Classification_QML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with Quantum Computing and Machine Learning\n",
    "\n",
    "In this notebook, we explore multiclass classification using Qiskit Machine Learning integrated with PyTorch. Unlike many tutorials that focus on binary classification in quantum computing, our approach extends to multiclass classification, providing a more realistic model for complex, real-world scenarios. We enhance our exploration by experimenting with hybrid models—combining Quantum Neural Networks (QNNs) and Deep Neural Networks (DNNs)—to achieve robust and potentially more accurate classification results.\n",
    "\n",
    "The dataset used is the well-known Iris flower dataset, frequently employed in machine learning for its straightforward yet effective demonstration capabilities. While not overly complex, this dataset serves as a suitable foundation for illustrating the power of quantum and hybrid approaches.\n",
    "\n",
    "The notebook is divided into three main sections:\n",
    "\n",
    "\n",
    "- Section 1: Introduction to Classification with Qiskit Machine Learning <br>\n",
    "    Here, we provide a brief guide to the Qiskit Machine Learning library. We implement a Variational Quantum Classifier (VQC) alongside a classical Support Vector Machine (SVM) as a baseline for performance comparison.\n",
    "- Section 2: Advanced Hybrid Modeling with QNNs <br>\n",
    " This section dives into two of Qiskit Machine Learning’s primary modules—EstimatorQNN and SamplerQNN. We use each to construct fully quantum classifiers as well as hybrid models, demonstrating both the pure quantum approach and the benefits of hybrid quantum-classical systems.\n",
    "- Section 3: Results and Analysis <br>\n",
    "    In this section, we compare the performance of different models used for multiclass classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import required packages\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from qiskit.circuit.library import ZZFeatureMap,PauliFeatureMap\n",
    "from qiskit.circuit.library import RealAmplitudes\n",
    "import time\n",
    "from qiskit_machine_learning.algorithms.classifiers import VQC\n",
    "from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n",
    "from qiskit_machine_learning.connectors import TorchConnector\n",
    "\n",
    "from qiskit_algorithms.optimizers import COBYLA, L_BFGS_B\n",
    "from qiskit.primitives import Sampler, Estimator\n",
    "from IPython.display import clear_output\n",
    "from matplotlib import pyplot as plt\n",
    "from qiskit import QuantumCircuit\n",
    "import torch\n",
    "\n",
    "from torch.optim import LBFGS, Adam\n",
    "from torch.nn import Linear\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1: Introduction to Classification with Qiskit Machine Learning\n",
    "\n",
    "Classification is a fundamental task in Machine Learning (ML), with a wide range of applications. Many introductory ML tutorial start with the Iris dataset, a classic choice fo its multivariate, multivariate structure, making it ideal for foundational learning. <br>\n",
    "In the context of Quantum Computing (QC), many tutorials remain focused on simpler, binary problems du to the current limitation of QC technology. Here, out aim is to bridge this gap by applying quantum techniques to more realistic, multiclass problems. <br>\n",
    "To establish a performance baseline, we first implement a classical Support Vector Machine (SVM) for the classification task. Then, we extend this approach to the quantum realm by performing the same task with Qiskit's Variational Quantum Classifier (VQC), offering a direct comparison between classical and quantum models.\n",
    "\n",
    "This section is based heavily on the Qiskit basic tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data\n",
    "data = load_iris()\n",
    "\n",
    "df_features = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df_target = pd.DataFrame(data.target)\n",
    "\n",
    "df_features.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(\n",
    "    data.data, data.target, train_size=0.8, shuffle=True )\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(train_X)\n",
    "X_test_scaled = scaler.transform(test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1 Classical clasification with SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results SVM\n",
      "accuracy train: 0.9833\n",
      "f1 score train: 0.9833\n",
      "accuracy test: 0.9667\n",
      "f1 score test: 0.9674\n"
     ]
    }
   ],
   "source": [
    "#Train and test SvM\n",
    "svc = SVC()\n",
    "_ = svc.fit(X_train_scaled, train_y) \n",
    "\n",
    "train_pred = svc.predict(X_train_scaled)\n",
    "test_pred = svc.predict(X_test_scaled)\n",
    "\n",
    "\n",
    "print('Results SVM')\n",
    "print(f'accuracy train: { accuracy_score(train_pred, train_y):.4f}')\n",
    "print(f'f1 score train: { f1_score(train_pred, train_y, average='weighted'):.4f}')\n",
    "\n",
    "print(f'accuracy test: { accuracy_score(test_pred, test_y):.4f}')\n",
    "print(f'f1 score test: { f1_score(test_pred, test_y, average='weighted'):.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2  Variational Quantum Classifier (VQC) using Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features = train_X.shape[1] #num features\n",
    "feature_map = ZZFeatureMap(feature_dimension=num_features, reps=1)\n",
    "#feature_map.decompose().draw(output=\"mpl\", style=\"clifford\") \n",
    "ansatz = RealAmplitudes(num_qubits=num_features, reps=1) #ansatz\n",
    "\n",
    "optimizer = L_BFGS_B()\n",
    "sampler = Sampler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to graph performance\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()\n",
    "    return objective_func_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 75 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "vqc = VQC(\n",
    "    sampler=sampler,\n",
    "    feature_map=feature_map,\n",
    "    ansatz=ansatz,\n",
    "    optimizer=optimizer,\n",
    "    callback=callback_graph,\n",
    ")\n",
    "\n",
    "# clear objective value history\n",
    "objective_func_vals = []\n",
    "start = time.time()\n",
    "vqc.fit(X_train_scaled, train_y)\n",
    "elapsed = time.time() - start\n",
    "\n",
    "print(f\"Training time: {round(elapsed)} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results VQC\n",
      "accuracy train: 0.7333\n",
      "f1 score train: 0.7383\n",
      "accuracy test: 0.7000\n",
      "f1 score test: 0.6917\n"
     ]
    }
   ],
   "source": [
    "train_pred = vqc.predict(X_train_scaled)\n",
    "test_pred = vqc.predict(X_test_scaled)\n",
    "\n",
    "print('Results VQC')\n",
    "print(f'accuracy train: { accuracy_score(train_pred, train_y):.4f}')\n",
    "print(f'f1 score train: { f1_score(train_pred, train_y, average='weighted'):.4f}')\n",
    "\n",
    "print(f'accuracy test: { accuracy_score(test_pred, test_y):.4f}')\n",
    "print(f'f1 score test: { f1_score(test_pred, test_y, average='weighted'):.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: Advanced Hybrid Modeling with QNNs \n",
    "In this section, we explore advanced classification techniques using two core modeules from Qiskit Machine Learning: SamplerQNN and EstimatorQNN. Using each module, we develop both fully quantum models and hybrid models that integrate classical neural network layers. This approach allows us to leverage the strengths of both quantum and classical computing, aiming to enhance model performance and robustness for the classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert data in tensor\n",
    "X_tensor_train_scaled = torch.Tensor(X_train_scaled)\n",
    "y_tensor_train = (torch.Tensor(train_y)).to(torch.int64)\n",
    "X_tensor_test_scaled = torch.Tensor(X_test_scaled)\n",
    "y_tensor_test = (torch.Tensor(test_y)).to(torch.int64)\n",
    "num_features = train_X.shape[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the EstimatorQNN\n",
    "def create_estimator_qnn(feature_map, num_features):\n",
    "\n",
    "    #feature_map.decompose().draw(output=\"mpl\", style=\"clifford\")\n",
    "    ansatz = RealAmplitudes(num_qubits=num_features)\n",
    "\n",
    "    qc = QuantumCircuit(num_features)\n",
    "    qc.compose(feature_map, inplace=True)\n",
    "    qc.compose(ansatz, inplace=True)\n",
    "    #qc.draw(output=\"mpl\", style=\"clifford\")\n",
    "    qnn_estimator = EstimatorQNN(\n",
    "            circuit=qc, \n",
    "            input_params=feature_map.parameters,\n",
    "            weight_params=ansatz.parameters,\n",
    "            input_gradients=True,\n",
    "            \n",
    "            )\n",
    "\n",
    "    return qnn_estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1  EstimatorQNN\n",
    "\n",
    "Since this is a multiclass problem and EstimatorQNN returns only a single output value, we create a separate EstimatorQNN for each class. The output values are then combined and passed through a Softmax function to produce the final class probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total QNN Parameters: 48\n",
      "Total Parameters: 48\n"
     ]
    }
   ],
   "source": [
    "#Quantum Classifier using only QNN\n",
    "class QuantumClassifier(torch.nn.Module):\n",
    "    def __init__(self, qnn_class_0, qnn_class_1, qnn_class_2):\n",
    "        super(QuantumClassifier, self).__init__()\n",
    "        # Attach the QNN to Torch via TorchConnector\n",
    "        self.qnn_0 = TorchConnector(qnn_class_0)\n",
    "        self.qnn_1 = TorchConnector(qnn_class_1)\n",
    "        self.qnn_2 = TorchConnector(qnn_class_2)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Get output for each class QNN\n",
    "        output_0 = self.qnn_0(x)\n",
    "        output_1 = self.qnn_1(x)\n",
    "        output_2 = self.qnn_2(x)\n",
    "\n",
    "        quantum_output = torch.cat([output_0, output_1, output_2], dim=-1)\n",
    "\n",
    "        # Softmax for n-class classification output\n",
    "        return torch.nn.functional.softmax(quantum_output, dim=-1)\n",
    "\n",
    "# We create one qnn for each class and we create different feature mapping\n",
    "feature_map_0 = ZZFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ0')\n",
    "feature_map_1 = PauliFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ1', paulis=[\"Z\", \"YY\"])\n",
    "feature_map_2 = PauliFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ2', paulis=[\"Z\", \"XX\"])\n",
    "\n",
    "model_qnn_0 = create_estimator_qnn(feature_map_0, num_features)\n",
    "model_qnn_1 = create_estimator_qnn(feature_map_1, num_features)\n",
    "model_qnn_2 = create_estimator_qnn(feature_map_2, num_features)\n",
    "model_estimator_qnn = QuantumClassifier(model_qnn_0, model_qnn_1, model_qnn_2)\n",
    "\n",
    "total_qnn_params = (model_qnn_0.num_weights + model_qnn_1.num_weights + model_qnn_2.num_weights)\n",
    "total_params = sum(p.numel() for p in model_estimator_qnn.parameters())\n",
    "\n",
    "print(f\"Total QNN Parameters: {total_qnn_params}\")\n",
    "print(f\"Total Parameters: {total_params}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Loss = 1.1423354148864746\n",
      "Epoch 10: Loss = 0.9923561215400696\n",
      "Epoch 20: Loss = 0.9709026217460632\n",
      "Epoch 30: Loss = 0.9588833451271057\n",
      "Epoch 40: Loss = 0.9533652663230896\n",
      "Epoch 50: Loss = 0.9504745602607727\n",
      "Epoch 60: Loss = 0.9487791657447815\n",
      "Epoch 70: Loss = 0.9477243423461914\n",
      "Epoch 80: Loss = 0.9474115371704102\n",
      "Epoch 90: Loss = 0.9472252726554871\n"
     ]
    }
   ],
   "source": [
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "optimizer = Adam(model_estimator_qnn.parameters(), lr=0.1)\n",
    "\n",
    "# Training loop\n",
    "num_epochs = 100\n",
    "loss_train_estimator = []\n",
    "metrics = {'acc_train_estimator':[],\n",
    "           'acc_test_estimator':[]}\n",
    "for epoch in range(num_epochs):\n",
    "    model_estimator_qnn.train()\n",
    "    # Zero the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Forward pass\n",
    "    outputs = model_estimator_qnn(X_tensor_train_scaled)\n",
    "    loss = loss_function(outputs, y_tensor_train)\n",
    "\n",
    "    # Backward pass and optimize\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    loss_train_estimator.append(loss.item())\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch {epoch}: Loss = {loss.item()}\")\n",
    "\n",
    "    model_estimator_qnn.eval()\n",
    "    output_train = model_estimator_qnn(X_tensor_train_scaled)\n",
    "    train_pred = torch.argmax(output_train, dim=1)\n",
    "    metrics['acc_train_estimator'].append(accuracy_score(train_pred, train_y))\n",
    "\n",
    "    output_test = model_estimator_qnn(X_tensor_test_scaled)\n",
    "    test_pred = torch.argmax(output_test, dim=1)\n",
    "    metrics['acc_test_estimator'].append(accuracy_score(test_pred, test_y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EstimatorQNN\n",
      "Accuracy train: 0.8917\n",
      "F1 score train: 0.8915\n",
      "Accuracy test: 0.9000\n",
      "F1 score test: 0.8985\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the model\n",
    "model_estimator_qnn.eval()\n",
    "\n",
    "output_train = model_estimator_qnn(X_tensor_train_scaled)\n",
    "train_pred = torch.argmax(output_train, dim=1)\n",
    "\n",
    "output_test = model_estimator_qnn(X_tensor_test_scaled)\n",
    "test_pred = torch.argmax(output_test, dim=1)\n",
    "\n",
    "\n",
    "metrics_estimator = {'accuracy_train': accuracy_score(train_pred, train_y),\n",
    "                          'accuracy_test': accuracy_score(test_pred, test_y),\n",
    "                          'f1_score_train': f1_score(train_pred, train_y, average='weighted'),\n",
    "                          'f1_score_test': f1_score(test_pred, test_y, average='weighted'),\n",
    "                          }\n",
    "\n",
    "print('EstimatorQNN')\n",
    "\n",
    "print(f'Accuracy train: { metrics_estimator['accuracy_train']:.4f}')\n",
    "print(f'F1 score train: { metrics_estimator['f1_score_train']:.4f}')\n",
    "\n",
    "print(f'Accuracy test: { metrics_estimator['accuracy_test']:.4f}')\n",
    "print(f'F1 score test: { metrics_estimator['f1_score_test']:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2  EstimatorQNN + Classical Neural Network\n",
    "\n",
    "This model combines the EstimatorQNN with classical neural network layers. Similar to the previous model, we use a separate EstimatorQNN for each class. However, in this version, we add ReLU layers after concatenating the QNN outputs, followed by a final Softmax layer to produce the class probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total QNN Parameters: 48\n",
      "Total Parameters: 208\n"
     ]
    }
   ],
   "source": [
    "#Define the EstimatorQNN\n",
    "def create_estimator_qnn(feature_map, num_features):\n",
    "\n",
    "    #feature_map.decompose().draw(output=\"mpl\", style=\"clifford\")\n",
    "    ansatz = RealAmplitudes(num_qubits=num_features)\n",
    "\n",
    "    qc = QuantumCircuit(num_features)\n",
    "    qc.compose(feature_map, inplace=True)\n",
    "    qc.compose(ansatz, inplace=True)\n",
    "    #qc.draw(output=\"mpl\", style=\"clifford\")\n",
    "    qnn_estimator = EstimatorQNN(\n",
    "            circuit=qc, \n",
    "            input_params=feature_map.parameters,\n",
    "            weight_params=ansatz.parameters,\n",
    "            input_gradients=True,\n",
    "            \n",
    "            )\n",
    "\n",
    "    return qnn_estimator\n",
    "\n",
    "# Quantum Classifier using QNN +DNN\n",
    "class QuantumClassifier_Multilayer(torch.nn.Module):\n",
    "    def __init__(self, qnn_class_0, qnn_class_1, qnn_class_2, num_features):\n",
    "        super(QuantumClassifier_Multilayer, self).__init__()\n",
    "        # Attach the QNN to Torch via TorchConnector\n",
    "\n",
    "        \n",
    "        self.qnn_0 = TorchConnector(qnn_class_0)\n",
    "        self.qnn_1 = TorchConnector(qnn_class_1)\n",
    "        self.qnn_2 = TorchConnector(qnn_class_2)\n",
    "        \n",
    "        self.fc1 = Linear(num_features, 16)  \n",
    "        self.fc2 = Linear(16, num_features)\n",
    "        self.fc3 = Linear(3, 3) #3 num clases\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = torch.nn.functional.relu(self.fc1(x))\n",
    "        x = torch.nn.functional.relu(self.fc2(x))\n",
    "\n",
    "        # Get output for each class QNN\n",
    "        output_0 = self.qnn_0(x)\n",
    "        output_1 = self.qnn_1(x)\n",
    "        output_2 = self.qnn_2(x)\n",
    "\n",
    "        quantum_output = torch.cat([output_0, output_1, output_2], dim=-1)\n",
    "\n",
    "        x = self.fc3(quantum_output)  \n",
    "    \n",
    "        # Softmax for n-class classification output\n",
    "        return torch.nn.functional.softmax(x, dim=-1)\n",
    "\n",
    "# We create one qnn for each class and we create different feature mapping\n",
    "\n",
    "feature_map_0 = ZZFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ0')\n",
    "feature_map_1 = PauliFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ1', paulis=[\"Z\", \"YY\"])\n",
    "feature_map_2 = PauliFeatureMap(feature_dimension=num_features, reps=1, parameter_prefix='θ2', paulis=[\"Z\", \"XX\"])\n",
    "\n",
    "model_qnn_0 = create_estimator_qnn(feature_map_0, num_features)\n",
    "model_qnn_1 = create_estimator_qnn(feature_map_1, num_features)\n",
    "model_qnn_2 = create_estimator_qnn(feature_map_2, num_features)\n",
    "model_estimator_hybrid = QuantumClassifier_Multilayer(model_qnn_0, model_qnn_1, model_qnn_2, num_features)\n",
    "\n",
    "\n",
    "total_qnn_params = (model_qnn_0.num_weights + model_qnn_1.num_weights + model_qnn_2.num_weights)\n",
    "total_params = sum(p.numel() for p in model_estimator_hybrid.parameters())\n",
    "\n",
    "print(f\"Total QNN Parameters: {total_qnn_params}\")\n",
    "print(f\"Total Parameters: {total_params}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Loss = 1.118337631225586\n",
      "Epoch 10: Loss = 0.9860469102859497\n",
      "Epoch 20: Loss = 0.7885076403617859\n",
      "Epoch 30: Loss = 0.6287564635276794\n",
      "Epoch 40: Loss = 0.5895841121673584\n",
      "Epoch 50: Loss = 0.5828325152397156\n",
      "Epoch 60: Loss = 0.5780918598175049\n",
      "Epoch 70: Loss = 0.6723870038986206\n",
      "Epoch 80: Loss = 0.5760970711708069\n",
      "Epoch 90: Loss = 0.6333550810813904\n"
     ]
    }
   ],
   "source": [
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "optimizer = Adam(model_estimator_hybrid.parameters(), lr=0.1)\n",
    "\n",
    "# Training loop\n",
    "num_epochs = 100\n",
    "loss_train_estimator_hybrid = [] \n",
    "metrics['acc_train_estimator_hybrid'] = []\n",
    "metrics['acc_test_estimator_hybrid'] = []\n",
    "for epoch in range(num_epochs):\n",
    "    model_estimator_hybrid.train()\n",
    "    # Zero the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Forward pass\n",
    "    outputs = model_estimator_hybrid(X_tensor_train_scaled)\n",
    "    loss = loss_function(outputs, y_tensor_train)\n",
    "\n",
    "    # Backward pass and optimize\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    loss_train_estimator_hybrid.append(loss.item())\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch {epoch}: Loss = {loss.item()}\")\n",
    "\n",
    "    model_estimator_hybrid.eval()\n",
    "    output_train = model_estimator_hybrid(X_tensor_train_scaled)\n",
    "    train_pred = torch.argmax(output_train, dim=1)\n",
    "    metrics['acc_train_estimator_hybrid'].append(accuracy_score(train_pred, train_y))\n",
    "\n",
    "    output_test = model_estimator_hybrid(X_tensor_test_scaled)\n",
    "    test_pred = torch.argmax(output_test, dim=1)\n",
    "    metrics['acc_test_estimator_hybrid'].append(accuracy_score(test_pred, test_y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EstimatorQNN + DNN\n",
      "Accuracy train: 0.9833\n",
      "F1 score train: 0.9833\n",
      "Accuracy test: 1.0000\n",
      "F1 score test: 1.0000\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the model\n",
    "model_estimator_hybrid.eval()\n",
    "output_train = model_estimator_hybrid(X_tensor_train_scaled)\n",
    "train_pred = torch.argmax(output_train, dim=1)\n",
    "\n",
    "output_test = model_estimator_hybrid(X_tensor_test_scaled)\n",
    "test_pred = torch.argmax(output_test, dim=1)\n",
    "\n",
    "\n",
    "metrics_estimator_hybrid = {'accuracy_train': accuracy_score(train_pred, train_y),\n",
    "                          'accuracy_test': accuracy_score(test_pred, test_y),\n",
    "                          'f1_score_train': f1_score(train_pred, train_y, average='weighted'),\n",
    "                          'f1_score_test': f1_score(test_pred, test_y, average='weighted'),\n",
    "                          }\n",
    "\n",
    "print('EstimatorQNN + DNN')\n",
    "print(f'Accuracy train: { metrics_estimator_hybrid['accuracy_train']:.4f}')\n",
    "print(f'F1 score train: { metrics_estimator_hybrid['f1_score_train']:.4f}')\n",
    "print(f'Accuracy test: { metrics_estimator_hybrid['accuracy_test']:.4f}')\n",
    "print(f'F1 score test: { metrics_estimator_hybrid['f1_score_test']:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3  Sampler QNN\n",
    "\n",
    "In this section, we use the SamplerQNN for classification. The SamplerQNN provides a probability distribution over possible outcomes, making it well-suited for multiclass tasks. Since our classification requires three distinct classes, we map the output bitstrings (representing the different possible outcomes) directly to our target classes. This approach leverages the probabilistic nature of SamplerQNN to capture class distributions, enhancing the model's ability to handle multiclass classification effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total QNN Parameters: 16\n",
      "Total Parameters: 16\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_features = train_X.shape[1]\n",
    "num_classes = len(data.target_names)\n",
    "\n",
    "def interpret_bitstring(bitstring):\n",
    "    # Convert integer to binary if necessary, ensuring a list of bits for consistency\n",
    "    if isinstance(bitstring, int):\n",
    "        # Convert to binary and fill up to the number of qubits\n",
    "        bitstring = [int(d) for d in f\"{bitstring:0{num_features}b}\"]\n",
    "    # Sum bit values to get the class (mod num_classes to stay within range)\n",
    "    return sum(bitstring) % num_classes\n",
    "\n",
    "\n",
    "def create_sampler_qnn():\n",
    "\n",
    "    feature_map = ZZFeatureMap(feature_dimension=num_features, reps=1)\n",
    "    ansatz = RealAmplitudes(num_qubits=num_features)\n",
    "\n",
    "    qc = QuantumCircuit(num_features)\n",
    "    qc.compose(feature_map, inplace=True)\n",
    "    qc.compose(ansatz, inplace=True)\n",
    "\n",
    "    sampler = Sampler()\n",
    "    qnn_sampler = SamplerQNN(\n",
    "    circuit=qc,\n",
    "    sampler=sampler,\n",
    "    input_params=feature_map.parameters,\n",
    "    weight_params=ansatz.parameters,\n",
    "    interpret=interpret_bitstring,\n",
    "    input_gradients=True,\n",
    "    output_shape=num_classes,\n",
    "    )\n",
    "    \n",
    "    return qnn_sampler\n",
    "\n",
    "\n",
    "class QuantumClassifier(torch.nn.Module):\n",
    "    def __init__(self, qnn, num_classes=3):\n",
    "        super(QuantumClassifier, self).__init__()\n",
    "        # Attach the QNN to Torch via TorchConnector\n",
    "        self.qnn = TorchConnector(qnn)\n",
    "        self.num_classes = num_classes\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Get probabilities from the QNN and select top `num_classes`\n",
    "        quantum_output = self.qnn(x)\n",
    "        return  torch.nn.functional.softmax(quantum_output, dim=-1)\n",
    "\n",
    "\n",
    "\n",
    "model_qnn = create_sampler_qnn()\n",
    "model_sampler_qnn = QuantumClassifier(qnn=model_qnn)\n",
    "\n",
    "total_qnn_params = model_qnn.num_weights\n",
    "total_params = sum(p.numel() for p in model_sampler_qnn.parameters())\n",
    "\n",
    "print(f\"Total QNN Parameters: {total_qnn_params}\")\n",
    "print(f\"Total Parameters: {total_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Loss = 1.1118794679641724\n",
      "Epoch 10: Loss = 1.0650984048843384\n",
      "Epoch 20: Loss = 1.0526026487350464\n",
      "Epoch 30: Loss = 1.0445497035980225\n",
      "Epoch 40: Loss = 1.040938377380371\n",
      "Epoch 50: Loss = 1.0405853986740112\n",
      "Epoch 60: Loss = 1.0401052236557007\n",
      "Epoch 70: Loss = 1.039971113204956\n",
      "Epoch 80: Loss = 1.0399222373962402\n",
      "Epoch 90: Loss = 1.0399057865142822\n"
     ]
    }
   ],
   "source": [
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "optimizer = Adam(model_sampler_qnn.parameters(), lr=0.1)\n",
    "\n",
    "\n",
    "# Training loop\n",
    "num_epochs = 100\n",
    "loss_train_sampler = [] \n",
    "metrics['acc_train_sampler'], metrics['acc_test_sampler'] = [], []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model_sampler_qnn.train()\n",
    "    # Zero the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Forward pass\n",
    "    outputs = model_sampler_qnn(X_tensor_train_scaled)\n",
    "    loss = loss_function(outputs, y_tensor_train)\n",
    "\n",
    "    # Backward pass and optimize\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    loss_train_sampler.append(loss.item())\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch {epoch}: Loss = {loss.item()}\")\n",
    "\n",
    "    model_sampler_qnn.eval()\n",
    "    output_train = model_sampler_qnn(X_tensor_train_scaled)\n",
    "    train_pred = torch.argmax(output_train, dim=1)\n",
    "    metrics['acc_train_sampler'].append(accuracy_score(train_pred, train_y))\n",
    "\n",
    "    output_test = model_sampler_qnn(X_tensor_test_scaled)\n",
    "    test_pred = torch.argmax(output_test, dim=1)\n",
    "    metrics['acc_test_sampler'].append(accuracy_score(test_pred, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SamplerQNN\n",
      "Accuracy train: 0.8417\n",
      "F1 score train: 0.8412\n",
      "Accuracy test: 0.9000\n",
      "F1 score test: 0.9017\n"
     ]
    }
   ],
   "source": [
    "model_sampler_qnn.eval()\n",
    "output_train = model_sampler_qnn(X_tensor_train_scaled)\n",
    "train_pred = torch.argmax(output_train, dim=1)\n",
    "\n",
    "output_test = model_sampler_qnn(X_tensor_test_scaled)\n",
    "test_pred = torch.argmax(output_test, dim=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "metrics_sampler = {'accuracy_train': accuracy_score(train_pred, train_y),\n",
    "                          'accuracy_test': accuracy_score(test_pred, test_y),\n",
    "                          'f1_score_train': f1_score(train_pred, train_y, average='weighted'),\n",
    "                          'f1_score_test': f1_score(test_pred, test_y, average='weighted'),\n",
    "                          }\n",
    "\n",
    "print('SamplerQNN')\n",
    "print(f'Accuracy train: { metrics_sampler['accuracy_train']:.4f}')\n",
    "print(f'F1 score train: { metrics_sampler['f1_score_train']:.4f}')\n",
    "\n",
    "print(f'Accuracy test: { metrics_sampler['accuracy_test']:.4f}')\n",
    "print(f'F1 score test: { metrics_sampler['f1_score_test']:.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4  Sampler QNN + Classical Neural Network\n",
    "\n",
    "Finally we add Classical Neural Network to the SamplerQNN model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total QNN Parameters: 16\n",
      "Total Parameters: 283\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class QuantumClassifier_Multilayer(torch.nn.Module):\n",
    "    def __init__(self, qnn, num_classes=3):\n",
    "        super(QuantumClassifier_Multilayer, self).__init__()\n",
    "        # Attach the QNN to Torch via TorchConnector\n",
    "        self.qnn = TorchConnector(qnn)\n",
    "        self.num_classes = num_classes\n",
    "\n",
    "        self.fc1 = Linear(num_classes, 16)\n",
    "        self.fc2 = Linear(16, 10)\n",
    "        self.fc3 = Linear(10, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Get probabilities from the QNN\n",
    "        quantum_output = self.qnn(x)\n",
    "        x = torch.nn.functional.relu(self.fc1(quantum_output)) \n",
    "        x = torch.nn.functional.relu(self.fc2(x)) \n",
    "        x = self.fc3(x)  # Final layer to produce class logits\n",
    "        return torch.nn.functional.softmax(x, dim=-1)  \n",
    "\n",
    "\n",
    "model_qnn = create_sampler_qnn()\n",
    "model_sampler_hybrid = QuantumClassifier_Multilayer(qnn=model_qnn, num_classes=num_classes)\n",
    "\n",
    "total_qnn_params = model_qnn.num_weights\n",
    "total_params = sum(p.numel() for p in model_sampler_hybrid.parameters())\n",
    "\n",
    "print(f\"Total QNN Parameters: {total_qnn_params}\")\n",
    "print(f\"Total Parameters: {total_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: Loss = 1.102861762046814\n",
      "Epoch 10: Loss = 0.8801552057266235\n",
      "Epoch 20: Loss = 0.7508866786956787\n",
      "Epoch 30: Loss = 0.6787429451942444\n",
      "Epoch 40: Loss = 0.6503905653953552\n",
      "Epoch 50: Loss = 0.6256065368652344\n",
      "Epoch 60: Loss = 0.6250995397567749\n",
      "Epoch 70: Loss = 0.633673906326294\n",
      "Epoch 80: Loss = 0.6173046231269836\n",
      "Epoch 90: Loss = 0.6103864312171936\n"
     ]
    }
   ],
   "source": [
    "\n",
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "optimizer = Adam(model_sampler_hybrid.parameters(), lr=0.1)\n",
    "\n",
    "# Training loop\n",
    "num_epochs = 100\n",
    "loss_train_sampler_hybrid = [] \n",
    "metrics['acc_train_sampler_hybrid'], metrics['acc_test_sampler_hybrid'] = [], []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model_sampler_hybrid.train()\n",
    "    # Zero the gradients\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Forward pass\n",
    "    outputs = model_sampler_hybrid(X_tensor_train_scaled)\n",
    "    loss = loss_function(outputs, y_tensor_train)\n",
    "\n",
    "    # Backward pass and optimize\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    loss_train_sampler_hybrid.append(loss.item())\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch {epoch}: Loss = {loss.item()}\")\n",
    "\n",
    "    model_sampler_hybrid.eval()\n",
    "    output_train = model_sampler_hybrid(X_tensor_train_scaled)\n",
    "    train_pred = torch.argmax(output_train, dim=1)\n",
    "    metrics['acc_train_sampler_hybrid'].append(accuracy_score(train_pred, train_y))\n",
    "\n",
    "    output_test = model_sampler_hybrid(X_tensor_test_scaled)\n",
    "    test_pred = torch.argmax(output_test, dim=1)\n",
    "    metrics['acc_test_sampler_hybrid'].append(accuracy_score(test_pred, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SamplerQNN + DNN\n",
      "Accuracy train: 0.9500\n",
      "F1 score train: 0.9500\n",
      "Accuracy test: 0.9333\n",
      "F1 score test: 0.9337\n"
     ]
    }
   ],
   "source": [
    "model_sampler_hybrid.eval()\n",
    "\n",
    "output_train = model_sampler_hybrid(X_tensor_train_scaled)\n",
    "train_pred = torch.argmax(output_train, dim=1)\n",
    "\n",
    "output_test = model_sampler_hybrid(X_tensor_test_scaled)\n",
    "test_pred = torch.argmax(output_test, dim=1)\n",
    "\n",
    "metrics_sampler_hybrid = {'accuracy_train': accuracy_score(train_pred, train_y),\n",
    "                          'accuracy_test': accuracy_score(test_pred, test_y),\n",
    "                          'f1_score_train': f1_score(train_pred, train_y, average='weighted'),\n",
    "                          'f1_score_test': f1_score(test_pred, test_y, average='weighted'),\n",
    "                          }\n",
    "\n",
    "\n",
    "print('SamplerQNN + DNN')\n",
    "print(f'Accuracy train: { metrics_sampler_hybrid['accuracy_train']:.4f}')\n",
    "print(f'F1 score train: { metrics_sampler_hybrid['f1_score_train']:.4f}')\n",
    "\n",
    "print(f'Accuracy test: { metrics_sampler_hybrid['accuracy_test']:.4f}')\n",
    "print(f'F1 score test: { metrics_sampler_hybrid['f1_score_test']:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3: Results and Analysis\n",
    "\n",
    "In this section, we compare the performance of different models used for multiclass classification. Although all models achieve reasonable accuracy, none of them outperforms the baseline set by the classical SVM model. This result suggests that, although hybrid quantum-classical models offer potential, they do not always outperform purely quantum or classical approaches. However, it is essential to note that these results are specific to the scope of this experiment and may not generalise to other, more complex problems.\n",
    "\n",
    "Furthermore, we show that quantum models can be successfully adapted to multiclass classification, demonstrating that quantum methods can relatively easily tackle more nuanced classification tasks beyond binary problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy estimator QNN train: 0.8917\n",
      "Accuracy estimator hybrid train: 0.9833\n",
      "Accuracy sampler QNN train: 0.8417\n",
      "Accuracy sampler hybrid train: 0.9500\n",
      "------------------------------\n",
      "Accuracy estimator QNN test: 0.9000\n",
      "Accuracy estimator hybrid test: 1.0000\n",
      "Accuracy sampler QNN test: 0.9000\n",
      "Accuracy sampler hybrid test: 0.9333\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy estimator QNN train: { metrics_estimator['accuracy_train']:.4f}')\n",
    "print(f'Accuracy estimator hybrid train: { metrics_estimator_hybrid['accuracy_train']:.4f}')\n",
    "print(f'Accuracy sampler QNN train: { metrics_sampler['accuracy_train']:.4f}')\n",
    "print(f'Accuracy sampler hybrid train: { metrics_sampler_hybrid['accuracy_train']:.4f}')\n",
    "\n",
    "print('------------------------------')\n",
    "print(f'Accuracy estimator QNN test: { metrics_estimator['accuracy_test']:.4f}')\n",
    "print(f'Accuracy estimator hybrid test: { metrics_estimator_hybrid['accuracy_test']:.4f}')\n",
    "print(f'Accuracy sampler QNN test: { metrics_sampler['accuracy_test']:.4f}')\n",
    "print(f'Accuracy sampler hybrid test: { metrics_sampler_hybrid['accuracy_test']:.4f}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(metrics['acc_train_estimator'])), metrics['acc_train_estimator'], label='Estimator', color='C0')\n",
    "plt.plot(np.arange(len(metrics['acc_train_estimator_hybrid'])), metrics['acc_train_estimator_hybrid'], label='Estimator hybrid', color='C1')\n",
    "plt.plot(np.arange(len(metrics['acc_train_sampler'])), metrics['acc_train_sampler'], label='Sampler', color='C2')\n",
    "plt.plot(np.arange(len(metrics['acc_train_sampler_hybrid'])), metrics['acc_train_sampler_hybrid'], label='Sampler hybrid', color='C3')\n",
    "\n",
    "plt.title('Accuracy during training')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph it is quite clear that a hybrid model does not necessarily perform better than a purely quantum model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Loss')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+kAAAJJCAYAAADWVnYlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADgpUlEQVR4nOzdd3RUVdfH8e+kF5JAAiT00It0kKoUQaoUeUQUC6CoiL6KXaygj6I8gthAlCaKBQuIiIUWUXpHihTpEAg1IZTU+/5xMkmG9JBkUn6ftWbNnTv33tkThmT2KfvYLMuyEBERERERERGnc3F2ACIiIiIiIiJiKEkXERERERERKSSUpIuIiIiIiIgUEkrSRURERERERAoJJekiIiIiIiIihYSSdBEREREREZFCQkm6iIiIiIiISCGhJF1ERERERESkkFCSLiIiIiIiIlJIKEkXERERERERKSSUpIuIiBQBBw8exGaz0aNHD2eHIiIiIvlISbqIiIiIiIhIIaEkXURERERERKSQUJIuIiJSzOzYsYNBgwZRvnx5PD09qV69Ok888QRnz55Nc+zevXsZNmwY1atXx8vLi7Jly9K8eXOeeuoph+PCw8N5/PHHqV27Nt7e3gQGBtKoUSNGjhxJVFRUQb01ERGRYs9mWZbl7CBEREQkcwcPHqR69ep0796dX3/9NcPjVq1aRbdu3YiJieG2224jNDSUNWvWEBYWRu3atVm9ejVBQUEAHD9+nOuuu46LFy/Su3dv6tatS3R0NHv37mX58uXExsYCcOnSJRo2bMjBgwfp1q0bjRs3JjY2lv3797NkyRJ27txJaGhoQfwYREREij03ZwcgIiIieSMxMZGhQ4dy8eJFfv31V7p375783AsvvMC4ceN47rnnmDZtGgDff/8958+f57333uOxxx5zuNbp06eTt5cuXcqBAwd44oknmDhxosNxFy5cwNPTMx/flYiISMmi4e4iIiLFxMqVK9m7dy89e/Z0SNABXnzxRYKCgvjyyy+Te8jtvL2901yrbNmyafald5yfnx8eHh7XGLmIiIjYKUkXEREpJjZv3gxAp06d0jzn6+tLy5YtuXz5Mnv27AHglltuwcfHh0ceeYTbb7+dGTNmJD+XWocOHQgJCWHcuHH07t2byZMns23bNjRjTkREJO8pSRcRESkm7AXcgoOD030+JCQEgMjISACqV6/O6tWr6dOnD7/88gv3338/devWpV69enz77bfJ5wUEBLB69WruueceVq9ezSOPPEKTJk2oWrUqkydPzud3JSIiUrIoSRcRESkm/P39ATh58mS6z9v3248DaNy4Md9//z1nz55l9erVvPLKK5w8eZJBgwaxcuXK5ONCQ0P57LPPOHXqFJs3b+btt9/GsiweeeQRvvrqq3x8VyIiIiWLknQREZFiolmzZgCEhYWlee7SpUts2LABb29v6tatm+Z5d3d32rRpw9ixY3n//fexLIuFCxemOc7V1ZWmTZvy7LPPJifnCxYsyNs3IiIiUoIpSRcRESkm2rdvT82aNfnll19YsmSJw3Pjxo3j9OnT3HnnncmF3tavX09ERESa69h73O2F4rZv386hQ4eyPE5ERESundZJFxERKQLs66RXrFiRm2++Od1jmjdvTvPmzenevTtxcXEMHDiQatWqsXbtWpYtW0bNmjVZvXo15cqVA2DUqFFMnjyZTp06UatWLfz9/dm5cyeLFi2iTJkybN68mcqVKzNp0iSeeuop2rdvT7169QgKCmL//v0sWLAAm83GX3/9RfPmzQvyxyEiIlJsKUkXEREpAuxJemb69evH/Pnz+fvvv3nttdcICwsjMjKSihUr0q9fP15++WWHpdXWrl3LzJkzWblyJUePHiUmJobKlSvTs2dPnn76aapUqQLArl27mDp1KitWrODw4cNER0dTqVIlOnTowLPPPkv9+vXz9b2LiIiUJErSRURERERERAoJzUkXERERERERKSSUpIuIiIiIiIgUEkrSRURERERERAoJJekiIiIiIiIihYSSdBEREREREZFCQkm6iIiIiIiISCGhJF1ERERERESkkFCSLiIiIiIiIlJIKEkXERERERERKSSUpIuIiIiIiIgUEkrSRURERERERAoJJekiIiIiIiIihYSSdBEREREREZFCQkm6iIiIiIiISCGhJF1ERERERESkkFCSLiIiIiIiIlJIKEkXERERERERKSSUpIuIiIiIiIgUEkrSRURERERERAoJJekiIiIiIiIihYSSdBEREREREZFCQkm6iIiIiIiISCGhJF1ERERERESkkFCSLiIiIiIiIlJIKEkXERFJZdasWdhstgxvYWFh2b7W8ePHGTNmDFu2bEnz3JgxY7DZbHkXeA7s3LmTMWPGcPDgQae8vjMcPHgQm83GrFmzcnxuWFhYjv/tRUREcsvN2QGIiIgURjNnzqRevXpp9jdo0CDb1zh+/Dhjx44lNDSUpk2bOjw3fPhwevToca1h5srOnTsZO3YsnTp1IjQ01CkxiIiISPqUpIuIiKSjYcOGtGzZMt+uX7lyZSpXrpxv13eGS5cu4ePj4+wwREREijQNdxcREcmlb7/9ltatWxMQEICPjw81atTgvvvuA8wQ6euvvx6AYcOGJQ+XHzNmDJD+cPfQ0FBuueUWFi5cSLNmzfD29qZ+/fosXLgQMEPx69evj6+vL61atWLDhg0O52/YsIE77riD0NBQvL29CQ0N5c477+TQoUPJx8yaNYuBAwcC0Llz5+S4Ug8DnzFjBk2aNMHLy4vAwEBuvfVWdu3a5fBaQ4cOpVSpUvz9999069YNPz8/unTpkuHPyv5+t23bxsCBAwkICCAwMJAnn3yS+Ph4du/eTY8ePfDz8yM0NJTx48enucbhw4e5++67KV++PJ6entSvX58JEyaQmJjocNzx48e5/fbb8fPzIyAggEGDBnHixIl049qwYQN9+/YlMDAQLy8vmjVrxty5czN8H3b79+/njjvuoGLFinh6ehIcHEyXLl3SndogIiKSE+pJFxERSUdCQgLx8fEO+2w2G66urgCsXr2aQYMGMWjQIMaMGYOXlxeHDh1i2bJlADRv3pyZM2cybNgwXnrpJXr37g2QZe/51q1bGT16NC+++CIBAQGMHTuWAQMGMHr0aJYuXcqbb76JzWbjueee45ZbbuHAgQN4e3sDZt513bp1ueOOOwgMDCQ8PJwpU6Zw/fXXs3PnTsqWLUvv3r158803eeGFF/joo49o3rw5ADVr1gRg3LhxvPDCC9x5552MGzeOM2fOMGbMGNq2bcv69eupXbt2cqyxsbH07duXhx56iOeffz7Nzys9t99+O3fffTcPPfQQixcvZvz48cTFxbFkyRJGjhzJ008/zZdffslzzz1HrVq1GDBgAACnTp2iXbt2xMbG8vrrrxMaGsrChQt5+umn+ffff5k8eTIAly9fpmvXrhw/fpxx48ZRp04dfv75ZwYNGpQmluXLl9OjRw9at27Nxx9/TEBAAF9//TWDBg3i0qVLDB06NMP30atXLxISEhg/fjxVq1bl9OnTrFq1ivPnz2f5MxAREcmUJSIiIslmzpxpAeneXF1dk4975513LMA6f/58htdav369BVgzZ85M89yrr75qXf1nuFq1apa3t7d19OjR5H1btmyxAKtChQrWxYsXk/fPnz/fAqwFCxZk+Prx8fFWdHS05evra7333nvJ+7/99lsLsJYvX+5w/Llz5yxvb2+rV69eDvsPHz5seXp6WoMHD07eN2TIEAuwZsyYkeHrp/d+J0yY4LC/adOmFmD98MMPyfvi4uKscuXKWQMGDEje9/zzz1uAtXbtWofzH374Yctms1m7d++2LMuypkyZYgHWjz/+6HDcAw88kObfol69elazZs2suLg4h2NvueUWq0KFClZCQoJlWZa1fPlyh5/X6dOnLcCaNGlStt67iIhITmi4u4iISDpmz57N+vXrHW5r165Nft4+lP32229n7ty5HDt2LE9et2nTplSqVCn5cf369QHo1KmTw3xv+/7UQ9mjo6OTe6Dd3Nxwc3OjVKlSXLx4Mc1w9fSsXr2ay5cvp+lBrlKlCjfddBNLly5Nc85//vOfHL2/W265xeFx/fr1sdls9OzZM3mfm5sbtWrVcnhvy5Yto0GDBrRq1crh/KFDh2JZVvIIhuXLl+Pn50ffvn0djhs8eLDD43379vHPP/9w1113ARAfH59869WrF+Hh4ezevTvd9xAYGEjNmjX53//+x8SJE9m8eXOaIfciIiK5pSRdREQkHfXr16dly5YOtxYtWiQ/36FDB+bPn098fDz33nsvlStXpmHDhnz11VfX9LqBgYEOjz08PDLdf+XKleR9gwcP5sMPP2T48OH89ttvrFu3jvXr11OuXDkuX76c5WufOXMGgAoVKqR5rmLFisnP2/n4+ODv75+Nd5Uivffh4+ODl5dXmv2p39uZM2cyjCt17GfOnCE4ODjNcSEhIQ6PT548CcDTTz+Nu7u7w23kyJEAnD59Ot33YLPZWLp0Kd27d2f8+PE0b96ccuXK8dhjj3HhwoVM37+IiEhWNCddREQkl/r160e/fv2IiYlhzZo1jBs3jsGDBxMaGkrbtm0LNJbIyEgWLlzIq6++yvPPP5+8PyYmhrNnz2brGkFBQQCEh4enee748eOULVvWYV9BrvMeFBSUYVxAcmxBQUGsW7cuzXFXF46zHz969Ojkee9Xq1u3bobxVKtWjenTpwOwZ88e5s6dy5gxY4iNjeXjjz/OxjsSERFJn3rSRURErpGnpycdO3bk7bffBmDz5s3J+4Fs9WJfK5vNhmVZya9pN23aNBISEtLEm15cbdu2xdvbmy+++MJh/9GjR1m2bFmm1dvzW5cuXdi5cyebNm1y2D979mxsNhudO3cGTMX6CxcusGDBAofjvvzyS4fHdevWpXbt2mzdujXNiAn7zc/PL1ux1alTh5deeolGjRqliU9ERCSn1JMuIiKSju3bt6dbrbxmzZqUK1eOV155haNHj9KlSxcqV67M+fPnee+993B3d6djx47Jx3p7ezNnzhzq169PqVKlqFixYvIQ7bzk7+9Phw4d+N///kfZsmUJDQ3ljz/+YPr06ZQuXdrh2IYNGwLwySef4Ofnh5eXF9WrVycoKIiXX36ZF154gXvvvZc777yTM2fOMHbsWLy8vHj11VfzPO7seuKJJ5g9eza9e/fmtddeo1q1avz8889MnjyZhx9+mDp16gBw77338u6773LvvffyxhtvULt2bRYtWsRvv/2W5ppTp06lZ8+edO/enaFDh1KpUiXOnj3Lrl272LRpE99++226sWzbto1HH32UgQMHUrt2bTw8PFi2bBnbtm1zGMUgIiKSG0rSRURE0jFs2LB093/66acMHz6c1q1bs2HDBp577jlOnTpF6dKladmyJcuWLeO6664DzJztGTNmMHbsWLp160ZcXByvvvpq8lrpee3LL7/k8ccf59lnnyU+Pp727duzePHi5OXf7KpXr86kSZN477336NSpEwkJCcycOZOhQ4cyevRoypcvz/vvv88333yDt7c3nTp14s0333RYfq2glStXjlWrVjF69GhGjx5NVFQUNWrUYPz48Tz55JPJx/n4+LBs2TIef/xxnn/+eWw2G926dePrr7+mXbt2Dtfs3Lkz69at44033mDUqFGcO3eOoKAgGjRowO23355hLCEhIdSsWZPJkydz5MgRbDYbNWrUYMKECfzf//1fvv0MRESkZLBZlmU5OwgRERERERER0Zx0ERERERERkUJDSbqIiIiIiIhIIaEkXURERERERKSQUJIuIiIiIiIiUkgoSRcREREREREpJJSki4iIiIiIiBQSJW6d9MTERI4fP46fnx82m83Z4YiIiIiIiEgxZ1kWFy5coGLFiri4ZN5XXuKS9OPHj1OlShVnhyEiIiIiIiIlzJEjR6hcuXKmx5S4JN3Pzw8wPxx/f38nRyMiIiIiIiLFXVRUFFWqVEnORzNT4pJ0+xB3f39/JekiIiIiIiJSYLIz5VqF40REREREREQKCSXpIiIiIiIiIoWEknQRERERERGRQkJJuoiIiIiIiEghoSS9ELsUG+/sEERERERERKQAKUkvpPZFXODmiSv4et1hZ4ciIiIiIiIiBURJeiH1246THDt/mRfnbydsd4SzwxEREREREZECoCS9kBrZqSYDmlciIdHikTmb2HE80tkhiYiIiIiISD5Tkl5I2Ww23hrQmHY1g7gYm8Cwmes5dv6ys8MSERERERGRfKQkvRDzcHPh43taUDfYj4gLMQybuY7Iy3HODktERERERETyiZL0Qs7fy52Zw66nvJ8ne05G8/AXG4mNT3R2WCIiIiIiIpIPlKQXARVLezNj6PX4eriy6t8zPP/9NizLcnZYIiIiIiIikseUpBcRDSsF8NFdzXF1sfHD5mO8u2Svs0MSERERERGRPKYkvbA6vgVm9obFr8DOBRB1nE51y/NG/4YAvL90L3PXH3FujCIiIiIiIpKn3JwdgGTgyFo49Je52flV4I5KLahYpxpT9pXmv/OuEBzgRcc65ZwXp4iIiIiIiOQZm1XCJjdHRUUREBBAZGQk/v7+zg4nY+cPw/4wOLoBjm2CiB1gORaMS7Bs7KQGpfqMo3rL7s6JU0RERERERDKVkzzUqcPdV6xYQZ8+fahYsSI2m4358+dnenx4eDiDBw+mbt26uLi4MGrUqAKJ0ylKV4Xm90Lf9+Hhv2D0URi6CG5+DRr0w/KvhKvNopHtX6ovvJ3ouQ/DpbPOjlpERERERESugVOT9IsXL9KkSRM+/PDDbB0fExNDuXLlePHFF2nSpEk+R1fIePhCaHto/zjcPhvbkzu58Mjf/OzRA4BSO78k8cNW8Pd3ULIGR4iIiIiIiBQbhWa4u81mY968efTv3z9bx3fq1ImmTZsyadKkHL1OkRnunk0nIq8w9qNpPHllMrVdjpmdtbpC7wlQJtSpsYmIiIiIiEgRGu4u1y4kwIunhg/hTpf/MSHuNuJwh31LYHJbWPk+JMQ7O0QRERERERHJpmJf3T0mJoaYmJjkx1FRUU6MJn/UKu/H1GFtGfypCz/HtOHTMl9Q89IWWPwy/P0t1O8LiXGQEJd0Hw+J8SnbHj5w3a1QtS3YbM5+OyIiIiIiIiVWsU/Sx40bx9ixY50dRr5rUS2QD+5sxogvEuly9hmmNvyH7kc/gBPbzC0r6z6BoFqmWF2TO6FU+fwPWkRERERERBwU+znp6fWkV6lSpdjMSb/anLWHeHHedgAm9KrAf2IXmKrvru7g4mZuru7g4p6y7+y/sH0exF00F3Fxg7o9ofkQqHkTuLg68R2JiIiIiIgUbTmZk17se9I9PT3x9PR0dhgF5q7W1TgZFcP7S/fyzC/hlLp7BN2vC8n6xB5vwY55sGk2HF0Pu34yN/9K0OxuaDwIgmrm/xsQEREREREpwZxaOC46OpotW7awZcsWAA4cOMCWLVs4fPgwAKNHj+bee+91OMd+fHR0NKdOnWLLli3s3LmzoEMv1J7oWps7rq9CogWPfbWZ9QezsX66p58Z6j58CTy8GtqMBO8yEHUM/ngbPmgOH14Pi1+Bw2sgMSH/34iIiIiIiEgJ49Th7mFhYXTu3DnN/iFDhjBr1iyGDh3KwYMHCQsLS37Olk5hs2rVqnHw4MFsvWZxW4ItI/EJiTz0+UaW/hNBKU83Zg67nutDA3N2kbgrsPtn2PwFHFhhis3Z+QRB7e5mWHzNm8CzVN6+ARERERERkWIiJ3looZmTXlBKSpIOcDk2gWGz1rFm/1l8PFyZNqQl7WqWzd3FrkTCvqWw+xfY+ztcOZ/ynKsHVGkNXgFm29XDzHe/ertUMJSvD+UbgG9QnrxHERERERGRwk5JeiZKUpIOJlF/8PMN/Ln3NJ5uLnx6b0s61Cl3bRdNiIcja0zCvnsRnN2f82v4lk9K2OunJO7l6oFX8f83ERERERGRkkVJeiZKWpIOcCUugZFzNrHsnwg8XF2YcndzutQPzpuLWxac3muKzSXEmLXYE2IhPtW2/Xb+CETshPOH0r+WmxcM+BQa9M2b2ERERERERAoBJemZKIlJOkBsfCL/99UmfttxEndXGx/c2ZweDbNR9T0/xETD6d0QsSvpthNO7oDok2au+6MbwCeH8+dFREREREQKKSXpmSipSTpAXEIiT87dyk9bj+PqYmPSoKb0aVLR2WEZCXHw8Y1wapepMt/3A2dHJCIiIiIikidykoc6dQk2KVjuri5MGtSUAc0rkZBo8fjXm/l+41Fnh2W4ukOfSWZ702w4tNqp4YiIiIiIiDiDkvQSxtXFxju3NUleR/3p77by9brDzg7LqNrG9KIDLHzC9K6LiIiIiIiUIErSSyAXFxtv3tqIIW2rYVnw/A9/88HSvRSKmQ9dx5p56ad2weoPc3bu5fOwcRZcOpsfkYmIiIiIiOQ7JekllIuLjTF9r+OhjjUAmLB4D49/vYUrcQnODcwnELq9YbbD3oZzB7N3XvQpmNkLfnocFr+Sb+GJiIiIiIjkJyXpJZjNZmN0z/qMG9AINxcbC7YeZ9Ana4iIuuLcwJrcAaE3QvxlWPSMWeYtM1HHYVYviNhhHu9cAHFOfg8iIiIiIiK5oCRduLNVVT6/vzWlfdzZeuQ8/T5ayfZjkc4LyGaD3hPBxR32/g67FmR87PnDpgf99B7wrwSlgiEmEvYtLrh4RURERERE8oiSdAGgbc0g5o9sT81yvoRHXmHgx6v5dXu48wIqVwduGGW2f3kOYi6kPebMvyZBP3cASleDYb9A49vNc39/W2ChioiIiIiI5BUl6ZIstKwv8x5pT4c65bgcl8CILzbx4TInFpS78SkoUx0uhMOyNxyfO7XbJOiRRyCoFtz3K5SpBo0Gmuf3/AZXogo+ZhERERERkWugJF0c+Hu5M2NIS4a1DwXgnd/3MOobJxWUc/eG3hPM9rqpcHyL2T7xt0nQo09A+QamB92/onkupDEE1Yb4K/DPzwUfs4iIiIiIyDVQki5puLm68Gqf63jzVlNQ7sctx7nt41XsPxVd8MHU6gIN/wNWIiwcBUc3wKxb4NJpqNAEhiyEUuVTjrfZUnrTt39X8PGKiIiIiIhcAyXpkqHBrasy+/5WlPFxZ/uxKHq//xdz1x8p+OHv3d8ET384vhmmd4Mr56Hy9XDvAvANSnt8o9vM/b/LzdJsIiIiIiIiRYSSdMlUu5pl+eXxDrSrGcTluASe/X4bj3y5ichLcQUXhF8IdEla+9xKgGrt4Z554F06/eODakLFZubYnfMLKkoREREREZFrpiRdshQS4MUX97fm+Z71cHOxsejvE/R8bwVr958puCBa3gfXD4cWw+Cu78DTL/Pj7UPe/9aQdxERERERKTpsltNKdztHVFQUAQEBREZG4u/v7+xwipxtR8/z+NdbOHD6IjYbPNKpFo93rY27ayFr74kKh4n1AQtG/Q2lqzo7IhERERERKaFykocWssxKCrvGlUuz8P9u4PaWlbEs+HD5PgZ+vJpDZy46OzRH/hUg9Aazvf1758YiIiIiIiKSTUrSJcd8Pd0Yf1sTPhrcHH8vN7YcOU+v9/5kSti/zlmqLSP2AnIa8i4iIiIiIkWEknTJtd6NK/DLqA60qh7IxdgE3v71Hzq/E8a3G46QkFgIZlHU7wsu7nByO0TscnY0IiIiIiIiWVKSLtekUmlvvn6gDRNvb0LFAC/CI6/wzHfb6P3+n4Ttjij45dpS8wmEWl3NtnrTRURERESkCFCSLtfMxcXGgOaVWfZ0J17oVQ9/Lzf+OXGBoTPXc9e0tfx9NNJ5wdmHvG//DkpWjUQRERERESmCnJqkr1ixgj59+lCxYkVsNhvz58/P8pw//viDFi1a4OXlRY0aNfj444/zP1DJFi93Vx7sUJMVz3bmwQ418HB1YdW/Z+jz4V889tVm/j0VXfBB1e0J7r5w7iAc21jwry8iIiIiIpIDTk3SL168SJMmTfjwww+zdfyBAwfo1asXN954I5s3b+aFF17gscce4/vvVb27MCnt48ELveqz7OmODGhWCZsNFmw9TpcJf9DrvT/5aPk+DpwuoGrwHr5Qr5fZ/vvbgnlNERERERGRXCo066TbbDbmzZtH//79MzzmueeeY8GCBezalVIEbMSIEWzdupXVq1dn63W0TnrB23E8kom/7yFszymHgnL1Qvzo3agCvRpXoGa5UvkXwJ7f4Mvbwbc8PLkLXN3y77VERERERESukpM8tEhlK6tXr6Zbt24O+7p378706dOJi4vD3d09zTkxMTHExMQkP46Kisr3OMXRdRUDmD70es5djOX3nSf4+e8TrNp3mn9OXOCfExeYsHgPdYP96NEwhMaVA6hVvhSVy/jg6mLLmwBqdAbvMnAxAg7+CTU75811RURERERE8liRStJPnDhBcHCww77g4GDi4+M5ffo0FSpUSHPOuHHjGDt2bEGFKJko4+vBoOurMuj6qpy/FMvvO06yaHs4f+09ze6TF9h98kLysZ5uLtQoV4pa5UtRq1wpageb7UqlvfHxcMVmy0EC7+YBDfrDxpmmyruSdBERERERKaSKVJIOpEnO7KP1M0raRo8ezZNPPpn8OCoqiipVquRfgJItpX08uP36Ktx+fRUiL8Xx+84TrNh7mr0nL7D/9EVi4hPZFR7FrvC0Ix/cXW34e7kT4O2Of9ItwNudAG83/L3c8XZ3xdvDFU93V7zcXPByd6WCXxdaMpP4HT+yu9kreHr74u1hnjf3rrjkVc+9iIiIiIhILhWpJD0kJIQTJ0447IuIiMDNzY2goKB0z/H09MTT07MgwpNcCvBxZ2DLKgxsaRpPEhItjp67xN6T0ew7Fc2+CHP7NyKaCzHxxCVYnLkYy5mLsdl+DRuwyjOQCnFneX/qFH5LvD7NMR5uLibBd3fFx9OVUp5u+Hq44evpiq+nG76ebsn7Snm5JTUMuFPaxz15O8DbHS9317z60YiIiIiISAlTpJL0tm3b8tNPPzns+/3332nZsmW689GlaHJ1sVEtyJdqQb50JWV6g2VZXIpNIOpKHJGX44i8lHSfdIu6Ek/U5TiuxCUk3RK5bN+OT2RlVEdui5nH7Z5rWEM7rsQlEBOfmHz92PhEYuMTibwcd03xe7i5UNrbnUBfD8qW8iSolAdBvua+nP1xKU/KlvKgnJ8nnm5K6kVERERExHBqkh4dHc2+ffuSHx84cIAtW7YQGBhI1apVGT16NMeOHWP27NmAqeT+4Ycf8uSTT/LAAw+wevVqpk+fzldffeWstyAFyGazJfdoVwjwzvkFjvvCJ/Po4rqZrY81BP+KJCZaXIl3TOgvxyZwMSaei7HxRMckbcfEczEmIWlfPBeuxKc0EFyKTd5OtEyyH3EhhogLMcCFLMMK9PWgvJ8n5f29CPbzJNjfi2D/pMf+XoT4e1G2lAdurk5dMVFERERERAqAU5dgCwsLo3PntEW8hgwZwqxZsxg6dCgHDx4kLCws+bk//viDJ554gh07dlCxYkWee+45RowYke3X1BJsJZhlwbSucGwDVLsBhiwAl7zrxbYsi+iYeM4n9fCfvRjLmYsxnL4Qy+mLMZyJjuVMdIwZqh8dy6kLMcQmJGZ9YcDFBmVLeRISkJK427eD/ZMSez8v/L3dclZUT0RERERE8l1O8tBCs056QVGSXsKd+RemdoDYaOj8InR81mmhWJbF+UtxnLxwhYioGE5GXSHigrk3txgioq5w8kKMw/rymfF0c3Hsiffzory/J+VKeZp7P7NdxsdDhfJERERERAqIkvRMKEkXtn4N8x4CmwsMXQTV2jo7okwlJFqciY7hRNQVTkSaBN5s2xN7k9DnZC69m4uNsvbEPTmB9zLD7pOG3pf386RsKU883DTMXkRERETkWihJz4SSdAHgh4dg29fgXxlG/Ak+gc6O6JpdiUvgVHJPfAwRF0wyf+pCTPIt4kIMZ3NQFR9S5szbh9kHB9iH23tS3s8Muw9Uz7yIiIiISIaUpGdCSboAEHPBDHs/ux/q3QKDvoASMpc7LiGRM9GxRFy4kpy4RyQl9RHJj81z8dkcZu/uaktO2EPsBe8C7EXwUvZ5e6iSvYiIiIiUPErSM6EkXZId3wzTbobEOOj1DrR6wNkRFSqJiRbnLsWmmSd/IuoKJyOvcPKCGXJ/5mIM2f0tUsrTLXlefDm/lFvZUma5ujK+Hmbt+aQ151XRXkRERESKAyXpmVCSLg5WfwS/vQCunvDAMghp6OyIipy4BLPkXPJ8eYd58ynbV+KyV8k+tVKebgQkJeylfdzx83LDx8MNbw9XfNxd8fFwxdvDLenePPZ0c8Xd1YaHmwuebi64u7rg4eaCh6vZ9nRzwc3VBXdXG+5J+1w1VF9ERERE8pGS9EwoSRcHlgVf3g57f4eydeHB5eDh6+yoih3LsrgQE89p+/z46BiHufKno82+85fiiLwUx4WY+AKNz2bDJOwuNtztiX1Scm9P5j2u2u/plnLv6eaa6rErnu6O+73czb3Zn3Y79fU8XF20jJ6IiIhIMaMkPRNK0iWNi6dhSnuIPgHN74W+Hzg7ohIvPiGRqCvxRF6O4/ylWM5fjiPqchxRV+K5HBvPpdgELscmcCnpdjkuPnk7Nj6R2PhE4hISiU1I2Y5JtT+bU+2dJnXinrqBwDQSpDQauCU1LLi52pK33V1TRgq4uSTdu9pwdbEfa3/O5jCiwC3p2m4uLqahIqnBwi3pmvZY3JMaElI3YmhagoiIiEjmcpKHuhVQTCKFl29ZGPAJzO4Hm2ZDjU7Q8D/OjqpEc3N1IdDXg0BfDyDvRzYkJFrEJSQSn2gRF59IXGIicQkW8QlJyX28eT4uKcmPTTDPJyf/8YnEJCQSE5dATHxKA0BMfNLjuFTb8SnHXYlLSDou1XbS9VKzn3chz995/nBJGolgHwngcVXjgoebC56uLri72VI1NjiOVHC/qsHg6kYCN1cb7i4uDg0Sbq4uSY0NqRskMtnn0Ehh9mlVAhERESlslKSLANToCDc+BX++Az+NgkotoEyos6OSfOLqYsPVJanSvKdzYwFTpC82qec/Js7cX4lLMA0F8VZSI0FKA4EZJWAaDeITEolLNA0M8QkWcYlJ9wkpDQ/xiRbxyfuTth3OMY0V8YnmuvbGiviE1K9tJTdYXN2okGilNCwUNS42HJJ+d1cbri4miXdNTuzNSAQ3F1vSZ8eGqy1l28XFhqvNfK5cbCn7XGw2XGzgYrNhS7pP/diW9NhG0v1Vx6V5nOo4+zVcXZLuk/aZ18Vh29Vmj9G8l+QYk98DKdtp3leq95zqHLdUz7u4kHxc6nPscZltNYaIiIhkl5J0EbtOo+Hgn3BkLczqA3d9C+XrOTsqKQFcXGx4ubji5e4KXs6OJmuWZRGf6DiyIDb1qIN4i9gEM3ogObm3NwIk7bM3OsTEpzRA2BsF4pMaEmLjUzcuJCaPfrA/n9zgkGClec6+LyHRSm7ESG+aQ6KFiQ+AhAL+SZYcjg0JSY0CqRoa7Il8cgNDFg0bLklJ/9XHYbNhg5RGjqTXdtjGfq2U7dTXMselHE9y48hV5yYfY29ESWlYsaU6J3XstqtiTr7H/B5weGxzbJRxsZE88iPDBiCX1DGlc4xLyntw/DmnHx9J7yvlZ5PyMyJVrPb3nbQ7+efmuM921fPmeqR6nPrzkryNLf396RyTXlvQ1bvSHpP2pKzalNJeM51rZHFN21VHZKcdK+3PKdXPJoPjrv7Z2K46Ib2fnX0z9b+Z/RgbjhdK/jxcdXx2/40ze/3M3mN617z6eJGiTEm6iJ2rG9w2Az7rC2f/hendYNDnppddRJLZbLbkoelFSWKilTyqIC7BIiEpebcn8amT/oSkYxMS0+6LT0gkwTKPEy2LhERz7dT7EhMtEi1ItCyspPuUx2bbvt/CNHykPs5KtT/1dVI/th+XkJh6X9J2cmwp9/Gp9yVy1XtIdUuKPz7RSvW+zPXtjR1XH5cVy4J4ywIstYWISImXF20J2bnE1Y0W2WrouPqoHDZapXfdNNfMxjnZeZ2rzX+kPbWD/bJxZOGnJF0ktYDKMHwJfHUnHFkDX/zHFJJreqezIxORa+TiYsPDxYYHRatxoSiwNxaYxD1VA0CqZN7ekJCQaBocEqyUBo2EVI0UGTVq2M+zSHts6vvUjRuQsu/q51NfK/UxpN4PjseZN5u8P71rJl71PJhGE4cYrJTH9vdJmvedfqOM/bmEDM65urEm9etY6RxDqpjTi8/+87G/l/Qal8xP2jxO2TY/N/s5qe9J59jk/Vipjkm9n3T3c9VrpH0m7Wtm9HxG18npNa7emRdxWFcdldHPhnR+lqn/jVK/fsrjdAKQfJUXP/NsXSJXL1R0PxCFvTBwTihJF7maTyDc+yP8OBK2fw/zR8D5Q9Dxubxp+hQRKWZsSfPd9aVCpGizrJwl9lc3BGT2XLYaCbLRYHPVYWliz0h28rei0mBxdaNN0s7MHqY9PN1LZNwYlN41s7NIWJ40SGTzGiEBRWDOYDbp76lIety9YMA0KF0N/poIYePg3EHo8z64eTg7OhEREZE8lzyvPBtz90Uk/2jMn0hGXFyg66vQ5z2wucLWr+CLAXD5nLMjExERERGRYkpJukhWWgyFu+aCh5+p/j69O5w75OyoRERERESkGFKSLpIdtbrCfb+AX0U4vRum3wwXTjo7KhERERERKWaUpItkV0gjeGAplK0L0Sdh+RvOjkhERERERIoZJekiOeFfEfq+b7Y3fw4ndzo3HhERERERKVaUpIvkVNU2UL8vWImw+GVnRyMiIiIiIsWIknSR3Lh5LLi4w74lsG+ps6MREREREZFiQkm6SG4E1oBWD5rt31+GxATnxiMiIiIiIsWCknSR3OrwNHiVhogdsPkLZ0cjIiIiIiLFgNOT9MmTJ1O9enW8vLxo0aIFf/75Z6bHf/TRR9SvXx9vb2/q1q3L7NmzCyhSkav4BELHZ8328jcgJtq58YiIiIiISJHn1CT9m2++YdSoUbz44ots3ryZG2+8kZ49e3L48OF0j58yZQqjR49mzJgx7Nixg7Fjx/LII4/w008/FXDkIkmufwDKVDdLsq1639nRiIiIiIhIEWezLMty1ou3bt2a5s2bM2XKlOR99evXp3///owbNy7N8e3ataN9+/b873//S943atQoNmzYwF9//ZWt14yKiiIgIIDIyEj8/f2v/U2I7PwR5t4Lbt7w2CazTJuIiIiIiEiSnOShTutJj42NZePGjXTr1s1hf7du3Vi1alW658TExODl5eWwz9vbm3Xr1hEXF5fhOVFRUQ43kTxVvy9UaQPxl2HZf50djYiIiIiIFGFOS9JPnz5NQkICwcHBDvuDg4M5ceJEuud0796dadOmsXHjRizLYsOGDcyYMYO4uDhOnz6d7jnjxo0jICAg+ValSpU8fy9Swtls0P0Ns73lSwjf5tx4RERERESkyHJ64Tibzebw2LKsNPvsXn75ZXr27EmbNm1wd3enX79+DB06FABXV9d0zxk9ejSRkZHJtyNHjuRp/CIAVG4JDf8DWPD7i+C8WSQiIiIiIlKE5SpJP3LkCEePHk1+vG7dOkaNGsUnn3yS7WuULVsWV1fXNL3mERERaXrX7by9vZkxYwaXLl3i4MGDHD58mNDQUPz8/Chbtmy653h6euLv7+9wE8kXXV4FV084sAL2/Jb5sZfPQXREwcQlIiIiIiJFRq6S9MGDB7N8+XIATpw4wc0338y6det44YUXeO2117J1DQ8PD1q0aMHixYsd9i9evJh27dpleq67uzuVK1fG1dWVr7/+mltuuQUXF6cPCpCSrkw1aDPCbC9+GRLiIe4KhG+FLV/B7y/DF/+BCfXh7VCYWB/+WeTUkEVEREREpHBxy81J27dvp1WrVgDMnTuXhg0bsnLlSn7//XdGjBjBK6+8kq3rPPnkk9xzzz20bNmStm3b8sknn3D48GFGjDCJzujRozl27FjyWuh79uxh3bp1tG7dmnPnzjFx4kS2b9/OZ599lpu3IZL3bngSNn0Op/fAe43hQjhYiekfmxgP390Hw36GSi0KNk4RERERESmUcpWkx8XF4enpCcCSJUvo27cvAPXq1SM8PDzb1xk0aBBnzpzhtddeIzw8nIYNG7Jo0SKqVasGQHh4uMOa6QkJCUyYMIHdu3fj7u5O586dWbVqFaGhobl5GyJ5z7s0dH4BFj0NUceS9gVC8HVQvn7S7TooWxt+eAD2LYEvB8HwJVAm1JmRi4iIiIhIIZCrddJbt25N586d6d27N926dWPNmjU0adKENWvWcNtttznMVy9stE665DvLgt2/gLuXSchLlTcV4K8WcwFm9oIT26BsHbjvN/AJLPh4RUREREQkX+X7Oulvv/02U6dOpVOnTtx55500adIEgAULFiQPgxcpsWw2qNcLat4EfsHpJ+gAnn4weC74VzbD47+5G+JjCjZWEREREREpVHLVkw5m6HlUVBRlypRJ3nfw4EF8fHwoX758ngWY19STLoXOyZ0wozvEREHD22DAp6BCiCIiIiIixUa+96RfvnyZmJiY5AT90KFDTJo0id27dxfqBF2kUApuAIM+Bxc32P4dLHvd2RGJiIiIiIiT5CpJ79evX3LF9fPnz9O6dWsmTJhA//79mTJlSp4GKFIi1OgEfT8w239NhA0znRqOiIiIiIg4R66S9E2bNnHjjTcC8N133xEcHMyhQ4eYPXs277//fp4GKFJiNB0MnUab7Z+fgr2Lr+16l8/DDw/CGjWciYiIiIgUFblK0i9duoSfnx8Av//+OwMGDMDFxYU2bdpw6NChPA1QpETp+Bw0vQusBJg7BI5vyd11Yi/BV3fAtm/gtxchsvCuuCAiIiIiIilylaTXqlWL+fPnc+TIEX777Te6desGQEREhIqxiVwLmw1umQTVO0LcRfi8PxzdkLNrJMTBt0Pg8Grz2EqAdZ/mdaQiIiIiIpIPcpWkv/LKKzz99NOEhobSqlUr2rZtC5he9WbNmuVpgCIljpuHKSRXqSVcPgef9YG9S7J3bmIizH8Y9v4Obt5wwxNm/8ZZEHsx30IWEREREZG8kesl2E6cOEF4eDhNmjTBJWm5qHXr1uHv70+9evXyNMi8pCXYpMiIvQjf3AP/LjWV3/t/DI0HZny8ZcGiZ2D9p+b4O782a7V/0BzOHYRb3oWW9xVY+CIiIiIiYuT7EmwAISEhNGvWjOPHj3Ps2DEAWrVqVagTdJEixcPXJNqNBkJiPPwwPPMicMvfNAk6Nrh1KtS+GVxcofUI8/yaj00iLyIiIiIihVaukvTExERee+01AgICqFatGlWrVqV06dK8/vrrJCYm5nWMIiWXmwfc+klKov3r87D0tbTJ9urJsGK82e79DjS6LeW5pneBhx+c3g3/LiuYuEVEREREJFdylaS/+OKLfPjhh7z11lts3ryZTZs28eabb/LBBx/w8ssv53WMIiWbiwv0eAtuSvq/9ecE+OkxSIg3j7d8Bb8lLd1200tw/XDH8738odndZlvLsYmIiIiIFGq5mpNesWJFPv74Y/r27euw/8cff2TkyJHJw98LI81JlyJt42ewcBRYiVDvFmj4H/h+uKng3uYR6P6GqRB/tbP74f3mgAWPrIdydQo6chERERGREivf56SfPXs23bnn9erV4+zZs7m5pIhkR4shcPtscPWEfxbCd8NMgt70Luj23/QTdIDAGlC3p9leN7Xg4hURERERkRzJVZLepEkTPvzwwzT7P/zwQxo3bnzNQYlIJur3gXt+AM+kFrh6t0Cf982w+My0edjcb/nSLO0mIiIiIiKFjltuTho/fjy9e/dmyZIltG3bFpvNxqpVqzhy5AiLFi3K6xhF5GqhN8ADy+Hwamh8O7hm479y6I0Q3BBObodNn0P7x/I/ThERERERyZFc9aR37NiRPXv2cOutt3L+/HnOnj3LgAED2LFjBzNnzszrGEUkPWVrQfN7wM0ze8fbbClV4td9klJ4LisRu+D84dzFKCIiIiIiOZKrwnEZ2bp1K82bNychISGvLpnnVDhOSrS4K/BuA7h0xsxtb9Av42MtC/6aaJZ88/SHh1dB6SoFF6uIiIiISDGR74XjRKSIcveClveZ7cyWY4uPgXkjTIIOEBNlln3LuzY9ERERERFJh5J0kZKm5f3g4m7msx/fnPb56FPwWR/Y9jXYXKHDM+DmBf8ug82fF3y8IiIiIiIliJJ0kZLGvwJcd6vZXvOx43Mnd8K0m+DIWvAKgLu/h5tegs4vmud/exEijxVsvCIiIiIiJUiOqrsPGDAg0+fPnz9/LbGISEFp8zD8PRe2fw83jwW/ENjzO3x3H8ReMOuqD54LZWub49s+ArsWwNH18NPjcNe3Ga/JLiIiIiIiuZajnvSAgIBMb9WqVePee+/NUQCTJ0+mevXqeHl50aJFC/78889Mj58zZw5NmjTBx8eHChUqMGzYMM6cOZOj1xQp8So1hyptIDEO1k+H1R/BV4NMgh56IwxfmpKgA7i4Qr+PwNUT9i02a62LiIiIiEiey9Pq7jn1zTffcM899zB58mTat2/P1KlTmTZtGjt37qRq1appjv/rr7/o2LEj7777Ln369OHYsWOMGDGC2rVrM2/evGy9pqq7iyTZMQ++HQoubpCYtBxb8yHQewK4uqd/zl+TYMmr4BkAj6wB/4oFFa2IiIiISJFVZKq7T5w4kfvvv5/hw4dTv359Jk2aRJUqVZgyJf2q02vWrCE0NJTHHnuM6tWrc8MNN/DQQw+xYcOGAo5cpBio1wf8K5sE3eYC3cdBn/cyTtAB2j4KFZtDTCT8NErV3kVERERE8pjTkvTY2Fg2btxIt27dHPZ369aNVatWpXtOu3btOHr0KIsWLcKyLE6ePMl3331H7969CyJkkeLF1c30mldpA3d+A21HZj3P3NUN+k8GVw/Y+xts+6ZgYhURERERKSGclqSfPn2ahIQEgoODHfYHBwdz4sSJdM9p164dc+bMYdCgQXh4eBASEkLp0qX54IMPMnydmJgYoqKiHG4ikqRuD7j/N6jTLetj7crXh07Pm+1fnoUL6f9/FRERERGRnHP6Emy2q3ruLMtKs89u586dPPbYY7zyyits3LiRX3/9lQMHDjBixIgMrz9u3DiH4nZVqlTJ0/hFSqR2j0OFpnBFw95FRERERPKS05L0smXL4urqmqbXPCIiIk3vut24ceNo3749zzzzDI0bN6Z79+5MnjyZGTNmEB4enu45o0ePJjIyMvl25MiRPH8vIiWOqxv0nwIu7rDnF/j7W2dHJCIiIiJSLDgtSffw8KBFixYsXrzYYf/ixYtp165duudcunQJFxfHkF1dXQHTA58eT09P/P39HW4ikgeCG0DH58z2L8/C6b3OjUdEREREpBhw6nD3J598kmnTpjFjxgx27drFE088weHDh5OHr48ePdph3fU+ffrwww8/MGXKFPbv38/KlSt57LHHaNWqFRUraikokQJ3wygIaQyXz8GH18NXg+HAnxr+LiIiIiKSS27OfPFBgwZx5swZXnvtNcLDw2nYsCGLFi2iWrVqAISHh3P48OHk44cOHcqFCxf48MMPeeqppyhdujQ33XQTb7/9trPegkjJ5uoOgz6Hn5+GfYth98/mFtwI2jwMDf8D7l7OjlJEREREpMiwWRmNEy+mcrKIvIjkwKk9sPZj2PoVxF0y+3zLQcv74fr7oVR558YnIiIiIuIkOclDlaSLSN66dBY2zYZ1n0DUMbPP1QOa3QPd/gsePs6NT0RERESkgOUkD3X6EmwiUsz4BJq56o9vhdtmQOXrISEWNkyHGd3h/OEsLyEiIiIiUlIpSReR/OHqbuakD18C98wDnyA4sQ0+6WSKy4mIiIiISBpK0kUk/9W8CR4MM5XgL52B2f1g7SeqAi8iIiIichUl6SJSMEpXhft+g0YDwUqAX56BHx+BuCvOjkxEREREpNBQki4iBcfDBwZ8agrI2VxgyxyY2RMijzk7MhERERGRQkFJuogULJsN2v0f3P0DeJeB45vMPPXDa5wdmYiIiIiI0ylJFxHnqNkZHlgO5a+DixEw6xZY+R4kxDs7MhERERERp1GSLiLOE1gdhi+GBv0hMQ4WvwLTb4aTO50dmYiIiIiIUyhJFxHn8vCFgbOg7wfgGWCGv0/tAGFvQXyss6MTERERESlQStJFxPlsNmh+LzyyBur0NL3qYePMXPVjG50dnYiIiIhIgVGSLiKFh39FuPMr+M908AmCiB0wrSv8/jLEXXZ2dCIiIiIi+U5JuogULjYbNLoNHlkHDW8DKxFWvQ9T2sOhVc6OTkREREQkXylJF5HCybcs3DYd7vgK/CrA2X9NBfg9vzk7MhERERGRfKMkXUQKt3q9YOQaaNAPrASYOwSOrHd2VCIiIiIi+UJJuogUft6lzTz1Wl0h/jJ8ORBO7XF2VCIiIiIieU5JuogUDa7uMPAzqNgcLp+DLwZA1HFnRyUiIiIikqeUpItI0eFZCu76FgJrQuQR+OI/cPm8s6MSEREREckzStJFpGjxLQv3/AClgiFiJ3w9GOKuODsqEREREZE8oSRdRIqeMqFw9/fg6Q+HVsIPwyExwdlRiYiIiIhcMyXpIlI0hTSCO74EVw/Y9RMsehosy9lRiYiIiIhcEzdnByAikmvVb4QBn8K3Q2HDDCgVAp2eg9hLcCHcFJa7EA5RxyAqHC4cB+9A6Dke3L2cHb2IiIiISBpK0kWkaLuuP0SPh1+egbA3Yc1kuHI+83MqNIbrhxdEdCIiIiIiOeL04e6TJ0+mevXqeHl50aJFC/78888Mjx06dCg2my3N7brrrivAiEWk0Gn9INz4tNm2J+juPhBUC0JvhMaD4IYnoMlg89yqDzWHXUREREQKJaf2pH/zzTeMGjWKyZMn0759e6ZOnUrPnj3ZuXMnVatWTXP8e++9x1tvvZX8OD4+niZNmjBw4MCCDFtECqObXoL6fcDNE/wqgFcA2GyOx8RehD2/wLkDZh77df2dEqqIiIiISEZsluW8SkutW7emefPmTJkyJXlf/fr16d+/P+PGjcvy/Pnz5zNgwAAOHDhAtWrVsvWaUVFRBAQEEBkZib+/f4bHJSQkEBcXl61riuQXd3d3XF1dnR1G8bLsDVgxHio2hweWpU3kRURERETyWHbzUHBiT3psbCwbN27k+eefd9jfrVs3Vq1ala1rTJ8+na5du2aaoMfExBATE5P8OCoqKtNrWpbFiRMnOH/+fLZiEMlvpUuXJiQkBJuSybzR6kFY9T4c32SWbwu9wdkRiYiIiIgkc1qSfvr0aRISEggODnbYHxwczIkTJ7I8Pzw8nF9++YUvv/wy0+PGjRvH2LFjsx2XPUEvX748Pj4+SozEaSzL4tKlS0RERABQoUIFJ0dUTJQqB00Hm2rwK99Tki4iIlLUnfkXEuOhXF1nRyKSJ5xe3f3qJNiyrGwlxrNmzaJ06dL0798/0+NGjx7Nk08+mfw4KiqKKlWqpHtsQkJCcoIeFBSUdfAi+czb2xuAiIgIypcvr6HveaXto7BhJuz9HU7uhOAGzo5IREQyErELNs2Gjs+Bd2lnRyOFTdxlmNYVEuLgib/Bu4yzIxK5Zk6r7l62bFlcXV3T9JpHRESk6V2/mmVZzJgxg3vuuQcPD49Mj/X09MTf39/hlhH7HHQfH59svguR/Gf/PKpGQh4KqmmKzAGs+sC5sYiISOZ+fMQsr7n6I2dHIoXRkXVw+SzEXjDbIsWA05J0Dw8PWrRoweLFix32L168mHbt2mV67h9//MG+ffu4//778yU2DXGXwkSfx3zS/nFz//dciDyW/fM2fQ7fDoXL5/MjKhERSe34Fji20WzvW5zpoVJCHViRsn14jfPiEMlDTl0n/cknn2TatGnMmDGDXbt28cQTT3D48GFGjBgBmKHq9957b5rzpk+fTuvWrWnYsGFBhywixUXlllCtvZnDtnZK1scDbPsWFjwKO+bBmmyeIyIiubdhRsr28c0Qfcp5sUjhdPDPlO0ja50Xh0gecmqSPmjQICZNmsRrr71G06ZNWbFiBYsWLUqu1h4eHs7hw4cdzomMjOT777/Pt150Mexz/kWKNXtv+oZZcCUy82P3h8H8h1Mer/8UYi/lV2QiInIlEv7+1mx7Bpj7f5c6L57Te2F2Pw2pLkxiolNGWoDZjo91XjwiecSpSTrAyJEjOXjwIDExMWzcuJEOHTokPzdr1izCwsIcjg8ICODSpUs88MADBRxp4TZ06FBsNluaW48ePbI8NzQ0lEmTJjnsGzRoEHv27MmnaFOoMUCcqtbNUK6emce2YWbGx53YDt/cA4lx0KAflK4Kl87A1q8KLlYRkZJm6zcQdwnK1Yfrkzpn9i1xXjyrPzINtn9OdF4M4ujwGjMiLqAq+ARB/BUI3+rsqESumdOTdMk7PXr0IDw83OH21Ve5SyK8vb0pX758HkeYfxISEkhMTHR2GFLUuLhAu8fM9popEB+T9pjzR2DObRATZYbH3/oJtHnEPLf6I9DnTkQk71kWbJhutlveB7VvNtv7lkJignNiOrw66X6V82IQRwf+MPc1OkCV1mb7iOalS9GnJD0LlmVxKTbeKTfLsnIUq6enJyEhIQ63MmXMMhRjxoyhatWqeHp6UrFiRR57zCQmnTp14tChQzzxxBPJve+Qtod7zJgxNG3alBkzZlC1alVKlSrFww8/TEJCAuPHjyckJITy5cvzxhtvOMQ0ceJEGjVqhK+vL1WqVGHkyJFER0cDEBYWxrBhw4iMjEx+7TFjxgBw7tw57r33XsqUKYOPjw89e/Zk7969yde1x7dw4UIaNGiAp6cnhw4dytHPSwSARgPBrwJEn0gZVml3+ZxJ0C+Emx73O+aAuxc0uxu8AuDsv7DnF+fELSJSnB1aBaf+AXcfaDIIKrcyQ94vnzVz03MqIR4Orc59cn3prIkHzDD8kztydx3JW/b56NU7piTpKh4nxYDT10kv7C7HJdDgld+c8to7X+uOj8e1/xN99913vPvuu3z99ddcd911nDhxgq1bzVCgH374gSZNmvDggw9mOYXg33//5ZdffuHXX3/l33//5bbbbuPAgQPUqVOHP/74g1WrVnHffffRpUsX2rRpA4CLiwvvv/8+oaGhHDhwgJEjR/Lss88yefJk2rVrx6RJk3jllVfYvXs3AKVKlQLM8P29e/eyYMEC/P39ee655+jVqxc7d+7E3d0dgEuXLjFu3DimTZtGUFBQker5l0LEzQPaPAyLX4GV70OTwaaHPT4Gvr7bfCnzqwB3fZey9qpnKWh5P/w10SzhVq+3c9+DiEhxY+9FbzTQNIoC1OgIuxbA3sWm+GdOLHsdVk6C7m9C20dyHs/Vid+hlVChcc6vI3nn8rmUoe2hN8L5pM6aw2vMSIySvDrOroWwfhr0fBvK1XV2NJIL6kkvRhYuXEipUqUcbq+//jqHDx8mJCSErl27UrVqVVq1apWckAcGBuLq6oqfn19y73tGEhMTmTFjBg0aNKBPnz507tyZ3bt3M2nSJOrWrcuwYcOoW7euQx2BUaNG0blzZ6pXr85NN93E66+/zty5cwGzDF9AQAA2my35tUuVKpWcnE+bNo0bb7yRJk2aMGfOHI4dO8b8+fOTrx0XF5ec7NetWxdfX998+blKCdBiKHj4wendsPc3M4R93kNw6C/w9DcJeukqjue0ehBc3M3wxyPrnRK2iEixFB0BOxeY7Zb3pexPHvKew6XY4mNh02yzvWth7mI6vMrcu3qa+4N/5e46kncOrQIrEYJqg38FqNjM/PtcOg1n9zs7OucKewv2L4cvB5lRIFLkqCc9C97urux8rbvTXjsnOnfuzJQpjstCBQYGcvHiRSZNmkSNGjXo0aMHvXr1ok+fPri55eyfPzQ0FD8/v+THwcHBuLq64uLi4rAvIiIi+fHy5ct588032blzJ1FRUcTHx3PlyhUuXryYYVK9a9cu3NzcaN26dfK+oKAg6taty65du5L3eXh40LixWrElD3gFQMthsOp905t+8C+zzJqLOwz6AkLSWe7RvwI0vh22zIHVH0CV2QUft4hIcbT5C1Oos1ILqNg0ZX+trub+2Ca4eAZ8g7J3vb2/mWHyAEfXm4rgnqVyFtOhpPnoze81q3scWmkadF3U35Wl/OrVPmAf6n6juXfzNIn6kTWmNz2oZt6/ZlFw4SSc/NtsnzsA3w6Fu38A12KU9sXHmpEUl8+aRohLZ8x2w9ty/n+7kNJvlizYbDZ8PNyccrPl8Bear68vtWrVcrgFBgZSpUoVdu/ezUcffYS3tzcjR46kQ4cOxMXF5ej69mHmqX826e2zF3A7dOgQvXr1omHDhnz//fds3LiRjz76CCDT185oLr5lWQ4/E29v7xz/jEQy1ObhpJ7xVbD6Q7Ov/xQzvDIjbR8197t+ylmrfdxlVZ8VEUlPYgJsTFpto+VVy+36V4TghoAF/y7L/jU3z0l1/biUAnDZFXsRwreY7TYPg7uvSRBO7cr0NMEMuX6zEuz+Ne+vfWCFua+esjIUVVU8Lvn/Rumq5rN64A/4/UXnxnQtzh+Br++CqR1hUiN4szL8txxMqAOT28CsXjD3HvjpcYg65uxo84yS9BLC29ubvn378v777xMWFsbq1av5+2/Tyubh4UFCQt5XKd2wYQPx8fFMmDCBNm3aUKdOHY4fP+5wTHqv3aBBA+Lj41m7dm3yvjNnzrBnzx7q16+f53GKAObLX+PbUx53HQuNB2Z+TnAD07NjJZrq8NlxJRKmdYWpHeCP8bmPV0SkONq3FM4fBq/S0HBA2uftvenZHfIeHQF7fzfbVdua+/1hOYvp6AazzJd/JQisAVVamf0HV+bsOiXRumkQd9EkUFei8u66F09DRFLxvtAbU/ZXMTWRSnTxuH+XmvtGA2HAVLO99mPY9LnzYroWqz+CfxaahrLzh82yuQDYwDsQgmqZwpJ1eoBL8RktUHzeiRATE8OJEycc9rm5ubFw4UISEhJo3bo1Pj4+fP7553h7e1OtWjXADGNfsWIFd9xxB56enpQtWzZP4qlZsybx8fF88MEH9OnTh5UrV/Lxxx87HBMaGkp0dDRLly6lSZMm+Pj4ULt2bfr168cDDzzA1KlT8fPz4/nnn6dSpUr069cvT2ITSVeHp81QyPp9of3j2Tun3f+ZdXs3fwGdRoNPYMbHxl2BrwbDye3m8fI3IaQx1O1x7bGLiBQH9oJxTe8Cd++0z9fqagrA7VuaveHm2+aClQCVWsL1w00v+v4/chaTvee9alszbDu0vZnve+gvaP1gzq5Vkpw/nDLaIPqEmSfd4828uba9qnv568A31fdWe4X303vMMOjM/iYXR4mJKT3pNbuYz2qn0RA2DhY+AWXrpIw2KAosC/752Wx3eQVCO5h/U+8ypiGvGE83Kb7vrAT69ddfqVChgsPthhtuoHTp0nz66ae0b9+exo0bs3TpUn766SeCgsxcrtdee42DBw9Ss2ZNypUrl2fxNG3alIkTJ/L222/TsGFD5syZw7hx4xyOadeuHSNGjGDQoEGUK1eO8eNNz+LMmTNp0aIFt9xyC23btsWyLBYtWpRmeL1IngqsAY+uhy4vZ3/+XPWOENwI4i7BhhkZH5eYAD8MN1/qPPygfh/Agh8egNP78iR8EZEi7fxh2JO0ok7qgnGpVW1jfodeOg3hWSzFZlmmbghA08Epw6JP/m16YrPrUFLRuGpJPfHVbkjZn8PlcksU+7+lb9LqO2s/hhN/5821k+ejd3Dc7xtkElGAI2spcU5sNfOzPfxSRnx0eNZ0PiTGwTd3Q+RR58aYEye2QeRhsxRjm5FQ5XpTa8AnsFgn6AA2K6eLcRdxUVFRBAQEEBkZib+/v8NzV65c4cCBA1SvXh0vLy8nRSjiSJ/LImDrNzDvQfNF5IntpnhNapZlWrA3zgRXD7j7ezMkb3Zf00NTrh4MXwKefulfX6Qoi48xjVj2JQxFMrL0Nfhzgmn8HLIg4+O+vssMf+30AnR6LuPjjm+GTzqZit9P7zafwcntzDDp22ZAw/9kHVNCHLxV1XyGH14FwdeZz/RbVSH+CjyyTktcZWTOQDPVoMurZqjyzh9NT/ewX689wfqgJZzZC3d8mXYZ1B8fhc2fQ/tRcPPYa3udombFO2a5wbq94c4vU/bHRMOM7mYkX4WmcN+v6Y9UKWyWvQErxkO9W+COOVkfX8hlloderXg3QYiIFISGA8CvIlyMMEMrr/bH20mFkGww4FPT8u/mAQM/M2uwn/oH5o0ww9REipu5Q2BiAzi129mRiDNciTKV0RPiMz8u9TJp19+f+bHJS7Etyfy4LUlJSv1bUhqJanQy99kd8n5im0nQvUpDuaS6OG6eUPl6s62l2NIXeymlsFud7tB9HHiUMr3bW64x2YoKNwm6zQWqtU/7fNUSPC/dPtS91k2O+z1LmSTXO9A0mPz4aNEYBWIf6l6/j3PjcAIl6SIi18rV3VT8BVj1gWOyvX66mQsG0PsduK5/ynN+wXD756Z3/Z+F8NfEAgtZpECc2gN7fsl6OogUX98OgZk9YEo7sxJGRonBPz/BxVNQKgTq9sr8mslLsW3IeA3o+Bj4+1uz3XRwyn77ih3ZLR5nX3qtahvH3l97cnhIxePSdfBPM9LAvzKUbwABlczcaIDFr1zb2t32+eghjcG7dNrn7QUCj282n4OS4kpUyhD/ml3SPl8mFG6fbYqrbf/O1HYozM7uN6NebK6moaeEUZIuIpIXWgwxc8BO707p3dn5I/z8lNnu+LwpWnS1KtdDr3fM9rL/wt5sViwWKQo2p6omvG2u6S2VkuPYppSevdO7zXzYaV3S78Ven9SI0/xe0/CZmYDKplfbSsx4Kbbdv5hl0vwqQo3OKfurtTNJyvlDcPZA1u8hddG41EKTkvSDK4tGj2RBs89Hr9MtpcZL64dMobfLZ2HJmNxf+0DS5+fq+eh2gTXApywkxMDxLbl/naLm4J9mFYLAGhBYPf1jqt8IPd4y20vGpvw7FUa7Fpr70BtK5HQpJekiInnBK8Ak6gCrPzBFbb4fDljQYhh0ej7jc1sMMcdgwff3w5l/CyJikfyVEAdbvzLbNlfzxXzPL86NSQrWqg/Mff2+0OEZU/zp2EZTj2N2f9PTCWYqxKG/zPBl++/RrNS2L8WWwZB3+1D3JneAi2vKfk8/U+kdUpK9jFhWSpJerZ3jc5WvN6Ogok+YHj9JYVkpy97VTtUD6uoOvSeY7U2fwZH1ubt+RkXj7Gy2lCHvJWm99H1JS6+l14ue2vXDocVQwIL5IwvvaIMSPNQdlKSLiOSd1iNMMnJgBXw5CBJizR+X3hOyrhbfc7wpqHMl0hRFiokumJhF8sueX83wZd/y0Hak2be56Bf+kWw6dxB2zjfbHZ+Fm16Cx7dCq4fAxd0sYfZJJ1OzICypZ69OT9NLnh21Us1Lv7qex4UTKcl76qHudtmdl356j6mU7eZtim2l5u4NlVqYbc1Ld3TqH4g8Ygr2XZ1IV2sLTe822z8/kXWtgqudO2hGQbi4pSTi6bEvxZZX89IvnjZJ44p38r8hPSEud9MB7Ouj18oiSbfZoOf/wL+SWSVh1085f638Fh2RMnQ/q+kvxZSSdBGRvFK6iikiBxB30cxZHDDNsRcnI24eZq5YqRCzruyPIzWEUoq2TUlD3ZveCc2Hmu19i00CJcXfmilmOHqNzhDSyOwrVR56jYf/2wCN7wBsJpHf8YN5/voMll1LT9W2phDZxVOmuFtq9rXRK7eCsrXTnmufl37gj8wLdtqXXqvc0vyOvlpxn5ceE5273m77EOrqN4KHT9rnbx5rCvGd+BvWT8vZte296JVaZL4iin16wpG1Of9bmpgIEf/Axs9MT/P7zeF/NeHrwaZy+vfD8+/vs2XBt0PhnTo5+9mf+dc0YLi4Q+iNWR/v5mGmlgBsmJmbSPPXPz8DFlRsbuoZlEBK0kVE8lL7Uab3IKSRWRrGPQfL5vmFJBV1cTfz2eeNgC1fmXmdsRezPj/2IhxeC2unmi8WH99gvkxoHrAUtKjjJiEHaHYPlK1lerasRNj6tXNjk/x36WxKpfb2j6V9vkwoDJgKD680vecAwQ2hxk1pj82Im4dZqg1SPmvguDZ6s7vSP7dSS3D3Nb3kETsyfo2M5qPbFed56ZZlktLpXdNftSQz6Q11T823LHQdY7aX/TdnDXf2onFZJaIVmoCbl/k3PrMve9feuxjm3A7jq8Pk1vDTY+azdDap57xcPTPF4fgmOJrLofpZxvC7KSSbGAd/vJX98+y1Gaq2MZXcs6PZPWaKyaG/Ct/qG8lD3W9xbhxO5ObsAEREipWQhvDEDjNHPb2el6xUbW16mhY+Adu+Nje70lXNl4Rydc29X4hp7Q/fam6n9wBXfVE88bdZLqjvh1kPuRfJK1vmmIS8atuUnsymdyUtv/QltH9cn8fibMN0U9E/uJFj0barBV8Hg782Bdy8S+d87exaXWD3z7B3iZnzDiaBOvWPGaJ+3a3pn+fmYeaY71tsqrzbe/qvZq/sXi2DJL1KazPsOuqoGYJdJjRn8RdmO+enzNlf8Q40vC17/z6Xz6UMMa/TLePjmg8xhSWPbYTfXoTbpmd9bctKWdYto/nodm4ephf28CoTT3ojKlI7tRu+usMUXgPz+ancEqq0giptzLZPIPz4CGz+AtZMNs/lpYQ4+O2FlMf7lpi/7RWaZH3uvmwOdU8toJJpJNv9M2ycBT3G5SjcfHMlKuWzV6/kJunqSRcRyWulyuUuQbdrMQwGzYHrHzC9Bb7lzP7zh00r+6oPzBeFL/4Dv78If881lZOxzLrrdXpAx+eg2xumlXzzF4V/qRUpPhITzWcOUoZTgkmY3LzNZ/XYRufEJvkv7gqs/cRst/u/7DXGBFbPXfVm+3rpR9eZ5BBSrY3exzSWZiSreemRRyHysKkzUjmDZMzDFyo2M9sHi9GQ99hL8NtLKY9P74a92awC/u8yM9WgbN3MGy1cXKD3RPM3avt32VsS78y/cCHc9GZnJ0Gums156ZZlVmJJjDejMx5YDqOPwNCF0OUV09jgE2iObZ203OrOBXD+SNYx5MS6T02vv2+5lHnYf03K+rz42JQRBlkVjbtay2HmfsuXEHc5Z+fml72/m5o+QbVNp0QJpSRdMjRr1ixKly7t7DCuWVhYGDabjfPnzzvlGkOHDqV///6ZHtOpUydGjRqVq9ikGLLZzBCv3u+YLwnP7INn9sOwX8yXmlYPmS8SZeuYVubOL8Fd38FTe+Cpf2DwN9D5BWj3KPR421xzyRjYMT/nsVyJvLb1bMH84bd/gZbi79BfZm6khx806Jey38sfGvQ12/YkXoqfbd/AxQizPra9Rkd+KV3VJINWIvy73DQQpLc2enrs89IPrUx/SpA9savQOPPhw4V1XvrZ/bBjXuZz7jOycpIZHRBQ1fy9gewliwB7koa6Z9aLblexqWmMBvjpcYg8lvnx9t7VKq1N4b6sJM9LzyJJ3/69SXLdvKDv+1CpecbLAIY0NI3nVgKs/zTrGLLr4pmU4e03vQydXzTbO+dnXajuyFqIjTZFOoMb5ux1a95k/p2vnM/dd4T8oKHugJL0YmPo0KHYbLY0tx49emTr/NDQUCZNmuSwb9CgQezZsycfonVUXBoDMvLee+8xa9YsZ4chRZ1vkBmeef39Zjj8kAXw6Hq4Yw50fMb0KPkFpz2v9YOm6jzAvIfgaA56MLf/AO82hEmNzBfg3Di9Dz5qBf+rZebJn8r/3yniZPaCcY3+Y3oaU2uaNEd4+w+Fp9fG7kokTO8G39yT84rTYiQmwuoPzXabh7Ne7zwv2HvT9y2F3YvMv6N/5ayHQ5e/DnyCzLD8YxvSPm8vGpfRfHS70BvMfWGq8H4lCmb1MQXIwnI4hPncwZSEvPt/4YYnTM/1kTVZ90gnJqTUB8hoPvrVbnrRJInnDsLMHpknpNmdj25X+Xpzf2afqc6enitRZrg9wI1PZ2/KQpuk1So2zspevZjsCHvTfHaDG0Gzu01jQO3upgFq5XuZn2uv6l7zppxPGXFxTVn2cGM+FJDb+jVMamwaQrIjPsbUBoASPdQdlKQXKz169CA8PNzh9tVXX+X6et7e3pQvXz4PI8xfCQkJJOamxTif2OMJCAgo1o0QUgR0f9P8sY+/YubcnT+c+fFxl82c+O+GQUyUaaH/8nZTzC4njm+GGd3N6yXGm3nKH7WCuffC8S25fjtSiF0+B7sWmO1m96Z9PvRG0/sZEwm7FhZsbFlZMsb0SO1aAEvHOjuaomnvb6Y2hqe/41SH/FQr1Xrp9oJxTe/MelUNF5eUwnPpDbXOqmicXZXWZsj2+UNmiHxhsGSM6QkHWDE+Z//Xfn8JEmJMI0f9vuBfARoPMs9llSwe32wKtXkGZL48WmpeATBsEQTWNH8rZvaEkzvTHpeYmPX66FfzCTT1WyBlOa+rhY0za90H1ky/yGF66nSHMtVNUr0199+zk53cCRtmmO2eb6V8dm94wtxv/QqiwjM+Pzfz0VNrdo+prXBkLZzMpJBiTu1dYhrnzx+CH//P1J7Iyv4/IPaCmbpXsXnexVIEKUnPimWZVjJn3HJYKdTT05OQkBCHW5kyKXO8xowZQ9WqVfH09KRixYo89pj5ZdSpUycOHTrEE088kdwDD2l7uMeMGUPTpk2ZMWMGVatWpVSpUjz88MMkJCQwfvx4QkJCKF++PG+88YZDXBMnTqRRo0b4+vpSpUoVRo4cSXS0WQM6LCyMYcOGERkZmfzaY8aMAeDcuXPce++9lClTBh8fH3r27MnevXuTr2uPb+HChTRo0ABPT08OHTqU4c9n48aNtGzZEh8fH9q1a8fu3aaS5cGDB3FxcWHDBseW9A8++IBq1aphpfp3WLlyJU2aNMHLy4vWrVvz999/ZxnP1cPdL168yL333kupUqWoUKECEyZMyDBmkTzh4mqK8gQ3MsNQvxxkvlyk59QemNY16QuDDW540gxZTog1vTIbP8veax5YYXpyLp026wvf/UNSq7hlkv1POsLnA4pnVeSS7O/vTGNQ+QZmyOjVXFygSdIw5C2FaMj7oVUpX5IBVr1f+BoRioKV75v7lsPM9IaCUK0duPuYRMu+NnqTO7N3rn3I+9Xz0i+dhYikRDGrJN3LP6WwV2GYl35wpSncBynzk+eNyF717n+XmzWzba5mqpS9nkD7xwGbGamQ2XXsS6/V7JyzURSlq8B9v5qh2tEnTaJ+9aivU7vM3xN3n5T16bMjeb301WmfO7HdrIYC0Ot/pshqdri4poxQW/Nx7qYU2FkW/Dba9JjX75syMgNMwcKqbc3f3zUfpX9+dETKEoSZFWnMjF9wyhz4vFqO7cTf8O0QMy3A3dcsSztvhBltkZl/kn7v1uud81EBxYzT3/3kyZOpXr06Xl5etGjRgj///DPT42NiYnjxxRepVq0anp6e1KxZkxkzZmR6zjWJuwRvVnTOLe5Snr2N7777jnfffZepU6eyd+9e5s+fT6NGpprpDz/8QOXKlXnttdeSe+Az8u+///LLL7/w66+/8tVXXzFjxgx69+7N0aNH+eOPP3j77bd56aWXWLMmZUiUi4sL77//Ptu3b+ezzz5j2bJlPPvsswC0a9eOSZMm4e/vn/zaTz/9NGCG8G/YsIEFCxawevVqLMuiV69exMXFJV/70qVLjBs3jmnTprFjx45Me/5ffPFFJkyYwIYNG3Bzc+O++8x6rKGhoXTt2pWZMx1/Mc2cOTN5GoHdM888wzvvvMP69espX748ffv2zXE8zzzzDMuXL2fevHn8/vvvhIWFsXGjiihJPvP0M3PVS4WYL5/fDks7pHfLV/BJJzi53RSuuft76Poq3DbT9IpZiWZJmqzmJu76yRS1i71gek6H/GRa+O+YAyPXmF4Zm6sZojerF8zoYb7cXThp5uVdPgcxF0yPfkKckviixL7sVvN7My4Y1jQpgdr/R94XXsqNuCuw4P/MdvMh0OYRsz3/YTOvV7Ln6AZTSdvFPSWBKQhuno49q1XbQlDN7J1rLx53bIP5nWNn73UNqm0KgWYleV66k4e8x112/CwP/gaq3WB+F399V8aNs2B+1/76vNlu9QAEN0h5rmxtkzRBSkNMeuzF5epkc6h7aqXKmxosla8386Nn902p5A4pvehV2+SsMKu9keXwVT3piYmmWJyVYBqic9oL3ewuM2LkzN6U5c9yY8+vZiSHqwfc/Fra5+296Rtmpl/bxT4VrUKT7H1WM9LSfCdm2zfXPoQ/8phZyi422nwHeGgFeJQyUyZWZfL5SUwwDUGQ8nkrwZy6BNs333zDqFGjmDx5Mu3bt2fq1Kn07NmTnTt3UrVq1XTPuf322zl58iTTp0+nVq1aREREEB+vuWMACxcupFQpx+Imzz33HC+//DKHDx8mJCSErl274u7uTtWqVWnVylTGDAwMxNXVFT8/P0JCQjJ9jcTERGbMmIGfnx8NGjSgc+fO7N69m0WLFuHi4kLdunV5++23CQsLo00bM9QpdUG06tWr8/rrr/Pwww8zefJkPDw8CAgIwGazObz23r17WbBgAStXrqRdu3YAzJkzhypVqjB//nwGDhwIQFxcHJMnT6ZJk6yXp3jjjTfo2NG0mj///PP07t2bK1eu4OXlxfDhwxkxYgQTJ07E09OTrVu3smXLFn744QeHa7z66qvcfLOZ//bZZ59RuXJl5s2bx+23356teKKjo5k+fTqzZ89Ocx2RfBdQySx3NLOXSZB/eRZ6TzB/kBc9A1uTqiJX7wADPjVLvIHpNejzPngHmoJCS16Fy2eh69i0idim2aYAkJVoqisPmOa4Vnz5+jDgE+g02vyx3vyF+cP95e2Zx25zMV9iPEqZBgfPUuYLkkeppG0/87hur4yXS5KcuXwOlicNBe01IesvgOFbTY+Oq0fK8Nj0lAk1X9wO/mnmK3Z8Jk/DzrEV/zNzVksFmy/JHr4maTuyFr65F4Yvzl6RKjD/l5a+bv5fdH4x++sVFwf2L9+NBoJ/xYJ97VpdTbIDKXUPsqNMKJSuZobjHlqVklza56Nn93dJ6A1mLn5e9aTHx0L85cyr06dn+ZtmTW+/itDtddObPXCWGbl0Zq/pyRw0J/0eyvXTzNJ1PkHQ6fm0z7cfZXo5t31j5pFf/W984YT5HQBQ6+acxW3nXQbumW/WZz/wB3xxG9z+GdTtmf2l165mr/AevsU0yNn/Hm39yvztcfeF7rlYeszTzwwTX/ORWY6tdtecXyM+NmU+fNtHzCoHV6vdzYwwOLndVH/v+Kzj88nz0XM51N2uekczhP/cATN/PLfTVa5Emb/nF46bqQaDvjDLK/Z4CxY8CsveMJ+PkHQK3B1ZBxdPmc99dusOFGNOTdInTpzI/fffz/DhwwGYNGkSv/32G1OmTGHcuLT/YX799Vf++OMP9u/fT2CgWQohNDQ0f4N094EXjufva2T22jnQuXNnpkyZ4rDP/nMaOHAgkyZNokaNGvTo0YNevXrRp08f3Nxy9hEIDQ3Fz88v+XFwcDCurq64pPqFHxwcTERERPLj5cuX8+abb7Jz506ioqKIj4/nypUrXLx4EV/fq4oKJdm1axdubm60bt06eV9QUBB169Zl165dyfs8PDxo3LhxtmJPfVyFChUAiIiIoGrVqvTv359HH32UefPmcccddzBjxgw6d+6c5vPVtm3KH+zAwMAcx/Pvv/8SGxub7nVECkTFZvCfaaZXZcN0k3zs/d3MI7W5mOT5xqfSzue02eDmsWaO3+JXzNzEy+fglkkpx/41ySTwYP7Ap37uaoHV4ZZ3zVJxqz80xcaunM84bivRDKOOv2KGPGZk1fsmSbj5dTOXUnJn54+m4Sb6pHkcvg3u+QECa2R8jr1gXL3eKcsVZaTpXSZJ3zIHOjztvDXTT2xPWZ6w1zvmyySYxObjG+Hk3+bn0O/DrK91/rBJLk4kTYPat8RcJ/i6vI+7oOxdYhKZpneln0DYnd1vRtCAWXatoNXpbnqB3bzhuv45O7dGJ9j0mRnZYU/Sk+ejt8veNaq2BWwmQb5wIqWBM6fOHzHJ8qbZZjTlf6aZxs7sOLYxpWjfLRNTEvxS5WDQ5zCjp+mlXPE/6PSc47kXT5sGOTBLjqW3HF6V683P4/Aqk5R2+6/j83uTqrpXbH5tPbqepWDwXPjuPrN+9zd3Q7/JKaMUQnOYpJepbqqeX4wwc+artTV/uxa/Yp7v9JxpwM6N1g/C2ikmUY74B8rXy9n56z4xnxnf8ubvbnpsNtOb/v39sGaKSebtBTkTE1N68XM7H93OxQVaDDV/wzfMzF2SnhBnpsWd3G7e0+C5Kb9Tm91tPn+7F8EPD8KDy9NOL7APda/To2CKThZyThvuHhsby8aNG+nWzXGJhm7durFq1ap0z1mwYAEtW7Zk/PjxVKpUiTp16vD0009z+XLGFWJjYmKIiopyuOWIzWb+MzjjlsMvLb6+vtSqVcvhZk/Sq1Spwu7du/noo4/w9vZm5MiRdOjQwWGodna4uzv+p7HZbOnusxdwO3ToEL169aJhw4Z8//33bNy4kY8+MvNqMnttK4PhrZZlOQw/9/b2dnic3djt59jj9PDw4J577mHmzJnExsby5ZdfJg+Hz0pO4snofYkUqHq9U75grf7QJOh+Fcyw9I7PZl5wqf3j0PdDk9Bvmm3+IMddMcWG7An6DU+YnvesCjeB+TLb7b/w/CEYEwmvnIOXT8OLJ2D0UXjuoFl+7qk98Pg2eHgV3Pc73PW9SYD6fmB6QTq/mNR7azNLMH3YElZ9aL40FEcJ8RB9Ku+nAkSFmwacufeaBD2otin0du6AqXx+fHP658Vdhr/nmu1m92T9Og36miXazh1I6bUsaIkJZmhwYrxJhOzLw4HpJbxtOmCDzZ9nvWTcwb/MVJETf4NPWdOTeXoPfHqT+X9S1H73JybA0tdgzn9MUvdBc/ju/pQGiKutnmwa0mrd7DhMuqCUrmp+fw1bZHo4cyJ5XnqYuY+9lPI5z25PunfplJ7BnFZ5tywzlPubu+G9xqbR6PJZ0yA5997s1QGJjzWFuaxEaHib6XlOrVIL0ygKpor47l8cn1/6minmWKFJ5v9/bxhl7jfMgsvnHZ/bcw1D3a/m7gW3z4bGd5j/n/MeNEP1PVPN/88umy3VeulJjS9LXzeNveXqpVRqz40yoSlzudd+nLNzL56GP8ab7S6vZP65bdDfvNblsymNoWAaES+eMiPKKmdj3fisNLvbTFc5vinnxV0tC35+0jRYuPuYqRZlqqU8b7OZ7wU+ZSFihxn1cfX5qeeji/OS9NOnT5OQkEBwsOOSQcHBwZw4cSLdc/bv389ff/3F9u3bmTdvHpMmTeK7777jkUceyfB1xo0bR0BAQPKtSpUqefo+ihJvb2/69u3L+++/T1hYGKtXr04ufObh4UFCQhbFHHJhw4YNxMfHM2HCBNq0aUOdOnU4ftxxZEJ6r92gQQPi4+NZuzZlDtGZM2fYs2cP9evXz/M4AYYPH86SJUuYPHkycXFxDBiQdn3X1HPtz507x549e6hXL/stp7Vq1cLd3T3d64gUqLaPpKxPW+tmGPGXY8GazDS/BwZ+ZoY171oA7zeFVR+Y57r9F7qOyX3PqIuLaUF39zZfWrzLmOXn/ILNH/zg68wXrtpd4bpbTWt/25GmcWHAJ6Z1vlJLMxfu9xdNb6h9LmNRlJgI5w7B7l/hz4nw/XCY0h7erADv1IJZvc1c4Lx4nY2z4KPW5ouSi5tZjmjEX3D/EghpZL4Mzrol/fmXu34yX6IDqmaveJGHb0qPp70id0FbO9V8GfUMgJ7/S/t8jU4paxX//JQZTXA1yzJDUGf3M1WtKzSBB8PMz61WV5NoLfg/s/xhTHR+vpu8c+kszBkIfyYVNQ1pZJK/7d/BxzeYIcipCz5ePJPSiOGMXnS7au3MmuY5Za/wHrHDFOE6tsEkhn4VzVD4bL9+0u/P7K6XHnvR9FhOaQ+f3WL+D1mJZpjvoC8c64D8OSHzhp6/Jpr4fYKg59vpH9PsrpTf+T88CKeTCvEe35xSS6Ln+MwbV2vdDOXqmznuqQstxsekNHLUzsb66Nnh6gb9p6TEDObf2DUXg4CT10tfC8c2pcTe651r77Ft87C53/q1+b+TXcvfMA0jIY2znqLh6gbtkirPr/rANMpASlX36h1yNk8/I75lUxorc7oc21/vms+RzQVum5F+4dBS5aBP0goBK9+DQ6mK+Z3cYZbhc/NKWbGhhHN64birex2v7ilNLTExEZvNxpw5c2jVqhW9evVi4sSJzJo1K8Pe9NGjRxMZGZl8O3KkEBSpyScxMTGcOHHC4Xb6tBkWOmvWLKZPn8727dvZv38/n3/+Od7e3lSrZv4AhYaGsmLFCo4dO5Z8Tl6oWbMm8fHxfPDBB8mv+/HHjq2NoaGhREdHs3TpUk6fPs2lS5eoXbs2/fr144EHHuCvv/5i69at3H333VSqVIl+/frlWXyp1a9fnzZt2vDcc89x55134u2ddg7ia6+9xtKlS9m+fTtDhw6lbNmyDpXbs1KqVCnuv/9+nnnmGYfruJTwCpbiBDYb9H4HntgJd31r/jjnRIO+5jyPUnAh3BSC6zfZuV/SwQznv3+x6e33CTIVgT+7xfQCZrSETUK8WTrp8FrYMQ92zDeJ6NENptp9VHiuVtzIlGWZuXtn/jXz8P5ZZHrM/pwAv74A3z8An3aBt6qY3rWvBpllwf7+1gwlTEj6knZoJUzrYtb2Pr0vd7Gc+dcUafrpcfOlsWJzePAP6PKy6dHyC4ahi0wyExttErhtcx2vYf+S3+yu7FfkbXa3ud8xv+AT2HOHYNnrZrvbaxlPjbjxKZOY2Hs1Uxfeio8xCdSip01S12ggDPvVVKr2DYLB30KXV83/jW3fwKed83Z5o/xw4m8zIuDfpWbo+IBPTYPDQyvgugHmC/i+xabg4/Sb4Z+fYf2nZv50hSY5ny9cGPiWNStfgJn3bF8LvFrbnDU2hiYVj8tqXvqFE2bk0cT6sHCUSa7dfUzhrpFrTPG0+n1Mr6N9CPTS1+C3F9KvIn5yJ6x4x2z3HJ/57/Ie48yQ9ZgoMzXjSiQsehawoNHtWS+b5uKSskzZ2o/NKCowo2Fio80Q5wpNM79GTri4mKrrHZ8zn8fsVu2/WpWk93VkrWlws7/f6nkw77lae9OQFX/ZNHRmx4ntKcf2fDt7vzOb3mV+vlFHTYMZpDSY1rwpp1FnzF5A7u/vHIspZubv71KWrezxdtqRHKnVvyWpUcJKarxMeg17L3rNm1KG85dwTpuTXrZsWVxdXdP0mkdERKTpXberUKEClSpVIiAgpZBG/fr1sSyLo0ePUrt27TTneHp64umZzSUVirhff/01ea61Xd26dfnnn38oXbo0b731Fk8++SQJCQk0atSIn376iaCgIMAknw899BA1a9YkJiYmz4ZlN23alIkTJ/L2228zevRoOnTowLhx47j33pS5Lu3atWPEiBEMGjSIM2fO8OqrrzJmzBhmzpzJ448/zi233EJsbCwdOnRg0aJFaYbX56X777+fVatWZTjU/a233uLxxx9n7969NGnShAULFuDhkbPWy//9739ER0fTt29f/Pz8eOqpp4iMzKTiqkh+yu1cPDA9jUN+Mi3oze6BOnnUg3KtXFxMb3+93qa3Yv1086Vmz6/mC0hiPEQdMxVoo46bwmhWNpbQsbma3n2fIDOkN6SJ6bkLaWyG7Wf0hT4+1jQWHN9shhAe32wKNMVfyd77cfWAsnVM0b3yDZJu9U3CFPaWKfi3a4FJmFoMgY7Pm8Q6M5YFkUdMsv3HeLMusrsP3PSSqcx9dW+al79plJn/sCkq9MMDZjh8u/8z85EP/gnYcla0q0prCKplirbtnJ+StKeWmAgHV5ie2vCt4FXaJCE+Qebet5wZPukbZO7L1c26yJtlwcInzJzfajekv567nYuLGaExtYMZmj9/pOnljI6AufckVQFPqtfQ7jHHz4CLC9z4pOnF++6+lOHvvf5n/r84ax5+RrbNhQWPmWSjdDWzGkNIUvJaoQkMnAlnXzY9eZvnwNH1JtGzu/r9FyU1Opqhw/vDUtY6z2rptavZ56+f3m2molw9Lzv6lBnKvn5ayv/9MqHQ6kHz/8Y+d9fOZjPDoH3KmiW61kw2Q6T7T07p/U1MMMW4EuOgTk9o+J/MY3R1N4XYpnY0n8dPOps50e6+5jOcHQ1vg2X/Nb9Dt31t5jHvXWyeq90t75fNstmg8wsmUc/OFKr0VGhskvzL58zN0z/tnPpria/NSPO7cd2n5ndiZr3zqZdca9DfjA7IDncvM2psyRhT/6XeLSkNStc6Hz21au3N35vTe8zvhOvvz/z4Q6vMewezMkbrB7N+jR5vmRFu5w+Zwnl939dQ93TYLCdOkm3dujUtWrRg8uTJyfsaNGhAv3790i0c98knnzBq1CgiIiKSq5j/+OOPDBgwgOjo6HR7Pq8WFRVFQEAAkZGR+Ps7ruF55coVDhw4kLwknJQ8b7zxBl9//bXD+ufOps+lyDU6vsX0dh5dn/ExLm5meKt/RfOl60qU6W2KiTIt/Vkl8b7lTLJuT9pjLyYl5ZtN72lCTPrnufsmJZtlk5LNcikJZ0BlM7w/sEbmX/pO7jS9GPbq1u6+0O5R82XRPs/x8jkzzPPYJjOc99hGM3zdrkZn6DPJJA2ZSUw00wjWJP3dbvuoKf7z5wTTA3LPvMzPv9qfE0wvYdV2cF+qebLnDsGWL80t8nD2r+cVYBKelveZZaPSs/Vr04Pj6mlqHJStlfV1j22E6d1NMnT9cDPy4cJxM1T+thlZV3a+eMbMq7Wv4914kKkkn9sCY5ZlRkBEHTP/jhdPmYYDh+3TJmGq0sb08lZrbz5LVyfSCXHw+8umABaYKtH/mZZ58b8LJ83x66eb/yOlq8L/bc7dUOTCYO9imHMb+Fc2BSxjo81nI6dF/ya3NUtcDvwsZTrHpbOmoOXaT8xa0WAaqG54MvtJ7dZv4MeRpoGx1s0m0fbwNQ0mv79kks5H1ma/qv7RDWYtcvuInC6vmgal7Fr1ofk9EFQLHlkHH7UyjW23zzbLmRVGM3unFJ/r8Ta0ycNlAuNj4N3rzP+9/0yHRrelPcayYP9y07B6ZK35/fPoesd521m5EgXvNjQjnprdbRovy1SHx7fk2VsBYPVHZuRGSCN46M/0G9/O7jefgy1zTKNT/T4wcHb2G2kO/Amf9QEsM93ol2dMw/PT+8zfwGIqszz0ak5N0r/55hvuuecePv74Y9q2bcsnn3zCp59+yo4dO6hWrRqjR4/m2LFjzJ5thtFFR0cnD0keO3Ysp0+fZvjw4XTs2JFPP/00W6+pJF3SEx0dza5du+jTpw+vv/46DzzwQNYnFRB9LkXyQGKiGXJ84A+TCPtXMqMI/CuaL+a+5TL+cmFZJum2J+wXTphhwfYlx07vyTqJ9woww0ArNjO3kEbmtbO7tFd2HPwLFr9qknAwiX71DibOs/+mPd7FzSzt0/ohM4w0u72glmWSg8UvJ+2wARbcNhMapq3lkamo4+bLrZVokqKTO0yhttTrI3sGQKP/QN3eppf34imT9F46bRLRi6fMfPCo46awkl31jiahrtsrJXmMPgUfXW8aLbq8knFF5fSs+9Q09tiVrQN3fJW9JB/MZ3DlJNMLaSUANjO8uEF/8wU3q1EtCXHm33j3IlP4KzIX0/dKhSQl7O3MKALvMqaX35683Pi06bXMbo/llUgzgqPy9Rk3ihQFMdHwdqhphAHz//XZgznvFf75aTP8v9WDpp7BmsmmqF5s0pDeis2g80um5zOnow72/G6mXMRfNj/vHm+baQfxV8zQ+BZDcna9TbNNvYTAmjByddpK25mJuWD+316JNEtxLnnVFBx7dr8ZdVMYLXsDVow3v3sfCMv7BqWwtyBsnKmJ8sDSlP2WZYalh70FR9eZfW5epje55bCcv86SsaYGgd31w81Sqnnp0lmYUM80Lg9fBpVbpDx3bJOZT75rQcrfvRqd4Y4vwSNnq1Lx24spKxKAqccwdOG1x1+IFZkkHWDy5MmMHz+e8PBwGjZsyLvvvkuHDmZO09ChQzl48CBhYWHJx//zzz/83//9HytXriQoKIjbb7+d//73v9nqRQcl6ZK+oUOH8tVXX9G/f3++/PJLXF1zOaQqH+hzKVLIxV4yvWf2pP3EdpN82xPyik1Nb0dBDAW2LPPlacnYtIl5YA1T5dl+C2nsuIZ9TqXu3fMOhKf+ydkXfbvPBySt9ZuU7NvV6ARN7zZzGLPTmGFfjmj9tKRRBUnX8qtohuQ2v9c0LPz9rWmceDAsZ0WjLMusM73ta7NE0IBPc5eQHFptln+yf2G3q9zK9L7W72vmtYPpOdu32PTc711setDs3LzMsHTfcmZotW/5tNsxUWY46qGVZjSAvefUzuZivmh7+MGtU7K/3FdxNKOnWV4MoHZ3uGtu5senZ8c8s+KFT5D5f2GvYRDcyDR+1O15bb8HjqwzNSGunCf5/0v1DnDvgtxd9+gGCKiS9fSY9Cx9zYyEsbmaRqfqHcz0p8Lq8nmTEDa7O+sRQ7kRHWEaLhJiTbHNyi3N77Wwt1JGcbl5mVE+7R/P/Sia6FMwqWHKlIk7voJ6vfLmPaT2w0Pmd13Tu80SlPuWmkbGg6kKsdbuZt5Ltfa5+/zFXYFPOprpX2AaLuyF+IqpIpWkFzQl6VLU6HMpIjmWEGeS0chjpqGgUvOs1y7PjX1LTOGp1g+ZW27smA/fJvUClq5qhqs3uTNnw0Cvdv6wKcy08TPT4w4pyYTNBYYvTb/6cFYsy1TFDqp17XNvI4+ait47f0yaW5rq61ilFqY398CfKb27YBLvOj3MvM0anXI2EiPusknKDq00tyPrTa9s2TowaA6Uq3Nt76eoC3vbLE8GZoWKG57I+TWiI+CdVCMKytWHzqOhXp+8m6sdscs0bF04bupIPLwq8zXs80t0hBl6bZ/K0+0NM82mJJs/0gz/rtrOJOv2UU1uXtDyflN0L7fJeWr2ERsubmaZ0pwuO5gdh9fAjO5mLn9gDVPgEMxrNhpoplPldDpIesK3mlodlmWG7Zeueu3XLMSUpGdCSboUNfpcikixZlmmcJx3oBnumJeFp+JjTCK8flrKGsltH4Xub+Tda+SFqPCUhP3QShwS9qDapqesbm/TO5fb4llXi481Sx4FVr/2ZaiKA3tSAnDf7ylra+fUz0+ZWhStHzbTP/Lq3yu184dND22DfnmzLnlu/TQqZamuR9aroSd8G0xNVTHezdsUXmv3WO5GK2Qk8qgZ+VHrppQlzfKaZcGUdmaUGJiVXFoMNT3dAZXz9rWOrDONiDU65u11CyEl6ZnITpIeGhqa7eHzIvnt8uXLHDx4UEm6iMi1OLHdfOG87tbCnZReOAm7fzZfWmt3K9pzvYuShDizZrmVAA+vzpt1p4u7s/vh4w7mM/rAsqJb3T8vfTssZSWRvE7OC9q/y2H5m1C3h3k/3mWcHVGRpyQ9E5n9cBISEtizZw/ly5dPXppMxNnOnDlDREQEderUKVRz5UVERIqV+KSh27mprVBSRZ8y0y48Szk7ksLBnlapwULSkZMkvYiulZE/XF1dKV26NBEREQD4+Phg038ycRLLsrh06RIRERGULl1aCbqIiEh+UnKec1evB1/SKW+QPKIk/SohIaaggz1RF3G20qVLJ38uRURERESkeFOSfhWbzUaFChUoX748cXFxWZ8gko/c3d3Vgy4iIiIiUoIoSc+Aq6urkiMREREREREpUHm4zomIiIiIiIiIXAsl6SIiIiIiIiKFhJJ0ERERERERkUKixM1Jty8LHxUV5eRIREREREREpCSw55/2fDQzJS5Jv3DhAgBVqlRxciQiIiIiIiJSkly4cIGAgIBMj7FZ2Unli5HExESOHz+On58fNpvN2eFkKioqiipVqnDkyBH8/f2dHY5IuvQ5laJCn1UpKvRZlaJCn1UpKgrDZ9WyLC5cuEDFihVxccl81nmJ60l3cXGhcuXKzg4jR/z9/fWLTwo9fU6lqNBnVYoKfValqNBnVYoKZ39Ws+pBt1PhOBEREREREZFCQkm6iIiIiIiISCGhJL0Q8/T05NVXX8XT09PZoYhkSJ9TKSr0WZWiQp9VKSr0WZWioqh9Vktc4TgRERERERGRwko96SIiIiIiIiKFhJJ0ERERERERkUJCSbqIiIiIiIhIIaEkXURERERERKSQUJIuIiIiIiIiUkgoSRcREREREREpJJSki4iIiIiIiBQSStJFRERERERECgkl6SIiIiIiIiKFhJJ0ERERERERkUJCSbqIiIiIiIhIIaEkXURERERERKSQUJIuIiIiIiIiUkgoSRcREREREREpJJSki4iIiIiIiBQSStJFRESyae3atdx6661UrVoVT09PgoODadu2LU899ZSzQ8vS0KFDCQ0NdXYYBSIsLAybzUZYWFiOz501axY2m42DBw/meVwiIiLZoSRdREQkG37++WfatWtHVFQU48eP5/fff+e9996jffv2fPPNN84OT0RERIoJN2cHICIiUhSMHz+e6tWr89tvv+HmlvLn84477mD8+PFOjMw5Ll26hI+Pj7PDEBERKXbUky4iIpINZ86coWzZsg4Jup2Li+Of02+++YZu3bpRoUIFvL29qV+/Ps8//zwXL150OG7o0KGUKlWKf/75h+7du+Pr60uFChV46623AFizZg033HADvr6+1KlTh88++8zhfPvQ7MWLFzNs2DACAwPx9fWlT58+7N+/P8v3ZFkWkydPpmnTpnh7e1OmTBluu+22NOd26tSJhg0bsmLFCtq1a4ePjw/33Xdfhte91vcFsH37dvr160eZMmXw8vKiadOm6R73zz//0KNHD3x8fChbtiwjRozgwoUL6ca1ZMkSunTpgr+/Pz4+PrRv356lS5dm+XPavHkzt9xyC+XLl8fT05OKFSvSu3dvjh49muW5IiIiOaUkXUREJBvatm3L2rVreeyxx1i7di1xcXEZHrt371569erF9OnT+fXXXxk1ahRz586lT58+aY6Ni4tjwIAB9O7dmx9//JGePXsyevRoXnjhBYYMGcJ9993HvHnzqFu3LkOHDmXjxo1prnH//ffj4uLCl19+yaRJk1i3bh2dOnXi/Pnzmb6nhx56iFGjRtG1a1fmz5/P5MmT2bFjB+3atePkyZMOx4aHh3P33XczePBgFi1axMiRIzO99rW8r927d9OuXTt27NjB+++/zw8//ECDBg0YOnSow6iFkydP0rFjR7Zv387kyZP5/PPPiY6O5tFHH00TzxdffEG3bt3w9/fns88+Y+7cuQQGBtK9e/dME/WLFy9y8803c/LkST766CMWL17MpEmTqFq1aoaNASIiItfEEhERkSydPn3auuGGGyzAAix3d3erXbt21rhx46wLFy5keF5iYqIVFxdn/fHHHxZgbd26Nfm5IUOGWID1/fffJ++Li4uzypUrZwHWpk2bkvefOXPGcnV1tZ588snkfTNnzrQA69Zbb3V4zZUrV1qA9d///tfhtapVq5b8ePXq1RZgTZgwweHcI0eOWN7e3tazzz6bvK9jx44WYC1dujQbP6lrf1933HGH5enpaR0+fNjhuj179rR8fHys8+fPW5ZlWc8995xls9msLVu2OBx38803W4C1fPlyy7Is6+LFi1ZgYKDVp08fh+MSEhKsJk2aWK1atUreZ/+ZHjhwwLIsy9qwYYMFWPPnz8/WexcREblW6kkXERHJhqCgIP7880/Wr1/PW2+9Rb9+/dizZw+jR4+mUaNGnD59OvnY/fv3M3jwYEJCQnB1dcXd3Z2OHTsCsGvXLofr2mw2evXqlfzYzc2NWrVqUaFCBZo1a5a8PzAwkPLly3Po0KE0sd11110Oj9u1a0e1atVYvnx5hu9n4cKF2Gw27r77buLj45NvISEhNGnSJE1l9DJlynDTTTdl/YPKg/e1bNkyunTpQpUqVRyuOXToUC5dusTq1asBWL58Oddddx1NmjRxOG7w4MEOj1etWsXZs2cZMmSIw3tNTEykR48erF+/Ps1UBLtatWpRpkwZnnvuOT7++GN27tyZ7Z+BiIhIbqhwnIiISA60bNmSli1bAmZI93PPPce7777L+PHjGT9+PNHR0dx44414eXnx3//+lzp16uDj48ORI0cYMGAAly9fdriej48PXl5eDvs8PDwIDAxM89oeHh5cuXIlzf6QkJB09505cybD93Hy5EksyyI4ODjd52vUqOHwuEKFChleKz3X8r7OnDmT7utVrFgx+Xn7ffXq1dMcd/XPwz50/7bbbssw3rNnz+Lr65tmf0BAAH/88QdvvPEGL7zwAufOnaNChQo88MADvPTSS7i7u2d4TRERkdxQki4iIpJL7u7uvPrqq7z77rts374dML3Ax48fJywsLLn3HMhyfvi1OHHiRLr7atWqleE5ZcuWxWaz8eeff+Lp6Znm+av32Wy2aw80m4KCgggPD0+z//jx44CJ3X5cRu89NfvxH3zwAW3atEn3NTNqrABo1KgRX3/9NZZlsW3bNmbNmsVrr72Gt7c3zz//fPbelIiISDZpuLuIiEg2pJc0QsrwdXsvrz2ZvTrJnTp1ar7FNmfOHIfHq1at4tChQ3Tq1CnDc2655RYsy+LYsWPJowNS3xo1apRv8WalS5cuyY0dqc2ePRsfH5/kRLtz587s2LGDrVu3Ohz35ZdfOjxu3749pUuXZufOnem+15YtW+Lh4ZFlXDabjSZNmvDuu+9SunRpNm3adI3vVEREJC31pIuIiGRD9+7dqVy5Mn369KFevXokJiayZcsWJkyYQKlSpXj88ccBMx+8TJkyjBgxgldffRV3d3fmzJmTJpHMSxs2bGD48OEMHDiQI0eO8OKLL1KpUqVMK7C3b9+eBx98kGHDhrFhwwY6dOiAr68v4eHh/PXXXzRq1IiHH34432LOzKuvvsrChQvp3Lkzr7zyCoGBgcyZM4eff/6Z8ePHExAQAMCoUaOYMWMGvXv35r///S/BwcHMmTOHf/75x+F6pUqV4oMPPmDIkCGcPXuW2267jfLly3Pq1Cm2bt3KqVOnmDJlSrqxLFy4kMmTJ9O/f39q1KiBZVn88MMPnD9/nptvvjnffxYiIlLyKEkXERHJhpdeeokff/yRd999l/DwcGJiYqhQoQJdu3Zl9OjR1K9fHzBDsH/++Weeeuop7r77bnx9fenXrx/ffPMNzZs3z5fYpk+fzueff84dd9xBTEwMnTt35r333kt3/ndqU6dOpU2bNkydOpXJkyeTmJhIxYoVad++Pa1atcqXWLOjbt26rFq1ihdeeIFHHnmEy5cvU79+fWbOnMnQoUOTjwsJCeGPP/7g8ccf5+GHH8bHx4dbb72VDz/8kH79+jlc8+6776Zq1aqMHz+ehx56iAsXLlC+fHmaNm3qcM2r1a5dm9KlSzN+/HiOHz+Oh4cHdevWZdasWQwZMiSffgIiIlKS2SzLspwdhIiIiOTcrFmzGDZsGOvXr08uZiciIiJFm+aki4iIiIiIiBQSStJFRERERERECgkNdxcREREREREpJNSTLiIiIiIiIlJIKEkXERERERERKSSUpIuIiIiIiIgUEiVunfTExESOHz+On58fNpvN2eGIiIiIiIhIMWdZFhcuXKBixYq4uGTeV17ikvTjx49TpUoVZ4chIiIiIiIiJcyRI0eoXLlypseUuCTdz88PMD8cf39/J0cjIiIiIiIixV1UVBRVqlRJzkczU+KSdPsQd39/fyXpIiIiIiIiUmCyM+VaheNERERERERECgkl6SIiIiIiIiKFhJJ0ERERERERkUJCSbqIiIiIiIhIIaEkvRCLiU9wdggiIiIiIiJSgJSkF1JHzl6i/VvLmBy2jytxStZFRERERERKAiXphdRX6w5zOjqW8b/uptP/wpi74QgJiZazwxIREREREZF85NQkfcWKFfTp04eKFStis9mYP39+pseHh4czePBg6tati4uLC6NGjSqQOJ3h6W51eXdQEyqV9uZE1BWe/W4bvd77k+X/RGBZStZFRERERESKI6cm6RcvXqRJkyZ8+OGH2To+JiaGcuXK8eKLL9KkSZN8js65XCIPc2vsIpb3j+etzv6U8XJh98kLDJu1nsGfrmXb0fPODlFERERERETymM0qJN2yNpuNefPm0b9//2wd36lTJ5o2bcqkSZNy9DpRUVEEBAQQGRmJv79/zgMtKNvmwg8PJD+0XD04416RLZeC+DcxhINWCCE1GvKfbl2pXKWqEwMVERERERGRzOQkD3UroJicJiYmhpiYmOTHUVFRTowmB3yCoG5vOPsvnD2ALSGGsgkH6epykK728Q9HgOlwzLMWPg26U6ZJb6jSClzdnRm5iIiIiIiI5FKxT9LHjRvH2LFjnR1GztXqYm4AiQkQedQk7GfM7cLxf7gUvptyceFUitkHm/fB5o9I8PDHtWYnqH0z1LoZ/Cs49W2IiIiIiIhI9hX7JH306NE8+eSTyY+joqKoUqWKEyPKBRdXKFPN3GreBIBf0u3v3ftYt+Rbgk6soIPLNgJjo2DXAnMDCG4EdbpD/T5QoQnYbE57GyIiIiIiIpK5Yp+ke3p64unp6eww8k2jurVoVHc0+yIe5c3lezmw7S9utG2mk8tWGrvsx+Xk33Dyb/jzHQioCvV6m4S9ahuT/IuIiIiIiEihUeyT9JKiVnk/3hnUnGPd6/Ppiv3csf4wPlfO08FlG308N3GjbQsekYdh7RRz8ykLdXuahL1GJ3Arvg0ZIiIiIiIiRYVTk/To6Gj27duX/PjAgQNs2bKFwMBAqlatyujRozl27BizZ89OPmbLli3J5546dYotW7bg4eFBgwYNCjr8QqlSaW/G9L2O/7upFp+tOshnq4OYf/kGPImlg8s2+npuoovLJnwunYbNn5ubZwC0fQTajgRPP2e/BRERERERkRLLqUuwhYWF0blz5zT7hwwZwqxZsxg6dCgHDx4kLCws+TlbOnOqq1WrxsGDB/+/vfsOj6pM+zj+nZn0HgIk9ID0qoBKEZVVQEQFQcWKKLoiroqor6JrA4UVFV1UQESw4YKVtWCJrNIRiCAIKCIllIRIS0JC2sx5/3iSSYaEEELCTMLvc13nOmWec8494QS4z9PKdc9qMwVbJcnJd7J86wEWbEjmu037SDuahx/5nGf/jYGBP3OZXyKReX+ZwiExcMEYOHcE+Ad7N3AREREREZEa4mTyUJ+ZJ/10OdOS9OLynC6W/3mArzck8+3GFA5l5WHDxeX2VYzx+4iz7MkAHAmoy95O91K71whqRYR6OWoREREREZHqTUl6Gc7kJL24fKeLn7Yf5KsNySzbup/dBzIY7FjC/X6f0tC2H4Cdrrq8HXgDyY0GcFZsJI2iQ2gYHUKjWsHUiwwmwM9+gruIiIiIiIiIkvQyKEkvXdrRPDbuTWPTrr+I3PQBl/z1DrWsNAB+dzXk6fxbWeFq5y5vs0FcRFBB4h5Mw+hgYiODiA0PIjYiiNiIQGLCAnHYNeWbiIiIiIic2ZSkl0FJejnlZpK9bBp+K/6NX246+TZ/Xqw7ge+PtmL3oSyy81wnvITDbqNOWCCxEYHUjQiiTnggdcICqR0eSJ2wAOqEB1I7zCyhgZpoQEREREREaiYl6WVQkn6Sjh6G/94Dv30JAeFw2wKsuA7sP5LL7kNZ7Dp0lN2Hsthz6Cj70nNIzcgmJS2b/UdycJ3EkxUS4KB2WCC1QgM8luiQAGJCA4gu2I8K8Scy2Cz+DjW3FxERERER36ckvQxK0isgLxveHwI7l0JYLIz4DqLjyzwl3+niQGYu+9Kz2ZeeQ0p6Nn9l5LD/SA77M3L460jhdi5H85wVCiskwEFEUFHSHlGwDgt0EBroZ5YAsx1WuB/oR2iggyA/B8EBZh0UYCfAYS915gAREREREZFTpSS9DErSK+joYZh9OaRuhFpnmUQ9tHalXDozJ9+dwB/MzOVQVi4HMnM5lFm0PliwnZaVR0ZOfqXctzibDYL9HQT5Owj2dxDoZyfQ30GQv51APztBBccK18H+DoID/AgJcBASYBL+0AA/ggv2QwL8iAjyc784CPTTSwARERERkTOVkvQyKEk/BenJ8FYfSNsF9TvDrV9AYNhpD8PpssjIziPtaMkl/Wg+mTn5HMkx68zcfI7kOMkqPJabT1aOk+w8J0fznCfVJP9UBDjsRAT7ExHs5679jwrxJyY0kJiwAGqHBVCrcLtgHRLgUGIvIiIiIlIDKEkvg5L0U/TXFpjVD44ehOZ94Ib/gMPf21FViGVZ5DktsvOdZOc6yc5zcTTPJPDZeU5y8l1lrrNynWTl5hesi7aP5jrJzM0nIzuf9KN5FX4REORvp054IHXDg6gTFkjdiEDqFu5HBLqP1QoJwE/980VEREREfJaS9DIoSa8Eu1bDO1dC/lHodAMMmmbai0sJlmVxJCef9Ox80rLySM8urPHP43BWHgcyczlwJMe93n8kl/1HcsjJP/Ho+YVsNqgVElBQI180Yn5MWAB1wgKJDg1wN72PCPYnIsj00VctvYiIiIjI6aEkvQxK0ivJlm/hPzeA5YSeo6HPM96OqMawLIusXKcZWO9IDqnpOaRmmJHzU9PNoHuFxw5k5lCR32C7jYKE3Z/wID93X/sAP9MHP9CvsF++2faz27DZbNhs5lwbNuw2wGZz75eHn8OG3WbDYadgbRa7zYaf3UZAKf3/i6+D/B2EBfoR5K8+/iIiIiJSfZxMHqrJqaViWvaDq16F/46CZa9AeBx0u9vbUdUINpvNPRJ9k5jQMss6XRYHM03t+4GCWvj9xWrk9x/J4XBBDX76UdP8PtfpwmXB4SxTm18dOew2QgMcHqP2hxUs4YWtBoLMGACR7u2iMQEigv0JVZ9/EREREfFBStKl4s65CY6kwMJx8M2jENMcWvTxdlRnFIfdRp3wQOqEB5arvGVZ5OS7SC8caC87j/TsfHLyXOQ6XeQU9Lk3i5OcPLPtdLmwLHBZYGFhWeZahfsuixPWpbsscLksnJblXjtdFi7LIt9ptk0MLrIL7n3sunC6PqfLIj3bdCM4lZ9dxHES+vAgP8IC/QkLMkl/eKBfwba/+2VAcICZCSDAT+MBiIiIiEjlUZIup+aCMZC2G9bMgi8fgHt+goCya3/Fe2w2m7vZeN2IIG+Hc9JcLousPCeZOWZgvsycotH8C0f0N8l7UcsBs21eRhS+nMh3mZcCh7LyOHSKrQn87LaCKfkc7sQ9OMA00fd32AlwmG4E/o6CfT87AQ4bfg67u6l/YfP/wi4AdhvYCz4rVLhVeKiwi8HxGgMUthKw2wqvbY4VbtttNihY+xV0O3CvHTYcdrt7399hw99hx89uJ8DP5v4ufg4bAY6i7+bvsKl1goiIiMgpUpIup8Zmg77Pwh/fQ1oS/Pgv6Dve21FJDWW329w12bEVHFLCsiyy81xFLQmOFh/QL5+0o3kcKXgJcCQnnyPZnvuFa2fBsP35LouMnHwycipeq1+TBBQk64UvJsxLieIvKmwex/0ddvz9Cs45JuF3b/vZ8LebY34Fn/nZzUsC8/LAZq5RcMzPbsr5Fbxw8LMXlfez24teSDiKXkw4Cl6Q+PJLBsuyClq0WFiYtTlevEzBmuMPVnHsGBLuFz8F40sUvL8pOGZK22z49M9GRESkJlGSLqcuIBQGvAgfXAcrXoeO10FcB29HJVIqm83mrvWOi6x4a4I8p4usXKd7Or6juU6O5hVNyZfndJGb7zJrp+Xezss3XQtynaYLQWGTf3dXgMJuAa6iNKtE4uW5onD8z5LlCxK7grXLZRI7l7u7goWz4H75LhdOl+VuZVC45Lss8pwu8p2mO8Kx28cOXGi+G2TmOiv8s/WmwoTdZsPd0qGwZYM5XtgqwZQvTGrNdsHaZiv2cy/WRYTCP5tjkm1X0b6r8PgxybgvDfFamMR7JPAUZfbFj5WW8HPssWOuV3gPSpzvec3ClwY2WxmfFbtf8XgLrl6iJUrxGI79zmXtlyhfzsE0T6TEfU9QoFyxl/j8xLGWPOfYz8vxfU8YR3ku4RsvisoV62l4RvTerGqcjheSJ7pDZf2Vf6Y9Is8Oak+jWiHeDqNSKEmXytGyH7S5CjZ/bpq93/4d2NVXV2ouf4edyGA7kcH+3g7Fa6yCcQXyiiXt7hcThS8j8l3m83wXea6iz80LC8tdJtfpIt9pyro/P2a7sEy+0yLPZbm3c50u8l1mO7/wuMsq2D9mu6DM8ThdFs5K++9RzVT4wsHzzYF+ZiIi4l1Z1bSCoDRK0qXy9H8e/vwBdq+GxNlw7ghvRyQiVchmK2hO7oBgHN4O56S4irUaOLYVgavg5YNHS4eCWu7C/eM1MS9MYI+tTeaYfVsZYwXY3McK9inaL74utZaakuMWFMZ2bLzmuGdLDMsyG4XfpXiZ4i0DKNYyoNSylue9PFsTHHO/Yp9xzDWPPb/E58fERolYSokd3PGXFuOxjj10oplrK/S6otT7eh489rYl9ku77EnGWnrxE1yjXD+zMi9RZveM8l/Dd1TG7Mano/VMeX7uNUll/Ex9qVWTlBRXDcdbOh4l6VJ5IurDJU/A1/8H3z8Dra+A8FhvRyUiUoLdbiPAXpjEVq8XDCIiIlKzqT2yVK5z74D650BOGnw71tvRiIiIiIiIVCtK0qVy2R1wxStgs8Ovn8DW770dkYiIiIiISLWhJF0qX/2z4fyRZvurByHvqFfDERERERERqS6UpEvV6P0YRDSAQztg8QvejkZERERERKRaUJIuVSMwHPpPMtvLpkDqZu/GIyIiIiIiUg0oSZeq0+YKaHU5uPLM3Okul7cjEhERERER8WlK0qVq9Z8E/qGQtALWve/taERERERERHyaknSpWlGNoHfBVGwJT0F2unfjERERERER8WFeTdIXL17MlVdeSf369bHZbMyfP/+E5yxatIguXboQFBREs2bNmD59etUHKqfm/Luhdks4ehBWTvV2NCIiIiIiIj7Lq0l6ZmYmnTp14rXXXitX+e3bt3P55ZfTq1cv1q5dy2OPPcZ9993HJ598UsWRyilx+MHFBbXpy1+DrIPejUdERERERMRH+Xnz5v3796d///7lLj99+nQaN27MK6+8AkCbNm1Ys2YNL774IkOGDKmiKKVStB0EsZNh3wZY9gr0GeftiERERERERHxOteqTvmLFCvr27etxrF+/fqxZs4a8vLxSz8nJySE9Pd1jES+w2+Fv/zTbP82AjH3ejUdERERERMQHVaskPSUlhdjYWI9jsbGx5Ofns3///lLPmThxIpGRke6lUaNGpyNUKU3LftDwXMg/Ckte8nY0IiIiIiIiPqdaJekANpvNY9+yrFKPFxo7dixpaWnuZdeuXVUeoxyHzVZUm544Gw7rz0JERERERKS4apWkx8XFkZKS4nEsNTUVPz8/YmJiSj0nMDCQiIgIj0W8qNnFEN8LnLmw6HlvRyMiIiIiIuJTqlWS3r17dxISEjyOfffdd3Tt2hV/f38vRSUn7ZInzXrdB3DgT+/GIiIiIiIi4kO8mqQfOXKEdevWsW7dOsBMsbZu3TqSkpIA01R92LBh7vIjR45k586djBkzhs2bNzNr1izeeustHnroIW+ELxXV6Dxo0Q8sJ/w40dvRiIiIiIiI+AyvJulr1qzhnHPO4ZxzzgFgzJgxnHPOOTz5pKlpTU5OdifsAE2bNmXBggX8+OOPnH322YwfP54pU6Zo+rXq6G+Pm/WGj2HfJu/GIiIiIiIi4iNsVuHIa2eI9PR0IiMjSUtLU/90b/twGGz6L7S+Aq6f4+1oREREREREqsTJ5KHVqk+61DC9HwebHX77EvYkejsaERERERERr1OSLt5TpxV0HGq2//ecd2MRERERERHxAUrSxbsuegTsfvDnQti53NvRiIiIiIiIeJWSdPGuWk2hc8EI/gvHw5k1RIKIiIiIiIgHJenifRc+DI5ASFoOfyR4OxoRERERERGvUZIu3hdRH86702x/cT9kHfRuPCIiIiIiIl6iJF18Q+/HIKY5ZOyFLx9Qs3cRERERETkjKUkX3xAQCoPfNIPIbZoP6+d5OyIREREREZHTTkm6+I4GneHiR832Vw/BoR1eDUdEREREROR0U5IuvuWCMdCoG+RmwKd3gcvp7YhEREREREROGyXp4lvsDhj8BgSEw66VsHSytyMSERERERE5bZSki++JjocBL5rtH/8FexK9Go6IiIiIiMjpoiRdfFPHodDuanDlw6d/h9xMb0ckIiIiIiJS5ZSki2+y2WDAZAivDwe2wrePezsiERERERGRKqckXXxXSC24errZTpwNvy3wbjwiIiIiIiJVTEm6+LZmF0H3f5jtz/8BGfu8G4+IiIiIiEgVUpIuvu+SJyG2PWQdMIm6ZXk7IhERERERkSqhJF18n18gDH4THIHwx3eQ+La3IxIREREREakSStKleohta2rUwQwid3Cbd+MRERERERGpAkrSpfroNgqa9IS8TJg/ClxOb0ckIiIiIiJSqZSkS/Vht8OgqRAQBkkrYMXr3o5IRERERESkUilJl+olOh76TTDb/xsP+zZ5NRwREREREZHKpCRdqp/Ow6BFP3Dmwmd3QX6utyMSERERERGpFErSpfqx2eCqKRAcDSnrYfEL3o5IRERERESkUihJl+opPA6ueNlsL3kJdid6Nx4REREREZFKoCRdqq92V0P7a8BymmbveUe9HZGIiIiIiMgpUZIu1dvlL0BYHBz4A75/xtvRiIiIiIiInBKvJ+lTp06ladOmBAUF0aVLF5YsWVJm+ddff502bdoQHBxMq1atePfdd09TpOKTQmrBwIKp2H6aBtsXezceERERERGRU+DVJH3evHmMHj2axx9/nLVr19KrVy/69+9PUlJSqeWnTZvG2LFjefrpp9m4cSPPPPMM99xzD1988cVpjlx8SotLocttZnv+KMhO8248IiIiIiIiFWSzLMvy1s3PP/98OnfuzLRp09zH2rRpw6BBg5g4cWKJ8j169KBnz5688ELRaN6jR49mzZo1LF26tFz3TE9PJzIykrS0NCIiIk79S4hvyDkC03vCoR3Q/FIYOgf8g7wdlYiIiIiIyEnloV6rSc/NzSUxMZG+fft6HO/bty/Lly8v9ZycnByCgjwTr+DgYFatWkVeXt5xz0lPT/dYpAYKDIMhb4F/CGz9Hj68BfJzvB2ViIiIiIjISfFakr5//36cTiexsbEex2NjY0lJSSn1nH79+jFz5kwSExOxLIs1a9Ywa9Ys8vLy2L9/f6nnTJw4kcjISPfSqFGjSv8u4iMadoUbPwS/YPjjO5inRF1ERERERKoXrw8cZ7PZPPYtyypxrNATTzxB//796datG/7+/gwcOJDhw4cD4HA4Sj1n7NixpKWluZddu3ZVavziY5r2ghvngV8Q/PEtfHgr5Od6OyoREREREZFy8VqSXrt2bRwOR4la89TU1BK164WCg4OZNWsWWVlZ7Nixg6SkJOLj4wkPD6d27dqlnhMYGEhERITHIjVcs4vghrkmUd/yNXw0XIm6iIiIiIhUC15L0gMCAujSpQsJCQkexxMSEujRo0eZ5/r7+9OwYUMcDgdz587liiuuwG73eqMA8SVn9YYb/gOOQPj9K/j4NnCWPm6BiIiIiIiIr6hQZrtr1y52797t3l+1ahWjR49mxowZJ3WdMWPGMHPmTGbNmsXmzZt54IEHSEpKYuTIkYBpqj5s2DB3+S1btvD+++/zxx9/sGrVKq6//np+/fVXJkyYUJGvITXdWX+DGz4wifpvX8LHtytRFxERERERn1ahJP3GG2/khx9+ACAlJYU+ffqwatUqHnvsMcaNG1fu6wwdOpRXXnmFcePGcfbZZ7N48WIWLFhAkyZNAEhOTvaYM93pdPLSSy/RqVMn+vTpQ3Z2NsuXLyc+Pr4iX0POBM0vhevngCMANn8On9wBznxvRyUiIiIiIlKqCs2THh0dzcqVK2nVqhVTpkxh3rx5LFu2jO+++46RI0eybdu2qoi1Umie9DPUlm9h7k3gyoPWV0D/SRDZwNtRiYiIiIjIGaDK50nPy8sjMDAQgO+//56rrroKgNatW5OcnFyRS4pUrZb9YOh7YPc3Td+nnA1fjIZDO70dmYiIiIiIiFuFkvR27doxffp0lixZQkJCApdddhkAe/fuJSYmplIDFKk0rfrD8K+gyQXgzIXE2fBqZ5h/Dxz409vRiYiIiIiIVCxJf/7553njjTe4+OKLueGGG+jUqRMAn3/+Oeedd16lBihSqRqfD7d9Bbd9Dc16gysf1r0Pr3WFT+6Ev373doQiIiIiInIGq1CfdDCDuKWnpxMdHe0+tmPHDkJCQqhbt26lBVjZ1CddPOxaDYtfgD++LThgg3aDoOsIaNID7A5vRiciIiIiIjXAyeShFUrSjx49imVZhISEALBz504+++wz2rRpQ79+/SoW9WmiJF1KtXctLH7R9FcvFFoH2lwJbQdBk57g8PNaeCIiIiIiUn1VeZLet29fBg8ezMiRIzl8+DCtW7fG39+f/fv3M3nyZO6+++4KB1/VlKRLmVJ+hZ+mw+YvIPtw0fGQ2iZhbzfI9GlXwi4iIiIiIuVU5aO7//zzz/Tq1QuAjz/+mNjYWHbu3Mm7777LlClTKnJJEd8Q1x4GvgYPb4WbP4FzboHgaMjabwaae3cgvNQSvrgfti8Gl9PbEYuIiIiISA1SoerArKwswsPDAfjuu+8YPHgwdrudbt26sXOnprSSGsDhD80vNcsVL8OOJbBxvqlhzzoAiW+bJSwW2l0N7a+Bhl3BZvNy4CIiIiIiUp1VqCa9efPmzJ8/n127dvHtt9/St29fAFJTU9WEXGoehz+c9Te4ago89AfcMh86D4OgKDiyzzSPf+tS+HdHSHgKUjZAxcZjFBERERGRM1yF+qR//PHH3HjjjTidTv72t7+RkJAAwMSJE1m8eDFff/11pQdaWdQnXSpNfi78+T/49RP47SvIyyz6rHZLOP8u6HyrSfJFREREROSMVeUDxwGkpKSQnJxMp06dsNtNhfyqVauIiIigdevWFbnkaaEkXapEbpaZxu3XT2DLd+DMMcdrt4Q+46DlZWoKLyIiIiJyhjotSXqh3bt3Y7PZaNCgwalc5rRRki5VLjsNfpkLi543/dcB4ntB32eh/tleDU1ERERERE6/Kh/d3eVyMW7cOCIjI2nSpAmNGzcmKiqK8ePH43K5KhS0SI0RFGmaut+3FnqOBkegGXhuxkXw6V2QttvbEYqIiIiIiI+qUJL++OOP89prr/Gvf/2LtWvX8vPPPzNhwgReffVVnnjiicqOUaR6CoqEPs/AvWugw3Xm2Pq58GoXWDgOstO9G5+IiIiIiPicCjV3r1+/PtOnT+eqq67yOP7f//6XUaNGsWfPnkoLsLKpubt4zZ5E+O4J2LnM7IfWgQEvQduB3o1LRERERESqVJU3dz948GCpg8O1bt2agwcPVuSSIjVfgy4w/Cu4/gOIaQ6Zf8GHw+DjEZCl3xsREREREalgkt6pUydee+21Esdfe+01OnbseMpBidRYNhu0HgB3L4deD4HNAb9+DFO7we++O3WhiIiIiIicHhVq7r5o0SIGDBhA48aN6d69OzabjeXLl7Nr1y4WLFhAr169qiLWSqHm7uJT9iTCZ3fD/t/Nfqcb4bKJEBzl1bBERERERKTyVHlz94suuogtW7Zw9dVXc/jwYQ4ePMjgwYPZuHEjs2fPrlDQImekBl3grsXQ4z7ABr98AFO7wx/fezsyERERERHxglOeJ724X375hc6dO+N0OivrkpVONenis5J+gvl3w8E/zX7nYdD3OQjScyoiIiIiUp1VeU26iFSBxufDyKXQbRRgg5/fhek9TfIuIiIiIiJnBCXpIr4kIMT0SR/+FUQ1hsNJMLs//Pg8OPO9HZ2IiIiIiFQxJekivii+J4xcBh2HguWEHyfA2wPg0E5vRyYiIiIiIlXI72QKDx48uMzPDx8+fCqxiEhxQREweAY0vxS+HAO7VsL0C+CKl6HDNd6OTkREREREqsBJJemRkZEn/HzYsGGnFJCIHKPjddDoPPjkTti9Cj4ZAVu/h8tfgMBwb0cnIiIiIiKVqFJHd68ONLq7VFvOfFg8CRa/AJYLouNhyFvQsKu3IzuxLd+ZZvut+ns7EhERERGR065aje4+depUmjZtSlBQEF26dGHJkiVllp8zZw6dOnUiJCSEevXqcdttt3HgwIHTFK2IFzn8oPdjMHwBRDaCQzvgrb6w4nXw5Xdth3fBf4bC3JsgI8Xb0YiIiIiI+DSvJunz5s1j9OjRPP7446xdu5ZevXrRv39/kpKSSi2/dOlShg0bxogRI9i4cSMfffQRq1ev5o477jjNkYt4UZPuZqq2doNN7fS3j8End0BuprcjK90v/zE1/5YTti/2djQiIiIiIj7Nq0n65MmTGTFiBHfccQdt2rThlVdeoVGjRkybNq3U8itXriQ+Pp777ruPpk2bcsEFF3DXXXexZs2a0xy5iJcFR8E1s6D/JLD7wa8fw8w+cHBb5d/rj+9hdwV/x1wuWDenaH/bj5USkoiIiIhITeW1JD03N5fExET69u3rcbxv374sX7681HN69OjB7t27WbBgAZZlsW/fPj7++GMGDBhwOkIW8S02G5x/F9z6BYTWhdSNMONi0/+7six6AeYMgXeuhKyDJ39+0nLTLL/Qth99u2m+iIiIiIiXeS1J379/P06nk9jYWI/jsbGxpKSU3m+1R48ezJkzh6FDhxIQEEBcXBxRUVG8+uqrx71PTk4O6enpHotIjdKkB9y1GBqeB9lp8MF18OPzpha7oiwLFo6HH541+3lZkDj75K+z9n2z7nAdOAIhfQ8c2FrxuEREREREajivDxxns9k89i3LKnGs0KZNm7jvvvt48sknSUxM5JtvvmH79u2MHDnyuNefOHEikZGR7qVRo0aVGr+IT4ioB8O/gnPvACz4cQLMvQGOHj75a1kWfPdPWPKi2W/ex6xXvQn5ueW/Tk4GbPqv2T73DmjczWyrybuIiIiIyHF5LUmvXbs2DoejRK15ampqidr1QhMnTqRnz548/PDDdOzYkX79+jF16lRmzZpFcnJyqeeMHTuWtLQ097Jr165K/y4iPsEvAAa8BAOnmlrrLd/Am70heX35r+FywYKHYMVrZv/yF+H6DyAsFjKSYdP88l9r42emBj6mhZnnvdnF5riSdBERERGR4/Jakh4QEECXLl1ISEjwOJ6QkECPHj1KPScrKwu73TNkh8MBmBr40gQGBhIREeGxiNRo59wEI76FyMZmILk3LoQPh504WXc54Yt7YfVMwAZXvQrn3WmS/3PvNGVOZrq3tQUDxp19o+k/X5ikb19i5nwXEREREZESvNrcfcyYMcycOZNZs2axefNmHnjgAZKSktzN18eOHcuwYcPc5a+88ko+/fRTpk2bxrZt21i2bBn33Xcf5513HvXr1/fW1xDxPfXPgb//CG0HApZpdv5GL/hgaOkjtTvz4bO7TB9ymx2ufgM6F/3u0fV28AuC5HWQtPLE99+/FXatNNfqdIM5Vq8TBEVCTpq5joiIiIiIlODVJH3o0KG88sorjBs3jrPPPpvFixezYMECmjRpAkBycrLHnOnDhw9n8uTJvPbaa7Rv355rr72WVq1a8emnn3rrK4j4rtAYuO5duHsFtL/GJMxbvoGZl8C7A2HHUlMuPxc+vg02fGSmc7tmNnQaWvJaHQuOrXz9xPdeVzBgXPNLTX95ALsDml5otrf9cOrfT0RERESkBrJZx2snXkOlp6cTGRlJWlqamr7LmWX/Vlj6MqyfC66C5uaNu5sa8m0/gCPAJPWt+pd+fupmmNrNJPv3rYXo+NLLuZzwcjvTh/3ad6DdoKLPVs+Erx6E+F4w/MvK/HYiIiIiIj7rZPJQr4/uLiKnSe3mMOh1uPdn6DrCJOVJK0yC7hcEN/zn+Ak6QN02cNbfwHLBT28cv9yf/zMJenB0yes1623Wu36C3MxT/04iIiIiIjWMknSRM010E7hiMty/HrrdA/U7w00fm6bpJ9LtHrP++T3ITi+9TPG50f0CPT+r1QwiG4Ez17wgEBERERERD0rSRc5UEfXgsgnw9x+gaa/yndP8EqjdCnIzipLx4rIOwu8LzPY5N5f83GaDZheZ7W2LKha3iIiIiEgNpiRdRMrPZoNuZvYFfppu+p8Xt+EjU0se1wHqdSz9GoVN3jVfuoiIiIhICUrSReTkdLze9Dc/vBN++8rzs8La9bNLqUUvVDjCe8p6yDxQNTGKiIiIiFRTStJF5OQEhJh50wFWTis6nrLBJN52f+hw7fHPD6sLse3N9nY1eRcRERERKU5JuoicvHPvNHOqJy2HvWvNsbVzzLpVfzOvelmaFvZL/7HKQhQRERERqY6UpIvIyYuoB+0Gm+0VUyE/F9bPM/vn3HLi85tdbNZK0kVEREREPChJF5GK6T7KrDd+Comz4ehBCIszc6mfSJMepib+8E44uL1q4xQRERERqUaUpItIxdQ/Bxr3AFc+fPuYOdbpenD4nfjcwDBoeJ7ZVm26iIiIiIibknQRqbjC2nRXvlmXNjf68ajJu4iIiIhICUrSRaTiWl0OUU3MdqPzoXaL8p9bmKRvXwQuV6WHJiIiIiJSHSlJF5GKszvg0qchKAoufPjkzm3QGQLC4eghM3WbiIiIiIgoSReRU9R+MDy6E1r0ObnzHP4Qf4HZVpN3ERERERFASbqIeJP6pYuIiIiIeFCSLiLeU5ikJ62AvOyyy+5YBhvngzO/qqMSEREREfGacsyVJCJSReq0MnOrH0mB3aug6YUlyxxOMlO8bf7C7Mc0h4vHQrvBYNd7RhERERGpWfQ/XBHxHpsNml1kto9t8p6XDYtegNfOMwm6zWEGqDuwFT4ZAdN6wKbPwbJOd9QiIiIiIlVGSbqIeFdp/dK3fAtTu8EPz0L+UWjSE0YugQd+hd7/hMBI+GszfHgLvHGhKa9kXURERERqAJtlnVn/s01PTycyMpK0tDQiIiK8HY6IpO2Bl9uCzQ53/g9+fB62fG0+C68HfZ+F9kNMrXuho4dgxeuwchrkHjHHGnSFvz0OzXp7lhURERER8bKTyUOVpIuI9712LuzfUrRv94Nuo+Ci/4PA8OOfl3kAlv8bfpphatwBzr0DLn9RibqIiIiI+IyTyUPV3F1EvK+wyXvh9t0roO/4shN0gNAY6DMO7v8Fzr/b1MavnglLX65YHC6naTqfkVKx80VERERETpFGdxcR7+v1IORnw1mXQNuBJ18LHh4L/f8FtZrB1w/DwmcgqjF0uKb813Dmw/yRsOEjCK0Lt34BdVufXBwiIiIiIqdIzd2Pw+l0kpeXdxojkzONv78/DofD22HUPN8+DiteA0cA3DIf4nue+Jz8HPj4dvjty6JjIbVh2H8hrn2VhSoiIiIiZwb1SS/DiX44lmWRkpLC4cOHT39wcsaJiooiLi4Om/pPVx6XCz66FTZ/bqZsG5EAdVoev3xuFsy7Gf5cCI5AuGoKrJwKyb9AcC0YNh/qdTpd0YuIiIhIDaQkvQwn+uEkJydz+PBh6tatS0hIiJInqRKWZZGVlUVqaipRUVHUq1fP2yHVLHlH4Z0rYfdqiGoCd3wPYXVLlsvJgA+Gws5l4B8CN/zH9Ik/egjeGwx7f4agSFMj36Dz6f4WIiIiIlJDKEkvQ1k/HKfTyZYtW6hbty4xMTFeilDOJAcOHCA1NZWWLVuq6Xtly9wPMy+FQ9uhQRe49UsICCn6/OgheH8I7EmEwAi46SNo3K3o8+w0eP8a2L3KfH7zp9Do3NP/PURERESk2qtWo7tPnTqVpk2bEhQURJcuXViyZMlxyw4fPhybzVZiadeuXaXEUtgHPSQk5AQlRSpH4bOm8Q+qQGhtuOljCI42ifgnd5jR2wGO/AVvX2mOB0fDrZ97JuhQUIP+KTTuATnp8N7VsHPF6f8eIiIiInJG8WqSPm/ePEaPHs3jjz/O2rVr6dWrF/379ycpKanU8v/+979JTk52L7t27aJWrVpce+21lRqXmrjL6aJnrYrVbg43zDV9zX//Cr59DNL3wuz+sG+DGcV9+AKof07p5weGw80fQ3wvyM0wNe87lp7e7yAiIiIiZxSvJumTJ09mxIgR3HHHHbRp04ZXXnmFRo0aMW3atFLLR0ZGEhcX517WrFnDoUOHuO22205z5CJSbTTuBldPN9s/TYep3eHAHxDREG7/BmLbln1+QCjc+KHpq56XaZrAb/uxqqMWERERkTOU15L03NxcEhMT6du3r8fxvn37snz58nJd46233uLSSy+lSZMmxy2Tk5NDenq6xyKnn81mY/78+d4OQ85U7QfDpc+Y7ezDEN0Ubv8aYs4q3/kBIXDDPGh+KeQfhTnXwXf/NP3eRUREREQqkdeS9P379+N0OomNjfU4HhsbS0pKygnPT05O5uuvv+aOO+4os9zEiROJjIx0L40aNTqluH1Zamoqd911F40bNyYwMJC4uDj69evHihXqRytCz/vhkiehzZVw29cQ1fjkzvcPgus/gNZXgDMHlr8Kr3SEhePMIHQiIiIiIpXAz9sBHNsn17KscvXTffvtt4mKimLQoEFllhs7dixjxoxx76enp9fYRH3IkCHk5eXxzjvv0KxZM/bt28fChQs5ePCgt0M7Zbm5uQQEBHg7DKnObDbo9eCpXcMvEIa+D398B/97FlLWw5KXYNVM6H4PdLsbgsoerVNEREREpCxeq0mvXbs2DoejRK15ampqidr1Y1mWxaxZs7jllltOmLgFBgYSERHhsZwMy7LIys33ynIys+MdPnyYpUuX8vzzz9O7d2+aNGnCeeedx9ixYxkwYABgxgDo0KEDoaGhNGrUiFGjRnHkyBH3NQpffHz55Ze0atWKkJAQrrnmGjIzM3nnnXeIj48nOjqae++9F6fT6T4vPj6e8ePHc+ONNxIWFkb9+vV59dVXy4x3z549DB06lOjoaGJiYhg4cCA7duxwfz58+HAGDRrExIkTqV+/Pi1btiz3z0KkStls0LIf3LXYJOx120JOGvw4Af7dEZZMhtxMb0cpIiIiItWU12rSAwIC6NKlCwkJCVx99dXu4wkJCQwcOLDMcxctWsTWrVsZMWJEVYfJ0TwnbZ/8tsrvU5pN4/oRElC+P6KwsDDCwsKYP38+3bp1IzAwsEQZu93OlClTiI+PZ/v27YwaNYr/+7//Y+rUqe4yWVlZTJkyhblz55KRkcHgwYMZPHgwUVFRLFiwgG3btjFkyBAuuOAChg4d6j7vhRde4LHHHuPpp5/m22+/5YEHHqB169b06dOnRBxZWVn07t2bXr16sXjxYvz8/Hj22We57LLLWL9+vfvFy8KFC4mIiCAhIeGkXliInBY2m2k632oAbPwUfvyXGZBu4TOw4nXoPgrOGQZhdbwdqYiIiIhUI15t7j5mzBhuueUWunbtSvfu3ZkxYwZJSUmMHDkSME3V9+zZw7vvvutx3ltvvcX5559P+/btvRG2T/Lz8+Ptt9/mzjvvZPr06XTu3JmLLrqI66+/no4dOwIwevRod/mmTZsyfvx47r77bo8kPS8vj2nTpnHWWWZArWuuuYb33nuPffv2ERYWRtu2benduzc//PCDR5Les2dPHn30UQBatmzJsmXLePnll0tN0ufOnYvdbmfmzJnurg2zZ88mKiqKH3/80T2YYGhoKDNnzlQzd/Ftdjt0uAbaDoINH8Gif8GhHaav+g8Toe1V0PV2aNLTJPYiIiIiImXwapI+dOhQDhw4wLhx40hOTqZ9+/YsWLDAPVp7cnJyiTnT09LS+OSTT/j3v/99WmIM9newaVy/03Kv0u59MoYMGcKAAQNYsmQJK1as4JtvvmHSpEnMnDmT4cOH88MPPzBhwgQ2bdpEeno6+fn5ZGdnk5mZSWhoKAAhISHuBB3MQH7x8fGEhYV5HEtNTfW4d/fu3Uvsv/LKK6XGmZiYyNatWwkPD/c4np2dzZ9//une79ChgxJ0qT4cfnD2DSZh3/ARrH4L9qyBXz8xS+1WJlnvdD0ER53avSwLsg7A0cNmhHol/yIiIiI1htcHjhs1ahSjRo0q9bO33367xLHIyEiysrKqOKoiNput3E3OfUFQUBB9+vShT58+PPnkk9xxxx089dRT9O7dm8svv5yRI0cyfvx4atWqxdKlSxkxYgR5eXnu8/39/T2uZ7PZSj3mcrlOGMvxBgB0uVx06dKFOXPmlPisTp2ipsGFLw5EqhWHP5x9o1mSf4E1s2D9R7D/d/jmEdMcvv0Q6HQDhNYBvwDwCwJHgBmYzhFoEn6AnAw48Ccc2Fq0Pliwzk4zZWI7QI97zTRzDv/jxyUiIiIi1UL1yT6lQtq2bcv8+fNZs2YN+fn5vPTSS9jtZrzADz/8sNLus3LlyhL7rVu3LrVs586dmTdvHnXr1j3pgfxEqpV6neDKf0OfcbD+Q5Owp26Cte+Z5XhsDpO05x8t4+I2sPvBvg3w2d9N8t/tbuh8q0aYFxEREanGlKTXEAcOHODaa6/l9ttvp2PHjoSHh7NmzRomTZrEwIEDOeuss8jPz+fVV1/lyiuvZNmyZUyfPr3S7r9s2TImTZrEoEGDSEhI4KOPPuKrr74qtexNN93ECy+8wMCBAxk3bhwNGzYkKSmJTz/9lIcffpiGDRtWWlwiPiEoEs67E869A3b9ZJrC71gCeUfBmQv52WAVa51iOYsS9NA6ENPcNGuvdVbBdnOo1dScv+Yt+GkGpO+B7/4Ji16ArsPh/JEQUd8rX1dEREREKk5Jeg0RFhbG+eefz8svv8yff/5JXl4ejRo14s477+Sxxx4jODiYyZMn8/zzzzN27FguvPBCJk6cyLBhwyrl/g8++CCJiYk888wzhIeH89JLL9GvX+l9+UNCQli8eDGPPPIIgwcPJiMjgwYNGnDJJZeoZl1qNpsNGnczy7Gc+eDMgfyCxZkDQVFl91/3D4YLH4bu98L6ebD8VTPC/LJ/w4qp0OFaaDsQAsMhIBQCwgrWBYv95Ma9EBEREZGqZ7POsLmt0tPTiYyMJC0trURCmJ2dzfbt22natClBQUFeirD6iY+PZ/To0R6jx0v56JmTSuVywR/fwrIpkLT8xOX9gk0CX6cVxHWEeh0hrgPUbqn+7SIiIiKVqKw89FiqSRcRqSnsdmjV3yy718BP02H/FsjNhNysgnVGUdP6/KNmyUw1ze8LOQIhtq1J2OM6mub1obUhJMYsfoHe+X4iIiIiZwAl6SIiNVHDrtBwZsnjlmWa0+dmQu4RM5Xbvo2QsgFS1kPKryaR37vWLKUJCIeQWiZhL0zeIxtBdHzREl7PvDQQERERkZOiJF1O2Y4dO7wdgoiUl80G/kFmCY2B6CbQoHPR5y4XHNpelLQnr4e0XSaZzzpoBrXLzTDL4Z3Hv48jAKKaFCXtce2h3dVmED0REREROS4l6SIiUsRuNyPJx5wF7QZ5fuZyQU6aSdazDhQtR1LhcBIc2mGWtF1m1PoDf5il0NePmjniu94GDbqYFwYiIiIi4kFJuoiIlI/dDsHRZok56/jlnPlmSrjCpP3Qdvj9a/jrN1j3vlli20OX4WYE+rJGsHc5TdK/f6vpO4+tILm3gc1elOjb7Gap3QLqtFFTexEREam2lKSLiEjlcviZZvTRTYCLzLFLnjJzxCe+DRs/g32/woKH4LsnTO36OTeD3c/UvO8vqIHfvxUObjPT0Z2MoCgzzV2THtC4B9TrBH4BJctZlnmJkLwOkn8xy76NZsT72PamiX5cR7MdUV81/yIiInJaaAq2YjQdlpxueubkjHT0EPwyDxJnm9r1E3EEQK2zihJly2USbKxi25gB8fZthLxMz/P9gs1Aek16mP7x+zYWJOXrTfP98giOLkjcC6apa3w+RDdV4i4iIiLloinYRETEdwVHQ7eRcP5dsGuVqV3f/IWpwa7dHGJamGbrMS1Ms/qoxmB3lO/aznxI+QV2roCkFbBzORw9aKaYKz7NXCFHAMS2M7Xt9TpBbAfISTc1/SkbzGj3+7eYFwvHXiO0rqmxb9zdrOM6mlYEIiIiIqdANenFqFZTTjc9cyJVzOUySXbScpO4p+81c8AXJuV1WoPDv+xr5GWbGv/CxL1wejpnrmc5/1BTY9+4OzS7GBqdr77xIiIiAqgmXXyQzWbjs88+Y9CgQZV63aeffpr58+ezbt06r1wjPj6e0aNHM3r06OOWqarvLiLlYLdD3dZm6Xp7xa7hHwT1zzZLobxsk6gnrYCklaa/ffZh2L7ILIv+BWFx0HagGSW/UTcl7CIiIlIuStJriNTUVJ544gm+/vpr9u3bR3R0NJ06deLpp5+me/fu3g6vxlq9ejWhoaHeDkNETjf/IGjS3SxQUGP/u0nadyyDPxLgSAqsesMsSthFRESknJSk1xBDhgwhLy+Pd955h2bNmrFv3z4WLlzIwYMHvR1apcjNzSUgoJTRmb2kMJ46dep4OxQR8QV2O9RtY5aut5tB7Lb9aEay/21ByYS9ZV8zmn1upllyMoq2czMh9wgERZhB6uI6Fg1Yp1HmRUREajy9yj8RyzrmP06ncSnncAGHDx9m6dKlPP/88/Tu3ZsmTZpw3nnnMXbsWAYMGOAuN3nyZDp06EBoaCiNGjVi1KhRHDlyxP3522+/TVRUFF9++SWtWrUiJCSEa665hszMTN555x3i4+OJjo7m3nvvxel0us+Lj49n/Pjx3HjjjYSFhVG/fn1effXVMmPes2cPQ4cOJTo6mpiYGAYOHMiOHTvcnw8fPpxBgwYxceJE6tevT8uWLcu83nvvvUd8fDyRkZFcf/31ZGRkAPDuu+8SExNDTo7nFE5Dhgxh2LBhHsfeeOMNGjVqREhICNdeey2HDx8+YTzx8fG88sor7nJ//PEHF154IUFBQbRt25aEhIQy4xaRGsovEFr2g6unw8N/wI0fQqcbIDDSJOw/vwtrZsH6efDbl6aJ/J418NdmSEsyg90d2mEG1PvhOfjPUHi5LbxwFrw70Exdt/5D2LUaMlJMTX555GbB3nXm3IXj4OMR8M1jJpbti02f/TNrqBoRERGfo5r0E8nLggn1vXPvx/ZCwImbUoeFhREWFsb8+fPp1q0bgYGBpZaz2+1MmTKF+Ph4tm/fzqhRo/i///s/pk6d6i6TlZXFlClTmDt3LhkZGQwePJjBgwcTFRXFggUL2LZtG0OGDOGCCy5g6NCh7vNeeOEFHnvsMZ5++mm+/fZbHnjgAVq3bk2fPn1KxJGVlUXv3r3p1asXixcvxs/Pj2effZbLLruM9evXu2vMFy5cSEREBAkJCZQ1vuGff/7J/Pnz+fLLLzl06BDXXXcd//rXv3juuee49tprue+++/j888+59tprAdi/fz9ffvkl33zzjfsaW7du5cMPP+SLL74gPT2dESNGcM899zBnzhx3mRPF43K5GDx4MLVr12blypWkp6eX2VddRM4QhQl7y35FNexJK8ARaP6ODwyDgDCzHVC4HQJHUgtGmF9v1n/9DlkHzPnbfvS8hyMAIhpAVCOIbFywbgSW05z31++mOf7hpBPH6x9qRtWv3QJimpvR75teaEblF6mOnHnwv2fh53egy3C4eKz5vRQR8VFK0msAPz8/3n77be68806mT59O586dueiii7j++uvp2LGju1zxhLFp06aMHz+eu+++2yNJz8vLY9q0aZx11lkAXHPNNbz33nvs27ePsLAw2rZtS+/evfnhhx88kvSePXvy6KOPAtCyZUuWLVvGyy+/XGqSPnfuXOx2OzNnzsRW0Gxz9uzZREVF8eOPP9K3b18AQkNDmTlz5gmbubtcLt5++23Cw8MBuOWWW1i4cCHPPfccwcHB3HjjjcyePdudpM+ZM4eGDRty8cUXu6+RnZ3NO++8Q8OGDQF49dVXGTBgAC+99BJxcXHliuf7779n8+bN7Nixw32dCRMm0L9//zLjF5EzSPGE/UTqtoFmFxXt52VD6qaCxH2D2T6cBOl7zEjzh7ab5URCYsyo9rVbmnnjM/+C/X/Aga2m9j4vs+DFwPqic2wOaHgutLgUml8KcZ3Ur16qh8NJ8PHtsHu12V/6Mmz5FgZN8xwMUkTEhyhJPxH/EFOj7a17l9OQIUMYMGAAS5YsYcWKFXzzzTdMmjSJmTNnMnz4cAB++OEHJkyYwKZNm0hPTyc/P5/s7GwyMzPdg5+FhIS4E3SA2NhY4uPjCQsL8ziWmprqcf9jB6fr3r27RzPw4hITE9m6das7qS6UnZ3Nn3/+6d7v0KFDufqhx8fHe1yrXr16HvHdeeednHvuuezZs4cGDRowe/Zshg8f7n5BANC4cWN3Yl0Yv8vl4vfff3cn6SeKZ/PmzaVeR0SkUvgHQYPOZinOmQ8Ze+HwLkjbVbBOMmubDWq3gjotCxLzVhAac/x75OfC4Z1FSfuBPyDpJ1MLv2ulWf73rJkjvvklJmE/628QUqtqv7tIRfz2FcwfZWZeCIyE7qNg1ZvmBdfMS6DXQ9DrQfA7iTFvstMgcz8cPQRZB03XlGO3g2tB51vMNI8iIhWgJP1EbLZyNTn3BUFBQfTp04c+ffrw5JNPcscdd/DUU08xfPhwdu7cyeWXX87IkSMZP348tWrVYunSpYwYMYK8vDz3Nfz9PecLttlspR5zlaP/o+04gxu5XC66dOni0ZS8UPGB2Mo7avqJ4jvnnHPo1KkT7777Lv369WPDhg188cUX5Yq9+Hc4UTylNYE/3s9ARKTSOPwgqrFZTpVfgGnmXruF5/HDSbD1e/jje9N/PjMVfvmPWbCBXxDYHWaxHbO2O0zT/uDooiWklud+cBTUOsvU7J+Jf2/m55gXI6mbTQKZutn8TC4YY16wVGcup+m6EVobHP4nLl8Z8nMh4Un4aZrZb9AFrpllnq9z74CvxsCm/5qpEn//Cq5+w3TrOJ5DO035TfNhT2L5Ylj9JjQ6H86908zscDIvAkTkjKckvQZr27Yt8+fPB2DNmjXk5+fz0ksvYS9oovjhhx9W2r1WrlxZYr9169allu3cuTPz5s2jbt26REREVFoMZbnjjjt4+eWX2bNnD5deeimNGjXy+DwpKYm9e/dSv74Zf2DFihXY7fYTDlhXXNu2bUu9johItRfV2Ixa3/V2kwDtWmmmmdu6EFI3Qv7RyrlPYKQZxb5ewYj29TqZZvmOGvTflbQ9JtErnpAf2GrGDzjW+g+h621w0aMQVs1mE8lOg5/fMzMaHE4Cmx3C65mxEgrHTCg+hkKtZpWTxB/cDh/fBnvXmv3u/4BLnipKkkNrw3Xvwq+fwFcPmq4jb1wEFz8KPUcXPWuHdpjEfON82Puz5z0CwiGk8AVTrWIvnWqZlyt7fjYJ/a6fzPLtY9DlVuhyG0Q2OPXvWJ0488xsFRrTQuSk1KB/9c5cBw4c4Nprr+X222+nY8eOhIeHs2bNGiZNmsTAgQMBOOuss8jPz+fVV1/lyiuvZNmyZUyfPr3SYli2bBmTJk1i0KBBJCQk8NFHH/HVV1+VWvamm27ihRdeYODAgYwbN46GDRuSlJTEp59+ysMPP+zRXLyy3HTTTTz00EO8+eabvPvuuyU+DwoK4tZbb+XFF18kPT2d++67j+uuu87d1L08Lr30Ulq1asWwYcN46aWXSE9P5/HHH6/MryEi4n1+AWYguaYXQt/xkHnA/CfccppR5i0nuPJNDarlNOu8o6bJ8dFDxZoGHyq2f8AkqjlpsHOpWdz3C4K6bU2ybrOD5TILVsG2VbQfXAuimxS0LIg32yEx3qudz881SeDuVQUJ22pI31162aBI8z3rtjFdE7b9CL8vgNUz4Zd50OsB6DYK/INP61c4aQe3wU9vwNr3zXNRyHKZ8RPS95iXPMcKjobWA6Dt1WYshook7Bvnw+f3Qk46BEWZ2RVaHWdcmPZDoMkF8OVo83P+33izbj3AzKpQmOSDee6a9DQ14m2uhPBy/N8g4zlIfAcSZ0NGMix+AZZMNtc/946SrVWOZfc30zCeygB3lmV+79IKfu5puwvWBfsZKeaFWM/RZn0q98lINr/D+/+AA38WdJcpGOfCcpquMb0egibqBihSHkrSa4CwsDDOP/98Xn75Zf7880/y8vJo1KgRd955J4899hgAZ599NpMnT+b5559n7NixXHjhhUycOLHENGQV9eCDD5KYmMgzzzxDeHg4L730Ev36lT4wUkhICIsXL+aRRx5h8ODBZGRk0KBBAy655JIqq1mPiIhgyJAhfPXVVwwaNKjE582bN2fw4MFcfvnlHDx4kMsvv9xjQL3ysNvtfPbZZ4wYMYLzzjuP+Ph4pkyZwmWXXVZJ30JExAeFxpTdz7288nNN3/fkgkHrkgtGtc/NMDWZx9Zmlpd/aEHi3sTU2IbVNX3qw+pCaB2zhNUtO/m1LPPiIT/bDOCXf9Ss87IKjmUV7ecdhf1bzEBle9eaz4uz2aFuO9NioG4biG1rkvPwep4vE86/C7Yvge/+CcnrzJR5q9+CS56EDteVPnCfy2mS5JQNsO9Xk4wVxpR3tCDuo0XxOnNMk+wuw6F5n4q3WLAs2LEUVk4ziS4F3b/qtIZud5t4czIKxkxIMot7/IRdpjn50UMmsV/7flHC3u5qaHqchN2yTJK5v2D2gqSVsPFT81mj82HIW+bPuyzhsXD9B2YqxAX/Z1o4FDZnL0zM2w2CNleZZ+RkhMfBxY9ArzFmmsVVb8LOZbD5c7OUl18QBEaYlzhBBevACLPtchX78y3251y4nZ1mBoIsy4E/TKuC5n1MrE16lC+ujH2w8TOzpGw48X22fm+WJhfAhQ9Cs97lf3nmzDdjZYTHnXwX1KyD5vdw71rzexkdDw26mi4QlfH3lkgVsVllzW1VA6WnpxMZGUlaWlqJhDA7O5vt27fTtGlTgoKCvBRh9RMfH8/o0aN9frqxPn360KZNG6ZMmeLtUNz0zImIlMHlMiPWp6w3iZzNBthMAmUrWGMr+s9+5l+m3OEk85/6jOTy3ysgzCTsjgCTWOfnFK2dOQW19RUQHA0Nz4NG55p1gy5m2r3ycrlMErXwGZPQgukKcOnTJuaU9SYhT/nVNJ/Py6pYnOH14ZybzYBn5RnfwLLMC4EdS03/65QNRZ8172OS87P+Vr5EzOU0CezGz0wtduZfRZ8FR0PrK6DZxebP86/f4K8tJjHPSSt5rZ6j4W//PPma+PS9sHC8uXfry6H1lZXfxWDfRpOs//rpiZNaV37l3TckxkzRGNmwYN0AIhqapvm/zDUvNwqf70bdzGB6LfqU/LPLTjcvHNZ/aMamKP47YXOYl2ExBVM3Fp/GMe8oLPs3rPsAXAXjINXvDBc+DC0vK/nCyeU0z/X2JbBjCexcYV7WgXmZFdPcdI8ovE9Mc5N8O3Nh77qCpPxnsz604/g/l+IJe8Ou5vfK/xT+L+bMM79/dn8zjabIMcrKQ4+lJL0YJUwV4+tJ+sGDB/nuu++46aab2LRpE61atfJ2SG565kREqlBetmnie3iHSd7TdptB74785bl25p7cdf2CzOIfYv5T7x9SsB9sloj6BYn5+SaJqIzm9nnZ8NN0WPKSac593NiCTe18bHtzb/+QUuIMMXG68k2Ctu4D0+UAAJtpmtxluJkqsDDZPXrI1DLvTjStBPYkmtHMi9+30/UmOa9zCv/OlpWwH8tmh+impsa+Tkto0a/mNKd2OU3rg5x0UyOeXbAuvu/wK/qz9HgGC44Fhptn8URdJA5ug2VTYN2cot+F2A5wwWhodTn8+T/Y8KGZuq54y5AGXaHDteZlTHT8iQfHS9sDy1+FxLeLxrGo287U4NdpVSwpX2a+Y3F2/6IEvzQ2u3lxRClpTa1mUP8c85wc+NM8uwf+KFnOXjAIp93PLDaHeYHg3i4YENOZA7lZx7ReyCz2YsVmWskUJv8Nupp9u6Psnw9AzhHz91Repnn5EBR54nOk2qhWSfrUqVN54YUXSE5Opl27drzyyiv06tXruOVzcnIYN24c77//PikpKTRs2JDHH3+c22+/vVz3U5Je+Xw9SY+Pj+fQoUM88cQTPPTQQ94Ox4OeORERL7Msk/gUJuyWyyQ7joCCZDzQc+0I8O4c8ZkHYNHz8PO7ppY5rr1JyOM6mKVWs/IlA8Xl55ga0sR3TA1pobA4k/Sm/Fp6UuMIMIP7tepvBkWr7Kn4nPkmYds039SQRjUuSMhbmSWm+an12RZP6cmw8nVYPauopt/m8BzUMKYFdLzO9OmPOav065zIkb/MfVbNLKohP1ZghGl6H38BxPcyz3Z2mnmhcGBrUb/3g3+a7cLxDyIaQoNzTFJevzPUP7v0QeuOHjI17bsLujjsWVP2C6FTFRBmYmrQxSyufNMyJm13wVLQ/SP7sOd5EQ0Knvc2ULe1WddpZbo7HMuZb/7c8o5Cbqb5uy2qUfX9HbEs83dTbmbBSxCn5zgkxZfCMUpqt/TpcTuqTZI+b948brnlFqZOnUrPnj154403mDlzJps2baJx49KbWg0cOJB9+/bx7LPP0rx5c1JTU8nPz6dHj/L1oVGSLr5Ez5yIiFSIZVXNgHgH/jQvANbNKZm01GpmagUbFiyxHTS1WE2UddA0y/9pmklmw+uZpLzjdaZJeGU9d0cPmfusnGaSsSbdTULetBfEdSr/GAmWBUf2mRcKFe2iYFmmm0z63mMGv3SVHAizeIuFgJBirRkKjh09XNDqZLVJ/vesPf7LiNIERZqWKUdSjl8moqFpHVOYkOdlld4iyGY343HUblnU/aB2C/OypXCchcy/TLeA0paMFPOdAsNMy4yAgnXxbZutqFVBiRYGBeN22Bzmz9Pub1rn2P0K1v7muGWZFy25mWbJOVI0IOnJGLXStFrwUdUmST///PPp3Lkz06ZNcx9r06YNgwYNYuLEiSXKf/PNN1x//fVs27aNWrUq9rZWSbr4Ej1zIiLik/JzYcvXZrTuuI4aaOtMlJtpanljmp9864yTUVgzWpX38CaXs2gwyd1rIPkXk/hGNixaohoXjRlQWEt+9LAZe+GvzZD6W8F4DL+deKwNm90MmGm5yh77IDDSdCGo6DgWp5NfUEG3A/sxY5PY8RijZNh/T627TRU7mSTda6O75+bmkpiYyKOPPupxvG/fvixfvrzUcz7//HO6du3KpEmTeO+99wgNDeWqq65i/PjxBAdXXtOGM6ybvniRnjUREfFJfgFmyjE5cwWEnp6Ex2YzNa01ld1hanfrtoHOJzGrUnAUND7fLMUdPWRenjnzCmryQ00tfkBowdgEgeZnWtjCYP+WgqnxCqfI+8OM0eEeeNFmXhBEx5vB/6LjzVgP0fGmFUV+tqnVzskwNdw5GaZlQE7BYlnHtCgILWpZEBBq4nG5zAsBZ17R2r2db+INDDflA0JNLX1AWNF+TX2BUwavJen79+/H6XQSGxvrcTw2NpaUlNKbd2zbto2lS5cSFBTEZ599xv79+xk1ahQHDx5k1qxZpZ6Tk5NDTk6Oez89/fiDrfj7m8FRsrKyKjXpFzmerCzz9rLw2RMREREROa7gaGh03onL2Wxm2rrwOGh6oednedlm5gy7f/Xut16DeX2edNsx/VosyypxrJDL5cJmszFnzhwiI81oh5MnT+aaa67h9ddfLzWxnjhxIs8880y5YnE4HERFRZGamgqY+byPF4vIqbAsi6ysLFJTU4mKisLhOPPeEIqIiIiIF/gH+XTfbfFikl67dm0cDkeJWvPU1NQSteuF6tWrR4MGDdwJOpg+7JZlsXv3blq0aFHinLFjxzJmzBj3fnp6Oo0aNTpuXHFxce44RKpaVFSU+5kTERERERHxWpIeEBBAly5dSEhI4Oqrr3YfT0hIYODA0vtA9ezZk48++ogjR44QFhYGwJYtW7Db7TRs2LDUcwIDAwkMLH8TDpvNRr169ahbty55eWXMxyhyivz9/VWDLiIiIiIiHrza3H3MmDHccsstdO3ale7duzNjxgySkpIYOXIkYGrB9+zZw7vvvgvAjTfeyPjx47ntttt45pln2L9/Pw8//DC33357pfchdzgcSqBERERERETktPJqkj506FAOHDjAuHHjSE5Opn379ixYsIAmTZoAkJycTFJSkrt8WFgYCQkJ3HvvvXTt2pWYmBiuu+46nn32WW99BREREREREZFK49V50r3hZOanExERERERETlVJ5OH2k9TTCIiIiIiIiJyAl6fgu10K2w4UNZ86SIiIiIiIiKVpTD/LE9D9jMuSc/IyAAocxo2ERERERERkcqWkZHhMaV4ac64Pukul4u9e/cSHh6OzWbzdjhlKpzTfdeuXeo/Lz5Lz6lUF3pWpbrQsyrVhZ5VqS584Vm1LIuMjAzq16+P3V52r/Mzria9rDnVfVVERIT+4hOfp+dUqgs9q1Jd6FmV6kLPqlQX3n5WT1SDXkgDx4mIiIiIiIj4CCXpIiIiIiIiIj5CSboPCwwM5KmnniIwMNDboYgcl55TqS70rEp1oWdVqgs9q1JdVLdn9YwbOE5ERERERETEV6kmXURERERERMRHKEkXERERERER8RFK0kVERERERER8hJJ0ERERERERER+hJN1HTZ06laZNmxIUFESXLl1YsmSJt0OSM9zEiRM599xzCQ8Pp27dugwaNIjff//do4xlWTz99NPUr1+f4OBgLr74YjZu3OiliEXMc2uz2Rg9erT7mJ5T8RV79uzh5ptvJiYmhpCQEM4++2wSExPdn+tZFV+Qn5/PP//5T5o2bUpwcDDNmjVj3LhxuFwudxk9q+INixcv5sorr6R+/frYbDbmz5/v8Xl5nsucnBzuvfdeateuTWhoKFdddRW7d+8+jd+idErSfdC8efMYPXo0jz/+OGvXrqVXr17079+fpKQkb4cmZ7BFixZxzz33sHLlShISEsjPz6dv375kZma6y0yaNInJkyfz2muvsXr1auLi4ujTpw8ZGRlejFzOVKtXr2bGjBl07NjR47ieU/EFhw4domfPnvj7+/P111+zadMmXnrpJaKiotxl9KyKL3j++eeZPn06r732Gps3b2bSpEm88MILvPrqq+4yelbFGzIzM+nUqROvvfZaqZ+X57kcPXo0n332GXPnzmXp0qUcOXKEK664AqfTebq+Ruks8TnnnXeeNXLkSI9jrVu3th599FEvRSRSUmpqqgVYixYtsizLslwulxUXF2f961//cpfJzs62IiMjrenTp3srTDlDZWRkWC1atLASEhKsiy66yLr//vsty9JzKr7jkUcesS644ILjfq5nVXzFgAEDrNtvv93j2ODBg62bb77Zsiw9q+IbAOuzzz5z75fnuTx8+LDl7+9vzZ07111mz549lt1ut7755pvTFntpVJPuY3Jzc0lMTKRv374ex/v27cvy5cu9FJVISWlpaQDUqlULgO3bt5OSkuLx7AYGBnLRRRfp2ZXT7p577mHAgAFceumlHsf1nIqv+Pzzz+natSvXXnstdevW5ZxzzuHNN990f65nVXzFBRdcwMKFC9myZQsAv/zyC0uXLuXyyy8H9KyKbyrPc5mYmEheXp5Hmfr169O+fXuvP7t+Xr27lLB//36cTiexsbEex2NjY0lJSfFSVCKeLMtizJgxXHDBBbRv3x7A/XyW9uzu3LnztMcoZ665c+fy888/s3r16hKf6TkVX7Ft2zamTZvGmDFjeOyxx1i1ahX33XcfgYGBDBs2TM+q+IxHHnmEtLQ0WrdujcPhwOl08txzz3HDDTcA+ntVfFN5nsuUlBQCAgKIjo4uUcbbeZeSdB9ls9k89i3LKnFMxFv+8Y9/sH79epYuXVriMz274k27du3i/vvv57vvviMoKOi45fScire5XC66du3KhAkTADjnnHPYuHEj06ZNY9iwYe5yelbF2+bNm8f777/PBx98QLt27Vi3bh2jR4+mfv363Hrrre5yelbFF1XkufSFZ1fN3X1M7dq1cTgcJd7epKamlngTJOIN9957L59//jk//PADDRs2dB+Pi4sD0LMrXpWYmEhqaipdunTBz88PPz8/Fi1axJQpU/Dz83M/i3pOxdvq1atH27ZtPY61adPGPUis/k4VX/Hwww/z6KOPcv3119OhQwduueUWHnjgASZOnAjoWRXfVJ7nMi4ujtzcXA4dOnTcMt6iJN3HBAQE0KVLFxISEjyOJyQk0KNHDy9FJWLeKv7jH//g008/5X//+x9Nmzb1+Lxp06bExcV5PLu5ubksWrRIz66cNpdccgkbNmxg3bp17qVr167cdNNNrFu3jmbNmuk5FZ/Qs2fPEtNYbtmyhSZNmgD6O1V8R1ZWFna7Z8rgcDjcU7DpWRVfVJ7nskuXLvj7+3uUSU5O5tdff/X6s6vm7j5ozJgx3HLLLXTt2pXu3bszY8YMkpKSGDlypLdDkzPYPffcwwcffMB///tfwsPD3W8mIyMjCQ4Ods9FPWHCBFq0aEGLFi2YMGECISEh3HjjjV6OXs4U4eHh7nESCoWGhhITE+M+rudUfMEDDzxAjx49mDBhAtdddx2rVq1ixowZzJgxA0B/p4rPuPLKK3nuuedo3Lgx7dq1Y+3atUyePJnbb78d0LMq3nPkyBG2bt3q3t++fTvr1q2jVq1aNG7c+ITPZWRkJCNGjODBBx8kJiaGWrVq8dBDD9GhQ4cSA8+edl4bV17K9Prrr1tNmjSxAgICrM6dO7unuRLxFqDUZfbs2e4yLpfLeuqpp6y4uDgrMDDQuvDCC60NGzZ4L2gRy/KYgs2y9JyK7/jiiy+s9u3bW4GBgVbr1q2tGTNmeHyuZ1V8QXp6unX//fdbjRs3toKCgqxmzZpZjz/+uJWTk+Muo2dVvOGHH34o9f+mt956q2VZ5Xsujx49av3jH/+watWqZQUHB1tXXHGFlZSU5IVv48lmWZblpfcDIiIiIiIiIlKM+qSLiIiIiIiI+Agl6SIiIiIiIiI+Qkm6iIiIiIiIiI9Qki4iIiIiIiLiI5Ski4iIiIiIiPgIJekiIiIiIiIiPkJJuoiIiIiIiIiPUJIuIiIilc5mszF//nxvhyEiIlLtKEkXERGpYYYPH47NZiuxXHbZZd4OTURERE7Az9sBiIiISOW77LLLmD17tsexwMBAL0UjIiIi5aWadBERkRooMDCQuLg4jyU6OhowTdGnTZtG//79CQ4OpmnTpnz00Uce52/YsIG//e1vBAcHExMTw9///neOHDniUWbWrFm0a9eOwMBA6tWrxz/+8Q+Pz/fv38/VV19NSEgILVq04PPPP3d/dujQIW666Sbq1KlDcHAwLVq0KPFSQURE5EykJF1EROQM9MQTTzBkyBB++eUXbr75Zm644QY2b94MQFZWFpdddhnR0dGsXr2ajz76iO+//94jCZ82bRr33HMPf//739mwYQOff/45zZs397jHM888w3XXXcf69eu5/PLLuemmmzh48KD7/ps2beLrr79m8+bNTJs2jdq1a5++H4CIiIiPslmWZXk7CBEREak8w4cP5/333ycoKMjj+COPPMITTzyBzWZj5MiRTJs2zf1Zt27d6Ny5M1OnTuXNN9/kkUceYdeuXYSGhgKwYMECrrzySvbu3UtsbCwNGjTgtttu49lnny01BpvNxj//+U/Gjx8PQGZmJuHh4SxYsIDLLruMq666itq1azNr1qwq+imIiIhUT+qTLiIiUgP17t3bIwkHqFWrlnu7e/fuHp91796ddevWAbB582Y6derkTtABevbsicvl4vfff8dms7F3714uueSSMmPo2LGjezs0NJTw8HBSU1MBuPvuuxkyZAg///wzffv2ZdCgQfTo0aNC31VERKQmUZIuIiJSA4WGhpZofn4iNpsNAMuy3NullQkODi7X9fz9/Uuc63K5AOjfvz87d+7kq6++4vvvv+eSSy7hnnvu4cUXXzypmEVERGoa9UkXERE5A61cubLEfuvWrQFo27Yt69atIzMz0/35smXLsNvttGzZkvDwcOLj41m4cOEpxVCnTh130/xXXnmFGTNmnNL1REREagLVpIuIiNRAOTk5pKSkeBzz8/NzD8720Ucf0bVrVy644ALmzJnDqlWreOuttwC46aabeOqpp7j11lt5+umn+euvv7j33nu55ZZbiI2NBeDpp59m5MiR1K1bl/79+5ORkcGyZcu49957yxXfk08+SZcuXWjXrh05OTl8+eWXtGnTphJ/AiIiItWTknQREZEa6JtvvqFevXoex1q1asVvv/0GmJHX586dy6hRo4iLi2POnDm0bdsWgJCQEL799lvuv/9+zj33XEJCQhgyZAiTJ092X+vWW28lOzubl19+mYceeojatWtzzTXXlDu+gIAAxo4dy44dOwgODqZXr17MnTu3Er65iIhI9abR3UVERM4wNpuNzz77jEGDBnk7FBERETmG+qSLiIiIiIiI+Agl6SIiIiIiIiI+Qn3SRUREzjDq6SYiIuK7VJMuIiIiIiIi4iOUpIuIiIiIiIj4CCXpIiIiIiIiIj5CSbqIiIiIiIiIj1CSLiIiIiIiIuIjlKSLiIiIiIiI+Agl6SIiIiIiIiI+Qkm6iIiIiIiIiI9Qki4iIiIiIiLiI/4f9qONAODdzcMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, sharex=True)\n",
    "ax[0].plot(np.arange(len(loss_train_estimator)), loss_train_estimator, label='Estimator')\n",
    "ax[0].plot(np.arange(len(loss_train_estimator_hybrid)), loss_train_estimator_hybrid, label='Estimator hybrid')\n",
    "ax[0].legend(loc=3)\n",
    "ax[0].set_ylabel('Loss')\n",
    "ax[0].set_title('Estimator models')\n",
    "\n",
    "\n",
    "ax[1].plot(np.arange(len(loss_train_sampler)), loss_train_sampler, label='Sampler')\n",
    "ax[1].plot(np.arange(len(loss_train_sampler_hybrid)), loss_train_sampler_hybrid, label='Sampler hybrid')\n",
    "ax[1].legend(loc=3)\n",
    "ax[1].set_ylabel('Loss')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_title('Sampler models')\n",
    "\n",
    "plt.suptitle('Loss', fontsize=14)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss function during training indicates that most models learn effectively, with the exception of the Estimator model and the Sampler model, which reach a plateau. This suggests that these models may require further investigation, including hyperparameter tuning, to improve their performance and avoid early stagnation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Work\n",
    "\n",
    "Future efforts could focus on hyperparameter tuning to optimise model performance. This includes adjusting the number of neurons, selecting the best ansatz and feature maps, and exploring other parameter settings. In addition, an in-depth analysis of computational costs and run-time performance would provide insight into the practical advantages of quantum classifiers compared to classical models, which would help clarify any genuine advantages of quantum systems for classification tasks."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "QML_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}