{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal Fusion Transformers\n", "\n", " \"Open\n", "\n", "\n", "The Temporal Fusion Transformer (TFT) is a cutting-edge deep learning model designed for interpretable, multi-horizon time series forecasting. Developed by Google in collaboration with the University of Oxford, TFT combines the strengths of recurrent layers for local processing and self-attention layers for capturing long-term dependencies [[1](https://arxiv.org/abs/1912.09363)][[2](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Forecasting/TFT/README.md)]. This hybrid architecture allows TFT to effectively handle complex temporal relationships and select relevant features, making it highly versatile across various forecasting scenarios [[1](https://arxiv.org/abs/1912.09363)]. Its ability to provide interpretable insights into temporal dynamics and deliver high-performance predictions sets it apart as a powerful tool for time series analysis [[1](https://arxiv.org/abs/1912.09363)][[2](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Forecasting/TFT/README.md)].\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Datasets\n", "In this tutorial, we will explore the application of the Temporal Fusion Transformer (TFT) model to various time series datasets, including synthetic, air passenger, electricity consumption, and stock market data. The TFT model, known for its ability to handle complex temporal relationships and provide interpretable insights, is particularly well-suited for these diverse datasets. We will start with the SimpleSyntheticDataset, which generates synthetic time series data with configurable trend, seasonality, and noise components, making it ideal for testing forecasting models. Next, we will use the Air Passenger dataset, which contains monthly totals of international airline passengers from 1949 to 1960, normalized for effective model training. We will also analyze the PJM Hourly Energy Consumption dataset, which provides hourly power consumption data from a regional transmission organization in the United States. Finally, we will apply the TFT model to stock market data obtained from Yahoo Finance, allowing us to forecast stock prices based on historical trends. By the end of this tutorial, you will have a comprehensive understanding of how to leverage the TFT model for various time series forecasting tasks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simple Synthetic\n", "First, let's import the necessary libraries and define our hyperparameters.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", "from fintorch.models.timeseries.tft import (\n", " GatedResidualNetwork,\n", " InterpretableMultiHeadAttention,\n", " VariableSelectionNetwork,\n", ")\n", "\n", "# Define hyperparameters\n", "input_dimensions = 10\n", "hidden_dimensions = 64\n", "dropout = 0.1\n", "context_size = 5\n", "batch_size = 32\n", "sequence_length = 10\n", "num_inputs = 3\n", "number_of_heads = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by using the Gated Residual Network (GRN). The GRN is a key component of the TFT model, allowing it to handle complex temporal relationships.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GRN Output shape: torch.Size([32, 10, 64])\n" ] } ], "source": [ "# Example usage of GatedResidualNetwork\n", "context = torch.randn(batch_size, context_size)\n", "grn = GatedResidualNetwork(\n", " input_size=input_dimensions,\n", " hidden_size=hidden_dimensions,\n", " output_size=hidden_dimensions,\n", " dropout=dropout,\n", " context_size=context_size,\n", ")\n", "grn_input = torch.randn(batch_size, sequence_length, input_dimensions)\n", "grn_output = grn(grn_input, context)\n", "print(\"GRN Output shape:\", grn_output.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will use the Variable Selection Network (VSN) to select relevant features from the input data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of x_a: torch.Size([32, 10, 3])\n", "Shape of x_b: torch.Size([32, 10, 4])\n", "Shape of x_c: torch.Size([32, 10, 5])\n", "VSN output shape: torch.Size([32, 10, 64])\n" ] } ], "source": [ "# Example usage of VariableSelectionNetwork\n", "variable_selection_network = VariableSelectionNetwork(\n", " {\"a\": 3, \"b\": 4, \"c\": 5}, hidden_dimensions, dropout, context_size\n", ")\n", "\n", "# Create example input data\n", "x_a = torch.randn(batch_size, sequence_length, 3)\n", "x_b = torch.randn(batch_size, sequence_length, 4)\n", "x_c = torch.randn(batch_size, sequence_length, 5)\n", "inputs = {\"a\": x_a, \"b\": x_b, \"c\": x_c}\n", "\n", "print(\"Shape of x_a:\", x_a.shape)\n", "print(\"Shape of x_b:\", x_b.shape)\n", "print(\"Shape of x_c:\", x_c.shape)\n", "\n", "# Call the forward method\n", "vsn_output = variable_selection_network(inputs, context)\n", "\n", "# Print the output shape\n", "print(\"VSN output shape:\", vsn_output.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will then use the Interpretable Multi-Head Attention mechanism to capture long-term dependencies in the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Attention Output shape: torch.Size([32, 10, 64])\n", "Attentions shape: torch.Size([32, 4, 10, 10])\n" ] } ], "source": [ "# Example usage of InterpretableMultiHeadAttention\n", "attention_module = InterpretableMultiHeadAttention(\n", " number_of_heads, hidden_dimensions, dropout\n", ")\n", "\n", "# Generate example input tensors\n", "q = grn_output # Use output of GRN as query\n", "k = grn_output # Use output of GRN as key\n", "v = grn_output # Use output of GRN as value\n", "\n", "# Create a mask (optional)\n", "mask = torch.tril(torch.ones(sequence_length, sequence_length)).bool()\n", "\n", "# Call the forward method of InterpretableMultiHeadAttention\n", "attention_output, attentions = attention_module(q, k, v, mask)\n", "\n", "# Print the output shapes\n", "print(\"Attention Output shape:\", attention_output.shape)\n", "print(\"Attentions shape:\", attentions.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use the output of the VSN as input to the attention mechanism." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Attention Output shape after the vsn: torch.Size([32, 10, 64])\n", "Attentions shape after the vsn: torch.Size([32, 4, 10, 10])\n" ] } ], "source": [ "# Example of using the InterpretableMultiHeadAttention after the variable selection network\n", "q = vsn_output # Using the output of the vsn.\n", "k = vsn_output # Using the output of the vsn.\n", "v = vsn_output # Using the output of the vsn.\n", "\n", "attention_output, attentions = attention_module(q, k, v, mask)\n", "\n", "# Print the output shapes\n", "print(\"Attention Output shape after the vsn:\", attention_output.shape)\n", "print(\"Attentions shape after the vsn:\", attentions.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's visualize the attention maps to understand how the model is attending to different parts of the input sequence." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Example of plotting the attention maps per head\n", "batch_index = 0\n", "fig, axes = plt.subplots(\n", " 1, number_of_heads, figsize=(5 * number_of_heads, 5), constrained_layout=True\n", ")\n", "\n", "for head in range(number_of_heads):\n", " attention_map = attentions[batch_index, :, head, :].detach().cpu().numpy()\n", " ax = axes[head] if number_of_heads > 1 else axes\n", "\n", " # Plot the attention map as a heatmap\n", " im = ax.imshow(attention_map, cmap=\"viridis\")\n", "\n", " # Set labels and title\n", " ax.set_xlabel(\"Key Sequence Position\")\n", " if head == 0:\n", " ax.set_ylabel(\"Query Sequence Position\")\n", " ax.set_title(f\"Attention Map - Head {head+1}\")\n", "\n", "fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.7, label=\"Attention Weight\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will train the TFT model." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import os\n", "import lightning as L\n", "import matplotlib.pyplot as plt\n", "import torch\n", "from lightning.pytorch.callbacks import EarlyStopping\n", "from fintorch.datasets.synthetic import SimpleSyntheticDataModule\n", "from fintorch.models.timeseries.tft import (\n", " TemporalFusionTransformer,\n", " TemporalFusionTransformerModule,\n", ")\n", "\n", "# Define hyperparameters\n", "input_dimensions = 10\n", "hidden_dimensions = 64\n", "dropout = 0.1\n", "context_size = 5\n", "batch_size = 32\n", "sequence_length = 10\n", "num_inputs = 3\n", "number_of_heads = 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by using the Temporal Fusion Transformer (TFT) model. The TFT model is designed to handle complex temporal relationships and provide interpretable insights into time series data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "####### TFT ########\n", "TFT Output shape[batch size, horizon, number of targets=1, number of quantiles]: torch.Size([32, 2, 1, 3])\n", "past length: 6 future length: 2\n", "Attention Weights shape[batch size, heads, sequence length (past length + future length), sequence length (past length + future length)]: torch.Size([32, 4, 8, 8])\n" ] } ], "source": [ "print(\"####### TFT ########\")\n", "\n", "# Example usage of TemporalFusionTransformer\n", "# Define hyperparameters\n", "embedding_size_inputs = 64\n", "hidden_dimension = 64\n", "dropout = 0.1\n", "number_of_heads = 4\n", "past_inputs = {\"a\": 3, \"b\": 4, \"c\": 5}\n", "future_inputs = {\"d\": 2, \"e\": 3}\n", "static_inputs = {\"f\": 4, \"g\": 5}\n", "quantiles = [0.05, 0.5, 0.95]\n", "sequence_length_past = 6\n", "sequence_length_future = 2\n", "\n", "# Create an instance of TemporalFusionTransformer\n", "tft_model = TemporalFusionTransformer(\n", " sequence_length_past,\n", " sequence_length_future,\n", " embedding_size_inputs,\n", " hidden_dimension,\n", " dropout,\n", " number_of_heads,\n", " past_inputs,\n", " future_inputs,\n", " static_inputs,\n", " quantiles=quantiles,\n", " batch_size=batch_size,\n", " device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n", ")\n", "\n", "# Generate example input tensors\n", "past_inputs_tensor = {\n", " \"a\": torch.randn(batch_size, sequence_length_past, 3),\n", " \"b\": torch.randn(batch_size, sequence_length_past, 4),\n", " \"c\": torch.randn(batch_size, sequence_length_past, 5),\n", "}\n", "future_inputs_tensor = {\n", " \"d\": torch.randn(batch_size, sequence_length_future, 2),\n", " \"e\": torch.randn(batch_size, sequence_length_future, 3),\n", "}\n", "static_inputs_tensor = {\n", " \"f\": torch.randn(batch_size, 4),\n", " \"g\": torch.randn(batch_size, 5),\n", "}\n", "\n", "# Call the forward method of TemporalFusionTransformer\n", "tft_output, attention_weights = tft_model(\n", " past_inputs_tensor, future_inputs_tensor, static_inputs_tensor\n", ")\n", "\n", "# Print the output shapes\n", "print(\n", " \"TFT Output shape[batch size, horizon, number of targets=1, number of quantiles]:\",\n", " tft_output.shape,\n", ")\n", "print(f\"past length: {sequence_length_past} future length: {sequence_length_future}\")\n", "print(\n", " \"Attention Weights shape[batch size, heads, sequence length (past length + future length),\"\n", " + \" sequence length (past length + future length)]:\",\n", " attention_weights.shape,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will use the Temporal Fusion Transformer Module (TFT Module), which is a more modular and flexible implementation of the TFT model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TFT Module Output shape: torch.Size([32, 2, 1, 3])\n", "Attention Weights shape: torch.Size([32, 4, 5, 5])\n", "Loss: tensor(1.4470, grad_fn=)\n", "Loss: tensor(1.4289, grad_fn=)\n", "Loss: tensor(1.3757, grad_fn=)\n", "Prediction: torch.Size([32, 2, 1, 3])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/marcel/Documents/research/FinTorch/.venv/lib/python3.11/site-packages/lightning/pytorch/core/module.py:441: You are trying to `self.log()` but the `self.trainer` reference is not registered on the model yet. This is most likely because the model hasn't been passed to the `Trainer`\n" ] } ], "source": [ "# Example usage of TemporalFusionTransformerModule\n", "# Define hyperparameters\n", "sequence_length_past = 3\n", "number_of_future_inputs = 2\n", "embedding_size_inputs = 64\n", "hidden_dimension = 64\n", "dropout = 0.1\n", "number_of_heads = 4\n", "past_inputs = {\"a\": 3, \"b\": 4, \"c\": 5}\n", "future_inputs = {\"d\": 2, \"e\": 3}\n", "static_inputs = {\"f\": 4, \"g\": 5}\n", "sequence_length_future = 2\n", "\n", "# Create an instance of TemporalFusionTransformerModule\n", "tft_module = TemporalFusionTransformerModule(\n", " sequence_length_past,\n", " sequence_length_future,\n", " embedding_size_inputs,\n", " hidden_dimension,\n", " dropout,\n", " number_of_heads,\n", " past_inputs,\n", " future_inputs,\n", " static_inputs,\n", " quantiles=quantiles,\n", " batch_size=batch_size,\n", " device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n", ")\n", "\n", "# Generate example input tensors\n", "past_inputs_tensor = {\n", " \"a\": torch.randn(batch_size, sequence_length_past, 3),\n", " \"b\": torch.randn(batch_size, sequence_length_past, 4),\n", " \"c\": torch.randn(batch_size, sequence_length_past, 5),\n", "}\n", "future_inputs_tensor = {\n", " \"d\": torch.randn(batch_size, sequence_length_future, 2),\n", " \"e\": torch.randn(batch_size, sequence_length_future, 3),\n", "}\n", "static_inputs_tensor = {\n", " \"f\": torch.randn(batch_size, 4),\n", " \"g\": torch.randn(batch_size, 5),\n", "}\n", "target = torch.randn(batch_size, sequence_length_future).float()\n", "\n", "# Create a batch\n", "batch = (past_inputs_tensor, future_inputs_tensor, static_inputs_tensor, target)\n", "\n", "# Call the forward method of TemporalFusionTransformerModule\n", "tft_module_output, attention_weights = tft_module(\n", " past_inputs_tensor, future_inputs_tensor, static_inputs_tensor\n", ")\n", "\n", "# Print the output shapes\n", "print(\"TFT Module Output shape:\", tft_module_output.shape)\n", "print(\"Attention Weights shape:\", attention_weights.shape)\n", "\n", "# Call the training step\n", "loss = tft_module.training_step(batch, 0)\n", "print(\"Loss:\", loss)\n", "\n", "# Call the validation step\n", "loss = tft_module.validation_step(batch, 0)\n", "print(\"Loss:\", loss)\n", "\n", "# Call the test step\n", "loss = tft_module.test_step(batch, 0)\n", "print(\"Loss:\", loss)\n", "\n", "# Call the predict step\n", "output = tft_module.predict_step(batch, 0)\n", "print(\"Prediction:\", output.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we will train the TFT Module using the SimpleSyntheticDataModule, which generates synthetic time series data for training, validation, and testing." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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wvrqtQ+cSY43TgMFgjYiIiIiIiIgGvJtfXwMAuPvdDRmOTOUJRlSfR2OdC8YYqw0cDNaIiIiIiIiIaMBrb5VYOBpTfe4LRYT7OhmNMVkbMBisEREREREREdGA194is7aAukpte71P+bizFWtsBR04GKxliTse+i5+b4iIiIiIiCiT9oZZrX71XLUdDV7l40gspj28XfhT7MDBYC0DiyW+LSQUCvXymZARny/+rwY2m62Xz4SIiIiIiIj6KjFXO/rej/Hw4k1pj28NqIO1mtaA8nGkszPWWCAyYFh7+wT6OqvVCrfbjbq6OthsNpjNzCL7CkmS4PP5UFtbi8LCQiUEJSIiIiIiIlq3rxX//GIXrjl2PIpy7Kowa1u9F/ct2oifHjEGTpv+z5KtfnUrqBimRTo5Y62TuRz1IQzWMjCZTKioqMC2bduwY8eO3j4d0lFYWIihQ4f29mkQERERERFRH/LjZ7/AvpYAdjf58feLD9QNs/63qR7zJpfrPl5bsRYSlhloW0GjMQk3v74Go8ty8LMjx3b+5KnfYLCWBbvdjvHjx7MdtA+y2WysVCMiIiIiIqIU+1rirZsfrKsBAEg6k83e/bbaMFhr0wRrYpWauLwgGpPw37X78M+VuwCAwdogw2AtS2azGU6ns7dPg4iIiIiIiIjaKRqTdCvWVm5vNHyMthU0HI2hLRDGW6v2ot6TLLwJRqJYvtX4efq6urYg3vu2GqfOqESek7PL24vBGhEREREREVE3qG0LoNkXxoTyvN4+lUHJaTMjEI63bG6saUNMJ1mraQ1CkiSYTKaU+/RaQf/89jqlMk0WCMewenez8rnR8/VVTy7Zgr99ug2hSAw/Pmx0b59Ov8NJ/ERERERERETdYPZfFmP+X5dgV6Ovt09l0PGFIkqoBgArdzTBF4qmHOcPR9EWjKTcDgCt/tRW0HfW7NN9jtV7WpTPwwaLDfrqJtBGb/x1Nvs4/qojGKwRERERERERdaM1QuhCPaO+TR0SrdvXCn84NVgDgNrWgO7trYF44OZKbA0NR2Nw21NnfK/f1woxMxOXHIiMArfeFk6cb9DgvCk9BmtERERERERE3cjcf7oCB4w6T1D1+bd7Ww2P/clzK3HPe+tTWkXlCq7iHDuAeACV60idqLWn2a/6PBTRD6i0m0T7Cvl8w5G+Gfz1dQzWiIiIiIiIiLqYuu2PyVpPq2tTB2tr01QNbm/w4dGPtuAHf/sc766Nt3q+vXovPtpQBwAoyZWDNQluR2rFmrjIIH5cdhVrUb1tCr1APt9QVL+ij9JjsEZERERERETUxcTQhBVrPU+uWJs9qhiAfog1NN+p+nz5tkZc+dLXaPAEcdPrawAApbkOHDymBEA8gDLrLCVo0FTHban1YHu9N+U47TkYBXA9TW5dZcVaxzBYIyIiIiIiIupi4pwtvTCGupdcsTa+PBeFbpvuMWPKcpSPJ5TnAoiHX298vQdtgQhy7BYs/c0xGF7kAhAPwtoCqYsOGjQVa+f/fTmOuvdj+DXLEiKaIM1oFltPk1tB+8r59DcM1oiIiIiIiIi6mDhny8KStR4nB2tleQ6MLs3RPWZsWa7y8RVHjcX4IfHPX/x8BwDg1JnDYLeaYbPEo5NwVEJbIJzyPPWaijVZtWYpQlhbsWYwi62nJVtB+8b59DcM1oiIiIiIiIi6mBisxSS22PW0urZ4qFWaaxysibfvX1mAsjwHgPjMNQCYP7kcAGBNBKPhaAytfp2KNW8o5TYA8AbVx/bZijU5WOsjQV9/w2CNiIiIiIiIqIuJoYl2aD11v821HgDx8GxGVaHuMXKQBgBjSnMwRPgcAKYNjz/Obo1HJ95gRDcM085Yk326qR7vfVutfK69DvrKTDP5PPrKzLf+pleDtSVLluD73/8+KisrYTKZ8Oabb6rulyQJt956KyoqKuByuTBv3jxs2rRJdUxjYyMuuOAC5Ofno7CwEJdeeik8Hk8PvgoiIiIiIiIiNbH6JxJjYNGTvMEIdjTGq84mDc3DyVMrdI87bnI5Dh1XgquPGQerxawK2nLsFhQlZrPJraBGlWmtOnPXAOCud9fj8he+xMaaNgCp10FfqVgLs2KtU3o1WPN6vZg+fToeffRR3fvvvvtuPPTQQ3jiiSewfPly5OTk4Pjjj0cgkOxTvuCCC/Dtt99i0aJFePvtt7FkyRJcdtllPfUSiIiIiIiIiFKIoUmEFWs9amNNGyQpXpFWkhv/pcdps2DBTw7GL+dPBKCuYKsqdsOUWDoht4I2JoK1XIcVr/98Lk6aOjSr86ltjVe0aa+DvhJkBRPnwYq1jrH25hc/8cQTceKJJ+reJ0kSHnjgAfzud7/DqaeeCgB4/vnnUV5ejjfffBPnnXce1q1bh3fffRdffPEFDjzwQADAww8/jJNOOgn33nsvKisre+y1EBEREREREcnE0ISBRc9aXx2vEJs0NE+57d9XHYZfvboKW+o8hq252mBNZku0gjb74osLClw2zBpRhAKX/rZRLZfdAiD1Ougr1wUr1jqnz85Y27ZtG6qrqzFv3jzltoKCAsyZMwfLli0DACxbtgyFhYVKqAYA8+bNg9lsxvLlyw2fOxgMorW1VfWLiIiIiIiIqKuIoUkkxoq17hCLSZB0FkOs3xf/GX+/inzltqnDC/DutUfg0HGlhs9XlutUPq4qSgZrdos6OslzxmuUHFZLVucpLy3QXgd9JVhTlhewsrJD+mywVl0dH/BXXl6uur28vFy5r7q6GkOGDFHdb7VaUVxcrByj54477kBBQYHyq6qqqovPnoiIiIiIiAazoDhjrY8EKANJMBLFcX/9BBc/80XKfRsSM80mluel3BdNE3IOyU9WrI0odikfy62gsnxnvFLNYVNHKib1YQo5uNIGaX2lQiwckSvWor18Jv1Tnw3WutNNN92ElpYW5deuXbt6+5SIiIiIiIhoAFG3grISqKst39qILXVeLNlYl1K1Js80G1bkSnnc6TOHAdAP3cqEWWxDC5LVa3IrqEyuWHNqKtaK3Xbdc5UDtZQZa30kcJWvT16nHdOrM9bSGTo0PgSwpqYGFRXJDR41NTWYMWOGckxtba3qcZFIBI2Njcrj9TgcDjgc+sMLiYiIiIiIiDpLDCnSVUlRxzT5khs6faEochzJeKOuLR6siTPTZKfNGIbKQpeqTVRW6E7OTCvLSwZr2lbQ/MRsNadNHawV5dh1N4eGIvHvv3YraF8IsiRJSraC9pEKuv6mz1asjR49GkOHDsXixYuV21pbW7F8+XIccsghAIBDDjkEzc3N+PLLL5VjPvzwQ8RiMcyZM6fHz5mIiIiIiIgI0FSsxRhYdLV6TzLA8gYjysf+UBRtic/1gjWz2YSDx5ToLh4wmUz4/fcn49LDRmPWiELldqtF3eOpVKxpWkGLc/Qr1pKtoH1vK6h4Tn1l5lt/06sVax6PB5s3b1Y+37ZtG7755hsUFxdjxIgRuPbaa/HnP/8Z48ePx+jRo3HLLbegsrISp512GgBgv/32wwknnICf/vSneOKJJxAOh3HVVVfhvPPO40ZQIiIiIiIi6jWhaHJelbYFkDpvT5Nf+dgTjECevl7viVer2a1m5DnaH3n86NDRKbfZNBVruQ795QVFbv0tofIMM+110BeCLPEc2hv0SZIEk9FguUGkVyvWVq5ciZkzZ2LmzJkAgOuvvx4zZ87ErbfeCgC48cYbcfXVV+Oyyy7DQQcdBI/Hg3fffRdOZ7Ikc8GCBZg0aRKOPfZYnHTSSTjssMPw5JNP9srrISIiIiIiIgLUIUWm5QWBcBR3v7se3+xq7uaz6h9iMQk7Grxpj9nT7FM+9oWSIWZdIlgry3V0WeijbQV12+OBmrZirSjTjDVN5WJfmLEmXqftOZ8bX12FY+//BF9sb8STS7YM6nbnXq1YO+qoo3RX48pMJhNuu+023HbbbYbHFBcX46WXXuqO0yMiIiIiIiLqkJDYYpchdHjqf9vw2Mdb8NjHW7D9zpO7+9T6vJvfWIOFX+zCnWdMxXmzR+ges1tTsQYA1S0BfLyhDoB+G2hHaVtBXfZ4lOISZqw5bWa47OoKNlnfbgVVB2tiFZokSXhx+U7MGF6IqcMLlONiMQlvfrMXoUgMZz+xDEB83txFh4zq0XPvK/rsjDUiIiIiIiKi/kqvYu3lL3bh8Ls/xObaNtWxm2s9qs/D0RjOenwprv7H191/on3Qwi92AQD++sFGw2P2NCeDNW8wAkmScPpjn+GhxZsAAKW5XResaVtB5Yq1WSOLlNsC4RjswvbQ2aOLMSQR7oWUVlDt8oIYdjf5cPxfl+DVL3d32fm2R1C4TiVJvWjj/e9qcMuba/H9R/6nekydJ5gSCn6xval7T7QPY7BGRERERERE1MVUywsSlUo3vrYauxr9uPn1tapjtZVOm2s9WLmjCW+v3pu2y2uwaguE0ewLK597ghGsr27DvpaAcltXVqwZBWvl+U4MK3QptzuE4w4YWYQjJpQBSH7/tZWL4WgML6/cjQ01bbjhlVXwhSKq+zfWtGFDtTqE7WraOW+b6zw454ll+HB9DbbUeXQfs7vJl3KbJxDWOXJwYLBGRERERERE1MXEwEI7WysQiao+dwsthaFITJkvJknqiqLBxgT9GWligAYA3mAUi9fVqG7r2mBN0woqfL8eu2AW7FYzvj+9UhXAOaxm5XOjirVQJKbaTvraV3uUj1v8Ycz/6xIc/8CSbl1yoG1P/eXLq7BieyN+/OxK1esUA95djX5oeYKRlNsGCwZrRERERERERF1M3QqavurMJrQQNnpD2FafrAjyh6J6DxnUWvzq6ihvMIIP1tWqbivL1V8k0BHaijWxwnB6VSE+v+lY/PWc6apWUKfNAkfic2V5gc6MtagQur67dp/y8Vc7kq2VbYHuC620LZ3VQmgpBmte4TrUq1jrznPs63p1eQERERERERHRQCRuWNRWBWnrsMTwrN4TVG3E9IejKMLg8MX2Rry3tlr53GipZ5um7dATjOC7va2q27TveWdYzeoTcWtad4tz4iGeGKzFK9ZMiXNJLC/QVC4GozHYhWDLG0xeByt3NAq3R5Sv0dW0m0CN3rVmXwi5jniEpFexxmCNiIiIiIiIiLqMWAkUjaVv5dMGa9vq1cHaYCFvmMyk1a8OcfY2+5WA6IT9h+KTjXWYv395l52XyWSCzWJSwjqXTT9KESvbnDaL8nkwol+xFgzH4LAmbxPbfldsSwZrvm6sWtS2mcYk/fNp9oXx/rfbkOu0YndzasWaN8RgjYiIiIiIiIg6IRqTcOWCr5DrtKpCFu3Qem0plk8Izxo8IexoYCsokFrZJ2vVVKzJQWSR24bHL5wFfzgKt71r4w6bxYxwNP690FasydStoGbl82QrqGbWXjgKp9BuGUzM3guEo1i1q0W5vTtDK20rqLgrIyBclx9vqMW978e3tJbnp86vY8UaEREREREREXXKtnov3v023so4bXiBcrs2UNHyCYPfdzf5Ud2anHM1WCrW2rP9VBviyMFaWZ4DJpOpy0M1QN0OahSsOVStoMmKtWQrqPo1BsJRVcVYMBz/uLoloGrR9AV7rmJN/D4Ewsn7Xlq+U/m4pjWY8jzRmARJkmAy6t8dwLi8gIiIiIiIiKgLiEPdV+9OVhxlWl4gtvp9ubNJdd9gqVhr8oVTbjMKaVoTywvyEjO/GrwhAMCQPGc3nZ169pjLqGLNoqlY02wFjSaCtZzE4wPhmKpiTG69bPKFVM/r68aKtdRgLfmxWBm4V7OJVc9g3QzKYI2IiIiIiIioC+xqSh3qDqRWKmnjIrEV9Mvtjar7BkvFWk1r5uBG1pqoWBtaoA7ShuSltih2FTGAMqqIE9t/HVaL0AoqqZ4j1xl/fCCirViL4oVl27FYs+G0O2esBTWtoFEhWav3pFampdPgCWU+aABiKygRERERERFRF9jdmDrUHYi3gqZrdRRbQb2aECXAYC2FXEk1tMCJTbUe5fYyndlfXUWsOrSY9SvptDPW5KAtFFUvL8h1WFGDIPyhqKpirS0YwS3/+jblebtzxpp2e6pPs0ijPRq8IYwqzemS8+pPWLFGRERERERE1AV2NRkFa5JqZpa2wzFdRdJgaQXVC9aMxnXJraCVBS7V7d3ZChrRLqDQYbfqV6zJ4VkkJles2QAAgUhMdV0Y6c4Za9rlBaK6tvZWrLXv+IGCwRoRERERERFRF9jVaNQKGlMNgpftbvLhihe/xJ7m1MfJVVHd2QbYl+gNxDcq8pOXF1QUqoO0sm5sBc1GasVa/HuoLC9IVIfJs+GC4WjaYEvWFRVr4WhMt/pRO2NNVK9p7Zw5olD5WK9qr9E7OFtBGawRERERERERddKKbY1Ysye+sKA0Vx3wRKISgpFkqBFLVD/96pXV+O/aauV2py35I/qYREtdf5+xJkkSVmxrRLMvfeiiV7FmVM0lt4JW9OCMtWzYtTPWNFtB5e2wuYlgTbsV1Ehnw1VJknDqI5/h+AeW4OudTXhXuObSfX1tUDZvv3Ll45HFbtV9//v10Th91rBOnWd/xWCNiIiIiIhogNlc29atmwRJbWudB+f83zLl81lCZQ8QDy+C4dTtj2v3tqiOO3hMifLxfhX5AJIz1iRJUrZK9ifvrNmHc/5vGU555DPlNkmScMkzK3Dx0yuU2XN6wVrQIFRs9Scq1lJaQftOxZrDZk5pBZWXWCjLC8LpW0HlAM6bZttmIBzFxxtqdavRojEJP3nuC/x8wVf4bl8rdjT4cPpjS/GzF7/Expo2AKnLC9I5ckKZ8vGYsuQstapiF4YXueGw6m9LHegYrBEREREREQ0gn29twLz7l+D0R5f29qkMGuv2tSkfDyt0YUxZrur+SExdsRaMxFDdElAq12RThxUoH8vVWP/8Yhf+75MtuOjpFZhz++J+12731jd7AQA7hcUOjd4QPt5Qh0821imvp1ZnnpdR6NOWqFgbWeJGVXE8XKsscGJYkUv3+J4iNkc6rBZheUH8+ywHbErFWiSKcMQ4LK1MtLqmq1j7/b++xSXPfIHfvrE25b4N1W34YF2tqipSJs9Py6ZiDgCOmFCmWkwwdkjyGhcr9QYjbgUlIiIiIiIaQN74ag8AYENNW4YjB5e9zX48+MEm/OiwUZg0NL9Ln3tHoxdAPFR74dLZ+HhDnep+XzCCzbVe5fNt9V4cfMdi1TEmE/CTw8dg6ZYGzB1bosywqm0L4o7/rleOu/HV1fAEw3jk/FkpLad9kV5Fll7lnbyQQBSMxLepmoQtBsFIVAncCt12LLruSOxp9mNYoavXK6bElyVuBZXDqy118Q2m4xKhVCAcRTBNsFVZ6MLGGk/airV/rtwFAHjtq92475zpqvvSVa3K34NsgrVHzp+Jk6ZUwGw24Yb5ExCMxFTVgb39vve2wR0rEhERERERDTAS+l+7YE9485s9+OfKXXhu6Y4uf+5diWqssw4YjjFluSjPV8/+2tsSwM9e/DLtc0gSUOCy4bUr5uKX8yfCbdcPKz5YV4PPtzZi4Yqd7T7Pt1fvxZNLtrT7cZ2hF9yIYZu8bdNjsPlSW7UmLy4wmeJLAJw2C8aW5cJp6/1wpyTXrnxst6hbQb3BCLbVx8PVA0cVAUi0gqZpxRxWGK/A6+icvSZfalgpk58zm+UJxW47zImg96pjxuOX8yeq2l7FjwcjVqwRERERERENIFl2dg06ctVPuuqfjtrREA/WRiQGug/J73wlmUsTFB07aQgWr69VPu9IkHTVS18DiLf1dXXVnhG9VkcxLJODHU9QPwQKhmOq1ypXtuXarUrY01eU5jrw4qVz4LJbYDKZVMsL1u1rhSQB5fkOJTAD0l+PlYnj9I7RVvLpaUrTNuxPtJfKm0rTcdhSgzOxSm2wB2uD+9UTERERERENMPIweFKTlwd0x5ZNOVgbWRIP1srznOkOz4oYJu1fmY+L545S3d/eMMMvzOnq7JbJ9tBrdRQXOYSiMUSiMQTC+omwOJsOAFoTFWt5zr5ZJ3TY+FIcMDJekWazxoOvcDSGb/e2AgD2ryxQB4UB46oyOYCTv19//3QrTnv0Mzz60WYcfMdibBLavfUqHJs0m1gvPHiE8rH8+yCb5QV6rZ6qRQ2DPFjrm1ciERERERERdQhjNX2BRECjtz2xM0KRGPa1+AF0ccWaEJSU5TkwaWie6v72hmNiyNKTw+bDOsGNGJaFozF407wWbeDWnHgdhW673uHdztKOKjn5fQ5GYvg2sQF2/8p82CxmWMwmRGOS0tqqR6lYS8xK+/M76wAA3+xqBhBfVCLTC9YaNcHaTw8fgxZ/BP9etVeoWMscrOlVR4rX0GAP1gb3qyciIiIiIhpgxMHw2q2Tg5lSsdbF1Vp7mv2ISfFh9WWJge5OmwVzRhd36nnFVtCyXIfy3LL2vg4xWMt2E2RX0PtaYpVUOCLBk6Yd8oh7PsLidTXK5/LrKMqxdeFZZs/ajmBNXF6werccrMU3vzoTYZT8+zVHJxgrdMdfo89g/lyLsPDBbU+tm2r2Ju/Pc1gxrNAFV6KtU65YkxccOHXaPWV6wRlnrCUN7ldPREREREQ0wMSEVtB0bV4tvjC+3tk0aFpHA5HuaQXdVh/f9DiyOEc182rhZQfjk18d1eHnVQVreQ6YTCb8/Kixym1i5V2zL4T73t+AHQ1eGGkSQpZsBtZ3Fb2toKoZa9GoMkPMqL3z0udWAgC+3NGEbfXxttuiHq5Yu+V7kwEAD543I+vHyIFTIBxTtvTOGlEIILUKLM+pDgqvPHqscg14DbZ7NvvEYM24Yu2aY8fjo18dBavFrARw/lAUuxp9+OC7+Ny+YyYNMXwdehVrDlUraO8vjuhNbAUlIiIiIiIaQMSczB+OqloKRcf99RPUtgXx7I8OwlETjX+oHiiCiSCqq4O1b/fEZ2ftV6Fu1TSZTBkXDJw/Jz7z6qXlOzGmLEd1n7YVFABuPGESrGYTHvpws6oV9BcLv8GSjXVYsrEO/7rqMN2vpa5Y67kwVbcVVPgehISKtQKXzbA18t211arNqsU5PRusXXrYaPxgdpVuZZgRsV1SkoDhRS4MSWyM1V4buU4rEL+U8OB5M3DqjGFoTCwfCIRjqkpU2d5ECzIA3d/nctvsxKF5KM1NVlMC8d8Hj360GaFoDIeNK8WZs4bjP2uqAcQDTvH7oFfNpqpY68HW4r6IwRoREREREdEAIlYypZsnVtsWBAAs+q5mUARrcsVa0GBIfnvEYpKykXJtYnbWlGEFKcdlahu88fiJcNutOGhUEQ4dW6q6Ty9Yi9+eqDgSvrdLNtYBAFYl2g31NAvBWijac8sLQjohnqoVNBpT2kVzHcYRxT+/2Kn6vDdmrLUnVAMAm6ZFUl5qAKRu2hSr9eRKNbEKzadTtbaz0ad8rHelycGcWN0nP7cvFMX66ngV3YUHj8CwouSm0ooCJ9oCnuS5ZlhewFZQIiIiIiIiGjDEtjE5fNnX4kddIkjTGiw/FHdVxdonG+sw7Y/v41/f7AEA1bZHLWuGSp5chxV2qxmnzxyuVDLJtDPWkrerZ2SJbZ2lucZhU5NPbAXtuYq1kLCoQG47FgPfcDSmtIKmC9Y+SYSHsmJ378xYaw+bRR13zRqRDNacmrBKfO1yqOqwmpVlCXpz6HY2JIM1vSpE+XsuzqOTw7pAOIra1gAAoDzfqWwgzeZ1yOem9/FgNLhfPRERERER0QAjDrX3h6L47RtrcMgdH+J7D3+qu8xgsLRxyVVSnV1ecPHTK+AJRnDNwm/Q7Athd1O8HW9yZX7KsXqBhChd8CYGa6WqirVEK1/idcjbJgEo7X565OolQH/uWXcRAx+5nVE1Yy0SU0KjnDTBmvbSLerhVtCO0P7emjo8Gb5q2yvzhRlr8vfeZDIpgZv4/ZO1Cu2a2iURsZikVCkWCxVrTrtcsRZBnScetg/Jd6pmvDX5wqoKOnF2oEysYhss4byRwf3qiYiIiIiIBhivEBxtqfNgwfJ4C11Na1A3ULENkmAtIFSsddXChu8S1Wojit0ocKVWUFnasUFSyym0AZbm6LSCJr7PX+5oUu4zmk8GqFtB9eaedRcx8InoBWvRZLCWa7C8QE9PLy/oCG0gNaE8OYdPOxNNrFgT56/Jt9d7UoM1USgSw74WP77/8P/w8he70BoIK2FkoU4r6L6WgBJ6lmkC2UZvSBX06WEraBJnrBEREREREQ0gYkWWtv0zHI3BabOogqXBEqyJLZPBSCxlePyd/12PHLsFVx873vA5xPetOMeObYktnOOH5OoebzN3/L3Nd9pw80mTYDGbUeBOrWZatrUBx9z7MaxCVVyrP5zyPDJVK2gPVaxJkqSEafLXddosCEbEVlAp2QqaZoaZxWxCkduO+kSVVU8vL+gKqvBM0wqao9MKKj6mwaPfyi0LRWO44z/rsWZPC258bTUOGn2U8ngx+JJbQbfXx6/d4hx7SjAWjUlp23IBdTUet4ISERERERHRgCHOWGvQtI/JFSoBYYC/zdrxqqr+RJzr5Q9FVcFagyeIJz7ZApMJuOzIMYZBwZ7m5BbG4hw79iTaQIcX6c+nMneiYg0ALjtibMptYovo1kQ4ImsLRhCNSbqVcqqKtR4I1rbUeXD9P79R3RbWWSARjsbQlkXF2uHjSxGKxJRgrbAfzFgTjSh2qz4Xrz+7xaxqDRW/x/J70pChYi0cjWF7Q/J6qEnMT9PO3ZOfW24jHZKn3z6cl6F6kBVrSYP71RMREREREQ0wvmAyQGr0aIO1eKAhbhjsTFVVfyK2HwYi6jlrzYlKL0lSt1M++tFmHH73h0pIsSGxRRGIh3O7lWBNHZp0J20LoVZbIIzv9raiLaCuXmsUt4L2QCvobf/+LmVLqW4raCS5vCDHYcXdZ07DyBL1+3ntvPG4/fSpqvbE/laxNqo0R/W5uBXUbjWrNsiKwVqO0gqavmItHJXgEa5d+docpgl9tZWa4sbZaxLVmj+YXZUxWHMwWFMM7ldPRERERETUjwTCUVXVlFY4GlO1+Wkr1uRAxSe0i0a7aN5YX6etWBOJYZrYTnnPexuwq9GPvy7aCABYLwRrrf4wdjfFtzJqw4vu5LKlBmt2q1kJOt5dW42THvoUF/x9ueqYZm/PtoLqLYmQrz91K2gM3kQYnOuw4JyDqvDJr45W7jebgGvnTUBloUsV4Oi9D33Z6BLjijWbxaRaziCGp3ntmLHWLFy7a/fEQ83KAvW16dYEs+XCNtqrjxmH166Yiz+eMiXjNW21mCFngdwKSkRERERERP3C2U8sw6F3foh1+1p17/dpwowmn1HFWvK4SA9uiOxNYpWUP6x+n8QwrVVnAUBtYlbdsi0Nym1twQh2NqZvBU2no4sNtMEIEJ9lJrdGPvHJFgDAaqFaLBqTlHZLoGcq1oYWOFNuM1peIAebuY5kRdozPzoII0vcWHjZIcptYoCjt6myL5oyLL4t9rzZI1S3OzVbNWNCwC2+ztwsK9Y8wYhqc+hXO+NLLSoL1demtuJRbAW1Wsw4YGQR7FYzrj9uImaOKMTtp081/Jpy0DnYgzXOWCMiIiIiIuon1iSqUP71zV7sV5Gfcr/Y4glA9YM2kJyxJh4n3zaQRaIx1RD9QNi4Yk3bQgnE55O1BsL4fGuD6nY57BhW2P5gzd7BpRF6raD5ThsKXDbUtAaxqym1olEbJPbEjLV8V2rcIH9dccaauhU0+dqOnjgER/9qiOrxY8r0l0T0Zf+87BDUe4IYWaJuBRVnqtksZlXFmhgayq2gDd70wZqWHKxqr01tpZ/RjLXiHDve+Pmhab+G3WJGIBzr8LU8UAzuV09ERERERNQPSQbtm9qKNe0mQd2KtdjAr1gLaiq0/CH152KY1upPrVhr9ofx8YY6RGISxpblqKrGXDZLVvO+zj2wCk/+8ADl847OpdLOyLKaTXj4/JnK/LGokNDI14m2LbMnKtbE8Ez7dQPaVtCQXLGWvvbnR4eOwjkHDle9j31djsOaEqoB6hlxdqvZ8Pd0tssLjGgr1rTXz5D81MrCbDkSz8UZa0RERERERNTniT94x4yCtaCmxVHT1hjSbQUd+BVrKcGathVUDNZ0KtZafGGlDfSYSUNUQ/SHF7myakscUeLG/P2H4rp5EwAAd5xh3GKXjrYVdNNfTsTcsaUocKVuyZRfp7ZCryeqFH3h1BlrSiuoELp9taMZOxris+pyMgRrTpsFd581HfP3H9qFZ9o7xEoyu8Vs+Hs6L8tWUCMVhergTHv9jO1EFaBcqWa0RXewYLBGRERERETt8uqXu/HPL3b29mkMeJFoTBWmiWGYXJS0vroVN7yySllooG0F1QpHUreCDoZWUG2wlK4VtFXZEJp8X5r9YTQl2mqrit2qNsdM89XOO6gKZXkOXDAnPmPrmnnjserW+ThpakUHXkm8bVAkh3r5OsFaky/+WrRBojZo7A4BneUFSiuoULG2bGsDWvxhlOc7MLE8r9vPq68QK8niM9b0j5PDRqPfpydkCBm1ywu0FWujS1Or6bIlV90V5aRee4MJZ6wREREREVHW/KEobnhlFQDghP0rUOAe3D9QdZdQJIbjH1iCigInXvrpwQDU4U8oEkM0JuGEBz4FEG+h+8Mp+6e0gmrJFUP+Lm4FrW0LYNWuFhw7aQjMHRzK350yVaypZ6xF8PpXu/HljibltmhMQrM/HqzJ88xk4zOEQXeeOQ3RmKRaVtBVv2/EofF6FWuH3vkhTp85DJfMHaW6vSdmrGnfYyAZ7OoFewt+cjCKsmipHSjEYM0EYP7kcjz1v20pM8/kVlDZiGI3zptdhfmTy/HO6mr86LBRePfbauX+QrcNzYlAtTjHnjKTT7wO3XZLp9o47zxzKr7b24rJOvMeBxMGa0RERERElDXxh+V41QmDte7w7d4WbKv3Ylu9VwllxBbF7Q1e/PHf3yqfexLD370ZKtbkVlBvSD3jqrNOeOBTNHpDuOvMqTj3oBGZH9DDMlWsqbeChnH9y6tSnmNLnRcAkOe0qlpBs6my6ugG0EzEYC3fqf/j/X/W7MM5B1apbuuJGWvynxU3nzQJr3+1B+ur2xDW2QoqK80dPKEaAJQIIWKjL4Q5Y0rw9tWHoarYrTou16EOxmaNKMTPjxoHALhmXvzas1vNyvf0kDEleO/basQk4IjxpWnPoarInfb+TPavLMD+lQWdeo6BgMEaERERERFlTQzWwka9S9RpVnMyMPGGIsh32lQD9j/dVI9PN9Urn1sS7YA7G31pn1euGPJ3cSuovH30w/W1fTJYS11eYDyLrsWfOmMNAOra4jOu8pw2VdvlxKG9174otvXptYIC8de+t1m9KbRHKtYS7/HkigK8Y4tXVCkVazrVbG774IonxMrO+rb4758pw1JDqlyH+vuqbeUE4rPO5GBtyrAC3HbqFATC0YxtylXF7d9mS6k4Y42IiIiIiLImBhI9UfUyWImDzD2J0Ee7iEDkSQRla/e0ADCuogpHJbywbDvufX+jclukC0MWMRDsS7QVaumWF+xrCaR9rnyXVfV844Z0fPh7Z4khy7H7lWPckFzMGlGY0t63pc6j+rwnK9ZcdjPslniIJAd62q9vt5gH9WZJvbZZmXZTqkPnfbJZkiFdjt2CsjwHqordGZdq7DfIWzi7yuC9comIiIiIqN0CKa2g1B3E91lu82zVqaS6/Igx8WMCcrDWCgA4cFSR7vOGozHc8q9v1bd1YeVhX5yvBrRvxtruDFV/eU6bKnzTqyDqKWLIMro0Bx9cfyRe//mhOFmzGGFroo1VFupgmBoIR7OudpNDeKfNoixcMGoFzXEM7q2S6WiDNb3rTVxokU3l3y3fm4w5o4txWeLPD+ocBmtERERERKSQJAnr9rXqVjHtavSpWspYsdZ9xOBHDn3adCrWJiQq07zBCFp8YaUV9KBRxbrPqxeGdm3FWt8M1rQVa8Gw+jWLbbZ7M1WsOa04cGQ8uCzs5eUdRqGedpFBV1SshSIxHH73Rzju/k9UG1NFa/e04PqXv8GWOk+yYs1mgVUO1pTlBervx2BrA5VVFDgzHqNdXuDQawUVAlbtsgI9lx42Gv+8/BDkOTkjsyswWCMiIiIiIsVjH2/BiQ9+mlLVVNsawOF3f4TLXvhSuU1vAHkgHMWXOxoR5fy1TgkIwY9SsRZQV6wVuGwoz3cqx6zdG28DrSp2KbfL5MH29Z5QyteK6MxYa/AEcdVLX+HTTXXtOu/uGtLfWRlnrBnMVdMymYAcuxW/mDcevzp+It668rAuO8f2mJSY63bWAcN179fOW9taH69YcydCl47MWNvV5ENdWxDbG3yq4LemNYCPN9RCkiT87dOteP2rPTj2vk+UINhtt6a0ggY0waa2Kmuw+NtFB2JMaQ4ev2CW4THaaj6nLTXGsasq1lj919MYrBERERERkeKBD+Kzt/6xYqfq9q93Naccq1f1ct0/v8GZjy/Dk0u2dsv5DRYBVcVaOPFfdcVaodum/NDdFojgb5/G3/MZVUUoy3Ooji1IVFbJA/hFeq2g1yz8Bm+v3ocfPrWiXectznrqS7TD8sVgKBaTlPAyk1yHFWazCflOG648ehxGlHRuq2JHLbzsYDzzo4Nw4cEjde8v1ARrctAtV7LptYLGYhI21rQhZhCKm4V5XeKChxMeWIJLnvkC731bjXpP6vXlslmU2XvJVlBNxdogbQWdMqwAH95wFE7UtO6KHFaLKjhzWNO3gmZTsUZdi8EaEREREREpjIbP64Voem2F/10b3/4nhzzUMaoZawH9GWtuuxV5iUq0Pc1+fLyhDnarGb84ZhyGF7kgzi2XA5Wa1tQ2R20raCwm4X+b61OOMyI+vq9WrAXSzFjzhiLItsAyv4+0zhW67Th64hDD99toEYB8HYQjqS/4vkUbMP+vS/DA4k26jxWvyRZ/GKFIDC2+MJp88evy7dX74NJpU3TazbBZk62g0ZiUsol2sFasZUtsOdarWLNZk9fBYG2r7U0M1oiIiIiISGE1qDjSa/tMN6epj+Yr/Ybe8gJtxVqew4ocTSBx9gHDMb48D06bBUOFdlA5UKnVqVgTW0FbA2Fc889vVPcbzdNSzlW4DiwZthD2Fm3FWkBnhp3FbILe6YthUZ6zf4QW2lZXmRwM6lWsPfrRFgDAQwbBmhhGtvjCOP6BJZh+2/vKbU2+UMqfE2ZTvE3RJrSC6v25kcMwKK2JQ5Nbfp06FWtsBe1dDNaIiIiIiEghthSJmn2ps7n0wjaZqY8GLP2FX5hBlVxeoK5Yy3FYUoK1ykKX8vHwouTH6SrWwrHk11q4Yif+vWqv6n65IsnwXIUQp69+3+VrVZ41J55zU+LaLnLbUZGfOkxefB+1s8v6qoNG6y+vkM8/0/KCl1fuQq3mWgmE1BVr2+rVm0abvOGUJRFuuxUmkwm2RCVsJCbpVroO1lbQbO1Xka98nGkrqF7VIHWvPh2sRaNR3HLLLRg9ejRcLhfGjh2LP/3pT6p/MZEkCbfeeisqKirgcrkwb948bNqkn7ATEREREVF6Rlsdm9oZrLFirXP0KtZaNRVrc8aUpFT6iLPVxI+1FWs5dgtGl+YAUA+yl0O0USVu5THiJthM5xqJtX8ofk+QK9aKcuwAgEAkCkmS8OLnO/DR+loAQEmOXXdm2jAhWDPawtnXzKgqxMLLDsaSXx2tuj3fFb9e9CrWRDe+uhr3vb9RdZtYsab354FexZr8fsmtiqFITHe7LVtB09uvIlmx5tBp8xVbglmx1vP6dLB211134fHHH8cjjzyCdevW4a677sLdd9+Nhx9+WDnm7rvvxkMPPYQnnngCy5cvR05ODo4//ngEAulXJBMRERERUSqjirVGb2rVUvpgjclaZ+jNWJMr1m46cRJumD8Blx42GhazSfWDdFluMkwrFT7Oc6orlc4+sAp/OnUKAHUrqPx1T5pagRHF8ZBpX0v6n618QiWT3uyuvkAOhQrd8WDNH4rixc934HdvrsW9iQCpOMeOkcU5KY8tz0tWsYUzVHr1JQePKcGIErdSpQcIM9aiMTz+8RYcdc9HulWMQHwLqEgM1vY0pz6m0RtKqViT54HJf66EozHs0QlqORcsvUlDkxVrDp0ZaxFhSKC2ipW6X58O1pYuXYpTTz0VJ598MkaNGoWzzjoL8+fPx4oV8c00kiThgQcewO9+9zuceuqpmDZtGp5//nns3bsXb775Zu+ePBERERFRP2Q0DL3RmzqbK/2MNQZrnaFXsSZvYpw7thRXHTNeCSvEah+xSk0M1uyawLQ4x67M0xMr1gKJFlSnzYKKgnigVN2SvmJNDFzCGSqheov83pUn3p9AOIYFy9Wbb4sNKtbELYt99fWlUypcE/lCwHrXu+uxvcGHxz7arPu4Rq+6Ki0gtCfvaUq9JoKRWErYLjebyddqJCZhV2M8sBOv1Vy2gqY1tixX+bjVn1rxJy4Q0atoo+7Vp9/xuXPnYvHixdi4Mf4vCKtWrcL//vc/nHjiiQCAbdu2obq6GvPmzVMeU1BQgDlz5mDZsmWGzxsMBtHa2qr6RURERERExssLmnQr1tTVKTGhakLO1bbVezHv/k/w6pe7u+4kBxBJkrC51pOymVMMMdqCEUiSpLRpFuWo53yJFWtimDahPNk+pq1ELHLblIHyYrWL3DLptJmVeW17WwKIplmbKc4ry9Ri2FvkYG1oIiysawtifXWb6pjiHDtGZgjW+urrS0e8JsSKNZnRa6r3qIM1MUDdralmk3mD6tDHF4p/Ll9roUgMuxOh3JjSZHUgK9bSs1vNOGH/oSh02zB3bEnK/RHVn738R42e1qeDtd/85jc477zzMGnSJNhsNsycORPXXnstLrjgAgBAdXV8lXd5ebnqceXl5cp9eu644w4UFBQov6qqqrrvRRARERER9SPykHGtRp2ZStqKNV9YHGIf/+8f//0tNtd6cMMrq7ruJAeQV77cjXn3f4LfvrFWdbtf1Qoahj8cVd7vokQ7oywstHKW5Cbvmz+5HD89fDT+eu50ZcaVrCjHDqs8UF5sBY3IwZpFCaE2Vrdh7p2Lcc3Cr3Vfg1hdl2kofndatasZi9fV6N7XnAglyxPLCfTCJKNWUHEYfG++vo4S24PzlWAt+T03m0y6r6vJF1KF5eLygt06FWtAahjnDcYfI7aCKsFaWfK95oy1zB6/cBaW33ysMidQJH4/qef16WDt5ZdfxoIFC/DSSy/hq6++wnPPPYd7770Xzz33XKee96abbkJLS4vya9euXV10xkREREREfV84GkNrQH/To9gKKlYpNXkzLy8QN4fKM+z7Y+tcT3pocXzx2j9Xqn8mEcOqtkBEacuzW8wpw8nFY8XKNLPZhN+ePBmnzxyeEpgWuTO0glqTraD/21yPmtYg/rumOqWyDuj5VtBAOIr739+AjzbUqm4/9dHPcOlzK7G1zpPyGLliTX5Nekpy9VtBnTYzfnbkWADA706e3JlT7xVi2Fqgs9XUajYp748oGpNUt6tnrKVvD5bJAabSChqVlNltY0qT7Y3cCpqZyWSCw6r/Pun9vqSe06dj4V/96ldK1RoATJ06FTt27MAdd9yBiy++GEOHDgUA1NTUoKKiQnlcTU0NZsyYYfi8DocDDofD8H4iIiIiooHs9Mc+w9o9rVjx22MxJE8dNNiEVlB/OIpchxWxmKS7BVCscrn//Q146MPkrCZvogXMZevTP3L0uuIcu6r6JxyN4cZXV+P975KVV55gRKm4KsqxpbR6icsDjNg0Lb5isCa2kckhncNmVuZxyQFqKBrDzkYfxgjzngB1K2hPVM788uVVeGfNPhS6bfjm1vkA4i21st1N/pRzbJVbQfONg7Uitx0FLhtGlrixoyHZ6mi3mPHrEybiiiPHosCdGkz1dXqtoKKYBLT4U39/A0CDN6RUSPnDma8zIzYhxN2jU7Hm6ifbVvuqdK3a1P36dMWaz+eDWfMvKxaLBbHEP3+NHj0aQ4cOxeLFi5X7W1tbsXz5chxyyCE9eq5ERERERP3F2j3xGcMfra9Nuc8sVKzJ85FaA2Ho/dwmzlgTQzUA8CVawLTVVYPd1zub8Ls31yjVfWJbpyRJ+M+afXjj6z2qx3iEijVtGyiQXeBh0ww0L3TbYE18rxu9Ibzw+Q4EwlElWHPaLHDqhB2balOrwcQW4O6eQba1zoN31uwDkGzvBNSBnt6IKWV5QbqKtUSA9P51R2Dl75JzvGNSvFqoP4ZqgDpYy3elBt1tgbAyv09LXGDgzyLANSJXrPnDUexLLMMYLcxYs3PgfqeEY6xY6019+p+Pvv/97+Mvf/kLRowYgf333x9ff/017r//fvz4xz8GEP/D7dprr8Wf//xnjB8/HqNHj8Ytt9yCyspKnHbaab178kREREREfZxeBiLO25J/kNZuB5SlmzcVisYQisRUlSiSJA36wdqnP7YUQLzK7P5zZigzrwCgzhNMaa8FAE8ofbCWDe3ygnyXTVXlcsuba7Gl1qPaCuq0pYYdm2racPz+Q1W3BUI91wr63b7k4rny/GRgJIaLJqivsXA0Bm/iHEty7LBZTLqVdcWJlkmH1QJHbvK6jUn9uxpIbAXNsVthMZtU3/vWQEQVUooaPMltwNplJelMryrEql3NGJuoSrMmrr+djT7EpHiQJi/HAFKvT2qfCGes9ao+Haw9/PDDuOWWW/Dzn/8ctbW1qKysxOWXX45bb71VOebGG2+E1+vFZZddhubmZhx22GF499134XQa/0sEEREREdFgJbbMRXUCAzEYkVsMGwyCNb0QSOQPRVUbFf3hKLf/JXy3Nx4QhYX3cE+TH/nO1PdHkoDtDV4AqRtBAeC4yeVY9F0NDh5TbPj17OLsNROQY7egLaAOoF74fIeyFdNpNau+dzK9ijV/Dy4vECur9EJgIPW6FueE5TltcNosCEfj1Zh2q1k552KD0DLSz9vsSoVgzWW3wG4xwx8TZ/iFVfMRRQ0dqFgzmYC//fAAPP3ZdlwwZwQAwJ5oBa1riwd1JTl2OIQqNQZrncPlBb2rT/9fLS8vDw888AAeeOABw2NMJhNuu+023HbbbT13YkRERERE/ZQYhkV1qotCOsFafVsw5Tggc4jiDUVUs71a/GEGawny+yzPogPis8G0s9Bkm2rigVahTvhzz1nT8K9v9uL70ysNv54YXOS74nParJqxO9GYpFSfOW0WOHUGpcvnIerJ5QXiEg3xWhXPIahpjZWDtTxnvFrLZbOgLRB/38tyHcogfr1ti0D/n1+V60iGsU6bBTaLCeKuglZ/RHd5AQA0CFs+s52x5rCaMSTfid+cOEm5Tb7W5MrLfKd6VuA4zUw8ap8IW0F7FWNhIiIiIqJBRKw60avEEYMR+di6RDuYuDEUyFyx5gtFVJUURj+8D0by+yx+P/Y0+w3f0w01bQD0q6oK3XZcPHcUig2CIUC9vEBeSmDXqRLyCTPW9CrWttR5UoKmnlxeIC7RMKpYC6Rsq41fd4WJGWni6yrNS7aTaqumzpw1HPlOK845sKoLzrz3jChObjp12yywawLTeMWa0Yy1ZKjuD2cX3ujN5pNn/Ml/5uQlKjNX/m4ePvvNMf12fl1f8ZfTpgIArjl2fC+fyeDEfy4iIiIiIhpEAsKcpIBOBUo4kgwr5OUFcvvW6NIcbBZaAcWKtRy7RZljJfMGo6qgrtUfAcXJ77P4nu1u8qWEY5UFTuxtCSjve2EHAwhxeYEcalh1quPkgMVpM+tWrAUjMexq9GGUMHg+0IOtoGIAJFbppKtYkzeCyhsxxbl/5x5YhTZ/GLNHp7bR3nv2NISjU/v9YH2X3YKVv5sHs8kEs9mEoQUO1Auz01oDEexrCeg+tt4bQjQm4fdvrcWSjXVZfT2Hzvtl11xr8jUoLlagjjt5WgUOGzefAWUv6d9/QhARERERUbuIlT2eoE6wptNeJwdrI4XKF0A9zLxE5wdkbyiiej5WrCXJ74tPaAXd1xxIqVgbUaJ+z9NVpaUjVqfJFWt6wZrMabPAadf/cVE7Z83Xg8sLxIq1cFRSZgamq1iTr7tCV2I5gRCsDclzYPEvj8SdZ05L+Vomk6nfh2qy0lyHcu0cOFIdInqCEby9eq/u4xo9IXy4vhYvfr5TuW1IXvowTK9iTdt2LC7toK7BUK33DIw/JYiIiIiIKCsBoZ3LG0ytIBMrjuTARA7WhhY4DY/V4wtGVa2BrQzWFE2+EN5duw+NwgyrBm8o5T0doQkzu2IraL4rXi1kMxv/OOi0xofcm3Wyt42JtlSZanmBJljb1ejDaY9+hn+v0g9u2qtJs0hDbi3MZsZasmIt+bpzndZBt6lWrzovGIlhYnkech3qprYGbzBlscG04QVpn1+3Yk1zW57Okg6i/orBGhERERHRICIGELrBms7yAnnG2qSheapjxeqqiE6lkjcUUT0fK9aSYhLwsxe/QpvwPWjwBlVVgIBOsNbBijW9GWtmvdQswWEzw2QyqaqP5MBks6ZiTWwFDWuCwTv/ux7f7GrG1f/4ukPnrdWkmQUmz1lTBWuRmKpyTm4fzddpBdUGSYPBQaP0t8deeMjIlNsavaGUsHTa8MK0z69XsaZtYc5zsrqKBg4Ga0REREREg4gYgnh0gjX18gL1jLVpwwtx5xlTccncUQDUFWt6ixB8oagqaGkNMFhLp8GTWrFWpQnWMrXhGdFuBU3HZEqGaGIItX9lPgBgU20b9jb7sbk2XrmWbnlBtpsks9WkqZ4Kx+QlEMlr+Z73NmDWnxYp2z6VVlCd5QWDMVgrM7iGjhxfhpik/v41ekOobVVvBZ7agYo17Sy1fAZrNIAwWCMiIiIiGkTSBWvRmAQxH/OFopAkSRl0XpbnwHmzR+D0mcMAqCvW5E2RT1w4C6fOqAQQr4jjjLXs+ULRlPdoSF6y/XZUiRuVha4OPbddZ3mBEYfVrLRHitVHU4fFA5XNtR6c9fhSnPzQ/9DsC6kWMISiMWXuGQDkd2HLXzgaQ1tAfc0qFWuaxRltgQju+u96AFBaGQsTgaJDWMqQO0hbEt+/7gg8cO4M1W1VxS5ocjXEJGBznbpCcXRJDrTE77NexVpJrrrSkq2gNJAwWCMiIiIiGkTStYJqB8/LQY9chST/cCyHNEGdirXx5Xlw262J59fOWNPfCnrPe+vx42e/UMK5gUKSJMSyfE1yq+a+ZvV2xiH5yUqfIyeUdfhcbDrLC4yIwYhTmEc2vjwPVrMJgXAMe1viixY21nhSZueJ1Ytiy5/eFtr2kFs6xZFo8jXrD6e2Iq/d0wIgvtkSSC7YEKuyBmPFGgBMKM/DaYmAHADyHPFZc+J7I8+k21itnqnnsluw7KZjcMVRY5XbxKUaehVrDqtFeT6AywtoYGGwRkREREQ0iIjLC7QVa9pZSv5QVGkDLXTblEofhxKsJYMSORSzmk3ISbTa+bKcsfboR1vw4fpaLNlU16HX1BsavSH8+e3vUgb5i3707Bc48cFPM27KzLFblFa5fS1+5fa7z5ymatubM6akw+ermrGWIdRwWsVgTd02qZ35tr3BmxKsie2sYsgiX0sdJVee5TttyvMmg7XU0G5rvRexmISGRMWlHAxHhLBXLwQaTMaWxavPfnToKABQVayVJMIy7RZYp82CigKXauaiOPtPDHFFpULVGivWaCAZ3H+KEBERERENMuqKNXUYoR087wlFlMUFJcIPznLFmhigyAGHxWyCO1EF5A2pW0HbMsxY025z7Mtufn0N/v6/bTj+gSW690uShI831GFDTRu+3dsKQH/BAwC47FYl9NnbEq9Y+9XxE3HOQVXIc1hx4MgijCnLwdETh3T4fO2qirX0oYZYpebSVK+NKlW3AW6p86gWMADqyseAEL7WJoK11kA440ZZPRsSIWaR26aEN8lWUP1qyI21bWhMVKyV5sRDykgs+bUH20ZQrWcumY0/nTYFvzh2PABgxohCAPHgq9hgUYZ8TYihZLGwrVZv3iKgnrPWlS3CRL2NVzMRERER0SASCKVrBVX/QFzfFlTaN8U2LrlyLRiJz9MymUxCxZo5WbEWjKpClmCGMCVDYVef8s2uZgBImUklE8MFeeaYtiJQZjWbUJwIfeSqLjm0MJlMePWKuYjGJFjSbPHMRKwiytT+qG4FVX88SjNfS263FImvU6yQrGsLotEbwqw/LcKYshx8+Mujsj7/ZVsacNVL8c2ihW67Uv0oh2RGSxI21XjQ4IkHa8WJ8LI/XWfdbUSJGz8sSW4DffC8GXj0o824+JBRuPf9DbqPkasfxbl9OcI1ZRQgi9WXXF5AAwkr1oiIiIiIBhHV8oJQRDVoXtuyWOcJKlVm4qws8QfqcFSCJElKkGS1aCrWIsnn16tSEr++diNhfya+l3LGZlSlFYhEUaqpDtK2KHYmVAMAm/B8TnvqcHmxok2cdacN1kaXqltBV++OB2sum0Voz0w+Xgy86toCWLqlHgCwtc7brvP/ameT8vHRE4fAapGrJuWKNf33dk+zXwn65KrLaIzJmpGKAhf+fNpUjC/PU8JeLbnKz25JXhticJtNxVoegzUaQBisERERERENImLQIUnAk0u24sh7PsLOBl9KRVVdW1DZwijORBJDn2AkqtokajWbkOuQZ6ypK9b0KrbEH8L7U7AmIf25iiHaql3NWLhip2Gw5g9FU7Ymipsru4I4Y82ls7WxwC0sGRDaN8W2UKfNktIKKl8f+S6rEs698dVuJcAV23tr24IZ580ZqW2Nt8hefuQYXDNvPGyJoDFZsabfCrotEeDlOqxKSGgU/JCaOBPNbjHjrjOn4rkfz07eJvw5oA7b9b/HOY7kdZfvYvMcDRwM1oiIiIioy0iSpMwzor4poNmeeMd/12NHgw9/euc75QdiOThrC0RQn5ixJgZrYnVTKBJTzayymE3CVlD18gK9YEld2TVwAg/xdd/29nf4zetr8MG6Wt1jg5FYSnWQvYuH6tvMyecbXuRKuV8M0MRrRAzhXDqtoLICl02pirv3/Y14cPGmlOeqbQ2qKhj1Wgb3NPvx71V7U7ap1rTGr8PKgvi5yxVrYWXGmn4r6Nb6+OB9cV7YyVMrACBlEQOpie9ZSa4d5x40QrWZVhWsCcFtJKr/+1iscNMLd4n6K8bERERERNRlbvnXWrz4+U48c8lBOHpSxwetU/cxmkXlDSbbNotz7Gj0hhCMxLCtPl7xI7Zumc0m2C1mhKIxBCIxOIUfpOMz1uI/ZqRUrGmCtSZvSKl4AoCB1KGnFyKu2dNseHxqxVrXBmtmswmf/eYYRKIx3TY8cROoWGXmsquXFwwrdGFieR7C0Ri8oYgSeOU7baqtr299sxe/PmGS6nqraQuog9ZoTAnIZCf8dQnaghH4QhGce9AI5fbatnjFWnl+PICUK/DSbQUFoFy/4vt7zoFVqCh0YeqwAt3HUFyJ0Lp5iM5GWjFgF0M2o4pAhxDeDvalETSwMFgjIiIioi7z4uc7AQD3vLeBwVofFTAIICIxSQk97FYzyvIc2N3kV2Zh5WkG3uc6rWj0hvDfNfuwL7HJEpC3gsbDmPhWUGHGmmqRQRQz/7RI9ZzRflSxlulU9YI1o6oqACh0qcOurq5YA4BhhamVajIx9AgI566dsWY2m/DOLw6DBOCCvy9XgrUClw3VrcLm0cTrEa+3Vbuacfj4ZMVTIByDW7N4Ut4wunhdrRKs1bUFlY2iZXlOAEjdChrWT2XrE4sLSoSKQLPZpKq8In2HjSvFwWOKMaOqCNfOG59yv1EraMQgIT9sXGnXnyRRH8BgjYiIiIi6XEfnKFHHxGISNtS0YUJ5XsYh90bBWiwmKWGQzWJGcY4Vu5v82FIXb6UTW0Hlzxu9Ifz5nXWq261mk1Kx5g1GEY7oV6zVJgIZkVELWX+k3bAKpLbhynIdViWIknX1jLVMxK8XShOsAck2zOGFLqxI3JfvsqnClfzE9SJeb02+MJZvbVA+D0aMg0a56un1r3bj+pdXKbcPSWyWtMoVa/KMtZD+jDVZiWY5BGVWnGPHwssOMbxfrKq0Wcz44cEj8cLnO/DL+RN1j58yrACvXXEIKtMEvET9EWesEREREVGX43DwnnXv+xtw4oOf4o7/rMt4rFHLXCQmKYGozWJWAgz5e5mraR/UBm0AYDbFq4HcifbBdDPWPMHUICRd0NLf6FWsBXRe35ETyrDgJ3NS3s/uqFhLZ2iBU/d29fIC9TmJs9oKXDYEheAwTwnW4rdVJp7//e9qlGOCBkEjkAzntcFtmRysmbUVa+r3Vvt+alttqfO0FWu3nbo/vvzdPBw90bha+YCRxagoYLBGAwuDNSIiIiLqckbbD6l7PPbxFgDA3/+3LeOxRhVrUSFYs1tMSoAhS6lYc6TO6ZLDjpxE22gwEkMwop6pJSV6KJt94ZTH96frxig6liQJN7+xBne+mxpyat/7e8+ejud+PBvTqwqR79RWrPXMj2qPnD8Tc0YX4/ffm4zZo4oBAEcIbZLykHmTST1TCwCGCcFafqKCUUt+zfP3H5p6nyZoFJcZyIHZuLJc5bY8YbOnXVleIFesqa8dbdurOC+MuoZ4PVhMJphMJr7PNCixFZSIiIiIupzRjB3qfUazqLQVa2W56gomvVZQLbkN1W03bmMMRWNwWC1o8aeGMP0pWDOyudaDl5bv1L1Prt6aXJGPJy86AMOLklsptcFaT1WsfW9aJb43rRIA8PiFs/DG13tw+sxhyv1ykOWyWVIGzqvO32VTVY15g/GP5WBt5ohCPLtU/bW1FWviIgv5WhxR4saK7Y3x+4UqR6t2eYGmFbSy0IX11W3CubJKqqv1dFUlUV/F3wlERERE1OX05ktR3xBIDNDXLiOIxSSEEt83m8WcWrHm0LaC6lWsxcMOh9VsOOtNDs+adCrWgv0oWDNaXuBNs6DAmwiGHDazKpQC4ssgRL0RWpTkOvCTw8eoqo7kijVx1ppMrArTBoO+UASSJCmLEKqK1a8XSK3gaw0krwl5w6jRvEarsLwgFpNSWkHl7aGykSWpX586R7xG+Sc+DWYM1oiIiIioy3F5Qd8lt98VaYa5RyVJWTRgs5pRVayu8MmmYk2uIjKZTIZVa8lgTadibQBcN5E0r0EOE7UtlUC82i9XCDt7qhU0E3mumksnWKsoTFY1aucqekNRhKMSoonb9SrGtEGqHKYBQEOirVSsaptcka98bEsEt5FYDHua/dCOdRS3gALACJ1gjzrHmmFRCtFg0Tf+tCYiIiKiAYXBWt/lDxkEa5oZa6NKclT3Z9cKmvzxQt4MKpN/CJfDsxa9ijWD+W/9SbqqO7n91agaTXxP+0qbnVyp5rClno+4SdRlV9/vC0ZUM9QKXDYUaDafat+rVn+ynbPJF0I0JinPMTTficcumKXcb0uEk6GohE218ZbPCeXJeWw5mopMt51TkLqatjWYaLDqG39aExEREdGAEmEraI8yarvUkqRky1yxWxNyhKPJYM1qRqVm+Lu2VVG3Yk04jxxHMnSxWUxKBdbAqVjTv8a9OttOZXKLtF7FGqCuChNDq960f2UBSnLsOGJ8me79d505FadMr8TJUysxfXiBcrs3FFVaPeXFB9r2Ym0rqFixJknxa0SuWLv55P0wqjQZ9srVkZFoDBuqPQCAiUOFijZL8lpMN/OPuoZRazTRYMDYnoiIiIi6nLYtjLqXxWxSWu7S+XhjHdoCETisZowsyQFQp9znDUVVM9YsZhNcNosSxGmDHr0ZaxZVsJb8UcNmMcNuNce/RroZawaLFfo6SZKU6h1vyDhYkxlVo4m395VW0LI8B1b8dp5heHvuQSNw7kEjAADP/Gg2/r1qL37/1rfwBiMIJDZ1Oq3xxQdluQ5srvUoj02pWAuor4kGTwjBRMWaU/N+2IQZa5tqEhVrQ5IVa2LIW+hKvVaJiLpK3/jTmoiIiIiIOiybWUeSJOG+9zcAAC6eOyqlesgbjCihlxxalOap20VF6WasAeoqITlYA5Jhim4raL+qWEsSg2RPMHM7q1Gw5hAq1oyq2npDthWRxTl2nD4rvlE0EpOUoMyVuBa011wwYlyxBgAN3qCySdWhmfEmX/Nf7WzC61/vAQBMGJqn3O8Wgt0Ct/F1TF1D4voCGsT6zp/WRERERDSgnPHYZ1izu6W3T2NQyCb42Fzrwdo9rbBbzfjZkWNThtFHYpLSxigHa9oB8KJ0W0EB9Yw1MViT2z31WkH7U8Wa2PomzhRM1woqMwrNxCo1cz8dDO8Writ5AYFcbaadsRYIa2esGVesaSv4bInP/7u2WrltQnkerj5mHA4aVYRTpldi9qhiAMAlc0d2+PVQdkYW52Q+iGiAYisoEREREXWLr3Y245JnVuDLW47r7VMZ8LKpWHv/uxoAwGHjSlGcY1eqiESPfLQZQHx5AQBUFjrxzS7959OfsZYMP8SKIbvFpIRJ6VpB+9eMtaRwRAISRVG+bII1o4q1PtL+2RlWixkOqxnBSAwNniCA5AIE7ayzTBVrLf6wEr45NUGwTXPNTyzPw4hiN345f6Jy298uPhDf7mnBwWNKOvGKKJ2XfjIHX+1swolThvb2qRD1mv7/JzcRERER9VlyxUpHfLmjCVf/42vsa/F34RkNTGLFmmQwRfyDdfFgbd5+5QCQUrEmkgOum07cD6W5dlw7b3zKMfm6W0HFijWhFdRqhj0xoy0UiUGSJGVDpurrRtq/FTQQjiLSC4FcTHifw7Hk1+9UK+gACNaA5Hy9xsTvf4eyWVQTrGkr1gLqUNIfiiqtw9r3xipU/ZXlOfDOLw5LqdwscNkwd1xpv63+6w/mjivFVceM53tMgxor1oiIiIio24WjMby0fCcOHVeKccKA8XTOfHwpAKDJG8KLP5nTnafX74mBQjgqwW5V/5DbFgjjm13NAIBj9xsCILUCSDR1WCEAoKrYjS9+O08ZzC/SbQVVzVjTbwUNRmLwhqLKhkyRdph9JoFwFEfc/REqC11488pD2/XYzhLPv+taQQfG9kq33YJGbzJYd9nMyu2iQCSKXY0+BMJRjC/PUyrWnDYzAuEYfKEogonlGdrrVbzWSnLsqqCNiKgn8U8fIiIiIuoSRpVSAPDc0u34/VvfYt79n7T7eddXt3bmtAa0Bk8Qb63aC7FgS97iKdrXEoAkxavMyvOdAKDbCgoAb111KH4wu0r5XC9UA/RbQcWAL9epDtYcQiuotuVPFmpnsLa7yYfatiC+2dXc41VrYttqOCIsL+jEVtABU7GWCFW1raDnHFiF4pzkIoFgOIbD7/4Ix/11CZp9IeW6GJq4Rn3hiGHFmhhOihtoiYh62sD4k5uIiIiIel00ph+sRWMSPt/aaPi4Rd/V4JWVBoO80P4qJgB4+Ytd+NkLXyKgEzINJPct2ohf/ONr1CcCDAC47d/fYWNNm+q42tb4/UMSgQWQWj0kmza80DBME+m1ktqEGWvDi1zKx3aLSVheEIUnoB8+tfd73eJPPo+2jbA7SZKkCgHFkC2rGWtGFWu2gfHjmdsRvzbkVlA5WCvOsWP5zcfi+uMmAABqWgPKY7bUebCxOn7dTiiPb/f0BCLKe5tSsSZca7kM1oioFw2MP7mJiIiIqNdFDIK1eNWK/n3eYAQ/fX4lfvXqauWHcK1wByqRbnxtNd79thoLlu9s92P7k50NvpTbXvtqN05+6FPVbbVt8QBjSF5yy2e6GWvZ0AvfxIq1kcVu5WPVVtBIDB6D8EmvYi0QjmLxuhr4Q6khaWsgWfnWrLNltLtor3V1K2jHZ6zNGT0whuzLQVe9R24FFebtWcxwJgLEHcL1+/GGOvjDUZTm2jFjRCEAoFlYcJE6Y02ojmSwRkS9iMEaERERERkKhKO487/rsXK7ccWZzCgA29cSgEHmhq93NisfG82mam97oEis5BqIjJZDaOeX1bYlKtaEYE1vxlqOQRWbkbvPmqb6XAw7RpbkKB+bTFBtBTUK1rRbIgHgjv+sw6XPrcRvXl+dcl+r0FKqt2W0u2ivdfFzo9cmMgrWTp1RiXvOmoYPrj+icyfYy+RqSHmuX5lw3QHJWXI7GrzKbW98vQcAMHdsqdJK2iSEpdpgzSZcazmOgTGbjoj6JwZrRERERGTob0u24olPtuCsJ5ZlPDaiM4wekOd76d/3hRDYhQyCOW0ot6XOoxvA6OlMKNcfNHqNg0NfKII9zX7EYpJuK6jejLWfHTm2XV//nAOr8MdT9lc+FyvWxBCvti2oXl7QjhD1uWU7AAD/+mZvyn1i+6feltGuFItJ2NXoS2kDBYBTHvkMLyWqI72dmLFmMplw9oFVGDckr/Mn3ItyhMUVZhPww0NGqu6XK9a8QhXi7qb49t/DxpUqwZwcllrNppTlBDaL2AqaukiDiKinsGaWiIiIiAxtqfNkfWw4ph9i1bQGVI2g0ZikBDArdySDtWA4cwj2wXc1+MnzKzF7dDFevvyQzOfUwwPte5IkSYbtswAw+db3AADfn16pBJti2OUWKtZOnzkMJ02twNETy9p9HmLAYRWCNbPw8b7mAA4aJc9Yi7VrxprDajacvSZWrDV3c8Xane+ux5NLtuKgUUX43cmTU+6/+Y01OH/OiKy2gtoG+AZLt1BBdsr0SowtU28CTrf9dNbIImxIzFprSlzfeksdrKpgjRVrRNR7Bvaf6ERERETUKdkMsZcZVaztbvKpqs7kip5oTFK1gooVa0YVbguWx6uXVmzL3JoKpLZEDiStgUhWr++rHU1KK6jYkidWrOU7rThucnlKVVA2xJY8caC8KBSNdXjGWrr5WW1CQNdVwZokSbrXn1xd+cX2JrzypfGyjWxmrA2U7Z9Gjt2vHMMKXbhk7ij86bQpKfcbvX6r2YSRJW6lYq3Rp15+ILKZxVZQ1osQUe/hn0BEREREZEgvV2vyhmC1mJDnVLdfGQVrH6yrVVVKeYMR5DttaPAE4RNawT7dWIdnP9sGh9WC335vP9VzBMJROG0WmNsR9AEDu2ItXbWaqDUQhr0tHmSUC62gYrjRmfhRbGu0WNTfH5MJkDMqccaaUVVXJCapKhqBeGgiz5KLxSRVJVy65QWLvqvBO6v34i+nTzUMXrbVe1Gaa1euZUmScMHfl8MfjuK1n81VfS25VRFIblnVisYk+LPYRDtxaP9u9czk6IlD8NlvjjG8Xy8oA4ARJW7YLGYl9JWD1owVa07+WEtEvYd/AhERERGRIRPUQYk3GMHMPy1CkduGr245TlXRptcK6rJZsK3ei231ySHlnkAEKEgdvH/foo3Kx7NHF6vua/aFMbTAohv0aUWEMK2/Bmu7m3woyXHozkGTNWS5mMETjCihpxhwit87gwLBrBi1ggLAPWdNxw2vrMLVx4xTQpJQJIao0TaLxP3hWAybatpwwMhi1UbJBm9IVXWnagX1qyvWfvr8SgBAVbEbv5w/MeXrrN7djFMe+Qy5Div+cvoUnDpjGIKRGJZuaQAA7Gj0YXRpfAGDPxRFXVvy/TZaGpFuvtoRE8rgsplx7bwJmDQ03/C4wcCoYm1Mabxl1K257nUr1rgVlIj6CP4JRERERESGNDkJ1le3AogPFfeGoqofaLUVay6bBSdOGYrXE9v+ZHIbYLqKK+1styZfCEMLnFm1pob6ebC2udaDefd/gsoCJ5bedKzhcUbhjpYkQamiEpcXqI7pRM2aGKxZNBfMWQcMx6HjSjA034n7E8FpKBpDNCwpx2tDtll/WoQDRxXh0031uPOMqaoKsJrWAMryHJAkCSaTSbW8wKgVtKY1oHv7+sQcL08wgkc+3IxTZwxDRDiXgPB1dzX5VI81unYbPcbfkzNnDcOpM4YZ3j+YOGz6wdrYsniQqQ3W9JY9iNeduCyBiKinDezmfiIiIiLqFG2O1SAEB02acEEbYs0aWYiDx5akPKc8gypdsCZWuAHJ0CSbRlBxTld/nLH20fpaAMDeFv1ASJbu/asqduHW701WVQa5bBbDyp7OVayJM9ZSv0MVBS6YTCZVK6gcrha57SnH+8NRfLqpHgBw61vfok1o99zXEkBNawCH3vkh7n9/Q9qKNZk27JPVCxV/cktpNKofrO1sUAdr9QbVgqt2N+veDrRvXuFAZ7S8YHiRCwDg0gRlehVr4rXGVlAi6k0M1oiIiIjIkHam2Z7m5JypJp9xsHbeQVW4/5wZGF7oSnnObCrWtMFaiz9+bDbZhBisBbKYd9XXmA2CIK10raCf3ngMfnzYaNUcvJLc1BBLlqYzMyOxmijd8gNxeYFXCdZshsfLxzYJlWjVrQE8t3Q79rYE8NCHm9POWJMZBVpiSCxvKRXbmQPCllq5Yk0O6doMtpo+u3Q7AP3WRMZqSU6hYk2uUgOAktx4m2+OpmJNr3XUptoKymCNiHoPgzUiIiIiMqTNJPY0icFaMtS4//0NOP2xpQCAUSVu3HnmNJTnO1GZJlhL18qoDdbkr5XN8oKgEKwZBSB9mSXLBCabVtB8VzJwKMkxDtbS3ZeJPc2MNdVxiXAkGI0p35eidn7d6ha/Kshr9eu3gopbPS0G14xYdeYNRRFLLE6Q/WfNPvxtyVZIkoSdjfFgbUpl+tlo8pbbC+aMSLmPBWtJYsXaJYeOxg9mj8D0qkIcM2kIAKTMFtSfsSa0gjJYI6JexGCNiIiIiLImbkYUW0Ef+nCz8rH4A29FYepML69SsWZccSWGY0CyOi6bcEJ87De7mnHJMytQ25a+rbIvsQjvn5SmRzObraD5qoo1R8r9D543A/P2K8flR45p51kmpZuxJlJVrIWyq1jTqmkNosCVfIwYjokVa+K2WaNzatDMQ/OFo6qqyxc+34G//GcdNtV6sKsxft1PrizIeI4VBU7MHVeacrt2EchgVpJrV6rSzpg5DHecMRX/uvJQJUCzW8yq75texZp4PyvWiKg38U8gIiIi6vcC4SisZlPaNjTqGG0bndgKahTsiN8HvVlK2bSCasmVSeL5RKIx3e95SBPKfbyhDg8t3oQ/nzY166/Xm8QKq1A0ZjiPSu/9K8114Jbv7ad8nifMnirWqQ47dUbnB+qn2woqkl9HKBJTWi/1zimdtkBY9fVErYEIojEJFrNJucYA43BSOyfNE4ikLOAAgPq2oDLLbVSJO+M5jinLUW0ylY0qzfzYwcJtt+Lda4+A1WLSrTYzmUxw2yxoS3wfHTrvpxiCMlgjot7EP4GIiIioX/OFIpj+x/dRVezGh788qrdPZ8DRxiS7he2IRjOtbBl6Gb2aYM1ls6g2P+pp8acuLwhGDII1nU2g8sKE/kAMpwIh42CtXlNxNaY0B4t/eaQqfMwXqrs60+6Zjt2a/HoWc3Yz1jyJ70ehsLzgkrmjcPCYYvzsxa8Mn6PVH0kJTk2m5PKFFn8YxTl2VbBmdG1pg7VVu5tVQaSsxR9WnqPcYKuqaFihS1VhddeZU5HjsGL/LKrdBpOq4vRBo8ueDNacOhVr4vxEp8GWUSKinsBgjYiIiPqlcDQGm8WM1btbEI5K2Frnzfwgajdxppk3GFHNVWs0CNa0VUt5Tqtq1pm2Yq2iwImtiZlquQ4rvKFIypZKcUi9LBCOqqpdGjxBRGNSSvACdF+o1B0kCJspI1EUQL9dUttKa7WYUioM87NcXtAZYgVZulBV2QoajcETjH8/i4VgbXJlPk6YUqF8PqYsJ+X3dVswnNImnOuwAhLQFoyg2RdCcY5dCW8BdVuoLBqTlOvPZjEhHJVw+Qtf6p53sz8MX6J1tSwvtZ1Wq7LQpZoJNnt0CUaX5qR5BOlxC3PWHDrBmbhwgxtXiag3MdonIiKifmdXow8zb1uEP739nWrOTqwzqw1Jl/jz6vrqVtV9Ysgm0laRieEOoBOsCXPY3HYLSoVZYHIFkdyKJw6XFwOWWEzCAX/+ALNvX6xUt4nc/ahVTAwG/Tqh0ObaNvzrmz0praB6HY/i8oLinMyhUEdkO2NNDkeafSFl46a4vEAO5W46cRKqil34i07rblsggmBE/Z6MKHajIDGrrTnxvfcIQa7eZtgmX0gJZjJVTrX4w8r55jqsKRsrteLBWnZhIxlz2ZPXrlOnavOoiWWYXJGPCw9OXRRBRNST+nywtmfPHlx44YUoKSmBy+XC1KlTsXLlSuV+SZJw6623oqKiAi6XC/PmzcOmTZt68YyJiIiou327txWeYATLtzWoKqr0WgCpc8T398P1tar7mgxmpGmDhANHFak+9wYjiMUkJZgbmp/cHOq0WVCenwyAKgrioVtrIigRQycxMKkT2vr2CO2qsmCGVtO+JBRVV6xpzbt/Ca5Z+A3CmnlgUZ1gWVWx1m2toEKwlqZyaP+KfJhNwBahCk1cXmBNtJFefuRYfHrjMThgZPK6kYOqVn84pSJxRlUhChPP05K4pjwZKtbkxQVFbptqGYKeZl+yYs1ttyDPmf744YUuVfuuXaeNkTLLyVCx5rRZ8J9rDu83sxOJaODq03/KNzU14dBDD4XNZsN///tffPfdd7jvvvtQVJT8n+zdd9+Nhx56CE888QSWL1+OnJwcHH/88QgE+s/mJyIiImofOUALRWKqCplgmMFad1q8Lh6snThlKIA0yws0c7b+8P39cd5BVbjokJEA4vPOWvxhJQgaWpAM0pw2M2aNSP5db2hBPHRrS1QiieGpWLEmbisV205lmWa49SXiUPZAhmtaDB+iOiVr+cLMsJ5oBU1nSL4TR0woUz63W8yqVl5tICsGUvJLawtEUt6T6VWFKHTFX1uTL4QWXxhr9rQo9+sFa3Vt8SC2JNeRcfB9iz+kXD9Om0V3DpuostAFq/BazGxT7BDxupo4NL8Xz4SIKL0+HazdddddqKqqwjPPPIPZs2dj9OjRmD9/PsaOHQsgXq32wAMP4He/+x1OPfVUTJs2Dc8//zz27t2LN998s3dPnoiIiLqNXLGirVwJRvtPeNJfqFtB2wBA2SLZnKgO0rbgagOSohw77jxzGg4fHw9VWgNhVLfG/xG00G1ThStOmwXnHFilfC4PgZfbO8Uw7ZEPN2PFtkYA6m2lei2qei2VfVU4TSuodsNlsRCW6VWsidVV7d3AmS27EIBk6sY+64DhysdD8h2ajaLGP5rI12EkJqW0+ooVa82+ME54cAke/nCzcr+2FXRXow8/ef4LAEBprj1jsNbkTbaCuu0W1UIIPRWFTtVz5tj7TxtyX7Jsa4Py8Qn7D+3FMyEiSq9PB2tvvfUWDjzwQJx99tkYMmQIZs6cib/97W/K/du2bUN1dTXmzZun3FZQUIA5c+Zg2bJlhs8bDAbR2tqq+kVERET9hxisRWNCBRMr1rrd0Hwn9q+MV480+kKQJCmlBdcoIBmRmGW1udaDnY3xds3hReq2OafVginDktsT96uIf63WQDj+tYTQ6Z01+3DO/8X/zrenSQzWUivp+m3FmqYVVPs6xLlpejMGxWC0pNtmrCW/iLh4Qc+JUyrwi2PH46eHj8bjFxygCuWsaWaRRWOSUp2q3eY5tiw3Gaz5w9jXou5c0VasfbCuRgnKLpgzMmOwJofAQHxTZaaKNYfVAqfNgreuOhRvXnkoXBlmspG+Hx06CgDwg9kj2E5LRH1an/4TauvWrXj88ccxfvx4vPfee7jiiivwi1/8As899xwAoLq6GgBQXl6uelx5eblyn5477rgDBQUFyq+qqirDY4mIiKjvCSXChlA0hkhUf5h9V1i1qxlvr97bpc/Z32jDGrfDogycD0VieOzjLfjFP75WHWMUkIwbkotchxW+UBQfb4i3lQ4rdKl+aJZnKS351dG4+aRJ+HHih+twVEIgHNPd+BmLSdjbnD5Y0xtg31epZqxpQiFtNV6pUIWm1woqzmHrroCnPQtELGYTrj9uAn578mRMHV4AmzX5WLtOS+mvT5gEALj37OlKACa3cQLAL44ZB4vZpLSCNut877XBmrwx9LQZlfj+9EpVxaSeGiFYc1otaVtfDxlTonw8bXghZlQVpn1uMnbtsRPw1MUH4i+nTentUyEiSqtP1yXHYjEceOCBuP322wEAM2fOxNq1a/HEE0/g4osv7vDz3nTTTbj++uuVz1tbWxmuERER9SNyhVQwEtNsicw+PJEkCaYMs49OffQzAMCokhxVFdVgog1r7BYzXLZkQHPPextSHmMUPFjMJkyvKsBnmxvw9qp9AIBhhW6l3ROIt4ICwIgSNy47YiwkKV6pFI1JaA2EdRdU1HuDmVtB+1GwZlSx9tzS7SlBr9jeqbe748jETLNJQ/O6+CyTxN9H7V3Mq2oF1blurjhqLM45cDhKch24570NaPGHlYq1P582BRceHJ/bJ1esVbekzlnWhqpy0Fbojr93mSrQ5Ao4p80Ms9mkmi04e3Sx0o78xW/nKedBnVfgtuHY/cozH0hE1Mv6dMVaRUUFJk+erLptv/32w86dOwEAQ4fGe+1rampUx9TU1Cj36XE4HMjPz1f9IiIiov5DbAUNCz/J61Uz6flqZxOm//F9vLBse1bHb2/wZj5ogNLO7XJYzbCYTTCnySStae6cWRVfTNCWqBqKt4KmBmsyk8mkDOBv0dkICQB7mwPqVtBE8HHWAcPx5A8PAKA/wL49PtlYh2sXfp0y36s76C0vCEdj+P1b3+KL7U2qY8V5X2JbtKwsz4GvbzkOb111WDedrVpMp2ounWxaQUty4y2s8oZTOVgTrxt5s6e4xELmC0XgD0Xx7tpqhCIx5VrIcVgS/82u1kAOlK86ehyG5Dnw1MUHYmSivRmIv9fZLnIgIqKBo09XrB166KHYsEH9r6AbN27EyJHxf5kaPXo0hg4disWLF2PGjBkA4tVny5cvxxVXXNHTp0tEREQ9RAnWojFEDLZEpnPnf9ajNRDBLf/6Fj88ZFTG4y2DcKtfIBzF26v3oaZVPc9Kbtu0W82GGyv1Ko9k2ta4YUUu1UB+p84spXyXDU2+MFoNgrU9TX51xVoiWLNbzUr7Y7MvjJe/2IXdzX5cdMhIlOa2b97YxU+vUM7ltlO7tzVNDNbk5QU7Gny6x4pVmnrLCwAorbs9oTMVa7Y0ywuAZGWZ/DUcQghblKg+292U+j7FJODHz36BZVsbcPNJk+ALxUNdd2KpQLbBmnz80ZOGYMVv4zOeZ1QVIhCJ4byD2P1CRDRY9elg7brrrsPcuXNx++2345xzzsGKFSvw5JNP4sknnwQQ/xfMa6+9Fn/+858xfvx4jB49GrfccgsqKytx2mmn9e7JExERUbcJJoIHSVKHadkGa/Icr3TEsMecrjxrgLrnvQ146n/bUm5XgjWLcbCm3QoqmjOmGA6rWfleDSt0obZNmGFlS50DJlcjGbWCfr2zCZ5EBRyQrIZzWJNtq9vqvbjxtdUAgEZvEH8+barhOaazq1E/4GqPmtYAPttcj+9Nq9Qdyh6KCDPWEsHZppo23ecSZwy2N9TqDpmWF2iJ14olw+8zccMpoK5Yk1swWwMR6JE3TL60fKfS1i1fG3lZBmtOnT83SnIdePgHM7N6PBERDUx9ulb5oIMOwhtvvIF//OMfmDJlCv70pz/hgQcewAUXXKAcc+ONN+Lqq6/GZZddhoMOOggejwfvvvsunE5nL545ERERdSexaskrBCrBLOdoleUlq5UaNBsGZeLQ93StjQPVW6v0lzbIrXvptvQZbQUF4uHIEYm5XwBQVeRWbwXVCS/kFsBmn37F2guf79A/V6tZN6j7aH2dKjhtj67Irr738P9w/cur8LdPt+rer9cKuqnWk3Kc227BZUeMUT43qljrSe19W23t2PaYr5mFphesZVJR4FKqAN2JasZ020hF3O5JRER6+nSwBgDf+973sGbNGgQCAaxbtw4//elPVfebTCbcdtttqK6uRiAQwAcffIAJEyb00tkSERFRTxDDFXEwuV41kx4xNNMLLLTPNRgr1ozm1YkVa0bSVawBwPenVyof57usqpBOLwjLd8UDletfXqWqTJPJ1W/asM9hMeuGIXua/Ybf954gb7X8cH2t7v3qYC1RsaY538uPHINvbp2PMWW5ym19IVjLtBVUS7yOMlW7ifPkAPX3u8CVXbvrvha/MmNNvjbSzWYUf+u7bX262YeIiHpJnw/WiIiIiLTEH4TFofRBg9ZErbZAcgC9UYud+DUG44w142AtHkakqzTKVAH0/WkV+PUJk/DEhbNgMpnSLi8AAJ2Z/LpOm1Gp+txuVW8wBQD5W/mRQaiVSQcL3XQZXVfaYC0Yiaacb5HbrgRLVcUuAMBh40u77uQ6SLtFNhNxxlqmh2q3d4qVjtqKNafNjN+cOElVnQrEQ1WvZsbasEJXytcqSywnmCpsA3ayYo2IiHQwWCMiIqJ+R6wm8wrBWrM/rFvRpNUqbHY0rFgTgqXerwPqeWGD6j+lFTRNxVqmINJkMuGKo8bihCkV8ecSgjWHTmA3JD/zooGjJ5Zh2vBC1W0Oq0Vp95N9b1o8fFu9uyXjc+rpymvBqGM2JFRUeoNRfO+h/6Vc14VC9dbCyw7B9cdNwL1nT+/Cs+uYKZUFmQ8SiHPVSnLTV52lBmvC4gOLWRWuHTmhDD87cmzK9tpwVML2+viWX/namD26GLedur/quNmjinHsfuUoy0uOl3HrhL5EREQdCtYikQg++OAD/N///R/a2uL/yrt37154PL1XUk9ERESDhxh6+UPJwOFPb3+HqX94T7UpUY844HynwTB68WtEsy2ZGgBaA2G8/tVuRAxa+sStoEbCnWgHdOiEFz89fIzhFs+fHDYaf7voQPz94oOQqxlCrzdjbfaoIgDAlrqO/b21o7PZ9Jg1AWQkGsOC5TtUVZR7hbbVH8weodzuFl7rsEIXfnHseBT34PZPrXevPRx/OX0KTp85rN2PfeHS2XjsglmoKEitHBMVuFKr0kRlwjWS64gfK261lSv75N//ciuoyWTCRYeMwoTyZFutXHUpVrxxxhoREelpd7C2Y8cOTJ06FaeeeiquvPJK1NXVAQDuuusu3HDDDV1+gkRERERaquUFIXWIJklAbav+QgKZWLFm1D4aiiafN8vRbQPCL19ehetfXmV4v1wlZEtTsRbOcjur8pxC+OXUCeyqit147IJZuo+94OCROG5yOSxmU0p1mt1qTqmAO2BkMQBga723UzPJ/KEo3l69F61CW3F7abdgPvPZdvz2jbXY15LckipvTM13WnH76VOU29v7Hne3SUPzccGckR2aR3j4+DKcNLUi43HaOWpiKyigrmzUVreZTcDwQrfqNu31Il7T8gKOIUKwptemTERE1O5g7ZprrsGBBx6IpqYmuFzJf1U6/fTTsXjx4i49OSIiIiI9YiuoP5RanZbph/s2oWLNaOFBcJBWrC36ribt/dlUrBlVuxk+Z4aKNSC1WkkmborM0VasWcwwaarCJg7Ng91qRigSw54mf1bnp1eldsu/1uKql77GVS99ndVz6DGbTGgNhLFwxU40+0L4eGPq3LfaxKKDPKcNJpMJp0yvRGmuA/P2K+/w1+2vijRz1LShqVixluNQX0dHTxyC4UXqijjtMgKrKliLXzdiWKcN4oiIiACg3attPv30UyxduhR2u/pfjEaNGoU9e/Z02YkRERERGRFDL6/OTLVImhKzUCQGv7hJ1KDyR7y9vUHRQCaHYHqz0GRG89mMOGypgYaWYbAm3K4NPhy21HO0mE0YU5qD9dVt2FLvwYgSd8oxWuL3X87YXv1yNwBgyca6jI8XiSGdxWzCXf9djwXLd+K1r3brVtDJIbD8Oh88bwaiMUkVAg0WRZpWV224OyQ/OQ9NbgV96uIDsWD5Ttx11jQ8v2yH6nhta6dNuPaUVlAhrNMuwiAiIgI6ULEWi8UQjab+y/Du3buRl5fXJSdFRERElI5qxlo49e8l6YKdNk3rXjbBWmdaBgcaexatoJFoxyvWjGoNjYI18Tz0Ktb0jCnLAQBsMVhcoSUGubFOzlgTKyTNJhPe+7YaAPDF9ib402y1lSvzTCbToAzVgNTNnymtoELbZm7i/Tp2v3I8fclBKM11oEyzHEFb1WbTrVhLhnWcsUZERHra/X/l+fPn44EHHlA+N5lM8Hg8+P3vf4+TTjqpK8+NiIiISJcYdPh0WkFDEXX4IUkSmn0hAOrFBfHn0l90IAYggylYs1nSt9EqraDpZqy1t2JNqDzStm7KtIPq9ejNWNMztiw+pH5LnTer8wsK4W1ndxcEQsn3xmJOngugXsShlW8QLA4mha70FWviooFcR2oIpl2A4dQEc1aLWLFmTnlOzlgjIiI97Q7W7rvvPnz22WeYPHkyAoEAzj//fKUN9K677uqOcyQiIiJSCQlhmF4rqHZu2oOLN2HGbYvwv031HapYG0ytoNqwQUsO1NLNWDOaW2dEDNMMcjXDwE2ktxVU9Xni3OVZWzWtAWRDDHLb+9q0fOHk9RqNAcOLkq2o2xv0N9QCqcP4ByPt91O7/EEdrKUGkaoNnzZLyixGu07FWqlQ5ZZp2zAREQ1O7f4/9PDhw7Fq1SosXLgQq1evhsfjwaWXXooLLrhAtcyAiIiIqLuolhdk0Qr6wAebAAA3v7EGt58+1fC5VLcP0lZQh82MtjRLVbNpBZ06rKDDX789A+JPnDJU81j1X221rYLyzLWSnHjA0uBJvz1WJgZrAZ3rTUuSJPx8wVcoy3PgtlOnqO4TKyxD0VjWraX5TlasZaJqBXWk/pgjVqzpXWfqirX4x+I15AkYVxQSEdHg1aF/+rJarbjwwgu7+lyIiIiIshJSLS/QCdYMqtBikoTWRMVaaa4d9Z6QKjRRfY3o4KxY04ZRWum2gh46rgRHTijDxXNHtfvrXjdvAjbUtOLQsaVZHb/q9/ORl6FCTTtsXv68OFGFVO8JZfW1xEqlbIK1jTUe/HdtfHba77+/v1JZ9cxn23DXu+uTzxuOGlZMarEVNM5hNRv+ni3LS85Ds+q0NJfmOVJuE6m3gqZe30aLNYiIaHBrd7D2/PPPp73/oosu6vDJEBEREWVDtbxAZy6VGIrFNBsdW/1ysOZAvSdkGGyoBtYPpmAtwywzhzJjLTVkmDKsAJcdMbZDX/eaeePbdbzRMoPZo4rxze5mnHXAcEwcql6sVVEQD15K5Yo1b5YVa8JSAaNQRyRWOPrDUaV66o///k79vJFY1u2F+WwFBRCvNDP6HojvUY499f3KEarUvDp/bui1ggLAbafujze/3oMfHza6Q+dMREQDW7v/D33NNdeoPg+Hw/D5fLDb7XC73QzWiIiIqNuJYZhPtxU0GWzUC+HJnmY/fvP6GgBASaJqKRSNQZKklBleg3XGWsaKtTQz1hzdvK3SaTMjkGZzJgAs+OkcRGOSatD8ExfOwsMfbsZ958wAkPzeB8Ix+EKRlBZSLXUraOZgTULyevEFI7ptifLzpgvqXDaL0urMirU4t92KJl9Y9z6TyYQHz5uB3U1+TK7M171fpvd9FMM0sXrtokNG4aJDRnXirImIaCBrd7DW1NSUctumTZtwxRVX4Fe/+lWXnBQRERFROmJFmt6IKnnGmiRJ2Fzr0X2OA0YW47PNDZCkeHCm3YapnrHWuYH1/Umm6rx0raDpFhp0BZfNkjHYslnM0C5vPGFKBU6YUqF87rZblJCuwROCuzhTsJYMb4OaIFevO1A8R72ttbJQJJo2WBtR7MaGmjYArFiTuTLM4Dt1xrAOP7fVoGKNiIgonS7528/48eNx5513plSzEREREWUjEI6mbOs0EotJqoo0PXIoduu/vsX5f1uecv+D583AFUeOTTle9RyDdMZapq2X6ZYX9ESw1hVMJpOywKA+iwUGYitoQNO6adV5H8Q5bHLLoV5gGYzE0s5YG1GS3BjK5QVx7Vlu0V52neUFREREmXTZ336sViv27t3bVU9HREREg4QkSTjkjsWY/sf3sxoMnyn4EY954fMduvefNLVCFQLpBmuDdMZapmH66VpB7d3cClpe4Mx8UJbkdtBGb+YFBmJVWTgqqWao2XQqm8TrWK5Y05vplakVdESxEKyxFRRA14WrelQVa918LRMR0cDR7pryt956S/W5JEnYt28fHnnkERx66KFddmJEREQ0OATCMWVm0s5GHyaU56U9PptgLZzhGLnaymI2IRqTVOGGlOgtHawz1rKtWNML0WzdXLF239nTcdVLX+PKo8d1+rlKcuLBWkMWm0G1CwbEz/Ves18nWPMEdZZsRGIIJZ7r/nOm4+WVu/D51kbl/qoil/IxK9biTppageXbGpXvX3tNG16A1btbUjbKAuoqNbaCEhFRttodrJ122mmqz00mE8rKynDMMcfgvvvu66rzIiIiokGixZ9dC6gsU0UVAIQTxxS6bWjWDDq//rgJysd2ixn+WFR5zmhMwhmPL0Why6baKBkdTMFapoq1NDPWutuYslz855rDu+S5iuVW0Cw2g2rfE3FumtWs1woqzFhLBGqegF7FWnLG2ujSHCy87BCc8dhn+GpnMwBgqFChl8cZawCACw8eieIcOw4cVdShxz96/iz8ddFGXHp46oZPo62gRERE6bT7/9CxQTS8l4iIiLpfsz9ZMSQHFjsavFixrRFnzhoOs9l4qYAReQab3JJX5LbhzSsPRU1rEAcJP5DbrWb4w1GEolEEwlFsqfNg1a5mAECFEGroVay1+MJ45ctdOGVGJYbkdV2LYm/L9P460lSs9af8sTTRCrq32Y9fvrwKB4wswvlzRugeq23XFMNa7dILQF2x5k1c0626wVqyFVRvdl2uI1mlxmAtzmI24fvTKzv8+KpiN+4/d4bufWJIyhlrRESULf4fmoiIiHpVixBSyNU95/7f56huDcAfjuKiQ0apjs8mWAtFY4hEY0rl0OJfHoXiHDtGluSojpPDjD3NAZzx2FJVqFHXlqxkkivWQpGY8phb/rUWb63ai9e+2oP/dlEVVV+QsRXUEp9xpbe8oD/NopNnrL34+U4AwGtf7YYECRfMGZlyrLYVtNmXDIPNptQARtwcesMrq7Bg+Q5cfUxq+6okJa95hzX+voqVgNOrCmA2xcMgzvzqfjar2ArK95uIiLKTVbB2/fXXZ/2E999/f4dPhoiIiAYfsRVUru6pbg0AABau2KUK1hq9Ifzm9dUZnzMUicEnhBs5Dv2B53LV1TOfbUupKKrzqIO1+97fgL99uhVvXnkoJg3Nx0frawEA6/a1Zjyf/iIakzK2vaZrBY1J/SdYK89PrTJ87KMt+sFa2LhiTe81+0PqIO7rnc1Yt69N9zzka16vEjDPacM3v5/f7UshKM4mhGl6lYhERER6sgrWvv7666yezKTzL3ZERERE6YjBmk+zOXFHgxcAsK3ei0c+3IzqVr9quLuRcDQGb6ISyGo2GQYTDlv89s21npT7tBVrD3+4GQDw69fW4F9XHoriXDvadAbS9zdtgTC21XsxbXihqhqwOMeuuzEzXbDWn2bRHbtfOewWs6pCr84ThCRJKX+n1baCNgkVa3Lbscivs912Z4Mv7fk4dFpBAS4t6Eli+6eFFWtERJSlrIK1jz76qLvPg4iIiAYpdbAWRUQIOryh+OyzHz2zAtszBBMiMVjLcVgN//FPDtx2N/lT7hODtYgwY1YO+4pz7NjRjnPqi2rbAjjpwf+h3hPEW1cdipHFyVbZ5Tcfi2l/eD8lJJIrefTCygJX/wmBch1WXHLoKDy5ZKtyWygSbx922dUVjqmtoMlrNqIzfzgQTr1tZ2P6a6U3l0JQnBhqcsYaERFlizPWiIiIqFepWkGDETRrtoR+u7clq1DtsQtmYd2+Vjz84WaEoxK8wXgYkusw/uuOI02IIS4sECux5FCl2G1XbvOFInDb+99fq25+fQ3qEy2v6/a1KlsoTaZ4pZ/VYgI0S1v1AqCZIwoxstiN02YO65kT7yLXHzcBDqsZR0woww+e/ByRmIRmfwguu0t1XLqKtUi2FWsZgjV5xpre7DrqGWL7J7eCEhFRtjr0N8CVK1fi5Zdfxs6dOxEKqVsEXn/99S45MSIiIhoctBVrTZr2wy213qyex2o2KaFESKhYc9v156sB2VcHRWMSLGaTErBJkqQKQPY2BzBuSG5Wz9Vd6tqCeHnlLpx94PCst5R+tzc5H66mNai0gtosZphMJt2QR65UE9+7C+aMxFkHDO/M6fcKp82CX86fCAAodNtQ7wmhyRvGXxdtRCAcw6WHjcaC5Tvw8srdqseJ4W9YZ9lDUCdY29Mcr4o0m4CKApfyuSwZWDLQ6S2qraBsBSUioiy1+/8YCxcuxNy5c7Fu3Tq88cYbCIfD+Pbbb/Hhhx+ioKCgO86RiIiIBjBtsNagCda0bXhGrJZkELS5xoPFieUCOWkq1rIN1iIxCRUFybBqT7NfdV57m1NbSXvaFS9+iXve24DLX/gy68eICxuqWwNKsOZIvI96EY/cViuGbgNh0HthogJxU20bXl65G2+t2osLn1qeEqoB6q2gEZ25cnoVa7Jfzp+Iz35zDCqF68lqNsGSqJA6bFxZh18DdY7NOrCuaSIi6hntrli7/fbb8de//hVXXnkl8vLy8OCDD2L06NG4/PLLUVFR0R3nSERE1O94gxE4rGZY2daVkTivyheKpAzM17bhGbGazcoPwyu2N2LF9viSg3StoNluW4zGJIjLHzfVeFRztPa19H6wtnJHE4D4BspsRKIxeITlCzUtAWWQvxw4pltMJb536Vpq+4vCxHw48f1rC+gvp2jyJq/Z+LWhXngQSBOsydejw5aspBQD3pOmDsUTF87C/pX8B+ueZjOLywsYrBERUXba/begLVu24OSTTwYA2O12eL1emEwmXHfddXjyySe7/ASJiIj6mxZfGPv//j18/5HPevtU+gX1jLVoJ4I1k24FWle0gkZikmp75NZ6r6ZiLZDV8/QlHs1G05q2ZMVaMlgzfrz43g2EgftyxdrXO5syHlvbpv5+a6vW0lWs5TnjwZpRMGkymXDClApUFbsznzR1KauqCrP/X9NERNQz2v1/jKKiIrS1tQEAhg0bhrVr1wIAmpub4fP1781YREREXWHplnoA8WHwlFmrEKz5w6kVa9oACAAOH1+Ku8+aprrNYtafCZa2Ys2aDN1KcuyGx8VikmqW1r5mv6pirS+0gmYjEo1BSpTetfrV72t1S1B5jXJQlq5oRwyG7Bbj8LK/KHTHK9ZW7W4xPEa+RrTz0bQLDPS2gsqSFWsDK5gcCMT2T1asERFRtrL+v7gcoB1xxBFYtGgRAODss8/GNddcg5/+9Kf4wQ9+gGOPPbZ7zpKIiKgHtfjDyuD7jjALP5DFdOYvkVqmirWa1tRqsF/On4iZVYWq26wWs25rZ7oZa2Kl0JB844H/kZikVHMBwL6WAAJCxVpNW1D5eOmWeuzKsAGyu63b14qb31iDLxLtsED8fTzur0tw1hPLEItJaEzMCZO3HzZ4g8omVbsyYy1NK+gAm0clt4KmE02EktrgLBxTf562FTRRsSZeew5r/w8mB4KBNjeQiIh6RtbB2rRp0zBnzhxMnToVZ599NgDgt7/9La6//nrU1NTgzDPPxFNPPdVtJ0pERNQTvMEIpv/xfRx5z8cZj/1mVzNOfuhTLN1cr7rdLPTP+dL8gE3x7ZrNfv0Za3JwU92SGqzlOa1KQCGzmk2q4eMytyO7VtADRxYZHhfVVqy1+BEUwpXaRPj31c4mnP+35Tj87o8Mn6snnPjgp3hp+U489tFmAPH3+aKnVmBbvRdf7mjCeU9+jtMejbcqjyxxw2YxQZKAi55eASC7ijUxeBgIFVdFaSoWZUeM118sEIlKiERjuO/9DVi6pT5tK2hVUbzFUwzTBsL7NxCIwZqFW0GJiChLWS8v+OSTT/DMM8/gjjvuwF/+8heceeaZ+MlPfoLf/OY33Xl+REREPWrtnngbWL0niJteX42a1iD+dtGBum1BP3/xS+xtCeD8vy/H9jtPVm6XhCn33mAkbSviYNcWjCAaE9+vZMVaZYET2xt8qNapWMtzWOHUzE6zWkyw61SZ5NqzW14wvaoQx+w3BLWtAfz6tTWq48LRGMJCu9++loAqaJOr6v63SR2ydkY0JnW6HU1+L3c2+rChpk25fYVQyVbotmNLnVf1OL3lBUPyHDh1RmXKMdqP+6sCg4o1swn47DfHYH11G6JRCW+t2ptyTCQaw6tf7sbDH27Gwx9uRnm+Q/e5xg3JVWan2a36M9ao91iFPz+sbAUlIqIsZf1/8cMPPxxPP/009u3bh4cffhjbt2/HkUceiQkTJuCuu+5CdXV1d54nERFRj2gQ2hD/sWIXPlxfi+XbGjIeKwoILYN688EoaZ9m6L8/nAzWyhOtmfoVazbk2LUVa2bdGWvZtoIWumw4euIQzB1bmnJcUNP6V9MaUNomAaDJF0YwEkVbIKx9aIc0eUOYc/ti/Oa11Z16HnmovtF2SwDId1pT2t6UVlDh5uU3H4vfnjw5eYwYrA2AQe/yjDUth9WCigIXjp44BKV5+oFZOCapZir6Q/oVaweNKhaed2AFkwOBzSy2gvJ7QkRE2Wn3/zFycnLwox/9CJ988gk2btyIs88+G48++ihGjBiBU045pTvOkYiIqEcs39qguxHQaOOjUWAjzlfyBdkKmo526L83GFFmrsnBmk8npHDazLCYTaqNn0bLC3KybAUtSAQregGLL6wOpmJS6ubH2tZglwWp/1y5C/WeIBZ+scvwGEmSMgZ58jmmmxmY77LhrjPViyD0toKaNCtCxRBiIARDRW79VlCnsGTAaMFFJBpTbQZtNQgyxYo/ByvW+hwrlxcQEVEHdKo3Zdy4cbj55psxcuRI3HTTTXjnnXe66ryIiIh61ObaNpz75Oe699W26QdrLpt+YCMGa6xYS293IlgbPyQXm2o98IWiMJni71+ZQXUQkAx5chxWJXizWfSDtXT7I8RKK7kVMNdhxSnTK9HkC+HICWX48zvrVBVIlQVO7BWq6HIdVniCEdS2BVSBiiRJKWFUtox+pn/6f9uwrd6L207dHze8shqvfbUb//nF4Zhcma97fCBx3nrhpCzfacMZs4YDAK5/eRWAZNBjTnP+ZrMJ+U4r2oIR5DkzD/7v6/KF12CzmJTWX3EWWmmuQcVaVNJ9j1/66RyU5DgQjERR2xrEwWNKlPvKC5LLMuxcXtAniNc7lxcQEVG2OhysLVmyBE8//TRee+01mM1mnHPOObj00ku78tyIiIh6zLp9bYb31ei0IgJQVUvJYdpv31iLLXUe5XZfiMFaOnLF2vjyeLAmBpFGIYYo12FFXWIjp8Vsgt2a+sNwuqoucdmBHKyZTCY89IOZAID3v42PupC3QFrNJgwrcqmCtRHFbny3rxU1rUF4hGAtGInBaRC+ZmIUaN329ncA4pVPr321GwDw+Cdb8HDifLXkirV0AW++K/7XQfH9VirWMpznI+fPQpMvhOIsBv/3daNK3Shw2ZDrsGJCeS4+2lAHAHAIFWsuzVy/fKcVrYEIIrFYSvUlAMyoKoTbYMbfiMSsNYAVa30RK9aIiChb7QrW9u7di2effRbPPvssNm/ejLlz5+Khhx7COeecg5ycnO46RyIiom4XjMQM79vZ6Mv4+F+/thoWkwmvf71Hdbs20Hho8Sa8tWovFl52cFbB0UC3pykeRowbkgdAPa81XcWaTFwMYbOYYbekBll6M9NkEWEBgd7werk1TA6obBYzhha4ACRbhuVgrboloPp+B8MdD9bEH+qf+t82lObaccr0ZBthm+rrGFejyeedLuCVK7XE61Gu/EtXsQYAR0zQ35LZH+U5bfj4hqNgsZhUs+3ShV5ueyJYi0rYpxPAO9NUoonB2kBopR0IxMudM9aIiChbWQdrJ554Ij744AOUlpbioosuwo9//GNMnDixO8+NiIiox9R7gob3GQVr4kD4f32TuikQgGrAPQDcv2gjAODhxZvwx1OntPc0Bxy5ymfckFzV7S6bBblpZqPJxPlpFrMJNqFibd5+Q3DdcROwf2WB4ePFOWl6IZgcLMmtoHarWTVny2YxobLQBQCoaQugyZdcaPHSip2YUJ6LyZX5CIZjGFWa/T9CisHan97+Dk6bGcdNLk8eILS3pguFA+EYYjEJnjSz/vITgWJpXvJ1KV9/kBXtFCW+t2L7pyNNOCYHr6FoLGXJht1qhjlN1ZOqYo0hTp8gfrdYsUZERNnKOliz2Wx49dVX8b3vfQ8WnX8NJiIi6s/q24yDtV1NfsRiUsoPya1ZbIA0qhTaWOPRvX2gqmsL4g9vfYsL5ozA3HGlWLqlHsML3diTCNaqilxw2SxK0JXvsqYNNGRiGGbVLC8YXuROG6oByRZPI9bEgH6xYk0ccu+wWlCeH6/0qmkJKG2pAHDXu+tVz7X2j8erKuyMxGKps9kC4ZiqIioqDI4LhKOQJONBcoFIFL50raCJirVi4XXJyw4yVawNVNksFjCb4tccEP/eh6LqaynT91oOZAEoCzuod40uzcHIEjfynFble0tERJRJ1sHaW2+91Z3nQURE1KvSVayFIjHUtAVQUZD8QTgSjaUdCC8TWwPF8GNXk34VXIMniOIce4eH3vdVf3r7O7yzZh/eWbMPd505Fb9+bQ2mDMtHTWs8LBpW6EKR2wZ/SyJYc9qymjslttpZLWbVMoJsQqxM30Nt1YrDalZtDXXazEol2pc7m1RVjFq1rQHkluUa3g8AW+o8OP3Rz3Sr58TKyUAkKnwcS1u15g9F4U3zOuW3zCq8d63++OsYWFdh9lTBmk19HV548Ai8+PlO3HzSfsrWVr2q1gNGFqX9GmIIXGOwIIV6ltVixuLrj4TZZBpwfwYTEVH3Yd05ERENSI3eELbWZV8VVu+Jt/CNMWjXq29Ltvi99201Dr7jw6yeVwxuxI/3NMer4EQfbajFAX/+AH9469usz7u/WLOnRfn416+tAQCs3dOKmBRvpyzNdWBIfnJLYr7LljJ36twDqwAAN580SbnNKYQe2oq1nCyCtenD01e0WTWbAW0WkypYc1gtOHhMCcwmYFdj6vB6UTZB7J3/XY/WQAS1OhWUu4Xwxie0dgbDUQTTVN75w1GlAk2PxZz610G5GnOwZgsOm3Er6K3f2x9vXXUofnzoaKWqSS8oP3rikKy/XnWLcbBPPctqSd/CS0REpMVgjYiIBqQfP/sFjrnvE6zZ3ZL5YCQr1v5wyv44fHzqsHux7fPyF75MW+EmEivWxHYvSQJ2NPqwt9mPBz/YhAZPEHf9N946+NyyHVk9d38SSlNRVeCyw2w2KS2V8dtsKYHGqTMrseYP83HZEWOV21JbQZM/EOdkMaPt7AOrcPvpU/HB9Ufq3q+tWEtpBbWZUeCyYXpVYcavlS7ckqVr6RSrosQW42Akpqpg0wqEo/AatCTP268cR01MXUCgBGuDtGZNrFhzairW7FYzpg0vhFkIcvWCMb33VSsnsWV03BAuASMiIuqvGKwREdGA9M2uZgDAQx9uyup4OSgrzXXozvZqNZiBVJrrwIuXzsHwIpfu/V6DYA0ANlS34ooXv8RfP9iIq//xdVbn2R9JkoS2NPPo5AqwcrFizWlNqVhz263Ic6o3d4rBmsVsUj0mm1ZSi9mE8+eMSFmeoNyvKdmya1tBE9fK4eOMN4/KjMItUZpcTRWsia2dgXAUgXSbQUMx5ToUg8fTZw7D3y8+UHf7obwhdNBWrKmuo8zLC+SW5nn7DYHbbsGc0cWqGWpG/nXVoTjvoCrcc9b0Tp4xERER9RYGa0RENKAt+q4mpeVSKxqT0OiNt3qW5tlTZioBxosKCt02HDa+VLXhTyRuBdUGa02+MFYlKuqWbmkYsIPiG7whtKaZPVbo0gnWXKkz1tz21IBD/F6ZTOpWUG0w1xHZVKwBwCkzKpHntOLoiWUYZhCopNvMmY2dQqtpSsVahlZQuQ1VDswAwKXzfr7ys0Nw5IQy3Hd2POgZrHOm1FtBja8jW6KNVg7WJg3Nx7LfHItnfzQ7q68zbkge7jxzGqoM/vwgIiKivo/BGhERDTjadrpVu5vTHt/oDSEmxatzit121UB8mTzMPahpuctzxud4GYU4YsVas08drGmDtoGYYexq9OGcJ5alPUauABuSp24FTa1YS/2+aL9XqmCtC7aYa2esaSvWZOOG5GHVrfPxzI9mo6o4GazNqCrEQaPiQ+yzagVNc98usWJNM2NNr2JNvjbFGWuqYE1nQcJBo4rx3I9nY0xiycIAvCSzIga26YI1+fqQZ+IV59hR4LbphpZEREQ0MDFYIyKiAUe7IXFDdVva4+U20GK3HVaLOWWmEhCvWGvwBPHm13tUt8vFcNqWQZnY/qdtJ23xh1UhzUAM1hZ+sRNb670pt4sBT4ErXgGmbgXVq1hLXUag3Z4pVph1RcWaVVOxZreYVdtG/UJLpjzwXDynYYUupYItm2AtHXFen3gtBSIx3WCtJCf+vl789Ap8tbMZAFCam6y20wsqtXT2GgwK6q2g6VpB1W9QifD+EhER0eCQeV0WERFRP+PXbF/cVJt+O6jcBlqcCCKMZqxd9/IqLNlYp7q9KfFYuTpIK92MtRZ/GMVuu1LJFhPyQEmSBkQbntG2w6pilxJo6s5Yc+nNWNNpBU0Tnhl9T9pD255rt5pV3xe/TqAlVtG57RbldXg6GayJmoVrKRqTENBZDlGUY8f2BvW2ykytoFqDd3lBtq2g6venJMdhcCQRERENVIP03yGJiGgg025INArW5LZOOfzKSVQi6VesRVJCNQBo8sWDtV/On4ihQjAk8wajWL27Gcu2NOgGa3muZMWauGk03cysvm7hip04/O4PsammDXWJ13TPWdPw1lWHwmQCfjC7CgXC607OWEuGEk6bJSXg1As4tBVrAHD1MeNw8rQKzB5V3OnXYtWUbNk0raHaEBdQtxHmOKxKhVtnK9ZE8nUna9Z8DsQrMLVKhGDNnaYSS2YenLmaZnlB5lZQmRzOExER0eDBijUiIhpwtKHU5prUVtDlWxtw/t+X49cnTFQqpXIc8aBBDGuKc+xo9IYMt4LK7XZVxW58fvOxuPmNNXhp+U7lfm8wgoueXgFvMIKTplYAAIYXubC7yY9WfxhhodJIntMEAG3BcL+d0/Sb19cAAG57+zvUe+KBT1meA9OGF2Llb+ehyG3HdS9/oxwvV6yJYVsgHE0JsfQq+A4fH9/GKbZn/nL+xK55IQAsKTPW1N+TTBVrLrtFmWXW2eUFohbNvL66ttTKwBxH6l/z1K2gWfw1cABUTXaEesZa9q2gpWwFJSIiGnT6VcXanXfeCZPJhGuvvVa5LRAI4Morr0RJSQlyc3Nx5plnoqampvdOkoiIep08b0puwdvbEkCbZqvnr19bjWhMwu3/Wa9sTHTZUivW5IH6rYGwqjXRbbdg1ohCPPSDmarnzdGEYW3BCJp9YYSjErbUxSvn5A2iLf6w7mwsAPCk2aLZX7QFIkrgU5Z4H0tyHTCbTaoQrSBRWSUGZzOqirJqha0qduPTG4/G0puO6cpTV2hnrGnDPr3vn3j95NgtSsClrVgLRWL4bm+ratmGdvGGEW31Y50nNVjTW05Qlte+VtDBW7GWfG/0Klhl2uujiBVrREREg06/Cda++OIL/N///R+mTZumuv26667Dv//9b7zyyiv45JNPsHfvXpxxxhm9dJZERNQXyFVEQ/IcSjC2WdMOKoZkcrAmz/ASf6iWg4gGTwghobrs0fNn4fWfH4ppwwsNn1drd5MfgDpY06t4Arp2HldPisWSwZDJBDR61cGaTBWsCR8vu+kYvP7zuZg4NC/rr1lV7Ea+M3VTZ1dImbGmqVAKR1ODMLHi0W23IjdRCakN1u5ftBEnPfQp/r16n3JbLLtcTTVjDdCvWNMLzsQZYHrBm9YgzdU0raBpKtaEVuECl021lZaIiIgGh37xf3+Px4MLLrgAf/vb31BUVKTc3tLSgqeeegr3338/jjnmGBxwwAF45plnsHTpUnz++eeGzxcMBtHa2qr6RUREA4dcReS0WbBfRT4AYNWuZtUxqmAtEXjIwZq6Yi3eJrqzMTkE/l9XHoqjJpbpfm3xB21ttY+8pKAqEay1DsCKtQZvctZXsy+MmBR/H7RD3fVmrAFARYELs0YUoa9I2QqauG6uPHosAODmkyalPMahqWyUK9baNMHa6t3NAIA1if8C+hVweqKaBE4vWNObPye+71ltBR2sraDi8oI0FWtiBWMJq9WIiIgGpX4RrF155ZU4+eSTMW/ePNXtX375JcLhsOr2SZMmYcSIEVi2bJnh891xxx0oKChQflVVVXXbuRMRUc8LJmasuWwWzBkTH2D/+dZG1THiD86+RJjhsqfOWBuSGKgfSQQZhW4bplcVGrYpikGMGGKI5Iq11kBEqZbT0oYw/cXeZr/y8bZ6LwCgOMcBiyagyheDNXfmajPt43uKdsaaXJF0w/yJ+ORXR+Gnh49JeYxDrFhzWA1bQeUKRvm/AHS3e2ZDL1jTC85cdrPwceZg7Q+n7A+nzYxfHd91c+v6A/WMteyWF3BxARER0eDU54O1hQsX4quvvsIdd9yRcl91dTXsdjsKCwtVt5eXl6O6utrwOW+66Sa0tLQov3bt2tXVp01ERL0oWbFmxsFjSgAAn29rwPKtDfjhU8uxudajaunzp2kFHaJpYSzS2bQoEoOYfINgbVRJDoB41VHQIEjpjxVrm2ra8NjHm1Nu1xvoLoY+ha7MgURvBWtGFWsmkwkjS3J0A1YxmM2xW3S3gkZjkhJC7mpKVkMGDSrW9DbOirYmQkz1eaj/mnfouJKUNtVMpgwrwNo/HI8rjx6X8diBpCOtoEPyHYbHERER0cDVp7eC7tq1C9dccw0WLVoEpzP9Xyjbw+FwwOHgX36IqP+QJCnlB3hJkhCK/n979x0mV13vD/w9ve1sb+k92YQU0gmBUBISkIsgKFyMCLkIgokQuHoBlaYiiPeKojSlqiDIT7q0GEJoaSQhvfeyu8nuZvv0Ob8/Zs6Z7zlzpuxsn32/nifPsztzZvbM7EzIvPmUcNIPfX2VR2gFnTAgD06rCfWtAVz1p8iYgBv+8iWGFDmV41uUVtDIfxbFapVks8H0WIQP2m67/n9my/JssJqNqpltWr1xxtp1z63DMaFiTaZ9DgFAnNGf6HkSmbqpJVEb6KUzQ0sMtJxWM1zR15W4FbS60atUQaoq1hIEa4OLnKhq9Cb8mXqvJXH+27qfzkO+04ImIbBNNpRfpN182ReoWkGTVKyJraAjS3I69ZyIiIioZ+rR/1Jav349Tpw4gSlTpsBsNsNsNmPlypV49NFHYTabUVZWBr/fj/r6etXtqqurUV5e3j0nTUTUwfzBMC76/af4/l+/VF1++z82YcrPl+FEkg/bfdGy7dX454ajACLBmsVkxMSBeapjDtS0qCrW5Llgyow14UO1y2ZWbfosSNG2OP+0MgDA8GJXwoH6+Q5ryoCuNwZreqEaoB+siZWAxjSq0bSVY11FG+glC1lix4hVYeqKtf+3/ihu/MuX2F3dpBxT3xpQttZ6A/phq9w+3BZiiFbitsFiMqrCNEOfXU2QmqpiLdlWUOHvkRGlDNaIiIj6oh5dsTZ37lxs2bJFddmiRYtQUVGBO+64A4MGDYLFYsHy5ctxxRVXAAB27dqFw4cPY9asWd1xykREHW7j4VPYWdWEnVVNqsq11zceAwC8vO4Ibpk7qjtPsccIhMK44S+xAFJue0sVYlU1RMJJveUFDosJuQ4LWqLtoqlaQYcUubDmJ3OR57Dg1pc3xl3vtplhNRuRazfrzsWS/eaDXZgwIA9zRusvSehpJCnxOku9YG3GsELcfsFojEwzjEgnfOsM8RVrqc9DfP24bCa4oltBPYEQfvTqJgDxW2qP1XtQUW6BL6hfsTYkQbA2uiwHu6ubda9r9gXiLnNYTDh3TAlafSEMLHCkfCx9lXrGWuKqYIvw+kj3tUxERETZpUcHa263G+PHj1dd5nK5UFRUpFx+/fXX4/bbb0dhYSFyc3Pxwx/+ELNmzcIZZ5zRHadMRNQuvmAI72yqxNmjilEanakUEgKLQEiC1dz2Cpq+okkzl8wefW70Zkn5Q7HKoOpo1Z9DbgUVPkjbLSYMKnCiMhq+5aUxaL8s+rvTq1jLd0UuE8O+XLsZjToz1b777FocfOjihD9n/aFTWLnrBBafP7LbW4JbEixhAID548riLjMYDG0KhLurYs1gMMBkNChbOK3ptIKa1XPM5OUFIu1MtKN1HlSU5yauWCvSD9bcwmvMajJiTLkbW441AACuP2s4/rHuKL4xeYDq8Ty/aIZueznFiL/nZH/HNnhi4eUItoISERH1ST06WEvHI488AqPRiCuuuAI+nw8LFizA448/3t2nRUSUloM1LXhr03Fce+ZQ5DkseGzFPjy6fA8G5Dvw+Z3nA1DPovL4Q7CajcqHfCA2TJ2gtNPJ5Io1cWC7zCcEGEorqCW+Ys1uMWLm8EKsPRjZKpqqYk3k1gnW5NvnC/djt5h0g7VUrnjiC+Xn3DAnfjtlV6pr9itff3jbHPzsja1Ye6AORS4rpg4pbPf9d1fFGgBVsGZJ4/0mVrk5rSbYzEaYjQZlppqeo6daIUkSvAkq1hK1glpMBvzvtybhkWW78cdvT0aRy4aH3t+B788ZgWHFLmy6d77uLDWGasmZTbHfWbLQ+kBtbPGE3t8zRERElP16XbD28ccfq7632+147LHH8Nhjj3XPCRERtcMlf/wMTd4gDtS04JGrTse/t1cDUM+qEiurWgNB5MGiCpAyDdY2Hj6F+tYAzqsozfDsex5txZoj2topbqCU1bX44y6LtYIKFWtmE2YOK8IfENl2mZ9GxZpMbyi/HKgVumLBmkPn/Npiz4mm1Ad1srrWyPM5IN+B0WVu3HvJOPx11SHcOq9j2pS7q2INUM9ZS2d5QVhIw51WMwwGA3LsZtS3xrdmDsh34Fi9B7uqm+EPhZGoo7ZU2ApqtxiVyjaLyYhvTh2Ib04dqFz/+MKpytftfW31ZZdM6o/Dda0YkKRl1s7/sUFERNTn9bpgjYgom8hB0Or9tQD0q2E8Qotda/Rrsf0olKQKJplvPB6pdvr8zvMxID87Zi0lbgWNDxcO1rbEXebQCdasZiOmDMlP+DOSydWZ7SYvPyjOic0dsyeoiDEYgHBYSlmtZewB1Ud1LZF5cQXRVtfT+ufhoSsmtvt+zxpZjM/21uA7Zwxp931lSgz10mm91qsoLXRZdYO1q6YPwm+X7cbmo/UJ20ABoDjHCoMhUsFanmvHwWilVDqtqZSZR646PeUxP714LDyBEL4/Z0TnnxARERH1SPzXGBFRDyBXuFh1BqO3CNshPTrBmifJbKtEvIHYbWqbEw/Q7220raC2ZK2gwfgQQ57FZhZ+D0aDAU6rGSNKXACAOaPSXyagV7Emt4IW5witoFYTHMI5vr/0bACRECWdIK8j2/pqmn14Zd1htPrjf+7Hu04k3PxZG20FLXTFLypoj6eumYqXbpiJ73djq6vJ1LaKtWKdZQ3FOs+L1WxU5p/trGpCg07wdtu80Xjhv2bAZjbhp18bi1vOH4lBQltoOudDnWdIkQt/vX4mzhpV3N2nQkRERN2EFWtERD2A3P6l9yHZI4Rg8tdisCZXudQ2+7BsezUumdRfd1i6qNHT/lbS7iRJErYdb8TwEpdqMUFcK6glcSuoHvm4XLsFF0/sh0AwjLLcSCDy2g9m49gpD8b1z037PPWWF8SCtVjQ4rAY8fS103D9C+tw7yWnoaI8Fw6LCZ5ACA2eQMqFCR2ZrTy+Yh+e/fwA/MEwrpk1VLn8/a1VuOlv69E/z44v7pqrus0H26rw8Ae7AABFrvRn0KXDZTPjzBHdG1qIFWvpVIhNGVyAOy6swLDiWABWlBP/vAwqcGBggQOlbhtONPmw/nBd3DE3zhmuVFJ+7+xIuHjTX9fHzqcXvn+JiIiIsgmDNaI+rNUfhC8QRkEHfxCm5I7Xe1DZ4FENdJcr1vSCtRZf8lZQedj5f73wJTYdqceGw6fw8DcnJT2HeuH2gWBmraTd6f2tVbj5xQ04fVA+3lg8W7k80fKCtgZrAPDYt6eorstzWFSbPNORq1ex5tJpBbWYMHtkMbbetwDm6Gsgz2FRgjU9QWH2Xke2gsotnbWaGXR/WXUQAHA8uh1V9H0h6CnMwr9P/EJ1Y/8026ZvPlfdGqgXrOU6LDAYDJg0KB/Ltlfjw23VccfoBWc5wuuKFWtERERE3Yv/GiPqw6b98t+Y/ItlaPTqf3CnjvfX1Ydw5kMf4YonVmHrsQbl8rBOxZo8p8njF1tBI1/rtYJuOlIPAHhnc2XK8xBv7w+1vZW0u/1zw1EAwFfRxyxr9mlmrEW3Iaa7rU+sfusIejPW5PZQMWiRK+vMwu8/1xE5LtH7s1WoZOzIYE1elhEIqVtltwiv12SyMVgTN7aO7efO6D70WmQHFUQq2uaMjrQXv7e1Ku4Yk858vRyhItVq7v75ekRERER9GYM1oj5Mrn7afryxm88kex2pa8XJptgMs6dW7lO+PlzXqnwtKRVrsQ/JzdEP8y0plhf4gupgzJRGyCLOctKbNdbTJQrK4pYXKBVrqQMzgyEWxHUUvRlrcngqVqzpVR3J1XGJKtbE2XuZLrDQI1dniVVaobCUcNabX/P6ycZgTTZlcH7G8+yKNRVrQ4qc+J8LxwAAvj1jMP5z+qC078tli73+WbFGRERE1L34rzGiPkr8IN6RH8opptEbwNkPr8D0B/6tBGfi0oBTrbFWO/lXILeEAkCTLxKotPqTz1jTLi9ItUFSe/tAqPf9/h0JgrXGdsxYc1hMHboEAIhtGRXZohtAxQBKnKMnk4O1I3WtceEpoG4R9urcPlP+6OtBDMx2VTUpX7usJnx5sA77TzYDiH/96YWJvd3g6LKAxeeNzPg+ioSKtWvOGIKVPz4PA6MVayajAVfPGJz2fYkzFBmsEREREXWv7PvXLxGlRWzz0rZ8Ucc4LmxPbPGHkGMzwxeIPdf1QtWYHLyJ1WNyhZC4nfG9LVUocFpVywe8AfXvT691TEvVCtoLK9bEwCoclpQwMX4raPqtoB3ZTikrybHh3DElMAAocFmx7Vgjzo5uDxR/Ty2++GowuY30wfd24qOdJ/DK92eprhdfF94O/B36oyGeX/h7YXtlrKq1xR/CN59cBQA4+NDFaNZsD813ZF/F2ovfm4lDta3t2vwotv7qzeobU55+i6mbwRoRERFRj8FgjaiPEj80B3thxVJvIG4SPNnkiwRrQgByShgOLxeqiZVH8rwwsTLps701+GxvDWYMiy0+0FY7pRMQ9fZgTQzKmv1BZftm/Iy19CvWtLftCAaDAc8vmqF8L0mSblWcbrAmbBRdcyB+W6T4utBWjbWHXMEovlbFtmWtVuHcl84bhTNHFHXYufQUgwqdGFToTH1gEmIraL7Oltd05wAC6oo1bgUlIiIi6l781xhRHxUQPjQHw70vWOkNxGDiZJMPkiSpAk1xM2dYaRUVK9Yi13sC8aHLwZoW5WttG6C5rRVrvXB5gUicF6edAybPm0sUrBkNXTsTLFGraZNOsKatapIkdQAuVqzptYpmSm/G2tEEwZovGFJmAA7Id2DpvNFptSL3RWIraKKZf+m8dwFNsGbi801ERETUnRisEfVR4lwtvflOlLlgKIy739iK/7f+qHLZiSZv3JKAemHGmvzb0GsFFSuTYvcXW4jgDYZVoUtbW0EDwd5XsegTXrP1qmBN3Qoqh8aJZrLZzCacX1HaCWeYnoJo5dJMoQJRpg3W/JqWbXGpRUdWrMmBmtginqhircUXUirWxIH6FE/8fYYS/M+Mm88dkdZ95bAVlIiIiKjHYCsoUR8lfmhu7cAP5QSs3l+Hv64+pLrsZJMvLlg71RpfsSYGRnKwlio08fpDqvs2pvicfbi2FTuFYfS+XjhjT3y89Z5YQCk/Z2PK3HDaTJg0MB+AeiZbrt2sLDmwmo24+z/GodETwKWnD+iCM1d7a8lZeGvTcVwza0jcddpgrdUXwr82V6LZF8R3Zw1VtWB6O7BiTf67wZ9GK2iLL6gEfOlsXu3LxEo+eYGF1q1zR2FUmRv1rX7c8+a2hPfF5QVEREREPQf/FUzUR4nBREdWuxCwu7op7rKTTb64WWZ6W0HF34syY82ffPaXNxhSzQczIHHFmjcQwrn/uwLiItjeOGNNFawJAWVzNDB78pqpGFbsUi4XK9bcdotqe2iew4I/fXdaZ55uQoMKnQk3TYY1rZ9VjV7c/o9NAICK8lws216tXNeR72H5uZUr5LyBkKpCUtTiDyotqaxYS+22eaPx2d6TuGRSf93rzSYjvj6pPyRJQkmODeMH5Okep6pY44w1IiIiom7FYI2ojxIr1hisdaw9J5rjLotUrKmfZ3E2GHSWFzz03k6M75+X8vfj8YeUQEl7H1oNnoAqVAMiwdr7Wyvx/tYqPHj5RFV1V08lPpfyrLpwWFI2VLrt6v+8mYWqHjEA6smh4sAC9bD81ftrla+vfGqV6jrtZtj20FasHT0VqVZz28wwGKAKJVt8QaVVmRVrqd06bxRunTcq5XEGgwEXTeiX8PoczlgjIiIi6jH4vzmJ+ihVKyhnrHWovSfiK9ZO6LSC6i0v0B7znWfWpK5YC6gr1pLNzNMLkvzBMJ5YuR9vfHVcFd70ZD4hSGqIVv41+4PKdlUxeEhGO7esJzljeCF+882Jyvdf7Ev8u0kWpraVXxOsyW2gAwudcZsrm32hWMVaLwhks4UYDmuDciIiIiLqWgzWiPooVqx1nGAojE/3nESLLwhJkrC7OkHFWjQMkmdnhYRPxLGtoPG/i0TVSLZoC5g3EFZtw0z2+wzoBEmBUBgef3ptpz2FXiuo/LjNRkNcAJRIqAenEgaDAd+aNgijy3IAAKuSBGtimLpmfy1Ou+d9vLHxWEY/Vw7UfNGlGC98EZkXOKYsBzaL+p8Nqoq1NMNMaj+XUB3o4/8YISIiIupWDNaI+ii/sAmytZeEKT3VU5/sxzXPrMVPX9+Cmma/auOm7GSzT6kEctvNcGqqeyREArpgG4KeIUWRVkF/KKz6mcGwpBugAeptsDJ/KKwEVR3ZUtiZ9FpB5WAt0QbQ3kpusRSrErW8gZCyGfaOf25Giz+Epa98ldHPk187gVAYH+08gZW7T8JqNuKWuaPihu63+IKsWOsG4iKEnlx1SURERNQXMFgj6qPED2OeXhKm9FT/++EuAMAbXx3HHp3FBQBQ2+xTAgib2Yh8zcZHSYpvA01lcGFsOH9ti3q4vLzpVdIMwBcDtwtPKwcQqVCSq+k6sqWwM+lWrEXP3Z4i4JF6bpGarnSWAoQl4Ksj9fjBi+txvN6rXJ4oYE14P2FJCV/9obDSfnrltIEYXpKjVEnKIltBI69rzljrHj15TiARERFRX8BgjaiPCqi2grJirT3EoEZvcQEQCT7qWiKzwKxmE/Kd1rhjfvj3jcrX/7z5zJQ/d3BhbLh9TZNfdZ03EMKe6iZM+cUy/OmTfcrlcqA6uNCJsf1yAURCKrkCrNcEa0IYfLC2BeGwpARr2VqxlsqD7+7Eu1uqVKH59uONbfpZ4m39wTB2VkVuP3FAPgDEBWvNQisot4J2j3TbnomIiIioczBYI+qjVMsLOGMtY9qKoN0JKtYA4FQ0WLOZjch1xIclH+08AQCwmoyYOqRAFZzpKc+zwRoNOmqa4yvWfv7OdpxqDeBX7+5ULperWywmg3LbQCismqvVG4itoHtPNOPFtYfhTbMVtJcVrKXdYrn+8Km4y748FLksqFO5tmpfLf61uVJ1WUATrO2ojLyeK/q5ASCuFfR/P9yN16Oz3Fix1rV+fulpOHtUMb49c3B3nwoRERFRn8ZgjaiP8jNY6xD7Tqor1NYfig83ZHUtkZZFm9mYNISQq4KKcmJVbQPyHXHHlbrtsEePPdmkDdaCutVncnBiMRmVYM0fFGes9Y7Xgny+l08ZAAD426pDabeC6j2XPZm4FGBEiSvhcXqLGDYcPoWTTT5Mf+DfuOu1Larrrv7zaix+aQMO1bYol4lthadaA6hr8cNoAEaVRoM1S+J/NrBirWt9d9ZQ/PX6mQw0iYiIiLoZgzWiPkocYs+toJnbUalutdtZlaRirVVuBTUmraqyRa8rcsWCtWlDC+KOc1pNcERDJG3FmjcQ0g1a5GDNao4Fa55ASFma0Hsq1iLneX5FKYDIcohYK6j+f9pe/N5MzK0oxYOXT0ChK74Vt6dyCq+VoUWxYO2KKQMxtl+u8nvUs+N4I15ZdxinWgP4+9rDyuVh4bVxQghl9ZZbDC1yKa8zbSuoyMWAh4iIiIj6IP4rmKgPCITCsJjUH4jFyhRPL6lS6ok+3nVS9/ISty2uiqxOaQWNBWJ67NFgqMhlUy4bXebGpnvmw2gEJtz3IQCgPM+uzFc6qdMKqpORKNtgrSYjrKbIZsFmb2zGXq+pWIueZ788OwCgvtWPlujWzESh5eyRxZg9shgA8Nx103HHPzfjrq+N7YKzbR+xYq3AZcX7S89GXbMfZ0Yfy1m//ghHT3l0b3ugtkW16VWSJBgMBtV73iAcrzcIf0y5W/la2woqctn4TwoiIiIi6nv4r2CiXubvaw+j0RPAjXOGw2AwpDz+YE0LLn70Uyw8Ywh+IoQInLHWfpUNHmVG1YB8B47VR8KNgQUOOK2muGBNrlizWVJUrEWrggqFVtChRS7kOSObRB+5ahKOnfJg4sB85X4O1baq7sPjD8VtBAX0W0GbfAHl+l4TrEUDoFJ3JFgLS0B1Y+T5ThZayiYNysf7S+d03gl2IHHGWqHLioryXNX1eq+l4pxIKFvT7MOBmlirpycQgtNqThim+0Pxl4uts8kq1pxpzoIjIiIiIsombAUl6kUCoTDuem0LHnxvJ97RDB1PZO2BOrT4Q/hkt7qySgzWuBU0M//vy6MIhiXMHFaIeWNLlcsnDy7Q3dRXJywvSBZCyO14YivokKLYIoNvTB6IJeePAhC/EVC+jUfTCiqHbEqwZjbCaorcVl2x1vNbQYOhsNK66rab4Y5WSlU2RILNbNuSKFas6bWwyo/XbTfjlRvPwMSBeXjiO1MwNrpwYOvxBuXY2ubIa1Bs//YFw/h0z0nc99Y2NHnj/y7Iscd+fvIZa/x/dURERETU9zBYI+pFWn2xD8P/++Eu1ZykRCobvACA2mioI1MtLwhEqpskSUrrPimisjHy3J45olgVKtw6d6RuZY8YrCULfxq9kQoycXbW0GL9ofVyeAJENn2OH5AHIFKFKP4q5QovudXPKmwFbdK0gh491YrHVuxFQ2uskq2rfLG3Bv9cfzTpMeJr12Y2Id8VqeQ7Vh/5fWRb5ZSqYs0ZH6zJFWszhxVi5vAivLXkLEwfWohx/SKVbWI1o/waFCvWPP4QrnlmLZ7/4iD+uvpQ3P3nCK/tZK2g2nZzIiIiIqK+gP8KJupFWoTKskO1rQnnKomqGiPH1LX4VaGZOEtJkiLBy28+2IWKe97H7urEA/gpRm6btFuMuPT0Ach3WvDjBWMwstStG0CIM9aShT8NnkigJQYVOQmqgWaNKFa+HlXqRq4jEjJ5/CGEwvHtvrqtoGKwFgzjm0+swm8+2IW7Xt+c8Bw7y7efXoP/fnUTdiVZAuETquqsZiMKomFTZbQVN1mbbW8kbn3Uq1iTK8rOGF6kunx0mTvuWPk1KLZ/e4Oxr4/Xx/+d4hYq1vQWJYwuy8HFE/thqFBVSURERETUV7Bvg6gX0c5C05uHpCVXrIXCEhq9AeRHQwixFRSIBDGPf7wPAPDIst144jtTO+KUs5pcBWYzGzGm3I2Nd1+gzL2zCy1zVpMR/lBYdXyyOWDyaLRLT++Pt746jnPHlCQ8dpYQppTn2ZUNkp5ACC1ChWOLL4hClxX+aJupxWSEJbq8QKwA8wZCqIpW4n2yuybFM9B5jjd4VEPzRfLzaDEZYDIalGDteJYGay5b7PEU6ARrN587AmW5dlw5fZDq8tJcW9yxcuWq2ApaI8wCzLFZ4m4jXqZXifn4wikYWar/uyIiIiIiynYM1oh6kVbNLLSA3tpHjaposAYANc1+IVhT3/aYUKny3tYqnPGr5XjqmqmYNCi/HWec3XxKxVok+BCXSYgVa/lOC04I4YXVnHx5gcxpNePvN56R9JgSdyw8GVjggDF6Dh5/CE3eWCunXO0oB6pWs1E3JPEJLYJhneUHnUlctpBsLYcvWmElP8cF0aUOLdGwyJ5lraBOzfICrelDCzF9aGHc5XrHnooGa+KSiv3CcoNgOH7GnmrGmk4lZn9huQERERERUV/DVlCiXkSsQALiq870VArBWm1zLNwRW0EBYM2BOtX3VY1e/OT1LZmcZp8hD/rXm5cmhlYFmrlYqSrW2uqZa6fhgnFluG3eaCWEafYF0eSLBbEt0a8DQaEV1BR/DuLygkTBWiAU1t042l5i5Vyyjbdi5R8QX8WVbRVrZmPstaQXliUibwYV1eq0gu4/GQvW5DZkkXrGWuxcxg/IxXdnDVG1qhIRERER9TX81zBRL9LWirVWf1D1QVlcYKAN5Vbti2/709sQSDGxyqn4/0dhs4jte+r2ulQz1r41dWCbzmPu2DLMHVsGILaZ8WSTD2L2JYeycnglLi8QifO29PZY1Lf6Mff/VmL2yGI8evXkNp1nKmLYqxerHav34PnPD2DK4AIAQrDmzO5gTfw95drT/8+29nkBgLqWSLguLi/Yd7JZ+VovWEs0Y+2Za6ejLNee9vkQEREREWUjBmtEvUiLv20Va2IbKKAO1rQVa5/tjQ/WGjwBSJKUtHqoL0u3Yk1bZWRNshX0jcWzlW2OmZCriyob1EPo5VDWLywvkGesicTFAHpVae9vrUJtix9vbTreqcGaXmT8/b9+ia3HGgEcABALL+VWUFlHVgP2BBXlbiyaPRT98xxtei9azUbk2s1oFALyf3x5FHkOCwYXxhYNiFWteptgxYo1kzH285NttiUiIiIi6ivYCkrUi7T6tBVrbQvWjtd7lLBEe1uxBVDW4AmgqtGrW8WSjcJhCQ+9txPvbalM63h5TpXNolexFrssX6cVNFH73OmD8nUrydIlV6xpf/fN0Yq1QDC6vMBszKhizSWELL5g6uUZbeETgrWgzms7EqrFJGoFzbbAx2Aw4N5LTsMNc4a3+bZFOu2gf/70QML3tG4rqFCxJrYH23Ve90REREREfQ3/VUzUi2i3ggZTtIJWasKVJz7ehxv+sh5A4jbSSyb1h9UU+6vhyY/3YdL9H+KxFXszOeVe5d87qvHkyn24+cUNqstfWXcY97+9DZIk4elP92P2Qx/hSF2rMOsrPsixC5cVaoM1S3rLCzKRE90gWdmo/t23apcXmBIEaymWF4iVeLXN/rjr20OsWEtnfmBfaQVtD201n2xXdbPu5UGdNNUlhMDiS0L8e4KIiIiIqK/iv4qJehHtjDW/TviwZn8tLvzdJ1h7oA5VmnAFiIRHwVBY97YAcOdFFdj5iwvx9Un9AQAvrDoEAPjNB7vae/o9XrWwuVMWDku4459b8NznB7HpaAN++a8dOFbvwYPv7VBCKL3KHbFiTVtRlWjGWkcEQnJFmTYTa/bpbAVNsbxAbz+BWFV2Uuf5ag/xNekLphOsRc6/KEcTrGVZK2h7iM/F6LIc5evtxxvSvg+x/VN8SbBFnIiIiIiIwRpRr6KdsaZXsXbNM2uxs6oJVz61Spmz1S9PPWD86CmPUh2knf9V4LTAaDRgWLEr6bnsqW7CCZ3grjcT8gOlZfbIqVbd65u8QSFY05uxJiwv0FQNWU3qGWtluTYMLXLib9+b0a7zB9StmqJWzfICi8kAi7ntwYgYeJ3o6GBNVbEm4YmP92H2Qx/heL1H93g5vBTnhQGsWBOJVWUfLJ2jBOb7a1oS3SSpztgGS0RERETUmzFYI+pF0pmxJlb9yHO2zhldojpmf02zctsiIVizmmMtiv3z47f9ybc5Xu/BBY98ghm/Wp7Jw+ixjEIFjtx2u1tomRO75HzBsNAKGv9XqT1ZxZrFqKpYmzOqBB//+DxMHVLYvgcA9aB5kVyxJodXFpMxo1Y+sVW0oyvWxJlt/mAYv35/J47Ve/Do8j26x8vPu9NqVoXHrFiLsQi/Y4PBgKHRwDzTfKycW0CJiIiIiFQYrBH1ItqKNTFE21PdhN98sFN1vTxjbf5pZXj6u9MwfkBk2+T+ky1KSFYsDDcvcFqU9q5+eY64n3/sVKRyaNvx2BD5bKpgEWeKtUSDqN3VTcplQU2rohyspapYi5uxZjapbhPS2xKQoYQVa5oZaxaTEWaTUVWFl47ODdb0Z6wlalu2Cc+hWGHJirUYiyb0HZ6iEjWVC8aVYfF5I/Cna6a2636IiIiIiLKF/icwIuqRtDPWxFbQCx75JO54uWKtPNeBcf1z8dWRemw91oj9NS3wR28rzqcSh8DrVawdqmvF0GIXzKZYGuMJhBJuuOxtPEJw2ewLohTArqpYsCYufGjyxrYn6gVrFuE50g7Xt5qNqrlVgQ4M1nI0vwuHxQRPIKSEsvJjkKvVrGaj7kbYRFQz1po7thVYbAX1pzFjrUQIhQcXOvHFvloADNZEJZqtoKlavFMxGAz48YKKdt0HEREREVE2YcUaUS+i3QqaanNibUtka6PcJje8JPKhev/JZgSCehVrsQBIr2LtcG38XKZGTyTs21PdhC/21qR8DD1ZqyZYAzQVa+HY8y0/bkC/FVQc7J7vUs9Y0x4fCqcfbKXisqlDJXn+WIvO8gKg7ZsdO7NiTRWsCa9tAwyqakFZaW7stSu+XtkKGnPL3FGYMjgfv/rGBABQWkGJiIiIiKhjMFgj6kXkAfRyKCKHJOEkFU82sxH50eH5w0siWwH3nmhRgosStxCsCQGQXkvhodrIIH+xsqvRG4AkSbjgkU/w7afX4Ehda9zteosWoSJQDtbEIe9ikNkYrVgzGQ2qOVYyscPSYTEpQ+OB+IURAZ0lFJkym4yq4G5QNFhr9sbPWAP0q+2S6cytoL4EFWsGQ+z3ISpzx6oq+wkVlnpBZ19V6LLitR/MxrdnDgYA5DksKNZsUSUiIiIioszx0wdRLyIHP3mOSAAmBzLVTYlb8vrl2ZXqqTFlbljNRtQ0+3A4GpKJH7Lznck/cO87GRnkL1Z2NXoCqoClozdFdiVVK6g3iGAorAp4xJZJf5LFBUAkDJKZjQb8/j9Pxys3noHnF01HmWYAfEfOWAPUCwwmDswDAByMVhuKW0GB+I2xqYgVa4frWlEXrYrsCOJz3SIEaQZEtrBqiRVr8uME1NWCFG9oUXzVWlmuTedIIiIiIiJKhcEaUS8iB1r5SrAWCSIO1iSuEivXbEucMTSyedKvbAWNfaCW7zeRLw+eQjAUhkeo7GrwBLBTmEMG9N5lBi2+WGjU4g/Cq5nzJc5VkyWq+DIINWsGgwEGgwEzhxfh3DGlccemaultK7HacOawQhgMQE2zHyebfLHlBdFAcEBBfMtvMmKwVtPsx3eeXtNhCyzE9s9TrernulHnuRcDyoryXDx69WS89L2ZHXIu2Uxvzpp226dc5frjBWO65JyIiIiIiHqr7Jg4TtRHyFU8csWaPHfqoM7sM9mAfKfq+zmji/GZMAutWGgF1WtplOU5LGjwBLD5WIOqYu21DcdwsjlWpeZrwyD8nqZV1QoaUoVIgH7VlD1BxVpb2hE7umLNKcwY65/vwJBCJw7WtmJXVRMCQfXyggH5bQ3WIr/fy6cMwBsbj2F7ZSNONvlQmpu88q2+1Z+yIlKsWKtvjVXCeYNhVNbHV2WWutVVVmK7LSU2rESvYs0OoEH5/o4LK3DWyGIMbGPwSkRERETU17BijagXkVsV5WBN3uyZLFgb28+t+v6c0eqKKbFtUNxUCQD3XTIOQGQA+pkjigAAX+ytUTZMAsC/tlRi7YE65XtvUB1G9SatmlZQbbCmN+fLlqBi7ZwxJZg1vAg3zhme8udaO3EmWFGOFWPKI6+BnVWNccsLUgVr2tBPfk6mDC5QFiPsOdGc9D7e/OoYTv/5Mjz/+YGkx/mE184pIVh7e9NxfO8vX8Ydn5eiwpL0DderWNO0BA8scGBQoZNttUREREREKTBYo4QkScKxek+HtXmRvmZfEJ/uOam79VAkSVJsxppTXbF2KEkr6Gn981Tfjy7LUbV9iZVV2mDt2jOHYsWPzsVSIVhbe/CUqhVUy5ukYk2sQuqJxIq1Fl98sKZXsZaoMs1iMuLvN56Bn3xtbMKf9+srJmB4sQv3XnJahmesT2ypdFrNGFOeCwDYVdUkzFiTW0Gd8Xcg3pemHVZuj7VbTBhZGgns9qYI1n7y2hYAwH1vb0/7Z9W3xrd+ajH0yYzeZtA8h0W1IVYOTYmIiIiIKDkGa5TQ7/69B7Mf+gjPfJa8yiRTR+paVRUqfdWNf/kS1zyzFk+u3Jf0OF8wDLl4KE8zYy3ZAPlx/XJV3xsMBpw9qlj53mIyoii6pfI8zfwvg8GAYcUuGI0GlOdFKpuavAFVZVf8eepf987m4zj958vwyLLdymWSJOH3/96D97ZUJry/riTOWGv2BeNCwma9VtA2btUUXTV9MD760bm6M6/aQ9uOO1apWGuKzViLLi8QK9aMOjmVNljzRcNGu8WIkaXylll1sLarqgl/+mSf8rOKctIbjK8K1jypgzXKzNAiF+wW9X/+7RaTKpDt38YWYSIiIiKivorBGiX0++V7AAC//NeODr/vDYdP4eyHV+Abj33R4ffd23yxrxYA8OKaw0mPk1vjzEZDXCuoXouiTK5uE80ZXaJ8bTEZsPy/z8H7S8/GuP65ccfK5NZBXyCs2p6plahiTa5akl9XALBqfy0e+fdu3PzihoT315l8wRDe2XxcCSY9AW2wpqlY88WHPW2ZpdZVfJowbPyASNXizqpGJTxUZqwJM7TMOjP2tEGpUrFmNmFUNFjbc6JJdczFj36KX727E09/GgnlxQrJE0k22PpUywt6dnVjb2a3mPDsddPx7HXTlMusmt99snmLREREREQU06P/5fzggw9i+vTpcLvdKC0txWWXXYZdu3apjvF6vVi8eDGKioqQk5ODK664AtXV1d10xpSu1zYcBQBsr2zs5jPpOVINsD9e7wEQmYUkh1xyK2hLktZMPWeNLIbZaIDZaIDTaka+04qK8sShGhALkHzBUNzPu+7Mobh4Yj8AiAujZHqPrroxccjSFX7/7z1Y8tJGfPvPqwHElkPIX3vSWV7Qjoq1zqINwwYWOFDksiIQkpQQVg5OxDll2uq0yH0lqlgzCRVr6hl/wehr+aOd1XHns+144ve8WGmXrAPdajLiwcsnJD6AUjpzRDHOryhTvrcJFWxyNSMREREREaXWo4O1lStXYvHixVi9ejWWLVuGQCCA+fPno6Ul9iHutttuw9tvv41XX30VK1euxPHjx3H55Zd341n3bV8dqcc/1h1JeRzHtsVLFawdi25F7J/vUKpL5FY7vRbFinI3nrtuuu59Fbis+PN3p+GxhVPgsKYXDMnBmj8UjmsF7Z9vF4K3BDPWdB5euJsXiL6zOdKCurMqUnGlWl6g1wqqUxmobanrCbRD/Q0GAyYPzlddZhEq7eR24Xlj1a3AQPzvUw5ObdFWUIMBqGn2oaohPiSVKwFPCfPSth1riDtO5k8xZxAA/nP6IGy9fwGunjE45bGUPrFirTjN1l0iIiIiIgLMqQ/pPu+//77q++effx6lpaVYv3495syZg4aGBjzzzDN46aWXcP755wMAnnvuOYwdOxarV6/GGWec0R2nnXX05i4lctljnwOIhD9nCXO8tJirxQtp0sZQWMJLaw5h+rBCVJTnKhVrA/IdSrVRIKzfClqcY8P7S+ck/XnnVcSHKMkkawUdkO/EwdrIAoW2VKyJl4XCUtzyhM4m/jxJklTLC3RbQXWXF/S8irXHF07Bj1/djDsvqlAuO31QPv6944TyvRikvLF4Nho8ATz96X7VMYBOK2gg1grqspkxcWA+Nh2pxyd7TuLKaYNUSzhiwVqsrVMOMQEgHJZw95tbcarVjyunDdKtmBP98PyRWDR7WKduUe2r5HZhgMEaEREREVFb9KpPJw0NkUqHwsJCAMD69esRCAQwb9485ZiKigoMHjwYq1atSng/Pp8PjY2Nqj+UmNnY9pfJjhQtnqxYi6etWHv1yyO4+81tuPB3nwKItYL2z7fDHG3VCgTDCITCiavEOpAcIPmC+hVr9uj1iWas6W2XDQuXpQpVOoOY44nLIYBIFWB8sBY/Y60nVqxNHJiPD26bowpPJw8uUB0jBmtWsxElbhtsOm2tu6qacNVTq/DJ7pMAAG8wtrwAAOZEA/RPdp9ETbMPn+6pUW5b7wnA4w+pAsnDdbENttsrG/HimsN4d0sVFr+4Ia71VmQxGfDf88egMLpogzrGB0vn4LnrpmP8gDxlZt5V0wd181kREREREfUePbpiTRQOh7F06VLMnj0b48ePBwBUVVXBarUiPz9fdWxZWRmqqqoS3teDDz6I+++/vzNPN6ukm6sFhEqVYIq2RtasAe9uqcTrG48p32uDta+O1Ku+jwVrQsVaKKyaCybTC7HaS2kFDYZVlV1AZAC+PKPJFwxBkiQYDOrqM92KNeE8vYFQ2m2pHUWsWNOGhS06FWtyy63dYoxVbvXAGWt6Jg7Mg8EQC7Ut5vjqQL2Q8PZ/bAIArDmwFgcfuliZgyY/7jmjS/CHj/bis701uOC3K1Vtn5IEbDxySnV/h2pjwdrJZp/ydYs/hIM16lltop5YGZgNxpS7MSa6NfbvN56BjYfrMbeN1axERERERH1Zzyu1SGDx4sXYunUrXn755Xbf11133YWGhgblz5EjqWeC9WUmQ3rtefXCB+pU1UesWAN+8OIGLNseW7ShDSPDmidJb8ZaMBwbRi+2x3XG0ysuL9CGUMUum1Kx9tznBzH9geU4oVlMoPc7l7eaRu63OyrWYq9tbUBZ7wnEVVC1RB/36YPyde+jJ3PbLRhZkqN8r7f10Z4ivJIkSalYk4PUyYPyYTIaUN8aUIVqsrUH6gDEKuQaPAHc+c/NWH+oDjVNPtWxu6ub4m4vY/tn5yvOseGCcWUwdnFLNhERERFRb9YrPqksWbIE77zzDlasWIGBAwcql5eXl8Pv96O+vl51fHV1NcrLyxPen81mQ25uruoPJZbu3KsGT2yOkjhTSQ+DtXhhTbCmfY7EGWtyK6g/GEaLLxJ05NhiBajaUK4jyBVDYSk2a+zhKybiX7ecBaPRoKp2qmn24ZnPD6hur3dOXiGg087y6gpiKCaHhbl2M6wmI1r9Ieypbta93bDiWEB19JSnc0+yA4kLDMw67+tU1Xf+UFh5XcrHmk1GJXTV88XeWgCRduESd2R218vrjuCKJ1apqtcAIBBK/Lq16gSBRERERERE3a1Hf1KRJAlLlizB66+/jo8++gjDhg1TXT916lRYLBYsX75cuWzXrl04fPgwZs2a1dWnm7XSDdbEijWxxUuPxFbQONrlBeJ3zb4gGjyR57dfnl2pNhIr1lTBWspW3LazCcGZXMl1zpgSnNY/MvRcG8rEBYU69ylWhHVHxZq6FTTyPOY6LBhdHgnO1h8+pXs7p9CyevRUq+4xPdHpgyJz1qxmY1yrLoCkARmgnp8nHpusmmztwUjFWr7TiiGFTtV1f1yxN/VJp/EziIiIiIiIukuP/qSyePFi/O1vf8NLL70Et9uNqqoqVFVVweOJVIjk5eXh+uuvx+23344VK1Zg/fr1WLRoEWbNmsWNoO0kVg+Z0hyyJgZrtamCNeZqcbTPifi93DLntJrgtltgkZcXhMJKsOYSgrXOeHr1KobEmWjaUCYuuNE5KVWwlmDpQWcSW97k4DLHZsZp/SJh4f6T+jO/7BYjbpk7CgDwPxeO6eSz7DhTh0SCNTGEFaWqWPNFf18Gg2b5gc5rY5LQLgsABU4LyvLsuvc7rNiV9OcCDNaIiIiIiKhn6tHLC5544gkAwLnnnqu6/LnnnsN1110HAHjkkUdgNBpxxRVXwOfzYcGCBXj88ce7+EyzT6tPDNbSu43Y/lnTnKIVVPxaZ9B9X/XVkXosfXkj7vraWNVgf21VmlyxFmkFla+LhSKdEVwajQZYTAZVu55TCGJShTJilWIoLMFkNMDTza2gYjvk3hORts9hxS6cNiAX+DJ2XI7NrPwOgMgssiXnj8T1s4chz2npsvNtrzHlbtx3yTiU5eoHXMk2nLqsptjCBrNJ9Z7VC71mjyjC1mMNylKOAqcVjd74RRsAMG1IAQ4kWVwA6M+EIyIiIiIi6m49+pOKJEm6f+RQDQDsdjsee+wx1NXVoaWlBa+99lrS+WqUnhZh62MwydwjkVzxA7StYi3ZXKW+5rrn1uJgbSu+/9f1qvCxJUGwlrAVtJNKAsXNjFazEWYh7NALZd7YeAyzH/oIm4/Wq37n8rZNb3e3ggrh0M6qyOD8MeVunNZfPXdRW+HlsEaCpd4Uqsmumz0MF03op3tdsnDUYTUp4af2d60XrDmtJtWyhHynFbfMHQmz0YAfLxijaqedPrQw5XmnalMlIiIiIiLqDj26Yo26j7j10RtIXkn09Kf7YTAYVK2gp1oDCITCCatMxOqlYDgMa8/OeLuM+ByK4ZgcWrqUYE1oBfXqtIJ2UlZpNRuBaGYqBiOAOnQDImHg0le+AgA88K8dqsfjCYTgsplVraCpXmedQSyU3Ha8EQBQUZ6LUWVu1XE5djPQGPvelqI6r7dKVrHW4gvhmc8ORI9TP369VlCDwYDpwwqwK7rpc+LAPEwcmI/dv7wIBgPw7pZK5Tkf1z8X+U6L6vWvddbI4jY/HiIiIiIios7GYI10tQhtb8kqiRo8AfzyXzsAAJee3l913akWP0oTtJyJAkEJsGZ4ollMDMfkNls5zFIq1kKSqpptTJkbu6qbsOC0sk45J7FqyKkJV2yaUKZeqGD0BsMI61SsqVtBu3d5wY5KOVhzw20zw2ExKcFfXMValgZr2nBU5AmE8PK6IwCAygav5nbq3/3oshx8e8ZgGI0GnDG8CKNK3RhTHgkr5bl2Q4tcSrBW4rZhwoA8fLqnJu7nfrB0Dj7dcxLfOWNI5g+MiIiIiIiokzBYI11ixVowLCEYCqva/mQ+ocqosl79Yftksy9hsKZqBQ3HApXHVuxFkzeIOy+qyPTUewUpjZIyscKrriU2WB8AzNGFEv5QGM3+WLD21+tn4N0tlbh86sCOPmUA6gDFpQmbtFVMXx2uV742aUboKa2gQpjWHTPWtBtvHRYTBhc6YTAYUJZrw8HayMZPt137WLOzwjLVnDzZGE1Fn9gKajQAH952jvL9f0xUB+6yQpdV9fWkgflxwZrbbsaY8lgoR0RERERE1NMwWCNdYsUaEKkm0gvWvMImx+MNHtV1dS2JFxj4Q7HbBaJfB0Nh/OaDXQCAhTMHY1Chs+0n3ksEw8mDNZPRoDwvAFDXEum/lMMsqzkSCAVDseUFLpsZpbl2XDd7WGeccvTnxl4DOdqwSVPtdKw+9no4XNequk5+3Xj93bMVdO2BOtz31jbsO9msunxUWY5SUVXqticO1pJUdvVmYmBYnmuHBAnVjfHzEu/6mjr4Flu+093emeuIPacWkxETB+bFHVPgZCkrERERERH1bNlZdkHtJi4vABLPvxJnZB2vVwdrYpufll+oVJKXI7QIm0jFUCkb+VO0PYbCkur5kCvWXDZ1K2ggJCkz1rTtip1BbBXU/jxtK6hIuyVWaQXtpuUFVz61CtsrG+N+ZpFQRVWaa1O+1ltekI3EirWpQwrw2R3n460lsyEW9l07awjOHVOqup0YpunNW9MjV7INLYoE6JMG5ccdU+BisEZERERERD0bgzXSJYY6APCnT/ejSjNXCVAHI3IRljwHzJNkGL0YnMnVa2KY11lbLXuKVMEaAFQ1xp5vecaayxptBTUJraDR35W2gqwzqFpBrclbQZPx6AZrXd8KquW2x7Z8lrrtupcDfSNYM5sM0UqyfFXbb64jfhOqVVWxlt5zM7ZfLj5YOgdvLJ4NACjLtePyyQNw7pgS5ZiCXrh1lYiIiIiI+hYGa6SrVVOx9tTK/bjmmTVxx+lVpZVH56ola+3Tr1iL/UxvF7YFdpVj9R7c99Y23PXalrSqs8QgU26r1W4F1baCdrbkraDp/3Ui/3493dQKmojYnliWpGJNuxE1W4jBqdjeKT7eXLtOsCbcTrvIIJkx5W7kC+2ev73qdDy/aIbyfSFbQYmIiIiIqIfjjDXSpa1YA4A9J5rjLtNrES3NtWF/TUvaFWsBpWItdnyi1tPeSpIkfPOJL5RtivPHpd7a2SwEjUrFWrQVVK4QCkux67RzwDqDGJrEt4Lqh002szEuSJRfG+Lv2ZtmxZovGMInu2twxvDCuEqy9hLvryxXrFjTBGuW7Pyr02IywmQ0IBSWlPAWUG9B1XudWVWBnCHu+kzlM1gjIiIiIqIejhVrpEtbsZaIXnhWEm2hSxas+UOxVk8lWBOCpK6ct9UVAiFJCdUA4ERTfFutrEJnA6K2Yk1cJHGyKTJcviuqe5LNWEtUsXZa/9y4y3RnrKVZsfbql0dxw1++xGMr9qV1fFvkqlpBYxVr2jApW1tBgdjvUaxYc1iTt4KKgWu6ywvSUehiKygREREREfVsDNYIK3adwAtfHFRd1pJk8YBI2wpqNRuRF22nS1Z1JraCBnRbQbOnYu3Nr47hw+1VqsvkMExmMRkwtMiJv10/E/3y7NBq0iwoEKuCaqOhW2EXDHoXQxNt66ne1liX1YR+eY64y32BECRJUv2e0w1T5W2jxzTLMjqCGKCJywu0lXHZ2goKxOasmY1taAXNYCtoOmYOL+qw+yIiIiIiIuoM2dnPRGkLhMJY9Nw6AMCUwQWYMDAPAHCiMXFFlUhblZZrN8MerWp6Zd0RfLitGk9+ZyoGRzf/iT9XFox+3apqBc2OirUjda249eWv4i4/oQnWzhldgqevnQ4AeG3D0YT3Jy8MsBjjw4uu2KCobgVNHS7lO60YWBgfrHkCIfhDYWXhBZD+8gJ5C2qzNxB3XYMnAJvZqBrC/9dVB7Fy90n88dtTUi5YEIO1kpxYwKkNER1tWNTQ28jPkcWs3woqzqGTidVt6W4FTebD2+bgUG0rpg8tbPd9ERERERERdSZWrPVxm4/WK1+L7Yn7T7bEHav3gVlbWZZrtyhtcpUNXmyvbMQd/9wcdzuxYk3eCtqchRVr8vwzLW3FWkhImJLNlXJGwyyj0QCTMRZ8WE1GuLqgispmSby8QE+uw4JzRpfEXe4NhOH1q8PTdCvW5NeJdg5ggyeASfd/iLn/t1K5TJIk3P3mNvx7xwm8v1VdNZjofGV5Tgt+dvFY3HVRharN1m4xwmjsuDliPY38O7aqWkHFGWvJlxdYOiBYG13mxgVpzCEkIiIiIiLqbqxY6+NW7atVvpbDnkAojMN1rQCAESUu7IuGbHpDy7WtoG67Oa4qaO/J+KUH6oq1SKgkznVLd5B9T2dKEMBogzWxcqvAmXiulDjXzGIyKIFcgcsCg6Hzwx6rKfa7dVlT//WR5zDrVh15AqG4asd0Z6zJbbFNPvUcwC8P1gFQt4iKlYHmNIbq52pe4987ezgAYEdlo3KZM43H3ZvJFadiK6gYnGmfI+31HdkKSkRERERE1NPxE1Aft2p/LFirboyEEIdqWxEMS3BZTSh1J96MCMS3grrtlrhgrbZZHSIB2hlrcsWauhVUkiRIkhR3297EgATBWrM2WIs9zsKcxBVrYkui2A5a6LLpHd7hUlWs/e6q07Fw5mDl++lDC5VNk6JmbxB3vqauZEy7FdQXaQFt0QRr4mtRft1sPx4LxMJpvJQSbRlNtCEzG9mjv2OxFTQsPHmpKtZsDNaIiIiIiKgPye7SC0pKkiRsOFSvfF/V6IU/GMYX+2oAAMNKXGgVwgpxI6QsPlgzxwUPeoGGX6hYC0QPaBWCkl+8sx0P/Gs7ynPteOeWs7tkMH9nECvzRPEVa7Enqcwdv7xAJlaJWcxGIHo3XbU9UT1jLf6vj8smD8Blkwfg8ikD8OG2aiw+byQA4F+3nIVfv7cTFpMRH26vxofbq5QgV5buXD25FbRZE6yJM/p8wTDsFhO2C5VmnjQ23eoN5gcSD/LPRsqMNeExB4QtvnoVaZ21vICIiIiIiKin4yegPqzZF1QFYycavfjJ61twz5vbAAAjSnJUVUGBcHzwoZ2FFmkFjX9ZaQMm8ftAtHqtRRN8hCXgeIMXW481pPuQepxEwVqrpoVWnLFWrrMVVOYSFgaIYUZBkrlsHSnZVlDR1CGFuOtrY5WQpqI8F88tmoFpQwsAIC5UAzJYXqAJ1vQ2jIrBmvY516NXlQmo20j7TLAmPOagzntfZOvgGWtERERERES9BT8B9WG1zerB+kdOteL/rY9tpBxS5FIHazohUfyMNYtuq9yxUx7V92IrqPyhXTuMXvkZvXiRgT9BsKY1vCRH+TpZsCbO9xpe4lK+7qqKPjHMcycJ1hLRe23Iyw3SX14QeT34g2HV60gM2uTLd7YxWEsUmlkSDPLPRgMLIltc++XHtrkGQ8n7aFUz1hisERERERFRH8JPQH1YbYu6amh3dWzJgNtmxvxxZepgLRj/4VobeuXaLbDrBA8Ha2NbRkNhSdUe6o9+aNfOzJL15g2h/hRhUZHLiqtnDMIdCyqUywoTVJ85LCbVrLJJg/KVr7uqYk2UrGItkTzNeS6aPRQ3nTMCQFuCtYDytfiaafDELvcFQ5AkSbXIQBsC60m0AMIsPO/aGYLZ5o4LK/DqTbNwwdjYVs5UAbGFraBERERERNRH8RNQH1YTrVgbVOhQXX7ltIHYdO98jB+QhxYhjNCtWNPMxXLbzcpWQdFXR+rx2w934WSTLy5sCob0W0GVn5FGINJTBTSVPiVu9ZKBWSOK8ODlE5EnbAI1Jtgkql0WMGlgvvJ1V1WsiQFLJi2RxZrFDIVOq7IQYe+JZvxj3ZGktw+EwqpZbGKVWkNrLFjzB8No9gVVxyZ6faXD0ocqsly2yCZX8XXYP0kVJcCtoERERERE1HfxE1AfJreCjilzqy4/v6JM+VB954WxSiq9qhVvXCuoWbdV7nf/3oNHP9qLRc+vjbsfObDLxlZQbRhZnqsOKBKFEHqFUyU56lBu0qA85Wu9uXadQaxaTFTdlUypJljMd1lVQez//HNz0gpFbVWjGKzVt4oVa2ElOJa1J6AVB/lb+mBw9JOLx2L+uDK88F8zdK9XLS/I8uCRiIiIiIhIxE9AfZQ3EEJVoxdApIpKHj4+sjQH88aWKsd97+xhyodp/Yq1+BlryUKercca4yrW5KquhBVrWRSsleWqgyVbgpBGr7VTe1sxpBNnr3WmRMsY0lWsCQcLnBalYk2mXUogavImCdY8sSDNFwzHbV4VZ6wlKApMSFxe0BeDo1K3HX/67jRlHp4WK9aIiIiIiKiv6ppP49SjNHkDuPB3nyrzp4pcNjx4+QSs2leLn/3HOJiF4MBgMGBseaSiTdvWCOjNWDPrDqgX7T3RrPo+EArjD8v3YP/JFt3jvf4QTrX4sXp/LeaOLetVH9y1c8OKXDYYDVBmzCUKafKdFtS1qCuutEsNDAYDnlg4BZ/vq8GF48s77qSTkJB8iH0qeQ4LLCaD8loqcFqRo5nV1uILxgVwNc0+fLanBkOLXarLE1Ws+RMEa4FQGBaTEUaDAWFJ/VguPb1/wvMWZ6yJ2zIpgssLiIiIiIior2Kw1ge98dVx1VD3ohwrLp8yEJdPGah7vDyYPBSWEIomQvIQfW17nc1iUgVrw4pdOFCjDsw+3n1C9f3Bmha88dXxhOfrCYRw/QvrsOFwPZacNxI/WjAm1UNss0AojGc+O4CzRxXjtP55qW/QhvsVOawm5DosSgiUKCTMd1jiLivLjZ9zddGEfrhoQr8OONP0XHfmMLz65VF8Y8qAjG5vMBhQ5LIp1ZIFTivKcu2448IK/Pr9nQD0W4KvemoV9p1swRnDC1WXN3sTLy842eRVHfvvHdWYcN8HePX7ZyKkCdXW/mRuXJinPW+ZhcFRHBsr1oiIiIiIqI/iJ6AsFg7rVxe9+qV6QHxRkkABUM+UOlLXiqm/XIb73tqGf22uVAV0QKSyxyYEawXO+IDo8701qu8rG7xxx4g8gRA2HK4HALy09nDSYzP1p0/246H3duLiRz/r0PsNaCrWbBajautnohBiyfkj4y7TzmfrDiVuG9b8ZC7uumhsxvdRJCwwKHBFXh83nzsCQ4ucAPRbgvdFqxlX769TXd6SrGKtOVKx1k+o9PMGwrjm2TXQ5GoozbUnXBqhxWAtntUUe88zWCMiIiIior6En4Cy1Od7azDhvg/wl1UHVZcfqm3B5qMNqsuKU2yUFFvfnvpkP+pbA3j+i4NY/NKGuONGl7lVFWtmY/xLbOuxRtX32pY9LY8/Fk41eQNJjszc+1urOuV+te2zdrNJ1dIpBhKi8yvKsOJH5+LhKyYql5Wl2MzYVTJZWiByqILX2GvPFW0JTTZjTUs+1hsIqdqSfcEwapoirbSDC52q24gBXCbYChrPYmZFHxERERER9U38BJSlXttwDC3+EO55c5uqFfOrI/Vxx6asWBPCsUShx+d3no+v7pkPh9WkCh5MRgMWzhwMAPjmVP1W0+rGWMWa3uIDcUuk3pw3ANh8tB61zckDumSOnGrN+LbJaDeg2i0m9MtzKN8nq+4ZVuyC0xYLoXpCxVpHEBcB2IWQTQ7WWhNsh9Ujvx4bPeqwzBcMKRVrQ4qccbdrDwZH8VRbQVmxRkREREREfQg/AWUppzUWWPz50/3K15uORKrV5lbENn8WpqhYMxoNyvB2b4INnXkOixKMiBVNJqMBP7t4HP56/Qz86hsTkKczO6wlOqft65P64x/fn6Vc7rZH7i/VVtCtxxrw9T9+jnm/XZn0uGTaW8WUiHYDqt1iVLUmpgohDIg9l3oz1nojvSpGAHBFX7MtbalYi85YO6X5/YnLC4YUueJu1x4M1uKJr+NEm26JiIiIiIiyEZcXZCmxskxstdx8tB4A8LUJ/VCcY0MgFEZxTvJgDYiECcFwCFUJ5qHZE3yYNhkNcFhNOHtUCQBgbD933JwsWaHLqqpgKnHb0OQNxi1I0PpiX2Rm26nWACRJanOrojb86kja5QV2iwn98tMP1uo9sc2gevPqeqNEs8wStYImCnOB2Dw27QbVO/65Rfla2wraXqzIisetoERERERE1FfxE1CWahK2JbZGw4dgKIxtxyPzzSYNysOvvzkRv73q9LSCKLm983CdfsukOcGHabMmRBnbLzfhzyhwWmE3x4K1UnekRdUTCKlaRCXN5Hm3PRY4ncygHfRgbaxV1mXVn3mWqfhgTV2xlqq6J1d4bO2dbdZTXDA2Ui2prV7MkVtBNcsLTrWqQzMg9rzJr/PalsS/94pyd+Ynq6Ojg7psYBNmBbKij4iIiIiI+hJWrGWpZl+sNa4lOrNqf00LPIEQXFYThhfntOn+5IqUBs0sq0Wzh+I7ZwxJeDttddLUIQV47vODuscWuCyqAK3EHQmgvIEQcmwWeAM+5RzyhaH3rUJF26HaVpS6k7dMVjZ48K/Nlbhy+iDk2i3Ye6JZuc7XwdVressLxBlrqYK1i8aX479mD8MZwws79Ly607dnDkGO3YzpQ9WPyWmVK9bUFWraajQg0hZ7uK41Fqw1xx8DAB8snYN++Q7d69rqueumY8PhU7h4Qr8Oub9soqpYY0UfERERERH1IQzWspRexdqRaLXZsBJXwna8RBJVocytKMOIksQhnbZi7aLx/QBs1D22wGmFTbUxMlLR5AmEVFVqJ5t8qmBNHFx/sKYlLrDRuvpPq3GwthWbjzbg0asn44SwPCEYlhAIhTus6kYb1EWWF8SCP0l/F4PCbDLinkvGdci59BQmowHfmBy/yCLHpj9jTW/+XbkSrEWu01tc4babMabcjVA4xZOcpvMqSnGeMJuQYhisERERERFRX8VPQFlKnFMlV3Qdr/cAgKpiKl2JgiZxXpieCQPzVN+bjAa8tWQ2ynJtGFWqDuQiM9ZiP0dZXuAPqQIqbbunWEV3qDb1ds+D0WM+2nkCAFCvqcJLNtOrrbStoDaLUdUCqVeN1VfJM9a0wZrec1SaG2kTbowGyDU6x7ij92dqY4hMbWcyGiA/zQzWiIiIiIioL+EnoCzV7NUJ1qKLB/rntX27pDxjTas8wabKt5echdsvGI3rzxoWd93EgflYfddc/OC8EarLC5xW1eBzeb6YJxBShV3iMgZAXbF2KMEMOD1yFZy2IirVFtK20FteIM5Kk8NDEoK1NGasya87+XdfF20FzRWezxw+t11KDtS4vICIiIiIiPoSfvLMUmIrqFwBVBmtWOufwcwpq1l/qL8chmhNGJgXV60mMhgMcVVwBS6LKnQqiLZ7NvuCqpbJuGDNK1astSBdoeidaufG+QKZzVkLRkO0N786jhnDCjGo0BkfrEWfx2eunYZP99TgsskDMvpZ2ciltILGgs2Vu0/inje3xR1bFg3WtMsL+uc70FjVBCC2DIG6hsNigjcQVlWdEhERERERZTt+8sxCvmAIfiHQ8QXDCIbCSsVaJsPcrULF2umD8nGgpgVnjSpu13majZpgLRqkXXPGEOw50aTMs9LOITvRlLgVNNHWUj3y6K16TUVUJhVrq/bVYtHzazGsOAc7KhthMRmw54GvwR83Yy3ymOeOLcPcsWVt/jnZzKUsL4iEZav31+LaZ9fqHlsWrbr0BEIIhMLK8oL++Q7slIM1uyXudgVOC07pzGyj9rtl7ijsqGxs82IUIiIiIiKi3ozBWhYS20BlrYEQKhuiFWsZtYLGQrChRU688v0z2t3yZTXHwjqn1QR7dHHBLy4bDyDxrLPKBq/qezFYq28NoNUfVDZMJiXF3z7Zz03mlpc3whsIY0dlI4DYNlC/diuoRb/yj2IVZvKyjfvf3p7w2FK3Tfm6yRtETbNcsRZ7bbt1KtbOHFmMf22u7JDzJbVFs+PbvomIiIiIiLIde3aykNwe57KalK2czd4gquQZaxlUrInBWq7DAptZPSssE2LF2phyd9z1tgRD0KuiAaFMG4wdr1cHb4nIraDa5QUefwfOWNPZCkr6YssLQmjxBbGzqjHhsTk2sxLE1Tb7lCUG4mIOsRX0tR+cidvmjcbN56jn+hERERERERG1B4O1LCS30rntFjitkSDncF0rAiEJRoO62iddFnP8ts72MgrB3C3nj4q73mAwwKETRGkr1ho9kccrHytvP00lLM9Yi7YGytsjvcG2z1hLtHhSO2ONGxMTk2esNfuC2Ha8EZIUWVKw8e4LcOdFFapjnVaT8jqUt7yajAaUCK9tcXnBlMEFuHXeKNVGViIiIiIiIqL24qf8LCRXrOXYzUoV0N4TzQAiQ9/NGbRwijPW3DqzqzIxtp8bbpsZ540pwbljSnSPcVjjg7XqRi/C0QFp/mBYmYk2tl+k6i3dYE2SIptB5Yo1edNkJhVrRp3qvXUH65QtpWPK3Fhy3kgO1E/CJbSCbj5aDwCYODAPBS4rvqFZ8uC0mpWtsQdrIgsrCl1WVRCr91w7rerrBxU68PR3p3Xo4yAiIiIiIqK+g5/ys1BTdEtmjs0MKVqVdSwaNhXlWDO6T7EVtKMq1opybFh/9wUwGQ0J20rFoKQs14aTTT4EQhJqWnwodduVNlCDIdJOuuFwvbKkIR0t/hBC0ZCuPM+OY/Ue+IIdE6x968lVytc/+4+xOHuUfnhIEXKwFghJWH/oFIBIsAbEtqnKHELF2v5osFbksqoqAvVep+IW24kD8/DSDWd04CMgIiIiIiKivoYVa1ko1gpqVob4n4xu0kxrqL8O1Yy1DqpYAyKtkaZEfZSIbdEEIqGI3Oonz4trFELEAdHZcelWrAGxjaBWsxH50TbBTCrWUo2bs7Rz0UNf4BJem2sO1AEAxg+IBGs2i/r5c1pNyHWoK9aKc2yquXx6FWvi9do2XSIiIiIiIqK24qf9LKQO1iKVPnKw5tJprUxHZ1SspUNsBbWZTSiPDqevbPDiRJMXVzzxBYBIiNJfCNbe2Xwcr204mvL+f/FOZPNkvsMCe/RnZbIVVK9iTcTZaqmZjAZlk2ddSyTwHFzoBBC/yMJiMiqvwwNCK6j4POfovE7FykjtxlYiIiIiIiKitmIraBZSZqzZzPAGIlU5Nc3RirUMZ3xZzR0/Yy0dYhWT3WJEmduOTYhUrH204wTqo4sHHBaTEqwdqGnBkpc2AgAmDMjDqLL4jaOyD7ZVAwDynRal3dATSF3JJEkSTrUGUOiKtNYmKboDAFhZsZaWXIcFTdFgGADynZHnV69VWK6crGqMVC8W5VhhE1pGXSle60FWrBEREREREVE78dN+FooFa5ZOqVjLc3RdHivOhLOZjSjPiywYqGzwojZa1QQAV0wdiP5CNZvsw+3VqvuT56lpBUISHNbIYzzR5E0Zutz/9nZM+cUyfLrnJADAmCJZYytoevKd6tA2N0l1pLZyUtsK6k4RrLEVlIiIiIiIiNqLn/azkMVkQKHLigKnRan4OtncvhlrZqPYCtp1FWtFLpvytd1iUoKXRm9AqcK76ZwRWHzeSJTl2eJmnb27pVL1vT+oH6YcqGlRKtae+/wgrn/hy6Tn9fwXBwEAD7+/K3JBiq5CiylFSRsBUAdrbrs56QZbecaarMhlVc9YS9GynOi1QERERERERJQuBmtZ6L/nj8GGuy/AD+eOgtMWCYuiy0HhsmVWsRYMx0KIrpyxJrdaApGKNXkgfbM3iNrmSMVacbSqzWY2oSTHprr9tuONSrUegIQbP3+8YIxqntvK3SfTOj8pmqilmsvGGWvpyXfEft/a6jUt7RKNQpe6FVRveYEowBlrRERERERE1E78tJ/lnJrWz0wr1sTgyGHJLJzLhLoV1KSEKU3egDLgXjymX3TOmkjeHAroVyn96hsTcPM5I2DXPK5EbaN6WlMFa2wFTUueEKaJIZueslx1iFqUo65YdNuSB3N+toISERERERFRO/HTfpbTBmmZzljzCgP99QbJdxZ1K6hRae9r9gWVVtBC4ZgB+fa4+/D4Y6GXTydYG1rkhNFoiAvWGjyBuGO15EpA8Wfo4Yy19OQL7Z2pKtbG9stVfV+cY1UtLEhVnckZa0RERERERNReWfNp/7HHHsPQoUNht9sxc+ZMrF27trtPqUfQBmmZbgX1pKjI6izqVlCT0oba5A0qywuKhGPkBQYisf1TrxVUntVlt6jfDnXCcgQAeHvTcdz75ta4QCYclnQDO5GFraBpEcO0PEfyYK1fnl01U60ox4ZClxWPfXsKnrl2WsL5bPPGlgIAvnvGkA44YyIiIiIiIurLum5YVid65ZVXcPvtt+PJJ5/EzJkz8bvf/Q4LFizArl27UFpa2t2n161cmiBN2xqarlQzxDqL2OZpt8RmrDV6AjjVIs9Yi1Ws6bWCevyx0EusvJPJAY62xfVUqzpY++HfNwIAhha7lMskKb3Qka2g6WnLjDWDwYDyPDsO1bYCiIXIF0/sl/R2f7h6CjYeOYUZQwvbebZERERERETU12XFp/3f/va3uOGGG7Bo0SKMGzcOTz75JJxOJ5599tnuPrVuJ1Z8AVC2hLaVTyeQ6gri+ZuMRmUj6fEGL4LRGWgFrlgAo9cKKoaCenO15LleQc1MNW3FmmzdwTrVfacTrHEraHrETZ+pZqwBQFlu7Pedbouyw2rCmSOKk24cJSIiIiIiIkpHr/9k6ff7sX79esybN0+5zGg0Yt68eVi1apXubXw+HxobG1V/slWRZktmphVrXz+9PwBgnGauVWcrcMbCFW8gFLeR1G03qzZB9hNaQQcXOgHEKsre2Xwc33zii7ifkRMNG7VB2inhe3GGmlwhBUTmsKWarwZ07Vy63kysUktUsWYUnsozhrHqjIiIiIiIiLpPr28FrampQSgUQllZmerysrIy7Ny5U/c2Dz74IO6///6uOL1uV6StWMtwxtq3ZwzG8GIXThuQ1xGnlTaTkKI0+4JxwZr28Q0qdMJoAMwmIwYVOnC4rlWpWFvy0kbdn2GM/ozLTh+Axz7aiyZfEABQJ7SC1rb4lK93VzcpXzd4At02fy4bpTNjTQ5MAeAH541EXasf88eVd/q5EREREREREWn1+oq1TNx1111oaGhQ/hw5cqS7T6nTFHdQxZrRaMCZI4tTDpTvTM3eIBwWkyps01bkFbqs+N1/TsYfr56M3GjbaLrz4crz7Fh/9wW4cc5wAOqKNbGaLRCKtYwGwxJqmmKhG7WPesaaOjT9fzfNwlkji/Hn705TLrNbTPjlZRMwZ3RJl50jERERERERkazXV6wVFxfDZDKhurpadXl1dTXKy/WrWGw2G2w2m+512cZhNcFpNaE12q6YacVaT2A0Rloqc2xmNHgCAOJnyAHA1ydF2lbf21oFQH9hQSJWs1FpP31l3RGcPaoEc0aXJJy3BgBVjV7V9xMH5uHJ70xFrsOChU+vwaSBXVvl15slawWdNrQQf/vezK4+JSIiIiIiIqKEen3FmtVqxdSpU7F8+XLlsnA4jOXLl2PWrFndeGY9h9g+mWnFWnd64BvjMbTIif9ZUAEAymZQABigswVUZo9u+fQEQpAkKeFxWoXRZQiN3iC+++xarN5f26ZgzWk1oX++Azk2M95cPBs/v3R82j+7r7NbTLCZI38t5XdjdSQRERERERFROnpv+ZLg9ttvx7XXXotp06ZhxowZ+N3vfoeWlhYsWrSou0+tR3BYYmGaM8OtoN1p4cwhWDhziPK9GBQOLXLq3QQAYLdEAhpvIKTMTUtHgaYFcclLGzAjyZD8qgZ1sJZjYyDUHovPG4m9J5oxoiSnu0+FiIiIiIiIKKnel7LouOqqq3Dy5Encc889qKqqwumnn473338/bqFBX+UQwjRxPllvJQZrQ4pcCY9zCBVr9S2BtO+/QNNeWtPsx7tbqhIe/5dVhwAAFeVuDCxw4JpZQxIeS6ndMndUd58CERERERERUVp6fSuobMmSJTh06BB8Ph/WrFmDmTM5i0nWG9s/kxHnxA1JWrEWedzeQBinWvVbOWcNL4q7rDzXrny99qdzMagw1m56fkVpwp83tMiFp6+djnM4SJ+IiIiIiIioT8iKijVKTmwFzQY+YRnBwILEwZpDCdZCccHaBePKMHVIAb41dWDc7QYVOvH4wikoddtQ6rZj1vAiHKk7CgA4d0wJPtp5QvfnSUh/jhsRERERERER9X5ZU7FGidmzLFgTFwlYzYlfwvKMtdc3HsMNf/lSdV1Zrg03nTMCRTn622G/NqEfpg2NzFWbMrhAubwkx4ZvzxwMi8mAJ78zRdW2uKOyqe0PhoiIiIiIiIh6LVas9QHDS1zAju4+i45Tm2RDp0gMFAMhdTVZqy+U9s+bOiQWrOU6LHjgsvG466IKuO0WXDi+H8pybfjp61vxg3NHpH2fRERERERERNT7MVjrA354/kgcO+XBJZP6d/epdIhvTRuIJz7eh7NHFSc9LlmlXnMbtoSK2ynLcu0wGAxw22ObPxfOHIL/mNAfeU5uAyUiIiIiIiLqSxis9QFuuwWPLZzS3afRYW6dOwqTBubhzJGZB2ut/vQr1oxGA177wZk4dsqDkaU5uscwVCMiIiIiIiLqexisUa9jt5hw4fh+KY9LtrShLRVrQGTOmjhrjYiIiIiIiIiIywsoa8nLC/TMHlnUhWdCRERERERERNmIFWuUtfRaQR+5ahJafCF8c+rAbjgjIiIiIiIiIsomDNYoa4nBWkW5G/d9/TTMHFYIg8HQjWdFRERERERERNmCwRplLYc1FqyNKnPjjOFs/yQiIiIiIiKijsMZa5S17ObYy7uAWzuJiIiIiIiIqIMxWKOsJVasOa0sziQiIiIiIiKijsVgjbKW3SwGa/GLDIiIiIiIiIiI2oPBGmUtozG2pIDBGhERERERERF1NAZr1CewFZSIiIiIiIiIOhqDNcpqRS4rAODsUcXdfCZERERERERElG1YxkNZ7eMfn4sGTwADC5zdfSpERERERERElGUYrFFWc9stcNst3X0aRERERERERJSF2ApKRERERERERESUAQZrREREREREREREGWCwRkRERERERERElAEGa0RERERERERERBlgsEZERERERERERJQBBmtEREREREREREQZYLBGRERERERERESUAQZrREREREREREREGWCwRkRERERERERElAEGa0RERERERERERBlgsEZERERERERERJQBBmtEREREREREREQZYLBGRERERERERESUAQZrREREREREREREGTB39wn0BJIkAQAaGxu7+UyIiIiIiIiIiKg7yfmQnBclw2ANQFNTEwBg0KBB3XwmRERERERERETUEzQ1NSEvLy/pMQYpnfgty4XDYRw/fhxutxsGg6G7T6fdGhsbMWjQIBw5cgS5ubndfTpEvRrfT0Qdh+8noo7B9xJRx+H7iahjZNt7SZIkNDU1oX///jAak09RY8UaAKPRiIEDB0kqaywAABBHSURBVHb3aXS43NzcrHhBE/UEfD8RdRy+n4g6Bt9LRB2H7yeijpFN76VUlWoyLi8gIiIiIiIiIiLKAIM1IiIiIiIiIiKiDDBYy0I2mw333nsvbDZbd58KUa/H9xNRx+H7iahj8L1E1HH4fiLqGH35vcTlBURERERERERERBlgxRoREREREREREVEGGKwRERERERERERFlgMEaERERERERERFRBhisERERERERERERZYDBWhZ67LHHMHToUNjtdsycORNr167t7lMi6lEefPBBTJ8+HW63G6Wlpbjsssuwa9cu1TFerxeLFy9GUVERcnJycMUVV6C6ulp1zOHDh3HxxRfD6XSitLQUP/7xjxEMBrvyoRD1KA899BAMBgOWLl2qXMb3ElH6jh07hu985zsoKiqCw+HAhAkT8OWXXyrXS5KEe+65B/369YPD4cC8efOwZ88e1X3U1dVh4cKFyM3NRX5+Pq6//no0Nzd39UMh6lahUAh33303hg0bBofDgREjRuAXv/gFxL19fD8Rxfvkk09wySWXoH///jAYDHjjjTdU13fU+2bz5s04++yzYbfbMWjQIDz88MOd/dA6FYO1LPPKK6/g9ttvx7333osNGzZg0qRJWLBgAU6cONHdp0bUY6xcuRKLFy/G6tWrsWzZMgQCAcyfPx8tLS3KMbfddhvefvttvPrqq1i5ciWOHz+Oyy+/XLk+FArh4osvht/vxxdffIEXXngBzz//PO65557ueEhE3W7dunV46qmnMHHiRNXlfC8RpefUqVOYPXs2LBYL3nvvPWzfvh3/93//h4KCAuWYhx9+GI8++iiefPJJrFmzBi6XCwsWLIDX61WOWbhwIbZt24Zly5bhnXfewSeffIIbb7yxOx4SUbf59a9/jSeeeAJ//OMfsWPHDvz617/Gww8/jD/84Q/KMXw/EcVraWnBpEmT8Nhjj+le3xHvm8bGRsyfPx9DhgzB+vXr8Zvf/Ab33Xcf/vSnP3X64+s0EmWVGTNmSIsXL1a+D4VCUv/+/aUHH3ywG8+KqGc7ceKEBEBauXKlJEmSVF9fL1ksFunVV19VjtmxY4cEQFq1apUkSZL07rvvSkajUaqqqlKOeeKJJ6Tc3FzJ5/N17QMg6mZNTU3SqFGjpGXLlknnnHOOdOutt0qSxPcSUVvccccd0llnnZXw+nA4LJWXl0u/+c1vlMvq6+slm80m/f3vf5ckSZK2b98uAZDWrVunHPPee+9JBoNBOnbsWOedPFEPc/HFF0v/9V//pbrs8ssvlxYuXChJEt9PROkAIL3++uvK9x31vnn88celgoIC1b/z7rjjDmnMmDGd/Ig6DyvWsojf78f69esxb9485TKj0Yh58+Zh1apV3XhmRD1bQ0MDAKCwsBAAsH79egQCAdV7qaKiAoMHD1beS6tWrcKECRNQVlamHLNgwQI0NjZi27ZtXXj2RN1v8eLFuPjii1XvGYDvJaK2eOuttzBt2jR861vfQmlpKSZPnow///nPyvUHDhxAVVWV6v2Ul5eHmTNnqt5P+fn5mDZtmnLMvHnzYDQasWbNmq57METd7Mwzz8Ty5cuxe/duAMCmTZvw2Wef4aKLLgLA9xNRJjrqfbNq1SrMmTMHVqtVOWbBggXYtWsXTp061UWPpmOZu/sEqOPU1NQgFAqpPpwAQFlZGXbu3NlNZ0XUs4XDYSxduhSzZ8/G+PHjAQBVVVWwWq3Iz89XHVtWVoaqqirlGL33mnwdUV/x8ssvY8OGDVi3bl3cdXwvEaVv//79eOKJJ3D77bfjJz/5CdatW4dbbrkFVqsV1157rfJ+0Hu/iO+n0tJS1fVmsxmFhYV8P1Gfcuedd6KxsREVFRUwmUwIhUJ44IEHsHDhQgDg+4koAx31vqmqqsKwYcPi7kO+ThyB0FswWCOiPm3x4sXYunUrPvvss+4+FaJe58iRI7j11luxbNky2O327j4dol4tHA5j2rRp+NWvfgUAmDx5MrZu3Yonn3wS1157bTefHVHv8o9//AMvvvgiXnrpJZx22mn46quvsHTpUvTv35/vJyLqcGwFzSLFxcUwmUxx29aqq6tRXl7eTWdF1HMtWbIE77zzDlasWIGBAwcql5eXl8Pv96O+vl51vPheKi8v132vydcR9QXr16/HiRMnMGXKFJjNZpjNZqxcuRKPPvoozGYzysrK+F4iSlO/fv0wbtw41WVjx47F4cOHAcTeD8n+nVdeXh63sCoYDKKuro7vJ+pTfvzjH+POO+/Ef/7nf2LChAm45pprcNttt+HBBx8EwPcTUSY66n2Tjf/2Y7CWRaxWK6ZOnYrly5crl4XDYSxfvhyzZs3qxjMj6lkkScKSJUvw+uuv46OPPoorRZ46dSosFovqvbRr1y4cPnxYeS/NmjULW7ZsUf2HY9myZcjNzY37YESUrebOnYstW7bgq6++Uv5MmzYNCxcuVL7me4koPbNnz8auXbtUl+3evRtDhgwBAAwbNgzl5eWq91NjYyPWrFmjej/V19dj/fr1yjEfffQRwuEwZs6c2QWPgqhnaG1thdGo/qhrMpkQDocB8P1ElImOet/MmjULn3zyCQKBgHLMsmXLMGbMmF7ZBgqAW0GzzcsvvyzZbDbp+eefl7Zv3y7deOONUn5+vmrbGlFfd/PNN0t5eXnSxx9/LFVWVip/WltblWNuuukmafDgwdJHH30kffnll9KsWbOkWbNmKdcHg0Fp/Pjx0vz586WvvvpKev/996WSkhLprrvu6o6HRNRjiFtBJYnvJaJ0rV27VjKbzdIDDzwg7dmzR3rxxRclp9Mp/e1vf1OOeeihh6T8/HzpzTfflDZv3ixdeuml0rBhwySPx6Mcc+GFF0qTJ0+W1qxZI3322WfSqFGjpKuvvro7HhJRt7n22mulAQMGSO+884504MAB6bXXXpOKi4ul//mf/1GO4fuJKF5TU5O0ceNGaePGjRIA6be//a20ceNG6dChQ5Ikdcz7pr6+XiorK5OuueYaaevWrdLLL78sOZ1O6amnnuryx9tRGKxloT/84Q/S4MGDJavVKs2YMUNavXp1d58SUY8CQPfPc889pxzj8XikH/zgB1JBQYHkdDqlb3zjG1JlZaXqfg4ePChddNFFksPhkIqLi6X//u//lgKBQBc/GqKeRRus8b1ElL63335bGj9+vGSz2aSKigrpT3/6k+r6cDgs3X333VJZWZlks9mkuXPnSrt27VIdU1tbK1199dVSTk6OlJubKy1atEhqamrqyodB1O0aGxulW2+9VRo8eLBkt9ul4cOHSz/96U8ln8+nHMP3E1G8FStW6H5OuvbaayVJ6rj3zaZNm6SzzjpLstls0oABA6SHHnqoqx5ipzBIkiR1T60cERERERERERFR78UZa0RERERERERERBlgsEZERERERERERJQBBmtEREREREREREQZYLBGRERERERERESUAQZrREREREREREREGWCwRkRERERERERElAEGa0RERERERERERBlgsEZERERERERERJQBBmtEREREvcB1112Hyy67rLtPg4iIiIgEDNaIiIiIupnBYEj657777sPvf/97PP/8891yfn/+858xadIk5OTkID8/H5MnT8aDDz6oXM/Qj4iIiPoqc3efABEREVFfV1lZqXz9yiuv4J577sGuXbuUy3JycpCTk9Mdp4Znn30WS5cuxaOPPopzzjkHPp8PmzdvxtatW7vlfIiIiIh6ElasEREREXWz8vJy5U9eXh4MBoPqspycnLiqsHPPPRc//OEPsXTpUhQUFKCsrAx//vOf0dLSgkWLFsHtdmPkyJF47733VD9r69atuOiii5CTk4OysjJcc801qKmpSXhub731Fq688kpcf/31GDlyJE477TRcffXVeOCBBwAA9913H1544QW8+eabSoXdxx9/DAA4cuQIrrzySuTn56OwsBCXXnopDh48qNy3/Jjuv/9+lJSUIDc3FzfddBP8fn+HPbdEREREnYnBGhEREVEv9cILL6C4uBhr167FD3/4Q9x888341re+hTPPPBMbNmzA/Pnzcc0116C1tRUAUF9fj/PPPx+TJ0/Gl19+iffffx/V1dW48sorE/6M8vJyrF69GocOHdK9/kc/+hGuvPJKXHjhhaisrERlZSXOPPNMBAIBLFiwAG63G59++ik+//xz5OTk4MILL1QFZ8uXL8eOHTvw8ccf4+9//ztee+013H///R37RBERERF1EgZrRERERL3UpEmT8LOf/QyjRo3CXXfdBbvdjuLiYtxwww0YNWoU7rnnHtTW1mLz5s0AgD/+8Y+YPHkyfvWrX6GiogKTJ0/Gs88+ixUrVmD37t26P+Pee+9Ffn4+hg4dijFjxuC6667DP/7xD4TDYQCRNlWHwwGbzaZU2FmtVrzyyisIh8N4+umnMWHCBIwdOxbPPfccDh8+rFS0AYDVasWzzz6L0047DRdffDF+/vOf49FHH1Xun4iIiKgnY7BGRERE1EtNnDhR+dpkMqGoqAgTJkxQLisrKwMAnDhxAgCwadMmrFixQpnZlpOTg4qKCgDAvn37dH9Gv379sGrVKmzZsgW33norgsEgrr32Wlx44YVJw69NmzZh7969cLvdys8qLCyE1+tV/axJkybB6XQq38+aNQvNzc04cuRIBs8IERERUdfi8gIiIiKiXspisai+NxgMqssMBgMAKAFYc3MzLrnkEvz617+Ou69+/fol/Vnjx4/H+PHj8YMf/AA33XQTzj77bKxcuRLnnXee7vHNzc2YOnUqXnzxxbjrSkpKkj8wIiIiol6CwRoRERFRHzFlyhT885//xNChQ2E2Z/7PwHHjxgEAWlpaAETaOUOhUNzPeuWVV1BaWorc3NyE97Vp0yZ4PB44HA4AwOrVq5GTk4NBgwZlfH5EREREXYWtoERERER9xOLFi1FXV4err74a69atw759+/DBBx9g0aJFccGY7Oabb8YvfvELfP755zh06BBWr16N7373uygpKcGsWbMAAEOHDsXmzZuxa9cu1NTUIBAIYOHChSguLsall16KTz/9FAcOHMDHH3+MW265BUePHlXu3+/34/rrr8f27dvx7rvv4t5778WSJUtgNPKfqURERNTz8V8sRERERH1E//798fnnnyMUCmH+/PmYMGECli5divz8/IRB1rx587B69Wp861vfwujRo3HFFVfAbrdj+fLlKCoqAgDccMMNGDNmDKZNm4aSkhJ8/vnncDqd+OSTTzB48GBcfvnlGDt2LK6//np4vV5VBdvcuXMxatQozJkzB1dddRW+/vWv47777uuKp4OIiIio3QySJEndfRJERERE1Pdcd911qK+vxxtvvNHdp0JERESUEVasERERERERERERZYDBGhERERERERERUQbYCkpERERERERERJQBVqwRERERERERERFlgMEaERERERERERFRBhisERERERERERERZYDBGhERERERERERUQYYrBEREREREREREWWAwRoREREREREREVEGGKwRERERERERERFlgMEaERERERERERFRBv4/J73PxPIE1ekAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "\n", " | Name | Type | Params | Mode \n", "----------------------------------------------------------------\n", "0 | tft_model | TemporalFusionTransformer | 75.9 K | train\n", "----------------------------------------------------------------\n", "75.9 K Trainable params\n", "0 Non-trainable params\n", "75.9 K Total params\n", "0.304 Total estimated model params size (MB)\n", "140 Modules in train mode\n", "0 Modules in eval mode\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9ee9fab634444a4787809a4d6f3e948e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from random import randint\n", "# Get predictions\n", "predictions = trainer.predict(tft_module, datamodule=data_module)\n", "\n", "# Concatenate predictions\n", "all_predictions = torch.cat(predictions, dim=0)\n", "\n", "# Number of batches to plot\n", "num_batches_to_plot = 5\n", "\n", "# Plotting\n", "plt.figure(figsize=(15, 5 * num_batches_to_plot)) # Adjust figure size\n", "\n", "for idx in range(0, num_batches_to_plot):\n", " batch_idx = randint(0, len(all_predictions) - 1)\n", "\n", " selected_batch_predictions = all_predictions[batch_idx]\n", " _, _, _, selected_batch_target = data_module.test_dataset[batch_idx]\n", "\n", "\n", " plt.subplot(\n", " num_batches_to_plot,\n", " 1,\n", " idx + 1,\n", " ) # Create subplots\n", "\n", " plt.plot(\n", " selected_batch_target, label=\"Target\", marker=\"o\", linestyle=\"-\"\n", " )\n", "\n", " for i, quantile in enumerate(quantiles):\n", " plt.plot(\n", " selected_batch_predictions[:, 0, i].squeeze(),\n", " label=f\"Predicted Quantile {quantile}\",\n", " marker=\"x\",\n", " linestyle=\"--\",\n", " )\n", "\n", " plt.xlabel(\"Time Step\")\n", " plt.ylabel(\"Value\")\n", " plt.title(f\"Target vs Predicted Quantiles for Batch {batch_idx + 1}\")\n", " plt.legend()\n", "\n", "plt.tight_layout() # Adjust subplot parameters for a tight layout\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now succesfully trained a TFT model on the synthetic dataset" ] }, { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "bat" } }, "source": [ "#### Electricity\n", "\n", "The electricity dataset is a widely used benchmark for time series forecasting tasks. It contains hourly electricity consumption data from various households, providing a rich and diverse set of temporal patterns. In this tutorial, we will leverage the Temporal Fusion Transformer (TFT) model to forecast electricity consumption. The TFT model's ability to handle complex temporal relationships and provide interpretable insights makes it an excellent choice for this dataset. By the end of this section, you will learn how to preprocess the electricity dataset, train the TFT model, and evaluate its performance on forecasting tasks." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Electricity dataset size: (143206, 2)\n", "Electricity dataset size: (143206, 2)\n", "Electricity dataset size: (143206, 2)\n", "Electricity dataset size: (143206, 2)\n" ] }, { "data": { "image/png": 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xWvUb++7evTtatGiBL774QtL9U1JSUKJECSQnJyMqKsrudxkZGTh16hRq1qwpeYkoqYvvERERERER6VXs5Wvo/ukaAMCwDtXwwcCmYhtERG7jRI50k4FGRERERERExnU+6To2Hr8suhmKmbgkRnQTiPxyLTNHdBOE0k0NNGfWrFkjuglEREREREQkg84TVwEAZo9sj051yghujfyWHrwouglEPlu8/wKenrULL/eth2d61hXdHCGYgUZERERERESasS32qugmEJGD137Pr6/+6X9HBbdEHAbQiIiIiIiIiIiI3GAAzYGO91QwPL43RERERETGZ4FFdBOIyEFqhrnrnwEMoBUKDAwEAGRlZQluCbmSnp4OAAgODhbcEiIiIiIiUorFBPEzBgmJ9EfXmwjIKSgoCBEREUhISEBwcDACAhhb1Aqr1Yr09HTEx8cjOjq6MNhJRERERET6tWT/BaRm5uC+NlXtbj99JV1Qi9RjBVfXEOkNA2g3WCwWVKxYEadOncLp06dFN4eciI6ORoUKFUQ3g4iIiIiIZPDUrF0AgJZVo1G3fGTh7UcupYhqkmp+3XIGHwxsKroZROQFBtBshISEoG7dulzGqUHBwcHMPCMiIiIiMqDPlx/Ft8Nai24GEZFbDKA5CAgIQFhYmOhmEBERERERmUJ2bp7dz9w7jIi0iIW+iIiIiIiIiIiI3GAAjYiIiIiIiISJNcGmAUSkfwygERERERERkaqsNus0j8en4WJyRuHPFouIFomXl2fFm3/ux/ztZ0U3hYicYACNiIiIiIiIhDp0IVl0E4RbcfgSZm09g1d/3ye6KUTkBANoRERERGRK205dRY3XF2HFoUuim0JEhKTr2aKbQERuMIBGRERERKZ03/ebAQAjZ+wQ3BIiIiLSOgbQiIiIiIiISCibkmh2/zaTnFyTPnEinWAAjYiIiIiIiFTlGCQza9DM1hcrjopuAhG5wQAaERERERERCfXj+pOimyBcfGqm6CYQkRsMoBEREREREZFQW09dLfy3xSKwIURELjCARkSmcyUtE3l5XCdARERERERE0jCARkSmsutMIlp/sAKPTt8uuilERERE5ERoUKDoJhARFcEAGhGZyvRNsQCANUcSxDaEiIiIiJwa0LSi6CaowsqdE4h0hQE0IiKVWa1W5OTmiW4GERERkeriUzLQZdIqTF513OV9goPMMUyVMqH7+85zWB0Tr0JriMgTc5yZiMj0Lmuo7tlDU7eh86RVyMjOVf3Yt365Hm0/XMEZTyIiIhLii5XHcC7xOv634qjopgi3+2yS29/HXr6Gl37bixG/KFt6xGq1CumXEukNA2hEZHibT1xBmw9WoPf/1uKvPXGim4MNxy/jUkomtsde9XxnGZ2+cg2HL6QgITUT645dVvXYRERERAAkTWgeu5SqQku073JapirHeWbObjR4eynOXk1X5XhEesUAGhEZ3k/rTwIATiZcE9wSsa5eyyr895ec9RWGy3eJiLTl6rUsTFl9HBeSr4tuimFZrVa8tmAfvl1zQtL9Z2w+rXCLNEIjKwIW7bsAAJi97YzQdqRkZHOVBGkaA2hERIKI7B8ciEsRd3ATG/LjFtR5cwmOx6eJbgoREd0wZu5ufLLsCIb+uFV0Uwxr15lEzNtxFpOWxohuiqZsOXUV2QIn1rQUrNp/LhnN3vsPo2fvEt0UcpCRnYtLKRmim6EJDKAREZmQRXQDBBHZUczOzcOmE1cAAB8sOiSsHaQPp69cw+4ziaKbQWQK62+UNTh52dyZ6ko6cJ4Td85sO3UVnyw74vL3SvZaJiw+jHYfrURCqjrLRD35eFl+cHXx/ouCW0KOukxahfYfrRTdDE1gAI1IIxbuPo/NNwbXJE5WTh4SbZY6KkntUI525hjFOHopFa3eX164pFdtObk334Gk9GwhbSD9uOWTNbj7m02sR0NEhvDu3wcL/20x6yyeCz9vOCXkuN+vO4mE1Exh/SJH61mfV7MupxUdG6k1XtIaBtCINODIxVQ8P28PHvxxi+imGJI3HbU+/1uLlu8vR1yS8nVQuNuRut5aeACJ6dn4YNFhIce3/RxqackEaduxeBbSJiJj4SVQY+z6J+KaQfpyLStHdBOEYACNSAPOJzHDQEnedAZOX8l/L1YfiVeoNTc9MXMn1h9LUPw4pD0SNiAjk0hKz8LO01eFBFWvXsvCJ8ticIrL1ohIRWYM0mw4dhndPl4tuhlEsjHj9xhgAI1IE8x6AlLLoQvO626sO5qAT5bFIFdgNOM9myUNSuOKCbFsM9Dy+KWnG7p/ugb3fLsZq2KUD9o7enXBXkxZfQK3f7Ve9WOTdi3YeQ5fcKdmUtDec0mim6C6YVO34oyLJfnuegQbjxt3WeOWk1cweeUx5HFWUZfM2pdlAI2IDO9CsvNdYx7+eRumrD6BP3adU60tIpfuOR559KxdqgbwzM5iE8JkX5EKFNTDW3FY/QDajtP5mxRcy+JycjPLy7Pi2Tm78fny/KDZy7/txRcrjmGfCYMcIlzLzMGU1cdxIsE8uzNn5ojbdVJvvlhxTNXjqVmf7oEftuCz5UexcM959Q6qA7l5VoyYtg2futlcQgtEJiCIxAAaEZneeSf1ziwK5Ws9P2+P3c9pmWLqB2Tm5GHR/gv4ZVOskOObkdUmhMkaaOQoQHCKaEZ2Lv7YdQ5X0rSxG5tZ5OVZhQ9CdpxOxD974/DVSvuBevJ1bnaiholLYvDJsiPo9dla0U0hErIqJvYKS9nYWns0HquPJODr1ced/j7xWhYyc8RPfJk0fsYAGpHaDsYlq1KgnrTprz1xdj9fSuFgVS1aWsLK+Bk5ChCwLZ3t53DC4sN4cf5e3P+DeTazEZ1hZbVaceuX69HzszVCg2haGIiZ2cwtp0U3QXWcRNIWvh3akpntPEMzL8+K+JQMtHx/OW75eI26jXLKnB8cBtCIVHT2ajoGfLUBnSauEt0UzToen6p6Z/5SSiYe/GELlh64oOpxSV1ausxrvW5E3o3lA2P/2Ce6KaahVgbatlNX8dSvO4tM5Cw+cBEAcDzePMvI7vx6o9DjX8/OxZFLqTh9JR0XkjmxRkRiHHZRK5i0Y+Pxy2g27j+8siC/X3YxJQPJ17Px65bTuHotS0ibzJqBFiS6AURm4qqYPeVbeuAinvx1J1pUjcbC0Z1VO+6cbWcAAJtPXlHtmGRuuRoPoB26kILVR/J3iJ0wqJng1piDRaUMtPu+3wwgfwdO20MmpDIblojIjDTeJSEAj/y8DTl5Vqw9mlB424vz9mBlTDx+33UOfz6t3ripwPmk66hXPlL144rGDDQi0oy52/MDWXvOJoltCJHSNN5ZFV2TyeicLV8KVLkI2tZTVzlocrDmSLwpP/v8HBBplxpzK1bBnRItldjQAtv33F1W+Mobu3fvPpOkcIuc+3iptjc5UAoDaEQq4gXCvTVHEjzficgLVqsV3609gTVH4oV//2wHqdyBzLxiLqag3UcrMXvrGbvbRX8+CRg+bTtmbRVTj4pBLDITfty1JS7J+W71JJ7oWp3uZJm0fiYDaOS17FwO/Mj4BNTzVtzklepuha4FG45fxsQlMRg+bbvopuDA+eTCfzvb+VVLOLhRziu/7UNCaibe+HO/3e2uXvOcXL4balq8X71amErt9iyX4dO2Y/+5ZM93JCK/iM58PXX5mtDjO/PAD5sx+LtN+GrlMVVrfP284RTe+/sgN7qQwKwvEQNo5JUP/j2Eum8uwZGLqaKbokuuatyY6QR09VoWxv9zyGPBUl645LfahBl+rna8nb/jrMotAd5fdFj1Y5L2uBooTd1wyunt0zfHKtYWI04UyG3t0QSsO6rMuVP0sikgf8OQy2nO69/l5lkx6FuxGy2I4Or1IBJB7u7wmSvpeGLmDqy+sfxPq7acvIrtsYn4fPlRPD9vjyrHTMnIxvh/D+GXTbHYduqqKseUSvzVoiitb4ilFAbQSLKxf+zDTzc6+P9bflRwa/SJYxXgzT/34+eNp3Drl+vd3k+tgtqumPSaYBqvLlB/d0mVS1z5RUdNNbx9fmYArT4Sj6dn7USikxl8nufcu5aZg0d+3oaHf96G61nGXKoycsYOvDh/r8vfZ5swA/JkgvaycUheGdm5WHn4EtKzcgp/Fp0F5opc3eH41AysPZqAJ3/diWUHL2HEL+Iz8wt4eo6bT1xWpR1nrqQX/vtympidLR3xOq093IWTJLFarZizTf2MDaPhbD9wMO5m5tnSAxfRv0kFga1xTQuZAeS7wxdS8O5fB1G1VITophTS09efn37jGHFj+fLi/RcRO3GA4NZon+2yynSboFlGdi7CQwJFNElRqzSehULGJCIocPrKNVQpGYHAAAveXngAv+08hz6NyuPz+5qj2bj/0KBCFJaM6ap+w1TS7ePVyMj2XIZHRP/X1WqBAlpf7q6kgiwvLb4CopMdRGEGGrm15eQVfLHiKJq995/oppABPfnrTtFNcGnBznOim0B+eOTnbdgWexW/79LO+xggoaNxPuk6lh28yCXMBmbS/qbu7TmbhIxsebPQtDgonL+dk6VkPAt2nsMtn6zBc3N2AwB+u9HHW37oEraezN+R2FNpEaVlKlyQXUrwTJT5O9z31URcN8VOpN98wiKDVNm5ebjv+814/99DTn9v1r4qA2jk1gM/bMEXK44hNTNHdFN8kpSehWkbT2GJikWB3eHAST+vgagtoUke8ane17DJzs1DmoLnOimf/c4TV+GJmTvx7z5tnLNIPJ2cMg1vxC/b8cRM7U76yOXV39Vf3q41eumnkHTfrjkOAFikkfGAM43fWaZ4EE2KvWeT8OuW05oKjoj4Tv6567z6B9WYNUcSsO3UVZc1Ws2KATTyiR46F1tOXkGL8csx7p9DeGrWLtHNAQCkZugzECnCT+tPim4CmcTZq+k4ezUd3T9ZgybvLkNyerbsx7Bardgemyj5/ptPXpG9DaQ9UgYoSo5hkq/L/1nXi80nvP+OrVVoMwEis8nJ1U42lO0p9o7JG7DnbJKQduTkWXHapgZXAbWHXFtOXsVbCw9g5WHtLO8WkT23UmPL20WEM7X0PdUSBtDIJxqalHBpyurjoptQxJi5e0Q3QTc+4I6FJDNngf/MnFx0/Xg1un68Gudv1ODYeUb+nZf+t+KY7I+pR+P+OajJc7Mor7nI9jmZkHbzBx1MWOnRgz9u8Xgf1sIkUkZccoboJji1/3wyHvhhs+hm2BG1hO+E7XXIoA6cT8ahOLFLd7WMV0DnGEAjn1y5xi2+yTeOs2taShF3dMbJTCDpl7OPWppKWaFfrfQugKbhr4VPLqVk4LcdZzFtYyw+WXZEdHM0w1XdlxSbzyXjZ+o6eilVdBNIZnO2ncEfGqqHSdqm5VphZpZ4LQtX0uQbf6Zn5eD2yRtw21frkZXD99yZGZtj3f7eYF1VyRhAI59sj03E6Svc5pv81/qDFVh+6BISnNSsyhacOjxn+xmhxycyivYfrcQrC1hbibTvyrWswn9rsci/0aVkZOPjpTGIuShPVsjltEyM/WM/Xpy/l4NkN1YcuoSnft2JpPQsz3fWIXcDfS1N5DprSm6emPblKHzcfeeSvLp/y/eXo/UHK2TbzCXl+s2JqgwN1J7zRMTVaMtJ+VdkGAEDaOSz5YcuiW6CYWjn0q2+q9eyMGrGDoz4ZVuR3zkLqhHJydl3LyvHPN/IvWeTsD2WHSQ1nb3qObM1NcO8tcnI3D749xC+WXMC/b9YL8vjXbPZGCZPQ4ESrRk5YweWHLiIT//LzxBOzcg2xRI+cu3bNScUffyjl3z7fNlOchiVHmqNmxkDaOSzzJw8/LT+JBbtu4BNJy6Lbg7pgLuZzWM+Xki1ijPd2iO1QzJ1g/gNLNTIvszLs+KuKRsx+LvNimycQM6lSFg2/P1asZ/B1Ixs4RnARpWVk4fh07bh+7UnhA6S/tkbh4W7tbfL3L5zyaKbYOq8w/iU/InLThNXoddna3EwTvz7oTRRNcakyBOUfQZA0V3J/SFXxuDZRH2VadFi+F+73xxlMYBGTuXlWfGlh6LXX648hg8WHcbo2bsw5MethQW4yXu2FwO5UpN9dTE5A2uPJiiS0t5i/HLZH1OLFu27gHpvLcHcbVwCqkd7NTCA+3tPnOLHSMu62Tk+c1VMbUJRS1O0LlHwMqqm7/2HWz5eLbQNWqDEJgILd5/HmiMJmLAkRvbHlup6Vi6enbMbz8/bI6wNWsaz0s1d49ccSZBlQlBkIAhwP9DX0hJOR0fjWZNRKYO/09ZmEbYupdzcZOOYhj8D2v3mKIsBNHLq3/0X8L8VR93ex/GCej6RATQ5NHh7qSKPe+ryNby6YC9OXXZfu67DhJV45OdtWH1E3u2bRXee1DR69i4AwOt/7BfcEmm03HlU2v7z4oNlzmSpkP0z9vebn89fNsUqfjxHh+JS0PS9ZYXLRFYficeWk1dUb4fSrFar1+e/gza7gonKjtDqLnl6l57lPKtDzdMwM6S1Q+vlUBbuPo96by3xexMGPdSY0gLHoH0ckxOKMEOX1fYcLToj3R0zvBfOMIBGTkmp0ULyUeP8M/THLZi/4xyG/LhF0v03HZd3ILv8sLY7iWrwFLwkZTm70G88Zt7l54v2Xyj8t+NyPTWCNu/+fQDpWbmYtDQGF5MzMGLadjzwwxa7mkV6Z7VaMfi7zbj1y/U+Z9slX8/GhWRzDKK2nLyCTce1853kJgLq00KdMjXf9VEzdqh4NO8di88vr/Hi/L1+PY4G3tZCi/Zd8HwnQTIddgDNztXQC6ewqyaobUbGwAAaGdZ6Ew+MnSnIJrggKKvANh3ZGWddBFFtLZBy3f+6ULaD5h6frvH78ch3W09JK5aflZNn2J3ItGR7bGLhv22XKGw6YZwstJw8K3acTsSRS6lFlsl6492/DsrSHq0OUC6nZeKBHzbjgR+2YMhPW025iYKGyzCpyrGweHJ6NqZvisWVNN82FfJ1J8HcPCs2Hr+s2TpQStlzNkl0ExRh+ykoWCXg7HeifbdW2cL9WiZ1klnJYKwc/X5XrqRl4p+9ccj0MRtTi5cIs163GEAj09DTEkLWBcr31x6xBY4LZl798dL8Pf43hBTj6pvW53/rVG2H1ii9rPekw+5uExaLqwelB0kydepv+1KenQ3lNnFJDLacvBngvpbJ5V6Ub8y83Xj374N4dLpvmVrzd5z16e++X3cCQ3/aiqESs/a17uq1LDwxcwdWxbhfDRCvwO7ncUnX8fhMbWfaaYXt8n1AfNBEzfGIFoIx3yoYwLz3u814ds5ujzXGXdHiyFBLmaVqYgCNTMNV9klWTh6WH7qEZAVnHZz57MZW4c7IsYThZEIaFu4+r5v6Vhq4bhaxTWLGkjsLVSgG7y+dfERUlaDAIELLHDuuSmchONZ4i71yc+ZZ6XNW4rUsIRMqWpgYueghE1gUrWbGKbGJgC3b7Kh955IxbeMpRT6bRy6mYteZRM931KA1RxIAAHt9PCf5Up/XYgEW7Miv+aWFTWXk8NHiw1h28BIe/UX9QNZL8/dio8xlQeTkrv95Ifk6vlt7AqtiLuG5Obs9rqbwl+M5R/QOoc/N2a3asfZL/K4puXumlF2yfVWQYbfkwEXFjkHqYACNZKOFmQN3XBXl/mz5EYyasQMPT92qansmrzqOc4npOJmQhidm7pT98Xt+thbPz9uDv/cWDeDc++2mwsLdavHl46HxjxQZgMjP2Joj8ZoJcDs2Y95237I2pBJVW+pQXApavr8cw1Q+3wPwb1debXxMZOMYMHP8NCjdn/hl4yllDyDRwt03s6xHz96Fcf8cwkIFMq/7fbEOg77ZZLqJAV9p5LQsqwU7/dsEwB/nNVAI/2SC6+WBq2Jcb5p1//dbMHFJDB79ZQf+3huHVxbsU6J5mmVbK1Vp7/4trVTBRIE7GBMBDKCRjLQwu+6LP3bld1ZFzDJuOHYZ78hU28aV3WeSity243QiJi3V1gXI2afn8EXvtm62Wq04FJeCjOyby39mbo71r2GkKqvVivNJ11ULLIk8aw2ftt1tx10rlHgvHAMktodQ6j25mJyB277KX8Ioos6aP3U5t8X6nw2rFV+sOIpW7y/HrK2nC29TO8vivX8OqXo8V5xl3h255N11zxsXubOqZFru0R44n4xXfturm/dT6xPsBeMAZxxrV565ouxmUGevig82ap0SG4242rxIif6PViZO5aB0lrZWMYBGspmvcMaCEf24/mSR3e/oJm+XUC45cBG3fbUe9363qfC2txUOUJK8Jq86js4TV+Grlcdx9VqWoh0NT0ulRk7fjqMKDmYBYLNGCub/vTcO648liG6Gop4QXINH64NItXxxo/7LWwsPFBZod3xtDjnUARJF6UxJUeOotCxzFcb3lta/q7dP3oDfdp7D8/OUWV4n9zJijb+cmmbk187XPocS582CJduOr/dvO+TP3JTSfK30Dck5BtBINnvPJXn9N6kZ2dh1JhFWqxUHzicrWgfFyBchX42evcsuW0tRKvRIf7tRLPjAeW0Mvgq8JzEtnYDPlx8FAPzvRpbKmwsPKHasLSfdd1BWHI7HsJ+UXepnO05Rc+fBl+bvLXLbQ1O3qXZ8x7OBGrOYRqllZBRWK9Dk3WWYuTm2yOdh7VFtBHONOrv+5y5xy/mcsVqt2HTismZr4WnVcRk2OnLmiszvg+g6Xp548z1XYpMFd3INlK3k6C8d1Aj+ft3NcjcFG3GsPOx+Iw5vXcvMwX8HL9qNx1Y6rE6IT8lQfaUXz8euMYBGsvHlAnnXlI0Y9M0mjPvnEG6fvAFtP1yhQMvcU+Pa5C6LxtnLplZXY9G+C/h1S/4ymuTr2ej56RqVjlxUVo5xM/F+2RQrugluufp0Ho9Pw9sLDyBOYP2S2Vv9qBvlQUZOrsfvv9KdZdulCGruPPi74AG02yWcBhov2O6oFnMxVdUgqV68/ddBBDh8ILQw3j5yMRXxKfJ//237SjtOF82yPijzBJCz/ofWvmN/743DkB+3ovfna0U3pZCS2c+XUjJMtfpAA19nt7x5q9Oz1N0h+A+ZrtXHFM6mV5MS38z07Fx8v/ZEYaF/ZyYuyd+I4zEfdwQu4Ph5GzN3Dx6fuRNj/9jv8m/afbTSr2P6oo+GzsdawwAaycaXC2RBUc+CAIOa0fW8PCuOx6dC6UoXT8zcgdu+2uBVpleOiq9DYnr+DMPMzbE4aXPhkH0Ao0KPXWNjAjtvLXR9YXSUnJ6Nd/86oPhOiJ7c/c1GzNxyGk/9Kv8mF0aXkpGN5+fuxuoj2q9xpqS8PCt6fbbGy7/S8jfZO0cu2gdDJq86Lqgl2qaFgJmtc4np6PfFOtw+eYPsj20bNHdWF2/Dcd9r5TljW+9Pa69zgf8O5Wd0aCfjQbkXav+5ZLT/aCUGfbPJ8509uJzm2+t1XeUgkNYjaFq+4sg1iZeQZpwNRJQIbn+75gQmLIlB8nXXk1xyZXw6ZjyuuJHR9udu+TeQ8YeUTFStTcaohQE0ko2WvkMxFz3P4I7/9xB6f77O5w6IVMsOXsLhCykud710VmOloDOpJsegnZwnRWZdAL9ukZ5J9cGiQ5i++TQGTtmoYIs8S72xnbdRl75ZYFFsQPm/5UexcE8cRkzb7r4NGh9YAP6dCw7EJeOEm93PCo8h0/G0xvG5XNbZIEatYsfuMhJFULIGW8F5VS0XdFJoXlusii07LMj+3X9e3HW14TtLkeKmX3Za4UL5RHphex7Y5WRTNq1Jy8xBoooTEXrowyqBATSSjZa+Q/2/WO/xPmovq3M2EDmRcM3pyee6gAK/Sg3s3vv7IJq+958qu95p6TPoj8MSAsByupBszl2flNjJqcCFpJuD1ksprgewtgF0I3ZEXAcLHCMmRe+x71wS5m8/K0sQx9UOW2pbIWByxB9TN5xS5ThKF+v3VqaCJQUCA9R9rl+uPFr474KvkoFi1B75GghTKnhse90RmXG383Siy9/d+91mFVtCpF2XkjPw+fKjbvtxWtLk3WVo+f5yzfR5jEo3AbQJEyagbdu2iIyMRLly5TBw4EAcOXJEdLM0Jy8vvxi/v/WkfBlQWyz5yx60uj2v6MGpq1fFeQ00+RqblC6tg+ZNhpQ3CgKVSw5cVOTxbWnzk+c9tTdBGDXDnEs0F+w8p0qmy/drT7r8nVaLlMdcVLZeSpGMIyevw51fb8Srv+/DGhkKyqud9eNKikbaIdX8HSrtrq2t+JmikwqO9d6UdvaqOSdICvjWJ1XuPTpzNb3w3ylulosZica+3kU4+4w8+MMWTNd4/VqvaLOroXmpmTn4auUxjPSz7pktNfqd7mq5yal5lWhVjqM1ugmgrV27FqNHj8aWLVuwfPlyZGdno2/fvrh2jWnGtn7acBK3T96A0bN3+fU4vgRTjl5KQ5dJqzHun0N+Hduo3M32OZJzUL3Qx11ulJyBV8qaI9rYuU1vDl/Q1q6latkoc60hV9ZJ3Kpd7kFGZk4u1h5NKFLvxttdYf3ZBcyX5+R4tOOXlNlpjrQjzSGwKHrCy7E9clI5Ac2O6NfVFVfNSpchG9/2fHLgfLJqA0tXbPspcSbJ/tb+LpxFbT55Be9qYAd1bb9yYojI05BzybVG80x8UrNMMdFNEEI3AbSlS5di+PDhaNy4MZo3b45ffvkFZ86cwc6d5syccOZ6Vi4+WhwDAFgucJmI1nccFMXVEkY1lq74Uox+5o3dOfVKq5mQajqXmI7528/qfodTJXcrS8tUJgPANgju7huu5Pd//D+H8MjP2/DCvD2Fty3ad8Hrc/THS2N8b4TEp2fUr6vGx40eyf35zHOxQc5aGbIM5fSTgktXDfpRV8T87f5nQNp+gu/9bjN6SNxtXI3r5j6D1he1ZbVaNR8E0vL1R+vBRxG0mrlP5qGbAJqj5OT8i06pUqVc3iczMxMpKSl2/xnZm17s8ufOyQRjzvbviJWeAaYmuXfcckZ0MXoRThj0c+yNHp+uwau/78Own7Z6vK/VasXG45cxcvp2zdVEUyqAlpKRg/k75Nki3pFth9xdzSMl+8aztuZnEi89eHP59PTNsV4/zo/rfQ8mZOc67+g6Pm3bTUy0PJjxlt6fi9yfz9f/2CfvA3rpclom3l54wOP90tXepdDkXH1NdspQtNvXAEScCpsvuGrZ7zvP4ZXf9iLnxrXvfNJ1zNt+Bpk5+vpcPvzzNtQcuxjH/Ni9UMkJNFOR6VyekZ2LYT9txY/rXJemUJrer6ukf7oMoOXl5eH5559H586d0aRJE5f3mzBhAkqUKFH4X9WqVVVspfr+2OXf9re5eVZ8s+Y4en62VqYWacs8GWYyzSY3z6rb7CXHXUX1yp/dsAqCF9tir+KKh00i4lMzMfSnrVhxOB6vLhA7yDUC20+fu5pH7vq0O08nIjld3zVyZvoQsDPS7LLen4ncAxWpAWulMjNf/32/8OxqDv7y5UgIjPyz174ExZ6zSXj0l+047kdAxpm4JGmTRuuOJmCpjPVcXV0aXvptL37beQ5/3SjB0fuztXjt9/0ud3PXqnUyZJZuP3VVhpaQPxejmIspeGn+Xpy9mo55289iw/HL+HDxYfnapiCr1YpDcSmaKqrPFTL6p8sA2ujRo3HgwAHMnTvX7f3Gjh2L5OTkwv/OnmUAxZ23Fu7Hx0uNuzEDs6Cd23rSdedkwFfr0XL8f8jI1tesp5GsjomX5XESPWwmYfv1uKjCzLs3tLZDnxS2HSRfd/u859tN6PM/fU9onEt0PjB9aOo2yY9htnO3FjvXeXlWrD+WIHlTGn8pFUSNUXmHY605Kbj+V4EJSw6j0TvLCjPFpX7FB07ZiFUx8Xj0l+2SjyXl+/TOX56zEq1WKx7+eRue/HUnElLl2bXcVYZugYLr9vUbfTC16nZqifbOhurSwuXvzq834vdd5zBqxg7dZeeuPhKP275aj9snbxDdlEJqZLeSsnQXQHvmmWfw77//YvXq1ahSpYrb+4aGhiIqKsruP3JtzjZtBBh3xCoz22R7EXIsqi23jOxc9P3fWklLRUTbcToR513MvsZcTMW1rFxT1OkwuhWH5QnEiaDHjCTbBEh3O1raPTMnPeV4mQZqzg+oPFfLp1ydc4CiGToajCdJ5u3gx2q1FmadaEHB2zd3+1k8NHUbBnylziDkz93+ZdRrmciA8HNzdos7uI3v155EVm4evlhxzKe/P5eY7vlOXkh22A1zm4esp3/3yfMd/WSZcSetSR5auPwVrEQ5eknZ3bml8uY1+X1n/rVE9OYhRqWFAK8IugmgWa1WPPPMM/jzzz+xatUq1KxZU3STSCH+1EqQqtm4ZYo+/r/7LuDopTThS0WkenyGfNsza4Ues5ackWsp6sQlHgrBG+Pl0gxvss6Ox6dKWs6khpWHL2G7jJMYvuw4+KxGBvkirDmSgOdtNn3QisX7LwBwH/iUU5LOly5r2Z6zSaY/3e/1sLHSJA8bp6iVCWaUUhTkO61lJGthQtOb/tWWk843cCPyh24CaKNHj8avv/6K2bNnIzIyEhcvXsTFixdx/bq2il2T/9To2HlKm/fXpRR9pecejHO/rEXkjLmvnYfdZxLR53N9L38DVBxIiu8TueQuGGq1WvHBv4cwf4dyGbR/7PJ+owGpH9v5O86i9+fr8OSvO9UJ+no4xGPTd2Dwd5uVb4cX9LyE09uv1Xv/HFSkHZRPC2NR0W0YOGWj5k733hb697f9V67ZZ/ZKeU9EvG+f/3fU7uftGt0MS0k6Pv3Lgrtw+sfXEhp65+/HZvmhS5LuFxSom1CSrHTzrL/99lskJyeje/fuqFixYuF/8+bNE900cuKvPb4vvzh9NR3zd5yVPSNDrYvQ5bRMv9PyjXK+T7zmf72c3R5mil15/Y/9qmQzKo19J/c2nbiCnzacUnTjgxfn7/X6b6R22lIz8gvbqrbEVvUlnL79ndZ2glXL6SvyLk0jksuhuBQMnLIRm3RWh0uZvp9yF+bDF25OaGbl5uFFhTJS2bXQB1+yuJWimWCeQcZISvJ3HPnS/D2S7hcUqJHPhMp0E0CzWq1O/xs+fLjopmmC1ra2HjN3j89/++2aE3h1wT7M3nZGvgbZ2ONjQEaqXaf9nyHUyjXKX3IsP8jJtUraxCCXSx38o+HPnLvvA5d6GdOL87wPWvrq8/+OoP8X65CmoV26tEaOZTu+TIot3n8Bi/Zd8PvYBbRwbdVCG3zx6C/bsedsEob8tFW2xyzIMPc209ybl1BrS+A8cdzw5w+F6gEWvCrnk66rtjSb9M8o5VHUJnUztrF/7NPVxm3BAboJJcnKnM/agOq/tVR0E2SnVKr6wCkbFXncAlqYoUmWOagg8hkt3n8BDd5eip83nHJ7vzf/3K9Si9Sls74/3WDWZQOOfO1sb1axbslXq44j5mIqZumkZqUeXUnLRLNx/3n1N9cyc/D0rF0YPXsXUjOMEyjX66nhqgwZ5a54u1GRvy+h/0EA5d5EuXbe9uSz/44g9vI1dJ64Cp0nrlLlmEZjtVrx5p/7Fa13rLVlu1qogeZrC0QGpqT2CedsO4tfNsX6fTy1hqLFw4LUOZDGMIBmAHJk3mgx2r3vXJLoJvhEfPgMGDlD+hbvWldwIRn/7yG395u7XRu7yJI4Kw5dwsrD0uo2KE2zg2SVT1AamE/I34Ro9q4it8enZiDWZmeugozZj5fGYObmWLWapwv+Bhzm7ziHdC93v87MuZmxdl2DfRQlaWVTEaUdv1Fm4cxV5ZYuO5vU3HLyCi4kX8eJBOllHuxP6cqd2H5c736yUC4Hzqfgjq/V2VXXqNYfu4xZW8/g7YUHRDdFV/w9v3mTVWp7T6VXILnjzVj9YrJvdbRFZNve3bKy6sfUAgbQDCDbzxPRrjOJaPC29jLY9FoLRguZJ1qbsSJSiu1s6MgZO/DY9B24poGleL6cB1QJNok/PXnNcUmTt2IupmKHk6X17T5cie6frrG77VBcCr5ZcwJv/yVPMX8Rl4OM7FxsOXlFliCMyIxq29o/cr2OGrg8S/K7DxuXKE6Bj0LMxVSf/s6b99HZoPL7dSfRccIq9PpsLc4nXUeut0tIBU4M5MlYrqKgBif5RsTr99SvO336O9sJCZEOnE9GvbeW4JXf1CnToIE5POTk5qHHp+puaubvtU7qtT+EmwiQWX3sYbtuo1CrwzNrqzK120Q6Fp+Gg3HeLbEwq5iL7nc09ZaIjroSx8zNs+JKWv7OZ+cS07Ej9qr8B7nhTwVqxng7sydyptOZedvP+D3Z4gs5Bhj+NjvHi12XjVAH7clfd+KBH7bgq5XH/H6swhpVAiKvW2yW8WphYkpNF3zMQFCUQd+C/eeSsftMkld/s/zQJXy39oQyDfJg8QH5agLqghYiIBqy5MBFn+pej5yxQ4HWeO+DRYeQZwV+26nBSQKFnLx8DZfTMp3+7g2dl5/RwioDERhAI5LZ2qMJfj+G1op0jv1jPwZ8tcHt4DIrJw9xDoVozXhi7f/FetFN8IntZ07OsWrBAPy+7zej9Qcr8O5fB9Bl0mrc+91mxYL3P2+UfwnMrjPeZXVmZHsf9Zmn4DLk137fj+ky1NXwlpJ1k8i5NUfyr0EzZKrLk5SehY3H/atJd8XF4MGdIxdvLq+T65wk8pqUkJqJe77dJCm7zGTxQkXJlUXpOIkycYnn69f1rFz8vOEUzsi4ouK4AXYXNwpR9cA2n/D+fKyVjbZ4brM3W6GkC7WudVqo+y0CA2hkGucSr2PK6uOKHiM+VYOzxjJKdDMYvmvKRnSauMou84YXSv+p9Roq0RE8kZCGem8twbSNp7DzxhK66ZtvDuq/WeP/DL6zYPOFJPm/h2osf/hk2RFFH3/bqauqd/hrlimm6vGc8eY5y11DRO99S4vFgjdlqO/zk4dNYJyxXcIpVwaaiCzMAh8vjcHO04mSAiBJfi5b9sSnl1Pnn2URun68GuP/PYRun6wW3RQykKOXpC99vpaZg6k+nH+VInJYYPRTmK+XSavVitNXrulux2KRGEAjU1F6gDr8Z+MU7/fW4Qv5SxcX3lg+dyUtE20/XCGySSTYPd9uQnauFeP+cb8BBLl3KSUDCamZWH8swa8OTnZuHrK8WM4ohwCj91gNzgJg0T4xS8YsCtRAu5TifSacXLxZzlww0RBzMQUvzNsjawaTr4y6sYGSQW5Xy7b8obUVCqRtHyw6jPc9bMIllSyfPIExGlGH9vb6pfbE2w/rTuKWT9bg/X8Pq3tgHWMAjUhGhy7IW/9KjwoG+K0/YPBMT5TolCelZ8v+mI60sKW60r5aeQxtP1yBh6Zuw6L9vgczVh9JQG6e/gbBes7i4oSu72yXhuj9dczOzcN5hxIHUtw5eSP+3H1eEztra2QFmOykfrb8ffqZOblYHRPv56OY45qndRuPX8bx+FRdBDNXxci3M3mODCcBOcZJRl82qPb1bsKN5ehKlD8xqiDRDSD/GaHoMdnLtVqRkZ2LsOBA0U2RhGm/yhHRWTZ438QnIjvK2202XFhzJAG3N6vk82P5uj26SLGXr6lyHCU+96IHu5dS9Pd+OyPHEs7fBRatHvLjFuw/791GPA9N3YqsG1lfRy/JW/dq7VH/Azl6IUf/5KuVx1CqWIhfj/HRosN2JQxIv4b+tBUA8PWQloJb4pncWbcJqTcfz5dxishl9KK46lu4ylDNMuFrpDfMQDOAIz5uA16AsQ/tGfvHfjR9bxmu6SQ4artj2PTNpxFvkEGbt7RSpFVPbF8zVztLaT393VZ6Vg4el3m3KzkHz5fT1C3qL8dM8ZIDF2VoiWdKXAtFZyjc//1moceXi79vzVsL9+Ol3/bK0hZfbI/1bhMSAFh/7LICLcm3y8tdJ83u8+VH8ZaftQBnb5OnWLjocwqZm209WF8mNuS4zPo6oaLEN+fnDafw9sIDPgXqXY3xsnysucsRiHoYQCPSqOxcq9c7/4niGDj6aLFx19Hn5lmRkuF8aaJRZtbUDKrfMXlD4b8vJYurT+SJ1Eyin9afwn+H5Fsy4ciX3QxFOR6fplr2mDsbjisXiPBEruL3gP3MvxRJ6dmI9bN2llbKEviTRXQiIQ2/blFmpzOSJjs3D0cupjp9H30dLErlKYjP3eq0jQFDbTl79eY1JcbPBA5faSnxY/y/hzBzy+nCjbKcUau9jpPQyRLKqDz6i315gNVH8rOTebpyjwE04pfEhtaWIiZ6WUMqPSsHJxPEb3G+SoY6H1o16NtNaPbefz7Vs/Fk1tbTuOfbTXa3qdV5tN09VU22A3S5lruJ/BonKrx7nlYCGp5cz8pF78/X4oqbnXvVskylDDZnFsi4bNBs9Ulsr8d/7Drv8+NkZhtjYkPPnp61C/2+WIdftxRdwjhm7m5Fj/3P3ji3v9+gYKafLW6oQkZgmxk76JtNbu6pPTOdnH/8YZs8cC3L+QoKX/jah11uM3k7f/tZNB//H6asPu7y/tm5eUXGa6Nn7fLt4CbDAJoB+HtN1ljMiGx8veqYV/fv/dla9PxsrUKtkV5ANMWL3cb0Zu+NQNMSP4q5O2ry7jLsiL2KN/884HYWS0mjZF526I3MnFzk5OZp+lwkNZCp9HOQ0g6r1epx0Ki05OvKbyAhN4tF/iUQogLTRvO1m0EAaV/BwO7H9UWDwGot0XZF7kG1KxkM5BLJ4sxV6ZnVtpmf/8q8o/S+c0myPl4BXyeTbQN6BfUWP1l2xOX9t526WuS2dBkDgUbGAJrOpWfl+LUrG2nbtUzvTmRxChcI/23HWUUf36zSMnNw73faqVWkdlZqq/HL0e3j1S67DN52JoycVSvltVhy4CKenaNsVocnenwPtBzAJd+J3siBSE5bT10R3QS6ISdX+rkljzVyZZUjsWSKkiuLbN9Sd++vt/2h7FwryhT3ftMSb4NfBZthOKPDLpyqGEDTuVd+24dZW1nbg9SRpMOsEjUd8HKXNaPrUb+spPtdy8qVNfhr9gv/LkFZjLa09B4YZdhitk1KPv3vqCyPc/SSmDo9RuZr3TJvMkc8ufXL9fjg30OyPZ5ebDqhnQDazC2nsf5YguhmCPP8vD2S7jdjcyyajftPtuPquS6cXAEtqTtVKnvVvPnov+30PsHA1fv4z944VCwR7vXj+bvRCUnHAJrOyZF9psdMAaUwA4H88cEiY2yecDJBnsLv3m5v7kvHSq3vrNTzpNJ1FK9qoKaYJLyuyO6HdSdFN0GX/Kmf5gyX5QLTNFCP7/CFFPy0QXw7zGr3mUS8vfAAHpq6TXRTNO+dvw4izcWOi76wwooD55OxcLe85zY1ZHuRteeO1K6Wq66I3BsyrTvqfS1FrWZHJ6VnabRl2sEAGmk+aHQlLVPxXZpIGo6J3TNKhojUWneeeBuc96VGjLOdT5V4F7RynpTS+dRCU7U0Qy61JZxMUsZFhUsLqO2BH7Sz3F6UvQrV/jGKi8kZmtjQSUlxSep8r3leLurU5XTcPnkDnp+3B5tOiNtlWiR/+zlyZsMC7idPfek/igyuaWHzJ60LEt0AInfOXk1H149Xo0bpCKx5pYfo5qiOHQfPtBK0SsvMwX4FlnBqdYZKCm87DUkudrB09zjOsnJEfm30+27JS0vnLl/fk7TMHBQPZTfJX9+sEbsBgNzBbxaDJ086TFgpugmkEd+uOSH7Y87ZdrN0z/H4NHSqXUb2Y2id1Gx/i4vOCPtq7knpwkWEeLfKxEjYMyTFTiJnr6ajaqkIvx6jYOem2CvyzhS4orUTqjeDUDVmO121R9RgOTfPiu6frkZwgPhk2hU220cbweELKX4/hhpLnWIuqlPfSEsBIS2zWq1Ivp6tofwz6axW+2DLztOJuKWetDp+5JqWajapZfmhS6gUHYbGlUqIbgqRrPLyrAgIsOh6ck9Nk5bGiG6CIXmaOz97NR1/7j6PRBcTs3JzFajL/52L2930lM6oNO51Ruqk07fDWivbEA0TP+ok4ZxtYyuHrh+vVuRxzSTpmvSi/d8oMMvlyPFkf+ZKOm75ZLVq28A7SkjNxNmr13Hysjw1u/yhVBtELYXztdbWxeQMXLtR6+OCl0u3jNAd18pST1FeWbAPLcYvN0zQJDk9m7un+SkpXezmM2oP9NceTcCoGTsw4KsNsj5ucno20rPkq6Mkynt/HxTdBPLRU7/uRIcJK5Gaod53+kpaFnp+ugZTVovNZPVFRrZ3uyKSdJ4y0O6ashGfLz9apE8WczHlxt/L2x65e+opGdo/1wcF6HGqVB4MoBFpmDe1qEQM3N9fdAinr6Tj+7ViiltraQb0q5XHRDdBuLik6+gwYSXafrjCp7/XcvDpssSCs1r6TIqwYOc5AMCzc3YLbslNkosNW+xrOx2MS0bz8f9h5IwdyjTMJKR+d5Si9nll5PTtijxu8/H/ocm7yxR5bE/u/Fq+YOAvm2JleyzKz7TxxFmtUF8sOXAR8amZ+HffBdUm975efRwnL1/DJ8uOqHI8OX23VvmJba06ezUdd369AX/vjVPk8f876H7Vh6tJ4B2x8u1Sbntt8fYy8/rv+3BIhpUeJAYDaAb2+X/uLzbxqRn4bYf32+6qSe2hqC876MWnZChWJPm6xmavNp+0zyrJkalTpneiB4haseXG5yM9y7fPbZ5MI113qfS+mrxS2uy3loOA5Nlcm9oyMzfnZ9auiokX1RzSIbl2mXNGVDLkvnPy1/ckeRyM8zwIV6IOl1qTRXIF/0Q4oEBdXL14c+EB7DuXjOcUmky7fE3f/e6528/iyV93im6GS1L60ebNP2MNNEP7atVxJKZn463bGyI0qGihv7unbML5pOsCWqasX1VcTpiTm4d2H+UXi415v78ix7BarXjpt72oXbY4Rveo4/p+KnRmjsfb11kLFFx7TCvBCj138OTk7+vw0m97nd6uhbc5J08/77FWvhd6E3NBnXp6pC5+H+SXfF3sslzyzl97zsv6eInpWYgM4xCSXFN6me8FlXaBlcrMwSQzYgaawc3cchrTXaTLGzF4BgBvLTyg2rHSbTLElCpU+c++C/hj13l8suwIsnLcDOIFDBLMvP7d6Hx5Z793siOmNxJS9T2jCGgj2Ee++XtvHN8/DXtuzm5k5nif3Wq0ZdVaqMm3XcZlUKQ/Hy89YriNk5TgazY+eVZQe/nz5Ue9yrC8mQgg/jyqeyYeAjKAZgJxGovSa5m3p1Pbme0Ahbbps01//nef81oCSelZ+GO3vDOMUgQygAaAGQ4FTiaI38wB8G0ptnzHFnZoIkP7e28cZm894/mOGnLmSjrOJcq7m9q/+y/I+nikbwmpnvv4SlyWFu5RpraVO2Pm7sbO04mYvPIYXpq/V+i13pGzmltG2UxHqy6lZOCrlccwaWmM5MkVpWowaueT6L+nft0pacMaUZucaQHzb3UsRWJ6rJYuMFoXn5qJytHhku9vWwNMjdOIq5po7T5cqcLRi5J7YOAtrXyytdIOLRk9a5dsj5XrZcaFEjXQiEi8RB929BTZBRozbzdiZd6hedfpRNzZvJKsj+ktnmG1Y+KSGDzUsYboZijGdgzz1544/GUTuBvSvhpaVy/p8TEmLolRpG22Xv99H354uI3ix3Fk5u+i7S6nHOpKc/VaFlq9v9ztCqJjDuV6qChmoOnYrC3ezcTGp2awVpMHW7yYLbqclonWH9zcbVDkoD1L0Pu6V0Bh4fSsHAz+bpOpdzfSg0UyZkkMnLJRtsdSHntxembbCb+YwuxtPcvKycMTM3dg66mrwtoQn5LpU9DPHS3sYsmznHZcM/EywUwJG21l5eSp0l+03cGZzMH2PKiXQGar95cDAHI0UApAzxhA0zGpO9ZZAWw+cQXtPlyJum8uUbZRMrPd/njnaXGdYGdmbLbfrECpGmhk77s1J7A9NhETl8SYIrtSWP0eDfUGTsmcweGLEy6Wp/665TRWHmYtGC3z9TtkgtOLof2x6xyWHRT73TTsNcqgT0uPpMzdaqW8gtykfAyNVgOR9Ecr3emLyZwUlAsDaCZgtQIP/rhFdDN8svdsUuG/52w7q/jxPlp8GGPm7pbW6XW4T5wKmzJsEziTrgXLDl7EV6uOi26GqsxcY8DRfwcv4uGftyHeQ80XpV4xx6WkB84n462FB/DY9B2Ft103cTYAkZaocU0Wpcbri7D0gMBaaLwsaYbR3wpXk1dEtv3jZ2bvdnPPoq5eU28nYa2EcEfN2OH5Tl4wc7UUBtBMwNNgU21j/9iHzhNXud9RUpAr17Lw1544xFxM9XhfxxOiGgX1/xJQtNUVVxsaKOmJmTvtftbK5L5hsww05vGZO7HuaALG/XNIdFMAOM+ME1FY2ZERZ9z9KT/gTRDaiK+dkVxOy8R/By9KKnCclWvs9/LJX3dhvKBzYcE36mBcMubvOGvaa2BapufPoVmduaJ8jVwf5roNx+BPz62NJy4X/nuFFysBft5wSvZgkh7eh/3n1S+7Y1QMoOmY1Miv6CUMjuZsO4vzSdcxYclhr/5OzYuglMGaY3sCTRaK93a2x8iU/myyk24frLqcmun2vmqdKrzd3IB8F3PB86SGK2oHxVbHxOOJmTuc7spG/pm99Qwen7kTL87b6/G+ZgiG/rzxlJDjFryyA77agFcX7MOKw/FC2iFatgYngrXizikbFD+GlO/46hhjfza1OvJIsOmnXUrJwL/74uw2XpPD2D/2+/R34/+Vf+JBq+8DAExaGoP9CtSs1vJzVhoDaDp2SedrmadtjBXdBFkFqJCBRuKkZ+UI2XV088kraPLuMrz390HVj60ltkHtCx7OfUoFHDPcFCxesv8CDsapM7unhZpweiJHgHvEtG2Ss2xG/LIdyw5ewoeLvJskIumWHrwouglk48jFFNFNMC2t7jqdJPPmGb66LGgiI1ml5y9ykxR3ziXeXELf49M1eGb2bszcctrNX+iPXZdAm19DAMC3a07gjq/lD2hr9dyjBgbQdGz6ZmOdiLSEdafIUeeJq9Bl0uoit2fl5GHBznOKHXfn6UQA0ndeO3A+GYkGz3w5c1X9QCYALHazs+hTs3ZhwFfKz7gD+Z1Rs/Emm2jn6auF3xvAuzodrmJkq48keD0g1Fr5BFKfUXPg2EPKp4XxowaaoGmiXp+dZ9QJbP27T2AtRInSb9SGXXc0QXBLxOB31HgYQCPdOJ6Q5vb3VqsV11Rc6uY4oHvgB31u1EDSJLoYPH+0+DC+XHlM5dY4t/P0Vdw+eQPaT1jp92NpLYh8JU17QUGRS8SuZeYgz0RLSJcckJZxdD0rF/d8uxn3fLupMGPQ6DVwtExkbSwtnMM8ZcsSGUlGdi7+3hun2iSepBpoyjeDJMoxWJ/FLoBurKdGHgSJbgCRVLY7cjrz6C/bsfqIerMbosYFObl5GDNvD+JTMjDv8Y6mXjoqcmBcMDiXmhmmhjU3Pv9ybNDhT9F2Jeh1J2GlNH53GcoUD8GOt/qIbooqvl1zAq/1b+DxfrbLd69n5SIsONCr84QWMkqM5Pt1J4Ud2ww10Ii0ZNLSGEzbGIsGFSJVOZ6kb7hKHUXHw2ghgK81Jw22o6rUjxavRMbDDDTSnfNJ153OassZPJPS8RZ1Qjx5+RoW7buA7bGJOHSBdUdE+fS/o6KboChRxalJustpWU6DpWbOuHJ27pZjCae3j0P5Ji6JEd0Ew8vMcV2bUbljamuChbShYDmhlJ3s5aCl3V/jHTc34vVCdUI/DyZ8v83cJ2IAjXSlzQfL0XniKjw/b4+ix0nwsMuf2cWncFmKSMsPXUKPT9d4zMr0h9FmCuWgna76TRkCBs+aZvMmFXTu5OrkOfbN528/i84TV+HopfzB4s7TiThiM3DMzDZmkEEvS4fNkgGSeE18sXYzF5MWjS+9B4JeIL4tRZ1Puu75Tn7QUDzVFMz8GWcAjXTl8o06SH/tiVP0OF+tOu7xPic91GRTg8UiZqndsXjxz10L1J7tysjOxfdrT2DUjB04dfka7pqyUbFjsVPuGTtr2lYQQPHmffLmvq/+vg/nk67jld/2Ij4lA/d8uwn9vlhX+PttsdrcHc1fJy9r4/y/5kg8bv1yvcvdb7mE0xz6/m8trqSZd9LTLIFiIr3iN9R4GEAjckJKYGTZwUsqtMSzudvOiG6CMKIHSGoHUP634igmqLQkisEhfTBTx0zKednfj+1FN9m1roLK2blWnE1Udmadiho+bTsOX0jByOk7nN/BJOcws2fMH72Uhsk2k54pGdl4YuYOLHGza7JcNBG80kATCqjdFC3VQHOkZlbmmStidiYnaXSStO01M0+0M4BGqrqQrI9BhpbPCbZ9AQssOCdg4KaV1+dQnNgacGpfE3edTnT7ezn7iWa+MErF18jeysPamFTwR66PPV0t1eIxm+TrRZcw5uTmaedCpbD9551n4JmJbSb+5JXHsOzgJTw1a5fAFqlHSx9z1c+CDgc8cD4ZX644VrjJk1m8+/cB0U0gN/7Zq+yqKVIfd+EkVV1OVWdrayP7c/f5wn8vPXABeSYeuM0WnH1n5EGztx1QU9TFM+7b7bedpxPxmKtMIMHk+pq6yzYx6gyzP/7ac97znRQQe/ka+n2xTkih++Px6hRPt8VAPrD3XBKmbTyF4Z1qFJb6MAu+/zfdPnkDgPzX5LledVH4gwBqHpWbeuT779Al9G9SQbXjSR0DFNRJJeNgBhqRzszdfjNoJKVWmyLYYQOgvXiKnP3ESyneLQuatPSIfAfXqEyHeoMGjp967ZCLOlRqs3tPVDxPqT2RYbVacfaqtpftfK3S9cnxtf9ixVFhA8ren6/zfCeZXb3mOmC04pD+s0KlOHA+BeP+OYTF+y+KborqNLGM9Ab1l3A6P+/GXLy5OiFO4cL1pB1P/rpTdBNMpVmVaNFNEIYBNCJnPEQicgQU7i+QlG6/XMXMg/j1xy4LPb7ar31WrvsDivwspGWK3wlOad+uFhSwdiNeI/WPtHgaknsXTnfUDqDVHLsYXT9ejY8WH1b1uN5Qa7OZDIfdTrX4WVTSlyuOufzdFyuPqtiSm65n5eKLFUdVL7NwIiFN3cxwDcSutJSBppnrkc1H4Ns1J4S0Qc0Nvsw8DhAl2WEsZroLD4DgQPOGkcz7zInc2Hs2ye3vr2Vqp77CvB1nVT+mlmY8Rdp4Qt0AnqfPpUhmWMIWl5yBr1fdHKweFFyDDwB6fba2yG0nNLBDsJIyc3Kx/1yy04GyqI1F1BrAFOzEW+CHdSfVObCOmG0wmeVmoH5V0HLGL1YexRcrjuG2r9YLOb5I8akmKGegEVr+rk/dcEp0E0hBCWmev+dWqxWpGdmaCnKTPBhAM6GkdHH1IZQa3OSpPXrX0MkwNSNH9WOmZap/TC0aMW276CbYEXmRNnI9OFuf/ncUx29k1mi1gyzi+6nG219wjFEzduKOrzdg+qZYt+2wOLlNKWploH2y7IhqO/HqlTnORM7N3XYGw6dtQ3pW/jkgLllMMOfgeXGTC9cFF5Dfd1bd5ezpWdqZ0FVbnhXYdy4JWRqsAXbkopi6V+P+OYh3/uKmAqpz0v9+7fd9aPref9gR634DMNIfBtBMqMX45aoVNDyvQu0Bq9WKu6ZsVPw4tgI0FEAT4WsNLmVTyszNsaKbIJntGP6thftVO25WTh7iktQdqD0/d7eqx7PV+/OiWV9aYvRY5rqjCQCA6ZtPF/ndtlNXC/9tUTGiPHurOhuaaDVoqyVmCeY78/of+7HmSAKmrjfv52TZQXPUfSPg2Tm7cOfXG/HC/D12t5v1FJCakY1pG2MxY/NpXE7TxnJaI/P0MZu/4xwA4IqbOpWkTwygmVTf/6lT6FbpzLBXftuLl+bvVX0bdzUHZlqk5aWEcnv7r4Oim+CTX7eot0PpbV+tx6EL6mYcLNzDbcG1RETQItfJ9WXfuZvXgu2nriI3z6pKZuaSA+YrXu6Ou7pcSjPp2NlOquAscVFdJLUP6+x5injuona8Fa2g/uGifRcEtyTflpNX8Pfe/L6JiM9BXp7tv3kmVBVfblMJEt0AIl8lpGbit53nhBzbzOEztYsCk/YdV6lYOEkjoh8n4phnPOxCOeKX7XitfwP5DmjmE78NKbHS/60QU7xeLcnXs1EiPFh0M8iB2uchZ98FEYGTMXP3oF/jCggLDlT/4Mjf6bJSdLiQYzsjqhbmAz9sAQA0qhgJERcMUc/bkZmzgMkcmIFGilLyHKr2rme2zJyAtsdE2WcFFu/XxuymK7vPJOKAylmYpF2ZCtQAWhVzyW2nWK3T8SfL3Nf/chxAzNmmXiamWZj5+lcg5bq2dx0261t0ME78dVDUJks5AjOOrgjarEKrLiRnqHqeLLjuudtQRCnOSvW8/692d4cmkgMDaKRbSwQENZKvZ+OV3/Zi84krqh9bK974U73aWlrx9KxdopvgUkpGNu7+ZhNun7xBaAeatCNGgeLFj/6yw2ltoYzsXMzZdgaXUtSpgTdl9Qm7n+NTM/DfwYuFyzlFzKuovXxZPI2HZ3gaVJ3FAvy7Lw4vzNuDDIFF/B3PUWqdl+wI+now68deTm4e6r65WMix1Q6inr2aH8T6Z6+AcVF60cmEnzeatwYjmQOXcJKilEwnfu+fQ4o9tiufLjuC33aeE7Z0lMjRVZuZ39w87e1ERcq7kHwdFUsov3xmy8kr6N+kgt1tw37aih2n1dlhytnVpNena5GamYMP726Coe2rC81MNjur1YpNJ66gXvlIse0wYQRtxaFL6N2ovLDjf7z0SOG/G1QQ+/7bOn3F/TJvfzEbU7t2n01Cdq6Yc4HFou5mY+eTruPYpVS8/6/64yIiM2IGGpEXPNXcITKbU5evCTv2ysPcbQ0Anpujzo6kzgaLagXPXCkolr46Jh5A0Qy0q9z9SjXLDl7E0J+2ouvHq4S2w4wx1JEzdqh+zKwc5xM2RxTIgPVVepaymyk4rYGm6BFJCqtVfBF9tbPQPll2xO7nZQe5sY2Sen++rrDmHZkPA2ikKNvORXJ6tqY6Vr7gbCNpjeix4jWBu709Nl39QaMWbY8VG8TSgsQby0gcs4/SZPx8Tl4pbmdJPSjYfa5gZzxRzBhAEyHbRb2lP3afR0JqZuHPF5MFLKO8Yfi07cKOrTaRE7xa7BuLjJ9p4eVQopQDucbLjrkwgEaKSrIptNtx4kq8smCfwNYQGZtFi71YIhXsPJ2IpHTn2WYnE+TJkvxpA+u6AK4Hy4v3ayPjYSkzL1QZwV9zk91lO3hPdPG9NAJndcdEXYcvs5B/ISvMt5Q7Pcu+9qBaH0N2O/MVTNaxFqE5MIBGqnE8uesRrxOkNaIv1iN+Mc8Mv9mJ2l1Oqt1nk5xmH12Xsaj5dQNcx/w1f8dZ/Ln7HPacTUJmDl8Ps1p7JEF0E4R77++DRW77c5eYGrmi+wJkQ+UaaACw4fhlhyao0wAG0G56Yd4e3D55g8vsXDIOBtBIUcVDA4XXIZDLgfPJWM0OI2lM7JWb2TXnE4tuJ64026U6JM6GY5c938kElL7cNB/3n7IH0IHv157EC/P2YuCUjWj9/grRzQGQv+OeKOcS03EuUVv1UbeosFM4VxQAC/fESbrN6K5n52ovgCd0CaeFKwIEOaPwxiHu/Ln7PA7GpWDLSeXPvyQWA2ikqMmrjqPVB8vx0/qTopvit4embhXdBKIiHv3lZh2wRfvV38LcrF7T2OBx9rbTopugOE+ZZJnZeVB61JTFmWU7ctaY84eoebqsnDx0mbQaXSatRoaMmY7+2nsuGScS0kQ3g1QkMnw1+LvNeEalzWyksFpZk8qs8btun6wWfl3KFFwHlJTHABop6q89cUhKz8YHiw6LbopfElIzC4tUE/mrUokw0U0gP83bcVZ0E+wULNdQOgtg3D8H8cO6E4oew5VW45e7/f2P608ij/1WU8pTOfulYHBqO1BLzdBGMLHAiXgG0ExFcMRo0T6xE3i/77RfOisyIU7KRMt3w1or2gaTxs8AAJOWxAg9/pQ1x4Uen5THABqRBG0/1MYyFTKGFtWiRTeBjOZGb/nl35TLjIu5mIJpG2Px0WIxnVNPg5JTl6+ZrnA05RM1WI5PvbnDpNYyPrSyhCwrh1FtNUzfHIvh07YVZkJeTsvE+mMJ2ltaqZCXfttb+G/Rz/mn9Sc9ng/6N6mgaBu08v0X4UBcstDj7z6TJPT4pDwG0IhcKJjNOnBe7ImYiMiT7BuD1N8VLGCth41gtL7RASlD7Qy0gBuD0/5frC+87WBciqpt0IthLH+hijVHErDmSAJmbs5fzt/9kzV4aOo2/L3XfDXZ1h+/LHQyZXvsVZwTUJPWltrnRC0x8VMnlTCARuRCwWzWk7/uFNwSY+nbqLzoJhAZzn+HLil+DD30SZmBZk69P1+LnacTVTvepZQMXE6z30DlkZ+3qXZ8KbQSStba0la55Wpso6wPFx/GheTrhcuL15hw86usnDyhQZQMDdTAmrFZnbqoWpy00tY3koyIATQiN85cSRc+i2Q0nwxuLroJwnF2jEh+Fpj3u5WWmYNVMZdMu1zuQnIGHvxhi2rHu/ubTWjzAUs7kOfNTUS4/asNhf/WXnhDHSa9FBCRCoJEN4BIy7p9slp0EwynRHiw6CYIZ9ZBPikrNUPZjU72nk1S9PHloMZXy2q1aq6+zBMzd2Dj8St4tHNN1Y+tldcjKzcPa4+aL9vGFQ28JaZgW2/rikNWoihXrmUV/jtBI20iIjIKZqARCRAZxtg1Ecmr6Xv/qXYsLWY6WSzqBKc3HL+s/EG8kHw9GxuPXwEA/LzxlOrH19KEgNaWUZJ5nL2arsni4euPaet8pRbRGwmYhSYD9XzvSWEcxRMJEBoUiFQYuy4IucYsPNK7DhNWYvXL3UU3w45afeY0jdV06jJplegmkAZprTaXUVkBXEi+jq4fc8WClvDTb157z3HzN1KW7jLQpkyZgho1aiAsLAzt27fHtm2cbST90eSMjUaIWIKktqd71BbdBJ8kpWfhqs3SEDKvq9ey0HycehlvUly5lmXKTQREF2nXYg0oM9p1xn4ThYlLYgS1xHz+3H1edBPIkfkuBUVcTM5Q/BhM9nKOGZDGpqsA2rx58/Diiy/i3Xffxa5du9C8eXP069cP8fHxoptG5JUGFSJFN4EEigzTXwZabp4VLcYvR6v3lyODA2YiuuGuKRtFN4EADPpmk93PJy9fE9QS8/l46RHRTSAHegjsP9iuqqKPfyIhTdHHd2bfuSTVj6lFp3j+NTRdBdA+//xzjBo1CiNGjECjRo3w3XffISIiAj///LPophF55X/3txDdBM0yQwaJHmembINml1mUmLRKf18t3Tsen4aVhy+JbgaRENY8rirQoqdn7RLdBI9aVy+l6OOr0dV0/Oy/OH+v8gfVgbsdJjTIWHQTQMvKysLOnTvRu3fvwtsCAgLQu3dvbN682enfZGZmIiUlxe4/Ii2I0mEGklp0GFsyhTnbzhT+Oy8PSEjNRHqWtmpBEalx+uBguajHpu8Q3QQiIRYfuACeEsgXRvjcbD11tfDfa48m4Hi8+llvWpR8Xdld0Uks3WwicPnyZeTm5qJ8+fJ2t5cvXx4xMc7rPEyYMAHjxo1To3lEXuEAjPTmx/UnC//d7RMWSyZt0mN2JxHp19ojCbCotQUwGcKD7aoBUH4skKfCZ3J1TDwe6lAd36w5zqXMZBq6yUDzxdixY5GcnFz439mzZ0U3iYg8MMMAODwkED0blBPdDK9cSuGyTTl0q1dWdBOIiIhIGHX6uQ//rN5GewyekZnoJgOtTJkyCAwMxKVL9nU2Ll26hAoVKjj9m9DQUISGhqrRPCKvmDUBrX55z5snGD98BkSEBCE0yNDzF+RCSCDfd737eOkR9G3kvN9BROZiseRvskPkLa5GIdIn3fTkQ0JC0Lp1a6xcubLwtry8PKxcuRIdO3YU2DIikurP0Z1EN4GIyC8nL1/D0oMXRTeDiDTg0AXWVybvmGChBZGh6SaABgAvvvgifvzxR0yfPh2HDx/GU089hWvXrmHEiBGim0bkFYtJp50iQjwnvVqtwPBONZRvDJEAL/apJ7oJhrZwT5wqx7mYnKHKcYhI205fSRfdBMO4rak5MnsLAmgWA6xHiUu6jpmbY0U3g0hVPgXQcnJysGLFCnz//fdITU0FAMTFxSEtTdmdN+6//358+umneOedd9CiRQvs2bMHS5cuLbKxABFpz1sDGhb+e/bI9mhUMQrlo4ousb6enYt372ikZtOIVNOoUpToJhAREQn38/A2dj/3bGCO8Zz1RrESI8ylx1xMxdt/HRTdDCJVeV0D7fTp0+jfvz/OnDmDzMxM9OnTB5GRkZg0aRIyMzPx3XffKdHOQs888wyeeeYZRY9BpKS+jcobYM7JeyO71ir8d6c6ZbB4TFeMnL4Dl1Ls6xrm5llNkaFngqdIZFhcgUNE5J+qJSNEN0GIsOBA0U0gIj94nYE2ZswYtGnTBomJiQgPDy+8/e6777arT0ZEzlWKDvd8JxMr2N7bKH59rL3oJhARERGRBnAcQKRvXmegrV+/Hps2bUJISIjd7TVq1MD58+dlaxgRmUHRPI7gQGOlZhUPc36aNULtCyKzsrIKNBER+cDT5SMsOAAZ2XnqNIaIvOZ1BlpeXh5yc3OL3H7u3DlERkbK0igiI7NYuHyvgLNOhBmWbwIA42dE+vXBosOim0BEZChmmZiwuikCUKlEGDa/3kvF1hCRt7wOoPXt2xdffPFF4c8WiwVpaWl49913cdttt8nZNiJDYubRTc66EGZ5dczyPIncGdmlpugmEAnVtW4Z0U0gIhU5Gwe0rBaNXx9rj5UvdUfJYiFO/oqItMLrANpnn32GjRs3olGjRsjIyMCQIUMKl29OmjRJiTYSGUqAxURZVsRAGZEb5sg3IHJtxqPtRDeBSAjHrrBZrgc3d+G8+QKEBgWgS90yCA/hBgNEWud1DbQqVapg7969mDt3Lvbt24e0tDQ89thjGDp0qN2mAkTkHGNn7pnl9WEQ1Xza1igpuglEpDG8FhCZk+03PySIgTMivfA6gAYAQUFBGDZsmNxtITKsYiGBuJaVXzvQjJ3l4qHOTzXO6l0YbVtzs8yokmfcup6IiMgFE3eYPrm3megmEJFEXgfQZsyY4fb3Dz/8sM+NITKq6IgQXMu6LroZmuOsrxQc5PXKcl2pWoqZumZlkvrIRG4N71QDv2yKFd0MIhLOfBPKwM2+gO18evmoMDGNISKveR1AGzNmjN3P2dnZSE9PR0hICCIiIhhAI3LQv3EF7D+fXPhzt7plBbZG+4zWnXJ8PkPaVXd6OxGRGURHBItuAhGRMLc2qSC6CUTkB69TPRITE+3+S0tLw5EjR9ClSxfMmTNHiTYS6VqJ8GC7pYr1yhcX2BptMWNGTsGMowlX8ppe9/r5wXNXS5qJyJwKBtQ9G5QT3BIicawmWcNZqyzHAUR6Jksvvm7dupg4cSKGDRuGmJgYOR6STKpXg3JYGRMvuhmyM0eXQB4MLJERfTO0Ffo2Kg+A2YdkbhZ+A4r46sGWOB6fhitpWVhlwD4QkTNm7+/xXEikT7IVGwoKCkJcXJxcD0cmNXV4W9FNkJ0VVuTZplrxelnIWWDRSB2KMsVD0LBiFMoUDy28zTjPjrxxW9OKCAo0dn0/X5Tkcj5TqRwdjkc6VUfjSlEoVSxEdHM0IzgwAA0rRpk+oEDmZsZVCUSkP15noP399992P1utVly4cAFff/01OnfuLFvDiIzEtlMQwB6yaYQGBSIkKABbxvZE3beWwGoFOtcpA4CBNFPjm1+od6Py+PS/o6KbQSpY/kI31ClXHBaLBf880wV5VivqvLlEdLOEerlvPdFNICJBOBwg0ievA2gDBw60+9lisaBs2bLo2bMnPvvsM7naRWQYViuQZ/IENG+esxE7FEGBAdj1Vh9cSM5Ao0pRAPLPnURmF8jvgVMDmlXEon0XRDdDViXCgwvPewEBFgSY8mp40z2tquCZnnVFN4NIGMczgNkS0Mx9BiStua1pBSzef1F0M3TB6wBaXl6eEu0gmdQpVxzH49NEN4NsWG3+19YfT3fCoG82qd0cIVx1iqxO8vVDg4yzzG1k15qF/y5ZLAQluWSJiCTo1aCc4QJoZC/AyeiZS9iIiEiEwADjjL+UxlfKYDiboU1NK5co/HfBDHyraiVFNUfTjJCZ1bthOSx9viuGd6ohuimkQfr/hMunUnS46CZokgFOg0RERB6VKc7JZdIXSRloL774ouQH/Pzzz31uDPmvZbVoHGMGmqZYrcBbtzfC6iNrAZhz8OzqORt1tj0oIAANKkS5vY8ZPwdEtl7pVx8RIYGim6FJRtpMhZxzFiS1mm4Rm3n9+HAbJKVn4ZUF+0Q3hVQ0qGXlwn9zoiRfoLN0XCINkxRA2717t6QHM0LmiN693K8+5u84J7oZ5CAy9OZXzdXXZOojbfDY9B0qtUgbzDxYiArn7oNkbt3qlhXdBM14qEN1zNxyWnQzSEUMktqLDAtCakaO6Gaopk+j8gBg6gCaxWJBsyolsO9cMjrUKmXYSVVbj3BlQhFmeN/1gHFM6SQF0FavXq10O0gmESFel7UjhVUoEYrIsJvBknAXGRcFuzOSOZRgAM20ONmUr0HFSL4WN7w/sIldAI0vizmZeSB5W5OKmLfjrOhmaFLzqtHYezZJdDNkV7FEGKY+0hYLd5/HPa2rYOkB4xcwDwq8eXJvVT2/lEtwoLQTfo/6ZbH6SIIi7SIi6VgDzWDY59aOaSPaYnDrKni6ex2EhwRi8XNdsWRMV4QGmW/JUtVSEaKboCoOfm9iHbii+PnIZ+ZggSetqxuvRibfbns8D5iXt+/9O7c3VKYhgoUFB6JsZChGdauFUsVCTLcqoVxkGLa90Qt73ukr6f5GXblgrnddexpUiMS6V3p41Sd7oG1V5RqkAz6lK+3YsQPz58/HmTNnkJWVZfe7P/74Q5aGEeldj/rl0KN+ucKfG1VyXxPLqG5tUgFjb3Xe+TPqAJoDo5tGdq2JXzbFim4GCVameAgup9n3F/KMegLw0/sDm6BKSXNNOpjRY11qFrnNzN8Is1w3977bFyGB3uYvmOTFMQHHpdvlosK8+Fvje6F3PfxvxVGnv6tVthhOJlxTuUXGt/T5bl7/TWSYuVe8eZ2BNnfuXHTq1AmHDx/Gn3/+iezsbBw8eBCrVq1CiRIlPD8AycZZZoeZO196Z9Sx5LfDWqNaaeeDQaM+Z7opwCyjInIrOoK7bLnieC2vUtKYO5PyTGCvbvlI0U0gAUqEB7ss5eHOnc0rKdAacZx1Dfo0LK9+Q1RWPNTcgQdnBreuUvjvkCDXoYnaZYur0RzT8mZIZvbyG14H0D766CP873//wz///IOQkBB8+eWXiImJwX333Ydq1aop0UZy4a0BxkzpNiuzpa4D+XU9jEhKcWjzvdtUwNzdjptC3XSUzeLYh7fivTsbO/1do4rGylrmOc8zM50bnu9d1+7nTqwD69b4u5yfJ/SmYokw/PpYe2wd26vI77zJxtIrVxPKUhg1aPFCn3qF/3Y3FrIgvw4ckWhe915PnDiBAQMGAABCQkJw7do1WCwWvPDCC/jhhx9kbyC5FhQYgJIR9uvhrTpO6ZFaRJOMY2BLY82oEpF0Rh0MeCPYyVKugldl/pMdMe/xDuo2iITSbw/Oe46ZJnc0qyioJaSm8Xc1QZe6ZUwRLJNbkEG3SbS9Drpb3myxAN891FqNJhG55XUArWTJkkhNTQUAVK5cGQcOHAAAJCUlIT09Xd7WkUdm6mwZnY5jnz6TkqlF+la6OJfu/fpYe9FNEI7fdO8VDw1C+1qlRTdDNvwMkC3b3ckBcwTUI3xYumk0fRoZf5mmUl7uV190ExRXpWQ4HupQ3envioUEmXIjNrV4k4Rj/LO1e5IDaAWBsm7dumH58uUAgMGDB2PMmDEYNWoUHnzwQfTqVTQdl9Rzr80acj1iMIUMQ8JH2SwdaXZ2gC517ZcmPdSxhpiGCGTC+QFy4EvdJ7PR8yoCb93XRt99Vl+wl0u+uq9NFZQ3Qdae1QqXZQ0qRefXB/3nmS5qNomcMfnJTHIArVmzZmjfvj2aNm2KwYMHAwDefPNNvPjii7h06RLuueceTJ06VbGGknO2fa3372oiriEyMGMNMFuBARYsGdMVI53szKVX04a3Fd0EzXq4Y3W0r1lKdDNIgOd61sFcLs0jF4yaieOYcUTm5mxypWopY26gQdJNHNQUtzWtILoZmlOjTDHRTVBFnoShYMVo4wcSRXD20pfhKhKnJAfQ1q5di8aNG2PChAlo2LAhHnnkEWzcuBGvv/46/v77b3z22WcoWbKkkm0lJ2xnK8NDAk0egtK3sOBANKwYhbdubyS6KbLpbNKiwFKGvxEhQZj3REdUMMGMopk916tukduCAgPQoVZpt7U+yLzMWg904ejOqGmSQaIrZu/DRQQbe4dCf95fsyQnPtCuGr4ZyjpXjszy/jv65N5mRW4z62shQpPKJZzebvZVY5J77127dsXPP/+MCxcuYPLkyYiNjcUtt9yCevXqYdKkSbh48aKS7SQXCgIUBRFinlT0pUEF59vYR0dwpl5OvTW8NfqHd+s7c1RJlaP1n41Qys132UxZt2Zamuarp7rXRu+G5dGhpnHqnkn1zu2N0MKguzKTdEY/J5p7yEnkmeM5oFmVaDENMaFnetQpcttr/Rs4va9BE+Ul83r6u1ixYhgxYgTWrl2Lo0ePYvDgwZgyZQqqVauGO++8U4k2khsTBzXDK/3q48+nO4tuit/MGM2OcrGkpaVBBhKeTrBqdZbfvr2hKscp4M0SLC5rcq15VeczX3oS4MeuWVFhxs7GcGd4pxqIeb+/6Gao6rX+DfDTI238+sxoVa2y7jPLzN4ZL2Ts+JHpGXV5NpFS+JVRT8OKUUVuKx5q3n6oO36tH6lTpw7eeOMNvPXWW4iMjMSiRYvkahdJVCIiGKN71EHVUhH5Nxig8zW8Uw3RTVCNqwCSAd5GTakcHY6xtzqfRZHTwBaVAABPd6+t+LGMrFxkKMbf1RhD2zvfiUlP3PX9PCVllSkeKmtbtKpspPPnGRZsrKLzo7ra17c0UxH1rhKX8zNT0dyM+Pa/IsPOiVFhQewXmpxZJtSaVNL/xKlR1S1XvPDfZo9r+hxAW7duHYYPH44KFSrglVdewaBBg7Bx40Y520Ym9eYAdbOFSBw1O8tP3FIbA5pWVPQYEwY1Q8z7/Z3O4rjCAWNRdcoVx8MdayDQCJk4bqZPPb7zBnj6UkQ6meE04qzzmwPs61s+bKLdWHmWI3cKBmZv3Ga8/t+dzSsV/tvX01rd8s7LfZBx3d+mqt3PRq8pvPWNXlj0XJcimyXYfmcK+gVGX+qtB0bso3nDqwBaXFwcPvroI9SrVw/du3fH8ePH8dVXXyEuLg4//vgjOnTgrmLku4ITYqDZv5Uw5iysSAXLJt5QIThrtKwZEYz0+fcnBvjBXU1QrVQEShqgJmL3+uVc/5KnfMPz9J020neevFfQ7evRoBw+HdxcbGM0ipNt5jLxnqaim6Cq8lFhaOwh+8xyM4KmW5VK6GvjMA7JnZMcQLv11ltRvXp1TJ48GXfffTcOHz6MDRs2YMSIEShWzNy7JhH5yoh13yJt0sw9PTspJ+aKMl5sKkeHY9sbvWR7PEe80AB9Gvm/YUOegQYK7r7j7gZEMe/3R6c6ZbDqpVuw9Y3eSjRNVY92qYmvHmyJu1tWFt0UEsAQ2aQqYGaFwWvuuPkauFrKTuZk5np5js98dI/aqFYqAo92riGiObL68ZE2opvgs8aVbq6uMeL41RuSA2jBwcFYsGABzp07h0mTJqF+ff/X9JP89Nz5KvgymviaYQje7JwoJU4ix8fBdvCmtYGcfr+xzrWrUcrl78KDA/Fszzp4uKO02mbaeqd84+7j5u69L8hkDAoMQEiQX+VKNSE4wII7m1dC/yYVRDdFMwwUJ/ZoWAf333mrw/8TGYXt99zdNa1URIjibSF9sc0+N3NA7ZV+DbDu1R6INsB3pHGlEnjTi6XqWuj/LRnTFY93q4VxdzYpvM3EH0cAXgTQ/v77b9x1110IDOTyJC0zU4ecjKemTe2DETdmml7sq59gfUig+AudlhULDcRLfetj/F1N3N7vsS75xdaNcDoLMGEv49meRbdCL9C3UXlMGNQU/z7bpfA2871C+fQ84eUtT1lFBZnLZu/DmOH5l48Kxd/P2O8cb/+8jfUi2F4C3AVBjHyp2P12H7w1oKHdeZ88a1oluvDfZl3Ca+TvhRTd6oqtfWexWNCwYhTeuK0hSkQEo0R4flC3RwM3ZTlMwMB50kTi1Crj37JmM14mn+lRBw+2r1b487t3NMZr/Rvg7NV0ga2S7rthrRHgQ3abbUq0EbgPCtx8fb4Z2gpPz9pV5B4bXuuBKiUjFGiZIG4+EnXLFcfRS2nqtUUl7uoAWiwWPNiuWpHbitzPtGE1Y3J1XpgwqCk2Hr/Mpb1ORIYGITUzR3QzZGeEJeneqFLyZlZ+rbLmLHlTslgIRnatJboZwoQGBSAzJ8/rvzNr0MwTs7wqB8f1w3t/HxTaBsfP4PrXeuBCUgbqVzD3xiZMlzCYoED9Djr03HZHS57vKu2OxnnKhdraLOHzlHJue15+qW+9Iss/9VSQv6+Ptb8iw/RfIN5WnsSezW0udkQ1VPAMQOlirpccjO7hOlNLz+o77BhXt1xxlCnuur6Ps7OEGbKzzDQ2cvVcH2xXDV8PaYVgZu8CsH+dTPTxQAWbWqfOPiudapcGANxhs6OlXtj2g2qU9j2AZqbPg9HMf6IjWlaLxoInO/r8GEZbwil9xUbR5633a6fU/k0xAfUgW1SNdvv7qLBg0wfPAAbQDCcyLBhjb22A1/o3EN0Ur01/tJ3oJsgmNMi/wI+eZ50ekljfypGrzkHVUvoIqPiSfWZE7j66Buv/SdLTTZp7uI4CxN6w/QjMf6Ijlj3fjd8PksRxYCHnJjJ6Y5u5ZFSzRrZH74blMOmeZoW3ObuEfDusNb64vwUmDtLnzoS31CsLwPf+Eelb86rR+PPpzmjjpkasJ3oeFzgTEeq6/2OsZ1qUlt/KDwbal1gxWuBWLgygGdATt9TGU91rY/2rPUQ3RbItY3vZZS7ZOjXhNpVbQ77qXKe0V0X6pczChAUH4sC4fv40i1TEa609d50PDfehZBNg8S24bIYlnGZ4/wtIfa6OA4upj7SVvS1aZvv0jZihuuLFW+x+7lynDH56pC0q2WSfOxtclggPxsCWlYVkZMhh2vC22PV2H7SqVrLI7968rSGCAy34yENwUMuDblKGbWauP9mLeuasC1W6uP43E3AlyKG/9EinGmIaQm4xgGZgesncAdwHUuSMfocFa+sj7+qZda9vn7Vyf5uqyjdGBrl5VkU6ef5sa8/C/upy9pkuWN468sbmAK44LvNrXb3oYIO0z/YzUC7ScwaRs1N8MTez00ZhtIwCd/Kkru12ULC5gBkZ8TsQJeH9NOLy7YAAC0q5WM4/qlstHB7f32lwzQg+ulufWYNaUzARdWi8uSaUHQNKQH5g8aCOJ9bdneEcf9ekcgklm0I+4siSTGXbm/ooXvuIQ5r/BIWWLUTKPJvr7RhJjSyTLW/0UvwYdJOzLbe/HtIK/z7bBY93c15EuFhIIP58uhNWOmQnGL0uklHjJ7ZLM6qV9jyRY3se+GBgE3SoVarwszKgmfNaeUZg0LffL492tg+ym3kJp9HqYwKQVPfVqOdFd4J8uNYtHN3Z8500oKB+HXnP2SRLRIjxJxUCAyx4pGN13N2yMqq5SAbRazYqANzpppaj1ibWjL8WwDfGHp2Qbih5Imx3Y2nonc0rIUqFDml0hPRjuEquc+xMKVE/qErJcGx4vScWPydxwwMJrFarV0v4fJlpLlM8FNUlDMoLOM76avGi++tj7UU3QVEhQQFoUrmEy2xSi8WCltVKooQX3x3Sro61SuO+NlXw1oCGku7fsOLNgrTDOlTH3Mc7FgYPmnH2VXEdavlel0duIzrXsPvZaPVXQp1MMLjSpnpJDDfY8h0pk2YRIcbLvPNF9/plXf5u/hMdPRb71gqDfYVVpUYoRe6JdLmMu6sJ/nd/C7fXgGEdqunyfFEpWrv1LZ1NglNRfJXIpT4Ouwo+2E65ZYSuAlv/PNPF78cee1sDzH+iIz4Z3MzznWVQXsKSpQKDWlYBADTwckeT2SP9D7gsHN0ZJcKDERIkX+/Gx1U6XgkPCcCal7v7/PdhwYH474Vu+O+Fbn61o1yk610FvdWlbhm7n/W85KFW2eKKPfa6V/RT19HMLBYLPr63OUZ2dZ5x6EiLQW0zefKW2oofQ+qkusViKSxZMLR9NQQGWHze4VjvLBYL3ruzsehmyEpKOYbu9csZOvNUKnd9jNpljV8P69YmFUQ3wRSCAvUb4fxgYFPse7ev6GbI5q0BDfFwxxoAgN4NXW9ApaS65ez78K6WnpsdA2jk0l0t7FNMm1eJxrzHOyh+XNvZBm8K0rsSFBCAdjVL+b0zphIGt6mC35/qhAVPdXJ5nyecLHvrVKeMk3uKl2e1IjhA+mnF10xlf7MS6pWPRL3y/m3DvE7BTTqGtK+m2GMrrU455QJoWqth6D9tpeprkZGzF6Se/xyvxQDQuFKUbO3wp8akVN5kG78/sAl+e7JjYfDoh4fbKNUs1blajlRAa8t35BYuIVskMMCCKUNaqdAabXPM1jNibTh3HCcWzUiN04Hes3x9Wf6sRQfG9cPIrrXwxm0NMfOxdpj8oJhzoO3n4f27GiPMoLvF+8sYnzpSRIDDSXVQqypoX0vdWgZ6PK97M8i3WCxoXb2k2wFMwclLjmCi3bFv/L+cF+jykWGoWiocg1pVxvBONWRvs5aEBQd6tVxXpMGtq4hugiy0OnwY1kG/AU8t0ON5Xh7SPtFf3N+iyG2eAjHetsJx1llu3lxnQoIC0LZGKUPWQPzh4Tbo3bA8/nja9aSZUZU2YSbDsz1930nV3XmxYJBbycQ1As2gSWX5Jkq0Zki7/H5TR5XHlVpVMA4MCQpA17plJU02KM1xYy+6yXi9E1IM10W7FxhgQe2yxfDZfc1lebxne9ZBzTLFCosq62EnluGda8BiseDz+1ootvREjY0HpFIrWcDfjLRPBsvzmZTCl3fH3d8osbxM7uLLPerLm2r/YDtzBeTkDAbpiTfLGm398FBrRTM9laDV4LfaapYphp8eaeNyx0Ujv04v9a0vugmqU/o56+HzUtKEgVO5PNuzLl7sU89lreLmVaOFLfXz1wt96mHmY+3w0yPGyTA2msoltVurTTRGRMgtZ8WF5Z6pLuumzoMcmQlqZTcMbl0FK1/qjjrl/FsaWOClvvWx+uXuNwur62Bph7dB1pplxNbxcLY0Soo/VcgesJ350VMGmdyfUtsLuFxfZbmLL7taQhDiIYPG1Vf6dpXr/0SFybeEz7EIvBSPdKqBkV1qYpaT2o5aCphrRd/GvtcGEpUUrMTSxE9VnBjQiiHtq+GDgU1EN8MnpYszkCJV9dIRRfuu2u8CFvHX6M6qbN5lVGHBgXiuV100crNk39/kBiXrW7sTHJifacUaqNoze1R7TLqnKZpViRbdFM1iAI1csgC4p1XRIveOSzv98fPwNlg6Rr5dIEVSOlDnbd+pfJT71Fu50oOdDXqlKhYahD3v9MHBcf0k/42717lEuHcdtU/ubY7mPgRTWt7IHjB6vRq11HRXENnmNY7y8v1Vi6vPQVS4PjqGW97oJdtjVS3pfTZZaFAg3rq9ETo7qe1o5OWdvpw9CnY2lbN+pF5f4nt1NLEglaf3tVW1koovt1WKXj9notx3YzONltWii/xOL6+lL/2rAoFGPvlrQEhQAOY93gE1y9w8n9zdsrJuSpOQf17qUw+1yhbDd8NaF/ldp9plcH9bc62E8BYDaOTWva2r4I+nO+F3N0Xu/dGzQXmUdrPGWl/ZB9pqq6tNE6YNb4tpw9siIsT/wX3zqtF2g15fBs/RESGyzUBtHtvTq/uHBAVg5mPtcGuTCnYXEXdZNLYZHL0a5u8OV720/0vQ3GVF6b3Iqyt/P9MZdzSvJLlgdFhwINa+0l3ZRsmkcaUo/DKindv7FPci80upOiFLxnSV5VxA6ig4F+ipoHjZyFBUNekyXTmVCA/G3S0ri26Gz4xY005ujSrmZxrd1aIyWlYria1v9MJvT3QEYN+nC76RdfTlAy3Vb6RK7vRxhQBJc4+Tutb/u7+FxkYypJTGlaOw6qXu6M/dbn3CXjO5ZbFYXNbqMLuykaFISM0s/Nnb7CelhAYFIDMnDyM618C4fw4V+X2PBvLXS1j7SnekZea4XY4rF3cXd18CAVFhwfjWYQambY1SmLf9LNKzcovcf/mLtxT++/2BTdCyWjT6+bGkqkCjSlHYczap8Ge9xsy8ycprViUakx90PwBwfLTqpcUu+3XG2TNe5KJmia3OtZ3vMubsrS8WKn9B2R8fboOGFY1bpFjrPC3xdcbf04KIrNlNr/fUbPBkUMvKuKN5JYz4ZbvopgAA6pV3nV027s7GCAyw6Ch0aq8rd1X0aM6oDtgeexW31C8LACgfdXOTgBIRwRh3Z2MEWG4WHG9XsxTevK0hPlx8WEh7lRQuYPe/aSPa4ssVx+z6YlpmAVAywrel0YPbOM/gNeqELQHNq5TA3nPJAIBykdyAxB/a7NGQ6iK9yIQo57A08NfHfF/C54mWz+O2O2d2ql0aT/eQv9i5L74b1hqLn+uK4Z1qSLq/HJ3x6qWLoXElZTc5GH3j9X3njkZu71fL3XJAD2Y82g4v9K6H/o0rOB2o1ixTDLXL3hzgFA8NwsMda9h1cn31ar/66N+4An56uGhBVdu2tK1hroC2kVfJBrgqSKXh854nBn67ZGF7TWtWxfdzpp6+F1oNngHA5/e3UGRSqXK0b8WXa5UtjvlPdMSql24p8jvHvpfeuKoVSTeViAhG70blXX5nHulUAw91rGF3m8vriAn4099z5VYnGTnFQgLxxm0NZD+WvywW4GUfN6qQsxwPad+AphXt+md62JhOy3g1IwDAqK61isyGu8pMmHhPM7ufK5QIVWyr24JOhG0NNm8ptXQkKMCCrW/0wuaxPTF7VAfFC6VKHTAFBwagUaUo2WaRtBKweaVfAxwY1w89G5R3e7+/n+ni8zG61SuLMb3rCumQRkeE4LuHWqN3o6LPz/atNNsOjWRuRpsNX/1Sd7xxWwMcGt/Pr+fma/zMjDshuqJkxvSbN2rV+aJdzVKoZTNRM/WRNni1f/3CZdy+BueI1CS1NIM/xvSqq/gxgPxg0/1ttNP3qloq/xzQoVZp7nJKdsKCGdpRA19lg5PaP29ZLRq3NbWfdanhYofEytHh+GbozQtjhRLhkDvvYHSP2niwXbXCXRptC95LzZabNbI9NrzWQ7GllaWKhaB8VBgqllCnMyuq5o2nOk5qKi6hVpqU+0gheuDuMjlJhma1clKUWC6RoTe/b01lmOHSw0YNDSvIuwzSWe1HHbwMAIDK0VwWUMBZbcQaZYrh8W61/a475+vnobYXGRtrXu7u20F0Qsl5EsdBzND21TDjUd+upb0alsfT3esUXpOqlorAz8PbqLIbNGnfHc3V3bVZKk8bWfmrV4Ny6i3z1Ng8zqzHOuC9Oxrhhd71RDfFJ1JXyEjVx8nEsxkseLIjXr81PzOydtlieOO2Btj1dh/nd9bYZ1jvGEAzuLbVSwEAyhQPxVsDGhZ+0Rw1qxyN12+VPmNq+z2UK2Bh65V+DTBhUFOnv9v+Zm9Jj9G5ThlU8aGovSdjetVFuxql8Ong5rI/ttwKdlF1x7ED4rizWbHQoMLCto60fj6uX973zEXRz+27Ya1RulgIPnPyOWvsZktzT8KCA9CtXll/muZWCZsdnOTY6VUPcaMKJeQNGjkb2OvhdQAgSz1Ao/h2aNHdrfxVEED3fUJF+pnN1SSalvT1YeBU7MZ56ZkedeRuTqEONsW5Q4MC8OHdTWU97/ZsUL5wN2g5vX9XY9kfU25tqmsjK14ryiq0AkTr/OkHueLu7KiljVuqlY7A8M41ERLk/zC+uR+lBHxVSuasuWIy9DVtqT2+K1Pc/esx6R7n4+GK0eF48pbaODS+H1a8eIvbyTku2ZUXA2gG9/XQlnjyltr4/amOGNm1Fp68xUWdLkv+ILBzHWV2evOX7dc+TEBhUVu9G5bH/Cc7qr6rWP3y3ncWPruvOWInDnB7n6qlIvBMjzp4rX8D7H2nLz66uylub2Y/oxmgwTPFlw+0wEt96mHgjZ2anC0jlnuJjpqXn5bVSmLHW71xT+sqdhlJFlgQ4UdnoUJUGAa2qHzjGNH+NtMtLV6up41oK7oJbjWtXAKtVRogyv3+fPlAC9kzN7X4GZJKiQFXwetRUYa6i7a0MzT0zvcPeR+k3PdeP6x66RYM61BdgRblsx3E6Gnc4lhfS6QmlZ33eQbqeCdSJXh7zo0KC0INh+zYKiXDMaCpvJlsip9TFPhiuWyzH09mrIvEBdEKsvvrlo/EwtGdsen1/F3sfdncxlvudrr3hdyftUrRYXjLj2X43lo4urPb39/f1vny4YLl/BEhQR7PA56CdOQdDQ6LSU7lIsPw+q0NJO9cp9VlQqKX09kqqD2gtndub4ThnWrgHyc1vvxd8/5yv/p4qnttlIgIRkhQAIa0tz9Zf3Jvc5QpHoIP727i13HkdFeLyni2V12MH9gEb9zWAH89c/MCNP3RduhWrywm3dvMzSN44OQjV83Jkiwlufrct6lRyufHfKRTDdQoUwx73+mLBU+abwlQj/ryFwyX0z/PdnFabNvZUlatdYiUKBavoVO/pgxp72vwp+jnyNmM9Z3NK/n4+Mob1bUmZo/M37zIl75BYIAFtcoWt/vb3a6WvZAwWu2P6t3ql7ujgUPZgTmjOiBK5nIn/r5/nr7bql4aLM5LK3gyZUgrPNalpgINsucq2GzrlX71EeoiY61F1WhUuhGM+eFh+TOnHUUqXDPaG1FOygL58l67MnuU5432pKyWKsi2budj//+BttV0PSGpNQygEQDzDlLualEJh8b38+pvon3cMtpfJSKC8d6djdH0Rrr1ihdvwaiuNfFEt1pYOqZb4f3keC8dU30bVozC9jd7Y6jDoE0Ln5uosGA83q22XWHlW+qVxYxH28lWbPn3pzpiYItK+PgePwJyfnB8ncf0qou3BjTELz5kU9Utl7+stUREMAJ1sHsXB1H5nC2H/UaBJYKkbQWDSn+W7jgugWtUKapIx1oL53ZnggMteHNAI3SqU8anv3e2wx4AWQtxO2YuyDkYI9efzSduqeX3Y0sJREjlmOWlBXNGdUDp4qGqLEfUQ/1SqXz5Bj/dvTYGNKuoyu6z3wyR1heQcl5vViUaG29ko+nFo53zg5Q96nu/TP7NAQ1Rp1xxu9scvx+BARaUCA/2aefsqjbBsYp+lPuYMrQVFj/X1efsvaBAi3Yv7DrEABrZ8fV6J+Xvutb1rcMLyDvT9GKfeogKC8L7A5tg4qBmHos51ytf3O3vRalTrjjeHNAIY29raFerxllNuud75+9UNKqrtJmwNtVLolHFKLuUfmezgc2rRHvZav2oYZO12bp6KXzxQEuUk3nZlC8slvxlzCO71ircZMPbvydl2W6yIpc3BzQqcpsvnTlbDRWoISM3vX5c5a7xIh+9vqL5/AlGvX17I0W+mwXuaVUFc0Z1wMiu/gdy1FJQAkFPut/IIq7kMBitJkNZjflPdJRtc4a/n/V9R3CluKpnK3VzLi0pGSFPFtO7dxS9tjrjbdDx1f7aW7opdYxXOTpcF/UQCzSvGo097/TB1Ee8n1SOCAnCE93cn7NPfHQb9r7bF2FB/pUQ8ufqGxwYgEYS+2x/uDiH1dfoeFaPGEAjO3J0QFyZ7sdujrUcdg4rCMa1q+l9Kutzvepi77t98VCH6pKKnA9qVQWv9Kvv9XHUNv6uxhjVtabTQfWYXnWx6qVb8MZt0tb0BwUGYNFzXTDFzWCjSeUoXbwuvnL8zJH35AjW6W0OOyQoALd5UUtm89ieePO2hihVLMTtLn2Vo8Px+1PyLbld9FwXjxma3w1jhptc5Apc2y6TfamPbzuw2X6nKriYFPB3oCC3h27UK3upr++7zpUID1a0HMSQ9tXQsbY268i6UqVkhMvBllZVjg7Hzrd6Y/Ur3e1ulyPhKSIkCC2rlSw81/o6SVG9dASiNLRMzZ2fHm4jbGWFt2yzb+T6Lo/o7HliWUtlZHxVLCTQq/6UqyW9tzXV5kZB0REhCPBxVcUdzSuhQQXPm44936euT4+vtlbVSmLX233slqdaUHTTOPIdA2hkx9dZKNuT8obXeji9j68nNgB487ZGeLBdNSx4siMAYOojbbH0+a540ccBhOPFsGeD/BlNV7s2SjmxivZwxxp4c0Ajpxd6i6VozRdPPN33mR51UUyBHVi1Qq1C7t7yd8ctva2qMNIyEGcqlgjHqG61sPOt3l7v0ufPS9O4kueBYX8Xy93IM7mHW8/2rIPmVaMxqNXNAur+XFMLrHvV+fVayQL7vhh3Z2Osfrk7HneSKeBLJq5UY3pJHzC5umQaYOytOaWLhyI0KBAzH2uHkhHBmDXSc50hb7SuXhJHP7gVfzupOSvFhwOd75qnRb0a5vd/5V5yKveVe/FzXfGWTSa2xSJ/YKtm6WJoX6toEFy7GcXSOb5W9X0c1xitdETDipEICw7E0udtSuG4uIJ3ql0GP7jYuOaDgc5rRIsaJ5UqFoIdb9nX9jR2b1pdDKCRTwrqjzjbRlpKMURvlYgIxoRBTQuLp4cEBaBBhSh0qFUa793RCPe3qYpX+tXH0ue7omnlEl7Xhpr6SBscGt8P97WtWuR3Vqv+gg7kvyHtqmHCoKZY9dItoptiNwCzzXBQYlvqOaM6yPZYWq79I/cOrf7yZSCgRh0b0fQ682+xyHvdeKlvffw1urPfu1AHOQTdXNVSK8jOdjWppLaAAAtqlinm9PMgNSvNU0D496c6FrntBR8n6Wxp5RNcpWQ4qpS0zzrV8tdLStu61i2L3e/0Rec6Zfx+Lo4Zt/7UGZS61EptJW4se+zgJEg0vHON/A06JBQ9l0LufnOjSlGK1W1d+nxXzBrZHjXKFEOLqtFFsr3dZSLu0sAGJFL7AravnqfyNWYw/4mOqFPO+TXO1VjW1conV5NOIoOvWj6/6x0DaCZUsN7fdmeYgu9YwQmgYLcPV0qEByPm/f6Fs3OudnZRw/DONTHp3mYY3aMOGlSIwj/PdimskSGVxWJBREgQHulYHf+7v7lCLTWGgroTbWpoM0NLLkGBAXiwXTXUKqutmgG2A0jHwZAc9LYECYDHpcTVb8yst6wWXXhbIHsWuhAhYZm9Hmjl03aLiyLLg9tUcXq7HFluSpMykTCoZWWn9UFtta5eChMGyZc5VFDQelhHbWTzRUcEY/bIohMkWp0gdNwpUmlyZNwuGdMV297opfqgubmXS03vblm5yG2hQYH5G3TU9r1esZrkODMVrC5pUCEKnW02Jmll01fwRK5abFqj1fOCPxwnTt2VAerXuDxe7luvSGkNuV6XKUPyS+R4uzmZP5NoRnxPRWH42YRGdK6Ju1tWRnpWLqZuOGX3uxpliuHQ+H6S1knbfom/f6g1np61C2NvlVZjS6uCAgNwd8sq2BGbiFlbzxTeXsGPnVOMZvPYXrielSvrrmXkXr3ykSgfFYrSxewv/r5k57j7k88Gyxs8lmO22NkF//uHWuOJmTsLfx7dow4+WXbE5WM82K4a2tcspcqATM1wg9Y6Q0q0564WlbHkwEV0rFUaA5pVxMwtp9G9Xlnc/8MWnx6vaqlwnL16XeZWenZHc20Uaw92sSPcY11q4Xh8GubvOAfg5nnis8HNMWDyes191mxJaZs/gcB+jctj2cFLXv/dN0NbY/eZRLT1oVYrAW8PaISosGCnwR5nCkpx+MKfbDNbDV0U6VfarFEd8M3q4/hmzQlJ94+OCMGCJzsiNCjQYz/ilnplsfZogqTHrVYqAmeupgNQIUPax0mwytHhOJ/k/hogZ7kTNUg9P4s8jS9/oRusAEoKqre3772+GPrjViSkZnq8b8Hy4Gd6SlvCLzWIavtZGdCsIvo0utXrc0+3emXRv3EFpyvASD26yECLjY3FY489hpo1ayI8PBy1a9fGu+++i6ysLNFN0y3HgqG2X+qIkCCvLwjNqkRjw2s9MaCZffFsT0Wq9aBPo3JoUrkEPry7iddLQ40oLDiQwTOVBQcGYONrPfGvgrt6DWhWEfe0dp6F4q1372iEcpGhGKfQLk79GnuXKWAB0LJaSZebhhTUVtS6hhV9X05Xt5y0TMrK0eGICAnUTCZuSFAAfh7eFqO61UKl6HC81r+BXxMawQEBuK1pBXSpo26WRX8vP7NSeFsfsCC7qkXV6CK/Cwyw2GWfFjx0o0pROPnRbT630QgaVZSW3ePYawoPCUSnOmVcBi21wNtsfTWViAjGO3c0QlOJ2VX+1AfVQaKlW8VDg/Bq/waY+kgbrHulh6TSE21qlJL02kqpNTxxUFN8Ori5rBvdeOLrpmeLx3T1+m90/vHwiRwB0E8HN0f98pFY90oP1C0fiXrlI4WVz5BrUw9nw+M5j/tW+sSXwH1ggAXfPdQaz3pRnxPIH+cbfeWQmnSRgRYTE4O8vDx8//33qFOnDg4cOIBRo0bh2rVr+PTTT0U3T7eCAm+eBZTqPLSpURLn96g/2y+n8jd2KRvaXhvLMMicgjQ8CHM0onNNDO9UQ5aZ2Vvql8WHiw+jmB9L+ZwFzmw7hwW1Ff3xxm0N8NHiGHx2n3KBp4iQIGx4rQe6TFoNIL8jFWAB8mScVu7dsBzevaOxTxk7/mSAqKV3o/KFuxHXeH2RrI89e2R7DPlp642fLJrbfObBdtUA5NcMK1UspEipBlc1C/35HoeocN6S8xDOsmaNUmuwrkOtnz+f7oSW1Upi5+lEQS3SDi3X6/RGr4buy68oJSw4EAMlZgr6a86oDjh8IQXd6pbBisPxRX5fvXQETl9Jd/n3JVzsLml0/nTH3rytIb5fdwIf3u3dEvd7W1fBvTJNzPoiJDAAXz3YsnBJtdRzubvAluO81UMdqntc3fDR3U3x0eLD+OrBFpKOr5Q7b2TCN68SLbQdRqCLAFr//v3Rv3//wp9r1aqFI0eO4Ntvv2UAzQ/lIsPwaOeaCA6yyF5McsdbvZF4LQsL95x3eR81Z6q8ZVt3JyhAP4ELMp8qJcNxLlF7QWq5ljXUKx+JNS93R+nivmc9+pMB8lyvuvhq5TGXvy/ojD/erTaGdaiueGHeKiUjMP6uxggKCEBYcCBCggKQkZ3n8e+8CQH4utzNVYaf3HxdTtitXlm7bIrD4/uj4TtLZWrVzc11gPzBitJB77taVMan/x31+u8iQoIwukedIrcrESiqIzHz0R89G8gXNLizeSW8umCfT3+rhaVc7oSHBNq9x66WG97Tqgp+33VOrWapqlKJMMQlZwAANo/tiY4TVglukfZJOSs4/egrFHfuWLt0Ybas46YoANCuRim3ATQgv27qJ8uO4H0XOyc6slgskq47xUICcS0rV9JjihAVFozLaZ6XMDrqWLs0RnatqflznDPe1DZ8pkcdnL6ajpZOsrRdkfKSDGlfDQ+0rap6TVHbWr/lo0JhsVhwVwt1At1Gp9vIQHJyMkqVcp81kJmZiZSUFLv/yN47dzRSpG5ZmeKhqFs+EqFBrgdUratrN5XUdomrWoNCIl+seqm7V/d3tZTP2a5cWlGjTDFEOqTff34j0+vrIS0VPXb/xhUwvFONIreHBQdg8oMtsdJmqYxau1o93LEGhrTPzya6pZ7zovAAMNfFsoK97/YtctsTt9RCdEQwnuxeW55GKsjXcdmglpXtanfq/dwerWDxasdBQTc3nzNn7m9TFXe3rIzvH2rt+c5+krIMZlTXWpIeKyw40OX3xhOtDy0d21fwHtcsU8zudiMv8+nbuAJ+e7Ijdr/dBxVLeF9ipHxUKIbeOPeSe3LGz/o1dh4k71K3DFpUjcaQ9tWw6fWemD2yPdpL6MuM7lEHxz68FW0lZp+HBbs/x2x6vSdmj2ovSza7L6S81lVLRbjcLMbpYzo8qG3wbMWLt+Cju5uiX+PyRc7xZfyY7JTDcz1vTg45rgiYOOhmwf53bm9U5G9f7lcfkx9s6XOg8JGO1VHaRYkbERvyBARYsP3N3tgythd3XZWZLl/N48ePY/LkyR6zzyZMmIBx48ap1CpyZnjnGlh+6BJua5pfGy00KACZOXked8MiImmk1lDY9kYvpGbmoFyUff2o9a/2wK4zibi9mX9Fzv94uhOqutj2WwmDWlXBHc0rScouk7LjZs0yxXDq8jXJx3+oQ3VNFIa3XXL2YLuqmLPtbOHPrauXRGRYEIqHBtkt03e2hGXsrQ3xWr8Guth1UcomN86ouRRPjVdRzYWF00e0Rc2xiyXfv0mVEniogzbKHvRvXAH1vVhOW8shoNS+ZmkArrNQtWLu4x3wgJvNNVx9XkoVC8Hql7v7/L2SopNGdne+t3UVNKl8s+5X2xolsT02EXe3cp2VMfOxdnho6jYAQHR4CD68u6ndJlNa9etj7TF722nULReJL91kUStFzo1H6pV3/v0NDgzAwtGdC3+uFB2OrSevSHpMqZnpARbgBQ914CpFh6NSdDi+X3tS0mOq7Z3bG6F7vbJoX7MU0jJycGtT/2py1ilXHHXKFceQ9tVwPD6t8PZHOlbHK/0bYPqmWPQWtJz4xb718WLf+kjPyikSNGpSuQROfnSbYv2ccXc1wbt3NEatN6RfK5Umquac0QnNQHv99ddhsVjc/hcTE2P3N+fPn0f//v0xePBgjBo1yu3jjx07FsnJyYX/nT171u39SX5RYcH459kueOpGVsPvT3XCLfXKYt4Tvs3wEpFvykWFoXbZotlnVUtF4K4Wlf3eMbNVtZKqX6g9dYBHdqmJhhWjcGeLooGu1/o3AAAM65CfTfDXM53tft+lThk0qBCJeuWdZ+xpcSnDhEH226EHBwZg51t9sP7VHpJq/DjrVD7bs+hSP9Hk3BVZqc+s48dD71W0vP68a2jbzoEtvQt0l4sKw5IxXbH+1R4A8pcvTRvRVvMTf1KyiG0/77blKWqWKabobuPVSxfzfCc/efrErXzpFrvgGQD89EhbfDO0ldNslAJd63qXfakVXeqWwTdDW+Op7rXRo35ZvHeH6+fojpTNShyzGEVqX6s03r+rMWaPbC/L4y0e0xXlIsMQIOEcWLA0XurOsXLx1LJHu+Qvv4wICcL7A5ugU23Pm+hI3y3z5ufjnTsao3hofokAbyYtlOAq48rf4FlEqP1Eg+OjBQRYJG/aRPoltDfw0ksvYfjw4W7vU6vWzbT7uLg49OjRA506dcIPP/zg8fFDQ0MRGsrIq5Y0qVwC0x9tJ7oZRGQCb7kZFA1qVQWd65RBuRsDyqiwYMx7vANGTt+Bd+5ohHtbV4HV6rqzFRyovQCardAbmYkFGYo1ykTgyKVUrx/npb71MXnV8cKfN73eE50mFq0btOJFz7u+ySl24gCvNwFQo1B4meIhuJyWhS519Dno1qstY3uhw4SVTn9X10X2ijuO9cF61C+H/e/19SoLT2ssyB9UrnulB4ICLX5Pmnh1bA2cLssUKzoeKBEeXLhCwqjCggMxbYQy/e6/RnfGmavpaKZQUfLGlaJwMC6lsPi5VA91rCFbGwoCSSUigjG4dRXkWeGyRmC7mqWw952+iApXd3hdrVQE2tUoheJhQahdthh+XH/K78fsXv/mNSzCTbkD2/iqBr7migsNCsSSMV1x65frAQDhTgJ1laLDccwmM4+MR2gArWzZsihbVlon8/z58+jRowdat26NadOmIYCF3YmI7PRqUA63Na2IqqUicN/3mxU5RnCgBdm52sku8Ud5h+Ws7WuVxt53+xYGzQoGfc5m4IdpZHmaq+QAx+y8DwY2RbHQIL/r91SKdl43SI1i8Y76NiqP/w5dQv3ykT4FB915qEN1zNxy2uu/++uZLliy/wIeaKd8naQImZfcBdr0q2r4kDFUpnjozQLVKkdM3GVPydUST1l4WggSSVGttPSl9j3ql8WO2EQEBwXg6rUsl/crCHRomh/vT4MKkYi5mOo0m7mA1IL0ejb/iY52fYvmVaPR3EXB9UrR/mc0LhzdGcnXs1GmuG/JEAU7co6/qzHe+esg7nLz/jma/mg7pGXk2PUTPhmcX1PL3SYbJRSsTelKQIAF857ocGOzAyvWH7uMmIv+XRMtFgveub0REtOzUMvJ6oUCVru/8euQutGwYhTeH9gEf+0+X7jCisxF2/noN5w/fx7du3dH9erV8emnnyIhIaHwdxUq+LeOm4hI7/59tgtmbzuDF3rXK7IkTe6L+/O982uB9G1UHn3+t07Wx9YCZxlnjsGobW/0KlJLTmscn0XZyFB8fl8Lvx6znaACya58/1BrZGTn4dk5uyQF0Lzp3Bfzcble5ehwjJRYsN5fQYEB2P5mb1itVrT7yHn2lTdsXx5n2UnfDm2Fp2btKvz5sS41MXXDKTzbsw4GNKsICyzo94Xxzgla9tfoznh2zm68cVsDRR7/5+FtkZNnRe/P17oNoP3zTBe3dX/UGFcHBVhQPDQIaZk5sj/2vCc6YteZRHS9sdPu7rf7ID07f7fFNUfi0a9xBZ+DPFpWLjLUbuOVdjVLoXmVEth7LhmNKznfxfX3pzoiITXTbdBFquDAAL9e1+Uv3IK0zByUKhaC/k0qoKwXj+Vug54Cj3TUxkQacDPAb7FYUC4qzO8AGpC/9NOXNpjBQx2qa6bOJ6lPFwG05cuX4/jx4zh+/DiqVLHfQUTK2nwiIiNrUrkEPrq7qd1tW9/ohc0nrvi1PCUyLAipGfaDkdLFQlTJrvFV9/plseZIAu5pJX23KU+e6l4bC3adQ1J6NgDnwQVR1LwEFiwHrVOuuF3h4N+f6qheI2xYLBbJO2nWKVcc/RqLm3BT6n1Ss+7grQ7nkrdvb4S3BjQsHDR5swmHkjrXKY3rWbnYdSYJgHqDOjWWCDtqXjUa627UagNgt7TI0RPdvJ9MsVgsCA60uP38zh7VXhObj1gsFux8uzfy8oCG7yx18nvfH7tEeDB61C9X+HPJYiEo2Kt0aHtjD6IbVIjEg+2qocKNSaNpI9ph8f4LLjfRaV1dOxMtIUEBKBWUvwSzXKR8k16bXu+J7bFXMUCjy3/VHBtXK6Xe5lFEWqGLdZDDhw+H1Wp1+h8RERVVPioMA1tWlrxLp62tb/TC7JHtC7f8rlgiDO/c3gi9GpRzulvZ873r+t1ef73Srz6aVymBr4e0wt53++LTwc08/5FEpYuHYu0rNwepQTooIeBrBpU7HWrlD4wWPdfF7nbRA6bHunjO+Fr+Qje7TAoqqsuN7JraZV0v32xbIz9sUPnGUl7b4JRtnyxSYMH9iJAgzH1cmaDuEDdLoLWQfOFYu63Aqpdu8Wrppjuv9q9f+O8OtUoVFiRf8WI3vHdHI6FZOaFBgZKD6iSNxWLBhEFNMebGdb5UsRAM61Dd6W7OZlEpOhx3taiMIIk7eaqtkYvsQCWEBQdi33t9cWh8P9WOqXW9GuYH2yPDdJGnRD7gO0tERHbKR4WhfFQYrFYrFjzZEXXLRaJERLDLdP6KCu7eJtXoHnUKd8BSQonwYDzXqy5gtQqpceKKY3Hfn4e3waQlR/DZfc1lO8b6V3tg4/HLGHQjqy80KLCw7ljPBuU8/LXyOtYujWnD22LEL9td3sebLKTW1UvCKtO+md3rl8Wes0m4pb72NxUoWSwEB8b1Q5iboPuUoa0wfVMsHmjrPgu1fxPjZfsBwEd3N8VHdzfFhMWH8f26k2hfsxS2nrqq3AFl4u8OorYB+ae718H2U1ex+kgCRnS+eU2oUy4SdcpFYvw/h/w6llI0EN8kUsXzverhUFwK1h+7rMrxosK00yfSgqH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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Electricity dataset size: (143206, 2)\n", "Electricity dataset size: (143206, 2)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "\n", " | Name | Type | Params | Mode \n", "----------------------------------------------------------------\n", "0 | tft_model | TemporalFusionTransformer | 1.4 M | train\n", "----------------------------------------------------------------\n", "1.4 M Trainable params\n", "0 Non-trainable params\n", "1.4 M Total params\n", "5.523 Total estimated model params size (MB)\n", "100 Modules in train mode\n", "0 Modules in eval mode\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Electricity dataset size: (143206, 2)\n", "Electricity dataset size: (143206, 2)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d5ff9f9ccab24ef2b0a4bfc4cb79d1f3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from random import randint\n", "\n", "# Get predictions\n", "predictions = trainer.predict(tft_module, datamodule=data_module)\n", "\n", "# Concatenate predictions\n", "all_predictions = torch.cat(predictions, dim=0)\n", "\n", "# Number of batches to plot\n", "num_batches_to_plot = 5\n", "\n", "# Plotting\n", "plt.figure(figsize=(15, 5 * num_batches_to_plot)) # Adjust figure size\n", "\n", "for idx in range(0, num_batches_to_plot):\n", " batch_idx = randint(0, len(all_predictions) - 1)\n", "\n", " selected_batch_predictions = all_predictions[batch_idx]\n", " _, selected_batch_target = data_module.test_dataset[batch_idx]\n", "\n", " selected_batch_predictions_inverse_scaled = (\n", " data_module.test_dataset.scaler.inverse_transform(\n", " selected_batch_predictions.reshape(-1, selected_batch_predictions.shape[-1])\n", " ).reshape(selected_batch_predictions.shape)\n", " )\n", "\n", " selected_batch_target_inverse_scaled = data_module.test_dataset.scaler.inverse_transform(\n", " selected_batch_target.reshape(-1, 1)\n", " ).reshape(selected_batch_target.shape)\n", "\n", " plt.subplot(\n", " num_batches_to_plot,\n", " 1,\n", " idx + 1,\n", " ) # Create subplots\n", "\n", " plt.plot(\n", " selected_batch_target_inverse_scaled, label=\"Target\", marker=\"o\", linestyle=\"-\"\n", " )\n", "\n", " for i, quantile in enumerate(quantiles):\n", " plt.plot(\n", " selected_batch_predictions_inverse_scaled[:, 0, i],\n", " label=f\"Predicted Quantile {quantile}\",\n", " marker=\"x\",\n", " linestyle=\"--\",\n", " )\n", "\n", " plt.xlabel(\"Time Step\")\n", " plt.ylabel(\"Value\")\n", " plt.title(f\"Target vs Predicted Quantiles for Batch {batch_idx + 1}\")\n", " plt.legend()\n", "\n", "plt.tight_layout() # Adjust subplot parameters for a tight layout\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "powershell" } }, "source": [ "#### StockTick Dataset\n", "\n", "The stock market is a highly dynamic and complex system, making accurate forecasting a challenging task. The StockTick dataset provides historical stock price data, which can be leveraged to predict future trends and movements. In this section, we will utilize the Temporal Fusion Transformer (TFT) model, a state-of-the-art deep learning architecture designed for interpretable, multi-horizon time series forecasting. The TFT model's ability to handle complex temporal relationships, select relevant features, and provide interpretable insights makes it an excellent choice for stock price forecasting.\n", "\n", "We will preprocess the StockTick dataset, define the necessary hyperparameters, and train the TFT model to predict future stock prices. By the end of this section, we will evaluate the model's performance and visualize its predictions, gaining valuable insights into the temporal dynamics of stock price movements." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import logging\n", "from datetime import date\n", "from pathlib import Path\n", "from random import randint\n", "\n", "import lightning as L\n", "import matplotlib.pyplot as plt\n", "import torch\n", "from lightning.pytorch.callbacks import EarlyStopping\n", "\n", "from fintorch.datasets import stockticker\n", "from fintorch.datasets.stockticker import StockTickerDataModule\n", "from fintorch.models.timeseries.tft import TemporalFusionTransformerModule\n", "\n", "# Set logging level to INFO\n", "logging.basicConfig(level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will define the parameters for loading the stock ticker data and setting up the TFT model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Parameters\n", "tickers = [\"AAPL\"]\n", "data_path = Path(\"~/.fintorch_data/stocktickers/\").expanduser()\n", "start_date = date(2015, 1, 1)\n", "end_date = date(2024, 6, 30)\n", "\n", "# Create a dictionary mapping from tickers to index\n", "ticker_index = {ticker: index for index, ticker in enumerate(tickers)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will load the stock dataset using the StockTicker class." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ds y unique_id y_scaled\n", "count 2388 2388.000000 2388.0 2.388000e+03\n", "mean 2019-09-29 20:31:57.587000 86.952830 0.0 -9.521511e-17\n", "min 2015-01-02 00:00:00 20.674532 0.0 -1.118944e+00\n", "25% 2017-05-16 18:00:00 33.900538 0.0 -8.956561e-01\n", "50% 2019-09-30 12:00:00 54.201355 0.0 -5.529273e-01\n", "75% 2022-02-10 06:00:00 145.265697 0.0 9.844678e-01\n", "max 2024-06-28 00:00:00 215.944229 0.0 2.177699e+00\n", "std NaN 59.245288 0.0 1.000209e+00\n" ] } ], "source": [ "# Load the stock dataset\n", "stockdata = stockticker.StockTicker(\n", " data_path,\n", " tickers=tickers,\n", " start_date=start_date,\n", " end_date=end_date,\n", " mapping=ticker_index,\n", " force_reload=False,\n", ")\n", "\n", "print(stockdata.df_timeseries_dataset.to_pandas().describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will define the hyperparameters for the TFT model and create a data module for handling the stock ticker data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n" ] } ], "source": [ "number_of_past_inputs = 168\n", "number_of_future_inputs = 24\n", "embedding_size_inputs = hidden_dimension = 80\n", "dropout = 0.1\n", "number_of_heads = 2\n", "batch_size = 1024\n", "\n", "# Create a datamodule\n", "datamodule = StockTickerDataModule(\n", " data_path=data_path,\n", " tickers=tickers,\n", " start_date=start_date,\n", " end_date=end_date,\n", " ticker_index=ticker_index,\n", " batch_size=batch_size,\n", " workers=0,\n", " past_length=number_of_past_inputs,\n", " future_length=number_of_future_inputs,\n", ")\n", "\n", "# Setup the datamodule\n", "datamodule.setup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will inspect the first few batches of the training dataset to understand the shape of the input data." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch 1:\n", " Past Inputs shape: torch.Size([1024, 168, 1])\n", " Target shape: torch.Size([1024, 24])\n", "Batch 2:\n", " Past Inputs shape: torch.Size([732, 168, 1])\n", " Target shape: torch.Size([732, 24])\n" ] } ], "source": [ "# Iterate over the first 3 batches of the train dataset\n", "for batch_idx, (past_inputs, target) in enumerate(datamodule.train_dataloader()):\n", " if batch_idx >= 3:\n", " break\n", " print(f\"Batch {batch_idx + 1}:\")\n", " print(f\" Past Inputs shape: {past_inputs['past_data'].shape}\")\n", " print(f\" Target shape: {target.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will create an instance of the TemporalFusionTransformerModule and set the precision for matrix multiplication." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "past_inputs = {\"past_data\": 1}\n", "future_inputs = None\n", "static_inputs = None\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "quantiles = [0.05, 0.5, 0.95]\n", "\n", "# Create an instance of TemporalFusionTransformerModule\n", "tft_module = TemporalFusionTransformerModule(\n", " number_of_past_inputs,\n", " number_of_future_inputs,\n", " embedding_size_inputs,\n", " hidden_dimension,\n", " dropout,\n", " number_of_heads,\n", " past_inputs,\n", " future_inputs,\n", " static_inputs,\n", " batch_size=batch_size,\n", " device=device,\n", " quantiles=quantiles,\n", ").to(device)\n", "\n", "# Set the precision\n", "torch.set_float32_matmul_precision(\"medium\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will plot the entire dataset to visualize the stock price trends." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_all_data = datamodule.train_dataset.df_timeseries_dataset.select(\"y\")\n", "plot_all_data = plot_all_data.to_series().to_list()\n", "\n", "# Plot all data\n", "plt.figure(figsize=(15, 5))\n", "plt.plot(plot_all_data, label=\"All Data\")\n", "plt.xlabel(\"Time Step\")\n", "plt.ylabel(\"Value\")\n", "plt.title(\"All Data\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will create a trainer with early stopping and train the TFT model using the stock ticker data.\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "INFO:root:StockTicker dataset initialization\n", "INFO:root:All datsets loaded sucessfully\n", "INFO:root:loaded dataset:TimeSeriesDataset(n_data=2,388, n_groups=1)\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "\n", " | Name | Type | Params | Mode \n", "----------------------------------------------------------------\n", "0 | tft_model | TemporalFusionTransformer | 351 K | train\n", "----------------------------------------------------------------\n", "351 K Trainable params\n", "0 Non-trainable params\n", "351 K Total params\n", "1.405 Total estimated model params size (MB)\n", "96 Modules in train mode\n", "0 Modules in eval mode\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aebff83b421d441b9ee6fd102291317d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get predictions\n", "predictions = trainer.predict(tft_module, datamodule=datamodule)\n", "\n", "# Concatenate predictions\n", "all_predictions = torch.cat(predictions, dim=0)\n", "\n", "# Number of batches to plot\n", "num_batches_to_plot = 5\n", "\n", "# Plotting\n", "plt.figure(figsize=(15, 5 * num_batches_to_plot)) # Adjust figure size\n", "\n", "for idx in range(0, num_batches_to_plot):\n", " batch_idx = randint(0, len(all_predictions) - 1)\n", "\n", " selected_batch_predictions = all_predictions[batch_idx]\n", " _, selected_batch_target = datamodule.test_dataset[batch_idx]\n", "\n", " selected_batch_predictions_inverse_scaled = (\n", " datamodule.dataset.scaler.inverse_transform(\n", " selected_batch_predictions.reshape(-1, selected_batch_predictions.shape[-1])\n", " ).reshape(selected_batch_predictions.shape)\n", " )\n", "\n", " selected_batch_target_inverse_scaled = datamodule.dataset.scaler.inverse_transform(\n", " selected_batch_target.reshape(-1, 1)\n", " ).reshape(selected_batch_target.shape)\n", "\n", " plt.subplot(\n", " num_batches_to_plot,\n", " 1,\n", " idx + 1,\n", " ) # Create subplots\n", "\n", " plt.plot(\n", " selected_batch_target_inverse_scaled, label=\"Target\", marker=\"o\", linestyle=\"-\"\n", " )\n", "\n", " for i, quantile in enumerate(quantiles):\n", " plt.plot(\n", " selected_batch_predictions_inverse_scaled[:, 0, i],\n", " label=f\"Predicted Quantile {quantile}\",\n", " marker=\"x\",\n", " linestyle=\"--\",\n", " )\n", "\n", " plt.xlabel(\"Time Step\")\n", " plt.ylabel(\"Value\")\n", " plt.title(f\"Target vs Predicted Quantiles for Batch {batch_idx + 1}\")\n", " plt.legend()\n", "\n", "plt.tight_layout() # Adjust subplot parameters for a tight layout\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "In this tutorial, we explored the Temporal Fusion Transformer (TFT) model, a powerful and interpretable deep learning architecture for multi-horizon time series forecasting. We demonstrated its application on various datasets, including synthetic, electricity consumption, and stock market data. Through the use of key components such as the Gated Residual Network (GRN), Variable Selection Network (VSN), and Interpretable Multi-Head Attention, we showcased how the TFT model effectively handles complex temporal relationships and selects relevant features. Additionally, we visualized attention maps and evaluated the model's predictions, gaining valuable insights into its interpretability and performance. By the end of this tutorial, you should have a solid understanding of how to leverage the TFT model for diverse time series forecasting tasks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "- Lim, Bryan, Sercan O. Arik, Nicolas Loeff, and Tomas Pfister. 2019. “Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting.” arXiv [Stat.ML]. arXiv. http://arxiv.org/abs/1912.09363.\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }