBytI6hwY+FM4gObyWSC4wgRrTczeI54NnPEPLpzhNQn1Z0GsgcYXt8qk11nmAic+7GxsRBg4PTRNg7TYqEyEZ+XGrghc+FkERLvh/HpUza7dG4H4wwCSv0piOLU1FSoDfcMiBtO5gvniFIG1pGwaIxMAI4e7aAMql5vbE3piB1zgMDzPY4Zi6Q9oyAprF9A4DhkEUVJm3GufP9oyojocyaztNXsunXrwsKuuAQhl8uFenEUToy24kR6mZvzGXzHXIGgIQP03eeeLA+px4WFhdR6DZcHBJx3wSsgrauVYpRGSiNRXjvq4+JgBQ5n7Gy5vHvwEitlSSkZjNcxABpI6dJMD4qlxpav6JB6fWk75oWFBQ0NDSmTyYQNJjDAbmDhN7aEpu3wOfK+uLio/v7+FCKJPMAPZARoUwyeuA6NA2o/1RsedqSQOXD5cNSLdwE6sPAWqlQqmp6eDrqkWCymxsDrlEES29raQoZQUiif8p303KHPZrOamJhQb29vOKyvXC5rdHQ0pZv8njgwwCH3Z6IvWBw6PT2dctScN7kWueUz5+846HUgzp05b7MHF8wHAThjyO9YdtwJivu3kkP9YCdHomP0n3EHIWaTFsavWb890Hae9++aBQf87z5QrNuk9PoF+CwOZv0+d7i9b77zE+CLl+8xv/g47sDH9i/ug/OS61R0pINxDqQhi7SH/xlj33nRgwGXfx9r+hxnMH38fGxcjt12+7PQG/AB29nyXJdHxhEb4qBeJpMJZ3nBawRytMHlzufPP3d5z2QyKpfLOuCAA3TXXXdpYmIiNe4OwjBm6OIYuJcapfAdHR0aHR3VDTfcoIGBgZRucT6iVBQ+dHvrm6tIjfNLqOTgGId9of1aDA4Tea2/MzOD74zkDCY1lCHPnJmZCch1jEAyMaBlvlUhDOLvcKaNlQnKgPtxqL12N5PJhEVKUmOXp+np6ZB+HRsbCyk1jCRoF87pwMBAOAl7YaGxwwvtq9VqGh0dDWg9Tg1OqUePjpowxgQyGFvGbWJiIlVTJy0JLMLB+LvwZzKNM0N4LuNFQEOEj2CQTkNw4wOpPMtAhmZkZCTlUHnJHW0hOAPN9AyR17kvLi6GsWVXH8ZQWhK2nTt3an5+XuvXr1exWAxCi3BSfsY4upMJYoFwe5Dt2SJ4ijYhH27UYtSFHSvcMfMdIBgHnAmEHpmLkdXVRh7w0090ho8nvMZ8uVPtaDhzEmcf+N51hJReH8L/fho5hpb38TntRHbYhc6NS7Va1fj4eMheoCN4Fvuvc94HQTC8LKVPOvcDRqenp7VmzZoU6u08w9gxlvAfcsPYoUPJbLoDIzXWBMVzFqP/zIvbAtB39JiDLR0dHcrn8+HUb4w/WwRLSm3EQF/8XcyDo6bICM9g/pgX3+3Jg7xYz7ke98wA1+C4+ho7d6jcucA5dcTZ0WApfVicOx/eNsY0rkGHGB9/VhxsuB2OnWZ/z2qieOwgn1Ps3u7du5dlgOMggnu8DAb+4ced/9iJdRvNNX6v6yEHav2eOMDhORA2Ap2EgzgzM5NatOw+gwey7ldBbu+cj+O/HfABMHPdE9s5ng15toK5Yaw9KOZdvm7DfQl+e6AcgynulDO/6Chf38laR8YUve4b7Xjwga5Dl+Gn+Rwhq15J4aeIN+Of/v5+rV27VnfccYeGh4fDdxxa7IGM8wo6BZ3pOoSMREdHh4aHhzUxMRH0ous1xhfdGdtP5xHXFW1tbWHtsgNKe6L9ymjESI2TC5A7uRDKnM9dIXudmZfDQNS/s+Uhn7nSdiUhpRfgxYyJ8HnUDkrvhoo2M8le6kXg4fV/7jiDcPuZIWx9SukOzgaLOCcmJoIBixGxGCnHcM3NzaVO7B0dHQ0GHLRUWnKMGAee7crPT8DGmSFAchTTUV0vqXJD5rXTni3BKfBSNucf2uNIgaSwlSVjhqPu5Vs4AqyZyGazGhoa0tzcnDZt2qTu7u4wTu4kef0qc06wQ5DrBoxxI6jmOg9iPDD0PiFHuVwuBGfuUPqhcDwTmUP+VqtzAHnpFD/NEDZ4vBlI4WiakyOE/OadbswILHh+jGh6HXIm01j0i2z39vYGdAc+Ghsb09DQkMbHxwOijy5D5lggTaaOoJssJcYQncL/fhBToVBIbWMLn7hBoP3uAHR0dCzLvhGwkWlDNjGOzZzmZhlL5ACjB4/Sv7Vr16YyKCxQlxq7QrmDTzrfkVKyTrlcLsg5jgLBablcTgWvXOvZTne6XCa9xMadKwj5LBQKYT953hU7q64vPLhxFBTd4vrdszS8E5AldrjckWTumQ/PMjHeLlcxsrrayAMM5IyxglfhUTLokM9HM2I+XM/gnMXAXxxMxM+JHd6YeJbzUbPrKBuEd5F9+BWH1wNIb2OcheQaqeFY+7h4nxzQ8ee5g814xUGM6yT3NXK5XMjAOsH/jI0D2+43+Pi540w/eK8HD/gkDjB7+7mGufBMo2eq4CUqERhXD5R8jlw/e5vb2tq0bt06dXd36/bbb9fY2FgYfzbTiPvr5IEdbfBKFildKkv5vwPZACiTk5OBR+LSVh9X5oTxKhaL+wx67nOggbJzQ+NGmQb5moPYkeA7d5pRsrOzs2EbQhcCnyDOJogRGRjfEaVmCIO3F4b3iN6ZpV6vh5IHHHavq6a0wQOwOBvBpFE2hIHhPARfMFqtVpedDk5bqVFmvOi/o+mMiy9QwiHB2UeYPHiKHS03WoyRK01H/sk4UKpAVod+ky1iO0wOzMGoUoPNu3CCnNwhI4Ci3AtB4joCh2KxGNo0OTmpu+++W5s3b9bAwEAQHpR3e3u7pqamUmtWmBsP6BBs+s073bFxWYgzNv4da2TcoQZNdZ7EQeUakIpKpbJ3gX2QkjvBsRPlQZoT/BSftk1ACFKF0Wkmzzwf48X8OKLMe3yuXR6y2aX1W7QBPhwdHdWdd94Z1h55lg+d6cgd88+cuxw7KME4gTpVq9WgGwjePQhgXFwnucGDF8muMCYEN+xsh75x9N53u3MHwjeH4HtkD505OTmZegbnizD+jmB6qRBzIimUcsalAtlsVoODg2F3LN4Tl5VwrQdMjn46uMEY+zvQM2SCvEbZM/ieCYudNwdfOIdEaji/nlnlGfAH5M/3gLoZiOcOOX/HSPRqJHf46YfPkQce6MxYrzgP+T08x+eU6/0Z7mtIy3eein83c/ZdXnmHAyRcg03ku/b2dlUqlWAv4uCR97qf4KAV7WeMvF3YfPjIz/GKQS7PktAu15noWP9x/82DEx8Dxh1gAUDHA3HXQ/H8+3yhQ9zWuq7nWrYHd/5ynUmQ4sCyl5y6/UHvtLW1pRa0O9C1fv16dXR06J577tH4+Hjov2+IE/cp/tv1ZswDDqIAQlerVQ0ODqaCMNoLP3DoovMIugtwn59SqRQOnN0b7XegASMR6bqippM0xEuVUJ6xEwDzMxl+Mjf3MZAgwK5cY7QdJzZ+l78HhvcJcoFdXGys1UiSJKSxpqenA0KCYyo1apzn5+fDVrgwJTW9jlRKCguD4yjR038wP+UIrvycwT3V60iDByBx9NvMMHkQ2WxcfXykdCkQ7fBAbnx8XMPDwxoeHtauXbuCEHEtwUZ3d3cwhMViMTg7XgbiCIOX0WFM6B8KoV6vpxZ07d69W/l8Puwx7YaX+k34K5PJhCyDX4uiYxcsBBDed15tpoAcKffUJP2an58P6LYrFQIoP8NgtRJ9o08eVLnB8KyRtHw3E75Hngh6mROu5zMPNvwZ6AUPeGIkmr97e3tVKpUkNUCTe++9V3fddVdq3Q19cQDCMxY808uO3IijnzyTEZclNXMgM5lMQAqRY9aO+IJH3um6AuIe5DyXy4WtnL2siayNb3LBSbL0O0kSdXd3q7e3V+Pj4ymAaHR0NFU669lTCDmiTBO590Cku7s77EqFrqPN6A03nDF667KGrkPvxHyLruYwP07r9Wcxrh4oocsYMwep4BMPQuEFZISxRIe4LsSG8l7m2WXKHRAPiFYrMYfwYqx74WEqB3w+44Ch2f/x5/HvuC18546ZZ19jcv+nGfnnvkMSeiEGZl3HuK2h6sLXTME72E/koxnwiCzg+Lr9Qv/ik3hFidvieKy8jCqeT4g+eDBBgO/yDBjIu+Kxdj1EJYQ/34Mc9y3iOWK8GV/GtlAoqLu7OzwTHw7d6bqM+4rFYlhvt3PnTg0NDaVkElA1HhPa5MG0+0DOtw6gONA5OTkZsskEQFK6PJO2eqbGAzO3HRwovC+0z4EGtbtMUGyEGQRXkhgPOuoDx4R6mo8zEGLE0YMAHG5/FgzJADMQOB+gQB4kecTLPfSL0oJMJpMKMpwJ3OHH4CKUIO0efPG/p+HcqfK6R9aAYFQ8SkbgPJp11N0VHEKKQuF6R8K41r9zJy1WPM2MlZ94TomU16tXKpWQ0eFZ9M2zH7VaTePj42EBPmtD3BmAByFKKDxI8DHzbQ537Nih+fl59fX1BUR6ZmYm7Oqze/fuZagI7/TxdcXs5WPwmTs0Lh88i/ZRMuJKgucwLl5u1ix9vNoIhUbQhLGEJ+BJSvngQUfT42A7RvI8QGWs0FvO387byCfGPEb5CoWCBgcHg+7o7OzUbbfdpm3btqXO0HHwwDM18Cd9cUDGMxPwGOWZTgQRXnLJM5wITpoZfdcfjDO86PoQR0ZSyMCyNosDKX0rbXZVox/I98zMjGZmZjQ5ORkyhzyLUkbP3DK+DmBVKpXUOpquri51d3erVCqFuaOvPIt+FQqFABYxl/CKB6B853MTZ708AM3n86kTe/06+BFd4MEBepL/CdQIeuF3PkfmeY4HKN4f56fYeXRgCSc4RktXE2F36vV62HrTHW4cID+Znrmm780cOan5+TuMbeyIQW6PpHTZUAzyxWCG66iYCJ55FrqJjV+8ogJ9JylsnV2pVFIAA211cMfb16wdHryyVtadWPcjAI4c3I2dZgcNGacYZIl9LMbCA6ZmmRx3hB2F9+2NXSaYjzgLjl7yMUUGCa4YQw7vcx3sFQseeOLX1Ot17d69O5yTwXubOfMeBHGtt6FZgBUT44R+3LFjh7q7u7Vu3brwTAegXQ8677iuY/zZJGNvtF9rNEBYqJ11tFFqTL5HfpJSAYdf76v3JQUnFYPFILhiYTBiA+vBh+9K4ynxOFLmWgS+UCikEDMYlLUY7jTSLq5jtyhSlC7I7oA6suJOM8pCapwzAXOQQmeMcRYYA5QRht/Lj1BOvIuxgJlixnbl5wgkf7vjhiCjVEDdq9VqanG4O8nuMLrixtlZWFhIlRV0dnaqVCqpXC6nFBspcb8ml8uFuWIcQHdQvrRt48aNyufzqtfroUZxYGAgHHDjxsHn0x1feDquwfTASErvf+3fuYGKlSaG0xHy+Lmrkegn/YtLzJhfNygu61wPH8VZJ4ggLV4H5E6HOwKOpsd8mclkwg5nCwsLYTej7du3hyCbQIP2ooBj3YXhAP2KnVk3WmyrSlDgyLY7CLSdUkDQLj8jhMWjHshyr9SoQ2ZOMGb0AafOx9PnhQDI+Xh2dlYTExMaGxsL+8J79lZSKkDhGYALPBf9504a5M4W2/vSNgKCrq6ucDo7G1hwnYMmMWjmYAK60xHWYrGYWsgvNdal8EyCI2+vL9DnM+d7b5sHFdgSD6xd79AHLzl23ieQQw5WM2BBkBaXjkgNnqICAfJxcz72gDweV2n5QcTwAM+Mr3Pb4brEr+N7b7eDn1L6INHFxcUgz1zjKDu+TqVSCcE976CPgAB8hpzHfOBgrY839h4Ql/57kI/8epmWj0U2m035NsgZ/Ohj4wAqoAVtabZG19voB4dyXpEDGPhFANtkwRgPByjdZrmeZu1wb29val7gL3yYjo4O9fT0BF4ZHh4OYDLPdz815lefAwIjfmLg0cEQyPkbAJZdETdt2pSqAHL942PN+Lr/6Bvw7I32a5/MONBwgZOUmkQ33I7e8xxHjLiG57uxipWAO+Qu0C6ktMG3jPSozj9jwNiZibaPj4+H1KtH7wgUzitOMY4vTgCTBSP4REoNI4QB43uMOUxVLBbDVrwg/Pl8Puzckslklu3vzPs9U+Lj6AweK0XeG6PE3BfPuQdDHvG6g0N2gmt8bAgaaANngTB3lKlVq1WVy2UVCoUUMuXPzGazYaEu/OR12owRypiaf04lJq05MjKSQopi/nYHwBEP+hA7BjGCxthQduaKEt6IAw1XcnHt+WojeIv5Wckpcgfas6j87Y4Tz+V+l3WcOCnNw/CNo9D8DT9h4CmZYjc0RzkxWpxxg0x7MEw/3NGljQQcjhbFpQNshNHb2xvWI/AMTsVmzVNnZ2c4gZua23K5HMryfFwWFxfD2g8/gduD6sXFxVTZAo4z6698blibEp8zwlqxxcXFoK/IOtB/5pYsBGOInvAsbiaTWQYA0U7WMWUyGQ0MDAQdmsvlNDIykip7ddly3cb4uC73OSFjzzq0OKDkOgcZ3D65c+AyXa831vB5hixGY902+vf+zlgG4gBqtQYaMVLu+gOecv3ZzGF2XeHPjQFMv8/5g/viRcXcHwOacVAeO/huG/jxNQA4xehFX+gsKZxV42s0+d7XB/i4eZCNHvL20p/YdsWgjuuSOFjyPrkej8fD/ROfk9jext/7OxzwYYyoMIltZpIkAXxkRzzmpVQqBR3FJhW00YNCSqpqtVpYc+aBBsByuVwOfR4dHU2tdSAg8b75j5OXS7kv0mw+fFycPDAnEEcXA/J7MAEwwjPRtyu1cSXar0AD487LmRiEDUWHg+aM6gPp0a6ncYla5+fnU0Lm7/cSoFj5wgheb8Z3GEgPeECl/KA7nFsXWpjXd4shavZFVhh1+u6LmRir9vZ2zc7Ops5dAIWSls7t8DH2mkGcDUoYPKgg+PMFU4xT3G++i5F7noUC8PKIGOlx5sah8XHGkcSR8IAAIx2vk2F+XXknSRKMB4tK+/v7Q9bG0V0EkfeTsaC/ZIJYTLd7926tXbtWPT09AXHl5Mvx8fGUU+COiI+FC2WMJHiAGgeUUqNUhDSrBxKOcjCWfObvWG3kigqnNDbscbDq4+XGyXndjaujbVLjFG0HEhzVcwctNiaSAs/An+inLVu2KJ/PB7DBy4UcBfISSsiRfWSG3dWQVS+p8rKhcrkc+NwDWwwboAnyRSBBFprxog2zs7NhISTtJQhEz8DfoIPj4+Opbb5B1zC8GE+CL7b09SCxUqmoVCqlMgWUhCEH8Y478IO3ibVNzDFyj77k8Lb+/n51dHRo9+7dYS2dZ4Zi59Uzax5UNnP+vA8x/yDnnZ2dmpycDP1324Kt9C2NmacYaKN9zvOxAxk7iG5jAa1iBHW1kPO7l1t6Bom1lA5GuN50vc29Dj5IyzMMzZyr2EeJx9yBDb83DgT9/kymkY1nHl3W6QclOhMTEwFE8Of4ODmvu/6jHMrXcnJvnBnzv+MSm3iM3BmWGptOOAjnvoa33YOQZsFwHFwS+HtZIXrNwTp/Pvcjb+hsvi+Xy6F93A9wG/uWlPzHWU+2QMen9IOK6TO2qVngTN9pe6yLmwV73ke3oxBtxGes1WrasWOH1qxZE95NVQ/vzmYbmww5OOe7B+6N9vvkL5QUwt0MIaTjdDR2jDBmUnpRMxPC3tDu8Pp1fp8Lqgc1DLA7he4ktre3q1QqqVQqBQWcyWRClsKFcW5u6XRbyqP8wD4MEqgdihzl4AEME4wBxPCztz73O9Lr9cu+IN8NiqMFHmy4I9YsWPCyrRjJoH/8uHD6OPI/c+HKiTajZCqVSjC4HgjRFgwHzgbvZjtLgpSpqalwgnk2mw2lFrw3RpGd51hrVKvVwuE4/f39SpIkbFvJHtGOdLtg03+UULOALTb2HjRg2Hxc4Q9HY9xZdZlqpnxXC9E/9ALIO/PvfOUlJMiDr3FijN2YIX+e7YLi5/IZz4EYZ2QNx5dneUkGMsdZO8whypnPkEfPMGJ48/n8sh3HKN/EiKKvCHp8e1h39qX0IXp87/XN7hRICiWPcckp1/p17JjEmgc36vA1QAylWsViMXW2CDrRkVbaTvkkcl8oFAKIw/Njx9t17sLCgsbGxoKhxznYuHFjAClAIt1exXxEv9zO8S4PbAn+6A88yDPRc+hysjAEc37Gg48l5A6w999tHtc5COROCm3nf8ZuteoR+ur99LHJ5/Nh4SvfIxPOOz7/nqGO0fXY4fM1nlL60Lk48HBHeiVAxdsCvyCLnt2knfgW1WpVo6OjAeRD3p0/0Zveb28T+gT76g6+62Xa77YeueBZnk3iPfSLcY3LqXyM4vGOsz6xb+JVIm6jsRdkQ339ZuwfwjcOJFUqFY2NjYWNafyAO+6JbVOtVgvl3blcLpw+PjMzo7GxsdR2vh5oNAMyGDsHAvC5udfH0ccylmkfS2878pDNZsNWur29vUHf4gv7vLkN9IqcfaH9DjSSpFEbySQ7ogwi3cyRjIXWnWAUx8zMjKanp1UoFFLrEOII1+/jJw5enDm83CqXywVUnHb42gwYoFqtamJiQpVKJZQDOCLo6Tje5TXOLKhk5yjGwcfRHXgMKk4On1ObyHNdIbki8nQtAUe8TauXbPA7FnQcHf4ngGDeEFSP4BEOlKSXVxQKhXAORpzJQtBRVL44i89cmaEoODGZ09E9kwQvwFNxVsXXsnB4Yl9fXwhy4QdX4DiQXqfugQdj5cKII+UK09F52hKXR3lQwm8U2mp1DqB4fVW840UzB4ixAZ2ENxxRSZJG3S5onCtmabky93e5AvXzdOAZz6TiNLP+ABSf+XPdQCmTAwRk1pIkCYviASAKhUIwUOwYk8vlwra67kw2yyQ7ikmbvB98Rr8dZURmfYOG2Jmgf/zPzj/MgTtqzK2kVCmCGygMcLlcDnbDwQnftckDUZ5DRnBxcTH0jedzcm17+9K22AMDA6rX61q7dm3YiQ4Zl5Yf9sdnUvOtONEhfIZj63qRPjH22BAcINbgYDs948d1vNtBC57rbXFbwpy6Q+563jOrq402btyoe++9N4yT9xU54kBcqSEfZD9cR0tpnwL+drmR0iAmP76hjY997ITHIAbkQUnsy0iNs1voA88C2BgfHw/61JF8z4T4+/EH3E66PfOARlJqa1sv6fbx9ne4jovfzdwgtzEwRx/hZ5c7xo35cP0VZ4s98OZ75nOlgJDvBwYGtGHDBg0ODqqrqyuMcTabXnPn8+N+a7FYDGv5kmQp08E2xA4SOn/wDN8S3H0sACrKz13n+xz7XDi/xIGv8z5gSyaTCRv2bN26NWx04aCJzwmUzWZDJnxvtN+BhtTYlxkjA5NJaUehWRlMM3TGd3lJkiScKUE9PtciIAxY7GB7GtUVidQY8Pb2dg0ODoaatIWFhRCxgiJPTk4GR4LFwyh476MrKP8fYcboFAqF4FC4U0m7PKDwQ+UYX65hfQbP98ACpeSMwRjEp2b7zl0euMTKMRZqmNsDI0c7QEd87t3wktVA4Nx5d7SDd5RKpcAXtBEHEiNNuRp9xfA7OktbXbHirIDEctAhc+PKmPbQD0fHUPJu4MlMuXKhD26wKCfxTAbP91pb561Yoaw2cnBBahiXZgqRMSfQJvshNXjEnULIMwguXz6G8BBy5UiXX0s2YGJiQj09PQHpmZiY0Pj4eED4eRbvjPkVGQHlB0QhsOQ3hoZAw8eGtWSgjzic3OubMRCY0F+AFNrlh11yDe11x8MPkYzRRXjV5yt2lPx61qggP2QveD+ZGp7BWGSz2ZBByufzQV64x+cNPYSsUuZ11113qbe3N+z6sm7dumWotzvlMVDGXDm/8k4OO0QfIf+xM89mFAQQPu+MiTta8EO8DsnLMzyA8HFnnj1YcVu6mumOO+6QtNw5h3fq9XrQ7dgFz24yx3GGg2fCc87TUkPn4PO4PXR/BIrlxR3DOPCL2xLzscvnzMyMpqamguy4o+2BqdQo+3Qbiu6DfAzjzJ5nIPnbedB1BM+C3M/j3S4XjCVyhdy5L+ltdDlwcMp9QNfrzHkcSLrNnpubU7lc1mMe8xht3LgxXNvR0aE1a9ZIkoaGhjQ0NBQqLbwyAR3U2dmpnp6eUCbqC/W9HR4AMJ5kzNFlACdeJuprM5oFtj7eHmy5f+dZV3jCdVq9vrQb1gEHHJA64NXttRMByr7QHxRogB76hMYpMXfMQGvcoeU5PvhMMgrbA5XYCLgTESsCZwKpYfByuZzWrl0bFiZKCjtNTUxMaGRkRLt37w7bsmI8HE3wNJ3UQBa9PW6UHHlzg4SRR/AR2Hw+HxQKe4FzDWPhaS/f0cQdC3foHOWlzcwZDi0OEf2kr+4we3rZHSRShZJSwUK9vlQWwuFWpOUc1XYUhKwRbSdj4WtlCEoRcEkh2OA6F6QYQQdN5n9fI+CotQeC7jDQTkchmT8v2fK/XW4YS7b6dIMTBya0wxFJD/xWI3kf4aEYiYkNN7X6zQJ8qTlq4xkzSSlD5CCHlEbdINA/aYmnQQ/ZPIByR4wC5ZBSI5OFnCOP7gBNT09LUngOMs5iZtZCwL8TExNBxiYnJ8MCZ7IEfpYE40tg5EYNPeMyQH9Z+0WfucedC8aSRdA46m70kBc/zI954R2AJswT6yfgB8aO+QE5IzuMHLgTzfWxTLHmatu2bTryyCO1sLCg7u5u9fX1hcWQMS/BOw4MxKiqX9/e3h7W17m98GB5bGwstJ0MCG2PQSvGER3MHHEvz42dW8aZtrmj4cj3agYsWOgvLT80LpvNhq3o0e0+Hp51dKcMuxb/H//t740zQi5n0ErZE57hbXMZisFKeAA7iN8RBxs4pH64Ke/y9+P8+tpOBy65xgMp2oYudv7yLKUDE7TPg28fU/ctmgGaDtKg1+mHg3fwMwCkr2fr6uoK62nd5uRyOfX29uqJT3yiurq6VKvVUmvC4JU1a9aou7tbt9xyS9jFDjnv6OjQwMCAyuWy5ubmNDExEWy/+6r4m+7vIocEEj4/3l/3zbjG9bnrAwcR4rlnfr0qwEHTjo4OVSoVbdu2TevWrVu2/gwf1wNtTyTsif6gQENqpIpZzMKES+ntAD0L4UIYI2R8T+fZLcAzFHzvyjgOLGIh4W9JGhgYCNkFqXEGw/j4uO655x7t2LFD09PTqYVVjtjTRpgM4WSLNBYUYfw8WqeO1w2D12vPzc1peno6RIi53FLqf/fu3WFReL2+VD7DYs8kSQJi4VtEQigC5ghD7/XfCD9zxUJKSSHjEy8wd+F2JJ4AiX7imHkpip9yClFqRXrSU8I48F7P7TzofIRSQ1nwznhjAt/jGie2Xq+Hk+dxbpl/xpH+uuMBwsH/sVKmjV7W4rXZ7sCgBL2Ej+c4+uCGazUS4wGPePYrRqCYB8ruHJFyJ15q1LzicLvhRxH7O2Inj3dybYxAVioVTU5OhkyUpHDwUWww6SOodLNTw92ph68JMpA9+jUzMxNk008EJkMiNcAY9KeftQH/+SJy18U4NhgTnuvILW2VGucUUcfrsukGjaCEQAxEFtkmQ9PX16fOzk6Nj4+HXeF4L+2KwQI3gL52BzADoq1sRUvf+/r6tHPnzpSO8IAjDmhj4Ia54R2ceM56Oy+fY5tNAjNHceF/5o8AxDOa9Il1gmwqwqFhOJ0xPzBezo/+7NVIlEi67oeXstlsWE8ZAxMOzMVzC62E3HKtO3nOI7EeafacOCD35/lcYQ/RJdhcwIdCoRD4AAcRXYJfQLASt9mzFdhlt+neVg+u3K7FdhjyDIjfg4wi0/F38TzGfpu3WWps++tBM76Rl4xLDcCwu7s7+Dm+Xu3EE08MsuHnk3nfqbrYuHGjbr/9dvX396uvry+Ue1YqFd17770qFoth9yonz0R4oBFvEsTnyDZls+4zxn5BzE/MsX9OG3gX/ELGHBAnl8tpeno6gOwbN24MPhm22m3t/uiRPzjQYHIcnUfZ+WIpBsXTYXHgEEeqKFuPcl1gYoF1xuVZ3IcCLxQK6u3tTSGQ5XJZO3fu1A033BBqOuP3Sg1mhSEYfBzSxcXFlGLzSM+Rcy+LokTLo996famcBmcARpyfbxw8R3qYhaY4865UpPTCtTjS9flBAbgT58gPQgdjgTq6gLsjIjVS9wRIpVJJ4+PjIUslKWzZS402AsBhXDHaz5zgoPA5QaM7eygGd6o8UHNn0hEBxp92xooXcqTL09qx88F98UIqLxeJn8W8ukJ255hrVjt5cEi/+NwdPleqtVottZVzjDIyh15aGKM6riNcP0GuX5zfULTsze76yhfoeUCJY4dT4NcA1LDRAfPKIVsur579pDzKnXnPChKkIqc4LOgI+k8JIXqGNvg4gMT5HCFfyA/ABfPhwYVvW807kyTR5ORkeD9r8diyl/GiP+hc5xHG0XUBGRocEAAF2ssYuOyVSiUVCgVVKpXwfNd7McIKuopcOkiDLBMAUe6HHvJdkFxn8Dc/6FoCE8+yAkLAB/BYvV4PZcbNZMdljb75c1cbuZPvjlQ2mw2lK9hj159xwADF4BWy5QEA5LZ9JYc7tof+WewY+rwjz4CS9EtSah1YJpNRb2+vxsbGAqjrdjJ+Jp97cEv7FxcXg1y6Hwa/c523hfb7eDqow/euoxyUjEEOSakxd3AhrjLweXPfxMECxtaBJTIdDiJt2rRJa9asWVYpAcXBeblc1mGHHRayrwDByB/61f1atzE+fuh7bw9AjOtX13kxkMX/MZ/5HPHe+G/mZGZmRn19fRoZGUkFQ1NTU9q+fbs2bNigUqkU9Bn8423YF/qDAw0fLIyLIw17EjbvsAu9O1YYYvZUjyN/F6YYaeI9vKutrU0DAwMBTers7NTg4KC2bdumW2+9VRMTEyHIcGeBCQHx83rlZjW2MAcIue+3j7Fk72YWKmLgPaNAnTX35vP5ZdkgkE0WtE9OTgan3g0Kvx09RYAZszj95YFgilnalm8TiDA3E1LGg7IMn9vFxcWQXaJvlICUy+VwAB/fuVLk+V4Hmck0TiYlAHMlJjXWztAOeLdWq4Ugim0yffxiBAF+gKdjg9OMp+ETF2buB7EmI+bPc8cbR8PR2tVO9MuNd1yemM1mA9Lf29srKX0yrC9ipq7ZnSj4HePpaFocTDqCyP9uADknA8NPts13WZIaupHPeb8bQWQP3nUj6cEkxsQNEPxBW2MHAmcU3eMOB/wHoukZNM/kIEfICeNGYAP6Cs+2tS2d++OlPpJCoIWsUxoLSitJExMT6ujo0OzsbDhMyh1xz4wwt6xX8wyrZ1B7enqCDh0YGAgAhqN73d3dGh4eTgWMUgOxY4zdVsVAl/Oo1CjvQt7n5ubCeh54O36el1rCD4wNwBJz60R2bHJyMmSN4mc7H3kAt69OwoONPOCLneD29vaQdUS+fBx8PNCjPM+zf16y7c6zO7a0IS7niR1wyJ00vnedBH+j87FXlFdyP9+tWbMm2A36QsDNe8hyeRmeZzQ92+96D4BCSq/xZBziH+STfsZ+Gk6xyxLXoJ8hHxOfX3dsPdj3/6UGwJMkSfCnuMarDwYHB8NYAuRSbsb7sSd83tnZqZ07d6pSqai7uzuUp7o9517/3G0OvOcyCtAKP3oWxPsT85DznfMV4+1rBhlHAivs2ODgYNgR0OViZGRESZJo/fr1KhQKKRsDTz3gpVN0yNNVHl2DAPoCHikdbMQOmUflCwsLAdWOmdUdtWbIAP8jLD09PZIaDmdvb6+mpqZ07733hlp5hNB39sFB9jRcvKDbkQcmCEFFGc3MzIRyIN9uDkQQJB2EE6XDWFHXjKDAJG74FxcXNTU1lUq14WxQ3tBs3QBjyOcx0uulEowvzkW8YJr5dSXFWPq5F45EME6guKAPBGYdHR2hzpK5YRGnO2q0w9OT8BF8WCqVUlsEE9FTw9nd3R0Wi3oJnAfHnllwvnMj49kXd5wh+uEBI4gL4+YoNk6gI67/nYj5BsHxPsMfIN3VajV1OJLUKNXEwWR+PFDkb6mhyB0Q8b8d0MCAFovFcMijr9taXFwMuwl5iZ+vK/GDQ7lHUthJRGrs/sS1nqnw7bbjNLojbnwGOOF6V1Jw9l0+s9ml9Q/xjlPMRcxvZCKcJwkuMJYe8MP/tVpNU1NTGh8f1/DwcNjqmgXu2WxWk5OTWlhY0PT0tNraGrtJocvJgiInZD/z+bx6enqC/OJw+99+PgmOPGvi4pIPDLwHm+5I+LjCry67jD/3zMzMpDbB8PF0p9CzI7Ejsbcs5uLiYsjGuhw4EAMPYoN8fd9qIncq6RcyOj8/H8A8t91+bez0MR+O+Mfy5jbAgS8H2mLkmHv8Gqe4RAenFpuLT+VBhj8vl8ulTrOm3fgZnGfTLAvO2OATuXMcA4LoGAf54v7GwXfsFPs5OvH1zus+F+4n+Zw04wUP3j3AxIbQZ/ozOzsbDmFlrvr6+tTT0xNKLL2qAfB3fHxcu3btUnt7e1hnVyqVUutjV8pSuh5x/8H7j87nB1l1ANzHFp51QJR2xPPDs33t7dzcXNjGF73vu2QNDw+HDZTQ065X9pXuU6BBB3yBMw1mkmi8Bx0u6D4BsQNFNA7S4MGGKwwpnTLyGj1KnZikYrGo9vZ27d69Oxx04xkMhN3b6gEIAk7fQawcJfLdjEiZ40xhxHBm+cyzMaVSKbzLt8aNI1mpUeKAwzI9PR0WioJcoBBj5NMRgFhYmTfInQ8yWO5oQygsSjwIdGZnZzU2NqaRkRHNzMyoVCqFnXEcCfGgw5meDIjUKJdirGmHL5akJKtWq4WyEXiQ8eF65orAsru7Oyz4dcSE/jkCjPDC7yDTXu5A+2mzK2+EPs6keeAUK3zfMWS1kjvq7sziNLuhcaM3Pj6u/v7+IDdu2HHspeXBM+/gvvg6FLM7EmQw+vr6giOHoubEetZfcK4EsuTKP3YCkEP4FCNASZQHKBCoGmuCuJbnu2Gh/Q76uIGTFJ7jGWnu8QXIBPzSkv4pFAqpcyPgSddj8ChZn/Hx8bBIcn5+Xl1dXZqamgoZqCRZ2mmQDAdjyA5OBENJspTSdyeQUrNKpaJ8Pq/e3l719vamwCXAAxBjL6GSGuWlZOSdR+Mx9mDUedTBBefLubm5UAbl5aCuM+EPeN/1xf4SvOEAXxy0uG1ejRQ7tNLSHPb29gZ+8JPpvSzGxwTQzp1eL62L7Z8HBVBsj2MwwK+PecMd6dimOriCHnGn1X0sfB0yZr6hSuwUuuNOm7wdzXwzQDdkCHJ9A5hARs2JhdiMuVcVxLYxDppolwOeHvS7T+R9xrYCTHg/GTPmA7ufJEsVJy6jrJ1FNn0NqvsO3d3dqZ2cfL5jOYuBC/gXn9ADE+efOMhw0N11VBzk+A/twvdkzCnvZEG4jzcZ5v7+/mVrePeV7pdAA6PjOwswgVKj7jF2XumIo45SemDdWPv7+MzRGhcYhMbT5ygd0o3uSHpdN8hfLAjuhEsNpxO0nj2TQTi9vIa2u5FDIVKOgcGCIVgUWK+na6pdQByd98DBmTR+Ln3gs/hev87b7wEJCih+l6TUtsDOD4uLi5qcnPz/2PuTH9mz7S4bXxGRbfTZnZPnnOp9G2PjAciAhAQDhiAQI/6GdwKCAZY8Y2QxspBAQkwYIsGEAQwQEjIgEEJCsmzLCHzvrap72uwzMiL7jIx4B/l7djzflZGnTtm36ve66mwplZkR32Y3q/2stdcu8w9hg2oShoyIgqDwDOYAJLPZbJaUJ/ZUZLpgzaErjBEaaRggBt6YzD4c0GQrgEajUalWhYGWBYrpluuykCFyhyCHJ5zLz1o59eK70jy+eWiiDfPp9C6axUmszKX3LtHMp14X5i7LIb6zgCbFcmVlJY6Pj4tj4BNn3W9v3nMfWE+e7xQFO0DcA+0yfviAyKYV2Hg8LqmVyAqcFe+XQJGxDwIlanq0o+xoHicQ12p3h6E1m81KFRXGm1OPMk9ZrpGmmBE+Ozs2lqH/6XRaQez5zpFoIsibm5txe3tbKb95e3sb7Xa7VFQBNMiyjb+dhmonzqCAP7eRAEh0enpaKojRB9OAI+Gmi4x2vmubTGYbym08mi7/NMuRLGeh0U6nEzs7OyWfHGfXoKHBSUesIqpIPd/b8bRMcV8wwL1efpYNPH5nZ4MIJLqy0WiUCBUgWJZpfIZDgnMVMTtI0kVHbDu4L5ZZ9MkywU5vBi15rmW1nwFf01e+c8ugkHmPvgMuut+W1wYY2dA8HA6L0TzPsRoMBhWbg/UGHPBaMA84snYM4DOX4zcokcdpe5C5yY4F8i4DcchVyynTdLYx+B75y5oRyajVagX88Qnndm5vb29jb28vFhYWotvtRrPZrDiH79L+xI5GxCzVid8QIsJyHrPac/UEZWFozyo/g2ZBj9cJ47OjHsacTCYFYV9dXa04SCxAPlHUSIJrv9NHNjXnGunut/NBcRggVC9+RFQ2S+IcdTqdgsRjIIPOmbnNqEZznafN/M8zwMyQVprzUI+8V8CGmAmfe9vtdhkT/cIpI/UEFLnb7VbW14irN0wTufGJ7vQdA8PIB+gEipfa+tAUiikiijELWm0BnJEfmh0Oz0HmFe/vATHCuLPx7UhGRnD+tDfnnUbcP3E+IzisxatXr6Lf7xceB0nKoAPPm4dUZ6XIbwv/R48exdraWjES4XEABdISoDnKNyP/TLNsjrYhYFlH1JKKTKT6IFMiojj2pBEtLy9Hs9ksCgODm+pHpGYZ8XTkxwgac0YfLYuhc4MaETNZZpqdTmdlqqkaSESBn+Xl5eh2uxWgAjnLc29vb0uqg2mEa5xiYD5jTBF30V34s9frRa/XK9FteJ659OGGVsh26Fxw4yEk0UCHgSZ4OMsQ6N7AQt5X8MdtNtb4P8vD70JzpMoFRiLu0HSnBtr+sJyB5pDfNoy9vgZCrA8j4sFrLJtyulREVHQzfDaZTEoFSqfl0ADdSCeFl6BfyzH4wX3OwAwNgxr5aYPZdhD7j5gf+N+2Hy2DqMyxHTrbHTyTeXQak2UB72XsREz9vfejshYXFxfRaDTi9evX8eGHHxYb7ujoqMyTwW/upRoU6+HoO5EP25vWQ47KmA/nOafoemxR5pLx2vHgXbnN423LIeQMDi0Fe1zBzPtFx+Nx7OzsRKPRiFarVYDed41q/EIcDQt4RzUgRisJmJn/c8uOiQk2GyAZjY+YpU1BGDwD4sZbjYjY3t6OhYWFODg4qAh6e4VZmCAEImaIyWAwKEYHTo6RcwuvTqdT2YSJB87cMT4EDsTA4mJguE429zHXTrOwcDFSx3yaIM1g/t6ebRY+fAezorwdBUL4IPAtNFgvDLGsrHu9XnS73eIAIEyMrIAIIZzN0PSJ8aPUx+NxOaOEjaJc70MRcTpAX42M8Q5ojLk3YpQZHmbn74yKew34YQ247l1RhP+vN3jM+2HM446W8dn19XXs7e3Fxx9/HE+ePCnfOYpqR8+OJ7SbZZCdEsLoT548iSdPnpSUJk6QPzk5KUY0yBZIVsQsqonDkR0M1hchjqPJd95vsLi4GGtra3F9fR2j0Sg2Njbi0aNH5fTwVqtVSpuCgJl+G41GqeaE08IYoU8b5kTXsjOBc2IEFV5h7wHzGhElR94IK3PBPNNc4c3omyvpOVWSeeJdRhzdx4hZiqWjCpzhwXg3NjYKDZK2xTu4Brnh9FmvJfRHf9xP6zPLPes103d2vH+RvP5dkh20Wq1W9CERX1BmaNTFDJh7O3NZnmcZnB0DDFdHubJD4Z/sWHq/A7ra42k0GsUYxnC2UzqdTktGBsCZQUWDgC4Cw/voP99lBz8iim3B95YJEffLsnO/HbnM65a5ee+ugWY7GdZ5GWlHFtlm8zwxJ81ms4CT9GNpaSmOj49LFBrnlPFjOzFG+s3aMcd2JmguyIBe84b3bF9mHUcBEcBU2wfczxpmACY/3zTD/6wP9ur5+Xk8fvy4XMc+RMBw7r28vIw3b97EBx98EO12u5QMf5f2C3E06DwGnEPfWbCZoT1xRhFybuRDAtIGAoTFHgsUKtd5wzqLx6mrHIQFck16kycZhmfTNnmHEbM84FwmlpSOiCjvoB8OPxn582EwRkVgGlB6FHHOQTXDwjjZocv/Z8K04eC5zSFfiJVxWFDRX55H3zE4oBe+c9qakfzRaBS3t7eVylusH8zN/qDFxcXilBApmUwmZX6MzLgiEAbE2tpaxXnid6fTqSALGaFgPhivn5Fp3gIDlDyjndnRcFoVz/kuNMbhKkTQFPOUhTPX7+7uxtOnTwtPR0Rl/s23OI12/uwM0nhns9mMp0+fFoN9NBoVB4OUSlD5iFnaD1FSRzJYY4ygjDbiELjca6vVin6/H81mMy4vL+Po6Cim02m02+2S9lOv321ebLVaRVGSZ8tYiPaxYZSUJwMvtVqtRAJZByIhliURMzqGd9ibwnwCFNAcrXMaGDyO4maDf0QUkIbILXKWZ3hvjlOyPJeOEBKBPjs7K87Y+fl5dLvdAvo8evQobm5u4sWLF8UZY46y0WP+9F4WK2/+hl7RSdlIspHL38i9DKC9b7Pm+Yq4Mwrb7XbZ9wPNsl6UjMdAtJPttbKOsG1ig9BACPSb12meo25Z4+fn9Cz+9/uzg3x6eloMPPjqbag2utX63DYFv+30+OA6z9k83WM7xU5VNn7z2LMzZn7I62QH3GvHOOY5Y9hArvwHHwKq/O///b/j13/914sTgtyo1+uVKm6Li4vx8uXLMjdOOyVSbKeE++g78jRHl3IDsM3nKNn2sPzLdobncx4ww1rU6/Wir9g/vLS0VNkXh21mW+v09DR2dnbiww8/jGazWfbNflX7hToahK/whOY5G/M8MDdPlhHd7LHawEXRGFmImOW8ElqlEoO9xel0WgxUyis6vMQpvhitpPngvPBu3ufcWBqpCiwgGyrpO/OysLAQ6+vrhShB5xuNu5O3cVa4DwXmHGQEHwTiSgNGYLIxndcHImNNnKrgtXCo1f8bYaE/GDwYFgg5jBzCwQgBHK+Li4sYDof35o21ioiyAS7ijsk5MwXEFVqk4ZyMx+PizEyn03j69GlFqIL8uG55Rr9Mlw4pW2nZCIFXMKIc7rVBYyMHWv3TnFv9toZxmBW700nM+1Qu2tzcjIhqaiU0ZYVmwWuahUZ5B/syWq1WTKfT6Ha7sbe3F6urq9FutysboVFgOV0S4ZtT36wkcBboI0Yy19hgQlFhgMNLPIP7rfAdwUPB8QxkFBFCgwoR1fxyjA8bYnlu4V8j9tnAom+8r9VqlXSmg4ODwuvIHBwk5DdpVMhlaAElz32kglHsgb7V6/UCBI1Go1hdXY0f/ehHsb29HfV6PR49ehSTySS++OKLSvobUbQsJ42Yvm0ekaNUE5znRMyTJ3as37dqmwcwttvt2N/fr6RNcS3pVKxBdibmORcRsygVPDdvzSKq8oT1Nq/bEETP85mLU1jPeg8U15Ly7X0XvN+GuR2KiKjIEMBPH07n1CXeBziDDOZ5vNOyxXNih8LXu2X9apvC8sLXZD07r8GPtkWQpa5kGTHT/7u7u/G7v/u78au/+qsFLOLdvu/LL78saakGS7kWsAbZAb3ZkQQMAdBmzsz/6JGcAua/c8t0nMePI0q1UObJ+185/JMjJSh8Av3Y3j45OSnnhtimelv7hTkaEbP8cw8GBgR5zGH7zPBWkDYQTMQRVQ/ZeWVmapgKlAsEzmgafQDtQGkgnEDVUWgeF42+opQxFmFsGwf2pkENnj59WmqhY4QglMbjcVHCEbNN647+2OmAWE2o9k5tkNlgsEOXhWNOu+Jv5tVCwE4M88X8YDwwLqI69Xq9fEcoLyLK/6Rz4PBhlFlQ8kyU+cXFRSwsLBSHA0ST9bEjhYO4s7MTk8kkPvvss+IcQiOtViuurq5iOByWOWVcOdqQ/+czhDbGj6v25Hudi2kH5SHh/ae92cFiPe1oRFTP3jk9PY29vb3o9/sV5DmimgpgmrTyM+3zbGiq3W6XiAD8sr6+HtPp3Wb0k5OTyvrkPVven8U5OaR3oIT4n+pR3W436vV6kU/Qaz5V3IqW/RxO90GumSfsnPuHSF8GDlZXVyvGiOcLfsVZsQNtBI/+WKYjC23IRNw5ZoPBoDwf4IQTwuFz5pK1wQmwvMr7WRh7RJS9GLyfddnY2Ijl5eX44IMP4vz8PF6/fl2J0iKDTFuWrRng4n//7Sp3lqVZt713Lr66bWxsxGAwqEQr2W9JZDTLyNPT06JHDWIY7IqoyhivTU51y47gvP/9vAzm2ZHnfwxRR8lwSs7OzuLk5CQmk0kBMninQc2ImRxwtBSDM2Jm8Nowz85t1v+Z3uc5Aka+5+nAbPeZlzw/BjVomcf8uftrpwX5Z5kMHxKRuL6+jp/97Gext7cXv/RLvxRbW1tFLl1dXcWXX34Zn3/+edTrdwf8kWJkY56xATqxlqTA2j7C5puXaYKcddZNHm/WW8yXgUqvj4FnrmPOcFobjUaMRqNot9txcnJSSXHPa8W4B4NBLCwsxPb2drxL+4U6GkySd+Ub4fWkmDkeIqJstHGdn4dxYIIjBYE8O6cv8J6MItMoWUmu9WQyO3iP3GsUJg6ViQZjIqP1PjyLcRiZYFGpJc/GaZgXYskGtlHDzKBcZ/TP6UzMqdEJC1cbH/OQBOcoOqrDe3kfocBG4+4QrTdv3kStVisbao2qGPlbXV0tmzZx/G5uboq3TR181pqx2VPHQKHaGGO3QGLck8kkTk5O4vPPP48/+2f/bPT7/WLoRUSsra2VmubzhKvp18af6ZnPEX5WYFk4+zP//112NGy4OrKTnebLy8s4ODiIR48excbGRuU7z73RQv/4vVae0AttZ2cnRqNRnJ+fx9nZWdkYbqUQUd3YDsjAOQ8rKytxfn5eeJPoDfyNLFpeXo7RaFRQ2X6/X4ocrKyslLRNUDro0nyKLIK3KKvqvVHew4Zss/x1dSsrLgNGzFUGG1gH+mODJqO3yKatra2IiLIPK0dxrBCd4sE1zke38498dqpXRBSZMhgM4g//8A/jz/25P1cO7/roo49iOBzGcDgsY/e+Q6OP/GTHLusi5nmeEetnvW/v1lgH1obzM3K0K/M550z5ADr4mLU2QmsnOeL+RvKHdGOW/b7ftGHdDHhoZx0b6uTkJE5OTio6A4PVm50txxyZiKimMvvdHgcy1oCkDeRsE0Dr5nHfZ/Bhnh5EFrhffMffyJpsHzIGxka/sMuyvkCXAFQ6rX06vSub/Qd/8Acxnd6V2uaZyJ3FxcV4/vx5XF5extOnTysVpjwe7B2A0XyIZq1WK9k1BnC+CqD0uOcBmxncmUwmlT0WfO4oB1kVKysrcXR0FJ988kmhB6fhOvpvGtvd3a2kj7+t/UIdjYjqOQFGYs3MTJAnis+NMnhQ8wxdG7gs5MrKSsn3xaFgIxBO0HQ6LYoKRU8ONk6GUyJANnN6Av0ej8fF4MWzJa3Hp4mTL8wBehAwhjhoBe/zpmTey56MLODoE2PCkHHqgcOCFqwwCYLVDDBPeFjguG9meDMgBvn19XVsbm5Gt9uNs7Oz8nx+ImaVDuywEU1gnkg5IqcQx4z5JQWL/jhdo1arVdKsmDvmhMjST37yk/jzf/7PF2eDPuH1w3hWKvMQLiNX9NtIS0Yys+CZh2x8F40So13QjQ0w0xNzc3R0FEdHR5WDFrneihTDH3rLssTGBfsaELSHh4cxGAxiOBxW0pkQ1Dn1jXvZ55AjmewJYhMnz+l0OiWScnZ2ViJ70O/q6mpl7wJR0Yio9CunbUwmdwUSHPHF2CIK6qgIcoEUsYiq454NZUcUmVc7FBkRw5GzoQ7KeHBwECcnJ2UfBemqlgn0m0OySKe0cYnMJFXg/Py8HPLJ/jsckJOTk/jiiy9ifX09arVarK2txbNnzyr77ZClNgagK/7mB1oiF9vpKjnN4LvIx99GOzo6KuDiyspKrK2tlfVF5kbcT88GNHIqYETV0UOGZLrlOzsK83SwfyNrsiFo4JDrXBULvUwE/eTkpOLEwxPcB13Z+Hekgj4bYHBKu/vE9bnvfGddbXo2+JkjLIwrg2Y8Fz7ypnjGk1Pvs96mj86ccZU7jGZK/SLLmH9kCzYiII3lkumDaPazZ89ibW2tyBj4HZpkjTg40v32flTTS8QsAgG9OQJq+y6DE7Y38hzbpnMAAFuo0WiUqDn7T5aWlmI0GlV4wY44tvL+/v69tZ7XvlFHw0j9vB+nPNgjp9mLyu+IqKIAEXdIVbvdrjAJRiJKHkZi0a6urmI0GpWqJCyGmQBkzIu0sLAQGxsbxThwdIPryNHLm6ro/6NHj8peBdIEIqKk+kRUI0DdbreE+3EenP6Qw4QwHnPow/9Iy+B+BNQ8hCNvejbSmcNqXA9DsF4IoE6nEx9//HG8efMmptO7HHg7bxF3qRSPHj2KTqdTjDKfEgzawxpT3o89IEZIeD9r2Wg0SunQHOGwYX96ehqHh4exublZUi7q9XolqsE7TCt+J/Rg5JPnY2A67SbzCv/zPH/+XWvZ2LVi5rc/n0wmperK5eVlARcstBGwVohOa+K3nQTQfNAsBCprx1qSpoGxn0tNR0RlzYmUEh1h7TnX4vT0NEajUWWvBs+mn0YPI2ZVlezMW7kis5BrGOkup2nEEpmA3LC8u76+LsYP99EvlC1zvbi4WJwi5gugYHl5uaRAUbXq4uKi8PmLFy+KE4XCAySq1WrRbDZjdXW1HNy3tLQUm5ubRb6sra0VveJNoABBGBYAQjisbLhdXFyMJ0+exP7+fhwfH5c5ZM5BK5ElGTDLspDxIrOzIfC+ff1m3uX8jOfPn5cUW5oN0YgouqLZbBY9FlGNQCBfs27L77aezAAGv31/RqYjqqXuI2Y0AX+enp6WCpk8jx9olfeRveF9CE475f9stMKzdlYygOv3W99ZBmCrGdx0pHMeMs+Y8zvyvDEGopdZNxiQzvzKNciy6XRaWX/kLHIYZ8PPgy5cXGJ3dzeur6/j0aNHBUQgQ4XxI/d5jvsNSOoIFOO0c2hAxnNu+4OW59f7cnPEHXl6dnZW5NjZ2Vmsrq6W1N7sZDAu2zmkkn9V+4U7Go4S0FkUoCfNE2PhYGM3o8QRVbQAlA6B0+/37002+yxsmFJSlsPjXBEGo5jn2mkCncSgxWhh4kEZIEbGgjKmobhRtgsLCzEajQrKCcHBqHjpKF4WfDqdlv67Nn7EzAmDaCH0vKnWTkQmeKN/EVHyor1WdtyMiNBvmGk6nRYEstfrlVxpDsyB2Ov1erkGYYVzRDrZ8fFxGaPXE6eu3+/fS1WAHsw8ODKOcBBJ6vV6MZncpbK12+3CqCsrK0VIOAI0TyFlpIx5Zd4zT9gh4e9sXM9TWN+FNi8flZaRZCvS8fiuahGnhWcanBdlmwdmYFwsLCwUVP3y8jJOTk6KnMD4dRGCbrdb5AFyLyLKXgw7QxbwOAzwNWlOKysr0e1270UaptNpSaNCoUFDEdU0D8+V94JZbuCYmEZxqkDg2efA93mfAgZPxKzKCk6GS0DmWuuMFXDFBtHa2locHh6Wd5MCtry8HL1eL9rtduEReBcZA18S4TJqOJ1Oy8GgRIt8ZggGBvc/evSoRLaNPnO9ZYuBDH64hn1ivNeO7XeRj7+NxtxnUIA1zMZ0RHX/Eil682yLebLGBqcNY3jU77CBazsi4v5624ZxJsd4PC7gmp0XDFQMaTu6nhvsAjvodrjgb55NlgS2ge0gy4d5IGR+d/7hfbYBudfX2MHwZ/zPu+njPAcGOY6d5up2+TM+b7ValfL6VPiDf9Hv2GqO5rBPaHt7O1qtVtTr9UqUwtHNZrNZsRew4ww0ZqfCTo71l1u2qWncgw2H0+H0wul0WsCt1dXVGA6HsbW1FUdHR2U9nB7PD3OADH6X9gt3NBgkStdovhc9RzUyQ9nDNvNiLNuQpN48xj7M5jw0iIt8RzsYVrgQy3g8Lvmf9MnNxIoTwbNQeo1GoxI2Gw6Hsbe3VxibKjZER5aXl+P4+LgoV+aNlAEW1ggiDSfH3iYIrZ0ciITcQRjPBguCzOuCk2EkwtEbR5GyF2xEAmUPqmCEFsSVCmAYVkSQmCufzAzaTN9vbm5iNBqVQ8tyoQAb8KSksGGLKBH9OT09jeFwGP1+P1ZXV8tpoSCjGBeZ9s2k0Lodbm8EzwLGUSRHBS1IvosRDfjJEUw7CkYdzd84mRcXF9Hv9wv9PaQQs9A2ioih+vLly1heXo7r6+tS3QrUHb7EGeYd2cEAjcQAAtlqtVpl42pElH1GlKfFcb66uirKi7NzoDd4nfm4vb2tpILSH+gGHuW+eQCBNw0yBs+h5TX9tkxEaeM88QyceOQwkUEcOdM9zv/Z2VkcHR0VviFdbG1tLZrNZlxdXcXx8XEBL4zoHh8fx9HRUQEx2u12SUeNmOkTzs1pNBqxublZZCv7Cykd7CIgzHEGCWzEMV92OpvNZmxsbBSHFPp71/zm963aoDv0COdQOV3WxqhtCyKhRCD9vcHGbHPMs1Gy8WXDOKfJoQu534URDHLd3t6W/VzIPOwJnmEjFD42Ld3c3MRwOKzsMbXthOEfMUvvos3TVTa68zowL9lRiZg5GJbZno88ngw8+50ZoJ5neNsxwPbJ6Y3oGWwi9AEyttFoFGAJoITroQ0isURmx+NxbG9vx9raWmUPm/vDGD0fzI8dC48nfzbPuWM+5jnHONPYxLVarZLV44h9q9W6dzL46upqOc8l78/AuX/X9o04GgwU4U3HaSgt/qaZac28fMdv5wHWanebJpeXlwsa5wlmcnd3d4ti89kJRp8gEIwOow2MJ3udKN+FhbtqNTgpERGDwSBub29LvjObOXE+dnZ2YjAYVDYrDwaDSh6hmQ3GN6rpSk6eS06qZP6cD+ox2NjwM0ys3Gd0xMIZp+ohRomISq5jp9OJ4XBYSXlCGDJPRi05FHFhYSG63W5ZQ9ey9vtxxui/cy0RPPTZG8M8NyCxFxcXxVnD2bIzQtQlIuYKS6cDZcfBxgp/Z4eWZ/Gc77pxYoFpJBBa9TzZ0cQB4HrTcl4LHBreY3kzmUyKY3F0dFQUdgYQLJsADqBTNnRjXDtiSkQU/l1bWyt0ydkvGKkoAKqX1OuzdCponvEZdYyIAkRMp3eREPpoI8r87bFZNmZFz5wis3D44V87cvQDNNAgBH2Bd7iv0bgrGIH88oZT1t0VAanQh+KkuAT9HQ6HJUrU6XSKwwEavrW1Vc5jYdzn5+fRaNztDfOm8IhZqXJHL7wGGRDAqOz3+yUaQ/M8vW9fr+Fcrq2txevXr++d4TKPJ0yTzoX3dQZxbHO4WW/aSLS84m/utxPCPiUbbjjMRNoA1nJxAT8fwx7+AgyB542Iz0vTMs95T4fnhJZBC/N6RDU92ryQHQHLcvQstldO40bGGQjJTqBbjohk0Cmi6lgZ4CDli/nBYSD65egEMu3m5qak7O7u7sZkMon19fWyz4E+ev1t4xqE9Bis42ybZefKtoTp1mvrzfTQRkSUQ2ChlclkUklXZV8fNGqbxyDou8qvb8zR8EZHJgSEN0+sG4zozUVm4IioMEar1Yp2u13JxXXlgZOTk9jZ2bkXBmcR7K1no54fFIZPlPXCEgGAINh8OJnMStEhBMbjcezv75dQOig588LmIXu7KDQzmJkF5WokwAaH75mHzjBGmnNUHXWysrdxbafRggoGJiqB4GDDvkN5dhbIWccQarfbxbkaDofFqKCfGA00V8yhzC2fgRrbocCQr9frxaNnDBcXFyX6Qn/zJrgcXuRez7GdVCOiRkMREDmlxfyT1+q71rIT7NQh+BDByZx4Y73nzevBOkXMzqwwCsn/yJ3pdFopDIFDj6LlPhQOm5f5DX1SuQP00nuJGFej0aiAJQAhGN29Xq+yB4Q5yGBAjnKanhxtNTpnGmW+AVUsA3ke/IajgYNnJWaapl8Z0YUPbODQh1arFQsLC3F6ehqvX7+OiBnIMhgMYjAYVPLA6/V6AZkwFPiNg8NaXlxcxEcffRTr6+sl2sE8sPbMGcU9PCbmz2ited3GG/eMx+NotVqxubkZP/vZzwoA9r798Rq6CJ4kojkvEpGdiYhZyoevpzkjwHoi4v5BcFzH5/CijVk7FzbaHGXnGoBI+rC+vl7SDNFTfhZGL/LC80PLDgSNsdlmoGUwEbmBnLXh7TllPi0v8hzlPtBss9jOm+f8zdOxuS8AkcwpcszrhKPGnAMm4tRcX18XZ+/29raAWoAxjpbQn9vb29ja2ir3zXMybG/YseB/f888Z7vN9oABIBpOm8E1g5TsE8SWwjEaDAbRbDbj4OCgoqNwQPK737V9I44GncvGUc6LzB010cNQDxEV3ufGxkYJZ02n05J+MxgM4tWrV7G/v19q0yNcMOSoWALjkpYDYdJ/Oxn0g5QgFhIjg+vZlL68vFzqX9fr9RL6t2ETERVjgFxAoyZeWDOiQ2I+hAeD2fNoIy0jvvOMNK8j6weT+TsYlz7a+ANlhEEx+vv9fgwGg5KigXBF4RPhoR/krYOiHh0dlblmDekn44AmcOYol+t0Lc+hhaJPkiV9wnPkMDJjt4DAmMhr5v/n/di5Na+YBr7LjkbEbI7mOckeO8YnvJ0VmZEzbxT1fPNcK3CuB1kEvGA/iJ0f0oFcJQlacDoV+3x8Avp0eldi1eu9urpaqqzhbCB/skxw9MfOEvRqhcqPkVDmEHomUkCk0emb8JkPCYQfmXfAFIx/UFZ4P6Iqa3LaBH0E3QSkODw8LHPNWmeDy6klvMdopeX5/v5+3NzcxOPHj8s6gk622+2ImG1yNDppOiGn2w4SMsg0jExYXl6Ojz76KP7wD/8wjo+Pv5aSft+qDT7h8Elkow05z6+Nfu5Hx0fM1sq0Q+O58EFOXbJefSiKYt1CqqTHUqvVKhUJcZJJ4+O8Kd4L0k7KGA0ZwV4vdJGBSngHPQO/Yd9kYBcAx3rVzoblJ89mHjJAnIGIrHN9vW0Mz4sdEuuEDNChG+yoOUJr2cH7XUzGGRgUpIiYARfMIRkmPIP9DVtbWyV1i2cBYFlXPUSrpkPuy9kMthfsTGYgKTtrZ2dnpYRvRLUw0NHRUXz88ceF1g3a5whW1rdva9+oo2FkjEXLnlpEVVFaKc5LoXKkg5KmTiPqdDqxv78fX3zxRdlUSD94vysj2JB2ugFMnSu0eHMjSpVIRaMxOxwHAs6eLwp1MrkrY2kUzg6MozaUVe10OhUHxXNoJIdnwrzeCD8PrWA+MsLoPltRY7T5OdnJQAFYIMJs3lOBUc8zQP/ytUYQ2+12tNvtOD8/L4ixBaaZotFoFJSYE8m73W6JUjB3FpA2rqjG47x4RzAQTHYAzIRGvTMd2ljk/szARmLMV9+H5nkwQmlHITvgrAn3WDhGzFAyeMfP83WkLfX7/YKAEhG9vb2tlMD2GS8IbWgF1JXn39zcxNHRUekvqT9U+rACb7VaJe2n1+vF0tJScbh5LwrYqTwAD9A0kRj2ZRlkIMUCmsTBIneX5r1qOF2OdpL2ZbDCG+DJF7eR5ugQGyZzigZ5z3YojQTywxohzyKqe5lYd9ZsNBqVOXzy5EmlzHin04lOp1PO+5lHezlFxMAA9zjtZTKZxPb2djx58qS8+/vCx7/ohtPbbrfLhlw+zwi314//bbzyYychRzki4t7aGhT1O9E5NIMD8KCNtYgofMe11iXQGnyL4QzgkfUQad/OgEBnzDNWAVOI2jo7IwNd1j2ODud5Qi54TmxoW19a9noe0YU5omqjOkf+vT45gsB7kQ3ITgNQzAUy1QV2XDKb9TIgZh09GAyiXq/HxsZGrK6uVvbE2M6AVpgLA+wGKmzL5PWbB9a75C5p+tgdlEy2XEJntVqtGI1GERFl43yr1Srl/L0Gzi54l/aNORoRMyMJzzwbTFwTUXU2jFY5qhExq61MdSEm6fb2NjY3N+Pm5iZevHhRvFgrRAxq3n97e1sQN1IaYFYzmz1mJnc6nRbjwoe63N7elnK5zsd2ageeroUexoVD7g4P93q9SjUa5syIBePEOcppCVbyIA52AucJGL8ne9CZ+AjhWqjx/zwlcH19HYeHhwXlgSmXl5ej2WzGxcVFeTbRJgyNxcXF4jC8rQJUrVYrKCxIFqWDSd9ySVErHAtWnm0lgNGZ78vNzuw8wZjT1ubxUHZIvk/tIeeK6J+Fto1Bmo2NyWS2ORKlGjFbX/Yb9Hq9iIhiQIOIgQY5gpnRaxQYyskpWHasI+72OnS73bi4uCjGfEQUI7vVakWr1SplXUlZiohKipWjsRj3OC1UFckFIFBayEnmykAMvJE3MuM0TafTsnnSchKnwClYrAsAD/IS1NiprBFR9mRhUJ6ent5DiqleR4oqMsIOAc+yc8o4Kb6xsbERW1tb5fputxvb29vFMXR0KmJWPMD7xJCTWQZNJpOCMv/gBz+IFy9eVMqWvm9fr0GfnU4n9vb2CgDg+f+qZkPX12MMc42djnmZFjT/b+PUn3vfp3U41aVwdi2/kEvInclkUgqmwO/wDM+kkArvcTTTY+Ueg4c56ss8ZJk6D5zJDnl20rnP789r5e8NvGET+foMyrmPthNdshYZHREVcHg8HhcHgTlE7jpNE33tiCZy0X1DX+QUJmwX273WGwY9rQvmRWo9buaaZzsajTziKAen4dHOz8/LfsHRaBTdbjf29vbKvj3bwsjYr9O+cUdjXi5ZZm4m1MIC5B7GtzFRr99VDSGkxf+EflyFAvSDSAELgMJh0smhnkwmJd+XUCQbM4lkYHw4WgFBo3BZWAwMjGUEB/3mMBly+uaVSaPqCkRG+tE8JjOjodRz9MFMYcHKd/kzoz5+ttcQRvE9NtgdQeL5vV6veNCUusRg6nQ60e/3Y2VlpUSmbIjgNNze3hbhjXDlXTCYK2JEROXwxs3NzSIMTH8wP+uwsLAQrVarcnYCc4Wz4TFbkXiuLCwR8nm+mDMrQkdDvo9I6ENOXN4fY8PVwtoOBb+98S9iVjlpa2srzs/PS1oTRrVL3QJQWIGyNigTyzGADHg34g412tjYiE6nE6enp0U50Q+cCyqicL9pinxvHAIcCfM/ssznhJyenpbvkQfQGw6M94SsrKyUeYQevdEdekWm8kzPLdfxHPcPNJF1ojIXvLi+vh6Hh4cFyQXAwHna3t4uxgLliW3wI3PsSMHb7KkbDofx4YcfFl5bXV2NTz75JBqNRjkFl/77Ocgho7gGciJmVdWePHkSW1tbBRx5iK7ft4ebKyleXFwUeWwjmfa2CIcBPvjWa8izbExmhyI/0/LGBmOtNjvbBt7FFuAdLrccEYW2zs/PS4EJF16ZV84beUPJZzfrjWwXwL/zInVv0zekBZp38zzNm5+8XnmdbCc69TLPvf83ryP/sTkYawb8sj1KJICoUafTKSn2AFQ8v9lsFvnmKAj0yXPInsj6wbRi28z7cw28OEqVIx5+DnNmUBzA38U3AKPq9Xopwb6wsBB7e3vxwQcfxN7eXplD5C1jol/vCnp+o46GlY9Dy3h62bt1FCNixgz8UEMaoy9iVosar5Q86MlkUhBycvV4po0TDHs7HhFRnAS8t5wy5A199JcylnaseBfKiHQIiIYSfZzcnb1s5sApVZkpjbKYye0w5DCqG9d7jRgTxkJGeTBM5kUQeB/EaWPD4b9erxenp6clNMdYOY2ZcDLvNAKLQRYxyw3HeLRTBA2ybhgH0+ldROr4+Dhubm7KGR02DDy3HOiFcZfDpTkszDPM7Ha88t/QivucUSWe+V1zNBYWFuJv/+2/Hf1+PyLuxvi7v/u78fu///tfOVbWyVUxPGdZQUEz3tCLwbu2thYff/xxRESMRqPCz8PhMI6Pj+P8/LzUt4f+HWHEKMCgJ5qG0eqoBOMirQqazp/DMxGzyANV5ow0EakBzbfiwviIiOLwkMKBvHHaBrLTh+tZWbtiFHPnHHkiFcgs6BgHy5W74EdC9RgIRH/gKyIWRCFYQ9Iovf+q0bjLU8epy/oG2U6fcKL29vbi7OwsPvnkk+j3+7G2thYrKyvx4YcfRq1Wizdv3lSqydAfZLwdEa+Bo1xLS0vx2WefxcnJSQwGg8o979u7NSqxnZycVA5Ew/CZh6hH3N8gzmZfvqNZfhig8jPd5r3Ldgu2C+lP6AnrZ2jEtsnNzU3ZzBwxKyhhFNxGNQ3g1Ii7UWxneczL9GA+sn73+PjfDgb9duQzt2wo22b5quuz4+Q+ZTAa+c76WWY54gmwHDE7l4vIEuDy2dlZ2b/lNUTn+FnMG/aBK4hZNuTUd+wEg+m2cWzw26Fzc0UxbAn66/1yOUoUEaXi6fr6ehwdHcVHH31UPjd9GnD/OnbI13I0rFTzgB5qOarhz/1cOxlO/7Gn7pKkKFuUo9FFqpb4oCxPKgucSywa8XaJSm8AAj1EcbEhjYO5PGbehyPUaDTKRrBWq1UMYBRy9o4Zd7PZLN60GQ8F6jBYDn3yXI8/G7omZv5HQGE08B3r5ef7XlrOj85OZa1WKyFg6t47XzEiioN4cHAQt7ezzdwIUhgIlIJnr6yslLMuRqNR5TuY2crm+Pg4xuNxOS/DigVkdzqdHepF2tZwOKzQP2uJseb59m/WMSMqDqGyxn4Oc/NdS51aXl6O3/zN34z/+l//a0k5/Lt/9+/G3/t7fy/+23/7b2+9NyPoEVVaZF6Zc1KjTJPT6d0J9b/2a79WymWiLFD08BjKns8iokS5cnoNyoroBA4IRjWOzGg0iuFwWN6FfGm1WgU4WV5eLnvSiKRCN4uLi+VMD0AROxiuGQ/owTwRtTVSx6Zr53s7qmbnAdlA8QYjcDbAcAKQnyjpen1WMcqyCGMMZBDZ6XWh/4AG9Xq9OHRZlzg3Hp4lKs2mc971f/7P/4nNzc344Q9/GI8ePYpWqxUff/xxjMd3JwJjRBgQMpiQwRcaRsfTp0/j5cuXlcjY+/bubXV1Nfr9frx48aJSSTLrmHmGUJb9gHnZWM9Gr7/PUQQbydhEjpJjN1j/EH10RUbLLM4HslzBOIXnsWtspEKH8K3nA57CBsEpn7cflt/zNiAzB543gxR2Cvx3jmbYOXjI0chpxV6/7GRwLYY8kRbv47UMcxTDMgu+tqxhPdbX16Pb7VYyJgzA0Den1EOjbLjnPQDnBlicik3/oO0cHcuOM7/pF+di3N7eFpDM62l7s16vx+npabGbjo+PK9WncOiR8Rlk/6r2tRyNf/AP/kH8zb/5N8uijcfj+A//4T/EP//n//zBwztsHDl32MYtk2wPG+XF/z4gyuci2NmBWJaWlipGoFHxiCiGgpX1eHxXUnU0GpXca4jOxgyGCkyJh2cDmYgFROYNSKenp6X/hOSMspspIHiHJu2U4bXboLUwozlXkGss2HKDmI2YGv105IPv+cyGQq4lbbTH6AfjIUUN1IGD95rNZjk87/z8PB49elQcPEcnoC9SP1qtVqyvrxdB4ZQSp4VMp9OyqXw6nZZN96w/904mk7KvY2FhITqdTpycnFTy262sLPhpdi68Pka2siPIPc6B/6618/Pz+Mf/+B/H7u5u1Gq1+I3f+I34a3/tr32lo4Figy49lxHVND940uh+xB2//uhHP4rNzc1YXl6Ofr8fnU6n7B8i2mhnH5lB6gYywOdL4DAQOTHyT4rg7u5uOTuH/nS73YiYyS0jmqQqQOtEKHzIFw4yQAj8hOKs1Wrl4EAjovxGVjykAKE/5t7v4zsbLqQSkDPtKAb8h6OAnAGR5RwT0wmbuafTaUm9JFrEIZ30wQAVzyeK4bQvpxSgB0AROSjwww8/LOercG1EFATUn2WgwUbk4uJiPH78OIbDYXlvptv37eG2ubkZ9Xq9UvY1OxoRVcM2G6fwr50RG2o5FS5HMub97795fsQslTeiasjWarWy18s66ezsrOgV9PXt7W1xyCOqlYX8O6K6L8P7EZCTfO+IiwFG8/W8OeA67/2w7uMZbrYN5q1RBuNYA/fLjgX9cp88v9htABwRUUknc0YK7/O6M+/oFRdzubq6ivX19ZJV4zW2nYV8Zs34nn4S6TBIThQNXcF6uj85umN7gDEAGFHx9OTkpOx5dTSFcbOXkGpUL1++jE8++SRev35d3u+tBozRFdTe1r6Wo/Hpp5/G//gf/yP+5b/8lxER8ezZs/hn/+yfxe/93u/Ff/7P//nB+1ggjHQ3e8QQSP5B2dvA5rmgWkQwjEA7PIkB6r7YWBgOh+WsBjMF3q+jBuwFwGBAaRrtjJgZ/hHVjWS3t7cl5w9F0+l0yrMI/U+n05K+EBEFDeTdKHKIEsJ0+Vb6YcfJDDYvOkGDYO3M+Jm8y2vMO/KccY/TmEABVldXC0KJwEWRMA84CQsLCzEYDAoCiaCz88o4SEXDACEdhnEhjLiXA9dubm7KHiAMOBApjDPWpt1u34ua5LCnHQuP3Sgo19H/jNTYYf86Ics/LW1paSn++l//6zEYDKLX68Xf+Bt/I377t3/7ne515O2huUEw2jlnHR4/fhzr6+sxGo1iMpnEcDgsip5nco+dZ0cPuIaIBWOCljmv4ezsrJRrhS+zkkOBUbscOgEwcCpVxCz/HxrGoMDxwNG4uLiITqdTjGyKJFim0OA5ZCzjzHRph4d5wKmyU4eSdb+ce00lq/F4HGdnZ3F6elrK7BppjZghdpStjohytsbq6mopLQ5gYKPKUS/Wif/ZtA949Pz582i32+W6fr8fn376aUmns9GDDHZU0mCM+Xc6ncb6+noBQc7Ozu6BD+/bw20ymZQodEbPs8FqgMvGqHkDmwR97edFVPfb2Qj1e2gGzmy4m4fgDQMVONs+HJTnwyv8jogiZwyyRMyyTqDJHBG3UZ7TdzxWzp/K0QhHVYzi217LDkV2gjwvvobncL2dCSPxjHGek2U9jH4ej8cVIAbZbWDb9pNT0iKqEY+rq6tKAYm1tbVi9znCg0z16ez0yWvC2UnIQsAPxmjaZW5MU+xvQ7Y4lXAymcTz588roBHOp+WhHe+rq6vodrvlwD6A1sXFxeKEMF+TyaQAPV/VvvYejf39/fijP/qjiIj4+c9/Hs+fP49Hjx699Z4c1cgogj3wHLoj3J4dEtB/NkVTRpXKDJzqmp0KlDthSedcz/Oqs9JGmRBl8eF6oOIsNIaIkVQIm9AWzMp9LlmZ92g4NGehYcbFkGJuYVYLV+7ls2ycmckcrYiobqrNaEQOm2JcOPXCRn6tVisbXU9OTioo6GRyl9LGwYfe8Hp9PTvVcmlpqWzYYsykxFk4k1KxsrJSCVe7JCmMfXx8HIuLi7G5uVkOS8uGCuPlNHjS5uhDRNwzOPy915E59HWOYHgs39WIxtLSUvyVv/JXotlsxl/+y385fuM3fiP+/b//9195H8Izo2n+n/nGuDT6trq6Gs+ePStzOxwO44svviiHfFJhijQnzoXJip30KNdiB80nz5fKSYAhKGxoG/51pNQRFdI419bWimy0cWTHxcUmHBVj39Pi4mIltO77Qb7M+1ZQFL7AePI8EH0gwmGjC4OItDOex1kkpImxfqurq5UqPShgHDorVoxGv+vs7CwajbvDEIlW0VfkC/NtlJl3nZ6exueffx6dTiceP35cHIQPPvggfvKTn5Q0N8oZI78wjCwrGC8GDqdaE7F5nz717q3b7cYXX3xR8scdybNjQcuf2WHOabXzgApHQ2ww85nluXnfSD/Xky4znc42f8Pvg8GgnErvFF7Le2wF61HLAH6wMaA5O8J5bPkz6/PsPNhx9/125m3Q+1l2QJzmlKNBTqOEl/I4aRnEswNTq9XK4cDYVsgNbEZsAFI5nXaK0c+c8w7Q/6Ojo5hOp/H48eMCJJmusGdxBLjPNhFrTXpVRFQqCeY9XKYv3ocesb3A//v7+zEej0sEO9N3dqrZj3J7exuDwSA6nU45hsGRJOb5XcGRr+1o/O2//bfjo48+ilqtFs+ePYvz8/P4T//pP731HnvlbvM8YRsF88LeDlGhRDH0XEq12+2WWvAOTeFcsAfDfTCSgHdnAYbSMlKH8cF45gkj73PIkRtC/hAVudvMGfs/7CRgwBhZQDlD6Hxmo558QKPkEJCdiXyfGdlRC3vcnmP6DXMZkczO0uLi3dkn5+fnBU10M90YWSaFZDqdxtnZWdze3ka3262EKy1sQQ2N4lDqDXqy8ByPx4VJSdXyfLDO0Jpzrfkchs9ImqMaGUXmd0a8bSx+FyMao9EofvM3fzMODg7iH/2jfxS/8iu/8k7jrNVqpaqHD+HCuTffWSky751OJ9bX1wtw8Pnnn8fe3l5cXFxUjF9Oqkf5QNvsvYA2MFjg+aOjo1JUAqMYxWU0lc9vbm7i9PS00ORkcpcXSxQCgCJHClCczun2fg6up1+cpIxccEoTjg3fEeUlrXMeqoYzvLq6WiK+bhgiWa7akYmY5fwCHMDrPt8D5+z6+rpUmOIdzCv3Uerz6OgoDg8PY2VlJdbX18tm9wyCLC7eHQJLBOjk5CR++tOflijJ7e1tPH78OI6OjopDxvwzV1/VkBPNZjP6/X7l9OL37d0aut2AwjyjB3ltXUWzo2G9nY3fiCoS7+8sy81D2fnA6c+pzo1Go6S4UFHNkRaX4iYF0WPleRlQyQCm+2og0M4J19pQ93MzUIr8cPQhRwhsj3BP1nPZeXgog8JradsFfspRLOwNDkAlahQRJaXK9ywtLRV70UCB94l5TgBCIyLW19dLSXQDv05hGwwGRX448gnQRCq4U/WxXyKq0RrPKe/Mc8YYkFO1Wq04G4ybdWMNANWWlpZiZ2cnPvzww3jz5k1JZ6XPThd/l/a1HY0vvvgimcUMKgABAABJREFU/vt//+/xl/7SX4pnz57F3/k7fycODw/feo8NJyPqVs52HiDkbABCOFQI8vOsNEHuOIUbo/fk5KRS+jajGPzNcymLCyGw0BgcNrwxTjNC0Gq1CurF80BFG41GqZ8fMTvcD6PD5SgbjUal0hFK0ZUPMpLCMyKqBx06TPhQdMmCjHebKZ3jx2f87XQQDHkbyuRIY6zx8/Lly2IkEarjfgStHQP6UavVyh4Nz1cOIyIcMApwTHKkDcNxPB7H4eFhWffNzc2CBtNPDDAqUZnW7WB47rKDYcZ3f4z6ZBr9rjU7qf/kn/yT+Ff/6l/FX/gLfyH+5//8n2+9D2cdR8OKH6ec5xuZ438qCy0tLcXu7m4cHx9X5t7OPb+Ngjtqxc/CwkKcnp4WQMOGvmUZqDpGrjdpQ+84IshB5Aw0wViN0Jk/50XVTFe3t7MiCxg1duiNuMFLpKEyVqJEACIY3HboJpNJZVOlDRxq1qPkHZH1xnecQXiT+y8uLopTkQEd9sBRlnE4HMZoNIqFhYXo9/slNcrVaCKizAlO2WAwiM3NzfLds2fP4vj4uFTMY81wBg1YGajx/HMQa6/XK87suyKE3+cGT73NgcgoOZ/7GjsauRkc83rCGxH3i5xk/evPcBosv6FjnFYaNIw84wBbdDYOAvzo6KDlAz/oRsaNPDKI63t5R0SUlJ/sUGQdF1E96BB5CQ8j4yx/562Ni9tk0BQe4108D2Pcxnd2xiKiYotERIVXm81m+Z7qgcgyZA9jggb4n9LkEXcOhyMjTuUfDodxfX0d/X6/ssemVruLvOA41+v1kupE/7A3TL+WrbY9+BwANuIOEMeOAqA3YOY1Ozs7i8XFxTg+Po6PP/64OEHLy8uF93IBpa9qX9vR+L3f+734N//m38S//bf/Nv7pP/2n8Q//4T+Mv//3//5XpnNAaL4OYxgFZ68ZBWQ0fmVlJTY3N4vigNEhZBu3rnNPXh0KyUYl/YqY5eK5TKIRUkcNMDJwQIhEeM8Aub0wPnsEIGicKkL6NpzX19ej2WwWp4r58ZwwVn8GooAxn9EDxuC9MjADfeA3jGxU3SgGgp4x2oCGkTKiwdxgsLH+zWazGC+np6eF4fw573DInNLAOBYgQe12u4JCOfrjPmNg+fAj6IK1PD8/L5uUSY9xtKHRaJSzEKALnDPPPX3OStBOpJuFgen2u2aMTCaT+MlPflKE9eHhYfz2b/92/K2/9bfif/2v//VW2bKwsFBx1KGriOq+KEcBTX+rq6tFPrBhDn51KJ4+sN8HlAoDFaXBHgwb5igayx0UtqMPVjo4r9A20Q3ogPe5cg58bcfGBkjETLkic7NDZTTO5TFxhCaTu8PnvMej2+0WOubkYu5BVvMDiOJIjJ1BwAR4FAWKXKbPzFu9Xi+Vu0DvvDeG53gvBONgs3+z2YyNjY1SrcoIMeCQIzzT6V2VssePH5fS2sgXDAbmMEciswFEVMMRtPft7Y1054eMnPz5PLTXcj5/l+XzQ8a0m9O+4QXudwqg9fFkcrdJlyidHWp0uGkaO8BRWYMqt7e3JeUS2yqXYjcAY0MV/kdeIFOcQfLQ2A1s+BpHHhibbYYMurk/9BVnLGdXvO23+Y3ntlqtStoU/UMW0ifGa5CHFElsAQPjOAQ3N7ODPTc2NsqYSZcCYMU2ZW15p89IYsxZdtuZMu1msJ3IBHKaZ1g2utnGrdfvqk91Op2YTCZxdHQUm5ub8fr16/Iu7yP6xhwN2s3NTfzWb/1W/Lt/9+/ir/7Vvxq/8zu/89brLWgjosKQNj5xHlx2kes2NjbK3gUQMxRtxCxkxd4IKhU5581eNf2IuDOM19fXK6g3hjzMgrLPxioEwMZtyoPxXowdDEcUzMLCQknJoYE0gLb5XRYyNHvvPIf64BgljN0eOQaxnYB58+JwMtfOczgQIERsHObz2Gz8W+DV6/V48uRJXF1dRavVqpQPZc2M/FpREMmALsn5Zu+GhT7NKVg4iKSMYBRxD87N3t5eNBqNcko76PdkMinveZvS8mc2gmxIZyFp3sk89F1pFxcX8f/8P/9PJe3kP/7H/xi/8zu/85UABpsinXqSFb2dDZQ487+/v1/4++LioqTvkeJDyLjb7Zb9F05vQC5cXl7GcDgshQqgJ1It2VNk9MnpTBjZrrxGhTOcfcaBkoL2oPeIKOd0ECJnz4SVt583mUxKCil8SaqhN4PbycFZwHkjhZF0J9IdGSdz6fQPnhsxcxbgeZcGdR9sVKH0iegeHh6WDcKWWUSSzTPeg0IlwKOjo+j3+6WcLUqf+wBjnAZLOeyMAhvBhV6y48G4FxcXo9lslsNLoZP37eFGCl1ENVsio+OmAzfL0HmyNhvUltXZCbEdY7nO34BeOKs29tnXZxAK4C1iFjHBOQa8tNMBbZ2enpZ0yOm0WvQiR9qQI9bDBrG4xxEf8+C8OWUObOhzLe+0DOJz2xS8xw699y5YRuX3zwOQnVlB6itRA2w1yy8incgf+NdOJGVrvUGcfqEvbm9vY3NzM1qtVpHngEU5OsEcI/9drIZxefwG2NFBdp5opNxnPiAtN+9/sw0KmNRut2N/fz8+++yzsqncc5j54W3tazkav//7vx+7u7vl/+fPn8dv/dZvxV/8i38x/st/+S/3jCw3CwQ6aOfCuY0IYDshnOAMwVuY816cjMvLyxiNRiV0BMND1KTkUCkqK6aIWY43zgmLacXHgrMAoA+uHmFEICso3skcRNwJl/X19UpqEQrbhoDnJ3u9Cwt3pTNhGCOytBxZooEeMKfMnc8jscOUhQWCArTSCpfrI2bIKmOYTO4OwltfX4+Tk5NyFgZrTeSGPjJOjCnQR4cKr66uYmVlJTqdTvHQ3RgnxkCj0SgHOHKiO/MNyrO/vx/tdjt+9Vd/NabTaWFm0HHvVTHzW8BaQfl7C/qMVCA4v8rw/tPacm47SuarGkahnVvLmIjqRkSUNog+GzDtmDu1EMQbWrSMwak+ODgo9AJvwpeAInYSbSw7jZN3GujAqI+IijyChogAuIqK94xYeT00F84JtgHuCih8h1JdWVmp7HOAl91HlB2AAr8JxUfMDrxz1BJF5ui2FSsOC46OwQvSGHifU9cc8cxzEXFXuery8jKazWZsbW2VevkRdzLn/Py8yG3kytraWhmjQStSuci7NjBj5wNd1263Y319vXIg1vs2v4EQZ6P/be0hAzk7HdnJeBtQxPMyaMo9i4uLJU0GWQ4tUG2KctjYQ6Y3PoOe7Xicn59XCj6gI3ESzBOAExEz496plTkqQLNxzPxko9/2gNOquCbvg5gHNvNs7nXENu/zYP64Dp3I+N24xuBOxAxoWFlZqRzIzGdsnkZuOr0b0AhZb3AX546N/UQxTFNcY32PrCPizgZ0251eG9uithG8LoyLili2KU5OToocgg4s2xuNu5T+ZrNZSq9zZMQ8J+9d2tdyNP7Fv/gX9z771//6X1c80ocaxIjCM2NZQTF4e5G9Xq+kOJAmASpHQxGwD8N5zExwrXYXxux0OhWUDuHOoiMQCHXRf3vLoNzepOjFIxrhUpcwPSinHReYHw+bRXdKg9FFiNHRDZ5h58NCxII0EwzXZqHCejkvNM8DnxFN8IZUwrcWdi4hjECFoTc3N8vG8EajEe12uyDGePSEBGF0C04LF/pOXni73S5lhO105L6Qv40BBN2aUW9ubmJtba30hapDp6enFaMr52j7x4LZtGrHIn/2Nmf++9ZqtVp0Op0iW5hHI8kRVUQHgIGIHor34uIi9vf3S8S0VrtLX0T42/BmXUajURwcHBTFY2XIelKC1Q5/Bi/MT6Q8Yjx0u91yGB+K207EwsJdWW/XSLcS4m9kT8Qsaoqijpg5N+ZHo5hECrwHisaYkImeqwwERcz2cDkyjaPFNXkTu9MU6RPv9QZHohtUT2FtbWwgO00D9AcDkPu3t7djc3OzlLGOuKt6RFWgra2t4qyyzqwd/QH9NEpspb64eHegYL/fj9PT01L++H2b38ifp2X7IzsH8wxdOx55ro34R9w/lMxGNj82mtEhlJLGCEcOkSYHQLa1tVVKOdMn9KbvpVAEPGqA0mPLWQPwK7TO9yDw1k88M/94TuHDeQCl58Rz7+fzDK+bAUvkpFO2vZa+z/NvuZvX39FDZB+RCUAjCnsgR9EFriiJvPE+TWgE0JMo6Wg0isePH5cqTrbB0AGM0XRDxNqgRB6TbS7rJObXZ7NkoIaMG8ukHLEi9arRaMTr169jfX09dnd3yzV5/b6q/bFTp9y+jlCEsVlIo1YQqjc+rqysxNraWkRUT7KkPCMLPhgM4vDwsIS22HDN5He73aJQImbhfZQZRmFGnVDElEjjnIXRaFQ2P3ozMMqPGu6c7steA88DSAZlGG0M2Htn8R314V6UJvNn9JPnWQDOY0ILJzscGV0g9GvkPecH+h3MH8gNhO1qYdxjJby2tlapLc1csGl0YWEh9vb2CloJqrq3t1cQHWgk4k4I4oBeXV3F2tpamT+nVTAHpHCwrtAT4wQdBdVk74+jY1Y+WaDaWLJCYD2yM8c8zxM43+cGX2M8+vN5Tob/5jvWAqN3NBpFs9ksKTS1Wi2Gw2HFeeEk78FgUAxw5+s6pcmIJA2kiageygu5uLa2VqKe/X4/er1eBZ3kb0L+djBcmQZDglQu7/vodDpRq9WKY+yUVO8xQA4QYTRw4JQIIp7sWcgoLMoQXoFHfWK3EUz4gzQyyzBvtCTiQ5lenDvmFpnCmOgLciIiKmUfeS86hXXnzAsbV51OJz744IPY398vshEQw5EWpzhAf0YykVXtdrsALZRKf9/uN0CniPvRSzfWP39nufsQQpuR+2ww827LEN/XbDaj0+kUAxMdjQHnVEieb33I8ynH7pPCcfjRC1zHWHOWA7RuQIt3W6dgP2BneH5ppmH330DoPCfQ85yNVF9vYDQ7DRm0yI5e7p91q0GSyWR2gB3lyxcXFwtg1Gw2o9VqlZKwAJ3Ma0RUDk+kAprnDUfu6OgoarVaAci5H53B+tluQh5jJ2XQN8+Z6YWxcswDz85RGWS15TnNe4o7nU68efMmfvVXf7WiC9ir8q72yC/E0XjXZsKYF8WwUcn1vV6vpAJxDx4pXun+/n7x4IwqglxisJNegOCPmJ3iDUJo5o2I4nleXV2VkC0ODIRLP1BgztuNmCFg9BlhAYOwv6DVapVccBwbPydHf7JiRuB5o5gFoJk7Iwl8ZqLPyEhGF3KeIr+dYw1zQNgYawg+EzjPILd9f3+/pCBMp9OCfq6ursYnn3xSjJDb27t60U65YE4ob8lcUPmm1+tFr9crxgF9JCUMgyYiiuChL5TjxemARrymTieJqFbnyuH5HGXCGLITB+28b7O2uLgY3W63wrPwesT9jYkRszr2dgQbjUZJmex0OtHr9cp+AxceODs7i/39/XJCvZ1Po5HeIEhzCex6fVZwAiOAszEAJdrtdqH729vbIj94BjxKFJFriHJw3fn5eRlzq9UqEULkI44Q8syKyAZxp9MpaYWuWsJ7+N/IJCldKMqsmHAYHP3IEVQcF8t1ryv8tbh4VyabalEHBwcFMIBPDSw5rRS9gDwl3QEkeDQaxfPnz+Pm5iY++OCD8m720n344YflsE9SpRxpho+d5mEAx9Hefr9fQKz3UY357euCLTa482d8jk7KtJVliHWjda6Rf5xG9J+f43N0oDmiDq1Wq6DnRDCGw2EBOkxT0A060DLNkX6PBdsFI9d6zzxhm8nOmh0Zt2zUM7fZ9vC1eX2yM/JQ1Ck7Mh6vwTsj/Nbl9HFlZaVEKMlcwb47PT0tB9a12+3o9/uleii2nuUcoILT4wA5We/Hjx9Hv9+v2EXIGOsO7/lFNjsaYjsMOsTWwPaLmB0qDa0YeG02m/cATebUOov9HOPxXWW0jY2N2NnZKcAMY3iX9q06GhFVFBHGNjpmhwODE6KBOdgzcHp6Gi9fviwCGwVvpAzmshLDIGFhG41GQR98hgVGQ46SoEyscL2YoJ71+l1pOlcUsEGKwgFZB8WkXGcWLow/e8V2zCJmynceWuP/Myo07xm8i/FhTGAkQPQ2jOlXfoedReYX4x6mRUiDyr5+/boIP9YCwwmkF4RrOBxWkAXTBOtCv0E919fXo9vtlj5mpAA0tNVqRa/XK+Xrzs7O7gle+mmHzcasQ9fQuBnVUSL+zg7H+zZroPMoW+Y8o4huRrltOEyn00IL8C8l/a6vr2MwGMRwOIyjo6OKPDH6HzHbvOewvx1ozs0h1x9Zsra2du+EYmSded7Py3vBLFcjojgPEff3d0BP0GO9Xi8VtZy2BG97XwTjQ24gVw1OGKzxepHDHHEnz0gf4VqMHYx91gl5MB6Py0ZGohbIAKIlOGpHR0cFfOB5jpJGzFLGSH2y0UnFP+gBHt7c3Cz9qNVqxdmo1WpxcHBQSY9gjRmDIy0GkZi35eXl2NjYiMFgUEnZeN/mt3k6bF7LhqyBtxz9zFFpeMbf8X02cEGvfQK0c/vhSQMi3Md5WlRue+iAT+wXbBB0tdNgrOs8N4wX2eiKl3zPuB1t8TOcopMjC/7sbdEmPn8oqmRA2sZv/t79zVGTiFl02/xvIBrH36mkOHkXFxclwru+vl4qWebN4uiJyWRW0c7jv7q6ioODg6jVavHo0aOy9llGuO+Wr5bnXkPSx71XI8t+7xd0JVKDHzglCwsL9yKvg8EgWq1WvHnzJn7wgx/E7u5ueQ8Azbu0b93RyKlS3othAm80GkXQs6nbOXZ7e3vx/PnzUpOdzZ3Ov0bZIxxQ2KDjIAkwAvl4KKyIuBe2t5Lg2RCv836n02klxOvwHYRcq9XKJlMQ1Ol0WvYngHryfBuuEVUnwAvO/2b0t4UpaX4uHnW+z0iHjV8IHuUJcdupw3Hj2Y1Go+xHcdWelZWVaLfb8ejRo7i8vIz9/f1yLSfpggozDlI7SGviVGcQypzOQd1sTo+H1jyndoJJz/B4bLTZyWC+vFbzohHZCM7OhJGLHE1636LstfJGP6PwRn0iZuFsnxfBGnNGCgAG9HJwcBCDwaBSvQmeR9AbWcqh/YWF2aF+KJaVlZWyDwM5lfcqgGIyHjYbRkSlXCxRA2iPSkzQuVM0cBjG43HlJNyM4Lt2vt+HEiNKkZE15hj5aNpfXFwsaY8+RBVZ4HRZnMSIKI4eCp3x+Cyem5ubYqQBNDDX5CIzXn6c5sacuP84eREz0IX8eCLaoJy12t2hnR988EHU6/XY3d0t/YyYHfiFvrETmn/X6/Vot9tl/9d7nr/fvs6cWPfZSc9yNjfoxDpxHmjhz+F30mSQ+UbCXYjAhiX0Op1OS2qmo+YGqiJme5icgk4/+e19lfCQ9bcdbjcDv7zb8+LmMbztmb42O4cP6TZHMbNc9RrYAeMa+slYSN3kfoDdiFlxAcs1p5qdnJzE6elp9Pv9WFtbKwY8QJMzKJrNZllr+sPaYbRvbW1V5CYgCpkXplF+nBlhhwrACx1IlJz/nZplXeU15UBZ0v0d+aU4Bvqo3W7H6elpKeP9roUrvlVHA+KHAcyoDjvy2draWplECKbZbMbx8XG8fPmyCHxyngkJOWwEIsX7cQDsmdbr9YKKRkRR4Fzj9BUjzRFRDGXnYLJIPkWctIPFxcU4OzuL09PTiJhtzCQlKCIKose7ySs0iuBwrFEBxmjE1kSJQMiIQEY9LGB9D8yYFbOdMaOTrj6F04SBboSSnETmGaVO9Qc2AJIvOZnMygO62sN0Oi2RMPbNMHbmjEgUBgBnrLRareh0OhVUNR+YaHQAVDUL22z08l7Po+mfa228vU+benur1+vlJFbPjRE9FJBD2RHV8rcrKyvxwQcfxHQ6jcPDwyLsr66uYmdnJ46Ojkq+PAY8edbznHDAE9B7aMd7J6Cpi4uLgnKCEGHMYzyY9gEmcDyoi84Gd4yPHHFtNBqVNC/kptOzImZhc2jVqRU+8InrbXRY0TO3Oe2T58FXTvG0PEPmXV5eVqI8yBOnQSDzmDtvvgWswUlkbRinIzo8h2pa9I154Sci4ujoqBhuyJirq6tSpWoymcTOzk4lcuMUiWzsZiNrcXExHj16VOjxfau2jz76KJ4/f175bB6AZoP2IdDNtGwjz0b52wxyngPtEm3DsIXuQdWtLxyxI+JB2o7P1kDvOrWJ3wYnTLPoGtsDNsqzw8CzzI+Wk/yfjWA/B9k7D+TM65PXJvPA26IcjMnPQmbZiEbnZ0cEPY5dxoZwywb3g3WihHCv14u1tbVyP3YH8xsRRe4YcEBu+zBT1gmZSCaM54tMEOxn2wM4Bp5/2xnIGOwu6wavPTqm1+vF8vJycXLr9XqpYLqzsxPb29vxR3/0R6UvHGL6Ve1bdTRsVNvpsOPBAoCAsVAo7aWlpXj16lWp5uIIBgaBlStEAhGhZPHECI9BiCASMDmKhOuNDhqBZnG73W4xfDc2NorBi0Jrt9vlXThQlLMFtYuYnSjOYufUDJgL58cbTnmXDS4jHcwPXi5MwhhYK5g9e9E2oPk/Czb/rtVmGzippOHxEEFi/h3Wg4k4l4TUMrxp1h9hSzoJhhT5hCC/jJXnMkdU/SDk2O12Szg0Iio5sNADRolLIqM4ct6k19ARPQshhLJ/jHi+b7O2sDA72R0ZwbyjkOchmY50rK6uxqeffhr9fj9evXpVoqOkZILi8Azy5qGjXMyAinY2jo2ONxp3ZQMPDw9LFC8iotfrVQQ/tMr3pEcRwWs2m8Vxz4d9ci/GSUbCmK/j4+OKMspAg0EBIgkGfGys+4eGjESJEiWxYULf4RsbDchB5oXoMPxKVMQgCkY5Y5xMJsUpxHGCHyOi4nwyFitxH5aIfDEQ8uWXX5b9NZYJH3zwQXE26DNGpiNjzDWyCD3GGm9ubsaLFy/exgbfy8acZOP/IVT8qyIgNoptXOdn+n05ZYrvAQtt2AMYeM3tJJNJQXom9o/lvuVNRFRy7p3xYGDUzgkNYDSDvjly4XEaGPNczAMtPWd2qLKz4fbQvfl9nk/LrHk2TLZ/XGYX2QUQQUEXR6atj5G1vAvwaW1trVQWc4SCdyA/eN/S0lKsrq6WA4mR46zbQxksPI81YI0Zk8ES22IGkZhTRyso0AMtAF65lPt0Oo2zs7PY2NiI/f39ePbsWdEzKysr8ebNm7lrmtu36miYsE3cdjL4G8VMpQ82+1GRYzKZlNQJDFUYy8LbyD6KCZQQ48H1km2Is6kb4W9UDvTw6uqq5IqDXIGMUQ0J9M4eLf1bX1+PXq9XQdFhDB9WM51OS5TDgg1ngTZPMFh5IkidqwyDWnBmpyIbwRZMGHoZnbTXzZpA1C7/yBxDE7m6VavVisePH8fLly/LoXkYXVR8oAQyiOb5+XkcHR2V1BU2dLuqjueDZy4sLMTJyUmMx+NyMJ+RItJOIma1s6ETTh628nlI2LJGjuZxrZ0LR9Let1nDyWOurJRyyNm8YMfxs88+i6dPn1b29rx69Sp2dnZKRA0EEnDCcgWeXF1dLaWTMTQyIn5xcVHKbkdEiXhy4CDpgMg3hHm73S5lWtnfAQ8hT5BtOSrs+cBQcZQHw7lWm1Va854wRzb8XMsdGzV2rKyouB4kz1Fr5pJ54cBTlC0yloNX4Rs7VMh0EETvqeNEcFDd8Xhc5DSOGtFw/gaVNg/XarUiH+hzRBTQa3t7u8iAVqsVT58+jevr61IJMSKKw0X6Fc4UDgbzxbMpKfn+XI1q+ypZaFDBkXfL2OxgZ6OWdYZOLdN9nWUL9GQeRx64iqIBJ5dCJYJnQxGn3tFBohcRMznjCCq8By3zXujP85dBL4MF2bmYN0+eF0ckH3LW3Py576Vf2ASWI14LRzFyf5kvbD5kG3Nhwx1n4/j4uGQ52KZyehrfkZq9vr4e6+vrlXQ53gFgw5pQ8OP6+jpOTk5KpJtxsz+VlFzPa+4T9MEYvQ70wWWNndbriBDj4ztsYUe0ke/1+l3p8MePH8fz588rFQO/qn2rjgaETsuEatTYB51gBEI8OB0gVjbuON0RQ5SGAqekGYvmXOJ5YS7OhICgnVtt7xGPlzEQCmu325X0KgwDPNherxcrKytloXkOihOmhcgRKkYwIWzunZcSALHZMXG+JuM2omOjmLEaqafPRkgy49NgvIw22FiJqO6JQRBwkF+j0Yif//znZZMnRv5kcncAEpEhynlCGxYSRDkQ8BFRUlmMBpFCs7CwUDaB8T9zaDTdKSVG0Tw3OFDMh3NhsyJkHd9HM+Y3TtuFhuahcfX6LD0yr8knn3wSP/7xjyMiYjgcxtLSUrx48SJ2dnYqOfR2mO3QUBkOh8eRNQwONpFzYjW0h4D2YXtsjB6NRuW6hYW7MtkAFkYfFxYWCgoXEQXlR7Y415a+44ggNzhRHCQNecC7I6rn5+QQPUax9z8RzYU3mEsf0AftI799ou7FxUWZE8sFp6RaVsGr9Xq9orQd0Y6IMlfw7s3NTTlRfnd3t0Skut1ubGxsxPr6eslZNu04ZYSUz6Ojo2g0GvH06dPi5LTb7fjggw9iPB4XwINoKRvgGQOyA1nB/Lbb7ej1erG/v/+LYZrvSHtX48b6yWBZbgaDTF+mt6zT+Myy2/uecDJWV1djNBoV0MIAKw706elp4U/0ldO9MX6hFRwKaMrROmQDxrEdFtsDzl6wfuc7g5Dz9JMBRY8JnsyGuufa4ENeG8ZI/xxl4L3mk3lZFdzDHJl/kVHetI2Op6BELjubAVQAp4WFhbKfL2evkKLEPU6rpJ2cnJS9gThVBksddXIkNGe32DlCJiOfoQfLHGSwZTJrOJ1OC72ie2q1u+qFvV6vlLp99epVxeH9qvatOhreBR8xY1YGbkaHuG30QmDNZrPUlOY5Jm68L6ffgIg5zx5v1tcxeSwYAgNUMedeuvFOp0HRL3vsGEcYtt4cibCB0DIKbqOJ5zufkrHxfL87o+bcg9Fsw4zn2MCwIPJ4HQa2t8zz3W/WjPcYCbURhFPnSMjq6mo8e/aspDpNJpMYDofFYCN1gjVut9ulnvnNzU3JrWbjeKfTKULeqC1/s3/j9va2nPAaMUNLSPOyYGk2m3FycnKP9vMeATtTVmQR9w+HfO9oVFutNiuiYOHqFDrLFvMMqS4//OEPS9WgtbW1+P3f//0SScjRPXiE7zqdTmxtbZX7FxYWijMAXz5//rxUNmNPGBEwaDtitlH65ububJ7FxcVYW1sr54M4pRD+oT+kfTrd0CkCjr5wDXIGR5z55B6DQchLEC0b7paZ5l2MmuyIRET5nvGyR8VyzMAD+iBiJi8ioiKDcFBsVKLgl5aWyuZ/lOfq6mqpVmfZNh6PYzQalcP69vb2SqU5Ipvsg3H+PPNBEZFut1uu4TwgnByDHo760n/G56jbxsZGHB4evpcB79CyIZydiof+tzGZQTfL5QzGWU+CTqMHiVTC946oAUIcHx+XzArrEZ7BOymzCr95r4YPFYZ+4DE/wyCuHVuP2cAuv7Ms5B3ZYbD9FjGr5maQNq9B1oHoX4Oftte8BjzLNom/s6PDu9DBzCmp9BcXFyVdmv0ITkmHR10tzBHibCc0m82KPQHPI/94JpXF2OfAOpLK5T1dWeZZdkVEkfvMKXIXOxXnhSgLYwMEwm6DRn0kgA8gJRV1fX099vf37wH6D7VvzdHAs4YgnVrA/9lzNgqXPXszlyeNzzgdlueBMjh1wGgVqADMS18cQkcYQLBEJRz2tvK3B1uv10s1JBQ/ewBQ/t5QTjQDwYRxAiJHv4x8IKAYm1MdGCvNis0IAs/JDgnvYJ4w5ow4ZJTCgofrmDu+Z+6NSDNn0+k0VldXS/Uo9u08e/YsXr9+Xan4wEY6EGYEPXmQ5N/jyWOs9nq94uzlDXg0UqLq9Xo5WZxGv0BhCX1Cn8w/9GABmBGxjBqxDl+VKvB9a4uLi+UMFAvfiJnhbgfOSFutVotnz55Fq9WKo6OjmE6nsbe3F8PhMCJmJSJzWtbS0lJJXaLyndcWpJpnnZycFLqmdDb8TKoVYwGVj4gSkXNE19E17xNA2bF3wkoeBe2KNzhhzBM8eHZ2FtfX17G6ulrkBkYNEQbOEYKO6QsN+U0KAUqNPkZEkVnX19dxfn5e0EA71chBnztiBWonJitvHDl0DGvIoYQY+6yLN6XDs8hwnofT0Ww2y+GJrVarpLjgwETcbRInqnp6ehqTySQ2NzeLbIKOANwAXWhG1jFuONOF6Ov7Nt+J+JM+z3rS0feHrjc98hsHnawH5D0nUKPv7GDgxDuixbPQlXYG6KPBWd7H/07VZq5M+3kMHqedKTu3vj6i6mjQLz+DsTvqnN9lR8Xy2qnC2Dh5zm2vYJf5bzshOWsCGYETwObnWu2uCAhpTd6HBnjA567qxGdUxHz06FFJkzIQzJiRndgt7Xa7AN7T6SwdlyiEQV/0memG50K30IH36DjLB+cCpwL9YTumVqsVncR+1fPz82i1WvHixYv4pV/6pdjf339nPvxWHQ171fx2xMIEm5WJK0rZiMeAZwEnk0ns7+8XdAqjnFxJFgAUjc8oORlR3UzI/ywMDIwyZBMx7fb2tlQkiJgZ1JSrQ+Gsrq7GYDAoRIRxBFI5Dy03msh4s/BBSFlYZoSE5xqt4PnzBGwW7EYH6A8CGoECM+eITHZ4CM3lNCqYOOIubxtjiHe32+2SMkEqG0KdPFTQZYQmigCPnkPXMB4xEBz2pN+TyaSgFmtra5UKYbyH/na73aJcsmCfJ1z9Lj/TBuX7NmukJDiSFnG/GpuVJnO4vLwc29vbJS/65uYmXr9+XYmOmoeur6/LIX4OY/OuiLu0zJ2dnTg/P4/hcFiccUcukX04pj4DAnlD7i97iHBGHAWExnFgFhcXK44wNEgqEGWcAThQ2vAqCKojuhj5LidLepSNlohZtATQBfCEU9WRNbwP2UsOMXxu8CijaPTb+7pQ3q5CBQCAQY9Cp2+MbTgcFnlv/eLDz2goY6KnjcZdGu/W1lap0ILiHwwG8ebNm/jxj38cy8vLZW/Is2fPYjgclhQo0u6clsu4eVZEFHCs3++/dzRSm+cEPASkzdNf1o3ZkM86K78PeQ3/s24YrXYS7PRPp9M4OjoqESp0IYaeHRQblfz2hnLADewqX4vRbEef+QCIc0ppNvQNfiFTcgrZPBvBdh08jb00LyJncDWDbV6zbJfkZ/la+unxW8a4j0b3MfQ3NzejXq8X24wopCOzyEE7pczRcDgstiVR73lOETYL6bI+0LnRuDvagT19yGvLR35cDY/xsy+ESCrvRH/hQHhMWR8w16Tj8j+gx+3tbXS73XLo4Ve1/786Gh6ACQTkjTQkQpB4cRFRwtgw5NLSUuzv7xcvi8llMUmZccnTpaWlohAjoihBiNbRDztF9I/wlkNUPAcCvb6+jp2dnTg5OansDXn06FGcn59Ho9EoB8aBrPNOI5g5tcweP8y6uLhYwnY2BswM9o7n5ddlQWsBbeHMM3kW33tteZ7njL6T7mSHz6Fahw0RCF63jY2NuLi4iJOTk+JMInxBGMj9RiBwVgnfY+yxoX86nUan0ymfec68+f/m5iaePHlSOQEeAQyK0O12i1NpJjd92dm20MLRep82Nb8RVXK4PMsVOx/QQK12d7jawsJCHB0dFQMd59LhaPaBEclACXhPFxWkjo+PSwqelQrvh96n02lxaJ2WxEZk0gFRfre3t5USqRSuIB0I5QD9YnAgM31uBZESp19AezZIkDdEWlBwABvMp505p8TieKBIeaaBAJx+AwHzDAHLOZQ90RWfcYGBxncRUd7P2JlHnCgiUFnv0GfzLOsYcad4z87O4vDwMJaWlmJzczOePn0a6+vrsbBwd27H7u5ufPjhh9Fo3FUZa7fb8eTJk+KEEk2hhC8y1HKU35PJJHq9Xuzu7lYMivdtfgMUsgMxr2XHIyPHzqTwd9kYpqF7cF4jZgAldHV0dFT2a8ELzWYzut1uBWTNOoWGc1Cr1YpDg5w4ODioACTYA8g3jOm8T4C5yBGKPDcR909Wt4HvDBVkoGU0/O935KIV9IX7kDX+znOOEW0AhPlAtmTE32AnfeM+wGzKplMQxGs8nU5Lqjv2qFMosS329/fj5uYmtre3S6VRzx3raQCGe5E9zWazcsixgaS8NoAw6IDV1dUYDoeVLArLcfrPOkVEKaCR9Zf3Dp6fn0ez2YyXL1/GkydP5qaJz2vfqqNhZp3nbETMUk1Q6D7uHCRsMpmUMmG12t0pz69fvy4pNkb9UJQYlt1ut4SCUGQoKPqC0Uq/UOIYLEbaW61WeR+VI87OzsqBMC7nirGM4YyCZqwQAkqUvsAc/HbKgOfXCHhGdJhbPucHZsuCl/fBuDCjnQv+9z0R1fr5EfcrV9EfxlarzareICDsLJE64vzmWq0WW1tbMRqNiofuMwXIja7VauV8DNYtp8BFRGF0p7tAF6wLTszp6Wm8efMmtre3yz4SxsK1GJSj0aiMeV5EL6NI2UB9H82oNtbTxqnlR+Yl+A76BJHhoEaijNAazjobsrNiIcpxcHAQBwcHJT2T71k/yx+UPZG46fTuECiqpTkv2We2OIpAAQOUUq1WK0qOlC4OuiTtysaSoyHcC/BCn3nX+fl5SaNCpjA2fhwNyGeGwGc4+66Yko2p3Gzs+XdEVNK12CfHc5FHpoFarVbSJ5k7+JG8bD8/YnbqLv13iigIsivPnJ6exu7ubjx+/DiePXsW/X4/jo+P4/HjxwXMWVpaiu3t7Tg+Po4XL14UWYDzx/sM3piOKXrx3tH4+i0j2/mHtcCQc5Q/R0TfBs45DS+nKdXr9Tg6OorBYFCJwBIJJDpOnxzts1OObRMRBXRA51IcBR1KdgbFb7AzMPhBzq1fnOJDv43+27GwbYGMcDEa2xz8diqY54GWIx8G3Pwuz1XWk8gqpwXZVkEPZKCC+WWPnM86IjuB74l+Ov3N6dbQwHA4jOl0Gk+ePKnsubNsQ16ORqNyrMLNzU2hp3lOsNfHv7m3Xq+XSDZABv1DRtueioiSGss8Z9sRAAeZenx8HB988EEBb7+qfWuORp4kb+6b54Sw0EZ7ELQYdu12Oz7//PPY39+/V1fdCDEbfZhUNmiyoTRidvw6ytMKCAGDM+Kyid7syT3ehwAxsqjn5+fFIAZBdUqP928YlbGRDqPMIz4rRqdW2cDPz8vNEQZKb9rZMLMzBhwcUECMFCMKvBNFjbECgmtEAkaIiFKmjwgGY1xdXY3Nzc2CHmA81Wq1chrzxcVFKQTAhk6ugckd6WF+WEvOcuH5oFDn5+fx05/+NLa3t+ODDz4oOdtGvtlsbprH8bUAcboPzfPxvs3awsLs5F0jXt4UGXH/AC6uJyJARTnKFBLxpNIPPETZWaKTh4eHsbu7G4PBoJKygwFifsRhcDoTjjR9JuJAZMP0yOFNLplNNASZxh4uEPJut1txfFDo/HaFO3io3W5XnILJZFJSEJFFONkYMKBuPscDBYaDThlxNljaULF88rrZqHC/nYKFw4GsRF7Pk1GNRqOcdYNcYb2RV6S28TeyzMZYrVYraQRO0XJJ3aOjo9je3o7b29t48uRJWauIuyppn376aRweHpbDD1krTp6n73YomK9Wq1X2Eb1vD7cMfpm+kAVZpmbHImK2IZpmwxk6N2hE9BOZ5D6cnJzEYDAo6+tzZSKiku5bq9UKr7k/Nzc3cXJyUklVAax0X+ygwj/QN2PCnnJqsOfLgCSfZ8PT8+30HAOYWfZmYDnbH9xnO8BgktctOzPzdKXXlD66zy4vbdmGoe9KpwBMBnF9foZTYZFX7MPa3d0tkRLLF987nU7LcQ18D53gPMxz3vIc2A7znPHcbCOj40jXtQ2U55IsD3TGy5cv4+nTp/Eu7Vt1NGhOFUFBZUej3+/H5eVlQdZQIhFRPM2jo6M4ODi4t3BMNPXQrXhgwHq9Xg5OgRHzYkVEQbAgiIODgxgMBhVk0ZtsnEaAYWPUjZxb0FL6wvjMjJmZEAwwIcSUhdJDz5n3nrc1MyYpFxbkVu78tnHjH6dvgUaTZkTLzoadNqMhKysrJQ/10aNHBRHgwEbmCRSHMwxub2+LkepIEWOZTmc5/kYeyHnEoCCkuri4GG/evInV1dX47LPPClKKMUmZUBxUG0kW8tnhyCjc+zZrFr4RM6QM2jEwEVEte4jjz76q0WhUIhukMFDS1KWRJ5NJ7O3txevXr+Pk5KSgO9A5DkU2FJeWliqpOhFRom+TyaRsAuQZ5m87zkTHvAeCNA0rh1wBhDlYXFysGNo40K1Wq+x3abVapZIfxg5ykXmGhi27I2ZVUCKq1V7svKOwnc6AnKXMrOWN5V1GlHHO8p4NjCreZ4cBWYlzVK/Xi7Ppg6u8P+/6+rpSSSjLhogZCky0naISjx8/jqdPn1Yi40+ePIkf/ehH8Qd/8AdxcXFRQAcir8g85gcUNSJKAYL3Z2rMb/OMzHnGUnZEImZAAYZvBimyU0Hj/3a7XYBMH/w2nU4LT/GZq1FFRCWlD16z8wy4QdEBeNBpR9DiZDJLvcY2Mfhn4JE5e2hsdrBzNAN5amfM8tef8XxH62z8ei0yUGB57ohEvjf/9nrPc5oiovIuZI/7S8oszgL8ig2Xjz0gauqUJH5fX1/H3t5ejMfjWFtbK44gctl7eLwhu9G4q2SGfJhnr+X14wcZ57SuvFZscDd9eo5yq9frJS1scfGurPcHH3ww99rcvjVHw0qDAZkwPDjOlkA5IgRAmC4uLuL169elXvnx8XFlwTEiMZBNsBEzBcfn1JLP4U9KMHKgF4yMZwix2EBtNBqlvj7XYBh53wGCgIZQMfOCGqI82cRlrxfCchk1M2Se44y4Mh9cb6HF72zw2oibJ3i5Hga2I0k/HK6G0ezk+f28A9QBQTse3x2QdX5+HicnJ2VumFvWg8pgKHXKmPb7/ZLvD6Ob6T039Pny8rKk7CFEXr9+HZubm9FutyvzCOJ0dnZWSXnLKI4dDNr7iMb8Bt3YgHTqCTTmNTWfHB8flwgZka5+v1/2boDsoxRPTk7ixYsXsb+/XwCGiKjIMIQ5sgG5A3KNIeuCElQx6vf7ZdM39yBv4GkrAcYLiEEJV5QTedgoS4xT37+4uFjSOonUIP9wVjhbg8ZcIvMYu6MRrn5FpNMyl894Hjns9BfZwZ43xo+8M28BIjFvvB+5TSQJPsKZMd0sLy8XQ288HpeUPHQM/WFdMRSRSchbyyicjS+++CJub2/j2bNnZS9XvV6PH/zgB3F8fBw///nPC/iBXGKuPB+1Wq0Abp1OJw4PD//EPPSnvWU5SXNkf94184Ab5Do0lpHgiPungBsUhZeI/Hv/EhWMSLfBvsjAIwAeBuJ0Oo3BYFDSK0HLrTsAqxyxIeLmSBw0Sgqfxwo9R1SB4Hl6iL9tV2D3eP7nGanZBjE4kt8zz3nwT3YYshPk37YBkB98bnnEXODcWE4gD0hPdXU6R1Ynk0kpj+siFjiH7Au8vLyMra2tIpfJhkH2UyQC2nAaLLLfmT22qTwnRLnJqMDRdEoZfc1rnpvnrV6/K75Ehb1Xr149eJ/bt+Zo4CxEVOs62/GAcbvdbiUXcW1tLSaTu1zFvb29ePPmTQmrmYFQFE5zyUIDg4BwZURUFCcbYqhOYgcBxcgCdzqdMrZ6vV4Uu1NkeK5DdZRP40AUFKrTv2yMG8XwhjDGBRFmRMZobsR9Qez/MWbys/nb+dpGGXJIkr46iuJ3M5eEEO2gMY6FhYWSckYu9jyBBhr66NGjGI/HJQWBMCDGktMxMIbG43FxUEF26T9jY08NDIkSYb8HTHt5eRmvX7+OX/u1XyuGG0YOZy0YPcsKy3zh93t93re7ZseC5vlzyzSOEXtwcFAOUFpbWytRDM50mE6nZd/XwcHBPcCDd/E8+NzVqFhvSi1zDznZOLls2KZ/jhhMp3flD30itY11IjERd4Y8kV6qLJlmG427SiZGb1Fa7Bsh+mvFZtkB71IVb54jl+cbvkXhIvudgsVaIZvhA+YtohqlBJBBZmF0UQXQ+/ow4pkXlDDvxiFBvqJnkBGsuw0K+msgyaVNFxcXi1N6dXUVn332WUEmV1dX44c//GFcXV3Fz3/+8zK2xcXFYhQY3DG9tVqtkjr6vlXbQ4bS24Aa6NNRO0cV873mDd5HFNT6Hb4gvZJiD0QzrT8xZolOnpycFPDMmRU803oVGwE6NECCLPB+U+vNiOrBvtC1bQfP61cBXrYz8r38nW2xr/qb8TJXNnj93PxZtvdYS+SeMzAiZlFYdAvgM9FJ5BURXoBM5IZT84g8s7YAjshhl7223I+4cxS73W7ZBB4RJTJhQBIadISLMTAf6BkqRLHXxCCd1yX/nZs/ZwvA6urqOwMf36qj4b9tQHsC+/1+RMwIF1SwXq/Hzs5O7O3tFeWwsHB3cm6/34/BYFBSZ+xFooAcisNLxCEAmYawcg13DthxCUyM/oiZoQ/65o2bGKs4L6Q/uKxbxOyQHRO9w/Xe++Awv5F/mCoTj5uNf9o8JuY6K9bsNXtzGe+H6C1snJ5kw8IpbxgLMIgFqBUCa8u9rBnr2u12Kxu1ImYH+BmNBhHGOfFJz4wJOgMRNpJFJQmuoWJQq9WqvJv9IHlePad+rgXye0fjfsPAnNfs6EbcT91B4Y7Hd4ezUe98PB5X1mxnZydev35dNlxGROF39me5mpJD5fAM9LS1tVXps3ng9va2OMUYxNAoYe3T09PKYYCmFSJ6KFCPkcP/SL2i0U9+GB8gjR0Tv5P5jYjikCC7DajAL0b9+M75zgZOmF+nOpEeZuel3W5XeBcZi+OADLaTQZQDuQ5w0Gw2i0yxkcX6LS4uFn1C+i5jYr1xQJEDjOX6+ro4GhiAP/7xjwuAsrCwEJ988klMJpP42c9+VpzChYWFoidcCAC6weD5vpe6/fGPfxwvX76Ms7OziJjpEtpDBlN23iJmoCcIMvdneR9xf7M0BV+cMgUNIE9arVahHZBqG8/Q62g0KhEMeMvOhKN1EVFAA4AH+JRnQ4+g4oyV8TPeDHYZcPQcme7nOSGWE3zm6MU8p+2hdXnIachrO885yf3jf2cmcL3XIadtusodabbs68UZ4FqMeOSnnQLWB/5GFr169aoczIoecbVS+oRdk484mEfj2Dj0q9VqxcHBQZkDp3XlZ2Tn7asaz3n8+PFXXhvxLTkaVv4ZFTBDg/JPp9MScYDR2ITpNAmUI4YiRifMm6uKeO8ERES+PcapDeulpaVKKU2HPXmOUxVyVRKERkQU75e0DcKpTlFwFQDe4Yo5znOEGIzC0My4ZjwjjHYqHhIuRl5MlDb+MxPbeZj3fEcvHCrlf3LEvZfGFR+4nzzXy8vLWF5ejmazGYPBIG5ubqLZbJaD+kiNyKlmlDZlDwXOBSWHTZ/zUEzSVNgsTt8R7PQZ59AIAr/zfOeozXtH435zhAr6Q2lkXouYL5CbzWa0Wq2SV0ta5MnJSbx58yb29/crBxo53QcQwsas0xSgParbgZKhKLy3BIMAJQZ6hiOEUcx7oXVkIs9xOpWVPXzqvH5SlYySYbhSIc+RG1IVp9NpOUQKJ8G8gZI0OBBRdf6Qh0YF4TXKjjN++oiBxnqzlsjk/GN6oJEShkPiqAVGGDKbuUWmM3egmY7oOvqEc2l0cjqdljWMiPjhD39YCklMJpP48MMPizNLGV7oCToxDT6U2vN9az//+c8rYGCWk28zVPmez6EzPs+Gl4Eho/21Wq3stSTiDfps/WBHAEfTdHZ2dlYO8HM6Nb+bzWbFgI2IQrPQuXkJmnVaoWWCnRcXnMlz5bFi6/B8y9ocfWC+5tkf+R3ZUaFZtj7kfMwzhueBqvSHMdN3RwSwMQwUIRtYS+Zkb28vdnZ2Yn19PR49ehQrKyslZQpbw84rYA+gBO+9vr4uDsrW1lZxNLBzDMxzaHHELLJrEC2P046GQS6Dmb7vofl7W6vVaiUF9F3at+JoGBnAoPLgISa8dBYMx+Py8jL29vZK6LHX60Wj0ShVnyJmZS9BhXAeIEqQapCw8/PzIuRJp8HYjIgS9SCMiUNglK1en53CSR9Q0vaYcSxQcGwuNrJnYcaz7IRFzE49zgLAQsDoQ8T9sy1spETMoj/Mkx2OjAwg8NzPiPuhfo/fXr2NG78rGwqgj3YqXTrTaCqGPD8gvPQVRJf15hl2QKkEdn5+HpeXl6U+PkKew3SYD+e812p30Q3S6DAGTBN2Cj3+h1AJnvO+3W/Qk+kUWscQtKNoXiXi9OzZs1hcXCwnudbr9djf3483b94Uox/BbyfRkVEaMooUKWi10+mUEoPea4HTwRk89J/qV0azSOMimgqIQYUbDJlGo1GiqBjT7AviGcwHyBb/E3H1JtaIKJWmiBLY4HLKgTdAErm1Q89aWFGyFgaTnA6F/MDA5pk819Ek3s1zuZ6xYOg7UmQniXQTnJuI2cnOGAjeWOucbmQ3a45cz2mV0+k0nj9/Hre3t/Fn/syfKRtMG41G/NIv/VJJ7WL93gYweF/N97VhH0TcL4ka8dWIuD9zifiIakWfbLNYZpP+WK/XS+lq9JH3/aBroEd+iHr5XI2sg+v1eqWErdfcfOJ5sLHPe+Az3mObJWJmvAKWOJqS549xWD+9zdnIa+LPH3r+POci99W6E37L9orllR1SR1ad/WJaYr3JhkBeACI6E2J9fb3IewCh6XRW8j4iyllp7G+IuAMvDg8Po9FoxNbWVnkf9osBbDsYBmpMU45WRMzkmPmFZvvFLTu6b2u3t7f/3zpHA2UTUWVkM29EVKIZEXcLwZHnx8fH0Wq1CpFzZgaEAvOw0JRURHFz4i7nK4Aq5XCfFw3li6LzBkTGUK/XK1Wn6BP9QZnPuycf7mMDlWaFbU/eRpSfa+ZDEJkYUeqMex7qkIWInQGjmPl+Ow4Z8fC4UNQIHTs2GCu8g/fiIDrnlI22KPVWq1WYivfB9JRks/InPQXUFEPn8vIyDg4OotfrRbfbLe/kPYwBpdJoNOLZs2dlHcnbzozPuj3UzAsZ5XnfqjSfBaXpzmAGgjviDh38+OOP49GjR3F8fFzWn70Y3sgH3YG2uyTq4uLsMD8Evw1PeJt+uboHkTTSA4nGOZ/X+3wyPRIt8WZm0Djn4dpRsfKn4ZCYf0HlHclkHkEXb25uSvpPxIw/Qf6csjFvPxlgT0QUR8nyHufOciwiKufRsOnWQJL3jHAwHlFl9MXp6WmZI6LFBr1If+h0OuUQNZwy5DNRbWgQh8TOmPUB63d6ehqvXr2KRqMRP/rRj6LdbsfR0VEsLy/Hj370o1KdyE5mRqMxAEnlfd/up8wYfMipTlmmopszOsz95pls/K6vr0ez2SynN3ufDusOnWAIU8no8PAwdnZ2ip4j2mFda8fasoE+8Fxv8uW+8Xh8r1iCK6sho3CMkV22FTxmN8vbeeCk5XMGLPk7P8PyxpF/+C8DdRmgs2MGwDgvquoUOReLsPMDmItMwL6guZ/I3tPT01hdXY1nz55Fu92upNL53fzmHI6IKFWcGo1GPHnypPQfu5DrsaEB0nLJ20yjjIOzeixHMq/MW9uvalwzz4mZ1741R8OD4H/nCJIKhdJcWFgomxSPjo5KdZiTk5OywSViZpxCNIuLiyXvmnMXOD0aJZcJx4RmR4PvXWISxmbDIUICg4L8Wt5nZ8OOEQ3lbDTURg73mREySs67MorL/OT8Q8ZlRrAgY50ioiLEnDYGQ9i4871GJ+lLFtyOjnAP85OFC8wFbcC0KHrGSd3+Wq1WDBkcTNJlqACCQeLcaOZwPB6XkHav14vNzc3yPEdUPKbr6+tKYQEbMXzGPBj1nScw3oZqfl+bnVOMW0e2vDam+dvbu7LGP/rRj+LDDz8sTutkMokXL17E7u5uURrQIEYeedWkHEE/yAEMXNAujIla7e4g0f39/cqhTxFR9pZxKCAOA5VCLBMiokRAyNGv1WpFTiDPiATi+ICaR8zoDX6F14laZIXMHHozNsavD8J02onplecjv7kHJ+7m5qbs8yBVypER1hgAgDOLSC1DJjFPKFb6S4TGDsloNCqbcw26APbA+0QkKImNHL69rZ77k88v4DtHnaFPTtS9vr6ON2/exMLCQjx+/Lhs2F1ZWYkf//jHMRqNYm9vrzg0OKisKc6OIy3f9zYPfDM9Z8ArN0fYoGWnzhkY5TnNZjPW1taKvomIYrSjf5AhTpsaj8cxHA5LFCMj8k5Ngqe9L5OomZ1q/gcoQ454Dwf2Fc3pd/7eNoPn0nOanQMb/p5LwMS8Pm62P7AxcHgMoLo/85r7B68wXsbnSCv7L6wjfK2dD+Q6/Wu323FxcVHSTVnHs7OzeP78eaysrMTGxkZJ6zdghC7qdrvlbB++Pzg4iNXV1WK7Grgl3Z6qYp5vaNS2k9e51WrF8fHx3HnLc5jX6F0cjncFQ7+11CkbVPMIlwiAD7iDgVZWVkouY+n4/4+JLNBRNijmw8PDchoni47hgCfvkrIIchDsTqdTFpowKX0mLI/iJref9Bs2FqMwTey5jC3jANEzSlqv10vlIitI/jcCADFj6BiZzREM3xNRLeHH/w5Jwiy52SHgury+ThvxO4kGYCBgMHI9oV4/jz7xPow/5ub4+Lg8dzKZlD02GHowJcgtxhPvIF8fB4QKMldXV7G1tRW9Xq/ihEVE2dDFZjHnVDqkm5GfhxjZzuH7NmuskUvyIVxZL9YMhQcNfPrpp/Hpp59GrVYrmy6/+OKL2N3dLZEM5AP0ZWOcaCqGPvSGUWgH8/z8PEajUTnbxak00+nsICb4k9QZDJOzs7NizEfM9i+RFgjCRVgcerfh4Nx/HPqImcHklADLJsAReBTUHyfBe1Z4nuUCyg+Hazq9O4iKyJIN53q9XvbGORJJ1ADU3ntKIqIyl5bBzIGNS9bURSOQARhm2em/vb0tFciQW6TYkY7gaBcOFeei8G7WzU7Xzc1N/PznP4/Ly8t4+vRpqdHf6/XiBz/4QYm0OzpueUR/vs8nhVu35c/n/U3L0YyI2XlKEfPTSbKjERGxsbFRbBIc0E6nU1Kyvb8rIkrk4+DgoPABUVJHx1hneA2nFn0O0AnfYGzaqObQUWQgANrFxUUFVMjzgg1i5yHPCTqXH8sPp1zZLqFfnnfrc8+3IyvwCv+7D3YMDZhm3uddTjfHZuBdRKTs5Pk5yDI7Kdh5eQzokcFgEGtra/Ho0aNi2+QoC/Lx6uqq7Mc7Pj4uB316PI1GI9bW1mI4HJY1z/TJ39mGQ2/lCIbX+F2djHdxPB5q31pEw4rZ3i8NhkeAYzBE3Bmkw+Ewbm9vo9frVTZlg46hDDm1F4bp9XplITPyaeIBkVheXi451ygSFs+heBvvnODrA7OIeqDIRqNR2SPAUfNGRXF86Je/d1jTuci07N1GzDbNopiceoVH7ygG3yO4HG3I+zKYi4iZ8OYdZn6cBNAY94+oA6iskWmENISNILBRg6NnQ2NlZSV6vV4lqsQJyq4KgvGPA8hhS+PxOI6OjoqRETEr5el3ra+vl371er1KFIo5ZO05wG+eo+f5e5vj8b7dNQvYrFSYe/jYcubJkyfxS7/0S9FoNOLg4CDOzs7iyy+/jNevX1dS9Ry140A7F2gAELEci4jirLoIxOXlZamKM5lMSrQCGVav14szQuSE72mWK6RBnJ+fx+HhYTFAIqpVYaBv6Gk8vit40e12S+EC5417b4JRMRQpRhFjdgU3gxgGQEjLcKSAPXKOZmMEMWbrBKe2Zt7IY+BvSi42Go3KCfCchWRUkbnymhN5stHEuiMzbNyTRkbak4sAeA8eugj5dnNzE69fvy5gFIdIbm1txZMnTyrnMQCSWC9EzJyv72vk0/xPywZsvj7/7/S+7MxG3D8/A7m+trYWNzc3xfHnxOfBYFDKlxIBXVxcLFkVpOxlQ93vAyjEJqGPAAu2fWjQMn30Pp9sZzm7xI6CbQhHc2ygZ3ozmm4bj/scmZmn8+wMOBuDeyx3udZj4b6cMWH5l8FYPrPt5Sp58J7tO2wLQGycfDJlkA+2pYbDYVxcXES/34/19fVYXV2tVBXk9+LiYqyvr8f5+XksLy/HcDgs9hJ9nkwmRba48qltpIecQ/rNfbnNczK+ifaNOxpZKUdUBQIExGYlvqfM6WAwKMYaoUc21rgawPHxcUlTAJXD+WDRvDfBKU2tVqtyEBOKlb7beEAB8A5vRofoXCmCkClEASphhWulYeXNfRgC9MdOgSsU0OeMiMOMRv3cvD7ZoHc4lbmwwrMRbcfCG9xZb4RSxGyzPbndCGcayC/zx3hJmXC0ZjyelSdFSNDXi4uLgvBg2GDYORcSRgSFoDylx3B5eRk7OzsFjcQYtVHnUHmz2YyDg4MHeSMLTsb9fTUevqqxVqzLPCfaaGej0Yj19fX45V/+5VLz+/r6Ovb39+Pzzz8vFee892JjY6NEMngfihR+I0KBgegwPOk+GBpW2JYHHMqHDIq4418QUegTugcgubm5icPDw0qhgoj7KaAoUQ57W19fL4f7OboH35Gy5ZKzVtB8b2OAOcGJsmOCnCLs78pujI1+GjyBR+3ARERJVc2lqplzIiDI78lkljJJdIdS6YzNchDnYzgcFsfI4AfgEHLcctSpVqPRKI6Pj6Pdbke73Y61tbVoNpuV6lPoo93d3fjss88i4m7zfr1ej6dPn8be3l6MRqPSt4zYIksf2uj5XW82PA3U0LLR9BCKn6v7uOWoBv+vra3F0tJSjEajmEwmJR2XXH30EKl1BwcH5VqMRmib9xtUtIEbMeNjnI+IWTQSA5Koivd5MGYcIgraIH+urq7KKeWu5ohMyJEHbI+I+2dmzIsCWG+ik/N6OLqY3weNOwrA/NmOsF3CNY7M2MmPuL93FVuP/iGrcD6wnewwLS4uxubmZgExvG/TDgLA93A4jEePHkWv17sHaE0md9k1jx49Kof1DQaD2NjYuEeP3W43Tk9PC2ie6TQ7GhF3sgbQ813aNxXV+MYdDXuROT2H71BkRqaNMmNYwmhnZ2fRbrej2WyWkpRUoMLAYPEhtFxTenV1taRBQMjj8bggbxDeyclJhTggfDZYURLSBmZeDJTPZDIpecKE0lutVkG/6CvKl+ejoHGsYBITHArxoWYhZmH3EOGY+RzCg3mZL+638OEeoydGSoxOkEZCZMMl4hzmxIjgOawvKON0Oi0bPnd2dqJer8f6+npMp3eFAIzWYpRgsJIuhXODMiGlAyELLQ4Gg1hYWIhf/uVfrqCengcbStkwyuiDUSO+f9/uN6JiKA6nDJhW+c0a9Xq9ODw8LArviy++qGzWIzWKKlHQY8RMwcIPINikTaD0McQxag1oQDvQug+A5B2NRiOOj4/j5OSkpHdGzFDOq6urko63tLRUIrePHz8ucpWqJ8wTKR0YFA6hYyAADqCQSMvyPFrBRsx4GWcMueNoUkRUohW8y6lpRi7ZU2KlzbONtHoTJHzMfC4u3h16d3BwcK+qIIafN3dfX1+XqBDos/fpAHyQfsn93osF+AEAhRN5cnISCwsL0e1249GjR7G5uVlkCnoG2bS8vFzoKSLi2bNnlRRQ5ty6FEP1++hozANi3hbNmNeQ9RmJN0KfdTlABI4tabKNRqMAohjw9Xo9dnd34/T0tBidWQeY15wyxbv4cXowfMEYKHbDdzi8tkmYr7W1tWi324V+sXOgXTsPOdriqJ6Nec9fNlLpp3ndetH2oPXnQ+tq3cr/9J+xZIA18w1jQfbb+UPGcE1G+21nYKcim+08IT9sM7x+/TpGo1Fsb2+XNQDg4hqX5B+NRvfOlGP/8dnZ2YM2dW7QzvHx8b3I0rfZvpWIhsOPDtPZM4WglpaWyiE4KGMTBROFUcmx7iDVLArGqdMtyLeOiOKxogwXFhYK8g1BWSlGRCEcGAxFC1KF4qMm+mQyKSgX30OEvt/jj5gRltFDf2clzBxjSPAemgUTghMEhWbiswPiaALCgrXw+nKfn+ExOP2EhhCwsEN58783+WOkGE3MiK9TFUCbm81m9Pv9UvMcxcyYcE7pq0PeW1tbheltwKGg9vf348MPP6wYvA75cg80xXs95++CyL1vdy07dVZm/A8fNRqNePr0aWxtbcXe3l5cXV2VPNfBYFCMyH6/X4xNaB7ewEBwGg60iGzinTgBpDEYpXfUhWe4WtTCwt0GX94zmUxKeN401Wg0iuN7cnJSxt9utyvOEqlWRBYcaYSPvdeJ6iTb29vlwElkoJ0QQBqDQrzHjhH0y5gZKxFhrjW6SApbRFSu4W9kAfPJnijvYajVasXQIpKBblhYWIh+v1+iHNCTo6+tVqvIzrOzsyIj7ABxH+/Fscq6hmv29/fj+Pg4Dg8PY2trq7zDkdV+vx9nZ2clFaPT6VRSqNivAw143fj/+9zmRYUtX7PtAK0ANBpU8HU25qbTafR6vVhdXS2HYVJ5bjy+O/eGiNl4PI6XL1+WiKX32jQajVKMhIYd4uyPXKgBpxP6J23P0T/4ABniPZ8eJ2PzZ1nnzPs/z+s8xyKvhb9ztIa59fpERMk8sO3hdWX9+H9etI/nIz/dHEnlOqdrIxOcym7bFQeTdeCZRHuRm6TDM+aVlZW4vr6OL7/8MjY3N+Px48dRq9UqJ4A7UuUCNuic29vbaLfb0Wq1ikxgbi2f0Bc8j/M0DB49tI7fVPtWHA1PSPYSaQj1brdbvHQj5hFRFPBwOIwvv/wyImYH2vA9VZrImcOgJ/Lg6ib8pqpUp9MpSsAH6yAUKBOGp8niXl9fF2MUg9d148nDhVjJr7awYZ+CURT3wVEXI5I88+LiolI1iua0KT97HtFZIM1zICJmKQaMG0ZxNCcLtWx8wNgwspnWqCWMSl9tHCFYEQ4gykTDWJvhcBhHR0fx+PHjePbsWfR6vcLE9JM+I5gIMXOmxvb2dgwGg8L4plEMCCsFjAwiKe12u3KKbUZxmOesEN+3WcsGAHTDXGIQYPitrKzExx9/XAxPnMizs7MSLSXlx8a8DfHRaFQipa5Wh1LCuAdQ8AbriFnde0AAV6PxtZTijpjlA0fMilNgCCPHyBXGKep0OuXwSPOZjSQb7nbied7a2lrZM4ACRAnzLKdYMAcR96vUWG4jL3wWBdfYCcupZ+YHUledYgmYwFi9LjZqvAcHYMeGmdFpaAkljvJ3xMyOKP8bFLN8ou/j8Thev34dOzs7RQ4ZiJpMJsXZgM7W19fj8PCwOJZuOcr0fXY0sj7LzUZp1puOaFgeGywyP21sbJS5dsnRs7OzOD09LfcfHR3FcDgsZasB0eys22bB9sEgtH6jlDR7Q09PTwsazj5Q7gEMgUfhFa5zlGNeFMKZBhH3sxrc7zwGA7LZeeBaZOs8HWgnIQN3+Zp50QY362H+51nMQR4Ta0kfcfoAhkxPthHQH8PhsFJsxu91+v3NzU3s7OzE6elpfPjhh9FqtSLi7gw59A7Az/X1dbTb7fJegMuNjY04Pj6ugGoZiPC8IOOxQbPTOK/N++5PYpd8axENh7/9nQcOEmfkn4Ui7WBnZydev35dqexgRR4x24iNV+qIB3mM5DXiZETchcaIkLAQpE1YMYE8OfVgMrnL1yQsBtoAQTrUjjKhopbnyA4FCKhP5zVxRcwW38wzL4L0NkFsJALjzfmavNsMz+ZojAr2SZAGYceGeafZYHOfM9rhSI6rkFlgehwoD6II/o7Tnjc3NwsjOxVuOp2l5jl1BVrp9XpF0TiFDORzYWGh5FXiQDln3OVGaaxRVjrvnY37zalSNBSH1xo6WltbK0YAqUwnJydxdnYWm5ubsbm5WYloAlBcXFzE0dFRyWsGPSQ8nhFQ80hEVMqtmnfgd8q8UteeCCu5vqQkkWqFgYLzBG8R/SUqB29wjw8EhE8wkpeWlorS6vV6sbW1Fd1ut9A2TjSRXjtBKDSUaI5W8i7kg/dTwc9suobvbBCY9p1KgqI2ckeEB+eAZzYajbKnA6AH5wpndDqdbfys1+slHYIx0y/Wx1ENaM57WUBicVL4HqeGyA5pez/4wQ+i1WrF9fV1nJ6extOnT0skBrmxtrYWR0dHlU2rBoUiouTpPyTfv6stO7cPXZO/gxaRyTZYM5hBq9VqBUkGbHJ65XA4LHQ3GAxiMBhErVYrmQ2kWEXMwDL6Av2h37B5bm+re8Bwlh0ZcR8A6NiTSso1soh3kUoYMQMArXvn8eM8J4Pv/Tfvm6e/8ufwBN+xVu6roy3z1nHe59alvMd2mseaZRIy1Taf73d2DXO3urpa0iJHo1FZW1e5c8YLfTo9PY0vvvginj59Gs+ePauALawz6e7YwshD9tzt7OxUHG3bfp4LdEiulPVttm/c0XCeXDY8bVhOp9OCAETM0pRubm6Kp/+Tn/wkdnd3i6ELQkklCFApFs2GpJmUiIIN7Ovr6zg+Pi75iyZ2lBmHbkVEpboLyrjb7RY0kspXVsiMCaMCJBOChlioZgEz0Q+IKEcmnIqAN46yMwMzVtbEDouZ2kY2P0b1aBgezBXvMRJhVBQjxWuO8uc+b4qzwcb7nPNuoYQhxzy5HxgaV1dXcXh4WFIe2u12iX5YWRhBZNyg33ZGhsNhcRR3dnZidXU1NjY2iuFjw4WQO83vmCeU37dqs3MHag2dQJ9G8fv9fmVTH4qYKmSg4Sjk6fQuX/7169dxeHhYMZRpVpQY+aPRqNC10/JQXEYbMWYNKJivoEmfk5AdZtI2kJ+gqRxISY12O+kY3vyOuEsZo6oWKGsuf0kkAkPIcixihubbMWE88C5ziLHkMcMDTomMqFbRYhw4EV4P8z8gA9HN6XRa9vGBBuNcIFudMoGc5f956RSM17KHH/aAGJHmx5Fu8rWvr6/jk08+iX6/XzZ4bm5uFscYvccZHBcXF4W2TI+84/sW1XgbGMN31k805smnY3sekSvZRul2u8WIR59AM9Dv2dlZHB4eVmjCaeDWY5nWoRsOdyQSC385ykDkHkepVqsVJ5Q+IyN9xhQ8RzoOzf1i3gxs5usc5bHdAs8+FGXAELbN4jWzTLQTgsy3c2lU3vf5t59tx8ZOCHycI76sCbKNdyJjHC0iCsweOfbqsCa2c+1ATSaTOD4+joWFhXjy5ElZN/qDvOr1ehUAZ3FxMfr9fhwdHRXww9Envy9itk+DE8m9Ju9ia/wiohvfqKNhRN2huoxeYwyikG381+t36VT/9//+34JKw7QgDRADytBoO+kBKAEYCGWD4oIxEQoRd0KJlAg23mGkcj251HiwRDPIxcX5IQrCGC8vLwuKisGEYwWjw8igkCgtPmNMKPWcr+7UEogXhstokIkOojUx2WGB+eehD8wb76nXZzX9cRL4cf8J9WbGtyJlXVi7iCiGGO+aTCYFLXbZ3IjZKZaNRqMc4NVut2N7ezvW19fj5OSkUqWMe0lzc+Udlz2m35SsZO3pt+mAZmHq+WXM71u1ZeEJvcIX/g402Qi4oxZOzSMt4eXLl/Hq1auyKRcDIqKaLgOKmPdnwcNGoHgvvEgUodvtFqTcxjqykT5GzBSfU42IXEwms9PAB4NB9Pv9ipHE3gnmY3V1tUR6V1dXC53CW4TrbZRgUAFc2Ejnfxx/5gDHAmVrNDHzMP1zRBV+QlYjd3F4kPWsHUifeZ3719bWikzGMQNxBuAwkEI0G+cm7+Gin1SJymluGKs4C0QiOMeANhgM4ic/+Ul8+umnxRlqtVrx+PHjcugb4NXh4eE9Y8ny4vuWPkUU56FoBjLZOimDVqRN+fqHohv1er3I79XV1aIHneZ7fX1dQCzOVkFfGPQiSmYDula7S1WkQpXBVhr0jy1ihwM+8Bi4nh/klFPHHYFg3HkeM715rrg/RzRtX9BsCNuJyO80XbOGmbatJ/3MbKhn+4V73Cd/l6MC6AFkZE4BR14idwASyUCJiBLV4MwM7xEhGkwBm62trXtrPhqNitymoiqZMOw5oz8GHPLcAzQZgJq35l+nYbe/S/tWHA07GJkwUTSgBISlIZbl5eV4/fp1nJycFALGSPCBeDc3NzEajcpC4bAgDIzwg1q4WknE7LAtDAKUXERUFAqExymRECGhKaNRCJuIKBVHbm7uTjnHAEDwOLQ2DwFAiSGcMHayoDCjWZjkCIavpxkFNWpQq832MDiU6+u534gr76J/WYFbMMEsOEhWFKwZjMqzLEzZrE1eq69dWFgoa9tut4uQRzgsLNzVQ+90OuVgHFfrwQjh2kZjdh6KHdvd3d34+OOPi/HAPIMcuw6259shdRsU79usZSVkvsgolssX93q9wp9ELCljeHR0FG/evIn9/f1yMCP0a2cgYuZ4omzZkAn6jTzBADVdUJSg0+lEp9MpUU0rNZoNbSKgrsFuBwCFh0PcbrdLKJ/5IRKHY8V42GDKOJC38JOjDo4YkZbE9UYAWSPkpMu6Tiazk8wjZuicK1U5BcTRKxDciCjptdxjtNgop2UjbTwel/1WyAd4mz4TwXa6mx0OlDXfs/6kjgKiMH84VdAe4yJ9YnV1NR4/flxSeZ88eVLO/qASGCcRG5RhfN+3Mrdra2uxu7tb/rdBbFAtN4NX6FxHMkwrtldI6W6328WJNI9OJpM4ODgofM45OaQ94SBHVFNAsTeOj48rqZmuJMV18JujMAa5ckQX58UgAc37FLKzZtCG5/O/59BzOi/64fVwy3rNRr4den9mJ2Geo50dk4ciH8hMZJtlKPaN195AkmUXtka2a5k75BuFOZCDgMu+h/6ORqO4vLyMx48fl4NOI+5kNHtMm81m5Uy4tbW1En23HnT0i4Ytw17RX1RbW1t7p+u+cUeDSfVGYS9KvX63uRrlZKN5aWkpXr16FYPBoBADHnzErHyikTTCWYSdYNqIuIcGXF1dFWSKalFs4GUDNwRBSUuU7PX1ddnwRcrB5eVl6RsH9OD18gODg1oRkbGDZMbBSIbwaZnhMqOiEB3BiIiKgs6erQVGNnaNSCKobOwhcFhn1tPRK+bfzI9jZsGHA8b7eMfb0rx4PpviNzY2ijDhGqJPWag45aVWq5UD+WBgEEOQZ68HCBfrtr+/H61WK7a3t0tp5IhZniQGgwWiBaSjTu/b/WZUMmKmXGzcYfQtLy/HxsZGdLvdslmPA4wODg7iiy++iP39/WI4wD8IbEfenBudUzJ9mnzE7JwEkCgUE+l3pFFhoNqAMOIEL+AQ4OCCmK6vrxdUzGF88redGkDzZ3aaMJwvLy+LEcaPQQeUJmMlBQ1AIyJKimmWTdkJcZoRjkS/3y+ATMQM2PFZHc6tR3ajfFkHZM48Z+PJkyeV625v7zbZ2rlDdjg6Q9nQ09PTYhgYJSRd0oYc9IAeyQbb2dlZvHr1qlSkajab0Ww2S9Up7wmgZXSZeZhnXH8XG+XL3eah7/4ug2LwTTak0bf+jHXBoXNUzlkPnU6nbO61IwuvWQ9SaCIfEOfzpQA3XbjEETt0pCsXWX5ZtzwUMZ1Op/dsM8+b+SwDk/McgnnOBn+zRg85G3YSvXdyHgKfHSHkiq/3NZ4XvzNfTz/zfglkcb1eLzRg+ejCH9iSyERAJ+bIcsoO6/X1dezu7sbjx4/L5nDAUE6jH4/HZS8XBxSzLwxw1UARjfQpihZkmeiWaeBt1+3s7Dz4vdu3do6GnQt/Rz6182FJFeKMDKIHoL4YpyidiJlCgoGazWZxQAhdkY+NIkRAEN2gAggChTAmBMiGPzMuXi+CBGeC+0FGQe4giOXl5XIIz2QyqaAfmalxupzbTcPgtWHvvTDzmNtMD/EzjyasHIpljSKqpX6NxmY0CYHolCsMdUcycBRtzMwTdDglFhgw7+LiYqUqB44ejXAlOZUYDvQBpsaBXF9fr0QkMPZwNugzubgoDxCIlZWVclgTxtI8oQn9ur13NO43o3wR1dPoQeIjZuUiNzc3Y319vYAE3k/z+eefx/HxcUUBYGBAXwAWOAM2IB0xg5ZJSarV7h/6aT4aj8fF8XD6BqldPJfvQKOITLTb7ej1etHtdovxwRhAxUmbQlbSTz/Xed3QP9fPi5aSAkmlPwwlGwR2SGwION0xK0DkIo0ccpwADC1HSekDNABvOX0ARzH3jcY7mQ/myYcauq/IflLVOCAWegN0wIDAIDD94riwwZ9qMJ9//nn0er1SjWppaSk2Nzfj4uKipG2ajrzmNl6+T22eTvPf/t4OtyNoBsKsM82XLvKCY4Djd3JyEhF3e56wL3A4HW2PuKN7ClIY0IuIYsSi8+A/0w/AVkS1+It/Z1QdAxgnBn4AHIH33R90nPttPWw7zHzriOs8Y3Xe79z/eZEK30P/PCf00SBKtnuywWw55TEiUxy1NABIdMrp8lRzYt+vi2ewVmRMOEqdIyLohsPDw/IcdM9wOKzQGCmevV4vDg4OKjLegL2jn6T/fR3b4quckndt33hEww0iMXGA6FP3vNPplJM2d3Z2ClNvbGzEaDQq3jjKh/QpIw2ELUEYSA/gECQUpo1VngkyZcWPEWEjB4LD0bDjgSNjp8klxlAYRGCInHiObJhAgChaiMWCwDnPdrjoV0YcjCbOQxmcVhAxS+0BwWN9MfIQVjBGFkxczxyRu8x7LTyc05rz6WHUnGpCwzji/c4Px/m00eMKNNAUjube3l4pMmBECSPSGwJ5B4iHy4P6oLUc1jSa7FD8e0ej2rLiMBKZUx8w5La2tkqqTMQdTb98+TJevnxZNuDmZ4BEGhWy48FaQ684FiifyWRSjMWImYK304Ei4vmOXBLa7nQ65R4futdoNMomZ5zdiCqfm6dzNIK/iejSJ+85gme9SdvRHcCZyWRSNr0jj0y3yBgilCB7rJEdO6evGvFD9gKk4EBxPzKYeTN6bP1jYMMGl9c+IkrUi/HnzZus/9bWVjx+/DjOzs7KKe8c8EmknepeyCrLZUAQxj0YDOKnP/1ptNvtMi+UXCd9iipVyD/zQwbxvsvtbcbSPOMyy1M7bdZTpn/z6+bmZgXJhqcvLi5iNBpFp9MpdgPOaMQs3RdbBj1vWjN9kRLIvaYd6w1AOCPilimOWHgsGcC0DeMfO/TIqGz0+/Ps8Bkw9efmac+9owima0cYvE55vZkvy6DcX+Qi77HMcuqn98L5Hr/XIA7rTkEObE4KcjgySwP0xtlj3y19v7y8jN3d3RLdnEwmcXp6Gv1+v9i8yCcAa/aCGDR1n7kPeszzm+f0Iefij+t0fKOOhj10G8kmGqo53NzcxNnZWayvr8fy8nK8ePGiOAugQ/V6vaIciSCAQIEEUf+cg6dWV1djMBjEyclJeYYNd/qDAo2YVVwBgUJBoTwgWA6FQumRswzhYxygTJeWlkoo3A4TDhHzZaPEAgWmsaHNNQgwDAXG4JAdTJQjGDnCYGcGwjQTOxIRMWP0iJmwNzMaWWR+s1JwKNh5hyCGME5GTjEAWFf66nQUfvr9fqysrJRqLhgGzgl39IkN4h988EHlJHk7HpSoJD8XYWGBPZ1WUz74zHPBPOR0l+97gx/sSKPoImZRPRzRfr8fjx49KkoAB+KnP/1p7O7uVlKlbHxiNCOniEzipIKacz2KxDnSyAjSm6AlOxTczztQCsgGGzkZhWWsTsexsjWYk538iNm+jHwAGDJ6nvI30IGj0el07oEh3Ae/k7aKUwRI4eg1aao+LA++Rh7bqed+jDaii3b4Le+sF9xfy2jPX8RsYz9psp4jO6ftdrvorYuLizg/Py/7P4bDYSmHimHIgYwg2jbqLi8v48svv4xOpxOfffZZoWscDVIvOdfF1bfet/tOheVsjvTkjeD8NrpM29zcjHa7XcAjgKbb27tzdnxQLkCl7YrRaBR7e3slP59oHZFAZIJBNSPo6CXvq/L/ETNe5TM77WRTEA2BV0C3DdJ4DiOqYCT9pF9+N/xv543P3b+HnGMDRNaD1vO+/qG1N3hn58BzDN/xLsafbQrsCM+JnSucMqIO0MZgMCh2K1k49Xq9gE+WpdACa+p5Yv/g9vZ2sZFt/1GZEBsa23YewGKdQ2rXH9e+yE78u7ZvZY9GZmomBMZjUVmA29vbGAwGxWjHMFhZWYnBYFAhfiYNZBHlSQSD71lUcuRhIjsTzsHLRkzELFeSCElElAoyHKyCQck1EBZEvbq6WoST8/V9EqUXMws/Oxk806hjxP2wqv/OTovXxGtlp8HpHHYQ+NvRFBwGrnEu+2QyKUIaZcn6gHrCvOQ40reIWV19hEHuqw0v3sdzmePpdLYpk4MXCUVScADawli6vb2Nvb29ct4ANBMRJd8eB4k8efcF4eGqM/TbBi+NOXvf5rd5DhqCv9lsxqefflpJO7i8vIw/+qM/ijdv3hR5YjTv5uam5OiT5wy94zxifDo9EfryeTytVqvsJ7ACg5Z5fsSMXqFv3r+4uFhAEjv+vNP53vX6XUTRxmtEVXn7+RjvTv/jc6LH3GvggutcFhQHjNQB5hN+dtlc3tnv94sD5MglCtd7v+BT5ItTAhwlMiqcIxWeExs+gEXIL+bJOfA4jfQvy0TGwGFoV1dXsb6+HhcXF3FychLD4bCUKiUCg0GIrqOhgz7//POSj809vV6vssfH+9mM5Noo+741G5LZyfC64SjMi2RY/0Xc5ds/fvy4GH7YIKTooudw2g0QjsfjODg4KDnxdpj5ydEFaNBZH9lgZp+jQcyIaiR/Op1WDgbkOvoAIGpbwPc7OhwxSxM12PjQGrh5LTIIyXh4Z3YOmCvmxffxve8xUOq19xgi4p7dYFlhu2oeIGqQhPkEyOKwZ+yA4XAYjUYjtra2Svo2ssR8SvTj+Pi4Aha7uAxyyAe7sq7X19cFyMnn7FhOQCMceO31+SpH7hfRvhVHIzMwA6TklvMeiVKwb8L7AFDCEDy1xclfBlne398vigABQWSDfjk0jsIDeXSKAWlUFkY4JAgFI22EtrmGfE36AIOwqNfX16VcH7nVuUqMFTjMgAGM0qR/oBa8D2MWpvWa0HczZFZa8wwC5tCb5rjWxjX99HMRYlQAon8IUAQgyp9+ux448+00ApiU9DT6HREFeSTEeHp6GuPxOHq9XjEUKft5enpaNtcRlWA+z87OYm1tLR49elS+z4gxzI1zEzGLFj2U7mVDKSLeo5WpQaPZyaVZgT979qykmCBDPv/883jx4kUlkmGUHP6ImCFXnJJN9AAjGHrAaCa8nfcdOT3HyCC0i7zjPvrAs1BmGUTA+bDhavlK/5ANlgV2xkDYAE+gT5wEzy3vRN4xfvO2DXrAAtKxSB9lT1yz2YzhcFgileQ6k7fsNEl+00eex3xGzPLbzV/0w3xp+WQ5PJlMSpqYHUQMSMsuO1IGWJD5OAjdbjdGo1Gcn5/H4eFhkUnMg6PKyGlKpH755Zfxwx/+sALOtNvtsuePaBoRZcv074OjkcdoEC7PQdZb3n9gR98GK/9vbW3F4uJiDAaDQu+kU/NedBD7s3A4d3d3K/rNEQnbBxjw8A/X8j8pON4bSuqkZZjBBNMsMsJpjGRPMAZHSi2rPC/WX9mhgN99r8GyeU5GRsYtX7JzyG8DoxFR4WE77bVarZJe5neQVcDaMS6exXOJejjSAIBdq9WK7mePVqY55uDNmzexsrIS6+vr0e12K3wKwLmwsBBra2tFBpLpQkR2d3e3ko6L/McuYk8dstzzk/mE4iQGT+wM5rX9upGLh9o3njqVDXJ7n1RowHsjh4xcRxuQTBoCFxQNhGE4HMb+/n5JhyEkj4LlcL9Op1MciqwMIS4YEgYn5ES1CIgPBRURxbPFwHVozZ4jBqudqoj7m8EiZtEHe6kY3AgkhBOM4tAeSOV0Oi2ooo0S53X6vTyfdXIUhe/5nQ0gh1Cdc+lyxJ6/jDIihFlb0EAjQPSF9YFePAdGkW9vbysh7snkLrfcBhPGGedgUPmGe1xRZjgcxpMnTyqKwagv6+10MvqRw/heM/6GJt63WXsIrTJN9Pv9WF9fLxU5IiL29vbiyy+/LBv//TzWJhv1GOKggjgoKGn2kaFc3Q+QRnLtnbpohQfvGuWCnpyza2VpYwRlCBhC/8yn3IsMIFxvuQtSZuVFhR0ijvQNmcp+M6ce2VmzoWHlxny62g7jOj8/j+FwWHiVyBBzh0zmeuYV5yAiKt8zT44q2TmhnxglNlZsKMCLRkSZW+bUBhZpu0tLS9FsNmM8Hke/34+zs7M4Pj6Ok5OT4gDzXAMjV1dX8fz589je3i5GZkQUegKIcv6+x5Ply/elZRAMes5OSd5U72aHrdlsxsbGRtnQv7GxUXEO0P3wVsTdhvBXr17F3t5exRkh5TIbpHbuc0TCMoBoFjScHXwj0jjn0LxTp+CjiCg6lRQuR/Ky0+WIn+WEQUXbD/SZeXqbsTrPycsR/sy7OCPoW/9wn+Wked72B3zLOjBORy68Po54REScnp4WRwEdkvt8e3tb9gNeX19Hr9erFC5BtyBbea5turOzs9jd3Y2tra2SuUOfl5eXy2GtRFEcXctzDYhtMNN8k/Vs/v+P63h841WnsmccMQv5Iog5gAo0iTxULzgOgyeUjTLPnz8vUQwM2ogo9edRiisrKyXnFWVpwexQP/n4EBcpWaQ7sTeEHFyH9/B+LZhQ0K56xLOpVoCHCxprjxUlBuLJ3Nqwt7FjpCMbriYs+hIRRQEa9bUQsRFvlMRnYrhP9JH5453MNc8zQ3u+EZpGgRGYFowYBNS8twIAdVheXq6UAMUJs5HvSmbk5PMsNl7d3NzEwcFBHB4exo9//ONymn0W4oyTXMqIWeTCxpnphv9tEL9vdy0bEjQUyvLycnzwwQeVM3Curq7iZz/7WYlSOWphOobOkROkuLGJfzKZRKvVKudguNSo6RujMx/maOceh8BjAmlz37Ii5x005ANyEYQOxcnz2EgMOmojjLEje3Ck2u12Bf0DpOCdIOpO9QCtt/ELvSN/BoNBHB4exnA4LONEhiAHqBZHTjtzybPtlNFvR0GzvgFMsJNno870RZ89To+D9XJFPCPCPM+IOWvgGvo2vlxKmLNMRqNRvHjxIj755JMSqSM94+zsrBLl8bi+Ly2PFdmZm0EsWq70ZlqyPNje3o6IqGQ0QItGsiNmBv7FxUUcHR2V1EzTgB1j7ACnY9FP+Ap7AZ51VHFedohpkR/mAFAWwNX62eCjEf0cCTL46Dn1GB1NsX3Avcgd+J7+uv/Qe363wVq+t31IKjbX2unAuM7RGo8R0AAbjLl2f7jP54A5cmzgKdsEEVFsu/X19Wi32xXZafun3W6XCqisE0C603gBWpeXl6Pb7cbr168r40M+Y7sCymALmX789zw9+ydt38pmcP/PxGH4Gy2kJvDR0VFRhEa9Efggzm/evImDg4OyiZNrMBDIbyUNgkou3vAbESXsCdrBAoNIEpm4vr6OtbW1IiQIc2HU8NtCjwXHYwbF5NnHx8clGgJaARFZmYJy2Ijh+TA3SEbELFUJQmYu/Z0VX0QVrTMjIhBcKs/IS0RUDKXc7GRaSPAsFLOJP2J2TgrGCsaUDbcsODBSRqNRjEajaDQaBeEmdQ7G4x7ODuC9NpaMBg2Hw9L3q6ur+OlPfxo/+MEPotvtljAszIxxAGNHzDYLZpTN73PK1/s2a1ZY0K+jEFtbW0WRolhevXoVOzs7FaXgucX4jpid5up0PT4ndaXb7VboHKfCzgIGKgbpPBTQjkNWuji8joZ5P0VEtVQsvJ3RNIAbKqZZ+fDuiKicQeEIC864jQT6DbhiAxu5brSM97EBmh+j8TlX3WmgPAsjjYpQrBlzzF4OlxnPTonBFHjS6W5ujNmGyDzjlh+jz0acDapERCnjjlNBf6FXnnV7exs7OzuxsbFRaIS0OoA0G8BZz37X2zxDKCPgGRVHZuQzTWw4wxOcmwESDW1ztpbBReyI4+PjOD8/j16vV1LjeH7uL3oK/YstgH4HzEBu2GCljzb0PQ/j8biklOOgIOdwcDh40GlkXJ/BF8+dZeND13mcBiizATsvEpL7kNfQKU7ug+W6r8deRPabXzx3BgadPQMI4BRJA6OAm7Y/Ml1Z7rL39+rqqpQnt2wio6PRuDuMjwgvYz87OytFZwCZoCeXvs32oe25hYWFsk9jnlz7qvbHdUK+MUfDRGhhaCQHFCDijrlJm6I2tY1HL9xkMomdnZ0YDAaVDTBMJDmMRC8wuG1MsoFzYWGhpEHgLRNJmE6nlXr47Xa7oBhUAjEKjRODsmAxI2ZoBX+jXFC8lDu04QBxW6HjgERESftBKXvuPZaMjiJUM0psArWQ83c5HInRzrzAODAj6+VqLdngY27sNDFHCwsLJZQIk9P/jDRCA1dXV3F6elo27C0uLsbR0VGp6sMGX5wM0uqgS9bTZeQmk0mJXpj5X7x4EZ9++mmpMEG/LbxWV1eLUMJxYsyOqGA4fV/THx5q0KyVCDyNUb2xsXEvNer169fFoIuYOXSTyaSAC9BbLlqwuroa/X6/ABCk3rm8pdF4+pSVpg0N7vN+gYxI+1mMg/FbicBTPmMBZ4lIgNFb0xtRAOQVNNtqtYoswZj1GNmgHlGtMkf6j+u0Ww5QkclnbsAPbpYX8CZzznu9Wd/IJWOzvskAitcgyzvLNGQG/TMY4qIVGHI5AoXxYXSTvlNFkfQnp640GndVa05PT2MwGMTu7m5sb29XaAWaRSewFjaQvm/NhizrOQ+sgTc8T8wr89doNGJ9fb0YknyPPQAPoFuXlpbi6Ogojo6OYjqdFsB0NBpFRFQiEdAJ8sb9J0Mjb/iHFxxRtGFvEBKZgIzq9/vR7Xaj2WyWvZvQMPYHfcsRMsss/rct498AIfCQwdHsTCBjs8NEs1zPkf3slLg5gkFfmWvuY56Qvzhgtq0YD3zpPZrYFmTh0D+Aa2dFMBbGiyyBBikUsb6+XqlIxZqvrKzE2tpaDAaDQkPT6TQGg0FxEAE32d+6ubkZz58/r0RfLN+gP8ZMyw7HNwFafKOOhoX+PEeDxWWfBUjPcDgs35tgJpNJHB4eVk4jREjc3t5Gp9MpniJKl7AkjkZEVE5aNZOQgjEvrxflTSSjVquVSlYIOlKpEFCdTqf0k81DGAQQLwIM5YHC5HsEGgQPEyEYc5g0I4UY4xawTuOwEoMQuZfPYCzKCDM3OFQQrwWojWnWmT6YEb0RmvW2cLZydWEAR5IoPWhmzQ7B2dlZnJ+fx8XFRaytrUXEXd4zueAYQiBA9Xq9jI+1I2+fkpYIrufPn8ePf/zjklPtUCVG3HQ6LU61mx0lHJz3jka12RGDHp0z2+v1IuIOEXL0idLELoqAQiedCAMWZ7levysX6HroKAcjVRklclSDvllgY5TQMprq50bMDCV42wqb/jglwPszUPaOBJs3cYThM6IEGCNEhugjc+PoAX1i/MhZGy3wsu8dj8elIAOyw+Olf8ytC4IY9UeO5Gig5WVOBTFvcp0jo4wJJ81Gmx0oHDE21Xr/Bs6HZbqdSVJheCepq+fn52WN2Bx/cnJSUE9kIVENI6EZ3c7G23ex5bHzGTRJc+QJXU/L6VKAFo7msWatVqvsBSTyvry8HIeHhzEYDCpoNpvCT09P7zm0pkk7rgYlHG1Hbtgege6hd56Lw4lzwQFxfk/EbEO8o7HZGbDsmheJ8Nxi6Bq9N/Dn9ZlnBPs6OwXe5/XQ+32fIyCOSFrO+H059c3AuPeY4JjQcuoRzzdAiuPhAhd+L/s69vf3Y319vQDtNOwHnFbed35+HkdHR/Ho0aNKSfaIu1LMOzs7FZmV55m+EC3+40Q1/jjtG02dck5cRNX5YCHq9bsKLzCKD73xaa+np6fx5s2bODw8LHsxlpeX4+LiomzcsudOmJA8RxgBZsyGA9/hnXqDqBVNrVatthRRNfCJqFjgGYFD+aAwQUpxrFyWjn5603dGPFHkPJt7He6LiDJnEDrzbSOH5zpsi2ePs+H8ZBtx7LOxAmSuaTakmR8cq4j75XidagTNoJQxXuxQcSo4VRscdoZOcFaHw2FsbW1VjDWcxYxosq7M3eLiYnS73RKhuLy8jJ/97GfxK7/yKwUVzuFap55BM4zT82Ah/b7N2jzDArrt9XpFeHPt8fFx2U8F3S0sLES3261EJu0s4GRQjQzj3cYojr0ROaeD2tgzKueN5fSRd0Mv/kHmOJ3TZZNtxNq4ZJyO9CIfXLnG5WFBUaFlOwvICBv88BROG3/baHG6E6km/O/IHn3g2Va2pKcY2cRZQR4TgaRlp4Q1w7Ei2sA15jc7VuypMLDCmp+fn5eoAzqn2Wzee4ZP/2W+SbfrdDoVeW3wB9SclDNSfiPiXgETy3Ibet/lZrDKET/aQ0CNN4JnnQfdAg7CMwsLC9Hv94s+4tmtViuOj4/j+Pj43nNAynEG+DFwSFqjo/3wIbRiukeXWD5EzBByG/reL0CqoCMOfI9cyXo3RzGy7DWf00yHtgE8NxmE8Lrxd3YQsnzztQYa4Ol5TpLnGD5zHx1hNsjKe7jW9gR7Z8m2cEYHv82HtgG5Fjtzf38/Li8vywGzthvRSZwjxzuJ4ntvSqPRiM3NzQKEz5t/aJqIqteZd75LM7jxLu0bjWjw24xsjxHlSavX6wWZ5gTthYWFODk5iZ2dnYIig3QtLS3Fs2fPYnNzs0QimEQQSQt6Jpl+2ICHEe2IRMw2S0GAMCrKFOKzc+MojBmCkLkVHZ5ldmT428ioIx5mNppTKzIhuA+M24iemdZpWzaaeZaFE4rNOat8jiLk+ThTFiQgIn4WAmteihXz4HFdX1+XaMX5+XnJZURY0w+MRgQDm6cIMWMMUmYOh4sxMQeMjZzd29u7A8SeP38ev/7rv15owHRtRncUw07Ge0fjq5sdeCs206CdVyv3fr8frVarXBcxC3uzhwtDLqcWwFMuWsHzEfLwFMrCaKRpkT7BG9lR4W+MB/OzDf0MOkDj5JW72IHlLDLAiCYGPAYVsiGnehkRQy5lQ8rK8ubmplICEiOHdzAOp06ynvm9PC+i6qAbeaY/dlo454a5cJlxz0nmWVIaWCcj5k6HopIX9GMEk6ioUUYcXtLTbm5uCqKJIcKYr66uih5jTZ0i58jTuyr9P+3t2bNn8erVq3sOhQ1x60Z+z9sI7s3QtVqtAhRNp9Not9tlEz4VEFk3zvQyyGdasb5GlwKEordw9Ov1ekn5Rj8bvLRTjl1g/rd9Y2AXG8Pfz4u0ZcfBzXaM5zPLRkd1Mi3aXrB9xGf5OgNx5nHey3fmR/fROt+fm7fmvRdetQw3WBoRxc6AFiLuKulBR0QkbWsxN3a2DBScnJzE9fV1PHnypICc2J04G0Tga7VaDAaD6HQ6JfLKHHS73VhdXS2ACn0ySI3twhkvDzl+X9VqtVo8e/bsK6+L+AYdjbxAXlgb696XYM8TxXd6ehovX74sOfSNRqMI30ePHlWqMjUajZI+BQHYGMk5js4/5nsIgnu9WG54kWZ+FFP2/Kl2NJ3OwrcIEQu7nHdr4x+Fx0Yu5tHowjxhYGayx28hnYWAmW4e2u6UJO7neTAITGJnBZSQcWfkwXnXVGriPTZKjOqQemDBdH5+XuaPRkUX9mggyCk5SfiStcV4cloIyh3GRQCA9uLgkONrWri9vY1msxknJydFUfga1ve9o3G/WXllxMrViOxc2KhdWVkpm+VsfOMY93q9aLfblYPlvNZ5/5Plgr9zaqNliOWN05J4niOByKLM0zZorGgz8AD/mb+96dTPh864B8Pn7OysyEbmhP/pD2h9PmGcSKP/NkLnMXks9Xq9VLzCEeF+y3ccGRQwFXVIVzH6mNM4DQBkOWY0le+pT08E1WvI95aBHJ7lTZ7MmdM9FxcXS4ESnAmes76+XubHTpsNFIxH1gVZZt32XW44GZaT5rd5DWPcvJWdDmQFa02efESU1FsOSdvd3S3AoXWS6c7gEunhEbNCOAasADewBbg262scWevlbIfAY9CM08MZt+0M3uO5sCOQ5zjbGMw7fOFnPaTL8r12Viw/7cDM64PBX6+D5z/zhHnK/bHsHo/HlTRRF3gBpAEkqNfr0el0iqyCb9Ev2BUAqayBnaKIOwdmZ2cnnj17ViIO2BqkeHtvyMHBQTx58qSUKMd+2tjYiNFoVJEJzBPr5DK5X9Uecj6m02m8ePHiK++P+IYdDStYe5FWNEy4w9ugjJ9//nlEROUshclkEuvr67G5uVkYlAkFEbDghZicHsO7YWoTHeGsiBlyDuEaYfDnfp+ZAqKCCZwWdX5+XsroWinDNJlAaGbEbIRw/TwEwB4168N3+bkWbF5Ho4wRUUJvPBvD3E4BDYEBMzqUaSVKH/IcgDTbILu9nZ2jQSTj4uIi9vb2ot/vFyOHtK+FhYWS6sGak2/5/PnziIjY2NiIfr9fnEYUidfeaOXi4mIJky8vL5fa2uwHYkwXFxclp9vpMxZw7yMaX92sRFk/IzUoifPz81hdXS17tlgDNlBDY71er6SxYAwYcZpnnMCjNIxH+ga/40yavvkfQ4MUUEdIImYyhT1BEbPIgfcEuOob/M3fjuYYTSO6wr4JfrjOxg7GtmWe0ycxiJ2qCA/bOZpMZqmwlrs0Oxnw6TzDIzvoRCkNJGUHAp1CY15qtVpR3sw/ZSWn02msrq6W/VjMO7TD+yKipIdxCOnt7W1Jv7MDiDzi79XV1RKdAF1sNO42I0PD9J915b3sGYA+bHB912WIjVma6WmeAZUBg6xfcVZ5fsRdlbCIKPqDZx8cHJS9fNZp6BQ7rez5IJPB6VG831FSpx7ZaYFvnevPM5CHNsItIwFPrPOhYeRlnkvmyPNh281j9TX0bR6QOG8dH/o/Oz4ZYPK1yLUMzuKI28h2+qefxXrZ9kPWsL62VZgbZATyrdVqlbPWMvgaMTvIF4CB/rNG4/E4Dg8PS1o3UfDV1dWYTCYlWjEe353ntba2VorNkK7ZbreLw2xny44OOifbiszHLxqw+EYdDRNARMxlbLwwlB/lKX/2s5+VEzkRtrVaLR49ehTr6+ulUhRVYYxmWRHYcHDfQKmdmpMRNhA0iBJBZc+Z93lD9mQyKQ5ETplijO6bP8uKPmK28Fb03izPZqx5Hqedh/y5x5CFioUX0QiUsFE81pn14Zl8Z+MZZrTD6VCuBfI8YWd6suOJcQI9jMezilHe8+NQpyNFbNK+ubmJ3d3dODs7i8ePH0ez2SwlBzG6OKAHB4Q0lU6nE/1+P8bjcRwcHMQHH3xQEW6EMpeWliqCKDsaDyFy39eWaRchnZWZI4ScpbK0tBTb29txeXlZMQ6gvbW1tSJHEPYYq86VtgHghuC2/JpMqvsMHH1kbaEnbw53uqLBAKd4ZYTec2CnmHsjZgALBjkOMGcMcdifFS+pUcynjWQDDcgNwB73i++dkkqJYMZDlDEbWJY9RvetsB2dMVJL3xxddooC0QPWzakUyDnu42BVR7dsoLJ+t7e3xVmhJOp4PI6NjY2yX8ZKnbEQBQEwc6rYxsZGTKezfWoGyKiYZlDHNPmuSOWf1paNRINl864FCIAvH5ozQIvJZBLdbresCfctLt5VLyTaDq37BHAa2RXIEO+T4DPe/Tb5gjPBM5ETpsVsTGZdb9DOvOXneh78YycjgwdZXpnf8trQMpBpe8OOEnZXXlP3bR492IGin3zOGiIDaY688ttAEXLDoIlTVDHqkTGckUFUAdlL+hrygsb7mEfK2hKtQK7zflImIyKOj4/j6dOnsby8XN4XcRcZJZ0rN0CLpaWlchSE5zHLj1+E0/GNRzRY9Bzy9+E3XI+x/eWXX8bx8XExbslb3N7ejo2NjeKx5cpRCGWHlJ0OhfGejWMbvhgcGUmzYHLKlRnYC+IqMGZQnmWBUKtV065wGvw9SGhuvBtEkvQNGAwkzsas+5SFCUzuKI7DgRHVChE2mP1OCzSEKQY6yjsbFEZ16YsdQaO9zAXVsHA8BoNBpSpYr9crZ2vgUMCszAdOxNHRUQyHwzg4OIjz8/PY2tqKfr9fBD4MPhqNKps5MfK63W5MJncHbnGabL1eL8qJDYCHh4dlnrJA+y4bCH+cZmfZSOY8Y7xWq5VNtJeXlyXfGmPSYAByxLXSbUzyv5FieBM+QIEYKaMf0LARMMaAo8H4ssI3uGFU306EeQT5atSbcUZEyScGAKnX62VPExEeUPOIKI65ZRb8T+SC/uZKPvOAClJSnFJoh9HznR1JN+bP80C0h//tNMBbef8JshSDz59jODpaS4WpPL85DcL70UiTWltbK9fjbNBH5NHFxUWlFDsHhvX7/Tg5Oak4qcwnjoZR23mg0ne92SDO6Lo/y/utaOZPHI1G465yk/UdhtzOzk6FTh2dj5g5GFS2BDSEtyxfzPfIDNsp1v/8ttzLcsPOinkyN8tT6Nfzka/zD9d6zIAr+bm2LeatUf6e8XjPgaNz3GOALgMrfo7th2zr2LAnFcpACnxPCWrAlohZWj99wfYC8AaEIK2aMvvIXcYGHdFnR7+vrq7i8PAwPv3004ojh85B73FyPWAPsoczn+iP9RS21vLyctn3/HWiGF6zd23fqKPhZibHCIQQHMLb2dmJ/f39YjCQb//pp5/G5uZm9Pv9YvThaLA4hJDwNGE8G/beyB1RjU64r96AxTscxoTIspAwyp+Fgvc4WFj5Gt9npuC3lY6FF/12/rIJNBs8/t4og4XBQ+vqEKVT1KyEIVynGiB4MVByyNYhaJgiYpaqZuFZq9VKuV3G6HQDxoNX3+l0Sh4ldHF5eVk2VN3e3pZNf3t7e3F6ehq7u7txenoa29vb0ev1KkgUz0EhIKgwXEajUWxubhaUE+GVw/Nmfv+8b7Nmgcbf2UG7vZ2VIWbDLPxwe3tbKrycnJxU1pL0JGg6oppmYQE9D+kxb9JYQ3gCBM2OrTdZcz994B02yiOqe7JsXEB38BQ8TqQUg9YIvvd2WHaB6hGqz+/FuEIOIQesYJEHlO5mrgBfFhYWYn19vShUO9iMhz0a3qvBHLlZOSNfABGgD1A70gXs6EAD/DjFJGIWxeZ+3mkjz44Rc0s0lfQo55BzDXN/e3sbR0dHxfGNiAJMrK+vx+npaWXvCzRDnrh1HXNkgOa71ubJx4x8Z6fDBRxYu7xvgTVaX1+PiJmMx6F78+ZNiV7kNB3oe3NzszgZ0KWBtGwI+390nIG0eQ4F78vPyzqe66xXskPzUF+yw5qBY8sP7rd9Z8eY7+c5DjzbwKJ1olN7PC/cj6FPX7IT53nxfBhY5m+/xyAyMhwehEcBtOivo+W8C0ASOzYiKnvFkD/wrft+e3sbBwcH8fjx48qaoLM4SmE0GpVUK0AlKtt5D51brVYrOtLz+bb/fe/XtVG+EUfDnreJk8ZmNp+ECMKzt7dXEPqrq6tYXl6Ozc3NePz4cfR6vaIsyG2G2CAw0DkIx8apiQpmcAiPfmePjc8gAibalanoh50UiAfCtYFgj9zOCmFZOwJ4zEbDzCRZ6EVUz8hAGRttYDOQCduGh5kPwyiXr/VGTzOz+4Uw9wZGnu/UAzbIOpqEwsQ4w1CxUFhYWLi3edvCmvExR2tra2WDpw0/+gN6eXZ2FicnJ3F4eBg///nP46OPPoqNjY2KIXF9fV32AWCAkYOLAEIQkvZB/7JTl1Hr923WLNRyRMOKz0ZArVYrCgj+PDk5iYWFhVJhDLrMdGKkm3WB7+xY2yGg4Ww7RRAaRWnZ8LCzhELAwDG/zjM46IujLzyTvQXIWO5zgYaI2QGV/o75ZSNjvV4vecGeM/aY2dBmrkEJG41GJdpKaeGrq6toNpvlmaZ9UhRxMiKiOECse1bWKHuqWUVEBYjACeF6O0gRM8XL38ijbEAwPp6TFbOdGJ7vMubcbwSUa46Pj6PX65U9X8he5sPvsOFlg9PG5nddljCPdlTnGUDMddZPEdX9owAMKysrleyIhYWF2N/frwBGpMuwRu12O9bW1opTaXAs2xMZPLAOnve/gVKeY6M+OyUZmLFz4TG75WfRDKSgQ7Od5Pfm53pt5oExEXHPaeF5WUfa9uJ/21fIWOwe7ADTivlinrPl7+yQ2PZwmXqyJ0iHqtVq5fd0ercvEDnqog8Rs1Lk2Rlzmt3Z2Vns7+/H1tZWBZiIiFLcxM4Fcnw8Hke/34/Dw8OKLV6v14vMNcD2dSIaPOvrtG/8wD4GBwPV6/VSF5zvEcJ7e3vFYGCvxkcffRTb29uVw7Mw5mxw887pdHrv8Dh79N5YaaHkygz+bh5x89uCax5i4XSMrCghaL8Lo8iCZDqdFoVkoucnO3U8i7Eb4eQdXEefnFOZhRVCEcPJzG+Dh/dnow1nA6K2E2OlSH+MuDq/3AaSGTFiJjihGSMfXG/BM5nc5VGurq6WaiIoGYynlZWVslH4zZs38fz587i8vIz19fXKBvFms1nZ0M+Y6Q/0gcNByp+NWH4yTb5vd828YOTNBib0urq6GsvLy3FyclLurdfrsb+/H+PxODY3N0tBAJcJtWOMgWkF7nWKmPFqxGwTulMlaI6qogCn02pNfp4RMUu55ForX7eM7BvtIp+YuYFvrCDH43Epx+pUR+TG8vJydLvdEoKnXLTfbaMjGxs+SR0ZQpoWxpwNRZwm5HatVqtEIJAHljHInTznRHAoDpGdA4wF79mhT5ZH8CR7PFh3G1lcx/jdJ+YZ8Mt7/ChCYVDm5uamHP6GzI+IYvhSYYt3Qa+O2kML3wdHwwba25p1kufIvAWf9nq9Qh8AbJPJJI6Pjwtt1uv1ivO8sbER6+vrlTWxDuR9BgPREZY52V7K17nfuf9ZTmQHOH9u/WnQc978+h22f3KfGTfNzgPPeMjxYHzuF32zfTHP+XB0F6CGtfI60Lc8Dj9rXrUv5o8sFKcrMo56vR69Xi9qtVqcnZ2VjBz2W7CmrpKKXGPMWb8hNzkNfG1trTwLh8SHSKPLsHPZv8z7TFvYkewhm+c4fF1n4m3tG4toZIYxI3S73crg2+12DAaD2N/fL0xeq9Xi448/jg8//LBsIMRgnZebysRlLxulGxEVQ59r7ByY+OYp9ozeWzFzjRnFC4UhQnPuYE5RsOKKmBkfOElG5rJxj4B0SpFDpRZgDwklGNdMxjtgKiO9jMVpDIzXkQW/y6FO3ufcd68nY3E/c1oJSgHmgS6yADZT4fSSj+vKLxgX7KvY3d2Nvb29ODo6Kme3eMOwUZjxeFxST3gX6BdODH13hOZ9RGN+syLIxpSVP9Glk5OT8tnCwkIpKtFqtSonLfNs1s+pfwYYsiEdUXV+2HNh+cN1RpeNeGdUE7ohEuAqNx4rSsah/YgZrzrdFKScxj3Ly8vRbrcLT1qORswODvO+FA4hdXMKkVOMGDN7uzAWRqNRJQrtUo0GYFD4yBOUJvdZWdJf3uF3AvQQ6crOfKPRKHyanU47mePxuPQHPWBZ5XHb0YC/c544a8hvI8WXl5dxdHQU6+vrlX2HyFLGwtw7qsFvO8nf1WZj7CFHw+vsio/znsOaU1YUeeDyxqwVYGi73Y7Nzc2SBmiHJRdnscHslC1sGTvtb/sdcX8/wrwx0UzL/uxtDkC+3pkUnk8AAXS95aTHnWUiz7R+Zt74justd23cz9MJEVF0MgCLC3TYScjghOciYpYRYpuMvnmdDRpxTafTKdeR+kgBGPM7/My1PB954721R0dHBQxhfsbjcQEsKL3s+a/VaiV9Ktu9zMXiYvVgYs/r141yvK19Y44GvzPxkZ5CmgkG4uvXr0tlnoiIjz76KD7++ONybgTIgkOgEVER0jmFaJ5iiKjuM8hh6OyF2/Dw/xZM8wx/G+RW4vTVaJ4jCr6WzwhbWqm7JCZzZpTPG43slIEMgvKbkT0fOZfS/eBdIPisTXbWUNpZQHrM/M9vRzoQ6tmpjJhtyMLoYq1RtDAvRpOFjg0hxrKyshLtdjuOj49jNBqV8eHcrq6uxmAwiFevXsWrV6+i2WzG9vZ2BWFhHPx9eXlZKlBEzPagoPR8n+993+63PC+stc9ZIOUEY6xer8fx8XGcnZ3F+vp6qe6DQ4GSZz1QPnZUbWDS4B8rK0rAoijMVza2M6jgXH2fF2GZ4NSqDAQguzCwKQVOBM2GPEqY/XE8Gz6r1+ulxCxKC2OcHGWUksEQHBr2QOUUCXjNstAAkFO3SHfzM5C1ls02OPjbaZb1er2SwsBeEMsI7vF+HCK32bCALrz/A30FDzOO5eXlygGyBhWYVwzZiKgUJcH4ODo6KlWreHez2Szri3x2BNXPsP75LrZscLpl/elKhp4j/67VamUfF3OHDqW8Me329jZarVY8fvy4ZGcAavh5doYi7tsO3JMBv7yeflb+f96c5GfMMxazQem/GT+f5YyH3Gx3wX/cY72GrHC6IuPnB5mVAc28fh5DttHc0PvmURrrZRli4MY0ZueH/+v1u0NgSVGlr7ZZVldXo91uR7vdjuvr65JBgXzOqbO8w+AEMoZzM5AREbOIKcclGIwaj8fR7XZjf3+/Yot73ShykPnoF+lkRHxDjkb2Qu1skLZweXkZzWazpK8cHh6WzZxPnz6NTz75pISijFRDyFb8RgQiqps0+dxh63kevwnOjoCZ1Yxhw3ceQ3thbUibqXwPAg4lyLMx5j1WBJQVrH/zvY+YtwDlbyMrRst4htM37LDAUA5HZ4GBss8bXpkb3jHPoDAzOFXBghBGIkdxNBpVjBEMK2/uNOqCksYgwajc2NiI1dXVOD09jbOzswoSgtMyGAzi9PQ0Dg8P4/Hjx5W+OcViMBgUemd97GiYRt87GA+3eU6GjXiMRVJMQHBGo1EcHh6Ww44wtI1q2dm3AstGAmly0DaGpBUsNGBZAp2i7Ky4aVnhMkbeC49i5OLI+BpQNRuhyA7666paRlDhX5A0rmesCwsLMRqNKnLCm7+ZJ/OAU39Ad53O6b5y+F+ODpifIqqnsDvNlfd7HWzcuUSt15vnsceLdWAfiQ/k4z6qlNlh8R6KlZWVaDablY3p2YnKxQcMnrCuV1dXcXx8XKL5yKfFxcWCjs4D9N5miH5XmnXmPHAu4n7FqWwg22g1T3sf0uLi3cGRdq6n02m0Wq2Szm1wMTt9pvn8vuzkRNzf6+D1tOH/kIPhZz1kkzAO/zYan20JOyG2Q+z02DHPLetyA55873cA/mUbjTVmvXEg7AB4vMwdsiiPd54D7nV0n5ELyFW/k43h2Fu2sXgWMuDm5iZarVY5v+j8/DyazWYcHBwUHeG+uqgP79/f348nT56UOYRWDQR7ewCp2jzf61Gv14s8452Zftz+JM7HNxrR4G//UAKUyWi1WvHmzZs4Pz+Pq6ursnvehzf5d8T98loWPBCfHQwbn67wk5EErvH1XGNhNc+5MJP7f7/HQggl6zAYyps+QmA51Opn8TcECpPMS42ah+owF3YwmEs7F36e7+Mznpf3Z/iZ8xwO5ohxwgw+JMfXMkesKz/UqkeJO0/Whp2RFoQcgtapKwsLd4fkDIfDuLi4KEjk6upqPH36NPb29uLNmzdxdXUVn332WSVfPyIqG1lzBMOKwyjLe4fjfrNCo2UjezqdRqfTqdDHeDyO/f39YjCw1hm9M2CQI3g2BHBkjFrmfjoaGBGVNJ6IGc1ZUTGOnEJEP+YhpMjBWq1WKo94XweGvTdqg+y6v1YwKJ3FxbvSqVRLyml+9Nl0asOJd5uf+RsnEOPt9PS0IPQRUZSkowqOGGaDzifl5rRL1gMDkn5bJnudHa2xEzNvrw7vqdVqZZ5arVbFSJ2X3gtNTKfTUn2O982LCJ+enkaj0Sj7WXBS6Af6IUfAvuuOBs108bY2Lx3OtFSr1crpzU6Nibjb74MDznpvb29Hu90ua8FaG/yz8T4v4pSN2mzgzXMYrTeyA5Btofw7y7aH5s5GMuOgOcvB/cugRXY8kMfwk6O6Bigw3LmOa2gGbJGt7h/9znsoaDliAA9nnsnz73l0dJv+so6kMTHWLOeJjrNXE9na6/Xi6OiopG5Dg3ZWaOPxOI6Pj2NjY6PQJntTs63DGqysrBQwh3kwCAR47Jb1ZKajrytbvtGqU2ZuJgGPjk3Ii4uLMRgM4vb2btf8hx9+WMmrMwrJM2yoYiDaA2RispKOqFap4RlZ6dpIRojP8/b8Lo/dyKWNC8Y1j9m92HYmcugwG6o0K2Wjee5bZkSnXvCMiKqwIU3EyJENM+ZinjOSHQq+d964BSCCzE4OSB7vw3gxcokRZSPDkSfmdGFhoaRwQE/QnWt3M/5G425z4MrKSoxGo4IOYMDs7+/HYDCIFy9exA9+8IOSTtFsNuPs7Kyk8lDNZjK5y0t3Prvn4b2jMb+ZN+cZUNAVBvF0epfTimFgY8y/szPutXfkcN5pvhFRUZgRs9Q4mp1Ivxe5QapUNoDMmxmBQpbQTyPv9frsIFLeiYKB9rjWRjXnEuFcu3CDD7gDVfP+jRwdwknn/UbTiLyweXHe3ivLJ95FH3JeNdXcWHf0ANUK7SRapgA4Mb/ZseBQTowC5on8fWiHz7Ix4Jz27MBCZ+gZPschw5BBzo1Go8oZPuyXOT8/rzh3rBlRLGjYxuF3pWVg4G0NHWZ9bv3NNc1ms6IboBF4ho23jx49KgUSTAOWUfMyBubpyGwf8Vk2ek3ztgXybzvQbtneeJtzZsclX5PtHL+fvlu+zetDHlc2YnO2CnzpKEQGjPgMewA5nOfPTpC/s401L+sig7C2gSyz4T1ksm0KaM+RUOwA0iKHw2GlXK6dPd5Vr9fj8PCw6CRvfq/X6xUwiftWV1fj+Ph47nx4Q7jHxt953f+47VvZDA7hoKDIUUOJXFxcxNLSUnz44Ydl84orlGR02sa0DWwYJBtyGf2HILLzEDFDszJB0iwIsvefFwgC82ZErs/XOqxoZ8Jou5F6yh5GzLxSruMZPNPGexYEMBqf29OHuS1MarVZxMWoHd9lBC+P1eO0Ycj6+f1ZUTNPOZ0h4q7akM/IcH9gXIS/HcfMfAgHDBD+Zq/QaDSK6+vr6PV6hSZxNj799NNSbYLzOziEkndmmv2uGQHfRDPfGHlh/nDoWGM2RJPeBG3ze56izsoVJBlDArmAQ2xUP58rYRrPUT0rN+jHstJ8bx4w39JfKhnxHtOX9y9xYKWdIMuYs7Oz0keKJRAFgB9tmJv3I+6XZs5ygz5QBYoohPeskDbgzZzmzQysEMmJmO1NY4wGO2yU+h3cw/3oFu5jjur1enFmrBugKyohZifSst/oLOO9vr6OVqtV+oG8yZGp8/PzAlog90FDSRHk/egEyy+nw35X2vb2drx69Wrudx5rBg2z7ma9kBPWO+g5HLXl5eV48uRJOTzYkXNowgh7TiWGvvz8efonA6P57xzhn+eI8Bs5k+eFlh0HX+d5eoh+kDmeY3jIUXoDMsgO6z6DsfAsc+WqdfMcpXlGMPc5o8Cb8/OYLDuRW57PLIe41jLR0QenrHrPGmN3kRt4H3v3/Py8VF01OGQwIiLi9evX8dFHH5V5o5Q38shpqtC35ajXjg3hdqDy3GbnlGuePn06lzZy+0ZTp8xkEVHQNry1hYWFouQ+/PDD2NraqqQFkP/GZPAsFs/KyO/1+600HJ7OSOE8prPiYWIxXI0sIgCcGmSHwkTt0BaCgGfRfL37ZqXJ/NnIsCNG37gvCzu/y8/PITSYCEb3Zk08aJcbthPhuXR/HE1yCpTn2I4H47YxicPhNDOf82Hhb6fH82SDzcaf00SIXtCner0ep6enBdmMiHLIZLfbja2trTg5OSlOBmO185uRmSxI37dqe0jxTqfTYjgyh5PJ7JDGeYZ8xP0qKjyL57L52emSIGURs0pECHNo0/LAfGpFZEckYhaNycLdyJSdXugRhYURb0BiMpkUQ5wa625GDHEm2GdgFL/ZbFZoP8uN6XRa9ia42emKiFKaFblPygBr45SsLNMM5HiNcSpweHCmJpNJ5eR1r7ONwIjZJmxkumWUjSEO42T8NgC4lkpe6Dhk12AwKHPieeOcE+YY2cpa4zABzGEsYZC0Wq04PT0tY/A7TEfZOf8utIODg7eirTZIc0pTxP2oAUAF0W2DTryDKAb8x9o48mUHj/dkoAPZ8DZnIzsPlmHZKZnX7GxYn2RbZN5n867J32WbxUClkXgDavTbfbLTYBlqfU1UKevHeX20c2WadyEIr42bbaVsf7mfdja4x2CQAWFHF51tYQcUeU1WBeVoifxCT8g2+H06vYvaP3r0KM7OzmIymcT5+Xl0u92KzrH9kteSvjtjJMv5efzltr+//9bvad/oZnD/3Wg0ygFNnNxbr9fL2QT9fr8wZS69iKMRMVM2oGEYmCYCK3H3KaJaztLMy7PnoR9+lj+z0+C9HxBdNop4Ln3wotohmkfY+RkRs8O2srfv6EUOnTtk575xDylItdqsQoSvM6N4gz6Gt715Wna65kVZ+AxGs9E2b28HginPoQ03z6PT6hYX705Qzjns0M7V1VVlzwv3o7ja7XbZhN5qtWJzczPevHkTr169qhhRGH6kkJgec0TufZvfsmLJ6FK3263wrzeHe3/GQ+lSbqwTkQw7J14jaNMOvRUpdMQ9WfF4DDhJHh/9zIbxvAgC/I8Curm5idPT07i6uirXzwNgPCb4l7FdXl5W5AfOeEbskA88B4PLPG7+BVUknRDnYN66Wgnnii5GSK+uriqHCbqqE+90OpI3r5MS6ogD3+XIsGlkOp1Vsfp/2XvzaFmvss7/W1VnrKoz3XMHSEImkCnS0qSV2EESupFBBlFp6IS2QZZ2sJd2gp32R0OUJCC0IhJWN0GxbVC4IRJX6DCoCCouEVzKIEg7NNAhHUJyk3vPVKdO1Rmq6vfHWZ99vu9zdp17c3PTwL3vs9ZZp+qt993vHp79PN9n2HsjT4k87N+/X7Ozs+l+jBSPJuHBlLaNMNLNAAXIKJdVeCZJoWLRuS8gdwDjoNcX1J8O5Km3xyOPuDvY98/oUam4XTH9xhle0g5GoL/dc+6yxe/xd+UcH1J+8S3lUPecURKBIde83Jxxkbsvd831YzTumCvRoBhmFOTe521wTMBYxHTz+GzsP8df0TngqapRJub63+sbo7T+P+I1jE8iEC4nvQx3dDoWwjAAX3EoYK1WS9td12o1LS8vp23Hkdu1Wi0dDujj745613+O1XJ9nOMvJ5dre9EpNzSovAs9GodyAbChOKanpwuhZ/fWuRXqub45gyFHCFxXTtKOIHDvZk4xR4PAf5eKqU/OwG6YRK9SNGzcQHCvK/dEDwh9EQ266Kl1qxtvvVvhMawK8HFDgN+jEcdn9xDA8L6I0gG2vycaW87YDr583Ya/3/kDAOMTPwpkgItfdyVP//mkdGFFOYAe9t3HCDl48KCOHTumr371q3r0ox+dDBMHotQ5em6o3zAeLqm4zzr9RuoKMsfH3dMaYo52nOvc45EM5yepGN6PvOv86eMYFZrLRa55ugBKGnJ+Ru65YiNczi4mgHBPW3D54kCANiMXJCUDn1C6O088H9jTGXgurpOLgI62+1zC++9rWzz1wtvEu31+e3qX7yhH30K0AcXt9XLHFetAqGu3202H/zHfva+87ZS1sLBQSPt1/oJ3OFwrrstgPOhHB1jIEcZhfX09rdXAyeMedp8HpxsNc8zkvNyeMiOpMPaAQteR3l+DwSBFqqTdzkb4MwJHdxbwORoJw/CE4xP/H+8b1v7YB/491z+x3BOpoxtL0bmZc2LknEXxc6x7pVLcgSq2I9fO+OdOXfox4jCX8/4ef14qRjaYry4/Ik50fITshl+8TGnnHCCPsI6OjibZ444ySSmaWq1ur9d4xCMekWT3xsZGWu/hThnfECGOZZwjritiHx+PB4fRw3YyuDeKzmP7OADtxsZGSo/iGQcHeGtgOk+VccDL/2itScWJmvN4x1BaZDiuYZR42W4xe32cmR2EuLHjRgbKH8aNDEsZDlSi9R8BihsiKFBvY27CAZp5hj7wveN9QuXCkNGQ8XUT3rf87u2Uih4l3uGRDAeV7CjjaSX0hU+oGLbkvX7dFQIACPBB3wPC6KfR0e1D4ABrKysrOnr0qJaXl9PWzCgiSWlNErzsxpwL5JJ2KGcwOlD13xgXPxfD1xIwv1xWjIxsn6EyOTmZFcZuXHi6VMy3jt4v7nHjw+duNGT479ELAD+GhHsOWQvkHlVXLNTJo5q0xedi9PzxO9EG5hdzkTUWyGbqgrxxfkaJxbxtFjQ3Gg2trKwkpUgfu3HjDinmIXWJUWNX5pJS3ZAX3hZ+9/7y3HsWmjPnAQ2U5byBPO33+4UT3zlPww07jwQBViuV7W3fHbD6eg2X99VqNZVL9I3F656+6jIr8ud3Op0o8JSKc9DxgRurPkd8TtRqtcL25O7swBB3vvD3uJERsYXXw9sUf3PD5kTaH8FivJfyfb4fDzi6sRqdbl6+A/T4PY5LBPR+P7qU+eT6OWIyJ7/f6+19E/sn1/ZcX7se93o61nGZ7s8yl5GPvvsWdYwGrNcZuQ2PjY6Oanl5OS05aLVahbQ+5F2j0Ugy2mW+14u6+7bf3j+5/jhZelgiGvx5jjQTnrA84DV6jd0D5t4mz4/mPf4+ws65333yO7PyPTKHP8d9bhjElAbuj4vJ/F6IAY0DmwPeMeLg3gMY0ZnE2+aLj2MaCOTvi4KL8mgzytsFdhTa3gYHVYAEN05iG3PRFTcGUf6AB573VCvK8HHifVGY+Xd4ESXu9aCObgB5+aOjo5qZmVGv19Pc3JzW19d13333aXZ2NrXBPcXu/fU6eF+WVCT3Gkk7/BkPyxwbG9PKykoBrLuBzRi4jGBrUpcRvAPezY1VNH6d99xh4nMTvnFnh89Jj86RMuP8Ah/FiIJ71H3Nkqc90n7+A+SjzAHsVyrbmxzMzs6mrcYHg0Fhtyqfs+7p43f608dpa2tLjUZDc3Nz6ZA/vMYoYfqi1+ulNEbqRZ+SzuJ9DDCnfwaDQcFgYExoH9/dKKvX65KUDABPuXPjjBRTaSc9ihQHHBWdTict3vbDtCYnJ1MkqlLZcaI5wMKREWU4Rgpy0bdYdQ+qgxTAxplGcYwdY8DzLvul4vkLLNanLLCJe5Td0PCyc6AzV78IjIc5IPxzDry7fMoZHP6d9/o7humdnGGRA/n8lsMs8TepuCDbcRLzXFJB9uYMhROhOAb0r/eZ40//i2Df781hO94hFY8AQFe5EyA6RKrVakrZZfG3tJ22R5kx/ZqIx9LSks4+++y0Vq1W29nIhENLo8HtMt/XuT1cdMoNDZ8kTr4QHA+ih4o8d9b3Yu90Oru2JI0eAr4jnHPpPTFVxS08B8rOYPwOQ+FdigYL78eqzFm4vA+mcmPDAYwzJ22M0RSfANQpFxGAooCKRpq/G0XHdfeqxfZQpvdbzlPjky7WN9aH/sZQYtLF/nBjyOsBxegG7YrRDq6Rp+i71vg1DEkHaQCr9fX1dLL43Nycjhw5opWVFZ1zzjmSdtKtEABLS0tDPSolFcl5NDoBiFy48BwfH1e3201C2VN2kA14cer1egKArjx8XjrABYzGKIDXIXr/AIReb5df7jBAVvG7b3qB0cF3+BZ+jf3k87pSqRTWOUjb87rT6RS2gc3NVU/Z6ff7aU92N/wAzr7DCalGKNB6vV4A5tPT01pbW9NgMNCBAwfSWgsWeXOukrQjOz2lMe4oFucTaUlsVoEjInpK3QgiusXiSvqP7WPhKfq81+ulXV5Q6P3+9qJMaUcmra6uanp6OqXm1Wq1lPNPWb6w3R0itB9ji/F3xw/pVDHNxvXj6WpoOMCOBK9EHexzEBkS9ar3Oc94hNGNu1hufAfl898dJtznho4D3djWYX0Q35Nrk99/PEPGAX7u9yhz/Llc3XPecsc7Hn3z+3lHdIrSXzGS4RTxikeeoiHk4+zti33kWSe5/oljR5lgDOQHbfI1bpISDmZHQceNlUolbd3daDRSWZxJNDs7W8DU9Xo97SYVo7+OK5E73OttOVX0sEQ0HOAzuKRN0TC2sSUE7JPTwTbKxjvdJ7S0ewcBFIhUXOOQu1cqHkXPoFBOtDojyPABid4JB7s569cnk9fdrw9jeDd4PKXAJwr1cWGbs+B90rrg9nQFL8vrOkyRRWGAAgXARA+1l1ep7Jyo6r/H/nZeA/Q7QPC+34sH3LD0d7nRy/kXtAFFA1iq1+saDAYJMC0sLOj8889P76bPSZ/ysch5okrappwDQNqJVkhFo9IPaHP5A58glzhozdN63CBwUJoTug5iIk9GsOgg3kGuGxlE0pBt7pV3hZTbLhECGPFu+NWVHc+5AUUdMCboD+pC1Ic+whvv7/BF+ERmmCf1ej2lGCJfeR8pAL62gIXV1J1oBe0lgkDUgfnvctbloC+EdFnkskPadoZ5mhrPTkxMpDJcN3HdFTTOESIrtHVra0tTU1NqNpup3EajkXYHw9CAD6inG0Hch+zxXarY+AAeRx7mnD+nMzlIjul1PheYp/A8Tkx40OcCxhzbjko78odyhzlYh8l01517YRPuddnnMsTfEQFuBNG5Og3Tffw2rC25e92h5210R6HLJ5ezXqbfk3PIOU7I1S/KY2REzrlMf0YM5/hEKjpioyMqGhXRkRTr7O0mguH4CVnj65GbzWY60E+Spqam0jzHMePtwwnCGXVet2jUMRd8LIdhkpPFKg9LRMP/6FgWw0k7RkO73S6AYGgwGKRcV4SlA4zIaD6wCF2/5pPawbiD55xFzEA7iOd90XCIjOpKMt4XAUJun3W3ZP25aNhw3b1slBMnYwS9PMsznmrhyt0Fs7cxjnsUfvEdXHdjxaNE3OuCyfuPCeJ97J4pP6SH+ntfeB/7WMZ0GX8Xnzn10++lLvB2o9HQzMyMNjc3dezYsUI0hnQHdorJ9WOuX890yilBN7J9QS3jj1MjlgPoazQahdOno0Hv6x6cHLREb6XLqVhn5xnnfcLpftozvOQpMrzDDWZXVL5FNOl5KFYMAjz3yDSAOafKwqe+M1Sz2UxpU+Pj45qYmEjeerbCxbjACGJ8fGvyqamp1H6PxLDWAPnl6xsgdqUiSuJrPqgnf5IKfSapsADc5TH6yRdZ8j6ep29jaowbGRh+8IwbqJJSioSnQ0xOTiZeYZcsX+xPtBQgTN854IXnvA1Eb9bX1ws6DWPodKUoH/jseiU67NzxEMG2GxnIDPgzF3nwzznZELGKg76IYWIb+Ow6MQJpvy8HpGNZUUblDAoH0e60zPW93x+NIY/sehuGlTUMyEbMljMm/Lnj/Z5rkxsS/tn70PEDGGBYtCY3Fv6bA39f/4czLD5TrVbT1vmepoqM3NraUrPZLGA6X6vhdfI6RzkTKWfsPVh6WAyNaDzgqQEMNxqN1BkOsnmG0DXP+VZd3OtK3AWHT3omCX+xE52J4mQEqEYA7OAkgn6vowsq91o78M1NcvdIUZ57MHKCzq1wSbtAknuE40R3T473k98XjYOckON7NG6iwKKefh8KGiBEWR5WjHWKxgmAwJU6oIn3ueHgSiEaSfAm7SZP3LcFdjDqYzE7O5v60r0GbiA56DgVk/h0pqhcGDs31Hu9XgKIpB65dweq1WrJyIgLtd04dQ9QzrhwBYEzhPJ9ngMWIxDyyIoDYW+ryyB2ZQJUwl85ryrlu/z1fH/qDDh2EE1bOB18fHw8nTHQ7/eTMuJ7VMzUw+USc8XXaZFmxHvoq8FgUEiX4j8GCWNGH/lCaU+JcMPeoyg+fjijxsfH04JfdnMi9xmjNW43yaFtpIu53MhFVNFzLg+IyjSbzZRGzL1+aCFGWJShzmfwkUfuGJeow84UYsyH6S2f1z63mZeAN4wOeMKzB7zMSBEbuK5xwCftXkcQ51COr7yduToMm4teJ5erEcBzH/2XwwWxX6NOc2fhsH6K9Y/4wRdhR7wUMz/c2In6PRobXo6/z8vku+On+Ay8gPxybOPO7lh3vxadGDzvG89QB3aac9mNbATzIGu3trZUr9fTqeCOsSHkhR9HsNf4nCw9bGs0Ymez0I2dMtwic4OAA6ZouC+Q416nSmVnS1anGPpyhUg5vHfYRMnltflvDkhpuxsFUdBEZQH5Vq1x8nsurj/nTIxhgmKJ5MrWhZ57SCMzRUMDRvYJ5+30/o5tgfzdUbD1+/2C1426eeqaP0N/+MIxn7AeTaDO8JKPrbfF+51+Ifrgi0MpOyp0aZtf9+3bl/gyenaix8QpCtmSdm+PyBgg3N0h4J4caQdwM96ejy8Vd7nb2tpK3meueYqNzz9kjc8JaXeqkstArgMI/ZwWl13cNzk5mQxt5IODBq47mKZO7mjw9BlfwM19RCHcWPfTs+kXN8hRbg74GQ/OePDd63xe0/+dTidFoxwMkH/MjmLUywFPXOTuMsTHhHr5VtPR4eFrddxxQOqW5+HTN3Nzc+r1emq1WgkE0NfUw+UhsoPPHpEAMGD8OH+xtoQxqtVqaa0NYzYsPdBTSXNA6UwgNy7p7xhdjHyEYT0YDBKuIGXFZYDPbcc7Oczg749APlLEJ1AErcfTFccDiLHdEZNE7OJtcIxBn7oMGpYOdTzKpTchk3mv68+oB6ij47E4RtQv9p8b7o4FcvfH5+g7+sXbDr/F/vZ+jZiMcnydoa+TlbblJ/zIphjIOiJwvItjJejj6Hx3nTqMHqrB8bAYGtLuHEQa5QeMSDu7DCDcfVEbz7MAkfthBBQ2Zfu7ooCNXuvILBGg+6DGyeVCOzJszjDxevhz7hHNGVJR2ECsa/HdRRxMuAcsevHdo+kHteTqF70DLkAc9Hh9/TfvB571+xHo/ufGD4rTvZNOOSECEPGDsgActD/2M2VBgLNcfXjW0zGcl+G7ffv2FQSiE2uSIpVGxm5yXsoZxNzjQNKNP8apXq8ngSzteHIAwnHdkFRUMC4LnPdd3iHkozyQdnYNQl7xWwSllE2KlxulnmbooN95H7DtXkTuZ4E2Rs7a2loC/KxLGAx2toTl8ElPC6JueMwqlZ31cPSZH/rpYNfnDGVinPC5Wq1qbm4unXpNn6EMq9VqOjXXo7D+u0dHo0ffPdJEzFmk7Qd6YejxDtaUTExMpPQlFmQiI7xNgCTnNWQPJ/7Cs/1+P60rgm9dd7JQk76KctZlZdRrjAH9ciaQzzlp9wY1rgv9mi/WRW4PwyQuFyJQjxjDAbTLH/f4H298ck7GE+mDYUaJ15H2uFPOn+VzdMp6H0a8ENODc2s4Hccxx+B/+sQzWrzfqStGtkdZvY/iHBkWXYlYRtptYESwHTEOctdxg/dJzuDnOrIgOkDd4cMc900juEZ92d7dlypMTU1pZWWloDvoQ39PlB3OJw+VHvY1GpIKigIvjQ8a3iE8Pc7QKA2ANcTiN2dW3s8E8IGOAJfyY/pLbmJ6iF4qgtSYPhEjGm7w5LwSLjTcIPL+9Lp4DnU04vxd1IP7Y518YkD+HACfejnj8Q7STKKnxydLbpI6gztI53kfIzcSYnTAx9XH2xWI7zjj457jiyhUotEFIPODuPAyxhBvo9FIbSWfW1LaTSK+IxphJW2T97ODSmknLcXzS31cAbaTk5MFkAuoGAwGCcD5e6QiOHDl5crO5x51pXzqBTCnPHhIUsEbjmfdHSIYBm4IYATQXp97sS/oJ7ZI9Mgv858NDZhf7NjF7yg6AL6ktK4AwEyb2XlNUmGXKYwV+tJl5dramlZWVtTtdtPzIyPbuz9xVgGKlvp1Op0CAKSOvp6CvvJoV5QVDs49MjAxMaHV1VW12+30fDzY08/DaTabqtW21xxyYrjzBLxHu3u9nlZXV1WpbBvAjO/MzEzBIPOD+AAiyA4WmDMvGCsMFtrJ+1yen27ketllAH0X561UjHbyfL1eLziWMBZdf8ey3Nngssmf8cgKFJ0LJyL7I8j1th/vGa8X36NRsde7coaK6zvSA5GtES/xXtfDyEafH66r4Vt412Wj4wHmmBspkMv0aCjEdufGIRpdsQzq5I4fd6C6ERkdU/58LCtGqXkOeTc+Pp7Wy3E//x3TYnhEoxqZ4Sm57qQ+noHxYA2RU2poOHB2bwwNY20GxGTc2trS2tpa2vovKm+8ZHQAXsEIDOnoXEg0Byyj9euf4+SIoJPP0VhwC5XyYp62T9IocHgfINujHZTlqRTUDe+NL4qkLr7ewz0TPmk9asM95CFHIYWipvycceXt8XfTZtrJ+/jzvvMJG/kM4J8bE8hTCDwE6V4N93L4s1EAkcIQFZi0s9uPG0IAvna7nQ6DY09/N/5K2pvi3KD/EajuaY/RBoCc57C6zGFBM89GQ8YVG8R3590IGBzQ+qFzbN0KD3qUolLZ9vKzdz9yc3R0VKurqykcHvvDFaFvC87cZE1HTDvwOsLfEPfVarVkpLTbbXW7XY2OjmpqakoTExPpD55nPNbX1xNo63Q6u9IP4/g64JeUIgcYXseOHdP999+fgDy7LwE+MN5Z8wEYJypBmzxtjHEjZYp5ylxl21rGwNPIPBJUrW5HXJrNZjKM4K/Il7yXftjY2FCn09H09HTq86mpKfX7/bRuI6a2Oc+4ceVb/zLmDjiIipwONDc3p4WFhcK1nF53cOXyw0GuVDxLwMGZb0Tgz3lZblDkdGqUKbwv6sAHQyeqN6L+jTglp4Mi1vH7XXdTj3ivg2MMkGjM+LhEeetGCHMp9jn//R087zgolzYY9Xru2jCDY5ix4WU54I9RDY9ex7GIZXt5yCrfKASHiKdScj9rSl3PeHpVfLdHbT1ieiI0Nzd3Qvc9bNvbRu8gAtlBKAB5YWEhbSPoaSYR/E1NTSXB6++KBg7M5zny0m5h5JEIH2iANOQTg3cMM2R80uQmNp/dePD7qZMbTkwoqbjrVgT3/Hl/OLPnDBre7Xuz81vM93YPLu+grjEMTJ/Gvs8ZF97XKFK/xw0Br3M0aKIR5fn6PEMUx7cJzXmbvEzu57uv16BOvvWtL1pFeLKtLTuXuEGVq0NJ25RTAsgIwKbvSy4Vc+/jIlnmMmdBUK7PafeARxAcZY2Ppc8v37YbRwpzzOdkVMS9Xi+Be9qJMoiOEcohRcyjD/B+TOvBQMcrhrxzGdDpdAo8zJo6DjED3LKwXtpxCngaGgYBxoE7ErxPmE8+L2kPEcFGo5F2/nG5gdJ10LG5uZmMjpmZmYKx5fM6GqbwBAZHvV5PBoUDfTd0SaVCTsKPnrLKmDEOLjs3Nze1vLxciKDRtxhKAF7nU+QYsoW64Kzz/nAAdjqQY4BhRB955II/lwVucLpnGMM/znXIcUe8TvkuUxycInecH+M9Tq5PIyaKFMuIwNLv4/8wjMTzXo/4e8yscD3tc2uYIci9rs95Fzybe9bLd8eBp096arm0k70S+yfntPH7Io7J9UM0NmPf5t7j/eO85/jDncRR7iOPYpoajhd/n5+XFHmWsXB945QzzqATmYvSwxjR8Ar5PvFScTElnjKEsK9+pwyUjofRotWJ4ojgWdqdA+mD6vV2EBzb5MzjCj9nZPj/yGBxQnn9XPDwXPQ4OhP6/+gtcGXs9aDMnIXujMw9pB75OxxcMZniCasodj7HvnXwHo0V7vHFmz5pueaTMhpfPj4uSL2PAE/ugfBx8snuwp5rCDm8Afzui9TgVyYxW3jmdnkYpmjOZHKlDfn4+GLZXq+Xohek9yAznBeQNw48oJzXyZWIywqXDcwrxh4Dp9vtam1trRDBANADOqUdnnRPGGFswK5UXIAOT7FwmnZ6nzDPSO9zPkcZseYA5UzUjTphCMDbHFCZM7hpCzzuW0IT0fOUKNrkxgDG+MbGRnJ2cP7EYDDQ8vJykvduaDAe6IuZmZl0hocDfR8D+tJPW19bW9PMzEzyCvopvpTfbDYLJ+nSJudZd2S584S+IsLqxka1WtXKykpaU0RkyLcs9lx2+p9UKvoSHcn7T6coaqvVOu497iDIAW/vGyKPrgd9J0Qvw0F1fB/l+zN7yfPjjUd0pp3I2Lmccn0c3+uOyb3IcUSMdkT5GRcUuyMyvj8a+ZBjE8YjOk0g/x6NSZf57mSIhp/jCzeQYh2cHHPQnpwx5DrHcZuXEaNl/j7/zPo/+hR5goxy7IgcjymjjUYj7fgXeRid5WMe65OjE5mL0sNkaPigSzsnrqLAXYktLy8nhbOxsZEOPnMF7OsSUCJ0LALbFZ20+yRK/x4FT5w8EaBS78iUkdlyxoa/x6/FnDm8T+4Ji54SFzr0h4MdX1PhwtIZJkZnKNv7zAE2lrNPRAd6DsqcOSHSU6JXwI2+nOUfhYpP/JznwOsdvQ8oZX+fe54c8HkZjElOIFJnN8KYrJzpARhypcce975VLn1/OoCAh5Ncnnjutf8GwHXnhM/pdruddvrx83aizHLKGev+nLRzNhBjC38Arqkb6TC+vskVB9c6nY5qte31P6urq4V5zHvdC+786t5135+degPm8ZzXajXNzs5qdXVVKysrhTlOPeMp7Bgma2trKdWIdXNEKtbX19VqtdRut9Vut1MaFTLbD5lDXq2tranb7eqBBx6QJE1PT6ctZqvVqqanp9M8Yj0Ea/+oB1vWElWif/mLUQdJhUP2VlZW0km7eMUxmBgf3z0RGenGTjTAHMS6jEGub2xsaGlpKfFXq9XSzMxM6msOBYSviLK4YbW5uZkM7Mi/tON0XacRycFmvB4xCn2IvB4bG9Pq6mphrE5E11OOj3NOT/FM1OdOUb74e/gcy4vt9Gt+rxu98Td3IMbvscwIWHNA2vVvlJ8RxNL/eOo91ZDfvW7ULxoQUtHh6uPgcz7XT+5UHkbeH9EBFrFI7BN/b8QX8X7HQugOXx8q7azL63Q6hb4lfQqc2Ov1CulTsc3RWep9F+sV++9E6JQbGvx3gIVC8fMIRkZGtLq6mha40SFYbpQTO9aZ1fObc5MugllnRlca/pzfw3sjAI/Cyu/PCZ/oCUCpOkCIKUVu8XI9ts/7mskYjQGPCkQDJRpY/pxP6jipcqCf695fnmPpRpu3F4HiKQk5T0Lkgeg9jf2ORe/9yHe3/rnuwMPb6Z+dn6i7h45R6G488f6JiQlJSgekxeib92NJ2xTBvVSMOMR7fWcgT5vgP8La553zlBslDhalnbQmnAIYO1JxPZqH7tfW1hIYJsLg3nHaQ9vw4PtaID9zhXojU6mLXydVyVNAqI/zPIYAYBxDgusOuuhLjLNut5u2dl1fX08Gy+zsbALJpDktLCykBdzLy8tpAaOvQ5mentbk5GQ6w2IwGKTF1t/4xjfUbDZ14MCBtNCczRXYWQ794obL5uamVldX1el00mGDlUolRZh8RyiP5hDZYB3G1NRUktfUzY08eIB0NLad9AgV4D4qcvcq0+aVlRXVarW0GJ0T7OEDN06JXMCX6NXx8XF1u91CHeCnM8XQyEU04Ldo8MU0KkkFbOHlROPDy2PuD5PjOQC7l5ExjGI5OSMo955cBCOHK6K8iR58aYd3o+PSnYrRWPFMiehYi8Ab7IJeHtZ3uc8xy4Hffa5CrrujEZPro5yzMeLGaHx4XdwZHPsUfsqNLzLcMSvvRi57hDVimX6/n+Q8zs6Ik+OOicMoYukToVNqaERPAX8scMQrxKIWjk2XdjqTE2NRcrkQG4oe0OzK1idHBBLU0YFKzmqLgz/M0IlWYaRoQPhkj0A+vs8jNt52N5oQkP5+7yOEX2wTismNtOh5iM9E0JLzJLt3NYYGXYgAYry97tWNXokYdoyC0IUFUS/6OIYmI2D1vpBUiALRzmhgxPHNKX8XlqRn+XaeuchSNK5KKhrBDuzcY+Zj3Gg00voDF6a9Xk/tdlvSDrDIeTxdMMeUoMhHyCF2+omC29eQ+FkO0QiIHj83LnyraklZMAOvs9ic9/gzcd0T84Wdo2jHzMyM1tbWkvIitxcQT4Sl0+kk4D0YDNIuKFtbWykaQXSg3++n1CJXtBsbG5qamiqAPMC6L7xutVpaXl7WoUOHNDU1leaYy3fSwzA8eAfj7V7rwWCQnmdbWz9dl77sdDppN6xqtarFxcXEW3GDjxhZQIYjC5ynXOY4r9BPS0tLkqTFxcW0BgRDzOW+r2PxFDLfgIC6oXdPlwXhx6O9nDfRQCCtOwK9GPWIRkp0UuS8xRHYwzPRaxydeTwbHaFR9+UANeXy3bGEY43ovY9YJIJI18PDMFaUlxA6OYJy/xyxmDtlcmVGXOhl0mZ3xrjzKPZjrr25fo/k4xf7KbaHd7vBF/WP67Xcu8HHfg/O+bhewlNeoXq9rna7neZH1HfgsFONRR6W1CkmHX+NRiMxDEL62LFjKac05tMNBjvpOt5JDs5cWLsSl4q7I8XOcqvaBUdUwM7EcSI52N5rQCITOwDnWo6JvR5ePgo/ChY/nCVa1BEY8XsufxVvoHsVPPIQjQN/nvHz3GfaAhNHME9ZMZyZi8S4UeZ181xvxhV+oG4ePXD+cMBHXQAxfr8LpVx0C0AxOTm5S3HQJ7zP+8PHvkydGk6Mi8+7qPhrtVpK3/H0BcYaEOzK1eWEtHuDCOe7SqWS0nco2yMYcSz93R6piyH9wWCQFp363IOHHSz73I/GTzRE4rlDgG8iyD4HiEoA9D1iQt+jyNrttlZWVgq7SVFfoiKtVqsAiOkPP68Cr9rY2Fha+EyaJfMaLxzvveeeezQ9Pa39+/en9vT7fS0tLaVIgvcPY0Rb4A1fIO27WLmzAl4gcgNvsfMV2/A6LxB5wEhi3rseyil3X9vR6/VSnVqtlubn51OUjr6jPI/Y0T5262q327sirn7eyelO7oRzPBKjnNJOpIl+xVDMgdoIHBn76MXmf8QGe4FZKBon8T6XC9E4iRQzJWI5jjNy9eQ+DHi/zn05bBCzOGIb/T2568x/yon4JT7rzkHe5dFvB/rUy3HHXro31297/T6svbm+ju3jOefP+E7WyLmxUqlUCilUUtEoQbfgNPE6+txwh29s417jeDx62FKnqPzExIQmJyfTgMOsKAbud0aInRw9jwAOvNcIhxgyi99zDOoTxn/3dkQh44Bhr053JvJyvWz3WsT7uebtlopbUnp0JIIOf8a/O1iPgJz7+c29rW508Bv9Q14lICHmhUfh6wLMJ0ycPDzjII66uvcCAyO230GH1z0ait5O0leom9cjN5bON94v/l72Y5eU+DaOTVRuZzrllDpe2+jMAOj52gt4YXV1tbBQNpYhKT0X+cuBvssonkcOIJx9331fqwN/ScWUzriIzw3vKBOdl91p4Qa5p4Gw6J0+imfq8A7kcbPZ3DWXcAzhDcfQYFMDDBNSYmu1mubm5tLOT+ya5JGZbrdb4HWiBLQL+eH1ICKCEbN//37VajWtra1pYWGhsC7FF02SXuXnS7jB2e1207swijA6PHUDXtnc3EyL+71varVaOjuE6AoGGGPtvIYxSF3gIyJgrVZL9Xpdq6urmp+fV61WS7suRmORaAZjzPbvPl9yzo3vVDpeGxjLiCP477qAa54+kkub8nf79xjJcOxBXbxeOZnv5PrenRL+343WnL6I74+GSayTP5cDzTnDKToSvV7RKZh7V+7d/g439v16NGxiW1xG+rMe1aQcQHV06jxYim3N9an/93kby4CiozXil7hxEjKBqC6yM+KMkZGR5NTJ8TaZR6eaHpbtbSG8jFzD48I2gt6B3E8uuy9AjAzgjOSAFms1GiWUHRWsA2AYEQaMxkksj2dy1l8UMs5E1Dsqeu8/BxJOpN1QjoP2aLgwWeP7HBT7va5gpZ1QvgOayJTutXBAFYVczuPigIpnvL2xfO+bXF9RT+8THzMfZ89z9JQVT9XCIPZokbcrepT9JHLAlz+7traW9uZnVx9XUN6m05m+00FOSSWV9K2nCID573qW+6KjMEceSXR96WVADhhzQDoHvB20Dysv0rDrsU3DjIcoa6OTYS8DJNY3AmF/bq8ITM6Y2Os+MEEO/OeMCq83dfE6Of7xfs9FlqNBFftmr3Hif8RaESfEunhKK787DqGcSL4LnWNQdhB0Y8YdwkQ1HDN6n3gm0V5tfrD0sKROubePBXwO4NfW1nZ5AarVagJi0dPujfYcNwfK7lGnLjzL/2GgzhnWUzQiOPcBJ6ICVavFXZ7iRIX28moMA/MOxCkv5vgPBjsh/1y9o5EVDS/KiIA/N9lj+QBs74/ceg3vDx+nmK/sqVeRySkjtjUKBU9f8XZFD5f3g+eKe9oD6RBQHPvojR0Mdg5PY8w2NjaSB5R8cN8G8UQiZCWVVFJJZzoNA9BOcS2T62SPQqJL3GFFBGuYgVKp7EQZ43XXa1F3DjMacjTMObeXgUHfDDN4hr07V0YE68cD2sMMmPieaCDmMJ47OL0/ojEQ68C7InbI4a2cwytnvA4z4vYqL/a/18X7z7GsPx8duxgfXka1ur2JBalQjo/j5jq+1llSYf1iNIBIQ499+1BxySlfDE4nELJlC8BKpZK2pWV7SW/s5ORkKodrsSMBo86QTjlwT30iwJSKOfrRO+Gg08nBKm3GwOD+OCHcow7FyejXY59Sb5jHFxi6UeR95N95F+kAvmYhvs93jYiGW65viSp5e3wBdGRkL8/TWyqVSsFY8XsjxbrkdvGhjr7zFe3NCSNfBMh3yiVS5uc1cC987uNHaogbYmtraym9xJ8tqaSSSirp1JKnTvmfX5OKXnB0iesyd4TG/1AO9A9zlDnlfnOd4XWMRpO/Y9j/aOwMA/i5euVAt6/dzBlV/pzr5OjMdADN7+5EddyRq18sK7bT656LbETnrfdxLkMl1j/Xx7m6+e/OYxGfSbvTqHJth2+9b4hsuGOclF3aE/mWFEtPLQRX5gyNHD1Yw+OURzS8UQCr9fV1TU1NpZ0zsLwAWr4toec8SsVFyM7wvM+ZTSqmyeQYmvo5+HbLzsEfgslTkLw896z7MzmAHA0A77N4H++IgsHTnnxRoBtig8GgYL0OSx1zJmeBJEagvyfXJ96vnAvA/VF4uDU+zEvBs37gjxsI0WjzaEZM0/L7PPJFG1zJ+P3+OQo66kYf+bixV7WnYg0Gg7Q1JW1BkHiuuPerK5FhwqukkkoqqaTjU0xLdT3pGMWBVdTb0fkIxUOFI6iNADjWw++J5JF49FYuRSs+m9Nle4HBCISjEzG+w52PuXL9mqfpxDrHusUjBhzHDMNL1MVBe86RHO/z+sWMiFz7h9U5GpTDjMzYX7TNMaLjkliGY9RhTldfn8W7cH7GDBPfnIMoB+/mf1wPPYxOhMecHhZDg880xtNQOEnQU6xYxxEBv1u6Pmj9fr8gIIalIuVSh7x+caFcztJ3IeJebmeC3Ptz4S8mnxs8MRLixkzOWgf0RsESgakzgFvwbpRA7BTF+9yrE61wNxgA0DH050ZGv98vbGvsHgXnGU9tigaFG4ROPJMTFv5+qZj7GdPzvI+jEPAFvF7/fr+fQpA5vuGMASY2RimGBuOY49+SSiqppJLyFJ1PUf+hW3PpTzHC4YB+GPjnPV6Ov0varYuGPb8X5QBlBKk5b3fECP7OWIZ/j+A691zO4RXxVXTgDnvWf+P3mNUg7UROfEOOnBGYM4xyBlSsQy5qEdsW6xw/u7M6h73iOFGXaFhBbECTe6fzVYykkcbtY1CpVHadq+G/STtnZkSDfJhx/VDplKdOSTsH4/jCbn4jV11SYb9vGu3eZq6zf7i0k6oS73OK3gPq5mVED/deAsQNghywdcqlGg0GOzua5NKofNIMY1o3RDCMogCB4Xwnm/i+eO5IDnQPM7bc6PLt57ye8Q+mx+CQigdXRaOD6x5NYjIxVjwXhS/kgsnDh95P0aPg3qgYQYM3PbSIwRK9Kd6GTqdTOAQH3mHv+2ikev/nBHxJJZVU0plOKysr3+oqlFRSSQ+CTpkr1S0q/iYmJpIFmQtXNZvNtLc54N89/TzD/uoOwvy7W688H0/t9esODL0+EeB5GpOkQjRj2PM8F+sWU6z8fsrMvduNAjcMvK/cGxM9+m4QxJQhb58Ddu8fB8Rev5ja5X2CYeCGif/mB5L5IujYH1wnMsauW26Y+jh4H8TIkNczF5HhfqI7box4XQeD7YXejUajUAe/n+/r6+taXV1N/M172U0tRuWioVdSSSWVVFJJJZX0nUwnHNEoPawllVRSSSWVVFJJJZVU0olSmRxeUkkllVRSSSWVVFJJJZ1yKg2NkkoqqaSSSiqppJJKKumUU2lolFRSSSWVVFJJJZVUUkmnnEpDo6SSSiqppJJKKqmkkko65VQaGiWVVFJJJZVUUkkllVTSKafS0CippJJKKqmkkkoqqaSSTjmVhkZJJZVUUkkllVRSSSWVdMqpNDRKKqmkkkoqqaSSSiqppFNOpaFRUkkllVRSSSWVVFJJJZ1yKg2NkkoqqaSSSiqppJJKKumUU2lolFRSSSWVVFJJJZVUUkmnnEpDo6SSSiqppJJKKqmkkko65VQaGiWVVFJJJZVUUkkllVTSKafS0CippJJKKumMoq9//euqVCp6z3ve862uSkkllfQdSKUMOXEqDY2SSirpO5re8573qFKppL+JiQk99rGP1c/8zM/oyJEj6b5PfvKT6Z7Pfe5zu8p5xSteoWazWbh2+eWXF8r2v8c//vHpvuuvv16VSkVHjx7N1vG7v/u7dfnll6fvKKlKpaI3vvGN2Wde9rKXqVKp7KqTJA0GA733ve/V05/+dM3Ozqper+tJT3qSbrzxRrXb7V33044XvOAFu36jLr/6q7+artFXv/d7v5et280336xKpaKnPvWp2d9PBd1yyy266aabHrbyv53p5ptvLgHM/0MqZUgpQ043+naSISPf6gqUVFJJJZ0KuvHGG3XBBReo2+3qU5/6lN75znfq93//9/XlL39Z9Xq9cO/111+vD3/4wydU7jnnnKM3v/nNu67PzMw85DpPTEzo/e9/v6677rrC9Xa7rTvuuEMTExO7nun1erryyiv1gQ98QD/wAz+g66+/XvV6XX/+53+uG264Qbfddps+8YlP6NChQ7ue/chHPqLPfe5zuvjiix9SvQ8fPqzzzz9ff/VXf6WvfvWresxjHvOQysvRLbfcoi9/+cu65pprTnnZ5513njqdjkZHR0952aeCbr75Zu3fv1+veMUrvtVVOaOolCGlDDlRKmXIiVNpaJRUUkmnBT33uc/VP/tn/0yS9JM/+ZOan5/Xr/3ar+mOO+7QFVdcke578pOfrI985CP6/Oc/r6c85SnHLXdmZkb/5t/8m4elzj/0Qz+k22+/XV/84hf1Pd/zPen6HXfcoY2NDT3nOc/Rn/zJnxSe+ZVf+RV94AMf0LXXXqu3vOUt6fq/+3f/Ti95yUv0ohe9SK94xSv0B3/wB4Xnzj33XLVaLd1www360Ic+dNJ1vvPOO/XpT39at99+u6666iodPnxYr3/960+6vFNB3W5XY2NjqlZPLEiP17qkkpxKGVLKkFKGnHoqU6dKKqmk05L+xb/4F5K2lZrTz/7sz2pubk7XX3/9t6BWRfr+7/9+XXDBBbrlllsK1w8fPqznPOc52rdvX+F6p9PRW97yFj32sY/Nekhf8IIX6OUvf7n+8A//UH/5l39Z+G1qakqvfvWr9eEPf1if//znT7rOhw8f1tzcnJ73vOfpxS9+sQ4fPnzSZQ2jyy+/XB/96Ed11113pfSQ888/X9JOSsatt96q6667Tmeffbbq9bpWVla0sLCga6+9Vk960pPUbDY1PT2t5z73ufriF79YKD+XX03ayz333KMXvehFajabOnDggK699lr1er3j1vmzn/2snv3sZ2v//v2anJzUBRdcoFe+8pWFe/r9vm666SZddNFFmpiY0KFDh3TVVVdpcXEx3XP++efrf/2v/6U/+7M/S233lJmS/t9RKUNKGVLKkIdOpaFRUkklnZb0ta99TZI0Pz9fuD49Pf2glGWv19PRo0d3/eXymE+GrrjiCt16660aDAaSpKNHj+qP/uiPdOWVV+6691Of+pQWFxd15ZVXamQkH5D+t//230raTnGIdPXVVz9kgHT48GH96I/+qMbGxnTFFVfoK1/5iv76r//6pMvL0ete9zo9+clP1v79+/Xe975X733ve3flWr/hDW/QRz/6UV177bV605vepLGxMf2f//N/9D//5//U85//fP3ar/2a/tN/+k/627/9W1122WX65je/edz39no9PfvZz9b8/Lx+9Vd/VZdddpne+ta36l3veteez91///161rOepa9//et6zWteo//6X/+rXvayl+0CaldddZX+03/6T7r00kv19re/XT/xEz+hw4cP69nPfrY2NzclSTfddJPOOeccPf7xj09tf93rXvfgOrCkU0KlDCllSClDTgENSiqppJK+g+nd7373QNLgE5/4xOCBBx4Y3H333YNbb711MD8/P5icnBx84xvfGAwGg8Gf/umfDiQNbrvttsHS0tJgbm5u8MIXvjCV8/KXv3zQaDQKZV922WUDSdm/q666Kt33+te/fiBp8MADD2TreNFFFw0uu+yy9P3OO+8cSBq85S1vGXz5y18eSBr8+Z//+WAwGAze8Y53DJrN5qDdbu+q00033TSQNPjgBz84tD8WFhYGkgY/+qM/WmjHRRddNBgMBoMbbrhhIGnwuc99blddIO8rp89+9rMDSYOPf/zjg8FgMOj3+4NzzjlncPXVVw+tz8nS8573vMF555236zp1u/DCCwdra2uF37rd7qDX6xWu3XnnnYPx8fHBjTfeWLgmafDud787XXv5y18+kFS4bzAYDP7pP/2ng4svvnjPun7wgx8cSBr89V//9dB7/vzP/3wgaXD48OHC9T/8wz/cdT3yS0kPL5UypEilDCllyKmkMqJRUkklnRb0zGc+UwcOHNCjHvUo/et//a/VbDb1wQ9+UGefffaue2dmZnTNNdfoQx/6kL7whS/sWe7555+vj3/847v+TtUCw4suukj/5J/8E73//e+XtL2A8Yd/+Id3LT6VpFarJWk7hWEY8dvKykr2dzySN9xww4Ou6+HDh3Xo0CE94xnPkLSdp/zSl75Ut9566wmlBpxKevnLX67JycnCtfHx8ZRj3ev1dOzYMTWbTT3ucY874VSPV73qVYXvP/ADP6D/83/+z57PzM7OStr2AONVjHTbbbdpZmZGP/iDP1jwal988cVqNpv60z/90xOqX0kPH5UyRIXfShlSypBTQeVi8JJKKum0oHe84x167GMfq5GRER06dEiPe9zj9lzYd/XVV+ttb3ubrr/+et1xxx1D72s0GnrmM5/5kOtXqVSG/nbllVfqrW99q1796lfr05/+tF772tdm7wMAABZydDwgAUB6/etfry984Quam5s7ofr3ej3deuutesYznlHIWX/qU5+qt771rfrjP/5jPetZzxr6/PLysjqdTvo+Nja2K3/8wdAFF1yw61q/39fb3/523XzzzbrzzjsLwCWmv+RoYmJCBw4cKFybm5sr5D/n6LLLLtOP/diP6YYbbtDb3vY2XX755XrRi16kK6+8UuPj45Kkr3zlK1peXtbBgwezZdx///3HrV9JDy+VMkSF30oZUsqQU0FlRKOkkko6Lej7vu/79MxnPlOXX365nvCEJxx395AH45E8HrH7iCtBp7W1tT13KLniiit09OhR/dRP/ZTm5+eHKtsnPOEJkqQvfelLQ8vityc+8YlD77n66qs1Ozv7oDySf/Inf6J7771Xt956q77ru74r/b3kJS+RpOMu6Lz66qv1yEc+Mv396I/+6Am/O0fREylJb3rTm/RzP/dzevrTn673ve99+tjHPqaPf/zjuuiii9Tv949bZq1WO6m6cF7AZz7zGf3Mz/yM7rnnHr3yla/UxRdfrNXVVUnbAObgwYNZz/bHP/5x3XjjjSf17pJOHZUyRIXfShlSypBTQWVEo6SSSjpj6ZprrtFNN92kG264IYWuT4bOO+88SdI//uM/6lGPelTht7W1Nd199917eurOPfdcXXrppfrkJz+pn/7pnx66SPNpT3uaZmdndcstt+h1r3tdVqn9zu/8jiTp+c9//tD3AZCuv/56vfzlLz9u+6RtEHDw4EG94x3v2PXb7bffrg9+8IP69V//9azylqSf//mfL2zxeTwv6F7e22H0e7/3e3rGM56h3/qt3ypcX1pa0v79+x90eQ+WLrnkEl1yySX6pV/6Jd1yyy162ctepltvvVU/+ZM/qUc/+tH6xCc+oUsvvXRoH0En0/aSvjVUypBShpxKOh1lSBnRKKmkks5YQlnecccd+pu/+ZuTLudf/st/qbGxMb3zne/c5fV617vepa2tLT33uc/ds4w3vvGNev3rX6+f/dmfHXpPvV7Xtddeq3/8x3/M7iLy0Y9+VO95z3v07Gc/W5dccsme77vmmms0Ozt7Ql6wTqej22+/Xc9//vP14he/eNffz/zMz6jVau25t/4Tn/hEPfOZz0x/xzvwq9FoaHl5+bh1c6rVamnnHei2227TPffc86DKebC0uLi4671PfvKTJUnr6+uSpJe85CXq9Xp6wxvesOv5ra0tLS0tpe+NRqPwvaRvXyplSClDTgWdzjKkjGiUVFJJZzSRZ/3FL35RjUZj1+/Ly8t63/vel30W79rBgwf1i7/4i7ruuuv09Kc/XS984QtVr9f16U9/Wu9///v1rGc9Sy94wQv2rMdll12myy677Lj1fc1rXqMvfOEL+uVf/mV95jOf0Y/92I9pcnJSn/rUp/S+971PT3jCE/Tbv/3bxy1nZmZGV1999QmlPnzoQx9Sq9XSC1/4wuzvl1xyiQ4cOKDDhw/rpS996XHLOxG6+OKL9bu/+7v6uZ/7OX3v936vms3mcfvw+c9/vm688Ub9xE/8hP75P//n+tu//VsdPnxYF1544Smp0zD67d/+bd188836kR/5ET360Y9Wq9XSb/7mb2p6elo/9EM/JGl7fK+66iq9+c1v1t/8zd/oWc96lkZHR/WVr3xFt912m97+9rfrxS9+cWr7O9/5Tr3xjW/UYx7zGB08eDCd6VDStx+VMqSUIQ+VTmsZ8q3d9Kqkkkoq6aERW1PutS3gYDB8u8XBYGdryQezNWVOfL7vfe8bXHLJJYNGozEYHx8fPP7xjx/ccMMNg263W7gvtx1kjnLbZQ4Gg0Gv1xu8+93vHlx66aWD6enpwcTExOCiiy4a3HDDDYPV1dVd9/vWlE6Li4uDmZmZ425N+YIXvGAwMTExaLfbQ+v6ile8YjA6Ojo4evTonm06UVpdXR1ceeWVg9nZ2YGktE3lXuPY7XYH//E//sfBIx/5yMHk5OTg0ksvHXzmM58ZXHbZZdmtQePWlLm+hjf2os9//vODK664YnDuuecOxsfHBwcPHhw8//nPH3z2s5/dde+73vWuwcUXXzyYnJwcTE1NDZ70pCcNfv7nf37wzW9+M91z3333DZ73vOcNpqamBpK+bbapPF2plCGlDIFKGXLqqTIYhFhNSSWVVFJJJZVUUkkllVTSQ6RyjUZJJZVUUkkllVRSSSWVdMqpNDRKKqmkkkoqqaSSSiqppFNOpaFR0ncUff3rX1elUtF73vOeb3VVSiqppJJKKqmkkkrag76lhsZ73vMeVSqV9DcxMaHHPvax+pmf+RkdOXIk3ffJT34y3fO5z31uVzmveMUr1Gw2C9cuv/zyQtn+9/jHPz7dd/3116tSqejo0aPZOn73d3+3Lr/88vQdoFupVPTGN74x+8zLXvYyVSqVXXWSpMFgoPe+9716+tOfrtnZWdXrdT3pSU/SjTfeqHa7vet+2pHbKYG6/Oqv/mq6Rl/93u/9XrZuN998syqVip761Kdmfz8VdMstt+imm2562Mr/dqabb765NIJKKqmkkkoqqaSS9G2yve2NN96oCy64QN1uV5/61Kf0zne+U7//+7+vL3/5y6rX64V7r7/+en34wx8+oXLPOeccvfnNb951fWZm5iHXeWJiQu9///t13XXXFa63223dcccd2RM8e72errzySn3gAx/QD/zAD+j6669XvV7Xn//5n+uGG27Qbbfdpk984hM6dOjQrmc/8pGP6HOf+9xx940+Hh0+fFjnn3++/uqv/kpf/epX9ZjHPOYhlZejW265RV/+8pd1zTXXnPKyzzvvPHU6HY2Ojp7ysk8F3Xzzzdq/f79e8YpXfKurUlJJJZVUUkkllfQtpW8LQ+O5z32u/tk/+2eSpJ/8yZ/U/Py8fu3Xfk133HGHrrjiinTfk5/8ZH3kIx/R5z//eT3lKU85brkzMzOFUyRPJf3QD/2Qbr/9dn3xi1/U93zP96Trd9xxhzY2NvSc5zxHf/Inf1J45ld+5Vf0gQ98QNdee63e8pa3pOv/7t/9O73kJS/Ri170Ir3iFa/QH/zBHxSeO/fcc9VqtXTDDTfseZjN8ejOO+/Upz/9ad1+++266qqrdPjwYb3+9a8/6fJOBXW7XY2NjalaPbHgGpGvkkoqqaSSSiqppJK+venbco0Gh4rceeedhes/+7M/q7m5OV1//fXfgloV6fu///t1wQUX6JZbbilcP3z4sJ7znOdo3759heudTkdvectb9NjHPjYbZXnBC16gl7/85frDP/xD/eVf/mXht6mpKb361a/Whz/8YX3+858/6TofPnxYc3Nzet7znqcXv/jFOnz48EmXNYwuv/xyffSjH9Vdd92VUszOP/98STtpXbfeequuu+46nX322arX61pZWdHCwoKuvfZaPelJT1Kz2dT09LSe+9zn6otf/GKh/NwaDVLn7rnnHr3oRS9Ss9nUgQMHdO2116rX6x23zp/97Gf17Gc/W/v379fk5KQuuOACvfKVryzc0+/3ddNNN+miiy7SxMSEDh06pKuuukqLi4vpnvPPP1//63/9L/3Zn/1Zarun3ZVUUkkllVRSSSWdSfRtEdGI9LWvfU2SND8/X7g+PT2tV7/61frFX/zFE4pq9Hq97NqLycnJ7OmdD5auuOIKve9979N/+S//Ja3z+KM/+iO9973v1R/+4R8W7v3Upz6lxcVFXX311RoZyXf7v/23/1bvfve79ZGPfESXXHJJ4TdOHr3++utPOqpx+PBh/eiP/qjGxsZ0xRVX6J3vfKf++q//Wt/7vd97UuXl6HWve52Wl5f1jW98Q29729skaddalTe84Q0aGxvTtddeq/X1dY2Njenv/u7v9D//5//Uv/pX/0oXXHCBjhw5ot/4jd/QZZddpr/7u7/TWWedted7e72env3sZ+upT32qfvVXf1Wf+MQn9Na3vlWPfvSj9dM//dNDn7v//vv1rGc9SwcOHNBrXvMazc7O6utf/7puv/32wn1XXXWV3vOe9+gnfuIn9B/+w3/QnXfeqf/23/6bvvCFL+gv/uIvNDo6qptuukk/+7M/q2azqde97nWSlE2DK2k4LS0tpc8c8TMYDDQYDFSpVNJnadv46/V6Wl9f1/Lyso4dO6Z77rlHd911l+677z4tLS1pbW1N/X6/sA6sVqtpdHRU3W5XW1tb2tjYkCRVq1WNjo5qdHRUIyMjGh0d1fj4uEZGRlStVjU2NqaNjY1Ul+npadXrdY2NjWlkZCSVW6vVVKlUVK1WValUVKvVUvnValWDwSBd87Vj3MN17h0fH1elUtHW1pYqlUqqH/Xd2tpKdfJ3j4yMpHIps9frqd/vS9qeMzwbj1OK32u1mqrVaiqf8sbHxzU6OqqNjY1Udr/f12AwUL/f1/r6ujY2NtLY8Sz3UFcvn2epJ/WjTvQXY7+6upre0+v1Cu/Y2trS+vp6el+1Wk3XqW+v11Ov19Pm5qZ6vV56l7eD57w+uboNBgONjY2p0WikesJ/jBnlbG1tqdvtpn7wNjvBR4PBINVPkjY2NlIdNzc3U7s2Nze1ubmpbre7K6p+utLFF1+sY8eOaXNzM/HR1taWtra2NDIykuYt5LzKespms5mi6/Q3xPfIC5I0MjKSdHqUT87zIyMj6fetra1UlpcNRV5w3o/k8yjO0UjIl2q1qq2trUKd+Q0+Rf7wXpdNufJjW1xO0SbkUrVa1cTEhMbGxjQ2NpbkLvXa2NhI/53ve72exsfHtbm5qbW1NXW73TSWsa78bzQaSYaSck2f8U5kob+3Wq2m78wtnqUttM3HGf7IjS11xAHq8mcwGGh9fb0wTv1+XysrK3rggQc0GAySnvH2uTx2vo08QT3QC7x7a2sr6TD6cWRkpFAv5lKtVtOxY8eyvLUXfVsYGsvLyzp69Ki63a7+4i/+QjfeeKMmJyf1/Oc/f9e9/+E//Ae97W1v0w033KA77rhjz3L/4R/+QQcOHNh1/aqrrtKv//qvP+R6X3nllXrTm96kv/iLv9DTnvY0feADH9DExIRe+MIX7jI0/u7v/k6SCmlWkfjt7//+73f9Nj09rWuuuUavf/3rTzh1zOlzn/uc/uEf/kH/9b/+V0nS0572NJ1zzjk6fPjwKTU0fvAHf1Bnn322FhcXh6atdbtdffazn9Xk5GS69qQnPUn/+3//70IK1Y//+I/r8Y9/vH7rt35Lv/ALv7Dne7vdrl760pem+171qlfpKU95in7rt35rT0Pj05/+tBYXF/VHf/RHKX1PUmGh/6c+9Sn99//+33X48GFdeeWV6foznvEMPec5z9Ftt92mK6+8Ui960Yt03XXXaf/+/Q9byt7pTghNF5YuxP2eCOZHRkbUbDZ18OBB1Wo1NZvNZEyg4DAe6vW6ms2mJiYm1O12tbCwoPvuu09ra2sJOIyMjKjRaCRliALf3NzU6OioxsbGkiECkHQlgDEh7SjhvYwKPvs9AGqUNoDAAc3IyEhBCTo5KAK8oExy4GVY+dTJ24fidwMQ5eVl12q1gqKt1WppvHyMJQ01fBw89Xo9dTodra+vp/+xHx1Ejo2NFerkoMqBAG3z53mft9GVdOTNRqOhqamppJx7vV5qa61WU6/XS/3HtU6nU+gn6sCYcr+PoySNjo4mA8PbA6gYGxvTmUKATfrJQXLOGGT8xsbGksPA+dHnXwRuDiKd5+KzECAaHqaOm5ubWl9fT6CT35kjHo2PYw/FegxLQXb+kFRwTgwzHOI89H51OR1ls8tjB69u0ExMTCRHiSStr6+r2+0m4L+5uZkM6c3NzcLz9MHGxobGxsbSPIl9E50jlFur1ZLhkRtD6ouBQf+hA5A/UVe5bKAcZLb3J/2DLIh9juOL905PT2swGOjYsWMFOY+8cAMjGhvUxduKTJK21xp3Oh11Oh3VarVkaHvdMBofyrrYbwtD45nPfGbh+3nnnafDhw/r7LPP3nXvzMxMAtxf+MIX9E//6T8dWu7555+v3/zN39x1/ZxzznnolZZ00UUX6Z/8k3+i97///Xra056mW265RT/8wz+8awG7JLVaLUnbaVDDiN9WVlayv1999dW66aabTsjIinT48GEdOnRIz3jGMyRtM95LX/pSve9979Nb3/rWAih6uOnlL395wciQpPHx8fS51+tpaWlJzWZTj3vc4044XexVr3pV4fsP/MAP6L3vfe+ez8zOzkraXmz/Pd/zPdnJdNttt2lmZkY/+IM/WIiQXXzxxWo2m/rTP/3TggFS0qmhnFdR0q5rKJPx8fFkROB9abVaWl9fTwoO8OWRjHq9roMHD+rCCy/Uvffeq7vuuisZJa4wUbIYHigeFJmDcAcq0m5Dw+fbMGUfn3Fl4mCfqEb0bkWgEA0L91rmDA8MHO7x8iE3DDwagELDwAOkeHSHd0g7yt3H1PtG2nYmbGxspMiDA27KySlcfqNujBPevNHR0QJAd0+0G2cOZCLorNfrmpqaUr1e18bGhtbX1xNveP/zDkmJt9wgcSBFvzkv+Gc8j5C303n9TKDNzU1JRTAHxegY3xuNhubm5jQyMqKJiQk94QlPUK1W09LSku677z51u11JeUOc68OiB/A9xqRHF3lueno6ySGANu3AEHXay/ni781FxZyPHHw6mOR399jzm0c5nLjuzoher5faHfsOkI5hTx+PjIwkTLC2tpai1Mg1l639fl/tdjvxuxtkPiYuZ/kPkB8bG0trPTHWY18Pi5TkZKHzRa7fnaKjyWUNcx4DgnJnZmZUq9W0uLiYZAtlREPJ65SLbhAZd4MCZ8z6+voumc3nYQbpidC3haHxjne8Q4997GM1MjKiQ4cO6XGPe9xQy1wqphHtBbgbjcYuI+ZkaK/OvfLKK/XWt75Vr371q/XpT39ar33ta7P3YURgcOToeMZINLLm5uZOqP69Xk+33nqrnvGMZxTWvTz1qU/VW9/6Vv3xH/+xnvWsZw19fnl5WZ1OJ30fGxvbtQblwdAFF1yw61q/39fb3/523XzzzbrzzjsLwiOm0OVoYmJiV/Rqbm6usIYiR5dddpl+7Md+TDfccIPe9ra36fLLL9eLXvQiXXnllcn4+cpXvqLl5WUdPHgwW8b9999/3PqVdGIUhbcLt6honQB1KJB2u61KZTu1ByHqxoErQpRKo9HQd33Xd6nVaml1dVWSkiKMwCGWgxCPcisqPurqbY3eRq65AqcMFLQbA+455Fk3wLyN7nGLhkUuGjI6OpqUjF/3tCveRbjd76NcB/ySdtXBDR43gEhpIkUiGlp49SnDvYlu7HjKAoDevcv899Sr2BfeFsDA5OSk5ubm1Gg0tLW1pU6nk8qO3tzIHxgek5OTBS+mAwQ8mM5L/ixtdeODMT+R9WmnCzlvM34ekYpRvNnZWU1NTaX5deDAAf3n//yf9cEPflC9Xk/f/d3frV/7tV/T6upqdl5IOxGIaBRjAHoUNIK0fr+vjY2NQtTUvcvRMMgZ+bQtZ1wMM0QAs5FyBkSUbdFw4LMbGdI2cPc0MUkp9YbfV1dXVa1W1Ww2NTMzU5BzjCXRCh/TWq2mdrutdruter2uXq+3KzXTy4pyOfJGTCuKspV7vYwoD48HwN2w8/HxZ9354fPaqdFoaGRkRIuLi4kveS5Gp9yoje8nsoMzjd9wTLhM3tzcTOXtFTE7Hn1bGBrf933fV0hbOR4BuK+//np94QtfeEjvxqp1IO20tra25y5HV1xxhf7zf/7P+qmf+inNz88PBexPeMITJElf+tKX9KIXvSh7z5e+9CVJ0hOf+MSh78PIuuGGG074rIo/+ZM/0b333qtbb71Vt956667fDx8+vKehcfXVV+u3f/u30/fLLrtMn/zkJ0/o3TmK0QxJetOb3qRf+IVf0Ctf+Uq94Q1v0L59+1StVnXNNddkvQKRTjYiU6lsnznyl3/5l/rwhz+sj33sY3rlK1+pt771rfrLv/xLNZtN9ft9HTx4cOji+Vx6XkkPjaLgjqDYP7vnBxofH9fc3FxBOLpn0RWfg/GpqSkdPHgwGRqABSgCfxRVXGMQowZet6j0YlsBFdETmvOeuoeNeeJrRfhOH7pRwfdcCkX0hvmzEZCj3D1K4fm97hXMKaoY3RgMttccsN7AIww+1g6aqBP97+lO3gbudfDonlJvEyDOAYd7GqenpzU9Pa2RkZGUAuPtdK8w4+DjRJuigve+cGMlAmdPSeM++mAYMD1dyfmMPnZjk98rlYrm5uY0NTVViBhWq1WtrKzo9ttvV6/X0+Me9zg97nGP02c/+9mCh5/xdGM+Ak3GFKAeQSXlMY6kEdVqtbSGjMhdTIHh3bE+LneiDIrvd/kTjdGJiYm07op7HGDHqOX4+HhBvgB+MRgYD+QYEVhklLRthDQaDU1MTGhyclKbm5taWlpSrVZTo9FIEUycDp1OJ/UvEQ/qKikZeS5Hve/oL9Z0TUxMpPm/vr5eiHBEZ5ekEzLgGZ9oMLoR4w4Qv8dTdONvExMTmp+f19jYmFqtViE6wV/OsHV5jV5xx010uPDO0dFRra+v75KPD5a+LQyNk6FrrrkmpRGR/nIydN5550mS/vEf/1GPetSjCr+tra3p7rvv3hOEn3vuubr00kv1yU9+Uj/90z89dKH30572NM3OzuqWW27R6173uiww/p3f+R1Jyq5NgdzIevnLX37c9knbhsTBgwf1jne8Y9dvt99+uz74wQ/q13/917MGgCT9/M//fGHNwfEiKSfDjL/3e7+nZzzjGfqt3/qtwvWlpSXt37//QZf3YOmSSy7RJZdcol/6pV/SLbfcope97GW69dZb9ZM/+ZN69KMfrU984hO69NJLh/YRdCYp94eDFhcXE385OOS7tONRAgRyjyvaSqWiY8eO6ejRo2q322lR6MGDB/XoRz9aU1NTBWBOeaOjo5qfn9c3v/lNSTvC1j2UDjI8rJ9TbLQhKqyo9F3pRMAfFYkrMf7c4+5eMQfKDla9n5wAEx5RGKa0omLDk+ugLpdTHoGBRwkcYEXjxNvq/2PEyMGHe/y87bTBozARCOY8uLxzZmYmOaB8IbzXI+dp9Os+5h6R8Lp4/2FYAJK8nbF/pGI66ulOc3NzyTngvAM/M94eyYB8Ps/NzaVx/cpXvlIwbAHMUc5IO0Cf8WIxsQNs509fV8C6ATeaAfzwgKcMQjk5kXMkxPkLj7hsnZiYSAaPl0d9IX8metO9TswtnAbR4PaoMAbGzMyMKpWKZmZmNBgM0iYOGACsxyJNFtnLeyiP9QRueFP3anU7zXEw2FlDxYYheO/RF4yxy3N4ySkXUYr96zLGx8HHkr7hO3V0vpK2DamZmRk1Gg11Oh212+3UVx55iuPuhiv87Cmo0VlGeWNjY6mPTnbZwXesoeGAe68F1sejf/kv/6XGxsb0zne+U//iX/yLwoR417vepa2tLT33uc/ds4w3vvGN+tM//VO99KUvHXpPvV7Xtddeq+uuu06ve93r9F/+y38p/P7Rj35U73nPe/TsZz97145TkTCybrzxxuO2r9Pp6Pbbb9e/+lf/Si9+8Yt3/X7WWWfp/e9/vz70oQ8Nrf8Tn/jEPaMskRqNhpaXl0/4fmlHUDvddtttuueeex6WQwWhxcVFzc7OFibkk5/8ZElKi0xf8pKX6Oabb9Yb3vAGvelNbyo8v7W1pdXV1WTsNhqNws5JJT04chCaMzJiSoAbGXjB2+22jhw5osXFRbXb7bSosN/vJwPivPPOU71ezxoKU1NTmpycTKCOXVF8kbikwnMupAEH7ll0g8EpAtucsUH7ogefOtBv7uEGaETA4caKl+3v9TJzKT9897ahlHILp3mWqIe3s9/f2QHK789FMXmngzffKcqNqgjqAUvep9TDIw14xXO7AgGeJicnd42De4B9PJ1HfbcX2uxjl+s3j6A4eaqct2l8fDylte2Vpnu6ETLX5QHjCGicm5vT9PT0rnVCzI2DBw/qqquu0oUXXqjrr78+rZWMRgVjw/U4Nry33W5rc3NTU1NT6T5fj8N89nGEx6izpOTZr1QqaT2Hg3+ehe/cI50jogFra2uq1Wqq1+uF3Zhy8z3nmWdtk/c55AB/dHS0AJZ9Ptbr9bT7FEZDt9tNkR1fS4eBEetEmdSHjTpYY8b1arWaNu9gIb47NjBo1tbWUpmMh6enettyfeTRHB8b7yP4JxotvM/b5ulx7mQbGRlJhrPvvgfvOJ96P/Ee5B7XGZdopJDet7Kykt3F9UToO9bQkHbSiL74xS9mt6tdXl7W+973vuyzeOgPHjyoX/zFX9R1112npz/96XrhC1+oer2uT3/603r/+9+vZz3rWXrBC16wZz0uu+wyXXbZZcet72te8xp94Qtf0C//8i/rM5/5jH7sx35Mk5OT+tSnPqX3ve99esITnlBIURpGMzMzuvrqq3XDDTcc994PfehDarVaeuELX5j9/ZJLLtGBAwd0+PDhPQ2lB0MXX3yxfvd3f1c/93M/p+/93u9Vs9k8bh8+//nP14033qif+Imf0D//5/9cf/u3f6vDhw/rwgsvPCV1Gka//du/rZtvvlk/8iM/okc/+tFqtVr6zd/8TU1PT+uHfuiHJG2P71VXXaU3v/nN+pu/+Rs961nP0ujoqL7yla/otttu09vf/vZkxF188cV65zvfqTe+8Y16zGMeo4MHD6ZzYUo6Ps3NzWVTDXIGRs6DR9gbIUpainube71eWkzIxg2s36hWt3f5mJubK+xAhQJDGbph4lGJGNXw+kXl7QLdQYy3x739MV0mes1QoJIKKQ651IGo/Lyvc/XwvGFPncoZRu7dl3YiRl5mTMMA1PuiewCze/bxMDKOvljdd3SiTbzDIxc+Xp6W5DziizJpn2/B6Tw1Nja263lX8F4nttuNO0q5oRHTZLgOwIkRGL/XU2rOpIgGa2Mgxh2PMEZGzDjw+Xfffffpl3/5l3XllVfqR37kR3TzzTfvkjtQNLad1z1Viy1Yp6am1Gg0kuPCx8l5141SvmNMVyqV5P3H4ACER2NHKnrMkW8TExNZRwl9louSeHupt8+FOO/dWPe55n1EOez2xW8bGxtpMXjOAUJfeVqRz3X63xc5exqp9xXle8TX10q5se+Gncv9nBPAn8+NRe65yGeuH+jbWK47cHyt19raWjJy6QeXWYxBfD+/42Sh3K2tLY2Pj6vZbJ608+I72tCYnZ3VNddcMxRwf+Mb39CP//iPZ3/zVKDXve51Ov/88/Xf/tt/04033qitrS1dcMEFuuGGG/T//X//31DPwIOlWq2mD3zgA/qd3/kd/ff//t/1C7/wC9rY2NCjH/1ovf71r9d//I//8YTP9yCqcbzIweHDhzUxMaEf/MEfzP5erVb1vOc9T4cPH9axY8dOaOH18ejf//t/r7/5m7/Ru9/9br3tbW/Teeedd1xD47Wvfa3a7bZuueUW/e7v/q6e8pSn6KMf/ahe85rXPOT67EWXXXaZ/uqv/kq33nqrjhw5opmZGX3f932fDh8+XFi0/uu//uu6+OKL9Ru/8Rt67Wtfq5GREZ1//vn6N//m3+jSSy9N9/3iL/6i7rrrLv3Kr/yKWq2WLrvsstLQeJA0zAOz1/0Ifk9twrNVrVYLC4Yh9875b9VqVRdeeKG+8Y1vFHY9QVnFdRpOUWHkvKC5371eOfAeFY6Dc2lnkWc0EJz8HdGzn3s33z1dAIMplk8fAeziu1xROph2ZYnydSDibfb3xDYC3jBOoreT/qctMb0N8BSjNLzTvbcAqZgO4oaS9yGL2T11gXflxse9jIC/3KJfN3J97ChjrzlzulHOMGesp6am0oLjYXPAP3/gAx/Qq1/9ar3whS/U7//+76fyo6Ea5zLkxqC0PTfZLWh+fj6VBR+5M4XnJiYm0m5yjUYjLXrGiMFoAHT7uijqyFzxc4Hgi5GREY2Pj+/y3iPTfJ1XbC/Px35h/gPKid7gKHCDanZ2VtPT0ym6Qr3YWc7XW3mf40zJrRcgPZZ6ubHmDgNS0TD4+Y0xIRWLfsvJbi+P8l3ORWDP3HXZRZ/knGj+2dcV+jNO1I3NT+r1urrdrlZXVwvnC1Wr1ZTWFyMa9CEynG3c4ZOJiYnsjqonQpXBMHOspJJKKun/MZFnDUWhG71ReIjb7XY6tO/YsWNaWFjQ4uJiAnjuyRkfH9dZZ52l6enpFKL3lBjA7vr6uhYWFrS2tlZQXLnFtu5pzxkIkgqGQLwHpZrz4vn9gFnfzpY/tnlFofluMJ6ylPOS5fqbe3KeN68jwAcg7wrRAUH0lHq5DqD9WQdYlOFeafegRm+qH8rnQMeVaS6CANFv9B3leJoYwIlnAXP9fj/tIMR7YgQq16cRGHkEifGg/vHsFJ7jeq/X02/8xm/set/pSI961KPS4W6ecsO6LNY7+BzwyNfU1JS+7/u+Tx/72MdUq9W0b98+XXrppfrEJz6ReMUX6Mf0okjOK857s7OzmpubS4cqQm5sIKM8SgZVqzsH7XlkEd6LZTnQRUYBNjudTlofwnWAqufp+1xk/gHY4X+XiRhabOaADPEoU7PZTMYG8wX5tbi4WHAGeNSGlMBer1dw+Hg63OjoaErJop4ekRkMBlpbWysc+Ef59IkbZr77Fu9xA8/lhzsTooyLDhDe6dEEN0i4jpxx+RXTJnMOL8at0+kkPcizbuTmnvX/OOwYo5NJn/qOjmiUVFJJpzdFD9JehCJASZCni8D3Pd97ve0TZln46QsbpW0hOz4+roMHD6Y83ghQoxFAHdyDHtMqchEMX9fh73cwE9+B4Pc+4hmMjvgeyL2EnjedIwdYTv5OX+PgCs497tHI8HLcaPP0MPdiRoMgPo8SjOALcMLz7i31dDTKArR4HdwI8Hq68YYHuFKpaG1tLW1R6tt8+rNuVOSMN/hhGKCIY8h3Ty85VZH47wSKvAcAnJubS0aGNDxC2mq19PGPfzyNPd+H3X+iBM8AjFdXV9NZP3EDAd4xNjaW+IG5x3xHjtHm0dHRtAYN3o6RMSITfroz9xIZwVGAIcPiX+d/+JRyMOzcwGBBOTtXjY+Pq9vtpkXclLG5uamFhQUNBoPkJfeUJI+M0l4cB2x7Gx0VHikiZWpiYiI5ZbzOcRthaQfQY1jQb5QV138hI9hxzqO6vubMeSFGWuHXmJrq90fZHNeH5Bw67mCamppKm5+48eqGJOUM6884fx4slYZGSSWV9G1DURhDw0LLTigmVxB+IBvEdqSrq6uamppKig8ACsjz+323GfcMuVcoeg8dZLvyd4PBP3u43ct1xeZeNe+b6DUH/B4PIHn6WFQ0OaDr7/O6+32+ZsGjNO6Zc/IthN0DSRscPA3zFrqRGNOJPErAWOJRdsNkbGxMjUYjtSf2gbfZDVfe0e/3tbKykjaR4ABJP+U4jr2DAwcBboABlHgPvOD3xcXDPHemUC4K1Gw2NTk5ucu4j1ErdwxIxXMhoqODeerjkKuDz2X38g8GAy0vLyfwurGxkQCrOy+YAxhJgG2PEEDswBQNaWThYDBI5wohEzc3NxO/+zazbO/qAJ/564ci+voqlw2dTkcbGxuFtNXJyUlVq9V0mrZv2ezRt+Xl5bRYm/OLKJ/IJnOq0+mk+nKOhstbHEUeveVQxMXFxZQyBnlKJf3muwy6M8DlEU4t0iNd/rETFHXyqDXXAPK+WYXLH+c93ufyyMvP6cVo6ETnQ9Q5bnzESAv9dDJUGhollVTSdwTt5U1xcI+SJIQegTRCv9VqaXJyMp3Z4uF6B4ARwHpI3oW9AxavaxT2XIvpV/E714YZKbwzgn4H1m7w8N5otHlOeQ5A5J7x/6QauGGDQvR96QHgEbCvr6+nVAcoZ/zEesS+9XHye+kLN17GxsZ2rZnwKBjKHzAUPZGey16tVtXtdrW8vJy2yBwZGUlpefCKb1QgqTA2nqriHnk3lJznSKHwSIb3gV87E8g9s2wS4Iu/c3NLKvKipxANozgfnRzswi8+rm4MrK2taXp6OkXhqEvc7YzrRC8mJiYSf/g20EQqZmZmUplE1ph3jUYj8VOz2Uw8Ci8RdeAdRC7o283NzTRH3aFBHeHZXq+XQHelUtHk5GRKQ+p0OqmepKeura2p19veoIO1I8gFDGbfiQqjnf71cXTZx5xDPpFGRLpYdCJ4+lWMcDPX4a/oxIEvcEJhrMQoU44HSYXNRRqI+ExNTaVIF0YRDgwiRjGV0vkndy33Pxoc0IlkFOxFpaFRUkklfVtRLqoRDQUpb3h4+sHU1NSuMHYUrK1WS81mU+Pj44WtJx3gObl3J6Y2Re+QU/S+ezujEoq7uXj5ERh5e1zZOjiO0ZXYB56jHMm9cHspG4AOKQYevQBc5faEdwXt7XYPfhw77yuPEji5IecgwtvpqU+xj6k/kQJXxIwZz29sbKTDs7hGCgn3kq4H+KKvcu2Lnnn3IHtqh6fDRGPSyzkTyKNB0va2qXjUpd2ph7FvoqOC5zxSFO/PRfn8d8j5j2c6nU4C+75RBZ8d3Mc21Ov1tJYCYwN+wJtO5IPdm3CO+Pbc8ZBNeJk/j57AY0QPeN6jLTH/3417T89CDvhCdYjfeAdrKarVqiYmJoZurOARA+aIp5sh29fX1zUyMpLWfeRkatQBbozAG7wPg8X7H/BP9AgDjGdjxIByI/V6PTWbTR04cCC1hXGALxqNhqamplK7V1dXtba2llLEhjmJ/l/LhtLQKKmkkr4jaBjY5DNAEU8dKVKAhYMHD6rZbCYv2crKilZXV9VqtdLajqggKdc9x8OAh1TcjcSVVC5S4Z5LB6Bx0Xb0zvNsDMXHHGLId4VxEIpy8voT2aFclLTX28cgRnscoKMY8XK6p5G2k9rmfRfHl/73d7tx4P0Y6+b1i4oXAygCCfcQe4424CumeZGGgScZEOh1xGsJ4Z2NdfXIBeMMX9CP/n7v95i2kzMcT1dy7zRGhq91iPdCMRIYt7+NRkkOEDqfSjugPZemwnwDzHOGRY6f3QkBcT4HW+Y2m03VarUELj3lqFqtpoguazFcjvFbp9NJAJUIpDsf3HlD/aSdhc8eJYDvXU75Ym6PcDC/2JaVe1z+EMmhP4lQECXwMzJcB9CvpKZh2GDYoQMwvFh8TztoJ89HOY3MQte4TCHa0O1203ePPub0h8sg2l+r1XTw4EFNT09rdXU11ZnxIwXO9cHk5GQ6XT2uQ/GxzBnbkMvMnNPsZKg0NEoqqaTThiqVSjo/IHqzn/jEJ2pqakrf+MY3VKlUdNFFF2lxcVH/+3//74KidI+ypyK5Qsu9Vyp6Oh0IRoCPBxXiuytLLzsaGwAi2sg1ngWYRhDkZTmodaXjKQK5sH/0kHkfkR7kCpP7fLcc71+Utqdg8Yz3T66/XXn6fa5cpZ3Tjd3gccPHvbRe3wj6SK3iGpEMjNS4EN0NKurnIMv7P3o73bCiXOoIX9N+3/o0ppWcCYShBfDz/PzjkR/IFudFpBzvx9/dWImOEYxG1hz4ZhSRD/DM+3WiAqQXLS4uJp7yzRCkbZ6fmJhIkV2PnLBl7tLSkpaWltLaijhv3DDBUIHPqSdGDpEC73v6gkgIdaxUKuk0cto0NjaWIjC828/8oP6kxbrjxA0bNvngna4D2PbVI1hR5uBQALwTNcRAoQ7j4+NqNBqanJxUs9lM85x7SHWTts90y601c+PTeWYwGGh+fl6zs7OF6NTY2FiKvrMVMISRCU1PT6vRaCQDFMMHXhxmbER5GnniZKg0NEoqqaRvGzpRcCDtzvVHeBK+lpQUCQDgq1/9qr785S9rdHRU+/fv12WXXaZ/+Id/UKVSSbuHsAjQ68J3B9UOBl2BRCDv7XLw6IaLe8xif0QvVPRkOzCK73cvOfd7aoj3W8zJBRT5dy/X+wVg7sDYIxluaNEWT53w6Eoc173C/DGqxTu83bEM73/ve56L6VQ8z/14P1mQ6n1A+f5OBxIYGDEHPKbHeP29XTlgyxiRnsZ7Tnbh5ncieTQDIJqLZkhF/opGhpSXQXG+xKinPxd5JxL3+aF08KHvkgdw5d1swY2nnjMSANecFcLY9/v9AthcWVlJRsbm5qYWFxd19OjRAgCN/cR1+sh3g6pUKmkOMIcB5QBsdvcjGuKyjHGDh2dnZ7WyslLYmIN73chyLzvlMQ/9lHF+cyPC06kw5DDakI3w0ObmplZWVgqHQfKfyEq73db09LR6vZ4ajcauE2QG8ysAAQAASURBVM5HR0f1iEc8IhmFe8ky55V6va6zzjorOTOIVmDQrK6uFvowx2edTie1f3p6WtPT02lLX4wmX0MU+ZPP9PlDSbcqDY2SSirp24aiwNwrxOuEEkMZkhPvKTV8Zk0GSr7Vaml2drYAmAlJe5QhJ4Cpo4ON6GX3Z+J/V6a50LqDVl8vAKDwE69j/fy794UDIu/vXCQhRxFgufKPxpm0c5CWR3N4LmeoeYTB1zVEZZ/r22g4eR0rleLBgdFT5/3jqU4R3OO5Jd/bDRtAnqdgAPrwOkfPeTQ43KvtgAl+jOAWgOrgIWeUnM4EMJ2cnCycAyHlNxFgrDyyFH93Phvm+d3LOIl8Gg16XxMmFZ0iHg1jvQHe/VarVTiDQ9o2Wo4eParZ2Vnt27cv8RvrGkZHRzU7O6v19fWUKnX06NG0jS1tjFFWfmPtlfMii5Dd6cFnFm/3er20xgJQ77u1Ec0kIjI1NVU498ijqqQEcc0jzr49b7PZ3OW4iMYcBpo7ELgXOVCr1TQ1NZV2ueIZl/eDwaCQtsZWsvQZcuDss8/WyMiIjh49usuR4vzS6/U0OTmpRz3qUcn5xUYZjM3Ro0e1urqadBVOCz/Dx/mX/pK2ZVez2SxEOHKGSuRV16MnQydsaDAYk5OTKTRJ7qorPRrtljAVxYPVaDQKO3JQPgyANxKh7WV4XRAS0s4WiVNTUylnjlQI3uNKgO+Vys52ZGtra4nJR0dHdejQIZ1zzjnphM5Op5PyI9fW1tRqtdRut5PnDkaP3rFms5mUVrfbLTA9E9CZO6YsuJBjkdH4+PgupUl5kYHIERwfH9fU1FQ6kVPaCWm6UiYEh2Dxg8Cc2chD9DAk18nf5N0TExO6/PLL9f3f//06cuSIJKnZbGpiYkL333+/vvSlL2lpaSnt2uAeHfLs3QtIPqWDgQh2XPh7Sgf9Nczr6otA2dnB0x/gIakYYqZ/EKaMGwKKtnS73eT5YcKzEwl7fR8PXD+UMOa3M0VhFvshAsphoJn5FcEzc3J0dFRzc3Pp4KqDBw8WgKaDPwS6e8jiwuUIhr2O/n6PjESjJIKVmObj97nnOrYvKkP60T3jDm78Ny/L2xA95DlAkksL8FC9g2SXf3sZSNQ1ejFzxhzPxv5yj12sE3zj/eeLZWPEgTr5+/yeKJtcLnnkI4LUHPDI1d373OsyOTmZdEucC2cCcQicL3Aexi/oFtdbOV0LJsnJFaeckeHvjWXHueqpVpOTkwWQTIpQvV7X2tqaVldXd50h4e3Ca37hhRdqampKy8vLBSC+vLys1dXVtMUr9XMs5I4A/gPeiRZTl9h+n1Obm5uFtQ9gibm5ucIWwr67lb+XPkPe5hw03keMjac9eZkuSx2vRceHL4QHwLNNNU4oHwPauri4qHa7rY2NDc3OzhZ2CJOkAwcOqF6v64EHHth1hoe0s1vaoUOHUhqZb8e9sbGhpaUlHT16tLDDlOsDDFLWykSjA55mnNnZy/WRG2FQTg4+GHpQEQ0Ubs6zFBVEjll5Hivaw5vkoPmuGnS8C2l/L0DYmdZ3AeBeOhtl6KCYaxMTE2o2m5K2J/vU1JTm5+fVbDa1ubmppaUljY6OampqSo1GQ/V6XXNzcwk4EmKLiyt9wWalUkm5kb4NIv3HpJ+cnCws7hsMBoUwfexbLwcjzZWtL7qamppSvV5PTAYjw6C+9Ztvbed1iYeBeVu5DyCOofXd3/3deupTn6rBYJBCxiwCo996vV767F4d7x/qE9MEqBt96MrcDRcHLHgt2Ycc/vJtLVlQ5e+LXhAvM3rg3auyV/jRd+R5KCHK73Qi3JyLZAzzAklFb3QE/q6ULrjggiTIB4OBPvShD2nfvn1JzrgccaUZQSHj5afTDjN6vP5xkWgOfDD3c8aU12kvIzt6tiIY8utuYOfAOxSjD5TjPOtjBohy7yNlRgMjlkd90AG+YNzbSju4PszzTH+4UeUAlHna7/eTE4Z7I8D39QA+fvCae5sd6LiecopyKQJj9xj7eS5uvCFPcXxVKpXCdsFnAnFmBmMk5beBpt8wSnLgiXkYDbe9jOIoJ5xHochTAFl+Y6ci5svo6KgmJyeTHoq7MuVoMBio2+2q2Wxqeno6AfnV1VXVarXk2GLDDPiSugGOwRLMYTZuoL7srDZsByf0tJ8F0u/31e12E99ygKFjIvoPA8nTe3wcfJE25JjS5RqyzR15Lhd41h2P7jRnPNhkgPqCWaFOp6NvfvObWl9f16FDh5KTlvImJiZ01llnqd1uq9VqpcX3ktIBsWNjY2q32+kdvBtDMvaT6w42phgZGUk4z41p5/VarbZrW1x0mu/SFfn+ZOhBp05FJTrMcxInA+Afpd/tdlMOmQsA32HAPZLSbi+OexTdKqdMLFJXRN7ZRGQoB8HjjM0Wcgh5hDfMQD04gZJoB4DZAQx9h/Bw5Vyt7pymya4ZlENEwBnBLXw/sIfj5j2dwiMnrsilbeE8OzubLHbGpNVqpbIZNwfN1NsngQsrgNz8/LwuuOCCZJRJ0tLSkmZmZlSpVLSysqJWq6VWq6Xl5eXUn7Xazuml8A596Ifj+OJN6ugKGj6IAoxIXA7MevoNvLi4uJisf0/ncB73MLzz6LDoVM5gP9Pp6NGjOvvss3cpASiCMb9Ov8ODnquMHFldXdXf//3f68lPfrJarZYOHDigRqORypR2xjECcQfK8BsGuaTCnPQ578a+zx33luaiaxEkRSDjwNp5id/gPX+Gd/v3HIiChkUAomz3/952j1p4lMiNda+Hl+VbaaIXfL7784AQPH9xbjk/RQPF+8h1hRux6AY33nDMxLnt6WxehgMj78fYvz4WcZzdiPa5gROMqEatVkv66UygJzzhCQUP+zAwDk9GkBrlsvOPG6c544Hr8Xs0QqSiR9hxTgTOg8EgHajX7/d17Nix9LwD22iQNhoNzc7OqtVqaTAY6BGPeITGxsZSyk6z2dTy8rI6nU7hXW6ou9cfByXkkQI3aB204hDlHgA2c9rbTwqr61XPjvE+9PN4fJG44zjWgkhKGSoejQaDQC5vfVc5DCrqFQ0Kx6kxC6HX6+m+++5Tr9fTWWedlaITyHicm+yKyAGHHBbqZ5CgN0ZHR9XpdAoROx8Tl73UEyMvlxHEc+6w4ppj4dhnj3rUo3Qy9KANjZzQy1HOaxO9Q5QHCMDy9Z0IXNF5yooLd1dWng/szB49bdwLSMc6r1QqyeNOigv522tra2kAHARjIE1MTKjRaGhmZiYx0GAwKDCp1wlmhaF9y7TR0VHNz89reXk5gf7ojYfRfUs5+h6lzp9b8eQYI0Tq9bokpYNzKB8jI3roYVwMsSisq9XtY+8f8YhHpCjRwsKCvvnNb+r888/X3Nxc6otOp6MHHnggvdtDhTH3HCCBQHKQ5+FnN368Tm64IuQqlZ1ToV1I8t4oyFBUEfT5ZxeQ7imFXIlFIX+mGx3nnHNOoV/2olxfuVzwXF48gUePHtXm5qbuvvtuPelJT9LIyIjuv//+QlnRKPVrkO9U4h5nT9P0OR7b5IDb91qHl6IHNnpWeS9exmh0RaDjDptoYPjONlzfK0Q+DETFfkLpxXZzv88T5izjFuW1RxHcWUT/0AbmOA4avycCtFwfu7PE2+q6ztvl8pwxcYeHl+t9Okxv5saZ+93YcUIeIctXVlaSIXSm0F133aVDhw4V+FzavYDeAT335oyMiBmkHQcEz+QMmtw8dOIZ19/MA/S+tLP4utls6r777kvvj5sPePvGxsZSOijvx3HI6dxEMsBJlOttdQcHdaHu4CqiLmAI+N3PAaFfWDQ9NTWV5hHPgo3cKUTbJicnC447N7KR54PBQFNTU7sWo7OGIaZF+3jzXgwk3/EKY2diYiIdmEcEw4G5byZA5gNzfWFhQaOjozp48GDhxHYyN9bX1zU+Pq79+/cXMCl1dCdZt9stnNXj8s8d45J2bZfMWLtDxp0sOd6lDURkqNfdd9+tk6EHLYmihwVyYOfASxquUJhkDGqz2UxWnQsH7mWS+UDH9/tARE9yrBeD4Aes+ASRpHvvvVe9Xk+HDh1KYa52u73LwkVouBDBE4AlT/+whsHbSEQDCxwDiEnM2hVACR4J39ua9nuOHQp4fX29AMxrtVrafeKBBx5IO1nAbA7Q3Ehxz4MrZiYbi844+ZNJv7W1pfvvv1/79u3TWWedlSbi17/+dR05ciSt9aBcJqXXxY01VwbufYbX3Evphif3YOkz6Xg3/SsVvaGutCMQ5F4HJHF+UFf/3Sd9zrN2JlOuH6KX2ik3tx20+TPr6+taWFjQV7/6VT3mMY/R+vq6lpeXdwEzNzi8Xni4XWFFoEl0kmfgV1/QTb5vBCexzfCVexF9nri8jB5TynAjKBorObANUf4wj6+3je8+J3w++TjhWKF8BxrUi/uik8rL8PGJfcDe83H8aaP/j4YfssHBZUxn9b6Mstz5JTofIrkMiOPs4zHM0UEZkjQzM5MAz17G4ulGZBrkeNh5F90cDRKpGFHz8XWPsKfy+Lq/OK+cP6NTCrkAX0g7J9O7zADD4BT0dGSvL+Xs378/gUIWEAOe3Uiv1+uanp4u8FJOltFXniqNMeG6lTlMyqGkwna6ngpP/8HnGCxRZ+Pl91RB5mOM1JJpgCwl5Tk6HzwllbJ6vV5a0+rzkHJWVla0vLys8fFx1ev1FP32dbAelXIjqFKpaHFxUWNjY2kBNv2II5fnwF9xHKjjyspKAc86j+XwuPMj+DPqN4wy53/nV5fFjoVOhk46dSp6nfaqRPRgRUEwPj6uZrNZOKrevVuSChMFwO1M5BYhlhjvjha7TxImLblttA+jZnNzUwsLC5KUUow2NjYKuZSubBA+7nFgslIHdmPwPkTJsMPB8vJy4eRPmLFarRYsb8/j4w8vBn2HYcM7/JAgJin/+/3tHMrV1dVCnRmD6IFxzwsGBsKRMD4RgZWVFf3f//t/dcEFF6hSqejv//7vdffdd6ddNHg/Bh18wJ8bF/SJ51PnwH80/hDKLqzdWGJcCaHCO85nLow8cuT85RMVPubPx9uBWmlk7JA7CKAIDuO9Dkz5zn/30iHkjx07punpaZ177rn6u7/7u7R4MXotfazcIHQj1UP3bjC7vIQA31GG5oC0A+1YJwegMarBc86zrix8jkTy/vWUSb8//gcoUB9ARDQy+I/yQ7m6M8FlorffnVKjo6OFnXg8yiop7aPvhlzUT25c+RgM63efz95Progpw+UG/TiMnKejIUmfRKMqGjjUiS0wFxcXs2N7uhJRefrMxw6Cd3K/RWOZa44RpB0PtqSCt3eYZ9ivOSClLAAsTrm1tbWEFyYnJ7W6ulrAQNGhQplsbdvtdtVoNDQ3N5dSqJlvgPx6va75+Xnde++9yZHIOzyds1bb3lzHt0n1qEouIkT/0CceVeQ7RgT4iTrS1w62PYLDbzGVi3JcN8d6efpUpVJJZ1F4hkyUOy732bRleXlZMzMzmpqaStfpG+739M3BYJA2vyENDicUcxynNXjGzxzZ3NxMu3Z5m6JTZZhujH3g191JG+UObfLI7EPBJw9pjYYzfM6rFBW+K2CusbiGP4Q0jEEZbo1yn4NTB6MwHqDRO887Gy8fYaX19fXCRPIDV7rdru69914dO3Ys7XABw/iuCTGMhaXtE4p2ILAA5p1OR/V6vZAeFBcDkcoTgYu3xxkEwUG/ulKkPMaCSEmr1UqT3PvT1y14qlij0VCz2Ux5hL5I1b0Jo6OjWlhY0P33369ut6v7778/CVIECe/DY4RioA3uAYlKxS145zsP/To/Me7SzqRi/IkUwT9uqEWBT93du8B3300jzpkoNKKQOBPpRNvvPByBdA44StLXvvY13XnnnWnXk2q1qnvuuUf333+/7rnnHs3MzCT5Aoh1meLgzgGpO0fcoxWNdHcIeFnRaJCKa6scrPq8z3m3omx20Osgwp/LGQ18d5AzzNPuhjLexpj26MaHb18Zd9DhHp5lTnofuBeYw7lwEnj0lQix14E+iE4IrzP18L7nfsqOhqcDIQcqbmxEB5v3uffrMF6OdYv34vmemZlJ6VNnCkUZH/tZKq6JGAZI3cjwPH36Mhoebmz4HPG54vzn+Iiog7+T3zAKyNdHZvBu102e30+ZvV5PrVarkJ5dq9XUarXS+RsrKytqt9uF+RUdXuzg6evQaFd04IK56KfZ2dnCSeXeFzgio9OEFHbmJDJomG6Mep+xJTvF8QH4gXHwqF9u3vp3r/fCwoK63a5mZmYKa0/AksgidpaqVqtqtVqq1+tpbS4GBn3K2RbgO9boDltvy6G0PmYxPd/xHXKC6xGfu4xy7BedZCdLJ2VooAw8zw+FgGfYJ5RPfgQ038kdjOFMIhKe45sTHrzDn6UzYSSAQrwfAMlk2NjY0OTkZDIg3MOxvLysb3zjGzp27Jiq1WrawWnfvn1pu1jAhLSjQBFEhNpQtITBPCzb7/fVbreTxy56yxzYuMBxwwLm8txEL8sXv/uYci9rS5y5XXFjdfvBOKRf0V4WmCMwnckXFxeTJyXnOXKPJjzhzE7d3Xvh7WZMecYP+fF2O4DxvvI8St7T6XQSYKEfnZdY8xI92bTBwWX87AqqpOPTXgrH/xD6Pu9XV1d14MCBwvwBYM7Pzyd+QIGzUNGjpw54oyHgHmiukfsb+dUNDOer6FmKMi/KA4jnY/9EA8TvdxkCuRHk15w/o0HPtcj7rtyJUkYPfSzTy8hFclw2eLTX11G5I4e5Gd/Bfx8X71sH/sPqKe2kZcbr3mc5IOtGmuu3aES4rInGnfOLO9ImJiY0OzurBx54QGcKRZ7MOTlzzk/nVx/H8fHxpO9cP0ajl7IYMwd8vg2qgzv0ME7FSqVScODhxPN0a4BszvM+PT2ter1emFvtdjvVk/UDbLzCrk/sSOVA0ueLg1TKAi8RPaGv3MlA3dj5CHk6MTGRjKLBYJAWrDMW7pDEwKNvkeVxvkoqpLy788DBNm3BmeynaLuujpGDyCvcAyaYmZlJRz74pkHuYOXvvvvu0yMf+ciUMoVspA/RGxgh6+vrCbt5RMajt7TT+yNXb+9j/w9mRT6Dv6LzzvntZOikDA2puCWfe/OGkRsZ0nYDHcxvbm4W8veip90ZJ+eBi2ktgEZp59TIuLaDyeG5eqzV2LdvX4pmtFot3XPPPWq32wXAzUEt7XZb8/PzaQLRVg51ImfWD5uh3h6FiEYbfevRHPdQokijNx0L2Q0VwDSGAJ4HF7I5zwoCwg3HKLR9Zwcmc6vVSqdY0tZjx45pcXExRRI8Z9GVJSDCcyzpBxc+tMc9KYCPKIzdOw1PSCrsNx2fYUtBz4N3/vMwvOfsu7By74BPYvd4edj6TDc4oofK6XgeWhQ4niIUVBSWPpb+u4N7xmKYYcGYUy+Xfcx9P4HVnSHOhzmjIfYHdchFCSjL6+U8FfvO5bQbIf4Xn/FrDiZc1lFmzLnOtdHb6srQ2+jvc6Dn9yIzmfsAIDdC2FgijlFsWwQY/nuuvhGIxP6L44knFXLHh+u16An38feyo6xyvYrhzCYiZwKBEZyiwes6jN+jkTEY7Oz25LzHHPMIB797FgE86ZEzwK2DZuQOh+n5XHY9yk6QriMdR4FV8KC7vq/ValpbW9PS0lLiCxYzV6vVdK5Wq9Uq8BOg3nfRo49cv7oDxvGEzyXeMxgM0rEAfohgPHiQdyA/omzOjS397Otv3Ahkx07qi6HlmShQdPDEueVzeGtrK+2Uyc5b0TFAqhjpVPfcc4/OOeecJJegkZERNRqNlLbOjnGkpfN8zKxwinI/x//0C8cykFbvcs/lrMupnDFzonRS21K4ZeNKxgc7BwqiMnYPzvr6enaw3JPtYXyPpjhTRIHvlraDSQYqMk+/v33kO9ENBpjniHi4cu90Ojpy5IgajUaK0ODFQ8BgDLnyde8pDMBAM9GZENFAIy0spmxJO0yLt8L3avbDbZyYgJ6yBpPDoJThOZW+oNPLZ4xYT7K4uJgWZyJoESoYHj7WvsaG9sWcULfSo0Dw/nSlgBKnjqR6xbUu3mfc7ykdUVk5v8GHzq+ueLwNOW/amUw50J4zvqJ3xb2NDi4p0ynn1fRyIMbMQYIDesbTI2Pr6+vp4EVpx0CJCjvOP1cKlO1pmP7fy/G24NnzFMl4P2W6k8PBNsqROruR74rfjQwv32UzgMXBE3VFdjkA9HfkjDF+Q+7U6/UE6sj7Jv3AFaOD+chPERhEHvD+4v5oDLhcoByXO8g597x623JGioM15zWnKAdp4+bmpiYnJ3XgwAGdqeSRIcC/g3lpt+zFyJiZmckaI9GozoFqxxfufHL55OPlDgyeQ4dyMB7PR6OyUqnowIED6WA+jC1wx8LCghYWFtTr9TQ7O6vBoHj+VLPZTIcJk6KDLvfUUXe6uEGMDmXHyEajsWvTlEqlUti2n0MVe71eOkeLfnWHG2PWaDQK6xY88udj7VgQR6s7s9HvHq32tDDfmpdyvA0RT7g84+DDiYmJlLbmBgFZFTiz77333rTtbXROrq2tqd1up3eASVhQzm5alO/rTCgH2e786lGQtbW1wu5ZtC/qmJxR8f/M0Mh5pXwhC430lCDudyAp7fZY+up/rFDWI6BMSOtxkAlF4bBXRzGQDgj9M9EHrsEwnpfoHoZer5dy9yYmJjQ1NVWISjDohEvdiqQPXUGisAHYAHYWiEfhBJPgQcf69fUO7OLgig5mZQLDfI1GIxk0vhezL7av1+sF4UAf8vvGxoaWl5cLuYjOB5VKJYWHoyczGl7ueXbh1O/3C6ek40FxI9aFoo8F/cMBRpTroMKNKPgQEBUBQwQHLgii4eGGRgQfZzJFAD6McuAM8pS7KDSjIcOzsf/5HsGAe3d4FwYrEc5oWPDeWE9XWNHwjEaQtzeCFz7D68gSyOuc41e/L5IbPfCzt9/72OuHQeAAhXpvbW0lZQqgiGW7AvY5TNomjgjfvc/naXxn7CPXY8OiiMPmJv3uCt3HKs5v/vt6lGhMR5mcc7bxm4+lAy8vZ3NzU1NTU9l2nY6EfHW+l3b3ZfzNx5UdE3NzF/7DMRbLiAaAOyCiYy+ON3ORewGgHOjmcsedafV6XQcPHkxrAbxeCwsLeuCBBxJ47na7yQmKQ0TaNjbgE19fiY6Lxn+1Wt11PgOYY3V1VZOTk5qcnExzmvk6MjKS/lMep537fMG4mZqaSmsgqLPvfoXezzk+/EA95p2niCEb6E/WennEx1OaGTscJz7mlAcxD9EL/ix4qd1u6+jRozp06JDq9XpKkwJbRZmKgTI2NqZ6vZ4O5eTd9Ic7Z52fILJrfA2t19sNYMr0e+LcejB0UqlTnsrj6yAcYOUGwgVx9J5RLiDcwXS/308pSlDOk+YK1dOk3JvgW9LxP9bXLXmUVq1WSxEXQK9v57axsZEWNgOeOXgPoIwwqFaraes5F44+oR1oM3E43t6Ptvc8yl6vl6xVn2hR8bti4h3cww4YbhghdKrVasobpC+dfDu60dFRra6uamFhQevr6ylsSh0wogj5OsjwNSb0sadN+YTHuxl3C2LcB4Pt81A45McFDPfxPgc0zuO8F55wxRGB4zDQRX8j7PBYQe4tLqmYg54j7/+cV9rBpwtQL5vnfUwY/+jRYk66QkMBYNhHr6bX1evlig4ZmGuXOzkicPX2RqOIueJ19TLcUPB3RWM5x48xT9zr5X0lqRANpn5493OKDPIUQmQNXtGoN5jLLjfjvIttjP3MWEad5QZENKz28uzFFF2uOU/TDo8we91yRk4cR2Q6/93Z48DhTCDXsT426JMIkmKKSa1W0759+1IZ0RHkDicHasP62ecfoDvyIPVCb7Kb5eTkpI4cOZJ2G4JvXKeMjIzo7LPPVr/fTztGshnLkSNHtLi4WDBs/bA36s1uUqzpqdVqWllZkbRbh0bDyB1uyE3qIm1nfrBJDI5HMNTS0lLCjbOzs6k/GQe8967PHcPxeXJyMhkFbtjxGUeiR6z4nfdFXmH7WmQQKbCUkYuWYgyxFs+d7PSVpBQtY7v/xcVFTU1NpciV40JfY0M96bPJyckU6aI+8JFjG8fCHORMO/0/5HoryreHKktO+kQfHywq4gwYPdI+yegMZ1BpJ8VE2sn1ZTBdEeTe7RakM52DDO6DqANlIbCjUun3+8mLxu/V6k7+N6lWLEj0UG089K5SqRQOy3Nr2wGCG2UOQpnIbgFzn1+PSpKdDLyfHFSR7sCEQSj4vt4RsPnuX9KOxetbzrJlMWNCpIOxcbDt45PzItIvjBMWvr/PjR8iMpOTk1pZWSns4OHKx/vdIyeuSNwI4jfemQNlXI+gw8fGgdIwYHemURz3+Ju026DzvnNPTgRlrhyknbQYF6rurIjvc0Xl+6j7HI7g1suVit7uYQKfd+UMWpehsZ7wtStTrkdnD+V7uzwiEOvMd+aDG90udynfd97yurhM9fIdVKCsmb8xjTL2Ge/2uY/OcIDoOsDn4bAyvc/9Pd6u2DYiz24sQNTH0yg9PSRnaDjvQQ4+4/h4XaIj6HQn52nI1zD6ff65Uqlofn4+ebXjvTHazvMemc/97gaF34dRAI9Xqzu7IQF0l5aWJBWzFVwuHDhwoLCYHJB533336YEHHijIP3eKuONBUsp0GB0d1f79+9OBj0RSfDGytxcZ6/qLOY/jhZSn6enpQlned4yRyzJ3zoJH6Ef6Y3JysqBHKSe3TifKfv7i/HBHkjuESS/zg32HAW+cu7wrylaMkm63m1LbDh06lNI+iXywxpb2RSdWvV4vyAWvj2d0YMTEE9GjQyyni4Z9Phk6KUMjKhDP4YvCO1riDvS5Rn4tv+GViws1IZSP/3dPOZPCPXzDFCn1dkUclbkvOKR+MDULxMk9dCL3D4APIzPhBoNBOkOElDD3jI6MjBSMEUnJgxrz++Jkok2Vyk4+Iu9DuOEFQxCRPoRQIL8xAm5Pl4oGkQs18jY9ZYjTa9fW1lLeMulg9C3jENM/eBdCmpNDPfrAve5xwXPS7XZTegt1JWeV8fdoSiSfnCgkvzcCE4QOfO7C2sEG790LZJe0myIA45rTMAHqQDIaBLnvCG7ux8DGsHeHgJftkbcIYKLBGevkYJLvvNvbEfsjB5JzfeD35sCs18GNg5zciffybveu+W/RuKtWdxaUOnCiD6PRBQB0eYcuQW64ke/RyVx/unyL9/l4RUODsj0HPI5v7A+XGURbPd0Weeaecy/Do7v87o6g2IYzhdyw87QlfovYxI0MUlikYtr0MAeQzzUoN95SkdfBARi8jB2OvfHxcT3wwAO7QLAbn9PT02njGe4bGxvTvffeqyNHjhQAJHMWhyfgOYLXkZHtMz2IRHCeBylPjk3AHo5lKpWddVcY0MjITqeTtppGr1cqlRQB4D3Sjm73OYzO5LrLS49muMMuOgLQ1S5nvR88chyd4Mg1NtLx83/cqEBH+Lb8/Dk+xdnA3Od08Ha7nc5ympiYSOml8A3jydrZ8fHxhAe9zS6vc6lSe8kFl4enkk7K0KCj8IJHhYJV6zl8kXzyEWKv1+vJupd2Upi4Hw8AKUwoJpSUW9jsQcwkcIEDeZoBv3m9XBiwJRnglUNjMHSkHWWGkGLXKbzqLD5ykIrFXq1upylxIGClUil462FA937E8BZ1ZlIAcHx7W35jVw0WrlIWz0VvmTM8ShXB7IKB+iGQeIYx7ff7mpmZUaPR0OrqahI2vgOVRxq4Bm+R88mYoZSZxHEy+cFA09PTGh0dVavVSuHEpaWlAojxCRstftqBEQMhRFzgcj3Wz5WQp5ac6on9nU45o+t4RljO6HXjIAKD+Lu/O0eulFASPr5ujLiCg5d8XU8EsjE6EPsgghg3BOJ7YgTVgX5OOebaSVk548Tn415ga5jX3x0uDryknUhIfIZ6uUFOG93wiX1InXA+OOWMT94XFTa/I1/coICikeF9BOUcE71eT6urq+p2u5qdnU2eagco8Ic/G40+7y/nxTOR3LiPETM3Eubn5wuOPs/Nd2eQ4wTnC8idYj5PXPehM8AUDvIlpZRoohmOT6j32NiY5ubmCrJofHw87YxJGpVvrOOOWBxrbtRHvUadYjTF08fBKPCkp5/Sz+g8dj/r9/tpzvuZN7wfYwhM5X1Ev/M7333DCa8D44Y8cOPAiT7y80boF3cKQsgt6u/rOh0zsZmPz3H0vS9I39jY0NGjRzU3N1c496fZbKalAu54pjzGh22NcXghH3q9XjIy4KM4P6L+c7kejY2Hanw8pIiGtDu1h4kRvfuxQVzjszOddyiM62k8rphgPhfA3MvOTJ7zG1NfPNIQvSE812g00uKn8fFxNRoNVSoVra6uFgwY3z0pAnWfDC7sYFTqjgBkovHOwWCgY8eOFVK3XLn4Lk5ROLmywwBgBwPqxkKxdrudtn/ziAjrP/wAGe/XXq+XJtbW1pYajYampqbSKeQutHq9nprNZhpvyl9aWipEP7xdfMbIiN6GOCkclLjwq9Vqmp2d1dramlZWVgrrVhBa9B88QD87+Iieb6+zj4MbLbyHSJfzwfFA9JlC7Xa7cPKrU874kHaDWpcF7qWOYxZlUgT/7mlDeWBc+CJ/r4Mv/I+yDiXl74kA0dvq170tuX7JgeZohLjM9GdOxKiL90Rjw+UMn1HgEagTSfT3x/SWnCHEfS7XXAahwN35Q9+7tzInM7y8GEHwciJ/8ZzvUOdyI77Dy3aDCOfcsWPHktPNDdMIZL1P41jtZUSeKRSdX9LuCOi+ffs0NTVViEoyz/kORUOF8py3YoQvyiQ3rh3LkC5Tr9d19OjRAkD2elerVe3bty+NPZ7zzc1NHT16VGtra0lHcgq1tLM5AliHdGUcYTgi3Xjgd+8DPjvQhjCm3KnpBoIfrgxfu4POU84d/7khhoOUNvk9nv7M7zzD2tZhMhkDoN1up5PawZC+q5OffwbOBLt4qn80Tp23nJfAda1WS/fee68uuOCC5HBlUT1rMSjP08M8C4TzStgtkw1uHOdGpxBjk3OsQi6rHoqxcdJrNOKEcwODjnZLOCp4rruHaG1tTXNzc7u8NmybhtfHQSGLqKgH7xkdHdXc3FzqdDz3KCKp6KFEATrzwvjNZlOzs7PJ4q7VailawqSIYBbGdeDfarVSuI4+4XkmGpOO9rF1GpERBALee/rQt61zwZbzvnQ6HQ0GgxSaYxIsLy+nBWK8n63iOp1OYmJvr7SzbgQLv9PpJGOpXq9rbm5Oc3NzaaE5dW82m2kcIM4yiRPCjQwXKA543JPBAnEEMcofrwzeFRS8/3feQzgCmiSlDQXiAWHSjsfSUykcDAFGEWD++5nsgYSWl5dT/mmOhhkbUtEBEo1ErsWypGIONfM7eqfYoCLuDIL8QFk5cHQA7WF7dxIQAcy1w72iDmC87m6AxN/i51wfxPfm+jdntOxFrszdUBsZGdmVK0x/cx8yzuVwNJB8nqDko8eZst0YirLD/7sDwR1guahE/B5BafSaOznveF9RZr/f18LCgtrttg4cOLBr/N1h4fWM7xoMBmlh75lA8/Pz6bwIPPiQ9y00Nzen6enpBMiY39KODI987xGSaIByTyTGljULOA49mlev19VoNNRut9P2pXE8K5VK4QwKd4Sy4crk5OQup5bXy52qvpsVetENAp9LfkYG/IocRDZ6JMSxEwYfuMfvd8zlxrEbRt6P4CIHvZxw7vVkTvg2t+6ciPLS5zoHFjcajcK63JhK531BOhVpTLzPMaK3w7ELWGR1dVWrq6sp02N0dFTT09OFM1QgT3l3owYDlmi7ty+XvZPjVecb5z1o//79u547EXpIEY24Z7mkXYaGK+1IUWmsra1pZmamYM1jYcJcMdeZdzi4BtyTbsTJlViH0u7tLt1bxnU8//Pz84VUJCYRQBUGbbVaWltbK5zNQDsB3vxBrIXA8w/YBWxVKpUkDKXiNrIIR1fKACPvcwfiY2NjySvLom8AAMLH+5IUI+6nv6Wdg/Xco8jWayz63tjYSBb7zMyM5ufnNT8/n4QS53BMTk5q3759WllZ0djYWKHN3ocxL3svge/hYQxb/vr9vmZnZ9OYeWgxN6mdP+gDF+D+fFy74jzuqSEOXnPphWciPfKRjzwhMCvlFfswoBzJAQRji/JzTyAGPOmiGPrIl7izSc7L7MofxeMe11xoO/K1K+PYNjdeHFxEcJz7758jwMn1m193xeRloPQj3wNqer1eYW66HvAIsY8R+obfYv3dQHQZTT+gjDF0fH56n7txGIEJ97uBAtjJOQmik8d1TvQQOt/0er3kpNq/f7+mpqYK+e1elr8n8h/e3zOB0DvDFgT7/Jybmyus2WMu+7hEfvZynF+dF3PGqG8SsbGxkRb/T01NpbqOjIyk7bHdu+zlEYl3fV+tbp/hhcNLKupAj0z4onja63qtWt1OD/fIhK8r4B04ApjPgGu883jk3ThmrhMpcrzmfEzdaJuPBfitUqmkFCPHP55mz3uRNV6+jyljFHGEtBO9oO9I2XY94GM/MTGhsbExrayspHo5DiCyGx2kGCr9fl/333+/LrzwwlSfZrOp5eXlXZtc8N83AgBPsJA+GgwxGhHlbk5n+fvoWxzRD5YeckTDwzcwpR9MxL3RQJB2C3uswPn5+bQbgkcApOK6CfdCxXQnqNvtFqxhT2Hy+niZ3M/aCpQYi89pJ973VquVwnPOAFEAubXLxASMb25upoXTKERO8KbOcdCdoXwNBMrWQW3OkveUMV+4zD0AfcBWPJWSXHXucUs65iKzje3i4qK++c1vqlqt6uyzz1aj0UgREAzKdrud6s8e4Xgu3ctBX/j7yFll4tPHGAcYWvDX1NSUBoNBqoO/w/nTQ845HpZ2vKuMsy8S5nrcrKCMZOymvYwMB6DOq057gWnIga1HE/13z9v2MSQ66soyGhMIbvfy8Z2xZy7l2u6KwCOgrnw9KhKNDTdMvJ+iRz32d64vXTm6jIgeda87n+Ncyim06KyKUQSAjZcTvZ0+5t5uvw9vMocqcm+MKEW+yQHJHJiJzzHm0Yh0kBrnvre52+3qyJEj2tra0uzsbOE5H5MIGPz9ZwqhM3ybc8gBnhsZOAQ9IuXzMfKYX4Mnc7KGMee98Nfm5qZWV1cLZ1r5gXLORz6+vV4vbRTAxgHwMeWypT6ywtdloKer1e31oHjPPapQr9dTyioA2yMvktL5GGA+nKHU0Q0FHLM+f+L8jsayG4hxnS787KnVyB+X3y5f0eU+N+gfN3YYMzfSXW6DbciS8HQplzU4UBhzyue7OwlcR9CXq6urarVaabMcDlT0NTsu/31NDX0LT3LdnaIRa0SHzV7Ec37ExIOhkzY0pJ0JBaMBQL0xw7zAOa8AKUmS0sSKITYHf66AXcFCTF63XCH3zDuDI6iwYpvNZppgtHN1dVX333+/VlZW1Ol0EhhmcjgT54wAgLcrakAsbUIARu+MG1eEy7juxEShHtQfAB4t2qgsHVhhdCBgMExcQALeo0cigkaMhzvvvFO1Wi0t0Oad5H664Yd3j/pg/EnFxd5RSRCN8PS0sbExLS4uFgwyDmlqt9up7+lj59Ec70Tw4SlRbmjwG0Yb/YWCOJNAwYOh6ImJFEEzz7giG+ZtJjQeQbKkgseMncqigRiBO3OGee8gNgLgarVaiAjnAI7LPVeQUda5keFtyP33utE3wwy2aPTw3YFz9Ih5ucyv6L1HLvGcyzifx3yOfeqRbeofjS7/HI2ter0uSYVdXSJQzPUDn13+4nxhDju4yPGmG2kONhxIuT7s9Xp64IEH1Ov1NDc3V9hu3IEC0WVvS9QJpzvBE3E+MVdnZ2dTVMBPwo6OOOaa6+t42G3kOWmHN4YZkvBMt9vV6urqroNmY5mM8cjIiKanp5PDjPUcnI3A/MC4QBax4xOYCvCfqzvZCvBXt9tNHvJarZayQty4qtVqajabSY9h4LDuI6aNoutoE/LM05y9Hx2fMCbodKkYzUW/0sfRyeDO75ga686iSH6ddvohhF6Gy6l6vZ4MFOeDmBpKW0gva7fbaaOefn97A5tWq1VoMzw5MTGRyqQeGDTumM/JOP8edWyuH7w/ToYe8jkabs1FQ8MXLvs9OaUm7QjLtbU17d+/Pz3jTBktX8gFsytcynDPgrR7a0asd0JlDspHRkZS9AKvQrvd1tLS0q68zqhwIliF0Qh5eh2IiIyPj0sqGlOeooQQ9O3ZEBI+Sd2bNgyoOGjiWQSEb43mINk9GNXqTvjUvQ0+LvQP9xOlWVlZSeHkiYmJNHHwJBPVIJpAmHtjY6Owj7YDEMaf8fSDH1kA7weG0VfT09NJCQzjTwdNcR7Qj77Fpbff02/8L2eMnckU+2KYkeF8nvstegW9rGq1ms534Tv3o5zX1tbS/uPMPY9K+DsYd8jLwnD1bRsjGPFUSuct5+tctIJ7oqMlAlzvowi8mQfRmHDy+90DOIzivPRnARnxffQvcpTnPCoQQX6ODwAu7lF1wMP8bTQaCXB6e4b1m78LGembAcS+yhka/o5cJMP7wMdlMBhoYWFBGxsbOnDgQNIhfh/9ulf9T2fCMSgV+5nPHskARLuDCl51Y8L5x9cRuUPR3+Py3HnW9UkEuBB87c46nsPpCvDft2+fOp1OyqJwneqOEd6DYwz5g17EGOH9HGrLzlL0gztYSMcDTzAXfWE0bQHH+PP0DcYFjgbK8V02vX+4xzGN/+6ZKrQX44LxcyyEPPB+jrzjmIV5xm6bm5ubCbN4P0K8v1KpFNaJsJ2tG1G8k7ZjtHE4M6nkHvllHH2xOrzG2th4EHKM3PJMjPg4fnee9fS0B0sPKaLhoAuhh3J16zl6qRj0yADSjlc4hhQJI8XdSqrVasof9PSfqNw8QuDWcqVSSVvPurdsfHw85VEuLS0VTm6EqQHYtIv/tC0ysit0mDbmlDqYxehASACCUaS0K4YX4yTx/oVZ3WNDWJXzLOgHgJikXetxGE+ENfWKDE2YGOVM/3a7XS0uLqrZbKYDakhV4zu5n4wH2/gxseO2wrxjZGQkbQ3MAVr0M+1jXBy8NBqNwsYCPq4RnPl78WQ7eIihU08l43NpZAynaNDlyMcEcgfIMG8OPMbYANzW1ta0sLBQACMoCMYz8oODQZcBGBQYzChe98xRX/jPlZ0rv9zn+Bf7JRoX0k5YPToaIjnI93514OYOJb8+rP+jkeDOH0+Lcg+fGx7+LmSr9390lBC9nJiYKOx8515/tpHlPbl+pHzaDAEacm2kXjF91Psn16+xvZ7yIO1EYA4ePJj0FWV7dNvH5kwh5pNnTdAH+/bt08zMjNbX11Mkgznm0UP0E7/FRbxudCDzPR8/OiCcHyHnMY8qwPsxfWp0dDTt0MgJ3v1+Px2q5yCy1Wqp1WolnEAqMhQdgB6N471uRHlkAuOk3W6n8ySov6d3s2ELchU5yxoaKJ7n5dEG3k9dXGYiv3znLMd9TowFOMYdsS6jc30T5Z3jHu5lHSmRcTf4vd44p+mjjY2Ngi6I2SQelZKk2dlZ3X///er3d7JUPB2XtvqznJvG+trIc7TZdY/PHa+PO+JOlh5y6pQ31JUygtuFnyvXGOVwr3ytVlOn00nnahC627dvn5aXlyWp4I2EKV3AuKInZOfCnjqQmwiwrlR2tsf1xWK0ixw1DtpjwqOwYlTFjR4Ugnv+I8jAQ4HQ6/d3DqECfLkXCwPBmY6+ZQIyQSjTPTHOTAgQyL0keENIk+J+FC7pQBg+CF/fAYS6S0pCCwt8enpakrSysqJer5cOD4rAoFqtanl5ucD0btRWKpW0gF+SWq1W8tIwfs6b8Cr5soR+eS99HUOrzmsAJp8LeKBQSFEROR+UtJtOxHOSMwbjn48ZCnNycjKNB3NnZWVFq6ur6nQ6adw86uTjFw3NmMaDvMLb5Qu/ve45Y8hlQc6Q8Dbnfo9gmfewviwnB+NzPpdynne/37+70QWP+3v4zTdAiG2OfRGjuJAbAA4e+M6Y4QDYv3//rmiSe+i8z10/Oejw39FXsb+9n2K7nU+9rf45lwrrEeS1tTUdOXIkHTLnEWpvv/fpmUARDNFf8/PzmpmZSWshIQyJ2O8+P/w6jir0MdvAu0PEdQJjESOUrm9zUcio3z1Fp9FoaHR0NG0zD95YX1/XwsKClpeXE096lCbOC5+fOcOn2+2m6ASpQl4WTsdqtVpYE8DvpFth5IMN4FFfn+L8Th9HXemL2T1lGlzksof7wR2+CN4d1x458nHOOYeH8YevucSxSb/GKAEb7uBAdQe5O6Yp3w/jY9xpG/wV5/j4+HghRczrRx+7zHEZx3EK3h/+PRpeD5YeUupUTqm7cpaUohM03K1Xj4bwPB78lZUVzczMFAT8/Py8VldXC4tjGFgHAIBpUiCk3Yf/AdAbjUYhrMh9y8vLBc+b5xcyoRqNhiYnJ9Vut7WyspKMEgAsDCYVPbR46DFe4mI0FKJv5+oWJ2DGmc2FGLtK+eJtFLwLBNpDBAAr2EGyW7lYyESV/PRQBEs0NHjOQYUDEoR2tVpN62EeeOABLS8vJ2VKuBIjcGRkJEWYaJsfNDg/P69KpZK2O6zX65qenk5rMxwgUEciH26M+ZhEMEvfeUoHhIBCsFFGBFgl7aZhnpMIACD42Pua+708F+Ce01ypVLS8vKxWq1XYLc7/x8iTg0acChizUjFszj0+b+GbXCjajdUcWIz1iEAmVw7zzMF07NNhCsR5n+/er9ETyP0+L7wuLqvda++K3CMcriSj0ZOLNvpvyK84f91b6rnMDli8n/w6wIoxdRDn73bg4/3Db9FgyxlazrfIuWq1moClJDWbzV16EPl9JlHk/Wq1qtnZWc3MzBS2rnVnnD/nY+U84anU7XZbrVYrzSU3xp1/Y72cN/0d7niLRgD/0XcASAwdnmUBMTrUnZN+Zo9nGUQDOAfUHfwCeKenpwsOBDdwPaJEFgKb+XC+h6/fYPdNnJWOlXx8GEtwhs8H+B4sx7vZaId3QZ5eBz7yc8HAQr7WQlJhzrqOAV/1+/1k+PmW+p7V4PxFlKFS2dkxij7AcTszM5PwIfX2CLuX59iSnUQpGyMvyinHjp4q5zI8rp2j306GTtrQwHrCSygVQZgbG54mJakw+E4wW7VaTVb75ORk8jI2m03t378/TSTK90gIHUFOv1Q8vAdBPDY2pqmpqcKJ34CDBx54IG2Nu9eWiuPj42m7L58I3s56vZ4UFFas76QFAGVdAvfwPP3mxggT1w/CA0z4QXCAGgRQDKF5zriD+Wq1mryxvtiMiYXAiES7MRYBc54/CfPCJ0RMWq2WBoNBWqjZ6/V05MgRzczMaHZ2Vo1GI0VMSLFisZUbrRhN7HBFP9K37ulhXACU8AV52z7WjJN7zOj3mFbjz3g0Az6KoLikE6MInnMe4WGfXTnh/BgMBmnverZ3xuD0yCzCHZ5mTnrKohuPzJvoEcKTJynNX35z4xuKSj16SVFyxzNIHBBHxxDlRQDm5M6B6KmjjjHK415dz0nG0YQs8D6jz12p+zNeZwfm0QhxGYanMWeEuqzmuytwB2P+XtqcGzMfo+jMiIYH73Awwn3RQKONyKlut6ulpSVVKpXkLPPIB/x9phHzYXZ2VnNzcwWjN6YmQYyLg3/m6ebmppaXl7MREQdl/n74Kjfekgo6HczgWIaxBzSj67wNg8H2QW9LS0uJJ5xnMBZ8m2k38tHjtAkcB9B2Tz2gnE1ZmE+DwaBw1gSbtLhTpVqtprqjl5F9rJPztlN35CXtAWv65ho4d+gXxjA6AJAf9IFjL99FNKbARfkQdbbzzGAwSNgLYwN8ISmNNfgGAwdDFgzhKVG+cBxHKe12Rw+yF+PS244scGPDjRp0mBu9PBP1hDthHiydlKExNjam//E//kfKF6xUthe8vOc979Ff//VfF5SEN0Aqhmti5RlILLdjx47pwgsvTDse+V7ZeBVibqGkNAkZOFeCbmQA1KvVamKO++67L3nq3fvsTMNE9fy3kZHtnSG8vW78wNxY3RhHKESYjQXXrvQA7hxZ73sZx8Xo1M+VvO+PLakwOSiPPscr68YJoMkta08VIlzKNT+Ui1xMV6gOtjxkRyTGtx08evSo+v1+MjYYE7wYo6OjKZ2tVqtpampKlUolHXQoqWCAYXS4x8+FL+3xRVTuRYmeMycHBm5cUGb0yJ6JQOBkKYIvqdjfw8Chj5d7zyqVStrm1OcdgILx5o/5hyKDD6M3yusGXzlIde9n5CXK8efcszcMtOQ8lN4fkWK/DfO+x36MZbl3No6Tgzf3wkXjizmPgnXQJOXPO3KD0d8LcADIuDKOc5Ey3Qvs4xbb7xTTXxkr12Xu2KJ8BzBeduRplzfDdOVgMEiOtEqlkk4SzxmFZwrh0MQ5hUOS33ysnNAl7iVnZyhwBE6BmMfO824sRF3BmDC2fu4FPOxpM/DW+Pi4pqen03NuhC8uLmpxcTHd6waI80jka37HUUL/UGdAvjvJ+J3y0KVsZz89PV3wolMWferZFY1GI63dcKejtLPOJsow8BnOPp/3HHiHo9b7gjkAX0A+J+kLxpbnY0QiYoCY7UH/MEYetYmyAkczKfcYUXFDkWp1e9MS0qdWVlZSm3BcU3/XRbVaLekz5/lhDhGeoQ3Mg2G69mTopAyNarWq8847T69+9avTNqXf+73fq9e85jX68R//8eT59VQpVzD+3RnYAfDW1laaSPv27UvWuzOjL2byiEK73U4D4cAfI6PZbKYdEarV7ZSdwWCgI0eOpENzaEMuj3dkZKSwvgAF56kxDCQAnmiJn14ZwS11pB3kGaI82aGJ9jsDOwPUarXkXfAFVFxnD273IrqlDDOPjY1pfHxcq6urknaEIff5xMQAdGUXQYVHUCSlvmMSOPDzXMpjx45pfX1dBw8e1NTUVBIs1Wo1efNWVlZUq21vt+cT2BdgSUrGWhQcniuKB4B6R+DhhkoUxHxGUA8T9iXlyY3XYeQgLXqZIuBzxev8CK8RJXPD3L05bnD7O30Nj9eL65SJcwE+9/QqacfDFoEK/6MR4MA7AlFvbzQMhhkeuef9GQc+sZz4HHM7evoAAe4MQVaSZ40M8X6NRkKM5Ph7PI9c0i4QgT7gN+qciyzmDDreGdsdP0eg5+MKHzno9P7z3+IY+Ni4AdrtdnXs2DENBoMkC3nXyXofvxMJY3Xfvn2an58vgD7AZiT4hR2T4NV2u63l5eUkG/htr/6MkSznRWQIZeBoxLnoc9pl0czMjEZHR7W6ulpI71leXk5ZF27QOx5yo8YXFztPYliQHeLGMHrQHanU2eUcBq9vNAGQjoedSipsTe/zyUEu7cGjL6kgI3jWZWJMRXPZ65hEKspmHzt/znEOcglDB97hf5SDHsXwheLMf9LMq9VqSkUnTZ5nud+jX4xH5Gf6DOcsfOtOcq+j94Vj5IhVHJs575wMPaQ1GktLS2q1WhobG9M//uM/pnQbr1C0opxZ3VIH6Eo7g8ikP3DgQEpvwDvO4MWoiVv93rlYmc1mMzEwUYhKpaIjR46kCY0S3NzcTKF3rGq8HbyDtBsUsoeuPeWJ0KSDa5jYlYJPyImJCU1OTmowGKQQZrVa1cGDB5OSJtVD2gnJ+mF1jIEDa7/XwVTO81ipVLSysqKFhYUUWoSpfSs6SWl/b84dceBNPQD5PiYISOoFaIEH4p7n8/Pzac/pfr+fFkUy/pTnXkXSwtz48ro5oKJvGGO3+B0k8NlzR+PE3Gv+lJSnXITAKSfwopHnQtbvYczgZZQt8yuu0YhrlfzduSiVK5loYObmRDTKkY/uVXKjweWFK2be44YP74l9FQHTiQDSCIq9HS7LvM3SjnMiB7BdNrozJFd/lw8eZYoyzA1/d1p531Cep5LQt3sZGV6fE5m/3BMjGrHPXZ7E+5BDzn/ehkple1t00iqQhZH3zwSan5/Xvn37kp7Biebj5eDfFw0DmFutVtqQhPvcc43McIdcpOgUiDzs6YNSccdKxn58fDztlOV6aXV1VUePHt2V0u11xLgA9KOPnb/c0cE5YNSJuQqGoB9xJFK+p5rSf4Bm6uvrJRyE4zR1/euyi/p7yrPvEOcGAeTYJuIxT593GeORDdcPfp2IA++MW+DH/z6mnU4nOWx9lzBJaYt++o2+8npGWefGEv2Uk/FuOER55U7tKHOjMyZ+Plk6aUNjbGxMP/VTP5WiDE996lP1mc98Ju2I4YRlR2dBMKBPwBjSwuJ3a4vf6/W62u12GiDehfUXQ/CTk5Npqzi84ZVKRQsLC1pdXVW/v7ODkk+KSqWSwoN+AjhC3pUHQJa2YDwx2XMTiH7hWcCGn3KKodNqtdIe1PV6Xevr67t2qooM70qLHbbc48Hk8XAcAnhxcTGBLfeEEJ7zhedLS0uF7SQRHh5ZAHDRdnJJmTAuyFyADgaDdFBiv9/XIx7xCE1NTaVdpNiOGE9UTJlzwMb7HGjE/7Hv4vh66piDKIQbfM1vPuHd8CvpxCgC3ZynV8oL/Rw4k4pCNTd+/CFTmL8YKj73Sa3A6RA9aXHMmeOuPHLep3jN57cDHr83ZwTkgHEOjA4zzGI/7zU+Dvid3KhCvgIc3NBwD6IbGHg5PVrC/xhZdKeAz38HM4xrbHdsR6486hSNq+jccZ01zEChDJwk0diK/Zczsjc2NlJkY2pqak9D/XSkffv2ad++fYl3XO5KO0CW8XcjY2trS91uV8vLywl040yLnnIoGhw58vkOX7rzy+9hPnC/75ImKe02dfTo0ZQq7FkbODA9FdmNEMdQcW6yeBn55vWrVCppLnpalddZKurKRqORUpp8jjA2Hp1ARrrejeDZ9aiPpb8/pxN8vZ0veKZevsbYMZHLJoi6+flcyA+fr8gyn6MYSOA5d5pilBHZoE5gFMrGeeyRX8fV9Jkbr95PMQLkGNsdE27IMRYu905Wppy0odHv9/V//+//1eLiol71qlfpU5/6lN785jcXFnqjAPyAoSjsHXwyIQBxHhFgizoHrnQQzOapSzAPg8LOQzA0KUTHjh1LhyGRLuVesn6/n06MjtavhzMB7UxCBBgeg4WFBY2NjWlubi6VT5sRWkQ8YEpnOryqHs6k3aSBMS5uxLhAdOHr7fWTj3lHv99Pi+BgQvcsYJzwmX7hPIxaraZWq6XV1dX0btpHlMgnuHtv8cRQDsZHv99PqQJbW1s655xzND8/r5WVlQRgWOBWrVbTWhsHc7yLfnJjgXbS9+7V8nsd0DkIcWPMjTLe6+QgpKQdQnnu1TcO1ocBaH53I555ELevBeAxLxhHT6tCLnkZzBlpx4vmxjt1caBLPbyeTsyxCGKcz/h9GPB3sO1KL2dwRcMiV6dhADne7zIzjoMbGe4UicY3Rhxl+YJ7f87bmasH9fM5KO3kgVOv3LyEYnTe35e7njPo3ODw8rwsN2Zdv8Av1MX1gHt0Gcf19XUdPXpUg8FA09PThbae7rR//35JKszfCD75j4FRrVbTpjGtVquQHQDfwHexjMjjkMuciHVw4Pm90VlSqWzvAgTecR20uLiolZWV9Dw6FF3FJibRKw2+4jNzjLnElvYYu15/5zHSs7w97lD1eelOU+9LnDM4IgHUnt5FXWgb9Qb7cF80JH1r+pxB5WPLfd6mmKpOf4AZwUL0DzoCQ8znPnOYtoMDiTgScRsMBqn/2bUUY9GNhZGRETUajbQ+l3HsdrtpjS/Y2yNWrDmM+jTyJe9zhw36ZthzD4ZOWhJtbW3pYx/7mO666y594Qtf0Lve9S599KMf1Ve/+tXspERAQs6AgD//DBNsbW2p0+mk3RcGg+1cVNZSAAA8NOcTnHdyuiaTY2xsLHkIABoeXYiLhVkDEHeJcKZjb+1Wq1VYSMV/8ogd+PiJ5OTXYTz5Vpy0lz51byDf6fOoXF35r6yspLCngy5XiJTtxgehPyYJO1A4WKM/WDze6+2cYdLr9VL0AyDWbDbT6Zoe7apWt7ftq9W208A4Y4PxYYLdfffdOvfcc1NqXafTUaWyfQAj+fc8454GB1n+G3XOeVOcn92Y4DuCz++JCmqYR6mkHfrSl76k888/X7Ozs0PviSAiB1alnW2kHex7BA5eJw2RbTD57ruP+QYNDpDjTm14qKTdRgb8ED1rvjbKvU5RGfjnaCQM66dI/owrslhWnAO58qKcH/Y++svf0e12C7sVMofoR9+hyz2e7pzIRQnoV18w6o4Xd8B4+xkLn5sO1LxPhxm4/O51csPBZWyOZ6m3O3bQg8g91zu8zwHb1taWjh07VtgM40wgdHZ0CMX/nla0tbWVFny7Zx4CvLre8Hucn+MccvnvhmUEajljfG5uLs0H6ry2tpayC1hT4ljHnYqS0tqLzc3NpBc9JQc9BL/7gXPOWxMTE5qamirk/XMf85H60B62yncjiT5DxuJkdSepp4gxj3G8AvRJb49jQLSADRIoB16I0VF/lvrRX91uN2HEGIGKujsaOH6/RzPBsqurq5qcnEzPgLXYndT1hbSTDSRtY1i26AdPY8D4wYE4W8k4ibul0Y4ol4bJ8ehgPxl6yC6ParWqu+++W7/5m7+p1772tXrVq15VUOLRc+XPSTuN4j4XFAwaJ0g3Go20yJc9rTudTgEgMvFIdYDxOaGS9KHR0VHdd999aWBZ+0FZHkGQlISYL1bs9XrJuFhdXdXKykphV4QIFpjg+/btS1YsYJ4JS/skaXV1dReze6hRyu+A4gzjURdCpJCnDtAPfsifTxbChbQP6xsBsL6+XghFUw7tp+98AWi/39fq6moytkgHI6yJMdpqtdLaGvoCxr/vvvuSMGSHEHZqYEE3uZC+xoT+8NQ997og7Og7lLmHFulv2so1f84BR1RWZxIQOFF6ylOesmc0IwfQ3OCjX0dGRpJjAj6FL+FBeJJrfvAk17mHMjBeUTCUz64ovp0kvA84dgcD5F7+nBHgzplo9OY8rZGicb1Xv0ZF43Iglpnzdrm8cNkTPazuCfUTrqWiMyaCNJ+TUbY6+HFPLHWiDECO193b7qCIz96ueL//HvvLgYbXhWvxWQeXOeDqMiMHXGkn8jmmMJ/OdODAgZQC7bzuWGJ0dLTgSUdnO+bwyFtu3nh5xzP0ud/Ldg+9GyK8C2dF1Oukdvs5Df5+L9fTC6Wd1B1PA4vA0Xl3c3MzAVbmJ9u+sz41PuNOApyx/PHeGM0A3MfIAQ5G9CyRBHb5RDZTL7z5YEHGjuc9U0baSQeK2Ro4NtjqH4cpfRDnGuRpeN4/0o7MdgOl0+kkDOp92Gw207t9IXq/v5NKX6/Xtby8XIg0SUr8TJ+j6+LGN1GveKQ15xR1uTg9Pb2nA3AvOilDo9/v68/+7M/UarXSZPnYxz6miy66SOeee66+9rWvpXupcMxndcJqh8li6ByLnMNfpG1mwMsflZFP7JGRETWbzTRoMCfpQg4YaZtPPixRt3r9gD7a59YpoUD3qkErKyva2trS1NSUpqenE0BBmHgYEaXBxIBpYXp+o2+jNwOh46lZDr6icIlj5gtjeQ8MzEJsV8y0d21tLY0fk4mt8FyIsyAMYM8OG1NTU5qbmyt4o/EuUw/A4eTkpJaXl7V//35NT08X1mwQhcEIioLVhT1jT73hETcycoaBC13G3IV4ND7cGHwoHoIzkVzxu5J1AYkB4PurY1Dmohek/5F6kDM0ohOEqAjn/LgCd6+iC3iMafeqR4Iv+N3Tvdwbx/dhqTF78VXOaxUBlRsRwwyUOI8iGKc9fp8DfJwBUnFbS4/u5MaW+zyC6/0c3+/j4e1wpevlHs/IjX2Zkyl+r8uB3H+XtdH5gC7xcocZg7GN/f729vBnCt11113ps48JoIrIITpsZWWlsOg78lHO0Oe+OB9cB3IP/91gic5VrvkOhgcOHNjlqPCIBN5plyF4skktmpiYSGcqjI2NpXa6/On3d9ZBOfYBS/AbhK70sy3AArQFY8d1PO3CeZgzAmN/+KYZ7rT0KAzzmnaR9oYsIX3ax9PfFXGfj78bcr1eL+EZ+hMe8vfxO9iEBePOH45V2+12cp5GI0tScojQf2DRRqORjE76wI02jEra6m30seQZHBt8jvyMzK7Vtnf2XF5e1snQSRkaGxsbuu666xL4pzPe8pa3aG1tLS0GhuJagmhNu1KFYEg60zuCE5+lnc6MzMt3LGzPR3TQwSAysN7Z3AM46XQ6Wl5eTjtgcV/M+/VJJmnXORKA4V6vp+npaTUajYJh4WDUw4luGLj31BnDPbooZCZP3PHKow1OML73OX3qERvfcYKymJS0z4WX9xf3016Emy8CZ2J7DqnnSa6vr6cDHdndoVLZPum5398+uZ2I0dbWVvJA41UBMHo/0M++3sY9JE6erufjG4FXziPm4KGkHRoG9CKIywFd+JqtAvv9fkoJIBWKP/jCjQvOk0FYuwxyz5uk5C2SlNZ+YTDDP9RPKq698DngxoTPCzcwYuQigvAc5QyKnHHrYCoHaON4eLn+/pxTJcp4V4rsxOIGgwON6Pjx97nDKs5RrnlbXGk68ETJAmSknW00c22mPZ4Sm0tBcAeDf3Z5is5x8OSGLTQsbdMjNjlZ48bimULOI/xH95My1e12tbKyUtgyVsqfPO/ZA3Fe8Iw7Pvy3GH3y+538PqIZbgDwOzzigF/ayfrgPlLNcexVq9V0LpXzFnoc7OI5/r4dq7fBd19yvnfHCClMRDCiI4/3eLn8+TgxR4kySzubroABkNE+bj6OLn+os2MRx22Or1xOUr7PUz/RPMd34JC4rbfzCOPiaXA8X6/X0/ixqJ7xnZycVL1eT7vMMea+eJ33uJxww9n53qP1MUrjbXOcdDL0kLa39crAmCz0hZxh6RQqDNP7oW5Ykljo5L2hxMfHxwtpOzlPs09mDk0ZDAYJtAJq3VL2d7t1CABh4KMXzq1UV47uUeca+dzso81e/pRHyJDJ68qXSRoVs69rcO95Dsh6bqoDfTc+GE/KxlABZCEoJiYmUm6rC6zNzc1d+aO02z2aHh71HEKEFP0Nn83NzengwYOpbQglchTJXa1Wt0Od5FniMcKI47/zX8zLpt9Yy+JjTP/wO+mBLgxd8ee8NSiSCGhKypP3ofO0Kxr4nfxXT53sdDpaW1srzGM3OJjnbki4cwMjAx72ReHwiG8+EQHt6OhoOqjKd5KLaVFOOQMjet9yn72/YrnRWHFjzZWTAwOu+bsjqHVnh5flcgT5QP8yr32+0d7oiXMFnTMwvA3eVnfaxHvc4CMtxA9qjEo79pfrjRiJ8Dx2/80NDgcv/t0NE5fv/rw7xOK45gDxmUKMneMLj2RwmjpnYMX0EyeA8zBjLRoZvD/+Hst33c53aGpqKjmtANxgHY9kEB0gu4I64kClbDzrlNlut5NjGN3qc4R0U7AXGQXeJk8NRfd7H6PXceJRH/S687b3A5kccU0E84E2xDQfB78+zz0q5fOPMWUOu6M4Gj+ux73+6AwMDjcAuadarSacRzqVO4+pP/3j/SopOVA3NjYSZuWZRqORzphz54UbEvSR90XOmcLz0SFC2+iXXH8/GHrIazSidzHnHZOKi5TjdUJO0bqVdpQx285NTU2ltAXKBFDG8lEilEP+v6c5uTDAumPRUbfbTYuK3YMUIzDOmB7ac0NhMBikNCnahCeVCQzTR0XljOnWqb/bLdoIINyD4EYR7UFAUBYAwJmRNCXfGWtkZESrq6up3jGygwLH2GLiYYy4MKA/ebdvM4zhsr6+nvZKZyK4h7FSqaSxajabqtV2tiN2YeCTkOcd+NM/pE/Rbz6B3evDM4AnlIK/x+8ZBpxL2k3H6x/3OjFm9DH8t7a2pna7nf57rrEbFk4udN0L6DvTwM/uBCGXFv5mPuXqLBWBLNd8Ljp/eT1zQAWKDoO9QCd95XLQDQTmjN8b3xW9YW44uDL3e5hDzLuYojKsnj4P41ySVDACXa54X3rf8Hu9Xi/wA/c4KJGKRoTPZ+rlBoODJKIXfi2WzbPUARBDH7lXNsoSKBpIZxI5eMXrz/dOp6OlpaVCSgvPSLsjzdHI9v48HthyHR+vR6PQ9RIOMoC+pEIaJ04+eGdlZSXtQuQgvdPpFAC1O02i9xr5BIZgp0awkmMBX2PGH/IWA8MXI2PIgLlyxpm0sy7Lx4TvuQggfcnfsHRVQL2TGw++OyDzy8fXDfvocBgMBul8L8ev1MUdUuAmokq8z50vg8H2JkfuOB8dHU0OU/iQ1LhKpVLYZSwahFHWHs+xCS4jssr6lMnJyfT3UOghRTRckdFxrqhykzgOlofLKMctLl+jAfE7kRC3kp14l29nF5UJ5EbF1taW2u22BoNBWr3PZOBZZx73kE9MTCQr1SMkTFYEirTD6DA24UYGm/b7GgOftG788JtPFhaZxz4HAPT7/cIOUD5m8YC7Xq+XFtTPzs4mD+1dd92VmJ9+jrt/MPZMNF9XAg8wWRCwGHxe506nk05EfeQjH5neWa/XJRUPa8QjMzs7m8LJ1CEHOOA3B54uVF35MGmdv6MHJs6T2O8+L0o6eXJQyx+LGVEGGBjswAF/wo8OqlGSKPbBYGebW6Kr8Kmvu+CcHAeFGJ4+Rx0I+f+c0en85gYVlHsu93mY0eL3UG6MAMT3RoAfwW4sO3oZc+3xe7zv/Jo7AZCrPO+GB/e4MYfDCUPQ5Q31Iv95ZWVll/Hk74p1i/WO0QpP0ZV20q1if9L3bthyzQ2xHAj2tub44EwhNy59zQInffsC6jiGXkZMqYogjvv47/0/LJIBRT3hoJJrpJ6T8oQe7/e3t5xfXl5O+t0PeuP9yKzoxIAfHbi60eqLpCUljCUVo/8YcVwDv/n5Gfx5WwHtvIuxIhPBjaWIbbx/pJ0ICO31Oc07PVsDgyc3dz1q4XLS2+mOBY/MgNcwsnx+Ok+RduaOKvAKbSYVHP1EG7mOUbi6upoySiLvOx94PVyeReeMY0dvZ7/fT2np9Xo9nVR/MvSQIhoIWE8j8gZEcm+M/zmDxImMhc2g9vv9FNKDeQAWnq/oTMwg04F4ICcmJlKu5urqalogDmNSN19YShlY94PBoLC3MWcwYBFC1IfUDspgjQY5k4RFeR/ef4QfkR1Ccz7BfK0Ak9pzMN1DgXfET/d2gYTRw7soG4VMf3a7XT3wwAOanJxMByCykxQGCov44RVCgZ6G5hENP+ODvnOg0uv10gK3AwcOJCMJTw6pdhhSCO7V1dUCWGT8fE1KHLO4bkdSIaXM6+fA0MuAN9wgHgYuSxpODnLdG8x3FC58wpbH9HkMNTNOeMpccXAP85EohXv54Q/IZQaeLgjehqIyiqDV5ZUDSygCplw/RXL+i3I49qnXm//e15F3mb/uxInti0qc98UIRVSIOF2iPHDgH+eZ9wNrcfDUNhqNpC/cY8oiWuS5v4vPDkJ9zNyREw0N/w8PunElFT230QhDPnkKKH3uqSlezyjLTnfCycB8BTO0Wi0tLy8XHENS0UBwnole7SinY7/mHJxOuXkajf6trS3V6/Wk65EbODt5fmlpSceOHVO73U462BdYUy74w/uGd/n2rXixfU4yLz2N0J2Anmbkjk024JicnFS3203GDvc7xvNnwEvwsNfFd8AkYsyzOGupr2Md+oJ5w2dPv4rGEO9mHsXrjLtjXO4lw2YwGOw6MNHlIViSDWq8Du5A8XQsIiMYF9w3PT2dtrqN/DWMPFITjSf0FbIDLMr31dVVbWxsaN++fcd9T45Oyfa2Tgx6BFsuPBk0/+NZwLov4hwfH0/GgW9rRtpRrAPAlfUizsCeysOEZHE3HgLqgdFBh/tZG1j9LphIm5iYmEipO0xGf3ZsbCyBYoA+YFlSMgDoN9+SEQOAvvRIDYzKe9kRJ1r8eBN8ASxtiBYw78eIox/HxsbUbDZ11llnSVJa2NTv9zU5Oanp6ekU+mPbWZQ5QoTyeE7aAX0AG/oUA21ycjLdzynga2trOnjwYPKo+FoU39FqbGwsHcwExbQxNwTcAxvXVuSUSwRycWecHEgp6eTJhWxcSE16FALdvcg8w3/4BCHvKY6eiuUAknnEuLph4Xwj7Ris8KR7xBD6znPSTmjdlQI0DLw4WPfrDpb4jyKhH/057hsGzGLEw5/1uuaUoIMNB2leBwfTDn6iMeIGj7ff2+vOhM3NTbVarSR7ZmZm0rymrvV6vbAWLzoT+OyRF2/fMIMJp5HzoDuu/H5/plLZ2c3HN/ZwsAYwyDk4zhRCjrt+wchwQ1Qafi6AG64eyY5zAXKHA+TzLT7jc5zxZoxxnqKbAdm93vb5U7QF3eVRjjiXfc7wHZ7xdBs3mjC6qQ918x1Ap6amUrvRcZ5d4ToWXQ8A92wXok31ej21mzLAS+hZnnX5wrrbKNNzfU47cPziYPUyo35wORLLcn5wuQT+ct5xme/8RpvJsgDnUh6RJOpMCne329Xc3JwkJT3VbrezPOg86v997H3cuYYz2vmfZzY2NrSwsLBrHpwInZI1GtFSpWIu+FwZuMHhi4ilnbMmKEPayZ/zxUfNZjPtHONWMwxLKMtD7r1eL0UtOBGUAfXtxKIXA2HOdxhnbGxMjUZDs7OzhYgBgANGYbGo34Oh4lED38lA2tm2DsOL9QIwHkLA6817fH96DwE6eRtRykSHUPaeWuJCUto26Kanp9Xv97WwsJAmyMjISEqzYpcvxqbZbKaUM6JJ/AYwdKCHQPJQq/ebpLSeZnp6WgcPHtTU1FTqNzwC8Nq+ffvSieXwG6kVbmTiVYY/nM9doLnhEQ1meMUFM/eX9OAogoJoqLmhiND3HcdcDnhkYWJiIuXn52SZf48KI0bdtra2UvTRvVk4LVx4uwCPbXHecUUQ7xmmXI7Xb7kISWxvrtwI6uM1qXiQnJcLYYDH9zF/MAii3PayiBDEugyT3w4OGKeVlRWNj4+nw0DdqRQjHVFZOxDx6Bd9G40hN0a9XRHE+vN+j0c0PCrtgMH7N47pmUC+roGdpdrttqRiBEEa3i/e99F7LRUXGbunn/uHGRnOL84XvI80bMCctJPC2+l0tLKyksC7G85Q9FTTH6xHwZHi9XHs5Q5I6uVb1aKPW61WMopIk6YcPPmkB7FWEuyC47RarSZnrK9t8HUalOPGhmMR8InPU+pKOzwizbjhdAJPONj26DPZJS5vohyN/MJ3HJseYWFTIyfnVTfq4Kter5cwCbjqvvvuS+slNjc31Ww2CxF7eDXK8Fy9uTcnh92pzPi6k/tk6KQNDe/46OGDkWMoCoZ248CZ2xUFVhW7F7jiImcNhnLPA95vBmx9fT2BXtJ3OGWT0B5MDzMQKaFtXj6gvV6vJy87k5qQO9vUMVl98NxjwiB7HzgQdc++p2mxiw0KF0bD48+kRzHRFxgyHk2I4+l55g6OPdQmFResUk/fIYMTMOlfNx5rtZqmpqZSpArL3NOaPEKE8cYJ4p76goGC56fT6WhmZkb79+9PJ5RKSsbl6OhoMgzZNcsNAe+fwWCQBKrnVnof4GXMAbQIGHJekpKOT8OAuH9mngDmfOtQZAvywoUlSiV6yT0iFp/3+6LzBCWLjPI6okzj+p4c2PSoQeQlv+7X9jIS4rXIj9GgGFZ2zoDIGUvu8eNeB1fxPdLO+gVkmveTGxy+Li7WgXHIpei60QiPuJFC3VzRRgcZ1yPIoNw4x+nn2Be0KWfcukHjQIt2ebTL9aq3/UyTNaQ3x0Xf0UB3cqwSjdJ4n6TCnJeKGzTsBe6iY0FSQcfj2XePODhlaWkpORijoc/Ye3qezzs3yAGxvj5SUsJsDiij3qVtrJWlvaRFe6oadXUvPW11w8LlLe+Ijhiu4TDyd7vDxI1rn+serfG2xYhzHPORkeLZay6THMdEvvGx9ohoNH647juLMYYYKs47RLFGR0d19OhRPepRj9La2pqmp6ezEQbnt8g3e2VVRCdoNMj3mkvHo4e0GBzyQfJJl5t8Lkw97YcJFu91QO6TptvtqtFoFO4h/9ZBM8YL21suLS0lgOll+gJQmIPdjnwiAEJ9ByXPe/R+4HA8mAglAOhnm003djAWMBi8H4mEsOUZwsAtT5QTdZB2GzdeT7fsfUw5lI8Ur2ih0z94Azi1cmFhIU0cDuTzRfGDwSDtHY0nWVLyflB3QAYT0rdbi94FIg7VajUZLCsrK+lQxOnpaU1OTiYeqFQqajabqtfrOnr0aEFZu0fBI02e8+r9xf30dfQs0Ofe/jjBS8pTNNSGAW43xH1OYigS2cQQjWtzInh3EMCYYZxEJY88QmFyRg7vnZqaUqVSSeuw4vvc6RLBfuyLCHJyoNaNn7361O+nPAdSsS4OAHLgOI5HNCYc0MW2UI57/fnzE49df+TSBZiPUXc4KAFs4YRww9GNDu+jCBQ9vYTfHIxIxd2pPC3D648RQTswepEn3gYMXuddTzVxGRbH5Eygfr+vdrutpaWlwvq5eI+0d9qqG6k+7p5+7fzlxh2/RaPc3wef+zVOW/Z0wfX19YRVfDy9XRsbGyk9HJ5xPiENkLROeI73dzqdwtlmDvwxInDukQlSq9WSEUfZlUolbfdK9kcEtTndlzPWcd7FdU3etxFv8t/xo3vh+Y7DE5niGIPy3aChPkRskPVeJ+SN30/ZjBlY1Nd7uKxtNBpp4T/9ioOUd7Tb7ZRdgWEHLooZF7Gf+RyNjIjHh8mN6GQ5GTplazRyk8q/57w2AFYX8DEk6Z3mArvVahUYZmpqSs1mM8sgLGR54IEH1G63CykNzlzRi8bzvqUrBgkgBSsdRmLC4Q3AOOl0OmnyIBAY3E6nk+4npWh0dDQtmu71eqneeNfJ73NPFu+mbfQti2RhdHak4D4PrZKitbKykoARBgNlephzfX097eiDl5DD0Xg34dNOp5N2/mk2m5qZmdHm5maKfrCzAoILocB4uceRyIgbKq6kB4PtxXO1Wk1nnXWWzjvvPM3PzycvUbfbVbPZ1IEDBxJf4B1hnCHObkFweZqFh2jhRfqVaw7mojelpOE0zKiIv0Ug6945aWfdD89FQeqKn3nNvQ5ckRPMF98ggXVbAEuU8vr6umZnZ9O88Oigg1RfZOnyJypmb7P3zTAjLPZnTlnzjHvGvQ+iB/JEycuhj3N6Ymtrq7BuyiMJtNvrHtuJ/EN5QzEigmex0WgkmRjrFZVt7KfcO9xQ8TH1PnPQNGx8KN8XeEOehks7eL9HN+g/NzjOBFpdXdXS0tKuNEPnXb8m5SNH/ow7nrwMz06IqYL+rL+TOvlOitL2OiEMgn6/n3atXF5e1srKyq6IeaVSSdGb8fHxtBaU+vgOVJVKRZOTk6lsoh/ILXSxLwz3jAKcM67n4HcwkK81xcFD2rEb0zwLX8O30QHh6ct+PRdNop99DrsxRTt8Haa04yh1zOfOWDcq/Trzrt/vFzJt+OMd7myAX+jbGC2lLY1GI2EmxoZdrFwmE2nB0c06GOfFSO6EceeH822cG26kQTFV7cHQQ951KqcAopIAhPtveME9rEYZDtpc8XuKw2CwnQqF0iC6AbivVqspx+3YsWM6duxY8qwjjNwLNRgM0laphLnoWM8PZKJjQBCBYBKTPsTBQIBvnsUzTiRjMNjZj1lSisj47lL0m3vlYEYH3A52mBj8Rt/iraBerVYrGU+SklFEed4P9KtHeRhb1o3w3oWFBbVaLU1MTGh2djalmrFQ3neakHa8iPQJa0Uo3xWqH+THmGOE+W4ZRGVYGHzBBReo2Wyq1+vp2LFjWllZ0czMjObn51Mf8CyC06MaREPcwJFU6H+MHfgs51lz78DJTtzTleA3afcaIv4PA6wOIJjT0WPl857fGTcHm67sUKyulJAJ7LaCUkCIo3R9m2rKi1Ffv57zmvLd+SZnRJwoOU/mohgRNHFfrs9z7+c5H6d4j7cnBwQdJPh4MQbUif7gu8tp90zjbcUJ5HrAddgwcB77J/KgA1Ovn68R8kPXvFycKe6AAMgh/9g1C/mL7HM543VChp0pxM5S7uGGojNC2p0COBgMdskASbtkvbQjo3JRpFxkg++ATQev8/Pz6Xfm/8rKSjqQzWXU9PR0wfnA2LvD1SNnLjtwfDj4bTQahXoSJfPsAU8B8jUd4DrknUdEHHiT7YEzlnfFCCAg3LESWR8OjHk/4+nznflGOURyODjZ30VUgPJw/kTnIOT6pFrdPncHh1NMiXJ55DIKfeDrW7xc39oWeUFfg9vgBXZMBYvGOtBON/Sc151PvV9yeMT74lsS0WBwYSCEojNB9NS5ocHkznkIEM50vDeQwVlfX9ehQ4dUr9cL1qWf53D//ffrm9/8ZgG4+mQgJQiwIamQm+iMwHX/3u/3004m7AwgFU++lpRCbyxORvCgfJgc0s5EJkTJO+lz6uh5g5VKpQCUEQIoVyajhzb9MDuY2D0K0TsTc38RjAglQpMINtbBkK9OugLGFQYC7aedeAvcA+ztlpTqTbt9/ACFRIq63a7uu+8+dbtdnXfeeTrnnHM0NjamI0eOqN1uq9lsFowN6kNkA+PSFZHPAed3N/B8rH1CO8gsqUj33HOPzj333KEAVdodcvd0N6Jw/M4cA7j5Qj/v/42NjbTbELIEPsJ4dwGOYpienk7eJY+MTkxMFOYz0docRYMo/nYi5PN1L4PgRMs5Ho8OM0qizAAM+b3RExg9mIAk+tlTmZhb0diKxqWnNCKLc30cwWc0AF0HuAGR40mei2lmDihieodvcuGA1WWKgy3WFfo5L7zPATDy/Uwij7pD6FLXWT7e9FsEsk7uCfZxRx9KxYggZUevNWCfOs7NzSXACs9vbm7q6NGjyUsN6GcNaQSD1N2jkcwvnF6A1Jgq6sQCbXekebq04ybuh9ygdUxCH1AnZKQbT/Q/W7Y6+JeUUrZ8DCMelHY2h4nPewYBdUIPRHmAnPaF6/QFODUCc/AMhow7SWJ/ML5sGuJOLp4lukV0wzNNfHMjeD2OT5Td0YnjfeN87EbqMDoZfQI95NQpb4wrqZw33gffQaaHq+hAT0OIITQmMJZptVpN520QGt/a2tLdd9+tb3zjGwksrq2tFba8pf4+SAACQAKDj5Hh2+b5zke0k3I5WRPmgNHxcHn6EkbFyspKUjjOiK64pB2vSUw1cyXFPaz1cAES6+vgLFq71McFKBOHxd4YZZ4r2uv11Gq10oFp9KOfmr24uJgma7PZLAgdyqNOLnjoA/rGd6mhfr5dMcZGq9XS1772NfV6PT3mMY/RxsaG7r///hQZazQaWlpaSu8isoUHkagGPOsKyz0Xw4R5BLdupJS0Teeee276PKxvnBcZI/7gTd+9yNNYXPF1u900F32bZwwMdwhIxegsdWD3IghjGY8yHjUcHZE/ho09vBH5RVIBWMTfhvWVf3YQfSLGbm4chn336y6T4nUHIfzm+sEdQD633EuYaw/lsnkE/e3pL973yBrvD78/prnl+tbb4MZejGbEtKloLBNF9brilOHQVql4+rlUzPv3PnUD7XSnubk5LS4u7gKOw8j7N45D7j5pd8QUwlh0T7bzFOTXxsbGNDMzk3QtXveVlZWUGeCpxJTrDgvKQj+5I8QdddQRR4iX4REQwCzzFv3a7/cLOpZIgRsiPjeRsw7yvVyXX/zf3NxMazcxoskQIW3Q+xjnJvXy32hPlJGMmzuAwBiSks5YW1tLC+JZh+rOcKKKjCljldM1tMONCXdoo0s8QkZZOFxpH+s2er2eJicn0y5g7lyIfEr94uYZMdrifcQ1xzh8ZnvdB0unTBKhvBm8YUCLjqRxMLNb5FIxDJjz0tVqNc3PzyejhMXR7PX89a9/PUUy6GwGFhBf6AgTym4U4RFlcrEmAXDuedp4HDjPwYGPgxVPDwFY43XlFEbvB3aL8snjBgB94lu0IXiYLHHSIUgQFm4IeZqUewnc2KO9vpaEdiCEMCzwCvsheizYZuJxcitb53kIl4nu6WQYJd4XeAKgnKCXpLvvvluzs7M6cOCAVlZWUp7p9PR02kYQJYT3sNfrFfIhvQ/3AhBOOS9sSSdP0bvnRoafAM6cw6DAqGD+kloXPdAo9pjjTMRC2kkBYL4A9tw50Ov1Cml3OFNywDXKpag8+B+9p7ln3JvrsiI+lzMSvJyTIfqR8Ykgns/Mc+6LQMKVpacBSDspLG4kVCqVBJhcp+Tyl5HdOLqoc1TKzhfeL5Qb9ZMbvr5zmVQ8fNCVei56w+/wpqdUOICjL5Cl7jw7U6jT6RTAczRi4xj6HI9OtpwzYK+54B72nAGfcxpwhgtj1ettr8MEPHKAH550n9OsZ3Js4dE7B90Oqv9/9v6st7EkydOHjaRWblIolozMqsqqru7GzMUMur/CfH9gbgYYzKCqOrNyjVUrSa0k3wu9j/M5Jj+UIiNy/tUZMkAQeXiOH1/MzexnZu5O9AQ+su3Fveyoiayz/ETfWa55VyoMatshOGGYj+4jRwa8TgRHIWtInOJIu12vTG4TfYIetx2V06TgFwMrUtyxSzKYcZQntwOZBY84W8J8wzgYbLCrKJF2dBERlMViEYPBIKbTaTUNyrxnGWoZZnBom4c21nje9tWH0EcBDVfIE9VoOIfKACI5wmHh4PSFXDYTif3vyXkbjUYxGo1iuVzGf/zHf8SbN2+K8MXopSOdJwdTMkkYNOf2I8SYqM7xdUjRCJ0/FkZzb1a8TBgmAXl8l5eXMRwOS1/7HfYiuG/bGIV6ZYVMekdeTGihaOOJenjcGE+f2o5R3u/34/j4uAiEm5ubmE6nZScLdnM6OTkpZ2H0eqvTe0ejUYk0ZPTN+/Dydbvdxva3/sOgtID58ccfYzAYxNOnT+Onn36K8/Pz8i4iS45ywcc7OzuNk+BNfrf7DKrxxyM1KXuW828RKwPPMgIwa+WE8Ly6uorpdFp2HfM6Ju6HN6y8nUfssUJ2WDhHrHLyLfgBy8gdpwp6JxL/j7jrJa31Q+4zUwYm+XO+hz6secXa3st9bcac76/JFpQnc8xGv+VZre04AQzscoTX+sjymXe7Hq5nTjHJvJj7kfegQ7w+x7LdctA76NX6lvqdn5+XNFN4KGIlB73+0YZbLb3kt0zOZ89OtYg6H3vcDYIz3+bybJO0vcPf0f2UsbW1FU+ePGk4ymazWZydnRU+NiA3f+M4dUoOPMs2/tneYp6xhpGUbPML3zGOkXFEV7BdbNcAdJF5Bug4R5nb8Gh2LHu+w7fMF6ehew4668FgIfO77UXe47VQjIcNf8rCxup0Og3QwWnm2dFrWWXHbd44BhlA+YyNZRb283A4LI4wFoePRqMyRqxNfvfuXaMPst2cbQ3/znfqlq9bJ0ZEsX0+lD7qHI220JQHzGi807ldXR+xmnxevE0HMomch5oBx97eXnnn9vZ2HBwcRLfbjb///e8FZBC98OEz/k95GBosHp7NZmX9gr3wMKq9ByB8vAFMenYpYnE4f4CsXu92RxpHOsi/zYbqcrlsKCYLhYi4M8FhYvdtRH27Wxu9TAZPbBt3lGEGhkkjoixqHAwGxYg7Pj6ON2/eRMRqATzb6vEeT+Rer1eE4XQ6LQci2gjDs+C8T0er6BOPH22bTCbR690urjw+Po6NjY3Y39+Po6OjsmUykRYAqNPm8G6wS5UFehaGHiPmRhZ4NYP6c6ZsePm7+S6iuV0fRh6ygwgGoXe2D4SytykiSpSD9xncMl/I4c1n5NjI8byCXwDW8JJT/7Jn0QqzDbDWDGE7GNqAQ36Wd9AOt9+/ezza+DUr3LZxNXD3eFgW8d2pA9mYsIFSk2WWc07BsoFpI90RaK7lfnBUxeXwfoMMRzLcN7wrR9fNb91ut+zIx/3wGPrAxhh6hXfVIjifA2W+NtE/HsMaGPbzLg+ZYbljY7c2r2pyjEMiMdTn83lJ13GKEuWix5FfRO5r8g6+yLzM+wApAB8boURpMZB5Jssj2gSozu23rHHaEfcgR3MGBnzLPLOtQTvoA4+bF6V7ParfaRlhh0RE3BnPDFKphx1QRGJ5tx2Z7jfsINcfhxPtxz5xnyG7B4NBsSUZc+QMDnTkYuZf2m5HC+R7s2y3/PtUzoqPimhggGevd02JRUTjEDsrCBivJgiYLEb5w+GwbBl5fX17QuJgMIi//e1v8f3338fNzU1Jv7EhbEbjPaDjk5OTYuhipDKRczgbNI9x612nYCTqTc4hC4Col1OyTAy214jYYKBNBjkwuoUZn70Yv2b4eoERY5FPEc0eSOpl8MH784ncbCfL+BHBYE9owBV1pb/pA6e5RERZVM4uVrQdoMK48OdDChlvUrAQ2Ah871HtHEyEHvXb3d0t2xVn4zB7oLyg3btYWZg8go27ZI9bVthWoHZOMPc4/wXeYf7xF7FKeYIXKNsKCfKYwW/wjOeDFRg72I3H48biQvMHbeM/CqfNK+u+aXMEQNlRUbsHshfM9+XnDFKyovJfLps+rDk3KJf+NaAAYHg7SstWxtNjwzhYPra120oVpe/tR62kc1/nMbCR5XQ8iPryXhus8IP5MOLWezidTu8YEfCgo2oYlvBgTmH5rVMeD77Txx7HiNX8sEG+br7k3yCnTkc00zkZN/Nhr9eLJ0+exOXlZXHIoespz4uFmS9XV1dle/jJZBKTyeSO7YCTj7a67ZaXEVH0rnWvbTKiFMgyZCsL2ukv1law1s1Ax+mLlBWxcjJn49q6FIeEjV47Q7IDyjrUzgbuYRxY98J1xt/j3OaMcV0cmWD7fvOSwRXy37t52omBfWLdY5uKg/k6nU7Zlp/3kQFydnbW4AXLr3VAw/xsey63+WPpow7sy2gpC2R3uL3NDseYOeyxcRn2bkVEjEajwoSDwSAODg5iPp+XSAYpNE57YMJQl4go5ynMZrPG4TMGOt6XmrojWDBUeM6o13l13W63bIGJIc6E4ZCv5XJZdmeyImOBIEYuk9nvMxAyszCZrYDpU4wFC0meQYDwDM/DgEwIG1YeN3KIB4NBPHv2rGzXx3UWiSNgHQYGHDCBF4tFWb9BVOH8/Dxms1kBmN6VwwrEgIz1FaTX2PhEALkOPqDNgpXUKdLOMm/YaLNRZuVXmzOP1KRan2RlAhlgTCaTwh9sCOBFuQbtGGwG71xHkC+Xy8bJt17zhRGMbIFn8RZ629uI5hoSysjyL6IJWnN/2MjPjoWaB8rl+lrua/5ynWogwmOzThllwBKx2sbc92CoZOOBvsIZxDgzzxw1ZHwcuUB+MX41Lz/8gGHD/Pbue07Dy3O2NhYYITm1D51hg8TAyGAM55ejtRGrvG73BXI1R9zzWpPfOtWMw/zfvJqNvTbvreW6gbPfg9FXk1t2GmAkos82Njbi9PS0saYMJ0a32y0RWbIs8gYXjDv8cXV1VXZ/dNutj8xL6LmdnZ07dgT9leeP7QHbSz4J3DKM/rGdYEeDy7Zd6OyO/F7bH04ztW1iIEP9zAPIlQxQHEnJ8jmDHJ5HztSiXURaer1eSYWkXfRLzX7mHaw9xgE+mUzi4OCg9NPe3l5Ju7NDA8qA1OQxMC+bd3xfm9PmPvok52i0TVIz9+7ubkkZcGgQYxqGNWPZk4Mh0Ol0ylkIi8UihsNhMR6Hw2HxeDkPziiRENRkMikLf5nkNnTpeDwWNmIxXEnHAL07pYf3sRCbd7MTCiEvECmCan9/vxH5oQx+x0NLv2Kk058Rq63QAFzZIxux2q2B+zGU+G13d7dRdmY63k39GJvslaTc7777riyIdT8iFOh/GB1j3+UihIhEsOMPh/95jUbO02fM8D5sbW01Tol2f2ajjTK9W8T29nbJ/ecewKUFuicoPMA1f36kW8qKOn+3AiQFDoAxnU5LNMMpLBaejI89TzY4IlZ7nvM+xhGFwXkwRNWQYVaQlINMoRzn4+b5kgketEeSOvl/ra+yMnd5NZ7kGct0A7ssB2r//WyNXB7f3Q/2LroMy1OvJ7ORbgPK9UE+21vrQ1bROY5SRaw8y1b8NkDc9uwxdNmUa5DJMwZSJnY25FBaR2fsFbbTCQ+19cY6A+O3Rtn+sMHaxpdcy8ad5wlj6ffU1vD4dz/vFKVutxsHBwdFtvhAXsrEoRgRDZmGfqRM7rNtk+sPf9E2ZA/19DNOXebPet5z1e21zePoLkY3QNlzh50/Wd9h52gGInkc7OBpM85t70Sstqr3Opmc7p0dBtlJUnNw2WZgHmNfWIYi27rd1U5ijqTnurtd6J29vb1i9+AIYW0sDtC8ULvGF/7v+/zuDLbc1l9KH32OBgxbU5Z0Nl78jJ5gZjotI3WXwTNMsvl8Hv1+PzY3N8vBcF988UXZxtSols6+uLiIo6OjmM1mxQghlYZJi1JidT91gbFB/hjQpN4ApPCKTSaTO9EAGJE+o98YYBb7YAjTvl6vF5PJpLQDr2pENJSuPWf0N3/2uvA7p35GNA+tiVjt6GLvG21xZARlbgDpkH6n04lnz57Fzc1NHB4eNiYfUY3lcnVK6ebmZuzt7cVgMIirq6s4Ozsrhj3vyhEBFHO/34/9/f3Y29uLTqdTAJrB8NbWVjx79qwYFL1erwBVpwHSvuylstDe3d1t7AhDX3rdDXxYS5HLhtcjrSfPR/jfO0kRwUD4ZwEeEQ1FvlzeRiuIZsLnTifc2dkpc96eNBQl9eC9LBZkPsM/OTUhG0a0zUapQRDfMwiuGb0RTSdPlqf8z0qT/zWgYFnlZ2seStcjvytHlCybanVDFjOetXxqruc55XdGrIwL1oqx9XcGW4BJOxdcV4+VyaDJbfeWtdSDMbbzgXKvrq7i+Pi41B0jxfxj2WeDcLlcreezgfw5UebrdQCD7zVDirnu5/LY5eijnYoGGRG348FOQXyeTCYNJ2e3242jo6O4ublpOENtdJO1Qc4+deM9V1dXJU3buon5a1lo4LFYLMqz2cvuNtB2zsWKiLLGhDLQtdSPvkSeYitlJwPzCluQ36irPe/cm+Ub16inHRXMJZwKtjtzVJXnM5Bp4zlsE8BGluWUg37Iay5sE/r99B8O9vl8HkdHR/Hll18W+TAcDsvW/9YRtNM2Y5Z3tXljvl3nPHoofXREw5WqTVTQltGuBa5zSmtKLHsLLLAxEnZ3dyMiSlSDjoHxARgnJydlazjqb6Hh0Jfz76zwUQQRK8GBsdzr9QrzMgF92BdrCoym+/1+SenxxKOveAcerOFwWAwj19+eCKImjnRQP4ht7Lx9nL2dPEs5FlQeD4NNewM97t6el3UQRs3cT/7hV199FTs7O+XU8pzLzDbDKFoMBxb/zmazcpBaFvqErlnHwz1sW5sVjoUUgIqxQ2jY20T6Qp6cVhTuO6d6PFKdsiLhOymKAAx4zJ4bBHMeWzxQ2UCPiJJKt7OzUw6RsreIcaMuXoCI581zzTIvAwSu2ZsH2ahuAxa1vuJ/NoLys1mZ0Q/rlKqNeTs4XC7lZdDDNYxl5n6tn1yuHRgoZoMbG/82ynifjXnmMduUDwaDcuIybZ/P5+WU3ryuwu10+6zDrLcchXYfO+3LKbtEaon+Qm6PZbR1Udaxn6sTIwOJiGb0LoNaxqKWWscOlfAqaze99hHdaj6LWJ20zdjt7+8XecEOmTjYIm7HkHU58J1z/62L3VbrYUfK0G8+s8uOwojVuksDdYxuAwXKnc1msbW1FYPBIHZ3d4uzrdPpFBDltHEMbuwSsjXoEzv82uYYdpXr2GYoWw5nmVlzkjJ2OINcF+uTdcZ2drDQfkdt7DCAB+xsdZ0YA/cRn0ejUdm4xs5snCakUea+cZtqdc+yonb9Y+TJJ0mdomK1gfDBSV5lb8PLaNBC0t4b3uFJZmMWr3Sn0yn5a2dnZ3F8fFxO2mRCAwy2t7dL+gMTC+M1IgrwsEKxQtje3i5KCw8CaVggVIfrvDUhzEO6j40Ue7AwwlF8ESuBhTcdps5pX85dpB9R1AhMPGAwYka1NqIQajA9AgMhgoBD4Hnr3GfPnsV8frtXOEAB0MdEYYvi58+fx2w2KyFJEDltimieZmk+JJ3p/fv3MRwO49mzZ2WxPsIRYqzwzBANsyeF+uHFZiIjLACKgA33nfmb8szXnjufq1GQqdYXGWTwOadaZpCcnRrwv/PkuR8vdr/fL9EzZJc9Wxb+KC7kGuPN/PdBma6zycZBNtxtMOf+8D3ZsMy/5ecy4MjRB5dRe86Gfv6f/9xegwDkFrLP9Xf0JANHnCmMY/bS1cBUNrodNT89PY3FYhHPnz8vesQeW2SswaD5y23jPsbR0TOnuxrgIT87nVsP5+vXrxvGAuT3RjS3CfUc4J2fm/PC/WC53dYP/IYMibjL84DR4XBYeC475Rjnmozyfb3eaqdMAIB3wsNRRspcRBSnqeuELsSQR4fbqKZdrANhbWw2Hu1MtMzgeba2Zc6ig334nuvQ6XSKPUOf4N33+rWcBsV7DdgNFmxz5Lno79gG2Js5Ldq8YruBZ3mvU/u9uN2plFnOZFlAtgyHGruvI1YZI8gYbKFaZNdruLBj4JODg4PSd5wqbjlj3nHbnalhublObqybT/fRRwMNDMFa4yKisZUj98MEGKYWomYKrnkiIwDssWZh3HK5LAflnZ+fl7MZKDdi5aHAQABc+J1MohzG8uSC+Wgjk8lREH6jnTAxf56MNzc3JW2q17vdQ5nICBNpa2ursafyYDC4o7zoYyYtgpE0IgQGk8EKyhEQG03OBc1gxR4CCIHoic57Nzc3S+oaIJFIB8Lx9PS0GBI7OzsFFPn0cPp7uVyW3xeLRZlsNzc3xbuyv78fX375ZUH99pJQd4xKFufZQ2TFwf0YroSD8epkkEh/+L8NLoPrR2onC3j+slCmvwER8CHzFB5xjjQRrppBH9E0ED1m8CZg27yBsQpf2eFgA5x5kfkEynXi/ppy43vtmp/3NeRZNspzJMT1rtUjK22/x/LB0ZPscXZbLcsMMiyTqQvzMBsUGVzAL1a4XJ9MJiXt0qkxbGOcQYLrlevDNZePEYMjzDwMf15cXMT3338fk8kkhsNhI0LC+5DDTh+GfG4B9DktBnc06r77II9lPmQYmyIi7qyPiGjyqw3mGm8wzj70Db3FnGE3KUczkF82rP1u2y4GvxjytI/IBHW0B537AbvwHE5Z7DvSfbC7nAqKrp5MJhERpZ2Ub/CBbqQNuW053Ys+tYMCyovXTZ7nnnO8G/lrueXIhtdOMX60yU7abMxnnU4ECMBlRyzPkOlisBHRXKDNd+zXnZ2dmE6nsbu7W5yYg8Egjo6Oqn2SZW2WFW4LssZpb9Z7v4Q+OonT3pk82CA2OhhyI7OB5RCPDTwmBB3CAXwgOLz+l5eXcXp6GsfHx3F0dFQGj3CflZEViNN7vCc5dWUHJDM7Qp8QIoxPOg0KiFw8DFRvH8t7MHQdEWFi2oPhKIpPLGbiYtDYEKaeMCRbqdHmbNDQ9xjq7mPXmTLZqjYzM5MtMydbP9obYhD1+vXrRqjRgBJl7TxHe5cBooC5nZ2dmM1m8fPPP8fl5WU8ffq0EdVg8uAp2traiqdPn8a7d+8aOY8AIkC1Jz/A1cLQxpYjU/bC5PnySPenBmVDPRvLNsy4F0WBgQbvM2YGLc4NNlggypHnW0SU+RHRPIvDSjxipUAMPGirDdls2NuYrfGM3+fPGdjmfs3XfN9DQEsNIOfybSQwbjYK3SceP3vevNOXnSAYRtnApL8xFmsyLtcPA59xhx+2trbKhg/U0wZKmy7DMWSnBHKAtXHI4/Pz83j9+nUcHh4W54f7zGXbAcR9GxsbxYmDPPyctraFMi+675xqzH+nVZr/0G/oj7zW0rzk+c3zkMHmaDRqrJv0JhWTySSOj4/LDpYRzW1uLXPsRLQBSJnenpksCM8nfsMuYr0EepA+5Awq9PdsNiu7HwEcut1uYz0sehfQQju8RinLGAMixsX1tDyk/XmdFmNFf9rBA/m619bZs48ucYQj62p0iDcQcl2z/up2u43DhWtpWYxDRJQ1v/AnDgWvz7q5uSmRkOPj4xiPx3F6elrG0gCpNidcN0f22yiD2F9CnwRoUAl3MozukBv34D2oebW4xr1ehElDQZcY40QQ6PjDw8NqriBGImU5hcLnWqAkIqIwh++LaDLubDaL0WhUmBRDGmZnsWrEyigBxeK9AlxQxnQ6jcPDw8aCK5iPOiyXzTQv+o1JQFQBBQ/TIoC8kJkoAc/u7u6WvbstaCmL+uQUI3sqyGmlzhcXF/H27ds4OzuL09PT2NzcLP0AP8DICELeSftQoIDXDHRpC0IMgHBzc1O2EXz69Gns7+83ti5mInMq+BdffBFv3ryJ6XTaCK071cO5vUx88y/lArT4bKMpL1r+3KlmvGayUuK7BTfes4go0UFv7uCd6QzKI+5GEaiTPV6E0Y+Pj4tsYq7ZeUEKzGJxu0DQG2LUDP5s5HteGLjnfrgPELT1axsAyb/la1nO+70ZPNXK9r3ZeMMJkw02yxXkGGPsg/HoJ2Q1IMV1x+hxzjSRW4AodUYPuD+soHMf2GilHhhBXgfCLlHUkRQueCun23ks3EYMQeqXU28/Z8qpVDWy0QXZoPd6ySwbssOgBuz5/uTJk+KQM/9xzhSHskHIEnS/HZURKz6jTt5F0hkT8Bi8Z8cpPOn2oCt53p7tiCjAgrnEn/sGgOLohLeWRV7auWsQCPHdcyHLEttDBl5ez+Gykc8GnzhLbWdi2zk64f9EdbBvHCGxHOR5xi8f8ue6MdbYitS35ljDRj0+Po6nT5+W9u/s7BS71/Vw3T3WWcbn6EV2Ev9SW+WTbEuRhXxENAYvIu4IaTxHGKoZZBixW1CMRqPG1qQRUcJI79+/j+Pj42IcstgZpAdDWgAbMSJglstlw0Bmm1W+u74wB3V1XrYnEWVTDguX6SdvYYuiOz8/L14xFCHCgT7huYjVwTCe9PZIIHQAZTbKmXgwNluoeeyYFPa2IagsAL0AivFDuBC16XQ6cXJyUhY0EQ6kr7a2thopBIAiFH/EakEvu1x4UR3vImRJ3c7Pz+P9+/cl/e7JkyeNfsMzs7GxEV9++WX89NNP5XTeNgIIkbZHmxHU9pDkCe858UgRf/3rX+Of//mfI6K+NiNi5XX03GJOAzJIkzM4xkvnlCuega/zOGQng1M94cmNjY1G9Ate4qT58XjcGO/crqyUavzB/f6cvVNQDSS4brV7LHcNEGxQZOOiDUBkAONrlvPU2+VyLRvXljuUi1GOnGGsJpNJY52C5ZQNT+stA1fkMGPr9VXuL9fNusvv9Ti7fU41scfXgIeyDbKoH+21XLLzw23+nMhe4oi7a3X47KhaRDQABbLCjoEM9HE45TlFGYxnp3ObGuO0PHgLh9tsNitzG12Cww/9wblNmRewF3xGlDcFsP70lrCOCppf3T4bnXzmHVz3+gGew+bwFrfYRHY823nTJuMtC3yt0+k0wFUeJ4Mq18vj6AgJmRIGEbzL8irLNXQ+0RzfY5DjNnmzkMwrgLPpdFoACsRaG0AoUXnbcP1+v6R+U6blV54fkOvM73ZgeFx+CX309rYOU9sTRMgxe5GoNA1nwnhQM1Nwz3A4LOhtOp3GeDwu2xAeHh7GmzdvivE+GAyKYQ0TE2GgDgYfzt9mr2KDCnIrQeI5z9GGPIxKXWBe1lsw+Zi8TEwDITOpd5lCWDgag7DxYnD3mwUGbSViYCOLMaUfPMEpJ4NKRxI8ib1bBXzS6XTKgjjaenR0VBZkW+hvb2+XM0XOz8/j7du3pT8jVulXDhdiIHCIH9Eo6gQQoX5siYpS2dnZKXubM5FfvnwZ3377bWO3Mnuus4LIi1Sz56km3B9pRf/yL//SMLhrBgJ/9vIg9Imiep7BG3nBPpTnSsRqrQV/KHvLEPgAfvHiyeVyGYPBoBwuyjy34Q2/QjWPGHWhbp5n1L1m6GTKwMTva3smK0zuzSDA5efn8xha8fl3PGj+7mdpJ/PLO0Ihl2azWSOlIRsZ1NOgLIOO3JeUX+uXGuXnrPeQ1U6PQj7gzDo7O2uk6hkEOdLhumRZA9/jGf0cKBtzEXcX/ea5Tz/t7Ow0xtRpS+Y9g2WAnB0clusRq/n14sWL2NraKsAy4tZ+OTs7K4f1Wo+QoQEftJ3twrv4fT6fF0ec14k6fcpzGGCLzQKPRUTDdiNVClsBwONF4b1er2QnANC9jtJRDeYufZztFR+ebDvJoNsAxvI4G/eOOBnYQNnJAP/QXuwpRywy0Xb60vdkZ9ByuSxADeCVHQTsCDqbzUofwG92HlxfX5ez4xg/25jZ1nA72+Qb7XdKqkFploUPpY8CGij7jM663W7JgzfKqwlsC0k63vczUNvb22XCYtSzXdrPP/8cb9++LQard4LJXqVer1cW87BYnAGw8sKggCk8oXgvqTJ4DFgPMJlMCvoEZVq5MnnYntPeJ+cs2uiPWAkzyOss+GzPAf1vdA6Dkc9roUVYL6eRRTRPK7ZnITOux9jrVIyuiTYQKSCFyAvQLi8v44cffiiCdrFYlMmEwKI/EAZ4fehHeyjpL3jNYzebzeLk5CS+/vrr+OKLL2JjYyPev38f8/ntAqsnT57E69evGzzpOWBvDm2zh5t+yYAvl/VIt3SfIWdlztzBsYDDwEAc5WaAEhENQ4H7iHqxrgq+dtoLSofFwp6/1I2IHII/Kyj41/PFcywbxDVesXHCc3m+ZSdPBhsPGQuDBBvs+b78OYOamtIl2ofxgoxknmT5TR9jkPEbco7y1gEFjzXj79PfXd/shOGPOlNXL9zmHflZdI8BUMRqpyhHOoiC0Tbe64gbBl3NgeeoyOdAz549i9evX5fvHsfMqzam6Cd4D0ejIxYQ8jvLdKf2cJ1nd3d344svviiyiDNhTk9Pyw6HyAKf44SjczabFdmWDVhHWyNWG+8g1+Afp3xSv4iVxxpZSZl2gnj9Yz5fw3yMJ9518TPZSYF9QSo575vP52VBfDaAbTg7slHTyxl885ztnZrRbP2MHYRz2aDE7eczMsRrNyxz7UDLAM+AAKcWdgpj5ahLtjl6vV7Z0dPnZtBX98lrzxfrPffrcrmMP/7xj3f67CH0UZKIgaJSTFoMVgxVM6cVKILTXvGIFZMiKDc3N+P58+flELeNjY2SQnVychI///xzI0WKQcVIB3jM5/Oy4Pjk5KSxIBlFh8EN83v3BOrN1rZ4PPBK0CcwEsYvqN3rDqwkvEuNDZjsgXGamXPQUbIwXY7k4NHNRrEnjRU70Yw8Ocyw9sYhiHiHwQn9Rh0iovAIC+gt6HiXQSDb3pKmRq4rZeHpY8E/fc3pm/Ym29gHsMzn88IPW1tb8eWXX8Z8Po+3b9/G1tZWvHjxIg4PDxu7xliQ2bOOp4dF+zXj0PPnQ4y+z4mygWCDjd/d1zaoGRMDDlIFTYBb/mMEOgISsdqOMuI2/5jTXRHKFvYRqwWDHOKZvWaWmzXDPfMF5eZ5lZVFNog9P3MfZvBgRexys0LNgCGPSQ3IuI12UGQvGXIE48jGuutmJwr9gbOi2+3e2bjDQMuyztt1slFIbmMGC3ncPKZZH+b+8bO+x17Uzc3NOD09jf39/XJ+kp0mGXw6zQt5A899TumYr169Kp/tnW7jR+tbe+pZQ+HFxHbw2ZGQZbujY8zZ58+fx+bmZpyfn5e5eHx8HO/fvy82AEDHZN0ZsdqBCr3v6OxyuWxE9q2n4Q+f57VY3Kbv4AB1n2D80waDYGScHQLcw/v4nTMk6EfzL1GGTme1mQPjkVO6IZ8zRt14v8ly0CDQsoK6eF5bpmTZCijwuVt2VliGYofZaW35Zbk0n8/LInuXCQE2AGPoJ/obxwT8c3x83NBD5nHrn8xjBqteTwpZB3zzzTfxS+ijt7f1oPAdgzDfG3HXUKgpLQaev729vbJfMN7LwWAQl5eX8c033xQDMa8XQEhH3J5cOZ1Oyw4i5EViUDD4Bjk+NZi6YtR4RxEG19unMvkBDSgBTziUA/cT6h6PxwWgmLI3IXto87jwTMTKe+F+3djYKMa8FatDlxF1gW2DwYxtxuVznrhM2K2trdjf34/Dw8MC7gAHl5eXBfFfXV3FZDKJy8vLGAwGjSiHDUJPuu3t7ZJW50Xa3e4qTQEF40Nz/vrXv8bu7m589dVXcXx8HBcXFzEej2M0GjX6ygALYEn/wDsYrxYMNg545yM1qQa+smFnUGrZYqEeESVyaANzuVyWLZAB+Sg9A2z4hLVD5nnKiFjtKx8RJc2A+hjkooQzQMjzlXvzJgw2OkzuC/9uPs3P1Pq55uGrjUWtLMv1tus2ACNWzo7shDIggOzFtFOj7Z2ec9kDSL8gK3BauazsWMn95DJ5p8eyBjoMmvLzlMn6tsPDw8ZhsdwDZX50GXYyPdIt2ZBC76KT6TOi0NgSBo+mHNnIKZDM4cFgEHt7e2UcyCI4PDyM8/Pzsp09NoDnKesbGH8Db3jMaU7YLWRXeD2HZaY3R+E5+JY5s7Gx0QBGtiEc4ciRHJwuNWBvBwJ1BggALHD4ZmcHn/HSZ2BmuXifnMuRjCyPPd8yAR7ysQg15wH9g07xPHWb5vPbXedI+XVZ3W73zu6qOMe9odL5+Xk8ffq0tCuvxWG8a31l2WE7kt9zqu4vjZJ+sl2n+OwFKq4Uk8aDmvP6I1aImPJ2dnbi5cuXZevafr8f4/E4rq+v49WrV6WjYFaDHRYen56exuXlZVkwheHJLkue0BlQeEs6T66I1VZkEc2tbgmh2bMEg3pSLhaLYsQgJAA/ju5YMeYQPYxMv9mL54nHJI1YGTdG3BYOPgnVY7bOAHQZHnvAAIDD3jnSS46Pj+P09LQRwgbsETmKiLJ71+7ubuzt7ZX0PMADnhr+YyRytgbeErcbfkAJvH37Nv7yl7/E7373u9jf34+ffvqp7LH/9u3bOwqBfrJBSDvxtvtgQt6by3ikW6p5H/0/95nBcM0wtFecKBdgludxKHhBH84D5js8s1zeRjCzkYcHj3uRHwbdNlDgb8uJrPSyIydfz1S7dh/VHD35XTboa7/7v4EefzZWsrL17/amcd3y1vKL+yB7Ci1nIStYxoQDW53Hzb0e79wPlJOdOO4f6xK3kfIMlLjf9x0eHsbOzk4cHBwUWWpHip0/GKI2dnJ9PwdqA4UGuNm5t1jcru1BP+VzcTK/G6haFtlIQ/+8ePGiGHmMPZFzHIl53C0fsv42X1JXdCvnONmh5kwO614cKDjIeBeGf860cAqVt93Phjb9TEYFNgl2hNPDka2uq9uWnSceC8+pmlOFuZftTdM6ued1N/xmvQ//AEjt8HW9uQdnaNZdlg/n5+cNBz1yKiJiMBg01ucw1vALYwIoqaX9uVyDjDw3fJ/lMOP2S22Vj14MTiWpCEYhE8y7BEFmymwQZ4X74sWLGI/HcXV1FYPBoJyK/erVqwaTTSaTwrR4Ci4vL8sOQ6BRFmxGrA7vQ+iTzsOiUkc7zEyAKe9VPhgMYjgclucjouzQwFa7TLR+v9+Y5JSdy0WIkA7kcCUM78gD/WgPKwxrBUf/ZwOACQ+4sgIzoOLdeeKz5TBlOZTsd9KHw+GwhAOn02k5adueEgtS+si7aJAHD2Ckbq4vRgV9Tf49p4jTVkDH8fFxvH79Op4+fRpv3ryJy8vLxh773ukGAY4gQABTVwNCG045X/aRVmRFsk6wZS+SvdDwwdXVVdndxfmu2XvuQ5gQqIAH+Ia5wzVkFaDEfI6ShQ+IwnnxI3/ZaKmBqaxUH9qPuZx19/leX29TWLUxqnntsiESUU//ch0yMEFGW97Z4PF6sly3bHBjpJG6mSMFTkVyJCWPR66z+bHmGfXYGqTkCBwy8tWrV7GxsRFPnjxp8BeAI689c/0s8z8HagNWdkDY6UWUHGdfduh5XmYZ4/62YWmeYDtbG6jsNMmp4BGr9B5khtcM4RhDN43H42IbkN7MONuxhR7mN2QU9oLXddI/6EfqShaGeczecRZ+O/3Q9lDEasE2etDGsR2jth8tuyNWACIb0C4vy8caUGz7zvjwW228+S07UwBsTn+zk8XvwuHo3+2sAkA4i8UOhX6/X3ZTXSwWMZ1OYzQalT6BP05OTu70k9vrPjC/2iEXsYpO+Z6NjY3GGXAfQh+9GNwD3e/3q4iSDjPj+9mI+naLu7u7ZZepTqdTFoP/5S9/aXgLnX9INOHw8DBOTk6KIMeQoN4RKwGNIU9aDozqw/NyCtDNzU0MBoOy2HNvb6+cmglCnU6nMZlMCrjxoV87OzvlVGLnVaLQiIxE3AIWhIf7p5YyhSLKiN4TC2Ob1CT6AcHona0sCKxEIRtLfHcd+v1+eS+hUdZTMPH6/X4Mh8NSJ+oN43uLYRSB+4bdPBg/0pbMIx53FmuzGM+eC8Yar/f+/n6cnp6WyAo7ENnrYe9rLUXNubXus/z5kdaTgQQ85rnDf4DpyclJnJycFMCfAbZBM9fxtsEXKFAbsigDHBWUg+LG2YDyJmLh1E4D+CwDzQ9ZGWSD19ez8yCXkWVsm7Fcu8eGdBu/thm2PGdZko045olljQ13vKSeyzamkS1csyzt9XpFzjktNkcyHCVw/Wwsup/hE7edZ3IagtuSQYDBsetzfX0dP/zwQywWixLZ4DnGPPe9x+hzlit5jjCO6AvOVNrf329EMZze2Aa0bePUQDzr+nBQRax2sePZrLvhTUCi+QhH6Ww2K4DB291Td0f/vSsmjk/kG7LIazecReA68JxlnHnQWSm5n9D5bWAsX6NeWY5Q7zZ5le+17uUZ19vjldM381jmOrotdoI4Qu3+4Z5Op9M4kNmy2+0hsmEbeT5frRUm9X8+nxewAZBjgx3X3Tzi/rFzw/X02OVMGRz1v4Q+OnWKwfTiKZjPAhcPTEafnrAMFAPJoWqz2aykTJ2dnRVjdDqdFuXDlmCdTifevHkTZ2dnRfH7QCfvRYyxipKj7gys8/DyQDhNCoOaVB6MVBRcRBRvvRel0gdEEIh62MCh3/Di41GNiIbnnugLTEW9rLh5n5Uxi+j7/X5jLYkVF8xJPfLEzIYNfYj3hHscMsX4Gw6HRXBiHDJp2aOaZwxwSHNh567l8nZf8t3d3Xj27FnxuBBmpO3eWm80GhVBjuAAjJJLOxqN4vT0NObz24MOj4+PG7wAuMhK30IOfmmLXnzOBsE6qnmWDCoiVl4zhCURxNPT0zg5OSm5xg6r2ytJmYvFoqFkIRuSPHN5eVl2F2MrZd5hj7M9dp6LBqlWoJ6jloO5L3xvzcCMiDuKMTscajyXDWKXmZVwvrdt/Gy88/4c9UC+GEB6XBaL1UJd5D2eNTtTAJ7M76xANzY2Ym9vL/b29hr56RAy25/d/tyXWUbm626jKevGGtEnNzc38fPPP0dExP7+fgGryJgMPvy+dWPzW6NaNMPtv7q6KrsLemt3RxjRMVlO1wCynWG+v9vtxsHBQXkn4MWpL66z9bTH8fLyMt6+fVsi+Yzp3t5eAeSeUxinzjRgHRqpnhAnfOeILH2CPcc1In/uC+YX882pUcwd7D0M8Tx/Ilagjc8eS+riaEEGEDnt1HLOUcNcdpaVlm+1+W0e8P3UydEiZ5zwPGVg63kDoMy7pIe7jyKi2EPO8tjd3S22Dk7bvCakBpTpI+RtTpMCVHBMBTz5/xnQsMDLXm2DiGxc82wW7Fa0e3t7xShmYjCYk8mkGPT9fr/sO/zzzz/HZDIp78PL1ev1SmoTRgUo0R4rTwra5VxFJvX29nbxMhDJMBhAiHh/apAnnnwYNC9mHg6HZQKORqNyABUHFbL9HnWn3z0B+WwQR3875cBeHJ7zGNpDYK+u38FEMlgDwXuS9Xq9Rsh4OByWhf2swTk8PCyCnL52hIw+9PkGjhZcXFzE69evyxkcRFTIweeMFIBmt3u7FTO8BGjgsMRutxuDwSCur6/LbkMm+sf5svZQ0lb6Pa/nsMJ4pDq4qP13PxOBYl96/vtgKO4lOgF5O2QrBmRB3n0G8I9HHOOEsXSutKMkWSE67zmiCQhsdEfc3TElewht0Nf6kDIpKyv6PKehrJTvAxnZ+1fjayt81x2F53QQzw3LY8Ypp7pxH0aOwd98Pi9OH/SDz12hTtZVtKXf71cNRRtJrm8t0lDrJ/exyQYCRipgA5Bk/rdBxvf7gMznQuiKw8PDmEwmxYim73zWknWdqTZGlJ31webmZhwcHBQHBxkSeW2gHR3oh8vLyzg7Oytb28JzyArKQQ9ZLmWeo50Rd9d3ECHpdrsxHo+L/rQ82ti43XSHNjAvI1Y7bgFk4Dvmoo1Vyx1HOKiz+dQAhn5yyrMzZNxvtj9r8hSHseeoHci2Uy3bsoPB0YoMXMxrORJumUZdeTdtpx/RO6zjsTxbLpdl51X6cTKZxP7+fiPljv6pyR63wQ47+pP/8AvpxuzW+v8J0MhGbc1oyiErG8Y01CFtMxPnImxtbcVoNIrz8/PCfK9fv47FYlHSZU5OTuLHH3+M8/Pz0kHUKXsOPaEADACAbrdbwqsMct5t6OzsrCg8QM+bN29iMBgUAROxmtC8B5RqheZJSU45Rjr9hyFMOSxqd3pHRDQO6yE6geLJCyQZM6dHOW2J8WVyWJEhUGoeW6N2yvBCMrd7d3e3HJbHvf1+v+EVYJJyoB7pZzUvhYUxW9M+efKk5DgDGG08wh/7+/uN7YcvLi5iNBrF2dlZqbvbxLNeN2MFhmC1AMtel2zkPVLTiM2CPiIawhnP9mQyibOzs5jNZsUDyO/Zs8N48HtEc0E/97FjXL/fv3MIErKKZ7lO3eBnC27KtuePOlghM1+zwU/51INyc5vavHOUnQ2jHOExgLCTwDxr726W+X6/+yCDD/cZfZjnjMeIvrMMpxxvO01ZrhvR6VoaAA4E8vQZQxuCnH1wdnbWAHpul40W85X/ZyDjccmUZQRrNrrdbsnNzv1uGZOdQp8T0V+Lxe2OPO/evSu79RjE2+jLxr/7M6J5zpejjXk+PXnypOhVO0Ax1CjTO1vh/IK/eEfevRPjHp2ajXQD3Zyqa2cfvIqdhM6OaPIqThSMXLzbGxsbpT9t4FsWum84/JZ6eX5gA+JkyDahZaHHJ3+3zsjO0tp40kbPGe6jvFq0ocZrfi/PEfV2Kp7rSMYG8gaZ5D4HbJinI6LsuEq/seU6dXU0xGnhAE33mcGv+cVZNPAIGSS/hD7ZiT41r6OFPmQEaYbIBi5G7fX1dTlN+uzsLCIiRqNRiU5sbGzE69ev49WrV8XzT2fs7u5Gv99vvB8kT6RhPp838uYwuF1fTxzv57xcLmM4HMbPP/8c29vb8fLly5jP5yUNyYYyk9M5iyg20oiIUnjtBJER2kXfGK07tQiPK95b0rEQJE6xoi88Ie25Q5DQB1aQKPLs0YVyKJY6A86IzBCZMO8Armg/7+t2uyUqwX14JuEzPCCM08XFRbx69Sp2d3djNBrFwcFBI7/VEazxeFxydmsL7f1OhLa9n9ljnde4uK9rgu+R6lQzxDAUj4+PYzqdxmw2K+l0yBjGx7wRsUq3wsB2+uXW1lbxdHvRN3MNonzql3kpIhpKxoqI+uBU8DoPG6zZiKHurkPEXSXh/1kuZ7JyzP1tY8Ge+mxoY+DUgJDLzUZRdmi4PL/DXkHuybu92ID0fGMRfp6fvI+I92KxWhDrtTRW3sgr1xUDIRskNbBnQxCjqjamHlf/3dzcxE8//RQvX76Mvb29ch1gZK8o7f8ciT45Pj4uEXKM1oiVc8AOSIP2DNAjmpsXZOcHtLm5Gfv7+8V+ICPDaw+JkuL9ZvORmlHrlCFHW+34tByrZTfYcQDxrLMtMjDBLjEwcnq6s0lms1nRt7zfzkKAPO/w/HOqo0GGF6dH3AUVNVDocs37ng/ZCWCgkO9HxthxWJPJuU18JuMCGy0b9fSnDfhsW+CItV5AR02n0+h2u2WrZOSKo2iQeSPrhiyLAUGbm5sla4cNjO4DXm30SYAGDbCXz//zIEREQ7Hn/Hsr5d3d3RgOhwVE0NDf/e53cXp6Gv/3//7f+Omnn4rHICLK1rZmdjoTxQNouLy8bOyl7/p5L2qIxeGUzWp/GIM0Ca8v4DmnzWRvOO1GSPqkWgSV+9X5pBaQXghugdjpdBongXvcIlYGtL0LFjaebAZijqgAhGx0ea0Lip9ojA0SFoTbI8LE9OJvG++AKKdS0OfL5TJms1npE1LSvvjii8ID9mJ3u7cpVCzwxwAlTYFJVhPc9D/35rHOc8XX1gmvR1qR+/36+jomk0ljF46Ipvfb/GUwbGOV8XH0Aj5jfiLYDeBrRrw9bwB7e5fhIYMODB14njrVDE3eY17hu3m4dj9lGshkQ8n3ZrmR782feQ6ZbYVuMM64eP7k+eSIgstCTtrZYpDhcrnXeeaus51cvJM8dgCmQQvjRWTZzgX/UW6WFY7CZdlR8zLm/ofoyzdv3kREFAecDWfPgV9qFPxnJyLa7MCTD7+1Q4uxrYHoGmWw4fF88uRJiVCMx+MCTuFNywg2I2FM7cm2MYxDIqK5npVFw3YywusGG9SZzRScTuoD9QBEkHWY+5A64PCz0xbDGtnG9tE4DTPY9zw2D3c6qzUiNQdJnst5rtRABv1XG0vzRZbtjqbYBrIe8fOWg7wTJzL6w3Ystg+6LGIV7e12u8UGzRkko9Go9PfNze2p4azVYGz5nTIBcbU+taOIsaNvcdz+0h2nIj7RYnAoK8iM0l1xPOYug45koDY3N+PJkyclPM61TqdTTvcmVQpPOek1KBvegbFKh56dnRWPAoYoHvBOp1OMCxsJ3E/9ESxmWNZrIHBgFLym9pIDKOg7vB6Eq+y1g/GsmPDS0P6I1Ymh9Km9sMvlsgEGDAztIZnP5yVliOv2kDgilYVZ9mZ6gWYbsCOC8eWXX5b6gOQx3A1gLLCXy2UjdSKiud2j34+A7vf78fLly7I4n/J92CJeGAsXGxAeExu6AE3AKJGomhcsC6VHqi96hpgj3q6a+QkfISsQqigthDFlErUgLYrrtVTPHJHyPLEC8P0odfjBXvLsYbOx6jlXM3gy2HDdayC47XkbTP7tQ4DvuvfZYcP3HAFyVNFAjOfoHztAsuFOmfSjo7oGQDmKTjtdPwwCZKMNP2SUZVweI5dlvUjbHP2gvrSBz77X/ej3XV9fF7BBfrb7C3DrFIrPhegbzmVCV9gpwP/s4MxOoIimcZqdjpbfnU4n9vb2imOCU92zrru5uYnT09N49+5dXF1dldRMfuOd1BO7xPVmDmBc2tnHvGK+8WeAwX9SdXgP6YGU4zVo3qJ2sbhNScPRh4yDDw2q4EWnWfEb9g32EtFFU5sssgPBtoijJrXxcj9mZ0htY4VcF8txj1d28OQIKzKLlDnLMsBGp9Mp9qflIXLJdnWv1yvHA0RETKfTYvfe3NxuP+z1FLkv6G/3seWtbXPeif78JfTRQINKUpm8l3L2pBlsONSWGaDb7ZbFSHimQc+z2Sx++OGHePv2bWxubsbz58+L58D5k0RMSGM6Pz+PyWRSDHnCc+xowiQdjUYNoxwEn3ebwTvPfXjkMSyNZnkOI+n6+jpOTk6i01ktNvTptL1erzAf7WYy5xQOr3HwZLESxsuK4Dg+Pi7CiLHxOpGIVRqVjQQmkD2JvIvJ7vQwGxQ3NzfltG0EMuiZ07g3Nzfj+++/L/UFdGBY8i57mp2CxZoeFoFTT28KwA4kL168iP39/ca2uLSbc1s8ZpRVE34WbNnD4miUwUn2rD5SxH/8x3/En//854i4u6gZ0GbvMALf2xLDv04HiGieSI83L3uf4fXsJOE+HB2eN/Am3m5OA87rzsy/Ec0tpzO5PibLW9+bPXFt3/Pn/M5a+evuz8o7j1muO8YFUU0rYsbTRrfH1m3hfiv/iOb6uwwa7W3N443cnkwmMRwOGw4qwAEyyZ7fWnTEhqK9026DZYCdRzni4/53fa+vr+Pt27fR6/XKmg17wx0F/hwIO+H169dFB3DdgN7kszOyXLeRnOdrxN05R1YD+hmjMWKlf0n1PD4+LkZbTo3J74lYpfCgo20z4SStRbQibnnHaxPtyIiIAhacukMd5vN5DIfDclYVbTk5OSl96aiGjX3q6LNASPmx/Mwg0ONBPQwIDGBoH88apDN21sl8z05Syua+7ECk3Zl36G9vu23+8FhiwLPJDHLFNhSOAfoUGYX96b7DTsVhjpykHd4il/oAUKhPTTdk53Guxy91in4SoOFJAhoyQmSAUfAIe4cCs1Gwt7dXOpiV/KPRKBaLRXz33Xfx9u3buLq6KgZ8v98v5y0wGCj5zc3NODk5idPT02KIoFh2dnYaC0h3d3fL4SpeO9DprE4bZ0tLchOhi4uLcj4EggZBYoMbRqMODo3i3ZvP5wWl4rWw8ZqNWn7nXWWA///9ShudD2hvfUSUujiVA4JJnauIt5hyuI8xzd5+76DB+5fL2ygD6Lvbvd0EgIiK3wEBLniHUyQoY2Pjdvvh6+vrcmAi/YFh+ObNmzg/P48//elPMRqNimeGyT+bzQrYYOEofU2bPRnhV3jd+d+MVVYiNcPic6Y///nPd/rDniH+A4IB7dfX18XLFhENuUJ+sNMTIlayCn522VbGOdLq6JxD6U61NHjhHhs08JlBvNubqQYA/Jv5yvfWwIojnrnM2v1tZWSg4Xe2UU25cT0bgxj4WZbYiKcvvdEG5fuzDQi/L2KVZrdcLovhhVKPaK5j6ff7xUGU3+G6URdARAYWGI4GTb7fdc7jGREFbLx+/bp4N10HDMLPhWazWbx69apEku2wjGiOD31og9hkHrFsz/cydp1Op6ztw8GJgwzQAcg4PT1tjDu6iHVaBhIGjd5hEUdkxMoZCD8g8+Bpl4/eNRh2FII6AxxwsOGQ7fV6cXp6GtPptMGHTtc2qLMz0vV0ZglOB2/Ewn+DxIgVCAI4MS70ked4BiS0OzseLH9dBn1jI9vlGBSy46hTsbOco15Eubz+1Y4zFoiTwQMxlsg5yvOOeOyACg9gi2aebaMsuy3bso76UPpooJFznhkcEC6Tysralc5eNDr7yZMncXJyUhbL4I3/7rvv4t27d9Htdss5CKBmwADejOvr6+I9wHDnPoQBBq0NAAwJjAqMAnIiI24N99lsVsJhNzc3ZXFOxO0EPD8/L6ldRGVgMBbXeMco+oc6gHpz3qaFZTYycgjdAAdl6jKzsRzRDJflsbEgycrWXgjqyn9QP0ADvnHuKIp+Z2cnDg8PC/igfuSCEgWh7kQyNjdXJ6PivQakEWWxwccEPTo6io2NjXj58mVDuGG8sr3tu3fv7uRmYrjaYGDcDCKzoZjnRM2r/TlSm6Hq9DWPgXnVOcikRnmxp+eA54bTmTyvUOrME/gMxWnQ2eaV95zIi8uzEVkDEwY465SE+87tg7KC9TW3OT/PO7PR7vszCLB8ctk2qK3QM3Cw8nWZPGPj3ekxloWdzir9wEChNkbwF+9CYXvLasplDHO+cgZcua+dKuG+oWx7rGtA0WPv61dXV/HmzZvodm8zAOwAyWkov2WyIZtBtHktYjVWXmzMvZkyKMdYhN/Q4YPBoOFgteONBd9OZbFnGQeF52XmUfgjZzPQdpxtBs2ADJyQrlPEipcjosEzmSdJRXU9er1eWYdqx6U/GzzRJrIN8vwxT9sJQJ87NdxjxfzOwLw2XyD3KToAuWKj2s4gyy5+9/zCrkM32P7KcgFwRVn0qZ3d2EK8hzLIAsF+xd7BqU30bLlcFue7gZLrkh1M7pc8NtAXX3wRv4Q+yTka7igbXfbiZG+Av3sAu91uPH/+vKxVADxsbm7G+/fv4/3792VQ8b47tOaJc3p6GkdHR2VrWAsNBJN3Zep0OmVSwTA2OqkTZThScX5+XhAqIIO6YZzQFt7tdSNeuA7Dokghp4bBfA6X5jUSRuNWsLTPINBKrtPpFOFn4eVF3QgFL8C2Z8S5kvynPbyDcn0PWwLO5/OGB4htAxHogLHpdHpnsd/W1lZZjHd5eRmnp6elLdTPnsOLi4t49+5dLJfL+N3vftfw/MADg8GgHN7nVCjzMUKdNC57150ekQE4QOiRbqlNAEY0D+yzRxABe3BwUMbPvM3/HE63UuMepzqxfe7NzU1Jx2MeOeeYejhaR32z/LMydJTDoN0gynXNIKQNoJhs7FtB1+63w8HP+vkamLgPCGUl5v+un99j54+VuuVIVuI1ryr3ONXBaVsYL94y9+bmpqSLZJBW84LnNtAf9rxm8ARZdlkG16hmGM9ms+J8Y12ZDZfPgThbymAzO8oy2eHJ7wYQLstk8L9cLkv6rcvi3dPpNN6/f1/sBv7Mv/Cac/dtoDOurCeNWKVXo2dxjLqd6CPklJ0BOOvM/yY86oBtp8Rj5GLwsntmTtVmvlnv2lPvuWR+hX8tu7GZ/IydC257nj9Zh9RAiB0gfM5rXWxDtJHXxNghZrnO897tMDth2K7W442zazAYNMoEuAH6WAi+s7NToq+WQ3a4uI+yowknsOfGuravo48CGjSextV+h1DytTzBiJXBRjTDSH9vb68sVGIQ3LFMRLZzPT8/j5OTkxIy7PV65brzt1msycBFRCkDkMNEBpQsl8sS+vKpoqyT8H7dGMMGLT4tlHIjVuGpvG8yoUsULoxg4zRPcsqjn526YePASpFnMP65n4ljrwTlepcJT+Yc/jUQBLAhYHnGi/GXy2WJIszn85hMJvH27dsYDAbx9OnTxtZ34/G4eGYYCwwTJqojOfQH+aIGkUdHR7FYLOIPf/hDObGcNLj5fB5PnjyJyWQS79+/L0LObaAfEIr2wtN+Aworwke6JfNnvm4DirkBn5KOaOUJ7yIrIppgwsKX+XZzs9o7nN9ns1nDC2gAAMDwCb4+eRhwHtHcutBzyWAjK7I2j2v24rlO/ryun9d5ujLA8TP+3PZ77X5HDWrvtNMkt81zyDntGFO0xXIv90OOJnhnnZy6myMebuvW1lYjfSQbs1mu5n6v9Q/1y2mreRzzuDB+Z2dnsbW1FQcHB40dgj4Xmk6nd9Jn182BDCJq/X0fwESOPH36tMgZ9NjNzU2cnJzEyclJTKfTBn9nZ0nEKiqB/Mq8441b0HG0EQCS06OI5mOoezt7O2kiouFYZE5grNoOcNpSdho4K4O+BOi4fwFF6GzmYnYmMEYcDMjz9IH/TAYqfM9zrY18n4EgcsbRU+yM7LCKWEWL0FGut+WcwYa/R9yel8HhoshAxsSHKJLFg95yRAZexIZxn7i/3Zf0AWW6H969e3dvH9bok2xvi9LHmxKxMqCNDp1mkhmS/8+fPy+H4N3c3MRwOCw5+ZRBLjYGI9EA0mB41sLEwIDOB4RERIl6MJHwWEZEnJ6eFqPSzHR4eBjb29tF8bHWoNO5XRhOG0H4/X6/sW7ECw35M1NT/xwOtkecCZtRfhaSMDLGflaytNlRBoMRAy5HMGgb73Xo1ZOP51k3sVwuS24jdeQajE70otPpxNu3b+P8/DxevXoVZ2dn8eTJk+j3+9Hr3R629+TJk7i6uiqLo7rd24MVaQu/TyaTsn7DwhphB0D9+uuvyxjSrsViEV988UVJibMHhklqAxhqA9We1I90S5l3fT2iua4IB0Ov14uzs7PGXLGXC573HKHMbrdb1vEQIcuL93FOXFxcNMC+vY7+77TAbCRnQ8YRPQOODBwiVoaN5WVuC5+tNPJn3m2wkBV2DWzkcfFYtZVf++y5wfjQz/a88jtyhvma+8iAzf/zPcgl+IR353dyr0Eo/R+xAps2ZmrGod9vB07udyjrl0xtoIM6HB0dlbMccpt+65TnQXaA1fou553XgETEat7W+PLg4KB4n5fLVWYCO0uxIDwbdhGrKAQyBWcJ73a6b6ezcrDy3bsb2iaIiGKXWP6g57kXfkOO2fD3GRDYWOy6GRHFC29gTBlEOehTzwMv0ud52x70o23F2hib9/P4WsbYDmnzyGc55/HPzhgDMvdXdmhQHqDfu466zIjVWT6Mo3l2MBiUgxxpw+XlZWMDoIgoEa/pdBr7+/uFv30gteWi+SAT/e7sE4/VL6FPkjqFgvXCZwzQfC9GNOSw3NbWVrx48aKkIgEMDFpYG7FYLEp+Wq/Xi8lkEkdHR3F6ehoRTcVL+I9OAw3iPQc1bmxslJO9bSQwOai3DX+epx8YwLOzszg4OIjBYFAWiW9tbZWDlmAuFlyhjNiliq03YSgMe7/XKVYRdxc3wSxMDDMJ/eEJwDXnJkMWZg4JRqzSQ+y58AJ38wEChAWXjDX9RxSIOoDgR6NRAZDb29txdHQU79+/j+FwGP1+P8bjcdle8M2bNyVa5d1ter1eObCPukGeeLPZLL755pv44x//GF9++WXhi5ub223jvvjii/j2228bighvOG3xdQS5+81jlJXg50w1b7e/29jr9/sxmUxiNpsVb1NEM2XKjg3KMbC8urqKk5OTEolkHUb2VlO2d3ChPswVntvd3b1j6KwDkwYu5NdmUGTgkcFDDcBQdyvR/KyV9IfwIM9k4zy3pzaWHkfPE+qYFZkVpJ+n7hhPtXZYSdrAt5w02LCxlsEF765FITNYc/v97nVkRZ4NXrdl3Xvn83m8f/8+Njc3GztnfQ7U1mc1IzSiebaOr+cy26LO2C3Pnj0r8j3i1pE2nU7j3bt3cXx8XJyQme8pc3d3t7GRgT3mbL1OXTD8vUOUATg84N2d7OijHuychg3CZjvdbvdORgORWvQfOo1NdyB777MT2fPNMseAh/bncaLtnlc2mGtj7v9+l6+3kfUE92eQgh3rSI7HFPBhsMPCbAMf+gQQ6vR05EGv1yuRDcom0wL7abG4PVdjOp0Wpzjt8EY6ue0ZOLsPrI8sO38pfZJdp6gEStor6o2MsocfVE45w+Ewer1ezGazUgbpLWdnZzGbzQo42N7eLh19dHQUh4eHjYVI9taDPheLRRlIJpPrGHHXaMboNXq1h83rNryGgXpsbGwUcEEko9vtFhB1enpavATOpXQ+YkaSNeVIvTPDGJXbs2bUyvi5nxgbPhuEYBiwvqXbXS10pc30gQEH78Eowwinzn4OoYZXhvE5OjqKk5OTGA6HcXFxEUdHR7G7uxsvX74s3hTCvbyH9CcOZEP429hCeG5tbZXt4r777rvY3t6Or7/+uuwydn19Hc+ePYu3b9/G2dlZw2D1ia32vDJmOYz5CDDqZEO25gFn8wXWU3EN4GwPDGlMdjawOBP+IIrodDfmv9N9AKGU7/dErLyQPO90PngCHrUgt2KueV/d9gwy1j2XPWc2eMyXD+FDOxVyeRlQ+B1tysnvZw6S4uq0t1rEIZfr350iR10Yd+Sk78kRIuSec+4Z6zYAZaMGPvF9Hl+/x+1AjngcfR/3rOMNnEmHh4exsbER4/G4eu9vmTLQrFEG51lvZqdGW1l7e3tlC3ocDJ1OJw4PD4s9glyC9yhzMBg00ioBK9bBeUdHZAUGve0XO7XyXLWTFZ6PWBnETi9HV1IOG6IAXLCJzIvwPPYCOh97x84h3s3zOZ048z//s+y7b2zzczXKY55Be77PTiqDOJzDGexaLnU6t+tzfRyCZQT9enl5eWctLBkYpDGhw/IZabu7u0WvYWuQiu96m+dqEYrM99bFv5Q+icvDA8aWswYQEc1zFuxpcYfv7+8XRiXVaTwel0XdLOrFW3N5eRmvXr1q7CYFCue7PY02yCNWaA8DeT6fl/QmdqwxioYBzs/Py7vyrkLe6cZgibYz+egPmM+DS7sZZIcRMV7oa3sIXEbOiyTkh+c9YgWE+E70hN/sSfBaFPqYdDOnIVmwOCzrsba31+Phcz5gatZtOOWM9Diem8/nJUxNviIhQxv0Th/z9pUOb9NPi8UiJpNJ/O1vf4t+vx/Pnj0r2xkPBoN4+fJlWRjuU+kpJ09K2u7+/lgvwedA7h/mHNftrTMYRyja2UAEczKZxPn5eSOSZwXB3PKGEzkKG7Fab2EBjIxgQwOvJbL8y23he1aw+XPuk2ywZgM/K9lcVvbWrlPeNqB9ze+r8bKNttrv9IPlt0EZdbIHkd9t0NNe84K9x8i9msfV99oZFBElDxsHgduQQbB1Re4jO3Pcn5Dl8rqxyH2XCYPx9PS0bF7wOZH7BL4ymVdq883jm9dLeU4RRcdAZGOXyWQSb968aUTSIewidKJ5ER48Pz+P0WhUAG/Eav2EveqsW3CZtMmLeG3Aop8NcAxQsTNytOf8/LzYLL5ub3zESt/jyETXOQ0892PN4Waw4XbZ4Pe4ZbmR+YE5brno323rmHfyu8w72bGLzDHgsGzmfsCBHbBuix3Mlm2kCPu5y8vLshDcoIIIFeDP9eXZNlmdnSg1R/cvoU+SOgVz8tkLqa0sah5eM+1gMCgTkDSj7e3tcg4CqSsbGxtxenoaP//8cwnh0XkO9TDoXrC5tbVVDGOeYwIZeTNBXe75+Xm5F/DCROdeJiTha85hoK14PRBCgC4WgeFNo514QHN/04dmYgsBM5bHB8Fizy9Czf1hVG9B6L52HwOaXKYZFMCH599Aw4TB7r3I8RCzm9R8Pi9rKXZ3d8vkPT09jePj4xgOh/HkyZPY29uL0WhUwAB94hO/AR2k/hkI93q3C8T/1//6X/E//sf/iIODg/jhhx/i7OwsxuNxLBaLAmqZkDVB57HLxpr/P9KK2ry3liHwH14xeCyiqYSm02mcn5839rD3XMjGuoWreTzfQ7TVdSNFkvpxne/2QFK/DBj8jNuSv+f5kxVxBgNZWVsh3tf/64BCLqONny07+I5xE9Fc6J3TM2vRhxq4wcjxO5jbXLOHlzrYqGR7SfrAB3l6jPyfOteiz3nO13aWytdz/7cZSP7svplMJjGdTqvj8FukGg+b1zNIzFGm2jN5Lrp/9/f3ix5hQ5mrq6viBON55NHGxkY568QRNniVskmJIb3bfA9/OU2UbAvPmQyusqM17zJlsO11HE514r2ur9+D7eL+xTno+ZXnga/nuey2+7OdAQYZHh+uZ9D/EIPZDpg8z/JcQ45SPnMYG8Lyi3cT5QL82eG1XC7LYdJ52YB3Ro24BYD9fr+sn+n1emVLW6f753I87pAdQnkMDHB/KX2yiAYdjWeGcBLRARikllJig2E+n8dgMIh+v1+eY8ETJ2///PPP8fbt24KavWMBiBDDFJDBZKN+5DGi3GAMG8SkQvAcZcJwnJtBTnavd7vLDPXngL+IKOAhounpXywWxXheLBaxt7dXdm/Y398vTJUnMmSvgKMe3qXChrOvOSTL9Vxm9i7wLEDQkQEmixmTMUUgWvgQvULAUb+8axXpTEwkeIkIVw69kmI3m83i+fPnMR6Pyx7zCAJ4ivFl7Dg1HN5kAfn/+T//J/7t3/4tlstlHB8fx5dffhn7+/tlJzQLYQs+e9k/xCh7pLtkReH+5Lo9zxG3O9GwyNs5xpZVNvo93zESGEsEOkYA/Ot3It9y9COi7pnLStIKss2gtgOBtnOfKT/nOex7IlbRZjsuKLutnPw5K99anWp1sYyhDy2/TK4X4+NUUKdG0U/L5bI4qZib6AcWzHonRBxFGUz4tGc7YHIbstHj8Xe9DHYiVgDJfVsrx9Q2Fozx0dFR67O/VbrPkIKykRqx4i8bhTnVZ7lclpOyuQ/AcXFxURxg6ILFYlEcm86oABwQTXf0xekztj/Qc+jTiGa6tduZ06rgVSJ01q/uBx/y5oXb9AWyDkcg9ec3bCrq7jrSH2SKOPIM+AFMYGNYn1IXp7Db1qjNGTtULJuzQV2bZ/S7nVO1e7OczA4qnrEzvtfrle33s7McHsBB7es4RanXbDYrm9ZcX1/HeDyOs7OzRpTKdcjOibY+MMhe1/aH0idZo9EGHsj3i4iGt9uCHGbc29tr7Mds43hnZ6fsMvXzzz/HDz/8UIxRHzoTESVcB3M4LWZ3dzdGo1FjW1rIQoYyvNsCTERINO8yYyaGiYbDYanHaDSKnZ2duL6+Lp5VT4KIKNvbgdgBHCBfhyTtoYB5ebc9EbzfC7zdz/YoMoYsxMfDj2AxkZ6EkON3xoXyEQYOESLsSJEDjNHvzpv3+h5Ch4CzXq8Xx8fHsVyuFuwTZQCAsKPQ06dPyxbIjJujVQZo0+m0nINxfn4eu7u78be//S3+6Z/+KV6+fBl//etf4+rqKp4/fx4//vhjMW4yH/Fn4wLKxtUj3VKbdyuiuU7AgINxZjwvLy/L7mPmQ3ukHPp2eZ1Oc8tiHBI+rNPK1grcbeA+e/T8ezZQTa4bc9vKzErSbauBCPoyA19+833mxfsM3JxaYAXr8vzObAQaWDmCwdzNbbYy9259/E6U1jnsfifyG3mBftrd3S2OLa/vyh5vR65zP3u+Z4MH2WUeqxkrGXzkd6z7rWZg50PRHqmZwuZrbfye+7zTud1SHf2K/XF+ft44HHg+vz1keDAYFH3raDlyiZ2jrIPRWd1ut6Tlulz4jGwPysOI5zt8Rzoxz2Fz2U7CJnOKNB50nLSUbcdet7tKH89rSkjpQa8yn7z4G/mNnnQWhwFOzTljWWjD2mDLz9hYzgZ0jQ/cLwZOWT/5s6MbTqfyb5YfTney8yziNqW73+83yux2u2UTlOXy1qlKiiQ7v3KQdObfXOfaOpQ2ylGyD6VPkjoV0QxX2TDE8POiaiNre7FIn4pYnVvR7/djb28vDg8P48cff4yffvqpMDBG92g0KoiaCcrgeG9pJg8TljrbSCcVx95KJnfeRo77eA+nEe/s7JStWVnYSHtQghgrXkRMxGM8HhfDhvrBZPP5vLEI1qd2Ep71zhSMA21h8jq0Rz92u6scQk8qjAX3FwvIbPBRnpE9bXDagfkEUGQDnW1rh8NhnJ6elkNnENTOS724uCg7jdHnTFrvALVYLMqhe7u7u6Ufa94XzlU5Pj4ufHB8fBzffvtt/Nu//VsR8Ht7eyUyZMBW8zTaq+D5kif8I9XJhqznEHOZRYtnZ2cNI9QGKAKf8YIvvd2yd8Czs8OgESUOP/rAN8AwZVFvv5P5ZOVHG21cG1hF3E0F4TPU5nnL9+bnaVMGvJ7HlNOWnpmVL21j3uYIVCZ7N536lEFUdq44ksHY4sTAcHK/MqbIUsb//Pw8nj17VpwRpJf6vfCFxy33UY66+DPfme/uB3tOa1Qb+zaqeSsf6e76i9yPnt985zn6FFCaPcxnZ2dlV8SIKCnTEc0D7Pge0TTekFOObJHB4U1uqAuOSJypyDBSxA1szs7OGtkb6E/ziddAArZ9HecmThOcn8wpZKmzI7A93G7PAzz5yEOijDmlnvvtzIYsGyw3rXdrQD0DlwxO8jsiVrtLOa3Wczzfz3VHxmv3wCc1OwAbmN8Bt7ZjWazP+JDGl2WAZUdNhjBm1p3ZgfVL6ZNENLIHx0aUPT2mGkBh8sxmszteh7dv38br168bC2/t+XYKT8QtqGBhtwU43m7v/MJv2WsPw3shFGd64HmgXRgmPk32+vo6Xr9+HcvlsgAoJiITHWWHEHBqFgZK7lcmNca7o0D2gFoQ2PiykLGHBCaj7rzT78ZT43CojQnnkXrcGQcEKn0AyPI4UP9erxej0Sg2NjbKQmx2NeO+0WgUNzc3MZlMYmtrqxy01+v1igBGKLLP9NOnT0sqzGJxu+ibsfROQez0EHErFN+/fx+LxSL29/fLeRzD4bCxmN+eB/N6NrIy8HikFWUD28ZwjkRcXV3FdDqN4+PjmEwmjQgGXnEb0cwZ+t9RPsAzcxBgbmXp+QBPU9fLy8uyp/719XVxHPj+mrK08qJu9rhlA7VNWdgQ4XtWev7s+zz3Lb95T5bxEXcXTGaPHddtaDjyZweH84h5p0GFN+bwOogctcAwc5scEXIbmOc4irrdbkm1Q3e4/3nnYDAoG0FkEOh5b4eXQVkGXh67df1tqhlc+funMBD+s1BbP+HBz79/aBQZfuKMErzH6BVAxnK5LI6yHIGIWEXtofl8Hqenp8WmsV7AzohYgXpsF2wVIiJ2vuTIGc5e2ywG1C4f3c2cQ57RHsCQoxIZRGFfsN4U+VnjcTsAvKMlfZ7HN+vXbLy38XzNgQPZiZplmp/lHXYgOdU2l5XfQbq2nZI850M2s9MBXrNM7Pf7JSWUTXCQoePxOA4PD0t7DZ5cVzt4aVsGJ7zzYx0XnwRoRNwN0Wekyf+aJw9Gcz5at3u7u1TE7cF4nOrtCIPRM+dfDIfDGI1GxfDnfT5gBqXCYGJkYPhyAvjm5mbJo4NRSKfxwS1MEIzXiNuTWiNWHji86wYmEVHOBGGXmoODg9jf3y955fRZRDM87x0pHL1wpMIpWAZ9CCcDDbwL8/m81Mm79jhXleedvmZDzhMmL1bDgGLSOexn0GKhCII3H7GegsX9V1dXZT0FtLu7W/iFbUkBK6PRKJ4+fVrGkH4jZ5KQJAfhLBaLODk5ifPz87K17s3NTYxGo3KiuOsP0V4ru3Uems+dvv322/jTn/5U9RTBk0QHiWYdHR01FLWFo50BEc1wOPOKQ0Gt5DzX4A3PMxRFTqN0tJZ7DVSsaFHWeW0HfFTzjvNsBubMsZpCqF13PTJAcR1cj3VGrxW560hZGPXZCMqAPIMUy+ncDwZgBh827jOwd3SJ+4liUi9SV9AJBmHok729vWK82Wjid0ev4Lm8CDdidYCq9V6tL9f1fxt9/fXXH3T/f2Zq8yi33dtmVBngRzRz1NFX/EZk/OzsrOiDfr/fOP8iR0OZC36OLAxsIHTW+fl5I+UInW8Aj16xl53fIlaywU6aiCgyyxtaOGXUUUNSr6bTaZFz6Hn0NDYT+pxNMSynnFqOcwfHoUFG5vccCc6pVJAdE7Z37Ky1jDcg8LzjPsubGkCxTHMU1vdb5+MYY0yzE5ldP9FZ9D/1cwZNRES/3y9AA1uIQ2V5plYv/nL0pnbNv30MfZLF4G1eOv/GZM2r8SNWgh6FwQTodrtxdHQUr1+/LmXA5BiHCAcMhYyuyVHkHATnVdtYBmCgZPByk4OJ4Z9DrqRtkceNAQxIII3JCpb0CrxnzvlESM1ms+h2u41Qoo3w5XJZ1lEQJgM08R57AJn0vV6vLIw18IlYRYgcvu10OiXFiMlg0IQApT+9hmWxWDS29oUAH3gS8ZJQJsCT+uetkhljDodkss1ms7KIijGnTZTp7WsvLy9jf38/dnd3y3a4nO5OfwIAUSQ+O+Tm5qax3755v23CWvhk5fBIEX/84x/vgAwLRlLeLi4u4vDwMI6PjxsHRUWs8nWdDkc5zAHmEN5HewKzPPO+5xErY5W5An+hdAC1Dok73dDGs+WiDXPzT/Z+Z4+ZyzPQapPLNcCb73Fd7gMbebx8v8ePsUAmGRBmLyl962fcD3hX7XyyIUM5jm5YyfqQMhR2TgW9vr6OwWDQSIMjejkajcpZCW3t9lg6TbhNybfJAY937v82o3q5XMa3335b/e1zopp3OaJpvELr5PZisShnX5ydnZVNSWazWUnXZPMary+KiMZaw4iIyWRSImLoeC/09tzAW23ZlvmIqIJ3+8zz1fZTXscKb7vP0LssSO73+2WRMW05Pz+PyWTSmDeUy3omO1MwpOkbwIZln3+nLjkS6FQyAwXLNdtK/s0yxM86bckOajsLag4f+psyDHQMNhhP6m870mUCZtmQxutdsF0Zd+wobJ+9vb2yrtYgqI2n2yi3M4OwX0KfLKKRvVN5AkfcZQb+7+/vx/b2dlmct7OzE+PxOGazWfzwww9xenoa8/k8hsNhHB8f3/EyjEajYviBGr1XNJMXrydMzkSnfCP1iGgwBoPstQwoqcFgUHIySX/ijAkWSmGIEw0BiLBYjEnEojLeZQWVvTBmYt6Bx8STxMrcBhgRBox8BFZtwqDISUNC2NDfCCsLHSIltI1J48N/bEA4AsZ6GYACyh6QhnF4cHBQojaHh4fx/v37AtIcuXI7CTlGRIlS7O/vl3EjQuKUNgTu+fl52W0LQUl/AHrc/mxw1TwMHyoIPhfKYBnexQuIwmHewvN48XJfo1QdvjYIBlBEROO9OAysjHAKEK1gvgBI4bmrq6sSmc0gIKcJWCZSt6yssoeNazXlmRWv57P71yCtVqbLzVQzjnNd7c3ld88NGwHICadgErXlPgAGC2QzELNytQHg9zCuHBJLfbyODZ3AVqZeaLtcLstpvFkp20ljXVWb/67zfX2d+/i+3z/GMPjPSNm2qAEJ+Dp7tiHzqudqt9uN0WhUZDzrJ1iMTWTAHmgI/Tifzws4xTnY7XbLZjd2PmQggTcbQv85ZcpRDGSgHa92mHhBONkVbO6CzsPJmTdBcIQW5xu2Q8QqjdQ2gg1r5jP2WHbe2o6gT3J6skGEqTb2lpU1yvrXUSLmrwGL5SUy2vPckcssm2kXfMI4W0Y424XxjFit1/C6HU6iZ80o9WRReO63dTKhZptbj9QcVw+ljwIa+aX2hGUDKys1Kk26EFEAzp6Yz+fx5s2beP/+fWFM0N7p6WnZ7hZvN5MxohkyYlDtNWfCOf2BiYlHfnd3t4RGeT87AMAspFblSYFQcLSh2+3G+fl52dEIMISXhLxwlJHzMLmW+502Waln4JFD+HlNxnw+L8ZRxCqsSfkQ/Uj0KL/LYdec22yjKSIKoMxePgteyAvNnJcKcPQito2N20McydW3oKYf6UuABOkPp6enZWthBC79S47kixcvyjWALULIgMHzAp7KxoXr1+bJ/BwpG8H8AfgBGefn53fmhA1EvEYoLW+XCGVj3wsPrQSzRw0lz7ii8PEUGvDbi4dBkPnD86jmTLCBZECQ+83z3n3ZVlatjNz/ucy2+2vvWleHXB/mqSOqjqy6DgAM5JdlS0Rzq1jmJLKfNhFF97oZ5Dd1wWAidTPrsfF4HG/fvr2z0QXtbouQuT1un/st9/NDgYP79nOir7/+Or7//vsH3Vvry2y3ZDCCs4l5jq5Bj+PswOkVsfLuz+fzODo6amybbp638YnTivvQQ9bX3W63yCdAtI317ACLWNlm3mwBvsJx552e8vNkX7huy+WysZMjcpb3IKexh/IiZZxB2GMum+9ef+Jx81z12NUARh57qDZXciTXOhow1SYLeTf8YGdt7f38bj1FWdiWs9ms2GYAVG9WgQ09m82KjezxywdKZ1mSnaHZKeSxXi6X8V//63+90/aH0CdJnaopE4MJGCKnB0REHBwclK0GZ7NZHBwcRK/Xi5OTk3Lqt/cuPzg4KBMAZO0dEWpetIuLi8Z+zzYiWL1PnQibkZfvLV5ZJ+DwOaErDAzKw+PmE6PPz88bHs+IKGCDOtv7z8Q2Y9s457MNoWyo8IwFFcYRbaPtCL7s+WRMYVKv3aDvKdvAhvpzD3W1MHfoFwF1cnJSQCXGIJPQws8LSIl4ffnllyV1xd5GxtUA1CltHLb07NmzePHiRVmb4TAt4A+lgoFhrwT3ZsXlvnT/5n7+3Ml9aGDodRn0PUoIEIGh6ggZefbwpb3NBtUoe88fG4gW4jgLHPnkXdSd9Lv9/f2G4ep2ZieAwQbl1PolX8v/+Wxjwv3Ju7K3rQYy2gzXzL9ZceXn83OOFjMu2TBwOpRTSlwfb5XdZhwg7+wBJV3SMtOGv50k0+m0gBLrGzb/QLe43zKIhZAVdlTU+umhwCLTL33uPzt999131XabXzw+NeMyO0chjOTNzc2YTqdlkwdOzPY22hjsePkXi0UcHx8XR2aONKKDiN5n/o2I4gzkeetQ5J3XdyCHWJRNPRwhNRC/ublpHFqLdxyDGTnFgmOcZ15/ggPO12gn4MbAAEBGhgR1AWA4C4V21BwydhBkIz47INaBjja+yY4UOxezg4JxM9iwA9b8Zt3B+6gv5eDgoAxAA3KIzAyiF9jP8CIyBrJTw+mlkJ0jbg/3LZfL+D//5/+09tk6+iigkZVMRotWOHkyMzmePHlSFA6ThB1+yL+2Ut/f34/hcFgW4DJoKG4YgcEB8ROqY5IsFouSwmNEaS8Ukwzj0uFIlNvZ2VnxlFrRoWhYYM7EsWFJH+CxYDE5ExeFRn2yd49+xRDLaNy5y/Z22NiKWEUxzORO//B9NeXpVCvvbpEXP3oCQfZkMn4YkUbvCDgLYOqFp4n6A/KY8O4zymZzABsiBpZffvllY+x82OL29nacnZ2VvnU+pMcWqgnBPIce6ZZsdNXAhoGblSXfedaHZVo529BD+SErPH8R5p5DzHdSprzojkWiBvSsGfI2ykTeXG/a7TY7JN/WT9xvY9myOHurqGvNiZDLzAq9Vs/8m8tbB0wYJ4+fZVLuC8s3fs/t8CJans0RRr5jMDFWrpNBkGXm0dFRPH/+/E6bxuNxY/ttR1D87uxNhhdzRCPTQ0DD5wgsMrX1QbZJuOb0YnvF/Rz2QLfbbWw1u7+/35Ah3gnRBuh8Pi8ZGTghO53b9E0isvAFqUqAD9sveKbRPa6vU6byugsiDXbO2PmH3oUwXFk32ev1yinVOGyQi96a1l50dmuz85PfSBd35MPr6+gLz0fbNNlJnR2wtomyDMpOFlM2/B01rjlKeJ/tzlw2OsT2WZajlF9zoPAb63BxZiA3cHwgF0m/Y62pn82g2rZo1g+5nZ+KPklEI+Juyo4H3MarPYucls3WcHTc9fV1HB4eNjqYcypAvE+fPo2Tk5NijGJQMqhMVoxHL0yyd4t7nZcIOS84IorXzGkYCCwWHns7VNZjRDSjJ/SJF5QNh8PY3t4uC8roUxNGrScCf97f3YxrgWiD2h7WrBztPUQY5HoYgFAGE4mQslEzdXYOa2ZyfvMid8pBONJuysMYZNwWi0U5GZx2Y1h4YSj3OjIBb7x58yY2Nzfjyy+/jE7ndpvB0WhUjFIEbs1TlA0+t63mUXkEGU2yQZoNTfjSvFZbG0PePYuvbYBGNHclojy/H2HOwX83NzcFaLIjm8/ZcFkYBciOs7Oz4nVn4Xk2znMk0p64NkOf+1zvGn9l50O+j+92KtSMs3Vj5bpnoJgBuI15G/URq6hSRPNQL+SNy8jjhWOH3W8oy0ZAr9drLKw1ODBP+TsRa2QFjgwiaC6PMYEPeAfOrewlzWN1H9XG+JHupxqIzwAjywBH0EljwcGQI50ZdHc6t1kPHCbrheCU6yi/DXOvH8zGIIDGa0WI8CMnqR/6kPlQM3BxdDpqSJ8QTcBm8Xo0e+mZl7TBOj9nnSCDOfm80+mUw4tx1mAfeq7nrAjXP9uYzGfrZMqh/XkeeeyzTDVlGeq0Tdpdk39O/3ZkJ7fFTlg7zUjhRw4B/ugr0vpxeDCO3hQgz4X82VRzTq27/yH0yRaDUyEPNJQVBAz59OnTiFgtSAKVcfgNAGE4HMZwOCy5uZ3O7RkH3W43zs7OyqCxC4Inv+sHw+7t7RWFYfCBQeEoAECF94LuyU30rkr2WpHTmHeUsBeLCAZKEIOKtlnJ8h+mtXFj5srhvIhmPmM2aHgnE8JGG7974tpgo38NWNznRICy0QgIsKHmOnW73cbiW8aA8okC8Sz9w4Qfj8cll5/n6cu9vb2yVTK7jNEnCMTz8/P4+9//HhERX375ZTnlHZ64uroqu3DYs5EpA7r8WzZyH6m+ToB+Mj+gwPgNRwU7v9k4NfjmGYeheZ75eXNzE69evYqzs7MyrjgycGxgiHK/14wARCJu06yQESwYzfzu3FzPn5rDwdfoBzsc8r0Z/Doi1FZuBjrc7/8ZJNf4P99TAxhOXfM9VrQGIuYR6xuPDfI/G49O84QXbDi5TtlxYkMHEINxhWPEbabfXUfX4UPm/SO4+HDKfZaB/UOIMUIfz2azov/Rk94+m/FmQxcDTmdsRKwO94Qf7Qi0Y88AxsDDsoaoQE6tQtdan8Pv/hyxMmw5bNQOUv/3DpQ+wC5itd6sZgdyH21l50pvzQowyTal7ab8O/PKTgPLrxq1zaf7HDf+zeXbvsgyifsNoHJmC9edSpnb541xLi8vG3qFfuHME7/Tdch1XwdC8vV1/fkQ+mQRDSsRewmy4jSy57A1DMudnZ04Pz+Pn376Kc7OzsqOLcPhsJTrAR0Oh7GzsxMnJydxfHzc2JaUzmfymOnZESqHH5m0XIuIsn5iPp8XIbO3t1c8ppwizaB5oQ5bo5JCgXENmmeXqhz6vL6+LhPP6zQyc/Lffc5YZC+g+z6HJw1CrFCpj9OMbFRTtj2RnvSQ0T6/2SPAuxgTrmE0cC/t9Mmn9i4hxBeL24WaNuTJP8Wr6XQ3e1gcAfvmm29isVjEf//v/70xqQFKw+Ew3rx500hHy4ZVniP+DMh7pCZl5WoPM3zAPIIXGFN785xOiIFoOYXyQq5cXV3F6elpnJ2dlXMUbJiyAwtldbvdxjosOxHgedaH4cDICyprwCJ74szjjuR5Ltn4rpVB+Z6XNSDzUO9Vm/LJPO4/rlmW0P85Kuh7c7pmzeNm+e7DrzDA7NSw8WSPMGATh9FyuSzrvbgG7zgKurOzE2dnZ42IkMfCaVQ5ukP9s4GxztDJ4+D7H/rcb5ly+82rHpc2wJFByWAwKM4IdsjEEeqyut1uHB8fN5yfroP5x3PROsoZC/Cmo26kMGFUopu51wusDerhPeaTdTd1cNqyMySoC2UxF6hzRBTbCIeK5VzEag74/C6DJhvf2Z6pybfaWOXxt/25bk7kMtcZ1p5nGdBkeebyGGPS0vLaDT7nhf8uA+fmzc1NzGazBjC7vLyM0WhUol7Ym9YP1NFjmgHRr0WfdDG4FXCmrPSGw2E5BTwiSs7z0dFRHB4elv2jd3Z2GguyCS3RyawFOD8/bxzahQHJu52vTedSJyYoO5Fg2JLK5JO0yY+jHLyotIszFzCG8L7D7Bi6i8WipGNsbGyUiYoQcC4l/WYjyTnk9uzCoFn5ZkON32ywZ4+hy6UMJi/rHGwEAIyYjNQfb58NB4QhXkXAipX4YrG4c/gR9yHg8KJ4P/3lcln60/XiHJSIKFEJG1Z+frm8zZV9+/ZtnJ6exsHBQWmnPV20j761oQG/2VCpGV+PtKJsFPDfXm7Gm3m2t7dXnocn4VOnADoFC/4CKBCBPD09Lfzqxd3z+byxM5wVd0RzBxFkIPWbTqcxHo8baYvIArczIhpKis+WpzZKM6+5blnBZMdE7bPfkRVQbUwyD2cZn++xF5b57/SPDCD4jvzMIMpGW643Y+0UNzs8HBnO/UAqLrqJZzEC6HfeRVk2cCxjGet1EcxaFOk+yobshzz7W6Q2ozNf95zKfV4bA5+zgqwhgglfdrvdmEwmjZ2lrKMY/5zbz7hjF2AHZZlA3WiPDUb4mXvg1Yho7F7p9HGnW2E0Mz/tYCRN2PxuEI+9Qnnercppi7TT9kceF5zDvMMAi2ue+x6nNpBOX9d4IPNJjWrX28q0jZaf9Wevw83jmO0f9xXp97PZLM7Pz2OxWMT+/n7Rd3aMQbV1Gm5HjbL8f0g/3UcfDTToBHuqIAtP0mhgOqIUPmei2+3GyclJLJfLgtzZzcALL/lMJIN1EUREvPgJkOCF1eQd2pvV6XTKlrZWeigXLyrm9GlABkLDC5JtlAwGgztG5tXVVQEaKF0Wi9kTRgqRPRPZa2YQw+SnHJ6xV9QAxIKS8pxHzkJ42mtPjaMsCDruxeDKAop6eNs6ysIw5B0YhUwUACfAwREo+pCIA/1hzyO7zABg8ISza4OjC84v/eGHH2I8Hsfvfve7AhhQEqPRqMH32fhyf1kAWSA90v2UgXbEajwNYJ2OwFhhzAIsGI/JZBKTyaTID3gY4G/wyym95s2IlfyDL21wwn/T6TQuLy/LeRrO83aEImI1D3KucvaQ2VtlvvL7sxHtfrSX0M+2kWVJfi4r3HxPjZgzpDu15WBHrLbrdGSA8qmXQRvy2h5hnnM/1P5nEIF8Rw7b6MspI37eDh7LXnYp8jj6fvdhG32IUfQ5kw1Ok8cj4i5f+HeAKqkqnFtgz/Pm5mYcHx+X87jye61X2KkqIkpkIiIaBv/u7m6JLJh/zX/wFQDGRip18joN5kmO8mfwQB1wAqBruYc+ilhtuOGsC887k20qdLJ1PXPOYIP32BnBeNkZY6eN55PnomVmBpmWiZCjPlnG2e7hc3ZauC9rDiXba7ahzTO5TsvlaoE3DjJS+L0ugzoRmcoyyWmpNTKQNH2MfPlooJENABqQw91ZkNpAw4sEsw0Ggzg+Pm4YtJTJmoeTk5OydzD3wPDZu89ntjSMWB0aYyMBT7nXeNir2ev1GrsZOUyZc/W96wPHzjNhHBJDSXEgXZ4oFh72ftiA5ponjA0V73Jjxqff8mT3ZMmHm/EeBBlCKU86pyOYL+z1dz3wQPIOG5D0ERPk8vKypL+5HIOL8/PzODk5ifn8dtMB1sNQhnMe8QygUMiD5ACc2WwWf//732N/f79sH2evz2AwKMClBrjpewgAVbvvkW6pBiwYX9IWCUOzaNGeH/jQypQyOWtlMplEt9stxm7EKl3BysV8ZaOW+7MAZhMD70uPo4X5lA3MbPjYE5hBRu4jk5Vg7k/LJytZvy8/U7u/BizyM37OfG6niXPLs8HD816DZn2Q+8TvQIbYuWRvKHxh/WBAZwcGxpNTLWlXLWe8BrxcX7ct81j2XD/SpyUbhxFxZy5HRIPHFotFw15g1zH4lrE/OTmJk5OTBu/mdVcQaxx8wG/ebMXvJwqC7ODd6HJnPcC3jqr63CfqRLoVej1vEMMfW/TiAEWvY7wCaPhei2bglMtGv881s1HPnPKWthErp64Bh+esbQD+W7ba+eOxtjO85nSoOVWy46aN12gPdawBFmxI2z/0P45yeAh+AOzO57e74X3xxRcFnOasmH6/H4eHh6UvautAqG+bc+g+p9FD6JMBDT5HNLca88T1IJPKZMO80+mUXajY3hQD0Tv9vH//vuxU5UGy95Ly2X/ayHy5XBYPNotoXF8UFJ5NrvMejA7niDPpUFqDwSD29/cjIsoJjbSJk6sBRltbWyWaYi8cIIX6QjATDEU0xVEa8kqpD5OTNC+EC6CJSeA2wvRW1lz3FrWOVFG+wQptzWFTR2sQUvATwomQ8nQ6LWPDzj9G/RycSJSh1+vFZDKJN2/exGw2iy+++KKkSjFugEaiNniXTk5OysQkRez09DT+4z/+I/b29mI4HJZcyaurq3j+/HnZitkesZqRZu9QTcF87lQzYrOx2uncnuh8fn5e5lBW7qzLMrhgESJCOiKKMqUc7x5n/mPuXl9fl7Vh3W63GA4RzR1lmFs2BFDAEXcNzuz5tiLI4MG8lXkse/1r3jv/noGBlX4el+yRawM9tecti+xZNRigXHtGmVPuP/rJC1jtHbQB6fQEt9EK3YYbssr1sQ7IoCGPpcfEcrxmALkfa0Akl3fftUdqUlsf2Q7xfIA8Pjs7OzGfz0s0I6LpbT47O4vj4+NW7y9jC1De2dlpgIxsAGMI+iDi7ODAlkF/EFVAZvGsN8+xHebNDyjPuoh5ZVnCZii7u7uNA3WxBWzo1yIITudG3uVF7/SPvfDMIc995JqdD5kMQvK4ZudOWxlZbmSijrWx93OWL8gtlw/YcN/T/9hCTj+nn87Pz+Ps7CzG43GMx+PyDHYoC/vtpGYMHkIfcu999MkP7Mthl6x0bCwwETC2US5eXGsgMJ1O4927d8Wo9n10bF6ExLkcp6en5TvKf3t7uxgXw+GwbK/mLW47nVX+v5l7sbhNv9nb2yvvY0IcHBzEixcvGmFyyJEbb8PJdYAFgogF7kRTeI+ND28FS7sQOggCMxm7e5mRs9FgBGxlz3vN0A4X29BG4DFRMNzsRSQ1KiJKv3vsEUy8l0gKXkn6hOgEUQsAx+HhYUl1e/LkSYzH44YnyOsrqPvBwUEjNE6djo6O4rvvvot///d/j36/H8fHxzGZTIriICffBkj2gPLZ9z3SirJwqxmtu7u7ZdcSnkHoMx/c1xisb968KTucRMSdtT+MKXMDbx3RT3iBSIgdIFakBhZWRFY4zHcoGzpWivd5uO2Ncj/4twxIHLmMaKZlZO9gHgenZuYwvIFhzUtmoMGcIFrgOjKfI5rnA1BXnsXz6no7PcR6wvLOEVP6mUXklj84T6zPMMyop+VfLcJlI4++z06c/FzujzZgWfNMwgOfMxjJ42E+rAE9k3/H6PV6Pgz1m5ubODw8jIho8BH/WV8KSBgOhw2d6HnmNZnwqucXgANClsDr+TenEjFXDDIA0Dhns07mnTjWmAPYaRjMtMMpXtQbOWFPPe213YGtwOnrjJmButvEuNpuQT7kvxp5rtVkl8G/nSO18ixnM8CgTOpnYGQQ1+msUjOxR3gvBxb6GaJMOF3Pzs7KBkX0O/zucTGf1tqe+6hmx/xSmfJJgEb2DEGAiDwYKFm8TaSfwLws2uVeohivXr1qKPSIZv6jF3x7/2iUCOXh8R+Px8VoZZCoN+VsbGzE7u5u8WayODwiygTb29srE6Pf78cXX3xRUjkIA3IvAKPT6ZRdAuyx88JF8vEcDp3P58WL6rzmiFuhSHtgMhZFAzxQcuQeI2iMuI2wc98YbXuNhRk8hwv5zB8Ti3sRdNSDfuJdRFggDgZaLBYlWkSECsE+n8+L55qFw4eHhzGbzeLFixel/yKae6EDbNlZhD7DA/PDDz/E73//+/jqq6/i6OgoZrNZ7O7uxsHBQdk3veZ5Nm/Za/FITfqf//N/xh//+Mey6DILw93d3TLWKCsbj44cYui/f/8+Dg8PC9/5RForFvOzjRIWfnoOTiaThpMhGwt5UwQibrw/pzOuIwN/G/J2NrgcK12XwX/LRf/mOZ77nndnJVwzgH3dSoo5zjk7RCKyIWiZxPscrbLcsMxwJAmlnr2qgAO+AxRMo9Go6As7v5AFTuH1AlvqlwGCoy12Ellf2iBuow9R8nt7e/H1118/+P7/7PQhsvQhIIyxYr7m3yIi3r9/X/gig9aI1cYwERHj8bgREYHn4J08r/nf6/VKVgb3kt7Luzh7A1lDuV4EntdAIYs2NjZKZJi5NBwOS0bGYDAom9/gfPG6V2wbywb3Qwb11ut2MGC75LVp7s8sz5hXHlPL0mz050wD19k627Ko5rxocwBkJ06NPG7ZEenUOMZiuVyWczN8+nrE7SYFl5eXZVfT0WjUOAaASFxbeqBtO9tttME65WNtlU8W0TBz1RRUZj4rpNFoVNJXUIAI+6urq/juu++KobyxsVEmHoxG56IEzEAsuMKjMBwOGyErJtxyuWwsALVRHLHaFYvoAykVe3t7jUXcVioo1aOjozuhzZpHEWDgsKqZ0TnfMAkM2+/37/Q55XHCNde8GNHAEGOaSYVg4R20CY+ex8BjT9sAkTXlavDEmHstitE0BPCzF9kCh7MzrMC9w4N3sCIUntfF4AFYLBbx5MmT4t3GKLm8vIy///3v8bvf/S62traKQBiPxyWlzh4J5ofbAs9mA+eRIv793//9DlizB45rXotlzxcKk3n7/v37cpCRwT4GJ/wwGAzKIs+IKHOQ33gHnk2isPCegQhAGL7a3t6OZ8+exXg8bqRdme8sZ6ykzP81jxnXUSy16GSWx9n75t/tdbMSzu80IKuNlZWx34sctGPCcshRahtHBlHIIeQG48F3Iqj5WXtYmaOe20RdLy4uot/vl/EyaLF3GLDhM31y2zPAy8ZKHsN1nsP8W+0erh0fH8fR0dGd3x9ptenDfQCfyCNGvj3qHCjsMcMYN3gga4GsAnQc3mpkmZ2Adr6x8JcyndlAedhEvMeg1dG5bDTD+9Tz5ub23K/xeByj0ajIPerolC0boMhGl49d5Tlnueu0aRyfPqMkO0ts91he2tbzfKgBDK7X5lib19/OBcv6LDuzTKUf8nsoPzsk+L+9vV12lPJWwBBjx7NkpxwfH8doNCp6EXBsAOYoWS26arsr27+M04c4O0yf9BwND6YVB8o9T4Dl8nYtw97eXhH2vd7tQik6/JtvviknNQ8GgwIqLi4uCiLf3t4ukxZmwBO1u7tb0PLu7m70+/1GjjYdD/JbLpfF229i4DBWdnZ2ot/vN4xnABBnaPR6vTg7Oyu7UcCkTNjpdBoHBwd3ECYokwlLv9GHXtuBYOEZCwEAEed9oBDN6AiriCjGOB7HxeJ2C17ugWn5jCCmXghT6p1ToGiLoyH2Kg8GgxKJQolzWjsAiDQ26k7bGRf3Q/bw4ImOiLKl6bNnz6Lf7xcAA89isNJvAI6bm5s4OjqK09PTUsfz8/NyZgrpU/CFF+/xZ2PqkZpUE74Rq3nH+hyumTj/IiLKoYzwnfkABwA8AQ+T9oiRCe/jxWMtlZW5wWMGmBFR0jL39vbKs17o6KiEAXnNuLchy7vtgauBhzYlR3n28tUcRG3jUxuvbFgbAHHNKZvus5xS6bIxVixDMfwcMbdDxGNjw496GFBdX1+XNXDITr4bZPKsHVH5pPH8n/7FCZE9mbzThlIbuPtQ+tjnf2v0ofztXaFsUJ+dncXJyUmxPQC+6Eb0ESnf6GuD48ViEZPJpNzDvIiIho4AVFAHZ4k4C8BOC2QZMnC5XO2ahzPV+qfT6TQMfWwb1sfm+cRGF8zJbKdgC0Ss0g9xJF9fXxdbK6eJY9MQ6YxYgULKqslEOxazzKA+/EGOyGY56PvyXDQotMwzIDJIcj8bqNrYpx38OeWeMWZXUvqN60TniXKROo6jC/vIOiPLHtfBbXC9MpD7UPpkEQ0rjqzs5vN5wxuJ0O31evHs2bMyUVH6+/v7MZlM4m9/+1u8e/euGOWnp6ext7dXQnlek0F+LbnWgBBHIAhNoewxNhAWniQYxwgYJgXloYCYzF4IzGJ22mykb0PCBqcFBgrZfWpQ4BQlmMn5zJ1Op/RHRDOnj1Auwoh68axBAOCJcbQH1+NuwGEl7PfnUJwVt+/d3t6O5fJ2VyjvLsV4MVl8/grCjBSVTme1rsZkgEdf/PTTT/Hll1/Gs2fPSj96XcpisSi5tYAwnnv58mVREuxsdXh42ADSkI2qmkfkke6SDU5kBnxiZYsiJcWJHcc6nU5joaKjWgjpg4ODePLkSdmqkT88bvCUo2kGtfZ+ZcILuLOzUz5b6VmAGzTbMEe4G1zZoOe+7CGrAQLLkgzoDGCy06gN+Pjd1DEDDp4B3FlXWNGjPFmc7wWqy+UqRY5nvPFGNuizcuQ5CBnHZ7ZDdz+hrE9PT+Pm5ibG43HDYea2seCSa9lQySDCXkM/Yzleo1yO/3/u9NVXX5VDe2tkfnAaXgblnoMYwNPpNLrd2zSq8/PzeP36dZFF3hXIxjGOTjuvDHzh0el0WvSKjWHvYtjpdBq7U2U+ccTN77Dx7XRd7KTd3d0YDoflnaQLIvOQhRFR5i6OQupmcE/d2BCGeWTHp52Bfi7rceroOW/npOchbbTssh3ieWPbhee47qiM5R6ymueynHaZ2QnkNri/6EfXIWK1eQWRCsYRuwT7yNE0NizA4Yn8tAMtgwjboW4DZN7hvtFo1DhP7EPok0U0IproMismlDafF4tFPH36tJGr3+12Y29vr6SnvHnzpiB9p/Qsl8vY399vABQEwfb2dtl1iE506NJhpaurq8YhOaRwgRoxWAllAThssBMdmU6nhfkwUhxix1AGhPDb8fFx7O/vF0MW5E9ZRtIO+WeEauM1YpWXyX8Y0aE37t/Y2CjrPhxipVzq4kXx5I7ifcgpYwgtJorzsRF8TEQLKcrKk5TfEeAcqIWQzhObdtEOvlvIMYavX7+OXq8Xz58/L+XbMxQRhaeY/N9//328fPky+v1+2dqQ9DX61sLU4NLXHqmd6DOn19moBHhfXl4W8MChWYwdPHV+fl7S/QaDQUmjxGsHEGB9Dh47QAXGLWXagDRQoN48l3OYDbRrfLCOJ7JizZSNpppR2gZuM7i477rHxwaMf7Nn1utfHIkCQCIncrQPGec1G474RjQPzGMccERljx33mW9sOPAOp1Bi9LEmJ4/X9vZ2DIfDYowxVgZB7i8MRRscGaDkvs/X8m9tQPBzIaKXpjZ+tXFpjznPcB9rKZfLZZnL33//fSOqin6EkBHwAfOf9Xvmd9YpRURxbtjO4Y86YkPkDWaoA/PE7bU94FTrnZ2dePLkSTmM0AY0MrHX6xUniZ2uzL0M9LGjvP0tbWb7VYMGsg7od+SkHX7MDc+PDPZrzhI/Yz7wXHQ/MQ+z4yI7XEw50lqTqzxvA9/zPNtt3W63yKTt7e04Ozsreo5+B7waaHCIn3mcLfzNnzVHCKDOwMnyy07SdY61dfRJU6dsULVVyBVmWzaYjMny9u3b+OGHH4ohG9E88ffs7Cw6nU4JW8Pc5KjxDjwL3r51PB4XjziMT0oEeboYoL1er+T8e+JgqACSiHbgCaeORHIwUoyWI6KAmG63G4PBICJud7RBwGEY411HUDDh6Wuf/GhB6RQiAxQmclZsbUZw3obVk5ddEfIkw7Nk5E37EWykRVEnL5AFkPEdoQdfsTAfkASIoh0RUZQDQg5hYiOC/mSv6adPnzbWlSDo2NYUnlgub73oe3t7cXp6Gr3e7RogL9rLwox206ZHukv2HCEX3G/ZO4VCmk6nMZlMGimRNjp3dnbi+fPnZQMI5haglV1P4CnejQKlHAMNe7qsmOxdpyy3z23I7aasNv7IyjMrqlqZ/ObvuR5uh59xnf2f/rdXzP+tB0g9ZBwtx1DUzA/I62y8AxVR6WyoR6wAjN/vqAh9itwlr51nbQh4swzm//HxceEfeIsyR6NRnJ2d3TEC6RP4wIDUY1rjidoY1oyfzxlgQO/fv7/DfxF3ed/UBkJ5jvWc8/k8RqNRzOe3Ow+a75xJgHzAlsE+wdm3WCwKUCB6Z4egdRjvdTZERBQn53K5LHrXThXkoXUp/6kTaZxE4uxcQwbitEXnep2Kd8jEgYiutZ2DnOK6F0Iz32iXHTLMK2RF5m/Pu/x7zQHE9ftkm/nEztxaeZRj8GZHRQY01ll+3joky9ZOp9MAGwANZ+nwHu8kikOcXRRz2TWiLgZz1J8+mk6nJcPkQ+mTAQ2jHr7bEw1RcdKfIlY5ghsbG/H27duyDaU7Jq8R4KyD/f39kmMP+iIXmlMT7SXvdDpxenpaJg/pTVdXVyV/HwOdyIYnRrd7myqDoY+XnwlzdnYWEbeAgX2MmeBMSNo6mUxiuVzGyclJ9Hq9ku6U059sVDB5iRI4RxjhZy8Az9HPFogoYZ+KzX8bMJRDP2IUeK9vwI/5gQnotAS8iAZEGYjRr/Q9bUZYE9ZGEG1sbBTPDDto4IVkMrKmxxEmFnNTn6Ojo9jY2Igvvvii8C7GETxxcHAQ3377bXS7t7m6RMcAXBjGHgOHsbNX7ZGaBG/7uw1He1nwCp6enhanBGMeESV68ezZswJqvZkAwtXXSQ9kzjOGjnBko89zhLlYU5D+nedzWiWGN7/nvjGgsWzwPRHNnar4XjNMDTDsjGh7v/vflPmbspiLzAkbgVbGfp45ztyJiJLe6pA+fZU9nBnQWydx3YYbv7m9+STwiFsd8f79+3j69GkjPYS+Hg6HZY1Q5gfGmmfo72x81eRDDZTWjJ7PmbKh2HbNRmDbPYvFbarxYDAoem4wGMTJyUlZM8G4X1xclDWltjVIW/E5WwBR9Jqdb6zvzM4MwHJ2diDrrFuoF9ECdq2ENw8ODmI0GjUcLRErHt3Z2Yn9/f2Sdk6UhS3BnfHAvMGGyJuqOK0bwxgHgeduxGodR012tY1dDTTw3Q6f2rXa/KmVVfu9Ns8sxy2X/Z7smMgAhD7Mdke/3y8ZMxFRMkvYOhm5YbDKAcIeJ96Z03IdzXAf2cm6DqA8lD5p6lT2cPm6/zuUBsgYDAZxdHQU33//fUHsNNSedtIbiFYAKpxSQ+iPTsKDYEMBxB7RRLHeweri4qJ0OGAB4DCdTsvJ0wzW5uZmARgwARET8uuoF0KChdpGxygfh03xonkC2zC3ceWohAGDBVL2uvtgKytnl22mow4ICJ+0zp+9uYyjjQ0vnHPUxGOPd4ZxwzMCwKN+IHmfHsp6CZ+d4ns4iA2az+fx/v37iIhGGpVTOY6Pj0veKrtnEE62IGZ8bYy0eYAfqU5WNuZfhO1sNovj4+M7Z2ogA9iekTGxooannDdsHoHfMg9bmbqO2YiveVSzkqopQhRSDQxAnofreCg7CqxkXJ82ZVIz2vK7DSr4Dfnh9CR7avPzli/ZwLbXzt623NeOVHqcDFyoF+mv9InbAP8YDGC8Ucbbt2/jxYsXxcBE7o1Go3j37l1jfN0vNQCZlfy6cXyk9VQDXrW5Y8+t+dl93O/3i85iU4h8ZoZtEqIflAOwxo6IiMY1l0FdvF6Ua94hMq8RcvQMHmUNWrd7m+nBmlbWECJHDbSRiRAyMGK1/sNr07A9sOMiVmvHaBfPG5iw7tJef9Zd5jHyvMi/5XGzfMhOEs+r2jWXm2Wueciy1FEI328DnbrbjuJZywTqzTuIMjkzguMTzs/PS2qwU9Ot2yaTSTx9+rSU7bRS8wr/a3Ilg7ja/PhQ+uRAw5Mt4q6nYGNjo+S7szCbHZp+/vnnePv2bUTc7vaBxz9iZdh6N4TlchnHx8exvb1dJtRisSgnbzvvLAsWAwmHtTE88XIbsePBYOGpGYnTq4nEwFAsHs77GTPICAiM17xo1Gtb8va2vAMBlNvp9/BnEFIzhnzdUQmPpcOF9BshW3vuHGbLSgDBacBhQwKgxvjhGfK5JwhVjEIDjt3d3djb2ytj5vpRZyIygCwUwvHxcczn8/jiiy8KKKHPABqj0aiMAeXQ/wZ4tfnAb49UJ/O2Bb49/ldXV3FyclKUKqBvNBrF/v5+8UgaNMBzjJn/G2gAMHln9pibh/LcchtqPB9RT4exYdym8GpK1nWwrMzvalOcLretvvm3DJqRdzbYSSvxFo05auF3RaxSn+jv7H3L70b+5Odw1vD5+vq6rNFxm3iHo7lEwvN8pXz0y7t37wqvEPnGicEOP07bsjzP/Z/Bax6DR5DxMKoB8Nq88/0mz3lSmReLRVmHhz4z7xLZ8tgxxl47Bq84wgHBVzgs8zqOiChRDJ/5ZPJubltbW7G3txfj8bhkT+CMtX73+7Gt7FilLbwfyhEIf7as5TdHOIgUIiPcd5YfNXI0wu/FsUcZHu9cr3Vy09QGcNpkl3/Pfcfv9/EfdcLR4QwJ+pBdpciWsf3FZgV2yO3u7jZsEOqQI8GWSXkO2V75pbLokwINKgGawwvv3zqdTozH4yLc2XL28PAwfvzxx3IdMHF+fl4mCbsg2AM2n8/L5I2IMpm8Q4M9zb1erxj/NuJRTgwYiJIJQXpRXow1nU7LNqdOl2AXCVJrADNMOBgJw4f7socPQwkhFNFUyPS312y4rymf71bQTFy/z/fRFxaAeaLSbzY4LCiNrO3NZJJ7lwUmZl70biFK39fSuJg4+f29Xq8AV7cRoUc7mMw+O+MPf/hD8R6woI/35bUf2UC2oHG7ax6RR1pRFnb8MQ8AGZ6fe3t7JaUFTxsK1goJx4Lzih3F4N51IMC/ZcPV41xT6vn5XCbXaXubMcTvOfrh37i/puRyOe7rWl1q1/Lz/h0D3u3L60l4n8/UIcpEFDobBG6Xvzv1LLcfnskpKMgI94V3EeJZAxvo7OwsNjc344svvijpJcvl7RrA4+PjO4YP5eFAcbptdhJ9CD2CkCa1GY4QY++IVUQTPAA22TxiY2Mjjo+PG1vHA0DQj6TOdjqdYrd4LZFT5Hjehh18SAqV+brTWaUQGqhio/AsG1xwRlRt/WPEKopBXaG8q6T1aXZwGiBwf04r9TjYFoiIUn+vI832AeXVnKhZrnnuZCCSAfs6+WZZ0za3HvKbQavtAK5ZPtT41am92DxsgITj1Gnzlt0ARdKo3H7skizPavX5lLLlky4Gj2gq0hya6fVudzTY3d0tOfOECn/66acSwYCJ9/f3i3J4+vRpmSx0GIOBMCB0yECifGxwRERZPOr8fRieezud1WInjE6UFQOFMkUwOBWDdhOytNcKAzciCoiCyGnOW+56sZXTp+gHMxwTNnv66NccuiMtzBEpL2DzvYyBDwD0+KLUARHZALBA8Ray5+fnjd0qENa0ift4lvxTBLAXneNJJfpEXu2TJ08K0Ly+vo7JZFLS78yrRNsIlf/hD38oQAYPFmFuduvKAm8doLB395HWk5UGNJ1Oy1ooIpzj8TgGg0FRlk6JiogGmPBvOUUqA/X833OhzSOV677OSKe8HInMZa0DJPl7rl/Ny1drR5uBVjPeMsDKbaWfUXxEbS03vaCbLYRRrBgfjJ/35Ud2IpchR6+RM2zwwXV7UTH24YV+v99YO4gBR1k2hK6vr+Pdu3exv78fu7u7ZdMQovNW7jYaLIeRaQY/Jvf5I6D4MKrNTfq91tceG5/gPRqNyjkFBs9sMsNz6AF+9wYGjlgAonNkwlE8G9kszLYjEj7n/egiUrPtTOEZ94ttCjta3Q+WhdlgJ0pog9r97OczAMjAuxYdyMa6r2W548/ZBm1zwuT3ZbmfAdFDZb7nNfPezmA/43o7cgshl+AFMn/od1L8ve0xG9aQ5QIvum/yu9z/GfTQjk9hq/wqazQi7oacI24HaW9vL7rdbkyn07I2Yzabxdu3b8vk45l+vx/Pnz+/MyEYwF6vVxYaRURj+1gGmn2iEeooPZ4h1HR2dla2lwVURKw88QiNnI41HA7j5cuXxci2ATMajUpkhjbN5/Ny0B/5ibwTAxpjnTMhYPQcWcggzv3vXQkwnFG6KNCsxIkOdTqr8B0RBwstdpEh3Yu6Ua49lfP5vKQiYKCbVzA2MPS92wuCmfEw4xuE5H5wGowjV0TFhsNhzGazODo6isvLyzIeeBuZ4NfX1/H27dtYLpfxxRdfRL/fL4vNGX8vUs2T0uAl56g/Gg73k4U7PHZ1dVVkx+7ubgwGg7LnOzxPRNLeONZWeb1GTRlZUeS6eC6u8z7XjPr8W77/Pn7I73NdaobEfTxWa4ONMBse5mM7MmqKKaLpocdgslywnIqIMmbZKxfRjFRkb2eug+uFPPKOfBBymM84VfD+uV/s6fOmFoCU4+PjePnyZXE6sHsZW6Dm9rptHr+H8ECmRxlyl2rgzDaJ05d9n41ODmkl/fL169dFF87ntztQRaxsEes01iD4nY46wFvYKNgpREci7uowPsOnBvNeuxkRDePWc8Rz0YY89SG6EBHFoWlglueFoxwG1u7fdREO5oUBTI0sO9aNm+VLdvble9vKaXO01Cg7C9ra6D7Jafz5OYNO2gJ4RQ4+efIkNjY24uzsrKQLI6NdJ4+to1bUi3qsAx75/o+RN58UaETc9RxENENw4/G4GLRMrMPDw5hMJo0cetKknj59Wk7W9gTxgr3Ly8vipSaVyREAH84EEqQ+jlQgFE5PT8v2YCw+7nZXp8Dau/b8+fN4+vRpwxjvdDrlTA6fgmmggABxSB/j3At9TI4y0NeACBgxK38UKcrVIXt7NhCkZngiPHhwYExHSniO+lMP726FR8a7TRlMmE8Y58lk0vD0EbEy6FsulyXUTLlEZwCmFrpE0G5ubgqIOzs7K5ENeCJ7Mt+9exeXl5fx5z//uYAU1gNhWLDNnyNL/u825jF6pBVlg9IKKSJK349Go4a3jzHPEYu865yBRE35rPtuT9m6Z/L9bovvzQI8v6vNaMqGu8v2czUA4ne5Ldxr50RE0xNnymNUAxHZEHCk1W0lqkukD4/y7u5uQxH6IFFkmfsi19lpq91ut6S/EglZLlfbhOfFrjbeIlayjvbiwT45OYkXL14UPYOhenx8XKKgXhuSQZupJr9/KQD5nOWL50TuB+uunLcfEWWdDeOLU5TsBnagYlt6QERENNaUwuusI0TP8V52yiQKgb6yjHCkD1BB/bnG97xG0sY/98NftW1oc4QNfW65aaM9ZzJAORri9+e1Tly3HKN8X8OJuk5O1pxA1OU+cOlrtc+Zh/JcbptvGdRFNNc7wA+Wje4HbCn4kD4bj8fR6XTi5OSktNd1oc7Ys2x6xDu9Uxk2Du0xcILa+v5D6JMf2OccQr7DlHgIyDEjt/X9+/cxnU6LN6Db7cZ4PC5GtPPrUUYoJvaiRoEAUPAATCaT0nEGFBFRPJ/ezxqDcXt7O/r9fglJ4jFlMBASBwcHJWrCNQYfA8i7ENhbAFjy7yg7GICFjPSL12wYGHj7OUeAcmjUBgGhN8gMlZnOKQb2cHA9e20QZF4MR5lEiwwyKAsDgu9e8O3JBiCgHwCaEbcTEfLCXvqU5wAfEatt+uAxA6rZbBZnZ2exvb0df/zjH6Pf78fBwUHM57fnpIzH43JoH4SQ8WJT+M7C5pHayQYr4721tRVPnz4t84v5mc9kycoxK5uawMyGew7d5zrVFNx9Cqf27pqCrXnxfX9WKLmcXJc2cLROUeY+qPE3Zdv5YeWX0zczKLCnlWsAxOzUyIY4c9m73dl5QeoW70EGsWOZI5PIidyH6Jv8DtJbp9Np2XyENpN+ZeVvB9G6caiNge/LfZ9/8zOfI2XgmynLA/QggBPDni1dyQIALOKgwlaBrq+vYzabldQVomlE4tmgBJm1sbFRwAq6245F+M8bDli2cQ9tYr7Ac+hLz1HAQ3a+2OaIWG2K4JS/mtOA+vlaHgc7OMyn2GsZkGTetjzJ93J/DSBYB2Q52jZ/as4at8M6YB0QgbKBbrCRZabfm9tfi0Zhk2KD8A7zNU5yLxvItE5WtDlEfgl9cqCBcUol7eXd3d2NXq9XvNC9Xq/kW7N4ir2cMQq73VXaw9nZWREIk8mkeKfZQx/vMu9w6s5sNitpQ0w4h0Odq72zsxPj8bhEJcxkKBLywjnV22ExJiA5yZ1OpxwN3+msDoGiDxAk7JQUsco/Rkhx6iyK1R42COAUsTKcqQvvwssS0Zx0CCt73xC0TApAGIKZtKHlctlIKTIY8OTina6XvRtMGLZ4A2DxLNse87sXyrHAnz7a3NyM6XRaQsKMC6CFd+bcb+rDPtQs6o+I+P7772M8Hsef/vSncoATuz+QOlETRgCODMIf6S79+OOP8dVXX0XEij+dNoBsIFrIfM8eN/6soCKa42LBno13K+k2hZX/r1NS+f5sxLsMh8FrStHXnB6R32lye2ptaONJgHetnrWyDCYMNmpgxotSXRdkojeZMGjhvbyDOe50Ky9E39raisvLy4Z86ff7Ja3SoAf9YqMPGYTMQC5hUHKWAt9JKeVZ+gNHFu9a168Podo4m/7pn/7pwWX9Vigbhfm3vM4z/46jkANhJ5NJ2RxkOBwWByH34zjDcJ7NZrGxsRGz2aw44UajUdlmG37EuKe+zBs73pgbjmTY0WdDFf0dsVpbhH42nwF0agDA+toZCfRX/u8+ZB7VZFXWgzldOo9d7X02zGu/QdYBli3ZhqvNm5qOyG29T29ncFFz9gAA1qVcZULGoRtYMgBP0z8+/4ddzvJupdkpdB/VHCQfSp8caOQwjBE1ufAXFxdxcHAQERGnp6fl7AkMCZ80jVGIcfn+/fsymfEYeD3Fzc1NzGazsg6DUBGLuTyBYXy9VIbCAAEAAElEQVSHL4lieGs4yiJ1ip0dhsNhbG5uNiIiGEHeuYoJiFHsFCsmMntdUy+EEdGfiGh41ehfGAfjGmHhCInfB5N74Xuv14vZbFaAGswLoHBKk9d3ePtG+g5D3h4Lpw/QPu4DyOS+oX1ObcPomc1mBcRh3CPsr6+vy+mtpL/RBwhDxswLrpzLTd8AfhG4FxcX8fe//z3+9Kc/Nba9vbm5ieFwWAwX6u+cbvfFI7XT8+fPI2Il9OGHi4uLsvDbID2i6b2ygU45WamYD2sKx+Wse64GPNruy1S7Zlm57t78LsgyId9jAyyDBIPfrMRdn6yg3X5HG+B36wLGygDBhoABoWWp77fcQ751u90iA5zywfuRQ51Op8hPG3QoZxtJyCaec2TCu9nhtbZcpi+2t7fLbogGjrU+bhv3dVQbp0zffPPNg8v7rdA6kAGPRDTz42284yUmkoCD0qlClEe0A/sD0ElWBevIvGbMOhkATJ09/vA/fzgLMyCJaIIS0rF5xnMwYhUtxK7h+TaZwjOun41483Tud8qppQdlx4ijJB6b7IBw2QZofh/XaVvWCfnedXPIMijPV/+ey83P+DnLQoNAj7dTy/KaLtrEYX43NzcxGAwKn2K/bG1txdnZWYlurHNMGHzwPQOzdf10H33yNRp5MM00TOJut1tCkCzI7fVu12+A8jHMWJh1dXVVFnvT8IuLixK25lAd1mvYa40Ba8YHRCAIHIUYDAaxt7dXvA4RUcBFNtwjmp4whyFZl4Eh7t8Iz/J+e2Wd1uSIhJWj03EsZPidelKGGdeTxznKRGsiopFbCojheacTeMK7XwB42cNJvzgyQ5kY6iBxlDhjD/ChjXhmZrNZnJ6eNtbZHB8fx8XFRQGDm5ubsb+/3xCw9uBsbm423n92dtaIZvDu4+PjODw8jH/5l38pO2N54b7TMXI43MbSI9UJb6FTALIxaIM3e61y/j+UwUHN08R9EfUQe1ZYFsBZcWaDv2Zg8nwNBEDZ4K8psKzMbJxnxecyIKdJ2tjPyhk5Unt3rT+zoZDHjrnfFvEwEMpRBf6TmmkecF0BDLwHmdzpdBpbH9ug83kHvJMykEOOTiPH3L7d3d04OTm5I5vbjJ3c14/0YZT5HMrpPBlomFfRl0SziWwjfzIQYO0GupByNjY24unTp0X3WrcbaEREySIwj3Y6ncaawYjVzpDUuWbcOhXKRq37hgwNbBGn8bh+fkfNKOde7Af3vWWP303/ZdnIvY4U8b42EG4gkSOdBhpZvub21GR1JuzYXCZ81KZX3JZa/9F3mUdzXVxvgz4DxuVy2cgwcXRjPr89T+P4+LiqX2rA3DZjTvH6JfSrAI3cefP5vHHuAIYz3ufr6+ti2LMVKUqAxmHgdbu3h+nc3NzE2dlZKQ/vM4axwQVhKjzcHOoF4zDgg8GgAAq2yrQX3MZ+9qjCOEQ+8HhgGKMMyQGlfihDG7QRqxCn1yfs7e0VD3/elQUh6d0iNjZud91iDQP3e2LQX0awThcDjBig2GtoJWlgyS5XTk1ySBelj6Hg9RmdzmpLzIgVgxtcefGbc67JlaWP2ByA0zQHg0FjYjmvlbLZJg7w43sBG5PJJF6+fFl2l8F7hbczl8l/p4o90l2yIIaYfzaAucdANxu2EXUwYCHa9v78vSaQa0oh/1YT4rW2WtllQwLKYMnXcrse2g4bB/zRZ8zv+yg/39Y/tM2AJpdDO3h3TXlTFkaelWsGLVbKRJpJ8/RhjSzOJWJKXbvd1VaezrnnnfZow5d2zLjNNcVdo0fA8eFUM/BqfJ6NcoN9r5VAlnv7cu5H952fnxddhH2Ds5L/ntsZcGCoYw94e9qsg2mD11ZAlhsQ2RRuv99Xkz2ADztY/byzJHL0oWbgm2wf1Ax+g6yIVap1DaC7vNo7s0GfeSPPv5reWOfMqV3LbfK1tnnOOGObZbIdbTvRdtdgMIijo6M4Pz+PZ8+eFXvu+vo6+v1+sZO8Frf2Dst+/29r24fSr7JGI2KlLHJKEaCDCU3ef0b23H91dRVv3ryJt2/fRqfTKV5sFklbgPg4e7wMKJetra3o9/sxHA5LelbEbb4u22OOx+Oy04kXU7Nomz/qj5DByPdiadpGig7CiwnFsxF3w+A7Ozvl2HnWHXgBsZ/l3REr0JGFBP1OGhCGPYoVgcaJ56x3QIEiINxOiHo555TvziFcLpcNjwy5zE5xcJoDwJAtZ3u92wNonJ6EUU/ONH01m83i/Py8EaLc2NgoZy90u7c7iHE/vLa7u1sA69bWVrx48SLevHlTtvy9ubmJ/f39uLq6ip9++im++uqrBpB1FM1zAeGMYfRI7QQv1pRnRNOYRk5kwGHjwV40ys//H+qpyffUvH725tWezXzB/7b7eaZNAbp/4DMvsswyuaZAkC9ukz14H9IvNeWdDb6Iu3m/NmByufzGZ2+e4ffk/PscjcBIGwwG5fl+vx8vXrxonLJ7eXkZr169irdv35ZdhqbTaZyenjZkL7qN/nYONTKZ9uJcW6e02/p7HW+2GUTrAO5vkf70pz/Ft99+29puG9Mm85h3e8R5mQHvYrEoh/bCB8PhsNgX1nvZCIb/7KzsdDoxHA4jYuWwNLDhWdcfMMIczQ7TiOaGCujXvJkL98G7OEr5b+diRFQzAaifMwO4J/c/f85y4HkvRud7lpXuh6wjarI09x+UdUJNPmWqyWfXxe+uybYa8KXu2L61tppsG3ucvZ7NtiV1zuvGHtI+k+v6r//6r2vLaaNfbXtbyN5Ho7GIKAbddDot5yIgkJkkr169auTkR9xuI3d+fl4OWsN7HrHagxojeTgcxng8LqHIzc3NGA6Hxejf2dmJ0WgUOzs7RdB4cbg7OYdbqWtWhCBKPGf24OVdiPCYcR6ADzHEexbR3BLOdcjpJF7ciMdte3u7eGvm83kJ/1thGsjYG2BB6UVFrOWgn/HGoLANGvKipeVyWdaecK+p2+2WiISFvQ/goz/p64jbc1Rms1nMZrMSiUDAetEwuYw+XIv0J851ob5ffvllvH79Ok5PTwsYxRAhEjedTst76CvzoPv0czMAfgm1eZf4syK2h9D38nxWBNno5nN+1t/XAZFsUK8DDPn+dfe1/Zb7xuR2GFC4PesUaS03+j5Cptf61eOWdQKyqlY3j3WuU60vstfSaXeWYabNzc0YjUbx/PnzBj91OrdR59///vfR7Xbj1atXZbczovBZVqKD7BDKzjPkN3XNOww9tL/dr+t++5Dyfgv097//vfU3OyPz/LYzgkNre73bjVksv+0MQ3+yEyXb4GPM8Z6rq6uyAyJ60HzsLAl0M7obw7+mk/lv8GIbxc4O5p7vyUABJyC/4VzlvZQJ/+OwdBYG/E7E2Y4Kp/vYSen1ng+Vi7kfsjzIuoD/Wfa3/W4nlfsng5baHKzJ1wxmahEWHCyZT12PGkjD/vFmRzhR6N+IKOuQe73Vuto2ylFxt2mxWMRf//rXtc+30a8GNPLEhsnsiSdkfXx8XCIFLKLqdDrx/fffx/HxcQMdkwZEp1t4e5ISueDE4Ihb5bK3t9dYa8HqfecH0sHerYm0LOpOWwAnBgGABBgIJMo6CBOTg0mHR80Ltbybk7242XvrvrXXA7SLJzDvduJ6ELVgrYJ3S6IP6WNSn7wrkCdNp9NpeFH4c5SLNiI0ARUAGXZ3uri4iNFoVAAKbWUbQpS9IyFeAzKbzUqkptPplJSqJ0+elJQ0wo3cxwR9+vRpXF5eljUegJ4ff/wx/vmf/7m8294op06ZagbgI62oBgxqIIP5Xose3FduRHMxaM17dB+4yPf6eg3Y1AzkNmX2oZRTc2x0MHetmP3eWlucbnYf1SIkuV657fbG5XJsrNf6cV3fci0bUTbYoK2trTg4OGgFkpubm/Hll1+WM5WQRThJnL5An7kNuV7uT55zH/n+R/p05LFFPnM9ornFOnqE++2Mi1ilA3PIK7rJqdWOMmAXYFtYB+KYQ6Z50xUDZci8Qdl2csKHNuTt/XY/eKF5loOOZvA8gMvrW3LUOIN687jltOeb5UOuq9v8ENnYBiIzH6wDHNkGqclu17ftHZnyPTVAYr3WZiPQ7077o+9JCWVdUESUReJHR0cNfsqR7dyP3FOzFX+p7fKrAA0rvYzk/J1dlljw692fvv/++5hMJsXj7xx+GxdsK0oaDmBid3e3bBeLR5/TNxko7yftznf0IyKKweoF3xGrbRQtvDwQzjn0ZPTuEQ5xoWQdZncuJALC3g6EYza6LEidV+q1Kw755zGkHOdrEmXyGAMsWECHwKQM5/zRhswPWTgul6tFawY4nU6n7K7A2o7pdBrL5TIODg4K6On3+2UdDxEQ1mt44SiRj5ubm3j27FnpEwCWI2UvX76Mfr9fhO719XW8fv06/st/+S8l5YpFVycnJ2vnxSPQ+DCyV86COofMTdmYzkogK6V8LSvFiLo3iusRzfUgtXrlOn6oYWmj9D6iDjnCsO5+P/ehdauBl2xQWPY79SmifQtFgw+iw5Yd5gnenZW5xxL5OxqN7qQ35fdubW3Fy5cvyyJKHGPX19dxenra0HNOa8gpsbTZxp15pgZy1/VxLUKT3/e5URsP+Pd1/YM9gFOOxd5sCOLMCXa/Y9MQ6/VseKNb0elExhzJMACx1znP9wxO8/yxfMqOT571ZjSep7zbDkqnjTNfSRWknwxQDHhyv2I3oOu9A1yWlTVD3O3N8j/f3+bYqP1e+55TOKkTfd8Ghvi/js9qDkjaxXtzVMWOpJw+ZYc9u7cSWQNoQN5Ih3q2yR63v6YfP5Q+OdCIaG7f5UlkoYrn3N9pyPfffx8///xzw0Cm8zGUSZvCo0DqEZN4MBjEkydPYjQaFa/CaDRqeAm63W4jRJgVko3kiNXuVawbYPCZnDbYGdTJZFLat1w2D3uyN8M5iY4S1BZvu64gcEBWNvIx/B1SpQ0AOIQG5QPmEA4Y+Y6uUBa0WCwau31RDsKKOuXnzfyehLQtCxfqwWGPXO/3+/H8+fOyiJtF4aenp6XPDw8Py7oeL+Z/9+5dXF9fx1dffVU8AqSvsc5nPB7H5uZm2S7u+vo6Tk5OyiFNx8fHpR5QbRLXokmPdD/ZcxxxN084U02Icv2hz9Q8ahmg+Pq66MqHgIQ8r3LEIpfZVq49dhk01ZSvAdVDwYaVEEqwjbKRkMvI97p9WekiH6wYKZd55vcYeLHuwrLe7fB3opjI+fl8Xub46elpQ05QD8toe3nb6vrQiMbnDCR+CRlwYpQxJpDtE5yaZDigFzCuI1brMfAie0v3iFWmhuUTzqutra1io5gvqYd5MO9WGLGSBU6JsW7l2axjavLEnnvaTrtoh+0Tp4b7HAgDEoN+7DKexfbhum2rGjDApmmL6tiJmYGX51OWeVn21PrY37PToqbTa/VvAzsZ/OZysAORpVmW0CeW0d7N1edmsDTBO4uue3cb3ff7Q+hXiWhg9GZvFRXu9XqNHRnIoe/3+/Hjjz/GmzdvGuEfJhR5+zAqaTP7+/tlF6lO53Zx1XA4LIzOOxwOjGjubMXkcb4uCiR70WiTlVpWHrTX4SqfiA2iR4nBLBG3IIV7bVx7kTrtcD1q3kHnTJpZrLw9mSyEakDLitPCmfo5fxPGns1mpb8NkLJRl9OuiJjwLoc0u91uyanlAEgveAIsAGaXy2WMx+OyYxTbFnoNzXK5jK+//jpGo1HZaQbwxIGL7OLF7jQnJyfx9OnTUofhcNgQNlY4n2LCfi6UFQa0zphvK2MdOOBa7VmPo5+peXjWebTaFJtBhIHIOi/mujJrVDPo27xZ6/rjIeQ+43v2jFlGWp66jOyU4jryoebR9Vhnpc499jzXnBhel+fUzsFgEO/evSv3sM7P8h2dx3vtvIFsNLX1fY0yz7YB30e6pTb+zcZk/g2+BBSwxbsXSXvL+U6nU4AmvJUzC7A7DDKcupQNUs/znDLNPV4LSN3tqOV3g3O320477oGvqZPXNTrjwHPAz/IbZVIf2sv7sSfanBkG6tb9plrfub21axls1eaP+zPLmVx+1gu5LMse93d+HrINh83qyGi23dwebKmdnZ2ymQ0bKbHREfaMnf7uizYd2NavH0q/CtCIuGsc8BuIGCQeEcVAOzk5ib/85S/F8HYIC8DhDmYNBuswMD5tcBKxcE6kO5fy8Eh5cL3FmhmLiZON+IykQfsuy94TM6HPYoDJLLSsWJnErqsVpycK3w2QqKfTszC48SRkZuZ30soAEl7cT50YWwSFd7uy18FAyQie9yLMLKCcnnV5eVlSpwCK3jRgNBoV7yP9v729Hfv7+3F6elrOcCEkTr//8Y9/LIdLskCfd+/s7JT900ndevHiRQGkRKroDz479eOR7qcs3MwjbWQBmpVpDShAnoeUU1MObcbeh5LBg//X7sn1zd7Nh76vZtjXgEFEVNu+jmr9keudFeW6MpAL65RfBl4ZdLjNGIYQHufMFziDbm5uot/vFwOOrddxKuH0Go1GpT3Z+1rTg1zPAMz3/xLKRmOb4fBbpzyPIQPJNrLhi3OKjUYY54hV+jD3A0JcBv/Rlc6bz85OPqOfHLHN/Ok0K0cjIByEtrd4h/uH3/y87RLq7FQwzyXXO7/HNpD1PPPPzlbKyjt7eTzdT35XTceuI7e7Blzyb9nB0+YIctk1qjnLXBePscck24cuI2/xzh/gOGeWcIZGjuita0eWrx9LvwrQaBt4GoJ3mC1f9/b2YrlcxjfffBPT6bQY6Ah2Gw8sGH/y5Ens7++XlKmdnZ3G7kp0lIUAgsYTCOM4e+8NihyGJxLjA3o8IA69UYft7e2S5oOhzOT1egkvks5b1OZFbCjPNgGQJ6hzRkG3mfDCQS6T/qD+lGPD3+/udDqljzJgY+yd0+lQca/Xa+RjR6yAIDyRNwXY3d0tQBIg0Ol0ysYCl5eXMR6PS323t7djb28vjo6O4vDwsOwkcnFxEd9//3384Q9/iMFg0NiKGa8lh0hyP4YG+1VjyDw0JeKRVtRmXN6nUDLI8LNcs2Gdy/L1hxhpNYM3K7CsoPjcBhJqCiDXJZdVa3+tXIByBmIfA5hMNSBXM/pM636zcePyTZYnPOP32jGUUzDW6aj83t3d3YhoLlhHTpOC5QXsyLuaR9DGILyQDcpMNV69jz5HmZP5LX/2nMtOLTsM83jzuyMH2cDz2lGDAnamciaC62CQkevGpiwZMEQ0U80j6tFb35fbUTOuuR/7xE7JdfIyX3efY18xB5k32AB5jHIbspw0aGrj8TZZ5PcYQEXUZbXfmWV2dnKY92rvzGXV6pTvs7w2wIxoboIDUCOdjzWplItd5EMn4eF1YOJTOit+lTUaecJnBUp6yXw+j/F4HIvFIv73//7fcXR0VAzOyWRSvMN0JtuLPn/+PEajUQEZg8GgeBbISYOypysjSBu+Vk5+zguZUCR89zuWy2UxvhlIjHLu5TfvQGUF6S3fMOBholx/o1P6k4lMu7yomnUZRBdcBuFQDGjemSMp1NP96Xa5TijViFV0wlsGctARZWYvhe/N63RIo/N2uoAbr1tZLBZlTY0VA32xs7NTIhyHh4cFOHz//ffx3/7bfyvRMfqC97DYnPJ7vdtzPtgyl2cQDozH5+hp/BCqKQd7erJgtOfH964rMytoX/8QAy0ro4jVVo8171j+vA7UZMXHszWQ8hDvk5/NSvFjjNJc/xrIsCLO/Z3rUQOJWX57LpkvbCxm54jvqekB95Pf5/mNjuAetvOmDAMH9EQ26NAHtf75pfQ5gopMbf3nfkb+Z0L/LZerQ/sYO9JR2A6f+c1z3MN26owFDtFsYKPz4C1TbW2rbY7sDDGP81wt3SjrVc85ysjRBeyEmryp8Wu+jzJtVFv2eI1Hdgxwf5bTbe3I4+06up9r4MhlPNSDb/mSr9fKrT3fBkbMq5YVtsHgB9uVlLuxsRGz2azYQchDLxPgfwZL6+rzsfTJgUYO+5jm83nxEmOA9vv9+Omnn+KHH34oXuiI27z++fx2m1qMw2fPnsWzZ8/iyZMnMRgMSg4+E9oLuzNyIzXGC5ZgQhv83jKOgbLhaG+VDesaGrYHhCgN12y45jrACF740+12G/1m74knD4YtnviI5iLEHJKFAGnT6bQRxqU8hGUOp3qs+Y3vBlwwdq/XK0LMwMyLzZxWZbDHf4+BPTAW4l7El40Pgz36aXt7O4bDYZyensZkMombm5t4+/Zt/PnPf25MQhTK9vZ2iXJcXl6W3c8cSqcPMVAegcbDqM3Ts86gqvWt768ZGPne+8bGYMIGaS0SkQV3VkJ+X06XaIt81EBWjnDU6sL1bIjn+uW0h0zZ2whPt40L12tjk8fYjg3akyMDueyaUWIZ7bx5yqyR+9EOIco+PDyMV69exYsXL4qDBp3U6XSKLLBO8Bo3rvm8IvinNiaZHgoG28DT50gGGHxflzoF/+/s7BQ94lQUNhlxxoHnP/IffiCS4YPvIlaGvNewUoaBs+cl+i3rfn53G7nWJvvyPb7XoMAORveRy4DyvfR9NuLR3W6HN4fxO2rvrY1Zjc89p9rmVQ2A1N7rMcltrUWec7tdpmXlOrDBNfgkn31BOdg4fh5bii2Y0SO7u7tlreyHOCay/v2QZ02fHGhYqeTOZyLzGaOM05c9SD4YaW9vL54/fx4vX74sO0mRLw/AsHHPxIRBzMw5HGqmJ8rQ6XRKONT5vAAWA4gMarxIijoQ4mLBMqFzh0VhXN6HULSnzjn+VirUueYZoFzew0S3ocGCNj5vbGyU1CQoC+m20GEOLWOI01Z7+exFoa9oJ+SFcz6p3H1kg42yUBgRUQyCiCieKfiCOpAStb+/X9ZuTKfT+OGHH+J3v/tdY1E3k/fJkydl+1zqDh/Ze2Yl8ksn6udC3333Xfz+97+vKs3sJYNncupBNuQf2ufr7qtFJbIQblPoAPU2AJXLr31fV6+aJ27d9YjVnGsDwPcZt+sUcw1EZDmUn8kyq9YWGwZ2GLksgyqXTzl4U7M3kLpariArz8/PYzabxatXr+LLL78sctoLXVmXlmVR5sMsfx9Cv4R/O51O/OlPf3rwO37rxNjWPPQQUX0Ix1tElLWH2Bd5PuMEzJGMiLsbWKB3vPtUxF3nJdcoI+LuwnDLIuqT15RmQJHntued7RqXUdNf2Qhf5xzIHnTbKtyfDXFTrr+v1WRA7d6aYV+rp0EEbXIbTXYmtDmVslxYN5/d1zyDwzTXBVvJsgd78/T09M7WtlmHPSSC86nslV8tdSqijqRBWXiR37x5U7Yg9aRhsFmM9fz583j27FmMRqPGYnLAiieXQ+UYy/a8ZyMZxcT7MUxRDAALe0W4Zg8Vigyw0e12i2HrnDlPaHu+XI6BEga6vWYoMStfyqHtXjth4ZQFC8rX/YE3ptPplLUQMLYjMihsp3blfGjqQfqB11vQh0xATyTaRBtyRGldhMBgxNEX6km7p9NpbG1tlYjXcrksC76Pjo7ip59+im63G7///e8jIkpEBj7x6e+0CbCWIxk2lh6pTl9++WVENAU0fWdAYYO5DcSZXz6UskK5z6Buu69mNNA+v8u/5RSfdfXyNfi8Jn9r9TRfZieF65Prl+tfMwpyeZnvcz9leYcMqxlNNhhqc8uyqAbulsvbFE4blQYh3MP3q6ursk/969ev4/e//33DoZKNpYhoeCHX1aUWBcvUBvruu/+bb76p/v5bpdp8rM0Hj5mjB6TBobM8xpeXl0WXe30Ev7OhTUTcARmZcsTC/F2TFTzjreprcxSqAYp1QN7ZBFl3u1/bvmML2BZxGTVQ4b7LjqP8ftcfG8Htq8nJTLU2PCSC2KavXWfss9w/uQ0Z6NTqnYEJOgx7wvzS7XZLWqf72bYZdTOINp/9v6JfBWhE3GUSG4scetPtduP169cR0fQSkkK1vb0dL168iCdPnpRIhhfb8py9zUxG75wEc7ahS09ucv6t9DwhcsSBd5sBAD82sm30QzaC8YSwqBnyZGIRd87b5D6XGxENYMJCoevr6+KloR0wct75AnJKmo17pxEgaLxGhGcMwqh/TjXwxOU5gzDq5cmKgQ8osucY46Pb7RYF4iiaAYIPaloul40Q+Js3b+Lnn3+O8XgcBwcH5TA/eIxoEHVy2honCNtL9EjrKW/dGFE3FqxwagRPtvV59ujwPStsK52asmwDG1mpZC8VxDtrxmRWWDUvPWXUPtvzVVO0GcihfDMIQu7lNtaoTXm2GRC8w4Ci1hc15YhccR/md7YBQgzHDKzyu4+Pj+Pi4iLG43HM5/OYTCZxenraiGzYQcW5SW0pttQ7GxK8ex2t6x/elfntc6LMe3n8M4/4vogoegD967V16Ao7qiD4EF1gnZt51PVCf9a2q+V7dtYyV+yAs0yqOV3W8ZV5EhlgHZ7r5Hqum9N+b03mtEU33Y51jo/crprszPWs6YO2edRGNfnGWNdSqXK7qVetLnnsuMdObh/wDF1dXTVS+ZbLZcMu4b8zOx7q8LxPJj2UfvXF4Pame3Jubm7G5eVlOegsH8C2sbERz549ixcvXsSLFy/KolxPYE9kr4WwV8q5kAgOKwfq6Z0hPBEIkWfPuxnC6T0GHtyLYZ3DV6Qo0a5+v9/YnYL0rZwuhcBBEOWtf53q5XbN5/PilXG+MIDFTG2mtxHiLW3JH/SYeaGb+5qIjNG3Q4EGa9QJ47+23oQ+8jgwudiVykI4G36cwUFfRKxCkZRJCPynn36Kb775puwwA2iczWblID8LEm9ikPnpkdZTFuRthqIjGTW6L50oA1u/v/betjqtU7APoWxI1Ork32yw1ICS7zf/ZaBTAz7IExsXlJFBXRvgyvPT9a4p+RoAz46KdZTvs5ygD3h3vjabzRoH92W6vr6OV69exebmZuzu7paoO1vgsm0pzopu9zZl8+zsLC4uLsq91Mc6x2NzHz0UMDzKl1uqgQ3zdgYgjM3GxkbZGhQdZGOe7dMpFyKNG4DiDWEoP/NnNiazIW9w6rlWm5umPKdyxKAWWfQ7vBtkbmcbH9bmfq0u6Oa8fslyoi36m+VMW33a5nIGHm1y2/ziZ7ITKju8sJ2c4dIWxc3tqYHCXF9sr5ubm0ZqOXzpVD5sF9cVGWU+dDm/Nv2qEQ2MrbzzEI1+9+5dnJ+flx2ljo+PS87Zs2fP4quvvornz5+XbUkNCjBOfQAOKT6uQ1ZwGfl5EF1nDEoLDMo08CBNJmKlWDFe7f0wIKIPOp3V9ojsYGIDyqd+siUrwMT5xQAZG+zZm+/TS0kNMpig3oADyBOMNhtMMKYY6Dl3NYMsC1YiKXiKFotFWc9iQuhTBwwFPjPZKDcvnqN97iP6Yzwel40HUDTmHaJAr1+/ju+//z7++Mc/lsWf7O6Ap4B+os/hiTbj9ZHuJwvwiPruJuuoBi5MLu8h4eT73tcGTvL7PiSFzvfbSM3l+p0ZXNTAkZV8Bg65TW2AwsaEjYUcfWwDatnA4DnW6PkdNXK9XSfLYssP1+P8/Dy63dv0VjtvkBWvXr0qGzz0er1G6sxsNisOBaefbm9vl1RgZJRTHuwgyuNYSz35JfSxz/9npCdPnsTh4eEd8JBBBr85zc4OMcaXA1jtOLQ32UCAM7/Q0zWyfshl2NkWcTe1q1YGv9UAq/nL/On6WffnfrEubWtLlhlZ1mWZkQHGOkdCnu9tBnt+T1tda/3V1qZ18t2y2/8zmGuT0blOD7nHchzezE6fxWJRFoZTJ4OSjY2Nws8c5Ge+uE8XeZyfPn269t42+lXO0cheLaOo5XJZFnAfHx838t2599mzZ/H73/++nJXBWQh4DC4vL8s1Ot7RBgxQRzl4P/9tbMIsXLPBnkEK6TcIGKcxZTCEAHNqThZWFnT8t+AhqmEglBdLR6yM2yyIvDh9uVw2jqinn2A2fodp6RuDM+qcQ4UspM+e+xpQsacoopniRZsQwFa+XsDKNdrFfwx/C26UjoGN2zQej8vid28swJiNx+O4ubmJ9+/flzQ+JjvAGD6jbHvCHuqdfaRbykasBW1E89DJ+8BBBiZ+zmVnT5b5jLrwv81jlinfy+cPARnIFpdV4/82RZ9/y22mL+zgqAGS3Ibsectght8sX3yf+8DzMadL3tfXlFcbz9xnbhcOsOPj4zg/Py9bpC+Xt+u23r9/H5PJpMg75KxTTK+urmI8Hhe5g7y2vCNKn/vTPPxQ50OWH21y5XOUNz6TIc/VzO8mrrFJDdHzyWRS9Gd2vPlsLp+ezTtwCGYnZ/6cjXwDkVzfDJbyMwblBrPU05kYuY9y/dqcEjWAkfs6R1Vr5deim1mOe+7WHEvMoewoyfIo/+4ybB/kvmijmtPKn23Ttcnd3L/rAE52niB/Mti4uLgohxNjY6M7ABfL5Wr9KWU+dA1jm957KP1qEY2sVN1JpE2dnp6WcwsWi9sdqfr9fgEZBwcHMRgMGl6FiNVgRqxOnfZ6BQbFACAbslkxUR4Cw959KzLWAxjImIGyQXR1dRXn5+dl8C1MvDjH52dYwDmtBw877TbowLDOB9+x9oN0oppBQFsvLi5iMpmUdTQGiKR1uf72lDhq4W3ssmcxvxPhPp/Pi5LOhgf320DI/c11o3QD205n5c2ppdxxEON0Oi19xrhx1sZ8Po8ffvgh9vb27vQ3fWxBRP3sxfnYCfu50bq+WqcYat6rzLM1RZivuR7ZcGkjK8B1oCK3rQ2Y1O51ndruycrMwMIywBEIGyREZbMzIr87gwcbFOZ9P2tD20aD54+VoOdTzXDMOznV+s46xGmjZ2dncXZ2VuQNOskGG3LCJ0OzUBxj0/qF91ruuc3upzag1jbObfQ5AYtMp6enDX40eT5m4ho6nawCe33tlDJfkM3gLfHtwIuIxpzju+2NvKYwZ1DwjFOuajIorxnxs7VrWRf73iwHmJ/cm8G85UOud/5ck1l+f02+1Kgmj2uGvd+dZYbvrfV5Wz2ynKqVl7fWv0+ut9kFtmO4z85ogzbburyfxeIRKwd9LSqS2537hve/f/+++tx99P/kwL6I1YLii4uLGI1GMZlMymJZ0NbBwUHs7++X3aV2dnZia2vrzl7UEbdhbzzrpBhBLOa1we4Jx28ZbETcjXowmJST15nYELahfXFx0TD6M7LO+3A7L5R2ewE7ijeHQw2GHC2hngCYnIbFSdi8321iDPn95uYmzs/Py/a8tQXfTh/jvxeRMy7uPxscOWychZ4jBfSTJ6EVgMujztnwd39wjRSK6XTaiETRL6PRKGazWXz33Xfx1VdflffN57fnvTiqQ7uzIH2k+6nNy4NxVwOumczbOapR83JBH2ro3aeI2p7h/31goXY9Ozdqz9m45pna+zPZyw5Ib1PmNUWegUUuz2NBednbys50GYjkOiJ3cpTKMib3GeSoN++Et7iG4s6RYejq6iouLy/vrO8jPxpZ0GbEAObcn4+OiE9L1pHwhvknj83m5mZMp9Mybo5OWW4AMrBNIprrUXMKlQ3zbMtQtoGG65ZBCp/dxmwQtrWvdn8NuEfc3dAgO3Pz/HeEwA4dOw0y+OD3X6ob28Cl65hBw33zq+056KERaeso90Ub5Xe6DHQe/JPBR7fbLWdnGGywRgzdmSN0tTpk/fUp7JZfLaKRDUqMNgzyo6OjuLq6ahiAX3/9dYxGo9ja2orhcNiILnQ6K28SRiYnO8PE2YPuyWQFlCdG3mUBBWsPhTvbypd7rKg4tRrB4/q4b7xwaGNjIwaDQSO9J3sSaSfgC0bLu0txHUXJydi0j3EgynF5eXkn1Eu/u428q+Y9dP9ayHqCsXgyIkof5baZyXPqG+Q1OvbG5vAr7fC++QhMxiOveYGfZrNZdLurnUSo/3A4LN5PeOTi4iK2trbKTmpZkJnHH+l+sqFau87nGmVgkef9OkM+ywtf9/NZyfpa5tWHAIc2kJDr1laHiLuHQbWBNagGUpiH656pXfO7DCzch/7NkRG/H5ljWWlwkoEZc7nmrLDRl/vCbbdx5HpZXtr54ufRQRcXFzEYDIqy7/Vut27ncM91fJy9ow8xmB6pnTxGBr41rzmgo9/vFyO62+02Ilq1SEGn0yk6OAMD8yheZevHDDBMBhuZDARqfJznvsvMc83zq+Z0MbiCmGPZaeNyKcPphbU6ZdmTI6w1MONn3Qdt17Fv/Fv+XLuW61br8xrRJ1n+e37X5nDWG7at2iJE8FLegWq5XBYwYbsVR7l3ObOszNG3Wrs/ln4VoOGJB/X7/cb5CbPZrOzesVzeRjOePn0a+/v7ZbuufLImCwRhdpiJ3Pps6NYEAYPE9Wyoci2v2q8pQHZGYtDyLhUGGjwDIOEZ3oNnjAnnhfOUFdGc8LQlL/ByvUlpatuDebFYlDphkEOO6gCuKIPQcm4jjE19UdKkYVBPp6A5TYs2ZmFGuTk8zbXsOTUwoo3d7u3Cz2yUGTSyw0xExNnZWelH8hw5BOf09DR+97vflYXhERHD4bDkdZtHXP9H+jByH1og36c8atctNGtgoU0R5GdzmbVxrYHNrDhtnOR7spHucn9JfWvAxPf5N8sAG+y5PnZqWCb5mkFGNgKZp56rjLcdHQY/WdZYnrkeNgoto7KBY+Ov0+mUaLjbj76wYscgIHq9u7vb2N52d3c3jo+Pq6Aq68YaWYZlegjvf25Um5/mPfOZ+xYHEgaaMwfQe3mXH//P1/3dMp/vOULia3leRNwFzW2goEa1+2oOQvMj77JzsWbwt73Lz/k68w89Wrsny65171wnAyOaRnqeczWD+qH0kPe5jh7Ph66HqNUdwtYx3/hebBTzPEDDjmocqdyXZbXr/ynoVwEaTh9hMrHYiogEu03hUfjiiy9iPB7f2eI1e3tsbHpAUE45D78NZEBcWy5Xi9TzxLZCtYHryIrXcmSgYGaxQia0xY5SHmiHKW1sRURjr28MadpK/wLQrLQpd7lcltO6aT9rXdyX9E9W5ryHBegWHB6bTmeV522vcp4k1JW+MuDyOMAT9Ef2OvKMU85cl8ViUXKquZ+2eJcw0teIbPC81wuxOxVRqMViUaId+fBD06fyEHwuBL/kg9RsiHJfRDOH+j6FUjPK141P7bcMcH1vBgu5Dlmh1IwA17NWj9wX+TkrbSvnmjJ3WW5XTm2ibK7bAVQzliLa08k8T5fL1and1iHun1zvPP7uW+Sfo8vUuw2kscucd97J6TY83+v14vLysshgp3ZyRkeWX8iXrNQzYP2lCv6XGE+/NcpzL6Ipiz0/ut1uWZfBeGILoKOdMZDThomU1+adbQuTHXgZEGdwkIG+KcuH7KTIQMb3eq7md3u3TRu1Btmuo/nWKWauA5+Z29nozvLTdcvzNcs0y7Va//gdvt/Xav1ZK9PvXzfP/A7L5pqecFm5P/0O24HwIk7aLKu8YQE2D+9yRkjui/v665fSr3oyOEwSEWXNQUSUcDPbCz5//jz29/ej3++X3ZWcW+bP9kRErA6dM7PlVKg8ef0/4u52uQyaU2x4xkrV3jOYyZPMz9cEAPexc5HTh7ywx3U3AqWefjdM1gbEOp1OARnUA8HixeKmra2tEg7NRhzjzCQggsK4WKhY8BHJIX0rC2+MDr/DHqZszHhnL/gOUGqAulwuy1ZvAJQs9GgrY+IxJ9pxcXERFxcXsb+/X4DbeDxuRMIyPYKM+ykL4ba/iPb5lRWA52TtPea9h4KPbATbyM/f1wGO2ntcTlt9a3V1G/I7LB9yuY7wZS8m84Y5lT2r2UD2mLjcWhtNlk+5r2qyJre3VqbrlYFLNvByWVbePJedO75/MpnE3t5eWf9G5LPW19kBlOvr9uRxuY8+d5ARcTedL4PePMabm5tlvajTprAl7ERjh0E7+zzGdn4BRiPunkMDZR7P/J7v5Z6aPKCObXPZZWfHH9fIkPAchi+9rtR1sk2VI4Q2aLkH+y4bzlmn297JVJNF+XPNQK7J8ofMmaxjnOJ03/MGcPedXdFWFu21QxvbzbIEG9UbFGRdQPYPUVqPQU0+fwqZ8qut0TB1u82D3m5ubsrEHg6Hsb+/3zDS2AHICwIhDzapUQYHmdH5705HGLCgi2u+DyHjMCoGp5VpjnZYyGRAkd9joMJ7iMhYADjnkefxwOfUHCsx7s8GWs2AsWfNTAvw8+SnbjXhaOPj+vq6hKN51sCI53zQniMVXl+SjZa8jgV+AIQ4KkTbHXmazWaxtbVV/hB6vB9vwe7ublnHQvt2d3fvCGW2jCPC4b6qGbqP1E5tHh9HirKhW3Mg1MrIBmXb+2r3+X2+Lz+TwURNhuV7siLL9CHCvlYP5kQGBrkdbcZZTrXKz1qWZQ9np9O5o8xq/UYdc9nMoxoYyf1Ti5xYdlg2ekMN9xMOEitxt9Pzmqj0dDotZ27MZrOG88YGkWX9ur51mzLVANgjrSjPq4jmukGPR7YJSCfOxjNlIPMXi0VxqpFBYN6OaK5xsJ2wztjN98CH1pm+Pzv+Ml+4bTWescMSnYYuZDe1vM7EGzDA0/7LHnmvY+W7z8tyfzwkqsc724ziGtiogZH820NBSE2+rjPM/Zsdn7+E8tqXnEGCncMGFRmEkvJum25dW22vrdNP99GvHtEwosVzfX19HdfX17G9vR17e3sxHo/LDkXkyRPJqC0qyoI+T5SIaPzmVCyuU54HI6N8Cw3Wl/g+16v2GYXFxMjexIgVMnZak9emuP2uTwY6MK4N7ZwHSsShzdNngeTn3JbsGcoGCfcul8tGyNlAKBsl7AzltAmnXDk1y6Fq97fb4L7KgtnjyXgQOnd/dDqrk89Ho1Fj7BD8/X6/eAjYmctAg2eygfRI95N5HQDI9axEfL3GozUlkg1mG5BtoNB1yAa6+av2/H3X2hwB69rRJvytdGp1XVdH5lFOg3K9avXwcxHRkGnIlTxH29rg9yKzc5/V6mXHQ1v7LNPyphaW0egTZACOJmSu07EibtNH2RQCwxNPeS1Vy4DjIQZo/tx2zyOtyDxXiz5ENMEDaXAeV9sZLqPT6ZS054i7KZt5TLJTkP/m9ayr7qO8jiI/Z13YVm5uJ/cbdOT5lNdh5Pe3gaFcF+woMlZcn7bvEXcjf7nsGoDJOqMNDLT97jJyvdpARq3NEXcdHOuATq5jtgkto7hnuVw2HKOdTqdkaOAEMV+3RVlct4+VMb/aYnBPcjoW5j0/P4+Li4sYDoext7fX2I8aYe/cM8hGBIagwYMnh+9z2MmCxWlXvCfvuoKh2Eb2ttXISsaIH0XuBdI2yK+urhrrDniXy6uh8nWTYbFYxMXFRRGQtYlqkGFUnPue/7UdMigLAwPF6zQq+oyyqfvOzk5R7NSTujtCAUikfAOhbGz6N97HWHO6NznV1C+n443H4zg9PS39z85oNlbYfabmlbUgf6R2evfuXTx58qR8r4FzKxTzvMFGxF2HQFaYWXnWyO/KBmENHLQZ5VmZPFRwt4GYNsV5X92ysZP7phbt8HenIEJZjrn/cW7kMmp1zUZPNn4Y26ygI+6ulTEfcN0GpJ0hNoTcHt6xtbUVFxcXDc8sOofIfK/XK4vCIQyAzHc1xZ1Tu0yZvx/KO19//fWD7vutkeU4dkeOKuPggg/YJcwHsEKkmhigQldXV43t9bNuzvZKlgs1gz1fzzq/FsWolZF5zpG9zFP53bUovO+1wZtlT77ftk+2r9zGiHYnLnKjFjXJ99sDn8vI8qT2rvvml+V47uN1YKEGsu6LbFjWZcdIRDNLxDzv9CmcJOz6meWd+7NW/09ht/zqEY2Ipmc/Isq2f+PxOPb29sohIhDhy62trZLLh4JjwjtykZnLE53ffKiSw6JWVNkbZoGVB4c68Q5veWvm8bZiEPX3xLHipH6k+vC+3C7XpeZl8PZ6RIey4cT7HFZzeymb/7kvnINqzyWCzcrZXh23CwRuHrBQyFEqgFhtwueoDHXj3fayGugsl7f733OqK31EW9na9vT0NLrd2zNCUE5Pnjwp3kuHlt2X9Ms6QfRIUVIQ1nmmI+6GvyPuKogMGv0s969TGPelt2QlkRV2rWzf9xBeaDMs7dVrA1C167V3t/Wz22cF7UhdLntdXTIodF/4mudtrS05/Ylnch2y989tcR2d0gEhP5jHW1tbjQ00LB+Rsy5nuVw20mkyEGozhPz+j6Hlchk//PDDR5XxWyGDxgxAMf4Bkzj4IlZzi41sMq92Os2Damtyhe/82RaiLtmBl6NsNtRrxnF+n+eB67MOqBq8+J1ZxnmOen7UgAvfsQPWgeSaMWtwYX2en8uy0OV5rtXebb74EKqBiixfsz7gM/9ts7os6l5znriett3ylsJkAWB/kdbpZzzWLtPtoT4fa7f8ahENGtPtrg47MtMOBoMGyPDkcPgarxBludzMfO5AT868yj6j4lon2nueDRUjf8jMgqFp5Rdxd/tKngeIwBgII57JIIj+zMa7Ddl8eFweH5S4hQB94jCx+8zRItqQ65zBAe9zu2kHz1BvRy3sVckTzs8ZMCC4LdizUZQnK8+y24i3ScYrwDt2d3fLYWKj0SiWy2Wcn58XL9jp6WnDI5brbt55pDrRr/cZXRaQFu41gZx3SYFqCob3ZqCzDvT42azUDDhrwKOmmE1tCtDzaB1IyMDIc8I86khrVujmY3vVkCHmbc/1miJ2eR6rLF/twPD9jga39ZuvtynJrPy9Fs/PW+ZZmS8Wi8ahp8vlskQvqDcylHrksclAyLzyELrPQFoXif8tknVTzXCrfUeXbG1txXQ6vdNnrCeAPzyvedbb1dvh12bYo7NrPFwDErZpsjHLPTU+yP3g+tjZWisDvVdLq7H90xYhyHPL9gNttyPB99n24X+W9dTdcynPn9o11z/3VRtYqlGWN1lPZPmS76F+vt8OlLz21s/msrBxc/TYm+wsFovGRjV20Li/LN983W37JfSrRTRc0dxZ29vbZYcph5A6ndVuBJTDZHZObEZmvMNKgrAowt4H8Nngd26bdyCy4Y1QMXmiUl6edAYUGPDc2+v1GqcctyFzpwURCcpb2sIAXqRuA5x3eL2DhQ79TFSG30mdypPKkZjM8BmAMba5Pr5Gvxtw8ltuD33CugquMY6uE32ex8mRHv8xMfFgwTs+zHA8Hsd0Oi0pXjc3NzGdTssaI4fSPQfsVXukdrpPwLd5vqyIPBe9kDEiqvOmVl7+y8o9359/y8/V6rqu/evq5/Lb6lCrC+3PBn++7noybzJgsazLdcjGnuVhrp8dMsiOmuK208ROBOQG/VZzIBnM5MNETfAKusKE3vFuOQYWfj4iiqc79z31qK3PaEtry9TGV1Cb4flbpqyH7BVGHmcbodvtlkXJrNHIqT04njqdTmO8zR+MZZtzDf1K/eDDNgOaumWbgvtqkQ33Q0325PrU5h/tov/agGqbQc737Mig77yDF32WnaG11PXsHIIyyMifa07W3Ib8ufZ9ncy2rLIduE4212zDLIsAo+v0k+uKzPI28D6fjPfhgHYEy7rRMtzZPB9LDwYajwbSIz3SI/3ahKIyZYMxG7B+FrIH3ErPIHsdILaxagM337/OW5aVcAZEte8uu1am+8N9kI34fD2X4f6oOYVc/+wEsULKAMx9mo2obFRYQfPdCyX9Wx5nt4kNInJ93HacO7W+tJHSZiTwOwZp3hykprxxiJnX8IBT11zf/M4a1YCE++RzAxltlJ2EPtiW8bDB79RhAKfHsOYoYj7gwDP/ZEPOIAH+yLLJmReZd7I8yZ9rjokan9T4g3nEbzZY+d/mKOPZWiTU73LmRj4cOBvrbfVfdy/XapTlHN89L9vqXqsD457H1/Vr0wO5nOyErfUxvznSnvkK2ZSjrH7v1tZWOVbC9cr9k/vuY+lxdeojPdIj/cPROmPrIR5flF/24BEFs4EZsQIZGWDYeHNZVnbZM+06ZO9TzWDJ4Kmt7W2/WfnU/mp9SvvcXtfP4MBkYyp7h73oNpdX65fcp/ZEu18MBPyb/0N5l0Kn7zr6ncckormWh2iz34HxCW/5edrjnf3Mc47K57VoHouaIfhQJ9/nDDD+9Kc/lc95Ttp492/wB/fwG/IBnsmAkHf4O5SNwIi7gMPXDCJyGlMNXPsd5puaDPLnmnMil4Vs9J/nZu4H96N/t1yo8bL7rpZ+CeWUnTxnkTWMWc1QdrZIli/raN1cMl/lZ7Jz577yc589BBhmmeoyDKYBGo6MsIygBtDa+sZ8+a//+q+tbVpH/0/O0XikR3qkR/oQqnlwap6cbLRn71r2Etn4y/mpLqP2me/rvGy5/vlzTWFmr31OJ6p5xGogIoOXWvQgG0q1OmFYQDVDH2XVttj6vr7JIAQPr1MJqEctndV1tXfR78je4XX1MciwMZTTLEmzyUZFRHPjD+ftR9xdJ0SZXmNXA5L3GayPdEvffvtt47vnXAZ3EXdTkTBW2e5zd3e3kaJd40XGi7I5xK9mXK8bx1rEogb4a7KuBpjbQIr/5/pleWhHQe4v6lyTyZ6HOQ3S5dQoz2HXuY2ys8LAhzIzwDDwsLwzcFpH9zkC7mvzOrmW5XMt0lKL8mbgTFmkcFInUuSRtXzHOVIr2/30l7/8ZW3ftNEj0HikR3qkfxjKQtmpTPb41jyA2atnJZlTYiKaOfPr0rXWGfXrlMp9CqumgPjfBgRy29rKcRnOD4eyQV8zFCDyf31PVmxtbbGXuAYw6KfcHuqfoySuq1M9clpE9qrylw1FPtfWdiwWi7LVttvBZhlOSwAoeQyzl7WWpkF/m78z3We4PtKK8lyHh2qGE5R5z1uDUkaWPVzPoJptROGpdcDQcswgoDZPslxbJ2fa3uk51BY5zHIz828N2Pv+NjBk+fwQ4FBrV5vjxjLc6a+OYkU05aDBh8HJunUdtf7M7ff//Fvu21xOXqPqKHsGtW63ZbF3oOK5y8vL2NnZachrj0cNhLmf3U8f0j+mR6DxSI/0SP8wZIUX0TR8+dzmKasJ/jZPXlaKFu7ZmOB+qM3Ib1M8uX0P+V5TyPcprdxGKwhTVhbrFDobavieDILWGcc1D2iuU80Da2MRnsieWRQzACAiirfOu9fV3mnKuyLauGOzCi+0jLjd0OT8/Lz0x3K5bOxOlA0B83Ftkad5PvfdOnoEHLeU+67b7RYg6N2/IBv52ZObz1axIZh3n/I7I6IBqqmHy8h18LqhHNmoyQDzUlsd8juoh+dOLgNe55kMftrawP0PkWN+N5spcL1mwOZnswxxmbW1VRlU1eYc5eY+bKvPffPNemMdMHJdPDZ8zimg+d2Un48JgKccMQE4U5YX5cMD3gnN7TVQ/xgHx+MajUd6pEf6h6GaN8550whhC3LuaaNaznBNiecy2vKw19W7zZPo+9qoTVH6r60N9hpmJfUh5DawtadPP85lWknW6mWjxZ9r+dK5fEcmvBuOy8GDt7W1FTs7O8WoxLuX35uNJkchcnQGRcsW1h4f3kudnIIAZQ8gu9S15Z4/hM/yWD3k2udK2ZCEan3k9EAbggYhEXdPGDd/QZkH8juz8c/5K37G7/X3dQA9v9Pfs+e6rQ/m83k5IT23PbfX768BH/M2Zfsvl1ure209QX7GRnA26uk7z8VadGpdv7T9VnM4rQNjuY2ur3kCuZYp81Lb+OQzMuhvj51332sDPv69BvI+hB4jGo/0SI/0D0M1QZZ3gcrrF7KXKCvAXD5KxwK1TYm0Ke3avQ9tV5v3rw0EZa9izaPHb22L+h7ilczPYUTX0s5yvdre589W7nn82p6tXc8K3oCi1h9tQM1GpdMIstFwc3MTNzc3sb293ShjZ2enbDnus3MiomFMZcOgzVtaS0dZR7n/P9br+J+Zan1Bmtt94J4x90GLjlzUDGrurxllEXe3gHY5ea45ZSZTW0T1QwxitzX/vk5OZllSe68NYhvyeY2X5W6eAzXDO/+WP9faVpMlmWopsm0g8JemCeXn21Iieb//dzrN7ffb1mPwv01/2elBdM9ykO343W6Dwlzmx/bHI9B4pEd6pH9Iqnn2IpoLI7OSWmdQ1zxcNhgi7p6Lk5ViBjRt9a4p7vw531OLqNwHEKwUnNOb0wWyEqm1h+/ZaHe/5jxue7ugvLCxVveHGAW5TpCNIEdGbCS4rTklintqY+k6d7vdsg3nYrG4k4Ljwz23t7eLF5j6cv5OzSDIvJrPe2njoZpR1Pb750bM5Y2NjbLbDoeUeX6bMl9h6DGWPOMF4Qan/GbHBXORcfVOQG3Ahd9rtI4nct1rv98nE/M8yGtSLG9zn1k2u02+XpMDtTVRtbrV2t8mV9z3bnutjTWZ5z704vgs89bRurbUQFQuE5ljQJpltvVSjZ+4x++7uLhopJP6IOLsiPE7/P1j6BFoPNIjPdI/DK3zQmchXsszXqcQnH7AfXlxXTYY1inuXO8P+e765s81ZfqhBmSb96mmuFA+TlOgDxaLRTG2bQzXjCbGw0ZW20Gni8XqANNcH667HXlsnbLURlaqjorl/HOPjfmDVCfo8vIytre3G/Xd2dmJq6urss7DGxdcXFzcMWSdg+1TeDFsvVizzdv8SHfJIAADCh4zMXY5dcSGXW3dRsRqjYYjHqYaGPZ4Rqz41tfa5vpDvfT53flZ89E6eep2Mo99SKENctc396PrkKmtTdlxVKNaZLUGbtrqkgGSjWfXa10Ud11b1gFF67P8TA1sZHCXy7csyXzmc33cR07bRHYCamrrHn3vY0TjkR7pkX4zZAGcvbxZmXBfNsgQwjYsEchQG1iwB8/GR0S78Z490VBWFuu85y6npkyzUszvys/k1IU2yoYP73JfXF1dNVKLcvn2jLnO3mox95evZ+VXAwJ+JueH19pkI5G6ZA9lW7+1pXdcX183FsYvl7cpCLu7u9Hr9UoEA+IQt1pueo6y1IysbIQ8Ao77CSPLp7xH3E0DgTzX7emNiMbWoDl1MwMIKBv1Bsz5xHnG18AoOz5cbk3O1eZBDXS0gZva9xrAraXTZMryaLlcNvppneFek2W5nbXIQpsjZp2jyG2qydUatQGoLPNynfK8ztdqDg/+O0XYICW3q3aCN7xvwHR9fV12n4q4XW90eXlZbS+8S4Sljd8fSo+LwR/pkR7pH4bev38fEc3ogw0H/mwgQ9nrzX8OUaOsmtCuGZ72mq97nz/zV0sd8HM5pca/1d5RM36tcGoeO1ObV6oNcAH0ciTDfetIBmPkVKNM9+V7+51uRy3FIvdJBni5jaTUGHDUvJ6UV1sMu1wuG6dHdzq3aTaDwaDc4/obeOBNbDP4HDmp0S9R8H/4wx8++Jn/zGRvLmkiPkOgRjk9xMbZ5eVlQ1bU5h6/2RDLfO5UzEwA2CwvaANltBniNTDSBlD47v/Z6K3NjzZj/D6Du80hwPU2uZrLyW2u3Zfl9n0AHqrJkKxDctS0rQ75c43naiCjBkbyX62tvo5+M0iBn7nv6uqq8R6isLRxXbtYR7QOjK2jx4jGIz3SI/3D0M7Ozh1B7M/2Svv3rFy4ZuMx4i6QWGes5zJqHnE/t+7aOkM4X88e11zPWrm5L7iWPei5fO5zvWkvniy/Pyu/NoCV+9D9Vutn908+vb1mHGXKXr9MfhbDwVEYgz+Udq0ctorEC83uVqSYGJxdXV013pHTwVz3+4BGW1tqRgjXfvjhhweX+Z+dlstlWUPj6JujcyauE7WAL9jKuObcsAxgTjBubfxXMyBrwAVqi9jWDFn/XntfvrdWnyxb8lznfvokG5ttMg/jNV/zu3Nd2r63zUW/13Kw1q9tBnKbo4W+yTK1Jvvb3lPTCeui4rleBnrWX57/TlfNB9UaQHS73TupoD4gNdcv16ftvKSH0iPQeKRHeqR/GBoMBtWDzmqpOVnoZk9f9hbVjAGXs06Z+f7aO9sENPQQb1DODb+P2srzOhPfa8Mit8Xk7RD9bC6Ld62rXzZiskHt8fAWjDVCiWZwaeMig9M2IGlDKIOcWr/y3M3NTVxdXcX29nZERMNjjme607ldCO4D/dzeWgQmH2jo+rfROu9um8H1WyV4IR/uaX6HuB6xijQtFovGzmH0ofPaDfojVl5eyqwBgnUgORvSWablFMKHUBsgabu3BjK8RuO+591XtXJqBnQGLMj3GrhoAygAyVpKV47c3ud8WOfg8fhmWgcq+N1zsfZ7DSTWwCl95OdynUjtzM9vbGzExcVF3NzcxOXlZakPEQ/kVK1vPEYP0WFt9Ag0HumRHukfiqxk+G7lYqWcBeA675aN7bYoQTZU/T97umqKuK08K0jKilgfaXCZtXJdjt+dFW1WoDlvOoOymlfVuewmR5ba0rLawFjtP2PK8/ksjLz2Io/RfalhlE9Z/Kf+OV3FIJd6Xl1dxdbWVjkB2mCH+2ezWcnLN+DAcM3nCOTv3F8zHNuMsEwPNU5/C+S+capfBqfZICVCtVgsyg5VABUWy/J7bfcoNhrIdTBfGDDYA10zGNcZxNC6cb/PqM5gO9/DqfeANs/JPMdzWZY/tfd7DpNiSSQwy92aUZvlbw3EPMQwbuu7DCRrctbyItcty+Is72oyu1b/3BcZiOVyrA9rEWyfg3RxcdFw5m1ubpZNK1xultkev19Cj0DjkR7pkf5hCCHn1Bl7KUlXycZ0NnZRlLW1Cniha94kl5Xr5d8pa130wB4tK2vuyelYtfC4QZHf75SiXE976LJRkRUK746IhsfMnriaV7jmxcwezNz3HpvsiawZDTXDpQYuavWycWfKY8W9jkZkgJrfTcRnd3e38KlTcQAabhf1IgUl90uO9NRA3X3A4nMmAwoMwZx3zmdvOXt9fV34xOeiwD9OIfR8hTye2fDL7/U9+bsN2ZpBV5vH637Pv7WBGr73er3Y3t4uaWQADkCy50bN6VMD+PmdPhzTz7IguQ085P6rOWlqRnct2mGqyfSavMvjntONclnrQJLfZeBZa6uvZblRe99yudqwIt8DaF4sFkVucb3GaxmwwZNEcj+UHoHGIz3SI/3DkBUEBh0HptnjXtvWNHs1+ZwFrg1RvzcbpzVF5PJQAC6j9mxEU2D7mkGDQYmVipVf9vrlz7lsv9cGse+3N73mbQMEtQEexuP/x96bh8l2VXX/3xp6qK7q7uq+t++QezNDgGAkEoFggAAvEIZANMwIJCCCMpiI4AsCQhhVlDAoIOADmMEoCILwEgwaQA04kcgQCJnHO/Rc3V09Vp3fH/377Pqe3afvkNx7k9yc9Tz9dHfVGfaw9hq+a+2149C+RwqyUDhXkFnkkQf+zyK/P+uarOfHn3n7475loYgYYzgaGALtdlvNZjNsGge1jdPRnDyS4rQng3VP9EB0RoaGhjQ/P59KfctykCHWBsYXRtTCwsKaNDxS5dYrpUy52ywej0GNWH7EbYLWk3FZ/VjveVDs7ErpsaFQAvwaO+P+LpdPMZDi38fke1r83mKxGCIo7tTsiWL5uF5/vd17us7bn3XN3nTCek7W3j5br71ZBL/G8ojvvC3MYRzV4MR3P9/HnfE48u3zDC9v3759r23NotzRyCmnnO4z5GgVQhFDzR0NR5dj5YlwjPN+443N/k4Es6NLMQq1nkLPMuDXQ+ZioziOiHhb94RexoiWtzcLyc1CVV1h781Q8PH06z2NYE8pBbGydkcjfiZt9rSpfaE9vZv2xaleblRmRRviZ0lak0LDZzyTaEbWHKznWGUZOln8tj/j8UAiPwWZMY5PrfaN3PztDqCfrRFHGkFyswzNLMM83lQdAwlZTgkUrxe+z1on6xnI8bPWkyVSRy6ud+3ejOz4viwwwcfeAQZ+AJK43yNUcX/3BE7sCXSIx8rf49/HoMqe1u2eZObewJT1nru3cc/iGQcqlpeXw34j+ufpU8vLy6l9NB41z2qPOyWbNm1a0499odzRyCmnnO5T5A6FVyBy49ArxbijEBu+bgxguGI0u8GRZQT45zzD25flhKyHBq5H++LIeFv8954Q0b29M2sfxp5Qt70pU39/PDbuzLih7/d528vlckBY45xoT89aLzUiVpbx3/H/tCmuNsT7Y97o6uoKUTZQWtpFpak4IkUuuvfF+5EV0fAxznIU92Q8PtCo0Wiou7s7yIXYiHRyY1JadVI45d2RYObRHdP1Iqke3cp6rwMK6621eB69jVnG/J7mPeYZNzqzro3vKxTSFevoVxYAw0+8Hl3+0pcsGe3/x2cn0dd4HnzsXJbHwILv9/L7Y+eP93DtelHULB7Yk9xcb7z9+eij+Jr4f6KuWYCK6x1PN5aU2mO0uLgYzvfh3q6uLjWbzTVOE2PFM2u12t12NArJ/oxQTjnllFNOOeWUU0455ZTTPlB+YF9OOeWUU0455ZRTTjnldMApdzRyyimnnHLKKaeccsoppwNOuaORU0455ZRTTjnllFNOOR1wyh2NnHLKKaeccsopp5xyyumAU+5o5JRTTjnllFNOOeWUU04HnHJHI6eccsopp5xyyimnnHI64JQ7GjnllFNOOeWUU0455ZTTAafc0cgpp5xyyimnnHLKKaecDjjljkZOOeWUU0455ZRTTjnldMApdzRyyimnnHLKKaeccsoppwNOuaORU0455ZRTTjnllFNOOR1wyh2NnHLKKaeccsopp5xyyumAU+5o5JRTTjnllFNOOeWUU04HnHJHI6eccsoppwcU3XLLLSoUCvrc5z53bzclp5xyuhfpc5/7nAqFgm655ZZ7uymHLeWORk455XS/JhQFP729vTrhhBP0+te/Xrt27QrXffvb3w7X/M///M+a55x77rmq1Wqpz574xCemnu0/D33oQ8N173rXu1QoFDQ2NpbZxl/4hV/QE5/4xPA/hm6hUNB73/vezHt+/dd/XYVCYU2bJClJEl100UV6whOeoHq9rr6+Pp100kl697vfrbm5uTXX049nP/vZa76jLX/6p38aPmOsvvjFL2a27eMf/7gKhYIe85jHZH5/IOjSSy/Vhz/84YP2/PsyffzjH8+doPsJIX96e3t15513rvn+iU98on7hF35hzefLy8v66Ec/qkc96lHq7+9XrVbTox71KH30ox/V8vLymuuPOeaYlPypVqt69KMfrb/+679ec63Luosvvjiz3aeddpoKhUJm2ySp1WrpiCOOUKFQ0De+8Y3Ma/Ym9w4Uvf/979c//MM/HNR33Bep2WzqXe96l7797W/f2025R5Q7GjnllNNhQe9+97t10UUX6c///M/1K7/yK/rEJz6hxz72sWo2m2uufde73rXPz92+fbsuuuiiNT8f/OAH73Gbe3t79Td/8zdrPp+bm9NXvvIV9fb2rvmu1WrpRS96kV7+8pdLWu3Lhz/8YZ188sm64IILdOqpp6YcLKevfe1rmU7W/tIll1yiY445Rv/5n/+pG2644R4/L4sOpqNx9NFHa35+Xi972csOyvPvKeWOxv2PFhcX9Ud/9Ef7dO3c3Jye+tSn6rzzztOWLVv0R3/0R/rgBz+oI444Quedd56e+tSnZgIGJ598cpA/73rXuzQ9Pa1zzjlHn/70pzPf09vbq0svvXTN57fccouuuuqqTPkC/cu//It27NihY445Rpdccsk+9etg0cF0NF72spdpfn5eRx999EF5/j2hZrOpCy64IHc0csopp5zuC/SMZzxDL33pS/WqV71Kn/vc53T++efr5ptv1le+8pXUdSeffLK+9rWv6Qc/+ME+PXdwcFAvfelL1/xkRQf2l575zGfq2muv1f/+7/+mPv/KV76ipaUlPfWpT11zz5/8yZ/o7/7u7/SmN71J3/3ud3X++efr1a9+tS666CL9wz/8g6699lqde+65a+476qijNDQ0pAsuuOAetfnmm2/WVVddpQ996EMaGRm5140QSVpYWFC73d7n60GgS6XSQWxVTg8kOvnkk/XpT39ad911116vfeMb36jvfOc7+tjHPqZ//Md/1Ote9zr99m//tr7yla/oz//8z/Wd73xHb3rTm9bct23btiB/3vzmN+vf/u3fVKvVdOGFF2a+55nPfKauuOKKNRGHSy+9VJs3b9Yv//Ivr9vGiy++WI985CP1u7/7u/qHf/iHTMfnvkj7285SqaTe3l4VCoWD1KKcckcjp5xyOizpyU9+sqRVw9jpDW94g4aGhvYrqnGw6LGPfayOPfbYNajjJZdcoqc//ekaHh5OfT4/P68PfvCDOuGEE/SBD3xgzfOe/exn65xzztHll1+u73//+6nv+vv79bu/+7v6x3/8x312srLokksu0dDQkJ71rGfpec973kFxNJ74xCfq61//um699daQAnLMMcdI6qSFXHbZZXr729+ubdu2qa+vT41GQxMTE3rTm96kk046SbVaTQMDA3rGM56xxpHL2qNB6tydd96pX/3VX1WtVtPIyIje9KY3qdVq7bXN//3f/60zzjhDGzduVKVS0bHHHqtXvvKVqWva7bY+/OEP6+EPf7h6e3u1efNmveY1r9Hk5GS45phjjtFPfvITfec73wl997S7nO6b9Ad/8AdqtVp7jWrccccd+qu/+is9+clP1utf//o137/uda/Tk570JH3mM5/RHXfcscdnjYyM6KEPfahuvPHGzO/POuss9fT06Atf+ELq80svvVQveMEL1nW05+fn9eUvf1kvetGL9IIXvEDz8/NrAJtDRYVCQXNzc/r85z8f1gNACqlb1157rV7ykpdoaGhIj3vc4yRJP/zhD3XuuefquOOOU29vr7Zs2aJXvvKVGh8fTz0/a4/GMcccozPPPFP/9m//pkc/+tHq7e3Vcccdl5mmlkWXXXaZTjnlFPX392tgYEAnnXSSPvKRj6SumZqa0vnnn68jjzxSPT09etCDHqQ//uM/DoDJLbfcopGREUnSBRdcEPp+X9Bb+0u5o5FTTjkdloTy3bBhQ+rzgYGB/TK4W62WxsbG1vwcKITvxS9+sS677DIlSSJJGhsb0z/90z/pJS95yZpr/+3f/k2Tk5N6yUteonK5nPk8Uqq+9rWvrfnuvPPOu8dO1iWXXKKzzz5b3d3devGLX6zrr79e//Vf/3W3n5dFb3vb23TyySdr48aNIVUkTqN6z3veo69//et605vepPe///3q7u7WTTfdpH/4h3/QmWeeqQ996EN685vfrB/96Ec6/fTT9wlpbrVaOuOMM7Rhwwb96Z/+qU4//XT92Z/9mT71qU/t8b7du3fraU97mm655Ra95S1v0cc+9jH9+q//+hpn7zWveY3e/OY367TTTtNHPvIRveIVr9All1yiM844I+Tlf/jDH9b27dv10Ic+NPT9bW972/4NYE6HnI499li9/OUv32tU4xvf+IZarVZYp1n08pe/XCsrK7r88sv3+M6VlRXdcccdGhoayvy+r69PZ511Vio983//93/1k5/8JFO+QF/96lc1OzurF73oRdqyZYue+MQn3muRy4suukg9PT16/OMfH9bDa17zmtQ1z3/+89VsNvX+979fv/mbvylJuuKKK3TTTTfpFa94hT72sY/pRS96kS677DI985nPDLJ2T3TDDTfoec97np761Kfqz/7szzQ0NKRzzz1XP/nJT/Z43xVXXKEXv/jFGhoa0h//8R/rj/7oj/TEJz5R//7v/x6uaTabOv3003XxxRfr5S9/uT760Y/qtNNO01vf+la98Y1vlLTqRH7iE5+QJP3ar/1a6PvZZ5+9X+N3n6Akp5xyyul+TJ/97GcTScm3vvWtZHR0NLn99tuTyy67LNmwYUNSqVSSO+64I0mSJLnyyisTSckXvvCFZGpqKhkaGkqe85znhOecc845SbVaTT379NNPTyRl/rzmNa8J173zne9MJCWjo6OZbXz4wx+enH766eH/m2++OZGUfPCDH0x+/OMfJ5KSf/3Xf02SJEn+4i/+IqnVasnc3NyaNn34wx9OJCVf/vKX1x2PiYmJRFJy9tlnp/rx8Ic/PEmSJLngggsSScn//M//rGkL5GPl9N///d+JpOSKK65IkiRJ2u12sn379uS8885btz13l571rGclRx999JrPadtxxx2XNJvN1HcLCwtJq9VKfXbzzTcnPT09ybvf/e7UZ5KSz372s+Gzc845J5GUui5JkuSXfumXklNOOWWPbf3yl7+cSEr+67/+a91r/vVf/zWRlFxyySWpzy+//PI1n8f8ktN9l5A///Vf/5XceOONSblcTn7nd34nfO9rL0mS5Pzzz08kJVdfffW6z/zBD36QSEre+MY3hs+OPvro5GlPe1oyOjqajI6OJj/60Y+Sl73sZYmk5HWve13qfl+/X/va15JCoZDcdtttSZIkyZvf/ObkuOOOy2wbdOaZZyannXZa+P9Tn/pUUi6Xk927d6eu25vcO1BUrVaTc845Z83nvP/FL37xmu9i2ZAkSfI3f/M3iaTku9/9bviM+bv55pvDZ0cfffSa63bv3p309PQkv/d7v7fHtp533nnJwMBAsrKysu4173nPe5JqtZr8/Oc/T33+lre8JSmVSmGuRkdHE0nJO9/5zj2+875OeUQjp5xyOizoKU95ikZGRnTkkUfqRS96kWq1mr785S9r27Zta64dHBzU+eefr69+9au6+uqr9/jcY445RldcccWan/PPP/+AtPvhD3+4fvEXfzGgjpdeeqnOOuss9fX1rbl2ZmZG0moa1HrEd41GI/N7ohp3Z6/GJZdcos2bN+tJT3qSpNW0hhe+8IW67LLL9im96EDSOeeco0qlkvqsp6dHxeKqWmu1WhofH1etVtNDHvKQfU4X+63f+q3U/49//ON100037fGeer0uaTWKlFUxSJK+8IUvaHBwUE996lNTkbFTTjlFtVpNV1555T61L6f7Lh133HF62ctepk996lPasWNH5jX3ZA3/0z/9k0ZGRjQyMqKTTjpJF110kV7xilfssTDF0572NA0PD4eo6WWXXaYXv/jF614/Pj6ub37zm6lrnvvc56pQKOjv/u7v1r3v3qR4zUpKyYaFhQWNjY3p1FNPlaR9kgUnnniiHv/4x4f/R0ZG9JCHPGSfZMHc3JyuuOKKda/5whe+oMc//vEaGhpKyYKnPOUparVa+u53v7vX9t2fKHc0csopp8OC/uIv/kJXXHGFrrzySl177bW66aabdMYZZ6x7/Xnnnad6vb7XNKJqtaqnPOUpa368vO2+0J42G77kJS/RF77wBd1www266qqr1k1rwADBWMmivRky++NkObVaLV122WV60pOepJtvvlk33HCDbrjhBj3mMY/Rrl279M///M97vH96elo7d+4MPxMTE/v87iw69thj13zWbrd14YUX6sEPfrB6enq0ceNGjYyM6Ic//KGmp6f3+sze3t6QFw0NDQ2l9lBk0emnn67nPve5uuCCC7Rx40adddZZ+uxnP6vFxcVwzfXXX6/p6Wlt2rQpGIv8zM7Oavfu3fvY85zuy/T2t79dKysr6+7VuCdr+DGPeYyuuOIKXX755frTP/1T1et1TU5Oqru7e91ndXV16fnPf74uvfRSffe739Xtt9++x7Spv/3bv9Xy8rJ+6Zd+KazxiYkJPeYxjzlg6VOjo6MpWTA7O3uPnpclCyYmJnTeeedp8+bNqlQqGhkZCdftiyw46qij1ny2L7Lgta99rU444QQ94xnP0Pbt2/XKV75yTQrc9ddfr8svv3yNHHjKU54iSYedLMhO8s0pp5xyup/Rox/96D1WUYkJg/td73rXfhncWUSZyPn5+czvm83mHktJvvjFL9Zb3/pW/eZv/qY2bNigpz3taZnXPexhD5O0utHxV3/1VzOv+eEPfyhpFZFbj8477zxdeOGFuuCCC/a5hCzlLi+77DJddtlla76/5JJL1m037/z85z8f/j/99NPvUdnGOJohrZbBfMc73qFXvvKVes973qPh4WEVi0Wdf/75+1SV6u5WoeLMke9///v6x3/8R33zm9/UK1/5Sv3Zn/2Zvv/976tWq6ndbmvTpk3rGmuxg5PT/ZOOO+44vfSlL9WnPvUpveUtb1nzva/hk08+OfMZ663hjRs3BmP0jDPO0EMf+lCdeeaZ+shHPhJy+7PoJS95iT75yU/qXe96lx7xiEfsUTbAn6eddlrm9zfddJOOO+64de/fF3rUox6lW2+9Nfz/zne+8x7tG8uSBS94wQt01VVX6c1vfrNOPvnksAaf/vSn3yNZkOxlf8emTZt0zTXX6Jvf/Ka+8Y1v6Bvf+IY++9nP6uUvf3mQf+12W0996lP1+7//+5nPOOGEE/bavvsT5Y5GTjnl9ICl888/Xx/+8Id1wQUXhPSXu0PUYL/uuut05JFHpr5rNpu6/fbb92iEH3XUUTrttNP07W9/W7/927+97kbvxz3ucarX67r00kv1tre9LVMZUhnlzDPPXPd97mSdc845e+2ftGqAbNq0SX/xF3+x5rsvfelL+vKXv6xPfvKTmUpfkn7/939fL33pS8P/621ghe5OuckvfvGLetKTnqS/+qu/Sn0+NTWljRs37vfz9pdOPfVUnXrqqXrf+96nSy+9VL/+67+uyy67TK961at0/PHH61vf+pZOO+20dccIyktt3r/p7W9/uy6++GL98R//8ZrvnvGMZ6hUKumiiy5ad0P4X//1X6tcLuvpT3/6Ht/zrGc9S6effrre//736zWveY2q1WrmdY973ON01FFH6dvf/nZmmyBKV7/+9a/X6aefnvqu3W7rZS97mS699FK9/e1v32O79kaXXHJJCpTZm+Oyv+thcnJS//zP/6wLLrhAf/iHfxg+v/766/evoXeTuru79exnP1vPfvaz1W639drXvlZ/+Zd/qXe84x160IMepOOPP16zs7PBaVyPDhc5cFikTuUncx4eJ3NeddVVete73qWpqamD+p4seiCfQvxAJgzur3zlK7rmmmvu9nP+z//5P+ru7tYnPvGJNWjZpz71Ka2srOgZz3jGHp/x3ve+V+985zv1hje8Yd1r+vr69KY3vUnXXXddZiWir3/96/rc5z6nM844I+Qjr0fnn3++6vW63v3ud+/xOmk1UvOlL31JZ555pp73vOet+Xn961+vmZkZffWrX133GSeeeGIq9eyUU07Z4zur1eo+pTg4lUqlNYjjF77whUy9cCBpcnJyzXtBq0mfesELXqBWq6X3vOc9a+5fWVlJyb1qtXqvyMGcDgwdf/zxeulLX6q//Mu/1M6dO1PfHXnkkXrFK16hb33rW6GqkNMnP/lJ/cu//It+4zd+Q9u3b9/ru/7v//2/Gh8fX/fQPmnVYP3oRz+qd77znXs8pJJoxu///u+vWeMveMELdPrppx+Q9KnTTjstJQv25mjs73oAgInX5KHQ8XH53GKxqF/8xV+UlJYF3/ve9/TNb35zzf1TU1NaWVmRpLBP7/4uCw6riAYnc37sYx/b67Vzc3N61rOepe985zs688wzde6556pYLOryyy/Xeeedpy996Uv6+te/vgYhOPnkk/V7v/d7kqQdO3boM5/5jM455xwtLi6GsmpOnMzpSJ50907m3JuhcjDp/e9/v573vOetm65xIOiqq67SBRdcoHPPPfceoct3hy699FL9+Mc/PmAbfHO6/xBpRP/7v/+biQhOT0+vCxawrjdt2qQ//MM/1Nvf/nY94QlP0HOe8xz19fXpqquu0t/8zd/oaU972l4P+Dv99NPXoIhZ9Ja3vEVXX321/viP/1jf+9739NznPleVSkX/9m//posvvlgPe9jDUilK69Hg4KDOO++8fdoU/tWvflUzMzN6znOek/n9qaeeGg7ve+ELX7jX5+0LnXLKKfrbv/1bvfGNb9SjHvUo1Wq1vY7hmWeeqXe/+916xSteoV/5lV/Rj370I11yySX3ONVjb/T5z39eH//4x/Vrv/ZrOv744zUzM6NPf/rTGhgY0DOf+UxJq/P7mte8Rh/4wAd0zTXX6GlPe5q6urp0/fXX6wtf+II+8pGP6HnPe17o+yc+8Qm9973v1YMe9CBt2rQpnAuT0/2D3va2t+miiy7Sddddp4c//OGp7y688EL97Gc/02tf+1pdfvnlIXLxzW9+U1/5yldCWeV9oWc84xn6hV/4BX3oQx/S6173OnV1dWVed9ZZZ+mss87a47MuueQSnXzyyWuistBznvMcveENb9APfvADPfKRjwyff+hDH1pTvKJYLOoP/uAP9qkPe6NTTjlF3/rWt/ShD31IRxxxhI499lg95jGPWff6gYEBPeEJT9Cf/MmfaHl5Wdu2bdM//dM/rTlT6WDQq171Kk1MTOjJT36ytm/frltvvVUf+9jHdPLJJ4e0uTe/+c366le/GmzPU045RXNzc/rRj36kL37xi7rlllvCeTwnnnii/vZv/1YnnHCChoeH9Qu/8AvrgtP3WbpXa14dIKI82cknn5z09PQkd955Z+r7rBJur371qxNJycc+9rE1z/vzP//zRFLyW7/1W6nPjz766ORZz3pW6rPdu3cntVotedjDHpb6nPJyZ599dlIul9eUf3vf+96XbN68OXnc4x6XWV4uSZLk5S9/efLIRz4y+chHPpJUq9VkdnZ2zTX3dnm5A0kf/OAH15SZO1S0XinNnO775OUl90TrlWxNks462p/ytlni8+KLL05OPfXUpFqtJj09PclDH/rQ5IILLkgWFhZS12WVlM2irJK7SZIkrVYr+exnP5ucdtppycDAQNLb25s8/OEPTy644IJMObFeGcvJyclkcHBwr+Vtn/3sZye9vb3J3Nzcum0999xzk66urmRsbGyPfdpXmp2dTV7ykpck9Xo9kRTW557mcWFhIfm93/u9ZOvWrUmlUklOO+205Hvf+15y+umnZ5YXjsvbZo01vLEn+sEPfpC8+MUvTo466qikp6cn2bRpU3LmmWcm//3f/73m2k996lPJKaecklQqlaS/vz856aSTkt///d9P7rrrrnDNzp07k2c961lJf39/IikvdXsfpj3JH0omZ629xcXF5MILL0xOOeWUpFqtJn19fckjH/nI5MMf/nCytLS05vos+wP63Oc+l+LnPa0RJ5cL//M//5NISt7xjnese/0tt9ySSEp+93d/N0mSztrI+imVSnt89/7Qz372s+QJT3hCUqlUEknBFtmT/XPHHXckv/Zrv5bU6/VkcHAwef7zn5/cdddda8rFrlfeNmusYzmSRV/84heTpz3tacmmTZuS7u7u5Kijjkpe85rXJDt27EhdNzMzk7z1rW9NHvSgByXd3d3Jxo0bk1/5lV9J/vRP/zQ1/1dddVVyyimnJN3d3ffbUreHlaPxd3/3d0m5XE7e8IY3pL6Pleztt9+elEql5MlPfvK6z3zSk56UlMvl5Pbbbw+frcd8v/zLv5x0d3enPmOhf/7zn0+q1Wry8Y9/PPX9wx/+8OQNb3jDugZAs9lM+vv7kz/5kz9JduzYkRSLxTX115Pk0DgaWULEnY477rgjecUrXhEW1oknnpj81V/91ZrnfPSjH01OPPHEpFKpJPV6PTnllFNCn9YTWHtyOn7+858nZ599drJ58+akp6cn2bZtW/LCF74wmZqaSl130UUXJY985COT3t7eZGhoKHnhC18Y6lQnSbYxmTsdOeWUU0455ZRTTveMDqvUKT+Z8y1veYuOOOKIzOv29WTOK6+8Updffrle9apXrXvd/pzM+du//duSOidzfuYznwnVJWJa72TOPZWlO1h00UUX6VWvepUe/ehH69WvfrWk1RxUSdq1a5dOPfVUFQoFvf71r9fIyIi+8Y1v6Dd+4zfUaDRCKtKnP/1p/c7v/I6e97zn6bzzztPCwoJ++MMf6j/+4z/0kpe8RGeffbZ+/vOf62/+5m904YUXho2b61ViWVpa0hlnnKHFxUW94Q1v0JYtW3TnnXfqa1/7mqampjQ4OChJet/73qd3vOMdesELXqBXvepVGh0d1cc+9jE94QlP0NVXX616va63ve1tmp6e1h133KELL7xQklSr1Q7mkOaUU0455ZRTTjkd/nRvezoHgvKTOe+91Knf+I3fSLZu3bomXeJFL3pRMjg4GE7nPOuss9ZNEYP2J3Xq6quv3mto+JZbbklKpVLyvve9L/X5j370o6RcLqc+z1Oncsopp5xyyimnnA4sHRZVp5zykzkPHSVJor//+7/Xs5/9bCVJkjrh8owzztD09HQ4gbNer+uOO+7Qf/3Xfx2QdxOx+OY3v6lms5l5zZe+9CW122294AUvSLVty5YtevCDH5yfxJtTTjnllFNOOeV0EOmwczSk/GTOfaEDcTLn6Oiopqam9KlPfWrNCZeveMUrJHVOuPy///f/qlar6dGPfrQe/OAH63Wve53+/d///W63/9hjj9Ub3/hGfeYzn9HGjRt1xhln6C/+4i9S5TCvv/56JUmiBz/4wWva99Of/vSwO30zp5xyyimnnHLK6b5Eh9UeDSg/mXPvdCBO5uS8gJe+9KXrHvpF/eiHPexhuu666/S1r31Nl19+uf7+7/9eH//4x/WHf/iH+1ReM4v+7M/+TOeee66+8pWv6J/+6Z/0O7/zO/rABz6g73//+9q+fbva7XY4gyTrYLN8H0ZOOeWUU0455ZTTwaPD0tGQ8pM590YH4mTOkZER9ff3q9Vq7fWES2n10J0XvvCFeuELX6ilpSWdffbZet/73qe3vvWt6u3tvVunYJ500kk66aST9Pa3v11XXXWVTjvtNH3yk5/Ue9/7Xh1//PFKkkTHHnusTjjhhP3uX0455ZRTTjnllFNOd58Oy9QpKT+Zc290IE7mLJVKeu5zn6u///u/149//OM194yOjoa/49Myu7u7deKJJypJknAKO07avpyC2Wg0wumZ0EknnaRisRhO3zz77LNVKpV0wQUXrDkhNEmSVJvuzinEOeWUU0455ZRTTjmtT4dtREPKT+Y8FCdz/tEf/ZGuvPJKPeYxj9Fv/uZv6sQTT9TExIR+8IMf6Fvf+pYmJiYkrW6I37Jli0477TRt3rxZP/3pT/Xnf/7netaznhX2wZxyyimSVuftRS96kbq6uvTsZz87M0r0L//yL3r961+v5z//+TrhhBO0srKiiy66KDg/0qqz+d73vldvfetbdcstt+hXf/VX1d/fr5tvvllf/vKX9epXv1pvetObwrv39xTinO45sakfh5N0PGl1f9SRRx6pxz72sbrpppt05ZVXqlarpdZXkiRqNBp685vfrB//+Me64oordOaZZ6pSqejiiy9e42BCxWJRxWJRSZKo3W6n3rs3Wu+Ze7p+vYiZPyt+bldXlwYHB9XX16fe3l719/eHPUaDg4Pq7+9Xd3e3KpWKenp6VC6X1dXVpVKppHK5rEKhEPrI89vtdvi/Xq+rWCyqt7dX5XI5fF4oFFQoFNRut1UsrmJRxWIxNe58x3v8Xa1WK/zmfq7zsSiVSkqSJKQ10r6VlRW1Wq3wQwokv32cCoVCuCZZPRcqvHt5eTn8np+f1/T0tMbGxjQ5Oam5uTnNzs5qZmZGs7OzmpqaCp9NT09n8gPt3t/5X+85WbQnXrmntD88fjjTSSedpGuvvVYPe9jDdO21197j+TyUVCwW1W63VSqV1NXVFdZ8d3d3+OFzfrq7u1Wr1dTf369SqaQjjjhCT37yk1Uur5p/PT09YW2zjkulUmrfaalUUqFQ0OLiYliD7XZbrVYr9T3ruVgshjXra7xQKGhpaUntdju002XU4uJi+LtUKqlUKqnVamllZSU802VS/B7aw9/wfJIkWlhYCO/mM/rA74WFhaCHVlZWtLi4qPn5ec3MzKjZbGpubk7Ly8vasWOH/uM//kNTU1P3C/6J5abToWj/Ye1oPOhBD9JLX/pSff7zn1/zXa1W0z//8z/r4x//uC6++GK9+c1vVpIkeuhDH6oPf/jDeu1rX7uuw5BFb3rTm3Tuuefqkksu0bnnnnu32vuDH/xAP/vZz/SOd7xj3Wue/exn6w1veIMuvvjilKPxgQ98YM21pVLpgDkaH/rQh/TqV79ab3/72zU/P69zzjlHj3nMY7R582b953/+p9797nfrS1/6kj7+8Y9rw4YNevjDH55KEXvNa16jSy65RB/60Ic0Ozur7du363d+53dSKWCPetSj9J73vEef/OQndfnll6vdbuvmm2/OdDQe8YhH6IwzztA//uM/6s4771RfX58e8YhH6Bvf+IZOPfXUcN1b3vIWnXDCCbrwwgvDXpAjjzxST3va0/Sc5zwnXPfa175W11xzjT772c/qwgsv1NFHH507GoeA3HhDgaAAtm7dqpGRES0tLekRj3iErrzySm3fvj11Zk2r1dL//u//SlrdD7S0tKTbb79dP/jBD/YoWDGGUVZ8viehm/XdvgjpPQn59b4rFovq6+tTT0+PKpWK+vv71d/fH4wDN+BRyCh4lC9ji3HijkSxWAxG+OLioiqViiqVSrgnVtwY/TgxvJ+oIk4D73BjgHGOnQV+VlZWtLy8nJoXHx+cGu8T32EkeHtxWpeWlrS0tKTp6WlNTExofn4+RE99/vhpt9trnN2s+eK+u0vxvXGf4uvytM4DS7OzszrxxBO1c+fO+4WR6ARvsi6ltTzTarXU09MTjOWlpSUtLi5qcXFRPT09Ghwc1OjoqLZu3apWq6Vms6lKpRLWNY6Mrw1JwXFhrRQKhZQscHnq69/XPPckSZJyZOhDV1fXGsDDn+fy0tcp7XTZ4u+fn5/X0tLSmnFyEGZlZSX8j2xk7JBRhUJBzWZTP/7xj9VoNO43/HNvt7OQ3NstyCmnnB6wtGnTJs3Pz69B3dvtth772MdqbGxM/f39+o3f+A29/vWvTylYKEkS/cEf/IGuueYafeMb39hvoYoRy7NiBbse3RPRuZ6DwWfValUDAwPhNz9EN0Ao+/r61NfXF5DMrq6uVH9Q0BjiKG8UfU9Pj1ZWVgIy6ugminxlZSU1Pij/3t7eNe135Y/BgnHhBtHy8nIwAjyi5O1zRLJUKgWDBXIkkr+JYoBCzs3NBYcDowtkcm5uTlNTU5qZmQm/G41Gau9aPGc+nvGcHQiif84fd+f5sXPS19d3tyoLHo50ODluRDb4QQ44IADPAlo8+MEP1oknnqiHPexhYf0TDeFanoW89edICmuRqCWAAuvdySMIrGN3NHiWgxxOyALe5WskXicOGvCsZrOphYWFAFjwfgcnlpeXU2AHDsbCwoJmZ2c1OzsbIqP//u//rptvvjmkaN/fKY9o5JRTToc1kSYTEwrk5z//eVBw9Xo9ta/m7kYYYnJFl2WE7IvBt6fvslDqPV3b09Oj/v5+VatV1Wo19fX1qVKppCIcpDvt7Vmx0+TGAikJpVJJlUolRERQ6hgqpC94+zHmHf10BFLqODeeGpHlRPj3Pk58HqOVOBc8BwcC4wD0kugFbSoUCsEQ8vSt2IDa25i6QeTGzoFQ2M5rMT/uz/PjqNDhZFzn1CHWpcuvJElCZMB5nvWysLCgxcVFjY6O6qijjgqAgUcNWVdxNCJJklS6pNThtXgt+brIito5kEFqlTsqcTokax+5kLXu/H0rKysh1ckdD382UR8iszFw0W63g0ORJIl+8pOfaMeOHanoSE57p9zRyCmnnO41mpubW7OPQFpNe3z0ox+ta665JijHJz/5yfr7v//7NcYgqP+2bdtSRnFM66WirGfc7cnp2B9ypRh/FlO5XA5OBlGMarWq3t7eNT+FQiFEMDx9Iatf/pkb3bwTA8LTJxqNhtrttnp6etbsr8BokRRSDrLSnnhXV1dX5h4LqeNo0I447crTRTy9qdVqhZQQz612B4Z30o4sAx6jaj2n18fO07hiByoe6wNB8XjeXf7LoxmHL4HIS2l+iaMO8Pj8/Lzm5+c1OTmper2uer0e9i709PSk0hxxAnAuWH/uUPh6iB0K5Eycvkm746hEvH8iC5iIwQdkQVdXV3jeysqKms1m2PORJW/dofAxY5yIcvD5z372M+3cuVPNZvNeT0W6v1HuaOSUU073GnkI2+nWW2/VZz7zmZBW9fa3vz21+dBpfn5e73vf+9b9Pgstl7LTU7IM0djIXu+5e+tn1mduoJbLZQ0ODqparYa0qEqlEqIa1WpV5XI5GANspmR/Bn9n9QfyzZls5i4Wi1paWgoOAfeVSiUtLy+r0WikNpq70c61zKMjjv5+RyH5PEY/pbV7O1D8SZKkcqYXFxdDmpSnd9Buf38Wyrq8vKyVlRUtLCwEJyU2Otabx0O1qTqLZw6WQ5PT/Zu8AmO8xryoQ7vd1tzcXABvdu7cqd7e3pBC5bKYtRWvJV8nbOqOo49+bpX/zVp1hz2WC04elfG2xE6IP4t9Z+vJfWRKXASE/4mGAjwsLy/rZz/7mcbHx9VoNNbs88pp75Q7GjnllNO9SllGEwYl38WIbIyMNRqNA/JeKTuVKgu53ls6yr4Yg1xTLpc1MDCgSqWiarUaHAucDaIY7KFAuXd3dwcHY19SZHgfVVzob6lU0vz8vFZWVtTX1xeMdVKn2u22FhYWUtEQUrdAFbk+K3LkKQ44Hjg1WfnfIKcLCwuam5sLFWB8T0eM8Gdt5sT5jNOrMDa8PRgf+zJ+69HdjTrsK603tjkdWorlw56ipFm/DwY5P/Mu5ALrhY3RksJG8LGxMW3fvl3d3d0hgsGa93XiexzcsHdHJu5/DNg44ODtxHj31Cm/F6Of9QnogcyhXWzcljqgBZETLxrhaVk4MJ6Syd8LCwv6+c9/rsnJSc3MzOQl8O8m5Y5GTjnldK9RrHizDLUDqZz3xVDkOkfDsww7V6ZZiNy+os+lUknVajVUfqJcrW/u5DefE2Hgt1drid/pRoAb/BgIKNdCoZBKoXDDIO7D0tJSqEIldYz8OK0IRyWrfKanaYGesnHTN2jSJkc/PfIR94V++gZzvzYeJ4ypfUUqs6Jeh8KQjNvglEc6Di55alBsCK/n3Gc5GfHPgSYKN8RRDW//wsJCKtoxOzuriYkJjYyMpMrQutNOxFXqpCR6P3HS4+pycVs8jcn3g/la9VLYvo7L5XKqzK2nZrF+XZbxjjhtK9774fu94g3hN954o2ZmZkL0Z0+plTmtT7mjkVNOOd1rVCqVdOqpp6q/v18zMzMaHx/X9ddfH4T/eilHB4viZ7tCjQ2Ke5JKBZVKpTUbvr1ajCtVkEZPmQLZc+M7yxD2ik1EQlwRS+nqUvQvfi7GQfyuuHKXo4eSQhTB+9PX1xeMAHcuuJf3dHd3p9LiYkON9xOZ4T0YJnEeOIYE93Z1dalarSpJklAvf1/n0p99qJ0Np9zB2HcaHh4O5zvtieL14aCD/6wX3VzPweAzR9cPJOEwAz7EUQ0iGh4VmJmZUV9fXzhrQ9Ka+9yx8nN3HASIi0PEQI3LoPgzj4x4RToADXdCACt4J2BEuVxeE/WM58LnJ94Twt/z8/O67bbbQhRj586d61ajuz+Tl4o/mJQ7GjnllNO9RisrK9q8ebNOPvlkfeQjH9HZZ5+t448/Xl//+tfXXHugjKm9pT0diPSUfbm+WCyqv78/OBr87unpUW9vb+oQLleqEOUo4yhK3Dffi5EkyZroB0hlXImFe+MoRRzJAQWk3/4c3y/B/zhNS0tLKcNhvYP34s3uscHmzg/jQ063R2zK5bJarZYqlUpqbwd98fbvzaiIeSj++1Dt41iPDnYK1/2Z9uRkuNMcI+cxQp+1IZpnxM5FzNdunDvifqDmjDXZ3d2disyy/qrVaigvizyYmZlRuVxWX19fCthw8ihkLA94D2mIvnbXSzVzpwQ55ymZfr+3nz56tMMBBG9Plkzk+9gZlFbTdHfu3KlGo6GdO3fqrrvu0tzc3GG5niYnJw/Je/bZ0XjoQx8aQvdsCPQ6yJL0sIc9TA972MOC94c36kzri2twcFAbNmzQzMyMbr/9du3YsSNUNPDSjVRDgSkJ08cLl7YkSRLqpoOSUW2B018XFxfDQiF/t9VqacuWLRoZGUnlEtIePzmSNnkN62q1um6OoaTghTti4OigI5ZdXV3q7e1NKVnGn0VeqVQkKYXEFQoF9ff3q91uq7e3N1RjcJSTFAnqyfNuFiqLlU1VtMc3ZXIPG6dY0L7Q4zJxGC++odXRT+eleG49fxr+c0SX91N5gnYSTiX9BISH+SDXnf/d4IF/HWlZXl5Wb2+vtmzZklI05XJZlUolfEZJPIwO3tXX16crr7xSs7OzAW1l061vasU4IjddkjZs2KAjjjhCxx13XOqgw/szJUmiZrMZcoWvvvpqnXXWWZmOxj15R9Zn+2uMHUhFg7InTYrzMLyELZvCe3t7U3Ihpqza9b5nI4420I/YeYnlK46Br1WXI7ES982jcd62O0S+zpAfHsFyJJL1Exs8fOe/XV77mmbNk3/OZ54yASra3d2tarUa8r33hWLHy43He4tiXt2TY51T2qh1vt6Ts+EOCfoIig1Y1obrFnfIPSJwoPjG9S1tHBoa0saNG1NRP3f0Z2dnw6ngWWOCfMhyHGL+d9ni99M2H7+sIhM814ERj2DE5PZmDIjszYljT9jU1JRGR0c1Ojqqm2++WTt27Aglcg9HR+NQ0X5HNJx5mFRC1LfddpuOOuooVavVkFsrKeUkSB3hPzU1pXa7rXq9rq1bt6q3t1ejo6OpjZ8oDK844CE5ae0ptrFi8sXR09Oj7u7uULnEc403bNigrVu3hvAbhJIvFAqq1WqSFA6C8qovroTjw3JcSTsa5ofk+KZOFJ1Xe3EHCueJMpcY+/39/erp6QmOVG9vbzC6Uezz8/Oanp7W3Nycent7w6mg1NNmw6SkgLQuLS2FNlCqkrQPNpHGCOfi4mLK+ECJF4vFVHiXtiIYfRwDo1reptfrdoMmPrCMA4cYM8abz2MH2MO8OIA4g+SsLy0taWFhQY1GQ4ODg2vCtH19fWG+lpaWUoqkXC5rdnY2VFKKKwTBazEiynUHGvW6LxEn1j7kIQ/RlVdeuQZtiulAjMHennEwx5l1xVkZRDM4kK+3tzc4HfCTO9XOOy5P3Il2Q2G9FDB3QmIHW1KQNS6HuBbwxYEff2Y8f95mNnojB7wdcYqK6xz/DHIHww0Nv9ZPMHYQBgLMcEdJ0h6djdggiv+OEd97k+4LbbgvUpYxHf/tRjT6wtdhVnoV5OsJ/qJ8qqcHItt554FyOHhPV1dXsLP6+/vVaDS0adOmlJ0C38/Ozqq/vz8Y+FnV2Dyq4Zuyfe05uOCOPeQOeSyDmJtYBviZF1DWmR8eVfHN7A5g+v1zc3MaHx/X2NiYbrnlFt12222anp4O+1kAKvJ1dPfobqdOOUMx+HNzc7rtttv0kIc8RM1mM1UuzREyV3zs4qfCysaNG1WtVoOTwomOOAvxCbOOjjkTeN1nPu/r6wsL2B2AVqulWq2mbdu2hbC6CwzPg0bh9vb2BlSadriCwRP398T5iz09PeH9oPxSpwSc5xHzbIxmqrFgOM/OzgaDeGJiQgsLCyqXy8Exmp+fD84fTgph0g0bNqher2t5eTmcgJkkSUBaiQbQL19wjMnCwkJos48dp476M6QO+pBlPNNXNw5cmHt1jThS40IKBxJHijkEFaaKjhsEbsRhtNRqtfDuSqWirq4uLSwsaGhoSN3d3YFXl5eXg/OGQ+t5493d3dq1a1dos0c0GEfnIb7PQkwPFxocHJQkbd++Xb/7u7+ra665Rt/5zncyrz1c+t3d3Z2KZODs85uTvjmUj5+enp7U3ozYSYjJAZDY6PUogBtavhnUFTQVmySFdvCdO+sefeTZ8QFfcSlZR0VdV/B8nuMUR2hiZ0ZSqj9cw1gCNsSGkEdUofWcjSx+jKMse7r2UFK9Xr9X339fpBhJz3I4YvkMmOWf8Swodo6xXdzJAIDzteCGsuuze8o7PHd4eDgAkc1mU1JaRsCz7fbqadqkcMZrynnbgTT+93HwNR2DIr7WvZ8OJPKZj5Xfg550WxQgYz0nCdsD4HZmZka7du3SHXfcoTvvvFPT09Mh4gLonG8Cv2e0344GG26cXBHccccdOvbYY1WtVoOhllW6TOoYUzMzM5IUFkBPT09A0TFgHUWIPWcYLw7lO5KMEU86EEi/JNVqNW3YsEF9fX1rDGWvmCJ1EHEMVk6hdUQgXiju4Xsko7e3NyhmR+vdmHekEqOWhTM7OxvmYn5+PqQIkeLlgnB6ejrUyi8UCurt7dXmzZtD+pokjY+PhyhKtVoNc8L7PQrhzgbv5G8WOAgt4+O8wPj4/9wbR4ZivsFIYHwlpXgSvpM6DixRmLm5uTCncYUKNxJoL8ae811fX5+mpqa0tLQUUGkcP4/2eW49z5ifn1/j3LiCiQW1O+Ye3TtcCIV300036f/9v/+npzzlKSneuidKNp5Xf969ldbim7/ZAM4P+zI8ekE/XCbFRkwMnGCgoGSJxrrRL3VS9Pjc5SdOemxkJ0kSZIukVMpfDEjQX+Yz3mxOG+Loh/fPN53G7YgNGSl9CJgf4OXyAlkQG399fX2hTT7uhUIhsy6/tyOW83EUhmvvLWKd5ZSWq1k//r0XX4hLSUta42j4WvRMC4/+ubNBNoifCZMFnt0TeQU/7ty5M+j7YrGoubm5kO4bAw+SQkYCcim2r2IAjL95n9thPvax/HKZ7CmffO9nWriTEcv1LB2aJVeQRQCDu3bt0s0336yxsbFU6j7zHkdLctp/2mdHgwF2o8eNeyZ6YWFBd911l4488shUmNHDbJAbwuwFGBoaCgpp165dwbFxRJv2OEPHObwoFlKLyMFzJQtqPzw8HPY7IARc+XoqlSttDASv8e4ICX30ttNO0pTciGCMcEJoDwxfrVa1bds2LS4uqtFoaHR0NKRw8XtpaSkY7Ag398jJ/96yZYuGh4fDc2dnZ8M+E5wNn2/+pk3xOHtqHGPEKcfz8/OamZlJ3cc4+b6ErP0tjCULnbn29sUoEGknVPBBWLXbbdVqtfB3V1fXGsfHiTnGgYOHcDwnJyfV3d2tWq0WoiZUz2E+PW1wYWEhhSDxTF9PvkcmRrQQloeTo0F6WZIk+vGPf6yXv/zlGhkZ0e7du1PX7U3AO18w5o5M+nNQOIy1pzIcTCoWixoYGAiOhm8E9/MyiHh49MKje26EZK0XyI3rODLI+onTsogAzs/Pa3FxMTgKcXoq5EqYdFSoUCiElENX0i5D/drYyWbtumFCf7zP/rn3MwsU802j7DH0sfJUF3c0isViiK7H5LKHZ9zXKCun/YFK7mTEDodHKojEe2TRje2svVHS2gpHnl7LeiEzwfcNOWofr3H4eX95q1gshr2ei4uLuuGGG1StVjUyMhJSnt2pitdgu90Oh2PGQILzO/ZUbJOxruOURHfGvU/IZMaD8XIwdj0H0UEWnwOXXTgYi4uLmpqa0u7du7V7927Nz88HOegpWlnze1+gPfHdfZHuVkTD0bO4rnGSJLr99tt11FFHpbxBjHWMYO7xCgMLCwthYZMeFIfz3dN1D9bRXjeqHXlz5UpVERBpGBXjnLaB3rMIfVE64k7aUWyUo9QdSezq6lKlUlmDPEoK6RMwvC9yNrQPDw8HA2V8fDxEKzByQc3pB45LT0+P+vv7tXHjRtVqtZTgZKzYXyOtphaBaiwsLKSQGBBDFieVKuJN++12O6AmjUZjjSAjDWNgYCCkkTDfvgGr3W6HqAGfE6nKOmuAaNnCwkJIFyMqhNHAPgo34OAtIix819XVFdLK4BtJajQaoUACv2dmZsL+HXdS2HvEvHtaS7ynyFNH/B7QncOFSqWSNm3apHq9rmKxqKuuukpnnXWWPve5z4WxXk+AxgZB7GBk/cSpejHCFW9OPpD9HBgYCIfyDQwMqL+/P2z29h9PkYrTktzhgLKcZFe63obYYHf+crSWqG25XA5RVAdqpPSGzqzosgMs/I4RYU9TpC1uVLhR59E/J/rpMi+eOwdMeB5ygnf6/hZ3xorFonp6esLetnj9uV7yseX3fdkAeCCSOxhZTgc6xX+cH/weB4liiuU4Mh9DmiI0rCP/nLXmdhUOx77KJoAyHHvo5z//uYrFon7pl35J8/PzKTAAO8ifQV98PyvXsmbilHj+9rXIWnAHA5si7o/rYrcXvU20GcAAR43vfB8MYOPi4qJmZ2eD3dRsNoMN446SZ3/EIPehpD2BSlmUNdb3Bfmzz45GPOAxGuUeaqPR0OTkpPr6+lJIIUznjCqt3TC1srISNuLEucgwsG+QdQXHws+qzuLM29vbq4GBgRA54FmOuiFgcCQ8quN99rSuZrMZHB2vruRGPRuvfc+ACxLaCELrqQqQt79arapQKIQTdGkjezBqtVrKgcGQQaHPzMyEiBLC9I477ggbqaenp1P17b2aBcY77ejp6Uk9H0eFvmG0DA4OamRkRPV6PSC5GACO4GK8swF7amoqoK3ehkKhEFA7/uaUUDcueb8LbOcnX6g9PT1BsPKc/v5+1et1Jcnq5va5uTk1m82AHBUKhYAIr6yspCJezLEbMI5eYdT5uvA2OZ8eLpQkib773e/qe9/7ntrttr72ta9p27ZtexSSrBlPNcr6QV7FAho+xsGIK8E46oVyvSfCuqurKzgWVJLif6Ju7Mvwg/qk9J4Dl3lxtCY2dmOET1IKgMHxdqQUfvVIqNRJD/FoQBzZcNTSCy24zPOoiUe6WZdEt+LotJMbLo5wesQT4jP/3gEi5BttW1paUl9fX5A1OH1zc3Mh3bZSqQQZxPjS//uKUs9pzwRPupxA7+Bk+MGYDhS6THFd7Qa6o+hZ+sXTp9CJXkyFNsWyKXZs9hThwMnA/uC5gBk7d+7UD3/4Q5188skhtdj5OTZsXc7E7/X1DHEPdpjLXP7H+I8d9zhK4eMctyXea4VOBRjhvWR9TExMBCDQ55P9lACYvi8OAOpQrW3aleVguHzbU1QjdjTubcdjvyIaCOusPRr8pvPj4+Oq1+upw5Z8AP2kWAxCJn12dlZjY2MptLdQKKQiDVLH6XDl58gDbZY6BilMWC6XU+lS/htDOUbhyN8DXSPHkna6gQ2SDzLonr+HUIvFYip9xytMkc89MDCwBlVhs720iq6ziR4BRtQEIUJEiTGLFzjRk0KhoOnpaTUaDUkKlcGYr9jZpP9UkGk2m6pUKhoaGgrtnpubC6kig4ODa5Bn3u17THDEMO57enpC39x4Z1ybzWbgz/n5+eCk9vX1qVQqaXZ2VrOzsyklgCOHQI+FG21jL01vb6/6+/s1NDSUMoharZYWFxdTm+dwEFEk/M0mfEdr/V2sCUdyaBM8dzg5Gq1WSzfeeGP4f3FxUT//+c8lZQtSHAwcUM+fJofa0xwcEHHEDZnk4XTP2W+1Vs9boESyl0p2hbkeuWInasp6dCeDvsQpGlJ6Xxrt5/eeUK74O/oLXxHddWfEN6e63HKl7u91wMFTHBzFZeNsbOADtNAWd1owxPw5Hi2JeSJ2tGJjxx0RR5MZk3K5HFJF3clzw7NSqWhubk4LCwtaWFgIzgcAzb7wQ073HWJdeyQDsMzPr8FIR8/5mvIUWKljgPvv2ID2qAZpu6wHrybpjvp6m8U9whcb8UTrHLxDFpGmSYT+2muvVaFQCPtqIddPHklED/lYxJFRiHYjN/w6rsXmcQDBv4vXvetMbBLXicgw5Pvi4qJmZmbUaDQCCMyYMBcrKythLyq2DnYZ4w+f7Mm5uycU2wL+kwWWZelGKdvRcKc3/hwb+GDTfu/RAOF1pceEuVIfHR3Vsccem4peMEkIcoxFP4dheXlZ09PTarfbobwjhvF6itb3Y7ChGzRSWnvCJRENFjj9c2PFJxSBEXvtjIWPD4IDA5fvssbBFxLOBkijRwSWl5fDJi6MVk8r8xxzV9KkUOHRM08IIPqG0EqS1ZK0oHV48fFZE44U+nygyKempoIjdNRRR+m4444L6WkYNBMTEyFsCdFvT61ot9sBgWg0GqEfzDnj42ldCwsL6u7uDps7C4XVDdwzMzMpNNZRVYQJYwfCA+/RzoWFBc3OzqpYLKpWq4UN+h4p4aenpycVZcGhYs4YQzek4Jcso8oR+AcCueIBoXNjHOMc48ARSEcGXS658Ro7G6QyeP50VrUYD8nHxjA8jKL3NIyenh4NDAxocHAwHJjl38cplm7EuMzzv2ODgOshN0jiZznBn4AGjLk/z40F1wMePfY9V7wrK+fcQRsvfOFjGhsXyC/WWdz2eAyQ294Od+q9fx51dIfUN4UCEKysrIS1z74UZJArdTfMfPyyKMtpjPnAn5VHUO4+OTiI09vb25uSJzgfXBOvP+c/KV1hyTMapI7Mcecc5Bzgqbu7O6Tzom9YI8godzicDyC+cwffgRfKaDtII61mL5TLZW3fvl39/f1rdJGvk9jJkNLpVVK6jHUWYAA5D2dFMeLIjdscUjq1OgaMcDBmZmZCkRYHPJA1vqaxc4hoIgfpoxfvOVDnaqwn2z1yluWAxHPvsmY9pyL+PwZmDibtV+qUpJSgZ9LcUMYxYHOyG6OxEiCS4ecfNBoNFYtFbdmyReVyOYX2Mzg8wyMaxWIxVBhaXFxUrVYLRmGMmmUZI+4lx15r7FnHHrW3S1qtdJR1EJwLKBiWNCAWCozAvgX2k8zNzYXa1/4+d1p8UfumZcYLowMjFwHK/g8Qd56LM+P7aHzs+ZsFy9hUKpXgGA0PD4cqF41GIxjaRF88nBtHf9rtdigh3Gw2Q7iTsVpcXAzCAOOHOUIowLMYj84vCO7YkOF5OIgIb8bL3y91ctURUo5+YJyS4+3oSCx4GWt43ufX+/hAcTSgcnm1YAHGAIgdqQG+P4d1h2xypxXy6JCkMEegYSirLKfD+czlGe+CT9w4BQDhzIy4uhTtZ115xNYNnawomDsNrjRi2eMGQ0xZBr2nUrpi428QWYpPlErpClUuS7MUJHKG8Y4Vo88ZbXLlGPfJ9Yv3ic9iYwfd4c6OtxNnkWc5elwoFILcoT3OH1mK3cfXjQLXK/FcrNcX+hOn/OWRlX0jzoQi6h9HMpAz8IbbCLGxmzVfbhe5rPb1CoCFjMDh8JRN51N3NrxwgTsEjtID2PH8gYGBUPQG49qjHuPj4yF9sF6vB5COZ8eAmPNxbP84sOAOfRYQAnGdRya4x8fc++62APKafaVEIB2gjh392KAnI8PTIgGNiVwyvtgUd1cXr+dU+N+8xyPcMSDh8iVLFrjcip0Ml2MHm/YrouHMFn+HAeUCfWFhIYXsu3EcpzBVKpWAKJHr7wxWKpXW5MW6MSgptYhIm6nVaoHxKEXrqVVxCg8/WRUH6L8bMRALpVqtqlqthsn3DenSqlFDXj8OhitJhAUeOY5buVxWs9nU9PS0BgcHUxEkNo65gHFUHoPehaUbMzFyzKKTlHKY3MiCWZkDBCWHHg4PDytJkhDdQHhhYFcqFQ0PD6tQKASji34zPryPUCjUarXCuSHwkTuICElSIrq7u9VoNFLRHvro6BPzxJi0Wq0UUu6fF4vFgEKhuHgu56NwLc9hYYMGI7QYRw+1e1twAPe2OfpwpJ6eniAPMMh943ScchRHxVwhxgYb88Nv+AOl4mfOuCEJz/vGyDivlx83ZGi/7/1CnuBQeElWd2CkdHnw2OmI17cb4TFy6LKO7xkzqaOwPMWKz2mHl9lGVjKWMUrmypE1RKTPoxfr8TXjHTss8Vy6jvL1F+uJeDykTppX7Og7yMSz4BXWMG2I9V9s+LvR5AZFVl9iih0Ub4v/neUI55Qmj2RgbHv0cT1ZAvkcxnIlnk83Cn3NoT+kTiQf551IuBdgiYEOn2OfazeQkTe1Wk0jIyOplEj676mCpEmzPqmm6HKFvvhYuHx1xz9eC67vkBfx89xpcQOaNed9xMYE9OPHjWru9/fEIKKkVDEdB7Z49vz8vObn50Nb6AM23L7Seg4G8tudQxwNt9fibQvOa25vYE/E/BIDP1lO38Gg/d6jIWmNIoG54kHwlCPfQwFzFAqFwPyg1s1mU729vamTqVGEeJBMku/ZyELuECDFYjGcREtKDz/O1PyUSqVgIFDdyVEODHjPofSKDBgobkSAznOYnqfvSB1BBKNj6LRarbDZu1AoBM8dQcI7WFQIL0J9brwwh65cvc9xP9z4IJRMTrNX5+nu7tbQ0FA4l6NcLmtubi7FN+Pj4+ru7tbg4GAq0jI7O6vbb789CAhvN3PIxm4cDpAK+AkjlH5Vq1XNz8+nUmzidCyiEHHqFosVQca9OHu0JZ5bHBecAoQHi352djagVB45yVro8Lcbthh9sUNyfydXSE6FQiFEATACcTpAH90wcAPBBThjmeVoOHrmxhtFADxNCuTd2+qIpyt2T5tyHoyjMX4KuDsgDnYQbXH+Z3zom68n+so4+J4xN3I8ohZHOpIkCeAF4xGjaHGqoaf0eQQ05m9vS/xd7BjGhgnk/eA+2u1RYTfG6Kenw8Tpahg/rdZq5b5msxlOCx4fHw/vxEgjGk1kPk6f8vGiv3tCVD3i7c6Igw0emc36HSPf8VjlpBRYwTpkrcZyJJ4jaS2yLK0t++/GLOvIbRYpnY3gwBO/sYt8ftHPzLXzmc8xfMoZYaT64Wjwt6eISaupwTgc2CAO6PCu+Hc8JrG+lTp7KWirywF3xLjP06Fi8EBSKmXZ1zvvw1GID/pDL8dOIdkV6HgqXLodxD5Wp/XKXscUr2d3MFxH8H+sX2K5HctE2uAAbSyX3Pk4VE6GtJ+OhgtCZ6RY4PMZjOqGruesVyqVEHb2U6lB8TGsYZi+vj41m80UM3t7Qqf+f8WxuLgYFhOlXXEO3INFQdGmkZERbdu2LZSflJQyPNxDdJSZMKSH85aXl9VoNHTXXXeF0og4R7GnjZDxXGLQBYwq3unOhTtX7XY75Wz4GDM2kEcCKPu2sLCgJEmCwE2SJNT4x2jB6CoWV/cpEJL1Cj2u1DGEms1mmIOxsTHdeeedmpmZ0fj4uCYnJ4MgqVQq2rRpk7Zv3x7aixPEvBUKnfK6SZKEKmWeutdqtUKkLC5bGo+D848bJgglBDTkRh4oKGOCYMQQcSPMHRD+d4XjwsIFLjyFEDxcaMOGDRodHU19RmpdtVpNGfBsqIYXWROsb9ZVHHH0qJ0be+7cw3sujB2FY/OyO3lxhBCehy/d6cCpoCgCzrG0dt9I7ADQNozoeA26Ena+RF4jD53P6L//pk+sFVdUnublY+v3xzohjhzQN987wVz4HGTJdNaQt93Tnfgsju4wFo4O+pgyv/4381ar1VSv17Vt2zbNz89rdnZWzWZTMzMzajabQV7Oz8+H8cLwYU69pCZpld73LGONefMIDMAI/O6OXrlcDgCVO6f+w6GsOSlEG2Mn3x0NaW0pZj7z31LHSJU6usSNal/XLovi9e2OBt/h8A8NDQWglOh8LCscXJA6hTP6+/vXFDlw3ey6DWeIc6+QAVR3cmPY++/9cWTebST41IEUN5od7GHsHJH3SCPtpyoUY0HbHLR1QJh2OGgHP3DkAe1wB6zVaoV0bAd3kRfrHejp4+Fr29c4c+AOrkfI4/vXi0S4E4bM8HRgTwVmbO9zEQ1Xhs6U7o0yePztysq9MdKl8JAxnAqFQlj8XseY95GqQtg6VhpOLGgEP5uDea8jyvSDhTkwMKChoaHAxBitPlF4sSxMFitGB0aSM/u2bdu0tLQUhIWXwvWFSzguDnm5UpmdnQ2Cg0gQ70YYeI41gsINVild/xrhxfXlcjlUx2m1WkFRdnd3q16va3BwULVaLTwPQYDhR6oUFaMajYZ27doVztyg/wMDA0EZt1otTU9Pa3p6Wrt27dIRRxyhoaGh0E7GgAiYb8ak3S5UHNFwhzc2rrKQITeKcMbY+0L+NmPKd81mMwgoN1iLxeKaMKu/k3fEnyE8HJFwgXt/p9jJYO2AMiKIqdzkFafc0YiFtNQBRmI0PkaWmRNXgFLH8OdgS9ZSsVgMbeEzj6TQBpQQhm4W0u9tdCXrCBlyM5ZTyEBf176GPTrHOLoj6wY5SpNoTldXVyjKwfphjXlpaTcqfHzc2PIIks8H4+uGhbfH5XqWQeXvWk8RO5jjjgqGCOPLjxt43IssZV8ZgIpXCnKHAvk+NTUVqveVy+XwN+2LjVj4wdvN9YBM8CtGGvPi6VyMPfMlKURkclLY2+UpmA5oZEWTJKV0UBzhANxzZ48f5olMAed7KV090+0ndF1fX59GRkYCmr68vKzx8XHNzMykIgKSgt72vWJEawA2iOggS1x2tduds3pI86a0M8Ckr5XYgfdx8bXI2sNeYv25MYysccfFoza8iza7XMTG5BkOCPm68HXv9/B8ql667GIM3Xh3BwmZFOtldyZiecU9HvV2HZZV2MTfJ6WzU1yn0XeP1DtI6ynB9zlHA/KOQlnhYGkVwY5Lq/ohOPEguIHhyIyHw/zdrkxjhEFSCB2WSqWALjPhKFVXWigUNypjdDMOg/s+ABYTThKnT2JMs9BdOYGUsdBwJDyVzJ04SaFkMOlG7IOA+avVatg3QJ1+D63jJFHPv7e3NxXZ8ZQUFpjUQUgIOS8sLGhiYiIYLY7Akd5FFYulpSVNTk5qYmJC/f392rx5swYGBgIiuGHDhrABFWU+Nzen66+/Pjh/CPNCYXWvw8zMTHB+CoXO4X8YVy7YKImLU8BzsoS91EFncSIh3/fiQpVNVfTdo0jejhh14LcbWrEAcMQLZOJwJMAARxnZsOkH27kyRTl4eNkdDX673IjD3Mwp88P6klb5oF6vh+gB8gy5wo+nPsWGidThCym9/8YVDIYOc+0KOXZCPUpB3z3q4kigOyLc7+OBAw5f+sZ00HqXD55OxvO8T7THHYp4XLgv/tsjM74GPIXADZvYaXTl61HD+LensnlbPSoBGutAjwND3l4imO7wsFfPEcSY9+LomwMRWeR86jrDARUQXHdiD5VBcX8gLyQRAwGscTfgWJ8OskoduQKoyFijZ8joQDd4FkJsT6CjYye8UChoZGQkACzwLqDl+Pi4pqam1hwwh03icsn3hvk69T4DOMRrFZCMcqi8A37zYhy820HMODLhh++6bEKvYty7Ue6OH86hOzCeOeAOCGPia8v7F9sKWVFYB2A8uuoyBvvOx5C17X+7I8gPOs11gs+Tz20cBXKecT5zHnN+IC0sC+g4WHS39mhA3nFnTogOgQa5wsHTR1hS7tEVPRWXMNpcIUjZ5dQcwaLCi6SAdMPIVMXCEEcRxAKbdyCcOBGaBUO7urq6gqGMVzwzM5PaIzI7O5uqMoXyZKM0BjbvxNienJwMKU20f2hoSCsrK5qcnFSr1Qol3CihiRHAeFAaFiLliZK5s7OzYaHgGOD8MWduSGFooNhZGJ7DyOb7RqMRQo8bN24M17uBVyqVNDg4mDo9m0W0vLyssbGxlMDB2HFj3+feFzc84uiKL1AX8C6oy+VymA933hwJdXSE9qDoEa44Rh7BcsPRUXZHnFkfCDZ483BzNHAS2Y+B0gJ5Yx37pmo3+mNDwQ1xV06sZzfcHTV3YzdWEl4S2hWFywjIn+m8hjERp8R41AyjI0a4YqUBvwKMcE2M6tMHV8AeuWFsGG/awbPhXQd+XLExVoyfE+vaDY94w/XKykrYBOvPcATQjZrYweD/2KlwR9EdAj5zXeVplbzDHYRYkcfP5lnoGq6Dj11XuA719sfOms9pDHAxD8wr44medAfb+SCnVXLngh/OzHA9Ia2OIWff+EGxHmGLCzg4X1CxyCONgH1eEt0NW94rKdhOnraELKnX69q+fbuWl5dDOp+nERUKncwE0gHhnxi0cKDQ9abLTkmhkijjlCRJqPAU7y/wlCx31nHA4gN13en3dUUf+NwBYc+miCO7Po/ubMSy0+W9y1AHAfjOZbbbhkTI2u1OpUx/L89zgNudp1jm47g4b9DGGLiBYuASXnI57NHnubm5Nc84WLTfEQ1nUEfrJKUmS0pX/YnDazgT7XY7MOTS0lLI4ZdWUWLQGqmzmcgFfWyk8R0IU6VSCcqb1CuYWlLw7GASR/Ec2UcIcOIzC7S7u1v9/f2hH3j3Ht3AOCTNh70hjryTt93d3R0WM4Z9kiSpsDsOyMjISIg4ELafm5tLKcpSqRSMXJi3r69PW7duVb1eV7VaDXsbisWiZmZmAorH2McRKPrgVRqWl5dDXj1jtbKyorvuuisIoZWVleBQ0mcE59LSkjZs2BCMccKcseHuC9x5AIPFnQ9HICWlnhcbhx6lYm7hbyJPLnhIn6Ntfk6Jo6CsB5wtNx6yyAWzR/SyBNzhQIVCYc3BkihFDpfyDeCsR9aZC1A+A/1j/DEspPTGRD8zBn53wYsRAV/Fedyez8065R2OPOFQu9JyBJ09RDi1fi1RHtYnTo4blB59c6DEHdf1yCOdjBXrgGd7qejYQPB0JK531DRG73xeQN0d0XfF7EAW5PrHQQSfW3d+JKXGqlAoBPlFyWxPawFQgteIuNNXxpd58sNmMV4pIDI1NaVyuRxkL+PIT+wAuH70PuBQIAM8Qs0PcscRWk+dyh2NDnV3d6tSqej5z3++Nm7cGHg1SRLt3LlTP/3pT9VoNMLaHRwc1MDAgPr6+jQ9PR0yCaS0QyCt3SieJKt7HAHHcDjRu17YhDlGP5VKJfX394c15usEnmi1Wurr6wsAXuxkeGEVCMe32WwGh8cdcXdUWctulMObtJE17baYlC6xj250eeT7Pdy5iYHC+CdJkpCVgaPhYJynWzFWtMfHzu1Xlze8m2j6wMBAeDa2F2PnxPx2d3en9hLzTo/4xJEUd3pjGUq/HTRjftzxcP3jgGYs471d92lHw1Ej73jMqFKnPCpM50iRn43gXvPi4mLYOO3MGeedOjOBEjCQMBWb4FZWVsKim5ubC0YCkzI/P586CVRaZUyUTqvV0vj4uEZHRzU+Pq75+XkVCgVt2LBBfX19qtfr2rhxo4aGhrS8vJza3IxAKJfLqU3VvjglpapquUKtVqsh7cvDuK1WK1SVWF5e1ujoaEglcpS+XF6to42i37hxowYGBoIxR5oYER7fDCopOCsYZrSR9joqwIbXWq0WHKbBwcFwHZumZmZmNDg4GPZxEC0YGhrSzp07U2MAT7mDGfMeC9SFoadR+HMYf+6nbRBCwfmS8Qe1ZAEjsH198D3o0fT0dDCq/D0xKhujPm40egRwb+kV9yfy0rX8oCDZU+VpVHHalBuzpNe50sJ5cEcEnkDJsjcoNspRNihq1o8jiyhQdwhd+EtpOcl37qw7EiWlU0JJn8QxHxgYSKFuUmefEAaDI6feBifvL441/Oz3xMY8Mtz7Hz/H0UhXjihDjz64EnVjB3IZ6X2O0UraSJ89WuVriTOJaLfLEORbs9lUtVrVwMBACs10Q5Dxc13hEYtSqRSi1CCUcYoq13n/4r7HiCaRHXc0SFN1YyQrrS2nVSKN+elPf7qOOOIIjY+PB54eHBzU9PS0/u7v/k4//OEPtWXLFtXr9QD89fb2hn04yGIAVXQJdoTPG3ICWbSwsBAcUoxWlyGxLQX4wt5A/440a5elFIGBT7iedmQ5vnFBg9gYjoEUiGwRqYOe4yy4YxIDCg4Qx3ICfnZb0//mXW4/MhaxDHFZFGe/uA3Id4yxAwwecarX68Hh8NK3pJ07+EQfHAyjfzzP2+VRB/rl0Q/vU+zQIkeIfjlY6bLRZSgZOgeb9uscDZ9gD2W7ovCIhiO4bmiB4nF4m6OThOlBDFkoHkL2heiGGuSK2hWBtMpEKHcEtBt3UmchYczMzc3pxhtv1I4dO8LGcBQ/i6fdboeTKMfGxsKp10Q8EABeOg0DH8aiHZ63x6L1Ezv53lNJNmzYoEqlotnZ2fBc0A5H2qjcI3WMh4GBAUkK6Mr4+Hg4/bpYLKaiF/H4+0IEmaUqxsTEROh/vV7X0NBQSM/gXd4WDHwcTebA+cYVvCOAzLsrZ49yeZrLelEO+oHh2mqt7s+A/xgH+sBC5vluZJIqh/HHWPNO1oxHaWIB6TzujotvdLu/kxvuOBSVSiXwqde3Xy9VqlwuByUbo0UIXM9TRhjjyNRqNTUajVR0w/nbDUGcZfgGXnDDwhUfz+B+P0Q0NpRdljlPQ8giZAP3wD+xMoL3HQyQlJKLrAdXwHF/nQ/hZfrowEiM5rojISnVTnja0xHjaKQ7a7FTAsXfAyIAliA3HBTxOXKQxAtmTE1NaXp6Ohh3GAgY8Z4K4yl7GJ7wCHKaM6XiaKYjmg4ouA7lO+YgNlCIKNM23wt0qA7kuj/R4uKiKpWKCoWCdu/erbe97W1KktWqkY94xCP00pe+VM997nM1MTGhoaGhwFOue6vVanA44DdAKKkjD5xn3SHmeaVSKbQlTomFxzgXCnDMC1HA37SBd+H8IC8dfHRn3J0IL5YidaKdvkGb62K5SFt41p5AaGQG4wAfeyTDHXDGDOL59MOBizgi605RLOP4nnlw+xIZ5bZnTA5mkSJP2wCEHBh2p837xf8+V7GOix0LlwH+m/b4/JCa6jKUaw9VGvZ+RzTc4PfJd0ZmoldWVoLy9vz/WFHhvTHBhJ+cMN6gLJTGw0UYIAhaFmNvb29AoDmjwyeDFJz+/n6Vy2WNj4/rpz/9qSYmJsJ7SGfYvn172FQGQzQajWAkI/hvv/32UMEFYUToL0lW8/9pf1ZYnGdz4natVgvj5Q4A7XBj1A04FgPjSZQCD7zdboea8RjZExMTKWZ0xRwvVqnDuFNTUyEFbW5uTrt371aptLoPY2RkRFu3bg285JtPW62Wtm3bFpwmoiLwhs+zIyQxIuLhUzdcPI1CUkB+EDSk6+EUohAwRNjcBxruQs3zQ7nP28hYOU+7cwv64DndGEf0B0PmcKE4h7i3tzc4GTgY8UbDOOLkFeU8DQI543mvUrp6DHPoudRSuhodvOdGAzwURxJcaUhKIf8ghlnPiY1qdzqLxWLYS+Ybix21zkrZiNFBdzr8YKv4/bGDy+cOhDh67+3l7yx0nu9BAWMAiXXi8sadlmKxGORzXC3H87PRFYBCvuYAyGKHyp1ExlhaNUopyOEGjjuk7tBlPbNcLqecDPrqcsifR3Te2+zviFHK9YAHl9E5dQhbREpnSaysrOiaa67RIx7xCD360Y/WEUccEUBRSan15JWc0KPNZjNkLLgz4Q4p84Hzie0hpTM0nE8pb89zPUUOvc570HFuuHraM2CdG6a+dpEVGPqSUnopjmZwP5ul3T6Q0uc7oGNjR9vb4A5JbNy7c8+zfcx8Tbvjz/yy5tx+cLAhduJdjrtsZW6wUbEd/fwlUtpdFsRAictJZJnLNpdvfm3cbniBMeHZsSNG5gg2V8xzB5P22dHIQpKz0DdHk1m8Xg4QY9snlH0A3OtonzMFTBYPuv/P9TDIzMyMJIUF1t/fH07dxkgZHBwMyIUrmImJCd16660B3ee5/f392rhxY6g2EQuewcFBLS4uanx8XLt371aj0dDs7Gzoe71eT+1F8c2gvlD4H6eETVfMgW+oxFBljDHQ3bsl/Yccb8+R5r0zMzNhsYDW+HMY4xh1cyUf70NxdGJhYSGU5iuVStq6dasGBwdDGLfVaqUOMyNE6VU1XAjinNEPSvHBG576IHUQdOYKwQx/Dw8Pq1arqd1uh5Qn7mUc2J/BfhIfCycXtlLacHOjwu9FMBLZc+WAk3E4bQZ3oUrN90qlsiaVyq/z9CeKH/jp4FKnCog7kSBEUjqPFYXS39+vmZmZNcrKhT1KPEsuOYoHCuiKxRElR/Gc3CDnfyKjksLYsP4xSOlXlrKNUyD4jMinty1OLYiNYfoOKimlS2RL6dxh+hA7ZvC9O9oOVMXj4mvJ15zPuSOwkgKoFBtGHo2nT/5+vue9rsvWM97dEHNjydNX3JhBjtBulyHMA9E8H1sKivBcxtP34UHOm4eTzLinxLz4377WGSvXvZ72wnU4/+ipZrOp+fn5VFo2vE8xCQedvGqYlD4rBifCdSxVPN3hdpDF1zCyb2lpKZwP4ftI6Dv84ZFZ+g7/0Hc+j9e/jyt87GlTDogyvp5VIKX3cMTABr/RgzhOsXMSAy5ubHvKllf2c7CD/71fLku9kpvbRYxPnCnB/Lr8Xc9O4Fnxvj+3dVx3uP1LW2I5GztN/u7YMTnYtN8RDc//wsiDYmTLQ/Q+ADGqy2R6CNiRRTdiHalz8vd1da3W3fd8RveyQRbYgIniQnDgJLAfAcSdSAeLG0eFVA9JuuOOO3TzzTen7ncvfGFhQbt379bCwoLq9bp6enpCOhVMSRulVWHkec9zc3MaGxsLKUegk4wx/QL1YEzYuOyeviPuLDKfE6IpvJ9nOcLBs1zJY/z4AmbO6GOr1QoH9o2MjGhkZCR1ZgdIEuPLRnfPIaetvvgYD+6Lw8u+gTgWZAghL82XJElwEulno9EIed5OsSBxp8J5IMtQ8bFy9DLeo+EG8uFAtVpNrVYrVJdiT4aH/ZkvDy17WqJvlo4dC99TAPrjoXtH6fv6+lJ7k+BZNzbdCHbjXeqk/yEHY8QtNqBd+fja8PtKpVJIC3OexKHnmR7Bkzq850rZeZA1TXTXFbe3j+9cuaGMYzQw7p8DQN4ujzzzG4MrHh9f245Kch975WKDUepU7Jmbm0u1gfmO5ZMjhFIHtHKe4f+4X54O5g6HRx8YDww+X9Mui1gHREZpB2mctMGfjbxjf6DvG8spTdgakBt5W7Zs0fHHH6+pqSmNjY2lUuZ8/TjQ4eizH8oYI/sOpmKnOE+5bouNzDjihe0yPz+vmZkZVavVUFxlZWUl2BNE5dvtdlgP1Wo1yCrazbvckfb17+1wGct1fg92A/0jUoxc9WpbjIPPDUZ9vI7ISOHdDu4gAz0FOkk6KbOuR2KnCXLng7FtNpshzR0eADDGFiL9ExnFMxgD9kEUi8VwHADtpl3rAUKMic8Pz3J54jLa+8TcxE6yl+M/FLRfezRiD9L/pqMwAJOwuLgYqhIwWEQsYDA3qskx9YFmwbjC5D4XArSDHEby7GE4qVPxCmbjNylP7XY7eN+eokE6BzmVVCep1+shdHbbbbfphz/8YcjBpu1xDn6r1QrVrmA2FhLIrY8xY+jIJv0HfXEF5OMSL2Su9VxIH3/6ixJzD5u5o1/MOU4HTk4c4vQNkaRPVatVFQqFEO3ZtWuXtm/fri1btoSx5d5isajh4WH19PSEqh/ubLgxSNt4tx9M6EiGV45h4cfpZcViMSh9crbZg0KExQljJEZleL8LDudXR0MdCXFDhL8Ptz0amzdv1uTkZNgYCZ8jpN25gK9xCvxMDTc0XRm6AOY361VKC+xisaiBgQFNT0+H/x2dzjJ8vSoV8+9omkcM3LF3o9zbB+8ie1BULme5r1KpBIfDN3G60c4adgfIjYFarZZKo/Jwu+dPe4SDz3hO7IS58qfvMaIWGzMYAPE4ecot3/E9bWS9EAHo7e0Nn4MKU4SCdvg7fFxjJ5I2ozdwsryd/HhkhT1oDjQkSZI6+MujErSB1EEMQze4enp6NDg4GPSa8zLAEobLwsKCpqamgpM1NDR0AFft/Zvcoe3q6tJxxx2ncrmszZs369RTT9XGjRv1xS9+MSVzfE3Bew6E8INhG4MMzFfshPg6YVN1HEVYDwnnOTjcrNdYtrCulpeXNT09rWazqZ6eHvX39wenw/newUrvh+tV1pE7Sp7i7e9m3JrNpmZmZtakbbrMRxcyZt5v7AKPHgAUuxx3kIaoj7fVxw6Z53LVwSNsT847m5mZCVWn4mt5JnxQq9VCujU6G9vPeSu2X1wfOX+4Y+EyOQaZeDb7XeP5KxQKqYIFh4LulqPhyoxBjhWspIDA9/X1BQM5djLciUBhQyg8LyHozBAPrit/FotPHgZ8vIiTJEntmYBxUX4YmxD/s0GbzdPj4+OBCWNjkx9HA6h05RED+o+j48a6l+lESfb29gbDmz7yvZROqWBsYmTFw/gePYGcQWkjStTT4vw9zgf0nTHDQPIICSjc5OSktm7dGkLS7hCAOk1PT2t+fj4l/FmUvNM3rDkyhDBz9Bh0k0pHCGV4EoO2VCppdHQ0JZTi57rR4OPhAprPPXLh8+6bTT0tDT44nBwNzprByfA9GL4ng3lmn5I7yo4EOfLtBinCGQPbhbnLLtINkB3x82IZVKvVQjskhXRDR9i41n+ktREO3kd6nxv5PgbS6hk1k5OTQXE78u3voAINMphxkJRyJLz/fEe0gLa4swAo4Q5NFo87WOQGFVFYDGjvI22P5bSklMyIEX9QOkAmUGJKnMd7/BwEcJDAr3F5jXHpDicOiJcwRfeMj4+HdYw84zm8k7+Rj6TAuIODbCWvH55Abrmjg1wnwjE2NqbJyUlNT0/v65I87IlU52KxqJGREb3lLW+RtDrWU1NTuuyyyzQ+Pp7aOwg5kBoDBzwDciCrUCiEc6n4HBkGf1SrVU1PT6eiLb4e3EmR0ntS3T6CN2ib3+82CWucdEye6XsT3Ul20MXXiQMqGLisOXiTseRZXlYa+wVbBF3rQC/PxHZB5nmaNGMay1W+j+UT40Af6F+8jxPwYnh4WK3W6v6p6enpADzCB74nBp2NQ4JNisPvDhrz5gCtp09BpVIp7FuE/3hHLHOxtdAT8B/P5PmHKjtinx0NN4oQ/nwupRE7Z+hWa7WCh1cc8vxYnusEI5OGAxOD/EPx4BYKhZCr7QucZzL57vX6IvLPYRhSM3gWSECtVtPc3JympqaCo0I+sE8oitCVrqMGCwsLIRzoIXuUiRs3HqKl3yg/93D5jugLhrMvtjgH2f8mCuLKzhnfnRPfFOobGEHZ4shDu9050IbIEwYg+2KmpqZCGLter4d5W1lZCZWIpqamNDMzswap8PYlSScCEzs+PteMIc4k4898eJrFhg0b1Gq1UofysOCdFx1l8bY5XzK//LC3BUMXYUzfee7h5GhQ2ABnjrnz3Fd3KDwl0kPOvi48LQ6eQ5mjxNyxZn3CM5VKJZXy6EpB6oAWXm6S9Vav18OJ9XFUBYrRKtrKhnhf8zhcGKMAIlQ143wdp9gAIbyPAc7BiPBd7DRwsBdrmHd6RAN5iqxgDHxNxcgqfUU++pko7vQ5uTzzdeBK2ev4Y7TQB+a3Wq2q0WiEsXV5H4MFvC/LOPH2lUqlUFHQ5SA8UalUND8/n0K+iTx45ArQA5CD8XLAC/6lAhFjw73O5+74DQ0NqVBYra6U0yq5YzA5OamvfvWr6u7u1qMe9Sht2rQprEMoltXubErpU6YdYMKZdAdgZWUl8Ivfj2wBfYdPWV+sXdYrBm+sX+CFGATmN2g7kQGpIy+8yIiDhZ4m7fIq1rsOgiHD44IVjKvbDm43IhPoP7Ld5QQ8DgDiKVbe9tgmjde0RxIcgGRNQW4fFoudql8bNmxI9cFTx2PQhzb39fWF/ZdZ0YoY0HI+ILWf5xL1JGrCZ9in6Kfu7m7Nzs4Gx8jHJc52OVi0z46GC1LIJ8SZOVYYi4uLGh0dVX9/v/r7+yWljQSEr9TZsLSwsBA2MUkdTxmjHoZx5pLSm5hiRc7fjma7QOd/0KeBgYFwGJ+0Orn1el2VSkWjo6MhBEY4rd1ua2RkRO12W1NTU8FJ4t2+058frvFa1B6Cd6QcJiYiwDhKaceBUCQLu1js1FWPlRP34ijFVXGkTg1v5sCFJOjHzMxMEAgzMzOp0CtjzWJkHwzzwSF+btQ0m03t3LlTxx9/vI4++uiwQHnf8PCw+vr6QlUsN+DdKMJhcoUdCxFJa059x9lAqUxOTobDEDdu3Kj+/v7g7MQOQ2ygOorEOzEC3ZGgb77WcO59z83h5GiwSRKB6CmLsZLBUYiNADeAYz5wge7IkYffXahLCg46ijyOeiCgWacIeuZ/YGAgpYCywBgprcBQsDjSjjBS5cz3RiXJaprN3Nxcih+cB+kb69ANJo/eufIGQCD9yA8oZTxjQ8PnyedFSp9jwjM8ZcCvdcADeeyOi28iZbxZ46xv+uNjzXy5jHWAy50+KAv88u9BWImAMX7MI5WCXJk7LznPJUmSqirG+2OknH0bcR+Qa/CFP4O15RH5Bzq5Ib60tBSiqt///vf19Kc/XU9+8pP19a9/XfPz8ynwzfkkTjfit4OALmN47/LyckDhXQ+5vvbyxGQtOAhGCrDUOdQYGegIufMs64CS+KxXAAWK4zg44vrIZaD3mTXq1fWyooe+1gF7PLUd2eO2j485fI2txhh7RNPfH48768nnzeUDRrs7695mbDVvrz/H96F4NMjnAplCZBJyJ4Y5jufMz0Xhe8YKMENSOMUeh7Wra/Xog3q9Lkmanp5OAWuxnDtYtF+OBg1E4cQevCsIOuCG8OTkpGZnZ1MpNEzy4uJiKBXnaUySUkYHjoGj+y5cQcYdnfLFESsfj644gt3f36/BwcFUHwYHB1Uul7Vjx47QBsLSMEq5XNbw8LAkaXx8PDhFjEEY+HKnjO3CwkIwtkFKvd0oMoQKDAkC4Qf9xSGxGFkBpcBwddSEZxPmhMkZ63K5rNnZ2TVRFYQHXjcLA/TSkRfOTpE6QorwLYcXkW8+Pz+v6667TtPT03roQx+qTZs2aWZmRs1mMwg3SvCS5uECEAPVBZ8LMNpfLpfD4uRsFaInxWJRo6OjYf8OcwZa2NPTo6mpqZSAgWccmXKEhffSHnc24pA1Bpb/f6iEw6EgnFc3Pt2xkDqKij00GHCsef52NAxeIDTvBR2YQym9Jl2GYfTjEPMeKW2oOBru5bGps8/6ixFA3sd6r9VqoRKdh/FLpVI4G4f+OHjBacU8L/7NOpA6aaue4kcEReqU4vVoCgrWnY14LOBr50uPTBGV8X45COHP83FivflzfQOto7nuVPq+Mt7VarUCkuzz7GPlz3VZCcFTDnoxFwA83d3dASAjWssz3WBzZ9h1G+32CClgUU9Pj2ZnZ1PGJLzle7gA77q6ulJGa06rNDg4qC1btqRsBDIvvv3tb+uZz3ymHve4x+k73/nOmv2MzvduGLpDjI0SG4wQjo3U0c+slRgIk5Sqsid1ijFwj8u72Fnnb0kBXHCHgsgo9gw8hO6mby6Pabf/hv+87bHczCI3xB28YX1kEYY9upzPXDZlOQzMT5yWFetibBbud+AHXcN96G5vC+t4aWkp9MlTxXzeaLf3lzEB8K7VaoH3kP9c76BvT0+PhoeHU3qTNnZ1denII49UT0+PxsbGwnj42S8Hk/arvC0Cm0GKU3wwrFwBxIoDz88rpkgdxMadCj53VKFer2twcDC8j+tJZ4kPS6IN3C+tLSNHf6TVyRkYGAingdKW/v5+zc3N6fbbb9fg4KAKhdVSraTv8I7FxUW1Wq1QAYJSro6Sg4YVi8UUwuB1mUEbqEhFW2g/EQYMT5BNnkP6hxNzhvLq7u7WwMBAQE3L5bLq9foa45x5YO55T8zsxWIx9J+a0sViJ4+9XC6HqACGAY6Ho7JUl5JWnagdO3ZodnZWD37wg3X88cerp6dHjUZD7fZqhZZyuayJiYlQhhje4KRzhASpXAgk+IPcaDdyC4VCqFjDwkSxsw4KhUK4j3xs3h8blvEaQagxfx529nniu9jxOFwIYcq4Sx3+RyEwH/V6PSjdONUQISylT4531MfvIXrih1HF6YpeXQ/CAHeAAyMQw9OViTtD/OaHCmc9PT2q1+spvsEpQP457zp63dPTo76+vrBnCXIZHDvgtJ216n2hJr/vpaM9yEKPuLEmfGxJz2BcPOWVe5HPWcYEMg85TbifdeCRX5ef7rQwd/wPj/h+P8bbkUeUc2xI+djFBqc7sJyh5JFo3u+6jHd5OokbK0Rf0G89PT0BgMGBIZrqJ1N7xNqr+xyqFIn7A23ZskVbt24NdoODcjt37tT3v/99Pf7xj1ej0dA111yTWrNSJ6+eexwVd6fPiXlot1f3Znouf1z0ApnDOzw1zu0VwDZHqKW08+82jp8ZhayjMibryvWwp/zx/Ng592IFWU6SO/U+zlBWxMhlJjIwjlq4g+hy0+WJz4k7ZEQeSDdibOk/mRW+x7evry+AwUSdXMfzDNI4AYH8HBYHEbGfYmcIPqHwA/LTv8MGSZLOOVC9vb3hmADGGD7C7i6VStq2bZv6+vq0a9eu4GAeCtpvRwMj04Wr1DHapLTC9snAAID8b/fSffIYZM6uQKn6+RgcljI0NBTy3H0x+N8gnFJHIUFJsppSMDIyEj7r6urS8PCwGo2Gbr311rAwms2mms1m6ph6X3SSQjSDcxlQiF1dXakqUihiKhWADLAJyaud+Nhy0vfU1JQKhdUzMGDmgYGBVEUMXziUwhsYGFClUtH4+HhQdvRBUnAWmA/m2xFOjA8WKoKUZ8SIHQYIz+Re5ozDvNzZXF5eVrPZ1HXXXafJyUk94hGP0NatW4Nxj8cfO7CtVkvz8/MaGBgIgtcRSIydoaGhkLpAGlt/f79WVlZ0yy23pNK8PDfcBUW1Wg0OLhQbePx2FANB5BvA4UWEngvowy11CrQbg9aViwvgarWqgYGBVDg7jhQxZrFyQnlx7glGqTvvKCCPDKI4PDVGShsaDp6w1pjXWIHQFgxkeJCUPWSZp0J4FMv5z50q8nFZd1QacYTN2xE7sIw58srvZSwYK6/g4iH8eF4YIyJJ/O/3uPPF2iRdg+vgf1eaLnMbjUZoF8YAzpLzE/Pc398fylU7+gu5A0I741QFN/a411O5nKecZ5AZ7ni4HvU58jQSnomxgEFK2qGDD+5k8+P8mpO0YcOGNSCk1MlcuOGGG7Rp0yY94hGPUKPR0G233bZGNkHwroNFse5z2YGe46R4AEHP0EAfxLoDcrnn0S3nvywQF53KPfE5D+hf+ggfusPiETrWAXLLwYcYWOF53B/LZ+Siyyp/F8+SlLJJGHt3/NzZo53+LggnE+eBsUuSJKSqEYngLK9arZaqTInch/x9pEPH9mGWvGGeAZ84MNrXMMAz75ieng42Kxkhzgtuc8CfrVYr2Du7d+9OHUR9MGm/HQ3QE6/PzsC6s+HK3xkhRtYgFh9GOINM7ny1Wk0dkMPE80xQLgxz0DoPbcWpS67sQPNGRkZC30qlkoaGhtRoNHTLLbeEe6emptY8P15o1NPmIED6SLsx+N2I97ChIxZ+cI+kkELSbrdTedYICgxuHA2MKNI0SPnh/a5A6Q/PxENngSCsYmHUarXCwUWMnx+W5R62l2Lk3SBwVOzCcXDUZmFhQbt27dL3vvc9nXTSSTr66KM1NTWl2dlZVatVHXPMMdq1a1dqkzgLzPvhBsbg4KDq9XrImwRtGhoaCilTIB/wiaMpzlfwelYeqM+rp0rhYLgx5XwV742K+fj+Tl4JyY1CN5BKpZI2bNgQeJXPUYCOkLlSckSLvzFmMfCIRKCcHGFizikTyHPj90vpUL8rNXcwuJcoGOCDp+EUi8UgH1gbVLKS0lXHHJF1WejGUCxjXQm5EeR8GUd2eG6MqgLSxIaN99dlImuf8UJ2SJ300HgteDQPB8qNLPrF98wr8hujnHbikOCQrYey0m5Hk7nG0yh8bfq76C+/fSxpG/X3fX6IevMbPUUtf0rfOnhDdMMj336/y6OcOmdWXHPNNQE087SlJEn0P//zPwHxbbfbKSDE1zq850Astgw8GoNc6E/ARPjEZZLLDfgGPcu7fT9lbNj7mvSzPjxC4dFiB1M80u6yMHZwpI5xjE3ozoTrPpfL8CdODGsMmeF7WX3dxf10PYg8gbIA5qznSUoBm8xNoVAIhRe83Pzk5GSwvzwqgbPFj7ffZRpz6mAY7WaeAKG9jcwFYwef4WRQ4MPf6+PNM+hLpVLR9u3bw57pg037tUcDgesbhGBaJtAHKDYG4oiGhwkxhpvNZshLY+M1qBjCkwXq5ym4IvIF5MIBtNBzkaGurq7gZLDgh4eHNT09rdtvv13S6gROT08HxCz2VMvl1RJ1y8vLYT9Hq7Wa0sWmbxcOpVIpeMdu8MOQrmS9xCE53bS/r68vGORTU1PBUcBLx0Pu6uoK4TgWdKFQCP2ROpWjYGxfEIuLi5qentbk5GQQmDg8oL9S55AxKid5KDk2PMhn5nsOcHMkH8VQKBRC1ODqq6/WzMyMfvEXf1HValVjY2Pq7e3Vtm3bdOedd6b2zdAP2opBx0apcrkcChbgtLDBH6TFBZnzOwvXTwn3ftJXd7rdiYj3ZiDk47+hwy11ypEs/8wV3NDQUDCw3Nh1pROjeqwvR4B8LJm3paWlcLAjaUFZKDDyifsdmULmxGiV87qkkL6A3GMdo2xAB5Fz0ur5Ph4ppQ9ZCpx+xQa/G9puMLhREDsYkPNz7DT5e13eZyGJjBm/HfV1YCWOuOD8+XN5thv1fgYOMhcjiAMPnS+YY0dj437HSCz858CO8ynj5w4Izg3rWVLYf8Z+MB9DdxJ4JxuW4zxxrmm1WqF6G7KTtsaRtZw60aerr75axWIxxRvuqF177bUpPelgRMzrDgh5eqBHn92RgPcACl1uAdi5PcWaQEb5GnIjPV6PxWJn36RH1XytuY3mstTb7PLQZQhtQ5/6fkJ3qkD3IXdY3GHmO377evd15bLIwSQca9rgfVxcXEydu+GgqacfQg7k8G7sTD8AmYiHRym93TyLZ7j89X4zzg68MQc8D2eWSDY2JP2I5ZaPE7yzsrKiubm5kLJ7KGi/ytv6xLvicxSJwXMGiIWvtLbslwv5crmsTZs2hfSVWKkR6SBH1RcAjOEMTJUmPzgFom2Ek1jMGzZsCHsyeN709HTYC8EprEmSpDZAt1qtUDu5UChofn4+MCHPps9ens03N5EKRriTsSLq4qUbWQwY+sPDw5qbmwtIDegAypY0Lca+r68vlGy96667lCRJUIyE9JMkCRWWGo1GKkVrbm4u5IezmXp5eTkcBuQ17UEwCA176J/5oZSfI33uwZOr3Gg0dN1112l5eVmPfexj1d3drdHRUVUqFW3evDkYG44+ozTgNSIZpGsRBSqXy+EAR6kTwo0FmLQqHNigDj/Fxih864gucw6SxBrjGW6IxevwcDr1F+PQ04Wc5yuVikZGRlJIl5QdMmd++J814qgRvAYfSqsRg76+vnA+DjIlllfuCDhqJGnNHMbzVi6XU+VLQdXdqHbUqlhcPYNofHx8jQGZZeiSfkb7MVI9tcIjpu7M8k7faC0p8283ZhhnPncE1GV17HS7os3al8Q73EmJ5yR2ptwQ8nQG1jUH4QE0EdVCJlJRxo0QH2Mp7ajFCtznhPkmEov8m5+f19TUVChmwfz4e7xfni/uToijnD7X3d3dobSyjy3poDmtEoBjbJj7vjDnYXhzZWUl6Dp41fctwK+Me8wrOMPwJnLJ0xHRv+zR4TP/3lM83cl14x8D1NOCXNe6TPA9EfTT+8E6YXx8Pwnf89udba53B8z53I1vH68YVGA9O/DpcocqVm5D4tzwXnQFoKrbr+5QxKW6OcuLuYijGA4Serqj85YDhjFQ5v/7uS0+t/TLo9lkuvC3g9QxIAo5qCStykbfh3swab8cDbxSD8O5oooR+fU8N0dYnOlrtZqGh4dVqVQ0OzubMlIx0t17xkiUOvmzvjhi9MsXhgvrgYEBDQ0Nhb4NDw9raWlJt912WzDKG41GiEqsrKwEZILNiKVSKaRFwXj8hoFcQCTJanRkamoqMCJ5eaDWi4uLoU6777dYXFzUhg0bQriMVCHGiLM/CAlT3UtKn16Nw4ATQ+WmJElSJTXb7XY47dyjDPPz8yHnFPSY9DrOxHA0dXBwMFTW8ZzUcrmcOg3eU1vgMQQDqAj9vuGGGzQwMKBTTjlFKyurZXb7+vqCcUrbYkSvp6dHIyMjIV2LNIb+/v4w9jiIVKshauUHJOJMZoWxeWeWkevGZZYT785VvA693vn9nZifrAhCoVAIhzdKaUcsRrjgYTfGXGA72OFRJZ6D81av14Mz6zLKDUlXcnznnwEe8HeptHpGAvIP5cXeKoAD551Go6GxsbEU3/MsnkPKAjIjLqftBogbtKSL+amxjoj6jyt+R/WzHLv10DzGEdnhc+NRDp/feDyZjxh0yrregQD4AfClUqmEQhhuHFar1SADfU59TnwtxohorPOYI2QcshKjwsfF+d0dKUcvvbgGMhLZ7WsIsIsS4zgpeUQjTS5vY33kax3gEF0AuTPsABTfxbKD1CLWIfesrKyEjAS3lfr6+sKp7rHh6EZrHM0oFovBUXF0XEpXhuJZlLb1YhLoMncaKEJAX9DNHgXgfnfY+HE5gb5rtVphz6bLGa7D0XY5Hstt2uPj77LF90B61JJIowO1cZSEdergLPNLG+k/oIGkVJ88Kjk3N5eSIe5wwH/+TJcJLo8Yf7eH4/GDGPu4j/ThUNkS+31gH5PP4Hv+VzyIrhgg/597KpVK6kA/N+ZJbUmSJCD9MG+tVkshwk7uxXpIzpkEL3JgYCBMcL1eV6FQ0O233x6Mj7m5Oc3OzgZjFMPAkQ8USRjY/x8pwGDGsJiamgqGNIgtJx2TcsRCX1paCqlaxWJR/f39SpLOqbP0L8szjZEvHwtX6m4Eg57Fm5NZSLSbyAWGiiMDKETmBQQAg35sbCxEV4aGhkLfcB49IuZODYuXd9Gnubk53XrrrXrQgx4UUJbu7u6woZ9DuphvlMrIyEjKeWKvAOPZbDaD4+X5u87vjhDxmQtxN/jcWWIdcZ0bv34Nn8Xr8HCi2MB1xU9U0wWtK1Z/hpRt+LvykDpFJ1x5wRc4cfAC4x8jaI6WxfLNEULeSx4siBopiMvLy9q9e7c2bdqUKiVL9BByZ8RRQtrrGw5j+ZrldPmPG79Zhi/tjvsfjyvkMjamLCeEe/x7nzcp7YQgA2JkFGXK2Phz3NGZm5sLMrVWq6Uq0pH6SspnjK56WzG6+MzRa9pEu2ZnZzU3NxciThh4bgB4BBddR3/jjcvwJDxD/2g/oBkyd2FhQbOzs5lz8kAlxo15ciBPUkhXI8VN6hQwcIc6C7x03gYlx25hDfv1yBq3mRxJ9+/RG/E6ljpRDC9Z6jyMvsJWQd+7/eCIPGOB/mKNAFLwv1dG8nu9v9KqAc4eTHjc0wSJvEgdYBvZRySCfbysNXfo/P3+HWMjdexCzwLBCXQwCOAqSZLUHr2Yd3iO6xWP9ri+8nNZXL7RR0BV7uM77okdzNgGja93PRHLRdcLh4L22dFw4wkF54qPz51B48lmcdLxYnG1XC2nl8ZGwszMTGqzKO9H+FKr3ks/emjLN/dg/LqQKJVK2rhxY1iY/f396u3t1a233hoMVErmgsjTD4zc+fn5gEjAeMXiauWIsbGxcHAfEQ82HoGoke83ODgYFGF8YA+RFhakh3HdWKANjur5eMDIjmw4IkMKAYaSG00sHk8D8Ln2aI0bx27swNhzc3Oam5tTs9kMAoY0K1LD4rxIKS20WbikIE1MTIT9J/Pz88HZKBQKajQaqXQ69quMjo4GIYNwRxDRn56enlD61x0D5hmkww0gRxrdOHAk3dP4PKoROxmHm2MRkyPuUkeIV6tVbd68OcgEonOMpwtXRwJd3rgznWXMunFHO0AwKZtMUYfYWeW9LrPgB3i3XC6n9lLhoONkEAq/7bbbwjtcuZFS4elaHj6H5+I+xMibKyNHv5Ch8DIRO1f2sSJCVsTgTZy64BQrVe4BlXNHkr+RVxgXPo/e71gGIfP8vbzLDRwiluwFZO23Wq1gcPgZSK4zspxjfmNwMc7NZjPkf8MT7BVj/HxfjRuSyHLIdZcjnW40uFNEul5OacKx8E3M8Mfi4qIajUY4gNfvceNRUkoeOT84+d4hX4Pcz7qBD3gPhq8fWuz7ANxZAfxzQ1lKgwLsfyVF0HWM83EMxvrzfB8hz/eiQL5hPis1LZZnLmeQ8f4+5gU54adfx/KCecHOQ27EDjs2DXOLrkZmeOokbeM+Bzhi4MGdKv6OZYfLIP+OSCTPY/07XyGL3A6M+S0eixjsj1PXsoDMg0X77GhI6QOUmGQMAJjXO+hM4x4g17A4nNFcWK6srGh0dFTValXDw8MqlUohtx+jd2FhQZOTk5LSpRVRmq6s3AtMkiSUeG21Vism9ff3a8eOHSFlgXAiyBf7KGKjEC99ZWUlIATT09Op0yJxZnzjDoKt2WwG1JP83RhpkdIbwtzzZvG4koEpHfVwxvKxYP5Y0IyfK3WuZYEiEF2ZMy4e0aC/XMMCwSgDbWs0GhocHAwOF+gMpX1pi6cP8P2GDRs0NjYW3gfvdHV1Dk6DTwuF1XNSqHfvKBaOG3xYKBRSG3FdAIPwOKrlQsKdEV83/sM9UOzMr2e4HU7kjgY81N3draOOOipEAt3IjtHd2Oh14zieM+d/RxCd76XOOQ6c0i2lz96RlJpXnud114vFogYGBkI/UbIgpM5XMYDiTg9RVHcaeD7/uwPrvIhMIpUC5MxTBlyx+pjSD9rmyi9Gcnkf5DLIyQ0ZT/ny5zJnnirG+DEPscwplToV6nxOfJyyHPaVlZWQEutpneyFA4HFAIzHP0ZTkaUYFpTljh0Gzk8g7RTegjA6XG66A+ZOj/fLjRjGtFgsBnQ8p1XC+PeDHdHZRBLddpE6Bqg74lJ2ZTmoUOics+QGIHoQ3e6RbKmTkgMfUCnPdb+vP0qb+nslBf0KsEX6kDvc9BX5gZ0EL3v/nPccWEHOMKb003Ux18eyB0IPu33gTr3LeU9BdmAT8JVqcisrK8Fm84gNAAvjH4PZnklBgRv2dfkYu+yjnW6v8Z3rGvroQIqfieYRD57jKa4xr/nn3iYHQ9wWYfzcITwUtN+OBowFA4HWuxPhoWxfEK7ApA46FlePckZeWFjQ6OiojjjiCG3ZsiWkM6E4CYU74glDw/TsYXAvsLu7W/V6PSzcarWq+fl5jY6Ohj56yM4Pt6KPOBGLi4uamZnR5ORkKOlKFMTTGXy/AePA4mg2mykkzpmA+2EyL//LYmTRMQ4uDN3gldIoJM9FsMRldln0njaF8cK7yfNst9vhhG2iDaC6vJMQpD+bRbeysqKpqanQR8oae6EBxh3+GRkZUV9fX0hHKRY7VUS4nv0qUqeOfrzRG0ORhYnBS7QIRwwB7YKScXfB54gLz3eDkLUUG75uEMfr5XAkd4QRjkceeWRIi/Qxdj53YzY2cv1/jxTERipyAlSTOSgUCqHMsZ/JIKWVrTsbXt66WCyGiAhGA44p8x5fDxro8w/P0U6MkeXl5ZRCjfvsaYY+Zg44+P4n2oGRzEZUxtN5lzmKjQRXXrGzh9Pk73CDPXYC3AGTFCK6npJG25BbUAymeNWx2ECEVlZWy1YuLi5qeHhYfX19IYoyMDAQ5gtHzg0iiHGDqCzFd07IKOSI8zApWW5IOP+5IRKPnz/HwQ6/PieFdQ1/TExMaGpqKhjyMZgQO9X8zQ9z5cCGpJAu5fe7IY0MifeDxZuXWXcUoHHQoVarhagC63t5efW0byL7WSm58JVHUTxa4Xzn0UqXI34d7aZtpFUxjt5/z4Dhe7fNnN+lzmZqPiOLwd9NyjdADuOAXiZdjHeTPUHRGj53oBX7wY878PK1tN375zYNn/k44rg4KIaT6GsUO44IlI+9g0o8w+fF5YCDIPFcMJaHgvbbnXEmlRS8Rg8DutJxir0vZ854cUOtVkvT09NaWFgIQv+OO+5QrVbTyMiIjjjiCC0sLITTm5lUQqPxJi7IK8xguHM+BoIoLs/KJGIgsvl4eno6KH6pM6kg3ggOxgcl5ntPcDQod+mLKFbM9C9GSni3Cz+Yj9Afxj8CJ04DwcD2CIYjpSwyco09JW1+fj4IN/aT9Pf3p+a0Wq2GxVMqlVIoAeFdBD6Cg7NUEFyMI05No9FQb29vWLCeIsZC9DQWDACMNgy5QqEQytv6/cyVI9ZubLmD4Q5OjDa6wPc15MbcA8048HSfYrGoLVu2hCpojtBKnTB2jJhnOR+gbZ56EDskjqLFDh28QeU35tSNfdrvG7AxOF1xOkDD9+5or4dWeQUU9gx5RTv4L46cxYYAxgTOPDKM9iPbHMX0PSrI9CwUEUVMu2mbG/fe1yyjW+rIwjgqA8W51XyH8+/tQTYyLzESy3vj9i4uLmp8fFz9/f1Bfi0sLKi7u1uVSiVVGdDz9b2v/vee9Bq57RhM8Irnd8e87W2HJ+l7bEwyX/BfTh2qVCpaWlrS5OSkJiYmwvpwOeDyN9a9Ujplz+fF5z+uIuQoMn8D2nG/kzvnvq49Wu8peUmyWgWSktg803WL/3jfeL7vB3G7zq93fvQ1lPUuz96I5W8MErnNGMsJByz8/Buiz0Rs/LBmgPAs54DvSb2fm5sL+7Y4rw257dklyEdkS2yfxXo7CyTwPnq02XlJUijRDV+6HsRu8dQv2hA7abybeXQw6FDJhrvlaCDUMBJwNmKUJw7L+CQgxN0AjBEBF9K7d+/W8ccfHxCImZkZFYtFHXnkkdq4cWM4JdoFMBuW8FSZrJ6eHg0MDAR0HI+VBUy1Ds+jdw+Q/QXkcsZlJ+NIDYYvjOApICj+qakplctlDQwMhH0KpA+xWKVOqUIYzaMj/jfGjoda4/QCGB9kDUTfmdYFAk4QfMBJmWNjY5qYmAj55zhx8/Pz2r17t1qtVii9G3vWzDsIMosbFGJubi6k2HHy7+bNm0OpUD9Mr9VqhYgS4+iHeOFMtturB9Ywt6AhjB986SeCY1DCWyDkbuQwLrTFeZ4fN5igGG2KlcHhTI4ODw8Pa+vWrSoUOpGvGBFijTv65cY688Qhd/BzbOS6sRgjx1mC2pF/Rxpx3MnxlzoHukExwEKkjnbyXtrJc11RYpRwICbrmTFaTxagYDz8zu+4jDQRTV+jDh7F8ptx88/8rAdPvYid69gI8c3R3p4YsWMcXDHHssz3TeGIcK1HRWLjh3ZirAwPDwd9wLNY94VCIbMyGfPvesANMt4jKcjxLFnrfXN+jOVCLCN8LpziVKAHMo2OjmrXrl0hUoZzx4+DS54yl2XIQS43mG+P3jkf+48DfFk2EPKFQ4hx7MkSoF0LCwup8vu0wTMInHd8PXsbuM+dWOSApBS/+w8pSbGh7Si/8/XeHAq+i514no0OBfSlb24nUQzByfsiaY39QHUogEtsEniAZ5TL5bBP1fde+Hu8Pe6M0jciHNiHpMthx8zOzoaIu/MLthdtov/xniHmL3Z2ue5QyoT9djQcPaFTCF/fbO1IijNZ/Ky5uTn19fWFjZLO9DgF0qp3t2vXrpDzuLCwoPHxcdVqtWDYEhVAYLAAaCsGCvfgWCBsSIECBUXQoAQ5v2FiYiJEMHzhocR8IeFJ+3c+JrHxQ91rHAWvhuSREUckGXc3fBzRwjjDs2+32yH/PUbMYlQjNjTcM8Yb5wyRSqWiRqMRhABtQvHTZtrnqXduVHhuoqSg/EEX5+bmVCgUNDg4GBwxFjbRoq6uLvX19YUqYmwSd55kHHGwaAeCWkpvqHPjxQ0j2uupJS54XGjHEQsX1v6d88rhTIzv4OCgtm/fnkLb/RopvTnRI3h83m63Q8oA85uFjqMoWFfuYCCU+WE+vOCEf89nXgkFQ5P5daXqDjZCH6WxsLAQIhcOCsRIqxsSzj+xMnf0iue4ImcPGTKGtcj93kapA4r487gGg8fXcVyZ0NMLIcrJxg44Y95ut8Op2DxDUpA9zCVOjit4n3P6QfukdNoAbfd583N9PPUOecHaj8ce2eYOIG2Io+u+ybRYLKYivHtKJ4lli68Pj3C5k57TKl133XVr0lhiA9mdCnicOeIHio1lX+843vH4xwahg7S0g+9IQ2JNVavVlKzizCdAT5cb8APkvOH8G69dUvjI9nC96cY+vOfp1i4XYj51feo6ljXrgFCs//zzWJf6Z0QcHAB2mykGB2gndhS2xtzcnKrVamr/C/cShfBKo+4EuGPqjorLZArbMKbIJ1JjpU6WB985TwCYMi6+T9erEbrNCXiN3Xuozte5WxENEHkYFeMAQew/TEyMwrhw9E1K0tr0Hzy+3bt3a8uWLdq0aZN27dqlrq4ujY2NaXBwUA960IN03XXXBedC6jC/p8GQl8fEkqu3sLCQOngNpUX61MLCghqNRtgc6J4hBEMXCoVUPXYcCBY93rIjYH19fRoaGgr9d4HGM6nG5IYFub6E/1jojLOnQZEn7mOMMwUy4KeWgtrRdvek3egip5lKLqVSKZw4ifMWG0jwgBshPlcIVuaQNClO4KavW7ZsCZv1Uersm8GRHBgYCMgwCIFHI7jPBRnGBHzEnDqiHqO16/E873HhGOfCwj/+2QPB0SgWV/czHHnkkert7Q1j7OTKCoTanWNyWWdmZlIIXGwMODJO5TInRyUd5fL0IdrhipcfNwhdsTqKRX9iY9gjJvCb74dzx4o2uBxgDDxC5LLQ3+Gy2Hmv1WqlFA/jwTuR+c7nPs7uBMYOlRvb/Pg+FZ+j2AgBpHDAo91uh1QmdAQgFGkOPr4OCngpU59v7sXxKhTSm9LdKMSRiHnR59PBGfriTiM6CFkT949+eZTIjdgsQyx2bGnrA0GW7Csx7lDsaDhfZzne8Ti7nHFaWloKhqQbwTHw4KAshD5yw5JDfLkvSVbP4pqdnQ17Cdvtzn4xdDmyzvsJb8PXbhu4zOKZ8Sb0WM5x1pSvFZ4FxevEnTfGMV6zLi8YE9enrncZQ7JRPOKI/QIQ65EK1h/98cyYVqsVqp8ODAykwCXsImxGdI+vY+eReP6JZjgIJK3aTD09PeH9DnDE/LKwsBDOReIzbDl4jrl2vkI34GwfbLpbW849quEGmqPDjggykFnMx/8oAX8mSo97qUdOqtXS0lIoQ7tlyxaNjIyk9mr4c1BsCG42emNUg8Q7KtZsNkOKFA4GyJmHxSWlmIRqEfSpu7tbw8PDKcSOPG3GzJUuOcFegs+FDoYF/erv71ej0UgZC67ciBbBgIwB6AgeMyiGCwOES+wUoMAYM05xHx8fD3mvnvNIPzhHhHAvC2llZSXkLEudtA5ObI5pZWUlFAJIktW0LiIckkJkqlKpaOPGjZKkjRs3BscnRjuYW/gNgQTCgWECT4M8OGKZRe6IxM5DjPb49w8Uw6BWq+moo45SX19fWAcYcc6LUvqEdpcxMzMz4XCrGG10cqc/NtpiZYdw9px5R6597twgj1Fy1qSjlDyL/GJfA6wzDGLkHMo+RvEAC3gfvOQOWewIeUTCjXuQQKmT/uV86MavkxvP3kePZHqEhvGIx9H7hMHC9wA+UsfhQUkWCoXgdLBGPXqKAxIje17QwtOtSKWdn58PYJJHK+mHR0+g2Mjwde1t4H6PssSoryOTyEN3gN3Yy+I/PtvTmnggEmPtc45eZP7dyYhRduQ/5Eaqk8+Nf0Yb3Inxtet2gqdfxRWskHs4GUQ8vMysRypoixchQQ+yBrwfXnKVtvO3p3OzXgHwGM/1IvwuK+Ixjq+J15Y7g26j8B0pU8hVzzZwwMgzQXgGOp35ZY6QBYuLi6rX66nN4wCtXg7ZHVFfd77+AUbckSqXy2HPCLYvNpLLEh8TABe3tbN4Leatdns1XZ3iOweb7pajEUctkqSTNgQa7R6ah9ViRMA33bgCc+UorQ4apVA9QkBt6MnJSW3cuFETExOpRewhrGJxNXefcBiKYnp6Ws1mU/V6Xc1mMyxg9hyQchMvAgS7Tzx9RyhQ3aper6vdbodSrqQB4TAwhr29varX6ynmpz42qAMChc/YvJwkq+d+4FysrKyeOuql3UqlUjht3NFL36PAvOEpO9rnY4pjgDFVq9W0efPmgLBAbLovl8saHBwMC6nZbIZzRnACQWGoFIWC9VCf19ZGyM7MzGh2dlYjIyPhlN+VldVqMWNjY+rv7w+oBAKRvHUEooceMdSoPIUBjIB2xMvTnRzBZSzXcyJ8rN1A4f8HgrNxxBFHhNPYffN9jMy64mTcl5eXNT4+nkp9iwW8G9rc60rSoxx+PW1wJF5K13znWldo3IuTCs/4fLZarbB/DCcjK8LlvOmpPVwLj7u8RZayVmLHwSMpjJkbUd5/NxQgB3DcWXNyhUh7eHdsjLlcjYn5RFaxnyVJklQ1HncWfLM7c428IWUA+UkfWecYAm58xQZmzAfeZ+97lqHEeMVRHt8HEjukvIuzXVyvwntu0Mb6NW5nTkqt3/gwPtZDHPXweXfHHIqNceY5No55TrxvzNeuG+kYvfFZHOTw+/p3HYIsdSAMXcVawU5CdoGix2se/UsfsAmgYrGYQtUBIn0t4AQ4/zPO8fsYz1iOYP9gG9E2+tHV1RVS8Ymw8CxALJfZGPE9PT2h77yH9UjbsDWoSlqv18O7vbop73OdkwVKVKvVYPfRfpwkbGLG3FO+Y16L5ZE7YLEc94i82yaHgu62o+EhdFdeMXLFonbP0r1IJtL3C0gdNIxBg0kWFxdD1ACDWlrdw5EkiTZu3Kjdu3evEbilUkkDAwMaHh4OeYfu+dIvSSEc6SiaK328SGktgkqUoFKpBAFAibn5+Xk1Go2wiYi9DaQ9ofDIbUb4wfQgeR4pYZHzmS9eSnN6mgCedJIkqlarkjonseO4sfjc8EX5UnIUR4fUs2q1GqIXS0tL2rFjRxgTlOrMzExYPBxAMzQ0FBi+p6dHjUYjoJMIej+MCKHr80JO/uLiokZHR1UsrlYv2rp1qySFTWHVajWM8ebNm9XV1RUO8+N97mw4+iCl94ww91lrw3/D825kupHqhrUL5QcKDQ0NpRRIFnrGWOFcomi9rHXslMTC3pWbX+f/O5rMGnJFIK2d39iQcETczwBBTlLX3hE27nVFhNx0ZYBMpS8eZfP8aleO7gSxDn2DtI9BHL1xdNNlsysnl4tZlOU4ZyFzWWPPnHR3d4fTrtns6hVx6L+nNHk/MHKYT0+d8ve4U4m89H0l/GYvoSPikM+hR1b2NM/INZ7n38PHrVYrRPO9be5cu5OStX5yWiU2VUsdwCg+r0paezaBlL0R3I26eMx9bcArfi3/Y6DH553Arx5hhKdjm4DnOc+4vEKfco2n07jtliSdczwwwumT23lE43xsuBe563oulu8uk7mXMfdMmFgmSQrZHlyHkc54s94ddCBjA5Ca9HapU46a/VgeVYZPGEsKygwNDQUwBxCTSlE+Dzh4rFWe6zqlWOwcvOzj6dFVp3jcnPf43gEunwPeeShTKu+Wo+Gec4zYogScwVBse0JZfBDcM4sROa6l6hSVhWZnZ9VsNrVp0yYNDAwE9IsFVq/XA5LPJJC2RI1lSqtKndJitIuFi/EgpfMoe3p61NfXp0qlEkq6sjHV0Q+Mc9KN+LxWq6ndbqvRaIS0Kk9vwvlgfFCqpCWxwIaGhlKnpbsSppqVjz2GO8ZPrHRJH2MMcL5wDhkv+tTd3a2jjz46jDHOEIKA+aPaikeWBgYG1N/fn9qQ76lbHlXwDWoIgdnZWc3MzKhUKml2dlZjY2PaunWrhoeHVSyubppjb0x3d7e2bNmiUqmk8fHxlJPh+3lwcIm2xFE3xok2IlDgG1/sjl7HiI8LYcbogUJeESkWnu4gsy4mJyc1Ozub+p57syIZkBsE/p0bhvCtgyHc678dZfOIg88rSpiyi17sImveYySKNvA+5y/fRAiPeqU5/gb5l9LpGr5hnX670R2/N1ZiPqY8h2f7d1l/ZxnCri9ihJO+1ev1ANhkVXzCIHK54Ncgu5k3lL2fQxAblu5IYdDFkQ13kvzeGP2OjVH/ztPiHIDwv4lsDAwMpCIgsaND310/5Y5Gh0CN430aDlqsh0T79W7AOd/6WuJaBwB8/pknBx38fgcu/N3wrjvCXI+OcUOS/wE7HNxZL2q7uLioarUa9DaFamJHxh0Lb7+DANguvBObwDe5c6+DcTFg4jIZnmYNzs7OBtuTgweRcz4u7mhSwQtZi13j2Q5EOxh7SWE/4IYNGwJYTHtI55cUUsIdqGBPKbLb0/Xpq/NYb29vuDaLXO5kAWHON4xbVpW/g0l3+1jA2KjiNx4kXmSW8pfWhsz9Gp7lRqV74YQMMe5dac7Pz2tgYCBsSi6VShocHFS1Wk0hfbRhfn4+pEpRErO7u1uDg4Mhf82FCo4UC4hTOYmsLC+vHl7n+x3Yr0D409GF5eVlTU9Pa3JyMuREEtWglGW73U6F99hfQv+ljtNTLpfV398fNohjlOBF43whGN3h2bVrVwpFIDe8p6cnCBsWCIxNVAinjZ/l5WWNjo6qq6tL9Xo9bGRDkPumdIQm8yitIt2UvPU0J0nBwUIYooBZXCz80dFRTU9Pa2RkRMcdd1zYJ8M4FgoFbd26Vd3d3dqxY8cafoyRLdrpKAnGpS/2rMWLEeHPjlEeXxdZyBm0p+/ub4TSyzKWWK/w78LCQtj/g5xwo1xa62zERha87wUHsoSxGxEoOCldFpI2uKJwhxO+9blH2bvBEX/uBRd8rovFYjjXI0b3UPzsf/K1hFyIjQovZuFouJPzaez48OPpg7HhG1Pcb/oVP9ffzzhQyANZgMGIwQUY4AYWBp6vvRgBBhBxo4TxxbDxcXNHydvrazkGCmLnOSuqEfOizwn3ALb4Ztb1HLp4DHJaJY8OMKaeluggArLd0Xl4gvn2TAKpkxYlpXk7a+04mOlFMBwoyXIicJThB2Qa+pjiGBjsDgJwradD0Z9yuRz2G7CWKpVKAC64njY60OEgjRO6k/H2SAvRA3fCGRPWJHLKbUYHgMhYcNQfh4gxclnKHj23AzyiiD3ndh/yHBnRaq2WoC0UCgHExs4D9PT1Cx9wejo2Ms+Ej2Iwgr95rsshj7jFMjWWofCIP8Pn8mDT3XY0nGH4YQKcqbjWGQOFx4J0Ze4Lzr1aDtIjt9nDfFIHoSONpl6va2lpSf39/WGDMIY1Rnyj0QiVptjrAVNyQNzk5GTYrwEjcKYD6VsoeA9HsUBwBHAiVlZWQn4l+xhA+2mrb9amMoDnV+K9s4hmZ2dTXjDMg7MjdWpE016ps8GS52D8I9AQWk4xwoAjRNswvjkzY+fOnbrpppvCO+v1ethH4Y4aBpYbeERzisViatMbhr4f7geSS4UyF2STk5O6+uqrdcQRR+iEE04IFbEInR5xxBHq7u7WrbfemopEIGAYD1c4LjQZY099gGIBwPjyHl/8btw8UCIanr8fOwhEMQqFgmZmZjQ5OZkSrlAWmuyRDuQLY+uoWIyg+/pZz+h2g8IjhKxNEDE28nKvzyvGgp+RwTPcgPS0CcbLn4MSZl3H8tXbxWfcA39721xW+5z4NTzPnS+uj+/LGr/YcMsaa38naH+5XA7nj0xNTQUgCd0BOsi+DB8nN/JoM1WB2B+E7ILvGDPkIiV1XWnTRkeh10PAeX8cafAoRAw0xIZEobCaNjg8PJwyGLwdMTFGOa0S68DlvJ9nIqUNWZfFztc+126EZ/FcloxyHuJen8PY+IwjFUQ75ubmgh2DbeS62/vnERh3NrDFXA/5evbojtt7DjQQKUB3xxHeQqFzWKk7VT6ejD33Qy5vPDLEeykMQ3sBknw+PR3K1wTyxW0j5sIjNc4PtN9LqRP9YY8oFaEc4Mb2jFPdfE5pk9sxOEbYQC5zssAJb6/LCbc3XLYdbLrbjoaU3hTuSgiEwHNm3SCL73fkwNEzwlaOLPlBXpQVa7fbYdMw5TG3bt0a9hnwfAzj5eVlTUxMaHJyUtPT06mNqI5mdHV1aWRkJHj5PT09qtfr4VpPo0Ih0g4WHxvVkyQJkRVPOSK1yseO8B8ePX1kjOgTDhJj5Xl/MLJ/7kYC80I/pA7yRapHHF5zJJ72sEg9dMi1GzduVKVS0e23366xsTGtrKyE6lhTU1Pq6+vTli1bJHVCmKVSZ58IURW+I72MDftUY8GhIvrh7XCeueWWWzQ5OamTTz5ZJ554oiqVSpj/zZs3a2VlRbfffnsqjO2GFnMC+TW+OZ/vmCen2ODM+tyVz+FOLlzdQMWIb7fbmpqa0tTUVEpZxwIWcoNPSleRitEiV6jumMTOBnPjRgDoOYRSo6qZO+isN9YpjoWjdMgS5F3W5ktvd9wvxs5BFWSmI+juxEFx/92ZcqMi5ks3kGNywwSFuR5KG899lpGD7GZ/HUimR3NceZNO6sYQ7XSjknHHEMBYclCK9jKHjJ3vwXGEOosnnRec/Nmu9xjXLKcEgIhxcAfOQQx/Zx7RyCbGydeAG7KMnTsQsU7wsZc68x2DIe54ZOkBNx7hM4AWdzCQF6D4/DSbzQDWeaTDnRWADYxh3uWOFWsBHebGLv3HpmLs0Ne0F/7kmYyRZ5/wLHem/TufG89CYc3heC0tLWlqaipkQGAbraysBL2BXZMkSepsDE8zjXU433Mv7XdblXd5pdCVlRUNDg6uAbYAdB2I4Dmxo5ElMwDanZfi37G+Ysz420EbB5UONt2j1Cl+O6PxmYfE3MjNQly4BwbC63Wjg88cySsUOpvIfV8I51T09fWFSgDsz5iamtLo6GgIt7kS9PCZ1Dlsb/PmzeHAN3dIYDwYEcWDYdRut0NFqL6+vmAQY6BjSM/Pz6eqQLFRGgXk5fbYzO1RI56JgPFQaJIkocKVV1ZwtITFhbcNA7ri5j3+v9Q5ZNAjGp47ijFGShmRDEm68847tWvXLtVqNW3cuFFHHnlkeAbtwzkplUoh9Yw9Nz5GSZJoZmYm8ABVZnzvSaGwioz/93//t5aWlnTaaaepp6dHd955p0qlko488siwP8SVgAsewsigEpKCERTzsxun8d/xdb6m4r8PZ3LjnrlDwS4tLWliYkKNRiOlDF3hxU6FGwkIcT6HXCj7ZzHqyHNcULM23KDnnRya5ff4uvGUKHdmWSdeqcqNe2SAp0FCtCdJOpXSkFvIVPoDCue5ztVqNSX/UEDedpcxniaYJct9HrPmOStFKJ4Hdxp8vTCXPT09GhkZCQgmEQxJAZjI6ofPiwNg7gAi31jTpDq4wcdY4Ij43o/YiXV+iz/3/vr+nXh8vP+MF1USaX8cRXHjLX7nA51iZ9n5xPWYf+9gm6dG8VkMcEhrI60+F762eJYj3+g6eA7+AgT0syKQHxj/ZEi4/nJ9DAhM/zyyISmcZ4RD7nIjBmDol7fV5U2cyeLXMxZ+De/xe9iPRDYL80XUmD7jDHj7aA92ITacn9dEu0ulUkjFx15ykJpraSPjgY3hAHuhUAjni3G9H8bp/BFHiVjPPN9lk4P3PMNBHeYbfnb7x/nY+e5Q0D1yNJjwLA/Kd+77fg3I/2YAMFgdFXaULa5M5aE8/h4eHg4H32HAd3V1qVaraceOHbrxxhvDASy+CZBIAuT7P8il492+36OrqyucUj46Opo6xRHngRA9obN2u50qmUqKkHvtVDJw5R47Y+58eJQkVjwuOD3aInWQWEdS3FHxiJMTz3Y00HOHUebSarrUUUcdpfHx8YC6eHla34RHDjrvQwHjrGJQMA+Mr6TUJjeQP0eMPNz5k5/8RN3d3Tr99NNDBaNKpaJjjz1WU1NTIVrmwjFJOtU45ubmUgIBpMPnwccpNqyItPi68fc8UMgVlqcJLiwsaGxsLGzulzrC0X98XmI0yNGyGGWM5YgbmVDWvBSLxRBZkxRKPFMi2/cHsHZYH95n/o/3Tvh1kAM45F/zOfKSctpZ6x6EMQs186iEG9CeeuQKzNMheIfLBjcc+D7eeBuPqVOMlDqa50BKV9fqQZxEhHkHDgK8wpr3e12W8h7nA/QXOgFwB5CBcWF8yWV3By/mGe+z6zRkWmw8xAYEn3lUg7GKr4McbMojGh3y1CnXVa5nPQ3Wq9q5Yyely9QyPz7HscPnIAd6jf+lzvkKzousXYrWuAHpEUMpXRIa8LVQKAQjnXf6d8gQ+glQx15U578YKPP7IPjNQRDvI9dkUewc+7j7Wp6fn0+dDUaEh3XEuiTCwdhgdzD/zDtjMD09HdZVb29vOA7Bs0tcdhSLq/vmvOgGz3ad5OvQHU1oT5HQ2PZyvRdHxLLkqct5H3sArUNB9yh1yhdqbFD5QLAwJKUWr9TZyBgbAlwLo3hOX7xxy68fGhpSrVbT3NycyuWyNmzYoHK5rJ///Oe6+eabNTc3p1arlaq3HCP8fm4FUQ8pfX7E0tKSJicnw2Fu7XY7dWCcV1pwxc7fGAx466BwjqbhnHCfC0faQygyVpyxY+Hj7qE/nzMWLAsCx4+2u9DCyHGhy4LgXkcMBwYG1NfXp7vuuktTU1PBqevv7w/GO0p3YmJCSZIEBxHkkEgQm67IfyQMykZRTinHQaRtjh4sLS3phhtu0EMe8hBt2bIl3FOv17V169aQQuX9py9+erovbvjA10e8TnysGbvYWX8goY+sYQocFItFzc/Pa+fOnSEtMFZ0rvCykETInQcX8rEwjkPI8ff+v+97ArHiLBhHtX0tsmZAyjBQPec6fr+jguz1KBQKAa0kVO8yxfvMvTji8dkQsSJ3go/d4OaZniLpgACKlb+RnzEQFOuJOOpA27I2qntJS0ANRzIxMpwvMAA86gC6iEERrznkJP1nPpeWlsLePJ9HZBWgU+w0+Ny4jIiNV5cXjK8jshDXkjrqKTCxXInfmVN6rxVrirFjrMiRZ52iP3wPppTW6R759s+znAwn+J4ImhOp1wCY8KI7G87fOK2SAkjqKX7o3VarFeTt4uJi4CP65XYZtofbGr6us0CcmMdjB8vv2ZPOi9cEjlUMAiF7vO8O0jYajWBPYGMBbjM+8ABtX15eDg5NqbR6Nlq1Wg3j5qCvR3pd/rid4LaS99fty7ivDhL4HlXnHecJnuVRYddJ8IM7aIeCDpijwUQx4K5k3OlgElzRuZB1D5TF7uFqR+k8rFUqlcJ+iuXlZfX09ITD2W666SbdcccdARFgwFdWVkLFpe7u7hBVaLVaATXEK3YF3Wg0ND4+HpwWqSMsent7g8fsKGJfX1+KIaQ0Ukl/q9Vq2BTuJ2qDgqJU/ZAyBIHUCZshDB1J9HGOUTX67ErOxzsWKvwfpznAA6AmrdbqZi36v2nTJq2srITTuWMjiVSyZrOpnp4ezc3NqVqtatOmTUG5czJnpVIJz8exKJfLYT8Hm+4xRlwYEVEaHR1VX1+fNm7cGE6H37Ztm8bGxkL42B2yJOlUjvDvQED80J7YiYiVvQsdF5wPJKNgaWlJtVotGM3NZjOs1ViZu2EmpevSx2gOfBVHOZx/MSbdQIvXh/O2R0ExCDhvJ3YkcWo92sX7eL+vF9qKQeupmBg4vvGb9EpHzTxSiYGEIsGJR1Ygp3x8fS8Xz4uR8BhUcKTODROXGR4V4R53Xugf7eZ7T0+UOhvhXe7Fp347oBDLF0pps1k21i2x8RIbTtyDfunr60uho+12O5wj5LLU+Ynnut6M139sSMRzAJ9Q95/vnZdjAC5r8+0DlVjTnoYjpc8W8Fx816/uFGelcnIN5Ai9f+Y/klJOBs+ZmpoKBwYT0aAdsXPt6x1gww1y1iCRDMDMrq6uwMfYP+wTI+Lu0TyXnw6aQbE8iOWhj1cWb/sz4jFy8II+wOvYOm7P8E7ax9hgR2Q5bLGsJ+tCUtgb2t/fr8HBQdVqtQAG855Y3+BoMGZ+jY9RDPrEcjQeE8bY7UmXKbzTQXpsXvrK34eC7rGjwe/YkIpTb7IQHvLaHSFg4FBaXjrNkTIUb7vdDuVoBwYGwmF+W7ZsUbvd1k9+8hPt3Lkz9QwGnGexwKjUwDNBzBFKk5OToRTuyspKKDnmZ2XEHjxIlyslmIjULGk1vWhwcFDDw8MaGBgIVaq43xmN/hONgVlopzMpYyh1UgM47AoqFjsbpdrtdnCufNMkqAHIBilSGAQoYV9MfX19waBjASRJEg4u5PmtViuci0AOKqXqKpWKVlZWNDExERBNr0NNP3yPBBW5CGmyad4V/NLSkiqVihqNhiYnJ9Xf3x/a0tfXp6GhoZC+FhtvCGWcVsLHzLFH8NzhcL6INwPH6yr+7HAlHOskWa2kduedd6ZOlZfW5jo7oCGtTYPKuhfKcujitevXotSQCVLHyG61WqmqRTGCj/Ljft97Eef4AhwgT7zohd/v75mdnQ0V29zI92vdsI1LVPvadMfH+d3P63CgyGWM53zH85TlNMdGr7cRw8nzp132eQSk3W6H84+YC/avOfIK4u/GpUck3BFyGet84ukhABDIvP7+/iCniLYWCoWw18v5NF7XPqZ7usbHk2s9KuNAX5ZcyTLoHqjEPMUpKW60uhMfO87wEQCk82bM8/FcZjnuRHOxg9rttkZHRzU3N7cmUkEGhfMpf7sNBcqOHHKZipPR39+vvr6+ICsc9ACI8DMnaL87G1JH98b7V2P70McjS3bHDreTOyVuoAPgVCoV1Wo1TU9PpzbCs1e3u7s7ZC1Q+hYnIt7Txl4Nj3r5fHFwKHLcC/YgvzzrxoFx5zN3hBwgiO1pd66cDx1U9kiPR9NjoN9Bdo9+HWy6R46GlGYiBikeLFe+McNwH8RgO0Ifo2UQKDKOBe+Esa699lrt3r1bc3Nz4XoWHveRD+6h+jhNhw2p5O3zDJS+I5HOmIQia7Va2JfAKbe+qSdJEm3evFlDQ0MaGBhIKW2Emm/UJD0IY5oSd+QpInTcuwWNYIxoCwJO6qR7sQj431MkGB8WmS8sKvF4OkiSJKHyA7yxadMmFQoF3XrrrSlkBoelVFo9+6RcLmtgYCDsgUEIkDtZq9U0MDCQOiEdRxEljAPA2RpEUticPzU1FRxNolmFQkHDw8OanJwMfeY0dxxSxs8rc9E2nCbIUQk3auJ1EgvhBwL19/er1Vo9NX7nzp2pDXNZDpiUdjz4zKMaWcoqVmYgQlmGcDz+blS7gnfAAzQyntsYjfIiDi783WDwtYYBIHXSllxJc+I9iqNSqaQAB3dsiAB6frLUAYXiCIAbuEQ6PJUHZeppA/H8xBsu3UihffTF58MNCXeEGKM4ikUBjoWFBQ0ODqYOMqWP7qS5w+QGpadD8l5/lyt/CopQrATZ1263QyqK7ymL13eWLPB3rWd4+XyQzsV9rlN9PnJHo0MeHQQsg4fdAcWZiB1c+Nr/Zx34fLoc8b89OgII6fePjY2Fkvoe2YQ3vQISRj4yiCgE645+0i/6gTMct9tlI3wWG8YY1953KV1G1+VzvHYcqHCKnf147Hz9AG7wU6vVVCis7vH0wgqSwhgz97xrZWUlHA3gctfTx5IkXagDJ430y0ajETJj3GaNnwXAwfh7FJ2++d8uI7Av4mv47TrE7W3sUddVHs293zgadNaVqYesXJBKHc/MQ5JOLF5PM0BRMCAwMsxeLBZ1xBFHBAS+t7dXmzZt0s0336xdu3aFRUe5NVKlWLR+yqxv+lteXtaOHTs0MTGRql1M22A62uxly2gfxge5fYwBSALl6Vgg7A1xY8IRlixGpN0DAwPBiAYlgZzZHbnAqwe18IP9XOH5xjj3ll3IsGgYVwRBsVjU4OBgQGPImUaA3XbbbQERZmHgFHV3d2tsbCw4VrGDNz09rUajocHBwdTBiQhXwseO3g4NDalcLoc2zc/Pa2pqSrVaTf39/WGcXOF4LjjjyTjhnDAeHr514RojjO50uNGznqF8uFK73dbMzIx27Nix5swWLxLAOMJTWWitrxkMxJhiZC3rXv/Ntcwr/Ef7mEOUHf/7WuM9RCs8paFQKITUCK71EpGxAcD/7tCzZ4poLk4A70F+0CePyHjbfH0jH+JxRjb5j4+TI3euF2KHOsuoyDKu4zZ4ZTzeDzpL6WznI4CQeD8JMgiDg4gI5clxEhwko80YeF7ZDjDLUyp7e3vX7OOKHd7YoPK+xs60GwUulySlQBL6FfN6TquEweWyWlIwIN24Q964bmddxZGL9TI3oNjmKRQKqUqUhUIhpGSTCuyOsTv26HC3FYrFYgDqSF2mbY6Y0x8QfWwaDGrS/pyXWPu0O5bD7rBL6bRLlwPed3eoab+PLX1zPemZGIyN7x/BhnEDvFgsBrvOnctCYfWwvSRJgo2HUY6tEpe+dRlFu7EzqtWqarVa+Aw57LaT28wx6LXeWDlfuRyN1zg8CH87/+C08pu23C8cDSm90ckHiE7AqM44XOcoO0qbAfd0IAYZh4B3tFqrG5rq9XoYvC1btmh+fl47duzQ9PR0UOAg1v58Bh6hjRLbtWuXpqeng3HrwoF3d3d3hxPKJYU0Hc6CAPlG6ZTL5RBtqFarwZlZXl4OyLyXnqM9jBGCiOtZVCgykH2YD/SSnHKiNlKnsgXPRvh4lSsUKCkdvhikjlDgeVIHtYzTDnAuvBqDJI2MjGhmZkY33nhjKs1jcXFR3d3doRqUtLYqDvPHhi1qY/f396u/vz9EVzzisLy8rMHBQY2MjKSUyszMjKampoKRAapO5Q3GGh6k/wh7DJNisZjaKObjEAvlGCWL188DhZrNpnbs2BH2HDi5A5b1uZQ23Nwwi//2a/gbB8ERqKzrJKXkBgaGR/OkNEIN/0mrPIPxwBy74cszfVMviKE7A74HwyOoOOe+z4B3IEfoI32LUUrABciVmBtaWePq78z6zFMC/DsfW+d9jxzx29FYfxZtxhAgisz+ORQ+B6Ey1qQ5SkqNCwraUVN3MjBqeC8Gq4NZHhl2w9T5NzaynPfWc0ri69wIcyOWv2OeyWmVqBLmKDCOO/zqABHjC8XpaVmOpM+FG5W+xly/F4vFcICwA2RJ0tkP5u1BXgBE+jNpI8+lqIa3FYO03V4twY+8oo0OnLn94evfHa2YVx2UcKPfKSui4WPqoAay0kEUj+wRzfF9sRB238rKivr6+gKwQ4pUb29v6vBkL9yBneZZJtgytJm2cmAie0gdIGYPousAxs+dwJiHXN450OzAkOsP5BCZHR7dyIqOOe8cTLrHEQ330HwAYQK/zlEsGNgRMCmt0Jkkz3OT0mgQCHWSdM5sYBPV3Nyc2u12SEdaWFgIZUlB8Nn4VCqV1Gg0dPvtt2tmZia8v9VqBaZDUXkJV2l1k2G1Wg35zL5hi7FBsKH4MOyTJAmVDFB0nprEWLmRDtO4F49j4PtWmAePJHjKGIY1jogrP+bEHUNHUDCGSqXVQxIxnHzzFCkFhUIhGO/sLcEhKxZXK1LdfPPNa1KO6I8vuBjB8UU0Pz+v2dlZzc/Pa3BwMIRMMSg9WlUoFMJ+j3a7HU5XHRgYCPwX5+Ay3swREQ13qEnL8z1AzL/zvwvp+LMHkrNx5513BgfcDa8YmJDWRiNiNCim2CiVsjdnokTje/1vF9ouh5BxfE4qA9ELkEmpc1iSK11X4q4kfRwkpSqXuPzAEWadkDuMPIT3WOc4HDHCnzV2PsYQ7XGHwgGiLON5vXfwnNjx9mvdwWAtZqGs0moa3tTUVLjPnSmirb4JHRmGHIydK8YV+e/GZ+wYSelKcm4c+H2u0/g+dkTicYkdvnh+2JzujmYsKx9IMmVvNDExkdrTh+GK886csG4dpY+NQQcpmF/0BOT6Cj53UIp75+bmArIOb3rb4GevlJQkSXBwHYSDbxYXFzU7O6t2ux0McZc/XV1dGhoa0qZNm9RoNMIZYyDz7HFwe4h1gV6N5XTs6ALeMaYxT/raQz46sOiywe1CH9ve3t7wjq6urrA/ijHhfT4X2BfxmBPJVD9koQAA8sZJREFUiIuRxOsfPgHgkFb3Tc3OzqpWq2loaCi1dtnTG0c53e7ycXRgJUv2uGNGBN2j7Fzj+2i9cAmp9oeCDmhEwwUs5N6fEwiclE7DcQcF8pAVTE3oenh4ODCmh7wbjYZarVY4odEnwTf09vT0aHZ2VrfddluIYvjiwVjESeG9xeJq9SM/8CouVevGJQxZrVaDMOrv708pMzxNiJQmBA7MjqHMOLN5m4iIV5zw673ajAsy9oDAhJ4qRUUnV3B878Y2/+NoubfujO9z0dXVpXq9HgTDzp07Q3sQFJLCO3AUJIUIDcIAfmo2m0G4speDjWLkXEod5SIplDmGdxwlcMOBvlEyFD6Iedc372dtfHXk29eQGxd7W3OHC83Ozq4xWt3piA1Pv05Kp/jE9/k1biBkKSr+dqPZlRLrEsfZ17a3o91uBx5k7XEtis/f7+/zVBj4Dl4CqSoWOxu03fmHp1zWcr87645UOkrnxoIrZJ8bV7bx9/H88H7/PnYUfa5j3YFREUcCXb643vCS08ht0Fo3fMrlckCNC4VCQDDRH8hSSamIBv1HdvsYYTzFgJkbOLEh4fJ0vTFyxyd2liF0EyCb991lUh7V6JCvR/YoejTR07P5zrMh1ovwudMbzynP5r2+/ngHPIle8Xtd1ri9486zRy4424d9idgD8Cl6tru7Wxs2bAiONE4MRjv7EZ3/vFpXkiTBafPx8P6jawFW/ceNaF//noHiciQLyPDPfM0ha7HFWP9EcDzllGuZG+cJr7blAIbrdkBk2kCBoHq9HtLmkyQJKfO+Ad2dCJ7l8+1AjEe6sFPgZ3eWPZOD1HN4xZ2QQyUXDmjVqRj5gqHj3FEmDwHo6BETSdTAFQ1Mx3W1Wi2gUe5x4yzUarUw8CgRz9lrtVq69dZbNT4+HpjGw5QYxLy7t7c3RAQKhUKoeEJbpU5YlraSx0vOZHd3t1ZWVjQ5OZkK34NkwhyuND1PkEXFcwcGBjQwMBDucyEGgpEkSfDUfcMiSBhpBbt37w4pLI6WOMrg6AphQIzzuOQjjgDzhwCDF+CH7u5ubd68WXNzc5qcnFStVgtVnVDihDFBLlw4wSNUgcLQA0FAyBHBcsHI9xgUCHjQZQSMo4WOfPjp4wgaomTwTmxYxIiFX5OFbsa0N0fk/kRuaEtpoyxGsGIUiN/uEMaGqf9eL/Lh7+Vvv9efhxJ3w5PPORcH3nHF4I6/G6+sVV/38KE7M76JEWOXkty+d4goI2vMK/t5ukGMnrmzkeXs+W8f03geY0M6vnY9g9mf7/LGHTifwzhigWExNDQUojq+/j39ATDGdUnsQOBwePTaDSjGl3EDoJE6CC37cbIMRf8/5rPYEItBiDgVDeMhdjDg0dzJSBMgFRFAL3fq4KLLb6mzsZo15Pzuf6PTnVdiJyReKysrKyGrIU499uiA8zOAnFdBq1QqIQXKU2jgQwxxjOzl5WXNzs5qbGxMlUolGKUAgdgOEG1xwx+55WmeLnPhX+w+b49/78CJyyhkIc/jvbHMbjabmpycDIVxWL84kx6VxDbys4jIssFm5X/IZTPXYPvEMsJlf7u9esYa7aVCXVYfGEO3DeAZ+MRljTsNfO4OBWm7tNV1EXbLoaADEtHgdywQ17uexcwCjSfIUQVH4HzBl0qlsLGbAXNHpK+vT1NTUymmxqMslUqampoKh8fB2AgJjHN3LiSFNByiD44QuIJ3JYSzMDExoV27dqVCoJT13LZtWzhIxtEPR1JceLHY2LPipWWdkRCgRBo8MsTCJnLgKAbEYvOIhEehGFfahgPAuzz/lM1mzCmorOepUqcaA5/KUowj1TkI9/E5C83PPuF9S0tLGh8fTwk6UrwQJK1WK5TT7erqCk5OPB7wL04f6DJpZ/AfDo2jFvx2JChr3TwQiTFwlN8NTBfiPoaxcexORkxumPFOfw6fZckv+MUjpq4MOdQpVjrrOTxxNIu2u9HrqU2k/aGI3KiXlEqjiB1Zr7Djz+Z7b1scrfCx8zHxdzh5vzzK4sYS98aOu88vY+FrhLHhWuQo5NFJlxEYXFzv9zKvjpoiC3kmkUtHoOMxcpmM7Ip5NqbYkYiNCv/e76Gfzlsx8kx73PBbb108EAm9JXV4Nt6v48Y1QJcbdcj/GBzCDuB5LlPcSIa/XXZ52jFrFTsAgGNubk6zs7MBoeY+StaTmiwplHZ3ewYHisMeJYXzo2gT78JIZyxctzsfAqy6DecOl8uVOMrn4F7sfDOm7mD5d8g3ZCL9JzLESeGDg4OSFGwdnjs3NxfanySJhoeHg6MGWIiccNsOu8MzS+AHdxQw9BuNhorF1RRxB739fufJeAz4oZ1Z48Tn7mQwFvG+DJeD8bMOFt0jRyMLhXFPLEZx+R60KBZ+MTrlixBGwoDFkHRh7ClITAyKlQ3Qy8vLuvnmm7V79+7ARKTCsCBBtDg0D2SASImkgJ7TF7zdZrMZIgPT09NhozehSp5PuTVnBKlj3CNAHIVzgYXR02w2U4aNGx9ZKKDPizsnfq8bFaD0ca6kCw3P2+R+F1ruCDDGnFvR09MTSkTynhtvvDH0t93ubFZjLDAmyH93HqSWPhuyGLuFhQXt2rVLzWZTw8PDGhoaCo4YvOKOyuDgoDZs2KCJiYnUWPo4uUPlG26JFE1PT69BL2NDkPHiO/+9Hh1ORkNsaLkCc+PPEdsYPV/P8HVHJeZtfzbX8tv5P87xTZJOEYuFhQU1Go0QpWNtxtE2d4ZcwCOb3EnydB2QOIAX9lrEzj6ygHVHFIMUStaqG/b+m3FnDbrScwfFgZA9KSiXMz6ebhz7fMQORyzL3An1MXXDn3eBDJOegHwgMsE4gTD6Wm6320E+gegy9qTM+inA/k5HO2N+9DGLdSSUxYt8Ho9tLE8kBQSXseBars8yYh6oFOsx+AJDEWfTC9T4HLoOjte4lF5XDjpK6TSqeB174QZHqGdnZwMI5+3yNEu3S5rNZti/laWvkGk4RO12O6Qau1G/spIu25rFu/A6aZ2elZJV0cjlgQNFWci687kDOP7bQQr2xriT6GAothZAdKvVCmg/GSDYeKSQ1Wq1sAeXcfQUeecHbFLmxm3QRqMRnLbl5eUQPYLv4jGN+dXlr4+7k5/n5JvCAcfQPXzvwMXBpgOWOgWxQHwROQpVKBRSIcIsBZ2FuEnpvFcEM4hdf3+/uru7NTk5qYWFhbDh2RXvXXfdpdHR0ZSDgfHLRJByhZLH6ySaQV/9lOClpSWNjo6Gdy8tLalarQZjoaenJxjWGLfNZjNcK6U3lblX7HsGXNmhPFGGHopjoXskxyMk8ZzxP142Idl4I2ucOxrPHRELhEdcDtaNCEKXCJCVlZWQLzo+Ph5y98mnxolk3nkXh3WR0uaOLGPowmlmZiacPr5161Zt2LAhtSlveXn1YMZKpRJyV3fs2BHmxA0C5y+cVa7zKjg+xu7Y7emzBwr5niTIAQbGj888bSQL6PC8ZyjLcI0dGH+3r3FH3t2BnpqaCqfOI9xxFNzokDoASJIkqYOz2FfFPgrknUdnkS84OLTXkbY435tx889wJOi7Ow9Zig4FGRvO7iA40XbmiWtidD4GNOLnuixzgyR+jjsrjCXRXeSuH9IJ+aGL7XY7VQLUZRf3ucG3tLQU0E7kjz/fx4z59kqJtNcdgZh/s+Syj5vLIQcxQKhjMAle8fNpHugE6u9ApDuyrdbquT7IdSh2KGIedOOX77OcTzfIXb6VSp1zmBYXFzU1NRUi/Di86EJkkTtJINikD9NWovfweGxvZUWD3Umnb/z2frhMzQKBYhDN1/y+kK8/fwf/u93jqaONRiPIRaIIXpFrcHBQ/f39mpmZ0a5du4Jdypi4XUgUieIyAN30x0FS5C8RaK8ENTs7q+Hh4cAf8CDj4XLT7Thf5+4M+/XoIAfZPY3XgezY9jgUdI9Tp6R0NCMWni6sJYVcew8zeZlbFmFWyAqFLKVRYBDvlZWVULqsWq2GUrdTU1O65ZZbND8/HxY+YTYMFyo/ETJjcXr1IN7HQVALCwuampoKZz14+J4yajMzM2Ez0KZNm5QkSXBISLnhHeyf8LQxz1n0yjO+KV3qHLbnxhjKHzTPlRS/GWueSR95ptTZWwFa6HnLjuz4b8bJDRuiUnjU9BXEhrYeeeSRuvHGG9VsNgOveDUf5q9Wq2njxo2SOt48IV9PJWEBEqVqtVqamprS0tKSZmZmtHnzZtXr9RAhI8eTyMj27dt15513hjNPaEP84ylk8CopWbTd14v/nYUWrUexkXd/Ju8v/Ol7FpzP4CHkAjztc+CGaNY7YgcFitFI0ClfMzi2rN84xzVO58HQ9xQHqq2xhtxZd6QzK9LIODj/YGDzDNYqIAXXA5h437w6jm++9Lxu+u7olyPCtK/dbqf21fFcl9/S2j05WQ62Ozj+2+fKv3MnBKKaHHJ6fn4+FdVgPLwv3l4n5sllFjLajQ7aiFPnEV8HPLwPznvxXPv33udYTngqhBcDcNmbRzQ65HPt6WXwU3yujZTmOZ4RG9o8J3ZEYqACoBVe5H74a2ZmRmNjY6lUTHeK3LnwPnHIH7YE7WAvp2cUuJxi76O3d2BgIOy3zHLy47Gkfz42/B3f49fGfL23eWOc/Dkui8iQiG0P1q/vvyFNyvexkpKGkU4/FhcXNT8/r2azqVqtFgrTePaJ2xzwF58jhxz87enpCZvGY+cv5qHYaXV5EjsZHtFwPRKnuzn/HWw6YHs0nNyzjRUAaI+jP54CICml0CBPT/L3Etqan58PYbAkSYJBfMcdd+j6668PyBUKFaIMGegzHqYjQ454UtFoZmYmtJFN0CsrK5qZmQmKzfsireYENpvNgFigrFD8fqolbS0WiwE9k5Ty0LmGtrtD54zJXGCguZJ2Y4eF6VESUgj8UD2EHM+OjQ4WNHPm7YpDdThFbJDCwKdUJQfxYRSVSqWwz8ZP/wZB9DJuEMaD50RWKhUtLCyEfTpbt27Vtm3bAmoMfxIt2bZtm2666abApzhN8DM/jhr19vZqZmYmJUDdsYjXzr4IW+f9w4FiweoKmzl0ZQVf+vky/qws5R9HWd2g8+toB8givMI8T09Pa2JiIrXWnLfjZ3tfPHLhjgHv8Lb75+7MwJPIKJ6FQsG48EioRzd8Q7SPuadlgpLGTkbsrHk/3ZDIQvNj4yt2qh0UcScjVrJO/k5fT1yLnAB4IN/cwSV+U1kPWeoGaMyfklJlIUE5PQpOf3xs40qB3uf4M8Yxy9FYT5Yw55664qBITh3CKHPnmjFaWloKVbxYB7G8cH50Qzn+POZ7dzKktHzyFCnmzN/tkTbf1ItMcXkgpYGDwcFB1et1FQqFYH+4rq/VagFdX1xcVLPZVKlU0vDwcOBr3+dJ29cDCWJ7IqZYpuxN7zkg4MZylozwqCRyD7uFscOGA5z1NUokp1gsqq+vL7V2cBoooU9kE6fDAVgfI9+zg/3g4FlW+pKPX5Yd7WMAPzM+8JNHU6ROQRFPm7pfpE45xQaUCzgGt1gsphaaC3t/Bn/DQFkKulQqhYPv8ODIr+P/m266KWwI57kYyuydINzsShfvtlgsBhQAJ2F+fj54pjAiioyJ9L0EXV1dYUNWu90OIU0UVb1eDwrKxxIv2UuoukHNNfFGLCldqUJS6u/4Pfz2xcgGbTx7qmW5AcF9nvpCG91wcWHjIV5Ha3EiEDiLi4saGRnR2NhY2OcwMjKier2udrsdwpcLCwvBQGCBkTIBuhujDBDCmrG58cYbdeONN+roo4/WMcccEwxZoiODg4Mh0uIIBO/2PSQIBs/td+QlXjMPZPL170rJ0RaUgSP+8TgyJ/wP/7li3JPic0Le4NCUSiVNTEwEWYJAd6XnhjnrNM65duOfPrjSiZ0KT3/gGldaEI4RffV0R19XLiP8b54Zbxr38Ynlh/9PP9zIwimC5+mnp0C4Iezj5yCPR7TcCPf2Qx4VkhQ2xWK8eelbByxIU3EHkshF7Dj6OxlvDm51h8MNJ+aLnHmnLBkQ68PYIcn6vN1uh/OSHATyucpplfx8GdaIpGBDYLTDM7Hc9vmJ+diNfeflcrkcSrNzL3Kj2WyGVGrWQa1WCzIAsE/qzCX6iQi9y0ZsEk8hhi96e3u1YcOGgNzjYHR1rZbb55DiQqFzsDCFW7xPrGmps8fEx4a/Y0Andqy5P4tiuZU1B/zGcSTKwHfuECFnmFs/VwTbzA9p5nPGDtnlaVjLy8thrx4AKZkRLksLhUKYf1/zAKZ+LfrC+xfbcFxL31x2wce+hzV20NBfvp/lYNIBS52S1gpIJ1+IUoeJ4pxqmANlwA/Xw6wbN25MndqI98rG35/+9KeanJxUT09PyPvHU+/r69Pg4GBqUzGLlgkslVYPovv/2PuTHkmz7E4PP2Y+hNtsPsaQWZnJLLFIAmQTIEFtWiCglZa90lJAfwXpI2illZaCBGjTCy20EfQBBKEXIiQ01BqKYpHFYrIiIzMyInx2t8Fns//C8Vx73uOvRWb1P4dgR1zA4e5m73DHc37nd849l9M6r6+vy6JnsXFWB2kmaQ9GEIuYMIuIhfJjARiYO5ORGXwUNwvEQAOXHH2eYz5R8EzguslM2JcNDTN7DlWZz+eFOWXCe1MabfB84Nn+baMRN6eZuE6nE5999ln8/d//fRwdHRWBwxghADhHBYPk7u4uut1u9Hq9stAYMytq9oA8evQoJpNJnJ2dlZCqi4uL+NM//dMyDoz/7u5uTCaT+Oabbx7MUea2GRXG1oDJczwzWzbU35eSQ5MajcV5NI5frQuz5DdrgzlqFt0yxgprGUPO+mMerq3dnzLN4Z92Q1tBYpx4vwVAJSIq65b1h2EB48Qct2fXJA3zxfvHqCds5Hz+cGOmPcw2VtwHdX2TDZdlbGX2FDH3HT9tryfFz8iggP4wWeXr/C6upU/9nedR9vBgSCJLGC/0gdvO3Fym6wjbBZiSjII2YbDWeYsyCcFn1nkRD5lf7uMeh9bm8sHYWBRHN3g8IdWc4p5C2DDrxnPUmMayhgLINLa5u7srodUYGBi3rFf2hY7H4wohw+ZknuNUzV6TjcbC+4EcspxE7zJvqKvDB23E0MZM2tDuHEZaRyDVGQuUPL89t+vIoywLms1m0ftev9bLkL9EUDCeEL+PHj0qm8LtMTKRQv8iU5C/7P3sdrvR7XYroegYJ+436yaXjBO41uudz7IHgzowptQx/yZ6pG6P5A9RvlePBorBAjN/hwJyfLAnn0MIIhYbKW3NbW5uxs7OTgHZWIobGxtxdHQU//AP/1Bi/plMm5ubZYIhSLgmIsoziKtnQ7LTzbVarZIizSEGxP/yOZYtCoz3sS8DobWxsRG7u7uxtbUVvV6vkl/ZC9opXG1sGDwwaez6I1e2+zQLIguJ7Ca24OBzgB7MACwJJRuFHjcfwkexcGb8IxbCeXd3t5x1ghep1WrFcDgs/YxVTqpiQiT4m7HBs3RyclIMnPPz88rJoNPpNP7+7/8+Li4u4p//838en332Wczn8xIq99lnn5VNemaQ6owO+gvFRruWga73zciIWMwxz3eEoRMl5LSRCFfWHPc42UFW9nzu3/6O8WL+Mq+Io2XcPEdRIJ7n/p41Rvs4W8FhiLSBNcrctSKyojeAziE+fA977/6izQYkGPco57eB1Nwu77+wcrSh4ZLHxMDCY2/gkO9xoX+yYua9sJY8H0ImYmEcXV5elj13jLn3u/FMhyLl8bC8Axje3t6n1fRBgvP5/X5C3lW35g2eqLd/eyz9nIjFXhAbmNnY/lCihNPZ88eBvGZ4mUuZcWd+G4Bb7mddCzlmBhkPaZ47vBedwn7PiCi4CbISo9jv5342MjsbGZiA5zE3fO4TdUZnMqds9GdjhJI9P9lwzjIiF7c/f+97TSJRTO5ZJjucCeONPSzGRhFRQtcnk0msrq6WE9VN8nrMeb/x1fX1dZyfn8dkMolerxeDwaDsSzZuqiMtcnttXNWRkximNha8ib1u34b/5ufHKN+boVE3Mez2iqjuj7B170737zyZV1buswU8ffq0KG1A7qNHj+LVq1fxxRdfFC+Dd9z3er3Y29uL8Xgck8mkArA7nU70er2YTCbx4sWL4sqkXtQ5hy5htHhjVqfTKanTcg72wWBQXFrE03LmQ6PRqJx2beWBe83gw8CM52OUeAEyDnmR0Ldkarq+vq6ETnhM80ZsM662uGmrGZ6NjY24uLgofQ14NKhxhgvGLOJesHY6ndjd3Y2Tk5PCOhDqAOB//PhxYZvIjU3deFaz2YzhcFgJXTs9PS3hdmw6py2vXr2Kv/qrv4qNjY34xS9+UULg+v1+fP755/HLX/6ygA8EGgIMA4+4cBgpxuN9NCiWFSv7iKiMHXPebNx0On2Qg9yA3v9HVMNs8ncGdcxp77OhLt5nxJg7RMX1RglboTiLG3u3cJej6K3QkV/O5U497Bn12vOcoi8cquX2zmazEmKArKDPeRZtphhQ59A1X5O9PtSPPqLUKVfAGPfUGY0ZrKBjKPQ9z0Ke3dzcxNHRUUkIkT3lAAqHV3mD7Gw2K0SRjWPXyX1N/vx2u10OVGU+t9vtGI/HlXYbaNJ+v9vGF3PERjn1wMPvOW79+qFUmeCIBQlmUMfnNryXGYYUZI31M+HPDpk8ODiIs7OzyknS3hTeaFQT3/BOEwvsR0X3c11EFKN2MpmUe5zh0olkIEMJ+XNa/xwCusxQWOYB8Xy17PR6zrJ6GfjOz8wywe9xIob5fF4wFp9lgsbhnISIz2b3p3ozLqSltWzDsMn6xR6D29vb+Oijjx7oN4dae365Tyzf+Zw+yOvZRjHto03GaRgX6JRsRP9Q5XvNOsXf+TMmgNPv0VCz6igxMwZ+ztraWjx58qRkB2CRra6uxsuXL+M3v/lNYa2wahuNRmG9fUgN95MG9+uvv443b94UkAhQNDtGHcir7tACDpTLng1iAFdXV0tIz+HhYUQs9nDQTp86zqQhxtuuMDMKTPx2u10mm69jLByChRBD4FxcXJT9JgAa3mHwwaR1SIsXtwULRgehTMR74sYlthHDz+eBwNZyGN7Ozk7c3t7GwcFBMYgePXoUx8fHcX5+Hi9fvoynT5/GxsZG9Hq9AuZWVhYnI8PawhLN5/cejgyuzP6cn5/HX/3VX8VwOIxnz57Fb37zm5hOp/HRRx/F119/Ha9fv66Mh+P6mecwmpl99BqpWz/vS7Fy5H8zLnzH546XtdLJ/WahbA+pf2f2PrNlzA0MUZgtDPLMeiM3WD+kv3X4wuXlZYVkYf2vrq5WQqFms1kxmlH09AOyK6KadSqzX6xlhyrSHvrYIZrZEHPJ5AX9ZVljhs9jYiC9jLWsU7J1a4F7Monlwviw/pk3GGerq6sFdCHz6ANkY24PAASPBP1H3W2AWTYy9g5FdXhdXaFv6uRCHfB1HfDQ2AjKff2+F+OO29vbmEwmERGVTE0RD/chUVhbea9UnYzB+8DzR6NRnJ+fVw5RI20yRBW4hjnGHF1fXy8gkXVL3QCYt7e3cXx8XDIk8Tn7LO3xInT87OysyJpWqxV7e3ul3sYQJiwtKzLhYT2XScncj+5vA+Kc8ttRA5YBGfTTTxsbG5WkOeA5r/tGo1E50JAUtvYOGQuCkZDJJA5wuJujGcBzjEO/369gqkajUfaaOnok95kJokw4uC+zl8KYw6F5mdT6Mcr35tGIeLhJLQtLJg6LiO9YSAySWXGH+Ozu7ka/36+cbdFut+P58+fFyABMR0TloDy7iDY2NmIwGMTd3V28evUq3rx5U1yMdrU5ppdQh4jF5CMNHvXtdruFxebgHN5NpgKY87W1tVI/h93AgJqpiIji7lxfXy9sA/UApAHC2AeBsjXrh0EDMLZRUWfkAegYSxsZxLRaUfN86sXntMkeG9ID82x7YwBB7XY7zs/Po9/vl3jWZrNZQrhghXw9Maw2FFutVpyfn5d9Nuvr6/H06dO4uroqJ3ey8LxZ7ODgIP7qr/4q/sW/+Bexvb0d33zzTfR6vfj888/j8PCwwgq4L5kDABTiaCOqm1zrDPP3ydhwX5hd90Z+QgQIN0RxMlff1m/Zk5HZs7p7/Bnj++jRo+Il9UFLFIfijEajEiJlpjyHgfp+nut+YU6SLhk5iEKlPf4us95+V2YRTSpk7wBjkRWaleAy0Jv7vs6g9jtsZPg+f+Z61LGhPMftRx4hz548eVL2YNHngEGuJYOgvayMIe3PjGluN5+RVMPprvmOz23QUNzW3F/fZoj50Nlcpw/lvjg0ESLQSWoiqvIBMPY28gJda2PTCVQgDDl0z3IPfYSR4XXF/IDUILqB+5BF4/G4hGIRkbGyslJS65swxXsDERoRJdNWRFRIx/l8XuQSdWEe534wUUgf2ljzPo46mc38dhia621Cx/KENcE9eLw9bnxPkhtwIeufbJd5fkREwXTeqwEJTMQGSYK4l9BuvEnUlVAq5Huz2SxpbuvkYC7GcRkzMEesS21Q2CPD5/9kPBp5wrhzssuHSee4ZAMArFYWm8OqVldX49mzZxGxOKdga2srvvnmm/i//+//u3L2xOrqagwGgzKxqdPd3V1JSXZ9fR2vXr2Kg4ODymZh55tnEpAu1e3xHoVms1mALZu0jo+PYzQalX4BLLH4t7a2SvYk3JUII/oz59Fmw6LrAdhiATDRvQnVixlPDIsSC97xe/STJylCzowIgMeTlrGxEOD8EoxH+hgrnxR7Z2dnlbjT2WxWhGGz2Yy9vb3Y398v7DLXEKIWce9SPj4+jvX19ej1ehUmN2IRywqQ2NjYiL29vbIxjz6AXZrP5/Hy5cv41a9+FX/+538eh4eHMZ1OY29vL4bDYbx586aMm43iiEU4EJmwzEgYAOd18z6V9fX1Slpq5r03q8EaEfMOMPQ+KcdWe/26LDMy6mSWFabBNXOfcCeecXl5WcIhHLZngOB32aNgZhNZl5lBQvxY68g7y0unt42ICnhCnkYsvCAOVajz+lBQrPQHc9xr3PeZzWQ8PQY2aDJIyeDBsivXa9masZGELEIurq+vx3Q6LWyjCRn6i31b9K/3E/od9Cm/3W7krNlz6sRmdP6nz3J7aaMJPIdO+Af5i86wp/xDqRbmAmG6eZNuXgsm/JbNQQx+yxzPm2azWTYZI9cA+5l8MECOWIBsdONkMonRaFR0ytXVVZydnVX25lAAsOhzNoBjcKDPI+5lzJMnT2JjY6OA4Yh4MPeoGzjI89Yy1UYZJGNdOGVe884yau91vp7/7WEkLJ0oCXuFu91u6V+MMb5HRpPFstlslvBS5EDEIhIHTILsIGIjkz6WYzc3N3F2dlaynaK7wBkQ5SZJ3FeZ2Ml6C51pcsSeLstY3v1PyqORO5TPXHDn5hg+gy97MvLzd3Z2ot1ux9nZWVxeXsbjx4/j7u4ufvnLX8bx8XFhHRlwu90A0zDtb968KWEvxM9GRNkQxgZwA22nTcvt4xqsf8KSIhZGEeCWBQ6QbbfbBbScnJyU6314n5WQz9pgoRMahlBxGlpPVntJmMxsVALoIDhYuMR20pfUnTF0W5nEZvA85gYdZudYoLSNmPbJZFIyKJCKb2NjI05OTko78e6wmGjHaDSK169fl6xjZOpx/6CYmSN7e3tlDiBAEOxff/11/Nmf/Vlsb2+XQxifPn1aTppn7jKvCfsCAL5N6Wew8D6V0WhUEdwoztlsVs6jOT8/r3gUe71eAYBZ8UTUhzD49zI2OCKK0ZrZcj/bhjoGKqmv8bTYSLBb3fIRecJ8Q6Eu25DaaDQqrnFnyTP4h7HDIPeGaF9b11+Z1fXf2ZDwvbmPfI3BdFaQZkb9nmyM+28/28+zLolYHJQIkQLAX1lZKXvw+J5Ul+5zZOXq6moJvWJsqJOVN6QRhEwmuXIfZc8F/7/NOGAu2djw823AInthVT+U+4KBwTqJqA9Ny+uE6zJTz7XWrcgHeyY5rwUCzeucOQoZSbm7u082c3x8XPZcYExiDJ+dnZVnoPN5BmuAyAtwx/X1dSHkmCedTqcSZYF8zXLLc8/9k70O3o9rkpJ+WwZycz/aE8K91pfej2BPkYE0/QbR0Gg0Sp86VBec0ev1otfrRUSUPSvOHEZ/WLfjwaKOmRjwvggOROQ7e2GyrMt9nr0VGBPuhzxn6zwZ/6Q8GpSsvG1xWqlG1Cs0AzU8HZSVlZX46KOPitXX7XZjd3c3/u2//bflBE0D0WazWTIHOFzn8vIyvvrqqzg9PX2wGYo4OSxNhDUgPGKxMRsgCSgAsAIyEGTtdjt6vV40GvexgByGc35+Hr1er7jdMHas+Fj8BjbuU4QJYUJMZCaT3fTca5YAwRex8HKcnZ1VlHRmJxgL6kB7KAg1jJfhcFiELWDJdaTvEFB2KaLcySrWarXi9PS0zAHiXHF5ktbNAp6xe/nyZbRardjZ2SmCwKEMjn9n8+ZoNCp1hvkYj8eVlIA8z8YgsZu0B8aZcAzHTWYwmoXK+1AwMuhDA+Pz8/M4PT0tJ8TPZvdpjJlzJF2oU4L2IBiI+rPMYMIaeq8OhXXEczFAMcSbzWaRPYRN8WwDSr/TbBOF+cJeMQx3KzMKc4w5yXMdwkfhHczROsCUjQbknd+3zKizx8Ptq5vTfJ5BXfb6uD6AetcBQsT/ZxaQ37Cp7OPa39+vhMsC8BxD7VBaQCLx3w4/pb3WWxir9C1kC/VBDmRjI5f8meUl7c3eIIgeP8N7Bd73wnrKRoSZY+u8OpnsPuda1qgZ7YgF0IPU6nQ6Rfc4BJHrIVXYv3hxcVF0itcbABkgDptv+eeNvxAVkCnoMYzSnZ2dB3saHFVCW+xhQEbkvvH+ktzPtNVeAp7NmuaZWXZnkB2xSABh2crRAvQ7baH+EJKse+/rms8Xp8O7Do8ePYqtra1y9pm9HcZt1MHEj/uNxBQR98mDIDg2NjbKfqE8z+qMB8s6hxrz443fvscGzz8pQ8OTkOJFhkL0oqIjsLbNijMJmeS9Xi/6/X7J3rO7uxvj8TieP39eYTk5fj6fZLm+vh7j8ThevHgRzeZ9/BzMNR1ul/POzk5lnwEuT7wMFIwNJg/19+Cx4RlwzBH2Tj/GZjAMMgym7DKzMpvNZiWki3ANx35ml7+BAM9lwXkRWkjZSGTR+nMbP5ldMGOBYZbf44NqULgG4jY4Wq1WzOfzktXr+fPncXx8XJ7jeGrc4ihbPDVXV1fx9OnTePLkSWHM8cgQRsE4knHCOan39/fLCaucDMpmLxSLQR7Pnk6nxegwqKTfDMbyOvr3vXgOY0QydziJezqdPkiccHR0FJubm+X8mkxs5AI4s1Hha7mfOcP1EdV0lnjLvMnbz8CN7vSSvNfGf8QiQYHBJ0SFwxXJyZ6Zf3sMMfxNoHgfC4yi5ylty8QPZZnhRltZb3nuIg8z8ZSNrAycssHItZY3fG/iA13Cd54LlqHeQLu5uVmSSyB3rq6uShgDst97ayBCIKVarVbZl+c+8143PoNRNghYBmSXGVq0yca1+4hncnCfZe37JFO+a8lz3n1sgJznIp+5T+uyCPEs1kOzeX9Kt/c8ZE/3aDSKw8PDklrVMou5l71vlmVEQ0Csos+YF6T2BVchc/r9fsFgnn8ZC0QsDAn6B2zgsGzrYu53v9HHNrbdDnsjIGQo2bDOcsZGCuPCmgSDYZRRf7czonpujg0Iwir7/X7x/jBXuN5GprGvEwBwHcmIIMrZc8O413k4jbPc19mbk/soX/NPztCIqGderNQc5sAkZfFgEFhpRywm5fb2doV16vf78eLFi3JI22AwKIwVFjtxhs1mM16/fh2vX79+YAGbZSLUwFYqITEYLrDnnoS4pAHth4eHlU3LgGnCpNrtdjnlutVqFQBhNz1Kz+w/k9lAI+IenHCYmBexF7bZM57nBQVIp130083NTSUzE+/Lz6IPWdRsnCW7C5vi6SdAklk2Z89x/fCQDAaDMneePHlSFiKb7Ofz+YP0ung4eGej0YivvvoqDg8P49mzZzEcDks7HWbhedLr9QoDdXh4WNlc12zeZ+44OTl5wOgyh7yfYHV1tRKHyfh4sb9vgABDHmMRJUw+8/F4XNl8DSvdaDTi5OQktra2KsCUv70+bFxwDSUbE95HlIEgdWINcE82NmygI7ec6IB1CrGBR5J6O84XuYAcYY1GRGHV5vOFt5L6ZEUCQZFBVERUwIHbQMmgweC9DkyYfHC76KfMyNeNRWZBbRDlfsygPmJxUBdy2c+lz6+vr8tBncwvAxV0AHMBo5gxcEiST2Y3IKN/qTcEEX1QFz6Sx4HrMqnj93jMvSnV93wo92V7eztOTk7K/9mzbPlgpjob0/wPvjETbzDN3xiq9p4Bok9PT+Pw8LByrhPPw+hF7iALkG2eTxFR5I3Dhgwyra8pAF3LSssLt9uEo7EB3mDwjDECxbLFRhJl2f/GBRkgu21e48hcMN18fh96TrRCo9EoUSX0bUQUUtPeBY5AwCg4OTmJ8XhcIiBID+x2udAfbBrHaz0ej6Pf75exIoTKxfLORm+eF3mtZxle9zMYDOLHKN+boVHnHrNVyUQhJCmDZysXW4Wrq6uxvb1dBqLb7UZExNHRUdze3pZd/CgDFCqL68WLF3F8fFyeawALMGZTtgU2wAfwQVpDgAOLfTKZxOnpaRwfHxdQawXjeNmNjY3odDrx8ccfV/aGeLMRYIqNWtSh0WiUtKzuNy/arFzqwh7MBtiKx6gCUHGt3Wx1bHBeULAa9C39hwsPIehQJwtyu3kx8NiLsba2FoeHhyUd7+bmZszn8+L9sZBmPBF6tHt19f6sjS+++CKGw2F8+umnJRbT7NF8Pi9zam9vL7rdbiUzkJUH40x7UTq8m/0aeD88R7xWMsB7HwpnE+RDhsbjcZydnVU2/XMtAhaDnnWR52OdwF/2nRnriMU6ZE1PJpNSRxvdlDzPWNN4VN02r1GUuxn5iKphQF29ngn9i1goscz4W54a+LDmDAr4MXOY5+MygGFZnkFaBhnZmOd9GYDk8bRxkg0b95v7yiAjg5H5fB7D4bBytgnyNoMs9Mnt7W0l/ai92Dc3N2W/HcYfxexiBkkO+6gz9Oo+5zk2vJDhFDwxGSx+KPfYIZMOuf/y+OffFOaz52WWOWAgAKY3Ap+fn8fBwUFMp9PKadQQMOAAMITnMV5g5odPxEaXgomoK/sMeDYEBfPeoUD0i2VlNsQ8z02C5nVaR+74/8ze1+GVunVhAx65iyzne2+QRgbjKb69vS0pbiF8bLjxXHQ2aXPpe75nbIfDYckQmtsN/uh0OoVIJsIFjOQ1a/mXjYYsi5EhNsb8fe5H+gxs/EOX38nQ+PTTT2N3d7dMoMvLy3j+/HkR1HkCMViOK6Z4MmaXoxcL6VyJUfRZDN1uN1ZWFuneHDc7nU7jV7/6VXEV2rXJe9lIjKLIDBMLFEUB8CUlKmw6lm8GjzluMCKKpwXjBjDr/OwYMgYlGGvEl/K/QbpZDnsd8oTlvjoQZqMCkISb1p9bMBto5HAtj3dEFMCOUs95ranDyspKXF5elrGjj5rNZgkti1gcQAOQo+/M3iBQbm5uKvfhoiacinA57iNzFXN9Z2enzB28II7F5z5vCnefkA6Pw7reV+PCBaFNTvKIiOl0GkdHR5UNuygA2GdCDnMIAddi+Jmd8/cRVVDvsEiDMtJG5pSHubAeMlvPerdCxHNjI96AMntJmDs8x2vY6zGz8vnwS/YXmP13n9BfDkPMnro8V01YRCxCmbjfBIoVL33pcCJ/l2WTmeE60GICib6mbchhGwD058rK/QFqzD1Aie9BThOSYgDl8Df6DuDgfkC/EKYJIbKsne5fGxqeK3xuoMU8yLHZH0q15D7JDHlEFDkeUd3H5fuZw57PfM7asJEPxjg7O4uDg4OYTCaFfDOu4odxRBcCli3viILIOodrHN3RbreLkeEwVeQEocnoLBtXlmG01+1jXtqbUWdU5L6v+8xz3u+l0FeuAzJxPp9XSCPWGhEaYAqvZ/rR/Ut/R0SRnc6CCdakXF9fF09Hp9OpyFofVOxoD7CKCRi+oy8xAo3lciis+6jOS0pfLjPcfujyOxka/8V/8V/EX/7lX8bz588jImJzczOurq7iX/7LfxnHx8dlkCl0AEKXRYbVxkBl1iUv4rW1teIZ4JmdTqcA77u7u2LBki7z7/7u72I8HlcWG4aIT8CEBfCmP5SMs0WxGXA+n8fJyUkJ6wBMeMFaOQMOEBZMUvqFLFmcGu5Un2z05If3IHysmL0fwqxsRDXnvSeXQ8AiorKhiLFxHLqNC/9tZpW2wzxHRNnYikBGcbsOKysrJQSK53mjLIYisaSOQX758mXMZrPodrslB3ij0Sj9PRqNyhygLjaAX7x4EYeHh/HZZ5/F9vZ2mTObm5vFuMDNjGfKwpr6OyzMLLFDcpx4oG7Rv2+gACODvTBkmXJ2MorXF0YqjF9ENVuQSQULaP/NuHkztUMazs7OSsYru6i5jvlsMM3nPMfC3V5IZ6VjffJ85jzry8/yXjbLTpSM2ThvZkQhWz7w7NxPluOZKFlmaNmos/zx9QbIZuLoH4M4A24bOhm4GMTl6+rqng00fsbjccXryrrGGDSjbDmX642OIHyUe0y+oIM8d5f1b5YJ2cCgeH8ZIZq8Hx32oVTL24xoz6sch8/3BoiZyDNAx3gFQI7H4zg4OIjxeFxJm49My0YG33M2hsc16xpfH7EIsUGmEK7NntD5fJFghwQ1eF0sk9z2TMh6rXn95/6sWy/uM+rve3iejexMTPNe4xA+IxTp/Py8rOV8lMAnn3xSMgYS1gaRhXHmM1F8ICbje3FxUQ5UvLm5ifF4XLJX0afGYBg7zEPL3+wZc7/lpEoR1YMOM/lgjOF+9Hc/RvmdDI3V1dX4H/6H/yH+6//6v46I+wPx/qf/6X+Kv/zLv4z/+X/+nx80lnvMvvs7FoFZA7v0Ixa5qAG/hBoQRxcRhSVqtVpxdHQUv/71r2vDLrrdbuzs7JSc0kwKFA/KxazE2dlZ8VzAHCC0yWpEfZ2ik8kIQLHrjYVgQYUyY5FyHXXzRKXuAAv2OiBQvJDdn17smTUE8Jk9xSgwYKkTBmY4cP/xnZkBUuO6XW8DlGxUp29XVlaKoCR1JyebTyaTSpt8ngcGAQwvbIdZyouLi/jbv/3bePz4cXzyySexublZTvLEEzGf34fKra2txWAwiNevX1c22/mH+2Cq7FHb2NgoAjwDizp259/ngmeI1I2wbGbF7FK24Gw2m5WMTOzNqgOmdcwx85txilgA5tPT07L3yWPEe5j3Tt6AQvE9JgAiFmcI8U42ChpUeP3yY68Fz0OR0n5kk99vgMv/mTU3A2pW1EDMxhl14/Mcosn92dChHlZwJixcP7cztyXXnX7NdbDStjHiPuF/jISIe69r3icHwMsMrmUdspcQO0gJEw2Ww4RSut42JCyvc/hVLjl7GYfDsm68Jj6U+5L7mmJZUQdsfU8m6yjsIzM4vrq6eiBXHB3Ab3Sdw2kYP/BURHXOz+fzktAEWcLzmKfGX5y7QdrWjz76KAaDQZkrjpQwpshyNCIqa8jrLRtymVBwv/nzZePE8zNxk5/rfprP74lgQttJVQu+Yg/l1dVVfPPNN3F0dBSz2azsy4QYJ5skhx36faw9EhHZuzUajaLf78fW1lZFNzF3CGVzH2T5bFnr/rIupNTpjm/7+THK77xHY2NjI/r9fkREfPzxx9FqteLw8LB8ny1WGwreLGVmKSswswFMdCYJoTcR9/HNk8kk1tfXo9PpxJs3b+Jv//ZvC2vNu9bW1kreYtyWtgxZzIQvsUkQr8X19XW5HksYDwvAG1YSb4k3JHNiJ5a0DQ0saDMU7Xa7AhZ8vZkLgy36zt4O6ugJxSTODCTjY8OPMcKQmc1mFWPIgi0vPAtH7qWfrPwA+TlWG7ciAJDwPPqHNJXr6+vxe7/3e/Hq1asYj8cl1IGFTwzk3d39qazj8bgwQ1bazNGXL1/G1dVV/Pmf/3n5DDcrxujV1VXxRNUtcsaR/sVVakHhg5No9/tYENIYqfSJUwabgc7MD9d7rjuEIaIqoLPCJBQzYrG/yAkGHB4YUU3vnIkR3u0QrAww+clxwzxvbW2txGsb5FBf5AXhDZZHXuPMb0J2WBde97zfc9DzkHdbbuf5Xsfkmoxw32eDLY+LDQA/L4OI/Hd+h729eQ4YsFBXTptvNBqVcD0SPzQai83YNhTtsfFzLQ/5m0O6eC+yvNVqlVBKCvM6GxZmJHNYVCYusvf8Q3lYcv9mAzYD6KzPWGe5eM8nBsN0Oo3j4+MKS45+Yo4Qgschtva0onvRjczBHN2Axzy3IyLK9RsbG/HkyZOKl4MMSuA1J1CJqO6jyN6xTCJ7vrlvMwjOxTKAOpuEoC02orjG99m4Yl8VGZ3QxY4UaTQWWcE2NzeLVxyvj0OkjZOQ1ZZ1hNEjU9APNzc3MRwOY3Nzs2Ao4zDutzGVvaF5HOhfYyv6IMtmGyjLjJQfsvxOhsZsNot/+S//Zfwn/8l/EsPhMG5ubuK//W//2/jf/rf/rdZKbTQWIUletAYOZtDyYvZibzab0e/3o9m8zzTEOQYrK/fpJF++fBl/8zd/UznzYjabRa/XK4OLEcHAcOBNp9MpMdrHx8clBZoZepSDwxwiqjHKg8Egtra2KqEgTHh+YMZarVYJ/wI4e1F6c7utdiZizmLDZznG2GwEXpNs+ZMBwX1uAWr3vr/3QqKOfO7nI5wajUYRprQtb0YzC0QubGck4hlkjmEfBftmGDNCt/B4NJv3ezU2NzdjPB4Xo4M68rvf70ev14vnz5/H5eVl/MEf/EElmwRjg8G0t7cXNzc3FSaecXaf8VmdQZ2ZqfepGGgB6FCUdm/PZrNiwNOHbLAkfpmyDFhlI8NKgHFiLA1MzcI3GtWYZ8AnHjSHOzGfWQMYVZlx9PMBEZANhJ16kzHGq9+fvRlWRPZQWqbSRrffMsX9FFGfbSe737nfhiLPoz8zweFnvw2A+KdunJk/Nk74jLrRXzl8t9vtFkBCGIV1EXITDxRjZ3LG+oK55f04pMRknhDyCwjKbeKZOfyBNjpk4ubmphK2e3l5GdPptAIeP5RqqWN3LZdd6sAeYNPXMofQhc3m/Zlex8fHRWf4WrwbzMGc5CFisckbPQHxBwFKZAP/O0WzU14zH9lDwF4i9KRxg+eWDQ76xxvMvR6t/0xmRFS9qhR0vTGj5RP9mD3LmTioGzP35e7ubjkQGaMIQsSGEmvchBVryEDeyW5IXGKvuAkowikPDg7i5uYmdnd3Kxk3kQOs39xfNgjcxjrDuI78yIaFf36s8jsZGs1mM/7Vv/pX8d/8N/9N/Mmf/En8V//VfxV/8zd/U3EBUgy66ly9CMy68BEXlPjW1lbpRDZPDYfD6Ha78atf/Sr++q//uihM6rO1tVU2jFO37JEg08A//uM/xv7+fgHPLEy7wvAcUH8Ab8QiRtB7PRAi29vbMRwO4+7u/gyQbrcb3W63GBoGMyw8nkUGA8BtttydCQWw7uuswL1w+QxBCaOSGU1CgFiU9ItZtfwdwHo+X+y3yOEhLBh7p3LoFu3GUIAluri4KJt0nR54d3c3jo6OHrBAxF7SjuFwGMPhME5PT2M0GpW29/v9ci5Ds9mM8/PzeP78eaytrcWTJ08qDDr1Gg6HMZ/Py74lBKQFLoLJgs8C0v+/b4YGOc0Byhj4NuwjqpvoDN7Z6FcnR9y3jF0mPBirPLZZPjH/PC8jFqFQMF8G8NQBQ9lEBUofwyJicdAUio51FrEAHbChrAWemxU1MggDCE8rwJb28kza7v5xP2agWseIeT67//kuf+465Gv43wZDHZmV70fu2MNjY4PrHPJo0OT05YROIFepL3OAcwoAgCZc6EfG5+bmpqTS3d7eLufrzGb3B4vWnd7u/nTolNvhfT0YFpAXZOfDOPpQHpYsd7P+i3gItCl5szh7GxgXsg1CgJmNj4jilYSUtG52HZBRJqTwMtrov7u7K6F4zHH0JfJ0MpnEfH5/HhV7EJ0lyWsmrzu+NwmZ9x6YpGQNOTEB89ryOJMNlu8828SNSQr6wXLa14FxOp1OISBZs3d391m/7J00sU19bUzyvSNLCMPCEw1ZRQRLxMLzcHp6Gjc3N/HRRx8VL5LHOpNadfKVuvg66kQdPV7Z+5HJoR+j/M6hU2SE+df/+l/Hf//f//fxX/6X/2X8p//pf1omsH98OupsNisCmkGKeNh5tmgjoligPkBvPl8wfMfHx/HVV18VJc+1uKrtUopYbIzu9/vRaNzn4n/58mXZD3B3d1eAbcQ9APZhNsRWE3tLfVutVgGSCHcm3e7ubmHd2bBMXegrgwNCI7L1auCVwQXX541BdoXyN1a6rfdGo1Fxu3rB8z7veUGpO5SAd5rdQ/BwDka/36/krcbDYZbPYUawM9TL+12y+xHmiPHnoEYbl7e3t2Wsnjx5Ejs7OyWrEMwPhs6jR49iNBrFF198ETc3N/HZZ59VDt9BMO3t7cVoNIpvvvmmGHm020YrczYLM4/x+1aY03jjGFMbDlngotSvrq5KatsMFiyMUWYGqu5/vrcSRRER3mIlYIPG4VoRC2XBZk7em8MfYct6vV7Fy8r1rCc8oLyDRAhWthl4e48Txjtts1FhQGAg5HAvs2PZgMIgpF1mGbMX1sCA50VEpc11hkQuNhqsUPM19rZ4XHMd7JVAJtmYsK5yqMbq6n0qS4AcBq/XdJ0n4uLiIo6Pj+Px48fFk4G88R6/unbZCDRBZeBEwZPCswxoPpRF8fjk+cw8tizxmjHgZs7gIUVvnZyclPFhzcxms2i32+X/drtdDgFlLrrg2SeJCrr3/Py8NkKDdK2EiluW2fPKnH/8+HF0u90KeYl8onh9m0ij7RSTmfbmZ29p7nvLbxOhfi7FYZzIgyxPKKwZIkboQ8hKh1kxnshen5sDtrNhBCntfaMYc5BnXENdkBcHBweVQ2jpM4eduh3LjIM6IqqO4MnYPJMWP3T5nUOnXP7H//F/jH/xL/5F/Gf/2X8W/91/999VGgg7B+BE8NOxKHq78rnP70HpmqVCyLPxu9vtlrRj6+vrsbOzU1gdT1qMkEePHsWbN2+KgcLm3pWVlcoBfKurq+VwllarVQyEbrdb6shmUtKZsYgBCOwlwDDxoYDewAcwQek4y5HZOSs+gyIANOFRHodcnDUBITafL9IDM+mJZcQwQBCZaUUoIXysVBFw3ijL5isDdcaADVds0Hb/AEYJY+L51JGNtRcXF/HFF1/EkydPYnd3t/Q/no1Go1EOgLu5uYl+vx+ffvppRERJIEAbMJQmk0l8+eWXMRqN4g//8A9je3u7vI+Y7k8++SROT0/j/Py8zNv19fVKxi3mRGaN3was/n0vZOBy6EcGnB6PiIWM4KwAAGJENetGZuMMIqys+EHAM49Ip2zZY/aIZzCXqb89NM1mszCKKDuUVlZurC/mi9cVctKKiflPX9EngBHawrMMts0A0qdmGOu8Bp6vmeHNyq3O48H78lh5nLMe8Di6Xr6nrk70F4YE8t9GUCa4TH7Qj83m/V4NyzbagjxBXjmW28yr+5n5wrk6kCp4dHNfunj+2duGPCN0hnUB0eV+/VAiHj9+HPv7+w/mJ/3E+jMhF/EwrM1zsdlsFm8G908mk5hMJoUEhXCCuCCCYH19Pfr9fkn1zXsI9SYL2uXlZdk7xkG4zHP2HfZ6vdImgDI6O6KavhaSxt532okcMrkIDqNudUas154jSTILz7UUkz/Ug7nM934Gv/EW52Qe/hsc4eiIyWRS1gdZJHPI7sXFRZHVkDvUAaxGv1Dfq6ur6PV60ev1KimzeS4ERrPZjNPT01hZWSl7wexRcf9Yp9GuTMTxucfBcjYTeMzbvb29+DHK72Ro/Kt/9a8qG9dGo1H85//5f16AWsTCcjIgNFtki9AK1MUTlEPZYAS8afj58+elPk+ePImzs7MC6rHcDY673W5cXV3Fr3/96zg4OIj5fF7Y7YgocfZ1pzkDhIil7Pf7ZeMPHgj+vri4KExiDrWgNBqNoswiFoqVGE/6zhu2AKn0Ke0zcwDwouRQJSap/0ehYvzYi8F3KEEbOTAEMHF4OAwQGGN7KrxgvTm+2WxGr9cr/Y/7PyJKfzabzQLgfeBQxP0GrM8++yz29/fjyy+/jDdv3sTm5mY8fvw4tra2ymnAKGIABAzUxx9/HJPJpLLXgzZdXFzEy5cv4+TkJP78z/88/oP/4D+ITqdT3KW9Xi8+++yz+Ju/+ZtKqAVsiFmgvFHea+d9KyhC2PdMZhjw2oC1AefvLWDr2EG71i2sfV+73S7r2GuJuqBgUFwYGk4PzbtoG+sdYOEQKSsrK32Ai2PszXxlL0kOYWQOI4fsgaXNlMySZy/Qtxkf2Uih7b7XnqNsJPg6PkPe2wgyGKq7N9fT7bP3hGLPB8AENtqn0z969Kh4qiFHbFQgj1utVkRExdjMc4wfxspGoNuS22GjiPthSAkLQd5QR6dP/lDuC2npI+o3ylK8Vih1wPnu7q4YDxCh8/m8krL70aNH0Wq1yl5QQiYZZ0cEnJyclH2iYA+n1+Y+suwxT+7u7g86pW7oOYygiIher1d08d3dXWxtbUW/3y86N7fPHgPmbyZa7P2xPMvrPMsd2p7XI9fYWFkm490fNuohLr0fDiIVfT+dTovRT1ijDSk85vQPY8t76RvexTuQ991ut7TNe4cx9O7u7hPUoAuoP0TXMs9mHcmTSXvri/zj+Z4TUfxQ5XcyNP7P//P/fPDZF198Eb/97W8LoIxYbDA2g5UVkyeyP/fka7VasbW1FaPRqDBLKPkXL17E1dVVbGxslMxChMFgWDABOWjl5cuX8cUXX5RTHGGXYIMiFiETEYvN01h9LFqEd2YmUSowTBgtbhsCA2PCG7za7Xbc3t7G8fFxWWi49vDYIJSy58SbkyyQsmJm0QKiWZRXV1fFIvfGcEAd/UR9AS2z2awIUD7n3Sw4+pJnWlB43nhuIMiurq6i1WqVvPOuA+/3mG1ubsbv//7vx7/5N/+mKIHxeByHh4fxySeflHAH7wmxsdvv96PVapXzEyj8PRqN4n/9X//XiIj4i7/4i+j3+/HmzZu4vr6OJ0+exMuXL+PNmzcV0EM7zZJRaMP7WjD8WDMuXjfz+bwYl4BBG9Z1QJO+zQrNwt9eT2QLaxuwYOMCpgsjBKaMNQc4NvOMrPF6BCAy9oDEvOmbumX2nbVJnVCMGCiESWCYE8bquW4w67lqcAWAWWZ4+DnZgPN6tqfKOmGZwfK2z+rqUDdnskfEQII+tiFJX5sVjYiylytikXWRRBSuH3KN8FmMT9fN4BISJwNa6p6BWu4T6g+xRVIEZDBzInu53/cCQVhXMrEQsdBH6Fuzzr6HMUFvI9NY1xh/rHlkCsw8qfRHo1EJ7cFwIAzOBg1kXEQ1MoGxB7A2Go0YDAbFc2KgSxIE5BZtRK4i20xCMl/rjHzaA0HgUuet5ll1oNpeuzqwbUKjzjC315I1xToFb83n8xL2iKyGwLq5uT8P4/LysgDy29v7AzkHg0F5D16RZrNZ5LjHF+8RY4/RR53G43EMBoNYXV0tmGRjY6OSBtl9Z+PL3iPL9kzo2CD0z9vWwvdZ/v8O3HSlYc5g4jyR6gwKOoeJbZZgZWUltre3o9PpxPn5eWG719fX4/Xr1/H69eviKm6322Wz+MrKSuUMDQb1b//2b+PVq1cREcX74MnpeDpYIbwjDvfiO4cn4AaLiCL08XKQP/nq6ipGo1GZNHhPmIiEfRkwRERFyaOYmGCEgeGVQeDRrzAhXtyOrUTZOQYdhRoRlc1T1BvFaePDJ3Uz3nbFEl9qIZ2FJTGl3gNhjw+Lj/aTVeXq6qqSi569F9vb23F6elraenJyEhERR0dHsbu7G+12O3q9Xlns3kTJMy4vL+Po6CjG43G5jnr+H//H/xFPnz6Nzz//PPb392M8Hkev14uPP/44Dg8PK8yy+531gfG8DFC8L2U6nRbmr664X8xOeQ5ltquuH5eBaFIVO8TSgJF02pPJJEajUWU9RCwMJb8nA2y+x2AxweH1YmOINcA6soFiwAzBAXuN59OZUpCntD2D9gwYsnGWWcdsGBv8ZmVPn/hzA+u33UOds3GR311nRHoM3F/ZsMohUfb04p2Yz+8zxkBGkeGOjIY+/Mwga21trbInje+RcZlkcF2ZyzkSwCTSfD6P6XQa4/G4EEQmepCTAKgPpb54bkTUb/5GVzF3zbxHRIVMQK5MJpNiZBDGhOfA+gTZcnp6Wjm7ZXV1tYRUAUAJl4JcQE55ryXjbyMHmXN9fV30HkkimLPGa55ryJ/sibcxkY3ljAv93Lq16O+8lurIiCyv8nP8P+OZ9QS4a21tLY6OjipnZEBsep8WaxgvxPHxcbTb7Xj06FF0Op3iJaGv7KVkLMFqrGfqzhzgHJP5fF72yzIu7o/c38ZVdV6NbFz8FOV7kT6eSChXW5l1Qi4rBq7jGaurq7G1tVUsbZjty8vL+Oqrrwq4ZO8E7N7a2lpsbm7GaDSK1dX7A1P++q//uliaTB4KTMXt7W202+1ygA0GRbvdrpxT4Y1CAHs2wnvTE4Lp7u4uJpNJnJ6exuvXr4s77ejoqNSVUzlZ3CwYwAd1oQ58jxBZFh/stKwRUWE1uQYPQXbdIzgZOwQdiygLZbtXWTz2DCHs2LTukAWUrc8UoW4IUgvW+XwRfoLgRdnjjfmTP/mT+Ou//usHp7ZPp9NiGOzu7pZUc4A+lMDd3V30er2IiDg4OIiTk5PiQUPY/O//+/8eH3/8cXz00UfxD//wDzGZTOLx48flMEGeBfPuUDeMy/fRuHCBtakDm7mYvWe8DNgcj2+wnBWY34Wy5Fn22EUsziPAoLDBzbPy+uNzJ6iwB9FryPHI+YA17uedrA1CqczSQTy0Wq0Ku+6+MICy4WDGj/8NOrJhwWeZGHK/ZhCQv4uoZpLx51aijIu9IrkdGYBEVFlm7vc99Cn1oB0UriU+e3NzM05PTyvGBF5PUmKb7HFd2NNnD6rflQ08f+6573pTD9jWzKrzN6z6B4/G8pLnD/1lz1YGvllWeb4iizhniVTrnmdgAsJFTSyytrrdbvFSEaKMZ8MeB342NjbKQXOcFUb9eQ5ZFjngGI8suh6MAMbwIZUR1bVi3c13lj3ZS8dv95Xnah2JYN1gAwRZXUeKeKxySKkJDvq13W7H2dlZxeMEqUVfe4xZv5CdHOK7srJSDuezkcK4YphABtB3d3f32cL4DsLAUSyemxFR8ENEVHBzbqPH6W1E3A9dvhdDIysd/19nxXrieNL5WaSAJaMHXog3b97E2dlZsTxZzFn57+zsxG9/+9v45S9/WQAdEzODjo2Njdje3i7KImIR/kUb8FIwySMWueKZTDDsbG52CAOnYne73WKxwqTyTAQJypFwJPoP8E+fIWDI+88C4JkRi0P1+PFBQjwXS95sDW1y9gS7N+k7mA7+ZyxtZDCubGgzCwQAcKw9QtSx6Z4jCACe2+v1Sh9icOzs7MSnn34aR0dHZTM6925ubkZExMuXL+P09DQ+//zzkn6YtjabzbJxrNPpFE8VhtnV1VW8efMmfvWrX8Wf/dmfxYsXL2I0GsVHH30U29vbcX5+XjGeqTvGrkPc3Mb3rTCO38XQgJVjDGB7HE7nZ/jvDOCY62Rm4XPLMbwtKCkO2ULAE1LA35Z7zBWHy3ld8dvEzM3NTVlPzGNYNzOXmf0jBMthVzyb3zZgbFi5fwxkHYucx8BrOhsRfl429kxOWB/wHOsC3+P7/PwMEH1tNjDrZJe9xTaUsrGAR6DVahWgRh8SRjcej2NnZ+fByfTMT3ugHRJivcfvOiY3A1zqSagNnjITMrPZIow3j+OHsiieuyYKDdrqgLIBNZ85nHl1dTUuLi7K3zc3N3F0dBT7+/uFVGNussYhvZiDyA4iO1ZWVoq+h/WGkMBjsre3F1tbW3F2dlaZ6zzfoZTIJeQMhkXuG3S9IxIyQWTZ4TWQi42BujHgf96TAXOdnnAodw4dB0cQXnh9fV3WMWQyGOLy8rJkDF1bWyuZ4UykshdrdXW16AMMAgzIfr8fm5ubZaM3WanQAZybgZ5YWVmJ0WgUW1tbpf+dtTV7ZpHpDiO3YZn70obNT1G+V49GZqYottjNmjmuzK6f+Xweg8GghLI0Go3Y3NyM6XQaX331VQmZGg6HlYwD3oz3m9/8Jp4/f14EBwKBQeV3r9eL7e3tSvpcGxkYAPxNvZm0TLT5fF4BF51OJ0ajUdk/AUhAwOBmY+LbTUY4hFPNsvGZ0Bv3HUYQdbby994If2egYXcjngdCuAAxZn55joUwACcrUL43YHJ2Dd7barUqQsssKyyuY0gNjOi3VqtVvENcP51OS71wE5upmEwm8atf/SqePn0av/jFL4rhRf5z5trOzk50Op1yMA/uzpcvX8Yf//Efx5MnT+L58+dxe3sbe3t78eWXX0bEguVBoJNbnbWwjMF8X4rZs+8iCHMaXAzDbHxmIJtDkxqNRmH/ssfACsksOs9pt9slYQDP4iRY/0REZb8GdWGtZiPUngTe6f7hehQeoDJicTiV34Ni8tkQ/nF4Yx1wpy8z8M8AwaEC2YAxCMnGA+0wMeI229vDtdnrkJ+XSS63I7N5OQTW84P6Or6e55OKlHrjyTg9PY2NjY3odruVQ9LoB/obOW1PQ50nhDbAyGZmkrApGHP3O9cR/gpR96E8LJ5D9hzZo8G8qjN6XRhjZAfG/3Q6jZcvX8bXX38d0+m0EJEmFCKq5/NQt5ubm6J3kC2ux+bmZgXc393dFS8bcgzcwN4CPuM8L+ph+eI+sJwwYUHfZG9u1mt5nbq/KXnt+RnM72z42TA0aWG5SxvAcWArQk0J0+ccGuuWm5ubIut5HniPrISErWFMguXOzs5ic3Mzfvazn8Xm5uYDeYDBwR7FiCgHelJ/70GkvQ6hpx8sgz0XspFhouLHLN+LoeHKezLVlcwIRFRPAI+47zQ27RL71uv1ypkZeCDs8kMRR0T89re/jf39/Wg2m2VhsakTi5RzNuxStiVs1o54XSbZ2dlZjEajErPN4PpZgHSf3kn2on6/X66hr5j49GE+OApli2cEa7vRWJxW6zASL0QvQI+VgRIKkwnsUBAKkz8zbNmYiVhsCvfEp22uE2OIgYeA9UF8FhyAAxS9+5x+XF9fL8L34uIi/v7v/77Ucza7d1uT3WttbS3Oz8/j17/+dezv78cf/MEfxGAwiMvLyxiNRsXliZCGWcJLdXJyEqPRqAhrDv5D+DSbzeIKRUjgtTJ4eR+NjIiIra2tODk5ecCO5WLDmbnJ33VsMMBgmWB1CmWzQzYwDDrm83klS9RgMChsNvuUDLRhJgEeKCS8lIQ48R7kg/dSGHzztzfMA0BZY64rc91r2n1guYmM4fkGBsvCH7z+M6tbN3b2ttbN+fxsG1h87nBc37fMSPK4Z2OOgofX76BvTYLhMXB4Be2yp4lMNq1Wq4AGnmem2BuBkW/LDB7/0Ia7u7uS7pSw4mx82qsBkPlQHpaMQwzgbPjyv+cGhTVgYMjcOjs7i1evXsX+/n5Zr3j28S44zbdDOHk269PJZSAcYc0decB8W1lZiZ2dnYrHgj2TRI3kzFUG7sxxyFnamYmBTBR7DdtDVNf3Xo/2MuZrwAP2LmTsGBElzKwu8gTPQr/fj1evXsX19XX0+/2CJcBzW1tbMZ1O4+TkpGJoRSzS35oIbjabhZRFHjNPyFoH7rOcZkzH43EhMIm+iFikzKWvXbLRl73WmZj4Kcv3Zmgsc22xSCzMs5VlVy9CHQbm9va2LKjRaFQyTGHZI/BxZf3qV78qzDLMApvILy8vi6VPHfE+kA7XSrTZvD+Ij6xFhFJMp9MSD03mApQ1DLqZMDwpbCR2LC/vJOSKRY0hASNJ+2gz7IYFg/sXkEP/8l1mbmz04cXgmRhYLFSfXksdWOSEF/AeC2gzrAZR1Jn3YTDhJbIhwn1mOb2vgvbkzeOffvppUf60gXZGRMlkdnFxEW/evImjo6N49uxZPH36tByyOJ/PCxtCauP9/f1ijGDckL2LFL20gZznDrsxoKRP38dyfHxc/n6boUGxMkQG8L9/m+HJiqjZbJasQBivzWYzzs7O4uTkpALcIxaAjXXkc1tQap7fnBbPtd5jxLvsNXVIJ7G+yE2MZ/rHbJWzZtlocdw3oNQKyGDAcsPKNDOHlht18p17DER4l4kPGzJ5rP1OZEydIZFZ0TxvXM887pbtBo/cx+cAGoplTL/fL8k/cugc9ZlOpyVUdTgcVggRz1HGhzFijrvvs1HHnHC2NodM8Q7a9sHI+PaSwbJBtb+n2MCm2IhmbI+OjuKbb74pSVY8H6+urspeTYgIn3HFb/Qc4wiBBrnJ/yYlmSfeG4js4HA+7wmLqHr9TD7Y8M0yg3raELDuz/Mx/52L+88ej2x41MkBvltZWSn7dp1AJCIKJouIQhSenZ0Vfc249/v90n94vh25Ajbhnd6TQUardrtdDIXj4+O4uLgoIfrIAOrnEHf6FNlqGZzlMsXyPfenDbM6uftjlO81FYWFof+nZE+GF6uVEUAbC5HnTafT2N7eLiwBp2uyQH/961/HmzdvIiLK51iHm5ublQ24OXcxCp6JijFweHgYR0dHZcN3Zk+5lywTxEmSbYr3Oc2gJ1REFIWBFwO2FUPDgoj7AfcAC4cX0Nde/JkFzIYJqTB5Rt5g5HY3GotN5HZdZg+MhQ0GFIqZtj169CjOz89LHwDiGA9n+UGo8t3Kyn362wzezS61Wq3Y3d2Ns7OzEmLG4UYGB7z/8vIyDg4O4uDgIP7ZP/tnxdAERGIsPnv2LE5PTyteK6dAvb29LYc1Mdfdn2Y8vCbex2ISoq54/nE9INvK0UqSkhlwryd7Ioizj4jCAHvTdcQCbEZEJXc64REoIBIQYNxycKm9LB535iDnd0QsQjbNWjGXkFP2ErC+7OmxQZP7x0rLAMshOrzL9fQ4ZMOlDrzn8cnFXgqAhWWX9YLrnw0Jj3EOE6NdJlesg7I3iLqzRhkLwmuRFxzOybOYV/ZSRNwnPOh2uxXvRgakrk8dqON6gAmbgpljXGeDm88I5fhQ3l6yJ8l6kt/Wpfx2NIPHbzwex6tXr0rCC4qJC1jsra2tGA6HFYLV+7asXyFfWfO834ksrq+vYzgcllByExARC4+qcUQ2rO3lMOi3UZYNfWQOJWOHjCeWFRsrfnZ+H+s1y3n6JmIRjmZiiQQPrKn19fVy6LK9AxCahMxOJpNCGnPoJn3HM9j07XVOHQ4ODmI6nZYsqdxrfWL5m71mJhs8Ftmz5PH7qYwLl+/N0PDA58VJsQGSJ5I7EZCbhSPxr2dnZwWADofD6Ha78cUXX8TLly/LPQhw0o8BSlh4LFgDbFxrt7e3cXR0FOfn5zGZTMrpz2bTIxaABABK5oeIRSYBrGtb2nXuv06nU1JTIrQAO574VpzU2x4I9zELm7bSL7Sf/zMb6MlrwMEzHY5hxUsd8uZwLxyeRfYmsn053S9giTRzs9msGBQ2WEiHSx+w0RLmhzHrdDolMwTzEKOD/RhnZ2cVhurVq1dxc3MTf/zHfxw7OzsxHo+LYcVGsV6vFysr9/tpSATA2Di2lR8zDmaY3udiwGgQmA1hF/ZlobCdOYWSmS4rIYfL8D5yqfOMm5ubEqboeHuMScsD1gFKCZDp+cbcgnjIsftWEDzD9bHyZn7hcTSA5vn0B97g3N9WTIQpsn55PjIqAwmvZfcv9c8eoEw8ZcWXdYeBDXLH9bDBk4ksf2c5Zt3iZ9GPNtw8b1ifGIv2egMg+G257FA6vBukS/f9eKTwuLtfc39xPeQGQJQ5Ydlpg409cR/Kfdna2qp4UikZqHrOZ3niOQnh6TnE/k3kij15s9mssN3MR6Iv8mGejK/ZdvQt4wxJxtku9r7RLq6zLIDUM87ymrHhYlIRWWiypA7ss7YcsZKvcbFczW2wAW9Zk0kF9zGfEW7N97QFrOVxBNtQ/1arFZPJpBgdyFQ2gOPJcL/ZOOQ7Ij4gpC8uLmJnZ+cBUUN98px0G20c0m++1rqlTub6+VtbW7Vj8X2X783QsGVWxwS45ImAAsjXuBBTyESZzxdegufPn8dXX31VJhNMVKfTKWFWDM58Pi+eDlKaGey9fv26HJgDgGk07k95xONAmBNMEe3FFWkmYjAYFJaC2F3SyjGxqJsnpE/yzeydF3kGPBELNsQMgpWrmVADYAsDCxWK2bdGY7GPxMIEcNVoNErGHreD99JuwByuQ9rjcwZ4N33IM0ejUTQajZKfmnbkjD8odwTLbDYrqSp593A4jJOTk3LffD6P09PT+OUvfxl/9Ed/FB9//HFlYzmhM3t7e9FqtSrGjYWxC/UzA71M6L4vhfkYUZ+NZNk9ZqhttPO9FVI2OMwsW/lQDJQhEzgrJoc3mGX2ejEYwOOGIouIEpONjAFseq0zDwmFYG673sgQu+Ht8XH4ZgbrFNadgZV/O+TRJX9m481rl/7IwCcrSHs/6u7NRkXWMXU6x2FJjBVj7zE0g53fbVIMggOv6v7+fjEWMNDwvNozEnHvkYfs8jPt6TYgrWvL8fFx5ewWPMs2OJy1iDnDHPxQYqmRkQmAvCYySEan2NhvNO5DnPCOZoKOWH0M14iohOJAprHmnAUKWYRsur29Le92YgdI2JOTkwKcjbOcrRMylHkO22/Ch7nE/KZ/6sgcPjeRwXdeSzbY+M06Ye1kueT7kIn8n0MP/W7Wt5OIQAb0er2YTCaV/mPvrM/FiIiyzldXV0v4maMoeG/OGsZ19Hez2Sx7Oz/++OPodrtl36llXdZf2aAyXnKbc794LmdcXbcWfojyvYZOIdS9+dcKJV+bFU22vKzIGHRbcf1+P16/fh1/93d/Vzp5ZeV+I/nGxkYlPMVMJD+DwaCwPa9fv45Xr14VcEzsfsS9sOj1esVAmM1mheEGIPAuJmiv14unT5/G9vZ2YbMuLi4KW0EbrJxhHwmdsoCw18LAgUVjJjR7g7JVTF/zP14YFJJBiYWaBQCnjloQZePRG9AMsFD4GALeb2MDi2ebreb99BthazDIPmCRft7c3IyTk5Oyt4a+Jg9+q9WKjz/+OFZWVuLk5KTM4dlsFufn5/H//r//b0yn0/ijP/qjYsTQXxigXtDeLEqd/WP2Iwvc97FkEJlL/i57MgywLVOyEefx4H8rTzPCCHHPX9/neuXn8iyUEmAP8ME+L9YscgPwWsdssQZZWxERvV7vQSwy17G+LEeWKXr6oE65ZdmNYrNM4Tl1XgaPbzYEl41zNiyWMXJmAvO7s7KmfvzOIVsev/yeDG4ARb1er4AGgxfrHT9/NlucBs95QrxjNptV9nW5Tnd31bOYzs/PYzgcVry59vZaZsKw2lvyoTwsnsc2Ag3cMqEXsQhBwjC4vb0te/GazWbx1g8GgyJLPA8jooQ9YUDO5/dZN+fzefkMzzsZxpwNcmNjIy4uLkooH54U5Eiv14t+vx+z2X0iFBuj7CdzBEhEVCIUTPZhREOUWp95jRroRzzcVL+s+B7kT51RYm+vn5mNHb7nf8gb+p32EMo9n9+flk3yH+Q32IQ6ed8G6w6vFPWzB8pZRbmGZ3/yySdFjjMWTiqQ9Rjyy3jYMsZyhbJM5v5Y5XsNnZrNZoU5YdIaYFGyYWFPiJUbixtPAaEubAifzWbx4sWLOD09jbu7+5RuT58+rcTSOrsHA4jLixRkr1+/jqOjowJYIxbWO20gXhrXNS7OtbW16HQ6MZ/PS57rvb29+Pjjj+PJkyextrZWNpgyEbnP/QFTaiYMBsILFFBvIUi7AP9WvHxnRUxdeB8KzgvTypl+sJV+cXFRrqUNLCLGEcHM+xhD2oSwY0FGLDa+Yd0jLJvNZmU/hq137gVs3t7eVsaSUK3j4+MKyOj1eiXcbXV1teS9vrq6itPT0wICb29v48svv4xmsxl/8Ad/EOvr6yVFIWPf7Xbj/Py8KPnM6GZmx2OTBcn7VOwFeBv4zMWhIt6rUVfqgGRmwkyMoAQIczRhQticFXBWalzrmGjkBoxkRFSSP3Av8cIYQfzN+rJHD3Did/E9BrczkWRDw3PR8dkRCxaedVwHJOoMAeS3ZdAygOF7PE51RFOeD76mjjwxIxpRDd+i1Cny7HXMBglzw55cTlquA6LIMu6B1by9vS2hLmZDkcU2QM7OzmI8HpcsZ8fHx3F6ehpbW1txfn5eZDEginE3EOGaD+XtxUCNdezxyUYvYdERi6yHrPH5fB7b29uVcXACCHuvms37TdqExEF4np2dVfZ4UD+SlEREnJ2dFZl0cXFRvPVk5xwMBpXkOGCQfE5XxMIDg/7le8uRiHggP1wA9MgwyrLr+S4XcIufYW8z9baXwuvZ4YwOKbTeACtQV8YOjzM6gFPebYwRLRERxZDnOwjMRuP+bI3JZFJCqzniYHX1/mDFN2/exEcffVS8TPQ39QUjgy3szafvsgfJ41pH0vzY5Xv1aFBms1llIfhE6nxdnbcDBQoj1+/3i7C+uLiItbW16Ha78dvf/jYODg4q2YciFhuBALMGvLAMk8kkfvOb38TJyUn5rNvtluxANzc30el0otfrlcV5fn5eBi1v3L69vT8/YXt7u2QWuL6+jsPDwzg+Pi4Tcjabldh+u7exnl1XCoYTAAMhkMOJhsNhpf0UGy6uP++lr3wKKEIBsON44IiqBQ2bkzeBOX7SYWuM63w+LyEpzgxl5Y7iNLvBogOY3d3d78/A/Tifz0t6YZgXQt6m02nJFIQLNOJeaOzu7paTPmGNSHd3e3sbX3zxRdze3sY/+2f/LPr9fjGCLi8vo9vtxu7ubhweHhZvl/sqezAMZt7nkkHqdy0YF2YH6wRrZnc8pw0qmLtkvIOgyHs/IqIi7EkxnY0eFJaNIMgJyA5+ZrNZkSdmtAj5c8pL3uOkElaWvAv2kXnI3EPmZM9ELmZ0DbIMtjJhZBBixWZyxICJenkucP+31ettXg8bG/SJ5a2/p3ge8VPndeQ53E+GOfQdfcqmUdoIqOF+2Om9vb0SQ44XmPkREYUwQXYBnshixO/Ly8syh8moRnuRfR/KtxfmRCYlHKXBnGOsCKPFQID99pxDp+SwGp51e3sbh4eHJUMhuIdzUpwIAjmC/iCJCfMK4qzdbhe5cnp6WtqEJxhgjYffhKCJGwhbJ4jhc//Oa8Vrytd6bS9b55a5rkf2ZPg6E5bUB8BOvzs5D1kjz87O4vDwsALmMQJ7vV7lXC5kA/9b7vF5s9msHDmwvr4eOzs7MRqN4uDgoIw/Y3p2dhYbGxvx0UcflXYwFuz1sbFB25AvlnV5HJb16Y9dvvesUxbUdunSeBYgPwbFXMcCaLVahS0aj8exsbFRXM7j8bjsyyDGHgDpcxIioliQgOsXL17Eb3/72yKcYetXVu6PkLcSQsDjDuU59ppELNKaDQaD6Ha7sbq6Wg5UgsWChby4uIitra3SR2a1ASTesGpXPKAXIINnJmcsaDQapb4oJxRuxELR41mhHs7THRElXIxrbDxGRCUEisWQ2VPGNX/PfMD9C/jhHQZSbPwG/CMwuAaghVFigLS/vx+Hh4dxfn5eBKbnG25Nsk7Q3na7XQQSwgfPxp/+6Z/GRx99VAEFxFq+efOmxN27vbyTYjb/fS4G/HXCsI4RR8YYyDM/rQzxCrqfAQaeZ6urq+XsHoN62EiHT7CWbm8XmcUwoPHo5fobGMNeAlg56dcAxul3HRrFerNSBOByvZ+V12L24LBGLB/qlLxZXT5fBhKoRx6zzKot83R4TrztfRmwZOPG4IZ+B6xkgssGqT2l9C3PBbzYY0yfsWew2WyWBCQAT+oDOMGAHI/H5TRgAAbhoPP5IpWmyRXKZDIpOsxj6mt574c9Gv/upY4thyQzUXFxcVFJCBNxP/cgs87OziJikb0OfHB8fFxh0+/u7koCEz5jPRgvmJWH9AJ7MEfBX+zvuL6+jsFgUM50cHSDMRnFxgfEnglR+sKGuYlCy428lv2bvuL7/GNgbT2RiUyvD9axDQ4IJIessrm71+sVrMfzjo6Oiof4/Py8GGYYD61W68GeLJMrYADO0trd3S24GAxze3sbo9GojGMmeehT2mtCo04mZ8Ily92fonzvhoZ/87cnfMS9EuFUzIgFu4bwvry8jMPDw/jss89KdqLJZBIbGxvxs5/9LF69ehUvXryIyWQS6+vr5WRv3jsajeL09LScc0G2KoyT8XhczkMAEDKB2CzV7Xbj8vIyjo+PY2XlfgNgv98v30csQDaMx83N/Qme/p4Jwf4AlE+jcZ/lgJh/FA3fw1g6k43T9fJcJmAGAgb3fM//nqQISRalFwz1wR3MAkFI4kZE4NIfvD9n3QFUwaZgTCEAHf9p1tEx7NSLd3J4Hu+kDhaKZK568+ZNXF5exieffBKbm5vRaDQqeehhqPf29sriZ7MfLunr6+v48ssvYzAYxB/90R8VcImXiQMi3W6vA68Ne3ze14ICcKkDoPkzs1RmsjITyb1+DiQGijBiYXBPJpMHz3Go3/X1dZycnBSDgrWAV81hVShw6m6GD3IAJR4RhRjwHi1kRUT10DcbsA4lsHFEHYgNp9jIyIDbMtwywuDD91shus9zyITvs/fJY+sxdshaHvss0/J4W+65Da6Xx971tKLP3g3eg57gGY1Go2zKdT8whs4QlYEa4BSvxnw+L6QR9TOYoi4rKytxdnYWFxcXRcdZjxqA1q2xD6Va6uRwDpuMqM4zNnSbsDI4tOEKmB2PxyVUG88FxBl6CyyAkYqn3mf3HB8fx2g0KjrLBgZpVldXV4veZj6ura3F9vZ2wV7ocM9XrzU+854NG8smDZd5lyOqxEGdF9P/1wFie0L9vddwNmZYB4wHa4PxAJfZs9zpdGJra6t4Cp1kwclnzs/Py/lZ3W43tre3i07hejAlfQeWYD36wFeICss7cGX2LuW5mrEf789EzU9ZvvfQKSsq/20GEiUNuOUoeG+6PDg4iF/84hfR6XTKGQhra2vx7Nmz+OKLL+LNmzexsnJ/gqOzwJDZA+MEwPub3/wmXr9+HaurqyUblRe1F4jT52JgWNigNMiY5D0SuLWJ6YOdWltbK2cyjEajclAP+ZjZfI7VjtcEgBOxcKMjFBxmBZDIE4qFRf0db22FakaN+2irY0k9ngA9DAW8QjCyhEhl4XB3d1cZJxb6zc1NOVGZPraAwDhjoY5GozLuxEjyPlgcUkEifN+8eRO//e1v4/j4OIbDYezt7ZU0fzB/ZqypN8zF2dlZmVuvXr2KP/zDP6zkzMYr5rF0QXjUCYz3sfT7/bKZlrIMWLqYubWh4XVsF39WQJyLwNxCGTudoY358Xgc4/G4xNezHghZ4PmQHxwS6gMkzbDZaOB76sPazN5Bh4OyNqzQqQf30QfICpMSef65DR4DPuOeZV44g4267/x33vNRx8p5nLnHYNlKlO8NzjPQyUZnJsVoH++gD21oMAaQGPbUA/LwnrswBmaj3e8kCcmMteev38/npF5vNpslxSrAFBlJfX9qoPGul2yYOqwwjwXf29DgWgwGrqVgcD5//rww4xELLGHCkueiJ3Nc/mg0KvoKw5VMmWwqxmgFU4GrOGgYvOX1COvuZDU5dHSZTM1kBHXNfWzjq24MMs7IOjKTNsvG0vILfEN49Ww2KzgBTNZut8tmevQ5nox2u10SeSDzIxZGCFiGRD/dbresOwhV47aIKKFtyAwMTs4Sow/xlDPelq+ZZLFOdH+9C+UHObBv2YSpu94WnBUFKccA7yxMgGhExM7OTrHGYSojFsAb0PBv/s2/KQfYIPABjEzAdrtdSX92e3sbg8HgwcblnJ8e4cA7CaeAYbCLdGVlpYCqRqNRzs4gppL3RkQ5xI69APSDQRX9kzNZoZQcJwhTZk8HExfQxSJiwkYsgHFmFOyBYiGjYHku8YWENVFXFDqGGorYMehkp6KdMHN4L9bX10smD5IBwN74MDd+2I+BJ200GsX+/n58/fXX8dlnnxVvFfXCSKLwjMFgEBsbGzEcDuP58+exubkZ29vbZY6SqGBnZye++uqryhyvm//vu7FBer1lMsLFBghrxaFTliH89hhaIN/c3BSyIStSPI3X19dxdnYW0+m0bOR2yB7GNQLfYTXE4Bs42LNg9pmCsR+xOEHb8bo2FACdFJ4D65mNAu8XAECbkWSdZOCd6+h3uV9p09s+473ZAPA7DGT4v85YoO7ZYKj7zPPGbactZgQ9F5zQgb5Entrooy6EmXpOUFcICIgl5AnyLstSe5iZG/z2mDo82XsbvQYwxj+UtxfLY68Ns/aeR/wPRuA8C3QtYwWxOB6Po9frFWacaA2ve5JNrK6uFmLD68PXrqysRLfbLe+GHIONJ+07JBlsPQQmbWMOIWMMVK2DbXAh67iGH6+Nuv7NBhyyJxPU1ovZoKlb3xSPk/Ek/cWaNaGIrie9MDJ6Y2OjeKLYc8X6xIOJFwvi2XJ1Y2OjkI42ChzJsLKyUvZ5gWEwVHi2D362XDTeQq6bwMn//5TlB/FomA2CobEwpXhCZGuXcCYfBR+xOB386dOnhdWLWCgCrPdWqxVv3ryJf/iHfyjhUcTBWVEDRObze9fVcDiMwWAQjUajuMYQILyHUCwWGynjmCzOWHR5eVk2nPf7/aKMMtvIPgEbBV4sDtOizVxLaBCTkcmcAbwXrBUyC8RjSJ879tHALrOLjKdZPns9SCNMxi7qSFYH7gHM0UdW7D4nhffhiux0OnF6ehqnp6fFFc0zYAthhjBi2fg9Go1iZ2cnfv7zn0ev1yv3Y6jBRFxeXka/34+f/exnxbD44osv4tNPP43Hjx/H5eVl7O/vx+rqanz88cfx6tWrAirq3J953r/P5bv0Qb6GuZg3AVIy62b2ud1ul7HNIDfiHrAfHBwUj1iel/aCIPCZl8gS9l5g5EImoDBwrRvkeg1DlgAcqJ/DoGAsTVRAhlCf7EanbxxL7R/3c12/LitmG+mH/My3gQsKbGBWpq5LBivIqTqQkecNz6ZPs4HltvhdBuvuS3uLSSiCvqOPkSUYm06WgiwF9GXDN2KxT88ZpWgn5AjXeU3kdn0o915U2OuIh16uiGp/eY+OP+M+CEZkA9EXHARLGnX0+tOnT+P09LSE/TrEDn09Ho8LAOYnIqLT6RSZAsawJw3i1F4unt/pdGIwGBTdhXyxbGCOZdKAd9rAz55Rr+OMN9yv9mi6r+vWeB0BZZngOvpZlrO0DxxiQ837MNvtdgm5X1tbqxCZEYvwWm8Ex2i7vr4uG+4xBmez+yMY9vb2ynlq9m4yNmzYj1gcMYB+wlOCbKDeNnBz+y3Xc//kwl6dH7r8aKFT/t4CMiIeKB1cWoQpWOGcn5/HYDCIjz/+OL788stiyBDnCvP/4sWL+OabbyobmQmBuLy8jF6vVzZpRdyHxuzs7ES32y3PcPYONn8D+NlfgMscNsDAZDZb5E13diM2c2NMwVjA1iM8YCp4lzcLemJlL4TDdmyY0A8uZjV4pr0n3lNhkBWxUIKOfTTYMjti4wiliCBx6JEZOIyWiChtbDablQNuMNTwMiAYCLdjHOztgqnA+zSbzUq2qJ2dnXjy5EmZb3jQMDxInwyrMRqN4je/+U18+umn0el0YjweR6fTKQbraDR6wKxmNrXOjfy+lsw852IA6L01KLa6ezJwgwV0iBFz+OLiomQNg2zI57h4AzbkAusI49x70giBiIjKyb08D4WDEua6TqcTEQvFShtZj3xnYsdywWDdnk73S53xW+eF4Po6AF7HXub+9z0eL3+evQ1ZUdYxnf7bgDDLkzwXLMd839sYQAOXbBDARvI3z2ccTI40Go0CcEwu+R25PrQvJzbgmWZi7QHPIXEfSpS16JIxig2JOiOd8UT327ifz+dxcHAQo9Go6ECeAbi17IKYwCBFRjQajbLXgr2G6NVms1kITrxls9msGCLMf3Qdem9lZaXgKsJ8YOedVjm31wQidbM8oB2AccsNyyWeZzmdATTX1BEfBtz8b7lA3exhWSY70OFgpdlsEXYYEeVgZrwOkJiMFQSv90MxNvZsv379Ok5OTqLX68XOzk6JUqEOjDf3kzmMensvs+doxs2OuLFsexuJV7cWfojygxgaZpQ80PxvcJA7KiLKAru+vo6jo6OSX9gKamtrK05OTuLs7KwoUZii3/zmN3F+fl5xiXsiwDawN2JnZ6fE4mGsWNA4dvru7q5y+A1gHJAwm82KkCeLEW7JiGrqXxSTJzZKxJlxMEAQZN50GhFl8zj9Qx86LSzv5jl2rVsR+v2EhlAHK0TayjNYXAAh6sN7aYsB1u3tbRF6XrQIPRsu1Pvm5ibOz89LStwsnBHMGxsbMR6P4+joKG5ubkoua8LECJfDk8TZGN98800cHx/Hs2fPSro5lPvu7m6Zi3d395lB2u12vH79Ol68eBGffPJJMVo++eST2N3dja+//rrU3yDGff/B0Hi4eXsZOMrgLoO0HLKShTMMHgW5cH19XTbm+trhcFiMVhhD5r5ZdLNUyBgDlYgoipwECiYUIA9sfKOsnASBtcY+twxqTd6wwZH6Igv4354DnknbUXwYKnWkEYV+zsZODtcxOLFnIz93GdhZZmRwjw0+g3P6KdeTfqK92SvCe6xHPKZuH2ua7D53d3fR7XYrHmUbevP5ItW378/MsOcqBgXeNeYqjLXDBN1/bwMa72NZtncuYjGPssFrstPkkDfus0729/fj5cuXlcQx3OuoAEJmYNkB/RywaNmC9wFdhveDxDUYP5CXNk6Y64QEsxZMHBKiQ7upr+dsBv/GcMhR7snrzMVyOq97ezw8JnUEQTZEXC/Gyzo3y0LkL8YG0RWEyuJpQJYi+wmJnc3uD252f0cscJzD2UejUQnXfvbsWTx79ixarVYJJed5ZP2kDSa2vTHd/VCHrz2v37b+f6yQyh/sHI3MruXQqWy5WuERajCb3WcK+uyzz2IwGFRyiq+ursbTp0/LZszt7e2YTCbx61//urhFDbz536nn1tfXS8YOBorPHJYBS7G2tlY2eZJmdTKZFIHgQ7M4W6HT6RQvhxlO0mKurKzE+fl5RUHyOXVyFqaIqIQQ2QCiX+0RcBy3jQiPA/egFFGUWNtMeFvvPjgvs3wod+pG2j1C0KiHN7UC+J1/HqOFPjOwckhSFiyMNXOKszDIFuTDd/BssP/j9vY2zs/Pi1fkF7/4RWGFCH9x/Xd3d2M2m8UXX3wRT58+jc3Nzfj666/j8vIytre3i1s7z3szXQjVD4Cg3kCou8aAwHPYSimiymCjrC0TGo1GXFxcVNZxxIKRQzYcHR2VOY48Q1Exd1C4eEB94J83WUZECYtgffK8OvY0onrOA6ybAQDziLVheWCQawDLu7gfZWcQXudVyHXLAN5eFhcDdhvcHk/eUxceketSN0cMQnyf95nZAHEdHfJQB/qtz/JazQbU7e1tHB8fR6fTKdkN3YeMxdraWkX/+Dfzwoab03vTx+zxY+zQP8xP2vahfLfC/K1j4m18O6tixP2aOj4+jtevXxeD4O5ucQ4VRgM4wiGfrDW8lYBakw7j8biyJ5W1xvxylEVElD2Ro9Go7N1Bz/Mc5olD8tzeTJbQN7zf4D6iaiz4WVnmZllEoX+tEzMp4We4niYF6Ds+t9ckYuFFAfO0Wq1CHhpLmOi8vb0t3gWyRc1ms2LAtVqtuLq6Kntj7CWCPI+I2N/fj7Ozs/i93/u9+NnPflbWLn3isDjas76+XjIiuj+z3MwEyruCK34QQ6NOaOa/7dXwDxOUgRqNRnF4eBhPnjwpIBxrv91ux+PHj0us49/8zd+U058ZODrem8jZFAyzzenOLEgWvEMoCLlwrmtcaFaKZllPTk4KuMCAIZ0a5y1gIDFR8+ZV7nUmBLNnGDAwqFaG9Cd18+K1AjU7y28mvRUZlj4TODMjKFPCzMwsOrTAbKAFF3V07LrbEhFFUEYsTrtdW1uL6XRamAmnGwWQXV1dRafTKf19dXVVPFV2e9ul/eLFixiNRvHP//k/j52dnVJf2hwRZVPx0dFRHBwcxHA4jH/8x3+Ms7Oz2NnZKXGfmZFxyMTbQPX7VKwYIr6djWFe+LoMDPw84mntgRuNRvHmzZtKWloKCqfVasXW1lacnp4Ww9ihE8ydRqNRDPOcFpLn27PgurJ+mVv+HgaNNYaRj4FDXZiTAFjmmYkJK2euBdgsm4eWB5klz4yilXoeV4+bn8GayAxm3Zjm4nArX+//HW7hv3NYgdnbnOnHAIb5YyPQz6fvp9NpvH79OiaTSWxtbRVAkdne7ImwDmGOI9e86ff2dpEi2Z5nACPzYlkGvA9lUTyHKTbQmdf8DcEAIGw0GnF6ehrffPNNMQJvb2/j7OysnK3FWLHWPSbMIUhGEkjwnZNDIFswMvifMyBI3024FO0jvA59GLEIszN+4G+TJLmfMnBn3tvrwxzMpIP71ADb32P05+uRRZaP2evH9eCKbLy7cC264cmTJ3F4eFiR8Xd394chk8gB48ykk881I7sVkSecAUaSmoh7+XJ8fBzdbrdyaCdtcL/bW5aJFMtKy6F3rfwghkZEVYF5kPkcYYxQRQjD1qFE7+7uN1fzmRfdbDaLwWAQd3d38b/8L/9LAXRmgayEyRrEoodhYBFeXl4WdpNFb0ueTd6ACkAIG9bH43G5z5uzABoGCBcXF3FwcFBABIKB1Ji4PTudTiWvPsINoYMi4tlmV/jtyWfXGkrPYMyeJwQFwhQhhbB0jLvZ3IiopHNkoxTg33tN8I6wsMhFzUZan/btBYWxZncx4R94lgx8XO+NjY1iAMIs293JHEL4/OM//mOsr6/HJ598UtpktgaD9OzsLD7//PNYWbnPcb+3txfD4TBevnxZMWBQQpkJ+lAW5W1sjGWJ2d46gGrlZG8m8/Pk5KSs+16v90DpeS5sbm5W1hJzCaWYjXpAg8MGfa6GjWpkEcrIBrwNb9YDygcCAuDpQtpU3hlRzZZDXW3kZC9EHg+vK4qV3tvCJfidFWJmOOsMEcsZkxd1hrqNpgwg8/Vui9+HXLCOyu+jDWYULXc5MRxiw6GXNqqzR8jzD+8yQAZdhR6MqHq4bZygT93+D6W+5DmYv6MwxjDbfH9zcxP7+/uVZCmE80Ys9nKY3LKhi+edOeA9PWAKxhtAjw7DwDg5OYnJZBJra2uFEPRm40ajUc5/ILz50aNHsbOzU+amZaTXAHKHuQoeoT9oZ916c99ZhvCZ/zch6vv4zoZJ9nSYCHAYPiWDdAyR1dXVSuIOQustkyPuN+J3Op2SFIa1PhqNSr+Cg8BjTjbEdzc3N2U/6Xg8jrW1tdjc3CxJBehr5lg2OixjbEhluWlS+acuP5ihQcMBVSh2ezL8N53lznN4Sc4wA9BfXV2Nk5OTCiBmcngxttvtstHbE436+YRVMhERfrSxsVEWluNq8YrwDDJJ9Pv92NraKgwUhgVtRqjc3NzE4eFhDIfDePr0aXGtbWxsRK/XK4aKFRCLmslnFo5FioGGcmI8AD9MPlytDqEwS8fzM4jjfVjvjCHPI0c32RzYcO0xysDI3iCe71NRvVEtu0f5m1zXFkCOu0fYwzB4AXIdp7uPx+MYDAYxHA6j0WjEixcvYjabxeeff14xPsmkRYgcecqJ6+/1eg+YSQMT5vn7WjITbWX/XcDRMuY7GxvIAryQzeZ92kn2S93e3sbh4WH0er2iXL2umNcff/xx5bA+wCehB6yVXEd7NVjP2Rhm8yGAkfs4D8gMOzIEuYrxEVE93CoD15xkAfKA6+pAN9facKNP8zjAbHq/h5+bx9uy32NeJ2+yIUl/1HlqbVjx2+/KHg/eXecZcRiIdZANKt/rv123169fx87OTjnUExnMcxlXPLUeP0JLIbEYRxumgCzrSp75PsuY71Lq5o3nh+cvJCBjMJ/PS5IZ1j86JiJKuNtwOIyVlZXKmVMGh97065AbzsgAf9gbYW/X6elpAcfo0na7Hdvb2+Ww38vLy5JeN+KeJOv3+5V9nmwQRv+brKUO3pPBtZ7zuc9oY5ar+Rqvlzpjhf/pdxPPvJc+9f4nCGWMC0dS0EaiZQijBRPS5qOjoxISy7PBOu12Ow4ODgrWibg/ogGygTBaby5nDp2dnUWjcb/vGKP16uqqYEDLQPeb5Y/JhWU68acsP5ihEfEwv7IFvdkZJkF2JeK+7vV6cXd3Vw6qibgPhdrY2Ijnz5/H6elpbG5uxu3tbbx69apimfb7/cJMs0AAsGzGcvaGi4uLcmDf7e1tdDqdUn8GGOXcaDTKs87OzuLg4CAajUZJY0Y7mGAIGBYyAmE2u89K1ev1KgrTDCMABSBuow3hYvASscjKgxDI39v7Aatpo8NC0AuZOgFwKDA2ZlGur6+LAPX4GnDb44AA4F48SNSD981m9xkd7GLGKPS+EsLLxuNxnJyclHjMXq9XYh43Njbi8ePHlc3ihEqxH+ji4iK++uqrmEwm8Sd/8idlAzp7StikiVfm+Pi4HKDkteB+N1v6vpbBYFDSB/6uBVniMJMMGCKigHwAPUAeDyTPwntKUgiIDBQLawcDlnXMmkFBsH584CRGrr1ZEYt9SpzTERHFq4d89HrmGRFR0leybp1xxB4A/mcOIg/u7u7KGrJyp388L+sMj8xOZmXHNb43/+0+jaieE5GZTtfRRkX2oFhW13lPzAJyjZ/LmNYZX8hK/jYJw//ZCOV5eNBub29LKJXTmNP3PBNWGjmfQ6IMrNwOdGn25H4oby+en57HNlYjFusuIgpmwBvRbC6yVbrfWW/WlxELxpuIiqOjoxiNRmVdQiiYEGB/IsYNqVBzwhVAMAlXrIPQVcgNkmQwfyPiwaF/tMPzL+8BdWhYLvkz9zff+5q8Z8z3MUb8b48jOIM1k3UD17DmcuSNDXawG8Y/aYe5D9m7vr4ew+Gwcpaa95H6fJTNzc3KKeI3NzdFBw6Hw4rxtL6+XrwrbmvGZhFRGWP64l3BFj+ooZEZKy8COoHfLEAUJAqn0+lEr9eLiIg3b97EbLY4x+Lw8DBevHhRQnK2t7fj9va2eDh88u/KykqxVB37BxjY2NiI0WhU3I+3t7dlb4JDuXCHwoSurNxnKTg4OCiWKycLR0Q5KJAYvbW1tZL69ObmpnhO2M8Bg054mRlPL8QMBhx6FrEQlLyTe82WorQAzSw8XIAAFASIWbfsaaGP7+7uKqFLpNCLqG7E4gwL9phMp9OyZ4aFZBekQbmF/93dXQldWl1dLa5j5wifTCbx5s2bOD4+rsS0Y8CyUDc3NyvhYswXBPL19XUcHBzE//V//V/xH/6H/2GJ16Qe9NvW1lY8f/48ptNpJfOXPXgOnXpXhMFPUbzBLeLhfgCDv2XFexO4Jys12EXmvb0AWQmz74b5Y8BBfcjQcnFxUcaPMUVWsCYgOvBiehNos9ms7LXCqDUAvbu7K0wZxIfDSJmb9gpQZ88t6pP/RrZQH55hA5/Plnkm6v6v+47PrCCpI3KF+mdjyYDeBiL1oV2WU8vY0exxcMn32HDiGYyVjUev8YjFHhB7aW9vFweqkUbb1zAmeHzRV7SHzcQ2xgBKzG2PP9cgpz6Uby8m1Sy76f9Op1OiNBqNRozH47i5uXkQmsm85aDGyWRSPN48n1Dtg4ODmE6nJYsmm5LBOmQzQyfyTjYgE0YZsSDcwDboOP7nrCiY9Yio6G0z/DzP893khkmMvHbzusnei0xs0vcRUXle3Rp1wUCKiIpn1+MJCcRzp9NpGWfXl1BFwqnoV3R9t9stmMQkOed5kSjo7u6uZByLWHi1tre3S7g/m8hJGHN4eFiIdWQf4eU2ADNucEi2C/rhXSg/uKGBEGZTkC0uK9O6ydFoNEooQ0TE2dlZHB4exmeffRZ3d3fx93//92XQGYinT59Gt9stlqcBsxX85uZmyY3MJDw6OiqTkQmAoGfAptNpRRnf3d3F8fFx2Vy8vr5erFNABqnSYAYwcBAoJycncXp6GhcXF/GLX/witre3i2fA7lXa0Wg0irWNl2Q2m5VsWQZbZECw9Uz/A4CI84Y5YZF5HGgv/5vJoG6MJUALABRRzfLA307vZgOA9+JarDsojXowNoSeUFj0KPbxeFxclBiKnJo6Go1ibW0tdnZ2otfrlRCWjY2NODs7q+wbWl1djYODg/jX//pfx1/8xV9UTl/nvna7XTamM3cNjGxsZ2H8vpXM8JndjXj7hvAM4liPfg7F7LNDTrj+0aNHlaxiKGXSDdrYns1mhXRYW1uL8/PzSiy8w/UcnsncxWPi8AOMW+YGudTtmQM8YAxbttEXTpPr95sg8PcRVaPD/W3W1t/ZeLDys6GXwyKzcbEMOGAk5Pfwjvzb1znmnWdxncFKJru8odWGbi7en+e2OD59Pl+cicBn3szvPTV4T7PhtLGxEcfHx+WdfF7HFFueENtNrD1zq06/vs/lu5AXy7wZkI+E2RK2tLKyEltbW7G/v18J0yF0Bo/H+fl5kQ/IDfaIobPW1tZie3u7pL7lhHCiLPCCWs8yv+rmB3oLVh2PC/ORcCHO4LB3xmvK8tO6mveYQPMeDvc7v3OYU9141M3ZLEP8f5288LXIWI8v44RHeTQaxcHBQUmPP5lMYjgcFl3B4Yv0ESQtESXgm8FgEBcXFxVCY3t7u0StmPBh7wsp+H/+85+XMPrZbFbJWua+tGfW3t8s395W3mbAfZ/lBzU0IhZWlRUuApuOyBOK67EmzQLDXr9586a4lGCSV1buz1F4/PhxHB8fxzfffFNRxAwujMLR0VFMJpOSynQ+n0e/368cQY8woU7z+Tym02nZn0BBAMFQrK+vR7/fLzmuAS8OY0IAAXrm83klPMxZYrzgmVzeIDQej2Nl5f5kWvoa8Ot9K/SvPT0oQ5RUp9OpgHkDDbMCDucizM3jzvcYGxGL8zlok69loWIgoKBJ1wmozGwtdaQPnQYZA2p/f7887+rqqmT/irjPQsa4s/G23+/HYDCI1dXVwkACIsmEdnd3F3/5l39ZQCP9MJ/fezWod2ZG8+/32dBYVr4NkObvMoNrpZOBMXMH1slK0fMMBWBvSBbos9mseOEmk0lhGm2Y2kOB+5yU11aMZr2zB5gUqJeXlxVGlM+Y/xHVVLlsCIdU8HvMRFIHexc8DryL9uffGVA4VMvMW+7jiGrmOb+Tv/OPx95eD2R83mjLO6mXn+/QRRthZlqtP7IhzPNpL5nuCJ1AnnlONZvNGA6HcXNzE9PptITaQrggr3kXTDryJHt33S8YGxBGHvcP5b5sbm4WQ47ifjRQy/0LS80aG4/H0Wg0CpkHC898sVx68+ZNHB0dFT3NXPA+m16vVwD/6elpMTLyvMj7vfL/3mfRaDQqJ6FDfF5dXcV4PC7M/Gx2Hy3CqeGE9oHDrOuZr5YTlg/MT/qDayhvM3ztmeU+5r1lfN0YYVhZTrOO83hYLoA3wB7j8Tjm8/vwWnu9MUrALvv7+5VoEHt/8D5H3GMMsKDlFEYjWU4hN7e3t0t/UH+veepvY8NGh/9+WxkOh2/9/vsqP7ihEbGI5cdiNxBGyViAmu3nejqt3W7H8+fP46uvvoqIhYu50WjEYDAojNHm5mZERBwdHRUF3Gg0otvtxsrKStlgdXZ2VsBBr9crgDOzB94zkN1nuEdJLdftduPx48fRbrfLZM6ZCs7PzwsDzgFeuDFHo1HJ60x2pX6/X8kKweRjItlLkpUaTKfHw4vOwtMb3JwhA2ub/jK7B1Djb6cKzXGWDrvCELLiZH+H3b82CiKibOxlodIWFuH19XXJwkE2EDbjI7BhjDEWvfmOObm+vl420hFbTVzs6upq7O/vx7/9t/82/qP/6D+qeO3YB0JWiWxQuK7vu0cjl9+lLzIo8O8MrjIwXVlZKS5sx9lzDc9l7w3z2sY9xizGJVmorPS51h4FGEzkC+CUtQfw5T6uM/h1OCdAFKYVwOC9IADWDMxzn+d+syfAIQ4G7NkwoFB/s29WlB67DEgyyeGxzvda1tE3ngfZw+L3M9boGNcB2eXQU99vo8zPchjLfL7wiHNfp9MpwA4jEWMWedZqteLo6Kg8l3lBP2Ujw0YcfQFQNGH1ocQDIyOifv8Aeovw4/l8XkgF+h1DHt3V6XQqIH88HserV6/izZs3RcehJ/r9fiE78Oz7jC5Iy4jFPsper1eZN4wrABjZA1Zh3rJnkjlNvYkCOD09jdlsFltbW0WWmcyLWOyf8nuRff4uGxw24LMnFAxjWZLXruUBzzTZmEE164ViwwVSEJkAYcp7nFmLMLP19fWCEdEH9nhDdg8Gg7JZ3MQrz6Bu/GacLi8vy0b+iPvIHTaDe/3nuZoJniyPs8ysKycnJ2/9/vsqP4qhwQASSsTE8WYbGPdms1nAJ6FAhAdhCPw//8//E1999VVsbW0VJcDGbiZio9GI4XAYzWazxD16IDEyWGhkkGIDD3swGGgLD5QbyqvT6VT2lLABmEUYEeUgF5hH6jgYDIoL7u7ufsM7oJ5TrvEwUCcDHhYJKXCze833GUBZ+bJYWRxO/0pYmdPbOZyp2WyWA6QiouxhyT8YSJkR5nvCmAiVY5HawHA8PG1hvCyUqPfp6WlJX/r06dNimDBGuK0tdKiXQ1oAkDBM/v7LL7+MwWAQf/Znf1bqjSDr9/tlD06Oy68DfB/KomSFtOx7/jbwNSjk/4iHqZ/tBatjyxwqwOZvQBsxu1ZwjUajsJGslxzmBBCkWEkClE3AwHIT6kecNEAVDxwKlDhi2pENDeqZ3et1xEXuq2WKzQDcDJu/81jYAPu2kkkR18FrPb/f7zZ5wHu9hn0tfYE85Lk2hLI3g3a4PdzT7/dL+B3gARKJ6zhAlLmFDEYeuf02/Ljfhg/zBZkD+LT3/UN5WLLxaAAasUhXzDpzmnbGBpKMc7Fev34dX375ZUVvmwDAKO50OpXNw4QyQXaRKRMZx3xm7rGvAByAPsfLQqimN3yDtSBkR6NRIQ7JzgkBAhFqGWAw7k3jOUSxTo6Dlaz/8hoyQcH/jI/XKGPHj/uI7/K6nM1mlayT7JGbz+dxdnYWt7e3MRwOC9GLrGVNW88TQgXeGAwG8dFHHxWiFFIh4uG+WsYRPAimAV9ZH9XhTgp6Bd2R++RdKD+KoRFRZQBZrHnfhoHr7e1tiZPjbITt7e344osvyvkTL168iM3NzXj27FkBxghnQPFwOIxutxsHBwdlszeW6XQ6LZ4Tb+SyssT6J96StHUwAwB+b/7EA0CcHYDj8vKyEi7lPR0IHRYhbjRibekfvjerCXjhWhtICCHYGBcWJPsKyKwQEcXook/N9nIvxmEGThFR3Iss8larVdyH1Nl9TrtQiiwcBDNtN8NLm724GH+HrHQ6nfj93//9CjsA8Ke/OIyQ+ZhdxTDHw+GwGBtc+6tf/Sr29vbiD//wD8v1gAq3BeM6uzffFWHwrpTcHxng5u/qWDIzYVY2zt5ipeTrI6rehIuLixJbjQGJ8shGI8DYe7xgPg3qUVTIC1z2OYwH5UKmG8cDe2O5jWSADQCX51My4UCfU3eek8ciGwh+juvs+2zgZIOBd/m6bBzZ+MnzwSDQn9srZE+KxyliASp5Rp1Xg/Z6DWeGMSt0z6mIKJlmDBiYf4zfZDKJwWBQDAX2DBGa41IXRuv5i64gFGRlZaWyf+1DqS8eQ4f2MSfRn6w31gtkFRih2WzGP/zDP8TBwUEhnSAgwD0RC4xydHRUPO7MdTyTNhxgzrkfbwjhcRgyEVHO33KIc7/fj4gFVmm32yXrJvoc48GeRN6Pl8yhkHUGgY0NrzG+z2FVBt91xr/rYR3u9Y/syGvX9bAxA/lJmLUBf0SUUHR+IH0dRu6D+wg3m06nMRqNotvtlvowRu5H1iqpy4fDYSF0Mx5mDhFpQ/uyfDSxbYzxLpQfzdBAAGI0YBXyOWA8ohquEBHlPAo2b6O0Abm2Bs0UcP/Nzf0BKezFwIOCEGZxosQnk0nxYLAQNjY2ot/vR6/XK+kuAR9sZCZ0AbCMAmk2708VPzs7i9FoVPpha2urxE/DmGAxR0RhJ1lICCaDfi+s9fX1IkS4xnn2MWyYkFzDpkQ8J+TBdx5phAITmDbAzvA99bd3hbrd3d1nm/J1CMiI6qZKxtNMCosalgWwRniZx8vs5mAwKCCn1WqVettQbDQW+crfvHkTo9GoHHbmUz+5fjweFy/V7e1t/H//3/8X//F//B+XPoINsZeNccnC5kN5WLJHIvfTtxkj+fvZbFbZmM88NzPHfSgOYphvb29LJjqM93a7XUlHDfjDWEa2OeuYM0MhKwxmvGcDAoBnOO6YuWTGlcQDw+GwtN1z3Eo4M470S1431NvGgkG//0YWIaf8uYkArvMa8P1OdZ69ExnAL1s/Bhc2cjJw9DP5nfWI2+p+c92d7pKCbOJ7QlpoG0RJo3G/7+3s7KySRhkP/uHhYaVddWuBuZANDcKVDeQ+lIclEw3WcXwOZqEQtmgDlLF9/fp1HBwcVDztYAuYaqetBmvk9YSnv9FoFMxDmFXEQq8yZyA2uZcf6skP13hPabPZLPoKXWdQjYzLZADfu942FkxGWL5kkoFi+Zjnu2WF+4p16xBB+tkyYD6fF+KVNczaYX1ibODh4fyRRqOaWAcMcn5+Xgmr29zcLCHfrGMMfohqCkkjwDitVqtgLggjMpxF3JO15+fnS4kX5q49t+8KvvjRDI2IhYKn41HIDLyFM4aHrbtOp1MmSsR9Dn6Mj6urq5IylknM8wCQ7XY7Hj9+HIeHh2WyRFTjaImrh5Hf2NiIra2t2N3djW63W8KEeCZ1h6nY29srAgaFe35+XtxqsJ0YI+vr67Gzs1MAdKPRKFkhZrNZ2aQ+n8+L0gCgAELYv2Ewg4LHK8CCynteAAYImyw0MuCLeOhWpt64lnkuC8YMhJlfmB6DFJ7NfbyPZ3jPB0zvfD4v+2M8nwCGuEEROhyew3Xs29jb2yvXn5ycxP7+fhwcHJRNlRGL81sIpWIuXl5exldffRW///u/X+rI+DNHcz/6/3dFILwLpc6IyCUzaBEPDW8Er0NH+CxicYCjSRDIA+YLpzrjgUNWkAbb+8I8xw0YI6KEPRF/zVxkHpK+lvVjUEBdMVq5D+CR1yX94lBH+sfr1muMvsng3sreypnn2eAxu2ZwYEMlA/sMJHgestlepzpjycWGmOdFrr89rTYocrv8TgMsxg62EdmfQ5ww8gjTYF4wT5hnZ2dnERElww3vYF4hRwA0BqnuY/cjRobb9aEsL9n4ZoycyMZZiwDmZvUnk0m8evWqjBtEmdPsQ4yRzj1iwfqDPwxKyTxFKA57/yA+CWuKWKTwtoEE3oJRZy6BA8BhYAl0G8/BwLDhRftZa14vzEHkkAkDGwr0uYkDCmvLxGMmkfw/YwPmycSn2X2Dcbx+ENYQ2pxhgrE+m81Kv1i/EHmDkcdeDPbfRSxC8jlQsdvtxnA4LKFxlqmE0YFXOAiYNnp/Lu23FywTI++toWGQabe/PRsGv5n5Rfgi2AeDQVEUhLMQcxgRlZh6XGQ7OzuxtbUVo9Eo9vf3YzqdlgFHkLDgd3Z2otvtltAqWOzr6+vKfoGI+8Wxvr4ee3t7cXV1FW/evCngN+I+68DW1lZELPKaAwba7XZ0Op1KmxFksBbz+bycMm3XOf0KWDELmRe2wTrvYkFg1ftaTvr2ZkYLYscrNhqNsqmc/sMwIiyEPsaDg7AyI+GNnIz9yspic2VmRxAojlnnwCMEKf1HmxFKGEgnJydlXDc2NmI4HFY2bLIRDMFFvGvEvRE5n99nK3v16lUcHh6WPTu3t/epD3d2duKrr76qGHjvkhD4Kcu/q5GVwbDnLZ9TCEmxjOEaDlc8OzsrigbljMxgzsBionzyybEGJMw7MuIxl83u4y0jRr/VahXGizUO6OY8GkK5mD/IS5hNPDUGlxggKH33PfXyuuPd7tc6AsBAwf2fge+y8bIRAWixIWgjxXtWzBRnL0WeWxGLfR7Zs2LQYQBBySGP7juHszhrEAQWBI+NKYe8IAdfv35dUpuORqMYDAaxtrZWkoq02+2S5ZAQlzzXXW/6zqEiDtf5UOpLNkz57TEH+OLZIESRTd/ffPNN8XxsbW2V87xms0X4tRPAoLORIT5jo91ux2AwqJw8TUQCsog15LXEvPQ+jOwFAUfRBuYuZAnPQP5QTNbl8D3qY0PM2MC4ISJKG9zPljn2sprEqDNWeHde/yY0XaeIKEYi0TPX19dxdHRUwD4Z4+iH0WhU1idZ4gjDJewaw8PGn8Mxm81mHB8fx5dffhmtVit2dnYqGQ0Zl8FgEBFRyVpJpIxlO7Ir4738/7tQfnRDg4mKm87KhYnMpCLjAkqQgWZB8h0KBEFMNgdyT8MuAyDI3jCfz+Ply5cFDGBorKzcZ63a3d0tdT8/P4/xeBwRVXBk44SJS8xlt9uNJ0+eREQUFxxgBSButsuZbWBRmeyAILLadLvdci3vRUjA5Ecszg8wg28FxTWAdAsWPnOIEpOcDWpmjFnQCEN+W8l6o3lmScwoMicQNISg2DDyMzCS7KJmgzlj5TSTCMtms1lOp+YQQRiOXq8Xf/zHfxxv3ryJ/f39YlSw8FEAZAU7Pj6OL774Iv7iL/4iHj16FDc3N5U5y3x+Gyv7vpXPP/88nj9//iC0IzPry0oey8xUs08GsJXZ3bu7+9PAMU65FxARsdjXwRrgDA0UDmwYCg/jnD1gq6urDzJNMQ8xWgldxEPB++yJtDLFPe/Ma9nw5h1mwszUe0MkCjiz+nxvkL7MI4chAmhBtnNNnbfJz7MxSB38uX/7e/7OIMTXmqyq+856x3XIc+W7tANZbnBv1pe/kTHsXSRxBIaGWUyeuyzkxPXJ4zyfzyvpMj+Uh8VGtoGaveyMG2QAJMTNzU18/fXXlbDlra2tok8iopAKeM/xMKDzW61WDIfDGA6HRW95M7YNAeaBwT46ibb4Goz4iMVe2UajUYhE6oSsscfM0SFgqZWVlRIVkD25uU9t4PP+OqPeMsDeEgNmr197JfPzXB+Teo6IwAOJoTIej2M0GpWEQzybs9soBwcHsbOzU4gh9ArJQTCgsqcXrEv2SpKLfPzxx2U/x3w+L3uA0StgRcK0OffE/cg+IRsa7xqZ+aPTHHSAYwYBskxKFMXZ2Vlx5zWb9+dedDqd+NnPflYAIROPTvfiZhGZbcKCj4jCGgH+CWnqdDolzzXAgMLCB4zYSCGmu9PpRL/fL88DkLBRbDweF+DstHYwGa1Wq5xcaSMsIopw4EDCfr//YM8FCiaiyt455tuCh/9ZGOxJwChhMTrG1AoPBs9GDO93fDg/vCcbRBGL9I4GLWZuzYaymPM76dfHjx8Xg9OpfhlPABn1X11djePj4xiNRkWodTqd+PnPfx7D4TC+/vrrODw8LAIdIPD06dNYW1uLi4uL+Prrr+NP//RPo9frxatXr8ocHgwGcXR0VO5jbN415uHHLr/97W8fgLo8l5eVrKgcGmRDA5ni62ezWUkx/ejRozI2EVEMbuYna8destXV1QIIYCabzWZxdRtU8AxABYwjQIEQCTNoGfADYrzhj988C6MecIrsQcagYJEJZhINlL1eWXf5mrpxMgjPgByAY4aPz/O1HnfkRX5/zrzCtR5jZBRtNtlCO3NbMrBxuzwu/s7tQK6jm/BsGTTN5/Ni1MJ+Hx4eljAOZHxEVBJ4eDNq9mpTMiuP4WKw9KE8LHXzNnsF+Y5kNvP5fRgbkRHcP5vdh0x99NFH8fz58xIWOZvNSlIUdFS73Y7Nzc3Y3t4uOAE5xnijn2xsO9FEXl/Ug88zCWPjBULQ8pP1bn3Jno3sCbRnxR4L/udvG26ud16zWYYwDqwf7vf6B0sYfzh6wGuaaBqv2Yh7kprsXMPhsIS50jbjTQ7+xRg0oQrp6/V5fX0d3W63sr+Y97948SKePHlS0ugzNxgbPBm0o9VqxdnZ2QOymGfSL+8atvjRDQ0vCodPuWNY1MfHx2UBNhqN4or8/PPP45e//GVFCbJwx+NxOTvDbF7EIoaRycCCffToUezs7MTe3l6x3COinAxpBgIAbO8Aypw4bFh73g/YtieCTdHsrcgL7tGjR2UjUDbAMvjJLBf/w5ga3Ecs3I3e74KANLjiGREL4UQ6N48Xz/CmVQst3uc68Tf38j0LtM6I5Ht7JShW5HwPCOx0OhXDMKfDjLiPoSeMzXMApvrx48eFed7f3y9Z0VZWVoqhwdkd+/v7sbOzU84d6fV6JetMXgvvu1ejjjmuK3WMWcRDF7xLJhEi7ucc4Up4BPBOOBXxfL5IYICciogiC5x+FLKE8Dqvd5QR7cRlj5fFG9Jpgw0Kewvcfu51FjPIDJ5F6I7DFh12gTwzKLDhQUHB87dBTzYwfI1ZfMaa9em5n9/v53DfMsbUOqPuezP7zBcrZeqEXDH54XeY7DFoAoBxDZ5lyCMnksB4td7iXZubmyWE04YhMfuMl+dYBhv0JfOUeZmB2fteBoNB2ReTi3UmxQAXLyBj+M0338Tp6ekDjyBs9ObmZjEAer1eOWeh3+/H7u5uSefud/r/bEwwtzJjjbfNhmhen6xHjI+IeEA8sCk6A3UbQOxVc//Yu5ENG67JxEKWefzt38ZatMEyZDablWyZGGZup7FlJjlYi9PptOh5wlI3NzdjfX29hEHb4CeMiiyEnPeGtxsym+iTRqMRX3/9dSV6gnpBTt7e3sZnn31WCbmyjESO+7wd6k8xCWJS5G2FMK0fuvwkHg0G3UqSSWAr+fz8PFqtVjlYC5A7HA5jMBjE/v5+cT8CKE5OTkrGJDb1bG5uVsIm+B7333A4jKdPn5bTnNm87UwzAHrqMJvdH27DogOscLI27KTT7nJozmAwKBPTgJ6JBVDmZEkrQ9gH7uGHe7Nit4Bh8gFQiDWPWIROwJ6Y9ccgirgHLxcXF4X15Z1mXtjHgIHoDfoR9XnpLfgiqrGNCFjaQF0RGChhngeQox3UDSGPMHDM6d3dXQmzwhWKYXJychKj0Sh2d3fjs88+i4gorvKnT5+Wg/8AiK9evYq9vb1iCK6urkav1ytAgz7g9zIQ/aG8veAK52/PqWazWQ484n8EO5nfvCb6/X7JIBdRnZPeK8UajoiipFgLsFuz2azIANa547JZk8gIyAtv4LWxwwbQiMW8sUcUQ4qNpwaWKN+8x4A2mo3kM8sgCv3kvRy+3gylWUWu8W+HhblOJjhMjtStDeSCwbrrQZu8ryQDjTpvqAEW/ZBBu+vo8fTzbeCx+Rfvmq/jOTCphOyZUKHvmSMOMc7rgTabiYZA+lDuCxEPuRjM50yI9jIw/ufn5/Hq1au4vb0t8p35hO5YXV2Njz76qIQ99/v9GAwGMRwOC6jnuZ7H2bD0Os2eFtadjQva42exLhxmiXyoY78xllmv9IO9ocYf4LC8jrmPOtWVbHygh10vG/u5n5rNxYHFlqc5WsZjzL1gRkhj1iub8iGO0Q30G/Iaw9/YJWLhdeXMtmazGc+ePYt+v18IT0jV/f39ePz4cZlDYCba7tA22m5D02sc+fJdMAXbAX7o8pPsELNXw5aZWadGoxHT6TSOjo7iZz/7WVk0bKJ+8uRJnJyclElze3sbr169qmT3wbi4vLyMra2tch0CuNlsloUPuGRSttvtePLkSVxeXpYsVI3G/QF7GBgRCwYExspZJgit8SZnAO3a2lo5SJDC5HdfEDbmv5lIhGIhPDAgmHD8xkiIiLIAM/NKf2TQbmVN+7DgI+4nNRvebbQ4bz/Cx8YG769jahGY/g7gxb3MI9cvIirhKgaLCE2AG6yDMwCZteD8Dk4Cv7y8jDdv3sTjx4/j937v9yLi/oRZ9vqgbObzecmCxlheXV1Fv9+PjY2NCpP2vnsz3lbcN8v6CUaZv228OZ4VBXh+fl7WnI1zlG6v14vRaFQ+m06nFXkAMIiIQjzM5/NywBX1wLhGpgAqCbNEoaI4WQv2CPq3WVan0ua5yBjkQQYR/E396CMMNYxkkx4RD/dMGfR7bbnfeWZm17LSM+NZd202MEykWD5aBtgwoj3ZkLFhYHbX/YRh6BBJ18lsoQ0dng9Z1mg0KudXsPGXTHuW/RiN3keWDTcDmmyk8X4z0g65+a6ew/ehZKMrA1zvn/J8tLF/d3cX33zzTQGyFxcXFV0wny/i7TEiOUgT/W8Dw0San+F5nb0UfGaPi9vBZzyHz+1Vox6AXtqOoWxShHsiql5I5iKkrGUqxZ/n+xkDF3s/siFkY557kaWESLPp3gbWbDYrByMyLqPRKE5PT8uaiVjgRvbY9fv9EtZ2e3tb8WBARKAjTCLc3NzEyclJTKfTgonAf/SzydPb29s4PT2Nzc3NMhaMC/XjXe6nZX/Xyd1cbLj9kOUnMTTMUDMR7NHgGk5bZCAePXoUo9GoDNbOzk6cn5/H5eVlvHr1qlhnKysrlbAgckWjyDEEOKWVfRxssGQyEvpAJoF2ux3b29uFmcTDQIwei4JwCStBL1raTxpbCxXcpuybcOaSzNJFLIQkAsuMC0LKAseLyUKDCcwCMijierfXGaocWsK1tvR5h5Wm62jGzqDBoGA+n5eFzfWuH/PFgsyMSGZeIxYb9EkicHV1VfHoeL9Is3l/OvSrV6/i6dOn8bOf/SyePn1aACkCmxTLJycn0Ww2K6EUzqGdFcaHUi1ZkVgoUzy/DcYi7lMKR0QJnbu5uYmXL18W48TgGEXVarVia2srTk9Py/g3m83iJWB+ra+vR7/fr2SkilicjZGZLcsBG/UO+aI+KDmHatnQ8LpymKMVOfLSBrfXegYlXjc8w/OzDsD4b4+J5bfH0YovGwn57/xuP8uywv1bx7JmkE77ud/fmeWENEL55zSmtBm55z61HKNvMTjxjF1cXMRwOHzAsnq8Laey0cUcwLtN+5HjnEFFn+YQiw/lYclzznuomBsm5kajURwfH5e+vbi4qJyMDbkHMUkIjnWtx9ng398xl1j/2dPoeW5duKxksGqDwzrcoVxeJ/5xpIUNGeZdBrF5jVPnOlKAuuIRNMawl5E2+G/Wq0kJn+0Tcb8uLy4u4sWLF3FwcFD6nVTAEYuwafbDmrC5u7uL4XBYTnF3v62srFT2hTr9ufe7EPLVaDRiZ2cnGo1GHB4eRrN5H2LltoDLqKejMfKYZzn7LpSfzKNh48Idk5UKbqydnZ0CPmHknz17FrPZLL755psysIB6JhagfDQalfSlT548KfGSCAkUCwf68YxHjx6V8Bg2gFJXjB+EPK41jAjqSj1gJCwUbPiQZQLmBOOE/nF8KO9h0VMXJibZEAw2XA+7hrkXNzsgxieFo9hot117sMcsKguoulALxsSM3TLmDQHiDZJWAj7kDK+TDZ0slFHsXrjkIydGP2JxaCTPYrMuudI///zz2N3djW+++SZev34dV1dXhalaW1uLN2/elBhNPGWczOp18KHUl2xo1BVvUMxhU+12u4zl2tpaHBwcFEaZezKT3mjcx9vCZKFw2A/BPqyVlZWy1lmTnpOE7mGQsz7NCmJM2LBmzkIwwLZn9i+iGhJFH3muuz/8vT8ziDLzmQ1gGxPZaMnr1QZF3fe+zoAlopqFzixmNkjcJr4zuMEI4Dn0f35vBh95PtBGSBV74Sl53wQyCQMErymHc2F07O/vl1S2luf8NlCjX7x/JAOzu7u74u2iT5Gx9gZ/KN9eslEN2cCYNBqNQkbQr7e3tyU8jrVPFin2ijHvrP+Md0wO8J29+3X1tPEbUU3J7HXs/31tRNUrwnP9vw1pikliMEM2XrKxUWcg1ZEJNj7AMhkjGkNmzyPtpS4Qp2CTRqNRsgCCGex5hlTGa31zcxOdTifG43HR8faGQjY6VHF1dTW63W7xaKIfbm9v4/z8vNKH3W63eLtms1kcHh6WsC3wHHqEd7oP6Af//66t9580dIpJkq3PiOpEnkwmsbu7WzoakEve+dFoFOPxuExsnuHFjfEAiPbx8VjIPmil1+tFt9st2aNQwhgFLEQrfEA8MfsRUQ5mgVkyKGITOSFRVm4828YGXggzuxZebFY2KM/GDe3wpm0LGOLNSblmZYwxxOKxkMBA4rRkFi3jaKHiReHNsHXCxOwjz8OozDHuCIuIxUE5pAu1kPX8QIjh7SIrhPs4Z9zgXI3V1dXY3t4uMZhmVk5PT+PZs2flAJ+rq6vCjFsIvItC4V0pywwMihMdGJBj+F5cXJQ0w8fHxyXMzx7IiAXA9NrHE8K6BvgDYiMWKZ65lvmPMZJDBzyfWYNmnjGE80ZfG1AGPxTu553ZE2ilnvu1bj5mJjPXwWs1A3++W1byd5b3WRa9bfz9HtfZBIYNlwxkfF8G7b7WsoLf6Al7ntxHgBPruFxX9nLt7u5WwlYxTvCO+R4ID8Yhs5gO8aKgDz2WH8rDwtpkPPkbIxOZ4P17XMcag2B49OhRPHnypBiSGCnMgTpPXCYGIqr4xXOLksOmIh6miaWONt4NWCMWEQ0ZU1hu5B/rQ+MAh3rRzuxFzF7GPA6ZWHBdsveCjfZZLvEOR5qgxwmdHo1GsbGxUfbT4HH0njv+5pwMQrMbjUYhrjk0GMLa+1TIOHl3dxdv3ryJ09PTaDQaJTvhbHa/19c4dDabxatXr+LZs2elX1n/kBpZrtHfdcbWu1B+slN8vLABe9nQMMOE4OYHq3I6nUa73Y6Tk5PKoHCdXV2DwaAMMNYsTLwZfDweCHZYISYVG4YQ4kxiXFxMFthwFgKMP/sE7OL35FhdXa08DzDjfR4sNgQboJnUbDmvssEzizULEq6NWHhaLJR8uE3Ewk0YsRBWEVV2xgIoYpGNwywe9cr5oT3esHWOA2WfBQrZghXXKF6pHP/setO/NgIt8DKw46Cdly9fxrNnz0oaXcbm+Pi47AnisEKyG1m4fhuYep9LnZDMn9mbEbEYf9YWXiRCIQF9xMGyRrMyGw6Hsbm5GdPpNMbjcSVU0fKl2WxGt9stRIHDCW04MMfNTnrdoRh9TSYe6gC82e38eQYcVsD53Qaivi4bKFmB1f228WH5bQLI1+ZCfTODm9ejFSrv8jPdd8jvDMb4u27NO9Y69wvhtOgMk2XUfXX1PqsfngzebePh7u6uZKjzIaYOb3FYbbvdjvF4XNpD+8waZ1YTHYse/FDqSwaoEQs9wjlIyIuDg4MK2GXM0TEfffRR8ZIbAzBns/fV6zUDfq7xPMz/m+Fm3TiUiettnNYRlnm922hwPWivCcF8nfvQhpWNHn+f6+FnmUiiXgbfrDfWlGUZ12WjB/3ghDusYfYBz2aLg54B+aPRqHx2enpaIQnY12kcyHuJpPnmm2/i9va2EF79fr8cEGiseH19HV999VV8/PHHpS+MXzw/PH/zz7tSflJDIyIeDDKfGUwT8wa7T0jCq1ev4uLiIj7++OMYj8dxfHxcYRcMIre2tsqGHjMLxNf3+/3Y3t6ObrdbUs6hdBqNRvGekFnKYJ46ZRcjoVYsEjwOLCazZIQAoWSwhjFoIqqMJswWgsyuNQu3zNgZtNjgwMAhLIpnA5Dv7u6zfo1Go8LeEw7Sbrej2+2WtlqYmY3jHbTX9fP4O17VrKA39jI+Nzc3cX5+XjwYzeb9RjDYBdydg8EgNjc3K/1OG5hjhEo9evQoer1eNJvNEmc5m83K+SiDwSCOj49LCMSTJ0/i9va29A0GH6FTCA7n2/Y6yGDhfSqZmXWpY5YpzCnGk/nfbC7CppjrzoE+m83KOTY5YQFZ0pBJeb8Fa8WZoAipi1goNgt55AfzwErZa9NKvO47PsvhDnxWx9TTv6wvvssGde7r7Mmom6/Z8LJMydfnfQh1z3Z9vQk3K0sbUA6vyu32+902yz2DMLOQmfjJbaHg+b2+vi5eS7PBJlSyYcSzyD7z0UcfFd1AWV9fLwQb9QPI8P5sNNUZezmu/0NZXow9WFvIBfr37OysQvwxDzY2NuLZs2exublZiDb2WuLVQv9FPNzzQMnkVvZYLFs3EVUPQJ7DdQQGcwWw7nrYaMnrwnPPRAVGQZYrbpvvy8/1+mF9ghciFkcU+J3IIMt8t8NkT6PRqJySTjsyEcI6g1hFxg8Gg3ISODjFHhGOQfAY393dFTzy7NmzkvVwa2srBoNBCd+CaIJYPT8/L3t8ct947HM/mlB+V8pPTnPAuFhAOh0ZYUjX19dlU87q6mrJSMW1e3t7cXZ2VgDszc1NYTaJm7QQAYTe3d3F9vZ2PH78OB4/flwmdMRioeX47ByeBfDPDBSxfhb0mY1wijvY/ul0+oB5JxwKoWajBKvelj33Zc8F/ZvBhr0umaUAeAGYbIwQhsZCAUx7Mxl1NXvjk465l+t4Js8g5aNZRuqNsceCt5AD9K+vr8f5+Xk5wbvT6ZR5xL0ASLPO5MFnj8WjR49ia2ursFwrKytxfn4eJycnZWP4b3/727LBHLc5fZhBUBaI72PZ2tqKo6OjB4CyTlHm7+3RMFj3mTfz+X2oG4oDeXN0dFT2ffX7/fIMz0Nkxmg0KjG37NXiXaw7ywOzi9kbZq+f37eMCODabJC4T8zwUayI61gw+scMH/LXijkrsTogz2/fl0EA/ZEZR+pkmWNPqOvsvuBZvNcy1XUzwMiKORtFVuTUAXnkvvG1gMdOp1P2eRHGi2eaPRsQZZ4HkF2vX78uCQzcbxSIIzwZ3ni8bDyQ9fP5vHjyP5SHJa8nh8XN5/OSJQgyajKZVM476XQ60W6349NPPy0ZLFn3JBNAdxrM5/HzGvV8zca/dXNuh6MHeKZlh724ft6yZ7p/6sgP3gdBAMC2PHVIH+uizpjP+jCTUCZjXB+IujpdWkegYGxkQtQeWK7D+xCxkJlk3jQuwbt5dXVVDBHIKPphNpuVMKvV1dV48uRJREQ5a8fHDYABDw4O4qOPPip1qgv5NXnyLhkXLj+pRyOzLgyaT7pms+V8Po+Tk5MC+g8PD8tpvLe3t9FqteLx48fx5ZdfFiHQarXKBlysTCYcMfNPnjwp6WqdOtKZApxpyeFSuFPNjgJQ2u12SXfHRIqoZhXBeLi+vi7Cy8yj6xJRDfOBgQXY+9koKf42EDGoyGAub2jlfd7HQF9QAFoYWRFRDBmum88XGXsMXAzQYH+olzfZOnNTRJQ9Ohg2Dr2zQUKdptNpYRf5HO+WBRHPzxuvMOSsrInHJyvFxcVFDAaD6PV6lVOkcc3ifs2s0fte6k4szqx+RH14UN0PRi79jSscoMdY4JVgTB17y3sddmfDtI5V4m8bH3WGgUEGa5H16zZm76iJCl9rwzrXA8DMNQYSBvYZyNjQyaw59xoEUZ/MgNoraVDg90YsNrD6WXV9XNeHmclbBoJc36x7/E7qb0PQHhlkEjLGchIgsbKyUuQ5hAXkidvAnLq6uiqHw/pk4IiFx5rMO7TJZwl53GgL9eVn2QF172NZ5kXN/YdhSEjMyspKjEaj4h0FcLZarfjss89ic3OzMneQNybW8lzMxoX/9k+WA/6/DsS7rS4OqXKhzXkN8nm+z+/znPTa5d3eD4p8rAPLlkWWtQ4TtIytkxeZ6MgHn1Ify1bLJydc4D089+5ukZo2ezTm8/s05+CN8/PzssfH/dRoNGJ7e7vonfX19ej1euV69hITaXN+fl6SDDAO1heZWMjj+W3lx8IiP6lHw8IwYtFoADcL9Pr6uoRDEMtGhiAm1uXlZQwGg9jd3Y3Xr1+XUCdAhzud53322Wdl8zLAAhYa5t7A2hPdAMIAlbAq9pBgAbNYWCC3t7cFoGLEIMwMKmDZ6R8De8cumqHkXjOaGVBnZQ0oX1lZKaeVY0yR/tXtxCDC8AOMI2wYQxYGyjrvp6gL77KnBaGRQ+EYAxtrzlnv/RC0fTKZFGCLYOB7G0auE3NiOByWDWFc6zoeHR1Ft9uN3d3dcjjP9fV1yW5GscDznP9QFiWD8zpQgDKycrJiQDbc3d1V0iJH3O+x6ff7MZ/PS1IIM2WsVVhqlDenxlrZ2gMWUQWpntNeb3XzvM5gMYDgcxsY7heUTwYWVqj5XcgI6r7MgOB3ZlVzvQyCHJJJ39rw8LV1GaGyEZHf6feZvMh9l+vsPuEZ2XtgWep60k8OvYxYnNJrgqPX6xVvKAaHQ2FhMK1/0Amcs8G8arVaZW8GzLHr73ljltYs5zK2+n0sn3zySbx48WIp++s55HGLiJIIhJCq1dXVePz4cWxvbxed0GgszsyB5beMoViv89sYg8+yHPC1+fqIKlHj6zNpkw10l+zt8zV+J31UZ0Qxfw3kM3bKMtAyNXtC3GYbPpYJrAnIDoevGWvmUMRsHPFcZws0CcwaRL/w7MvLy+L5Zq0Tak46fcKyeRah2icnJ2UfCGuWcHXS397d3VWy4GV55zZ+m6HRaDRKWNYPXX5SQ4NOwTKkExk4XM8ceEbHv3jxogC+iKiAzN3d3Wi324X9sdsL0Li7uxvb29vR6/UKc24Qz94Kh/fwDIQGg0k4lTdGW6DkSUp4FO5UM+bUxWFTGCosfAsz+ojMSjZoAFlZuOUFHrEALWZfLBy4hueaOYBxs6VtxYlHIiIqnhDGnMJ7CMtCMNAnMBMobcAf10RE2aTFWDWbzcp5JggWUp0+efKkzCuzHV647Ptw2MPl5WV5B0IWVvLJkydFePD9cDgsB0lm1oH++lAWxa7/iHpDw+vVa4+5wBoxCFxdXY3Nzc2ymRfPkw9jYh0Sl+25zjk7rCuUletkRVq3nyL/zorZc8EGgK9HCVnhLwMSmfE001UHeuqMHn+flbGZUPc59TYIcP/wLIci2Xjknhz6kOcC9THplNuRWVj3QzZmKMgvjwkyJYenAmQghSaTSfGQEb5LKCWhuxAuhFkNh8OIuD9t2iGXd3f36S9fvXpV2usxyaF4mclctj/mfS7Pnz9/6/fMqfl8XqIWIu51t/FEq9WK3d3d2NraqiSBIYKBuZyNjGwI5O+WrUHLFj/Hn3Gd74l4aFxYliBfwGIG+XU6ypilzuCpk2v+O7PwWTaYCHCbchau3EZwVm4jY+A1gBHhJEO8wwYO7QWXIdvoO8tYdAT7MTn0E8+Eo2II/eR+dNLJyUlcXFyUeeekNmAhHyHAXKwzDL/N0JjP53F0dPTWa76v8k54NBhE/meACA1isFqtVpycnMTf/d3fldSlLH7SfvV6vfj444/j5cuXcXh4WMAvimhnZyd2dnaKtwPFAfNAFiqMHCsbTz7yHDNx6haBJznuMEAqk5rJj6IyiImoZqWwQGFRUT+8EAZCLKZs5WbgYkMKBZqfQZyxwwUM9LDwvZhtcUdEOZHThkjeJ2HWzoqdAjjJC8mgDHDJBnF7YGgbrMH19XX0+/3KIW4IFvoUcEAsdkQUg2c2uz+HheQAp6enlVTM8/k8BoNBjMfjWubog5FRLRgQdYrR4+01YfbXhmWjUU1rvbe3V8bMhvDZ2Vmsra2VWOvsDbPXgvuzEe/fdWFJWbkabHgOZC9AZvoiHm4Cdt9lj4Gvq1M8nvMYCZn9zoRA9o7memZPg+uW351lZTYGDDocflFHUri9/u3+zsaKn58NDwCXZTHvZo5mGUS2ocvLyzg/P6+EVXjTOP1zcXFR5h7k02g0KqBkNpuVFOi5XQ6TMaD0D2PwbaDjfS51awRZ3e12S5gUYwkw7Ha78fTp08q6I0WqN4Nn4iQD72XAue465nGdJ6Du+ctIBvShjQzWxduM0kwC1MkhkzOuh+uW12xe9xk081y8n5lUoJ05DJXxynKZ54I/MCDAovZecB9r3gl68uGvjD3h7peXl9HpdOLx48cxmUwqh0qvrKwUbEuYVavVKun7G41GIc4mk0khnMGpHBSIDDMuetdwxU++GdwdZJfyzc1Nyd6ABbixsVE2366ursZgMIhms1kWN7v4G437kxZxRTeb96nHNjc3S2Yph0YxMRAMWOsZ2EREYTxhqpjcZvp8fUSUzAUAIQqgxYvDBwIyuR365TAu3gE7S4YkTzR+c23eHO5FjNHC+21g2NDg++zBsPHkiW7FXPddFrI8yxvKYZtZnHy+urpaACP/29uBkrBgpZ0XFxexv78ft7e3MRgMKmduwBpwH+9pNO43/83n88KIMy6wmZ988kkZLwwSvBp5w9YHAFAteJAs6OuAkt31+W9fy34M0j7zGaABQ2RjY6Oc3mtAm9e01/vbUqbm9Ze/qwMHWUHkdeC2WWFmQ6zuWfmZeb2ZnVsWasM6tts+tz97IPisrk5W/n5GHuvM9mYZkq93+7LHqe4a94vfaUMntzWDFuYsMh5ZNZlMYjKZFPnV7/dLEhKu73a7ZS7SvlarVTaHNxr3BNqrV6/KPLBs9PhkbxMG8QePxvJSB9LtIcXYHI/Hha1ut9uxvb1dzkaytwMdZO9cfm4d0bDMsPC9dXN42XrPstO/83OMCSx76/CEjfNcZ9rm9ZLfzTXcnzFAfkbuh4hF6mHXIZM+fk4e50w2gFvQ6chd8AbrksP/5vN5ITFtHIHZIKscIcHBfMfHxzGZTAqOxMNJGC97NmxITSaTsgdoNrvfC0aWVcbL7X7XyIWf3KMRsQih4n8WLSFQDHK73Y6Dg4OyV+Pu7q5kjGHQLi4uSgjWkydP4tWrVzEajUocZafTKYfucbgd+a/NznsBUNiATeYArmGi1lnl5FJnAtrdjXVsowHXXQakGAiElbHpkMXBM61YLBDypih7ACxkHFM5nU5LWuGIqNQxIoqhlkFZNkDMBDKWLA4APQZAHVMD8OdejMObm5uYTqeVeMz5fF6Ehjf/MqaOyec7TupsNu/PRQBERlQPPOR/fpOaEqah2+3G5eVlyUIVEWWeImAYR8//d0kg/JTFjJXDFbLSiqgP5TNQbTQWSQx2d3eLQciYs05WVlZiMBiUzHZ5vwDAOhsI8/kijNFMUh3DSD2tZJcBDNqW35nbzbq1MsolGxEGEHzv9uR7ud7v42/aagOl7jluO9dnI8Vtr6t/7iP3ifu77t1Zhhso1b0r9/e3GWtmgi3XV1dXi9G8vr5eUiyTAnV/fz9Go1G02+3izUBu3tzcxNnZWfGe3t7elk3iDsfJIDL3K3I2Zxn6UOpLnmvNZrPI9kajUfaFdjqdGA6HZT8mQBDPqUOLKF4jGTTnv7Nsq5M9dffx23PCMqluvXnOGgs4pNFyoM6woE1OtmPCgba4n7Puy+uZe8APPNORFqw/19+fuQ9z32SZZTwAbrDuMZYjo6kzhhrb5H7BSwxe6nQ6cXh4WMEd7NflEOdmsxmj0ai022T1dDqNVqsVEYuTz5eFor8r5Z3waGTrGEOCPQcAhtXV1Tg6OiohPoDg4XAYW1tbZUCYoLe3t7GzsxOffPJJEfj8dDqdSipWewoAGwB4vmNzqEEz19orwYADQFZXVyssPNdh7NjbwKTE4MiglInHpngmHCDJSs+L3cLLC8BghLAi+hcDyfcRTobnB4FaB4zMfFqA2VvD/g3uccykMzZwr5mG+XxeDk/kABzqRRgCxYIuGw/T6bTUAYCQQ8F4r8eJzGaEPxDDj6GBkmJTOGP6XUDW+1icLQpP3jIm1vPY88/KibAT9kXZ4OY+58iHlUY5LcvgUgf+83riujo2sA7U8l0GIRGLrC0GGtmwMkMIMHDWm8zs58/4nEIbkAl1Hg7LBj/DQMJeV895Gxx1YKSuThnk2Btr+WKdQt/wTtex7h3+3h4U3lHH0jLXXB/0FffhHSYZxZMnTyp7OCwLm81mCbPY2NgoMdukVrZ3ONcn/3wo373ktQiJFrE47X0+n8dwOIx+v190t4m+vPmbeyMesut169/r0jo1ExWWKxm4+zs/29f5vdzndYEsdDSC17ZlYzZmbaRQ6sB+bndeh7z3bd/7mcvkQ13f8LeNqIgo0RusLbyODie3x9K6h3eA4cA4mRyC1F5dXY39/f24u1ucpwMJPhgMynOIVrm8vIxer1ewx9raWjlk2uTPu7juf3JDgwJ4Q4ByeFqjsYizxqrzZsPr6+sSRxmx2MxL3Pze3l7s7e2V9ICdTqeSD9+g0ynoPHlIVwvw9YF2ZriZZB5s75GwdW1L3ZPYyor3m01EaSEIXBcmdsRDhc//CEsEp2PNHXJUZ6Hzft7tNHEYVHUgifbiMbq4uKiwm/QfdeRvh3nVGSzUhxM2p9NpJUOWBfyjR49iOp1WFDShUvTR1dVVyR7V7/drQ1Zgw6fTafGAGMg2m/cJC87OzmJvb68Iq/l8XpgKg7gP5b5kL5PBdZ2xYQVskM53eL5QFBifKE9CGDGes6fRisPvi3gIiKmnvYF1is/GBfVGUWVw4H1JGWjUrW2vJSvWDGZcnwxGbKCZLMn1qOuXDGBMnuSC/OA5eU1n4iKX3HbXwWvVQDz3He20zMzA3e1+29+5jZZNjUajMpabm5vFs/nzn/88RqNR0VXci47Z39+P3/u93yt1Gw6HcXx8XP6v6/8MOrP8/lAelrp5hj6kME/QDeAH+pjNvNzLc/PzvYb8GXOe+z2v8/X87Xm+7H25Tcvmg2WKPRIZmFtGG2gblLsu2Rj355l0cV3c715Puf7Za5T7P8tyh4ED4hk340ywB7IQgysn/ZnP54XUBA+ZkMwGYqPRKKG629vb0Wg04ssvvyzXXl5elkREHByNXIKIsIck95377V0yOH5yQyMDTgO6bMGzYRoGOuI+ppu8xZubmzGfL1JWPnv2rBgVw+GwosiyRUookic1jD1ChcJE4llMOCYEJwozKQ3mDUh4D5OUZ+S80dmlyWRFYQOIrdxtKGS2guf47IAs3FiUHqeIKB6NvEk+szFe5CxYwCAhRj5fxOym2QQLN4wyv9NKNYdUYfkzZ/JhjAgPnsl7JpNJzGaz4sEyg4NLlbpwngaeNAsTwC5twdD4oPwXBY+D+wWiIbP4lGxEWvAzdxD6sI14SQ0K2FTHOs1rYBk4qBPgXmdclwt1zZ6RvD6WKQieaXmZZaeVKmuE67wm/V02eM2YZ5CTAa7HJ8trt62uLa6TwbqJggy+skHjdtYBFo+jmdZsMPmZdX1e910O0bBM97sghnjG3t5eATUc8MZeQuYRB3Z98sknsbJyf1DccDgsz3b/5v6v61/rrw/l2wtz3yzxo0ePYjgcVsCm92VY1xv0Wl5RuMa6JxOcXFdHfPjvOpCe78tGSJ4ree24vtmQzmve/7vfcvFartPffgaf1YUDZUPEujy/x6QpCXmurq4K2TkajYrurqsr2Iw2OvGNDReIcda6w7jBdrnfVldXo9/vx87OThwdHRXilw3i6+vrsbW1Vd7jCJP5vLqfN8u6d6385NKHTsejYSYN9pgFfHt7G/1+P05PT0toDTH1bLADYOzs7MRgMCj7MFZXV2NnZyciFqEyCAzAaN4ovbGxEe12OyIepnUlxMj3RCwyGRiYNhqLY+9tPEQslJ5PheQ6W9FeDAbfVu5eEDkNGxOb6210cR3/5/hfAxPc/TYEl7GGFqRebPYCkb7N/ZFT2NoAs1Flt66NP9zXgFXmkduJN4zMUhFRSWHr8cV4bDYfnvSKoUcIlT1Op6enlb00jcb9RnJS3bqv3tdC/2eFQ6ghY52Vndej5xRjiUHMnGU9kKkOo5S5EhGV+cqz6pj1OkDvYqPVdXR76hR7VqT2FtYp3WXzJj/H7fD3GMJ1KR0NnL2e6wCLAa3f67rzeZ2H1N5KrnfbXWffl8cgg6x8jcFTBja5zzMQy+3N4+Tf9Dd6wuNLuN7t7f1JwoRFceJ0o3FPAk0mk+I5Rf+hi9yfGVi43jbcPxgay0sdOEP/eF4R5sb44yFF33ptU7L8sEFhMFtnZPCdjZ089nXGRF6Hef1m2ZP7wJiCe5APdc+3gWHCJc/PvI5yG3N769plY57vTEjkvjJpyXhdXFzEeDyuhOUT8QIxxXoB77Dp3z9ZjvE8k9SsechfsJD34G5tbUWzeb8nw+fDtdvtGAwGhZQg6Q9hlpCgGCi5vEu44p2RPrY8IxYDN5/fx+FfXFzE2dlZYYAAiRELhv74+DjW19fj5z//eezt7ZVrbWxsbGxEt9uN6XRaBLCBDP+TWcLCxqw9BpAXhhW6vSURD5kzp7ZlMtIOJjmKCkOF7x3nb6af3wbgBs95kWCZZw8PzyIkyIoYcMbCBRAaVNFG95e/p35mC3x2BnWnLwwAMciIk/S7GBe+73Q6cXl5GePxuBKaQwgeRo3btczDgieD/vCYNpvN6HQ6MZlMYjqdFs8FXjbA9HQ6jeFwGC9fvqww8e9zYcMbhXFnni5Tvnl+eV36gETPI4QyxjsywYCxzqjg9zKFbrDs+3MooY2J7PKve2fdHpW3Gfd17B/fLSMDkCW+j/9dx+yFydf6fcuMIAMxkyX5+/xMGx78nZnjOvDkuud+9Gf5O49jnRGVCR+31x5xfw8hZbkHA3p8fFxkFefx4H0djUYl9Jdzn6hbXZhKBqR8TirMDyViMBgsPSmdsXVYcMR9H2dSCvIvA+e6wrz1HKhbZ54z6Hs+px4ZrPNsSpZTdeti2Vp5W5/UeR15TiZpI+qzzWXDI7fNxYRI9hhHVDeys8ZysT6hP33uWja+iEDIYXDoDWMy7kG3+F1cQ5Yye1dyJAUHAq+u3h+pcHp6WvZj4CnjoEgwMToNEhUsSD99VyNjMBh8p+v+/y0/uaGRBaMZoPF4XIwD0gTipfDCYsJxJgJxlBgKZJYC3JMaF/AYsZhwXA97kTcFO2SIicsAu04YCwbAefCZoN6A6n7h3RGLRcuzzPghvHieBYOFmkG/QRDvajabBcDP5/ehSAcHBw+ECEBtdXW1pHozcKgDHxlkzedVd76FKu5Dj41ZORss/I/B5IxThMYwboTeUT8zUn4/nhZCrwB8PgE0h3CtrKyUfSLUiZOBO51OnJycxHg8jn6/X7tZ/X0tmQF0KBD/Z7d2RDwwMl3YeGtlvL6+XmLhSQiRM8NkYF337FwMNPMaNrDOija3KyvKtwHnZeDe69sgxB7KtxkJGeTQDzao/Jzcdko2xFHcVoIZrOf2WI7UeXLqDKVMdOTfeZ1bbuQ+cBhM7vs8Z+ueZcPT+sPG1MrKSgnrPTo6in6/H4eHh3FxcVH0zPn5eTx+/Dgiong1snHtvqnrp9yH73vhLANKniMRDw35vI7JbknCj3yN9WFeQ35PJlLyGqirp/9fZuQsA/j5s1w/G9j8X7e2c32Wycvcj9kbmOua16+xYV7nrCu3P/elcRcGhvdfWG4v60uH1dposQ4ygeB2+vBp15t2IGdWV1ej2+2W8YA0j1hkO3W4O/Wqk92/S8lr4Ycq74yhgaVIR3Ga4ubmZqyvr8fR0VHZzNvv9+Ps7KyEP1xdXcV0Oo1OpxMfffRR9Pv9slgwMjjBO2KxeMx6k16QA7ty3D6TLCsPCxl7FiiZ1c+ApE4QWRFn1oP9J1xLAWT7ZG2HY+V3+IAaLHnahbUfEdHtduPs7OyB+5062LPAe1nIEVWGzx4XM/psyka5slkr75fBG+EUj7SB92IAwWpjNBDehPsRoYCQcHgK32Ps0CdmsMyw0MfEXNqdzgF+b968icvLyxKOd3FxUQEM72uxYLQ7edk1EYv1yzyIWKwzwksoHleMjJwEIrNk/ryONbRi5HuHLbqe1M1tyQrZrGUGBsv6LCtft3dZHe1ZzNfn9vsnt4c61H1Wx6ZlsFEHMty3dWRFDpmoey46JHuSsqGS2+Y651CvXOxN8bNsXC0bdwwuh9biDZ3NZvH69esS8onR4jM4SHySn103BjbqIEY+lPtSx3zbcLVhSB+iVyGJLi8viwfcHniDVj/bngyKZYyNds+/bMTnNVlHSGTDaRkQpw7LDJV8He2wnOYdxj51z/s2QzcbGcsImjqsmIF77hO8T6SQrSMnWMN8Zy8F6w+MEREFa+GtsAfMOOLq6qqSmTR7ISnGn7PZLPb29oqhMZvNykF+0+m0XON5Rt0pdcR2XalbCz9E+ckNjYgFi2mQikeDU5sxBq6uruLx48clyw8TaH19PT777LN49uxZbG1tlcnV6XRK6tFs5TMpOBvDwiAbAt7QzeBY+Rn8WFky2LaKrUi9qLiO/mAyW/gQY+5MBwi8vPmZ9zDpWSx+33w+rywq3o/lTEYvzvuwcrXBg/fHWbd8bVaALuyTWQY26GdAf8RCeLhfuZe68TxYJ8aH9lAMPAxM2XNBvD/9Qr/bg2TQ0Gw24+zsrOznYP/P1dVVtFqtaLfbcXR0VAvm3seSweSy7ylea3jiIhYhbF57ADfCLJnjWennEMk6T8ayevA3dfguZVmY07eVOgOCz73OMnDJRnFENfQgy0QTHpkM8Ttd/+zJyCEPb9v7ZQ8HxTLYpa6/6kB/NiTrQBntq3tn7q+6/3N98jywfEA2Ol0713U6ndja2orRaBSDwSBOTk7KHg3ACiAjG9d1xfI9ewk/lIfFcth6p9FoPEhzOx6Py/hZT3nMs0GbdRSlzlim1Bks/rsOiL9tTrpevibLEpOirofXcj6slLlsPGODzex7rp/lzTKCA0Mu38v/ed27GPRDZlqng9kgZ5GL7JUAWzmk19k7MynlsQDX+owweyR8rAKEKCRpr9creIN+vbi4KCF8lqku0+m0pL19V8o7YWjkSUnHXlxclI6+uLgoDHO/349erxcnJydlwj99+jQ++eST2Nraiohqbnuz6ZTMFrCPI6LKglrhekMR7+A7LzQ/NzMSbjP1YjIT6sTzsIB9iIwtdhs2GBBsmM5K2kZBHVPivmdh8D0C1fs/uNaLh98ZcNTFb3I/78bqx1hqNBrFQODQOy9Q6ujFzfe8n8WIIUa7CT84Pz+vGA6576kzG8ERHIyHD82hbznIZzQalWsJAWMOcfjW++7NoKC4fpfrPRfszSCLFQY6ezZsrPoeGws5dpp3GbRHVEN0KN9mYNSB+WX32VDIgCIDiLcZLAYxfiefObyH4ud5HS/by2Eln59jYFJnILkPMtCx/M1rJJMKuX8sVzJ4qWtDNjbqDK26NtQZe26X32ldYvaR/W6NRiP6/X70+/1CnjUajbJvA4KEvWSev7lNbnddPT+Uasn96P18eDHQUWwmrkseU9fHEHAG4ZS3GX8GkMt0xHchQ+qMEbfbf3td1hlGeT7ZS2yCNhsq+X1Zbrmueb2D2+xt8POyQcRvG254YDJxwn4M2mMs5TXaaDQq+zp4x+rqatHtYEfXrdlsljO5jOdy6CbYx22DRItYhH1jjKD/PP8oFxcXlays70p5ZwwNu3rpOKcpJQyIwdna2oqjo6O4vr6OJ0+exM9//vOSeg4QSFYpQnEc3mCgT/paJirWLpMIpelwHmcUyEoox+lmZqAOaPCsnAqR68yew8hSN7OzCMUM9jFYIhYT2iFONiB4h+tpjw73O6uP7/O42nBxexnvZXtI3JcIeO4HsJN1gWfCMjAHsuIw07K2thaDwaASmpAFdxY8vN8giixYPJcQtHa7XfYYTSaTisem1+uV930wNn539y1zJXvUCFsjjNBGJAaHWaiIxRjXGQ91gDFf9zYQZwCb133+P7ev7nd+ZwaZrnMdSM4Knr6yzM3snL+rKwb1rH+Pp99pubvMcMrtXQbk3wa+bEC4btYvbucyAOjrLc+zRzp7OeqAFO+wRxn9Ylmwu7tbvLUHBwdxfX1dvKJkmrG8r5sDuY8+GBn/bgWdhVGxuroaJycnlXnAHGHuezzqyM1c6oyJZTqhbszr5ECWW5kEqCt5DSxbn8YTyNnsHf62eWdDvq4OxgDgDkKgM8bKeCKTKu4fnmEi0u3htzFXxmdgBQhQ6g1+8h5Q+sgniNf1u3UZ9z169KgcWk0/QMK6GGvOZrM4Pz9/J9f7O2No8GP32N3d/YmJPreBRe/0lBgZjx49KgCUECKAxMXFRays3Kcqs7Fga9yCAgvVWQRYVJ7grj8L0d6AiIcZZurcXVjYzoLlhZ1dtFjcAF0vdCb3fD4vm5Ecs+c4VC9uW/aESlkpI3jpv3wWgQGNN967b/FCAEj8fpRps9mspPNlEdmTtL6+XvGy4Gmy8eRxQujDTNAvHMaIMUq/kNLORm8WWLgnYR6azft9P1dXV2Vf0OXlZUwmkzKfbm5uotVqPdgI/76WZWD7bYU5w6Zu/u52u5UxJ7SOMfNGvTzv64yMDBrqWL7Mhvt+K7JlITjLFPQyUL3MQKkD33Vgsw6UvO2+umKgYMVvQ4Lr8nsz2K8LRfL/y0CLZaPHxHV/m+GSwVedEZiNiWyUZTCz7H+3JRsh19fXBYg0Gvcp1ff29uLy8rKECyMTeTYeX0rd3HVd6vrvQ7kvnmN53NGvzWazgDwOaqWPvY7RdxHVM3OWzWEbKbm8bS7m+nN93TV1sqnumcvWu9do3Vp2fXM78npfVupkHHUH+yG/HfXheW5Mlb2y4A2IJs5TiqiSXHVkqj+nbugbiknN29vbguOoDzpqGXnLM6knoVHr6+sl7HcymZRontzvDuv+XSIDfszyThgaEQ8BB5MsIso5CxbUs9ksdnd3Y3NzM7a2tooBAcjG9eQ9EwgJhAdAso6NYIIy0V1Hs5ooIMc7Zys5K2a3GVDutIX+zhY9n0dUDzT0YvaCcPiYFav/ziFV9CFGGxOf+MacGccWvvs/GzPUyWDPwtrjYCPDC9Fzw8bPzc1NYReoF6drNpv3p21iKJDRBWaD+bGxsVEyaPmsDvoY48/CiFz4tBvPGMCAU8OPjo5iZ2enhPU8evQoNjY2SmrXOsH9vpR/l81orL+1tbWSTQ4XNUb+eDx+EOZnI79OuWbl67HhvblkpWYlnhV0Lm8DrMiMLA8yAPF1GdjmPqsDL9lg8nNyik8/izVoht5r1vXNss/rOSv1ZeEkdcB6mZFm0FEnb/k7AxyDKst3/8+77HXN7fa77OGxLEOOwIwiqzgband3N2azWXz99dcVz4dlRR14WzbX3kfZ8ruUuvngucSYmVU2DvB8R8/VhWL6XV4bEcuNiLrP64xYr+n8zPx/lm3+Lnsgfb/fV+eRX2ZsLGtXljl+lzECeKLZbFb2lHrtZdnB+IFN2GzdbDYL0biyslLIbOqFQcMzLKvAasgKkwTgRB/o6DabXHS/2gsLeZZJXfrIhI4L+PZdXefvjKHBpMqWZrPZLPHuGxsbcXt7WzZj/eEf/mFxLUVUrcm8j4AFMZlMotPplLzFEQ9ZHwA050TwHBYXhg4LLRsQFkB1C8HKCXYKcOsFxXMBxVlo5NAAp8ul3Swa2sH1ntQGRHyH4YYHiYPmrFzt8ue5PAuWn2wKBkPz+bxi9ed+sSByJg+uR0i4f1iYGA64WfGs4PHA6qfPWKA+8dPzIseAWig0m82yb4g0lW5Lq9WK8/PzOD09jeFwGBsbG+WALsY9G1HvWxkOh5UDDL9LYW5bkfd6vQowOD8/f5ChxwZtLhkIL3N1u9SxfVZ2+Zn8zkZD3XP9DH7XPcPX1BkX2fgxoLISzWRJHQCpq5MBi+tjME1dHFOcZSbXGOx43RmU8LfXYjbMXHL93CcGepksWgbg8vhkcJr7yvOkDmTNZrOil5DZpF+fTCalL6hrzqpVNz70D4W18KE8LHWAPoPOR48eVVJm1405c4QxtBHC957Xfq/XSTakl82vZZ/XAfss9/Kz83MN7i3TvN7r5OjbDIss92xE8Owctsj9PlOojjzI65LvMAaMS9DD7LmYzWZlHyb/g4vAXtQHw4XnItfAjGAPoi6MQy3PICQta7jHeoysquATDBaMJ/ru/Pw8Dg8PPxga31Y8sSPuB5s0fxcXFyU1KDH1T548iWfPnsXq6mqMRqOyacuGhg0Onou16VhuM+QMnt1QTAJPfK43u8d3bpP/5jvey3OzQmNPgEExk59noXCI2fXE90mReCeazWbpTwwb5wA3Q0FoEJY/oUYZHOR20z67O7N3wGAdz0dmbm21u8/X1tZKggDHl9voI43xfD4vTAWLF8+GQQreDU6LJstWRHUPSwYrngNs1rQwgS3Bi8H8ZI7jTbPQ/rFSzb1L5d9l45pBMXOFceOEdp954jHkPsbBY2YDug6wWhFGPIzl52/WcB1Q+C4lAxd/5vdluUEdc53z/35+vtdgnvdkI8ftzPX2M/g7t8cKt85IyiAmK8+6MCa/3+OagY9lUX52XTib+y17glmzdeFfb1P4uf/5jBTtyPJOpxOffvrpUq/IsrrWlXdxg+hPVbLhV2egom+Q2cj5ZXH2frb3gnoeIo/q3vk2b2B+Vx2Yd7FscBuXgXHPWYNqPsslhx16TtowyWsh63SuzQRCJg3ycxzOHlFNCOJ28x1ttJeAbKQ8A6MDPEDdMsZDN7DufT6QjUv6AF1lmcdv9nxQX7ApbeNe2re9vV1JBgHOuru7i/Pz86Xe4HehvFOGRrZ0KaQGxWXZarVib2+vbPimswldYWBcbDFmUOeJ4LMT6hZKRJV5M7BkIdWFZ3giZjaWSU04FIYCz15dXa3s+2AhooywnDudzoOMWNSD9/p/hwV4ceIiZkGxF4F6LgNi3nBPnzjsLDMq7uMcUmWASFudOpdn0i/25pC61qFe/O1xt1BjPxAGmPe5eCyWzatms1nup74RixPHr66uKimWmYf/rmD035fiNMPftTA2zPuNjY3i7VxZWSlnlDisz2vNXslMAJiByqVOkFuxZrm1bM77f3sus/LmGRlc1IGiZfMnr7MMvpcZVnXGTt3nNkayHMlAJ3tYcluyzMx9loFEXT/UGUB1fVPXXgMkv6Nu3PifOePMNrntGawhMyKqgOT29jYmk0kJ71hfX4+NjY3iXbZnJ6I++xltyePyrsZu/xSl3+9XvKiW4cYOyHATUhBHy4rXUzYkLYeyMZ09dHXroq7UrVOD7vxdvtfGxdveVYdz/K5lXnnLOPcDf9uAzhjKIUkYfN5H6QQzkLcml10HjEYbBxFRDnGNWBCk9Iv3gho/2CBi7oDtXBf6Ju/NMB7jfBb+9xhwPdE1Ozs7FV3lSJe3GZ7vQnlnDA0KA0InAvoxNprNZjx9+jQ6nU6ZWL1eL66urmqVnENvCBXiPc6axDvsKvPCNWjICzqDAU+UPBmzZ8SgHHcYIN8Cw16GvNBoFx4QM4n8dmYlKyD/HxEVF6K9Og5FssGRjZn8XIoPqsrCNBuGjUajtIfFjaFBH9lAy4LO7SOsykKK+mSB6f7CaHW+bHu+DJ4ABGdnZ9Hv98vnvBOW0gIQgwRhukxQfygPi5VQs9ks+zQAAJeXl8Ubh6LI4SaMZ8TDjZt1QtuGbyZE6hhty4NlijuD6LrP/D7PeeZrds1nYyi3xeuyjjDJ78tGmn/bUM/tr2M9vVcs91mdAc+z6+Rr3bV+f13/1X1f127+rgMsrovlrI21rD/qAF+ee8gjdBye+fX19Wi1WnF2dlbbz8tApEsOo3rfS12opglAmGU2D1sP4B3PpdFY7LXMuABsUWfccu/bjMZvM1qp/7L/M9FgoyE/L6+Buu+zzjcRt8yopw/sMcnr2u8w0+97LDe8Pu3l8N5cCms1G/gG+egACEbqYl1T59Fm7IlgYP5kvJfvySRPxo5gQs5Nu76+LieEmwTN4/YulnfK0HDcGZ1MyM7q6mqcn5/H48eP42c/+1m02+3CJDUajZI5iGKrtNFolExUAF2sW1uFmfF2yeyzJ0dWKHnRez8C1jWTxIA4MyX52QZH1Pf09LR4c6z8aWNeYDYSaBNtNpBm4l5dXZXxqFOcdhfaes9uR4AGm7IcNuXxp9BHNpjIvOADCulDs9tkfppMJmUONJv3oWNOWRwRFQHj3yicrCAYL2eM8Hka4/E4er1e8XCQKQLw69hLMyAfyncvrAMEudPW4g00S5VP8LWB6/GnLAOoVnRWRC4ZmFPepgiWAec6QsIy4NvmjQFFRFUu+Zl1BpGNEdcr90OdTMxkhte15YHf4/WUxynL1ToA9LbCcy0b8/jUgcAcrpQNPt+bDa46Ay8bV7m9EVHkHHKVUFDPZ/fVMiPW/bTMeP5QFsY0esRhT+12O25ubqLT6ZRkIgDRurmZU9NT6nSnQXZdnequ5T352ryW6wwT61L+r5vDy4xWg37XC2yALsvkBf3rub9M1hiHuX8A2sz9LP9yP3Ktx8PGP30I6Yxxj6GI98rECN97P4a9CZADPlbB+KrO2GPt2yNKP/C3ydHJZBK9Xq941iBiv41oeBfKO2NoeCEY7MMkbGxsxHg8ju3t7djd3Y35fF5h8IfDYZyfn1cmPAO9vr5eNt96oQEYGVQPWs72FPFQ6fp3/tzu17znwHs/5vPFKZNZiORFbbDB37e3tzGdTithHyg1AwwbOvSBw6M8yQEKZJrKVngdWKjz7rDAc2gGQtv9VTf2XvwYCBYWGXAyzu12u5ygjvJwVioWL+/G8MFYMYvqE0QtnKlrs3mfvYxMUxzmtLGxUb5vt9ulry2YPEf/KQiLd6F4DgLQfHhWPhEVhZNBP3OrDmBmOZHfz9qqW5t+ntd8nZzw93UKetmcMGh1XXMIVw7HeNuzl+15yJ6Juv6wYcE76xTg2/qV8m1GVG57lk11Rswyw8B9UWcg+rdlWv48/87g5m3zA5mfdcR0Oi2EBXPcuiOnE68ry+beh/KwOELAHg3k9vX1dZmT6ASPqYFlRHWvVvaEe9zrSp6rbwP/+afuGZkgqVt735UUyZEKdfLBWCATt3Vrw+/E6DPAB6hn74D7P7fPa8VhTFxP//usLN4LHiBkiXo5DNzhceiSVqsV7Xa7kq3PHtw6L6/JkzrPEPeBd/kML+g/FQLhnTI0GBhbi+zgb7Va8ezZs8qBKFi4AI9ut1sApZVDZt3rLOCIqiFRx+RlBZVBtI0FhM8yMBMRDxZbrqMXC4ssg3Ym9OXlZSXtWw7Ryv3smF0WGM9GoMKiZUPIBowFT51QzKEaFIdo2TDytQhoFpVBPwWhwDgTV+tF7SxT1JHPYLtzG2woYkhkBgMjgntQNufn5+U0auZnr9eLy8vLB9e6DzIg+lDqC4I94n6cHz16VNJOev3zPWOQw1Uouc8ziM9gPiuIZYA6A13WQlbWGRjXtdfr096ZOhCRgYANAb+PZ9cBmQyq+czryvIg90k2cNzXdf3qd2bg4Do6DCgzxRnku61vM3KyIZbHzeNpGVJ3Hf1ieWkiJ/e9vRPuh5ubm7i6uoper1fk1MrKStm0yhx+GxB1/1hmfiiLkscdwEmYr0OHAaTZAwrJ5aiBiAVIzPNj2Xrj3oxR8ne8k2J5kudyRDww+pfhEfeD/6+TT3XPcx9m8vZtsslA315ErvFndQREljOWIcaQNga53ns6APqOLsHbgF4xLmButFqtytltdZjIxlXei+Hr3D/eE8L1nlM2TN718s4YGhkMe6I61u329rZsuiXsCcYSFtmnP3Kwn5/vOD1bqvmwHepiQG1llhU3vzc2Nsr/GeDwOfH7/O+JZhDhvQFWFt4oxWLkffP5vCikXE+eZxBvIJYPJbTXZz6fVzwjy4QN7bORtWxBOXsU2a24t9lsVg6rwitFOJcFPP0wnU6LB4TzNbK3hHd7s7hzZ2N84A5nbtmzwRiwl8Tz4ubmpqQCZlzYRGjAklmuD4bGtxcMSvoOBQL4zooKAW8wnIFCnWvfYNnkAt+7Plby2YhwsWHwbWOdwXwG8ZaPy+71O97m2chrMz+zrp5W+LlfeG9m8PjO8vC7ZOcyIPH/+R7XMxtXbl9de2xQZFlbB478O8tBz4OsR/LYG4Dkvry8vIx+v1+uabVa8c0331S8ccv6LM/htzHo73MxiOTsKHSzDUV/xm/kSU6H7uLrsjGQ13j+vA5A+3f+LF+f53CdgVG3fuqwWN388bzy2rKuNaGZZQLz3vfxLJMJJmTdR5m0yLI4X+PEMXW4xGvW8s3ZqmgzYboRUYwMsnt6PDxOWS5meYU85D2eT2CvbLhlGf6ulnfG0Ih4aBHbnQ+YjIgYj8eFxfTgNJvNysnftoDv7u6KN4QBxHo1SOGdBu4o3zyJsncB0OvYwQxyMAry4sgWMM+3gLLFn++JeJiFpNFoVEAw7fZkzQfVeJE7NAVhzPvt6jcIsOfFfQVAtOCiTmRbuby8LGNAvVqtVuV5OavVfD6vpAE2iGe+4GL0XhTmmOca7bYHhB8O9OOZs9ksut1umWN+fqPRiOPj4+j3+yVfNmFdfJ89GR/KdyvMo+xdMnPvEMKIeCCsTRQsU7R1BkYdK1y3BjKQ9PO/TSl4bvDOXF+zfH5fHbDnvXVGUFbKvK8OOHNdZvTz87IC9b08vw7M1/Vp7qu6PrV8rgNZNmzy+Obn1ynwunbxvFx/y+EMIpysILfH/2fD0HvSICuYE/bU55JBzoeyKIytMQAe816vFxH3uvz8/DwGg0GcnZ092B9pwsJ6NMuBOqMhy54cGZHrap32bevCciu/19fw97L+qbs3P8N143q/u468oe5m470++d7Yz1EuuY6UTEbk/vG78HDUeaV5v8/IWFtbK/swXScnCIDQzv3knzrPcp0MMT41rkKGNBqN4sF3FMu7XN4pQyMiKpOKxWXwPpvdbxAfjUbR7XYfMF2cuszZBp78BtoZZLpkIwCDxpM1W8KZWbUg4/kGwzaCIhbZsXKsLu8wwDdY53rSqHI9YN3xgn6vWQqegfJyWA/PbjQaJf7dRosBDG22IMxhFWYCaR+eBTb+Uz8rbowdwCJ9x8ncDrcjZV02qvwulMt8Pi+bwWgvxqGFQLPZLAkHms1mmWfOXuV5Q1273W7xLrVarTKWzKn88yHE4e2ljsm2MM9K13M1G3oG7CiViIdegqyY6wBrNkrq6gKZsUzZZyMjG7GuYwb07oc6pZM/573Uh/lu2ZANKLc9z1O3PQOdLMtczPj6+XXP9P0Z1PEsFwNDy6Y6Q6eujXUGUza8sszLfcx7PI4Ol/Jz81xYXV2N6XQag8GgAjYjooT21BEVuY7/FIDIj1nQmzl5jMNjm81mTCaTIrNJOcz9JuW8liKq7PgyoF8H4JcZh35Pnazj3rpxzl63ZXPaJRsadWuyzgBaVo/c3mxkZTnKdzD4jsIA+NfJFRs9df1iOZPJ3rr+MBbLRIYNw5WVxXlhlp3oHcsh18njk9e3PTnci/5gIzh7QSEj3mX88M4ZGgwWqbzoPGfpub6+jpOTk3LmQUR174EP7bMis8ueSeswpbwY6lz02UI1ODcwoHB/XYy/FxfCj3f7XgOArBxt3Nj9ZpDgd1ho0X63y+3AiiabwsnJSUyn0wp7UMeo1QlJuwM9Vg6JY/PV7e1tbGxsVELeODPk6uqq1vXoDd42Hni2AZ43ftmwGY/HxTjDcLG3a319PTqdTjF2IqJ2kz1jNhqNSrrbiCjxmx6XZYzk+1K2t7fj+Pj4OwOhLKytsJYpdRvk2bPncKsspJexTVasGWjWGRiUOiPjbcCZdtYx6NnAcd/UAd63gXbLOSvrLA89VzM75zHxO3LYQ65/HcipA1KZnMDw4jkGBRH1m8mzzK0DPxk05brl/s1z0MXPZI74vJ+3AUuHxiInr66uSqpsZJDnTR2I5O/hcPigP97X4sgAysrK4mDbfPJyli38rgPZxhTL5lkdk15nfFhHeE3keYmMqJN9ru+yUnddHYZxHbNHIId7u2R9bRDu/91/1q8RC9mZvYW5v7yuchRKnUFkHZxlkteusVg2RHxPJqSznM7/Z1mHriI83Lis2byP0nCWS67vdDoxHo8fJEN5V8o7ZWjQqRganU6ndDr7HlZWVmI0GsXd3V2Mx+PodrsPYiTX19ej3W5XMg1FLCYJ8ft1oN+bveqYp+wBMXjM4IP/AcFWCMtAEX/7c9cls/1k1WEjtA0WGz51sdxmSOk/0rPR1yi7RqMRm5ubJdUc+xMIB8gAwYZYBmJ4E1g49Ov19XU5Bf7m5iZ2dnYqwJw60z48Be4/3mWvRF7IeTxR/hcXFzGZTKLZbEan04nr6+uS4pbr2RyOUWTj1fOMvr24uCjPILMVCix7jt5Ho+Pk5OR3uj6vjYgqA+0QS64hZDIDBRsZVgCZgcrvz0A0A4oM+nJ9/dvvyAptWalTlnUgnt9uT+6rZYZBXsvIgToAVfd8K/1cx6yc6753X9T1td+byR1Kln11BkSdss999ra+9neZzMrG2LL9dnX9btLp8vIyHj16FOPxODqdTpEteDXyPK0zOM7Ozh70z/tahsPhg7M0ALf0p+eKAbH1vEGsPSHcU0ccUPLa5/kZ5C4D/3kN5LLMWM7v8vfLPB+us9vJGuf6vPe1DlQba/kar2Wnv8egBgeyh9N96PtZ7/4/92XumzrCtW4cvJYzxqnrp9z+3P/U1X2Zx4XrSMVL306n04o3nrn7rpZ3Ctl4UXO679raWlxcXJQJOJvNKif/ksnHhY7H8otYLCIALRPBE9J/8xxPvOwhcWhWdp/bGs6TNj+/LnTLAiui6qpzHRuNRmxsbES73a6kdjVzZk9I/tt9FhFln4SNGlzMa2tr0W63y7scLpbrld/l77ynhv/rwhjoBzMHPIPUu/RNPvHd9Wo2FznOee58vvB0zOf3HhB+ptNpHB4exvn5eYnn5P2EqDG2MF8I2sxmjUajkgs/IqLT6VQMoTpB9T4VG+TfpeS1FLFwg9uo5Jn2bGYFY2VNPfJm8vxe35e/pw5+fx3QMKD0+Gdg7efld/t+f19nvNSB5QzYvQkxX5/Xpj/PAMve4rr61tXZ92Yjxv2V60DJ+9by+Pi9eUz8LL8/k0dvA40mNbKcdZ/zPmRaJqdogwmIiChhmBA8nBvja5fVzX34odyXZUZXnvsRC/lU5900SeT1kGVA/pv1Uafz/n/tnd1u2zoQhCm0aYoURS9b9P0frMgL9KJNazl2dC6KkT+NlnIOjnHiIjNAENuiyNWKPzvLJanPFRlnXt439GYOq2fsoSIZLgtlcjLu7dLlcRuL4/DT0/qk79baIsRZZ2RxLKfevA+r+mHv2ygXQ7CpW/YfPH3cI0QoB50y7G/9/VAntDf90EARYK9j7N+3nFMvjWdToHRUQWutff78+aVFCF4x3GBtbbnuR14dXfcNGpz4kly4F8wHThrBHAQ4yNBjx3I4QDuxpEwcvCuSrrxIqnzwlKHseqvyorwME6DBLbkq8uVGdGunmaLKcKMO+ByUl4abk48tY5/voHpu5k1duIw+C3vOeKv07++AebCukpx4iIzu0wJw6URbanPGPXg+qndZHcDmXunWltutMsSHOwoKFWnmNZfJHQ89QlwZq+fqfCVTVa9dfv1nW6106G3f+y+h6vvYN1CnlEl9AUO0nLRsOY9dV04iPC3LYhvzfpHPrrGEeu05UFnmNE1zmLjKlUNTYZM8nb73bJzJvzZc71xLEASBwWcBFX6ngY0DAMMaW1t79N3z2NraO0ejY2vg5T1VPhpEmF9ry4GIIZa9PFzOc0azy0SiQnkkS7VmzZ/JQRlIHGSUORFxg46oPI/UOY2OnrHDenAuhKH3/hwuz5ZO/N1WHl/NuHJbbS+Hhog2odjv94sdcEjGKpwjScEfcDMVh7cbvTduzMLZ6Yp49gxU9iEkmu6weG7drRwc/N5zkFR9oc+6kXxVDhblS9nY/kSOaYR7+69mnn2DHM0sPD4+rtbb+DPonXAGQvp1GSmXZPX36ff3+mHpgJEpW/2LCAUJ283NzbxswPs/9i16ttvb20U4+TUhRCMIgr8Gmp3goM4T7rUTD9O1tpytkNeNYW/uTXYDoTKASQgqsqLfeD4PB4nKAK48nZKfIZY9+Xi9Ik1+jXqlQeHPQ91wAK0IDfUpLx+fjQZGNVi7roWeR9XvI5GrdMz3S7guqzJ6eXod8BmRSqd6nyIblXwKKVH4MBeDUpc9osF0QY1hGGZjzo1cfaaB+f79+4VRSE8zf6fxzfbr7aDymFd1vTLwq+9eBv/rGg1gDxt1I9wN7WqWwNusG/HsL6o2MwzDIiRKuuS90rPSqm24PHSgaDaAOtZ76IVccn0UZ0lUjsYWzZZrDNqaVa1IBvXqu2Ap3JvrVZSv1hjzHDbtQPXz58+rbOshGkEQ/DXgjIbIhtYQceDWoFV59HzdUmVYnjPclN69kvy9iuF20OPXk0XPTdJRycJndI8ojWgOaNQl4c9frSdgWuap9FV4EPXieuC6Ksrqz+gGoRssvXSu00pnDtefP78TDteX6kYv9IykmCF8btzsdrt2d3fXpmma63ZVboXnpHmtEMnwmbxevfP1gnJWVGTPHRieF9tftU6zem9Vv+QbvTANPelV/d/v93NonuqaDFzVT6XVs0oHWqDsm+cImiGq+jgSEM1QuA4YQsXzYyST9KaZYOrbyUbPedLaaVbc+xRd93Ub3l/c3NwsHDVVPdhqg7r++Pi4OoxWBJjv8eHhYd6dc5pO67S0bjZEIwiC4D9AAw0HHB1KqYFAO6dVRr46dI+f99kJN8q3wqQIDl7uqVRZXo5+9zx7BnWPBFVkopKNz61B2e+lXtzD2tp6EX/lbdXvvvWsy0PDrSItXkbPK1oRhuqeSmce4uH65Hf+1hvU/d0yLI35SlZtqU2CQcKhXRhpnJ0LxwjOQ1vhC17H+buTZL2XrRAbvaNqZzQPtXJC0jN8BdZbbxOejv/1eb/fz/JTzook64yr1tq85TL7YYYB0lAehmGxSFt5cGfLarMXkQp58znj5zPD3I7Y24cMdz1Ttfum5PJZY7Z3OkrYZ9Kho2euiJ/3b8xTMug+kTzNxvC9Pj09zSFjrBs+Jl4bQjSCIPgr4B2qvnOgnKapffjwYbFtssB4Xoa29AxzH7zdkDjnAXeiwvQcuGjQM//KYGE5lI/hQpTTwyB8rQnDCiqDlempY3r7GOIgWVU+PZfaUrzSF403Jw9MS4LDvCiT3+uGmBMq99xSv9Qj4brqGfvMg4uImb8MXRlV8rBKHyLHLIPG8RbR2CJZwQmqO759vF+Xh1wG9DiOK0+8tz99drLg30lifRbA36HPaPlnl0W/qx/ijotKp5kMfVefwi3saSAzZNXXt3CGw8/cYP/jz0lCoM8iNuxXXHa9O+Wh7yQGuo8khfKSHPk7ZGgTt+B1p4H06+/F+9eqjqmdsz75du0iVD77KT14nteCEI0gCF4MOovluYYQvVOtnYxnLqZT/C4NaA5OVYfsRip/rzpvN0jdw+X5Vh7oyjPu9yvdOUO3RzI8j2k6HVDJnbgYl+yez6oc/t4L3WDsN41rGh7u6e3pSflRx9UArvLdC8j75emsZKYn14lhr84QNOy9TrF8wsNHPNxF9VuGnfTGMrcMDLWJYI2KvG21dxJ3r/dVveL9NPTdk1+lq2Txfq/37qs6JnC3Ms68VmtISDLoqCCRUN/CvLwNuJPGiRFn/PRd/bXWI6h8kQXOvrDtVO236lPZb/P5OTPg1/Qb+zOmcWJI4sjnF9mrZooVQiUnkEgX34XeI8v6N+Po/40QjSAIXgxfv35t9/f3z+4gfeBq7RQGcHd3N8fL0ojWdTf+3Ju+hcoLThl8YHIwxIEDlOe75Y10o3prEGeYDvNg7LKXz1kR15HKrQ7o4jP6oV2Sz2OypTMaX5SdoRaum54X12cl+JxOkPxkcX+vzNs9sCQ6TsxcX25wtdZmQ0LpaXzwEFTpkcYX03uaHoZhaF++fOlef23w8BY3bvUuSIKrELlxHFdOA85c0pglqvUcFYFn26gMZf9cyci1ByQLMsx1XkPVtrT2zeu7rpOwqByG+mydsUXQeUAiUfVfOihXsug0dxF16cFJhIciVjI5YdCf+gr9TkLB/55XRQLZF/p3/eb9g8sv3QoiJzxb7NoQohEEwYvh27dv/yq9G4IaZBTDrnhdbX/Y2mkxHwf8aoDgoFANEO7BklHssyyCe+zc26UBtjIQeD8/VzMiLlNFdLa8tE4avAwN/G4w0RiQ8VYdeFitJWBe1UJYpXGvseu4Rzz9M3XtRqGTQzcG3Cvpz9PzYvJa5blkndE1hWVw9qIXZsFZqC0cj8d2f3+/meY14ePHj6uTwd2wlRHOa/qs3e00i9raug5U/ZTy9Vh61SseYqvfOcOlmS1fJ8J6LRk4I0B5GHLEmUW2M4ZVeb/C9RFsN8xf+fEg1aqPdVTtkn2nDGqmk4NJW5l7P0By4Qa9Oyb8DA+VI/IksiFS43rhzlB8Lz5DyT8nEJSZs0Y8BPDNmzft9vZ2dkoojx8/fpRrUK4B17lyJAiCwODeYg6C+/1+Dpli2uPxOHvmPJ/KePPvXtY5g74aNKqy6Gl343TLcKw8j/zPwZZlVgsFK/JTzQowL382hkdRXwxlUxiAZJMRosGTxo9DA7t7IasB22Vura0MJumBhpJfo+Hhhovr1z291ftS3q57eptbOy0CZew38/Dn75HkSoZzZOQ1oUdsqaNPnz7Nn4/H47wAl4b5NE2r+Hn9V5to7XSyu6+XUpnKfxzHuU2xvnNdwffv32cSwTh9hdZp3cg4jov7uI5Nz7Db7RZ6UD7a0UiGPBeCy9hWeuX7+/fvRRvSuSQMEePWqzTaORs8TX9CgHQvyYyer7U2pxEZlPGvtRP6XTplO6T8h8OhPTw8zHl4nyC5hmFou92uvX37dkFq3IHx69ev2fHixEfpD4fD7ARjP8q/cRwX+jocDm0cxzkP6UeOtXfv3s0HeV4jhik9UBAEQRAEQRAEF0ZmNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDhCNIIgCIIgCIIguDj+Acb58mzwTaT+AAAAAElFTkSuQmCC", + "text/plain": [ + "
Figure 2. A three-layer neural network example.
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xy8Est9bDcPi" + }, + "source": [ + "The neural network described above is a relatively simple mathematical equation with three inputs ($X_1$ to $X_3$), five intermediate values ($Y_1$ to $Y_5$), and one output ($Y_F$). It also has two distinct sets of parameters shown with the letters $w$ and $b$. As a neural network is usually an extension of linear equations, these parameters are called weights (shown by $w$), and those which do not are called biases (shown by $b$). \n", + "\n", + "You have probably seen more complicated neural network architectures than the one above. But, one point that this figure can help you better understand is what some of the frequently used terms in deep learning really mean in a mathematical context. For example, a \"node\" is a fancy name for intermediary outputs of each of the calculations happening within the equation ($Y_1$ to $Y_5$, and $Y_F$ are nodes), or arrows between the nodes show how some outputs of previous calculations are regarded as inputs for subsequent calculations. Likewise, a layer is a collection of nodes at the same level (i.e., they do not receive inputs or send outputs to each other).\n", + "\n", + "However, deep neural networks are much bigger and more complicated than the three-layer network we presented above. Instead of three inputs, they usually receive hundreds to thousands or even millions of inputs. For example, a neural network that receives an image as its input actually regards every pixel value of that image as an input variable to its equation. Likewise, neural networks that work on text data regard each word in a given text as an input variable. The general principle, though, is still the same. A neural network is an equation that receives multiple inputs and generates an output that we desire to be meaningful (e.g., a correct prediction). \n", + "\n", + "\u200cBefore proceeding, let's use some real numbers for the parameters and input variables and see how it looks. Lets suppose that our model receives three clinical input variables for patients with pneumonia and predicts their chance of living as the output. We can assume the input variables as follows:\n", + "\n", + "* $X_1$: patient's sex (1 for females, 2 for males)\n", + "* $X_2$: patient's age divided by 100\n", + "* $X_3$: patient's white blood cell (WBC) count divided by 10,000\n", + "\n", + "And we can assume that the output variable should always be between 0 and 1, where values closer to 1 mean a higher chance of survival. Using the sigmoid function as our activation function for the last layer, we can ensure that our model always generates a value between 0 and 1.\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + ">\n", + "**Note 1**: all values and parameters in the following figure are imaginary and chosen randomly. It is also very unlikely that such a small model can really predict a pneumonia patient's prognosis. We just made up an example for you, so don't take it seriously!.\n", + "\n", + "---\n", + "---\n", + "\n", + "\n", + ">\n", + "**Note 2**: we used the sigmoid function in all layers of this example network. However, many more alternative activation functions could be used instead of the sigmoid (at least for layers 1 and 2 that do not necessarily need to result in values between 0 and 1).\n", + "\n", + "\n", + "---\n", + "\n", + "
Figure 3. A three-layer neural network example filled with random numbers.
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EStOXNAyQG7m" + }, + "source": [ + "Now that we described how neural network equations can be useful in helping us solve real clinical problems, let's go back to our own example in this notebook and use it to explain the big picture of neural networks a little better.\n", + "\n", + "Here, we are looking to build a neural network that will receive pixel values of a CXR as its inputs and then rely on its parameters to calculate a meaningful output for that specific CXR. For example, we may expect the output number `1` (or values close to 1) for CXRs with pneumonia and `0` (or values close to 0) for patients without pneumonia. This is similar to how equations work in the world of math. We provide inputs to them and they generate outputs for us. However, there are two apparent questions ahead of us if we want to achieve such a desired neural network:\n", + "\n", + "1. What type of neural network (or equation) should we build? How many parameters should it have? How should those parameters be organized in different layers and nodes? How should we connect those components together? These questions can all be summarized in one technical domain of deep learning: \"*architecture design*\" for neural networks.\n", + "\n", + "\n", + "2. Even if we know the exact architecture needed for our neural network, how should we know the appropriate values for its parameters? In other words, we need to know the values of an equation's parameters before we can feed any input values to it and expect meaningful outputs. What we do to find the appropriate values for a neural network's parameters is called \"*training*\" of that neural network or deep learning model.\n", + "\n", + "The following two sections will address each of the above questions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_DZLvNqSdIb8" + }, + "source": [ + "### Neural network architecture designing\n", + "\n", + "We previously tried to mathematically describe a simple example of deep learning models for you. Having such an equation-oriented model of neural networks in mind will help you better understand the many different architectures introduced across the literature for neural networks every day. Look at the following fantastic figure, for example.\n", + "\n", + "
Figure 4. Variations of neural network architectures.
Source: https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464\n", + "
\n", + "\n", + "Each node in this colorful diagram represents a different architecture. Putting their details aside, each has a different number of layers, nodes, connections, and activation functions. Imagine these components of deep learning models as Lego(TM) pieces that data scientists combine in unlimited ways to build their new toys, aka models, at the end. You might ask: how can I know which architecture works best for the problem I am working on?\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + ">\n", + "**Note**: The words \"model\" and \"network\" are used interchangeably in this chapter.\n", + "\n", + "\n", + "---\n", + "\n", + "Deep learning is not only a science but an art, and the above should help to explain why. You can look into the literature of deep learning (and you should) to see what others have used for problems that are similar to yours. Take note of scientific comparisons made to illustrate different architectures' strengths and weaknesses. But at the end of the day, there is rarely a clear rule for the correct architecture for a novel problem. It is your innovation, intuition, and art in combining the pieces available to you that determines how good or bad your final model will perform to address your problem of interest! On the same note, data scientists usually evaluate many different architectures for a specific problem to finally find the one that works best for them." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FyoYaFami-bt" + }, + "source": [ + "### Neural network training\n", + "\n", + "Once we have selected the architecture for our neural network model, we still need to figure out what values to use for the model's parameters. In deep learning, this task is accomplished by looking at the available data (the training set) and \"learning\" the appropriate values for the parameters from that data.\n", + "\n", + "Let us go back to our example once again. You saw before that all the CXRs we downloaded are already labeled; i.e., one or more radiologists have already labeled each as either 'normal' or 'pneumonia'. In other words, we have access to thousands of CXR examples (or input data to the model), in addition to their actual outcomes (or the ground truth *labels*), which a deep learning model should predict. The training task is to find the best set of parameters that will make our neural network predict (the mathematical output value) the closest outputs to the ground truth labels. This process happens iteratively and is called \"model training .\"\n", + "\n", + "Recall again that we generally do not train on the entire available data set.\n", + "Instead, we first split it into training and validation sets (or sometimes, training, validation, and test sets). We then use the training set for training our model. We already did this splitting when making our data loaders (look above and remember how we built separate data loaders for different sets). Next, we build our initial deep learning model with random parameter values (later chapters will describe this 'initialization' in greater detail).\n", + "\n", + "Of course, this initial model will not make any meaningful predictions for us, but as the training iterations go on, the model will gradually change its parameter values to make better and better predictions. Such gradual improvement becomes possible by providing feedback to the model regarding its current parameters and performance, and more importantly, how their values should be changed to improve the predictions. This feedback mechanism is the heart of model training, and we will discuss this further in Part 4.\n", + "\n", + "In summary, we need to train our neural network on a portion of labeled data that we call the training set. The model will use this labeled data multiple times (each time is called an 'epoch') during the training and gradually change parameters to find their optimal values in order to minimize the predictions accross the training set labels. A model trained like this can then be used to make predictions on unseen (unlabeled) input data. Applying a trained model to unseen data is usually called \"inference.\"\n", + "\n", + "---\n", + "\n", + ">**Note**: Most neural networks are trained on labeled training data, and this form of learning is referred to as \"supervised learning.\" However, supervised learning is not the only approach to training models. Sometimes training is performed in unsupervised, semi-supervised, or self-supervised manners. In these variants, human-labeled training data is either unavailable to the model or is available differently compared to supervised learning. More on this in later chapters.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-QVLqFULw2wt" + }, + "source": [ + "## Part 3: PyTorch models playground\n", + "\n", + "With that as background, we are ready to start coding again. An excellent way to understand the training process is to compare a model's performance before and after training. This is what we will do in the current section." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FoJ21m46oMK7" + }, + "source": [ + "### Creating a model\n", + "\n", + "The following cell calls the `build_model` function, which we will use throughout this chapter for creating PyTorch models from scratch. Please note that this function will not train the model; we need to code for that separately.\n", + "\n", + "The `build_model` function does two important tasks:\n", + "\n", + "1. It uses the torchvision library to create a model using the architecture the user requested: e.g., to build a ResNet50 model, we can code:\n", + "```python\n", + "from torchvision import models\n", + "model = models.resnet50()\n", + "```\n", + "or if you want to create a VGG16 model, you could type:\n", + "```python\n", + "model = models.vgg16()\n", + "```\n", + "Our code in the following cell does exactly the same thing in this line:\n", + "```python\n", + "arch = 'resnet' ## Is this correct??? \n", + "pretrained = True\n", + "model = eval(f'models.{arch}(pretrained={pretrained})')\n", + "```\n", + "The only difference is that our code uses the Python \"eval\" command to build a model based on the value of the \"arch\" variable. This means that the function can build a ResNet or VGG-based model depending on the user's interest.
\n", + "\n", + " ---\n", + "\n", + " > **Note**: You may also note that we can pass a `pretrained` argument to the above command. We will introduce this argument and what it does in the last section of this notebook.\n", + "\n", + " ---\n", + " Isn't it interesting that we loaded a relatively complicated model like ResNet50 or VGG16 in just one line of code? This is the magic of deep learning frameworks. Many of the complicated architectures are now available to developers in just one or two lines of code (1 line to 'import' them, and the other line to actually build the one you want). However, there are still occasions in real practice when developers need to develop their models from scratch or change a pre-loaded model.\n", + "
\n", + "\n", + "2. When we build our models using PyTorch, they are almost always designed to throw out 1000 values (or, more accurately, a linear vector of size 1000) as their outputs. This is the case as many PyTorch default models were built to classify the images from the ImageNet dataset (which had 1000 classes). However, we only have two classes in our example for classifying CXRs. So, we should change the final layer of the model (also called the final fully connected layer, as it is not a convolutional layer) to have two output nodes instead of 1000. This will cause the model to create two values (or a vector of size 2) instead of 1000 values.\n", + "
\n", + "\n", + " ---\n", + "\n", + " > **Note**: Here we have specified 2 output classes, but above we said we would have a single output ranging from 0 to 1. A binary output (2 classes) is a special case where there is usually no effective difference and it is usually better to have fewer outputs. Why might we want to have 2 outputs rather than just 1?\n", + " ---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "84lx39aLa3Ov" + }, + "outputs": [], + "source": [ + "# Building a resnet model\n", + "\n", + "import torchvision.models as models\n", + "import torch.nn as nn\n", + "import shutil\n", + "\n", + "@make_determinate\n", + "def build_model(arch: str = 'vgg16', pretrained: bool = False)-> Callable:\n", + " \"\"\"\n", + " Build a resnet model using Pytorch.\n", + " Parameters:\n", + " - arch (str): baseline architecture of the model that could be called using\n", + " torchvision.models.arch command.\n", + " - pretrained (bool): whether or not to use pretrained weights.\n", + " - model (Callable): built pytorch module.\n", + " \"\"\"\n", + "\n", + " # Loading a model with the user-specified architecture from torch\n", + " if 'vgg' not in arch and 'alexnet' not in arch and 'resnet' not in arch:\n", + " raise ValueError ('Only resnet, vgg or alexnet models can be loaded!')\n", + " else:\n", + " try:\n", + " model = eval(f'models.{arch}(pretrained={pretrained})')\n", + " except:\n", + " raise ValueError ('The name of the architecture is not valid!')\n", + "\n", + " # Replacing the final fully conntected layer of the model\n", + " # Please note that we have two classes, and therefore, the final FC layer\n", + " # will have two final nodes\n", + " # The VGG network has no FC layer, so we directly change its final layer\n", + " if 'vgg' in arch or 'alexnet' in arch:\n", + " model.classifier._modules['6'] = nn.Linear(4096, 2)\n", + " else:\n", + " num_in_features = model.fc.in_features\n", + " model.fc = nn.Linear(num_in_features, 2)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jNqLmboSzwvf" + }, + "source": [ + "Now that we have a model, lets see what its architecture looks like. For PyTorch models, we can use a library called `torchsummary` to see the layers of our model, the shape of each layer's output, and the number of parameters in each layer. Such a library is extremely useful if you are building your own model. For instance, it might help you detect that your model does not fit into your GPU's memory and you need to check what layer(s) are oversized.\n", + "\n", + "It is very easy to use the torchvision library. You only need to use the \"summary\" command, and pass a PyTorch model and an input size to it. Please note that your PyTorch model should already be on GPU to work with this command hence we use the \"to(device)\" command to move our model to the GPU device that Google colab has assigned to us." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xq5Ej3IB8HW-", + "outputId": "e8a41d6d-9ddc-4850-e735-b2e7240fe654" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 64, 224, 224] 1,792\n", + " ReLU-2 [-1, 64, 224, 224] 0\n", + " Conv2d-3 [-1, 64, 224, 224] 36,928\n", + " ReLU-4 [-1, 64, 224, 224] 0\n", + " MaxPool2d-5 [-1, 64, 112, 112] 0\n", + " Conv2d-6 [-1, 128, 112, 112] 73,856\n", + " ReLU-7 [-1, 128, 112, 112] 0\n", + " Conv2d-8 [-1, 128, 112, 112] 147,584\n", + " ReLU-9 [-1, 128, 112, 112] 0\n", + " MaxPool2d-10 [-1, 128, 56, 56] 0\n", + " Conv2d-11 [-1, 256, 56, 56] 295,168\n", + " ReLU-12 [-1, 256, 56, 56] 0\n", + " Conv2d-13 [-1, 256, 56, 56] 590,080\n", + " ReLU-14 [-1, 256, 56, 56] 0\n", + " Conv2d-15 [-1, 256, 56, 56] 590,080\n", + " ReLU-16 [-1, 256, 56, 56] 0\n", + " MaxPool2d-17 [-1, 256, 28, 28] 0\n", + " Conv2d-18 [-1, 512, 28, 28] 1,180,160\n", + " ReLU-19 [-1, 512, 28, 28] 0\n", + " Conv2d-20 [-1, 512, 28, 28] 2,359,808\n", + " ReLU-21 [-1, 512, 28, 28] 0\n", + " Conv2d-22 [-1, 512, 28, 28] 2,359,808\n", + " ReLU-23 [-1, 512, 28, 28] 0\n", + " MaxPool2d-24 [-1, 512, 14, 14] 0\n", + " Conv2d-25 [-1, 512, 14, 14] 2,359,808\n", + " ReLU-26 [-1, 512, 14, 14] 0\n", + " Conv2d-27 [-1, 512, 14, 14] 2,359,808\n", + " ReLU-28 [-1, 512, 14, 14] 0\n", + " Conv2d-29 [-1, 512, 14, 14] 2,359,808\n", + " ReLU-30 [-1, 512, 14, 14] 0\n", + " MaxPool2d-31 [-1, 512, 7, 7] 0\n", + "AdaptiveAvgPool2d-32 [-1, 512, 7, 7] 0\n", + " Linear-33 [-1, 4096] 102,764,544\n", + " ReLU-34 [-1, 4096] 0\n", + " Dropout-35 [-1, 4096] 0\n", + " Linear-36 [-1, 4096] 16,781,312\n", + " ReLU-37 [-1, 4096] 0\n", + " Dropout-38 [-1, 4096] 0\n", + " Linear-39 [-1, 2] 8,194\n", + "================================================================\n", + "Total params: 134,268,738\n", + "Trainable params: 134,268,738\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.57\n", + "Forward/backward pass size (MB): 218.77\n", + "Params size (MB): 512.19\n", + "Estimated Total Size (MB): 731.54\n", + "----------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Plotting the structure for a Vgg16 model\n", + "\n", + "from torchsummary import summary\n", + "non_trained_vgg16 = build_model(arch='vgg16', pretrained=False).to(device)\n", + "summary(non_trained_vgg16, input_size=(3, 224, 224))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "erU9ZdcN2t-f" + }, + "source": [ + "### Using a model for inference\n", + "\n", + "Now you can do what you probably expected to do when you started this section. First, let's test our untrained model to see its baseline performance on differentiating between the normal and pneumonia CXRs. This is a good checkpoint for us to learn how to apply a PyTorch model for inference.\n", + "\n", + "----\n", + "> **Note**: The code to use a Pytorch model for inference is the same for both trained and untrained models. So we can build an `evaluate_model` function that receives a model and a test data loader and then shows the model's performance on that test data loader. We can then use this function to evaluate the different models we will develop throughout this chapter.\n", + "---\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "CCy0IFUbJoNg" + }, + "outputs": [], + "source": [ + "# A function to evaluate a model's performance on the test_loader\n", + "\n", + "from tqdm.notebook import tqdm\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sns\n", + "\n", + "@make_determinate\n", + "def evaluate_model(model: Callable,\n", + " test_loader: Iterable = test_loader,\n", + " plot_cm: bool = True) -> float:\n", + " \"\"\"\n", + " Evaluate a given model's performance on the test set.\n", + " Parameters:\n", + " model (Callable): the pytorch model to be evaluated.\n", + " test_loader (Iterable): test dataloader to be used as the test data.\n", + " plot_cm (bool): whether or not to plot a confusion matrix.\n", + " accuracy (float): accuracy of the model.\n", + " \"\"\"\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " model.to(device)\n", + " model.eval()\n", + " labels_list = list()\n", + " preds_list = list()\n", + " with torch.no_grad():\n", + " for batch in tqdm(test_loader):\n", + " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", + " outputs = model(inputs)\n", + " preds = torch.argmax(torch.softmax(outputs, dim=1), dim=1)\n", + " labels_list.append(labels)\n", + " preds_list.append(preds)\n", + " all_labels = torch.cat(labels_list).cpu()\n", + " all_preds = torch.cat(preds_list).cpu()\n", + " accuracy = (all_preds == all_labels).sum().item()/len(all_labels)\n", + "\n", + " if plot_cm:\n", + " cm = confusion_matrix(all_labels.numpy(), all_preds.numpy())\n", + " ax = plt.subplot()\n", + " sns.heatmap(cm, annot=True, fmt='g', ax=ax)\n", + " ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels')\n", + " ax.set_title(f'Confusion Matrix - accuracy: {accuracy:.2f}')\n", + " ax.xaxis.set_ticklabels(['Pneumonia', 'Normal']);\n", + " ax.yaxis.set_ticklabels(['Pneumonia', 'Normal']);\n", + "\n", + " return accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DR-ankIwD7Ui" + }, + "source": [ + "Please note a few things in the above function:\n", + "\n", + "1. Whenever we want to use a PyTorch model for inference, we first use the `model.eval()` command to put it in the evaluation mode. Conversely, when we want to train a PyTorch model, we put it in the training mode using the command `model.train()`. You will learn the difference between these two in the following section.\n", + "\n", + "2. When we iterate through the data from a PyTorch data loader, it returns a batch each time we call it (typically once in each loop of an iteration). Depending on how we specified the batch should be made, it may include different samples for a given batch, though all training samples should be returned over the course of a complete iteration (a.k.a. 'epoch'). Because we built our dataloaders using MONAI dictionary-type dataloaders, we can access the imaging data and labels of each batch using the `batch['image']` and `batch['label']` commands.\n", + "\n", + "3. Like the training, inference also runs faster on a GPU, so we send our images and labels data to GPU using the `to(device)` command. You don't need to change anything else in your code for running the commands on a GPU instead of a CPU, and that's a big benefit of PyTorch!\n", + "\n", + "4. Look at the following line of code and try to understand what it does:\n", + "```python\n", + "preds = torch.argmax(torch.softmax(outputs, dim=1), dim=1)\n", + "```\n", + "If you remember, we assigned two nodes to the final layer of our model in one of the above cells. This means that the output of our model will naturally be a one-dimensional array (or a vector) with two values. These values look independent from each other at first, but we will use a trick during training and inference to make them meaningful. The trick is to use a mathematical function called `softmax` that receives an array of 2 or more input values and change them so that they sum up to 1. You can look at the following image and check its source page to better understand how softmax works:\n", + "\n", + "
Figure 5. Softmax Activation Function Explained.
Source: https://towardsdatascience.com/softmax-activation-function-explained-a7e1bc3ad60\n", + "
\n", + "\n", + " Now, the two values of the final node will no longer be meaningless. The first value (the one in index 0 of the output array) denotes the probability that the input CXR is for a normal patient, and the second value (index 1 in the output array) denotes the probability that the CXR is for a patient with pneumonia. Well then, how should we say what the model's prediction is for that input CXR? The predicted class will be whichever class has a higher probability value. Therefore, we can use the `argmax` command to get that array index. The result of the \"argmax\" command and, therefore, the result of the previous code line is an array of zeros and ones, and the length of that array is our batch size. In other words, we now have the model's prediction for each input example in our batch.\n", + "\n", + "5. Finally, please note that there are 2 outputs of our `evaluate_model`: First, the accuracy of the model, which we calculate by dividing the number of true predictions by the total number of predictions our model has made. The second is a performance table, also called a confusion matrix. It is easier to introduce this matrix when you are looking at it, so let's proceed and use this function to evaluate the performance of an untrained VGG16 function on our test data loader." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 504, + "referenced_widgets": [ + "d6b9487396c74c9ebbb4710825738c8a", + "97adb5724faf4ed29ab150d8ac3ae6f8", + "a1d0339b8c214afe9d489a97b6f01695", + "04a41bbdd14440cabbef6e603d6734bb", + "eedd4f505bd74f188b1f2d2f81ebc574", + "f3a95a66a4f2421a96a8533d79eb9826", + "2712b5d9230f488eac997e5f83d41a03", + "3503548ea4224ea59ce76aa5d43c8172", + "667485a1eda04009a4939cf0a024bf5d", + "d1cea112d2af4516908f4cb4aeff735a", + "c207aca462bf4e918bf581c4d5717104" + ] + }, + "id": "rzwivQLRIrux", + "outputId": "9228c58a-abb7-43ea-efea-853d5b9f164b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d6b9487396c74c9ebbb4710825738c8a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/39 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluating the performance of a non-trained model\n", + "\n", + "_ = evaluate_model(non_trained_vgg16)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X7eKE7a7rT29" + }, + "source": [ + "The figure above is a confusion matrix. It gets its name because it shows how many times the model confused one class of inputs with another (pneumonia versus normal in our case).\n", + "When we ran the above cell, our model had very poor performance, confusing many pneumonia and normal cases with each other. But don't be discouraged. Remember that we haven't trained it yet! In many cases, an untrained model will even classify all cases as one of the classes. In our scenario, for example, the model may predict 0 pnueomonia or 0 normal cases.\n", + "This might be a surprising result, but it can happen rather often when you use an untrained model for inference and if a class is very rare or very common. If accuracy is used as a metric, and some classes are rare, then the model can be very accurate by always saying it is not the rare class. However, we know that it does not mean the model is good.\n", + "\n", + "---\n", + "\n", + "> **Note**: Please note that your confusion matrix may be slightly different because the model's initial weights are random and they may be different from the initial weights for our model. However, the performance of this untrained model will almost always be very poor.\n", + "\n", + "---\n", + "\n", + "In this section, we learned how to create a PyTorch model using the already available architectures in torchvision library and use it for inference. We also observed that, as expected, an untrained model with random weights will perform very poorly in doing a task, including our task of classifying CXRs. This means we are ready to move to the next section, where we will train our models.\n", + "\n", + "
**Coding Practice**: In the above code, we used a model for predicting the class of an entire batch. However (and in most real-world applications), we will need to use our model to infer from a single image. It should look trivial to you how to do that, but it is not a bad idea to practice it before we proceed. Look at the following code cell, where we have loaded a single CXR from our dataset as a NumPy array with shape (224 * 224 * 3). See if you can feed it to your model and get the predicted class for it by yourself. Of course, the predicted class may not be right as the model is untrained, but the goal is for you to practice inference with PyTorch models.\n", + "\n", + "---\n", + "\n", + ">**Hint**: This is what you need to do in order:\n", + "1. Resize your NumPy array to the size (224 * 224).\n", + "2. Convert your NumPy array to a PyTorch tensor.\n", + "3. Add a channel dimension to your input tensor in dim=0 (i.e., your tensor should have the shape of (1 * 224 * 224) instead of (224 * 224)).\n", + "4. Add a batch dimension to your input tensor in dim=0 (i.e., your tensor should have the shape of (1 * 1 * 224 * 224) instead of (1 * 224 * 224). This is necessary as PyTorch models expect their input data to have the batch format.\n", + "5. Use the \"model(img_tensor)\" command to get the model's output for the image and then apply \"softmax\" and \"argmax\" commands on the output to get the final prediction as we did above.\n", + "\n", + "---\n", + "\n", + "You may need to look at the previous chapters or search online a little to find out how to do the above steps, but that would be a fun and easy coding practice for you, so enjoy!\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CN1-Vj_GN8pc", + "outputId": "9334347b-c9dd-49a5-d567-1be644633c62" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(712, 1088)\n" + ] + } + ], + "source": [ + "from skimage.io import imread\n", + "\n", + "img_array = imread(train_imageinfo_list[0][0])\n", + "print(img_array.shape)\n", + "\n", + "#### Type your code here:\n", + "\n", + "\n", + "####" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_K9JBLqgZBlw" + }, + "source": [ + "## Part 4: Training implementation\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j-AvF3-3gWUf" + }, + "source": [ + "We said before that training a neural network consists of letting the model make some predictions, then checking the gap between its predictions and the known values. The gap is the penalty, and that penalty drives changes in parameters so that the gap descreases as the training goes on. This cycle repeats many times, and the model will see each sample in the training data multiple times. Now, it is time to expand this initial explanation. Before we jump into PyTorch and work with our own scenario, it is not a bad idea to play with this concept with a much simpler dummy example." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KClFRYW37nt8" + }, + "source": [ + "### Training a simple model\n", + "\n", + "Imagine we have numbers 0.01, 0.02, ..., up to 1 as our inputs, and a label for each input is defined based on the following formula: $y = x*0.2 + 0.06$ (e.g., if x is 0.3, then y will be 0.12):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WRnlQ9uDiXxe", + "outputId": "4796352a-0d8f-4a99-b67b-d8b1ecf38f19" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The first ten input values: tensor([0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700, 0.0800,\n", + " 0.0900])\n", + "The first ten label values: tensor([0.0600, 0.0620, 0.0640, 0.0660, 0.0680, 0.0700, 0.0720, 0.0740, 0.0760,\n", + " 0.0780])\n" + ] + } + ], + "source": [ + "input_tensor = torch.tensor(range(100)).float()/100\n", + "label_tensor = torch.tensor(list(map(lambda x:x*0.2+0.06, input_tensor)))\n", + "\n", + "print(f'The first ten input values: {input_tensor[:10]}')\n", + "print(f'The first ten label values: {label_tensor[:10]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Et7EFLQaXb55" + }, + "source": [ + "Now, let us say we want to train a very simple model (actually, a linear model with the general form y = ax+b) to learn to reproduce our data. In other words, we want our model to learn parameters a and b as 0.2 and 0.06, respectively. You perhaps remember that PyTorch models always start with random parameter values, and a model we create will not necessarily have our desired parameters. It could be any model like the following dummy_model we created using vanilla Python:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "A5FN5xVRkDCd", + "outputId": "f50629e1-379e-41de-b562-bdb5eaae79ed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The first ten predicted values: tensor([0.0400, 0.0401, 0.0402, 0.0403, 0.0404, 0.0405, 0.0406, 0.0407, 0.0408,\n", + " 0.0409])\n" + ] + } + ], + "source": [ + "dummy_model = lambda x: x*0.01 + 0.04\n", + "output_tensor = torch.tensor(list(map(dummy_model, input_tensor)))\n", + "print(f'The first ten predicted values: {output_tensor[:10]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qZPZVW2WYsZT" + }, + "source": [ + "As you see above, the outputs the dummy model predicted are different from the actual labels. But how different are they? If we ultimately want to optimize the parameters of our model during training, the first step is to know how different the model's performance is versus what we expected. In the world of machine learning, the difference between the observed and expected performance of a model is called its \"**loss**,\" and the function used to calculate that loss is called the \"**loss function**.\" As you can imagine, we do not have a single loss or loss function for all machine learning problems in the world. As deep learning models are diverse, the loss functions to train them are diverse. Even for a single model, more than one loss function may be used.\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + ">**Note**: The terms cost (cost function), loss (loss function), and criterion (criterion function) are often used interchangeably in machine learning. Also, the letter \"*J*\" is often used to describe the overall loss of a model.\n", + "\n", + "\n", + "---\n", + "\n", + "Now, for the sake of our dummy example here, we are going to use a relatively simple but widely used loss function that is called the \"**L2 loss**.\" This function is calculated given two input vectors y_true and y_predicted using the following formula:\n", + "\n", + "
Figure 6. L2 Loss Function.\n", + "
\n", + "\n", + "It is called the L2 loss because the difference is squared (power of 2). Can you guess what the name might be for a loss function where the difference is used (without squaring--power of 1)? Yes--it is the **L1 loss**. If you try to code this yourself beware of one thing: an error should always increase the loss, so you need to take the absolute value of the difference.\n", + "\n", + "\n", + "---\n", + "\n", + "> **Note**: All loss functions in deep learning act on two input vectors: y_true and y_predicted. The reason is that regardless of what mathematical form those functions have, they all reflect the difference between the expected and observed outputs of the model.\n", + "\n", + "---\n", + "\n", + "The L2 loss is very easy to implement in PyTorch (remember **2 means power of 2 in Python):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "itfpfPbMmZFJ", + "outputId": "5a051546-7cd7-45e7-b18a-7ce02120d35e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The current loss is: 1.6015435457229614\n" + ] + } + ], + "source": [ + "dummy_loss_function = lambda labels, outputs: torch.sum((outputs-labels)**2)\n", + "loss = dummy_loss_function(label_tensor, output_tensor)\n", + "print(f'The current loss is: {loss}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ScoW_N9MdUb3" + }, + "source": [ + "Now you might wonder what this value of loss means! Well, that is not an easy question to answer. Most of the time, loss values don't have a 'real world' meaning. What we care more about, though, is how the loss value changes when the model updates its parameters. The general assumption is that the closer the predicted and observed labels are, the lower the loss will be and vice versa. Feel free to change the code cell above and see how the loss will increase or decrease as you make your dummy model more or less different than the actual formula we used to create our data. You can also see that the L2Loss function will penalize outliers much more than an L1Loss function because of the squaring of the difference. If you REALLY wanted to penalize outliers, you could use higher powers.\n", + "\n", + "
We can also use the matplotlib library to plot our expected and current models on a graph. As you see below, the orange line is our current model, and the blue line is what it should be, with the yellow area denoting their difference. Please note that this yellow area is not the L2 loss but is only a visual measure of the difference between the two lines." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + }, + "id": "jEZlUaDx4twV", + "outputId": "f600a7f2-fefa-4649-8633-4a81f91a4228" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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The answer to this question is the cornerstone of deep learning and is called: \"**gradient descent**\". Gradient descent consists of two main steps:\n", + "\n", + "1. In the first step, we calculate the gradients of the loss function with respect to each parameter (weight or bias) of the model. These gradients have valuable information. They tell us how the loss will change if we increase or decrease either of our parameters. For example, say d_w and d_b are the gradients ('derivatives' if you remember calculus, and hence the 'd_') of L2 loss with respect to the parameters of our dummy model. Suppose d_w is a positive number and d_b is a negative one. In that case, decreasing the w and increasing the b will both decrease the loss and therefore move our model one step closer to its desirable form. In more complicated models, where later layers of parameters are built upon earlier layers, the gradient of loss with respect to parameters in earlier layers is obtained by applying the chain rule of calculus to gradients obtained for the later layers. We recommend this blog post to become more familiar with what the chain rule is and how it helps in calculating the gradients in deep learning: [Deep learning and chain rule of calculus](https://medium.com/machine-learning-and-math/deep-learning-and-chain-rule-of-calculus-80896a1e91f9>)\n", + "\n", + "2. Knowing the gradients of loss with respect to parameters, we can update the parameters using the following formulas:
\n", + "new_parameter = old_parameter - d_parameter * learning rate
\n", + "where learning rate is a number denoting the pace of updating the model's parameters. More about learning rates later, but for now, you can just use the value '1' as the learning rate though in the real world it is much less than 1. This second step is called \"**optimization**.\"\n", + "\n", + "The above two steps happen in each **step** of model training, i.e., for each time the model goes through a batch of data. For each batch of data, the model predicts the label for each member of the batch, the loss is calculated by comparing the known and predicted labels, the gradients of the loss with respect to all model parameters are calculated, and finally, all parameters are updated using the gradient descent approach. The following figure shows how we change the values of a single weight parameter in a stepwise manner to redu e loss as much as possible:\n", + "\n", + "
Figure 7. Illustration of gradient descent for a single parameter.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", + "
\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + ">**Note**: A \"**step**\" in model training denotes each checkpoint when the model has seen a single *batch* of data. On the other hand, an \"**epoch**\" denotes when the model has seen the entire training data set once. Therefore, an epoch consists of (number of data points / batch size) steps. Actual training sessions often have 10's to 100's of epochs.\n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vNNzyPjZuM2s" + }, + "source": [ + "It is worth pausing here to reflect on the process we described above.\n", + "\n", + "First, note how the gradient descent process is simple in concept but complex in practice. Unlike our dummy model, which only has two parameters, actual deep learning models have millions of parameters, and updating each of those in each step is not something any human mind can or even regular CPUs can do efficiently. We almost always rely on the parallel computation capacity of powerful GPUs to train the model. \n", + "\n", + "Second, note that selecting an appropriate learning rate is critical for training a deep learning model. Like the [story of Goldilocks and the 3 Bears](https://americanliterature.com/childrens-stories/goldilocks-and-the-three-bears), it is critical that it be 'just right'. A learning rate that is too small will hinder the pace of parameter updates, but if too large, it will cause the loss to oscillate and prevent the weight from finding its optimal value or converging to a global minimum as described below:\n", + "\n", + "
Figure 8. Small and large learning rates.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dS2fLuafyecG" + }, + "source": [ + "Furthermore, the loss landscape for a single parameter is not always as straightforward as in the above plots. The gradients for a single parameter may become zero (so that the loss curve forms a saddle point\") or change direction during the training and form what is called a \"local minimum.\" If the gradient is 0, the parameters will not be altered. Even more insidious are local minima where the loss will falsely look like it has the least (best) value. It is important that we not stop our search in such a local minimum, and that we continue on to look for the \"global minimum\", where the loss really has its lowest value. Things are more complicated when you think that we have millions of parameters, and the loss landscape is many dimensions versus the 2D curves we show below:\n", + "\n", + "
Figure 9. Local minimum and saddle points.
Source: https://www.ibm.com/cloud/learn/gradient-descent\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G8S_lG5U0lk5" + }, + "source": [ + "Considering these challenges, do we have any tools to control and optimize the gradient descent during training? The answer is 'yes'. Here are a few tools available to data scientists:\n", + "\n", + "
**Tool 1 - Learning Rate Schedules**:\n", + "As we said above, selecting a reasonable learning rate is core to smooth training. In simple deep learning training sessions, like in this chapter, the learning rate is constant during the entire training. However, there are ways to alter the learning rate during the training or even give it a 'schedule' to change constantly in a way you desire. Data scientists usually use learning rate schedulers to overcome problems like local minima or the problem of vanishing or exploding gradients.\n", + "\n", + "---\n", + "\n", + "> **Note 1**: **Vanishing Gradients** is the term used to describe when the loss gradient with respect to a parameter gets very close to zero. Conversely, **Exploding gradients**, occur when the gradients become very large. Either way, this gradient can be multiplied by other gradients during the chain rule calculations making the resulting updates either zero (no update) or very large, in which case they dominate all the other parameters. Both cases can lead to poor training. Therefore, we always prefer to prevent our gradients from getting too small or too large during the training. We will describe how to do this later on.\n", + "\n", + "---\n", + "---\n", + "\n", + ">**Note 2**: When training a deep learning model, there are many value choices we need to make that are different from parameters. Unlike model parameters that are learned, there are no simple ways to learn these values. Examples include the learning rate (and any scheduling), the number of epochs over which you train your model, and the batch size. Such values are called \"hyperparameters,\" and the process of finding the best hyperparameters for each training is called \"hyperparameter tuning.\" Like selecting the best architecture for your model, finding the best set of hyperparameters is a matter of experimenting and using general intuition, science, and the art of data science!\n", + "\n", + "---\n", + "\n", + "\n", + "
\n", + "\n", + "**Tool 2 - Loss Curves**:\n", + "Another helpful tool to understand the course of optimization is a plot of the loss value for each training epoch, known as a 'loss curve'. Unlike the above figures, which illustrate the change in loss landscape with respect to a single parameter, a loss curve plots the overall loss of the model for each epoch. Put another way, the loss curve shows how the loss is changing during the training. If the model is learning efficiently, the loss curve will tend to descend during a training session (although oscillations could be normal). As shown in the figure below, we almost always plot two loss curves for the model. One (shown in blue) is the loss of the model computed on the training data and the other line (shown in orange) is the loss computed for the validation data. The comparison of these two curves during training gives valuable information to the data scientists regarding their models and how well it is learning the task versus learning the training data set (overfitting).\n", + "\n", + "
Figure 10. Example of training and validation loss curves
Source: https://bit.ly/3tYfWXF\n", + "
\n", + "\n", + "**Tool 3 - Optimizer Algorithms**:\n", + "Gradient descent has been very influential in the history of deep learning but is nowadays one of the simplest algorithms we can use to optimize our model's parameters. Many more algorithms are available that change the parameters more effectively. These new optimizers are more resistant to problems like vanishing and exploding gradients. As discussed later in this chapter, they help avoid overfitting or underfitting.\n", + "\n", + "Optimization is a complicated process, and deep learning frameworks like PyTorch have made the work of developers much easier by creating optimizer classes. These classes will take care of the entire optimization calculations for us! For example, below we show how to create a simple stochastic gradient descent optimizer in PyTorch that we will apply to our dummy training. We only need to pass a learning rate and model parameters to the torch.optim.SGD class and PyTorch will create an optimizer instance from that class for us in a single line of code:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "u-HAup-dNd38" + }, + "outputs": [], + "source": [ + "dummy_optimizer = torch.optim.SGD(dummy_model.parameters(), lr=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "221gb5EtJorp" + }, + "source": [ + "Perfect! Now we have our data, loss, and optimizer. With all these in hand, we are ready to train a model in PyTorch. We only need to run the few lines of code per step of training (i.e., per each batch of data that the model sees):\n", + "\n", + "\n", + "```python\n", + "# Get the training inputs and labels from the training dataloader.\n", + "# Prepare your loss function and optimizer.\n", + "\n", + "model.train() #Line 1 \n", + "optimizer.zero_grad() #Line 2\n", + "train_outputs = model(train_inputs) #Line 3\n", + "loss = loss_function(train_labels, train_outputs) #Line 4\n", + "loss.backward() #Line 5\n", + "optimizer.step() #Line 6\n", + "\n", + "\n", + "# Log the training loss at the end of each step, if needed.\n", + "# Using loss.item() will return its raw value as a standard Python number and with no gradients attached to it.\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e9LH3NlKLHU4" + }, + "source": [ + "Let us go through the above six lines:\n", + "\n", + "* Line 1: As mentioned before, each PyTorch model can be either in training or evaluation mode. When in the training mode, PyTorch will keep track of the gradients of the model's parameters. This is required for training.
\n", + "* Line 2: This line makes sure our optimizer has no memory of the gradients that happened in the last step. It resets the optimizer, making it ready for the new optimization.
\n", + "* Line 3: You already know this line: it feeds the inputs to the model to obtain its predicted outputs. From a training prespective, this step is usually called the \"**forward propagation**\" or the \"**forward pass**\"
\n", + "* Line 4: You also know this line. The loss is calculated using the loss function we defined before.
\n", + "* Line 5: Calling the backward method on loss will cause PyTorch to collect the gradients of loss with respect to every single parameter in the model (unless the requires_grad=False for a parameter, which is a way of freezing some parameters--we will describe this later). These gradients are then stored in the x.grad attribute of each parameter x. So, the model itself preserves the last gradients of loss with respect to its parameters. This step is usually called the \"**backpropagation**\" or the \"**backward pass**\".
\n", + "* Line 6: And finally, the 'learning' happens here. Calling the step method of an optimizer will do the optimization operation and update all the parameters based on their gradients, the learning rate, and the optimizer.\n", + "\n", + "---\n", + "\n", + "\n", + ">**A note on training vs. validation loops:**
\n", + "As we mentioned before, the above lines of code are executed for each training step. This means these lines are executed within two inner \"for\" loops! The first loop iterates through the number of epochs we use for training. The second loop iterates through our training data loader, so we loop once for each batch produced. This inner loop is called the training loop. When the model sees all batches, the inner loop ends, and the outer loop goes on to the next epoch. This will restart a new training loop, and this cycle goes on until the last epoch.
\n", + "In almost all standard deep learning training sessions, there is a validation loop following the training loop. The validation loop also runs within the epoch loop (i.e., it is run every epoch). However, it is distinct from the training loop. In the validation loop, the model iterates through the validation data loader (not the training data loader), and the model is in the evaluation mode. Although the loss is still calculated in each step, no backward pass and optimization happen based on the validation data. The reason is we want to use the validation data to evaluate our model, not for training it. This is an important point: models should never 'see' (i.e., be trained on) validation data during their training session. Failing to do this will result in models that look to be working very well but that may perform very poorly on a fair and real-world evaluation!\n", + "\n", + "\n", + "---\n", + "\n", + "
\n", + "\n", + "The below lines of code are the validation loop of a model. Please note the differences between this code block and the one above for the training loop:\n", + "\n", + "```python\n", + "# Get the validation inputs and labels from the validation dataloader.\n", + "\n", + "model.eval() \n", + "valid_outputs = model(valid_inputs) \n", + "loss = loss_function(valid_labels, valid outputs) \n", + "\n", + "# Log the validation loss at the end of each epoch, if needed.\n", + "```\n", + "\n", + "In summary, the following schematic figure demonstrates how training and validation loops are organized within an epoch loop:\n", + "\n", + "
Figure 11. Schematic shape of training and validation loops
\n", + "\n", + "\n", + "It is important that you now reflect on what you have learned as it is the basis for all things going forward. If you feel ready, it is time to show you how this process works in action. The following code will train our dummy model to learn the data we have. Please note that:\n", + "* As this model has only two parameters, we train it on a CPU.\n", + "* We log the epoch number, the raw value of the loss, and the output tensors of the model at the end of each epoch. Before logging, we put the model in the evaluation mode to not accidentally change each parameter's gradients anymore.\n", + "* For simplicity, we do not use any validation loop in this training. We will do that in our main training for the CXR classifer.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "8ca2d16eb39c4bdfa69573a26a47599c", + "7cf5259a599d4b9eaafb1daa7d5a3b86", + "dfc2b08cdcde41428a7d6f1f3a1e4286", + "7742bfd105074ddbb3887b36847ea7f8", + "cb25585a197e492196b2914aae4f8d8c", + "39f3c7a1908e431d85e08675bef6ff30", + "888038374c0342b499265dd8d9b1e830", + "2989c32e4fae4719b8d52b39ffcf5b31", + "696130216db349588b93356709096633", + "2214303bad7e40829ab8fe9d52507ba0", + "353f42dd9ae645979ab410f6a7f04b5f" + ] + }, + "id": "15pVKTgr8S6F", + "outputId": "42e1c367-a142-4f26-b4ed-92dfc2f14a1b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8ca2d16eb39c4bdfa69573a26a47599c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(3, 3, figsize=(15, 15))\n", + "\n", + "for i in range(9):\n", + " index = i*max_epochs//9\n", + " ax = axes[i//3, i%3]\n", + " epoch, loss, output_tensor = epoch_log_list[index]\n", + " ax.plot(input_tensor, label_tensor, label='ground truth labels')\n", + " ax.plot(input_tensor, output_tensor, label='predicted outputs')\n", + " ax.fill_between(input_tensor, label_tensor, output_tensor, color='yellow')\n", + " ax.set_title(f\"Epoch: {epoch}\\nLoss: {loss:.2}\")\n", + " ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EXI2CLKwP0uD" + }, + "source": [ + "As you see above, our model gradually learned to simulate the ground truth model we used to generate our data. If you print the parameters of the final model, they should be close to the orignal *w* (0.2) and *b* (0.06) we chose for our ground truth model:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Pl-22wwGQZSm", + "outputId": "5f2f8504-cc4e-4aaa-e66e-94c0cfe20e8f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The final model's weight is: 0.198\n", + "The final model's bias is: 0.0616\n" + ] + } + ], + "source": [ + "print(f\"The final model's weight is: {dummy_model.weight.item():.3}\")\n", + "print(f\"The final model's bias is: {dummy_model.bias.item():.3}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VMAPo_7LQZ1y" + }, + "source": [ + "Wow, these numbers are very close to the target values. Of course, our dummy problem was not difficult, but anyways, seeing a model training well is always exciting! We can also plot the loss curve for the model to see how it decreased throughout the training:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "id": "bk0SIi6hQgy3", + "outputId": "e9a47cf2-8960-4d7c-ae7a-e3f4ffd7202d" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
Figure 12. Binary cross-entropy loss function
\n", + "where $N$ is the total number of samples, *$y_{i}$* is the true label for the \"$i$\"th sample, and *p(*$y_{i}$*)* is the model's prediction for that sample. Cross-entropy measures the relative entropy between two probability distributions over the same set of events. So, $H_{p}(q)$ means to calculate cross-entropy between the real data distribution $q$ and the predicted label distribution $p$. The distribution $q$ is simply calculated using probability weights from the distribution $p$. For example, if the true label for a given CXR is normal, and the value of the model's final prediction (after applying the softmax function) for the CXR is 0.3 probability for normal and 0.7 probability for pneumonia, then *p($y_{i}$)*=0.3 (i.e., the predicted probability for the true label is 0.3). The BCE (and its more generalized form, the categorical cross-entropy) are widely used loss functions for classification problems. Although we cannot discuss the math behind these formulas in detail, you can check the following blog post to learn more about them: [Understanding binary cross-entropy / log loss: a visual explanation](https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a)\n", + "
\n", + "2. As opposed to our dummy example, we will have both training and validation loops here. For each of these loops, we will log the loss value and the value of our accuracy metric.\n", + "\n", + " ----\n", + "\n", + " >**Note on the difference between performance metrics and loss functions**:
When using both the BCE loss and the accuracy metric, two questions may come to mind: First, why do we not use the BCE value as our metric? And second, why do we not use accuracy as our loss function? The answer to the first question is that the output of the accuracy formula is usually more understandable (or tangible) to data scientists compared to BCE loss values. For the second question, not all metrics can be used as loss functions. Although accuracy is a very interpretable metric, its formula is not differentiable. This means we cannot calculate the gradients of that formula with respect to the model's parameters. In summary, not all metrics are appropriate as loss functions, and not all loss functions are appropriate as performance metrics. However, depending on the problem, sometimes these two may be similar.\n", + "\n", + " ---\n", + "
\n", + "\n", + "3. Another new thing we do in this training is to save our best performing model during the training. If we do not save our model during training, it will be lost. The process of saving a model during the training is a simple form of \"model selection.\" Model selection can be made using many criteria. Here, we follow the most common approach, which is to save the model with the lowest (best) **validation** loss.
\n", + "\n", + "4. Finally, we will run this training on our GPU, since working with images take much more memory and is not efficient on CPUs (it will take 10s-1000s of times longer depending on CPU and GPU).\n", + "\n", + "\n", + "With the above points in mind, please take a look at the following code cell which provides a function to train our CXR classifier. Please note that this function receives our model, data loaders, loss function (here called criterion), and optimizer as inputs. We should also give it the desired name for saving the model and the maximum number of epochs we want to use to train the model. This function will be used many times but with different parameters in next sections of this chapter.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "0pMHGSlFEykp" + }, + "outputs": [], + "source": [ + "def train_classifier(model: torch.nn.Module,\n", + " model_name: str,\n", + " train_loader: Iterable,\n", + " valid_loader: Iterable,\n", + " criterion: Callable,\n", + " optimizer: torch.optim,\n", + " num_epochs: int,\n", + " plot_curves: bool):\n", + " \"\"\"\n", + " Train a classifier model using pytorch and the given parameters.\n", + " Parameters:\n", + " model (Callable): any pytorch module,\n", + " model_name (str): name of the model to be saved.\n", + " train_loader (Iterable): dataloader for training data,\n", + " valid_loader (Iterable): dataloader for validation data,\n", + " criterion (Callable): any loss function,\n", + " optimizer (torch.optim): a pytorch optimizer,\n", + " num_epochs (int): number of epochs to train the model,\n", + " plot_charts (bool): whether or not to plot the training and validation loss\n", + " and accuracy curves.\n", + " \"\"\"\n", + "\n", + " # Sending the model to device (preferably GPU)\n", + " model.to(device)\n", + "\n", + " # Releaseing the GPU memory. This is not necessary, but a good practice to do\n", + " # before starting new training sessions.\n", + " with torch.no_grad():\n", + " torch.cuda.empty_cache()\n", + "\n", + " # Building a saving directory for models\n", + " model_save_dir = os.path.join('Best_Models', model_name)\n", + " if os.path.exists(model_save_dir):\n", + " shutil.rmtree(model_save_dir)\n", + " os.makedirs(model_save_dir, exist_ok=True)\n", + "\n", + " # lists to log the epoch values\n", + " epoch_train_loss_list = list()\n", + " epoch_train_accuracy_list = list()\n", + " epoch_valid_loss_list = list()\n", + " epoch_valid_accuracy_list = list()\n", + "\n", + " # Starting the training loop\n", + " for epoch in range(1, num_epochs+1):\n", + " print(\"-\" * 20, f'\\nStarting epoch: {epoch}/{num_epochs}')\n", + "\n", + " ## training\n", + " model.train()\n", + " steps_train_loss = 0.0\n", + " steps_correct_predictions = 0\n", + "\n", + " for batch in tqdm(train_loader, unit=\"batch\"):\n", + " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", + " ### Zero the parameter gradients\n", + " optimizer.zero_grad()\n", + "\n", + " ### Forward + backward + optimize\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " ### Accumulating the loss and number of correct predictions in step\n", + " steps_train_loss += loss.item()\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " steps_correct_predictions += (predicted == labels).sum().item()\n", + "\n", + " ## Measuring the epoch training loss and accuracy\n", + " epoch_train_loss = steps_train_loss/ len(train_loader)\n", + " epoch_train_loss_list.append(epoch_train_loss)\n", + " epoch_train_accuracy = steps_correct_predictions / len(train_loader.dataset)\n", + " epoch_train_accuracy_list.append(epoch_train_accuracy)\n", + "\n", + " ## validation\n", + " model.eval()\n", + " steps_valid_loss = 0.0\n", + " steps_correct_predictions = 0\n", + "\n", + " for batch in tqdm(valid_loader, unit=\"batch\"):\n", + " inputs, labels = batch['image'].to(device), batch['label'].to(device)\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " ### Accumulating the loss and number of correct predictions in step\n", + " steps_valid_loss += loss.item()\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " steps_correct_predictions += (predicted == labels).sum().item()\n", + "\n", + " ## Measuring the epoch validation loss and accuracy\n", + " epoch_valid_loss = steps_valid_loss/ len(valid_loader)\n", + " epoch_valid_loss_list.append(epoch_valid_loss)\n", + " epoch_valid_accuracy = steps_correct_predictions / len(valid_loader.dataset)\n", + " epoch_valid_accuracy_list.append(epoch_valid_accuracy)\n", + "\n", + " ## Printing the logs\n", + " print(f'train loss: {epoch_train_loss:.2f} | \\\n", + " train accuracy: {epoch_train_accuracy:.2f}')\n", + " print(f'valid loss: {epoch_valid_loss:.2f} | \\\n", + " valid accuracy: {epoch_valid_accuracy:.2f}')\n", + "\n", + " ## Saving the best model\n", + " if epoch==1:\n", + " best_valid_loss = epoch_valid_loss\n", + " weight_name = f'{model_name}_Epoch{epoch}_ACC={epoch_valid_accuracy}.pth'\n", + " torch.save(model.state_dict(), os.path.join(model_save_dir, weight_name))\n", + " elif epoch_valid_loss < best_valid_loss:\n", + " weight_name = f'{model_name}_Epoch{epoch}_ACC={epoch_valid_accuracy}.pth'\n", + " torch.save(model.state_dict(), os.path.join(model_save_dir, weight_name))\n", + " best_valid_loss = epoch_valid_loss\n", + "\n", + " # Printing the best model\n", + " print(f'\\nTrainng was over. The best model was: {weight_name}')\n", + "\n", + " # Plotting the loss and accuracy curves\n", + " if plot_curves:\n", + " epoch_train_loss_list.insert(0, 0)\n", + " epoch_train_accuracy_list.insert(0, 0)\n", + " epoch_valid_loss_list.insert(0, 0)\n", + " epoch_valid_accuracy_list.insert(0, 0)\n", + " fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + " axes[0].plot(epoch_train_loss_list, color='blue', label='training')\n", + " axes[0].plot(epoch_valid_loss_list, color='orange', label='validation')\n", + " axes[0].set_xlim(1, len(epoch_valid_loss_list)+1)\n", + " axes[0].set_title('Loss curves')\n", + " axes[0].legend()\n", + " axes[1].plot(epoch_train_accuracy_list, color='blue', label='training')\n", + " axes[1].plot(epoch_valid_accuracy_list, color='orange', label='validation')\n", + " axes[1].set_xlim(1, len(epoch_train_accuracy_list)+1)\n", + " axes[1].set_title('Accuracy curves')\n", + " axes[1].legend();\n", + "\n", + " # Loading the best weights and returning the model\n", + " model.load_state_dict(torch.load(os.path.join(model_save_dir, weight_name)))\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jrAhXo59vMiQ" + }, + "source": [ + "Alright, now let's proceed to our first training and see the performance of our trained model in action. For this initial training, we will use a VGG16 architecture for the model and let it train for 10 epochs. Please note that each epoch will take a few minutes to run (depending on your GPU), so grab your cup of coffee and give yourself a little break!" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "357dcdd72abb41e898df659daefd5d10", + "759a8fe7f71c4257ba423bfd93e1e376", + "cf479fdccb9a4078b463ee54fe37e279", + "bff20abf942d40178a72637de02e312e", + "1f107310246647e0a6f3ca512e8d55bf", + "5a8816a5bebf4212bcd91d80eec80b61", + "0ad6acd499904cf6bf9ebbe07ddb5f3d", + "de2a41970f04422baa02676120876232", + "a718673190c14f01a01a20aac04e36a3", + "06d79c695cf3402ea13230cc8d8e9e8c", + "04afade849d84d6d95efc99301837dd0", + "e3e30cc3b7a54c0eb67c543a0cef2b67", + "4be6277b68de45909f965b0deedf3f95", + "fd41ecc70bff4704a199de99714c3cb2", + "34f5cc4031f04d72838a4ffc93d6a983", + "c8b7a4e5b480474985127c8efb8d66a6", + "82b30678c2954080aa6b0a4914c958e4", + 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"Starting epoch: 1/10\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "357dcdd72abb41e898df659daefd5d10", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/654 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Doing a base-line training with a non-trained and not-pretrained model\n", + "# You may need to vertically scroll within the code output window\n", + "# in order to see all of the output.\n", + "\n", + "vgg16_model_1 = build_model(arch='vgg16', pretrained=False)\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "learning_rate = 0.01\n", + "optimizer = torch.optim.SGD(vgg16_model_1.parameters(), lr=learning_rate)\n", + "num_epochs = 10\n", + "vgg16_model_1 = train_classifier(model=vgg16_model_1,\n", + " model_name='vgg16_model_1',\n", + " train_loader=train_loader,\n", + " valid_loader=valid_loader,\n", + " criterion=criterion,\n", + " optimizer=optimizer,\n", + " num_epochs=num_epochs,\n", + " plot_curves=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 504, + "referenced_widgets": [ + "c31ed7b98add492d954928ed54c7be3f", + "918f22ec37d4492d92bbad4128ab55e9", + "336a4b5fb1a440ae916e2139b3185912", + "0784eda61e3344fc97cdf20f79418900", + "31232d276056491ab150f4af05d226a1", + "3735bf51e36b4a27a6ed1be76f67d3c0", + "f56b7a0733f144928519d60d04ce8813", + "0241b8d8a4d24223b92a81b248a6af74", + "c6cfac4b80a64d7f89c870e2a073bf91", + "3461f19d5ee946c6b0e47a10622a3be7", + "e4d750ae0933409693dd64921c61c396" + ] + }, + "id": "s96uQASUCm6c", + "outputId": "44163c7a-2070-426b-e4e0-cdfa783f5ef8" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c31ed7b98add492d954928ed54c7be3f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/39 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluating the performance of vgg16_model_1 on the test set\n", + "\n", + "_ = evaluate_model(vgg16_model_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "48MQfr8byKTh" + }, + "source": [ + "First look at the loss and accuracy curves. Can you see where overfitting has started? What do you think is the reason for those oscillations in the loss curves?\n", + "\n", + "Now look at the confusion matrix. How do you interpret that? Does our model have a more challenging job detecting the pneumonia cases or the normal cases?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8R_t42m8O1la" + }, + "source": [ + "## Part 5: The concept of fit\n", + "\n", + "You have previously learned the definition of fit, overfitting, and underfitting. As a quick review, whenever we train a regression model, we are trying to find a curve in a multidimensional space where most of our data points fall on or very close to this curve. Speaking more simply, a fit model is nothing different from a well-trained model. Unlike a well-fit (or well-trained) model, an underfit model has not learned enough from the training data. The curve from such a model will not cross as many data points as a well-trained model in the same poly-dimensional space.\n", + "On the other hand, an overfit model is a model that has learned more than necessary (e.g. the specific examples, not the overall idea) from the training data and will therefore not generalize well to the unseen data (e.g., the validation data). The mathematical representation for such a model is a curve that crosses almost all of the data points in the poly-dimensional space, even the outlier data points, which a well-trained model should ignore naturally. Please take a look at the following figure to remind yourself of these concepts, and feel free to review our previous chapters if needed. The first row of the figure shows under-fitting, appropriate-fitting, and over-fitting for a regression problem, and the second row denotes the same concepts for a classification problem. Looking at how we defined an appropriate fit for a regression problem above, can you now describe what an appropriate fit means for a classification problem?\n", + "\n", + "
Figure 13. Comparison of appropriate fit, underfitting, and overfitting
Source: https://towardsdatascience.com/techniques-for-handling-underfitting-and-overfitting-in-machine-learning-348daa2380b9\n", + "
\n", + "\n", + "For the sake of this chapter, we will focus on the most common reasons for underfitting or overfitting and propose a few solutions to combat them. But before that, let us first specify the exact situation when overfitting and underfitting are occurring!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BkIPlpHs2y1d" + }, + "source": [ + "### Standard training strategy\n", + "\n", + "Although not always true, we generally train a neural network by pursuing the following three steps:\n", + "\n", + "1. Train a model to the point where it clearly is overfitting. As a general rule of thumb, every deep learning model will overfit the data if the training is continued long enough. In other words, if the model is not overfitting after an 'infinite' number of epochs, then something is probably not right in the training setup (data, model architecture, etc.). We will discuss a few of these scenarios below. On the other hand, it is also undesirable if a model overfits after just a few epochs. This warns us that the model might not have learned enough from the data. Again, this is something that we should be able to deal with.\n", + "\n", + "2. When overfitting starts, we can use different techniques to postpone it. For example, suppose the initial overfitting happens at epoch=10 for training. In that case, we do our best to postpone it so that it now happens a few epochs later, hoping that will improve its generalizability and ability to learn more meaningful features.\n", + "\n", + "3. Finally, one should save the version of our model's parameters that were in place around the time the model started to overfit. Practically, we do not usually pick the model the exact moment the overfitting started but choose our model from around that point and based on a desirable metric (e.g., accuracy). For example, if the model started to overfit at epoch 10, we may check epochs 9 to 11 and pick the model from either of these epochs which have the highest accuracy. This will ensure that we have a model that is not overfitting to our data and meets our expectations about metrics we care about.\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + "> **Note**: Remember that the usual way to detect overfitting is from the loss curves. Overfitting starts when the training loss is still decreasing, but the validation loss is clearly increasing (some jitter is normal due to noise). If you look at the following image as an example, the red dashed line denotes where the overfitting starts, and the yellow area may be where we prefer to pick our final model from. Some data scientists prefer to end their training when they hit a checkpoint like the red dashed line below. That approach is called \"early stopping\" and may help consume less training resources (e.g., GPU).\n", + "\n", + "\n", + "---\n", + "\n", + "
Figure 14. Loss curves showing overfitting
Source:https://www.baeldung.com/cs/training-validation-loss-deep-learning\n", + "
\n", + "\n", + "We provide code snippets for saving and loading weights for PyTorch models.\n", + "\n", + "In order to save some weights:\n", + "\n", + "\n", + "```python\n", + "save_path = 'path_to_save_the_weights.pth'\n", + "torch.save(dummy_model.state_dict(), save_path)\n", + "```\n", + "\n", + "And for loading a model from disk, we have:\n", + "\n", + "```python\n", + "dummy_model = Build_model(arch='vgg16', pretrained=False)\n", + "weights_path = 'path_to_save_the_model.pth'\n", + "dummy_model.load_state_dict(torch.load(weights_path))\n", + "```\n", + "\n", + "---\n", + "\n", + "> **Note 1**: Weights (or, more generally, the parameters) are saved in PyTorch, but not the model's architecture. Therefore, to use these saved weights, it is necessary to build the empty architecture for our model first and then load the trained weights into it.\n", + "\n", + "---\n", + "---\n", + "\n", + "> **Note 2**: PyTorch weights are traditionally saved as files with a \".pth\" extension.\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-il_3aZ3QAss" + }, + "source": [ + "### Under-fitting\n", + "\n", + "If the training process never displays overfitting, the model is probably underfit. Even if the model's performance looks good, you should still find out why it is underfitting and address it since there may still be room for improving the model's performance. In the case of underfitting, there are three aspects we should check first in our training session: the training length, learning rate, and the architecture of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G9Yae2asV7ND" + }, + "source": [ + "#### Number of epochs\n", + "\n", + "As mentioned before, one of the most common reasons for underfitting is that the model was not trained long enough. Look at the training session where we trained vgg16_model_1. You see that the model's performance at the end of the first epoch was not as good as the final model we saved in the later epochs. The reason is apparent. At the end of the first epoch, the model had not had the chance to learn enough signals from the data.\n", + "Addressing this form of underfitting is easy. Just let your model train longer. Do not forget our general rule of thumb that a model should be trained until it overfits the data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fc5V45PdWFPe" + }, + "source": [ + "#### Learning rate adjustment\n", + "\n", + "A second reason for underfitting is using learning rates that are too small, which results in slower learning. The following code snippet shows this phenomenon by training our model for three epochs and using a minimal learning rate. You should see that the model is not learning significantly during this training and, therefore, will not appropriately fit the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 463, + "referenced_widgets": [ + "659da3c7f3264128a1b1f58a5ace9a50", + "e9a3bb30beae479fbb0557cd6188d2cb", + "217d3931f5db4f438b71ceb4eb0071d7", + "1905a03f4aaa4b85894787e52152c4d2", + "f060ebeab1924388ab575c6ba126c4be", + "54e2c4a62ae54c5baa95f7f79dab9b10", + "536854a4e52a4f59b7c3764cc72eb56a", + "87e4d60158284bfb975c9cb8ef6eb183", + "da61a126ac2948ff8b92192bcc39af53", + "418f4b8f32c64d08a12a23eedaa7e00e", + "4edf01ed3c9848cd94b74c1ffea26ecc", + "1b9b80fba1a641c8bdfb9d897fceffb9", + "dcfc0aa0c9874904b228e1ae792c3086", + "350fec89290441449c93cc6daa24f920", + "5e2543e514774d61ac9885cb568264cc", + "aa44ff0034ce4fb690dbfaed1a2a5865", + "600047e6d4d24b73b995ee06716004cf", + "b19d6d6bc60a4ff7b6c0082a860878c3", + "94544211e32e44a29cf91fde57fe2470", + "7dfc2a31e5df4e6dbf1f0ea2e871fd3a", + "e35eadbb74764b158bc55ef75a19fca9", + "80dd6ff925884a9281aae126c883ce02", + "b0ad5654e72a4c7bbd17194a9a616ae4", + "98a18ec77c694859a4970a97739a8811", + "03410778d0f3440fa41f39c88dcf1379", + "9705a8ed883b4a188fbe363ca8557ca0", + "9d4d0d9d4e6147aea56e03b7f713a707", + "3ed49b83d81c4011bf5fdf412651b9c4", + "4fd5965d686c4632a77e698098d6f897", + "17e958585182475495b3009798a55b0a", + "23ee93d280b74e1a83f8a4a8c11ec9d9", + "391e00a28b234ac3bb1aa4be302054b5", + "f4a37190bbfc492f94b569a05791988e", + "ebd7e699c61a426da814258bb88a28ca", + "610365b950c9480d93dae44161ff0949", + "01c1fe7360d34b6280d8d2c9ecbbed73", + "55f57e24af4e4c26af4abf5052714ad8", + "e735587b93f743a8a3af580a41c5a180", + "e05fcf46cc9b4035a7cc564dee67f618", + "83b7cc610b9b432f9d5f89f2d49c1134", + "dc6106e86bb540bca688a76c237cf43c", + "4e9514807b8041fb8a2b568ebfed4337", + "97ebbf2e50914ac0a91a6c8d730a630e", + "7adb09d9617f4f3a8491dedf9c1eb8be", + "d5ee8aade2a84829b458baf37eec2434", + "c767d1d134ea4b9f986b057994d57d62", + "b2c3366f6d6b4c3c9b9823553348647e", + "aa9bb9d0827f43208113561919589186", + "7c9165cadc4548089df21e0bbcf573fe", + "73e2787c445c405e9eb320412e14b012", + "c45a3682f2a644dabb42f44f1db6e101", + "e260f56a676b4ceb81288aa800445e2c", + "f5c0d2b1f28b4e7ead0bc5bc168006d0", + "c5d4cadc93eb448ab0a3e352d600a56a", + "4e9674e4d2684062a7152f166daf310d", + "17e398e452b94e78961e9f9155d9dc84", + "1aa724f2644d42fa98b10bf7b24dbcb2", + "a5f009c1b0324aaa90a1007911395f73", + "7a9feadc34964fefaf795ee4b30a9b3a", + "477fd96f99524d2a903be6490197ace4", + "234cac5e95e5471fa73d38307b2e0a93", + "d97caf463e504cfa88f14b9ab81e65aa", + "c2c7829c0c7f410bb1bd578cc855573a", + "7d46f45037e04f6cbb6ea1882070acbf", + "072ae8119723480a97de638743d3cb6e", + "f308688fa38644cfbe29586bc86733c3" + ] + }, + "id": "FesBMb27Vody", + "outputId": "6432c0fa-3386-40f9-8b8a-b58e27295111" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------- \n", + "Starting epoch: 1/3\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "659da3c7f3264128a1b1f58a5ace9a50", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/654 [00:00**Practice**: If interested, try to down-sample your CXR images to 8 by 8, and create a linear PyTorch model that receives 64 inputs values in its first layer and generates two output numbers in its second layer (do not forget to apply the softmax as well). Try to train this model and see how it will underfit the data yourself. Do not forget that we have about 5000 training samples, so a linear model with more parameters than this number will easily overfit our training data. Can you say why?\n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "JPut_xzHhpGd" + }, + "outputs": [], + "source": [ + "##### Use this code cell for the coding practice described above:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NYTAI4pFRQh_" + }, + "source": [ + "### Overfitting\n", + "\n", + "Although underfitting may happen for the reasons mentioned above, the problem that is much more commonly seen during the training sessions is overfitting. As mentioned above, we always want to postpone overfitting as much as possible to give our model more time to learn from the data and improve its generalizability. Here, we will review four aspects of the training setup. Investigating and improving these may help with combatting overfitting. At the end of this section, we will deploy several of these strategies to run another round of training, with hopefully, more resistance to overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x_ns_kk6lfV8" + }, + "source": [ + "#### Training data size" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VZnLmmqEiYHk" + }, + "source": [ + "It may look trivial, but we should mention that the most potent way for addressing overfitting is to add more data to your training. When your model sees more data during the training, it will have a more difficult job to memorize all data. Instead, it will have a higher chance of learning the patterns present in the data. As you can imagine, the problem with this solution is that finding more data is often not very feasible.\n", + "\n", + "---\n", + "\n", + ">**Note**: If interested, build a data loader with only ten training samples from our pool of CXRs, and train a model on it. See for yourself how overfitting will show itself in the few starting steps of your training!\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_W03U5alDje0" + }, + "source": [ + "#### Data augmentation\n", + "\n", + "Data augmentation is a well-known strategy to combat overfitting. Although this technique is never as effective as adding more unique training data, it will still help the training. The fundamental advantage of data augmentation is that the model will see variations of data points in each epoch. For example, a single photo may be used in a slightly rotated, horizontally or vertically flipped, zoomed in or out, or brighter or darker variations when the data loader calls it in each epoch.\n", + "\n", + "Although the list of possible augmentations for imaging data is very long (and newer techniques are introduced every day), there are two challenges that data scientists should consider when augmenting medical images:\n", + "\n", + "1. The first issue is to decide about which augmentations to use. Not all augmentations may make sense for medical images, and some may make the model's job harder for no reason. For example, in our scenario for classifying CXRs, it does not make sense to augment the CXRs by vertical flipping. The reason is that CXRs would never be acquired upside-down!\n", + "\n", + "2. The second challenge is how much to augment. For example, it may make sense to rotate the CXRs for a few degrees to make the model think these CXRs are new data. However, if we rotate them as much as 90 degrees, we will be in the same situation as described above, where the augmentation has no value. Even if such limitations do not exist, estimating the benefit of augmentation is difficult. In fact, the types and amount of augmentation is another hyperparameter one needs to tune to achieve a good fit.\n", + "\n", + "---\n", + "\n", + "\n", + ">**Note**: As an exception to the above scenarios, if our CXR classifier is expected to see non-standard CXRs during the inference time (e.g., flipped or highly rotated ones), it may make sense to add such augmentations to our pipeline. Please note that augmenting the training data adds complexity to the model's data and makes the model fit more computationally demanding, so this complexity must have some returns (e.g., delayed overfitting).\n", + "\n", + "\n", + "---\n", + "
\n", + "\n", + "Recall that adding augmentation is often what we do when building our data loaders. So, let us build another data loader using augmentations and visualize their outputs below to see how much they differ from the outputs of the previous data loaders we used." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 930 + }, + "id": "JOtAEq7EMr3g", + "outputId": "acb8894a-76e7-4ae0-eeea-0509df207b43" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
Figure 14. Cross-entropy loss with L2 regularization (weight decay)\n", + "
\n", + "\n", + "As shown above, a new term is added to the loss function, called L2. The higher the model's weights are, the higher the L2 will be, and thus, the overall loss. Here, lambda() is a hyperparameter to tune and determine how much penalty a model should receive from weight decay.\n", + "\n", + "Fortunately, implementing weight decay in PyTorch is very simple. You only need to pass the lambda value to the optimizer class when building your optimizer. Then PyTorch will automatically implement that during the training:\n", + "\n", + "```python\n", + "optimizer = torch.optim.SGD(vgg16_model_3.parameters(), lr=learning_rate, weight_decay=0.001)\n", + "```\n", + "\n", + "In most cases, start with a value between 0.01 or 0.001 for lambda and gradually try larger or smaller values to find the optimal contribution of L2." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aG2MpE2jD6Ao" + }, + "source": [ + "#### Architecture selection\n", + "\n", + "From the discussion above, you can see that the more parameters a model has, the greater the tendency for overfitting. Imagine such a model as a smart but lazy student. It will learn the easiest way to solve the riddle of training data. If that is possible by simply memorizing all the data points you have, it might perform even better without having to learn the underlying principles.\n", + "\n", + "Albert Einstein famously said that things should be made as simple as possible but no simpler (https://www.brainyquote.com/quotes/albert_einstein_103652). This is particularly true for deep learning models. The challenge is knowing what that right size is. The question of how big a model should be is, again, a matter of experimenting. A common strategy is to start with a model that worked well on a 'similar' problem, and if it overfits quickly, then reduce complexity and if it underfits, increase complexity. Repeat until satisfactory results are found. There are some computer-based strategies to automate this that will be discussed at a later time.\n", + "\n", + "Another important point to mention is that smaller models are not 'dumb' nor more challenging to train. Yes, they have lower capacity, but they may perform better than a more complex model! In fact, tons of research in deep learning has been done to build small but smart models. ResNet models (which you should be familiar with from the previous chapters, otherwise, check [here](https://towardsdatascience.com/review-resnet-winner-of-ilsvrc-2015-image-classification-localization-detection-e39402bfa5d8)), inception model (check [here](https://sheng-fang.github.io/2020-05-05-review-googlenet-v1-v4/)), and EfficientNet models (check [here](https://towardsdatascience.com/efficientnet-scaling-of-convolutional-neural-networks-done-right-3fde32aef8ff)) are among the well-known examples of such novel architectural designs. These models will always beat a model like VGG16 in terms of overfitting. You may also design your own architectures to achieve even smaller but smarter models than what is already known.\n", + "\n", + "Fortunately, working with ResNet models in PyTorch is as easy as working with VGG models. Lets' try a ResNet18 model and compare the number of parameters it has with our VGG16:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R9ZNSurnm0Xj", + "outputId": "82d1d417-6519-4781-a26c-82423703ae1c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 64, 112, 112] 9,408\n", + " BatchNorm2d-2 [-1, 64, 112, 112] 128\n", + " ReLU-3 [-1, 64, 112, 112] 0\n", + " MaxPool2d-4 [-1, 64, 56, 56] 0\n", + " Conv2d-5 [-1, 64, 56, 56] 36,864\n", + " BatchNorm2d-6 [-1, 64, 56, 56] 128\n", + " ReLU-7 [-1, 64, 56, 56] 0\n", + " Conv2d-8 [-1, 64, 56, 56] 36,864\n", + " BatchNorm2d-9 [-1, 64, 56, 56] 128\n", + " ReLU-10 [-1, 64, 56, 56] 0\n", + " BasicBlock-11 [-1, 64, 56, 56] 0\n", + " Conv2d-12 [-1, 64, 56, 56] 36,864\n", + " BatchNorm2d-13 [-1, 64, 56, 56] 128\n", + " ReLU-14 [-1, 64, 56, 56] 0\n", + " Conv2d-15 [-1, 64, 56, 56] 36,864\n", + " BatchNorm2d-16 [-1, 64, 56, 56] 128\n", + " ReLU-17 [-1, 64, 56, 56] 0\n", + " BasicBlock-18 [-1, 64, 56, 56] 0\n", + " Conv2d-19 [-1, 128, 28, 28] 73,728\n", + " BatchNorm2d-20 [-1, 128, 28, 28] 256\n", + " ReLU-21 [-1, 128, 28, 28] 0\n", + " Conv2d-22 [-1, 128, 28, 28] 147,456\n", + " BatchNorm2d-23 [-1, 128, 28, 28] 256\n", + " Conv2d-24 [-1, 128, 28, 28] 8,192\n", + " BatchNorm2d-25 [-1, 128, 28, 28] 256\n", + " ReLU-26 [-1, 128, 28, 28] 0\n", + " BasicBlock-27 [-1, 128, 28, 28] 0\n", + " Conv2d-28 [-1, 128, 28, 28] 147,456\n", + " BatchNorm2d-29 [-1, 128, 28, 28] 256\n", + " ReLU-30 [-1, 128, 28, 28] 0\n", + " Conv2d-31 [-1, 128, 28, 28] 147,456\n", + " BatchNorm2d-32 [-1, 128, 28, 28] 256\n", + " ReLU-33 [-1, 128, 28, 28] 0\n", + " BasicBlock-34 [-1, 128, 28, 28] 0\n", + " Conv2d-35 [-1, 256, 14, 14] 294,912\n", + " BatchNorm2d-36 [-1, 256, 14, 14] 512\n", + " ReLU-37 [-1, 256, 14, 14] 0\n", + " Conv2d-38 [-1, 256, 14, 14] 589,824\n", + " BatchNorm2d-39 [-1, 256, 14, 14] 512\n", + " Conv2d-40 [-1, 256, 14, 14] 32,768\n", + " BatchNorm2d-41 [-1, 256, 14, 14] 512\n", + " ReLU-42 [-1, 256, 14, 14] 0\n", + " BasicBlock-43 [-1, 256, 14, 14] 0\n", + " Conv2d-44 [-1, 256, 14, 14] 589,824\n", + " BatchNorm2d-45 [-1, 256, 14, 14] 512\n", + " ReLU-46 [-1, 256, 14, 14] 0\n", + " Conv2d-47 [-1, 256, 14, 14] 589,824\n", + " BatchNorm2d-48 [-1, 256, 14, 14] 512\n", + " ReLU-49 [-1, 256, 14, 14] 0\n", + " BasicBlock-50 [-1, 256, 14, 14] 0\n", + " Conv2d-51 [-1, 512, 7, 7] 1,179,648\n", + " BatchNorm2d-52 [-1, 512, 7, 7] 1,024\n", + " ReLU-53 [-1, 512, 7, 7] 0\n", + " Conv2d-54 [-1, 512, 7, 7] 2,359,296\n", + " BatchNorm2d-55 [-1, 512, 7, 7] 1,024\n", + " Conv2d-56 [-1, 512, 7, 7] 131,072\n", + " BatchNorm2d-57 [-1, 512, 7, 7] 1,024\n", + " ReLU-58 [-1, 512, 7, 7] 0\n", + " BasicBlock-59 [-1, 512, 7, 7] 0\n", + " Conv2d-60 [-1, 512, 7, 7] 2,359,296\n", + " BatchNorm2d-61 [-1, 512, 7, 7] 1,024\n", + " ReLU-62 [-1, 512, 7, 7] 0\n", + " Conv2d-63 [-1, 512, 7, 7] 2,359,296\n", + " BatchNorm2d-64 [-1, 512, 7, 7] 1,024\n", + " ReLU-65 [-1, 512, 7, 7] 0\n", + " BasicBlock-66 [-1, 512, 7, 7] 0\n", + "AdaptiveAvgPool2d-67 [-1, 512, 1, 1] 0\n", + " Linear-68 [-1, 2] 1,026\n", + "================================================================\n", + "Total params: 11,177,538\n", + "Trainable params: 11,177,538\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.57\n", + "Forward/backward pass size (MB): 62.79\n", + "Params size (MB): 42.64\n", + "Estimated Total Size (MB): 106.00\n", + "----------------------------------------------------------------\n" + ] + } + ], + "source": [ + "resnet18_model_1 = build_model(arch='resnet18', pretrained=False).cuda()\n", + "summary(resnet18_model_1, input_size=(3, 224, 224))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mjAWLvRtz1PY" + }, + "source": [ + "As you can see, our ResNet 18 has about 11 million parameters, while our VGG16 had about 134 million! This is a huge difference! You will shortly be even more surprised when you see the performance of ResNet18 is also much better than the VGG." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "paSvtepMXId-" + }, + "source": [ + "#### Transfer learning and fine-tuning\n", + "\n", + "Last but not least, **transfer learning** is one of the best techniques to reduce overfitting. Not only does it help with a better fit, but it also reduces the dependency of training on training data. This means, with appropriate deployment of transfer learning, the model may learn the same or even better with a smaller fraction of data at hand. This is very useful in fields like medicine when adding more data is not always feasible.\n", + "\n", + "There are many ways to define transfer learning, but consider the analogy introduced at the beginning of this chapter. Remember that each deep learning model is a huge mathematical equation with many parameters. We told you that these parameters often begin with random values when the model is created. In transfer learning, these parameters are not randomly initiated anymore. Instead, they are imported from another model already trained to do a task similar to what we pursue. This source model is called a \"pre-trained\" model.\n", + "\n", + "Imagine our VGG16 model to classify the CXRs, and suppose we already have access to another VGG16 model that others have trained to differentiate viral pneumonia from COVID19 pneumonia. Although this second model is not exactly doing what we want to do, it is doing something similar, and perhaps, it will use many features that are similar to the features that our model should learn. In mathematical terms, many parameters of our model will probably end up having values close to values of the second model's parameters. Therefore, if one starts with this second model and starts to train that for the new purpose, training will probably be much faster and smoother than when training a randomly-initiated model from scratch. This process of just updating weights rather than training from random values is called **\"fine-tuning.\"**\n", + "\n", + "
Figure 14. Transfer learning vs. learning from scratch
Source: https://medium.datadriveninvestor.com/introducing-transfer-learning-as-your-next-engine-to-drive-future-innovations-5e81a15bb567\n", + "
\n", + "\n", + "It is not always possible to find a model that does a 'similar task' but it is often the case that transfer learning from a model that is fairly different can still be better than starting from random values. The good news is that there are a number of pre-trained models (e.g. the ImageNet database) for a variety of purposes that can be very useful for transfer learning, and if you have limited data, it is valuable to start with one of those models.\n", + "\n", + "According to Wikipedia, The ImageNet project is a large visual database designed for use in visual object recognition software research. It consists of more than 14 million images of natural (not medical) objects that have been hand-annotated by the project to indicate what objects are pictured. As a tradition, whenever well-known deep learning models are introduced, developers pre-train them on ImageNet and release their weights. This means in the worst-case scenario, you can find access to the weights of a standard model that has been pre-trained on ImageNet to use in your project (unless you aim to use a custom architecture of your own or others). Although ImageNet is not a medical database, transfer learning from it to medical tasks is better than no transfer learning in many cases. The reason is that the ImageNet database is huge. Therefore, models pre-trained on that will have a memory of many features, at least some of which may be useful for medical training sessions as well.\n", + "\n", + "If you check the script for the \"Build-model\" function above, you can see that it loaded pre-trained weights from a model pre-trained on ImageNet to our VGG16 model. Do this now and see how an ImageNet pre-trained VGG16 will perform in evaluating our CXRs:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 540, + "referenced_widgets": [ + "934314848e4046a3a354914f55933036", + "ef88e819f9eb4afa8253a44aabd88063", + "2968d7e7534f4bf7a37b8dbb65f677e0", + "35514fe35fbd48db84a8764b4810039a", + "f35a628ac81d434da3a2450ab51c9f5e", + "a42b50a06c434339882d0622fde17e10", + "e3db85ad5025451486b9f81c7c051b51", + "a3b058c4f92f49709e691ae4f9d1ea9c", + "97d389e26b4a419f864055b4592640aa", + "1d12bf40a0404f97ae78615416341650", + "ce21b2a432424b43bb705893a0bdc4e5" + ] + }, + "id": "u0kLJjOvrc_O", + "outputId": "0847371a-d575-4059-a79d-4b3532aa05a1" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 528M/528M [00:02<00:00, 192MB/s]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "934314848e4046a3a354914f55933036", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/39 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluating the performance of an ImageNet-pretrained model\n", + "\n", + "Imagenet_pretrained_vgg16 = build_model(arch='vgg16', pretrained=True)\n", + "_ = evaluate_model(Imagenet_pretrained_vgg16)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pdZ6EbLJv0Mv" + }, + "source": [ + "This shows that the pre-trained model was not that successful in differentiating our CXRs from each other, but that should not be a surprise since that was not the ImageNet task. As said before, models pre-trained on ImageNet are trained to identify natural objects like animals, cars, etc. A model pre-trained on ImageNet will probably not do better on a medical imaging task than a randomly-initialized model. If we want to see the value of transfer learning, we must perform fine-tuning.\n", + "\n", + "To emphasize again, fine-tuning means to start with a pre-trained model, freeze 'most' initial layers, and train the remaining layers (by 'freeze' we mean that the weights are not changed). The intuition behind this technique is that the initial layers of the models often learn low-level features (e.g., lines, edges, curves, circles, etc.). Subsequent layers recognize combinations of these (e.g. 2 lines at a 90 degree angle are a corner and 2 circles might represent eyes). As a result, in an ImageNet pre-trained model, we should mostly rely on the first two or three layers. Anything after those layers are likely specific to natural images and, therefore, not helpful in understanding medical images. Of course, if you happen to find a good medical imaging source model for your task, then feel free to use more later layers.\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + "> **Note:** This is the most basic form of fine-tuning. More advanced forms also exist, which are beyond the scope of this chapter.\n", + "\n", + "\n", + "---\n", + "\n", + "To see this in practice, load a ResNet18 model pre-trained on ImageNet, freeze its initial layers, and train it on our data. We will also use L2 regularization and data augmentation to focus resources to avoid overfitting and to improve performance. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "6uq1lhFobWQ7" + }, + "outputs": [], + "source": [ + "# Defining a function to freeze the resnet models for fine-tunning\n", + "\n", + "def freeze_resnet18(resnet18_model: Callable,\n", + " children_num_to_freeze: int = 5,\n", + " print_children: bool = False):\n", + " \"\"\"\n", + " Freeze a resnet18 model up to a user-specified children module.\n", + " Parameters:\n", + " resnet18_model: The resnet18 model to be frozen and returned.\n", + " children_num_to_freeze: The number of children modules to be frozen.\n", + " print_children (bool): Whether or not to print the children modules.\n", + " \"\"\"\n", + " for i, child in enumerate(resnet18_model.children()):\n", + " if i < children_num_to_freeze:\n", + " status = 'Frozen'\n", + " for param in child.parameters():\n", + " param.requires_grad = False\n", + " else:\n", + " status = 'Unfrozen'\n", + " for param in child.parameters():\n", + " param.requires_grad = True\n", + " if print_children:\n", + " print(f'******************* child module number: {i} - {status}')\n", + " print(child)\n", + " return resnet18_model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IBCgfYvgbcrg", + "outputId": "ff190878-4b28-4497-f4d5-7d2d08023733" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 44.7M/44.7M [00:00<00:00, 118MB/s]\n" + ] + } + ], + "source": [ + "# Creating a resnet18 model and freezing its inital layers.\n", + "\n", + "resnet18_model = build_model(arch='resnet18', pretrained=True)\n", + "resnet18_model = freeze_resnet18(resnet18_model,\n", + " children_num_to_freeze = 5,\n", + " print_children=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "80f8aacfefda40a1a24607d9feb18300", + "54d9c23c950b4f9c9da8a4863beda42c", + "fa5299a353174d0aacb1a1091831466a", + "f0d9bd40fbba40aa830d038dcc268626", + "99f3e94159cd4aae89dbd6a6c920a214", + "320cc641d56a47b18837d32d4fe8b47b", + "098cdb190047415aa1d9454499a9f851", + "c5d8db254d0a429092608fcb7cef30d9", + "f87065d55a7a43f5bb033bcbacd5cd21", + "4365c83108bf4786bf137f90d610d3ad", + "13587f074cf44e3bbc0be021eb8aa92b", + "95c80ca8c63a482092ddb19d79410ae2", + "d43a6838d436464c8c8d435e0ea662dd", + "42701594c37442f99bf1e96d076d777c", + "299f9c5f4e12429f9a85c5b3cf3ab232", + "c279b86e9efd4b38b067d7e41d9a7aef", + "1b160f8f26bb4fa7b016baba40592b1e", + "9eecb503496745af82d152a24f23fdb2", + "5cc4a8cfb7aa46a58d47fbb8551cb922", + "48aad42d3cf54a2195d789fee8c89b1b", + "6788ef14c2694c43a9763c38d24ee16a", + 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"version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/654 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Training a model with resnet18 architecture and pretrained weights\n", + "\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "learning_rate = 0.01\n", + "optimizer = torch.optim.SGD(resnet18_model.parameters(), lr=learning_rate,\n", + " weight_decay=0.001)\n", + "num_epochs = 10\n", + "resnet18_model_2 = train_classifier(model=resnet18_model,\n", + " model_name='resnet18_model_2',\n", + " train_loader=train_loader2,\n", + " valid_loader=valid_loader2,\n", + " criterion=criterion,\n", + " optimizer=optimizer,\n", + " num_epochs=num_epochs,\n", + " plot_curves=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 504, + "referenced_widgets": [ + "2a07be74eafa439aa8ca0355f71e0f06", + "6359b0b788a94cc2b4187289a2c51530", 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This is why you should always be familiar with as many deep learning techniques and tricks as possible. Now that we have reviewed the most common techniques to battle overfitting, our discussion in this chapter is almost over. Feel free to review the above concepts and redo the training in different variations. Try to include or exclude different techniques we introduced to see how much each will affect the model's performance. Do not forget: Deep learning is, of course, a matter of science and art, but it also is a matter of experimenting and having perseverance!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dTUNSbHvO566" + }, + "source": [ + "## Part 6: Conclusions and further reading\n", + "\n", + "This chapter described how to train a deep learning model. This included a discussion of how a neural network resembles a mathematical equation. That was followed by a definition of training and how to implement that in PyTorch. Next, the concepts of fit and how to combat under-fitting and over-fitting were covered. The discussion was enriched with many practical examples and Python code.\n", + "Nevertheless, this is still only a tiny fraction of the world of deep learning. Other resources that may be useful to understand the concepts include:\n", + "\n", + "* [A Beginner Intro to Neural Networks](https://purnasaigudikandula.medium.com/a-beginner-intro-to-neural-networks-543267bda3c8)\n", + "* [How to train neural networks for image classification \u2014 Part 1](https://medium.com/nerd-for-tech/how-to-train-neural-networks-for-image-classification-part-1-21327fe1cc1)\n", + "* [FastAI course - SGD from scratch](https://course.fast.ai/videos/?lesson=4)\n", + "* [Anrew Ng's deep learning specialization on Coursera, courses 1 and 2](https://www.coursera.org/specializations/deep-learning)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LwT17BWFKzl4" + }, + "source": [ + "---\n", + "\n", + "##***Feedback***\n", + "\n", + "*Now that you have completed this chapter, we would be very grateful if you spend a few minutes of your time to answer a short survey about this chapter. We highly value your feedback and will do our best to leverage this to improve our educational content and/or strategies.*\n", + "\n", + "[Click here to begin the survey!](https://docs.google.com/forms/d/e/1FAIpQLSddhdaAmeHmrKKRNXCLIQH6_mnIC3KR7XlDIVWGt3FSQhPDhQ/viewform)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "include_colab_link": true, + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.12" + }, + "widgets": { + "state": { + "00db50bbbb074131bc4992ec5c3906f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": 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