diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 1970c46..53c8c16 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -12,11 +12,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn import datasets"
    ]
   },
   {
@@ -37,11 +40,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes = datasets.load_diabetes()"
    ]
   },
   {
@@ -53,11 +56,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes.keys()"
    ]
   },
   {
@@ -73,13 +87,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _diabetes_dataset:\n",
+      "\n",
+      "Diabetes dataset\n",
+      "----------------\n",
+      "\n",
+      "Ten baseline variables, age, sex, body mass index, average blood\n",
+      "pressure, and six blood serum measurements were obtained for each of n =\n",
+      "442 diabetes patients, as well as the response of interest, a\n",
+      "quantitative measure of disease progression one year after baseline.\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "  :Number of Instances: 442\n",
+      "\n",
+      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
+      "\n",
+      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+      "\n",
+      "  :Attribute Information:\n",
+      "      - age     age in years\n",
+      "      - sex\n",
+      "      - bmi     body mass index\n",
+      "      - bp      average blood pressure\n",
+      "      - s1      tc, T-Cells (a type of white blood cells)\n",
+      "      - s2      ldl, low-density lipoproteins\n",
+      "      - s3      hdl, high-density lipoproteins\n",
+      "      - s4      tch, thyroid stimulating hormone\n",
+      "      - s5      ltg, lamotrigine\n",
+      "      - s6      glu, blood sugar level\n",
+      "\n",
+      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
+      "\n",
+      "Source URL:\n",
+      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
+      "\n",
+      "For more information see:\n",
+      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
+      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes['DESCR'])"
    ]
   },
   {
@@ -97,11 +156,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# 1. 10 numeric attribute (age, weight etc.)\n",
+    "# 2. data is the baseline and target is a quantitative measure of disease progression one year after baseline\n",
+    "# 3. 442"
    ]
   },
   {
@@ -115,11 +176,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# yes, consistent as in data description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442, 10)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes['data'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes['target'].shape"
    ]
   },
   {
@@ -156,11 +257,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn.linear_model import LinearRegression"
    ]
   },
   {
@@ -172,11 +273,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_model = LinearRegression()"
    ]
   },
   {
@@ -190,11 +291,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_data_train = diabetes['data'][:421]\n",
+    "diabetes_data_test = diabetes['data'][422:]\n",
+    "diabetes_target_train = diabetes['target'][:421]\n",
+    "diabetes_target_test = diabetes['target'][422:]"
    ]
   },
   {
@@ -206,11 +310,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes_model.fit(diabetes_data_train, diabetes_target_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "152.718929651221\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes_model.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.81774793e-01 -2.38292415e+02  5.10802601e+02  3.27726454e+02\n",
+      " -8.09873327e+02  4.89841345e+02  1.00217410e+02  1.81974785e+02\n",
+      "  7.41715416e+02  7.66594057e+01]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes_model.coef_)"
    ]
   },
   {
@@ -231,11 +382,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "y_pred = diabetes_model.predict(diabetes_data_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[197.59839294 155.14324745 172.75435806 111.60251429 164.65611632\n",
+      " 131.10577235 259.0612676  100.4923271  116.98458327 124.2054576\n",
+      " 218.37929583  61.18798499 132.34126512 120.33334972  52.61053967\n",
+      " 193.95740798 102.50966048 123.53318546 210.98571577  52.52007012]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_pred)"
    ]
   },
   {
@@ -247,11 +418,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[233.  91. 111. 152. 120.  67. 310.  94. 183.  66. 173.  72.  49.  64.\n",
+      "  48. 178. 104. 132. 220.  57.]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(diabetes_target_test)"
    ]
   },
   {
@@ -263,16 +443,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# Its almost impossible to be the exact same as one is the prediction and one the baseline"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
     "# Bonus Challenge 1 - Hypothesis Testing with `statsmodels`\n",
     "\n",
@@ -302,8 +484,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -311,7 +495,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "#### Interpreting hypothesis testing results\n",
     "\n",
@@ -326,8 +512,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "# your answer here"
@@ -351,11 +539,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = pd.read_csv('../data/auto-mpg.csv')"
    ]
   },
   {
@@ -367,11 +555,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 22,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.head()"
    ]
   },
   {
@@ -383,11 +683,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 398 entries, 0 to 397\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   mpg           398 non-null    float64\n",
+      " 1   cylinders     398 non-null    int64  \n",
+      " 2   displacement  398 non-null    float64\n",
+      " 3   horse_power   392 non-null    float64\n",
+      " 4   weight        398 non-null    int64  \n",
+      " 5   acceleration  398 non-null    float64\n",
+      " 6   model_year    398 non-null    int64  \n",
+      " 7   car_name      398 non-null    object \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 25.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "auto.info()"
    ]
   },
   {
@@ -399,11 +721,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      70\n",
+       "28     70\n",
+       "27     70\n",
+       "26     70\n",
+       "25     70\n",
+       "       ..\n",
+       "368    82\n",
+       "367    82\n",
+       "396    82\n",
+       "381    82\n",
+       "397    82\n",
+       "Name: model_year, Length: 398, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['model_year'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "auto['model_year'].max() #oldest mdel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['model_year'].min() #newest model"
    ]
   },
   {
@@ -415,11 +799,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             0\n",
+       "cylinders       0\n",
+       "displacement    0\n",
+       "horse_power     6\n",
+       "weight          0\n",
+       "acceleration    0\n",
+       "model_year      0\n",
+       "car_name        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = auto.dropna().reset_index(drop = True)"
    ]
   },
   {
@@ -431,11 +843,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    199\n",
+       "8    103\n",
+       "6     83\n",
+       "3      4\n",
+       "5      3\n",
+       "Name: cylinders, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['cylinders'].value_counts()"
    ]
   },
   {
@@ -451,11 +879,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto.drop(['car_name'], axis = 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2790</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2130</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2295</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "387  27.0          4         140.0         86.0    2790          15.6   \n",
+       "388  44.0          4          97.0         52.0    2130          24.6   \n",
+       "389  32.0          4         135.0         84.0    2295          11.6   \n",
+       "390  28.0          4         120.0         79.0    2625          18.6   \n",
+       "391  31.0          4         119.0         82.0    2720          19.4   \n",
+       "\n",
+       "     model_year  \n",
+       "387          82  \n",
+       "388          82  \n",
+       "389          82  \n",
+       "390          82  \n",
+       "391          82  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "y = auto['mpg']\n",
+    "X = auto.drop('mpg', axis=1)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size= 0.8, random_state = 55)"
    ]
   },
   {
@@ -469,11 +1026,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto_model = LinearRegression() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_model.fit(X_train, y_train)"
    ]
   },
   {
@@ -503,11 +1080,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "y_pred = auto_model.predict(X_train) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7642440320149999"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score( y_pred, y_train) #r2 score based on SSE (error sum of squares) > value should be as small as possible\n",
+    "# r2 score should be as close to 1 as possible. the higher the better"
    ]
   },
   {
@@ -523,11 +1130,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "y_test_pred = auto_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7432347770491917"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score( y_test_pred, y_test) "
    ]
   },
   {
@@ -541,11 +1168,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# The r2score of the training set is higher than the r2score of the test sets, which means its fine, but could be improved.\n",
+    "# overfitting: big difference between r2 train & test\n",
+    "# underfitting: bad scores on both sets"
    ]
   },
   {
@@ -561,11 +1190,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "y = auto['mpg']\n",
+    "X = auto.drop('mpg', axis=1)\n",
+    "\n",
+    "X_train09, X_test09, y_train09, y_test09 = train_test_split(X, y, random_state = 55, train_size= 0.9) "
    ]
   },
   {
@@ -577,11 +1209,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto_model09 = LinearRegression() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_model09.fit(X_train09, y_train09)"
    ]
   },
   {
@@ -593,11 +1245,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# train model\n",
+    "y_pred09 = auto_model09.predict(X_train09)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7713047863048625"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score( y_pred09, y_train09)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test model\n",
+    "y_test_pred09 = auto_model09.predict(X_test09)"
    ]
   },
   {
@@ -609,11 +1292,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6257388409051768"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score( y_test_pred09, y_test09) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# yes, but depends on each time, running the model.\n",
+    "# sklearn cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6187419270694543"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "cross_val_score(auto_model, X, y, cv=10).mean()"
    ]
   },
   {
@@ -629,7 +1354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,63 +1369,326 @@
     "Follow the documentation and initialize an RFE model using the `auto_model` linear regression model. Set `n_features_to_select=3`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the model and print the ranking."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting estimator with 6 features.\n",
+      "Fitting estimator with 5 features.\n",
+      "Fitting estimator with 4 features.\n",
+      "Final selected features: \n"
+     ]
+    },
+    {
+     "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>cylinders</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>15.1</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>71.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.1</td>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>313 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     cylinders  acceleration  model_year\n",
+       "0          4.0          18.6        78.0\n",
+       "1          4.0          16.7        78.0\n",
+       "2          4.0          15.1        80.0\n",
+       "3          4.0          16.0        72.0\n",
+       "4          4.0          14.5        82.0\n",
+       "..         ...           ...         ...\n",
+       "308        4.0          16.5        80.0\n",
+       "309        4.0          14.5        71.0\n",
+       "310        6.0          17.0        82.0\n",
+       "311        5.0          20.1        79.0\n",
+       "312        8.0          11.5        75.0\n",
+       "\n",
+       "[313 rows x 3 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# for X & y\n",
+    "selector = RFE(auto_model, n_features_to_select= 3, step = 1, verbose = 1) \n",
+    "selector.fit(X_train, y_train)\n",
+    "\n",
+    "kept_features = selector.get_support(indices = True) \n",
+    "kept_features = list(X_train.iloc[:,kept_features].columns)\n",
+    "\n",
+    "X_train = selector.transform(X_train)\n",
+    "X_test  = selector.transform(X_test)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=kept_features)\n",
+    "X_test  = pd.DataFrame(X_test, columns=kept_features)\n",
+    "\n",
+    "print(\"Final selected features: \")\n",
+    "display(X_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feature importance is ranked from most important (1) to least important (4). Generate a model with the three most important features. The features correspond to variable names. For example, feature 1 is `cylinders` and feature 2 is `acceleration`.\n",
+    "\n",
+    "Perform a test-train split on this reduced column data and call the split data `X_train_reduced`, `X_test_reduced`, `y_test_reduced`, `y_train_reduced`. Use an 80% split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 2, 3, 1, 1])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selector.ranking_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "X_train_reduced, X_test_reduced, y_train_reduced, y_test_reduced = train_test_split(X, y, train_size= 0.8)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Fit the model and print the ranking."
+    "Generate a new model called `auto_model_reduced` and fit this model. Then proceed to compute the r squared score for the model. Did this cause an improvement in the r squared score?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto_model_reduced = LinearRegression() "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Feature importance is ranked from most important (1) to least important (4). Generate a model with the three most important features. The features correspond to variable names. For example, feature 1 is `cylinders` and feature 2 is `displacement`.\n",
-    "\n",
-    "Perform a test-train split on this reduced column data and call the split data `X_train_reduced`, `X_test_reduced`, `y_test_reduced`, `y_train_reduced`. Use an 80% split."
+    "auto_model_reduced.fit(X_train_reduced, y_train_reduced)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# train model\n",
+    "y_pred_reduced = auto_model_reduced.predict(X_train_reduced)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 57,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7338327064704717"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Generate a new model called `auto_model_reduced` and fit this model. Then proceed to compute the r squared score for the model. Did this cause an improvement in the r squared score?"
+    "r2_score( y_pred_reduced, y_train_reduced)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# test model\n",
+    "y_test_pred_reduced = auto_model_reduced.predict(X_test_reduced)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8454189364005489"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score( y_test_pred_reduced, y_test_reduced) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# while the r2 score of the train model went down a bit, the r2 score of the test model improved well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6187419270694543"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(auto_model_reduced, X, y, cv=10).mean() "
    ]
   },
   {
@@ -719,6 +1707,151 @@
     "\n",
     "* Data integration specialists who are business or content experts but also understand data and programming. This cross-disciplinary track brings together data, technology, and business and will be in high demands in the next decade."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Decision Tree Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor # bec. linear regression\n",
+    "from sklearn.tree import plot_tree\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score \n",
+    "#mean_absolute_error, mean_squared_error should be closest to 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dtr_model = DecisionTreeRegressor(max_depth=3) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dtr_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = dtr_model.predict(X_train)\n",
+    "y_pred_test  = dtr_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#r2 score, absolte E and mean sq E to check/compare regression models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7061290530668518"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# train model\n",
+    "r2_score( y_pred_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4758292645007983"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test model\n",
+    "r2_score( y_pred_test, y_test) # bad score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5756660877301583"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(dtr_model, X, y, cv=10).mean() #bad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fgwQe8cNcZlB/3bzNlr1fhdnH+ctX2XfkOPWrVQj3ckVERGIrBW8REREgb86sjBrYDYA3u7Zh5oIVuLu70r9TKwBG9OvKzIVf8O0Pp2lWpypX/rpBziwZKV+iMMYYMqVLQ9liBQG4cOUqa7fv5/eD68mULg0Afd5owZfffMdna7bw0dh3Quyhe9umNK9XPcw+06VOGWotTUpPPh73DsUL5sXH9xmrtuymbqc32btiTogh+X8//sovZy8wb+LwF/58ADq+NYbtB47w1MubelXLs2DKyOemqdK6F6d+OYu3jw9dWzXi/bd7hWvZIiIisZmCt4iICFAwd47A/zfGkNIzKflzZQ8cc3d3I1niRNy++zcAbzSrR4MugyhYqw01ypeiduWy1K5cBhcXF3745SyWZVGsXodg38Pbx4cqYRx9jugzvnNly0yubJkDX5cpWoDLV68z67OVIQbvZeu3kz9XNkoWzheu5U8bMZCR/bty9uIVxnwwnyETP2LO+HeDTbN81vs8evyEn34/x4ipnzBzwQqG9O74yuskIiISGyh4i4iIAG5ursFeG2Nwd3N7bszf3x+Aovlz89uX69l35DhffXuSHkMnUDBPDnYsnYW/vz/GGL5evwh39+DLiBc3bqg9RNap5kGVLJyfdTv2Pzf+5KkX63bsZ/Sb3cO9rDQpPUmT0pPc2TOTPGliarTry9C+nciYNnXgNP/+f94cWfH386fPyCm81b0dbm76yCEiIq8vbQVFREReUeKECWhetxrN61ajQ7N6VG7Zk/OXr1IkXy4sy+LmnbthXl9tL6Knmofkx9/+IE0qz+fGN+w6gLePL20b1X6p5f3LsizAdhO50Pj7+/PMzw8/f3994BARkdeatoMiIiKv4KPFq0mTypPCeXLi7u7Gmm37SJwwAenTpMIjfjzaNKpFj2ETmTJsAEXz5eLePw84fOIHsmZIR5PaVUJcZkRPNZ+9dA2Z06clX86s+Pj6smrLHrbtP8yq2ROfm3bpuu00rFERz2RJnqvNW76e+Ss28OOeVQDsPHiUe3//Q9ECeUjoEZ9f/7jIiGmfUKpI/sA7ma/cvJu4ceNQIFd24ri7cfL074yeOZ+mtasQN06cV14nERGR2EDBW0RE5BUkSuDBrEUrOXf5TwyGwvlysXnRTDzixwNgweSRTJ23jJHT5nLt5i2SJ0lMiUJ5qVy6mMN68vF9xvCpc/jr5m3ix4tL3hxZ2bRgOnWqlAs23R8Xr/DNyZ/YvuTDEJdz9+9/OBvkDu3x4sRh4erNnDl/GW8fHzKkTU2jmpV4p+d/17C7urky49PlnLv8J5YFmdKlpnf7Zgzo0sYxKysiIhKDmH9PFRMREYmJjDFtgZX/vi6YOwcnti2Lwo4koko17MTPZ84FHWpnWdaqqOpHREQkolyiugERERERERGR2EzBW0RERERERMSBFLxFREREREREHEjBW0RERERERMSBFLxFRESiyOHj3xM/V3nu3Lsf1a2IiIiIAyl4i4iISKjmr9hAyYYdSVW0JqmK1qRyq57sOvhNsGk27zlEw65vkbF0feLnKs/h498/t5y+I6eQr3pLkhWsSsbS9WnZZyi/n7vkpLUQERGJWgreIiIiEqr0aVIx4Z0+HNu8hKMbP6NKmeK06jeMn3//73FfT556UaZoQaYOHxDqcooVzMOCKSM5tWslWxd/gGVZ1Ov8Jr6+z5yxGiIiIlFKwVtERGK9zXsOUbJhR5IVrEq6knWo2b4fN+/cA+DClau07DOULOUa4lm4OmWbdGHnwaPB5s9dtTmT5iymx9AJpCxagxyVmrJux37uP3jIG4PeI0WRGhSo2Zr9Xx8PnOff08h3HjxK6UadSFqgKuWaduX707+H2eu3P5ymZvt+JC9UjWwVGjPgvWk8ePQ4XOviCA1rVKR25bJkz5yBnFkzMW5wLxIl8OD4qdOB07RrUoeRA7pSq1KZUJfTvU0TKpQsQuYMaSmaPzdjBvXk+q07XPzzmsN6FxERiS4UvEVEJFa7cfsuHQePoX2TupzatZJ9X8ylXePagfVHj59Sq1IZti+ZxYmty2hSuwpt+o/gzPnLwZYzZ9laShTKx7FNS2hetxo9hk6k8+Cx1KlcluNbllKhZBG6vvM+Xt7eweYbPmUOE4b05ejGz8iSMR3Neg7hyVOvEHs9feY8DboMon61CpzYuow1n0zmp9/P0Wv4pHCtS0gad3ubFEVqhPkVXn5+fqzdvp9HT55SpmjBcM9n7/GTp3y+cQcZ06Umc4a0r7wcERGRmMItqhsQERFxpOu37uDr+4ymdaqSOX0aAPLnyhZYL5Q3J4Xy5gx8PbRPJ3Z8+TWb9hxkWN/OgeM1KpSmV/tmAIwe2I2Pl6wmW+YMtG9aF4DhfTuzbP12fjl7geIF8wbON6xfZ2pWLA3AgskjyFGpKWu27aVLq0bP9frhopW0qFedQd3aBo59PPYdyjTpwq27f79wXUIyd+Kw53YGvKzTZ85TpXUvvLx9SOgRnzVzJlMgd/aXXs6nX2xk5PS5PH7ylFxZM7Fr2cfEjRMnQr2JiIjEBAreIiISqxXKk4OqZUtQokEHqpcvRbVyJWlapwopkycDbEdfJ85ZzK6D33Dj9l18nz3Dy9uHgrlzBFtOwSBBM2ECDzzix6NArv/GUqVIDsDtu38Hm69MkQLB5sufKxu/hXJTsR9++Z3zl6+xfueBwDHLsgC4cOUaJQvlDXNdQpI+TcqwfjzhkitrJo5vWcr9Bw/ZvOcQPYZOYM+KOS8M/fbaNKpF9fIluXH7LrM+W0n7gaP4cvV8POLHi3CPIiIi0ZmCt4iIxGqurq7sWDqL46d+4cDXJ1i2fhvvzZzP3hVzKJQ3J8OnzmHvkeNMHtqPHJkz4hE/Ht3eHY+Pr2+w5bi5B99kGmNwd3cN8tr2X/+AoPwq/P0turRsyIAurZ+rpUud8oXrEpLG3d7m6Mkfw/y+d07tD7MeJ4472TNnAKB4wbyc/Pl3Zi9dw/xJw8O5ZjZJEiUkSaKE5MiSkVKF85O2ZB027zlEuyZ1Xmo5IiIiMY2Ct4iIxHrGGMoULUCZogUY0b8Lxep1YP3OAxTKm5NvTv5E+8Z1aFq7KgBe3t5cvHKNnFkyRsr3Pv7jL2TNlB6wHV3/9Y+LtG9SN8Rpi+TPxa/nLgaG3Jddl5BExqnm9vwtf7x9fCK0DAsLy7IivBwREZGYQMFbRERiteOnTnPwm++oUaE0qVIk48df/+DqjZvkzZEVgBxZMrJ1/2Ea1KiIu5sbE+csxss78sLg1LnLSJk8KWlTpWDSnCXEcXejdcOaIU77do8OVG7VkwHvTaNbmyYkSuDBmQuX2fnlUeaMf/eF6xKSiJ5qPmr6POpUKUvGtKl5+PgJa7bt5fDxH9i0YHrgNPfuP+DPv27wz8NHAJy/fJUkiRKSOqUnaVJ6cv7yVTbtOUS1ciVIkTwp127cZuaC5cSNE4e6VctHqD8REZGYQMFbRERitSQJE3Ls5M/MW76e+w8ekSFtKob17ULbgLuBTx0+kD4jJlOjXV+SJk5E/86t8I7E4D3+nd4MmzKHsxeukC9nVjZ8Op0EHvFDnLZgnhzs++ITxs1aSK32/fDz9ydrxnQ0qlkpXOviCDfv3KXrkPe5efseSRIloEDuHGxZNDPwhnEAO748Qs9hkwJf9x01FYCR/bsyamA34sRx58jxH/h48SruP3xEKs/kVChZmENrPiVNSk+H9S4iIhJdGCsC16KJiIhENWNMW2Dlv68L5s7BiW3LorAjm8PHv6f2GwP489sdpEieNKrbiVFKNezEz2fOBR1qZ1nWqqjqR0REJKL0HG8RERERERERB1LwFhEREREREXEgXeMtIiLiAJVKF+Pp2aNR3YaIiIhEAzriLSIiIiIiIuJACt4iIiIiIiIiDqTgLSIi8gK1OvRn0LiZUd2GiIiIxFAK3iIiIrHE5j2HaNBlEBlL1ydl0RpUbNGD7QeOBJtmw64vKd+sK2mK18azcHVKN+rEio07w/097ty7T7YKjYmfqzx37t0PVjt95jw12/cjWcGqZKvQmElzFhP0saWHj39P/Fzln/s6c/5yhNZbREQkutPN1URERGKJI/87ReUyxRkzqCfJkyZm9da9tO43gj3LZ1OhZBEAPJMmYWifzuTOlhl3d1d2HvyG3iOnkCJ5UupUKffC79Fr+CQK583J9Vt3go0/ePSYBl0GUb5kEb7e8BlnL16m57CJeMSPz6BubYNN+/3OFSRLkjjwdUo951xERGI5HfEWEZFYa9HqzWQu24Bnz54FG+80eCwt+wwF4MKVq7TsM5Qs5RriWbg6ZZt0YefBsO9Gnrtqcz78bGWwMfvT0X18fBk1fR7ZKzbBs3B1yjfryr4jxyNnxUIxc9QghvR6g5KF85E9cwZGDuhK0fy52bb/v6PeVcoWp1HNSuTOnplsmTLQv1MrCubOztHvfnzh8ucsW8uTp1682bXtc7XVW/fy5KkXi6aOIn+ubDStXZW3e3Tg4yWrgx31BkiZPBlpUnoGfrm6ukZ85UVERKIxBW8REYm1mtetxv0Hj/jym+8Cxx4/ecr2A0do06g2AI8eP6VWpTJsXzKLE1uX0aR2Fdr0HxHh0597Dp/I4RPfs+yDsXy37XM6NK1H897v8tNvf4Q6z7R5y0hRpEaYX1//79RL9fHo8ROSJUkUYs2yLA5+8x1nL14JPCIemlO/nmXmwhUsmjYaFxfzXP34D6cpX6Iw8ePFDRyrUaE012/d4fLV68GmLd+8G1nLN6Jux4F89e3Jl1ofERGRmEinmouISKyVLElialUqw+qte6lVqQwAW/cdxs3VlfrVygNQKG9OCuXNGTjP0D6d2PHl12zac5BhfTu/0ve9cOUqa7fv5/eD68mULg0Afd5owZfffMdna7bw0dh3Qpyve9umNK9XPcxlp0udMtx9zF+xgWs3b9G2cZ1g4/88fET2ik3w9vHB1cWVWWMGU7ty2VCX8/jJUzq9NYYPRr9F+jQpOX/5z+emuXnnLunTpAo2lipFMgBu3LlHlozpSJPSk4/HvUPxgnnx8X3Gqi27qdvpTfaumPPC4C8iIhKTKXiLiEis1rZxbXoNn8iTp154xI/H6q17aVqnKvHi2o7MPn7ylIlzFrPr4DfcuH0X32fP8PL2oWDuHK/8PX/45SyWZVGsXodg494+PlQpUzzU+ZInTUzypIlDrb+MLXu/YsS0T/j8w3FkTp8mWC1RAg+Ob1nKo8dPOHjsJEMnzyZz+rRULVcixGW9PWEWZYsXomntqmF+T2OCHwn/9wzzf4dzZctMrmyZA+tlihbg8tXrzPpspYK3iIjEagreIiISq9WrWg5XF1e2HThC1bIl+PLY/9i++MPA+vCpc9h75DiTh/YjR+aMeMSPR7d3x+Pj6xvqMl2My3PXLfsGuY7c398fYwxfr1+Eu3vwTe2/gT8k0+YtY9qny8Ncn80LZ7wwpG7ac5BuQ8azaNpoGlSv+Hz/Li5kz5wBgML5cnHm/CWmzf881OB98Nh3XL1+ixWbdgEErnuW8o14u0d7xg3uReoUnty8fTfYfLfv/g1Aas/kofZasnB+1u3YH+b6iIiIxHQK3iIiEqvFixuXJrWrsGbrXu7eu0/qFJ5ULFU0sP7NyZ9o37hO4NFcL29vLl65Rs4sGUNdZorkSblx67+Q6eXtzdkLlykccMp6kXy5sCyLm3fuUjmMI9z2IuNU83U79tNz2EQWTh1FszphH6H+l79l4e0T+o6G7Ys/xMf3vx0LJ3/+jV7DJ7Fn+WxyBPycShctwKjpc/Hy9g7cufDl0ROkTZWCzBnShrrsH3/7gzSpPMPVp4iISEyl4C0iIrFem4a1aNjtLS5d/YvWDWvi4vLfvUVzZMnI1v2HaVCjIu5ubkycsxgvb58wl1elTDE+37CDBtUrkCJ5UqbO+xzfIME0Z9ZMtGlUix7DJjJl2ACK5svFvX8ecPjED2TNkI4mtauEuNyInmq+Zvs+ur87nslD+1OhZBFuBByBjuPuHrjcqfOWUbJQPrJmTIe3jy+7vzrGyi27+WD0W4HLmbd8PfNXbODHPasC1yeou3/fByB3tsykCHgUWOuGNZk0ZzE9hk5kWN/O/HHxCjMWrGBE/66Bp6DPXrqGzOnTki9nVnx8fVm1ZQ/b9h9m1eyJr7zOIiIiMYGCt4iIxHqVShcldQpPfjt3ic8/fD9YberwgfQZMZka7fqSNHEi+nduhfcLgveQ3h25fO0GLfsMI4FHfIb26fTcc60XTB7J1HnLGDltLtdu3iJ5ksSUKJSXyqWLRfr6/euz1Vt49syPIRM/YsjEjwLHK5Yqyt4VcwDbXc4Hjp3BtRu3iB8vLrmyZWbRtNG0blAzcPq7f//D2YtXXup7J0mUkO1LZjFo3EzKN+tGsiSJeLNrW97s2iZwGh/fZwyfOoe/bt4mfry45M2RlU0Lpofr+eEiIiIxmbG/Rk1ERCQmMca0BQIfql0wdw5ObFsWhR1JRJVq2Imfz5wLOtTOsqxVUdWPiIhIROk53iIiIiIiIiIOpOAtIiIxXbBTt/z8/aKqD4kkIfwb6vQ8ERGJ0RS8RUQkpnsY9MWFK9d49PhJVPUiEfTw0WMuXLlmP/wgKnoRERGJLLrGW0REYjRjTGrgL4LsTC6YOwd9O7YgYQKPwDtqS/RmWRaPHj/hk8/XcfrM+aAlfyCdZVk3o6g1ERGRCFPwFhGRGM8YcxCoEtV9iEMctCyrWlQ3ISIiEhE61VxERGKD9wGvqG5CIp0XMD6qmxAREYkoBW8REYnxLMs6CDRG4Ts28QIaB/zbioiIxGg61VxERGINY0wqoCnQCEgPxHnJRSQAMgL2F4Y/Bv5Ed9e2Z7D9vBLYjVvYfl6PX3J5PsA1YCuwybKsWxHuUEREJBpQ8BYREQGMMfWAjUBcu9JWoJVlWd7O7yr6M8bEBdYBDe1K3kBTy7J2Ob8rERGR6EWnmouIyGvPGNMY2MzzoXsD0FKhO3QBP5sW2H5WQcUFNhtjGjm/KxERkehFwVtERF5rxpiWwHrA3a60CmhjWZaP87uKWQJ+Rm2A1XalOMAGY0wL53clIiISfSh4i4jIa8sY0w5bWHSzK30OvGFZ1jPndxUzBfysOgDL7UpuwGpjTFvndyUiIhI9KHiLiMhryRjTCVjB89vCRUAXy7L8nN9VzBbwM+sCfGZXcgVWBPzMRUREXjsK3iIi8toxxvQAlvD83cvnAr0sy/J3flexQ0D47gnMsyu5AEuMMd2d35WIiEjUUvAWEZHXijGmH7CA50P3LKC/QnfEBfwM+wEf2ZUMsDDg30BEROS1oeAtIiKvDWPMW8CcEEpTgcGWnrEZaQJ+lm8B00Iozwn4txAREXktKHiLiMhrwRgzDPgghNL7wHCF7sgX8DMdBowPofyBMWaok1sSERGJEkafM0REJDYzxhhgNDAuhPIoy7ImOrml15IxZhQhB/D3LMsKaVxERCTWUPAWEZFYKyB0TwBGhFAeYlnWDCe39Fozxgwh5FPPJwKjddaBiIjEVgreIiISKwWE7unA2yGU37Qs62MntySAMeZNbDeyszcDeFfhW0REYiMFbxERiXUCQvdHwIAQyn0sy5rv5JYkCGNMH2yPbrP3MTBI4VtERGIbBW8REYlVjDEuBDyP265kAd0ty1rs/K7EnjGmG7CQ5x/rNh/op8e6iYhIbKLgLSIisYYxxhVbmOtiV/IHOlmWtcL5XUlojDFvAEt5/ikri4GelmX5Ob0pERERB1DwFhGRWMEY44YtxLW3K/kB7S3LWuP0puSFjDFtgBWAq11pBdDFsqxnzu9KREQkcil4i4hIjGeMcccW1FrZlXyBNpZlbXR+VxJexpjmwGrAza60BnjDsixf53clIiISeRS8RUQkRjPGxMEW2pralXyA5pZlbXd+V/KyjDENgfVAHLvSJmw7T3yc35WIiEjkUPAWEZEYyxgTD1tYq29X8gKaWJa1x/ldyasyxtTBFrTj2ZW2Ay0ty/JyflciIiIRp+AtIiIxkjHGA1tIq2VXegI0tCzrS+d3JRFljKkObAPi25X2YtuZ8tT5XYmIiESMgreIiMQ4xpgE2MJZVbvSI6CeZVlHnN+VRBZjTCVgJ5DArnQQ206Vx87vSkRE5NUpeIuISIxijEmELZRVsCs9AOpYlnXM+V1JZDPGlAN2AYntSkeA+pZlPXR+VyIiIq9GwVtERGIMY0xSbGGsjF3pPlDLsqz/ObsncRxjTElsp5gntSsdA+palvWP05sSERF5BQreIiISIxhjkmMLYcXtSneBGpZlnXJ6U+JwxpiiwD7A0670HVDbsqx7zu9KRETk5Sh4i4hItGeMSYktfBW2K90CqluWddr5XYmzGGMKAvuBVHalU0BNy7LuOL0pERGRl6DgLSIi0ZoxJg220JXfrnQdqGZZ1u/O70qczRiTF/gSSGNX+gXbzpebzu9KREQkfFyiugEREZHQGGPSAYd4PnRfBSordL8+LMv6DagMXLMr5QcOBfyuiIiIREsK3iIiEi0ZYzICXwG57UqXgUqWZf3h/K4kKlmWdRaohO13IKg8wFcBvzMiIiLRjoK3iIhEO8aYrMBhIIdd6Ty20H3R+V1JdGBZ1gVsR74v2JVyYAvfWZzelIiIyAsoeIuISLRijMmB7Uh3FrvSWWynl19xelMSrViWdRnbke+zdqWswOGA3yEREZFoQ8FbRESiDWNMHmxHuu1PGf4VW+i2v75XXlMBvwtVsP1uBJUR25Fv+0sUREREooyCt4iIRAvGmALYbqSW1q70E1DFsqwbTm9KojXLsq4DVYGf7UrpsIVv+5vyiYiIRAkFbxERiXLGmMLAQSC1Xel7bI8Mu+38riQmsCzrFrbw/b1dKTW2u53bP/tdRETE6RS8RUQkShljSmAL3SnsSsexPZ/5rvO7kpgk4HekOnDCrpQCOGiMKe78rkRERP6j4C0iIlHGGFMGOAAksysdBWpZlnXf6U1JjBTwu1IT+MaulAw4EPC7JiIiEiUUvEVEJEoYYyoA+4DEdqVDQB3Lsh44vSmJ0QJ+Z2pjuyt+UEmAvQG/cyIiIk6n4C0iIk5njKkK7AES2pX2AfUty3rk/K4kNgj43akH7LcrJQJ2G2OqOLsnERERBW8REXEqY0wtYCfgYVfaCTSyLOuJ87uS2CTgd6ghsMuulADYaYyp6fyuRETkdabgLSIiTmOMqQ9sA+LZlbYAzSzL8nJ+VxIbBfwuNQW22pXiA9uMMfWc35WIiLyuFLxFRMQpjDFNgE1AHLvSeqClZVneTm9KYrWA36mWwAa7UlxgszGmsfO7EhGR15GCt4iIOJwxpiWwDnC3K60E2lqW5ev8ruR1YFmWD9AGWGVXcgfWB/xuioiIOJSCt4iIOJQxpj2wGnCzKy0FOlqW9czpTclrJeB37A1gmV3JDVhtjGnn/K5EROR1ouAtIiIOY4zpAizn+e3NAqCbZVl+zu9KXkcBv2tdgYV2JRdghTGms9ObEhGR14aCt4iIOIQxphewGDB2pTlAb8uy/J3flbzOAn7negOf2JUMsMQY09P5XYmIyOtAwVtERCKdMWYAMD+E0gfAQMuyLCe3JAIEhu8BwIchlD81xvR3cksiIvIaUPAWEZFIZYx5G/g4hNJk4B2FbolqAb+DbwNTQijPNsYMdnJLIiISyyl4i4hIpDHGjABmhFAaC4xU6JboIuB3cQQwLoTyTGPMcCe3JCIisZjRZyAREYkoY4wBxgR82RtpWdYkJ7ckEm7GmJHAhBBKY4H3tcNIREQiSsFbREQiJCB0TwKGhVB+x7KsmU5uSeSlGWPeAaaHUJqMztYQEZEIUvAWEZFXFhC6ZwAhXRM70LKs2U5uSeSVGWMGAh+FUJoJDFH4FhGRV6XgLSIir8QY44ItpIR0F+helmUtcHJLIhEW8Bi8kO7IPxt4U+FbRERehYK3iIi8tIDQPR/oYVeygG6WZS1xflcikcMY0wX4jOefQb8A6KNn0IuIyMtS8BYRkZdijHHFFko62ZX8gY6WZX3h/K5EIpcxpgOwjOefALMU6G5Zlp/TmxIRkRhLwVtERMLNGOOGLYy0syv5Ae0sy1rr/K5EHMMY0xr4AnC1K60EOlmW9cz5XYmISEyk4C0iIuFijHHHFkJa2pV8gVaWZW12elMiDmaMaQqsAdztSuuA9pZl+Tq/KxERiWkUvEVE5IWMMXGxhY/GdiVvoLllWTuc35WIcxhjGgAbgDh2pc1AG8uyvJ3elIiIxCgK3iIiEiZjTDxsoaOeXckLaGxZ1l7ndyXiXMaYWsAWIJ5daSe2nU9ezu9KRERiCgVvEREJlTHGA1vYqGFXegI0sCzroPO7EokaxphqwDbAw660D2hiWdYT53clIiIxgYK3iIiEyBiTEFvIqGJXegTUsyzriNObEolixpiK2I5yJ7QrHQIaWpb1yOlNiYhItKfgLSIizzHGJMYWLsrblf4B6liW9a3zuxKJHowxZYHdQGK70tdAfcuyHji/KxERic4UvEVEJBhjTFJsoaK0XelvoKZlWSed3pRINGOMKQHsBZLZlY5j2zl13+lNiYhItKXgLSIigYwxntjCRDG70h2ghmVZPzq/K5HoyRhTBNgPeNqVTgK1LMu65/SmREQkWlLwFhERAIwxKbGFiEJ2pZvYQvdp53clEr0ZYwoAB4BUdqUfsZ0hctv5XYmISHSj4C0iIhhj0mALD/nsSteBapZl/e78rkRiBmNMHuBLIK1d6RdsO61uOL8rERGJTlyiugEREYlaxpj0wFc8H7r/BCopdIuELeBvpDJw1a6UHzgU8DcmIiKvMQVvEZHXmDEmE7bQncuudAmobFnWOac3JRIDWZb1B1AJuGxXyg18FfC3JiIirykFbxGR15QxJhtwGMhuVzqPLXRfdH5XIjFXwN9MJWx/Q0Flxxa+szq/KxERiQ4UvEVEXkPGmJzYjnRntiudwXZ6+RXndyUS8wX87VTG9rcUVBbgsDEmh9ObEhGRKKfgLSLymjHG5MUWujPYlX7BdqT7L+d3JRJ7WJZ1DagC/GpXyoAtfOdxelMiIhKlFLxFRF4jAY8+OsTzd1/+EahqWdZNpzclEgsF3Mm8Cra/raDSYjvtvIDTmxIRkSij4C0i8powxhTFFrrtnzd8Etsjw/S8YZFIFPA3VQ3b31hQqbDd7byI05sSEZEooeAtIvIaMMaUxPacYU+70rfYnjN8z/ldicR+AX9bNYDjdiVP4EtjTAnndyUiIs6m4C0iEssZY8oC+4GkdqWvgVqWZd13dk8ir5OAv7Fa2P7mgkoGHDDGlHF6UyIi4lQK3iIisZgxphKwF0hsVzoI1LEs66HzuxJ5/ViW9QCoi+1vL6jEwD5jTEXndyUiIs6i4C0iEksZY6oDu4CEdqW9QAPLsh47vyuR15dlWY+ABsA+u1JCYLcxpprzuxIREWdQ8BYRiYWMMbWB7YCHXWkH0NiyrCfO70pEAv72GgE77UoewI6Av10REYllFLxFRGIZY0wDYCsQz660GWhmWZaX05sSkUABf4PNgC12pXjA1oC/YRERiUUUvEVEYhFjTFNgIxDHrrQWaGVZlo/zuxIRe5ZleQMtgXV2pTjAxoC/ZRERiSUUvEVEYgljTGtsH+Ld7UpfAO0ty/J1flciEpqAv8l2wEq7kjuwzhjTyvldiYiIIyh4i4jEAsaYN7B9eHe1Ky0FOlmW9czpTYnICwX8bXbE9rcalCuwyhjTwelNiYhIpFPwFhGJ4YwxXYFlPP+e/inQzbIsP+d3JSLhFfA32g1YYFdyAT4P+BsXEZEYTMFbRCQGM8b0Bj4DjF1pNtDHsix/53clIi8r4G+1NzDHrmSAz4wxvZzflYiIRBYFbxGRGMoYMxCYF0JpBvCmZVmWk1sSkQgI+JsdCMwMoTzfGDPAyS2JiEgkUfAWEYmBjDFDgI9CKE0C3lXoFomZAv52hwCTQyh/bIx5x8ktiYhIJFDwFhGJYYwxo4BpIZTGAKMUukVitoC/4ZHA2BDK040xI53bkYiIRJTR5zMRkZjBGGOAccDoEMrDLcua4uSWRMTBjDHDsZ3JYu99YKx2tImIxAwK3iIiMUBA6J4MDA2hPNiyrA+d3JKIOIkxZjAhX/c9BRih8C0iEv0peIuIRHMBofsDYFAI5f6WZX3i3I5ExNmMMf2xPa3A3ofA2wrfIiLRm4K3iEg0ZoxxwfZhu69dyQJ6W5Zl/9xfEYmljDE9gU9DKH0CDNTjA0VEoi8FbxGRaCogdH8KdLcrWUBXy7KWOr0pEYlSxpguwGfYnu8d1CKgl8K3iEj0pOAtIhINGWNcgcVAR7uSH9DRsqyVzu9KRKIDY0x74HOefzrNMqCbZVl+zu9KRETCouAtIhLNGGPcsH2obmtXega0tSxrvfO7EpHoxBjTElgJuNmVVgKdLMt65vyuREQkNAreIiLRiDEmDrYPzs3tSr5AS8uytji/KxGJjowxTYC1gLtdaT3QzrIsX6c3JSIiIVLwFhGJJowxcYF1QEO7kjfQzLKsnc7vSkSiM2NMfWADENeutBVoZVmWt/O7EhERewreIiLRgDEmPrARqGNXego0tixrn/O7EpGYwBhTC9gCxLMr7cK2087L+V2JiEhQCt4iIlHMGOOB7UNzDbvSY6CBZVmHnN6UiMQoxpiqwHbAw660H9vOuyfO70pERP6l4C0iEoWMMQmxfViubFd6CNS1LOuo87sSkZjIGFMB21HuhHalQ0BDy7IeOb0pEREBFLxFRKKMMSYJsBMoZ1f6B6htWdZx53clIjGZMaYMsBtIYlc6CtSzLOuB87sSEREFbxGRKGCMSQbsAUrale4BtSzLOun8rkQkNjDGFAf2AcnsSieAOpZl/e38rkREXm8K3iIiTmaM8cT2obioXekOUMOyrB+d35WIxCbGmMLYru9OYVf6HtvOvbvO70pE5PWl4C0i4kTGmFTYPgwXtCvdBKpblvWL87sSkdjIGJMfOACktiv9BNS0LOuW87sSEXk9KXiLiDiJMSYttg/Bee1KfwHVLMs64/yuRCQ2M8bkAb4E0tqVfsV2hs1153clIvL6cYnqBkREXgfGmAzAVzwfuv8EKit0i4gjWJb1O1AJ23tNUPmAQ8aY9M7vSkTk9aPgLSLiYMaYzNhCd0670kWgkmVZ55zflYi8LgLeYyoBl+xKuYDDAe9RIiLiQAreIiIOZIzJBhwGstmV/sB2pPuS05sSkddOwHtNJcB+R1824KuA9yoREXEQBW8REQcxxuTCFroz2ZV+B6pYlmV/6qeIiMMEvOdUBuwvbcmMLXzbn5UjIiKRRMFbRMQBjDH5sJ1ebn/95Glsofsv53clIq+7gPeeyoD9ExQyYDvt3P4+FCIiEgkUvEVEIpkxphBwCEhjVzoFVLUs66azexIR+VfAe1BV4Ee7UhpsR77tH3coIiIRpOAtIhKJjDHFgINASrvSd9ie033H+V2JiARnWdZtoBq296agUgIHjTFFnd+ViEjspeAtIhJJjDGlsD2nO7ld6Ri25+Xec35XIiIhC3hPqgF8a1fyBL40xpR0flciIrGTgreISCQwxpQD9gNJ7UpHgNqWZf3j9KZERF4g4L2pFvC1XSkpsD/gvU1ERCJIwVtEJIKMMZWBvUAiu9KXQF3Lsh46vysRkfAJeI+qg+0ymaASA3uNMZWc35WISOyi4C0iEgHGmBrALiCBXWkP0MCyrMfO70pE5OUEvFc1wLYTMagEwG5jTHXndyUiEnsoeIuIvCJjTB1gOxDfrrQdaGJZ1lPndyUi8mosy3oCNAZ22JXiA9sD3vNEROQVKHiLiLwCY0xDYAsQ1660CWhuWZaX87sSEYmYgPeuZsBmu1I8YEvAe5+IiLwkBW8RkZdkjGkObATi2JXWAK0ty/JxflciIpEj4D2sFbDWrhQH2GiMaeb8rkREYjYFbxGRl2CMaYstYLvZlZYDHSzL8nV+VyIikSvgvaw9sMKu5AasNca0cX5XIiIxl4K3iEg4GWM6YvsQ6mpXWgx0sSzrmfO7EhFxjID3tM7AEruSK/CFMeYNpzclIhJDKXiLiISDMaYbsJTn3zfnAz0sy/JzelMiIg4W8N7WHfjUruQCLDPGdHV+VyIiMY+Ct4jICxhj+gKLAGNX+hjoa1mWv/O7EhFxjoD3uD7AbLuSAT4zxvRxflciIjGLgreISBiMMYOAT0IoTQcGWZZlObcjERHnC3ivexOYEUJ5rjHmTSe3JCISoyh4i4iEwhgzFPgwhNIEYKhCt4i8TgLe894FJoZQnmWMedfJLYmIxBgK3iIiITDGjAamhFB6z7Ks0QrdIvI6smxGAWNCKE81xoxydk8iIjGB0WdHEZH/GGMM8D4Q0ofHYZZlTXVySyIi0ZIxZhgwOYTSeGCMdlCKiPxHwVtEJEBA6J4KDAmhPNiyrJBOOxcReW0ZY94CPgihNA3bzkp90BQRQcFbRAQIDN0fYrt5kL1+lmXNdXJLIiIxgjGmHzAnhNJHwFsK3yIiCt4iIhhjXLB9aLR/JI4F9LQsa5HzuxIRiTmMMT2wPevb/rGL84D+euyiiLzuFLxF5LVmjHHF9mGxm13JH+hqWdYy53clIhLzGGM6A4t5PnwvAnopfIvI60zBW0ReW8YYN2wfEt+wK/kBb1iWtcr5XYmIxFzGmHbA54CrXelzbDsz/ZzflYhI1FPwFpHXkjHGHVgOtLYrPQPaWJa1wfldiYjEfMaYFsAqwM2utBroaFmWr/O7EhGJWgreIvLaMcbEwfYBsKldyQdoaVnWVud3JSISexhjGgPrAHe70kagrWVZPs7vSkQk6ih4i8hrxRgTF1gPNLAreQNNLcva5fyuRERiH2NMPWxBO65daRu2nZzezu9KRCRqKHiLyGvDGBMf2ATUtis9BRpZlrXf+V2JiMRexpiawBYgvl1pN9DMsqynzu9KRMT5FLxF5LVgjEkAbAWq2ZUeA/Uty/rK+V2JiMR+xpgqwHYggV3pANDYsqzHzu5JRMTZFLxFJNYzxiQCdgAV7UoPgbqWZR11flciIq8PY0x5YBeQyK50GGhgWdZD53clIuI8LlHdgIhIZDLGuBhjShpjMgS8TgLs4fnQ/Q9QQ6FbRMTxAt5ra2J77w2qErAn4L0aY0yGgPdwfUYVkVhFb2oiEmsEfFDbApwA/jTG9AD2A2XtJr0HVLMs64STWxQReW1ZlnUcqI7tPTiossA+Y0xP4E9s7+FbFL5FJDbRqeYiEmsYY+pju47wX095/oY+t7Ed6f7JaY2JiEggY0xhbDtFU9iV7N+zG1iWtcNpjYmIOJD2JIpIbNLO7rV96L4BVFHoFhGJOpZl/QhUAW7alezfs9s6pSERESfQEW8RiRWMMR7ALZ6/a+6/rmML3Wed15WIiITGGJMbOASkCWWSx0Aqy7KeOK0pEREH0RFvEYktGhJ66AZICmR3TisiIhIO2bC9N4cmAdDAOa2IiDiWgreIxBbdXlCPDwxwRiMiIhIuA4B4L5imqzMaERFxNAVvEYnxjDGeQI1wTHrB0b2IiEi4XQzHNLUC3uNFRGI0BW8RiQ1aASaMui/wBTDaOe2IiEg4jML23vwsjGkMtvd4EZEYTTdXE5EYzxhTFPjebvgJsAvYBOywLOu+s/sSEZEXM8YkBeoDzYA6gIfdJMUsy/rB2X2JiEQmBW8RiRWMMYOAwcA9YBqwWXfCFRGJWQKeUNEUGAIkBz6wLGtWlDYlIhIJFLxFREREREREHEjXeIuIiIiIiIg4kIK3iIiIiIiIiAMpeIuIiIiIiIg4kIK3iIiIiIiIiAO5RXUDIjGJMcYFKA+0BEoBiQj7+dESvVjAQ+AEsA44almWf9S2JCLyetA2NMbTNlQkAnRXc5FwMsbEAzYA9aK6F4k0O4HmlmV5RXUjIiKxmbahsZK2oSIvQcFbJByMMa7AVvSBITbaCTSyLMsvqhsREYmNtA2N1bQNFQknBW+RcDDGVAYORXUf4jCVLcs6HNVNiIjERtqGxnrahoqEg67xFgmflvYDb3doQP7sGXB10T0KYwo/f39+OX+VmSu225daAvrQICLiGM9tQ4e98xYFC+TH1dU1KvqRV+Dn58fPp39hyowP7UvahoqEg4K3SPiUDvpiSMeGjO7ePKp6kQhoUR2MMcxYvi3ocOnQphcRkQgL9h47cug7vP/eyKjqRSKgTcvmuLi4MGnazKDD2oaKhIMO1YmET6KgLwrmyBRVfUgkKJTzuX+/xFHRh4jIayLYNrRQwQJR1YdEgsKFCtoPaRsqEg4K3iLhE+xxJ5F9evnl67dJXKkziSt1pniHYS817/sLNwTO+/GqXZHaV2wVwr+fHmcjIuI4wbehkXx6+aXLV3BNkAzXBMnIV7TUS807etyEwHlnzpodqX3FViH8+2kbKhIOCt4iDuTn50/NvhNoPWxWsPEnXt4Uaz+Mt2YuCza+ccbb7J3z3+l3W7/6jsaDp5O14QDS1e5N1V7vs/PrH4LNM7h9ff7YNIv0KZM7bD1eRb2BkwN3CAT9avHuB4HT+Pn5M37RBgq2eoeUNbpTsNU7vL9wA8+ehX5z1KA7KYJ+7Tv+kzNWS0REnMTPz4+K1WvTuGXbYONPnjwhb5GS9H1zcLDxnZvXc3jffzugN27ZRu2GzUidOQdJUmekbOUabN2xM9g8Q98exLXzv5MhfTrHrcgrqFanQeAOgaBfDZq1Cpzm4cOHvDVkOFnzFCSBZ1oqVKvF/05+H+Zyg+6kCPq1e+9+R6+SyGtP13iLOJCrqwvzR/SgQrf3WL7jMG/UrwTAe/PX8szPjwl92wSbPnnihHgm/e+MvK9PnaFysbyM7t6cZIkTsHbfMdqN+pidHw2jXOHcACT0iEdCj3i4ukZsh7OXtw+PnnqRImnknDG2YsIAfH2fBb6+9+Ax5bqOpmnVkoFjH67cwcJNX/LpiO7ky5aBX85fpfekhcSN48bQTo3DXP7GGW9TMHvGwNfJEieMlL5FRCR6cHV1ZcmCeRQrW4nFy1bQtVMHAIaNHsuzZ8+YPml8sOk9PZOTIoVn4OvDR45SrXJFxo8ZSfJkyVi5Zh3N27zBl7u3UbF8OQASJkxIwoQJI3wU3svLi4cPH5EyZYoILedf61cux8fHJ/D13Xv3KFqmIi2aNgkc69HvTX4+/QtLFswjQ/p0fLF6LbUaNOH0yW9Jny7sHQk7N6+ncJBT/pMnTxYpfYtI6BS8RRwse4bUvN+7FcNmr6Ry8XxcuHaLz7YcZOdHw0gQP26Y8057s32w18O7NGHPsR/ZfuT7wOAdUd/+/Acrd3/NpoMnmDKgPe3rVoiU5Sa3C8LrDxzHI24cmlT57zTA46fPUbdcEeqWLwpA5rQpqVu+KN/9eiFcy0/tmTRSehURkegpR/ZsTJ0wlsFDR1C9aiXOnb/I/IWL+XL3NhIkSBDmvLNmTAn2+r0RQ9m5ey9btu0MDN4RdfTYt3z+xWrWbdzEh9Om0KlD2xfPFA72QXjN+o14eHjQspltp/TTp0/ZuHkr61Z+TpVKtu32mJHD2L5zN/MXLmb8mFFhLt/TMzlp0qSOlF5FJHwUvEWcoHuTamw/8j09Jyzgyo079G9Vm7KFcr3Ssh498SJporA/bLzIpb9us3rvN6zac5Qbd+5Tr0JRFo3uTY1S/90wZdCMpazZdyzM5Zz4fBIZU3uGOc2/lu84QvPqpYPtbChdIAeLtxzkzKW/yJ0lHb9fusbh739jcIf6L1xeh1Gz8fLxJXuG1PRrVZsmVUq+cB4REYl5evfoxuZtO+nUvTeXLl/hrQF9qVCu7Cst6+GjRyRLljRC/Vy8dJkVq9awfOVq/rp+g0b167Ji8UJq16weOE2fgW/xxep1YS7n9MljZMqYMcxp/rV42XJat2gWuLPh2bNn+Pn5ES9u8B348ePH4+ixb1+4vBZt38DLy5ucObLxZv++tGga9llmIhJxCt4iTmCM4aN3OlO47btkTZeKUd2avdJyFmzcz1+3/6ZN7ZffU//oiRebDp5g5e6jfHv6D8oXys2QNxrSuEoJEnnEf276kd2aMaBN3TCXmTacR5y/+/UCv168ypyhXYOND25fn0dPvCjVaSSuLi488/PjnTca0qNp9VCWBAnjx2Ni39aULpgTN1dXdh79gc5j5zJ/RA/a1IqcIxgiIhJ9GGOYP/tDchYoSvZsWV/5UWRzP13I1Wt/0aFtqxdPbOfRo0es3bCZ5StXcfTYcSqVL8fwIW/TomkjEiVK9Nz040aN4O03B4S5zHRp04bre5/47iSnf/2NhfP+u/lbokSJKFu6JJOmzaBA/rykSZ2aVWvXc+z4/8iRPVuoy0qYIAHTJ42nXNnSuLm5sW3HLtp27IrXgrl0aNs6XP2IyKtR8BZxkuU7DxM/rjt/3b7Hpb9ukzvLy93IZdvhk4yet5YlY/qQKc3LX0O25av/0W/qYnJnTseRReMokD3svewpkyUmZbLIud57+Y7D5MuagRL5gn8Y2HjwBGv2HuOz93qRN0t6fjp3haEfryRL2hR0bFA5xGV5Jk0UbIdAsTxZufvPIz5auVPBW0Qkllry+Qrix4/P1Wt/ceHiJfLmebnLrTZt3c67I8ewctkiMmd6+UeCrt+0lR59B5A3T26+O3rohY9ES5UqJalSpXzp7xOSxcuWUyBfXkqVKB5sfNmiT+nepz+ZcubH1dWVYkUK06Zlc374MfSbjaZI4cngN/sHvi5RrCh37t5lxocfK3iLOJjuai7iBCd/u8CHX+zk8/f7U7VEfnpPXoSfn3+4599y6H90H/8pn47sQb0KRV+ph3oVijFlQDvixXGnSs9xtB85m61ffYe3j2+I0w+asZS0tXuF+fXnzbsv/L5PvLzZ8OVxOjao9Fxt9Ny1DGhThxbVy5A/e0ba1i5P/1a1mfnFjpdatxJ5s3H+6s2XmkdERGKG/538nqkzZ7F2xVJqVKtC11798PML/ekX9jZs3sobXXuydOE8GtWv90o9NG5Qjw+nTSZe3LiUrlSd5m3fYOOWbXh7e4c4fZ+Bb5E4VYYwv678+ecLv++TJ09Ys34j3Tp3fK6WPVtWDu7ZwYNbV7l85jTfHj6A77NnZMn8cjsWSpUowR/nX3xvFRGJGB3xFnEwL28fek1cSPu65alVphCFc2aiVKeRzFq1k7c7NHjh/BsOHKfP5EXMH9EjQtcxJ0uUgL4ta9G3ZS1+vXCVlbu/5p1ZK+g/bQlNq5SkTe1ylCmYE2Nsd0ePrFPNNx08gbevL61DOBr9xNv7uWdqu7q6YPlb4V8x4OdzV0ijG62JiMQ6Xl5edO7Rh04d2lG3dk2KFi5EwZJlmf7BRwwbMviF869Zv5GuvfqxZMHcCF3HnCxZUgb2683Afr05/cuvfP7FKgYOfpee/QbSomljOrRtTfmyZQK3oZF1qvnaDZvx9vahfZvQT49PkCABCRIk4O+/77N3/wGmTBj3Uuv2408/k1Y3WhNxOAVvEQcbu2A93j6+TOpvu9Npas+kzHzrDXpPWkTdckXIly1DqPOu2/8tvSYuZELf1pQvnIubd+8D4O7u9txdw19GvmwZmNC3DeN6teLL706zcvdRmrw9g1nvdKJt7fJA5J1q/vmOI9SvUAzPJM/3W7dcET5cuYPM6VLYTjX/4wpz1uyhbZBr2Md+uo6Tv11g26yhAHyx62vc3VwpnDMzLi6GXUdPsXDTAcb1fvlr9kREJHob8d77eHl5MXPKBADSpEnN7A+m06VnXxrUq0OB/PlCnXfV2vV07tGH6ZPGU6l8OW7csJ0ZFSdOnAg9PqtA/nxMmzSeyePHsu/AQT7/YhV1GjVn3scf8EY722NCI+tU8yWfL6dxw3p4eiZ/rrZn3wH8/f3JkzsX585fYOjI98idMydd3vjviSgj3hvH/777nn07twCwbMUq3N3dKFq4EC4uLmzfuZu5CxYxZfzYCPcqImFT8BZxoKOnzvDpxv1s/WBIsBuYtahehm2HT9Jn8iIOzBsd6vxLth7kmZ8fw2avZNjslYHjFYrkZufHwyPcn6urCzVLF6Jm6UI8ePyUx0+9IrzMoP748wbHfjrL5pnvhFifPqgDExZt5O0PlnP77wek8UxK54aVGdqpUeA0N+7e5+Jft4LP9/k2/rx5B1cXF3JkTMMnw7rp+m4RkVjm8NdHmTN/AXu3bw52A7M2LZuzaet2uvbqxzeH9oU6/8LFS3n27BlvvTuct979b5tZuWJ5vty9PcL9ubq6UqdWDerUqsGDBw949PhxhJcZ1Nk/zvH1N9+ye+vGEOv/PHjAyDHvc/XaXyRPloxmTRoyYcwo3N3dA6e5fuMm5y9eDDbfpGkzuXzlT1xdXcmVIzuL5s3W9d0iTqDgLeJA5Yvk5u+Di0OsLRvX74XzR0a4Dq/ECeKTOMHzdzePiJwZ0/Dg8NJQ64k84jN1YHumDmwf6jTzR/QI9rp93QqR9qxxERGJvipVKI/Pgzsh1tYsX/LC+SMjXIdX4sSJSZw4cm5I+q9cOXPg9/jvUOutmjelVfOmYS5jyYK5wV536tA20p41LiIvRzdXE4lG6g6YTKXuY15qnqnLtgTc6Oyeg7oSERGJ/qrUqk/J8lVeap4JU6YH3OjsqmOaEhEJoCPeItFA+pTJ+WHlVADiuL/cn2WPptVpUb0MQIjXUYuIiMRmGdKn48xPJwGIE8f9BVMH17dnd9q0bA5ACk/PSO9NRORfCt4i0YCbmyvZM7zaHUWTJ04YoRutiYiIxGRubm7kyJ7tleZNnjxZhG60JiISXjrVXERERERERMSBFLxFREREREREHEjBW0RC9fWp36nRZwKZG/QjVY0eFO8wjI9X7Xpuui2H/kfJN0aQonp3Sr4xgm2HT75w2YdO/kqNPhNIV7s3OZu8yXvz1vLsmV+waTZ+eYLyXUeTumZP8rd8m49W7XxuOQs27qdEh+GkqtGDYu2HsXL30VdfYRERkUhy/foN2nfuTr6ipXBP5EmXnn1DnO7Bgwe8+c5QMmTPS/xkqclVsBhrN2x64fJXrFpDsTIV8UiehlSZstOpe+/A2qXLV3BNkOy5r9179wdO89WRo1SoVouUGbORwDMt+YqWYuas2RFfcREJka7xFpFQJYgfj94tapA/W0bix43Dt6f/YNCMpcSPF4ceTasDcPz0OTqPm8eILk1oVKkE246cpOOYT9j7yUhK5sse4nJPn/+TFu9+wOD29fl0ZA+u3/mbQTOW4efvz8R+bQDY++1PdBs/n2kD21OjdEHOXP6LgdOWEi9OHHo1rwHAos1fMubTdXw8pAsl8mXj5G8XGDhtKckSeVC3fFHn/JBERERC4O3jQ4oUnrw7eBCLliwNcRpfX1/qNGpO0qRJWL18MRnSp+fqtWvEjRs3zGXPnvspU2Z8yNSJ4yhTqiRPnz7l7Lnzz023c/N6ChcsEPg66PXsCRMmoH+fXhTMnw8PDw+OHvuWPgMH4+ERnz49u7/aSotIqBS8RRyg3sDJ5M6cjvhx47Bi19e4urgwpGNDujWuyvA5q1i3/xiJPOIzukdz2tYuD4Dvs2eM+GQ1Ww59x70Hj0iZNDGtapZhXO9WAPj4PmPiZxtZs+8Y9x8+JnfmdIzu0ZwapQo6bD2K5s5C0dxZAl9nSZeSbV+d5JufzgYG73nr9lKpaF6GdGwEQO4s6Tj8/W/MXbeXJWP6hLjcDQeOkydLOkZ0tT1/NHuG1Izv04pOY+YyrEtjEnnEZ/Xeb6hbrkjg98maLhWDO9Rn1sqd9GxWHWMMq/d8Q6cGlWlZo0zgNN//dpEPV+5U8BYRiaGq1WlAnty58IjvwdIVX+Dq6srId9+hV/cuvD1sJCvXrCNxokSMHzOKN9rZdtb6+vryzvBRbNi0lbv37pEqZQratW7J5PFjAfDx8WHM+El8sXodf9+/T97cuRg/ZhS1a1Z32HpkyZyJj2bYnliyYfOWEKdZsvwLbt2+zaG9O4gTJ07gfGG5f/8fRox5n42rV1CzetXA8YIF8j83radnctKkCfnmrcWLFqF40SKBr7Nmycymrds5cvSYgreIA+hUcxEHWbvvGAk94vHl/NEMbl+PYbNX0nbkx+TImIZDC8bStk55BkxbwvU7fwMwf/0+th/+niVj+vDDyqksHduHnJnSBi6vz+RFHDn1O5+N7s2xpRNoV6cCrYfN4udzV0LtYcbybaSt3SvMr29+PBPudfrx7GWO//IHFYrkDhw78cs5qpUMvrGvXqoAJ06fC3U53j6+xLV75Eu8uHHw8vHl1JlLAPiEME38uHG4dvseV27csU3j60u8EJZz8rcL+D57Fu71EhGR6GXlmvUkSpSQY4f2M3TwIN56dzhNW7cnV44cnDhykI7t29Kz35v8df06YDsCvHnrdlYuW8SZn75j1eeLyZUzZ+Dyuvbqx1dHjrJiyQJ+PHGUTh3a0rhlW3786edQe5g8fSaJU2UI8+vI0W8itJ5btu2gXJnSDHz7XdJlzU2B4mUYN3EKvr6+oc6z98CX+Pn5cfPWLQoUL0PGHPlo1qYDFy5eem7aFm3fIE3mnFSsXpv1m0IO///64dRPHPv2BJUqlo/QOolIyHTEW8RB8mRNH3hEt3/rOnzwxU7cXV3p27IWAMM6N2bWyp0cP32OJlVKcuXmXXJkTE25wrkwxpAxtSelC9o+NFy4dov1B45zeu0MMqa2PWe0V/MaHDr5C4u3HuLDwR1D7KFr46o0rVoqzD7TpXzxY1TyNH+LO/cf8szPj2Gdm9CtcbXA2s17/5AyWZJg06dMloSb9/4JdXnVSxXkk3V7WbXnKC2rl+HW3w+YutT2geDG3X8Cpxn68RccOHGaqiXycf7aLWav3h04Tea0KaleqiCf7zhMw0rFKZYnKz+cucTnO77C95kfd+8/Ik2KpC9cNxERiX7y583DmJHDAHhrYD+mfjALd3d3BvazXcc8evi7TPvgI7759gQtmjbm8p9/kitHDiqWL4cxhkwZM1KuTGkAzl+4yOp1G7jw249kypgRgH69e7L/4FcsWLyUT2bNDLGHXt260rJZ0zD7TJ8ubZj1F7l46TIHvzpC21Yt2LZxDZcuX2HAW0N49Ogx0yePD3mei5fw9/dn0rSZfDB1EsmTJ2P85GlUr9uQX74/joeHBwkTJGD6pPGUK1saNzc3tu3YRduOXfFaMJcObVsHW16mnPm5fecOz549470RQ+ndvWuE1klEQqbgLeIgBbJlDPx/YwwpkyUif/YMgWPubm4kTZSA238/AKB9nQo0fns6RdsNo1rJ/NQqW4hapQvh4uLCj2cvYVkWpTqOCPY9vH2eUalY3lB7iKxnfO+ePYLHT73436/neW/+OjKnTRF4irxt/YJPb1nWc2NBVS9VgIl9WzNk1gr6TvmMuO5uvNuxEd/8dBZXF9uJOJ0bVubitVu0HfERvn5+JPKIT58WNZm8ZHPgNO92asTNe/9Qs+9ELCxSJUtMu9oVmLVqJ66uOqFHRCSmKlggX+D/G2NIlTIlBfP/N+bu7k6ypEm5ffs2AJ3at6N2o6bkKVyCmtWrUrd2TerWqomLiwvfn/oRy7IoULxssO/h7e1N1cqVQu3BGc/49vf3J1XKFCz45CNcXV0pXrQId+/d4+2hI5k26X1MCBtTf8sfX19fZk2fQq0ath3hKxYvIF22PGzbuZvWLZqRIoUng9/sHzhPiWJFuXP3LjM+/Pi54P3Vvp08evSIb//3HcNHjyVL5kyBp/CLSORR8BZxEDc312CvjTG4udqPgb+/BUCR3Fn4ec0MDpz4mcPf/0bvSYsomD0jWz4Ygr+/hTGGQ5+OeW658ePGCbWHGcu3MXPF9jD73DBtMOUK5w5zmizpUgKQP3tGbt17wOQlmwODd+rkSbhld3T7zv0HpLI7Cm6vf+s69GtVmxt375M0UQKuXL/D2AXryZw2BWD7eb3fpxVjerbg5r1/SJE0EYdO/goQOE38uHGYO6wbH73TiVv3HpDGMylLth0ikUc8PJNEfIeDiIhEDXf34JcRmZDGjMHf3x+AYkULc/6XU+zZ/yUHvzpMl559KVygAHu2b8Lf3x9jDMcPH3huGfHjxwu1h8nTZzJ5+odh9rlj01oqli/3EmsWXNo0qXF3c8c1yOeDvLlz8eTJE+7cuUvKlCmemydN6jQA5Mv737Y7SZIkpEubhj//vBrq9ypVogRLl698bjxrlsyA7RrxW7du8/6kqQreIg6g4C0SjSROEJ+mVUvRtGop2tWtQPXe4zl/7RaFcmXGsixu3vsnzCPc9iLrVPOg/C0LH9//rp8ulT8HX/7vF95sWy9w7Mv//UKpAjleuCxjDGlT2L7/ugPfkiFVcorkyhJsGldXl8Ae1x/4llL5c5AyWeJg07i7uZE+VXLAduO22uWK4OKiI94iIq+TxIkT07JZE1o2a0KnDu0oV6Um585foGjhQliWxY2bt6hauWK4l+eMU83LlSnNqrXr8ff3D9xunT13Hg8PD1Kk8AxxnvJlbafQnzl7jgzp0wPw6NEjrt+4SaZMGUOcB+DHn34mbSg3WvuXv78/3t4+r7IqIvICCt4i0cScNbtJ7ZmUQjky4ebmyrp935I4QXzSp0yGR7y4tKpZlj6TFzGxXxsK58rM3w8e8/UPv5MlXUoaVS4R4jIjeqr5/A37yJw2JTkzpsECjv14htmrd9G9yX/XePdpUZM6Ayczc8V2GlYsxvYj33Pkh9/Z88l/p8V/umE/Czbt5+SKKYFjH63aSY1SBXFxcWHr4e/48IsdLBvXN/AU8bv3H7Lp0P+oWCQP3r6+rNj5NZsP/o+dHw8PXMYff97gu1/PUzJfdu4/fMKctbv59eJV5o/Q3VhFRF4nH378CWnSpKZIoYK4u7uzas16EidORIb06fDw8KBd65Z07dWX6ZMnUKxIYe79/TdfHf6arFmz0KxxwxCXGRmnmp/60XbztocPH+Li4sKpH38mThx38uXNA0DvHl355NNFDBoyjH69enDp8hXGTZhCnx5dA08z/2T+Aj75dBG//nACgFw5c9CoQT3eGjKMebM/JFnSpIydOIVUKVPQoG5tAJatWIW7uxtFC9suWdu+czdzFyxiSsBd3gHmzFtAliyZyJ0zJ5ZlceSbY8z8aA59eugabxFHUPAWiSYSesTj41W7OH/1JsZAoZyZ2TBtMB7xbM/ynDe8G9M/38Z789Zy7fY9kiVOQPE82aj4EkfAX5afnz9j5q/lyo07uLm6kjVdKsb2akm3xv89vqR0wZwsGdOH8Ys2MGnxJrKmS8XSsX2CPcP77j8P+ePKjWDL3vftz8xYvg1vn2cUyJGRVZPepFaZQsGmWbXnKKPnrbFd354/Bzs+HkaJfNkC6/5+/nyydg9/XLmBu5srFYvmYf/cUWROm9JBPxEREYmOEiZKyMxZs/nj/AWMMRQtXJAdm9bh4eEBwOJPP2HStJkMGzWGq9f+InmyZJQsUYwqL3EE/FUULxf8GvLtO3eTOVNGLvz2EwAZM2Rg99YNvDNsJMXKViJN6lR06diekUPfCZznzt17nDn7R7DlfL5oPoOHjqRxy7ZYlkX5smXYt2NL4PoCTJo2k8tX/sTV1ZVcObKzaN7sYNd3+/n5MXz0OC5dvoKbmyvZs2Zl8vvv0Us3VxNxCGNZVlT3IBLtGWPOALn+ff3FhAE0rFQ8CjuSiNh2+CTtR80OOnTWsqywL3QXEZFXYr8NXb9qOU0bNYjCjiQiNm3dTou2bwQd0jZUJBx0EaSIiIiIiIiIAyl4i4iIiIiIiDiQgreIiIiIiIiIAyl4i4iIiIiIiDiQgrdILHfkh99IXKkzd+8/jOpWREREYpRDh7/GNUEy7ty5G9WtiEgMp+AtIlHu6KkztB42i9zNBpG4Ume+2HUkxOn++PMG7UfOJmO9PqSu2ZOK3cZw5tJfgfV6AyeTuFLnYF+dx8511mqIiIg43eGvj9K4ZVsy5siHa4JkLF2+Mszpe/UfhGuCZMycFezpHty4cZOO3XqRLmtuEqVMT9HSFfhi9VpHti7yWtFzvEUkyj166kW+bBloW6c8vSYuDHGaS3/dplbfibStXY5tHYeSJJEHZy9fJ0H8uMGm61CvImN6NA98HS9uHIf2LiIiEpUePXpMgXx5eaNdGzr36BPmtOs3beG7k9+TLm3a52qdevTh77//ZuOaFaRKmZLNW7fTqXtvMmZIT6UK5R3VvshrQ0e8RSLJ1q++o2znUaSq0YNM9ftRd8Bkbt37B4AL127RZvhH5GgykDS1bEdqd31zKtj8BVq9zZSlW+g9aSHpavcmb/PBbDhwnPsPH9N57FzS1u5FkbZDOXDidOA8/55GvuubU5TvOpqUNbpTqfsYfjhzKcxej58+R90Bk0ldsye5mw1i0IylPHj8NFzr4gi1yxZmTM8WNKlSEhcXE+I07y/aQLWS+ZnUvy1Fcmcha7pU1C5bmAypPYNNFz9uHFJ7Jg38SpLQw2F9i4hI5Ni4ZRtFSpUngWdaUmTIStXa9bl58xYA5y9cpEmrdoFHYkuUq8z2XbuDzZ8tbyHGT55Gl559SZI6I5lz5WfN+o3cv/8PbTt1JXGqDOQuVJy9+78MnOff08i379pNsTIV8UiehpLlq3Dyh1Nh9nrs+Amq1q5PwhTpyJgjH30GvsWDBw/CtS6OUK9OLSaOe48WTRvj4hL6R/vLV67w1pDhrFiyEHf354+9HTt+gj49u1OmVEmyZc3C4Df7kzFDev733fcO613kdaLgLRIJbt69T5dx82hXpzz/Wz6J3bOH06Z2ucD646de1CxTkC0zh3B08XgaVS5Oh1GzOXv5r2DLmbtuL8XzZuPworE0rVqS3pMX0u39T6lVpjBff/Y+5QvnoseET/Hy9gk236i5qxnXuxVfLRhLlnQpaTn0A554eYfY6y/n/6TJ29OpV74IRxe/zxcTBvLz+T/pN+WzcK1LSJoNmUna2r3C/IoIf39/dh89RZ4s6Wj6zgyyNhxA5Z7j2HDg+HPTbvjyOFka9qdUxxGM/GQ1D588DWGJIiISXdy4cZN2nbrRsX1bfvn+OIf27KB929aB9UePHlGnVg32bNvID98eoVmThrRo25Hfz5wNtpyPPplHqRLF+O7oIVo2a0qXnn1p36U7dWvV4vtjh6lYvhwdu/XCy8sr2HzvjniPyePHcuLIl2TNmoWGzVrz5MmTEHv9+fQv1G7YjIb16vLDt0fYsHo5P/38C936DAjXuoSkXpMWJE6VIcyviHr27BntO3dnxNC3yZsnd4jTlC1dinUbN3Hnzl38/f3Zsn0nt+/cpXrVKhH+/iKiU81FIsX1O/fxfeZH4yolyZQmBQD5sv23oSyYIxMFc2QKfD2kYyN2fXOKzYe+491OjQLHq5cqQI+m1QEY0bUpc9buIVuGVLSrYzvF691OjVi+8wi/XrxGsTxZA+d7t2MjapQqCMDcYd3J2+It1u3/lk4NKj/X60erd9GsaikGtKkbOPbh4I5U6DaG238/eOG6hGTOu115arczIDLd/vsBj556MXPFdkZ1a8a4Xi05/P1vdJ/wKR7x41K3XBEAWtYoS8Y0nqT1TMpvl64x7tP1nD7/J1s+GOKw3kREJGL+un4DX19fmjdtROZMtm1lgfz5AuuFCxWkcKGCga9HvPsO23fuYcPmrYwc+k7geK3q1ejTszsAY0cN48PZn5AjezY6tm8DwKhhQ1jy+QpO//obJYoVDZxv5NB3qF3Ttu1dPH8OmXLlZ+Xa9XTv3PG5XmfMmk2r5k0Z/Gb/wLFPZs2keLlK3Lp1+4XrEpKFcz/m6VOvMKeJqLETJpM8eXL69OgW6jRrli+hXedupM6cAzc3N+LGjcsXSxdRpHDBUOcRkfBT8BaJBAVzZKJK8XyU6TSKaiXzU6VEfppUKUGKpIkBePzUmylLN7P7mx+5efc+vn5+ePn4UiB7xmDLCfo6oUc8POLFIX+Q0JsqeRLAFkSDKlUgR7D58mXLyO+Xgh9N/9epM5e4cO0WGw+eCByzLAuwnRJfIm+2MNclJOlSJgvz5xNR/gH91atQjP6t6wBQKGdmvj9ziYWbDgQG7y6NqgTOkz97RrKkTUW13u9z6swliuTO4tAeRUTk1RQuVIDqVSpTqGR5alavSvWqlWnRpDEpU9p2/j5+/Jj3J01lx+69XL9xA1/fZ3h5eVGwQP5gyykU5HXChAnx8PAIFnpTp0oJwK1bt4PNV7Z0qWDzFcyfj99+OxNir9+f+pFz5y+wdsOmwLF/t6HnL16kVIniYa5LSNKnSxfmzyeivjpylGUrVvH9scNhTvfe+Incu/c3e7dvJoVncrZs30nnHr05tGdHsB0fIvJqFLxFIoGrqwtbPhjCiV/O8+X/TrN8x2HGLVjHzo+HUzBHJkbNXc3+4z8zoW9rsmdMQ/y4ceg1cSE+vs+CLcfd1TXYa2MM7m7/jf179fO/QfRV+PtbdKxfiX6taj9XS5cy2QvXJSTNhszk2E9nQ6z96/qeT1+5Z88kiXBzdSVP5uAfTnJnThvi6eb/KpYnC66uLpy/elPBW0QkmnJ1dWXP9k18e+J/7DtwkCXLVjByzPsc3L2dwoUKMmTEaPbsO8C0SePJmT0bHh4edO7RGx+f4Gdaubu7B3tt24a6B3sN4G/5v3Kv/v7+dOv8BoP6932ulj5d2heuS0jqNWnB1998G+b3fXDr6iv3fOjwEa7fuEH67HkCx/z8/Bg2eiwffTKfK3/8wvkLF/lk/kK+P3Y4sM/ChQry9dFjzJm/kIVzP37l7y8iNgreIpHEGEPpAjkoXSAHwzo3plTHkWz88gQFc2Ti2E9/0KZOeRpXKQmAl7cPF/+6RY6MqSPle//vl/NkTZcKsB1d/+3CVdqGcl124VyZ+f3SNbJnCP17h7UuIXH0qeZx3N0olicrf/x5Pdj4uT9vkDF16EcRfrlwFT8/f9J4JnVYbyIiEnHGGMqWLkXZ0qUYPfxdCpYoy9oNmyhcqCBHv/mWN9q1oXkT26VZXl5enL9wiZw5crxgqeHz7Yn/kS1rFsB2dP30r7/RoV2bEKctWrgQv/72OzmyZ3uldQmJo08179OjW+DP7l91G7egTcvmdO9iO53+32vaXe0OALi4uuLv/+o7KkTkPwreIpHgxC/nOPTdr1QvVYBUyZPw09nLXLt1j9xZbEdoc2RMzfbDJ6lfvijubq5MWboFbx/fSPv+0z/fRoqkiUibIhlTl27B3d2NljXKhjjtW+3rU733eAbNWEqXRlVJ6BGPs1eus/voKT4a0vmF6xKSiJ5q/uiJFxeu3QRsR+T/vHmPn/64TLLECckYcNfyQe3q0mnMXMoVyk2lYnk58sNvbDhwgpWTbDe0uXDtFmv3HaNWmUJ4JknI75f+YuQnqymcMzNlCuaMUH8iIuI43574HwcOfkWtGtVInSolP/z4M39evRZ4E7CcOXOweet2GtWvh7u7G+9PnoaXd+QF1UnTZpIyRQrSpU3D+CnTieMeh3atWoQ47buD36Rc1Vr0GfgWPbt2IVGihPx+9izbd+5m/uxZL1yXkET0VPNHjx5x7vxFwHZE/s+rVzn1488kT56UTBkzkipVSlIFnGb/L3d3N9KkTkXuXLbtY57cuciRPRv933qHaZPG45k8OVu27WD/lwfZtPaLCPUnIjYK3iKRIEkCD749/QefbtzPP4+ekD5Vct7t1Ig2tWxHnSf1b0v/qYupM2ASSRMloG+LWnhFYvAe16slIz9ZzR9/3iBvlvSsnTLouedb/6tA9ozsmj2c8Ys2UG/gZPz8/cmSNhUNKhUL17o4wg9nLlL/zamBryct3sSkxZtoV6c880f0AKBBxeJ8PKQzM5ZvZ+jHX5A9Q2o+HdmDOmWLABDHzZWvTv7KvPV7efzUm/SpklO7TGGGdWmMq6se4CAiEl0lSZyYo8e+Zc68Bdz/5x8yZkjPqKHv0CHgbuAzp0ygR5+BVK5Vj2RJk/Jmv97P3Zk8Iia9/x5Dho/izB/nyJ83D1s3rCZBggQhTluoYAEO7d3B6HETqVqnAX5+fmTLkpkmjRqEa10c4bvvT1G9bsPA12MnTGbshMl0bN+WJQvmhmsZ7u7ubN+4luHvjaNxi7Y8evyYHNmy8tn8T2hYr+6LFyAiL2SsCFwrKvK6MMacAXL9+/qLCQNoWKl4FHZkc+SH36j/5lQubp2NZ9JEUd1OjLHt8Enaj5oddOisZVmhH44QEZFXZr8NXb9qOU0DgmpUOnT4a6rXbcjNy+dIkcIzqtuJMTZt3U6Ltm8EHdI2VCQcdBhIRERERERExIEUvEVEREREREQcSNd4i8RgFYvm5cHhpVHdhoiISIxTpVIF/B7/HdVtiMhrQke8RURERERERBxIwVskGqg3cDJvf7g8qtsQERGJcarVacCAwUOiug0RkTApeItIuG396jsaD55O1oYDSFe7N1V7vc/Or394brq56/ZSvMMwUtXoQZ7mbzH4g8959CTsR7+c/O0Cjd6aRqZ6fclYrw8NB03lu18vBNYvX79N4kqdn/vad/ynwGmO/PBbiNOcvfxX5P0QREREXsH16zdo37k7+YqWwj2RJ1169n1ummp1GuCaINlzXw2atQp1ubdv36FOo+ZkyJ6X+MlSkzlXfvq/9Q7//PNP4DSHDn9Nk1btSJ8tDwlTpKNIqfIsXrbiuWXN/XQh+YuVJoFnWvIWKcnnX6yOnJUXEV3jLSLh9/WpM1QulpfR3ZuTLHEC1u47RrtRH7Pzo2GUK2x7ksjafcd4b/5aZr/bhXKFcnHpr9v0n7oYbx9fPhnWLcTlPnriRbMhM6ldtjD73hwFwPTl22j2zgx+WT+TRB7xA6fdOONtCmbPGPg6WeKEzy3vxOcTSZbov2ewpkiaOFLWX0RE5FV5+/iQIoUn7w4exKIlS0OcZv3K5fj4+AS+vnvvHkXLVKRF0yahLtfFxYUmjeozcdxoUngm59z5iwwYPISe/QexZvkSAI4dP07B/PkY8tZA0qZJw579B+g9YBDx4sWlXeuWAMxb+BnDRo/j0zmzKF2yBCe+O0mv/oNIliyJnuUtEgl0xFskAhZvOUj2xgN59swv2HjX9+fTZvhHAFy4dos2wz8iR5OBpKnVk4rdxrDrm1NhLrdAq7f5eNWuYGP2p6P7+D5jzPy15Gn+Fmlq9aRyj7HsP/Fz5KxYKKa92Z7BHRpQIl82smdIzfAuTSiSKwvbj3wfOM3x0+comS87bWuXJ3PalFQuno82dcrz3W8XQl3u2SvX+fvBY0Z0aUruLOnInSUdo7o14/6jJ/xx5UawaZMnTkhqz6SBX3Hcn99/mDJp4mDTuLrqrU5EJLr59LMlpM2Si2fPngUbb9+5O01atQPg/IWLNGnVjnRZc5MoZXpKlKvM9l27w1xutryFmDlrdrAx+9PRfXx8GD56LJly5idRyvSUqlCVPfsORNKahSxL5kx8NGMqnd9oR7JkyUKcJnnyZKRJkzrwa/+Xh/Dw8KBls8ahLtfTMzm9u3eleNEiZM6UiepVK9OnRze+PnoscJrhQ95m/JhRlC9bhmxZs9CnRzeaNm7Ixi3bAqf5YtUaunfuSNtWLciWNQttWjanR5dOTP/go8j7IYi8xvRpVCQCmlYrxT+PnnDw5C+BY4+ferPz6+9pXatswGsvapYpyJaZQzi6eDyNKhenw6jZET79uc/kRRw59Tufje7NsaUTaFenAq2HzeLnc1dCnWfG8m2krd0rzK9vfjzzUn08euJF0iBHl0sXyMHP565w/PQ5AP68eZddR3+gVplCoS4jZ6Y0JE+SkM93HMbbxxdvH1+Wbf+KjKk9yZs1fbBpO4yaTbZGA6jZdwKbD/0vxOVV7jmWnE3epOGgqRz+/reXWh8REXGOVs2acv+ff9j/5aHAscePH7N1xy7at7GdWv3o0SPq1KrBnm0b+eHbIzRr0pAWbTvy+5mzEfreXXv146sjR1mxZAE/njhKpw5tadyyLT/+FPoO7MnTZ5I4VYYwv44c/SZCfdlbvGw5rVs0I0GCBC+eOMBf16+zaes2KlUsF+Z0Dx48JFnSpIGvvb19iBcvbrBp4sePx4nvvsfX1/el+haR5+lUc5EISJYoATVLF2TtvmPULG0LltuOnMTN1ZW65YoAUDBHJgrmyBQ4z5COjdj1zSk2H/qOdzs1eqXve+HaLdYfOM7ptTPImNoTgF7Na3Do5C8s3nqIDwd3DHG+ro2r0rRqqTCXnS5lyHvhQ7Jg437+uv03bWr/t3FvVbMs9x48ot7AyVgWPPPzo03tcrzfO/Tr0xJ5xGfnR8NoN3I2H3yxHYBMaVKw+YMhxI8bB4CE8eMxsW9rShfMiZurKzuP/kDnsXOZP6IHbWrZvn8az6R8+HZHiuXJho/vM1bv/YaGb01j50fDKF8kd7jXS0REHC9ZsqTUqVWDlWvWUadWDQA2bd2Om5srDevVAaBwoYIULlQwcJ4R777D9p172LB5KyOHvvNK3/f8hYusXreBC7/9SKaMtkuX+vXuyf6DX7Fg8VI+mTUzxPl6detKy2ZNw1x2+nRpX6mnkJz47iSnf/2NhfNmv3hioF2nbmzdsYunT59Sv25tFs//JNRpt+/azZeHvuLIgf/OHqhVoxqLl62gaeOGlChWlJM/nOKzpcvx9fXlzp27pE2bJsLrJPI6U/AWiaDWtcrRd/JnPPHyxiNeXNbuO0bjyiWIFxAYHz/1ZsrSzez+5kdu3r2Pr58fXj6+FAhynfLL+vHsJSzLolTHEcHGvX2eUalY3lDnS544IclDuCb6VWw7fJLR89ayZEwfMqVJETj+9anfmbF8Gx8M7kiJvNm4cO0WQz/+gomLNzGqW7MQl/XU24d+UxdTqkB2PnuvF37+/sxevZu2wz/iq4VjSRA/Lp5JEzGgzX/XmBXLk5W7/zzio5U7A4N3zkxpyZnpvw89pQvk4Mr1O3y8epeCt4hINNS+TSu69e7PkydP8PDwYOWadTRv0ph48eIBtiPg70+ayo7de7l+4wa+vs/w8vKiYIH8r/w9vz/1I5ZlUaB42WDj3t7eVK1cKdT5kidPRvLk4d85HVGLly2nQL68lCpRPFzTfzB1Eu+NGMqZP84xaux43np3OPNnz3puum++PU6HLj2ZNWNKsGWPGjaEGzdvUaFabSzLInWqVHRs34bpH36Mq6trZK2WyGtLwVskguqULYyrq2HH1z9QpXg+Dn33K5tn/rcXftTc1ew//jMT+rYme8Y0xI8bh14TF+Lj+yzUZboYFyysYGO+Qa4j9/e3MMZw6NMxuLkF3xj+e4Q4JDOWb2Pmiu1hrs+GaYMDb5QWmi2H/kfPiQv5dGQP6lUoGqw2ftFGWlQvQ6cGlQHInz0jj728GTBtMcM6NX6uX4B1+45x6a/b7J87KvB67M/e602m+n3ZduRkYLC2VyJvNr7YeSTMXkvky8aGA8fDnEZERKJGg7q1cXV1Ycv2nVSvUpkDB79i99aNgfUhI0azZ98Bpk0aT87s2fDw8KBzj97BbkBmz8XFBcuy24YGOVXa398fYwzHDx/A3d092HTx48cLdbmTp89k8vQPw1yfHZvWUrF82Kd4h8eTJ09Ys34j40aNePHEAf69LjxP7lx4Jk9G5Zr1GDn0HTJmyBA4zdFj31K/aSvGjRpOnx7Bb3gaP358Pps/h/mzP+TmrVukTZOGBYuXkihRIlKk8IzwOom87hS8RSIoXtw4NKpUgrX7jnH3n4ekTp6ECkGOrh776Q/a1ClP4yolAfDy9uHiX7fIkTF1qMv0TJqIG3f/ewyIl7cPZ69cp1DOzAAUypUZy7K4ee+fMI9w24uMU803HDhOn8mLmD+iB00C1imop17euLoEv32Eq4sLdp+Bgnni7YMx4OJiAsdcjMEYg79/6DP+fO4KaTyThtlveKYREZGoES9ePJo1bsTKNeu4e/ceaVKnonLF8oH1o998yxvt2tC8ie3SLC8vL85fuETOHDlCXWbKFCm4fuNm4GsvLy9+P/sHRQrbLgkrWrgQlmVx4+YtqlauGO5enXmq+doNm/H29gm81v1l+fv7A7brtv/11ZGjNGrRhjEjhvJm/z6hzuvu7k6G9Lb7q6xdv5H6dWrh4qLbQolElIK3SCRoVbMsTd+ZyeXrt2lZo0ywDVSOjKnZfvgk9csXxd3NlSlLt+DtE/ZNSioXy8vynUeoV74IKZImZvrybcHunJ4zYxpa1SxLn8mLmNivDYVzZebvB4/5+offyZIuJY0qlwhxuRE91Xzd/m/pNXEhE/q2pnzhXNy8ex8Ad3e3wOXWKVeET9buoWieLJTIm50L124y4bON1ClbOPBo96cb9rNg035OrpgCQLUS+Rk9bw2DP/ic3s1r4m9ZfPDFdlxdXKgcsGPhi11f4+7mSuGcmXFxMew6eoqFmw4wLsi145+s3UPmtCnIkyU9vs/8WLP3G7Yf+Z4V4/u/8jqLiIhjtWvdkrqNm3Pp8hXatmoRbBuaM2cONm/dTqP69XB3d+P9ydPw8vYKc3lVK1dkyedf0LB+XVKm8GTStJn4BjnLLFfOHLRr3ZKuvfoyffIEihUpzL2//+arw1+TNWsWmjVuGOJyI+NU81M/2m7e9vDhQ1xcXDj148/EieNOvrx5gk235PPlNG5YD0/P5M8t45P5C/jk00X8+sMJwHa99t27f1O8aGESJkzIL7/9xtCRYyhTqiQ5smcDbM/xbti8NX16dKVd65bcCNgx4erqSsqUtsvFzv5xjuP/+44ypUry9/37fDj7E07/+htLFsyL0DqLiI2Ct0gkqFg0D6mTJ+H3S3+xZEzwvciT+rel/9TF1BkwiaSJEtC3RS28XhC8B3dowOUbd2g74mMSxI/LO2805Madv4NNM294N6Z/vo335q3l2u17JEucgOJ5slHxJY6Av6wlWw/yzM+PYbNXMmz2ysDxCkVys/Pj4QC827ERxhgmLNrEX7fv4ZkkEXXKF+G9Hs0Dp7/7z8NgjwnLlTkdayYPYsrSLdToOx6DoWDOTGyYNpj0qf770DH98238efMOri4u5MiYhk+GdQt2GrrvMz9Gzl3D9dt/Ey9uHPJmTc+6qW9Ru2xhh/1MREQkYqpUqkCa1Kn49bffWbl0UbDazCkT6NFnIJVr1SNZ0qS82a83Xl5hB+9h77zFpctXaNq6HQkTJGTEu4O5fiP4oykXf/oJk6bNZNioMVy99hfJkyWjZIliVHmJI+Cvoni54NeQb9+5m8yZMnLht58Cx87+cY6vv/k22Cn3Qd25e48zZ/8IfB0vbjwWfLaE386cwdvbh4wZ0tOkYX2Gvv1W4DTLVqzkyZMnzPxoDjM/mhM4HvR7+/n5MWv2XM78cQ53dzeqVKrI1wf2kCXzfzeIFZFXZ+yvgRGR5xljzgC5/n39xYQBNKwUvpudSPSz7fBJ2o8KdpfYs5Zl6e5rIiIOYL8NXb9qOU0bNYjCjiQiNm3dTou2bwQd0jZUJBx0wYaIiIiIiIiIAyl4i8hrR2f6iIiIvBptQ0VejYK3SPgEe27Jg8dPo6oPiQQPnzx3fWDoz6UREZGICr4NffAwqvqQSPDw4SP7IW1DRcJBwVskfK4FfTF33R6F7xjqweOnzF23x374WkjTiohIpAj2HvvRnLk8ePAgqnqRCHjw4AEfzZlrP6xtqEg46OZqIuFgjOkDBNvSxHF3I3PaFM89s1qiLz9/fy5fv4NPkMfKBOhrWZaelyIi4gAhbkPjxCFrlsy4urpGUVfysvz8/Lh46TI+Ps8d4NY2VCQcFLxFwsEYkxq4BMSL4lYk8nkBWSzLuhnVjYiIxEbahsZq2oaKhJMO1YmEQ8AGpTG2DYzEHl5AY31gEBFxHG1DYy1tQ0VegoK3SDhZlrUXqAscAPyjuB2JGH9s/451A/5dRUTEgbQNjVW0DRV5BTrVXOQVGGNSAcWARICJwlZyAhPsxlYCW6KgF3tNgLZ2Y6OAP5zfSiALeAh8b1nWrSjsQ0TktaVtaLg0QdtQkVhFwVskBjPG7ANqBBm6BWSzLOtxFLUUyBiTELgApAwyvN+yrJpR1JKIiEggY8x+oHqQIW1DRcRhdKq5SAxljKlM8NANMCU6fGAAsCzrETDFbriGMaZSVPQjIiLyr4BtaHW7YW1DRcRhdMRbJAYyxhjgK6BikOG/gByWZUWbB4wbY+ID54G0QYaPAJUtvfmIiEgU0DZURKKCjniLxEw1Cf6BAWBidPrAABDQz0S74Yo8f6ReRETEWbQNFRGn0xFvkRgmYE/9t0CpIMNXgFyWZXlHTVehM8bExXYzmIxBho8DZbXHXkREnEnbUBGJKjriLRLz1Cf4BwaA96PjBwaAgL7etxsujW09REREnKkB2oaKSBTQEW+RGMQY4wKcBIoEGT4P5LUsyzdKmgoHY4w78DuQLcjwD0Bx7bEXERFn0DZURKKSjniLxCxNCf6BAWBsdP7AABDQ31i74aLY1kdERMQZtA0VkSijI94iMYQxxhX4CcgXZPg3oKBlWX5R01X4BfR/GsgTZPgXoHBM6F9ERGIubUNFJKrpiLdIzNGa4B8YwLanPkZscAP6HGs3nB9o5fxuRETkNaNtqIhEKR3xFokBjDFuwK9AziDDPwFFLcvyj5quXl7A9XWngIJBhv8A8lmW9SxKmhIRkVhN21ARiQ50xFskZniD4B8YAN6LSR8YAAL6fc9uOCfQIQraERGR14O2oSIS5XTEWySaM8bEAc4AWYIMfweUiol3Mw14hur/gOJBhi8CeSzL8omarkREJDbSNlREogsd8RaJ/roS/AMDwOiY+IEBIKDv0XbDWYEuUdCOiIjEbtqGiki0oCPeItGYMSYecA5IH2T4G6BCTP3QAIF77I8CZYMMXwVyWpblFTVdiYhIbKJtqIhEJzriLRK99ST4BwaAUTH5AwME7rEfZTecAdv6ioiIRIZeaBsqItGEjniLRFPGGA/gApA6yPBBy7KqRVFLkc4YcxCoEmToJpDNsqwnUdORiIjEBtqGikh0oyPeItFXP4J/YIDnr+uK6ezXJzXQNyoaERGRWEXbUBGJVnTEWyQaMsYkwnaXUs8gw7sty6obRS05jDFmN1A7yNBdIKtlWQ+jqCUREYnBtA3VNlQkOtIRb5Ho6U2Cf2CA55/dGVvYr5cnMDAqGhERkVhB21ARiXZ0xFskmjHGJMO2pz5JkOEtlmU1iZqOHM8YswVoFGToPrY99vejpCEREYmRtA0FtA0ViZZ0xFsk+hlM8A8MEHv31P/Lfv2SYvs5iIiIvAxtQ7UNFYmWdMRbJBoxxqTAtqc+YZDhdZZltYqilpzGGLMOaBFk6CG2u7PeiaKWREQkBtE2VNtQkehMR7xFopchBP/A4A+MiaJenG0MEHRPYCJsPw8REZHweBdtQ/+lbahINKMj3iLRhDEmDbZnjsYPMrzCsqw3oqglpzPG/J+9u46KonsDOP4dEQVEBBSxFezE7u7u7u6f/drd3d3d3d2tr/Xa3WIXKj2/PxbX3WVBlNgFns85nuPenZl9ht2Z5965d+6sBBrpFP1Ac8Xe3UQhCSGEiAQkh0oOFcLcSY+3EOajL/oVBj9gmIliMZVhaPb7J2s0fxchhBAiOP2QHCo5VAgzJg1vIcyAoijJgA4GxUtVVb1vinhMRVXVe8Ayg+L2AX8fIYQQIpCAHNHeoFhyqIbkUCHMhDS8hTAPA4BYOq99gBEmisXURqDZ/59iA/1NFIsQQgjzJzn0F8mhQpgpaXgLYWKKorgArQyKF6iq+sQU8ZiaqqqPgYUGxa0VRUkV8dEIIYQwZwE5tLVBseRQfZJDhTAD0vAWwvQGAZY6r72A0SaKxVyMQvN3+MkSzd9JCCGE0DUIiKnzWnKo5FAhzJI0vIUwIUVR0gFNDYpnq6r6whTxmIuA/Z9jUNxMUZS0pohHCCGE+QnIoc0MiiWHSg4VwizJ48SEMCFFUVYBDXWKvgMuqqq+MVFIZkNRFGc0j4ax0SlepapqYxOFJIQQwoxIDg2a5FAhzI/0eAthIoqiZAYaGBTPkAqDhqqqr4GZBsUNFUXJZIp4hBBCmA/JocGTHCqE+ZEebyFMRFGUDUBtnaKvaK7UvzdRSGZHUZT4wCMgrk7xRlVV65goJCGEEGZAUZSNQC2dIsmhBiSHCmFepMdbCBNQFCU7+o1ugClSYdAX8PeYalBcO+DvJ4QQIhoKyAG1DIolhxqQHCqEeZEebyFMQFGU7UAVnaKPgKuqqp9ME5H5UhTFHs0Ve3ud4u2qqlYzSUBCCCFMSnJoyEkOFcJ8SI+3EBFMUZR86FcYACZKhcG4gL/LRIPiqoqi5DVBOEIIIUxIcuifkRwqhPmQHm8hIpiiKPuAsjpF79Dcl+ZhopDMnqIocdFcsY+vU7xPVdXyJgpJCCGECUgO/XOSQ4UwD9LjLUQEUhSlCPoVBoCxUmEInqqqX4GxBsXlFEUpbIp4hBBCRDzJoX9HcqgQ5kF6vIWIIIqiKMARoJhOsTuQWlXV76aJKvJQFMUGeAAk0ik+qqpqCROFJIQQIoJIDg0dyaFCmJ70eAsRcUqiX2EAGCUVhpAJ+DuNNigurihKSVPEI4QQIkJJDg0FyaFCmJ70eAsRAQKu1J8G8usUPwPSqqrqZZqoIh9FUayAe0AyneIzQCFVTmZCCBElSQ4NG5JDhTAt6fEWImJUQL/CADBSKgx/RlVVT2CkQXEBQCaIEUKIqEtyaBiQHCqEaUmPtxDhLOBK/UUgp07xQyCDqqo+pokq8lIUJRZwG3DRKf4XyCNX7IUQImqRHBq2JIcKYTrS4y1E+KuOfoUBYLhUGP6OqqrewHCD4lxANROEI4QQInxVR3JomJEcKoTpSI+3EOFIUZQYwFUgi07xXSCzqqq+pokq8lMUJSZwA0inU/wfkF1VVX/TRCWEECIsSQ4NH5JDhTAN6fEWInzVRb/CADBEKgyhE/D3G2pQnBWoE/HRCCGECCeSQ8OB5FAhTEN6vIUIJwFXlK8D6XWKrwNuckU59AJ6Qq4BmXWK7wBZpFImhBCRm+TQ8CU5VIiIJz3eQoSfhuhXGAAGS4UhbAT8HQcbFKdH83cXQggRuTVCcmi4kRwqRMSTHm8hwoGiKJZoZg111Sm+BOSWWUPDTsBst/8COXSKZbZbIYSIxCSHRgzJoUJELOnxFiJ8NEe/wgCaK/VSYQhDAX9Pwyv2rkAzE4QjhBAibDRHcmi4kxwqRMSSHm8hwpiiKLGBe0ByneKzQEGpNIS9gCv2Z4B8OsXPgLSqqnqZJiohhBB/Q3JoxJIcKkTEkR5vIcJeG/QrDACDpMIQPgL+roMMipMDrU0QjhBCiNCRHBqBJIcKEXGkx1uIMKQoig3wAEikU3wMKCGVhvATcMX+KFBUp/gVkFpV1R8mCUoIIcQfkRxqGpJDhYgY0uMtRNjqgH6FAeRKfbgL4op9YjTfhxBCiMhBcqgJSA4VImJIj7cQYURRFFvgEZBAp/iAqqplTRRStKMoygGgtE7RO8BFVVUPE4UkhBAiBCSHmp7kUCHCl/R4CxF2/od+hQECX0EW4cvw750AzfcihBDCvHVBcqipSQ4VIhxJj7cQYUBRlHhortQ76BTvVFW1iolCirYURdkJVNIp+ojmiv1nE4UkhBAiGIqi2KPJofY6xZJDTUByqBDhR3q8hQgb3dFvdEPgZ2OKiGH4d3dA8/0IIYQwT93Rb3SD5FBTkRwqRDiRHm8hQklRlPhortTH1SnepKpqbROFFO0pirIJqKlT9AXNFfsPJgpJCCGEEZJDzY/kUCHCh/R4CxF6vdCvMKjAEBPFIjSGoPkefrJD8z0JIYQwL5JDzY/kUCHCgfR4CxEKiqIkRHOl3kaneLWqqo1MFJIIoCjKaqCBTtF3NFfs35goJCGEEDoURXEGHiI51OxIDhUi7EmPtxCh0xf9CoM/MMxEsQh9w9B8Hz/ZAH1MFIsQQojA+iA51FxJDhUijEmPtxB/SVGUpMB9wEqneImqqi1NFJIwoCjKEqC5TpEnkFpV1ZemiUgIIQRoc+gDILZOseRQMyI5VIiwJT3eQvy9/ug3un2BESaKRRg3HM338pMVmu9NCCGEafVHv9EtOdT8SA4VIgxJw1uIv6AoSkqgjUHxIlVVH5kiHmFcwPex2KC4bcD3J4QQwgQkh0YOkkOFCFvS8Bbi7wwELHVeewEjTRSLCN5IwFvntSWa708IIYRpDEJyaGQhOVSIMCINbyH+kKIoaYAWBsXzVFV9bop4RPBUVX0GzDMobqEoSmpTxCOEENFZQA5tblAsOdRMSQ4VIuxIw1uIPzcYsNB5/QMYY6JYRMiMQTMpzE8WaL5HIYQQEUtyaOQjOVSIMCANbyH+gKIoGYHGBsUzVVV1N0U8ImRUVX0FzDQobqwoSgZTxCOEENGR5NDISXKoEGFDHicmxB9QFGUdUFenyANwUVX1nYlCEiGkKIoT8AiIo1O8TlXV+iYKSQghohXJoZGX5FAhQk96vIUIIUVRsqFfYQCYKhWGyEFV1bfAVIPiegHfqxBCiHCkKIobkkMjLcmhQoSe9HgLEUKKomwFqukUfUZzpf6jaSISf0pRFAc0V+zj6RRvVVW1holCEkKIaEFyaOQnOVSI0JEebyFCQFGU3OhXGAAmSYUhcgn4viYbFFdXFCWXKeIRQojoQHJo1CA5VIjQkR5vIUJAUZQ9QHmdoveAq6qqX0wUkvhLiqLYobli76hTvEdV1YomCkkIIaI0yaFRh+RQIf6e9HgL8RuKohREv8IAMF4qDJFTwPc23qC4gqIoBUwRjxBCRGWSQ6MWyaFC/D3p8RbiNxRFOQSU1Cl6DaRWVfWbiUISoaQoShzgIZBQp/iQqqqlTRSSEEJESZJDox7JoUL8HenxFiIYiqKUQL/CADBGKgyRW8D3N8aguJSiKMUjPhohhIiaJIdGTZJDhfg70uMtRBAURVGAE0AhneIXQBpVVT1NE5UIK4qiWAEPgCQ6xSeBoqqcGIUQIlQkh0ZtkkOF+HPS4y1E0MqhX2EAGCkVhqgh4HscaVBcGChrgnCEECKqkRwahUkOFeLPSY+3EEYEXKk/D+TWKX4MpFdV1dskQYkwpyhKLOAukFKn+AKQT67YCyHE35EcGj1IDhXiz0iPtxDGVUG/wgAwQioMUUvA9znCoDgPUNkE4QghRFQhOTQakBwqxJ+RHm8hDCiKEgO4DGTTKb4PZFRV1dc0UYnwoiiKJXATSKNTfBXIqaqqv2miEkKIyElyaPQiOVSIkJMebyECq4V+hQFgqFQYoiZVVX2AYQbFbkBNE4QjhBCRneTQaERyqBAhJz3eQuhQFMUC+A/IqFN8E8imqqqfaaIS4S2I7/0WkFW+dyGECBnJodGT5FAhQkZ6vIXQ1wD9xAEwRBJH1Bbw/Q4xKM4I1DdBOEIIEVlJDo2GJIcKETLS4y1EALlPKXqT+xKFEOLvSQ6N3iSHCvF70uMtxC9N0a8wAAySCkP0EPA9DzIoToPmdyGEECJ4kkOjMcmhQvye9HgLQZDPojwP5JdnUUYfAc+ePYfmcSg/PQHSyWNwhBDCOMmhAiSHCvE70uMthEYr9CsMoLlSLxWGaCTg+za8Yp8SaGmCcIQQIrKQHCokhwrxG9LjLaI9RVGs0dyHlESn+CRQVCoN0U/AFfsTQCGd4hdAGlVVPU0TlRBCmCfJoUKX5FAhgiY93kJAO/QrDCBX6qOtIK7YJ0XzOxFCCKFPcqjQkhwqRNCkx1tEa4qixAEeAgl1ig+pqlraRCEJM6EoyiGgpE7RG8BVVdVvJgpJCCHMiuRQERTJoUIEJj3eIrrrjH6FAQJfqRXRk+HvICHQyRSBCCGEmZIcKoIiOVQIA9LjLaItRVHsgEeAo07xblVVK5koJGFmFEXZDVTQKXqP5or9FxOFJIQQZkFyqPgdyaFC6JMebxGddUO/wgAw2ARxCPNl+HuID3Q1RSBCCGFmuiE5VARPcqgQOqTHW0RLiqI4oLlSH0+neKuqqjVMFJIwU4qibAWq6RR9BlxUVf1omoiEEMK0JIeKkJIcKsQv0uMtoque6FcYVORKvTDO8HcRD+hhikCEEMJMSA4VISU5VIgA0uMtoh1FUZzQXKmPo1O8TlXV+iYKSZg5RVHWAXV1ijzQXLF/Z6KQhBDCJCSHij8lOVQIDenxFtFRb/QrDP7AUNOEIiKJoWh+Jz/ZovkdCSFEdCM5VPypoUgOFUJ6vEX0oihKYjTPHLXSKV6uqmozE4UkIglFUZYDTXSKfqCZndXdRCEJIUSEkhwq/pbkUCGkx1tEP/3QrzD4AcNNFIuIXIaj+b38ZI3m9ySEENGF5FDxtySHimhPGt4i2lAUJTnQzqB4saqqD0wRj4hcVFW9DywxKG4f8LsSQogoTXKoCA3JoUJIw1tELwOBWDqvvYGRJopFRE4jAR+d17GAASaKRQghIpLkUBFakkNFtCYNbxEtKIriCrQ0KF6gqupTU8QjIidVVZ8ACwyKWymK4mKKeIQQIiJIDhVhQXKoiO6k4S2ii8FATJ3XnsBoE8UiIrdRaH4/P8VEnl8rhIjaJIeKsCI5VERb0vAWUZ6iKOnRn0kTYLaqqi9NEY+I3AJ+N3MMipsqipLOFPEIIUR4khwqwpLkUBGdyePERJSnKMpqoIFO0Tc0j7B4Y6KQRCSnKEpC4BFgo1O8WlXVRiYKSQghwoXkUBHWJIeK6Ep6vEWUpihKFqC+QfF0qTCI0Aj4/Uw3KG4Q8HsTQogoQXKoCA+SQ0V0JT3eIkpTFGUTUFOn6AvgoqrqBxOFJKIIRVHio7liH1eneJOqqrVNFJIQQoQpyaEivEgOFdGR9HiLKEtRlJzoVxgAJkuFQYQFVVXfA5MNimspipLDFPEIIURYkhwqwpPkUBEdSY+3iLIURdkJVNIp+ojmSv1nE4UkohhFUeKhuWLvoFO8U1XVKiYKSQghwoTkUBHeJIeK6EZ6vEWUpChKfvQrDAATpMIgwlLA72miQXFlRVHymSIeIYQIC5JDRUSQHCqiG+nxFlGSoigHgNI6RW/RzMLqYaKQRBSlKIotmiv2CXSKD6iqWtZEIQkhRKhIDhURRXKoiE6kx1tEOYqiFEW/wgAwVioMIjwE/K7GGhSXURSliCniEUKI0JAcKiKS5FARnUiPt4hSFEVRgGOA7gn7FZBaVdUfpolKRHWKotgA94HEOsXHgeKqnGSFEJGE5FBhCpJDRXQhPd4iqimNfoUBYJRUGER4UlX1OzDaoLgoUMoE4QghxN+SHCoinORQEV1Ij7eIMgKu1J8BdCfleAakVVXVyzRRiehCUZTYwD0guU7xWaCgXLEXQpg7yaHClCSHiuhAerxFVFIR/QoDwHCpMIiIEPA7G2FQnB+oYIJwhBDiT0kOFSYjOVREB9LjLaKEgCv1/wI5dIofAhlUVfUxTVQiulEUxRK4DbjqFF8CcssVeyGEuZIcKsyB5FAR1UmPt4gqaqBfYQAYKhUGEZECfm/DDIpzAtUjPhohhAgxyaHC5CSHiqhOerxFpKcoigVwFcisU3wbyKKqqp9pohLRVcDv8QaQXqf4OuCmqqq/aaISQgjjJIcKcyI5VERl0uMtooK66FcYQHOlXioMIsIF/O6GGhRnQfM7FUIIcyM5VJgNyaEiKpMebxGpKYoSE82V0XQ6xf8B2eXKqDAVRVFioOlByqJTfAdND5KvaaISQgh9kkOFOZIcKqIq6fEWkV1j9CsMAIOlwiBMKeD3N9igOD3QyAThCCFEUCSHCrMjOVREVdLjLSItRVFiobkPzUWn+F8gj8x+KUwtYJbgi2gmhvnpEZBeJiwSQpia5FBhziSHiqhIerxFZNYC/QoDwCCpMAhzEPA7HGRQ7ILmdyuEEKYmOVSYLcmhIiqSHm8RKSmKYgXcA5LpFJ8BCkmlQZiLgCv2p4H8OsXPgLSqqnqZJiohRHQnOVREBpJDRVQjPd4ismqDfoUBYKBUGIQ5Cfg9DjQoTo7m9yuEEKYiOVSYPcmhIqqRHm8R6SiKYgM8BJx1io+qqlrCRCEJEaSAK/ZHgGI6xe5AalVVv5smKiFEdCU5VEQmkkNFVCI93iIy6oh+hQEC3wckhFkI4j61REAHE4QjhBCSQ0WkITlURCXS4y0iFUVR4qK5Up9Ap3ifqqrlTRSSECGiKMo+oKxO0TvARVVVDxOFJISIZiSHishKcqiICqThLcyeoihZgNZoJoJxAEYYLJJPVdXzER6YEH9AUZS8wDmD4oHAJyAtsFBV1esRHZcQImqTHCqiAsmhIiqQhrcwa4qixAeeA1YBRZ46/wfYrqpqtQgPTIi/oCjKdqCKTpHu79kTSKaq6vsID0wIESVJDhVRieRQEdnJPd7C3JVEv5JgZfD+4AiMRYjQMvy9Gv62S0ZgLEKIqE9yqIhKJIeKSE0a3sLc2QTzngqsVxQlTUQFI8TfCvidrkPzuw2KdQSFI4SIHiSHiihBcqiICqThLcydRTDvKUA6At+vJoQ5Gonm96oEs0xwv3chhPhTkkNFVCE5VER60vAW5i4kv1H/cI9CiNDzC8Eyck4WQoQlyaEiqpAcKiI9+YEKc/e7q5fuwICICESIUBqA5vcaHLlaL4QIS5JDRVQhOVREetLwFubOIZj39gJuqqo+jqBYhPhrAb9TN2BfMIsF93sXQog/JTlURAmSQ0VUIA1vYe5SGSnzBf4BKqmq+iZiwxHi7wX8Xiui+f36GlkkVYQGJISI6lIZKZMcKiIlyaEispOGtzB3hlc2PwOFVFWdqKqq3JcmIh1VVf1VVZ0IFELze9a11wQhCSGiLsmhIkqRHCoiM0VVg5uVXwjTUxRlDNAcuAtUV1X1o2kjEiJsKIriCGxBM1PrUlVV+5k4JCFEFCM5VERVkkNFZCMNbyGEEEIIIYQQIhzJUHMhhBBCCCGEECIcScNbCCGEEEIIIYQIR9LwFkIIIYQQQgghwpE0vIUQQgghhBBCiHAkDW8hhBBCCCGEECIcScNbCCGEEEIIIYQIRzFNHUB0oShKOiA9YG3qWMQf8QM+AGdVVf1h6mBE+FMUxRrIDzgCFiYORxj3A7ijqupdUwciIobk0EhLcmg0JcesWZLj0cTkOd7hTFGUdkAnIKupYxGh8g3YAfRVVfWJqYMRYU9RlJTAWKAKEMfE4YiQ+Q+YparqPFMHIsKH5NAoQ3JoNCHHbKQgx6OJSMM7HCmKMggYbuo4RJh6BJSQE1XUEtDoPgK4mDoW8VcGq6o6wtRBiLAlOTRKkhwahckxG+nI8RjBpOEdThRFqQpsM3UcIlxcVlU1p6mDEGFHUZRLQA5TxyFCpZqqqttNHYQIG5JDozTJoVGQHLORlhyPEUju8Q4/jQwLHOPFJaGjPYpiinDE3/D28eXBs1eGxTkURcmoquotU8QkwpaiKJkw0uh2TRyfWDHlFm9zo6Ly5pMHH78Guj2tESAN76gjcA51dMQ5oROKJNFIw9vbh/sPHhgWSw6NmozXe+PboyDHrDnw9vXlwdOXhsVyPEYgaXiHA0VRYgKVdcua1yjL9P4dsbCQinxkc/fxcwo06Iqnl7ducTVATlJRQ1XdF1axYnJi2v9IkzSBqeIRv+Hn50+POdtYvv+ibnFlRVEsVFX1M1VcImwYy6GtWjRn7sxpkkMjoTt375Ijb0E8PT11iyWHRiFG6701yzF9YGc5Zs3M3cfPKVC3s9RpTUQeJxY+4gE2ugV9WtWTk08klS5VMmqXLWJYnNQUsYhwofdd1iySTRrdZs7CIgY96xY3LLYB7CM8GBEeAuXQgX17Sw6NpNKnS0f9OrUNiyWHRi2B671t6ssxa4bSpUpG7XJFDYvleIwg0vAOH7EMC+La2hhb7q89efmaODmrECdnFXLUbP9H6w6btUK77tTlm8M0rqgqbpxAT8MI9B2LSEvvu4xrE/uPN/D09Uccqg7AoeoA8naY8kfrjlx5QLvujC0n/vizo6u41ka/Jzkuo4ZA36OdXdxw/cDHj58Qw8qWGFa2ZMz2Z9M9DBwyTLvuxCnTwinCyM02rq1hkRyrUUuY1nufvHhNHLeKxHGrSI5qbf9o3WEzl2nXnbps01/HEJXFjRPou5HjMYJIw9vM+Pn5UapFb+p005+g9/sPT7LXaE/X0bP1yrfOHMaBReO0r7cdOk2VjoNIWbIRzoXrUqxpT3YdO6e3Ts8WtXmwfzlJnc2rV698m37aCwK6/2p2GaZdZt66XeSt+z8SFalLoiJ1KdGsF3tPXAh2u7oXKXT/7T/1b8iDk3sKoyU/P3/K9Z5Hg5Er9Mq/e3mTp8MUeszWn0dm49Dm7Bn3q5Lg/uELrSeuI2+HKcSvPpCOUzcG+oxutYpye1lfkiSIFz478ZeW7btAhb7zcWk4kpQNRlBlwELO3Hyst8zkDcco2WM2KeoNJ03jUdQfsZybT17/dts3HrtTqd8CEtceQqbmYxm/9jC6E32e/O+h9mKE7r+7z99ql5H7fEVI+Pn5Ubh4aarVqqtX/v37dzJkzUGH/3XVK9+zYysnDh/QKzt2/AS5CxTGOl58UmfIwtwFC/Xe7/tPT14+fkCypObVaVSiTHntBQHdf5Wr1wp2vadPn1G1Zh1sHRPilDQFXXr0wtvbO9h1fkeOVwEBddxmPamjU6+DgDpu1TZ0HTlTr3zr7BEcWDpB+/rV2w807zuOHNXaEjdHZdoOmhzoM3q2rMuDQyvNro578/4TGvUcReaKLYnjVpFRc1YGWmbConUUadiVRAVrkbJ4fWr/byg37j3+7bav33tEuZa9iZ+3OmlKN2HM3NUEO3m2HI4mI/d4mxkLCwvmD+tGgQZdWbb1AM2qlwFg0PRl+Pr5Mbp7S73l49vHJYHDrwr7iUvXKZYnG0M6NsHBzpZ1e45Rv+do9s4fTaGcmQGwtbHG1sYaixihu+7i6eXN1+8/cHIImwbD6on98fbx1b7+8PkL+et3oUbpQtqypM7xGdG1GWmSJ8FfVVm14xD1eo7i5MopZE0X/JOgts4cpreMY7xAV+CF0GNhEYM53WpTtNtMVh64SOMyuQEYunQfvn7+jGhZQW95x7jWxLf79Qhwbx8/4tvFoVvtYizdZ/wCka11bGytY2MRI3SZ0NPbB48f3iSIFzaPID95/RE1Cmclf8aUWMe2ZPb2U9QespTj0zqTOommQnPq+kNaVcxHjrTJUFWVMasPUmPQYs7O6opDXOO9HV++e1Jz8BIKZk7FoUkduf/iLZ2mbcImdiw61yist+yZmV1xiPtrtEkCO3m8uvgzFhYWLF00jxx5C7J46XJaNm8KQJ8Bg/D19WXi2NF6y8d3dCRBgl8V9kePHlOpei1aNGvCiiULOXnqDJ26dscpQQJq1agOgK2tLba2tqEeVuvp6cnXr19xcnIK1XZ+2rRuNd7ePtrX7z+8J3ueAtSuWSPIdfz8/KhcoxbxHR05fmg/Hz58pFnrNqiqyowpk8IkLhF9WVhYMH9ETwrU7cyyLftpVqMsAIOmLtHUcXu01lvesI7r7e1DAvt49GhZhyWb9hr9DG0d1yIM6rjffuDkGDZ13B+eXqRI4kzVUgUZPnOF0WVOXPiPNnUrkStzOlRURs5aSeV2A/h3y1wc4xkf9fPF4ztV2g2kcK7MHF81lXtPntNu0BRsrK3o2qxmmMQuwo40vM1Q6hRJGNm1OX0mLaBEXjcePHvFgo272Tt/NHGsrYJdd+I/+kNy+rdrwN6TF9hx9Ky24R1aZ67cZNWOw2w+cJLx/7ShcZVSYbJdw5PKxn3HsbGKTc0yvyrjlYvn11tmaOemLNy4h/PXbv+24R3fPi6JEjiESawi+nBNEp+hzcvRf+Fuirql5tGrDyzec54do1sRxyr40VkpnB0Y11Yz38y2U9fDJb6zN5+w9vAltpy6ztjWlWhQKmyeCrKgp34P4eQO1dh99haHLt3TNrw3DWuht8zc7nVI2WAEZ289oULejEa3u+HoVX54+TC7W22sY1uSKaUzd5+9Zfa2k3SqXkivZ8zJPo7ehQwh/kaa1KkZN3oE3f/pQ6kSxbn/4AFz5y/kyP49xIkT/O9r7sJFJEmcWNvozJghA+cuXGTS1OnahndonTp9hmUrV7Fh0xamThxPsyaBJof+K46Ojnqv127YiI2NDXVqBd3w3n/wEDdu3uLx3VskT54MgHGjRtKmQydGDRuCnZ1dmMQmoq/UKZIwsntL+kyYR4l8AXXcDbvYu3AccWyCr+OmTOrMxL6a2yu3HjgZLvGduXyDVTsOsXn/Ccb3bkfjqqXDZLu5sqQjV5Z0AExYuN7oMtvnjtR7vXB0LxIXqsPZyzepWDyf0XXW7T7CD09P5o/oibVVbDKnTcWdh8+YsWILXZrWkNEmZkYa3maqTZ2K7Dh6ltaDJvPk1Wv+16g6BXP8XcPZ49sPHOxC17v7+IU7a3YdYfXOI7x6955KxfKzeHQvyhT4dS9cl1GzWLv7aLDb+XfjLJInThiiz1y27QC1yxYN8mKDn58fmw+ewuO7J/ncjFfydTXoNRpPLx/SpEhC50bV9HrShQhOqwr52H32Fu0nb+Tpm490rFaIAplSmSyeJ+4fWHf0CmsPX8b9w1cq5MvIgp51KZUjrXaZ7rO3suHo1WC3c2ZWV5I72YfoM719/fD08SVe4PkOtDx+eOHvr2JvG/QyF+48JX9mTS/6TyVzpmXUqoM8ff2RlIl+NRZK9JiNt48v6ZMnpFfdEhTJ5hqiWIUw1KFtG7Zt30nTlq15/OQp3bv+j8KFCv52vbNnz1GmdEm9snJlSrF85Sp8fHywtLQMYs3gPXr0mBWr17Bi1RpevnpFtcqVWLVsMeXK/Krkt+/chVVr1gW7nRuXL5IiRfIQfebipcuoV6dWsBcbzpw9R8YM6bWNbtDsr5eXF/9eukyJ4sVC9FlCBKdN3UrsOHyG1gMm8uTlG/7XpAYFw6hz6G88fu7Omp2HWb3zEK/efqBS8fwsHtObMgV/XcjuMmIGa3cdCXY7/26ZG+I6bkh8/fYDf39/7IOpw5+7eouCObNgbfVr3pPSBXMxfNYKnrx4TapkicIsHhF60vA2U4qiMGNAJ7JUbYtrskQM7tj4r7Yzb90uXrx5T4OKJf54XY/vP9i0/ySrdx7izNVbFMqRhX9a1aVG6YLGJmZgYIdGdG0S9JV0gMRO8UP02Rev3+Xm/SfMGdwl0HvX7z2mZPN/8PT2xtbamrWT+pMlbaogtxXH2orR3VtSwC0jMWNasOvYOZr2Hc/8Yd1oUOnP/y4i+lEUhSkdq5Gj3WRcEjkyoHHYXAH/Ex4/vNh68j/WHL7MuVtPKZg5FT3rFqdqwSxGJ4Tr37A0/6seaDZ+PYkdQz5h1ciVB7C1ikWFfBmCXKbfgl1kdUlM3vQpglzmzUcPksTX7zVzstdUKl5/8iBlIkecHeIyqUNVcqZNhrevH+uOXKbaoMXsGNWKQlmCH9kihDGKojB35nTSZMpKaldXRgwZFKL13F+/oVRJ/TzhnDAhvr6+vHv3nsSJQ16p9fDwYP3GzSxfuYpTZ85StHAh+vf5h9o1qxM3buBjcfjggfTq3tXIln5JkiRxiD77/IWLXL9xk4VzZwe7nPvr1zgn1G84JEiQAAsLC9xf/37+BiFCQlEUZgz6H1kqt8I1eWIGd2oa4TF4fP/Bpn0nWL3jIGeu3KRQziz807oeNcoUNl7H7diErs2Cnx8hpHXckOo9fi7Z0ruSzy3ovPv63cdA97MnjG+vee/9R2l4mxlpeJux5dsOYh07Fi/evOfRc3cyuIbsqvZP2w+fYcC0xSwb05sUSf78CtyWg6foOHw6GVySc2rV1N8O5U7oaE9CR/s//hxjlm3dT6Y0KckdMCxHV7pUSTmzZhqfPb6x9dBp2g6Zwp75Y8icJqXRbSVwiKd3QSBnprS8//SFKcs2S8NbhNjKg/9iHSsmL99/5rH7B9InD7ur2iGx/fQN/jdjC+mTO3F0SieyuASfTJ3sbbUN2tCav/MMy/ZeYMuIFtgFMRRw0OI9nL31hD1j2/723jrDoW8/J4H5WZo2mRNpk/26zzVvhhQ8ffOJmVtPSsNb/LXFy5ZjbW3N8xcvePjoERkzBF2Z1RXk7/UPh3Bu3LyV1u07kjFDev49e4psWbMEu3zChAlJmDBszjOLli4jS+ZM5M2T+7fLBrVfMmRVhKXlW/dr6riv3/Ho+SsyuAZ9wTY8bDlwko5Dp5LBNTmn1s74fR03vr22QRsR+k1ayOnLNzm4dMJv548Iq3OUCH/S8DZT/964y6SlG1k/ZSALN+yh3dCpHF4yPsSTt2w9dIrWgyazYHgPKhUzfl/I71Qunp/xvb6zauchijbpQfnCeahfsTjli+QhdqzAw+vCaqj59x+ebNx/goHtjd/nFsvSktQpkgCaRvS/N+4xc9U25gwJ3DselNxZ0rNi+6EQLy+it0v3njN103FWD2zM4j3n6TRtE/vGtQv15C1/omK+jIxpXYk1hy9RqudsyuZOT93i2SmbJz2xLQOfysNqqPnc7acZteoA64c0I1c64xf/BizazabjV9k+qjWpEjkaXeanhA62vP70Va/s3edvmveCuVCQO10yNp/4L9htCxGUCxf/ZdzEyWzbtJ658xfSok17Th099Nucmsg5YaCe3jdv3xIzZkzixw/+t26oWpVKTJk4juUrV5O3UFEqVShHowb1qVShPLFjBx61ElZDzb9//866DZsYNnjAb2NM5OzM6TNn9crevXuHn59foJ5wIf7Wv9fvMmnJBtZPG8zC9btpN2gyh5dPitDnflcukZ/xvduyavshijbsSvkiealfqQTli+Y1XseNwKHmfSbMZ8PeY+xZOBaXZMGPanFO4MDr9x/1yt5++AwQZp1hIuxIw9sMeXp502bQFBpXKUW5QrlxS5+aPHU6MXnZZv5pWee362/cd4J2Q6cyf1i3UN3H7GBnS6eGVenUsCo37j9h1Y5D9Bg3j04jZlCjdCEaVCpBgeyZtFfUwmqo+aYDJ/Hy9qF+xeIhitPfX8VLZ+bWkLh256FMtCZCxNPbhw5TNtKwVE7K5EpPNtckFOg8jWmbT9CjTsTd72hva037qgVpX7UgN5+8Zs3hS/Sev4MuM7dQrVAW6pXITv6MKbXHY1gMNZ+55SRj1xxi3eCmQd7T3mf+TracuMaO0a1Jl+z3szHnSZ+CYcv24entg1VA5ebI5fskdoxLCuegj8n/Hr3C2SF8n+UsoiZPT0+atWpDsyaNqVCuLDnc3MiSMw/jJ02hX+9ewa6bP38+tm3fqVd24NBhcufK+cf3dzs4ONC1cye6du7E9Rs3WLZyNf/r3pM2HTpTu2Z1mjRsQKGCBbTHcFgNNV+/cTNeXl40blD/t8sWyJ+PUWPH8/z5C5Il0zwe7cChI8SOHZtcOf/s+eZCGOPp5U2bgZNoXLU05QrnwS1DavLU7MDkpRv5p1W9CIvDwS4unRpVp1Oj6ty491hTxx07h07DplGjTBEaVC5JgRw6ddwIGmrea+xcNu47zt5FY0nv8vuRrvncMjJo6mI8vbyxiq2Z8PXw2cskdopPyqTOoY5HhC15jrcZGjxjGZ7e3ozt0QqARAkcmNy3PaPnrebG/SfBrrt+7zFaDZrE8P81o1DOLLi/+4j7u498+Pw12PV+J3OalIzu3pK7exazeFQvvnz7QdVOg1mjc/UvoaM9qVMkCfZfzJi/v5q5fOsBqhTPT3z7wLOnDpq+lFOXbvDk5Wuu33vM4BnLOPHvf9Sr+KsBNHjGMiq2+3Vlf+WOQ6zbc5TbD59x9/Fzpi7fzPz1u2lfr3Ko/iYiehi+fD+e3r6MbFkRAGeHuExoV5Vxaw6F6JnV/z18yX8PX/L1uxcfPX7w38OX3H76JlQxZUrpzIgWFbi+qDcLetbl63cvag1eyrqjV7TLONnb4pokfrD/YgbTuzBt03GGr9jPjC41SZM0Aa8/fuX1x698/uapXabnnG2sOXSJhb3qYW9rrV3G44eXdplhy/ZRbeAi7evaxdywjm1Jx2mbuPnkNTtO32DapuN0rFZYW8GZs+0Uu87e5MHLd9x6+pphy/ax6+wt2lTSf6qBECHRb9AQPD29mDx+DACJEjkzc+okho0czfUbN4Jdt33rVjx/8YJuvXpz6/ZtFi1ZxrIVq+jZLeQjrIzJkjkzE8aM4un9O6xatpivX75SrnI1Vq5eq10mYcKEpEmdOth/MWP+vv9k8dJlVK9amfjxAzcKZs6ZS8ZsvxrUZUuXInOmjDRr1YbLV65y6PAR+gwYSOuWzWVGcxEmBk9fiqeXN2N7tQEgUQJHJvfryOg5q0L0zOqrtx9w9fYDvn77zsfPX7l6+wG3HjwNVUyZ06ZidI9W3N23jMVje/Pl23eqdhjImp2HtcskjB+6Oq63j482di9vb16/+8jV2w948PSldpluo2axcvsBlo7tjb2dLe7vPuD+7gMe339olxk8bQkV2/TTvq5boTjWVla0GzSZG/ces+3gKSYtXs//msiM5uZIerzNzMl/rzN33U52zhmpN7lDnXJF2X74NO2GTOXosolBrr940158ff3oPXEBvScu0JYXyZWFvQvGhDo+CwsLyhbKRdlCufji8Z1vP378fqU/cO/JC05fucn22cONvv/6/UdaDZzE6/cfsbONQ5a0qdgyY6jezJPu7z7w6Lm73nrjF67n6as3WFjEIE2KpMwZ0kXu7xa/der6I+bvPMvWES30JjCrVTQbO8/coNO0jRyY0D7YbRTtNkvv9d7zt0me0J5rC/8JdXwWFjEonSsdpXOl48t3T7798A71Nn9atOccPr5+tBy/Vq+8QckczO5WG4DFe84DUG3QYr1l+tQvSd+GmscMvv74lUfuH7TvxYtjxebhLfhn7g5K9piNva0VnaoXolP1X6NzvH39GLR4D68+fMEqliUZUiRk3eCmlM2dPsz2T0QPx0+cZObsuRzYvUNvArP6deuwZdt2WrRpz5njQQ8fdXFJxa6tm+jRuy9z5y8kSeLETJs8IcweJWZhYUH5smUoX7YMX758wcPjW5hs96e79+5x8vQZ9u3cbvT9d+/ec+fuPb14dm7ZRKeu3SlcojTW1tY0qFcn0PPOhfgbJ//9j7lrtrNz3mj9Om6FYmw/fIp2gydzdMWUYLdRsN7/9F7vPnaOFEkScmvP0lDHp6nj5qZsodyaOu73sKvjvnrzQS/2h89esWjjHorkzsreReMAWLB+FwCV2vbXW7d/+4YM6KCZZNn93UcePX+lfS9e3DjsmDeSHqNnU6RhV+ztbOnStCZdmgY/AlWYhjS8zUzhXFn4cmGb0fdWjOv72/XDonEdUna2NtjZBp75MTTSpkzKt0s7gnx//rDuv92G4TKNq5QKs2eNi+ilUBYX3m0dYfS9JX0ahGgbH7ePCsuQgmRnYxXkxGd/IyQXBkKybz8b6boyp0rE7rFtglyna62idK1V9LfbFuJ3ihYpjM+3z0bfW7dqRYi2UaxoEf49eyoswzLKzs4uzHuV06VNi7+nR5DvDx00gKGD9O/9TpEiOTu2bAzTOIQAKJwrK18u7TT63ooJ/Y2WG/p2dXdYhhSksK7jpkzq/NvYQ7Jv80f0CFSWJa0L+5dM+OvYRMSRoeaRXNnWfSnUsNsfrTN2wVoSFqrDM/e34ROUENFUpX4LKd591u8X1DFh7WGS1R3G87fGGwdCiIhTrHQ5chco/EfrjBg9lrjxnXn67Fk4RSVE9FS2ZR8K1f+z2zrGzltNwvw1efZK6rjC/EiPdySVNGECrm2dB0AsIzMaB6dt3UrUKafpTYrvIPdsCRFaSRLY8e9czVVoS8s/m5W1daX81CrqBkB8u7AdQSKECJlkyZJy94bmKQCxLGP90bqd2relQT3NxKcJjNxHLYT4M0mdE3Btx0LgL+q49atQp0JxAKNzBQlhStLwjqRixrTQPlLrTznGi4tjPJkdWIiwEtPCAtckf1fhdohrg0NcaXALYUoxY8YkTerUf7Wuo6Mjjo5/9mgxIUTQpI4roioZai6EEEIIIYQQQoQjaXgLIYQQQgghhBDhSIaaizD16u0H+k1ZxNXbD7j/9BUNKhU3OhP5F4/vDJ+9gi0HT/Ph8xeSOTsxtHMTapUtYnS7K7YfpP3QaUbfe3RwBQkd7Xny8jWZKrcO9P6WGUMpWyiX9rW3jw/jFq5jza4jvHr7gYTx7enapAYdG1T9y70WIvLacfoGS/ae59rDl3j5+JI+eUJ61ClOxXwZtcusPnSJTtM2BVr31cahWMWyDHLbl+49Z/jy/Vy5/wIVyJ46CYObliVXuuQAeHr70GP2Nq4+eMnd52/JlzElO0cHPoaFiM5Wr13PhMlTuHvvPnZ2cSldogQTxo4mUSLnINe5e+8effoP4uTp03h5eZM5U0aGDOxP+bJltMvEsLINtN7sGVNp30ZzDB49dpypM2Zx/uJFPn/+QprUrnTt3ImWzZuG/U4KEQmcuPgfQ6Yv5d7j53z39CJF4oQ0q1mObs1q6S239eBJRsxawcNnr3BNnpghnZtRtVTB325/zc7DTFu+mbuPnmFrY03ZwrlZOKoXAJ5e3nQZOZMrt+5z59EzCmTPpH0M2U9tB01m1faDgbab0TUFF7fMDcWei7AiDW8Rprx9fEhgb0eP5rVZsnmf0WV8fHyp2nEw9nZxWDGuN0mdE/Di9TtiB1OBr122CGUK5tIrazdkKl7e3iR0tNcr3zpzGFnTuWhfO8bTr1w07zeB56/fMWNgZ9KkSMKb95/44RV2zz8WIjI5deMRRbK5MqBxaRxsbdhw7ApNxqxix6jWFMycSrucTWxLLs3vqbducI1ujx9e1B66lLK507NvfDsAJq0/Sq0hS/lvUW/i2sTGz18ldqyYtKmUnwP/3uXzN89w2UchIqtTp8/QtGVrJowdTbXKlXjz9i2dunancfOWHNy7K8j1qtSog6tLKg7u2UWcODbMXbCI6rXrcePyRVKndtUuN3/2TCpXrKB9HS/er8moTp89R5bMmfinRzcSJ0rEvoMHadfpf1hZWdGwft3w2WEhzJitjRUdGlQlc9pU2FjF5syVm3QZMQMbq9i0rVcZgHNXb9G091gGdmhM1VIF2XH4DI3/Gc2hpRPJky1DkNuevWobExevZ1T3VuTNloEfXl7cf/JC+76fnz9WsSxpX78K+05e4PPXb4G2MaF3O4Z3ba63Tv46nahR9s+e1CDCjzS8zUT5Nv1I75Ica6vYrNx+EIsYMejduh6ta1eg76SFrNtzjLi21gzp2ISGlUsCmgZsvymLtL3GTo721KtQjBFdmgOaRvCIOatYu/son754kN4lOUM6NaFMwZzhth8pkzgzsbemkr31oPHnnq7YfpC3Hz+xf9EYYllaatcLjrVVbKytYmtfv/34mWMXrjF78P8CLRvfPi6JEjgY3c7BM5c4cv4q/22bTwKHeCH6bCGMqdx/IemSOWET25JVhy5hEUOhV90StKiQlwGLdrPh2FXiWsdmYJMy1C+RAwAfXz8GLt7D9tPX+fDlOwnsbalTzI2hzcoB4O3jy+jVh9hw9AqfPH6QLrkTAxuXpVTOtOG2H2PbVNZ73adBKfZfvMPuszf1Gt6KouDsEPIJa+49f8vHrz/o16AUKRNpJp4a0Kg0G45d5f6Lt+RIm4w4VrGY0rE6ADceu0vDW4SZEmXKkyFDemxsbFi6fCUWFjEY0Lc37du0pkfvvqxeux67uHEZOWwITRo1AMDHx4eeffqxactW3r//QEInJxo2qMfYkcMB8Pb2ZvDwkaxas5aPHz+RMUN6Rg4bQrkypcNtP86cO0+ypEnp3qUzAK6uLnTu0J4uPXoFuc67d++4d/8+c2dOwy1bVgDGjhzO1OkzuXz1ql7D294+XpA95/37/KP3ukPbNhw9dpzNW7dKw1v8kfKt+vyq4247gIVFDHq3qU/rOpXoO3E+63YfJW4cG4Z0bkrDKqWAgDrupIVsOXiSD58C6rgVSzCiWwsgoI47ayVrdx3W1HFdkzOkczPKFMoVXCihkiNTWnJk+pWPUyVLxPZDpzl16Ya24T1r5VaK5slG7zb1AcjgmoKj568yc9U2lgXR8P70xYMh05eyduogShX4VUfPkvZXJ1IcGyumD9LUea/ffWS04R0vbhzixY2jfb3j8Gk+fvGgSbUygZYVpiENbzOybs9R/teoOkeXT2LXsXP0nriAA6f/pUzBXJxYOZlVOw/TacQMSuRzI7FTfGav3cH2I2dZNuYfUiZJyIs377n7+NfVsXZDp/HomTtLRvUiqXMC9p28SJ1uIzi+cjLZdHqEdU1YtJ4JizcEG+eWGUMplDPzX+/njqNnye+WkZ7j5rHz2Dkc7eJSs0xhereqi2UIHxuxeudhbKxiU6N0oUDvNeg1Gk8vH9KkSELnRtX0ltl59Cw5M6VlxsptrN51GOvYsShbKBdDOzfF1sb6r/dJRE8bj12lY7VCHJzYnj3nbtNv4S4OXrpL6ZzpODKpI2sOX6LrjC0Uy5aaxPHtmLfjDDvP3mRhr3qkcHbg5bvP3H/xTru9TtM38/jVB+b3rEvSBPHYf/EODUau4NCkDmR1SWw0hknrjzJl47Fg41w/pJleI/p3PH54Y2+rfzz88PYha6sJ+Pv7k8UlMQMalSZb6qBnnU2T1AnHuDasOPgv/9QrAcCy/RdJ5mRPhhRysUuEv9Vr19O9S2fOnjjC9p276d6rD/v2H6Rc2dJcOH2cZStW0aZDJ0qVKE6SJImZPmsOW7ftYM3ypaRKmZLnL15w5+497fZatmnPg0ePWLV0McmSJmX3vn1UrVmH86eOaxu4hkaPm8CY8RODjXP3ts0UKRw4lwEUyJeX/oOGsGPXbipXrMD79+9Zt2EjFcuXDXJ78ePHJ326tKxYvZY8uXNhbW3N/EWLiRs3LoUKFNBbtluv3nT4X1dcUqWiZfOmtG3Vkhgxgp7+58uXryRNmjTY/RHCmHW7j/C/JjU4unIKu46eo/f4+Rw49S9lCuXixOpprNp+kE7DplEiX3YSJ4zP7NXb2X74NMvG9SFlEmdevH7H3cfPtdtrN3gKj569YslYzcjJfScuUqfLMI6vnkq29K5GY5iwcB0TFq4LNs4ts4dTKGeWEO3TlVsPOHv1FgPaN9SWnbt2m/YNqugtV7pgTuat3RHkdg6duYSfvz9v3n8iV412fP76ndxZ0jGmV2tckhnP/SGxbMt+SuTLTgrpYDIb0vA2IxldU2gP3i6NqzN5yUYsY8akU0PNvcf92tRn8tJNnL16mxqlC/Hs1RvSpEhCoZyZURSF5IkTkt9Nc1/mw2ev2LD3OLd2LiR54oQAtK9fmSPnr7B40x6m9utoNIZWtStQs0zwQ1KSJAzdc0ofv3Dn2IVr1C1fjM3ThvDk1Wu6j52Lx48fjOneKkTbWLHtAHUrFNXrBY9jbcXo7i0p4JaRmDEt2HXsHE37jmf+sG40qKSp+D96/pozV24SO5Ylqyf049PXb/QaP49Xbz+wakK/UO2XiH4ypEhI34aaq/Odqhdi6qZjWMa0oH1Vzb1cveuXZNrmE5y//ZRqhbLw7O0n0iSJT8HMqTTHrJM9+TKmBODRq/dsOn6Nqwt7kdzJHoC2lQtw7OoDlu49z6QO1YzG0LJCXmoUNl7p/ylx/JA/y3TBrrO8fP+ZuiWya8vSJE3AzP/VJItLIr7+8GbejtOU7zOfE9M7kzpJAqPbiWsTmx2jW9Nk9CrthYEUCR3YMrwF1rGDHqIuRFjJnCkjQwcNAKBH1/8xbuIkLC0t6dq5EwCDB/Rj/KQpnD57lto1a/Dk6VPSpU1DkcKFUBSFFCmSU7BAfgAePHjImvUbeHTnJilSaOYo6NyhPYcOH2XewkXMnj7VaAzt27Sibu2awcaZNEnQF7AKFSzAqmWLady8FT9+/MDX15cypUqydOH8INdRFIUDu3dSs14D4jklJkaMGDg6OrB722YSJ06kXW7Y4IGUKFYUW1tbDh05Sq8+/Xn37j0D+/Uxut2du/dw6MhRTh4JfA+pEL+TMXVKBnRoDECXpjWYvGS9po7bqDoA/do1ZPKSjZy9eosaZQpr6rgpk1IoZ5ZfddzsmYCAOu6eY9zas+RXHbdBFY6cu8zijXuYOqCT0Rha1alIzSDmEvopJHXctGWa8O7jZ3z9/OnfriGt61bSvvf63UcSxtcfdZkwvgOv330McnuPnrvj768yfsFaxvVui6NdXMbMW02F1n25tGUeNtZWv43J0Ku3H9h/6iJLxvT+43VF+JGGtxnJkjaV9v+KouDkGI/MaVJqyywtY2JvZ8vbD58AaFSlFFU7DsatejtK5c9BucK5KVsoFzFixODK7Qeoqkqu2vonHy8fH4rlzhZkDBHx/EN/fxUnx3jMGtQZCwsLcmRKw4dPX+kzaSGju7VEUZRg1z939Ta3Hj5jwfAeeuUJHOLRtUkN7eucmdLy/tMXpizbrG14+6v+KIrCklG9tMNxJvVpR7VOQ3j9/iPO8Y0PURfCmEypflViFUXBKZ4tmVL+urJsGdMC+zhWvP3sAUDDUjmoMXgJudtPoUSONJTJlY4yudIRI0YMrj54iaqqFOikP4mgl48vRbMZv3oPYfsc8J1nbjBkyV4W/lOPFAl/HQt5M6Qgb4YU2tf5MqSgSLeZzN95lnFtKxvbFD+8fOgyYzN5MiRnQa+6+Pn5M3PrSRqNWsnhyR2JYxUrTGIWIihZs/wamaUoCgmdnMiSOZO2zNLSEgcHe968fQtA8yaNKVupKumzZKdM6ZJULF+OCuXKEiNGDC5duYKqqmTOkVvvM7y8vChZvFiQMYT2Gd83b92iW6/eDOzXh3JlSvPK3Z3e/QbQvlMXli1eYHQdVVXp3K0HCeLH5/ih/VhbW7NwyVJqN2jE+ZPHSZpU09Af1L+vdp3sbtnw8/Nj9LgJRhvep8+cpVGzlkybPIG8eXIHel+I39EdNq0oCk4O9mTWqfcGquNWK03VdgNwq9qGUgVyUK5wHsoWzq2p4966r6nj1miv9xlePj4Uy+MWZAxhVcc9sGQCHj9+cOHaHQZNXUzKpM7aIfIAhrVYVVWDrduqqoqPry8T+rSndMDtoIvH9Ma1VCN2HztH7fJBn2OCsnL7Aezj2lKlZIHfLywijDS8zYhlTIOvQ1EClSmKpuEKkCNjGm7sWMjBM5c4duEabYdMIUtaF3bOGYG/v+YgP75iMpYxLfS2odtLbCgihponSuBAzJgxsbD4FVd6l+R89/Ti3acvOAXcex2UpVv3ky29KzkypfntZ+XOkp4V2w/pfLYjSZzi690Dk8FF03vx3P2tNLzFH7G0MBiSqYClhf7xpiiK9ph1S52UKwt6cfjSPY5fe0jHqZvI4pKILcNb4B+QmA9N6hBoG1axgz5Vh9VQ8+2nr9N+8kbmdK+tN6O5MRYWMciRJikPXr4LcpmNx67y2P0D+8a1wyLg77SgZ11cGo5k55mb1NPpURciPFha6o+sUBTFaJm/vz8AOXNk5+Ht6+w7cJDDR4/RvHVb3LJmZf/uHfj7ay7anj91LNA2rK2Cvk0ptEPNx46fRL48ufmnRzcAsmXNQhwbG4qWKsvIYUNInjxZoHUOHznKjl27ef/qGfb29gDMzjGVg4eOsGT5iiB7tPPlyc2XL194/fo1zs6/LiCeOn2GitVqMmzwQDq0bRPsvggRFMO6qKaOG3S+zJExDTd2L+Hg6X85dv4qbQdNJks6F3bOG6U9Ho+vnhqonmwdO+iLumE11DxVMs1F9yxpXXjz/iOj567SNrydEzjw+r1+7/bbD59IGN8+yO05B8xLlDH1rwvc8eLGIbGTI8/c3wYbizGqqrJi6wHqVy6pnUtJmAdpeEdydrY21CxTmJplCtO4SimKN+vFg2evcMvgiqqqvH7/kWJ5gu7hNhQRQ83zu2Vi/d5j+Pv7a+8lu//0BTZWsUlgH/yQWI/vP9h84CTDOofscSbX7jzUm2itgFtGthw8icf3H9p7uu89eQmgHa4kRHiys7GieuGsVC+clYalclLmn7k8fPWBbK5JUFWVNx89KBJMD7ehsBhqvvnENTpN28TsbrWpVuj397apqsqNx+5k0enxN/TD2wdFUYgR49dV/hgxFBRFQVXV336GEKZgZ2dHnVo1qVOrJs2bNKZA0RLcf/CAHNndUFUVd/fXlAimh9tQaIeaf//xXe8iNaB9HdRx9P3HD4BA92rHiBFDe5HBmCvXrmFlZaVtrAMcO36CKjXrMGRgf7r9z/jwXSHCi52tDTXLFqFm2SI0rlqa4k168ODpS9wypNbUcd99pFjeoHu4DYXVUHNd/v4qXt4+2tf5smXg8JnLdG9eW1t2+Mxl8rllMrY6AAUChtDfffycpM6a27c8vv/A/d1HUvxF3fTExf948PQlzaoHPReEMA1peEdi01duJVECB7Klc8UypgXr9h7DztaGpAnjY2NtRb0KxWk3ZCpjerQie4bUfPzyleMX/8MlaSKqBfE8wbAYhnP1zkMAvn77oRlCe+chsSxjktFVcyWvTZ0KzFu/k38mLKBdvUo8efmGkXNX06ZORe1QnLlrdzJv/U4ub9Z/7uCm/Sfw9fOjXsXigT535Y5DWMa0wC19amLEUNh9/Dzz1+9mRJdm2mXqVijG2IXraD90Gv3bNeDz12/8M3E+NUoXCvRYMiHC2qytJ3F2iEtW18RYWliw8fhV4trEJkkCO2xix6JOMTc6TtvEyJYVcEudhI9fv3Py+iNSOTtSpaDxUSahHWq+8dhVOkzdyIgWFSiYORWvP34FIFZMC+12x605RO70KUidJD5fv3syb8cZbjx2Z1KHqtrtzN95hoW7znJ+TncAimdPw+Ale+k5ZzvtqhTAX1WZuvE4MWIoehcWbj99g4+vL++/fuebpxf/PdRcCMvqGnRjRIjwMHnaDBIncia7WzYsLS1ZvW49dnZ2JEuaFBsbGxrVr0eLNu2ZOG40ObNn58PHjxw9fgJXl1TUrG58DobQDjWvXLEibTt2Zs78BZQrrRlq3v2fPuTMkV17r/mWbdvpP2gIB/fsImnSJBTIlxdHRwdatm3PoP79sLa2YsHipTx89Ej76LAdu3bj7v6aAvnzYm1lzZFjxxkyfBRtWrUgdmzNqLijx45TuUZtOrRrQ6P69XB3fw1oRrw4OTn99T4JERLTl28mkZMj2dK7YhkzJuv2HNXUcZ0TaOq4FUvQbvBkxvRsQ/aMqfn4WaeOa2TiXQh9HXfO6u2kSupM2lTJUFWV05duMG35JtrU/XXLVcdG1SjbsjcTFq2jaknN48SOX7zGwSUTtMvMXbODeWt3cHmbZq6GtKmSUblEfv4ZN48ZgzpjbxeXUXNW4uQYjwpF82rXu/XgKd4+Prz/9AWP7z+4evsBAG4ZUuvFuWzLPvJkTa83lF+YB2l4R2JxbayZunwzD56+QlHALb0rW2YM1U7CMG9oV8YvWs/AaUt48fo9DvFsyZ05XbD3eIeFgg266r3effw8KRIn5NauRQAkS+TE9lnD6Tt5EQUadMU5vj1Nq5WmT+t62nXef/qiN0P7T8u2HqBqyQI42NkGeg9g/ML1PH31BguLGKRJkZQ5Q7po7+8GsLWxZuecEfQcP4+iTXpo7n8pnp/hOo1zIcKLrXVsZmw5wcNX71FQyOqamA1DmmETMDRuVtdaTFp/lCFL9/Ly/RccbK3JmS4ZRbKGvAf8Ty3ddwFfP3/6LdxFv4W/ngtcKIsLO0e3BuDzN0+6zdrKm49fsYtjRTbXxOwa04Zc6ZJrl//w5Tv3dGZoT5fMiTUDmzBu7WHK9p6HAtr9TZrg1+0kdYcv49mbT9rXRbvNAuDj9lHhtMdCGBfX1paJU6Zx7/4DFEUhh1s2dm/bjI2N5gLU4gVzGTV2PH36D+L5ixc4OjqQN3duShQrGm4xNW/amK8eX5k1Zx69+vQnXjw7ShQtyrjRI7XLfP78hTt37+Hjo+l1S5AgAXu2b2XgkGGUKl8JHx8fMmZIz5YNa8mZIzugGYY/Z/4Cevbph7+/P64uqRg2eACd2rfTbnfZilV8//6dSVOmMWnKr7knUqZIwaO7N8Ntn4UAiBvHhqlLN/Hg6UtNHTdDarbMGv6rjju8O+MXrmXglMW8eP0Oh3hxyZ0l3R+N8vxTfv7+DJq6hCcvXxMzpgUuyRIzvGsLWtepqF0mf/ZMLBvXl+EzlzNq9ipckydm+bi+es/wfv/ps94M7QALR/Wiz4T51OkyDFWFAjkysWv+GL2J1Wp2HszTl2+0rwvW0zxe7NvV3dqyz1+/se3QaSb0/nUsC/OhyJC/sKcoSmLgpW7Z86NrgmwsCvPXa8J85qzRexTEfFVV5awWBSiKMg9o+/N1uyoFAj3bWpifTx4/cGk40rA4iaqqr0wRjwg7xnLo+1fPcHCQOTgiq649/2HGrDm6RZJDoxCj9d4T63CwC9/JesXf6TVuLnNWb9ctkuMxggT9sEYhhBBCCCGEEEKEmjS8hRBCCCGEEEKIcCQNbyGEEEIIIYQQIhxJw1sIIYQQQgghhAhH0vAWv3X84n/EyVmFdx8/mzoUIYSBk/89xKHqAN5/+WbqUISI9o4eO04MK1vevXv3+4WFECZ3/MI14rhVlDquiBDS8BZRwtdv3/lnwgIyVGxJ/AK1KNn8H/69cTfQcveevKBBz9EkKVqfBAVrUbBhV24/fGaCiIWIXtw/fKHDlI2kaTyKRLWGkL/TVE5df6R9/81HDzpO3UjG5mNJUnsotYcs5cFLabwIEdaOnzhJtVp1SeaalhhWtixdvjLQMoOGDidjthzYOibEMVEySpevxOkzZwMtd/7CRcpWrELc+M7YJUhEoeKl5KKDEGHs5L//UafLMNKUbkIct4qs2HZA730fH18GTllM3todccpXA9dSjWjedxzPXr0JYovCVKThLaKETsNncOjMJeYP7875dTMolT8HlTsM4uWb99plHr9wp3SL3qRM6syueSO5sH4Wgzs2wdbGKpgtCyFC67PHD8r3mY+KyrrBTTk3qyvj2lYhQbw4AKiqSuPRK3nw8j0r+zfi2NROJEtoT/VBS/jm6W3i6IWIWjy+fSNzpkxMnTgea2tro8ukT5eOmVMnc+3iOU4c3k+qVKmoULUGr1+/1i5z7vwFylWuRrGiRThz/DAXz5ygZ7cuWFpaRtSuCBEteHz3JFOalEzo0w5rq9iB3v/u6cWV2/fp3boep9bNYP3Uwbxwf0f1joPw9fUzQcQiKNLwNiPbDp0mb93/Eb9ALZIVb0C51n15/f4jAA+fvaJu95G4lGmCU8HaFGzYlT3Hz+utn7FSK8bMX0PbIVNwLlyXdBVasHHfCT599aBZ3/EkLFSHbNXacvDMJe06P4eR7zl+nvz1u+CYvyaFGnbj8s37wcZ67uptyrXuS4KCtUhTrhldRs3ii8f3EO1LWPvh6cXWw6cZ3qUZRXNnJXWKJAxo3xDXZIlZsGG3drmhs1ZQskAOxvZoRY6MaXBJlojyhXOTLJFTuMQlopYdp29Q6H/TSVx7CC4NR1Kp3wLefPQA4NGr9zQcuYL0TceQtM5QinWbyd4Lt/XWz9Z6AuPXHqbj1I0krzeMzC3Hs/nENT57/KDlhLUkqzuMXO0mc/jyPe06P4eR771wmyJdZ5Co1hCKd5/Flfsvgo31/O2nVOq3gCS1h5Kp+Vi6z97Kl++eIdqX8DBt8wmcHeIyt3sdcqVLTspEjhRzS0365AkBePDyPRfuPGNih6rkSpectMmcmNyhKp7ePmw6fjXc4hKR1+at23DLnQ8b+wTET5yc4qXLaRuFDx48pHrteiRO6YqtY0Jy5S/Ezt179NZ3SZeJ4aPG0KJ1O+wSJCJF6vSs27CRT58+0aBJM+LGdyZdZjf2HzikXefnMPKdu/eQI28BrOPFJ3eBwvx76XKwsZ45e47ipcsRx8GJZK5pad+5C1++fAnRvoSHiuXLMXrEUGrXrEGMGMargY0b1qdUyRK4urqQOVMmJo8fw9evX7ly7T/tMj1696VjuzYM6NubLJkzky5tWmpWr0a8ePHCLXYReW07eIq8tTsSP291khWpS7mWvfXruF2H41KyEU75alCw3v/Yc+yc3voZKzRnzNzVtB00GecCtUhXtikb9x7j0xcPmvUeS8L8NclWpTUHT+vUcQOGke85do78dTvjmKcahep34fLNewTn3NVblGvZmwT5apCmdBO6jJihX8cNZl/CQ/kieRjWpTk1yhQmhqIEej9e3DjsnDea2uWLkS5VMnJnTc/0Qf/j9sNn3H70NNziEn9OGt5mwv3dR5r1m0CjKiW5tGk2+xaOpUGlEtr3PX54UrZQLnbMHsHZtdOpXrIgDXqN4c4j/WHSs1ZvJ3fmdJxaPZWaZQrTdsgUWvSfSNnCuTmzZhqFcmah1cDJeHrp9yL1n7qYEV2ac2LlFFySJaJm12F8/+GJMdfvPaZKx0FULJaPs2tnsHbSAP67+4gOw6aFaF+Mqd55CAkL1Qn2X1B8/fzw8/MndqxYeuXWVrE4c+UmAP7+/uw5foGMLsmp1mkIKUs2okjj7mzcdyLYuIQAeP3xK60mrqNByZycm9WNXWPaUK9Edu37Hp7elM6Vjs3DW3Bi2v+oUjAzTces5u7zt3rbmbP9NDnTJePolE7UKJSFjlM30XrSesrmSs/xqZ0pmDkV7SZvwNPbR2+9wYv3MLRZeQ5P7kgqZ0fqDV/Ody/jPcE3HrtTc/ASKuTNyInpnVnRvxE3Hrnzv+mbQ7QvxtQeupRkdYcF+y84u8/dJHf65LQcv5a0TUZTpOsM5u88g6qqAHj5+AJgZRlTu06MGDGIZRmTszefBLttEf24u7+mQZPmNG3ciJtX/uXYwb00bthA+77Ht2+UL1eG/bu2c+XCGWpWr0ateg25feeO3namzZxNnjy5+PfsSerUrknz1u1o1KwlFcqV4/L50xQpXIgmLVvh6amfC//p25+xo0Zw4fRxXF1SUblGLb5//44x/12/TtlKValSuRJXLpxh87o1XPvvOq3adQjRvhhTsWoN4sZ3DvZfWPL29mb+oiXY2dmRPVtWAN68ecOZs+dInDgRRUqUIVEKF4qWLMOhw0fC9LNF1OD+7gPN+oyjUZXSXNoyj32Lx9Ogcknt+x7ff2jquPNGcnbDLKqXLkSDHqMC13FXbSV3lnScWjudmmWL0HbQZFr0G0/ZInk4s36mpo7bf0LgOu7kRYzo2oITq6dp6ridhwZTx31ElXYDqFgsP2fXz2TtlIGaOu6QKSHaF2OqdxxEwvw1g/0X1r5+05yTHOzihvm2xd+L+ftFRER49fY9Pr6+1ChViBRJNL1AmdOk1L6fLZ0L2dK5aF/3bl2P3ccvsPXQafq0rqctL1UgJ23rVgJgYPuGzFi5ldTJE9Mo4KTQt009lm87wM0HT8iZKa12vT6t61OmYE4A5g7tSrryLVi/9xjNa5QLFOvU5ZupVbYIXZvU+FXWvyMFG3TlzYdPv90XY2YP7sIPT6+Q/bEMxI1jQ75sGRi/cB2Z06TEOb496/ce59y1O6ROnhiANx8+4/H9BxMWb2BQx8YM79KMYxeu0XLgROJYx6ZC0bx/9dkienD/8AUfXz+qFspMioQOAGRK+atym9UlMVldEmtf96pbgn3n77D91HV61ft10alkjrS0rpgfgL4NSzFr2ylcE8enfskcAPxTrwQrD/7LrSevyZE22a/t1StBqZya43Vm15pkbjmejceu0rRsnkCxzthyghqFs9K5RmFt2aQOVSnabRZvP3n8dl+Mmf6/Gnh6+Ybsj2XEY/ePLNp9jg5VC9KtVnP+e/SKPvN3AtC2cgHSJXMieUJ7Rqw4wLTO1YljFYvZ20/z8t1nXn/8+tefK6Kml69e4ePjQ+0a1UmZMgUAWTJn1r7vli0rbgENRIABfXuzc/ceNm7eysB+fbTl5UqXomO7tgAMGzSAKdNmkDq1K00bNwRgUL8+LFm2nOs3bpI7V07tegP79aVcmdIALJ4/l+Sp07N67Xpat2weKNaJk6dRt3Ytenbroi2bPX0qOfMV5M2bN7/dF2MWzJnFD88fIfpbhcbO3Xto0KQ5379/J3HiROzftR1nZ8254uGjxwAMHTGa8aNHkiO7Gxs2baF8lepcPHNS7+8vxKu3HzT1wjKFSJFE8xvKnDaV9v1s6V3Jlt5V+7p3m/rsPnaOrQdO0qftrwtRpQrmom29ygAM7NiYGSu2kDp5EhpVKQVA37YNWL51PzfvPyZn5nTa9fq0bUCZQrkAmDu8O+nKNmX9nqM0r1k+UKxTl26iVrmidG32qzE8dUAnCtb7H2/ef/rtvhgze0hXfgRxsTw8ePv40G/SAioWy0dS5wQR9rni96ThbSaypXOheF438tTtTKn82SmRLzvVSxfCyUEzZOvbD09Gz1vD3hMXcH/3AR9fPzy9vclicLDrvra1scbGKjaZ0/wqS+hoD2gaorryZcugt17mNCm5FcSkY1du3efBs1ds2v+rt/hnz9WjZ+7kzpI22H0xJknC+EG+FxILR/Sgw7BppC3fHAuLGGTPkJo65Ypy9faDgPj8AahUPB9dGlcHwC29K5du3mPe+l3S8BbBypIqMcXcUlPof9MpkT0txdxSU61QFu09yt88vRm35jD7L97G/cNXfP388fT2JXMq/QZt5lSJtP+3tY6NTWxLvUavk70tAG8/689QnjdDCr31MqV05s4z/d70n67ef8nDV+/ZcvLXkFDt8en+gVxpkwW7L8YkiR+6oaP+qkr2NEkZ0kxzIS9b6iQ8ePmeRbvP0bZyASxjWrC8b0P+N2Mzro1GYREjBsXdUlM6V7rfbFlER27ZslKqRHGy5spL2dIlKVWyBLVrVMfJSXPb0Ldv3xg2agy7du/hlftrfHx88PT0JGsW/QZt1qxZtP+3tbXFxsaGrDqNXmdnzYXjN2/1j7UC+fLqrZc1cyZu3ta/teSnfy9f5v6Dh6zfuElb9vN4fPDwEXnz5A52X4xJmjRJMH+dsFOiWFEunz/Nu3fvWbB4KfUaNeX0scMkTpwIf39NTm3bqiUtmzcFIEd2N46dOMHcBQuZM2NahMQoIods6Vwoni87eWp1pFSBHJTIn4PqpQvj5BhQx/3uyeh5q9h7/Dzubz/i4+urqePqdDgBZEmXSvt/bR1Xp96bML49YKSO62ZYx03FrQfGh2BfuXmfB89esmnfcW2ZNoc+f0XuLOmC3RdjkkRg49fPz4/W/Sfy+cs31k8bEmGfK0JGGt5mwsLCgp1zRnD+2h0Onb3Msq0HGDJjOXsXjiFbOhf6T1nMgdP/Mrp7S1InT4KNVWzaDJ6Ct6/+kFTLmBZ6rxVFIaZOmRJwb4gakDT/hr+/SvPqZencqFqg95IkjP/bfTGmeuchnL58M9jPfXNqQ5DvuSZPzL6FY/n2w5MvHt9J7ORI0z7jSJlU06iJb29HzJgWZHRNobdeBpfkbNQ5uQphjIVFDLYMb8GFO884cvkeKw9eZPiK/ewc3ZqsLokZtHgPhy7dY0TL8rgmToBNbEvaT92It8GkJpYx9e/uURRF75j9eeuWf0CS/xv+qkqTsrnpWLVQoPcSx7f77b4YU3vo0t8O+X6+PugE7+wQlwzJ9RsS6ZI7MW/HJ+3r7GmScmLa//j8zRMfXz8SxItD6V5zyJ4mabCfK6IfCwsL9u/ewdlz59l/8BCLly6n/6ChHD2wF7dsWenVtz/79h9kwthRpE2TBhsba5q1aouPwS0chpOAKYqiV/YzX/qHKl/606pFM7p36RzovaRJkvx2X4ypWLUGJ06dDvZzv74P/T3iceLEIU3q1KRJnZr8+fKSLrMbC5csZVD/viROpLmImCljBr11MqRPz7Nnz0P92SJqsbCwYOe8UZy/dptDZy6xbMs+hkxfyt5F48iW3pX+kxdy4NS/jO7ZitQpkmrquAMn4e2jP9LKMqZ+syVc6riqSvOa5ejcuEag97R13GD2xZjqHQdx+tKNYD/3zdnNfx3zT76+frToN47rdx+zZ9FY4tvbhXqbImxJw9uMKIpCPrcM5HPLQL+29clduxOb9p0gWzoXTl+5ScNKJaleSlOZ9vTy5uFzd9KkDJsr3+f/u41LMk0i/fbDk5sPntAwiHtW3DKm5tbDp6ROEfRnB7cvxoRmqLmuONZWxLG24uMXDw6euczIrs0BiGVpSa5Mabn7WL9CcO/JC5InThjqzxVRn6Io5M2QgrwZUtC7fkkKdJ7GlhP/kdUlMWdvPaF+yexULajpQfP09uHxq/ekSRK6kRw/XbjzjFSJHAFN7/qtJ6+pXyKH0WWzpU7C7advcA3ms4PbF2NCO9Q8X8YU3Huh/4ihBy/ekTyhfaBl48XRPGXgwct3XL7/gv6NSv/154qoS1EUCuTPR4H8+Rg8oB9ZcuRm3YZNuGXLyqnTZ2jSqAG1alQHwNPTkwcPH5IuTZow+eyz5y/g6qrJZd++feP6zVs0adTQ6LI5s2fn5s1bpEmd+q/2xZiIGmpuyN/fHy8vTZ5OlSolSZIk5s5d/cd23rt3jyxZgh8qL6InTb0wI/ncMtKvXUNy12zPpn3HyZbeldOXb9CwSimql9bcIuXp5c3DZ69IkzJsLryev3Ybl2Sa/Pbtuyc37z+hYcDwdENuGVNz60FI6rjG98WYiBhq7uPjS9PeY7n14Al7Fo0jUQLHcP088Xek4W0mzl+7zZHzVyldIAcJHe25euchz1+/I4NrcgDSpkjC9iNnqVQ8H5YxYzJm/hq8vMPuIB6/cD0JHOKR2MmRsfPXYmlpSd0KxYwu26NZLUo070WXUbNoWas8cW2sufv4ObuPn2fGwM6/3RdjQjvU/MDpS/ir/qRPlYwHz14xYOoS0qZKSpOqvyrt3ZvVpEmf8RTKkZliebJx/OJ/bNx/gnWTBoTqs0XUd+H2U45dfUDJnGlxsrflvwcvefHus3ZW7jRJErDzzE0q5stITAsLxq89jKfP3zdUDU1af4QEdnFIFD8uE9YeIVZMC2oXczO6bNeaRSn7z1y6z95Ki3J5sbWOzd0Xb9l7/jZTO1X/7b4YE9qh5h2rFaJc73lMXH+EmoWzce3hS+btPMOgJmW1y2w9+R/x7eKQPKE9Nx+703fhLirly0TJHGmD2bKIjs6eO8/Bw0coV6Y0zgkTcvnqVZ49f6HtfU2XNg1bt++gWpXKWFrGZPioMXiGwYXdn0aNHYdTggQkSZKIEaPHEiuWJQ3r1zW6bO9ePShQtATtO3ehXetWxI1ry+07d9mxazfzZs347b4YE9qh5h4eHtx/8BDQNKafPnvGlavXcHRwIEWK5Hz58oXxk6ZQpVJFEidKxNt375g1dz7PX7ygbu1agKbh0at7N4aOGEW2rFnIkd2N9Rs3c/b8BWZMnRyq+ETUc/7abY6cvUzpgrlIGN+eq7cf8Nz9HRkCRiGmTZmU7YdPU6lEfixjWjBm7uqwreMuWBtQx43P2HmrsbSMSd0KxY0u26NFHUo06UGXETNoWbsiceNYc/fRM3YfO8+Mwf/77b4YE9qh5h7ff/Dg6UtA0yP//NVbrt5+gGO8uCRPnBBfXz8a9RrNpRv32DBjCIqimQQOIJ5tHKOPIBOmIQ1vM2FnG4czV24yZ+1OPn/1IJmzE31a19POBj62R2s6DJ9O2VZ9sbezpVPDqoFmbQyN4V2a0W/yYu49eU7G1CnYNG0QcayNP986azoX9i8cy7BZKynfph9+fv6kSpaIqiXyh2hfwsMXj28MmbmcF6/f4RAvLtVLFmRIpyZY6sySXKVEAWYM7MTExRv4Z+ICUidPwoLh3SlfJPAEVULosotjxdlbT5i/8wyfv3mSNEE8etUtoZ0NfGSrCnSZsYWKfRdgb2tN+6oF8fQOu4b34KblGLh4N/dfvCNDioSsHdyUOFaxjC6bxSURu8a0ZtTKg1TuvxA/f39SJnKkcv5MIdqX8JAzbTJW9m/EiBUHmLjuKMmc4jGgUWlaV8ynXeb1x68MWLyHt588cHaIS/0S2fmnXvidM0TkFS+eHafPnGXmnLl8+vSZ5MmSMbBfHxo3rA/ApHFjad2+I0VLlcXBwZ6unTuFacN7zMjh9Orbjzt375E5U0Z2bN5InDjG50jIljULxw7uY9DQ4RQvUx4/Pz9cXVJRvWqVEO1LeLj47yVKlquofT10xCiGjhhFs8aNWLJwHjFjxuTmrVssWb6C9+8/ED++I3ly5eLYwX1k07kvvtv/OuHt7U2vPv15/+EDmTNlZPe2LTKxmgjEztZGUy9cs0NTL0zkRJ+29bWzgY/t1YYOQ6dRtvk/mjpu4+phW8ft2oJ+kxZy7/FzMqZOyaYZQ4ljE0wdd/E4hs1cQflWvX/VcUsWDNG+hIdLN+5RoXVf7euRc1Yycs5KGlUtzfwRPXjx+h27jp4FoHD9Lnrrzh3enSbVyoRbbOLPKGoo7iUUximKkhh4qVv2/OgaHOxsTRRR0I5f/I8Kbfvz5NBKEgQz+Vl012vCfOas2aFbNF9V1XamikeEHUVR5gFtf75uV6UAY9tUNmFEv5z87yFVBizi/sr+xLcLevKz6OiTxw9cGo40LE6iquorU8Qjwo6xHPr+1TMcHBxMFJHG0WPHKVmuIm+ePyZBApkp+E907fkPM2bN0S2SHBqFGK33nlhn8kdZHb9wjQqt+/Lk6Bqp4+roNW4uc1Zv1y2S4zGCyHO8hRBCCCGEEEKIcCQNbyGEEEIIIYQQIhzJPd7RXNHcWfl2acfvFxRCRLjCWV35uH2UqcMQQgDFixXF39PD1GEIIUKoaJ5sfLu629RhCKElPd5CCCGEEEIIIUQ4koa3EEIIIYQQQggRjqThHUmVb9OPHmPnmjoMIUQIVe6/kH/mbv/9gkKIcFeiTHk6d+th6jCEEKFQvlUfeoyebeowhAgxaXiLcLXt0GmqdBxEypKNcC5cl2JNe7Lr2LlAy33x+E6v8fNIXbYZDvlqkLVqWzbtPxHstntNmE/hRt1xzF+TjJVaBXrf08ubtkOmkLfu/4iXtzrl2/QLdnunL9/ALk81ctfp9Gc7KUQUcur6I8r2nodro5Ekrj2EvB2mMGOL/rG4bN8FKvSdj0vDkaRsMIIqAxZy5ubjEG1/3ZErFOk6g0S1hpC60SjaT9mgfW/s6kM4VB0Q6F/SOkPDcA+FiDxevXKnUdMWZMyWg5g2drRobfyJP1++fKFLj14kdUmDlZ0jaTNlY/3GTcFuO4aVbaB/cxcs1Ftm/cZN5MhbgDgOTqRKm5EJk6cG2s6sufPI5JYTG/sEZMiag+UrV//1/goRFW07eIoq7QaQsnh9nAvUolijbtrnbv+0YtsB4rhVDPQvJM8zX7PzMPnrdsYxTzVSFKtP6wETw2tXRCjJ5GoiXJ24dJ1iebIxpGMTHOxsWbfnGPV7jmbv/NEUypkZAB8fX6p2HIy9XRxWjOtNUucEvHj9jtixLIPdtuqv0qhKSW7ce8Khs5cDve/n749VrFi0r1eJfacu8vnrtyC39fGLB20GT6F4Hjdevn0fup0WIhKLYxWLdpULkCmVMzaxLDl76yk9Zm/FOrYlrSvmB+Dk9UfUKJyV/BlTYh3bktnbT1F7yFKOT+tM6iRBP9943o7TTNl4jGHNK5AnfXJ+ePvw4OU77fudaxSmRYW8eutUH7iYHGmThs/OCmHmvLy8iJ8gPn169WTB4iVGl/Hx8aFc5Wo42NuzbuVykiVNyvMXL4gdO/Zvtz9/9kwqV6ygfR0vnp32/3v27adRs5ZMmzyB8mXKcOvOHdp27Iy1tRWdO7QHYM78BfQdMJj5s2eQL08ezl+8SNuO/8PBwZ4qlSqGcu+FiBpO/PsfxfK6MaRzUxzixWXd7iPU7z6SvYvGUihnFu1yNlax+W/XIr11rWLHCnbbs1dtY+Li9Yzq3oq82TLww8uL+09ehMt+iNCTHu8ItmjjHlKVboKvr59eefP+E6jbfSQAD5+9om73kbiUaYJTwdoUbNiVPcfPB7vdjJVaMXX5Zr0yw+Ho3j4+DJq+lLTlm+NUsDaFG3XnwOlLYbRnxk38py29WtQhd5Z0pE6RhP7tGpAjY2p26FzpW7H9IG8/fmL9lIEUzJGZlEmcKZgjM7kypwt225P6tKND/SqkSZnE6PtxrK2YPqATLWuVJ2nCoBsDAB2HTadR5VLky5bhz3dSRGlL9p4nXZPR+PrpH7OtJ66j4cgVADx69Z6GI1eQvukYktYZSrFuM9l74Xaw283WekKgnmTD4ejePr4MXbaPzC3GkbTOUEr0mMWhS/fCaM+My54mKbWKZiNjCmdSJnKkXonslMyRljM3nmiXWdCzLm0rFyBb6iSkTebE5A7VsLWOHWxsnz1+MHz5fuZ0q0O9EtlxTRKfzKkSUbXgr0qHrXVsnB3iav+9+eTB7WdvaFwmd7jus4g85i1YRKIULvj6+uqVN2raguq16wHw4MFDqteuR+KUrtg6JiRX/kLs3L0n2O26pMvExCnT9MoMh6N7e3vTd+BgkqdOh61jQvIULMK+AwfDaM+MS5UqJdMnT6R508Y4OjgYXWbJshW8efOWrRvXUbhQQVKlSknhQgXJkzvXb7dvbx+PRImctf+sra21761ctYYqlSrQsV1bXF1dqFShPH3/6cn4iVNQVVWzzOq1tG7ZnAb16uLq6kL9unVo06oF4ydOCZs/gIiSFm3YTaoSDQPXhfuOo27X4UBAXbjrcFxKNsIpXw0K1vsfe4yMmNSVsUJzpi7TH+lhOBzd28eHQVOXkLZME5zy1aBwgy4cOPVvGO2ZcRP7tKdXq7rkzppeUxdu34gcGdOw4/AZveUURSFRAke9f8H59MWDIdOXsmBkTxpULknqFEnIktaF6qULh+fuiFCQhncEq1m2CJ+/enD43BVt2bcfnuw6eo76FYsD4PHDk7KFcrFj9gjOrp1O9ZIFadBrDHcePQvVZ7cbOo0TF6+zZFQvzq+fSeMqpajTbQTX7j4Kcp0Ji9aTsFCdYP+dunTjj+Lw+PYDBztb7esdR8+S3y0jPcfNw6VME3LV6siouavx8fENZithZ/76Xbx+/5E+retGyOeJyKVG4ax8/ubJ0SsPtGXfPL3Zc+4WdYtnB8DD05vSudKxeXgLTkz7H1UKZqbpmNXcff42VJ/dafpmTv33iPk963JqRhcalMxJg5Er+O/RqyDXmbT+KMnqDgv23+kbj0Mcw7UHLzl/+ymFsqQKchlvXz88fXyJF8c6yGUOX7mPn7/Km08e5O80lUzNx9J49Eoeu38Icp0V+y+SJmkCCmYO+rNF9FK3dk0+ffrMwcNHtGXfvn1j285dNGqgaXh7fPtG+XJl2L9rO1cunKFm9WrUqteQ23fuhOqzW7Zpz7HjJ1i1dDHXLp6jWZNGVK1Zh6vX/gtyndHjJhA3vnOw/06cPBWquLbt2EmhAvn5X/eeJE7pSubsuRg6YhQ+Pj6/Xbdbr944JU1B3kJFmbtgIf7+/tr3vLy9sIptpbe8tZU1z1+84MmTp5plvIwtY8X5ixdD9PkieqpZ7mdd+NdoxW/fPdl15Cz1K5UAwOP7D01deN5Izm6YRfXShWjQY1To68KDp3Di4jWWjO3N+U2zaVy1DHW6DOPanYdBrjNh4ToS5q8Z7L9Tl67/URwe3/XrwgA/vLzJUL4Zacs0oVbnIVy59SCItTUOnbmEn78/b95/IleNdqQp3YT63Ubw6HnQdQRhWjLUPII52NlStlAu1u05StlCmqvR2w+fIWZMCyoW1QyxzJbOhWzpXLTr9G5dj93HL7D10Gn6tK73V5/78NkrNuw9zq2dC0meOCEA7etX5sj5KyzetIep/ToaXa9V7QrULBP8lbMkCeOHOI5563bx4s17GlQsoS17/MKdYxeuUbd8MTZPG8KTV6/pPnYuHj9+MKZ74Hu3w9L1e48ZPX8tR5dNwMLCIlw/S0RO9rbWlM6Vjg3HrlI6l2YUxs4zN7GwiEH5vJoRElldEpPVJbF2nV51S7Dv/B22n7pOr3oljG73dx69es+m49e4urAXyZ3sAWhbuQDHrj5g6d7zTOpQzeh6LSvkpUbhrMFuO3F8u2DfB8jcYhzvPn/D19+fPvVL0rJCviCXHbnyALZWsaiQL+gRI4/dP+Cvqkxaf4TRrSvhENea8WuPUGXAIs7N7oqNwXA6Lx9fNhy7SrdaRX8bq4g+HBwcqFCuLKvXrKN82TIAbNm2g5gxY2qHNrtly4pbtl/HwIC+vdm5ew8bN29lYL8+f/W5Dx48ZM36DTy6c5MUKZID0LlDew4dPsq8hYuYPX2q0fXat2lF3do1g9120iTGR22F1MNHjzh89BgN6tVl55ZNPH7yhM7deuDx7RsTx44Ocr1hgwdSolhRbG1tOXTkKL369Ofdu/fav1HZ0qXp1qs3+w8conSpEtx/8IDJ06YD8MrdnVSpUlK2dGkWL1tGzepVyZ0rJ/9eusyipcvw8fHh3bv3JE6cKFT7JqImB7u4lC2cm3W7jlC2kGZE0/bDpzV14WKaXJMtvSvZ0rtq1+ndpj67j51j64GT9Gnb4K8+9+GzV2zYc4xbe5b8qgs3qMKRc5dZvHEPUwcYn+OnVZ2K1CxbJNht/1FdeO0OXrx+R4PKJbVl6VIlY86wbmRN54LH9x/MXrWN0s17cXb9TNKkNH671aPn7vj7q4xfsJZxvdviaBeXMfNWU6F1Xy5tmYeNtZXR9YTpSMPbBOpXLEH7odP4/sMTG2sr1u05SvVShbT3cXz74cnoeWvYe+IC7u8+4OPrh6e3N1nSpvrrz7xy+wGqqpKrtv5JxcvHh2K5swW5nmO8uDjGi/vXn6tr++EzDJi2mGVjepMiSUJtub+/ipNjPGYN6oyFhQU5MqXhw6ev9Jm0kNHdWqIoSph8viEvbx+a9xvP6G4tSJVUKgciaHWLZ6fz9E189/LGJnYsNhy7QrWCWbAKmIfgm6c349YcZv/F27h/+Iqvnz+e3r5kTuX815959cFLVFWlQCf94a9ePr4UzeYaxFrgENcGh7g2f/25P+0e0wYPT28u3nnG0GX7SOHsQP0SOQItN3/nGZbtvcCWES2wswk6yfv7q/j4+jG2bWVK5kirWbdnXTI0G8Pe87epWUT/PLTj9A08fnhRv2TgzxTRW6MG9WjZtgPfv3/HxsaG1WvXUatGdaysNL+/b9++MWzUGHbt3sMr99f4+Pjg6elJ1iyZ//ozL125gqqqZM6hf9uDl5cXJYsXC3I9R0dHHB2DHy4aWv7+KgmdnFgwZyYWFhbkypmD9+8/0KN3XyaMGRVkDh3Uv6/2/9ndsuHn58focRO0De82rVrw4NEjqteph4+PD3Z2dnTp1IFhI0drL1QP6t8H99evKVS8FKqq4uyckKaNGzFh0hQsLGRQpQha/UolaT94yq+68O4jVC9d+Fdd+Lsno+etYu/x87i//YiPr6+mLqzTMfWnrty6r6kL12ivV+7l40OxPG5BrhemdeFDpxkwZTHLxvUhRZJfdYR8bhnJ55ZR+zq/W0by1+3M3DU7mNi3vbFNoaoqPr6+TOjTntIFcwKweExvXEs1Yvexc9QuH/S5SZiGNLxNoEKRPFjEiMHOY+conteNI+evsn3WcO37/acs5sDpfxndvSWpkyfBxio2bQZPwds36GFbMWIoBNxypeWjc++Mv7+KoigcXzEZy5j6PbvWVkFPwDJh0XomLN4Q5PsAW2YM1U6UFpSth07RetBkFgzvQaVi+j1niRI4EDNmTL0e5/Quyfnu6cW7T19wcogX7Lb/lvu7D9x6+Iz2w6bRfpimcePvr6KqKnZ5qrF5+hBKF8gZLp8tIpdyedITI0YMdp+9RTG31By7+oDNw1po3x+0eA+HLt1jRMvyuCZOgE1sS9pP3Yi3wf1rumIovzlmVc0xe2hSBywNRmNYxQ761D1p/VGmbDwW7P6sH9Lst8O3UybSNBYyp0rE208ejFtzOFDDe+7204xadYD1Q5qRK13yYLeXyFFTaUmf/NdFt3hxrEjkaMfzt58DLb/iwEXK5UmPs0PYVHZE1FG5YgUsLCzYtmMnpUoU5+DhI+zbuU37fq++/dm3/yATxo4ibZo02NhY06xVW3y8g8uhMbT3Lf+ke7uTv78/iqJw/tQxLC31J/60tgr6FovR4yYwZnzwMwzv3raZIoULBbtMcBIncsbS0lIvh2bMkJ7v37/z7t07nJycQrSdfHly8+XLF16/fo2zszOKojBu1AhGDx+Ku/trnJwScOjIUQBSpUwBgLW1NYvnz2HerOm8fv2GxIkTMX/RYuLGjUuCBMHPrSKitwpF82JhEYOdR89SPG92jpy7wvY5I7Xv95+8kAOn/mV0z1akTpFUUxceOAnvYG5DjKEYOY59Ax/Hx1dPxTKmfh61DmYSswkL1zFh4bpg92fL7OF6E6UZs/XgSVoPmMSCkT2pVDx/sMtaWFiQM1Na7j8NeqI05wSaeR8ypk6hLYsXNw6JnRx55h66W91E+JCGtwlYxY5FtVIFWbf7KO8/fcE5vgNFcv06WE9fuUnDSiWpXkqTiD29vHn43D3IScQAEjjEw/3dr3slPb28ufv4OW4Bw3TcMriiqiqv33+kWJ6ge7gNhcVQ8437TtBu6FTmD+tGjdKBKxf53TKxfu8x/P39iRFDc4X8/tMX2FjFJoH974fE/q0kTvE5v36mXtmCDbs4fPYKayYNIKVOr7yI3qxiWVK1QGY2HLvKh6/fSegQV++e57O3nlC/ZHbtRGGe3j48fvWeNEmCPjYSxIvD649fta89vX249+It2Vw1Q9azuSZBVVXefPSgSDA93IbCaqi5Ln9VDVTZmbnlJGPXHGLd4KYUyJTqt9vIlzElAPdfvCNpAs3FNI8fXrz+8JXkCe31ln3i/oET/z1i9cDGfxSniB6srKyoWb0aq9eu59379yRydqZY0V/DQE+dPkOTRg2oVaM6AJ6enjx4+JB0adIEuU0npwS8cnfXvvb09OT23btkz67Jlzmyu6GqKu7urykRTA+3oYgYal6wQAHWrFuvl0Pv3ruPjY3NHzV+r1y7hpWVFfb29nrlFhYWJE2qiXHtug0UyJ+PhAn186OlpSXJkmmGw65bv5HKFcprYxHCGE1duBDrdh3h/ceAunDuX7nr9OUbNKxSSjtRmKeXNw+fvQpy2DVAAgc73N8a1IUfPcctfWoA3DKk1tSF332kWN6ge7gNhcVQ8417j9Fu8BTmj+hBjd/Uq0HTm3393mOyBtPDXyB7JgDuPn5OUmfNse7x/Qfu7z6SIrHUYc2RNLxNpF6FYlTrPIQnL19Tt3wxvQSVNkUSth85S6Xi+bCMGZMx89fg5R38c/yK5cnG8m0HqVQsHwkc7Bi/aL3eVb60KZNSr0Jx2g2ZypgercieITUfv3zl+MX/cEmaiGqlChrdbmiH16zfe4w2g6cwultLCuXMgvu7jwDEsoyp3W6bOhWYt34n/0xYQLt6lXjy8g0j566mTZ2K2iFyc9fuZN76nVze/GuW9gdPX+Lxw5NXbz/g7ePL1YCJMTK6JidWQI/ErYdP8fbx5f2nL3h899Qu45beFUvLmGROk1IvXicHe2LFsgxULkSdYm7UGrqUp68/UrtoNr1jNk2SBOw8c5OK+TIS08KC8WsP4/mbyQGLZHVl1cF/qZA3A/HjxWHS+qP4+v6a2ChN0gTUKeZGx2mbGNmyAm6pk/Dx63dOXn9EKmdHqhQ0PsoktEPN5+88QwpnB9ImTYCqwpkbj5m55SQtK/4aqTJt03FGrTrIvB51SJM0gfYCglUsS+LFsdJuZ+Gus5yf0127PxXzZaTvgp1M6Vgde1trxq4+RAL7OJTLk14vhpUH/yWRgy1lcgb/ZAMRfTWqX5fyVarz6PFjGtSvq3c8pkubhq3bd1CtSmUsLWMyfNQYPD29gt1eiWLFWLJ8OVUrVcTJKQGjx07QmxwsXdq0NKpfjxZt2jNx3GhyZs/Oh48fOXr8BK4uqahZ3ficC2Ex1PzK1WuA5lndMWLE4MrVa8SKZUmmjJphqR3atmbW3Hl07fkPnTu04/GTpwwdOYoO7dpoc+jMOXOZNWcet65pJrPasWs37u6vKZA/L9ZW1hw5dpwhw0fRplUL7WPI3r17x4bNWyhetAheXt4sWb6CDZu3cPTAXm1sd+/d49z5i+TPl4ePHz8xZfoMrt+8xdKF80O1zyJ6qFexONU6DuLJi9fUrVBcvy6cMinbD5+mUon8WMa0YMzc1b+vC+d1Y/nWA1Qqnp8EDvEYv3Ctfl04VTLqVSxBu8GTGdOzDdkzpubjZ526sJHOIQiDuvCeo7QZOInRPVpRKFcWbUdZLEtL7XZHz11FnqwZSJMyCV88vjNn9Xau33vENJ37zueu2cG8tTu4vG2+dn8ql8jPP+PmMWNQZ+zt4jJqzkqcHONRoWjewIEIk5OGt4kUzZ0V5/gO3Hr4jKVjeuu9N7ZHazoMn07ZVn2xt7OlU8OqeHoFf7Lp1aIOT16+oV6PkcSxtqJ3q7p6V/0A5g3tyvhF6xk4bQkvXr/HIZ4tuTOnC/Ye79BavGkvvr5+9J64gN4TF2jLi+TKwt4FYwBIlsiJ7bOG03fyIgo06IpzfHuaViutN5Hc+09fuPtYf7hNpxEzOPHvr1kkCzboCsDNnQtJGXDfTM3/DePpqzeBlvl2aUcY76mI6gpndSGhQ1xuP3vDwn/0Jzkc2aoCXWZsoWLfBdjbWtO+akE8vYNveHevU4ynbz7RcNRKbK1i0aNucdw/fNVbZlbXWkxaf5QhS/fy8v0XHGytyZkuGUWyhrwH/E/5+fkzbOk+nr75iIVFDFwSOTK4WVlalv+VxBftOYePrx8tx6/VW7dByRzM7lYbgA9fvnPvxTu99+d2r8OARbtoMHIFqqqSP1NKto1oqTexmr+/P6sPX6ZBqZxyj6gIUvFiRUnk7MzNW7dZs2Kp3nuTxo2ldfuOFC1VFgcHe7p27vTbhne/3j158uQJ1evUx9Y2Dv37/MPLV/ozAy9eMJdRY8fTp/8gnr94gaOjA3lz56ZEsfCdADBnPv0L4zt27SZlihQ8unsTgOTJk7Fv5zZ69ulHjrwFSeTsTIumTfQmknv37j137v563J+lpSVz5i+gZ59++Pv74+qSimGDB9CpfTu9z1qxag29+w3UzDeRLy9H9u8hb55f97n7+fkxZfoM7ty9h6WlJSWKFeXU0YOkSiUXr8XvFc2TLaAu/JSl4/UnPhzbqw0dhk6jbPN/NHXhxtV/XxduVU9TF+42nDjW1vRuUy9wXXh4d8YvXMvAKYt58fodDvHikjtLuj8aDfqnFm/co6kLj59P7/G/LkoVyZ2VvYvGAfDp6zf+N2I6r999xM42Dm4ZUrN/8XhyZ/11Yfr9p8/cffxcb9sLR/Wiz4T51OkyDFWFAjkysWv+GJlYzUwphvdCiNBTFCUx8FK37PnRNYEeGyAij14T5jNnjV5jfb6qqu2CWl5EHoqizAPa/nzdrkoBxrapbMKIREh88viBS8ORhsVJVFWV56hEcsZy6PtXz3AI4jnWwvx17fkPM2bN0S2SHBqFGK33nliHg53M0WGOeo2by5zV23WL5HiMINKdIIQQQgghhBBChCNpeIePQMMI/PyCnt1YmD9/P3/DIhkqEnXofZd+gb9rYYb8/I1+T3JcRg2SQ6MYI9+fHKtRi5FjVnKpuZI6relIwzt8fDUs+O/uYxOEIcLKtbsPDYu+mCIOES70jtfrj92DWk6Ykf8eGh1RLsdl1BAoh1699p8p4hBh5OrVQN+fHKtRi5F67yNTxCFC4NodqdOaitzjHU4URbkOaKcddrCzpW+b+iR2ctTOMirMn7ePL3tOXGDjvuOGb9VWVXWTKWISYUtRlNqA3sPqaxbJRrk86Yll8Mx7YXoq4P7+C+PWHubzN0/dt66rqhr8c9REpBEohzo4MKh/X5IkTiQ5NBLx9vZh1+49rN2w0fAtyaFRjNF6b7uGUu81I94+vuw5fp6Ne48ZviXHYwSRhnc4URRlCDDU1HGIcPEdcFJV9bupAxGhpyiKDfAW+PtncAlzMERV1eGmDkKEDcmhUZrk0ChIjtlIS47HCCRDzcPPAkDG2URNI+QEFXUEfJcjTB2HCJVHwEJTByHClOTQqEtyaNQkx2zkJMdjBJKGdzhRVfUlUAI5CUU1g1VVHWvqIETYCvhOB5s6DvFXHgElAs65IoqQHBplSQ6NouSYjZTkeIxgMtQ8nCmKYg2UB+oA6Yg8w1njAwl1XnsDD/5yW3ZAUp3X/sCdv9xWRPMDPgAHgA2qqkaWuMVfUBQlPZpjtQzgCJjzTd7p0b94+oK/nyAlNRBL5/Ub4P1fbisifAfuork3f6+qqj9MHI8IJ5JDAcmhIhKJRMdsdMyhcjyamDS8hVGKouwFyukUzVZVtdNfbssZMJwqOr+qquf+Nj4hojNFUfIDZwyKE6mq+vovtzcb6KBTtFdV1Qp/G58Q0Z3kUCHMl+RQYSoy1FwEoihKLKCIQfGRv91ewInspkFxyb/dnhAi0PFz428rDAEMj+8iiqJYhmJ7QkRbkkOFMHuSQ4VJSMNbGJOHwEODjoZym4cNXpcI5faEiM4Mjx/D4+tPHTV4HQfNeUAI8eckhwph3iSHCpOQhrcwxvBK4FVVVd+FcpuGJ7XCiqLEDuU2hYh2Ao6bwgbFoao0qKr6FrhmUCw9akL8HcmhQpgpyaHClKThLYwxPFmE9kogwDFAd0IBayBfGGxXiOgmP2Cl81pFc3yFluFxLpUGIf6O5FAhzJfkUGEy0vAWegJmoyxoUPzX96b9pKrqB+CKQbGclIT4c4bHzWVVVT+GwXYNj/OCiqJYGV1SCGGU5FAhzJ7kUGEy0vAWhgqg/0gEf+B4GG1b7lETIvTC+t60n46jOd5/io3mfCCECDnJoUKYN8mhwmSk4S0MGV4JvKiq6ucw2rbhya2Aoijm+nxHIcyOoihx0AyT0xUmlQZVVT8B/xoUS4+aEH9GcqgQZkpyqDA1aXgLQ4YniVAPkdNxAvDTeW0JFArD7QsR1RVCc9z85AecDMPtGx7vUmkQ4s9IDhXCfEkOFSYlDW+hpShKXAI//iCshuCgqupX4IJBsQyVEyLkDI+X8wHHVVgxPN7zKopiG4bbFyLKkhwqhNmTHCpMShreQldhIKbOax/gVBh/hsz6KMTfC4/ZknWdBHx1Xsck8GNXhBDGSQ4VwrxJDhUmJQ1vocvwhHROVdVvYfwZhsNwciuKYhfGnyFElKMoSjwgt0FxWA5jJeB4P2dQLBV7IUJGcqgQZkpyqDAH0vAWusJrpkddpwFvndcWQJFw+Bwhopoi6J+zvdEcT2FNZk4W4u9IDhXCfEkOFSYnDW8BgKIoDkBOg+IwrzSoqvodOGNQLFcDhfg9w+PktKqqP8LhcwyP+5wB5wchRBAkhwph9iSHCpOThrf4qRig6Lz2BM6G02fJrI9C/LnwnC1Z11nAS+d1DKBoOH2WEFGF5FAhzJvkUGFy0vAWPxkOhTmlqqqX0SVDz/BqoJuiKPHD6bOEiPQCjg83g+LwGMaKqqqeBJ4QSobKCRE8yaFCmCnJocJcSMNb/BTeMz3qOgfoDu9R0PQWCCGMK27w+jtwPhw/T2ZOFuLPSA4VwnwVN3gtOVSYhDS8BYqiOANZDIrDrdKgqqo3cMKgWE5KQgTN8Pg4EXAchRfD4z+roigJw/HzhIi0JIcKYfYkhwqzIA1vAYGvBHoA/4bzZ8o9akKEXETdm/bTRcDwMUjFw/kzhYisihu8lhwqhHmRHCrMgjS8BQS+9+S4qqo+4fyZhlcDMyqKkiicP1OISEdRlMRABoPi8BzGSsDxf9ygWO5RE8I4yaFCmCnJocKcSMNbQMTem/bTJeCLQZmclIQIzPC4+AxcjoDPlXvUhAgZyaFCmC/JocJsSMM7mlMUJRmQ1qA4vIfgoKqqL4GvBspJSYjADI+L4wHHT3gzPA+kUxQlaQR8rhCRhuRQIcye5FBhNqThLQyvBH4ErkbQZxteDZSr9UIEZnhcRERvGsAV4JNBmRyjQuiTHCqEeZMcKsyGNLyF4ZXAo6qq+kXQZxue/FIripIygj5bCLOnKEoqwNWgOEIqDQHngaMGxdKjJoQ+yaFCmCnJocLcSMM7GlMURSHiZ3rU9R/w3qBMrgYK8Yvh8fAeuB6Bny8zJwsRBMmhQpg9yaHCrEjDO3pzAVIYlEXUEBxUVfUn8NVAqTQI8Yvh8XAk4LiJKIbng5SKorhE4OcLYc4khwph3iSHCrMiDe/ozfDK2xvgZgTHEGjWx4BeBCGitSB60yKsUh/gBvDWoEyu2AuhITlUCDMlOVSYI2l4R2+BhsipqqpGcAyGw3CSAWkiOAYhzFFawHAG1IgcxkrA+UCGyglhnORQIcyX5FBhdqThHU0FXAk01UyPum4D7gZlMlROiMDHwSvgjgniCDRzsvSoiehOcqgQZk9yqDA70vCOvjIAiQzKIrzSEHA1MNBQuYiOQwgzFGiInAl60yDw8ZkYSG+COIQwJ5JDhTBvkkOF2ZGGd/RleEJ6BjwwRSDIPWpC6FEUJQbmiPn7RwAAtBJJREFU0ZsGcB94blAmFXsR3UkOFcJMSQ4V5koa3tGXOdybpv1sg9dOQGZTBCKEmciM5jjQFaH3pv0k96gJYZTkUCHMl+RQYZak4R0NBVwJLG5QbKorgQCPgCcGZXKPmojODH//j1VVfWSSSDQMzw/FA84jQkQ7kkOFMHuSQ4VZki89esoGOBqUmeRKIMg9akIYYepHoBgyPD/EB7KaIhAhzIDkUCHMm+RQYZak4R09GZ6QHqiq+tQkkfxieFIqriiKhUkiEcKEAn73xQ2KTVapB1BV9Qnw0KBYKvYiupIcKoSZkhwqzJk0vKMnc5lwQpfhSdEecDNBHEKYWnYgnkGZSSsNAQI9EsUkUQhhepJDhTBf2ZEcKsyUNLyjGUVRYgLFDIpNXmlQVfU5cNegWK4GiujI8Hd/R1XVFyaJRJ/heaJYwPlEiGhDcqgQZk9yqDBb0vCOfnIBcQ3KjpogDmNk1kchjMyWbJIoAjtq8NoOyGmCOIQwJcmhQpg3yaHCbEnDO/oxHNpyU1VVd5NEEpjh1cAiiqJYmiQSIUwg4PdexKDY5L1pAKqqvgJuGRTLUDkR3UgOFcJMSQ4V5k4a3tGPuc30qOuowWtbILcJ4hDCVPIAcQzKjpogjqDIzMkiupMcKoT5khwqzJo0vKMRRVFiA4UNis1lCA6qqr4BrhsUy0lJRCeGv/f/VFV9a5JIjDM8XxRRFCWWSSIRIoJJDhXC7EkOFWZNGt7RSz7AWue1ChwzUSxBkauBIjoz5940CNxzYI3mvCJEdCA5VAjzJjlUmDVpeEcvhveSXFFV9b1JIgma4UmyoKIoViaJRIgIFPA7L2hQbFaVhoDzxRWDYrlHTUQXkkOFMFOSQ0VkIA3v6MXcrwSCpvfAX+e1FZDfRLEIEZEKALF1XvsDx00US3CkR01EV5JDhTBfkkOF2ZOGdzShKIoNmpOSLrO5N+0nVVU/AZcNiuWkJKIDw9/5pYDjwdwYnjcKKIpibXRJIaIIyaFCmD3JocLsScM7+igI6D5WxA84YaJYfsfwaqAMwxHRgeHv3Bx700DTg+Cn8zoWgYf3CRHVSA4VwrxJDhVmTxre0YfhlcALqqp+MUkkv2d4ssyvKIrh4yGEiDIURbEl8AQrZllpCDhvXDQolh41EdVJDhXCTEkOFZGFNLyjD8OD2uyGyOk4CfjqvI5J4Ee4CBGVFEbzO//JFzhlolhCwvD8IZUGEdVJDhXCfEkOFZGCNLyjAUVR7IDcBsVmeSUQQFVVD+C8QbEMlRNRmeHv+1zAcWCuDM8feRRFiWuSSIQIZ5JDhTB7kkNFpCAN7+ihCGCh89obOG2iWEJKZn0U0UlkmC1Z1ynAR+e1BZrzjBBRkeRQIcyb5FARKUjDO3owPCGdVVX1u0kiCTnDYTi5FEWJZ5JIhAhHiqLYAzkNis15GCsB54+zBsVSsRdRleRQIcyU5FARmUjDO3qILDM96joDeOm8jgEUNVEsQoSnouifi73Q/P7NncycLKILyaFCmC/JoSLSkIZ3FKcoSnwgu0Gx2VcaVFX9QeChfHI1UERFhr/rU6qqepokkj9jeB7JoSiKo0kiESKcSA4VwuxJDhWRhjS8o75igKLz+gdwzkSx/Cm5R01EB5Ht3rSfzqE5n/ykoDnfCBGVSA4VwrxJDhWRhjS8oz7DE9JJVf0/e2cdF8XzxvH3iqiECgiChIICBhJ2C3Y3dnd3d3frz+742t3d3d3YgYqNSsn+/jg8uOMIJe7Aeb9e+9Kdnd17ht2dzzwzz8zKQVqx5M9Rn6PjJkmShVYsEQgSgLDn2VUtWafnpv1GluVAIn+uRTTsBckNoaECgY4iNFSQ1BCOd/InKc5N+81F4LtamugNFCQnvNT2v6N47pMKYo6aILkjNFQg0F281PaFhgp0GuF4J2MkSbICcqklJ5lGQ9iowkm1ZNEbKEhOqD/PJ2RZDtaYUzdRr09cJEmy1IolAkE8IzRUINB5hIYKkhTC8U7eqPecfQOuaMOQOKAeMiQaDYLkhPrznCRC5CJwGUW9EhHRYy9ILggNFQh0G6GhgiSFcLyTN+ov73FZlkO0Ysnfo94bmF2SJGutWCIQxCOSJNkAzmrJSWY0DSCsPjmhliwaDYLkgtBQgUBHERoqSIoIxzt5k1RXeozIVeCLWprosRckB9TF9TNwLfHNiDPq9UoZrVghEMQ/QkMFAt1FaKggySEc72SKJEmZgWxqyUktBAdZln8Bx9WSRW+gIDmg3vg9Hva8JzXU65VskiTZacUSgSCeEBoqEOg8QkMFSQ7heCdf1IX1I3BDG4bEA2LVR0FyJCmvlhyR68AntTTxjgqSOkJDBQLdRmioIMkhHO/kS6QFJ2RZDtWKJXFHvTJ1kCTJQSuWCATxQNjza6+WnCQbDWH1iljASZDcEBoqEOgoQkMFSRXheCdDJEmSSPorPUbkNuCnliZ6AwVJGfX38z2K5zypEqnREFYPCQRJDqGhAoHOIzRUkCQRjnfyJBtgq5aWJHsCIcreQNFoECRl1J/fo7Isy1qxJH5Qr1/sgKzaMEQgiAeEhgoEuo3QUEGSRDjeyRP1nkBf4J42DIlH1Csl0RsoSJJEMZqWZBv1YdwF3qqliVA5QVJFaKhAoKMIDRUkZYTjnTyJVCEl8Z5AiFypWhP5+40CQVIgO5BJLS1JNxrC6pdIDXtt2CIQxANCQwUC3UVoqCDJIhzvZEZYT2CkEBxt2BLPPAReq6WJSkmQFFF/bl8Bj7RhSDwTKZRVjKgJkhpCQwUCnUdoqCDJIhzv5EcuIKNaWpLuCYQoewPFHDVBUiTSJ1CSwWgaRH4/LYGc2jBEIIgDQkMFAt1GaKggySIc7+SHek/gM+CJNgxJACI1GiRJEs+wIMkQ9rwml2+PqvMYeK6WJkbUBEkNoaECgY4iNFSQ1BEVbvJD07dHk0NPIEQOwzEHcmvDEIHgL3EFMqilJYcw1t8jauJbpIKkjtBQgUB3ERoqSNIIxzsZIUmSHuCplpxcegKRZfkpkUceRKicICmh/rw+lmX5mVYsSRjU6xsvMaImSCoIDRUIdB6hoYIkjbiZyQt3wFQtLVn0BEZArPooSMokt0+gqKNe35iiqJcEgqSA0FCBQLcRGipI0gjHO3mhXiE9lGX5pVYsSTjUKyXPsFEKgUCnkSQpJZFH05JVo16W5RdEXl1WNOwFSQWhoQKBjiI0VJAcEI538iK5LjgREfVKNj2QRxuGCAR/SB4gnVpasmo0hCFWThYkVYSGCgS6i9BQQZJHON7JBEmS9IGSasnJrtEgy/Jr4J5asugNFCQF1J/Tu7Isv9GKJQmLer3jGVY/CQQ6i9BQgUDnERoqSPIIxzv5kB8wVks7pgU7EgOx6qMgKRJptWStWJHwHFPbNwbyacEOgeBPEBoqEOg2QkMFSR7heCcf1ENRbsmy/E4rliQ86r2BxSVJSqUVSwSCWBD2fBZXS052o2kAsiy/BW6rJYtQOYGuIzRUINBRhIYKDU0uCMc7+ZDcV3qMyDG1fSOggBbsEAhiS0HAUC3tmBbsSCzEysmCpIbQUIFAdxEaKkgWCMc7GSBJUhqgmFpysm00yLLsB1xXSxaVkkCXUX8+r8my/EErliQOmkbUUmvFEoEgBoSGAkJDBbqN0FChockC4XgnDwoDaSLsy8AJLdmSWIg5aoKkxL8yN+03x1HUQ79Jg6KeEgh0EaGhQkMFuo3QUKGhyQLheCcP1Od+XJFl+ZNWLEk81HsDi0iSZKAVSwSCaAh7LouoJSfb0TSAsPrnqlqymKMm0FWEhgoNFegoQkOVCA1NBgjHO3nwL81N+80JIDTCfmoiV8wCgS5QFIi4cNEvkv9oGog5aoKkg9BQoaEC3UVoqAKhockA4XgncSRJMgIKqSUn9xAcZFn+AlxWSxaVkkAXUX8uL8uy/FUrliQu6vVQYUmS1BfHEQi0itBQFYSGCnQRoaEKhIYmA4TjnfQpBuhH2A8BTmnJlsRGvTdQhOEIdBH15/JfGE0DOIliZOI3+kRewEog0DZCQ8MRGirQRYSGKhAamgwQjnfSR70n8IIsy9+0Yknio175FpQkKa1WLBEINBD2PBZUS/4nGg1h9dAFtWQxoibQNYSGhiM0VKBTCA0VGprcEI530udfW+kxIqeB4Aj7KYHiWrJFINBECUAvwn4wiuf2X0GsnCzQdYSGhiM0VKBrCA1VRWhoEkc43kkYSZLSA/nUkv+JnkAAWZa/A+fVkkWonECXUH8ez8my/EMrlmgH9foof1i9JRBoHaGhQkMFOo/QUFWEhiZxhOOdtCmJ6j0MBM5qyRZtIVZ9FOgy/+JqyRE5AwRF2E+BYgRDINAFhIYKDRXoNkJDhYYmK4TjnbRRr5DOyLL8UyuWaA/1SjivJEmmWrFEIIiAJElmQB615H+q0RBWH51RSxYNe4GuIDRUaKhARxEaKjQ0OSIc76TNvzw37TfngIAI+xLgqSVbBIKIeKJ4Hn8TQOSwzn8BMUdNoKsIDRUaKtBdhIYqEBqajBCOdxJFkiRzwE0t+Z/qCQSQZTmQyAttiDlqAl1A/Tk8Ffa8/muo10vukiRl0IolAkEYQkMVCA0V6DBCQxUIDU1GCMc76eKltv8duKgFO3QBMUdNoIv863PTfnMBUF8Mx0sLdggEEfFS2xcaGo7QUIEuIDRUgdDQZIRwvJMu6hXSKVmWgzTmTP6oh+HkliQpo1YsEQgASZIsARe15H8xjJWweumUWrJo2Au0jdDQcISGCnQKoaHhCA1NXgjHO+miHoLzr/YEAlwC/NXSvLRgh0DwGy+1/W8ontN/FfX6SYSyCrSN0NBwhIYKdA0vtX2hoaoIDU2iCMc7CSJJkjWQQy35n200yLIcDJxQSxa9gQJtov78nZBlOUQrlugG6vVTTkmSMmnFEsE/j9BQVYSGCnQQoaGqCA1NJgjHO2mi3tP1BbiqDUN0CLHqo0CXEKslq3IV+KqWJnrsBdpCaGhkhIYKdAmhoaoIDU0mCMc7aaL+sh2XZfmXVizRHdR7A50kSbLViiWCfxpJkuwAR7Xkf3Y0DSBspOK4WrJoNAi0hdDQyAgNFegEQkMjIzQ0+SAc76SJWOkxMteBT2ppolISaAP15+4TiufzX0esnCzQFYSGRkZoqEBXEBqqGaGhyQDheCcxJElyABzUkv/5RkPYaMUxtWRRKQm0QaQQOVmWQ7ViiW6hXk9llSTJXhuGCP5dhIZqRmioQIcQGqoZoaHJAOF4Jz3UewL9gNvaMEQHiTRHTZIkSSuWCP5Jwp43MTdNM7eAD2ppYkRNkNgIDY0aoaECrSI0NFqEhiYDhOOd9FB/yURPYDjqvYGZiTyyIRAkJFkBO7W0f340DSCsnlJvQIlGgyCxERoaNUJDBdpGaGgUCA1NHgjHOwkRRU+gqJDCuQO8U0sToXKCxET9eXsL3NWGITpKpDlqYkRNkFgIDY0RoaECbSM0NHqEhiZxhOOdtHAGrNXSRAhOGLIsy4hPogi0i6a5abJWLNFN1N9PG8BJG4YI/kmEhkaD0FCBDiA0NHqEhiZxhOOdtFAPKXkNPNCGITqMem9gKdEbKEgMwp4z9XdUjKapch94o5YmQuUEiYXQ0JgRGirQCkJDY4XQ0CSOcLyTFpFC5ERPYCTUK2krIIc2DBH8c+QELNXSRKMhAmH1lfgkikBbCA2NGaGhAm0hNDQGhIYmfYTjnUSQJCkFGhaF0YYtOo4P8FItTVRKgsRA/Tl7ATzWhiE6TqTFYcSImiChERoaa4SGCrSF0NDYITQ0CSMc76SDC2CuliZ6AtWIojdQhOEIEoNIIXJiNE0j6u+nBYr6TSBISISGxgKhoQItIjQ0dggNTcIIxzvpoN4T+ESW5afaMCQJoGmOmnjWBQlGFKNpolGvAVmWnwBP1ZLFiJogoREaGnuEhgoSFaGhsUdoaNJGVKRJh0grPWrFiqSB+t/GDHDThiGCfwZ3wFQtTbyjUSNWThYkNkJDY4/QUEFiIzT0zxAamkQRjncSQJIkPcBTLVn0BEaBLMvPUcxTi4gIlRMkJOrP1yNZll9oxZKkgXr95RlWzwkE8Y7Q0D9DaKhACwgN/TOEhiZRhOOdNMgDpFdLEz2B0SNWfRQkJpFWS9aKFUkH9frLBPBIfDME/whCQ/8coaGCxERo6J8hNDSJIhzvpIF6hXRPluXXWrEk6aCpNzClViwRJGskSdJHjKb9EbIsv0LxPdKIiIa9IKEQGvrnCA0VJApCQ/8coaFJF+F4Jw3E3LQ/55jaflognxbsECR/8gHGamnHtGBHUkPMURMkFkJD/5xjavtCQwUJhdDQv0NoaBJEON46jiRJqYDiasmiJzAGZFn2Be6oJYs5aoKEQP25ui3L8lutWJK0UK/HSoSNfAgE8YbQ0L9DaKggEREa+ncIDU2CCMdb9ykAGKmlHdOCHUkRMUdNkBiIuWl/xzG1fSMU9Z1AEJ8IDf17hIYKEgOhoX/HMbV9oaFJAOF46z7qFdINWZb9tGJJ0kM9DKe4JEmptWKJIFkS9jypj6aJMNZYIMvye+CmWrJo2AviG6Ghf4/QUEGCIjT07xEamjQRjrfuox6CI3oCY89xQI6wbwAU1JItguRJISBNhH0ZxXMniB3q9ZkIZRXEN0JD/x6hoYKERmho3BAamsQQjrcOI0mSAVBULVk0GmKJLMsfgGtqyaI3UBCfqD9PV2VZ/qgVS5Im6vVZMUmS0mjMKRD8IUJD44bQUEEiIDQ0bggNTWIIx1u3KQJEDOsKBU5oyZakilj1UZCQiNWS48YJFPXab1KjqPcEgvhAaGjcERoqSEiEhsYNoaFJDOF46zbqISOXZVn+ohVLki7qvYGFJUky1IolgmRF2HNUWC1ZjKb9AbIsfwauqCWLUDlBfCE0NO4IDRUkCEJD447Q0KSHcLx1G7HSY9w5CfyKsJ+KyKGHAsHfUAyI+OmOXyieN8GfIVZOFiQUQkPjjtBQQUIhNDR+EBqahBCOt44iSVJaIi9iIhoNf4gsy1+Bi2rJolISxAfqz9EFWZa/acWSpI16vVZIkiRjrVgiSDYIDY0fhIYKEhChofGD0NAkhHC8dZfiQMoI+8HAaS3ZktQRc9QECYGYmxY/nAZCIuynJPLnZQSCP0VoaPwhNFSQEAgNjR+EhiYhhOOtu6jP0Tgvy/J3rViS9FHvDcwvSVI6rVgiSBZIkpQeyK+WLEbT/gJZlv2B82rJYo6aIK4IDY0/hIYK4hWhofGH0NCkhXC8dRcxNy3+OAMERdjXA0poyRZB8qAEqvVnEIrnTPB3iDlqgvhGaGj8ITRUEN8IDY1fhIYmEYTjrYNIkmQK5FVLFiE4f4ksyz+Ac2rJolISxAX15+esLMs/tWJJ8kC9fssrSZKJNgwRJH2EhsYvQkMFCYDQ0PhFaGgSQTjeuklJQIqwH0Bk0RP8Geq9gSIMRxAX1J8fMZoWN84CgRH2U6CoBwWCv0FoaPwjNFQQnwgNjV+EhiYRhOOtm6j3BJ6WZTlAK5YkH9QrdQ9JkjJoxRJBkibsufFQSxaNhjgQVr+pL3wlRtQEf4vQ0PhHaKggXhAaGv8IDU06CMdbNxErPcY/F4CIYUwS4KklWwRJGy+1/R8oni9B3BArJwviC6Gh8Y/QUEF84aW2LzQ0fhAamgQQjreOIUlSRiC3WrLoCYwjsiwHAqfUkkWonOBvUH9uTsmyHKQxp+BPUK/nXCVJstCKJYIki9DQhEFoqCAeERqaMAgNTQIIx1v38FLb9wcuacGO5IhY9VEQH4jVkhOGi4D65568tGCHIGnjpbYvNDT+EBoqiA+EhiYMQkOTAMLx1j3UK6STsiwHa8WS5Id6GE4uSZKstGKJIEkiSVImIKdasghjjQfC6rmTasmiYS/4U4SGJhxCQwVxQmhowiE0NGkgHG/dQ6z0mHBcBr6ppXlpwQ5B0sVLbf8rcEULdiRXxMrJgrgiNDThEBoqiCteavtCQ+MXoaE6jnC8dQhJkmwBZ7Vk0WiIJ2RZDgGOqyWL3kDBn6D+vBwPe64E8YN6fZddkiQbrVgiSHIIDU1YhIYK4gGhoQmL0FAdRzjeuoV6z9Qn4Lo2DEnGiDlqgrgg5qYlLNeAz2pposdeEFuEhiY8QkMFcUFoaMJyDaGhOo1wvHULTT2Bv7RiSfJFfS5RNkmSMmvFEkGSQpKkLEBWtWQxNy0eCavvxIia4G8RGprwCA0V/BVCQxMeoaG6j3C8dQRJkiRET2BicAP4qJYmegMFsUH9OfkA3NSGIckcMUdN8McIDU00hIYK/hahoYmD0FAdRjjeuoMDoN5rLBoN8Ywsy6FE7mEVvYGC2KD+nBwNe54E8Yt6vWcvSZKDViwRJCWEhiYCQkMFcUBoaOIgNFSHEY637qBeIb0D7mjDkH+ASI2GsNESgUAjUYymiRC5hOE28F4tTTTsBTEhNDTxEBoq+COEhiYqQkN1GOF4axFJkuwkSTooSdIdoJXa4aOyLMvasOsfQL030BbYI0nSPUmSemnDIIFuIklSL0mS7gF7AfWVQcVoWgIQVu+pN8haSZJ0J6y+tNOGXQLdQ2io1hAaKogVQkMTH6Ghuk1KbRvwjzMXKBvFsZSSJJWRZflwYhqU3JEkyQQoA3wB0kc4VDHs36mSJB2XZflyYtsm0C0kScoHTA3bza52+DNQVpIkX1mWPyemXckdSZLKEFmbiob9mxOYA1RPVKMEuorQ0ERGaKggtggN1Q5CQ3UbMeKtXWyjOVYHOCRJUp3EMia5ExbqdByYjWqDQZ3o7ovg3yG658AExXN0XIRYxh+SJHkDh4Da0WQTvfWC3wgNTUSEhgr+EKGhiYzQUN1HON7a5W4s8lRNcCv+HTIDbrHIJ+YFCiB276cbkRd0Evw9VWKRR7yfgt8IDU1chIYK/gShoYmP0FAdRzje2uV8LPKIOTDxxwvAJ4Y8H4FHiWCLQPd5CHyKIc8jFM+VIH6IzWI7sak3Bf8GQkMTF6Ghgj9BaGjiIzRUxxGOt3aJ6eHfBPyXGIb8C4R9tqIJEBBNtgtiQR4BKBcouRBNlp9AE/E5lHhlNbA5hjyi0SD4jdDQRERoqOBPEBqqFYSG6jjC8dYu14CQKI6dBZqJCil+kWX5HNAYiKphICokQUSieh5koLEsy+J5iUfC6rumwLkosgSjqDcFAhAamugIDRX8IUJDExGhobqPcLy1iCzLAcBrDYd8gBqyLP9MZJP+CWRZ3gL0juKwEAFBRKJ6HnrJsrw1US35Rwir96oDjzUcfi3LcmAimyTQUYSGagehoYI/QGhoIiM0VLcRjrf2eaa2HwhUlmX5vTaM+YeYASzTkB5dWJTg30NTo2EpMDOxDfmXCKv/KqGoDyOiXl8KBEJDtcMMhIYKYkZoqBYQGqq7CMdb+wwiPGTrd+jNAy3a808QNveoLXA/QvJFWZY/aMkkgQ4S9jxcipB0H2gn5jAmPGH1YBNU68fB2rNIoKMIDdUCQkMFsUFoqPYQGqqbSOLZ1z6SJGUF6gIbZVnWFBoiSCDCvh/ZDMW8l3ViPqBAHUmSUgANAH1gpWgwJC6ifhTEhHhGtIfQUEFMCA3VLqJ+1C2E4y0QCAQCgUAgEAgEAkECIkLNBQKBQCAQCAQCgUAgSECE4y0QCAQCgUAgEAgEAkECIhxvgUAgEAgEAoFAIBAIEhDheAsEAoFAIBAIBAKBQJCACMdbIBAIBAKBQCAQCASCBEQ43gKBQCAQCAQCgUAgECQgwvEWCAQCgUAgEAgEAoEgAUmprR+WJCkl4AnUAhyA1NqyRfBXhADvgD3ALuA7kBuoB7gCxtozTfAXyMBn4ASwWZbl1+oZJEnSB8oANYHMQKpEtE8QPb8AP2AfsEOW5S9/cxFJkjKguL9lATNAL74MFERLEPAa2A4ckGU58G8uIklSFsAbKAqkA6R4s1AQV34At4FNwBVZluWYTpAkKT1QHagImCPeR10iCHgObAMOy7Ic/DcXkSQpG1AXKAikRbyzicVP4D6K9/F8LN9HY6AqUBnIiBZ9KMFfEQg8AbYCx2VZDtGKFbIsJ/oGZAUeo2jsiy3pb9+Bizpgh9jiZwsFRgJShHc2F/BKB2wTW8xbIFD/L+rlNkCwDtj/r29+QP4/vHcSMF0HbBdb7LYTQLoY7ml9FO+ytm0VW8zbKyDXH76zesASHbBdbHAJyBDD/aqMoq2rbVvFFj/bYyCrVnxgLTjd9sAzHfiji01sYot+Gxn2zuYE3uqAPWKL/RYC1P2Derm1DtgstvDtE5D3D+7fTB2wWWx/tp0GjKO4n3VRvMPatlFssd/eAjn+oKNsuQ7YLLbw7RpgFsX9qojoBEuO2zPAPrH9YCnsoUo0JEmaC3RM1B8VCAR/gwxYA9OAhlq2RfDnvAIyy7IcGl0mSZJSo2g0pk8UqwSxZY8sy1ViyiRJkjOKkElB0qOdLMuLIiZIkpQCeIGi7hUkLdbIstw4pkySJBUEzieCPYI/o58sy5PVEyVJuoNiAEKQ/Jgry3LnxPzBRJ2fIEmSHlAnYpppurR0aFwTs/RpkSQxtSWp8DMgkLU7D3Hn0dNIxzrUr4yDtSVSCnE/kwq/fv3izLV77Dym0haQgAZAtYiJGU2MaV25MOmM0iCJ6Wg6QWBICNtP3eTKw5cRk22AIihG1qKjLGpOdxH7dJR1NkVfT9zfxMA/8BerL7/l9ZegiMnlJEkylWX5Uwyn11VPaFCnJvnzuqOnJ6YE6wKyLPP85StmzVtMaKhKP1g9YJFa9iKoOd0FCxbE29ub1KnFUji6gCzLfPnyhblz5/L27duIh6pLkmQgy/LPGC4R6Z1tVDovLvZW6Il2U4IjA74fvzFvx2mCQn5FPFQPUHG8JUlyQc3pdnGyp0HVMhiI9zHJIMsyH798Y/6a7Xz6+i3ioTqSJHWTZflXVOfGN4k64i1JUg7gbsS081sW4po9W6LZIIg/QkJ+kd6jAhGfoYm9WtK5YVUtWiWICw36TmTXsQsRkw6hcMyUXF/cj8yWZolqlyBmZFkmT9vJPHv7MWLyQFmWJ0R3niRJE4D+v/ddrAzZ38FNdIQmMh++B5N3ymVCQlU0uZIsy/uiO0+SpH1Ahd/7zRvVY8mc6QlkpSAurFy7gVadekZMCpRlOU3EBEmSBgDjf+87ODjg4+Mj3kcd5MmTJ2TNmlU9uYgsy+eiO0+SpHNAod/7HasVZVybGINbBPHMnvN3aDzuP/Vkw4gdJ5IkdQDm/d43N03P46PrSZlSdGomRW7c86Gwdwf15JyyLN9LLBsS+3NiJhF3DA3SCKc7CZMypV6k8c7Cbjm0Yosgfijsll09ySTiTiazdMLp1lEkSaJAjszqybEJHzeJuJPfTkQfaYMMRvpkzZBGPfmP71+RgvnjyyRBPKPh3qQOm+oREROVc4oUEe+jjuLg4IC1daQZAX/8zhbMGaneFiQCBXNk0ZScTm3fJOJOvtzZhdOdhHHLkQ1Dg7/S2XgjsR1vFfVIKcLgkh0pU4pPwydlNLyTKjdUT0/cX10mZeT7E5sbplovi1BHraEhzPTP719K8YUbXSWKe6N+j8X9TEJouD9/UecKXdUGGvQSYnwfhd+S1PnLdlK8Id72ROLN+w+06DsWj6otMHYtR7tBEyPlufPoKY16jCBXhSYYupRhzJwVMV73/cfPVG/bn6xe9TDxqIhTmQb0GD2TL9/8VfIdPHURr0ZdyFigKnbFalG3y1AePn2hkmf9rsMUqt2ODPkqY1/Sm1b9x+H7XiVsVRCGr98nWg6ZTh7vrqQrVJf2I2ZHylOx/TCMC9SJtNXpMTbG66/bc5wijXqToVgDMpdtQdvhs1SOHzp7ldKtBmLl2ZjMZVtQv/cEHj5T/fT2hn0nKdKoNxbFG5K1QmtaD53JW7+Ypov+m+w8c4vaQ5fg2Hg0dvWGUbb3HPacv6OSZ8X+C1TqPx+HhiPJ0mAE1QYt5OztpzFe+8qDF9Qcshj7BiPI0mAENQYv4vKD8Hfv3vO3VBu0EOemY7CqPQSPNpMYtXIfQcHhn5jsNH0DptUGRNqKdJoWb3+Df5GzT79Qcf4Nso4+R5EZV1h50TfGcz7/DKHr5ofkGHeBHOMu0HXzQ778VP0c6MnHX6i++CbOY8+TZ/Ilxh54RsivxF3I9F/i1NkLpDbPjHuR0rHKv//wMYqVr0Za62xkyJKTcjXqqRy/eOUa5WvWx9w+l/L4hctXVfJs3LqDfCXKkdY6G1ldCzJl1jwEcSMoKIhhw4bh4OBA6tSpyZw5M7NmzYr2nLFjx1KsWDGMjIyijQxYvXo1Hh4epEmTBnNzc5o1a6Y8FhAQQIsWLXBzc0NfXx8vL6/4KpIgBk7feoJXrzlYeQ/Ho90Ulu6Nec25z/4/aT99I5kbjiJzw1G0n76RL/4xTasX/AnbDp6kWtv+ZC7hTcaC1SnZsCu7jp6JlG/Oqi14VGuFWb4qOJZpSI8xs/D/Ebt74ffpC9lKN8Awdzn8Pn1ROXbrwRPKt+iFWb4qZCvdgHHzVqlMa33z/gMt+o3Do1orjN0q0G7wpLgVOBERjnciERQUTAbT9PRu3YACUYRj//gZQBYbK4Z3bYm9baZYXTdFColqZYuzac4YbuxZwcKx/Th2/iqdh4c3yJ++fEO9rkMplteVs5sWsGvxZAICA6nVYZAyz9krt2g9cAJNapTn0rbFbJg9irs+z2jVf1zcCp5MCQwKJoNJOno3r0UBFyeNedZM6ovP3sXK7cK66ejppaBWmSLRXnvuut0MnrWS7k2qc2HddPbMH0kVzwLK409fvaV+n4kU9cjJ6dVT2DVnOD8Dg1Qc+rPX79Fm+CwaV/XiwrrprJvSn3tPXtBq6Mz4+QMkM07fekwJt2ysH96C4zO6US5/dpqOW8WZ20+UeU7dfEytEm5sH9OGQ1M642hjgffwJfi89ovyuv4/A/EesQwrs7Tsn9yJA5M7YWmWljrDlvDtRyAAqVLq0bB0XjaPasWF+b0Z17Yqqw9eYszqA8rrjG9XnXsrByu3W8sGYprWkBrFXRPuj5LMef4pgKar75HfLi37O7jRpbgNQ/c8ZfedD9Ge12XTQ269+c6qJjlZ3TQnt958p9uWR8rjd3y/02z1XTyzmbC/gxtz6zpx4P4nxh16ltBF+if59PkzLTt2p7Rn8Vjl3757H41bd6JJvTpcOr6fUwd20LJxA+Vxf//vVPFujLWVJSf3b+fUgR1ksrSkcp3GfAvr0N578AhN2nahTfPGXD9zhNlTxjFz3iLmLFyWIGX8V2jYsCH79u1j4cKF3L9/n40bN+Lm5hbtOYGBgdSuXZsePXpEmWfWrFn07duXPn36cOvWLY4ePUqNGjWUx3/9+kWaNGno0qULVaqIudaJxbO3H6k3agUFc2Tm+PTO9PT2pP+iXew4cyva89pMXc8Nn9dsHNacTcNbcMPnNe1nbEwkq/8NTl26gWehPGyeO4azm+ZRoURBGnQfyenLN5V51u8+wuBpi+nXtiFXdyxh8bh+7D95kT7j58bqN9oPmYKbhunGX/2/U7VtfzJmMOXkuv8xdWBnZizbyKwVm5R5goKCyWCSnt6t61PANWlNcdX5GKYKLXqRPWtmDNOkZtXW/ejppaB/+8a0qV+N/hPnsX73YdIaGTGieysaVS8HQHBwCAMmz2frgRN8/PwViwwmNKhShtG92gKKGzb6f8tZu+sQn7/6kz1rZkZ0a0W54gWiMyVOZLGxYuqgLgBsPXhCY578rjnIH/YATV60JlbXzWCSnrb1wxedzmxtSbsG1Zm8aK0y7ertBwSH/GJUzzbKVW77tGlIpVZ98Pv0BXPT9Jy/fgcbS3O6NvcGwMHOmo6Na9F7bOSR3LhQsf0wsjvYYJg6Nat3HSVFihT0a1WHNnUqMGD6cjbsO0FaI0OGd2pIw8peAASHhDBwxgq2HT7Lxy/fsDBNT/2KJRjVtSkAQcHBjJm/jnX7TvD563eyO9gyvGNDyhbJE6+2RySLdUam9GkNwLYjmtdRMUufVmV/04HTGKZJTa2yRaO87udv3xkx5z/WTelP6ULuyvTcjuFzka7ee0xwyC9Gdm6svJ+9W9SiSscR+H3+irlJOi7cuI9NRjO6NFI8Gw62VnSoV5k+U5b8XYGjoOrABTjbZcQwtT7/HbqMXooU9KlfipaVCjN48S42Hr9GWoPUDGlagQal8wIQHPKLIUt2s+PMTT5+/YG5iTF1PT0Y0aISAEHBIYz77yAbj13ls/9PnO0yMqRpBcrkdY5X2yMyoV11lf3+Dcty4OI99py7TVEXBwAW9Wmgkmdap5rsOXebw5fvk83aXON1H758z6dvPxjYqBxZrBRz4wc3Ls/GY9d49Oo9eZxsyWptTtYI52fOaMrpm485F2E0Pb1RGtIbhc9L2n32Np/9f9KobOLP5/VedhtHcwMM9FOw4do7UkgS3Uva0rSAJSP3PWXrTT+MU+vRv0xmvN0tAAj+Fcqo/c/YfecDn36EkMFIn9pu5gwqp3iug0JCmXL0BZtv+PHlZwhOFgb0L5MZL0eTBCvHqotvsUybijFVFPfXycKQq6/8mX/6NVVyZdB4zsP3Pzj66DPbWrtQILPi/Z5YLSu1lt7mkd9PHM0N2H7rA04WBvQuZQeAQwYDBpfPTMcND+jlZYdxau2GKpau6k1OZ0cMDA1Y8d8G9PRSMKhPd9q3bEqfwSNZs3Er6dIaM3pIf5o0UOhBcHAwfYeMYvOO3Xz4+ImM5hloWLc240coOm+DgoIYPm4KazZu5tPnL+R0dmLUkP5UKOOV4OVp27UPTRvWRZZltmzfHW3eX79+0aP/UCaMHEyb5uFffcqZPbzz9N7DR3z89JnhA/vgkEUx93bk4L6s2biF+498yJ/Hnf/Wb6ZqxXJ0bNMCgKz2WejfswuTZ86hU9sWiTon28vLi5w5c2JoaMiyZcvQ09NjyJAhdOjQgV69evHff/+RLl06xo4dS9OmCs0MDg6md+/ebNq0iQ8fPpAxY0YaN27MhAmKdRh/jzyvXr2aT58+kTNnTsaOHUuFChWiMyVOHDhwgEOHDuHj44O5uaI+tLe3j/G8UaNGAbBp0yaNxz9//szAgQPZtm0b5cqVU6a7uoZ3WhoZGTF//nwAbty4wefPn/+yFIlD1cGLcba1UOju4SvopZDoU68ULSsWZPCSPWw8cV2hu03K0aCUoh0UHPKLIUv3suPMLT5++4F5emPqerozorningYFhzBuzWE2Hr+m0F1bC4Y0KU+ZvJoHFuKDpfsuYGWWjkntFO2U7HYZuXz/Bf/bdorqRXNrPOf+i3ccvvKQvRPaUSinQj+mdapB5YGLePjyPU62Fglmb2yo0KK3wmcxSM2qrQcUPku7RrSpX5X+k+azfvcR0hoZMqJbSzWfZQFbD57g4+dvWJiZ0KBqaUb3bAMo2rij/7eCtbsOK3wWBztGdG9JuWIJ57NMGaj6ha3BnZqy78R5dh45TbF8infn3LXbFHTPoSxHFhsrGlUry/ZDp2K8/pxVW/gZEEC/do3Yf1JlQV/W7TrCz4BAFo3th0Ga1Lg4OXDv8XNmrdxMt+beSJIU5lMpbNx64GR8FDnR0HnHGxQh0F2be3N83f/YffQMfSfM5cCpi5QvXoBT6+exevsBOg2bilfhPFhnNGfuf1vYcfgUK6cMIYu1Ja/e+vEgQlh1+yGTefziNcsnDcbG0px9Jy7g3XkIJ9fPxS2H5sXeJi38j8kLo3eGty0YT7F80ffOJjSv3/mx/dApShQItyOPizMp9fRYumkPrbwr8yMgkP+2HyBf7uyYmyrWFCjknothMxaz++gZKnsV4cPnr2zac5QKJQtF9VN/zYZ9J+nSqBpHl41nz4lL9J+2jENnr1G2iAcnVk7iv13H6DxmHl4F3MhkYca8dXvYefQ8y8f2JIt1Rl69/aASVt1h5ByevPJl6ege2GTMwP4zV6jbawInVkzE1dleow2Tl21myrIt0dq5ZeZgiuXJFW/lXrHjMHXKFcMo8sIOSg6fu8av0FDeffhMvnrd+er/g3wujozv3hwHWysA8uTISkq9FCzbdoiWNcvyIyCINbuOkS+XI+YminVBCrplZ/ic/9hz4iKVSuTnw5dvbDp4ivLF4r8zYtOxa3SqWZxDUzuz9/wdBi7axaHLDyibz5mj07qw9sgVus/ejKe7I5kypGPBztPsOnebxX0bkjmjGa8/fOHRq/fK63WeuYmnvh9Y2KcBNubpOXDpPg1Hr+DwtM64Omj+tO3UDUeZvvFotHZuGNFS6UTHBv+fgZgYG0R5PCjkFwHBIaQ3Nowyj6ONBWZpDVl18CJ9G5QBYMWBC9hamJAjs6XGcx6/9uPwlQdULBj1Z0NXHbyIl7sjmTOaxrI08cvWm360K5KJnW1dOXDvE8P3PeXYo894OZqwp50rG6+9p+92H4o7pMcqXSqWnPNl792PzPV2xs4kNa+/BvH4Q3g4Wq9tPjz9FMCcOk5kSpeKIw8/0WLNPXa3c8XFykijDbNOvGT2yVfR2rm6SU4KZVFfK0fB5Zff8MymuqaKp6MJG6+9J/hXKPoa5v9dfuGPUaoU5LcL71QrkDkthqlScPnFNxzNDQgKCSW12loXaVKmICBE5sZrf4o6aP9z6Ws2baVHp3acObSTnXsP0GvgcPYfOkaFsl6cP7qHlWs30q57X0p7Fsc6kxWzFyxh2669/Ld4LvaZ7Xj5+jUPHj1WXq915174PH3KqoVzsLXJxN4DR6jZsAXnDu/G3dVFow3jp85iwvToO3Z3bVhNiaJRa9C8xcvxffuO9csXMGbyjBjLffnaDV68ek3q1Kko4FmB175vcXXJyfgRg8njpmjoZ3fMRgYzU5auWsuQvj0AWLziPzLb2uCSQ9H5FxgURBq1TwkZpEnDy9dvePbiJfaZ7WK0JT7577//6NWrF+fPn2fHjh306NGDffv2UbFiRS5dusSKFSto06YNZcqUwdramlmzZrF161bWrVuHvb09L1++5P798E/Bt2zZEh8fH9asWYOtrS179uyhWrVqXLx4EXd3d402jBs3jnHjoo+Q27t3LyVKlNB4bNu2bRQoUIBp06axcuVKDAwMqFSpEuPGjcPY2Piv/zYHDhzg169fvH37lly5cvHlyxcKFizI1KlTNa1InmTYdPw6nWoU49DkDuy9cI+Bi3dz6MoDyuZ15ujUTgrd/d9WPN2yKXR311l2nbvD4r71yZzRlNd+X3j0Kjxaq/OsLTz1/cjC3vWwyZCeA5fv03DsKg5P6Yirg+YIzKkbjzF90/Fo7dwwrDlFXew1Hrt47wWlPBxV0krndWLt0asEh/xCX8N86ov3n2OcJhWFIiwkWjhnFozSpOLCvedad7xBMRLctVkdjq+dxe6jZ+k7cR4HTl+ifLH8nFo/h9XbD9Jp+PQIPstWdhw+zcrJg8liY8Ur3/dqPssUhc8ycSA2lhbsO3ke787DOLnuf9H4LGtUBuA0sW3+OKUTHRv8v//AJF249hX2cGHtzkOcv3aHQh65ePHmHbuPnaVCiYLRXufa3UdMXbqek+vm4PMssoZfuH6HonlzY5AmvI4tWzQ/o2Yv59kr31hHBOsqScLxzumYhSGdmwPQrXldpi5eh37KlHRuqvgk+KCOTZm2ZB3nr96mVgVPnr9+h1MWW4rlc0WSJOysLSmcRyH+j5+/ZsOeI9w78B921oqGb8fGNTl67jJLNuxi5rDuGm1oU68adSp4RWuntaXmUa/EoHmfMew6eoafAYFU8izMgjH9lMfsbTOxc9FEmvYeTa+xswgNlXHP6ci2+covllAkb26WTxpMq/7j+RkYSEjIL8oUzceicf01/VycyJnVjsHt6gPQtXE1pq7Yin5KPeVnyAa2rcv0lds4d+M+tcoU4bnvexwzW1MsTy7F/bSyoLC7IjLg8UtfNh44xZ0d87CzUlS4HepV5tiFGyzZcoAZA9pptKF17fLUjmbkGcDaIv5W7750+yF3fJ4zd2inaPM9ffWW0FCZiUs3MalXK0zTGzNh8UYqdxzO5Y2zMEyTGnsbS7bPHkbzQdPoPXmx4n5md2DLzCHK6xRxz8HSMT1oPWwmPwOCCPn1i9KF3Fk4vGu8lek3OTJbMqCRosezc80SzNh0HP2UenSorgj97NegDDM3H+fCvWfUKObKi3efcbQ2p6iLg+J+ZjRR9lw/efOBzSeuc31xf+wymgDQrmpRjl97xPK9F5jaqaZGG1pVKkStGMKuM2WIvcOzaPdZXn/4Qr1SeaPMM2bVAYzTpKJSoagd5LSGqdk5vh1Nx65i+qZjgGJEe+vo1hik1lfJW77vXG74vCYwOITmFQoyrJnm0SXfj185dPlBpBH4xMQ5wohu+6JpmHPqFSn1JNoUUQhiTy9b5p5+zaUX36jqkoFXXwLJmiENhbIoVky3MUmtHDF++jGAbbf8ON8jLzYmCqFtWSgTJx9/YfWlt4yvqrlx3DS/JdVcNI9M/8YqXaooj73zD6ZEVtV7YGGkT0iozMcfIVimjXzuO/8gMhjpq4xoSpKEuZE+774FA+DlaMKic2/YdP09NXOb8/57EDOOv1T+pi6QK4czwwf0BqBn5/ZMmjEHff2UdOugGGEZ2q8nk2fO5eyFS9SpUZXnL17h5JiVEkULIUkSme1sKFpIMdri8+Qp6zZvw+f6eTLb2QDQuV1LDh8/yaLlq/nf1PEabWjfqil1a1XTeOw3Npmsojx28/ZdRk+azumDO2P9vfInT58DMHzsZCaPGYZ9lszMW7yc0lXrcPv8cawzWZE2rTGHd27Cu2lrJk7/HwD2me3Yt3UtBgaKjrjypT3pOXA4B44cp6xXCR49fsL0OQsAeOP7NtEdbxcXF0aMGAFAr169mDBhAvr6+nTvrmjPDBs2jIkTJ3LmzBm8vb159uwZzs7OlChRQnE/M2emaFGFHvr4+LB27VqePn1K5swK56ZLly4cOnSIBQsWMHeu5jDSDh06UK9ePY3HfmNjYxPlscePH3Pq1ClSp07N5s2b+fz5M127duX169dRjmbHhsePHxMaGsqYMWOYMWMGZmZmjBo1ilKlSnH37l0MDaPuONVlcmTOyICGis7czjWKMWPzcfT19OhQTXEf+9UvzcwtJ7lw7zk1iuVW6K5NBormslforoWa7p68wfVFfbCzMAGgXZUiHL/uw/L9F5jaoYZGG1pVLBiz7ppp7vgEePf5G17uqo6jhYkxIb9C+fD1O1Yazn37yZ8M6Y0i18HpjXj72T9Sfm2g8FkUawh0a+7N1CXrFW3cprUBGNShCdOWrOf8tTvUKl9S4bPY24T7LJkyqvksR7l3YDV2mTIC0LFRTY6eu8qSjbuZObSbRhva1K9KnYqe0dppnTH2Psv8tdt59daPRtXCvy5bv0ppPn7+SvkWvZGRCQn5RaNqZRnTq02U1/n+4yct+o1j2qAu2Fiaa3S83/p9xNpStQPF0tw07Ngn4XgnBrmdwxtekiRhYWaCi3P4yJW+fkpM0xvz7uNnAJrULE+1Nv1wq9ycMkXzUaFkISqUKEiKFCm4dvchsiyTt3orld8IDA7Gq2DUo4FmJukwM4m6AtE2E/t3YlCnZjx4+oIRM5bQd8Ic/jeiFwC+7z/SefhUGlcvR90qpfH//pPR/1tO096j2bt0CilSpODuo6f0GT+HAR2aULZYfnzff2Tw1AV0HTmdxeMHxKutLhHCpiVJwsI0Hbkcw3sv9VOmxCSdEe8/KhZbaFK1FNW7jMSjThdKF/KgQrG8lC+aR3E/7z1GlmXy1+uh8huBQcF4FtAcqgSKMHD1UPCEZMX2w+TKlpn8UcwH/02oLBMcEsKUPq0pU9gDgCWju5OtYhv2nLiEd/livPX7RNdx82hUxZO6FYrz7ftPxixYT7OBU9kzb4Tifj5+Qb+pS+nf2puyhT3w9fvE4Fkr6TZ+AYtGaq6o/5Zc9uGNY0mSsEhvRK4s4Wn6KfUwMTbgfZgoNiqTj1rDlpC//RRK5XGiXP4clMvnTIoUKbju8wpZlinSWXXRsMDgEEq6Rf3pQdO0hpimjZ8G1K6ztxi+dA+L+zWMckR54c7TrNh3nq1j2pDOMOoIhp+BwXSbtZkCOTKzqE8DfoXK/G/rCRqPWcmR6V0xShPu3C3t1wj/n4HcevKG4cv2MGPzcXrVLRXpmmsOXya9URqqFI6/aIw/Jadl+N/6t+OZI2N4mr5eCtKnSYnfd4WjWc/DggYr71J81jU8s6WntLMppR1NSJFC4uab78gyeM25pvIbQSEyxRyirnNNDfUxNdSP8njsUA0JlpE1pEZ3Rth5MvxuB3o6mjC0fBaG7H5Cr22PSKWXgh6etpx/9g1dWTDeLVd4Z5EkSWS0MCd3rvB5cvr6+piapOfde8WIWLNG9ahYqwE58xenXClPKpYrTaVypUmRIgVXr99ElmVci3ip/EZgYBClShaL0gYzU1PMTP8uYiMwMJDGbToxadRQZTh4bAgNDQVgYO9u1Kmh6OidP2MSh4+dZPX6zfTr0ZmfP3/StlsfChfIx6pFc/j16xfT/jef2o1bcf7IXoyMDGnTvDE+T55Ru3ErgoODSZc2LV07tGbUhKmx7gSITyLOg5YkiYwZM6qEUuvr62Nqasq7d+8AaNGiBeXKlcPZ2Zny5ctTuXJlKlWqRIoUKbhy5QqyLJMrl2r9EhgYSOnSUS9eZ2ZmhpnZ33dWh4aGIkkSa9asIX16RSfp//73PypUqMDbt2+xtNQcIRSb6wYHBzNr1izKly8PKCIErKys2LlzJ/Xr1/9rm7VJZN01JleW8L+Rfko9TIzS8P7Lb93NQ63hy8jfcTqlPBwpl9+ZcnnDdPfxa4XudlFdAyYwOISSrlFHBcSH7kaalSGHlynqcyIfk+Xo6+3ERKPP4qTms6RT81na9setSguFz1KiEBVKFFDzWVqr/IbCZ/GI0gaz9OkwSx8/Psv2Q6cYPHURKyYPIrN1+DN28uJ1Ji5cw4whXSnglhOf56/oO2Euo+esYFiXFhqv1Xv8XAp7uFCznObIl9+o3+PfC6slhy8rJgnHW1/tcw2SJEVKA4nQUMWNyZPLmTsH/uPQ6UscO3+VdoMm4po9G7sWT1JW7ifXz410jTRpoh4d0fVQcysLM6wszMieNTMZ0qejbLMeDGjfBNtMGVmwdjuGBgaM7dNemX/JhIE4l2nAuau3KZrPlSmL11LALQc9WylEyDV7NowM0lC2WQ9GdGuFbVhPW3ygHj6k6X5KSITKikaSR46s3No2j0PnrnH80k3ajZiNq5M9O+cMIzRURpIkjq+YGOm6Bqmjvp+JGWr+IyCQzQdOM7h9zAJvlUHREM3hED5ikt7YiEzmprx8qwjHXrBxH4Zp0jCmW/iqrEtGdSd71Xacu3Gfoh45mbp8C/lzO9GjaU0AcjvZY2iQhvJthzC8YyNsreIvOiNSOJgkoa8Waiuh6FQAcHe04drifhy58oATN3zoNH0DuR0ysXV0a0Jlxf08PK1LpFDfNKmjdrLiK9R8x+mbdJi2gXm96lG5kOZ7P3/HKcauPsCG4a3I5xz9yNam49d46vuR/ZM6Kj/FtqhPAxwajmTX2dvULxXe2WcbNtKQI7Mlv0Jlus/eTLfaJVU+8SbLMv8dvET9UnlIpa+96ltfT1X9JElz2m+xdLU25lyPPBzz+cLpx1/osfURuSwNWdcsV9g9hz3tXCN9yiyNftTrf8Y11DyjsT7v/YNU0vy+h5AyhYSpoea/bUbjVPh9D0YOe05BUcYPP4KxMA5/PtsXtaZdkUy8/RZMegM9Xn4OZPyh52Q2jbqTJjHR11d9lySkyGlSuKbmdXfl0bVzHDhyjKMnTtOqUw/ccudi/9Z1Sk09d3gP+mrPpEGaqMsbl1DzN2/fcefeA1p36UXrLooO5tDQUGRZJrV5ZnZuWEX50pFHe6ysFDqWM3v4ehEpU6bEMZsDL14qnqW1m7bx5OkzTu3frnSiVy+ag7lDLrbt2kvj+nWQJIkJIwczdtgAfN++w8I8A4ePK+Y1JvZoN2i4n1JU91OhqXnz5uXJkyfs37+fI0eO0Lx5c9zd3Tl48KDyfl68eDHSNX6P+GsirqHmmTJlwsbGRul0A+TMqeggev78+V873pkyKUbHInYkpE+fHmtra54/f/5X19QFIk2FkTS3rZS6m82Gawv7cOTqQ07ceEynGZvJ7WDF1pEtle2ow1M6RrpGmlRR60xcQ80zmqTl7SfVUer3X/xJqZcCsygcektTY/w++0eug79+J6PJ309JiE9i08YlwvuYJ5cTd/avCvNZrtFu8CRcs2dl16KJyntzct3/ItWv6tNdIhJfoebbDp6k9cCJLB7Xj6qlVKNER85eTr3KpWjpXRmA3M4O/PgZQKfh0xjUoanGT68dO3+Vl77v+W+HYvHY3wuVO3jVo1er+ozs3gpLczPe+ql+Uendh88AZMygnel18UmScLz/hnTGRtSu4EntCp40qVkBr4Zd8Hn+CvecjsiyzFu/j3gWiv18V10PNY/I74o2MEgx2vQzICDS95d/7//O++NnIHopNOeJuIS/tkhnbEjtskWpXbYoTaqWolTLgfi88MU9u4Pifn74hGf+2M9VScxQ8y0HTxMYHEyDStGH/QDKEPoHz15hY6kIo/X/8RNfv0/KUPqfAYFR38+wivxHgIb7Gbb/e1RPm6QzTEPN4m7ULO5GozL5KNdnLo/ffMAtqzWyLPPu0zdKRDPCrU58hJpvOXmdzjM2MrdHPWoU03yt/209wYQ1h1g/vAVFomhMRORnYBCSpPj6wG9SpJCQJAk5rGNJE7IcSsivUH6FykTUrtO3HvP4zQealE+4RVUSirRpUlLNJQPVXDJQL48F1Rbd4snHAHJbGSHLijDsYn8w/zmuoeb5bNOy756quJ/0+YybtZHG+d0A+eyM+R4UyqUX/spQ+Usv/PkRFEo+O9UIGkmSlL+/7aYf1ulT4ZpJ83z1pEC6dGnxrlkN75rVaNaoHsXKVePR4yd4uOVGlmV8372jVImoR7jViUuouU0mK66dPqySNn/JCg4dO8GmVUuidH7zubuROnVqHjzyoXgRxTzE0NBQHj95RvnSXgD8+PkTSZJIEaH+TJEiRZgTo/rO6unpYWOtcOzWb95G4QL5yGihG+2AmEiXLh1169albt26tGjRgsKFC/Po0SPy5MmjuJ++vpQqFTniJiriGmperFgxNm7ciL+/v3JO94MHDwDIkiVLlOfFRLFiimfy/v372NraAuDv78+bN2/idN2kSDrDNNQs5krNYq40Kp2Xcv3m8/jNxwi6608Jt9jPe49rqHmBHHbsOXdXJe3YtUfkcbTROL8boED2zPgHBHHh3nNlqPyFe8/5HhBEwRyxj37RNVR9lvJ4NeqGz/PX4T7Lh094RjPCrU58hJpv2nuMdkMms2hsP2qVLxnpuMZ2pl4KonMZdi6cQFBw+JSry7fu02HoVPYvm0q2zIr6oaB7LoZOX0xAYBBpwgbQjpy9TKaMGchiE/X0o6RCsnS8Zy3fiJVFBtxyZEM/ZUo27D5MOmMjbCwtMDRIQ4OqZWg3eBIT+nbAI5cTH7984+TF69jbZooy/CE+Qs2v31V8cuab/w9SSCm4fvcRqfRTktPRHlCstn7XR/HJmYDAIN76feT63UcYGxqQLYvigZz33zYWrN3GtV3LAdhz7CwfP38lj4szxoYG3Hn0lMFTFlDQPafynIolCzN75WbGzl1J/Sql+fb9B8NnLMXWKiN5cilCnyt7FabziGksXLeDcmGh5n0nzMUjl5NyLry2mP3fDizNTXFzdkA/pR4b9p0knZEhNpYZMEyTmvoVS9Jh5P8Y16MFHtkd+PTVn5OXb2NvY0mN0oU1XjM+Qs1v3Fd8aurr9x+kkCRu3H+Cvn5KcmZVbfSt2HGEqp4FyWAS+ffmb9jDgg17ubpJMfLjlMWaqp4F6Dd1KbMGdcAkrRFjF67Hwiw9lUooVrCuUDwf/1u7i3GLNlCvQnH8fwQwYs5/2FqakyenwlmtXCI/XcbOZ9GmfYpQ8w+f6T91KR45siodeG0xZ9tJLE3T4prVGn29FGw6fo20hqmxzpAewzSpqOvlQacZGxnTugru2az59O0np24+xt7KjGpRrHQa15C3Tcev0XH6Bka3qkzR3A68/fQNUHzq6/d1Z24+ztjVB1jQqz6ONhbKPGlS6StXHF+46wyLd53lwnzF/FkvDyeGLdtL73nbaF+tGKGhMjM2HSNFCokSbopFZdYduUKaVCnJlcWKVPp6XH34ilEr9lO9WG5Sq/VwrzpwifzZ7VRC+ZMCC868xjJtKlysDEmZQmLbDT/SptbDOl0qDFLpUdvNnJ5bHzGsgj2umYz4/DOEs0+/ktk0NZWjWGE8rqHmTQtYsuyCL8P2PqFpfksuPf/GhmvvmeMdPh1k790PjD/0nPXNc5EpXWqcLAwp5WjCgJ0+TKqueNcG7PShrLMpjubho4HzTr3Cy8mEFJLEnjsfmXPqNfPrOqOnK7Hmf8j0OQvIZGmJu6sL+vopWbdpG+nSpsXW2hpDQwMa1a1N6049mTxmGHncXfn46TPHT50lq31malWrrPGacQk119fXVwmNB7CwMCd1qtQq6dt27WXwqPEc2LYeG+tMpEuXlvYtmzBywhRsrDNhn9mWuYuW8+nzFxrXU8y/LOtVkv7DxtCl90C6tG9NaGgok2b8D70UesqOBb8PH9m0bSeexYsSGBTIiv82sGn7bo7s+vu5yInJtGnTyJQpEx4eHujr67NmzRrSpUuHra0thoaGNG7cmBYtWjB16lTy5s3Lx48fOXbsGFmzZqV27doarxnXUPNGjRoxevRoWrZsyYgRI/j8+TPdu3fH29ubjBkVkQpbt25l4MCBHD58WOnEP3/+nI8fP/L06VMArl27BoCjoyPGxsY4OztTo0YNunfvzoIFCzA1NWX48OFkzJiRqlWrKn//zp07BAUF4efnh7+/v/I6Hh4ef10mXWLO9lMK3XXIhH5KPTaduK7QXfN0GKZORV1PdzrN2syYlpXCdPcHp249UehuEc0LJMZVd1tVLMji3ecYuHg3LSoU4MK956w5cpXFvcM7cHadvc2oVQfYNroV1hnSk90uI2XyOtFz3nZmdq4JQM9526lQILtOLKz2N8xasQkrCzPcsmdDXz8lG3YfIZ2xITaW5gqfpUoZ2g2ezIS+7fHI6cTHL185efEG9rZWUfsscQw137DnCG0GTWJ873YUy++Kb9gIdCr9lMrrVvZU+BZ5XZwp4JYDn+evGTV7BZU8CylHu+et2caCtTu4tnMpAE72tiq/8+HTVwCcHeyUiz3Xr1KacfNW0W7wZPq3b8TDpy+ZumQ9gzo2UQlBv34vzKf6/oMUKSSu33tEKn19cmbT7Q61ZOl4GxsZMn3ZenyevUKSJOVCYoZhq0kvGNOPiQv/Y/C0RbzyfY9p+rTkd81ByT/oTfobini3V9nfc+wsma0tuXdQEcL+5v0HlTyPX7xmyYZdlCjgzv7lijmvHz5/4cGT8NUO06ROxeINO7n/+DmBQcHYWllQvUxxerdpqMzjVTgPyycNYtrS9cxYuh6DNKkp4JaT7QvGY2SoaCw2rVUR/x8/WbBmGwMnzyedsRGeBT0Y01vz4mSJibGhATNXbcfnxRskScLN2YEtMwdjGLbi4fzhnZm0dDNDZ63k1buPmKYzJr+LIyXzRz3HOz4o2qSPyv6ek5fInMmCOzvmK9MePnvN2Wt32fG/YRqv8eHzN5UV2gEWjezOgOnLqNdrvGLelXsOds0doSyvVwFXlo7pwYyV25i5ajtpUqeiYG4nts4aolwxvUm10nz7EcCCDXsZNGMF6YwNKZk/N2O6NotkQ2JjbJCa2VtO8PiNHxISrlmt2TiiFYZhUz3mdK/L1A1HGL5sL68/fMHU2IC8znZ/1BP/pyzfd56QX6EMXLSLgYt2KdOL5XZg13jFO7lkz1mCQ37RapLqlJOGpfMyt6eiofDx63ceRlih3dkuI2uHNmfi2kOU7zsXCZTltTFXiExKvRRM33iMx2/8kGWwszChdZUidKqh+l3iL98D2HnmFuPbRT9KqIsYp9Jj3unXPPmgGE3MbWXIqiY5MUilEOdpNbMx68Qrxh58xpuvQZgYpMTDxpiiDrYxXPnvyWyahlVNcjBi3zPlp8VGVbJX+ZTY14Bf+PgFEPIrvAt/dh1Hhu19SqNVipGa8tlNGVNZdQrDkUefmXXyFUEhoeS0MmJpw+yUdkq6IXJpjY2ZOnseDx8/QZIkPFxzs2vjKgzD9GPJnGmMmzqLAcPH8vL1G8xMTSiQ1wOvEtFHFSU0X75+5f5DH4JDQpRpE0cNRV8/Fa069eDHz5/kcXPl0M6NWIeNrudwdmTb2uWMnjiN4uWrK9oQrrnYtXEVtjbhX1VYtX4T/YePQZZlChfIx+GdGymYL+E+YRmfpE2blsmTJ/Pw4UMkSSJPnjzs3btXudDYsmXLGDt2LP369ePly5eYmZlRsGDBPxoB/1OMjY05dOgQXbt2pUCBApiamlKzZk3lJ84Avnz5wv379wmOMGI2bNgwVqxYodzPk0dxD44ePYqXlxcAq1atomfPnlSrVg1ZlilevDiHDx9WWVitcuXKPHv2LNJ1dCHiLz4wNkjN7K0nefz6A5Ik4Zo1ExuHNccwbERxTrc6TN14jOEr9vH6w1eF7jrZUiKaOd5xJYulGRuGNWfQkt0s3XseK7N0TGhTReVTYl9/BPLwlR8hIeHRJot61aP/ol3UGb4cgIoFczA5CeribxQ+y8YIPks2ts0bF8Fn6cPEhWvCfBa/MJ8lOyULav7CQHywZMNuQkJ+0XfiPPpOnKdML5Hfjf3LpwIwoH1jJEli1P+W8+qtHxlM0lPZqzAjurVU5v/w6auKzxIb0qc1YteiifQcO5vi9Ttjki4t3Zp70y3sc8e/KeLdUWV/z7FzCp/qwOo/LW6iIiVmpSJJUhHgzO/9dMZG+J7fkWi/L4h/jHOXVYarA5xePRn37En3Ex3/Ov9bs5MB05dHTLoCKJf2trUw4ebS+F1sTxB/dJy+gXVHrkRMmiTLcrSfJpAkaQGg7GFrXciKUZWjnwsvSBjKzr3O3bc/IiY1kmU52ol6kiSdB5Tfb1n8v2m0aJw0F4xK7jx59hwnjyLqyYayLCu/rSdJ0kRA+VmSZs2aqTiWAt0iS5Ys6nPFK8uyvDe6cyRJug8oFxpYNaARVaMYVRYkHJ/9f+LQeIx6srUsy29+70iSNBBQLl5QpVQRNs4elUgWChICq8I1+OqvorNFZVk+m1i/H/XKNQKBQCAQCAQCgUAgEAjijHC8BQKBQCAQCAQCgUAgSECE4y0QCAQCgUAgEAgEAkECIhxvgUAgEAgEAoFAIBAIEhDheMeBExeuYehSBr9PX7RtiiAeOHH5FsYF6uD3+au2TRHEA6du+mBabQAfvnzXtimCBOLMky/YDD/Lx+/BMWcW6BzHTp0hpakNfh8+xpxZoPMcO3YMSZLw8/PTtimCBOLUzceY1hjMh69CV5MiJy5cxzB3OeG3aBHheCdz5q/ZRsFabbAsWA3LgtXwatSFvcfPqeRpN2gihi5lVDbPhl20ZLEgJnz9PtFuxGyylGtJhmINyFevOycv31Ye337kHDW6jiJLuZYYF6jDicu3tGitICZ8P36l4/QNODYejVXtIRTuNI3TNx8rj5tWG6Bxm7XluBatFsSW2SdeUXnBDbKPu4DrxIs0/+8e91RXLhfoMCMnTCWlqY3KZpPdI1K+B4988G7ahgxZcpLWOhsFPCtw9/7DxDdYEC2/fv1i6NChODg4kCZNGhwcHBgyZAghET45J8syI0aMwNraGgMDA7y8vLh9+3Y0VxXoEqdvP6HhmFXkajkB0xqDWXP4SswnCXSGU5du4N1lKNlKN8AwdzlWbdsfKc+2gyep3m4AmUt4Y5i7HCcuXNeCpX9HsvyOtyAcGysLRvdqi2NmW0LlUFZvP0D9bsM4vWEertmzKfOVLpKXxeMHKvdT6YtHQxf5/O07ZVsPoohHTjbPGIS5STqevHqLhVl6ZZ4fAQEUcstBg0olaTt8thatFcTEF/+fVOw3n8K5srB+eAvM0xnx9O1HzE2MlXnurRyscs6hy/fpOmuzyrdOBbrL2adfaF7QCndrY2Rkphx5QYOVdzja2R1TQ31tmyeIBdmdsnF45yblvp6ensrxJ8+eU7JiTZo08Obgjg2YpE/HvQePMDYyVL+UQMtMnDiROXPmsGLFClxdXblx4wbNmzcnderUDB06FIBJkyYxdepUli9fTvbs2Rk9ejTlypXj/v37pE2bVsslEMTE959B5MxiSYNSeeg4Y1PMJwh0Cv8fP8nlaE/j6uVoM2iSxjw/fgZQyMOFBlXLRJlHV0kSI97bDp6kYK02mOWthE2RmpRv3pO3forQtMfPX1O3y1DsS3pjnr8KRbzbs+eY6ufYcpRrxLi5K2k3aCIZC1TFqUwDNu09yuev/jTrMxqL/FVwrdSMQ6cvKc/5HUa+59hZCtVuh2meihSt24Ertx9Ea+v5a7cp37wnGfJVJlupenQdOZ2v/uEhOdGVJSGoVroYFUoUIlsWG5zs7RjZvTVpDQ05f/2OSr5UqfSxsjBTbmYm6RLMpu1HzlGoYU/MizfErkxzKrQbytsPnwF4/NKX+r0nkLVCazKWaESxJn3Ye/KSyvm5qndg/KINtB8xGyvPxmSv0o5NB07z+dt3mg+ahmXJxrjX7szhc9eU5/wOI9978hJFGvUmQ7EGFG/al6t3faK19fyN+1RoNxSL4g1xqtyWbuMXqHz/L7qyJAQzVm7DytyURSO7kd/FCXsbS0oVdCOHg60yT8PKXgxqW49yRfNGc6X4Y+eZWxTrOoNMdYbg0HAkVQYs4N2nbwA8efOBRmNWkL3pGGy8h+LZfRb7LtxVOd+t9QQmrT1Ep+kbsKs3DJeW49ly8jpf/H/SatIabOsOI1+7yRy5Ev7u/Q4j33fhLiW6zcSq9hC8eszm2qOX0dp64d4zqgxYgHWdoeRqPo6ec7by9UdArMqSEMzcchxLs7TM71WffM52ZLEyw9Pdkex2GZV5LE3Tqmx7zt2hhGtW7K0yJJhd6uy584Gyc6+TbfQ5XCZcoM7SW7z3DwLg6ccAWq65h8fkSziOOU+F+Tc4eP+TyvmFpl9h+rEX9Nj6COex58k/9TLbb/nx5WcIHTc+wGnseYrNvMrxR5+V5/wOIz94/xPl5l0n6+hzVJx/gxuv/aO19dKLb9RZeotsY86Tb8ol+u98zLeA8NGs6MqSEKxplov6eTKSw9KQnJZGzKrtxIfvwVx8kXDPlTpbd+4hT7GyGGfKhoWDC6Wq1OHtu/cA+Dx5Sq1GLbHJ7kE6G0cKeFZg176DKudncyvE6EnTadWpByZ2zti75GfDlu18/vKFRq06kt7WiRz5inHgSHgUxu8w8l37DpKvRDmMrLJS0Ksil6/diNbWsxcuUapKHdJaZyNzrnx06tmfr1/D/1bRlSWhSKmXEivLjMrNwlz13Rs6eiLlSnkyZcxw8rq7ktU+C5XLl8HO1iZB7NmyZQtubm4YGBhgZmaGp6cnb9++BcDHx4caNWpgZWWFkZERefPmZdeuXSrn29vbM2rUKFq0aEHatGmxs7Nj/fr1fP78mQYNGmBsbIyTkxMHDhxQnvM7jHzXrl14eHiQJk0a8uXLx+XLl6O19ezZs3h6emJoaIiNjQ0dOnTg69fw6VzRlSUhOHPmDNWqVaNatWrY29tTvXp1qlevzvnz5wHFaPeMGTMYMGAAderUIXfu3Cxfvpxv376xZs2aBLNLnZ1nb1Os2ywy1R2OQ+MxVBm0iHefFXXfkzcfaDR2Fdmbj8em3gg8e/6PfRfvqZzv1nYyk9YdodPMTdjVH4lLq0lsOXlDoauT12FbfyT5OkzjyNXwqIzfYeT7Lt6jRI/ZWHkPx6vXHK49ehWtrRfuPafKoEVY1x1BrpYT6Dl3m6quRlOWhKB8/uwMa1qeGsVykyKFlGC/Ex2Ktn47zPJVwaZobcq36MVbP4UuPn7+mrpdh2HvWQ/zAtUoUrcje46pRqLmKN+EcfNW0W7wJDIWrI5TmUZs2nsszG8Zi0WBarhWbq7mtyjCyPccO0ehOu0xzVuZovU6xcJvuUP5Fr3IkL8q2Uo3oOvIGRr8Fs1lSQgqlizEqB6tqVW+JCkkzfevUfVyDO7UlPIlCiaYHQmFzjvevu8/0rzPGBrXKM/Vncs4sHI6DauVVR73//GT8iUKsmvxJM5vXkjNciVp2H0E9x8/V7nOnFVbyOeagzOb5lOngidtB02kRb+xVChRiHObF1IsvyutB4wnIFC1ATZoygLG9GrLqQ3zcLDNRO2Og/jxMwBN3HrwmKpt+lGlVBHOb1nIupmjuHnfhw5DJseqLJqo0X4AFvmrRLvFll+/frFxzxH8f/yksIeLyrGzV26RpUQd3Co3o9Owqbz7kDAv1Vu/T7QYPJ3GVUpxecNM9i0YTcPKnsrj338EUK5oHnbMGcbZNVOpUbowjfpN5v5TVYdq7rrd5HNx4tSqydQuW5T2I2fTash0KhTLy5n/plAsTy5aD5sZ+X7OXMHork04sWIS9jaW1Okxjh8BgRptvfXoGdW7jKJKyQKcXTOVtZP7cevhUzqOnhOrsmiiVrcxWJZsHO0WHTuPXyB/bieaDZyKffmWFGnUm/kb9iDLcrTnJRRvP32j9eS1NCydl/Nze7F7Qnvql8qjPO4fEEjZfNnZMroNJ2d1p1rR3DQbv5oHL96pXGfejtPkdbbj2Ixu1CruSqfpG2kzZR3l82fnxMxuFM3tQPtp6wkIUp3LO2zpHka0qMSR6V2wtzKj/sjl/AjQ7ETdfupL7aFLqFQoJydnd2fV4KbcfvKGrjM3xaosmvAevhTbusOi3aJjz7k75He2o9XENTg1GU2JbjNZuOtMlPfz3advHLh0jyblCkR73fjk3bcgOm16iLe7Bce6eLC5ZW7quFsoj38P+kUpJxPWNsvJwY5uVM5pRtv193n0/qfKdRafe4OHjTH7OrhRzSUDPbc+osvmh5R2MuVABzcKZ0lL1y0PCQgOVTlv9IGnDC6XhT3t3Mhsmppm/93jZ9AvjbbeffudhivvUC67GQc7urG4QXbu+H6n13afWJVFE01W3cVp7Plotz/BP+gXoTKYpEmcqCLft+9o1LoTTRt6c+v8MY7u3kyT+nXC7fH/TsWypdi3ZS1XTh6kVrXK1G3WlnsPHqlcZ9a8xRTI68HFY/vwrlWNlp160qRNFyqVL83lEwcoUbQwzdt3JSBAVR/7DxvNuBGDOX9kDw72malevxk/fqg+G7+5efsuFWs3pFqlclw5eZBNqxZz/fYd2nTtFauyaKKKdxPS2zpFu8XE42fPyJwrH47uhWnUqiOPnz5THgsNDWXX/oPkzO5EZe/GWDm6Urh0ZTZs2R7jdf8GX19fGjRoQPPmzbl79y4nTpygadOmyuP+/v5UqlSJgwcPcv36derUqUPt2rW5d0/VMZsxYwYFCxbkypUr1KtXj+bNm9OoUSMqV67MtWvXKFmyJE2aNIl0P/v06cPEiRO5dOkSWbNmpUqVKvz4oXnqxM2bNylXrhzVq1fn+vXrbN26levXr9OqVatYlUUTlSpVwtjYONotOooWLcrRo0e5e1fRAXznzh2OHDlC5cqVAXjy5Am+vr6UL19eeY6BgQElS5bkzJkz0V47vnj76Rutp6xXaNH/erB7XFvqe3koj/sHBFE2rzNbRrbk5MyuVCviQrMJa3jwUrUDat7OM+R1suXY9M7UKp6bTjM302baBsrny86JGV0o6mJP++kbI+vqsr2MaFaRI1M7YW9pRv3RK/kRGI2uDl9GpYI5OTmzC6sGNub2U1+6ztoSq7Jownvkcmzrj4x202V8/T7SvO84Gtcox9UdSziwYioNq6r5LcULsGvRRM5vnk/NssVp2GOkBr9lq8Jv2TiXOhVL0nbwJFr0H0eFEgU5t3k+xfK50XrgxMjt3KkLGdOrDafWz8HB1oranYZE47c8oWrb/lTxKsL5zfNZN3MENx88psPQqbEqiyZqdBiERYFq0W7/MjofT/zmvR/BISHUKu9JZmtLAFycHJTH3XJkwy1HeMh0//aN2XPsLFsPnGBAhybK9LLF8tO+YQ0AhnRuwawVm8iW2YbGNRSV68AOTVm5ZR+3Hz4hX+7syvMGdGhCueKKRu6CMf1wKlOf9bsP09I7ssM7Y9kG6lTyonuLesq0mUN7UMS7Pe8+fIqxLJqYO6o3AVE4ErHl1oPHlGrUlYCgIIwNDVg3ayS5nbMqj5crXoDqZUtgb2vFs1e+jJq1jMqt+nB64zxSp0oVp99W543fJ4JDQqhZpjCZMylG9VwcMyuPuzrb4+psr9zv18qbvScvse3wOfq39lamlynsTru6FQEY3L4+s9fsJKtdJhpV8QKgf+u6rNxxhDs+z8mby1F53oDWdSlbROFMzR/WmexV2rFh30la1IxckcxctZ065YrSrUl1ZdqM/u0o2qQP7z5+ibEsmpgzpCM/oxCw2PD01VsWbdpPl4ZV6d2iFjfuP6XPlCUAdKhX+a+v+7f4fvhKcMgvqhdzJXNGUwByZbFSHnd1sMbVwVq536d+afZfvMuOMzfpU7+MMr10XmfaVCkCwIBG5Ziz7RRZM2WgQel8APStX5rVBy9x99lb8jiFj+73aVCaMnmdAfhfd29cWo5n0/FrNKsQuRd09pbj1CrhRpdaJZVpUzvVpGT3Wbz/7B9jWTQxq1sdAgL/fmGvp74fWbLnHB1rFKeHtyc3n7yh/4IdALSrWjRS/rVHrmBskJpqRV0iHUso3n4LIviXTFWXDNiapAYgh2V4CK2LlREuVkbK/e6ethx88Ilddz7QwzP8XnlmM6FFQcXfs08pOxaefYO9WRrqeigc3x6etqy7+p77737gbhPeeO7haYuXowkA02s6kn/aZbbe9KNRPstIts47/ZrqLhnoUCz8mRtfNSsV5t/Azz84xrJoYnKNrJE6A+LCsL1PcbEyJJ9d4oSsvvZ9S3BwMHWqVyVLZsX9yJ0rh/K4u6sL7q7hz9OgPt3Ztf8gm3fsYnCfHsr08qU96dimBQAjBvRhxpyFOGa1p2mDugAM7tuDZavXcevuffLncVeeN7hPDyqU8QJgyf+mk8UlP2s3baV1s0aRbJ06ex71alWnV5cOyrQ5U8eTv2QF3r33i7Esmlg4azI/AzQ3OmNDwXx5WDpnOtmdHHnn58e4KbMoUaEGN84eIYOZGe/e++Hv/50J02czclBfxg0fyNETp2nariuGhoZUrVjur39bE69fvyY4OBhvb2+yZMkCQO7c4dNO3N3dcXeP8PcfPJidO3eyadMmhgwZokyvUKECnTp1AmDkyJFMmzYNR0dHmjVrBsDQoUNZunQpt27dIn/+/Mrzhg4dSoUKFQBYtmwZtra2rFmzhjZt2kSydfLkydSvX5/evXsr0+bNm0eePHl49+5djGXRxOLFi/n5U3PHTWwYMGAA3759w8XFBT09PUJCQhg8eLDyb+Hr6wuApaVq/WJpacmrV9GP/MYXvh/DtKioSwQtCrfH1SETrg6ZlPt96pVi/8X77Dhziz71SinTS+dxok3lwgAMaFiGOdtPk9UqAw1KK9pAfeuVYvWhy5F1tV4pyuRVdEj9r1ttXFpPYtPx6zQrH7nDd/bWk9Qq7kqXmsWVaVM7VKdkzzkKXY2hLJqY1aUWAUEh0ebRZd68+xDW1i8Ze7/l+Dm2HjzJgPbhgy9li+WjfQNF+3NI5+bMWrGZbHY2NK6hqFMGdmjMyq0a/Jb2jSlX7Lff0henMg1Zv/sILb0jtxFnLNtAnYpedG9RV5k2c2g3inh3VPgtMZRFE3NH9iIgUPOAliAJON5u2bNRqnBe8tdoTZmi+SlVJC+1ypfEwswEgO8/fjJu7kr2Hj+Hr99HgoNDCAgKUnEsAXJnD983NjLA0CCNysOTMYOiQnj/8bPKeYXcc6mc5+LkwD2fZ2ji6u0H+Dx/zea9x5Rpv8etnrx4TX7XHNGWRRM2ltGPxsQGZ3s7zm1eyOdv/mw/eJJ2gyayb/k0ZfnrVi6tzJvbOSt5XJzJUa4Re4+fp2a5EnH+/Yi4OmXBq4ArBRv0pHQhd0oVdKNmmSJYmCrmKH//GcD4RRvYe+oyb/0+ERzyi4CgIFwcs6hcJ7ejvfL/xoYGGKZJjUu2cKc3Y9ic5/dqKzcWdHVWOS+XYxbuPXmh0dard314/NKXzQfDe7l/j0Q+eelLfhfHaMuiCeuMcQsPDg2VyZszGyO7KDqV3LNnxefFGxZu3KcVxzu3QyY83R0p1mU6pTyc8PRwokax3JinVzhO3wOCmLj2EAcu3sP341dCfoUSEBSCi30mleu42Ic7uMYGqTFMrU+uCGkWJgon5f0X1fC0gjkyq5yXK4sV99VG039z/dErHr/5wNaT4aGuyvvp+4F8TnbRlkUT1hmivtexIVSW8XC0YXhzRSeSWzYbfF77sWT3WY2O938HL1HXy4M0qRJvbnAuKyOKZ01P6TnX8MxmQvGs6anqkoEMRgobfgT9Ytqxlxx68Il334IIDpUJDAkll5pDm9My3Dk3Sq2HgX4KcmQMz2NurLien9oK5fls06qclyOjIQ/eRzFi+vo7Tz8GsOP2B2Xa7+CBp58CyGNjHG1ZNJEpXero/jx/xKj9T7n4/CtbW+VGL5FCIN1z56K0Z3Hci5WmXClPyngWp06Nqspw6e/ffzBq4jT2HDjEG993BIcEExAQiJtLLpXruLrkVP7f2NgIQ0MDFafX0sIcgHfvVVe0Llwwn8p5uXPl4M59zaGPV67f5NHjp2zYukOZ9vsd9XnylIL58kRbFk3YWGeK8lhsqFSutMp+4fz5cMpThJVrN9Kzc3tCQxWdMtUrVaBn5/YAeLjm5vLVG8xbvCLeHW93d3fKlClD7ty5KV++PGXLlsXb2xsLC0Vb4fv374wcOZJdu3bx5s0bgoODCQgIwM3NTeU6EfeNjY0xNDTE1dVVmfbb8Xz3TrU+LVKkiMp5rq6u3LmjOnXtN5cvX+bRo0esX79emaa8nz4+FCxYMNqyaMLGJm7h+xs2bGD16tWsWbMGFxcXrl27Rvfu3XFwcKB169bKfJJaiKssy5HSEorc9pnwdMtGsa6zKJXHCU/3bNQomhvz9Io69HtAEBPXHVHo6qdvEXRV1aHVrKvheSzC1hJ5r/blj8i6asn9F5qnc1z3ea3Q1VM3lWnhuvqRfE620ZZFE3HVVW3jlj0rpQrnIX/NtpQpmo9ShfNSq3wJVb9l3uqY/ZYI+8aGYX6L8x/6LYZhfovaaPpvrt55qPBb9h1TpoX7LW/I75o92rJowsbSPMpjgiTgeOvp6bFr8SQuXL/DoTOXWbFlL8NnLGb/8um45cjGwCkLOHjqIuP7tCdbFhsM06ShzaAJBAerNt70U6oWVQL0U4YvkPK7Qv0ton9DqCzTok4lujbzjnTM2tI8xrJookb7AZy5fFPjsd+8v7Q72uOpUumTLYtCrPLlzs7lW/eZvXIT80f31ZjfOqM5NpYW+DyLfr7s36Cnp8fOOcO5cPMBh89fZ+WOI4yY8x/7FozG1dmeQTNXcOjsNcZ2b4ajXSYM0qSm3fDZBAer9n5GvHeguH+a7+ffh2CHyjLNa5SlS6OqkY5ZW5jFWBZN1Oo2hjPX7mo89pu3J/6L8piVuQk5stqqpGV3sOHlOu18vkVPLwVbR7fm4v3nHL36kNUHLzJq5T52jW+Hq4M1Q5fu5vDlB4xuVZms1uYYptanw/QNBKnfTz0N91MvRYR9xb9xvZ9NyxegU43ikY5lypA+xrJownv4Us7deRrt777cOCrKY5amackRYT43gLNtRha8Px0p75nbT3j46j1L+jeM9vfiG70UEuua5eTyS39OPPrMuqvvmHD4OZtauuBiZcSo/c849ugzQytkwcEsDQb6Kei+9RFBv1Tvlb6eaqNVkiBlhDSJsHc2DrMmQmVomDcjbYtEdras0qWKsSyaaLLqLuefR/+JwYeDC8Vo24h9T9l+04+NLVzIYpYmdgWKB/T09Ni/dR3nLl7m4NETLFu9jsGjJnBk1ybcXV3oN3QU+w8fY9LooThmdcDQ0IAWHboTFKQamaOvr9o5IUkS+hEW4YwXDQ0NpXXThnTv1DbSMZtMVjGWRRNVvJtw6lz00wG+vIz96uPGxkbkyuHMI58nAJhnMCNlypTkzKEasp4juyMbtuzQdIk4oaenx8GDBzl37hwHDhxgyZIlDBw4kOPHj+Pu7k6fPn3Yt28fU6ZMwcnJCUNDQ5o1axbL+6mvsg9xv59t2rShZ8+ekY7Z2NjEWBZNVKpUiZMnT0b7u/7+Uc8f7tu3L3369KFBgwYAuLq68uzZM8aPH0/r1q2xslI4q76+vtjZ2SnPe/fuXaRR8IRCTy8FW0e15OL9Fxy99pDVBy8xatUBdo1tg6tDJoYu28vhKw8Z3bJiBF3dRFCI6hSciBoKv3U1YjtJ8W9oHKaqhcoyTcvlp1ONYpGOZTJLF2NZNOE9cjnn7mge4PrNy/XD/9rmhEZPT49diyZy4frd8Lb+zCXsXzY1zG9ZyMHTlxjfpx3ZMttgaJCaNgMnxcFvicP9Cw2lRZ2KdG0WecqOdUbzGMuiiRodBsXst1zc+dc2J3V03vEGxcNVyMOFQh4uDOrYlHzVW7Fp31HccmTj7JVbNKpejprlFeGjAYFBPHnxGqcstjFcNXZcuH4XBztFo/v7j5/cefSURtXLa8zrkdOJuz7PlE7un5ZFE/ERaq5OaGgoQUFRh8f6ffrC67d+WFkkzOJNkiRRyC07hdyyM7BNXfLX78Hmg6dxdbbn7LV7NKzsSc3Sil71gMAgHr/yxTFz3EYtfnPx1kMcbBXC+v1nAHd9ntMoinnZHtmzcvfxC7LZRf3b0ZVFE3ENNS/snoMHz16rpD189ga7THGPjPhbJEmiYI4sFMyRhX4NylCk83S2nryBq4M15+48pUHpvFQvphhJCQgK5qnvRxyt46dH9OK9F8pFxr4HBHH3mS8NSmteVM4tmw33nr8lazS/HV1ZNBHXUPNCObPw8JVqp4nPaz/swkLyIrL6wEU8HG2itCUhkSSJ/HZpyW+Xlp5etpSac50dtz7gYmXExedf8Xa3oEouxX0ICA7l2cdAsmYwiJffvvLym9JR/RH0i/vvfuAdxbxs10xGPHj/E4dofju6smgiPkLNh+55wo5bH9jUMheOFvHzd/kTJEmiSMH8FCmYn6H9euJWpBQbtu7A3dWF0+cu0qSBN7WrK6ZPBQQE8PjpM5wds8Zw1dhx/uIVstorIpa+f//B7bv3adogcuc0QB43V27fe4Bj1qhDGaMriybiGmquTkBAAPcf+uBVQuFopEqVivx53HnwUHWhzoePHpPZNn7aIepIkkSRIkUoUqQIw4YNw8XFhfXr1+Pu7s6pU6do1qwZderUUdrr4+ODs7NzDFeNHefOnSNrVsWz8f37d27duqUMT1cnb9683L59G0dHR43HYyqLJuIaav7jx49Iq9Lr6ekpOxgcHBywsrLi4MGDFCigCNcNCAjg5MmTTJ48+a9/909RaFFmCubITL/6pSnSZSZbT93E1SET5+4+o0EpD+WXLRS6+gFHm/hps128/wJ7KzPgt66+pUEU6524ZbXm3ot3ZM0U9W9HVxZNJPVQc/jd1s9FIY9cDOrYhHw12rBp3zGF33L1Fo2qlVVGlCr9Fvv4WYzxwo27OIS1W8P9Fs2RNx65nLj76BnZMsfkt2guiyZEqHn06LzjfeH6HY6cvULZYvmxzGDKtXuPeOn7npzZFELumMWWnYdPUbV0UfRTpmTc3JVxagirM3HBaszN0pMpoznj560ilX5K6lcprTFvr9b18WrUla4jp9O6XlXSGhpy/8lz9hw7y/9G9IqxLJqIa6j50GmLqOhZCFurjHz7/oMNu49w4uJ1tswbC4D/95+MnbuCmuVKYGWRgWevfBk2YzEWGUyoXjbyyGBcuXDzAUcv3KBsYQ8yZkjP9ftPePXWT7kqt2PmTOw8dp6qngVJmVKP8Ys2EBif93PpJsxN02FlbsqExRvR109JvYqaw+l7Na9JqZYD6TZ+Aa1rl8PY0IAHT1+x5+QlZg/qEGNZNBHXUPMuDatRpvUgJi3dRJ1yxbh+/wnz1+9heKfw+ZIfv3zjpa8fn8NWpXz8whcTYyMsM5hgaR7ZoYsLF+895/j1h5TO44yFSVpuPn7FK7/PZLdTjAw4Wpuz69xtKhfKRcqUekxaeyjSQi5xYeqGI5inN8LKLB2T1x0mlX5KvD09NObtXseT8n3m0nPOVlpWLIixQWoevHzPvgt3mdGldoxl0URcQ+I61ShOhX7zmLL+CLVLuHHj8WsW7DzN0GYVVfJ9/RHA9tM3Gd069ospxheXX3zj5OMveDmaYGGkzy3f77z+EohzmAOZNYMB++59pEIOU1LqSUw79pLAkPibEz3zxCsyGOljmTYV04+9RF8vBbXcNHeedCpuTbXFt+i/8zFN81tilCoFj/wCOHj/I5OqZ4uxLJqIa6j5wF2P2XLDjyUNspM+TUrefVN0vBml0sMotV4MZ8edcxcvc/j4ScqX9sLSwoJrN2/x4tVrcmVXOGJOjlnZvmsf1StXQD9lSkZPmkZAFAtO/g3jps7E3DwD1laWjJk8nVSp9GnoXUtj3r7dO1GsfDU69exP25ZNSWtsxL0Hj9i97yDzZkyKsSyaiGuoed+ho6hasRyZbW14996PsZNn8P3HD5o1CJ8T2bd7Jxq07EDxIoUoVbIYx06eYf2WHWxevSROv62Jc+fOcejQISpUqIClpSVXr17lxYsX5MqlCC91dnZm69at1KhRA319fUaOHBlpgbS4MGbMGCwsLLC2tmbUqFGkSpWKRo0iz9cH6N+/P4ULF6ZDhw60b9+etGnTcu/ePXbu3MmCBQtiLIsm4hpqXq1aNSZMmICDgwMuLi5cvXqVadOmKTsPJEmiR48ejB07lhw5cuDs7MzYsWMxNjaOspzxzcX7zzl+3YfSeZywMDHm5uPXvPL7ovzahUJX71C5UE5S6ukxad0RAoLjz1GduuFomK6mZfK6o6RKqYd3Sc0dId3rlKR83/n0nLtNVVcv3mNGp5oxlkUTcdVV/5+BPHmjmG4UGirz8v1nbj5+jUlaQ+wsTOJ07dhw4fodjpy7Stmi+bE0N+XaXU1+y+lwv2XeKgKC4m+AbeKC/zA3TU+mjBkYP281qVKmpH7lUhrz9mpVH6/G3eg6cobCbzEy4P7jF+w5fo7/De8RY1k0EddQc/8fP/F5rlhPIVSWefHmHdfvPcIsfTrswtZT+vjlKy/evOPLV0U71+f5K9KnM8LS3Awrc7M4/X5Co/OOdzpjI85evcX8NVv5/PU7tlYWDOjQhIbVFL03E/t3pOPQKZRr1hOTdMZ0aVonXh/gUT3bMnDyfB48eUlOxyxsmjMWI0PNjTTX7Nk4sGI6I2cto0LzXvwKDcXBNhPVyhSLVVkSgrd+H2nVfzxv/T6RPq0RuZ2zsm3+eOWCcXp6Kbj94Alrdhzk81d/rCzM8Czoweqpw0ibAN8gTWdsyLnr95i/YQ9fvn3H1tKc/q3r0iBs1HlCzxZ0Gj2X8m2HYJLOiM4NqhIYj47aqM5NGDhjOQ+fvSZnVjs2TRuEkYHmsM/cTvbsXziaUfPWUrH9MH79CsXexpJqXgVjVZaEIJ+LI+um9GfE3P+YuGQTdlbmDOnQQLnQHMCeExfpMGqOcr/L2HkADGxbj8Ht6serPemM0nDuzjMW7jrLF/+f2FiY0Kd+GeVq4GPaVKXbrM1UHjAfE2MDOlQvHq892cOaV2TI0t08evmeHJktWTe0OUZpNC8ImNshE7sntGfs6gNUHbiQX6GhZLEyo2phl1iVJSHI62zH6sHNGL1yH1PWH8HWwoTBjcsrF8T5zZYT15FlqFPSI8FsiYp0afS49Pwby8778jUgBOv0qejhaatcDXx4RXt6b/eh1tLbpDdISdvCmeLV8R5UNjOj9j/Dx+8nzhkNWdE4B4apNDusuayM2NLShUlHnlNn2W1+hcpkMU1NxZxmsSpLQrDyouLTSPVXqM6D7eVlS+9SdppOiVfSp0vHmXOXmLNwGZ+/fMXOxprBfXrQOGw18CljhtO2W2+8KtfC1CQ93Tq0jV/He9gg+g0Zxf1HPrjkcGb7uhUYRaEtbrlzcXT3FoaNnUTpqnX49esXWbNkoUbVirEqS0Lw6tUbmrTpjN+Hj1iYZ6BQ/rycPrBTubgbQI0qFZk/YyITps2m58DhOGV1YPm8mVSpEP3qv39D+vTpOX36NLNnz+bz58/Y2dkxdOhQmjRRrPsxbdo0WrduTYkSJTA1NaVHjx7x6nhPmDCB3r17c//+fVxcXNi1axdGRpqjRdzc3Dhx4gRDhgzB09NTcT+zZqVWrVqxKktCMHv2bIYOHUqnTp149+4dmTJlom3btgwbFv4Fin79+vHz5086d+7Mp0+fKFSoEAcOHEi0b3inM0zDubthWvQ9ABvz9PSpX0q5GviYVpXoNnsrlQcuUuhqtaLx6ngPa1aBIUv38OiVHzkyZ2Td0GZR66q9FbvHtWHsf4eoOmhxuK4WyhWrsiQE1x69otqQ8E6v8WsPM37tYRqWzsPc7pqjbeITRVv/NvP/28bnb7/b+o2VXzGa2K8DHYdNpVzzXgq/pUntSCuTx4VRPVszcPICHjz97beMicZvycqB5VMZOXs5FVr0DvNbrNT8lqjLkhBcufWAiq36KPfHzFnJmDkraVKjHAvH9gNg99GztB8yRZmn84jpAAzq2JQhnTVH4OgKUmJ+hkiSpCKAcqWqdMZG+J6P/zlQ8cGJC9eo2LI3z09twTyaxbL+dYxzl1WZH3R69WTcs8dPiGJ8cuLyLSp3GM7Tg8swT8BvlCd1/rdmJwOmL4+YdAVQxm7bWphwc+mAxDYrEqdu+lBt0CIerR5KhmgWafnX6Dh9A+uOXImYNEmW5f7RnSNJ0gKg3e/91oWsGFU5+lVLE4MzT75Qd/kdbvbLj1k0i58lJ8rOvc7dtyqfZmoky/La6M6RJOk8oFzGf/H/ptGicfx2sP0Nx06doWy1uvg+uol5Bt0egUgsnjx7jpNHEfVkQ1mWlbHTkiRNBPr93m/WrBkrVqxIJAuj5tixY5QqVYr3799jbi4WT/pNlixZeP5cZeGqyrIs743uHEmS7gPKMI1VAxpRtUjifakiKk7dfEy1IUt4tGoQGdIlf1397P8Th8Zj1JOtZVl+83tHkqSBwLjf+1VKFWHj7KjXbdEmJy5cp2KrPjw/uUn4LdFgVbgGX/1VdLaoLMtnE+v3df473gKBQCAQCAQCgUAgECRlhOMtEAgEAoFAIBAIBAJBAqLzc7y1RcmCHvy4fVjbZgjiiZL5cuN/cbO2zRDEE8Vds/Fp5wRtmyFIQIo6pOfVyEhhuYIkglfxooR8eqVtMwTxhJeXF4k5NVGQ+BR3zcqn7WO1bYbgLylZ0J0ftw5q2wxBDIgRb4FAIBAIBAKBQCAQCBKQZOd4V2jRi55jZmnbDEE8UbH9MHpNWqRtMwQJSNWBC+g7f7u2zRAkIN7LbjN492NtmyH4S0pX9aZb38HaNkMQT3h5edGlSxdtmyFIIKoOXkzfBbq5cLEgMhVa9Kbn2NnaNkOQSIhQcy2x7eBJlmzYyfW7jwgIDCJHtiz0a9eYqqWLasy/YfcRWvQbS0XPQmyZO05jnt8YupSJlDZzWA/a1q8GwJg5Kxg3d2Xk8wzS4HdpNwBv3n9g4KT5XLv7kEfPXtGoWlkWjot2ceR/mu1HzrFkywGu339CYFAQORzs6NuyDlU8C6jk++r/g1Hz17Lt8Fk+fvmGraU5wzs1ok65YlFe++iFG4yZv47bPs8wMkhDoypeDO/YiJQpwz+ptPngaaYs28Kj568xN01H+3qV6NG0psp1Nuw7yfSV23j0/DVpjQwpVdCNcd2bxfu3vZMrp28+ZuTKfTx65cfPwCDsLExpWr4AXWuXVOZZsf8C645c4d7zt4TKMm5ZrRnUuDxFXOyjvK7fF3/aT13PnWe+fPz6A3MTYyoVzMnQZhVJbxT5U3c+r/3w6jELWYaXG3VzddXkwIVnX/FefhtHcwOOdPZQpu+8/YG5p17x9GMAwb9kHDKkoW2RTNTziPq7tOuvvqPXNh+Nx673zY+58b+xantC88b3LX2HjOLqjZs89HlCk/p1WDp3RqR8s+YvZsHSlTx78YoMZiZUr1SB8SMGY2yseSXnFWvW07pzL43HXj+4TkYLc0ZOmMroidMiHTc0NODrq0dxKte/yps3b+jduzdXrlzh4cOHNG3alOXLl6vkWb58OS1btox07s+fP0mTRvOnQgGeP39O586dOXLkCAYGBjRq1IgpU6aQKlX4Z7P279/PiBEjuHXrFqlTp6ZYsWJMnjwZZ+eovxsviD2nbz1h5MoDPHr9np+BwdhZmNC0XH661iqhzLPiwEXWHb3KvefvFJrqkIlBjctSJJd9lNddc/gKnWdpnlr4YMVALEyM47so/wQKv2UX1+/5RPBbGlK1VBR+y54jtOg3noolC7FlbqTV41U4eu4Ko2av4PbDJxgZGtC4ellGdGulbOc+e+VLzgpNI9s0fxzlwz6P/HtVd3Wu7lhC9qyZ/7S4CYZwvLXEqUvX8SyYh2FdW2GWPi3rdh+mQffh7F8+lWL53FTyPnnxmkFTF1Asn2usrz9nZC8qeYbPj0yfNrxB0aNFPdrUq6aSv0rrPuTLnV25HxQUTAbT9PRu3YClm3b/afH+OU5duY1n/twM69gQ03TGrN93kob9JrF3/kiK5VF8zzI4JIQaXUdhktaYleN7Y5MxA6/efSC1ftSN7lsPn1Knx1h6t6jNwpFdef3uI90nLODXr1DG9WgOwIHTV2g1dAaTe7emXBEP7j99RZex80iTOhUd6lUG4Oz1e7QZPotx3ZtRpWQB3n/6Ss+JC2k1dCa7541I8L9PcsDIIBXtqxUjVxYrDFPrc+7uM3rN2YJBan3aVFG8a6duPqZWCTcK58yCQepUzN1+Cu/hSzgxqzvZrDV/gieFJFGliAtDm1XALJ0RT958oO+87fSYvZllAxqr5A0KDqH1pLUUcXHgzK0nCV7mf5XPP0PovvURxR3S4/tN9fuqpgYp6V7SFkdzA1LqSRy6/4k+233IYKhPGWfNnVjVc2eglKOJSlrPbY8IDJGF0x2PBAYFYZ7BjH49OrNo+X8a86zduJUBw8eycOZkihctxOOnz2jXtQ8BgYEsmj1V4zn1alWnQplSKmmtOvckICCQjBaK97p3lw60b6naMCxfsz7587jHQ8n+TQIDAzE3N2fAgAEsXLgwynyGhob4+Kh2bEXndP/69YsqVaqQIUMGTp48ycePH2nWrBmyLDN7tmLk8cmTJ9SoUYNu3bqxatUq/P396devH5UrV+bRI9GREh8YpUlF+6pFyGVvGaapz+k1d5tCUysXBuDUzSfUKu6q0NRU+szdcRrvEcs5MaNLlJpaq7grZfI6qaR1nrmZgOAQ4XTHgVOXbuBZKA/DurVU+C27jtCg+0j2L5sSyT958uINg6YuipXfcvP+Y2p1HEKfNg1YPL4fr9/60W3UTH79CmV83/YqebcvGIdr9mzKfbP0aSNd7/L2xZhGSLfQsU+r6Uyo+eINO7EvWYeQkF8q6S36jqVul6EAPH7+mrpdhmJf0hvz/FUo4t2ePcei//RajnKNmLFsg0qaejh6UFAwQ6ctwrF0fczzV6FYvY4cPHUxnkqmmSkDu9CnbUMKuOUgWxYbBndqRp5cTuw8fFolX3BwCM37jmVEt1Y42GaK9fVN0hpjZWGm3AzSpFYeMzYyUDn27sMn7vo8o3mdSso8WWysmDqoC01rVVR5gGPLki0HcKjQKtL9bDlkOvV7KxbFevzSl/q9J5C1QmsylmhEsSZ92HvyUrTXzVW9AzNXqYYlq4ejBwUHM2z2KpyrtCVjiUaUaNaPQ2ev/nEZ/oTJfVrTu0Vt8rs4kc0uE4Pa1iNPjqzsOnZBmWfVjiO8//iF9VP7U9QjJ1msM1LUIyf5XByjvO6mA6fJ4WDH4Hb1yWaXiRL5XBjTtSkLN+3j23fFp1/X7j1OpRL5aVe3Ig62VlQsno/eLWozfcU25WI4F27cxyajGV0aVcPB1oqCrs50qFeZS7cf/nWZl+09j3PTMYT8Ur3HbSavpdEYxXdnn7z5QKMxK8jedAw23kPx7D6LfRfuRntdt9YTmL3lhEqaejh6UHAII5bvxaXFOGy8h1Kq52wOX3nw12WJDR6OttQp6U7OLJZksTKjfqk8lM7rzNk7T5V5FvVpQLuqRXHLZoOTrQXTOtXE2CA1hy/fj/K6ZumMaFWpMB6OtmTOaIqnuyOtKxdWue5vRizfi4u9FTWLxb4TLi6suvgW90mXCPmluqhS500PaLnmHgBPPwbQcs09PCZfwnHMeSrMv8HB+5+ivW6h6VeYf/q1Spp6OHpQSCjjDj4j39TLOI45T6UFNzj26HP8FCwG+mz3oa67BfnsItd9xbOmp2JOMxwtDLA3S0ObIpnIaWnEhWdfo7yegb4eGdOmUm56KSROP/lKg7xRj5LHBwuXrcLa2Z2QkBCV9CZtOlOrkWKU0OfJU2o1aolNdg/S2ThSwLMCu/ZFv0BPNrdCTJ09XyVNPRw9KCiIgSPGkcUlH+lsHClUqhL7Dx+Ln4JFgX1mO2ZMHE3zRvUxMzXRmOfshUsUyp+XJg28sc9sR+mSxWnSwJsLl6LWCAMDA6wsMyo3PT09jp44TasmDZR5jI2NVPK8ffeeO/ce0LJJw3gr34IFC7C0tIx0Pxs1akSNGjUA8PHxoUaNGlhZWWFkZETevHnZtWtXtNe1t7dnypQpKmnq4ehBQUEMGDAAW1tbjIyMyJ8/P/v374+nkkVt16xZs2jRogVmZlF/C16SJKysrFS26Dhw4AC3b99m1apV5M2bl7JlyzJp0iQWLVrE16+K9/jy5csEBwczfvx4HB0d8fDwYODAgfj4+ODn5xev5fzNsn0XcG42LrKmTl1Po7GrgDBNHbuK7M3HY1NvBJ49/8e+i/eiva5b28nM3npSJU09HD0oOIQRK/bj0moiNvVGUKrXHA5f+fv2QWzwcLShTkk3cma2JIulGfW9PCidx4mzd54p8yzqXY92VYrgltVaoakdayg0NRrbDFLrY2maVrnppUjBiZuPaVI2X7zav3jDLuxL1o3st/QbR92uw4Awv6XrMOw962FeoBpF6nZkz7Fz0V43R/kmzFi2USVNPRw9KDiYodMX41imIeYFqlGsXicOnk5ov6Uzfdo0oIBrDrJltmFwp6YKv+WIBr+l3zhGdGuJg2307yLAxr1HyZEtM0M6NyNbZhtKFHBnTO+2LFi3g2/fVb63jVn6dFiZmym3VBoGrizMTFTy6OnpRcqjTXTG8a5TwYvPX79z5OxlZdr3Hz/ZdfQMDauVBcD/x0/KlyjIrsWTOL95ITXLlaRh9xHcf/w8Tr/dfshkTly8zvJJg7m4dRFNalTAu/MQbtzTHBoIMGnhf1jkrxLtdvryjT+yw//HT0zSqTb0RsxcQhYbK5rUrPBH1+ozYQ52xWpRvF4nFq3fSWhoaJR5l2/eg5O9baSR9rhQu2xRvnz7ztEL4X+D7z8D2H38IvUrKUJzv/8IoFzRPOyYM4yza6ZSo3RhGvWbzP2nL+P02x1GzuHkldssHd2D82un0biqF3V7TeDmg6dRnjN52WYsSzaOdjt99c4f2fHtx09M0oX3ru46foHC7jnoPXkJWSu0Jl+97oxduJ5gtUZURAKDgkmTWrViMUidioDAIK6GPZ+BQcGkSRU5z6t3H3j+5j0ABd2y8+b9J/acuIgsy/h9/sqmg6coXyzPH5UpIrVKuPLF/yfHroX3/n8PCGLv+TvU81Jc1z8gkLL5srNldBtOzupOtaK5aTZ+NQ9evPvr3wXoPHMTp289ZmGfBpz+Xw8alslHw9EruPnkdZTnTN1wFNu6w6LdztyO/SjyDZ9XXLj7jGK5HaLMExTyi4DgENIbG8b6um8+fGXn2VsUVbvu/ov32H/xHhPaVY/1teJKtdwZ+BoQwsnHn5VpP4J+sf/eJ2q7WwDwPegXpZxMWNssJwc7ulE5pxlt19/n0fufcfrtXtt8OPP0K3PqOHG4kzv1PCxoseYet32/R3nOrBMvcRp7PtrtfDQOMsDyC7688w+iu6dtjDbKsszJx1/w8ftJIft0sS7bxmvvMdBPQZVcUTsT8UHdWtX4/OUrh46FN7q/f//Bjr37aVyvNgD+/t+pWLYU+7as5crJg9SqVpm6zdpy70HcRvVad+7F8dNnWLVwDtdOH6ZZw3rUbNiC6zdvR3nO+KmzSG/rFO128sz5ONlVpGB+rt+8zdkLik7e5y9esWvvASqWKx3ra6xatxFDAwPq1KgSZZ6lq9bi7JiVEkULxcneiNSrV4/Pnz9z6NAhZdr379/Zvn07TZo0AcDf359KlSpx8OBBrl+/Tp06dahduzb37kXvnMVEy5YtOXbsGGvWrOHmzZu0aNGCatWqcf369SjPGTduHMbGxtFuJ0+ejPL82PLz50+yZMmCra0tVatW5erV6Dvaz549S86cObGzs1OmVahQgcDAQC5fVrRB8+XLR8qUKVm0aBG/fv3i27dvrFixggIFCmBurnmkNa7UKu7Kl+8BHLse3vZUaOpd6nl5AOAfEETZvM5sGdmSkzO7Uq2IC80mrOHBy/dx+u3Os7Zw+tYTFvaux+lZ3WhYJi8Nx67i5pM3UZ4zdeMxbOuPjHY7c/tprG248fg1F+49p1g0U7PCNdUg1tddd/QqBqn1qVE0d6zPiQ11Knjy+dt3jpy7okxT+C1naVhVMeXT/8dPyhcvwK5FEzm/eT41yxanYY+R8eC3TFH4LRMHcnHLQprULI9352Ex+C1rsChQLdrt9OWbf2SH//cfkf2WWcvIYm1JkxrlY3WNoOBg0qROpZJmkDq1op2rNjjUsMdIspSsS+km3dl6QHWA5jfF63fGwas+lVv35fiFa7EvTCKhM6HmpunTUr5EAdbvPkz5EgUB2HH4FCn19KjspQjjdMuRDbcc4SEG/ds3Zs+xs2w9cIIBHZr81e8+fv6aDXuOcO/Af9hZWwLQsXFNjp67zJINu5g5rLvG89rUq0adCl7RXtvaMvaV8/w123jl+55G1csp0w6dvsSmfcc4tznqECtNDO3SAs+CHhgZGnDs/BUGTp7Ph09fNP6NAoOCWL/rML3bNNBwpb/HNJ0x5YvmYf2+E5QrqnDCdh49r7ifJfID4Opsj6uzvfKcfq282XvyEtsOn6N/a++/+t3HL33ZeOAUd3bMw85K4Rh0qFeZYxdusGTLAWYMaKfxvNa1y1O7rOZ5Kr+xtoh9I3nBhr28fveBhpXD5/8+efWW45duUa9CCTbPGMSzN+/oNXEx338EKMPG1SlbxIP/rd3F2j3HqFu+BO8+fmb8EkVPqK+fYlSxbGEP+k1bxuFz1yhV0A2fF77M+m+HMk8W64wUcc/B0jE9aD1sJj8Dggj59YvShdxZOLxrrMukjomxIWXzZWfjsWuUzaeYprDr7G309FJQsWBOAFwdrHF1sFae06d+afZfvMuOMzfpUz/yWgSx4cmbD2w+cZ3ri/tjl9EEgHZVi3L82iOW773A1E41NZ7XqlIhahWPfqQ4U4aYQ5JcWozD78t3QkJD6d+gDK0qFY4y75hVBzBOk4pKhXLGeN3Wk9ey99wdfgYFU6FADuZ0r6s85vvxKz3+t5mVA5uS1jB1NFeJX0wMUlLKyYQtN/wo5aQIo9579yMpU0iUCwurdrEywsUqfCpLd09bDj74xK47H+gRC+dVE08/BrDtlh/ne+TFxkRR3paFMnHy8RdWX3rL+KpZNZ7XNL8l1VwyRHttq3Spojx29+13ph97yc62udFLIUWZ72tACPmmXiYoREYvBYyt7EBpp9ivlbD+6jtquppjoJ+wPfGmJiZULFuKtRu3ULGsIlR62669pNRLSdWKCq1xd3XB3dVFec6gPt3Ztf8gm3fsYnCfHn/1uz5PnrJu8zZ8rp8ns50NAJ3bteTw8ZMsWr6a/00dr/G89q2aUrdWNY3HfmOTKeYRlOhoWLcWHz59onRVb2RZJiQkhCb16zBhZOwXj1v+33oaetfEwEBz4z8wMJA1G7fSr0fnONmqjqmpKZUqVeK///6jYsWKAGzdupWUKVNSrZri7+bu7o67e3h4++DBg9m5cyebNm1iyJAhf/W7Pj4+rF27lqdPn5I5s2KuZJcuXTh06BALFixg7ty5Gs/r0KED9erVi/baNjY2f2XTb7Jnz87SpUtxd3fn27dvzJw5k2LFinH9+nWcnJw0nuPr64ulpaVKmrm5OXp6evj6+gLg4ODAgQMHqF+/Pl26dCE0NJQ8efKwd+/eONkbHSbGBpTN58zG49cpm1cxj3zXuTsKTS2QAwBXh0y4OoRHP/apV4r9F++z48wt+tQrpfG6MfHkzQc2n7zB9UV9sLMwAaBdlSIcv+7D8v0XmNqhhsbzWlUsGLOmmsXcIenSamK4ptYvTatKUXdWjVl9UKGpBXPEeN3f/HfoMt4l3TBIHb/TepR+y67DynnGOw6fJqVeiuj9luPn2HrwJAPaN9Z43ZhQ+C1HuXdgNXaZFFFTHRvV5Oi5qyzZuJuZQ7tpPK9N/arUqegZ7bWtM/6B37J2O6/e+tEobHAUIvgtm+ZHc6YqZYvmZ/bKLazZcZB6lUvz9sMnxs9fDYCv3wcAjAwNGN+nHUXyuKCnp8fuY2dp2mcsi8YGKQdnrSzMmDm0G/lyZyc4OIQ1Ow9RuXU/9i+bQvH88TewGFd0xvEGaFi1LO2HTObHzwAMDdKwbtdhapYvoewJ+f7jJ+PmrmTv8XP4+n0kODiEgKAgcjtrboTFhmt3HyLLMnmrt1JJDwwOxqtg1KOBZibpMDOJ/QhHdGw/dIrBUxeyYvIQMoc5/36fvtBu8CSWTxr0x6HeAzuGzzNzz+nIr1+hTFq4RqPjvf3gKb79+EGj6rHrmfoT6lfypOOo//EjIBDDNKlZv+8kNcsUDr+fP//f3n3H13Q/fhx/n+xhJaKi9q4V0aL2KlWj1KzSb0qVjq8OqkZbVO1YLVpFa9RstGipTW1tdaCDFrUJIomEyD6/Py5JbpIbQk7o9/d6Ph73j5y7zs3nnvv5vM9nnFiNmxOidbt+1oWwCCUkJik2Pl5VypW84/fcf/gfmaapml3fsNseF5+gxrUcn+30zZ8307kid+Kb737Qu9M+1/wxA1SiSOpQUtM0Vcgnv2a885KcnZ1Vo1JZhUdGa8jU+RrzepAMI2ND/7E6gRr7epDeDP5ML73/kdxdXTW4d2ft+fWQnJ1sA1Z6dWih42cv6OmBE5SQmKh83l56uVsbjZ39hZydbY859M9pDZo8V4N7d1bzOoEKDYvQO9M+12vjZmnOyMx/pG9H16Y11O/D5YqJjZeXh5uWb/tV7etXS+mBvxYbrwlLN2vjvsMKDY9SYlKyYuMTVaXU7U+bSO/AsbMyTVN1/2u/kFFcQqIaBZR18CzJJ6+XfPLefs+zI2vHv6SrsfH66a9Tem/+OpUo7KtuzR7O8LjZq3drwfoftHL0C8rn5Xi+4U1jX2irwd0e09GzlzTq8w0aOme1Puhn65V8cfIXer5VHdV6KPcXCOkYUEgDVh3V9fgkebo5a+XBMLWp7CsPV9t3KyY+SVO2ndHmvyN0MTpeCcmm4hKTVbnwnf+vfzt/TaYpNflov932+ERT9Us7/t318XKVj9edNa7iEpP1yvIjGtaypEr4ZF1eedyctfGlAF2LT9auf65o5IaTKubjoYZlbn3i5qfT0fr70nV92NHxFJOc1KNrR/XuN0AxMdfl5eWpJctXqlP7NilzYK9di9H7E6Zo7cbNOh96UQmJCYqNjVNAlcp3/J6/HvhNpmmqWt0mdtvj4uLVtJHjhSR9fXzk62PtYo/bd+/VuMnTNGPSWNV+pIaOHT+h/kOG671xkzTy7bdu+fy9P/6kPw//rXkzP3T4mJWr1yn66lX9p9udnUDOyrPPPqtevXopJiZGXl5eWrx4sTp37pymPK9p5MiRWrNmjc6fP6+EhATFxsYqIODOG56//PKLTNNU5cr234m4uDg1a+Z4pICvr2+WQ8RzQt26dVW3bup6NvXq1VNgYKCmT5+uadMcX+Ems/o27fbQ0FD16dNHQUFBeuaZZxQdHa3hw4era9eu2rp1q5ycrBkw2rVxoPpN+0oxcfHycnfT8u371b5eVfs6ddlWW50aEZ2mTi18i1d27MA/52x1aj/773RcQqIaVXPcvs6xOnVcH129Hq+f/j6t9xZsUInCPurWNGP7e/aavVqwYZ9Wvt/rtupUSfrx8CkdPn1RM9/I+WNRkp5p+5hefHdSam75dqueatHIPrfMXGRRbultt92WWwIdPs83fz755s/J3DJHCya+bZ9b3p2o+ROyl1ua16+pcQP7asDYGXpx2CS5u7lpyIs9tPvn3+TsZDs57eeTX6/3TO2MeKRqRV2OiNLUuSEpwbtC6eKqUDp1FMujgZV18lyoPpi/nODtSKsmdeXs7KQ1W/eoSZ0a+u77X7R69oSU+4dOmqVNu/Zp3MAXVbZkUXl5eOiFt8crISHB4Ws6OTmlzHO9KSEhdWhvcnKyDMPQzi8+lquL/b/Dw8Nxz0jw7MWaOHtJlp9n1axxtxy+vWrjDvUeOl6fjh1st6L5n0eOK/TSZbV5IbUhkJxs+xx5A1ro56/n2n3BslIroJKirl7ThbBwFfazrwTnf7VWrRrXkX82enNvV6sGj8jZyUlrtv+oJrUC9N2PB/X1jGEp97/94QJt3rtfY14PUrniReTp4a6+I6bblU96ToYhU/blmXa+W3KyKcMwtH3BBLm62Pcmebo7Ls+J877SpHkrsvw8Kz58J2WhNEdWbd2rPsOnafbI1zKsaF64oI9cXZzt5ptULF1MMbFxCouMcrgAxKs92qlf9ycVGhahAnm9dfL8JY34aLFK3fixMwxDo179j957pbsuXI6Un08+bfvRNlzoZvCfPH+FalYtn7LSedXypeTl6aHH+7yrES93VzH/Oxs617LWQ3JyctLaH/5U4+rltP3AUa14P7UyGDb3W235+W+Ner61yjzoJy93V700NUTxWZaxU4YyTkhKnSqRbNrKeMuUfnJ1tm/8pB+Wn9bkkO80dfl3WX6ekPd6qV4Vx0PHJamkv+1YqVLKX5ciozVh6eYMwfuTb3ZpzKKNChnxvB6pcHvH6c35aBWKPyCfvN5qPeQTDXy6mYoVKqAdB49p9+/HNWHpFkmSKVPJyab82r+tSS+3V88ncm44a3rNK/jI2cnQhr8i1KB0fu3854qWBKX24L+/4aS2HY3UsJYlVdrXQ56uTnp95VHFp5sXnpaToYy/y2kebytjaW3fanJJ1/N8M/BnZtqOM5q+82yWn2fRs5X0aMmMjY+L0fH6+9J1DVh1VANWHb2xH5JpSiVG7tXCHpXU+MYCaU5OhkoXtPV2Vi3iraNh1zV9x5nbCt5Lf76gKv5eCngwdxb5adOyuZydnPXN2g1q1riBtmzfqXUrUuuuQcPe14Yt2xQ8apjKlSktLy9P9XzpdcXHxzt8zczr1dR6+Ga9+v2WtXJ1ta9XPbNY9Grc5GkaPzXry+qsCVl0V8O3h48O1tOd2qt3UHdJUrUqlXTtWoz6vv6Whg3qLxeXrJtFcxcuVWC1Knok0HHd/tnCJWrTsrn8C+f8HP62bdvK2dlZX3/9tR577DFt3rxZGzduTLl/4MCBWr9+vSZNmqTy5cvLy8tLQUFBOVKe+/btk2u6OZWOev0l21DzsWOzvgrLunXr1LBhwywfkx3Ozs6qWbOmjhxxPAfY399fu3fbz0sNCwtTUlJSSk/4Rx99JG9vbwUHB6c8ZtGiRSpevLj27NmjBg0a5Ng+p9WyVkU5OTtp7Q+H1DigrLYfOKYV76Wu2j5s3jpt+eWIRvV6Ik2d+qXi080zTsvJMJSueJWQ5vE3201bJr2cod3k4eb4eJi8fJumfrk9y88TMvw51cti6LgklSyctk69qgnLtmYI3p+s3qMxizYpZMRzt12nStLCTT+pWukiCix3dyMrHGnVuI4tt3y3R00evZlbxqfcP3TSbG3a/ZPGDeyrsiWKysvTXS8MDb5FbjEyHo+ZtHN3LpuR4ffVw93xiLjg2Us0cc7SLD/Pqk/G3nJBtFWbdqr30An6dOwguxXN/zxyQqGXwtWmzyC7fZWkvNVb6udVnzrMLa8911mvBnXS+UuX5ZMvr06eDdXwD2xTbR2pFfCQFq7Kep2JWtUq6ct1Wbf7ctt9Fbw93N3UvkVDLft2sy5HXlFhP181rJU6ZGrvL7+re7sWeupx2/Dd2Lh4HT99TuVLOh7O6OeTX6GXLqf8HRsXr7+Pn1L1SrbehuqVysk0TV0IC1fjR29/vmtODDX/ct136vtOsOaMHawOLe2HfzxStaL2rfrUbtvIaXMVGXVVU999TaWy+DKmd/DwUXm4u9nNN5akE2fOa/uP+7V8xqjbfq3s8HB3U/tmdRSyfqcuR0arcMECavhw6pDGvfsP65nWjfVUM9vZ6ti4eP1zNlTlSjjuDfXzyZ8yxPrmc/4+eVYBFW1hqXrF0rbyvByhxjVvfwGqnBhq/uXG3Xrp/RmaNaKfOjxWN8P9das/pJANO5WcnJxytvzoqXPy8nCX3y1GTxiGoSI33n/5hp0qVthPgQ/ZB0RnZ2c9+IBtmO3yjbv0aLWKesDXFgRiYuNSeshTHn/j7/QhNzs83FzVrl5VLd+2X+FR1/SAT167Oc/f/3lC3Zo9rHY3FgOLjU/QidBwlXOwGqkk+eX31oXw6JS/Y+MTdOTMJQWUsQ1ZDyjzoEzT1MWIaDXMooc7vZwaap5WcrKZ4STCjJU7NH7JZn0xomeWlxHL8nVN24mGm6+9e8Ybdvev/f5PTQn5Tpun/FcPFsyZM9iOeLg6qXUlX604eEnh1xJUKI+r6qYJrvtORalz9UJqU9n23YtNSNbJ8DiVKei4QV7Qy1UXrqY2PGITknU07LqqFrH1nlT195ZpShevJqh+6dsvk7sZau6fz01bXrFfgXrBvlDtOHZFn3WrqOIFHDdokk0zyxMNN12LS9LqPy5raPPcG7ng4eGhju1aa8nyFQoLD5f/A4XUuH7q79Pu7/fp2W6d1bGdbb5ybGys/jlxUhXKOe6R8fMrqPMXLqT8HRsbq7+OHFWNANuoosCAqjJNU6EXL6ppQ8c93OnlxlDz69evZ1hsx9nZOUNDNzNXr17T8lWrNWbYUIePOX7ylLbt3KOVS+bd1X464uHhoU6dOmnx4sUKCwuTv7+/GjdObT/s2rVLQUFB6tSpkyRb2Rw7dizLS2AVKlRI58+nzuWNjY3V4cOHVaOGrU1Uo0YNW3mGhqpp09sfzpwbQ83TM01TBw8etBtun17dunU1evRonTlzRsWK2dqPmzZtkru7ux55xLYAV0xMTKbfE0lZrplztzzcXNWubhUt335A4VExeqBAXtWvWirl/u8PnVS3poFqd2O+sq1OvaxyRR3/7vnl89aFiHR16tlLCihja2ul1qlX1TDg9ntic2qoeVqZ1qmrdmn80i36YnhQlpcRS+/q9Tit2vWbhgXl/IjOmzzc3dS+eUMtW7NVlyOiVLigrxqm6V3d++vv6v5kcz3VwnZyKSW3lHL8vffzKaDQsPCUv2255XTG3HI5Qo2z6OFOLyeGmn+5bpv6vjtRc8YMUofHG9nd90jVCtq30n5q7Mjp8xUZFa2p77yqUrdYaM0wjJT3D1n3nYr5F1KNyo5Hhh08fOyWnYa2x2TdJsht91XwlqRubZqr3YuDdfJMqJ5u3cxuOE+5ksW0essutW1WT64uLhr78eeKjXN81kiSmjxaQ5+vXKc2TevJz7eAgmcttjvTV75UcXVr+5j6vhOs8W+9pMDK5RV+JVo79x1QqWJFUg6W9O52qHnIt1v1wtvjNW7gi6r/SIBCL9kOMjdXF/kWyCdvL09VKW8frArkzaPEpCS77TMXr9Kspau0f818SdK33+3RhbAIPRpYWZ7u7tr+468aNWOBnu/SRu5u9g3OBSvWy7+Qr1remFOf3oFDtl6f6KsxcjKcdODQUbm5uqhSuVK3/Tm7PtFIT706SifOXlDXJxral2eJIlq97Qe1bVxbLi7OGjcnRHG3KM/GNavq89Vb1bpRLfkVyKeJ876yL8+SD+rpJxrppZEzNPaNngqsWFoRUVe18+c/VKpoYbVvlvl83Lsdah6yYaf6jpiuMa8HqUGNyrpw4+SAq6tLyuu+0KmlZi1fp7cmz9WLXVrp1PmLGjP7C/Xp3DJleNsnIWs1K2Sdfv0ytdfng4Wr1LxuDTkZhr757gdNWbBKn48bkNIICIuM0srNe9TwkaqKj0/QwtVbtXLLXq2flXqN59YNa6rfmE8058v1tqHmlyM1ePJcBT5UJmUu/J3q0jhQnUbM1akL4ercONC+jB/005rv/1DrRyvLxcVZwUs3KzY+6zJuGFBWizf/pFaPVlLBfHk0OWSr3aqh5YoWUpcmgXrlg+Ua3buNqpd9UBHR17Xrt39Uyt9XTzpYQOVuh8XNXr1bJQr7qnyxQjJNU3v/OKEZK3fq+dap36kPv9quMYs2ataAp1WuaKGUxo6Hm2vKNblnr9mjT9fs1Y+fvClJWv/jIUVEx6h6uaLK4+GmQ6cuasS8tapVsYTK3DhBUbmkfYW1/8gZOTkZGbZbpUNAIfVYeEinI+LUoZqfnNL0Qpcp6Kn1h8PV8iEfuTgbmrLtjOISs26Y1i+dT8t+vaTHK/qooLerpu04o8Tk1OBT1s9THQP81H/lUQ1vWUrVingr8nqi9p6IUgkfd7WunHlFejdDzV2dnfRQuuHxft6ucncx7LZ/uP2MahTLo5I+HopPStaWvyP11YEwjWpdKuUx8344r3k/hmrHq/Ync7/5PUxJyaY6BNzdMZddz3TpoNadeujEqdPq1rmD3TFavlwZfb1mvdq1bilXFxeNCp6i2Ni4LF+vacP6mr94mZ5s9bgKFSyocZOn2fXIVChXVt27dFTvV/pr4ujhqlG9msIjIrV9116VKVVCHZ5snenr5sRQ8/2//S5Jioq+KicnJ+3/7Xe5ubqp8kO24NnmiRb64OPZqhlYXbVr1tDRf05oxNiJatOyeUpv90ez5+njT+fpjx/tF+8JWfmNEhOT1L1rB4fvP2/RMhXxL6xW2VisLbt69Oihli1b6vjx4+revbtdeVaoUEErV65U+/bt5erqqpEjRyo2NjbL12vWrJnmzp2rdu3aqVChQhozZoxdj1yFChXUo0cP9ezZU5MnT9bDDz+s8PBwbdu2TWXKlFHHjh0zfd2cGGq+f/9+SVJUVJStPPfvl5ubW8qw95EjR6pOnToqX768oqKiNG3aNB08eFAzZ85MeY0ZM2ZoxowZKQvMPf7446pSpYqCgoI0efJkhYeHa9CgQerTp4/y5bO169q0aaOpU6dq5MiR6t69u6Kjo/X222+rePHiKeHcKl0aV1en9+br1IUIdW4ckEmd+qdaP1pJLs7OCl62VbFZjCCTpIYBZbR4889qVfshFczvrcnLtykxzW90uaJ+6tK4ul6Z9pVG92p1o06N0a7fj9vq1LpVMn3du65T1+xVicI+Kl/UT6Yp7f3zhGas2mU3x/vDFTs0ZvFmzerfReUe9Mu8Tv12rz799nv9+HF/u9dfues3JSYnq2vjwDvex9vRrW0ztXtxqE6eDdXTbZpmklt2p+aWmQsVm8XoE0lqUjtQn69crzZN6srPN7+CZy9Jl1uKqVubx9T3nYka/9aLCqxUXuFXorRz30GVKubvOLfc5VDzkLVb9cLbwRr3Zl/Vr1kt5eSAm6uLfPM7yi3eSkxMl1uWrNKspd9o/+q5Kdumzg1Riwa15ORk6OvNuzT50y+0cPK7Ke3cRV9vlKuLi6o/VNY2ynLb95q19BuNHvBCymvMWLhCJR4srMrlSio+IVHLVm/R6q27tWTq8Dv+zFa474J3o9rVVdjPV4eOndSCSfYLgUwY/LJeHjZJLYL6q0C+POr3n063/AIP7POMTp4NVddXh8vby0OD+vbQ+TQ94JI0a/QgTZi9WO9MmaOzoZfkkz+valZ7SI2ycSYpuz5bvkaJiUl6a/zHemt86sIkDWtV14b5U7J4pr3LkVf09/HTKX+7urho9rJvNCR4ppJNU6WLFdGwfs/pxWeesntecnKyFn29Qc8+1dLhUvt1O9tfP2/ttr0q8WBhHd6U9RD7tBo9UkWFCxbQ4eNnNH/sALv7xvfvqVdGfazH+7yrAvm89d9ubRV3i1D2Zs+OOnn+orq9OUHeXh56q1cnnb9kf+miT0b8V8Fzv9KwaZ/r7MVw+eTLo5pVyqlRzZxd0TKtuSs2KTEpSYOnzNPgKak9HQ0erpISgIv5++nr6cM1dOp81Xt2oAoXLKD/PNnMbiG5y5HROnLSfmXujXt+1cS5XykuIVHVypfUF5MG6/H69kObl67drnenLZRpmqpdrYLWfTJSNaukLizz7JPNFB0Tq1kh6/T2BwuUL4+XGtWsqtGvBt31Z29QrYwe8Mmrw6cv6tNB3e3uG/1CW7027Su1HvKJCuTx1EvtGig2PutGQv8uTXTqYoS6j/5ceTzcNaBrU4WG269E/dHrXTQ5ZKtGzFunc5evyCePpx6uUDxbZ+uzKynZ1Mj563TqYoScnZ1U2r+ghj/3hF0j4bO1e5WQmKTng+2PkWeaPayP+9t6fsKjrunI2dQVaD3cXDRv3Q/668xFxSckqqhfAbWpW0X9O2d9Zjo31SuVT4XyuOrvS9f1cRf7BYtGPFFKb359TB3m/qH8ni7qU6fILYN3v4ZFdToyTs8v/Uvebk56tVExXYi2P/anPFVW03ac1ZhNJ3U+Kl4FPF0UWDSP6pW+swXbckpMfJKGrjmu0Kg4ebg6qayfpz7sWE5PVUvtKQiPSdSxsIxhZ+mvF9WqUkEV8MzdqrdJg3ryf6CQ/jz8txZ/ar8Q1qTRI9TntTfVpHUH+RTIr9de6nPL4D2kfz+dPHVaHbs/rzx5vDV0wKs6F3rB7jGffTRFYydP05ARY3Tm3Hn5+hRQrYcD1aRh1iOL7lbNRvZXAFmzfpNKFi+mYwdtq6G/M/B1GYahEWODdeZcqPx8fdT2iRYaNWxwynPCwsP115GMqwPPXbhUHdq2kk+BApm+d3Jysj5fEqKgZ7pYegmbJk2ayN/fX3/++aeWLVtmd9+UKVPUu3dvNWzYUD4+PnrjjTduGbyHDh2qEydOqH379sqTJ4/eeecdnTtnXw/NmzdPY8aM0aBBg3TmzBn5+vqqdu3a2eoBvxM3e91vWr16tUqWLKkTJ05IkiIjI9W3b1+FhoYqf/78qlGjhnbs2KHatVM7FMLCwvTXX6mXdHR2dta3336rV155RfXr15enp6e6d+9ud0m1Zs2aacmSJQoODtbEiRPl6empOnXqaP369fL2Tl1M0goNqpbWAwVu1KkDn7a7b/TzrfTa9JVqPXSOrU59st4tg3f/zo116mKkuo9dpDwebhrQpYlC04wqk6SPXuukycu3acSC9Tp3OcpWp5YvpoZZzPG+W0nJyRq5YEOaOtVXw4Me1/NPpJbdZ+t+sNWpE+2/5880q6GPX7e1ncKjYnTkbMZLvC3c9LPa1qmiAtlYAf1ONKpVXYUL3sgtE+0XaZww6CW9PHyyWjw3wJZbnu2o2Lhb5ZZuOnkuVF1fGy5vL08N6ttd5y+mzy0DNWH2khu5JexGbqmoRrUdj/S4W5+FfGvLLRNm6q0JqSe2GtYM0Ib5k2/7dS5HRNnlFknauGufgucsUVx8gqpVLKOQ6SMzdApOmLVYp85flLOTk8qVLKpPRr2ZMr9bsq2O/vak2Tp3MUye7u6qVK6kVnw8Wk80sm4q3p0wbmd4VY69mWHUlbTn5t/58ngr9IdvsngG7nd5qjZXcprv0O5FE1W9onU/1LDWjCWrNWTq/LSbfpGUkvKLFSqg3+YOye3dwm16eWqIlm39Je2mYNM0Bzt6vCQZhjFLUspy/70f9df7rbOe5w5rNP/4gA5dsLtuaXfTNLOclGcYxg+SUloon86Yop49ns7iGbhXjp88pfKBGaYheZmmmXLtPcMwJkhKmSQZFBSkBQsW5NIeIrtKliypU6fsLg3V2jTNLJc+NwzjL0kpY/8XDumutg56lWGdyKvXVbrH6PSbHzRNM2XehWEYQyWlLFTQpmldLZ/+fvrn4F/Ev057RV21q2frmaa5N7fe/765jjcAAAAAAP+L7mnwzs3eduQOivTf7VbHJMfs/S0nyocS/nfjGL1/3UnZUJ73N35z/704Hv9/utdFmNvB227iWExs7C3nOuD+lv5amBFRV+/RniAnhGcsP7vJgVExsUpKsm5FV9ydiOjr6TdlPbnTxu53OfJ61vMFYQ3TNBWR8X+f7fKLiIzMqV1CDguPiMxsc/pGkH15RkQI96ekpCRduXIl/ebs/+ZezfC7jVwQkfn/PX352ZcVbdx/tdi4eMVkXPPido7ZHJPbwfu00pzcS0pK1pDgmYTvfyHTNPXluu+UlO6yGu/PXKJLERkqIvwL/H7khOYsX59+s93FUKNj4jR28Sa7FTZx75mmqY0/HdbGnw6nv+vkbTz9RNo/Vv0Wpj3Hr3BmPxclJpmaufucQqMy1IXZLr+pH83WX0eO5tSuIYdEXrmioe+NSb/5tGma6X9MT6T9Y82aNVq7di3H430mISFBw4YNU1RUVPq7sn3MTvlyu05d4ARLbrp6PU7vzl2bfvOVG7e0TqT9Y88vv+vLdds4Hv+FYuPiNWTiJ+k7j0zZsmmuydXF1STJMIztkuwu/ubi4qw8Xp4yZDh4Fu43sfHxuu5g5VvDMJTP29Pukgq4vyUmJSn6WqZnf5tK+lBSQNqNri7O8nJ3zTDiAfdGfEKiYjJeii9Jkr9pmhmXe03DMIzSkv5Jv93LzUluzpSv1UxTup6QnNl1wE9IKmPeopI2DKODpBXpt+fLm9fSlbVx+0zT1JWoqMwa6x+apvlG2g2GYRSSdF6SXeF5eXnJ3d3xteSRe0zT1LVr1+wuuXbDAdM0A2/1fMMwekmam357Xi93OdNuspxpmoq+Hqfk5AzH4wLTNHum3WAYhrekS5Lslkb39HCXR7pL9OL+ZcrU1ZjrdpemvWG7aZpNcnNf7kXw7ilp3q0eB+Ce+0e2lVdflTT1Hu8Lsu9r0zSfup0HGoaxTdL9c/0ySNJI0zTfu9WDDMPwlHRcUmHL9wg5KUlSHdM0f0p/h2EYqyS1z/U9wt3qb5rmB7d6kGEYPrLVrwWs3iFky2OmaW5Nv9EwjPmSnsv93UEu6GWa5vzcfMN7cWptgaRR9+B9YS3G3fxvOSWpxY1hkB9Kmn6P9wfZs0dSdi7S3knSAYv2Bdl32/XkjUtRNZN00dI9Qk5KktQjs9B9Q5DSXHoV/wrTZasrb8k0zQhJLSRFWrlDyJY+mYXuG16WtCk3dwa5YpRsdW3uMk0z12+SDEm9JO2QlCxbaOP277wdkjRSUnNJX0m6fh/sE7c7v12UNFNSqUyO2Vck7b0P9pGb49sxSeMk5buD3+WCkibLNkfxXn+O/6+3nyUNkOR8B+VXUdJnksLvg8/BLfNbnKRvJD1+G+WZT7Zj+dh9sN/cHN/2ylY3GndwzAZI+ly2ecX3+nP8f7wlSFonqf1tlJWnbG3dQ/fBfnO781uybNmzl+7gmM2JW64PNU/PMIwHJJWQ5HFPdwTZlShbSDtupvkSGYaRR1JZSXnv1Y7hjiTLVvn/ZZpmlstaG4ZRRFIxSUw4vH8kSgqTdMy8yx91wzZxv4IkX6WbZwrLxEs6Z5rmmbt9IcMwXGUL4fklFk65j1yT7fjMsBpXVm4cj2Ul+UlysWLHcEfiJJ0xTfP83b6QYRjush2zecUxm1tiZGu/RmTnSTeOx9KSHhDH479NrKRTpmne09Fh9zx4AwAAAADwv4zlEwEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAAL/R9lgctGe+IljQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
+    "\n",
+    "plot_tree(dtr_model,filled = True, rounded=True)\n",
+    "plt.show()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -737,7 +1870,49 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,