{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMJREfiXOxGxb85UfV4nibH"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Neural Network (feed-forward) 1-layer to learn a sine function from samples.\n","\n","Activation function ReLU. Hidden layer size = size (play with this parameter)"],"metadata":{"id":"7rOF78RfkxsK"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Generate data\n","X = np.random.rand(300) * 10  # Input data\n","Y = np.sin(X) + np.float32(1e-3) * (np.random.rand(300) - 0.5)  # Noisy sine function\n","\n","# Convert data to PyTorch tensors\n","X_tensor = torch.tensor(X, dtype=torch.float32).view(-1, 1)\n","Y_tensor = torch.tensor(Y, dtype=torch.float32).view(-1, 1)\n","\n","# Split data into training and testing sets\n","split_ratio = 0.75\n","split_idx = int(len(X) * split_ratio)\n","\n","X_train, X_test = X_tensor[:split_idx], X_tensor[split_idx:]\n","Y_train, Y_test = Y_tensor[:split_idx], Y_tensor[split_idx:]\n","\n","# Define the neural network model with sigmoid or ReLU activation\n","class NeuralNetwork(nn.Module):\n","    def __init__(self, input_size, hidden_size, output_size):\n","        super(NeuralNetwork, self).__init__()\n","        self.layer1 = nn.Linear(input_size, hidden_size)\n","        self.layer2 = nn.Linear(hidden_size, output_size)\n","\n","    def forward(self, x):\n","        #x = torch.sigmoid(self.layer1(x))\n","        x = torch.relu(self.layer1(x))\n","        x = self.layer2(x)\n","        return x\n","\n","input_size = 1\n","hidden_size = 50 #10\n","output_size = 1\n","#learning_rate = 0.01\n","#num_epochs = 1000\n","learning_rate = 0.001  # Updated learning rate for Adam optimizer\n","num_epochs = 10000  # Increased number of epochs\n","\n","\n","# Instantiate the model\n","model = NeuralNetwork(input_size, hidden_size, output_size)\n","\n","# Define the loss function (Mean Squared Error)\n","criterion = nn.MSELoss()\n","\n","# Define the optimizer (Stochastic Gradient Descent)\n","#optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n","# Define the optimizer (Adam optimizer)\n","optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","# Training loop\n","for epoch in range(num_epochs):\n","    # Forward pass\n","    outputs = model(X_train)\n","   # loss = criterion(outputs, Y_tensor)\n","    loss = criterion(outputs, Y_train) / X_train.size(0)  # Divide by number of data points to have square error\n","\n","\n","    # Backpropagation and optimization\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","\n","    if (epoch + 1) % 100 == 0:\n","        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n","\n","# Test the trained model on unseen data\n","with torch.no_grad():\n","    predicted_Y_test = model(X_test)\n","\n"],"metadata":{"id":"tbEJSJzDE4hT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Plot the original sine function, training data, and predicted values on testing data\n","plt.figure(figsize=(10, 6))\n","plt.scatter(X_test, Y_test, label='Original Sine Function in Test')\n","plt.scatter(X_test, predicted_Y_test, label='Predicted Testing Data', marker='x')\n","\n","plt.xlabel('X')\n","plt.ylabel('Y = sin(X)')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":531},"id":"sLAB9FPND0Ru","executionInfo":{"status":"ok","timestamp":1693142563744,"user_tz":180,"elapsed":477,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"2fb8ad00-c8a4-4e02-ad10-586ecbf39610"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import mean_squared_error\n","\n","def nrmse(y_actual, y_predicted):\n","    return np.sqrt(mean_squared_error(y_actual, y_predicted) / np.mean(np.sum((y_actual - np.mean(y_actual)) ** 2)))\n","\n","def pcorrect(y_actual, y_predicted):\n","    return (1 - nrmse(y_actual, y_predicted)) * 100"],"metadata":{"id":"C-aUVPN6wpqK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Convert predictions to NumPy array to\n","# Evaluate the model\n","print(f'MSE: {mean_squared_error(Y_test.numpy().flatten(), predicted_Y_test.numpy().flatten())}')\n","accuracy = pcorrect(Y_test.numpy().flatten(), predicted_Y_test.numpy().flatten())\n","print(f'Model Accuracy: {accuracy:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AluBLjLrtZeK","executionInfo":{"status":"ok","timestamp":1693142577753,"user_tz":180,"elapsed":292,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"cd14467c-eeb5-4b97-cde7-772ee9b98220"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model Accuracy: 98.87%\n"]}]},{"cell_type":"markdown","source":["Neural Net. Multilayer version (2 hidden layers)\n","We increase the model's complexity by adding more hidden units and layers to the neural network. This should allow the model to capture more intricate details of the sine function."],"metadata":{"id":"Dof6ghkfm05-"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Generate data\n","X = np.random.rand(300) * 10  # Input data\n","Y = np.sin(X) + np.float32(1e-3) * (np.random.rand(300) - 0.5)  # Noisy sine function\n","\n","# Convert data to PyTorch tensors\n","X_tensor = torch.tensor(X, dtype=torch.float32).view(-1, 1)\n","Y_tensor = torch.tensor(Y, dtype=torch.float32).view(-1, 1)\n","\n","# Split data into training and testing sets\n","split_ratio = 0.75\n","split_idx = int(len(X) * split_ratio)\n","\n","X_train, X_test = X_tensor[:split_idx], X_tensor[split_idx:]\n","Y_train, Y_test = Y_tensor[:split_idx], Y_tensor[split_idx:]\n","\n","# Define the neural network model with ReLU activation\n","class NeuralNetwork(nn.Module):\n","    def __init__(self, input_size, hidden_size1, hidden_size2, output_size):\n","        super(NeuralNetwork, self).__init__()\n","        self.layer1 = nn.Linear(input_size, hidden_size1)\n","        self.layer2 = nn.Linear(hidden_size1, hidden_size2)\n","        self.layer3 = nn.Linear(hidden_size2, output_size)\n","\n","    def forward(self, x):\n","        x = torch.relu(self.layer1(x))\n","        x = torch.relu(self.layer2(x))\n","        x = self.layer3(x)\n","        return x\n","\n","input_size = 1\n","hidden_size1 = 10  # Increased complexity\n","hidden_size2 = 16  # Increased complexity\n","output_size = 1\n","learning_rate = 0.001\n","num_epochs = 10000\n","\n","# Instantiate the model\n","model = NeuralNetwork(input_size, hidden_size1, hidden_size2, output_size)\n","\n","# Define the loss function (Sum of Squares)\n","criterion = nn.MSELoss()\n","\n","# Define the optimizer (Adam optimizer)\n","optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","# Training loop\n","for epoch in range(num_epochs):\n","    # Forward pass\n","    outputs = model(X_train)\n","    loss = criterion(outputs, Y_train) / X_train.size(0)  # Divide by number of data points\n","\n","    # Backpropagation and optimization\n","    optimizer.zero_grad()\n","    loss.backward()\n","    optimizer.step()\n","\n","    if (epoch + 1) % 1000 == 0:\n","        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n","\n","# Test the trained model on unseen data\n","with torch.no_grad():\n","    predicted_Y_test = model(X_test)\n","\n","# Plot the original sine function, training data, and predicted values on testing data\n","plt.figure(figsize=(10, 6))\n","plt.scatter(X_test, Y_test, label='Original Sine Function in Test')\n","plt.scatter(X_test, predicted_Y_test, label='Predicted Testing Data', marker='x')\n","\n","plt.xlabel('X')\n","plt.ylabel('Y = sin(X)')\n","plt.legend()\n","plt.show()"],"metadata":{"id":"th4ZwmycIHjP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Convert predictions to NumPy array to\n","# Evaluate the model\n","print(f'MSE: {mean_squared_error(Y_test.numpy().flatten(), predicted_Y_test.numpy().flatten())}')\n","accuracy = pcorrect(Y_test.numpy().flatten(), predicted_Y_test.numpy().flatten())\n","print(f'Model Accuracy: {accuracy:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JMw54_utMF4X","executionInfo":{"status":"ok","timestamp":1693143202859,"user_tz":180,"elapsed":412,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"2026bf60-4f22-4ba8-c8d0-7f34a0f30f46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MSE: 0.00021622916392516345\n","Model Accuracy: 99.73%\n"]}]},{"cell_type":"markdown","source":["We use now a NODE"],"metadata":{"id":"16sQuK9hI-I5"}},{"cell_type":"code","source":["!pip install torchdiffeq"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zh4kUQSoJG5u","executionInfo":{"status":"ok","timestamp":1693143957218,"user_tz":180,"elapsed":5520,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"ee23b516-6899-4358-a4b9-b7c137807497"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torchdiffeq\n","  Downloading torchdiffeq-0.2.3-py3-none-any.whl (31 kB)\n","Requirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from torchdiffeq) (2.0.1+cu118)\n","Requirement already satisfied: scipy>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from torchdiffeq) (1.10.1)\n","Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scipy>=1.4.0->torchdiffeq) (1.23.5)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (3.12.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (4.7.1)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (3.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (3.1.2)\n","Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->torchdiffeq) (2.0.0)\n","Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.3.0->torchdiffeq) (3.27.2)\n","Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.3.0->torchdiffeq) (16.0.6)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.3.0->torchdiffeq) (2.1.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.3.0->torchdiffeq) (1.3.0)\n","Installing collected packages: torchdiffeq\n","Successfully installed torchdiffeq-0.2.3\n"]}]},{"cell_type":"markdown","source":["NODE with hidden layers"],"metadata":{"id":"iwytFGupsU_0"}},{"cell_type":"code","source":["import torch\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from torchdiffeq import odeint, odeint_adjoint\n"],"metadata":{"id":"TqtX1moyNZR1","executionInfo":{"status":"ok","timestamp":1693146621759,"user_tz":180,"elapsed":306,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}}},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":["Fitting sine function, using as Data a sample of its derivative (cosine).\n","\n","Esto NO es correcto. Debo usar un modelo con parametros. Por los momentos esto solo halla la solucion de dy/dt = NeuralNet donde NeuralNet hace un fit de los datos correspondientes a derivada de seno. Falta construir el predictor"],"metadata":{"id":"CJRojqw5NmVs"}},{"cell_type":"code","source":["# Generate  data\n","X = np.sort(np.random.rand(300) * 10 - 5) # Input data\n","#Y = np.sin(X)  # Sine function without noise\n","Y = np.sin(X) + np.float32(1e-3) * (np.random.rand(300) - 0.5)  # Noisy sine function\n","\n","dY = np.cos(X)  # Derivative of the sine function\n","\n","# Convert data to PyTorch tensors\n","X_tensor = torch.tensor(X, dtype=torch.float32).view(-1, 1)\n","dY_tensor = torch.tensor(dY, dtype=torch.float32).view(-1, 1)\n","\n","# Split data into training and testing sets\n","split_ratio = 0.75\n","split_idx = int(len(X) * split_ratio)\n","\n","X_train, X_test = X_tensor[:split_idx], X_tensor[split_idx:]\n","dY_train, dY_test = dY_tensor[:split_idx], dY_tensor[split_idx:]\n"],"metadata":{"id":"i1wjOckkOBIP","executionInfo":{"status":"ok","timestamp":1693146625090,"user_tz":180,"elapsed":296,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["# Visualization of sine  training data\n","def plot_data():\n","    plt.scatter(X_train, dY_train, label='Training data', marker='.', alpha=0.5)\n","    xgrid = np.arange(-4, 4, 0.1)\n","    plt.plot(xgrid, np.sin(xgrid), label='Sine function', linewidth=2, color='red')\n","    plt.xlabel('x')\n","    plt.ylabel('y')\n","    plt.legend()\n","\n","plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plot_data()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":388},"id":"SIUZ7ucUQbRw","executionInfo":{"status":"ok","timestamp":1693146646536,"user_tz":180,"elapsed":889,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"4e470459-78c1-448b-f754-07c86f4cf4f7"},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Define the dynamics of the Neural ODE\n","\n","dy/dt = ODEfunc(y,t,theta)"],"metadata":{"id":"8qwVSMHrOKzU"}},{"cell_type":"code","source":["#  Neural ODE dynamics as a Nnet of 2 hidden layers\n","class ODEFunc(torch.nn.Module):\n","    def __init__(self):\n","        super(ODEFunc, self).__init__()\n","        self.fc1 = torch.nn.Linear(1, 32)\n","        self.fc2 = torch.nn.Linear(32, 16)\n","        self.fc3 = torch.nn.Linear(16, 1)\n","\n","    def forward(self, t, y):\n","        y = torch.relu(self.fc1(y))\n","        y = torch.relu(self.fc2(y))\n","        dydt = self.fc3(y)\n","        return dydt"],"metadata":{"id":"4nKp72JNOn0O","executionInfo":{"status":"ok","timestamp":1693146653108,"user_tz":180,"elapsed":447,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}}},"execution_count":53,"outputs":[]},{"cell_type":"code","source":["\n","# Instantiate the Neural ODE model\n","ode_model = ODEFunc()\n","\n","# Define the time points for solving the ODE\n","##Deberia usar X_train y despues extraer parametros optimizados y evaluar en X_test\n","## pero NO veo como\n","#t_points = torch.linspace(0, 1, X_train.size(0))\n","\n","# Define the time points for solving the ODE\n","t_points = torch.linspace(0, 1, X_tensor.size(0))\n","\n","# Solve the Neural ODE using the odeint function from torchdiffeq\n","# odeint(ode_model, dY_train, t_points, method='adams') ## method ='euler'\n","# The odeint function from the torchdiffeq library uses the Dormand-Prince (DOPRI5) solver by default.\n","# The Dormand-Prince method is a popular explicit Runge-Kutta method for solving ODEs\n","# and is suitable for a wide range of ODE problems.\n","# It's a higher-order method that provides good accuracy while also adapting the step size dynamically to maintain stability and efficiency.\n","solutionRK = odeint(ode_model, dY_tensor, t_points)   ##Aqui deberia entrenar primero dY_train\n","\n","# Solve the Neural ODE using the odeint_adjoint function from torchdiffeq\n","solutionADJ = odeint_adjoint(ode_model, dY_tensor, t_points)\n","\n","\n","# Extract the predicted derivatives from the solutions\n","##there are several trajectories (see all with solutions, a specific one solutions[n]).\n","##We extract the final time point: -1\n","predicted_dYRK = solutionRK[-1]\n","\n","predicted_dYadj = solutionADJ[-1]\n","\n"],"metadata":{"id":"qlJXABsvsX6P","executionInfo":{"status":"ok","timestamp":1693147274257,"user_tz":180,"elapsed":342,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}}},"execution_count":61,"outputs":[]},{"cell_type":"code","source":["# Plot the original derivative and the predicted derivative\n","plt.figure(figsize=(10, 6))\n","plt.scatter(X, dY, label='Original Derivative')\n","plt.scatter(X, predicted_dYRK.detach().numpy(), label='Predicted Derivative odeint', marker='x')\n","plt.scatter(X, predicted_dYadj.detach().numpy(), label='Predicted Derivative adjoint', marker='o')\n","plt.xlabel('X')\n","plt.ylabel('dY/dX')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":531},"id":"m8RJwwp-XtLd","executionInfo":{"status":"ok","timestamp":1693147281361,"user_tz":180,"elapsed":881,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"afad50d8-2a66-42b2-b63a-0028755544b8"},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1kAAAINCAYAAADMabVmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxU5f7A8c+ZYYZhZF+GRRA1y6yMJKXs1jWLpPrVtbRcsrI0rcxrSYtaoiia2qI3MzMt08yt0qxrXS0sWy1NyhbNckNUcBCQRYSBOef3x8joyDYoq37f9+XrxjnPOec5h2XmO8/z/T6KpmkaQgghhBBCCCHqha6pOyCEEEIIIYQQ5xIJsoQQQgghhBCiHkmQJYQQQgghhBD1SIIsIYQQQgghhKhHEmQJIYQQQgghRD2SIEsIIYQQQggh6pEEWUIIIYQQQghRjyTIEkIIIYQQQoh65NHUHWjuVFXl0KFD+Pj4oChKU3dHCCGEEEII0UQ0TaOwsJCIiAh0uurHqyTIqsWhQ4eIiopq6m4IIYQQQgghmomMjAwiIyOr3S9BVi18fHwAx4P09fVt4t4IIYQQQgghmkpBQQFRUVHOGKE6EmTVomKKoK+vrwRZQgghhBBCiFrTiKTwhRBCCCGEEELUIwmyhBBCCCGEEKIeSZAlhBBCCCGEEPVIcrKEEEIIIeqJpmmUl5djt9ubuitCiDOg1+vx8PA466WbJMgSQgghhKgHNpuNzMxMiouLm7orQoizYDabCQ8Px2g0nvE5JMgSQgghhDhLqqqyd+9e9Ho9ERERGI3Gs/4kXAjRuDRNw2azkZ2dzd69e7nwwgtrXHC4JhJkCSGEEEKcJZvNhqqqREVFYTabm7o7Qogz5OXlhcFgID09HZvNhslkOqPzSOELIYQQQoh6cqafegshmo/6+D2WvwRCCCGEEEIIUY8kyBJCCCGEEGdk3759KIrCL7/84vYxixYtwt/fv8n70RDatm3Lf/7znwa/zgMPPMAdd9zR4NcRZ06CLCGEEEKI81hGRgZDhgxxFuyIjo7m8ccfJycnp9Zjo6KiyMzM5LLLLnP7ev379+evv/46my6fkeuvvx5FUVAUBU9PT1q3bs3tt9/O6tWr6+0aW7ZsYfjw4fV2vuqCx1deeYVFixbV23VE/ZMgSwghhBDiPLVnzx66du3K33//zfLly9m1axfz5s1jw4YNdO/endzc3GqPtdls6PV6wsLC8PBwv5aal5cXFoulPrpfZ8OGDSMzM5Pdu3ezatUqLrnkEgYMGHDWgZHNZgMgJCSkUQqf+Pn51ftooKhfEmQJIYQQQjQTdlVj0+4cPvrlIJt252BXtQa93mOPPYbRaOSzzz6jR48etGnThltuuYXU1FQOHjzIc88952zbtm1bUlJSuP/++/H19WX48OFVjrR8/PHHXHjhhZhMJnr27MnixYtRFIWjR48ClacLJicnc8UVV7BkyRLatm2Ln58fAwYMoLCw0Nlm3bp1XHvttfj7+xMUFMRtt93G7t2763y/ZrOZsLAwIiMjufrqq5kxYwZvvPEGCxYsIDU11dkuIyODfv364e/vT2BgIL1792bfvn3O/RXT9aZOnUpERAQdO3Z0PqOK6YL33HMP/fv3d7l+WVkZwcHBvPPOO27dV7t27QDo0qULiqJw/fXXu1wfYP78+URERKCqqsu1evfuzZAhQ5xff/TRR8TGxmIymWjfvj2TJk2ivLy8zs9QuEeCLCGEEKIGtnIbS7Yv4fkfnmfJ9iXYym3YVTtbsrbw6Z5P2ZK1Bbtqb+puinPAut8zuXbGFwxc8AOPr/iFgQt+4NoZX7Du98wGuV5ubi7r169nxIgReHl5uewLCwtj0KBBrFy5Ek07Gei99NJLxMTE8PPPP5OUlFTpnHv37uWuu+7ijjvuYNu2bTz88MMugVp1du/ezZo1a1i7di1r167lq6++Yvr06c79x44dIzExkZ9++okNGzag0+m48847KwUWZ2Lw4MEEBAQ4pw2WlZWRkJCAj48P33zzDd999x3e3t7cfPPNzhErgA0bNrBz504+//xz1q5dW+m8gwYN4r///S9FRUXObevXr6e4uJg777zTrfvavHkzAKmpqWRmZlY5tfHuu+8mJyeHL7/80rktNzeXdevWMWjQIAC++eYb7r//fh5//HG2b9/OG2+8waJFi5g6derZPj5RDVknSwghxHnPVm5j5V8rySjIIMo3iv4X9cfoYWTmTzNZvH0xqnbyjdyLW17Ey8OL4vJi57ZQcyhj48YSHx3fFN0X54B1v2fy6LtpnD5ulZVfwqPvpvH6vbHcfFl4vV7z77//RtM0OnXqVOX+Tp06kZeXR3Z2tnN63w033MCTTz7pbHPq6A7AG2+8QceOHXnxxRcB6NixI7///nutb+ZVVWXRokX4+PgAcN9997FhwwbncX379nVpv3DhQkJCQti+fXud8sGqotPpuOiii5z3snLlSlRV5c0333QuKP3222/j7+/Pxo0b6dWrFwCtWrXizTffxGg0VnnehIQEWrVqxYcffsh9990HwLJly/jXv/7lvM/a7iskJASAoKAgwsLCqrxOQEAAt9xyC8uWLePGG28E4IMPPiA4OJiePXsCMGnSJMaOHcvgwYMBaN++PSkpKTzzzDNMnDjxjJ6bqJmMZAkhhDgvVYxGjUwdSdelXXlhywss37mcF7a8QLdl3Ri4diBv//G2S4AFoKG5BFgA1mIriRsTSU1PRYi6sqsak/67vVKABTi3Tfrv9gabOnjqSFVtunbtWuP+nTt30q1bN5dtcXFxtZ63bdu2zsADIDw8HKvV6vz677//ZuDAgbRv3x5fX1/atm0LwP79+93ue000TXMGVNu2bWPXrl34+Pjg7e2Nt7c3gYGBlJSUuEzl69y5c7UBFoCHhwf9+vVj6dKlgGPU6qOPPnKOLtXnfQ0aNIhVq1ZRWloKwNKlSxkwYIBzvadt27YxefJk5/14e3s789OKi4trOrU4QzKSJYQQ4ryTmp7K9M3TOVx8uMr9qqbye87vbp9PQ0NBYcbmGfSM6olep6+vrorzwOa9uWTml1S7XwMy80vYvDeX7hcE1dt1O3TogKIo7Nixwzl97VQ7duwgICDAOZoCjtGbhmAwGFy+VhTFZSrg7bffTnR0NAsWLHDmH1122WUu0/fOlN1u5++//3YGh0VFRVx55ZXO4OhUdX0WgwYNokePHlitVj7//HO8vLy4+eab6/2+br/9djRN45NPPqFbt2588803zJo1y7m/qKiISZMm0adPn0rHmkymOl1LuEeCLCGEEOeV1PRUEjcmolU5bnDmNDSyirNIs6bRLaxb7QcIcYK1sPoA60zauSsoKIibbrqJuXPnMnr0aJe8rKysLJYuXcr999/vHOFxR8eOHfn0009dtm3ZsuWs+pmTk8POnTtZsGAB1113HQDffvvtWZ3zVIsXLyYvL885dS82NpaVK1disVjw9fU9q3Nfc801REVFsXLlSv73v/9x9913OwNKd+6rYqTMbq8579NkMtGnTx+WLl3Krl276NixI7Gxsc79sbGx7Ny5kw4dOpzV/Qj3SZAlhBDivGFX7UzfPL3eA6xTZRdnu1zvp8M/sTlzMyjQLbQb3cK6yUiXcGHxcW8kwd12dTFnzhyuueYaEhISmDJlCu3ateOPP/7g6aefpnXr1nUujPDwww8zc+ZMxowZw9ChQ/nll1+c6znVJVg7VUBAAEFBQcyfP5/w8HD279/P2LFjz+hcxcXFZGVlUV5ezoEDB/jwww+ZNWsWjz76qDN/adCgQbz44ov07t2byZMnExkZSXp6OqtXr+aZZ54hMjKyTte85557mDdvHn/99ZdLcQp37stiseDl5cW6deuIjIzEZDLh5+dX5XUGDRrEbbfdxh9//MG9997rsm/ChAncdttttGnThrvuugudTse2bdv4/fffmTJlSp3uR7hHcrKEEEKcN9KsadVOEawvIWbHdKLU9FR6rOzBQ589xPzf5jP/1/kM+3wY/1z5T8ndEi7i2gUS7meiuhBEAcL9TMS1C6z3a1944YX89NNPtG/fnn79+nHBBRcwfPhwevbsyaZNmwgMrNs127VrxwcffMDq1au5/PLLef31153VBT09Pc+ojzqdjhUrVrB161Yuu+wyRo8e7SysUVcLFiwgPDycCy64gD59+rB9+3ZWrlzJ3LlznW3MZjNff/01bdq0oU+fPnTq1ImhQ4dSUlJyRiNbgwYNYvv27bRu3Zp//OMfdbovDw8PZs+ezRtvvEFERAS9e/eu9jo33HADgYGB7Ny5k3vuucdlX0JCAmvXruWzzz6jW7duXH311cyaNYvo6Og6349wj6LVJdvxPFRQUICfnx/5+flnPWQshBCiaX2651PGfDOmQc6toBBqDmVd33V8mfElozeOrrH9rOtnSTXCc0hJSQl79+6lXbt2Z5TjUlFdEHAZZ60IvBqiumBjmTp1KvPmzSMjI6OpuyKEW2r6fXY3NpCRLCGEEOeNilGm+qaceCs8Js4RwE37cVqtxyR/nyzrawmnmy8L5/V7Ywnzc31DF+ZnanEB1ty5c9myZQt79uxhyZIlvPjii87S4UKcLyQnSwghxHkj1hJLqDkUa7HVrbysy4IuY3vudpcy7gpKletkjYkbQ3x0PFuytmA9bq3qdC7ybfm89strdI/oTqwlVvK0BDdfFs5Nl4SxeW8u1sISLD6OKYJ63ZnlMjWVv//+mylTppCbm0ubNm148sknGTduXFN3S4hGJdMFayHTBYUQouWwq3bSrGlkF2cTYg6pMnipqC4IVBto6RQdgy8ZTGLXxCoXKtbr9NVe50ymJFq8LIy7apxMH2zBzna6oBCi+aiP6YIykiWEEKLFs6t2Fvy2gHe3v0u+Ld+5PdQcyti4sS7BS3x0PDOvn1lpnSxvgzdXhFzBNa2vof9F/TF6OEonGz2M3HfJfZWuWV2Z9jOZkmg9bmX0xtGSpyWEEOcICbKEEEK0aKnpqSR/n+wSXFWwFltJ3JjIzOtnVgq0ekb1rHXU60zEWmLxN/pz1Ha0zsdO+n6SLGYshBDnACl8IYQQosVKTU9l9MbRVQZYcHI64IzNMyoVmdDr9HQL68at7W+t17Wr9Do9Sd2TzujYo7aj/HT4p3rphxBCiKYjQZYQQogWqWJh4dpoaGQVZ5FmTWuEXjn0atuLBy998IyO3Zy5uZ57I4QQorFJkCWEEKJF2pK1pU4LC2cXZzdgbypL7JrIyz1eJsAzoG4HtqxCckIIIaogOVlCCCFanNT0VCZ+P7FOxzTUGlk16dW2Fze2uZE0axqbDm5iwe8Laj2mW2jVBTWEEEK0HDKSJYQQokWpKMFeYCtw+5gwcxixltgG7FX1KnK/HuvyGH6efjW29ff0r7ZqoRAt3QMPPMAdd9zh/Pr666/niSeeaPR+bNy4EUVROHr0aKNf+1SKorBmzZoGv05TPefanMn3oW3btvznP/9psD7VJwmyhBBCtBgVeVjuLCRcQUFhTNyYJq/Yp9fpSe6eXGObid0nNnk/xfnlgQceQFEUFEXBaDTSoUMHJk+eTHl5eYNfe/Xq1aSkpLjVtrEDo7Zt2zqfi5eXF23btqVfv3588cUX9XaNzMxMbrnllno7X3XPqC7PubnbsmULw4cPd7t9UwbUEmQJIYRoMdKsaXXKw/L39K9Uvr0pxUfHM+v6WYSaQ122h5pDZY0s0WRuvvlmMjMz+fvvv3nyySdJTk7mxRdfrLKtzWart+sGBgbi4+NTb+erb5MnTyYzM5OdO3fyzjvv4O/vT3x8PFOnTj2r81Y8w7CwMDw9PeujqzVq7s+5LkJCQjCbzU3dDbdIkCWEEKLFcLd4hZeHF4/FPMbGfhubXeASHx3P+r7rWZiwkBnXzWBhwkLW913f7PopGllJPuQfrHpf/kHH/gbi6elJWFgY0dHRPProo8THx/Pxxx8DJ6f4TZ06lYiICDp27AhARkYG/fr1w9/fn8DAQHr37s2+ffuc57Tb7SQmJuLv709QUBDPPPMMmuY6An36NLbS0lLGjBlDVFQUnp6edOjQgbfeeot9+/bRs2dPAAICAlAUhQceeAAAVVWZNm0a7dq1w8vLi5iYGD744AOX63z66adcdNFFeHl50bNnT5d+1sTHx4ewsDDatGnDP//5T+bPn09SUhITJkxg586dzna///47t9xyC97e3oSGhnLfffdx5MgRl/scOXIkTzzxBMHBwSQkJACu0wWvueYaxowZ43L97OxsDAYDX3/9NQBLliyha9euzn7dc889WK1WgBqf0anP+dlnn+Wqq66qdK8xMTFMnjzZ+fWbb75Jp06dMJlMXHzxxcydO7fGZ1VaWsqoUaOwWCyYTCauvfZatmzZ4tLGne/Dt99+y3XXXYeXlxdRUVGMGjWKY8eOOfefPl1QURTefPNN7rzzTsxmMxdeeKHzZ7emZ9IYJMgSQgjRYrhbvOLVG17lkSseabZT7xpqjS7RQpXkw7t9YdGtkH/AdV/+Acf2d/s2aKB1Ki8vL5cRqw0bNrBz504+//xz1q5dS1lZGQkJCfj4+PDNN9/w3Xff4e3tzc033+w87uWXX2bRokUsXLiQb7/9ltzcXD788MMar3v//fezfPlyZs+ezY4dO3jjjTfw9vYmKiqKVatWAbBz504yMzN55ZVXAJg2bRrvvPMO8+bN448//mD06NHce++9fPXVV4AjGOzTpw+33347v/zyCw899BBjx44942fz+OOPo2kaH330EQBHjx7lhhtuoEuXLvz000+sW7eOw4cP069fP5fjFi9ejNFo5LvvvmPevHmVzjto0CBWrFjhEoiuXLmSiIgIrrvuOgDKyspISUlh27ZtrFmzhn379jmDhpqe0enX2bx5M7t373Zu++OPP/j111+55557AFi6dCkTJkxg6tSp7Nixg+eff56kpCQWL15c7XN55plnWLVqFYsXLyYtLY0OHTqQkJBAbm4u4N73Yffu3dx888307duXX3/9lZUrV/Ltt98ycuTIaq8LMGnSJPr168evv/7KrbfeyqBBg8jNzXX7mTQYTdQoPz9fA7T8/Pym7ooQQpz3yu3l2o3v3ah1XtRZu2zRZZX+dV7UWYt/L14rt5c3dVfFeeb48ePa9u3btePHj9f94KMHNO0/l2vaRF/H/x/NOLE947TtB+q305qmDR48WOvdu7emaZqmqqr2+eefa56entpTTz3l3B8aGqqVlpY6j1myZInWsWNHTVVV57bS0lLNy8tLW79+vaZpmhYeHq698MILzv1lZWVaZGSk81qapmk9evTQHn/8cU3TNG3nzp0aoH3++edV9vPLL7/UAC0vL8+5raSkRDObzdr333/v0nbo0KHawIEDNU3TtHHjxmmXXHKJy/4xY8ZUOtfpoqOjtVmzZlW5LzQ0VHv00Uc1TdO0lJQUrVevXi77MzIyNEDbuXOn8z67dOlS6TyA9uGHH2qapmlWq1Xz8PDQvv76a+f+7t27a2PGjKm2j1u2bNEArbCwUNO0qp9RxfUrnrOmaVpMTIw2efJk59fjxo3TrrrqKufXF1xwgbZs2TKXc6SkpGjdu3evsh9FRUWawWDQli5d6txms9m0iIgI58+AO9+HoUOHasOHD3dp880332g6nc75e3X69wXQxo8f79IXQPvf//5X4zOpTU2/z+7GBjKSJYQQolmzq3a2ZG3h0z2fkmZN45luzwCOghanqvi6ORS5EKJO/FrDA59AQFvI2weL/g/2/+j4/7x9ju0PfOJo1wDWrl2Lt7c3JpOJW265hf79+5OcnOzc37lzZ4xGo/Prbdu2sWvXLnx8fPD29sbb25vAwEBKSkrYvXs3+fn5ZGZmukxL8/DwoGvXrtX24ZdffkGv19OjRw+3+71r1y6Ki4u56aabnP3w9vbmnXfecY7U7Nixo9L0uO7du7t9japomoaiOP7ebNu2jS+//NLl+hdffDGAy2jRlVdeWeM5Q0JC6NWrF0uXLgVg7969bNq0iUGDBjnbbN26ldtvv502bdrg4+PjfFb79++vU/8HDRrEsmXLnPeyfPly53WOHTvG7t27GTp0qMs9TZkyxeV+TrV7927Kysr4xz/+4dxmMBiIi4tjx44dgHvfh23btrFo0SKX6yYkJKCqKnv37q32fi6//HLnf7dq1QpfX1/nNMqmJOtkCSGEaJbsqp0Fvy3g3e3vkm87OU0q1BzKA5c+wKd7P3UpghFqDmVM3JhzMrfJrtpJs6aRXZxNiDmEWEusBJLnGr9IRyBVEVgt7OXY7gywIhvs0j179uT111/HaDQSERGBh4fr28NWrVq5fF1UVMSVV17pDAhOFRJyZuvReXl51fmYoqIiAD755BNat3YNQBuqoEROTg7Z2dm0a9fO2Yfbb7+dGTNmVGobHh7u/O/Tn2FVBg0axKhRo3j11VdZtmwZnTt3pnPnzoAj+ElISCAhIYGlS5cSEhLC/v37SUhIqHMxkoEDBzJmzBjS0tI4fvw4GRkZ9O/f33k/AAsWLKgUFOn1Dfs3p6ioiIcffphRo0ZV2temTZtqjzMYDC5fK4qCqqr13r+6kiBLCCFEs5Oankry98kuwVUFa7GVRX8s4qUeLxFgCjjnA4/U9FSmb55eKaAcGzf2nAwoz2t+kXDn/JMBFji+bsAACxwBQIcOHdxuHxsby8qVK7FYLPj6+lbZJjw8nB9//JF//vOfAJSXl7N161ZiY6ter65z586oqspXX31FfHzln+uKkTS73e7cdskll+Dp6cn+/furHQHr1KmTsxBChR9++KH2m6zGK6+8gk6nc673FRsby6pVq2jbtm2l4LSuevfuzfDhw1m3bh3Lli3j/vvvd+77888/ycnJYfr06URFRQHw008/uRxf1TOqSmRkJD169GDp0qUcP36cm266CYvFAkBoaCgRERHs2bPHZRStJhdccIEz3yw6Ohpw5I9t2bLFWXDDne9DbGws27dvr9PPYm3cfSYNQaYLCiGEaFZS01MZvXF0lQEW4Fwj68UtLxJriT2ni0dULLx8etn6w8WHGb1xNJ/t+6yJeiYaRP4B+PC0NYA+HF65GEYTGzRoEMHBwfTu3ZtvvvmGvXv3snHjRkaNGsWBA46+Pv7440yfPp01a9bw559/MmLEiBrXKmrbti2DBw9myJAhrFmzxnnO9957D4Do6GgURWHt2rVkZ2dTVFSEj48PTz31FKNHj2bx4sXs3r2btLQ0Xn31VWeRhkceeYS///6bp59+mp07d7Js2TIWLVrk1n0WFhaSlZVFRkYGX3/9NcOHD2fKlClMnTrVGQg89thj5ObmMnDgQLZs2cLu3btZv349Dz74YJ3f2Ldq1Yo77riDpKQkduzYwcCBA5372rRpg9Fo5NVXX2XPnj18/PHHlda+quoZVaei0Mb7779fKZiaNGkS06ZNY/bs2fz111/89ttvvP3228ycObPafj/66KM8/fTTrFu3ju3btzNs2DCKi4sZOnQo4N73YcyYMXz//feMHDmSX375hb///puPPvqo1sIXNanLM6lvEmQJIYRoNuyqneRNybW209DIKs4izZrW8J1qIu4svPz0109LoHWuyD/gmoM15DPXHK1mFGiZzWa+/vpr2rRpQ58+fejUqRNDhw6lpKTEObL15JNPct999zF48GC6d++Oj48Pd955Z43nff3117nrrrsYMWIEF198McOGDXOW727dujWTJk1i7NixhIaGOt94p6SkkJSUxLRp0+jUqRM333wzn3zyiXM6X5s2bVi1ahVr1qwhJiaGefPm8fzzz7t1nxMmTCA8PJwOHTpw3333kZ+fz4YNG1xKrUdERPDdd99ht9vp1asXnTt35oknnsDf3x+dru5vswcNGsS2bdu47rrrXKbIhYSEsGjRIt5//30uueQSpk+fzksvveRybHXPqCp33XUXOTk5FBcXO0flKjz00EO8+eabvP3223Tu3JkePXqwaNEi5zOtyvTp0+nbty/33XcfsbGx7Nq1i/Xr1xMQEAC49324/PLL+eqrr/jrr7+47rrr6NKlCxMmTCAiIsLdx1dJXZ5JfVNOVOYQ1SgoKMDPz4/8/Pxqh8SFEELUj3m/zOO1ba+53X7GdTO4tf2tDdOZoxlQZMUW1oUlm/aRnltMB187A2IC0R87zC+5Rg5oQVh8TMS1C0RfeAg8vcHkVy+X35K1hSHrh7jVVhYybnolJSXs3buXdu3aYTKZ6nZw/kFHmXaXIheRlQOvBz5tsOIXQoiTavp9djc2aFE5WV9//TUvvvgiW7duJTMzkw8//LBS9H26jRs3kpiYyB9//EFUVBTjx49v1IXIhBBCuMeu2lmyY0mdjnF33SwXJflQWuR4s3paIJWXtQ9LcBADLvfH+PpV2Mtt9C+dyM9aB3woZrFxOrlf5hCk5NMRAyNLXySTYGJ8i1huSMEcEAb3rqqXQMvdhZcBpv84nZ5RPc/JKZPnBU9vaHXiZ/nUIhenFsNoFeJoJ4RoEVpUkHXs2DFiYmIYMmQIffr0qbX93r17+b//+z8eeeQRli5dyoYNG3jooYcIDw93rrYthBCieVjw6wIKbAVutw8zhxFrqTqJvloVi74ey8Z+xwJY0hvKS+lXOpHDWgArjCnk4EvKZ9cy2fM4euB9YzJ9S5M5TADh5BCmy3OcS7MRzFFAYXZJCmablWLAXFpUL0FWXQLIw8cP88avbzDiihFnfV3RBEx+juC8Ivg/lV+kYwSrHkdJhRANr0UFWbfccgu33HKL2+3nzZtHu3btePnllwFHZZNvv/2WWbNmSZAlhBDNSGp6ap2mCSooZ7YeVmkRHMuGvH0cebsfQVoJHorKB8ZksjVfwnVHMao2JhrfQQE0Dcd+z2QmlD1AsHLUeaojmj8GRWWFIYVonZV01cKosiRW+0RQH+NJsZZYQs2hlYpeVOf1ba9TUl5CYtfEeri6aHQmv+qDKJkiKESLc04Xvti0aVOlMqAJCQls2rSp2mNKS0spKChw+SeEEKLhVBR4cJe3wZuZ1888sxwkv9Zs7P426aqFUPLI1nwp13R4KCrhuqPkqD6EKAV4KCplmo6nS4dRpukwKCrTjAsxKBplmkKmGkC4Lo/VnsnOAGuALYltBd5s3pvrGDGz/on96AE27c7ho18Osml3DnZVc+TfWP90tKmBXqdnbNzYOt3e23+8LYUwhBCiGWhRI1l1lZWVRWhoqMu20NBQCgoKOH78eJUL302bNo1JkyY1VheFEOK8t+DXBW6P1rTyaMVX/b7C6GE8o2vZVY1xG/LAlsQKo2ME6lRBukIAyjQdd5Ums40OGMrsTDMudLaZUPYgO7U2rPZMdm4bXTaCTIIAyM3Nhg2PYM/8nSOaN4klk537Opjy+Ng0BbMtByyd4IH/1jgFLD46npd7vMzTXz+Nqrm3uObE7ydyY5sbJT9LCCGa0Dk9knUmxo0bR35+vvNfRkZGU3dJCCHOWXWdJjjl2ilnHGABbN6bS2Z+CZkEMbqs+vylCWUPsI0OxLCLyYZFLvsmG97mNcMrLttmGeYSTg4AYZ7lHM89hF4rI5Q8VhsnEE4O4eSwSJ2AuSQTVBtk74CCzFr73KttL17854tu32NRWRE/Hf6p9oZCCCEazDkdZIWFhXH48GkLOB4+jK+vb5WjWACenp74+vq6/BNCCFH/7Kqdid9PdLv9Y1c8dtZlyq2FJQCEk8Msw9xq2002LOIuvuQDz2QMJ6YOjrMNoUxTMCga4bo8MtUA+pQmk65aiNZZWWFMIca3iM6XXMJj9kTKNcdLbLguj488x/OhcQKRuhznNfL1AeDp41a/e7XtxWMxj7l9n7PTZrvdVgghRP07p4Os7t27s2HDBpdtn3/+Od27d2+iHgkhhKiwJWuL29UEAzwDGNZ5WPUNSvIh/yB2Vas6B+pE/pPFx0Q4Oc6pgpmqvzMYAshRfSg/kYP1oucCZ4B1V2kyX6pdOKL5O9sGK0cp03QMsCU5A63lhhR+276dLwqj6Fua7Dy3Rck/WZUQOKAGc3PBeMZ/kctb3+zBVq7Cga2OkvLVGHb5MFp5tHLref165FfJzRJCiCbUonKyioqK2LVrl/PrvXv38ssvvxAYGEibNm0YN24cBw8e5J133gHgkUceYc6cOTzzzDMMGTKEL774gvfee49PPvmkqW5BCCHECe/tfM/ttre1v636HKMTZdmL87IYWOYoPlHh9PWr4oKO875pCpE4AqyKIhflms5ZXTBT9SeEo3gooAHjbEPZhmOdrEyCUFQIUvIpxcAR/MkkiFGmKc7rZJU6Xlq30YFHbY+zwHOWS3c1DSbaBpNJEO/+uB+AtZ/+lw88J6E3mmDEj+AfVek29To99196P69ve92tZ/bct89JbpYQQjSRFjWS9dNPP9GlSxe6dOkCQGJiIl26dGHChAkAZGZmsn//fmf7du3a8cknn/D5558TExPDyy+/zJtvvinl24UQoomlpqfy+f7P3W7fs03PqneU5MORXRzLy8J8LIPZJeOduVGXs4s5Jc9hPpZBcV4WlBahN/ngHRhGumrhEdtoSjBQpunoW5pMH1sK6aqFTIIZbHsWOwqKh4nnn/o3Sf/XiTu7d+L3ngsJfOIbdA99zs4+qYwdEM/yYVezemw/zMPXw72rCAx0rG8VTg4TDe9U6rKiwBues4jB8aFhDLt4z5iMHjt2WwkUWSsdU+Hhyx/GqHMvJ63EXsLYb+pWnVCIhvTAAw9wxx13OL++/vrreeKJJxq9Hxs3bkRRFI4ePdro1z6VoiisWbOmwa/TVM+5Nvv27UNRFH755Zcqv3ZHc703aGEjWddffz2aplW7f9GiRVUe8/PPPzdgr4QQQtRFXUu2B3gGVL3o8IkRrGN5WdybO4z/GOY4c6Omlg3iNeMrGBSVDDWEkRXrV+kU/If/l59/3YN1Qy435b9IMEf5jQ74mPT8J2IWXS6M4u3rOqPPigdvC0b/KIZed9q1A6O4MgquPHXbibWM4tppdDDlsUhNdsnBOpWHorLKM5mksgeYbFjknJbYzzaRlWFdqC6M0uv0DOs8zO1iIev2rWPqP6aeVbEQcW574IEHWLx4MQAGg4E2bdpw//338+yzz+Lh0bBvE1evXo3BYHCr7caNG+nZsyd5eXn4+/s3aL8A2rZtS3p6OgAmk4nQ0FDi4uJ45JFHuOGGG+rlGpmZmQQEBNTLuaD6Z1SX59yUoqKiyMzMJDg42O1j6npv+/bto127dvz8889cccUVZ9BL97WokSwhhBAtX11KtgOMv3p81VPeSosozsui1bEM/mOYwyjbSGdu1HzPWc7A5THbv0+uXwVg8qNnXBe+HXMDM4fdxkMD7mL5sKv5ZUICs4bfxv09YzB66CDyyiqn7dVGX3iIj01TXAKsA2oww0tHu+R/eTjX3jqZ9/Wz1oElm/bVeP5hlw+jlcG93CyAlB9S6nwPounYVTtbsrbw6Z5P2ZK1Bbtqb/Br3nzzzWRmZvL333/z5JNPkpyczIsvVl3R0maz1dt1AwMD8fFxr/hLU5g8eTKZmZns3LmTd955B39/f+Lj45k6depZnbfiGYaFheHp6VkfXa1Rc3/OFfR6PWFhYXUK7pvzvUmQJYQQotHUtWT7g5c+SK+2vVw3nihyYWsVTt/jzzkDq9nGOSwvd51W+JjtcX6lA3CysmAFvU6h+wVB9L6iNd0vCEKvU87spk7n6Y3ZL5CKeRcH1GDutk3kM62bSzGMU1WUjAdIzy2u8fR6nZ7J10x2uztrdq8hNT3V7fai6aSmp5KwKoEh64cw5psxDFk/hIRVCQ3+/fP09CQsLIzo6GgeffRR4uPj+fjjj4GTU/ymTp1KREQEHTt2BCAjI4N+/frh7+9PYGAgvXv3Zt++fc5z2u12EhMT8ff3JygoiGeeeabSbKTTp3qVlpYyZswYoqKi8PT0pEOHDrz11lvs27ePnj0dv9sBAQEoisIDDzwAgKqqTJs2jXbt2uHl5UVMTAwffPCBy3U+/fRTLrroIry8vOjZs6dLP2vi4+NDWFgYbdq04Z///Cfz588nKSmJCRMmsHPnTme733//nVtuuQVvb29CQ0O57777OHLkiMt9jhw5kieeeILg4GBn2sqp0wWvueYaxowZ43L97OxsDAYDX3/9NQBLliyha9euzn7dc889WK2O6cU1PaNTn/Ozzz7LVVddVeleY2JimDz55N+VN998k06dOmEymbj44ouZO7f6aqwA69at49prr3V+v2+77TZ2797t0mbz5s106dIFk8lE165dK800q2q64FdffUVcXByenp6Eh4czduxYysvLXZ7tqT9Dbdu25fnnn2fIkCH4+PjQpk0b5s+f79zfrl07ALp06YKiKFx//fU13tfZkCBLCCFEo7Crdp779jm32z8a8yiJXRNdN1YUuZifQO/nV7Cj2Nelut9Y40qX5s8ZljpztCw+prO+B7eY/GDgSpTQy8g3htGvbKJzMeJtdGBM6VBOn/k+2bDImaMVHWiu9RK92vaiW2g3t7s0Y/OMRhkREWcuNT2VxI2JlUZ5rcVWEjcmNmqg7OXl5TJitWHDBnbu3Mnnn3/O2rVrKSsrIyEhAR8fH7755hu+++47vL29ufnmm53HvfzyyyxatIiFCxfy7bffkpuby4cffljjde+//36WL1/O7Nmz2bFjB2+88Qbe3t5ERUWxatUqAHbu3ElmZiavvOJYq27atGm88847zJs3jz/++IPRo0dz77338tVXXwGOYLBPnz7cfvvt/PLLLzz00EOMHXvmuYqPP/44mqbx0UcfAXD06FFuuOEGunTpwk8//cS6des4fPgw/fr1czlu8eLFGI1GvvvuO+bNm1fpvIMGDWLFihUugejKlSuJiIjguusc85XLyspISUlh27ZtrFmzhn379jkDqZqe0enX2bx5s0sA9Mcff/Drr79yzz33ALB06VImTJjA1KlT2bFjB88//zxJSUnOaaVVOXbsGImJifz0009s2LABnU7HnXfeiao6FlEvKiritttu45JLLmHr1q0kJyfz1FNP1fisDx48yK233kq3bt3Ytm0br7/+Om+99RZTpkyp8biXX37ZGcSNGDGCRx991BkUb968GYDU1FQyMzNZvXp1jec6Gy0qJ0sIIUTL9cavb1BcXvMoTYVQr1AevvzhyjtOTBE0H8tgnjqRASSRSRBzy/7FDM83nc2m2/oz0ONLZ47WKNMU4toF1tet1M4/Ch78FL/SIja2CmfJpn3sPlLE9h+/ZLrnWygKlGs6l5ysDzyT6WdL5r7ut7h1iXnx8+i6tCsa1ecqV8gqziLNmka3MPcDM9F4KvIUq/peamgoKMzYPIOeUT0btFqkpmls2LCB9evX8+9//9u5vVWrVrz55psYjY7cvnfffRdVVXnzzTdRFMcI8Ntvv42/vz8bN26kV69e/Oc//2HcuHH06dMHgHnz5rF+/fpqr/3XX3/x3nvv8fnnnxMf71gPr3379s79gYGO31+LxeLMNyotLeX5558nNTXVuTxP+/bt+fbbb3njjTfo0aMHr7/+OhdccAEvv/wyAB07duS3335jxowZZ/SMAgMDsVgsztGwOXPm0KVLF55//nlnm4ULFxIVFcVff/3FRRddBMCFF17ICy+8UO15+/XrxxNPPMG3337rDKqWLVvGwIEDnc94yJAhzvbt27dn9uzZdOvWjaKiIry9vat8Rqe79NJLiYmJYdmyZSQlJQGOoOqqq66iQwfHaPrEiRN5+eWXnd+7du3asX37dt544w0GDx5c5Xn79u3r8vXChQsJCQlh+/btXHbZZSxbtgxVVXnrrbcwmUxceumlHDhwgEcffbTaZzJ37lyioqKYM2cOiqJw8cUXc+jQIcaMGcOECRPQ6aoeK7r11lsZMcKx2PyYMWOYNWsWX375JR07diQkxFGYKCgoiLCwsGqvXR9kJEsIIUSDs6t2Fv6+0O32Y68aW+WbSbtPBAPLklwWAO6nfMF045su7QZ6fOmSo7XckIK+8NBZ30edmPzArzVGDx1Dr2vP893KWGWa5MzB6luazHI1nrtKkyk7sTbXB56TMGa5V6zJ6GHkgUsfcLs7X+7/8gxvRDS0NGtajXmKGpozUG4Ia9euxdvbG5PJxC233EL//v1JTk527u/cubMzwALYtm0bu3btwsfHB29vb+cb/JKSEnbv3k1+fj6ZmZku09I8PDzo2rVrtX345Zdf0Ov19OjRw+1+79q1i+LiYm666SZnP7y9vXnnnXecIzU7duyoND3ubNdL1TTNGfhs27aNL7/80uX6F198MYDLaNGVV15Z5bkqhISE0KtXL5YuXQo4linatGkTgwYNcrbZunUrt99+O23atMHHx8f5rE6trO2OQYMGsWzZMue9LF++3HmdY8eOsXv3boYOHepyT1OmTKk0/e9Uf//9NwMHDqR9+/b4+vrStm1bl77t2LGDyy+/HJPp5IyC2r4PO3bsoHv37s5nDfCPf/yDoqIiDhw4UO1xl19+ufO/FUUhLCzMOa2yMclIlhBCiAa3JWsLpfZSt9re1OYm4qPjq9y3eW8u2wq8GUCSc0HhF06MYGkajLU9xAjDx84crSfKRvJu4AJaBYSBp3eV52w03hb0RhN2Wwn9bBOdOVjb6EA/W/LJdbK8LW6fMrFrIpnHMlm3b12tbT/Z+wlPdn1S1s1qhrKLs+u1XV317NmT119/HaPRSERERKXCA61auRZaKSoq4sorr3QGBKeqGCmoKy8vrzofU1RUBMAnn3xC69atXfY1VEGJnJwcsrOznbk9RUVF3H777VWOjIWHhzv/+/RnWJVBgwYxatQoXn31VZYtW0bnzp3p3Lkz4Ah+EhISSEhIYOnSpYSEhLB//34SEhLqXIxk4MCBjBkzhrS0NI4fP05GRgb9+/d33g/AggULKgWnen31fztuv/12oqOjWbBgAREREaiqymWXXVavhVLcdXq1QUVRnNMWG5MEWUIIIRrclsNb3G7b/+L+1e6rKF6RSRBTyga5LPQ7xvYQ72k38LUtxhmAvRu4gFYDF0NwB8fIUlPyj4IRP6IvsrIyrAtLNu0jPbeY6EAz93W/BX3WtY4Aq44VDadfN50fMn/gaOnRGtvlluTKlMFmKsTsXmDibru6atWqlXOqmDtiY2NZuXIlFosFX1/fKtuEh4fz448/8s9//hOA8vJytm7dSmxsFcsx4BgtU1WVr776yjld8FQVI2l2+8ncwksuuQRPT0/2799f7QhYp06dnEU8Kvzwww+132Q1XnnlFXQ6nXO9r9jYWFatWkXbtm3PuuR97969GT58OOvWrWPZsmXcf//9zn1//vknOTk5TJ8+nagox9+In376yeX4qp5RVSIjI+nRowdLly7l+PHj3HTTTVgsjg93QkNDiYiIYM+ePS6jaDXJyclh586dLFiwwDnV8dtvv3Vp06lTJ5YsWUJJSYlzNKu270OnTp1YtWqVy8jhd999h4+PD5GRkW717XTuPqP6INMFhRBCNLza04YA8NR70jX0xJSiE1UET1VRvOJydlVa6Pcxw8eEk0MmQTzqMZniVlGOEazmEGBV8I+CyCudUwgn976Mode1P7uS8To9t7e/3a22Pxz6QQpgNEOxllhCzaEoVF3hUkEhzBxW9XpxTWDQoEEEBwfTu3dvvvnmG/bu3cvGjRsZNWqUcxrX448/zvTp01mzZg1//vknI0aMqHHx37Zt2zJ48GCGDBnCmjVrnOd87733AIiOjkZRFNauXUt2djZFRUX4+Pjw1FNPMXr0aBYvXszu3btJS0vj1VdfdRZpeOSRR/j77795+umn2blzJ8uWLatyXdWqFBYWkpWVRUZGBl9//TXDhw9nypQpTJ061RmUPvbYY+Tm5jJw4EC2bNnC7t27Wb9+PQ8++GCd38i3atWKO+64g6SkJHbs2MHAgQOd+9q0aYPRaOTVV19lz549fPzxx6SkuC7PUNUzqk5FoY3333+/UjA1adIkpk2bxuzZs/nrr7/47bffePvtt5k5c2aV5woICCAoKIj58+eza9cuvvjiCxITXYsW3XPPPSiKwrBhw9i+fTuffvopL730Uo3PY8SIEWRkZPDvf/+bP//8k48++oiJEyeSmJhYbT5WbSwWC15eXs4CJfn5+Wd0HndIkCWEEKJB2VU7vp5Vf9p9uiGXDXFMZztRRZBFt2LPy2DT7hw++uUgqqpxg/d+Vnk6Fvo9oAYxrHS0S45WJ3M+a57tj3n4erh3VfMJsM7EiUDTrmrOZ7Bpdw52VXMEoCWONwg92/Ss5UQO83+b3yglwUXd6HV6xsY5Kt6dHmhVfD0mbkyzmeppNpv5+uuvadOmDX369KFTp04MHTqUkpIS58jWk08+yX333cfgwYPp3r07Pj4+3HnnnTWe9/XXX+euu+5ixIgRXHzxxQwbNoxjx44B0Lp1ayZNmsTYsWMJDQ1l5MiRAKSkpJCUlMS0adPo1KkTN998M5988olzOl+bNm1YtWoVa9asISYmhnnz5rkUqajJhAkTCA8Pp0OHDtx3333k5+ezYcMGl1LrERERfPfdd9jtdnr16kXnzp154okn8Pf3P6NAYNCgQWzbto3rrruONm3aOLeHhISwaNEi3n//fS655BKmT59eKUip7hlV5a677iInJ4fi4mLnqFyFhx56iDfffJO3336bzp0706NHDxYtWuR8pqfT6XSsWLGCrVu3ctlllzF69OhK66x5e3vz3//+l99++40uXbrw3HPP1Vp8pHXr1nz66ads3ryZmJgYHnnkEYYOHcr48eNrPK4mHh4ezJ49mzfeeIOIiAh69+59xueqjaKdvmiBcFFQUICfnx/5+fnVDokLIYSoWmp6KtM3T3dr8WGzh5nvB36P3lYER3ahrRqCkrePDELpVzKeTILoyVYWmGbhwcniEb/SgXBynFMEi1tFOQIsv9a1XrNZqyhXn5fFwLIkthWczCmL8S1iuSEFc0AY3LsKu9GbhFUJWIuttVYbrHjTPvP6mdXmvom6KykpYe/evbRr184lub8uqvp9CTOHMSZujHyvxDlv586dXHzxxfz99991mr7aEGr6fXY3NpCcLCGEEA2iYt0fd0qMA0y9dir6gkOwchDHCnJ5qOhhpmuzidYdZoUxhdfK/sXzxrfwQKNc0/GgksKvOD5ZzSSIUaYpJwOPpi5yUR9OKVf/qvosIxnlDChnl6RgtlkpBsxHdqEP7sDYuLEkbkxEQanxmTdmSXBRN/HR8fSM6kmaNY3s4mxCzCHEWmLleyTOebm5uXzwwQf4+vo6c85aOgmyhBBC1Lua1v05XahXKGOvGkt8aDdYdDv2rN9phZ3p2mxG2UYy2zjHpYpguaYwvHQ0u3wvZum9V3CkqBSLj4m4doHoC691BFgteYrgCRXl6l9Vn6WN7girPJMZYXuc8YalROuspKsWJpWO4K1VQ1BahRB/7ypmXj/TrZHDU0uCSyGM5kWv08v3RJx3hg4dytatW3n99dcbrDJkY5MgSwghRL2rbd2fCs90e4Z7Lr7H8Ul9/kGOFebSCjtlms5Zhn15eU/GGlc6j3nWNpQvuBIKStEpCr2vOGVaYEufIniKinL1IxnFKs9kDIrqrKaYrlocASizUPJOrP9SWuQcCZm7bS7zf51f6zUaqiS4EELUxYcfftjUXah3UvhCCCFEvXP3zXuQKchZ6MKuafQtfpZ01eJcsDdaZ3UJsOBkFUE4WdL9XFRxb7/SgRG2x132zSu/zTnCl65a+OKqEws9l+Sj1+m5Ovxqt67RUCXBhRDifCdBlhBCiHoXaAp0q12IOcRZ4KFwXi/yj5czwJbkDLRONct2p0sVwXBynCXdz0UV9xZODuMNrou+TjMudAZYA2xJPLfmD469kQBvJcDh7S2uJLgQQpxrJMgSQghRr1LTU3nu2+dqbOPyJr8gk+O5B/EvOcgKo2Pdl9fK/lXpmLs9vmGUbaQz0HrfNIW4oOMNcg/NQVy7QGJ8i5xVEw+oQeRprVzaTC1zrG+zwphCq+IMtOwd8OaN6AsOtaiS4OcSKdosRMtXH7/HEmQJIYSoN6npqYzeOBrrcWu1bVze5GfvRHv/AfKKbRxQg4nWWVltTOJ541sux5RrOiJ1R5htnOMMtLwDw9CbfBr0fpqSvvAQyw0pzhGrx2yPY9Ncg6K5xlmsNk4gWmdF00AB7GUlkLuH+Oh4Zl4/E4vZ4nJMqDlUyrc3AIPBAEBxcXET90QIcbYqfo8rfq/PhKyTVQtZJ0sIIdxjV+30eK8H+aX5NbYLNYcyNm4s8X4d4bVuaGXHUYADahAGygnVuR5frunwOJGjZVBUMgglo+erXHPV1edEFcFqnZhGefTIIQYffdiZg2VVfQhRClFOGaDSNFAUKNMUjmj+hEa0QTdgKfhHYVftUhK8kWRmZnL06FEsFgtmsxlFqXq6phCiedI0jeLiYqxWK/7+/oSHh1dqI+tkCSGEaFRbsrbUGmABTL5mMte0vgYObEUtt6HDESRE6nIo11zflGoajCkdyijjR0TrrNjRExkWRtS5HmCB4/7uXYVP9t+89tY9ROIY0fq3bSSLPF8gkCJnU0VxlLY/ovkTrsujPKsA3cpBMPi/6E1+UhK8kYSFhQFgtVY/kiuEaP78/f2dv89nSoIsIYQQ9eK9ne+51W6rdSvXBF2K3TuUQeokljABg6KiaeChuE6uyNb8+J7L+d52Of/zn46/fzAMWHruB1gVTH7oQy7EOzCM9CMaA2xJAJRqHpxe06IIM+G6vBMjfnaO52Xilf03RHVtgo6fnxRFITw8HIvFQllZWVN3RwhxBgwGA3r92Y/2S5AlhBDirNlVO19mfOle47JSRzXBI4dILx3LXSTzgedEDKcFWLmaNxZdPiuMKQzTJePzyGdg8jl/AqwKJj/8h/+XtF92w5rfHZUVdUedUwQr+CvHsJ+YUpmp+hNy/AjaO/9CeexH8I9quv6fh/R6fb28SRNCtFxS+EIIIcRZG/vNWMq1crfaxgVcTHFelrOaoIZjFOZ0pZrBWQzjPdNU9Ipy/gVYFUx+3NAplNTAF04WuVAc0ylPpVdUctVWhCgFeCgqalkJHP7Dkd8lhBCi0UiQJYQQ4qzYym2s37ferbZmDy+6BHd2roUVrbPypvFF/Dnm0k7TIFyXB2gc1ILxC4oAT+8G6H0L4ulNq1beaJxa5KJydcUA5ZizUMjwkifQ/jcG3u0rgZYQQjQiCbKEEEKclZQfUtBwr1Dtg8V2Sub1IruwlAG2JA6rvlh0JyvllWkK5ZrOOUoTqcvB2+SB0nvO+TuKVcHkB3ctBIPZWUUwRFdI2WmPXlHArul4pHQ0E4xLUI7ug2PZUFpU5WmFEELUPwmyhBBCnLHU9FTW7F7jVlujBg8d3IVvaRbvGydxMfsIUgpd2mjoeLx0BGWnBFq+vgHgW7mM7nkp9BKU+z+m3MtySpELyFNdFynWUJliXOhcY2vTFS9UOxJoV+1sydrCp3s+ZUvWFuyqvTHuRAghzmlS+EIIIcQZsat2kjclu93+5qJjzhedSN0RphvfRHfaCJgOjQws3FWazAeeySh6Ix53LZRRrFOFXISXfxjlWUcwKHZHkQvFdSqghwLhSh4H1GBG2UYy58t/o/3VBuW+VS7PMjU9lembp3O4+LBzm3MdM1msWAghzpiMZAkhhDgjC35d4Na6WAAKCg9lu24L1eWjO21BXQ9F5VXjHLIJYHPPFXiM+glCL6nHXp8DTH4wYCm6sMvI1PxPFLnQKhXBAFhR1oPZxjlEcZj8nEMuUwZT01NJ3JjoEmABWIutJG5MJDU9taHvRAghzlkSZAkhhKgzu2pn4R8L3W4f37of95Qmc0ANqnJ/uabwUOmTzmIYnwe9wD+6XCalx6vjH4WuVwoWpdBZ5OLp0mGUaa4v608aVzmnDPYvHY/dJwJwfP+mb55eZS5dxbYZm2fI1EEhhDhDEmQJIYSoswW/LuB4+XG32t7c9mYi7HeTSRB325LJVn0rtcnDl+20ZYAtiWyPcFoFhEk1wdoEtkdvNGFHzyOlo/m38SMMJwKuCooC5ZqOUbaR7Dzux+a9uQCkWdMqjWCdSkMjqziLNGtag9+GEEKciyTIEkIIUSd21c6SHUvcauujb8XUf0zjQF4xABbyCFQqV7kLUfJ53zgJgH23vw/3rpI8rNr4R8GIH2HAUpJN7zpHrB6zPe7SzENRmW2cQxg5fLcrG7uqkZ2z061LfLnfzQWmhRBCuJDCF0IIIeokzZpGga3ArbYD8ou4+4VVbCvw5nJ2scozGb2iVtk2UneEVZ6TCI3+SgIsd/lHoTf54uFjIT1fY5RtJLONc1yalGkKBXhxDC/mfLmbb7Zu4ynzdAhQqjnpSUt2LCE2NFaKYAghRB3JSJYQQog6cXd0w0vVeCw7g9kl47mcXcw3zsTjlADLqvqRqQa4HBOmy0dvk/Wc6sTkh+XRtTyre4LZxjlE66wcUIPI0hzP1qBoBFGIN8cJJ4fZJeO55mgGFnvta5spKJKbJYQQZ0CCLCGEEG5LTU91e6qgZ2EPDpwoZPG6cRaBysnRr0zVn962KfSxTXYWw9AAXXAHWRPrDOjLjrGg1evOKYN325K5s3QyB9RgACJ0uXxonMD7xkkngjALytG7az2v5GYJIcSZkSBLCCGEWyoq0rmjlYcvGYduZoAtiXTVQmtdHsYTo1iZqj99bClkEkQmQTykn0KxVzhK6KUw6H2ZKngmPL0xB4RR3CqKUaYpzmd7t22iM9AK0+URqTtCumphgC2JXdau3Bhxl1unzy7Orr2REEIIJwmyhBBCuKW2inSn+lfrUYCOTIIYXTbCZd9jZU+QiWP0amTPC/gkaSDmRzbAg/+Tku1nyuQH967CPHw9q8f2Y2TPDgBkEsSospEuTUeXjXA+/2ivbm6dfn/h/vrtrxBCnOMkyBJCCOEWd3Ox7rvgDnpE3ghAODnMMsx12T/LMJdwcgD4R4cQ9DoF/FrLCNbZMvmBX2v0OoV/dHCMXtX2/OPCuhJqDkWh5iIYc3+ZK4sTCyFEHUiQJYQQolZ21c7qXavdanvNllVMX5FKODmsMKacyAEKxqr6Uap5EK2zssKYQoxvEXHtAhu45+enuHaBxPgWOZ9/umqhT2myc7Hniud/dfsQxsaNrXJR4tNJAQwhhHCfBFlCCCFqteDXBRwrO1ZruwC7ne75B5lvG3tKkYVg9Nix6PIBjUNqINE6K8sNKegLDzV8589D+sJDLDecDLAG2JJI0y5y5sid+vzjo+N5LOaxGs8nBTCEEKJuJMgSQghRo7osPtyzqBw9EK7LI0Q5SpYWgB474bo8ALI1f8Z7PkNxqyjMAWHg6d2APT+PVVEIA07kaJmmVHr+bXzbuHXaw8fcy8kTQojznSxGLIQQokZ1WXz4qiKVTDWAcF0enko5YeRRke5Tqul5wDaGiff2wRz6L8cbfMnDahgVhTBKi1jtE8HmvblYC0uw+JiIaxeIvvBal+cfYg5x67QvbHkBk4dJFicWQohayEiWEEKIGrlbvtvXrpFgy8aODqtaOXjK1vwpwosf9+ZKoYvGcEohjO4XBNH7itZ0vyCoykIjsZZYtwpgHC09SuLGRCmCIYQQtZAgSwghRI3cHeW4J78AqxpApC6HQKXQZZ+mwUTbYLIIAjeKLIjGpdfpGRs3ttZ2FQUypAiGEELUTIIsIYQQ1bKrdlRNxc9Y86iTn93OI/n5BCt5lGs6PE4sPFxBUWCa8S3CyaF7++CG7LI4Q/HR8cy8fiYBngE1tpMiGEIIUTvJyRJCCFGl1PRUpm+eXvMCxCcGpSyZ16Jq72FQNMA1wNI0R5Bl0eWzynMSoSE3NFynxVmJj46npLyEcd+Oq7Wtu9NIhRDifCQjWUIIISpJTU8lcWNizQEWEKZqzLJmM6v0SyaVDkarYiZgjuZDpuoYHbHo8tHbihqiy6KehLYKdatdkFdQA/dECCFaLgmyhBBCuLCrdqZvnl7jArV+Bl8WlPuzIsvGxUWtiNZZedT4EfbTCicc1VoRrCvEjo5MLQB92GXgG97QtyDcUZIP+Qexqxqbdufw0S8H2bQ7h5jgLoSagmspgQHPffucFMAQQohqyHRBIYQQLtKsabWOYOWXFWAr1AiyHeYAQWSqAbQ+sRbWqbwpJvNEMYxcj1CUO+dJVcHmoCQf3u1LcV4WA8uS2FZwcr2yGN8iRntnM85XQVGqL1NiLbaSuDGRmdfPlJLuQghxGhnJEkII4cLdXJsZ5fGkqxYidTkYKXPZV6B6nSiAoRGsHCVLDcA/tI2MYjUXpUUU52VhPpbB7JLxhJMDQDg5zC4Zz//lZjCtQCPEs/opgVJpUAghqidBlhBCCBf7C/a71e4R2/8YZRvJYdWXIJ1rnpWnUsbjpSMo03QYFI0QXT66G5NkFKuZsPtEMLAsiXTVQrTOygpjCrHKX6wwphCts5KuWphflETKddNrPI9UGhRCiKpJkCWEEMIpNT2V17a9VmMbBbCUa9xqy+I14yt4KJUnlHkq5YwzruCR0tHY0aM3ekFg+wbqtairzXtz2VbgzQBbEvvVYKJ1VlZ7JjsDrAE2xxTCn//e5tb5pNKgEEK4kpwsIYQQwMmCF+4oz+1LprqESF1OpX1lmsIRzZ9I3RGSTe/CgKUQdhn4R9V3l8UZshaWAFCEF8cxuewbXTaCTIIIJ4eLf3oFQvS1ns/dBauFEOJ8ISNZQgghAPcKXgAMKbRjy7mAp20Po542iKVpYFA07Og4oAbjHRiGvu01EmA1MxYfR2DVjkO0Vw657JtlmEsMu1hhTOH6ooNY7FqNlQZ1io68kspFT4QQ4nwmQZYQQgjA/SlfFx3P431jMi8a56M77d13tubHATWYSF0OJoOOgAHzJQ+rGYprF0iMbxGvGudgUFTKNMfbgTJNR7TOygcnpg4eUC0oR++GGsIsVVN56qunpJy7EEKcQoIsIYQQgPsFL+xlvkTqcghRjlKuub6M2DDwmG0U6aoFz4DWUk2wmdIXHmK54WSRi7tKk9mvBmNQVABn4PVv20h2WbvyaNtR6Gp5yyBVBoUQ4iQJsoQQQrhV8AJAKfNhasE4DqhBeCrleJx4U25V/TikBhKpO8Js4xwmeT6JecgaGcVqrjy9MQeEkWuMYIAtiT1EVMrN2qOFs5cIwsnhoi0zUVGrPZ1UGRRCCFcSZAkhxHnO7YIXGozLycFCvjO4OrlLIQcfMtQQonVW5hjnoC871kA9FmfN5Af3rmLvbe+TSVCVuVlmbLTnECuMKZRq7uVcSZVBIYRwkCBLCCHOc+4WvHj0aD4Dj2fxunEWARS67AvVHSWIQh6z/ZujptaYA8LA07uhuizqg8mPKy67tNrcrChdtjM3y17m69YppcqgEEI4SJAlhBDnOXdHH9qWlVGmKbTW5eGplJOj+VCunSyIoKCBTyg+j3wG966SqYItQFW5WemqpVJu1vyCR1DLqv9+KiiEmcOItcQ2VteFEKJZkyBLCCHOc26PPpS1wnDKwsNBSiEeikaZppCpBhCuy+Md/WT0iiIBVktxWm7WNjowumyES5M9Wjj7iKT08O1A5TqDyoktY+LGoNfVvqaWEEKcDyTIEkKI81ysJZZQc6jzzXJV1DI/Xi54klKt8hr2j9meoI9tMumqBdUrWKYJtiSn5WaFk8N/DHNcmpix4c1xygsvI8jahxC76+JooeZQZl4/k/jo+MbsuRBCNGsSZAkhxHlOr9MzNm4sQKVAq+Lr0sO3s5s2PG0bXun45wxLAehvS2JXr8UyitXSnJKbtcKYQhvdkUq5WSuMKcSwi8WFy/hsfwZz8zRmdB3DwoSFrOu7TgIsIYQ4jQRZQgghiI+OZ+b1M7GYLS7bQ82hPHLxZMoLLyOGXbxsnOeyv2Lx2hXGFALNBq7s2LYRey3qS025WVUtUPxScRIJnQbRLaybTBEUQogqVJ73IYQQ4rwUHx1Pz6iepFnTyM5PJ0RvJrZ9AqBja+obvGFLxqColGs6Em2P8KThA6J1Vueb8Pe9pqIv/Cf4tW7qWxF1dSI3qxh45Piz7LD5McCWxAqjI/ACRxGMDDWEAbYkMm3ebN6bS/cLgpq230II0UzJSJYQQggnvU5PN/+LuPWbeXT7ZBzkH+KX3//gZWY5A6xszZcHDJ8x1PaUc7TDjp5WvoGSj9VSncjNMg9fz/DbewCQSVClIhiPlz1GJo7AylpY0ujdFEKIlkKCLCGEEK5Ki+BYNuTtI/OVGxmz7Dv2lnpzUA0kG1/CdUcJooAivHjEYxLHWkWhD7sM+i+VfKyWzOQHfq0J8zUBEE4OswxzXZrMMswlnBwAglt5Qv5BKMlv9K4KIURzp2iaptXe7PxVUFCAn58f+fn5+Pq6txijEEK0dBs3p9FubX9njs5zZUOYblhApC6HdNXCmpg3iLsihrh2gegLDzlGsCTAOifYVY0+099jdsl4onVWSjUP8vChXNM5v/8DbEmE+HiywpjiWHha1kUTQpwn3I0NJCdLCCHOQ3bV7si9Ks4mxBxCrCXWWcDArmqM25AHp+TkvOs5HYB01cJAWxLaXxoj7wxEr1MkB+scU1EEw2yzckANAhQidUc4oAVzQA1y5N8Zk6FUwVx2hGLAXFokQZYQQpxCgiwhhDjPpKanMn3zdA4XH3ZuCzWHMjZuLPGh3fjlrwwy80vgRE7Oas9kZ7vnyoZwiCDIL5HCB+eqU4pgjLCNJ7vQ5gy2D6jBZKkBhCj5eCrlpKsWRpUlsdonAqkxKIQQJ8l0wVrIdEEhxLkkNT2VxI2JaLj+6Xesh6Uxs9yP2MM5/F/BOACX6nIApZoH/1f6PLuI5JUBV9D7ChnFOieV5ENpEd9lezLozR8JJ6fSz0LFtMFMglg+7GpnwG0rt7Hyr5VkFGQQ5RtF/4v6Y/QwNtWdCCFEvZLpgkIIIVzYVTvJm5IrBVgAGhoKMF05ynrbIcd0sIppYmowHtgJ0+XhqZSzyPgCd9smYvExNfYtiMZi8gOTH0f2HgQclQafKxvinDYKMLpshLPSYIF1H7T2YObvb7F4+2JUTXW2e+mnlxh8yWASuyY26i0IIURTkiBLCCHOEwt+XUB+afWV4DTgsF5hvTGEW23ZAGRpAYBGmC7PJT/nfdMUwoNuaJR+i6ZTEUh34ABvGV9y2TfLMJcBtiQAenw3lpk7zLytL650DlVTefuPtwEk0BJCnDekhLsQQpwH7KqdJTuWuNV2Kr0p1RyfwYUpec6KcnfbkrnbNpF01YJ3YBh6k09Ddlk0A3HtAonxLWKRcQaeSjngCLxPLYDxgeckdEX7WayrHGCdatEfi7CV2xqj20II0eQkyBJCiPNAmjWNAluBW22zyqMZanvKZdtzZUPIJIhMglgdMx//4f+VanLngYpKg5G6HA6oQRxQgwlT8gCFLDWASF0OrZUjvOEdhqrUfC4NjZQfUhql30II0dQkyBJCiPNAdnG2ew3LTYQU+zLVsNBl81vGl+jAAQCuvuJyCbDOFycqDR5rFUX/spMjmZG6I4Tp8gBHMZQl+ivdOt2nez/FrtobssdCCNEsSJAlhBDngRBziFvt7svP54PTynUDzoIXMb5FxLULbMiuiubE5Af3ruLPhBUc1BwjmaPLRrg0GWp7ijxbG7dOZ1NtLPhtQUP0VAghmhUJsoQQ4jyQV5KHTqnhT74GvuUqTxYcJlJ3pFLBiwNqMJG6Iyw3pKAvPNRo/RbNgMmPAyeC7XBymGWY67J7qmEhgXkX4e6CMO/ueFdGs4QQ5zwJsoQQ4hyXmp7KU1895VJW+3QaoGT9H+U1FLw4amqNOSAMPL0bqeeiubD4mFzWykpXLfQpTSZdtRCts/KecRoeBR3dOld+aT5p1rQG7rEQQjQtCbKEEOIcZlftTN88vcq1sSoo6Cg5eA8HCntUKnhRsRZSJkH82GMJ3LtK8rHOQ3FBx3nfNMUZYA2wJZGmXcQAW5Iz0Poo91eMisGt87mdIyiEEC2UBFlCCHEOS7Omcbj4cI1tNFQ0eyvCyalU8GKWYS7h5ADga2krAdZ5Sm/ywTswjHTVwkBbknMR4kyCGHgi0PILDGPYpfe7db4vM75syO4KIUSTkyBLCCHOYe6OGLT1ya52KtgKY4oUvDjfmfzwH/5f9tz2Hppfa5ddml9r9tz2Hv7D/8uwLv/G1+hb6+nW7VvHZ/s+a6jeCiFEk/No6g4IIYRoOPsK9rnVLln50GUqWCZBDLAlOQMvR8GLa+G0N9jiPGLyo2dcF77tqrF5by7WwhIsPibi2gWi1zkWydIDgy4exOu/vl7r6ab8MIUb29yIXqdv4I4LIUTjkyBLCCHOUTN/msnbf7xdYxsFhVCvEK4pg+IyGFWWRKbNUdgikyBGmaaw3JAiBS+Ek16n0P2CoGr32zX3KgfmleaRZk2jW1i3+uqaEEI0GxJkCSHEOeizfZ/VGmBVGHPVOPSh3TCXFrHaJ6LyKEXhtY4AS/KxhDsU95tKAQwhxLlKgiwhhDjH2FU7KZtS3Gr7yOWPEB8d7/jC5IceKo9SyBRBUQfdQrsxn/lutXV3kWwhhGhppPCFEEKcY9KsaRy1HXWrrR1ZFFbUr25h3dwqfhFqDiXWEtsIPRJCiMYnQZYQQpxj6jQFq/rls4Q4I3qdnknXTKq13di4sVL0QghxzpIgSwghzjF1mYJlO9aOTbtzsKsa5B+EkvwG7Jk4X8RHxzPr+ln4GSvn8fl7+jPr+lknp6kKIcQ5SNE0rUV9jvnaa6/x4osvkpWVRUxMDK+++ipxcXFVtl20aBEPPvigyzZPT09KSkrcvl5BQQF+fn7k5+fj61v79AchhGhqdtVOrw96YT1urb6RBqrdzLG/xwM6YnyLTlYRvHeVFLkQdVeSD6VF2E8pnhLsbUDx2sPW/V+Ah5Fura+lW1g3GcESQrRY7sYGLarwxcqVK0lMTGTevHlcddVV/Oc//yEhIYGdO3disViqPMbX15edO3c6v1aUOpQ9EkKIFurujnfz2i+vVb3zxGdr5qybOIaOcHJ4rWQSZtsRigFzaZEEWaJuSvLhnd4cz8uif3kyvxb6OHdd7lPISo938AoIg87DQAIsIcR5oEVNF5w5cybDhg3jwQcf5JJLLmHevHmYzWYWLlxY7TGKohAWFub8Fxoa2og9FkKIxpWankrCqoTqAyzAR4VZ1iN8ULqSWOUv3jdOIlJ3hFLNg6G2J7H7RDRij8U5oSATe9YfeB3PZG7ps4STA0A4OcwtfRav4479FGQ2cUeFEKJxtJggy2azsXXrVuLjT87h1ul0xMfHs2nTpmqPKyoqIjo6mqioKHr37s0ff/xR43VKS0spKChw+SeEEC1BanoqiRsTOVx8uMr9PY6rLMg8zPJ9xVxc1IponZXVnslE6o4AkK35sbdQz+a9uY3ZbXEOsBu9Oaw6ps1E6nJ435h8IoBPJlLnCLiy1VbYW1aGghBCnLEWE2QdOXIEu91eaSQqNDSUrKysKo/p2LEjCxcu5KOPPuLdd99FVVWuueYaDhw4UO11pk2bhp+fn/NfVFRUvd6HEEI0BLtqZ/rm6WjVlAtUUPjG4Iel2I9oXQ4ep5Vuz1IDuNuWTBZBWAvdz1sVAuCHbBP3lzxDqebIQojU5ZwI4B0BVqYaQLmqp3jFUDia0ZRdFUKIRtFigqwz0b17d+6//36uuOIKevTowerVqwkJCeGNN96o9phx48aRn5/v/JeRIS8GQojmL82aVu0IFoCGhupRSH+PwRxQgwjT5bnsL+dknozFx9Rg/RTnpk17jlCEF0e0ykngVs0PO3oidUcw5/0JKwdJFUshxDmvxQRZwcHB6PV6Dh92fRNx+PBhwsLC3DqHwWCgS5cu7Nq1q9o2np6e+Pr6uvwTQojmzt21sTSPY3goqsu2w5o/kbojrDCm0NErn7h2gQ3RRXFOU8giiCTbg5w+IzCQQiJ1RyjTdOixo52oQiiEEOeyFhNkGY1GrrzySjZs2ODcpqoqGzZsoHv37m6dw26389tvvxEeHt5Q3RRCiCbh7tpYk/mAMMV1FMufQg6pgUTrrKz0nIK+8FBDdFGcw7pfEEQ4OUw2LuL0Ir4eioqmgUFRSVctLOwwB/xaY1ftbMnawqd7PmVL1hbsqr3qkwshRAvUokq4JyYmMnjwYLp27UpcXBz/+c9/OHbsmHMtrPvvv5/WrVszbdo0ACZPnszVV19Nhw4dOHr0KC+++CLp6ek89NBDTXkbQghR7/JK8tApOlRNrXK/omkEl2tcX5oLiiNHRgHCdHl4KnYUTeMQwYQHRYCnd+N2XrR4VweX8IHnJForjhysI6oPwbpC535FgXJNx79tI9m/9ThRl37OC1tmuExxDTWHMjZurCxSLIQ4J7SoIKt///5kZ2czYcIEsrKyuOKKK1i3bp2zGMb+/fvR6U4OzuXl5TFs2DCysrIICAjgyiuv5Pvvv+eSSy5pqlsQQoh6l5qeylNfPVVt0Qs00IBnc3MwK+UncmR0ROpyOKAGAQqRuiMc9wpH6T1H1sgSdaa3FRGqKwDNEcBX9bOoAsWYCNR/ypNfbai031psJXFjIjOvnymBlhCixVM0Teqp1sTdVZ2FEKIp2FU7CasSai56oSn4HezFF7a38VTKndvTVQsDbEkA/M9/Ov7BEXDvKgmyRN2V5KO905vDB/ehahoRuqNoGi5TBzUNniu9nx8vTMWq11NpXiGOKpih5lDW9V2HXhYtFkI0Q+7GBi0mJ0sIIURltVUVBFAUjSx7G4bannLZ/lzZEDIJIpMgfuyxRAIsceZMfih3zMNs8qwUYOVprSjXFBQF7vBbidXDo8oACxxVMLOKs0izpjVi54UQov5JkCWEEC2Yu1UFAzwymWpY6LLtLeNLdMCxbqCvpa0EWOLs+IbjExBKOXoUBWeVwQDlGMfwQtPgiId7o1Nf7v+yATsqhBANT4IsIYRowepSVTBaZ3UsCqs5RhE8lXLeMU4nxrdIyraLs2fyQxmwlCxTe9JVCw+VPknZiZ81P6UYRYEQu3sVBJfsWEJqempD9lYIIRqUBFlCCNGCfZXxVY37FU0jrLycBJuVbM2PEOUoHopjiKFc0xGhy2W5IUXKtov64R/F4ovm0N+WxAauZELZgy67Y0tKMZSZK62ldToFhRmbZ0hZdyFEiyVBlhBCtFAzf5rJ4u2Lq29w4p3smJw89ECIku8MsDLVAPqWJnPU1BpzQJiUbRf1JizEQhZBxLCLyYa3XfbpgXE5OVSdkXWS5GYJIVo6CbKEEKIFspXbag6wAFAYc7iU+OLjLlutqh99bJPZRgdWXDJPCl6IenVf97ZcwS4+8JyI4URQn6P5OKep3n08mz75NrfO5W7OoRBCNDcSZAkhRAu08q+V1S487KTAMiWu0mZVOfmnv8jTIgGWqFfGrJ9ZZUp2BliZagC3lT5P39JJzhyt247nuXUud3MOhRCiuZEgSwghWqDvD37vVrvupu8qbQtT8njfOIlwcujePri+uybOd94W9AYTGpClBtDHNplMgthGB+4qnUSZpqNLiY1QtbZJg5BX4l4wJoQQzY1HU3dACCFE3aSmp/LtoW/dahtdXgaAVfND1XSE6RxvWiN1R1hlmkRoyA0N1k9xnvKPgsc2o+TuIdC/PUN+t7FlXx6tjHr6xMah8/oHejSeKTnIk98n1XiqF7e8yI1tbpSFiYUQLY4EWUII0YLYVTvTN0+vvaGmoQP6FxSRqQbwsG00xZhYZJxBpC4HgDBdPjpbUcN2WJyf/KPAPwojMOyfjn8nOaYABmTVfpqK4hfdwro1RC+FEKLByHRBIYRoQdKsaRwuPuxGS4VBR4vIUQN4xDaaV41zeME4nwdsYzhEMHbFgM5yCfiGN3ifhaiKu0UtpPiFEKIlkpEsIYRoQdx9w2ksimZ91u18SwmvGucQrbNi9tAzre81hEZ/hd5W5AiwpOiFaCLuFrXYX7i/gXsihBD1T0ayhBCiBXH3jWlywd/4KcXOACtdtfCQbhKxnS9D7x8JloslwBJNKtYSS6g5FKWWVbPm/jKX1PTURuqVEELUDwmyhBCiBan1jakGwWUat9qyWO2Z7AywBtiS2Fbgzea9uY3bYSGqodfpGRs3Fg2t1rYzNs/ArtoboVdCCFE/JMgSQogWpOKNKVAp0NI00ADD4Rs5tRbb6LIRZBIEgLWwpJF6KkTt4qPjeSzmsRrbaGjOAhhCCNFSSJAlhBAtiF214+fpx72d7sXf099ln1buh9fB3iwo/dRl+yuG1wjHUVHQ4mNqrK4KUbWSfMg/iF3V2LQ7B2uet1uHSQEMIURLIoUvhBCihUhNT2X65uku1QUDjH7cdtzG1QXFzDzcl1cMc51TBKeWDeI14ytE6bJ5zziZkaapxLULbMI7EOe9knx4ty/FeVkMLHNMYdWbczFH136ou/mIQgjRHMhIlhBCtACp6akkbkysVL79qC2fJbpibLYjvG+c7AywRtlG8pxhKQZFpUzTEaXLZrkhBX3hoSa6AyGA0iKK87IwH8tgdsl4wsnBXtwOpczbMd+1CgoKYeYwYi2xjdxZIYQ4cxJkCSFEM1exAHFVBQIcWxSmBgahOxFQTSkbxOxTqgr2syVzrFUU5oAw8HRvapYQDcHuE8HAsiTSVQvROivvG5OJVXYxLifXkWF4WqBVkXc4Jm4Mep2+8gmFEKKZkiBLCCGauVoXIFbgiEHH/4wWDIrKAs9ZLlUFf9Y68GfCCrh3lZRtF01q895cthV4M9T2FKWaB5G6HFZ7JjPweBbjDpehlPu4tA/1CmHm9TOJj45voh4LIcSZkZwsIYRo5txN+H+FBG5jifPr58qGOKsKHlADuFICLNHEKqpbFuFFHj6Ekefc9/7RRzDnBfCc7zT0hgJ8DEFc0/tt9P5tmqq7QghxxmQkSwghmjl3E/5H8bnL128ZX6IDBwCpKiiah4qfQwUo11zfgsw1vMIHxhRut2VxaaE3LxUngW9UE/RSCCHOngRZQgjRzNW+ALFGSJnKrbYsDqjBWFXHiJWnUs47xunE+BZJVUHRLMS1CyTGt4gVxhQidTkcUIPI0gIACNPlEak7wgE12GXxbLtqZ0vWFj7d8ylbsrbIosRCiBZBpgsKIUQzV7EAceLGRBQUlwIYyolCAc/m5pCtBaDHjkWXDzhGCiJ0uSeqCl4Lfq2bpP9CVNAXHmK5IQWzzZEzONT2FO2UTBZ4znK28cCOBoSRw9f71pKUtsQlJzHUHMrYuLGSpyWEaNZkJEsIIZq5mhYgDi7XmGk9QnzxccKUPMJ1jhyXA2oQfUuTOWpqLVUFRfPh6Y05IIyjptYMtT3FLONc5hpfcWkSoBTSjkweD5jKuxkvVSr6Yi22krgxkdT01MbsuRBC1ImMZAkhRDNW3QLEUUosP/zVgeJiXzobJ4LuuHP/YdWfu23JZBLEikvm8UivGKkqKJoHkx/cuwqfkkKi5qzl4rL9zrXcRtgeZ6JhCZG6I7ztOZ3bgsNwLFLgOk1WQ0NBYcbmGfSM6iml3YUQzZKMZAkhRDNV7QLEpfn8WvIFgXorCjo0xfVNaIBSSCscQVeRp0UCLNG8mPzQKwpzPOc4AyyDojLesJTJZfdRpun4zcvAYQ8PUKrOQ9TQyCrOIs2a1sidF0II90iQJYQQzVCNCxArjs/2fUI/5H3jBMKUPJf9RsXOEuM0wsmhe/vgRuqxEHXg6Y3qFUy6auGu0mTn4sTzPWdhUFSydO5NtHF3eQMhhGhsEmQJIUQzVNsCxJqikG3QkWkuBqBMU1z+P1yXxyrTJK4OKWn4zgpRVyY/vr5qHv1tSWyjA8+VDXHZvaT0TrdO4+7yBkKIlqulVhiVnCwhhGiGFv+x2K122XpHPopB0UhXLYyyjeQ14ytE6nII1RWgtxU1ZDeFOGOBgSFkEUQHDvCW8SWXfdPLPuOOsmA0j6LTU7IAUFAINYcSa4ltpN4KIZpCVXnJLaXCqIxktRAtNYoXQtTdZ/s+46sDX7nVNsR+8m/B6LIRbKMDd9uSyTOEog+7FHzDG6qbQpyVijWzFhln4KmUA5ClBXBADaK9zspzOUcAzblMwak0NK4Ov1peC4U4h6WmpzJ64+gWW2FU0bQq/noJp4KCAvz8/MjPz8fX17dJ+tCSo3ghRN3YVTs93+tJXmlerW39yjW+ysigorZaumphgC2JTIJYdU80V14UJUUvRPOVf5Di+QmYj2VwQA0CFOdixB7YCdPlkWr2YmpQEEc8qv5MWKfoGHzJYBK7JjZu34UQDcqu2rlm+TUUlxdXub9iNHtd33WNXmHU3dhARrKaueqqi7WUKF4IUTdp1jS3AiyAfx0r5IBqoc8phQNWGFOI8S3iissulQBLNG+nrJl1ty2Zu20TSVctROqOEHZivbfrjpWh+3skZfmXV3kKVVN5+4+3mfnTzMbsuRCigY39Zmy1ARa0jAqjEmQ1YzVWFzuxbcbmGTJdQohzyJf7v3S7baciEwNsSRzSghhqe8oZaC03pKAvPNSAvRSiHpxYM+vHHkvIJIhMghhdNsKlyVDbU+wmAg/f32o81eLti7GV2xqyt0KIRmIrt7F+33q32jbnCqMSZDVjtVYXawFRvBDCfXbVzto9a91q61euMbVgHAArjSm8YJzPWM9nKW4VhTkgDDy9G7KrQtQPkx++lrYAhJPDLMNcl91TDQsJDNiAotSc2aBqKiv/WtlQvRRCNKKVf62scoChKs25wqhUF2zG3I3Om3MUL4Rwn9tTBTU4ntUbIzpWGFOI1lnxMxl495Gb0Cu9HAGWTBUULURFAYzZJY6f5XTVwuiyEcwyzCVaZyXB63PWuvF2JaMgoxF6K4RoaO7+Lps9zM26wqiMZDVj7kbnzTmKF0K4z90PTLoVlxNdFOwMsNJVC4PVieDbGvxaS4AlWhR94SGWG07+LA+wJZGmXcQAWxLpqoVLygvcOk+Ub1QD91QI0Rjc/V3uFd2r0Yte1IUEWc1YrCWWUHMoSlWLhOCorBJmDmvWUbwQwn3ufmDyaEEOqz2TXd6UbivwZvPe3AbuoRAN4EQBjOJWUYwyTSGTIAAyCWKUaQq32wPQuTFzKMQkHzgKcS7of1F/dErNIYqCQtLVSY3UozMj0wWbMb1Oz9i4sSRuTERBcZmfWhF4jYkb06yjeCGE+yo+WLEWW6sueKOBobwVsSWlzm2jy0Y435RaC0sara9C1JsTBTDMpUWs9olg895crIUlWHxMxLULRF94LfftWMTinStqPM3LW1/mprY3yWuiEC2c0cPI4EsG8/Yfb1fb5oFLH8DoYWzEXtWdjGQ1c/HR8cy8fiYWs8Vle6g5lJnXz5R1soQ4h1R8sAJUGsHWNFCAZ3LyOPUt5CzDXMLJAcDiY2qkngpRz0x+4NcavU6h+wVB9L6iNd1beziqZPq1pkfbXrWeQgpBCXHuSOyayIOXPlhpREun6Hjw0gdbxNp4MpLVAsRHx9Mzqidp1jSyi7MJMYcQa4mVT+uEOAdVfLBy+gLkHnZfxhw5wsDjWZUKA6wwpjDKNIW4doFN2HMh6lFJPrzbF45lY79/Ld/v2+vWYVIISohzR2LXREZeMZKVf60koyCDKN8o+l/Uv9mPYFVQNE1zr0biecrdVZ2FEKI+2VX7yQ9WVLj4w2fwOZbhzMHKJIhwcpzFL4pbRWEevt5R+EKIli7/ICy6FfL2cYBQ7tTdT0l07SXaFyYspFtYt0booBCiIbi89jXTQQV3YwMZyRJCiGZIr9PTzf8i8IrAbmhFsVcwuWV2HimfQKbNlzByKMKLUaYpLDekyNpY4tzi15qN3d+m3dr+ROsOs7J8MX3LAin3OAZK5WJQCgqh5lApBCVEC5aanlppFkeoOZSxcWNbZHqMjGTVQkayhBCNodKnd74XoF/Wj+K8LAaWJbGnQEcrjpNFEJ3M+Sz1SMHgG4p5yBr0ZcdkbSxxTrGrGtfO+ALyDzpHa1PNXiRagh0lYU4JtCryFyVPWYiWKzU9lcSNiZWKPjXH328ZyRJCiBaiyk/vTEEkHrNy67GDzFbHM4CTUwQXlE8iUD1C8XG9I8CSKYLiHLN5by6Z+SVAEKPLRrDaM5n44uPMtB7h6cBoyg3Fzrah5lDGxI1pNm/AhBB1Y1ftTN88veqqumgoKMzYPIOeUT2b3dTBmkh1QSGEaEIVn96dGmABWI/nMMZPx0qvUGdxi1jlL943TiJSd4RSzYOhtiex+0Q0Uc+FaDgVyxGEk8Msw1zn9vji46zan4spvT/HDw5gyAUzWNd3nQRYQrRgada0Sq+Bp9LQWmT1UAmyhBCiidT46Z3iKNn+RpCBdDWYaJ2V1Z7JROqOAJCt+bG3UC8LEItzksXH5FLYJV210Kc0mXTVQnudlQ/KFxNSEMXV4Ve1qE+2hRCVuVsV9H87drJpdw52VXMUxynJb+CenR2ZLtjcleRDaRH2KhdoPCR5GEK0YLV+eqcoZBsU/jRBtO3kdpum5wHbGLIIkgWIxTkpLug475umEIkjwBpqe4oivBhgS3IGXu+ZphAa0BPyS+S1UIgWLMQc4la7xd/ksrD4B27w2c8cw6uYAyPg3lXN9ndfgqzm7MQ6IRWJ79sKTlYOi/EtOllRrBn/gAkhqvfl/i/damc3FMMpQZYODTOO4EoWIBbnIr3JB+/AMNKPaDxke4qXjK8TTAF325KdgVYOPjw993PeNryAV0AY3P+RvBYK0QLFBMcQ4BlAXmle1Q00DaXcF3txO2LYxXxbMh5lKsWAubSo2f7ey3TB5qy0iOK8LMzHMphdMp5wcgDHHPXZJeMxH8ugOC8LSouauKNCiLpKTU9lyY4lbrUNsdud/12u6fBQVF41zqGjV74sQCzOTSY//If/lz23vUeQj5FOSgaRuhzeNyYD0N+WxDO24bxkm4LX8UzsWX9AQWbT9lkIUWep6anc+uGt1QZYiqahAONycuilbGWVZzIeioqmwdO2Yc06L1mCrGbM7hPBwLIk0lWLS+L7qXPUB5YlNesfMCFEZRW5WLXSNMLKy4ktKT15LAqH1ECidVZWek5xTBsW4lxk8qNnXBfeeTSeHPwBnIFWhJLDIuMMInWODx8Pq77YjbJOnBAtSXWFn04Varfz4uEcBh7PYr7nLDwUFYAszZ+tRUHNOi9ZgqxmbPPeXLYVeDPAdjLQWu2Z7AywBtgcUwg3783FrtrZkrWFT/d8ypasLdhVe+0XEEI0idpysU41JiePU9P6PRU7ChqHtGD8giJkAWJxztuS40Xf0okcUIMBR6DlKALjCLAOqMH0LZnID9kydVaIlqKmwk8AaOBXrvJJxiESjhe77LKqvvSxpTT7vGTJyWrGKn5wMk9ZJ6TC6LIRZBIEwJcZqSSlLThnVsgW4lznbiWl+woKiS8+7vy6TFM4ovkTrssj3zMMpfecZjsXXYj6smnPETIJ4gHbMyzznIpFOVlRLEsL4G7bRDRg61/7uPqCANdFvS2xUn1QiGao1g8bFcj30LHRGEgvm+tolQ2j87+bc16yBFnNWMUPzunrhADMMsxlgC2JbJ9Mlqe/W+nYw8WHGb1xNLOunyWBlhDNjLuVlHq6BFg6DIqKXdNxSAsmPDgKfMMbqotCNCMKPhQzyziXQApdd2lgIY/Zxjn8uDuAhA+8OXz85IcY8oGjEM2Tux82lnqUuhR+Ktd0ROqOsMKYwlBlYrPOS5bpgs1YXLtAYnyLqlwnJFpnZakxBe+wj2o8x6TvJ8nUQSGamVhLLKHmUBSUqhtUkYs1wvY46aqFSF0OXka9jGKJ80b3C4JoxyE6Kfud+RgVwnR5rPJM5m/vQlJ8i10CLABrsZXEjYmkpqc2ZpeFELVw98PGMLXc5WsPRaVc0xGts/KB1/PNOi9ZgqxmTF94iOWGFJccrDTtImeOVo45H7tHYY3nOGo7yoLfFjRSj4UQ7tDr9IyNGwtQOdDSHFufPHLUJRdrvGEpo2wjSVctePhFyCiWOG9cHVzCPNNslwArSwvAqjo+ZFAUlelBAVDFhxYV+R4zNs+QDxyFaEbySqop117hlA8bM1V/+pQmc0B1pMlUBFp+/sHNOi9ZgqzmzNMbc0AYxa2iGGWa4szByiSIUaYpHDBb3DrNuzvelRcXIZqZ+Oh4Zl4/E8tpv8d6uy/PHi7l5uPFlUavZxvnMMnzScxD1sgoljhv6G1FWJQCADLVAA6owYQpeVh0jtysNJMnhz08qoqxAEeglVWcRZo1rbG6LISogV2188KWF2pt93ROHlbVnz62FNK0i7jbdjLQUnR6lP+b2axfCyUnqzkz+cG9qzCXFrHaJ4LNe3OxFpZg8TER1y6QtD0+8N2YWk+TX5pPmjWNbmHdGqHTQgh3xUfH0zOq58lEfRUu/vAZfI4fdo5eZxLkXHw1WmdljnEO+rLb4URJayHOeb7hFPpdxLHcLO62TSRcyXEpBLVb5+PWadzNARFCNCy3KuwqCmZDAA97TiTT5vgdzySIEZ7Ps9Ij2bEAeciFjdDbMydBVnNn8gOTH3oc89JPFds+Ad8tUymwFdR6GnlxEaJ50uv0Jz8AKcmHgDCKgVFlSWTaHNMgKkavlxtSMAeENevpEULUO5Mffye8y6jF36BApUJQvuXuTcrZX7i/ATonhKgrd9+TFvR6jg879a80yKAvvM7xOtiMR7GgjkFWamoq8fHVV+hRVZXnn3+e8ePHn3XHRO30Oj33dbqP17a9VmtbdxMMhRBNqJbRa33htS3ihUWI+nZlx7aE+m5mdsl4Z57y6LIRzDLMJcFm5cXyKI7olWqnDALM/WUuHfw7SKVBIZqYu+9JrSVhAHQPLoHWp7z2+bVuqK7VqzrlZN16662MHDmS4uLiSvt+//13unXrxuuvv15vnRO1G3b5MPw8q3/DpaAQZg4j1hLbiL0SQritJB/yD2JXNTbtzuGjP4vYdMSxfEP34BJ6X+xN9wuC0OsUxwuLBFjiPFRTIagDqoXnco5AdYuanqChSQEMIZqBmOAYAjwDqt2vaaCW+ZH8/nH6TH+P4vkJ8G5fx+tlC1KnIOubb75hw4YNxMTE8N133wGO0aspU6Zw5ZVX0rFjR37//fcG6aioml6nJ7l7cpWloCu2jYkbI4sxCtEcleTDu30pnp9An+nvMXDBDzy+4hcGLvihRb+wCFHvaikEdY0SzGNq7dNopQCGEE0rNT2VWz+8lbzSaqoLahoKUHr4dsLJY3bJeMzHMijOy4LSokbt69mq03TBq666ip9//pmxY8fSs2dPhg8fzg8//EBGRgbLly+nT58+DdVPUYOKKmXTN093SSQMNYcyJm6MTI0QorkqLaI4LwvzsQxmq+MZgKPQRTg5zC5JwWyzUgyYS4tkBEuc39yYShuZ+R38mFLrqQ4fqyXhXgjRIFLTU0ncmOhcWqEqYXY7Q46ovF9kZNYp68SOKktitU8ELWnIoM6FL0wmE7NmzcJqtTJ37lxatWrFTz/9RMeOHRuif8JNlaqUmUOICY5h25FtfLrnU0LMIcRaYmVES4hmxO4TwcCyJGarjjyTFcYUZ55JS35hEaJB1FAICr/W5B0srfKw01X7CboQosHYVTvTN0+vNsDSNFDsXsxNz+NCnZWBJyqIOivt2rzZvDe38u9+M1bndbJ2797NP//5T7744gvmzZvHZZddxvXXX89HH33UEP0TdVBRpezW9reSX5rPrR/eypD1QxjzzRiGrB9CwqoEWfVeiOaiJJ9ffv+DbQXezgXGo3VWVnsmE62zkqGGMMCWxLYCxwuLEKJmNeV4nEk7IUT9qa1su6IAHsf5t+FOl+2jy0Y4pwdbC0sasov1rk5B1pw5c4iJicFisfDbb78xfPhwvvvuO5544gkGDBjAfffdx9GjRxuoq8JdFcOxp/8wW4utJG5MlEBLiKZ2Iher3dq7CSeHTIIYXTbCpUkxRorwAlreC4sQTSG0VWi9thNC1J8v93/pVrt7PF0HbWYZ5hJODgAWH1O996sh1SnImjBhAm+88QarVq0iJMRRflGn0zFmzBh++uknduzYwaWXXtogHRXuqWk4tmKbVFcSoomdyMUKtB1ihTGFGHZVWvunvZJJOw4BLe+FRYimEGuJJdRccwAl1XaFaHx21c7aPWvdattJzSNdtTCsdDQZaohzKn2MbxFx7QIbuKf1q05B1h9//MGgQYOq3HfppZfy448/8sgjj9RLx8SZqW04VkOT6kpCNDK7amdL1hY+3fMpW7K2YGsVysCyk1MEPzgxRbBMc/xJLtN0GBSVV41zWuQLixBNQa/TMzZuLMqJ/52qYptU2xWi8aVZ09zKhQy02wkq9mOUbSTjDUs5itkZaC03pKAvPNQIva0/dQqywsPDa9yv1+tJSko6qw6JOjptjZ3/7djp1mHurrYthDg7qempJKxKcMmPjH+/F39o+/i3baQzoAIwKCrpqoW7SpOdAVhLfGERoqlUVNu1mC0u20PNocy8fqZU2xWiCbj7nvO6QpXRtpHMNs4hWmfFl+NM9HyS4lZRmAPCwLP2ZRqaE7erC86ePdvtk44aNeqMOiPqqGKNnbwsBpY5EuT15lzM0bUf6u5q20KIM1ddudo8Wzam1u+y9+Dd7CmNoKNywLlvdNkIttGBAbYkPvGdRmALfGERoilVVW1XqusK0XTcfc/5V8GNzgArXbXwiMckPhozAOOxfzleB1vYUiZuB1mzZs1y+To7O5vi4mL8/f0BOHr0KGazGYvFIkFWYzlljZ3X1HHcTTKZxe1QyrzRPApPlGpxpaAQag6VOelCNLDaytUCKKHrMO53LWoxyzDXUa6WIPbe9j6BF0W1uBcWIRpdST6UFmF3WUOrPQntujpGgm2y1pwQTaUiX9JabK36NVHTCLPbWW5/F70ODqjBDLU9xS6bH1vT8+h+QevG73Q9cHu64N69e53/pk6dyhVXXMGOHTvIzc0lNzeXHTt2EBsbS0pK7QsBivph94lgqO1JSjUPInU5vG9MJlbZxbicXMdsdM31B7lijrrMSRei4blTrlYzFHLEXEi6aqHPKVMEK5J8r7jsUnljKERtKmZ1zE+gz/T3GLjgBx5f8QsDF/xAn+nvUTw/Ad7t62gnhGh0FfmSQKV8SUfMpTAmJ8+5HqQHdl4wzseH4hZdXbfO62QBJCUl8eqrr7osQNyxY0dmzZrF+PHj661zomab9+ayt1BPtuYPQKQuh9WeyQw8nsVzh0sJtrsGWTInXYjG4+4c9B26AAbYkkjTLnJZL0tysYRw0ymzOmaXjHeWew4nh9kl4zEfy6A4LwtKi5q4o0Kcv6rLlwwpVxl/uJT44uPObWG6PCzk0YrjLbq67hkFWZmZmZSXl1fabrfbOXy4+k9uRf2yFpaQRRB32yaSpbkurtijuIzUjAM8f9CGf/a/WHDTW6zru04CLCEaibtz0N/VDXEutJhJEKNMU1pskq8QTcHuE+FSrXOFMYVY5S9WGFOcuR0Dy5Kw+0Q0dVeFOK/FR8ezvu96Ftz0FiE5dzDj4HE+P3CA/scPk6UFkKWe+l5WIdTXs0VX13U7J+tUN954Iw8//DBvvvkmsbGO3J6tW7fy6KOPEh8vb+Iby6nRfbmm49QR2DAljwNqMM8XPEcmQdiL20oSsBCNqLY56AoQ6hXC2ieeZGt6/okcEhNx7QLRF17bIpN8hWgKm/fmsq3AmwEkOQOr1Z7JAKSrFkeOo82bzXtz6X5BUNN2VojzjF21V3r/qR2/gGLrhVxu1NCfGO4JU/JAgQNqEKAQqTvCYt0kx+uhX8vMyTqjIGvhwoUMHjyYrl27YjAYACgvLychIYE333yzXjsoqhfXLpCOpnzmqylE6nLIUgMI051ch8ADx4LDHj6/88zmlygoO+LcF2oOZWzcWBnZEqKBVMxBT9yYiILiEmg58yOvehajh0flN34t9AVFiKZQkbORSRDPlQ3hXc/pzn2jy0Y4R4oLrPugtYd8eCFEI0lNT2X65uku+cmh5lBuCBnGMbyw4o+HZncEWCeMKvs3mVoQK4wp+HgFt+gZHYqmadWXvqrFX3/9xZ9//gnAxRdfzEUXXVRvHWsuCgoK8PPzIz8/H19f36bujqv8gxx9/Sb8Sw66RP6nWullYUqoidPzDCve5EmOlhANq6oXmTBzGGPixsjvnhD1YNPuHAYu+IEOHOATz2fxVE6mM1SMZAH8z386/sERcO8qCbSEaGDVLWFS8aHj8QP30rbQn0XGGUTqcpz7K35nNWD24OuI69SukXteO3djgzoFWddddx29e/fmX//61zkZUFWlWQdZJfloS/qSeXAvqgqRuiOkqxZGl41gtmEO4boj9IpqjVWvrxRkwcly7uv6rpOpg0I0oKqmS8jvnBD1w65q9Jn+Hq+VjHO+WcvSAijXdETqclw+hCw2t8b88OcyWixEA7KrdhJWJVRbYVdBQVfuw+p0K+1P5E2OLhvBLMNcZx7lKNMUVo/th15XxRvYJuZubFCnwhfDhg1j06ZNXHnllXTq1IkxY8bw3XffcRaDYeJsmPxQes8hwGx0BlgVVcrutk1kvdGC1aPqAAtAQyOrOIs0a1rj9luI84xep6dbWDdubX8r3cK6SYAlRD3SFx5iucExbb5U86Bc052YfqSQpQYQqcshUneEck3H/uOe2DxMbMnawqd7PmVL1hbsqr2pb0GIc0ptS5hoaNg9Csgx5zvfux7Sghhqe+qcqrBbp5ys+++/n/vvv5/S0lI2bNjARx99xN13343dbuf//u//+Ne//kVCQgJeXl4N1V9xOt9wvAJbU6zoGVXmSO4Fx9z0l70GAGtqPYW7paaFEEKIZsfTG3NAGPnlZRQeLydSd4QyTecyfV7TwENR+dlUzPCP+pBXluvcJznKQtSvuixhMsr2HAArjSnk4MtYz2d5y/jyOVFh96xysir8+OOPfPzxx3z88cfs3r2bG264gXHjxvGPf/yjPvrYpJr1dMEKVa50b0Lx2s2wz4fWevjChIV0C+vWCB0VQgghGkBJPv/b+jeT/7vdWWHwdMu9wng+1Cg5ykI0sC1ZWxiyfkit7Uzp/fEobuP8nT1qao3PI5+hV5RmXWG3QaYLHjt2rMrtV111FVOnTuW3337jt99+48YbbyQzM7NuPRZnzuQHfq3R6xS6XxBE74u96R5cQqwllgBj9Wv1KCiEmcOItcQ2YmeFEEKIemby45Aa6FhrzjaSMs317U2JpmNGUEAViyngTMyfsXmGTB0Uoh5ULGFSHUXTCC7TiDzu5QywMtQQBqsTwbe1I2eymQZYdVGnIOvyyy/n22+/rbHNBRdcwOjRo7nrrrvOqmPiDJXkw7t9KZ6fwN0vrOLQngQ0zTFV4lQVH+SNiRsj+SFCCCFavEBvT8LJYbZxDgZFddn3m5cBu+EYiuQoC9Hgvsz4kpLykqp3aqABz+Ue4UPPyUTrrJRpOo5iZk+Bjs17c6s+rgWqU5DVt29fbrjhBp5++mlsNltD9UmcjdIiivOyMB/LYHbJeEIKwyk5eC+6ctd5raFeITI1QgghxDmjjT7P+an46SNZWTr3UtAlR1mIs1NRuj3fll/lfl9V46KD3YkvPu7cZlBUfDlOK4471707F9QpyHrhhRf4+uuv+eSTT4iNjeXnn39uqH6JM2T3iWBgWZKzOssKYwqXFxlZs/8ICzMP8/RhG+HZ9/LxHf/Dz9NPqisJIYQ4J1zRIZLjulaUaToMikq6aqFPaTLpqoUwtbz2EwAh5uqn2AshamZX7UzfPL3S2lhOmoaXZmep7QOXzWWajlG2kWQRhMXH1Ag9bRx1qi4IcPXVV/Pzzz8zfvx4rrnmGm666SY8PFxPs3r16nrroKibzXtz2VbgzQCSnJ/orfZMBkBfbGGU7TmyfTJJWHULebaTn9hJdSUhGkA1RWni2gU6StM248ReIVoafdkx2phtGIpVZ1noTIIYYEtiKSmElpdzWK+nqjmDFetGSo6yEGeuttLtKAqHPTz4zctAt5JSAOeHIrONc3jUYzJx7QIbqbcNr85BFkBpaSlWqxVFUfDz86sUZImmUzHMmkkQo8tGOAMsgNFlI8j2ycTU+l3yTpvtebj4MKM3jmbW9bMk0BKiPlTkR+ZlMbAsiW0FJ6fsxvgWsdyQ4ihRe+8qCbSEqA+e3pgDIyhWdJWWNBltmsLoYymM83UEWKd/zq6h0ffCvo3cYSHOLe5Ot83Wn6wFMML2OOMNS4nWWXnXYzL6wn+eM4uF1zk6+vzzzxkyZAjh4eFs3bqVTp06NUS/qvXaa6/x4osvkpWVRUxMDK+++ipxcXHVtn///fdJSkpi3759XHjhhcyYMYNbb721EXvcuCqGWcPJYZZhrsu+lwxzuSM0GLWqA0+Y9P0kekb1lGIYQpytU/Mj1fEMwPGpejg5zC5JwWyzUgyYS4skyBKiPpj84N5VmEuLWF3l6PG1eGZvZfrPs6v8tP21ba/xwd8fyKwOIc5QoMm9UagQ+8kUlfGGpYyyjWS2cQ6BvqEtfm2sU9UpJ+vhhx/mtttuY9iwYWzatKnRA6yVK1eSmJjIxIkTSUtLIyYmhoSEBKzWyuthAHz//fcMHDiQoUOH8vPPP3PHHXdwxx138PvvvzdqvxtTXLtAYnyLnFMFT52TnmPORzMUVVtdCeCo7SgLflvQeB0W4hxVVX5krPKXy+/mwLIk7D4RTd1VIc4dpy9pckVrul8QhF6ngF9r4jv8i/V91/PYFY9Vebi12ErixkRS01MbueNCtGyp6ak89+1zNTfSNMLKywkq9nO+N43WWZltnMMkzycxD1lzTn3oWKfFiC+77DKWLFlCly5dGrJP1brqqqvo1q0bc+bMAUBVVaKiovj3v//N2LFjK7Xv378/x44dY+3atc5tV199NVdccQXz5s1z65otYjHiU+UfpHh+AuZjGS5z0sPJ4dHAqbwYaqz1FH6efnzV7ysZzRLiLGzancPABT8QTk6lxVFP/d1cPuxqul8Q1IQ9FeL8YlftJKxKqDZ3pCI/a13fdfI6KIQbKioKVlvwAkDTUIBxh8uYnfec871pxetjcasozMPXt4ipgu7GBm5PF0xMTKRHjx4sWbKEJUuW1Nh25syZ7vfUTTabja1btzJu3DjnNp1OR3x8PJs2barymE2bNpGYmOiyLSEhgTVr1lR7ndLSUkpLS51fFxQUnF3HG5unN+aAMIqh0pz0d3VDgHdrPUV+aT5p1jS6hXVr2L4KcQ6rLT8ykyCXdkKIxlFbcv6pa2bJ66AQNau1ouAJoegZXWBnvn0ymZx8bzrKNOVkjvI5NFUQ6hBknV6uPS0tjfLycjp27AjAX3/9hV6v58orr6zfHp5w5MgR7HY7oaGuK0iHhoby559/VnlMVlZWle2zsrKqvc60adOYNGnS2Xe4qZw2J33rzn3kHc3FOzgala48+cOHHFeP1XoaWStEiLNTU37kLMNc50jWuVSuVoiWwO3kfHkdFKJWtVYUPGFqz5lc5X8xN/tE8Mvvf5BV6kFgYIgzX/JcrLbrdpD15ZdfOv975syZ+Pj4sHjxYgICAgDIy8vjwQcf5Lrrrqv/XjaicePGuYx+FRQUEBUV1YQ9OgMmPzD5oS/JJ+67YS7VzYz/396dx0dVXg0c/907aybLJJlkkgAxstStVihKlNZWaVNp7aJVURR3hVpAFFDBJRIEFVygRUCEuiCooOBSqxWMFZe6gKaitdq+KIQly2SZTJZJZrv3/WOSIYEsA2bP+fbD+zYz905O6JA757nnOSdlLJbUjmvNZVaIENEJaSEKXAWUectItaUy2jkag2qI7I9c1nBwD9bMwFSWmlZG9mjNsC7sV+1qhegLor2+yXVQiI5Fuxix5dtitGNGka3t59S3J0Fsari7buN+yf7oqHqvP/zww2zdujWSYAEkJSWxcOFCzjnnHGbPnt1pATZJSUnBYDBQWtoyWy4tLSU9Pb3Vc9LT04/oeACLxYLFYvnuAfcGrXU3K/8ZlqT3wVDf6qwQAKfVKbNChIhCfmE+i7YvarGKF5k5l3hiuATC7zpsZk9TDfpzpgXhFbx+eoERojca7RxNmi0Nl9fVZomTgkJFfUU3RyZE3xPtYsTa9yr5u/c1XrAuZAiN18x+3l33iLoLNqmurqas7PDMtaysjJqamu8cVGvMZjOnnnoqb731VuQxTdN46623GDt2bKvnjB07tsXxEG5B39bx/U3r3c12cUd5NQpAGz1PyhrK+HPBn7s1ViH6mqaNvoeWSUS6k5V9Gt4fGZvJDOvCyB6sphp0b2xmv6xBF6K3M6gG5mYf3iyrOR2dW9+9lSWfdP4ecyH6k6ZFC4XWF+4VXScloHNKfYAN5gUMoZRCzcm2sU/2+wXGI+ou2OTKK6/kvffe4+GHH47MqPr444+59dZb+clPfsLatWs7PVAIt3C/6qqreOyxx8jOzuZPf/oTzz//PF9//TVpaWlceeWVDB48mPvvvx8It3A/66yzWLRoEb/+9a/ZsGED9913HwUFBZx88slRfc8+112wmfa6mz0Xk859qbFgCLR5/jXfv4ZZp81q83khBqqou5P9eiOGQD2hVmf2FPXLGnQheq0GD/hqI/8e39q7lef3LkJvd3okPHzWw5xz7DndFKQQfU/ToiPQ8u6wHv4/S13l5HjrgXB33Uv9uej2wbw/52fh8Qp9TLS5wVHdyVq1ahW/+tWvuOyyy8jKyiIrK4vLLruMX/7yl6xcubLjFzhKl1xyCQ899BB33303o0aN4rPPPuONN96INLfYu3cvxcXFkeN/9KMf8eyzz7J69WpGjhzJpk2bePnll6NOsPq6Q7ubNbex6np0NdjWzSwA1v5nLf6gvytDFKJPiro7WfU37c7skQRLiG7S4IH1F+JdPZ4LFj3PpWs+4sn3yjpMsADu/fheQlqow+OEGKhysnJYcvYSnDZni8e1oJ2MAz+PJFgQ7q5bhINiTwPbd1d2d6jd6qjuZDWpq6vjm2++AWD48OHExsZ2WmC9RX+9k/XnuAz+kmrq8DVuG3MbV5x0RVeGKUSf8/q3rzPnvTkdHrf4J4s5d9i53RCREKJdrcyQLEvYR8zgDVGd/sT4J6SduxAdaGoE9fev/sva9ypJ9Saw0Xxvm3Mi/zxxFOeN6nslg116J6tJbGwsp5xyCqecckq/TLD6uqbuZk0JVqHmjEzYrjPXd/wCwAcHPujiKIXoe6Q7mRB9S2t7lEeEPFGfL+3cheiYQTUwJvE4fu0YhdObGEmwmj5/7tdSIv/+Mqjo9yNMvlOSJXo3Q00Rz5kOJlgT/bkU6Mcx0Z9LrD8mqtd4v+h98gs7bvkuxEDS4UZfFNJt6dKlU4heYvvuSnZWxzHRfzDRei30GEmh6MoAZcFEiCg0luWe+PeL2GSZ3+LzZ7HuAHR8upEs1cUL1oVkO6Jb8O+rJMnqzyxxbXY3y/fnouo6HQzoBmDx9sVSjy7EIS467qJW2z83JV5zsudgUA3dHZYQohWt7VE2AHeVV4LeZrNdAFkwESJaTaOD6otJoYr9WgoT/bkA4c6CagVlup39WgpxyekYrPE9HHDXOqo5WaKPsNrh8s3YfLW82NhNqbKyjHRLkB+c9CvuevPf/N31tw5fpsRbQoGrQOrRhaD12VjNpdnSmJM9h5ysnG6OTAjRlqaypAwqWGo62KDrHG89F1QFeDGx9T3KCoosmAgRpaay3GXaXeF9WLpOhhL+N9d0V+uyQC4LzvsBPxs1rN83f5Ikq7+z2sFqxwCMHWyEt6fjdZcw4W+57Kw+E+vg/8OU8N8OX6a0ru1OakIMFE1tatsaYDpt5DQmnzJZPpAJ0cs07VFe1nCwhH5mYCpLTSuZX1XMcb50HkhNRDN4I+ek29JlwUSIIxApyyU30g/gRUse0LLhRUzqMf0+wQIpFxxYmm7j1u1jWcNdZFBByDsiqlPdPncXBydE7xbSQuR9mNdmgqWgsPn/NndzVEKIaBy6R/k6/y0U6Y7IHq1J9SW8X1HHEz9ezOLT5vDEuGW8ceEbkmAJcQTaGx00MzA1sm2l6bj+TpKsAaTpNu7eZt1dskI1UZ2bZEnq4uiE6N3WfL4Gj6/tbmSR2Viugm6MSggRlWZ7lOda7uAB82o2mhcAcINxPpXmQRCTwui4Yzk3/0HGbL0Xg7+2h4MWom9pqywXYKlpJRlUtDiuv5MkawDZvruSb6tVPMQR0FWyVBf3qy9Eda50VhIDWUgLse6rdVEdK62eheiFmvYoT9nC+mm/4KQEP1mqi+etC6nyBjm3+nZ+v/9SiledD+49UFcGPkmyhIhKgwc8Bzg1K4kTbdUtRgdN9s1kn5YaWdwfmVBL9tDkno64W0iSNYC4ahqIpZ4EvJiU8JT70Q0+0oLBDrsM3vn+ndLKXQxYBa4Cqv3VUR0rCxJC9FJWO9gHY0gcwkc/XUuh5iSTUjaYFzBIqeBx80MMoZRCzcm2sU+Cve8NSRWi2zW2bfeuHs8Ni1ezKnh3JMGa4Z/OXaZnqMIWSbSeMy3AUFPU01F3C0myBhBnvJUSHMzwTyegh/+nNwBzK9wo6Cjt9LB1eV3M2jZLEi0xIEV7d8pusUurZyF6uZCmc/tb7hYzs1605EU+GF7qz+X2t9yEtChmnAgx0DXb75/ne5BqbJEEa5l5OVmqiwTqmWeZjTc2E1tSOljiejrqbiFJ1gCSPTSZE20elpmXY1K0SKKV461niauclHaGMjZt9peZWWIgivbu1OUnXi6dBYXo5bbvrqTY09Dm5vwiHBR7Gti+u7KHIhSi72ja71+oOTlGLcdOLQsDkyIJVqHm5AbjfFbN+QO2KVvg8s0DorMgSJI1oBhqinjGeLBOdpr/pshzOd567iuraPd82dgvBqrRztGk2dIig4Zbk2hOZPIPJoe/8BwIl1AIIXqdps5mGVTwJ9PyFs8135xf7doj/46F6ECkbbv/YKK1xrI08llzoj+Xr7x2Pi10h0twB0iCBZJkDSyWOEwJaZHbuLmmtS2eLlOjG5smG/vFQGNQDczNngvQZqL1q4zpbN9dRci9D546F9ZfKB/QhOiFnPFWMqhgo3k+x6jlBHSF23zXR0oHN5gXMJJdnP7OFfDUb6FqX0+HLESvJW3b2yZJ1kBitWO79mXmW2ax3PwnMtVKdJ3IxSVdC0b1MrKxXwxEOVk5LDl7CU6bs8XjqUEd6/7zWP1GHLPWvEbxn38u3cmE6MWyHfW8YF0YSbBMis695ie4x39FJNHaZMkjseEAWum/YeMkWTARog3Str1tkmQNMIZAHcvNKyIJlqIQubg4vHbSgsE2G2AoKKTb0mVjvxiwcrJy2HLhFp4Y/wRXDbqB+w74eXPfPjb5NjJa+R8bzAukO5kQvZzBGk9ccjqFmpM/+GYR0FVMisYqy1JWBn4X+Tqoq6h6CL3BIwsmQrQhe2gyIxNqW7Rtv8CX1/LO8ABq296cJFkDTeNAxjpbJtf7Zre4uDwfHMeccjdAq4mWjs7s02bLxn4x4IS0EDtKdvD6t69T4CpgZMoP2fzJCdxXfSf7pTuZEH2L1U7ilFfJP/0J/sGpXOTLi1wLF1v+EkmwjIpGoebkiRHLZcFEiDYYaop4zrSgxR6sAv24Ft07B1Lb9uYkyRpoGgcy/vWHa3grcnFRMCkac80b+UV9PQ+WVqAGW2+vOee9OSz5ZEk3By1Ez8kvzGf85vFcu+Va5rw3h2u3XEvOC+dQpn0i3cmE6KusdlIGDwNgJyOY2qwRFBBJsCb6c3nk03pZMBGiLY2L997YTGZYF0b2YBXjYIZ14YBr295cdJ0ORP9itfOVNx6o5FsGUayncIxysJnFNs9EvEYfFsd7QGNNYSNN13jyyycBmHXarG4OXIjulV+Yz6xtsyIjDJq4/WVYB68n5sB5LPVtbPHcUtNKJvpzKcYxIDf6CtFXNN9LcpfpmcOevzcwKfyB0Rtg++5Kxg53dHeIQvR+jYv3Nl8tL8YPYvvuSlw1DTjjrWQPTcZQc2Y4wRpAXQWbyJ2sASor2QbAGL4iU2nZLfAu01OtJljNrf3PWvxBf1eHKUSPCWkhFm1fdFiC1UQB4tJeZkgbNegZVAzIjb5C9BXZQ5M53uqJ7CVpmh3ZZIX5z4xkFzAwO6MJETWrHeyDMagKY4c7OG/UYMYOd2BQlQHXtr05SbIGqCvGHssPlV08ZlmCokDTFixdhxfttvAnyDYSLAjf0dr4v41tPi9EX1fgKqDUW9r2AQqUmxReN6cz0Z9Lke7gOv8tkUTrBetCsh313RewEOKIGGqK2GhdGEmwTI0lglN8MyNfb7LkcQq7ZMFECHHEJMkaoMwl/2KTZT4mRY90GQzoKooC+4zRVZHuq5bZIaL/envv21EdN58LANhoXsAD5tVc35hoxSWnY7DGd2WIQojvwhKHPTGFIIZIgjXRn8tWfUyLZhirrMtlwUQMWM0bP+0o2UFIC/V0SH2G7MkaqOKcGMxWQv4Gpvhu5m7zOrJUFwCZwejmZWUmZHZlhEL0mPzCfNZ9tS6qY5VgLC+Y5zNELQcNYhIS+TbnecadMmzAlkgI0SdY7SgTn6HuqYupqiyP7KWEcDOMi3x5LDc/QnxKhiyYiAEpvzCfRdsXtajqSLOlMTd7LjlZOT0YWd+g6HobQ5EEANXV1djtdjweDwkJCT0dTueq2sfL7/+Lm983MJJdbLLkYVI0/MCYYzPRoM2SQVVR2XHZDsxGc3dGLESXC2khxm8e336pIICuYwzG8fLeMrLUcny6kUnqYp6981rMRikSEKLPaPDw9uffcsdblRR7Du69yrBbue/nybJgIgaktho/KYQ/Fy45e8mATbSizQ3kTtZAlpiJPlglg7dYZl6OSdEAMANXeap50p5ApJbwEFeddJUkWKJf6nAvVjMPVu4hSw2XEZXpdvbXm/m00C1dyIToS6x2xmX/kPdP0w/vjKa2vTdZiP6qvcZPOjoKCou3L2Zc5jiZndoOSbIGuGMM7hadlZoSrVluDyFdYX1ifPiOViMFhZ8O+Sk/GfITQlpI/nGJfqe0LroEa1J1DTnecIJVoiUxwZ9HibRtF6LPauqMJsRA19Fio45OibeEAlcBY9LHdGNkfYvUtAxwo0YMoV6NjSRYPt1IiZ7Efs3BrVVVfLD7AJeXQVz9scRpOjo67+x/h2u3XMv4zePJL8zv6R9BiE7l9rmjOm5ws72LQQ4uNkgXMiGEEH1Zmbes44OO4LiBSpKsAc4QqOMYmx+TorFfc1CmJ5KuuAGF/ZqDWEVjaKieWutuag+pmnB5XczaNksSLdGvJFmSojsuFL7HW6InMUQtZ4N5AcfHeMgemtyV4QkhhBBdKtWW2qnHDVSSZA10ljhsyYPwxmYy1XIfE/zzKNSc4U5pKBzQkngspXHv1SF7s5pqdRdvXywtPUW/kRabFt1xofB7Pqir7NdSyFJdbLQsxFBT1JXhCSGEEF1qtHM0aba0SJOLQykopNvSGe0c3c2R9S2SZA10VjtcvhnblC3MuSSHYhxM9OdGEq0imxeX0dBml8HmdblC9AdNF5c26TqpAY37PHMa/51UADpFpGB3DAJLXLfFKoToGTI7SPRnBtXA3Oy5AIclWk1fz8meI/vyOyBJlggnWvbBlNf6ACjGwczAVADKDNH9A5K6XNFfNF1cWl3Ba5x4UVP6e/6ln9BsQaKCpBgTynnLpdWzEP1cfmE+52w6h2u3XMuc9+Zw7ZZrOWfTOVI6L/qVnKwclpy9BKfN2eLxNFvagG7ffiSku6CIaNqwn0EFS00rAUgNRbc6t7dmb5fFJUR3Cmkh7BY7l594OX/79lXcvqrIc2kaUDWBXTWnkU4FtcQww7qQ50wLsCWlQ0JGj8UthOh6+YX5zNw287DHXfUuZm6bydKzl8qHT9Fv5GTlMC5zHAWuAsq8ZaTaUhntHC13sKIkw4g70K+HER8ipOlcsOh5ljXcRZbqolhLZGpgBlUjnqTCAHobJYMQvn0sKxuir2ttun2SBucOP59BljGkhjJISB2K2VvESVsuQ7OlYLv2ZQyBunCZoNzFEqLfCmkhztp4Fh6/p81jbEYbH1z6gXwIFaIfk2HE4ogZaorCK/J+F5oOqUo1mm6ktuR89MEvtzmYuIkMphN9WVvT7asUnWe+fQnrAZ2ymrFk8BUvWBcSSylYDBCoA/vgHopaCNFdPin9pN0EC8Ab9PLY548xddTUbopKCNFbyZ4scZAlDltSOvVWJxoqRkVjkyWPQTWpmMrPbDfBkgYYoi9rd7q9Et6dFZf2Mj9UvmaDeQFDKKVQc7Jt7JOSYAkxQGwv3h7VcU//52lphCGEkCRLNNPYadAw5R9M8OdFBhRvsuRxcqD91bsm0gBD9EUdTrdXFMpNCnfYF5OlutinpXKpP5fb33IT0qTiWogBoe11xhbqAnWy4CiEkCRLHMJqZ92XAf6lj+Ai38FE60blrahOl8F0oi+Kerp9Y7fNBkzUEEOxp4Htuyu7MjQhRC8xJm1M1MfKgqPo8xo84DlASNP58JsKXvnsAB9+UxFeWPQcCD8v2iV7ssRhCiu9AOxkBHcHruZ+8xOMbvCRFgxSajC2upqnoJBmS5PBdKJPinq6fWO3zRFKESfzLR9yMq6ahq4MTQjRS4xJH4PNaMMb9HZ4rCPG0Q0RCdFFGjyw/kK87hIuDeSys/rg/MeRCbUHO+pevlkaPrVD7mSJw2Ql2wAYyS7uMT0FgAGYW+FGQefQbSsymE70dR1Ot9d10oJBRjeEZ8kpCjxoXk0GFZHRB0KI/s2gGrjq+1dFdeynJZ92cTRCdCFfLV53Cba6fSxruIsMKoDwiJ9lDXdhq9uH110CvtoeDrR3kyRLHOaKscfyQ2UXmyx5mBSNgK5wv/8SzqrzscRVjjMUbHF8WkyqtG8XfVrz6faHUhqnXMytcGMAirVE9mspDFHLecG6kGxHfTdGKoToSccmHBvVcc/+91lpfiH6rFD8IC4N5FKoOclSXWwwL2C08j82mBeQpboo1JxcGsglFD+op0Pt1STJEocxl/yLTZb5kQTrG30wlxnf5gbfTM6q87F1XxGri8r4QcWPWenWeaMaco6gVl2I3qhpun2CKaXF42mhEEtc5eR46ynRkrjAv4AJ/nkUak7iktMxWON7KGIhRHeLtrTY4/NI8wvRp4S0EDtKdvD6t6+z7l9vsbPaxkT/wUTrRUteJMGa6A+XEMqe5PbJnixxuDgnBrOVkL+BP/pvJte0jizVxd3mddzgm8kqy1JOaQjxYP1WBqsVeANg89VKXa7o83KycthdmMWibX8nyVjMvcpGcnwVRIpgG6sJi3Hw9PEryL3wdHnfCzGAjHaOJsGcQLW/usNjpfmF6CvyC/NZtH1Riy67sSPslJX+lpm1U3nRkhd5fGZgKsWE9xzKnuT2yZ0scbjETJj6MYbr3+SR+XdynZoXWcm427yOO/zX4tbjGaxWUKg5uSRwO2v3vcV9H93Huv+swx/09/RPIMRRS4mLwelN5OWG5xnfPMEC0hU3L5jnk0EFJ594kiRYQgwwBtXAFSdeEdWx0m1X9AX5hfnM2jbrsDEmitFDzOD1TEhc1eLxpaaVkT1asie5fZJkidYlZsKQU/lsXxW7GhJb3DJ+0PIXhjQmWL+3/4Tdgxbz0KcP8tx/n+OBHQ8w5tkxLPlkSU//BEIclWOMVbxgns8QtRyA/ZqDC3x57NfCK3dD1HJeMM/nGGNVD0YphOgRDR4mH3sudnPbCywKCum2dOm2K3q9kBZi0fZF6Id2NCPc4Al0nkhR+VZzcoEvr8UerZEJtWQPTe72mPsSSbJEu5puBRfj4M7AtS2eu9x+Gn7HDg5tN6jpGk9++aQkWqJPGpVmIFUNz//YrzmY4M+jQD+OCf6DiVaq6mFUmnTSFGJAafDA0+fhf+wcLEU56Droh3bb1QF06bYr+oQCV8Fhd7BaUBRKjEYuMV5FgX5ciwX350wLMNQUdV+wfZAkWaJdTbeCR7Cfx80PRR73A9WOL1BoWu043Nr/rJXSQdF3NA5eJD4Dd9z3IglWMQ7SqaCWmEiiVZ94PAa7dFUSYkCpLiZU8iUx9cWs9zxJzIHz0IMt72g5QyGWnDxNuu2KPiHafYNuY/iDXjEOZlgX4o3NDM/JssR1cObAJo0vRLuyhyYzMqGWFQ2LsSjh1u0lehIvxJvR2squGmm6xsb/beSKk6KrXxeixxwyePHb6lnEUk8JDjKoYIN5ARUkcJt1Hv+Xs5lxpwyT/VhCDDAhcxwuLY4MKslQq/irbx1Tv5nB5PhHwVRHaijEKfUBzIFn4fuXyu8I0etFu2/wrvGnk2Q4EWe8leyhyRhqzgwnWPIeb5ckWaJdhpqi8GRvf0VjqZTCELWcYnNGVOfvq97XtQEK0RmaD17U7mIiuRQ3S7CyVBd2q4ktN4zGkDikp6MVQvSAj8qsLG64mRct8zAqOhlqFS9Z7gmXdvghqKsYFY0qt4tE6bgr+oCRKSNJsiTh9rlbfV5BIc2WxhU/HNey/NU+uJsi7NukXFC0zxKHLSmdKutgJvjzIvOBTgzWRHV6ZkJmFwcoxHcXzeDFq7R5kCAXFiEGqg+/LWc3g9ilDz5sL1ZTghXQVfY3WPjI/T9e//Z1dpTskKHEolfKL8zn3JfObTfBAmR/4Xcgd7JE+6x2uHwzH/9rF8WvlADhGQkbqvN4KDkJDdrclKUqKpccd0m3hSrE0dq+u5Kd1XFMJDeSWDXNBWkavFjsDw9eHDvc0bPBCiF6iEIs9cTRcNhlz6hoBHWVd2It3OsIUv7OjMhzabY05mbPlX1aotfIL8xn5raZ7R6TZktjTvYced9+B3InS3TMaifBeSwAGVSw1LQSM3CVp3EY46FLeo2uOukqzEZz98QoxHfQvIvmzMDUFs/J4EUhBMDY4Q4UwEig1ee3xVqY6Uyh3NAyAyv1ljJr2yzyC/O7IUoh2hfSQtz5/p3tHhNriuW1378mCdZ3JEmWiEpTA4zm5VPvl8zkgqrgYW8iVVG55vvXMOu0WT0SqxBHqqmLZtMiQnMyeFEIAXBGSgObLHmkN454aC4ELHIkhb9opbpDR2fx9sVSOih63GOfP4Y36G33mLpAHQWugm6KqP+SJEtEpakBRlOCNdGfS4F+HFtL7mDTtz5uq6jkIq/ObSdPYcdlOyTBEn1Ka4sIMnhRCNGcwV9Lmhqu4AjqLROpAquFUqOx7ZkmQIm3RD64ih4V0kI8/eXTUR27o3RHF0fT/0mSJaLT2ADDG5vJDOvCSPlUMQ5usS7kwlAS80zpXPH9K6VEUPQ5bS0iyOBFIUREQgY++/DGJhc6gWaJVqkhusYApXXtDH4VoosVuAqoC9ZFdayu6eHZkQ2H37kV0ZHGFyI6jQ0wbL5aXowfxPbdlbhqGlqdmRDSQnxS+gnbi7eDAmPSxjAmfYx0pxG9V9MiAjAjEG5yAQcHLz5nWiCDF4UY6Kx2rFe9wDd/Ppeh+gFMikaJlkS66sZtiG7Nuq1ObkJ0h2iHDwO8+nYd1/9jfPjad/lmGUlwFCTJEtGz2sFqx0B4AzBV+6B2D34tiac+91FY6SZg+Rvvuh+l2n9w5WM1q7Fb7OSNzZNNlKJ3OoJFBCHEwGVQDaRZQph8WmR2JEBSSIvq/CRLUhdGJ0T7oh0+TMjMY9VPYFNdeAGbzH07KpJkiaNTtQ9Wnk7I38Alvnn8Sx+BMf7fWAevD19yDilL9/g8zNw2k6VnL5VES/ROhy4iNCeDF4UQAJY4ElIGU3TAD8AQtZxCzcnyht8Df+/w9LTYtC4OUIi2jXaOJs2WRqm3jbJVXQcU5pbVMqyxfH5GIJcX4wchtUhHTvZkiaNT6yLkb8BAiOfNeZzC/7Ck/bXVBKu5RdsXSXclIYQQfZPVjnLecpJs5kiCNdGfy9d1P0EJxLU50gTCd7FGpozsxmCFaMmgGrjl1FvaPeYaj4dJ9SWR9/bO6vCMSHHkJMkSR8Wf/kMu8s0joKuYFI3b7YtRTdXtJlgQnhci3ZWEEEL0WQkZxCQPPqQRlIq39HxAaTPRcvvcnPvSuTIvS/SYJZ8sYc77c1p9Tg1aedhVzix3eLuHzIj87qRcUByVdR/u4V/6CC7y5bHJkofb2EF21cyRbLwUQgghepU293CeQY1hNA98fB+lDeWtnuryupi1bRZLzl4ipfOiWy35ZAlPfvnkYY/renh9/LyaGs7x1kceX2payUR/LsU4ZEbkUZI7WeKoFFaGB9ntZAR3B64mNRR9CWDUGy+FEEKI3shqB/tgDKrC2OEOzhs1mLGDjZyTdBKv/v4N4oytNwnQCd/lksHEojv5g37W/mdtq8+FR7vpvJJo5P80J5N9M9mnpcqMyE4gd7LEUclKtgEwkl3cY3oKtUHDGQziMhjaHcbojHEy2jm6u8IUQgghul6DB9ZfiNddwvnKFdSmtj1bSEePDCYekz6mG4MUA9WCjxag6e10wFQUNOD62LN4pm4d1dgiiVZ4RuSZ0gDqKMidLHFUrhh7LD9UdrHJkodJ0dB0lczSxuSpnY2/Ps3H2/ve7qYohQhPuN9RsoPXv32dHSU7ZPVYCNH5fLV43SXY6vZxufZEVKdI6bzoDvmF+bz8zctRHXtmzHsMUcsZrhSxyDwNb2ymzIj8DuROljgq5pJ/sckyHwMaAV3lIl8eO30jGKGnUp+xlWpD680+pZW76E75hfks2r6oRbvaNFsac7PnyvtPCNFpQvGDuM4/mwe1BZyouYGOW7VL6bzoaiEtxKLti6I+/oRgLQCVJLB06kWYDRfLjMjvQO5kiaMT58RgthLCwMX+PHYyAoBdNb8g5f+uIykUaveO1p3v3yl3FESXyi/MZ9a2WYfNA2naeC4dvoQQneXT/+7hVt9yQMHpjSMtGERp4xqooJBuS5fSedHlClwFbc/Eak7XUXWdS6prCegKf/DdzI7KmHCJoCRYR02SLHF0EjNh6scYrn+TjQtuJPfXJ3Ll2Cxyf30ic6afibuDvVneoJe5783txoDFQNK0ete0ybw52XguhOhs7qpKHFQzRC3HomjMrXADtJpo6ehc+L0LuztEMQAdSUnqVZ5qVF3hIt98PmcEH35T0YWRDQySZImjl5gJQ07FbFS57ifDuGd8JtedYqGyjda1h9qyZwv+oL+LgxQDUUerd803ngshxHcVl5LFRH8u+7UU0hU3Od56lrjKcbbReXfFzhWMe34cW/ds7eZIxUASbUnqL+u8zHJ7cJOAi6TGR9uuRhLRkSRLdI6mzkqP/YKnXvooqlN0dDb+b2MXByYGomhX72TjuRCiU0QKNw5+MM3x1rNlXxHT3FXh8vlDPrO6fW5mvzObJZ8s6a4oxQAz2jmaNFsaCm1UFuk6zmCQRWXhu1apiocXzPPJoIKxw1K6MdL+SZIs0Tl8tXgri7B5D7C+ZjmEouup8sGBD7o4MDEQRbt6JxvPhRCdobaskA3mBQxRKyjRklo8tzE+Ifxf2vic++SXT8odLdElDKqBudnhrRmHJlqKrqMAt1e4MQABPfz8ELWczZb5nJHa0M3R9j+SZIlOEYofxPTADAK6ilXR+G1dVVTnvV/0vjQgEJ2uo9U72XguhOhMSYnJVJDAfs1BkIPddQusFsqNart7lAHu/fhe2SMqOl1IC2G32Ln8xMtJNCe0eC4tFOKB0go2um+gWEvCpOiRRCvNUI3BX9sTIfcrkmSJTrF9dyX/qMnkIl8eAV0lr6IyvOG3nQ6DTeZ/MF8uLqJTtbt61/j1nOw5GNTWRw0IIcSROPX4Y3nQMh1QGKKWs1dLYbJvJl+pSR2eC1DZUCl7REWnyi/MZ/zm8Vy75VrWfbUOt99DUijEBVU+nigu5dW9xTzqnotLTyJFqULTwaTohFAxpB4HCRk9/SP0eZJkiU7hqgnfVt7JCKb6b8IMXO2pjurcKn8Vn5R+0oXRiYEoJyuHJWcvwWlztng8zZbGkrOXyJwsIUSnMdQU8bj54UiC5SGOu0zPsL7h/KhfQ/aIis7S1giTKlXlJbuZPUo8F/vmA7DJkteYXCnUW9MwpP8ALt0grds7gQwjFp3CGW8FIIMK7jI9A8Ast4cCi4WdMdYOz99evJ3TM07v0hjFwJOTlcO4zHEUuAoo85aRaktltHO03MESQnQuSxy2pHS8QJ5/OvN8D5OlulgVfJHfBxPRjB3vb5E9oqIztDvCRFFQdJ3HUsyc5tnNQtNajIpGQFeZ4Mvj+ZsuAFuCJFidRJIs0SmyhyZzos3DquACslQXAV3BpOic3uCLKslqa0OwEN+VQTUwJn1MT4chhOjPrHa4fDM2Xy2rYjM47z47q4J3M0x1cVu5yqI0M6C3uTdL9oiKztLhCBNFodRo5MKEZzE2aOg6XOmby2eMYN2XAa77iSRYnUXKBUWnMNQUsSnmvhYJVkBX+azmZ9Gdj9xZEJ2swQOeA4Q0nQ+/qeCVzw7w4TcVhDQdPAfCzwshRGex2sE+mE8L3XzlTWCiP5dCzcmk+hKu8bT9+0ZBkT2iotNEPcLEEH6/Verx7Ca8/6qw0ttlcQ1EcidLdA5LHLHxyYTqijApIQK6iknRuDvwLucFkwgZfO12V3r080c5Lvk42ScjOkfT3DZ3CZcGctlZHRd5amRCLc+ZFmBLSofLN0tZhBCiUzXtUS7GwczAVF605DHL7eFkn59bHZktSgcTzYmMSR/DV5VfEWeKY0z6GEm2xHeSbE2O6rjUxkHZdqWONNyU4CAr2daVoQ04cidLdA6rHSY+gyHjZOpsmVzln4tPN5ClVnB7eTWgdNhp8M7375Qug+KohbQQO0p28Pq3r7Nj/z+pcZdgq9vHsoa7yCA8aDGDCpY13IWtbh9edwn4pEWtEKJzNd+jvNS0MvL4Od56Xiqswlp4Cb6KH2NVbVT5q3hz75us/nw1k9+czFnPnyVjTcRRyy/M587372z3GEXXSQ8GyfKaCeoqRkVjkyWPHyq7uGLssd0T6AAhSZboPImZcNWrfP3LDZTqiTRttLqk3sUf3VUdzgnxBr089vljXR6m6H+at6qd894crv3nHH6SnMBzMelkqS42mBcwWvkfG8zhPYOFmpNLA7mE4gf1dOhCiH4me2gyIxNqW/y+ucCXR6HmZJjqYpZlHZbkf9KgHV6a5fF5mLltpiRa4og1dRR01bvaPEZpXOyeU+HmhsBsLmwcu2NSNDZZ5mMu+Vd3hTsgSJIlOpfVzn4tiTpiKNMTIw+HOkiwmjz9n6flbpY4Im21qg0aargvzRxJtF605EU+8Ez0h0sIt++u7KGohRD9laGmiOdMBxOs6/y34NFt3Oifzreak7+kqNBK57fmFm1fJNdCEbX2Ogo25wyFWOIqJ8dbz1LTSlwkcZEvjxAGDGYrxDnbPV8cGUmyRKdzxlspwcE0/wyC+pG9xeoCdTKQUUStvQuLooQ/xjzgSKL5R5WZgakU4wAO7p0QQohO09TOPTaTuZY7eMj8KK9b7mCleRk3GC/AZTR2WNlR6i2Va6GIWkcdBZvMKvWx0j2HQs0ZqfLQ4pxw3VaY+nG4Ikl0GkmyRKfLHprMcLObZeblGBWNoA5jGqL/MCsDGUW0OrqwKAoETXUUWC2Rx5aaVkb2aDXtnRBCiE7T1M59yhbWT/sFQ2PqsShBhqjlXGl9KeqXkWuhiFa075W7uYQC/bhI58ss1cVGy70YEjIkweoCkmSJTmeoKWKDeSFZqosiLREVGNPgIzYUXenD3pq9XRug6DeivbB8pSZF9kQ0rd6NTKgle2h0XZiEEOKINLZzJ2EwV2jz2a+F754fr1VF/RIynFhEK9r3ijuYQToV1BLDDOtCvLGZ4U67lriOTxZHTJIs0fkscVgS09mvOTARQlXAANxTXhnuMNhBl8EVn62QTb8iKtFeWB5puOKw1bvnTAsw1BR1cYRCiIFs++5KPq+JZ4I/j/2ag9ENPpzBYIfXQWeMU4YTi6iNdo4mzZaGQttlqEnmVJ793U95K/kBPhiyghdvHo9tyhYZZdKFJMkSnc9qJ3bCoxhVjVS1BghfT87x1nN1VXVULzH/g/my6Vd0yN3gRlXa+TWm6xiCCZR5RwLhuTWyeieE6C7NZ2bNCNyIAbi9wh1+sp1Ey6f5eHvf290QoegPDKqBudlzAQ5PsxrXtuu//RGDX76YWO8+4kNuDIG68N1WSbC6jCRZoksYFIVUtQ6A/VoK1/tmE9BVZld5+KPb0+H5Vf4q1nyxpqvDFH1YfmE+t7xzC5qutXmMoig88PM8npv8I/48cRTPTT6DF+deLKt3Qohu0Xxm1jLTIwDhzm6ucuxa27+7qn3VzNo2S6o6RFRCWgi7xc7lJ15OoiWpxXMpQZ1BB37GJt9GhlBKoeZk29gnwwmW6FLGng5A9FMJGRjST6a8dD8TGnIpxsG9gcvIM6/n2GAwqpdY/9V6Jv9gMgbV0MXBir4mmna1qqLy4E8f5Jxjf3H4k3JxEUJ0g+yhyZwSX8NKXx5D1HDDnRI9iZNr4R/eA+QcMxi3qh7WbVBHR0Fh8fbFjMscJ9dB0ab8wnwWbV/UsglUKBZT1QnMb/iQc/0lGHgKVMIzIv256G+5ef80HYMa3XgdcXTkTpboGlY7XPkyb57xNMU4GMu/udu0HoDUKBtgeHweaWErWhVNu1pN10iyJrV7jBBCdCVDTREbjQcTrP1aCpf7bmeKfyb55lTcBkOb7dx1dEq8JXIdFG1qa06krtbhT/6UR/Rf0jw9nxmYShEOij0NMieyG0iSJbqO1c6F407nh8ounrbcR9OCybB6lYQoEy1pYStaE+37Qt4/QogeZYkjJimdkGqmiBSu9t/GA+bVLDcv4zHOjuol5PeYaE1HcyIVIJD2Vos5kc1HmMicyK7XZ5KsyspKJk2aREJCAomJiVx33XXU1ta2e87ZZ5+Noigt/txwww3dFLEAMJf8i02W+ZiUg3t8HUo9v/f4ozpfWtiK1uytjq7Nf6oGNHS8B1AIIbqE1Q5XvoLhhvdIu/kd7r/0Rwyz1nGMWs7tyt+iegm5DorWdFjRoUC5SWGL2UmJloRPN0ZGmGRQIXMiu0GfSbImTZrEl19+yZtvvsnf/vY33n33XaZMmdLheZMnT6a4uDjy54EHHuiGaEVEnBOD2UoIA7f6phDUw2+5mZ4y7KFQu92VVEXF3eDurkhFH5FfmM+KnSvaPUbXwRBM4ISXboP1F0qiJYToOVY7OE/AkDiEMSN/wFc//jMBXWWMr76xnXvrpylAui1dWrmLVkV7h7PSCOmqmzLdzn4thSzVxQvWhWQ76rs4QtEnkqyvvvqKN954g7/85S+cfvrpnHnmmTzyyCNs2LCBoqL259zYbDbS09MjfxISEropagGEJ4hP/ZhnT17DJs7mQl8eQV3FAOSVV4ZbjbaRaGm6xi3v3CLdlUREU3lEe3Q9/OFkTnk58XX78LpLwNf+XW8hhOgOIU1n5j8NXOTLQ9NVbq9wo6CjHHod1MO51w9SfsCOkh0y0kQcJto7nMdrHgo1JxP8eVzsn0eh5iQuOR2DNb6LIxR9Isn68MMPSUxM5LTTTos8lpOTg6qqfPzxx+2e+8wzz5CSksLJJ5/M7bffjtfrbfd4n89HdXV1iz/iO0rMpDT+ZABcJFGph/9h53jrechV3uGbcPH2xXKBEUB0DS8UBS6rDHBpfUm4k1Igl1D8oG6KUAgh2rZ9dyUl1T52MoKp/pvI8dazxFWO89B9yo17mN/c+yaT35zMWc+fJQuOooWOBhAruk56MIjDa2eiP9zl2Rebwbe/eZ7EKa/KCJNu0CeSrJKSEpxOZ4vHjEYjycnJlJSUtHneZZddxvr163n77be5/fbbWbduHZdffnm73+v+++/HbrdH/mRmZnbKzzDQjR3uIIMKXjDn4VQPlm4laRpaG52VQLoriZbe3hvdcM6RofDK3UR/Ljur46SLkhCiV2hqNpBBBXeZngHCC45b9hUxzV0VGRx7KI/Pw8xtMyXREhHNBxAfqunO6JwKN7cEplKMA4C7fn0i47J/KAlWN+nRJGvu3LmHNaY49M/XX3991K8/ZcoUxo8fzw9+8AMmTZrE008/zUsvvcQ333zT5jm33347Ho8n8mffvn1H/f3FQWekNLDJMj/SxrbpIlJmiG72R2ld+3cvRP+XX5jPuq/WRXVsaijEzGYXFumiJIToDZzxVjKoYIN5AVmqi4B+8GPYC/FxgN5WR3cAFm1fJJUdIiInK4clZy8hydyydDAtFGKJqzw8+LpZR8F0e0xPhDlg9egw4tmzZ3P11Ve3e8ywYcNIT0/H5XK1eDwYDFJZWUl6enrU3+/0008HYNeuXQwfPrzVYywWCxaLJerXFNEx+GtJN1SDBkFdwaiEsyxzMLq/6wd2PIDVaCUnK6crwxS9VEgLkfdhXofHKbpOWijE6AYffzat4GL/3RTjkC5KQoheIdtRzwvWhQwhnGCZFI39moPZpl/hMr7b4fml3lIKXAWMSR/TDdGKviAnK4efDj6b3/zpYS7XnuBEzY3Da+eWwBy+Z1oZ6Sg4w7qQ7KHJPR3ugNKjSVZqaiqpqR1v3Bs7dixVVVV8+umnnHrqqQD84x//QNO0SOIUjc8++wyAjIyMo4pXfAcJGRhSjyNU+hVGJcQBLYkUpYZxPjcpwVgqDAp6O8t3Vb4qZm2bxZKzl0iiNQCt+XwNHl/HHQJ1IKHkTDR9E5lqGc+b72G69V65sAghegWDNZ74xGQC7jJMioZPNwIK3xiib8r19t63JckawEJaiAJXAWXeMlKNMYxOGI4BleeDa0lsKImUyuvAdf5beNz8EFmqi+dMCzDUnAn2wT39IwwYiq6300O7F/nVr35FaWkpq1atIhAIcM0113Daaafx7LPPAnDgwAF+/vOf8/TTT5Odnc0333zDs88+y7nnnovD4eDzzz9n5syZDBkyhHfeeSfq71tdXY3dbsfj8Uhnwu+qah9snESVu5wrq/7ASvMyhqjl5NtimOVMCXexbSfRUlBIs6XxxoVvYFCjKzMUfV9ICzH2ubHUBztuN5tQcTLPVn0SKcMxKRre2ExsU7bIhUUI0TtU7cPz1MXUVbnQNBiilvOqOZ07BpujfomlZy+VBccBKL8wn0XbF7VoAOUM6Vjcv2OhZwsOqpnozwVgg3kBFSTwoGU6j5sfxpaUDpdvlv1YnSDa3KBPNL6AcJfAE044gZ///Oece+65nHnmmaxevTryfCAQ4L///W+ke6DZbCY/P59zzjmHE044gdmzZ3PhhRfy6quv9tSPIBIz4apX+fisdXzOCCb451GiJ0W6KyVpWrunSxOMgWlHyY6oEiyAuQ0FZKkuCjUnF/vzqIvNDF9YLHFdHKUQQkQpMRP7DW+QMmMbk40LKdScnOsvwRHU25yZ1ZyCIl13B6D8wnxmbZt1WIfdMhX2Of7KDMvPuaRZgpWluhhmq2f9tF+EFxolwep2PVoueCSSk5Mjd61ac+yxx9L8plxmZuYR3bES3cRqJ8F5LBDuChnUVVDC3ZUaKtzc7kzp8CWiHcAn+ocdpTuiOi5W0zjXX8I+LTXSrvbr8Rs49bhMubAIIXoXq51PDwT5ypvARHLZYF7AXRXlzHSmhBOtKLvuStngwNA0I1JvJQvXFQVF1zGlvU5qbRp/Mq2KLDbO0ObxYsJgUNvppiK6TJ+5kyX6j+yhyRxv9bDBvIAhagWlWiIQ7oYTjWgH8Il+IsqC5h956zEAXszUEu6gtF9LkgRLCNErNXU9LcbBzMDUcCc4VzkxUe7ikAXHgaOjGZG6olBuUrjDvjiSYMkIk54nSZbodoaaIjZaF5KlujigJZGo1AIwusFHWjAYme/QGlVRcTe4uytU0QucmnZqVMddUhN+Hw1TihlKEYB0FRRC9FpNv58yqGCpaSUQrupYVhpd8rS3Zm+XxSZ6l2gT6qaxODLCpHeQJEt0P0scdscgikhBBSxKEIAyPYk/lAfCx7SRaGm6xi3v3CIDGQeI/MJ8cv+Z2/5Buo49FOK0Bl+k2cUj5uWMTKiVroJCiF4re2gyIxNqI/tn9mopTPbNxOm14wwG27wONln/n/WyL2uAiLaCJ7WxIqj5bCxZbOw5kmSJ7me1o5y3nCSbmQzVzX7NwX4thXTFzdg6ndxSb4dvTNn02//lF+Yzc9tMXPWutg9q/BCSV15JkZbKRb48CjVns3a1Rd0UrRBCHBlDTRHPmQ4mWB7iyDWtY6H/Cn5Rrbe7LwvA4/ew5os13RSt6EmjnaNJs6Wh0Pp7QtF1UgI693nmRK6BG8wLZLGxh0mSJXpGQgYxyYOpsg5mgj+PCf55FGpOhqjlHKvXokW56Vf0T9EOH3aGQixtnGrvxcy3DGKiP5dK8yDpKiiE6N0scdiS0vHGZpJnuRU7tRyjlvOYZSkf1Z8Z1Uus+886WXAcAAyqgbnZcwEOS7QUPdwOo7b0fP6ln8BEf64sNvYSkmSJnmG1w+Wb+fisdRTjiGz8hYM1xR2RTb/9146SHVENH15QVkGON9zevWkvVjEOdv/mBWlXK4To3Rqvg7YpW1g1Zwp3GmZHSp7nKH+L6iWq/dVyN2uAyMnKYcnZS3DanC0ed2qQVjmBspqxQLiRygzrwvCMSFls7FF9poW76IeatXNvvvE3VboMDnjRtm3fYY3hRw0+gs32Ys2wLmTUyd+XlrVCiN7Pag+3c/+mgvfqs7iIPDZZ8sj21ZMQClEdxaLjis9WMCJxhAwn7odCWogCVwFl3jJSbamMyxzHuMxxBx8zxjA6YTgkZLJ9dyWumgac8VayhyZjqDkznGDJYmOPkSRL9Kimjb/LGsJ16T7dyKB6GylBjQqDgt5K2aACpNnSGe0c3f0Bi+4RZdt2FR1dBz8GSrSkZuURZ4J9cNfGKIQQnaSpA9xORjDVfxNrLEu5wlPDiuTEqM5fvH0x4zLHYVCjqwQRvV9+YT6Lti9q0bo9zZrCbaffQXxoNIGaQQTjrZCQjKGmiLGD48Da7Lon18AeJ+WCokc13/i7X3NQpicyWHFzQ3kgPI/x0O5KjbXHv8j6BQWuAqlF76eyM7KjO66hAUUBEyFy/VfLXiwhRJ/UvJ17rmkdAJM91dhDoQ67DAKyT7mfyS/MZ9a2WYfNxiqtL2P2tllcseEv3LThMy5d8xEXLHoe7+rxsP5CaOi4zF50H0myRM9qtvF3quW+SAOMS+pLuavUjyN06MUl/JZd99U6rt1yLeM3j5d27v3QaWmnYTe3U+Kg6yQ2tm3XdbjBN5O3OVX2Ygkh+qTsocmcaPOwwbyAY9RydB0MhDunRkv2KfcPIS3Eou2L0Fsr6VAUQMeW9gqgkUEFyxruwla3D6+7BHy13R2uaIckWaJnNdv4O+eSHIpxRDrjXFJfSv6+/TxRXMp5VQHQOeyXjsvrYta2WZJo9TMG1UDej/Jaf7JxVXdeeSVqY5fju83r+J61KrwXSxIsIUQfY6gpYlPMfWSpLgK6iqJAQFfI8dYz1R3d3QnZp9w/FLgKDruD1YKioJtqOCH2vciMtULNyaWBXELxg7ovUNEhSbJEz7PawT6Y8lofEO6Mc2fgWiC8kje6wcdrsfZw+eAhW7T0xv8s+niRlA72MzlZOSw9eykWklo8ntasbfviwMWRVrXPGKVVrRCij7LEERufTAgDJkVjr5bCN/pgArrKFE81aR0MJ06yJDEyZWQ3Biy6SrR3JGdY10USrIn+XHZWx7F9d/R3PkXXkyRL9BpNNekj2M/j5ocijxdYLQRN3nbnMpbWl0ob2/6mwUNO4olMPvZxKLySm0o1Hi8qZcu+okjb9tmmTdzjv4JCzQm2FNmLJYTom6x2mPgMhoyTqTQPYrp/BjH4MSkaBmBuhTs8HamNRMvtc/OrF38lVR39QLR3JJs6Mc8MTKUYB3CwgYroHSTJEr1GU6fBp8yLsShBAEr0JP6rRlf+teKzFXKB6S8aPPD0+dQ/lsOr737CMK+Zq+oOkO3zoesqlVosug4mRWOVZSkLAleQOOVVKRUUQvRdiZlw1avs/s0LfM4I5vqvR2vMqXK89SxxlZPWzogTV72LmdtmynWwjxvtHE2aLe2wocMRuk56MMjohnD1z1LTSjKoAA4uVoveQZIs0Ws0dRocolawX3OwX0shXXGTHIz+NRZvXyxlg/1BdTGhkn8T4y3i8cAcNlvyMCk6QV2lQo8nWa3DjxqZj/WY5U+Yq77t6aiFEOK7sdoZdfL3OdHm4UHzYy3G/eV463l9XxGxHXQcnP/BfLkO9mEG1cDc7LkAhydauo4CXFuuMcGXFymX32BewMiEWrKHJnd/wKJNkmSJ3qOx02CVdTAT/HmRToPj/a4O69GbSBvb/iFkjqNUSwAgTfWgokUSrDQ1vAm8TE/iBv9MQhgwmK0Q5+zJkIUQolMYaop4xnhwwbFMP3iH/l9WC3UGw+EblJup8lfxSekn3RGq6CI5WTksOXsJTlvL61p6KMTtpQGWue+kQD8u0ijs4IxI2Zfcm0iSJXqPxk6DH5+1jmIcFONgZmBqpB49Wm/vfbvrYhTd4qNyKxc2zGO/Fq4zVxUwKlokwdqvOZjgz+OKq27AcP2bMPXjcKmNEEL0dZY4TAlpFGpOpvlvwq8f/Ki23RpdOdjGrzd2VXSim+Rk5bDlwi08Mf4JFp8xjyeCSbxYaWBD6J7IHqxiHMywLsQbmykzInshY08HIEQLVjsJzmOBEjKoYKlpJRAuk5jm9rAiObHDl3ht92vMPm02BtXQpaGKrvPhNxUUE06kXjLfTbp6MMku0ZKY4M+jGAc7Ct2cNf7UHoxUCCE6mdWO7dqXueGBZ1hm/hODVTdBXcWoaFG/xJt73yS/MJ+crJwuDFR0mQZPeOZV/CCCdcMI1A9C/9Gp2DJMvAh8Whyg2GfGGW8le2gyhpozwwmW7EvuVSTJEr1OUwOMZQ0H5z/MDEzlIfdKnrWHcBvaT54qGyopcBUwJn1MN0UsvouQFqLAVUCZt4xUWyqjnaOh+Ty0Q6tiWnzdcQmpEEL0NYZAHSusK0hscLFfc2BAI0NxM6ahgdVE90F68fbFjMscJwuOfU2DB9ZfiNddwqWBcGv2JiMTannOtIDspHS4fPPBpMo+uIeCFe2RJEv0Ok0NMGz+g/MfaonhZv90flqzilcSO36NaOdMiJ61dc9WFn60ELfv4J2qNFsa52dOJYNaXjDPJ11pWSqarrh5wTyfCf55jB12eneHLIQQXc8Sh90xiKID4Q5yGaqb/ZqDzHoNeyiER1Xb3ZcFB/coy4JjH+OrxesuwVa3j2XaXUwkl2IcZFDBsobwZyMvYPPVyp2rXk72ZInep7EBhjc2kxnWhdQSw1rzIh4xL2dH9e+ieolo50yInrPkkyXMfmd2iwQLoNRbyur/zmNm8kKGqOVAeA/WBb68yB6tIWo5m63zOSNVZoIIIfohqx3lvOUk2cwMUcsp1JxM8Odxo/8m8sqjHzgrC459Tyh+EJcGclt0Dhyt/I8N5oPVPZcGcgnFD+rpUEUHJMkSvU9jAwzblC28OPdinrrsRI61eslSXTwaeBFDILbNKjEFhXRbemPJmeittu7ZypNfPtnm8zqw2mEkxMEmFwX6cUzwH0y00tRqDP7a7glYCCG6W0IGMcmDqbIOZqI/FwjPRGraoxyNvTV7uzJC0QW2765kZ3Vci86BL1ryIgnWRH+4hHD77uiTbdEzJMkSvZPVDvbBGFSFUSd/n2sIz4MYprq4vSI8dO/Qlu7hwgmdOdlzpAa9FwtpIRZ+tLDD40qNBj60D2Kq5b4WnZSmWu6jPiYDQ/r3ISGjq8MVQoie0bjgGH/DVlLjLZE7GXu1FD5xXUtKUOtwtMmKz1awdc/WbgpYdAZXTbhCo6nDcnMzA1Mj18Om40TvJXuyRK/XtKpzHbfwmuUOLql34XDFsMiRRKnx4Fs4NaRzu55ITprUn/dmBa6Cw0oE21J9zp28dOIlbN9diaumoVknpZ9IJyUhRP9ntWPw1bLBvABbIJxg1WAjz7SOq0t+B4Pzw4lWO/uzbnnnFh7iIc459pxuDFwcLWd8uE1/8w7LTZaaVjLRH96j1XSc6L3kTpbo9ZpWa2qJwU08EG7pvmVfEYP3/grLgd9w3wE/W/fu40eVVeG2p6LXOpI9AqmO4zGoCmOHOzhv1GDGDndgUJVwJyVJsIQQA0GzfcqLzdMZoRQxRK3gKd9fSSo/tcMGGDo6s9+ZTX5hfjcFLL6Lpg7LzfdgXeDLa7FHa2RCLdlDk3s6VNEBSbJEr9e0WqMAwWZDGQ3AU4HnebnheX7rL2G/5mSafyofuf/H69++zo6SHYS0UM8ELdoUbVOSZGuy7K0TQohm+5SXTr2IChIBGKJWMEX7IOqXmf/BfLkm9gFNHZab78Eq0I9rsUfrOdMCDDVFPR2q6ICUC4per/ncrCFqBT7diAENo6JFhtTu11K4zvJrQukrmfzOwVW9NFsac7PnykDGXmS0czRptjRKvaXtHnfn6XfK3johhIBwomW1s+P/yrnFN48XzPMZopZzvOYBoisbq/JX8UnpJ5yeIaMverWmO5fAjEAuxf7wnKxiHMywLgyPuElKD5fMi15N0fUOdk0OcNXV1djtdjweDwkJCT0dzsDkOYB39XhsdfsaO8spkdbeTTbHOMhLs4W/aFY6oTS2w1hy9hJJtHqR/MJ8Zm2bhd5Gm8hrvn8Ns06b1c1RCSFE7/bQlq9Z/vY3jGA/r1tux6CEGJ85iFKDocOyQYBTUk7hmV8/0w2Riu+kwQO+WkLxg1rZk1wke5J7WLS5gZQLit6vcVWnyjqYCf48pvlnEGhWNhgCHk2xhL845CKjN/5n0ceLpEyiF8nJymHJ2UtIs6W1eDzJksTDZz0sCZYQQrQqfI2z0YCKjgGYWxFdIyGAL8q/wB/0d1FsotM067Ase5L7LkmyRO/XrI2tPcbIMvNyTIoWSbQKrJZwl8F2VvFK60tZ88Wa7opYRCEnbQxbfvEEa37xONcfn8u1wxdz/2mb+PkxvwDPgfBKnhBCiIixwx1kUMFK858xKhoQbgT1xyjnZunobPzfxq4MUQjRSPZkib6hsY3ts6YFJOvhzaD3Biax2rKUMkN0+3ZWfLaCEYkjpGywN6jaBxsn0VBdyfz6O/nKm0B4BPEOfha/l+WmR7AlD4LLN8uKnRBCNDojpYHN1vkMIjwvslhLQlHgDx43f0lMIKB2vHa+r3pfV4cphEDuZIm+xBKHKSGNQs3JDP907jSF68pTQ9GXAS7evljKBntagwc2TCJU/G9i6/axKng3GY0fGEayi8f8d2DzHsBbWSTt+IUQohmDv5Y0tRoIN3y6wH8Pt/j/gAH4da03qtfITMjswgiFEE0kyRJ9h9WO7dqXmW+ZxTLz8kh70/s8c0gJ6uGBjB0o8ZZQ4CrohmBFm3y11NVUYiBEQFcjcz/OUXawyZIXKQWdHriRUPygno5WCCF6j4QMDOknU21JZ4J/HrHU87j5IQByKypR9PavhQoKw+3DZbGxB4S0EDtKdsiImQFEygVFn2II1LHcvAJb4OD8iGIcpJacD4Nfjuo1Suvabx0uOldIC1HgKqDMW0aqLZWRKT/kovo7WKXNI0t1RRKt1ZalAAR0lYt8eez0HcP23ZWMHe7o4Z9ACCF6CasdrnyZ2IYaUlf8k5W+PCxKEAC3nsiEKh/PJ1rCiVYr+5R1dP6Q/wcZb9LN8gvzWbR9UYvRJfK/Qf8nSZboW9qYH1FWM5Zjqt247e92+BIP7HgAq9Eqv9i6wdY9W1n40ULcvoPdr5LMqRQZxjPRmxuZaN/cNP9N7GQEAK6ahm6NVwgher3GPcobzAuwBSrYrzlQ0BmsVnK7WwFS2ZRoQWt+ziFJV6m3lJnbZrL07KVyLexiW/dsZfY7sw973OV1MWvbLBkx04/JnKwOyJysXqiV+REZFj+j0lXO3Xo1pQ3l7Z4us7O6x5JPlvDkl0+2+pyuQ8OByxlXW8+axjtYTZrfoXxu8hlyJ0sIIQ7V4IH1F1JVXsSvquaSRTHPWu5Dbcyj/MDT8XaWJTnQ1UCb3XdtRhsfXPqBDH7vIlv3bOXWd29F07VWn1dQSLOl8caFb8j/Bn2IzMkS/deh8yNOiCP7n5MxP/07bjv5Dx2e3jQAV5pgdJ2te7a2mWA1iUt7iUfMf27xWPM9WifZqskemtyVYQohRN/UONrk47PWUYyDPWRQzsFOrGbgX96foBuC7Y438Qa9zH1vbjcEPPDkF+Yz+53ZbSZYEP48InvF+y9JskTf56uFujJw7+GkF+7Guv88tGBsu6fIL7auE9JCLPxoYbvHKAqETHV8EWMioKtM9s2kUHNGml5kqS5eiLk3PNleCCHE4ax2EpzHAuERxUG9ZTL1a+s/onqZN/a8IQOKO1lIC7Fo+6Kojy/zlnVhNKKnSJIl+j77YLaNfZJCzckQStnk20hG6alRnSq/2DpfgaugxR6s9pSoRi7y5fGmPoaJ/txIohXCQGxCMljiujhaIYTou7KHJjMyIbw/a5Ba1aKx4JGMN5n/0fwuiG7gWvP5mhZNLjqSakvtwmhET5EkS/R5IU3n9rfcTPTnsldLIUt1cb/6QlTnVjRUSMlgJ3tr71tRH7tWvS7S5KIYBzcY51MXm4kh/WS45BkZRCyEEO0w1BTxnGlBZKTJrb7JkURrdIMPs9Z2qVpzW/ZskWthJ8kvzGfFzhVRH59uS2e0c3QXRiR6inQXFH3e9t2VFHsaiCeGeqxA+OKSFgxSajBC2+XoPLDjAdZ+uVbaqHaSJZ8s4Zmvnonq2GSznWdvnsenhR5cNQ04461kD03GUHNW+A6WJFhCCNG+xo67VSGNGVV/YIX5z5EtWAbglAY/n9isHb6ML+SjwFXAmPQxXRtvP3ekZYIAc7LnSNOLfkruZIk+r6nN91CKGKaE9/AYgLkVbkCHDvpnNrWyzS/M79pA+7k3dr/RYbOL5u4cezdmozHcvGTUYMYOd2BQFbAPlgRLCCGi0dgAI37S06y0/JkhagUAlVp4X/Jkjyfql5Ly+e+uwFUQdZmgqqg8fNbDssDbj0mSJfo8Z7yVDCp4xLw80jgB4Kw6H0td5ThDwaheZ/4H86Vc4iht3bOV2969Lerjr/n+NZxz7DldGJEQQgwQVjsGSxxOtRqAUs1OglIPwOkNPixRlgzKvqDvrrQu+n1YD/70QbkO9nOSZIk+L9tRzwvWhZGa9It8eZEGCjneerbuK2J2eVWHr1Plr+Kxzx/r+oD7maY2tXpHtwwbTTphErNOm9XFUQkhxACSkIEh/WRKdTsOpQZj44LjAv/lLHBVhocTtjMWVfYFfXf5hfk8sOOBqI79XeY1/PyYX4DnQHjmmeiXJMkSfZ7BGk9ccnpkiO1ORjAzMPXg80AoFN0g6VU7V7F1z9YuirT/OZr687Mdp4Dra7mwCCFEZ7HaUX8xnxSlNpJgXemby++MHzGqzsJVVTWtn6eHty3LvqDvJr8wn1nbZnXYWVfXQQsk8MzW73HBoufxrh4P6y+U62E/JUmW6PusdhKnvEr+6U9QjIMMKviTaXmLQ9KDgaheSkdn9juzZX9WlI6k/hyAYAxD1t9IaNVP4Onz5cIihBCdJXkYBrOVEAYu9uexmwwcVJOhurmmMsDi0goSD2nrnhbSWHLCdbIv6DtoWmzssJpD11EAX+nvOIVvWd5wJ7a6fXjdJeF5n6LfkSRL9A9WO1f/6kxOiQ/PCzlGLY/szQroKr/0lxIfiq6cDWDx9sWyPysKT34RZaMLPfznjnI3QyjHoPmpr5ILixBCdJrETJj6MYbr32Tjghu57twfM49wS3eHWsfYOp0T/u8i7jvgZ7GrnIcO1PLG3v38/O95sHd7T0ffZ0W72JisadxR6udntfVstuSRqZaxT0vl0kAuofhB3RCp6G6SZIl+w1BTxAbzglb3Zmm6ypVH0GWpxFtCgaugC6Pt+y7926W8V/RelEfrTKjycWm9C4D9WgoTg3lyYRFCiM6UmAlDTsVsVDl5cCL4GyJPJan1rLH8md/6Szi1xsg5vkqMCoAOr0yFBg8hLcSOkh28/u3r7CjZIYuNUYi2K+M15SEurS9htWVppEnXNP+N7KyOY/vuyi6OUvQESbJE/9E4L8Qbm8kNxvnsZAQT/bmRRGuypxp7KNTu5t/mpJ1t2x7Y/gD/rvh3VMcqus6DrnLurgqv9O3XUpjgnycXFiGE6EKumga2cSq3+qYcdtlzKtUoSvhyqAB19Q3k736D8ZvHc+2Wa5nz3hyu3XIt4zePl/L5DkTblfGfvjNafD3NfxOfMwI4OIpG9C+SZIn+o3FeiG3KFqb89iwAinFEmmAYgLzyStqdTtyMtLNtnT/oZ91X66I7WIeHXOX80lsfeWhGYDrFOAC5sAghRFdxxoeHEP+TH1Cux7V4LpJgKVCoOTlH/z2ztt97WNmby+ti1rZZkmi1Y7RzNGm2NJS2PlvoOunBIKtCL7d4+E7TM2QQnmvW9L+V6F8kyRL9i9UO9sGkJ4R/YWVQwVLTysjTOd565pb6QW870VJQpJ1tO+Z/ND/qY0/zBjinWYIFsNy0TC4sQgjRxbKHJnOMoZIN5gWkqrVUay1/3ypKuLLgEv+dVDnebrVxQ9Njsk+5bQbVwNzsuUArS7h6+NE5FW6aejdWaPHs11LIUl1sMC9gZEIt2UOTuy9g0W0kyRL9UvbQZEYm1Eb2aO3VUvivNoSArjKpvoS5pT5oZWyIQviicuH3LmTLni1Sk36IkBZiy+4tUR//s4Zw2+Dmf8+D1EpeMM/nJFu1XFiEEKKLGGqKeMF6L1mqi1ItgXjl8MoBG15Ctv2oprb3LOvosk+5AzlZOSw5ewlOW1qLx1ODGg+VlpPTbLGxHgvT/DMo1JxkqS6eMy3AUFPU3SGLbmDs6QCE6AqGmiKeMy3A5g83wbjRP51HzMsbN5sqTKovQS1NZ7EjiZCpLnKe3RSHrhpYsXNF5LEkSxJ3nXGXTGYHdpTswKf5ojtY17mkujZSkhLQFSr1BNJUD0PUcjZb7sFQ81OwD+7aoIUQYiCyxJGaPpjSfXUt92A1u92SpHg5yfgF0aRPsk+5fTlZOcQETuHKZzeQZCzmHl5gvL8scgfLpdnxY2KIWs4y83JuDkxnffIaYpPSwRLX7muLvknuZIn+qVkTjBnWhXzOCK7z34JPN2JSdAK6wug6Ay/vLeeJ4lLmlDZwhduPx1+Lx9dyRc/tczP7ndks+WRJD/0wvUN+YT6z35kd9fHn1HkxE76gB3WVi3zzOd+/kP2ag6BiIkYuLEII0XWsdtQzpuJUqw/bg3Wfb2Lk62nK21G9XEVDxYCv7Giz+2KDBzwHcNUECHmHY6w+kdG+IM3HO98QmMkE/7zIHaz1yWuIvXQtXL45vNVB9DuKrkfZam2Aqq6uxm634/F4SEhI6OlwxJFo8ICvllD8IJ76527WvPY+m8zzGaKWtzhsv+YghMJ1WWZKDYaWy3yHePCnD/LLob/s6sh7naZp9h0OW2xkCIX4dO+ByAXmNt/1PK//DIAZp8Uw66eDICFDLixCCNGVSv8Dj/4InfAg3ELNyUR/LsU4uIi3edCyBk2Bc47JpMygdPgbfiBXdmzds5WFHy3E7XNHHkuzpTH3hzPI2bYMr7uEC+vv4CuvnRHs5zXLHViUYOTYfVoqF/vvBuDviYtITBkkCVYfFW1uIHeyRP/V2ATDoCqkxFsowcEE/zxK9cQWhxkVDZethlKjsd0EC+C2d29j656tXRh07+MP+rnnw3uiS7B0HXR4oLyyxQreNNNfI80uxv5wJDhPkAuLEEJ0tbST4NqtKI7vUWfL5BrmRbq7bmIcC4zT8Mekc3vjdbHNDnmNBmplx5JPljD7ndktEiyAUm8ps/55J695S7DV7WNVcB4j2cXj5oewKEECjU22ArpKploWbkISbyb+hq2SYA0AsidLDAjNu9hpOi1aAKUrbvLVtMNPaoWOzux3ZrNUWUpOVk4nR9n75Bfmc8+H9xx2YWnPNR5Pi46CAV2NdFH6o/EeaXYhhBDd6ZhsmPwWsb5a3owfxPbdlbhqGnDGW8keei6GmmnkWOJYUrqDRdsXHdbGvTVPfvkkJzlOGhCVHVv3bOXJL59s83kduDPWzonlPoapLjZZ8iLDhk2KRqHmZIZ/OsvMyxuvhQsxKFskwRoA5E6WGBCyhyZzvNXDBvMCMtQqXFrLX27pQf8Rvd6ijxf1+9r0phLBaBMsQ8jAw65yZrnDe9oadJX9miNysclSXbwQc690URJCiO7WrLJj7HAH540azNjhDgyqEm4+ZLWTk5XDlgu3cNuY26J6yVvfvbXfV3aEtBALPlrQ8XHGai4xXsVeLQWTogFEEqyJ/lx2MoI/Gu/BG5uJTfYjDxiSZIkBwVBTxEbrQrJUF/s1B35MLZ4f53eTFIo+aSqtL2XNF2s6O8xeI6SFWLR9UdR7sABWlBa3uINlQWOp/wIKNScmRSOEgdiEZLm4CCFEb9PYuAFUKqstUZ82+53Z/XpQ8Zx351Dlq4rqWLdR4ebA9BaPzQxMjZRnTv7NT7BN2SJlggOIJFliYLDEYXcMoogUQGGIWk6h5uR2/7UAGIC7yitbnZ3VlhWfreiXq3ghLcSzXz8bVckIgNI4zf4MX0Pk766pa9UM8yvc5J9OnS0TQ/rJcMkzcnERQojepMED6y/Eu3o8Fyx6nmVbS47o9PkfzO+XlR03vnUjWwqjnwuZFNRZalrZ4rGlppWR/cjp9pjIXUMxMEiSJQYGqx3lvOUk2cyRBGuGfzo3GP8WOeQcbz1XVlV3sO23pVveuYU3dr/R+fH2kPzCfMZvHs8DOx6I7oTGrKppmn1T35AqPSYy0f4ZxxpiL1sLV78KiZldE7gQQoij46vF6w43bljWcBep3gS0YGzUp1f5q/ik9JMuDLD7PbD9Abbt3xb18WrIxsbgWrLU8GzOC3x5kVbtG8wLGJlQK/uRByBJssTAkZBBTPJg6mIzuTlwcBNqoeZkim8mAV3l1qoq5pT6QI8u1dLRufXdW3lox0NdHHzX27pnKzO3zYz6DhZAsqaxxNVymn21ZuVc/wNM8M/DZcwID1pMGSGrd0II0QuF4gdxaSA3khRsNN/LoJLsxm6x0ZV2bC/e3sVRdp+te7ay7qt1R3TOwto6hjV+npjoz6VAP46J/oN/p8+ZFsh+5AFIkiwxcFjtcPlmYi9dy/rkNZEEa6I/l636GK7wzaVIS+by+lLuqQp2/HrNrP3P2j6daG3ds5Vb3701+hN0sAc13tx7oEWCBWBT/DhxU4yDt05/UurPhRCiF9u+u5Kd1XEtkoI3A2u5xlMd/YscSQlILxbSQiz8aOERnXPNCZfxW1s63thMZlgXRvZgFeNghnWhNLsYwKSFuxhYrHZIGUFsUjpeYEYgl2J/HPF4uc38PIoKJSSRVRkDwbGQ+k7UL732P2s5JfWUPjekMb8wn9nvzI76+KaFzTElQzGz/7DnjIrGJkseF/nyuHDcjWCUtRwhhOitXDUNQDgpmBmYyouWPABmuT28HPgp7tSdHb5GnDGORwoeAQXGpI1hTPoYDKqhw/N6mwJXwRGNLDlv2HnMOv12GDkVm6+WFw9rkZ+MoebMcIIli40DjqLr0W7zH5iineos+pgGD/hqCTX+Qqx27eH0bZNI9BUT1FWu8M3lQ04iYcR96MaaDocUN0m2JvOPCf/oMxeXkBZi/ObxR1QiqAXsnFmawSr/1lb/WpqaXoQwYLj+TRhyaidGLIQQojN9+E0Fl675iAwq2GBeQJbqijz3rebkvKxEdENDtJdBAOwWO3lj8/rcPMnXv32dOe/Nifr47Zd+Qkx9uSRRA0y0uYEsMYuB6ZCZITmnj2aWdjMBXcWoaDxtuZ+LlW3cXlF5RC9b2VBJgaugi4LuXCEtRN4HeUeUYF1fFmD8rjMOS7CCzfawKUo40VKNZohzdmbIQgghOln20GRGJtRGEqy9Wgr/1YYQ0BWGqS7uKA83hDqSJXmPz8PMbTP7RHv3kBZiR8kOXv/2dSoaKqI6R9fBV/4Tpj34BN7HfgHrLwwv3grRjJQLCkG4Jv0fNZlcRB6bLPMwKToPWP4C9WAvtTE3zYEe5TJembesi6P97vIL88n7IA+PP8qLgg4pQZ3ptcVoljUtEqz9moNp/ptYYf4zQ9TGC5QCyoWPSzdBIYTo5Qw1RTxnWoDNH96nfKN/OsvNyzApOroOl9aXQGk6ix2JhEzeyHmK3nGPqEXbFzEuc1yvre7IL8xn0fZFLRYbVUVF07W2T9IhWHMiJ5Ydz2OWOzAFNLyAzVcrd7NEC3InSwgO1qTvZAR3+q9r8dy59V5uLQ0Q7VzeVFtqZ4fXqfIL85m5bWb0CRbhH7229HwOaCmYml1Ui7RkJvjz2MkIJvjzKCKFEEYU58kw9MzOD14IIUTnssRhSzrYuOFzRjDdP4OArkYqE8bWBXl5bwVPFJdyX2kFt1VURtWEt9Rb2murO/IL85m1bdZh1RztJ1g651cF+GnRSWyy5GFSNAK6yvTAjYTiB3VxxKKvkTtZQgDOeCsAGVQwzfTXw54/4DmL31ve5aVEY7v7s9Jt6Yx2jgbCJQgFrgLKvGWk2lIZ7Rzd46t5/qCfu/551xGdo+o6pgPnUVYzloWKkdWWpZHn5gWuinRSuuDsbNLOeAeDvxYSMmRFTwgh+oLGzrtNjRue+uduFrwGF/kOVnYcq5QDMLhexaT4eNqWHvXLv7nnTYBecQ2E8LX5k9JPyPsgD72d1VMVFY2DCVdSKMTcMjfn1nuh8ToY0FUu8uWx03cM23dXMna4o8vjF32HJFlCEK5JP9HmYVUwXJMe0FVMysFfrnPNG6EKIIOXEk2ttqtVUJiTPQeDami1BCHJksRdZ9zVY90H8wvzmf/BfOoCddGfpOs86CrneN/zLFRMrDD/ucXTd5me4d/+YRTj4MzvpWJIlAuMEEL0OVY7WO0YgJR4CxCu7Fjgv4J7LE9HDjMpGoWak/UN5wOvR/XSz/33OZ7773Ok2dKYmz23R5thtHZtbouGxi0VVTzd8Huqg06eCq5lmOptccxd/mvYyQjgYEWMEE2kXFAIwjXpm2Lua5Fg7dccuPWWU+8bSn+N5cDvSAi2XP1Kj3Gy5Owl5GTltFmC4Pa5mf3ObBZ9vKjLf54mTRt6H9j+ADO3zaTKX3VE55vKzuT42niyVBdrLEsjpRFTfDNlmr0QQvRDzSs7ppheO+z554LjWBN8AWcweETdMFxeF7O2zeqRZhj+oJ/c93OZuW3mETV7Sg0FecO3jkHeeO4PTDrs+ZtNL5JBeC9y09+bEE2khXsHpIX7ANHggad+S6jk3xgIUag5meGf3rKZAxDUoUxPwqm6ed2czp8Yz7yYf/DjmCQME58llDAoqpboWfFZvHL+K11aOnEkK3at0QIJ1O2ay8XKtnATkEaTfTN5Ux/Tot2vNzYT25QtYB/cWeELIYToASFN5zcLn2NVcF7jwqOCSTn8o2K+LYaZzhTCnY6ie20FBWeMk4VnLqSyobLLS+lDWog5785hS+GWozr/8aJSsn0+AjqoKBia/T2EdBVD4529KYb5vJ57KQa1n0xlFu2KNjeQJKsDkmQNIFX7YOMk6jyVTHJP5s+m5WSpLoq1RJxKFYYWHfVSmOafwTJz+JgQBgzO49hxTi7X/nNuVN/OqBp58KcPdmrpRNM+sLf3vs26r9Yd/QvpUH/gcr5fY43U5Dcp1JxM9OdSjIOTbNVsirk3PM3+8s2yD0sIIfo6zwHqVo8ntm5fi8qOWKWBJOVguXmFHs8vzVfjTX8T1eht5wXbl2BO4IoTr2DyKZM7NdnKL8xnzrtz8Gv+Iz5X0XWcoRBb9hWh6i23Yut6+GdPUWsifz9V1sEk/vFNWWgcICTJ6iSSZA0wDR4o30Xdc1cRW7cvklD8RNnZ4m5OlRZDLbEMUcsjv2RDGNgy+DjmmI9gzxPw8FkPk2RNOuoGGSEtxPaS7az6bBVfVHxBQAsc0fc/lKLrzCkNsK3qUh41/xmjohHQFe70X8c001/JUsNtfm8wzueVOyZiriuWQYxCCNFftFHZ8Zh5KemqO3JYUFe4wDefzxnGhWMbODazhF1Vu/jHvn8c1bc1q2bOGnIWFx9/MWPSxxx1whXSQqz5Yg0rPltxVOeHe2HoLHWVc1Zdw2F38Vb6f8s67ZxIJUcQA4b0k1GuflWugwOEJFmdRJKsAajBA+svpKq8iF9Vhe9KNf0yPVRTgtX0/99NzGRa0pGVCxw6kyPRnMilJ17KsQnHHpZ0Ne9Y6IhxsKNkB4//+3GCWvA7/MCNGn8TzC31M6m+5ODDOlzmu4MPOVlKBIUQYiBorOyoqiznKs8fDiudb1Kq2Znsn82vf/UbahoClPi+ZIs77zt/+1hTLBeMuIBxx4xrc+Hx0Ouhruu8u/9dXv3m1SPef9ycFrBjKz2bTb6NbV73L/Ll4SKJDeYFJCY7sF/9gsyFHEAkyeokkmQNUA0eQg01/H7FP3nEd1fk7s3fgtlMM/+txaFNCVb47k4edcc9htvnbuOFj1ySJYnfDPsNCZYENv1v01HvseqII6BTV3o+mbUOXrTkRR53aXZ+519ISWOr9pEJteHBlVIiKIQQ/VeDh1DZ/1H6+CUMItzCfb+WwozAdFaa/kS6WgVAUFe5wJfH54wANBJG3IdurGl33MmRSLWm8qNBP6ImUIPVYMVmtPG1+2t2Ve2iIdR5Hf3sZjsXDJnLstd0QGWy8lfutGxocYzeWDrYlGhNP/8n/GLUcLkODjDR5gbSwl2I1ljtGHy1bDAvwBZwRcollpmXH3ZoU4I10Z9LsT+R2cfexOr/5nVaKG6f+7vtr2qPrgMKk9wBbvUU49LWYzKHWhzixxTZ0zx93HBm/uJ4DDVnSomgEEL0Z1Y7BkscaWo1aOEEa4J/HsU4mOKfxYuWeRgVHaOisca8hPP9CwC4uaKce9MsoOvonZBolTWU8cq3r3zn1+nIuRk3Yuck4Cuu5W/cYW6ZYDUtqOp6+Lr/onU+hiFvynVQtElauAvRFksctqR06mIzuakxwWpq8X6olYHfRYbyHmM9g0knHN7qtTdKD4W4o9TPh6V/xKUlkaG6SVGqgXC9fbGWxBC1nA3mBWRQwY9HpIa7J9kHy4VFCCH6u4QMDOknU28bxFTLfZHr3OeM4CLffIJ6OIlKU6t4yXI3L5nv5pJ6F0tc5ThCfaNQyhCMp37/5ax+I44Fr31FjvIpuZZnUZTwOuS9vokUas7I1gBFCVfXG0wWiHP2dPiiF5NywQ5IueAA19QI49mriPXuO2wPVhNdh+t8s/kHp/Lc5DMYO9zBJa9ewn8q/9ODwbdB17miuoZTao2c2ABZajnFWlJjB8WDvw6m+GbyhT4ssgdrP2lk3PwPDIlDejB4IYQQ3arBA75aQvGD2L67EldNA+U1Pha89hWnsItV5j8xSK1sccp+LYWL/LmU2aqZcU46Z2Rlcef7d3ZZuftR0XXG19UxtdTIpMaOuaewi82NHXV1HRb4LuMJftNiP3IIAwbHULh4HaSd1NM/hegB0eYGcidLiPZY7ZAygtiEZEIYWiRYhZqTOb7rIzXaqy1L+Vn8XrIzDOA5wMbfbuT7ju/39E/QQpo1hdxSP7dVVvFLfzlmQrh0Oxmqu0WCBXCn6RkAJvpzKdScxCWnY7DG90TYQggheorVDvbBGFSFscMdnDdqMCnxFiB8R2t6YMZhp8wPXEEJqYS8w6koPQnNO4Lbxszp7sjbZfEex42lRoapLl4wz+dM5XOWm5dhUnQCusJk/2ye4DcAFOPgBuN86mIzMaSfDFe8LAmW6JDcyeqA3MkSQLjT0rMTCLn+F2lp2zQr6md8ymrLUoyKhjcmA1uiE+rdhC54go98Q1n/75f5oHoFGp3QAfAoTQrF8ONqLws9c6mu13jBnNdqpyiARf5LuNT4dqTZx2WBXBac9wN+NmqYlAgKIYTgw28quHTNR2RQ0er1JKirXOjLYycjIo+NTKhlStwC7k9Q8bQy3LjbhIzUF19EsGZUY/zzGaKWR57eq6Uw3T+DzxlB7q9PJCXegjPeSvbQZAw1RbIfWcidLCE6VWImXPYChoyTqYvN5AbjPZHa9H9wKlPM9+G1DcZmd4K3Aqr2oj+Rw1uP5/L3jzOI+eomFh7wM8XtYXJVNWtq4MGT/tAtof/K+RumlNTwk6p9rA7NJxU3V/vn4NJav0hcanybGf7pFGpOslQXW5Mf4GcnOuWiIoQQAoDsocmcEl/TIsEq0ZMi1xWjorHZksdIdgGQQQXLGu7i15X7+HuZxrQTrsBmtHVrzD/1aqwpLuWVPbWk1mTyA3bhpBIjLZs93RyY3tgpEVLiLZw3ajBjhztkP7I4YnInqwNyJ0u00Ept+qErXB9+/BGn/eNiTI2bZtf7z2ac6QuGqBUHuxMBoLB07CSeLHm3S0K1aDp290V84xrTop48oKvs1VPJUlwYm60muvVY6vQYhqjlFGpObg5MZ33yGmKlVbsQQojmPAeoX/VzYuqLgYOdBwFeNOeS0ay9+x/9N3GX6Znw3l7NwVTLfcy5JAdXjZdP3JvIL96IN+jtslDTgkEoGU9dzejIdbBMSyBJqUFFRz2kAWLzSpWmPdZCNCdzsjqJJFniSIQ0nR8veotzqzdHuhM1CehKZDNtU9ciXTWSP/g4FsYZcfuqvvP3V1EZEXscU/Z8SU7tAfZrTm70T2c3gxhGEZsseS0adhyqWEsihIEhajl1sZnEXroWUkZIgiWEEOKgBg88fR6hki8p1RK4sGFepLojgwpeNOeSrlS1uAb6dCNleiJX+2+jlpjI7MXjrG7mJNzHl4nx7Bl0Cp+4v8b9HYYJAyTpCudW1/CzujpO9fnQGudaAWxubD3fXNP8r6WmlZFS+RnWhbw49+LwHSwhmpEkq5NIkiWORFOdOsDdyhNca8lv8XzzBKvp4lNvG4R5yhYKGkoPTq6vK+Pdsn/xt8I3Www2TotJ46LjLuKYhGNwGCyE3Pv4pKyAwLfvc6LHy32e29kfSuEUdrHGvIQ0tYqgrvKNnoENP5lqWYt4QrrKDf6bmGd6ukXJh90WQ4xjsNzBEkII0boGD1QXEzLH8VGZlQ+/Led/pbVs/U8p6VSQrXzFMsvKyOElehLpirtFspVCFQ+ZH2OIWsEBPYV4qxFbyhD+deZUynxu3q78N1v2/QOdtj+qWlQL1558LaOTjqeyrphkSzLHvTCTZF8JcPB6G9RVqnUryWrLu2YlehK/94W3ADSv+vDGZmKbsiVcIihEMzKMWIge4KoJT5/PoIKfmz4/7PlDE6ygrnCX70rOd8Xwr/9TMIficGRaGfnW7RxviGXcuFWEnH4qXZ+Takli9PHnY/DXQtn/ob8yHcq/YqwOIVSMisYphnu4PzSRXPM6nIoHXQ/Xxh+vHAAgoIOp2aKcS0/g3/owJvjzIrX1qYY6DBPWwqBRkmAJIYRondUeHlgM/DgRfvy9FD78poKt/ylFAWabNrU4PKirFOvheYxDlHJet8zFhIaiQLGWCGgk+Eo4sD/ACS/NYXTQw0+TTuCea/J5/uvnKHD/F6s5HptipKLehS0mheNNp5GuH0OSOYWT/nYNWnUR1/lvo6jhTl40302G6o5cd42KRrJyeFlisNnsy2IczLAu5DnTAmxJ6eEmF0IcJbmT1QG5kyWOxIffVDBrzWst9j+1VZ4X1BWMik5AV7nSN5dbzc/jpArQG/dvKXytZ/Ggej1PKPNRFYXglX/D//IMrFW7QA9ibJYwhXQFQ7NyxLb4dAMe4vDrRoaoFZH689R4MxuNecQkpcOVr0iCJYQQ4oiENJ0LFj3Psoa7ImV3MwNTI2V4+zUHBjQyVHeL85quh0FdpUKPJ031AODSElhknMpdyhN4FSsbM25jQvmj2BpKWBC8jGuV13Di5iH/Rcw2b2aQWolPN3K17zYeND9GqlKFRQkdFqdbj+VG/40sMq2JXAenqPO5+/JzOGOYQ7oIinb1u3LBe++9l9dee43PPvsMs9lMVVVVh+fous68efNYs2YNVVVV/PjHP+bRRx/le9/7XtTfV5IscSRCVfsp/tPPGELpwSYXbSQ9t/mu517zE42ztxTcJOBUwhcWTQc1srqnRC5ILt1OMtWRevKOEqpDn29K6HaTgQKRZLDKOpj4G7ZiUBS5sAghhDg6ngN4V4/HVrevRQOJ5mV4JVoS6YckWXD49UrTQQFCgLHxblSFHo+GilMNV2oEOXid9aNiaVzUDOrh6o6m5O1QAV3hIt98XCRJeaA4Yv2uhbvf72fChAn88Y9/jPqcBx54gGXLlrFq1So+/vhjYmNjGT9+PA0NDV0YqRjIDNZ44hOTD0uwWlvKmGvawA2+mY3H6pEEC4h0O0pVqslQ3ezXHBRrSTgVT+NqX/iAtl67iaKEZ378VxsSiWmR+S8oHCyL8MZmkpgyKDxoWNrTCiGEOFqWOGxJ6XhjM5lhXRhphlGMg4n+XPZrDpKUmlZPbZ5gBXXQUVCUgwmWokCKWkNq47VSUcCIFvnvFkWjXIuPJFhAqwmWroNJ0dlkycOJO3IdlPJA0dn6zJ2sJk899RQ333xzh3eydF1n0KBBzJ49m1tuuQUAj8dDWloaTz31FBMnTozq+8mdLHHEqvZRu/qXxNbtP2wPVrUeQzz1ka/LtXgeDFzCYstf2ny5gK4yzX8TdzdrThGtQGNHpW8ZxFCKeMS8nCzVRaV5ELt/8wKjTv6+lEUIIYToPK2MOkmJtbD4+bdY6buDIWo5+zUHRkUjXTn8jlZbDr3T1VElx6HnVupxONTaFueGMMB1WzEkZMh1UESt393JOlK7d++mpKSEnJycyGN2u53TTz+dDz/8sM3zfD4f1dXVLf4IcUQSM4mb+CQoCjoHLwL7tRTG+x5goe/SyN2nFLWGe81PtPtyJkVjtWUpQ9QKSrSkqMMIr9ZpLDMvJ456PmdEZMUu2TmYU4/LlOGKQgghOpfVDvbBGFSFscMdnDdqMD92+thgXhBJsEAhXXFTrCVFKjPa0rxyo7kjSbBu9U3mN/77G793YxUIYDBZMMSnyXVQdIl+m2SVlIRbd6alpbV4PC0tLfJca+6//37sdnvkT2ZmZpfGKfqpY7JRrt2KkjSMkGrmgB4e1FiMg8f5LbN9k3Fp8ZGOR827G7Wrg4tK8/vSTS1rs1QXryXcz+bLsnhx7sXhmnNpzS6EEKK7NJYR1sdkoKpKs2SLFnuMW+PRj7yET9MhoBsINt6xutH8CgAT/Hkc0B2EVDNKyvFw/VuQKJ/zRNfo0Rbuc+fOZfHixe0e89VXX3HCCSd0U0Rw++23M2vWrMjX1dXVkmiJo3NMNvxhG4bqYtLNcTzUOEcEFH6Slkno5ZdQqInUj+/XHFiUYKTePKgDHKwtByIrfyYlSEorde3hxIpI10GjoqGhkuxIJfm4zPBmL9nUK4QQojtZ7XD5ZmKqi7G+Mh1PuRFDg4+MxnLBYi2Jhf5J/NmyssU1L6iDQ219D1eTps66B88JXzeLtQRy/dcyz7yOLNXFX+PuY89vXyA9693wKJSEDFlsFF2qR5Os2bNnc/XVV7d7zLBhw47qtdPT0wEoLS0lIyMj8nhpaSmjRo1q8zyLxYLFYjmq7ynEYVqZIwJAgwdPYioBdyWmxgQLFFIVD8VaEimKu3Ge1eHdkVKVqsO6CwZ1cOvxpKo1GBu/NgAoCmrq8XDJM3IxEUII0XMar4fKFZuxVxeT8NINBEpqKQ3ZmeqfwSPm5RgVjSItGQWNZKUaMwcTrrb2ZBkam0FpqJiVEGV6AiHdyBC1nHnmdcy3zGK5eQWpSemkHn+MXAtFt+nRJCs1NZXU1NQuee2hQ4eSnp7OW2+9FUmqqqur+fjjj4+oQ6EQXcJqx371C3ieupjqqgqu8c/mAfNqQprKjf7pPGZeSobixqcb+aNvBo9algHhjbsZahWaDi49kQf9E7jV/DxlJDHTP5WnLItxKtX4k4YTM/5uDHFOSD1OLipCCCF6h6Zk66pXMFUX879v66naWkhFMAE0mOjP5Xj28BfLkkhS1TzBavrvzRtLGRUdlxZLSDdQjIP7DH9ktelhkhPSWHPtpRgCv5PGFqLb9Znugnv37qWyspK//vWvPPjgg7z33nsAjBgxgri4cL3uCSecwP3338/vf/97ABYvXsyiRYtYu3YtQ4cOJTc3l88//5z//Oc/WK3WqL6vdBcUXarBQ6ihho/KrHz6vz2YQ15OGTaEk7ddi1ZdxPWBOXxSn85w9qMA9UoMz5nmU4ONm/zTqbAdy6+O0UhPdXLq8VmckdIgZRBCCCH6lJCm8+l/9+CuqiQuJQtzXRE//Os5GLR6yjQ78YoXKwFK9CTu9U9iqWU5BmCP7uQYxYVBUahLOomPf3g/8anHcOrxx0rnXNFl+t0w4quvvpq1a9ce9vjbb7/N2WefDYCiKDz55JOREsSmYcSrV6+mqqqKM888k5UrV3LcccdF/X0lyRI9opUWuM54K6dmJfHFf/5Dic9IcnIq2UOTwx0ChRBCiP6kah9Ufos/cRgvfFJI8bdfUR2TSSAug7jyL4gxKZyRPZbT7ZUY0KVqQ3Sbfpdk9RRJsoQQQgghhBAgc7KEEEIIIYQQokdIkiWEEEIIIYQQnUiSLCGEEEIIIYToRJJkCSGEEEIIIUQnkiRLCCGEEEIIITqRJFlCCCGEEEII0YkkyRJCCCGEEEKITiRJlhBCCCGEEEJ0IkmyhBBCCCGEEKITSZIlhBBCCCGEEJ1IkiwhhBBCCCGE6ESSZAkhhBBCCCFEJ5IkSwghhBBCCCE6kbGnA+jtdF0HoLq6uocjEUIIIYQQQvSkppygKUdoiyRZHaipqQEgMzOzhyMRQgghhBBC9AY1NTXY7fY2n1f0jtKwAU7TNIqKioiPj0dRlJ4OZ8Crrq4mMzOTffv2kZCQ0NPhiD5G3j/iaMl7R3wX8v4RR0veO72PruvU1NQwaNAgVLXtnVdyJ6sDqqoyZMiQng5DHCIhIUF+2YijJu8fcbTkvSO+C3n/iKMl753epb07WE2k8YUQQgghhBBCdCJJsoQQQgghhBCiE0mSJfoUi8XCvHnzsFgsPR2K6IPk/SOOlrx3xHch7x9xtOS903dJ4wshhBBCCCGE6ERyJ0sIIYQQQgghOpEkWUIIIYQQQgjRiSTJEkIIIYQQQohOJEmWEEIIIYQQQnQiSbJEv+Dz+Rg1ahSKovDZZ5/1dDiil9uzZw/XXXcdQ4cOJSYmhuHDhzNv3jz8fn9PhyZ6qRUrVnDsscditVo5/fTT2b59e0+HJHq5+++/nzFjxhAfH4/T6eT888/nv//9b0+HJfqgRYsWoSgKN998c0+HIo6AJFmiX7jtttsYNGhQT4ch+oivv/4aTdN47LHH+PLLL1m6dCmrVq3ijjvu6OnQRC+0ceNGZs2axbx58ygoKGDkyJGMHz8el8vV06GJXuydd95h2rRpfPTRR7z55psEAgHOOecc6urqejo00Yfs2LGDxx57jFNOOaWnQxFHSFq4iz7v73//O7NmzWLz5s18//vf51//+hejRo3q6bBEH/Pggw/y6KOP8u233/Z0KKKXOf300xkzZgzLly8HQNM0MjMzufHGG5k7d24PRyf6irKyMpxOJ++88w4//elPezoc0QfU1tYyevRoVq5cycKFCxk1ahR/+tOfejosESW5kyX6tNLSUiZPnsy6deuw2Ww9HY7owzweD8nJyT0dhuhl/H4/n376KTk5OZHHVFUlJyeHDz/8sAcjE32Nx+MBkN8zImrTpk3j17/+dYvfP6LvMPZ0AEIcLV3Xufrqq7nhhhs47bTT2LNnT0+HJPqoXbt28cgjj/DQQw/1dCiilykvLycUCpGWltbi8bS0NL7++useikr0NZqmcfPNN/PjH/+Yk08+uafDEX3Ahg0bKCgoYMeOHT0dijhKcidL9Dpz585FUZR2/3z99dc88sgj1NTUcPvtt/d0yKKXiPa909yBAwf45S9/yYQJE5g8eXIPRS6E6M+mTZvGv//9bzZs2NDToYg+YN++fdx0000888wzWK3Wng5HHCXZkyV6nbKyMioqKto9ZtiwYVx88cW8+uqrKIoSeTwUCmEwGJg0aRJr167t6lBFLxPte8dsNgNQVFTE2WefzRlnnMFTTz2Fqsq6k2jJ7/djs9nYtGkT559/fuTxq666iqqqKl555ZWeC070CdOnT+eVV17h3XffZejQoT0djugDXn75ZX7/+99jMBgij4VCIRRFQVVVfD5fi+dE7yRJluiz9u7dS3V1deTroqIixo8fz6ZNmzj99NMZMmRID0YnersDBw4wbtw4Tj31VNavXy8XLNGm008/nezsbB555BEgXPp1zDHHMH36dGl8Idqk6zo33ngjL730Etu2beN73/teT4ck+oiamhoKCwtbPHbNNddwwgknMGfOHCk57SNkT5bos4455pgWX8fFxQEwfPhwSbBEuw4cOMDZZ59NVlYWDz30EGVlZZHn0tPTezAy0RvNmjWLq666itNOO43s7Gz+9Kc/UVdXxzXXXNPToYlebNq0aTz77LO88sorxMfHU1JSAoDdbicmJqaHoxO9WXx8/GGJVGxsLA6HQxKsPkSSLCHEgPPmm2+ya9cudu3adVhCLjf3xaEuueQSysrKuPvuuykpKWHUqFG88cYbhzXDEKK5Rx99FICzzz67xeNPPvkkV199dfcHJIToVlIuKIQQQgghhBCdSHZ5CyGEEEIIIUQnkiRLCCGEEEIIITqRJFlCCCGEEEII0YkkyRJCCCGEEEKITiRJlhBCCCGEEEJ0IkmyhBBCCCGEEKITSZIlhBBCCCGEEJ1IkiwhhBBCCCGE6ESSZAkhhBCtCIVC/OhHP+KCCy5o8bjH4yEzM5M777yzhyITQgjR2ym6rus9HYQQQgjRG/3vf/9j1KhRrFmzhkmTJgFw5ZVXsnPnTnbs2IHZbO7hCIUQQvRGkmQJIYQQ7Vi2bBl5eXl8+eWXbN++nQkTJrBjxw5GjhzZ06EJIYTopSTJEkIIIdqh6zo/+9nPMBgMfPHFF9x4443cddddPR2WEEKIXkySLCGEEKIDX3/9NSeeeCI/+MEPKCgowGg09nRIQgghejFpfCGEEEJ04IknnsBms7F7927279/f0+EIIYTo5eROlhBCCNGODz74gLPOOoutW7eycOFCAPLz81EUpYcjE0II0VvJnSwhhBCiDV6vl6uvvpo//vGPjBs3jscff5zt27ezatWqng5NCCFELyZ3soQQQog23HTTTbz++uvs3LkTm80GwGOPPcYtt9zCF198wbHHHtuzAQohhOiVJMkSQgghWvHOO+/w85//nG3btnHmmWe2eG78+PEEg0EpGxRCCNEqSbKEEEIIIYQQohPJniwhhBBCCCGE6ESSZAkhhBBCCCFEJ5IkSwghhBBCCCE6kSRZQgghhBBCCNGJJMkSQgghhBBCiE4kSZYQQgghhBBCdCJJsoQQQgghhBCiE0mSJYQQQgghhBCdSJIsNX01wAAAADFJREFUIYQQQgghhOhEkmQJIYQQQgghRCeSJEsIIYQQQgghOpEkWUIIIYQQQgjRif4f1OFlNRXqtzEAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["# Convert predictions to NumPy array to\n","# Evaluate the model\n","print(f'MSE: {mean_squared_error(dY_tensor.numpy().flatten(), predicted_dYadj.detach().numpy())}')\n","accuracy = pcorrect(dY_tensor.numpy().flatten(), predicted_dYadj.detach().numpy())\n","print(f'Model Accuracy: {accuracy:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DoxSxbUyYhyE","executionInfo":{"status":"ok","timestamp":1693147294245,"user_tz":180,"elapsed":9,"user":{"displayName":"Argimiro Arratia Quesada","userId":"14219514422153572380"}},"outputId":"a97699e7-bdae-408f-ef7b-840249ab5cc8"},"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["MSE: 0.006734320428222418\n","Model Accuracy: 99.29%\n"]}]}]}