{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Historical Consistent Neural Network\n", "\n", "This notebook demonstrates how to Prosper_nn to build and analyze a Historical Consistent Neural Network (HCNN).\n", "It begins with a basic HCNN and shows how the data and the training loop should look like and how to find suitable hyperparameters.\n", "Then we build an ensemble of HCNNs for more reliable predictions and use it to show the uncertainty of the forecasts. The ensemble is also useful to investigate the influence of the input features on the future, which we also demonstrate.\n", "Futhermore, we introduce the concept of Deep Historical Consistent Neural Networks and show how to build an ensemble of them." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Theory\n", "\n", "HCNNs belong to the class of Recurrent Neural Networks. The picture below shows the model. It can be used to forecast a time series. The architecture is special because it has no input in the common sense. All the data features are interpreted as targets that are equally important. So the architecture does not distinguish between input and output features. This is necessary because the forecasts depend on the future values of all features and therefore all features must be forecasted. This idea is common for Vector Autoregressive models.\n", "\n", "The network has an internal state, which contains interpretable and uninterpretable features. The first part of the state vector is interpretable as it is the model's expectation of the target features. It is mapped to the target layer where the observed data features are subtracted. Because the target, i.e. this delta between model expectation and real observations, is equal to zero, the model learns to reconstruct the observables in the first part of the state vector. In other words, the result of the output layer corresponds to the absolute error between expectation and observation. \n", "\n", "The lower part of the state vector is uninterpretable and contains model-constructed hidden features. They are used to give the model more flexibility in capturing dynamics and influences which are unobserved.\n", "To calculate the state of the next time step, a non-linearity ($\\tanh$) and the state transition matrix $A$ are applied on the hidden state. These update steps describe the implementation of the HCNN Cell that calculates the output and the following state for one time step:\n", "$$ s_{t+1} = A \\tanh \\left( s_t -[\\mathbb{1}, 0]^T \\cdot ( [\\mathbb{1}, 0] s_t -y_t^d) \\right)$$\n", "\n", "$$ y_t = [\\mathbb{1}, 0] \\cdot s_t $$\n", "Repeating the same cell in a row for following time steps gives the final HCNN.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide_cell" ] }, "outputs": [], "source": [ "import sys, os\n", "\n", "sys.path.append(os.path.abspath(\"../../..\"))\n", "sys.path.append(os.path.abspath(\"..\"))\n", "sys.path.append(os.path.abspath(\".\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "\n", "from prosper_nn.models.hcnn import HCNN\n", "from prosper_nn.models.ensemble import Ensemble\n", "from prosper_nn.models.dhcnn import DHCNN\n", "\n", "import prosper_nn.utils.generate_time_series_data as gtsd\n", "import prosper_nn.utils.create_input_ecnn_hcnn as ci\n", "\n", "import prosper_nn.utils.neuron_correlation_hidden_layers as nchl\n", "import prosper_nn.utils.visualization as visualization\n", "from prosper_nn.utils import visualize_forecasts\n", "from prosper_nn.utils import sensitivity_analysis\n", "torch.manual_seed(0)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preparation \n", "\n", "First, we set some parameters and create some dummy data. Most of these parameters are also relevant for the HCNN. For example, we have to set the length of the time series. On the one hand, we have to specify how many time steps in the past (`past_horizon`) we want to use to train our model. On the other hand, it is necessary to set the number of time steps the model should forecast (`forecast_horizon`). \n", "\n", "How many features the dataset should contain is determined with `n_features_Y` and the amount of observations that are trained in parallel in a batch is defined with `batchsize`. \n", "\n", "With `n_data` we specify the amount of time steps in the original time series. Sliding windows with the length `past_horizon` are extracted from this time series and are used for training." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "past_horizon = 30\n", "forecast_horizon = 5\n", "n_features_Y = 2\n", "batchsize = 1\n", "n_batches = 2\n", "n_data = 100" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "For HCNNs the data should be in the `shape=(past_horizon, batchsize, n_features_Y)`. Therefore, load a dummy data set and extract sliding windows out of it with the `create_input` function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# generate data with \"unknown\" variables U\n", "Y, U = gtsd.sample_data(n_data, n_features_Y=n_features_Y - 1, n_features_U=1)\n", "Y = torch.cat((Y, U), 1)\n", "\n", "# Only use Y as input for the hcnn\n", "Y_batches = ci.create_input(\n", " Y=Y,\n", " past_horizon=past_horizon,\n", " batchsize=batchsize,\n", " forecast_horizon=forecast_horizon,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The targets of the HCNN should be in the same shape as the input data, that is `shape=(past_horizon, batchsize, n_features_Y)`. Because the output of the HCNN is already the comparison between oberservation and expectation in the past horizon, the targets are zeros." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "targets = torch.zeros((past_horizon, batchsize, n_features_Y))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Single Historical Consistent Neural Network (HCNN)\n", "\n", "In this section, we show how to use a HCNN and start with the initialization. Then we discuss the training loop and create forecasts. At the end of the section we evaluate the model and analyze it." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "\n", "First set the variables for the model initialization. Some variables depend on the data and are already specified above. One important hyperparameter is the size of the hidden state `n_state_neurons`. The `sparsity` should be chosen depending on `n_state_neurons`, because of the models numerical stability and long term memory. In big systems ($n\\_state\\_neurons > 50$) as a rule of thumb the following should apply: $sparsity \\cdot n\\_state\\_neurons \\approx 50$.\n", "\n", "With the parameter `cell_type` we can choose the cell that is used in the model to calculate each update step. If we set it to `'hcnn_cell'`, the [HCNNCell](../api/hcnn.rst#prosper_nn.models.hcnn.hcnn_cell.HCNNCell) is chosen as described in the [Theory](#Theory) section. This is also the default cell. If we set it to `hcnn_gru_3_variant`, the [HCNN_GRU_Cell](../api/hcnn.rst#prosper_nn.models.hcnn.hcnn_gru_cell.HCNN_GRU_3_variant) is used. Instead of a normal transition matrix $A$, the cell applies a version of the GRU cell variant 3 from Dey and Salem (2017). The model has the option to use the values of the previous state for the next state and achieves memory in this way.\n", "\n", "For further explanation, have a look at the documentation of the [HCNN](../api/hcnn.rst).\n", "\n", "Dey and Salem (2017):\n", "R. Dey and F. M. Salem, \"Gate-variants of Gated Recurrent Unit (GRU) neural networks,\" 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA, 2017, pp. 1597-1600, doi: 10.1109/MWSCAS.2017.8053243" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "n_state_neurons = 3\n", "sparsity = 0\n", "cell_type = 'hcnn_cell'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "hcnn_model = HCNN(\n", " n_state_neurons=n_state_neurons,\n", " n_features_Y=n_features_Y,\n", " past_horizon=past_horizon,\n", " forecast_horizon=forecast_horizon,\n", " sparsity=sparsity,\n", " cell_type = 'hcnn_cell'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set optimizer and loss function." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "optimizer = optim.Adam(hcnn_model.parameters(), lr=0.001)\n", "loss_function = nn.MSELoss()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Training Loop\n", "\n", "The following code shows a training loop. The output of the HCNN has `shape=(past_horizon + forecast_horizon, batchsize, n_features_Y)`. The first `past_horizon` entries are already compared with the real observations and are equal to the absolut error. They are named `past_error` in the following and should be close to zero. The last `forecast_horizon` entries of the output didn't have that comparison and give the `forecast` directly.\n", "\n", "For all time steps in `past_error`, the `loss` is calculated with the `loss_function`. The values are summed up and the `optimizer` reduces this value." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "epochs = 100\n", "\n", "total_loss = epochs * [0]\n", "for epoch in range(epochs):\n", " for batch_index in range(0, Y_batches.shape[0]):\n", " hcnn_model.zero_grad()\n", "\n", " Y_batch = Y_batches[batch_index]\n", " model_output = hcnn_model(Y_batch)\n", " past_error, forecast = torch.split(model_output, past_horizon)\n", "\n", " losses = [loss_function(past_error[i], targets[i]) for i in range(past_horizon)]\n", " loss = sum(losses)\n", " loss.backward()\n", " optimizer.step()\n", " total_loss[epoch] += loss.detach()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forecast \n", "A final prediction with test data in the HCNN can be performed as shown:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad():\n", " hcnn_model.eval()\n", "\n", " output_forecast = hcnn_model(Y_batches[0, :, 0].unsqueeze(1))\n", " forecast = output_forecast[past_horizon:]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation\n", "#### Postprocessing\n", "Because the output of the model has different meaning for `past_horizon` and `forecast_horizon`, the `expected_timeseries` can be calculated by adding the real observation data `Y` on the `past_error` for the `past_horizon` and concatenate it to the `forecast` of the model. Then, we plot the `expected_timeseries`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expected_timeseries = torch.cat(\n", " (torch.add(past_error.squeeze(), Y[:past_horizon]), forecast.squeeze()), dim=0\n", ").detach()\n", "\n", "visualize_forecasts.plot_time_series(\n", " expected_time_series=expected_timeseries[:, 0],\n", " target=Y[: past_horizon + forecast_horizon, 0],\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Determine Dimension of Hidden State\n", "\n", "We use [neuron correlation analysis](../api/neuron_correlation_hidden_layer.rst) to check the size of the hidden layer (= `n_state_neurons`). If there is a high correlation (>90%), two state perform more or less the same task. Therefore, the size of the state can be decreased. Further explanation can be found in the [Regression Flow Tutorial](Regression.ipynb#Determine-Hidden-Layer-Dimensionality).\n", "\n", "For the analysis the hidden states have to be saved for all observations. So the forward path of the HCNN is repeated for all of them and the state is saved to the `states_for_correlation` tensor. The time step at which the state is saved is the present. Here the model should have the best possible state because it is the last time step where teacher forcing can be applied." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The most correlated neurons are the ones with indeces [1, 0]\n", "The according Pearson correlation coefficient is 0.5\n" ] } ], "source": [ "with torch.no_grad():\n", " states_for_correlation = torch.empty(\n", " (Y_batches.shape[0], batchsize, n_state_neurons)\n", " )\n", "\n", " for batch_index in range(0, Y_batches.shape[0]):\n", " Y_batch = Y_batches[batch_index]\n", " model_output = hcnn_model(Y_batch)\n", " states_for_correlation[batch_index] = hcnn_model.state[past_horizon]\n", " states_for_correlation = states_for_correlation.reshape((-1, n_state_neurons))\n", "\n", " corr_matrix, max_corr, ind_neurons = nchl.hl_size_analysis(states_for_correlation)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Ensemble of Historical Consistent Neural Networks\n", "\n", "When the model trains well, we build an [Ensemble](../api/ensemble.rst) to further increase the performance. With this approach, the overparametrization is mitigated and the solution doesn't depend strongly on the start initialization like with a single model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "\n", "We first have to choose the necessary parameters for the ensemble. The most interesting parameter is `n_models` that represents the number of submodels trained independently in the ensemble. When the original model has $sparsity>0$, the `sparsity` has to be given in the initialization because the submodels are independently pruned." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "n_models = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reset the HCNN." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "hcnn_model = HCNN(\n", " n_state_neurons=n_state_neurons,\n", " n_features_Y=n_features_Y,\n", " past_horizon=past_horizon,\n", " forecast_horizon=forecast_horizon,\n", " sparsity=sparsity,\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "hcnn_ensemble = Ensemble(\n", " model=hcnn_model, n_models=n_models, sparsity=sparsity, combination_type=\"median\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reset the optimizer." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "optimizer = optim.Adam(hcnn_ensemble.parameters(), lr=0.001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Loop\n", "\n", "The traing loop is similar to that of the single HCNN, but the output of the ensemble is of a different shape. The first n_models entries of the (additional) first dimension contain the output of the submodel HCNNs (`outputs`). The last entry of the first dimension contains the `mean` output of the submodels. We apply backpropagation on all the errors of the individual models for all time steps in the past horizon. The best prediction, though, is the mean of all these individual predicitions and therefore the `mean` output is used as final forecast and for loss calculation of the ensemble model." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "epochs = 100\n", "\n", "total_loss = epochs * [0]\n", "for epoch in range(epochs):\n", " for batch_index in range(0, Y_batches.shape[0]):\n", " hcnn_ensemble.zero_grad()\n", "\n", " Y_batch = Y_batches[batch_index]\n", " ensemble_output = hcnn_ensemble(Y_batch)\n", " outputs, mean = torch.split(ensemble_output, n_models)\n", " mean = torch.squeeze(mean, 0)\n", " past_errors, forecasts = torch.split(outputs, past_horizon, dim=1)\n", "\n", " losses = [\n", " loss_function(past_errors[j][i], targets[i])\n", " for j in range(n_models)\n", " for i in range(past_horizon)\n", " ]\n", " loss = sum(losses) / (n_models * past_horizon)\n", " loss.backward()\n", "\n", " optimizer.step()\n", "\n", " mean_loss = (\n", " sum([loss_function(mean[i], targets[i]) for i in range(past_horizon)])\n", " / past_horizon\n", " )\n", " total_loss[epoch] += mean_loss.detach()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forecast \n", "\n", "A final forecast with test data using the ensemble HCNN can be performed as shown:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad():\n", " hcnn_ensemble.eval()\n", "\n", " output_forecast = hcnn_ensemble(Y_batches[0, :, 0].unsqueeze(1))\n", " forecast = output_forecast[-1, past_horizon:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation\n", "\n", "#### Visualization of Uncertainty\n", "\n", "With an ensemble it is possible to visualize the uncertainty of the prediction. With the `plot_time_series` ([API](../api/visualize_forecasts.rst#prosper_nn.utils.visualize_forecasts.plot_time_series)) function the outputs of the individual models of the ensemble can be seen as uncertainty of the ensemble model. If the variance of the individual models is high, the mean output is rather uncertain, and vice versa. First, the `expected_timeseries` has to be calculated for the ensemble mean and the outputs of the individual submodels." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expected_timeseries = torch.cat(\n", " (torch.add(mean[:past_horizon], Y_batches[-1, :past_horizon]), mean[past_horizon:]),\n", " dim=0,\n", ").detach()\n", "expected_timeseries_outputs = torch.cat(\n", " (torch.add(past_errors, Y_batches[-1, :past_horizon]), forecasts), dim=1\n", ").detach()\n", "\n", "visualize_forecasts.plot_time_series(\n", " expected_time_series=expected_timeseries[:, 0, 0],\n", " target=Y[: past_horizon + forecast_horizon, 0],\n", " uncertainty=expected_timeseries_outputs[:, :, 0, 0].T,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Uncertainty Heatmap\n", "A more advanced way of showing the uncertainty comes with the `heatmap_forecasts` ([API](../api/visualize_forecasts.rst#prosper_nn.utils.visualize_forecasts.heatmap_forecasts)) function. For each pixel in the image a heat is calculated that is higher if many forecasts are close in the vertical axis." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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J+mth4cKF/PVf/zUvvfQStbW13HfffXR1dXH//fdP+BkpJf/1X//FNddcw7Jly3jXu97FrFmzaG9v58knn6SkpIRf/epXr0nXpz71Kf73f/+Xa665hg9/+MNUVFTwve99jwMHDvCzn/3shJvxTUbjP//zP/O73/2Oiy++mPe9730sWbKEjo4OHnjgAf74xz9SVlbGm9/8Zr74xS/yrne9i/POO48tW7bwP//zP8ybN2/M91511VXU1dVx/vnnU1tby44dO/jGN77Bm970JoqLi4GRa+Mf//Efecc73oHX6+W66647JpfIYDAYppUZWOnKYDCcgeSXtx1vuU6l3GVfx1uC82c/+5m64IILVDgcVuFwWC1evFj9zd/8jdq1a1fhmO3bt6srrrhCFRUVqaqqKvXe975Xbdq0SQHq/vvvLxyXzWbV3/3d36nq6molhCgsdZtf2vTuu+8e8935ZUofeOCBE9bl2WefVeecc44KBoOqoaFBfeITn1CPPvromOVRR9fz5ZdfVueee64KBAKqqalJfeMb3zipdjze8rZNTU2F42699VYVDoeP+fznPve5MUv8/vSnP1VXXXWVqqmpUT6fT82ZM0e9//3vVx0dHWM+Nzw8rO644w41f/585fP5VFVVlTrvvPPUV77ylcKSvFPRjk1NTepNb3qTevTRR9XKlSuV3+9XixcvPuazRy9vm+fVV19Vb33rW1VlZaXy+/2qqalJ3Xjjjerxxx8/brtOpP1o9u3bp972trepsrIyFQgE1Lp169Svf/3rk6rvZDUeOnRI3XLLLaq6ulr5/X41b9489Td/8zcqlUoppdzlbf/+7/9e1dfXq2AwqM4//3z1/PPPq4svvlhdfPHFhfN8+9vfVhdddFHh+1paWtQ//MM/qKGhoTHfd+edd6pZs2YpKaVZ6tZgMGiBUGoKsvgMBoPhdcIll1xCb2/vCePvDQaDwWB4vWNyNAwGg8FgMBgMBsOUYwYaBoPBYDAYDAaDYcoxAw2DwWAwGAwGg8Ew5Wg30Hj66ae57rrraGhoQAjBQw89dNzjOzo6uPnmm1m4cGFho6Oj+e53v4sQYswrEAicngoYDIY/a5566imTn2EwGAwGw0mg3UAjFouxatUq7r333pM6PpVKUV1dzac//WlWrVo14XElJSV0dHQUXocOHZoqyQaDwWAwGAwGg+EotNtH45prruGaa6456eObm5sLG3vdd999Ex4nhJhwoyiDwWAwGAwGg8EwtWg3o3G6iEajNDU10djYyFve8ha2bdt23ONTqRSRSGTMK7/LrsFgMBgMBoPBYDg+2s1onA4WLVrEfffdx8qVKxkaGuIrX/kK5513Htu2bWP27Nnjfuauu+7iC1/4wpiydef/Xy65/K/IbzzS1ethX6uXljkZaquyhePaOjy0dXhZOj9NWYldKN97yEt3n4c1S1IEg06hfPteH4MRi/WrkljWyLYmG7f7SaUF61cnx+h4YWOAgN/hojckiUQlCrBtwQubApSV2Cydny4cm0hIXt3hp6Yyy/ymTKF8aNjD7sPFLGjxUF8ZJxgOEhuKcbjDYe9BD/NmpyZdp1DQoaTIIRKVbDuFOvl9itVLRwZzk63TYMRi+14fjfUZGuuzCKCkyGHPIS97D/lmxE7H1gmOdHuIxgRL5o9oP9k65Zkq39ux10tRWNFQk8WyRrRPp53ydPd52dfm4+8+Mo+58efwewRYFuV+SblPcCQpSDgCJT0oIagM+ynye2iPpEg7AiUECkFVSZiAz0t7f4Q0Xo5EyzjYmuTQQD17t3ewsql7zI7fJ6rTsvnpgl/HZ8hOR/te3reffilAMiWn1U7j1UkAyZTgle1+lrRkpvF6mrhOG3f6WLEwRXFYFfrs6bbT6DoFK6u56txhktkACneWfV//HPxWljnlHYVjFZK26FxC3gS14U5ErjyLn55UE2HPEN5UGwDRoTg9vTZbd1rMrk1Nuk7lJXbBt/fMkJ1G+17er1s7PGzb458RO42uU17PY88G8XmZtuvpeHXa1+rF51VUldsEAyPap9NOo+u0Y5+Xs5amCPiVFvdGfp9izdIUX/7CG9GVh72Lpuxcb8rsmrJznS603rBPCMHPf/5zrr/++pM6/pJLLmH16tV8/etfP+5xmUyGJUuWcNNNN3HnnXeOe0wqlTpmBuPzX36KjduLsR2361cKHCWQQiHEyHGOAqUEUipGFU9YbjsAAkuONYWdu96so+adbAcsqThnTZKXNgUKetx/1ZjjFeA4AiEUcvSXSon0+imuKEYIRUVNOd1tPcSHomSzDkLZk66TRyresMrVlM6KSddp/PKTr9PR5VZOz4sbA2RsOSN2OrrckoqzV6Z4YaMfRp39ZOtUKJ8i3wPF+tUpXt7sL/jRZOvklp+6nQoIiaMEH/r4epZHfk5TSCF8PpS0kEJgC4myPDiWH0dahYUdbAS28OFIC4VACLfvyCJJWGE2dDXx9LODOEpwcEcrqcgwyhn5MTtRnXyeEb/OOmJG7HS07+V9+0+vutf/dNppvDq5fp3khY1B9yb6FOp0Yu2Tq5MA1q9O8PLmkT5yuu00uryioZKL35Bh/0AjSkmQwj2HAMszchYpBMKykO4hyJwgaQmEtBBC0X1kCICuth5iQ1HSiQwoe9J1Gt1nZ2wxI3Yat8/eFCCTlTNip9HaT3ydnZ7r6XjahVCsW5Xipc1+nGP67On/zR33OpvBeyMAn0fxxM8vRVd+E1o8Zee6Nr5zys51unhdzGgcjdfrZc2aNezdu3fCY/x+P36/f0xZNB4s3GiMxlECxl4HbvlRx52o3J6w/NgyBQwMecbRI8Y/XglsNfoogcyVKyVwlMRxhFuXU6xTdpSm/K3GZOo0cfnJ1eno8nwb2a+hTuNrPPU6KdynQIpj/Wi09pMtf611ktLVM55fH639xOWnZqfCp/O/YMIibDnuDZcAlb/REu6sBaNf+XL3jozRP1NSCqSUICwclf9FEtiOQJ10XQVZ59hrbbrtNKJx5IZ5YMiTq+/47e4eP17pa7NTQeOoOrl+7SnckIzH6bieRji2TlIqBiPj9ZHTZ6exXyqJpoM4jkQhEWr0gwY56v9Fzotdf8779OjyvD87jjyhTx6vTqP7bJXvJ6fZTqO1F/rs11Cn8TjVOp3cdTb119PxtOf7bHvCPnt6f3OPd53N1G9udoJyw8zwuhxo2LbNli1buPbaayf1ue17fadJ0eRxHKGVHtBPk256QD9NuukBQEgavBkc4RvvN2ra0bGNdNOkmx7QT5OjBEcitQgx/k3TTKBdGxk9J0Q3TbrpgYkHLLogPXrrm2q0G2hEo9ExMw0HDhxg48aNVFRUMGfOHO644w7a29v57//+78IxGzduLHy2p6eHjRs34vP5WLp0KQBf/OIXOeecc5g/fz6Dg4PcfffdHDp0iPe85z2T0tZYn+Fwp6fw5GcmEUIxuy57SnqElHj8PorKS6hrqgFgbksZtu3gKIfkcBzbmfwjgdei6XSgmx7QT5NOeiyv2x2dVdtKd2cFJUEfWG5IFABCYEsvSlgjZbhPem3pQQk5ZkZDCUnQjrK4po+OJZUADPYOk06kyCTGxvYeD53aKI9umnTTA3ppElJSN6eKs5baZKwKEDKn0Q2dkqNCQKR0J+us3L/5iT4p3bAVIaCoqAIA23ZQyiE26GBnsmNCAk9Kl0ZtZPScHLpp0k0PuJp0RnhfN+swARquOvXyyy+zZs0a1qxZA8Dtt9/OmjVr+OxnPwu4G/S1traO+Uz++A0bNvDDH/6QNWvWjJmtGBgY4L3vfS9Llizh2muvJRKJ8NxzzxUGIidLY312bEz5DCKFXnpAP0266QH9NOmmB0ApxUBKnx8KHdtIN0266QH9NAmhCKhexo0lmSF0ayOj58Topkk3PYBWWgwazmhccsklHC8//bvf/e4xZSfKZ//a177G1772tdcqzWAwGAwGg8FgOGVM6JThz5p82FS4tJjaOdUsW+5OwS9typDJVOPYDr1OD6mYe7ydyRznbAbD1BEqKQJgVvcrbAw20lY8C6Q1kqSaS4JViGOm6B1HMjpoRAIOUO6NUG13sqK5GICurhpiQzEimQxO1sZgmA48fh+Ll1fSUNFJUeUwQo4NJjg6tEAINw386DIpHJQSFAdLAcjalShHoRxFPBLFzrhLiU42hMpgMEwfwvv6GmhoFzqlM129HnRZDFgpvfSAfpp00wP6adJNTx5/IDR2jdQZRMc20k2TbnpAR00Cb6BYG78G/drI6DkxumnSTQ+glRaDmdGYFPtavTMtoYCjhFZ6QD9NuukB/TTppgfc+NqiknIcpBbR7Dq2kW6adNMDOmqS+IuqtEpU1a2NjJ4To5sm3fQAhWX6deX1FjplZjQmQcucDFKTHwkplFZ6QD9NuukB/TTppgfczZuikQGU0iP8Q8c20k2TbnpAR00OqWivNn4N+rWR0XNidNOkmx5AKy3jIbxiyl5nAmZGYxLUVmU5eNijxaIhQkxOTz4meHR+xtLllaxbEAVgvtpFaslq0ulq7KxNf0cvACnlnHQs+2Q1nW500wP6adJJT/XsagCs/j9y0LeE1HAtCmvMMe4Ossd+9ugyIdyyhoogy0NDzC06AsCyRXPp7aomFU+QjMZPKpZdpzbKo5sm3fSAXppCJWHWzY9T1LeHRl8wt8mkhSOsgrMq4fq6I62CXkdYhQ0rAVBuWbDUXZ4501KHY1e6y9w6ikTUTa472aVudWojo+fk0E2TbnqgsJerQRPMQMNgMBgMBoPBYJgGXm+hU2agYTAYDAaDwWAwTAPCen0NNEyOxiRo6/DgaDI16Ci99IB+mnTTA/pp0k1PHttbhS7L8+jYRrpp0k0PaKhJQGnAq4lXu+jWRkbPidFNk256AK20GMyMxqRo69BnZQWlxEnrEVJieV1Th0rC1MyuYumKKs5dOMyy+PMA+A5u56wVfjLLl5FJ1+DYbnzvYHc/qXjipPI0JqNpOtBND+inSSc9i5a5ORqUVbFhaxhIjtmMUwhxws05Rx8LMFAfpG5ZLfXpVgCW1ZfSvqiCgZ4hMqk02VT6hOfSqY3y6KZJNz2gh6Z8blxtYw3L7I2EBg+AdHMxlNeP8vpwLC8IgePxAeBIL0paKCGxLR+OdPtuJWQupwNKs30ALKiwsBfUYNtVKEfRe8Ttt5PDcRxOvJ+GDm00GqPnxOimSVge2ns8CIsxd/gzuZfL0fss6YY0MxqGiVg6P42UegyVpVRa6QH9NOmmB/TTpJsecH+raoJHEOixOo+ObaSbJt30gH6aHKU4Yvu1etqqWxsZPSdGN01SKpbMS2mjB9BKy3gIKabsdSZgBhqToKzE1mbaW6CXHtBPk256QD9NuunJE/AkZlpCAR3bSDdNuukBPTXFHevEB00jurWR0XNidNOkmx7QJejWkMeEThkMBoPBYDAYDNOAsF5fz/jNQOPPnHx+RqikCHD3Kli6oopzF0VZPvwM4pU/AjB8uJNKj5ezlvlJrVhAJuvGy9u2jXIU6WTypPfTMBgmi/RYrFuSBaAjtIahjb20HuzCUSMd8slME4tRC6hLKYhGimmsraS8uh+Ahmwrq+Yvo7OjlkQkTjwXx+LYI749+nuEkFhSIUTqtVXQ8Loknxu3dEUVobbtIMrc3AwBjj+I4w24eRiWF9vjByBjBbClB1t4yAovWdx4fEdJbCwyjodyzwAAZXYviyoF2VyeRt6P+3HzNOwZjJM3vD7wBgJYngzeQABpK+yse8+AlctFUu4eL3lmMndDF0yOhmFC9h7yahNf6yi99IB+mnTTA/pp0k0PuIOFw30l2iT0OQr2tvq0aiPd7KabHtBPkwBqPBmtwjp0ayOj58TopkkpONAeGHcj1ZlCl7YxuJgZjUnQ3adPcykltNID+mnSTQ/op0k3PeAONAaiIZQm28zm20gpfZ7E6WY33fSAfpqEgBJpo4Q+mnRrI6PnxOimSSlBz4Av/25GteTR5SHVRJwpSdxThZnRmARrluizsoKUSis9oJ8m3fSAfpp00wPgOA4L63uRQo8beykVq5cktWoj3eymmx7QT5OjoDWj36pTOrWR0XNidNMkpWLF/Kg2ekD/VaekJabsdSagz7D4DCAYdLSZ9hacWM/o/Izq2W7OxdIV1Zy7OM6K4adRf3yMvu0HAEgNJxFSUufxcPZiP6nlTQA4di2tjiLS65DB3XNgolyNk9E0neimB/TTpIser9/HKmsjAE/1nIXKtDHUJ3GcscrGexIkxbF5HEIKhJBkMza7DhYzp7wOgAXZbSwqa6NtyTz6uquwczHtmVQa5TgIKZG5c0jLyr1XhEPxGW+j0ehitzy66QE9NIXLigFYtygB+xXDxTUMVFUhpSBtBUiLAFnlIas8pB03FyPteMhkLRxHkLItMrbr37YjyDqCrC1oLHPzORr8nZTbPSys8uAsrCCby61zHMWAo0jFwM5kJtSnQxuNxug5MTppkh6LsupyyioylERKyaQV2UwGJ+sU+lYna+dyPd1cDTWqD8/nbkx13oYObWMYwQw0DAaDwWAwGAyGaUCcITMRU4UZaBgMBoPBYDAYDNOAkK+vrIXXV21fI9v3+rD1CBvHdvTSA/pp0k0P6KdJNz0ukt2HwuiyCqLjwI4DAa3aSDe76aYH9NMkgOriIEKjh5m6tZHRc2J00+Qowf7OUhyNErB1aRuDi5nRmASDEZ12dRUT6smPlkfnZyxe7sbunrckwarhJ8k+9ShH/rSD4c4o4MZKOlkH5Tg0ev2sb7kKgKw9i2zGxrEdogNDAKSd1AQxlRNrmhl00wP6adJDT3FFGcUHHgdgQ8cqenptwD7myU8+f0IIWcjHUFIhpBibqyEklsdCOYrurjgH+0sAaKwqpnpgD2c3l9HVU4OdcffuGB6I4tg2Qgo8XjdW3uvzIj0SIQTD/V6EHHS/T4sRkB52G0E3PTDTmoSUhdy4FdmXSc1exD5rDZmUqymVlaSykkzWzbvIuSLpDGRthW1DJqMKN022rVBKYdsOiQZ3XyRZV0ODt4Mq1c2iGkHWrsgdW41jOww6DinlHGcPJN3sZvScGH00+QIBWpY1YNuKaiCZyJBMZEgnM6QSbk5nJp3BzthkMxmymWzBFx3bxnHUSO5GblW/qdmvS59Bz3iYVacME7J+VRJLk9UMLKm00gP6adJND+inSTc9AAKHNyyPaaNJCofVCwa00QP62U03PaCfJkcpUgP7UUqfjU91ayOj58TopkngMLvoAAIdHsC46NI2E/F6W3XKDDQmgWXp5by66QH9NOmmB/TTpJse0O+HQjc9oJ/ddNMD+mnSaS+WPLq1kdFzYnTTpMtS5AY9MaFTBoPBYDAYDAbDNPB6C50yA40/M/J7ZwAEi8JUzapi0bJqzl+WBGD18BNkfv8bDv1hKz2bBnCyuXWsMw7pWIZMIoP0WDR73HNkm64im63FthXtudh0x1Fkc/sOGAxTwax5NRCPAXBwdxfNJQLLYyHUSCyykML1b8tyczIs92+Wx0Jabrnldcu8fi8+nwev34PPZzEcdzv2qFVGRWQzC9WL9C2/jGzW3V+jvXWIVDKDZUmCYXePgmDYi89n4bEglO7F8mbAEWRT6WlrF8OZi+X1sHSFm6MRPPBjNi18J0cOddLdaaGwyGYV2WyWbNbNu7Btty+2bQelcnlzShX2GgByuUgC2w4A4PUU46vNUi26qBZdLK51/T9rl5LNVuf2L1Ckk27/PzXx7wZDbg+NmgrOXePFiXiYvcBPNOllOA7RmEMs5iYdxWIZ4rEMqXiaZCJNJuXu65JJuTkbdiaLbdsF30wnk3/2fmpWnTJMyMbtfm1WM7AdvfSAfpp00wP6adJND7irmGzaHdJGk0Kwv7tWGz2gn9100wMaahKCQacZpdHPrm5tZPScGP00SVSoGZ1uJ/VpGwOYGY1JkUrrNd2lmx7QT5NuekA/TbrpAUhn9PnRAsjYeqzyMhrd7KabHtBPk6PhT65ubWT0nBjtNEn9/FpnTOjUDPP0009z9913s2HDBjo6Ovj5z3/O9ddff9zPPPXUU9x+++1s27aNxsZGPv3pT3Pbbbe9pnOOx/rVSV7YqMd6+pY8Vk8+bCpYFAagurGaxctrOH95irOiTwCQevRX7P/dZrpfGDjmnMO74qR6MijHYXaubP7FEnveVWTtKuzcF3UC0YHIMeFT42maSXTTA/pp0kXPypVlOOG5AET7hzjvrDQb91UgpBdp5ZZrtiykR+LxevB4LTyefJiUB6/Pwh/w4PfnwgZDFqGghd8HHktQWeJWTqAQsQjW/l284ewgzqpzAdheVsXQUBaPV1BS5J63olQQ8Dl4ZZaesiH6OotxHEGkZ2DGwwZ1sZuuemDmNYVKijhnccJ9sy3NS/uLoH8zB3tm4yiJk3VwVC501Rmb3CukwLJk4V8Ar8/C45H4fBbZrFuhI92KkL8Mq9ymSnVR6+kEwK4XZLIl2HYNylFEet3jM6THhKXMdBsdjdFzYnTR5PX7aF5cx4WeZzg4nKWxPESyopxhUUYkGyaSCgIwlAgyGA0zFHEYjtrEYm7oVCKWdpfDjafJpDOFkKpIr3OcJfRPDkuv51THcKasFjVVaGeOWCzGqlWruPfee0/q+AMHDvCmN72JSy+9lI0bN/LRj36U97znPTz66KOnfE6DwWAwGAwGg8Hw2tBuRuOaa67hmmuuOenjv/WtbzF37lz+9V//FYAlS5bwxz/+ka997WtcffXVp3ROg8FgMBgMBoNhqjGhU2cYzz//PFdcccWYsquvvpqPfvSjr+m8qVSKVCo1piybTWNJf+G9Um7iqhQKMcpvHAVKCaRUY/annKjcnf4Ux6zVn58WPXoa0C1XCKHGfMY9XCFzZVI4hU10lFIowBESZVkoj4XI2igpYZTTq9yXOQgQwtXsOJBb/10IBykdLKlQUmErVaiTJUc0nVqdxisXgBpTrgDHEQihRks/pjyvRwqFzUzZaWz5yGdPrU6F8inyPfcbjt0nYjrt5BbaOMp1RSFydpMKIRyEEK524SAFuX8F7oSsQOAgCv86jNTQdr9MiZz/SpRSOMr1b0ep3OZpEjfl20Yg8k0CeNyGVjbguNdV7m/TbaejfS/v2yP2myY7TVAnV1eu35nG6+l4dcr/3+jzT6edpBzZ7VjlvkSgRvYcEA75iKmj9yFQWIDbd+V1CgTkyvP9usB2/Ru3j3dy4SZj++yRNnCkQonx++yZstO4fbZU2NN4PU2k/cTX2em5no6n3dVD4Xd+snU6cfnJ1cmSI37o9quu/znCGfF75bj9p8ofm/dbx72XyL1Evu/I+aIlFYrXch+h1z4jR/N6W3XqjB9odHZ2UltbO6astraWSCRCIpEgGAye0nnvuusuvvCFL4wpW37WO7nmzTcX3nf1etjX6mVuY5baqmyhvK3DQ1uHl8XzMpSVjMTD7j3kpbvPw6pFaYLBkR+W7Xt9DEYszl6RGrMRz8btflJpwfrVyTE6XtgYwO9TCARnr3QHQ7Yj2bCjmNpaH6uXu8tvVtUM0VQXZW10N31/+AN9pbX0DYRJzF6D8PQhnn4FtaQZtaylcO7U/nY6nttJdPZc0jW1lO5qx5t+hjUNZ2E7cwmrNvqrUkQHM2SSaXYfsOjqEYU65TWdap1WLx0Z3Nm24IVNAcpKHJbOH1lSNJGQvLrDT3WFzfymTKF8MGKxfa+P2XVZGutdewgEc2Zl2dfqmzE7ja2TW15cZLN0/oj2ydQJptL3vLywMcDZK5JYo/Kdp9NOUlosLn6V7YHFFJWUU1+xj1BxiPPWpJEyy1C6nIFkObPLuinyx5CWxOsRpDz1pCij0nOAgJXG41H4PAoRmkVpkY0ztAeJ+70eO0tFZRVV0Xb2RQXxAUng1Y00NMdoKvFSuWAlnV39AAQ9GYSUNNcVQWKQwYEB/FVVnLs6TSrr5amnfZSHotNsp2N9TyDw+9S0Xk/Hq1N7pxfbgaXzp/N6Ol6dfBxo8xX6yFOp0ynbaWWa6oY0pb3PunpmL2DXn4ap86aZHT4IgBCCHUfq8fsUC+p6Ck85lbA4NNRMWVGaWSVdWB73pkR6A2QC8yjxDSGTbi5GxlbEBoro9NfiiF7s4W73WNnPgvJZZBY2Ue7rRiZcu6YTaQ62CVqPWIU65fvsmbPTWN8TCBY0Z9i+1z+t19NEdRK52/xQYHqvp+PV6YWNAVYuShEMjmifTjsJIQgWe1mxpA/nV/9LWVkNR0LlWMEgMhSiIRygvLKc3oRNP0GS4RDJoJ9UfR0RpxYr2UY6ESedUaTTDl3DVfREAswuaScxmCYVd3M0tu3xvuY6GWYeoZTSdugnhDhh4vbChQt517vexR133FEo+81vfsOb3vQm4vH4MQONkzknjD+j8X/e9RzprB/IP0Ga2RmNopAikRSAu78AlpdgUYjaOVUALFpWzbnL0pwde5Lkb3+BEpLdv9nMwOaI2xbjzGigFB4fVK+vBiFoPHchoUsvZ2f9lTy7uwKUw7atfXQe6iY+FCWdTOPYuafPKIIBV5P7VGRmZzTI6YknBI6SWsxogMLvg3gSrNEzSSddp1z5lPmeIhQAd2sIMap8MnV6jTNPXi9f/+d5WMJGCMnHPt/O3EU12MJfSPxWSHw+gc9n4fVJggE3+RsEAZ8iFFAE/YpwwJ2hKA2kCVtx/NL9sfSJJEIIKqOt+F55it6Xt1I2vxEuvgYB7ClaS0e0CIDKgJvAWxMYwJ+NYmWS7Mi08D+/TKIQHNzWSjaZmNEZjbxvR+P56+z02+n4dVIE/IpYQiIl03g9Ha9OEA46JFNuG02+TqduJ49HMHflfP71+l0AeJMR7nj+IqK9PaQyrt9KKVDCKgyc80nfltdCerz4AxYBv8Tvd8tDYS/hkEU4AMmU2xiJtKIkLJldlaWpqIvijDtYTlt+2jOz2HSoiO3bBug40AVAbDBCMp4at892lJjxGY1Cn50UOI6c8RmNE19n0z+j4ShF0A/J9MggaHJ1OlH5ieskpKSioYab3zmfS373IbKBMB4njb+8HKuqElVeg11ahaMU8VANQ95KBjMlDCRDDCV8oByGohCLuwni8bhDPJ5B4NC65wjRviHsTDa36MGpzWj84ReXois7b7hqys61+IHfTdm5Thdn/IxGXV0dXV1dY8q6urooKSk55dkMAL/fj9/vH1O2Zjm8sDF/Ez2Co0aHW4wqP+q4E5Uffd6R8mPLLAkrF6dyK0+4gSPuE2n3phrc6Xch3P8XgFAOwrYRo1YdEY6Tn80chXTLAamcXDhLfv5euiumOBLbcX+YQOE4Yoym/A/7ZOo0cbkYt1wpgT1Ou+fLx+hRM2Ono8stCauXJgt2m0j7yZa/1jqdSM902AnHHSjnp88RguaqHvb1NeIoWdh3QCFzLysXWjLikzkHH/NDLaQsTFHL/HUghHsWx0GiyH9ACIEQVuFzhXMIgRCCzHA7UIXKX1vTbKc8eRsdff1Pi52O1jiqTpaEVUtSE/oRnJ7raYRj62RJxaol6XE1TYed3Icbo/wLmF/Xz7a2WrePViLXr7rHipxvCSXJ99p5f3fJ+aUQIPJlY31Y5u5gpZSFvl8hC78Jx+uzVS4Ea7rtBBP02Uzf9TSR9pO7zqb+ejqe9hP32affTiJ/nyEkSkg6KmYzp3c/+d5aCbABmfNJmeuL8z7p9tkCVej18/8dubfIP6w8lTrpzustR+OMDxQ799xzefzxx8eUPfbYY5x77rkzpMhgMBgMBoPBYDBoN6MRjUbZu3dv4f2BAwfYuHEjFRUVzJkzhzvuuIP29nb++7//G4APfOADfOMb3+ATn/gE7373u3niiSf4yU9+wsMPP3zS5zzTGb1/Rn7vDIALlic5a/hxEr/+Obt+tQmAoe2xsZ/1uiPrihUlDO4Yxk442AmHjqd7ALBTWZodhyVXSdTCKwFQqhKAzoMQGxomm8rHctoYDJPFHw7S0vE0P7FvcN+H+qmqkfgaKvF5LQL+XIy0X+HzKkJem5AvTdjrhjaGrARBESeUiRBIDADg6+tFDvdDMuE+PbPd2GcnMsTQ9r0cfrmN7h0dNA8OA7DsgsPMmb2MtCdI8fAR9xy796Biw9jJFPXNZ7Nk+WIUFj2He4gctYeMwTAay+th6coaArt/AMCes97JYG+UhkaLYFEAR0m8Pjcs0Oez8Ps9+ALuLEUwYBEMSsJBQTioKMnlFpQGk5T44hTLYYadYgDah8uIJi0SaYuBdAl+nxuzHk4N0uhTZOfMIZstJZtx+2bHcXAcdcweSAbDZPAF/cyaV8NFxa+w/acvk7osROyZlwgWeymqKaKoroxgdRkAxbXVlNXWM7t6FvGSeiKl1QBE7BKGUmEiSR+RRIBo3I1ASSWz2JksscFh7D9TH329zWhoN9B4+eWXufTSkdi622+/HYBbb72V7373u3R0dNDa2lr4+9y5c3n44Yf52Mc+xr/9278xe/Zs/uu//quwtO3JnPNksW29nEM3PaCfJt30gH6adNMD4KDXTtxS6NdGutlNNz2gnybH0S+IQLc2MnpOjG6aRDZ74oMMBcyqUzPMJZdcwvHy08cbGFxyySW8+uqrp3zOk+WFTYHXfI6pwnZEQY/QxGdHa9IB3fSAfpp00wPuzVifPR+/Rw/HliiaSjyofn0GP7rZTTc9oJ8mR0l2dTYUdrrXAd3ayOg5MbppErZN6I/P5t55Z1RLnolyOgwzgz493hmAu8ScLot0Kc30gH6adNMD+mnSTQ+AwksMXTQpIJ510EWPi252000P6KdJEfYn0UcP6NhGRs+J0EuTEgK7ojy/nYsm6NE2EyEtMWWvMwHtZjR0Zun8/AomM63EXXli2YIML2wKgjWSn7FkRQ0XLnfjdNcMPkrsVw+x46HNRPcmCp/11/rwlbmmL53txvrWr51H3+x2urf3jDm2+4UBMsmtzFeKpbloNLXoKmBsngaAyqRYOj+pVRvpZDPQT5MOesprK2DfdjZFowDUN1Wxou4gJVWNBL1ZAtLNxQiqGP5sHF86ijcewRN1l/IUkQHUcITs4BCpHrcs0j1AvC9KJpEZ0xnbGYdod4yBTe4Sz8NHXgag+XAPNev3UlxVRbbH3YugZ/NukkMJMlmHNGWct+58hJTs2l5LPBIjkxi7hvt0ooPddNYDM6spWBTmnCVJnJfcnLiX22oJhbtY0TJIn9OAwsLvlwT8glBQEA4oQn5XZJE/S5EvSYknRphhwsnc/i6RLqz+TlR/L6LSzcOb3bSK7pI5DGWKEEKRwn3SHRKC4ng3zSGBPXcO6Uw5AI7toBxVyK2zpDJ99hmkB2ZWk/S4s7qh0mJWriwj+PiPGd6bRK1ejHzpSSLZGAMVEUJ1/QRK3VU7i+tKKKorI9xYR3FtLcU5360pqyEVqiBaVkmsrJjhbAiAWKKCVCJNNpMlGXUr6GQnl/+p0aThuLzecjQ0N4fBYDAYDAaDwWA4EzEzGgaDwWAwGAwGwzRgksENE5JISG0i/xQQT7p6dJmEU+jXRjrpAf006aYHXE3C8mnj2ALwZdPa6AH97KabHtBPkwIyjg+dHEnHNjJ6jo+OmojEtEqL0EjKuLzeQqfMQGMSvLrDf+KDpgnHEWzZW4SwKORnLFtVy4XLYqzu+S0AQ794iB0/20qiPTVmv4xAqR/psfCFfdSeNR+AwDnnE2rcSnH9XnobO+ndPghAqivNwKYIe+wtLMitab38ahux6I0IMZKnARAbgo27hDbrszuO0MpmoJ8mHfS0LKkDIehsdWPR154zm3N9G7D6tyGTMYi6+RQqMogdi2EPR0kMDpPsc8tjPcOkhpPE+5PEO928iVRXevwvO4r4Qff47Qd3MXR4gKqFdaQicQAOv9ROsjuDyjjU9P6K1auWArB0xZvpau1haAb3ItDBbqPRTQ/MnCYhJdWN1axOPYeYMw+AXXuTzJlbSeOCakK+LOAQ8qYIedKErARhhgmm3Vy3QHIAz0Afsq8LJzJIpqcXgKEjvQwf6SfWGyNcFQag7g3baFm8mOTsJURDVdjKXfVHKhtvOkZF9hCy2Mae3wJANluBbTso5RAbdH331R363Jbp5ke66YGZ9Wuv3wdAw9xaLpjXwe67nqP+nHJEdA+sKycTz6AcheWVhbyK5JCb86kcRTCWwDs0CIC3vAdvaQWhcCnZcBkpfwkAiZaVxGLVpBJp+nLnSMUTk+prJ9oJ3jAzvL7mb14jNZVZhNCjUxZCUVOR0UYP5DTp1kYa6QH9NOmmx0UxnFZMwYrUU4ISgnR1zZQskT1V6GY33fSAjpoUZAaNHx0Ho+fE6Kap0D9qtNeQLm0zEUKKKXudCZiBxiSY35RBF7tKAfNmp7TRA66m+XPS2miSQi+bgX6adNMDIHDoSTr6TH9bksS8+froQT+76aYH9NMkcBDJLkCPGV/Qr42MnhOjnSbp9o9olHegTdtMgJByyl5nAmeGSoPBYDAYDAaDwXBGYXI0zmC8fh/BkiIqG2pYtqqGi5YNs6rj1/Q9+AsAdvxkO+n+DP5aH7WrqwAIV4WRHgtPwEdxcwOe8y4GYFf1hcypaqKqYRPFTTsprtsPQMemDoa2xxjcGmVHcjMAC9NZlr0pi7X4GqSsROSmTLvbFNKKuqNsR6fnvwZdEVKybqUHlV2Jvdl90nvuohjOc7uJtW4nG4mSGnL3Ioj1xkjH0qSG0yR7UyTaU8c/t1fgq/BiJxzshBvrqzIKT4mH4rkhSmcVM9zp7t3R98oQ7U92M9Q2XPh8fj8Z5bEYPhQn88QjSKU4//9cxI5tNcSGhmd0Lw2DnkjLYvHyGnzb7+fIee8AILElw8Vne6iKdTNX9iCFwBsfxopHkNFB1EA/dsTNOUr3DxDpGiDaOUi8P068180ZiuyJozL5frUHgK5t3dQu20nN6i1UtczDqZ0NgPIFEOkkMpumKhFhSY37OXvhApSqQuX6ZykVQoz4vMEwEdKyKK4oA2Dlqgpa9v4Xj/+xl1V/dzbUl1G/ugknkQSlxuRTCMu93/CGA8hcjgeASqcRsWGkncWbSeLxu/6/sDrE8KKlRIdrSCfdXDvbtsnOYE7cVHOmhDxNFWagMQkGI5Y24RMKiMS8ei2voASDUY82khR62Qz006SbHgAEBNNxdHJu0dkHy8pnWkYB3eymmx7QUZPA7/MAk9t87LSi9Goj3Wymmx7QU5M/EUWn/lofJeNzpoQ8TRWvr9q+Rrbv9WmzmoHjCPYcLsZReugBcJRg54GgVm2kk81AP0266QEQQlI7eASpSdKsyNrIp1/RRg/oZzfd9IB+moSQVJUWIzVKmnWUYPs+vzZtpJvNdNMD+mmSyqG6p1W7/tGgD2ZGYxI01mc43OlBaXBzLy3B0qV++qIlLFtZw8XLIqw49HO6fvoLtv1gJ+CGiZQuDdOwpoGi+grAncYUUhJoqEGsfAM7Ki8F4KV95XTXlzJv2Sxq65ppqN8EQLh2O0eqDtL5fG8hlGTbj7ewKJFiyV+k8Cy6DilrAdgiwamIs3e/IpPMzPg0pxCK2XVZbWwG+mmaaT2W18M5xZvZY51PRY3bHS0feIJXN/cidu8nM5Ah2e1On4+EjRyLv9ZH0awgocoQAIHSAMUNFfhKi7BTaez8FHw6i5CCYHUZ/tpqskNu2Eh7w1YO/PpwwcfzeEo8CJ9FqnYWex/bhFCKlZf8nhUr3+oucZvJFJZxnE5m2m6664GZ0xQqKeL85SmyTwywqbcZgKoahzfI50m8/CJWfycSyEaixCNRkgPDRLsiJCNuKGCiP0H0QAI7MX7/WbwoxPAuN5xqeFec4V0H6dzcRfXiXVQtbXQ1NNQgwmEcIRCWh+qYG5ayqimLtWQxUO2uWoOi3N/HoTalhd108yPd9MD0axJSIi2LQHGIxoV1AFy0oIu+ex4DwFscJlpRS2lsAD8KpEB6LBDuc2zp9SC8XmQoiPQHwB9wT+zzgdeH8vpQlrewalV5z26W14WILJtHPF4DQDaTJToQOenwKd1XnUKjhw3TgZnRmASN9VltVjOQAqqKIlpdUEIo6ivjWrWRTjYD/TTppidPek5T4YdqxpECtaxFu1VVdLKbbnpAP00KGPAUodOGfUIoZtfqtVKgTjbTTQ/op0kJwWC4Ak3GYcCZsOqUWd7WYDAYDAaDwWAwGF4TJnTKYDAYDAaDwWCYBl5vyeBmoDEJuno92uxWLKRFWf0sltfXcPGSQZbtf5C27z/I7p/sKxxTdXYZs9c1U9xUX1hWTjkKT1kp9sJV7Co/j+f3lAGwffsgvQNFdM9upqWqkqY3uLG+FbUNhOo3Uly3m7ZnDwOQaE+x7QfbWRhPseCtKaylfwmAJeo4sr+eYEmKbDaCncnmvnNmcjWU0stmoJ+mmdYTKimibPOj/Kbq4yxb7opI/u5hEn/sJbk9irDH+o7wCsJNAUKVAQJlQQCKaoopaqgkUFuFt8pdxpmKKjIV9WT8xQSySWTazb2QmRQohR0IEw9X4c245fOa5hCqfJo9v9pHuj8DgK/Cy6zzavGEgxwc7KbruT6E7TD3sd9ywc2Xsn1bLfGhYVLZsXkd08FM2+1odNMDM6NJSEnlrCpWp57D29jIwQ43tGHNIvD98Tek9vazf/tOhOOQHEqRiqRJdmfIRrLjns9f66O40c07Kq4romRWBcVNdUQPdwPQtbWdjqd7iO5NEN17mM5X3WVvKxaWUtJQSqC8GMvnwV9xxC2PRjhrYQy59Cw8nmrA4eArxSilxzLNuvmRbnpgejS5m8G5vistC18wQPXsGtadVQLAgt0/4sXHdwMw3NqJhxCx3m48Pi+ekB9vKFi45xBeLzIcQhYVQzCMCrj9tvIGcLx+lMeHkhaOsAAIdB+g7sgrnFUfJLqsAYBUMkuHo4gNDWNnMoXlmSe6t9DJXuNxpoQ8TRVmoDEJ9rV6Z1pCAUcJ4tTNtIyxCElPrAqljsy0EsBtI51sBvpp0k0PgEQhX94+0zIKCOXg2bqTjK3PGu662U03PaCfJomiuP0gCa32ApAc6gzjqOPvSTNd6GYz3fSAfpqEUpT1HJ5pGWPQaTVOg8nRmBQtczJITZKvpVCE6AQ0+tFSDtXhXm0S1KVQWtkM9NOkmx4AB4Fz9lKUpUf3pIQku3yxNnpAP7vppgf00+QgGJ7VjNIqbMKhqS6mTRvpZjPd9IB+mpQQDFbPLqwapQO6tM1EuDNGU/M6EzgzVGpCbVVWm1XJhAA/g+i1NY2iNBDVZqAhhF42A/006aYHACFQ82ZpswSgEmDPrtdGD+hnN930gJ6akhXVaLXqFIqq0pQ2baSbzXTTA/ppUkIQL6lE6eTX+kgZl9fbqlMmdOoMJVhSxNyqOKur+liy+0EOff8h9j54EID6i6oBaLpwMcHmRqQ/gLLdtf5FMEhmziL2lK7nhf2VbN3SD0DnoR5ikQSDAyV0zyqmq2EZAAvmVjO7cjaN9RsI12wE4OAzexnYFGHH/+whHUsz7x3uHgVy5V/yihNg1rwqbBtig+4eBXYmO+N7ahj0pK6pluSBgxxMpHnLuVEA9n5pC8xdi6fEQ6jaT7DMD0CgLEhJQynhukr81RUj+RiVNWQq6okW1RHxVgIQyRYzkAwRT1n4PQ6+sOv/fstGCoesI4mlfXik65eNZ82nuawST+Bhdv96FwDFs0PUrZ2Pr6GegT7ozWne++gWVlz6O1auegedh0rJpFz/n4n9NAx6IS2LJStq8b76LSLn/gVWLmVuXWAjBx95icHyJQz+oRsxjq9YQfe5X9HcIEW1YUKVYcrm1RGa5YbIeurqcaobiFU2URrpBKBi71bqV2+la0sbR/7YQ6LdDYFqb++mnW5Kl4bxhryF/WXKD3dT3tPN2esShJa9AeU4PNMbxvKmsFPj54kYXl/k983w5nIsvH4fZTVlLFtVy6V1WwHo/JeHC3u5HPpDG3ZJC+nnD+LzSwKlfoLlIQKluT2NyovwV5TizWaRuHt5ASjLDf9S0sK2fDi59yiFPHKAZilJzr0EgGSqAqUUXa2CRCRGNuPm0Tm5+5oT5WwYZhYz0DAYDAaDwWAwGKaBM2UmYqowoVOToK3Dg6NHVBBKQSBcqtUcoRCCjFWlzQ6qjtLLZqCfJt30AOA4iG370EeUoqivUyM9+tlNNz2goSblIHfs18qPENAbLdFGkm42000PaKhJKbwHDoLSZzZBm7aZCCmn7nUK3HvvvTQ3NxMIBFi/fj0vvvjicY//+te/zqJFiwgGgzQ2NvKxj32MZPLkV6ozA41J0Nbh1eYmWilBIFyK0G2g4anSJlZTKaGVzUA/TbrpAXcVE7ltP0KTaXAJFPd3aqMH9LObbnpAP01CKeROffwaQAhJb7RUmzbSzWa66QH9NAnHwXvwEEKju3td2kZHfvzjH3P77bfzuc99jldeeYVVq1Zx9dVX093dPe7xP/zhD/nUpz7F5z73OXbs2MF3vvMdfvzjH/P//X//30l/pwmdmgRL56fZud+L48ysEwspqZlTScm2X1JHjAPf/xX7f9kKwJyrG2i6bBUAvnnzIBCCdArhc+PcUzXN7Ctaw4sHKtm8ZZDOQ+6669GBITKpNIlokqGBYvoH3PWyexvrmV9bRsvZDVTX1AMQrHqeA6Vb6Hi6h30PHcJO/wqA5kSSsxZfSXb1Wdi2Q8cBV29scHhG8jSkVCyel9HCZnl00zTTepatqsbTWQzA0iOPAvD45ijlH7qMumgHxTUlBKorAPBUViBrG8hU1JMoqqXT7+YiDWVL6E+G6e/3Mjjs1iESdYgMZ0kn03h9Ep/PjQv2eLx4PALHUWQyCiHd8v2Vs4k2vYXlb4Fs0s25SA4lCDY3kVp8FnbpLIJPtyJsm96XB4n+7hHOv+1yts6rJTowBDCt+2nMtN101wMzoylQHOL85WlSv+hhD4tZMMvNeyj606948fEu1HmrEc9tKuRo+Gt9FM0KEqoMUTbH9fOi2dUEGuqQNXVkapsYKnX3NBqQ1fSlSuiOBCgPuTHqc85dTf2irZSfvYX6NW6uBkDXhl7S/RmGtsdyygYB6J8/QPm+buYMDrH63F4cBeetPoeug0GiA/aMx7jr5ke66YHTq0l6LKRlESwKEyhy97soLi9i7oIqrlg1TNnvfwDAH763s/CZeFeWWMuynF+7/uar8OKvdnMuwtUhimqKKG6oIFhVhreyDACrpARPuBhCRTj+EI4v4J4wGSfT2YXo62fRard+2YWXI0QlHo9F75EB4sPu92RSaRzbxs7aKEeh8nt/SH0GPeMxkw+Iv/rVr/Le976Xd73rXQB861vf4uGHH+a+++7jU5/61DHHP/fcc5x//vncfPPNADQ3N3PTTTfxwgsvnPR3mhmNSVBWYmvyrN5dtySu4TgxlU6jy0pYAr1sBvpp0k1PnmxJqU6L8xAX+qxbD/rZTTc9oKcmVVc50xKOwUtMm2tNN5vppgf01KSbX+vUNuMxlcvbplIpIpHImFcqNf6+OOl0mg0bNnDFFVcUyqSUXHHFFTz//PPjfua8885jw4YNhfCq/fv385vf/IZrr732pOtrBhoGg8FgMBgMBsMZxl133UVpaemY11133TXusb29vdi2TW1t7Zjy2tpaOjs7x/3MzTffzBe/+EUuuOACvF4vLS0tXHLJJZMKnTIDDYPBYDAYDAaDYRqYyn007rjjDoaGhsa87rjjjinT+tRTT/HP//zPfPOb3+SVV17hwQcf5OGHH+bOO+886XPoF3uDmxF/991309nZyapVq7jnnntYt27dhMd//etf59///d9pbW2lqqqKt73tbdx1110EAm7MX3NzM4cOHTrmcx/60Ie49957T1rX3kNeLVYzsLwelq6qI/XL37C/v5dDDx8GoOX6JhqveAPW/EUAOL4gIpsGf5BkxWwA9gdX8PLBCrZsGaTjQHchvjyTSmNnbTKpNMl4kviwu6LA0GAJfQNF9MxezKI57r4FzWW1LKqqIFDyJw78+jAHf9MOQDr5W+beBJUXNGCr5sLa1h2OIh6JTnuehqP0sVke3TTNlJ78jqYXLo4gK89inu1j+LFHc6IcFjYXU7b2MlRlPclSdx+BwWAVg1TSnyqmP+pnsNM9x0BEEYlkiURixKO5/Ip4mmQ8hWM7SEtiedxcDGlJLEsipMCxnUKs7JHiAMPRUuTy61h6lbtjRt+zLyFCYfrK5uP3VtJyyVwEii3f2cbeR7dx1mW/ZfWqW+g4UAbk4oWnaS8N40cnZiY0VTVUc1bqaTx11fTFg7yhaAsArb99HhyHUPtewquKEUpR0lBMyexKwg1V+OtrkQ1zAEhXzaaveBYDVNGdKKWnx93PoG8I+gdshoYShMNuKF9D3Ryaa+toXreShoXbWLjW/b6Gs3fQveUgvXv6C/sdAET3JtxXd4w5h7tRCJa8Zz0VtUXEhxLYM5yjoZsf6aYHpl5Tvi8WUuD1+wgUhSirLqei1s2dq6sLc86yDCv2/YSN33ui8LnKs0pJRdLgkcS370KEJfaQ2/+l+zOk+908ouFdcfy1EYobBymuKyJYHgbc/TV8xSE8RWGsohAyd7+WGY4SP9JNaihGybC7t9Ly82y8iy8l4K9id7GP3k53j67YUJxMOkM2k8HJOoX9NZSj+Z4wU7ijt9/vx+/3n9SxVVVVWJZFV1fXmPKuri7q6urG/cxnPvMZ3vnOd/Ke97wHgBUrVhCLxXjf+97HP/7jPyJPoi7azWicjoz4l156iY6OjsLrscceA+CGG26YlLbuPo9GqxkIAn09CKVPDyiUosROaLMSllJCM5vpp0k3PQDCUZRk49rE2QohCAf8CE1yj0A/u+mmB/TTJByF1d6hV5+Nwhcs0mqlQJ1sppse0E+TUArZekQrv9albXTD5/Oxdu1aHn/88UKZ4zg8/vjjnHvuueN+Jh6PHzOYsPKbLp6kzbUbaIzOiF+6dCnf+ta3CIVC3HfffeMePzojvrm5mauuuoqbbrppzLrA1dXV1NXVFV6//vWvaWlp4eKLL56UtjVLUhqtZuAwuGQlagpHxq8VJSVtgeoZX7kkj5RKM5vpp0k3PQDKY9EWqkEPL3I74q6BCI7Q51rTzW666QH9NCmPRfq8dVr12Y6QDPcfQQo9rjbdbKabHtBPk7Isspedg8rdfOqALm0zEVMZOjVZbr/9dv7zP/+T733ve+zYsYMPfvCDxGKxwipUt9xyy5jQq+uuu45///d/50c/+hEHDhzgscce4zOf+QzXXXddYcBxIrQKncpnxI+u5MlkxP/gBz/gxRdfZN26dYWM+He+850TfscPfvADbr/99uM+eU+lUsdk7nu9STzSSz44QilwlEAKNWbfPEe5I2opxz4nmqjcdgAE1lEXh53r+61jfpcUAodsMISSFspjjfrLyGY1jgKhQAp35KnAHQQoG5G7jRNCIQU4UiGlQgpVKBe57xHYkHtCoJSDoxQCgSMtlBQIR6E8Fkp6SEkPXsdxGweQwkFK5dZNKrI5cUfXaaK62o4A1JhyBTiOKGifqNySilDIxhIKh+m303jlllQEgw7iFOtUKJ8i3wNXj0cqRgf9TKZObvnk7KRwfUw5juuvysYRAqkUypKkpBeHLCjXd4UQOEqhlINSrg+jRG7DStdHBQ5SODg5X5XCAeEgBUghcuUKKXIhU7l/8+UCd3lPZ1QLuXVwyGSyOFKSH/0oIQq63Zs09zOny05H+17et/OzLNNxPR2vTq5fuyvhTOf1dLw6CSAYtMf49nRcT+B6pFI2Tq6/y/u8XVyE8njAtt3+M68JUVisz8n1nSrn76ic+sJTWtdX3f+13WNyn8v7riMkKjcwVpbkmErljnGExM6mc9dErs+eZjuN22dLdVrtdLJ1OvF1dnqup+NpF8Lts60ps1PuN18y6rNq5B6BER9TQoCU7u+95b4E4JQUIb2j7kWUQthOwfcKx4sR/xx5icITbyd3x+EIiSNl4Tp21NH3LqPuL3L3GEhVKD+6DXRDzOBDq7e//e309PTw2c9+ls7OTlavXs0jjzxSSBBvbW0dM4Px6U9/GiEEn/70p2lvb6e6uprrrruOL33pSyf9nVoNNI6XEb9z585xP3PzzTfT29vLBRdcgFKKbDbLBz7wgQkz4h966CEGBwe57bbbjqvlrrvu4gtf+MKYskuvuJl157+zcCPf1ethX6uXuY1ZaqtGYgLbOjy0dXhZPC9DWcnI7dveQ166+zysWpQmGBx5grR9r4/BiMXZK1JY1sgFsnG7n1RasH712B0Yt7dXcNGiXjp3VTEUWoi8cSHVS8pomh0k2rSUI1k3fleks/iEh+qaWnbZs0kO99F5pJdY12H8iQzD/Yra8jiNdRmU4950dQ/4ONhh01A+QHVZGq/yofr87InW0xdpIGi30VE0i6pllxOomsfcOc9w+L9fJHvZORwOhPD0Zpi98XEuXKFwVAslzh46qzNEBzNkUmle3eYjlRasWxkfU6cXNgbw+xSrl44M7mxb8MKmAGUlDkvnpwvliYTk1R1+qits5jdlCuWDEYvte33MrsvSWJ9FCphVZ9M9O8ueQ75pt9N4dcpP9pQVOyxqGdF+snXKM1W+t2uf6ytnLU+NCRudTJ1OxU77OkLMqkkRanuCjcVzaPFsYMf+FP7deyi9YS19Vc0MF1WQUgGS8TpkoJLhtg6S8QFSqX5SKZvD/WUMp4qpC7biVUlKM1mKLZu97SUMDElWzuvDkg4IgRCCrfuLyWQ9nLVoECFEYdr31T3lJMMWmeIEW60w2VnrEFKydFEP8axDT18fA3FB8Io34cem6Km9DAwVsX3HYZasfYXY2V4SmSBP9geoLh48LXY62vfyvh0MKBLJ02enk/U9KaAo7LbndF5Px6vT5l0+wkHFulWpQp99uq4nmduPpWxBMfJPj3Nw0bkEEjvpP/ASAO0vDRJsDMHaxQRWzUYohSopprLcR7xiFtvTAeK4sevxaJDBxBKGBqPI+H6SSfd7h+OCXYcqCMp+Kurc/jOa8bGjt5T9lfNoKvVQMttdZrSkZhkVa9qYvWczBwYU/SoEQCqSJvvsLgY27aevVKAaKinr28cFZ/n5Q8xD60GHVYsS02qn0b6X9+uhSIate/ynrd872TpJKZhVa2NJpvV6Ol6dDrS5ffaKRWkC/hHtp2KnQECwZplbJ8tjITySw/FqFi8M0FTeAUB5OMPsnr0c+M4D9PWHcd6wlOJLQvhmVxAaGKCip5W9zbPwzr+OzHCSdDSDva0V9fRW1JrFqHmzSAAJYLD1EKHdbaRWr0RQhZWykLZF6e5DFCeG6Ju3FDsQJBNoIG1nKH15F96hQVS4AV9zhuUVLVQvLOFAbTMOHvyJXjJpm2zGxrYdNu+vxOuxmV8zNgfBMJa//du/5W//9m/H/dtTTz015r3H4+Fzn/scn/vc5075+7QaaJwKozPi169fz969e/nIRz7CnXfeyWc+85ljjv/Od77DNddcQ0NDw3HPe8cdd3D77bePKfv8l5/ixU3+3FOMwkN7DrR5OHh4pCnzP2o793sReI8p37TLN86TCHh5y9iEnnz5CxsDY8rDlSAtL+w4gHjoKSyvotq/AGa/gaAFTSH3O2U6i3AUacDrL8LrC7MvUcaBgQjtnd3AEO1dHjq6PThZO7cuswUWtHYGae0KEi4JU1ZZQlV9CRVAQs7CX15Bg5Wh7MgAnUP9AFh/eAknofA1BGguKaU9t0FgRMxjT1cvna3dJCJR0qnsuHWyHYgnxTHlAIMROaY834X29Fv0DVjHlB/u9HCky4MlFW9AFWwz3XYar06WVJy9MsXg8KnVqVA+Rb6X/4ZXto749WTrlGcydvIG4UiPj7kBm7bqOhwkg3v3AeA/uJ9Q0EeoqoZhu5xU0r1Jsv2ziCQVkaxDJJZhOOX+uB/sqyGVcBcxyKQzuU2sbDbuKXEH0DKX/O24N8Sv7ilHSFFYrMBxIJm22N01i2BDNbVVIzckIZWhqqqK4UyCubFBJNABcOgIDW076b/6Vg78KZQ7evi02elo38v7diIpsJ3puZ6OVyfXr5OvqU5Hl7/WOgkglhC8vHlUnz2JOo3WfqI6WV73b5fMd32hKajoK53LvEPuZqbt0TiUeinq72Z2qgOpFAFfPYJGvBLqKsvpEzUApBMlEIUsYQbSLQxGXD+PDqeANAPDARJZN1G3pKKIyuow9YDwl1NR7N6QNsSiBAYTZICSrjayhwYAiByOYh9wN5YUz20Cj0X529bRk2mhb3AnkJ52O8GI7+X9etcBtz2n63qaqE4ej0CtcLAdSE/j9XS8OgmhmNsIW3b5xmzYd6p2emmrO8D1+H0EioKU10JahUj4FgBQV55kVmQPhwEOHUG2dVJyYTWzZAvpSBykwBsfpnLXJuIdQ0S7YiQ6UtiAeHUnYuOuwnfKMg9UWvi2bMO334uvyIc34MUbsCDgofzAToQlSA4kiPbE8ITc9qvv3EtJSw2t1bV09M3CHnKfirUNzyUWS5OOp0mnszgqQyrjYcv+imPspBWnEPJ0JqPVQON0Z8QfOnSI3//+9zz44IMn1DJeJv/ug0Wks4Kjt4Nx1Mi095jyCXbtnKjcnrD86BKBkBLrqQ2QyiCEQDr5UCg3TATcf/NTsW6oiABhoZCF8BKlBLbKTekikLlpzHydHCVxlKSQziMkQkikFEhUYUpZ2DYiZRPethVr6bkjYWnCcs/hiFz9xCTr6tZ3vPK89onKbQe27fGTsXPTtdNup2PLbcd98jS6LcbTfrLlU1Gn7Xt94/r10dpPXD55O0lB7hqViLwPZzLUlIRIWBZCyZFpZiFxI0wEbjBM3odlwU8dZ+R6dxyByvm1KNRNFHxdjdEqXD8X1pg+Q+AmvvlLGrD6RlpIOAqp3EGMGpXqdjrtBCO+l/ftvB+dbjudqE6uX/sL4RvjcTqupxHGq5Ni+17/uL495XZyRvwL3PsIISQy3z8qhXAcKjv2YZX53RAz3FAZIQRSioKfj/i7QGEV/Eup3L95X8Xtn/N/F0IWVhCSo8JRhFII2x2ACNseuc6yNtgODcRw8BQSwqffTuP02dmxv09HM119uZMVJ3GdTf31dCLt2/f6cm00Xp892d/WnM86YlT/6d4rQM6P8z/njgLHRtg2UjlI5YASlB/ag5XNuv5l24jcl4mjvlQ4ErByx8icP0pEzp+lchCOQOS+Q2AVygu/FWL04E2O6f/z7ZF19MkXGQ+hUZ7WdKBVbU93Rvz9999PTU0Nb3rTm05J32DEQpc9J4UQiM4+rVZ6EErhGRzQpIUAhFY2c9FNk256crH1Po82q5cJIbB8IY1aCPSzm256QDdNQikC8WFN1LgIpQiLkUH7zKOXzfTTA7ppEoAvGtFETR691Lze0WpGA9yM+FtvvZWzzz6bdevW8fWvf/2YjPhZs2YVdj687rrr+OpXv8qaNWsKoVPjZcQ7jsP999/PrbfeisdzatVevyrJy1v8Ez4xmA6kx6J6djVLdv2YrndegfeJZ1n2l0uouOR8knOWkvUG8aZjACjLQ6KkjkPWAl5tKwdg89YI7fu7iQ5Ejln3XzkOtuPg2HZh5Sg7nSUVTxIbTjA8VApA/2Ax/XNXsXxtJXPKqwhVuwPDHQ9vp7NhNXzvdyz3erl83a3uOex6HKXoOujupwFuTu3pXp3KkoqzV6Rm3Gaj0U3TTOkpKnd9KfXMk3S85TrOOfw/7N8UAeCcO6/g2a4S+tQsBoYlQ0NuuF00GicaSZGIpUjGU6QTbkhJJp0mm8lip7PYuSe3ynFQjptMOzrx7uhVOvJPllLxBJlUmi1ei4Zqdx+aa1evQ+3fSenwYXYMpOhcdgVSSuZd+jSb925l3+93sPbyhzlrtTub2r6/lHQyOS17aRg/OjHTqckfDgJw/soskfvayF71LhZld3L4kecAyEayLLxhEcNvvJ7SmlKkEAyWNdFn1bp7ZfT66R10z9XXn2VwMEY0kiIWSZCMuyFp6USaTDqNnc4ic0m3wwNRBnvDdHWEaa8rZl9u5r+ptobmZUupX3CA+lXbqTnkhiXGDh6mZ2srffsGGN4VR3ksNmxo45IbEuzaWkN7IkUmMTaWfzqZST8a/ZQ53094LMGaJQk2bA2QzerxUG+q2kh6LCyvF1/QjdwIlxZRWVfO3JYyzl5sM6fI3Z9rdu+rDPzwf9n/y1aqzi4DYOH/uQBPywIYGiAzNESvU8LsvgMEy3sIlg0Sq4wSOeTehyTaR/JaRu+vMRrhFUhPrs1LPKiMGnNc5EiUxo5+Gq/ooXzVZcxZthiAgw3F9EdCxBKKeMImEXf73nh05nz4ZDiV1aLOZLQbaJyujPjf//73tLa28u53v/uUtY1OsNKCUxwwnU6UVy9N2tkM/TTppgcorHKiC7rpAf3sppse0E+To+GTVkejZUlBP5t5NNMD+rWR8zoLBXrNaLRU+nSg111hjtOREX/VVVed9OYiBoPBYDAYDAaD4bWh5UDDMDFev49lq2o48N8PUXvpZcyuXUvpJRcTqV9KxuMnkB4uHBsvqqVNzmPj4XK2bnPL2/d3M9w3dNwQD+U42Bk3XMWxbWzbJpvJkoq7U6DRSJJIpIzBljmsnPMWFr3NXS1lVfUjbOjy0fVgP5Z4mKW5J2WXr70VR9WwyVF0t+a0RaLTEj5l0JOGee4M5dCuI/itDH2/fazwN89l19K+z2F3d5ThSJpk3A2RSsVTpJIpsqkM2UwGJxcmZWftQphUfiUpGO1b9khYxARRTUIK7KxN58FuNmwpAmDRJRewcKCbUOc+QuESdicWIYTkwqsvpejJfQxujdL328c478NvBGDTgjqG+4ZIRmNT1UyGM4TyWneVmzek/0AqHCApfVRv+Dmbf+92eMIrqL3qYl6pWshA0RKElHQPBenql/QP2gwMJBgacFeDig8nScQSY0KlAGzbxsnaY8IBM6k0qXiC2FCMod5huo64vnu4toj9ddXMqqmiac5i6uYecXUO7Kdl1Q4a9uyj4+XdONJi/64hLu96iCUrbmawe5Dh3O+CnTk2xOXPiXyfIEYl4oMbUiSlQFoWPr8HIeJIy4JsdqJTnZH4AgECRSGKy12fqWooo2lOEataMqyVLxHauRWAyHN/YtO3N+Or8LLsnZcCkL7wzQz6yyjv3Y0Y3lTYXwiR30hOIrwnP3unMgo74/bddiJ9zN+Hd8XZvmsXkSODNF3azso1awFonLOGgeoaotkwCdtLLOWuzjUYC59Sm0wXr7fQqdfX/M1rZON2/4QrbEw3wnGo696P0CikQyhF6JUNEy9DMs3Yjl42A/006aYH3DS+ruQcjt4KbaYQQLi8AZ0SDHWzm256QD9NAiirrB67ed4MIxwb+chz2ni2bjZzHNi4K6iNHtCvjQSKhr5WrRam0f7WVsqpe50BnBkqNSGV1qU7dvHY+j1hkanUyMLgGqCbzUA/TbrpAbCV98QHTSNS6jf5q5vddNMD+mnKb+ynFXG9Emd1s1laMz2gXxt5nD/v2S/Da8MMNCbB+tVJLE1aTEnJ4fqFKI2SipQQRM89Dzx6/JhaUi+bgX6adNMD7lLyDcF9hT1aZhoFDPe1Mu4i9zOEbnbTTQ/op0kB/T2dWj2IUdLCeetl2ni2bjaTEtatiGujB/RrI4WgtboFpdFM3Ugsl54IIabsdSag32M6w4QIKSkqL+XS5REO/LqNOe9bSGjVArpql5GVXsLpIYRjkwhVAnBYNLO5vZyt26O07+8BINIzeFJLcObj25UDylE4WbsQK5xJp0nGU0SHyxiIlDE473IAVryxgppNrxBbU0rH0z1Iy90Vd7G0sFffglK1bM6dv7vV5Gm8nlm9qgyAQHcx89jDnqf2ULzI3VV5Y+giBnq307a3k1TSJpN2Y3adrJsrNHrpWqCQl3E8PzqRj+X9PDY0zIFd7oahL85ror55DeFdf6Is2Mv+xDIQFh3L30jL5X9g097N7Ht8N+uudP187eoP0ravbNqWuDXogfRYtCxxl5VVT36V8IK5+IYOcOR3zxaW6JxzdQPtK99E254shzsCKCz6+9MMDSaJRRIkosnCMrbZVIZMKj0mJwOO9nPXvxzbxs5kcjl0CeLDbn7QUO8wPR1FtFWH2V9TTEPNEgAayucye+1KahbtZ96SjTjAwV/v48j3f8Iln/hLDuyrIRV3c0WSo5Y5P1M5emO00fkY+VyCfD6GJ7e7u+X14PF68Pq9BIIePP4B/MUhsnbsz+a6lh6L4spSyqtLqa53d5lvnhNkQUOKpf5dBJ59jN5XdwCw5xF3Z+81719P/JK3AbBfLiAsk1Smt9C/Yx/xirn07mgl2RMh1psg2Zsas6xtHiso8ZR4Cv9v+SWWVyIsibTcm+Z0LEM2aiO8Asvv2io9mCXVlebw410MHvojDZv3A1B/3g7KZ81GlVXj+INk/W6+SbSyElhzmlpvCjhDQp6mitdXbQ0Gg8FgMBgMBsO0YGY0DAaDwWAwGAyGacCsOmWYkBc2BrRZ6YGszdyQLuvyuAhgbvQI2HpML9uOZjZDP0266XERbD9ci6P08G4BzK4oQqfuUje76aYH9NMkhCDhW4DSyI8E4Hn4KYQmIVK62cxRgg27StGkeQD92kgoh6odr2rjQ6DVwm7jI+TUvc4AzIzGJPD7FPHkzHmwtCzqm2tYcejn9IYs2qpWYVkesvgociJ4sklSvmKOyDkAbD1SzradMdr39zDUOwBwSvHjynFwcp8Fdz33TCpDOpkmPpwiEikHYHDhG1h9cTHLlc0W+/e0P9nt6vb8isXv8aBW/F+U4+65sVkpetogEY1h5+LuTwczbbPx0E3TdOuRHouLWtoBKNrfiPXq7xjaHmP1h1YD8IOdQWIDfUSHHWzbKfjr6H0yToe/KMchm0oz0NUPwKYt5bTUrGRdxQECbdson7UKIQTbhpq54o2XUfzUXoZ3xel95AkAzvvYG3l1obuXRiISnXJ9R/N696OTYTo0+QIBzl3tLoDR+S87abrlL/FufIYDT7QWjmm++mweOjyPrdv66O/PAoL4cIJUPEU6kSKby7MACnkZJ5t7lM8vsjNubgdAKpYkPhxjsC9IT0eYw5XuvgJVVUHqahqZVVXP3JULUUqx4oPbefWf/8Dl7/oFL6+8maFed8+l3kyWTGL6V6SaCpsJKcfNx7AsC5lbrMSyLKxcLobH68EX8AEQLPITCHoJhXwUl3rYJ1OkMh560japeEKLvJXX2kZev4+6pmoa5xTTUOu2x7zqGHP87ZS3baHvle3se3wPAPGDSea/tRnfW27kxewKANJZSWXpIM6ebRz4wx4SSQ8iFifVlUJlxi4tYAUl/lofRbVBAmVBAqVBtw7FAbzhAJ6gHyvgw/K77Z8eipKJJVGOU7CVncrQv7+Hrg29RPcm2L13HwD9+/som1NGcUMFls9TOEe4ohSWaJyj8TrjzBgOacLqpSltVnrAkgz09Wq227lDb/+ANvsfWFIzm6GfJt30AAgcls8b1iZfTgFtdhDdVp3SyW666QEdNTk0lrQjhT5+hFJ01sxDl0bSzWYChwV1vVrZTLc2wrLIXLBOqwRnXe5BJkSKqXudAZgZDYPBYDAYDAaDYRoQZ0jI01Tx+qqtwWAwGAwGg8FgmBbMjMYksO2Znabyh4OsWFlO+w9+yorbVrGHAFG7iFJvjEA6StobplPMYltHBQBbd8Rp3dPNYHd/YW30U10HPB8L7P6/u6+Gk7VJJ1MkE25ccDxWRnZuDXWX/l9WOg4bv/07ANoe60DIh1jyXgu1/CZXh6plK4zJ08h/z1Qy0zYbD900Tbcer9/H3B2/dN9UVHLkN08BUHnNlQDsfKGXCjtLJpkkmx353HTERivHIRVzr5X2/d281DiPxpVrkZufoSVwCCklT/Yt4vCSNzLv0ifZtGtTIZZ5/RW/4g2r/47D+ypIJ9w15O3M6dsx9/XuRyfDdGgqrizlnMAGADqGU5BK0f74S6S60pSvKgEge9GbeeXpGMWpOD3tvTiOIJPKYGcy2Fkbx7YLORkweV8fnasB7v4a2UyGdCJFIhIn0ufmXfR1hugoD3OoKsTB+jpQNhctXQlA5/d/zIWfeiNth2sBiA/HGM5kpn3viKmwmbsXhnfcfAyv390vwxfwEQj5CAS9hIt8hMNueWmJRUmRoDTsUB5MsGNbCfNLaonHMmQzmdOaU3iyvJY2yu/HtXhxKXPrHWqKhgCo9fZQGu1AxIeJ9USI7nX7wbLlRTTd9CZeCZ1HT7+bA9FQlqBmaA99G7YzfDCOWpzBTjr4Krx4iy0CZX78xbmcl/IQ4eoSglWl+CrL8ZSVujpKyiBUhBMIYwfC2J4AAKFkBJmMITLpkazuTJqK7g7q1hzgyEv7CvmfvS8P0vvyIP7aI0jPSJv4qgJc8NFTbqLTzxkS8jRVmIHGJHhhU2CmJRSQyqGkqnGmZYxBIUkGFiLljpmWAoDtCK1sBvpp0k0PgKMkL20N5TYqm/nYaCkl85wBujSKQdbNbrrpAf00KSRbDlbhOMduZDZjCIs5pX7aNNmITjebCSHpTLXMtIwxaNdGWRvvI0+7b7x63EALTVa+nIijN5L8c+f1VdvXSFmJjQ43PuCqyKYTmiWDK6Qd1UiT0spmLrpp0k0PgKKsWB9NSilieDXya9DPbrrpAf00KYqDafTRAyhFIuOgtFkPVC+bKaXwyxi66HHRrI2EwKmu0MiHQJOV0Q05zEBjEiydn9ZmpQclJLGhbtDo5keg8GUOa3NDZkm9bAb6adJND4AUiiXzktpoUkrRYRVr49egn9100wP6aRIoWuoHdVqcB3DoimW0WnVKJ5uBosJ3BMHML2mbR7s2siTZc1Zr40MASGumFRwfIabudQZgQqfOAPLTbOW1FVw4v5ud/7uXS3/9KbwiS0AmCdkRspaPblHP9s4Ktu5wYytb93Yz1DtIOpGa0jjb0ftq2LZdWP89m0xS70/ylH8Fl115K6ty05evfvv3tD56BGk9yNK/di8MteImoJZtQtBzuId4bt8BHeJfDaeXovLSwt4TFWuWcPCpVnwVXvbPvxaA7p/2UD3b3T9DOdPfkTo5v430DLJzay+vNjSzOLyZmvaNSAGlofls7mvi2jdeRvGTexjeFXd1P/IU5/7DtbyysIHBbncvjsRpzNEwzDxCSmbPryX4zH2AG4+e2LWLtmcPAzDv0kUAvMS5tO09SLAmRXxwGNsRKEcV9suYqj4vf558vkYhVyO3B1IiFmd4YJj+7iDdnUVI4dB4ThNrP7iGTfe8zOVvf4izV70bgP7uGtKJFMlo/Izqky2vl1BJEYGiIF6fm3fhD/rwBbwEwz5CIbesqNhLcdiitNjNxygNurmGFYFBSqwIJcleAoNHiNYsoKlU0D9QSzqZItofwT6D2uNoPH4fNY1VLG9OURscJCjd/iuYHsa2fKhQMXZ6JDlu8VvW0Ln8jRzpC1MUcPvGhkAP/n27SAzEaLpsFkOLKqj1tODze/EVh/CVleApLQbAKi2D0nKypdWkwxVE/G6ORtwqIakCJLIBkraHVNa9HQ2VZghUpvHJDCI3a2MJm6IFQ5QtO8iiJRspm/MCAO0bDjO4NUqqKz2mjvGuLFqj19OG087rq7YGg8FgMBgMBoNhWjAzGpMgkZCaREW6WJZHu21p0rYXNFGl0M9mumnSTQ8ACuJJnTQJfBpt2AX62U03PaCnpoRWfp2L8pf+mZZRQEebCcuHLr9poF8bCcCTTuqSMnJmcIaEPE0VZqAxCV7doU+HLAWUVbrLEOoSPuooSdvQLOo02YzGcYRWNgP9NOmmB8BRgo07AvqEawhJY8BBaCIH9LObbnpAP02OkmzZW4Tj6BNOp5REFDUjlR7OrZvNpACruHmmZYxBtzYSjkP1oZ0zLWMMQpNV1Cbi9bbqlBloTIKayiw9/RZqmpc0sLyumZoX1rBw/w9pA/obVyPjXQQDfhxh0UsNO7sr2bozxaG97hrTg90DpGKJ07KW/+h12/P5H04mQ0lgmJdeVsASrrj6VgDWOA6vfOtxDv6mHSEfBGDZX0tY8Xaghm1AjxvSTDwSnbI8DSEU1RX2jNhsInTTNBN66ptrOPjDfQBIr4dEe4r5b23mqf0NACSGD1Jdnp6xNsr7XjqZpKutmw1bill07goWbH8YASw8q5un9tTTuvhaWi5/ko27NgKw7/F9nHvZQ6xbfTuteyvdcyRSp+X6M350YqZDk8fv46xVJbT+vxcBCJaHOfKnnSTaU/gqvJS98SoAnt8iGO4doCwUo7tPopQ47QPpMX10Lu8om86STqRIxZPEI3EEio2bbS65+Rb4xit0/PgXXPCp9QC0r1rG8GCMnlSabCp9vK+aMl6rzYSU+IJ+qmdXU1YZJlzk7uUQDnsoCktKiwUlIbfdS4NpyvwRSq0hSlJ9BGPu76a3/Qiqt5tMdzdD7T0svzFMkVfStfpyhgeryaQyJKPuOaZ7jxE49TbK39yGS4tZvLSCptBBfE6SrHLbKOotw/Z68BcPkY6N7AETvvAidqYb8HscGoojAFRFD0IyQdWyJkIrlzMcqqBYOqhgmGygiLS/hGGfm6MRt0qIOSGG00FiaR/RmJuoHUtKkilIphSJpEMm406J+P1eAv4wfp/AyuV0eywIB+qpLJrHnHOXMatxHgAVS/9E2x8207dvADvl4A25HxA+c2urE6+vYdVrZH5TRpt9VpSCoWGdlpJ1ZwPn1AwjNAkzkUIvm4F+mnTTA/ppEiiGI0MojcIndGsj3fSAfpqEgJY5aW30AAipqPB3axP1opvNlBD0xbPatA9o2EZAd9arVRtpn2wt5NS9zgDMsM9gMBgMBoPBYJgOdBklThNnxnDIYDAYDAaDwWAwnFGYGY1JMBixZmR6MFRSBMDaFX56/+cRGq+sJ+4rxu9LgxD0UsOunkq27krTuq+XwZ4BAFKxRCE293SR31MDIJ206emxORLpRikJZy8H4MprbmONbbPh35/kwK/dZAwhH2TZuwWsuBEpatiaG+F3t05dnoZi5mw2Ebppmgk9q1aVM/TpGACtATdXo/Ga89i8eRCAbCarRRs5WZt4JMbB3V20z2tgTUkZCGjqe5ni0HW80tXI9ddcSelTewAY2h6j45GnOfeOa9mwaC7g5kklIvaUx+MbPzox06GpqLyE8+a00faSG99fv7aaw3/sAmDOJbPYPeeNAOz+fTfpZJKBQYHjONO6P0w+VwPcUCnHtnGyNplUGikVrW0+/rj4Mpb83wVs//4uLr3YzaM7f30jXZ11xIdjDPcNTks+wqnaLJ9/4Av6Ka+rZPHyambVWpSEXM2lgTQl/gRlnkGKUu7vYyjWg7ezE3q7yPT2kujsAaC7rYdo9zDRrhiRgymamxfgDHVw4U1z6V7RTCKapDeXd5V2UtO+aMWptlE+17NmdhWrW1KEM0NEveUMZt19LSLpAB7p0KAcol0xGtfPAWC4fjEA9UXDVEm3jSw7jVM3h0DjfPZWrqdvIMJwSQ0pO0Ai4yERt4gPuD6eSAniCUUs7pBM2iQTKQBSqSzptE0mnSWTypLNuLby+j14fR48XgvLcs9hWRK/30NRsZf9tU0sqq8CYMkb5zGvrpaqTduJ9wzhKw4C4Ksqm3zDTiPiDAl5mipeX7V9jWzf68OZgQ3ExkNKSUVZCVKjZdIcR7DzYAhHk2RQxxFa2Qz006SbHtBPk6MkWX+jVrPd2rWRZnpAP0266QFX04Gucm1ufHRrI2Hb1A4eQWqUC6ldG0lJsLROGx8CtLKXwQw0JkVjfUabRGelFMOxuGbJ4IpZNSlt2kgIpZXNQD9NuukB/TQJFDLTi6OHHEDDNtJMD+inSTc94GqqLYuiNFneVrc2UlIyGK7QaiEI7dpIKdLxAW18CNDKXuMixdS9zgC0DJ269957ufvuu+ns7GTVqlXcc889rFu3btxjL7nkEv7whz8cU37ttdfy8MMPA/D5z3+eH/3oR7S1teHz+Vi7di1f+tKXWL9+/aR0NdZnOdLlwZ7G61t6LKpmudOEb6jZz44HdnDOp6+hL5vkcAzSwUr29FWwdXeGg3t66evoJTkcB8Cxpz5kYzxGvkNRX5Gic/cQh/eNXADyDSu48trbOMu22XCva6v9v2wFfsaydwPLb0TIGgC2CUFXmyAeiRWWBVWOOqV6SDEzNjseummabj3SY3F+Szdtufd9rwwBEDnnL2j/f274iVCONm2UTaUZ7Omnu32YgbPOQkpB+bYnWbhsiGd3lnBgxTW0XPEEAK9s38CBJw5y7hW/YP3qTwJwcHeFu8T0FF+Hr3c/OhlOtyYhJQ1za2na8WP2drnLv3Zv7yMbyQJuOOD9e91+rb9jM9j2jLdRvh+1HQfHtrGkotQb54+vtHDZLW9nx//8E/seeBKAlfOW0Lb6/9DdWU0qliQVT4w5x+ngVG2WDwsqKi+leUEt5y1LMTvYSXGyH4BgvBdPTw+ip4Nsn1uW6u5loLOfocMDxPvixDrc+iXaU4XzKo/F7v0JAk++xPp5P+aCNX/L4GAdqbh7zGB335Qtx36ynEobCSkLIdiLllUxP7iHqCinPVHDkcEAALGkoKk6jbfrEEPbYyy/ebZb7glSYQ3hJ4nHdv08UlRPrHQxXckKXt3hJ5zZTa/dQCotSKcdUqkMmbQbCpVKZt3wqHSWTCpTCJHKZrI4toOdtXP3Km5lpEciLQvLshC5G2khBR6vB1/Ax5HDQTrnuEvnds87m7XrK6iveoaiA3sRntx6uHWzX2Mrn2Y0mv2ZDrSr7Y9//GNuv/12Pve5z/HKK6+watUqrr76arq7u8c9/sEHH6Sjo6Pw2rp1K5ZlccMNNxSOWbhwId/4xjfYsmULf/zjH2lubuaqq66ip6dnuqplMBgMBoPBYDC8rtBuoPHVr36V9773vbzrXe9i6dKlfOtb3yIUCnHfffeNe3xFRQV1dXWF12OPPUYoFBoz0Lj55pu54oormDdvHsuWLeOrX/0qkUiEzZs3T1e1DAaDwWAwGAyvd4SYutcZgFahU+l0mg0bNnDHHXcUyqSUXHHFFTz//PMndY7vfOc7vOMd7yAcDk/4Hf/xH/9BaWkpq1atmvA8qVSKVCo1pqyj20YKVRieKQWOEkihxtjbUaCUQMqxkYITldsOgMCSY+dB3XKFFLldSB0H5RlZbcIbCLlxkcpGkF8RRBXOI5TCQeE4AiHUmHA+BeOXv4Y6SaHo6bNysaM5DcLV5zgKRwiUEKAUymPhSOl+3nEKx0vhIGWuDlJhO2JMnY6r/ajygh4UcLrtBNZRw/bxyqVQdPV6UEqNKT/ZOhXKp8j3lHL1jPbrydbJLc/b6fjapVSFWF4lZSHG1HEUQiiUEghcu+U1zYSdRtdJCkUsHcZRg3k3zeVG2TiOgzNqGlxJiYMA5V6P+Tjqqfa9vG/nc7Sm2k7HLR/H96RQdPdZKMWM2enoOikFXb3W2D57EnU6nvZ8vo7AcY/JhWyo3IeVxxrjB6BQaqxfn0qdprqPkELRO+ilstohn+6npHRrpsjpz/uYytfkBNpPvU5jf0NOvk75vkMKhcBBKeX+XiqFEAJHKRwFAoGDyP0mgCMlSlooy33hOO5f8va0JFZHF0q5tVaOg8Ap/Cbna3Eqv0+TsdPY20m3zxZibFue8D4if1+Q67eUdH2y4KNKoHL/r4TAESN9sxIOSLffUypXF+Hk+nJBQpWA6/EIXBu43+W2Vf6Vb/f8e4SDkg7KdttXClenlA5SCBSuL44+h9v2eWfN9cHK/fZ8c2iUujo+um8oOMVoNdDo7e3Ftm1qa2vHlNfW1rJz584Tfv7FF19k69atfOc73znmb7/+9a95xzveQTwep76+nscee4yqqqoJz3XXXXfxhS98YUzZsrPeybVvvrnwvqvXw75WL3Mbs9RWZQvlbR0e2jq8LJ6XoaxkZEnAvYe8dPd5WLUoTTA40lFt3+tjMGJx9ooUljVyhWzc7gevj3OXDwKQ2PEqyUsuxLNkBSKbpmsgAAOD9Pf1448m6O/wEZBRVi93czSUUiQSkld3+KmusJnflCmcezBisX2vj9l1WRrrR7RPRZ2qK4fY1WpzeJ9g5bw+eg+EeDYVYN7661nuSDxOhhf2e9gXsxl4tY0y5xEuWXw1jpOlQgzSXZsmFsmQSmTZsKOYsC/B4rmpwk3VZOvkKMG+Vnla7ZRKC9avTo52F17YGMDvU6xeOjJgtW3BC5sClJXYLJ0/Uj4TdsrXaV+rl/WrklNQJ4el89PHrZPX76N47+MAqCXNqGUthGcFeKEjSGWgiwPtAZpm21RXuq+ZtlNpUZblc/vJDqfZ2F0C0seVlsWs4Q1UWw1s2w9LL30zIWFT9vgO+lUNmw7FaZzzLABnrWzh8e4Q9cUJaipH7DpVdWrv8pBKM+V2glPzPUcJlrakZ+B6Gr9OwzHJG1aNHD+V19NAPMg5yyJs3dOL89bLAIg//QqCBKEPXc2O+ReR2OnOmqt0DCHEGL+eSTu1dXhZ1JyirMR2Vw2Kb+GlistpvvZ+9juNqJIiMlv20rzqOVatOItIfxktFYNYueVxp9pOR9fJ6+Wk6ySkpHc4yJHeIGtWwPL5rZR37SU12IPd305JJsZBSoinbFKRBJlEltCOraT3dTK0dD2quA6KgSaQjzznDjJuugJ/mRdfkY+hfcPI/Ul2P/gMJVWLeUPzIiplGkdJnnyuGE9mgEXNyRPWaSp9b1+rlzVLUid9PdnCwwVr3XZfVrWLoSOH6Sm/mP1HJAzvyrW5xF+UIvbKq1BbweEGd1nbbHcXwhchVFVDVzpMbHiAeNZPJBGnJwKbtico9UepLunAchR+xyE6FOBQR4jG6mGqi+Mon0J5FIdjfnq6vSxqilFSmXUfOirFvjYf3f1eVi2MEww4CCEQQrDrUJihqIezlw5jeUAKiZSSA9sbydgWpfYgzpCHhuJllMwrZ74zQAbBQW8VEz9GNkw3Qmm0bNGRI0eYNWsWzz33HOeee26h/BOf+AR/+MMfeOGFF477+fe///08//zz44ZExWIxOjo66O3t5T//8z954okneOGFF6ipqRn3XOPNaPzNJ5/hcMfI8q3TMaPhDwe5+M3uJfPx0vt54sZ7uPxXn6J31kpe7KpC+avZukexf3cvPYf7SUSjYLudlco9nZmuJ+WWUDTPznLoiA/l8VNSWY4UDrPn17H+7GIur9yI/5duCNyGbz9HqivNvL+YQ/M7r2fHgrcB8IedZWzf1Et3ey/JaALHEWRSKYQaSWyfzIxG8+wsB9o8ZB2pzYzGnFk2B9osxCgxMzmjMbfRprXdGrMs8ema0QiUFPHzv9rCk2/+/xVmNGrWlXP4X57i37/+Co4S2IkYcxuzHDzs3rjO9IxGIOBh5eowl129GCEkl7d9m3jzSn7YfgFlxfDWxPcBePVT32Bge5xFN80n9IkvAvDN5xfz8tO7SA5FYNSqLFMxo9E8O8u+VreNdJjRaJqdZX+rFwRazGigYG5jhtZ2z0ifPYk6HU+7o8ATCPJ/bj2bv3jkZjb/15aCSKEU1edVMevfv8E//aoRgO0v7EJl07TMGfHrU6nTVPcRllDMa3KIeZr4u7+uoeyO6znwu04Azv7YOrLv/Bg/3L6cP/3hAEOdbp5kNpU+gfZTr1Ohzz7sIWvLk/M9j0WwpASlBM1LZnHZRSVcZf0O78Gd2L09CCA5MEi8a5BIez+JgTjYNom+JJF9Y3/jsR1QCk+Fn+LmEEV1JRzqK0a8vJ2mK+qY96m/5fe+63j8j+4+QPu3txPtHwJ7ZEAxHTMazbNtDrVbqGP67PGvJ4/fx9xlzQDc9JflnK3+xAZ5LnuP+Onpc9sgHLK4dNkATT/+NM//09Nc9P/eCkDy/DeT8hVhWwGGVTEoRW+qlPYBP61HFPt29VER6qU7UoKddZO87awik7YRQuE4btK3k3WwHQc740YtKDecAcdR2LYzpq5CSqQUOMqd0bA8bpllWXj8HorKSgFomFPGqmUBzqo/QmPPK3iGB1zN5bWUr7sWXUk++G9Tdq7AWz8yZec6XWg1o1FVVYVlWXR1dY0p7+rqoq6u7rifjcVi/OhHP+KLX/ziuH8Ph8PMnz+f+fPnc84557BgwQK+853vjAnTGo3f78fv948pq6+xaD0ich3mCI4ShZm8MeUTrHM9UfnR53URKNxpXClAZG03NA+ByA6jArVuB6Zk4fj8eUZvCqWUGHeFionKT6VOQkJ1pc2BdoHIdY2Oku70p7CQUhQ6TJG1EVkb6ThuvQo9rIWjJI4jR32XW6ejN7k6YZ1yeva3eU+5TuMxvp1GfmCPWy6htsq92RjvPNNhp9FYJ9BzUnUqIMYtH63dcUThR1Y4DjjuWvUIa9TNoCjYbbSmabVTAYGjBOVFydxTNvc6E0KAsECMXWFQOI7r0zJfF/d4Rx3rv6+pTqN9W43f7ser04nsdDLlY3xPQk2lzYGjbDbm+NNsp6PLLamorbI5ePhYTVN1PSEkUjmIozazE7Y96kbJ1Zef0TjarydTp+NpP5U6CQlVFTbRIVHog/N1kSrvx/lB0dh/j6/9FOt0VJ99MnVSjijccBd+a4RAogqbyUql3Po4ttvf5NVMsAmhyLrHCeVAUz1iww4EuWtdWIXreqRO09eXn7jPHv8+Iu+LQkhkzh8RI/cXCquw8aFQClkIcxJI4QZn5z8jhMy1g4MQUB5O0DVU5oYwKTkSHqtE7rfcDeHO28lxBMrJ/50x5a5aNwBLiJFykXvvOO7gw9Wcv7eQufsICpq15gxZlnaq0CpQLL/07OOPP14ocxyHxx9/fMwMx3g88MADpFIp/uqv/uqkvstxnGNmLAwGg8FgMBgMBsPUoNWMBsDtt9/Orbfeytlnn826dev4+te/TiwW413vehcAt9xyC7NmzeKuu+4a87nvfOc7XH/99VRWVo4pj8VifOlLX+Iv/uIvqK+vp7e3l3vvvZf29vYxK1PpiJCSovJSVi50x4ORnz+Pv9ZHpriSuKeInv4kXZ02B/YM0N/RRyIam/Y1vcdDOQ52Kk2kbxCgsGeCtf4srniL+wRpTSbLy//2LPv+/+z9d5Qc13nnD3+qOk/OOWOAAQYDDHIiCCaQoEQqUhKtQGllhfWuZUnWrn9r2n4ly3tsWZYt0Za1h7LXstayrGCKpEAFUiQYQSQiAzOYnHPonLur6v3jdvfMEIMwwAC4FPt7Th9gbldXfe9zn/tU3bpPeHoQeJLm/yKOUZo/gKoU02YxMTshtkEDHj/RUAQtFrvmmhppyIH8skJmnvn1graGvS387EyUWMIlQ7Z3PVosRjigc+KCeOG4q2U9joFzrKrbyRsXTPRuuR+AFXsPcOL8G/S/OMSue/YDsKN1Hf2dBYR9wWWvpZHGrUVmbja3rZyl90D3Rd817G3hJc96xvqF/7uuaXK91ZsHQ9dxT7t46Uwd/+Mj9zPwKxHj2PHMOTY3/JTdu+sZGysj5BWxfwFNQ7/ETsDNhqKq2DIcFCZqTa1bn8/OgvPEfvpzxtsG8U/5AQg5QwTHIkSdsUXPk1FnJ7siA4DMoizy6orJrC7DXF7O9IlJsrflM/TcGCVrn2bXJ2qZad0CQNAXIRaJEfbr0shkMTiyMlndUgxAs6OdWDyD8z1WBof86IntlcrybBrdx+g8cG7Bb60RL2FrNk69kEF3HgCDEyrDI0EmRz24J2coswaYHp1GiydctvW5pB/6vHv2/PYkkvUz3gzlTW/9FUVFURWUoEo0LF4SR4IRNK0Ew6jCqFQot4v5Flele7RdiLdZHQ3pRuPhhx9menqaL3/5y0xMTLBhwwaeffbZVID40NAQ6psi9js7Ozl48CC/+c1vLjqfyWSio6OD//f//h8zMzMUFhaydetWXnvtNdauXbskbiLY8dr7tpxQFAW/VihVBUzdmCcjCWgt4CMJZOMkGx+Qj5NuwOiUjfoaCZQ6ARllJBMfkI+TbHxgjpMskZryycjANjwEEr0kkE1GhgGj03ZpdAikePy4PGR37VpmSLfQAPjc5z7H5z73uUW/e/nlly9qa2pq4lIx7Xa7nSeffHJZeA2PW5blPMuB5EJDJhiGkpKRDPNoPh9ZIBsn2fiAfJwMQ2F02k69RO+kZZSRTHxAPk6y8YE5To4cCQw28slIBbHQkAiyycgwFMZm7Mm/bimXJBQZHkDSSEGeO+dbAM2N0VQu6lsNXdfJN4+m8lXLAFU1pJKRbHxAPk6y8QH5OKmqQVNtID3XLgPZ+IB8nGTjA/Jxko2PDgSbWxK1ReSAbDJSFYNV1X5RH0YS6DJtrywGVV2+z1sAUu5oyIq8HA2Fm/cmQTWZKKkuoqVwBIDxUwNU7SzHMJkZi5QSdHXQP2FlZnSWoNd/y+MzFOZkpOt6Kg2id9bNMHDEpGLaJnxb73l/nM1xjeOPHUrFaQCs+YSBsuYDmM1F9PSKrF9To268s17C/iCxSDT1uHelvs7nIwtk43Sz+TQ2l9PxlQuYc4TpiXvj2Pc9SN8TEykfZ5Mqn4wybWG626fQDZW2TVvYHDlOi72DU+YWjg2XA/Dh+/aS/1InrjNeRp49BMCOrac4vnY9zkknQbdv2eanTHqkqComM+TnRVFVFQNFijiqGyWjZGaesroS1gz9igM9oQXf20qt2Pc9yLFTEfwuLyBslSqZXsOcjLqHQlw4N8nI736Q0l1PATB5yEn3f77ChuaN9LY+hHNK+PhHw2GieuSGjPFSxkxRVcw2K7lF+TS1iDT1tzVOU3jkaU49fRLXGe+iv0vanqwaO1mlmWSX5ZBTU4qjUmS2NBWXoJVVE8irwWkpIj/6C+o2FDN9+EnafnKGbbVPcNs+Meed7nJCgTDTwxqRYOim6P1S9dpksVBcXcyONeJ+nDfbS3fRbfT0ePF7w5RVilSxm6pnif/sF0wecorrJOIINLOdGaWUrukCugeFjR4ZdDE77hbxk0E/mTUhIv5EdsgrxGBcrYzedBoU1SBZlzh5r0jdM0wqNks5aon4UV5o7KquccvwNttxeWssh9JII4000kgjjTTSSCONtxTSOxpppJFGGmmkkUYaaaRxM5DOOpXGpdAzaJEm0wMojLvzF1RzvtXQDblkJBsfkI+TbHxAPk66Ab1DVuwl6bl2KSRlJAsfkFNGMvEB+TjJxkdRFIrCLhRJgpxBPhnpBvQOyzX3pfdMeovEViwX0guNJWBq9uaKy5bpYGVTPmWzRwA4et5F63/ZQtySQcdoJj29ISAkRXwGiOwT82WU5BOPRPG7PAx2KihqBQDqth3c81CMTZrG8W8dScRpADzF6kd0LM3vpzBX+Mx25NoYHbYzO+4i6AsQDYkc2leqq/FmPjJANk43k49qNrF7o0pkMkrRljwAvH0BTufuxTXReUs4XQ0MQ2FiCrI14bt8tLuCDVUNFPQfo6GqhXOdwve5+7Z3sOKeAxw/c4zBF0VcVdXe/ezY2EpvRzGjgVAqbmk5ON1qGSVjFUwWMxabFU8YVLPon65pt7zmzY2SkdlmBaB1QyEzT/8CEHUYAIIDYRr21nI6dy/9nRMLxluGMXsz5jjpTI9M8drQBt7/rm0ATL3xHKMvTVG+8Wl2f7aVobX1AHhnfbgmZm5IXZilyEg1mcjKz6G2qZSda4Wv/uqhXzOw/9WL4jMclTYyyx1kFGaQXydqbmRWFmMrL0UtKSdWUoM7pxIAj1rIdCSPaZ8dl0/lwdvfhR9Y89HzXPhhN10/O0hzbR0At635GB5PKeFgBE3TUuN9I/V+qXpkz85gdUsJ65WjqbbzE0U4p0bJzHGwtknobvPAjzjxU3FMyfZ8TImSAlNZDXRNFdLeHWOoX9S2ck26CXpFfStd05iYVgH9xvZ73rmT/4sEQ3hmYKhHxe6owGEVsTOr83RKbhiTNJaKt9ey6jqxcU1EmkwPGDrNdR6pMj2oqiGVjGTjA/Jxko0PyMcpxSc91y4JVTVY1+iXhg/IKSOZ+IB8nGTjg6Ez6fJJlcVINhmpqsGGNWFp+MCNXegtCxRl+T5vAcj1ekVyOBy6RIVgDBxWTarKNApyyUg2PiAfJ9n4gHycknxQkCVNvHQyAnDY5MuoJJOMZOMD8nGSjQ8YxDRNmnkP8slIATLs8vABqYZrcbzNYjTeXr1NI4000kgjjTTSSCONNG4K0jsakkJRVXKL8ljXoGEZFZVJI5NRHOXFTGRW0dkdwB6JEvIGpIjPuBwMXScaiuBzuhnsFO89TKYKzFt3c9dDMTZF45x47A2ARKzGz1nx4Tj5a+8CoDi7ibb8QvqybEyPe/DOCv/b+XU1bkr+clWVWs6yw5bhYDcvc6LOjiPfAUDRviKeaM9Ixd3IjLA/gKYrtJ+fpf/hXawY+Fc2rBzmXKfwCz46WMpH991H4cudzJ70ADD07BG2b3+DN9ZuxTk+iz8WB94CW/uXgaKqKKqCJRGrYMtwkFuYhSMrTkZeJrquEAmEb+rcvJnIys8B4I5V03T81wvktWRhzxN+7qHRCBUP3MV/tmfgnnK+pfoe9gU5ccrDQ3e9E4DKPacYOTBJ+5Nn2dH6n+xY94cAjI+VEgoECXn9qToGNxuq2YQ9O4OK+lK2tGawRXsJAPezv6H36UHMOWaK1ueRVZIFQG5NMRnlRVhKSqCiGoBIQTUzmaU4KWImlMPMjNBnl1fB6dbwuCP4/SEKWkvpdzfy7g+9m4nz/8z4q9MUrfwNAK3/pYLZ9Q/g9ZYSDUfwO4U8bkT8yrVANZsoLC9id0uYzJ6TAMysuZPzr0RQTSqNq/LZUyHi40b/4gnc5/0Ubcmj9o7VBFdsAuCCs4LzXXEGemaZHZ8F5u69hm6gomMYyXism7OvkZxXhi7iNNxTLga6rWRmiLgSe1MZTTeFyTXiLeLytFxILzSWgPYeK5oc9gMDhQv9Dmn4AGi6XDKSjQ/Ix0k2PiAfJ9n4gHycdEOhf6oAXZdnwSibjGTjAws5mUy3mo18MjIMBc1ehUzOH7LJSDY+ApI/yL/Nsk69vXp7nXB7TcijwAoevxl5+AAo0slILj4gHyfZ+IB8nGTjA/JxUvCHbcjDB2SUkVx8QD5O8vHBnCnZG2j5ZCQXH5GWOA15kN7RWAK2t4Y5fs6Gpt94JTbbrFQ2FLMiZxyjzTXXXlxCn7+S8f5hNjV5OHHejiFJAmuTarBlXWRRGSXdp7wzoi/9HQomtRLztju54+E4GxNb8Kf+8SS9Tw+ix/dTc+84ANu37qFk9RYK84rp6rMxNiLcFJyTbgIekWIvmeo2ea0r8bkUlDe9aVBUBWV+4JbJhGEkt23n5H61LhLXwulG4mbyyS8rJPyzb1N/Rx3eMeFaVPmuu2k/O4UWi90STleDJJ8TbQqarjAxMMWRwWYai8upGj5EXfWHAejqDdN55/003P08sydFmsjBF0epums/O2/bQveFIsLBECBSPl+PW82tkpGiqql0tpm52QDkleRSVZNDmb2fM33F6IaKa9pDyBskHAyhxzV0Tczvm+lKdCNkpJpNVNQL94xVF37E8GSUhr21+CaEO2fZziJGNr2f9v83RSQx1jeSz/ViPidd0xjtm+L0vt0ANG9bScgVZvakh4GfvcjW1VsA6G+9F9d0MRORKNHEDtZyjeuVZJS0z1a7naKKYtatz+e2kg7UX4o0w12/bgNg9UMrKVi3EkuZSJGul1YTzq9gNqOM2XghADOhTJyTFmY94HJreDxBAAK+KEF/mJA/TDwSZqxslFF/Lav3vpfmD57l9TPP0/lUFwCbKn7B9g9W4tywmaCvlJGEa+SNdCtbih7ZMhysXlfGxsBzEAwAcCG0kvFhFxXV+dy+NkDhC98H4JUf9ZDdlEHTe7di27aTN9SNAJztMujrmsE5PkvIL2SkxWKp/t1qvdbjGpFACOf4LD12kYwiw57P3vU3ncpVw3ibLYTSC40lwGSS44E+CbNkfEA+GcnGB+TjJBsfkI+TbHxAPk6qIpXvBCCfjGTjA/Jxko1PWq+vDNn4SI901qk00kgjjTTSSCONNNJII43rQ3pHI4000kgjjTTSSCONNG4G3mY7GumFxhJwut120zIrZORksnpVFvnhNmJTU6l2vbKes71mXNMeXMNW4vGbl1LuStD0y8vI0HVikSgA3mk3fRdUVFMF5q13s/th0d4ai3Pmu2fp/8UIvgk/APXDY6za2U/emrspyl5BR77wse3PsjE9bsc76yUSDKXOrWsaely7Ih+Y8/lVVCHDZDyGoirCH91kSn2nmk3ocQ1DNzAMHX1eTEiy7c3xMm/2Xb4aTjcTN4uPajaxal0FZ/7XcXZ+9V34fnEEgOFNH2D619O3hNPVIsknOdcCHh9nznnZ++AuSo/9jI1bZwBo78zgSF8xn7hvH0Uvi5SRM8fdDD3/Btt2HeKNdbfjmRGxKd5p13WlwLyZMpo/Ryw2K7YMB1n52ZRU5gNQ35BDc20U9Fq0bBE4OzCYweSoB5/TR8gfJBoKA2JuGrpxU2I1boSMrHY7mzYVADD4L/vJanSQ11iZitFY8Y7NPDlUz/TIuYt89GXTa3gzJx3frIdjnSsA2LZtCxVTLiLeKH37hyjf9nMAbvtAM6NrS/C7/bijs4nzLE+nLicjRVUxJ1Iq55UU0NRSyu6VM5ScfIbOZ48D4OsMkt+aQ+m79+Gv28CkQ8RoOGMFzAQzmB21MOsWNtrljuP1Bgj6owR8YSJBEW8SDUeJx2LEY3H0eJxjZ3OJxKZ4tbaej933fhqOddK3X6Sc7/7ladZX/pzb7izC3VpDKCDOMRWNEwmGboieL+W+lldSwB0bovDCi+ibdgBwqsdCPKaxdYONTSM/4eT/fSH1u5bf2Ya691105Gzm2DmRgrynYxLn+CxBrx9tkfTcMui1rmkEvX6mRoQt7nKYgfxbR+gKeLvFaLy9llXXiUhULuWQjQ/Ix0k2PiAfJ9n4gHycZOMDEnJS5XtvJZuMZOMD8nGSjU8sLt9jkmwyko1PGnJBvhkkMbZvCGOSRGKqKhcfAJNknGTjA/Jxko0PyMdJNj4gIycdzdMHyPO6XjYZycYH5OMkGx9VhZbaKVRFnmBn2WQkGx8ARaLxWhSKunyftwDkewWVRhpppJFGGmmkkUYav414m7lOpRcakkE1i/KsheWFrKkKYtKiuLuEP2heSxaTZa10vuIiGghd7jTSIum3HA2HcU856W1XMFsqsWy+D4CdD0dYF9c49y9tzBx3A+DtO0lN3yTV902yc+MeSlaL/N6FeYV054m6Gs5JNyFvMHXuuBJHMeKp686vj/Hm2hiKqmAym1AUNSV/k8mEyWLGnPgAmCwmdE1H0zT0uE484a+qxeKJNg1N01L+q4ZuYKjKgrgNRZX8TcsNgsVmZc9GiDpjmCuqyK8rAuDZgVqC3sO3mN3SEI9EGemZ5PhsIw9mZtPoEjUzKireSXdvgAt776fhHuH3PHP8EEMHxqi+8xl23b2doQFRgyEajtzQXPvLhWTdDBBjmJGTRX5pHpU1eTStsAHQUj5Lg/cNBvU4NasKUFSVjsJiugvtjA5nMDPhwe/2ARAJhIlFoujc3JoaywFFVckpzuOeFf0AdD45wPrPtGDJzcGRnwGAsfd9nHjRQ9Drv5VUrxnRcJiOC04AnB+4jdzGDlaYTZzpOUvHk8cA2NDyDNtbfpeJ8dJUXZig23fDx1M1mVK1W2qbSrltfZzVQ79m7NevMnJgMnVc07s30r/m3fR6SnHOCt2dcRt4PHG8Xj9+r4ijCAUiRMNRoqEo8Vg8FX+gJ2LtDF1HVSEcDOOZ9XDmtJNVlbu5/aG7me78KQCzJz0M/uow9YVF3Lbm43i8Yn6Hg2FcE9p118u5VlgdYm6uaC5ni+dXeIfGcT+4C4C+Qx6KynK4I+ckw9/+Ka4zIr6o+ZEmzO/8ACetuznXZedCm4h3mBmdScVnyDpnDV1Hi8UJeoSdGR9QgYJbSyqNFNILjTTSSCONNNJII4000rgZUN8aLk/LhfRCYwk4etouTcYQTZeLD8jHaT4fWVwZZZaRLJCNk2x8QD5OClCbY2ZIIpcA2WQkGx+Qj5NsfHQdTnTkItOLfNlkJBsfAN2Qxw4thnTWqTQuCZtVLrcX2fiAfJxk4wPycZKND8jHSTY+IB+nuEQPGknIJiPZ+IB8nKTjY5FPsaWTkWR80pAL6R2NJWBDc+SGr9xtGSJ3df3KIirtIxCA4aODANTe1sApdx1Tw70ohnZT+CwFJvXqZaTHtVScRvd5FYu5AgDLxney9XfirFcVBl8XvtCe9gA9Tw7gn/JTNzxG844BAAoa91CUXU9nfiEDOXamxxI1Cpxewv4gegw2rg1z7KwD3TClamOoqoJqErEYqtm0IB7DYrMIHlYLFpsZm92C3ZFsM6FpOrpmoGk64VAMgGg4RiQUTeRfn/P1FXnYNQxDR0v44qvobGwRnOJzISQp3Gwf2KWM2fUgKz+XXeHn6NxdBDY7RbdvBeDUKXdKXjeb09XizXwMXdQbOH42yp07N5PV9hoAm1vuoq83zuHeQlbfuw+AogPtzBx30//cCTbffpD21nsB8LmD15Vr/0bLSFHVBXUzgFTtDFE3I05LVjsAxd0H8Rw+ymhFE6srelCB0pU7qFzfREdxHn1DGYwPixgG17SHkDco4qiiCZ/4eXFNy4nllpHJYqZ+dTkVB/8OgE6gcPNatGCI0k0rAThk3MZIT/9FOn0j+CwH3sxJj2tMDom6NmdDq7mzsgJLQR5lu8eYOCh89qd/+Tzb/lsLg6278cyKWKtoKIIWi113jZRLyUg1m7BnZ1BeL2Igtm90sDX6PO5nf0PfC4Op4xreXYNt37s4PFjJ4EgMbyJWJuiPEgpEiISixKLCbmsxDS1hnw3dSNVLMqtmFCWh/2aFprIR3mjLYmJwisPncli14yGaHhC6f7zzGIPPj5BZ/DJr80twr3sfAF5vObFI7Lrr5SxFRkkoqkp2QR4Aezab8P/kKbJqynlhtDIhi3G2bCnH/uy/88ZPeynfUwxA2SMPc9C0hzfazPT3OlN6cKX4DFn0en6droDLc+uIXA1usYvFd77zHb7xjW8wMTFBa2sr3/72t9m2bdslj3e73fzpn/4pTz75JE6nk9raWh577DHe+c53XtX10guNNNJII4000kgjjTTSuAkwbuFC4yc/+Qlf+tKXePzxx9m+fTuPPfYY+/bto7Ozk5KSkouOj0aj3HvvvZSUlPDEE09QWVnJ4OAgeXl5V33N9EIjjTTSSCONNNJII400fsvxzW9+k8985jN88pOfBODxxx/nl7/8Jd/73vf44z/+44uO/973vofT6eTQoUNYLMK7o66ubknXTMdoLAGaJlcAj2x8QD5OsvEB+TjJxgfk4yQbH5CPk2rIl6pXNhnJxgfk4yQdH10uPiChjCTjIz0UZdk+kUgEr9e74BOJRBa9bDQa5cSJE+zduzfVpqoqe/fu5fDhxdPM79+/n507d/L7v//7lJaW0tLSwl/91V+haVdv76Xc0ViK/9j3v//91MosCZvNRjgcTv3t9/v54z/+Y55++mlmZ2epr6/n85//PL/3e7+3JF5Hz9iX3pklQFFVsgtzAWhZZcKmh7CG3LjPCz/TzX+yg1Md4Hd5iMeNG85nqdB0ZUmc9LhGJBjCNemk87yImTCby7G0PsCGD+pk1wrFH3zpLCMHJpk4OIN35A1q+sYBKL9njNwNd1DctJ7CvHx6ckXu8LERO+5pLwGPn9PdVkxWsMyLxTCZTVisYmVuspiw2CzY7Basdgt2h5gSDocFR4aJzAwTGXZhRO02UtlHNB0CQREAFwjq+HwxQsEYwUCUSEj4icYiMSLhKFpMS/mOaprGmV47JquBatHREzU2DF1P1d2Yj/k1OG6EH/tSx2ypSNYlqWgoxfvjP6fxgS2gaUxtehcAIy9PXtSvG81pqViMTzQcpr9zktPbN7FbeR2AdfpJXindQVeXjwvvEHVhVtz7PDPHDzLy8gS1L/6C29/TCsDMbCkhf4h4LEY8oRvXy2m5kKydkaybkVcsbFJVXT5NK2ysLXey0n8C08uvADD2+mmGj44AJxjZUAZA+Y5+NmzYSlntVioKquksFL78Q8MZTI568Dl9hPyJujehcCJO4/r8+9+M5ZaRIyuT27Y46Hj0NwBU31uOWl6F3t+DaY8Y79fPGPic7kX7IZtew+KckrUITnaa2FNZj+p30/jAFiYOPgtA5/4udqzdz+59KxgdF64WPpePkDdAPBZLjSUs3WYtxkdRVax2O0UVxWzYWAjA7oKzGPt/Qd9LnUQmo2TUid/UPrCLk9l3c/aYH68zSCQs4jFi0Rh6XF/wcKSaVFSTiskk4vfURGlrUTvJlPqcbguhWoRcutqmOFi7kve/8wEAKk8NMPrSFL0HenEUPce29ws9d228G7+3NFUvB1i2mjlX0iOTxUxtU7mQk/EiXUd6aPrbD3P+lKh5kpOfwe11w5z90qsArPmU6MvJ0vdw6KRKX9c0zkkXgYQeXKl+hkx6nZRxOLj4g7YsWE7Xqa997Wt89atfXdD2la98hT//8z+/6NiZmRk0TaO0tHRBe2lpKR0dHYuev6+vjxdffJGPfvSj/OpXv6Knp4f//t//O7FYjK985StXxVG6HY2k/9hXvvIVTp48SWtrK/v27WNqauqSv8nJyWF8fDz1GRwcXPD9l770JZ599ln+/d//nQsXLvDFL36Rz33uc+zfv39J3PJyNECW7AqGZHxAPk4GuVlx5OED8nGSbcxAPk6y8QHZOBmKglZYIAmbJOSSkXx8QD5O8vHJzYwhDx+QUUZy8QG5uNxYPProo3g8ngWfRx99dNnOr+s6JSUl/NM//RObN2/m4Ycf5k//9E95/PHHr/oc0i005vuPNTc38/jjj5ORkcH3vve9S/5GURTKyspSnzev1g4dOsQnPvEJ7rzzTurq6vjsZz9La2srx44dWxK35sYoJkkkZlLl4gPycVJVaKoLSlUbR1WhqTYgDSfZxgzk4yQbH5CQk6oS3bgeQxbFRj4ZycYH5OMkGx9VMVglkb0G+WQkGx9AKi6LYhldp2w2Gzk5OQs+Nptt0csWFRVhMpmYnJxc0D45OUlZWdmivykvL2fVqlWYEpk6AdasWcPExATR6NXtxks1HNfiPwbCNaq2tpbq6mre85730NbWtuD7Xbt2sX//fkZHRzEMg5deeomuri7uu+++S55zMb+3eDyKSTVSH1URq2ZVMRa0K8l29erak6tvcU4dVdHB0DAMQ3zMJgyzCR1QSL45EOdbeB6AhW1qov3Nx16y/Tr7lDzf/D5d3NfFuCT6hEh1qRugJ4raGJCSgWEyoSe2HQ1ANxLuFoaGgtjeVUjIMcFnfp9UVU/JOMldSf2tJ86RaEcHNDCSn4SMDW3hJ8V97tyqoqfak1zUeXK/1eO0mO5daZwu3375PimKjq6o6IpC0hvMMIRsFuvT/P7K0Kckn/l9UhUdw9DRU5oHJP4SrnDiOwDDJPqebCfxi+vpkzh2ecdp0faEDisk7ZI+r6cKuqpimEypIlSGqop2Q7wNM1LzRl8wR26G7l11X69iPqmqIfo/r6+6kZCDYaAbV9a9i232Mo7TNdqI5O8uakdDN+aZvaQNNpvQURLjKo5TVT31u+vtk6Jc3K6qRuK+mNA9wxDzyZTglFgJ6Ig5ltQzhaQMFrPNl2o35t0j9AXzLHlv1g3m7FiCh66o8/RA9FVdxnFaio1IzExxf0TYWmVuxop5meCebBVy01Lcl6J7Fx0vwf1Jaijq8n2WAKvVyubNmzlw4ECqTdd1Dhw4wM6dOxf9zW233UZPTw/6PPe5rq4uysvLsVqtV3VdqWI0rsV/rKmpie9973usX78ej8fD3/7t37Jr1y7a2tqoqqoC4Nvf/jaf/exnqaqqwmw2o6oq//zP/8yePXsuyWUxv7e77v0I23Y9kjIwkzNmeocs1FfHKS2ay5s+PG5meNzC6oZYYktRoGfQwtSsmdamKA7H3KC191hxe01sbY1SUSf8KIv0c4TIorCvHf39dwNwrmIb5rNn0WNRHHaDqjINhQi6IYKxjp6xk5ej09w4t8oMhVROXbBRXKDRWBtLtbu9Jtp7rFSVxakun+N+PX3KzNCpTHA63y36tGVdBJNpbtKfbrcRiSps3zAXQ6OqYc4MKKIwknOCC11ZBIvXUryjhOp8B9WVNYTu9OMe9uJ3x+lw+lGfPcxM3I41lomjYpLGvGry6ytQMysZyjWIegMEfBEKMnQCmokxdz61pQEKc0KYzSZM5iiucB5hSqjImSDL5sViVrBYFKLmMmyZVjJifViUEBZV9De3oACL1YF/ZhDD0Ikl6mvoWfX4IhlEXYNEowaRmOjvoKeeSChEqW2QeMJ3NB7TMXQHFZUOakvcKV/mYEjlXF8WhVkh6iqEDmAYuHwmLvTaqCyOUF0mxs/AYGrWTHe/6bp1r7NX9GFTS2TBW7vFxglEBVib1WBD85wP7OV0r30og+L8GLetmeLUmXzq9+7EYTI4NrUaNTrDmoqzxItjC3Svriq+QLevdT5dje5dTZ9aVkZTeh0IivlUmBOloWCAge4QJYWrcJgVanpP0lq3kq7gKO1dovZE0Z77KXnxPBNaOWecNop6XgZge+MenLNlUDCN3SRkZuj6VfdJVaCyTMNhNwiFr3+cTnc6KC3SWFETTcQyGUSxE7CUsaYugkOZpTjPRYnFSUlPP9Gjr9AxGidaUkogVoOxvhZDzaLnyQGMXa2MjEK20YNjysf6mhoqV2/E65ygOsPCbKmBzxOlrS8Lf9hGY9EQhhZL1Zw51WZdFt0722kl02GwrTWSstnXavfWNGrUN8aoD7/EcPE61DfaueP3NzKiZBMtX81YrA6A8Z5BYpEoWxfRvXicBXp9LX1abltekKuldLtrQOjemlofDruO6j7H+eoKVlqjOLbupODRDAyTidl2N6cmFDb0/Jod6z6K5ukjOx4l6IsTj8Y5ei4DE1FaVwVF3I2hE49zVX1K6rXHG+N8t42qsji1VQaObDNlVT42lnWQmZNP/PABTjvt+Bq3QCOUN0C2a5L+tXvp7h6h2OSnuNBg1JmHK5DBqko3dmtc1FJSFIadxYS1DJrKxjCbDRGroSpMBKtRrTaqMvowmRRMZoi5NXqnc3BPTlGX08fsgItjDU0oqsqWB7YxNX0cf/M6zoXtFJ05jzUe5fY9xXhb66jNieN3ibGandVp67Zc9z23f1jY7HVNUey2OR1r77Hi8VvYsUlj45oxAHqPnsJRXcKZjD0UKp0ANK90oJ07gqcrzIb/bzcn68TzRUfnCIoryPSIDSteNq8WsSWGYVxW9zr7LORkLZxny/1stFRbvmltkDQWx5e+9CU+8YlPsGXLFrZt28Zjjz1GIBBIxTp//OMfp7Kykq997WsA/Lf/9t/4x3/8R77whS/wB3/wB3R3d/NXf/VXfP7zn7/qa0q10LgW7Ny5c8FKbNeuXaxZs4bvfve7/O///b8BsdA4cuQI+/fvp7a2lldffZXf//3fp6KiYsHuyXw8+uijfOlLX1rY9hevcvSMDT2RhSL54qJ/2MzAyJwok5Oto8+CguWi9jOdVuaH/CaL3JxszyCSVwvAHUVRTCafeJv45IsAlH359+gcz0fT+wiGFbr7LZzrsqb4ALi9KkdPzwVmJafmtNPErMt0UfvIhJmxyTnu19Mnk2qwLqRwrstKLC44HT+3cAsv2df5HE0WC5ZMCEVUOsZKsZSW0lzgpzIugtEcRpTirtOEDo2gDc4ZFuvsNDUzfTjqihkvLsLpFQFwYQoY8dmZHndRmaMzOpuFYoJxdw6zoQJsdlGMD8BuB1esnLBZxWFRybQqgIoNILMGqy1GRuJB0GwRBjavsBwFg6AmHia9MSugEratxBc38CdKJBvoxHQLvbPVqaDEeCRCdf40vpCV070F6AmB6DHxNm3GY2HGnZC7oaPp4g3N6JSF8WlLYoz0xDjp1617BuImcr7bskCPFhunZHswrFzUDovrnsUBM24zfvNK8n7+Eg2f2Db3NthSwPH2LCKBUKJf4nf9I2ayM/WUbl/rfLoa3buqPp2xpfQ6mYFm2mki2JFDZn0jddn94mAXYC2gZ0bFXp4PQJHlHNOAcqqD3FwXDXesAWDMng/E6R3NIuAWN1w9rl11n9TEXAuGFXT9+scJYNplYtbtwGKzYs1wkJWXRVEFhCggbORTlqtRmglF3k6CgH2gD/tgP6Fjs+iGCndtAUA5dIbs+BDlG2soKDIRt9QyC2TnlzCuFzIzazDq8uMPewE425dHJBAmHomiaxrxxPxZjj7NOE2c756zkddq9zoHrESzKvj0bSFGn3wxkRViI1XmCGhO3Pl1APjDzgXjNJ+7qhoX2exrGqdltOUm1ZzS7Xgic9DZLhsKsEKp5EN3ucgI9mEABZ1nAHA9OUDePeVYdzUCKqbcFfTNBpgZcxEKhtD1CNGYyhttWUKn5xVkvFKfknrd0W9J9WnKbSW/vAi1pJaMbFE0sCjmYfb4UQKviaJyWZ9uAaAwJwPXaCMdY5MYupFK3tE3VYSiKJjMIgBcNxRMJuh3VqaCvq0WEwYKVlQmoyuwWlWsZnBO+9ENhVBY4WRXHk1ZdewqnHvYVmZcqE++SNbmPGpLt4ABYSCu5NIxVs5EwjxEgiHAuO57LopBWZHKuW4LxptstqKK+dSyWxSQrBztxBUJgaLQM1MNgKMinwbtVwzENcyxCAXF4r45M1xK1/gk4MbjN3H0jLi/GYndj/nj9Obxm5o2cW7+PFvmZ6Ml2/Izi7sOyYLkPfBW4OGHH2Z6epovf/nLTExMsGHDBp599tnUC/6hoSHUeW8dq6uree655/jDP/xD1q9fT2VlJV/4whf4X//rf131NaVaaFyL/9ibYbFY2LhxIz09PQCEQiH+5E/+hKeeeooHHhDZFdavX8/p06f527/920suNGw220V+bme7cxfNvqAbyqKxR/ol0uJdql3TFYyEN5uiqiiKggIoyarSqipu6CjousrJ9sUyPSiLVuc0DAVtEY6Xar+WPum6chGnS6UGXMBRV7CQdEVQMTCJCt6JyagAqq6jaFpKFgCKYaAa4u2uqqooyW1ERcVARdPNdAyLLCBmExiGkJ+4xtxEMhDXFFuRSqpVUdREhdjEmCT5qCoKBooxr11REr83MJg7ByipawLEdTNdY0XoRhxdV1NZp3RDAQwMQ0l6B2AYSuJGaSwYJ8OYk+n1657CqQuXNsqXqvS6ePuldQ/FhBLXUBVI0VdUNF25SEfi2uK6fS3z6eq5X7pPsfjFei3GRsHAlNJTAEVJjLWSqDyf+ErRdBRdm/s7oau6Mdf/+f24Up+0N8216x0nRZ2zBWpiLid1FlShyYl5lupDwhYqcQ01FodfH5o7n6ahGjoqxty8UYRcDAyMhB1L9lVP6IHo99xD+PX0CeDUhcWz4VyL3dMNYZPm26CULFI35rnxWewci9vspc+n5bLli3FK6p5uqAm7Kn6uzhtvRdcSLuIKYErZuLnxE9yT15h7IL58n96s10ZifqTuC4l5o8KC+4FiJF11xJzUDVW4HZO0ryoKirDZxpztT94Lkp8kx7m/VTpHi1PteuIercx7EFMMQ8hE01CNeYJWlHkyScrVWIZ77uVttq4rc/bHEM6bijJvPismkrNPgdRDZVJuyWskdXj+YuZS3E9eYp4t57PR4u2LNqdedEqLW1wZ/HOf+xyf+9znFv3u5Zdfvqht586dHDly5JqvJ1WMxrX4j70ZmqZx7tw5ysvFKj0WixGLxRas0ABMJtMCn7OrQUlhPOVLeKuhKIZUfEBGTgYF2aGUn64MUDAoyApKw0m+MZOPk2x8QD5Ohqpg1FdelJ75VkI2GcnGB+TjJBsfJLPXIJ+MZOMDSMUlDcl2NGDp/mN/8Rd/wY4dO2hsbMTtdvONb3yDwcFBPv3pTwMi9e0dd9zBH/3RH+FwOKitreWVV17h3/7t3/jmN7+5JG4r6+LMumzE4zdGiTNysmhenQNAIRfw6XlMvHCY1R9uBOAN12pmRtvQ4xomFRprY8y6TIu+YbgVUJVr46TFYoR9wqdydnSGdosJVSlmunYHAGt3lVNRUk5u3WEGXrnAxEGxfT760hT+ySPUDE5QtnuE3PW3A1DctI7O/Fz6ckxkxabpdxVhc9iwO8xkOEzY7SqZDvFAlGGHTLtOhjVOhiVMhlm4STlMYexqCFs8iC0uuJmDIUzxMOagFzUcQLdnAhDNKiSUU4CvsACfloUvmnCpClvxBG14A+APiEVtMBCh0OSmZ6aESFgnFhH+q9FoHC0WJx7TiMdEm67paAnXA21ebvqkL3tciaPF5rbwrwXXOmZXi+yCPABubxijP2YQya9As9g5/lqyhsLF+c5vNKel4lJ8IoEQnW1TdG4UL0HWdp5mU84FXi9aQWencAtqe+A+Gvb+hqmjrzF2cJqy1hcA2PqRGiY23o3HVZKqsRL2B6+67sByymh+3QwgVTujsjafVStsrKsQ7kDJ2hnjh88ydHgIf09o7hwOM9rWZtThCdA1po668AwEKB+YpmzzAKs29wJQsmI7FTkr6SzOo3dAzJOJ0Qxc0x5C3iDRcDilE9dbU2M5ZWTLcLC2tQTjqS/jqBRvky3N68AwCK3awsH2DABck92XrJcgm17D4pyS/KdGZmj3raREPU6guIHqB0RM48jhMYafH6dw5fNs//RqAAY37KY724prOoDPHSDkF7oRDUeIRaIpF6okLjW28/lgFq41mXnZ1K4q444NUVa0/QyAvmcOMv6qcJsq2Z5P0f0izuBZ33o6O1yYLSYys2yYLYk3+yYFq9WExWrCahEvHS0WBZtNxWZVsFrAlvDksVt1LCYDi9nAZopi8mgMeotwTbkI+QMMd0/xem09ACvufDcrznTT/oNOZo676c04BUBT3hPceXcRTnc5Yb9w950dnyYeid5QvVZNJsrry7i74CQAbU+cYfufPcQvuzLIzBYH72p00v/os5TtLsLx8Mc50FkBQPvZcWbGpokEQlesnXG1fG4VJHrfsSjmvB7eHpBuobFU/zGXy8VnPvMZJiYmyM/PZ/PmzRw6dIjm5ubUMT/+8Y959NFH+ehHP4rT6aS2tpa//Mu/XHLBvjTSSCONNNJII4000rhWLGfBvrcCpFtowNL8x771rW/xrW9967LnKysr41//9V+Xi14aaaSRRhpppJFGGmmkcQVIudCQFWE9G8WkpYIgr3cLdD5Us4niqmLW1QTEuRUVmxKm/Qed3POfvw/Av5yLEfQmUs4hUstJslMJXB+n5LZ6yB9gamiKdkXB5c4FYLZhBevq82ksrmVN9atkFomgpOFXx/C0BzjX3kbNiIuaCZFEoGXLCEXVWynIrGZ2spDsqlwcdoVMm06mTSPDEsZhFu4ZDjWMTQ1jjwewxoJYgkL+plgIUySIEgujxBLpGGNRCAaITU7iH5vElHA1seTlkJufR35eAUZuIbHsAgAiGfn48gvx69l4Y8LNyhMyE/LkkF2RQzCiEgoJHQpFdCIRnXAoTiThThWLaMSiwp1K/Cva47E4elwn6AsQdF+f69SN1CPVbKKsVgRSNrT/B9G7StBNFnqtLQx2irFabA7JptuX4qPFYsyMznCkV2S8WVtVT1HfEVpWt/DCCx4ADnWVsvbed1D6UhuTh5z0vSRSTLaU72f3e2qY2rCCoE+4mUwPx6/atWK5ZKSaTZgsFuwZDrILhdtmcXke9Q05NNdprM3ppLT3IACBw4fpfuUCk0dnMWJzVzbnmLEU24hFPMTNIn2oETOITEYZ+NUorgE3lQMT4txbBtjYupXS2k2U5Yn0493FRQwMOpge9+Jz+vDEhatWPHJ1xaAuheWUUV5JAXe3eDj1hcOsfXgdALHcYkzhAG2WLbSfnQJIZVC7kXyWE5fjFPD4ONWlsntFIQYq/o33ALDynSc4+8/n6T3QxaZV+wG4fd8KCnJKGZvOYGIyh9lpYUe9Tj9BX5BIMJxy84xH46m6M2/W9SQfVBVbpnCtK6stZefmDLZ4nmbsqWcBGDog0reaHCoN961ncM2DALzxShy/O0DD6lJKiq1YLcJFxWYFi3nOLQrAZtaxm6NYTRomVcOqCvtqVWOYFA0TGooRZaIkn8yKLAa6cpkdmcIz4+L8OXFveqNmJ3vu38PYqVHc5/0pdy6L43VWFhRy5/pP43aJeNFIOILf5b1u96nLjZk9O4PNm4uw/2IuNf/0bQ8z8myAppVZAKwZ/Tkv7h/iru/+DgeMuzh1Qrgiz47OEPYFF2QJu14+twoycVkU6R2NNC6FntEcdN19q2kAIjtDe8/VFUu5WZCNk6KohC3Vib/kMD2KoqI7qiEsh6GRbcxAPk6y8QH5OCmahuX4GTRt8fiEWwHZZCQbH5CPU5KPIomTvaqqRFL3EDkg65jJhEtlr5IFtzK97a2AHE87bxGUFwalyWagKAbV5TFp+IB8nAxDx6LNpKrJygDD0FGi8nCSbcxAPk6y8QH5OBmqSryxLlWhWQbIJiPZ+IB8nGTjYxgGlrg89hrkk5FsfCCddUo2yHNXeAugvDAkTTYDVYHq8rg0fEBGTgZWbQZZdjMEDNToLLJwkm/M5OMkGx+QkJOioDXWS5XuRTYZycYH5OMkGx/DMMTLKknsNcgnI9n4gFRmaFEYirpsn7cC0q5TS0B2XiaOHBOaW/if6ixfnIYtw0HjmiKqrSINpJc86meO0QkMrHs/AP3fnURL+On/tiEpRy0WJ+j1MzmsEAqItIBebx5OTz7TtXto3lNJXbHIQJbfcIT+lzuZPelh6LkxfBMifqV2eILyHUNkr91Fe56D/KJJHKYIdi2ALRbAGvZhior0qmokiBoJQTgIkTBGIh7DiEYxYjERO5JwBzF0Ay0Ywts/xtSFcYxELj9LhgV7rgNHfgb2/GysedkAOHJzyC4sgPxi4jmFAIQdefRlGOSVWAmTTSAuCh0FYjZCMROhqJ1Awq0qHIFQxCAc1gmHNSIRwSMS0YhF4sxMeBKpCK8vTuNGwWKzsqY5D4Dxp56j/r5NxE0WTg7m4Z7qubXklglBr59zZ10ADH1wNzWH/h9bd41wrEDEO3R0eDj/rr2suOc5Jg+9guuMSHvbX3SSpsonub31vzI9LYqRhvwhvNMutGWM/boUVLNI+2m128nMy06lswVYtcJGS7mLlYETWF55lfHXT4v+vSmlrbVA5AM155iI+TS0qIE124wSV9BCGloi/sjTHiAwIuyaZ8RF+eAoVZv7KWzaCkBZUwulhQX0DjkYGc5Ipfv1zbqXNQ7uWmG126lvKqOl9wccmIySv2cXADHAV7yCo50ZTI/0AUg7F68F8UiUno5ZprauotjVTU+2qPze/M69ZL/ah68zyOBvjgOwou7n5DfuYrywitGKXIanhb0bn8xmZjKA1xUg4BH3zXAwRDwSJR6Ni/oribTdybFWFAWrw0Z+qYh127CpiDvyTuL//lN0PyPKbCd1q/qucux77+f1QeHeNNg7hi3DxrrVVipyg1hN4n5pVnSsagyrGkVNRIiYiWHWo5iMuCj+qgv7aoqLMTRQMXSNkhwbmQVezq0oxTvtJhqKMDEoYnIOn8th1Y53s2LvaU6cfyMlu6HnxrBlP8fG/1rG1OYPAeD1lDHaB0GPL5Xydzkh4jxLuGflEGf+p4irWvuBVl6YWIXVGmbHChGL4fmnXwMweccjHDwQY2JQxMsFvf4lx2ekcY1Iu06lkUYaaaSRRhpppJFGGmlcH9I7GkuAJ5wFxqWzitxMGAZMzpgx5NnRlZKTzZ6JbLVxMu02FEWRYjdexjGTjZNsfEBCToaBOjiKPITkk5FsfEA+TrLxQQGzPVuqe4hsMpKND0hlhhbFW8XlabmQXmgsAZO+QnRj9FbTAEA3FHqHLLeaxgLIxklVFDJz8m81jQVQFYW87ExCkiw0ZBszkI+TbHxAPk6KpmM63YFilueJTDYZycYH5OOkGwq9w1asDjn0SFUU7FlFt5rGAkg5ZhLxAcFJZqQrg6dxSezaEOXlcCFTQ+LvkD+AFotft0+joqrklRSwuckgI+IGYESpJPbir6l/sIrX+kW+eefk6dS1VMWgvjpO/7BZmkm1HJwMXU/EaQSIRYSvbDgYIeiL4HbnMtOwmtZm4btbX1rD2qpXGSo7IfL1J/zfff3nqRqYpvKuKXJWtlKkRlFDAYxwED0QQA+G0KLCDzwaiaKFo+ixOHpcS/nNGpqeknXSh1hJ+BO7h5xMveFaUEtAsShYCyxY88xYM4XRtWZZySzKJKMwC3vCZ9+cl0ustJoKm4Gam4+elQdA3JFDxJ5DOCebkCrynYd1O0HNRjhuJRg1E4zZAAhFVMJR6B904Jp0EvImY0iWroc3Uo+y8nPZ2ijiZi78uIu7n3gQl72I8+1eouHwLeF0LbgcHy0WZ6xf+DgfG11DbWEJNSMHaVr1MACHXxvjYGcFa/feT+mBc0weEjUiRl+dIqvkFVrKapjY+EEA3M4youEIIa//iv7b1yojRVVRTSasDqFLuUX5lFYXUFufQ0u9uObanE5K+w4ROHKY3lfamTgsfLuT+m7OMWMtMBNP6F1oNIKuKxgbV6Oc6kDREr72FiX1m7hX+MqPvjSFd8xP2dA0pZuHAWhu7aekdgvluQ10FeURCogaNyF/kFjo0npyJVyvHpksYh7nFOexbaONwW/+jLLdRcQqGwGRorLbWM2FttlUfaMbyedG4HKcDF1namSGdtcq7gmfIpIh5DG25j5W7nuNk50nGH5pHIDc6gMUbBqnoLqeuvJVTK6sA2CkooihmXxGx7OYmhAxGu5ZEa8RCYaJhsPEo0I3DFVBMXQaauO4YsU0rRP1J+5umsDxix9y4smzKT0CsJVaqb1vC+cK7uHMcz4AIsEIDRsqaS0dIy82jUkXxyuGhmpoqNrCGBol8epbN5lTb5kNFDSTFU21EFdMZPsukJOVQfOa9Qx15+Icm8bvEvearrYpDtev5H337aX85ECqjgZAz88HyCh6ij2fEffv6c23o6gK4/0m/K45G7jUWI1LjZktw8Ha1lKqDv4l3Z0iBtHx/Y/Q2R6nqdHB6pEnATj2i/M0vr+OlwYb6W3rI+AWsrvW+AxZ9ToNefD22r+5Tlh0jzTrUEWB0qK4VDFFMnLyGpKtpRXwmTOQZS9exjGTjZNsfEBCToqC0VApVZCjbDKSjQ/Ix0lRoLQgjiLDdi8i61QgHJFi9zkJKcdMIj4glRlaFOmsU2mkkUYaaaSRRhpppJHG8kP2ldAyY8nLoeHhYUZGRlJ/Hzt2jC9+8Yv80z/907ISSyONNNJII4000kgjjTTeuljyjsZHPvIRPvvZz/LII48wMTHBvffey9q1a/nhD3/IxMQEX/7yl28ETymwrjrGWKSUuCZWo85xhZA/iBaLYejGNcdqmG1WaleV0pzTT0TPBMBqxGn78RG2fOUTfOe0G4CwL5j6jW7A8LgZXaIt3eXiZOg68Xl5xvW4RiwSI+gL4fUW4PKWADBTs5c191axorSUnMrDDL4ucqx72gMM/GqUgDNE5u1Bpl1TxANhYqEYsVAMLRpHi4mx0mMaumZg6Ib4V7v0GComFVu2lcB0cEF8Bgj/9chklMhk9E2/msacY8ZekojbyLWhrJ5iwD9DVlEW9nwRj2HJycKRk0VWTg5KTp74aUYWekY2mi2TiD2XSJY4NmLKIIKd4twyei8UEAmITGjXUn/hRumRajZRUl3EmuhJAA7FDOKF5QxGqhjtG7usX7Jsun05Poaup/y1T571c8+9O8g/tp8d29wAnD5lo/OCi7Or9tJ073NMvfGK+F3MYPDVYbLKf8ltj9QAMLl5Fz53kMlonEgwdFl7ci0ySsZn2DId5JWIJAk1jSWsXZ1BS4WblUExVtZXX2Hi0GmGjgzh6wwuOIc5x4wl20TUGV/gL49qoHb0MZ/Qm+dIEr7OIKHxYXwTwje8bHiCgo2D5K3ZRGn9RjzeCgBmRmfxRKLXbFevR48UVU3FsVSvKOX2nFMcf3KA3V+7n5hV2OiwLZdTPRlMDAxeVX0j2fQarswp4PFxugvuXJlJgdUDQLunlr377iXvxU7c50VsSt/LPfjG3eRUdZBdf5aGujoAqqqbaKhbxXBJGYNVeQCMTmQzMRHANe0n4AkQ9gv7FYtE0eJRpn02qhrL2LNRkFpx5sec+cnrBAcWxuvU311DdM+7eb0jh/GhQQAyczPYuMZEpacdNRpKvTk2VBOGKmrH6CYxrprZiq6a0VQLUZOdWLLdMBMxbEQ0KxFNpcw6iz3mp7XGw9m6YrwzrlStF+f4LMfP5LH23nfScN9xpt44IM4R0jFiBhd+2s6Wsv8A4O4PV2JSGzhlURntU/HNCnlGgqElxWksNmaKqpJfVsjeVh9tn/oVje8X8j9qvQvDMNhZO4rv/z4HgL8nRO1fv4t/OOnGPeVM6e6tmGc3CjJxWQzG2yxqYcm9PX/+PNu2bQPgpz/9KS0tLRw6dIgf/vCHfP/7319uflIhMysLWcJaDENheNyCIUnwFcjHSTEMsp2TqYA/GaAYBvaxYWk4yTZmIB8n2fiAfJwUXUe90IciUbEv2WQkGx+Qj5NhKIzNOKR5EFMUlVyHVaQjlwQyjplMfACpuCwGQ1GW7fNWwJJncywWw2YTK/8XXniBd7/73QCsXr2a8fHx5WUnGdwuFyDHjVRVDZobo6iqHA+sIB8nQ1VxVjSgSxQwZagq/lXNGKocnGQbM5CPk2x8QD5OhtmEvnsjRqLiuAyQTUay8QH5OKmqwcpqH4ok91nD0JnyhdAleTEEco6ZTHwAqbikcQ0LjbVr1/L444/z2muv8fzzz3P//fcDMDY2RmFh4bITlAnRaESabBgKkJejSZK7SEA6TopCJEOuYksoCvHcPEm0SMIxQz5OsvEBOTkZpXLZf9lkJBsfkI+TAuRmXtkN7abBgHBsaelnbzRkHDOZ+IBct/zFkM46dQV8/etf533vex/f+MY3+MQnPkFraysA+/fvT7lU/bYie/AMd25YRTRWCkCnquCccBH2B4lFoql3MEv1dczMzWbTOgeFzm5684UM6+JdHGsP0LP+YUZfmAJEnuu3CwxdT8kzGg6jaRqxaJRIOErAJ/zLXe5cphtaWb+5mJrSKrJrDwIwcOAMoy9NMXXMBXUzOJ/pQPfFLnGlpWF+bQBzjpg+lmwTikVBC+loIX2h7zqihoA/0WaYo+j1Afy/GMZsMXBUit1BW44VS4aFjIIMHPkZAFizM7BmZ2DOdGDPziIzW8RoKBmZ4Mgkv2Erx9Y045oUtRlC3mvLg34jYLFZaVqdj6PjKAAZdXZi9hzahzPwTrtvLbllRjzhrz3YNcHxHWu5z/48Td7DAKxYuY9TR4c51FnL+nveQdWhTgCGnx8nNBqh5zftrK/4GQB7HqxmqrWCUCCEcyyWOu9yQFFVTBYzjqxM8ssKqFtZDMCWdRY2FXRR1vc6gSOCc8cr7Uy9MYsWErpkcoibmbXAghbSCY1GFpw7o85OxY5qnGsL0LtyUDQNd7vvkjEaIObExEFRn8M/EaBkxEnJ0Ch1rYPsaX4EgO6uMkI+fyoG6WZCNZnILRJ2ZmNrNhkv/BgAW+tG/KqY94N6HW3n3QQ8Pmnm3XIjHonSc2GGqe2rKPH1AXDMX01fwz4a73uR4+ePAcm4mzHsJdNklY2QVy30PK/hLJWNDZSvaKahag0AIyWVDFZlMziWyeRkCNe0iPPwuf1Eg0Eyc6Osas5hR/CXAPT/x6+YOe5ewCur0UHlO/bwUnw7F9pmiSfiDBpWFrK+aBDThA/Nlolmtot+mO3ETTbiJisR1QFA1LARMazENAuhqIVIXIxrOGYiHFMIRlQiEY312Zn47EXUav00r93AaF8eznFxTw75Awx2TvJafQMfv/udVB9qB2DgV6Kwb9QZ49yPjgCwpfIH3L37w9itKzlhNTHULXb/PDNuIoHQNdexALA6bKxcW866nn/lwFEX637+uwA802OhsU6lvu1pTj5/AQBHpY3etQ8x8uwk0XD4t1Z3ZUa6YN8VcOeddzIzM4PX6yU/f67q8mc/+1kyMjKWlVwaaaSRRhpppJFGGmmk8dbENe27GIbBiRMn+O53v4vPJzKHWK3W3/qFRnHULU3+Y92AnkGLVNkVpOOk66gn2+EymaRuOnQd9Y12kOQtknRjhnycZOMD8nFSDIOc8UFp9Brkk5FsfEA+ToYO4+58ZEm6Aio5OdlSBYPLNmay8YG3QNaptOvU5TE4OMj999/P0NAQkUiEe++9l+zsbL7+9a8TiUR4/PHHbwRPKZCjhaQxOIahMDUrV71F2TgpuoE6OHaraSyAohvQP5r865ZyAfnGDOTjJBsfkI+TYhg4PLN4JAqalU1GsvEB+TgZKLhDmdK80ENRyHA4bjWLBZBuzCTjA2+NrFNvJyxZO77whS+wZcsWzpw5syD4+33vex+f+cxnlpWcbDhyYpodJT8hvF74ECtKCX12C85JNwGPn2hI+C4vpa6GajZRuaKMreVDqFMx3BHhh7/q6C8o31PMS52l+JxDwMLYD1U1aG2KcqbTiq7LobTLzSnZX0NH1LmIa2jRONGQ8F0P+sJ4PHk4V1SxvuYBVr+zCoBVZSVkFh9h6Mg4weZWlBeOLdsj/Xy/8+INwnUwtzIXS4YNQ9cFv4DgF/GFiUc0YsEY0UAs0WYQXr8B5YVjaCENf4/wP/cz54eejP2wFpixZpmxZlqw5zmwZYt4DmumDbPDRsHGUbauW0/vhQJx7kBoybU0bpQeZeXn0tIQJ/B0FwCVW8rxZxTT3uElGg5f9rey6faV+CT11Dvt5th5jds3bsHRLvyyt7XczYVzNi60zXKm6R7W7XsNgNnuFwgOhHGf99P7S+HnvrrqCW5f91lmZsqIBMP4Zt0Ai+bYv1oZKYnsZmabFUdWBkWVRaxoKmJrs/h+m/UoGQd/w+ShUwweErUI5tfOSNbNAIj5tAXxR6W7hN7V7G4ia8d2RshiTcsJVAxmz/cy3TmNq82bivW4FPw9IcJTY/gnfZSOzdCSVwTAhg2/w+RQnoh/W0KdgSSuVY8UVcWW6aCqUdTr2Vk9TMefvUbNvgqihZWEbLkAnB/MYWKw66pjaWTTa7g63Z4Zm6HNuYa9oRMA5GfFOTlezkP77qPwZRGLMXvSk4pF8/eEmHaIuLGMmjHy63opajpPcXMjACUr1lBfvpaRoloGXNkMjQtPiPGJbJzTXnatD3Bn6Vl8/yTiYvr2D13Ea8U9K5nc+l4Ov24wO+4iv1iMyaY1UDp1nmBuJW57GRFD2MywZiOsWYnETASjyVgMlXBUJRyFYMggEhW2PRLViUQ0omGNaCRKTYaOObuarcZRNtT6OVdbjM/pBiAaiuCZcXH2jJOT77qTze8Q83vk4H7i3jiKRUnNp44fPE9TLMZdt38A+5b1WCzlAPR3qrinXYR9QXSuHOM5f8wMxNzMLsjjjs0K/X/xM0q259Pe+BAAwTd0bqseYOIHv8F1RtT72fDfN7C/pxy/a/ia5tXl+Mik12nIgyUvNF577TUOHTqE1Wpd0F5XV8fo6OglfvXbAV0i1zAFcDh0Cd6Jz0FGTkZOllR8UOTiJOOYycZJNj4gJ6coqiAmyT1eNhnJxgfk46QAJqLS6BCAoS1fUoblwPwxk0FMsukQyOArcHmkg8GvAF3X0RbJfjQyMkJ2dvaykEojjTTSSCONNNJII43fNrxVYiuWC0vu7X333cdjjz2W+ltRFPx+P1/5yld45zvfuZzc0kgjjTTSSCONNNJII423KJa8o/F3f/d37Nu3j+bmZsLhMB/5yEfo7u6mqKiIH/3oRzeCozQI//gQA31n2PQF4ZustTyI3V5If6aVmTErPpfIBz6/rsaV/C1tGQ42bCigaujX+EsasanCl//Cvx+g+TMP8p2TM8QW8QHWdGjvsUqVUOlGckrW1UjW1ABEXY1ghIAvH5cnh5mGnQCs211GdXEZufWHGewcxHx7IaqqoCaqFpssasp3XTWrKKoy97dpbktTURdub0YDUcZPTxIcCGMrtVK4QviS5zc3YCkpRrHZQdMwomK89FAQPRxBD4eJJ2oBRANhvBE3kXtKibhDhNwiric0Hk35v8//d85b3rWgbofJoRKY8rBz11neWCMc7t3TLoLupeX0vxFjpppNlFQXsSJ7HGe3CMYvbKqiK17FeP/MFf2CZdPtq+UTDYfpbZ/g3NYtbNVeB2CDcpIX6zfTeWaUQxfqaL7jXQDUn+uhbaADgNGXRE7+zOLn2fz7tYxteh+e2ZJUzFfYH7xoTK+Gk2o2YbJYxLlzsympKmLlmkJ2rImyKSb48ewzdP3mJBNHZhbEUpgcKuYc8yXrZtTsrKJ013oAjM2305m7hWg0SryuHkVRqGg+R2F7G841/UxdmMTdJWzjm2vMJBH3xpk66iI4G8aWK+on3PnZ22lfVYbf5SXsD1xG8ovjWvVI1NDIo6VZxMvVXvgBB466uP0bu4hbHEzoFQC0XfDjc7qver7JptdwdZyC3gCnOnTuasoEYEXuJE/3lNG9cR8New8AMHvy8MLzJnTJ1xnE1xlk8vQMBU0i1qJo5Xny155kfVMzdVXrGCmsB6CvMp+hCQdrV2jkHfgHDn3/TOp8tlIrkckouc2CQ9GD+3hifBW9HcK+NK4W8aKt2Z2YRgP05q1mbDabYETY8HBUIRQ2iEQMQhHBLRKJEw1r4t+oRiyasLsxjVgkTjwWR4vF6amrJe62sXZ1AQ1GD2tbWhntE94bsUiUSDDEWP8kr59fRfOe9wFQt/ckPU8OYC2wpHR+/NVptMgLNAWC3HnP+7Fv3i76Zqugu92ES3ES8gfQEjVBLqVX88fMbBP3hNqmcm4L/oqX9w9x13d/h3/rF/KoqYSKQz/m8Av9qd8XPvgOzpx0pezL9UJWvZYZadepK6CqqoozZ87w4x//mLNnz+L3+/nUpz7FRz/6URySZWdYbigTsyhk3moaCSi4vaZbTeJNkIuTAlg87ltNYwEUwBb0IY/Xr1xjJiAbJ9n4gGycFEXBYnWgRGW6gcolI/n4gHycEnp0q2mkoBAn61aTeBPkGzO5+IDsURpp16mrgNls5mMf+xh/8zd/w//5P/+HT3/607/1iwwA/X13oatyKIhJNdjeGsYkUXYF2Tjpiopn0zYMVR4jqCsqk43rpeEk25iBfJxk4wPycTJ0He/MMLpE6W1lk5FsfEBGTprQI0keFFVFJ49u4PqzMy0XZBsz2fgAUnFJ4xp2NP7t3/7tst9//OMfv2YySXznO9/hG9/4BhMTE7S2tvLtb3+bbdu2XfF3P/7xj/nwhz/Me97zHp5++ulFj/m93/s9vvvd7/Ktb32LL37xi0vildmQTf/fD5NT/BMANn/CgXXVHeRk5dCfY2NiVCy2XNOeVLpbLRZb1FUk6apTUF7EnsZxODbEZPU91Md7AThxcIbM//sIEy9OXtLVxGSSbzLdSE6GrqdS3QKpdLeRcISgP4zHI9LNzjY20tqYz4riagr6hqhfk4dJVcBkQlFUMJnEB0BRQVXEv4oC8xeSSqI9iYCP3JoTjJ0cJBaM4SjMAcBaU0u0djXBzBJiJhuKIfZtLVoEsxbBFAtjjQnXKVsoQKYvTn2lHd3pJDwt0kCGZjz4Jz0EpgNEfGK/IzQRJeqMpS7/ZteqgakRbt/3NNvWCxeW/s4Cwr7gklPcLveYWe12mlbnUxDqpLtnBoDK+3bQMZGDZ6b7lnC6XlwNHz2u4Zx0cqSjis0NqwHI7HqDzS076Gk3caFtlmMrdgGw5x2nmGybYOa4O/X7vmcHyal6ijs+2cDExlb8XuE4Nz0cJx6JXuRKcTlOqtmExWYlK1+k/SypLmLN2gJ2NXlZ53qJ8HO/AKDzF2dSaS9T5024TcW98QXuVIpFoWRrPrW3ryZ753ZmV94GQHtoFW0dJnINJ07TWlBM1K9cSXXNOsqbz1HQ3oarYwCAme4p3L1+IpOL7+n5e0L0/KYdgM07f8a2TZ9ntC+PaDh8Tak4l6pHiqpiz86gakUxG6vF3Jz5hxcxOVSsK1fht+ZwYSIPgJGevkXdWpeTz83AlTjFI1F6O2eY3LkGgEpPO1ZrBYf7S/j4vfsAyH+u7SI9mo/IZJTxyWkAJg7PULBuhJI1FyhsOU3zajFXautaGVndgKnjHG0/fDVl57IaHYSnhB1c9aCwdV0r3s2J5wJ4nV5KqorYulocWzh2llBBFacGchmfjBIOCZ2Z7x4Vi8y5SAn3qMS/CZdcXdPQ4yLpjYKGc9pE76ybgVX1tISOsKk+wNlakfrY7/ISj0Txu7x0nJ/i9YbNANz/3rsZevkHRCajFG4Sc3D2pIepoy6igVdY7fVz+zuFO6Fj4x2YLWV0W0zMjCoEvaJdi8Uv6T6VHLPMPOHCtWNLFs4f/ABbqZXJOx7BeVz05YNbRuj+m98QGo2Q1SieT7rq3snoExNLcrG9EmTUa5mRdp26Ar7whS8s+DsWixEMBlOVwa93ofGTn/yEL33pSzz++ONs376dxx57jH379tHZ2UlJScklfzcwMMD//J//k9tvv/2Sxzz11FMcOXKEioqK6+KYRhpppJFGGmmkkUYaS0XadeoKcLlcCz5+v5/Ozk527969LMHg3/zmN/nMZz7DJz/5SZqbm3n88cfJyMjge9/73iV/o2kaH/3oR/nqV79KQ0PDoseMjo7yB3/wB/zwhz/EkgiQTCONNNJII4000kgjjTRuDJalbvzKlSv567/+az72sY/R0dFxzeeJRqOcOHGCRx99NNWmqip79+7l8OHDl/zdX/zFX1BSUsKnPvUpXnvttYu+13WdRx55hD/6oz9i7dq1V8UlEokQiSzMymA+dQJDUdATq9GUP7Kho6CjKmIrUlGMBHcDVAMl4S+oG2AYCqpqpCpXqoqOkTiPoevoCbcgw2xKtBsX+RtqOmi6wdkOa+L7ZLuy4G8QBX10XUFRDOYnUbpkuwG6oaAqBsq89vnc52/6zW+HOU4CyqLcgQUcL99+cZ9QjTnuqoGqCtkr6BioYOjCzcowqIw4MTAABR1QUJgfKKYCqQ1k0QVxhCL6Nh/JXxkmE4ZJT+mBARiGga4b6IqOkhxPwxDthpFqUw2oNoUAA515upQ8l6JgJNy6DLMJw6yjxDUMNeHileKa0CkAQ2yTq4q+QPcuNU7z2zXd4HS77SIZX884iWtoqflhKIrwuTa0lO5fTvcMY6FuX43uLewTLKfuzddrVb3yfBJ9F+3JQ8TxOkZirJI6Z5jnYnUMs2muAJcxZ09MqkE88c1cnwQnTU+2L+RuJHgnzyH+Fdc2jHn6ZjJhmE0JHROug4ZZFVxMBqDP6Z5Z6KaeMAy6YYABhqEDKh5qyZ03/3TDEGOU4KQraup6htkEmpgr82UAYCTEntRtVdExqQY6i/f1UnZP0+HMhYU28mrsntAn0YekETRMJnQDYZ8NjeQ7OnWefYfL697iNpsl9Wm5bfl83VaUS8yzhNz1hKuNmNcaGCYSKpAaVwDimpDbmzqlxDUMRQGzSchTVVOxGIKrsJeV+LmQtB1qUmd0DLOBriiohoGRvOcmbL9h6CiKKsbIEGOkoCWko6CgoyjiWFXR0Q1xXVXRMRJthqonKlsLHTAMA0M3GHKXJmQmbLmhz83N5AiY1MRcMzSSs95QlMQ8mrPnyXmmK+qcfTSSzw06qjp3v9cVMYsvtnvCZhvMPUdgaHPzTNcTfRf/T9zW5njoQm4scT5dSvcWnWfX8RyxHLZcjlKGl0badepaT2Q2MzY2dl3nmJmZQdM0SktLF7SXlpZecgFz8OBB/uVf/oXTp09f8rxf//rXMZvNfP7zn79qLl/72tf46le/uqDti++7j3t+5x7OuIU2Nx08zPpdcUwFqylSwJkvlGdwKpPufgcl9mEyzNGU72fPoIXJaYXWpiiZWeLYmlVByjtfQLFnMDHlhNE3ALD+13281FlM2DvM9g3hBTyOnrZjsxpsWBNNPdBomsLRM3bycnSaG+f8hkMhlVMXbBQXaDTWzvn7u70m2nusVJXFqS6fSzk5OWOmd8hCfXWc0qK59uFxM8PjFlY3xMjLmfOV7hm0MDVrprUpisOhoyYe0Nt7rLi9Jrasiyzw3zzdbiMSVS7dp+a5xd2V+lSYG6exNoCqejBbZog6s+mfKsASA3whJnJryF+RS4bDQXZeHh6Pl1AonJjkChmZ2dgzc/G4Z4lFxXUNIDO7AKs9C69zgnh8TmaF9Tk0FpUxtWqUyIyXsQIRo+GwOvBnlNLphGBcQdMT1i97PWaiEBhIGUqz2aC4oQRbzTTB2QnMUeGHbw95qXcP4vSEmVBFlhNLIAwT05iOnsCbX06kvAaAeEBD6RslfrydC2fGqFp5EIDbNpTwRtxET4/O6obwZccpifYeCz6/iS3rwqmwlesdp8xcC6vyu5h2epg96cGor6S/dA3+sXY2Nrpwurmi7pUXzz2sX63uzfVp+XUvqddXmk+FDg/a9BlOltUCUBsaZGt+B21NDnRvDwNdeQCUrb6HFfeeZyYvhFYg0mVHgbbjvWyv/TGbN6tYNws9miyLcfqcwqzTWNAnVYGTbdaL+qSoKqe68iksyWLrOtGnkpJZqgtHaB07z8SB1+jyiex5kcYtKCV+1ANHMK+tId66Gi1mEIsbKBOzqK+exL63iXhDHbmVOdiqywjU1DJWtp2OoRimuJtZdx8Rd4gZfzYj41WUOMbx5McYysojL28z9Xc1UdbcyZDmwDrrJHvahdkVRH/xDdRZJ/6NOzEsc7ci97OHIBjm9IUZVu05xJ6teUyWxjh4VF2ijbCSk2XQumau/Up2b0WdTkNDhIaGMczj/eTZTfQe6Kbg03fSYyvF5YrhHbuAK1KCZ8bFuhWhJegeC2z21erewj4tvy1P6vbl5tPM6Axtw1YUQyMSdbMiu4s3eito33Mv3tlhcj/+HqzdU4SnY6hPvggZdvT7dyX0EYjHUZ54CUoL0PZsYhqYjkBGZ4iqo08RLijC2BLEXNTDufOjuDPqyWuKEKuvI756BdGxMBkVdsJ7d1Nsgze6DPK1Njat1KmuV6mP95CbYWMqGGfAnE1o/By2UJTJqWxcgQxWlEyRZ4piWMWCpXskG7fXRGujE5Oqi/c2hsHZ7gwiEYUtzb7Es6qBQwlzeqiQ84MrsGeq2IPH2dFcjoGJ0b5slFCI5kYPVnuEkR4XMd3K2M4PUP2xLrrHHfhLbQDktHqJPHkYl1bM8VEbxUdOAdDQHMDWej8FVgVfRZRgIjalfwgGhtVFbfmsy8SG5jhVDYnYFeNVzj4zzq7f28WBAajL6QMgeuo1ho96wWzC8cn3AHCu283aygleHzMtk+5ZKMrXqSxbvueI+bp3vbZcRhhKeqFxWezfv3/B34ZhMD4+zj/+4z9y2223LRuxq4HP5+ORRx7hn//5nykqKlr0mBMnTvD3f//3nDx5EmUJg/voo4/ypS99aUFb1/5/Y/p//Qs4hRLnNewAICM7FyNWjMsjzu8JBwEfvaNZRAJqKmBQ08S7hTOdViw2KwCWmgYyssQujCOvhvrepwBwv3oQ/V6Rm/roafsCHpoOkaiCgcHxs/bEGwgBt1ddcHxyak47Tcy6TBe1j0yYGZucU4PkJk3/sJmBkbn25M2xo8+CguWi9jOdVsyqwdbWMMfP2onGBafj52wXcYfF+xQMKxe1X6lPTo8Zk8WCLcNBdoEIjHOHc3FRRUVuBIKnycoSD1XZ2TlkZuWio2KgoCgKBpCdU5B6w2CgpN5iZueXpN58AVhMYtwrZ/qZaR+koLESAGv1ZhQFsgtriIQz0OJJOaugWrHkrcBqFkbVQhT3TD9VRRlU52dg84uFhjkigsWzon4qnWLBHppxE5z2EQTMg0Po5/qFzCejqYHKHB0iP7HgGetewfhsBxC77DgtnAUG2zeEOX7WtkCPrmecimqK2HpnLRXGKP0Ag2PU2+P8u7+BN855icfEjepSujc0aqasOJ7S7avRvYvfgi2f7h0/a0vpdTwho0vNp9EpC7bJSt5xlziuqNuLC3CGChnqj6KUiAVIZuYoAOqRsxjhefUr6m1AK1n5pYyFxLie6+3D7XMDeqpPpsRci0SVi2yEajZhskI4ZqLPJRanjvJccgpmYPQ8trAf+ysnAYh1JSq1KKAMj2MZm0QLaTAvCNw8MIR5cJjibZWU5q9Ht9fjATRLCZqlmImJGONTLhqKR5k1DKZDZZjz7eRl6eTmuciM9gBQpYaIuodw9fbj7JvF4/SISz/z6kKd1HQwDIo7T2F5/8MMn63l7IUImu5fko0wqVBfHV1gI69k9wZGrYQspZSuqaEmdzTFK3ugm+qMu6GoktG23JS9WIrumVTjIpt9rXZvuWy5WTWndDumXbpPOcVAZj0G0OA/QW92IzE9DopGTmE1laOdBF9tI3ohIHY0fEGx4EAkEUhh0plqB7CtyYAVBdhcs5RM92IrNOMClKPnoc6MeXAIm2uS8Ak3WZvzKHzfbgDCSgnnB4PEY3Gy6hrIcZwGoEyNMFtYQ59LwTMbIBZNvOQbyxcB37qGrumIzRmD0905iSQjBhg6mi52rd44n4mhi92F3duTu8dWqgqy0BUzrzpXJnrQhduncvSMg8z8XNYXJl23x3F4nahPdpKxXsxjW5aFCKBcGMDqm6AiZx0AOa0rGQX8RgldkwrToyKBRsATYDFbrijCZp+8kEU4uwqAj+/UGJ8UyQti9gbCiTTTDdGnGPQFwTAo6zsHwMnmP+V4uwdNDy2L7plUqCyLLZxn1/EcsRy2/PjZhcencWux5IXGe9/73gV/K4pCcXExd999N3/3d393XWSKioowmUxMTk4uaJ+cnKSsrOyi43t7exkYGOBd73pXqi25vWs2m+ns7OS1115jamqKmpqa1DGapvE//sf/4LHHHmNgYGBRLjabDZttobLaLBaUuIaSyH6iJmaToqigmOY9rCZdqxQ0XXnT5BNuI3M3PlNqa1FRVdTElFa0xPYzyoIHwPkw3nT+xFkWLVZjGAraIruJl2rXDWXR3Uf9Elx0PbFRbST5JG+kix9/qYI6i7dfpk+6ArqCrivoRtKVSQVEhilFUVILTEVRUBUVEgsNcY5kBrCUk8tce+L3KRaJ/6uGjqrrqKktbxLXUVEUE4piWvAb0Z4Y14RuCC7iA+LttJG4evK8qq6jJPRZ0fWU3inzsu8oho6qzumdkVgYXW6c5mO+291iY3Ut46QbQg5qQpaKbiT6p4qFQ2o+XFr3FtPtq+3THMfl070knytxNwyhh8lij8mdfQMV3VAxELqR0klNQ4nPy+w0z6ak7Ig+p5/z+yTGOmkj5nHQFUyJdiMxJ1BMqUx3SuK6ME+XLIq4dsK+LeCkz+mjisE8g5Xom56Ye/PtnwkUUQhTmafjKobQa02b6+slskmpuo6qCjnoC2zKYkdfbCPELuJiNvLyuqcbwp6r8+e+rieS082NCyxd9xa32ddg95bJls+32VeyHSTsmqok/6+JsVUUVCMxpsmxTOgSzOm6ASkdS50y0UHFMFANIVnFMBL6aRZ2L3FeRdPm3HYUMZ+Sc2q+jimqmpqHBkl3r8TxupFyT07eiw1dEdkM571U0hJtSd4kXkCpipIQwHx3v4Td05UFuqFgpHgDKHpi7if6lLLzqcsmOc7ZmMXGI2mz59/zVFVJzScUU2qOqsx329VT32vXMJ+SnN6sY5ebZ9fyHLEYlseWy4P5uvZ2wJIXGvoypkR7M6xWK5s3b+bAgQOpBY2u6xw4cIDPfe5zFx2/evVqzp07t6Dtz/7sz/D5fPz93/891dXVPPLII+zdu3fBMfv27eORRx7hk5/85A3rSxpppJFGGmmkkUYaacyHcW0l7N6yWLYYjeXCl770JT7xiU+wZcsWtm3bxmOPPUYgEEgtCj7+8Y9TWVnJ1772Nex2Oy0tLQt+n5eXB5BqLywspLCwcMExFouFsrIympqalsTNW72BTb+3nTf+6lUAzv/kJOtUlRW7PeTVbaEkqx6AooJs+vPtjA7bmR134XeJ/OLRUAQdEYyVzH99x0YD17+9StaHP06mJUTPT8S2cuvv3sW/nphBi8UvJpIGwIK6GiFdT7nkRENRgr4IXk8Oq0sK6IysQDdMiYAz0PW5rV0RtJb8/9yrl+R/56+rMxwKm2rewZq77JTaX0VPXI9gAGs8SMxkYtprZcopfhwMaegaKIoJs1lMNZvFQrElg2GtkbwsyMtJuOEV+cg1nGQFp8nyTgGQ45pCn5kiMjlNxOklNCv0KDjrxz8VwNXmpf/Xw9TcL9zttm/cQF/HtdXSWA6YEtncymqLqcuZQplXA2SqchODz0yhxWKX+vlvBXRNY3pkmkN9wv6sqqihuPsgrS2fZahbofuCqCXwWlUDH7z/AWrODdD/i5HU7/09ITr/8zXW1taxe+OnAHDOltMXjhDy+i9ZSyK5W6GaTFgdNnKL8qlsKGbDemFndtaMUHfhGSb3P0f3r3oX1LEwOVRMDhMxn7A1RizxDteiULq9kNrbRZ2DrB3bmWncRXtoFefbLfT3+wCYGnERcHsotwUYHJ9AN1ScU1lMjGczWFFEXXkBdSuFq0lVTQ+lq85R0N2Ju3NA9K9vGs+Ij+BQeEHdju6n+9hzx4/YtfnPGeopJBoSc+VG6ZCiqmTkZFHbWEJzhR9713lAjMm6jzYQyyig21nMUK9wbUzyeTsg6PVz6oLQvXtX57GucJCXYyUc6s4H4NP73sGKaJzRvH4mDzkv+r2j0obJJnRUi+hEJqNoIR1PewBPewCA8dOTFLf04MpowlFqpXCFOPfkGeFKtOaDOzmecx8AZ15yEvQFqGgoZ1tTiJzhMwBEims5PpDP2EA3YX8oFR9p6Lqoj6EbC+pHGLqRCsae35aCphENaQTcXto7vAw3NLLK+TpbGsWcaKspJuDxEQuFCfkD9HVMAPBK4wo+/NC76HupP1VfpOHdNWTszmC6cwZfZ5CT3zkOwFqPn80Ph7Ctexc2awnnLWLnY3JoGr/Le8laXLbsDDZuEi7jlp9/WTS+5xFGugwebB0HoPOvf506vvj+u4XszrjeVrqbxq3HVS003hyrcDl885vfvGYyAA8//DDT09N8+ctfZmJigg0bNvDss8+mAsSHhoZQb1F17sLi0lQxtluNpF+2TFuEsnEyUAnbVnINWZxvGAxUvOpK8hbmu7plkG3MQD5OsvEB+TjpOpzqzseeKY9LgGwyko0PSMhJ10QMR6HpysfeBGg6HG/Pkkc+zI2ZPe9WMxGQTod4C7hOpbNOXYxTp05d1cmWEmx9OXzuc59b1FUK4OWXX77sb7///e9f8fyXisu4EnR96ZVpbyRsVoNgWC6FlY2TYsQxFOutprEAKnFAHk6yjRnIx0k2PiAfJ6tZvru7bDKSjQ9IyCnDDoo8O/lWi04oIs/LKhBjJhOk0yHJkV5oLIKXXnrpRvN4S8A1O0ORJBUdTSpsaI5I9SZBNk4KOrboQGJXQ443ZAo6WXo/sPKKx94MyDZmIB8n2fiAfJxUFdbWe+gcz1qQqe1WQjYZycYHJOSkmkRa3NcP3momgJDP+pVBjrdn3WoqKSTH7NzQrWYiIJ0OcXF9jTRuLaSL0ZAZ/c5cVr/vo6xoHwSg9+lBOp85zYpQhMJNU+Q0inR1xdUtlOWV0ZlfSF+WjYlh8fbaO+MhHAyhxWKU14ssWjsiLzAx4SZYsIo6bxuvPy98K4u+9QkmvjG5wJ80jcVh6DpaLI6e8MfVNY1YNEokGCRHC9Pv8iYydCSPNxbEY+j6/NiMS78psljNhCP56I33sHYnKP2d4jeRCA7nCEWVlUzYMxlJuL/PTIXwe8PEY3M7YRYzGBVhxgaDZGTZyc5yAJCbnUledikFmY3kFYtUt7kVXnJjM2T6J3F4pshzCv/++PQ0kakZpioG6ds/RN/PXwdg567jHGveiHPSSdDtS8nmZkBRVawOkaVt9epcsrURLO4pHJWird1bj3u67aZwuZUwdJ2A28eZs24Axt6/h7JX/o0dd4xypKKAiUExhqfO+Fh13720vvMkk+efASA4IHLCTxycIb/2F9z2+RUAjG66F6+zmIlIlKgeuWhMFVVFTRRBsWU6yCvJp6axhM3rHews7QKg7PjTDD39Ij0/H0jFYABYCyxoIY2oc2HcQ3ZTBtXbqinZvg5tyx4A2rO30DZRSGdflLHBaZyToo9BXwAtEiIWieKedqHrCkFfAK/Tz8xkFuMT2QyWC1/y2rIC6lY2UlXTQ/HqdgDyezrxdA3i7J3CM+xJ+exrIZ2un7zEnu3v4MTaTbinXACEvNoN0Wurw0ZBeSFrmhxUWy4QGx5Ofeeoq2Uiu4q2YzquCRGD8NsebzQfWixOb4fQ3fHd66kcPUZ5+UN0XBApitvedT9rH4jjKDlKRsEFRg+JzJFRZwwjZhB1xsmqEalIMwrt5NZkEwvGCM6EU3ofHAgzMDIK72+iamc5cZ9oD41GqNlXQWTfhzmYSHM6NTyM1W6jZV0B6+KvQljYzN6sjZx/2YNv1kM8FkvFWyTjMBbEXyRwWV1SRZbKWCTKaN8UZ8caacxx0KSKul7N6zYxMTiJNxJFi8VxTcwCcOJUPq0Pvpe1H3qNN/5GFBv2T/ooW19JRmEmE9njzBx3A3D+Xy8QD/+EloeD2Jrfh8Ui3MTPmU2MD6j4XR5iiJtKMlZDUVWKK4u5d5VYbZz6bwdp+eQajsS2k5+r0nDqRwAceGmKrEYHgcEwg2vfLcbv11Pp54pbjPSOxlXg+PHj/PSnP2VoaIhoNLrguyeffHJZiKWRRhpppJFGGmmkkcZvE95uC40lbzD9+Mc/ZteuXVy4cIGnnnqKWCxGW1sbL774Irm5uTeCozSQLSWZpsmnrLJxSuYZlwmycZJtzEA+TrLxAfk4XSrX/a2EdDKSjA/Ix0mRLNOibPIB+TjJxicNubDkJ56/+qu/4lvf+hbPPPMMVquVv//7v6ejo4MPfehDC4ri/TbCb161oIjTrYSmKxw9Y5fq5i4bJ91QGXDXSrVA1A2V3tlqaTjJNmYgHyfZ+IB8nDRd4Y3zmZcu9nYLIKOMZOID8nFS4hrqUy+hSuLao+kKx845pJEPzHGS5YWVbDoEcr70mA8jUZR2OT5vBSzZdaq3t5cHHngAEAX2AoEAiqLwh3/4h9x999189atfXXaSsuDCuWFezr+Nd3zifQC4h/6V2ZMeevR2Kme9FI6L+IqaVYMU1bRQtno1xflFtOcKv9LhPhuz407C/iCbNwuf5eDPfkzBygomdAfmQ89icgjjcWByPQH34cuwMcjL0XF751e1vtW4dZySNTUAYkTR4sKv1qx78QUtzA+9eHPRycX8dheDyWxKxFsUoK68i5WOHACsI10o7mlKMzsI5TvwhUoA8PmsuGcD+Nx+YpFYimduVpzZ8SwsNgu2DKEbjkwbmVlWsrMtZGeL8+Zl55KfVUFBZoTc3AC5dcIfOic0hcMzTm31Gabapxl6TuT1r3/nU+zYsZm+jiLCviDAVdTTuP4xU1QVk8VMXkkBAOvrwsRVK9G2szTe3wjAD7rUFKcrQzbdXhofLRZnrE/4qB+bXMV7CwqpG36V5rW/w/SY8O8fG5jhYHstDbc/ROOpCwCc/efzqXN0/LSH7XU/AeCuj69gsrWWoC+Ia2KGeCSa4uTxmzFZzDiyMgHILyugflUxW1rM7Mg5Q+6rosZK789fY+BXo4Com2EtEDVPos5YqnaFYhF9K9tZRO3tq8ncsYPpFbtoC4gxbL9gob/fy9SIC5/TSyQo/OLj0Ti6FicvW8cTiAMK8ViMaChCKBDE5w4wMymCacdKsxgsLxKxGg3ivJVVfRSubCe3pxNv9yCnu0WWQyNmMPrSFPW//g9uv3cz/V3FAIwGQldZJ+bqxk01i/iWjNxsGlYVsbrMS1Z4lsCQsOcZdXaU0goGQ5X0ds4S8geu4trXzufm4uo4GbqOc1zEH5yebqHa8ytaG+OcOyXm9MHOcpTVD7Aiv5K6soNklZ4GYOjwAJ72AHFvHF+vOFZZpZJdZierJIv8OgitEO2BmRCuC37K39OEI8/E6NHu1PUbP3Ifvw5s5cI5UaciGopQu6aanSu9ONpPE6sSuvTGQCGjfd1Ew+EFdv3aYxIMcjKiuL0q3mk359oD7Ny3muqxIwBsWbmGtuoSAm4f8Ug0NSdGeiZ5+cJKPvOeD1L0opjfU0ddZJVmU7Smiro92diyBwAYfWmKjh/1EAs9ycrfCWHd8AEAbNZqTltURvtUfLPC9kfDYfR4nJISC83rS6g+9NcAdI9GKP7YR+geNbNn9Qxd3xRxX+YcM2a7ieq7yzk0VA2A33XsGmVxaRnJqNcyI+06dQXk5+fj84lA08rKSs6fFzdHt9tNMHi1DxJvTVTlTSND7QMQWRWaG6NSZVeQjZOqwopyD6oij9FRVYPGSq80nGQbM5CPk2x8QD5Ogk9EGj4gq4zk4QMScjKp+BrXYEiU3VEq+SA4NdUGUNLPIpeETFzSWMJCI7mg2LNnD88//zwAH/zgB/nCF77AZz7zGT784Q9zzz333BiWaaSRRhpppJFGGmmk8RaHgbJsn7cCrtp1av369WzdupX3vve9fPCDHwTgT//0T7FYLBw6dIiHHnqIP/uzP7thRNNII4000kgjjTTSSOOtjLfKAmG5cNULjVdeeYV//dd/5Wtf+xp/+Zd/yUMPPcSnP/1p/viP//hG8pMK05Mhpk9Gqd8rfCibH+7kcM/zeNoDRD0D+KeES1nh8CQ5jf2sbuyiuH4bRdlNAJzNKaE7w8r0mJN7Vor8153/8zzbvv5fydem6d1/iPWf2gDA10560S6TfcMAQiFVKk9EWTjpcQ1DN4gEdDzuKD6XJxWkql9lPMZiUFUFXddRVQVFySfWsA2AVfUOMiZ6sDrHqTbbiZWLaRXXCohEsgkHI4T9wndXi0XwejQ8s25UkxXVLDYVLVZrKmbDniHqrjgyrWRn28jOtpGTnUF+tojryc+spbAwSN3OWhrv6+R0z2kAun52kJ17jvFG89ZUrn+/y3NZ/+TrHbNkfEZGThY1K0VsSm3GGH4jB8+zx2n67w8D0H1yJlXn5EqQRY+SWCofQ9fxu4RP9fEzIe65YweZx37Nzh1eLrQXAjA+MEXH+RkOVrXy4LvfAUDRqZFUbn0jZtD2xAkAtq74D+7c/EWmp8uJBMP4XR4U1SAUMWGyWbFlZFJUKXSjcXUx29bqbDMdxvLcU3TsPyqu96qogWAtsGAtMBOeEjFDyfiM7KYManaIZB4lO1uJb7ydtqzNtI8X0NETAWB0aAr3lIegL0A0FE6Np6EbqCoEwyq6bmDooOmirk0yViPoE3ENydoaY6VZDCc4V5cUUFffQGVlMwUrL1DVKWIjhhM1hc7/xyFuu+tFjq3bC4BrwnlFvb7acVNUFVuGqGVTWlNMyyozpdZp1HCU2S4R+1TUVECopJ7zQ3ZmRvsva5evl8/NxlI4JWNTTrXFeGB1CRvsbTydUwdA23knUMB0zW7W3FZJabmIB8isOMxw6QUmjsykdM11xose0yhoyCerNJfMEpGtsnClQWGTn3C+DX+bm9Co0Lu1n1jNyPaPcOT5CK5JYdfsWQ5aW/NZ4/0lRML0528F4NxBUUMjWW9iOeUTDYcZ6p7i7KZaKi0itq6ZczSt3crE4CS+WXfquj6nm/OnpzlSt5cdH3kZgFeP72fk4DiZxdnkra6jNkfELVkzu+j/xQi9Tw8SDfyclR8Wct6780PYLKs4YTEx0iPiiDwzLmLhIFgyuXN9gLbf+yUA9Q9Wcbr4HdhCCmu6nuBAYu6s+tAK+p4dZMPv3c8/nfECEIssLEmwnDKSBTJxSWMJrlO333473/ve9xgfH+fb3/42AwMD3HHHHaxatYqvf/3rTExM3EieUqB9IFeabEG6rnDqgk2qLC+ycdJ1hbPdcmXC0XWFcz1Z0nCSbcxAPk6y8QHB6fQFuzScZOMD8o2bbHxAPk6KrlM20YsiSdYp2eQDglPXWBGKJHEssspIZrzdsk4tWVMzMzP55Cc/ySuvvEJXVxcf/OAH+c53vkNNTQ3vfve7bwRHaVCYG0GWtbKiGJQUxlEkCSoG+TgpikFxfkwaPpDkFJWGk2xjBvJxko0PyMdJNj4gHyfZ+IB8nAxFIZCZhyFJGnnZ5AOCU0FWEMOQg5OsMpIZOsqyfd4KuKbK4Ek0NjbyJ3/yJ9TW1vLoo4/yy1/+crl4SYmKXCd9neO8XLkCgJp7P8Cq8320/6CT0GiEUafY1fFN+MkbmCKnY4D8lj52broNgMKWHWRnFdCda6f60DcBmDIpBKvXUtB/jFMHJmn88hcBGP73icu6BqgKNNbGmHWZ0CSZUzJxMnQdI65RWxZlcspBfBlqQCmqgt/lZQxQFAVNywMg2rCRpioH2dO92J0j1BeJbW6qmtGNAjTNSN0UfM449RUhZt0mouFw6q1UNBRBNZsweUyYLGJaWmwWbHarcKdyWMnMFi5VWVlWcnNzmKzawL3vuJeiYwOAcI9pfOEJdt65hZ4LwkUn5A8k0qEujusZs/luUyU1JaxrFilWM6NuPOZcxg5M4vjOuwCYeWb2qlNMyqRH18on6Z4w2DXJiZ0buMN6gDW+12lZJ9ykvK4Azkk3x05ls+oe8YJm1btPMnP8QOocvk6Rxa/rh79hU/UKhjf/Dl5nKcORKKpqsKreTdtIFkUVJTStFW5IO9aE2RR6lfgvf07b/hPMnvSkzueotGHNNROaiBL3zk2IyrtKqN69hszt2wGYathFm7+R9nYzAwMinS2QSmkbj8YxDD2VPlRRFcwmlZW1kQUyMnQj5UKVlEfSjcrr9DM7nQ3AaEkWwxVFVJcUUFu3gpUPtAMw/PzPUSwK7vN+Qj/7D+782O0A9HQUX1Gvr3bcTBYzuUX5Qv6rC1hVNItVC2GKhRg7MgXA+v+ykZHM1XR0eAl4fNecKlU2vYalcUq6jPV3TjJ25yaqOp9nZdMGAF5/sZfzhoHTlcN0fRPNdSIVcWNRNauqXiOv5jRDR4S7sK8zmHA3jlPcopNXI9JiZ5QV4igvoj+7nJnXXyGjzg5A2Ycf4gf9tfR39hNPcKhvrua2FVOYj5xCb1jD0UHhtplMbXtD5BPX8My4Od1ewbY9wh26dPAo21evp/1sMUGvP6WTsUiUicFJXjudR8u9HxWcHzxJ/y9GGDo8gD0/i6ymxHNEXjaO/Ex6ftnH8PPjxEK/BmB1MMQ9dz6MbUsLxy3lAAx0q3hnFNY3G7QOPMFLh4Qr2T1PfJQfjGazodbDwP/vSWyl4l6Rv6KcuLcX/573M/IdkXJ7udzKFpWRRHqdhjy45oXGq6++yve+9z1+9rOfoaoqH/rQh/jUpz61nNzSSCONNNJII4000kjjtwbpYPDLYGxsjO9///t8//vfp6enh127dvEP//APfOhDHyIzM/NGcUwjjTTSSCONNNJII423PN4qsRXLhateaLzjHe/ghRdeoKioiI9//OP87u/+Lk1NTTeSm3Tw+Ey3mkIKBuD2miSJGBGQjZNsfAAwwOOXh5OMMpKNk2x8knD7zSDJDcsA3D65ZCTbuMnGB+TkZAv5ZQmFlFI+GBDRMm41ixRklJFMXNJYwkLDYrHwxBNP8OCDD2IyyfPAfTNxvsuCLcPNuTMzAByq2sHeB+5i6sIkM8fdC9L3BYZDzBQ7ye2eoHxgGIC1u4fIXb2P0vwa2v8/4Ye5Yu9qALyvvArAGznCh9vnPHdZLrqu0N5jXf5OXgdk46TrCm1dZparmruhC7/zoMfH+ICKponzxmL5RBtbWF1mI3+2B7tLpMWszwejai2QTzK2cUQ36JswoVp0lDel2jV0g7geR0ukDY1HYkSCYVSPCbPFjMVmAcBqt+LItOF0ZdOw+wGa3nsagJnjz9P5xCF23HmIYy27AXCOz+KPxS/pV349Y6aaTKn4jKbmIjZUihSqYSWLQmOaMeDQsEiZGvAcverzyqhHS+WT9IN2Tzk5er6K21ZvxNxxkp3bdgAwNl7MhVNDDPZM8WqNkNEj+95Pw/Fu+vYPYXKoKXsycmCSgoYnuOsPVjGxcRM+t0h/OebNprSuiLUtBexaJVJXtkz/Bv+vnqHjmXOpGA8Q6WvNdjOB4RBRZyzV3vj+Oqr37SC24XbasjYB0DZWQGdvZEE6WxB+58l+KYqKahFKbTKbMFks9E1moFqiqIn+J+M4krEaQCLlbZxoKEIokfLZ6/QzM5WViNUopOrOhwDIavwN/h5xzNkfnOK2ffsBONz6MDOjM/hm3ZeNl7jSuCmqiiMrk+pEWuaNjTGKjQlCpiyKJgZScsrdtI4XJ0sYG+i9YlzI5SCbXsPSOCVl7Zx0cnJqPVU+NzvWCd04/JLKzKiToC+Mx53LdJ2Ie5mquYM1e2ooq64np+4wAEOvtjN2cJrQaIThqXGCrUJPy1sNsqtL0V88iOaKsP5zdwBwquK9nNjvwuv0kJkrUsJu2pDLytEfYUSj9Jfu4sx+EYu0nKlt4WL5RAIh+rumObdBxFeUcpS1yllWt2xhamgKfyKGRI9rBL1+utsmeGlFKwDv/9iDBKZ/wtRRF0N53TTm5QBgb1xB2d58bLmZdD97gYmD4vkiFnye5kCAu+97GPsWkUrdbCmnv1Nlzdpyhv/6PynakgdAz9oPoPVC6/gvePHpQTZ9fjMA8VAER6WN17wb8c4cXza5XE5GMkD6rFNvM9epq846tX//ft7znve8bRcZANVl8mQwUhSD6nJ5+IB8nGTjA4JTRVFIGk6yykgmTrLxAcGprCCAIsm7O0UxqCgOSycjmcZNNj4gHycDhVBFFYYqR+pW2eQDoGCgh2elyswlnYwk4rIY0ult07gkxGS61SwEVAWqy+NSZVeQjZNsfEDcJCqKwmk9ugxk4yQbHxB6VF4QlOaGqihQWRyWSkayjZtsfEA+ToYC4YpqadIGySYfEA/RMi00ZJSRTFzSuM70tmmkkUYaaaSRRhpppJHG1eHt5jqVXmgsAYZhEAuHmRgU+aiPnculefe7qb/7DL7hE0Qm5/x3o84YUWcMX2cQZ6/wH63om6T6HZMUbbiD1xN+mGu/+GEYbuP0ix1U31vOz88JP/xoKHKTe5fG1cDQdaKhCH486AmfZV3TicULiK1sYk2xlUJnNwB29zj1+SpUrUFVhM+ygo5DmSWvKI94nFQ8hqEb6JqOrs/VJzCM5Pk1YrqRyiEfCUYI+oIEfWEOVVXy8F5Rq6L+yAX6fzFC469/ym0PiliAq605sBSoZuE+ac/OoKSmhJb1xWxeGaEm1AFAn62Flu4f0/LJNXzzjA9gWa//VkIkGKKrbYq2LTtZHz3M6lkRi7V13buYnixganiGc2dFnYrDFXu446E3GD/xI0KjwrcaIDQa4dz329jd+APuflc1E5PlKOhkm3yUlhdwW+M0q/pFDaOZZ35Nx1MXiExGUSwKec2iVoVqUvANBIk6YygWhaYPCB/zyvfvY7JlH23eBi60i9tBf5+H6TE3frePSCA8T0d1UevFZMJkNWNO1Hux2m1kZFpwZEbJLshFN1SioQixSJR4LCbiNBK6bOgGWiyGHtfm6XOIoDeYitWoLtkCwI4HWzjx2BvkNmfiaQ8w9R8/AeCe/3E/nW2lC+oWXAvMNivF1cVsXCf8/huzelDiBhHDju/Y8VQtgsDa3Zw7GsU367nmGhq/TQj7gpw4F+FdKytpCR8BoLhqC2N9k3hnvYSDYXxuEVszO5vLZMMq1taU0FhSD8Cq6lfIrzvJ4KFBfJ1BZo67AYj4ohSvC+FVV1BxWxHqe0T9iZfPZTI+0ANAzcoyAHbXDhH/1UnMLRs4OlTOaJ+wuctZQ2MxaLEYs+MznLwgeGzfuJrsobPsWLOeC+dELQ0ATdfRYnFmx2c5ekLY/vUPfJjmR2YIuX/F5CEn1szzADTkZmNa1UzBPXm05GbS8fNTQnYnPZwOvEJLMMQd7xSxMNbNezhiKaNG/w29Tw9y1z+8H4Anx8porgow8Xc/wVpgIf82UROn/Tv/SctHN/E3J8OpWjZp3Hq8VVyelgtp16klYHLGjCTFODEMufiAfJxk45OEK5ApzRsNGWUkGyfZ+CThCWeBLHqEgtOfIY1eg3zjJhsfkI+TYoDSN4oshGSTD4i5lm1R0jK6DGTikkZ6obEk9A5Z0CVZieqGIhUfkI+TbHwADFTGXHnSvNGQUUaycZKNDwg9mg4UgSKHCTcMhVFnrjR6DfKNm2x8QD5OiqGjHm9HkWTnSDb5CKgU21UULS2jS0EmLotBX8bPWwFy3KXeIlhRE0OVJPhSVQyp+IB8nGTjA8J16v/P3nuHx3Gdd9v3zO5i0XslABIEwQYWkBSbSEokRapb1eq2JMuO33yvX31JXqUqySdbSWzFiePYcRw7dtybJNuybBVLlklREsVOEGwgtfnJ8wAAqElJREFU0TuwKLtYbG8z5/tjdheECEoECRKH8tzXtReAg9md33nOM2fnzHnOc2bluaVZxCujjWTTJJseMPyoKGPEyLksAYoiKM8fk8avQb52k00PyKdJKCr66lppsk7JZh8DneGQjrCYNjoXMmmZjD+0rFPmGo0pUFIYo7PXSsBjxEu2nhpif001d23ZQtmJbnp3G2s3Yp5Y8j2WNJVApxE32trZiW/Ix5z2bnJqjZ3UY6Vz8P3iOZz1Yyz/75s5fcz4jA+KBVaUcT2SZLiUTtOl0pNYp6En1lLoAk3TiWkFROfPo7bAWGdT7G4m1TtEVZaCUmFsbqmQQ3AILLlFRGMQicT3zIjqxKIaumb8BNA0HV3T0WJGeWLthh6/ufSP+Tl6bIya668FYP3dB+l5+2c0Pr+ftdt2AHBg2U3vu+fAVG2kWi2kZhq+W1RRzLIVxWxY6KVGP0WK11hrMBTLpO/5l5n1yY/T88L5+fPFaLrUXIwePabhGnCyr3kWddWL4JQRf71uYzXdy5axz+1nqNcJwJ6jmcy/5n4Wf/Q49f9xOLmPQ3pVKoHOECd++i6r5j3PtVf9MUKAdyhGXU0Pc0/+mv4XfwdA8wttaEGdlHwbufMzkzrGOv3J9RmLH1hA2d3Gfj1ti+7kYG8ZLe1hBnqMfVBGh8cI+YLJmO7EWgzVasGWkkJKWgqp6amkZ6UCkJmdSm6OleIUHxF7MUKojI0GCHgDhAMhIsFwcj1Gcn8NoaNF9WRZLBpLrtXY12DovuH2O+ArB9E1QXpVKie+dwqAbXf+nOUrP81Q7whjQ4btpurbqtVCZl42i5cVs7rc2Pcm19eHI7OGdPw0v3KMRXcaexwdZyWdLcOEA8GpNf4U9MwUF6JJ1zQ6mgbp27aBigPPA1C3fLuxh8aIh5AvyHDY8F2/N8iYO5ehOdk4qjYCUHtNJbNmzyNn3l563jlJ9+uG/b1NATxtXYi7r2PR9SvZGTX2Ajp5dJCAx0dOYR5rVxp9z+zTPyYSjdI1ezNHXvLgdRrrIKdzDw2Y3D4hb4DW08a10rhiKetih1muHGHhknUMdhv9XcDtReg6YX+Q7hYHAG821ZC17RHqPB4OfOlNet4YAMCeVc+c/AKiVYtJ35LL0nj/2vLSAQb3uKj/5h6WeY29RjbdHcS+8jqcO49hL7Xj3GKsY/G3w1WeN9j1oybW/MU6RG4+AANvDzP/i39P548c026b97PRTCNJQi6TOHIMiU1MTExMTExMTEw+5AiUaXtdCF//+tepqqoiNTWVdevWceDAgfN637PPPouiKNx5551TOp850DAxMTExMTExMTG5DMxk6NRzzz3HE088wWc/+1nq6+upq6vjxhtvZGho6H3f19nZyV/8xV9wzTXXTPmc5kBjCvQMWNElmRrUhVx6QD5NsukxUPDG8mWZYZbSRrJpkk2PgULUWogiSYyAiPs1EsUMy9ZusukBCTXpAuVkG7LE4EhnnzhZ7iFkESWjjWTSIhtf/vKX+fSnP81jjz1GbW0t3/zmN0lPT+e73/3uOd+jaRof+9jHePrpp6murp7yOa/YNRpf//rX+dd//VccDgd1dXV87WtfY+3atZMe+8ILL/CFL3yB1tZWotEo8+fP58///M95+OGHp3TOngEj9j6Ru905MMLhY/msuOF6Zq3dj9dh5NBO5KsHSCu3Y0019h1wn/Dh2D2Cq3E/q/63sc+B1eOk9XeNAIxuugfX287z0iKEktQjC7JpupR6hK4n/cA/5kUIPb6+opDIwjkALC20UeptxR5wMVsx8rxTMZ/29BIqYwrhqELEcBOiMUEkCtGoIBKPXY9FBdGoTiyqE41pRCOJtRuCWFTD4/Lh6HKyt9E4X836u1ny4FGO/vcxgr/+OQDXfmwzTY3FBH0BosGzc8xPxUaKqpKSmkpeiRH/u6SuhC21oyx07cbq7CdQuQQAVRec/lkrjr+5Dc/w6ama9kPnRwGPjxPHnXQ+cC2zW04CkH/yTTYuqaR/oIjmY70AtJ0e5q3Kah648y5KDnUwuMcFgMWuklZuZ/Soh84f/4ZNTy4EBUbnzqb4wAt0/HIn7b/pTp4vvSqVnNlZCF0w2uIBIDwYwZKmsviBhZTc/RFOz7sDgN3NhTQ1e3A6PPjHjLVnkWAYXeikpNmx2KzYU439POzpdtKzUsnMtpOXl0p+ntGvFeRAcXaYqFZA0Sxj7wnHcBbDw0E8o0G8bj9Bn7G+IRIKo0Viyf01DPsa6zUSazXaTxsx7Uc23kzN3f9D6wudzL5xFt2dRix/1w9+yfbP387JEyX4x4x9Wqbq2zZ7CuXVpVyzNMRs52HDRhkFjEayWTH0Do0nfFz1xVsAeLbZzqjDddEx7rL5NVyYJqHruIdGOehYwayA4TMb1wzichfR0WbD6RglFG9vr8tDOBDG6w7idOYA4KiupnZWMQtvrWZ+1Tvkzt4PQPfeTtwnfCxcojB47cPs/l38e7ZvBEVVqVpUyqYSoz/x76wnY+1a9nZX0Nvacsn2z5jMPrqmMdJn7IN1sKmElVU1ZLQeYv2SVTQeLQKg1xuI76URxTNirF1rqB8hM30ht976KCvGfBz44h4AWl/oJKNoP4U5eQRmLcR2bTYAizLTsaW9S++OQQ5/7SAAywNh1t0b5M1/eo31/99m3h5dAMC80jCj3/4JAJk3fwS6mpN637Vex9jI8UtiH5DXr2VmOtOAh8NhwuGJ+67Z7XbsdvtZx0YiEQ4fPsyTTz6ZLFNVle3bt7N3795znuMf/uEfKC4u5lOf+hTvvPPOlDVekTMaU536yc/P5+/+7u/Yu3cvx44d47HHHuOxxx7j9ddfn9J5a2siqKocQ2VVFVLpAfk0yaYHjJsqNdgjTbYgGW0kmybZ9ADoQuByj0mT3lAIHc3XJ41fg3ztJpsekE+TsFpwz5mf3Ax1ppHNPmA8rdevXYUuyWymjDaSSctk6GL6Xs888ww5OTkTXs8888yk5x0ZGUHTNEpKSiaUl5SU4HA4Jn3P7t27+c53vsO3v/3tC67vFTnQmOrUz5YtW7jrrrtYvHgx8+bN40//9E9Zvnw5u3fvntJ5c7M1abajUpBLD8inSTY9AAhQtACyhAbIaCPZNMmmJ0E4EkGWDfsAREwevwb52k02PSCnpkhm9kxLSCKjfQBEaQGyXPsy2kgmLZeaJ598krGxsQmvM2csLgav18vDDz/Mt7/9bQoLCy/4c6640KkLnfpJIIRg586dNDU18cUvfvGcx002HRWLRbCodpT4l2kiV7MQOrqiICxGKEEiB7iwWhAWyxnlCoouEBYLenyjLV2MhzXrukiOxC2qILEfz3vTZRvlAkURWM4YuWu6AogJxwtA1xUURaAq51EujM1uVEVMSBGnC2M6UlUnTvqdWW5RxzUZGpUJ+sa1n6tOk5VfeJ0SelRFoHFhdTqz/L11UpJ1E4BAVXQUtOS9lhACIQR6/AVnpOEUAsT4scZPFWNS1QjRSOSVMH7XUeLPrxVFvOd343hd11HjFTSedikIXTd0xX31vXVKCDifdjLqm6ircV6h6+N1E8L4r9AM3xfjGs/8/PPzvfH3XGw7vV+d3r884Xvjei7kejLU6Oi6jh5XmjhEwWgfwPiZsKWqIqzxfsNiQcSP0VU16U9Je0Py2OTx7ykXVgvCqiY1jfuhNu5L8T5FVQUIkezfDB8z/Nvw8cR7deMsgnjKWpEsV9CSdTuzv1RVgYj3FTFdTGin5LkZv1b0uB2EatQJRTHK4n5t9DWT9xGJ387te/oE/zVsoxt+ZbVMiPNW3+PDF+Z7k/XZxs/p7PfG6/LBffmZvj2VOiV8xGgto59R0OK+HO8j1ITtxn3ccJbxfmO81Pj+FFYLQlHOSFesGz4jjD5PCIGixHtDAQgt2b9dij4iYZ/xftLoB898Yi7i9RDizHoy4XvI+FtPHi+U8es7cc0bT6jHvysSeoXVAtZEv64m+xRdUYzvkPi5E4p0AUr83UY/bOg///5war6X+G3CdXYR9xHT05fL88BjMqYzdOpcYVKTUVhYiMViYXBwcEL54OAgpaWlZx3f1tZGZ2cnt912W7IsMdtotVppampi3rx5H3jeK26g8X5TP6dPnzsefGxsjPLycsLhMBaLhf/6r//i+uuvP+fxzzzzDE8//fSEsq3bH2LtxoeTX0DDbo3u1iGO1NhZvnIrujBi11PqTyN2nSKyYjnB+cVkzjLyws+qayNwqJnR2csZvGoLANHuAcacqaSXxzjQZ2fZHCPWWq/UaGi0E44orFsxMf50f0MqaamCilINhTC6AE1T2H80ldxsndqaSPLYYFDlyCk7RfkaNXOiyXK3x0JjawoVpTEqy8b3/RgcsdLWbWNuZYySwvHyngErPQM2FlVHyc0ej1Vu7bIx5LRStzBCRrpOeVzTiZYU3B4Lq5eFsVjGL/r3q5M9RbCidnxwd7F1UhUoL9UYqojR0pVyQXVKSxv/4mhsnbxOR08LQh6dyvkuUrx9DHXG90jRFhIuAN17GovfiOm12PwUpWeRkd3D2JgbPSXxjWbHmj2HUMBHxGfkaNd0BU3NIGybjR7yo4RH4uWCgJ5LS18hoYFGFNcoAAe7M9hw6+3kvN3KkcYYojCfyt7fcU3dEmIeGx2tUeoWBifUqanNiK1dtTTMmXtkvbedFEVBtVhoGSll8bICiu3dzC87RWrbKbpGBpmfHqPbWol3bIQsjuG6+zpOnBomEgpRnB+jev54e3+Q71VVxCb49nS104X63tL5kaRf+wNTv57aey3Ygp0cb81Gr1gOQHZXI7UlO1mzYDX5GGu7At4RWk4p1F93CwWP9uCoaAfAA+SPtBLz9NAmqgjur0egECmaRdMv3mbgt33od18HQGqRDTUzBQ7sY7QvTHCjsReBalcpXJxHyYq5HC27ntOnjfUfoeF+smMaXWNZ5GUGmVPiQ1FULFaVoJaBM1JCRVGAHPsoqakR0lP9pGZmU1hoIz3SQSjkJ033Yxv2EBIKi6vsjI2N4UxJwZtvwxuAPncBvUN5zM7pQ0SjRMNRYtEYp9pTGfNZWbnAjRq/5RQITnWphKMqp5qCXH3fA+jqQUarMyl0BdDtdlrUeVSf/B3XLFtPkUXjaFs+pARZPG+8XYNBlWNNKWSkCdbWhZN99pjPxql2O/Nr7GxaOkyOo4nugJdsJUK4qI6w00XTyQ7s//tmDmQuJzUjh9OvuJhd6CRr9ngfdCG+F4sxwa/Px/cudV+en6Mlfbu58/zrpKpRjp30cMfcajqiqSjt77KybCHlaVYO2ksZdYywsHyESChi3JgowzSfqiAzLUyRJcipMTtDuXmUzb2eullz8cQUstb2oXcNoS9Zzv42O+2nBijJ9VFXPUJaVjpL5vfAkR0UaT66MwpozlmKu/kEC0udpIR1egZs1NZMbx+R+A6xqExoJ9USRtMVTp10cmr+UjIiXRSM7mbTygpiIoWhvnQylCA1c6IoqtGG4Vg7hw7byVhdxYLr7qE4v8pop+frOfHDE8ytqiFsySeUbtxHFK68ngUZGYSWORjsj+tzwrxXfwekMrT5ToJOo4+Ym97J0V/3serxVZxMqUDrPQFA8ecf5JWGMLFQ8JJ95za128jOnHidXcx9xHT05auWTHxILBsztYYkJSWFq666ih07diRT1Oq6zo4dO3j88cfPOn7RokUcPz5xfc/f//3f4/V6+epXv0plZeV5nfeKG2hcKFlZWTQ0NODz+dixYwdPPPEE1dXVbNmyZdLjn3zySZ544okJZY88vpt9Dfakk6hWG0VVELWWUG2J0NNYD4Cze4QwoOw5SkqnnfxVxcYHaBoBQPn9Aao/Y9wABEfa6Rt0wawU9PRqDhyP30xGo8nR+f6G1Ak6NB38QYW3DqQy4rJMcFq3R51wfOLSHHZZcI5azirvdVjpHxx3g8TTko4eq7EBT5xEB3K63YaC7azyo00pqIqgMN/KiMtCTDM0HTo+caT9fnUKhJSzyi+mTkpcz7DTcsF1Ovvpytl10lGw2OBISx55xXlUZRhTjPmoWCxWyvPSSQkZnW0wLYMBcrGkpJJdUElMN/TEUIkJUFOyUbON0AFNU9E1FTSIWfIJW/OA+MJxXQFC9AxnEFBzAahaXkyu31hYaT18HBSY+9DV/C40H9fYKSBwVp10IWjtsuFyqxP86L3tpKgqVnsK2cUKMZHCQKiaFflhqgP1RD19kF6CLcVOfuEsLEoM9ws7Gb3934EBhkctDI+c3X7n8r32HivegJL07elqpwv1vX0N9qRf63EbTel6UqBnKIOgfT7VGAuPoyFjgzGRWk7LkHFe58Aos+YZDyXK/YO0//rN5OdYlhjlyktvU74gYjxhtGsMBf0Q01Bf2AlA2uIMskoz0TQN/IFkuTXbSnHaQlgxF4stFbdiPKhpc7oZHfYAUVweO6O+FCyqBZvdRkq6nfRMGIvk4onkkmOxUZBhJSVFAFHSs3JIz8whLzZEhseLT9gIKwo5OTkEU0twe9JwBxQCWgwI0TNWhnfUb2zkFwwTDho3MvWnMpILrYXQsaYaI96RWA3VtjfofGEnudvKCGoaasCoU/Xd89htnc/xDuN9bu/ZfYQQcOx0Ck73eB+pxJ8iD7rTCaTMoyqjCWvQWFDeB6RkFFDe10To3dOk//FfJXf+aupKRQuPj8QvxPcURZzVZ1+Kfi9Zfh59uapYk75tPMk+vzpZbDZWFdtQgGpbCGGz48yfy5gnFYGPcNTCia5i/B4/sWgMm91Gagb4gim0O4uoyMgiL1MhJ88BjlayLIJUdzc9R49TtGY+TZYKoIuhsQxahoLklRayeN1sCodeBCBvbAiKSug4kMfpxghBrz9Zp+nsIxLfIVENIrHxdrLajaQHVcvAYkulOjJCMG8RL/fOw5iLaEy2kxr3ucz8TGYvBN2Wz+y0FHwdRmKIoSOnIQVyxwZJTxe4C4x+Pj3sBiCrs4Xhl3uSGtMfXoR60E3ZX3yUfaH5AMzOGaB30AXMoaggH63pCAD2rHRiuSlo+qX7zhUCjjSm4Bwd9+uLuY+Yjr58X8P5PeH/Q+SJJ57g0UcfZfXq1axdu5avfOUr+P1+HnvsMQAeeeQRysvLeeaZZ0hNTWXp0qUT3p+bmwtwVvn7ccUNNKY69ZNAVVVqamoAWLFiBadOneKZZ54550BjsumokdGMiQclbswUFRVQ41NKSmLqM6ahaFqyXJxRnph+VIVIHo8y3tknfhq/n61PCAXH8GRLbJRzHq9NMpt4rnJdKGfOi46X68rZhfFynbM1aec4fjKN5y6/0DpN1HMhdZpc48RyRTXClHRdQRcqgnhHrCjGTED8BWBRVVLtGWhCRcF4AShCBWHMHCjxsDoUdXyLU0U9I97mzClq45xGuSXpV4pm3IAZf1uSU7Vn10lhyHnupVoJuysoKLpRTyPIxoKiqqgKqInwLkUBRUFVVJSYhiARqqNMastztZ+mq5P69sW203vrdH7lCjHtbL+eyvWkKPEnWIoluSgu+WmKmmw/42f8IQYC5YwsR8mmj2mo8ZC07JifERFv2fixiqah6Ea8tCLGP0OJKcn3Ge1kSZ4zcX4jBMbwP12oCDGuNh58YbxPSXyO4ZOqqmBRVbKtFkbiPq+oavxYBeKhfQI1eT496UtGOyXaUAgFayLsA8NeSmy8TkkbACgWdD2hUZm0vQedE7PhqGecx7helAmhIIqiogrdsOMZU3y6PvnnT8X3zt1nT3e/9x6N79PvTdZnn1ed4u2nKEYrCiXhDxYSfYQev+51/czr/4w+MuEnGG6iIlC1GLkWDVTLeJ3i/erE60egqqrhU/r4fgLT30dMtE+yPzzjfYqiGHoUZbzvZ7w9RPxY/cz7BeWM61HTARVVGGFJatwhE6mrFV2f0Beo6CgdfVgS1xigKkryPkKNhxYC8XMY7TG1/nBqvjc4MnnWqUv1nTtePmlx8kGnrIhJbHK5uP/++xkeHuapp57C4XCwYsUKXnvttWSUUHd3N6p67nuCC+GKWwx+5tRPgsTUz9VXX33en6Pr+llrMD6IlYvD0mQzUFUhlR6QT5NsesCYQXA7h86IQZ5ZZLSRbJpk0wNGfHZPenFyPdhMowtBrzcqTbYgkK/dZNMD8mkSqkp3LBVZspfJZh8w1mnoN109YW3RTCKjjWTSMhl6ctXlxb8uhMcff5yuri7C4TD79+9n3bp1yf/t2rWL73//++d87/e//31efPHFKZ3vipvRgKlN/YCx3mL16tXMmzePcDjMq6++yo9+9CO+8Y1vTOm8aWkTm1XXNNxDoxw9Ucjam7ZStvxdAAJOPxabysghN8G+MHqd8TSiqLaCggWzcFb3o6cb4TFNLx4CoHr7XF5rCKNr55erXZlEz0wjm6bLpUfoOlo0RsDjQ+giOXMVixYSjZUSKbVRoXYAYA+6SA0KyuxesNgQiSd3qOgWC5rNiqYYl6Wm2tCwoGElKmxERTzMSrcS1S205WXR256Oa9BYo3HoaDYLt3+E2nv3svfpXQA4n/s5W/7sBk6dLCXg8Z+138D52shis5KZl82cBaVsXmnUb8XArxl56bekZKdjqVtDt9cIGds68D1SH1vMz44NXtDeAx82PxK6jn/MS/1RD+tv2wLAnO429Ib9bNg+n+7lywDYPxbA0eXk7eNzmLftIRbdZ+yvc+onLYS9EQqX5+LYPcKxHxxGWC2o9xUQfHMIBShYlQNAVmkm0WAUV9MY4cEIKfnGk8baB5aQf9edHCu9hXcaszndaKzRGHMaYUPpWWmkpKbEf08lK8tObl4KBXkWCnKMm77izCBFqYPkRxxkjPVhbTPWk2mOAfz9Q4QrFjGn7xgqgtLKGuYV1TBQWMmAN4c+Vy4AjuFMnCMhPK4APk8AgKAvOGF/jUjQeADUeGyQ9sfupXjdzxhpdFG9fS4AI4fctHzvN2z76t0crSkDIDDmJewPfmC7paQaoRZzFpZxXVUb2q92YFm1GoCBUCGz7X0c/W496//2Rn7YauyJMNh9Ci0a42KRza/hwjVp0ShdzYP03rAZgJJX/4tVV+eRvWA1memFnEwz/K6vw4LH5SESDBMNG7H9oUAIrzuAy5WLo2oWSyo/AsCim+ZTXfU2e3KWcGTPKGPx/ScsNhvzasu4Nqse514jJKhw60Ze7Z1zSffQgHPbJ+EPg93DHOyopbaymvTGPWxYtgaA08cLCXkDaNFosg/0u730tg2yz24l/erNbHvI2ONmgctH8/NtnHiunlW52WRsywVgOHseWWtuZW5GFtZUIxV/8/NtnPzhaYr/6QHqlXWU5Rs6Yi/8CICce+/FJWKc/FkrAFt3fYHWXzkueg+Y90NWvzaRhytyoDHVqR+/389nPvMZent7SUtLY9GiRfz4xz/m/vvvn6kqmJiYmJiYmJiY/IEh+4aC080VOdAAY+pnslXyYEz9nMk//dM/8U//9E+XQZWJiYmJiYmJiYnJ5EgS9XbZuGIHGjNBY2vKhMVHQtcJ+4P0tg1zoHcu968z1ohknOhEtQbIq9MZPephqNEJQMGCWeQsX0RmzRyUbmNq01lvZJ8pvXkLHbsd5x27r+ln65lpZNN0OfUkwqeCPj/6gHFCLaYRixUR0wqIlRmXWkV6O6Wak5SI39hfIJEgQOgoumbEJivjs3Ei/rtQLQiLNV5mQahW8straKgu5HS9EZbV3TrE7tlzqL7lXkp3GOkNj337BNs+8gtWrPw0g93DjEWN8IXEVPr52Mhis5GWmUHpnGLWrcpgfeS3AAw//ysGT/Sz+MFrGZ5Vh2/ECAPre/bXlP3xJ3F+e+SCbPlh9KNYOEJv6yB7u4wsMeXLrkZ/49fkN7zGNStmA9DfX0TbyT6aTg7zVmUdd953u1F+5JuMNfrJnpVJ4epcRg65EYoCP9uDogpKNxWSUWgkqgh7Q4w0ugkPRkgrt7P4o0ZmkJw776K+4GbePZlOU+MIHpeRUteebie3IIPMLDs5OUa4S2G+hcJsjeLMIIX2UfLDAwCku3uxNPUQcwwQ7B/E7zDCr4KjAULeEHr+EAMpMRQgq7KJrKoKcmfPpaq0huG5RthTf2kR/e4MBkYyGB42QqRGnQG87gBBX5BwIITfbYRzOftGeLNlOY8+tIW3/vRXSVvO/UgFHS/3snX3D1i35ikABjpyiYYjE0JE3ttuqtVCXmkBABtWp1P6zo9xdQ2QcstiAEJRG6Wtv+OkK4rvxoc59qJRP9+oZ1rWVMnm13BxmtxDLvb1rwLg9mgU9cAuFi0aJmvOJjLT5gBwPL2UzlYbLsco4YAR2hb0+ImFowT9QcZGcxkeMcL++ubWsaR2Fg0NqfS09RKLhycVzy5l4yorGe/8ikTC1YG6Wzn8shevc+yShgWdyz4Jf/CPeTl23E3P3RspP9nAVWIfAO/UbmCkb5iAezwVrBaN4XG66WpS2W2rIH3DzQBs+qSHaOAHdLzcy6mf72VZThYAORuzcdhmE1p+M5WpaQDYs17j5M9OM2/VIl5ypnJrqZHF7sh/76f24YWcLtzCks7fJM+5P/sWXAONl8Y4iXpJ6tcm8mAONKaA22P54IMuG4pkekA+TbLpMTKJpNlUFE2R5LGGfDaST5NseuKZ7RxOFJscU/AKYPOMoRRmfOCxlw/Z2k02PSCbJkVRiJKBPFH2ctkHjGs/3QJIk1lJPhvJ4z+TM50b9l0JyJGy5AphXV1Imh0nLaqQSg/Ip0k2PWDs/t49FpYmY4iMNpJNk2x6IL5z9V1bJ+wIPpMI1YJ7xRp0SbJggXztJpsekE+T0HUKLK2oqhyPpGWzDxjXfkfQktwVfKaR0UYyaZkMYxf46XldCcjzrXAFcObOlDIgmx6QT5NsekC+zkFGG8mmSTY9AMIm14S0sMgx6DkT2dpNNj0gnyYVOW6gE8hmHwA5hmHjyGgjE3mQ65vqCsRIcevi6Iki1t+8FYDixbsZONhC/tw8wmMRAp1G+r3B491kL12AsmApvd/6IQCWNBUtqNO/4nbcL7hmrB4mF09inUbIa6TtdGNs0qhphUSiRhxytLwaiz2ANzOTFBHFohtRx5ZoCEsshBqLoMSMuF5Fi6FEw6DFQNfHc8trGgjB7PxhViy9l542Y9dor9PDsaMu9ldcy8aHDF907P45XT/4JVs/fxsnqksJjBnx7+HYxFSgk5HY0daekUZRZRErVhawOb+B4A+eBaDpV83MWl+MZfEyBmKlFGcZfn76Z610/uWtBDz1F2nRDw9C1/E6x6hvMNZk1dy4gbXzG/EfO86SWW8AsHbFR3EN5eAadLO/PpMFN3wUgCX3HWHP53Yy2uGhdHkx4YURhMWC165SvqmYjLw0gqOGzw2fGCU8GCG9KpUl99SRfsc9ABzM2s67x+20nh7B6/ZjTzc2I50zr4B5VXYKszWKMozdlQtTRskL9pM+0oPq6CYa3xzV7xjGP+AiOOon4o+gRQ1/tKXZSM1Lw2+3EhgJoOgaAVeA1M4hMkvbyag8yeyqKgDKZs2juqSawaIy+sqNWPQBZz5DI1mMjATxuALJ2Pygz0/DESd33vMx0spfZfCkA4B51y+lb88gjd/9Ldv+x6jfkYWV+EY9hHz+Se2vqCqpmRnMqzXS4W7NPkjLszuYvXkpHVZj/Uih1Uv3sy9TdUs5u5x1OLqaAWN9jcnZhANBDh817H3Polq8e/ZiHR2jwu0is/ZaAHJWLCEtrZi2VBvDvcZaxYDHRyQUIhaNGmtyxgzfdblyGZpbQGiwm+CYji2++3ZNbSkbbXvpf/sIFTdvAuCX/fPobmm9pKltz4dYOEJ/+yD7exdyX0Ul6pFdAGxauZ7Tx/IJ+fzju97rOrFwBM/IKO2nLOxKrQAgbe3dXPUpP9Hgz+jdMUjTs8Zn1GZmEVxzJwOxUkILbwFgzgNZLM98hdMZ88hL0Uh97ccARFxRSh++n9P+LEp+/QpLHzPWHX33iDW5NsZEHsysUyYmJiYmJiYmJiYm044kkdOXDTN0ago0NNqlyWag6XLpAfk0yaYHAEWhID8PRZIHGjLaSDZNsukBQNex7twHuhxhJoquk9d8Qho9IF+7yaYHZNSk0jZYLI0e+exjZCjMyJuFLLdvMtpIJi0m5ozGlAhHJLk7jCObHpBPk2x6ACyqKlWQrYw2kk2TbHoQAoIhIHWmlSRRo+GZlnAWsrWbbHpAPk1RTa61PrLZB0BV5bp1k9FGMiPXPuqXHrm8VXLWrQixvyH1rL00IsEwPS1DHFhWDcB9a9ZiPd6JLc1Gxdpy2gaNfQ56dwxSvOQ0uZVzaX6+DYDZN86i+/V+3u2bRyS457y1WNTJ9cwksmmaCT1C15NjiJA3gNAFekwnFjNKY7EM3OkamYXV2K06dpuxHiM1LYydEClaEJtm3LDZYkGs0SCWsB8lEkKNxOORIyEIh1GHermqrp+jNcUAnHR6GOoZYe/xbGo3PwBA9e37aX2hk233/JTVq/8cR1cuQHLPgXPZSFFVUlKNm9jc4jwWLinm2vmDZP3+OQ78rAEAPSYovaoGf3ENQigsVox87QeBt+pVdO3Cnm5/WP0oEgrR22qsd3i3Yh5zV15Pdns7kT1vAbDpzoV0Ll3KIU+Avo4Rdp0y9iKY+5EHqDrQROerfQRnBylZUoyuqowsWklm1wmCQ14GDxt7lkRcUTJr0lhy71XYb7+X/fYtAOw5ZqWtaQTfqJ/UzFSq5hcCsGG5wqKcNnIDA6QN9gCgDPYQdQzi7R/CPzhKaMyI8U6sy0jJSCGjKIu0fGNtUGZFMdbSEjpSipjVXI8qdAJDowRH/Yx1j+BzuEnv6AMgY1YzRZXlFJbPZU5RDQBDeRUMlOXRN5rN4EgmQX98TVtHPwMdg7w5uJSbHlnD/md2A1Bzs4WFdy3g+HdOsuh33wfg6jVfpKctl0golIyJP7PdsFgoKCtg01XGV571Nz/AedrNwr9cjzdq7FGwMvQO7/yig22//BO+Xe/HN2qsp5mOPTTeq0cGv4aL06THNHpahgDovfl6yvq68ZxuJ/TufnJGjfUY61eOkLVsHZkZBZy2G7Yf6LbgG/USDUeIRWNEw0YfGPSF8I9lMSezjT7dRkGZsefJNasULK8/j2pRcK419pY59Lr/ku+hAR9sH6Hr+EbHqD/mY9uN15L2/H8CsKbuXd5augnXwAhBj2/C8dFwBPeQi5YT8TVw9nJSr7qfJY95ifheYODtYQBSs39HdVY2gcW3MxAwbBEsv55592bQ3TPI6jkDNHzX6Dtq7q7iaNlHKFa9HP3vY2zd8Y8ANL88MOM2mgksckz2nBMzdMrExMTExMTExMTExOQiMWc0TExMTExMTExMTC4DZtYpExMTExMTExMTE5NpR7a9tC415kBjCpwrBlGLRhkbGeXIcSMW8+qbt5NbtQtncz+FtZVU32q8qeUXHTS/2sjKytLke7Nn5VB5veCNoz60aPS8tWj6ufXMFLJpmik9iZhuHQj7g4zpOnp8Dww9VoC/agF2jwW7XSXV2M6A1BRBeopOqk3DbjX2EUizRUlNDZOaE8IugthjRr75lKgfW9iHzdnHbOdh6pbcBkB3azbuoVGaTw7xdtUSAO5++Dbaf/N1Wn7wMtd99U6OLzT2EUjsOTCZjRRVJSXNTnZRLgDVi0rZsCTC/LaXOf3CboJ9xhqSim0lpM+dTQDBLNsAmW/9BoBFD9bww+MDFxzb/mH1Iz2m4XW5ATh9Yph9sxfxkavW4n75FQDyDvyGa9fMYWCgkI7Gfk4eM9ZdvFO+iRseuon+fT/EsXeEhffkIIDyvlP4hrwMHhwm4jL6jpzaDGrvXYPllnt5V72W/UeNJ2ftzSP4xwKkZ6cxd34hVy83vunW8y72/XuJOgYZGzDO5x8cJewNEQ1G0aI69kxjP4PsWTmk5mWRUV5MyqxSlJJyAEIFsxnOKCNbj2KvrkBRFDIGOoj19RLsHyQ4MkZ4zNhvIeINktI/THpxN5nlTcbnls+hvHgucytn4yguZmzM8NFRhxP/mJf9h7185K6PQXyNxsjJLsq3riJrYQdH/2cHANf95DCHFtfidY4lY+IT7aajkp6VzoKlZVwrjOOP/HAPFVfPYnj2avIVY28Z8erLADQueoCu3w0Qneb9M2Tza7h4Te4hY++nPb2ruX31dWQDo0ebGNp7FIDcERd1651kLtxKRloJAMdTrfR1WBkbcRMJhokEjTU5WjRKJBSiy2fDmpbG/CXG9+QG7U063zxOzUev5dfDRr/W1XR59tA4H/tEwxG6WxwcWFfLjbMMzfqe19i0cRPNxwuS+1gk1kroMY1IKIQrfr2dOmrBnjKL1KseZskn/YS9LwHQ8XIvtvSXqPqjbILlNwHQ580lmL0Zi5JG4dv/wMkm4zth7b/dxzt9OTyY9gIO4EC+8Z3gHDg97TZ5L7L6tYk8mGs0poA9Ra5hqGx6QD5NsukBUInNtIQJyGgj2TTJpgdAs6bMtIQJaNO0aHo6ka3dZNMD8mlKkUyPbPYBQDe/Q65khJi+15WAOdCYAitqw9JkM7CocukB+TTJpgdAUQR5aiey5LeV0UayaZJND4BQVZxVi0GVIxWoEIJh1xgyfe/J1m6y6QH5NKkq1C0IoEqiRzb7ACAESqATIUmKVBltJJOWyRAo0/a6EpC8OUxMTExMTExMTExMrkTMNRrTRGIvDYB9y6u59+o1OI7+Ai0UoXRdLQDB0QC9OwZp/tVeqm4x4psDTj/z7tpE93HHjGk3uTQIXUfTdcJCR8RXfylCY3ZWgE73GJYUGykpxhNpm82C3W68UlNtANhT0klPhTS7INWmk55ixPimWqOkZ0Uoz+wir/0AqxYMAHC0phiv04NrwMnBhhwAlt90H8s+tYvj3znJ1rd+yPrVnwOgrz2f4XAEtIlx6IqqYrWnkJmXQ+U8I6Z6bV0KV0V34Hz1d/S9OYQlzXg+kVdViIhGyehvIkPXaP7l2wAs+PNP4vruyCWy6pVNIu5/uHeYgw05zN92AzXzTwDgOnSc5XNep33FvbiGsnENugHYcySLBdc9xLJHjnH4KwcZaRlBWCxEqsK49w+huaLk1WUDsOT+9Wg33sc7savZ16DT2WLk5A/5QmTkpFO9oIANy3TWau8Ygnb8mt6GNiK+ELGwEY4hdIE9K5XsWbmkFWSTUWH4ga2sDFFURrBgNv2Z5bii+QAM+jMZ6VNIj4C37CoURaV0yShF8/vIdHaQNdBFdMDw0dCwi4g3gH9ghJDL2Kcitc9BSnErFbMqKCmejavuRgBaTuQz1DVAT7OD3ddey9JHFwHQ+NMmKm9Yx6I76jj4L3sNbb/+Lpu2f42u5nwiwfCE9W4Wm5WiimK2rooR+MGPAPC1Bpn91FbalDzmjDUYn/uL/Sz71BKeP5GH19lxyfcf+DCQWCdxuMHLrK0bWXl1KvkWCyMHjgPgONRMnmuM+RudZC4x2jUrvZojaWV0tdoYHXQl1zDEIrHk+pqC0gKuXWn0mdEXfo7VbmFs490c+L2x1udy7KFxvugxDc+wm4PHY2y9aisA/u99g/UbdrNr0QZc/UZfGPL5J7wnUW/nwAgnGizYU0q5dfMnqPMaNjj472/Q/HwbqTkvsPDhDACCOdvpdWdQkTbGyR/sYPaNswA4XvVRtC5wP/ccSx5dxPfqjVu7sD94eYxgMiXMxeAm50TT5Jqmkk0PyKdJNj0Ami7XRKKUNpJMk2x6AJQL3BDxUiEknCCXrd1k0wPyadJ0yfRIZh8wHgjJhIw2kpkrZW3FdCGXt0rO/qOp0nSCmq5IpQfk0ySbHgBdqLQOV6ALOS49GW0kmybZ9IAxyMg/dghFkqe6KBYiaQtQFDn8GuRrN9n0gHyaNF3h4IkMs398PxQLRUXF0jxokNFGMmkxMQcaUyI3WwNpljsKyfSAfJpk0wMgyEgJIo8mOW0klybZ9BhKIlk5CEWSL1QhUDQfQqpHdbK1m2x6QD5NgpysGDLpkcs+gBCEw2GJFEloI6m0nM0fWtYpM3RqCtTWRD5wLw2A+mM+Nt18C7mzdzLaPkjOkvkAVN+6jtDY2wzucbH5q3cBsO8Lr5D9zP14drqnpMWivr+emUA2TbLoSeRNB/CPCnLnDHK8oxDFasNiMdZoWG0WLDYrVpsFW4o1WWZPtSbXbtjtxrGpqTZS7RlUl2ayOauFyrFjANQtuZHu1mxc/SPJ9UK7qufyyYceIO21f+Lkd1/num/fDkDDklo8rjEivhi1NSEOHEtH0xUsNivp2RmUVRWzsi4LgPU5x+CVV+jY1QJA5tw0AGKhCGMnW0jpHSDqDdDzhhGH7/+3OwkHjl2wvWRpt0uhJxFXHvD46GhysHt2FVXrjdh1teVbBHf+jmsfqqVzyWKO7DPiq7tahnizcjYP33M/JQfaGNzjQlgtsKAC/E0Ursqh9sFNAISvv593A6s5cCxGV+sI4YCx50lGTjo1iwvZsDTG2vAuYq//GoDevafRYxr2rFQyS4x1HmkF2aRXlGItLUMvKieQXwmAI20WI5E8hv0ZjHRaGBk1jOF2R/G4Pcwr6KG1ZyECC8VFZZTklzCrcCEl5cPke3sAyHR2g6OX2PAIkVFjjUbU5ycaCGIbcZGS38P6a4oBeHfxEtxDLnyjHnYfjrHtY/cBcPIH/8BYYyt5G9ZQvM7YJ+DIf+9my83vcGDp1YyNuAm4o1hUWDI/ysneQpbUlbDW9Uve/u/DANTcXYVv2bXkacOo+4y9NZz1Yyz/0l9y/I2hS7JHg2x+DRevKeHP3S0O9hTOJbJ4DVddbacoxdggaOjtAziOdhEa9VI6Znw/brnqBjJXrSQtvYS2JisjfU4Agl4fIhZl2UINe2Ux6wOvA3Dq9ydY/NAWXnbV0dXcBnBZ9tCA87dPJBSi7ZSDw2vXA7Cq+DnUN3/Dpi3X0nrCWMs0FApNWFeS+D3kDTDcM8xRq4VU+1xuvfkxAFb6Auz/4i6OffsEV2X8AoAlD2XjS78a0fguIw1eVvzkIQC+2ZTDynkBGv6rga07/pHml42+eCp7c10osvq1zOh/YDuDS94cJiYmJiYmJiYmJiZXIuaMhomJiYmJiYmJicll4EoJeZouzIHGFAgGVWki/wRy6QH5NMmmJ0EobAFJNtqR0UayaZJNTwLF65NHk4CIZgNVDr8Go90CIVWacG0Z/Ug2TQIIhi3Yzf7xfVBIEfLsDC6jjWTSMhnmQMPknBw5ZX/f/yfyYve0DLF3xTzu2rKOo1/9FaF+Y4+M9LXrWXxvlCPuXVira4z3DEZ4a2gpkdC7U9Ki68oH6rncyKZJJj3JeNxAmIPHbKiqB0VRUeI3ZqrVgqqoqFYVNb5uw2KxYLVZUa0q1vj6DQBbihVbipURZyYLr15FWfc+AFZUDnK0ppixEXdyvdDRhhwO3XErK/5oB3uf3sWCl78PwKbr/p3u1gL6W4M0NKVhsVmxWCA9O5PSOSXU1eWxqbIDgNzdL9Lyu3rGGo088JEx40tu4OgAMEBqbioh93jM9K7jGReV416mdoNLo0eLxnAPuTjakM2C8g0AXLv+MP2vvEnp3hfYvOYJBgYKAOhpdtDQ4KLm5hvY+MhBBvc8Z2SbemkPJevyWPzgtfi2PQjA7rE6Dh4N09PuJBQIkZ1vrLOZv6iQjUsjrPb/nvDrL9G3rxmAlIwUChdXk1ZeiqW0zNBWNAt/7mxc9lKc4VyGfMaanBGHhRGXhtsdwTMWJuA12jzoDxEOhOlVrKRlDgLQn5tBTl4aBQXplBRWU5prrPMorVhKUeUgOZ4eMkZ6ARDDg8RGR4n5/ERGXGQdfg2Aq1eupO1kHiN9g7Q1DnB4450AzL7xf2h5/RRrN29kwZ3rjHo/+RrRX/yAa+/ZQEdTQXL/gOOtdqqWlrBtuY/hL/8ULWgEkc+55wYG0oooO/UGLb89CEB6VSoH8j7CUHfLJdmjQTa/hunT5HWO0XRikHC4kNDSlaxZZ/hMSUoKys49jLQMERw19tmZ5XSx8ZpRsuo2kZFeTFN836DB7hF8ox4coTJuXxHD/8OfA5CSmYJ/673s3+HHM+w2dF+mbGvnax89pjE66GLPCWN/rKs3bWbgOz9i47Z32bVoDQCjgyNEiSB0gdDHFzNo0SgBjw9H1yCHbBbS7EsBuPnOT7DK5+fgl/ZT//VDAKzPyWDl3dn0f/vHlK7NoXmZcd0PvBnij4p/z1vAgfzbcA6cnk4zvH/dJfVrE3kw12hMgeKCGIoix1BUUYRUekA+TbLpAUNTUV5UGk2y2kgmTbLpARCqgphbLk3WKQVBQVYQRaJniYoiKM6PIsvzTRn9SDZNiiLITfdLk71MNvsACCEIFxbLc+1LaCOZtEyGLqbvdSVgDjSmQM2cqDSRAaoilx6QT5NsesDQNK8iJI0mVYGa2RFp9IB87SabHgBUFX1NLUiycZeiCCqLfVJ9wasKVFeEUSXRJKMfyaZJUaA8340sg0PZ7ANG2E1wbo00176MNpJJy2QIoUzb60pAytCpr3/96/zrv/4rDoeDuro6vva1r7F27dpJjz158iRPPfUUhw8fpquri3//93/nz/7sz846rq+vj7/+67/mt7/9LYFAgJqaGr73ve+xevXqadOdmM4dGxnlYEMx19x4BzkVO5NhCvNXr0HdcB3Lg2HwewEo2ZDPG0f8l20q2GRm0WMauqYRDUfQdGXCBmeKqkzY8VVVFVSLBUU1QqwSqXBViwWL1YLPE6R+/lxuzDLSzlb6TrG8dgs9bXmM9BohLI6uId46Np+ldzxM4SsNNHz3HQA2b3uT+uVb8IyMoigBUjMz0HWF4tnFLKsr5JoaBxUNLwLQ+fJuencMJnUF+8ITfoLPOP/1RujNc8eHptlqHz6ErhMOBBnoGOTdo0Za2ZqNd5Jz8hSDbx1iZfVvaVlxLwCjwz6G+12805DJ/M0fZ+ljx9BVlY68dBZfu5WxrffzzogRbnHoaJC+jhHCoQjZ+VksrC0E4JqlIVa6Xyfw6kv0HWglNccIbam8YS1i+To8OZW4rCUAjIRzGPKk4hxTcbk13KMRADxjIQK+EEFfiFAgRCxspM6MRaMoIkYkGMLpcKHrCt5RH6ODqQxlp9Gbk0ZuvnG+osJ8ivPzKcmppmSeG4CCOQ4yPX2kjfQh3E58jUZ/ufGqw+xetBT3kAv3kIs3jxrhV3/5yTvZcf9/EW5uJuUq43uhYtsR6r9xgI23v8HepTfgGnShqoKUNJ3lC4pY3vl9dnzvFLUPLwRgbOkW8jzdjLz+Jt2v9wOw5q+u5hsNVgIe36Vo8g81kVCIkf5hYtEY0UgxoaWLAVh7VRoVqWmob7yF45iR4rj1pQPMdo2xatsoGbXbyUgzfPREqpXBTpWikgirXb/l6O8aAVj+iS381r2Srub2y5bW9kII+4M0nTD6vsZVN1Ka8wLqGy+w4dprAGg7mYfXOUY0HEGHCeFTuqYR8PgY6Bhkf0o8jfnGNdx47yPUeYMc/W8jXXj9N/ewJisLT2eI1Y/eyHdOFwGQk6Pj/smPWfLoIr5Xb02GDpqYyIAcQ+IzeO6553jiiSf47Gc/S319PXV1ddx4440MDU1+8xIIBKiuruaf//mfKS0tnfSY0dFRNm7ciM1m47e//S2NjY3827/9G3l5eZeyKiYmJiYmJiYmJiZJzA37Zpgvf/nLfPrTn+axx4xNa775zW/yyiuv8N3vfpe/+Zu/Oev4NWvWsGaNsdhqsv8DfPGLX6SyspLvfe97ybK5c+dOWZvbY5FkQteYWJZJD8inSTY9IKEmAW6vRZZIBUA+G8mmBwABVvcoUDLTSgDDRmM+ufwIAR6/jXyJMhjJ5kcyaoqIjJmWkERG+wAoDicyXfuy2UgmLZNxpaytmC6kGmhEIhEOHz7Mk08+mSxTVZXt27ezd+/eC/7c3/zmN9x4443ce++9vPXWW5SXl/OZz3yGT3/60+d8TzgcJhwOTyg72SywWsGiGl4ihLHDo6oIzlyXlYhT1oVAV1WEakkeD6ArColJU2GxkLgsEp+bILHL5nt3udR00HVoarehnPE+TVcAMeF4gZGBQVHEhLjFc5afo066MOIKVVVM+Np+b3lCk3EGZUp1mrz84urU1G5L9joXWqeJGi++To1tdhTVaLeErwhAMFG7qoKiCsOSikCNn1dV9fGQK6Gji3F/Q+jxYwztqqqjoCUXV4p4+JUuBAoauoBT7alk5hr2URUdhHG8SNhUVY2dqAFimhFI/Z5KKTHNMLNqQVV0LHHdF+J7iIm+PVPtlPC9M/1aVcW0XU8Q90mMsEld15NfkLoAhBY/RifhxLquJ8Pr0luaUFYUG20bP1ZBP+P48c8Wup48Z6JNAXSUcd+IPyITwvCB8W9r3ai7Ynx2wmdVRYBi2ERo0NSVhmo16qqqevL4xAcpjH+uEDpCCBRFQdeF0VcKQ3HitEIXRn1UgeHucRsAwmox+lFhxGOLRFncr1VFoAuFtr4sqlbGP9tqQVdU1Pi5dSHQlbhvJx8NGr6Lmrimps/3Ju+zjf9Pd79ntOf59eUJTYoiLvh6UlWjf0q2txCAjh73Oz3eXwmMPkhX1LgOPSk28a04ppWN10FVDO8TWtyfxEV9P02lTgma2m0YUU7ifdvJEq9/0n9R0BXljOs4fh0oIt4/jt9HiPj/jb49YRPjZ9JHMXxYQaC+XQ+P1E647hPnVdDG66WKy/Kde6rNhqp88L3R5erL5Xi0YJJAqoHGyMgImqZRUjJxpF5SUsLp0xeerq29vZ1vfOMbPPHEE/zt3/4tBw8e5E/+5E9ISUnh0UcfnfQ9zzzzDE8//fSEsrUbP8ambR9PXtCDI1baum3MrYxRUjie17p3SNDV7GD/4iwW330XfbsN7fkt7aRsX4Fzw834jx8HwP5HD+E60g3A6mVhLJbxC6qh0U44orBuxcS41P0NqaTada5dE8LjM/JXa5rC/qOp5Gbr1NZEkscGgypHTtkpyteomRNNlrs9FhpbU6gojVFZNq79XHXqGbDSM2BjUXWU3Ozx9SStXTaGnFbqFkZIT9PJztTx+FROtqbg9limVCd7imBF7fjg7mLrpADZmTotXTZau1IuqE5paeNxtI3TUifoH7Li8yssrhnXHgxbaDiVSkF2jJrZ8boqCm6PhdMdaVSURKgsi8aLFYbdKfT1RWhv0Tg2z4hxznH3sqConRM15RSn9pCTGUOP+VBdft72LuGGT17Pzt/0IrIzad1ziLV1ZThqsgi7Q1TPjaKqgpJSN3MLB5h9+iC9L+1keOFKnCk1iLuNdMzqCzshPRX9pg1J7Uo0hvKrNyl/4EYGUvKp7GyleEXogn2venaU+XOiSd+emXYa970lNZGkXwem9XpSKM8bxuYx0gYfbstm87U3on//+7QfaWTRmlkAhOvy2HdYoe30MPtLVJbe8aDxBe/1MbBsDfsci3F1nTR8IBogs1Snx1vNotoc6kpOAZDb10Zr8yn0d5uxVc1Cu+E6AE5X1uKKlTLqWoBndIw03UEwPEYoGGPUa6OlL4cc6wgleX5S1Rh6uo4jaGF41MbcWUGK86PJAUooqnL0dDqL5obJyzEGRBaLhb7hXEYcucwvGyYcVRgas+FNs9KeORtNyaQ0ZYwM22zS84pIy/Yx2zGEVY/Rc/QwV8/PJjMYIegL0Hzcgc2icfDmjaT9vz00OFWKtTTmpgsq7r+R7oIBmvcdZf3KOYgV0DZYyMJKNysLj1D/ZifinutQ7lpLmRqiKWpHaTxNvyhFv7sQpb2P9Ns/indXC2uW+pKDr+n0vUgUrt8YTPr1+fjepe7L87K1pG+3XOT1pKohLCluWk5r6NE8KrJ7OeS14yhaS+FNBVSmvUIoNYOWQCajfkH2kRZyx0JsW7qZYDBAHiqDhVHmFx+hdfchI212RSUtq25hoPUUC4pHyIjptHXbmDf78vTlie+QN95NI8XGB7RTCHtaG8GwhbdPb+TjW7dw/Fg3VeFdAKxeUcXhY/lkWYcozQ2ix9doDA5baO1SmF0SoawkgB23ofHEHFJXbaX2Lh/5s4y1RSMnXBx+/TTpt9RyYtHNxE6eAGB5tY2jL/Zy3Y//BLXhGKuXeM9ZpwTT5Xun2mysqg2TahcfeG90ufrylbUTHxLLxpUS8jRdSDXQuFTous7q1av5whe+AMDKlSs5ceIE3/zmN8850HjyySd54oknJpR97ou7OHjMHh/xjztLR4+Vzt4zTGmxUFABmr2cuUEHwzuM/OGZVRuJALPSVfw9xuAjK3UO3qCRR/zQ8Ym5qBOj8/0NqWeVh8IKHr8yQQ+A26NOOD7hz8MuC85Ry1nlvQ4r/YPj2s9Vp8TT0NPtNhRsZ5UfbUrBqgrW1IU4eMxOJKZMuU6BkHJW+cXUyRLX0xWvx4XU6eynKxdXJ4sqWL08zP4G+8Tj40+YjToZC2cVdfzpbt+QDYczJV6uoigKVjuMRQrJKzRUzhE9tKYbC4tbezNAAS0coaSqjMWrjXLl9wdQgFnlq/Ft/RMCETeVhSHahqrRhUpKSTErCkdJce1H0XWKmo4w+ut2RDSuJKaBN2AMON5Dmh5hbmiQ+ub1+Jzign2vq9dKSWEs6dsz0U4J3B6Vg8fsSb+Oxa+16bqemrtS8arG2rLKpeVkKcfwA8XDnQQKjX00hjrm4g32Yk+HUMo8qjmILqBDj2BRVUCly1MFwECPk2gkSna+gkYK6flG+VzRjG+ghT7A5hujMmDs7eMu2IA3WgE+iCo5RC1ZjEZjuL0RfJ4wEGFoLIPhsXTC4TCxcIxYJApodPal0NWfgq5pqOisXm5kU2vqtGOxWlAtFmz2FGx2O/YMaHMUkJGVRlZeOvkWO4UY/iwyqknPCFJgHyU/7MCmGTcyc339NM+r4VhHO+5BJ6XzFMIxK3mF5YT278VitzLnFmMfjTQ9jPrCTspKVuK77s9oH3KhKIJUW5i8gjJsJw8z9ko3pQ8sByA9I4MSXx+uN98h1hZMNtSAO4e+067kQt3p9D2LKs7qsy9Fv5csP4++3Kpak74d1ZQp1wnGryeLzUZKRhqZuQpR3Uanu4rM8kzyCx1U+U7iBewhP5l7jyF0QenqORRW59MPpKamEUmbTZ8vyEJrG7mDPTgBe18v5fnZdB2t4kRjmJDX/4F1ms4+IvEdIsT5tVNmQS4ImLUCsoiQf/QQxR+9DYChQAkwwoDTTk+fsX8GgK4Zs48dvTZ6R1LIys8BoGqx8bMkFUTTEQBcL7Rhqc0kdM1GSvPyaXPlArB0VRrRQRcATf3F9J4OnLNOZ5ZPh++pCtjtOgePpX7gvdHl6ssPHpNrX4/3Yg40ZpDCwkIsFguDg4MTygcHB8+50Pt8KCsro7a2dkLZ4sWL+eUvf3nO99jtduz2ic5qtaag6cqEG3sYD/dIoCbmFhUVFYGiJaY34/9XFNREiEs8xAg463MTaPqkxQihTKJHmfR4IRS0SZz7XOXvrVOy/Bwadd0I0khoutA6TV5+4XUSQjHqwoXVaXKN01Un5Yy/Eh30uPZEmaKcaVejPOFjAhU1PkhRFWO2A8bratyoqyTmr5V4djNVJMKvEser6EJFEM9ypRj/UXTdeE/sDMMJYQw4zqqRsZ+C/h6fvBDfm8y3Z66dxu2v62e305lcyPWki/j8v2JJhhmoQiTbVXDGTvKKJZnBwwh3UUBREPHSRDsmWkM5wzcSfY5Cot8x+qpkGJ6iABaMoA41+ZlCKEYYiK7G9U70MV1X4gFmSrJO6Iau8f+d6WOGnyUrqxg+p6qqoSdePxUBigVdqBP6FEVVUTQNRVOSYRwKhm8n/FoXajyMx7CBGvfj5PGKkuyblTN8OdHO4r19/DT53uR99vT3exM0vo/vndlnJ1JlXvD1lLw+Ej2BMt62Z7aTpoEuUIU+oT1QLAiMhyhJXxUCVTX8ezLfm6xOk3ExfYRhF+P1Qe105nmM/lNDVcb76sTnnekDxk2nERKmndEfJI5XFVDjA18lpqGIRAiWiogfqyiWZPn49XLuOn1w+fn7nhHWNLlfy/WdazJTSJV1KiUlhauuuoodO3Yky3RdZ8eOHVx99dUX/LkbN26kqalpQllzczNz5sy54M80MTExMTExMTExmQp/aBv2STWjAfDEE0/w6KOPsnr1atauXctXvvIV/H5/MgvVI488Qnl5Oc888wxgLCBvbGxM/t7X10dDQwOZmZnU1Bhx5f/3//5fNmzYwBe+8AXuu+8+Dhw4wLe+9S2+9a1vTUnb4Ij1vKa89JiGZ9jNgSN+rrnhXgrmvwVA66v1LNq0jd7iq+h/0VijUf3b/w/vzrEp6QDjKcj56rlcyKZJNj1wbk1n5lQfL0v8phlPps+YRFBUBS2m0dE0SP0CY3+B0pwiKsMtLF28gZ72fAAcnQO4Bpzsrc9n5fZHmH3juwAc/spBrrv11+yvuxdXp4fauhJAZcuSMRa3vEDf8y9z+metyfNZ0lTSZ6disanYs42QF9VqwdU0RngwQu7STJwbjX0fgi9P3Z/Px0YzxaXUEwtHcA6MAHDwSDYLbrqRpevr6Xr9ANV7nwfgmhV/zkB/PgMdQxw+MsaCm+5ACJ2M/HZ29S6k4biXgW6n8XnRGHlFOSxeUsCmWi91jlcBcL/0En2HOsgozGT2zVfj2mS01TvDS2juiOF2+/GOGfHOQV+YoD9IJBghGomgRYwYa03T0GOasZD7jG84oeugCBxDKrFIFF0o6JqGpipEwxEioRBBnxHK4XPbGB22M5yVTl98L4+c3DQKCtMpys+kOLeM69a2AzDys+fZfM0hdi+sxTMyykj/MAA7Guv49KPb2fWZn7O43ViDEqvbQM3d+2n47yNsufPX7FlxL6BTlG1l3tGf8uYLnaz4zAoGF2wGoNDTQfPP38HXGiSzxtBxKGM7zoGmSa/F6UA2v4bp1aRrGmF/kDFdR4vP4kciGqFwKYF5d7Lk9iwA5tlfpfOtRtrfbCHo8lF+g+G7162/k6zUxSx0naR9RzNX/a+tADO6h8ZU7ZPYg+VEwzBdH78ba+qr2F57FoD1G9ZTbyujvycV95CbUPyaOHNfjVg4gsfpBqCrSWW3rYK0q2/mmkc9AIS9P6D9t/3UfqaY11tnE40Y4VJXR37P28CBgjtwDlz4etYLQVa/lhnZ9U030g007r//foaHh3nqqadwOBysWLGC1157LblAvLu7OxlSANDf38/KlSuTf3/pS1/iS1/6Eps3b2bXrl2AkQL3V7/6FU8++ST/8A//wNy5c/nKV77Cxz72sSlpa+u2ffBBlwldKFLpAfk0yaYHZNSkMjCWR75Ek5uy2Ug2PWCETWRnZ8OoHO0mo41AJWwpk8azZbSRbJoURaXYGqPjEg32pops9gFQNJ2ioAsUOTxbRhslQuxM5EC6gQbA448/zuOPPz7p/xKDhwRVVVXJDCHvx0c+8hE+8pGPXJSuebOjdPRYpXBiVRHMrYxJowfk0ySbHpBRk05ZzihQiCyRlLLZSDY9YKQF9Xg8Z057zSgy2gh07NoQclhIThvJpkkInaGYFaGafdG5EBaV4bR889p/HxKphGVFknH0ZUOOq/kKoaQwNiEn9EyiKHLpAfk0yaYH5NOkIMhN95+xe8HMI52NJNMDgIBgMMikKy1nABltpCCw6RcXxjedSGkj6TQJPNoZiQJmGPnsAygK3pRMzGv/3MikZTLMncFNpoVIKERXs4O3Vyzhzo9eC8Cb/89zzN+7C/WOlQQ6jXjTnT0LiYR2z6RUkyuA98aNCx2ELnANumg4YeyjUbutlvmegyybNcLJmiIAIxY4EKT91AA7FyzgoU/eCUD36//FwPd/wta/uZ5jagbLFnlQVJWlHb+k5ycv0vx8W/JcmTVpFC0qILs8n5ScDGyZ6QAEh0Zxtxmxw/NvWsLO4cUAaFHTn88XoeuEvEasdk+zg7cra5i35W6yT7TQ86phxzXzl9O68nbedPpwdDnZdWwOChpV2XnUn/bg6HEnY+LzS3JZvKSAaxePsbTvJVy/fhmA3gOdZM/KZs6tG3Becz9vOYy2OtTgZ6jPTdAXJBo20m0m1mWcuSbD0CqSms+nXkI3NpyMBDXU+DqPSDBE2BYi4PXjcRlZ/VzpqQz2p5GVm0ZufhqzNlwPQIn9V6S9+QvWX/VFOk/lMjYyCsCxI8MMffwTpFe9lLRR4YotzLnnBlpf+BZDP/oZ1/3FzYYG9yjNX38Ze0kKeXffRSfGOoGCPa8x8Lax5qPmBmOPgu+fSEm2hcnUEbqOpuuEhY4e90dd04lGYoRC+QQX3gTAijsymZ/1Mm2vHaHn3V78w28AMM89xjXXfoSm442kF6QSuOEhAPbuCOAZdqNPkulONhL1HuwZ4u2OpTy0fQ0tPzfWaG69tZ7Uq1bQmFdCV0cqzgE3AF63h0gwjBaNInSRTHvrGRml/ZSFt1IrSFv7UQBWf8pPJPw8oXkrOP6um1kVRtpy37M/ZvHH5vODeithf/Ay19rE5P0xBxomJiYmJiYmJiYml4ErZSZiujAHGlOgZ8AqTToxXcilB+TTJJsekFCTohAUBVLNNctmI9n0AAgUIpbCmZaRREYboUBGRmZyRmamkdFGsmlSgMyRAYKSBLHLZh8AhE5WRlpy35OZRkYbyaRlMmTXN92YazSmQM+ATZqLWwhFKj0gnybZ9IB8mhRFIUhhcqM/GZDNRrLpMVCJWgqTm3rNNDLaSFFUMjIzkxuZzTQy2kg2TYqikOl0mG32PihCkJWRntwIc6aR0UYyaTExZzSmRG1NhNPttnPuXnkmib009h/2semWBwEou3YnTS/sY8maAwS2Gel6Xz18YbGnqipYVB09bz2XA9k0yaYHpldTIr6/u3UIgPoFVcwqL6E82snSRasB6G7NxdEZZGxklIOH3ay+0/DFBfe9zqmftLD1rp8RqLyO2tZ3UBXo/tELtPyiA4DC1bkAVK6vJmdRNZbyCkRmLkrAC0D0nd0E+8KUbiok64YbOdgQSOq6GGRrt0utJxGT7XW5OXl0iN2VV3HzTdfR9F/PARB46QW2fnIZXYvKaazvpunkEIqio8/yMdRtQdONtRkAS5cVcO0iF7Wdv2HkN6/Se7ALgJyKHObctomRjffx1sBiDh0x2rC7xYF/zIce04jFdQhdTNgr43za81w2SrxXS+xsrKromoYSVokEwwCEfAH8YymMjdhxZqbyToGxL8wfX7uezmd/y5ZtDeyuqcbrcgPg6BpkR+cyPvrJjex5ytjcdfbDjXiWbGbpY+9w4nun2HbvL9AF9I/46HlrhPV/eTU9FRspDRh7dJz62dtJjQU3G2tCmt4dSrbFpUA2v4ZLo0mPaUSJAOBxuolFo0TDMYJhYwYusHgrq2/OZkFWJh0v76PvbQcAYe9u5rrGaBzIZOM983ltdBUAnadbZ2QPDZi6fRL+7nd7OXx4lFvuuh/92V0ApL3+EzZv9VGyfCUnCvJpaTX2bzlzX41oOJJc5xENR3APuWg+rmK3VwCQuuoBaj/p58WOLEYHXdxxQyYA9X90mK2/f5rmVwYuqQ9Phqx+LTPnkyn1/JHD5u+HHI/DrhByszVpmlRBLj0gnybZ9ICcmmIRuRYPymYj2fSAoSk9RZ52k9FGAEGLfaYlJJHRRvJpUhDFBTMtIol89gFQUHX/TItIIqONZNIyGX9oWafMgYaJiYmJiYmJiYmJybRjhk6ZmJiYmJiYmJiYXAYkyXVw2TAHGlOgtcs2pWwBkVCI7hYHO/uMnPX3fWwbb/7xs8x59SUWPmbsUt67a/CCtOhi6nouNbJpkk0PTL8mXdNwDxn7Cxw/UcDisvnURg+zpNQJwMn5xbiH3AR9fvrbB9l5agEAf/ypB2l+/p9o/94LLHnMRtdPfokiBG0vGjH9ldeXMWfrMgDsdavwz1qIO70UVeiUdu8HYLTd8N15N63kVPlNdL14Yb58Vp0ka7fLpScajjDcO8ze+hwWbL+big1HAeh88yS1S55l04o/Z7A/j+FeJwqCJs2GpgsKSvNZuiwfgM2LRljU8iuGfvMavYd6yJuTC8Cc269haOMDvN23gINHPPQ0G3HxXpebWCQ25TUZ7+V8bXTm/hqJWHQtGiUajhAOBAl4bRw/ZuzT0vvAnSjPv0bmzudYt/qf6Woy9sDwjXo4dGiUO+74BMo/7gRg5Le/R/vTzZR89DZO/vg03d//OUJRGAvYyFmYhv22jxLQ00h59xUAhvaPUr61mL43h2iruRWA4WeHp1zvqSCbX8Ol05RYdyh0gd+to8d0YlGjLBQsJLB0HWuvy6ImMwur/U0AencPcOJnB8mqKSd0w9Ps22GEB3lGRmdsD40LtY8WjdHX7uCtoaXccsMKAJp/tZdqn5+V6zoonnctJblzADgR31djuN+Ff8yX3AdD1zSi4QijDienjhq3aim2WdhXPkTDazFsdoVNmrFG6W3gQMEdOAdOT0u9p4Ksfi0zV0rI03RhDjSmwJBTHnMJoUilB+TTJJsekE+TIgTZShSXRD2fbDaSTQ8Y6W1dvjQEcjwak9FGihDYBgeJSuLbMtpINk2KEFgHHNJkwZPNPmBk5vKGsxDIsU5DRhuZWafkwlyjMQVWLg5Lk81AVYVUekA+TbLpAfk0CVWlW2QgVHm6AtlsJJseAFURLCh3oipyDDRktJFQVfyrrkJY5PBtGW0kmyZhsRBcsxpdksGhbPYBY1awMqfPvPbfB5m0TIYupu91JSDXMFRy0tL0KWUz0GManpFRDtWPAXDtHY9Qef1bHPvREWZ96p8B8Pyy54K0KBeg51IjmybZ9MD0axK6npxq72kb5Mj8eVQsKGVWrBuApYtW0N2WT6g9iG90jIZ6IxXu4fvuZuXjr1L/zaNk1zXh+U03Sjw8oebuKipv2Uhk+SYAWrNqGfDnERq1MC9nCHqN9Lf99Q5KNuSjbr+Nd1vyGRvpnJY6ydZul0uPHtMIeHy0nxpg15xqHrn5TgAyjnfS9as32bBwFa0rbuAdlw9V0bHbohTNymfJsnw2LzDadf7pF3C8+Bp9h3vJr86n6s7NADg2PMBbPTUcqh+jt3UwmSo2Go5MS1jKVG10ZniW0I0QG13TiEViODqNurzTXcu9W1fR8fI+tlzfyLvVZQC0HPHS1+7gzbHVXPO/jRSoR799hE0fa2KgZjMr/vj3HPnPeoTVgn53Ndd+/BractdQEW3n1I+NMJ2c2gyyZ+UQqAuxu9343KCv86Lt8H7I5tdw6TUJXUeLxgj6/Iz0G20ejUQJh4vxL1nK+quzmJueAUBa3k6af9tJ8frF7HKtoKvJ6Gei4cglUvfBXKh9hK7jG/Vw4Iifm2+6F4DQT/dw6peHmNU+QNmmDgpWGtdmcd1VHM3LpyUnlYHuUdzDRihs2B8iGo4QDUdwDowAcPKoBbutmLC3gzk11fh+9kMAlj62mO/WWwn5Lv8sh6x+LTOSjKMvG3I86jExMTExMTExMTEx+VBhzmiYmJiYmJiYmJiYXAbEtMY8yT5/Y85oTInG1hQ0OcIi0XS59IB8mmTTAxJq0nSyWk8jjyD5bCSbHgBdKLQ7ctAlWfQoo43QdNS361GQI05BRhtJp0nTKfE6kOXWRDr7AKDSNpCLkOQGU0YbyaRlMsw1GibnxO2xTPk90XCEnhYjleTOzoV8/NFb6Pn4d9jRZMQFx8JtF6hGuSA9lxbZNMmmBy6FJi0aBcDrHONU4xiLK+dQpxwBYEmpk8b5RbiHRvG7vQx1G/Hvbx4toO5jj5LxciP9zzViTVMBhSUfr6Xolu0MLrqOZn8VAF1ddjw+QWkBFKX1MvxuPQAxj8aiP7uGfbYtHDvqIhyYrp2qZWu3y6dHi8ZwD7loOJLDglu3AbDx1oMc+8YrFP76ea57ZBk9vcUAjI2GqKvL49qaAead+DkA/S/+jv4j/RQuKGTOHVvo3/AAAG93VXPosJvetkF8o2PJuPfpSxt6cTZKpL0FDf+YF4BDh93ccus9RH/+Dvk7f8Lq1Z8HoLc1k8CYlz2HAtz8wMcB0P7jMNEdv8V//1pK7r4b+89PAJAzRyd2w32EtBTS3/0NQ/uN+Pf1f3cNjiPtLLhlOT855jY+Ixq7YP3nh2x+DZdDU2KdRsgbAAyfi0VjhIJFBEJz2LDc8NH5OXnYMl5BveFe9v3Oj2dkNHn8zHHh9omFI3Q1DfDO1cZat7U31NL6u0ZaX2nH1eFkdruxjm7FpnaKFlxHce4cTuSW0N2RCsBIPN1tNBwhEgwDMNwzzDGrBeeo4MGVFo48bvTFW17/R5pfGpghW8np1ybyIMdjgyuEdXUhLJJkM7CoQio9IJ8m2fSAfJqExYJ+11aEVZ4vCtlsJJseAAWd6vxuEDN5EzaOjDYSVgvedVdLk8FIRhvJpklXVPpcHhRJ0jbLZh8AVdFZUTOKMK/9cyKTlskQYvpeVwLmjMYUsFjkalXZ9IB8mmTTA/JpEjb5ugHZbCSbHgBVleNmLIGMNhIWeQbQIKeNZNOkSxYPIpt9QL4baRltJDOy+filxpzRMDExMTExMTExMTGZduR7lPkhQ49p+EaNfTQOHRpl4z0fo/bh3bxQb+TFPjOfvInJxRAOBOlrH6KhbT5zlpUCUBrtZdmiOrpa8wn6AgQ8PgBOHXXwzsJtrP/kRva3q6z8f1ajCp3Mmz9Cc9kWjg0U0dFrTM27R/1kZ6ewsjpG1om3aTvSC8D82+bh3fYQew5bcHQNzXAs9YcDoetEQiEGOgZ5pyELgPmbHqTi8Ena3jhBXd1zXL3yT0Bo+IbSWFXTR/XRX9LzwhsADBwdoHhxEVV3b6N37X3s6pgLwOF6N/3txvqMSDAsdb8Ti68f6W0b5O2xlWzbuoTWX73Dtq+3ArCvqpi2o166mgbYvel6ABY9WMOxH+9lyR0dtJddw4pPrkVXVAYXzaNFXUKV0sWpH++gYFUOABlrVtP/jX3MefLP6PuhsW5JZptc6QhdR4vbNyx0Y9+UmE40ohEMGWuOfPPvZOl92ez3ldHT6pjR/TOmA6HreEZGebfeqMe2u+9hQcqLdL11msH9TkZbDgFQ2dJP5Q3d5K/eRnHdVRzLKwCgOdvOYK+bsRE3YX8IgEgoxGDvMFVLU9gY28HuqPFkfH/uR3AOnJ6BWppcCFdKyNN0Yc5oTIGGRrs02Qw0XS49IJ8m2fSAfJoUIbDtPoAiJBGEfDaSTY+BSsA6F0WRY9GjjDZShE5lcBhMG50T2TQpQDRtrlTZ1GSyD4CuQ/NAoTTLnWW0kUxaJuMPbY2GOdCYAuGILJe2gWx6QD5NsukB+TQpwdBMSzgL2Wwkmx4AXbIJaRltZJVkwWwCGW0knSbF9OsPIhqTa+2RjDYykQdzoDEF1q0IYZHEYhZVLj0gnybZ9IB8moSiELn+WoQiiSDks5Fsegx0MmMtCEkeacloI6GodKSXSvPYT0YbyaZJALZAC6pittm5UFVYUjkoye4wctpIJi2ToQsxba8rAbkeHXxISeTA7mt3sKNpEZ96+H4GfuSYYVUmHzaM9UAeTje6qJ1TAcBVdieLip2cmF/I6KAruV5opG+Ytw4VsPLWRynb9zb2FctQFYWD9g0ca06jvcPPmNPIe2+zW6mem0Gt9RjOdw+SUZQBQOm9t/MrZx1NJ/rxjXpmptIfQvSYsZdEy0mjj9g1ZwH333Ubgye+Sdfzr7H5b1ehC8Hp/DSq6l+i8xdvMHjCWGdQsrSYqnu20736Ad7qmE39ETcA/e2DeJxuYuGI9GsREvp8o2Psqw9y4y334Pvl3zB/1w8BWLnqH+hvG8DrHOOdeuPYrQ/fy+mfPUPa3lfwbPprZt9xH7oQBMNpRISV3L2/4sgeF5u/codxjowcIq4oh7O243WdmJmK/oGixzQioRBel04sGiMaNvYvCQQL8C26kd6WZrzO0IdizVc0HKH9lHEd77v6JtbelcriWTvJKDpM5+t9ALS+0Imnb4y5XX1ctbmLovlbACjKq6AxL5XuzlScA8aeIgGvn0gwTF5RJr7n/5Pln14KwLfrLYR8/stfQROT88AcaJiYmJiYmJiYmJhcBiRaEnlZkHyCycTExMTExMTExOTDgRBi2l4Xwte//nWqqqpITU1l3bp1HDhw4JzHfvvb3+aaa64hLy+PvLw8tm/f/r7HT4Y50JgC+xtSpclmoOly6QH5NMmmB+TTpCgw29khTQYTkM9GsukBIztPQVEJiiSR2rLaqKwgL/7bzCOjjeTTpNDprpJGj3z2MbJO9QfmSZMpUEYbyaRFNp577jmeeOIJPvvZz1JfX09dXR033ngjQ0NDkx6/a9cuHnzwQd5880327t1LZWUlN9xwA319fed9TikHGlMZbQH8/Oc/Z9GiRaSmprJs2TJeffXVCf9/4YUXuOGGGygoKEBRFBoaGi5Ilz3lwr7Uha4jdB3fqIcjh0c4XHInAY8vuafBhXKhei4lsmmSTQ9cWk2RUIihnhEaWlQaWlSc9jKKxQBLF6aQX1aAxWbDYrMR9gdpP+Vgp3MVwZs+zttii/E6lsKxoy76Okbwe4P4vUFyctOom+Mn58QuQqM+qm/bQPVtGzhe9VH21/sZ6R9O7n0wXcjWbpdbTywcwTngxDng5OAhNw1ldzD/tjU4jg0iXvop4uWfUXrwedqffZ2BhkFKl5dQuryEqntvoGv1g7zZNpvDh0fpbR2gt3XgsqzPmG4bRYJhOk4P8K6yhaprazj9052c/ulOti/ooXh2MZqm0do4QGvjAPUVH6Xy+jJO/XgHs2wDNGaspzFjPadDc5ib0sXpH71BwaocwpvvJLz5TkRXC4Wrc9l93E40HLlsezbI5tcwM5r0mEYkGMY/5sXRNYija5CTDQ7eaVBwdPRJtYfGxdhHj2m4h1y4h1y8fcTCbss2xq5/mOqHb2fZJ5aw7BNLSCu3M7R/lIbvHqD/R89Tsfv7VOz+Plsz9nHdyjArVxUyZ2EZcxaWkV+Sjz09jY11Gke+eYT8j3+M/I9/jKbjAzO6pkVGv5YZXZ++11T58pe/zKc//Wkee+wxamtr+eY3v0l6ejrf/e53Jz3+Jz/5CZ/5zGdYsWIFixYt4n/+53/QdZ0dO3ac9zmlG2hMdbS1Z88eHnzwQT71qU9x5MgR7rzzTu68805OnBhf4Of3+9m0aRNf/OIXL0rbitqwNNkMLKpcekA+TbLpARk16TjG/IA8j4Bks5FsesDIqNRjy0eocqS5lNFGCEF0rEeqzFyy2Ug2TQo6iypHpdEjm30AVFWgebukSa0ko41k0jIZ0xk6FQ6H8Xg8E17hcHjS80YiEQ4fPsz27duTZaqqsn37dvbu3Xte2gOBANFolPz8/POur3TNMdXR1le/+lVuuukm/vIv/5LFixfzj//4j6xatYr//M//TB7z8MMP89RTT00wromJiYmJiYmJicmVyjPPPENOTs6E1zPPPDPpsSMjI2iaRklJyYTykpISHI7zy4T613/918yaNWtK99NSZZ1KjLaefPLJZNkHjbb27t3LE088MaHsxhtv5MUXX7woLeFw+KxRYSwWwaLak38LAbpQUBUxYfNZXYAQCqoqJkQHK/Hc4ELoWNT476qIxxMqybIEiTjD947OjXKBoogJ79F0BRATjheArisoikBVzqN8inU6s9yijmu6sDpNVn7hdUroURWBxoXV6czy6ajT+HsvTTuRzD+voyDQdR1dH99CNFEnRRWoim6UK4ZPGrXDKI/H/quKQMEI/UuUGi8Foeso6KiXpE7jvj0T7WSUJ3xvXM/lvJ4SdlUUHRHfKVlYLOiKiq6Mr84QgK4a4nUUdD3erghUVY/XTSBUQSzed0y37xm2nuhjH1TXD24nw0eF0NAVNVlfXddRFT35f12oxtM91WLYR9eT/iyEQNcNuwmLBV0IVEUxfNliQUEzbCOMOb1L20dM1mcn6sp7jr88ffmZvn35rifjb+VMH8doSyXeb437/vRdTxdSp4R9Er59Ie00XkcdMHxVB/T43kVCHf+pqyp6XGlyJk7o8fcSt5EwLGaxoMcPGe+zL/93buK3M897udvp7HI5ZjHPhT6N8v7uySfPuge22+3nOPri+Od//meeffZZdu3aRWpq6nm/T6qBxvuNtk6fPj3pexwOx0WNzs7FM888w9NPPz2hbMu2h1i36eGkkwyOWGnrtjG3MkZJYSx5XM+AlZ4BG4uqo+Rmj8dNtvUIHF2DHG9MZXWtFzA6k8bWFNweC6uXhbFYxj2wodFOOKKwbsXEnZv3N6SSliooL9ZQ6sLoAjRNYf/RVHKzdWprxuNbg0GVI6fsFOVr1MyJJsvdHguNrSlUlMaoLBvXPtU6tXbZGHJaqVsYISNdZ1Zc04mWqdfJniJYUTs+uLvYOqkKzCrWqKqI0dKVckF1SksbDym6kHZ6b5103ahXbpbOwnnj2qeznYZGxwgNNpNhD9HSnUa53cXcrB6qF5RRltZBWopGOBjCYu3i6LF0UhZV0N/VhqoIVHeQUlsYZyiH3JJi5pf0MWd2mExnF01jULV8HiPX3MvQ6BjHTw+SG3WxpDLIvhHbtPleVUVsgm/PRDud6XtL50eSfu0PXJ7rafn8EBkZhp5UAuw5sZz5Nz1Eii+NI84IQlUQI0EsnQHKV89Cu+9+AA6X1NLVFKThpBNn7wC1swYNvyvRiMW4ZH2EqkB6mmHr6WqntcsDWFN66WyKUHnzPQz/7GlIS6W84Q2uWbGRdnuYaKyb+qYc9hzP5+ZPfpyBXx4m7eQ+nAUbUTMrEVoM3/HD9BUto+qRxZwK5ZORU4Ja/z+k//FD4DrKmqX+i+r3zrdOsRgT/Pp8fO9S9+X5OVrSt5s7L8/1NKFO4TDHWwXF+VHmZLuweXrJyfQyv9LCiRb7tH4/XUidEt8hFpULbidFNf4XHjzFO9HFjFbNorx0I/l3zAZgzsIjeH69k75AEY3hcrpPGXsSzRl5iatWdWIrXkpZSgoAwyU6zd0ZVImTdFy7mndiCwCozuvAm82MfOc2tdvITBesPcOvL3c7vbdOq5ZMHjokC2IaRxp2u/28BxaFhYVYLBYGBwcnlA8ODlJaWvq+7/3Sl77EP//zP/P73/+e5cuXT0mjImQJYAX6+/spLy9nz549XH311cnyv/qrv+Ktt95i//79Z70nJSWFH/zgBzz44IPJsv/6r//i6aefPsuYnZ2dzJ07lyNHjrBixYr31TLZjMbND+7Dak1J/j3VUbtQVLIK87l62wJ+//w+o0zXpXj6f6F1utxPwcw6fXCdUtLTmbt0Dgpw6w15rMk8icNSya/3ZXNkdxMo4HO6saWmsnDNAjauzabhhLE5X1+nC8+oBy0GxRWFqIpg46Yibit4F+W3z2GxqPhu/zRCwLMnF7NvVzujAyNEwrFLWqcPYzt9UJ2sNhsAOcX5rN0ynz9aWo/z85/D2eoyymdlMXRymFkrS5h9300AdCy/h7dbZ9Fw1E1/xyDBMeOmJbEQfKbrNNV2smdmsHbbUv5m3kvsvvtpUBSu+Y+P898Zf8GOX59E0zR0XWHzrcv4v5avsvOR73Ddjz/NrrmfQVFUVEVn04kv8+anf8S1/34nzVufQFFUMj/3Mco2reDx/v9D1/FW0/dmqE6oKorFhqIIUjPTyC3Mxdk3RMgfvGLrdNaMhtVYRzWrppI5C8pYXGOjrmyQ6qE9AITf2kHXzmP0v+sERaHkaiP2fe6WJWRccy2dFddwYqQMgFNtGl0dHr5y40He2vyXpO4/BMC//msjfrfvstXpA8slaKe3fr0FWfm7707fQOjzn5za7MW6detYu3YtX/va1wBjhnj27Nk8/vjj/M3f/M2k7/mXf/kXPv/5z/P666+zfv36KWuUakbjQkZbpaWlFzQ6+yAmGyXm5Vpxe+C96RJ1oZw5hzherk88TolfiQI1fjGDOOMY7T3Hj5dPVirIytRxe9T36FEmPV4IBW0SjecqP986TSwX5GZP1DS1Op2r/ELrdKaeC63TZBovpk6C3GwNt0ed9HOmq52EiIeaKBZUVUGJ9/aJ4zVdwRI/v6L5EFgABV2o6LpKYhpeF4rxGYqCIuJhV4qCooDAgi7OrMd0+R7kZp3t25e3nRIoaPrZfn05rqeEfl2Mn1fVdRRNQwDRjExg2Ah3i79PVVVQLPHjxz9D05Uz+ppL0UeM22g620nXFQQqqhK3gBAYnm1B15UzPlNBBZSYZvxU4uFUWgAFES8XKPFwFSX+ORP991L3Eefqs6e733uPxvet09m+femvpzP/UrBYDO26bvQ3mekaAe/F1On8yz+4ThPtcyHtJCZcx4Cioqhq8sY8ERSoxD9E0bT4p8bLk9f0eJUDMQGanvRno34z9Z0ryJmkv4aZ/M6V5vn5pMzk4/0nnniCRx99lNWrV7N27Vq+8pWv4Pf7eeyxxwB45JFHKC8vT67z+OIXv8hTTz3FT3/6U6qqqpLRQpmZmWRmZp7XOaVaDJ6SksJVV101IW1WIo3WmTMcZ3L11VeflWbrjTfeOOfxF0NtTUSabAYWVS49IJ8m2fSAfJoUBJZQrzT7MYB8NpJNDwAWC96aRaDKIUpKG6Gj+foRTH6TcrmR0UayaVIVwaKqoDR6ZLMPGDZyBHVpUivJaCOZtEyGrotpe02V+++/ny996Us89dRTrFixgoaGBl577bXkEoTu7m4GBgaSx3/jG98gEolwzz33UFZWlnx96UtfOu9zSjWjAVMfbf3pn/4pmzdv5t/+7d+49dZbefbZZzl06BDf+ta3kp/pcrno7u6mv78fgKamJsCYDbnYmY+pIHQdv9vLqaOOS5rP3uQPm2g4wmD3MADHW/OYt6aMIjFI7fx8utoKAQh4/ETDEXpbHPTmReg47UAXKr5RL5qmkZ2fw5x5eQCsrBgm/eQRYtmZ6Ou38dbQUgBONDjwjIyiazOXv/3DjBY1YqF9o2OcPDrIrtmrue3+Wxj67PcROvgdAeZeVcqcj95I+/J7AXizeRYNR5wMdAziH/Mm9ze5UvubSChEy8kBDq27lbk3GJkHm3/4W6772h9Rf6CQvpYeABqPOmj51McpWPUc7T9+mfJ/+RhC1wmExmj+8evkLs1kdNvDWDB8teUXHaT9w5cZ/qfJ06abXB6ErqNFjRj+kDeAS48xOyMGyJG2eTpI7G/hHHCiaTqesTycNcWMzL0FgKV3VrB47k7y5+6j/fcdDO4xQiPdbXupbu9nzvYuCldsBaB4ZR3HcguInDjOsk8u4Tv1xh11OBCcgZqZXKk8/vjjPP7445P+b9euXRP+7uzsvOjzSTfQuP/++xkeHuapp57C4XCwYsWKs0Zb6hlP8TZs2MBPf/pT/v7v/56//du/Zf78+bz44ossXbo0ecxvfvOb5EAF4IEHHgDgs5/9LJ/73OcuT8VMTExMTExMTEz+oJFoafRlQbqBBkxttAVw7733cu+9957z8z7xiU/wiU984qJ1BYOqNAEmArn0gHyaZNMDMmpSiGgpH3zYZUQ2G8mmx0Cg+PySBAVJaiMFrFYrkwaJzwAy2khGTYGQPHpktA8oWCOhDz7sMiGjjWTSMhniypxgvmCkHGjIypFTF5+bWIvGGOkbvujP0XVlWvRMJ7Jpkk0PXB5NekzDP2aspmxrcnJ6bhHrClwsKHRxqqYAAOeAC8+IizHnGHsO5eIfcxvv1TTs6WmUVOZTt9CYOaz21CMCftQV69hn28L+3T4AhnuGicazGU2rfsnabab1RIJhnH0j7KvPYdEN97PwtiMAjLb1Mvv+W2hdeh+7mo0Z34YjIzg6h5JhU5crZOpS2UiPaYw6nLx1rIK//NgdAOy876tcd/JnLKn7Pwz3GqFPI/3D7Gis49OfvJ5dj/+C6/7f1wFQhgfYscPB5q/exWuuRdxgM9bzOdJU3uyvJeTdM+2az1kXyfwa5NCU8FFN1wn6dI6clOfBx3TaJ+wPMurQCQfCBLxhRt1GaOrw/DUsX1fOnFlVZFftov2NYwAM7nFx6ictjHaNUrW1E4A1m6+jaNG1dN73cxb8/gs0vWDE0ifCs2YCGXzovZxrUbnJzCD5khm5KC6IJTfdm2kURUilB+TTJJsekE+ToggKsoLS6AE5bSSTHog/RcwrlObJnZQ2EuBR7AhVjpsOGW0kmyZTzwcjhEBUzZrRzEVnIqONZNIyGboQ0/a6EjAHGlOgZk4USb6zUBW59IB8mmTTA/JpUhWoLPZN3F18hpHRRjLpAWNPHl95lTQDDSltBAxZMqXJzCWjjWTTZOr5YIQQ6FfVmtf++yCTlskQQkzb60pAjh7YxMTExMTExMTExORDhblG4zIjdJ2w30xFZ3JpSaQ1He4d5nhTHtVXl1CoDrN0gREX3NVeQNDrIxoOEwlGiAaNTZFs9hTySvJZtDiHZQUdAKS09KKXz6Wx8DrePajS1WTEBQc8vhmNDf5DQeg6AY+PjtMD7Kycx2N3PYAuBDl9w7Qu3s5bTSUcbXACMNDhIODxX9b1GZeacCDIqaMOGj95HwAVW56j40cvse3zj3LskLGL8lC3g4b6IYYefoy08pcYevaX6KqKs2gBOYszcF3/KAPNClrDCwAs/9QKvnB4zEzNLBkf5v5Ei0YJCx1d04iGI/i9xn3A2FgBQ/PKWT7ndpbcOYfayt8DkDdnP62vdOLYPYKr0VhLNL97kOpt3bTrsC/zZlwDjTNWH5ML50L2v7iSMQcaU8DtsUgzXSmQSw/Ip0k2PSCfJgGM+eTRA3LaSCY9CdKJ4ZtpEXGktJEAq2cMXRJVMtpINk2mnvNBoDicMy0iiYw2kknLZFwhEU/ThjnQmAKNrXJlw5BJD8inSTY9IJ8mXVc43ZmOrsdmWkoSGW0kkx4AVVGYpQRokWT9gZQ2EjpZLafwaHLM7MhoI9k0mXo+GBVQ365HUcxr/1yYWafkQg5PvUKoLItKk81AUYRUekA+TbLpAfk0KYqgvDgsjR6Q00Yy6QEja4lL2KVZDCijjYSiECyrkCrrlGw2kk2TqeeD0QF9STVCks0YZLSRTFomQ+hi2l5XAuaMxhSoLIvRP2hFu8i21aLRi9aiKtOnZ7qQTZNseuDyaUrE5wc8ftqbRzhVXcbGWS5qClwAzFtYgHPAhW8kTFlBkD5HJpqukJmXTdX8QuqqAhS7mgHQ07LoLt/Au425NJ3ow+tyG+WXKL5dtnaTQY+uaXiG3RytH+bdiusQQiO98CQNjYUcPzaCo2sQAL/bixaNXfb1GZfSRnpMwzkwws4TywF4/OGb2fHId9n2v37BouWPAOAccDLUPcSOzmV89NMb2fO5nQirBf3u69j8yW38un8xBTmCYz/eC8CqH3yenu85LqudZPCj9yKbpg+7Hj2mEdHDaDGNWNSYRY6EInjdubhG8xict566TRUAzK6oInv2m7TvaGTkkBuAkz84jbPbTckj23npiLF+aaaRrc1A/qxTV0pa2unCnNEwMTExMTExMTExMZl2zBkNExMTExMTExMTk8vAlRLyNF2YMxpTYHDEKk22ACHk0gPyaZJND8inSQgYcsqjB+S0kUx6DBRS0jIBOWIEpLSRECjtfTOtIomMNpJNk6nnPBCCdK8L89o/NzJpmQxzjYbJOWnrts20hCS6UKTSA/Jpkk0PXH5NsXAE14CTxuYCaoqKKLM6AFg6L4/O1kICY17aehQycjIBKJldyLJFKcyzNaN6QgCMlC3jQG8Fx4+N4BwYIRrfo+NSxbfL1m4y6BG6TiQUwtE1yDv1OQAUFi7gRMMwgz1D+N1egBlZnwGX3kYhb4DjDUMAdD76MUo2vEjPD3/Jlr99EICTR/Jw9g1x6NAod9z5CZTPv4mCINPbieemz3NiR5BPXz9Ec6sR0/5uyvV4nEcumd7JkMGP3otsmv4Q9AhdR4vGCGl+wFizGQmFCXiDjI7mM1IzG4Dls++m9t45LJu7g+6KfQC0v9KNY9cgW/5hM82/HJRi3xHZ2gwMTSbyYM5oTIF5s6OokmQzUBUhlR6QT5NsekA+TaoimFcZkUYPSGojifQAKOik40BBjswzMtpIWFQiixZKk5lLRhvJpsnU88EIi8pwWJ7MSjLaSCYtk6GL6XtdCZgDjSlQUhhDkWSgrChy6QH5NMmmB+TTpChQXBCTZRYekNNGMulJYMctjSYpbaQoaLPKpBloyGgj2TSZes4DRcETBUWSbelktJFMWibjDy10yhxomJiYmJiYmJiYmJhMO+YaDROTDzFC1wl4fHS2DHOquoKiOU4A5uU5mbegANfACBarn7ySPHShsnBxHkuKh8j0DOLNmwPAUc986o/5cHQOEfIGpIgL/kNEj2kEPD5aGwdQFZ2sGj+O7ih+tx8tnpN/JtZnXA60aJShbmONxo7m5Xzi49vY9Zmfs/V//QaA+UvuYtThpK/dwY6xtWz+36vQFZWRpVW82b+YYMhPxf6fErvdiH//cb0gFl9rZGJyuRG6TmK/vSgRtJhGNBwlFAjjHcsDYGQ0l6F5m1i+tZKqMmNvjbzqd2jd1UKPrQaXwzlT8k0uEllmWS8X5kBjCvQMWKWJidOFXHpAPk2y6QH5NAkU+obsiDR55ppls5FsegCEUBjxZSNEaKalAHLaSBGC3JAbWeICZbSRbJpMPeeBrmNJzUeI/plWAshpI5m0TIYuu8BpxgydmgI9AzaEJNkMhFCk0gPyaZJND8inSYj4QEOSmzGQ00Yy6QFjgDjiy5FGk4w2UhDkhcZAkeNrTkYbyabJ1PPBKEJgSSuQRpOMNpJJi4k50JgStTURVFWOkaiqCqn0gHyaZNMD8mlSFcHCqgCqIk/IjXQ2kkwPgKroVOYNS5NdRUYb6YqKI7OYZIzKDCOjjWTTZOr5YISqEvP1SaNJRhvJpGUyhBDT9roSMEOnpkButoaCHPmiFeTSA/Jpkk0PzIwmLRpj1OHiVFMB80tKAKhM6WXpvAI62/IpsjvJzi5EoLKsKkJJpIeIPYtmbQEAh04Kulsc+Me8aNHoJdcrW7vJpMdoS6fxRVrkJ+i1oUW1GV+bcTlsFPQZ+w4cqR/h1vsfIaf2VYZ/8iwAm//P3TQdzcM95GLfYT+3PvBxdCHoi2ZSf9BDRUU2Td96lUWPPwBAy1sDl91mMvlRAtk0/SHq0WMaQheE9WByTw2AgDfEmDufoXnVrKy+H4BFpXNZNvdNftwTJBIIXjJNU0G2NgNZgiXPzZWSLWq6MGc0TExMTExMTExMTEymHXNGw8TExMTExMTExOQy8Ic2o2EONKZAa5dNmmwGupBLD8inSTY9IJ8mXSj0DGVSlC3PZLN8NpJLD4AQ0OXIQBdypGiV0UYKkJuZIU2iAxltJJsmU88HoyBwR4vRhWumpQBy2kgmLZOhXyFrK6YLc6AxBYac8phLCEUqPSCfJtn0wMxoErpO0Oenq3WIUzXG3hilc1Oozh1m/sJi2lstLFmYBsDczA6UkEZvyjzqT6UD0HqqH8+w+7LtOyBbu8mkR+g64XhsdnfIIsX6DLg8NkrsFTLQ4WBn91I++vA17H7yNQA2f+p1frdoGw1DLrqaBti96XoAht02hnq7uP8GK31vDqH954MAjD7XcUm1ToZMfpRANk1/qHqErqPpOkLoaPF9imLRGKFACO9YPi53DgCD87aybPscGr7hR4vO/HUP8rUZmFmnZMNcozEFVi4OS5PNQFWFVHpAPk2y6QEZNenMyeuXJjMPyGcj2fSAoaluQUAaTTLaSAidlHC7NBnVZLSRbJpMPR+MrgsWlI1Io0lGG8mkZTKELqbtdSUg1zBUctLSdEkm4Y2wAJn0gHyaZNMD8mlSEKRYooA8HZZ8NpJLD8Q1pcqjSUYbgUDRI4B9poUActpINk2mnvNBYE+JSaNJRhvJpGUyrpS0tNOFVDMaQgieeuopysrKSEtLY/v27bS0tLzve95++21uu+02Zs2ahaIovPjii2cd87nPfY5FixaRkZFBXl4e27dvZ//+/ZeoFiYm8qFFY7iHR2k87aPxtI++UAlZjLGoSie/IJWFxWMsKnGTGXXjSp3F0b5CTp5wcfKEC9eAk0goJEWIjomRDtNIian/QbVJor5+t5cDB0cJfuQT2EtSsJekMPbTH7NptZ2sghx8ox7eOqTxzmGdvoEQqqpw1eCLFKzKYcfxXHYcz02Gn5mYyIQe09CiMbRojJA3gGfYTV+bg5NHBzl5dJB3DkbZ2T2fkC8w01JNTM4bqQYa//Iv/8J//Md/8M1vfpP9+/eTkZHBjTfeSCgUOud7/H4/dXV1fP3rXz/nMQsWLOA///M/OX78OLt376aqqoobbriB4eHhS1ENExMTExMTExMTk7PQdTFtrysBaUKnhBB85Stf4e///u+54447APjhD39ISUkJL774Ig888MCk77v55pu5+eab3/ezH3rooQl/f/nLX+Y73/kOx44dY9u2beetsbE1BU2SB4iaLpcekE+TbHpARk0qXlEBSmymhSSRzUay6YGEJrs0mmS0kUBhwFsiTWYuGW0kmyZTz/mg0tSVgabLMSsno41k0jIZV8raiulCmoFGR0cHDoeD7du3J8tycnJYt24de/fuPedAY6pEIhG+9a1vkZOTQ11d3TmPC4fDhMPhCWWj7hhWawqJeHYhjPSgqiJQzggK1IWR9UBVJyZWPFe5cVEoWN6zgClxsVjeM++UKPf61Pj/RLxcAcSE4wWg6wqKIlCV8yi/yDolNGm6uKA6nV1+cXXy+lRUxdA5U+303nK3xxKv0/jnXOp2EopxLgV9/P3xsJsoGSDcCCHQhTDKhQAEqiJQVUOroopL1k4T6zTRt2eqnc6sU0KPQFzW6+n96uT1q4DOe+1+vnX6QO1TrNOYVwUuRzsJVEVH1wXCakGJaUYPKDRURY+fR0egEoyloSphQ4PFEreXiqK85/q7TH352X32uep6+fryhCZdiMt2Pb2fdq9PRVVB17ms19O5tHt9iT8mu86mv52UhKZ4n60qIpnQINF/j/msqKqxxu5C6vT+5VOv05h3ol/P9L3Re8tNZhZpBhoOhwOAkpKSCeUlJSXJ/10ML7/8Mg888ACBQICysjLeeOMNCgsLz3n8M888w9NPPz2hbMu2h1i35uFkjubBEStt3TbmVsYoKRx/ItwzYKVnwMai6ii52VqyvLXLxpDTSt3CCGlp40PuxtYU3B4Lq5eFsVjGL6iGRjvhiMK6FRNDx/Y3pJKWqnPL5gD9Q1Z0AZqmsP9oKrnZOrU140/wgkGVI6fsFOVr1MyJJsvdHguNrSlUlMaoLBvXfjF1ykjXmVUco3/IyomWqdfJniJYUTs+uLvYOqkKzCqOcaTRTktXyoy003vrZNzDK7R2Wlk4b1z75WgnpydAeqgZe0qM3s5cbPkeKrJ7UPPDiFEXAkEXKkMhP00tYwz1DLJinou8qhC6pl2ydnpvnebNjrKyNpz07ZlopzPrtHR+JOnX/sDlu57er04J3371rXSCIfWyXE/vVydVgfQ0nR170ll4idtpZa2XVLWRd3sLmf9X91PhaGX/j05TdddOrlldxlCvB8V9ktaBfFZU9bJoWR7HDjaT8ZmP4+k+RberkFmF4cvSTmfWKRaDO6/3J/16JtrpvXXKz9GSvt3ceXmup/erU8KvG1tTONFiv2zX07nqlNDz4hsZWK1c9nbqGxbMn+2jINMI8/7/27v3uKjq/I/j7zMjNxFQkasIXvCClZiGrtYvbzxAMxG3VdfcvLW5GrReNk0rQ9va0s3d6qGrqxlutabrPkJ302BNUze8YoJ3ErzGVVRABhlgzuf3BzE6OjCgM3M+bp/n4zGP4nCYec2ZrzPznTNzxrXKDWdPBiOyyzVUV6twd7vV7szb6fbrlH3OBcMGVqLypo7FcyM3V0KfhyxfJObmp/ZhcIU0usZ///vf8Zvf/Mb887Zt2zB48GDk5+cjKCjIvHzcuHFQFAWbNm2yeZ6KoiAlJQXx8fF3/c5gMKCgoAAlJSVYu3Ytdu3ahYMHD8Lf39/qeVnbo7F46W5knvL6ccav7axdryP87NEqHM5yN/dovUejhY4QFVnXVF2rNPs6WV9+79dJ/2PPoUx31Jh0LF5d0esIj/Uy4mCmG24/NoYzbiddC1cEdQ4EAAyODsPgLpdRrbrgYn4pegYBOp0OqqLHidKO+G9GDbIzL0KnI1RcLTN/h4YzXoF10avo1/vW2NZ6j4Zri1vjulZVWOzRqB/bB47WbSOt92jUjesqHMz0QN2+zOZfJ9vtdf9t0UIHn0BfjH+2O6K/ngkdqTi4fB8GfPsXvLm3D04d+h5tAtoCACLaF6DKMxIvZDwHv8d64qX8BBDpcD4r2+mvlFu/z27oujrnvvz2++wak6L5Hg3zfXaWO2pqdZrv0bD978yxt5OibwEXtxZw9ag7cpqXjxfaBbeG183vcCjTBap66w+02qOhAOjf+yYyjt0a11rv0XBtQdiVMgRcTZh/yW7n9fmyULudl6NotkcjLi4O/fv3N/9c/6S+qKjIYqJRVFSE3r173/fleXp6Ijw8HOHh4fjZz36Grl27Yt26dVi4cKHV9d3c3ODmZnlYxBYtXGFSFfM/pnoqKfV7DC2X37GereV3nu+t5VYXg0ix0qNYXZ9IgclKY0PL7+U6mW5rqn+q0dzrZH35vV8nIqXuukC726nh63T3+Tj8dqIf75kVPXQ6HZQft41Op4NOpwMUHRRFD6AWQN2Tamtj3t63053XydrY1u52ujWuVfMDqeP/PVlvvG1ySvX/zqxv97r1rS111H2E0mi7/W4nBaqqqxvDP37/i0IEnaKAoIOqKlBJ9+PbTeqW6VQVOhDox/HvzNvpdtbvs519O/24/I777PovOXPGv6fG2ols/ztz5n257X9njrudlB/b6/vr7r9v3U7W+p39mFs3CbA+rnk95gqtaDbR8PLygpeXl/lnIkJgYCB27txpnliUl5fj4MGDmDlzpt0vX1XVu/ZYCCGEEEII4SgPytGi7IXNR2YURcHs2bPx1ltv4V//+heOHz+OSZMmITg42OKtUMOGDcOKFSvMP1dUVCAzMxOZmZkA6j5UnpmZiUuX6nZNGQwGvPrqqzhw4AAuXryII0eOYNq0acjLy8PYsWOb1Zh5itdRXjj1APyauPUA2japJhNKi6+jtPg6Tp+5gbxKP7RSbqBjsCd0ICik4lJVMLLOEi7lFKOy7AYqy27AVOPcI1Jxu9249QD8mpzZQ6qKiuvl2J9xA/pnJkH/zCQAQPXm9RgQ1Qqerb1QVlKK61fKkJXdEj971A2nPs1GSfRUXLl8BVcua3NYc263GcCvSXosqbUm1BqrUXWjElU3KlFafA2Xc4tw9ISLbKNGcGqxhojsdnoQsPkwOADMnz8fBoMB06dPR2lpKZ544gmkpqbC3d3dvE5ubi5KSkrMP2dkZGDIkFvvxZs7dy4AYPLkyVi/fj30ej3OnDmDv/3tbygpKYGvry+ioqLw3//+Fw899FCz+ozV1nffaYVbD8CviVsPwK9Jp9Nb3b2tJW7biFsPwK+JWw8AVNeyeS0NAM9txK1Jemzj1sStR/DC6l5YURS8+eabKCwsRFVVFb7++mt069bNYp0LFy5g8eLF5p8HDx5sdZa3fv16AIC7uzu++OIL5OXlwWg0Ij8/H1u3bkVUVFSz+/r3rmJz2DS9jlcPwK+JWw/Ar4mIcO1KAatXRrhtI249AL8mbj0AoNMBfbqVAuDx8ibHbcStSXps0+mIVRPHbcSpxRpSyW6nBwGrPRpCCCGEEEL8r3pQJgj2wnzeJ4SwF1JVGA03YTTcRN65Kzh52R1VSku4oBpXyR8l5I8Tl1oiN/sqSouvo8ZYjRpjNUjl8YqwEPVqjdW4/H0hdhufwG7jE4h8IRJHVx/G0NZHENw5AGqtCWqtCXoXPf7P/QBaeLfA1xe7obK8ApXlFVrnC9FkpKow1dTAVFOD6qoqGK7f0DpJiGaRPRpCCCGEEEI4gUo/rRfvZI9GMxzMdGdzNAOTyqsH4NfErQfg16QoCgLa+cLiW5U0xm0bcesB+DVx6wEAVQWycn3BZWRz3EbcmqTHNm5N3HqAB+CoUz+xz2jIRKMZ3Fx53ajcegB+Tdx6AH5NJoZvjeK2jbj1APyauPUAgKsLr7HNcRtxa5Ie27g1cesRvMhEoxl69zSyOZqBXserB+DXxK0H0L5JNZmgmkwoK7mOM2fKcbmiHQquGfD9ldbIvtIWp0+X4coPV2A03DS/z93ZtN5Gd+LWA/BrcnYPqSpuXCtF+hEj0o8Y4fPsr1BbXgvXf69H/6jWcG/VEi29PRDVqxqmLz5Fn5n9kJFx3Tz+tcDtNgP4NUlP49RaE6immlUTt20EyFGnuJHPaAghhBBCCOEEnA4n7wzM531CCCGEEEKIB5Hs0WgGk4nLxwrrcOsB+DVx6wH4Nel0Oi7faWbGbRtx6wH4NXHrAQBV5fVaGsdtxK1Jemzj1sSthzuV4eciHUmhn9o+nPvwxKg9WicIYRd6Fxf4hvjjyegu6BFag/9+V3c3cDrzMq4XXkWtfH+GYE7R6eAXFgQAmD4tFD1WjMX5PRcQvv1zvLk5AADQr58vOk58GFFpK/DcqgBczy/WMlkI4STf/nuQ1gkNevqFU3Y7ry/X9rTbeTkKr5d7mGvtbQLAZV5GzHoAfk3cegB2TUSgGgPY9ABgt43Y9QD8mrj1AABBr1aA2By6mec24tUkPbZxa+LWA/BqETLRaIae4dVsjmag1/HqAfg1cesBODapMBnyoTC6Y+a2jbj1APyauPUAgE4huNf+wOYQNBy3Ebcm6bGNWxO3HoDNP/kGEal2Oz0ImN8cQghHMNXU4MbVMnx/thxXSnW4mFOCC2dLcONqmbxtSjwQSFVRfqUU5VdKsfcIIeBXv0TlhSq0Tk1G376+ePTRtujU+hqqr9Vgd83/oeJ6udbJQgjxkzu8rUw0hBBCCCGEEHYnR51qhps3dWzeYELg1QPwa+LWAzBsIsAEN60rLHDbRtx6AH5N3HrqKGjRgs9DHMdtxK1Jemzj1sStB+D/CY0HZU+EvfC5F34AHD3N5wmZqiqsegB+Tdx6AH5NBB3K0Akq5WudYsZtG3HrAfg1cesBACg6+Pr6oliDb7e3huM24tYkPbZxa+LWA9Q1caY+IJ+tsBd561Qz+PvWQlF4zEQVhVj1APyauPUAvJqMlTdRdLEYRRcu43pBCa4VXIWx8qbmn8/gtI0Afj0AvyateqqrqlBdVYWcUwU45Dca4T/viKyPdmJY+EUM7XIBHns3ofuErvg2w4haY7VT2+7E7TYD+DVJj23cmrj1AGDVImSi0SzhYTXQMZko6xRePQC/Jm49AL8mRSH4e12FwuiegNs24tYD8Gvi1gMARIQrbq1BTKI4biNuTdJjG7cmbj0AWLVY81P7MLi8dUoIIYQQQggn0PpdA87G6HVMIYQQQgghxP8KmWg0Q2m5ns3RDAi8egB+Tdx6AF5Naq0JN66VoyC/FjcrDLhZYYDK4IOznLYRwK8H4NekVY9aa4Jaa8L1wqvYfdQFYZOfQdkpA9r/dz3ap3+KqzszEPzcOJw7na/5q4jcbjOAX5P02MatiVsP8GAcdUreOiWsOpXjqnWCmaoqrHoAfk3cegB+TaqqILfAG6p6VesUM47biFMPwK+JWw8A6EDwOHECOmWI1ikAeG4jbk3SYxu3Jm49AP+jTj0o3+htL7JHoxk6BNWwOZqBohCrHoBfE7cegF+TohAC2xjY9AA8txGnHoBfE7ceAFABVIeGgsuLfhy3Ebcm6bGNWxO3HkCOOsWNTDSaoUNQLZujGegUXj0AvyZuPQC/JkUBAttWsukB+G0jbj0AvyZuPXUUVIeFaR1hxnEbcWuSHtu4NXHrAfgfdUpVyW6nB4G8dUqIn7CaqipUV9XAVNMCJua7m4VoiLHyJs4cK8Dx58chZNhnOPnRVyC9HpUhj+JowGiUX/le60QhhAAgR50SQgghhBBCiPsmezSaoaikBYjJnioiXj0AvyZuPQC/JiLgyjUXEJcg8NxGnHoAfk3cegAARFDO5QFMdtRx3EbcmqTHNm5N3HoAsGqx5kE5WpS9yESjGXIvuWidYKaSwqoH4NfErQfg16SSgvP57lDpptYpZhy3EacegF8Ttx4AUFQVuoxTUJh87T3HbcStSXps49bErQeoa+JMjjqlISLCG2+8gaCgIHh4eCA6Ohpnz55t9G/eeecdREVFwcvLC/7+/oiPj0d2drb59xcuXICiKFZPmzdvblZfl9Aa6JgczUCnEKsegF8Ttx6AYZOpFh38yqGQic37RrltI249AL8mrXvUWhOuFlzF11le6P78aBQfvI6ijDK0nvwz7MpyRXVVlSZdt9N6G1nDrUl6bOPWxK0HAKsWjlauXImOHTvC3d0d/fv3x6FDhxpdf/PmzejRowfc3d3xyCOPYPv27c26PFYTjWXLluHDDz/E6tWrcfDgQXh6eiI2NhZVjTxI7NmzBwkJCThw4AB27NiBmpoaxMTEwGAwAAA6dOiAgoICi9OSJUvQqlUrjBgxoll9Ae1qoTCZKCsKrx6AXxO3HoBfk6IA/r58egCe24hTD8CviVsPAEBRUO0fAIXJ13dx3EbcmqTHNm5N3HoAsGqxRssv7Nu0aRPmzp2LpKQkfPfdd4iMjERsbCyKi4utrr9v3z5MmDABzz//PI4ePYr4+HjEx8fjxIkTTb5MNhMNIsL777+P119/HaNHj0avXr3wySefID8/H1u2bGnw71JTUzFlyhQ89NBDiIyMxPr163Hp0iUcOXIEAKDX6xEYGGhxSklJwbhx49CqVSsnXTshhBBCCPFTR6pqt1Nz/elPf8ILL7yAqVOnomfPnli9ejVatmyJjz/+2Or6H3zwAYYPH4558+YhIiICv//979GnTx+sWLGiyZfJ5jMa58+fR2FhIaKjo83LfHx80L9/f+zfvx+//OUvm3Q+ZWVlAIC2bdta/f2RI0eQmZmJlStXNno+RqMRRqPRYlltbTX0Ojfzz0R17wXUKWQxg1YJIFKg05HF5xAbWm5SAUCBXmc5OzX9OIb0d0wH65YTFIUs/qbu8KRksT6h7lsyFYUsji3d4PL7uE563a2me7tO1pbf+3Wq79EpBBO0up0sl9/6W+1uJ8sXe+iOruZfp7rl9h57t8a2FreT5XW61aPV7XTndapvunX7Nfc6NaG9GdeprquuRavbqe78TVABUAu9+TooUOu2V2O3n1Puy63dZzd+nRx9H3H72Hbev6eG28332TqCSbPb6Va77X9nzn/Mrf8yOp3Vduc/5tb/3+2Xq+1zo7sfz/6XWXuu6ubmBjc3t7vWra6uxpEjR7Bw4ULzMp1Oh+joaOzfv9/q+e/fvx9z5861WBYbG9voDoC7EBPp6ekEgPLz8y2Wjx07lsaNG9ek8zCZTDRy5Eh6/PHHG1xn5syZFBERYfO8kpKS6u9dzKdBgwZRVVVVk1ocraqqipKSktj0EPFr4tZDxK+JWw8RvyZuPUT8mrj1EPFr4tZDxK9Jemzj1sSth4hnk6NYe66alJRkdd28vDwCQPv27bNYPm/ePOrXr5/Vv3FxcaENGzZYLFu5ciX5+/s3uVGzicZnn31Gnp6e5tPu3bvve6IxY8YMCgsLo8uXL1v9fWVlJfn4+NB7771n87yqqqqorKzMfLp8+TIBoLKysia1OFpZWRmrHiJ+Tdx6iPg1cesh4tfErYeIXxO3HiJ+Tdx6iPg1SY9t3Jq49RDxbHKUO5+rlpWVNTjB0mqiodlbp+Li4tC/f3/zz/W7foqKihAUFGReXlRUhN69e9s8v8TERHz55ZfYu3cvQkJCrK7zz3/+E5WVlZg0aZLN82to15MQQgghhBBaa85z1Xbt2kGv16OoqMhieVFREQIDA63+TWBgYLPWt0azD4N7eXkhPDzcfOrZsycCAwOxc+dO8zrl5eU4ePAgBgwY0OD5EBESExORkpKCXbt2oVOnTg2uu27dOsTFxcHPz8+u10UIIYQQQgiuXF1d0bdvX4vn2aqqYufOnQ0+zx4wYIDF+gCwY8eORp+X34nNh8EVRcHs2bPx1ltvoWvXrujUqRMWLVqE4OBgxMfHm9cbNmwYxowZg8TERABAQkICNmzYgK1bt8LLywuFhYUA6j5I7uHhYf67nJwc7N27t9nH/xVCCCGEEOJBN3fuXEyePBmPPfYY+vXrh/fffx8GgwFTp04FAEyaNAnt27fHO++8AwCYNWsWBg0ahOXLl2PkyJHYuHEjMjIysGbNmiZfJpuJBgDMnz8fBoMB06dPR2lpKZ544gmkpqbC3d3dvE5ubi5KSkrMP69atQoAMHjwYIvzSk5OxpQpU8w/f/zxxwgJCUFMTMw9tbm5uSEpKYnN26m49QD8mrj1APyauPUA/Jq49QD8mrj1APyauPUA/JqkxzZuTdx6AJ5NXIwfPx5XrlzBG2+8gcLCQvTu3RupqakICAgAAFy6dAk63a03Ow0cOBAbNmzA66+/jldffRVdu3bFli1b8PDDDzf5MhUi+ukcB0wIIYQQQgjhFGy+sE8IIYQQQgjxv0MmGkIIIYQQQgi7k4mGEEIIIYQQwu5koiGEEEIIIYSwO5lo2LB3716MGjUKwcHBUBQFW7Zs0bRn1apV6NWrF7y9veHt7Y0BAwbgq6++0rRp8eLFUBTF4tSjRw/Nejp27HhXj6IoSEhI0Kzpxo0bmD17NsLCwuDh4YGBAwfi8OHDTrt8W+P4iy++QExMDHx9faEoCjIzMzXtWbx4MXr06AFPT0+0adMG0dHROHjwoKZNU6ZMuWtMDR8+XLMea2NcURT88Y9/1KypqKgIU6ZMQXBwMFq2bInhw4fj7NmzDut55513EBUVBS8vL/j7+yM+Ph7Z2dkW66xZswaDBw+Gt7c3FEVBaWmppj2/+c1v0KVLF3h4eMDPzw+jR4/GmTNnNG0aPHjwXeNoxowZmvRcuHChwbG9efNmTZqAuiNejhkzBn5+fvD29sa4cePu+iIze7H1OO/MMd3UJmePa1s9zhzTonEy0bDBYDAgMjISK1eu1DoFABASEoJ3330XR44cQUZGBoYOHYrRo0fj5MmTmnY99NBDKCgoMJ++/fZbzVoOHz5s0bJjxw4AwNixYzVr+vWvf40dO3bg008/xfHjxxETE4Po6Gjk5eU55fJtjWODwYAnnngCS5cuZdHTrVs3rFixAsePH8e3336Ljh07IiYmBleuXNGsCQCGDx9uMbY+//xzzXpu7ygoKMDHH38MRVHwzDPPaNJERIiPj8e5c+ewdetWHD16FGFhYYiOjobBYHBIz549e5CQkIADBw5gx44dqKmpQUxMjMXlVVZWYvjw4Xj11Vcd0tDcnr59+yI5ORmnT59GWloaiAgxMTEwmUyaNQHACy+8YDGeli1bpklPhw4d7hrbS5YsQatWrTBixAhNmgwGA2JiYqAoCnbt2oX09HRUV1dj1KhRUFXV7j22HuedOaab2uTscd2U50LOGtPCBhJNBoBSUlK0zrhLmzZt6KOPPtLs8pOSkigyMlKzy7dl1qxZ1KVLF1JVVZPLr6ysJL1eT19++aXF8j59+tBrr73m9J7GxvH58+cJAB09epRFT72ysjICQF9//bVmTZMnT6bRo0c75fKb0nOn0aNH09ChQ50TRHc3ZWdnEwA6ceKEeZnJZCI/Pz9au3atU5qKi4sJAO3Zs+eu333zzTcEgK5fv+6UFls99bKysggA5eTkaNY0aNAgmjVrllMuvyk9d+rduzdNmzZNs6a0tDTS6XRUVlZmXqe0tJQURaEdO3Y4pcna47wWY9pWUz1nj+s7e7Qc08KS7NF4gJlMJmzcuBEGg6FZXwfvCGfPnkVwcDA6d+6MiRMn4tKlS5r21KuursZnn32GadOmQVEUTRpqa2thMpksvngSADw8PDTd8/OgqK6uxpo1a+Dj44PIyEhNW3bv3g1/f390794dM2fOxNWrVzXtqVdUVIRt27bh+eef16zBaDQCgMU41+l0cHNzc9o4LysrAwC0bdvWKZdni60eg8GA5ORkdOrUCR06dNC06e9//zvatWuHhx9+GAsXLkRlZaWmPfWOHDmCzMxMp47tO5uMRiMURbH4Ajh3d3fodDqHj21Oj/P1bDU5e1w31KPVmBZ30Hqm8yABkz0ax44dI09PT9Lr9eTj40Pbtm3TtGf79u30j3/8g7Kysig1NZUGDBhAoaGhVF5ermkXEdGmTZtIr9dTXl6eph0DBgygQYMGUV5eHtXW1tKnn35KOp2OunXr5vSWxsYxpz0a//73v8nT05MURaHg4GA6dOiQpk2ff/45bd26lY4dO0YpKSkUERFBUVFRVFtbq0nP7ZYuXUpt2rShmzdvOryloabq6moKDQ2lsWPH0rVr18hoNNK7775LACgmJsbhPSaTiUaOHEmPP/641d87+9XfxnpWrlxJnp6eBIC6d+/utFd9G2r661//SqmpqXTs2DH67LPPqH379jRmzBjNem43c+ZMioiIcHhLY03FxcXk7e1Ns2bNIoPBQBUVFZSYmEgAaPr06Q7paMrjvLPHtK0mZ4/rxnq0GtPibjLRaAYuEw2j0Uhnz56ljIwMWrBgAbVr145OnjypdZbZ9evXydvbW9O3c9WLiYmhp59+WusMysnJoSeffJIAkF6vp6ioKJo4cSL16NHD6S0PykSjoqKCzp49S/v376dp06ZRx44dqaioSNOm2+Xm5jrt7Vy2erp3706JiYkO77idtaaMjAyKjIw0j/PY2FgaMWIEDR8+3OE9M2bMoLCwMLp8+bLV3zv7SVljPaWlpfT999/Tnj17aNSoUdSnTx+nTBJtbaN6O3fudMrbXmz1VFZWko+PD7333nsO7WhKU1paGnXu3JkURSG9Xk+/+tWvqE+fPjRjxgyHdDTlcd7ZY9pWk7PHdXOeCzlrTIu7yUSjGbhMNO40bNgwh72qcq8ee+wxWrBggaYNFy5cIJ1OR1u2bNG043YVFRWUn59PRETjxo2jp556yukND8pE407h4eH0hz/8wfFB1PSmdu3a0erVqzXt2bt3LwGgzMxMh3c0tam0tJSKi4uJiKhfv3704osvOrQlISGBQkJC6Ny5cw2u48wnZU3pqWc0Gqlly5a0YcMGNk0VFRUEgFJTUzXt+eSTT8jFxcU8lhytKU1Xrlwxj6GAgABatmyZU9qsPc5r/RmNxp57OGtcN7XHGWNaWCef0fgfoKqq+f3RHFRUVCA3NxdBQUGadiQnJ8Pf3x8jR47UtON2np6eCAoKwvXr15GWlobRo0drnfTA4DbOf/jhB1y9elXzcb5u3Tr07dtX88+v3M7Hxwd+fn44e/YsMjIyHDbOiQiJiYlISUnBrl270KlTJ4dcjiN7qO4FP4eN7Xtpqj+8tSPGdnN61q1bh7i4OPj5+dm9416b2rVrh9atW2PXrl0oLi5GXFycQ9vqcbv/AxpvcvS4bm6PI8e0aFwLrQO4q6ioQE5Ojvnn8+fPIzMzE23btkVoaKjTexYuXIgRI0YgNDQUN27cwIYNG7B7926kpaU5vaXeyy+/jFGjRiEsLAz5+flISkqCXq/HhAkTNGtSVRXJycmYPHkyWrTQfpjXH+6ve/fuyMnJwbx589CjRw9MnTrVKZdvaxxfu3YNly5dQn5+PgCYjyEfGBiIwMBAp/b4+vri7bffRlxcHIKCglBSUoKVK1ciLy/PoYcobqypbdu2WLJkCZ555hkEBgYiNzcX8+fPR3h4OGJjY53eU3/fU15ejs2bN2P58uUOaWhu0+bNm+Hn54fQ0FAcP34cs2bNQnx8PGJiYhzSk5CQgA0bNmDr1q3w8vJCYWEhgLqJjoeHBwCgsLAQhYWF5u7jx4/Dy8sLoaGhdv/QuK2ec+fOYdOmTYiJiYGfnx9++OEHvPvuu/Dw8MBTTz1l15amNuXm5mLDhg146qmn4Ovri2PHjmHOnDl48skn0atXL6f31MvJycHevXuxfft2uzfcS1NycjIiIiLg5+eH/fv3Y9asWZgzZw66d+9u9x5bj/POHNNNadJiXDfW4+wxLWzQalfKg6J+1+Sdp8mTJ2vSM23aNAoLCyNXV1fy8/OjYcOG0X/+8x9NWuqNHz+egoKCyNXVldq3b0/jx4/X/H2QaWlpBICys7M17ai3adMm6ty5M7m6ulJgYCAlJCRQaWmp0y7f1jhOTk62+vukpCSn99y8eZPGjBlDwcHB5OrqSkFBQRQXF+fwD4M31lRZWUkxMTHk5+dHLi4uFBYWRi+88AIVFhZq0lPvr3/9K3l4eDhtLNlq+uCDDygkJIRcXFwoNDSUXn/9dTIajQ7rsdYCgJKTk83rJCUl2VzHWT15eXk0YsQI8vf3JxcXFwoJCaFnn32Wzpw5Y/eWpjZdunSJnnzySWrbti25ublReHg4zZs3z+JQrs7sqbdw4ULq0KEDmUwmh3Q0t+mVV16hgIAAcnFxoa5du9Ly5csddsh0W4/zzhzTTWnSYlw31uPsMS0apxAR3dsURQghhBBCCCGsk89oCCGEEEIIIexOJhpCCCGEEEIIu5OJhhBCCCGEEMLuZKIhhBBCCCGEsDuZaAghhBBCCCHsTiYaQgghhBBCCLuTiYYQQgghhBDC7mSiIYQQQgghhLA7mWgIIYTQRMeOHfH+++9rnSGEEMJBZKIhhBANmDJlChRFueuUk5Ojddo9W79+PVq3bn1f6ymKgi1bttx3y+HDhzF9+vT7Ph8hhBA8tdA6QAghOBs+fDiSk5Mtlvn5+d3TeVVXV8PV1dUeWQ+0+u1wr9tRCCHEg0H2aAghRCPc3NwQGBhocdLr9QCAPXv2oF+/fnBzc0NQUBAWLFiA2tpa898OHjwYiYmJmD17Ntq1a4fY2FgAwIkTJzBixAi0atUKAQEBeO6551BSUmL+O1VVsWzZMoSHh8PNzQ2hoaF4++23zb9/5ZVX0K1bN7Rs2RKdO3fGokWLUFNTY/59VlYWhgwZAi8vL3h7e6Nv377IyMjA7t27MXXqVJSVlZn3zixevPi+t9Hx48cxdOhQeHh4wNfXF9OnT0dFRYX591OmTEF8fDzefvttBAcHo3v37gAs3zq1fv16q3uP6vtUVcWbb76JkJAQuLm5oXfv3khNTTVfxoULF6AoCr744gsMGTIELVu2RGRkJPbv33/f108IIcS9kYmGEELcg7y8PDz11FOIiopCVlYWVq1ahXXr1uGtt96yWO9vf/sbXF1dkZ6ejtWrV6O0tBRDhw7Fo48+ioyMDKSmpqKoqAjjxo0z/83ChQvx7rvvYtGiRTh16hQ2bNiAgIAA8++9vLywfv16nDp1Ch988AHWrl2LP//5z+bfT5w4ESEhITh8+DCOHDmCBQsWwMXFBQMHDsT7778Pb29vFBQUoKCgAC+//PJ9bQeDwYDY2Fi0adMGhw8fxubNm/H1118jMTHRYr2dO3ciOzsbO3bswJdffnnX+YwfP97cVFBQgM8//xwtWrTA448/DgD44IMPsHz5crz33ns4duwYYmNjERcXh7Nnz1qcz2uvvYaXX34ZmZmZ6NatGyZMmGAx+RNCCOFEJIQQwqrJkyeTXq8nT09P8+kXv/gFERG9+uqr1L17d1JV1bz+ypUrqVWrVmQymYiIaNCgQfToo49anOfvf/97iomJsVh2+fJlAkDZ2dlUXl5Obm5utHbt2iZ3/vGPf6S+ffuaf/by8qL169dbXTc5OZl8fHxsnmdycjIBsLju9ScAlJKSQkREa9asoTZt2lBFRYX5b7dt20Y6nY4KCwuJqG47BgQEkNFotLiMsLAw+vOf/3zXZefk5FDbtm1p2bJl5mXBwcH09ttvW6wXFRVFL774IhERnT9/ngDQRx99ZP79yZMnCQCdPn3a5vUVQghhf/IZDSGEaMSQIUOwatUq88+enp4AgNOnT2PAgAFQFMX8u8cffxwVFRX44YcfEBoaCgDo27evxfllZWXhm2++QatWre66rNzcXJSWlsJoNGLYsGENNm3atAkffvghcnNzUVFRgdraWnh7e5t/P3fuXPz617/Gp59+iujoaIwdOxZdunRp9nX38vLCd999d9fyrl27mv//9OnTiIyMNG8XoG47qKqK7Oxs856YRx55pEmfTykrK8PTTz+NkSNHYt68eQCA8vJy5Ofnm/du3H45WVlZFst69epl/v+goCAAQHFxMXr06GHzsoUQQtiXTDSEEKIRnp6eCA8Pv6+/v11FRQVGjRqFpUuX3rVuUFAQzp071+j57d+/HxMnTsSSJUsQGxsLHx8fbNy4EcuXLzevs3jxYjz77LPYtm0bvvrqKyQlJWHjxo0YM2ZMs9p1Ot19Xffb3bkdrDGZTBg/fjy8vb2xZs2ae7ocFxcX8//XTwJVVb2n8xJCCHF/5DMaQghxDyIiIrB//34QkXlZeno6vLy8EBIS0uDf9enTBydPnkTHjh0RHh5ucfL09ETXrl3h4eGBnTt3Wv37ffv2ISwsDK+99hoee+wxdO3aFRcvXrxrvW7dumHOnDn4z3/+g5///OfmI2e5urrCZDLd57W/JSIiAllZWTAYDOZl6enp0Ol05g99N9WcOXNw/PhxbNmyBe7u7ubl3t7eCA4ORnp6usX66enp6Nmz5/1dASGEEA4jEw0hhLgHL774Ii5fvoyXXnoJZ86cwdatW5GUlIS5c+dCp2v4rjUhIQHXrl3DhAkTcPjwYeTm5iItLQ1Tp06FyWSCu7s7XnnlFcyfPx+ffPIJcnNzceDAAaxbtw5A3duWLl26hI0bNyI3NxcffvghUlJSzOd/8+ZNJCYmYvfu3bh48SLS09Nx+PBhREREAKg70lNFRQV27tyJkpISVFZW3td2mDhxItzd3TF58mScOHEC33zzDV566SU899xzFh9gtyU5ORl/+ctfsHr1aiiKgsLCQhQWFpqPXjVv3jwsXboUmzZtQnZ2NhYsWIDMzEzMmjXrvvqFEEI4jkw0hBDiHrRv3x7bt2/HoUOHEBkZiRkzZuD555/H66+/3ujf1b8ybzKZEBMTg0ceeQSzZ89G69atzROURYsW4Xe/+x3eeOMNREREYPz48SguLgYAxMXFYc6cOUhMTETv3r2xb98+LFq0yHz+er0eV69exaRJk9CtWzeMGzcOI0aMwJIlSwAAAwcOxIwZMzB+/Hj4+flh2bJl97UdWrZsibS0NFy7dg1RUVH4xS9+gWHDhmHFihXNOp89e/bAZDIhLi4OQUFB5tN7770HAPjtb3+LuXPn4ne/+x0eeeQRpKam4l//+pfF50WEEELwotDt+/2FEEIIIYQQwg5kj4YQQgghhBDC7mSiIYQQQgghhLA7mWgIIYQQQggh7E4mGkIIIYQQQgi7k4mGEEIIIYQQwu5koiGEEEIIIYSwO5loCCGEEEIIIexOJhpCCCGEEEIIu5OJhhBCCCGEEMLuZKIhhBBCCCGEsDuZaAghhBBCCCHs7v8BGWDT5EjaKY4AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualize_forecasts.heatmap_forecasts(expected_timeseries_outputs[:, :, 0, 0].T)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Due to runnability of this notebook, there's only a few models in the ensemble and they haven't been trained very well (e.g., small number of epochs). For a fully trained ensemble containing a larger number of models, the heat map look more like the following:\n", "\n", "\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Here, we see that the outputs of the individual models in the ensemble are spread out and where there is more variance and therefore uncertainty in the ensemble." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Sensitivity Analysis of the Input Features\n", "\n", "The ensemble model can now be used to identify the important features of the dataset for forecasting. We apply the sensitivity analysis on the future nodes we are interested in. Then we have a look at the influence of the previous time steps. It can be interesting to find out when the values of the present have the most influence on the forecast.\n", "\n", "Because the sensitivity analysis shows all features for all time steps, it is useful to restrict the analysis on one feature. The idea of the sensitivity analysis is described in more detail in the [Regression Tutorial](Regression.ipynb#Check-Input/Output-Sensitivity) and the [ECNN Tutorial](ECNN.ipynb#Sensitivity)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sensitivity = sensitivity_analysis.sensitivity_analysis(\n", " hcnn_ensemble,\n", " Y_batches[:, :past_horizon],\n", " output_neuron=(-1, past_horizon + 3, slice(0, batchsize), 0),\n", " batchsize=batchsize,\n", ")\n", "\n", "node_for_sensitivity = 0\n", "restricted_sensitivity_matrix = sensitivity[\n", " :, range(node_for_sensitivity, past_horizon * n_features_Y, n_features_Y)\n", "]\n", "# plot\n", "visualization.plot_heatmap(\n", " restricted_sensitivity_matrix.T,\n", " center=0,\n", " vmin=-torch.max(abs(sensitivity)),\n", " vmax=torch.max(abs(sensitivity)),\n", " xlabel=\"Observations\",\n", " ylabel=\"Input Node\",\n", " title=\"Temporal sensitivity restricted on one input feature for one output neuron\",\n", " cbar_kws={\"label\": \"d output / d input\"},\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Deep Historical Consistent Neural Network (DHCNN)\n", "\n", "Another possibility to improve HCNNs is to use a deep version of it, the [DHCNN](../api/dhcnn.rst). Multiple HCNNs are combined to solve the same task. The states of the lower level HCNNs are given to the upper levels. This way, the deeper levels have to explain only small errors in the state that the previous levels couldn't explain. Because of the additional levels and therefore additional matrices, the model gets even more over-parameterised when working with big state dimensions. Fortunately, it is enough to work with a small state dimension ($dim(state)\\leq50$) and no sparsity in the matrices. \n", "The memory problem, which was previously tackled by the sparsity in the transition matrix, can then be solved by using an `HCNN_GRU_3_variant` ([API](../api/hcnn.rst#prosper_nn.models.hcnn.hcnn_gru_cell.HCNN_GRU_3_variant)) in the lowest level of the DHCNN.\n", "At the end, the deepest model gives the most reliable predictions.\n", "\n", "" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "\n", "The `deepness` parameter is equal to the number of stacked HCNN models. As explained, we can decrease the hidden state dimension and need no sparsity." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "deepness = 3\n", "n_state_neurons = 50\n", "sparsity = 0" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "dhcnn_model = DHCNN(\n", " n_state_neurons=n_state_neurons,\n", " n_features_Y=n_features_Y,\n", " past_horizon=past_horizon,\n", " forecast_horizon=forecast_horizon,\n", " deepness=deepness,\n", " sparsity=sparsity,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reset the optimizer." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "optimizer = optim.Adam(dhcnn_model.parameters(), lr=0.001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Loop\n", "\n", "In the deep version of the HCNN there are some changings in the training loop. Because of the `deepness` we have an additional dimension in the `output` of the model. Now the shape of the output is `shape=(deepness, past_horizon+forecast_horizon, batchsize, n_features_Y)`. In the training we have to add the losses of all level of `deepness` and for all time steps in the `past_horizon`. Then this value is used for backpropagation. To compare the error of the model to others, we use the most reliable predictions from the deepest level. We receive it from the last level of the output: `output[-1]`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "epochs = 10\n", "\n", "total_loss = epochs * [0]\n", "for epoch in range(epochs):\n", " for batch_index in range(0, Y_batches.shape[0]):\n", " dhcnn_model.zero_grad()\n", "\n", " Y_batch = Y_batches[batch_index]\n", " output = dhcnn_model(Y_batch)\n", " past_error, forecast = torch.split(output, past_horizon, dim=1)\n", "\n", " losses = [\n", " loss_function(past_error[j][i], targets[i])\n", " for j in range(deepness)\n", " for i in range(past_horizon)\n", " ]\n", " loss = sum(losses) / (deepness * past_horizon)\n", " loss.backward()\n", "\n", " optimizer.step()\n", "\n", " loss_deepest_level = (\n", " sum([loss_function(output[-1, i], targets[i]) for i in range(past_horizon)])\n", " / past_horizon\n", " )\n", " total_loss[epoch] += loss_deepest_level.detach()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forecast \n", "As mentioned, the best prediction is in the deepest HCNN. Therefore, we will use this prediction as forecast." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad():\n", " dhcnn_model.eval()\n", "\n", " output_forecast = dhcnn_model(Y_batches[0, :, 0].unsqueeze(1))\n", " forecast = output_forecast[-1, past_horizon:]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ensemble of Deep Historical Consistent Neural Network\n", "\n", "Now we build an ensemble of the DHCNN to tackle the over-parametrization and the random initialization problem of the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "\n", "Building an ensemble of DHCNNs is as simple as building it for any other model. First, the DHCNN is reset and then is given to the `Ensemble` class." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "dhcnn_model = DHCNN(\n", " n_state_neurons=n_state_neurons,\n", " n_features_Y=n_features_Y,\n", " past_horizon=past_horizon,\n", " forecast_horizon=forecast_horizon,\n", " deepness=deepness,\n", " sparsity=sparsity,\n", ")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "dhcnn_ensemble = Ensemble(\n", " model=dhcnn_model, n_models=n_models, sparsity=sparsity, combination_type=\"median\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reset the optimizer." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "optimizer = optim.Adam(dhcnn_ensemble.parameters(), lr=0.001)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Training Loop\n", "\n", "Again with the [Ensemble](../api/ensemble.rst) class it gets a little bit more complex in the training loop because the `ensemble_output` has one more dimension: `shape=(n_models+1, deepness, past_horizon+forecast_horizon, batchsize, n_features_Y)`. Combining what we did in the above sections, the loss has to be added for all timesteps in `past_horizon`, levels in `deepness` and models in `n_models`." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "epochs = 10\n", "\n", "total_loss = epochs * [0]\n", "for epoch in range(epochs):\n", " for batch_index in range(Y_batches.shape[0]):\n", " dhcnn_ensemble.zero_grad()\n", "\n", " Y_batch = Y_batches[batch_index]\n", " ensemble_output = dhcnn_ensemble(Y_batch)\n", " outputs, mean = torch.split(ensemble_output, n_models)\n", " past_errors, forecasts = torch.split(outputs, past_horizon, dim=2)\n", " mean = torch.squeeze(mean, 0)\n", "\n", " losses = [\n", " loss_function(past_errors[k][i][j], targets[j])\n", " for i in range(deepness)\n", " for j in range(past_horizon)\n", " for k in range(n_models)\n", " ]\n", " loss = sum(losses) / (deepness * past_horizon * n_models)\n", " loss.backward()\n", "\n", " optimizer.step()\n", "\n", " mean_loss = (\n", " sum([loss_function(mean[-1, i], targets[i]) for i in range(past_horizon)])\n", " / past_horizon\n", " )\n", " total_loss[epoch] += mean_loss.detach()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forecast \n", "\n", "Analogously, the most reliable prediction is in the mean output of the ensemble and the deepest level: `ensemble_output[-1, -1]`. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad():\n", " dhcnn_ensemble.eval()\n", "\n", " output_forecast = dhcnn_ensemble(Y_batches[0, :, 0].unsqueeze(1))\n", " forecast = output_forecast[-1, -1, past_horizon:]\n" ] } ], "metadata": { "kernelspec": { "display_name": "prosper", "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": "3.8.5" }, "vscode": { "interpreter": { "hash": "a604604040b0261c277bc75aa34f15c6f86bb9bc8166d3b0f73ab3af3d1b81ef" } } }, "nbformat": 4, "nbformat_minor": 2 }