5. Model Evaluation and Validation/5.4 Advanced Validation Techniques/5.4.2 Hyperparameter Tuning with Cross-Validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b226b360-58e5-4668-9eda-f88a2c315de7",
   "metadata": {},
   "source": [
    "# 5.4.2 Hyperparameter Tuning with Cross-Validation\n",
    "\n",
    "### Explanation of Hyperparameter Tuning with Cross-Validation\n",
    "\n",
    "Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning model to improve its performance. Hyperparameters are the settings that control the training process of the model and are not learned from the data. Examples include the learning rate in gradient descent, the number of trees in a random forest, and the regularization parameter in logistic regression.\n",
    "\n",
    "**Cross-validation is a technique used to assess the performance of a model by dividing the dataset into multiple subsets (folds)**. The model is trained on some of these subsets and tested on the remaining subsets, which helps in providing a more accurate estimate of the model's performance on unseen data.\n",
    "\n",
    "Combining hyperparameter tuning with cross-validation allows for the evaluation of different hyperparameter settings by training and validating the model multiple times with different subsets of the data. This approach ensures that the selected hyperparameters generalize well to new data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7dc31f-44d5-4dae-af7b-8294c0795cea",
   "metadata": {},
   "source": [
    "### Benefits and Importance of Hyperparameter Tuning\n",
    "\n",
    "1. **Improved Model Performance**: Proper hyperparameter tuning can significantly enhance the accuracy, precision, and overall performance of the model.\n",
    "2. **Generalization**: It helps in finding hyperparameters that generalize well to unseen data, reducing overfitting and underfitting.\n",
    "3. **Model Robustness**: Well-tuned hyperparameters make the model more robust and reliable for making predictions.\n",
    "4. **Optimized Resource Utilization**: Efficient hyperparameter tuning can save computational resources and time by avoiding the training of suboptimal models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6f94da-6023-43f9-8a68-9b5e212ac9f7",
   "metadata": {},
   "source": [
    "### Methods for Implementing Hyperparameter Tuning with Cross-Validation\n",
    "\n",
    "There are several methods for hyperparameter tuning, including:\n",
    "\n",
    "1. **Grid Search**: A brute-force approach where all possible combinations of hyperparameters are exhaustively searched.\n",
    "2. **Random Search**: Random combinations of hyperparameters are sampled and evaluated, which can be more efficient than grid search.\n",
    "3. **Bayesian Optimization**: A probabilistic model is used to select hyperparameters, aiming to find the optimal set more efficiently than grid or random search.\n",
    "4. **Automated Machine Learning (AutoML)**: Automated frameworks that use advanced algorithms to perform hyperparameter tuning with minimal human intervention.\n",
    "\n",
    "\n",
    "**Refer to: [Optimization>Hyperparameter_tuning](https://github.com/AdArya125/Machine-Learning---The-Journey/tree/main/3.%20Optimization/3.3%20Hyperparameter%20Tuning)**\n",
    "\n",
    "These methods can be implemented using cross-validation to ensure the robustness and reliability of the selected hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6547b10-570c-45b4-89f7-52b7e3763dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec635165-26ac-4875-ad81-25a1498b71b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a dataset (e.g., Breast Cancer dataset)\n",
    "data = load_breast_cancer()\n",
    "X, y = data.data, data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20832ba8-d8f9-4a5d-9c86-85cda8136033",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Define the model\n",
    "model = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d067c0f-c8e1-4707-808d-860723a616be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the hyperparameters and their values for grid search\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21a80be0-b636-4a75-8540-d28bf93c7abe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n",
      "Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "# Perform Grid Search with Cross-Validation\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Best hyperparameters from Grid Search\n",
    "best_params = grid_search.best_params_\n",
    "print(f\"Best Hyperparameters: {best_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "611da9bb-3e65-431c-8e61-66b4e76e9bb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9707602339181286\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.94      0.96        63\n",
      "           1       0.96      0.99      0.98       108\n",
      "\n",
      "    accuracy                           0.97       171\n",
      "   macro avg       0.97      0.96      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train the model with the best hyperparameters\n",
    "best_model = grid_search.best_estimator_\n",
    "best_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict and evaluate the model\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da90445e-1f0e-404c-a711-41d445a3d46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeZxO9f//8eeMMTNmxr7MMGGMLbKbbJV1ylaoT7aEhrQhGSkSsmQrosi02EuU0ELChBSVJTvZhpnIWLJkxJiZ9+8Pv7m+LjN8Gp+8rzP1uN9u51ZzrjPX+3ld5zhzrtf1Pu+3lzHGCAAAAAAAALDI29MBAAAAAAAA8O9DUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAA3bebMmfLy8sp0GTBgwC1pc926dXrllVd05syZW/L8/4v092Pjxo2ejnLT3n77bc2cOdPTMQAAwL+Aj6cDAACA7G/48OEqVaqU27pKlSrdkrbWrVunYcOG6bHHHlO+fPluSRv/Zm+//bYKFSqkxx57zNNRAADAPxxFKQAA8D9r3ry5IiIiPB3jf5KUlKTAwEBPx/CYCxcuKCAgwNMxAADAvwi37wEAgFvuq6++0j333KPAwEDlzp1bLVu21M6dO9222bZtmx577DGFh4fL399fISEh6tatm06dOuXa5pVXXlH//v0lSaVKlXLdKnjo0CEdOnRIXl5emd565uXlpVdeecXteby8vLRr1y498sgjyp8/v+6++27X4x988IFq1qypXLlyqUCBAurQoYMSEhJu6rU/9thjCgoKUnx8vO6//34FBQUpNDRUU6ZMkSRt375djRs3VmBgoEqWLKm5c+e6/X76LYHffvutnnzySRUsWFB58uRRly5ddPr06Qztvf3227rjjjvk5+enYsWKqWfPnhludWzYsKEqVaqkTZs2qX79+goICNBLL72ksLAw7dy5U2vWrHG9tw0bNpQk/f7773r++edVuXJlBQUFKU+ePGrevLm2bt3q9tyrV6+Wl5eXPv74Y7366qu67bbb5O/vryZNmmj//v0Z8v74449q0aKF8ufPr8DAQFWpUkWTJk1y22bPnj16+OGHVaBAAfn7+ysiIkKff/652zaXL1/WsGHDVLZsWfn7+6tgwYK6++67tWLFir+0nwAAgH30lAIAAP+zs2fP6uTJk27rChUqJEmaM2eOunbtqqZNm2rs2LG6cOGCpk6dqrvvvls///yzwsLCJEkrVqzQwYMHFRUVpZCQEO3cuVPvvvuudu7cqR9++EFeXl566KGHtHfvXn300Ud64403XG0ULlxYJ06cyHLutm3bqmzZsho1apSMMZKkV199VYMHD1a7du30+OOP68SJE3rrrbdUv359/fzzzzd1y2BqaqqaN2+u+vXra9y4cfrwww/Vq1cvBQYGatCgQerUqZMeeughxcTEqEuXLqpbt26G2yF79eqlfPny6ZVXXtEvv/yiqVOn6vDhw64ikHSl2DZs2DBFRkbq6aefdm23YcMGff/998qZM6fr+U6dOqXmzZurQ4cOevTRRxUcHKyGDRuqd+/eCgoK0qBBgyRJwcHBkqSDBw9q8eLFatu2rUqVKqXExES98847atCggXbt2qVixYq55R0zZoy8vb31/PPP6+zZsxo3bpw6deqkH3/80bXNihUrdP/996to0aLq06ePQkJCtHv3bn355Zfq06ePJGnnzp266667FBoaqgEDBigwMFAff/yx2rRpo08//VQPPvig67WPHj1ajz/+uGrVqqVz585p48aN2rx5s+69994s7zMAAGCBAQAAuEkzZswwkjJdjDHmjz/+MPny5TM9evRw+71jx46ZvHnzuq2/cOFChuf/6KOPjCTz7bffuta99tprRpKJi4tz2zYuLs5IMjNmzMjwPJLM0KFDXT8PHTrUSDIdO3Z02+7QoUMmR44c5tVXX3Vbv337duPj45Nh/fXejw0bNrjWde3a1Ugyo0aNcq07ffq0yZUrl/Hy8jLz5s1zrd+zZ0+GrOnPWbNmTZOcnOxaP27cOCPJfPbZZ8YYY44fP258fX3NfffdZ1JTU13bTZ482Ugy06dPd61r0KCBkWRiYmIyvIY77rjDNGjQIMP6ixcvuj2vMVfecz8/PzN8+HDXulWrVhlJpkKFCubSpUuu9ZMmTTKSzPbt240xxqSkpJhSpUqZkiVLmtOnT7s9b1pamuv/mzRpYipXrmwuXrzo9ni9evVM2bJlXeuqVq1qWrZsmSE3AABwLm7fAwAA/7MpU6ZoxYoVbot0pSfMmTNn1LFjR508edK15MiRQ7Vr19aqVatcz5ErVy7X/1+8eFEnT55UnTp1JEmbN2++Jbmfeuopt58XLlyotLQ0tWvXzi1vSEiIypYt65Y3qx5//HHX/+fLl0/ly5dXYGCg2rVr51pfvnx55cuXTwcPHszw+0888YRbT6enn35aPj4+Wrp0qSRp5cqVSk5O1nPPPSdv7/+7xOvRo4fy5MmjJUuWuD2fn5+foqKi/nJ+Pz8/1/Ompqbq1KlTCgoKUvny5TPdP1FRUfL19XX9fM8990iS67X9/PPPiouL03PPPZeh91l6z6/ff/9d33zzjdq1a6c//vjDtT9OnTqlpk2bat++fTpy5IikK+/pzp07tW/fvr/8mgAAgGdx+x4AAPif1apVK9OBztMLBI0bN8709/LkyeP6/99//13Dhg3TvHnzdPz4cbftzp49+zem/T/X3iK3b98+GWNUtmzZTLe/uiiUFf7+/ipcuLDburx58+q2225zFWCuXp/ZWFHXZgoKClLRokV16NAhSdLhw4clXSlsXc3X11fh4eGux9OFhoa6FY3+m7S0NE2aNElvv/224uLilJqa6nqsYMGCGbYvUaKE28/58+eXJNdrO3DggKQbz9K4f/9+GWM0ePBgDR48ONNtjh8/rtDQUA0fPlytW7dWuXLlVKlSJTVr1kydO3dWlSpV/vJrBAAAdlGUAgAAt0xaWpqkK+NKhYSEZHjcx+f/LkXatWundevWqX///qpWrZqCgoKUlpamZs2auZ7nRq4t7qS7unhyrat7Z6Xn9fLy0ldffaUcOXJk2D4oKOi/5shMZs91o/Xm/49vdStd+9r/m1GjRmnw4MHq1q2bRowYoQIFCsjb21vPPfdcpvvn73ht6c/7/PPPq2nTppluU6ZMGUlS/fr1deDAAX322Wdavny53n//fb3xxhuKiYlx66UGAACcg6IUAAC4ZUqXLi1JKlKkiCIjI6+73enTpxUbG6thw4ZpyJAhrvWZ3Yp1veJTek+ca2eau7aH0H/La4xRqVKlVK5cub/8ezbs27dPjRo1cv18/vx5/fbbb2rRooUkqWTJkpKkX375ReHh4a7tkpOTFRcXd8P3/2rXe38XLFigRo0aadq0aW7rz5w54xpwPivSj40dO3ZcN1v668iZM+dfyl+gQAFFRUUpKipK58+fV/369fXKK69QlAIAwKEYUwoAANwyTZs2VZ48eTRq1Chdvnw5w+PpM+al96q5thfNxIkTM/xOYGCgpIzFpzx58qhQoUL69ttv3da//fbbfznvQw89pBw5cmjYsGEZshhjdOrUqb/8XH+3d9991+09nDp1qlJSUtS8eXNJUmRkpHx9ffXmm2+6ZZ82bZrOnj2rli1b/qV2AgMDM7y30pV9dO178sknn7jGdMqqGjVqqFSpUpo4cWKG9tLbKVKkiBo2bKh33nlHv/32W4bnuHrGxWv3TVBQkMqUKaNLly7dVD4AAHDr0VMKAADcMnny5NHUqVPVuXNn1ahRQx06dFDhwoUVHx+vJUuW6K677tLkyZOVJ08e1a9fX+PGjdPly5cVGhqq5cuXKy4uLsNz1qxZU5I0aNAgdejQQTlz5tQDDzygwMBAPf744xozZowef/xxRURE6Ntvv9XevXv/ct7SpUtr5MiRGjhwoA4dOqQ2bdood+7ciouL06JFi/TEE0/o+eef/9ven6xITk5WkyZN1K5dO/3yyy96++23dffdd6tVq1aSpMKFC2vgwIEaNmyYmjVrplatWrm2u/POO/Xoo4/+pXZq1qypqVOnauTIkSpTpoyKFCmixo0b6/7779fw4cMVFRWlevXqafv27frwww/demVlhbe3t6ZOnaoHHnhA1apVU1RUlIoWLao9e/Zo586d+vrrryVdGUT/7rvvVuXKldWjRw+Fh4crMTFR69ev16+//qqtW7dKkipWrKiGDRuqZs2aKlCggDZu3KgFCxaoV69eN5UPAADcehSlAADALfXII4+oWLFiGjNmjF577TVdunRJoaGhuueee9xmf5s7d6569+6tKVOmyBij++67T1999ZWKFSvm9nx33nmnRowYoZiYGC1btkxpaWmKi4tTYGCghgwZohMnTmjBggX6+OOP1bx5c3311VcqUqTIX847YMAAlStXTm+88YaGDRsmSSpevLjuu+8+VwHIEyZPnqwPP/xQQ4YM0eXLl9WxY0e9+eabbrfbvfLKKypcuLAmT56svn37qkCBAnriiSc0atSovzxI+5AhQ3T48GGNGzdOf/zxhxo0aKDGjRvrpZdeUlJSkubOnav58+erRo0aWrJkiQYMGHDTr6lp06ZatWqVhg0bpvHjxystLU2lS5dWjx49XNtUrFhRGzdu1LBhwzRz5kydOnVKRYoUUfXq1d1u9Xz22Wf1+eefa/ny5bp06ZJKliypkSNHqn///jedDwAA3FpexsZImgAAALgpM2fOVFRUlDZs2JDpDIcAAADZFWNKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsY0wpAAAAAAAAWEdPKQAAAAAAAFhHUQoAAAAAAADW+Xg6wN8hLS1NR48eVe7cueXl5eXpOAAAAAAAAP9axhj98ccfKlasmLy9r98f6h9RlDp69KiKFy/u6RgAAAAAAAD4/xISEnTbbbdd9/F/RFEqd+7ckq682Dx58ng4DQAAAAAAwL/XuXPnVLx4cVe95nr+EUWp9Fv28uTJQ1EKAAAAAADAAf7bEEsMdA4AAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsO6milJTpkxRWFiY/P39Vbt2bf3000/X3Xbnzp36z3/+o7CwMHl5eWnixIk3fO4xY8bIy8tLzz333M1EAwAAAAAAQDaQ5aLU/PnzFR0draFDh2rz5s2qWrWqmjZtquPHj2e6/YULFxQeHq4xY8YoJCTkhs+9YcMGvfPOO6pSpUpWYwEAAAAAACAbyXJRasKECerRo4eioqJUsWJFxcTEKCAgQNOnT890+zvvvFOvvfaaOnToID8/v+s+7/nz59WpUye99957yp8/f1ZjAQAAAAAAIBvJUlEqOTlZmzZtUmRk5P89gbe3IiMjtX79+v8pSM+ePdWyZUu3576eS5cu6dy5c24LAAAAAAAAso8sFaVOnjyp1NRUBQcHu60PDg7WsWPHbjrEvHnztHnzZo0ePfovbT969GjlzZvXtRQvXvym2wYAAAAAAIB9Hp99LyEhQX369NGHH34of3//v/Q7AwcO1NmzZ11LQkLCLU4JAAAAAACAv5NPVjYuVKiQcuTIocTERLf1iYmJ/3UQ8+vZtGmTjh8/rho1arjWpaam6ttvv9XkyZN16dIl5ciRw+13/Pz8bjg+FQAAAAAAAJwtSz2lfH19VbNmTcXGxrrWpaWlKTY2VnXr1r2pAE2aNNH27du1ZcsW1xIREaFOnTppy5YtGQpSAAAAAAAAyP6y1FNKkqKjo9W1a1dFRESoVq1amjhxopKSkhQVFSVJ6tKli0JDQ13jQyUnJ2vXrl2u/z9y5Ii2bNmioKAglSlTRrlz51alSpXc2ggMDFTBggUzrP83CRuwxFpbh8a0tNYWAAAAAACAdBNFqfbt2+vEiRMaMmSIjh07pmrVqmnZsmWuwc/j4+Pl7f1/HbCOHj2q6tWru35+/fXX9frrr6tBgwZavXr1//4KAAAAAAAAkO14GWOMp0P8r86dO6e8efPq7NmzypMnj6fj/C3oKQUAAAAAALKjv1qn8fjsewAAAAAAAPj3oSgFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACs8/F0ADhb2IAlVts7NKal1fYAAAAAAIBn0FMKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHU+N/NLU6ZM0WuvvaZjx46patWqeuutt1SrVq1Mt925c6eGDBmiTZs26fDhw3rjjTf03HPPuW0zevRoLVy4UHv27FGuXLlUr149jR07VuXLl7+ZePiHChuwxFpbh8a0tNYWAAAAAAD/RlnuKTV//nxFR0dr6NCh2rx5s6pWraqmTZvq+PHjmW5/4cIFhYeHa8yYMQoJCcl0mzVr1qhnz5764YcftGLFCl2+fFn33XefkpKSshoPAAAAAAAA2UCWe0pNmDBBPXr0UFRUlCQpJiZGS5Ys0fTp0zVgwIAM299555268847JSnTxyVp2bJlbj/PnDlTRYoU0aZNm1S/fv2sRgQAAAAAAIDDZamnVHJysjZt2qTIyMj/ewJvb0VGRmr9+vV/W6izZ89KkgoUKPC3PScAAAAAAACcI0s9pU6ePKnU1FQFBwe7rQ8ODtaePXv+lkBpaWl67rnndNddd6lSpUqZbnPp0iVdunTJ9fO5c+f+lrYBAAAAAABgh+Nm3+vZs6d27NihefPmXXeb0aNHK2/evK6lePHiFhMCAAAAAADgf5WlnlKFChVSjhw5lJiY6LY+MTHxuoOYZ0WvXr305Zdf6ttvv9Vtt9123e0GDhyo6Oho18/nzp2jMAVrbM4CKDETIAAAAADgnylLPaV8fX1Vs2ZNxcbGutalpaUpNjZWdevWvekQxhj16tVLixYt0jfffKNSpUrdcHs/Pz/lyZPHbQEAAAAAAED2keXZ96Kjo9W1a1dFRESoVq1amjhxopKSklyz8XXp0kWhoaEaPXq0pCuDo+/atcv1/0eOHNGWLVsUFBSkMmXKSLpyy97cuXP12WefKXfu3Dp27JgkKW/evMqVK9ff8kIBAAAAAADgHFkuSrVv314nTpzQkCFDdOzYMVWrVk3Lli1zDX4eHx8vb+//64B19OhRVa9e3fXz66+/rtdff10NGjTQ6tWrJUlTp06VJDVs2NCtrRkzZuixxx7LakQAAAAAAAA4XJaLUtKVsZ969eqV6WPphaZ0YWFhMsbc8Pn+2+MAAAAAAAD4Z3Hc7HsAAAAAAAD456MoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACw7qaKUlOmTFFYWJj8/f1Vu3Zt/fTTT9fddufOnfrPf/6jsLAweXl5aeLEif/zcwIAAAAAACB7y3JRav78+YqOjtbQoUO1efNmVa1aVU2bNtXx48cz3f7ChQsKDw/XmDFjFBIS8rc8JwAAAAAAALK3LBelJkyYoB49eigqKkoVK1ZUTEyMAgICNH369Ey3v/POO/Xaa6+pQ4cO8vPz+1ueEwAAAAAAANlblopSycnJ2rRpkyIjI//vCby9FRkZqfXr199UgFvxnAAAAAAAAHA2n6xsfPLkSaWmpio4ONhtfXBwsPbs2XNTAW7mOS9duqRLly65fj537txNtQ0AAAAAAADPyJaz740ePVp58+Z1LcWLF/d0JAAAAAAAAGRBlopShQoVUo4cOZSYmOi2PjEx8bqDmN+K5xw4cKDOnj3rWhISEm6qbQAAAAAAAHhGlopSvr6+qlmzpmJjY13r0tLSFBsbq7p1695UgJt5Tj8/P+XJk8dtAQAAAAAAQPaRpTGlJCk6Olpdu3ZVRESEatWqpYkTJyopKUlRUVGSpC5duig0NFSjR4+WdGUg8127drn+/8iRI9qyZYuCgoJUpkyZv/ScAAAAAAAA+GfJclGqffv2OnHihIYMGaJjx46pWrVqWrZsmWug8vj4eHl7/18HrKNHj6p69equn19//XW9/vrratCggVavXv2XnhMAAAAAAAD/LFkuSklSr1691KtXr0wfSy80pQsLC5Mx5n96TgAAAAAAAPyzZMvZ9wAAAAAAAJC9UZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADWUZQCAAAAAACAdRSlAAAAAAAAYB1FKQAAAAAAAFhHUQoAAAAAAADW3VRRasqUKQoLC5O/v79q166tn3766Ybbf/LJJ7r99tvl7++vypUra+nSpW6Pnz9/Xr169dJtt92mXLlyqWLFioqJibmZaAAAAAAAAMgGslyUmj9/vqKjozV06FBt3rxZVatWVdOmTXX8+PFMt1+3bp06duyo7t276+eff1abNm3Upk0b7dixw7VNdHS0li1bpg8++EC7d+/Wc889p169eunzzz+/+VcGAAAAAAAAx8pyUWrChAnq0aOHoqKiXD2aAgICNH369Ey3nzRpkpo1a6b+/furQoUKGjFihGrUqKHJkye7tlm3bp26du2qhg0bKiwsTE888YSqVq36X3tgAQAAAAAAIHvKUlEqOTlZmzZtUmRk5P89gbe3IiMjtX79+kx/Z/369W7bS1LTpk3dtq9Xr54+//xzHTlyRMYYrVq1Snv37tV9992X6XNeunRJ586dc1sAAAAAAACQfWSpKHXy5EmlpqYqODjYbX1wcLCOHTuW6e8cO3bsv27/1ltvqWLFirrtttvk6+urZs2aacqUKapfv36mzzl69GjlzZvXtRQvXjwrLwMAAAAAAAAe5ojZ99566y398MMP+vzzz7Vp0yaNHz9ePXv21MqVKzPdfuDAgTp79qxrSUhIsJwYAAAAAAAA/wufrGxcqFAh5ciRQ4mJiW7rExMTFRISkunvhISE3HD7P//8Uy+99JIWLVqkli1bSpKqVKmiLVu26PXXX89w658k+fn5yc/PLyvRAQAAAAAA4CBZ6inl6+urmjVrKjY21rUuLS1NsbGxqlu3bqa/U7duXbftJWnFihWu7S9fvqzLly/L29s9So4cOZSWlpaVeAAAAAAAAMgmstRTSpKio6PVtWtXRUREqFatWpo4caKSkpIUFRUlSerSpYtCQ0M1evRoSVKfPn3UoEEDjR8/Xi1bttS8efO0ceNGvfvuu5KkPHnyqEGDBurfv79y5cqlkiVLas2aNZo9e7YmTJjwN75UAAAAAAAAOEWWi1Lt27fXiRMnNGTIEB07dkzVqlXTsmXLXIOZx8fHu/V6qlevnubOnauXX35ZL730ksqWLavFixerUqVKrm3mzZungQMHqlOnTvr9999VsmRJvfrqq3rqqaf+hpcIAAAAAAAAp8lyUUqSevXqpV69emX62OrVqzOsa9u2rdq2bXvd5wsJCdGMGTNuJgoAAAAAAACyIUfMvgcAAAAAAIB/F4pSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsM7H0wEA3LywAUustXVoTEtrbQEAAAAA/vnoKQUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOh9PBwCQ/YUNWGK1vUNjWlptDwAAAADw96OnFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrbqooNWXKFIWFhcnf31+1a9fWTz/9dMPtP/nkE91+++3y9/dX5cqVtXTp0gzb7N69W61atVLevHkVGBioO++8U/Hx8TcTDwAAAAAAAA6X5aLU/PnzFR0draFDh2rz5s2qWrWqmjZtquPHj2e6/bp169SxY0d1795dP//8s9q0aaM2bdpox44drm0OHDigu+++W7fffrtWr16tbdu2afDgwfL397/5VwYAAAAAAADHynJRasKECerRo4eioqJUsWJFxcTEKCAgQNOnT890+0mTJqlZs2bq37+/KlSooBEjRqhGjRqaPHmya5tBgwapRYsWGjdunKpXr67SpUurVatWKlKkyM2/MgAAAAAAADhWlopSycnJ2rRpkyIjI//vCby9FRkZqfXr12f6O+vXr3fbXpKaNm3q2j4tLU1LlixRuXLl1LRpUxUpUkS1a9fW4sWLr5vj0qVLOnfunNsCAAAAAACA7CNLRamTJ08qNTVVwcHBbuuDg4N17NixTH/n2LFjN9z++PHjOn/+vMaMGaNmzZpp+fLlevDBB/XQQw9pzZo1mT7n6NGjlTdvXtdSvHjxrLwMAAAAAAAAeJjHZ99LS0uTJLVu3Vp9+/ZVtWrVNGDAAN1///2KiYnJ9HcGDhyos2fPupaEhASbkQEAAAAAAPA/8snKxoUKFVKOHDmUmJjotj4xMVEhISGZ/k5ISMgNty9UqJB8fHxUsWJFt20qVKig7777LtPn9PPzk5+fX1aiAwAAAAAAwEGy1FPK19dXNWvWVGxsrGtdWlqaYmNjVbdu3Ux/p27dum7bS9KKFStc2/v6+urOO+/UL7/84rbN3r17VbJkyazEAwAAAAAAQDaRpZ5SkhQdHa2uXbsqIiJCtWrV0sSJE5WUlKSoqChJUpcuXRQaGqrRo0dLkvr06aMGDRpo/PjxatmypebNm6eNGzfq3XffdT1n//791b59e9WvX1+NGjXSsmXL9MUXX2j16tV/z6sEAAAAAACAo2S5KNW+fXudOHFCQ4YM0bFjx1StWjUtW7bMNZh5fHy8vL3/rwNWvXr1NHfuXL388st66aWXVLZsWS1evFiVKlVybfPggw8qJiZGo0eP1rPPPqvy5cvr008/1d133/03vEQAAAAAAAA4TZaLUpLUq1cv9erVK9PHMuvd1LZtW7Vt2/aGz9mtWzd169btZuIAAAAAAAAgm/H47HsAAAAAAAD496EoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwzsfTAQDg7xQ2YIm1tg6NaWmtLQAAAAD4p6GnFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrKEoBAAAAAADAOopSAAAAAAAAsI6iFAAAAAAAAKyjKAUAAAAAAADrfDwdAAD+icIGLLHa3qExLa22BwAAAAD/K3pKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA6yhKAQAAAAAAwDqKUgAAAAAAALCOohQAAAAAAACsoygFAAAAAAAA626qKDVlyhSFhYXJ399ftWvX1k8//XTD7T/55BPdfvvt8vf3V+XKlbV06dLrbvvUU0/Jy8tLEydOvJloAAAAAAAAyAayXJSaP3++oqOjNXToUG3evFlVq1ZV06ZNdfz48Uy3X7dunTp27Kju3bvr559/Vps2bdSmTRvt2LEjw7aLFi3SDz/8oGLFimX9lQAAAAAAACDbyHJRasKECerRo4eioqJUsWJFxcTEKCAgQNOnT890+0mTJqlZs2bq37+/KlSooBEjRqhGjRqaPHmy23ZHjhxR79699eGHHypnzpw392oAAAAAAACQLWSpKJWcnKxNmzYpMjLy/57A21uRkZFav359pr+zfv16t+0lqWnTpm7bp6WlqXPnzurfv7/uuOOOrEQCAAAAAABANuSTlY1Pnjyp1NRUBQcHu60PDg7Wnj17Mv2dY8eOZbr9sWPHXD+PHTtWPj4+evbZZ/9SjkuXLunSpUuun8+dO/dXXwIAAAAAAAAcwOOz723atEmTJk3SzJkz5eXl9Zd+Z/To0cqbN69rKV68+C1OCQAAAAAAgL9TlopShQoVUo4cOZSYmOi2PjExUSEhIZn+TkhIyA23X7t2rY4fP64SJUrIx8dHPj4+Onz4sPr166ewsLBMn3PgwIE6e/asa0lISMjKywAAAAAAAICHZako5evrq5o1ayo2Nta1Li0tTbGxsapbt26mv1O3bl237SVpxYoVru07d+6sbdu2acuWLa6lWLFi6t+/v77++utMn9PPz0958uRxWwAAAAAAAJB9ZGlMKUmKjo5W165dFRERoVq1amnixIlKSkpSVFSUJKlLly4KDQ3V6NGjJUl9+vRRgwYNNH78eLVs2VLz5s3Txo0b9e6770qSChYsqIIFC7q1kTNnToWEhKh8+fL/6+sDgH+1sAFLrLZ3aExLq+0BAAAAyL6yXJRq3769Tpw4oSFDhujYsWOqVq2ali1b5hrMPD4+Xt7e/9cBq169epo7d65efvllvfTSSypbtqwWL16sSpUq/X2vAgAAAAAAANlKlotSktSrVy/16tUr08dWr16dYV3btm3Vtm3bv/z8hw4duplYAAAAAAAAyCY8PvseAAAAAAAA/n0oSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArPPxdAAAwL9D2IAlVts7NKal1fYAAAAAZA09pQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1vl4OgAAADaFDVhitb1DY1pabQ8AAADILugpBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArKMoBQAAAAAAAOsoSgEAAAAAAMA6ilIAAAAAAACwjqIUAAAAAAAArLupotSUKVMUFhYmf39/1a5dWz/99NMNt//kk090++23y9/fX5UrV9bSpUtdj12+fFkvvviiKleurMDAQBUrVkxdunTR0aNHbyYaAAAAAAAAsoEsF6Xmz5+v6OhoDR06VJs3b1bVqlXVtGlTHT9+PNPt161bp44dO6p79+76+eef1aZNG7Vp00Y7duyQJF24cEGbN2/W4MGDtXnzZi1cuFC//PKLWrVq9b+9MgAAAAAAADhWlotSEyZMUI8ePRQVFaWKFSsqJiZGAQEBmj59eqbbT5o0Sc2aNVP//v1VoUIFjRgxQjVq1NDkyZMlSXnz5tWKFSvUrl07lS9fXnXq1NHkyZO1adMmxcfH/2+vDgAAAAAAAI6UpaJUcnKyNm3apMjIyP97Am9vRUZGav369Zn+zvr16922l6SmTZted3tJOnv2rLy8vJQvX75MH7906ZLOnTvntgAAAAAAACD7yFJR6uTJk0pNTVVwcLDb+uDgYB07dizT3zl27FiWtr948aJefPFFdezYUXny5Ml0m9GjRytv3ryupXjx4ll5GQAAAAAAAPAwR82+d/nyZbVr107GGE2dOvW62w0cOFBnz551LQkJCRZTAgAAAAAA4H/lk5WNCxUqpBw5cigxMdFtfWJiokJCQjL9nZCQkL+0fXpB6vDhw/rmm2+u20tKkvz8/OTn55eV6AAAAAAAAHCQLPWU8vX1Vc2aNRUbG+tal5aWptjYWNWtWzfT36lbt67b9pK0YsUKt+3TC1L79u3TypUrVbBgwazEAgAAAAAAQDaTpZ5SkhQdHa2uXbsqIiJCtWrV0sSJE5WUlKSoqChJUpcuXRQaGqrRo0dLkvr06aMGDRpo/PjxatmypebNm6eNGzfq3XfflXSlIPXwww9r8+bN+vLLL5Wamuoab6pAgQLy9fX9u14rAACOEjZgibW2Do1paa0tAAAA4K/IclGqffv2OnHihIYMGaJjx46pWrVqWrZsmWsw8/j4eHl7/18HrHr16mnu3Ll6+eWX9dJLL6ls2bJavHixKlWqJEk6cuSIPv/8c0lStWrV3NpatWqVGjZseJMvDQAAAAAAAE6V5aKUJPXq1Uu9evXK9LHVq1dnWNe2bVu1bds20+3DwsJkjLmZGAAAAAAAAMimHDX7HgAAAAAAAP4dKEoBAAAAAADAupu6fQ8AAPxz2BxwXWLQdQAAAFxBTykAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1vl4OgAAAEC6sAFLrLV1aExLa20BAAAgI4pSAAAA17BZHJMokAEAgH8nilIAAAAO5pTeYxTqAADA340xpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGCdj6cDAAAAAFkRNmCJtbYOjWlprS0AAP5tKEoBAAAAN8FmcUyiQAYA+Ofh9j0AAAAAAABYR1EKAAAAAAAA1lGUAgAAAAAAgHUUpQAAAAAAAGAdRSkAAAAAAABYx+x7AAAAQDZncybAG80CyIyEAICsoKcUAAAAAAAArKOnFAAAAIB/FHpsAUD2QFEKAAAAAG4Rp9xaCQBORFEKAAAAAP7hnNR7jEIdgHQUpQAAAAAA/zpOKtQB/1YMdA4AAAAAAADr6CkFAAAAAIAHOeWWRif1HnPKe4Jbi6IUAAAAAABAJpxUqPsn4vY9AAAAAAAAWEdRCgAAAAAAANZRlAIAAAAAAIB1N1WUmjJlisLCwuTv76/atWvrp59+uuH2n3zyiW6//Xb5+/urcuXKWrp0qdvjxhgNGTJERYsWVa5cuRQZGal9+/bdTDQAAAAAAABkA1kuSs2fP1/R0dEaOnSoNm/erKpVq6pp06Y6fvx4ptuvW7dOHTt2VPfu3fXzzz+rTZs2atOmjXbs2OHaZty4cXrzzTcVExOjH3/8UYGBgWratKkuXrx4868MAAAAAAAAjpXlotSECRPUo0cPRUVFqWLFioqJiVFAQICmT5+e6faTJk1Ss2bN1L9/f1WoUEEjRoxQjRo1NHnyZElXeklNnDhRL7/8slq3bq0qVapo9uzZOnr0qBYvXvw/vTgAAAAAAAA4k09WNk5OTtamTZs0cOBA1zpvb29FRkZq/fr1mf7O+vXrFR0d7bauadOmroJTXFycjh07psjISNfjefPmVe3atbV+/Xp16NAhw3NeunRJly5dcv189uxZSdK5c+ey8nIcLe3SBWtt3eh9s5lDck4Wp+SQnJPFKTkk52RxSg7p+lmckkNyThan5JA4XjPDe5IR70lGvCcZ8Z44M4fEvskM70lGvCcZ8Z5kb+mvwxhz4w1NFhw5csRIMuvWrXNb379/f1OrVq1Mfydnzpxm7ty5buumTJliihQpYowx5vvvvzeSzNGjR922adu2rWnXrl2mzzl06FAjiYWFhYWFhYWFhYWFhYWFhYXFoUtCQsIN60xZ6inlFAMHDnTrfZWWlqbff/9dBQsWlJeXlweTeda5c+dUvHhxJSQkKE+ePP/6HE7KQg7nZnFKDidlcUoOJ2VxSg4nZXFKDidlIYdzszglh5OyOCWHk7I4JYeTsjglh5OyOCWHk7KQw9lZPMUYoz/++EPFihW74XZZKkoVKlRIOXLkUGJiotv6xMREhYSEZPo7ISEhN9w+/b+JiYkqWrSo2zbVqlXL9Dn9/Pzk5+fnti5fvnxZeSn/aHny5HHEge+UHJJzspAjI6dkcUoOyTlZnJJDck4Wp+SQnJPFKTkk52QhR0ZOyeKUHJJzsjglh+ScLE7JITkni1NySM7J4pQcknOykCMjJ2XxhLx58/7XbbI00Lmvr69q1qyp2NhY17q0tDTFxsaqbt26mf5O3bp13baXpBUrVri2L1WqlEJCQty2OXfunH788cfrPicAAAAAAACytyzfvhcdHa2uXbsqIiJCtWrV0sSJE5WUlKSoqChJUpcuXRQaGqrRo0dLkvr06aMGDRpo/PjxatmypebNm6eNGzfq3XfflSR5eXnpueee08iRI1W2bFmVKlVKgwcPVrFixdSmTZu/75UCAAAAAADAMbJclGrfvr1OnDihIUOG6NixY6pWrZqWLVum4OBgSVJ8fLy8vf+vA1a9evU0d+5cvfzyy3rppZdUtmxZLV68WJUqVXJt88ILLygpKUlPPPGEzpw5o7vvvlvLli2Tv7//3/AS/z38/Pw0dOjQDLc2/ltzOCkLOZybxSk5nJTFKTmclMUpOZyUxSk5nJSFHM7N4pQcTsrilBxOyuKUHE7K4pQcTsrilBxOykIOZ2dxOi9j/tv8fAAAAAAAAMDfK0tjSgEAAAAAAAB/B4pSAAAAAAAAsI6iFAAAAAAAAKyjKAUAALINY4zi4+N18eJFT0cBANwiTjnXp6am6ttvv9WZM2c8mgP4J6MohX+MlJQUzZ49W4mJiZ6OgkwcPHjQ0xGUkpKi4cOH69dff/V0FEdJSkrydIQbsn0h6NRzSXJysn755RelpKR4OopHGWNUpkwZJSQkeDqKZsyYoQsXLng6hiPt379fX3/9tf78809JV/abLZcvX1bp0qW1e/dua21mxblz57R48WLH5vs3+fPPP93+DR8+fFgTJ07U8uXLreZwyjHrpOskp5zrc+TIofvuu0+nT5/2aA7JWftn6NChOnz4sEczOOl67fLly/Lx8dGOHTs8HSVboiiVjc2aNUtLlixx/fzCCy8oX758qlevntWTxLJly/Tdd9+5fp4yZYqqVaumRx55xOoJ3MfHR0899ZTHv1FJ9+uvv+rtt9/WgAEDFB0d7bbY4pRjRJLKlCmjRo0a6YMPPvDYPvLx8dFrr73mqA/1Gzdu1AsvvKAOHTrooYcecltsCQ4OVrdu3dz+HXvK2LFjNX/+fNfP7dq1U8GCBRUaGqqtW7dayeC0c8mFCxfUvXt3BQQE6I477lB8fLwkqXfv3hozZoy1HJs3b9b27dtdP3/22Wdq06aNXnrpJSUnJ1vL4e3trbJly+rUqVPW2ryeAQMGKCQkRN27d9e6des8lsMpf4cl6dSpU4qMjFS5cuXUokUL/fbbb5Kk7t27q1+/flYy5MyZ0zH/fqUr57HJkydLulIEiYiIULt27VSlShV9+umn1nKkpqZq2rRpeuSRRxQZGanGjRu7LbbFxsbqpZde0uOPP65u3bq5Lba0bt1as2fPlnTlC5DatWtr/Pjxat26taZOnWoth1OOWSddJznpXF+pUiVHfLnqpP3z2WefqXTp0mrSpInmzp2rS5cuWc/gpOu1nDlzqkSJEkpNTfV0lGyJolQ2NmrUKOXKlUuStH79ek2ZMkXjxo1ToUKF1LdvX2s5+vfvr3PnzkmStm/frn79+qlFixaKi4uzWoCRpFq1amnLli1W28xMbGysypcvr6lTp2r8+PFatWqVZsyYoenTp1vN55RjRLrygbZKlSqKjo5WSEiInnzySf30009WM0hS48aNtWbNGuvtZmbevHmqV6+edu/erUWLFuny5cvauXOnvvnmG+XNm9dajg8++EC///67GjdurHLlymnMmDE6evSotfavFhMTo+LFi0uSVqxYoRUrVuirr75S8+bN1b9/f2s5nHIukaSBAwdq69atWr16tfz9/V3rIyMj3Qp4t9qTTz6pvXv3SrrS87FDhw4KCAjQJ598ohdeeMFaDkkaM2aM+vfv7/FvJI8cOaJZs2bp5MmTatiwoW6//XaNHTtWx44ds5rDSX+H+/btKx8fH8XHxysgIMC1vn379lq2bJm1HD179tTYsWMd8eHt22+/1T333CNJWrRokYwxOnPmjN58802NHDnSWo4+ffqoT58+Sk1NVaVKlVS1alW3xaZhw4bpvvvuU2xsrE6ePKnTp0+7LbZs3rzZtW8WLFig4OBgHT58WLNnz9abb75pLYfknGPWSddJTjnXjxw5Us8//7y+/PJL/fbbbzp37pzbYpNT9s+WLVu0YcMG3XHHHerTp49CQkL09NNPa8OGDVZzOOl6bdCgQXrppZf0+++/ezpKtuNlbPanxt8qICBAe/bsUYkSJfTiiy/qt99+0+zZs7Vz5041bNhQJ06csJIjKChIO3bsUFhYmF555RXt2LFDCxYs0ObNm9WiRQurF+cff/yxBg4cqL59+6pmzZoKDAx0e7xKlSpWctSqVUvNmzfXsGHDlDt3bm3dulVFihRRp06d1KxZMz399NNWcjjlGLlaSkqKPv/8c82cOVPLli1TuXLl1K1bN3Xu3FmFCxe+5e3HxMRo2LBh6tSpU6bHSKtWrW55hnRVqlTRk08+qZ49e7qOk1KlSunJJ59U0aJFNWzYMGtZJOnEiROaM2eOZs6cqd27d6tp06bq1q2bWrVqJR8fHysZcuXKpb1796p48eLq06ePLl68qHfeeUd79+5V7dq1rX1Yccq5RJJKliyp+fPnq06dOq7jJDw8XPv371eNGjWsXRDnzZtXmzdvVunSpTV27Fh98803+vrrr/X999+rQ4cOVm+xyJ8/vy5cuKCUlBT5+vq6iu/pPHFBmJiYqA8++ECzZs3Snj171KxZM3Xv3l0PPPCAvL1v7XeATvo7HBISoq+//lpVq1Z1O14PHjyoKlWq6Pz581ZyPPjgg4qNjVVQUJAqV66c4d/wwoULreSQ3M9rXbp0UbFixTRmzBjFx8erYsWK1t6TQoUKafbs2WrRooWV9m6kaNGiGjdunDp37uzRHFdfJ7Vr10533HGHhg4dqoSEBJUvX97q7blOOWaddJ3klHP91edwLy8v1/8bY+Tl5WW1d4yT9k+6y5cv64svvtCMGTP09ddf6/bbb1f37t312GOP3fIvWZ10vVa9enXt379fly9fVsmSJTNk2bx5s7Us2Y2dTxm4JYKCgnTq1CmVKFFCy5cvd30b6u/v7xrDwQZfX1/XH+2VK1eqS5cukqQCBQpY//agQ4cOkqRnn33Wtc7Ly8v6H43du3fro48+knSla+mff/6poKAgDR8+XK1bt7ZWlHLKMXI1Hx8fPfTQQ2rZsqXefvttDRw4UM8//7xeeukltWvXTmPHjlXRokVvWfvPPPOMJGnChAkZHrN9YXHgwAG1bNlS0pV/R0lJSfLy8lLfvn3VuHFj60WpwoULu24xfeutt9S/f38tXbpUhQoV0lNPPaUBAwa49Xy4FfLnz6+EhAQVL15cy5Ytc/UiMMZY3TdOOZdIV4qFRYoUybA+/XixxRijtLQ0SVfO9ffff78kqXjx4jp58qS1HJI0ceJEq+39FcHBwbr77ru1d+9e7d27V9u3b1fXrl2VP39+zZgxQw0bNrxlbTvp73BSUlKm54nff/9dfn5+1nLky5dP//nPf6y1dyPFixfX+vXrVaBAAS1btkzz5s2TJJ0+fdqt9+Ot5uvrqzJlylhr70aSk5NVr149T8dQmTJltHjxYj344IP6+uuvXb3Ijx8/rjx58ljN4pRj1knXSU45169atcrTEVyctH/SGWN0+fJlJScnyxij/Pnza/LkyRo8eLDee+89tW/f/pa17aTrtTZt2lhr65+GolQ2du+99+rxxx9X9erVtXfvXtc3Xzt37lRYWJi1HHfffbeio6N111136aeffnLdTrJ3717ddttt1nJIUlxcnNX2ricwMNA1xkrRokV14MAB3XHHHZJk9cObU46Rq23cuFHTp0/XvHnzFBgYqOeff17du3fXr7/+qmHDhql169a39La+9A/VTpA/f3798ccfkqTQ0FDt2LFDlStX1pkzZzwyeHJiYqJmzZqlmTNn6vDhw3r44Ydd+2bs2LH64Ycfbvngrw899JAeeeQR1zgSzZs3lyT9/PPPVj9MOeVcIkkRERFasmSJevfuLen/vqV9//33VbduXas5Ro4cqcjISK1Zs8Y13kpcXJyCg4Ot5ZCkrl27Wm3vRhITEzVnzhzNmDFDBw8eVJs2bfTll18qMjJSSUlJGj58uLp27XpLx/Fz0t/he+65R7Nnz9aIESMkXTle09LSNG7cODVq1MhajhkzZlhr67957rnn1KlTJwUFBalkyZKuAuW3336rypUrW8vRr18/TZo0SZMnT7Za0M7M448/rrlz52rw4MEezTFkyBA98sgj6tu3r5o0aeI6py5fvlzVq1e3msUpx6yTrpOccq5v0KCBpyO4OGn/bNq0STNmzNBHH30kPz8/denSRVOmTHFdr7311lt69tlnb2lRyknXa0OHDvV0hGyLolQ2NmXKFL388stKSEjQp59+qoIFC0q6coLo2LGjtRyTJ0/WM888owULFmjq1KkKDQ2VJH311Vdq1qyZtRzSldtcnKBOnTr67rvvVKFCBbVo0UL9+vXT9u3btXDhQtWpU8daDqccI9KVb3RmzJihX375RS1atHDdQpDeJbpUqVKaOXOm1WLZxYsXrX5Lfa369etrxYoVqly5stq2bas+ffrom2++0YoVK9SkSRNrORYuXOjqcl2xYkU988wzevTRR5UvXz7XNvXq1VOFChVueZY33nhDYWFhSkhI0Lhx4xQUFCRJ+u2331zfDtrglHOJdGVsuObNm2vXrl1KSUnRpEmTtGvXLq1bt87quBITJ05Up06dtHjxYg0aNMh10blgwQKP9HhITU11m8HsjjvuUKtWrZQjRw5rGR544AF9/fXXKleunHr06KEuXbqoQIECrscDAwPVr18/vfbaa7c0h5P+Do8bN05NmjTRxo0blZycrBdeeEE7d+7U77//ru+//95qFulKT8NffvlFklS+fHkrt4lf65lnnlGtWrWUkJCge++91/V3Lzw83OqYUt99951WrVqlr776SnfccYdy5szp9rjNWxovXryod999VytXrlSVKlUyZMmsF8it8PDDD+vuu+/Wb7/95jauVpMmTfTggw9ayXAtJxyzTuKEc710ZSD8adOmueXo1q2b1TFAnaRy5cras2eP7rvvPk2bNk0PPPBAhn3SsWNH9enT55bmcNL1WrpNmza5HSe2C9zZEWNKZWPx8fG67bbbMoxVYYxRQkKCSpQo4aFknjVnzhzFxMQoLi5O69evV8mSJTVx4kSVKlVKrVu3tpLh4MGDOn/+vKpUqaKkpCT169dP69atU9myZTVhwgRHnkBvtbJly6pbt2567LHHrnt7XnJysj766KNb+s1YamqqRo0apZiYGCUmJmrv3r0KDw/X4MGDFRYWpu7du9+ytq/1+++/6+LFiypWrJirJ0H6cfLyyy8rf/78VnLkzZtXHTp00OOPP64777wz023+/PNPjRs37l/1LdCBAwc0ceJE14VFxYoV1adPH5UuXdp6loMHD2r06NHaunWrzp8/rxo1aujFF1+02sviei5evKgcOXJk+FB5K+3fv18tWrTQkSNHVL58eUnSL7/8ouLFi2vJkiXW9lH37t31+OOP37DHmjFG8fHx/6rz/tmzZzV58mS347Vnz5639NbsayUlJal3796aPXu2q2dBjhw51KVLF7311lu3/FbkG0lNTdX27dtVsmRJa+d5SYqKirrh4zZ76tyo15yXl5e++eYba1mudu7cOX3zzTcqX768lS9iruakY3bNmjV6/fXX3f7+9e/f3zUovC1OOddv3LhRTZs2Va5cuVSrVi1J0oYNG/Tnn39q+fLlqlGjhpUc6Zywf0aMGKFu3bq5vgTxJKdcrx0/flwdOnTQ6tWrXV/snjlzRo0aNdK8efP+9QXmGzLItry9vU1iYmKG9SdPnjTe3t7/uhzGGPP222+bQoUKmZEjR5pcuXKZAwcOGGOMmTFjhmnYsKHVLE6wZs2aGy42xcXFmdTU1Azr09LSzOHDh63lGDZsmAkPDzcffPCB2zEyb948U6dOHWs5nCQpKcnTEVxmzZp1w8WWZcuWGV9fX1OrVi3Tt29f07dvX1OrVi3j5+dnli9fbi1HcnKyiYqKMgcPHrTW5vXEx8ebhIQE188//vij6dOnj3nnnXesZ2nevLlp1qyZOXXqlGvdyZMnTbNmzUyLFi2sZEhOTjaNGzc2e/futdLejWzatMls27bN9fPixYtN69atzcCBA82lS5es5XDSe/LEE0+Y8PBws3TpUnP27Flz9uxZs2TJElO6dGnz1FNPWc3Sp08f8/777xtjjElJSTF33XWX8fLyMoGBgWbVqlVWs8Bd27ZtzVtvvWWMMebChQumbNmyJmfOnMbHx8csWLDAahanHLNz5swxPj4+pl27dmbSpElm0qRJpl27diZnzpzmww8/tJbDGGec640x5u677zaPPfaYuXz5smvd5cuXTdeuXc0999xjLYcxztg/ycnJJjw83OzatctKezfilOs1Y4xp166diYiIcHtfdu7caSIiIkyHDh2sZsluKEplY15eXpkWgw4dOmQCAgI8nuPIkSPG39/fWg5jjKlQoYJZtGiRMcaYoKAgV8Fh+/btpmDBglaznD592rz33ntmwIABrj+mmzZtMr/++qu1DF5eXhkWb29v12KTU4qXpUuXNitXrjTGuB8ju3fvNvny5bOWI93+/fvNoEGDTIcOHVzvz9KlS82OHTusZXDKvjHGmHz58rktgYGBxsvLy/j5+Zn8+fNby1GtWjXz4osvZlj/4osvmurVq1vLYYwxefLkcURR6u677zazZ882xhjz22+/mTx58pi6deuaQoUKmWHDhlnNEhAQ4FaESbdlyxYTGBhoLUehQoUcUYCJiIhwfYA+cOCA8ff3Nx07djRlypQxffr0sZrFKe9JwYIFMy34fPPNN6ZQoUJWs4SGhpoNGzYYY4xZtGiRKVasmPnll1/Myy+/bOrVq2c1izHGHD9+3Kxdu9asXbvWHD9+3Hr710pISHAreNsUHBxstmzZYowx5sMPPzRlypQxSUlJ5u233zbVqlWzmsUpx+ztt99uJkyYkGH9+PHjze23324thzHOOdf7+/ub3bt3Z1i/c+dOkytXLms5jHHO/ilWrJgjilJOu1776aefMqz/8ccfTd68ea1myW5u7RzFuCXSZ8fy8vLSkCFDXD9HR0erT58+at++vapVq3bLc7z55pt688035eXlpffff9/185tvvqk33nhDPXv21O23337Lc1wtLi4u0/t2/fz8lJSUZC3Htm3bVK5cOY0dO1avv/66zpw5I+nKeA0DBw60luP06dNuy/Hjx7Vs2TLdeeedt3zA6muZ69wpfP78eavjOh05ciTTAbPT0tJ0+fJlazmkK92vK1eurB9//FELFy50TQu+detWq7fJXW/fXLp0Sb6+vtZySBmP2fPnz+uXX37R3Xff7ZrR0obdu3dneitnt27dtGvXLms5pCuzuSxevNhqm5nZsWOH67aFjz/+WJUqVdK6dev04YcfaubMmVaz+Pn5uSYJuNr58+etHrOPPvqopk2bZq2969m7d6/r7/4nn3yi+vXra+7cuZo5c6Y+/fRTq1mc8p5cuHAh0wH4ixQpYn0iiZMnTyokJESStHTpUrVt21blypVTt27dtH37dms5kpKS1K1bNxUtWlT169dX/fr1VaxYMXXv3t36e5KWlqbhw4crb968KlmypEqWLKl8+fJpxIgRVgdyPnv2rGscuGXLluk///mPAgIC1LJlS+3bt89aDsk5x+zBgwf1wAMPZFjfqlUr64NKO+VcnydPHsXHx2dYn5CQoNy5c1vLITln//Ts2VNjx45VSkqKtTYz46TrtbS0tEyHMsiZM6ejBqh3IgY6z4Z+/vlnSVc+SG7fvt3tpOzr66uqVavq+eefv+U53njjDVeOmJgYt8HtfH19FRYWppiYmFue42qlSpXSli1bMozdsWzZMqtjA0RHR+uxxx7TuHHj3P5YtWjRQo888oi1HJkNvnjvvffK19dX0dHR2rRp0y3PEB0dLUmuIurVYyKkpqbqxx9/tFJETVexYkWtXbs2wzGyYMEC6wMRDhgwQCNHjlR0dLTbcdK4cWNNnjz5lrf/5ptvSpKrsJw+qLh0Zd98++231gvLmSlbtqzGjBmjRx99VHv27LHSZuHChbVlyxaVLVvWbf2WLVtUpEgRKxnSlS1bVsOHD9f333+vmjVrKjAw0O3xq6dBvpUuX74sPz8/SdLKlSvVqlUrSdLtt9+u3377zUqGdPfff7+eeOIJTZs2zVUo+/HHH/XUU0+5ctmQkpKi6dOna+XKlZnuG1uDNRtjXBe8K1eu1P333y9JKl68uNUZXyXnvCd169bV0KFDNXv2bNcXH3/++aeGDRtmddZKSQoODtauXbtUtGhRLVu2zDVz5YULF6wO1hwdHa01a9boiy++0F133SXpyuDnzz77rPr16+fKZcOgQYM0bdo0jRkzxi3LK6+8oosXL+rVV1+1kqN48eJav369ChQooGXLlmnevHmSrnxBYnsiFKccs8WLF1dsbGyGL/BWrlyp4sWLW8shOedc3759e3Xv3l2vv/66a2KP77//Xv3797c+cZBT9s+GDRsUGxur5cuXq3LlyhnO9bYmTnDS9Vrjxo3Vp08fffTRRypWrJikK1+Gp8/uieujKJUNrVq1StKVASsnTZqkPHnyeCRHejW+UaNGWrhwodXBOq8nOjpaPXv21MWLF2WM0U8//aSPPvpIo0eP1vvvv28tx4YNG/TOO+9kWB8aGqpjx45Zy3E9wcHBrpldbjWnFFHTDRkyRF27dtWRI0eUlpamhQsX6pdfftHs2bP15ZdfWsshSdu3b9fcuXMzrC9SpIiVD5JOLCxfj4+Pj44ePWqtvR49euiJJ57QwYMH3S5Ax44d6yq02jJt2jTly5dPmzZtylBI9vLyslaUuuOOOxQTE6OWLVtqxYoVGjFihCTp6NGjrpk9bXnzzTfVtWtX1a1b1/WtZEpKilq1aqVJkyZZy7Fjxw7XALd79+611u61IiIiNHLkSEVGRmrNmjWu4kJcXFymPS9upRu9J15eXtZyTJw4Uc2aNdNtt93mmlVt69at8vf319dff20th3Tleq1du3YqWrSovLy8FBkZKenKh2ubhf9PP/1UCxYsUMOGDV3rWrRooVy5cqldu3ZWi1KzZs3S+++/71ZYqFKlikJDQ/XMM89YK0o999xz6tSpk4KCglSiRAnXe/Ptt99an0jCKcdsv3799Oyzz2rLli1uf/9mzpxp9fwqOedc//rrr8vLy0tdunRx9QzKmTOnnn76aY0ZM8ZaDsk5+ydfvnz6z3/+Y62963HS9drkyZPVqlUrhYWFuQqECQkJqlSpkj744AOrWbIbZt/DP86HH36oV155RQcOHJAkFStWTMOGDbM6q1qRIkX09ddfq3r16sqdO7e2bt2q8PBwrVixQt26dVNCQoKVHNu2bXP72Rij3377TWPGjFFKSoq+++47KzkkzxdRr7Z27VoNHz7cbWaoIUOG6L777rOa47bbbtPHH3+sevXquR0nixYt0vPPP+86hm81JxWWP//8c7ef04/ZyZMnq3jx4vrqq6+s5DDGaOLEiRo/fryrGFasWDH1799fzz77rNUP106xevVqPfjggzp37py6du2q6dOnS5Jeeukl7dmzx9q3oub/zzBbuHBhHTlyxDXbToUKFTK9NfffYNu2berUqZPi4+MVHR3tuv23d+/eOnXqVKbF73+DCxcu6MMPP3T1sKxQoYI6deqkXLlyWc+yYMECJSQkqG3btrrtttskXSnM5MuXz9rMwAEBAdq0aVOGnuM7d+5UrVq1rA5z4O/v7xrq4Gq//PKLqlWrpj///NNalo0bNyohIUH33nuvq8fwkiVLlC9fPlcvLluccswuWrRI48ePdzu/9u/f39qxKjnnXJ+amqrvv/9elStXlp+fn+varHTp0h6bxdMJ+8cpnHa9ZozRypUr3f4Np38RgeujKJWNJSUlacyYMYqNjdXx48cz3Kt68OBBKzlSU1M1c+bM6+bw1LS+Fy5c0Pnz56133ZSkxx9/XKdOndLHH3+sAgUKaNu2bcqRI4fatGmj+vXra+LEiVZyeHt7y8vLK8OYQXXq1NH06dMdcWvWv9nzzz+vH3/8UZ988onKlSunzZs3KzExUV26dFGXLl2sjivlFN7e7kMdenl5qXDhwmrcuLHGjx9vZUr5lJQUzZ07V02bNlVwcLBrPAvb40Y4UWpqqs6dO+dWwDx06JACAgKsnWvT0tLk7++vnTt3Zuiub1u3bt00adKkDMdG+tTu6YU7T7l48aJy5MiR6RgX/2SXL1/W7bffri+//NLqrft/xcWLF63fFpauSZMmKliwYIbbw7p27arff/9dK1eutJaldu3aql27tus28nS9e/fWhg0b9MMPP1jLIknJycmKi4tT6dKl5eNj/0YSpxyzKSkpGjVqlLp16+YqnnqKk871/v7+2r17t0qVKuXRHE7aP40bN9bChQuVL18+t/Xnzp1TmzZtrHz+c9L12uXLl5UrVy5t2bJFlSpVst5+dkdRKhvr2LGj1qxZo86dO7u6hF+tT58+VnL06tVLM2fOVMuWLTPNkX6LkA1OOEFKVwbOfPjhh7Vx40b98ccfKlasmI4dO6a6detq6dKlGe67vlUOHz7s9rO3t7cKFy5s7YL4oYce0syZM5UnTx499NBDN9zWVi+L8PBwbdiwIcPtRmfOnFGNGjWsFXOlKxfBPXv21MyZM5WamiofHx+lpqbqkUce0cyZM2/pWCPR0dEaMWKEAgMD/2v3ZltjwDhJQECAdu/enWHsMU/o1q3bDR+3WfhISUnR6tWrdeDAAT3yyCPKnTu3jh49qjx58riNSXar3XHHHZo2bZrq1Kljrc3M5MiRQ7/99luGglz6wNY2B4A9c+aMFixYoAMHDqh///4qUKCANm/erODgYIWGhlrL0ahRoxt+M23r73BoaKhWrlzpiKJUamqqRo0apZiYGCUmJmrv3r0KDw/X4MGDFRYWZq0n944dO9S0aVNdunQp09vD7rjjDis5pCsTfbRs2VIlSpRwjZe0fv16JSQkaOnSpbrnnnus5Lhw4YJ69+6tWbNmSZJr3/Tu3VuhoaEaMGCAlRySc47ZoKAg7dixQ2FhYR7NITnnXB8REaGxY8c6Ylwgp+wfb29vHTt2LMPfv+PHjys0NNTa5EFOul5Lv9sh/fyKv44xpbKxr776SkuWLLHetfha8+bN08cff6wWLVp4NId05faS5OTkDOsvXryotWvXWsuRN29erVixQt9//73bLWK2u296+gSdN29e14eTPHnyOOKWp0OHDik1NTXD+kuXLunIkSPWchhjdOzYMb355psaMmSItm/frvPnz6t69epWvhH8+eefXRcM6eN+ZcYJ+8wTatWqpZ9//tnj/4akKwPuXu3y5cvasWOHzpw5o8aNG1vLcfjwYTVr1kzx8fG6dOmS7r33XuXOnVtjx47VpUuXrI4/NmbMGPXv319Tp071yDeS586dkzFGxhj98ccfboX+1NRULV261Gov3W3btqlJkybKly+fDh06pB49eqhAgQJauHCh4uPjNXv2bGtZrp244vLly9qyZYt27Nihrl27WsuRPjPU+++/75GeL1d79dVXNWvWLI0bN049evRwra9UqZImTpxorShVqVIl7du3z+32sI4dO3rk9rAGDRpo7969mjJliivLQw89pGeeecY1QLANAwcO1NatW7V69Wo1a9bMtT4yMlKvvPKK1aKUU47ZJk2aaM2aNR4vekieP9enGzlypJ5//nmNGDEi0wkcbA5N4en9c/XQILt27XIbKzc1NVXLli2z+kWIk67XBg0apJdeeklz5sxxzeqJv4aeUtlYqVKltHTpUo9/o1KsWDGtXr06w7gANqWfIKtVq6ZvvvnG7USQfoJ85513dOjQoVuexdPdN99880098cQT8vf3z9At/lq2Bkh2gvSxitq0aaNZs2a5zUyYmpqq2NhYrVixwtoA8E7qlu5pTuy19fHHH2vgwIHq27dvphegVapUsZLjetLS0vT000+rdOnSeuGFF6y02aZNG+XOnVvTpk1TwYIFXWOgrV69Wj169LA6fXr+/Pl14cIFpaSkyNfXN8MH6t9///2Wtp9+a/T1eHl5adiwYRo0aNAtzZEuMjJSNWrUcM34mr5v1q1bp0ceecTK377/5pVXXtH58+f1+uuvW2nvwQcfVGxsrIKCgjw6M5QklSlTRu+8846aNGnitn/27NmjunXrZig8w56SJUtq/vz5qlOnjtu+2b9/v2rUqKFz585Zy+KUYzYmJkbDhg1Tp06dMv37Z3PWO0+f69NdPbTA1ed+Y4y8vLwy/bLzVvH0/rn6719mZYRcuXLprbfe+q+9vP8uTrpeq169uvbv36/Lly+rZMmSGbJs3rzZWpbshqJUNvbBBx/os88+06xZszw20J4kjR8/XgcPHtTkyZM91qvCaSdIT3bfLFWqlDZu3KiCBQve8N53Ly8vq7eqjRw5Up06dfLY/fjpFxSZjbGVM2dOhYWFafz48a6p1G1wSrf0Dz74QA899JDHziONGjXSokWLlC9fPjVq1Oi623l5eVm79efasa3S2/fEBej1/PLLL2rYsKF+++03K+0VLFhQ69atU/ny5d0+vB06dEgVK1bUhQsXrOSQ5LrV5npudY+cNWvWyBijxo0b69NPP3X7IsTX11clS5a02tsjb9682rx5s0qXLu22bw4fPqzy5cvr4sWL1rJcz/79+1WrVi1rHyKjoqJu+PiMGTOs5JCuXIPs2bNHJUuWdNs/u3btUq1atXT+/Plb1vbnn3+u5s2bK2fOnBkmkrjWrf4wu23bNlWqVEne3t4ZJmK5lq0PkgEBAdqxY4fCw8Pd9s3WrVtVv359nT171koOyTnHbGZ//9LZ/vvn6XN9ujVr1tzw8QYNGljJIXl+/xw+fFjGGIWHh+unn35S4cKFXY/5+vqqSJEit3T4iWs56Xpt2LBhN3z83zhW7F/F7XvZ2Pjx43XgwAEFBwcrLCwsw0Cmtqqx3333nVatWqWvvvpKd9xxR4YcNr7ZiYuLc9QJ0pPdN+Pi4jL9f0/75JNPNHToUNWuXVuPPvqo2rVrp0KFCllrP30A/lKlSmnDhg1W274ep3RL79u3r5566im1atVKjz76qJo2bWr138uqVasy/X9PctK/nes5cOCA1TGL0tLSMr24+/XXX60OKnr58mWtWbNGgwcP9liRO/0DSFxcnEqUKOHx21z9/Pwy7dGxd+9et7+HnrR+/Xpr4xmmpKSoUaNGuu+++xQSEmKlzRupWLGi1q5dm+H2kgULFqh69eq3tO02bdq4xn1p06bNdbez8eGtWrVqrizVqlXL9EsiW1nSRUREaMmSJerdu7erbUl6//33XWNd2eCkY/baCYs8xQnn+vQcw4cPV0xMjCN6tnt6/6SfxzydI51TrtdSUlLk5eXliEHosyN6SmVjTqnGOuWbHSeh+2bmdu7cqQ8//FDz5s3Tr7/+qnvvvVedOnVSmzZtPNJLx5OzIEnO6ZaekpKiZcuW6aOPPtJnn32mgIAAtW3bVp06dVK9evWsZHASp8yClO7aWxqNMfrtt9+0ZMkSde3aVZMnT7aSo3379sqbN6/effdd5c6dW9u2bVPhwoXVunVrlShRwuq5Pm/evNqyZYvHZ0KSpLVr1+qdd97RwYMH9cknnyg0NFRz5sxRqVKldPfdd1vJ4JQZXyVlmNQi/XjduHGjBg8ebO3axEmD33722Wfq2rWrBg4cqOHDh2vYsGH65ZdfNHv2bH355Ze69957PR3RisOHD7uKuNdOxHItW/vtu+++U/PmzfXoo49q5syZevLJJ7Vr1y6tW7dOa9asUc2aNa3kkJxxzHp6CIprOeVcX7hwYa1bt87jRSmn7Z85c+YoJiZGcXFxWr9+vUqWLKk33nhD4eHhat269S1v32nXa7lz59b27dsdMR5bdkNRCv84nj5BSp4tGP63MXmu5slZ1b7//nvNnTtXn3zyiS5evGht3Ia0tDS9+uqrHp8FSXJOt/SrXbhwQYsWLdLcuXO1cuVK3XbbbTpw4MAtbfO/zcx4NVtjajhlFiRJGW5pTJ9Fs3HjxurWrZu1AXF//fVXNW3aVMYY7du3TxEREdq3b58KFSqkb7/91urA3l27dlW1atXUt29fa21m5tNPP1Xnzp3VqVMnzZkzR7t27VJ4eLgmT56spUuXaunSpVZyOGXGV0l67LHH3HqOXX283nfffdZyNGzYUM8999wNewfZtHbtWg0fPtxt8pMhQ4ZYfU8yc+bMmQwzFv/bHDhwQGPGjHHbNy+++KIqV65sNYdTjlknzSDmlHN937595efnpzFjxng0h+Sc/TN16lQNGTJEzz33nF599VXXbbAzZ87UrFmzrPV8d9L1WuvWrfXQQw955Po9u6MohX8Up5wgPenaD7CbN29WSkqKypcvL+nK7Rw5cuRQzZo1rY3Pk5ktW7bogw8+0Lx583Tq1Cn9+eefVtodPny4Zs2apeHDh6tHjx6uY2T+/PmaOHGi1q9fbyWHk508eVLz5s1TTEyMdu/efctvo7i6t6UxRosWLVLevHkVEREhSdq0aZPOnDmjhx56yFpvnFGjRmnv3r0enwXJaVJSUjRv3jxt27bN9eHNEzN3jRw5UuPHj1eTJk0yHdjU1iQO1atXV9++fdWlSxe38Wh+/vlnNW/e3G1WIhu+++47t31je8ZXJ3HS4LdOMXbsWIWFhal9+/aSpLZt2+rTTz9V0aJFtXTpUqsfcmfNmqVChQqpZcuWkqQXXnhB7777ripWrKiPPvrIET3cbHPKMTtt2jQtXLjQETOIOeVc37t3b82ePVtly5bNNIfNL3mdsn8qVqyoUaNGuSZBSf/7t2PHDjVs2FAnT560ksNJ12ueHoQ+O6Molc0UKFBAe/fuVaFChZQ/f/4bjmNxK2/9qVGjhmJjY5U/f35Vr179hjls3qrmlBOkU0yYMEGrV6/WrFmzlD9/fklXppePiorSPffco379+lnNExcXp7lz52ru3Ln65Zdf1KBBAz3yyCN6+OGH3WbDu5WcOgvSxYsXlZyc7LbO5hTD6T2kPvzwQ8XGxqp48eKuqcJvv/12azlefPFF/f7774qJiXGNa5WamqpnnnlGefLk0WuvvWYlh1NmQZKkxo0ba+HChRl6M5w7d05t2rTxaHHZU5wyiUNAQIB27dqlsLAwt/PJwYMHVbFiRUcMMG5beHi4NmzYoIIFC7qtP3PmjGrUqGFt3zhp8Nt0ycnJOn78eIaxWEqUKGGl/VKlSunDDz9UvXr1tGLFCrVr107z58/Xxx9/rPj4eC1fvtxKDkkqX768pk6dqsaNG2v9+vVq0qSJJk6cqC+//FI+Pj5Wz7FpaWnav39/pvumfv361nI45Zh10hAUTjnXO2USFsk5++d6Ezjs27dPVapUsfZls5Ou1zw9CH12xte/2cwbb7zhGlDW5hgR12rdurX8/PwkyePdjK8WFxeX6aChfn5+SkpKspYjNTVVb7zxhutC79pig62xgsaPH6/ly5e7ClLSlXGMRo4cqfvuu89qUapOnTrasGGDqlSpoqioKHXs2FGhoaHW2k935MgRlSlTJsP6tLQ0Xb582WqWpKQkvfjii/r444916tSpDI/b+uPVoUMHffnllwoICFC7du00ePBgqwO8Xm369On67rvv3AZaz5Ejh6Kjo1WvXj1rRal8+fLpP//5j5W2/pvVq1dnOIdIVwqZa9eutZpl3759WrVqVaYf3oYMGWIth1MGNg0JCdH+/fszjB/x3XffKTw83GqW2NhYxcbGZrpvpk+fbi3HoUOHMj13Xbp0SUeOHLGWwynHiHTl3023bt20bt06t/W2iw3Hjh1T8eLFJUlffvml2rVrp/vuu09hYWGqXbu2lQzpEhISXH+LFy9erIcfflhPPPGE7rrrLjVs2NBajh9++EGPPPKIa0axq9n+EOmUY9Zp1/VO4KQ7LZyyf0qVKqUtW7Zk6NW4bNkyq7fSOel6zSmDv2dHFKWymavvUfXk/apXj4nkpOktnXKCHDZsmN5//33169dPL7/8sgYNGqRDhw5p8eLFVj+4nTt3TidOnMiw/sSJE/rjjz+s5ZCkJk2aaPr06apYsaLVdq/lyVmQrvXCCy9o1apVmjp1qjp37qwpU6boyJEjeuedd6yOW5AjRw59/PHH1mfdy0xKSor27Nnjut003Z49e6z+sXfCBA1XT5m+a9cut1vBUlNTtWzZMquF3ffee09PP/20ChUqpJCQELcesl5eXlbPbemSk5MVFxen0qVLe6Tbfo8ePdSnTx9Nnz5dXl5eOnr0qNavX6/nn39egwcPtpZj2LBhGj58uCIiIlS0aFGPzAb4+eefu/7/66+/duv9mpqaqtjYWKuDvzrp9q/HHntMPj4++vLLLz22f6QrX0olJCSoePHiWrZsmUaOHCnpSnHM9jf4QUFBOnXqlEqUKKHly5e7xsP09/e31sNCkp566inXDHye3DeSc45ZJ13Xp/P0uT7d/v37deDAAdWvX1+5cuVyFZZtcsr+iY6OVs+ePXXx4kUZY/TTTz/po48+0ujRo/X+++9by+GE67XMeHoypWzHIFtLSUkxCxYsMCNGjDAjRowwCxcuNCkpKR7JsnHjRjNnzhwzZ84cs3nzZo9keO+990xoaKiZN2+eCQwMNB999JEZOXKk6/9tCQ8PN19++aUxxpigoCCzf/9+Y4wxkyZNMh07drSWo3PnziYsLMx8+umnJiEhwSQkJJgFCxaYUqVKmS5duljL4SSLFy82efPmNWPGjDEBAQHmtddeM48//rjx9fU1y5cvt5qlePHiZtWqVcYYY3Lnzm327dtnjDFm9uzZpnnz5lazOEXfvn1NwYIFzfjx483atWvN2rVrzeuvv24KFSpk+vbtazXL5cuXzYoVK0xMTIw5d+6cMcaYI0eOmD/++MNK+15eXsbb29t4e3sbLy+vDEtAQICZNm2alSzGGFOiRAkzZswYa+3dSFJSkunWrZvJkSOHyZEjhzlw4IAxxphevXqZ0aNHW8uRlpbm+huTvl/8/f3Nyy+/bC2DMcaEhISY2bNnW23zWumvP7Pj1dfX15QrV8588cUXVjPNnj3b1KtXzxQtWtQcOnTIGGPMG2+8YRYvXmw1R0BAgNm9e7fVNjPTs2dPU7JkSRMZGWkKFizoOpd99NFHpnr16lazPPLII6ZGjRqme/fuJiAgwJw8edIYY8xnn31m7rjjDms5AgICXH97ncApx+zp06fNe++9ZwYMGGBOnTpljDFm06ZN5tdff7Wawynn+pMnT5rGjRu7znHpOaKiokx0dLS1HOmcsn8++OADU6ZMGde5PjQ01Lz//vtWMxjj+eu1dCkpKWb48OGmWLFibsfryy+/7JH3JTuhKJWN7du3z5QtW9YEBASY6tWrm+rVq5uAgABTvnx5VxHEhsTERNOoUSPj5eVl8ufPb/Lnz2+8vLxM48aNzfHjx63lSOeEE2RAQIA5fPiwMebKh4VNmzYZY4w5cOCAyZMnj7UcSUlJ5umnnzZ+fn6uD7e+vr7m6aefNufPn7eWI11CQoKZMmWKefHFF03fvn3dFpu+/fZbExkZaQoXLmxy5cpl7rrrLvP1119bzWCMMYGBga7jJDQ01Pz444/GGGMOHjxoAgMDrWY5f/68WbJkiZk6daqZNGmS22JTamqqGTt2rClWrJjr33CxYsXM2LFjrRbcDx06ZG6//XYTEBDgdmHx7LPPmieffNJahri4OOPl5WU2bNhgDh065FqOHj1q/QuI3Llzu94HT3v22WdNzZo1zdq1a01gYKAr1+LFi021atWs57l06ZLZuXOn+fHHH61fBBtjTIECBaz+3b+RsLAwc+LECU/HMG+//bYpVKiQGTlypMmVK5frGJkxY4Zp2LCh1SwRERFm7dq1VtvMTHJysnnttdfMs88+6/bl4YQJE8x7771nNcvp06dNz549TatWrcxXX33lWj9kyBAzcuRIazkaNWrk1r4nOeWY3bp1qylcuLApU6aM8fHxceUYNGiQ6dy5s7UcxjjnXN+5c2fTtGlTk5CQYIKCglw5li1bZipWrGgthzHO2j/pkpKSTGJiokfadsL1Wrphw4aZ8PBw88EHH7j9G543b56pU6eO1SzZDUWpbKx58+amWbNmrgq5MVcq+c2aNTMtWrSwlqNdu3YmIiLC7Nq1y7Vu586dJiIiwnTo0MFajmt58gRZrlw588MPPxhjjLnrrrtc3+bMmzfPFC5c2Hqe8+fPm61bt5qtW7d6pBhljDErV640AQEBplKlSsbHx8dUq1bN5MuXz+TNm9c0atTII5k8rXLlymb16tXGGGOaNGli+vXrZ4y50qMuNDTUWo7NmzebkJAQkydPHpMjRw5TuHBh4+XlZQIDA02pUqWs5bjW2bNnzdmzZz3SduvWrc2jjz5qLl265HYBumrVKlOmTBmPZMpMWlqatba6detmpk6daq29GylRooRZv369Mca47Z99+/aZ3LlzezKaR7zwwgtm+PDhno5hjLnxMZmUlGQtR4UKFcyiRYuMMe7HyPbt203BggWt5TDGmNjYWFO3bl2zatUqc/LkSde5zZPnOFyxcOFCU7FiRTNjxgyzceNG17VS+mKTU47ZJk2amP79+2fI8f3335uSJUtay2GMc871wcHBZsuWLRlyHDhwwPqXiE7aP07gpOu10qVLm5UrVxpj3PfN7t27Tb58+axmyW4YUyobW7NmjX744Qe36UALFiyoMWPG6K677rKWY9myZVq5cqXbmE0VK1bUlClTdN9991nLca2AgAAFBAR4pO30mSBq166t3r1769FHH9W0adMUHx+vvn37Ws8TGBjo8emvBw4cqOeff17Dhg1T7ty59emnn6pIkSLq1KmTmjVr5pFM58+fzzBOkc0Z76KiorR161Y1aNBAAwYM0AMPPKDJkyfr8uXLVqcX7tu3rx544AHFxMQob968+uGHH5QzZ049+uij6tOnj7Uc17K5L661du1arVu3Tr6+vm7rw8LCrA7WLF0Zj2bKlCkZZpQ5dOiQOnfubG2w8zJlymjw4MH64YcfVLlyZeXMmdPtcVtTc0tXxsUrUqRIhvVJSUlWx/e4ePGi3nrrresO/m5rFqSLFy/q3Xff1cqVK1WlSpUM+8bm+SQyMlKzZ8/OMN7Zjz/+qM6dO2vv3r1Wcjhl4hPpynsiXRlb8WrGAzMBOmWyAunKjIw//fRThixeXl7q3LmzlQzpAyR369bNrX1P7BunHLMbNmzQO++8k2F9aGio29iGNjjlXJ+UlJTpZ4rff//dNfGTLU7ZP6dOndKQIUOuez6xNamTk67XnDSZUnZDUSob8/Pzy3Sw6vPnz2f4h3krpaWlZbgAlqScOXNan4XAKSfIqwepbt++vUqWLKl169apbNmyeuCBB6xkSLdx48brzgJoc5rU3bt366OPPpIk+fj46M8//1RQUJCGDx+u1q1b6+mnn7aSIy4uTr169dLq1avdpmv3xAXo1QXKyMhI7dmzR5s2bVKZMmWsFhG3bNmid955R97e3sqRI4cuXbqk8PBwjRs3Tl27dtVDDz1kLYt0ZdD56x2ztj7kp6WlZXos/Prrr64ZUG3ZunWrqlSpog8++MA1K+KsWbP07LPPqnHjxtZyvPvuuwoKCtKaNWu0Zs0at8e8vLysFqXSBybu3bu3q31Jev/9963OHNm9e3ctX75cDz/8sGrVquWxQZK3bdumatWqSZJ27NjhkQzp/P39VaVKFb399ttq37690tLSNHz4cI0aNUrPPPOMtRxOmfhEcs7MXU6arOCLL75Qp06ddP78eeXJkydDFltFKafM7iY555j18/PTuXPnMqzfu3evChcubC2H5Jxz/T333KPZs2drxIgRrhxpaWkaN26cGjVqZC2H5Jz907lzZ+3fv1/du3dXcHCwx/7+Oel6zUmTKWU7Hu6phf9B586dzR133GF++OEHk5aWZtLS0sz69etNpUqVTNeuXa3laNWqlalfv745cuSIa92vv/5qGjRoYNq0aWMthzFXbmksW7asGTNmjJkxY4aZOXOm2/Jv89FHH5mcOXOa+++/3/j6+pr777/flCtXzuTNm9c89thjVrMEBwe7bvGsUKGC+eyzz4wxxmzZssVq1+d69eqZunXrmnnz5plVq1aZ1atXuy3/RoUKFTJ79+41xhhTtmxZs2zZMmPMle7GAQEBVrNMmjTJBAUFmV69ehlfX1/z5JNPmsjISJM3b17z0ksvWcvRrl0706NHD2PMlS7YBw8eNH/88Ydp3Lix9X87ycnJ5vnnnze+vr5m4MCBpm3btiYoKMi8++67VnM4ydq1a01QUJB56qmnjL+/v+nTp4+59957TWBgoNm4caO1HHny5DHfffedtfayi8mTJ5uAgADTsWNHU7duXVOsWDHr4/Y5ZeITJ3HSZAVly5Y1ffr0sXpLp9M55Zjt3r27adOmjUlOTnb9/Tt8+LCpXr266dOnj7UcxjjnXL99+3ZTpEgR06xZM+Pr62sefvhhU6FCBRMcHGx9PD+n7J+goCDXLY2e5KTrNSdNppTdUJTKxk6fPm1atWrlmtnG19fXeHt7mzZt2pgzZ85YyxEfH2+qVatmcubMacLDw014eLjJmTOnqV69uklISLCWwxjnnCCdonLlymby5MnGmP+7tzktLc306NHDDBkyxGqW1q1buz5E9+vXz5QpU8aMHDnS1KhRwzRp0sRajsDAQLNnzx5r7WUH9957r/nwww+NMcY8/vjjplatWuaDDz4wTZs2NbVq1bKapXz58mbu3LnGGPf78QcPHmx69uxpLUdCQoKpWLGiqVChgvHx8TF16tQxBQsWNOXLl/fYWHVDhgwxXl5eJmfOnGbdunXW2x82bFimHyAvXLhghg0bZj3P/v37zeOPP27uvPNOU6FCBdOpUyezbds2qxkqVKhgfdyZzERFRblmHLra+fPnTVRUlAcSGTNgwADX8fr99997JIMTJj5Jd/r0afP111+bOXPmmFmzZrkttjhpsoKAgADHZNm7d6955513zIgRI8ywYcPcFtuccMyeOXPGREZGmnz58pkcOXKY4sWLm5w5c5r69et7ZFxSJ5zrjbnyvowcOdK0bdvWNG/e3AwaNMgcPXrUIzmcsH8iIiJc4315ktOu15wymVJ242WMMZ7urYX/zb59+7R79255eXmpQoUKmd7LeqsZY7Ry5Urt2bNHklShQgXXGAo23XnnnXrrrbdUp04d6207UWBgoHbu3KmwsDAVLFhQq1evVuXKlbV79241btxYv/32m7UsBw8e1Pnz51WlShUlJSWpX79+rlsaJ0yYkKGr663SqFEjDRo0yCPHp1Nt3LhRf/zxhxo1aqTjx4+rS5curn0zbdo0121BNgQEBGj37t0qWbKkihQpohUrVqhq1arat2+f6tSpo1OnTlnLkpKSovnz52vr1q06f/68atSooU6dOilXrlzWMkjS5cuXNWDAAE2ZMkX9+vXTd999p71792ratGlq0aKFtRw5cuTQb7/9lmF8j1OnTqlIkSJWb311iq+++kpvvvmmYmJirJ3DMnO9fXPy5EmFhIQoJSXFWpbTp0/r8ccfV2xsrF577TWtWbNGixcv1rhx46zevne1Cxcu6Pz585mOTWPDf7tVzdbQAt27d9edd96pp556ykp7N/LQQw+pQ4cOateunUdz/LdbGm3dMn4tTx+zkvT999+7/f3juslZPL1/NmzYoAEDBmjIkCGqVKlShqFcbI4L6pTrNdw8ilL/EOm70VP38zqFk06QTnDbbbfpq6++UuXKlVWlShUNHDhQHTt21Pr169WsWTOdPXvW0xGtO3DggJ566ik9+uijmR4jnh4Q/t8uPDxcn376qapXr66IiAj16NFDTz75pJYvX64OHTpY+/DmJFWrVtWFCxc0Z84c1alTR8YYjRs3TkOHDlW3bt309ttvW8nh7e2txMTEDGNWfPPNN2rfvr1OnDhhJYeTnDhxQu3atdO3336rgICADOeTW328njt3TsYY5c+fX/v27XPbN6mpqfriiy80YMAAHT169JbmuFpoaKhKlSqlOXPmqFSpUpKk+fPn65lnnlGdOnW0ZMkSa1mcoly5cmrRooVGjRrlsQlYJGn06NGaMGGCWrZs6fHJCqZNm6bhw4crKioq0yytWrWykqNkyZJ65pln9OKLL1ppD/in2Ldvnx555JEMhVvjgTFakf0x0Hk2N23aNL3xxhvat2+fJKls2bJ67rnn9Pjjj1vNERsbqzfeeEO7d++WdKWn1HPPPWe9ap8vXz6dO3cuw+C//9YTZP369bVixQpVrlxZbdu2VZ8+ffTNN99oxYoVGWYButU2bNigtLQ01a5d2239jz/+qBw5cigiIsJKjhMnTujAgQOKiopyrfPUTDtOERcXp5SUFJUtW9Zt/b59+5QzZ06FhYVZy9K4cWN9/vnnql69uqKiotS3b18tWLBAGzdutD7gulNERETozTffdM2+5+XlpRdffFH33XeflcGA8+fPLy8vL3l5ealcuXJuX36kpqbq/Pnzjuh54QkdO3bUkSNHNGrUKI8M9JovXz63fXMtLy8vDRs2zGqmp556SoMGDZK3t7drXfv27XXXXXe5nXf/TY4cOaJnn33WowUpyVmTFfTo0UOSNHz48AyP2fxbfPr0abVt29ZKW8A/SadOnZQzZ07NnTvXowOd45+BnlLZ2JAhQzRhwgT17t3bNQPF+vXrNXnyZPXt2zfTP/S3wttvv60+ffro4YcfduX44YcftGDBAr3xxhvq2bOnlRySVKtWLfn4+KhPnz6ZniAbNGhgJUd4eLg2bNigggULuq0/c+aMatSooYMHD1rJ8fvvv+vixYsqVqyYa5aQ9NuyXn75ZeXPn99KDunKvnnhhRf08MMPu61fuHChxo4dqx9//NFKjooVK6pChQp64YUXMj1GbN+Ck5aWpv3792c6W2T9+vWtZGjQoIG6deumrl27uq3/4IMP9P7772v16tVWckhX3o+0tDT5+Fz5zmTevHmuY/bJJ5+0OrNodnDp0qVbPh31rFmzZIxRt27dNHHiROXNm9f1mK+vr8LCwqzOguQkAQEBWr9+vapWreqR9tesWSNjjBo3bqxPP/1UBQoUcD3m6+urkiVLqlixYh7JJkkXL16Uv7+/x9p3CqfcqoaMnHRLI5CdBAQE6Oeff1b58uU9HQX/ABSlsrHChQvrzTffVMeOHd3Wf/TRR+rdu7dOnjxpJcdtt92mAQMGqFevXm7rp0yZolGjRunIkSNWckjOOUF6e3vr2LFjGcYCSExMVIkSJXTp0qVbniElJUVz585V06ZNFRwcfMvb+2+CgoK0bds2hYeHu62Pi4tTlSpV9Mcff1jJERgYqK1bt3pk7LVr/fDDD3rkkUd0+PBhXXsqtvlNcZ48ebR58+YM78n+/fsVERGhM2fOWMmRkpKiUaNGqVu3brrtttustJldzJkzRzExMYqLi9P69etVsmRJTZw4UaVKlVLr1q2tZFizZo3uuusuV8EQUo0aNfT22297fBzDw4cPq0SJEo74pjotLU2vvvqqYmJilJiYqL179yo8PFyDBw9WWFiYunfv7umIVnz++eeu/z9x4oQjblVLl5ycrLi4OJUuXdoR/55tFy/ffPNN1/8nJSU55pZGIDupX7++hgwZwlhj+Ft4/i8Rbtrly5czveWpZs2aVgc1PXPmjJo1a5Zh/X333Wf9Hv2IiAglJCR4rCh19UXo119/7dajIDU1VbGxsdZuhfLx8dFTTz3luqXS0/z8/JSYmJihKPXbb79ZvShu3LixY4pSTz31lCIiIrRkyRIVLVrUYx8ovby8Mi0Knj171urtjD4+Pho3bpy6dOlirc3sYOrUqRoyZIiee+45vfrqq659ki9fPk2cONFaUSopKUmxsbFq2rSp2/qvv/5aaWlpat68uZUcmTl37py++eYblS9fXhUqVLDW7pgxY9SvXz+9+uqrmX6gtTWO4TfffKOgoKAMtyF98sknunDhQoZekLfSyJEjNWvWLI0bN851i5YkVapUSRMnTrRSlLp8+bKaNWummJiYDLcl29KmTZsM6zx9q9qFCxfUu3dvzZo1S5JcBcPevXsrNDRUAwYMsJJDunJNNGrUKI8UL9944w23n51yS2Nmzpw5o3z58nk0gyfNmDFD7du39/itr8iod+/e6tOnj/r375/p3z/GaL1yntu+fbtKlixp9e6UbMn2dH/4+/Tq1cv07ds3w/p+/fqZZ555xlqOjh07mnHjxmVY/9prr5n27dtby2GMMR9//LGpWLGimTFjhtm4caPZunWr23KrpU/h6+3t7fr/9MXX19eUK1fOfPHFF7c8R7oGDRqYxYsXW2vvRjp06GAaNGhgzpw541p3+vRp06BBA9O2bVtrOd555x1TvHhxM3ToULNgwQLz2WefuS02BQQEmH379lltMzP333+/adu2rUlJSXGtS0lJMf/5z39Ms2bNrGZp1aqVmTlzptU2na5ChQpm0aJFxhhjgoKCXNOob9++3RQsWNBajsqVK5slS5ZkWP/VV1+ZKlWqWMthjDFt27Y1b731ljHGmAsXLpiyZcuanDlzGh8fH7NgwQJrOa4+51+9pK+zpWzZsuabb77JsH716tWmXLly1nIYY0zp0qXNypUrjTHux+vu3btNvnz5rOUoVKiQ2bt3r7X2soNnn33W1KxZ06xdu9YEBga69s3ixYtNtWrVrGYZNmyYCQ8PNx988IHJlSuXK8u8efNMnTp1rGZxijFjxph58+a5fm7btq3x9vY2xYoVM1u2bLGWY9OmTWbbtm2unxcvXmxat25tBg4caC5dumQthzHGFClSxOTOndt069bNfP/991bbvtqxY8fMo48+aooWLWpy5MiR4ZxvU/369c2sWbPMhQsXrLZ7rWs/51z9+cf2e3L69Gnz3nvvmQEDBphTp04ZY64cx7/++qvVHH369DHvv/++MebKdfRdd91lvLy8TGBgoFm1apXVLNkNPaWyuWnTpmn58uWuWwd+/PFHxcfHq0uXLoqOjnZtN2HChFuWoWLFinr11Ve1evVqtzGlvv/+e/Xr18+tm/St/sapffv2kqRu3bq51tkcxDp9TKBSpUppw4YNKlSo0C1t77955plnFB0drYSEBNWsWdM1UHI6m99ivP7666pfv75Kliyp6tWrS5K2bNmi4OBgzZkzx1qO9HEjPP2NtSTVrl1b+/fv93ivrbFjx6p+/foqX7687rnnHknS2rVrXb1PbGrevLkGDBig7du3Z3rM2rrNJTU1VW+88YY+/vhjxcfHKzk52e1xm7MAxsXFuf7NXM3Pz09JSUnWcuzbt08VK1bMsP7222/X/v37reWQpG+//VaDBg2SJC1atEjGGJ05c0azZs3SyJEj9Z///MdKjlWrVllp57+Jj493zXR3tZIlSyo+Pt5qliNHjmR6TktLS9Ply5et5Xj00Uc1bdo0jRkzxlqb1zN79my1b98+w/hvycnJmjdvnrXeoYsXL9b8+fNVp04dt565d9xxhw4cOGAlQ7rZs2fr3XffVZMmTdzGc6patar27NljLcfw4cP1/PPPZ+iJ8+eff+q1117TkCFDrGWJiYnRhx9+KElasWKFVqxYoa+++koff/yx+vfvr+XLl1vJ8eSTT2rAgAGqXLmyDh48qA4dOujBBx909bycOHGilRzSlfPJF198oZkzZ6phw4YKDw9XVFSUunbtqpCQEGs5HnvsMcXHx2vw4MEe7dkuSdWrV9fzzz+v3r17q127durevbtHbiGPi4uz3mZmtm3bpsjISOXNm1eHDh1Sjx49VKBAAS1cuFDx8fGaPXu2tSwLFizQo48+Kkn64osvFBcXpz179mjOnDkaNGiQvv/+e2tZshvGlMrGGjVq9Je28/LyuqUfLDO7EL5ejls9wPfhw4dv+LjtQayv5oku2FfPfpTOkzPNJSUl6cMPP9TWrVuVK1cuValSRR07dszQ5fffYtGiRXr55Zcd0fX56NGjmjx5stu+6dWrl9vAyTZkdsyms3nMDhkyRO+//7769eunl19+WYMGDdKhQ4e0ePFiDRkyxOotHRUrVtTo0aPVunVr5c6dW1u3blV4eLjeeustzZgxI8N0zLdKSEiI5s6dm2F205UrV+qRRx7R8ePHreSQpFy5cmnv3r0qXry4unTpomLFimnMmDGKj49XxYoVdf78eWtZnKBEiRKaPHlyhqLtZ599pp49e+rXX/8fe28eV3P6//8/Ku3raSOkTiRpIwbDVJKxr1ERQrZGqkkjy8jaDBrJ0nxsGWStd3ZmCq3KUmlFtJIlW02oBqnr90e/zttxDjPv7/vd9XrFdb/dzm3qerndrsd0zut1rut5PZ+P5wNqWnr16gV/f39MnTpV7PO6Zs0aXLhwAZcuXaKiw8fHB5GRkTA1NZUa4G7Jw7oPkZOTQ0VFhYTPZGVlJfT19ak911RUVHDjxg2YmJiIvTe5ubmwt7fHixcvqOgAmu7h27dvw8jISEzLrVu30KdPH2r3MF/eG0D8uebn54fXr19j586dKCwsRN++ffHnn39S0aGpqYmsrCx07twZGzZsQEJCAuLi4pCWloZJkybh/v37VHR8yJMnT3Dw4EHs378ft2/fxrBhwzBr1iyMHj36k2uH/wXq6uq4dOkSevTo0aLz/FPevXuH06dPY//+/fjjjz/QpUsXeHp6Ytq0abzwkaXJ4MGDYWtri5CQELFnyeXLl+Hu7o67d+9S06KkpITi4mJ07NgRc+fOhYqKCjZv3oyysjLY2Njg5cuX1LS0NlimVCuGLye0fImUA9wGnd5nw4YNMDY2FmVuubi44NixYzAwMMDvv/9OrVMTn94boMlkfO7cuVzL4A3N2RxcZfa9T/v27fHzzz9Tm+9jfNiBkCsOHTqE3bt3Y+TIkVi1ahUmT56Mzp07w9raGlevXqUalFq4cCG8vb3x+vVrEEKQnp6OI0eOYN26dYiIiKCmY+zYsfj+++9x4sQJdO7cGUCTGX5AQAB1o2ZDQ0NcuXIF2traiI2NxdGjRwE0tXen3e3t9evXyMvLk9pBk9bfZfLkyfD19YW6urqoa2dycjL8/PwwadIkKhqaWbFiBaZPn46HDx+isbERx48fx507dxAZGYmzZ89S03Hjxg3Y2toCaPJOeh/aWQ7Nz/QPefDggZj3ZEvT7GHo4+MD4N9/h4iICOodNLt3745Lly5JrNtiYmKkZoa2FB97b3Jzc6kfyggEAty/fx+GhoaIjY1FcHCwSCPN9QAhRPQsu3jxIkaNGgWg6blLq4mSNNq2bYtvvvkGhYWFKCwsRH5+PqZPnw6BQIC9e/di4MCBLTa3oaGhREMaLmnTpg2cnZ3h7OyMp0+fYteuXQgKCsKyZcswYsQI+Pr6ShwgtQSPHj1Camqq1O8/WuukjIwM7Ny5U2K8Q4cOePz4MRUNzbRt2xa3bt2CgYEBYmNjsX37dgBNfn5ycnJUtbQ2WFCK0eo5ffo0hg8fDnl5eTGjcWnQ2iB8mIJ98eJFxMbGUk/B5kuQrpmioiIkJiZK/fJqyRT5rVu3Yu7cuVBSUhIrJ5UGzWADn4KG1dXVSE9Pl/refInG448fP4aVlRWAJhPc5gyCUaNGISgoiKqW2bNnQ1lZGcuXL0ddXR3c3d3Rvn17bNmyhWrAISQkBMOGDUO3bt1E3REfPHgAOzs7bNy4kZoOAPj+++8xZcoUqKmpwcjISLQZSUlJEb1vNIiNjYWHh4fUjRrNwPLatWtx9+5dODk5iRpHNDY2wsPDg3qweezYsThz5gzWrFkDVVVVrFixAra2tjhz5gy+/fZbajr4cHDXs2dPyMjIQEZGRuy9AZpKhMvKyqQ2imkpfv75ZwwfPhy3bt3Cu3fvsGXLFty6dQuXL1+WMPluabgOXgoEAtF707VrV7HAVENDA2pqasTKCmng7OwMd3d3mJqaorKyUtQ8Ijs7m2qZf+/evREcHIzBgwcjOTlZtLEuKyvjJAvnyZMnOHDgAPbu3YvS0lKMGzcOZ8+exeDBg1FbW4s1a9Zg+vTpf1st8d+wefNmLFmyBDt37qTWsOifkJ6ejr179+Lo0aPQ19fHjBkz8PDhQ4waNQrz589v0e/mffv2Yd68eVBQUICOjo7YPUSzSYCioqLUDKTCwkLo6elR0dDMzJkz4erqKirxbO5MeO3aNXTr1o2qltYGK99jtHpkZWXx+PFj6Ovr86b0hy8p2Hxi9+7d+O6776Crq4t27dpJfHm1ZAmSUChEZmYmdHR0PlluSqPElI+cOXMGU6ZMQU1NDTQ0NCTeG5r+SXzBzMwMkZGR6Nu3L7755huMGjUKS5YsQVRUFHx8fKiWqr1PXV0dampqJEpNaEEIwYULF8TKPJszc2iTmZmJ+/fv49tvv4WamhoA4Ny5c9DS0sKAAQOoaDA1NcWQIUOwYsUKXpRMFBYWit4bKysr3h1McEFxcTFKSkpgb28PZWXlj2bGtASrV68W/TcgIED0OQUABQUFGBsbY8KECVBQUKCiBwBKSkqwfv165ObmoqamBra2tli8eDHVYG4zly5dwpo1a8S0rFixAkOGDGnxuffv3w9CCDw9PbF582axjLXm94Z29lh9fT22bNmC+/fvY8aMGaKMsbCwMKirq2P27NlUdOTl5WHKlCkoLy/HwoULsXLlSgBNJbGVlZU4fPgwFR0AMHr0aMTFxaFr166YPXs2PDw8JDLYnj59inbt2rVoprVAIEBdXR3evXsHFRUVCbsFmuukp0+fioJ0RUVFGD16NGbPno2hQ4eKnm2pqakYNmxYi5bBGhoawsvLC0uXLm3x8slPMXv2bFRWViI6Ohra2trIy8uDnJwcxo0bB3t7e6oeaEBTtuf9+/fh4uIiOsDbv38/tLS0qHVLbo2woBSD0QK0b98eMTEx6N+/P8zMzBAcHAwXFxfcuXMHX3311RdZU2xkZIT58+dj8eLFXEvhHbdu3ZJqpk0rs69r164YMWIEfv75Z9Z2+f9nyZIl0NDQwLJlyxAVFYWpU6fC2NgY5eXl8Pf3p2qePGjQIBw/flzCk+7ly5cYN24cdTN6xr/R0NBAdna2qJyRAZiYmCAjIwM6Ojpi49XV1bC1taUW+K+srISrqysSExMhIyODoqIimJiYwNPTEwKBAKGhoVR0AE0bEjc3N+qlpYy/Jzk5Gf379/9ivS3/E16/fg05OTmqf6tZs2Zh9uzZnwwQEkJQXl7eokH4/fv3f/L69OnTW2zuD1FQUEDnzp3h6emJGTNmSM0GevnyJcaOHduiGaM6OjpIT0/n/PvvxYsXmDhxIjIzM/Hq1Su0b98ejx8/xtdff43ff/9dwk+wJeFLU4vWCAtKMT4b6uvrMWzYMOzYsQOmpqacalmwYAHOnj0LU1NTZGdn4+7du1BTU8PRo0cREhJCzZiYT2hoaCAnJwcmJiacaaivr0e3bt1w9uxZmJubc6ajmdLSUowfPx75+fkiLyng3z4ftDL7VFVVkZ+fz+l7w3euXLmCK1euwNTUFKNHj6Y69/vZoO/z9OlTdOjQgWpHs9raWiQnJ0sNotIsfX3fh00av/32GzUdAwYMwKxZs6jM9ykePHiA06dPS31vaJp6f+zz+uTJE3Tq1Alv3ryhosPDwwNPnz5FREQEzM3NRea3cXFxWLhwIW7evElFx/tcv34dBQUFAJo63tH0Tnqfp0+fSi3Vptlc431qamoktGhoaFCbv6GhASdPnhR7b8aMGUPdA2b//v3Q1dXFyJEjAQCBgYHYtWsXunfvjiNHjlDLfLx//z5kZGREWR7p6ek4fPgwunfvTt0XlG3yxSGEIDU1Fb1794aysjKnWgIDA6GtrY0lS5ZwqqOZtLQ0sazL5tI5mvCpcUJrgwWlGJ8Venp6uHz5MudBqfr6emzduhXl5eWcpmDziVmzZuGrr76i7tHwIR06dMDFixd5EZQaPXo05OTkEBERAaFQiPT0dFRWViIgIAAbN26EnZ0dFR3Ozs6YNGkSXF1dqczH+Gfk5eUBAHr06IGEhASxkoWGhgbExsZi586d1DrLZGdnY8SIEairq0NtbS20tbXx/PlzqKioQF9fn2rp6/jx48V+r6+vx40bN1BdXS3KLKNBXV0dXFxcoKenJ7WDJq1AXXx8PMaMGQMTExPcvn0blpaWuHv3LgghsLW1pZJN1+zpOG7cOOzfv1+sHKqhoQHx8fG4cOEC7ty50+JagKZukXFxcbCxsRHryFRaWgpra2uqHRqfPn2KSZMmISkpSZTxWF1dDUdHRxw9epSa78n169cxffp0FBQUSJg2026uUVZWhgULFiApKQmvX78WjdNu9FFcXIwRI0bg4cOHMDMzAwDcuXMHhoaGOHfuHNUsEDMzM2zfvh2DBg3ClStXMHjwYISFheHs2bNo06YNteeanZ0d5s6di2nTpuHx48cwMzODhYUFioqK4OPj06IeoB/Cp00+H4KXjY2NUFJSws2bNznf6zQ0NGDUqFH466+/pH7/0TgMqa+vh7KyMnJycmBpadni8/0dsrKyePLkicQzPTc3F46Ojl+kHcY/hRmdt3IOHDiAHTt2oKysDFeuXIGRkRE2b94MoVBItW710qVL2LlzJ0pKShATE4MOHTrgwIEDEAqF+Oabb6jpmDp1Kvbs2UO1tOZD6uvrMW/ePAQFBUn4F/n7+1PVwpcyCgDo0qULgoKCcPXqVU43b97e3tiwYQMiIiLETGe54MqVK0hISICuri5kZWUhKyuLb775BuvWrYOvry+ys7Op6Bg5ciQWLVqEW7duSX1vaHdWa2xsRHFxsdSTfJr+RVw/X3v06CEy4pXWRUdZWRnbtm1rcR3N+Pv7Y/To0dixYwc0NTVx9epVyMvLY+rUqfDz86OmAwBOnDghMdbY2IjvvvuO6ibyyJEjOH/+PJSUlJCUlMSZ0evSpUvxww8/YPXq1VBXV8exY8egr6+PKVOmUDPSHjduHICm/+8PS1nk5eVhbGxMtWSutrZWajlyVVWVRNZFS+Pj44NXr17h5s2bogORW7duYfr06fD19cWRI0eo6PD09ETXrl2xZ88etG3blnoXwveZOnUqCCH47bffONXi6+uLzp074+rVq6LAf2VlJaZOnQpfX1+cO3eOmpb79++LDM1PnjyJCRMmYO7cuRgwYECLdpb7kBs3bqBPnz4AgOjoaFhaWiItLQ3nz5+Hl5cX1aAUXzpXSgterlu3jnrwUlZWVmSEz3VQat26dYiLixP9PT78/qOBvLw8OnXqxHkGEt+aWrRKCKPV8n//939EV1eXBAcHE2VlZVJSUkIIIWTv3r1k4MCB1HTExMQQZWVlMnv2bKKoqCjSsW3bNjJ8+HBqOgghZMGCBURDQ4P06tWLzJ07l/j7+4u9aKGhoUFKS0upzfcxZGRkyJMnTyTGHz9+TBQUFKhqMTY2/uhLKBRS0zFu3Diirq5ODAwMyJAhQ8j48ePFXjTR0tISfU5MTExIQkICIYSQ4uJioqysTE2HjIzMR1+ysrLUdBBCyJUrV4hQKCSysrKcauHD8/Xu3bukrKyMyMjIkIyMDHL37l3R69GjR+Tdu3dUdDSjqalJbt++Lfr51q1bhBBCrl69SszMzKhq+Ri3b98m7dq1ozZf27ZtyU8//UQaGhqozSkNNTU1UlxcTAhpeq7cuHGDEEJITk4OMTIyoqrF2NiYPHv2jOqc0hg+fDhZvnw5IaTp71NaWkoaGhqIi4sLmTBhAlUtGhoaJD09XWL82rVrRFNTk5oONTU1UlRURG2+T6Gqqip6nnCJiooKycvLkxjPyckhqqqqVLXo6emRrKwsQgghPXr0IJGRkYSQpjUBTS2qqqqkrKyMEELI6NGjyfr16wkhhNy7d48oKSlR0dCjRw/Ss2dPIisrS6ysrEjPnj1FL2tra6Kurk5cXFyoaCGk6XkybNgwUllZKRp7/vw5GTZsGBkxYgQ1HYQQcvr0afLNN9+Q/Px8qvN+iJaWFtm7dy+nGgghJCIigowYMULsvaHNqlWryKpVq4iMjAz54YcfRL+vWrWK/Pzzz+Tw4cPkzZs3nOlrDbBMqVbMtm3bsHv3bowbN04sM6h379744YcfqOkIDg7Gjh074OHhgaNHj4rGBwwYgODgYGo6gKbTHVtbWwBNXYjeh+Yp3Lhx43Dy5EnqmVHNNJdRAEBcXJzUMgraLW3LysqozvcxtLS0MGHCBK5lAAAsLS2Rm5sLoVCIvn37IiQkBAoKCti1axdVf6eW7Fjzn+Ll5YXevXvj3Llzopa6XMCH52uzfwhf3h95eXlRhx19fX2Ul5fD3NwcmpqauH//PsfqmigpKcG7d++ozff27Vu4ublx2nkIaPKFa/aRMjAwQElJCSwsLAAAz58/p6qFL8/6kJAQODk5ITMzE2/fvkVgYCBu3ryJqqoqpKWlUdXS2Ngo1RxaXl6e6v3t5OSE3NxcUTYOl3z11Ve4f/++KMuCKxQVFfHq1SuJ8ZqaGqpdEQHg22+/xezZs9GzZ08UFhZixIgRAICbN29SXbNZWFhgx44dGDlyJC5cuIC1a9cCAB49eiSRed9SNGde5uTkYOjQoR/tXEmL5ORksWw6oMnoe/369dS6vTbj4eGBuro62NjYQEFBQcJbilaJmKKiIvX/d2mEh4ejuLgY7du3h5GRkYSxOQ0f3+YOlcbGxqypxf8jLCjViikrK5NqkqmoqIja2lpqOu7cuSO1pEZTUxPV1dXUdABo0S4T/wmmpqZYs2YN0tLS0KtXL4kHZEuXdPCtjOJDyAeG3jTZu3cv9Tk/xvLly0X36po1azBq1CjY2dlBR0cHUVFRHKvjhqKiIsTExHC+aeLL85VP9OzZExkZGTA1NYWDgwNWrFiB58+f48CBA9S9HBYuXCj2OyEEFRUVOHfuHNUuSNOnT0dUVBSWLVtGbU5p9OvXD6mpqTA3N8eIESMQEBCA/Px8HD9+HP369eNUG1dYWlqisLAQ4eHhUFdXR01NDZydneHt7Q0DAwOqWgYNGgQ/Pz8cOXIE7du3BwA8fPgQ/v7+cHJyoqYjIiIC06dPx40bN2BpaclpqXZERAS8vLzw8OFDqVpoma6PGjUKc+fOxZ49e0Qla9euXYOXlxf10vVff/0Vy5cvx/3793Hs2DFRAOj69euYPHkyNR0bNmzA+PHj8csvv2D69OmwsbEB0HTg2fw3amn4tsnnU/AyLCyM09LbZvz8/LBt2zZs3bqVUx3Nex4+0Lz+ePv2rVQLik6dOnEhq1XAjM5bMd27d8e6deswduxYMRPPbdu2Ye/evdQ6vJmYmGDXrl0YPHiwmI7IyEisX78et27doqLjfYqLi1FSUgJ7e3soKyt/tCa9pfjQS+p9ZGRkqHk5CYVCZGRkQFdXl8p8f0dkZCR++eUXFBUVAQC6du2KRYsWYdq0aVR1vHv3DklJSSgpKYG7uzvU1dXx6NEjaGhoiJ3GcUFVVRUEAgH1BUdycjI2btwoMvDs3r07Fi1aRM1svZlBgwYhMDCQ89p7vjxf+URzu2VHR0c8ffoUHh4eosYSv/32m2jjQgNHR0ex32VlZaGnp4dBgwbB09OTml+cr68vIiMjYWNjA2tra06MXoGmTp41NTWwtrZGbW0tAgICRO/Npk2bqHXt4hPl5eUwNDSU+iwtLy+nujm4f/8+xowZg5s3b8LQ0FA0ZmlpidOnT4u6nLU0Z86cwbRp0/Dy5UuJa7SNzq9evQp3d3exRg3NXWhpaqmursb06dNx5swZ0f377t07jBkzBvv27aPqW8QnGhoa8PLlSwgEAtHY3bt3RY0tvjQ8PDyQlZUlEbycM2cOevXqhX379nErkAPGjx+PhIQE6OjowMLCQuL7j5YxP58oKiqCp6cnLl++LDZO+7nWGmGZUq2YhQsXwtvbG69fvwYhBOnp6Thy5AjWrVuHiIgIajrmzJkDPz8//Pbbb5CRkcGjR49w5coV/PDDDwgKCqKmA2gyp3R1dUViYiJkZGRQVFQEExMTzJo1CwKBgFp2EF/KF6TpqK6uFnX/ocmmTZsQFBSEBQsWiNJ9U1NT4eXlhefPn1Mrdbx37x6GDRuG8vJyvHnzBt9++y3U1dWxYcMGvHnzBjt27KCi433eD6Jqa2tLdEVqaQ4ePIiZM2fC2dlZlMWXlpYGJycn7Nu3D+7u7tS0+Pj4ICAgAI8fP5Zquk7r9Jwvz1c+0bt3b9HP+vr6iI2N5UwLX7Ji8/PzRRl1N27cELtGM7D8frmvqqoqJ88xviEUCj/atUsoFFLdHBgaGiIrKwsXL17E7du3AQDm5ubUW5b7+Phg6tSpCAoKQtu2banO/SGenp7o2bMnjhw5wqnRuZaWFk6dOoWioiIUFBRARkYG5ubmnGXrNjcOKi0txb/+9S/OGgcRQnD9+nWxwzsFBQWpzQP+12hra6OwsBC6urp/e0hHq1Rt69atmD59Or7++muJ4OWWLVuoaGiGLx0JtbS04OzsTGWu1sKMGTPQpk0bnD17llMLilYJR15WjP8RBw8eJF26dBEZAXfo0IFERERQ1dDY2EiCg4OJqqqqSIeSkpLIYJQm06ZNI0OHDiX3798nampqInPi2NhY0r17d+p6CGn6+zQ2NnIy9/r168nRo0dFv0+cOJHIyMiQ9u3bk5ycHKpajI2Nyf79+yXG9+3bR4yNjanpGDt2LJk6dSp58+aN2GckMTGRdOnShZoOQppMMgcNGiQy8W7WMnPmTLJw4UJqOrp160Y2bdokMR4aGkq6detGTQch0k3Xm03PaZuu8+H5ykeePHlCUlJSSEpKCnn69CnXchjvkZGRQSIjI0lkZCTJzMzkWg6nyMjISP183r17l6ioqHCgiHveN8TnGhUVFd6YrjfD5XqNEP40Drp79y7p1q0bUVFRIXJyciIdvr6+ZN68eS0+/759+8jr168JIU3NRfbt2/fRF20KCwvJ6dOnyenTpzn7/H6sidHDhw+pGdHzieb14cdeNFFRUSEFBQVU5/xcYJlSrZwpU6ZgypQpqKurQ01NDScptTIyMvjxxx+xaNEiFBcXo6amBt27d+ekDOr8+fOIi4uTSIU3NTXFvXv3qGrhQ6najh07cOjQIQDAhQsXcPHiRcTGxiI6OhqLFi3C+fPnqWmpqKhA//79Jcb79++PiooKajouXbqEy5cvS3gAGBsb4+HDh9R0AIC/vz/k5eVFhtHNuLm5YeHChdQy+0pLSzF69GiJ8TFjxlD3yuFDluG7d+9w+PBhDB06lPPnK9CUBZORkSFhMFtdXQ1bW1tq5cCvXr3C/PnzcfToUdFJrJycHNzc3PDrr7+2eJmLra0t4uPjIRAIRO2XPwat8sq9e/di0qRJEkaztHnw4AEmT56MtLQ0USZsdXU1+vfvj6NHj1IrD2umsbERxcXFUj01pHlQ/i9p9huTkZFBUFCQWGZHQ0MDrl27hh49erSoBmnwoUTa2dkZiYmJ1NrXf4pBgwbxxnSdD+s1gD+Ng/z8/NC7d2/k5uaKfe+MHz8ec+bMafH53/cFnDFjRovP959gamoKU1NTTuZu9m6SkZFBRESE2D6roaEBKSkp6NatGzU9K1euhKenJ+fl4SdOnBD7vb6+HtnZ2di/fz9Wr15NVUv37t2pNxf5XGBBqVZMcHAwpkyZAqFQCBUVFSoptdI4ePAgnJ2doaKigu7du3OioZna2lqpf4eqqiooKipS08GXUrXHjx+L/CvOnj0LV1dXDBkyBMbGxujbty8VDc106dIF0dHREkGOqKgoql/wjY2NUlObHzx4AHV1dWo6AP4EUQ0NDREfHy+xObh48aLo80MLrhc3ANCmTRt4eXmJNo9cPl+BJh8PaZ/ZN2/eUA2kzp49G9nZ2Th79iy+/vprAMCVK1fg5+eHefPmiW2iWoKxY8eKnuN8MTZdsmQJ/Pz84OLiglmzZkkNvNNg9uzZqK+vR0FBgaib2Z07dzBz5kzMnj2baqlls1fQvXv3JEqRaXhqZGdnA2gqP8rPzxc7gFBQUICNjQ3VDsWA9BLp1NRU6iXSXbt2xdKlS5Gamiq1PLqlm7C8z+jRo+Hv74/8/HypWmiZjPNlvQbwp3EQnw7vBg8ejKlTp8LZ2RkaGhpU5164cCHWrl0LVVVVieYaH0LDPzAsLAxA07Ntx44dkJOTE11r7khIs3T71KlT+Omnn+Dg4IBZs2ZhwoQJVPdazYwdO1ZibOLEibCwsEBUVBRmzZpFTcuGDRsQGBiIn3/+WepzjfZnuFXBcaYW47/A2tqayMrKkq+//pr8+uuv5NmzZ5zo0NXVJaqqqmTy5Mnk3Llz5N27d5zoIISQ4cOHi8oG1dTUSGlpKWloaCAuLi5kwoQJ1HTwpVTNwMCApKWlEUII6dq1K4mOjiaEEHL79m2irq5OTQchTWnpcnJyZOjQoWTNmjVkzZo1ZOjQoaRNmzbk+PHj1HS4urqSOXPmEEL+/Rl59eoVGTRoEJkxYwY1Hc3zFxYWin5uTpHPyMgg2tra1HT83//9H1FQUCBeXl6i0p958+YRRUVFsmPHDmo63ufmzZvkjz/+IKdOnRJ70cLBwYGcOHGC2nzSaP5/lpGRIZGRkWJ/h+PHjxNvb2/StWtXanpUVFTIpUuXJMZTUlK+2HKo+vp6cvz4cTJmzBgiLy9PzMzMyPr160lFRQVVHUpKSiQrK0tiPDMzkygrK1PVYmNjQ1xcXMitW7fIn3/+Saqrq8VetJgxYwZ58eIFtfk+BV9KpI2NjT/6EgqF1HQQIr1U+/2SbVrwZb1GCCFCoZBcuHCBECK+Jti/fz8xNzenpkNLS4vcvHlTQselS5eIvr4+NR2ENJUMtmvXjigrK5OJEyeSkydPkrdv31KZe+DAgeTPP/8U/fyxl6OjIxU97+uqqqqiOufHyMrKIj4+PkRXV5doaWkRLy8vkp6ezrUsQgghJSUlRFVVleqc7z/D3n9xYUHR2mBBqVbOjRs3yNKlS4lQKCTy8vJkxIgR5NChQ6S2tpaahvr6enLmzBni7u5OVFVViZ6eHpk/f74oGEKT/Px8oq+vT4YNG0YUFBTIxIkTibm5OWnbti1VHwVFRUWpteaFhYVEUVGRmg5vb29iZGREBg8eTHR0dMirV68IIYQcOXKE9OzZk5qOZjIzM8mUKVOIra0tsbW1JVOmTJG6kWpJ7t+/T7p3707Mzc1JmzZtSL9+/YiOjg4xMzOTWqPfkvAliEoIIcePHycDBgwg2traRFtbmwwYMICcPHmSqgZCmhYR1tbWYl5S73/B0yIqKoqYmJiQbdu2kcuXL5Pc3FyxFw0+9NR6/6WgoEC6du1Kzpw5Q0ULIYQYGhqSvLw8ifHc3FzSoUMHajr4yuPHj8nGjRuJlZUVkZeXJ6NHjyYnT54kDQ0NLT63qakpuXbtmsT4tWvXSOfOnVt8/vfhm1dQUVERiY2NJXV1dYQQwolnkIKCgtS/SVFREdU1AUMSvqzXCCHk559/Jt27dydXr14l6urq5NKlS+TgwYNET0+PbN26lZoOPh3eEUJIQ0MDiYuLI9OnTycaGhpEIBCQOXPmkKSkJOpa+MSbN2/I7du3SX19PddSyNu3b8mxY8fIqFGjiLy8PLGysiKbN2+mehDxPnV1dcTPz4/qwR0hhCQlJX3yxfg4LCj1GZGamkrmz59P9PT0qGfBNFNbW0sOHjxIRowYQRQUFIiJiQl1DdXV1SQ4OJi4uLiQ4cOHkx9//JE8evSIqgYLCwvy008/SYyvXbuWWFpaUtPx9u1bsnHjRuLr6ysW/Nm0aRPZvXs3NR18o76+nhw4cIAsWrSIfPfdd2T37t2iDQtN+BJE5ROjRo0iY8eOJc+ePSNqamrk1q1b5NKlS6RPnz4kJSWFmg4+Ga4bGxtzlgn7Pjt37iSDBw8WywKqqKggQ4YMoZJRp6WlRQQCwT96ccXVq1fJ3LlziaKiIjE2NiaamprE2NiYJCYmtui8J0+eJH369CEZGRmisYyMDNKvXz/qGX+Ojo7kjz/+oDqnNCorK3nRSIIQQjp37iz1Htm+fTv1BhsMcfiyXiOEP42D+HR49yF//fUXiY6OJjY2Npxmnrx48YKcOHGCE1Pruro64unpSeTk5MSM6BcsWEDWrVtHXQ8hTQGyo0ePkiFDhpA2bdoQe3t70qVLF6Kuri7WcKkl+HBtoKWlReTk5Ii6ujrVDHvGf4cMIZR7jzNajJycHBw8eBBHjx5FZWUl/vrrL050PH/+HEePHsWOHTtQUFBAte0yXzh27Bjc3NwwePBgkUdBWloa4uPjER0djfHjx7e4hvr6esybNw9BQUEQCoUtPt/f8fvvv0NOTg5Dhw4VG4+Li0NjYyOGDx/OkTJuefHiBcLDw5Gbm4uamhrY2trC29sbBgYG1DRkZGSgsbFRwmfs2rVrkJOTQ+/evalp0dXVRUJCAqytraGpqYn09HSYmZkhISEBAQEBIr+YlubvPL249r6qrq4WGVrTomfPniguLsabN2/QqVMnAEB5eTkUFRUlfOFawmh8//79op8rKysRHByMoUOHivlbxcXFISgoiKoPzJMnT3DgwAHs3bsXpaWlGDduHGbNmoXBgwejtrYWa9aswdGjR1vUJ04gEKCurg7v3r1DmzZNdqHNP6uqqor925ZuoX7ixAksX74cixYtkuqpYW1t3aLzN+Ph4YGnT58iIiIC5ubmyM3NhYmJCeLi4rBw4ULcvHmTig4A2L59O77//nt4enqKfMfS0tKwb98+bNmyBfPmzWuxufnki7N161bMnTsXSkpKItPmj0HL34oP67UPefv2LeeNg969e4ejR48iLy9PtDaZMmUKp00dHj9+jKNHj+LgwYPIyspCnz59cPXqVSpzu7q6wt7eHgsWLMBff/0FGxsb3L17F4QQHD16FBMmTKCiA2gyok9LS8PmzZsxbNgw5OXlwcTEBKdOncKqVauorZMA4Pr169i7dy+OHDkCRUVFeHh4YPbs2SKP0m3btiE4OBhPnjxpMQ379u0Ta3wiKysLPT099O3bFwKBoMXmbSYvLw+WlpaQlZVFXl7eJ/8tre+/1ggLSrVyysrKcPjwYRw+fBh37tyBg4MD3N3dMXHixBbvhPQ+dXV1OHHiBA4dOoT4+HgYGhpi8uTJmDJlCtVOEADw559/Ys+ePWIdbmbOnAltbW2qOq5fv46wsDCRDnNzcwQEBKBnz57UNGhqaiInJ4cXQSlra2usX78eI0aMEBuPjY3F4sWLkZubS03LnTt3sG3bNrH3ZsGCBdQ/q3yhT58+CAwMxMSJE8XGjx8/jg0bNuDatWvUtAgEAmRlZUEoFKJz586IiIiAo6MjSkpKYGVlhbq6Ompa+MKGDRtgbGwMNzc3AICLiwuOHTsGAwMD/P7777CxsaGi4z/pYrNy5coWVAJMmDABjo6OWLBggdh4eHg4Ll68iJMnT7bo/M2MHj0acXFx6Nq1K2bPng0PDw+J75qnT5+iXbt2El3o/pe8H7D7O97vbNUSyMrKSozJyMiAEELF6LyZdu3aIS4uDjY2NlBXVxcFpUpLS2FtbY2amhoqOpo5ceIEQkNDxb53Fi1aJNWk93+Jo6MjTpw4AS0tLTg6On7038nIyCAhIaFFtQiFQmRmZkJHR+eT6xIZGRlqXUUBfqzXGNJ5+fIljh07hsOHDyMpKQkmJiaizuM0u0i+/zw5fPgwVq5cidzcXOzfvx+7du2iGggyMjJCVFQU+vXrJ/ZsKy4uhq2tLV6+fElFh5WVFW7fvo0hQ4Zgzpw5GD16tJj5OtCUqKCvr9+i33/l5eUwNDSU2pG3vLxcdIjWUsjKyuLx48fQ19eHrKys6PvuQ2h+/7VGWFCqFdOvXz9kZGTA2toaU6ZMweTJk9GhQwfqOiZNmoSzZ89CRUUFrq6umDJliujkmjYpKSkYPXo0NDU1Rdkd169fR3V1Nc6cOdPiraj5xvTp09GjRw+qWQMfQ1lZGQUFBTA2NhYbv3v3LiwsLFBbW0tFx7FjxzBp0iT07t1b9Dm9evUqMjIyqJ92AcDr16+Rl5cntXU6re5DampqopO29ykrK4O1tTVevXpFRQcA2NnZISAgAOPGjYO7uzv+/PNPLF++HLt27cL169dx48YNaloA4NatWygvL8fbt2/Fxmm9N0DTRu7QoUPo378/Lly4AFdXV0RFRSE6Ohrl5eU4f/48NS18QU1NDTk5ORIdI4uLi9GjRw9qAYdZs2Zh9uzZn/zOI4SgvLyc8+w6WvAly1BdXR1ZWVkwNTUV27hlZmZi6NChqKyspKKDwfin1NbWYv369YiPj5e6JqAZqCsqKkJiYqJUHStWrKCmQ1lZGQKBAG5ubpgyZQrVzO0PdRQWFsLQ0BAeHh5o37491q9fj/LycnTv3p1qkFtFRQU3btyAiYmJ2LMtNzcX9vb2ePHiBRUda9euhaenJyd7z/eRk5NDRUUF9PX1xcYrKyuhr6/f4oGge/fuoVOnTpCRkeHN919rpA3XAhj/7zg5OeG3335D9+7dOdUhJyeH6OhoDB06VCJCThtvb2+4ublh+/btIi0NDQ2YP38+vL29kZ+fT0WHh4cHHB0d4eDgILHRp4mpqSnWrFmDtLQ09OrVS6KMg2b7Z01NTZSWlkoEpYqLiyV0tSSBgYFYunQp1qxZIza+cuVKBAYGUg1KxcbGwsPDA8+fP5e4RvNERVFREU+ePJH4rFZUVIhKgWixfPlyUYByzZo1GDVqFOzs7KCjo4OoqChqOkpLSzF+/Hjk5+eLnXo1n8TRPO16/PgxDA0NAQBnz56Fq6srhgwZAmNjY4mSS1rU1NRIbFRotjrW0dHBqVOnEBAQIDZ+6tQp6OjoUNOxZ88eibEPSytlZGSoLUSfPn0qdRNJs2SAL4tuOzs7REZGYu3atQCa3ofGxkaEhIR8MmOopeH63mFIhw/3zuzZs5GcnIxp06bBwMBAauYHDXbv3o3vvvsOurq6aNeunZgOGRkZqkGp06dPw8nJSWoGJk0MDQ1x5coVaGtrIzY2FkePHgXQVJ2hpKREVUvv3r1x7tw5+Pj4APj3uiQiIoJqUkBQUJDY7w0NDcjPz4eRkRGVsrlmPpZfU1NTQ+W9ef87jy/ff60RlinF+KxQVlZGTk4OzMzMxMbv3LmDHj16UPPZmj17NlJSUlBcXIwOHTrAwcEBAwcOhIODg4T3SkvCp/T4efPm4cqVKzhx4oQo5bq4uBgTJkzAV199hYiICCo6VFRUkJeXJ5FhUVRUBBsbG6rlYaamphgyZAhWrFiBtm3bUpv3QyZPnoyKigqcOnVKVPZbXV2NcePGQV9fH9HR0ZxpA5o8cAQCAdUFenMaekREBIRCIdLT01FZWYmAgABs3LgRdnZ21LS0b98eMTEx6N+/P8zMzBAcHAwXFxfcuXMHX331FbVU/bKyMixYsABJSUl4/fq1aJx2WRbQ5CExe/ZsDB8+XBSYu3btGmJjY7F7927MmDGDio4PSytdXV1x7NgxtGvXjmpp5fXr1zF9+nQUFBRILNC5KhngOsvwxo0bcHJygq2tLRISEjBmzBjcvHkTVVVVSEtLo1r6w+W94+zs/I//7fHjx1tMB4C/9bR6n5b2t2qGT/eOlpYWzp07J/K24gojIyPMnz8fixcv5lQHn/i///s/+Pn5QU1NDUZGRsjKyoKsrCy2bduG48ePIzExkZqW1NRUDB8+HFOnTsW+ffswb9483Lp1C5cvX0ZycjJ69epFRcf3338PKysrzJo1Cw0NDXBwcMDly5ehoqKCs2fPYuDAgS06f/PzZMuWLZgzZw5UVFRE1xoaGkS+qGlpaS2q40NKSkqwefNmMRsZPz8/qt85rRGWKdXKefDgAU6fPi114UfrCx1oSjlOTk6WqoNmNo6trS0KCgokglIFBQXUNgcARAGWhw8fIiUlBcnJyQgNDcW8efNgYGCABw8eUNFRVlZGZZ5/QkhICIYNG4Zu3bqhY8eOAJo+v3Z2dti4cSM1HQMHDsSlS5ckglKpqalUAw1Ak0HywoULOQ1IAcDGjRthb28PIyMjkYdGTk4O2rZtiwMHDnCiqbi4GCUlJbC3t4e2tvZHT8JaiitXriAhIQG6urqQlZWFrKwsvvnmG6xbtw6+vr5U/SOcnZ3h7u4OU1NTVFZWipoCZGdnS3yOW5KpU6eCEILffvsNbdu25ewUHwBmzJgBc3NzbN26VbSRNjc3R2pqKtXssR07duDQoUMAgAsXLuDChQv4448/EB0djUWLFlErrfT09ETXrl2xZ88ezt8bvmQZWlpaorCwEOHh4VBXV0dNTQ2cnZ2pN5IAuL133vcXJYTgxIkTUi0O/pPg1f8rHz43s7Ky8O7dO9GarbCwEHJyctQ21QC/7h2BQEDd/1Qaf/75J1xcXLiWISImJkZUrv7hHqMlmmpIY/78+ejTpw/u37+Pb7/9VpS5ZWJiguDgYCoamvnmm2+Qk5OD9evXw8rKCufPn4etrS2uXLkCKysrajpiYmIwdepUAMCZM2dQVlaG27dv48CBA/jxxx9bPBjU/DwhhCA/Px8KCgqiawoKCrCxscEPP/zQoho+JC4uDmPGjEGPHj3EGidYWFjgzJkz+Pbbb6nqaVXQa/TH+F9z8eJFoqKiQiwtLUmbNm1Ijx49iJaWFtHU1CSOjo7UdGRlZZF27doRDQ0NIicnR/T09IiMjAxRVVUlQqGQmg5CCDl69Cjp1KkT+eWXX8ilS5fIpUuXyC+//EKMjY3J0aNHSW5uruhFg9raWhIXF0eWLFlC+vXrRxQUFEiPHj2ozP0hjY2NpLGxkZO539cQFxdHQkJCyLZt20hycjJ1Ddu3byd6enrE29ubHDhwgBw4cIB4e3sTfX19sn37dnLq1CnRq6WZOXMmiYiIaPF5/gk1NTVk586dZP78+SQgIIDs37+fvH37lrqO58+f86KNu5aWFiktLSWEEGJiYkISEhIIIYQUFxcTZWVlajoIIeTt27dk48aNxNfXl2RlZYnGN23aRHbv3k1Nh6qqKrl9+za1+VoDSkpKpLy8nBBCiK+vL5k7dy4hhJA7d+4QLS0tajrU1NRIUVERtfk+xahRo8jYsWPJs2fPiJqaGrl16xa5dOkS6dOnD0lJSeFaHifw5d4JDAwks2fPJu/evRONvXv3jsydO5f88MMPVLWEhoaS0aNHk6qqKtFYVVUVGTt2LNm4cSM1HXy6dw4cOEAmTpxIamtrOdXh6elJtm/fzqmGZrZs2ULU1NTIggULiIKCApk3bx4ZPHgw0dTUJMuWLeNa3heNoqIiuX//PiGEkDlz5hA/Pz9CCCGlpaVEXV2dmo4ZM2aQFy9eUJvvU/To0YMsXrxYYnzx4sWkZ8+eHChqPbDyvVZMnz59MHz4cKxevVpkdKevr48pU6Zg2LBh+O6776joGDhwILp27YodO3ZAU1MTubm5kJeXx9SpU+Hn50fl9K2Zv6s5p9UBaNmyZUhKSkJ2djbMzc1F5Xv29vZU66wBIDIyEr/88guKiooAAF27dsWiRYswbdo0qjr4wj/1JaCRtl9XVwcXFxfo6elJbZ1OM8uQL/CljTtfDNfr6+sxb948BAUFcd5F09HRET/++CMGDx7MqY4Pef36tcTpOS2PHr6UVo4bNw7Tpk2j3qhBGrq6ukhISIC1tTU0NTWRnp4OMzMzJCQkICAggGqWIR8aSQD8uXf09PSQmpoq1eKgf//+VM3fO3TogPPnz8PCwkJs/MaNGxgyZAgePXpERQef7p2ePXuipKQEhBAYGxtLrAloZQWtW7cOmzZtwsiRIzlfm3Tr1g0rV67E5MmTxUy9V6xYgaqqKoSHh1PR0dDQgH379n3UhL6lO1dKg2sfNCMjI+zevRtOTk4QCoXYvn07Ro4ciZs3b+Kbb77Bn3/+SUXHs2fPoKenJ/Vafn4+1ewxJSUl5OfnS1i1FBYWwtraWqx8myEOK99rxRQUFODIkSMAgDZt2uCvv/6Cmpoa1qxZg7Fjx1ILSuXk5GDnzp2QlZWFnJwc3rx5AxMTE4SEhGD69OlUg1J8KVdbv3499PT0sHLlSjg7O6Nr166c6Ni0aROCgoKwYMECURppamoqvLy88Pz5c1505aNNS7al/U85cuQIzp8/DyUlJSQlJUmYiX6JQanz588jLi5OVOLZjKmp6d92NflfwhfDdXl5eRw7dkzCUJQLIiIi4OXlhYcPH8LS0lJio0LTELiurg6BgYGIjo6WupGmVSLGl9LKiIgITJ8+HTdu3JD63tAMwDQ0NEBdXR1AU4Dq0aNHMDMzg5GREe7cuUNNB18aSQD8uXfevXuH27dvSwSlbt++Tf278eXLl3j27JnE+LNnz6h2fOXTvTN27FhOyweb2bVrF9TU1JCcnIzk5GSxa7TXJuXl5ejfvz+AJt/Y5s/GtGnT0K9fP2pBKT8/P+zbtw8jR46EpaUlp+8TX3zQZs6cCVdXV5Epf3PQ/dq1a+jWrRsVDQBgZWWFPXv2YOTIkWLjGzduRFBQEDU/YaAp8J+TkyMRlMrJyZHoDsj4AE7ztBj/FW3btiW3bt0ihBBibm4uKjfKyckhqqqq1HTo6uqSwsJCQgghpqamJDY2lhBCSEFBAVFRUaGmg0/k5OSQLVu2kPHjxxNdXV3Svn17MnnyZLJz505y584dajqMjY3J/v37Jcb37dtHjI2NqelgSKdt27bkp59+Ig0NDVxL4Q1qamqi54mampqofC8jI4Noa2tzKY1UVlZyUgLr4eFBNm3aRH3eD7ly5QoRCoVERkZG9JKVlRX9lybz588n5ubmJCYmhigrK5PffvuNrF27lnTs2JEcPHiQmo63b9+SX375hfPSytOnTxNNTU2x9+b994gm33zzDTlx4gQhhJDJkyeTYcOGkdTUVOLh4UEsLCyo6ejSpQuZP38+efz4MbU5PwZf7h1/f3+io6NDQkNDRRYHGzduJLq6usTf35+aDkIImTZtGjE2NibHjh0j9+/fJ/fv3ycxMTFEKBQSDw8Pajr4dO8wJBEKhaJna69evciOHTsIIYTExcURgUBATYeOjg45d+4ctfk+hbW1NRk/fjy5evUqKSsrI3fv3hV70eRf//oX2bRpk6iMj5CmPcbJkyepadiwYQNRVFQkXl5epK6ujjx48IAMGjSI6OnpkePHj1PTQQghq1evJlpaWmT9+vUkJSWFpKSkkHXr1hEtLS2yZs0aqlpaGywo1YoZO3Ys2bVrFyGEkICAANKlSxcSHBxMbG1tiZOTEzUd3377LTl06BAhhJDZs2eTPn36kIMHD5KhQ4eSPn36UNPBZ3Jycsj06dNJmzZtqC5yFBUVpXolFBYWEkVFRWo6GNIRCASkuLiYaxm8Yvjw4WT58uWEkKagVGlpKWloaCAuLi5kwoQJHKvjhrVr1xItLS0yYcIE8vPPP5MtW7aIvWhhbm5OnJ2debEQNjQ0JImJiYQQQtTV1UXPucjISDJ8+HCqWviAkZER8fb25kUAJjY2lhw7dowQQkhRURExMzMjMjIyRFdXl8THx1PToa6uzpvnK1/unYaGBrJhwwbSvn17UeClffv2ZMOGDWI+UzSora0l3333HVFUVCSysrJEVlaWKCgokO+++47U1NRQ08Gne0coFJLnz59LjP/555/UPVr5wqxZs8iqVasIIYSEh4cTZWVlMnjwYKKlpUU8PT2p6TAwMKB6qPwp+OSDxheysrKIhYUF6dKlC9HW1ibDhw8nFRUV1HU0NjaSTZs2kQ4dOoiesR06dCCbN2/m3NeX7zBPqVZMaWkpampqYG1tjdraWgQEBODy5cswNTXFpk2bYGRkREVHZmYmXr16BUdHRzx9+hQeHh4iHXv27EGPHj2o6OAThBBkZ2cjKSkJSUlJSE1NxcuXL2FtbQ0HBweEhYVR0WFpaQl3d3csW7ZMbDw4OBhRUVHIz8+nooMhHX9/f+jp6Um8P18yfGrjzhc+5SUlIyOD0tJSKjpUVVWRm5tLtSztY6ipqeHWrVvo1KkTOnbsiOPHj6NPnz4oKyuDlZUVampquJZIFXV1deTk5PD2/qiqqoJAIKBa8uLp6YkBAwZg1qxZ1Ob8GHy6d5pp9juj5b/2MWpra1FSUgIA6Ny5M1RVVanOz6d7R1ZWFo8fP5Yo83ny5AkMDQ0lvPNaCj75JzU2NqKxsRFt2jQ5zhw9elS0x5g3b55Yx7WWJDQ0FKWlpQgPD+e8xJJPPmh84dWrV5gzZw6OHTsG4N9luVxrAiAqZ2d8GuYp1YoxMTER/ayqqoodO3ZwoqO5rTAA6OvrIzY2lhMdfEJbWxs1NTWwsbGBg4MD5syZAzs7O2hpaVHVsXr1ari5uSElJUWsNWl8fDyio6OpagGaFhfFxcVSFzn29vbU9XBNQ0MDQkJCEBcXB2trawkvi02bNlHRYWJigoyMDOjo6IiNV1dXw9bWllrQA+BXG3e+wBevvEGDBvFmY21iYoKysjJ06tQJ3bp1Q3R0NPr06YMzZ85Qf87yAWdnZyQmJvJiY91McXExSkpKYG9vD21tbQnvk5YmPDwcLi4uuHTpEudmzXy6d5rhOhjVjKqqKlU/ug/hw71z+vRp0c9xcXHQ1NQU/d7Q0ID4+HiqjS745J8kKysr1qBm0qRJmDRpEnUdqampSExMxB9//AELCwuJ58nx48epaeGTDxofSEtLw9SpU6GtrY28vDykpaXBx8cHv//+O3bs2EG9wRTQZELf7KHYrVu3jxqxM/4Ny5RqxWRkZKCxsRF9+/YVG7927Rrk5OTEgkUtSVlZGd69eydh6lZUVAR5eXkYGxtT0cEnzp07Bzs7O14s+q5fv46wsDAUFBQAAMzNzREQEICePXtS1XH16lW4u7vj3r17nBoz8glHR8ePXpORkaF2Gvmp09lOnTrhzZs3VHQw/p7me4eLTcKuXbsQHBwMT09PqZt8mgvhsLAwyMnJwdfXFxcvXsTo0aNBCEF9fT02bdoEPz8/alr4wE8//YTNmzfzoltWZWUlXF1dkZiYCBkZGRQVFcHExASenp4QCAQIDQ2lomPPnj3w8vKCkpISdHR0JBpJ0Ay28+neYYjDh3unOejS3CH6fZrX0aGhoRg1alSLawGaGhRERkZixIgRVOb7FHv37oWamhpcXFzExv/1r3+hrq6OWjbMzJkzP3l97969VHQAwJkzZzBt2jSp3V2/xPW0oqIi/P39sXbtWtH9W1JSgqlTp+L+/ft48OABNS2vXr3C/PnzceTIEdHhu5ycHNzc3PDrr7+KBZwZ4rCgVCumT58+CAwMxMSJE8XGjx8/jg0bNuDatWtUdDg4OMDT01Pii+HgwYOIiIhAUlISFR0AvzI+GOL06NEDXbt2xerVq0WdOt6H5oOaZWw10Xw6O27cOOzfv1/q6eyFCxeodswC+NPGnU9ERkbil19+QVFREQCga9euWLRoEaZNm0ZNw/un1R/C9UL43r17uH79Orp06cJp1gVX8KXEEwA8PDzw9OlTREREwNzcXNTCPS4uDgsXLsTNmzep6GjXrh18fX2xZMmST352acDne+dLh0/3jlAoREZGBnR1danNKY327dsjKSmJs87R79O1a1fs3LlT4hAvOTkZc+fOpb4+4QPGxsYYNWoUgoKC0LZtW67lcE5ycjIcHBwkxhsbG/HTTz9R7V7s5uaG7OxsbNu2DV9//TUA4MqVK/Dz80OPHj1w9OhRalpaGywo1YpRU1NDXl6eWBkf0JS5ZG1tTa2lroaGBrKysiTS0ouLi9G7d29UV1dT0QGwjI8P8fDwgKOjIxwcHCQ+J7Thi6cGy9j6N3w7nQX41cadL2zatAlBQUFYsGCBqAw3NTUVv/76K4KDg+Hv78+xQrrU19dj2LBh2LFjh0SGLm3YQYgk7dq1Q1xcHGxsbKCuri4KSpWWlsLa2pqa35e2tjYyMjJ4VdLIYLQG+OSfpKSkhNu3b0tUXdy9exfm5ub466+/qGl59+4dkpKSUFJSAnd3d6irq+PRo0fQ0NCAmpoaNR188kHj0yHv+yXjysrKIIRQ//yqqqoiLi4O33zzjdj4pUuXMGzYMNTW1lLV05pgnlKtGEVFRTx58kQi2FBRUSEyBKSBjIyM1ADYixcvqG0g/0k9/pdYRqigoIB169Zh1qxZ6NChAxwcHDBw4EA4ODhQ38z17dsXxcXFnAelvLy80Lt3b5w7d05qxtaXRPMCgi+nswDg4+MDFxcXrFixgp0A/v9s27YN27dvh4eHh2hszJgxsLCwwKpVq764oJS8vDzy8vK4lgGgaWMk7XvuzZs3ePjwIQeKuC3xBJqMq1VUVCTGq6qqoKioSE3H9OnTERUVxdtGEtXV1V+k/xnfaWhoQH5+PoyMjKh70fj6+qJLly4SJYPh4eEoLi7G5s2bW2xuZ2dnsd8TEhJ44Z+kr6+PvLw8iTV8bm6uxGFAS3Lv3j0MGzYM5eXlePPmDb799luoq6tjw4YNePPmDVVfXz74oAH8OeT9WMn4rFmzoK2tjY0bN1LRAQA6OjpSKz80NTU58bZqTbCgVCtmyJAhWLp0KU6dOiW6Aaqrq7Fs2TJ8++231HTY29tj3bp1OHLkCOTk5AA0famvW7dOIlLcUowbNw5A00PwwzLC9zM+vjQiIiIAAA8fPkRKSgqSk5MRGhqKefPmwcDAgGqdtY+PDwICAvD48WOpvg20Sm6KiooQExPDeXCMT0gz0uZqw/TkyRMsXLiQBaTeo6KiAv3795cY79+/PyoqKqhqSU5OxsaNG0Uedd27d8eiRYtgZ2dHVcfUqVOxZ88erF+/nuq8zfDxIIQPJZ4AYGdnh8jISKxduxZA0/dyY2MjQkJCPumj97+GL40kAGDDhg0wNjaGm5sbAMDFxQXHjh2DgYEBfv/9d9jY2LTY3Fu3bv3H/7al/ZPev2/+Dlql2t9//z2srKwwa9YsNDQ0wN7eHleuXIGKigrOnj2LgQMHUtEBAMeOHZP6N+rfvz/Wr1/fokGpDzfS48ePb7G5/hMmT54MX19fqKurizJvkpOT4efnR9Xw3M/PD71795YIho0fPx5z5syhpgNoerYvXboUqampnHoI8uWQ19/fH/Ly8igvL4e5ublo3M3NDQsXLqQalFq+fDkWLlyIAwcOoF27dgCAx48fY9GiRVTLCFsjrHyvFfPw4UPY29ujsrJSZFqdk5ODtm3b4sKFCzA0NKSi49atW7C3t4eWlpZoY3Lp0iW8fPkSCQkJsLS0pKID4FfGB5+oq6sTdQ5JSkpCVlYWunfvjuzsbGoapHlqNJeN0TxRGTRoEAIDAzFs2DAq87UGuNwwfQif2rjzBUtLS7i7u0tkfAQHByMqKgr5+flUdBw8eBAzZ86Es7OzWDfPEydOYN++fXB3d6eiA2gKckdGRsLU1BS9evWSaCPf0gEHvpW+8qnE88aNG3BycoKtrS0SEhIwZswY3Lx5E1VVVUhLS6N2us+XRhJA09rk0KFD6N+/Py5cuABXV1dERUUhOjoa5eXlOH/+fIvO/U+g4Z/0T729aK4JOnbsiJMnT6J37944efIkvL29kZiYiAMHDiAhIQFpaWlUdABNpWo3btyQaodhaWmJ169fU9PCF96+fYtp06bhX//6l6gKpLGxER4eHtixYwcUFBSo6NDR0cHly5dhZmYmVpZ89+5ddO/eHXV1dVR0APzxQeOLLQdfSsYBoGfPniguLsabN2/QqVMnAEB5eTkUFRUlKlSysrKo6WoNsKBUK6e2thaHDh1Cbm4ulJWVYW1tjcmTJ0tEzVuaR48eITw8XEzHggULoK2tTVWHNL7kFPlly5YhKSkJ2dnZMDc3F5Xv2dvbU08jvXfv3ievGxkZUdFx4sQJLF++HIsWLeI0Y4tPcLlh+pC6ujq4uLhAT0+P8y5ifOHYsWNwc3PD4MGDxYJB8fHxiI6OpnaibW5ujrlz50oEODZt2oTdu3eLsqdowJeAA18OQoRCIVavXi1W4gkA+/fvx6pVq6RmQ7YkL168EK0JampqYGtrC29vbxgYGFDVwReUlZVRWFgIQ0ND+Pn54fXr19i5cycKCwvRt29f/Pnnn1xL/GJRUlJCcXExOnbsiLlz50JFRQWbN29GWVkZbGxspHY4ayksLS3h5eWFBQsWiI03l3DfunWLio5Bgwbh+PHjEmvnly9fYty4cVQDus0UFhaK9hhWVlbU1ozNCAQCpKWloXv37mKBj9TUVEyYMAFPnjyhqocP8OWQV11dHVlZWTA1NRV7bzIzMzF06FBUVlZS07J69ep//G9XrlzZgkpaHywoxfis4FPGBx+QlZWFnp4e/P394ezszItOKlzDl4wtPsGnDROf2rjzievXryMsLEwU+DE3N0dAQIAoS5YGioqKuHnzJjvF/xu4OAj5WIZFUVERrKys2HvDMe3bt0dMTAz69+8PMzMzBAcHw8XFBXfu3MFXX31FNfDBEMfIyAi7d++Gk5MThEIhtm/fjpEjR+LmzZv45ptvqH7//fbbb1iwYAEWLVqEQYMGAQDi4+MRGhqKzZs3UysT+1jToKdPn6JDhw6or6+nooNPuLm5QVNTE7t27YK6ujry8vKgp6eHsWPHolOnTti7dy/XEqnDl0PeESNGoFevXli7dq3ovTEyMsKkSZPQ2NiImJgYKjoY/x3MU6qVU1RUhMTERKldD1asWEFNR3V1NdLT06Xq+PDktiXZsWMHDh06BAC4cOECLl68iNjYWERHR2PRokVUMz74QHZ2NpKTk5GUlITQ0FAoKCiIsqUGDhzISZDq1q1bKC8vx9u3b8XGaflH0M4YaA0IBALcv38fhoaGiI2NRXBwMIAmw2TaQboff/wRq1ev5kUbdz7Rq1cvHDx4kFMNhoaGiI+Plwh8XLx4kVq5ON/gy0FIly5dEB0dLVHiGRUVxUmHwtevXyMvL0/qmqAln/XOzs7Yt28fNDQ0JIybP4SmWbOzszPc3d1hamqKyspKDB8+HEDTdzTt0pcHDx7g9OnTUr+HafpsAU3Z/snJyVK10MqKnTlzJlxdXUWeOIMHDwYAXLt2Dd26daOioRlPT0+8efMGP/30k8iTzdjYWKLRRUvxfgOJW7du4fHjx6LfGxoaEBsbiw4dOrS4jvdpaGjAvn37EB8fL/V5QitrKzQ0FEOHDkX37t3x+vVruLu7o6ioCLq6ujhy5AgVDc0QQhATE/PR/R+tZ9uECRMANH1um+HikDckJAROTk7IzMzE27dvERgYKFYyzmgdsKBUK2b37t347rvvoKuri3bt2klkFNAKSp05cwZTpkxBTU0NNDQ0JHTQDEo9fvxYtDk6e/YsXF1dMWTIEBgbG6Nv377UdPAFGxsb2NjYiBZ3ubm5CAsLg7e3NxobG6kGHEpLSzF+/Hjk5+eL+bA0f15oaaGd8t0a4NOG6e3bt3Bzc2MBqffw8PCAo6MjHBwcJLqt0iQgIAC+vr7IyckRGa+npaVh37592LJlC2e6uIQvByGrV6+Gm5sbUlJSpJZ40iQ2NhYeHh54/vy5xLWW3qhoamqKvlOkdUDiirCwMBgbG+P+/fsICQkRtY+vqKjA/PnzqemIj4/HmDFjYGJigtu3b8PS0hJ3794FIQS2trbUdABN3y8jRoxAXV0damtroa2tjefPn0NFRQX6+vrUglKrVq2CpaUl7t+/DxcXF1GHSDk5OSxZsoSKhvf57rvv8N133+HZs2dQVlYWfVZo0KNHD8jIyEBGRkaUqfU+ysrK2LZtGzU9QJPB+L59+zBy5EhYWlpyZqbdsWNH5Obm4ujRo8jLy0NNTQ1mzZqFKVOmQFlZmaqW77//Hjt37oSjoyPatm3L2d+EL4e8lpaWKCwsRHh4ONTV1VFTUwNnZ+cvumS8NcLK91oxRkZGmD9/PhYvXsypjq5du2LEiBH4+eefpbaBpglLkReHEILs7GwkJSUhKSkJqampePnyJaytreHg4ICwsDBqWkaPHg05OTlERERAKBQiPT0dlZWVCAgIwMaNG6l37+I6Y4tP1NfXY+vWrSgvL8eMGTNEJWFhYWFQV1fH7NmzqWnx9/eHnp4eb9u4c8Hs2bORkpKC4uJidOjQQZTt6ODgQD0L5sSJEwgNDRUrI1y0aBHGjh1LVQdf4FPpKx9KPAHA1NQUQ4YMwYoVKzjrokkIwf3796Gnp0d9w8hn+vTpg+HDh2P16tUi7xV9fX1MmTIFw4YNw3fffUdNS3O29o4dO6CpqYnc3FzIy8tj6tSp8PPz+9tMN8b/nnv37oEQAhMTE6Snp0NPT090TUFBAfr6+qIu27TQ1dVFZGQkRowYQXVePqOtrY2DBw+yv8n/T3l5OQwNDaUG58rLy0WG4wx+w4JSrRgNDQ3k5ORwenIONHVfyM/P51wHACxYsABnz56FqakpsrOzcffuXaipqeHo0aMICQn54jodCAQC1NTUwMbGRrSRtbOz48T4XVdXFwkJCbC2toampibS09NhZmaGhIQEBAQEUOsEyJeMLb5QX1+PefPmISgo6B93aWpJfH19ERkZCRsbG87buPONhw8fIiUlBcnJyUhOTkZhYSEMDAzw4MEDrqV9sbCDEEk0NDSQnZ1NrcueNBobG6GkpISbN29yUr7IV9TV1ZGTk4POnTtDIBAgNTUVFhYWyM3NxdixY3H37l1qWrS0tHDt2jWYmZlBS0sLV65cgbm5Oa5du4bp06fj9u3b1LTwiZiYGFGTkQ8Pzb60NSzQ9IxNSkrihSfqo0ePkJqaKrVkjmYTFqFQiD/++IN6eenH4PqQV05ODhUVFRIeaJWVldDX1//i1vWtFVa+14pxcXHB+fPn4eXlxamOoUOHIjMzkxdBqbCwMAiFQpSXl3OaIs8XDh48CDs7O2hoaHAtBQ0NDVBXVwfQFKB69OgRzMzMYGRkhDt37lDT4efnB6FQiPj4eKkZW18a8vLyOHbsGIKCgriWAgDIz88XZXbcuHFD7BpXKep8QSAQQEdHBwKBAFpaWmjTpo3YSXZLk5GRgcbGRolS6GvXrkFOTg69e/empoUv8KX09ffff4ecnByGDh0qNh4XF4fGxkaRLhpMnDgRSUlJnAalZGVlRe8JC0r9G1VVVdHG0cDAACUlJbCwsAAAqeWWLYm8vLyoTFtfXx/l5eUwNzeHpqYm7t+/T1ULX9i6dSt+/PFHzJgxA6dOncLMmTNRUlKCjIwMeHt7U9VSUlKCzZs3izIvu3fvDj8/P+r3dUBAALZs2YLw8HBO1wD79u3DvHnzoKCgILUJC82g1KpVq7B69Wr89ttvnGaC8uWQt9nD6kNqamqgpKRERcOHvH37FmVlZejcuTPatGHhln8C+yu1Yrp06YKgoCBcvXqV09bpI0eOxKJFi3Dr1i2pOmhFyj+V8fFhC/MvhZEjR3ItQYSlpSVyc3MhFArRt29fhISEQEFBAbt27aIa0Lxy5QoSEhKgq6sLWVlZyMrK4ptvvsG6devg6+tLLWOLT4wbNw4nT57kxX2SmJjItQTesWzZMiQlJSE7Oxvm5uZwcHDAkiVLYG9vD4FAQE2Ht7c3AgMDJYJSDx8+xIYNG3Dt2jVqWvgCXw5ClixZgvXr10uME0KwZMkSqkGp8PBwuLi44NKlS5yuTdavX49FixZh+/btsLS0pDIn3+nXrx9SU1Nhbm6OESNGICAgAPn5+Th+/Dj69etHVUvPnj2RkZEBU1NTODg4YMWKFXj+/DkOHDjwxb5f//d//4ddu3Zh8uTJ2LdvHwIDA2FiYoIVK1agqqqKmo64uDiMGTMGPXr0EPOos7CwwJkzZ/Dtt99S05KamorExET88ccfsLCwkHie0DL1DgoKwooVK7B06VLOPS9dXV1x5MgR6Ovrw9jYWOJvQiujjutD3oULFwJoCoIFBQWJWcg0NDTg2rVr6NGjR4vreJ+6ujr4+Phg//79AIDCwkKYmJjAx8cHHTp04MSnrrXAyvdaMZ8qtaHZOv1TD2ea3ReAJmPTnJwcXpQhMcSJi4tDbW0tnJ2dUVxcjFGjRqGwsBA6OjqIioqSaqrZEggEAmRlZUEoFKJz586IiIiAo6MjSkpKYGVlhbq6Oio6+ERwcDBCQ0Ph5OSEXr16QVVVVew6zRNAhiSysrLQ09ODv78/nJ2dOStjUFNTQ15enkQQuaysDNbW1nj16hUnuriCT6WvysrKKCgogLGxsdj43bt3YWFhgdraWmpa9uzZAy8vLygpKUnNKKC1NhEIBKirq8O7d++goKAgkVFAc5PPF0pLS1FTUwNra2vU1tYiICAAly9fhqmpKTZt2kS1EUhmZiZevXoFR0dHPH36FB4eHiIte/bsob6Z5AMqKiooKCiAkZER9PX1ceHCBdjY2KCoqAj9+vVDZWUlFR09e/bE0KFDJQLdS5Yswfnz56mWEc6cOfOT1/fu3UtFh46ODtLT0znNAG3G1dUViYmJmDhxolSj85UrV1LRwbUth6OjIwAgOTkZX3/9NRQUFETXFBQUYGxsjB9++IFqtqyfnx/S0tKwefNmDBs2TLRmOnXqFFatWvVFHnz/U1hQivFZMX36dPTo0YMXGR+Mv6eqqgoCgYBqSradnR0CAgIwbtw4uLu7488//8Ty5cuxa9cuXL9+XaJk7EuALwFuhnRyc3ORnJyMpKQkXLp0CQoKCiKPuGazYBro6Ojg7Nmz+Prrr8XGL1++jJEjR1I19eYLfDkIadeuHQ4fPiwR3L948SLc3d3x9OlTqlp8fX2xZMkSTjMKmk+qP8b06dMpKQFMTEyQkZEBHR0dsfHq6mrY2tqyZyzHNDY2ori4WKpXkL29PTUdJiYmOHbsGHr27InevXtjzpw5mDdvHs6fP49JkyZRC6QqKSkhPz9fYjNfWFgIa2trvH79mooOPhEYGAhtbW1eZLqoqqoiLi4O33zzDac6+HLIO3PmTGzZsoUXViVGRkaIiopCv379RM0kTExMUFxcDFtb2y/SZ/Kfwsr3PhM+rOP9UjE1NcWaNWuQlpbGMj54SnFxMUpKSmBvbw9tbW3QjosvX75clDWwZs0ajBo1CnZ2dqKMrS8RvrT1ZUjHxsYGNjY2oudXbm4uwsLC4O3tjcbGRmrZqEOGDMHSpUtx6tQpaGpqAmjaVC9btoxqOQef4Evp69ixY/H999/jxIkTopP84uJiBAQEUO8o+vbtW7i5uXFe4kIz6PR33L17V+p9+ubNGzx8+JCaDj4FxwYNGoTjx49LNF55+fIlxo0bh4SEBCo6rl69Cnd3d1Hnufehne0/aNAgnD59Gj179sTMmTPh7++PmJgYZGZmUu1GqKenh5ycHImgVE5OjoSZNC2ePXsm8h81MzOj6qcIAOvWrcOoUaMQGxsrtSyZZhMWQ0NDXgRg+GLLQStb7p/w7NkzqfdIbW3tF79H/ztYUKqVExkZiV9++QVFRUUAgK5du2LRokWYNm0aVR3JycnYuHGjmCHiokWLYGdnR1XHnj17oKWlhevXr+P69eti12gbETLEqaysFKUcy8jIoKioCCYmJpg1axYEAgFCQ0Op6HjfCLhLly64ffs2JxlbfIUFuPkHIQTZ2dlISkpCUlISUlNT8fLlS1hbW8PBwYGajo0bN8Le3h5GRkYiM/qcnBy0bdsWBw4coKaDT/DlICQkJATDhg1Dt27d0LFjRwDAgwcPYGdnR72Bw/Tp0xEVFYVly5ZRnVcaJSUl2Lt3L0pKSrBlyxbo6+vjjz/+QKdOnUQG3y3J6dOnRT/HxcWJgrlAk+dJfHy8RMllS8KX4BgAJCUlSXTrAoDXr1/j0qVL1HR4eXmhd+/eOHfuHAwMDDj97tu1a5coU8vb2xs6Ojq4fPkyxowZg3nz5lHTMWfOHMydOxelpaXo378/gCZPqQ0bNoh8fGhRW1sLHx8fREZGiv42cnJy8PDwwLZt28R8hFqSdevWIS4uDmZmZgAgUZZMk9DQUAQGBmLHjh1Unx8fwg55JWl+lvj4+AD492cjIiJCIsucIQ4r32vFbNq0CUFBQViwYIHIiDA1NRW//vorgoODqZ3cHjx4EDNnzoSzs7OYIeKJEyewb98+uLu7U9HB4DceHh54+vQpIiIiYG5uLkppjYuLw8KFC3Hz5k2qet7P2FJWVv5o944vBb4EuBmSCAQC1NTUwMbGRlS2Z2dnJ5FhQIPa2locOnQIubm5UFZWhrW1NSZPnixxavylwKfSV0IILly4IPbe0Cw9asbX1xeRkZGwsbGBtbU1ZxkFycnJGD58OAYMGICUlBQUFBTAxMQE69evR2ZmJmJiYlpcQ3O22PudqZqRl5eHsbExQkNDMWrUqBbV0RwcGzduHPbv3y81OHbhwgUqnXDz8vIAAD169EBCQgK0tbXFtMTGxmLnzp24e/dui2sBmkqhcnNzqXbL5DuEEGzevBmhoaF49OgRAKB9+/ZYtGgRfH19qa6V5s2bh4sXLyI8PFxsr+Pr64tvv/0W27dvp6JDIBAgLCwMM2bMoDLf32lp9stTUVGReMZy6Zf3pR/ypqamYvjw4Zg6daqoY+OtW7dw+fJlJCcno1evXlxL5C0sKNWKEQqFWL16NTw8PMTG9+/fj1WrVlEryTE3N8fcuXMlgmCbNm3C7t27RdlTtGEZH/yiXbt2iIuLg42NjViddWlpKaytrVFTU0NFx8cytjw9PalmbPEJvgS4GdI5d+4c7OzseJGuz2D8Hc3ms9KQkZGhVpb19ddfw8XFBQsXLhT7zklPT4ezszMePHhARQfQtF7LyMiArq4utTnfhy/BsWYtzesyaVsQZWVlbNu2DZ6eni2uBWgqmQsMDMSwYcOozPd3/Pnnn9izZ49Y5cHMmTPFgnc0aW5goa6uzsn8urq6iImJwcCBA8XGExMT4erqimfPnlHR0a5dO1y6dImqafbH2Ldv3yf3NrRLl9khrzglJSVYv349cnNzUVNTA1tbWyxevBhWVlZcS+M1LCjVilFSUsKNGzckTneKiopgZWVFzYhQUVERN2/elNBRXFwMS0tL6oaILOODn6irqyMrKwumpqZiG4TMzEwMHTqUWlcZvmVs8QG+BLgZjNYMOwjhF2pqasjPz4dQKBT7zrl79y66devGuVlzdXU19WxHroNjAETeTc0Bwve9gRQUFKCvrw85OTlqek6cOIHly5dj0aJFUr2CrK2tqWlJSUnBmDFjoKGhgd69ewMArl+/jurqapw5c4Z65uP7Pk7dunXj5HOjoqKC69evw9zcXGz85s2b6NOnD7XOouvWrUNFRQW2bt1KZb7WADvkZfwv4daFkvFf0aVLF0RHR0uMR0VFUY3kGxoaIj4+XmL84sWLMDQ0pKYDaMr4+O677zBixAhER0cjOjoaw4YNg5eXF8LCwqhqYYhjZ2eHyMhI0e8yMjJobGxESEjIJ0/W/9ecP38eGzZsEPmuNGNqaop79+5R08EnKioqRL4R79O/f39UVFRwoIjBaD1ERkbCysoKysrKorK5L9Vji09oaWlJfX5lZ2ejQ4cOVLVs2LBBzGPFxcUF2tra6NChA3Jzc6npKCsr4zQgBTR1pzI2NkZjYyN69+4NIyMj0cvAwIBqQAoAJkyYgIKCAnh6euKrr75Cjx490LNnT9F/aeLt7Q1XV1eUlZXh+PHjOH78OEpLSzFp0iR4e3tT01FbWwtPT08YGBjA3t4e9vb2MDAwwKxZs6h1VGvm66+/xsqVK8WCyH/99RdWr15N1aMnPT0d+/fvh4mJCUaPHg1nZ2exF00cHBwQGRmJv/76i+q8H+Lv7w95eXmUl5eLeXu5ubkhNjaWQ2XcMXjwYOzbt4912ft/gBmdt2JWr14NNzc3pKSkiHk5xcfHSw1WtRQBAQHw9fVFTk6OmCHivn37sGXLFmo6AGDbtm3Yvn27WMbHmDFjYGFhgVWrVrEyJA4JCQmBk5MTMjMz8fbtWwQGBuLmzZuoqqpCWloaNR21tbVSjTGrqqqgqKhITQefaA5wf2hMTDvAzWC0Nj5W+url5YXnz5+z7xwOmTRpEhYvXox//etfokOQtLQ0/PDDDxJZoS3Njh07cOjQIQDAhQsXcPHiRcTGxiI6OhqLFi3C+fPnqejw9fVFly5dJAz4w8PDUVxcjM2bN1PRATRlnrRt21aiTO+3337Ds2fPsHjxYio6+JQJXFxcjJiYGLHAnJycHBYuXCh2qNfSLFy4EMnJyThz5oyEj1NAQAA1HycA2LJlC4YOHYqOHTvCxsYGQFMHWiUlJcTFxVHToaWlRT349DF69uyJH374AT4+PnB1dcWsWbPQr18/6jrOnz+PuLg4dsj7HhYWFli6dCnmz5+PkSNHYurUqRgxYsQX67v5H0EYrZrMzEwyZcoUYmtrS2xtbcmUKVNIVlYWdR3Hjx8nAwYMINra2kRbW5sMGDCAnDx5kroORUVFUlRUJDFeWFhIFBUVqethiFNdXU2Cg4OJi4sLGT58OPnxxx/Jo0ePqGoYPnw4Wb58OSGEEDU1NVJaWkoaGhqIi4sLmTBhAlUtfCEmJobIycmRoUOHkjVr1pA1a9aQoUOHkjZt2pDjx49zLY/B4C3GxsZk//79EuP79u0jxsbGHChiNPPmzRsye/Zs0qZNGyIjI0Pk5eWJrKwsmTp1Knn37h1VLUpKSqS8vJwQQoivry+ZO3cuIYSQO3fuEC0tLWo62rdvTzIzMyXGr1+/Tjp06EBNByGEGBkZkbS0NInxq1evfrH3Tv/+/cmJEyckxk+cOEH69u1LTYeOjg5JTEyUGE9ISCC6urrUdDRTW1tLdu3aRRYuXEgWLlxIdu/eTerq6qjr4BP19fXk2LFjZMyYMUReXp6Ym5uTX375hTx+/JiaBjU1NVJYWCj6uaSkhBBCSEZGBtHW1qamg280NDSQuLg4Mn36dKKhoUEEAgGZM2cOSUpK4loar2GeUozPCktLS7i7u0tkfAQHByMqKgr5+fkcKWPwhRs3bsDJyQm2trZISEjAmDFjxDK2OnfuzLVETrh+/TrCwsJE5qrm5uYICAigXr7A4C8mJibIyMiAjo6O2Hh1dTVsbW2pdprjC3zxdgSAxsZGFBcX4+nTp6LW6c1w0YWPL5SXl+PGjRuoqalBz549Ocn+bN++PWJiYtC/f3+YmZkhODgYLi4uuHPnDr766itqpR4f+7xy4QGqpKSEgoICiQ6WpaWl6N69O3XPr1u3bqG8vBxv374VGx8zZgw1DVFRUQgMDISPj48o8+Xq1av49ddfsX79ejFfpZb0uuKLjxPjn/H06VPs2rULP/30ExoaGjBixAj4+vpi0KBBLTrviBEj0KtXL6xduxbq6urIy8uDkZERJk2ahMbGRiodTvnO69evcebMGfz000/Iz89HQ0MD15J4Cyvfa8X8/vvvkJOTw9ChQ8XG4+Li0NjYiOHDh1PRkZGRgcbGRvTt21ds/Nq1a5CTkxOZNdKALyWNDOm8fv0aeXl5UjdNtBZ+lpaWKCwsRHh4ONTV1VFTUwNnZ2d4e3vDwMCAigY+0qtXLxw8eJBrGQwec/fuXakLqjdv3uDhw4ccKOIevpS+Xr16Fe7u7iIT6feRkZH5ohfCnTp1EvlbcmVC7+zsDHd3d5iamqKyslK0PsvOzpYIELUkXbp0QWxsLBYsWCA2/scff8DExISaDqDJjzQtLU0iKJWWlob27dtT01FaWorx48cjPz9frDth82eF5r0zefJkAEBgYKDUa836WvqebvZxioyMhJKSEgBufJwAbss8bW1tER8fD4FAgJ49e37y+ZGVldViOj5Feno69u7di6NHj0JfXx8zZszAw4cPMWrUKMyfPx8bN25ssbn5YsvBVx4/foyjR4/i4MGDyMvLQ58+fbiWxGtYUKoVs2TJEqxfv15inBCCJUuWUAtKeXt7IzAwUCIo9fDhQ2zYsAHXrl2jogNoMqy8du0awsLCcPLkSQBNGR/p6eks44NjYmNj4eHhgefPn0tco71p0tTUxI8//khtPr7j4eEBR0dHODg4UN+YMPjP6dOnRT/HxcVBU1NT9HtDQwPi4+NhbGzMgTLu4ctBiJeXF3r37o1z587BwMCAdQD8/9mzZw/CwsJE3XhNTU3x/fffY/bs2VR1hIWFQSgUory8HCEhIVBTUwPQ1GRi/vz51HQsXLgQCxYswLNnz0RZFPHx8QgNDaXqJwUAc+bMwffff4/6+noxLYGBgQgICKCmw8/PD0KhEPHx8RAKhUhPT0dlZSUCAgJadEMvDb74W/HFxwkAdu7cicOHD0uMW1hYiHzjWoqxY8eKvEbHjRvXYvP8pzx9+hQHDhzA3r17UVRUhNGjR+PIkSMYOnSo6Nk/Y8YMDBs2rEU/w+yQV5KXL1/i2LFjOHz4MJKSkmBiYoIpU6YgKirqi63E+Kew8r1WjLKyMgoKCiQ2A3fv3oWFhQW19Fo1NTXk5eVJbGbLyspgbW2NV69eUdHB4DempqYYMmQIVqxYgbZt23KqhQ8ZW3xi9uzZSElJQXFxMTp06AAHBwcMHDgQDg4OzOicAVnZpka972cRNCMvLw9jY2OEhoZi1KhRXMjjHD6UvqqqqiI3N5dq1g3fWbFiBTZt2gQfHx9RdseVK1cQHh4Of39/rFmzhoqO+vp6zJs3D0FBQRJZQVywfft2/PTTT3j06BEAwNjYGKtWraJu/t58gLp161ZRyZySkhIWL16MFStWUNOhq6uLhIQEWFtbQ1NTE+np6TAzM0NCQgICAgKQnZ1NTQufqKurw6FDh3D79m0ATc+1KVOmQFlZmaoOvpV58gEFBQV07twZnp6emDFjBvT09CT+zcuXLzF27FgkJiZyoPDLRVlZGQKBAG5ubpgyZQrVaqHWDgtKtWLatWuHw4cPS9QMX7x4Ee7u7nj69CkVHTo6Ojh79qxESu/ly5cxcuRI/Pnnn1R0ACzjg89oaGggOzub85MCPmVs8Y2HDx8iJSUFycnJSE5ORmFhIQwMDPDgwQOupTF4gFAoREZGBuct5RmSDBo0CIGBgRg2bBjXUniDnp4etm7dKiqJaubIkSPw8fGR+h3QUmhqaiInJ4cXQalmnj17BmVlZVHWFlfU1NSgoKAAysrKMDU1pd4FVyAQICsrC0KhEJ07d0ZERAQcHR1RUlICKysr1NXVUdXz6NEjpKamSj00+7Br4peAqakpVq5cialTp4qNHzhwACtXrvwivQwvXboEOzs7rmUAYIe8H3LhwgU4OTmJDvMY/xxWvteKGTt2LL7//nucOHFCtNEvLi5GQEAA1QfBkCFDsHTpUpw6dUpU1lFdXY1ly5bh22+/paYDaDo9WLduHWbNmsUyPnjGxIkTkZSUxHlQysfHBy4uLrzI2OIbAoEAOjo6EAgE0NLSQps2baSewDG+TKSVllRXV0NLS4u+GJ7Al4MQHx8fBAQE4PHjx7CyspJoP92Spsh8pb6+Xuopda9evfDu3TuqWsaNG4eTJ0/C39+f6ryfgi/PdjU1NXz11VeczW9paYnc3FwIhUL07dsXISEhUFBQwK5du6jf0/v27cO8efOgoKAAHR0dsTJcGRkZqkEpvgTHuCzzFAgE/7gUuqqqqkW1vE/v3r1RV1cHFRUVAMC9e/dw4sQJdO/eHUOGDKGmgx3ySkJ73/s5wTKlWjEvXrzAsGHDkJmZiY4dOwIAHjx4ADs7Oxw/fpzaRuHhw4ewt7dHZWWlqFwhJycHbdu2xYULF0QGozRhGR/8o66uDi4uLtDT05O6aaK1yOFLxhafWLZsGZKSkpCdnQ1zc3NRMNfe3h4CgYBreQyesGHDBhgbG8PNzQ0A4OLigmPHjsHAwAC///67yHvkS4Ivpa/STmVpmSLzFR8fH8jLy2PTpk1i4z/88AP++usv/Prrr9S0BAcHIzQ0FE5OTujVqxdUVVXFrtPc5MfExCA6OlpqpznaZs2ZmZkf1XL8+HEqGuLi4lBbWwtnZ2cUFxdj1KhRKCwshI6ODqKiolq8g9n7GBoawsvLC0uXLuU00+LvgmM0s5O4LPPcv3+/6OfKykoEBwdj6NChYuXAcXFxCAoKohpwHjJkCJydneHl5YXq6mp069YN8vLyeP78OTZt2oTvvvuOig4+2XLwCT49Y1sTLCjVyiGE4MKFC8jNzYWysjKsra05af1cW1uLQ4cOiemYPHmyROCBFnV1dUhNTUViYiKSkpKQlZWF7t27f7HeAHxgz5498PLygpKSEqeLHE9PTwwYMACzZs2iMl9rQFZWFnp6evD394ezszO6du3KtSQGDxEKhTh06BD69++PCxcuwNXVFVFRUaLF1/nz57mWyBlcH4Tcu3fvk9eNjIyo6OATPj4+iIyMhKGhIfr16wegqStweXk5PDw8xNYnHwau/td8qmyP5vff1q1b8eOPP2LGjBnYtWsXZs6ciZKSEmRkZMDb2xs//fQTFR0AcPToUXh4eGDo0KE4f/48hgwZgsLCQjx58gTjx4/H3r17qWn5kKqqqv8oS+Z/hY6ODtLT0zk/NONLcOx9uC7znDBhAhwdHSU6V4aHh+PixYui5ko00NXVRXJyMiwsLBAREYFt27YhOzsbx44dw4oVK0T+hi0NO+SVhE/P2NYGC0oxPitYxgd/adeuHXx9fbFkyRJOFzl8ydjiE7m5uUhOTkZSUhIuXboEBQUF0b0zcOBAFqRiAGgy8CwsLIShoSH8/Pzw+vVr7Ny5E4WFhejbty9V/0C+wQ5C+Iejo+M/+ncyMjJISEhoYTX8oFu3bli5ciUmT54MdXV15ObmwsTEBCtWrEBVVRXCw8OpabG2tsa8efPg7e0t0iIUCjFv3jwYGBhg9erV1LQATfYXJSUlsLe3h7KysijLkCaBgYHQ1tbGkiVLqM77IXwJjvEJNTU15OTkSDSTKC4uRo8ePVBTU0NNi4qKCm7fvo1OnTrB1dUVFhYWWLlyJe7fvw8zMzNqPmjskFcSPj1jWxssKMX4rGAZH/xFW1sbGRkZnC9y+JKxxWdyc3MRFhaGQ4cOobGx8Yss/WFI0r59e8TExKB///4wMzNDcHAwXFxccOfOHXz11Vd4+fIl1xKpw7eDkFu3bkktGfgSDWf5SvOym3bAA2jazBYUFMDIyAj6+vq4cOECbGxsUFRUhH79+qGyspKaFlVVVdy8eRPGxsbQ0dFBUlISrKysUFBQgEGDBqGiooKKjsrKSri6uiIxMREyMjIoKiqCiYkJPD09IRAIEBoaSkUHADQ0NGDUqFH466+/pB6atXRGXzN8CY7xCSMjI/j6+kr4WIWGhmLr1q1/m636v8Ta2hqzZ8/G+PHjYWlpidjYWHz99de4fv06Ro4cicePH1PRwQ55JeHTM7a1wYzOGZ8V2dnZooyP0NBQlvHBI6ZPn46oqCgsW7aMUx0//vgjVq9ezXnGFp8ghCA7OxtJSUlISkpCamoqXr58CWtrazg4OHAtj8ETnJ2d4e7uDlNTU1RWVmL48OEAmp67H54efymsX78eenp6WLlyJacHIaWlpRg/fjzy8/NFXlLAvwMfLLDMPZGRkfjll19QVFQEAOjatSsWLVqEadOmUdPQrl07VFVVwcjICJ06dcLVq1dhY2ODsrIy0D6jFggEePXqFQCgQ4cOuHHjBqysrFBdXU21452/vz/k5eVRXl4Oc3Nz0bibmxsWLlxINSi1bt06xMXFwczMDAAkDs1o6hg1ahRiY2M5DY7xidWrV2P27NlISkpC3759ATSVA8fGxmL37t1UtaxYsQLu7u7w9/eHk5OTyOPq/PnzIm9fGhw5cgTnz5+HkpISkpKSODXm5wt8esa2NlhQivFZYWNjAxsbG9GDsDnjw9vbm2V8cExDQwNCQkIQFxcHa2trzhY5b9++hZubGwtIvYe2tjZqampgY2MDBwcHzJkzB3Z2dl90VzWGJGFhYRAKhSgvL0dISIiolXxFRQXmz5/PsTpu4MtBiJ+fH4RCIeLj4yEUCpGeno7KykoEBARg48aNVDQwPs6mTZsQFBSEBQsWYMCAAQCA1NRUeHl54fnz59RMkgcNGoTTp0+jZ8+emDlzJvz9/RETE4PMzEw4OztT0dCMvb09Lly4ACsrK7i4uMDPzw8JCQmiluq0OH/+POLi4kQNg5oxNTWlmv0CNGXd/Pbbb5gxYwbVeT+EL8ExPjFjxgyYm5tj69atIhN+c3NzpKamioJUtJg4cSK++eYbVFRUiDUYcXJywvjx46npYIe8kvDpGdvaYOV7jM+Kv8v4CAsL41riF8un/D1oenr4+/tDT0+P84wtPnHu3DnY2dlBQ0ODaykMnlJfX4958+YhKCjok6bNXzpclb7q6uoiISEB1tbW0NTURHp6OszMzJCQkICAgADmbcUxQqEQq1evhoeHh9j4/v37sWrVKpSVlVHR0djYiMbGRrRp03QmffToUVy+fBmmpqaibmu0qKqqwuvXr9G+fXs0NjYiJCREpGX58uXUyl/V1dWRlZUFU1NTMQ+YzMxMDB06lGq5Tbt27XDp0iWqnTulIRAIEBYWxnlwjMFv+GLLwSf49IxtbbCgVCunsbERxcXFePr0KRobG8Wu0erCZ2JigoyMDOjo6IiNV1dXw9bWlqpHj0AgEMv4GDhwIMv4YIjh6+uLyMhI2NjYcJqxxWC0NjQ1NZGTk8OCUu/Bl4MQgUCArKwsCIVCdO7cGREREXB0dERJSQmsrKyolkMxJFFSUsKNGzckylyLiopgZWWF169ft7iGd+/e4eeff4anp6dEVhBt3r17h8OHD2Po0KGct5IfMWIEevXqhbVr10JdXR15eXkwMjLCpEmT0NjYiJiYGGpa1q1bh4qKCmzdupXanNLgS3CMwW/YIS/jfwkr32vFXL16Fe7u7rh3755EnaqMjAy1E9q7d+9KnevNmzd4+PAhFQ3NHDx4kGV8MD5Jfn6+qOb+xo0bYte+1LR0BuOfMG7cOJw8eZJaqVFrgC+lr5aWlqLuZX379kVISAgUFBSwa9cumJiYUNXCkKRLly6Ijo6W2LxFRUVR2/i3adMGISEhEtlaXNCmTRt4eXlRa13/KUJCQuDk5ITMzEy8ffsWgYGBuHnzJqqqqpCWlkZVS3p6OhISEnD27FlYWFhIHJo1l421NH5+fti2bRvnwTEGv+GLLQef2Lt3L9TU1ODi4iI2/q9//Qt1dXWYPn06R8r4DwtKtWK8vLzQu3dvnDt3DgYGBtQ31KdPnxb9HBcXB01NTdHvDQ0NiI+Ph7GxMVVNI0eOpDofo/WRmJjItQQGo1ViamqKNWvWIC0tDb169YKqqqrY9S/R1JQvByHLly9HbW0tAGDNmjUYNWoU7OzsoKOjg6ioKE61MZpMkt3c3JCSkiLylEpLS0N8fDyio6Op6XByckJycjL1tZk0+vTpg5ycHBgZGXGqw9LSEoWFhQgPD4e6ujpqamrg7OwMb29vGBgYUNWipaXFC98ZvgTHGPyGHfJKsm7dOuzcuVNiXF9fH3PnzmVBqU/AyvdaMaqqqsjNzeWs61Gzqd37nX6akZeXh7GxMUJDQzFq1Cgu5DEYDAbjf8inyvZkZGSolmoz/p6qqioIBIIvdnPAN65fv46wsDBRdpC5uTkCAgKodsvasWMHVq9ejSlTpkgNLI8ZM4aalujoaCxduhT+/v5StVhbW1PTwhBn5syZn7y+d+9eSkoYjNaFkpISbt++LRH4v3v3LszNzfHXX39xI6wVwIJSrZhBgwYhMDAQw4YN41SHUChERkYGdHV1OdXBYDAYDMaXTHFxMUpKSmBvbw9lZWUQQlhQiiHiUx2yaNo+fExL8yEnbS2vX79GXl6eVH9WmoG6Zp49e4Y7d+4AAMzMzKCnp0ddA4PB+M/p1KkTwsPDJZ4bp06dgre3Nx48eMCRMv7DyvdaMT4+PggICMDjx49hZWUlkV5L65RJWteY6upqZi7OYDAYnynN51ks4MEPKisr4erqisTERMjIyKCoqAgmJiaYNWsWBAIBQkNDuZb4RePh4QFHR0c4ODhw6vH1YcCFS2h1HPw7YmNj4eHhgefPn0tcox0cq62thY+PDyIjI0XvlZycHDw8PLBt2zaoqKhQ0fHXX3+BECKa7969ezhx4gS6d++OIUOGUNHAB/6TUkpW0sgAgMmTJ8PX1xfq6uqihmPJycnw8/PDpEmTOFbHbz5+ZMLgPRMmTEBBQQE8PT3x1VdfoUePHujZs6fov7TYsGGDmGeFi4sLtLW10aFDB+Tm5lLTwWAwGIyWJTIyElZWVlBWVoaysjKsra1x4MABrmV98fj7+0NeXh7l5eViG1c3NzfExsZyqIwBAAoKCli3bh26dOkCQ0NDTJ06FRERESgqKmrxubW1tUUBF09PT7x69arF5/wYtra2+PPPPwEA+/fvh56eHoyMjKS+aOHj4wMXFxdUVFSI2rk3v2gGpABg4cKFSE5OxpkzZ1BdXY3q6mqcOnUKycnJCAgIoKZj7NixiIyMBNB0yNynTx+EhoZi7Nix2L59OzUdXKOpqfmPXwwGAKxduxZ9+/aFk5OTaJ00ZMgQDBo0CD///DPX8ngNK99rxdy7d++T12l9qQuFQhw6dAj9+/fHhQsX4OrqiqioKERHR6O8vBznz5+nooPBYDAYLcemTZsQFBSEBQsWiMyaU1NT8euvvyI4OJh15eOQdu3aIS4uDjY2NlBXV0dubi5MTExQWloKa2tr1NTUcC2RAeDhw4dISUlBcnIykpOTUVhYCAMDgxYt6VBTU0NeXh5MTEwgJyeHx48fc1YOpqysjKKiInTs2BFycnKoqKiAvr4+J1qa0dDQQHZ2Njp37sypDgDQ1dVFTEwMBg4cKDaemJgIV1dXPHv2jJqO5ORkWFhYICIiAtu2bUN2djaOHTuGFStW8KJrIoPBZwoLC5GbmwtlZWVYWVlx3tChNcDK91oxfPmAP378GIaGhgCAs2fPwtXVFUOGDIGxsTH69u3LsToGg8Fg/C/Ytm0btm/fLtZSfsyYMbCwsMCqVatYUIpDamtrpZb2VFVVQVFRkQNFDGkIBALo6OhAIBBAS0sLbdq0afEA0ddff41x48ahV69eIITA19cXysrKUv/tb7/91qJaevTogZkzZ+Kbb74BIQQbN26Empqa1H+7YsWKFtXSzMSJE5GUlMSLoFRdXR3atm0rMa6vr4+6ujqqOtTV1QEA58+fh7OzM2RlZdGvX7+/PRBnMBhA165d0bVrV65ltCpYUOoz4NatWygvL8fbt2/FxmmZMwoEAty/fx+GhoaIjY1FcHAwgCbPEdqpzwwGg8FoGSoqKtC/f3+J8f79+6OiooIDRYxm7OzsEBkZibVr1wJo8sJpbGxESEgIHB0dOVbHWLZsGZKSkpCdnQ1zc3M4ODhgyZIlsLe3h0AgaNG5Dx48iLCwMJSUlEBGRgYvXrzA69evW3TOj7Fv3z6sXLkSZ8+ehYyMDP744w+0aSO5FZGRkaEWlAoPD4eLiwsuXbok1Z/V19eXig6gKYC4cuVKREZGQklJCUCTv9Pq1avx9ddfU9PRpUsXnDx5EuPHj0dcXJzowOHp06fQ0NCgpoNvxMTEiKpAPtxzZWVlcaSKwScaGhqwb98+xMfHS22ckJCQwJEy/sPK91oxpaWlGD9+PPLz80UdS4B/G8/SCggtWLAAZ8+ehampKbKzs3H37l2oqanh6NGjCAkJYQ9qBoPB+AywtLSEu7s7li1bJjYeHByMqKgo5Ofnc6SMcePGDTg5OcHW1hYJCQkYM2YMbt68iaqqKqSlpfEiC+RLRlZWFnp6evD394ezszNnJ+hCoRCZmZnQ0dHhZP73kZWVxePHjzkv39uzZw+8vLygpKQEHR0dseYNMjIyKC0tpablxo0bGDp0KN68eQMbGxsAQG5uLpSUlBAXFwcLCwsqOmJiYuDu7o6GhgY4OTmJbDjWrVuHlJQU/PHHH1R08ImtW7fixx9/xIwZM7Br1y7MnDkTJSUlyMjIgLe3N3766SeuJTJ4wIIFC7Bv3z6MHDkSBgYGEs1gwsLCOFLGf1hQqhUzevRoyMnJISIiAkKhEOnp6aisrERAQAA2btwIOzs7Kjrq6+uxdetWlJeXY8aMGSKT9bCwMKirq2P27NlUdDAYDAaj5Th27Bjc3NwwePBgkadUWloa4uPjER0djfHjx3Os8MvmxYsXCA8PR25uLmpqamBrawtvb28YGBhwLe2LJzc3F8nJyUhKSsKlS5egoKAABwcHDBw4EAMHDmRlHhzSrl07+Pr6YsmSJZCV5b7/U11dHQ4dOoTbt28DAMzNzTFlypSPlly2FI8fP0ZFRQVsbGxEf5f09HRoaGigW7duVLXwgW7dumHlypWYPHmymG/fihUrUFVVhfDwcK4lMniArq4uIiMjMWLECK6ltDpYUKoVo6uri4SEBFhbW0NTUxPp6ekwMzNDQkICAgICkJ2d3eIa6uvrMW/ePAQFBUEoFLb4fAwGg8HgjuvXryMsLExkdGtubo6AgACqHV8ZjNZObm4uwsLCcOjQIU66vDH+jba2NjIyMlg2IeOTqKiooKCgAEZGRtDX18eFCxdgY2ODoqIi9OvXD5WVlVxLZPCA9u3bIykpiR00/D/APKVaMQ0NDSIjQl1dXTx69AhmZmYwMjLCnTt3qGiQl5fHsWPHEBQURGU+BoPBYHBHr169cPDgQa5lMKTw+vVr5OXlSfWxoOUxyZAOIQTZ2dlISkpCUlISUlNT8fLlS1hbW8PBwYFreV8006dPR1RUlERZMhesW7cObdu2haenp9j4b7/9hmfPnmHx4sUcKWO0a9cOVVVVMDIyQqdOnXD16lXY2NigrKwMLL+D0UxAQAC2bNmC8PBwidI9xqdhQalWjKWlJXJzcyEUCtG3b1+EhIRAQUEBu3btgomJCTUd48aNw8mTJ1nnJQaDwfiM8fDwgKOjIxwcHKh+xzD+ntjYWHh4eOD58+cS12RkZFgmDsdoa2ujpqYGNjY2cHBwwJw5c2BnZwctLS2upX3xNDQ0ICQkBHFxcbC2tpYwOt+0aRM1LTt37sThw4clxi0sLDBp0iQWlOKQQYMG4fTp0+jZsydmzpwJf39/xMTEIDMzE87OzlzLY/CE1NRUJCYm4o8//oCFhYXE8+T48eMcKeM/rHyvFRMXF4fa2lo4OzujuLgYo0aNQmFhIXR0dBAVFYVBgwZR0REcHIzQ0FA4OTmhV69eUFVVFbtOs3MJg8FgMFqG2bNnIyUlBcXFxejQoYPIE8fBwQGmpqZcy/uiMTU1xZAhQ7BixQqpLeUZ3HLu3DnY2dl90Z3L+MqnulPKyMhQ7ZalpKSEgoICCTuM0tJSdO/enbOuiQygsbERjY2Nom6RR48exeXLl2Fqaop58+ZBQUGBY4UMPjBz5sxPXt+7dy8lJa0PFpT6zKiqqoJAIKCaMvgpLynanUsYDAaD0bI8fPgQKSkpSE5ORnJyMgoLC2FgYIAHDx5wLe2LRUNDA9nZ2cwXh/G3NDY2ori4WGqZp729PTUdJiYmyMjIkOgEWF1dDVtb2y9y7WhqaoqVK1di6tSpYuMHDhzAypUrv8i/CV8oLy+HoaGhxP6KEIL79++jU6dOHCljMD4PWPneZ0BxcTFKSkpgb28PbW1t6rXNZWVlVOdjMBgMBncIBALo6OhAIBBAS0sLbdq0gZ6eHteyvmgmTpyIpKQkFpRifJKrV6/C3d0d9+7dk1gr0i7zvHv3rtT53rx5g4cPH1LTwSfmzJmD77//HvX19aJqh/j4eAQGBiIgIIBjdV82QqEQFRUV0NfXFxuvqqqCUChkVWAiYAAAEQRJREFUJdIMMZ49eybydzYzM2NrpH8AC0q1YiorK+Hq6orExETIyMigqKgIJiYmmDVrFgQCAUJDQ6lral7kMHM3BoPB+LxYtmwZkpKSkJ2dDXNzczg4OGDJkiWwt7eHQCDgWt4XTXh4OFxcXHDp0iVYWVlJ+FiwMnoGAHh5eaF37944d+4cDAwMOFmrnT59WvRzXFwcNDU1Rb83NDQgPj4exsbG1HXxgUWLFqGyshLz58/H27dvATSV9C1evBhLly7lWN2XDSFE6v1SU1MDJSUlDhQx+EhtbS18fHwQGRkpykSVk5ODh4cHtm3bBhUVFY4V8hdWvteK8fDwwNOnTxEREQFzc3Pk5ubCxMQEcXFxWLhwIW7evElNS2RkJH755RcUFRUBALp27YpFixZh2rRp1DQwGAwGo+WQlZWFnp4e/P394ezszFoe84g9e/bAy8sLSkpK0NHREds8sTJ6RjOqqqrIzc1Fly5dONMgKysLoOlz+eEWRF5eHsbGxggNDcWoUaO4kMcLampqUFBQAGVlZZiamkJRUZFrSV8sCxcuBABs2bIFc+bMEQsqNDQ04Nq1a5CTk0NaWhpXEhk8Yt68ebh48SLCw8MxYMAAAE3m576+vvj222+xfft2jhXyF5Yp1Yo5f/484uLi0LFjR7FxU1NT3Lt3j5qOTZs2ISgoCAsWLBC7Ab28vPD8+XPWlY/BYDA+A7Kzs5GcnIykpCSEhoZCQUFBZHY+cOBAFqTikB9//BGrV6/GkiVLRJt+BuND+vbti+LiYk6DUs3ZA0KhEBkZGdDV1eVMC19RU1PDV199xbUMBpq+94CmTKn8/HwxQ3MFBQXY2Njghx9+4Eoeg2ccO3YMMTExGDhwoGhsxIgRUFZWhqurKwtKfQIWlGrF1NbWSk0DrKqqonqqsm3bNmzfvh0eHh6isTFjxsDCwgKrVq1iQSkGg8H4DLCxsYGNjY2oFCw3NxdhYWHw9vZGY2Mj89TgkLdv38LNzY0FpBifxMfHBwEBAXj8+LHUMk9ra2tqWqT5kVZXV0NLS4uaBgbj70hMTATQ1FVty5YtrIMm45PU1dVJ7YCrr6+Puro6DhS1Hlj5XitmxIgR6NWrF9auXQt1dXXk5eXByMgIkyZNQmNjI2JiYqjoUFJSwo0bNyRO3oqKimBlZcVa2DIYDMZnACEE2dnZSEpKQlJSElJTU/Hy5UtYW1vDwcEBYWFhXEv8YvH394eenh6WLVvGtRQGj5EWtGwuo6NtdL5hwwYYGxvDzc0NAODi4oJjx47BwMAAv//+O2xsbKhpYTD+jhcvXqChoQHa2tpi41VVVWjTpg0LVjEAAE5OTtDR0UFkZKTIa+yvv/7C9OnTUVVVhYsXL3KskL+wTKlWTEhICJycnJCZmYm3b98iMDAQN2/eRFVVFdXa5i5duiA6OlpiMRwVFQVTU1NqOhgMBoPRcmhra6OmpgY2NjZwcHDAnDlzYGdnxzIbeEBDQwNCQkIQFxcHa2triQyYTZs2caSMwSf41C15x44dOHToEADgwoULuHjxImJjYxEdHY1Fixbh/PnzHCtkMP7NpEmTMHr0aMyfP19sPDo6GqdPn8bvv//OkTIGn9iyZQuGDh2Kjh07igLrubm5UFJSQlxcHMfq+A3LlGrlvHjxAuHh4cjNzUVNTQ1sbW3h7e0NAwMDahqOHTsGNzc3DB48WOQplZaWhvj4eERHR2P8+PHUtDAYDAajZTh37hzs7OzYiTAPcXR0/Og1GRkZJCQkUFTDYPw9ysrKKCwshKGhIfz8/PD69Wvs3LkThYWF6Nu3L/7880+uJTIYIrS1tZGWlgZzc3Ox8du3b2PAgAGorKzkSBmDb9TV1eHQoUO4ffs2AMDc3BxTpkyBsrIyx8r4DQtKMf4nXL9+HWFhYSgoKADQdAMGBASgZ8+eHCtjMBgMBoPBYDRz69YtlJeX4+3bt2LjY8aMoaahffv2iImJQf/+/WFmZobg4GC4uLjgzp07+Oqrr/Dy5UtqWhiMv0NVVRVXr16FlZWV2Hh+fj769u3L/IIYjP8SVr7Xynn9+jXy8vLw9OlTUUeTZmguLnr16oWDBw9Sm4/BYDAYDAaD8c8pLS3F+PHjkZ+fL/KSApqy6QBQ9ZRydnaGu7s7TE1NUVlZieHDhwNo6nbGZXdABkMaffr0wa5du7Bt2zax8R07dqBXr14cqWLwjXXr1qFt27bw9PQUG//tt9/w7NkzLF68mCNl/IcFpVoxsbGx8PDwwPPnzyWu0TSs9PDwgKOjIxwcHGBiYkJlTgaDwWAwGAzGP8fPzw9CoRDx8fEQCoVIT09HZWUlAgICsHHjRqpawsLCIBQKUV5ejpCQEKipqQEAKioqJHx7GAyuCQ4OxuDBg5GbmwsnJycAQHx8PDIyMpj/GUPEzp07cfjwYYlxCwsLTJo0iQWlPgEr32vFmJqaYsiQIVixYoXU9pO0mD17NlJSUlBcXIwOHTrAwcEBAwcOhIODAzM6ZzAYDAaDweABurq6SEhIgLW1NTQ1NZGeng4zMzMkJCQgICAA2dnZVHTU19dj3rx5CAoKglAopDIng/HfkpOTg19++QU5OTlQVlaGtbU1li5dyvY6DBFKSkooKCiQeK6Vlpaie/furCP9J2BBqVaMhoYGsrOz0blzZ66lAAAePnyIlJQUJCcnIzk5GYWFhTAwMMCDBw+4lsZgMBgMBoPxRSMQCJCVlQWhUIjOnTsjIiICjo6OKCkpgZWVFVVfHE1NTeTk5LCgFIPB+GwwNTXFypUrMXXqVLHxAwcOYOXKlSgtLeVIGf9h5XutmIkTJyIpKYk3QSmBQAAdHR0IBAJoaWmhTZs20NPT41oWg8FgMBgMxhePpaUlcnNzIRQK0bdvX4SEhEBBQQG7du2ibr8wbtw4nDx5Ev7+/lTnZTD+W16/fi3RJIB1pWUAwJw5c/D999+jvr4egwYNAtBU5hkYGIiAgACO1fEblinViqmrq4OLiwv09PRgZWUFeXl5seu+vr5UdCxbtgxJSUnIzs6Gubm5qHzP3t4eAoGAigYGg8FgMBgMxseJi4tDbW0tnJ2dUVxcjFGjRqGwsBA6OjqIiooSbaJoEBwcjNDQUDg5OaFXr15QVVUVu05rDctg/BPq6uoQGBiI6OhoVFZWSlyn2SSAwV8IIViyZAm2bt0qClwqKSlh8eLFWLFiBcfq+A0LSrVi9uzZAy8vLygpKUFHR0fUPQVoMjqnlSIoKysLPT09+Pv7w9nZGV27dqUyL4PBYDAYDAbj/52qqioIBAKxNSQNPlW2R3MNy2D8E7y9vZGYmIi1a9di2rRp+PXXX/Hw4UPs3LkT69evx5QpU7iWyOARNTU1KCgogLKyMkxNTaGoqMi1JN7DglKtmHbt2sHX1xdLliyBrKwsZzpyc3ORnJyMpKQkXLp0CQoKCqJsqYEDB7IgFYPBYDAYDAZPKC4uRklJCezt7aGsrAxCCPWgFIPRmujUqRMiIyMxcOBAaGhoICsrC126dMGBAwdw5MgR/P7771xLZDBaNSwo1YrR1tZGRkYGbzylmsnNzUVYWBgOHTqExsZGltLKYDAYDAaDwTGVlZVwdXVFYmIiZGRkUFRUBBMTE3h6ekIgECA0NJQTXc1bERYYY/AVNTU13Lp1C506dULHjh1x/Phx9OnTB2VlZbCyskJNTQ3XEhmMVg136TWM/5rp06cjKiqKaxkghCArKwubNm3CmDFj4OjoiIMHD8LKyop5AjAYDAaDwWDwAH9/f8jLy6O8vBwqKiqicTc3N8TGxlLXExkZCSsrKygrK0NZWRnW1tY4cOAAdR0Mxt9hYmKCsrIyAEC3bt0QHR0NADhz5gy0tLQ4VMZgfB6w7nutmIaGBoSEhCAuLg7W1tYSRuebNm2iokNbWxs1NTWwsbGBg4MD5syZAzs7O/aQZjAYDAaDweAJ58+fR1xcHDp27Cg2bmpqinv37lHVsmnTJgQFBWHBggUYMGAAACA1NRVeXl54/vw568rH4BUzZ85Ebm4uHBwcsGTJEowePRrh4eGor6+ntt9iMD5nWPleK8bR0fGj12RkZJCQkEBFx7lz52BnZ8faoTIYDAaDwWDwFHV1dWRlZcHU1BTq6urIzc2FiYkJMjMzMXToUKldxVoKoVCI1atXw8PDQ2x8//79WLVqlSgrhcHgI/fu3cP169fRpUsXWFtbcy2HwWj1sKAUg8FgMBgMBoPxmTNixAj06tULa9euhbq6OvLy8mBkZIRJkyahsbERMTEx1LQoKSnhxo0b6NKli9h4UVERrKys8Pr1a2paGIxPUV9fj2HDhmHHjh0wNTXlWg6D8VnCyvcYDAaDwWAwGIzPnJCQEDg5OSEzMxNv375FYGAgbt68iaqqKqSlpVHV0qVLF0RHR2PZsmVi41FRUWzjz+AV8vLyyMvL41oGg/FZwzKlGAwGg8FgMBiML4AXL14gPDwcubm5qKmpga2tLby9vWFgYEBVx7Fjx+Dm5obBgweLPKXS0tIQHx+P6OhojB8/nqoeBuNT+Pv7Q1FREevXr+daCoPxWcKCUgwGg8FgMBgMBoMq169fR1hYGAoKCgAA5ubmCAgIQM+ePTlWxmCI4+Pjg8jISJiamqJXr15QVVUVu87MzhmM/w4WlGIwGAwGg8FgML4AXr9+jby8PDx9+hSNjY1i18aMGcORKgaD3/CluRSD8bnCglIMBoPBYDAYDMZnTmxsLDw8PPD8+XOJazIyMmhoaKCmxcPDA46OjnBwcICJiQm1eRmMf0peXh4sLS0hKyvLtRQG47OH3WUMBoPBYDAYDMZnjo+PD1xcXFBRUYHGxkaxF82AFAAoKChg3bp16NKlCwwNDTF16lRERESgqKiIqg4G42P07NlTFMA1MTFBZWUlx4oYjM8XlinFYDAYDAaDwWB85mhoaCA7OxudO3fmWoqIhw8fIiUlBcnJyUhOTkZhYSEMDAzw4MEDrqUxvnB0dHTw+++/o2/fvpCVlcWTJ0+gp6fHtSwG47OkDdcCGAwGg8FgMBgMRssyceJEJCUl8SooJRAIoKOjA4FAAC0tLbRp04Zt/Bm8YMKECXBwcICBgQFkZGTQu3dvyMnJSf23paWllNUxGJ8XLFOKwWAwGAwGg8H4zKmrq4OLiwv09PRgZWUFeXl5seu+vr7UtCxbtgxJSUnIzs6Gubk5HBwcMHDgQNjb20MgEFDTwWB8itjYWBQXF8PX1xdr1qyBurq61H/n5+dHWRmD8XnBglIMBoPBYDAYDMZnzp49e+Dl5QUlJSXo6OhARkZGdE1GRoZqtoesrCz09PTg7+8PZ2dndO3aldrcDMZ/ysyZM7F169aPBqUYDMZ/BwtKMRgMBoPBYDAYnznt/r/27hBHYSgIwPBgkIRU7SnKCZrCSVBYNOEICDgFggMgMOQRcAjSqxAsYdWu2ax9JeX7bEVH/2lnvr5iPp/HYrFo/aJY0zRxOp0ipRTn8zn6/f7v11Lj8VikAvggohQAAHRcURRxvV7faqfUj6ZpYrPZxHa7beUaIADtsegcAAA6bjqdxm63i+Vy2fYo8Xq94na7RUopUkpxuVzifr9HWZZR13Xb4wGQkSgFAAAd93w+Y7VaxeFwiLIs/yw6X6/X2WYpiiIej0eMRqOo6zpms1lUVRXD4TDbDAC8B7/vAQBAx00mk3+f9Xq9OB6P2WbZ7/dRVVUMBoNs7wTgPYlSAAAAAGTX7ukNAAAAAD6SKAUAAABAdqIUAAAAANmJUgAAAABkJ0oBAAAAkJ0oBQAAAEB2ohQAAAAA2YlSAAAAAGQnSgEAAACQ3TePlX5gGtuTgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importances\n",
    "importances = best_model.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.bar(range(X.shape[1]), importances[indices], align=\"center\")\n",
    "plt.xticks(range(X.shape[1]), data.feature_names[indices], rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a67696-1799-49d5-a8ab-7194a92bfbaa",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Hyperparameter tuning with cross-validation is a crucial step in the machine learning pipeline. It ensures that the model is optimized for performance and generalizes well to unseen data. Methods such as grid search, random search, and Bayesian optimization can be effectively used in conjunction with cross-validation to find the best set of hyperparameters. This process not only improves model accuracy but also enhances its robustness and reliability. By carefully selecting and tuning hyperparameters, we can build models that perform better and provide more accurate predictions, leading to better decision-making and insights from the data.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": 5
}