{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A.I. Assignment 3\n",
    "\n",
    "\n",
    "## Learning Goals\n",
    "\n",
    "By the end of this lab, you should be able to:\n",
    "* Perform some more data preproscessing: checking for missing samples, eliminate them, encoding labeled classes\n",
    "* Feel comfortable creating a simple decision tree for a classification \n",
    "* Dealing with some feature selection techniques\n",
    "\n",
    "### Content:\n",
    "\n",
    "The Lab. has 3 sections: \n",
    "\n",
    "1. Preprocessing\n",
    "2. Constructing and fitting a decision tree\n",
    "3. Perform a feature selection based on the linear correlation between the dataset's features \n",
    "\n",
    "There are some exercises, put your answer code in the cells with the *# your code here*. \n",
    "\n",
    "All the work must be done during the lab and uploaded on teams by the end of the lab. \n",
    "\n",
    "If there are any python libraries missing, please install them on your working environment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using a variation of the famous **Iris dataset**. !DO NOT USE OTHER VERSION THAN THE ONE PROVIDED HERE.  \n",
    "\n",
    "This set contains measurements of various parts of three different species of iris flowers. The goal is to use these measurements to predict the species of an iris flower.\n",
    "\n",
    "The dataset contains 154 instances, each with 4 features: *sepal length*, *sepal width*, *petal length*, and *petal width*. The target variable is the species of the iris flower, which can be one of three possible values: *setosa*, *versicolor*, or *virginica*.\n",
    "\n",
    "We will start by loading the dataset, preparing it and splitting it into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the imports:\n",
    "\n",
    "# pandas for handling the data\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Seaborn is a Python data visualization library that offers a user-friendly interface \n",
    "#    for generating visually appealing and informative statistical graphics.\n",
    "import seaborn as sns\n",
    "\n",
    "# From sklearn we import some classes and functions for data handling, the tree classifier, \n",
    "#    the accuracy and the plot function to depict the tree  \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.tree import plot_tree \n",
    "\n",
    "# This class we use it to search exhaustive over specified parameter values for an estimator.\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from dtreeviz.trees import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "1. Import with pandas the file *iris_teach_2.csv* into the pandas DataFrame with the name *df_iris*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>iris_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  iris_name  \n",
       "0    setosa  \n",
       "1    setosa  \n",
       "2    setosa  \n",
       "3    setosa  \n",
       "4    setosa  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "\n",
    "df_iris = pd.read_csv('iris_teach_2.csv')\n",
    "df_iris.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. use the method *isnull()* from the class DataFrame to check if there are empty cells in the dataset. (Hint: check the documentation and use this method with respect to your DataFrame object; use the method .sum() to the result to count the empty cells on columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    1\n",
       "sepal width (cm)     1\n",
       "petal length (cm)    0\n",
       "petal width (cm)     1\n",
       "iris_name            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df_iris.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. We see that we have some empty cells on some rows. Delete these rows (hint: use the method *dropna()* from pandas.DataFrame class, with the argument *inplace=True*). Check the documentation why we use that argument (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html)! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "iris_name            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df_iris.dropna(inplace=True)\n",
    "df_iris.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Divide the dataset in two parts: a set **X** for features and **y** for target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "0                5.1               3.5                1.4               0.2\n",
      "1                4.9               3.0                1.4               0.2\n",
      "2                4.7               3.2                1.3               0.2\n",
      "3                4.6               3.1                1.5               0.2\n",
      "4                5.0               3.6                1.4               0.2\n",
      "0    setosa\n",
      "1    setosa\n",
      "2    setosa\n",
      "3    setosa\n",
      "4    setosa\n",
      "Name: iris_name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# your code here\n",
    "X = df_iris.drop('iris_name', axis=1, inplace=False)\n",
    "y = df_iris['iris_name']\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Create a **LabelEncoder** object to encode the classes from the target. Fit it with the *y* list, and encode *y* with it. (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "# your code here\n",
    "y_encoded = LabelEncoder().fit_transform(y)\n",
    "print(y_encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Divide the dataset in a training and a testing set as we did it in the previous laboratory with the sklearn function *train_test_split*. Check the documentation why we use for *random_state* a fixed value here! (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "4                  5.0               3.6                1.4               0.2\n",
      "33                 5.2               4.1                1.5               0.1\n",
      "145                5.8               2.7                5.1               1.9\n",
      "88                 6.0               3.4                4.5               1.6\n",
      "89                 6.7               3.1                4.7               1.5\n",
      "..                 ...               ...                ...               ...\n",
      "74                 6.1               2.8                4.0               1.3\n",
      "109                4.9               2.5                4.5               1.7\n",
      "14                 5.8               4.0                1.2               0.2\n",
      "95                 5.8               2.6                4.0               1.2\n",
      "105                7.1               3.0                5.9               2.1\n",
      "\n",
      "[112 rows x 4 columns]\n",
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "76                 6.1               2.8                4.7               1.2\n",
      "18                 5.7               3.8                1.7               0.3\n",
      "121                7.7               2.6                6.9               2.3\n",
      "81                 6.0               2.9                4.5               1.5\n",
      "79                 6.8               2.8                4.8               1.4\n",
      "32                 5.4               3.4                1.5               0.4\n",
      "67                 5.6               2.9                3.6               1.3\n",
      "144                6.9               3.1                5.1               2.3\n",
      "71                 6.2               2.2                4.5               1.5\n",
      "85                 5.8               2.7                3.9               1.2\n",
      "113                6.5               3.2                5.1               2.0\n",
      "12                 4.8               3.0                1.4               0.1\n",
      "37                 5.5               3.5                1.3               0.2\n",
      "9                  4.9               3.1                1.5               0.1\n",
      "19                 5.1               3.8                1.5               0.3\n",
      "59                 6.3               3.3                4.7               1.6\n",
      "107                6.5               3.0                5.8               2.2\n",
      "72                 5.6               2.5                3.9               1.1\n",
      "58                 5.7               2.8                4.5               1.3\n",
      "135                6.4               2.8                5.6               2.2\n",
      "30                 4.7               3.2                1.6               0.2\n",
      "130                6.1               3.0                4.9               1.8\n",
      "27                 5.0               3.4                1.6               0.4\n",
      "131                6.4               2.8                5.6               2.1\n",
      "134                7.9               3.8                6.4               2.0\n",
      "148                6.7               3.0                5.2               2.3\n",
      "111                6.7               2.5                5.8               1.8\n",
      "146                6.8               3.2                5.9               2.3\n",
      "47                 4.8               3.0                1.4               0.3\n",
      "31                 4.8               3.1                1.6               0.2\n",
      "22                 4.6               3.6                1.0               0.2\n",
      "15                 5.7               4.4                1.5               0.4\n",
      "68                 6.7               3.1                4.4               1.4\n",
      "11                 4.8               3.4                1.6               0.2\n",
      "44                 4.4               3.2                1.3               0.2\n",
      "149                6.3               2.5                5.0               1.9\n",
      "53                 6.4               3.2                4.5               1.5\n",
      "28                 5.2               3.5                1.5               0.2\n",
      "[0 0 2 1 1 0 0 1 2 2 1 2 1 2 1 0 2 1 0 0 0 1 2 0 0 0 1 0 1 2 0 1 2 0 2 2 1\n",
      " 1 2 1 0 1 2 0 0 1 1 0 2 0 0 1 1 2 1 2 2 1 0 0 2 2 0 0 0 1 2 0 2 2 0 1 1 2\n",
      " 1 2 0 2 1 2 1 1 1 0 1 1 0 1 2 2 0 1 2 2 0 2 0 1 2 2 1 2 1 1 2 2 0 1 2 0 1\n",
      " 2]\n",
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1\n",
      " 0]\n"
     ]
    }
   ],
   "source": [
    "# your code here\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.25, random_state=42)\n",
    "print(X_train)\n",
    "print(X_test)\n",
    "print(y_train)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model\n",
    "\n",
    "We can now build the decision tree model using scikit-learn's **DecisionTreeClassifier class**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a decision tree classifier object \n",
    "tree_clf = DecisionTreeClassifier(criterion='entropy', random_state=42) \n",
    "# Fit the classifier to the training data \n",
    "tree_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model\n",
    "\n",
    "We can evaluate the performance of the model on the test set using scikit-learn's *accuracy_score* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set \n",
    "y_pred = tree_clf.predict(X_test) \n",
    "# Calculate the accuracy of the model \n",
    "accuracy = accuracy_score(y_test, y_pred) \n",
    "print(\"Accuracy: {:.2f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Model\n",
    "\n",
    "We can visualize the decision tree using scikit-learn's *plot_tree* function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.3333333333333333, 0.9375, 'x[2] <= 2.45\\nentropy = 1.583\\nsamples = 112\\nvalue = [35, 39, 38]'),\n",
       " Text(0.2222222222222222, 0.8125, 'entropy = 0.0\\nsamples = 35\\nvalue = [35, 0, 0]'),\n",
       " Text(0.4444444444444444, 0.8125, 'x[2] <= 4.75\\nentropy = 1.0\\nsamples = 77\\nvalue = [0, 39, 38]'),\n",
       " Text(0.2222222222222222, 0.6875, 'x[3] <= 1.65\\nentropy = 0.187\\nsamples = 35\\nvalue = [0, 34, 1]'),\n",
       " Text(0.1111111111111111, 0.5625, 'entropy = 0.0\\nsamples = 34\\nvalue = [0, 34, 0]'),\n",
       " Text(0.3333333333333333, 0.5625, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.6666666666666666, 0.6875, 'x[2] <= 5.15\\nentropy = 0.527\\nsamples = 42\\nvalue = [0, 5, 37]'),\n",
       " Text(0.5555555555555556, 0.5625, 'x[3] <= 1.75\\nentropy = 0.896\\nsamples = 16\\nvalue = [0, 5, 11]'),\n",
       " Text(0.3333333333333333, 0.4375, 'x[1] <= 2.35\\nentropy = 0.918\\nsamples = 6\\nvalue = [0, 4, 2]'),\n",
       " Text(0.2222222222222222, 0.3125, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.4444444444444444, 0.3125, 'x[2] <= 5.05\\nentropy = 0.722\\nsamples = 5\\nvalue = [0, 4, 1]'),\n",
       " Text(0.3333333333333333, 0.1875, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3, 0]'),\n",
       " Text(0.5555555555555556, 0.1875, 'x[0] <= 6.15\\nentropy = 1.0\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
       " Text(0.4444444444444444, 0.0625, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.6666666666666666, 0.0625, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.7777777777777778, 0.4375, 'x[1] <= 3.1\\nentropy = 0.469\\nsamples = 10\\nvalue = [0, 1, 9]'),\n",
       " Text(0.6666666666666666, 0.3125, 'entropy = 0.0\\nsamples = 9\\nvalue = [0, 0, 9]'),\n",
       " Text(0.8888888888888888, 0.3125, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.7777777777777778, 0.5625, 'entropy = 0.0\\nsamples = 26\\nvalue = [0, 0, 26]')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RwTq5JRGCzHvrQ4cOBeDJkye0b9/eYD0vLy+8vLwKXMa6dety/Phxbt68iVarpW/fvsrogsbGxnh4eBAbG4uDg0Oe21i2bBmLFy9m2LBhHDp0iL59+3L8+HG0Wi1du3aladOmLyyDRqMhMjKSoKAgPvjgA/bs2cO///1vIiMjMTMzw9PTU0miJEmSVN7JxoCUQ//+/fH396dNmzaYmpry7bff0qBBAwIDAwkODiYgIMBg+fr16ytBdNnHFOjQoQN79uxRrpDT0tIM1tu9ezdr167Nsf8X3fNPS0vD2NgYIyMjXn31VV555RUgs7egbt26CCEICwvj3XffBeDGjRu0aNHCYBvPnj2jZs2aGBsb8+qrr1K7dm1UKhX+/v5A5u0GCwsLAO7du0fNmjUNEihlrQ+GAxplbc/Y2Jh69epx//79PN+HJElSeSIbA1IOI0aMwMLCgqCgIAC6du3K0qVL6dWrF9bW1jmW79ChA0ZGRri6utKlSxdl+qpVqxg2bBjp6ekYGRmxZs0ag/UL0zNw+/ZtRo4ciZGREenp6SxfvhyAFStWcPjwYVQqFc7Ozri5uQHg7u7O6dOnDbbh6+vLxYsXSU9Pp2vXrri4uHD37l2GDBmCkZERb775Jp9++ikAK1euRK1W07dvX2X9Q4cOsXLlSoyMjKhRowbr168H4KOPPkKr1VK9enXMzc1xdnYu0HuTJEkqKzIDYRVWUbLgzZgxg4iICDZt2qQ8UZAfd+7cYeXKlUqDoTDGjh3LunXrCj1ccUxMDJMmTaJXr17K0MtQcY69JElVg2wMVGHyB6nsyGMvSVJ5Ip8mkCRJkqQqTsYMSFIZiouLK+siFBszMzNatmxZ1sWQJKkQZGNAqlQ/SBVF1jH39vYu45IUHxMTE+Li4mSDQJIqINkYqMLMzMwwMTGpVD9IFUnt2rXZu3fvS3MaVARxcXF4e3uTmJgoGwOSVAHJxkAV1rJlS+Li4khMTCzSdjZs2MCxY8d466236NSpE61atcLIqPyEo2T9UG3btg1LS8uyLo5CdqtLklReyMZAFdeyZcsi/yBlDdRT3llaWsrIfUmSpFzIxkARJCQkFPmqWsokr5IlSZLKjmwMFFJCQgKWlpYkJyeXdVEqhYoYfHbt2jXs7OwYO3Ysy5cvZ+nSpezfvx+VSoWHhwdz5sxBr9fTvXt3EhMTOXfuXImVZdy4cdy5c4cDBw4YTF+9ejXfffcdAJcvX2bWrFlMnToVrVZLWloa1atXp3fv3syZM6fEyiZJUvknGwOFlJiYSHJycrm7D10RVeTgMwcHByXD4ZAhQ/jggw8QQuDo6MiIESNo0aIFOp0uxyBNeXny5Al16tQpUBkuXbrE3bt3c53n6+uLr68vAF26dMHT01OZt3//fjmyoiRJgGwMFJm8D1016HQ6AgIC2Lt3LwsXLqRBgwYMGDDAYJm2bdsCmaM4Ghsb5zuFcVJSEt988w07d+6ka9euzJ8/v0Bl++STT5g9ezZLly7Nc5nLly9Ts2ZNZdAmlUqFp6cnJiYm+Pv78+abbxZon5IkVS6yMSBJ+aDVagkODmby5Mlcv36doKAgrl+/nuuyu3btom3btjRp0uSF2zx58iQbNmzgzz//xNPTkx07dlC/fn0g/yM6RkVF0bRpU15//fUX7mvnzp3KcNIA+/bto2HDhpw/f54RI0YQGxv7wvUlSarcZGOgHIqNjSU5ORm1Wl3WRQHgjz/+YOzYsaSlpeHj46MMD5wlMTGRkSNH8vjxY9zc3FiwYEHZFLSE+fr60rJlS0JDQ1GpVLkuc+LECTZs2MCPP/740u3t37+fs2fP4uvri6enp8Ewyfkd0XHp0qVs3LiRJ0+evHC5ffv2ceTIEeX/hg0bAmBtbU2NGjVITk7GxMTkpfuTJKlyKj8Pg0uK2NhYIiIickzX6/VlUBqYM2cOK1asIDw8nM2bN3Pv3j2D+Z9++injxo3j559/JiYmhgsXLpRJOUvalClTWLduHfPmzSM9PT3H/IsXLzJt2jT27NlD7dq1X7q9ZcuWERISQlJSEh4eHrzzzjucOHECyOwZ0Gq1Of6e98cff+Dt7c2oUaOIjIzkiy++yLHMmTNnaNq0KY0aNVKmPXr0CMgc2TEpKUk2BCSpipONgVI0c+ZMXFxc6NatG5GRkUDmldkHH3yAWq3Gz88PyIwAX7t2LVqtlidPntC2bVvGjx+Pj48PFy9eRKPR4OzszLvvvosQAp1OR79+/fD09MTW1pajR49y7949XFxclH37+PgQExNTqHLHx8djZ2eHsbExGo2GqKgog/nHjx+nX79+APTt25fjx48Xaj/l2YYNG7CwsGDcuHEMGTKERYsW5VjG19eX+/fvM3jwYLRaLWfPnn3pduvVq8eECRMICQnhww8/5Pbt20Bmz4BOp8vx97zffvuNQ4cOsXXrVuzt7ZXP0OjRo5Vlnr9FkJGRgYuLC05OTrz11lt89dVXBTwakiRVNvI2QSkJDg5Gr9cTGhrK3bt3GTJkCKGhoSQnJ+Pj48PSpUuxsbHh0aNH+Pr6kpiYyIwZMwC4desWS5YsoVGjRgwcOJCVK1diZ2fHxIkTCQoKom7duty5c4fIyEju3r3LgAEDiIqKonHjxsTHx9OsWTPlBz27uXPnKleiWWxtbXNcXWZkZCiv69Wrl6NnICkpiVq1ainzr127VkxHrfwYP3688vq9994D4ObNm1y5coVZs2axfPlyfvrppxzrZT1a2KxZs5fuw8LCAgsLi0KVr1WrVgaPFW7ZskV57e/vb7CskZFRoRuGkiRVTrIxUErOnTvHTz/9pHT1Pn78GMh8vt7c3ByAFi1acP/+/RzrtmrVSunivXr1qvKjrlaruXTpEvb29nTs2BFjY2OaNGlCSkoKkHl1GBgYiIWFBW+//XaO7S5btixfZc+eWvjhw4dKkFsWU1NTUlJSqFWrVq7zK6vmzZu/9JaIsbFxrlf0kiRJ5Ym8TVBKrKyscHd3V7p7s67Inw9EE0JQo0YNg3vS2X+MW7VqpVzVRUREKA2J06dPk5GRwV9//aXcr+7RowdhYWFs27Yt18GI5s6dm+OedFY3c3bm5uacOnUKvV5PWFgY9vb2BvOdnJw4ePAgkNkD4uTkVNDDI0mSJJUh2TNQSvr168fx48fRarWoVCq6dOmSo/s2i1qtJiAggJiYGLZu3Wowz9/fnwkTJgDQrl073N3dCQ8P57XXXsPDw4MbN27w+eefA5mNCFdXV86dO2cQPJYlvz0Dy5YtM3iaoEGDBsTGxhIWFoavry+zZs1i5MiRrFy5EldXV6ytrQtyaCqN8vYUyNKlS1m/fj0dOnTIkZkQqs5TIJIkvZxsDJSi3H78s6eozf6F/csvv+S6jJWVVa4Beq1bt841EMzIyIgxY8YUusyQmUwnLCzMYJqtrS22trYANGrUiEOHDhVpH5VBbGwsiYmJORoDer0eY2PjUi/PmDFj8PLyUjIQPi/rKZBBgwYxYMAALly4gJWVVSmXUpKk8kDeJqjE/Pz8iIqKUiL9pcKpqE+BNGnS5IWNkKrwFIgkSfkjewYqgbyeQc/tmXOpYCryUyAvUxWeApEkKX9kY0CSXqAiPwXyMlX1KRBJknKStwkqCZ1Ox+TJk0t8P05OTmg0Guzt7Tl27BgAgYGBtGnTJs8eioqsIj8F8jLyKRBJkrLIngGpQI4ePUqNGjW4cuUK77zzDq6urkBmIp6s7vHKpCI/BbJ582a2bNnCpUuXcHNzY9euXdy8eVM+BSJJUg6yMVBKrly5gre3NzVr1sTExIQff/yRHTt2sHHjRpKSkujZsyeLFy9Gp9OxZMkS6tSpw6VLl/j000/ZsGEDV69eJTAwkE6dOmFtbU3v3r05efIkdnZ2rFmzxmBfx44dY+HChWRkZODm5sb8+fP54Ycf+OSTTzAxMaFbt24sXry4UO+jRo0aQGZ3eefOnZXpmzZt4vvvv2fw4MGFukotzyrqUyBjx45l7NixBtPMzMzkUyCSJOUgGwOlRKfT4enpycyZM5X0vgMHDmT48OEIIXByclLy0j99+pTDhw8TFBTE/PnziYqK4vjx42zcuJFOnTqRlJTEyJEjWbFiBR4eHgbR5kIIZs+ejU6nw9TUlMGDB3Pp0iX27t1LQEAAnTt3NkgvnKVPnz48ffrUYJqPjw8+Pj4G0x4+fEj//v25fPkymzdvBsDDw4ORI0eSnp7OgAED6NKlCw4ODsV5+KoUPz8/4uPjmT9/flkXRZKkKkI2BkqJl5cXixcvxtvbGxsbG2bOnMmxY8dYtWoVer2ey5cvc+vWLQBsbGxQqVQ0a9aMDh06YGxsTPPmzZUxAapXr65c3XXu3JnLly/TuHFjIDORzJUrV5RHxh48eEBCQgLz58/ns88+Y/Xq1Xh5eTFgwACD8gUHB+frfdStW5fjx49z8+ZNtFotffv2pV69ekBm6l0PDw9iY2NlYyAf5FMgkiSVF7IxUEpUKpVyr7dHjx4MHDiQjz/+GJ1OR926dVGr1QghlGWzr5cla35aWhpnzpzBxsaG6OhoevfurUS5m5mZYW5uTnBwMLVr1yYjIwMhBM+ePWPdunWkpqZibW2dozGQn56BtLQ0jI2NMTIy4tVXX+WVV14BMnsL6tatixCCsLAw3n333WI6apWfTqdj3759JTpy4KVLl5R4hSdPnpCRkcGpU6cMHlE8d+4c//nPf3B3dy+xckiSVH7JxkApOXDgAGvWrMHY2JimTZvSunVrhg4dikajwdLSElNT03xvy9TUlMDAQCIjI7Gzs8POzk4ZDEelUrFkyRL69OmDSqWievXqbNu2jZUrVxIREUFaWhrjxo3Lsc389Azcvn2bkSNHYmRkRHp6OsuXLwdgxYoVHD58GJVKhbOzM25ubvl+L1LJs7CwUD4fAQEBPHjwAPhfIKJer8fS0pIePXqUUQklSSpzQiqUmJgYAYiYmJhS37e1tXWp77MklfSxLMr2//jjD+Hg4CC0Wq3o27evEEKI7du3C61WKzp37iw+/PBDIYQQoaGhws3NTXh4eAhLS0sRFBQk+vfvL6ytrUVUVJQQQggrKyvx/vvvC7VaLaZMmaKsN2nSJCGEEEePHhXOzs6iW7duYsGCBUIIIYKCgkTnzp2FRqNR9lUUjo6O4vLlywbTQkJChLe3d5G2W5b1QZKkopM9A5L0ApUl8BMgISGBtLQ02rRpYzB9586dDBs2rKiHSpKkCkw2Biqg7I+sSSWrsgR+AuzatYuhQ4caTEtNTSU8PJx169YV6vhIklQ5yAyEZcDHx4fo6OhS2Y+dnR0XLlwAip49MDExkT59+tCtW7eXDnf7xx9/oNVqcXR0ZOPGjQDExMRga2tbKpkSi0tW4Oe2bds4fPgw8fHxfPzxx3z77bfodDr++c9/FjjwEyA6Opq2bdsqy2QP/NTpdJw6dQpXV1eaNm3KunXr+M9//sP777+fo3x9+vTJkY0wMDAw1/eye/duhgwZYjAtODiY7t27U7169cIdIEmSKgXZM1DJrV+/XhmWtqjZAwsy5O2cOXNYsWIFtra2ODo6MmjQIOzs7Pjiiy/Yt29f8b3BElYZAj8h84mCV199lWbNmhlM37lzJ//+97/z/R4kSaqkyjZkoeJ6PmDKz89PhISECCGEuH79uujXr58QQogePXoIjUYjHBwcxMWLF4UQQowaNUpERUUZBI/dvXtXaDQaIYQQsbGxwtXVVWg0GjF+/HiRkZFRqDJm7ed5sbGxws/PTwghxJYtW4SFhYVwdHQUq1ateuH2unTpIp4+fSqEEOLrr78W69aty3PZN954Q3k9a9YscejQISGEYcBclvIcQFicKlvgZ3bl5RhLklQ48jZBMfH29mb79u0A7Nixg+HDhwPw3XffodPpWLBgAatWrcrXtvz8/Ni+fTs6nY46depw+PBhg/m7d+/O0TWcnwGCHj58iJOTE71791YeI/Pw8OD8+fMcPXqUgwcP5hgaN7vnh7zNuheem+zBbi9bVpIkSSpb8jZBMcm6N5+SksL+/fs5cuQIycnJTJo0iStXrpCamppj0Jnc7itDZoBgVqDXkydPaN++vcF6Xl5eeHl5FbiMRc0eWJAhb7OP2CeHx80kAz8lSSqvZM9AMerfvz/+/v60adMGU1NTDh06RIMGDQgPD2f+/PkGP/gA9evX5+bNmwAGj5l16NCBffv2odPpiI6OzjGMbWF6BtLS0pSr9eezBwJK9sB27doBcOPGjRzbyGvI29yWNTc359SpU+j1esLCwrC3t39h+cqzsgr4PHDgAA4ODjg6OhIVFZXneomJiajVajQaDWq1mt9++w2A8PBwHBwccHJyYvfu3S/ct6+vLxqNhk6dOinpkNPS0nj77bdxcnKiW7duSmNmypQp1KtXj8TExGJ415IklQeyZ6AYjRgxAgsLC4KCggDo2rUrS5cupVevXrkOD9uhQwdluNouXboo01etWsWwYcNIT0/HyMiINWvWGKxfmJ6BgmYPdHd35/Tp0wbbyG3I2/T0dAYNGkRkZKTBssuWLWPs2LGkpaXh4+NDgwYNClTeqior4FOv1zNv3jzCw8N59OgRQ4YM4eeff851nfr163P8+HGMjY05duwYy5cvZ9u2bcyYMYMffviBhg0b4uzszIABAzAxMcl1G5999hk1atQgPT0dKysr3nvvPcLDw3n11VfZu3cvx48fZ/ny5fz3v//lyy+/5OzZsyV5GCRJKmWyMVCMWrduTVpamvL/66+/nusVZfZHv7799tsc821sbAgJCSlyeczMzJg6dSqbNm3CysqKsLCwHMssWrSIRYsWGUy7c+dOrqlpcxvyNjo6OtcEN23bts2xv5iYGObMmUOvXr0K8W6K17Rp0+jXrx9ubm4kJCQwceJEDhw4QM+ePUlNTSU1NZUtW7ZgYWGhrJN9HIHExEQGDx6MTqfjzJkzvP/+++j1eiwsLFi3bp3BLaDCiI+Px9zcnDp16lCnTh3S09N59uwZNWvWzLGssbGx8vrJkyd06tQJgJSUFCWPQcuWLYmNjUWtVue6v6yhqVNSUrC0tKRGjRq0bt1a6U26f/8+r732WpHekyRJ5ZdsDFRin3/+eaHWa9KkidJz8DJdu3ala9eu+VrWzs6OkydPFqpMxc3b25uvvvoKNze3HAGfpqamHD58mFWrVuUrGY+fnx87d+6kSZMmTJ8+ncOHDxs0eHbv3s3atWtzrJf1WGFu7t+/r8RzQGa8x71792jatGmuy1+6dInRo0dz+/Zt5ZZAnTp1iI+Pp2nTpvz6668vDeIcPXo0hw4dYuLEiahUKpo3b87Tp0+xtLTkyZMn/PLLLy9cX5Kkiks2BqQqqbwHfNavX18ZUAheHoRpYWFBREQEZ86cYfz48fz66698/fXXTJo0iVq1atGhQ4c8GxJZtmzZQkpKCt27d2fQoEH8/PPPtGrVil27dhEZGYmvry/fffddgd6HJEkVg2wMSFXW8wGf3377LQ0aNCAwMJDg4GACAgIMln9RwOeePXswMzMDMLhVBIXrGWjXrh3x8fEkJSXx+PFjqlWrRq1atXj8+DF6vd6g1yD77YP69esrcQG2traEhITw5MkTvLy8lFTIN27coEWLFgb7y9pGzZo1MTExUYa/znpPZmZm3L9/P8/ySpJUscnGQBHFxcWVdREqvJI8hkKIPIPdynPAZ7Vq1ViwYAFubm6oVColR8Xu3btJTU1l4sSJyrJnzpxhxowZSuzAihUrgMygwIMHD1K9enX8/f0xNjbOM+Bz6NCh3L9/n7S0NN566y3++c9/4u3tzdChQzlw4AApKSl89tlnBXoPkiRVIGWa8qgCu379ujAxMRGA/CuGPxMTE3H9+vViOz/JycniP//5j7Czs1P2Ud6z402fPl04ODiI8+fP57nMtGnTxN27dwu9jxMnToiAgIBCry+EEJMnTxYWFhbi3r17yrSsDIQdO3YUe/bsEampqUXahyRJpUslxHMPv0v5lpCQIJ+1LiZmZma0bNmyyNu5evUqa9euZfPmzdy7d48+ffrQu3dvfH19iYmJoWPHjsVQWul5p06dws7OjjfffJPTp0/z+uuvM2HCBN59992XxipIklT2ZGNAqvAyMjL46aefCAgI4ODBg9StW5cxY8bw3nvv0bZtW+WHSjYGSk72Y1ytWjUCAgLYtm0bqampDBo0iMmTJ+Po6FjkRy4lSSoZMmagCGTPQPEpTM/A/fv32bJlC2vXruXy5cvY2tqyceNGhg0blmdyHankvfHGG6xfv55PP/2UrVu3EhAQgJOTE2+88QaTJk1ixIgRBRrtMYusb8WnuHripMpDNgYKKSEhAUtLS5KTk8u6KJWCiYkJcXFxOb6gMjIyEEIYJNaJjY0lICCA7du3k56ezttvv83WrVtxcHB44ZWnDPYsObkd23r16uHr68uUKVM4cuQIAQEBvPfee8yaNYvRo0czceJEJf01QHp6OsbGxrmeQ1nfilde9U2qumRjoJASExNJTk5m27ZtWFpalnVxKrS4uDi8vb1JTEw0+HK6d+8eGo0Gb29vpk2bxjfffMNXX31FREQEzZo144MPPuDdd99VsuzlxczMDBMTkxxjPEjFy8TERHkUMTsjIyN69uxJz549uXbtGuvWrWPTpk188cUX9OrVi0mTJtG3b1/8/Pw4f/48wcHByuiYWWR9Kz551TepapONgSKytLSU96FLQNa95ps3b5KQkEDLli3566+/cHFx4ZtvvsHd3Z1q1fL38W3ZsiVxcXGyi7mE5afruVWrVvj7+7NgwQJ2795NQEAA7u7utGrVir59+3Ly5ElGjx7N9u3bDUa+zCLrmySVDNkYkModIQTu7u6EhYWhUqn473//y6hRo5g4cSJWVlaF2mbLli3lVVA5UqtWLUaNGsWoUaOIiooiICCAzZs3o9fr2bVrF6ampmzatKmsiylJVYYcwlgqd/bv389PP/2EEIKMjAyMjIy4fft2oRsCUvnWqVMnLl26RLVq1UhPTwdg8+bN/PHHH2VcMkmqOmTPQDlw7do17OzsGDt2LMuXL2fr1q1s3LgRIQStW7dmy5YtqFQqunfvTmJiojKufHE6dOgQ77//Prdu3TLIiZ/dpk2b2LlzJ2lpaXz00Uf07NkTrVZLWloa1atXp3fv3syZM6fIZXF3d2fPnj0YGRnx4MED7t+/zz/+8Y8ib1cqn1QqFePGjVPGX6hbty41atSgTZs2JbK/5+vb0qVL2b9/PyqVCg8PD+bMmYNery/R+hYYGMgnn3yipIV+PjX16dOnGTVqFPHx8dy8eVOJxSiJ+iZJgMxAWFhZGdeKI6vd1atXRb9+/ZT/nz17prweOXKkCAkJUf63trbO1zYfP35coDL8/fffIjk5Oc/t//bbb2L8+PE5pms0miJlxBOieI+lVDmVZH37/fffhRBCZGRkCAcHB5GQkKDMK6n6tmXLFvHZZ5/lOf/Ro0fi0aNHOeqXrG9SSZG3CUqZTqfj7bffBmDhwoV8+eWXOZbJGlteCFGgK6SkpCT++9//0qdPHyU/fX41aNCA2rVr5zn/m2++wdjYGDc3N4YPH64MWqNSqfD09KRXr16cPn26QPuUpJKWn/rWtm1bIPOzbGxsTPXq1fO17aLUN8jsaevWrRtffPFFjnmvvPIKr7zySo7psr5JJUXeJihlWq2W4OBgJk+ezPXr1wkKCuL69es5llu9ejVr167FwsLipY/OnTx5kg0bNvDnn3/i6enJjh07lOFuCzNiXm5u375NSkoKR44cYdOmTSxbtozly5ezb98+GjZsyPnz5xkxYgSxsbEF2q4klaT81jeAXbt20bZtW5o0afLCbRZHffPw8GDkyJGkp6czYMAAunTpgoODw0vfj6xvUkmRjYEy4OvrS8uWLQkNDc0zSY6vry++vr5Mnz6dwMBAg1Hqnrd//37Onj2Lr68vnp6eBtndCjNiXm7q169Phw4dAOjduzfffvstAA0bNgTA2tqaGjVqkJycLLP/SeVKfurbiRMn2LBhAz/++ONLt1cc9S1rCGpjY2M8PDyIjY3NV2NA1jeppMjbBGVgypQprFu3jnnz5inR09k9e/ZMeV2/fv0Xdt8DLFu2jJCQEJKSkvDw8OCdd97hxIkTQOaVilarzfFXUBqNhpiYGACio6OVrtVHjx4BcOfOHZKSkuQXk1TuvKy+Xbx4kWnTprFnz56X1jUonvr28OFDIPNWYFhYmEEmxheR9U0qKbJnoJRt2LABCwsLxo0bR1paGosWLWLMmDEGyyxbtgydTocQglatWjFr1qyXbrdevXpMmDCBCRMmcOnSJc6fPw/k/0olOjqaOXPmcO3aNdzc3Jg7dy7du3dn9OjRbNmyhV69evHjjz+i1WqpVq0a//3vf8nIyMDFxQUTExP0ej1fffVV4Q6KJJWQ/NQ3X19f7t+/z+DBgwH48ssvlV6wvBS1vq1YsYLDhw+jUqlwdnbGzc0NQKlvN27cYPTo0Zw5c4ZBgwYxfvx4hg0bJuubVGLkqIWFVJwj4d28eZOePXvSv39/li9fnusyWY861axZk59++qlI+ytv5KiC0svI+lZ8ZH2TciN7BsqB5s2bc+HChRcuY2xsXOCgP0mScpL1TZJykjEDkiRJklTFycZAGYuNjSUiIqKsi6H4448/0Gq1ODo6snHjxhzzt2zZgrm5Oe3btzeYPnnyZNRqNV27dlWuqEaPHq0EUNWpU4fffvutNN6CJOWpotW3BQsWYG1tjVarZejQoQDExMSgVqvRaDT07duXe/fuAbK+SUUjGwNlLK8vJ71eXwalgTlz5rBixQrCw8PZvHmz8kWTpX///kqwVJbff/+duLg4IiIi2Lt3L/PmzQMyGw46nY7vvvuOf/zjH7zxxhul9j4kKTcVrb5BZrIknU7Hrl27AGjWrBkhISGEhYXh7u7O119/Dcj6JhWNbAyUkJkzZ+Li4kK3bt2IjIwEMp8N/uCDD1Cr1fj5+QH/Sy6k1Wp58uQJbdu2Zfz48fj4+HDx4kU0Gg3Ozs68++67CCHQ6XT069cPT09PbG1tOXr0KPfu3cPFxUXZt4+Pj/IYYEHFx8djZ2eHsbExGo2GqKgog/mNGjXKkaGtcePGvPLKK6Snp3P//n1ee+01g/nffPMNb731VqHKI0n5UVnrG8DixYtxcnJi586dADRp0kTJbVCtWrUc9VHWN6kwZABhCQgODkav1xMaGsrdu3cZMmQIoaGhJCcn4+Pjw9KlS7GxseHRo0f4+vqSmJjIjBkzALh16xZLliyhUaNGDBw4kJUrV2JnZ8fEiRMJCgqibt263Llzh8jISO7evcuAAQOIioqicePGxMfH06xZM+ULJru5c+cqz0JnsbW1zZEKNSMjQ3ldr169XK9UnvfKK6/QunVr/vWvf/HkyRMOHDhgMH/nzp25poGVpOJQmevblClTWLBgAQ8fPsTV1RVHR0dlKO7ExEQCAgI4cuSIwTqyvkmFIRsDJeDcuXP89NNPSrKRx48fA2BiYoK5uTkALVq0UPL7Z9eqVSsaNWoEwNWrV5UvGbVazaVLl7C3t6djx44YGxvTpEkTUlJSgMz7hYGBgVhYWCi52LNbtmxZvspuZPS/zqKsUeReJiQkhDt37vD777+TkJDA0KFDlS/CO3fucP/+fTn8sFRiKnN9y8o4WLduXbp378758+dp2bIlT58+xcvLi4CAAGUZkPVNKjx5m6AEWFlZ4e7ujk6nQ6fTKT+Mz6dCzRqIKHtWtOxfDq1atVK6HyMiIpQvttOnT5ORkcFff/2lZEzr0aMHYWFhbNu2DW9v7xxlmjt3bo6saFldp9mZm5tz6tQp9Ho9YWFh2Nvbv/T9ZmRk0KBBA1QqFfXr11e+jCEzI9uQIUNeug1JKqzKXN+yMhWmp6dz4sQJ2rZti16vZ9iwYUyaNAm1Wm2wvKxvUmHJnoES0K9fP44fP45Wq0WlUtGlSxf8/f1zXVatVhMQEEBMTAxbt241mOfv78+ECRMAaNeuHe7u7oSHh/Paa6/h4eHBjRs3+Pzzz4HMLzVXV1fOnTunXOlkl98rlWXLljF27FjS0tLw8fGhQYMGxMbGEhYWhq+vLwcOHOCLL75QMhV+8cUX9OjRg+3bt+Ps7ExKSgqLFi1Strdr1y7lXqcklYTKXN+mT5/OhQsX0Ov1DB8+nHbt2rFz505CQ0N58OABa9asoV+/fsycOROQ9U0qgjIbPLmCK6sxwUNDQ8WkSZNynTdv3jwRFBRUquUpDnJ8dellZH0rPrK+SbmRtwkqCT8/P6KioujXr19ZF0WSKj1Z36TKRt4mqGDyGgXt+ShlSZKKTtY3qaqQPQOVhE6nY/LkySW+HycnJzQaDfb29hw7dgyAwMBA2rRpU+jhkSWpIiqtOgdw5coVatasSXR0NABLly6lS5cudO3aNc/4CEkqCNkzIBXI0aNHqVGjBleuXOGdd97B1dUVgPfee095dluSpOK1ZMkSnJyclP+HDBnCBx98gBACR0dHRowYQYsWLcqwhFJFJ3sGSsmVK1dQq9W4uLgo9xl37NiBi4sL9vb2fPTRR0Dm1UaPHj3w9PTEysqKH374gQEDBtC+fXvlqsDa2prp06fj6OjI1KlTc+zr2LFjaDQanJycWLhwIQA//PAD9vb2aLVaZV+FUaNGDSDzWe7OnTsr0zdt2kS3bt1k96lUblSWOvfbb79Rt25dmjdvrkxr27YtkPn4pLGxcY4shJJUYGUdwVhRFTQid/PmzWL58uVCCCH0er0QQognT54IIYTIyMgQjo6O4tatWyI0NFQ4OjqKjIwM8f3334s333xTpKeni9DQUDF+/HghhBD/+Mc/xOnTp4UQQgwcOFBER0crUc8ZGRmiU6dOyrYHDRokLl68KEaOHCkiIyMN9p9d7969hUajMfjbsmVLjuUePHggunXrJpo0aSJ+/PFHIYQQ9+/fF+np6SIlJUX06NFDRERE5PMoZpLRzdLLFOYzUlnq3Ntvvy3u3LkjRo0aJaKiogzm7dy5U/j4+OT7mAgh65uUO3mboJR4eXmxePFivL29sbGxYebMmRw7doxVq1ah1+u5fPkyt27dAsDGxgaVSkWzZs3o0KEDxsbGNG/eXElVWr16dWxtbQHo3Lkzly9fpnHjxkBmitIrV64oV0IPHjwgISGB+fPn89lnn7F69Wq8vLwYMGCAQfmCg4Pz9T7q1q3L8ePHuXnzJlqtlr59+1KvXj0gcwx4Dw8PYmNjcXBwKOohk6QiqQx17ueff6ZNmzbKvrI7ceIEGzZs4Mcffyz0MZKkLLIxUEpUKpWSiKRHjx4MHDiQjz/+GJ1OR926dVGr1QghlGWzr5cla35aWhpnzpzBxsaG6OhoevfurWT9MzMzw9zcnODgYGrXrk1GRgZCCJ49e8a6detITU3F2to6xxdTnz59ePr0qcE0Hx8ffHx8lP/T0tIwNjbGyMiIV199lVdeeQXIzJJWt25dhBCEhYXx7rvvFtNRk6TCqwx17tSpU5w4cYLevXtz9uxZ4uPjCQoKIjExkWnTpnHgwAElK6IkFYVsDJSSAwcOsGbNGoyNjWnatCmtW7dm6NChaDQaLC0tlVHI8sPU1JTAwEAiIyOxs7PDzs4OnU4HZH6RLVmyhD59+qBSqahevTrbtm1j5cqVREREkJaWxrhx43JsMz9XKbdv32bkyJEYGRmRnp7O8uXLAVixYgWHDx9GpVLh7OyMm5tbvt+LJJWUylDnpk6dqsQo+Pj4MHnyZMzMzBgxYgT3799n8ODBAHz55Zd06NAh3+9Hkp6nEllNX6lATp06hZ2dHTExMXTs2LFU992+fXvOnTtXqvssSWV5LKWKoaw/I5WpzpX1sZTKJ/k0gSRJkiRVcbIxUAFVlisUSaooZJ2TKjvZGJAkSZKkKk42BsqAj4+PksykpPdjZ2fHhQsXgMyAKgcHBxwdHYmKispzvcTERNRqNRqNBrVazW+//WYw383N7aVpWJcuXco//vEP+vfvr0yLiYnB1ta21FK4ShKU//oGmQGKWem8Dx06lOdy586dU+qmVqvl5s2bQOaTCVnr16xZk/v378v6JhWIbAxUcuvXr8fKygq9Xs+8efMICQlh7969TJs2Lc916tevz/HjxwkLC2Px4sXKUwMAR44coWbNmi/d75gxY5SxC7LY2dnJDIVSpVaY+gbwz3/+E51Oh06no3fv3nkuZ2FhwS+//EJYWBjvvPMO69atAzKfTNDpdHz99dc4OztTv359Wd+kApGNgWIybdo0jhw5AkBCQoJyRdyzZ0+0Wi1qtZpLly4ZrJN9oJPExERlkJ8zZ87QvXt3tFotEyZMoDge+IiPj8fc3Jw6derw+uuvk56ezrNnz3Jd1tjYGGNjYwCePHlCp06dlHlr1qzJ15VGkyZNlG1IUnGrTPUN4MaNGzg7OzNixAgSExPzXK569epKHoSnT5/y5ptvGszfsWMHQ4cOLXL5papHNgaKibe3N9u3bwcyK+Tw4cMB+O6779DpdCxYsIBVq1bla1t+fn5s374dnU5HnTp1OHz4sMH83bt3K12C2f9e5P79+0qmQMjMJJiVXS03ly5dQq1W4+vri1qtBmDv3r307NlTJjmRylxlq29XrlwhPDyc7t278+GHH75w22FhYdjb2/P1119jY2NjMO/bb7/lrbfeeuH6kpQbmXSomGTdK0xJSWH//v0cOXKE5ORkJk2axJUrV0hNTaVRo0YG6+SW6Qwy7wtmte6fPHlC+/btDdbz8vLCy8urQOWrX78+Dx48UP5/+PAh9evXz3N5CwsLIiIiOHPmDOPHjyciIoKNGzdy4MABIiIiCrRvSSpula2+NWzYEIChQ4eyfv36F25bo9EQGRlJUFAQH3zwAXv27AEgMjKSdu3avXA/kpQX2RgoRv3798ff3582bdpgamrKt99+S4MGDQgMDCQ4OJiAgACD5evXr68EAMXExCjTO3TowJ49ezAzMwMyU6Fmt3v3btauXZtj/1kZ0XLTrl074uPjSUpK4vHjx1SrVo1atWrx+PFj9Hq9wVXMs2fPlLiA+vXrY2Jiwp07d0hMTMTd3Z179+7x119/4ejoyLBhw7hx44YcPlUqdZWlviUlJVGrVi2MjY3R6XS0a9cOgHv37lGzZk2DTInZ62aDBg0Meul27tzJsGHDXnTIJClPsjFQjEaMGIGFhQVBQUEAdO3alaVLl9KrVy+sra1zLN+hQweMjIxwdXWlS5cuyvRVq1YxbNgw0tPTMTIyYs2aNQbrF+ZKpVq1aixYsAA3NzdUKpXShbp7925SU1OZOHGisuyZM2eYMWOGcs9/xYoVNGvWjFOnTgGZX4L79u1Tyjho0CAiIyMN9rd582a2bNnCpUuXcHNzY9euXcqXrSQVh8pS3y5evMi4ceN49dVXqVWrFhs3bgRg5cqVqNVq+vbtqyx76NAhVq5ciZGRETVq1FB6ETIyMvjxxx9ZvHhxgcopSYoyGSuxEqgIw4BOnz5dODg4iPPnz+e5zLRp08Tdu3cLvY8TJ06IgICAfC0bHR0tunTpIubNm2cwvSIcS6lsVYTPSHHXtzFjxojU1NRCl0fWN6kg5NgEhSTzexcfeSyll5GfkeIjj6WUG/k0gSRJkiRVcTJmoIji4uLKuggVnjyGxSchIeGFz6lXVFmfEflZKTp5DKXcyNsEhZSQkIClpSXJycllXZRKwcTEhLi4OFq2bFnWRamwKvtn0sjIiIyMjLIuRqUg65v0PNkzUEgtW7YkLi4u31dhgwYN4tq1a5ibm9OlSxc6duxIt27dMDKq+HdqEhISOH78OKdPn+bEiROkpKRw4MABmjZtmu9tmJmZyS+mIkpMTCQ5OZlt27ZhaWlZ1sUpdrdv3zZ4dr8kXb16lY8//rhcHsvU1FTCw8M5deoUJ0+e5Pr163Tu3FlJTZwfsr5Jz5M9A6UkJSUFvV5v8MxwZSSE4P79+zRo0KCsi1LlyMCw4lORjuWjR4+oXbs21atXL+uiSBWY7BkoJbVq1SrrIpQKlUolGwKSVIpeffXVsi6CVAmUaGOgsgYzlZWy6NqT57BwKno37LVr17Czs2Ps2LEsX76crVu3snHjRoQQtG7dmi1btqBSqejevTuJiYmcO3eu2Mtw6NAh3n//fW7dupXr7YHVq1fz3XffAXD58mVmzZrF1KlT0Wq1pKWlUb16dXr37s2cOXOKvWzFTdaz4lPR616ZKakEBtevXxcmJiYCkH/F9GdiYiKuX79eUqdMnsMKfK6EKN5kMlevXhX9+vVT/n/27JnyeuTIkSIkJET539raOl/bfPz4cYHK8Pfff4vk5OR8bd/e3l4kJCQIIYTQaDRFSqQlROkm5pH1rOLXvcqgxHoGKnswU2mLi4vD29ubxMTEUmv1ynNYOGVxropCp9MREBDA3r17WbhwIQ0aNGDAgAEGy9SoUQMAIQQ1atSgTZs2+dp2UlIS33zzDTt37qRr167Mnz8/3+XK7+2my5cvU7NmTWV8DJVKhaenJyYmJvj7++cY5re8kfWs+FS0uleelHjMgKWlZbkPwJFeTJ7Dyk2r1RIcHMzkyZO5fv06QUFBXL9+Pcdyq1evZu3atVhYWNC4ceMXbvPkyZNs2LCBP//8E09PT3bs2KGMpleYgX9eZOfOncqogwD79u2jYcOGnD9/nhEjRhAbG1uo7ZY2Wc+ksiQDCCVJwtfXl5YtWxIaGmow1O/zy/j6+jJ9+nQCAwMNBtt53v79+zl79iy+vr54enoaPEVTmIF/XmTfvn0cOXJE+T9rOGBra2tq1KhBcnIyJiYmxbY/SaqMKuxD7rGxsURERJR1MRR//PEHWq0WR0dHZdSx7BITE+nTpw/dunVjwYIFpV/AckKet/JpypQprFu3jnnz5pGenp5j/rNnz5TX9evXNxg6NzfLli0jJCSEpKQkPDw8eOeddzhx4gSQ2TOg1Wpz/BXGmTNnaNq0KY0aNVKmPXr0CIA7d+6QlJRU5RsCss5J+VHpGgN6vb4MSgNz5sxhxYoVhIeHs3nzZu7du2cw/9NPP2XcuHH8/PPPxMTEcOHChTIpZ1mT56382bBhAxYWFowbN44hQ4awaNGiHMssW7YMrVaLRqPh999/Z8SIES/dbr169ZgwYQIhISF8+OGH3L59G8jsGdDpdDn+nhcdHY2bmxvXrl3Dzc2No0ePAjB69GhlmedvEWRkZODi4oKTkxNvvfUWX331VUEPR6Uj65yULyUVmVjYaNwZM2YIrVYrHB0dxa+//iqEEMLKykrMnTtXODg4CF9fXyGEELa2tqJ169ZCo9GIx48fizZt2oh3331XeHt7i7i4OOHs7CycnJzEuHHjREZGhggNDRV9+/YVHh4ewsbGRhw5ckT8/fffQqvVKvseNWqUiI6OLtT7feONN5TXs2bNEocOHTKY36VLF/H06VMhhBBff/21WLduXYG2XxbDjhZkn/K8/U9ZDRFbnPu9ceOGsLS0FDNnzsxzmfT0dKHRaETPnj2LvL/ypjTPofyuLL7vSjk8c+GVq5iB4OBg9Ho9oaGh3L17lyFDhhAaGkpycjI+Pj4sXboUGxsbHj16hK+vL4mJicyYMQOAW7dusWTJEho1asTAgQNZuXIldnZ2TJw4kaCgIOrWrcudO3eIjIzk7t27DBgwgKioKBo3bkx8fDzNmjUjPj4eOzs7gzLNnTtX6d7MYmtryxdffGEwLXvO9Hr16uVo7SYlJSmJh+rVq8e1a9eK6aiVPXneKp/mzZu/9IrM2Ni40EF/UtHIOicVt3LVGDh37hw//fSTcv/w8ePHQOagGubm5gC0aNGC+/fv51i3VatWyn3Dq1evKh9UtVrNpUuXsLe3p2PHjhgbG9OkSRNSUlKAzC7HwMBALCwsePvtt3Nsd9myZfkqe/YxBh4+fKhETmcxNTUlJSWFWrVq5Tq/IpPnTZJKl6xzUnErVzEDVlZWuLu7K/cQs1qZz0c3i///WefsgU7ZP2CtWrUiJiYGgIiICKVynD59moyMDP766y8lAKpHjx6EhYWxbds2vL29c5Rp7ty5OQKd/Pz8cixnbm7OqVOn0Ov1hIWFYW9vbzDfycmJgwcPApmteicnp4IennJLnrfKpaIFnMXExODg4ICzszMjR45Er9ej1+sZNWoUWq2WQYMGKUGF6enp+Pn54ebmhpOTE3fv3i3tt1MsZJ2Til1J3X8o7L2b2bNnC41GI7RarZg9e7YQwjDDWb9+/cTVq1fF1atXhVqtFoMHDxZJSUkGy5w/f15069ZNdOvWTYwePVro9XoRGhoq+vTpIwYMGCBsbW3FkSNHlOU/+ugj4eHhUaT3+/vvvwtnZ2fh4OCg3OM6ffq0+OKLL4QQQvy///f/RK9evYSjo6P4+OOPC7z98h4zIM/b/1T0mIEtW7aIzz77LMf09PT0Im23sAYPHiyio6NFenq66NKli/j7778N5g8fPlyEh4cLIYQYM2aMOHr0qNi3b5+YNm2aEEKI7du3i08++UQIIcSXX34ptm/f/tJ9VoSYAVnncpIxA4VX7hoDJSU0NFRMmjQp13nz5s0TQUFBpVyiginvjYGSUhHPW3ltDFTWgLPly5crn4NBgwaJ3377Tfj7+4tt27YJIYSIi4sTPXr0EEII4eLiIubOnSs0Go344IMP8txnRWgMlJSKWOeylLdjWZGUq9sEZcHPz4+oqCj69etX1kWRCkCet4LJHnD23XffMXv2bAAl4CwiIoLQ0FAl4Oy9995Dp9NRp04dJeDs//7v/5g9ezYrV64kPDyc6tWrExQUBGQ+079v3z4OHTrEnDlzaNCggRJwlpSUlGfAWX66lV8WcNa7d2+mT5/Ov/71L6pVq0aHDh1o3749ISEhAISEhCjr/Pnnn7Rr1w6dTkdCQoLSHS29nKxzlVu5CiAsSXklNnk+0lUqX+R5Kx6VOeDs3//+N/v378fS0pKJEyeyf/9+3N3dOX78OFqtlq5du9K0aVMgM2FSz549AejVqxfnzp2jb9+++SpHVSHrXNVUpXoGdDodkydPLvH9ODk5odFosLe359ixYwbzxo0bR//+/Uu8DJVVaZ1DT09PzMzM+Pzzz0t8X6WhMgecCSGUFMRmZmbcv38flUqFv78/Op2Otm3b8tZbbwGg0WiU8kdHR9O2bdv8H8QqpDTqWUJCAs7Ozjg7O+Pi4sLVq1eVeYsXL1aCPM+ePVui5ZAyVZmegdJ09OhRatSowZUrV3jnnXdwdXUF4NKlSxU2ermq+eqrrwgJCak0Y8z369dPuVJWqVR06dIFf3//XJdVq9UEBAQQExPD1q1bDeb5+/szYcIEANq1a4e7uzvh4eG89tpreHh4cOPGDaUBZWRkhKurK+fOnTNIF5wlvz0Dy5YtY+zYsaSlpeHj40ODBg2IjY0lLCwMX19flixZgru7OzVr1qRevXrMmTNHefbeyMiIN998k08//RSAWbNm4ePjw4oVK2jVqhUeHh75PYRSMXv11Vf57rvvaNiwIYcOHWLJkiVs2rSJAwcOYGRkZDDehFTyylVj4MqVK3h7e1OzZk1MTEz48ccf2bFjBxs3biQpKYmePXuyePFidDodS5YsoU6dOly6dIlPP/2UDRs2cPXqVQIDA+nUqRPW1tb07t2bkydPYmdnx5o1awz2dezYMRYuXEhGRgZubm7Mnz+fH374gU8++QQTExO6devG4sWLC/U+soZ7ffz4MZ07d1amf/LJJ8yePZulS5cW/iCVc5XlHDZr1qw4Dke5ktuP/7lz55TXBw4cUF7/8ssvuS5jZWXF8ePHc2yndevWuab+NTIyYsyYMYUuM0Dbtm0JCwszmGZra4utrS0ALi4unDx50mC+iYkJoaGhObbVsGFDfvjhhyKVpzyoDPWsXr16yutq1apRvXp14H+jTrq6umJubs7q1aupWbNmkY6X9HLlqjGg0+nw9PRk5syZStDQwIEDGT58OEIInJyclPzmT58+5fDhwwQFBTF//nyioqI4fvw4GzdupFOnTiQlJTFy5EhWrFiBh4eH0jUImd2Ks2fPRqfTYWpqyuDBg7l06RJ79+4lICCAzp07GwQtZenTpw9Pnz41mObj44OPj4/BtIcPH9K/f38uX77M5s2bAYiKiqJp06a8/vrrxXnIyp3Kcg6lovPz8yM+Pp758+eXdVEqncpUz1JSUvj444/ZsmULALdv3+Yf//gHx44d46OPPmLz5s0vHCFTKh7lqjHg5eXF4sWL8fb2xsbGhpkzZ3Ls2DFWrVqFXq/n8uXL3Lp1CwAbGxtUKhXNmjWjQ4cOGBsb07x5cyVquHr16sqVQ+fOnbl8+bIyBntiYiJXrlxRomIfPHhAQkIC8+fP57PPPmP16tV4eXkxYMAAg/IFBwfn633UrVuX48ePc/PmTbRaLX379mXp0qVs3LiRJ0+eFMehKrcqyzmU8k8GnJW+ylLPMjIyGDlyJDNmzOBf//oXYBjk2bt3b3bs2FG0gyXlS7lqDKhUKuU+Yo8ePRg4cCAff/wxOp2OunXrolarEUIoy2ZfL0vW/LS0NM6cOYONjQ3R0dH07t1biaA2MzPD3Nyc4OBgateuTUZGBkIInj17xrp160hNTcXa2jrHBzw/rd20tDSMjY0xMjLi1Vdf5ZVXXgEys6h5e3vz9OlT4uLi+OKLL3INlqroKsM5lIqHTqdj3759JT5yoKenJ8ePH2fOnDlK/n1A6SZ/9uwZX3/9NR06dCjRcpSmylLPpkyZgqOjI4MGDVKmZQV5Ojo6yiDPUlSuGgMHDhxgzZo1GBsb07RpU1q3bs3QoUPRaDRYWlpiamqa722ZmpoSGBhIZGQkdnZ22NnZKYOqqFQqlixZQp8+fVCpVFSvXp1t27axcuVKIiIiSEtLY9y4cTm2mZ/W7u3btxk5ciRGRkakp6ezfPlyAH777TcArl27xuTJkytlQwAqxzkEmDp1KkePHiUtLY2zZ8/mCKSTyo/cgj0rexBaZahnJ06cYNOmTTg4OPD9999jZ2fHihUr8PHxYezYsXz77bfUrVuX7du35/u9SEVQUtmMyjoTVPaUm5VBVcxAWFHPYXnNQPi8P/74Qzg4OAitViv69u0rhMhM3avVakXnzp3Fhx9+KITIzEjn5uYmPDw8hKWlpQgKChL9+/cX1tbWIioqSgiRmcnw/fffF2q1WkyZMkVZLyuT3dGjR4Wzs7Po1q2bWLBggRBCiKCgING5c2eh0WiUfRXW8ymUR40aJd5//33h4uIiJkyYIFJSUgq0vaqUgbCi1rPclPWxrMjKVc+AJEmlpzIFoT1PBqFJUsFU2sZA9sehpIpJnsOSVVmC0HIjg9DyT9YzCSpxY0CSpBerLEFouZFBaJJUMOU2HbGPjw/R0dGlsh87OzsuXLgAZAbmODg44OjoSFRUVJ7rJSYmolar0Wg0qNVqJUAwi5ub20vTeeY2TntMTAy2tralknK3pJX3cwiwceNG1Go1Wq2WK1eu5LncrVu3sLOzo06dOgbvacqUKdSrV69CZio8cOAA3bp1Q6PR0LBhQ4MgtGHDhhUqCM3R0ZEWLVoYDEqUPQjNxcWF3r178/fff7No0SKcnZ3RaDR5BqFlpU/O+sutITB16lQ+++wzNmzYwKhRo4DMz8TJkyfRarWEhoYyfvz4gh+gCqIy1bODBw/StWtXnJ2d8fLyIjU1FYAnT54wevRoXF1dlcdYK9N3ZblQUsEIRQ3kGDVqlBKcVJKy7yc9PV28+eab4vHjx+LPP/8Ujo6Oea6Xnp6ujO9+9OhRMWLECGVeSEiI6Nu3b57DgGbJa5z23IYQrYgBhOX9HP7999+iS5cuIj09XURHR4u33347z2WfPn0qEhMTc31PGo1G3L17V/m/ogQQFqfKFIQmRMUKIKxM9ez69evi2bNnQgghZs+eLf7v//5PCJE5/PbPP/+cY/nnvytlAGHhlWrPwLRp05RHfRISEpQBe3r27IlWq1VGQcsu+4AZiYmJSqvwzJkzdO/eHa1Wy4QJE5TuyqKIj4/H3NycOnXq8Prrr5Oens6zZ89yXdbY2BhjY2Mgs9XaqVMnZd6aNWvy1VrNGtbV2NgYjUbz0tZ1eVCZzuGvv/6KVqvF2NgYOzs74uPj89xurVq1lMFwJKmkVdV61rJlSyWde/YUxT///DM7d+5Eo9HkSJcsFY9SbQx4e3srz4zu2LGD4cOHA/Ddd9+h0+lYsGABq1atyte2/Pz82L59uzLm+uHDhw3m7969O8eIaLllScvu/v37Bvmy69atm2Ps9OwuXbqEWq3G19cXtVoNwN69e+nZs6cyctuLvGyc9vKoMp3D55fNLaJdyh8ZhFa8qno9+/333zl06BCenp4AnDp1ikGDBnH06FG+//57zp8//9JtSAVTqgGEWfebUlJS2L9/P0eOHCE5OZlJkyZx5coVUlNTc4xulluwEmR++QwdOhTIvDJv3769wXpeXl54eXkVqHz169fnwYMHyv+5jZ2enYWFBREREZw5c4bx48cTERHBxo0bOXDgABERES/d38vGaS+PKtM5rF+/vkGsR/bzIUllqSrXs8TEREaNGsXOnTuVXoLXXnsNFxcXAFxdXTl//jzW1tYFKrP0YqX+7de/f3/8/f1p06YNpqamHDp0iAYNGhAeHs78+fNzdGHVr1+fmzdvAhg8u9yhQwf27duHTqcjOjo6x3jphWnttmvXjvj4eJKSkrhz5w7VqlWjVq1aPH782OCDDxh0idWvXx8TExPu3LlDYmIi7u7uzJo1ix9++IGdO3cCcOPGjRz7e9k47eVVZTmHXbp0ISwsDL1ez6lTp2jXrh0A9+7dIykpqZBHp2KqCEFopqamymfg0KFDeS6XV7Dn0qVL+cc//qF0uUP5DkKrivUsKSmJQYMGsWLFCmU5wCBIWz4dUjJK/dHCESNGYGFhQVBQEABdu3Zl6dKl9OrVK9eWXocOHZRx0bt06aJMX7VqFcOGDSM9PR0jIyPWrFljsH5hWrvVqlVjwYIFuLm5oVKplG643bt3k5qaapC05MyZM8yYMUOJG1ixYgXNmjXj1KlTwP/ysmeVcdCgQURGRhrsL7dx2iuCynIOGzRowKhRo3BycqJ69erKCJMrV65ErVbTt29fZdm0tDT69OnDhQsXiIuLw8vLi/fff79AZZMyrV+/HisrK/R6PfPmzSM8PJxHjx4xZMgQfv755zzX++c//6mkyX2RBg0acPjwYaZPn24wfcyYMXh5eeHr66tMs7Oz44svvmDfvn2Ffj8lpSrWszVr1nDx4kXmzp0LwNixYxk5ciT+/v68++67PH36FCcnJzp27Fig8kr5UFKRiRUlqnP69OnCwcFBnD9/Ps9lpk2bZhAtXlAnTpwQAQEB+Vo2OjpadOnSRcybN89gekV8mqC0FPc5HDNmjEhNTc3XspMnTxYWFhbi3r17yrTy8jSBn5+fCAkJEUJkRmn369dPCCFEjx49hEajEQ4ODuLixYtCiP9FimePzr57967QaDRCCCFiY2OFq6ur0Gg0Yvz48SIjI6NQZcwekX7hwgXh5eWlzOvSpcsL0wa/+uqrwsnJSQwfPjxf5zK3KPurV68qxyFLWT+9I+tZ4eT2XVlRjmV5VOWTDn3++ecvXWblypVF2kfXrl3p2rVrvpa1s7Pj5MmTRdpfVVPc5zDryiU/vvzyy3wvW9q8vb356quvcHNzyxGEZmpqyuHDh1m1ahXr1q176bb8/PzYuXMnTZo0Yfr06Rw+fJhevXop83fv3s3atWtzrPeiK/m8gtCaNm2a6/JXrlyhYcOG/Oc//+HDDz9k/fr1Ly23VHzKsp7lRn5XFq8q3xiQpMri4cOHBv9XpiA0QHm0c+jQobIhIEnFrMQbA3FxcSW9iyqhLI+jPIcFU9rH6/Tp0wQEBLBt27Yc854PQvv2229p0KABgYGBBAcHExAQYLD8i4LQ9uzZg5mZGZAZQ5FdYXoGsgehPX782CAITa/XG/QaJCUlUatWLYyNjdHpdAZBaDVr1ixQtsSCmDt3Lh9//DGOjo4GDaWSIOtZ0cljWAQldf/h+vXrwsTERADyr5j+TExMxPXr10vqlMlzWIHO1bNnz8T27duFWq0WgGjevLmYOHGiAMP7pX/88YeoVq2aOHjwoBBCiD///FPY2dmJnj17imnTpin3z7Pur2dkZAhPT0/h4uIi5syZYxAz4ObmJrRarXB1dRXnzp0rVLmfv4+/f/9+0bVrV+Hg4CBOnjwphBBi48aNOWJsoqOjha2trXB2dhY9e/ZUju2HH34ofvzxR4NlU1NTRffu3UXTpk2Fvb29WLFihRBCiE2bNglHR0dhZmYmunfvrtzbflHMQIsWLQQg3njjDbFhwwbx5MmTQr3vF5H1rGLVvcpKJUQxpKPKQ0JCQoXM2V5emZmZ0bJly1LdpzyHhVNS5+rGjRusX7+ejRs38v/+3//D1dWVSZMm4e7uzm+//YadnR0xMTHlNtp6xowZREREsGnTJqysrHJd5v333+eDDz5QeiFeZOzYsaxbt07JVFdQMTExTJo0iV69erFw4UJl+qlTp7CzsyMqKop79+7x1VdfceDAAV599VVGjx7NxIkTDR59KypZz4pPWXxPVgYl2hiQJKnohBCEhoYSEBDA/v37qV27NqNGjWLixIkGP6hZP2DluTFQUeR2LK9du8a6devYtGkTf//9N7169WLSpEn07dtXecRYkioqmXJNksqpx48fExAQgLW1Nd27dycuLo41a9Zw69YtvvrqqzyvrKWS0apVK/z9/bl58yaBgYHcu3cPd3d32rZty6effiqv7KUKTT5NIL2Q7L4snKJ0VV64cIGvv/6arVu38vTpUzw8PAgICECr1ZZ4EJv0crVq1WLUqFGMGjWKqKgoAgICmD9/PvPnz2fo0KFMmjSJzp07F3r7ss4Vjrw9UDSyMSDlKSEhAUtLS5KTk8u6KBWOiYkJcXFx+f5ySk9PZ//+/QQEBBAaGkrjxo3x8/NjwoQJNG/evED7lhHVRZffY9i5c2cCAwP5/PPP2bx5M2vXrmXr1q107tyZyZMnM2TIEGrVqpXv/co6V3gFrXOSIRkzIOUp677ptm3bsLS0LOviVBhxcXF4e3vneu/+xo0bBAcHM378eAD++usvNm7cyPr167l58yaOjo5MmjSJQYMGKYO05Jf8ISlehflx0ev1/PjjjwQEBHD48GHMzMwYO3Ys//73v2nVqhWQOQphx44d+de//pVjfVnnCudFdU7KH9kzIL2UpaWlrGDF4MGDB/Tq1QsjIyPat29PQEAAe/fupVq1aowYMYJJkyZha2tb6O23bNmSuLg42cVcTArT7WxsbIy7uzvu7u7Ex8fz9ddfs27dOj777DP69+/PpEmT+O9//8vMmTP59ddf8+z1kXVOKm2yMSBJpSA1NRUPDw+uX79Oy5YtcXR0pE2bNnz66af4+PgU2/DVLVu2lN2k5YS5uTlffPEFS5YsYfv27QQEBNCrVy9at27N06dP6dOnDxEREbzyyitlXVRJkk8TSFJp6NGjB2FhYSQnJ2NkZMSsWbM4e/Ys06ZNK7aGgFQ+mZqaMn78eGJiYli0aBF169blwYMHnDt3rkiBhpJUnGRjQCpR165do2HDhsyaNQuAQ4cOYWVlZZBqVq/Xo9Vqc+S7Ly5Lly6lS5cudO3aFX9//xzzQ0JCcHR0xNnZmX79+vH48WMAtFotjo6OaLXaXNcrCCEEZmZmNGnShOvXr7Ny5UpOnz5dpG1KFcv169dZtmwZFy9epHHjxjRq1KhE8xOUh7q3detWunXrhqOjIyNHjiQ9Pd1g/unTp3njjTeoVauWvL1V1sou+aFU3hXHcKDPDxn7999/i+TkZGFtbZ1j2dym5ebx48cFKsPvv/8uhBAiIyNDODg4iISEBIP5z549U15//PHHYuPGjUIIITQaTaGGrs7PcUtPTy/wdqWK72XnvTiH4C0PdS973Ro5cqQypHaWR48eiUePHhW6rmWRQxcXnewZkIqNTqfj7bffBmDhwoW5Du/boEEDateuXeBtJyUl8d///pc+ffqwYsWKAq3btm1bIHNEPmNj4xypa7NH7aenp2Ntba0s7+npSa9evYr9Kl5mrKuaSuq8l9e6l1W3hBDUqFGDNm3aGMx/5ZVXZMxEOSEDCKVio9VqCQ4OZvLkyVy/fp2goCCuX79epG2ePHmSDRs28Oeff+Lp6cmOHTuUe+wFHSlv165dtG3bliZNmuSYt3v3bpYtW4aJiQnTpk0DYN++fTRs2JDz588zYsQIYmNji/ReJKmklOe6t3r1atauXYuFhQWNGzcuUpmkkiMbA1Kx8vX1pWXLloSGhhZLtrz9+/dz9uxZfH198fT0NBiq1svLCy8vr3xt58SJE2zYsIEff/wx1/lZ21qzZg2fffYZy5cvp2HDhgBYW1tTo0YNkpOTMTExKfJ7kqSSUF7rnq+vL76+vkyfPp3AwEAmTpxY5LJJxU/eJpCK1ZQpU1i3bh3z5s3LESxUGMuWLSMkJISkpCQ8PDx45513OHHiBJB5daLVanP8Pe/ixYtMmzaNPXv25NpN+uzZM+V19q7UR48eAXDnzh2SkpJkQ0Aq18pj3ctet+rXr1+o2xRSKSnroAWp/CpoUM769evF3LlzhRBCfP311+Ljjz/OEcQUFRUlunfvLkxNTUX37t3FkSNHlHn5CWK6ePGi+Oabbwr0Pnr27CnMzc2FRqMRGo1G/Pbbb0IIIXx8fIQQQqxbt05oNBqh1WrFwIEDxb1794RerxcdO3YU3bp1Ew4ODuLYsWP53p8MZpIKq7CfnfJa9+bPny80Go1wdnYW77zzjhJQmFX3EhISRPfu3UW9evWEs7Oz2LZtW4G2n0XWuaKT6YilPBXHkLg3b96kZ8+e9O/fn+XLl+e6jF6vp3v37tSsWZOffvqpKEUuF+RQwlJhFednpyrVPVnnik7GDEglqnnz5ly4cOGFyxgbG+cZ9CdJUuHIuicVhIwZkEpMbGwsERERZV0MxR9//KEkEtq4cWOO+eHh4Tg4OODk5MTu3bsBSEtLw9HRkXr16rFv3z5l2ZiYGBwcHHB2dmbkyJHo9fpSex+SlJuKVt+yuLm5MXnyZOX/yMhIevbsiUaj4dNPPwVyr5tS8ZKNAanE5PXlVFY/nHPmzGHFihWEh4ezefNm7t27ZzB/xowZfP/994SGhrJ69WqSk5OpVq0a+/btw8/Pz2DZlStXsnz5csLDw6lRowZhYWGl+E4kKaeKVt8Ajhw5Qs2aNZX/nz17xsKFC/n+++8JCwtj9uzZQO51UypesjEgFdjMmTNxcXGhW7duREZGApmP333wwQeo1WrlhzPr+WKtVsuTJ09o27Yt48ePx8fHh4sXL6LRaHB2dubdd99FCIFOp6Nfv354enpia2vL0aNHuXfvHi4uLsq+fXx8iImJKVS54+PjsbOzw9jYGI1GQ1RUlMH8lJQUGjduTLVq1WjZsiWxsbGoVCqaNm2aY1u2trY8ePAAgIcPH9KoUaNClUmSXqay1jeANWvWGPQKnDhxAhMTEwYPHkzPnj357bffgNzrplS8ZMyAVCDBwcHo9XpCQ0O5e/cuQ4YMITQ0lOTkZHx8fFi6dCk2NjY8evQIX19fEhMTmTFjBgC3bt1iyZIlNGrUiIEDB7Jy5Urs7OyYOHEiQUFB1K1blzt37hAZGcndu3cZMGAAUVFRNG7cmPj4eJo1a6Z8wWQ3d+5c5ZGnLLa2tnzxxRcG0zIyMpTX9erVy3GlUqdOHeLj42natCm//vprrlcyWXr37s2gQYOYOXMmtra2dOjQoTCHU5JeqDLXt71799KzZ0+Dxw1v377N+fPniYqKIiEhgXHjxvHLL78UqG5KhSMbA1KBnDt3jp9++kl5pjhrUB8TExPMzc0BaNGiBffv38+xbqtWrZQr6KtXrypfMmq1mkuXLmFvb0/Hjh0xNjamSZMmpKSkADB69GgCAwOxsLBQUq5mt2zZsnyV3cjofx1hDx8+zDFa4Ndff82kSZOoVasWHTp0yLVHIMu///1v9u/fj6WlJRMnTmT//v0MHDgwX+WQpPyqrPVNr9ezceNGDhw4YHBro379+jg6OmJqaoqlpaWS66MgdVMqHNkYkArEysoKd3d35QshNTUVIEfGM/H/5yLPnvwk+5dDq1atiImJwc7OjoiICHr27AlkjmKWkZHB3bt3lSuGHj16sGjRIqKiotixY0eOMuX3SsXc3JxTp05hY2NDWFiYMppb9nVCQkJ48uQJXl5e2Nra5nkchBBKhkIzM7Ncv4wlqagqa327c+cOiYmJuLu7c+/ePf766y8cHR3p3bs3S5cuJSMjg7/++kspU0HqplQ4sjEgFUi/fv04fvw4Wq0WlUpFly5d8hzeV61WExAQQExMDFu3bjWY5+/vz4QJEwBo164d7u7uhIeH89prr+Hh4cGNGzf4/PPPgcwvNVdXV86dO5frvfn8XqksW7aMsWPHkpaWho+PDw0aNCA2NpawsDB8fX357LPPOHjwINWrV8ff318ZVGbIkCFER0dTp04dIiMjWb58OUuWLMHd3Z2aNWtSr1495syZk+9jKEn5VZnr26lTp4DM8Qz27dvHsGHDgMw4BY1GQ1pamlKmvOqmVIzKMOGRVM6Vdlav0NBQMWnSpFznzZs3TwQFBZVKOYpKZkOTCqs0PzuVpb4JIetccZBPE0jlnp+fH1FRUfTr16+siyJJlZ6sb1WTvE0glRt5DXby/L1ISZKKTtY3KTvZMyBJkiRJVZxsDEgVik6nM0hSUlJ+//13+vfvj1arZdq0aSW+P0kqT0qrnnl6emJmZqYECgI8ffoULy8vnJyceO+99wzyFUglRzYGJCkX77//Plu3bkWn07Fq1aqyLo4kVUpfffWVQUMA4D//+Q/29vYcP36catWqcejQoTIqXdUiGwNSsbpy5QpqtRoXFxclAGnHjh24uLhgb2/PRx99BGReefTo0QNPT0+srKz44YcfGDBgAO3btyc6OhrITLk6ffp0HB0dmTp1ao59HTt2DI1Gg5OTEwsXLgTghx9+wN7eHq1Wq+yroK5du0ZKSgrvvvsuLi4uhIaGFmo7klRSKkM9A2jWrFmOaeHh4cp76tu3L8ePHy/09qX8kwGEUrHS6XR4enoyc+ZMpXtv4MCBDB8+HCEETk5O3L59G8jsDjx8+DBBQUHMnz+fqKgojh8/zsaNG+nUqRNJSUmMHDmSFStW4OHhYZAjXQjB7Nmz0el0mJqaMnjwYC5dusTevXsJCAigc+fOuXYv9unTh6dPnxpM8/HxwcfHR/n/9u3bnD59mri4OIQQuLi4cPbsWYMkLpJUlipDPcvL/fv3qVevHpB7GmOpZMjGgFSsvLy8WLx4Md7e3tjY2DBz5kyOHTvGqlWr0Ov1XL58mVu3bgFgY2ODSqWiWbNmdOjQAWNjY5o3b65U/urVqyuZxjp37szly5dp3LgxAImJiVy5ckW5gnjw4AEJCQnMnz+fzz77jNWrV+Pl5cWAAQMMyhccHPzS91C/fn3efPNNJeFK06ZNSUxM5LXXXiuWYyRJRVUZ6lle6tevz4MHD2jSpEmuacOlkiEbA1KxUqlUSoayHj16MHDgQD7++GN0Oh1169ZFrVYjhFCWzb5elqz5aWlpnDlzBhsbG6Kjo+ndu7eSm93MzAxzc3OCg4OpXbs2GRkZCCF49uwZ69atIzU1FWtr6xxfUvm5YmnXrh0PHz7k6dOnCCG4ffu2knpYksqDylDP8uLs7MzBgwf517/+xcGDB+nVq1fBD5BUYLIxIBWrAwcOsGbNGoyNjWnatCmtW7dm6NChaDQaLC0tMTU1zfe2TE1NCQwMJDIyEjs7O+zs7NDpdEDml9qSJUvo06cPKpWK6tWrs23bNlauXElERARpaWmMGzcuxzbzc8VibGzMwoUL6dGjB6mpqSxcuFCmP5XKlcpQzwCmTp3K0aNHSUtL4+zZs2zdupXRo0fj4+ODs7MzlpaW9OnTJ9/vRSo8lchqHkrSc06dOoWdnR0xMTF07Nix1Pffvn17zp07V+r7LaqyPm5SxVUWn52KWs+yk3Wu6GRElCRJkiRVcbIxIJVbFf1qRZIqAlnPJJCNAakE+fj4KM8yl/R+7OzsuHDhApB5P9XBwQFHR0eioqJeuv7PP/+MSqUiMTExz2UOHjxI165dcXZ2xsvLSxlXfsqUKdSrV++F60pSaSjv9S0tLQ1HR0fq1avHvn37XriPx48f4+HhgYuLC2PGjCEtLQ2Q9a0kycaAVCmsX78eKysr9Ho98+bNIyQkhL179+YrlfCqVavo1KnTC5dp37494eHhhIeH889//pM9e/YA8OWXXyqPZUlSVVGY+latWjX27duHn5/fS7e/YcMGXF1dCQ0NpW3btuzevRuQ9a0kycaAVCDTpk3jyJEjACQkJNC/f38AevbsiVarRa1Wc+nSJYN1suc5T0xMVEZKO3PmDN27d0er1TJhwgSKI5Y1Pj4ec3Nz6tSpw+uvv056ejrPnj3Lc/kffviBbt26vTT6umXLltSoUQPI/FKrXr16kcsqSS9TmeqbSqWiadOm+dru5cuXsbOzA6Bjx46Eh4cXuazSi8nGgFQg3t7ebN++HchMfzp8+HAAvvvuO3Q6HQsWLMh3Ln8/Pz+2b9+OTqejTp06HD582GD+7t27lWFWs/+9SPbsZQB169bNM4NZRkYGa9eu5b333stXeSFzAKNDhw7h6emZ73UkqbAqU30riPbt2yvlCwkJkVkIS4HMMyAVSNa9wpSUFPbv38+RI0dITk5m0qRJXLlyhdTUVCVzX5bcEp1AZuDS0KFDAXjy5Ant27c3WM/LywsvL68ClS8re1mWF2Uw27FjB+7u7tSqVStf205MTGTUqFHs3LlT6SWQpJJUmepbQYwdO5apU6fi6upKhw4d8t2jIBWebAxIBda/f3/8/f1p06YNpqamfPvttzRo0IDAwECCg4MJCAgwWL5+/frcvHkTwCDveYcOHdizZw9mZmYASpBQlt27d7N27doc+89KiJKbdu3aER8fT1JSEo8fP6ZatWrUqlWLx48fo9frDa5izp49S0xMDN9//z2//fYbw4cP5/Dhw9y7d4+aNWsa3DpISkpi0KBBrFixgnbt2uX7WElSUVWW+paX3OpbrVq12LBhAwAfffQR3bt3f+l2pKKRjQGpwEaMGIGFhQVBQUEAdO3alaVLl9KrVy+sra1zLN+hQweMjIxwdXWlS5cuyvRVq1YxbNgw0tPTMTIyYs2aNQbrF+ZKpVq1aixYsAA3NzdUKpXShbp7925SU1OZOHGisuynn36qvNZqtezYsQOAlStXolar6du3rzJ/zZo1XLx4kblz5wKZVy4jR44sUNkkqTAqS30DGDJkCNHR0dSpU4fIyEiWL1+ea307e/YsU6dOxcjIiJ49e+Li4lKgckmFICQpDzExMQIQMTExZV2UF5o+fbpwcHAQ58+fz3OZadOmibt37+Zre2PGjBGpqan5Wnby5MnCwsJC3Lt3T5lWUY6bVP5UhM9OeatvQlSM41beyXTEUp5kis/CkcdNKiz52SkcedyKTj5NIEmSJElVnIwZkKQSEhcXV9ZFKBFmZma0bNmyrIshSVIxko0B6aUq649aSck6Xt7e3mVckpJhYmJCXFycbBCUIFnnCkYer6KTjQEpT2ZmZpiYmFTaH7WSVLt2bfbu3Vvpno+Oi4vD29ubxMRE2RgoAbLOFZ6JiYny2KRUcLIxIOWpZcuWxMXFvXRQkKNHj3Ly5Ek8PT0xNzenWrXy/7HK+lHbtm0blpaWxb592ZUuFUZ+6lxaWhrz5s3DyckJBweHYknyU95kZGTwxx9/EBQUxD//+U/eeuutl64j61zRyKcJpCpJRh8XjjxuklQ5lf9LuAogISFBDqlZCLIlLxWWrHOFU9Z1Tp63wimN8yYbA0WUkJCApaUlycnJZV2UCqeiB6Jdu3YNOzs7xo4dy/Lly1m6dCn79+9HpVLh4eHBnDlz0Ov1dO/encTERM6dO1fsZQgMDOSTTz6hRYsWQM7UsRkZGUyaNIlz587x+uuvExgYSO3atdFqtaSlpVG9enV69+7NnDlzir1sJUXWucIryzonz1vhlcZ5k42BIkpMTCQ5ObnE7j1XVpUlEM3BwYHly5cDmalWP/jgA4QQODo6MmLECFq0aIFOp8sxKExenjx5Qp06dQpUhvfee48ZM2bkOi84OJhq1apx/PhxVqxYwZYtW5QUsfv376+QAVeyzhVOWdc5ed4Kp7TOm2wMFBNLS0t5D7US0+l0BAQEsHfvXhYuXEiDBg0YMGCAwTJt27YFMkeNMzY2pnr16vnadlJSEt988w07d+6ka9euzJ8/v0Bl27RpE99//z2DBw/Gz8/PYF54eDj9+vUDoG/fvixatIiJEyeiUqnw9PTExMQEf39/3nzzzQLtszyQda5ikuetfJKNAUnKB61WS3BwMJMnT+b69esEBQVx/fr1XJfdtWsXbdu2pUmTJi/c5smTJ9mwYQN//vknnp6e7NixQ4kMz+8Ich4eHowcOZL09HQGDBhAly5dcHBwUOZnH2++Xr16yrjw+/bto2HDhpw/f54RI0YQGxubzyMhSVJlJNMRl0OxsbFERESUdTEUf/zxB1qtFkdHRzZu3JhjfmJiIn369KFbt24sWLCg9AtYSnx9fVm3bh2zZs0yGDM+uxMnTrBhwwa+/vrrl25v//79nD17lpEjRzJy5EiDR8S8vLzQ6XQ5/p5Xr149jI2NqVmzJh4eHjl+1LOPN599rPmGDRsCYG1tTY0aNar8fVxZ5yomed6Kj2wMlEN5fcD1en0ZlAbmzJnDihUrCA8PZ/PmzcrVZZZPP/2UcePG8fPPPxMTE8OFCxfKpJwlbcqUKaxbt4558+aRnp6eY/7FixeZNm0ae/bsoXbt2i/d3rJlywgJCSEpKQkPDw/eeecdTpw4AWT2DGi12hx/z3v48CEAQgjCwsJo166dwXxnZ2cOHjwIwMGDB3FycgLg0aNHANy5c4ekpCRMTEzyfyAqIVnnKiZ53oqPbAyUopkzZ+Li4kK3bt2IjIwEMq/MPvjgA9RqtXK/d/Xq1axduxatVsuTJ09o27Yt48ePx8fHh4sXL6LRaHB2dubdd99FCIFOp6Nfv354enpia2vL0aNHuXfvnsEY4D4+PsTExBSq3PHx8djZ2WFsbIxGoyEqKspg/vHjxw3uSx8/frxQ+ynPNmzYgIWFBePGjWPIkCEsWrQoxzK+vr7cv3+fwYMHo9VqOXv27Eu3W69ePSZMmEBISAgffvght2/fBvLfM7BixQq6du2KWq2mVatWuLm5ATB69GgA+vTpw7Nnz3BycuLkyZOMHj2ajIwMXFxccHJy4q233uKrr74qwpEp32Sdq5jkeSt9MmaglAQHB6PX6wkNDeXu3bsMGTKE0NBQkpOT8fHxYenSpdjY2PDo0SN8fX1JTExUIsRv3brFkiVLaNSoEQMHDmTlypXY2dkxceJEgoKCqFu3Lnfu3CEyMpK7d+8yYMAAoqKiaNy4MfHx8TRr1kz5kGY3d+5c5Uo0i62tLV988YXBtIyMDOV19vvOWZKSkqhVq5Yy/9q1a8V01MqP8ePHK6/fe+89AG7evMmVK1eYNWsWy5cv56effsqxXtajhc2aNXvpPiwsLLCwsChQuRYtWpRrw2TLli0AGBkZsX79+hzzC/tlV5HIOlcxyfNWNmRjoJScO3eOn376Senqffz4MZD5/Ki5uTkALVq04P79+znWbdWqFY0aNQLg6tWrygdVrVZz6dIl7O3t6dixI8bGxjRp0oSUlBQg8+owMDAQCwsL3n777RzbXbZsWb7KbmT0vw6k7Peds5iampKSkkKtWrVynV9ZNW/e/KXdfMbGxrle0UslT9a5ikmet7IhbxOUEisrK9zd3ZXu3qxW5vOBaEIIatSoYXBPOvsHrFWrVspVXUREhFI5Tp8+TUZGBn/99Zdyv7pHjx6EhYWxbdu2XAc+mTt3bo570s8/mgZgbm7OqVOn0Ov1hIWFYW9vbzDfyclJuS8dHBys3JeuzCpa4NLo0aOVc1ynTh1+++03YmJiUKvVaDQa+vbtq1zFTJ48GQcHB7p06cLWrVtL+60UG1nnKiZ53sqIkIokJiZGACImJualy86ePVtoNBqh1WrF7NmzhRBCWFtbK/P79esnrl69Kq5evSrUarUYPHiwSEpKMljm/Pnzolu3bqJbt25i9OjRQq/Xi9DQUNGnTx8xYMAAYWtrK44cOaIs/9FHHwkPD48ivcfff/9dODs7CwcHB7Fu3TohhBCnT58WX3zxhRBCiP/3//6f6NWrl3B0dBQff/xxvrZZkONWEoq6/y1btojPPvssx/T09PSiFq1QBg8eLKKjo0V6erro0qWL+Pvvv3Nd7t69e8LKykoIIcTt27fFkydPhBBCrF27VnzyySdCiMzzLYQQKSkpwsLCQqSlpSnrl/V5K2gZZJ37n7I+d/K8le/zJhsDRVTWFUwIIUJDQ8WkSZNynTdv3jwRFBRUyiV6ubI+bnntf8aMGUKr1QpHR0fx66+/CiGEsLKyEnPnzhUODg7C19dXCCGEra2taN26tdBoNOLx48eiTZs24t133xXe3t4iLi5OODs7CycnJzFu3DiRkZEhQkNDRd++fYWHh4ewsbERR44cEX///bfQarXKvkeNGiWio6ML9X7eeOMN5fWsWbPEoUOHcl1u48aN4qOPPsp1ur+/v8E0vV4vrKysDBo4ZX3eyksZZJ2rePsXQp63F5ExA5WYn58f8fHxBc5oV1VV5sClLDt37uTLL780mJaYmEhAQABHjhwxmL58+XKGDBmCsbFxgY5jVSbrXMUkz5sMIKwU8noG/fkfDOnFKnPgEmTmFLh//z5WVlbKtKdPn+Ll5UVAQICSiAjg22+/JTo6mj179uRr/1WNrHMVkzxveZONAUn6/2UFLmX9AKempgKFD1yys7MjIiKCnj17Av8LXLp7965B4NKiRYuIiopix44dOcqU356BrMAlGxsbwsLCmDVrVo5t7d69myFDhij/6/V6hg0bxqRJk1Cr1cr048eP8+WXX3Lw4EGD9yVJUuUla3olpNPpmDx5convx9PTEzMzMz7//PMS31dp6NevH0IItFotLi4uzJs3L89l1Wo1P/zwA2+//XaOVL7+/v74+fnh5ORESkoK7u7uALz22mt4eHjQu3dvpcFhZGSEq6srderUUXoWslu2bFmOxEO5XcUsW7aMadOm4eTkhI+PDw0aNCA2NpbVq1cry+zatYuhQ4cq/+/Zs4fQ0FDWrFmDVqvls88+A2DcuHHcu3ePPn36oNVquXv3bv4PYhVUWvVt9uzZdOvWjd69e3Pr1q0S319VIL8r/0f2DEiF9tVXXxESEkJiYmJZF6XY+Pv755h27tw55fWBAweU17/88kuuy1hZWeWaWax169a5ZvszMjJizJgxhS4zZI6YGBYWZjDN1tYWW1tb5f/nexiGDRvGsGHDcmzr0qVLRSqLVPxiYmL4/fff+fnnn4mIiGDevHls2rSprIsl5VNF+K6UPQNl4MqVK6jValxcXJTUlDt27MDFxQV7e3s++ugjILPV2qNHDzw9PbGysuKHH35gwIABtG/fnujoaCAzRef06dNxdHRk6tSpOfZ17NgxNBoNTk5OLFy4EIAffvgBe3t7tFqtsq/CyE9WPenF/Pz8iIqKUj4HUvGrDPXt8uXLShxKx44dCQ8PL9R2KprKcO6gYnxXyp6BMqDT6fD09GTmzJlKFPjAgQMZPnw4QgicnJyUHPVPnz7l8OHDBAUFMX/+fKKiojh+/DgbN26kU6dOJCUlMXLkSFasWIGHh4dBmlkhBLNnz0an02FqasrgwYO5dOkSe/fuJSAggM6dOxtEoWfp06cPT58+NZjm4+ODj49PyR2USk4GLpWdylDfrK2t2bhxI3PmzCEkJCTPp0Uqm8pw7ioK2RgoA15eXixevBhvb29sbGyYOXMmx44dY9WqVej1ei5fvqzcE7SxsUGlUtGsWTM6dOiAsbExzZs3V74MqlevrnQFd+7cmcuXL9O4cWMg85GxK1euKC3qBw8ekJCQwPz58/nss89YvXo1Xl5eDBgwwKB8wcHBpXQkJKnkVYb61r59e/r164erqyt2dnYFHsOioqoM566ikI2BMqBSqZQAsh49ejBw4EA+/vhjdDoddevWRa1WI4RQls2+Xpas+WlpaZw5cwYbGxuio6Pp3bu38kicmZkZ5ubmBAcHU7t2bTIyMhBC8OzZM9atW0dqairW1tY5PuCVqbVbkeh0Ovbt21fiowi2adOGFi1aAFXjvFaW+jZt2jSmTZvGkSNHePXVV4vn4JRzleXcVQSyMVAGDhw4wJo1azA2NqZp06a0bt2aoUOHotFosLS0xNTUNN/bMjU1JTAwkMjISOzs7LCzs1MGxlGpVCxZsoQ+ffqgUqmoXr0627ZtY+XKlURERJCWlsa4ceNybDO/rd2pU6dy9OhR0tLSOHv2bIXOY1+V1K5du0oNnlRZ6pubmxtCCFq1apUjcVRlVVnOXYX4rizR/IZVQFmn2Myei7siKevjVpj9//HHH8LBwUFotVrRt29fIYQQ27dvF1qtVnTu3Fl8+OGHQojMlKdubm7Cw8NDWFpaiqCgING/f39hbW0toqKihBCZKY7ff/99oVarxZQpU5T1slKlHj16VDg7O4tu3bqJBQsWCCGECAoKEp07dxYajUbZV2G0a9dOaDQaMXDgQHHlypUCrVvW562sy1BR65sQZX/uynr/FfXcyXTEklTOVJZgphMnTtCwYUOOHj3Ke++9x6FDh4r5SEmSVNHIxkAFl/35dqlkVZZgpqy0w927d8fX17fIx6UqkfWt4pLn7sVknoEy5uPjozwHW9L7sbOz48KFC0DmvTgHBwccHR2Jiop64bobN25ErVaj1Wq5cuVKnsvdunULOzs76tSpY/CepkyZQr169cp1wo38yApm2rZtG4cPHyY+Pp6PP/6Yb7/9Fp1Oxz//+c8CBzMBREdH07ZtW2WZ7MFMOp2OU6dO4erqStOmTVm3bh3/+c9/eP/993OULytjYPa/wMBAg2WePXumjItw7tw5g/EIqoLKVN8eP36Mh4cHLi4ujBkzhrS0NKDy1Lfsyvt5S0tLw9HRkXr16rFv376X7mfBggV069aNvn37KueprM+b7BmoQtavX4+VlRV6vZ558+YRHh7Oo0ePGDJkCD///HOu69y7d4/Nmzfzyy+/EBsby5w5c/IcvKZBgwYcPnyY6dOnG0z/8ssvOXv2bLG/n9JWGYKZ/v77b/r168crr7yCSqWqMoFoZaGk69uGDRtwdXVl6tSpLF26lN27d+Pt7V1p6ltZKcx5q1atGvv27WP9+vUv3f6FCxeIiYnh559/5ptvvmH58uUsX768zM+b7BkoAVmP/wAkJCTQv39/AHr27IlWq1VGsssue47sxMREJUHNmTNn6N69O1qtlgkTJihXlkURHx+Pubk5derU4fXXXyc9PZ1nz57luuyvv/6KVqvF2NgYOzs74uPj89xurVq1KvWVZtaXQVhYGLt27aJatWrMmTOHM2fOsGvXLo4cOUKnTp3QarXK44GdOnVSrs7btm2rXDWoVCpWrVrFL7/8wpo1awAM1nN1dUWn0xEaGsrhw4d57bXX8Pf3Jzw8nBMnTjB79uxCvYfXX3+d06dPEx4eTlhYGG+88UYRj0rZq6r1raJnJaxM502lUtG0adN8bTcsLEy5BdivX788GxilTTYGSoC3tzfbt28HMlNnDh8+HIDvvvsOnU7HggULWLVqVb625efnx/bt29HpdNSpU4fDhw8bzN+9e3eOruHcMt1ld//+ferVq6f8X7du3Twzmj2/bG6Ba5JUlqpqfWvfvr1SvoqYlbAynbeCyL7dWrVq8eTJkyJvszjI2wQlIOueU0pKCvv37+fIkSMkJyczadIkrly5Qmpqao4R6nK7rwyZ93WzRpp78uQJ7du3N1jPy8sLLy+vApWvfv36PHjwQPn/4cOH1K9fP89lf/vtN+V/OaRt8ZDBTMWnqta3sWPHMnXqVFxdXenQoUO+r0zLi8p03gq73ZSUlALdXixJ8pu9hPTv3x9/f3/atGmDqakphw4dokGDBoSHhzN//vwc3Vj169fn5s2bAAaPmXXo0IF9+/ah0+mIjo7G29vbYL3CtHjbtWtHfHw8SUlJ3Llzh2rVqlGrVi0eP35s8OEH6NKlC2FhYej1ek6dOkW7du2AzHubSUlJhTw6FV95D2jK8vPPP6NSqV4YlFQZAj+rYn2rVasWGzZs4NixY7zyyiu89dZbBTlk5UJlOW95ye28OTs7K/E9Bw8epFu3bvnaVkmTPQMlZMSIEVhYWBAUFARA165dWbp0Kb169cLa2jrH8h06dFDGtu/SpYsyfdWqVQwbNoz09HSMjIxYs2aNwfqFafFWq1aNBQsW4Obmpty7hswKk5qaysSJE5VlGzRowKhRo3BycqJ69eps3rwZgJUrV6JWq+nbt6+ybFpaGn369OHChQvExcXh5eWVa9S7VDCFCWjKsmrVKjp16vTCZSpD4GdVrG9nz55l6tSpGBkZ0bNnT1xcXApUrvKgspw3yIwpio6Opk6dOkRGRrJ8+fJcz5u1tTU2NjZ069aNOnXq8H//938FKleJKdGURlVAWWfVyq/p06cLBwcHcf78+TyXmTZtmrh7926+tjdmzBiRmpqar2UnT54sLCwsxL1795RpZX3cctu/n5+fCAkJEUIIcf36ddGvXz8hhBA9evQQGo1GODg4iIsXLwohhBg1apSIiooyyBp49+5dodFohBBCxMbGCldXV6HRaMT48eNFRkZGocqZtR8hhLhw4YLw8vJS5nXp0kWkpKTkuW5QUJBYuXKl0Gg0+Tqv2feV5fl1y/q8lZcyvEx5q29ClP1xK+v950dVPm+yZ6CK+Pzzz1+6zMqVK/O9vawrlvyoKI+veXt789VXX+Hm5pYjoMnU1JTDhw+zatUq1q1b99Jt+fn5sXPnTpo0acL06dM5fPgwvXr1Uubv3r2btWvX5ljvRWMG5BXQlNu94oyMDNauXcu3337L/v37X1peqXjJ+lYxVeXzJhsDkvT/q0wBTTt27MDd3Z1atWoVaB+SJFVNsjEgSdk8H9D07bff0qBBAwIDAwkODiYgIMBg+RcFNO3ZswczMzMAJTtclsL0DGQPaHr8+LFBQJNerzfoNTh79iwxMTF8//33/PbbbwwfPpzDhw9z7949atasWW4imCVJKh9kY6CYxMXFlXURKpTyerwqS0DTp59+qrzWarXs2LEDqFyBn+X1M1RelZfjVV7KUVGU2vEq0YiEKuD69evCxMREAPKvgH8mJibi+vXrZXLeKkIwkxDlL6CpPBw3WecqZp2T5618nzeVEMWQt7GKS0hIqBDPYpc3ZmZmtGzZskz2ferUKezs7IiJiaFjx45lUoaKqLwcN1nnCqcs6xzI81ZYpXHe5G2CYtCyZcsyrWCSVNXIOlcxyfNWfsnGgCRJBVZZ7/uW9ZWzJJUV2RiQqrTK+qNWUrKO1/PpXisLExMT4uLiZINAqnJkY0CqkszMzDAxMam0P2olqXbt2uzdu7fCDYzzMnFxcXh7e5OYmCgbA1KVIxsDUpXUsmVL4uLiXhrM9P3337Nt2zbc3d2xt7enXbt2GBsbl1IpCy/rh23btm1YWloW67ZlV7okVT6yMSBVWfkJZurYsSOLFi0qpRIVP0tLS/m0hCRJLyUbA9ILyUeBCkdePUuSVJHIxoCUp4SEBCwtLUlOTi7rolQ4FT0Q7dq1a9jZ2TF27FiWL1/O06dP8fHx4datW7Rv356AgACEEHTv3p3ExETOnTtXIuVYvHgxOp2OZ8+e8fXXX9OhQwdl3unTpxk1ahTx8fHcvHlTSf2s1WpJS0ujevXq9O7dmzlz5pRI2SSpMpGNASlPiYmJJCcnl8h958qssgSiOTg4sHz5cgD+85//YG9vz/Tp05kyZQqHDh2ib9++6HS6HIMw5eXJkyfUqVMn3/s/cOAARkZGHDlyJNf5bdu25ZdffmHAgAE55u3fv19pHEiS9HKyMSC9lLzvXLnpdDoCAgLYu3cvCxcupEGDBjl+YMPDw1m4cCEAffv2JTw83GB8g7wkJSXxzTffsHPnTrp27cr8+fPzXa59+/bRsGFDXF1dMTc3Z/Xq1dSsWVOZ/8orr+S6nkqlwtPTExMTE/z9/XnzzTfzvU9JqqpkY0CSqjitVktwcDCTJ0/m+vXrBAUFcf36dYNl7t+/r4yKWK9ePe7du/fCbZ48eZINGzbw559/4unpyY4dO5ThlvM7YuPt27f5xz/+wbFjx/joo4/YvHmzwWBMeclqRJw/f54RI0YQGxv70nUkqaqTjQGpVMXGxpKcnIxarS7rogDwxx9/MHbsWNLS0vDx8eHdd981mJ+YmMjIkSN5/Pgxbm5uLFiwoGwKWsJ8fX1p2bIloaGhqFSqHPPr16/PgwcPaNKkCQ8fPlR+2POyf/9+zp49i6+vL56engZDJud3xMb69evTs2dPAHr37q2MvPgyDRs2BMDa2poaNWqQnJyMiYlJvtaVpKrKqKwLIFUtsbGxRERE5Jiu1+vLoDQwZ84cVqxYQXh4OJs3b85xxfvpp58ybtw4fv75Z2JiYrhw4UKZlLOkTZkyhXXr1jFv3jzS09NzzHd2dubgwYMAHDx4ECcnpxdub9myZYSEhJCUlISHhwfvvPMOJ06cADJ7BrRabY6/52k0GmJiYgCIjo6mbdu2+Xovjx49AuDOnTskJSXJhoAk5YNsDEjFYubMmbi4uNCtWzciIyOBzCuzDz74ALVajZ+fHwCrV69m7dq1aLVanjx5Qtu2bRk/fjw+Pj5cvHgRjUaDs7Mz7777LkIIdDod/fr1w9PTE1tbW44ePcq9e/dwcXFR9u3j46P8aBRUfHw8dnZ2GBsbo9FoiIqKMph//Phx+vXrB2TeKz9+/Hih9lOebdiwAQsLC8aNG8eQIUNyzaswevRoTp48ibOzM8+ePaNPnz4v3W69evWYMGECISEhfPjhh9y+fRvI7BnQ6XQ5/p7n4+PDyZMn0Wq1hIaGMn78eKUsADdu3MDNzY0zZ84waNAgtm/fTkZGBi4uLjg5OfHWW2/x1VdfFeHISFLVIW8TSEUWHByMXq8nNDSUu3fvMmTIEEJDQ0lOTsbHx4elS5diY2PDo0eP8PX1JTExkRkzZgBw69YtlixZQqNGjRg4cCArV67Ezs6OiRMnEhQURN26dblz5w6RkZHcvXuXAQMGEBUVRePGjYmPj6dZs2bKD3p2c+fOVa5Es9ja2vLFF18YTMvIyFBe53YvPCkpiVq1ainzr127VkxHrfzI+pEFeO+99wC4efMmV65cYdasWSxfvhwTExP27NljsJ5er6d79+40a9bspfuwsLDAwsKiQOWqXbt2rrcGtmzZAkCLFi1yfdKgsA1DSarKZGNAKrJz587x008/KV29jx8/BjKftTc3Nwcyv7jv37+fY91WrVrRqFEjAK5evar8qKvVai5duoS9vT0dO3bE2NiYJk2akJKSAmReHQYGBmJhYcHbb7+dY7vLli3LV9mNjP7XOZbbvXBTU1NSUlKoVatWvu6VVxbNmzd/6S0RY2PjXK/oJUmqeORtAqnIrKyscHd3V7p7s67Inw9EE0JQo0YNg3vS2X+MW7VqpVzVRUREKA2J06dPk5GRwV9//UXt2rUB6NGjB2FhYWzbti3XwYbmzp2b45501q2K7MzNzTl16hR6vZ6wsDDs7e0N5js5OSn3yoODg196r1ySJKkikj0DUpH169eP48ePo9VqUalUdOnSBX9//1yXVavVBAQEEBMTw9atWw3m+fv7M2HCBADatWuHu7s74eHhvPbaa3h4eHDjxg0+//xzILMR4erqyrlz55Sehezy2zOwbNkyg6cJGjRoQGxsLGFhYfj6+jJr1ixGjhzJypUrcXV1xdrauiCHplIpb0+CLF26lPXr19OhQwcOHDiQY35VeRJEkoqDbAxIxSK3H//sKWqzf1n/8ssvuS5jZWWVa4Be69atcw0EMzIyYsyYMYUuM2RmsQsLCzOYZmtri62tLQCNGjXi0KFDRdpHZREbG0tiYmKOxoBery+TkRzHjBmDl5cXvr6+uc7PehJk0KBBDBgwgAsXLmBlZVXKpZSkikHeJpAqJD8/P6KiopRIf6nwKuqTIE2aNHlhI6QqPAkiScVF9gxI5Vpez6A//1SAVDgV+UmQl6kKT4L8f+3dfVCUxx0H8O9xoAjX8JKzhpg6iUYoIIP1BMJxcCcFlRffptYzBhoarUlVFESaxFYhRtSQAFVjxSKRTsCWSLWilRQUH4FiA5yRxEBgCKnUQTswqOGd49j+QXmGCyAHgnfH/T4zzuSeZ5/dZXcmz+/ZZ3cfQiYKBQOEmDBjXgkyGlNdCULIeNBrAmJUOI7Dtm3bJr0cX19fyOVyeHp6orCwcNLL0xdjXgkyGloJQojuaGSAkGFcuXIF06ZNQ319PX7xi1/A399f31WaFMa8EiQ9PR2nTp1CTU0NAgIC8Je//AV37tyhlSCEjAMFA2RC1dfXIywsDNOnT4eVlRX+/ve/4/Tp00hLS0N7ezuWLl2K/fv3g+M4JCQkQCQSoaamBu+99x7++Mc/4ttvv0VGRgYWL14MV1dXLF++HP/6178gkUhw5MgRrbIKCwvxzjvvoK+vDwEBAYiLi8OFCxfw7rvvwsrKCjKZDPv37x/X3zFt2jQA/cPmHh4ej90uhsxYV4Js3LgRGzdu1DomFotpJQgh40DBAJlQHMdhzZo1iI2N5bf6XbVqFTZs2ADGGHx9ffk96js7O5Gfn4/c3FzExcWhvLwcxcXFSEtLw+LFi9He3o7w8HAkJSVh9erVWrPOGWN48803wXEcrK2tsXbtWtTU1ODMmTM4duwYPDw8tLYaHhAUFITOzk6tYxEREYiIiNA69vDhQ4SGhqKurg7p6ekT3EqmLSoqCrW1tYiLi9N3VQgh/0fBAJlQSqUS+/fvR1hYGNzd3REbG4vCwkKkpKRAo9Ggrq4OjY2NAAB3d3cIBALMnj0bbm5uEAqFeO655/jvA1hYWPBPeR4eHqirq8OsWbMA9G8oU19fzy8de/DgARoaGhAXF4f3338fhw8fhlKpxIoVK7Tql5eXp9PfYWNjg+LiYty5cwcKhQLBwcET0TwmhVaCEGI8KBggE0ogEPDvfAMDA7Fq1Srs2bMHHMfBxsYGUqkUjDE+7eDrBgycV6vVqKyshLu7OyoqKrB8+XJ+trtYLIajoyPy8vIwY8YM9PX1gTGG7u5upKamoqenB66urkOCAV1GBtRqNYRCIczMzPDUU0/hBz/4wcQ1kIniOA45OTmT+hXBhoYGfkKiUCjERx99hBdeeGHSyiNkKqFggEyoixcv4siRIxAKhXBwcMDcuXOxfv16yOVyODs7w9raWue8rK2tkZGRgbKyMkgkEkgkEv7DOAKBAAkJCQgKCoJAIICFhQUyMzORnJyM0tJSqNVqbNq0aUieuowM3L17F+Hh4TAzM0Nvby8SExN1rjPRn6eeegrnzp3D008/jU8//RQJCQk4efKkvqtFiHFghIxApVIxAEylUumlfFdXV72U+7j03W7jqcM333zDvL29mUKhYMHBwYwxxrKysphCoWAeHh7st7/9LWOMsatXr7KAgAC2evVq5uzszHJzc1loaChzdXVl5eXljDHGXFxc2M6dO5lUKmWRkZH8dVu3bmWMMXblyhXm5+fHZDIZi4+PZ4wxlpubyzw8PJhcLufLehwFBQXsjTfeGNM1htBvhOgLjQwQQqbMxE8A6Orqwp49e3Dq1KkJbCFCpjYKBojBGrx0jUyuqTLxs6+vD+Hh4di1axd+/OMfT0TTEGISaAdCMmkiIiJQUVHxRMqRSCSoqqoC0D9vwdvbGz4+PigvLx/xuoEv8MnlckilUnzxxRePLCc+Ph4ymQzBwcFobm4GAERGRsLW1pb/bawGJn5mZmYiPz8ftbW12LNnD86ePQuO4/DCCy+MeeInAFRUVODFF1/k0wye+MlxHG7cuAF/f384ODggNTUVH330EXbu3DmkfkFBQUN2JczIyBiSLjIyEj4+PvjZz342Ie1CiKmgkQEyJZw4cQIuLi7QaDTYu3cvioqK8N1332HdunUoKSkZ9ho7OzsUFxdDKBSisLAQiYmJyMzMHDZtVVUVVCoVSkpK8Ne//hWJiYlITEzE0aNH8eWXX07mn/ZETIWJn9evX8fJkyfh7e2Nv/3tb5BIJEhKStK53oSYNP1OWSCGbLgJVVFRUaygoIAxxtjt27dZSEgIY4yxwMBAJpfLmbe3N/v6668ZY4y9+uqrrLy8XGvyWFNTE5PL5Ywxxm7evMn8/f2ZXC5nmzdvZn19feOq50A5jDFWVVXFlEolf87Ly4t1dXWNmsf58+dZSkrKiOf/8Ic/sOPHjzPGGOvs7GTe3t78OblczpqamvjfhjARTZ91oImfhBgfek1AxiQsLAxZWVkAgNOnT2PDhg0AgHPnzoHjOMTHxyMlJUWnvKKiopCVlQWO4yASiZCfn691Pjs7e8jQ8HCb2Ax2//592Nra8r9tbGz4d9nDqampgVQqxY4dOyCVSnXK19LSEm1tbaP+fYQQYizoNQEZk4F3811dXTh//jwuX76Mjo4ObN26FfX19ejp6Rny8Znh3isD/RME169fDwBoa2vDggULtK5TKpVQKpVjqp+dnR0ePHjA/x7t07VOTk4oLS1FZWUlNm/ejM8++2zUfLu6usY0bG5qaOInIcaHRgbImIWGhuLQoUOYN28erK2t8emnn8Le3h5FRUWIi4vTuuED/TfSO3fuAIDWMjM3Nzfk5OSA4zhUVFQM+ZzteEYG5s+fj9raWrS3t+PevXswNzeHpaUlWltbtYIEAOju7taqo5WVFQCgpaUF7e3tWmn9/Pz499aXLl2CTCYbvaGMlKFP/FSr1fDx8YGtrS1ycnIeWUZjYyMkEglEIpHW3zRVJn4SMlFoZICM2SuvvAInJyfk5uYCAF566SUcOHAAy5YtG/YzsW5ubvxna728vPjjKSkpePnll9Hb2wszMzMcOXJE6/rxjAyYm5sjPj4eAQEBEAgE/CuL7Oxs9PT0YMuWLXzayspK7Nq1C0KhEAD4yWbJycmQSqVa3yNwdXWFu7s7ZDIZRCIRPv744zHViwxvPBM/zc3NkZOTgxMnToyav729PfLz8xETE6N1fKpM/CRkolAwQMZs7ty5UKvV/O9nn3122CfJwUu/zp49O+S8u7s7CgoKHrs+YrEY27dvx8mTJ+Hi4oKVK1di5cqVWmmqqqqwe/durWOenp4oKioakt/du3cRGBg45Pi+ffuwb98+rWORkZG4d+8eH1AYqujoaISEhCAgIAANDQ3YsmULLl68iKVLl6Knpwc9PT04deoUnJyc+GsGf0+gubkZa9euBcdxqKysxM6dO6HRaODk5ITU1FStV0HjUVtbC0dHR4hEIohEIvT29qK7uxvTp08fklYgEMDBwUGnfC0tLWFpaflYdSPEFFAwQIzeBx98MGqa5ORknfMbyyeLjx49qnNafQoLC8OHH36IgICAIRM/ra2tkZ+fj5SUFKSmpo6aV1RUFP785z/jmWeeQUxMDPLz87Fs2TL+fHZ2No4fPz7kuoHlhcMZaeKnrjd9QsjjoWCAEBMw1SZ+EkImFgUDhJiI70/8PHv2LOzt7ZGRkYG8vDwcO3ZMK/2jJn5+8sknEIvFAKD1yggY38jA4Imfra2tWhM/NRqN1qjBSFpaWjB9+nRa6UHIOFAwQEZVXV2t7yoYFUNtr6ky8RMA1q1bh4qKCohEIpSVlSExMXHYiZ9qtRpBQUGoqqpCdXU1lErlsNsdE2Ly9LzpETFgt2/fZlZWVgwA/RvjPysrK3b79m299Z0x7KYXExPDvL292VdffTVimujoaK3dHR/ltddeYz09PTql3bZtG3NycmItLS38MWNoM0Imi4Cx7y0KJ2SQhoYGWos9DmKxGHPmzNFb+Tdu3IBEIoFKpcKiRYv0Vg9jQm1GTBm9JiCPNGfOHL3e1AghhEw+2oGQEEIIMXE0MkDIFGaokxkNEbUVMWUUDBAyBYnFYlhZWQ353gN5NCsrK37JJCGmhCYQEjJFjTb5Mzc3FwcPHoREIoGfnx9+8pOfYN68eTAzM/63h4wx3L59G59//jn++c9/4vr16wgLC8Ovf/3rR16n74mfhOgLBQOEEEKIiTP+RwBCCCGEPBaaM0BMGu2jMHaGMJRO/TY+htB3xDBRMEBMVkNDA5ydndHR0aHvqhgVKysrVFdX6+2mQv02fvruO2K4KBggJqu5uRkdHR3IzMyEs7OzvqtjFKqrqxEWFobm5ma93VCo38bHEPqOGC4KBojJc3Z2pu1njRD1GyEThyYQEkIIISaOggFCJsjNmzdRWlqq72rwvvnmGygUCvj4+CAtLW3I+ebmZgQFBUEmkyE+Pv7JV9CAUN8RU0fBACETZKQbikaj0UNtgLfeegtJSUkoKipCeno6WlpatM6/99572LRpE0pKSqBSqVBVVaWXehoC6jti6igYIGQUsbGxWLJkCWQyGcrKygAArq6u2L17N6RSKaKiogAAhw8fxvHjx6FQKNDW1oYXX3wRmzdvRkREBL7++mvI5XL4+fnhV7/6FRhj4DgOISEhWLNmDRYuXIgrV66gpaUFS5Ys4cuOiIiASqUaV71ra2shkUggFAohl8tRXl6udb64uBghISEAgODgYBQXF4+rHENGfUeIbmgCISGPkJeXB41Gg6tXr6KpqQnr1q3D1atX0dHRgYiICBw4cADu7u747rvvsGPHDjQ3N2PXrl0AgMbGRiQkJGDmzJlYtWoVkpOTIZFIsGXLFuTm5sLGxgb37t1DWVkZmpqasGLFCpSXl2PWrFmora3F7Nmz+ZvCYG+//TauX7+udWzhwoX4/e9/r3Wsr6+P/29bW9shT5ft7e2wtLTkz//73/+eoFYzDNR3hOiOggFCHuHWrVv4xz/+AYVCAQBobW0F0L9e29HREQDwox/9CPfv3x9y7fPPP4+ZM2cCAL799lv+xiCVSlFTUwNPT08sWrQIQqEQzzzzDLq6ugAAv/zlL5GRkQEnJyf8/Oc/H5LvwYMHdar74G8MPHz4EHZ2dlrnra2t0dXVBUtLy2HPGzvqO0J0R68JCHkEFxcXrFy5EhzHgeM4/qlOIBBopWOMYdq0aejt7eWPDf4f+vPPP88PGZeWlvI3o88//xx9fX3473//ixkzZgAAAgMDce3aNWRmZg771cG3334bCoVC69/AcPdgjo6OuHHjBjQaDa5duwZPT0+t876+vrh06RKA/qdoX1/fsTaPQaO+I0R3NDJAyCOEhISguLgYCoUCAoEAXl5eOHTo0LBppVIpjh07BpVKhT/96U9a5w4dOoTXX38dADB//nysXLkSRUVF+OEPf4jVq1fjP//5Dz744AMA/Tcif39/3Lp1i386HUzXp8uDBw9i48aNUKvViIiIgL29PW7evIlr165hx44d+M1vfoPw8HAkJyfD398frq6uY2kag0d9R8gYMEJMlEqlYgCYSqXSS/lXr15lW7duHfbc3r17WW5u7hOu0ej03WaGUgfqOzLV0GsCQgxMVFQUysvL+dnixHhQ3xFjRa8JCNGTgXfG3/f9meXE8FDfkamGRgYIMRAcx2Hbtm2TXs6aNWsgFov599zk8VC/kamARgYIMTEffvghCgoK0NzcrO+qkDGgfiOTiUYGCNFRfX09pFIplixZwr8TPn36NJYsWQJPT0/87ne/A9D/pBgYGIg1a9bAxcUFFy5cwIoVK7BgwQJUVFQA6N8FLyYmBj4+Pti+ffuQsgoLCyGXy+Hr64t33nkHAHDhwgV4enpCoVDwZY3H7Nmzx32tMaJ+I2R0NDJAiI44jsOaNWsQGxvL7xC3atUqbNiwAYwx+Pr64u7duwCAzs5O5OfnIzc3F3FxcSgvL0dxcTHS0tKwePFitLe3Izw8HElJSVi9erXWtrWMMbz55pvgOA7W1tZYu3YtampqcObMGRw7dgweHh5aO9QNCAoKQmdnp9axiIgIRERETF6jGAHqN0JGR8EAITpSKpXYv38/wsLC4O7ujtjYWBQWFiIlJQUajQZ1dXVobGwEALi7u0MgEGD27Nlwc3ODUCjEc889x28ra2FhgYULFwIAPDw8UFdXh1mzZgHo/yJdfX09/xT74MEDNDQ0IC4uDu+//z4OHz4MpVKJFStWaNUvLy/vCbWEcaF+I2R0FAwQoiOBQMBvGhMYGIhVq1Zhz5494DgONjY2kEqlYIzxaQdfN2DgvFqtRmVlJdzd3VFRUYHly5fz2+WKxWI4OjoiLy8PM2bMQF9fHxhj6O7uRmpqKnp6euDq6jrkpkJPmMOjfiNkdBQMEKKjixcv4siRIxAKhXBwcMDcuXOxfv16yOVyODs7w9raWue8rK2tkZGRgbKyMkgkEkgkEnAcB6D/JpSQkICgoCAIBAJYWFggMzMTycnJKC0thVqtxqZNm4bkqesT5vbt23HlyhWo1Wp8+eWXQ3bcm2qo3wgZnYANhLyEmJgbN25AIpFApVJh0aJFT7TsBQsW4NatW0+0zImgzzYzhDoYa78BhtF3xHDRagJCCCHExFEwQIgeGOvTpamjfiNTFQUDhIxDREQEv/Z8ssuRSCSoqqoC0P/+29vbGz4+PigvLx/xOrVaDR8fH9ja2iInJ2fUcuLj4yGTyRAcHMxvahMZGQlbW9sptcmNofcbAKSlpUEqlUKhUKC+vn7EdI2NjZBIJBCJRFp/01TsNzL5KBggxMCdOHECLi4u0Gg02Lt3LwoKCnDmzBlER0ePeI25uTlycnIQFRU1av5VVVVQqVQoKSnBxo0bkZiYCAA4evQov4yOjN14+q2lpQXp6ekoLi5GUlIS3nrrrRHT2tvbIz8/H2vXrtU6Tv1GxoOCAUL+Lzo6GpcvXwYANDQ0IDQ0FACwdOlSKBQKSKVS1NTUaF0zeF/65uZm/uM1lZWV+OlPfwqFQoHXX38dEzFPt7a2Fo6OjhCJRHj22WfR29uL7u7uYdMKBAI4ODjolO+1a9f4tfEhISEoKSl57Lo+SVOp3z777DMoFAoIhUJIJBLU1taOmK+lpSWefvrpx64fIQAFA4TwwsLCkJWVBaB/u9oNGzYAAM6dOweO4xAfH4+UlBSd8oqKikJWVhY4joNIJEJ+fr7W+ezsbP7Ld4P/Pcr9+/dha2vL/7axseE3w3kcg/O1tLREW1vbY+f5JE2lfvt+2uF2LCRkMtA+A4T838A73q6uLpw/fx6XL19GR0cHtm7divr6evT09GDmzJla1wy3MQ3QP9Fs/fr1AIC2tjYsWLBA6zqlUgmlUjmm+tnZ2eHBgwf874cPH8LOzm5MeYyWb1dX15jW3RuCqdRvdnZ2+OKLL/jfZmb0vEaeDAoGCBkkNDQUhw4dwrx582BtbY2zZ8/C3t4eGRkZyMvLw7Fjx7TS29nZ4c6dOwCgtU+9m5sbPvnkE4jFYgD9E/oGy87OxvHjx4eUP7CBzXDmz5+P2tpatLe3o7W1Febm5rC0tERrays0Go3WE+VIWlpaMH36dK0bvp+fH3bv3o033ngDly5dgkwmGzUfQzNV+s3Lywv79u2DRqNBZWUl5s+fD2D4fiNkIlEwQMggr7zyCpycnJCbmwsAeOmll3DgwAEsW7YMrq6uQ9K7ubnBzMwM/v7+8PLy4o+npKTg5ZdfRm9vL8zMzHDkyBGt68fzhGlubo74+HgEBARAIBDwQ9/Z2dno6enBli1btNKvW7cOFRUVEIlEKCsrQ2JiIpKTkyGVShEcHMync3V1hbu7O2QyGUQiET7++OMx1csQTJV+s7e3x6uvvgpfX19YWFggPT0dAIbtN7VajaCgIFRVVaG6uhpKpRI7d+4cU90I4TFCTJRKpWIAmEql0ndVRhQTE8O8vb3ZV199NWKa6Oho1tTUpFN+r732Guvp6dEp7bZt25iTkxNraWnhjxlCmxlCHUZjaP3GmHG0G9Ef2o6YmCzannXsDKHNDKEOxojajTwKzU4hhBBCTBwFA4QQQoiJowmExORVV1fruwpGw5DaypDqYgyovcijUDBATJZYLIaVlRXCwsL0XRWjYmVlxS+90wfqt/HTd98Rw0UTCIlJa2hooA+6jJFYLMacOXP0Wgfqt/ExhL4jhomCAUIIIcTE0QRCQgghxMRRMEAIIYSYOAoGCCGEEBNHwQAhhBBi4igYIIQQQkwcBQOEEEKIiaNggBBCCDFxFAwQQgghJo6CAUIIIcTEUTBACCGEmDgKBgghhBATR8EAIYQQYuL+B9bpDAM3Z9OzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the decision tree \n",
    "plot_tree(tree_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import with pip the package <a href=\"https://pypi.org/project/dtreeviz/\">dtreeviz</a> to visualise nicely the tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
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      "/home/danielcujba/Desktop/School/Anul2/Semestrul2/AI/.conda/lib/python3.12/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
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      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "Fontconfig error: Cannot load default config file: No such file: (null)\n"
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      "text/plain": [
       "<dtreeviz.utils.DTreeVizRender at 0x787154d93590>"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz =  model(tree_clf, \n",
    "               X_train,\n",
    "               y_train,\n",
    "               feature_names=X.columns, \n",
    "               class_names=[\"setosa\", \"versicolor\", \"virginica\"],\n",
    "               \n",
    "               )\n",
    "viz.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning the Model\n",
    "\n",
    "We can tune the hyperparameters of the decision tree model to improve its performance. \n",
    "\n",
    "One important hyperparameter is the maximum depth of the tree. \n",
    "\n",
    "We can use scikit-learn's GridSearchCV function to search over different values of the maximum depth and find the best one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'max_depth': 2}\n"
     ]
    }
   ],
   "source": [
    "# Define the hyperparameters to search over \n",
    "param_grid = {\"max_depth\": [1, 2, 3, 4, 5, 6, 7]} \n",
    "# Create a grid search object \n",
    "grid_search = GridSearchCV(tree_clf, param_grid, cv=5) \n",
    "# Fit the grid search object to the training data \n",
    "grid_search.fit(X_train, y_train) \n",
    "# Print the best hyperparameters found by the grid search \n",
    "print(\"Best hyperparameters:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a new decision tree classifier object with the best hyperparameters and fit it to the training data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=6, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=6, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new decision tree classifier object with the best hyperparameters \n",
    "tree_clf_tuned = DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=42) \n",
    "# Fit the classifier to the training data \n",
    "tree_clf_tuned.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.3333333333333333, 0.9285714285714286, 'x[2] <= 2.45\\nentropy = 1.583\\nsamples = 112\\nvalue = [35, 39, 38]'),\n",
       " Text(0.2222222222222222, 0.7857142857142857, 'entropy = 0.0\\nsamples = 35\\nvalue = [35, 0, 0]'),\n",
       " Text(0.4444444444444444, 0.7857142857142857, 'x[2] <= 4.75\\nentropy = 1.0\\nsamples = 77\\nvalue = [0, 39, 38]'),\n",
       " Text(0.2222222222222222, 0.6428571428571429, 'x[3] <= 1.65\\nentropy = 0.187\\nsamples = 35\\nvalue = [0, 34, 1]'),\n",
       " Text(0.1111111111111111, 0.5, 'entropy = 0.0\\nsamples = 34\\nvalue = [0, 34, 0]'),\n",
       " Text(0.3333333333333333, 0.5, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.6666666666666666, 0.6428571428571429, 'x[2] <= 5.15\\nentropy = 0.527\\nsamples = 42\\nvalue = [0, 5, 37]'),\n",
       " Text(0.5555555555555556, 0.5, 'x[3] <= 1.75\\nentropy = 0.896\\nsamples = 16\\nvalue = [0, 5, 11]'),\n",
       " Text(0.3333333333333333, 0.35714285714285715, 'x[1] <= 2.35\\nentropy = 0.918\\nsamples = 6\\nvalue = [0, 4, 2]'),\n",
       " Text(0.2222222222222222, 0.21428571428571427, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.4444444444444444, 0.21428571428571427, 'x[2] <= 5.05\\nentropy = 0.722\\nsamples = 5\\nvalue = [0, 4, 1]'),\n",
       " Text(0.3333333333333333, 0.07142857142857142, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3, 0]'),\n",
       " Text(0.5555555555555556, 0.07142857142857142, 'entropy = 1.0\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
       " Text(0.7777777777777778, 0.35714285714285715, 'x[1] <= 3.1\\nentropy = 0.469\\nsamples = 10\\nvalue = [0, 1, 9]'),\n",
       " Text(0.6666666666666666, 0.21428571428571427, 'entropy = 0.0\\nsamples = 9\\nvalue = [0, 0, 9]'),\n",
       " Text(0.8888888888888888, 0.21428571428571427, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.7777777777777778, 0.5, 'entropy = 0.0\\nsamples = 26\\nvalue = [0, 0, 26]')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ammnTppGTk1PqtI4ePaqs6927N5cvXwZgx44ddOzYEU9PzxJeFUmSpAogpFrtzJkzAhBnzpwpdRqXL18WNjY25ZepYgoPDxfDhg1TXmdmZoqHDx8KIYR48OCBsLCwELdu3RJCCDFx4kSxe/fuYqddWFrZ2dnC1NRU/PHHH0IIIebOnSu2bt1aqrSEEKJr167i3LlzQgghvvrqKzF16tRCzy9PedwzSZKkkpAtA3XQvHnzWLlypfI6ICCAr776igcPHuDm5oa9vT0ajYa9e/cW2Dc4OJi5c+cqr/v27av8Cj906BDOzs7Y29szatQoHjx4UK75NjIyokGDBgA8evSI7Ozsck8rOTmZhg0b0qFDBwBcXV3ZtWtXmfL1559/AnD//n1eeOGFUudZkiSposjKQB00ZswYtm3bprzetWsXXl5eNGjQgN27dxMZGUl4eDh+fn7KLITPkpyczNKlSwkLCyMyMhJHR0dWrFhRYLugoCCdkwcFBAQU6zg3btxAq9ViamqKv78/rVq1UtYFBASg1Wp56623yMzMLFVaLVq0IDMzk6ioKIQQ7NmzR5lHoTT5+vrrrxk8eDCmpqZ8++23xT5PSZKkyiQrA3WQvb09N2/e5NatW8q8Aq1atUIIwTvvvINGo8HNzY1r165x+/btYqX522+/cfbsWZydnbG1tWXDhg1cuXKlwHYBAQE6Jw8q7iQ/bdq0ITY2lkuXLvHdd98p+Vu2bBkJCQmcPn2a+/fv89FHH5UqLT09PTZt2sTs2bPp2bMnrVq1wsDg2QN1Fpavzz//nIMHD3L9+nXGjh3Lu+++W6zzlCRJqkxyOOI6ysvLix07dnDlyhXGjh0LwHfffUdKSgqRkZEYGhrSsWPHfBMPQe6Qu08H1OX9AhdC4OHhwbffflvkcYOCgtiyZUuB5R4eHiWa9a9169ao1WqOHz+Ol5cXrVu3BnKb7KdMmVKmtJydnZXZE3ft2kVycnKp0urTpw/x8fE4ODgAudf8tddeK3ZakiRJlUW2DNRRY8aMYevWrezevZsRI0YAuc+0W7VqhaGhIceOHePSpUsF9rOwsFCa0C9dukRMTAwAPXr0yLdPeno6Fy9eLLB/WVoGkpKSyMjIACAtLY0TJ07QuXNnAG7evAmgNO2r1WoATp06pXOyoqLSunPnDgAZGRl8+umnTJ8+vVRpNW3alNTUVOWa/PTTT8oxJEmSqhPZMlBHabVakpOTsbS0VKbeffXVVxkyZAiOjo7Y2tpiZWVVYL9evXphZmaGRqPBzs4OrVYLQIsWLVi/fj1jx45VWhOWLVuGpaVlueX5/PnzzJ07V5n2+K233kKj0Sh5v3v3LkIIbG1tle59165d47nnnitRWh9//DE//PADOTk5vP3228ov+9Kk9fXXXzNs2DD09fVp0aIFwcHB5XY9JEmSyoucqKiWq8mT3hw9epTPP/+cPXv2lDqNgIAAxo8fr1RayqI804LCz68m3zNJkmom+ZhAqraMjIyIjo5WBh0qjaCgoHL78i7PtHbs2MEbb7xB06ZNyyU9SZKkspCPCaRqy9nZmcTExKrORoXw8vLCy8urqrMhSZIEyMpAnZGQkFDVWZCKKe9e1ZZ71rx5c8zNzas6G5IkFUHGDNRyV69epWvXrkq0u1Qz6OvrP3NOhJqiYcOGJCQkyAqBJFVjsmWgljM3NychIaFEfeVL4s8//8TNzY2cnByaNGnCn3/+SePGjdm0aRNt2rSpkGPqkpCQgLe3N4GBgbRr167SjltRVCqVMnZCTZZ3X5KTk2VlQJKqMVkZqAPMzc0r7IN4zZo15OTk0LRpU8aOHYunpyd9+/bFyMioQo73LIMGDZIR+JIkSSUkKwNSmUydOhU7OzscHByUfvaSJElSzSIrA1KZ6Ovr4+joWNXZkCRJkspAVgae4erVqxX2vF16NhmJLkmSVPFkZaAIMhK/6lW3SPSjR48ybNgw+vTpw759+zh58iRz587l4cOH1K9fn+XLl9OnTx8AfHx82L9/P+vWrcPT07PC85aTk0P37t158cUXdY7aGBAQQGhoKAAPHz7k9u3bpKWlAVCvXj1lCGVzc3P27dtX4fmVJKn6kJWBIiQnJ5ORkcHGjRvp2rVrVWenzqmukej9+vVTvmxVKhU7d+7E3Nyc33//HQ8PD2WgpJCQECZNmlSitFNTU0s9KuGqVavo0KFDgZkm8wQFBSkTQq1atYqIiAhlXePGjYmOji7VcSVJqvlkZaAYunbtKiPU65iIiAimTZvGf//7XzIzM+nWrRt79+4tsJ21tbXyf+fOnXnw4AFZWVkYGhoW+1h37twhJCSE4OBgZs2axYwZM0qc3zt37rBr1y4WLFjAF1988cztN2/ezMKFC0t8HEmSaidZGZAkHZycnBg8eDCLFi3i3r17TJs2jU6dOnHjxo1C99m5cyd2dnbFqgjk5ORw6NAh1q5dy7lz5xg/fjyhoaGYmpoCua0Ky5cvL7CfVqslJCSkwHI/Pz8CAwN5/PjxM499/fp1Lly4gJubm7IsPT0dR0dHDAwMCAgIqJTHGpIkVR+yMiBJhVi0aBFOTk40adJEmRK5MOfPn8ff359Dhw4VK21PT08iIyNZu3Yt7u7uBbpl+vj44OPjU6y0jh07hp6eHs7Ozhw9evSZ22/dupWRI0dSr149ZdmVK1do06YNly9fpn///mg0Gjp06FCs40uSVPPJWQurkeDgYG7dulXV2SjShg0bsLS0xNLSkg0bNujc5t69ewwYMABLS0sGDBhAampqJeeyfKSkpJCens79+/eL/MV9584dPD09+eabb+jYsWOx0l62bBkjR45k1qxZzJo1izNnzuRbHxISgq2tbYE/XRWEX375hZ9++gkLCwvGjh3LoUOHmDhxYqHH3rJlC+PGjcu3LG+0yHbt2tGvXz+ioqKKdR6SJNUOsjJQjRRVGcjOzq7k3BR07949AgMDiYiIICIigsDAQJ1f9EFBQQwcOJCLFy8ycOBAJWitppk2bRpLlixh+PDhhT5fz8jIYOjQobz77rv069ev2GlbW1uzYsUKzp49i4uLC/7+/tjZ2REWFgbktgxER0cX+NP1iCAgIICkpCQSExPZsmULAwcOLLSi9r///Y/bt2/Tq1cvZVlqaiqZmZlAbgXoxIkTWFlZFftcJEmq+WRloAJt3LiRbt26YWtry/Tp05WJZ1QqFfPnz0er1dK7d2/S0tLYsWMHp0+fZsyYMdja2vL48WMsLCwIDAykV69e7Nmzh82bN6PRaFCr1cybN085jkqlYu7cudjb29O/f39SUlL4448/cHBwULaJj4/H2dm5TOdz8OBB3N3dUalUqFQq3N3dla5qT9u7d68SRT9p0iSd3dyqu/Xr12NkZMTYsWNZuHAh4eHhnDx5ssB269atIyYmho8//lj59X7nzp1iH6d+/fqMGzeOw4cPs3PnToyNjcvzNFi0aFG+boKbN29m9OjR+R5LJCQk4OjoiI2NDX379sXPz09WBiSprhFSoc6cOSMAcebMmRLve+7cOTFs2DCRlZUlhBDi9ddfFxs3bhRCCAGIAwcOKMv//e9/CyGE6NOnj4iKilLSaNu2rfj000+FEEIkJSUJc3NzcefOHZGVlSX69+8vdu/eraS3ZcsWIYQQH374oXjzzTeFEEL07dtXREdHCyGE+Mc//iG+/vrrAvlctmyZsLGxKfA3b968Att+8sknYtmyZcrrpUuXik8++aTAdiYmJvleN2vW7BlXS7eSXP+y3KuSCA8PF8OGDSv29hMnTlTuU11UWfdFkqSykQGEFeTw4cNEREQoQ/U+fPhQmYXuueee4+WXXwbAwcGhyHnrx4wZA+R2dXN1daVFixYAeHt7c+zYMTw9PTEwMGDUqFHK8mHDhgG58wYEBwfzySefsGvXLp39yAMCAggICCjWOQkds13rmo+gNs9RYGRkRHR0NEOHDn3mwDw+Pj4cP36csWPHVlLuJEmSSkdWBiqIEIIpU6YQGBhYYN3TM/rVq1ePJ0+eFJpOo0aNlPQK+5ItbPnIkSMJDAzExcUFZ2dnnU3QQUFBbNmypcByDw+PAs/6TU1NOXHihPL6+vXruLi4FNi3WbNm3L17lxYtWnD37l2aNWtW6PnVNM7OzsqgQnmCg4Px8PDghRdeyLdc1/P9qjJ48GBOnjxJ3759C31sc+/ePcaMGUNiYiIWFhZs27at1AMgSZJUs8iYgQri5ubG9u3buXv3LpAbmHX16tUi92ncuDF//vmnznXdu3fnyJEjJCcnk52dzaZNm5Rhb7Oysti5cycAmzZt4qWXXgJyWyDc3Nx48803mTx5ss50AwICdAaq6Qr6y4sRSEtLIy0tjdDQUNzd3QtsN3ToUIKDg4HcL8q8loraqroHfgK8/fbbfPvtt0VuU1sCPyVJKjlZGaggVlZWLF68mAEDBqDVahk4cCC3b98ucp9Jkybh6+urBBA+rXXr1ixdupR+/fphY2ODjY2N8iVrbGxMVFQUDg4OhIWFsWjRImW/8ePHY2hoiKura5nPycTEhAULFuDo6IijoyMLFy7ExMQEgNdee43Tp08D/zcGvqWlJaGhocV+DFFd1LbATwBXV1caN25c5Da1IfBTkqRSquKYhWqtpgQ/GRsbF7ruo48+Eu+//37lZaYcVUUAYW0M/MzzrODH8gr8fFpNKUOSVNfJmIFabMiQIVy5coXw8PCqzkqNURsDP4urNgd+SpJUNFkZqAXypqH9u++//75yM1ILiFoY+FlctTnwU5KkosmYgVrCwsKi0EpBRcjKyqJHjx7Y2NhgbW3Ne++9p6x7//33efHFF5VBeA4cOFBp+Sqr2hj4WVx1LfBTkqT/IysDUqkYGBgQFhZGTEwMMTExHDx4kIiICGW9n5+f8gU1aNCgKsxpydTGwE+Avn37MmrUKA4ePIipqSmHDx8GalfgpyRJZVDVQQvVWVmCnx48eCAGDRoktFqtsLa2VgLFFi9eLBwdHYW1tbXw9fUVOTk5QojcIDQ/Pz/h6OgounbtKiIiIoSnp6fo2LGjWLBggRBCiMuXLwsrKyvh7e0tNBqNGDVqlMjIyBBC5AatpaamCiGE+Pbbb4WTk5OwsbER06ZNE9nZ2eLJkydi4sSJwtraWqjVarF8+fJyuEK50tPThZ2dnYiIiBBCCPHee++Jzz77rMzpVscRCMtTbQ38fFpNvC+SVBfJloEKEhoaSps2bYiJiSE+Ph4PDw8AZs2aRUREBPHx8WRmZrJ//35lH0NDQyIiIvD19cXT05PVq1cTFxfH+vXrlQmBzp07x8yZM4mNjUWlUrFq1ap8x01ISGDHjh388ssvREdHU69ePTZv3kx0dDRJSUnEx8cTFxens/m5JDPl5bG1taVly5a4ubkpQXcAK1asQKvVMmXKlBo7a2FVGTJkCBs3bmTWrFlVnRVJkuoIWRmoIBqNhsOHD/POO+9w/PhxJQgsPDyc7t27o9FoCAsL4+zZs8o+Q4cOBcDGxgaNRkOrVq1o0KABHTp0ICkpCcidYrZHjx5AbgT68ePH8x336Wh4W1tbDh8+zKVLl2jfvj2XL1/mrbfeIjQ0lCZNmhTIc0lmysuTV8mIiooiPj4egNdff50//viD6OhoTE1Nefvtt8twJWuvogI/Y2NjZQCfJEmVRvYmqCCdOnXi9OnTHDhwgAULFtC/f3/8/f2ZOXMmERERmJmZsWTJEh49eqTsU79+fQD09fWV//Ne50WuP6v7lygiGj46OpqDBw+yatUqtm3bxrp16/KtDwkJYfny5QX202q1RVYIjI2N6devH6GhoajValq1aqWsmzZtmtIqIkmSJFVPsmWggty4cYOGDRvi7e2Nv78/kZGRPHr0CD09PVq2bEl6ejrbt28vcbqXL1/m1KlTQP4I9DyFRcMnJyeTk5PDyJEj+fDDD4mMjCyQdklaBpKTk5Xm/0ePHvHTTz/RpUsXAG7evKlst2vXLtRqdYnPs66o7F4giYmJ+R4BGRsb8/nnnwO59z9vebt27bC1ta20fEmSVLVky0AFiYuLw8/PD319fYyMjFi5ciUqlYqpU6eiVqtp27YtTk5OJU7X2tqaNWvW4OvrS6dOnZgxY0a+9U9Hw+fk5GBoaMjq1asxMjJi8uTJ5OTkoKenx7Jly8p0frdv38bb25vs7GxycnIYPXo0r7zyCgD+/v5ER0ejp6eHhYUFX3/9dZmOJZUfCwsLZRCjnJwczMzM8PT0BPJPrDRv3jxlrARJkuqAqo5grM6qWyT05cuXhY2NTVVno9JURW+CutQL5PDhw6Jnz54Flufk5Ii2bduK8+fPl/kY1a0MSZKkm3xMIElPqSu9QAC2bNnCuHHjCiz/9ddfadasGZ06dSrx9ZMkqWaSlYEa5OkmXqli1JVeIFlZWezZs0eZG+FpmzdvZuzYsSW8cpIk1WQyZkCSnlJXeoEcOnQIrVbLCy+8kG95dnY2O3fu5Lfffisyv5Ik1S6yMiBJT7lx4wYmJiZ4e3ujUqlYu3atzl4gw4cPL1G6eb1AunXrVmgvkBEjRjB79mxatGhBSkoK6enpNGzYECMjI0aOHEnnzp3x9vYukLaPj88zHwn8XWG//sPDw2nfvj3m5uYlSk+SpJpNPiaoYHlduSrb0aNHMTY2Vpqwr1+/joODA7a2tqjValavXq1sO2nSJKUrma2tbbEeRWzYsAFLS0ssLS3ZsGHDM7fPzMxkzJgxWFpa0qNHDxITE4H/uz4qlao0p1nu4uLi6NatG7a2tnzwwQcsXLgwXy+QYcOGlakXiI2NDSkpKUX2Anl6ToSkpCT69u2Lra0tEyZMKHMvEMjtCvrjjz8ycuTIAuu2bNkiHxFIUl1U1RGM1Vl5REJXVQ+A8PBwMWzYMOV1ZmamePjwoRAiN2LewsJC3Lp1SwghxMSJE8Xu3buLnXZKSoro0KGDSE1NFampqaJDhw7i3r17Re7z1VdfiTfeeEMIIcS2bdvE6NGj863XNU5/bZmboK71Anladb4vkiT9H9kyUELz5s1j5cqVyuuAgAC++uorHjx4gJubG/b29mg0Gvbu3Vtg3+DgYObOnau87tu3r/Ir/NChQzg7O2Nvb8+oUaN48OBBuebbyMiIBg0aALm/DLOzs0ud1sGDB3F3d0elUqFSqXB3dyc0NLTIffbu3cukSZMAGDFiBEePHkUIUeo8SJIkSeVHVgZKaMyYMWzbtk15vWvXLry8vGjQoAG7d+8mMjKS8PBw/Pz8iv1ll5yczNKlSwkLCyMyMhJHR0dWrFhRYLugoCCdXciKO9XsjRs30Gq1mJqa4u/vn2/Y4ICAALRaLW+99RaZmZlFppOUlISZmZny2tTUVImaL84+9erVQ6VSkZKSUqx813SyF4gkSdWdDCAsIXt7e27evMmtW7e4du0aZmZmtGrViqysLN555x1+/vln9PX1uXbtGrdv3y5Wmr/99htnz57F2dkZgMePH+Pi4lJgu4CAgDLNMd+mTRtiY2O5efMmI0aMYNSoUbRq1Yply5bxwgsvkJWVxYwZM/joo49YtGhRoenoquQUJ1q+pPtIkiRJlUNWBkrBy8uLHTt2cOXKFSXY6rvvviMlJYXIyEgMDQ3p2LFjvu5nAAYGBuTk5Civ836BCyHw8PDg22+/LfK4QUFBbNmypcByDw8PgoKCip3/1q1bo1arOX78OF5eXrRu3RrIfZQwZcqUZ6ZlamrKiRMnlNfXr1/XWXn5+z7Xrl3jhRdeIDs7m7S0NExMTIqdZ0mSJKniyMcEpTBmzBi2bt3K7t27GTFiBAD379+nVatWGBoacuzYMS5dulRgPwsLC6KiohBCcOnSJWJiYgDo0aNHvn3S09O5ePFigf0DAgJ0Di5TnIpAUlISGRkZQO7UuSdOnKBz587A/00sJIRgz549ysRCp06d0tllLS9GIC0tjbS0NEJDQ3F3dwdg/vz57N69u8A+Q4cOJTg4GMh9tNKnT59q1zJQXXp+QMl7a6xZswatVoudnR0uLi6cO3dOWff222+jVqtRq9U6Y1lKktb8+fNRq9VYWVnx1ltvKS0+Pj4+mJiYsGfPnhKcuSRJ1YVsGSgFrVZLcnIylpaWypzzr776KkOGDFFGkLOysiqwX69evTAzM0Oj0WBnZ4dWqwWgRYsWrF+/nrFjxyqtCcuWLcPS0rLc8nz+/Hnmzp2rfAG/9dZbaDQaJe93795FCIGtra3S7fDatWs899xzBdIyMTFhwYIFODo6ArBw4ULlV358fHy+L7U8U6dOZcKECXTs2BETExOdLRx1Wb9+/ZQv0nv37hEYGMjp06cBcHR0ZOjQoTRt2rTQ/ceOHYuvry8A+/fvx8/Pjx9++IHvv/+es2fPEhMTQ3p6On369MHd3V0JJi1JWqdOneLYsWNKJdbFxYWTJ0/i4uJCSEiIEiAqSVLNIysDpZSQkJDvdfPmzfn11191bpsXPKanp8emTZt0buPq6qpMTVwRXF1diY2N1bnuyJEjOpdHREQwc+ZMneumTJnClClTCizPzs6mZ8+eBZY3aNCgVFM2l8W8efNo27Ytb7zxBpDbsmJmZsbEiRMZNmwYqampZGVlsWTJEoYNG5Zv3+DgYKKjo5Xpffv27cvnn3+Ora0thw4d4v333+fRo0d06NCB9evX8/zzz5dbvp/urQH/1xKjax6BPI0bN1b+/+uvv5RKX0JCAn369KFevXo0adKE9u3bc/ToUWXOhZKkBbk9UR4/fowQgqysLFq0aFHa05QkqRqRjwlqKSMjI6Kjo3X+Si+uoKAgpfWiuA4cOFCi7fOa5p/u2VBeamrPj9L01gBYvXo1HTp0wM/Pj88++wzInS/hxx9/5OHDh9y6dYtffvml1Gl169aNfv360aZNG1q3bs2gQYOUR02SJNVssmWglnJ2dlZG+avOKrLbXU3t+VHanhczZsxgxowZbNu2jSVLlrBhwwbc3d2JiIjA2dmZ1q1b07t3bwwMnl3sdaX1xx9/cPHiRZKSksjKysLV1ZXhw4djZ2dXqvOUJKn6kJWBYvj7IwGpcpTHda+JPT9K01vjaaNHj8433PHChQtZuHAhkBvIWZJYlKfT2r17Nz169KBhw4ZA7nwKERERsjIgSbWArAwUoXnz5jRs2FDn5DBS5WjYsCHNmzcv9f5jxoxh5syZ3Lx5k//+979A8Xt+fP311wghuHz5cr6eH7NmzVKmF05PT+fGjRsFvmDL0jLg7u7OwoULSUtLAyA0NFSZzXD+/Pl069atwERJFy9eVPLw448/0r59eyA3huP+/fuYmJgQERHBjRs3lJiOkqZlbm5OcHAw/v7+PHnyhGPHjjF48OBSnaMkSdWLrAwUwdzcnISEBJKTk6s6K3VW8+bNyzSDXk3s+VGa3hqrVq3i4MGDGBoaYmJionRHzMrKUloVmjZtysaNG5VHDiVNy8vLiyNHjii9UIYPH06fPn3K7bwlSao6sjLwDObm5nI61xqupvX8gJL31li+fLnOdBo0aJBvnICypFWvXj2+/vrrorItSVINJXsTSDXSpk2bmDNnDpD7HP/pZ+w1TUl6fpS0t0ZlpeXj48PPP/9c6PgFUVFRyv+vvvoqP/30U7kdW5KkstMTcuo4qYaJiIjAxcWFGTNm4OLiwtKlS5Vf9GfOnMHe3r5qMygpIiMjcXBwAKB3794sWLCANWvWcPjwYSIjI2nXrl0V51CSJJCVgWe6evWqjBmoQn+PGUhOTsbBwQEDAwPq1avHxYsX6d+/P6NGjWL69Ols3LiRrl27VmGOpaclJCTg7e3N8uXL2bJlC6dOncLGxoZbt27Rpk0bTp48WWCUS1nmqlZZ43SkGkpIhbpy5Ypo2LChAORfFf01bNhQXLlyRQghRHZ2trCyslLW9ejRQ3z55ZfiwYMH8l5V47+8e5icnCw++ugjodVqlXX9+vWTZa6a/T1d5qS6QwYQFiE5OZmMjAz5a7OK5P2qTE5OxtzcnN9//z1fMNxvv/3Gb7/9RmpqKgsXLpQ9P6qpvF+a06dP5z//+U++dUePHiUzM5P69esDssxVtb+XOanukJWBYujatat8Dl0NWFlZkZ6eTkZGBk+ePCErK4snT57Qtm1bQPb8qO7+/e9/M3/+fAwMDJS/559/XqkIPE2WOUmqXLIyINUoDRs2VEbAk2oWIyMjLCwsqjobkiTpILsWSpIkSVIdJysDkiRJklTHycpAFTl69CjGxsbKQDPXr1/HwcEBW1tb1Go1q1evVrb18fHBxMSEPXv2VHi+fH19admyJba2tkVut23bNqysrLC2tmb8+PHK8nr16inT9ZZl+mRJKk9/L28nT57EyckJtVqNg4MDP//8s7JtZZY3CwsLNBoNtra2yvDTf3fy5EmlO+3f8yTLm1ReZMxAFerXr59SuFu2bMnJkydp0KAB6enpqNVqhg8fTqtWrQgJCWHSpEklSjs1NZWmTZuWOE8+Pj68/vrrOofCzXPx4kWWLVvGyZMnadq0KXfu3FHWNW7cuMKmJJaksni6vKlUKnbu3Kn0UvHw8FCm/K7M8gZw/PhxVCpVoevNzMxYt24dn376aYF1srxJ5UW2DFSwvCleHz9+zF9//UXXrl25cOFCge2MjIyUoVwfPXpEdnZ2iY91584d/vWvf6FWq9m6dWup8tu7d29lUpzCrFmzhjfffFP58GvZsmWpjiVJ5a245c3a2lrpedK5c2cePHhAVlZWiY5VHuWtOMzNzbGxsUFfX35cSxVHtgxUMCcnJwYPHsyiRYu4d+8e06ZNo1OnTty4caPAtjdu3MDDw4OLFy/yySef0KpVq2emn5OTw6FDh1i7di3nzp1j/PjxhIaGYmpqCuT+ytE18YxWqyUkJKRU53ThwgX09fXp1asX2dnZvP/++3h4eACQnp6Oo6MjBgYGBAQE4OnpWapjSFJplKS85dm5cyd2dnYYGho+M/3yLm96enq4urqip6fH9OnTmTZtWgnOVpY3qfzIykAlWLRoEU5OTjRp0iRfLMDftWnThtjYWG7evMmIESMYNWrUMysEnp6eREZGsnbtWtzd3ZXpafP4+Pjg4+NTLueR58mTJ5w/f56jR49y48YNevfuTWxsLCqViitXrtCmTRsuX75M//790Wg0dOjQoVyPL0lFKW55Azh//jz+/v4cOnSoWGmXd3k7efIkbdq04c6dO3h4eNClSxdeeumlYu8vy5tUXmS7UyVISUkhPT2d+/fv8/jx42du37p1a9RqNcePH3/mtsuWLWPkyJHMmjWLWbNmcebMmXzrQ0JClACjp//KUkEwNTVl2LBhGBoa0rZtW7p27crFixeB3AoNQLt27ejXr1++2eokqTIUt7zduXMHT09PvvnmGzp27FistMu7vOWVl5YtWzJixIgST40ty5tUXmRloBJMmzaNJUuWMHz4cBYuXKhzm6SkJDIyMgBIS0vjxIkTdO7c+ZlpW1tbs2LFCs6ePYuLiwv+/v7Y2dkRFhYG5P5SiY6OLvBX2kcEkPvrKDw8HMj94D1//jzt27cnNTWVzMxMZfmJEyewsrIq9XEkqTSKU94yMjIYOnQo7777Lv369St22uVZ3tLT0/nrr7+U/Bw8eBC1Wl3svMjyJpUn+Ziggq1fvx4jIyPGjh1LVlYWPXr04OTJkwW2O3/+PHPnzlWaHd966y00Gk2xj1O/fn3GjRvHuHHjuHTpEikpKaXK74QJEwgLCyMlJQVTU1OWLFnCpEmTWLRoEY6OjgwdOhR3d3cOHTqElZUV9erVIygoiGbNmvHLL78wffp09PX1ycnJwc/PT344SZWquOVt3bp1xMTE8PHHH/Pxxx8DcOjQoWIHw5ZHebt9+zbDhw8Hch+9jR8/Xom9ebq8RUVFMWTIEFJTU9m/fz+LFi0iNjaWhIQEWd6k8lPVMyVVZ2fOnBGAOHPmTLmnHR4eLoYNG1bs7SdOnCh2795d7vmoziry+kvVU0Xdc1neikeWubpLPiaoIkZGRkRHRxdroBAfHx9+/vlnpeuhJEklI8ubJBVNVgYqWXBwMLdu3cLZ2ZnExET27dv3zH1CQkK4fPmy0oRY0TZs2IClpSWWlpZs2LBB5za7du3C2toafX39fIOePHnyhMmTJ6PRaLCyslKaYB8/fpwvoKpFixbMnTu3Es5GquuCg4Np3759tS1vULwy9/777/Piiy8qZejAgQMA7NmzBxsbG7RaLb169SIuLg6QZU4qGRkzUMmCg4OxtbXlhRdeKLAuOzubevXqVUGu/s+9e/cIDAzk9OnTAMpzy7+PrmZtbc3OnTuZMWNGvuW7du3i4cOHxMXFkZGRgZWVFePHj8fU1DRfpaF79+6MGDGiws9HkmpLmQPw8/Mr8IX+4osvcvjwYZo3b05YWBgzZszg5MmTSmtIHlnmpKLIloEy2rhxI926dcPW1pbp06eTk5MD5A53On/+fLRaLb179yYtLY0dO3Zw+vRpxowZg62tLY8fP8bCwoLAwEB69erFnj172Lx5MxqNBrVazbx585TjqFQq5s6di729Pf379yclJYU//vgDBwcHZZv4+HicnZ3LdD4HDx7E3d0dlUqFSqXC3d2d0NDQAtt17tyZLl266EwjIyODJ0+e8PDhQ+rXr8/zzz+fb/2lS5eU8QkkqaTqapkrjJOTE82bNwfA3t6e69evF9hGljnpWWRloAwSEhLYsWMHv/zyC9HR0dSrV4/NmzcDcP/+fV566SViY2PRaDRs3LgRLy8vHB0d2bp1K9HR0RgZGQHQqFEjTp48Sc+ePQkICODIkSNER0cTGRmpjKV+//59evbsSWRkJK6urixevJiOHTvSpEkTYmJigNxIal1jqgcFBens+xwQEFBg26SkJMzMzJTXpqamJCUlFfuaDB8+nIYNG9KmTRvMzc3x8/MrMO76li1bGD16dIEBWyTpWep6mVuxYgVarZYpU6aQmppaYP26desYOHBggeWyzEnPIh8TlMHhw4eJiIhQZht7+PAhrVu3BuC5557j5ZdfBsDBwYGEhIRC0xkzZgyQO666q6srLVq0AMDb25tjx47h6emJgYEBo0aNUpYPGzYMgKlTpxIcHMwnn3zCrl27dE5aEhAQoPNDSBchRIFlJfkAOXXqFPXr1+fGjRvcvXuXl156iVdeeSVfE+3mzZtZt25dsdOUpDx1ucy9/vrrvPvuu+jp6fH+++/z9ttv5ytHx48fZ82aNfzyyy8F9pVlTnoWWRkoAyEEU6ZMITAwsMC6vF8gkDvN6JMnTwpNp1GjRkp6hX3xFrZ85MiRBAYG4uLigrOzM8bGxgW2CQoKYsuWLQWWe3h4EBQUlG+ZqakpJ06cUF5fv34dFxeXQvP+d5s2bcLDwwMDAwNat26NnZ0dsbGxSmXg7NmzPHz4ECcnp2KnKUl56nKZe3po8mnTpuULcLxw4QKTJ09m7969NGvWLN9+ssxJxSEfE5SBm5sb27dv5+7du0DuKGBXr14tcp/GjRvz559/6lzXvXt3jhw5QnJyMtnZ2WzatIk+ffoAkJWVxc6dO4HcL9y88cufe+453NzcePPNN5k8ebLOdAMCAnSOivb3DyVAeV6ZlpZGWloaoaGhuLu7F++CkDvD2tGjR4HcZtYzZ85gaWmprN+8eTNjx44tdnqS9LS6XOZu3ryp/L9r1y5ltMKnh1W2trYusJ8sc1JxyMpAGVhZWbF48WIGDBiAVqtl4MCB3L59u8h9Jk2ahK+vrxLM9LTWrVuzdOlS+vXrh42NDTY2NkrTpLGxMVFRUTg4OBAWFsaiRYuU/caPH4+hoSGurq5lPicTExMWLFiAo6Mjjo6OLFy4UJnS+LXXXlMinvft24epqSm//vor7u7uDB48GICZM2eSkpKCWq2mZ8+e/OMf/6Bdu3ZK+lu3bpUfTFKp1eUy5+/vj0ajQavVEhYWxmeffQbAxx9/TFJSEnPmzMHW1lZ5hJJHljmpOPSErgdWEgCRkZE4ODhw5swZ7O3tqzQvKpWKtLQ0nes+/vhjHj58yHvvvVe5mapg1en6S5WjOt1zWeZkmatLZMxADTdkyBCuXLmiTBwkSVLFkmVOqo1kZaCGKOwXyvfff1+5GZGkOkKWOakukTEDkiRJklTHycpALWFhYVHoL5mKkDc9rI2NDdbW1vmenRY2hrok1RaVXd7y5OTk4OTkhKenp7JsxowZdO7cGY1Gw2uvvVZkl0pJKoysDEilYmBgQFhYGDExMcTExHDw4EEiIiKU9X5+fkp3qkGDBlVhTiWp9li1ahUdOnTIt8zT05OEhARiY2PJzMxk/fr1VZQ7qSaTlYEKkp6ezuDBg7GxsUGtVrN161YAPvjgA5ycnFCr1UybNk0Zfaxv3774+/vj5OSElZUVp0+fZvjw4VhaWrJw4UIAEhMTsba2ZsKECWi1WkaPHs3Dhw8LHFvX2O3Z2dlMmjQJtVqNRqNRuiWVlp6enjLnwOPHj3n8+LEc6lSqMrW9vEHueAK7du1i2rRp+ZZ7eHigr6+Pnp4ednZ2OucmkKRnkZWBChIaGkqbNm2IiYkhPj5eGS1s1qxZREREEB8fT2ZmJvv371f2MTQ0JCIiAl9fXzw9PVm9ejVxcXGsX79eGYf83LlzzJw5k9jYWFQqFatWrcp33MLGbo+OjiYpKYn4+Hji4uJ0DpYSEhKiczx1Hx+fQs/T1taWli1b4ubmlq9/87PGUJek8lQXypufnx+BgYHo6+v+2M7KymLjxo0MGDCgVNdQqttkZaCCaDQaDh8+zDvvvMPx48eVIUvDw8Pp3r07Go2GsLAwzp49q+wzdOhQAGxsbNBoNLRq1YoGDRrQoUMHZeKSdu3a0aNHDyB3vPTjx4/nO+7TY7fb2tpy+PBhLl26RPv27bl8+TJvvfUWoaGhNGnSpECefXx8dI6aFhISUuh55n3oRUVFER8fD+SOof7HH38QHR2Nqakpb7/9dhmupCQ9W20vb8eOHUNPT6/IGRLnzJlDr169SjR8uCTlkV0LK0inTp04ffo0Bw4cYMGCBfTv3x9/f39mzpxJREQEZmZmLFmyhEePHin71K9fHwB9fX3l/7zXeUFBz2qKL2rs9ujoaA4ePMiqVavYtm1bgYlLQkJCWL58eYH9tFptkRUCY2Nj+vXrR2hoKGq1usgx1CWpItT28vbLL7/w008/YWFhwaNHj/jzzz+ZOHEiGzZsAOBf//oXiYmJ7Nu3r8j8SlJhZGWggty4cQMTExO8vb1RqVSsXbuWR48eoaenR8uWLUlPT2f79u0MHz68ROlevnyZU6dO0a1bt3zjpedxc3NjxIgRzJ49mxYtWpCSkkJ6ejoNGzbEyMiIkSNH0rlzZ7y9vQuk7ePjU+QjgaclJydTr149mjZtyqNHj/jpp5/45z//CeSOoZ43k9zTY6hLUkWp7eXt6VkQjx49yueff65UBLZt28a2bds4cuQIBgbyI10qHfnOqSBxcXH4+fmhr6+PkZERK1euRKVSMXXqVNRqNW3bti3VLGLW1tasWbMGX19fOnXqxIwZM/Ktf3rs9pycHAwNDVm9ejVGRkZMnjyZnJwc9PT0WLZsWZnO7/bt23h7e5OdnU1OTg6jR4/mlVdeAXLHUI+OjkZPTw8LCwu+/vrrMh1Lkp6ltpe3orzxxhs0btxYeTwwYsSIfPMoSFJxyLkJilDdxulOTEzE09NT5/zptVF1u/5SxatO97yulTeoXtdfqlwygFCSJEmS6jhZGahBLCws6tSvFEmqSrK8SXWJrAxIkiRJUh0nKwMVLDExEVtb20o/7tGjRzE2Nlb6UgNs2LABS0tLLC0tlUjkoqxZswatVoudnR0uLi6cO3cu3/r09HTMzc2ZO3fuM9M6efIkDg4OGBgYsGfPHmV53vVRqVTFPTVJKlJNLnPBwcG0aNFCGYBo7dq1z9znlVdewdbWFrVazfTp08nOzgZyeyDkpdO5c2eljMkyJ+kiexPUYv369VO+eO/du0dgYCCnT58GwNHRkaFDh9K0adNC9x87diy+vr4A7N+/Hz8/P3744Qdl/eLFi4scBOVpZmZmrFu3jk8//TTf8rymWPnBJNUGZS1zAK+++iqff/55sY+5adMmmjRpghCC0aNHs2vXLkaNGkVQUBBBQUFA7pwGeXOHyDIn6SJbBkpo3rx5rFy5UnkdEBDAV199xYMHD3Bzc8Pe3h6NRsPevXsL7BscHJzvV3Tfvn2VZ5KHDh3C2dkZe3t7Ro0axYMHD8o13wcPHsTd3R2VSoVKpcLd3Z3Q0NAi92ncuLHy/19//ZVvAJazZ8+SmJhY7AGFzM3NsbGxKXQoVUkqTF0qc6WRN7rhkydPlLEV/m7z5s2MHTu23I8t1R7yk7mExowZw7Zt25TXu3btwsvLiwYNGrB7924iIyMJDw/Hz8+P4vbaTE5OZunSpYSFhREZGYmjoyMrVqwosF1QUJDOsczzBiMpSlJSEmZmZsprU1NTZcjVoqxevZoOHTrg5+eXb7KVf/zjH3z00UfFOj9JKou6Vua2bduGVqvFy8uLa9euFet8Bg0aRMuWLWnUqFGBgZWuX7/OhQsXcHNzK1ZaUt0kHxOUkL29PTdv3uTWrVtcu3YNMzMzWrVqRVZWFu+88w4///wz+vr6XLt2jdu3bxcrzd9++42zZ88qTe6PHz/WOb7406OQlZSuD8nizDI4Y8YMZsyYwbZt21iyZAkbNmwgJCSEHj160K5dO37++edS5UeSiqsulbkhQ4Ywbtw46tevz9q1a5k4cSJHjhx55rEOHDjAo0eP8PHx4ciRI/kmK9q6dSsjR46kXr16JT8Jqc6QlYFS8PLyYseOHVy5ckVpevvuu+9ISUkhMjISQ0NDOnbsmG8cdAADAwNycnKU15mZmUDuh4aHhwfffvttkccNCgpiy5YtBZZ7eHgozwYLY2pqyokTJ5TX169fL9GEJqNHj1ZGX/vtt9/4/vvv2bBhAw8ePODx48c0atSIDz/8sNjpSVJJ1JUy16xZM+X/qVOn4u/vX+T2T2vQoAHDhg1j7969+SoDW7ZsKZcplKXaTT4mKIUxY8awdetWdu/ezYgRIwC4f/8+rVq1wtDQkGPHjnHp0qUC+1lYWBAVFYUQgkuXLhETEwNAjx498u2Tnp7OxYsXC+wfEBCgc5azZ30oAcrzyrS0NNLS0ggNDcXd3R2A+fPns3v37gL7PJ2HH3/8kfbt2wOwcuVKrl27RmJiIv/617+YMmWKUhEoLC1JKou6UuZu3ryp/P/999/TqVMn5XWXLl0KbJ+ens6NGzcAyM7O5sCBA/m2+9///sft27fp1avXM/Mr1W2yZaAUtFotycnJWFpaKjX5V199lSFDhihTmVpZWRXYr1evXpiZmaHRaLCzs0Or1QLQokUL1q9fz9ixY5VfNsuWLcPS0rLc8mxiYsKCBQtwdHQEYOHChZiYmAAQHx+frztUnlWrVnHw4EEMDQ0xMTEpVteowtKKiopiyJAhpKamsn//fhYtWkRsbGwZz0qqK+pKmfviiy/Yt28fhoaGNG3alODgYABSUlJ0PnbIyMjA09OTR48ekZOTQ79+/fLNn7B582ZGjx5drEeCUh0npEKdOXNGAOLMmTNVnZUSCw8PF8OGDSvWti+//HK5Hbe0aRkbGxdYVpOvv1Q6NfmeV2SZO3DggFixYkUpclU4Weakp8nHBLWUkZER0dHROn99/N2BAwfK7bglTStvAJRWrVqVWx4kqSpUZJl7+eWXmT17dmmzlo8sc5Iu8jFBLeXs7ExiYmJVZ+OZ5PjvUm0hy5xUk8mWAUmSJEmq42TLQDEkJCRUdRbqJHndC3f16lWSk5OrOhvlLu+ey3tfNeR1r7v0hCjmkF110NWrV+natSsZGRlVnZU6q2HDhiQkJGBubl7VWak2avv7Ul9fP9/YAFLlkmWubpItA0UwNzcnISGhzL/A9u3bx+LFi5XXbdu2xd3dnddee61WjAoWFRXF+vXrOXXqFFlZWUDugCu6xoovqebNm8sPpb9JTk4mIyODjRs30rVr16rOTrm7efMmaWlpVZ0NnS5fvsy7775bpdf+0aNHfPXVVxw5coRbt24py9etW4eNjU2Z05dlrm6SlYFnMDc3L3PBiImJ4YUXXmDGjBmMHTuWzp07l1Puqgd7e3umTp3KgwcPOHjwIP/617/IycnB3t6+qrNWq3Xt2lVe40oWGRnJu+++W+XX3tnZGSEEMTExhISE8N1332FhYSHfD1KpyccEklTDREZG4uDgwJkzZ+SHfyWT116qrWRvAkmSJEmq48rlMUFtjWyuKcrrGZ+8jxWvOj+PPXr0KMOGDaNPnz7s27eP69evM2zYMLKzs3ny5AmzZs1Shrr18fFh//79rFu3Dk9PzwrNl6+vL3v37qVNmzaF9o8PCAggNDQUgIcPH3L79m0l7qBevXpoNBog97Hfvn37KjS/RZFlrGpV5/JX5co6hOGVK1dEw4YNBSD/quivYcOG4sqVK/I+1oC/8rhXFTVk7N+H083MzBQPHz4UQgjx4MEDYWFhIW7duqWsnzhxoti9e3ex0793716p8nXs2DFx5swZYWNjU6ztV65cKSZPnqy81jXsbmmV5drLMlb1f+VR/mqrMrcM1PbI5uouISEBb29vkpOTy1Tjlfex4pXXvSqriIgIpk2bxn//+18yMzPp1q2bzp4fRkZGyv+PHj0iOzu7xMe6c+cOISEhBAcH52tZKInevXuXaGS/zZs3s3DhwhIfp6LJMla1qkv5q67KrTdBVUfXSuVD3sfaz8nJicGDB7No0SLu3bvHtGnT6NSpkzIV7tNu3LiBh4cHFy9e5JNPPinWePY5OTkcOnSItWvXcu7cOcaPH09oaCimpqYAhISEsHz58gL7abVaQkJCynRu169f58KFC7i5uSnL0tPTcXR0xMDAgICAgAp/rPEssoxJ1ZHsWihJddCiRYtwcnKiSZMmrF69utDt2rRpQ2xsLDdv3mTEiBGMGjXqmRUCT09PIiMjWbt2Le7u7gWmz/Xx8cHHx6dczuPvtm7dysiRI/ON33HlyhXatGnD5cuX6d+/PxqNhg4dOlTI8SWppqpxvQmCg4PzDbRRHW3YsAFLS0ssLS3ZsGGDzm3u3bvHgAEDsLS0ZMCAAaSmplZyLiuXvG/VS0pKCunp6dy/f5/Hjx8/c/vWrVujVqs5fvz4M7ddtmwZI0eOZNasWcyaNYszZ87kWx8SEoKtrW2Bv/KoIGzZsoVx48blW9amTRsA2rVrR79+/YiKiirzcao7Wd6kkqpVlYHSPNMsb/fu3SMwMJCIiAgiIiIIDAzU+QYOCgpi4MCBXLx4kYEDBxIUFFQFua088r5VL9OmTWPJkiUMHz680OfrSUlJypDHaWlpnDhxolgDZllbW7NixQrOnj2Li4sL/v7+2NnZERYWBuS2DERHRxf4K+sjgv/973/cvn2bXr16KctSU1PJzMwEcitAJ06cwMrKqkzHqQlkeZNKqsorAxs3bqRbt27Y2toyffp0ZUxylUrF/Pnz0Wq19O7dm7S0NHbs2MHp06cZM2YMtra2PH78GAsLCwIDA+nVqxd79uxh8+bNaDQa1Go18+bNU46jUqmYO3cu9vb29O/fn5SUFP744w8cHByUbeLj43F2di7T+Rw8eBB3d3dUKhUqlQp3d3ely9PT9u7dy6RJkwCYNGkSe/bsKdNxK5u8bzXzvgGsX78eIyMjxo4dy8KFCwkPD+fkyZMFtjt//jw9evTAxsaGPn368NZbbyld9Iqjfv36jBs3jsOHD7Nz506MjY1Lld8JEybQs2dPzp07h6mpKcHBwUDuo46nuwlu3ryZ0aNH53sskZCQgKOjIzY2NvTt2xc/P78aWRmQ5a3mlreaokorAwkJCezYsYNffvmF6Oho6tWrx+bNmwG4f/8+L730ErGxsWg0GjZu3IiXlxeOjo5s3bqV6OhoJdq5UaNGnDx5kp49exIQEMCRI0eIjo4mMjJSefPcv3+fnj17EhkZiaurK4sXL6Zjx440adKEmJgYIPdDMu+N97SgoCCdzZoBAQEFtk1KSsLMzEx5bWpqSlJSUoHtkpOTadGiBQAtWrQgJSWlTNeyMsn7VjPvW57Jkyezc+dOAAwNDTlz5ky+X9N5XF1diY2NJSYmhpiYGKZNm1bqY7Zv3x4nJ6dS7fvtt99y8+ZNHj9+zPXr15V7/cEHHzB06FBlu4ULF/Kvf/0r377Ozs7ExcURExNDXFwcU6dOLfU5VBVZ3mp2easpqjSA8PDhw0RERODo6AjkDhbSunVrAJ577jlefvllABwcHIqcWnPMmDFAbpcpV1dX5c3j7e3NsWPH8PT0xMDAgFGjRinLhw0bBsDUqVMJDg7mk08+YdeuXToHNQkICND5htZF6Bjd+e8BVIUtqynkfaudjIyMiI6OZujQoc8cmMfHx4fjx48zduzYSspd3SXLm1QZqrQyIIRgypQpBAYGFlj3dB/nevXq8eTJk0LTadSokZJeYW+ewpaPHDmSwMBAXFxccHZ21tmUGRQUxJYtWwos9/DwKPAMy9TUlBMnTiivr1+/jouLS4F9mzVrxt27d2nRogV3796lWbNmhZ5fdSPvW828b8/i7Oxc7P78ZX2+LxWfLG+1s7xVN1X6mMDNzY3t27dz9+5dIDfA5+rVq0Xu07hxY/7880+d67p3786RI0dITk4mOzubTZs20adPHwCysrKUptFNmzbx0ksvAbk1azc3N958800mT56sM92AgACdAU+6glnynn2lpaWRlpZGaGgo7u7uBbYbOnSo8uwzODhYqYHXBPK+BQM1774VpbZEn8fFxeHs7IytrS1OTk5KT4bMzEx8fHzQaDTY2try66+/Kvs8fvxYGWuhS5cuyvutupDlLRioXeWtWirrEIZlHRp1y5YtwsbGRmg0GmFvby9OnTolhMg/hOj69evFnDlzhBBC7NixQ3Tq1EnY2NiIzMxM0bZtW5Gamqps+9133wm1Wi2sra2Fn5+fstzY2FjMnz9f2NvbCzc3N5GcnKysO378uDA3NxfZ2dmlOoe/++abb0SHDh1Ehw4dxLp165TlU6dOFREREUIIIZKTk4Wrq6vo2LGjcHV1FSkpKaU6VnkNTVvSdOR9K/l9q6p7VRx9+vQRUVFROtc9efKk3I5TWikpKaJDhw4iNTVVpKamig4dOugc3vjll18WBw4cEEII8cMPPwg3NzchhBBffPGFeO2114QQQly7dk1069ZN2WfRokVi4cKFQgghsrOzxd27dwvNR1mufVn2leWtbJ+TQlTcMN61RZVXBipLUeOTf/TRR+L999+vvMyUo+r8BVMeatN9q8x79e233wonJydhY2Mjpk2bpnyAGxsbi4CAAKHRaISLi4tITU0V27dvF40aNSrw5fHBBx8IZ2dnsWPHDrFp0ybly8Pf3185jrGxsZgzZ46ws7NTvjwuXrwo7O3tlW3i4uJEz549y3TOmzZtEm+88Yby+o033hCbNm0qsN3LL78stmzZouzz6quvCiGEeP311/Ntb29vLxISEoQQQpiZmYn09PRi5aOqKgOVpTaVt7+rCde/KlV518KqNmTIEDZu3MisWbOqOitSCcj7Vri6HH3+6aef4ufnh5mZGf/85z+VJmobGxv27t1LdnY2v//+OwkJCSQlJZGWloa+vj4LFy7E3t6e0aNHc/v27TJd/9pIlrfar84MR5w3nenfff/995WbEalE5H0rubocfb5q1Sq+/PJLhg4dSkhICG+++Sa7d+9m6tSpJCQk4ODgQOfOnenZsycGBgY8efKEK1eu0KtXL5YvX84XX3zBP//5T7799tti5au2keWt7qozlQFJqitEHY4+37hxI1988QUAXl5eSmXDwMCAzz//XNnOxsaGjh070qxZMxo2bMjw4cMBGDVqFF9//bXOc5Kk2qxOPSawsLAotOZbEbKyspQR3KytrXnvvfcKbLNixQr09PQqNV81WWXfQwBfX19atmyJra1tpR63tOpy9HmrVq345ZdfAPjpp5+U4ZMzMjJIT08HYM+ePbRt25YXX3wRPT09hgwZwtGjR4HcVpWaOEJhearsMnbhwgVcXFywtrbGxsaGHTt25Fv/73//m86dO2NtbY2/v3+l5auukS0DFcjAwICwsDCef/55njx5gouLC6+88ooyEtvNmzf58ccf5dza1ZyPjw+vv/46U6ZMqeqsFIuVlRWLFy9mwIAB5OTkYGhoyOrVq4t8n02aNAlfX1+ee+45Tp06lW9d69atWbp0Kf369UMIwaBBg5THAcbGxkRFRREUFETTpk3ZunWrst/48eP54YcfcHV1LfM5mZiYsGDBAuXRx8KFCzExMQHgtddeY8aMGTg6OrJmzRpmzZpFdnY2jRo14j//+Q8Ad+7cwd3dHX19fdq2bcv69euVtD/66CMmTJjA3LlzadGiRb51UsWrX78+//nPf7CysuL27ds4ODjg7u5O48aNCQ8PZ9++fcTGxlK/fn3u3LlT1dmtvcoagViWCM0HDx6IQYMGCa1WK6ytrZUo4MWLFwtHR0dhbW0tfH19RU5OjhAit/uTn5+fcHR0FF27dhURERHC09NTdOzYUSxYsEAIIcTly5eFlZWV8Pb2FhqNRowaNUpkZGQIIUS+7jW6oq2fPHkiJk6cKKytrYVarRbLly8v6+VRpKenCzs7O6XLjBBCvPrqq+L06dMFuv2URFX3Jqgr9/Dy5cvCxsamTGlU9b2qCLU5+lyXquhNUFfKWB6NRiMuXbokhBBi1KhR4vDhw+WSbnUqN9VRlT4mCA0NpU2bNsTExBAfH4+HhwcAs2bNIiIigvj4eDIzM9m/f7+yj6GhIREREfj6+uLp6cnq1auJi4tj/fr1yqxX586dY+bMmcTGxqJSqVi1alW+4xYWbR0dHU1SUhLx8fHExcXpbN4szfSrtra2tGzZEjc3N+WXzZEjR3j++efzTQBSE9WVeyiVjIw+Lz91qYydOnWKrKwsLCwsgNxHCMePH6d79+706dOHiIiIslxKqQhVWhnQaDQcPnyYd955h+PHjytBRuHh4XTv3h2NRkNYWBhnz55V9smbmMTGxgaNRkOrVq1o0KABHTp0ULoatWvXjh49egC5Ec5/n4P96WhrW1tbDh8+zKVLl2jfvj2XL1/mrbfeIjQ0lCZNmhTIc2mmX80rPFFRUcTHx5OVlcW7777Lhx9+WLYLWA3UlXso6VZU9HlsbKwcPrYc1JUydufOHSZMmMA333yjBKY+efKElJQUfvvtN5YvX86YMWN09iyRyq5KYwY6derE6dOnOXDgAAsWLKB///74+/szc+ZMIiIiMDMzY8mSJTx69EjZp379+gDo6+sr/+e9zouMftbkFqKIaOvo6GgOHjzIqlWr2LZtG+vWrcu3PiQkhOXLlxfYT6vVFvlGNzY2pl+/fkrhuXDhgtIqcP36dbRaLWfOnFG6b9UUdekeSlJVqAtlLCMjg6FDh/Luu+/mmx7Z1NSUESNGoKenh4ODA4aGhvlmMpTKT5VWBm7cuIGJiQne3t6oVCrWrl3Lo0eP0NPTo2XLlqSnp7N9+3al209xXb58mVOnTtGtW7d8Ec553NzcGDFiBLNnz1amxUxPT6dhw4YYGRkxcuRIOnfujLe3d4G0fXx8it2cnJycTL169WjatCmPHj3ip59+4p///Cfm5uZKpDfkRu9GR0ejUqlKdJ7VQW2/h1LpVcX72tfXl71799KmTZsCYxv8+9//5ssvv8TAwIDBgwfz8ccfV1q+yqK2l7Hs7GzGjRuHp6dngbQ8PT05cuQIffv25X//+x8PHz6kefPmJTpPqXiqtDIQFxeHn58f+vr6GBkZsXLlSlQqFVOnTkWtVtO2bdtSzYFubW3NmjVr8PX1pVOnTsyYMSPf+sKirY2MjJg8eTI5OTno6emxbNmyMp3f7du38fb2Jjs7m5ycHEaPHs0rr7xSpjSrm9p+DwEmTJhAWFgYKSkpmJqasmTJEp0j6klVr7CeHzU5Kr22l7Eff/yR/fv3c+XKFWXcieDgYGxtbZkyZQpTpkxBrVZjZGREcHCwnNa4opQ1ArG6RWiWR9R3TVIbI9Rr6z2s6ntVV6LSdb1/yisqvbbMTVBby1hRqtP1r47q1KBDklSX1aWo9L+TUemSVLRaVxnIe04p1VzyHlaMuhKVrouMSs9PljHp7+QIhJJUR9SFqPTCyKh0SSpatWgZSExMrJJx348ePYqxsbHy6wdgw4YNWFpaYmlpyYYNG56Zxpo1a9BqtdjZ2eHi4sK5c+fyrU9PT8fc3Jy5c+c+M63MzEzGjBmDpaUlPXr0IDExEfi/61PdexvU5PtY2LUvynvvvYeZmVmB+/LFF18U+55Xphs3btCwYUO8vb3x9/cnMjJSZ1R6SeVFpQOFRqXrmishOTmZnJwcRo4cyYcffkhkZGSBtMurZSAvKh2o0VHpdamMpaSkMGDAALp27YparebLL7/Mt37btm1YWVlhbW3N+PHjgZrzWVkd1fmWgX79+ilzs9+7d4/AwEBOnz4NgKOjI0OHDqVp06aF7j927Fh8fX0B2L9/P35+fvzwww/K+sWLF+frN1uUb775hubNm3Px4kW2b9/OvHnz2Lp1a43uelhZynofC7v2RRk0aBAzZsyga9eu+ZbPnj2bJk2aVLtm2NoelQ6F9/yQUellV9llTF9fn8DAQHr06EF6ejqOjo4MHDiQTp06cfHiRZYtW8bJkydp2rSp0jtEflaWQVkjEHVFaPr7+4uvvvpKeT1v3jzx5Zdfir/++ku4uroKOzs7oVarxZ49e4QQ+SNb169fL+bMmaPs26dPHxEVFSWEEOLgwYOiZ8+ews7OTnh5eYm//vqrTHkPDw8Xw4YNU15v2rRJvPHGG8rrN954Q2zatKnY6W3atEkMHjxYeR0fHy9GjRpV4JwKM3DgQHHq1CkhhBBPnjwRLVu2VCK7hdA9DnxFRqjXpfv4rGtfFF33Rdc9r+reBBWhrkWll3dvAlnGilfGhBBiyJAh4siRI0IIIfz8/MQ333xT6LYV+VlZW1XIY4IxY8awbds25fWuXbvw8vKiQYMG7N69m8jISMLDw/Hz8yt2EE9ycjJLly4lLCyMyMhIHB0dWbFiRYHtgoKCdEYf581rXpSkpCTMzMyU16ampkqQVFFWr15Nhw4d8PPz47PPPlOW/+Mf/+Cjjz4q1vn9/fj16tVDpVKRkpJS7P3LW126j9Xt2kt1gyxjxStjV65cISoqSpnb5cKFC1y4cIFevXrRo0cPQkNDi5WOVLgKeUxgb2/PzZs3uXXrFteuXcPMzIxWrVqRlZXFO++8w88//4y+vj7Xrl3j9u3bxUrzt99+4+zZs0qT++PHj3FxcSmwXUBAQLHezLroKmzFaUqcMWMGM2bMYNu2bSxZsoQNGzYQEhJCjx49aNeuHT///HOFHr+i1KX7WN2ufU0ho9LLRpaxZ5exjIwMvLy8+Oyzz2jcuDGQ2zvk/PnzHD16lBs3btC7d2+la6tUOhUWM+Dl5cWOHTu4cuUKY8eOBeC7774jJSWFyMhIDA0N6dixY77IZQADAwNycnKU15mZmUDuG8nDw4Nvv/22yOMGBQUpo1g9zcPDg6CgoCL3NTU15cSJE8rr69ev6yxEhRk9erTyvPS3337j+++/Z8OGDTx48IDHjx/TqFGjIicnMjU15dq1a7zwwgtkZ2eTlpamzNleVerKfayO116qG2QZK1xOTg7jx49n+PDheHl55UurR48eGBoa0rZtW7p27crFixdLFfMi5aqw3gRjxoxh69at7N69mxEjRgBw//59WrVqhaGhIceOHePSpUsF9rOwsCAqKgohBJcuXSImJgaAHj165NsnPT2dixcvFtg/ICBAZ/Txs97cAO7u7oSGhpKWlkZaWhqhoaG4u7sDMH/+fHbv3l1gn6fz8OOPP9K+fXsAVq5cybVr10hMTORf//oXU6ZMUSoChaU1dOhQgoODgdzmwj59+lT5r9O6ch+LuvZdunQpxpWqWWpyVHpwcDAtWrRQmrXXrl37zH0K6/lx8uRJHBwcMDAwUILjoHKj0mUZK7yMvfnmm7zwwgu88847+ZZ7enoSHh4O5PY6OH/+vPLZK5VOhbUMaLVakpOTsbS0VKYxffXVVxkyZIgy+IiVlVWB/Xr16oWZmRkajQY7Ozu0Wi0ALVq0YP369YwdO1apIS9btgxLS8tyy7OJiQkLFixQnkstXLhQqbnGx8fn+wDLs2rVKg4ePIihoSEmJibF+jArLK2pU6cyYcIEOnbsiImJic5ae2WrK/exsGufkpJS6LPa+fPn8+233/Lnn39iamrKjBkzWLhwYbmdR21V1qh0yH0Pfv7558U+ZmE9P8zMzFi3bh2ffvppvuWVGZUuy5juMhYfH8/KlStRq9VKxXXp0qUMGjQId3d3Dh06hJWVFfXq1SMoKEhOl11WZY1ArMkRmn+PkC3Kyy+/XG7HLW1ald2boKaoyPt44MABsWLFihLnqbJ7E9SlqPTi9s7RRVcZEkKIiRMnit27dz9z+9oyN0FJVccyVhTZm6DkqsWgQ1XFyMiI6OhonbXYvztw4EC5HbekaeU1WbZq1arc8lCbVOR9fPnll5k9e3aJ9vniiy9YtmyZzuF1K0pdikqH3AFntFotXl5eXLt2rVjnI5VedStjhZGflaVXpwcdcnZ2LtZIc1VNRmwXrbrdx9mzZ5fbh1tx1aWo9CFDhjBu3Djq16/P2rVrmThxojK6oFQxqlsZK4z8rCy9Ol0ZkKTapK5EpT/9bHjq1Kn4+/sXub0kSc9WbpWBhISE8kpKKoHyvu7yPlacir62Y8aMYebMmdy8eZP//ve/QPGj0r/++muEEFy+fDlfVPqsWbOUGQbT09O5ceNGgUC0srQMuLu7s3DhQtLS0oDcaZbzJjSaP38+3bp1Y/jw4fn2uXnzJq1btwbg+++/p1OnTsq6Ll268Pvvv5cqL5VFlrGqIa970cpcGWjevLky+YlUNRo2bFjmSVfkfawc5XGvClNXotK/+OIL9u3bh6GhIU2bNlW6q5Wm50dUVBRDhgwhNTWV/fv3s2jRImJjY8vt/J4my1jVq8jyV9PpieJGExUhbwYyqWo0b94cc3PzMqcj72PFK+u9SkpKwt/fn02bNnHmzBns7e3LMXcV7+jRo3z++ef5+vQXZtCgQSUKRvvxxx+5ePFiucZrqFQqpdUCIDIyEgcHB/bs2cOwYcNKnJ4sY1WrvD4ra6NyqQxIklSxLl26xEcffURwcDBGRkY8ePCgRlYGfvnlF8aPH49Wq2Xfvn1VnZ1CJSYm4unpycOHDzl//ryyPK8yoKenx9ixY3nnnXdQq9VVmFNJKh91umuhJFV3Z8+eZcKECXTq1Indu3ezePHifFNk1zR5UenVuSIA/xeV/nRF4Gn+/v6cPHkSjUaDp6cnp06dquQcSlL5kpUBSaoGfvzxx3zzVpw+fZoRI0agVqs5evQon332GYmJiQQEBPD8889XYU4lyJ2H5I8//mD9+vUkJCTQvXt3BgwYQHh4uBK3sGrVKjZt2lTFOZWk4pFdCyWFfJ5Z8XQ9szx37hyjRo1i3LhxHDt2jA8//JBDhw7RsWNH1q5dy4QJEzAyMiqQloyOrnxPX3NDQ0MmTZrEhAkT2LVrFx9++CGurq707NmTBQsWcPv2bd58803MzMzo3bt3gbRkeascMk6gmKpq6EOperly5Ypo2LChAORfBf41bNhQXLlyRbnuf/75p+jSpYto27at6NmzpwCERqMRmzdvFk+ePJH3qhr+/f0e5snJyRE//PCDcHZ2FoDQarXCyspKvPDCC+LGjRvyHlaz+yXlJ1sGJCB36NmMjAw2btxYYDIXqXwkJCTg7e1NcnIy5ubmCCFwd3dX+sU3btyYL7/8kunTp2NgUHjRNDc3JyEhQf6qrCKF/dLU09Nj0KBBeHh4sHHjRlatWsVvv/2Gnp4effv2JS4uTmnhkeWtcvy9zEmFk5UBKZ+uXbvWuAj1miouLo5ff/1VeR0fH8+sWbO4f/9+gSlb/87c3Fx+uFVTGzduZOLEicprIQQXLlxg+/btvPrqq/m2leVNqi5kZUCSqohWq+XcuXNkZ2fz5MkTsrKyePLkCXZ2dlWdNakMxo8fT+fOndHX18fAwAADAwP09PSwtrau6qxJUqFkZUCSqpBsIq59DAwM6N69e1VnQ5JKRHYtlMrd0aNHMTY2zjeU7ODBg1GpVHh6eubb1sfHBxMTk2KNSFcWFy5cwMXFBWtra2xsbNixY4fO7dasWYNWq8XOzg4XFxfOnTunrKtXr54yPW9xpnKVpMpWHcteVlYWPXr0wMbGBmtra9577z2d2508eRIHBwcMDAwqPE9SQbIyIFWIfv365RtY5u2339Y5+11ISEiJv1hTU1NLnJ/69evzn//8h7Nnz3Lo0CHmzp3LX3/9VWC7sWPHEhsbS1RUFAEBAfj5+SnrGjduTHR0NNHR0dV+0Byp7qpuZc/AwICwsDBiYmKIiYnh4MGDREREFNjOzMyMdevWMX78+BIfQyo7WRmQSi0iIgI7OzseP37MX3/9RdeuXblw4YLObV1dXWncuHGpj3Xnzh3+9a9/oVar2bp1a4n3b9u2rTJJT6tWrTAxMdEZjf90Hv/66y/09PRKnWdJqig1qezp6ekpA2U9fvyYx48f6yxX5ubm2NjYoK8vv5aqgowZkErNycmJwYMHs2jRIu7du8e0adPo1KkTN27cKJf0c3JyOHToEGvXruXcuXOMHz+e0NBQTE1NgdxfNsuXLy+wn1arJSQkpNB0T506RVZWFhYWFjrXr169mk8++YTMzEzCw8OV5enp6Tg6OmJgYEBAQECBZldJqiw1sezZ2tryxx9/8PrrryuzVErVh6wMSGWyaNEinJycaNKkCatXry7XtD09PYmMjGTt2rW4u7sX+DXh4+ODj49PidK8c+cOEyZMYP369YX+6p8xYwYzZsxg27ZtLFmyhA0bNgBw5coV2rRpw+XLl+nfvz8ajYYOHTqU7uQkqYxqWtmLjo7m/v37jBw5kvj4eDnBUzUj22OkMklJSSE9PZ379+/z+PHjck172bJljBw5klmzZjFr1izOnDmTb31ISIgS0Pf0X2EfUhkZGQwdOpR3330XZ2fnZx5/9OjRfP/998rrNm3aANCuXTv69etHVFRUGc5OksqmJpW9PMbGxvTr14/Q0NByza9UdrIyIJXJtGnTWLJkCcOHD2fhwoXlmra1tTUrVqzg7NmzuLi44O/vj52dHWFhYUDur5O8gL6n/3Q1U2ZnZzNu3Dg8PT3x9vYu9JgXL15U/v/xxx9p3749kBs4lZmZCeR+CJ84cUKJQZCkqlBTyl5ycrISePjo0SN++uknunTpUq75lcpOPiaQSm39+vUYGRkxduxYpfvQyZMndW7bt29fzp49y4MHDzA1NWXDhg24ubkV6zj169dn3LhxjBs3jkuXLpGSklLivP7444/s37+fK1eusGXLFgCCg4OxtbVl0aJFODo6MnToUFatWsXBgwcxNDTExMREeUSQkJDA9OnT0dfXJycnBz8/P1kZkKpMTSp7t2/fxtvbm+zsbHJychg9ejSvvPIKQL6yFxUVxZAhQ0hNTWX//v0sWrSI2NjYEh9PKh1ZGZBKbfLkyUyePBnIncEtrynx6NGjBbbVtaw02rdvr/xaL4lXXnmF7Oxsnes++OAD5X9dQVEAzs7OxMXFlfi4klQRalLZs7a2LvSR2tNlz87OjuvXr5c6f1LZyMcEUrkzMjIiOjq6WH2YfXx8+Pnnn2nQoEEl5EySajdZ9qTSki0DUrlzdnYmMTGxWNsW1QVQkqSSkWVPKi3ZMiCVq+DgYG7dulXV2SjShg0bsLS0xNLSUokJ+LuoqCh69OiBRqNhxIgRZGRkALmBhP369eP5559n7ty5+fbZtm0bWq0WrVbLyy+/XKrR2iSpJGpLecuzYsUK9PT0SEtLU5bFxsbSs2dPrK2t0Wg0PHr0CIBNmzah0WiwsrJi0aJFFXkKdYKsDEjlqqgPp8Ke2Veme/fuERgYSEREBBEREQQGBur80n7ttdf49NNPiYuLo0+fPnz55ZdAbkDVBx98wCeffJJv+5ycHGbPns2RI0eIjY1Fo9Hw1VdfVco5SXVXbSlvADdv3uTHH3/MNzX3kydP8Pb2ZvXq1Zw9e5ajR49iaGhIcnIy8+fP5+jRo5w9e5azZ89y6tSpyjqtWklWBqQibdy4kW7dumFra8v06dPJyckBQKVSMX/+fLRaLb179yYtLY0dO3Zw+vRpxowZg62tLY8fP8bCwoLAwEB69erFnj172Lx5MxqNBrVazbx585TjqFQq5s6di729Pf379yclJYU//vgDBwcHZZv4+PhijQ9QlIMHD+Lu7o5KpUKlUuHu7q6zz/OlS5fo1asXkDuc665duwBo2LAhvXv35rnnnsu3vRCCnJwc0tPTEULw119/8cILL5Qpr1LdU1fLG4Cfnx8ffvhhvgGODh06hK2tLTY2NgA0a9aMevXqcenSJbp06UKzZs3Q09Ojb9++ShmVSkdWBqRCJSQksGPHDn755Reio6OpV68emzdvBuD+/fu89NJLyq/gjRs34uXlhaOjI1u3biU6OhojIyMAGjVqxMmTJ+nZsycBAQEcOXKE6OhoIiMjldnJ7t+/T8+ePYmMjMTV1ZXFixfTsWNHmjRpQkxMDJDbnWrSpEkF8hkUFKRzAJSAgIAC2yYlJWFmZqa8NjU1JSkpqcB2Xbp0Yf/+/QDs2rVL5zZPq1evHitXrkSj0dCmTRsSEhKYMmXKsy+yJP1/dbm8HTlyhOeffz5fZQRyZxvV19fH3d0de3t7Pv74YwAsLS05d+4cV69e5fHjx+zfv/+ZZVQqmgwglAp1+PBhIiIilHHEHz58SOvWrQF47rnnePnllwFwcHAgISGh0HTGjBkD5E6u4urqSosWLQDw9vbm2LFjeHp6YmBgwKhRo5Tlw4YNA2Dq1KkEBwfzySefsGvXLqKjowukHxAQoPODSBchRIFluoYlXr9+PbNnz2bx4sUMGzYMA4Oii0pWVhZff/01sbGxmJubM23aNP79738zZ86cYuVLkupqecvKyuLdd9/VORPokydP+Pnnn4mIiKBhw4YMHDgQBwcH3Nzc+Oqrr/Dy8sLIyAgnJ6d8cQZSycnKgFQoIQRTpkwhMDCwwLq8XyGQ+6v4yZMnhabTqFEjJb3C5gMobPnIkSMJDAzExcUFZ2dnjI2NC2wTFBSkDCT0NA8PD4KCgvItMzU15cSJE8rr69ev4+LiUmDfLl26cOjQIQAiIyM5duxYIWeXKzo6Gn19fWXyoxEjRrBx48Yi95Gkp9XV8nbz5k0uXLigtApcv34drVbLmTNnMDU1pU+fPjRv3hyAwYMHExkZiZubG0OHDlW6UC5fvlyp9EilIx8TSIVyc3Nj+/bt3L17F8gdhvfq1atF7tO4cWP+/PNPneu6d+/OkSNHSE5OJjs7m02bNtGnTx8g99fBzp07gdwo4ZdeegnI/UXk5ubGm2++qQyy8ncBAQE6h0b9+wcToDyzTEtLIy0tjdDQUNzd3Qtsl3fO2dnZLF26lBkzZhR53i+++CLx8fHcu3cPgJ9++onOnTsXuY8kPa2uljdzc3Pu3r1LYmIiiYmJmJqaEhsbS4sWLXB3dyc2NpaMjAyys7P5+eeflZE/79y5A+SW1cIeaUjFJysDUqGsrKxYvHgxAwYMQKvVMnDgQG7fvl3kPpMmTcLX11cJaHpa69atWbp0Kf369cPGxgYbGxuledLY2JioqCgcHBwICwvL11Vo/PjxGBoa4urqWuZzMjExYcGCBTg6OuLo6MjChQsxMTEBcnsQnD59Gsj9gOzcuTNdu3ZVuhfm6dixI//4xz/45ptvMDU15fz587Rp04Z3330XFxcXNBoNV65c4a233ipzfqW6oy6Xt8I0bdqUt99+GycnJ2xsbNBqtQwePBiAOXPmYGVlRd++fVmyZIkykZhUOnpC10Mdqc6JjIzEwcGBM2fOYG9vX+nHV6lUhT7z+/jjj3n48CHvvfde5WaqnFX1NZaqj6p+L9SF8gZVf51rEhkzIFVrQ4YM4cqVK4SHh1d1ViSp1pPlre6SlQGpWijsV8r3339fuRmRpDpAljfp72TMgFSjWFhYVHoXorS0NLy8vOjSpQtdu3bl119/rdTjS1Jlqooy5uvrS8uWLbG1tc23/N69ewwYMABLS0sGDBggh/iuQLIyIEnPMGfOHF5++WV+//13YmJi6Nq1a1VnSZJqFR8fH50jEwYFBTFw4EAuXrzIwIEDdfZYkMqHrAxIZZKens7gwYOxsbFBrVazdetWIHeecicnJ9RqNdOmTVMGH+nbty/+/v44OTlhZWXF6dOnGT58OJaWlixcuBCAxMRErK2tmTBhAlqtltGjR/Pw4cMCx9Y1dGt2djaTJk1CrVaj0Wj47LPPynR+f/75JydPnlRGEzQyMkKlUpUpTUkqidpexgB69+6t9DJ42t69e5Uug5MmTVJGUJTKn6wMSGUSGhpKmzZtiImJIT4+Hg8PDwBmzZpFREQE8fHxZGZmKkP7AhgaGhIREYGvry+enp6sXr2auLg41q9frzQDnjt3jpkzZxIbG4tKpWLVqlX5jlvY0K3R0dEkJSURHx9PXFyczr7SISEhOodT9fHxKbDtpUuXaNGiBZMnT8bOzg5fX1/S09PL8xJKUpFqexkrSnJysjKYUIsWLUhJSSnR/lLxycqAVCYajYbDhw/zzjvvcPz4cWXEsvDwcLp3745GoyEsLIyzZ88q++SNGmZjY4NGo6FVq1Y0aNCADh06KOOLt2vXjh49egC5w6UeP34833GfHrrV1taWw4cPc+nSJdq3b8/ly5d56623CA0NpUmTJgXy7OPjo3PQFF3zuz958oSIiAhef/11oqKiaNKkiWyqlCpVbS9jRSlspESp/MneBFKZdOrUidOnT3PgwAEWLFhA//798ff3Z+bMmURERGBmZsaSJUuUOcghdxpgAH19feX/vNd5w6w+60OgqKFbo6OjOXjwIKtWrWLbtm2sW7cu3/qQkBCWL19eYD+tVlvgw8rU1BRTU1O6d+8OgJeXF0uWLCkyb5JUnmp7GStKs2bNuHv3Li1atODu3bs0a9as2PtKJSMrA1KZ3LhxAxMTE7y9vVGpVKxdu5ZHjx6hp6dHy5YtSU9PZ/v27QwfPrxE6V6+fJlTp07RrVu3fMOl5nFzc2PEiBHMnj1baT5MT0+nYcOGGBkZMXLkSDp37oy3t3eBtH18fIrdXPnCCy9gZmbG+fPn6dy5M4cPH1aGQ5WkylDby1hRhg4dSnBwMH5+fgQHBysjKErlT1YGpDKJi4vDz88PfX19jIyMWLlyJSqViqlTp6JWq2nbti1OTk4lTtfa2po1a9bg6+tLp06dCswN8PTQrTk5ORgaGrJ69WqMjIyYPHkyOTk56OnpsWzZsjKf47///W+8vb3JzMykXbt2BAcHlzlNSSquulDGJkyYQFhYGCkpKZiamrJkyRImTZpEQEAAo0eP5j//+Q/m5uZs3769zMeSdJPDEUtA9Rq2MzExEU9PT53Tp9Zk1ekaS1Wrqt8LtbWM/V1VX+eaRAYQSpIkSVIdJysDUrVjYWFR63+xSFJVkmVM+jtZGZAkSZKkOk5WBiRJkiSpjpOVAancJCYmFphopDIcPXoUY2NjZaAVgA0bNmBpaYmlpSUbNmwodlr79u1DT0/vmU2oKSkpDBgwgK5du6JWq/nyyy+VdV988QXm5ubMnTu3pKciScVWk8vbyZMncXBwwMDAoNhDDG/atAmNRoOVlRWLFi1SlsvyVj5k10KpVujXr5/yoXLv3j0CAwM5ffo0AI6OjgwdOpSmTZsWmUZGRgafffYZ3bp1e+bx9PX1CQwMpEePHqSnp+Po6MjAgQPp1KkTs2fPpkmTJvKZrFRrlbW8mZmZsW7dOj799NNiHS85OZn58+cTGRmJiYkJXl5eyhgJsryVD9kyIBVq3rx5rFy5UnkdEBDAV199xYMHD3Bzc8Pe3h6NRsPevXsL7BscHJyvpt63b1+lsB46dAhnZ2fs7e0ZNWoUDx48KNd8Hzx4EHd3d1QqFSqVCnd3d50zov1dYGAgc+bM4bnnnnvmtk2bNlWGcm3UqBGWlpbKMK+SVBp1qbyZm5tjY2ODvn7xvoIuXbpEly5daNasGXp6evTt25ddu3aVR/al/09WBqRCjRkzhm3btimvd+3ahZeXFw0aNGD37t1ERkYSHh6On58fxR2uIjk5maVLlxIWFkZkZCSOjo6sWLGiwHZBQUE6JzoJCAh45jGSkpIwMzNTXpuamj7zi/r333/n7NmzeHp6Fus8nnblyhWioqJwdHQs8b6SlKculbeSsrS05Ny5c1y9epXHjx+zf/9+WfkuZ/IxgVQoe3t7bt68ya1bt7h27RpmZma0atWKrKws3nnnHX7++Wf09fW5du0at2/fLlaav/32G2fPnsXZ2RmAx48f4+LiUmC7gICAYn0Q6aLrg/JZ47DPnTtX54fks2RkZODl5cVnn31G48aNS7y/JOWpS+WtpJo2bcpXX32Fl5cXRkZGODk5kZaWVq7HqOtkZUAqkpeXFzt27ODKlSuMHTsWgO+++46UlBQiIyMxNDSkY8eO+SZJATAwMCAnJ0d5nZmZCeR+cHh4ePDtt98WedygoCC2bNlSYLmHh8czZw00NTXlxIkTyuvr16/r/AB8WkREBO7u7gDcunWLQYMG8cMPP2BnZ1foPjk5OYwfP57hw4fj5eVVZPqSVBx1pbyVxtChQ5WgxeXLlytTG0vlQz4mkIo0ZswYtm7dyu7duxkxYgQA9+/fp1WrVhgaGnLs2DEuXbpUYD8LCwuioqIQQnDp0iViYmIA6NGjR7590tPTuXjxYoH9AwICdE6BWpzpg/OeWaalpZGWlkZoaKjyRT9//nx2795dYJ+UlBQSExNJTEykR48eHDhwQKkIdOnSRedx3nzzTV544QXeeeedZ+ZJkoqjrpS3ohRW3u7cuQPA3bt3Wb9+PZMmTSpRulLRZMuAVCStVktycjKWlpbK9KGvvvoqQ4YMUeY51zWLX69evTAzM0Oj0WBnZ4dWqwWgRYsWrF+/nrFjxyq/bpYtW4alpWW55dnExIQFCxYoz/AXLlyIiYkJAPHx8fm6RD1LSkqKzmbQ+Ph4Vq5ciVqtVrp3LV26lEGDBpX9BKQ6q66Ut6ioKIYMGUJqair79+9n0aJFxMbGFlreAObMmUNMTAx6enosXbqUNm3alNs5SLIyIBVDQkJCvtfNmzfn119/1bltXgSznp4emzZt0rmNq6srp06dKtc8/t2UKVOYMmVKgeXZ2dn07NmzyH2PHj2q/H/q1ClmzpxZYBu1Wl3sIC5JKom6UN7s7Oy4fv16geWFlTeAzZs3lz2jUqHkYwKpxjMyMiI6OrpYv/gPHDhQorRffvllZs+eXaJ9vvjiC5YtW0aTJk1KtJ8k1QSyvNVOsmVAqvGcnZ1JTEys6mwoZs+eXeIPNEmqKWR5q51kZUDK5+9NlFL5ybu2tfUaN2/eHHNz86rORo1SW98L1YW8vsWnJ+SDTwm4evUqXbt2JSMjo6qzUqvp6+vn6wJWmzRs2JCEhARZISgGWd4qj3xfFo+sDEiKq1evkpycXKJ9srKy6Nu3L48ePeKFF14gLS2NR48esXPnTiwsLComoyWUkJCAt7c3gYGBtGvXrkrzolKpaN26dZXmoSLkXeMzZ85gb29f1dmpEUpT3gA+/fRTNm3aRP369TExMeHmzZt4eXkxf/78Cshl5frrr7/o378/enp6tGrViuvXr6Onp8ehQ4eUHgolJVusikc+JpAU5ubmJS4069atU7osmZub8+abbzJ8+HC6du1aEVksk0GDBskvKqnaKE15A/j++++B3DkxXn75ZYYNG8bAgQNp0KBBeWexSly4cIE9e/awd+9ekpKSEEJw6NAhPv7446rOWq0mKwNSmQwfPpzLly8zY8YMXnzxxarOjiTVeitWrKBp06YMGTKEevXqVXV2yl27du146623eOutt0hOTmblypVMnDixqrNV68nKgFQmTZs2JTAwsKqzIUl1xuTJk6s6C5WmefPmLFq0qKqzUSfIykAJlfY5n1R88hmflEeWt4pXEeVN3rfKUa73TkjFduXKFdGwYUMByL8K/GvYsKG4cuVKud23M2fOCECcOXOmzGmFh4eLJk2aiCFDhgghhDhx4oRwdHQU1tbWwt7eXhw9elTZdsKECaJp06Zi9+7dZT7us7Rt21ao1WphY2MjHBwcdG7z6NEjMXr0aNGxY0fRvXt3cfnyZWWdvr6+sLGxETY2Nsq5lUR5XuM8srzVzPIm71vNvHeyZaAEkpOTycjIYOPGjdUyQK42yItKT05OrratA/369WPPnj1Abu+AnTt3Ym5uzu+//46Hh4cyIEtISEiJJ1NJTU2ladOmpcrX8ePHUalUha7/5ptvaN68ORcvXmT79u3MmzePrVu3AtC4cWNlaNvqQpa3ilcR5U3et8pR3vdOVgZKoWvXrjIqvZaLiIhg2rRp/Pe//yUzM5Nu3bqxd+/eAttZW1sr/3fu3JkHDx6QlZWFoaFhsY91584dQkJCCA4OZtasWcyYMaNczuHv9u7dy5IlSwAYMWIEs2bNQghR7nPPlzdZ3momed9qFlkZkCQdnJycGDx4MIsWLeLevXtMmzaNTp06cePGjUL32blzJ3Z2dsWqCOTk5HDo0CHWrl3LuXPnGD9+PKGhoZiamgK5rQrLly8vsJ9WqyUkJKTAcj09PVxdXdHT02P69OlMmzatwDZJSUmYmZkBUK9ePVQqFSkpKTRv3pz09HQcHR0xMDAgICAAT0/PZ56DJEm1h6wMSFIhFi1ahJOTE02aNGH16tVFbnv+/Hn8/f05dOhQsdL29PQkMjKStWvX4u7uXuDXuY+PDz4+PsXO68mTJ2nTpg137tzBw8ODLl268NJLL+XbRugYXyzvuFeuXKFNmzZcvnyZ/v37o9Fo6NChQ7GPL0lSzSZnLaxGgoODuXXrVlVno0gbNmzA0tISS0tLNmzYoHObe/fuMWDAACwtLRkwYACpqamVnMvykZKSQnp6Ovfv3+fx48eFbnfnzh08PT355ptv6NixY7HSXrZsGSNHjmTWrFnMmjWLM2fO5FsfEhKCra1tgb/CKgh5c7u3bNmSESNG6Jyy1tTUlGvXrgG5U8umpaUpo7rl7d+uXTv69etHVFRUsc6jJpPlreaS9678ycpANVLUGzw7O7uSc1PQvXv3CAwMJCIigoiICAIDA3W+eYOCghg4cCAXL15k4MCBBAUFVUFuy27atGksWbKE4cOHs3DhQp3bZGRkMHToUN5991369etX7LStra1ZsWIFZ8+excXFBX9/f+zs7AgLCwNyWwaio6ML/Ol6RJCens5ff/2l5OfgwYOo1eoC2w0dOpTg4GAAdu3aRZ8+fdDT0yM1NZXMzEwgtwJ04sQJrKysin0uNZUsbzWXvHflT1YGKtDGjRvp1q0btra2TJ8+XZmgRqVSMX/+fLRaLb179yYtLY0dO3Zw+vRpxowZg62tLY8fP8bCwoLAwEB69erFnj172Lx5MxqNBrVazbx585TjqFQq5s6di729Pf379yclJYU//vgDBwcHZZv4+HicnZ3LdD4HDx7E3d0dlUqFSqXC3d2d0NDQAtvt3btXiaKfNGmSEnlfk6xfvx4jIyPGjh3LwoULCQ8P5+TJkwW2W7duHTExMXz88cfKr/c7d+4U+zj169dn3LhxHD58mJ07d2JsbFzivN6+fRsXFxdsbGxwcnLCw8MDDw8PIPdRx759+wCYOnUqd+7coWPHjnzyySfKB09CQgKOjo7Y2NjQt29f/Pz8amRlQJa3mlve5L2r+nsnKwMVJCEhgR07dvDLL78QHR1NvXr12Lx5MwD379/npZdeIjY2Fo1Gw8aNG/Hy8sLR0ZGtW7cSHR2NkZERkDv++MmTJ+nZsycBAQEcOXKE6OhoIiMjlTfO/fv36dmzJ5GRkbi6urJ48WI6duxIkyZNiImJAXK/3HR1cwsKCtLZHB0QEFBg26cD0CC32TkpKanAdsnJybRo0QKAFi1akJKSUqZrWRUmT57Mzp07ATA0NOTMmTP06tWrwHazZs3i4cOH+X69t2zZslTHbN++PU5OTqXaLyYmhpiYGM6ePcuCBQuUdR988AFDhw4FoEGDBmzfvp0//viDU6dO0b59eyB3fvq4uDhiYmKIi4tj6tSppcp/VZLlreaWN3nvqse9kwGEFeTw4cNERETg6OgIwMOHD5XZ6p577jlefvllABwcHIqcc3vMmDFAblc3V1dX5Y3j7e3NsWPH8PT0xMDAgFGjRinLhw0bBuT+EgwODuaTTz5h165dOvuRBwQE6Hwz61JUANqzltUWRkZGREdHM3ToUOUXd2F8fHw4fvw4Y8eOraTc1V2yvNVc8t5VD7IyUEGEEEyZMkXnuP15NVnI7eL15MmTQtNp1KiRkl5hb5zClo8cOZLAwEBcXFxwdnbW2QQdFBTEli1bCiz38PAo8PzK1NSUEydOKK+vX7+Oi4tLgX2bNWvG3bt3adGiBXfv3qVZs2aFnl9N4+zsrAwq9Cy6nu9LFUOWt5pb3uS9qx73Tj4mqCBubm5s376du3fvArmBWVevXi1yn8aNG/Pnn3/qXNe9e3eOHDlCcnIy2dnZbNq0iT59+gCQlZWlNGlv2rRJ6VL23HPP4ebmxptvvlno5CYBAQE6A9V0BbLkPfdKS0sjLS2N0NBQ3N3dC2z3dKBacHCwUvuu6WpLBLOPj4/SxNmuXTtsbW0B2LNnDzY2Nmi1Wnr16kVcXByQ+94dMGAAXbt2Ra1W8+WXX1bW6RSbLG/BQM0sb/LeBQPV4N6Vy6DGdURJx1/fsmWLsLGxERqNRtjb24tTp04JIYQwNjZWtlm/fr2YM2eOEEKIHTt2iE6dOgkbGxuRmZkp2rZtK1JTU5Vtv/vuO6FWq4W1tbXw8/NTlhsbG4v58+cLe3t74ebmJpKTk5V1x48fF+bm5iI7O7v0J/6Ub775RnTo0EF06NBBrFu3Tlk+depUERERIYQQIjk5Wbi6uoqOHTsKV1dXkZKSUuz0K2KM+/JKs0+fPiIqKkrnuidPnpQp7fKQkpIiOnToIFJTU0Vqaqro0KGDuHfvXpH7+Pv7i8WLFwshhDh16pS4e/euEEKIn376STg7OwshhLh375749ddfhRBCPHjwQHTp0kWcP38+XzrV4b7J8lZzy5u8d1V/72RloAQqouCUh6cLzN999NFH4v3336+8zJRRZX44ffvtt8LJyUnY2NiIadOmKR8CxsbGIiAgQGg0GuHi4iJSU1PF9u3bRaNGjQp8AH3wwQfC2dlZ7NixQ2zatEn5APL391eOY2xsLObMmSPs7OyUD6CLFy8Ke3t7ZZu4uDjRs2fPMp3npk2bxBtvvKG8fuONN8SmTZsK3T4nJ0e0bdu2wBe7ELkVC3Nzc537DRkyRBw5ciTfsurypVIZZHmr/DTLi7x3hZOPCWqxIUOGsHHjRmbNmlXVWal26nIEc55ff/2VZs2a0alTpwLr1q1bx8CBAwssv3LlClFRUUqwl/R/ZHmrueS9kwGEtUJaWprO5d9//33lZqQGqcsRzHk2b96ss6fD8ePHWbNmDb/88ku+5RkZGXh5efHZZ5/RuHHjYuWpNpLlreaS965wsmWgFrKwsCj0TV9RfH19admypRKMVt2J/x/BnBcEdP78ed59912gciOYDxw4wN69e4uMYC5uy8DTww1DbgRz3jDDf5ednc3OnTuVykyeCxcuMHnyZHbt2pUvsjknJ4fx48czfPhwvLy8dKZZV1VFefv000+xtrbG2tr6mfNmSIWTn5X/R1YGpHLh4+Ojc4St6qouRzADhIeH0759+3zzoD89x8LTUzMDvPnmm7zwwgu88847RV4jqeLFxcUREhLC6dOniYqK4ttvvy1yNk2peqmun5WyMlAJ0tPTGTx4MDY2NqjVarZu3Qrkjg7n5OSEWq1m2rRpSjNv37598ff3x8nJCSsrK06fPs3w4cOxtLRUxshPTEzE2tqaCRMmoNVqGT16NA8fPixwbF3DfGZnZzNp0iTUajUajYbPPvuszOfYu3dvZdKbmsDKyorFixczYMAAtFotAwcO5Pbt20XuM2nSJHx9fZUhUJ/WunVrli5dSr9+/bCxscHGxkZ5HGBsbExUVBQODg6EhYWxaNEiZb/x48djaGiIq6trmc/JxMSEBQsW4OjoiKOjIwsXLlTuyWuvvcbp06eVbbds2VLgEcHHH39MUlISc+bMwdbWVnmEEh8fz8qVKzl58qTSMnHgwIEy57ei1PbylpCQQI8ePXjuuecwMjKiR48ezxwAq6ao7fcOqvFnZbmEIdYRpY3e3LFjh3jttdeU12lpaUIIka8biY+Pj9i3b58QIrcL2zvvvCOEEGL58uXixRdfFLdu3RIPHz4Ubdq0Effu3ROXL18WgNLly9fXV3z66adCCKF0szl37pwYNmyYyMrKEkII8frrr4uNGzeK06dPi/79+yvHfrpLTp4NGzYIGxubAn8TJkwo9DwvX74sbGxsSnRt/q42RjfXpgjmwlSn+1bby9u5c+dE586dxb1798Sff/4ptFqtWLhwYYmuUZ7qdN+EqP33Lk91/KyULQOVQKPRcPjwYd555x2OHz+uPBsODw+ne/fuaDQawsLCOHv2rLJP3njyNjY2aDQaWrVqRYMGDejQoYMSId6uXTt69OgB5AamHT9+PN9xnw6Ss7W15fDhw1y6dIn27dtz+fJl3nrrLUJDQ2nSpEmBPJdk1jypdGQEc8Wo7eWta9eu/POf/8TNzY1hw4bRrVs3DAxqRyx4bb931VnteAdVc506deL06dMcOHCABQsW0L9/f/z9/Zk5cyYRERGYmZmxZMkSHj16pOxTv359APT19ZX/817nBbQ9a1xrUcQwn9HR0Rw8eJBVq1axbds21q1bl299SEgIy5cvL7CfVqutcW/yqiYjmCtXXShvr732Gq+99hoAs2fPxtLSssi81RR14d5VV7IyUAlu3LiBiYkJ3t7eqFQq1q5dy6NHj9DT06Nly5akp6ezfft2hg8fXqJ0L1++zKlTp+jWrVu+wLQ8bm5ujBgxgtmzZyszYqWnp9OwYUOMjIwYOXIknTt3xtvbu0DaPj4++Pj4lOm8pZKxsLAgOjoalUpVqcds3Lgx9erVw8DAIF9cQU1VF8rbnTt3aNmyJf/73/8ICwvjo48+KtG5VFd14d5VV7IyUAni4uLw8/NDX18fIyMjVq5ciUqlYurUqajVatq2bVuqqWutra1Zs2YNvr6+dOrUiRkzZuRb/3SQXE5ODoaGhqxevRojIyMmT55MTk4Oenp6LFu2rMznOGHCBMLCwkhJScHU1JQlS5boHERHqn6OHz9eqRWQilYXytvIkSNJSUmhQYMGrF27lueee67MaVYHdeHeVdvPynKJPKgjqjoQ7WnlEYBSHVWngKYHDx6IQYMGCa1WK6ytrcWWLVuEEEIsXrxYODo6Cmtra+Hr6ytycnKEELnBTH5+fsLR0VF07dpVRERECE9PT9GxY0exYMECIUTufbOyshLe3t5Co9GIUaNGiYyMDCGEyDe+uq6hkp88eSImTpworK2thVqtFsuXLy/ztfn7mO6lVZ3uW0WQ5a1q0ywLee+KRwYQSlIhQkNDadOmDTExMcTHx+Ph4QHArFmziIiIID4+nszMTPbv36/sY2hoSEREBL6+vnh6erJ69Wri4uJYv349qampAJw7d46ZM2cSGxuLSqVi1apV+Y5b2FDJ0dHRJCUlER8fT1xcnM6xCUJCQnQOUlRYM6aenh6urq44ODjwn//8p7wunSRJNYysDNRQec+XpYpTFyKbT548SWRkJD/++COrV6/m2LFjZb9wtZAsbzWXvHfFI2MGJKkQdSGyOW+44pYtWzJixAhOnTpVILhKkqTaT1YGJKkQtT2yOT09nZycHBo3bkxGRgYHDx5kwYIFJToXSZJqB/mYoJIlJiZWyQQVR48exdjYWGnGBtiwYQOWlpZYWlqyYcOGZ6aRmZnJmDFjsLS0pEePHiQmJj5zn/feew8zM7MC0epffPEF5ubmzJ07t4RnUnni4uKU4Uk/+OADFi5cmC+yediwYWWKbLaxsSElJaXIyOanh0pOSkqib9++2NraMmHChDJHNt++fRsXFxdsbGxwcnLCw8NDiYuoLepaedu0aRMajQYrK6t8w17XhPL2tJp8306ePImDgwMGBgbKNObPcubMGbRaLR07dmT27NnKcMuVet/KJQyxjiiP6M2qimwNDw8Xw4YNU16npKSIDh06iNTUVJGamio6dOgg7t27V2QaX331lXjjjTeEEEJs27ZNjB49+pnH/e2338SNGzd0Dsm7fv16MWfOnHzLant0s4xsrtw061J5u3v3rjA3NxfJyckiJydHjBgxQvz3v/9V1tek8laT79uVK1dEdHS0mDBhgti9e3exjtutWzdx6tQpIYQQgwcPFgcOHFDW6bpvQsjeBNXOvHnzWLlypfI6ICCAr776igcPHuDm5oa9vT0ajYa9e/cW2Dc4ODhfja9v375KoMuhQ4dwdnbG3t6eUaNG8eDBg3LN98GDB3F3d0elUqFSqZQZ74qyd+9epT/siBEjOHr0qFKDLUz37t1p3bp1eWVbquNkeSu8vF26dIkuXbrQrFkz9PT06Nu3L7t27SrP0yi1unTfzM3NsbGxQV+/eF+vN2/eJD09XWllnDhxYrFbFMqTrAyU0ZgxY9i2bZvyeteuXXh5edGgQQN2795NZGQk4eHh+Pn5PfOLM0/y/2vvzoOiONMHjn9RQTMxOiKEkJpBViWJCAPCeCEbFaOAiUiMVxI0inHXirvErY1xPDZugiXUpsoYq6LW5gCPaDxWTNZVzGqMRzYJYzgUJaUJisp6DYpGPCAw+4c/+xcywwDOMDAzz6eKKrun3+63+7Gnn+l++31NJpYuXcqePXvIz89Hr9fz7rvvWizXnLHuf628vBytVqtMazQapbV7U8q0b98etVpNRUVFk/ZJ3CUtm+0j51vD51tISAjHjx/nzJkzVFdXs2PHjka34SyeFLfmKi8vR6PRtOg2mkIaENopKiqK8+fPc+HCBc6ePYtWqyUgIICamhoWLFjA/v37adeuHWfPnm10iNx7vvnmG44dO0ZMTAwA1dXVxMbGWixnMBia9B/aGmsnXFNauTe3jBCOJOdbw2W6devGe++9x/jx4/Hx8aF///4NjovhbJ4Ut7a4jaaQZMABxo8fz9atWykrK1PGiP/444+pqKggPz8fb29vevfuXe8VNIAOHTpQV1enTN+5cwe4+58jISGBdevW2dxuZmYmn3zyicX8hIQEMjMzbZbVaDQcOnRImT537pzVE+nXZc6ePcsjjzxCbW0tlZWVbXNcbuHW5HxrWFJSktL4bdmyZfj7+9tc3pk8JW7NpdFoOHfuXL1t3Hvl15nkMYEDTJo0iU2bNpGTk8O4ceMAuHbtGgEBAXh7e3PgwAFKS0stygUHB1NQUIDZbKa0tJSioiIABg0aVK9MVVUVJ0+etChvMBisdjDT2H9wQHn2VVlZSWVlJbm5ucTHxwMwf/58cnJyLMokJSWRnZ0N3L3NN3ToUCWDfeKJJ5pwpFyXK7duvuezzz7Dy8urSY8p2vJbIHK+NXy+Xbp0CYDLly+TlZXVNvq8/z+eEjdbrMUtMDAQlUqF0WgE7p7fY8eObdZ6HUHuDDiATqfDZDIREhJC9+7dAXjxxRcZM2aM0otcaGioRbkhQ4ag1WoJDw+nX79+6HQ6APz9/cnKymLy5MlKlpyRkeHQYUp9fX1ZuHAher0egEWLFim/OoqLi+tdfO6ZMWMGU6ZMoXfv3vj6+irZdkVFRYPP+ebPn8+6deu4fv06Go2GWbNmsWjRIofthycYPny40qDoypUrpKenK6ML6vV6kpKS6Natm8113Lx5k3feeYcBAwY0aZujR49m1qxZ9OnTp978tLQ0unTp0qrtHuR8a/h8e/XVVykqKsLLy4ulS5e2yi/MhnhK3AoKChgzZgxXr15lx44dvPHGGxw5csRm3FauXMn06dO5efMmiYmJJCYmOmwfmkqSAQcpKSmpN+3n58fXX39tddl7X6ReXl5s2LDB6jJxcXHk5eU5tI6/lpqaSmpqqsX82tpaBg8ebDG/U6dObNmyxWJ+Xl4es2fPtrqNjIwMh4z05Wjz5s2jR48evPLKK8DdXw9arZaXXnqJsWPHcvXqVWpqaliyZIlFlp6dnU1hYSHLly8H7rZuXr58OZGRkXz++ef89a9/5fbt2/Tq1YusrCw6d+7ssHr/snUz/P8vl+eff95mufT0dF599VWlzo0ZOHCgnTVtWXK+WT/fNm7caH9FW5AnxK1fv371bvvfYytuer2eo0eP2l9ZO8hjAg/h4+NDYWGh1Uz213bu3NmsdScmJpKWltasMitWrCAjI8Nq//rO4Emtm7///nuOHTtGcnJyk/ZD2E/ON9fkyXGTOwMeIiYmpkk9mDlLWlpas08MR/Kk1s1z5syxmpSIliPnm2vy5LhJMnAffn2rSziOM4+tp7RuNhqNSqOnCxcuMHr0aP71r3/Rr18/m+XaCjnfWk5LHluJW8ty+PF1SD+GHqKsrMysUqnMgPy14J9KpTKXlZU5LG4NddtZVFRkjo2NNffq1ctsMpnMZrPZvHz5cqXrz/3795u9vLzMp06dqtc96sGDB82xsbHmuro6848//mh+4IEHzAUFBeZLly6Zg4KCzD/++KPZbDabb9y4YT5x4oRddbfWPWrPnj2V7lF79uxprqioMJvNZrPBYDBv27bN5vqGDh1qLigoUKYff/xxm8u3ZjfScr655vkmcXPN2MmdgWYICgqipKQEk8nU2lVxa35+fgQFBbX4djyldXND2vpbIHK+OYejzzeJm/M4MnaSDDRTUFCQUy5Uwjk8oXXzL3355ZfKv13hLRA531yTxM31yNsEQrRhnty6WQjhPHJnQIg2zJNbNwshnEeSAeExpHVzy5FjK4Rrk2RAuD0/Pz9UKhUpKSmtXRW3plKp8PPza+1qCCHug5e5oebEQriRM2fONLt1c21tLUOHDuXWrVt07dqVa9eu0bFjRz744AOrbxm4mgMHDvDaa69RW1tLly5duH79OoGBgezYseO+1uest0CEEI4nyYAQDfj2228ZNGgQKpWKiRMnkpyczMiRI1GpVK1dNYe5du0au3btIicnh5ycHGpqavjpp58cOp6CEKLtk2RAiAaYzWby8/OJjIykffv2rV2dFnfnzh2+//57IiIiWrsqQggnk2RACCGE8HDSgFAo7ue5umielniuLnFredIeQrg7SQYEcPeC0qdPH27evNnaVXFrKpWKkpISh11YJG7O4ei4CdHWSDIgADCZTNy8eZP169fTp0+f1q6OWyopKSElJQWTyeSwi4rEreW1RNyEaGskGRD19OnTh6ioqNauhmgmiZsQwh4yNoEQQgjh4SQZEEIIITycJAPCKbKzs7lw4UJrV8OmNWvWEBISQkhICGvWrLG6zJUrVxg5ciQhISGMHDmSq1evOrmWzuUKcXv66adRq9UkJyc3uIynxU2I5pJkQDiFrYtKbW2tk2tj6cqVK6Snp2M0GjEajaSnp1u9YGRmZjJq1ChOnjzJqFGjyMzMbIXaOk9bjxvAn//8Z9atW2dzGU+LmxDNJcmAuC/r169nwIABREZG8vvf/566ujoA1Go18+fPR6fT8dvf/pbKykq2bt3K4cOHmTRpEpGRkVRXVxMcHEx6ejpDhgxh+/btbNy4kfDwcMLCwpg3b56yHbVazZw5c4iKiuKpp56ioqKCH374gejoaGWZ4uJiYmJi7Nqf3bt3Ex8fj1qtRq1WEx8fT25ursVyn376KdOmTQNg2rRpbN++3a7tOpu7xQ0gLi6Ohx56yOYyrh43IVqaJAOi2UpKSti6dSv/+c9/KCwspH379mzcuBG429f9k08+yZEjRwgPD2f9+vWMHz8evV7Ppk2bKCwsxMfHB4AHH3yQr776isGDB2MwGPjiiy8oLCwkPz9f+bK+du0agwcPJj8/n7i4ON5880169+5Nly5dKCoqAiArK0v5ov+lzMxMIiMjLf4MBoPFsuXl5Wi1WmVao9FQXl5usZzJZMLf3x8Af39/Kioq7DqWzuSOcWsqV46bEM4grxaKZtu7dy9GoxG9Xg/ArVu3CAwMBOCBBx4gMTERgOjoaJvj3E+aNAkAo9FIXFyc8mWdkpLCgQMHSE5OpkOHDkyYMEGZP3bsWABmzJhBdnY2b7/9Ntu2baOwsNBi/QaDockXEGu9cnt5eTVpnqtwx7g1lSvHTQhnkGRANJvZbCY1NZX09HSLz+79egRo3749P//8c4PrefDBB5X1NfRl3dD85557jvT0dGJjY4mJiaFr164Wy2RmZvLJJ59YzE9ISLB4ZqzRaDh06JAyfe7cOWJjYy3Kdu/encuXL+Pv78/ly5fp3r17g/vX1rhj3JrKleMmhDPIYwLRbCNGjGDLli1cvnwZgIqKCs6cOWOzzEMPPcT169etfjZw4EC++OILTCYTtbW1bNiwgaFDhwJQU1PDP/7xDwA2bNjAk08+Cdz9JTtixAj++Mc/Mn36dKvrNRgMFBYWWvxZu6DcayNQWVlJZWUlubm5xMfHWyyXlJREdnY2cLdx3b1fvK7AHePWVK4cNyGcQZIB0WyhoaG8+eabjBw5Ep1Ox6hRo7h48aLNMtOmTWPmzJlKQ7RfCgwMZOnSpQwfPpyIiAgiIiKUL+uuXbtSUFBAdHQ0e/bs4Y033lDKvfDCC3h7exMXF2f3Pvn6+rJw4UL0ej16vZ5Fixbh6+sLwMsvv8zhw4eBuxeq3NxcQkJCyM3Ndfjt7JbkjnEDGDZsGBMmTGD37t1oNBr27t0LuE/chHAGGcJYAJCfn090dDTfffddm+rWVq1WU1lZafWzv/3tb9y6dYvFixc7t1L3qSWOscSt5bXVYyyEI0mbAeGSxowZQ1lZGfv27WvtqohmkLgJ0TZJMiDatIZ+Xf7zn/90bkVEs0jchHAt0mZAuJTg4OAGLzQtoaamhkGDBhEREUHfvn1d5tZ2W+PsuJ04cYLY2Fj69u1LREQEW7duddq2hXBFcmdACBs6dOjAnj176Ny5Mz///DOxsbE888wz9O/fv7WrJmzo2LEjf//73wkNDeXixYtER0cTHx/faE+FQngquTMg7FJVVcXTTz9NREQEYWFhbNq0CYC33nqL/v37ExYWxu9+9zulU59hw4bx+uuv079/f0JDQzl8+DDPPvssISEhLFq0CIDTp0/Tt29fpkyZgk6nY+LEidy6dcti29a61q2trWXatGmEhYURHh7OO++8Y9f+eXl50blzZwCqq6uprq52iw5s3D1uPXr0IDQ0FICAgAB8fX0xmUx2rVMIdybJgLBLbm4ujz76KEVFRRQXF5OQkADAH/7wB4xGI8XFxdy5c4cdO3YoZby9vTEajcycOZPk5GRWr17N0aNHycrKUgYHOn78OLNnz+bIkSOo1WpWrVpVb7sNda1bWFhIeXk5xcXFHD161Oq77GvXrrXa3e3UqVMb3M/IyEgefvhhRowYofTg58o8JW4AeXl51NTUEBwcbOdRE8J9STIg7BIeHs7evXtZsGABBw8eVHqU27dvHwMHDiQ8PJw9e/Zw7NgxpUxSUhIAERERhIeHExAQQKdOnejVq5cyHsBvfvMbBg0aBNztzvbgwYP1tvvLrnUjIyPZu3cvpaWl9OzZk1OnTvGnP/2J3NxcunTpYlHnqVOnWu3UZu3atQ3u572LVUFBAcXFxfYdtDbAU+J26dIlpkyZwocffugWd3SEaCnSZkDY5bHHHuPw4cPs3LmThQsX8tRTT/H6668ze/ZsjEYjWq2WJUuWcPv2baVMx44dAWjXrp3y73vT97rBbeyL21bXuoWFhezevZtVq1axefNmPvroo3qfr127lmXLllmU0+l0Ni8sXbt2Zfjw4eTm5hIWFmazfm2dJ8Tt5s2bJCUl8Ze//MUhoyMK4c4kGRB2+e9//4uvry8pKSmo1Wo++OADbt++jZeXFw8//DBVVVVs2bKFZ599tlnrPXXqFHl5eQwYMKBed7b3jBgxgnHjxpGWlqaMQldVVYVKpcLHx4fnnnuOxx9/nJSUFIt1T506tdFby/eYTCbat29Pt27duH37Nv/+97957bXXmrUvbZG7x622tpbnn3+e5ORkq+sSQtQnyYCwy9GjR5k7dy7t2rXDx8eHlStXolarmTFjBmFhYfTo0eO+Wt737duX999/n5kzZ/LYY48xa9asep//smvduro6vL29Wb16NT4+PkyfPp26ujq8vLzIyMiwa/8uXrxISkoKtbW11NXVMXHiRJ555hm71tkWuHvcdu3axY4dOygrK1MGPcrOziYyMtKu9QrhrqQ7YgG0rS5XT58+TXJystXhbV2Zu3dHLHETwnVJA0IhhBDCw0kyINqc4OBgt/t16QkkbkK4LkkGhBBCCA8nyYAQQgjh4SQZEA5z+vTpVmmt/eWXX9K1a1elUxyANWvWEBISQkhICGvWrGl0He+//z46nY5+/foRGxvL8ePHGy3z3XffodPp6N27N2lpaUrXvStWrCAoKIg5c+bc9z45kyvH7auvviI6OpoOHTqwffv2Jm138eLFaLVa1Gp1vfmuFjchHEmSAeEWhg8fzmeffQbAlStXSE9Px2g0YjQaSU9PV7rLbcjkyZM5cuQIBQUFGAwG5s6d2+g2X3nlFT788EN++OEHSktLyc3NBSAtLY233nrL/p3yAPbGTavV8tFHH/HCCy80eZujR48mLy/PYr7ETXgySQZEg+bNm8fKlSuVaYPBwHvvvceNGzcYMWIEUVFRhIeH8+mnn1qUzc7OrvcLa9iwYUrjss8//5yYmBiioqKYMGECN27ccGi9d+/eTXx8PGq1GrVaTXx8vHKhbsgvR7P76aefGu1J7/z581RVVSnv4r/00ktN/mXa0jwpbkFBQURERNCuXdO/ygYOHEhgYKC91RXCrUgyIBo0adIkNm/erExv27aN8ePH06lTJ3JycsjPz2ffvn3MnTuXpnZXYTKZWLp0KXv27CE/Px+9Xs+7775rsVxmZqbVQWkMBkOj2ygvL0er1SrTGo1G6TvfltWrV9OrVy/mzp3b6Kh55eXlaDSaZm/DGTwtbkII+0kPhKJBUVFRnD9/ngsXLnD27Fm0Wi0BAQHU1NSwYMEC9u/fT7t27Th79iwXL15s0jq/+eYbjh07pvQVX11dTWxsrMVyBoOhSRcQa6xd4JoySM2sWbOYNWsWmzdvZsmSJTafWd/vNpzB0+ImhLCfJAPCpvHjx7N161bKysqYPHkyAB9//DEVFRXk5+fj7e1N79696w1oA9ChQwfq6uqU6Tt37gB3v/ATEhJYt26dze1mZmYq3cj+UkJCApmZmTbLajQaDh06pEyfO3fO6oWrIRMnTrToRtfaNs6dO1dvG48++miTt9HSPDFuQoj7J48JhE2TJk1i06ZN5OTkMG7cOACuXbtGQEAA3t7eHDhwgNLSUotywcHBFBQUYDabKS0tpaioCIBBgwbVK1NVVcXJkyctyhsMBqvD1TZ2QQGUZ82VlZVUVlaSm5tLfHw8APPnzycnJ8eizC/rsGvXLnr27KlMP/HEExbLBwYGolKpMBqNwN1W8GPHjm20bs7iKXGzxVrchBDWSTIgbNLpdJhMJkJCQujevTsAL774It9++y16vZ61a9cSGhpqUW7IkCFotVrCw8NZvHgxOp0OAH9/f7Kyspg8eTI6nY7Bgwdz4sQJh9bZ19eXhQsXotfr0ev1LFq0CF9fXwCKi4t55JFHLMqsWrWKvn37EhkZydtvv608IqioqGjwufrKlStJTU2lV69eBAcHk5iY6ND9sIenxK2goACNRsOWLVt4+eWXlfraitv8+fPRaDRcv34djUbDkiVLHLofQrgieUwgGlVSUlJv2s/Pj6+//trqsvdannt5ebFhwwary8TFxVl9tcuRUlNTSU1NtZhfW1vL4MGDLeYvW7bM6nry8vKYPXu21c/0ej1Hjx61r6ItyBPi1q9fv3qPa+6xFbeMjAy7R0UUwt3InQHh8nx8fCgsLKzXeU1Ddu7c2ax1JyYmkpaW1qwyK1asICMjgy5dujSrnKeRuAnRdsidAeHyYmJiOH36dGtXQ5GWltbsC5EnkrgJ0XZIMiDq+fWtZeE4LXlsJW4tR46t8ASSDAjg7vNklUpFSkpKa1fFralUKvz8/By2Pombczg6bkK0NV7mpnZBJtzemTNnMJlMrV0Nt+bn50dQUJBD1ylxa3ktETch2hJJBoQQQggPJ28TCCGEEB5OkgEhhBDCw0kyIIQQQng4SQaEEEIIDyfJgBBCCOHhJBkQQgghPJwkA0IIIYSHk2RACCGE8HCSDAghhBAeTpIBIYQQwsNJMiCEEEJ4OEkGhBBCCA8nyYAQQgjh4f4H71hKobE0+5IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the decision tree \n",
    "plot_tree(tree_clf_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='brown' size = '14pt' face=\"Century Schoolbook\"><b>Feature selection</b></font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice prior to constructing the decision tree, it may be beneficial to perform dimensionality reduction techniques such as a Feature selection. This can enhance the likelihood of the decision tree to identify discriminative features.\n",
    "\n",
    "One way to do this is to examine the correlation between the variables in our dataset by plotting the Pearson Correlation among all attributes. We will use the full, clean dataset for this with the labels encoded. \n",
    "\n",
    "Recall: The *Pearson correlation coefficient (r)* is the most common way of measuring a linear correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>iris_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   iris_name  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_iris_set = X.copy()\n",
    "df_clean_iris_set['iris_name']=y_encoded\n",
    "df_clean_iris_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Pearson Correlation of Features'}>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(df_clean_iris_set.astype(float).corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The initial observation provided by this heatmap is very valuable as it allows for a quick understanding of the predictive power of each feature.\n",
    "\n",
    "It is evident from the heatmap that the *petal length* and *petal width* exhibit the strongest correlations (in absolute terms) with the target classes, with respective values of 0.95 and 0.96.\n",
    "\n",
    "However, it should be noted that these two features also have a very high correlation with each other (0.96, the highest in the dataset), implying that they may be conveying the same information. Consequently, utilizing both of these features as inputs for the same model might not be advisable. \n",
    "\n",
    "If we look up to the example we can see that the column 2 from X (*petal length*) is in the root. Therefore, further exploration and comparison of these features is required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "7. Drop the *petal width* column from the database and create a decision tree in a similar way with the example.\n",
    "\n",
    "8. Find the proper depth and evaluate the score for the decision tree model that you build."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "Accuracy: 0.95\n",
      "Best hyperparameters: {'max_depth': 2}\n",
      "Accuracy: 0.97\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.8333333333333334, 'x[2] <= 2.45\\nentropy = 1.583\\nsamples = 112\\nvalue = [35, 39, 38]'),\n",
       " Text(0.2, 0.5, 'entropy = 0.0\\nsamples = 35\\nvalue = [35, 0, 0]'),\n",
       " Text(0.6, 0.5, 'x[2] <= 4.75\\nentropy = 1.0\\nsamples = 77\\nvalue = [0, 39, 38]'),\n",
       " Text(0.4, 0.16666666666666666, 'entropy = 0.187\\nsamples = 35\\nvalue = [0, 34, 1]'),\n",
       " Text(0.8, 0.16666666666666666, 'entropy = 0.527\\nsamples = 42\\nvalue = [0, 5, 37]')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "df_iris = pd.read_csv('iris_teach_2.csv')\n",
    "df_iris.dropna(inplace=True)\n",
    "X = df_iris.drop('iris_name', axis=1, inplace=False).drop('petal width (cm)', axis=1, inplace=False)\n",
    "y = df_iris['iris_name']\n",
    "y_encoded = LabelEncoder().fit_transform(y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.25, random_state=42)\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(criterion='entropy', random_state=42)\n",
    "tree_clf.fit(X_train, y_train) \n",
    "print(tree_clf.get_depth())\n",
    "y_pred = tree_clf.predict(X_test) \n",
    "accuracy = accuracy_score(y_test, y_pred) \n",
    "print(\"Accuracy: {:.2f}\".format(accuracy))\n",
    "\n",
    "param_grid = {\"max_depth\": [1, 2, 3, 4, 5, 6, 7]} \n",
    "grid_search = GridSearchCV(tree_clf, param_grid, cv=5) \n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
    "\n",
    "tree_clf_tuned = DecisionTreeClassifier(criterion='entropy', max_depth=grid_search.best_params_['max_depth'], random_state=42)\n",
    "tree_clf_tuned.fit(X_train, y_train)\n",
    "y_pred = tree_clf_tuned.predict(X_test) \n",
    "accuracy = accuracy_score(y_test, y_pred) \n",
    "print(\"Accuracy: {:.2f}\".format(accuracy))\n",
    "plot_tree(tree_clf_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}