diff --git a/images/RBFSVM.png b/your-code/RBFSVM.png
similarity index 100%
rename from images/RBFSVM.png
rename to your-code/RBFSVM.png
diff --git a/data/boston_data.csv b/your-code/boston_data.csv
similarity index 100%
rename from data/boston_data.csv
rename to your-code/boston_data.csv
diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 3176602..1c3dae3 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -26,6 +26,21 @@
     "Since we want to predict the value of houses, the **target variable**, `'MEDV'`, will be the variable we seek to predict."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import datetime\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,29 +50,768 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
+   "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>crim</th>\n",
+       "      <th>zn</th>\n",
+       "      <th>indus</th>\n",
+       "      <th>chas</th>\n",
+       "      <th>nox</th>\n",
+       "      <th>rm</th>\n",
+       "      <th>age</th>\n",
+       "      <th>dis</th>\n",
+       "      <th>rad</th>\n",
+       "      <th>tax</th>\n",
+       "      <th>ptratio</th>\n",
+       "      <th>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.15876</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.413</td>\n",
+       "      <td>5.961</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>5.2873</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>305.0</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>376.94</td>\n",
+       "      <td>9.88</td>\n",
+       "      <td>21.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.10328</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>5.927</td>\n",
+       "      <td>47.2</td>\n",
+       "      <td>6.9320</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>284.0</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.22</td>\n",
+       "      <td>19.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.34940</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.544</td>\n",
+       "      <td>5.972</td>\n",
+       "      <td>76.7</td>\n",
+       "      <td>3.1025</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>396.24</td>\n",
+       "      <td>9.97</td>\n",
+       "      <td>20.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.73397</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.58</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>5.597</td>\n",
+       "      <td>94.9</td>\n",
+       "      <td>1.5257</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>351.85</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>15.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04337</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>6.115</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>6.8147</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>393.97</td>\n",
+       "      <td>9.43</td>\n",
+       "      <td>20.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      crim    zn  indus  chas    nox     rm   age     dis  rad    tax  \\\n",
+       "0  0.15876   0.0  10.81   0.0  0.413  5.961  17.5  5.2873  4.0  305.0   \n",
+       "1  0.10328  25.0   5.13   0.0  0.453  5.927  47.2  6.9320  8.0  284.0   \n",
+       "2  0.34940   0.0   9.90   0.0  0.544  5.972  76.7  3.1025  4.0  304.0   \n",
+       "3  2.73397   0.0  19.58   0.0  0.871  5.597  94.9  1.5257  5.0  403.0   \n",
+       "4  0.04337  21.0   5.64   0.0  0.439  6.115  63.0  6.8147  4.0  243.0   \n",
+       "\n",
+       "   ptratio   black  lstat  medv  \n",
+       "0     19.2  376.94   9.88  21.7  \n",
+       "1     19.7  396.90   9.22  19.6  \n",
+       "2     18.4  396.24   9.97  20.3  \n",
+       "3     14.7  351.85  21.45  15.4  \n",
+       "4     16.8  393.97   9.43  20.5  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston = pd.read_csv(\"boston_data.csv\")\n",
+    "boston.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "crim       0\n",
+       "zn         0\n",
+       "indus      0\n",
+       "chas       0\n",
+       "nox        0\n",
+       "rm         0\n",
+       "age        0\n",
+       "dis        0\n",
+       "rad        0\n",
+       "tax        0\n",
+       "ptratio    0\n",
+       "black      0\n",
+       "lstat      0\n",
+       "medv       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>crim</th>\n",
+       "      <th>zn</th>\n",
+       "      <th>indus</th>\n",
+       "      <th>chas</th>\n",
+       "      <th>nox</th>\n",
+       "      <th>rm</th>\n",
+       "      <th>age</th>\n",
+       "      <th>dis</th>\n",
+       "      <th>rad</th>\n",
+       "      <th>tax</th>\n",
+       "      <th>ptratio</th>\n",
+       "      <th>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.00000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.730912</td>\n",
+       "      <td>10.509901</td>\n",
+       "      <td>11.189901</td>\n",
+       "      <td>0.069307</td>\n",
+       "      <td>0.556710</td>\n",
+       "      <td>6.30145</td>\n",
+       "      <td>68.601733</td>\n",
+       "      <td>3.799666</td>\n",
+       "      <td>9.836634</td>\n",
+       "      <td>411.688119</td>\n",
+       "      <td>18.444554</td>\n",
+       "      <td>355.068243</td>\n",
+       "      <td>12.598936</td>\n",
+       "      <td>22.312376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.943922</td>\n",
+       "      <td>22.053733</td>\n",
+       "      <td>6.814909</td>\n",
+       "      <td>0.254290</td>\n",
+       "      <td>0.117321</td>\n",
+       "      <td>0.67583</td>\n",
+       "      <td>28.066143</td>\n",
+       "      <td>2.109916</td>\n",
+       "      <td>8.834741</td>\n",
+       "      <td>171.073553</td>\n",
+       "      <td>2.150295</td>\n",
+       "      <td>94.489572</td>\n",
+       "      <td>6.925173</td>\n",
+       "      <td>8.837019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.006320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.460000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.392000</td>\n",
+       "      <td>3.56100</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>1.169100</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>1.730000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.082382</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.453000</td>\n",
+       "      <td>5.90275</td>\n",
+       "      <td>45.800000</td>\n",
+       "      <td>2.087875</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>281.000000</td>\n",
+       "      <td>17.375000</td>\n",
+       "      <td>374.710000</td>\n",
+       "      <td>7.135000</td>\n",
+       "      <td>17.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.253715</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.795000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.538000</td>\n",
+       "      <td>6.23050</td>\n",
+       "      <td>76.600000</td>\n",
+       "      <td>3.207450</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>330.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>391.065000</td>\n",
+       "      <td>11.265000</td>\n",
+       "      <td>21.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4.053158</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.631000</td>\n",
+       "      <td>6.62925</td>\n",
+       "      <td>94.150000</td>\n",
+       "      <td>5.222125</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>666.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>396.007500</td>\n",
+       "      <td>16.910000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>88.976200</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>8.78000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>12.126500</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>34.370000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             crim          zn       indus        chas         nox         rm  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.00000   \n",
+       "mean     3.730912   10.509901   11.189901    0.069307    0.556710    6.30145   \n",
+       "std      8.943922   22.053733    6.814909    0.254290    0.117321    0.67583   \n",
+       "min      0.006320    0.000000    0.460000    0.000000    0.392000    3.56100   \n",
+       "25%      0.082382    0.000000    5.190000    0.000000    0.453000    5.90275   \n",
+       "50%      0.253715    0.000000    9.795000    0.000000    0.538000    6.23050   \n",
+       "75%      4.053158   12.500000   18.100000    0.000000    0.631000    6.62925   \n",
+       "max     88.976200   95.000000   27.740000    1.000000    0.871000    8.78000   \n",
+       "\n",
+       "              age         dis         rad         tax     ptratio       black  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
+       "mean    68.601733    3.799666    9.836634  411.688119   18.444554  355.068243   \n",
+       "std     28.066143    2.109916    8.834741  171.073553    2.150295   94.489572   \n",
+       "min      2.900000    1.169100    1.000000  187.000000   12.600000    0.320000   \n",
+       "25%     45.800000    2.087875    4.000000  281.000000   17.375000  374.710000   \n",
+       "50%     76.600000    3.207450    5.000000  330.000000   19.000000  391.065000   \n",
+       "75%     94.150000    5.222125   24.000000  666.000000   20.200000  396.007500   \n",
+       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
+       "\n",
+       "            lstat        medv  \n",
+       "count  404.000000  404.000000  \n",
+       "mean    12.598936   22.312376  \n",
+       "std      6.925173    8.837019  \n",
+       "min      1.730000    5.000000  \n",
+       "25%      7.135000   17.100000  \n",
+       "50%     11.265000   21.400000  \n",
+       "75%     16.910000   25.000000  \n",
+       "max     34.370000   50.000000  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "Q1 = boston.quantile(0.25)\n",
+    "Q3 = boston.quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "threshold = 1.5  \n",
+    "\n",
+    "boston_df = boston[~((boston < (Q1 - threshold * IQR)) | (boston > (Q3 + threshold * IQR))).any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>crim</th>\n",
+       "      <th>zn</th>\n",
+       "      <th>indus</th>\n",
+       "      <th>chas</th>\n",
+       "      <th>nox</th>\n",
+       "      <th>rm</th>\n",
+       "      <th>age</th>\n",
+       "      <th>dis</th>\n",
+       "      <th>rad</th>\n",
+       "      <th>tax</th>\n",
+       "      <th>ptratio</th>\n",
+       "      <th>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.15876</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.413</td>\n",
+       "      <td>5.961</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>5.2873</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>305.0</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>376.94</td>\n",
+       "      <td>9.88</td>\n",
+       "      <td>21.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.10328</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>5.927</td>\n",
+       "      <td>47.2</td>\n",
+       "      <td>6.9320</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>284.0</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.22</td>\n",
+       "      <td>19.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.34940</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.90</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.544</td>\n",
+       "      <td>5.972</td>\n",
+       "      <td>76.7</td>\n",
+       "      <td>3.1025</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>18.4</td>\n",
+       "      <td>396.24</td>\n",
+       "      <td>9.97</td>\n",
+       "      <td>20.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.73397</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.58</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>5.597</td>\n",
+       "      <td>94.9</td>\n",
+       "      <td>1.5257</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>351.85</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>15.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.04337</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>6.115</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>6.8147</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>393.97</td>\n",
+       "      <td>9.43</td>\n",
+       "      <td>20.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>0.06899</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.65</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.581</td>\n",
+       "      <td>5.870</td>\n",
+       "      <td>69.7</td>\n",
+       "      <td>2.2577</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>188.0</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>389.15</td>\n",
+       "      <td>14.37</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>0.10084</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.547</td>\n",
+       "      <td>6.715</td>\n",
+       "      <td>81.6</td>\n",
+       "      <td>2.6775</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>432.0</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>395.59</td>\n",
+       "      <td>10.16</td>\n",
+       "      <td>22.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>0.67191</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>5.813</td>\n",
+       "      <td>90.3</td>\n",
+       "      <td>4.6820</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>376.88</td>\n",
+       "      <td>14.81</td>\n",
+       "      <td>16.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>9.32909</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.713</td>\n",
+       "      <td>6.185</td>\n",
+       "      <td>98.7</td>\n",
+       "      <td>2.2616</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>18.13</td>\n",
+       "      <td>14.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>0.08244</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4.93</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.428</td>\n",
+       "      <td>6.481</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>6.1899</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>379.41</td>\n",
+       "      <td>6.36</td>\n",
+       "      <td>23.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>228 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        crim    zn  indus  chas    nox     rm   age     dis   rad    tax  \\\n",
+       "0    0.15876   0.0  10.81   0.0  0.413  5.961  17.5  5.2873   4.0  305.0   \n",
+       "1    0.10328  25.0   5.13   0.0  0.453  5.927  47.2  6.9320   8.0  284.0   \n",
+       "2    0.34940   0.0   9.90   0.0  0.544  5.972  76.7  3.1025   4.0  304.0   \n",
+       "3    2.73397   0.0  19.58   0.0  0.871  5.597  94.9  1.5257   5.0  403.0   \n",
+       "4    0.04337  21.0   5.64   0.0  0.439  6.115  63.0  6.8147   4.0  243.0   \n",
+       "..       ...   ...    ...   ...    ...    ...   ...     ...   ...    ...   \n",
+       "396  0.06899   0.0  25.65   0.0  0.581  5.870  69.7  2.2577   2.0  188.0   \n",
+       "397  0.10084   0.0  10.01   0.0  0.547  6.715  81.6  2.6775   6.0  432.0   \n",
+       "398  0.67191   0.0   8.14   0.0  0.538  5.813  90.3  4.6820   4.0  307.0   \n",
+       "399  9.32909   0.0  18.10   0.0  0.713  6.185  98.7  2.2616  24.0  666.0   \n",
+       "403  0.08244  30.0   4.93   0.0  0.428  6.481  18.5  6.1899   6.0  300.0   \n",
+       "\n",
+       "     ptratio   black  lstat  medv  \n",
+       "0       19.2  376.94   9.88  21.7  \n",
+       "1       19.7  396.90   9.22  19.6  \n",
+       "2       18.4  396.24   9.97  20.3  \n",
+       "3       14.7  351.85  21.45  15.4  \n",
+       "4       16.8  393.97   9.43  20.5  \n",
+       "..       ...     ...    ...   ...  \n",
+       "396     19.1  389.15  14.37  22.0  \n",
+       "397     17.8  395.59  10.16  22.8  \n",
+       "398     21.0  376.88  14.81  16.6  \n",
+       "399     20.2  396.90  18.13  14.1  \n",
+       "403     16.6  379.41   6.36  23.7  \n",
+       "\n",
+       "[228 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston_df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Next, we want to explore the data. Pick several varibables you think will be ost correlated with the prices of homes in Boston, and create plots that show the data dispersion as well as the regression line of best fit."
+    "### Next, we want to explore the data. Pick several variables you think will be most correlated with the prices of homes in Boston, and create plots that show the data dispersion as well as the regression line of best fit."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d9/783c_j055nj0b0y037qm_1zc0000gn/T/ipykernel_54259/1692776502.py:8: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your plots here"
+    "features = boston_df.drop(columns=['medv'])\n",
+    "labels = boston_df['medv']\n",
+    "\n",
+    "boston_df = pd.concat([features, labels], axis=1)\n",
+    "\n",
+    "corr = boston_df.corr()\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(14, 14))\n",
+    "\n",
+    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
+    "\n",
+    "sns.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "\n",
+    "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -87,7 +841,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# [\"indus\"], [\"nox\"], [\"age\"], [\"tax\"]"
    ]
   },
   {
@@ -100,11 +854,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "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>crim</th>\n",
+       "      <th>zn</th>\n",
+       "      <th>indus</th>\n",
+       "      <th>chas</th>\n",
+       "      <th>nox</th>\n",
+       "      <th>rm</th>\n",
+       "      <th>age</th>\n",
+       "      <th>dis</th>\n",
+       "      <th>rad</th>\n",
+       "      <th>tax</th>\n",
+       "      <th>ptratio</th>\n",
+       "      <th>black</th>\n",
+       "      <th>lstat</th>\n",
+       "      <th>medv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.0</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>228.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.348504</td>\n",
+       "      <td>4.015351</td>\n",
+       "      <td>10.827939</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.538482</td>\n",
+       "      <td>6.186281</td>\n",
+       "      <td>66.237281</td>\n",
+       "      <td>3.998012</td>\n",
+       "      <td>7.671053</td>\n",
+       "      <td>371.710526</td>\n",
+       "      <td>18.742544</td>\n",
+       "      <td>389.028509</td>\n",
+       "      <td>12.086798</td>\n",
+       "      <td>21.412719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.504425</td>\n",
+       "      <td>8.724793</td>\n",
+       "      <td>6.393417</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.103290</td>\n",
+       "      <td>0.428421</td>\n",
+       "      <td>27.103921</td>\n",
+       "      <td>1.788350</td>\n",
+       "      <td>7.442090</td>\n",
+       "      <td>151.906285</td>\n",
+       "      <td>1.771429</td>\n",
+       "      <td>11.118071</td>\n",
+       "      <td>5.633787</td>\n",
+       "      <td>4.971316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.006320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.380000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.409000</td>\n",
+       "      <td>4.903000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>1.345900</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>188.000000</td>\n",
+       "      <td>14.700000</td>\n",
+       "      <td>344.050000</td>\n",
+       "      <td>2.940000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.083672</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.860000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.453000</td>\n",
+       "      <td>5.888000</td>\n",
+       "      <td>45.800000</td>\n",
+       "      <td>2.501475</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>278.500000</td>\n",
+       "      <td>17.800000</td>\n",
+       "      <td>386.572500</td>\n",
+       "      <td>7.777500</td>\n",
+       "      <td>18.775000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.171310</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.560000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>6.167000</td>\n",
+       "      <td>69.650000</td>\n",
+       "      <td>3.665900</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>307.000000</td>\n",
+       "      <td>19.100000</td>\n",
+       "      <td>393.410000</td>\n",
+       "      <td>10.875000</td>\n",
+       "      <td>21.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.777345</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.581000</td>\n",
+       "      <td>6.437250</td>\n",
+       "      <td>91.125000</td>\n",
+       "      <td>5.400700</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>403.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>15.027500</td>\n",
+       "      <td>23.825000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.966540</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>7.416000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>9.222900</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>21.200000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>30.810000</td>\n",
+       "      <td>36.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             crim          zn       indus   chas         nox          rm  \\\n",
+       "count  228.000000  228.000000  228.000000  228.0  228.000000  228.000000   \n",
+       "mean     1.348504    4.015351   10.827939    0.0    0.538482    6.186281   \n",
+       "std      2.504425    8.724793    6.393417    0.0    0.103290    0.428421   \n",
+       "min      0.006320    0.000000    1.380000    0.0    0.409000    4.903000   \n",
+       "25%      0.083672    0.000000    5.860000    0.0    0.453000    5.888000   \n",
+       "50%      0.171310    0.000000    8.560000    0.0    0.520000    6.167000   \n",
+       "75%      0.777345    0.000000   18.100000    0.0    0.581000    6.437250   \n",
+       "max      9.966540   30.000000   27.740000    0.0    0.871000    7.416000   \n",
+       "\n",
+       "              age         dis         rad         tax     ptratio       black  \\\n",
+       "count  228.000000  228.000000  228.000000  228.000000  228.000000  228.000000   \n",
+       "mean    66.237281    3.998012    7.671053  371.710526   18.742544  389.028509   \n",
+       "std     27.103921    1.788350    7.442090  151.906285    1.771429   11.118071   \n",
+       "min      2.900000    1.345900    1.000000  188.000000   14.700000  344.050000   \n",
+       "25%     45.800000    2.501475    4.000000  278.500000   17.800000  386.572500   \n",
+       "50%     69.650000    3.665900    5.000000  307.000000   19.100000  393.410000   \n",
+       "75%     91.125000    5.400700    6.000000  403.000000   20.200000  396.900000   \n",
+       "max    100.000000    9.222900   24.000000  711.000000   21.200000  396.900000   \n",
+       "\n",
+       "            lstat        medv  \n",
+       "count  228.000000  228.000000  \n",
+       "mean    12.086798   21.412719  \n",
+       "std      5.633787    4.971316  \n",
+       "min      2.940000    7.000000  \n",
+       "25%      7.777500   18.775000  \n",
+       "50%     10.875000   21.200000  \n",
+       "75%     15.027500   23.825000  \n",
+       "max     30.810000   36.200000  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "boston_df.describe()"
    ]
   },
   {
@@ -126,7 +1095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +1104,8 @@
     "def performance_metric(y_true, y_predict):\n",
     "    \"\"\" Calculates and returns the performance score between \n",
     "        true and predicted values based on the metric chosen. \"\"\"\n",
-    "    # Your code here:"
+    "    score = r2_score(y_true, y_predict)\n",
+    "    return score"
    ]
   },
   {
@@ -148,11 +1118,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.2, random_state=0)"
    ]
   },
   {
@@ -175,11 +1145,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=10)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(max_depth=10)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Five separate RFR here with the given max depths"
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "forest_2 = RandomForestRegressor(max_depth=2)\n",
+    "forest_4 = RandomForestRegressor(max_depth=4)\n",
+    "forest_6 = RandomForestRegressor(max_depth=6)\n",
+    "forest_8 = RandomForestRegressor(max_depth=8)\n",
+    "forest_10 = RandomForestRegressor(max_depth=10)\n",
+    "\n",
+    "forest_2.fit(X_train, y_train)\n",
+    "forest_4.fit(X_train, y_train)\n",
+    "forest_6.fit(X_train, y_train)\n",
+    "forest_8.fit(X_train, y_train)\n",
+    "forest_10.fit(X_train, y_train)"
    ]
   },
   {
@@ -191,13 +1187,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Produce a plot with the score for the testing and training for the different max depths"
+    "test_score = [forest_2.score(X_test, y_test), forest_4.score(X_test, y_test), forest_6.score(X_test, y_test), forest_8.score(X_test, y_test), forest_10.score(X_test, y_test)]\n",
+    "train_score = [forest_2.score(X_train, y_train), forest_4.score(X_train, y_train), forest_6.score(X_train, y_train), forest_8.score(X_train, y_train), forest_10.score(X_train, y_train)]\n",
+    "\n",
+    "depth = [2,4,6,8,10] # --> x\n",
+    "\n",
+    "color_test_score = 'orange'\n",
+    "color_train_score = 'green'\n",
+    "\n",
+    "plt.bar(depth, test_score, color = color_test_score)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.bar(depth, train_score, color = color_train_score)\n",
+    "plt.show()"
    ]
   },
   {
@@ -213,7 +1242,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# When the depth is a higher value, the training model is more accurate"
    ]
   },
   {
@@ -276,7 +1305,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -290,7 +1319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/your-code/lab_overfitting.ipynb b/your-code/lab_overfitting.ipynb
index 3776411..e95a64f 100644
--- a/your-code/lab_overfitting.ipynb
+++ b/your-code/lab_overfitting.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,6 +30,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from IPython.display import display\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "# Makin' some data\n",
     "from sklearn.datasets import make_circles\n",
@@ -55,11 +58,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "# Define the gamma values\n",
+    "gamma_values = [0.001, 1, 20]\n",
+    "\n",
+    "# Instantiate a list of SVM classifiers with different gamma values\n",
+    "classifiers = [SVC(gamma=gamma) for gamma in gamma_values]"
    ]
   },
   {
@@ -71,9 +80,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jP3rt+Ljmq4UUFxWmuTeCIGQj9Q2NjN5tVlTjY1TC1h8+kudwkh+nsB1d0QY4KcrJrEl3MqmYOBCA3KEj0tyTzMIcPQUAVVye5p70HiopoghQjsz9fvW2cDT/6ynMyaEot/cL2x0rVlHocrJmHeRLJfSYKHTRJz4jQs/preNjcVEhxb1MtAuCkFqiGR/TspGjbMLYdDw2rYioDY6I2sRRiW/SkMmiVshudqxYBcCadWnuiCAIgiAIQhdSKmx9bm3fwu/WCp0xR08RUZtARNQKyUZErSAIgiAImUzKHVtxawV/CLKQWETUCslCRK0gCIIgCJlOyoStuLVCIOLWJo5KikTUCklDRK0gCIIgCNlASh3bvuTWSsKo4EgIcmIRUSskExG1giAIgiBkCykRtn3RrQURtV2REOTE4t9XKwjJQEStIAiCIAjZRMoc277o1godSGmfxCLJooRkIqI28Yweme4eCIIgCELvJunCVtxawY+I2sQgolZIJiJqk0fp+L6zwCsIgiAIqSYljq24tX0bCUFOHCJqhWQiojY5iFsrCIIgCMknqcJW3FpBQpATh4haIRWIqE0O4tYKgiAIQnJJumPb19xaEbXdEVEbPyJqhWSzY8UqEbVJQNxaQRAEQUgNSRO2fc2tlRDk7kgIcmIRUSskC38IspAcxK0VBEEQhOSTVMe2L7m1ICHIgUgIcuKQWrVCMpF9tclD3FpBEARBSB1JEbbi1gogojYRSK1aIZmIqE0eflErbq0gCIIgpIakObbi1vZdzNFTRNQmANlXK6QCEbXJQ0StIAiCIKSOhAtbcWv7NrKvNjGIqBWSjeyrTR6jR4qoFQRBEIRUkxTHtq+4tX5RK25tZ8StjQ8RtUKykRDk5CH7agVBEAQhPTgS2Vhfc2tBRG0gEoIcPyJqhWQjojZ5yL5aQRCExKPra2O+R+ajfZOEClvoe26t4ENCkBOHiFoh2YioTTwiagVBEBJLoKC11iyN+j5z9JRO94rI7TskTNiKWyvIwBEfUtZHSDayrzY5iKgVBEFIHH5RGouYDSTwvkCRK/PU3k9CHVtxa/smEoIcP1LWR0g2EoKcHETUCoIgJIZ4BW0wuopcPzJv7Z0kJHmUuLV9FwlBjh/ZVyukChG1iUVErSAIQmJIhqjtirVmaXv7ur62R3t3hcwmYY6tuLV9F1n16jkiaoVUICHIiUdErSAIQvykQtB2xf8sCVPufcTt2Ipb23eREOT4EFErpBJxaxOHiFpBEIT4SYeoDUQc3N5HQhzbvuTWiqj1ISHIiUFErZBsdqxYJaI2gYioFQRBiJ90i9pAgjm4IC5uNhKXsO1Lbq2EIHdHvvA9RzIgC6lAQpATi4haQRCE+MkkURuIZFPOfuJ2bPuKWwsSguxHQpDjQzIgC6lE3NrEIKJWEAQhfjJV1HZFXNzspMfCVtzavomEIMeH7KsVUoW4tYlDRK0gCELiyHRRG4i4uNlFXMmjxK3tm8iXuWeIqBVSjbi18SOiVhAEITHo+tqsErVdkWRTmU+PhK24tX0TcWvjR0StkArErU0MImoFQRASQ28SgSJwM5cehyKLW9u38ItacWt7hiSLElKNuLU9RwStIAhC4slmtzYYUg8384jZse1rbq2I2g7ky9ozJFmUkErErY0PEbWCIAiJJdtDkCPR1cEV0kePHNu+5NYKEoIcD7KvVkgH4tb2DBG1giAIiaUvCb1ABxfEEEoHMTm2I8vFre1ryJez54ioFVKNuLU9R0StIAhCcujNbm0wxL1NHzGHIvcFt1YSRnVGRG3PEVErpBpxa2NHRK0gCELi6cvCTsRteoir3E9vRtxaCUGOB0kWJaQacWt7hohaQRCE5NHX3NpA/HtvRdymjpj22PbbZUyy+pExiFvbGXFrY0eSRQnpQtza6BFBKwiCkDxEzHVgrVkqW/tShDi2QRC31ufWypcvdmRfrSBkPiJqBUEQkk9fdmu7IqHJqUGEbQDi1vqQEOSeIaJWSBc7VqwStzZKRNQKgiAkFxFvwRFxm3xE2HZB3Fof4tb2DBG1gpC5iKgVBEFIDeLWBkfEbXIRYbsTcWt9SAhyz5BkUUK6kKRR0SGiVhAEQcgERPQnDxG2AfR1t1ZCkHuGJIsS0o2EIYdHRK0gCEJq0PW1ItyiQLIlJ4eYsiL3ViomDuzzotaPuLWxIftqBSFzEUErCIIgZCr+bMky904c4tgKgLi18SCiVkgXkjQqNCJqBUEQUos4kD1D3rfE0eeFrbi1HciKUWzIvlpByExE1AqCIKQHCUOODUkmlVj6tLCVhFE+JAwidmRfrSBkJiJqBUEQhGxCxG3i6NPCFiRhlIQgx47sqxUyAQlD7o6IWkEQhPQgoiw+RNwmhj6bPErc2g7ErY0eEbWCkHmIoBUEQUg/EoYcH/5kUrq+VubmPaRPO7bi1opb2xNE1ApC5iCiVhAEQegtiHMbH31S2Ipb2yFqZUUoemRfrZAp7FixKt1dyAhE1AqCIKQfEWGJRcRtz+mzoch93a0FEbWxICHIQqbR1/fXiqgVBEHIHFIZhty6aX3Y871hji9hyT2jzwlbcWslBLmniKgVhMxARK0gCELfI1DQ1iyrCnpNxcSBna7LZpEr4jZ2+pywhez+kCcK+YJEj9SrFYTMQUStIAhC5pCqcFm/WA0laP0Eng8Uudk69xdxGxt9StiKWys1a2NF9tUKmUZfLfMjglYQBCEzSXYYcrSitiv+67Nd4AaKWxBzKhx9LnlUNn6gE4WEIMeG7KsVhMxARK0gCELmkQq3tqeiNpCaZVXt97duWh9xj24mYq1ZKkmloqDPCFtxa33IKk9siKgVhPQiolYQBCFzSUXSqHhEbdd2eoPABRG3oehTocji1grRIvtqBSH9iKgVBEHITFLl1iZK1AaS7SHKfnErpTu70yeErbi1PuSDHx2yr1bIVPpS/VoRtYIgCJlNMt3aVLipnRJNBRzPFpEriaW60yeELWTPhzQZSMKo6JF9tUKm0xcSR4moFQQhE4jkSvbVuVWqwmCT4dZGela2lQuSxFKd6fXCtq+7tRKCHDsiagUhfYweKYJWEIT00FWwhXMkA8UE9B1B4X/Nqdhbmw6ysVxQYGhyX3dve72whcz+MKaCjPuAaw2fLkItXgIuJ/qQg2Bk+n9Hsq9WENKHuLQdOGq2k/v9WrBt3MOH4B42CJRKd7cEodcSKFCjFWxdrws0EjJu3pUgUiVqw4UhG80tFHy/BqO1DU9ZKc1jRoKRnFy42bYXV0KTe7mwFbc2A93aZSswL7oMtXwF2jB8IhfQxx2Nfc8dUFiYlm7JvlpBSB8ian2ollbK//sa+ctWoQEUKA3uIQOoOfN4vBX90t1FQehV9ETQhqI3u2aJfJ+ipVsYsm1TPm8BpZ8uBtsGpVBa4y3Ip/qYOTSPHZX0vmSDwO3r4rbXl/vJ1A9eqsioD/WmzZjHnQrf+xLgKNtGae37eeV1jB9e6BusUozsqxWE9CGidieWzYBH/kve8tUAKHyiFsBZuZWBDzyJ0dCUvv4JQi8j0H1MpFjzt6fra7O+JEvga0j0+xQrFe9+ROnCL3xzR0DtNEbMpmYG//cVcjdsSnofupYLylR602cwVnqtsK2YOLBPi9pMdGuNv/8TGhpRltXtnLJtjI8Xoj6Yn4aeiagVhHQgoraDvO9WkrOxsn2yFoiyNUZTC0ULv0xDzwSh95GKkNpsqzfqF0GBP5B6QRtMMJoNjZQs+ppgGzL8x8rnLUxqvwLJBnEL2fcZTAS9OhS5r5Kpda3UM88FFbV+tGmi/vsCevYBKeuThCALQnoQUduZgsXfoXeG1gVDaU3BF0upO2z/FPdMEHoXur42ZUItU+uNhhI6mZIQqmsYcuF3K8Ner7Qmb8NmzIZGrKLUbGmrWVbVHpqcyUZaXwtN7pXCtq+7tZA5g2cndtSFPa0sC71te4o6IyHIQnbRm2rYSubj7hiNTSFFbfs1La0p6o0g9E5SKWoDyQRxEUvG50zDbGn1JdCLMEaarW0pE7Yg4jYT6bWhyH2VTAxBbmfwIMINSdrhgOFDU9YdEFErZBfZXsN29EgRtaGwykrRRujMxxrwlhSnrkOC0MtIl6j1k46w0MCwYn9Icbr3yvYEb3FRxBwsWim8Bfkp6lEHEpacWfQ6YStubYa6tYB97g/DpmRXXi/2D89ISV+ktI8gpBYJPQ5P4x7TUHZ4N6Jpr91S1BtB6F2kW9T6SYW4CCVms5nGSbugHWbI81opmsaPwc7PS2GvOhBxmzn0OmHbl8lotxbQPz4Pxo5Gm90HJw3Y5/wAdpua9H7Ivtr0sHBD+FB0ofciojYybaOH0bTbpKBRLVop3EMG0LjntJT3SxCynUybxCdLXGRSBuNEY+fmUDPHl3+l6xiplcJ2OamdvW/qOxZAt/JEGUpvF7e9ao+tuLWZ69YCUFSE9cpzGL++BZ7/H8rrBUD3K8W+9GL0FT9N7PMsC/W/V1EP/wu1fCUUFKBPOh7zwp9ijxyT2GcJYZm/bgcuU9bR+iIiaqNEKWpPPRpveT+KPv4co7UNAG0aNM3Yle1HH4x2ORP6yJy1Gyn8+HNy12wApWgZN4qG/XbHM3RQQp8jCOkiFdmPe0LgnkeIb+6WjhqzySKc41k/cyp2jovyeQtx1tUDPpHbMmoYWw87CE9ZaUL74theR+miryhc/j3KY9E2qD91u0+jaZfRvv2+YV5DpmuR3rzntlcJ276MOXpKdnw4y/ph33s33PJrWLYCXE6fS+tKcFiw14tx4aUYr72JNgyUbcP2HXDfAwx89HFqXnwFz8zdE/tMISh+UetNd0eElCOiNkZMg7pD96PuoL1xba5C2TbuQf0p23cGZVHcXvvld1E/qujDT+n3+jy0odpDoAu+/o6Cr75l28lH0rR78qNnBCGZZKqo9ROYMbknAqM3CdpAwjmfjbtOoHHyeFxVNZhtbXhKS/CWJD4KL2/tBgY/87KvZu7OhFV56zaSv3YDddN3ZeuRBwcVt9mSTAp6r7jtNcK2L7u1mR6CHJTyMthvVtKaV/f/E/X6W76/ByQcUJYFzc2U/+B0KpcsA2diHRChM/PX7Uh3F4Q0III2NspnTApytENYugYNi9iGu3JjiHZ8BIpe17pN9Ht9HkCnfb3K1mig7Pk3aBs+BO+A3jPZEfom2SD4YhUYvVXQRo1SuAf1T17zbW4GP/cqyrZQAXHPfoFbsvgbWocOpmFa8PHWL26zgd4obnuNsO3r9JYPZEKwbYwHHgmZFl5ZFmZVFbmvvULrCSeluHN9B7+olRDkvoWI2vCEEp/RiNdwhLu/q+jNebOzUxuIwrdnreiTxWw/bk5cfRKEdJFt+we71ruFzvO6bC7Vk20ULV2GcnsIFWysgdLPvgwpbP1kg2sLvU/c9gphK26t0InKKtSWyrCXaKeTnIULRNgmCRG1fRMRtcHpKmbjFbGx0vV5xpIVYTMwK1uTs2ZDsrslCEklG8Vf1/DkYOeE5JK3qTJszVwF5FTXorxeX5nKIGSTawu9S9z2CmHb18n2D2HCCVNSqB2tg2ZnFuJHRG1yKB0/ltGsythatiJqO5NuMRsOFc0WjGjGUUHIQDKltE88ZHv/sxkdJjFUrNdli2sLvUfcZr2wFbdW6MbAAeixo2H12vY9EV1RXi9tsw9Jccd6PyJq+x4iaDsIFLOZJGS7ovfbA156y5dzINh5w6Bl/OgU90oQ4ifbQpCFzKNl1DCKly4LeV4rReuQQRDBHMk21xZ6h7iV2WeWk60fvKSiFPZll4QUtdo08YzbhbZDZP9YIhFR2/cQUeujfMakdlHrGjQso0UtgHXmib6olSDntFLgMHFecGrYZFSCkKmI2ynEQ+OkXfAW5Id0ZJXW7Jg1M+r2wpUwykSyvc5tVs9A+7pbK6I2NPrsM2n4ia8urn8PhFYKDVgDB1H7zHMSapdARNT2Pfq6qPWL2fIZk9rFbKYL2nZ2GY112y/BNNEB31ltGOB0YN11M86p03ANGtZJtAtCJpOtE3Ehs9AOB5vPPAE7NwcN7QuA2vAJ3dqDZtE0fkxUbYUrXZTJZPPiUNaHIvdFJAQ5MpWqGH53G60nnUr+ow/j/PYbdFERzSedgmf3PXCsWIFZVYV7z70ihpMI4RFR27fwC1rom6I2W8KNI6GPPBjvlIkYz72KWvQVGAZ61u7YB++Lqt2OWvgFetqk9tdYPsN3Xyy1cgUh1WTzhFzIHNwDKlh3yY8oWrKMwhWrUB4vbYP6Uz91EobXS/6qtbQN7I9VWBBVe9m019aPPyw520y0rBW2fdmtBQlBjgblcOHea2/ce+0NgGPJEkqvuYqcX1zVfo01aDD1v76J5h+cnaZeZjcialPP6JGkLYFUX3Zpe4ug7cSwwdhXXuT7+/Y6zD/ei+OHP2uv/a3zcrHPOAH70nNxDRrWqWyQCFwhkxC3Vkg0dl4udXtNp26v6WDblH34CUOffgnD4wF8UYCNE8ay9YjZ2Pl5IdvJxr22gWTbfluZjWYZ4tZGppKibscc335D/yMPxfX5ok7Hjcot9LvsJxQ8cH+qutdrEFGbetIpKPuqqM22/bM9orEJxwVXo+Z+0C5qAVRLK8Zjz2Befzto3en1S3iykGmIWyskBa0Z8Mrb9Pt4UbuoBd9e28Llqxj2r2cxWtvCNlGzrCrr9tpCdu63zcoZaTavfCSCbFo5STV+Uascrk7Hi2/+DaqttVsWUH9qgJKbbkTV1aWii70KEbW9n9EjfT+l48f2KVHbJwTtToxnX4V1G1GW3e2c0hpj7geoL5a0HwsUtyJwBUHozeRuqqT4mxUESyWltMa5o46Sz7+Oqq1sFrfZQtbOSvtiGLK4tdHRVdQa1VXkzg1d2gKAtjbyXnohyT3rPcxft0NEbRoJ3Oeaiuf0JUEL9BlB68d47lUIkUUefJnkjRff6HRM3FshU8gmN0nIPoq+/rY9cVRQtKZ4cWTx508kla3iNlu+Z1k3MxW3VtzaUAQLQQYwKytDlv5px+HA3LQxCb3qffhDkIX0kAqR6XdpU/W8TKFrluM+w9baoG6EH2VZsDl4dk8Rt0ImkG2ukpA9OOobUHboOaQCHI1NUbWVzeIWsmMRKeuELYhbK3QnVAgygFVeEbkBr4Vd0T/R3ep1yL7azCFZrm2goO0rorZr2HGfo6Q47GltGlBRFvK8fyFAQpMFQehtWPmha9q2X5OXG3V72Spus2W/bVbNTsWtFbc2HMFELYA9dCht++yLNsKU9TENWk48KUk96x2IqM0c/IIzkeK2L++lhb4TdhwM+4QjfDVsQ6AsG/vYwyK2I+6tkGp0fa24tUJSaZgyMWzUn1aKhmmTY2oz28VtJpN1M9S+6taKqA1NqBDkQOpvugUMFXLy1nDV1dj9ByS6a70GEbWZR6LEZ18PO4Y+6tIGYJ95PJT38zmzXdCGgT1zKnrf3aNqS8StIAi9iZbRw2keNTyoa6uVwsrPY8ceu8Xcbs2yqvZsydkmcDPZtc2aWWpfdWslBDk84UKQA3HvPYvaZ1/AGjIUAP/am52XR/0Nv6bh+l8ns5tZjYjazKV0/NhOwjQWugraviZqoW+7tJ0o64f3kbvQu04AOsZHrRT68IOw/nYrmGEiXrog4lYQhF6DUmw55Rgadh3fLm79Y2TboAFs/NGpWIUFPW6+q8DNdJGb6a6tI90diIW+6NZCLwpBrqtDvfw6VFXBgAHo446G0pK4m40kav20HXQwVV99Q86H8zDXrEGXlNB66OHoosiOb19FRG3mUzp+LDtWrGoXqWvWBb+uq/jtS0LWT6DQyjhB6/GiPliIWr0O8nKxZ+8Lwwan7vlDB2M9dg/W8lWob1aAw0TvPQMG9iz3gGvQMNyVGymfMYnaL79LcGcFQcKQ+xRak7txC3kbNqMVtIwYRtuQgRBh72vCHu9yUn3c4dTO3pf8NRtQtkXr4IG4ezg+BsMfnlwxcWAncZuJ2sdaszRjo0mzQtiKW5v9qPv/iXHrHeB2+1b+LQt+9RvsX16NvvwnPRqcoglB7oZh0HbQwXDQwbHf20cRUZv5+EVqoMANd11fJJPDjtXCLzBvuB21bQfaNEHbGHf9A33kwVi/uRpyc1LXmQlj0RMS8zkRcSsIQrw4dtQx+LnXyKmuaXdMlda0DhpA5clH4y1JnTlhFRXSMC25kSh+gQudRW4mCtxMJCuELfTdX2gmrobEinr0ccxf/67jgNfr+9Ptxvzd7Vg5OehLLoipzWhDkIWeI7Vqs4++LFzDkdGi9psVmJff6Fvsg871tt98H7PNjfXnm9LUu/gRcSsIQk8xWtsY9vhzmI3NAJ2SOOVUbWXoE8+x/sIfoHN651ywk8gNOJ4JmihTXduMn7WKW5vluN0Yf/hz2EuMO+6G1taYmxZRmzykVq3QG8iGurTGA4+DtoNm3VS2xnh3PixflYaeJQ7ZcyskmnQkr8mWPZDxEvg60/2ai7/6BrOhKfj4qDWOugaKly5LQ89Sj38vLmRWNuVMSySVFY5tJqxMpINMWwXpCWrBp6ht28JfU1+P+mA++vA5UbXZoxBkIWpkX63QG8hkl7ad5hbUh5+ELyVhmhhvvI+doPDgdCHOrZBoUrG/tquAqFlWlRV7IGMh2GvsStfXDKl53UVLl0dxzTLqdp+W9L5kCu17cXf+O52fP79rm0lktLAVt7YXsKMuuuvqortOQpCTi4haoTeQFaIWoKk5rKhtp6Ex+X1JASJuhWzCL+S6Cr3esAcyUKQGE7Jd6XpNql630dJKuAwsCjBa2pL2/Eym6yJLNn3+kknGz1776i+qN7i1AHpUdL8/PTL637OI2uQgolbIdrKuNm1pMTovN/w12k5tduQkI2HJQjbQuml9p9DPUGRqeGgoAsOKo3l9oUjV6/aUlQatH+tHK4WnrDRpz890MuHzZ61ZmlHhyBk7gxW3tpcwbQp68kS0Efyjpg0DPXY07Ll7xKYkBDl5iKgVsp2srE3rdGKfcAQ63PdOGdjHHpq6PqWArPn9CBlJsifRflEbC+kWF5FIlKDtStcarImmfsaUsFEtSmvqZuya8OdmG5n++UslGR2KLG5tL0AprD//AfOEM9CWF2XZ7ae0YYBpYt31x6jL/WSTW+v8+isKHrifnA/nAYq2gw+h8ceX4N01MxcvRNQK2UpWubRdsH/8Q4x5C9DVNZ3HRwVKg33VRVBRlsYeJgeXkcugr79Dzf0Y5fHgHjyAxlkzaN1lVMpqUwrZS7L218YjDAJDQzNp/hoqpDqRJOu1N04YS9PYUeSvXovqom810DR+DM3jRifseZmCcrspXrKMoq+/xdHUjKe4iPrpu9IweQI4zKD3pPvzp+trM0K/yEw2g8jEtNkJYY+ZWK8+h953VqfDetZeWC//F/bdO2ITlRRllajNf+wR+s/en/ynn8Kxfj2O9evIf/xfDDhwX/KefjLd3euElPURsplsFrUAlJXi/ddf0UcegnYErDUPH4r397/CPvuU9PUtSaivv8Nx0oU4n3kVZ802HHUN5K1YzYBHn6Xf/+ZCNPuOBSHBJEIAZpJz5ndRE+nQhiMpr90w2HLK0WzfZ0+sgJI+Vm4O2/bfi8oTj+x1C2FmUzPDH32GirfmkVO5FUdDE7mbKxn46jsMfeI5VJs75L3JdtBDkYpEbtGSkY5txcSBGbXaJSSA6dOwn38Ke/MWqKqG/hUwbGhUt2ZbCLJzydeU/vwKX/iM5W0/riwLDfS77Cd4ZszEO2Fi+jq5EynrI2QzWS9q/VSUYd16HVz3U9SGLb59t6OH97oJGwCtbZhX/hpa21B2h4D1/73ok8W4hw2iafep6eqh0IdJhAD0O2fpJBUubTCS4hqaJtsOmsX2/fbAWbMdFLjLy0I6l9nOgJffxrltR6ekWX63OndLNf3nfkD1MeG3p6TbvU0nGSls+yI9dms3bcZ44j/w7TLIz0MfdTj6yMPA6Ux8JxPBkMG+nxjJJre24IH7wTTB6+12TuFLdlDwzweo+9Ndqe9cALKvVshmohK1jU0Yr72D+nwJaI2eORX7mEOhqCBFvYyMd+T0zgei0HOOdYuT0ZWUoN6ah9pRH/K8VlD04WcibIWUkgx3K12iImpRa9sUrFxDwYpVGB4v7ooy6nebjLekOK7nJ0vYa4cD96D+CW83k3Bu20HBmtCfRaU1RUuXU3Pwftj5eWHbSrW49Zf+SXfkacYJ277o1vY0YZR67AmM6270/UNrMAyM/76AHjcG69knYOiQBPYyPWRbCDJAzgfzUEFErR9lWeR8MC+FPeqOiFohm4lG1KrF32BecSM0NsHOtW819wOM/3sE655b0Gmoe9hNxO5E5cc2kQzVTjYIXuOLJWjTRFlW0PNKg6u6FtXmRudk19gvJJ9kJo5KpLuZLscsWlFrNjQy5OmXyKnZ5ss6rDUFK1bTb/5n1Bx2IHV77JaQvvS1+Xy85G7cHPEaZdvkbqmieeyoiNdmQvRAqsk4YZs0WtpQazaAUugxwyHD/sOMdYVDvTsP85rrOx/0TxTWrMM84xyseW/6nMMsJdtCkNuJJnwwA0IMRdQK7dg2zi1bUV4P3ooy7IL8dPcoJOUzJkUOPa6uxbzsemht3RnC1RHyqltaMH92A97nH4LBA5La164CNFYBG4pg7ejm+k7Py1iRmwFjn5DdJHo/XzRurbN2O2ZzM96iIryl0X2PUy0qonZqtWbIMy/jqt0O0JF1eOef/d/+AE9JMc279DwpU18UVIkh2vEx+nG0ZlkVFfSdhLwZJWyT8iVoc+N45FnMV95DtfqKOOv8XKwTDsN7zkngTO9b0GO39p77Qq56K8uC5StR785DH3ZIvF1MDV4v6tU3Uf9+ErVuPZSXU3DGD2k562x0YWYtQkSi7eBDMB//V0jXVpsmbYfMSXGvOpBkUUI7WlP4yWKK31uIo6HRd8hQNE+dyPajD8bOoJDdWPbTGs+92m0Ppx9la7Tbg/HsK9iXX5DwfgaKy0QJWQD18cc47r8f9fnnkJuLffzxeC+6CIYODfqsTBW59h7TcLz4RsjzWik8gyrErRVSSigxmLdmPeXvfUxu1db2Yy3Dh1Az5wDaolgYS5WoiGVPbd7aDeRU14Q8r5Wi34JFcQlbf19SJagc2+so+eJr8letQ9malhFDqNt9Gu6B8YUuR9Ilid7D3DJ8CJrwslWbBq1DYtNLqfpdZEI4ckYJW0jwm+714rzhzxhfL+ucpKK5FfPpV1BrN+L57ZUQosZqqoj5A9DcgvHxwrCXaIcD9dY72SFsW1sxfnQRxvsfog0DZdvodRso/WIxhf/4OzWvvIk9aFC6exk1jT++hPx/PRp0cPJ9ChWNF/445f0CSRYldKZk7nxK3ltAoPxTtiZ/yTJy1m+m8tKzM8K9jTVJlPHefJRthzyvbBvj3Y8SJmyTJWb9OG6+Gcftt/vG9Z0LZuq77zD/9jfc//sfet99u90T2I9MErn60APRdz8I2+uC/o6U1tQfsFcaeiYIncn/fg2Dn321W5bu3I1bGPrvZ9l09im0RSkwkhmWG2vd3YLv17bPtYKhtCZvUyVGaxt2bk6iupk08leuZvDzr4PW7e6zs66O4q++ZesRs6mfGft+/UBBG+r31rppfft1iRK43n4lNI0bTcGqtUHr92qlqJ82GTsvt0ft94Xw8IyxbZLh1hrvf4K5+Lvgq/ZaYy74EmPh4oQ/N1p66tbiCZ3quwMNbW09az/FGL//E+qD+QDtA63aOUA51q6l7MLz0ti72PFO3pXt9z0AhoE2O9aOtGmC6WDbPx/GGjsu5f2SfbVCIOa2HRS/twDovgCjbI25o57iDz5Nfce60KPMx2HKIXRc4+lhjzrwjpzeLhpVfnFSRK3x0ks4br/d94yAKBBl29DcjOukk6ChIWwbgX0L7HNayHHh/b/boLAAbaj2RRW9c9uM+9QjyTvv5PT1T+hThAxDtm0GvP6eTyx1OaW0Rtk2/d+KLldGMjMTxypqgZD723t6XTpx1DUw+IXXwbY7CUFl+35vA958n5xNlVG3VzFxYLseyR06IqwIDDyfSA1Tfcwc3P19hpfeuXXD/6cvWmD/HrWbSaWokklGzXATvYrgeOU9tBHa0NeGgfna+wl9Zqz0yK4vLkYPGUzYSn+WjZ66a0+7lToam1CPPhF65dCyyPn4IxzfZE6NrGhoOeNMqhd8RtOFF+GZMBHPxEk0XfwTqj5ZROuJqZ+0iagVulL4+dKw+x2V1hR+9jUEWRhMFT0t56N3ndAulIKeN030rhN61Ce/MPSOnN4uGJMhaP047rkn5GtRtg319ZhPPRVVWxkjcCeOw/vCQ9iXnoeeOA49ajj60APwPnwX6oarZR+ukFKCCcP8tRtwNDaFDAlVWpO7pQpnzbaon5NIQRFYozZW2gb2hzARLRrwFuRjRci6Gw3+mqqxEItILF68FOzuiw9+tKEoXbQ4qmf6k9dGErRdSbS4tfPz2HjuaVQdcyitwwbj7ldKy8hhVJ54JJvPOhEdR9WTVJV/SmaSt0hkRChysjaYqy3VQd3a9vO2jdqU2hpffnrs1gIohf3j8zBuuT1oIXutFOTmoM84JY4epoil36JaWsJeopUiZ/5HeHeN4z1LA97xE6j7453p7oaIWiEoju07Il5jtLah3G50GsLR4qlRa59xPI7X3w15XlkW1hnHx9RmssONg2JZqAULgoakdXRGYXzwAdbFF0fdrL///oRTaQlPLivFvvAsuPCsbqdcg4ZRPgNqv/wu9f0SMhZdX5vwxFGhcNQ1RNzrCODcUYenoixie4nMkhxvjdqGyeOpeOcj8HiCvz4FdbtPS8sCU+7QETEJ4by1G8OOj8rW5K3dGPJ8NCHH0RBrvyOhHQ4apk2iYdqkhLXpJ9n7bf37bNNFxsx0k/EG6+LCsK6mVgpdmr7Mu/FsrtYXX4A+aH/fawgYfLRpgmFg3/9XKE7R5Csewk3YhIQholboip2XF3Hiok0jrtXhnhKPqAXQu03GuuRs398Dcij4/25dcCZ6j+jK/aQi3DhdZIR7Gwb/50AQUo2VmxNV3lkrL3pXMxGhoPGKWgCd46LyxCN826UCohr1zp+W4UPZvvfMHrefSlT42MWwRBty3JM2s4HeGpKc9tluMj8E1uEHhJ+4aY196H5Je34oErKS4XJhP/Ew9q03weiRAGinE33sUVhvvIg++oietas1zF+IccvtGL++hcZHX+K7JRY1tUn6qEydjI7wH4PSmrZ9e7anoK8jyaKEUDRNnxQ2wZI2FE1TJ0KKF0XiFbV+7J+cg/eum9G7TW5fANRTJ+K98zdRJ40KJmjVihWYt96K45prsO66l5ULt7F+fZLW6EwTvffencR5N7TGPuCAHj+ia3hyphDv718QIhEulLd57CjsMFUzNOApLoo6eZSfeARpIkStn+Zxo9l47mk0ThjXPr54S4upmXMAm884ARw9KxVpNLdQ8uliKt7+gJIPP6NytcGqyn5YVmzub7TaoHnksE7mTle0oWgZ2Xks6bqPNlFkU1KmVIUkp4OMCEVOmh1+1IGYL7wFtdtRVucJnDYM9JABWHO6Z5NMBQlJhe1yoS8+H+vi88Hr9dWsjSd0pLIK8+wLUV8tQZsOLFtRqj14uIVTeZaCQ/filut3MH1q/ElX2iksRJ/7A3jgkaCTbG06cO+5F94p2RWGnAlICLIQDvewwTRPHEPe8jXdQrm0UmjTpH72rJT2KVGi1o8+eF+sg/ft2E8WZQb8oGHHbjfOn/4U84kn0KaJpQ1ybYvx/D+u5i4+mvZTbrjR4vjjQy8W9ATvVVfhOvPMoOe0YUBhIdZZ3cN5Y6WruE139mQ/5TMmSUiykHK0y8m2A/am4t353c/hC1GuPXjfHs25/KGgEN38N5GCNpC2QQOoOvFIqrT2rczFWSGk5NMvqXjvY9A2FiZo2J+F/PM/F3Jivz9z6Wnfc8Up32Ga4VcBYwnrrZ8xhX6ffIkOkuQLfKHIdXvs1v7vZAjarlRMHJg1wrE3Zknu3TPewgLcd9+AHjcK8K3c+Fd27F13wf3n6yHFe8eSFnfucMQnaj0ezFN+AEu/BUBZXhzaJ2DL2MbrHMXad9dxwNED+eTzxNYXtG+4jrb9fI6sf+XQ77BYI0aw7eHHEvq8voCIWiEiSlF75vE0T5uIVjvD0HaOIVZJEdUXnoF3QOpq0SVa1HbCMKKatIULO3ZceSXGziRNyrJw2B5MbHJwcy8/Y/KSZzjjdCcPPpjY75x94ol4r70W8O278qNNE/LycD//fEK3nWSSeyuurZBOduw1g5rZ+2A7zE7jo+1yUXX0HBonj+9x29GEJfsTRAVenxSUilvUFi35jv7vfOTLXaPBoS0c+LIqn8/D/HL7DdzwwEx+fMe+UUe3ROPaekuKqTzxSF9YdeC2vJ0h1lsPO5DWYYO7JYdKFtkkEpMtvtOVQEppHfkjVl9fT0lJCV9cfQlFCSya7v+QpQK1bDXG0uWAwp4xGT02PR++dBcuDoV68RXMH18W8rwHBw9zAZca9zNpgocv51UmLK9AJUXg9ZL/6qsUPPYI5to12BX9aT7zBzSf9UN0Ufr2QWcr89ftyDhR29zYwA/3mUBdXR3F2bD/O0r84+PS31xBURbU/AuGub2OvGWrUF4vnkH9aR07CsJklE8G5TMmpVXIBArabqxfT86ECSGTlNgoVrILE1mG0wmr17ipqAh6aY9RH36I4/77UZ9/Drm52Mcfj/fii2FYct4z3VwPZIZz667c2Otd24bWNqbc8tdeOz7Wrl5KcRz/l/snyYlMHhVtVmGjtY2CFasxm5vxFhfRtMsYdJgw5ViIJN6ywvnTmpH3PYajviHkvmQPDoazgSoG8eLv3+GIvTZHbDYWUe+s3U7JF0vI31n/tWX4UOr2mEbR7I4atqnSGz3NVp0OkulgJ1Lv1Dc0UD5mSlTjY0aEIqcCPXEM1sQxae1DXG5tXR3qzXegrg5Gj0IffKAv9DhBqFdeD1uw24mXM3ian9j/4JvvXCxa7GLPGdHU043y+bn5tJxyGi2nnJawNvsqmShqhczG6ldC4z7pSxYSt6j1eFDzF6G2VKFLS9AHzYIYSlWEFbWA+corPlcjhLA10ExgBRNZxgprIk88YXLllYmtAakPOABPHHtpY0XlF6c3a3IXJCRZSFVG5K7YuTlJyU4LKRKuWpO7aQs5VTVo06Rp7EisosKENe+qrsFZH76WtonF8fyPh42L+Ocru0QlbP0hydGE9nrK+1Fz2IFw2IGAT7D5l1HS4aJmSziyP1t3byJtwra3vZHREvPqhW1j/OkvqL/dj2prQyuF0ho9aCD23X9EH3pwYjrW0BA2kQxAAc3tf1+5ypEQYVtJEcqR2NDmvowkixKyjXhFrXr7A8zf/w21o65jfMzNwf7Jj7DPOS3sFo1IgradhgZfqF6EMbKIBkwTVn3fO+qwZsq+W9egYbgrQ5fsEIRYySZXLR5c1TUMfOlNcmq2tecP7q8UDVMmsPWIgxPiPBvuyHNBG4MiGrBsg+XrS6JuOxZx21VXpCssON7SP9uWr+p2rGzC2Hi61KdIq2ObTbHo8dJTt9b4/Z2oe+5tD+9oD4WrqsY4+0Ks55+CffeOv4O7jEPP+whlBXcZ/KF2fkqK40+QUomEGCcS2VcrZBvxlnNR8xZg/vJW/DM2//ioWtsw//JPAOxzTw96b9SiFtDjx6O83rDXeHCwmjFoDUXFvauMWaa4t+LaCkL0OHbUMfTx5zDcvnwp7cttWlO0dDlGSyuVpx4bd71aT7/SiDV/HVh8xyRAU1oYmyni1wrR7O5IhK4INV9PdsRAoKBdU9OxrWl0RVv7uWQJ3N6URCotM2Bxa6Okeivq/+4PnultZxY787Y7EtI3+5yzQopaP/fi24NbXGRzyAFtCXmuuLWJQUStkG3EnSxK653iVYWcUBn3/xuaWzod65ogKhrsY45B9+8fsuyOBwfPcQq1VOD1Kk49NbGZkTOBdCeVkkRSghAb/RZ+geHxBM0NoLSm8Pu15G6qjPs5VmEBTbuMDll2x8JgE0N4kyNQCs6Ys7ZHz/Enfgr301PM0VPaf8A3Xw/8CbwmGfiF65qanE6ituuxYG5uvPS2yIW0zYJ7y8pANPT0i6D+92rY0Ddl26hPF8HGTT3tWgfjd8H65dWAz50NxMJgHgfxID8G4Pqr68jLi8+RELc28YioFbKFhGRAXrEatXZDyIRO4HNu1byF7f8OlfE4Ii4Xnoce8mXeNDrnNvDgYCv9uYY7MU3NUUdZzJjRuxxbP+kWtxC/yy8IfQKtKVq6DGWHHou0oShaujwhj7MvORldlN9t8c+LiYXJOfwLZSqGlDdz9mGJF2c9IVDMBhOygXQVuJHIHToiZhOvq6ANdT4Z4rY3kfKZsLi1MdyzbXt0CaJqt/WgR93R11yJ9fe/oMaPaz9WQzm/N27kaPU62nRy4zV1XH1Z+CQB0SJubWKQZFFCNpGosj5qe13Ea7RSqO07gNhCj4NhH3447rlzsQ/pyGvQSg7/Vucwy/iUTQzjmGNs/vXv8CHL2U46xa24toIQJZaN4YkwFmmN2dIS/pow+EvoVEwciGv6brj//jusQ/fF3lmWzEbxNocx2/iAd5nD+GH1vHXXW5QUenr8zEQQzJmNFv/1iXRvYxGqkcRvppGOkj9p2WMrbm106KFDIu7r0krB4EE9fka39k49CeuUE6GqmtpKL08vGEPD1lxuG9jCGSfVMHBAYvbWiqhNDJIsSsgmElmrVg8eEPEapTV68IC4RW37M2fNwvPKK3i2baOtagcvLRjMd6sLuaAATjjRzaRJvdOp7Yp/z60gpAJdX5u2jMhZi2lg5eZgtobbNqbwFEcfPRfMmOo0nx9Ygfe6i+Hyc9Hb6pi3agzzvh/HQUrzm+lvc9D0xJWJ7AmB8/F4y9Co4vKEirZYBKvv2lUJ32+b6H221pqlSQvdDkdKha24tbGhTzgG/t9voKU1+HnTRM+ZDQP6x964bYNlgdPZ/ZxSMGgg5YPgsultQGL204KEIPcE1dxM3gvP4Vy6BJ2bR8vRx+DZY0/mr/c5VuLWCtlEwly3kcOwp01CLV0eNKO7VkBxMZ4f+LZQxCRqtQaPxzc+BpuJlZWRU1bG6ZPgdBJb1idbUPnFaUkm5Ro0jPIZdCSRsm1yV6whd7UvC2nbyKG0TBwHMi4KIYgnY23WoBT1M6ZQuvCLkNs1lNbU7zY5ZBNhswx7vaEjCvNyUEMHMHtoI7MPXBxrz5NCoEObKPzObaYvuuRUVlOwfBWG24O7ooyGyePROZ3Npd5U9ifljm3WuLW2DVt3hvhWlPXoP8m4VyoKC7F/9xvMa67vlnFOmybk5WLf9P9ia/PLrzD+dj/q9TdRXgs9cgT2heegLzgHclIT4tCn3NqWFvJeegHX54vA4aD1kENpm3Oor3RIFOS8+QZlPz4f1dDgm2RrTdFf/kzbrH1x3f4PKE/cIC0IsWA0NGG43XiLiyCKkhFx16oNgn3dpZgXXI320knc+pKYaDx/vhNVGk0uzZ1s3Ijj7rsxH3sM1diILi3FOv98vFddBQN7x3/6iSYucWvbqE++QM3/DDxe9OTx6MMPgrzcqG531Gyj/6PP4dy2o31vX/FHi/CWFLH1nJPxROHqC32TZCfMMVrbMJtbsPLzsHPTEz66fa8ZFH67Akd9Y1Bxu2P3aXgqyjodCxQ33ebrrW2YL7yF43/voLZuQzsc2AfuiffMY9BjMnNunwxBG+wZwcRt7tARVBDfZ62ksY4RWzfi8npozM1n7cARtLlyo3JtVZubQS++QcHqdWhDAQpsm4q5H1J9zBwaJ4/vcb8ymbSW+8lIbBvzpbmYz76OUeULM9AV/fCefATWKUfGLHDj/TLpc3+IVViAcdufYENHHT+9957Yt98C43cJc3eXvrz+Fsb5P/H93Z8Bef0GjJtuQ7/1LvbTjyZV3PY1t9Y1/yPKzz4LY8d29M49J4X334dn/Hhq//sC1oiRYe93fvE55WefCZblW9TwdOxLcX72CQdefg4fPP5y1CJZEBJB7vLVlLzzMTkbtwBgu5w07T6VHYfuhw4hSJKV8EfvOgHr4bsw7rgP9XVHGRg9Zgye3/4GfeoZUbelVq7EdfDBsH17+/ioduzA/OtfMf/zH9rmzYPhwxP+GrKZuMoAVVbjuPxG1PdrfQu1CnjmZfjz/Vh/+g167xkhb3UNGkb5Ls3k/OE+zCZfffXAhQ2zvpEB//wPW646H7uosAevTBB6hmtrLWUfLKRg5RrfVgilaBw/hm0HzuomIpONnZ/HxnNOo+KteRSuWN0ubq3cHLbvszs79p4JRBCzflracF3zB9SKNb6IFkB5vRjzPsH1wWd4brsae/fUh52GIpFhx+FIdEiyH8O22HvZ54yo2YS9M2pIac20td/y1egprBg2LkILMOiF18lfu8F3r61pr4vn9TLwpTex8vNoGdX7/k9LmbCtmDgw891arXHc9TDmGx90Pl6zHccDT2OsWIPn+p9GJSQSGVeuTzkR66Tj4eulqLp69MgRMCrG97K+HuMnV/hWyANW7tr//vFC1N//ib7qsoT1Oxh9xa01V31Pxaknws7C5YF7pR2rVlFx/DFULfgM8vJCtlF0959B66ArrYZlUbbkS/p/8hFb9zkw4f0XhGAULFpC+fNvdCrrYLg9FC78ktzv11L5kx92E7eJ3FcbDL3rBKzH7sFatxG1pQrvpL3QU6egCkpiasd5wQWdRK0fZVnoqiqcl12G53//S1i/F22KPgnfHkMzd1GwR+LW48FxyS9hk29xJPA9143NmJffiPep+2Bs6MU/x9sfYTY0hSyHZ7S2UfTJV9Qdul8Mr0YQek7OlmqGPvEcymt11NTWmsIVqylYvZ6NPzoF98AebB2LA6uwgKqTj6amsQlXzTa0w6R18MD2MGK/qI00P3c8/iJq5dpu8xFl2Wilcd56L23/uQdc6Z/jxePSdjVgBhF5nE5GSPIeK79keI2v4onRZc4+Y/US2pw5UBE6IiVnSxUFa4KH3Ct8UU395n/WK4WtWD0BGF98g+OND9hp2Lfj/7f5/icYC76Mur2ErhIZBkyfhj5o/9hFLaD++wK0tIYujWHbGP98NGx5oXjoawmjCv9+L3g8Qff+KcvCsW4t+S8+H7oBj4fc118NW1vYNh0MefvVRHRXECJiNLdQ9tLbvm0RXSc3WuOo2U7J+ws7HU+2qO3EyGF4zrgQPW1qzKJWff01xmefhfy+KcvCePtt1Jo1Peraok0N3X6UoaL+CXZ/JhFrUi419yPU+k0oK8j4qLUvcuqJ58K24fjgs/DP0Jr8r74Le40gJAytGfDa3E6i1o/SGuX1MuC1d9PUOZ/AbRk1nNZhQ8A027MZR1X/1ePFfOW9oPMZ2Pn6GpowInwnk03X0j3RUElRpx/lcHX6CTyXDLYtX9UtcVR+axOjqjaErM+ugV3Xfceara6QGZULl6/aGX4cHKU1+es3YYTI4ZPNpETYZsuGZPPld9BhQo21YWC+/E7kdtKQBSwSaum3YUsHKUBVVcOOyCU0YqWvhSAD5L3wXFhRqg2D3P+9FPK8amsLe7+vEY2jqbGnXRSEmCj48luwrZD/2SqtKfz0K19SOlIsaomvnI/66qvI12iNWhrdinw0Ijam/kUhdtONP5lUNBjvftSt3mWntiwL9dYHIc8DGG5vyM9iezs7I2aE7CVbMiLnVG4lp7o2bKKm3MpqXNU1Ke5Zd6J1af2ordtQO0P+Q6EdJsaq9CTl6lq+JxoCxWqgkO1K4PFwAtfv2gYjVg00tHZL2PMKKGptorg59Liv3B6IOEKC4Ulv6aVkkDLHNuPDkAG1NvgKcvt528ZYtzm6tpIY098jog0PSVIYSV9yawFUhNpwyrYxGkIPSrqgAGtA+MFQoWkYE/0ea0GIB0fNNlDh/8sw2tyYTR2f/WwQtQDkRpesKFwOgq4is6ciNlq6tp8pAjcqcdvcEtL9aSdsmRLQ40aFX4hWCs+AGBKHCUIcOLdtj+o6V2101yWLWEUtgHZFsWtR6+BVNpJMrPVogzmz0dBV4EZLT7SPw7I6bfcJfV3ocqCe8n4RIzBtlxNvQX7M/ct0ki5ss8WtBaAgnxCBuoDP/tf5ofdEQma6tQD6iEPD1sTVhoG9z95QWJDQ5/ZFtxbAO35CWEdCmyaeyaHT7KMUTRf9OHwbKNafdGY83RSEqOlaHiAUtsuZtGRRwUhEjVr7kEPQESZluqAAe7/u+zVTKWZDkSkC1/87iCRu9djIopQIe7/sU48JvxCtNY2zwvdDEBKFHaUpYLtSL/789ETUmqOnYO5xAHrCuLBiS1k2nHJKu3va9ScZxOLSdhW0PSWSuI31tZZNGMvois6LePX5RZ321QbDVorGvNDz9YZdJ/hKgoY4r5Wifrddw0ZyZispcWyzwa0FsA7eO3jNQj9KYR2yT8R2Ms6tBfTsA9CTJ/myTwZB2Tb6ykuT8uy+5tYCNP34krCrZcqyaDrvgrBtNF56OZ5pu2F3Ebf+fy/55W9pHTAo/s4KQhQ0Txkf1mXTStEybiRl+0wHUuPWJkLUAlBejnXhhSEXkrRSWJdfDgUdE4lggjbdZILAjeZ3YZ98VFhRitbYZx4ftg29x25Ypx3r+3vg8Z0/TVMn0jJJIlqEzrRuWp+UUj8to4ZhR1gcs3JctIxMTRRLV2IRtV0FqSqpwL7m5yHDrLVpYs/aEw44qN05DfwJ1mY8xLqXNjDkOBGEaidRc//NZYNodeYQaoS0UayvGIrbGTqCyM7NofroQwC6LUhopfCUlbJtvz0T0t9MQ5JHBWAdeSD0Kw46udGGAcWFWEfPDnl/prq1ABgG1n8eg3FjAN9ApJVCGwbaNLH+dBt6zuyEPrKvurUAzWecResRR/re44Dj/s9W/a+uxzt517Bt6IICal5+neXn/RR3UUcynLpJU1l4z8Os+UF4YSwIicQ9bDAt40YFXbVv/4xf7IsgyCpR62/vjjuwTzwRAO1w+L67O8t0WT/6Ed5f/xog6L7ZTCMTBG5Y13bUcKzLfeNXYIITjW/SpffZHfuEI8M/QCns/3c5bZf9CD2oI9OsVVLEjqNnU3vGMZCBvxuhd6KdTrbvu3vYa7bvu2f7mJIOIonarntVA4WjPvFYrJv+n+/76Z8/+o2SKZOxH30gpDEUSuT2hHS4tMHwJ5aKlliiV7VhsHDiHr4xrss+WRtFS04uX42ZGrGdxikT2XzmCbQO7TBAbKeTuj12Y+OPTsWOsl54tpHUb1hWlPgJpLAA913X47z+LtTmqvYvrbIsdP8yPLdeDf3CT6Iy0a1tZ9BArPffQL39LurVN6GlBT1xPPqHZ8CQwQl9VKJXyDIK28bY7itOrktKg1/jcLDt309ReN//UfiPv2Nu8e3N9k6aTMPPf0HLKadF9aiPar24fn4DK352Lblbq7FycnGXy74xIT3U/OB4Kp5+mbwVazoVfNcuJ7WnHkX+1Akp21cLiRO1ALhceJ54Au+iRZhPPIGqqkIPGYJ1zjno3XbrJA4zUcwGw99PbWsWbWpIWdkg5bbR9dvwDp+GY8PXQa+xLzgTPXwIxiNPo7773newogz7zBOwzzkVnFFMT5TCuOhHtBwzmx3zPgOtsYqLRNAKaWH7PntguD2ULvzCd0Cp9pqvvrqxoWszJ5NoRFU0glH/7CdYxx+D8fjT8P1qKCzAPuFY9MEHRlUGM7B9XV/b/sxokoPFWpe2J3PQD9fu6PTvA0aVRvWcwJJAwUr/5A4dQeumjsRayuPBcHuwcnNChgJX9RvA3OkHseu6ZQzZVokCvIbJmoEj+GbkRNpcud1CmIPRMnoEm0aPwGhuwXB78BYWgKP3hR8Hkr6lowxFDxuM+9E/YixairH4W19I1LSJ2HvtBmH2BGW0WxuIw4E+6nD0UYcn/VG9TtR6PBT+4+8U3H8fjk0bAXBPn0HDz39B6/Endr/e6aTxyp/TePmVGFu3gsPELisPH+4ewPx1O9r/rp0uWoakJ4RJEPzo3By2nncqzs1V5H+zEuV24xlYQfO0iZTtvVtKk0UlVNT6UQq955549+wI0Vq0qQECQo6zkUD3FpJXF9d48UUcf/oTxuefA2APGYI+4xjss04KKlT1YQdiHXYg1o568HigrLRne76UwirpuxFCQoagFLWz92XH7tMo+mY5jsZmvIUFNOw6HquoMK1dC2UyxVzzdcRw7Ouvjbs/0QrcWAUtRC9quwpZAKfDN8/3eO1O54OJXOVwob3RZ17P2VJNv/mfUrByDQqfe1o/fVfqB1QA3cOKtxf146Mp++DwenBaXtqcLmyj8/hYNmFsVM+28/Oww+QIyjojMgxJE7ZZlTSqK4aBvdc07L2mxXRbRru1KaRXhiB7vZSd8wNy33yjfQUWwPn1V5SfezZ1v/ktjT//RfB7DQN7YGzfB7+odYVZTBGEdOEZMpC6IR2f6VQni0qKqA3CoiwXtF1Rhkqae2veeSfOG2/stJVHbd6M+ss/UYu+xrrr5tBOQWl8v0/XoGGUz4DaL6VubW8iW0r9dMUqKmTHrPBhyakinGDpSYmcYAQ6lrES+OxQBlEsc+toRG2gYPUL2a4EHveL3FAOblfXFujm2jq+WcWwf/0XtG4PLjY8HkoWfcXkvFzWT5tNmyt4aLDX4cTrSF/SsZ6SLsMvqbPm3qL+I5E1bm0K6W1ubf7TT5L7xuu+WpYBx/3JdEpuuQnHiuUJfaaIWiEbSGW92mjrpMZL18RQvYmu7m1C2ly+HOeNN/r+HpBgTLGz/u+Hn6BefithzxMEITLhDKZoRK1/j2qomq/+n2DX9YRgiadiFbWR9tP6Ra3TYYQUtV3xXxfM4Q1a+7Zrn71e8u9/xidquyTgUlqT09LKfpuDb9nIdtJh+CVl5pzVbm0PEbfWR690a4GCB/8Rdg+JNk0KHnskIc8KDEEWhGwgG5NFhSLTMh0nA/9rS1RiKfOhh0Jm3AfAMDD+87+4nyMIQmyEM5hCzVvDCdmg7YQRuqnCL2pD8eHaHZ1Ebaz4hXAwcet/fiiMBV9i1DeFzCqttKZ842acMYQ1R7O/tq+SNEtI3Nq+S29zawGcy5eFLXWiLAvHN/GHTEkIspBNpCoEOR2iti+QKPdWLV2KsqzQ520btXp9yPOJwBeOnLqQeCH7CEzg05fxl8oJRldB2xMC702FuI1G1EJsLm0ogonbSO+TWrcp/MIfYNiawpbmmPoS7f7aSPQ2M1KSRyWAbgOE1qiPPkb960nUqjXofv3Qp5yAPul46KXptSHy4JLN6Lw8VFvoFTJtGOj80MWyY0FErZANxBWCvHo9xjMvo77+FlxO9AF7Y590JJT163ZpKkRtXxO0gSRk321hIdowwi7+kRc6cYkgpIpk1LBNNGZTM8WLvyF/1VqUZdM6dBB1M6fiqSiLuo1Qe2tDmTHJqGTRLm69vrbj2YsbikjCOR6XNlK7XffcBu617ZQdOTcHwo2NOxlUYbE9TUZsbzIjEz6D7k2ZtSIRdICwLIzLrsY8+Qeol19HLfkG9dF8jCuvxTzkKKjM/EG1J/TWEGQ/LSecGLb+nLJtWo4/Ia5nzF+3Q0StkFX0RNQaz/wPx6kXYTz7MsZ3K1FffYtx76M4jjsPtfiboPeIqE0u8Tq31vHHhxW12uHAOvGElO2RFoRsJXfDZkb+/V+UffgJuZsqya2spuSLJYx48AmKv1iSkGd0NWOSXZ4xWe5tpH4nS9QGay/ce2fvOzNkGDL46ne7+5XQWhidOSJhyOERxzZOug4Q6v/+gXr2ed/fd4ZmKdv3gdZr1mFe8BOs115IbSdTRG91awEaf/oz8p96MqgroU0Ta+gwWk48ucfty75aIZsonzGpR6JWff415h/+z/cPy/c9UgBao1tbMX92A97X/g3FvglLsjMgi6jtIB7n1j71VOxbb0Vt2NAtJFkbBpgm1tUhssYnEMmOLGQzRksrQ575H8rrQQXoIL8oGvDm+7j7l9M6fEjYdkKFlgYLQY41i3A4wtV9TbR7myhR+/SCtWHPn7nPqKDH/SHJ0dS61UMGYh08C2PeJ+16IBAFbNt/b1CK0RVtrKnpXvqnKxKGHJqELmP0xjcoFEHdWo8H4+8PdhqQAlGWhfrsC/jyq+R2LgJuN7z+di6PPlnA2+/l4vXG115vd2sBvBMmUvvMc+jCQjQ+B8Lv4HrHjKHmf69Cbs/CzGVfrZBNxLOP0fj3c+gQn3Nla2huwfifL3tust29UKJWa1jyuYPXns3h/dddNDcltRsZRY8Ffm4unjfeQI/1Tbb846MGKCzE8/zz6IkTgdRlthaEbKN4yXcotyfkHFIbitJPv4yqrVgiJ4OJQ3+ypQ/X7mjfm+r/2brRyfzXCljwRj71tY5OWYMjieBEuLeJFrX98pxBf/zXRBK/wfrmx68VPL+4EHvWDMD3e9SGgVYKrRQ1c/anccqEqMRqMtza3hZlm3DHtre9QeHotrd2xfeo2m1h79GmifpgPnrGbknsWWgee6qAX95cSu22jo3sgwd5uecP2znp2JYet9ub3Vo/bQcdTOW3K8l7/llcX3yOdjppnXMYbYceBhESA0RCRK2QTfQ0C7L65AuUFWavkdaoT77Ee8PvfNcnya0NJWqXfungD9cWsG5Vx3+NuXmas3/Swjk/awmXGL3X4M+WHKtrq0eNwv3llxhvvYXx1lvgdqN33x3rjDOgwBdip/KL0c31yei2IGQ9eWs2hD2vbE3+mvAJsHri1nYllDDcVm3wfzeW8PWCjkV8ZWj2O7KVS35TR16Bbq/5CqEdXOVwob3uoPVfIxFt2HQ4URsoaMPhP7+9xcPTC9Z2c2+7urb+1+VHFZej62t9/8jNwXPLVTTPX4j3tYUYbW14SotpmDoJq0sIciTXNlFubTIJl6As2SRM2PZ5txYgTFbIHl2XYB57qoCLrijHF9HfQWWVyRkXVPDsYzUcf1Rs4jYZbm1rK3y20KC5WbHLBJsxY0PvTYgVrWHtGkVLk2LYCJvikhjvLyig+Ufn0vyjcxPSHwlBFrKJnoYgtxNFAo32EOUUi9qV35pccVYxXk/n61tbFP+8O5+WFsVPfxlb1spkYNvw7VcOtm1VVAzQTNrNi0pgJHVNtWJbtUHt1kaOmF4Y282miX3UUdhHHRX2Mu/I6TjWLe55JyNQPmOShCML2Ye2ifhVDrNX0080BlMogRhK1DY1KH59bjlbt3RexNe24uM3ctm6xeSWh2vb7/ML3ESK22jDpqvXO9m01kFuvmbS7m6cAfo1WlEbSL88Z7u4PW7X0WzdYpKbrxk4LPa5vDVqCOrSk9kaIolZ2YSxbFu+Kui50RVtCRW1vTUnUkId27S/QZ6dMbXO5G8dDroSUV+Pdpgob5iyB5YFe++RxJ4Fx+2GX95cik/Udg29UyiluebXpRx3ZEvMk6REubVaw333OLn3bif19R2dmLWfxR33tMUtcF950eTPf3CxcoVv4HW6NCed6uX6m91U9I+r6R4hIchCSrFtlGX5Quh7oITiLqWyuRJKS9DVNaEnb4bCOvjQpIraUOG2D96Zj9cLth38/FMP5HLaeS1UDEzcQlusfPS2k7/eWsDm9R2Ty2GjLK66uYlZB3nC3BmZZV+b3H9HPovmd4zne+7v5q93wvTpiXvNyXZtXYOG4a7cmLT2hdSg62t9GWVT9kCN8np7PD7Gi2pzo2wdZIbWgVaK1mGh99fG4tZC9KIWYO6z+VRvMtG6e+9sW7H8SxeL5uWw95y29jZiEbcQft9tNKL2qXcaefDWClYs7nA7C0tsTr2kgWPObuY/C9cCsYlaPzlteXz0xBBeWNQfy+t7D4aP83DqT+phTufXGE6s5w4dEbHslE+8+sSt37mVhFHRk/3Jo7TGeP8THM++jrF8DQD2hNF4Tz0Ke/beCR+gQrm16vmXMH56VdjVNG2aMGYUet9ZCe1TNLz9fm6n8OOuaK1Ys87JJ5+7mLVHdEWiE+3W/v5mJ3//a/dB67OFBicclsfr77cwbETPJliPPODg17/MQQVsXvG4Fc/9x8GC+SavvtNCWRqiJkTUCsnGtX4zxfM+IW/ZKpTWeEuKaNhnJg37zoQwmb6DttVTt3bFahwX/QKaWsJM2gCHE+/ZP4zsWvSAcKK2brtiwfvOoJO2QN56KYcfXNyahN5F5oO3nFz/k6Ju782mdQbXXVDEHQ839FjcLvncwRU/KO4WTPT5AicHHQRz53rYc8/0CXpBiJfWTeuDlvpx1DVQuvBzipd8h+HxYrlcNOw2me2zZnYLEU0WRmsbQx9/DtfW2rDXKa3ZsWf4bWyxuLWBRNqX+s7zeWHNYsPQvPdifruw9bcVjbgFwrq30YjaZ+Y18JvzBtDW0nmEbKwzePSOEj75tp5dj++ZqG2uc/DSzZNo2u5CByx8blzl4O5ryrnwhu0ccE3H6+kajtxe9icG/M6tX9AmOvy4N0fZJmRWnU472/Hgf3Dddh9q5dr2Y2rlWly33Yfjn88k5ZndVr6qqjEuu9rniIT45muloF8p1mMPJHU1UGv4aqmTN9/J5etvnO0DUVV1dHtAq7fGtle020CjNaqxEVpiC2lev05x/9+CDziWpaivh3vujH1AAqjZCr+9Yefg2WXialmKzRsVf/lTavcISwiykAryli5n4D+eJG/5qvaxyaxroPTNeQx4+L8dUS4RiCsEWWscv7wNmltCloTRSoHpwP3QA6gxu/TsOWEIFLWVGw0+mefk60WO9sR5ddtURFFrmLC9Jv7/MlVbK2ZjQ1QhhX4sC+6+yTfJ7tpPrRVa+87H0GTA/XD7rwqwvGBbndu2LYXXA5dd6uhR26FQ+cWSREpIO86abQx/+GlKvlyKsXMsNN1uShZ9xfBHnsZRl5r94OXvf4yrZhuK4G6t/6u3bd89aB43Omgb8bi1gaK2sU7x9QIXXy900dzo683TC9aytSpU73zYtm8LQ1cCE0uFQzlcKIeLSk8uVfWaSruASoqi3lP77P3FtLWobmOYn2WvDSOnLT9sG6H44oUh3UQtdIzFj91Ryvbw6XV6RNmEse0/ySDtUbZJIqsdW2PxdzieeQ2gUwpt/98d/3kVa+/d0NMmJuR5Id3aJ/7jE7Uh7tMAZWVYH7wF/SsS0pdgvDMvh1/8uh/ffNcxAOw6yc2ff7edoYOj2wswJMrrKinqPNDYNvmPPULhff+H8/uVALTtuReNV/6c1mOOi9jes087MIzQ248ty+eu3vonNzmRM6F3azvc1j7LUvzncQc33uLGlQJ9KyHIQipQLa2UP/Oab7EpsHQEgIacdZsonr+I+tnhI0jiDUFWXyxBrQ2fFAWg7X8vwCGHxfWsYPj31G5ca3DXTQV8+qFzpz0MZRU2517ezKHHuzEMHTIMGXxjU/9BUewRDkHpvHcZev89lC6cD0BN/8G8c+RZvHf46ViO7ot2580a2f73Lxc62FoZPuJm0zqTbxY7mDIjtjT33y52sO770FMB21YsWaJYvFgxY0Z2uLZS9keIhoEvv43R1tbNkFBaYza1MOC1d9l81olJ7YNqc1P09Xdh65wqoOagfdixb/htbD11awG8bSYP3VbE+y/l4/X4xkHTaTH6wGoO+IGLwnI32zeahBK3ytC05gQPv43GuXUs+46iu+4k78XnUR4PdnExTeecT+NVP8cuDz9vfuubOha+PTikqAVAw8r5Zex2TPC9raHwuhUrPizvJmoDsbzwp/s9/P766M2XiokDg0YQpIJku7XpTBwFCXBs02lnm/+bG7J0BIA2DRwvzU3oM4OufC35JuzquwJUbS2U9UtoXwJ5691cjj59AN8u6/zF+naZk2POGIDWMLC/RdfEUX4MQzNhFw+77xY5DLnbwGjb9Pvpjym9+kocq75vP+z6fBHlZ59F4V13Rm5zi4poZLvdirodsbvda1cbGBGM6KYmxfZtqdtXI6JWSDYFX36L8npDr7FrTdHHX0CQunpdiSdhlFr2PTpCGRmldUg3Nx78orZqs8klJ5fw+fwOUQuwrcbg7psKeeFfuex/mBvTDP1eGAoOOz72fU6PLlzHmt/dya7nnU7Rpwvaj5dtreS0x+/m8ruvpdiEQpej08+jC9e1/7wwf3tUz9q6JfZxZePa6O6ZuyjxIdji2grpwlW5ldzK6pCCUmlN/toNOLbXJbcf27ZjREgoqg2FCjF3g/BubTC6urUeN9xycRnvPt8hagEsj8n37w7irbvHMeHAmnCGLdpWjN6vOmRpnHDOrevTT+h/yIHkPf8cyuPbTmHU11P49/+j/yEHYVSFF4ANO4zwohaf8G7aFrtz0VLvxHKHn0AaJlRt6FgcVA5X2K16meCUZkIfkkVCZtfpeoPUirVhS0coy0atWJv8jricEcOLtWmSrFoRtg2XXVuG1sHD1Gwbrvx//bjrtu34RqYuq5NKoxT85ffbo46SDhwY8156gfxn/uMT8DrQOff9bkp+dzOOpeH3F/TvryOGujkcmuKS2B2D4hIdSs+3o5SmoCD5bsT8dTtE1AopwVVZHXbMUYDZ2ITRGlqwxJ0FGXzJ/KL5ahUnduEvMPvxw/fk0VivsEJMfh6+J4/TzmvFlQtGCHF7/pUt9KuI/EICBemjC9cxrG4rP3jkDgDMAPFuoDG0ZvIXHzLr7ee7tRMocvuVRyf6yypiXxwoLI5u3CsoSuz4mKwEYYIQDTnVNdFdF2Hfa7zoaEoF6tDX+UVtqHl4oBkTSmwteLOAlV87g0esaMWmpSXklXroN6QVZQQZB5Rm6JQ6Ju3ZRL88Z8jar0HFrW3T76LzUW43yuocbaIsC3PTRkpu+FXQfvvbKi0jeL8CX4ZW5JfEnoPAlRc5ilFryC8MP/aGrKaSYnrz3lo/cc2w0/4G5Uax+pLTs32ZXQlnrevD54R1G7Rpog87pLP4bWxEvfUO6pXXYW34DGmR+PjTHNaud4TcI6a1YvVaJ8OGennmka2MHN75i7rLWC+vPL2VQ2dHXpEPNjAWPPgPdJgJtDZNCh59KGy7J5/uDTnpBDBNzXEnWeTmhrwkJMefbOH1hm/7kMMsChNfuagTsq9WSCU6ysRQoSZMcWdB3om9355EUra6ogI9Y0bHAa8X4/33MZ5/HvX55zHtR4XOora1Bd5+KSfs+AK+kNy//7eOSdM6T65K+tlcdVMT5/4sfN4Av5CFzqJ0n7nPhXc6lOKA158K2/bwqY3klXgI/T5qBgy2mLpHbGHIAHvs76GgKPykrLjUZuY+3vb3NVtI1GdY6H1oR3T5RKK9rqe4K8rwFBWGHSGV1jSNGxXQKY2rcisFy77H8e0qcgd2z5QcrVsLvozHKowaUIbm+/nlHHvjMkbtsb1TEk7DYTN5zlYO//n37W34EzRFI25z3nsXx4b1voohwZ5tWeS9+DxGbfeFCH8bBUWaPWe3hlyUBN9/IWP3Db4R9v13vgj6A5BTYDF0Sl1Y4Wxbin2ODP3/QzrDcoPRm91aSMAe23S+Qfb+e6DWb+60vzYQbSjsA/ZMej/0sUehh/0RtlR1+3JqANvGvuwS3wGvF+P3d6IefAQV4JTYBx+IffcfYWjoVO6h2LApuoF3wyYHZ5zUzAlHt7BwkYvqrSZDh1jsMd0dUz6rrpv4nUuXhBX2yrJwLvk6bJtjd9H88DwPTz7WXaCbpiY3D666NrpszV2ZMs3miGO8vP262W1F0jB8bvWV18ZXKiNaxK0VUkXLpLEULfwy5HmtFG2jhqJzQi8Qxu3WAgwdjD7sQJj7Uchxwnv11fiLDRr//jfOG29EBYSf2ZMn4/nrX9H77x/xcV3r1O7YZuDxhB/gDMMXrjxuUiv/eL6eNStNNqwxKCjUTNvDizPMGqpfzIJP0HZl8LqVYcdHQ2sGblzjm3mFGIhNBxx47hbe/OsIupds8/37il839ygoKCcHLriyhb/dGjoD7IVXteB0gbZ97+8eQxOzCuhPIpWMmrZS9kcIR/Oo4WjTCBv1ZzudtAyPfU4WE4bBjn12p/9b84Ke1krRPGYknvIyAHI3bqH/m++RU93hJOt/PIf3/FOwjjm4073RuLVOh8HWzWbYPaTaVjRszSG30OLQy1fTtN1JzRqfGB64SyM5Bd1Fqb/2a6hn+vfcHvXtN2jTDClswTeHdKxciTvIXlu/UD7jskYWf5yDVwcr2aaZcng1RRXd55B+ATuyvPP4t662ifff+YLZc2Yy86QtbP62mGDlMpWhmXVoK2MmhN9DnAmkwozMBGc6q2fZ3mMPgZycoPu3tKEgJ8d3TZxE3Aidk4P17JMweODOZxvonX9imth/+zPM8gls4/JrUP93fydRC6A+mI951EmwNbrwmED6Rxmm1r/cN3AYBuy7l5sTj2lhzxnRi9pQA6OOYKNqpdB5kbPR/f5ON5de6SEnp/NCxfiJNs+/1srYXXoeCvd/D7Rx9PH+169xOHxtlZTAw0+2MXOPxO/vC0RCkIVU0zp2FO7BA4KPj/hcgFCJoxLtdFk3/QK9+zTfs03TNz7udIq9P/kJ1lVXAWD+85+4fvxj6LKnSi1bhuuoo1ALFhCOrqIWoLjExogQpmZrKA0YR0fvYnHg4R523ze0qA3m0AbD48oJG9EC4HVG3s4y6aAdHHH5evKKO08A80u9HHzFGtb2Wxn2/nCcfkErP/5FMw6HRind/qfTqfnJL5s4+Rzf/1ehyiUJQrZh5+dRN2NqmBgI2LH3DLQzMVF/4aibOZXte8/0PddQO+ePvu9a65BBVB1/OAA5W6oY8uQLuLZ2dh7Vjnqcdz+C+cJbQGxuLUBJmU2nDIPdbtQ7I0Z8FPTzMHJmHSOm1wUVtX78YcnB8AvSlc2asNk9d6Lz8jr9u+te3ZHjvfz24W0MHtU5asV0Wcw4cQuzftA9gWEoUes/NrK8gPff+YJB4xs57KrvceX7Xqsy7XbXer8jW/jZbTu63R+J3KEj0hL1mgozMt0OdY8d27SHIQNU9MP9x2tx3XAXuqGpYz+ZbUNBPu7bfgHlpanpy9jRWAvfR738GurNd8DtRk/dFX32mTBo53v15VcYz74Q9HZlWejqrRj3P4T961/G9OiD9mtlQIVFdY1BqGTxAwfYHLhv/AWeg6VcbznhJAoeeSj0ipvWtB4bOTOyacL/u8nDpVd5+OBdk+ZmxfiJNtNn2nFXSMrLh/sfaWP1jW7eeMVBczOMn2BzxDFWzFmWY0VCkIW0YCiqzz2FAY/8F1dVjU9c+UN6FdSecDituwQvHQEJcmv95Odh/eOP2J8uxnj9XdhRjz1hCtZFP0ZP8wlemppw/OpX/u51Qtk2GnD+8pe4P/gg7KO6iq/8Qtj/MDcfzXWFTDBiW4rDT4hufIzk0HZl6V4HM3P+myHPW4bJkr2iW4CdNHsH4/ffwfqvimje4aCgzMOIaY07k+M52vsWmFE5GpSCc3/Wwok/bOXdV13UVhtUDLQ55Bg3xaXdJ7yJdG2BpLm2ghCOmkP2w2xupujblR3jo/JV1qifvivb9kt+xB8ASlF7yH40TJ1I8Vff4thRh52bQ+Pk8TSPHtG+6FX+7nyUDl1W0vHPZ7AOP8DXZBTiwi8uDz6xmcf+VBw6HForxh9Q2y4EA5k9Z2bE5zy9YC1n7jMq6PM3H3Aou//x1yHv1YA9eAieKVPbj4WqubvLVA9/ebGG+56ooaEyj5JixbCpdbjyugvncKI22LWz58zk7P/7irWLStmxOQ9HrkXZlK1cdMLgkPeFqssrJJe4QpEzIU5bT96Ftif/gvnuxxhLlgNgT5uIdfA+kBe/YonJVs/JQZ96EvrUk4KeNp76b9iQC2VZ8PhTEKOwdTrhDzft4MLLywkVpnb7TdtxOHwlKwwjuDmw5Fsnf3+4kA8X5OIwNUfMaeWS8xoYPdIKm+Gt8SeXUvDvx9BBMptq08SuqKD59DOjfj0lJXDcSdGVHYqVMWM1l16ZmrDjQMStFdKBXVxI5eXnkrtiDfnfrkS5vXgGltO4x1TsosKg9yQkYVQwlELvPQNr7xnt2XADEwgZr7ziq4Ed6nbbRn36Ker779HjxnU7H27/5wVXtrDwfRde3b2kj1KaY89oY9goG9v2jY3BxseaasUtd7SydtFEtNdg4NgWdjuqlsHjm8O+7K/3PpStg4ZTVr0Z0+66VcX3oPdOODdsG4GYDhi9e/DXWuhy0Oj28ujCdTGLW4CSfpqTzg4v8JWh0FFk0o4WlV+Mbk5NvVBB6IRpUnXCkezYayZFS5dhNjXjLSqgYdpk3P0T6zqFM4P8ZV/c/cupOfSAoNf0759D/vpN4R/S5saxbAu6y7S1a3nGrm7nISe18N9HcmiudaHtznMVZWj6DW1hY/O7KBNGlhW27zf1h+sC7L//HqycX8bKj8ppaXBS3L+NiQdvZeTuO6hr84QUt54RI1h35ImMePMljCDOrQLqr7nO53wE0FXUtl+voHxsI+OmRF6ojEbUjiwvYF1tEwCmUzN2n+2AL0t9qFBr8JlA2tuz7XPJoGLiwIzQbKmgR8I2I9zaQPJysI45uNv+gkSRMFu9qjp0oVb/s7Zt910TTaa8AM45swnLgutuKmVHnYlSGq0VpSU2v7uhjk2bHYzebQgbNzvIy7M5/cRmfvGzeiaN94VtPPivAi67pgzTAO9OV+O7FU7+9kARzzyyld0PD10g2xo7jtr/PEvZj34ADQ2+viuF8nqxBg2m9rkX0cV9MwOmhCALaccwaJ04ltaJkYu8pzLZTtesuGrLloh7rQDYvBm6CNtgIciBjJtk8dcn6/ndzwvZtL5jfHQ4NCef28q4CV4uOK6Eld+YKAP22M/DWT9uZc/9fROXP/6rhjf+MA5vW7/2HAA7tuSw7IN+zDqjklmnV4fsruV08veb/sFPfvdTBmxeh2WaoMHQNh6ni39fdTsbx04O/5pjIF5xGw3KUAl3bZOB1LMVoqFt8ADaBg9IStuB8+VgwqJ10/qo5tTGqsi1wHGYqKpqiMGtBcgv1My+dilfPDKeLd/595IC+BxPb8UCKt8/lOZNw/leGxRWbGf4zOWMmLIKpWDVBpvHrx6Dp75kZ+ENRUN1DhuXlDB82g4Ou2oV9WFE3pe33ImzqYGhH8z1JT30O9K2TcM119F8/oVAaKc2kFChz4EEc56F+MmE/bUQh2PbF5R/wn9JA/qDwwRv6ImbLimOWdT6Of+HTZx1ShNvvJPHliqTwQMt9p/VytGnD+Crpa72bQwtLQZPPFPAMy/m8/p/q3E64bJrytBadeqaZSlsW3P6+f356LM6hoT5lbcddDCV364k75mncS36DBwO2g4+hJZjj29PChMLbW3w9hsm69YYlJRqjjzGS0X/mJtJKxKCLGQjSXFrA/COnB601IseODCyqAUYNKjTPyOJWj9TZnp5+v0dfPmJg3Xfm+Tla2bN9vCPP+Xz++uKUIZP7GoLPp/v5NMPXPz85iZqR6zkzTum4G0zOiW28ydbWfifQfQf3crYPUO7jtsHDOWPf3meSV98xK6LPsDhdbNx9CQ+Pfg4WgtiX/TTGjZ+U0D1qjyUCaOmN1A2rMOh8IdI9zQ0OVoSKW4lHFnobUQqxRPpXCDKzIt8kWXD8M7f9XDRdn6eXrCWvFI49voVbNuYS9WKQpSCwZMamPdiLVXvHLlzMdAnKBtrSvnurb3Ztn4QU46ez44Fh+FpKAZUuyb2j48blpSw6Nmh7H3WxpCurZ2bx8J7/0XR4s8Z+fqL5NRtp2zyLjT/8EdYI33XRyNq/fTLc7Jjcy4blxRjeRX9xzQzeGJDp0icaNzawGv94cg9RRWXY46egrUmfOnLbCfd+2shAVmRezuJ/CXZp5+M49HHQ57Xpon+4RlxPSM3F048piPt+C9uLOXrb1zdwu+8lsJugzMv6M9B+7VimuANUinCJ3Y1T/w7n2tv8NDWBp9/ZtDaopgw0Wbo8I6QNF1YSPMFF9F8wUVxvYbXXzG59oocdmxXmKbGtuGGa1xc9FMP19/sCan7tYYVyxT1dYrhIzWDBie/Lm0kxK0VsoVUuLX+EORg2Mcdh87PRzUHD+3VhoGePh09fny3c9EmNVIKZs7yMnOWb7B7/3UXLz/tS34XmBXUXxro7pvzmXnqANxNJqFq9ihD8/lLFYzdsx6toXpVHs11DgrLPVSMbG2fTNmmg2/2nM03e86Oqq+hqF2fwyt3jmT7plxfSKCGD/QQRs2o58irNpBb2LE40NW9rdxoUF1pUFpmM2JMfAnzEhmSLOHIQjJp3bS+Pdw3VUQjamNBD+qPvesuqO++D1kJBKcTfdxR3Q6HC0P24y/RUzaslbJhvmRxjducVH18oO/5OnAu4xvUqpaNoqCsju0bOi82du644tt3+jPzpM0APPhiJbuXjSS/yGaXqR7MABXSMH13lk7fHY9359ikgYD+RiNq3U0mb9w7jg1flYLyVb3QtqJkcAuHXr6Kr1d8HPJeT4uLpm3FGA6Lov47ItbG9SfHCibWM4m+FIYMPRC2feUNSoqlvsdM7OOOQr3yRrfN/9o0oawf9k9/nLDHNTcrHn68MGT9RttWVFabvPFOXtg6r7al+OA9k/wCuO8eJ3U7fNcq5av/+vs73Z0Ebjx88J7Bxed07I32992y4IF7nViW4ubfdw9pefV/Jnfc6mLVSqNT3276vZsxY1MvcCUEWchGku3WQvcQ5HYKC/H+7nc4f/GLbqf0zo2v3j/8odPxeOuqPvtYLoapQyaVUgas/rjMNzkKMYxoW7H5uwJWLizmo38Ppq6yY/yqGNnC7As3M2zXprj66aeh1skzvx6Lu9lsf7afdV8V8cItoznjD9/vTCblo9DlYN0KJ6f9zsWWbzvcm10me7n0V83seUB8OQeyISRZEFJJokWtH8/FZ+K6+vdoFTyBlP2rX/iSlOwkXImfaHjzYb/bEWJ+qGw2fT2OYGVwAvG2maz5rJTl8/pTubwIfyq9fv0tzrisgUNP6VwDNtr+deXJj9by4T1T2LF+Z/4IrdrH7fqqXF65bSKDj/6KcV1+Le7mHFa8P5Oq5aPa9xi7CpoZvfc3DJu+Iu7kpaHIHTqCCkj5oktvR2beYUi4pa4U9v1/RV9wTvcU8nvMxHr1+Y4MyglgzXoHjU3hf8VOh8YTxbxm4wbFH37rahe14HNz33/H5LjDc6mqTMw3/4+/c+2cRAYpUaIVDz/g6Paspx93cMm5uaz+vnvfjj80j7VrUluiQkKQhWwj3W6tH+uyy3z1avv163RcjxyJ56WXsA86qP1YtCHI4Vix1AwpasEnHFsbHKGzhQbw6p9GUlfVOQ9Bzfpcnv/tGDZ+E33YWzgWv1KBuzl4zUltK6pW5bPm884LB9Wr83j5pglULuucLOz770x+cV4RH83teTmTRJf/ieYz0hNSuXdcECA52/X0rrvg+eN16MGd92XpokKsW29C/+ySbveEyo3iJ9ye1NbaCggyF+t4sEFbYxQh0sD8R0dStbLzGLR9q8H9N5fyv8cSMz5WLunH9rVFIcdHd4vJjm87m1aeVhefPXU4VctGdUqc5W7KY/m7e7Jq/m5RP9/pMEI64kLqiEnYlo/Psk2OPSSpG6BdLuzbb8Fa+hnWP+/Duu9uvB++jfXKszA6sfugclyRp2O2htEjvTjM0NcahmZrdfCPimUparcq7rsn/lpv69cpvvrSDFJcOwDtc2f9NDbAr3/pAnQ3MWxZioYG+P1N4Qf2ZCBurZBtpNWtDcC6+GLa1q7F/cILuP/5T9refhv3t99iH3po9/biFFahatR2oMkt8hJW2SqN4R8/u04CtcK24f2HhoR0fGPhu3mlQSdt7V0xNMs+KO107L1/DsHyqm736Z1uxh3XFwbdhpJqovls9IRUfK4FwU+yoxrt6ZNwP/Yn2u6+Ae+fbsH7wN1Y336OvuSCTuncu2ZCBl8YcjA31B+GHMj773yBYUbOeeAL140wDiuN5TWCjF2+fz95TxENdfGN5U8vWMv6TyvChg9rW9HwfeetLOs+m0RLXWGXUOuOvq39ZArN24NXEBA6MEdPyYj9tdADx7YvhCFDCjZAl/VDn3AM+rSTYWL3PWOJYOxoL2NGedoLSQfDshQ/v7S+PRNyV3wJA8AMI3wtS/H0vx2REj5HZMf2yAObYXS+7pWXHLQ0Q6iB1bIUb75msn1b0NMJR0KQhWwjaeV9AojZicvJwT7qKOyzz0YfcEBHjfKdLNrUkBC3cP/D3GHHNoApc7bhzLNDj6NaYVuhaoj7ztesy6NmXW58nQXamsInFtS2oqWhY4fR9s0utiwvCCmGtVZs22rw2YfxubbxhoQLghA95pipGMefAuedCyed7EuuEkA0CaOiYcTEKsKJVqVs+o/bSMmQapQKtWfflwcg3IKc5YX5r0fn/IbDbnGFfQ6A5ekQ+1rDpq93gW6itgOlbDYv7V5eLlvoK9tHA5EZeC9GKfh/P68PGtYLPrF6yAEtnHtWM3++1VeXy+HQnc4bBuy1T+QkI01NiqbQJSijYshQGyPCZn2vF0aM7LhmwzqFI8KczLIUWzYl/6MuIciCEJpEOXKJFFFnXNAKiqCiVRmavGKLqYdt44T/txbTqTu5Af6/j9itnvCWro/G2vijWor6e8I+Sxma0kEd2ZHrqyNHqyil2by+Z5n4k0GywpEFIdkkW0SYo6e0RxRGMl+CubWxMnDCOnIKmyGoaNVoFCP3+I6px35ETnETvrFp5/i0856KMU1EcnQNE2q2xD8GFQ9oi5DwSZNX3JGc0PaaeFpzwlzvezUtdYkJlRZSQ0yz/dzBw5PVj4whk+z0RHDuWU38+to6wCdUldLtYce77+bmqYdqAbjikgY+fquSM09uYugwixEjLc4+38s7H7cwbbodMdLElaPJj/O7X9EfDjvSCuOgaAoK4OjjOuLmSvtp7Cic4tJ+qUkgJW6tkE1kpFsbBYna2zlmgsVt9zVgODQovTOLpm9yllvk5eSbVpNTYDNs1ybO+etydj9+K6WDWymqcDN2zzpOuXkV0w7fRsQBEsgriT/ed9rhtWEfpW3FrnM6wlMCMySHvEcrivvFlyEZErPgkKxwZEHIVvxi1j839f+EIlgIsp+uYchPL1gbNAy5/dlOi5mnzSWnYGdyJ2UDO8dJw2bK0fMpGVxLblELs370GuMP/pyiAdvJKWwid0AVAw94l8OvXBXxNdo2lJT1fAzyv44JB9VEdGyH7bai/e+Gw0JFCLdWCpy5oWvwBuLx2hwwqjSqa4XkIeV+ejlKwW+uq+PMU5p4+PFCVq1xUFxkc/qJzRx2cGunCL89Z7h55N5t3QbGU87w8uB9YQY/U3PyaV4cCfg03XCLmwXz82hq1J2yOftCohW33dnWSUAfc4LF734duj3D0MzYw2bIsOQKWwlBFrKNVCbUyUS31s/3Rd9z0QM5fPNuGZUr8zBMGDm9gfH77cCZ0zFuFPf3sP+PKtn/R5Wd7ve2KVz5Vnum4u5oSga6GTi2JcT56Jl6RC3ffVBKzbq8IBM4zZTDtjF4fMdzBoxpoWRg286kVsEnfLl5mv0OiW7iFopElv4RhGzEnwk5XrrmeInFaAkVghxPQqOCsgb2vfB/VK8YQc3qodi2QfHAbQyZsoqcgtb26xw5XkbMXM6Imcvbj62rbaKgrJihU+rY/G1xSNGpgP2Oin98HDC2iYmHVLPs3f50He+UoXGV1jJst5UdxxQMmriWym9HB9lj60PbBoMmraUu7t4JqUKEbQC9za0NZPxYL7fftCPidcEGxinTbE44xcv/XuiejdM0NXn5cNnP4ysZ4WfMWM0rc1v47Q0u3n3bbA+jHreL5pe/aePIYzqvrg0ZqjnvIi+PPOjoFnLtDy+87sb4Jm2RkBBkIVtJhVubaBcukZl4H124jkKXA1wWe5y4tUdtOHI0+/1wC+89GOy99I1BB563JSElI5w5mlNvWc1H/x7Mt+/1w/L4JmN5xV52P2Erux/f+TUoA/Y7u5LX/jySUCU5zv1ZM/kJyo2SqaV/XIOGUT4Dar/8Lt1dEXoxPQ1DjkfM+vHP3aJ1a2PBdNgMnryWwZPX9uj+PU7dzMu3FvkS6HXbGqcZd+hmygb0rH9dszrvf+56ige08fWrg2ht8BkyptNm/AE1tA16FbNLxsBRe31D9YoRWF6677VVNmUjt1AyZCt1QfK0bG/xZHwN276ICFuhG8EGxrvva6NfPxePP+rYWfPWN0kaN97mb/9wM3pM4lbrx4zTPPafNiq3KDauVxSXaHaZoENODG/6vRvTAQ//w4Ft+/LMWJaitB/c+bc29jsg/jC7SIhbK2QT2Vj+JFEJo/w8unBdwtra7UjfrGf+E4N3Ore+8TGvxMvBF25m7F71CXtWTr7NnEs2sf/ZW9i2yVeHt2JkC2aI/83H71uHp3UD8x4egrvFRBkabYPp0Ew/qRLvjEog/oz8iXRtvSOn41i3OCFtCUKmEiho4zFVwonaUG5tuDI/ieT9d75g9pyZHHXdSj54cBQNW3Pwj4+m02a3YyoZfeR6nl5AzCLR/xoCw6mVAbsdU8XUI6qp3ZCH7VX0G9qKK9/i/Xc8QOf3qKCsgZmnvcOSV/antb7QF269U3wPHL+eyUcsZP22xNQhF1KDCNudJLXET5YQLpOeywW3/snNz3/p5r25DlpbYdKuNjP3sJNWvHrQYM2gwZEnSqYJN93m5tIr3bz5qoP6esWoMTaHHmHhSnKlH3FrhWwlm9zaRIcg+0VtoStx/wXuduQ2dj14O2u/LKK5zkFRhYcRuzWEFJzxklNgM3h8c+QLgV0P2c74/Xaw6tMSGmqc5Bd7Gbt3PbmFFo1u3/tx3qzElpvrKSq/GN2cuIUAQcg0UiFoAwnl1obbX5sIRpYXsK7WJwqHTGrgjDuXsGVZEXWVObjyLIZPr8OVZwNOtrd4eHrB2pjFbajXYDg0/UdHNz6WDK5lv4teYtu6QTRs7YdhWlSM2Ux+aUdG1NlzZsbULyF9iLANoLeGIcdCpAGyvAJOPTMDih4Gof8AOPv81PXNL2p74tY6GupxNDXiLivHdoXPyicIiSQb3VpIXAhyMkStH0eOZtyszBRlzhzNxAN2dDte6HLQ6E7cuJmQcOSWFqjaCkWFkB9/GRAh+1DF5Zijp2CtWZrurkQklv210WY1DkegCRFuzhbP3tpkoAwYMrmBIZO7L1T2y4tN3CbacVYKykdVUj6qMvLF6ca2MZua0Q4Hdl78ZeR6GyJsEbcWElf3LNPxemHBRwY1WxWDh2j22sfuWiIzJmIVtWWLP2PC3+9iwIIPUFrjzctn3UlnsvwnP8fdTxZWhNSQTZmQk5EwKhmiNtt5dOE6zt17JMuXmKxfY1JYpNl9Xw85Mcyb4g5HXrcOx+9/j/nUUyi3G20Y6IP3xbrkR7DL6J63KwhJJpb9tT0VtdEKWugQtfHsrU010YrbYCHIycLvOPupr3axdXUByoC8kUE23iYR5fHQ7+PPKfnia8xWX1m3liGD2L7fHjSPk/HRj/zvvhNxayMPlNnOc/8xufU3LrZWdwz0w4bb3PYnN3OOiKJmUAA9CUEeOO9t9r7iAgCU9k3+HC3NjP7PYwyaN5d5T76Ku0w+h0LyyMZMyJBYt1ZEbXcKXQ5Wf+vi3JtLWL284/0pKLI597IWzrq4NWlbTvyoVatwHXQQbN+OsnzjsbJteP9jHB99ivXAn9DTsjPaQBCg5wlKYxG0EFnUpmp/bU8IFLfQed9tYL9TIWr9zJ4zk+YdDj745yg2fFWCPwmfMkbTPL+VC/9fHTldAku0180gOhZldX1tXNEHyuNhyJMvkrulqn3+CJC7pYoh/32F6iNmUz9zao/b701kz1JOkhC3Nna3trkJmhojX5dJ/PcpB1f+JJet1Z1nZ5s2Ks47K4f35sZeHDwWt9Zoa2X3669AaRujS+Fdw7LIq9zE5Hv+EHMfBCFW+qpbm8hkUeHwehRtTQY6+TnrEsbWtbm8est41qzoPA42NRjcd3sBD/8ltnDgnvzeHFde2UnU+lGWDR4v5q/vAB1fcqpsDcMXsp+ezDUrKeq0jzYRotZPKoXh++980f5324K2JhM7jJfQL8/Z3r+nF6xt/+l6Ltn43Vp3s8nLt05k45IOUQu+UkDvv5THH35WRltbcgf80s8WdxO10GGS9H9rHmajJLkCcWyB9Li1WsOHH+ew5Dsn+Xmaow5tYdDA9M2EIg2YWsOLz5rc/zcn3yzxTX4mTLK45GdeTjvLm/TV/Hhwu+G3N7gIVu5Ca4VSmpuvdzF7TktUr6MnNWuHzH0NV33oSmiGZTH85WdZet3NeAsSVHtDENJEItzaZcsUL77si4PdbU8vYybEFlURSDL31frZvCyfT58fwNovikAr8ks8TDuylt1P2IozR/PWW4ujbuvww6cnrZ/B+PjJQdiW6lYyzc+/7s3jpLNbKesfWVj2JBxZrV2LOXdu6PO2Des3oT5fgt5jWkxt+3ENGoa7cmOP7hWyn4qJA6lZVpXWPkQ714zGod1aDe++7aC5CcZPstlnP5v563cAiQs/XlfbxMjygrja8CeQaqhxsfh/g1n5UTmWx8B0WYzfv5bpx1VSWBG8JGOiBezsOTN5/50vYnpNs+fM5KtX+lNfnROkVBHYtmLppzksnp/Lleckab+r1pR8viTiwl7xV9+yfb89k9OHLEKEbRpYtNjFjy4p5/vVTpTSaO3L7HvB2Y3cfdt2clKYSyhat/a2m5zc/zcXyuj4Yq1YZnD1ZTks/sLgtj+5M1bcznvHZMf20J3TWrFqpWLJVwbTpodfXOhpFuSiVSuxHQ4Mb+gkLabHTd7mjTTsMrFHzxCEcJTPmJQVbm1VFVxwvpN33zVA7ZzYaMWMWW5uuqeRigE9c+2SKWpXLij21YpVtE9+muucLHxmAIvfNRl13NuMHJAfVVsbtjd3EsHJFrkt9SZrPi8iWI1bP1rD3FdyOP381qT0Qa1cGfEaDajV63osbIW+S+7QEbRuWp+258fi1kbKdOzxwG+vd/HvRxxYlto5h1QMGOrlZ793Mnlm+AXApxesjUow+kVgInDvKOGFGyfjbjHRtm+csdwmy97vz+pP+3H8b5ZROrgtIc9KFIF7a5fNq/CXJA+KYWrefymfK89JjjmlPF4cUbixrprU7vnNVPp0KHJP9zvEw7KVDg49YQBr1vomWb4VcoVlKR76VyEXXJ569ziSW/vpAoP7/+a7xj8oAe2r+/96yMm8d2MP5U0VVVXRKe7qyuiu60kWZG9+PkThYlj58a2OCkK6icetbW6GIw53Mm/ezu+iVu1C8evPnPzsjBKaY4y2SnYIcluzwZt/HYHWncdHALRBa0057mUzom5veL/89h+At95aHJPbGyvNdQ7CiVoAw4Ta6tjGvVjCkXV+ZNGvAC0ZkoUMI9qMyNHMNaMp33PtFS4ee8gnaqFjHlazxeTWi/uzfmVm+VXraptoWXJMJ1HrR9sKd7Nv72om4Re1/hI/LXVOwo2RtqXYVh37HLh10/qoogi0w0RHco6UwnYFX7BI1aKOrq9NyXMi0aeFbTq4/e4S2toUVtcJEGBrxTMvFLB4SWr2D0Tr1j72kBPTEU6Uae66PXX7NWJl4MDoHJ6BEWrm9iQE2c+WOUd121sbiFaKuvGTaB6SXEdN6Jukwq1NBE89abBihWqftAViWYpN6wxefy72kJZkurXLPijF6/YtUAZFG6z9dJegrykSqRC4+SVeUOHHPsuCigHRuxGxJvvSe+2F7t8//DVOB3r/vWJqVxBSQSwZkUMRjahdsUzx7NPOoFsGbFvh9cKzD4TeyhRr0qjZc2Z2ywocK23b+1G9qrD7ot9OtK2oWlFEzdrULFpF+5oC69bml7gJZ9kapqZ8YM+3ykTEMGjaZXRYcatsm8YJ47odT1X4fSaV5eqzwjYdSaPcbnjmxXy8YSY4DofmqedS59pFk4xg6dcGljfcREXxxSKDhfMz8+N00ByLfmWaUAOTUppx422mTAs9cetpCLKfxtHj2Hj4segQtYWU1iy79BoyNp5bECLgHTk97r21jz8eftVbA68/G/0+plRkQa5dnwtGeNFnex2snt/zxEVdBW4iySu2GD2zodM2k64YCg49Lomhgk4n3uuvD3laK4V9xvFQmrhM24KQCqKJDIxG1AK88F9HWJPBthQL38qlrSV0Gz3ZtxqPuG3bXhbVdR8+NCre3HBxs662iXW1TZ1ELcCE2TVhg1psS3HQCR3vkfYG3zMcD9v32d3XdpBzWilaBw2gZfTwhD83G8lMJZIiUh2G3Nhk4PFEEC4attYk/9cSSybkgoLQojCQ22/JzHJBLhfcdJubncFsnc4ppVEKfvuHyHuEe+rW+vny1r+wZfbhANimie1w+CZsDieLf307W+YcFVf7ghCMbMoEW11NyARGAGjFttroFn9SkQX5rbcWs3FLVdCkIl1Z88l4vO74tmwkS9zu98NKTIcOKW7PvbyFfhXJnXVaP/kJnhtv9NWuNQy004k2fe+XPvFI7Ct/nNTnC0I6iFbUAtRGMfbZtqK5sftcpaclfvwiryfidl1tE9NmjIzq2pq1BVSvTI2p43dtA19T1/DjQCYdspWSgW1Bx0dlaKbs3crPftDZcQ4s9ZMI2oYMYsspR/vGRWgfJwHaBg9g8xnHizGyk8wKxk8R6SrxU1xkU5Bv09QcWiBpYMSwJIY0BBBt3dqjj7NY8pURYTVNsehTk43rFcNGRDcB2rEDvvnaQBmw23SbZCYDPvVML6apufU3LqoC9tIOH6m59Q43Bx0S+j2P1631Y+Xl8+k9D1OybClD3nwZZ0M9TSNGs+G4U3D3k/q1QvLIhqRRAKNGw9q1GjtE2JphaAYPiz4kNplurV9cjpu6lU+/3jXi9dpyUL1yCEN23RBV+7alqNtchm2ZFFbUk1PoS9zUVdwmIrlUxchWTvvdKubeP4ytazomaIXFNudd3sIZF/YsadSiTQ3sMTTKRVSlsG68Eev88zGffBK1YQO6pAj22xVGZn4YvSD0lGjnYt7CZrQdPmrBlaMpLOk8RgaWyukJ/kRSsWRJ9gvFIbvWYzptLE8EY0DZrJxfzsDx0QloraF2XT6tDQ4Ky92UDoltjAp8Tf5/h8KVZ3Psjcv48KGRrP+yFL99azo0B5/YzI9+sQPTLI3p+T2heZcxrLn8Aoq+XYGrugbtcNA0fjStw4ZEFLWtm9YnJGQ+Erq+Ni2VZgLpk8IW0lPix+GA837QxP2PFIbcb2XbcM6ZyS0SG2vd2rPO8XD3n5y0tXYvl9OV2trIwraxAX73axfPPOXA4/a1l5enOedCL9fd6E5aVuiTTrM4/uQWFs43qNmqGDJUs8fedlSLXPG6tYHUTZxC3USpnywkn1S6tYko8TP7xEbefSd0O7atOOGsyBOYZLu1flE5vF8+urSGgvI6mmpLwt+kNO6myGHUWsPaT8azZuEEPK057fcO2GUTkw5bTG5Rh8D1Z1BOhLgdOK6FH965kurVuWzfkoPt9PDL8/vh6uF43JOyPwAMHYp17bUA6OZ6HOsW96wDISifMYnaL79LaJuCkGw+XLuDA481+e99ocdHw9TMPqEZZxCdHG/pHL/wC8yU3FXkdnV1fffY7HpYFV+/NjjCExQt9dH1cd0XJXzy1HDqKjvG0/5jG9n37A0MGBe9sxxOzHYlv8TLEVevYuNGxSTnGEyHZtJMN8X9NJ7QxS4An9hL1B5UneOifkZs88eaZVVRJzmLB2vN0rQZh4H0+lBktbkatWIN7KhPd1cA+NVVdQwaYOEwg/+Hf90V9YwZlXzHNtoVQoDyCrj0Sk/kNpVmUIQETG1tcNZJuTz1eIeoBWhpUTxwr4OLz8nBTmI5X9OE/Q60OeEUiz1nRRa18SSMEoRMIKxbW7sdvl0BmytT16EwHHC4mz32cwcN+TIMzfS9PRxyTHT7l5Ll1gaKWvAtlE85ZlHkG7Uit7g54mXL5u7GivendYjanfduXTmET/59MO7mjrE7GaHJA8a0MmG/OoZPr+fJL5Mfzp1KsiGBmtCZdE6UVZsbV+VWnDXbItYQ7QmVFEU1F/tw7Q4ABg3TnPoTv/HRuT+GqSkutTnl4s7GSE9DkEMxe87M9h9/OG9gWG/geT97nrEJwxF+YqeUprA88ti+6pN+vHX3OOoqO6+41awu4OXbJlD9ffLCmbe3eCgod7PvEa3sPaeN4n5p3hQsBKXXOrbGZ1/jePhZjJVrgZ3JJ/bbHfuWX6fVJh800OajN6q44pf9eOXNvPb9ZAMqLH55VT2XX5zYuPyuxOrWgq9uWkWFby9qqLHdNDUHzLYYOCj8F/3Zpx18+blBMOdXa8U7bzl49y0vhx6ZmnDscCQqBFkQMo416zH/8k/Uh5+gdn6p7SkTsS8/H71X9KVpIDFJo/w4HPDHhxq4/4/5/O+pXNpafeOEK0dz7OltXPr/mnBEWNRPplvbVdT68ba5MHPcWG2hykJoHDkeBozbErb9xpoi1n++S9BzWhu0NuSx9tPxjJ/dsfrvd24TTaHLQaM7ghWRZFR+Md6R0xPu2grZgSouT0sJEaO1jfL3F1C05FsMr28u4ikuYvu+e1A/fdeU7mX0i1qnw7fAfvpPGynuZ/PsPwqpq/XtQVdKM2P/Ni66vp7ygR0CMt4Q5EhE63ju2JxL6eBWtm3II1TUn7YNxh9YE7Ydy6v4+DF/OG2X0kFagQULHh/OCTcvi6pfPeHMfUZ1+rfHa3PAqNKkPS9R1CyrooLEZPCORLrDkXulsDXmfYrz1ns7HVNaY3z8BcbJ52C99TKMjm5DezIYNsTi+X/XsGmLyfKVDnJzNXvOcONMUcWcWNzatjY478wcPnzfDDmWG6bGlQM3/DbyatsTjzkwDEK6sqapefJfjpiFrWXBN0sMmppgzFgdUWBHS0n1ZgbNm4vZ2kL9uIlU73Ogz/YVhAwnZImf1etxnHMFtLa2i1oA9e1yzJ/+Cuuum9EH7ZPCnvpYtKkBZShycuDK3zRz0c9bWP6NCRrG72pRWBz9dzoZbm0oUbvyg8ms/ngyKJtQohYUkw5bHNG12PT1KJSy0TpElIg22PDlGHY5aGmn8Xh4v/yIIcnbN7to3OaksMxDvyGJydppNjRQ9tZruGqqaRs0mG2HH42d1/n9iWmfrSAkkYqJAyOWP1FtboY+/hyumm2dxkdHfQMD3ngPR30D21I8PvpFLfg09VFnNXP4ac18v9RJW6ti6Cgv5YMSu682Uaz7ooS3/zp2Z4K9UAsCmomHbKV8RJh0zsDGr4tpbQj9erRWVK8q9AnpGPfcNu9wUrclB0euTfnIZkIUsIgNrxc19z3UN0uhdQfWvjOhrLT9dO7QEVSQupI8qSATwpF7n7B1u3He9TBo3e0rpGwb3dCIcfNt2I89kI7edWLoYIuhg1PnTPbErf3bXU7mf2ACKqRbO2Omze//7GbSrpEnnps2GCETw4CvVuX6dbGNKE/928Hdf3SyeZPvPsPQHHakxW//4I46kVVXFqyoZM9bf8WoV54DQCsDw7ZoHjyURXfcx7bpe/aoXUFIN+Yd9/lErdV5IqRsjVYK87d34X3zKXBG/u8hUUmjglFQpJk5KzbHMFlubShRW7NmgE/UAoQQozmFzUw89GsGTdwU8Tmt9fkR889721zYXgPT2V0kBxO3G78p4MPHBlO1qqPvA8c2c8C5Wxi2a/j9aIUuB48uXMd5s7osBGvNkAfvZcRdt2O0tYJpoiwLb0Eha2+4haqzzgHi2GcrCAkmd+gIWjetj3hd6aKvuola6JBkZR8vomHKRDzl/SK2FU2pn3D43dqgbTtgwvTgW8QyRdS2Njh45//Goq3QotZ0WUw/rpIZx4ePZgForHX5am5HyELfWOuKWtg21LhY8O/hrP+ytD2CsqCsjZknbWHi7A4HeXtL5O140FHqR707D+NnV6O21qBNA8Oycfz1X1jHz8H70x+kxSBJpWubTnrd5kHjo89RTc0h14WUZaHeeBuqt6a0X5lCLG6txwOPPugMK0QBLrvKw65To9sYW1YevnSQYWj6D4h+k+29f3Fy7RU5bN7U0UfbVsx90+S4w3LZsrlnIUOzrr+cUa8+j9La5/bbvgWI3Kot7HfRGRStTF6oiyDES8ikUVuqMT75opuo9aO0Rm2vQ83/LOpnJSIMedGmxG3BSLRbG0rUAqxfNA6lwoxXymbQ5I1RiVoAZ35bxChHw+EN6vwG22+7YUkBz908hurVnUtRVK/O47mbx7BhSc/2ow1+9AFG/+FmzLZWFL7/VwHMpkbGXX81/V94pkftCkK6KfliSdj9tNpQFH/1bcr6E+jWRkOmiFqA5R+UY3nDOLVKUzqklZknbkFF8TJzC71RlVbLLYpOhDZuc/LSzZNYv7i0U5m5pm0uPnxoFItfGdTp+q5hyKEY9Ol7GD84H2p8YfTKsn3jpG1jvjQXx18fi6qdbCYdWwj89DphqzZXt9e+C3mNbcOmzSnqUWbQE7d2w3rFju3hBxGHQ/P5Z9F/jE7/gTfsxM22FaeeGZ1LU12luONW/+DduVHL8tW7vPuO2Af3b1//gGHvvO77nHTBsG2U18P4B+6JuV1BSCXBwpDVpsir4towUBtTPz4qI759a8lwa8OJWv4/e+cdJlV1/+H33Dsz2/suLCxl6R3pgoigCPaCHXsvUWNJYokxJtEYuyYmPzWxx4LdKDYUxIIi0qR3WGBh2d53p9x7fn/MztapO3WX+z7PPrAzd+49Mztz5nzO51uAisJsz2HDAFKhcr//jk3vUXuRuufzCaHTe3SBxzm09TilhMXP9UHKjr2BpRRt7g8EpbGBfk885H58OLct+z98vzM/xMCgK6HrmGrrvPd+0CXmyiq/Tqft3hCVBX4siFqA4h3J3nwMkIKyPYlIP72MfuOrMMV5m1ckab0ayOrvPaTZxZoPetNYY0J2MG+cv698J4/6ysA3SpWHHgdkB9cfnJvH6idLEQejY66VbjnkV+RCMISqAnRn6XbClpQkzwmcrUkNTbGTrkQgbi34FymhaVBX5/+C9MLL7PTpK1HdVIVWVcnI0Rqnnunfgui9t0xeF2WaJnhvgYkG/+Y4wFkwqt9nH6KrniczRdPI+3Ihis3q/4kNDGIAmeJHs2hd9+u4cIYhd5Zw5NZ6ErXgLNriHYlm939Mab3L6TGk0Blu1+FiOqrFwYCp23yeZ9GitRzclkjlwTjPDocUVB6M4+A2z8/PRetNg/Rvv8ZU69lhF0DcoYOkrPbf9TcwiAmEQDf5+LwKgR6unoRBsuDHPTEjagFndXtfy0PhXfu2xhyvM/FsT5uuzrMcecF+v2p7OWyC7cuy3IjatqfcvizLYxiyu8JRSmkpync/eIyKch6koHy9vPnXSLTiOZzodsJWmz7Ra8U6KQRy1AgYmB+5QUWZzri1AH37Sfr00/E27UgpeO8tE/v2+idu09LgvU8bmTjF+aF3Lgyd5595nMZb/2v0u4/t/r0CxYf4tloFFeWBOUHx1ZX4mmoVTUOt979fmoFBpPDau3boQGTf3t6jucwmv4tHxUoYcjjdWm/kDDroPRQZqC1JpXSXfwsXIWDsGT+RN3pPUzEqcM1FSZm1TLnoGxLTvc87LiFeXezfRmZNifeFcPvNAlNFuV/nNbc6LpSh5gYGweBVRAhBzcihSC9rSCElNSPdVy6PJqFu6xMK+oyp9rGUcubLrv2frx63LYw58RBHXrivxblt2gSMT3Ew+6Zd9J/gn5veWGNCs3uXQEKR1JY6F6T+hiErVZV+HCQQNc62TNHId42EaxtNul/xqOwMtDOOR/1wUYdNb9ev+u9/F9Fy7bFAoG4tgKLAr35t5/e/9a4062rh4fst/PM//jmYvfMk73/ayKYNglUrVIQCR83QGDgosJi49EzpM4RFCEmqn9VUXe196vP6+txCtCcm4Ug+/Fx/g66Bx36dQqDdfCWmOx5oqtXbFgnol54L6ZF9bwcbhgyhdWt9hSC76D9pBwc29ge3rybNt234bCIzb/jUrzwy1aQz+pRVDD5mI6W7ctE1hZQeVaTnlfn9tdU3I5FN67YCvhdN8SmBhQxb+/q3EGvs6yw4FWwBKaPlj0Go8KeAVOXUCaRs3gYOrUMoqRSCxrxcGvL7hnOYnSaW3FqAQVPL+fntPBprTR4iR5y3rXq/N0OOLiMlx3e1diFg7EmHGHFsCXvXptFYYyI5y0bfsdUoJv/nGUuihhCyQ5pGa6QUzrzeANB69ECaTQi7l8dpOjI3p81N/lTsDjWNhXvDJqxd1ZGj0fan2zm2AI7r56OdOdfpzgqBNKlOnZKchP7MU8i5s6M9xIjRWbfWxSVXOph+jPcPtq4LFn6oUuXfRlkzI0dLLrnSwcWXOwIWtQBnnu1A0zxPSqoqmXuSRnIAL4FFVdh75vkIL4pZV1UKzroQ6StkycAgBpFzjsFx3+2QEI8E5/woBFJV0C8/D/1Xl/k8RyyGIYcaX6IWIDW3kqEz1+M93k5grUmkrKBHQNePT2mkzxF76DdhFxl9/Be1LpJ6F5GQ6n3uTkh10GdUbUDnrZp6NI29+3h0taSiUDtyDPUjRgV0XgMDd4jUrIi2D7FnZVA4fx6OZGdhNamI5vd6/YB+HDz3tMPOGOks5nidk363zadRIBTY9m12wOceNLWCUXNK6D+hKiBRC2BJ0Ok3vtIZLu0BqQuyxwcmNmVKKvLM07zX+jGpaMe1REW5xGUkQ5K7U4uh9nTPlbmq4rjxYhznn4J5436oqYGhw5CnnASJCb4f383ojFvb/FgB4yboLF8mvYpIh0NQdECQlha51g5DhknOucDelGvbdmyKIlFVuO1O/6rjLSuoxKI693kaevVhyw2/YcT/PdbBh9FVlcYeuWy7+uYQPQsDg9DhsXdtO+SZJ+KYOxPx1XeIg4cgPQ39+BngRwsLF901DNmfEOTWJGdX+3GUpKGycxWIO0u/7AQqx6+g4ZujPB4z/aKDeCkn0Eybtj+Kws4Hn2DklfORQrYpsicVBWkysfOBR0PxFAwMooI1L5eCX11G4s4C4g6VIE0q9YPyseWE3n2SDltQazSIvdza1qT2tOE70RaqSyKftzxh3kH2rUtzRkS3d26FZMj0MlJ7NfodhuxCv/u3qEu+QVZWNVeMh5a4Hsf1FzrrAbXC33ZUoSacrm206JaObTPZGeiXz0feczfy3LMOO1EbrFvrIi3da/X7NsdFmkf+buOSKxwoqkQIidK0+5bbS/L6+42MHuu7kJgrBLk1W6+/jTV/fITGni25H7qqUjjnVL55fSG2rMB2Fw0MYo7EBOTpc9GvuwT9/NMDErWhJNbCkME/t9aFKd6fzTOBOcF3mF2oyRi+g2OvKcQc71xcudwJc7zGsdcUMvr4ik6dt3LmcWx87V1qR41pc3v1hMmsf2shteMnBTdwA4MwEZ/Xzz9nTFGoHzKAiqOnUDl1YlCi1lNl5Fy6f+65yaKjqL5yxiRxSYGF/IaC7Px6Tr5jG4kZTb1nm2q+CEUy4tgSZlzVuU3Tor4j0D7/H3LOcW0jW/r1wfb7G9DOON7t4/x+b4aISLi20agK3j0d2yYiEb5SXy8oKlZIS5VkZfrffzUcSAmr15nZuNlCYoLOmFmC9Kzgd/FOm+fgb3/2fB5FkUyaopPbK3JurQuLBR583Matd9hZ9JlKfZ3TyT3mWC2g/tcut7YZISg492IKzppP2rZNqI0N1PYfhC0z8vkCBgZdFYcDCveD2QK9ekU/gq+q2MzBLc6d8t7D60jt0VaULlq0NiBRC5Deu4y4lHqsNQl4ciZUs4PsgUWdGnOwHFIWc+0L49n5cxp1FSaSMhwMmlyFOT64+bpq2gzWfbSY+F07sJSWYM3thbVffmgGbWDQTXDlGrpDSjhYJNCkoFdvibvsJrtDD7iXbWex1qkUbkjFYVPI6NNAzoD6oM+pmCQDplSw66dMjxWIpaYwaJp/RelCTa8RtVzw5HoKN6RSUZiAOU6j/4RKEtObhLZ/QX/NCJMF6bBBfj/0/z4PRYdgz15ISUb2yUHfs9HnOSKdbxsu19bbez+cdGthC4QtcflgkcKfHk7n9XeSsFqdH9ZjZzRy352VTD8y8jvz6zaaueLGLNZtbAlpiYuTXHW9nTvvtQck8trTt5/kwsscvPFKx5BfV7uL394T+efcmh49JRdfHviOnzu3tg2qStWIMd6PMTCIAfwNQw4WR/9xPsOQrVZ47FGVZ55VKSt1zhnDh+v87ncaF17k3AAMVRiyP25tQ43Kl//Xh10rUmkRn5KBU6qZ+6v9xKdoAYcguxAKDJ25gfULp3g8ZuD0zZgske/r2jcjkX0V9ZjjJcNnVIblGo0DB9M4cHBYzh0OLLl9yBoPZWs2R3soBocpUsJrbyfx8N9T2brdaRpkZkkuv8bOjbfamztDzMhP57s9lWEfj67Bynfz2PBFzzaVgrP61THr+j1k9g2gZ6Ibxp12kD0rM9AkHYpICSHJG1NFj0HR6zKhKNB3bDV9x/qTVhIguT2dPwDVZagDRnvt8+oKSY6UuC3dcqjbtRvq3qHIYaLwoMrUObm8+maLqAX49oc4Zp/Rk08WxUd0PNt3mjj2tJ5s3NLWVbVaBc/8w8xdtweXvwHwwCM2LruqJeRXbUrUT0uH5/9r5aijo+tWB0MHt9bAwKDT2Gww70wzDz7YImoBtm4VXHWVmb/8uWWXLRRhyL5wWAXv3TeQ3Stbi1oAwe6Vqbx730AcTfN4oG6ti96j9zLyxFWoZgfOUDZnmzShagyesYEBR24N9mkERWdFe3s6m88sFGG0/DGIGSId8qnt3tAmJPPPD6dx5U1ZbNvRsilXXiZ46hEzl50fhy3CPsH3L/Xnl4W5HdrflO9P5KP7h1NVFFz+a2bfRk6+axuJ6U7709nf1hn2mz+lguNv3hX1aB53eOpf21lcRpsvFzPSxaS6W/ufbuvYhrPM9N1/Tqe4RMXRrpiSpgmEkFx5Uxb7NhRiCV5P+sXfnkylvl64Le4kpeDNV81cd6OdwUM7H3pmNsMDj9q46XY7ny9Uqa0R5A/SmXuSFrHnGWpaF4wyMDAIDa+8orB0qegQ3eH6/W9/M3H2OTqkR2Y8W77LoLQgHndhwlIXlBbE895zNWQMD+46fcftptfIvRRvy6OxOhFLUiM9hhZiSQjt4ijgcTW5tsGSbDFRa4t8HpyBgasysjenq6uwfpOZvz6eBnQsWKTrgmXfqrz9uomLr2j5rIUzHLmiMJ6t3+S4vU/qAodVYc1HvZh17Z6grpM7tJb5T61j/7o0yvclYLLo9BtXRWpP/9pERotAC0e5KCLFbQ61SM3yK+800s5td8JY1QdIeYXCO/9L7CBqXUgpKK9Q+eizyBSqslphwftJHscDzrY3770Vmj2M3F6Sy69xcNPtdk49IzqitrwMXnvJxN8fM/PuAhP1nYhg8RmCbBBy3l7RfXYEY42s8SOiPYRmnn3Ge96DySR54YXgv3r8DUPeuCTDVzceKrcM7rRb2xqTRaP36L0MPGoLfY7YExVRqzkUDm7uw85lwylYNQhrbWQjiAwMugqhcsT8cbtcru3zryZjUr2bDC8/3zKvzchP92sMnXUXdyzL8tnyZuePmWj24C1VRYF+46oYd1oRo08ojoqolRIKN6aw9qNcfvmkJ+X7Qr9W91Xl2t8WVpF0bsPl2raPVogE3dKxDWey8u69Kg6H9w+4ySTZusMMBJeX4A9V1Qp2HxOOEFBcHINxHgEiJTz2NzP/95QZhwNU1Vmc5ve/sfCnv9m48NLA3ATDrY0cr/+4J9pD6PbESn7tzp0d3do253AIVqzVuTDUg/NAXYW5Q15XG6TAUd89KuYXbclj42cTcVgtCEVH6oItXx1BxqitfKGv5IQTj4j2EA0MYoJQtVcJNEdx82bp1YiQUrBrR8e1iTfX9oJp+Szo5HdsfZUJOjQ2bIvuULA1qCSYu3bERvm+BL78xyCqi+KdYl7CigWC3iOrmX3TLuJTQvv8PLm24H8UQrTaAHVluu3KPlxhyCnJvsN5dR2SkyJTITg9TSc+zvu1pIRevSNfsTjU/P0xM39/1ILd7lw4OzcYBPX1cMctcfzvPf8qZBlubWRxidr0hG65j2bQjkQfxqeiSJJSZETyawFSsm3NRe7cInSS0xsjMpZwUrq7B798OBWH1VlrQeoKIEAqVGwYTtEPk6M7QAODGCTSubZpyXpzW0JPJLZree2va9sZkjLt+Oozq5p1LImRL34XSmrLzSz86zBqip35wlJv2YA9uCWFTx8egu7DtHKHp+JeLtfWW9tNl7j1x4yL1Pu0O4jobitsw8WQgQ6GD/W+UJISzjwl+Jwmf7BY4KLz6ryGtug6nHNB7O20FR8SPPY3MzMmJTB+WALnnR7Pwg9VdDd1qKqr4OknPLUcEoDkofstbh/rDsOtjSyx2jy+OxBLYcgA556rYzJ5m48Ex54UXAhaIEWMRs0u9+ogIxX6HLE7qPGEA82usnf1QH54aTZf/+NUfnjxeApWDsZhc7+Bt/3b0U3rU3fPVVCxaRg1Zcbn0KDrEupovFCGevorCM6YuB7dQ9sbcKaOnXF25NZrQ44u89iGB5yFnoYcXdZcMDRWkBL2rErn04eH8NpNY3nrN6NZ8VYeteXu57iNX/TE1qC6fa5SF5QVJFGwJi2gMfjKe/ZX3IL393akQpK7Sy5vt1vdh7NoFDjDev90Z5XHhZKiSC45v47+fSO3u3X3bVWkpemoHsTt1Tc4yB8QW5PSpvUKx01N4B+Pmdm9U6GkWOGnHxSuvyKeG66MQ2v38n31hYq10Xui3L4ChfW/eH9LG25tZHn9xz2GqI0AkQhD9pebf+3MvXfnSphMkqFDdWaeEHzZT3/yawGGTa8id2id+zwyoZPQo5jcEfuCHk8osTea+em/x7J50XhqDqVjq4+npjiNLV8dwfJXZmOrb5vD1VCVSPXBTO8h18D2HwNbuMUKIjEVR/9x0R6GQRQJ17ouFP07AxEE82bsZejARrdmhKJILBa46pqOxsiM/HTsjtB3n0jraWX0iUU4w5HbIhSJJdHB+NMPhvy6wSB1+Obf+Xz51GAObEqlocpCdXE86z7N5d07R1O8M6nDY7Yvy/Iu4IVkxw+ZnRqPt5ZMoRa3Br7pdsI2Epx9egP/fLQci8XZ+sZsls2i8vx59fzfY5FtNN2/r8Z3nx5i8pFtd/lSUiV33Wvj3vuj22O2PQ4HXD4/jpoa2uxcuqo6f/qRyr//1VYMVVUK7+GEzcf5vr7h1kYGI6+2e+FPfi3A4MGSjz62k9m0RjCbZbODO3q05KGXKjFHsOicapac9cfdDD+mAqXVYlJRJWlDdnHURT+0uT0W2PTFeGpLXO2JXHNkU+pFeTLrP5nU5nh7ox+bR0Ky6ZeSoMaVbDF1uuWPgUEsEynXNs6i8+mDCxk9zFmDxWRyriEBsjJ1PnvnEAMGRrZ94tT5+5l0biHmhLZryNxhNZxx3xaSs32vISsa7D5/QsXmJTls/94pBluLVVcV5y8eH9yh2JWt3nuqmpQCa23g6VL+VKsOlbiFyLi2XT0cuVslvYWzaFR7rru8lnPPqOfN9xLZtcdEeprOuWfWM3xIdEJ+UwYl8O4nNnbtsLNtq0JCAkyZppEQgzVRvvpC5UCh58lASnj+GRPX3mhHbZqL+uVL7+GETfTt73mBarT3iRwuUWu4tYcn06dLdu6y8eGHCqtWCcxmOGGuztEzJKsO6BHLr3VhSdA54eb9zLj0IEXbnEnAuUPr+f6nVZjigq+GHEqstfEUbekD0v1cJaVC6c5e1FcmkZjuLAkfn9oAQvf4GAB0gSW1BvC9OWFgcDgRitYqriJSjYV7fbpredkN/PD391hWPJVFy3tgdwgmjbcx75R6LBYoIg7psLmtruupiJSrgFRnvnOFAuNPL2LMiYco2pqCw6qQntdAei/fKSOtBau31jgLftzTfGww6wIpYf1nPV0ZaG7uFzTWmNm9IoPB01tMppQcK5UH3Ld9A6c7nZrb+RSZ7/ZUes2FFiYL0mHzWVBKVpd5LCrVVQtJabs3hD2atjXdSthC+MJV3JGZoXPj1bURu54/DBwsGTg4tpP8f16uYDJJL9WlBYeKBAcLBX36OWeumcdp5PTUKS12X3FVUSWTpugMGOhe2BohyJHHELXhJ9bya1tjscB55+mcd15ozxuMY5iYpjFwsnNRsWjR2hCNKLRUHcjwLlABEFQWZjYLW0uCjZ5DD1C8rTfS7WMlqlkjdeBeIHIbwAYGoSZc/WwjLW6FgKN7LmfmrR0X/LnUuHX3ZuSnew17DRaTRdJnTLXfx7uEqj+9Xl3HuARuZ9cH1lqV6mLvLcwUVadoW3IbYTtidgk/vtbXrRgGp9s7bGZpp8ZkNinYHbpf4hagyOH823rrc+vtfW70tvVOt7GvIunWxhpFpPjsmxVLCD/NmtbHmUzw2N9tCNExd09VJfFx8MDD3sNlDLc2Mhh5tZEllvJrI4W/+bW+CEXv2pDTSTN76LHrMcXbEaJ9GKOzlcfIE1ajdPF2HQaHN+E2LkKZbxuIs+auz2cuNUhHbKWRucMfUevu+E6HJvs7P7Y7bvisEnIGeqi1gGT4scX0HFzXuTHREpLsz8aDr9Bkb+/zSOXadkVn2EW3WulH0q016DzTj9G99gIWQtK3v06vvLYT0OwTNN78oJGx4/U2x86YpfG/RQ2MHOM+L6W7urUZa1cy/p5bmXn+iUy/6lzy3/4van3nJ+ZQYOTVGhgER3peGULxkWMnJJl927oLiel1TL1sCTmDD0KregTJ2dWMP3sZvUdHd6EiFMHKQvcheGFh/0GUp/6DeumvUS+7FeXZV6G4o4AwMGhPsHmMpVsONecq+hIILlfOnbj1RDiKSAVKRYM9YFHrIhhxG5ekkZHX0GaOa4+uKfQe2XauMVkkp9y1jZHHF2OytEQ1JqTaOfKC/Rx9efDzY2tx60vg+pN3G07DTmloJG3FGvJee48+L79FzudfYznkrMHQ1d3gbheKfLjh7UMRq8w8TmPgYJ2C3aK5YFRrpBTccLMNxc22y/RjdBZ+1UjBHkFZqaB3niS3l+/CL93KrZWS0Y/8icGv/QddVVE0DSkE2SuWMfT5f/D9i+9R3yfyFfSMvNrui1GRNnJYEm30HrOHwnX57kOShU7u8P3OvNp2JKbXMf7sH7HWxdFQlYg53k5iRq3fUTLdBfHZ16j3PgJIhOYUAXLDFpSX30Z78k8wIDeq4zOIXUIRkuzC39BkVw5ie3KpocjRNiIv3OHI/hCKQlCunOBAw5KFgLGnFvHNcwPc369IEtNt5E+o7HCfOV7nqEv2MfncQioPxKOokow+DShe6kot+HFPQALeJW79DU32lHfrLSQ5Pq8f2XRegFoOlZD35ocoDc7+7QKIKyohbc0GSmcdReW0iZ06b6zQLVb7UQ1D3roN8d83Ea+/BXuisyPelcKQARQFXnmrkZwezqrSrmrHrj5pl1xp55IrvYfM9c+XTJik+xS13dGt7f/eGwx+7T8AKE19kYSUCCC+uIipN16C3w19Q4QhaiNPpPNr/amI3IFDh1Beew31+ecRK1awcn91xAtHuSNW82tdDJ/9Cxl9mhxZV2hx079puRWMOnG118fHJVlJ711BUmZbUds3IzHmn3vQbNuF+oeHQdOaRS2A0HWw2VFv/ROitCKKAzQIBpGaFfY1Xyj7hnp1bxutKN+uQP14CXLB68jy4J2yUFYf9kZn3dpQnGPI9DLGnlwE0Cq0WIKQxCU5OPF321G89N01x+vkDKwnq793URvMesZsUjCbFJ/urTBZECaLW5MqHFGowu6g91sfoTRa29bcl87XK3vpDyRuj72+7oHQbRzbiIchFx1Cuf4WlGU/Nt8khUCecDz6049Devj7BXZFt9bFgIGSr5c38O4CE/9730RNtWDYcJ2Lr7QzbboeUoehu7m1Q176F1KI5omoNYqmkbprOznLv6XkqFkRHZohaiNPzObXWq2Ybr8d9eWXEZrWlOUJ44aPZPtTz1E/rHOiPJStZmIyv7YJk0Vj0vzvKN7Wm8J1+TRWJxKX3EDe2AJ6Dtsfc+2JYgl1wYcghNtUPCEl0uHA9OlSGDM8wiMz6EqEugJtG/e2d1/UD7/E9NK7iPrG5vlRPvkS2l/uhnPbVtxzVyHZXXVklwvalehMNWch4Mj5+8mfWMGmxTmU7U3EHK8zYEoFw44pJS4pdgqots+99eTgCpOFIof7ismhLJiWvHk7prqOfZJdSCHI+Gk11tOmhuR60aDLC9uouLXV1ainngP7C9vcLKSEL5egnn0h2mcfOMuChhlhslBRAW+/bmL1ShVVgRnHapx5toOE2F23AZCSCldc6+CKa8NT0KQ7tveJP3SQ5L17vB6jm0z0WP5dxIStkVdr0AYpMV92GcpHHzldMlp2hRO3b2XMeaew9pOlWDsZLh9o4SiHXbBjeRq7V6agOQQ9BjbgUOMho1OXjxiKIskdXkju8ELfBxs0I5b9jNA8L2yFrqOu2mAIWwOfBBvy2R6XuNVffov41z9tvr15E6a0HPXGO9DTs5FzjgPcV0gOZTiylHBwcwo7fszEWmsiOdvKsJmlZPZpdHt8MLm1oabn0Dp6Dg1/XZFAw5Hd4Y/AdSduXSHJ7ensezNhzz6Pxgg4dUzCvgMUbjpINpErVhVKurywhci7teK1BbB3n9s3htA0WLcB8fGnyLPPDNsYXBPd4kUq110eh7UREM4J8qMPVB76i5nX37Myemz0iwwYhA7hZ4hx6xC8cGKEIBu0R/z8M+qHH7q9T9E0qKsj77mn2XX/o2EfS8UBC+//ZSA1JRaEIpESdv6UhhTnEHfKz/QetS/sYzCIMP7MfV6Er0Hs4wpHDkfbn3BTtm4/A9/9yu19rtasyh//gnb8sf63kOgk9kaFRU8N4sDGNISiI3WBUGDD57mMPrGIqRfuj1h+fjAtgMJJRoKZigZ7SMQttG0NBO4Frruc21C93z0J2g74e1wM0r3srAihvPG21z+6VBTEgnfDPo4dO+O4+hKnqJVSIHWBrjuj5isrBBecGU9lZdiHEZN0R7cWoKFnLxqye3hqxQaA4nBQPm5SxMYUi19GBtFDfeMNpMnznqmiafR4d0HYvzgddsH7fx5IbZnz/Sl1AbKpD7YuWL9wCpWFmWEdg0HkkePHIFXPiXNSVdDHDIvgiAy6MvF5/UKSa+siacduhNVzGx8hJWLHbti4uc3t4Wj9883z+Rzc5KydIHUFcK4jwSluN3weuuftjVhxgD3hWuOEKszblX8LHdsDuauWHErzrjEv17t+EWDtkQ1e5tBYp0uv/KNWNKq0zGsrLaHriKay2eHA9YZ/8Tkzuu4Ute3RNEFVJbz7Zrcw5QOiOxaMakZV2XXx1R53cnVFpSGnJwdnzQ37UIx+tdEj0oWjAkGUlvosXqY2NqBY3Ye6hYodP6ZRU2ppXqi1RSCEZPdPQ8M6BoPIo194psdQZAkgJfZTjo3omAzCQ1QLh3YStb7B68a0C1HaEn7qLu8yWKqLLez+KcPt+tHF2oW56K1aM0aqMFUs0lrchlLggmdx25727/fObLrUjB6ONJs9vgeFhMop4wI6Z6zR5VVPVHrX9slDlld4tPSlqiL7hbeoizBZ+PwTFc1LP1gp4YtPTVx9Q3hyWGOZ7ujWuthx2fVk/LKK3l9/ga4oKE0iQldUtMRElj/9CtIcXsFp5NVGn0gVjnL0HxdQRWTZp4+z9LkXcetITUWPiw/F8Dyya2WqM/zYrbB1OhQlO3ojZdgj/gwiiBw/Gu2Wq1H//jxSVZtFrlRV0HVst15BaYlRFbmr4yn3MFyEov0PgCM12asx4kJPS/B5nLsCUv6y75e0lthnDzRWmynbm0DOwJZiQ7HuroYTl7h1hSb7wp/XqnVocvuw5NYhyaF6v+vxcRw8+2R6vbMQdL1Zx7jybqvGjaJmdNeuP9BlhW00d+r0S+aj/O73Hu8XmoZ+8QVhH4fN5mvaEzSG1xSJOcLl1u7eYmLxB4mUFqmkZujMPLWBkRNtYV8QW8rLyH/nv/T59APMdbXUDBjM7vMvY8Xj/6bPoo8ZsOAVUnZtx5GUxP6TzmTX/CtozO0d1jEZebUG3tAuvRTTU095vF+qKkUXXBp2Nak5BNJHuqUn0WsQGMUHFRa+HceOzSpx8XD08TaOmWvDHO76iVYb4tPFKB98higqQWZnIs84Af2805BjhqO8+SGsWgeKgpw2Ce3CM3FkJsKazT5PbWDgIpQVkusG5aPFxzW3W2mPVARyyAAY5L5Pq4tgC0hpDsWXrnUeZ+++JkFn8Xft4xK/9kaFvT/lEF/SCylh+Hgbs05vIDnN+eq7E7euHrftCSjXVkoSdxWQuno98YdK0M1maocPpmrCGPZdfSFpK38heetOhEPDmptD5aSx1A8e0OV3erussIUoubWAPO9s+O+byA2bOoQ8SUVBzjy6uaJdqCmipVn36LE6y5cpaJr7N6GqSo4Yf/gVjwqlW6vr8NwDqSx6JwlFleiaM/Xgy/cSmTCjkTseryAuIWSXa0PKjq0cfeXZWKoqnTtrQFxJMT2Wf0fhnFNZ+cj/sf+Us8JzcR8YotbAE3LkSBzXXYf63HMdFm66qmLvkcuBa24M+zhy8hvY9XOqF3ErSc6u7urf4VFn4VtxPHJPEkjQJSgCvvxfHHn9NJ58rZrefcP0HVRbh3r9nYiN25ztfaR0pglt2Y7y1v9wPP8Y2mN/7Pi4ov3hGY9BVOhyRaRUlZK5M8n9aFFzmx8XUghQVOw3XuQ2T9Bd25/OktWv3msYMoBQddJ7N4TkeocjGQlminck8fljQ7DWqc1/7J++iufNp1O48+8VjJ3mFK8ucdsef1xbt9EEUpLz+dekrd3Ypgpyxo+rSF/5C4UXnEnp3JmUzp0ZwmccGxhbMZ0hIR7tgzeR552NNLfsDcj4OOTVl6P/9/mQJ16XlSs88a8Uzjk9mdPmxPPHuyyccIrDo6gFZ57tJVccPjkR4XBr3/tPMovecfZN0jVnYS7Xa752WRzPPRB8v+KEokJyfvyWjLUrEY6msHFNY+pNl2GurnLmbDcdq+jOjZTeX33C4FeeDfragWLk1Rr4g+PJJ3H8+c/ItJbPhxSCimPnsO79z7Fn5wR8Tm89bB12wZZv0/nwgXzevHMwnz3Zl8w+jU01Mjx7Ev0m7Qh4HAYtrPrBxEN3J6FrOAsXSlcBQzhYqHD7Jak4gs2EqapCWboU5ftlUNvS2kN95P8Qm3cgaKn0KaTzh72FqPeGv+q2QXSJlrkRLLWjhnHwrJOwp7dN8ZCD+2N7/G7kyCHO36tDk2crJRRuTGHxvwbw4R+H8/ljg6mvNJPSoxGheGj7okgGTy0nPsW55jic82s7S0OViU8fGYKtXgWc86OrgKG1UfC3mzIp2tdWK7R24f3ZxIjv2Rt1+14Sdu9Fraltvj1l3SbS1m50nqdV2qSQEmF30PudjxH27pmm2CUd25goGJCSgv6PR+G+uxHrNjhDnSYcASkpvh8bID+vsXDSOT2orhFNCzXBujUKug5TpmqsWK6iKLJ5QaGqEk0T3PdXK8NGdN2S3Z0hlG6tzQofvpIEHjJddF2wdGECF99SQ2aPwF2JxMJ9jP3r3fT8/uvmiacxK4et191Kfe8+JHkLfZKSQa89z47LrvdagTaUGHm1Bn6jKGh33ol2yy0oP/0EVitrM/tjz8sL6rTuetjWV6m896eBlO1NcKoaKSjelcDW7zPoNayOg1sT2+XaOj9rPYftp8/Y3UGN53DntWcTnOnUbjZYdU2wv0Bl2VcWZp7YiYqu9fWY7r4b9eWXEVYrADIuDv3sk9EvOQfx6RKP7c+EpiN+WIm2txD6BfeeM4h9IuXahirPFqBu2GDqhg4i7mAxan0DyePysRw5ufl+bfeGkKx1dQ2WPjuAncuzWuZBIdn3SzqpuY2oFh3NprRJyxBCktqjkakXtY1uCGd+7QXT8lnQzTbOtyzNwd6oOgVte6TA4ZB8/mYil9/h3LTwlG/bvv2POmA02q71qO9/gemNj4mvqiEF5zdb3ZABlMyZScZPazpEBLgQUqI2NJK8ZTs1Y2K3EGVn6ZLCFmJopy4rE3nsMWE7fXWN4JTzcqitFW3CRlyu4YrlKjffbuObJSrr1joF7rSjNa6/2cGs2YdPr75wuLXb1lmor/EulKUuWLMsjtnzAgvXiS86wDEXnYqlsrzNblpcWQlHPHgPpeOnoJtMKB7sDgHElxaTcHA/9X3zA7p2MHSnLx2DCBAfjz7TGepkK6zxq2hKoHz6RH/K9zcVomqaI12LtINbExl9fDn1VSZ2r0pF6oK4jEoGHbmLPmN3I4yYpU7jsMPK781ewxlVVbJsiTlwYWu3Yz79dJQffmgjXoXVirLgQ8TKdR4rH7dGrN2INIRttyZSRaRCmWfbjBBYezur2ib2CU9rnbUf9WLncmdbs2bx2vSZrSmOo8fgWjL7NrD9+ywcVpWEVDsjjith9ImHiEs6fNaQ4aBgdZrXJGapC5Z8auLyO1puax+S3D7X1vV+N73wDqYFC9ucTwBJO/YQf/AQptp6vCEVQfy+A4awNQgNUsKhYgVNF/TqqaF4WVy9/k4SlVWKx8WDokg2bVD49OtGNM2Z8+3tfN2ZUFdCdvgVeSOx2wNfrg977iksleUo7RZnrjNlrVnhtRdjx0eEFyMEOXbIGj8iYhWRo0V1laChTpCRpWOJ83xcyZ549m9I9nImwY6f0rjmP5tRTBKpw1eL19I3IzHkYz7c0DT3reZaIwG7zyKHHVHefhv1++/d3id0idi2078TGQnUhw1dLtfWDY2Fe4nP6+fzuPo6qCgXNNYLzF4K1mt2wfrPe+JpnSB1waFtKRx9RQHTL9uL1EHpuu1LYw6Hzdkb2Bu6Q2HBj3s6uOHuqiQ3U7APtZ2odSGkRPUhapuO7LbzY5eTQOqA0bHj1gaIlPDia0mMntaLvqP7kD82j0ETevPkMyl42nz+amm8t17K6Lrg26+dM5GqHp6iNlyVkPsNcXjMP2lBMHB4YLknis1Kv4/e6SBqWyOF8H4/0NCzF/W9wy9wjBBkg0ixermJm+encPK4TM6ensEpEzL5x/2JVFe6/wLeuy4ZX3U9G2tMlO2Lp6kui0GIsMRBXj8NITy//lKHISMDz+NSX3wR6eXLTArhLLTjBSlAThwT8LUNuh5ddU3YGn9CnHfvUrj52jhG5idy5NhErprRi8d/k87+Xe49qrKCRGz1vvwryYGNqVGfH0PVGzaWyBlU53UNKRRJj4EdRWj7Fk7CZKGIljRH5b2PwYuR46p27W3fUeg6DWFuSxotDkMZFGKKDqE88AjqEVNR80egzpiDeOEV3PXZ+d0f07nutiy2t5qE9heq3HlfOhddk+W27WODH+16HEa0SFj61mbm6Eyd3Yiiup+YFFWSP8zOkDGBCVtzVSWqzer9IEXBlpKK7sW13XHZ9SEvUtYeo7WPQVA0NKD+3/9hGT+eaWPymTT9CPo99iDmkuIOhy5eaOGWC1P5ZUXLe62hXvDuK/Fcd1YqjbUd3+vucjvdoRtzZMgRAs653PMXlBASkxlOObcpP1aXTMrzrwaFUlDgMX8WmoqhJCZ4FL9SVZCzjoLeuX5dz6B7EBP1VwJBSpK27qD36+8z8PFnSb3xr5geex6xa1+HQzdvM3HqnBQ+el/F4XDOe7ou+GlxPHdekMWuTR0FrK92Zy5c54s23W2dMer4Eq8t5aQuGDWnmIwEc0DCXpRU4MsJdhbV83BdIXAkJ1I7fJDHx2cP7+lX9EAs0qWEbcxNWlu3oc48AfHPZxEHDiLq6mHrdpS770M984I2FRx/WGHh7886Y0bahm85c2ff+ziJ9z7qGB5nUn1NOBKzCa+ublfnpx8UrrwwjsG9ExnYM5FzTo3n809UpHS6teEQtS6u+X01ObkaSrtdN0WVJCZJbn+4MuBoDkdyCrofW6P7T5qHNSvH6U403eYSunvPOJ+dF10V2IU7SXf7sjGIEDU1WGbPxvSb3yC2bEGtryPuQCF9nvk74046hoSd25sPra+Fv93pDCnW2y0EdE1QuFdl9bu9OlwiPsmB73B8iSWh+yrbuookNi0ax+InT2fRI2fx/X/mUrBqELoj/F/v8y5pZNqxdhCyjXOrqhIh4N4naknLCPzLSebkeHVkpRDIQf2R40Y5f28SuFJpyrEePADtT78J+LoGXZcu59pKSc4XS+n1/mck7DuAYrOj1Dagfvk9lhv+iLJsVZvDr78tk7o60aEThq4J7FbBP/+Q3mEdmNKzEd+dagXSh7DtyhWRrXUqqz/sxRu3juH5yyfw2k1j+fmdPBqqwp+JmZ1fz+TznAW4Wju3rv+POamIPmOqm29vLW7NJsVzn+KsDJ/XlkJQMXmc8/+ueRGni6vHxXHgvNPDboy4iHSkbZcSthBDk5eUqFdcD1XVbYpYCCmdu8lrfkH56yPNt//nlWRMJs8TjKpInn2pY65YYqKvLTeBzSZ48d8m3l1goqw04GcS0/z3RRNnn5LA4i9VGhucz/Xn5QpXXxzPX+8Lv+DKyNZ59K1Szr6mlrRM5985IUnnpAvqeeLdEvoOCjzMTktI5ODxJ3l1YxVNY9dFV7L4w6Wsv+PPVIydQM2AwRTNmsuy595kzf1PhD3u3AhBNggG0+9/j/jll5Y5sQmha5grKxh241XNO3KLF8bRWO85Z1PXBNuWZuGwtr/fFXTlDcH6RVls+CqD8v1eEna7IJWFmfzw4hz2rxmIw2pB6gp1ZSls+XIcPy+YgWYP7xxhMsGDz9Xwm7/U0W+Qc340mSUz5tp49r1qjjulE9WQAe2SS7wfICX6GSeiPfcIjofvQU4Zh+zfBzlhDI6//A7t1b9Daug7FBjEPjFngHggeeM20tY4c4LbzI+aDpqG+YF/QVkF4HRrf1gR77G9o64L9m43s2ND2zWRtdaMPxt/xTuT2Lwkm4Nbkz2aJOGsiBwu6qtMfPjHEax+vzd1ZRakptBQZeGXhbm8/4eRVBeHpiewO5YuXs3SxaupTPyU3sd/RnyPAyB0QJIzsI7ZN+3kyPktVaf9MRBc4cj6OfO8Fs+TQlAzahhlx89g/8VnUzt8MLasDKy5PSibeRQF112MrWfgLfe6CkbxqM7yw0+I7Z4LWAhdh9ffgnvugOQk1m+yeA330HTBpq0d39hxaSYU1Xco3X13WQCByWThossc3PegDUv4PrMRYed2we9/63wSWqvXzjW5P/u0hZyRCUw42kdYb5CkpEkuvKmWC2+qRdNCs8m15frb6fntVyAlSruQOykEe087h9qBzl52uy6+ml0XXx38RQPACEE2CIqqKtRXX/X45Ss0jaStm0hZ/TM1E6ewZ4eKyYTXnqcOq0pthZn03BaxpFp0/CmgtuaTli/xpLwUesxbRVxyeOeNcKNrgjXvH+V0ZmVrAet8PSoLs9n1wwiGzNwY1nGYTDDvYivzLraiac79tmBrkmiXXIL6r39BQUFLb+8mpKpCvzzkyceBSUXOnYk2d2ZwFzToFkSiQnKoWv6kr1yLFKKNqHUhAOnQUN79H/J3v2Hrdv++hwt3maBHy++q2b9Y5L1rMti7Jh0QpOU2Muv63fQYVOfrYTHPspf7U1Ma12HDVOqChmozS58dwOl/3Bry6y5dvBqA/llJzhuyyuGIpewpdb6mQsDAIycEdM421ZHHjkY/eQ7i868Qetv3jxQCaVKpOGoSAI19e9PYt3cQz6br0WUc21grGiVWrfFZtVY0NMD2HQCkpuhei2wAJCd1vP/4E+w+8shcj3Ee43AIXn3JxK03dH1n4tUXzV5NSUWVfPJ6ZKubhipyo2bIcJb9523q8/oCNIfd6SYTuy64nLV/eiw0FwoCQ9QadBaxaVNz71FPSEUhZc1KABKTpF/pFOb4tgu1/kfU+lHgrS11B3JZ8fosHNauva9bvL03trr4dqK2FVKwd80gv/OQQ4GqhqjQZkoKtq++Qp8+HaBNOoacfASO5x+F+K7/HWcQHsLl2oYs51BK4g4WuxW1rY8Ra9cD7teG7ohPbHtcSo6NtFxf4ciizb/Vh+JY+OBQyvfF+3XNWKWu3MyeVekec1ylLji0PYXyfQkhvW4HUduK/Owk8rM73u6ifa6tt3Bk/d//Qj//LKSiNIUYN60he2ZSeNHZ2P0IV+6udO1v9mhiUv1LbG1SQuecUc/3yz1/Eauq5Lwz2+6QFZHCccfbGTxUZ/fOjrkVeGi/LHXBR++buOHXdsYc4Wf1gBhk1c+Kx9AbaApPXNd1bemKcZP46pMfyP75B1J2bEVLSKTomOOxZWVHdVxGax+DoDH58dUiQarO4445wcZL//C8SaUokuxBdSSlt3XvkjIcjDqunI2LM322nmm5rkJ9RTKF6/vTf5KfbWNikKqDmQhFR+qed/8cjRYaqpJIyqyN4MhCRK9e2L/4AsfGjYivv0ItL0SfOBYGds2CJgaRIVJ9bYNGEaB7WUMK0TyPHj21kbRUnapqz591S7zkiKOs7FnX9hTjzzjA0ucG+j0sKQW6Q2H1h705/uZdfj8u1igtSPReFriJkl2JZPZtCMk1vYna1vTPSmLp4tXMmh2Ya9uGuDj0B+7BPm8m6k+/gM2Ont+HhqwErFs7FmcMhK5cOAq6iGMbizkTctYxXqs2AsisTBgxDIBLzq+jV08Nk5sKu6oqSUqU3HBVx8WHKc7CG+81kj9QNh/rdCjci9rmx5kk778d3X2Lgj2Cv95n5vwz4rnk3Dheed5ETbXvx7mIs4Cv/DmTuYtXzRKC0inT2X3hleydd0FMiFoDg2CRY8ciM7zvGAupUzljFgBDRmpMO9bWoUhb09nQJYw/q8jteWZddYABk5wTi6LKplKQvueFwvX5Po8JJ/ZGM3tWDGHlW0fz85sz2P7tKBqq/XcPFNW/TcvWx+2r8Ke/YWwhR41Cu/IK9HNPDVrU2or2U7Zmc4hGZmDQSYSgPr+f95ZVUqIfPRWA+Hi4+7YqLyeUnH5ZLQlunN0hR5cz6ZxCoGntKFxzpJez6YI9KzOwN0ZPImgOwc4fM1j01CAW/nUo373Uj9I9/kfoqV5q2rRG8fM4f/ElalvjEsL+0r7tDwCZ6WgnzUQ743jkEcO7bW/aQOgSwhZiqGiUi5HD0WdM9xiOLIVAv+EaMDudr5RkyVcfFtO/n9NxMJsk5qYPVHamzhfvF9Ond0s+Wus3b+8+ksU/NPCf/zZy1nkOTjhZw1demZRQWhK9N/jrr5iYMTGBf//LzLJvVZYuVvnDHRaOnpjApg3+jeu4uZrPUOTJs7p2nlwsYri1BkETF4fj5ps9LtykqlIx8zgaBg9tvu3P/6hhyjHO6puqKjGZnJV2LXFwz6N19B3nflfMZJGcdmcB5/9tB6PnlDFkahVxSb6qIAtnGG+UqCzM5NtnTmLrkrGU7e5JeUFPdv04jO+ePYmDm/zrLZg9sMirWwuSxIwa4lPbitm5c8d1fuAGBl0AkZoVk4ZIayqPHO8xFFkqCqSnIE8/sfm222+s4cZbGhBCoqgSk1k6NwKF5OSL6jnvV56jMsafcZDzH1/PEacWMWByBem9GhGKD2NGF1jr1KhURK6vNPH+H0ay5P8GsXd1Oge3pLJ1aQ4f3DuS5W/08StYssfgWkwW798DQpHkjaoJ0agDw5sADqT1j7v3efbwnp0ZUliIxufQCEUOAv0//0Q95yLYsAmpKAhdR5pUhENDXnAu8ubr2xw/ZJCDjT8e5IvF8Sz5zlnh7qgpVs44ud5toSdharlRSmdRjiHDJMkpOt8sUWmo9y4Qe+dFJwz5x2UKd91maRMa6Pp/ZQVceFY8P6xpINHHxtb8S+w8/YSZ+jrZoQWIEM52Eqdc1PULHMQKRgiyQSjR7rwTZccO1DfeQJpMCIejeZ6sGzGabU891+b4xGR47KUatm5QWfqZhfpaQb+BOnPnWUlJlby83Pv1pA5pPW2k97JRW26iaHuS5x6CQnYQfJHC1mBh1dtH47CZaLNBKRWklKz7eApJmbWk5lZ6PU96XhlpvcqoLspAus2zFQyctiWkG/i1tsCrwBsYGHSkIb8vxXNnkrPoG2gqIiXB6bglJ2J76A6UhJYIDiHgrj80ctnVzmi8gwcF9ZYGZp3WSM8+vtuZSSlISLMTl+jAkqBRecD7xp5i0olPcWDXIlsRWUr48qnBVB2Mb/q9qVVN01y+/rNcUntaGTm7xOt5LAk6o+YW88snuW5DkoWQDDm6lMT02GpllJFg9nszwV3YfXxePxoL93b6+uEIQ460MRnzwjamd92yMtG++B/i00WI9z6EsnLkwHy0i+fDkZPchgSoKpw8t5GT53pubN8+1GDJlyq332ihtERBVSUtEdCew5E1TXDehdFZhDz3tLPok7uCqJomKC2BD98zceGl3seXmQWvv9fIJefGU1Pt3KKTEoTifB1/82gF/YcYC61QYIQgG4QcVcX+wgs4rrgC08svU7d5G/asbErOPJfyOSchze43UYaN1hg22v+cp4oDFj55vD+lexKcoXbSc9ugZiT0HRed/LHCdfk4rJ7acAiEkBSsHMyYU1d6PY8QMP7sH1i54BhqS9OcrSSkghA6UioMnLaZ3mMKQj7+y6f2D/k5DQzCgTpgNNruDdEehkeqJ46lIb8vaWs2kFxVgUhORp82Hm3O0ZDcNuzWtS7s3Udy0+325qJCZpP3wEtbg8I3/85nz8pMZ79pXPOjZ9tTKJLBR5VhskgITfqp3xTvTKJ4Z8fWly1IflmYy4hjSxA+Yk4nnX2AmhILu37Kaq5HIBSJ1AW9R1Uz/dJ9IR17OHAVkJqRnx7toXQZYl7YQgyGIbfGYkGeeSryzFNDelqXW7viR4Ur5sc1i9m2xZQ859pefYOdQUMin38qJXyzRPVa9EkozmN8CVuAiZN1fvylnnfeNPHRZzqaQzByvJ3jz64nM6frFsaKRQy31iDkCIGcMQP7jBmsK6xBKJ23Dy+f2p+XlxeQbGn52qqvUnnn3kE0VDtva+vQtq0Y3zImnbTcCnqNjM6ipnRnrtf7pa5QsrOXX+eKS7Yy7YqvKN7em6ItfXA0mknKqqHPEbtJ6RFAQYMYRtZXYypYG+1hGHQxukoRKXtWBqXHz4B2Tpk7U6d1FB/4FrVSwqInB1O0tckskS3VxT21ABeKxJKoMeHMgwE8i9Cxf11as/h0j6C2NI7qkjjSenpPRVNMkuNu3M2oOSVs/TaLujILCel2hkwvJ29UtU9h3FWJz+tHNgTcliqWQpiDoUsI28OZxx50TmTuHQjXzNQibtPSJb+6xc6vboleeIWXvtGAM2TQHsDw0tLg6usdjDipEosauzNRRanCJ68nseTDBGqqFDJ7aMw9p56TLqgnMTl2i1wZIchdh6zxI7Dk+peDeTjwy+fZNFSbPCyCnLcJVUdqznlDUXVGzKrA0e8LFFN0KqrruoKnSBsXnhd1HVFUSe7wQnKHFwY5svAhdcnorBT+7/8U/v2cyu7dguRkuGC+xq9v1uifH+0RGhjEHoGaOgvafZcf2JTCwc2p7g+WAoRENelo9pZaMb1HVjP9sr2k5NjcPy7M6JozTFj6mCN1h39zpBCQO6yW3GGxXRleStizKp2NX/Tg0M4kFpoF46ZbOf2yOgaMilwtma5cDdlFTAvbWOtdGwmKSGnelSsphh++9944VVHhhpvtHHmUTnyCZOJknbgotvcTAsYcobP+F6VDXqwLRYFx4wNzW5cVVIZgdG2pqxbs22XCbJb0H+rAFIS2O7hX5feXZlFd0fK8iwtVXv9HCks/TuCvr5SRmh574tYIQTboymxe6rlPITgXSH1H1zHx9BKkhJ6DG0hI0Vi0yAFER9im9y6jsjDTY/9ZIXTSepWH/Lr7KuoDKhyl2QWlBfHouiCzTyNxiZ2PkGlsgJNONPPTT035clJQXg7/fk7lv6+qfP6FnQkTYm9+NDBwR/bwngG7Yb7OFw5BseOHTO/upxQoKpx0xxbsjSrpvRtI7REdQeuix6A6dM27gWFJdJDaI7qFQ6WEisJ4bPUmUnKsJGXYmTV7AksXrw6oMrLrXMtf78uGL3o2/70a7bBiSTw/fRXPdX+qZMavW44vIoVcWopeeQq5D+R9Gg63NloaLqaF7eFOVaXvHSm1KZf1uDm+iwdEiquut/Pr69wXJhBCoprggksCz40NlVtbVy145YlUvv4oAYfd+RqnZmjMu7KO0y+t81qJ2RNP3JFOdWV7MS+QEg4UmHj+b2nc/nBlSMYfagy31qCr0ljr/StMSmdlz/7jYme3vu/4Xez5eSge+5BLhf6TdkR8XM3X1+HnD3NY/VEOjTXO11c164w6rpxx5+/v1DlfeCqRFStEh8gjh0NQXy85/zwzW7ba8NBkwMAgZgi2OE8ksdaakD72o+yNKrlDa2MmLLfvEVUkZdqorzS7FeRCSEYcV4IaxVaPu39O5+d38qg62FTcS0j6HlHFtIud6S0FZXU+xW1BWUvh071r0tjwhVNYtn7OuuaMynzuz+lcekYD/fpLhMmCdLRsPngKue/M+7Q7uLUQw+1+YrpoVJhoXzSqR09nywtvOBzQr39s7XTPO1dj/qXOWGOlVd9e1SRRFHj631Z69PR/zKF0axvqBfdckcXiD1tELUB1hcorj6fy/N88hO14YedGEzs2WpomoY7ommDZF/FUlsXWx80IQTbo6qT2sHntySgUSXpubLUES8yoY/RJzsJQQrRadTb9P//IreQMdt+zN9xICV8904cfXs9tFrUAml1h/ZdZfPrAEKye6x66xdoIHy2I9xjBo2mC/fsFX3wR3vnRVtQ5UW7QtYn1taQ7p6y909U6kg9oLhzli5Qcm0/Bmpju+5hIoqgw55YdmCy6sxhgM860u55Da5kw70C0hsfWb7L46h8tVZudQxPsX5fG/+4bwYTR04C2wtUTs2ZPAGDD5z3bPdfWCATw2kud8yH9cWK7S26tixh6O3fkcAtDhrbFAVLT4LQzNVTV88LNYoEzzomtysBCwCNP2Xj2pUYmTnaGSKekSs4828EnSxo59YzA3eVQubWfvZnI3h0mjyL0swVJ7NzkeQKx22HfThOFu9XmXOLtGy14qzAITnG7Z2vsBEgYIcgG3YGxc8u8fvSkLhh9fOjDeoMlb2wBR17yNT2HFaJa7CgmB5n9Shh/9jKGzloftXEd3JrIxiWZuHWSdUHZ7kQ+etNzmxApYf8ehV3bVBqbqqkWFqjU13qfv00myaqV4e+7XrZmc9ivYRA7dJU1ZGecMl+FowCGzSzxmaox4jjPbXMqGuwRbfXjImdgPWc/uJFRc4qJT7GjmnUy+jQw/bK9nHznNme15ihga1D44dV+uIu2kbrA1qDy04K8ZsFaUFbXQeDu3CfZtsOC1tiSM1i8y0trOkDXBatXBh7O4s/7yiVqQ+3WRnNDKXZW2oc57d1aF3fca+ObrxOoqpRtKg0LIZFScN9fbaSlRWqU/iMEnHqmxqlnBhciHerc2i/eSfQamqOqkq/eT2TQyLYVRe12eOe5ZD57M4naaucXSmaOxumX1ZGQpOOrGAwQVA5vODDc2q5H2ZrNZI2nSxaQmpSXwsogKyO3Z+SxFWxamsGhHYluFgaSYUdX0md0x53zuXPHsWjRWvpmJHa4L1Kk55WTnvdTRK7lb37thq985OQBH74Rx7lXdLRtP3svjleeTmB/gXMBFp8gOfW8Rk48y7djLiV46P5kYNBtCaVT1r5wFEBm30ZGn1jEhs9zaS/GhCJJy21k9AnFIRtDKEnJsTHt4n3N4b2xwO4VGThsnov/SV1QsDqDxhq1WdwuXbyagrI6Gg71pHT1ZBoP9Qaca/gvd1Qw+bzCNpGN7pGYgxDzvnJtwxWCHK2NpZgUtrEeOhIu2pdyB+jbT/LxVw38+fcWvvxcbc5R6j9A8rvfWznj7NjJrQ0XoayEXHpQxZsI1TRB0f62O2OaAx76dQZrlsW1yRErL1F4+bEUjjqhsXmjwRMJSTpDRke3KIMLIwTZoLtgskjOvm8X37/Wi42LM5sWHRCX7GD8KaVMObvYXTtxAw9UFlm8ilopO86PAC8/ncDzTyS2CQtvbBC8/1o8a34yk5OrUVLkeUGoaYI5czvuOMr67tGyyMDAE+1Fhbsw5GCYeuF+krNs/LKwFw1Vzu99xaQzeFo5R164D0ti919DhoqakjgUVXqM+AOnuK0rtxCf4gxZmTV7AvvXp/L5q0PaHiedIvjAxlRyh9Wyb12ax7lXCJjdyTo6rlzb9uI2XE5tLBCTwha6TghJJOifL3nxDSuHigR79wiSUyTDR8puv2ALRyXkxBRJbZXnF05RJalpbXfGvvssgdXfuwu/c57nhy8SGD3FyqaVFvd5ZEJy6kV1xCUEM/LQYIQgG3Rl3PWyNcdLjr36ANMvKqJ0bzyKKsnu34gpisVFuioJKZpPxzYlta0ALdyr8PyTTZNbu809XRPs3qZy4ok6n37q/pyqKjlyqmTiRPd/L6OHrUF3JBC3tn1+rT9hyM2PFTDmxGJGzSmmbG8iul2QntdIXJIhaAMlLknzWCugzXHJLemBug7f/DsfKekwP0pdYLeqNNSoTSk1HUOchSJJTJKcM7/zKYetxW3727sjMZ1je7jQvjCAJ3rmSiZP1RkxqvuLWheh7lt77Gn1XsM+dE1wzCkNbW77/K1EL4n9TjGckqYzYoKt+XegOTf6mJMbOP+G2KnKari1Bt0RS4JO72H15A5u8FvU7quoD/Oook8gz3H4MRVeRa2iSE46p21o8cK34rxWktd1WLVK4de3OBdmroKIStOcOnKk5M03o9d33cAgWrhzazuDuzDk9igq5Ayop+fQOkPUdpIBR5Z7TToTQtJjSA3JWS3zWeH6VOorLR1ErQupC0p2pjD14r0IVTbPiwgJwilq3/rQSnp60/EOW5tWP/4Sn9evw0+4iHar1phzbKP9ghjEBuFwawFOu7SOJR8l0lBHh3ASRZEMHWtn3PS2C7cDBSbvif2aoGificfeKmXNsji++TiByjKFHnkas+fVM3ycPSY2IowQZAODFlx5tocD/vavHTi5mh4D6ynZk9BhzlNVSXKa5OzL2ubX7tut+mgpIjh0CO6/X2P+fJ2XXlTZvl2Qni4551yd007Tjfxag8MKb25tKKohG4SH5Ew7I+cUs3FRDzqmVTgF6eRz2lZsriqKd4pUL6lqAOacWk56cA1Ju4ey9RczJhOMm25l+sl1jBsTg4V0YpiYE7aHG77yJ1b+pPCf/zOzdImKrsGEyTpXX29nzkndf8ct1G4tQE4vnQdeLOPR32RwoMCEokikdOY7TJhh5da/VXbopZiYrFNT6XksQkiSU3UUBSbOsDJxRmy1FwEjBNmge1J1yMyahTls+S4de6NCeq6VsSeWMXp2RVT7HHZVVBOc9cfdfPZUXwrWpiKEBOF0FfoO1Hjg/2rJ7tH2dU1OcbZx07x8JVksErMZxo2T/P0fka/ibyvab1REPgyR1WVouzdEexht8JTb6K9b2z4MeYGX73ZbvcqmxTls+TqHugozCSkOhs4sZdScYhJSY6ubRldh6kX7EIpk46KeSOkM9Za6IC7ZwTFXF9B7ZFs31ZKo+WqaAYA5QePKk3sDLdF9doePJsQGbokpYWsUjWrLG6+auPNWC4oKmsO527N8mcKyb+O54dc27vlz1wzfktJZZdhsxq2TGS631kX+MAf//LiEDT9b2LHRjMksmTDdSt4A9yuzmac28O6/kz3mVkgJM05ucHtfLGG4tQadRdZXIxID7/EcLmptDmoLUnjvTwNx2JRmd7Fsfzxf/yePbT+kM+8Pu/1qC7Gvoj6q1ZHdoWsCoQSfcuJvNeTWxKdozLt3D2X74ij4JQWpQWp+LXddnuV2PLNPtbHwbc8tgFRVcu55ekxErRgYxAKewkA7WzTK3Xd7Q7WJj+8fRvWheGd+J4K6CgtrP+rF1qXZnP7HLaTkxEZBy0DRdUA6w6sjjaLAtIv2c8SpRRSsSsdWbyK1ZyP9xlehmjp+3/QbX4likugOTxOgJDHLynXn5bi9d0Z+eugGHwFiIeo2poQtHF5Fo7xNXLt2Cu681YKUAq3Vxpqr5c8z/7Bw1AydY4/vOs5tWSk8+7SZN/9rprJCkNSUEP+rX9vJ69t2QgiHW9saIWDMFBtjpvie2E88v57PFiRRV+MmfFmV9OitcUwMC1vDrTUIBlPBWhz9x4XkXKFo+XP51P68uKyAjx/ObyNqgeZwrwObkljxbg+OutBziwOIrXBkXRPsWz2IglWDaahMRig6OYMOMmDaVtJ7R74Xb1ZfK1l9rdTanF9AQrj/bp443c6YiXY2ru3YH1xRnE7t7bcH/j1lVEQ26G4E2t4nmDDkZa/0o7o4vkO3BqkLGqrNfP3MAE7/49aAzhlt9qxMZ92nPTm0PRkQ5AysZczJhxg4pSJsG2cVDR4MJIud3Gkt675qO9DqUNdmQ3yyxpgTD/HLwlzcV4UXXP3bhg51Cty5tdLRNTciIk3MCdvDDU9u7Wsvmb2Gd6mq5MXnTF1G2B48IDjzhHiKDopmcV5XJ3jtJRP/e9fEB583MGSYDLtb2xkysnUeeKmMh27J4OBeE6oqkTgXovlD7dz9j4qYqHjsDpeoNdxag+7E3tVp1FV4fk9LKfjliyyOPLfYr5DkaLu2uiZY/e50yna3LHylrlCyoxfFO3pxxBk/kTu8MKBzhrIw1uVT+3u8T1HgkRdruO+mZFZ8Z0FRJYoAh0OQliF5e4GDkSM7FxZuVEQ26C54a6/ir8vlbxhyXYWZPT9neGxBKHXBoe0plO9LILOv9035BT/u4YJp+T7HFgz+FL9a9X4vVn+Q50yPaBKIpbuTWPLPQZSeXMSR8/eHdEytBW2gz3/Bj3vaPH7wqXuot0q2f9ULoLnyvMUCl/62mmNO7dgXHNy7tZ0pHHW4ETPC9nANQ/bEqp+VZgHoDk0TrF4ZhTiMTvL731jaiFoXmiaoqZHcfG0cn3/j/HCH263tDP0GO8OXf/nRwuY1FlQVxk61xkxhKG8Yotagu1GyM8lnP0FrrYnqEjMZvb3vcseCa7t35eAmUdvOXZEKIFn/8RQy+y/EkhBY+kmgYcidJSVV8sSrNWzfpPLDEgs2KwwZqTH9OCtT84Prw2lg0F3wtxJtIEWj3H2/lxUkehS1rSnZlehV2GYkmD07lhHk0I4kVn+QB9Dmebn+v+7TXPqMrSJvVGhEn+s5d1bQu3vc/OlQXlzMss8TqKlUyOnl4KgTG0lK6bjpZ3fofocgy+qyTo0xHMRCGDLEkLAFIwy5NWYTuOtp1RpTTP31PHNgv+CrL1SPE62mCTasU3n10zoGjYrw4AJAUWD8dBvjp3eNcBAjBNmgu6KosilvzPdx/hIt11ZKKFg12MsRAl1TOLA+n/wp2/06Z7TaGA0ZqTFkZMtC2Xu15MhgKwqtk2PQNYilwlHeQpBD7daC//NeIPNjNNn8VY7X/tpC0dn0ZY+QCNtgRa03MnvonHZpnddjPBWM8tbmJ1be57FC7FljhxHeetceN1fz2htQNUnmnNg1qtpt2az4tXu4Z4s5Jt3arozh1hp0R669MNFrCy6EJK2nldQe/rkNkXI23aHZTDRWJ+FtE1MISU1xYC0fQvGcam0Or2HI3pB67CyajYrIBtEmELfWXzx9v/cYXIvJ4j1NTQhJ7xA5nOGmZHeS1/le6golu5OCvk44Ra0v7A692alt79Z2hdzaWIq6jQkVESv2daTwZ+K64GI7iUktTexbI4QECVdeF/0QEX+Ii/NvgWP28zgD3xg9aw1ilUl5KUGLntETHOQMqkO4mR8BkIJJ80oCThOIhtMpVD9sTQGKyT/7M1purTsm5XUuDNkoHGUQqzQW7g3oeF9urTvahyEH4tYCWBJ0Rs4pdvZPdXd+RTLoqDKSMnyvITMSzD6vF25Us++5z59jvBEtUesStOC9ArI7tzaWwpAhdqJuY0LYHo54c2sBMrPgtXcaSUxqErJNjbAURaKq8K/nrYwc3TWE4ITJOilu8ghao6iSSUd3DaEe6xghyN2bsjWbIx5eaSpYG1NiQwiY85udpPVqbPrdOb+4hO7EM4oZfXxglYSj5dqqJp3M/odAeF6YSV0hZ/BBv88ZTQc6VBiFoww6S7gX/KVbvFdbb483t7azYsDXxvXkcw4wYHKF8xpK2/mx17Aajr48MIEeTfInVTbP8e4Qimx+rsEQTlHrErDtf1wOrSdR68utNcKQO9JFsjQPTyYdqbN8XT3vvGHimyUqDodg8lSNCy910Kt31xC1AAkJcO1Ndp54yOw2JFkokmPPrCc9KwYSsroJhltr0N351Ul5xKVsoWhNJtu+T8dar5KZ18joOeX0GOC+yqQ/RCPXduC0rZQX9MBdXQUhdBIza8kZ6FvYhtKtdbX5MTDoisTCgj/Q9j7+FI3y1z1VTJLZN+2iaGsxW7/JprbUQmK6nSEzyugzuhoRoK0VierInhg+q4R1n+biaHST1iYkqklnxOySTp8/FAWyPOXGtibQnrQuURvrbm0shSFDDAjbwzEM2Zdb25r0dLjmVw6u+VXXXmTc8ls7hw4KXnvZjGqS6JqzubbmEEw8ppFr744dN6grY7i1BocTqkkybHoVw6ZXheR80aqQnJVfzKiTVrHx8wmABCkQQiKlQkJGLRPP/87nQtQlakPp1nb1/FqjcJRBKAk0DBk8u7WdLRoF/m9cCwG9htfSa3itX8d7wlUdORzi1h+hnpju4OQ7tvH5Y0Ow1qkI0RTDKMEcr3HCbTtIyQ4uD7Uzz6u9mA1UuHrCm6CFFlEbC5s3LmJJx0Vd2BocHigKPPSkjUuvsvP2G2YOFAqysyVDZlYyZKwNi5vJO6K4SqzGeu8eLxg9aw26CpPyUlhZWINQgv+81docJFtC91XmEreRdm37HLGH7IFFFK4bQE1JKqpJo8eQA+QMOei21oI7whqCLGVA82Nn82tDjVE46vAi1sKQ/SWQolHRIJzi1p81S4/Bdcx/ah07fsjk4OYUpBTkDqthyNFlWBI6H+3XWbe2Q16sP2X6fdA67NhXv9pYErWxRlSFbazZ1+Em1ieuSDBytORPD7Z8eJcV2MJWCbnskMInryex9OME6moUcno7OOG8euaeU09cvPOY3KWLGPTqv8la/ZPzMROnsuOy6zh0zPFhGVO4MUStQTiR9dWIxNRoD6OZy6f25+XlBWE5dzRCkuNTGhk0PXAh1pkQZLtVsP7LLNZ/kUl1iYW4JI2RsyoYd0opJDdy+dT+JG1aT+9//5Oszz9BsVlpGDSEg5ddzaHzL0GawzPXyPpqI7/WIChiYdEfaBgydCwa1Z5oF3EKtbgN9PmY43VGHFfKiONKg752a9w9Fynhxy/i+fSNJHZvNqGaYNKxjZx0US35w+zMyE9HKSoi+d57SHzjv6jl5Wg5Pai77HLqbrgRPdN/BzMQQRtLbawgNnVc1B3bWLKvI0EgYcjdnWUFlWE7994dJu65PIv6WoGuOV2Gwj0mXnoklW8+TuD+F8sZ/8qjDH/mCXRFRdGdpfGzVi4nZ8UyNt10B9uuuzVs4ws1RgiyQbgxFazF0X9ctIcREVyubbR62wZCZ0KQrfUK7/5xICV7Eppi+gT1lQqrPsphw+JMTrlvGxmLv2D49ZeBBEVzpsIk7NzOwD/eScZXX7DlP6+5FbexEoZsYBBKQh2G7A/BhCGHi9biFoIvuBTN5+PJrZUSnv1zGovfS2zTP/f7TxP4/tMEnnnRiiq3k3PCbJTycoTmXD+qJcWkPPk4iW8toOSLxei9enU8t5tiUL7ELMRm+LGLWNNxRlVkg6gSDrdWSnjk9vQ2otZ5h0BKwe4tZlbds4HhzzwB0CxqW/9/5D8fIeOXVSEfWziJ9heeQeSIRmXkcBAKEXT51P5hKXTkEoqx1D6nPZ3Nq/3u1V6UFiSAFLQuViV1gbVO5ecnshh28zUITWsWtQBCSoSUZHy7hF4vPefx/LEShmxweNGVwpBjTQwEQkaCuXm9seDHPZ1ykqPtPrtwJ8y/W5jA4vecG5qt++fqmkDqcNM1caRceXUbUetCaBrqgQOk33Yz0mHr8JNLTYcfX8SyqI1FoubYxqJ9HU4CLRrVmsJ9gu+/VdE0mDBJY/jIrr8jHk63duNKC4W7PYs8XRdM/voFdFVF0dw3MddVlQELXqbiiInhGmbIMHrWGnRFXHm2wVJXI9j5QwbCZiajl5U+o+oCrvjpiVh2bjsraq11CpuXZrRZsLVG6oJTDryHQgMCT32CJb1f/g8HrrkxZusS2Ir2G/m1hyHhWPx3xq0NBH/S1BZ08nte12D/+jTqys3EpzroO7YKkyU0a8jW42kvVD05ua2Pi9V1yyevt3VqWyOlYKT9F5LWrvD4eKE5iF/0Bbn7t0CfvE6Po/VGjSFq/SeqochdeccqEtRUwx23xrHwQ7VNifMp0zSefs5KXt+uLXDDlVu7fb0ZRZHoHhZuAJP15Si4F7UAiqaRuW51OIYXUowQZINIEyt5troOL/0jgdefTcBmzcTVKie1h5U5N+6n7+i6kFwnWpWS/aEzxaLK9sej2b3PvZNYia6oqLp7J1wAcQcLUaur0NLSAx6DJ4z8WoNYJVxFo1y0z691F4YcKLtWZPDDK/1oqDbjmh8tiQ4mn1vIyOM73x7HHa1FautQZV/HRgtvYci7NrlvTelivPS9NhRSIjZsQnZC2HYVQRurXW2MUOQI0JmiUQ4HXHpePJ98pHb4gK1aoTDvpHgqykM1wu6FouLJZ2jGRpzP82gW38fEArHwJWFweBAO0dHZcOTnHk3kpb8nYLO65kfnv9UlFj74ywCKtieEaIROAbmvoj4mwpJd4+hsBWRF9f162/Avuqh9jq3UpRGGbBAVwhWGHG63NlzsWZXO4qcH0lDt8q+c86Ot3sSyV/qz6aucsF3bFars6SdW8OQqK6r3x9mFn9GXFv+jNGV1WfMPOAVtLIvaWCYqwvZwC0OGwItGffm5ys8/qW1zRJvQNEHRQcGrL8bOBBEIywoqw+bWAoyfbvUYZufis7jT0b3MXrqiUHTsCaEeWkgx3FqDrp5n21kRVHpI8Oa/42mdH9pMUy79D2/kBje4dsRCzm0oetXm9G8kPsV7TvJCTvXo1gJIRaFq0pHoiUmdHoeBQagJlxAIt1sbaqSEn97o0/Sb+7XQirfzcNhiM40gmggBRxxl9boBuEge73X9CCATE5FHTnZ/XysR607MGoI2OKLm2MaifR0uGhvh1RdMzJ0Rz4j+iRw1LoEnHjZT5qVi+XsLTKhePli6Dm+9FvWi1jFJv8EOxh3V6GVikuy7+FJ0ixnpJhlPVxR0Sxy7z7skvAMNAbG0+2kQWbpL/qCU8N0iM7dcnMJJR2Rw+uQMHr0niT07PC8cFi+M8xqVIXXB3nUp1FWGdo6MprgNhagFUM2SCaeV4CmuRVEllbNmUz9oCLrq/m8gdJ3CG25pc1uwhcBkfXVQj29NV9/wMYgNwuHWdiZ885kFJax4cRD//dURvHr9OL54fDCFGzxvCpbuTqS62MPGXxP2BhP71qUFNI7DhTMur0P30BpXVSUyJ4eaCy5BKu4llBQCec0VkNRUgMqHiDXEbGgxQpHDzI7aFM49I5V7fmdh80aFmmrB3gKFpx4xc/zRCezZ7X7iKS4WaG7c2hYEZWVdb7fNnVsrJWxbZ+aDl5L48OUktm8IXqzd9nAlA4Y5cygUxbngcm0UHH92A0f/Oovl//ovWkKCcxJq9aMlJPLjM6/TmNs76HGEC6NglEG0MBWsDZkIkRJeeySDu69LZc1yMzXVCuWlCgvfiuPyk9L4YYn793hFmYIHzdWGxmo/DgqQaIjbnQUKZeuHk1E7h10rU9A9lwfwi8nzShh5bAUAomleFE3z5NBRGvc81cCml9/G2sfZrkQqChKQqooUgl33/Y2K4+Z2OG+wYcihDHXvLhs/Br4JZ2/PaLu133ycwOIHx7BvRQ6NNWasdU5B+unDw1j1fsd2MkBTTq0vJI3V3cMcsdapbP0mi7Uf5bLt+yzsjcFJm1GTbVx3bxVCyOZ1oxASISTpGZI33m+k5uEHkccfCzjnxTb/nn0m+l23exWyoaCxcK/Hn3ATq/m1EIXiUYdbGPKDf0lg3S9KhzxZXReUl8L1l8fx2dLGDoUl+/SV/LJaehS3Qkh69e7axaMASg4qPHJbBjs2WpoFqK4LhoyxcccTFWTnetg280FquuTh18tYsTSebz+Jp6ZCoVd/jePPqmfYEU7BWzplOl8s+pl+H71D1sofQQhKJ01j3+nnYk+N3Z1MIwTZoLvw1lsK//63czHQOu1C0wRCSO69MYX3f6ggLaPtXJeTq6H56PAjhCQxI/RtgKBF3LqKSoWrYnJBiZUD30yjescAEFC83OlGJ2faOfHWvfQZ1bkCWUKBOTfuZ/SccjZ+lUnZQTPD8y3MOcPG9Nk2TCawpvVlzaJlZC36hMxFn6I0NFA/dASHLrgYa9/+IX6mBgaxRSzk1hbtU/nXH9JA0mYN6Uq1Wv1BHrnDaskb1ba6fFJmx16pHREkZbovoNRVkBJ++SSXVe/1RncIhAJSh2Uv9ePI+fs9FsjyVDiqNXPObWDEpEa2fJnJurUqcXGS40/QOPt8B0kJNiAe/bUX0H9cgfL2e8iSUujdC23+uchBfaC+Cgh9eHz796WnjZfspn899VLuzkRluyZWVX6o2VadwttvxLnNkwXn4m3DOpU1qxQmTGor4M6/yMFH73v/81x0WdealNq3+KmrEfz+sizKi5sWtq3yYnduMnPPZVk89X4pCUmdE/CqCaYd38i04xs9HmNPS2fnJdew85JrOnWNQFEaG7BUVWJPSUNL7Pxi2HBrDVzYivZjye3j+8AY5J9Pqx4rmEspsFkln74bx/xr2n6Gjz/Nxj8fSMLuYQoUimTgpGoSUoK0Nn3Quh0QhE7gus63/6tZ1O3rC4imxa3z/roKEx/cP4ALHtpBTr7n+c0bQkDvYfX0HlZPrc3B5VM7ilVpsVB66jxKT53n9VwhC0N2aFBeCfEWSDWKUBlEl2i7tYveTmyKJvZgcCiSDV/07CBsM/s2kNm3nvL9rl7V7ZHEpzroM6Yq5GOOJOs/78nPb7V898mmZbTDprLslf6YLDpDj3FfVMxT4SgXdofOOTPSYYYdaPtFIx04+88KAUcdiX7UkS33hbjnbBshKyUVq/c4z5+Y4LXVWumWQ2QP7xmSMXQ1jFDkMLJlk4rV6j1cWFEkq3/u+Gc45liNuSc7EKLjgkFVJcOG61x4aXjciHDSOgx5yYcJlBW5L5Cla4Ligypf/y90lU2jSWLhPsbfexunHDWcE4+fyCnThjHpt9eRsmNrQOcx3FqD1kQr3DIU4ci6DqtXC69tuQA2rO64wZeWIbn2d65Q4LZzpKJKTBad8edFJs9y7txxbcKTg6me3PqxRwycRm1BP4+9FHVNsOK9Hp0etwtPojZQggpDrqvDcsctmI4/D/MJ8zHPPBv1itsQ33vuFekJo3/t4UU4w5CjzZbVFo/GCDid20PbkjvcLgQcdeleZ3pBhzWk8/ejLt3rs/pvLOOwKqx+31u6mOTnd/I85sp2FumwOUWtu/tCIGrdhRSXbi7C9uZiEm97jAH/eIEB/3iB/s++StqqdS27nV7Od7gRUcc2lmOyw4E/OWBSuj9OCHj2JSsP32/m1RfMNDSIpnNKTpuncf8jVpI6zmcxS3u3FmDpxwm+PpMsXZjAyRdGv8VGMCTt2cnMi0/HVFuNojkdJEXX6P3Vp+R+8xXfv/w+laOO8Pt8hltr0B0QAhQFNC+mqhCe59H51zSSlCx5/slEyktaFn+jxzv4zf11fFdpJZJfca2LOrV2cVvT2tF1d3/rc3z7SjpClUgPC1upC3YsT8NhE5gsnXNMa20xsDlaV0fc6fMQv6xDtFqFinWbMd38Bxz33oY866QoDtDgcCMUYqB0yyGyCS4UVDGBq/+sJ1y58e3pNbyWU+7axg+v9qN8X8u8k9LDytT5+8mfVNnpccUC+9enYm/0tsgW1FdaOLQtmV7DawM6t90RuBrujKh19z7rECUgJdmLviF99fo2W7imymqyF31D3MFDFJ9yvFv39nB1bbtH5ngMUkQKo8erpKZKqqu97LhJwYxZGuVl0FAvyOkpm1tfWSxw7/12brvDzppVCg6HYPRYjZzgN+mDonCf4N23TOzfJ8jMlJx5joMRo3wvrNoXjaqpVPA2YSMF1RVdP6hg3P13tRG1LhRNA2ll4t03s/h/33gNKwGjYJSBZ6IVjizrqxGJqZ16rBAwa5Zk6VI81hLQdZh8tJ36WqiuVEjN0GndYeb0+VZOPtfKxjUm6msFef01+g10Lkq+W+4UbsmWyH7NWesVeurHUbInAZNZMmBSNf3G1PLlV23Frq/qxo21vndGpS6wNyqYLJ0PuQ7WrQ22d6365JMdRC04Ky9LQP3bP3DMmgqZGUGN06D7Ea7etRD9MGRwti7cutbcHGLbHqHo9D2iCs0uaKgyY4rTiG+VftFreC1n/XUT5fsSqCu3kJBqJ3tAva+lRljRNShYnc6BjanoEnoOqWPglPKAN+esdf7Zzdbazs3/M/LTO9zmya0NRNT6myPrIqFgP+mr1wNtV8uu/6eu30Lt8MHUDx7g89qhItZrJRnC1oXDAVu2Oe2DQQMhOfj+fPHxcNUNdp56xNyheBQ43deRY3R+c3Mcq1Y4P6TJKZKLLnNwy29tuOoXJafAjFkhjqfoBFLCEw+beeoRM4riXJhK4F9PWZh3roPH/2l124/anVsL0KufRtkh1WMooqJIevePAUdB08hcvxpzdRV1ffOpHTDY74cm7d1NzoplHu9XdJ2U3TvI/GUl5ePc9zwDIwTZwDNlazaTNX5EeC8iJewtRNTUInv3hMwMTAVrcfQfF9Rpb7vNweLF7nt8K4okKUXy49dmHv1DEromUFXJsafYuOrWevoOcM6JJhMcMbnjPHH51P68vLwgouJ254pUPnuqLw6r0tRuTLD202yy8xs48x4TyZn+z2dpPWyeOvI0Y07QiEvqnKgNpVsrNm5E7N2LzM5GTpzotOL9QdcxPfdsB1HbfF5A6jrKR1+iX36ez9MZbX4OP8JdnMdf1JpaTDW1aAkJODI8F5/Udm/wO3px9ln1vP98EjYrblISZHNKwqs3jMNhda4hc4fVMGHegea8WyEgq18DWf0aOvW8QknlgXg+e3QItaVxKKrzM79lSQ+Wv96HE27fQc8h/hfDS8nxp0CW06GOBF7fhyXlOH5eDRYTZVoc0uT/91Hq6vVIRSA81DGQQpC2er1XYdtYuDfkRaRiOfo2YsI2ZsOQdR3x3AsoTz+LKHE2lpUJCciLL0D//e86JXCLaNm9vuW3dnZuV/jofROqSaI5RHOxlJ65kvVrlTZrgNoawfPPmPhmscL7nzUSS8V5X33BxJMPOxeh7cMHP3xPJSnZwkNPuJ9s2ru1AHPPrWfdT3Eer6frgjnnRHcy7vfhW4x4+mESiouabysbP5lf7nmQ6mGjfD4+efcOv66TsnObV2ELRgiyQXQQ3/yI+s+XETt2AyAVgZw5De22ayFIYTv7eMmjjzm443cqqgoOhwAkQkBCksRqFSxb3JJnpmmCrz+x8MMSM8++W83AYd5FnUvcRoKiHQksfLR/k7si2uTGle2N54O/DOCix7f7ndc28tgKfnzLcxiZUCSjZ5cHlScXrFub/PNPDPrzXcRtXN98m56fj+Nvf0Of573gFAA1Nc3fux4RArFzj99jMvJrDYIlELfWUlJG1pLvSdy1t9lFa+zVk7Jjj4Igw0DTs3Xu/lcFf/tVBlYrTRtdojn8OD7Fzq6fMtuI3kPbkvn04aEce8NuBk8rD+r6ocRWr7LwwaE01jjXMbrWsia01Zn49OGhnPPQRlKy/ROsvYbXkJxtpbbM4rZAlhCSzH71AQt6T2HI0uF+XF5zvEvKUR9+BtMvW4lv0qUJcRYqj5xAxVGTfEbpAcSVlHkUtQBCSizFniMXDsdw5K4f5xkkyp33ovzxAWj15SoaGhAvvIJ61nxo6GTFSZNTAJpM8K/nrbz1vwZOPUPjiAkas47XeOSpRoqLnW/q9o6lpgm2b1P455OhFzJVVbDwQ5W33zCxdrXiM8fVhcMBTz1qxpOFIHXBm6+aKD7U9rl4cmsBph7fyISjG90WyBJCMmlmI1OO7dzrHwoGvPEiE+69jfhWohYg45fVHHPJGaRs39J8m9pQj6W8DOFo64JoCf5VSXUket5AMdxaA38Ih1slPvkK0633QSthIXSJ+HY5pktuRuzZE3QRqZtu0li12s4112pMnqIz/kg7N/6+npxcHYetY5iypgkaGwQP3eX/pqO/6MD4ZQAAcENJREFU7qTmgN2rU9i4JIM9a5ID6he78oOcpv+5KfakC8r2JbB7tf9h2ynZdo6a75p72s6RQpGkZNuZfFax/wNsRSgKRqWs/pkxF51J0uaNbce2Zw+W+fNRFixoudFuh5ISnKvzVsTHI30u7gQysXsUETQIHeEMQ/YXS3EpfV55h8Td+9p86uOKiun95oeY1m7x+Fh/GT3Zxr8+K2HU6fvIHVZLj8G1jDnxEEOml9FYY+7g5ErpDKX79vn+2BpCu8SXEg5tT2Lbt1ns/jk9oPNv+y6LhuqO43WeV6DZFTZ96X+enVDgmKv2IAQd1pBCkSgmydGXd859dxeGDHgsGuWWimosN96Had32NvW7VKuNzG+Xk73om5YbpUSpb0DYOopn3WL2FbiDbjFMj9Yc3qHIq9eivPya27uEriPXrkP8903ktVcEdRkhYPoxOtOPaflSf/4ZU1MfRs+tgF572cwdf7ATQNSCRzQNHr7fzAvPmttUah4xSuPvz9gYOcZ7qPO6tQolxd4nMU2DxYtU5l/SdhHpzq0FZ1GYu/5ewVvPpPDZgkTqa53HJSbrnDS/nvNvqPGrAFc4MNVUM/qJB4COfyFF18BmZeRTD7L1htsZ9tyT5H7zFUJKbCmpFJxzEduuuhl7Wjpl4ydjS0vHUlXp8Vqa2ULx9GO9jsdwaw28EZZw5IYG1AefdpYuabcDJjQdWVuH5Y7fYH37vaAvNWKE5IknNMCpJF/7zM4//+pZuOqaYNNaM7u2qn67tr5CkjcvTefbV3rTUN1yTGK6nVlXHWDoUd7bYkgddq5Ic7tocyEUyc6fUhk02f+NgClnl5CU7mD5Oz2pKXFuliqqZOhRlcy47CCJaYGHIYcqBHnAn3+P0LSOubE4Zbj59tuxTpmC6cknUf/7X0RjI9JkQj/3XBx33YUcNgypWZFHT4YfViI0D+HImoacfXRIxmzQvYh2GHLOom8QDkfH+VFKJJD44ofYTpztXyVRL6Rn6/z5ATMLftxARoIZh03w31+N8zLfCDSbws4fMxlxnI+ICD8p3pnEN//Op/JAyyaTatE44uRDTJh3AOFD4+7+OcNraoXUBbt+yuDI+f5v0OaNruGU329lxZt9KN7ZUk01d1gNR87fT86AjgX6/Olh6y9e3dqXFiAqq926rQJIX72e6rEjSdq5h7TV6zHVOcda3y+PiqMm0TDAGTpcO2IIcUXu+/GCMxS5duTQoJ9LdyIiwjZWw5CV/76JNKkIh+fFgfLya2gBCNvWYcje2LFNaQq983xMdZWgvEzQo2dwPQIBfv9bC2+8YuqQ67tti8K8k+P57OsGBg72fJ0GPwoTKwo0BBg5bLbAxbfUcO51Nezb6RRv/QbbsXiOUI4IeYs+RrF5zs1QNI3cb7+i57KvgZaFv6WmmkGv/JvcJV/w7WsfYU/PZOu1tzLm0T+5PY8Ugp0XX43dQ8y54dYaRAvx1fdQ3+CxvJvQdFiyDMrKnOK3k0Wk3FGwy7+FYMFO38IWfIvbzUvT+eLpfrRfedVXmvj08f4oyh4GT/UsSHVNeBW14HQ77I2BOyijZlcw8tgKyvbFY7cK0nvZOt2f1yVqg3VrE3ZuJ2XdGo/3C4DycuKmTYPaWkRT7opwOFDefhvLxx9jW7QIOXww+pXzUZetRArRQSBIVUGOGIqcPM7nmIw2PwahwN8wZFNFFQn7Dni8XwCiqhbH50swnTIn4HHYHTpmk/v5oqHK3JxT6wlFlW1EaDCU70tg4YND0e1tx6PZVFZ/2Au7VWHqhd4FqXPu8z5HOmyBz4+9htVyxp+2UF1soaHKTFKGnWQf4cy+eti2x1uLH3c07i8gdelKryHEUgh6vbsQU119m3kvYd8BEhb8j+KTZ1NzxEiqx44k/ac1qA2NHedHIdDjLFSPj+1iTpHm8A5F3rXHq6gVUsK+fQGf1hWG7A1/W/UkJgYvanduF7z+svsCVs6wPvj7Y94dwUFDpNuQ4dboumDY8JZd92UFlR7d2vbExcPgUXYGj4q+qAVIKDqA9LHLKnC6CR2qHesaSfsLGPn0IwDsvOQatl5zi3MSUhR0kwm96dx7zr6ITbfc7fU6hltr4C+hDEcW+w+CycdnQNcxrVoasmu6GNM33q/jEpL8nx89iTnNAd++0hv3bTWc/uM3L/f2WJkUQDVLUns0J8G5xVnEpZOpLQpk92+k19CGTotaF6HoWWsp9P0+kwDV1c2i1oXQNGhowHzZZSAlctwotEf+AHEWpABpUpvnXjl6ONo/7vcrF83g8CEWwpDNld6jOMD5GVBK/MtzbZ3D6SkUFpyOoynOdzFRKQWmuODmCher3uuN7lDcriFBsP7zntSWeV+nZPVrQCiexy0USUafztdUSe1ho+eQOp+i1huBtPnx+h60O1Dqfcz1UmKqrXPr9gP0+Pxr1Lp69MQEDlw4D0eKUzRIRUE2FebRkhIpvHAeWpJ/KW+HC2EXtjFdFjozvfkN4pEwxcKefJqjqVCKp8tKZsxykBxEz3sX771lQlU9L7g0TfDR+yYavXwOc3tJ5p6keTyPokj65etMOzr61ZtDgTUjq8OCzB2e/oKKptHvf2+j1teBEGz+9Z0sWrSCLTfdQcFZF7Lt2lv4cuH3/HLfIx7fY0Z7H4NACLlblZqMX93t40L/Hj3uOJ34BO+iNSlFZ/zUwMLKLp/av0Mo7r71yU3hx57D+mpKLBzY6n3xMO6kMu+GhITRs6NXzCWUVZDtWdk+jxF0DGFvvk/TULZvx/zRGwDI2Ufj+Oot9Lt/jX7WyegXzsPx0pNoLz0JXirMGhy+hCMMOZCiUXq87803AeDFtXPhrxvochobzQ30HFLj1WyQumDglAq/zusNW71Kwep072kWAnb84D0qc8TsEqTueb0tdcGoOZ2rGRBKvG0qtMfde7CxcC+YTejmzgfECgApSVnn/E635WRRcMOlHDzrZKrGj6Zq/GgOnnkie351GbaeOV7PdTgSEcc2FsOQAeRZZ3hsM+BC1NUj3njbr/MVkeKXWwswYbLO9GPcC0UhJFI6KyqHgpIS4XPD224X1PhI/br/YRtZObLDmFXV2Xv36X9bmys8eysa1RUoPOF0r46tFMJnQr9qbSTxYGHz7w25eWy75tf8cu9DbPnVb6nrP9DjY40QZIPOEirXVj9+hs9jJKC+9DamgrVBF5FqTXIy/O63Gt4c0Et/1UBcJ6M7Wou8+kr/hHl9pfeFytiTysgbWde02GwZt6uC6ayrC0nOik77slCFILuoGz4KffhwPwo/eUZCm6JkJCWin3sq+t03o99+LXLcKL+dWqPNj0GksebmYE9L9boOkEDc4p865Jxpuzf45Tq7cxBd4nboKfuain+6WUMqkr5HVJLV370DWtFg7/DjicaajilsHa4nJA1V3ufRHoPqOOLUg00PaD1m53w5aFoZ+RMrvZ4jGgQahgxQurWYmtEjvM6PAl+B2WApbfUeURTqhg2idO5MSufOpG7EkLAZb96IabOyicM6FFmeOAc5fKjPiUl57O/+ORcBIAT859VGjjra6QqaTBKz2RnuG58A//eClanTQ3PN3F7SZ/Vji0X6bC3Uu4/k0yWNXHy5o9lNUVXJ0TM13l3YwMTJbcfrbxhyLGLLymb7lTe5vU8KZxNf6ataAqDFdz7HxXBrDQIlpK5tzxz0c0/zOj8KQHz8JRzyXNyis9x1t8b5VzmrpiuqxGSSKIrz55Jf1XPhdZ0L63WJO5fYS870bwPRlyg1mSXz/rCbafOLSExvOTa7fwMn3VrAESdGx60NtaiVumRSn1QcDz3k/L2T4lYAxIcu78TIrz08iIUwZACEoGzWtOZiaW4PAdRDZWgfLwr49N6cwwum5dNzZBVTrtqBYtZBSBRVb95E6zOmiuNu3NV8fHsRe8G0/DY/7Y9pTXyqvfm8npC6ICnDdwjw5PMKOfb6XWS2CjlOyrQx8ewDHHv9bp8FqGIFT0WjWhcfq5g6AT3O4nZ+bCpc7R0hkObYXAPGqlnpIqxvo5hX9mYz+rlneT1EAGLffti81etx/haNak1qGrz5oZWFixu47iY7F1/h4G+P21izpZ5TzwxNbgTAORc4vOpyVZWcfb7DL/cjt5fkgUdt/PEBG73ydDRN8M0SE+eelsC9d1qoqe76bq2LzTf9jk033YGjKeTINRE15PZizX2PoHhJupNCUDV4OPW9+wR8XcOtNYgV9NNP8LmrDKAs/REgpK6tosDL/1J4+5sKrrq1gTMubOSa39Tz7veVXPe7hqDSLluL2z6ja0nKsONxqSEkablWcof4rqBnskimnF3CibfspcfAOgBKdify+d/78fEj/ag44F9ET6gIh6h1oZ94Iva33oIezhYdrntkSgr2hx9GZmZ6P5fJhDzKe+9uAwN3RLsasovakUORiuI9A0EIzCs3ejmi1bFueqV6yvu8YFo+d9yazGmPrmL8/N0MnHmIoScUMvuedRx542bqsHoVsu3P1V7kurAk6AyYXO5d3AoYPN33xp1oOm7OrTvoN74ChKSuPI5V7+Xxv78Mp3BDCHLvOkkg+bXecIWzO9JTKbz4bGw5ThHYPD8KQfW40dQN6u/d0dV1ihITKN+6MyTjOpwIe1XkWFf2gNPO95VP6Ue5X3/DkNszboLOuAnhy03tny+59kY7z/2z4/hU1enUBhL2/NiDZv7+mKVNfkdDg+DVF0ys+FHhjn8LUpO7QbEPIdh23a3suvhqeny3BHNNNXV9+1M6ZTooCr2WLqLnN1+huNk1EFKy9YbbO130xHBrDTqLs/UPWHID31Rpj7u+eh1QBDQ2YipYi6P/uKCv2Z5TpySRm1eDUEI7p7gqJddrDo69ppCFj/SnQwEpIRHAsVcX+v1R3rUyhY8fym9zm5SCXT+nsX99Cuc/tIPMPM8V10NFqEWti0l5LYtP/fTTsZ58MsqSJYiCAmRODvoJJ0CCM1LFfOedbs8hhUA/99SQ5M8aYcgGoSCQ/NpmpPQdzSclwuZ7fZVLTQeDZEZ+Ot/tqfT6uEtm94XZrt9UIKfpp3NcMC2fBT/uaRa3GQlmJp59gH3r0nBYVbe5tuPPOEhiun9ryOpiCx/eNxJbveq0Lpso2ZXEp48M5fibdzJgcmWnxx8M7V1ydxsNgWDLyWLflRcQd/AQccWlSNVE/cB+aEmJxO8/SNLOArclC3UhqExMZa2SxwBslG/dSeawQUGN5XCiixj/YWT4UJ9FgqSqwoD8yIwnTPzhL3buvs9GamrbXbcp03T+t6iBPv38qy66c7vg7485BXL7vAtNE2zaoPDZG577T7Y53gHLF8fx2t9TePNfyWxZa/YZMh0NHEnJHDjxdArOvZjSqTNwJRKvfOhflEw7BgBdNaGbTEhFQVdV1t11PwfmnhrwtQy31iCWkP3yfBbYE5qOHJjf8pgQurZtxuJHEZZAcYm+3PHlnHZnAak92i5k0ntZOeOe3eSPr/XrfJoDvvxXX2fWWLv5UeoCW6PCNy/28utcUsKBrYn8uKAny17PZcdPqeh+BvKEQ9R6fP1NJvS5c9GuuQb9zDObRa3261/j+O1vnfUIVNXp0DY1ZZenHo9+27UhG5sRhmwQFYTAnpXhPaxUCLS8Hh1u9pRn215MzchPD5mb6C/t3dv0XlZO/+MWegxqOw/GJTmYetFeJszz3PaoPctf74ut3o1AborP/e6FfDS7f7uI1cUW1i7MZcVbeWz5OhtbQ+glTfv8Wm9hyG43R4TA2juX6nGjqRkzvLmC8YG6enZMGY+mKEicYlZv2j2tTErj2zFHgRDsLnWGUhrOrf+EzbGN+TDkJuTsWcjcnlBc4raQlFRV5OknQ5bnsKrOhCFHGiHgxlvtXHW9nRU/KjQ0CIYM1b32rnXHG6+aUVWJprmfeKSEr95JZu5ZjSz7Ip6qMoWcXhrTT2gkqZWo3rbezMO3ZFBeoqKanDnAbz+bwtAjbNz1VAUZ2bFfXVlLTOLHZ14nY/0a8j7/CFNdDXX9BrD3jPOwZnf8IvMXw601CAW2ov3Bu7aZ6cjZR8OS7519a9shFQHZWcijJgKEzbWdlJfCysLACnj4i8u57TmunCv+Vc3B7YnUV5pIzrTTc3BgIc97Vqc2VVh2j9QFBWtTqDhg4eC2RCoPxhGXqDF4ahVpPVscj7oKEx8/0p+ibUkIxVl7QdcUkjLsnHrHHnoN9RxBFC6nFtq6tT4RAscDD6BddRXqa6/B/v2Qk4Nj3umoiZ3LjzY4vAlHfm1nw5BdVE0YQ/aX33o+QEpssybhTzyfO9fWhbe+tuGitXub2QdO/+NWKgrjqToYjzlBI3dYLarJ/zVkfZWJvWvSvRSjEljrTOxemU5cokbxjmSEKukzupqcQXXNc7HmEHz/cj+2fZONs+SJRNcEP7zWl+mX7WXYMe7fJ96KZEUSl0hdHZ/P+qm9yT+0j7S6KjRFpTCrF8XpOW2i/XaXxjEgO/xRPt2FsIYid5UwZO3Zf6CedwlS09q4t1JVIbcH+l/u9XmazoYhR5r4eDjm2M6Lxj27hI+obUFpkcrVs3sgJSgq6Bo8/1Aal95ezakX1VO0T+W+qzOxNTo/uFqrtkc7Npj50zWZPP52KaauoO+EoGLsBCrGTgj6VIZbaxAqnOHII0JyLu23N2BatwlZWt5G3EpVAUVF++tdbaozusStSEwNyfVduMRtqEOSoUUEvry8gNQB1fS2dO6rsfKgBaFIr60xQPD6b4bisAkU1Xnsd6/2YuRxFcy+thCE5P0/D6C8sCm3XxfIpmC1+ioT7/95IBc/vp203I5hcuEStcG45XLAABz3Or9Dm938grUhGJURhnw4Eur8WuhkGHITVeNHk7R9Nwl79gEtYaVSCISUlM4+mqoyO9mFe4nP69epa7hCkqMtbjMSzGTkNZKR17mNqdqSOJ8VlhGSZS/3x1ZvQqg6SMGq9/LIGVTL3Ft3kphuZ9kr/dj2bTYgkDrN86NmU/j2PwOIS9I8Vlh2l2McaUccaHZi7SYL2/P8CzM2QpL9wwhFBpg+Fe3zD5EnzW0Ou5OJicgrLkFb9DHk9vT40K7g1oaS1DTpR4Vxia4LpBRoDue/dpvghYfSWPxBAgtfS8JmFehuFn+6Jti7w8xPS3z3iOuOGG6tQSgJycK/RxaO1/6Jfs5pyIQmsaUoyGOn4/jvP5CTxgZ/jQAIR0iyi/YVkwMlLknHS025Zhw2Z7MHXVOaFnqCTV9nsPi5PHb+lEbZvgS34ljqAodNYc0nHfvIhtOphQDdWi+YQiRqXRhhyAZRRVU5cN5plM2ahpbckobV2LsnB845haop4z0+1F04ci41bnM7Xfmf0RBh7opKdQZLkh/zqgRbg3ORKTWleR4s3Z3EJ38bStUhC1u/yW6Tn9uCACFZ9V7vgNPa3OXXugtDdkcgrn9nQ4pdQtjAN2FxbLtKGHIbxoxCf+lZqG+AmhrISAeLfy5sV3FrQ8Fp8zTeedOb+HKXCt9y3xv/TMHWKNA9hDKDswfb95/HM/2Ewydc7fUf9xii1iCkhNK1JTsT/a4b0X9zLVTVQFIiJHjefAq3ayt1GRbnFlpCk2ttDpIDdG4HTaliyb97e5nfXKstN/dLp7itqzA19TL3kO6hC7Z8l86sq1ry2sIpaqUuQyJqw5V7bWDQWYINQ25GVamcNonKqRNR6xuQqooeZCsr6bB1WFu6xJeroFQk3VuXuF3QFFnWmfVKWq6VjLwGKg7EexCmTbi5T+qCygMJrH6/t/eLSEH5vkSqi+NI6xn68F1PEQOBuP6GSA0vYftUdIkwZHckJkDPHn6L2sONWbM1JkzSUFU3TcGFN1ELICgvVmmo874glbpgyxoLbz2TTGlR9w8qMEKQDcJJSMM1zWbIzvQqalsTDjETKufQG5dP7c/lU/tTa3ME5N4mpGqMP7UU962DXPOjt009qCzyHa5nrVX55qVe7NlqptbmaB5vqAm1Ox5Kt9ZWtN9waw8jPBXtCZZgwpA7IARaUqJHUetJSLtzbb0xIz+9uahU+5/2eLuvMwTj3goBk84tbBKu3uZITyeQFO9K8tlXF2DFm33YuzbNZ9Fqg+5H91cNYeRwC0MGZ0HgV99pZPoxzkRbVZWYmooHJCRJ/Gg7TXqOBsLbcZLKcoW3n03m2hN68L9X/Kuy3JUx3FqDcBDNhX+oQ07bE86QZBetQ5P9FbjTLypi/GmlzUWfFFUHpF9FVoSQxCVqPhZuzmJ7az/N5sPfj2DPG6OwB9eVwiuGW2tgEDyeBLQnse4pJLk1LoHb+qe90PV2X2cFbzDiNn9iJTOv240pzjkvKqrubKsmJMKNYdIGKWiotyI1X9JFsmdNGl88PoQP/jCSugrP6yvXa9Tm0W5e93AULutqdJVo3JCHIneVJx4qDqcwZBfp6fDG+1Y2bbCxZJGJHcWNDBhhJyEe/nK9b6f+uNMbeOc/yV6OcJZ9d61bX34slayeGkef2P1Ckw231iAShKRCcieR9dVdMiTZRevCUi5x6y1EWVFh5uUHmXh6Cdt/SKOxxkRqDxv9xtXw0g3D0b0synRNMHByNcW7Er2MyPl8Xbm8X31sISU1kd88UB/YE/NBrLu1BgbdCVld5jbS0V1IsjfaizRf97mKUkFgoc3ti0oFwtCjyxgwqYLdKzKoLokjLsnBgCkVLHp8CGX7EjyHKQudrD5lFO9IQLdZ8OzuCmjKzS3fn8BnDw/huHt/Yf70fL/H6M4199TmJxLESlXkrhCNGxbHtis8cYPgGTlactPtds69oYajZts4YpqN7F5aU0hyRxRFMnKClXOvq2XkeJtf4SQu/vnHNOpqwruAjRaGW2sQTrqra+tyEiPh3AJtwn39cXCTMx2MP7WMafMPMWp2BSlZDoZOr/Iy70nMcToTTy9h4hnFzbf5RvDB6/GsW+Wzqp/fuF7TWHZrjTBkg2CIlCDx57reXFt/nNtgcOfo+our321Fgz1g99YcrzP0mDImnX2AMScWk5xpZ8TsYu9TnlToe8R2ek7/BiHwuNZs+xhBRWEiGz8O38ZuSMPZvWBURPYPIxS5kxyOYcjuWFZQ2fx/RYGb769EUZ0itjWKKrHES669pxqzBf74XDnzrqgjMcW/SdTaoPDXGzMCrnQXyxhurUGkKFuzOWoul6lgbdjETaTFLQQucFsz/eIiElIdHcVt0wJt9vWFmOMlR19SxOzr95OS4++CVvC7K1Kprgrd5l8oc5nDHZZuYNAZIiVI/L2ep3BXl7gNp8CFtpWXAxW4EHzV5KHHlNFreI0bwer8PW3YRirj9pLcfw8n3bmNHoNr/TyzZMsnfdi2rq2J4O45drcw5Ozhnru6dFcMYRsEh2MYsjssasvbaOyRNh54qYyRE1smByEkE4+x8sgbZfQf6lwExsXDJbfW8PLSQ8w9pw7FV24FsHlNHNs3dC9303BrDSJJNEM4wy1uI41L4LYuMuVL6KZk2Zn/yA6GTa9sM+f1GNjAGffsYfgxldTaHNTZHQyYWcx5T23kroe8F5JxUVcj+PSd4KtthqoKMoTnb26EIR9+hKtwVLTw5tp6E7dA2AWuy72FwFoLtRa3nRW4qkly4u+2M/aUIswJLfNoUqadaRfv5dx7Gpg1ewKzZk8gb1QNp/9xK+c+ss6PMzs3/D54vmMKnLvwbH/DkCNFsGHIne2f3FUJaY6tOmC0EYZswPBxdu5/sZzyYoWqCoXMHJ20TPcTpNkCo6fYWPSu7wJRqir58ct4ho4JblcwFjDcWoNIE9L2PwHiav8TLlw5t+HOt/VE64rErXNx25NsMZGSZefoX+1m8mUqtaUWLIlaszNba+t4vqL9Dqej66NSMsDijy1ccHXnaxGEw/kOh1trhCEbdFVKtxzy6KJpuzd4rVPTWnAVOdpuPoXaaJmRn96cf+tv7m3rlkCdyb0FMFkkU84vZMK8A1QfikNRITW3EcXDENJybSRl2qgrN+OrK8fKb+Kw25zrzlDQmXB2ZzjxzoBb/hhhyP4Tlj623Z0iUgy3lrZhyO7I7KGT2cP3jt/U2Y0kpujU1/iYPAU+WwV1JQy31iAaRKuQVLh627qItrh14avtzsvLCwJqzZPbR2fqTDvLl/peuNXWdj4IK5R5tWC4tQaxS7Tya/3BJW59mUTtXcX2QtcTnSlEFWjf3GAKS7kwWSSZfX1v0gkBI+cU8/NbeT6PlbrAZhWYLdJjGHL719VXGHK4w9ljpWhUV8IIRTYIitZhyJ3FbIHbH67AV6EUXYO8Af7ns8UqhltrEC1iwekKZ+uXSXkpEc237Qyd6Tf72wfqmlwGz89NVSUDh3Zufgy1qHVhuLUGsUqk82vbX9ubuNZ2bwg4r9NVaMrXjyuUOZCQ5s6GJne2sFSgjDnhECk5VnytIVMztKa2lE68VZBuTTjCkP0RrK5jDLc2MEImbA+XMGSjaJQTX25toEycYWPWaQ14npgkqommY7o+hltrEC2iXUgKDHEbKLl5Or97sBZvjq2mCc68qPO7+6EUtUbfWgOD4HCJ21AXLmotcsH/nN1oF5byhmqWnPL7rV4DWhRFcsIF9R5DmgPNWw7G9XcJVW/i1hC1ncdwbDuBEYbsJBRubWuuvquavoMcHSoqC0WCgBv+WEVKWtdesBpurUGsEG1xG066o7g9cZ6NOWdYmyootzw3VwXR085vZNL0wBePoSwW1ZpQ/52NMGSD7oQv1xZ8F5QKlkAFbigKS4WLlGw70y91vp7tqyoriqTfUAenX14HOMfuT9EoX0XLgnH9vYnbUIna7OE9Q1Y4qiuZl4awNYgZklIlf3u1jFMuqiMhsWXSHDrWzr3/V8FxZxpurYFBKIiFcM5wu3rdTdwqCvzh8Vp+/Yd6evRqmR9z83Ru+3Mtv3uwDhFgenE4Xp9w/l1j4X1rEFm6cqsVfwhE3EZK4PoiVkOTRx5fwtzbtpM9oK75tsRkndMuq+P+l8tISIyt74PMYYPIHDaIAdnWNj+u2w06R0iKR0VdyZdXIBa8g1ixChQFOeMo5LnzILljae9gMMKQnSwrqAy5W+siKVVy5R01XHJrDRUlKnEJ0mNF5a7G6z/uMUStQeRxOEjcsI2ELTsRDgf23B7UTh7bVCWZqBaSkvXVYSsmBS0FpYCoF5UKBaoK513ZyDmXN1JSpCAEZPfUPYbXeSNcebVguLUGoSWsrVakJGFvISkbtqI0NOBITaF67EhsuTnhu2YT3iokt6b1829fNTmUa2+XuHUVovIWnRjNwlLe6D+hiv4TqmioMlFWo3HpCb0xt7pMKIpGNRbuDWmOtiFiQ0uXr4oslnyDcvm10Nhk5wsQH38Kf30U7a1XYOL40F7PCEOOCGYL9MjToj0MA4Mujamsgh4vvo2pohophHMRt3knqV//SPm8E2D8iKhXSY6UuJW67BbiFpzubc/end/wC5eolfXVYQs1N9xag1DQWpQIm51e731C4p59zfMjiiB91Tqqxo2i5MRjCTgMopNj8jdktL3I99YeCDonfHOpoYgUpMPmc83b2bZA4RS3AI2WBq46Nd/tfdEsGmUQfrq2sN25G+WSq8FuR8imEAPXPzU1qOdegrbiG8juGnHhXQF3RaMqyxQWf5DAuuVxSAkjJ9qYc3Y9WT27h9MaCgy31iDiODR6vPgOalWTY9k8R0okkPn+5xRnpJF8zglRF7fhJpri1toISz6J4+tPLdTVCgYO1Th9fiNDRkZn4y6cojYcGG6tQbjI+XwJCQXO91fz/Nj0+UhduxFHagoV0yeHdQwu1zYQcdsab+JLHTC6g/Por9BtLW4h9O6tS9yW19mp25nF9u+yqCu3kJRpY8iMMvqMrkZ0MjDQU6izJ7e2w20RdGsNQk/QwtbXblE4UZ5/GTStZUJqhdB1ZF0d4vW3kLf8KuhrGWHILbQOQ1633MKDN2dgswmkDiDYuNLCe88n89vHKjjyOKMHl4FBNEjctB1TRZXb+wQghSD12xWUDOpH1vgRkR1cK8Ld39ZFNMRt0X6FX1+UyoG9KkJIpBRsWGXig9fiufzmeq66rSEShlAz4Qw/hvAVBjPcWoNQY6quIWXjNo+FdAWQvmINFUdOAJMa1rEEK2494U70tl6z+9srN1zu7VkT8nn01gzWfB+PUHSkriAUyc7lWfQZU8WcW3dgsnQuL9ZVsMrdGNvTPgwZur5b60+Ie3clJImS0cqvFZ99gdC87HrrOspni0J3PSlIWPAm2SceT+7QgfSYNpnkp55AVJSH7BqxTHu3trRI4a83Z2CzCqQucNVa13WBwwGP/iaDwt3h/ULoChhurUE0iN+6yxle5wEhJfE7doPu3MUOhTMmVqxBvf1PmOZcgOmUS1AefQb2Fvr12Ei0iJmUl9JcVCrchaV0HX57ZQqHCp1fs1I6/xaa5vz35acTWfRh5FJbwilqjfY+Bl2NxF0FPo9RG63EHwydO2cuLSf7i6X0/9dL5D/9Ij0/+Iz4fc750eUCBtNGxh+03RsCLkgVaGGpGfnpfrUFevmRVNb+EOc8t940T+rO+bFwQyo/vtbX5/XaU9FgdytqD0e3NpSbJF2Jrl0V2ea9spqAltzbYGlsJOucM8m84RosP69ALSnGtGUzqff/iZ7Tp6Lu2R2a68Q4rd3aRe8k4rCJ5gVbG6RASvj0zaQIjs7AwMCF100/1zES0PVmRywYcas8/SKm6+5EfLscUVqOOHAI5a3/YTrnWsSyn70+NhL9bVvjEnfhFLc/f2dmz3ZTs5BtjxCS159NwE3AUUhxiXiXqA/5+Zv+ZuFwa21F+w231iAsCM2/VCl/5lF/SNq6k34vvEnamg2Yq2sx1daRvG0nfV57n4ym+TFS4hYCF7iuysmh6ntbUylY/H5is5Btj5SCrd9m01jjf2Cpr2rLwbi1kfibGISGoIRtNMOQAeT4I5CqZ0dQqipy4rigr1NECmkPP0zc998BzjBncApnoesoxYfIvOxiwr5CiTFWfReP7mFSAtA1wc9L4yI4otjDcGsNooWtt/dQJAnYszPA5Fw4BCNuxdc/oL64wPn/VgtGoengcKD+5s/gISzaRTTFbTgE7orvzKgmz+eVUrBrm4mK0vDFIoc79NhFJHoTGxiEEmvPHI9hyC6kENhygo9IVGtqyf3wc9D1NqlzounzmfXtchL27APaittoCFxfdMa9hY6O6aZVFjSH97+A1BQObvGvu4lL1IbLrYXg+tYaRI6gHdtotvmRV13mdTdNaBr6FZcEfR1RX0/SS883C1p317Gs+wXLzyuCvlas4q5olObw/ThPbsXhwOs/7on2EAwOY+omjHa2P/NyTM20CW1+76w7prz+PtJDzxkhJdjsKB994fM80RC34XJvNQ2fC2fnceGZIyMhasP5dzLcWoNw0piXizUny2O6hhSC2uGD0ZISg75W2poNIKXH+UAKQdrPa5t/L91yKCoCF/x3b4Gg3FtfotZFTYPusfet63ZX+LGnvNrWY2iN4db6R9RbugZIlw5Flsceg37D1c7/t1pUuVxc7U+/h9Ejg76OaesWlLo6r8dIRcGy/MegrxXLtO9dO3y8DUX1vBhUVMnw8b4nve6M4dYaRAs9OZHS804BIZCtiiXJpp+GkYOpnTLO7WMDdW3FLxs9bvw5LyoRa/wrxhFpcQvhcW9HHuHA4XXxJsnqoZOZE9rq8eEOPW6+ThhDkA0Mwo4QHDrjBPQ4SwdxK4XAnp5KydyZIblU/L4DboucNg9FShL2HuhwuzuB2/4nlATi3rpCk6Fz7u2gUfamXBjvXDIvvVmwthayrR1ab4L2cHRrs4f3PGzzayGIqsjRDkMGQAj0P/8BOWkCyrMvIFeudjoUR09D/9W1yOOCn5SKSMHib+/aSJa3jCDu3FqAk86v54t3PO9m6prglAu9bwh0Vzy5ta7Fvyd3y8AglDSMGcah9FRSvltB4uadoGnYe2ZTc9QE6iaOcTZEbUfZms1kBdzf1sfcJ0RA82Okety2xiUCXZWTgaCqJ888yUbaX3RqqoTblA0h4JzLG/CSTRMwkQo9dhFyUSsl6Dq2koOGW2sQdmw5Wey7aj7pP60hZf1mVKsNR1Ii1eNHUznpCPSE+JBdS+JjlvRypzdRle3jup1tIeRqFeR35WSHc87xp3IywHdUMm56I+t+jEd3E7WiqJKxU63k9nVGZXoTr55widpu5dbqutvvbYMWgmr3ExPWtBDI009BO/2UlhzXEAtM+8hR6GnpKFWVnoeh61iPCUxImzZvQi0qQuvRA8fIUTEtjNu7tQD9hzq45u5q/vNgGooqmycn1/8vvLmGkRO8J/N3Z5rdWimZuGIxcz57nQE7NyIFbB82nkUnX8z68TOiO0iDbo+tby/KLjyDMimdqys/xFqg4lZOPgJWrPFakEVOHhfAqMG04kv0egskJiKPntmcCxxuQiVw4+Lgb/+u4fZLU7HbZHPIsVAkUoeps+zMv7oxJGOOtKANtZsu1m9GeeUdxDfLEQ4Hap9ckieMpnby/7d35+FRlWf/wL/POTPZN0IgQIAQQBZZREQRV1CRuu9LtS7Yzfq21f662dZaW/ta+9q+tb62ttpqq6XWWtu61x0XwI1VEMIeCBCykX2ZmXOe3x+TSSbJrMmcOdv3c11cSmZy5gmEyXznvp/7OQaI8LOHKFUCBfmoX3oa6peeFnwNacDrsM6KicjefzDqHBapCHRMSm4CsNLVjczDdWivqkbXmNGQmZEDZXjwTSbkhodbILGjgRI99xYIhs2mHzXhR8tHoe6g2ntcJCAhFKBkjIb/ujv2XIZEDAy1dqzWeppbUfTBOuR/sg2qz4dATjZa5s1C0/HzoOdkm7o2K0rPK4V0SfETUu/ZtZmZaPvyzci/7+cR20mk6oHv+OPhP2ZeQtfNWPUuCr/3XWR8sqn3Y/6jZ6H5v+9F9+IlqVh62pz72Q5Mmu7H84/nYuP7mZASOPo4Hy68rh3HLHJnG3K/aq2UuPzJX+Psl1dAEwoEJIQEpm7fgOnb1uFfV9yCly9cbtpayUWESGzTZ49kwq1+/eXwrFkb8TapCCA7G/oFZyf2wAdroN77G4j3PujtVJOjRiHwgx9A+/KX0/YGYCoC7twFAfz55SY8/VgWXn8+E12dAhOnaLjs+i4su6R72Fk9vG063aE2VdVa8do7UG+/BxCid2aGqK7BiOoaZFXuRv3nLkZKy9pE0Rj03NJyzNHByceBQOSnYF2i+YRjE7qW8Psx8q3VKNiwBUrPvxfd40HLvFloWHIS5IAnlfBQlmzIDVUwk67eJhhwL1hQgNPe68ZPfhHAW//KRVO9gqISHWde1oFlV3Ygr3Do20JiHTWUTLXW7FDrrW/E+Cf+AaXb15s/PB2dGLFmLfK3bEf19ZdDy+PpI+GG9GPVEm3IaRL6R9n6re/Cs20bcp77N6SqQmhacF+GlAhUVKDxsScSul7GOytRculFg96582z9FCMvuwgNTz2D7rOWpvzrGKpVVU0Rq7Xhjp7vx9Hzm9KzoGHK37Udufv3wldQiMZjFhj2gilUrZ25+UOc/fIKAIAq+55o1Z6W5Eue/i0+nb0QVZOHvxecKNUSDbfyxOOgfeOLUH/1SN/zIxBsmcrKhPbgfwP5Cfzwra2H5/pbgabmftuvRF0dvLfdBlFXh8APfzjsrysZww24ZeU6brurA7fd1ZGyNRkWaA8cgLJpE+D1Qj/xRCCv/0TSlO+rbWqBesfPAalDhL0ODf3pZlfuRv77G9B68nGpeTwiE2i5OTh0xfkY+/TzgNY3GTn0GrJu2WJ0jR+bwIU0jHvqOWRVH+pXZFECARSu3YSM+kYcvOrCqK2qoZBWMqMUXQf2JVzBTaY1GUgu4BYWAr+82wPc3Y139zb1ftzrGXqnRrQW5GSqtZFakEW3D1kHaoIdmmNGGR8opcSYZ1/pF2p71yIlPC2tGPXq26i59Fxj12EzQ36/2BJtyOnk9eLIY4+j4/XXkPvnx+DZtRN6SQk6rvosOi+7AjIngcl5UqLoW98IDlIZMGhFSAkJoOibt+Lw+s0Rn5i8n2xC5ptvAJoG34IF8J16uqXbl62kcMsmHPPT21G8eUPvxzpHleLTW7+H/RddmbLHGbi3dsnrf4emqFD1yNO7NUXF6W/8A49PvjNlayBKpVC4jUe//groC+dDfeo54JOtkBkZkIsXQb/kHKCkOKHHUh5eEQy1UVqa1XvuQWD5cmB8hJBdXQ31ueeA5mbIqVOhX3hhsB84RcID5McH+t7xH84+3GQYWp2trYX31luhPPts3xyA3FxoX/kKAnfd1a8NPJX7apXnXwX8gZgzZPJWr0XrSfP5s45srXPSBFTdfD0K129Gzs69ELqOzgnj0DJ/TsJHCuVt3RFsaY5ASImcvfuRu3032mdMHXx7tw95lbvgaW2Db302Mi9Y1Bvekq3eAollgGQruL37b/c29YbTZANurH214WsKF6laC4RVuzUNI995H4VrN0HxB48DkUKgbfoU1C1bbFg7cOahw8isrY96u5ASudt3Q21tg5YffBPS7YOjAKe1IqdQbxtyOEVB99nL0H32siFd07tuLbw7dkS9XUgJz759yFizCr6T+/ZeKnW1KF5+AzJXvRscOtTTsuU/6ig0Pv5XBGbEf9E5FNGGRtlNQeUWnHrjxVB8/d+ty6o7jOPuuA2ezg7sufrGlD1e+CTkSbs/jRpqAUDVNVTs/jRlj01khGC4Rfz9ttOnQLvzG0N7EL8fyguvxdynC0VAfeyP0H74o36f5/nmN6H+4Q/BThhVhQgEIEeMgP/3vw8G3BSLFnKB1AXdSNOZDWk3bmpCxpIlEHv39nvDVbS3Q/3lLyGqquB//HHIztaUD4sS23YGA2uUvYcCgPdIM4Q/AJnBCfNkb1p+HhpPOxGNp504pM8v3LAFUoioE5alEChcv3lQsC1Y9wlK3nwPwh8IzljQJfDq22g8+Xionz/f0OotMDjgJjxgKizgArFDbrxAO+RqrZQoffYV5FXu6tdGLqREXuUuZNbWY/+NV0Xd4zwcmYfr4w4dE1Iis64BHfmJnffrBs6eyiAl0NoK+Ia21zPeP75kqfsTm66m7g87asPnQ8nFFyDj/dXBNel67z4kz+7dKDlvGZSampSuM1y8NmQ7mP2Ln0Dx+aEMrJL3/HfWL++Gp71t2I8TaRKy3xv7e0gmcB8iowifH8KX+IC3ZI8BSkpzK0R3nOdqISD2V/cbXuS57TaojzwSfG6UEiLQc8B2UxO8V18NZeVK49aMvrNwBx4ZFOlXJLHuG35to/bQqg89BLFnT8Qz4YWUUJ9+GuKt1w15bHi9cSuxEoB0wM8hsqGAFnxOinFUTzp5mlviHhvkae4/2C3/k20Y/cpKKP7g/l6hy57/6hj57gcI/OklAMlN/w0dC5TImbfhQkcEJXP+7cBjgqL9Cr/fQKHHGkq1NnvfAeQPCLUhQkp4jzShcN0ncb+WoZCqmtBYDGngDAK7nWELDKFia4svsqMT4qGHoTz6BERtHaSiQC49A/pt/wUsmG/asvSSeIPZB98v+9l/w/vploj3E5oGpbkZeY/8Di0/vCsVS7QvKVG88WOUvfwsMlqa0T6hHFUXXw2pqhj9/rsxP1Xt6sS4V1/AvkuuHvYyBp5bu37BYpz5ylNRq7ZSCGw4bvGwH5coYVIid/0W5L/7ETIOB9ucustK0XrK8eiYOyNq0BjaMUBJyM2BVJQ45+ECwtvTCtbRAqW2Eeqjj0Z8sSekhBQCnh//GL7Fi1O/3gjihc+B1d1EPidVxK5dUB9/HGLfPsiRI6FdfTXkggXwPPpo8AiJKKTHA3XFk1DGpq6tO0Q/bSE8z74S/bGFQNeUcg6PorTK2ncAI9Z8jJzd+yAA+PPz0LxgLpoWzAM85n0vajnZ8LS0RQ07EoAWvi1O11H89pqYVb/iVR9hz3FzMXLuhKQqt8DQ2pOB5I8IAqJXYhM1MNQOCuQtbVBffQ/yk0+R7fEgZ1QpOqZMQv6mrZCKgIh2vrkECjdsRtOi1M8B6KiYELNCDwBaZga6ysak/LHtzHmtyO0dUC+5Gtj4Se8LJKHrwOtvQX39Teh/fAjyvM/EvETENuQU8C06GdqYsVBqDkV9ktFGjux3bFD2M3+P+WJPaBqyn3oy5cE2kaFRVqF2tOOE//dFlK5aCV31AAgeazL9d7/C3ss/F/fzpceD7MOHDFnbyrOuwOLXn4GQOpQBT06aUODLysa7iy8y5LGJBpESI557HfkfbIAMexLKOFiLkqdeQHNNHZqXnRb10w0Nt9lZkGeeArz5XtR2ZKFp0M89s/ecW+VvK4LzCCJUG4GeDpc1a4CDB4Fx41K73iFIV4jtR0p47rwTnvvu63tnXwh4HnwQ2oUXAgcPxm51CwSg7NyG6BsqhrG00xZBTiwDqg8N+hkne9becvoJBjyysZq27+r9/6JpU0xcCUVSMqM06rTbvM2VKH3+1WB3SM/HPK1tGLlyNXJ2VeHgVReZFm5b58xEZk1d7PvMndH7/1kHD8PbGrsbTfEHkLurCvUZGUkPlQoZGHCBJPfgJhFwkyUDvoiVWqBv3co7H8F77+8Af6DnjV2JcbqEb+QIaJkZ0UMtgm8YqK3tKV83EGxdb509A/mbt0U+kQVA08L5vZOwS2aUGrIOu7FHckmC8sBv+4XaEKFpgC6h3HIb0Ba/7dSIf2BQVTTfdXfPSV2RtfzoJ0BG32MrjY2xKxgAlAGtJ24z/47bMGrNOwAARQtA0TQougYhJSqejj+tWmgauosTq6ZHs2LN3kHVWgCoHz0eD37zf+HLyIIUAroQ0IUCCaArJxf3f+dBtBZavAOCHCNrx17kf7ABAPpPHe75oVn49gfIqIrdbtywfisAY9qStS9dC3i8wVkCA0ghoJ9/FjA5+KLLU7UBStX2hA6rF83DPw/RrtSHHoLnvvsABJ/rhKb1tmsrL7wQbAeOQaoqUDLCmMV5VHT+5FbI0uBzoBQi2HosBCAEGi9dhu4p5cY8tgGatu9C0/Zd2FMF7KkyezUUSazQpnR0ovSlYNt9eJAQCD5fZu8/gKKPNhi8wuha5s6Ef0RR8N/HAFII+EeOQOus6b0fU7rin5MtETwTFwi239ZvO5xUW3K4UIsygKTalAe2KCfSppyIaNcJX5fYtgven/4G8PmDW1l0vTfIehubkFnXGPX1eoiek5WS9UZSt2wxOiYHnwOl0vP82DPHoWXeLBw5aUG/+7t9cBSQZMVWLTdmSFHKBAIQjz0RvbopJWRnF8Qzz0LecG2aFxfUedXVOBLwo/AHt0M0N/dWY/X8fDTfdTc6rruh3/21KVMg162D0AIRryeFQGDSpJSu0U5Do3L37kLZay9GvV0KAd3rhfD7B1VMQ3SPBweWnW/UErFt1gn47q9fxKL3XsBRlRshhcC2oxfgg5PPQXdWAtO0iVIk7/31MduqpKIg//0NaCiPXY01rHI7tQLaw/8D9Qf3AtWHetuwpKpCv/w86N+8uf96x48FArH3CEuPB3JsAkdpOFEgAM+990a9Weg6ZHd33K4g7fyzjFohZGkJqm+5Dtmf7kTOpzsgAgH4xoxC+4K50ApNqHAPUahKGx5o91QBFdjFqq1NFHyyFdD16B0MEihcuwlNJ5ozpVtmZODA5y5F6bOvIGffgd7AJQB0lo/H4QvPhgx7o8o/ojDuNQUAf1FBv4/Vbzvce+btUILSwPNvAWOmKMcSa19t+Bo9T70IRDniXUgJ4Y/z80UItMw17rhG6fXg0BXnI6v6IPI3V0Lt6ESgIB8tc2fCVzrKsMe1s6RbkS29v7auHuJIU+z7qCrElq1R34Exqg05XMe116HjsiuQ9dorUA8dhDa6FF3LzgGyB48Mb7/uRuT87cmY12tf/vmUr9Eubchj334t9osyKaH6fNAVBRICQg6+3/Yv3gp/4dArEtGqteE6c/Px5rLP4s1lnx3y4xANV8bBw7HbqnQdGQcTO5DeqHAr585E4Lk/QXy8EWL3PsisTMhTTwCKB/8blWefDtz3EGRXd8QXJsFAfDlQVJSy9dmJWL8eorY2/h3z8iDb2wcNkJKKAnn8PMgTjjVkfb6a6mAHgKqic850dM6ZHv+TLChSqCX7yaitB2L01AkA3tY2CJ/fkCm4idDycnHw2kuRUdeArJ6jfzonlsEf4Ug1/8hidI4bg6xDh6O2sgby89AZ4Y3M+m2Hh9ya3LvWsMFMybQpRwq4QGIhN16g7VdFlhLK6vUxJ/FLIaBlZ0Ht7Br0ZyiFgJabg6YFx8Rd17AIga4JZeiaUGbs4ziEPdJLorISbAfIjn0/Q9qQB8rKQtcFF6H9S19B18WXRgy1AOBbdBLaP3d9xKdZqajwnbAQHddeZ+xaLUzp6oIU8b+NN91+NzrH9K/aBLJzsOW276Py5iEeT0JkMzJe2ykAPYljVQxrSxYC8vh50K+6EPKiZRFDLQAgLxfa7V8NvhQdUEGRqgoUF8P/4x+ndm02Ijo6EriTgHbLLZAL+re0SUWBPPdMaL+6K6F272QZOmE7jeKF2j1V/ffckjVE2o8oPZ7YZ6sgNKXb/GFmvlEj0TJ/Dlrmz4kYakPqli2GVNXBz4897f61554Z9d93aB/yUFuTww2lTTnUohwKqeGtyuGBd+DHooXa8LUACFbno8xn6Lu4RFfZWLTOmjboz7CrbAyqr7vcsHNsaWicNTxqRBHk8ccBa9dHr+AFAtDOGdo5tKYQAk2/fhCBqUch7zcPQK0LDg7Qc3LQceNNaPnBnUBm6qZV2mloFAC0TJsJJUqbdojm9eLAeZdi71U3oOSj1citroI/vxCHTzmj/wTBIUikWktkFR2zp6HgnQ+jT1kUAp2zpiV1TcOnJcchL1qGQEEe1AcfA3YHX4BJVYV+7jkI3PcLoNw+ezRTTZ8xI3hkRIwXb0LXoZ9+Ovzf+X8QW7bA89pzgNcbrNKONrZDK/TGiF2xUms8UTASasXsqMeyDEVW2cSIYa39qMko3BD5FAogGAY7KiaYOhk5Wb4xo1B9/eUoeWsVcvbs7/1417hSNCw+CV0TY1cBwyu3wPD3cA63ihsSXs2NF2RDZEtD/+8jVYU+fgzEgcgV7eDCBLpLS3Dk1IVoWHwScvZWA7qO7rGj4Rs9vNksqVQyozTl+2ttcQpOBEkFW1EQ/V0hq9C/8VWo1yyPeJtUVeDYY4CFCyLeno425CFRFLTd+g203fJVeLZtg9ACCBw1DTI31+yVme7wqWeic1QpshrqIr6Zoasqqs+/DP6C4F6T+oWnoH7hKeleJpEltC2ch/w16wB/IGJblZ6ZgbYFc5O+runhdsnJCCw+CaiqhmjvgBxbisCxiwHELcA4W2kp9IsvhvLvf0cMt1JVIcvLoZ0QPAZPzfNDXnKO4ctyQrWWodZ5OiZPRPeokciobxz8/AgAUuLIosivH63MVzoKB6++GGprGzytbdByshEoir//NiRUuR1ua/JAQ92LCyQeZkOiVYi1i5fC82DkIaOh74CWebOC983PQ+ucGRHvS9Zhn9JcguTSM6D98mfBH9iKEvxvzyhszJkF7Yk/xNz0n5Y25KHyehGYMwf+eccaEmrtNDQqRHo8+Pi+30H3eKEPaA/SFRUd4ydiyzd+YMhjs1pLdqMVFaB2+RXQszJ7pyuGJhDrOdmo/fxV0POG1sVg5LTkhAgBTJoAOWs6UFwET9UGeKo2QHa0QHa4d3K8/xe/gBw3blD7pFRVICsLvocfCh7/U7Uhreuye7UWYKi1u0HtyIqCg1ddCF9Pa69URHBStwCgKjh8wdK4FU4r0/Lz0D1uTFKhNlz41OTQr5Ssa4jTlBMVul6kqr92/hIE5hwV/HkY/jk9xz3VfWYJtPy8lK6HjOWsVuQe8vproC07C+Jv/4Co3A7k5kK/4BzIU082ZZKdKQIBqAcPBlvyxo1L+Ou2UxtySMNxC7Hyby9j2h/+D2WvPg8lEICvoBB7r7gOO5Z/ZViDoYicxldehoPfvRk5m7Yhc+9+AAJdUyaiY/Z0wDu8HwlmV24jCZ15KztaIHJ6pn9KCRw6BOHzQZaVxT3yxtbGjoVv9Wp4fvlLqI8+CtHSAun1Qr/qKvi/+hXIaUelNdT2DoyysdCRPmRf0dqRtfw87P/8Z5Gzuwq523cHp3SPLkHLnJmu2kupdHZB6eqGlpsDOWDuwsAKbrjhVHOHU8GNJlaoBYCuwweB2z6HwF9fQ+FHG+FtCVaCO8vH48iJ89FZYf3jc3h+bX+ODLYAgNLRkLfeEvf8qRDLtiHHIdrbkfX8c1APVEMvKUHnOech94k/I+/hh6D2TMP0T56Cttv+Hzo+d71jg33rUTOw9ue/wbqf3g+1uwuBnFxDBp6ErFiz17BrExlNZnjRvmAO2hfMSfm1LRVuNQ1i9Vpk/PFJICsT/s8uh7JuAzz3PwBlS3AvnSwuhvbFLyJw++1Rh/jZ3qhRCNx7LwL33AO0tUEiAHg8wUCb5lBrdxwE5SwlM0p7g1ovIdAxZRI6pkwyZU3plHnoMLKrDgCQ6Bo/DlIAxe9+iJw9+4JD+VQFrUdPR+NpCxEo6P86eeCfW6qCbqoCbrxQG1K/sx444Vg0HT8vOPFaVW21jxrg+bXhnBtsh8DSbcgR5Pz5MRR+/3aIjnbA4wE0DUXf+DogZb99ZZ49uzHi6/8Fz/btaLn7vyNeyyptyF0dAh+9nYmmegXFo3QsWNyFzCTOvpZeLwJpqr6wDZkoslBFbmTPKTFmBFyxYQvU2++BOFwHqSqALpH1i98B6D9BWTQ2Qr3vPijvvgvfSy8lPl3fBJoGvPmmQGWlgrxciXPP0zF6dBIXUBRIDwB40t56HGL3ai3AFmSniFa1dQO1tQ1j/vUysg/U9D4fCin7nY0LAELTkb95G3J27UX1jVciUFgQ8XrA4KALAJHGKyUawoYyaGrdRi/WfJQJxdeO00/MxDR1bdT7Dvq7F8K0Y5wodRhsbSr76b9jxG1fg0TPE1Ag+mTg0BCE/Ad/jc5LLoV//nER7xevDTmneh8m//VRlL3yHNTODrROnYHdn70RB5ZdmJLq6EtP5uCJ+/PR1aFAKBJSF8jO1bH82y1YelnnsK+fKqzWEiXGtOrtriqoN98O+P0AMOicwoGDYYSuA2vWQH34YWhf//rQHrO5Geof/wj1sccgDh+GHDcO2k03QVu+HMgffkfQO28L3HSTFwcOCCiKhK4DqgrcfLOGe3+uwRPnp3n4PmMzQi1bkGk4Uj0ZOSSrbCJKEDmUOZXw+VG24p/wNgWfEwY9Hw68v5RQO7tQ8vq7qLnsvKQeq/fPVdeR9+l2lHy6DcqhOiArA74T5wLXXgqUxp8uHKmKG67qgBfXfr0CH27IhRASwAhIKXDWcUV47PvvoaSwO/b6qB+7TkQGHDg8aihs14as6yj48Z19oTZB0uNB7p8eHfTxRKq1xes/xBmXLMHkv/4R2bU1yGhtwYiNH+P479yCBd+5JVhKGIZX/p6DR+4pRFdH8FtS6sGvrLNd4Ld3FeGt56zVIshqLVFizBgqpT76JBDwRz32Lern/e53Q3vAQ4eQsWgRPHfcAbFjB0RLC0RlJTzf/S4yTjkFqK8f2nV7rF0rcMEFXhw6FPy9rgsAApom8Nvfqvj612Kn2lCoDQ3USje2INNwpOMFtpv2KeZvqYT3SHPEI26ivaYUUiJ3xx6obe3JP6CmYcwzL2HM869B3V0NpaMLSmMLMv+zCpmfvx3+d1cnfqmeQVPhv+o3bceSyyqw9pPg60QpBaQMfiVvrR+Dz3xrKbp8/eOOUyr1RhzzY3cMtj3s1IbsXfsxPAeqkz7GQgQC8GyL/I55rGqt0t2FhV9bDtXXDSUswCo9LxrLXnkek598LMnV9PH7gL88EO3NBQFA4olf5SPOcbVpMbBaq2gBCH14oZ7I6dIabgMaxKvvDKrSxiOkhLJ7N5BkGAYA75e+BFFVBaHrfS18UgZ/7dwJ71e/mvQ1w919twpNCwXa/qQUeOwxFbsj5K7widCmtB7rOnzVewGwBZmsKxQM3BJu87dUDunzhJTwHmlO+vNGfLAeuTv39F6j93q6BLr9yL3/L+iq2jPksPnHF4/CwfocaNrg17GarmDLnhH459uDzzM3vVorZbAoFO0MXRoStiLbkNLUNKTPk0JA5kffHxFN2SvPI7P5SPQ7CGDKE49g97WfH9Jwqk3vZ6KtOdZ7LAJH6lVsXZ+B2cf7kr5+qo3I8uC4D17D0pdXYPKuLZAQ2DntGLx67uew8bjTzV4ekSWlrS25uxsixtaMWGRWJmRXW+/ve6coxyB27YL62mvRb9c0KM8+C1RXA+OT/7qbmoBXX1F6KxCRqKrEU0+p+N73g2+ymd12LD7ZCuWxpyDe+QBeTYO/uAh5J81H28J5wf5pm2G11vlC+20jDpNyGLWjc8jne+sZSXar6ToKP94Y9WYhJURzGzpfeB/tM6b27slNpgq54tXJ0GNkQ0Xo+MtrU3DN0mC4Nrta62luRdEH61CwaSsUvx9aViZa5s3CkYXzk5q87ZY3YpLl+mBruzZkAFr54HeeEiIlOi+5tN+HEmpD3rgWuuqBEqVkKqRE7sH9yDjSCF9x8i1DLU2JNQ60HDG3wSBUrb3sbw9g2Ut/gSaC6xGQmLxjE/7r/m/huUu+hBcu/aKJqySyrrSE2+wsyII8iJa2+PcNI1UVculpvUEwdEQQEDvgig8/jHttISWUtWuhDyXYHkHMUAsERxzUN5gfaAFAvPYO1NvvAUTf3mZPYxNGvPAmsit3o+76S20VbkOhltVa5+ut3Pb8figBN9mwYUaI9hUXwdvYFLEVORoJIFBYAN/o+Pthw3la2+Fp74h9bUVBdvUhtM+Y2neUUM9tiQTc+uYsxNqYp0sFtUeCQwFDodasNy+89Y0Y/8Q/oHT7ev/81a5uFH2wHnmfbkf19VckdW6uEW3I4YO67Mj1wRawVxsyAASmTUf38ScgY91aiAh7WyPtvZWqCm38BHReevmg+8cbGiUVJaHNvHKIL1ZGj0uslXd0mfktv4t2r8eyl/4CAFBlX8ti6P8v/NfD2DJ3EfZMtfcTA5FRDJ+YrCjQLzsPyp+fTniPrVQEoCjQbrii92ORAi4QIeQm+rwXb7pTFKNGAxkZEj5f9CdhTQPKx3YFH8akQAsAaG6Besf/AFKHCPujD608a8de5K9eh9ZTjzdleUPFUGs+UTDSsAFSA4VXb0MiHW0T7XOTER4T0xW2WubNQt6OPUl9jgDQcNrCpLvypJLY/eWAAaTJBNzyMW1obMmEHuUNQFXRMWVc66Brp52UKH32lX6hNkRICU9rO0a9shI1l59vzvrC12PTwVEAg61tNd/3vyg5Zyng8/ULt1KI3n596fUGj/4JBBA4ahoannoGMicn6ceqXXQaJv/tT1Fvl4qClqkz4C8sSuyCmobS997EqA9XB/+hzzsBo8fdgLpDasTKhFAkyiYFMOVof9JrT5VQtXbJa09DU1SoUfbVaoqK09/4B4MtURxGVm/1G66A8vq7kAdr+u21DX/TT6pqsKoY0IC8PGj33QFEOLcyPChGCrn6qadCqmrENxl7HzcjA/qiRQmvX6xbB/Vf/wI6OlAwYwauvPQLePLpTGha5BduigJ8btHH8FSZ9xwJAMrzrwF+P0SMQlD+mnVoPWWBLc5UZwuyew0MUsM5tiaRxwkP0kYHr44pk9A2fQpyK3cNLoIgNNkEwScWqQNCQf0ZJ6Nt9oykH0vLy4VvRGFwWFWU+whdR0fFhIi31W873O983KyyicDheqhvrIFobIIsLsIXTivFVyqXRl+DrmD5uTtMb0HOPFSLrNrogwR7B3S1tEEriF21ZRtydK4OtnZsQw7xHzMP9a+8gYIffh9Zb6/s+/ix89F8x4+gNDch4+OPANWD7iVnoPv0xYNeSKyqaopbrQWAw6edhfbx5cg+VN1veFSI0HXsuOmWhNadt2cnFt1yHXKrq6D3VDCmPvEwHil6HZeIvwKQ/cKtUCQUBfjyHS2mvw4ake1F+Z5Po4ZaAFB1DRW7t6RxVUT2ZVi4LSxA4E/3Q73/EeDlt/r23I4ZjcAXPgtUTIR470PA54ecORXyrNOABM4vjBhyC3OhXX4Z1Kf/EbFCLBUF2o03AsXF8dfd0gLvNddAff11SI8n+JwdCOCnGQ/gPzmbcKRjYLgNvgz96dd2YFSxuaEWAETlruCao7Q4CgCephYIn98250WyWkuAMS2fka6fln2+QqDmomUoXv0xCj/eCLUreBSOnuFF8/w5aJk9A3mVu6B2dsFfVIDWWdOT2vs58LGaTjwOo19+M+LNUgj4Ro5A56TIwRYIq95OHw387yPIfPm94POMIgBd4kb9OTw+ch0+PDIDmt7/Na0iJM49sRqnjXu/37XMkFlbF/c+AkBmXT064gRbwPjvSbtydbAF7NeGHM4/Zy4a/v0ClAMHoB48AL2kBFrF5N7buy6+NMZnJ056PFj90F9wyuevQFZd8ElBSNm777byi7ei+txL4l7H29KMU266HBlHgmeQKWEDXs5veRovZbXjlnF/w+6dfVXlKTP9WP6dFhw93/wXbQDg98b+fpEAfBlZ6VkMkQMY1ppcXATtJ98GvnUzxL4DkJmZwJTy3jO35fw5w7p8eMiVX78W+v79UFev6a3ehv6rL1mCwM9/ntA1vddcA+WttwCg3wCsib6deL97Fr624D38Z+2Y3jf/xpd24c6bd+PGiw8O62tJmQxvgttWrH8gA8+staZ0tSObZWAbtGFBTFXReOpCNC5agMy6BgASvpLiYKcfgCOjUteK2nLM0fA2NGLEhxsgFQGhS4Te+grk5+HQFecn1MGhPfYist4OBlRICfS8j5iFbrzWcDy+NeOfeGzXWej2B7eH5GX7cfNFlfj20ueDcwhMHgqW6HY9GWfbipHVWrvvrwUYbB1BLyuDXlaW1OckMjQqXPukKXjjuXcw/sV/YtyrL8DT0YaWabOw54rr0DxrbkLXmPjvp5DZUBdxYIGiazi760W8cPadeP0X38WRegXFozSMn2z+vtoVa/b2nlu74bglOOs/K6BG2bsnhYL1xy1O4+qInMGw6m1BPuQQWuiSkpMN/YE7Id/5AMpzr0LW1QNjRsP3xa9AP/MMQPqBjthvzol166G+/nrk26TEZGUvXsA5qHr5Aezcl4P8HA3zZrRYag6Tb85RyPrny1Fvl0Kga/KEIe83TpdUtiBXlANF06ak7HpuJgpGQrY0mL0Mw6W1eutR0T12tHHXBwAh0HDmqWibcRQKN2xGRl0j9MwMtM08Cq1HT4NMYNKy8PkxYvXaqLfnogO/rboYd/3ld/jkwBj4G2owt/wwcjP9pgfakI6KiZBCxBzapWVmoKtsTNxrGVmttfP+WsDFwdbObcipkkgbcrhAbh72Xnk99l55/ZAer+w/z8Y8r0voOsa/9G9M+Mo3McGirwPeWnoFFr/+dwi/D8qAr0UTCnxZ2Xh3ycXmLI7I5tJ2JJARVBVyyUnQlpzU+yEFgFL9SUKfrvzl0Zh7dYWuQ3y8EeOz6zH+eOv9/PLVVAPHz4V/VDE8DUeCZ1SGkQAgJVpOP9GU9SWL1Voym9OOIOouG4PaBEJbJNlV1VD8cd4c7OxC7vuvY8G8GcDIYIW2c0iPZgwtLxctc2eiYNPWiOFWAmg64diYFVvurY3P+v1ABrJzG7Idedtb43apeTra07KWRIVXawGgsWQs/u9bv0Z3ZjYkBHShQBcKJIDOnDzc/50H0Vpo73e7iMzUsH4rGtZvha+mOhiW3KKzM6E2XnR2Gb6UZIX+nho2VaJ2+RUIFBcB6Dk7XfQMNRQCjZcuQ/fUIR5XlyYcGGV9TmiXTFRW2URklU1EyYxSV4eaeKE2pG13Heq3HbbsGwH1Z5+O9qmTAAQnRkv0TY5umTcLR06OPzGee2tjc23F1s2SbUNOleapM5FbtTfqebi6oqJl6vQ0ryp522ceh+8+8CJOfO8lHFW5AVIIVM48Dh+e9Bl0ZyU/dZqIBrN19XYoJk0AtNjHE8ncHKAnNFpFb6jt2SutFRXg0K3Lkb11F7K37oTwB+AfU4L2BXPjTvq0ClZrrcst7cgDOa16myzfyBEpvZ9ZpMeDmsvOQ9aBGuRvroTa0YFAQT5a5s6Me0ZwyYxSQ0OtWjHb9m3IgEuDLduQk29DToW9V12P8a8+H/V2RdeG3OZshNARP5F0Zedh5dIrsXLplelbEJHLhIdbwIAzby1EP+9MKL96BNLni1i4lYoC/dJzAW/8/WjpMjDU9lJVdM6ehs7Z00xY1dCxWktW5oRw21g5+N9Y8fT4e898paPQVToKmbX1kdt4hUB36Sj4SkelZJ2GEgJd48eia/zYhD/FzdX6ZLm2FZltyOlXf/xJ2HPFdQB6WtN6SAASAvvPvQSHliwzaXWRhbchE1H6hVqTATi7NTk/D9oPbwNEMMSGk6oClI+H/sVrzFlbBFFDrc2lslpbYe2ua1tzUztyuFDFzs5BZ099Zr9fjZW7IgbegWrPOxPS4+n3+hHo2fLg9aD23DOMWrKpQn/XbEFOjGuDrVuZ1YYMABACG394Lzbc8TN0lPWdWdY5Zhw2f/tHWHvPA71HcZgtVrWWiNIvPNw6NeDK88+C9pt7IOfO7PtYTjb0qy9G4M/3A/nWaOV1Yqg16ngfTkROPSe0Sw6HXcNtY+Uu7KnPHPTx0MfihVtf6SjsX34l2mZM7Q23Ugi0zZiK/TdeaY9q7RAZHWqd9EaRK1uR3c6MNuReQmDvVTdg75XXI6u2BpASXaPHWCbQhmO1lsha+s68de7eW7loAbRFC6AdaQ4OiioZAWRYp8PIqaGW7MfpZ9rG4oS25HB76jNRUdKNxspdMVuT/SOLcfjiz6C22we1swtadhZkpnWeH1PN6H214ZzyhpH10oTBapDPNmQrEAJdpWPRNWac5UItq7VE1uaG6i1GFALjShlq04QDo+zFKS/Ch8OOlduKku6otyVauQUAmZmBQFGB40MtJc9aiYIMtaqqydxqrY2wWktkba7Ze2sRTg21rNbam5NaKIfCTvsuExkSFalV2W1CRzuFjnoymlOmIYcw5RARkW259tzbNHJqqA0xolpbUc79tUZz0ovx4Qidc2sXsaq2bsdBUcPnqmDr5jZkU4dG2ciKNXtZrSWyIVe0J5vAyaGW1VpncHvVNsQO4TZUta0o6Y4YcN0aekNVWiC9odaJ/3Y4PMpF2IZMRE42cLgU4Oyzb43m5FAbwr219iYKRkK2NJi9DNOFhknZQSjcNlbuihhkE2lZdoKBb0SYVaV1WucDgy1RDw6NInIGN0xPNpIbAq1Rx/sAPL/WDG6ekBySVTYRJYBtpiS7JcCGs0qYdTLXBNsa5Ju9BNOwDTlxbEMmco6+gBv8PQNufG4JtUbj/tr0YdWWrMrKQdaJbciAi4ItANfurwXYhhwPq7VEzsWAmxg3hNoQtiA7D6u29qvaOlV4oLVSmB3IaW3IgMuCLdmfCAQwct0HyGhsQOfYMhyZOx8QIiXXZrWWyNkcH3ClBLbthNh/EMjPhTxubkLn4Lop0HJglDOFqrYMtxSLp6kFmTW1gKKgc8I46NlZKb2+XaYaO7VaC7gk2LIN2RnGP/8MZv/v3ciqr+39WNvECmy842eoW3SaiSsjIjtxYsAVm7dB/cn9EDt2935MFuZDv/l66FddGPUNQDeF2hAjq7XcX2setiT3KZlRyqptGLWtHaNefhO5O/ci9EwoVQXNx8xCw5mnQHqGF4fsEmjDObFaC7gk2AJsQ7a7if9+CvN/+A3IAR/P3V+FRTdfi9WP/A31J5w8pGvziB8idxoYcAGbhtzKXVC/8C3AH+j3YdHcCvXnvwE6u6Avv6rfbW4MtOmq1nJ/rXlEwUjXV23tNCE5HZSubpQ98Qy8zS0If3tPaDoK12+Gt7kVh644f8jdf3YLtU6u1gIuO8eW7Enp7sLs/7kLEsDApx0hdQipY87P7wy24RERJalh/VZbn4Or/voPgD8AoesRb1ce+jPQ1AKg/9fnplAbwr217uD0F++UuMJ1nwRDbYTXiEJK5O7ai+y9+5O+bujs2ayyibYJtSFOrdYCLgi2bm9DdkK1tvTdN5HR2jwo1IYIKVG4fSsKdmxL+tocGkVEIZECruVDbn0jxPtro4ZaAEBAg/6PZ/sFWreFWiOP9wlhG7I1hF60M9wSABRs2BKz8CGFQMGm5J4P7ValDXHDvwlXtCK7uQ3ZCbLqDkMKEfHdtoH3a5k2M+nrsw2ZiMKFh77QWbiARduUG45AxGtWURSIxiOuC7Mh6RwYxTZka+AwKQpR2zuiFkaAYHHE09qW8PXsGmpDnFytBVwSbMneuktGxw21ANBVMjqp67Jam17vrtxo9hKIktY/5PZ93DIht7go/n00DW3tXYYvxcrYguw+DLcEAFpuNkRza9RwK4VAID83oWvZOdS6oVoLuKAV2a2c0oYMADWnnQl/XvSWcikEmo+ayWqtha18Y53ZSyAatlALb8P6rf1alU1tVx41EvrC+ZBKjOd7RUHHnOnpW5OFpKtaW1HOaq0VubUtOatsYr+zVN2s5ZhZMQdDCSnROif+60c7h9oQp1drAYdXbGuQ7442ZCnhXbcWGWs/AlQPuhYvATwlZq8qZfTMLGz+1p049q5vDxogJYUAhMDmb92ZsvNsyRgTi3OwO/7diFJObWxC9o69gKbBV1YK38SyYT9fRGtXDklHRTf0mMq15yNr3SZIKSN2tzSfcRL03BzD12NVrNa6Gyu3sSld3cjdsRtKZzcCRQVon1IOqKrZy0qZ5vlzULBxCzwtbYOeH6UQ6Jw0Hh0ViYVVu4ZaN72x4+hg6wbq7l0ovukGZGzcEAx5AIqkxMmnL8WG//41/IVF5i4wRaouuxZSUTDrV/cg80jfOXUd4yYEz7E96fSkrscjftKH1Voyi+j2ofif/0HOJ5U9HwCEBHylJWi4+gL4S1PzBmCkvavhbcsDJRt6o1WEwx8346YrUfzP/yCjrrH3Y3pmBprPPAmtJy9I6vGcIp3VWrK2gZVbBlwAUmLEqo8wYvXHUDStd5ZJICcbdecsQbtDOhD07CxUX3c5Rr/4BnL39B2DJBUFLXNnov6s0+K+0RmafmxnbqjWAgy2tqbU1WLUuWdDqa8HgH7vRI19701k33wN3n3iuWEfPG0V+y75LPaffzlGfbgKGY0N6Bxbhob5JwCxWvDIEspH5iLQ3WH2MshNdIlRT/wLmXv293V59DxFemsbMPrhJ1HztRugFRUY8vDRBjVFqu4O9VrhfOVlqLntJmRUH4KnoQl6Via6p0yE9Lr7Dbx0VWvZhmwPrN72KX7vQxS/92Hv70OvIdWOTox55iUcvOoidE62d5gL0fLzcOjqi+BtbELmoVpIRaCzfDz0nOy4n2v3lm61YrZrQi3g4GDrhmN+ch/5PZS6eghdG3Sbomko3rwBY1a+ikNnnWvC6owhvV7Unrx4WNfg0Kj0YbWWzJK1ay+ydu+LeJuQEkp3N/JXfYym885I67oMnUwsBHwTxsE3YZxxj2ET6Tjeh+yJ1VtA6ejEiDUfR7xNIPge4MiVq1DtkGAb4i8ugj+RgXs97L6v1k0tyCGODbaA84/5yXnyrxFDbYiuqJjw/D8cFWxThW3I6VM+MrFpg0SplLNxK6QiIPTIE9WFLpG7bkvagy0ZL53H+7AN2b4iDZYaSshNJjxYJUTnbd8NaNHPvhYAsg7Xw9twBP6RI9K3MAuxe6gNcVO1FnB4sHU65Uhj7Nt1DZmN9WlaDVF/K99Yx1BLplHbO6OG2hClqwuQkoPnHCid1Vq2Idtb+Av/oVa4EgkPoRZowPyAq3R2Bp/34hylqHZ2wZ+mNVmJE0KtG6u1gEODrRvakAFAGz8BYntl1DNedVVF+3i+nRyOQ6PSgy3IZLZAUUHMiq1EcN8VQ62zsFpLw2FkdSt07XTs8e06sA/12w5HvT1QUBD1tWP/++UNax2x9qfGWp+ZnBRq3VatBRwabAHntyEDQPvym1D4ve9GvV3RNFRdek0aV0TUh9VaMlPbgrnI/2BD9DsIgbaF89K1HEojVmvJyqwwwKp92mRomRlQun2I9NaeFMHhSoGC5AtFA8NstIAYaSa92WHXCaE2xI2hFnBwsHWDjutuRM5fV8C7ZTOE1n+vrRQC1Z+5CA0LTjRpddbDoVHpwWotWYG/rBStJxyDvA83DnrhJhWBwMgRaF0035S1kTFYrSW7CIVbs0ivB/VLT0fpC69BAv2eI6UQkB4V9WeekvR1kwmGke4zMOymM+g6JdS6bQryQAy2NiZzclD/3EsovON7yPn73yB8PgCAPy8fuz73BVR++RtssxuAbcjpwWotWcGRC5dCKypA/rsfQe3sAhA8u7Bj7gwcOf8MyKxMk1dIqcZqLdmFKBhpatW2dc4M6BkejHxrNTKONPd+vHPCONQvPQ2+0cmd852KYDjwc9MVdJ0Uat3OccG2BvmuaEMOkYWFaPq/3+LVL30HRdu3Qs3womnmHOhZ8c/mchNWa9OD1VqyFEWgZfGJaDllATIOHIbQNPhLS6Dn5pi9MkqxdB7vw2otOUX79KlonzYFGbX1wUFRhQUIjCgc8vVSHQzDr9d1YF+/NudUhVynhVo3V2sBBwZbt/IXFKF54clmL8PSWK1ND1ZryXI8HvjKy8xeBRkknS3IIazWUqqYWbUFEDz/unTUsC5RMqPU8GCY6mquUwItwFAbjsGWiFKC1VoiMgurtWRHZu+1TYVYk4+NFKuaC0QOuokOtrIThtr+HBVs3XLMz0CrqprMXoKlsQ05fVitJaJ0YrWWyHxmB8REBlFFu5+dMdQO5qhgC7jjmJ9IMlTF7CVYGtuQjcVqLRGZhdVaIhrIaSF2IIbayBwXbInCsVqbPqzWOlsilTFWsiid0jkwKoTf40R9zGpDdjuG2ugYbG2ObcjxsVprrJVvrGOodaBIQTZeiKhA5PDLMECplu4WZFZryS66DuxL6/mvTq+MWg1DbWyOCbZuO+YnHNuQiShVwgNDstWwSPevKO9/TYZcShVWa8kJzD7PluyBgTYxjgm2RAOxDdl4rNY6SyiApjIwDLzWwKouwwIli9VaInIThtrEMdjaGNuQ42MbsnE4MMo5jAi00YQ/Bqu5NFSs1hKR0zHQJs8Rwdatx/wAbEMmc7Faa3/pDLUDRQu5DBEUTboHRlWU8/uRKBIOjjJOKNACDLXJckSwBdx7zA9FtmLNXlZrDcRqrTOYGWoHCq2BVVyKhi3IRNbCwVGpEx5mAQbaoXJMsCWi9GK11hmsEGrDsYpLsbAFmYichNXZ1GKwtalVVU1sQ46CQ6OMxWqtM5hxBmiyIlVxGTTcidVaInIKhlnj2D7YuvmYH4qObcjGYrXW3tIdEoarXxUXbFN2m3S3zIdCLb+/iCiVGGiNZ/tgS0Tpw2qtc1i9WhsNq7juxBZkIrIrBtr0YbC1IbYhR8ehUcZjtdbe7FatjYYB1x3YgkxEdsYje9LL1unIzcf8EKUbq7XOYddqbSR7qvq+nqbtuxwT3IktyERWVTKjlBORE8BQm362r9hyfy2FcGiU8VitJatiBdeZGGqJyG4YaM1j64qtG7ENOTa2IRuD1VpncEM1kxVcZzDj742hloiGi6HWXLav2BJRerBa6wxOakOOJdIkZQYXe0l3tZaIaDgYas1n22DL/bVARmMDJj7/NHL37kYgPx8Hll2IplnHmL0sU7AN2Tis1pIdKZqGsrpqjGhuhNwuUFtcCugSUAQDrsWl84xltiCTK0mJ7D37kbtrL4SmoXvMKLQePQ0yg9v7hoqh1hpsG2wB9+2vXVXV1Pv/k/7+OOb+7A4ITYdUFUACRz32EGpOPQMf/eL30HLcV11jG7JxWK0lOyluqsfCT95HRsAHXQgAwOQDu9G2Mxfvzz0ZYAXXstLZgsxQS26ktrZh3FPPIbOuAVIRAASwXkfJG++h5uLPoGPKJLOXaDsMtdbBzZo2k6EqGPvGy5h39+1QAgEIqUMJBKBoAQDA6FUrseD2r5q8SnIKVmudwy17TXM627Fo4yp4Az4AgCIlFCl7buvASRvexb5dAeyp4h5cq0pHtZahllxJ01D25L+RUd8IABC6hNB1CADC58fYf7yIjMN1MS/Bicj9MdRaC4Ot3UiJGb/9JaSI/Fen6DrGvvUK8ndWpnlh5uHZtcZitdY53LC/dnL1Tig9L9QGUiCR3d2J8bX7AfQNmmLAtYZ0tiADDLXkPrk79iCj4QhEz5t94YLPmRJFH6xP97Jsi6HWemwZbGuQ79o25OyaAyjc/imE1KPeV1dVjH3j5TStjJyK1Vqyo7LaaigY/KIt3LjaA/1+PzDgMuSmX7pbkBlqyclKZpRG/Hhe5S5IEeltvyChS+RV7jRqWY7CUGtNtgy2bpWhKlA7O+PfUSjwdHYYvyAL4NAoY7FaS3ajalrM2wUAT8/WjYF4VJA5Qn/O6WxBJnKqWG3Cwu8HIlRrwykBLe593I6h1roYbG2mc0wZtMysmPdRAn60TpmWphWZj23IRBTSmluA6P0sgA6BlryCmNdgwE0/7qslMp6vpBiIUbGVAHzFRVHvw/21DLVWZ7tg68ZjfsKnIWs5Oai6+CroqhrxvlII+PILcGDpeWlaHTnRyjfWsVpLtrR7/JSYP9gUSOwdNzmhazHgGi9df6YMtURAy7xZcauxzcfNTdNq7Ieh1vpsF2wB9x3zAwTbkEO2fvXb6BhfPijc6qoKqShY+7P/g56Vne4lph3bkIlooAOjx+PAqHGQQL+dtqH/ryyfgeb8oqSuyYBrjHS1IDPUktWJgpG9oSlVIu2zDRQVov7MUwCg315bCUAKoHPSeDQfm9p1JEutmD3olxUw1NqDrc+xdSt/UTHeXvE8pj/8AMqfWQFvexukEKg96XRUfuk2HJm3wOwlpg3bkFOP1VqyNSHw8ayFmFy9E1P270ROd3AuQUtuAXaUT8eB0glDvnQogFWU94UyhqWhYaglMk5W2UR0HdgX8bbmE45FoKgQI1Z/jKxDhwEAWm4OmhfMxZET5gNROgKjDaRKlfAAGx4eZUtDv9u0PZsNXUei6yJrYrC1KX/hCGz+9o+w5bbvI6P5CAI5udByGEaIiCAEdk84CrvHT0WGvxtSKPB7vDH3liWDATc1GGqJzNE+bTLap02G0tUNoWnQcrITen40an9trGqomSGXVVr7sVWwdev+2vA25IGk14vuktFpXJE1sA3ZGKzWkqMIAV9G7GF7w8GAOzTpaOXmkT5EwSpr/bbDUW/XszITvo7REgmP0UJuqgMuq7T2ZatgC7hzfy1FxjZkIrKC8MpjBfpCG4PVYEa3ILNKSxQUqx15qNczgloxe0jhMfQ5qaziMtDan+2CLREZY+Ub68xeApHtsYobHUMtkf0YWa1NxWCoWK3KQOygO/C+DLP2x2BrYeHH/FCfFWv2slprELYhE6UGA25/DLVE6ZdVNhElQMx25ESvY5RUhsmB14oUdI18fDKfbYKtG/fXAoi5v5YoVVitJTJGpIALuCuAMdQSmSveXttYn2eUdBzjw9DqPrZKTdxfS2QcVmuJjBM6C9et5+EaEWoryvuGRDHUkhMYEfZC1dZkQ2ro/nap1hIBNgu2bsI25MjYhpx6rNYSpVekgOvEkBv6uowKtQCrtOQcRoa8ZMNtOkItkRFs04rsRmxDpnRhtdYdKsqNPzuUEtdvmrLDWpWNaj8OBVrA/n9GROkUmpIcry05HaE2HW3I5E62CLY1yGcbMvHsWgOwWuseRdOmOLIq6BTRQq4dw5vRodaOfyZEVhAeboH+Q6XCq7npqNSyDZmMYItgSxTCNuTUY7WWyFrsfC6uEaGWVVqi1AmF1vCAG/5xIjtjsLUg7q8lIiIgesgFrBXywrsBjAi1VvpaiYwiCkZCrZgd8+zVVDEryLINmYzEYGtR3F/bH9uQU2/lG+tYrSWykVh7cgFzwh8DLREli23IZBTLB1vur6UQtiETEQUNDJHpDLoDH4eBloiIrMDywdZt2IZM6cChUe5UNG0KKmDM8Stkrkh/pwNblwdKJDxGGzjGacdElCy2IZPRGGwtiG3I/bEN2RhsQyZytljhM1KFN9lrpAIDLVF/6dxnawa2IZORGGzJFtiGnDqs1hKR2VV7BloiIko1Swdb7q8lMgartURkBgZaIndiGzKlA3teLWRVVRPbkAdgG3JqsVpLQP9wQZQu4YOhGGqJogu1IzsN25DJaJau2BIBbENONVZr3a1o2pSE9lYSpQonHRO5mxNDOlkTy4NERESUchXlDLVEw+GkQMhqLaWDZYOt2/bX8pifwdiGnFor31jHai31YjsyGYltx0TDwyBIlDzLBls34v7awdiGTJR6DBpkJFZpiSjESVVnsj4mKSIX4NAoIjIaW4+JUs8JwZDVZ0oXBluyJLYhpx7bkGkgtiNTqrD1mCj17B4InRDKyV4sGWy5v5YAtiGnCqu1FAnDB6UKq7RExrJzQLR7OCd7sWSwdSPuryUjsVpLREZgqCUyll2DoZ3DONkX0xRZDtuQidKH7cg0VAy1ROljx6Bo11BO9sVgazK2IUfGNuTU4BE/FAsDCQ0VQy1R+tgtIKoVs223ZnIGywVbt+2vBdiGTETmYtWWksFQS5R+omCkLaq2dlgjORcTFVkK25BTh0OjKBEMJ5QMhloic9khOLJaS2ZhsCXLYRty6rANmYhShaGWyFxWD4x2CN3kbAy2JuL+WiKygqJpU9iOTDEx1BJZg1VbkkNrsnr4JmezVLCtQb7ZS0g77q/twzbk1OHQKCJKFYZaIuuxUrhlqCWrsFyqctvgKOqPbchE5mDVliJhqCWynlCAtFK4ZaglK7BcsCWi4eHQKCJKBYZaIuuySrjl0T5kJQy2JuH+2v7YhpxabEOmoWDVlkIYaomsz+xwy1BLVmOZYMv9tcQ25OFjtZZSgeHW3RhqiezDrHDLUEtWZKlkxf21RMPHai0NB8MMAfw+ILKTdIZbtWI2Qy1ZlqWCLRERWQOrtu5UUc5QS2RH6Qi3nH5MVucxewFutKqqiW3IYVas2cs25BRgGzKlStG0KWjavsvsZVCaMdQS2dvAcKvt2ZyS64aHZYZasjIGWyIHYRsypVJFObCnyuxVUDow1BI5hygYCdnSMOyAy0BLdmOJYFuDfO6vJRoGVmsp1UJVW4Zb52OoJXKeUBAND7hA/JA7sJWZgZbsxBLB1k14zE9/POYndVitpVRjS7LzMdQSOVt4MB0YchP5HCI7YbA1AffX9sf9tUTWxqqtMzHUErkLAys5HRMWkc2xDZmMFAo+nJLsHBXlDLVEROQ8pgfbGuSbvQQyCduQU4dtyGQkBiDnCL1Bwb9TIiJyGtODLQDXDI7i/trB2IZMZB+s2tobQy0RETmZJYKtm3B/LaXSyjfWsVpLacGWZHtjqCUiIqdjyiJTsA2ZyH4YiuyJoZaIiNzA1GDL/bXuxjbk4eHQKDJD0bQprNraCEMtERG5hekVW+6vJRo6tiGTWRhurY+hloiI3MT0YOsm3F9LRE7A/bbWx1BLRERuw6RFabdizV62IQ8T25DJbAy31hQ6oxZgqCUiIndhsCWyKbYhk9kYbq0lPNAy1BIRkduYFmxrkM/9tURDwGotWQnDrTWwSktERG7Him2acH9tEI/5SQ1Wa8lKGG7NxVBLRETEYEsm4P5aIudhuE0/7qclIiLqw2BLZCNsQyYrY7hNH+6nJSIi6s+UYFuDfDMe1hTcX0upxjZksjKGW+OxSktERDSYaRVbtwyOAri/NoTH/BC5A8OtMdh6TEREFB0TF5FNsA2Z7IThNrXYekxERBQbgy2RjbANmeyE4Xb4WKUlIiJKjCeRO0kpAQAtrW0pedBWSAjV+a3I7+9vBgAE2IqMv3+4DwDgS+xbjiLQfJ0IdAuzlzFkAV8ngL7nE6cIfT1t3d0mr8Sa1InjAQCjfHsAAFX7zVyNvZRPANp8QOHUCgBAaxe/x5wq9Pzh1OfHVL1+JCL3CT1/JPL8mFDKaG1tBQBUHHPiMJZFRMO12+wFpEBraysKCwvNXkbKhJ4fT/z5701eCRHZnVOfH/n6kYiGK5HnRyETiL+6ruPgwYPIz8+HEPatGBGReaSUaG1txbhx46Aozuli4PMjEQ0Xnx+JiCJL5vkxoWBLREREREREZFXOeVuQiIiIiIiIXInBloiIiIiIiGyNwZaIiIiIiIhsjcGWiIiIiIiIbI3BloiIiIiIiGyNwZaIiIiIiIhsjcGWiIiIiIiIbO3/A8uIjJrG92JiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -90,6 +110,8 @@
     "names = ['gamma = 0.001', 'gamma = 1', 'gamma = 20']\n",
     "\n",
     "# iterate over classifiers\n",
+    "classifiers = [SVC(gamma=gamma) for gamma in gamma_values]\n",
+    "\n",
     "for name, clf in zip(names, classifiers):\n",
     "    ax = plt.subplot(1, len(classifiers), i)\n",
     "    clf.fit(X, y)\n",
@@ -146,11 +168,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for gamma = 0.7: 0.9\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "clf = SVC(gamma=0.7)\n",
+    "\n",
+    "# Perform cross-validation and calculate the accuracy score\n",
+    "accuracy_scores = cross_val_score(clf, X, y, cv=5)  # Assuming you have the complete dataset X and y\n",
+    "\n",
+    "# Calculate the mean accuracy score\n",
+    "accuracy = accuracy_scores.mean()\n",
+    "\n",
+    "print(\"Accuracy score for gamma = 0.7:\", accuracy)"
    ]
   },
   {
@@ -162,11 +200,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for gamma = 20: 0.85\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "clf = SVC(gamma=20)\n",
+    "\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "accuracy = clf.score(X_test, y_test)\n",
+    "print(\"Accuracy score for gamma = 20:\", accuracy)"
    ]
   },
   {
@@ -178,11 +231,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for gamma = 40: 0.85\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = SVC(gamma=40)\n",
+    "\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "accuracy = clf.score(X_test, y_test)\n",
+    "print(\"Accuracy score for gamma = 40:\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for gamma = 5: 0.9\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "clf = SVC(gamma=5)\n",
+    "\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "accuracy = clf.score(X_test, y_test)\n",
+    "print(\"Accuracy score for gamma = 5:\", accuracy)"
    ]
   },
   {
@@ -204,7 +292,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -218,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,