diff --git "a/pca practice and T-SNE.ipynb" "b/pca practice and T-SNE.ipynb"
new file mode 100644--- /dev/null
+++ "b/pca practice and T-SNE.ipynb"
@@ -0,0 +1,3572 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0713720f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "308ed275",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d0=pd.read_csv(\"train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f66c5ca7",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>label</th>\n",
+       "      <th>pixel0</th>\n",
+       "      <th>pixel1</th>\n",
+       "      <th>pixel2</th>\n",
+       "      <th>pixel3</th>\n",
+       "      <th>pixel4</th>\n",
+       "      <th>pixel5</th>\n",
+       "      <th>pixel6</th>\n",
+       "      <th>pixel7</th>\n",
+       "      <th>pixel8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pixel774</th>\n",
+       "      <th>pixel775</th>\n",
+       "      <th>pixel776</th>\n",
+       "      <th>pixel777</th>\n",
+       "      <th>pixel778</th>\n",
+       "      <th>pixel779</th>\n",
+       "      <th>pixel780</th>\n",
+       "      <th>pixel781</th>\n",
+       "      <th>pixel782</th>\n",
+       "      <th>pixel783</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 785 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
+       "0      1       0       0       0       0       0       0       0       0   \n",
+       "1      0       0       0       0       0       0       0       0       0   \n",
+       "2      1       0       0       0       0       0       0       0       0   \n",
+       "3      4       0       0       0       0       0       0       0       0   \n",
+       "4      0       0       0       0       0       0       0       0       0   \n",
+       "\n",
+       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
+       "0       0  ...         0         0         0         0         0         0   \n",
+       "1       0  ...         0         0         0         0         0         0   \n",
+       "2       0  ...         0         0         0         0         0         0   \n",
+       "3       0  ...         0         0         0         0         0         0   \n",
+       "4       0  ...         0         0         0         0         0         0   \n",
+       "\n",
+       "   pixel780  pixel781  pixel782  pixel783  \n",
+       "0         0         0         0         0  \n",
+       "1         0         0         0         0  \n",
+       "2         0         0         0         0  \n",
+       "3         0         0         0         0  \n",
+       "4         0         0         0         0  \n",
+       "\n",
+       "[5 rows x 785 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d0.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "08a64546",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42000, 785)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d0.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "876cf7bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=d0.drop(\"label\",axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0724ad9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42000, 785)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d0.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1f248295",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l=d0[\"label\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f6f9596",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42000,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb6f7276",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#after droping the label column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2d320aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42000, 784)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3fa60053",
+   "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",
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+       "\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>pixel0</th>\n",
+       "      <th>pixel1</th>\n",
+       "      <th>pixel2</th>\n",
+       "      <th>pixel3</th>\n",
+       "      <th>pixel4</th>\n",
+       "      <th>pixel5</th>\n",
+       "      <th>pixel6</th>\n",
+       "      <th>pixel7</th>\n",
+       "      <th>pixel8</th>\n",
+       "      <th>pixel9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pixel774</th>\n",
+       "      <th>pixel775</th>\n",
+       "      <th>pixel776</th>\n",
+       "      <th>pixel777</th>\n",
+       "      <th>pixel778</th>\n",
+       "      <th>pixel779</th>\n",
+       "      <th>pixel780</th>\n",
+       "      <th>pixel781</th>\n",
+       "      <th>pixel782</th>\n",
+       "      <th>pixel783</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 784 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
+       "0       0       0       0       0       0       0       0       0       0   \n",
+       "1       0       0       0       0       0       0       0       0       0   \n",
+       "2       0       0       0       0       0       0       0       0       0   \n",
+       "3       0       0       0       0       0       0       0       0       0   \n",
+       "4       0       0       0       0       0       0       0       0       0   \n",
+       "\n",
+       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
+       "0       0  ...         0         0         0         0         0         0   \n",
+       "1       0  ...         0         0         0         0         0         0   \n",
+       "2       0  ...         0         0         0         0         0         0   \n",
+       "3       0  ...         0         0         0         0         0         0   \n",
+       "4       0  ...         0         0         0         0         0         0   \n",
+       "\n",
+       "   pixel780  pixel781  pixel782  pixel783  \n",
+       "0         0         0         0         0  \n",
+       "1         0         0         0         0  \n",
+       "2         0         0         0         0  \n",
+       "3         0         0         0         0  \n",
+       "4         0         0         0         0  \n",
+       "\n",
+       "[5 rows x 784 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ca353ffb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#taking some of the data to train:\n",
+    "data=df.head(20000)\n",
+    "label=l.head(20000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f5bf0629",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(20000, 784)\n",
+      "(20000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.shape)\n",
+    "print(label.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0b9cc18a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20000 entries, 0 to 19999\n",
+      "Columns: 784 entries, pixel0 to pixel783\n",
+      "dtypes: int64(784)\n",
+      "memory usage: 119.6 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "13574fd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 20000 entries, 0 to 19999\n",
+      "Series name: label\n",
+      "Non-Null Count  Dtype\n",
+      "--------------  -----\n",
+      "20000 non-null  int64\n",
+      "dtypes: int64(1)\n",
+      "memory usage: 156.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "label.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f435f9ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visualizartion technique:\n",
+    "#importing the matplot lib and to pyplot:\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bc9b9f68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20000, 784)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#we have to take sample data from the bunch of data to reprasention and an tooo analysing:\n",
+    "#and this the data that we have to work for now to train and and to analyze the result:\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd22d944",
+   "metadata": {},
+   "source": [
+    "# applying the PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "31a088cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#we take the sample data 20K fro our analysis: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b5d76cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data preprocessing - STANDERDISE the data :\n",
+    "#importing the preprocessing.stdScaler class from the sklearn laibrary:\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "std_data = StandardScaler().fit_transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "50768fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20000, 784)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#here we will chekc the shape o fthe standerdized data:\n",
+    "std_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "79a53cce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#now we will compute the co-variance matrix on the std_data:\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "co_data=np.matmul(std_data.T,std_data)\n",
+    "\n",
+    "\n",
+    "#here the data is tranposes and computing the covariamnce to the original data:\n",
+    "#from this we canget the samples for our result:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d87fa010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "co_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a1dbb55b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(784, 784)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "co_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a5a9f531",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from the co_data we can do cumpute on the eigan values and the eigan vectors:\n",
+    "from scipy.linalg import eigh\n",
+    "value,vector=eigh(co_data,eigvals=(782,783))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3a175908",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([581382.27258531, 801125.51413557])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1436a2c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.],\n",
+       "       [0., 0.],\n",
+       "       [0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0.],\n",
+       "       [0., 0.],\n",
+       "       [0., 0.]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "afb310ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector=vector.T\n",
+    "#transpose the matrix(vector):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a45ed57e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d942edf9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 784)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a3de69eb",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       -2.42014289e-02, -4.10744639e-03,  1.17896178e-02,  1.65781992e-02,\n",
+       "        1.70266903e-02,  2.05778342e-02,  1.95153707e-02,  8.53496708e-03,\n",
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+       "       -4.38328551e-03,  1.64532363e-03, -3.99259451e-03, -1.18230778e-02,\n",
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+       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "84f37cf9",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       -2.61638392e-02, -5.67895801e-04,  1.08144445e-03,  1.21601055e-03,\n",
+       "        3.40995342e-04,  5.76912839e-04,  5.43675008e-04, -1.77873027e-03,\n",
+       "       -8.87917820e-03, -2.84856231e-02, -4.88645937e-02, -6.03834884e-02,\n",
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+       "       -1.05946254e-02,  6.35802753e-03,  2.84618193e-02,  3.91761505e-02,\n",
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+       "       -7.35683334e-02, -9.64390906e-02, -1.00936065e-01, -8.63796699e-02,\n",
+       "       -5.19181277e-02, -7.91407109e-03, -7.04060004e-04,  3.85867260e-04,\n",
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+       "        4.17848302e-03,  2.78012252e-02,  4.31600536e-02,  4.78287697e-02,\n",
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+       "       -3.03731819e-02, -6.68942761e-02, -8.34141090e-02, -8.43020363e-02,\n",
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+       "        1.42003168e-02,  3.85090659e-02,  5.04643483e-02,  5.14667785e-02,\n",
+       "        3.06818228e-02,  5.71187199e-03, -2.38727870e-02, -5.56567970e-02,\n",
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+       "       -5.74124324e-02, -1.37536473e-02, -3.76559464e-04, -6.36551696e-06,\n",
+       "        2.93258856e-49,  1.04684335e-49, -2.35956277e-03, -9.07758227e-03,\n",
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+       "       -7.24298555e-02, -4.38702114e-02, -1.75995080e-02,  1.10250167e-03,\n",
+       "        1.93729587e-02,  4.13102905e-02,  5.21491213e-02,  5.01910890e-02,\n",
+       "        2.83416419e-02,  4.19239536e-03, -2.71033556e-02, -5.89515328e-02,\n",
+       "       -8.44109831e-02, -9.97854026e-02, -9.98249307e-02, -8.57082319e-02,\n",
+       "       -5.39114799e-02, -1.31628643e-02, -4.33658223e-04, -3.62681661e-05,\n",
+       "        1.74435053e-49,  2.50258715e-04, -2.64182895e-03, -1.08033804e-02,\n",
+       "       -4.31055548e-02, -8.26063269e-02, -9.80469659e-02, -9.79096406e-02,\n",
+       "       -7.87649794e-02, -4.92381180e-02, -2.24798756e-02, -2.03648998e-03,\n",
+       "        1.73163990e-02,  3.70363496e-02,  4.88272874e-02,  4.53404137e-02,\n",
+       "        2.57929883e-02, -6.25411388e-05, -3.40962928e-02, -6.55204552e-02,\n",
+       "       -8.87843560e-02, -1.00207332e-01, -9.62445250e-02, -7.81107032e-02,\n",
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+       "       -4.64653646e-02, -8.45557383e-02, -1.00756895e-01, -1.01674673e-01,\n",
+       "       -8.44942671e-02, -5.53027402e-02, -2.81896337e-02, -5.47582633e-03,\n",
+       "        1.43765533e-02,  3.29609071e-02,  4.34956813e-02,  3.79781095e-02,\n",
+       "        1.72558333e-02, -1.37621777e-02, -4.82142694e-02, -7.64730876e-02,\n",
+       "       -9.47091999e-02, -9.97937494e-02, -9.07667930e-02, -6.83635184e-02,\n",
+       "       -3.78072503e-02, -1.00323778e-02, -4.92754389e-04,  1.09108677e-03,\n",
+       "        3.46260290e-50,  1.72604321e-04, -2.84758133e-03, -1.60604308e-02,\n",
+       "       -4.73302440e-02, -8.24666754e-02, -9.76331996e-02, -1.00235807e-01,\n",
+       "       -8.55014793e-02, -5.92594302e-02, -3.26544145e-02, -9.21989283e-03,\n",
+       "        1.02301364e-02,  2.71096954e-02,  3.35299440e-02,  2.30349732e-02,\n",
+       "       -2.06631386e-03, -3.47202239e-02, -6.57357739e-02, -8.70044384e-02,\n",
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+       "       -2.78445599e-02, -8.16226690e-03, -2.94668797e-04,  1.61248142e-03,\n",
+       "       -1.93543422e-49,  1.52099166e-04, -2.57087314e-03, -1.64781628e-02,\n",
+       "       -4.45128859e-02, -7.58855518e-02, -9.13903418e-02, -9.55172266e-02,\n",
+       "       -8.58090713e-02, -6.53576406e-02, -4.09554994e-02, -1.97722958e-02,\n",
+       "       -1.74344665e-03,  1.20915887e-02,  1.50900590e-02,  2.12699524e-03,\n",
+       "       -2.49104105e-02, -5.39591832e-02, -7.76598718e-02, -9.11488608e-02,\n",
+       "       -9.23191607e-02, -8.27525614e-02, -6.41336711e-02, -4.16603606e-02,\n",
+       "       -1.89769325e-02, -6.21861246e-03, -9.60941439e-04,  1.43153169e-03,\n",
+       "        3.43491426e-49,  1.95358763e-04, -2.70455832e-03, -1.40903260e-02,\n",
+       "       -3.82841284e-02, -6.42023009e-02, -8.15754629e-02, -8.77920976e-02,\n",
+       "       -8.46685809e-02, -7.19667381e-02, -5.34437764e-02, -3.52355873e-02,\n",
+       "       -1.93638172e-02, -7.29601628e-03, -6.37367403e-03, -1.94075602e-02,\n",
+       "       -4.21672276e-02, -6.36959637e-02, -7.83634753e-02, -8.25213704e-02,\n",
+       "       -7.75481158e-02, -6.44683641e-02, -4.66712555e-02, -2.86948024e-02,\n",
+       "       -1.39588474e-02, -5.02384693e-03, -9.60800201e-04,  8.82067509e-50,\n",
+       "        1.47024560e-03,  8.22823241e-04, -2.74830396e-03, -1.08448626e-02,\n",
+       "       -2.87928630e-02, -4.87571627e-02, -6.50157710e-02, -7.44323310e-02,\n",
+       "       -7.56905955e-02, -7.00033459e-02, -5.91755278e-02, -4.49970311e-02,\n",
+       "       -3.15937275e-02, -2.08651032e-02, -1.98291957e-02, -2.96526228e-02,\n",
+       "       -4.42673969e-02, -5.76326508e-02, -6.39462010e-02, -6.33745930e-02,\n",
+       "       -5.58831060e-02, -4.38105354e-02, -3.08128525e-02, -2.04751172e-02,\n",
+       "       -1.09854011e-02, -3.72211084e-03, -5.07874202e-04, -7.53836201e-49,\n",
+       "        1.47024560e-03,  1.47024560e-03, -1.47149349e-03, -7.32124094e-03,\n",
+       "       -1.75001104e-02, -3.15334943e-02, -4.38068689e-02, -5.21722053e-02,\n",
+       "       -5.50798048e-02, -5.13952471e-02, -4.37751690e-02, -3.28535923e-02,\n",
+       "       -2.19110122e-02, -1.42692270e-02, -1.24757672e-02, -1.82964496e-02,\n",
+       "       -2.80439640e-02, -3.64896855e-02, -4.13459153e-02, -4.05255145e-02,\n",
+       "       -3.37491146e-02, -2.63353620e-02, -2.01415581e-02, -1.42126465e-02,\n",
+       "       -8.79015464e-03, -3.47315212e-03, -6.29952022e-04, -1.01957459e-49,\n",
+       "        2.94966644e-49, -1.34617968e-49, -7.35795577e-05, -2.92316627e-03,\n",
+       "       -6.32702181e-03, -1.20475041e-02, -1.84515083e-02, -2.31912693e-02,\n",
+       "       -2.45358279e-02, -2.14404936e-02, -1.52315596e-02, -8.38855737e-03,\n",
+       "       -7.71566241e-04,  4.05473132e-03,  4.58277173e-03,  6.05925450e-04,\n",
+       "       -6.61791917e-03, -1.26429011e-02, -1.75904624e-02, -1.86092174e-02,\n",
+       "       -1.80936854e-02, -1.63924800e-02, -1.32265212e-02, -8.17363940e-03,\n",
+       "       -4.80884517e-03, -1.17889112e-04,  8.09200003e-04,  3.23780374e-50,\n",
+       "       -2.05854182e-49,  3.64989355e-49,  7.37494875e-05, -5.41581730e-04,\n",
+       "       -8.22538102e-04, -1.73563303e-04,  2.43968337e-04,  1.18830276e-03,\n",
+       "        4.32477642e-03,  9.68416425e-03,  1.57295662e-02,  2.01928921e-02,\n",
+       "        2.41457043e-02,  2.40361763e-02,  2.15792670e-02,  1.65911423e-02,\n",
+       "        1.07963538e-02,  4.50556100e-03, -1.82081890e-03, -6.62058211e-03,\n",
+       "       -1.00992194e-02, -9.75715539e-03, -8.11284833e-03, -5.24231401e-03,\n",
+       "       -2.13774362e-03,  2.57708829e-04,  6.54490729e-04,  3.06420753e-49,\n",
+       "        4.84326935e-49,  1.68908599e-50,  1.09122578e-04,  2.32452388e-04,\n",
+       "        1.18852224e-03,  2.22137912e-03,  4.14530504e-03,  7.39386055e-03,\n",
+       "        1.20251124e-02,  1.73213469e-02,  2.30901147e-02,  2.84889736e-02,\n",
+       "        3.10031530e-02,  2.94513545e-02,  2.63767960e-02,  2.19858156e-02,\n",
+       "        1.63696140e-02,  1.03459498e-02,  4.05238714e-03, -4.65843709e-04,\n",
+       "       -4.04173991e-03, -4.32221407e-03, -3.67356775e-03, -2.71944435e-03,\n",
+       "       -1.09190003e-03,  3.01573300e-04,  4.40428596e-04, -7.49006312e-50,\n",
+       "        4.65704984e-50,  2.18297350e-49, -3.52513744e-49, -1.58050582e-49,\n",
+       "        1.49548504e-03,  1.82910415e-03,  3.20680453e-03,  5.28241190e-03,\n",
+       "        7.72044634e-03,  1.09725750e-02,  1.55568609e-02,  1.93382056e-02,\n",
+       "        2.11911393e-02,  1.97552119e-02,  1.69053536e-02,  1.41883772e-02,\n",
+       "        1.11740776e-02,  7.47255465e-03,  3.53431864e-03,  1.34960472e-04,\n",
+       "       -1.69272076e-03, -1.48844415e-03, -6.86431962e-04,  1.91229798e-04,\n",
+       "       -2.80661498e-04, -7.63516628e-50, -4.27927199e-49,  2.68228719e-49,\n",
+       "        1.57058444e-50, -1.70077015e-49, -1.41367382e-49, -1.30920271e-49,\n",
+       "        2.53732809e-49,  4.55984210e-04,  9.73248949e-04,  1.58867372e-03,\n",
+       "        1.40593258e-03,  1.90623575e-03,  4.23763016e-03,  4.65771701e-03,\n",
+       "        4.79939594e-03,  4.93293731e-03,  5.39699548e-03,  4.51771891e-03,\n",
+       "        2.78035778e-03,  1.53127348e-03,  6.95493664e-04,  5.31972584e-04,\n",
+       "        6.19149923e-04,  1.24334019e-04,  7.42072864e-05,  0.00000000e+00,\n",
+       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "03f7dd23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f4180eef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 20000)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_cord=np.matmul(vector,std_data.T)\n",
+    "#here the eigan vector will computed with ethe std_data:\n",
+    "\n",
+    "new_cord.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "da5ca35a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  5.43069721,  -6.24299872,   1.82861099, ...,   5.46973995,\n",
+       "        -16.30829738, -10.74668112],\n",
+       "       [  5.06086205, -19.29314824,   7.68449823, ...,   0.08480173,\n",
+       "         -2.96182323,   5.19074485]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_cord"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5e6de351",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  5.43069721   5.06086205]\n",
+      " [ -6.24299872 -19.29314824]\n",
+      " [  1.82861099   7.68449823]\n",
+      " ...\n",
+      " [  5.46973995   0.08480173]\n",
+      " [-16.30829738  -2.96182323]\n",
+      " [-10.74668112   5.19074485]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_cord.T)\n",
+    "new_coordinates = np.vstack((new_coordinates, labels)).T\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "df9bee54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_cord2=np.vstack((new_cord,label)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "ebbcbbad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  5.43069721,   5.06086205,   1.        ],\n",
+       "       [ -6.24299872, -19.29314824,   0.        ],\n",
+       "       [  1.82861099,   7.68449823,   1.        ],\n",
+       "       ...,\n",
+       "       [  5.46973995,   0.08480173,   6.        ],\n",
+       "       [-16.30829738,  -2.96182323,   8.        ],\n",
+       "       [-10.74668112,   5.19074485,   7.        ]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_cord2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "78722b18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20000, 3)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_cord2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "214a98a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b49c8b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating new data frame for our good reference:\n",
+    "df=pd.DataFrame(new_cord2,columns=('1st principle','2nd principle','label'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "368a8734",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array('1st principle', dtype='<U13')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b4d8d681",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50 rows × 784 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
+       "0        0       0       0       0       0       0       0       0       0   \n",
+       "1        0       0       0       0       0       0       0       0       0   \n",
+       "2        0       0       0       0       0       0       0       0       0   \n",
+       "3        0       0       0       0       0       0       0       0       0   \n",
+       "4        0       0       0       0       0       0       0       0       0   \n",
+       "5        0       0       0       0       0       0       0       0       0   \n",
+       "6        0       0       0       0       0       0       0       0       0   \n",
+       "7        0       0       0       0       0       0       0       0       0   \n",
+       "8        0       0       0       0       0       0       0       0       0   \n",
+       "9        0       0       0       0       0       0       0       0       0   \n",
+       "10       0       0       0       0       0       0       0       0       0   \n",
+       "11       0       0       0       0       0       0       0       0       0   \n",
+       "12       0       0       0       0       0       0       0       0       0   \n",
+       "13       0       0       0       0       0       0       0       0       0   \n",
+       "14       0       0       0       0       0       0       0       0       0   \n",
+       "15       0       0       0       0       0       0       0       0       0   \n",
+       "16       0       0       0       0       0       0       0       0       0   \n",
+       "17       0       0       0       0       0       0       0       0       0   \n",
+       "18       0       0       0       0       0       0       0       0       0   \n",
+       "19       0       0       0       0       0       0       0       0       0   \n",
+       "20       0       0       0       0       0       0       0       0       0   \n",
+       "21       0       0       0       0       0       0       0       0       0   \n",
+       "22       0       0       0       0       0       0       0       0       0   \n",
+       "23       0       0       0       0       0       0       0       0       0   \n",
+       "24       0       0       0       0       0       0       0       0       0   \n",
+       "25       0       0       0       0       0       0       0       0       0   \n",
+       "26       0       0       0       0       0       0       0       0       0   \n",
+       "27       0       0       0       0       0       0       0       0       0   \n",
+       "28       0       0       0       0       0       0       0       0       0   \n",
+       "29       0       0       0       0       0       0       0       0       0   \n",
+       "30       0       0       0       0       0       0       0       0       0   \n",
+       "31       0       0       0       0       0       0       0       0       0   \n",
+       "32       0       0       0       0       0       0       0       0       0   \n",
+       "33       0       0       0       0       0       0       0       0       0   \n",
+       "34       0       0       0       0       0       0       0       0       0   \n",
+       "35       0       0       0       0       0       0       0       0       0   \n",
+       "36       0       0       0       0       0       0       0       0       0   \n",
+       "37       0       0       0       0       0       0       0       0       0   \n",
+       "38       0       0       0       0       0       0       0       0       0   \n",
+       "39       0       0       0       0       0       0       0       0       0   \n",
+       "40       0       0       0       0       0       0       0       0       0   \n",
+       "41       0       0       0       0       0       0       0       0       0   \n",
+       "42       0       0       0       0       0       0       0       0       0   \n",
+       "43       0       0       0       0       0       0       0       0       0   \n",
+       "44       0       0       0       0       0       0       0       0       0   \n",
+       "45       0       0       0       0       0       0       0       0       0   \n",
+       "46       0       0       0       0       0       0       0       0       0   \n",
+       "47       0       0       0       0       0       0       0       0       0   \n",
+       "48       0       0       0       0       0       0       0       0       0   \n",
+       "49       0       0       0       0       0       0       0       0       0   \n",
+       "\n",
+       "    pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
+       "0        0  ...         0         0         0         0         0         0   \n",
+       "1        0  ...         0         0         0         0         0         0   \n",
+       "2        0  ...         0         0         0         0         0         0   \n",
+       "3        0  ...         0         0         0         0         0         0   \n",
+       "4        0  ...         0         0         0         0         0         0   \n",
+       "5        0  ...         0         0         0         0         0         0   \n",
+       "6        0  ...         0         0         0         0         0         0   \n",
+       "7        0  ...         0         0         0         0         0         0   \n",
+       "8        0  ...         0         0         0         0         0         0   \n",
+       "9        0  ...         0         0         0         0         0         0   \n",
+       "10       0  ...         0         0         0         0         0         0   \n",
+       "11       0  ...         0         0         0         0         0         0   \n",
+       "12       0  ...         0         0         0         0         0         0   \n",
+       "13       0  ...         0         0         0         0         0         0   \n",
+       "14       0  ...         0         0         0         0         0         0   \n",
+       "15       0  ...         0         0         0         0         0         0   \n",
+       "16       0  ...         0         0         0         0         0         0   \n",
+       "17       0  ...         0         0         0         0         0         0   \n",
+       "18       0  ...         0         0         0         0         0         0   \n",
+       "19       0  ...         0         0         0         0         0         0   \n",
+       "20       0  ...         0         0         0         0         0         0   \n",
+       "21       0  ...         0         0         0         0         0         0   \n",
+       "22       0  ...         0         0         0         0         0         0   \n",
+       "23       0  ...         0         0         0         0         0         0   \n",
+       "24       0  ...         0         0         0         0         0         0   \n",
+       "25       0  ...         0         0         0         0         0         0   \n",
+       "26       0  ...         0         0         0         0         0         0   \n",
+       "27       0  ...         0         0         0         0         0         0   \n",
+       "28       0  ...         0         0         0         0         0         0   \n",
+       "29       0  ...         0         0         0         0         0         0   \n",
+       "30       0  ...         0         0         0         0         0         0   \n",
+       "31       0  ...         0         0         0         0         0         0   \n",
+       "32       0  ...         0         0         0         0         0         0   \n",
+       "33       0  ...         0         0         0         0         0         0   \n",
+       "34       0  ...         0         0         0         0         0         0   \n",
+       "35       0  ...         0         0         0         0         0         0   \n",
+       "36       0  ...         0         0         0         0         0         0   \n",
+       "37       0  ...         0         0         0         0         0         0   \n",
+       "38       0  ...         0         0         0         0         0         0   \n",
+       "39       0  ...         0         0         0         0         0         0   \n",
+       "40       0  ...         0         0         0         0         0         0   \n",
+       "41       0  ...         0         0         0         0         0         0   \n",
+       "42       0  ...         0         0         0         0         0         0   \n",
+       "43       0  ...         0         0         0         0         0         0   \n",
+       "44       0  ...         0         0         0         0         0         0   \n",
+       "45       0  ...         0         0         0         0         0         0   \n",
+       "46       0  ...         0         0         0         0         0         0   \n",
+       "47       0  ...         0         0         0         0         0         0   \n",
+       "48       0  ...         0         0         0         0         0         0   \n",
+       "49       0  ...         0         0         0         0         0         0   \n",
+       "\n",
+       "    pixel780  pixel781  pixel782  pixel783  \n",
+       "0          0         0         0         0  \n",
+       "1          0         0         0         0  \n",
+       "2          0         0         0         0  \n",
+       "3          0         0         0         0  \n",
+       "4          0         0         0         0  \n",
+       "5          0         0         0         0  \n",
+       "6          0         0         0         0  \n",
+       "7          0         0         0         0  \n",
+       "8          0         0         0         0  \n",
+       "9          0         0         0         0  \n",
+       "10         0         0         0         0  \n",
+       "11         0         0         0         0  \n",
+       "12         0         0         0         0  \n",
+       "13         0         0         0         0  \n",
+       "14         0         0         0         0  \n",
+       "15         0         0         0         0  \n",
+       "16         0         0         0         0  \n",
+       "17         0         0         0         0  \n",
+       "18         0         0         0         0  \n",
+       "19         0         0         0         0  \n",
+       "20         0         0         0         0  \n",
+       "21         0         0         0         0  \n",
+       "22         0         0         0         0  \n",
+       "23         0         0         0         0  \n",
+       "24         0         0         0         0  \n",
+       "25         0         0         0         0  \n",
+       "26         0         0         0         0  \n",
+       "27         0         0         0         0  \n",
+       "28         0         0         0         0  \n",
+       "29         0         0         0         0  \n",
+       "30         0         0         0         0  \n",
+       "31         0         0         0         0  \n",
+       "32         0         0         0         0  \n",
+       "33         0         0         0         0  \n",
+       "34         0         0         0         0  \n",
+       "35         0         0         0         0  \n",
+       "36         0         0         0         0  \n",
+       "37         0         0         0         0  \n",
+       "38         0         0         0         0  \n",
+       "39         0         0         0         0  \n",
+       "40         0         0         0         0  \n",
+       "41         0         0         0         0  \n",
+       "42         0         0         0         0  \n",
+       "43         0         0         0         0  \n",
+       "44         0         0         0         0  \n",
+       "45         0         0         0         0  \n",
+       "46         0         0         0         0  \n",
+       "47         0         0         0         0  \n",
+       "48         0         0         0         0  \n",
+       "49         0         0         0         0  \n",
+       "\n",
+       "[50 rows x 784 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "fd30ac40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "e51e7d8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "28ff7a54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MsR/9aL4UDki9wkLmJNgc+nrsqrqZAmBIxLLxABYKIboBWCj/nhAik6Zd8544aZrBdgOjIuuXjTbUaU2gZvY6pDb4SGltwVH8Z2VNV7lvNkb3f7db5N9QGTB/dd26/7hmb5tXF+Xj8In4RpTGiuQTRYlc7bm2ZvyQfwjZ42fjkf85O9tkiLLDgFKofcVo1Z4Z8U64t0DRw+eej+29c074xlghxBIAkZWnwwB8IP/8AYDhdhxLS6y+r3rsnvfEaslZjd0fgsgvV57BevXbLc7rHw+3HtOo9bxbM2J9+c2OrDbqz5/VtCsdPF5hqq1Bi5k7I7PMTBIWac/RUtfq/TfsSZz5iqxwsjG2lRBiLwDI/7dU24iIxhJRLhHlFhXF/0zR6CHa6krKKvG6yWkP4pVMc10/M8vYPC5a1P5Uu/58M1UcvpU8HyVDrn9bfYIxIz6Os9olHk/OtGesiTE+nutGCDFZCJEjhMhp0aKF48d7btYmXz00JFEcL48uqTo1bW0yi/fiF0iiQgNLPE4m+v1E1AYA5P+tzf5kk1UqD8pwyvdbtacG8Js3Fkd3XX3W4l1CpBEpS3BxSp6t+0wWdlQr+UXkADcvGe3K6zWyq3qBiLIBzBJC9JZ/fwnAISHERCIaD6CZEOIRvX3k5OSI3FzjfcuVekwah4zm+jMXhgTLs5BSJzwJVxwegIxm0b0oRKAOKFW9/j9YlYnAyU6gtGNIq2/uDa84dD4ymuv3IgmWZyFY2QxpmdKkU4Gy1gic6Br7dZWNkJJ+DMGKJkjJOBq1vqrkVKTU2ae6LnpfjZGSrl8yDx1PS/nBS1AnS78dJHCyA1LrSw2Kau+PGUKkIFjeEql11QfVKI9lVOWx3iCqQlpDeWqNQD2UH7wUdVvNRrCqIVLSShAob4nUOtHlmYojZyOjaU3bRv20+iitkqZYCJS3QGqdIlSWnIb0hsbqnUUwDSABIv2E1yQjC0crws9j1YnOSGsQPoBJBOqCUmNPaRBL6G/RYvZ9DZS2Q7CyKdIb1bQ/lBcNRkbzJaCUioht2yK1ntR7p/JYL6Q3Cq9yUfscK7erPNYn7DgAEChrhdS6NZ0AglWZSEnTn8o4WNUAKWnSgDQRTAOlhHfdrDiaA1HZCHVa1PT2CpS2RbCyGYa0HotXRl6su38tRLRKCJGjud6ORE9E/wFwMYAsAPsBPAngSwCfAugA4FcAI4UQui0w8Sb6kooSDPzPQNOvY4yxxEH4+db4ZqSNlehtGTAlhLhBY9UgO/YfS0XAuYcgMMaYO3zcGMsYY8xZvkj0IujsEGnGGEtmvkj0qHR3Nj7GGEsm/kj0pe51mWSMsWTji0Qvdhh7mj1jjNVGvkj0js4GxBhjSc4fiZ4xxpgmTvSMMeZzvkj0sZ4ExBhjtZlPEj1jjDEtvkj0jDHGtPki0XPVDWOMafNFoi914NF9jDHmF75I9PuK43vQL2OM1Qa+SPTpxdFPN2KMMSbxRaJPDag/AYoxxphPEj1jjDFtnOgZY8zn/JHoyesAGGMscfki0W8CP3iEMca0+CLRF4EfDs4YY1p8kegZY4xp40TPGGM+x4meMcZ8zheJPuh1AIwxlsB8keh5RnrGGNPmi0QvuB89Y4xp8kei9zoAxhhLYL5I9FxHzxhj2nyR6PkJU4wxps0XiT7IiZ4xxjT5JNEzxhjT4otE3wRpXofAGGOW9C1z7gFKjid6IhpCRFuIKJ+IxjtxjGxR14ndMsaYay4ode7Z144meiJKBfAGgCsB9ARwAxH1dPKYjDHGwjldou8PIF8IsV0IUQFgOoBh9h+GG2MZY0yL04m+LYACxe+F8rJqRDSWiHKJKLeoqCiug3QCV90wxpLbxSedq7pxuhVTbXKCsOK3EGIygMkAkJOTE1fRPL1pX/ycNzfsAMk0K0IoXifidupcRO439MZpveFevR9qx9aLVe81pLPeakx27Ecv7mT7Tlhl5O81ek60tjO63OzrneB0oi8E0F7xezsAe+w+iKDwPyPZPtAU8b8T+3Z6v3rH8fL9UDt2rHhivcbq32PX+TDyHjj52UpkRv5eo+dEazujy82+3glOV938BKAbEXUiogwAowHMdPiYjDHGFBwt0QshqojoPgDzAKQCeE8IscHJYzLGGAvn+EgjIcTXAL528hjEvW4YY0yTL0bGQvAkCIwxpsUXiZ440TPGmCZfJHoeMMUYY9p8kei5RM8YY9o40TPGmM/5ItHzjPSMMabNH4meq+gZY0yTLxJ9Rb0sr0NgjLGE5YtEH0yt73UIjDGWsHyR6BljjGnjRM8YYz7HiZ4xxnyOEz1jjPmcLxI9965kjDFtvkj0jDHGtPki0XOJnjHGtPkj0XOmZ4wxTf5I9LXu0ceMMWacLxI9Y4wxbZzoGWPM5zjRM8aYz/ki0fdo3dDrEBhjLGH5ItGnclssY4xp8kWiB3GmZ4wxLf5I9IwxxjRxomeMMZ/jRM8YYwmgSDR2bN+c6BljLAE4OcKfEz1jjPkcJ3rGGEsATs7NyImeMcYSAFfdxMT96BljTItPEj1jjCW3YKKW6IloJBFtIKIgEeVErHuUiPKJaAsRXWEtTMYYY/FKs/j69QCuA/Av5UIi6glgNIBeAE4BsICIugshAhaPxxhjvuRkBbSlEr0QYpMQYovKqmEApgshyoUQOwDkA+hv5Vi6eK4bxliS+ynYw7F9O1VH3xZAgeL3QnlZFCIaS0S5RJRbVFTkUDiMMZbYqpDq2L5jVt0Q0QIArVVWPS6EmKH1MpVlqt1EhRCTAUwGgJycnLi6khKX6BljTFPMRC+EGBzHfgsBtFf83g7Anjj2wxhjzCKnqm5mAhhNRHWIqBOAbgBWOnQsCOHkmDLGGEtuVrtXXktEhQDOBTCbiOYBgBBiA4BPAWwEMBfAvc72uOGqG8ZYcnOyuGqpe6UQ4gsAX2isex7A81b2zxhjzDoeGcsYYz7HiZ4xxhLAUdHQsX1zomeMsQRwHHUd2zcnesYY8zlfJHruXMkYS34JOnslY4yxxOePRM9TILBkleZcvSxjIf5I9IwxluT4mbGMMcbixomesSQ3snyC1yGwBMeJnrEkdxz1vA6BJThfJPoU8sWfwWqjS5/wOgKWIITg7pW6UrjTDUtWA+/3OgJWC/gi0TNzSkWG1yEwxlzEiZ4x5ql3q670OoSEIHhkbAw8YMoUJz9QzH3J/H4eE/XxbNXNlvZxWGTaFI1/+SPRM1PyxSleh8AYAJ6nyi2c6GuhOyoe9joExmyTTHc0R0UDzXU8MpbZ6gj4VjfhNe3kdQTMAV5dlDjRM+aQa8qfjf/FY7+1LY5ElkylcTt4VVXFiT7RNe5g+y5r25fLK+tEl/hfXK+Jue3v/Sn+YzHXaH3zvg70xweByx07Lif6RNf1Uq8jYMmgRXfbd7k00Nv2fdZ2pFGm33HpW3jxpgsdOy4n+kTXspft9bVcove3oeXPeR2CYa9UjfA6BE2fZd7g2rHuvaQrhvRu49j+fZLo3U9c2WXTXD8mAODMWy3vIuiXtz1k3Ir4X3uGtT7cseQG7S9px1KO5Bn5PCUwJO7Xzgmcrb2yofWkmXnxQ9EL0+tb2ic3xjJ1kYPBrnnVmzgSWcvT4n/tuffaEsIl5S+rLi/W6U6X6LSqGRLFFtEegEacv19gef9X9GoVvfC3H+m/6Kli3dXcGOuxqsYdvQ5BR2J/4RJe3SaOH2KHcO62m6nTLRs3bufMQTNbWnp5o7rpNgViDid62eGbFwE3fW7uRde940wwSmrTO/Qd7fxxndA2x6MDJ+iFssWpXkcQpVRkAF0vA+BMNcNeZNm+z2SS5tFUu5zoZSIjE2hkcmqAvqOcCSbk1KHA6TchquxyyhnOHtcpd3wDpKR5HUW4FBtLWJc+Ib1nhiVeo/ha0QWoY/+Aur0PFOCmikdxZ/pE1fXZZR+b3meCXr4TEid6JWHgo1O3MYaXP+N8LABw5YtAel13juWGlBRgwiF79tXCQr28UkoKssumoUfZFHwd6G9tXxc+DIw2n7ASSfMGNQ25dtfRfx/sg6Oao7I1Lnq9rou5X+caOB3YbzudBmQHJVjxKk5uzl454F483eVWrNut0ujSqg+w/2fdly8O9MMlqWstBlELyjIdBgK/LtNef9NnQFUZkGqkh4nO5yNNegxfOTJQijrmYjSrWRfg8DZnj2FRt5YNHdkvxZs0B00ANs0EglX2BuQVswPhbMIlelnd9NTohS17qW7br30T3DwgkRtvfaDDOfrriYDmXYAm7eM/xk2fAY1cbEQd+g9HdvuXq3vauDf3ChHFp1xg6fUpzbsCALYEjX8GtgVrZ6O5TxK99RJ943pqdbX2f+i1b11VpIce+pzAJfiWdiYZhUtsepZqm9O113UdHLEg/vN8Tb94pn7WP14AEYWPwU+pbtcg3cYbc0X1paP3yakZ2HLZVAMbap+jB+7/E7YM/QL/CxgfUWq5msdI9a7CW1VXWzueTXyS6GNIqxd7Gy1jv7MvjmteQ7fT9T+UR896oOaX+s3UNzr9RkthLH3kEu2VZ99pad+azI7uTY2RvIx+4e76Dm5cKJs1sH+Q0sS+c9G77N+271dfAhYqSOVuW3ay5RnO1dGnW8gbsorI2vH63vQ6spToieglItpMROuI6AsiaqJY9ygR5RPRFiK6wnKkXjnldCDL4OjGWJ+3rpehd9vGupscOXd87OPU1d9HLO2b6Yzuq9/c0r41PbDGmf36QabKwBwAFan1cRzWRmImMmH0Ym3D4KdI79Ufg15l76qv7D0CmHAYSHOgzaZBc+D8P9q/3xislui/AdBbCNEXwC8AHgUAIuoJYDSAXgCGAHiTSOeynCiMNOpaafjVe+3gp4GO58W/b68Y+bJmdU++xz2q3dmMWyGNnbjwkahVd17Y2YWgbPDwNmDkFK+jCJORGiMNtelr27FaNjLSi42AFOvp6uPz5qmv0LiwO8lSohdCzBdChJrDVwAIDUcbBmC6EKJcCLEDQD4Ai33XEoVDCev8h4Dbv3Zm32aozYIoD6DR5dSdgKsU722v4dGrG7aWxk5c+njUqrZN1G/zwy6Dmhc7o58pGz57DbKAXtcaPJp91TihNrCbz82OWiecqi7qEz3OpVFdFzoatuwJtOyJgWf0QaKMlbCzjn4MgDnyz20BFCjWFcrLkosNE4i5LtXg7WbOHeG/d7pImqejkcrb1Fpvulr5S3rrLJ1tvPmwP0zyLXI7lTJGp4uM7+iUM6RzU6+p6RiqXOjB3Kx1eK+TEWcm3letXkYqdk68Cne5eeczQnvk+uDTWiG7eTzVYoqLUsfz1TcZtxwYtxydshrgwUu7Rq/3oItlzERPRAuIaL3Kv2GKbR4HUAUgNFpE7ZutetkmorFElEtEuUVFRfH8DbHFW22gm+BU9B8bK5CYo1otp0Sj85JbLl0a3Z93FqYMlH5Qu9u4dWbUoqF97O16N2lUP+RcJI+U7TPS9OuNlHOPn/MHdGgf/nCauy+y8MATpzhYcxvPozE7NKuP2Q+Y6N7ZoEX0soz6eL7v/JrfyUi5maQ7javdnZwwZmRCiMFCiN4q/2YAABHdCmAogN+JmtaVQgDKYkY7AHs09j9ZCJEjhMhp0ULlZCaTM2+JvU2HAbqrO6qVMob8zUQQBhPu2b83sU+FftZ6/NjG1IXFWNVAnbQ4b3A1kth1Z7ZDVqZcJ2zwbmB6w9uBZsZLvZmXPRq90OJUurZJbyDdCT1VLI2KjodeGxCloEfrhtiSqf+dCqMcmWrm4tOwteri8tQ4ZidNSQHOcre2wGqvmyEA/gzgGiHEScWqmQBGE1EdIuoEoBuAlVaO5Zjm3WJv09695gVSS2Ct+0j/6/UJN0tzal/5i6UswYS+bB0GAte+ZV8MdrrxUyBnTNgi1+4x9KZ1CHs/Y0fU54anzfVQUusZ0tTCYD5FYp0T7I+yxl2khB3fzuKPQ88lT0jdnlPTUT8jDcsfixwPoUN5wc2oD4z8wFgbVNwizoFHd75W6+hfB9AQwDdElEdEbwOAEGIDgE8BbAQwF8C9QoiAxWNps3Ly7lwI3L9af5urJsXez93fx94m3jgbtwXGzANunxN7W9soYu0k9/2PN343Ptzdr4iqHqk5rNXjx3i9jX9fr1Oiu86u73ir7cfRdeEj2J3aFjMCA7FpxEKguY316qFJ5C4zOV+U8iJeJ1Pq9mxF6Fz2Gg40dL8XjNus9rrpKoRoL4Q4Xf53t2Ld80KILkKIHkIINzOUOXUbS0Pp9ShLTVrftVCpW0+otNQtjmEFHQZIJRCrLn5Mffn5D4X/rkwqkaVGZfwmRwqqGv4WcM9y6/uJYldidKhk2vliZ/cfr1Y9cU/TyTiGTPkOM8Z5vO7fwPC3VUYaq0hJkapyBtxT/VcbmgfnqkkG2sDi1E1+KPfAB7S3GTBO+r9JeHvIRd0Vd76ddQYieswfk5olgwH31jy0wMmSWax9X/zn6GVqT8XRS+AjpwAvRDZcWvibQiN9x8wDvnoQKNoc/76UESVa+7AyoAfXAo0cejiGjQiIfSL7yndSdTKBfMXgJjungCYy2NiJqGQcU89hwGN7gc2zo9eNmgrkvi99RlVGpA86TXE3EOvpUwC86oFWO6ZAUDNyCnDXUveON+QFxRcm0TKQSWF3FjoXBK0ujEP/AfxWZTrfDgOkutc/bLQUXsg9pnufOFCy7n29dLcX+cjCJh1rpnnQuKiGFue3HS7N499zmOp21a54AbhNJVnFISykLoNMvXZFUG7/sftjHqpL12sze3h7fM8QzqgP1fe/5zDgli9N7CMx1d4SvZFBI1k9Ym9z29fA0V3mjh1qLGvVG9i/3txr3Wa0IU6t1NdrOLBDZa6giEbT8OPVldokbDDm/ND8Oh5WjTRoXtN+U1yosZF+fMcadjY2j7/yYtKkI3DqVcCKN43FGRFLaBATEYw/LMaOajw93QYDfzkIpOrcKTSwYeBewt0KWld7E70RmQa6e2afB8Dk1AUZcvLsNTzxE31mi5pb4e4x2hYGPQksfFplRRJ+cSzOJ6SqucrgGac8tE7633SilzTIkFJDahyPvhNCfo2VvH/VJOBEUXRXU70kb4TTF6NYWpkcm2MTTvQsXIY8+KRJe+DEAfnnDsAjO9T7giu/OBf8USPRJ5A7FwHBGB3A+ow01rhulkZf7IQy8H4AwGs3nIH/ripEzzaNAJNNJvGm0jcz78e41C+B4gKgy6VAM5MznpriVuFDcZwH8hz+m7T5JNFHvGlNOgBHf5V+Pv+PwI9vO3csK+wsXJz/B+lh0yss9nNv3VvqW9ysM/AvxchBrSmTq7ldalc5XhMD/cfbnhV7GyO9R+zkRinzsmeAyjLt9XUaA+XFQLY0rL9lo7q49xJ37kBC1UTz612JceOeBSpP1tz12mXUVGBj9GhoW1z/HtBaa+I1xXvrUZIH/JLodWeFfNLmRG8HB5Ji6KEUVhM9IFUpHQo98s5CrG7fJttUt2/ZjZ/Gvmtw23kPGtxQ5f2OY46feEg9Ocn+JA9Ijao9hwFzNboXW9F7hPY6r6uKZLW31w1zb7pUvQtxg5buxGBE6GlZkRO7aY090NL9CuDU36ivu/kLoOfwiIWJkQw0OTQJV5P6Un17lxbm56ph5vijRJ/MMlsDx/eFL7trCVB6NL79Ne0I7M0ztu3d3wNHC2JvZ0aDFlIjmhF/2Cj1vTbi7DulBPrx9frbNWwDlOwNX3bW7cC6TzRf8s4tOeiUVR/IulKqumiXE75B98uNxWhEl0ulf0pZ3YFfnRgwJlPO72KXP20F/t7NUmn/1NaN8NEd5yAlBbjxnR9tDE5Pgl9UHcIleiecdZvxbcd+C9z8ZfiyNv2Aziam0VUa9gYw6kNj22a2BNoZqLPWkyo/Qi9Uar9zUc26WN3UGrc13rulfnOgW5xzknQ8V3f1ZT1boWvLhtKozcgk74bR05zb9wNroj9fugwmwsyWwNBXgN8vjCOoGud3y0KdNA+eSaT62ZSXGR2YFfdx3MclerNi3caGRpmumqK9Td1G0v8ZDYBGbaR/8TrrtvD51us0BHpeU/P7gHHAnjXOlRhHfwys+bCm62CTDlI8hStrvjBWu8TFo8Wp7h8zXjEbuiOk1QWqdBpWlbRmwszIBCqO1/weTz7KuV118QnIM3YO+kscO/VQz2HS9+QSg1V1lz8Xexuuo/fAFS9Y38dZ6h9uU865R4qlv8UHcXe6CLj6n8AZv9PeZshf4+tF0qQjcOpQqUeBnmadgEET1EsuLU4FLnzY4NDwGIyWjEL17LfZ8LSu+1fbNtI0pitfNHZxGvVhfCM/I5l6VJ65q0AAqcgum2bi850YyRBpGcDQSdJTuIxoYWBApRHn3mfPfnTUrhL9WbcB8yy2utvwLEmkZUQPiTfrsb011SZOSE2TSutaLnkcWBprVk8CLn3C1rBiGjkF2LvWnhGSzbvEnvDOLufcJf2LRXm35jOJUcnhgSued/wQtSvR+0mseTV6DgMat9ffxoqLHpH+OWh7sDXaUxHSKYCoNKBVwq/bCOhk4slBCUz3War35Vrb+dWvAgueBIp3A8FKa/uqliAlcz0JUpXittpVdVObjJrqSknBSZdWTMKbgQQtwTr6sIpwqpe0rG7Sv3j1Gi7NoHnvj7Gr5yKFqsc0xJtK3U3BLt0/ODEmIA4+SfS19qYvQdn3laVELSXeqN1dM6k07yIN+Kl+rq6B79I4uxv2ffz9DY1VcbsKM4I/q24GPQl8dof2eiOt5cw6O7uWJUg3tWp2tNUkkltmAtsWOjY4qtbqdwMQrFKdy95N/kz0fa7XTvRqD9lgCSsg5IRqZ99mFq1Je3PjP2yVoHdtdtB7EPhtX7s2bQd/e2qTRJpuwIDs5vUxOXCV1B019Cg35lsJds/mvOzzgKbZrhzKnyV6pu6Mm7yOwJTZD1yAkxUBoKHOpFE+Vks7iMTn+veBTV8Z2LB2nlRO9CxhNaiThgZ1+CPqefuE18c3ovd10j+jkuFvshF/i5xUrxlQetjrKODLUkydhua2f2CNvQ+rZslhzDyg9IjXUXjOH4m+RXevI4h2zzKpTvzvLj4+LhY/lWLM9k/WmvOF+VuHAV5HkBD80RjbrDPw+L7Y28Vi56PeWvUy9sxZPwo9iMHJkbks8aRLo7WLhbmLMLdFOM8fiR4A0utZ30e9psBNn1nfT213zt3ShdfKrJws+XQdDAyZiHfqxzdZH7lxx5kp9zyrZ3LGUKW7lkpzKiURf1TdxHL9+8DSl4E0Gy4GLDYiey68LLkQAQPuwbSeZdi495jX0agb+KB0p9lnZPz7aNNX+pdE/JvoL34MyJLrx3sMkf4xlkT+7/IeKK0M4Poz23kdiimtGtVFq0Z1vQ5DXWoa0HeU11G4zseJ/s/27q/fjcBaB58ExFiEZg0yMGnU6V6HwXzAP3X0Trv2LZ4+IRGcnlyDvvzBR721ail/JfpMG3vNsMR0zWv29LBiCaNuujSfUbMGDj5Ip5bzV9XN2G+Bos1eR8GclJICpHBDr5/0OqUR/npdH/ymN/fScoq/Er3VB20rtU6uVnXGHHfa1cBlz9q+WyLCDf072L5fVsNfVTd2MvqAYMZqi7R60sPgWdLhRB+pkdyVrf053sZhp6byl5OnAWCsVrJUdUNEzwIYBiAI4ACA24QQe+R1jwK4A0AAwANCiHkWY3VHy1OlBy836+J1JPbpPQJo1Jbn/WCslrJaon9JCNFXCHE6gFkAJgAAEfUEMBpALwBDALxJRMnz7LWsblKjn18QAR3P9dekZowxwyxlMyGEcpxzA9TMhzsMwHQhRLkQYgeAfAD9rRyLMeaRlqdK/3e6wNs4WNws97ohoucB3AKgGMAl8uK2AFYoNiuUl6m9fiyAsQDQoYNPW97TTU6py1giadMP+FM+d1BIYjFL9ES0gIjWq/wbBgBCiMeFEO0BfAzgvtDLVHalOhmpEGKyECJHCJHTooUPp/W9fzXw0M9eR8GYNZktuOovicUs0QshBhvc1zQAswE8CakEr5yMvB2APaaj84PmPmrUZeF+9xlQUeJ1FIzFZKmOnoi6KX69BkBoWOpMAKOJqA4RdQLQDcBKK8diLOF0Gwz0utbrKBiLyWod/UQi6gGpe+UuAHcDgBBiAxF9CmAjgCoA9wohAhaPxRhjLA6WEr0QYoTOuucBPG9l/4wxxqzzUWdxxhhjajjRM8aYz3GiZ4wxn+NEzxhjPseJnjHGfI4TPWOM+RwJoTozgSeIqAhSf/x4ZQE4aFM4duK4zOG4zOG4zPFjXB2FEJpzyCRUoreKiHKFEDlexxGJ4zKH4zKH4zKnNsbFVTeMMeZznOgZY8zn/JboJ3sdgAaOyxyOyxyOy5xaF5ev6ugZY4xF81uJnjHGWARO9Iwx5nO+SPRENISIthBRPhGNd+F47YloMRFtIqINRPSgvPwpItpNRHnyv98oXvOoHN8WIrpCsfwsIvpZXvcqkbXntRHRTnl/eUSUKy9rRkTfENFW+f+mbsZFRD0U5ySPiI4R0UNenC8ieo+IDhDResUy286P/LCdT+TlPxJRtoW4XiKizUS0joi+IKIm8vJsIipVnLe3XY7LtvfN5rg+UcS0k4jyPDhfWrnB28+YECKp/wFIBbANQGcAGQDWAujp8DHbADhT/rkhgF8A9ATwFIA/qWzfU46rDoBOcryp8rqVAM6F9JzdOQCutBjbTgBZEcteBDBe/nk8gL+5HVfE+7UPQEcvzheACwGcCWC9E+cHwDgAb8s/jwbwiYW4LgeQJv/8N0Vc2crtIvbjRly2vW92xhWx/mUAEzw4X1q5wdPPmB9K9P0B5AshtgshKgBMBzDMyQMKIfYKIVbLP5cA2ASgrc5LhgGYLoQoF0LsAJAPoD8RtQHQSAixXEjv2lQAwx0IeRiAD+SfP1Acw4u4BgHYJoTQGwHtWFxCiCUADqscz67zo9zX/wAMMnLXoRaXEGK+EKJK/nUFpGcva3IrLh2enq8Q+fWjAPxHbx8OxaWVGzz9jPkh0bcFUKD4vRD6SddW8m3TGQB+lBfdJ99qv6e4PdOKsa38c+RyKwSA+US0iojGystaCSH2AtIHEUBLD+IKGY3wL6DX5wuw9/xUv0ZO0sUAmtsQ4xhIpbqQTkS0hoi+I6ILFMd2Ky673jcnztcFAPYLIbYqlrl+viJyg6efMT8kerUrmSt9RokoE8BnAB4SQhwD8BaALgBOB7AX0u2jXoxOxH6eEOJMAFcCuJeILtTZ1s24QEQZkB4i/195USKcLz3xxGF7jET0OKRnL38sL9oLoIMQ4gwAfwQwjYgauRiXne+bE+/pDQgvTLh+vlRyg+amGsexNTY/JPpCAO0Vv7cDsMfpgxJROqQ38mMhxOcAIITYL4QICCGCAN6BVK2kF2Mhwm/HLccuhNgj/38AwBdyDPvlW8HQ7eoBt+OSXQlgtRBivxyj5+dLZuf5qX4NEaUBaAzjVR9RiOhWAEMB/E6+hYd8m39I/nkVpHrd7m7FZfP7Zvf5SgNwHYBPFPG6er7UcgM8/oz5IdH/BKAbEXWSS4yjAcx08oByfdi7ADYJISYplrdRbHYtgFCPgJkARsut5Z0AdAOwUr6FKyGiAfI+bwEww0JcDYioYehnSI156+Xj3ypvdqviGK7EpRBW0vL6fCnYeX6U+7oewKJQgjaLiIYA+DOAa4QQJxXLWxBRqvxzZzmu7S7GZef7ZltcssEANgshqqs93DxfWrkBXn/GYrXWJsM/AL+B1Lq9DcDjLhzvfEi3SusA5Mn/fgPgQwA/y8tnAmijeM3jcnxboOgpAiAH0hdlG4DXIY9WjjOuzpBa8NcC2BA6F5Dq7xYC2Cr/38zNuOT91QdwCEBjxTLXzxekC81eAJWQSkZ32Hl+ANSFVDWVD6nXRGcLceVDqosNfcZCPS1GyO/vWgCrAVztcly2vW92xiUvnwLg7oht3TxfWrnB088YT4HAGGM+54eqG8YYYzo40TPGmM9xomeMMZ/jRM8YYz7HiZ4xxnyOEz1jjPkcJ3rGGPO5/wclPzSFLWoAcQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(new_cord2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7edb4d79",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Could not interpret value `1st principle` for parameter `x`",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [59]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatterplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1st principle\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2nd principle\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfull\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m     44\u001b[0m     )\n\u001b[1;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/relational.py:808\u001b[0m, in \u001b[0;36mscatterplot\u001b[0;34m(x, y, hue, style, size, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    793\u001b[0m \u001b[38;5;129m@_deprecate_positional_args\u001b[39m\n\u001b[1;32m    794\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatterplot\u001b[39m(\n\u001b[1;32m    795\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    804\u001b[0m     legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    805\u001b[0m ):\n\u001b[1;32m    807\u001b[0m     variables \u001b[38;5;241m=\u001b[39m _ScatterPlotter\u001b[38;5;241m.\u001b[39mget_semantics(\u001b[38;5;28mlocals\u001b[39m())\n\u001b[0;32m--> 808\u001b[0m     p \u001b[38;5;241m=\u001b[39m \u001b[43m_ScatterPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    809\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    810\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx_bins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx_bins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_bins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_bins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    811\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mci\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mci\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    812\u001b[0m \u001b[43m        \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_jitter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx_jitter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_jitter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_jitter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    813\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    815\u001b[0m     p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[1;32m    816\u001b[0m     p\u001b[38;5;241m.\u001b[39mmap_size(sizes\u001b[38;5;241m=\u001b[39msizes, order\u001b[38;5;241m=\u001b[39msize_order, norm\u001b[38;5;241m=\u001b[39msize_norm)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/relational.py:587\u001b[0m, in \u001b[0;36m_ScatterPlotter.__init__\u001b[0;34m(self, data, variables, x_bins, y_bins, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend)\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m    572\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m    573\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, variables\u001b[38;5;241m=\u001b[39m{},\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[38;5;66;03m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[1;32m    582\u001b[0m     \u001b[38;5;66;03m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_default_size_range \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    584\u001b[0m         np\u001b[38;5;241m.\u001b[39mr_[\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39msquare(mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    585\u001b[0m     )\n\u001b[0;32m--> 587\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    589\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39malpha \u001b[38;5;241m=\u001b[39m alpha\n\u001b[1;32m    590\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend \u001b[38;5;241m=\u001b[39m legend\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/_core.py:605\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, variables\u001b[38;5;241m=\u001b[39m{}):\n\u001b[0;32m--> 605\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    607\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m    608\u001b[0m \n\u001b[1;32m    609\u001b[0m         \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[1;32m    610\u001b[0m         map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/_core.py:668\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    667\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 668\u001b[0m     plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables_longform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[1;32m    673\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/seaborn/_core.py:903\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    898\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[1;32m    899\u001b[0m \n\u001b[1;32m    900\u001b[0m     \u001b[38;5;66;03m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m    902\u001b[0m     err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret value `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` for parameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 903\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m    905\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    906\u001b[0m \n\u001b[1;32m    907\u001b[0m     \u001b[38;5;66;03m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m    908\u001b[0m \n\u001b[1;32m    909\u001b[0m     \u001b[38;5;66;03m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m    910\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, pd\u001b[38;5;241m.\u001b[39mSeries):\n",
+      "\u001b[0;31mValueError\u001b[0m: Could not interpret value `1st principle` for parameter `x`"
+     ]
+    }
+   ],
+   "source": [
+    "sn.scatterplot(\"1st principle\",\"2nd principle\",legend=\"full\",hue=\"label\",data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "bb0158f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PCA()"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#incitialiting the pca:\n",
+    "from sklearn import decomposition\n",
+    "pca=decomposition.PCA()\n",
+    "pca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e788089f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca.n_components=2\n",
+    "pca_data=pca.fit_transform(std_data)\n",
+    "plt.plot(pca_data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "86f6920d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20000, 2)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "af957eaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca_data2=np.vstack((pca_data.T,label)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "b6715e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-5.06088033, -5.43072442,  1.        ,  1.        ],\n",
+       "       [19.29311167,  6.24344421,  0.        ,  0.        ],\n",
+       "       [-7.68449363, -1.82832579,  1.        ,  1.        ],\n",
+       "       ...,\n",
+       "       [-0.08484574, -5.46956027,  6.        ,  6.        ],\n",
+       "       [ 2.96175295, 16.30891386,  8.        ,  8.        ],\n",
+       "       [-5.19080478, 10.74615999,  7.        ,  7.        ]])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_data2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "6b013d46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca_df = pd.DataFrame(data=pca_data, columns=(\"1st principal\", \"2nd principal\", \"label\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "f9adf254",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YnOfmvvmjo8cVit6gz4umFCeeVI5QQggefPDBLvXxdhYZdiVqdDlLIunk5OOxBVKrVj9h+PzjGUzQlSxBV3pvj4fYm53e6LM+nVk4qVQJrOKEogT3NCRV5Km3AJn5Iyd+6Y8ZMyZlZNj+ySe8XV9PyNbxo2YNBpkxoKPTK1a8im2Pcq34PefL1dHHjfyPLZYCwuHkal+LpSDdj8GQdLIEx+vx3JtrUygU/RflpXya0bR8ORnt7Wmdm2hUYVQcpf9/YvGT3q6S/9yfmfrRJjLa2kBKMtramPrRJvKf+zOQPEauKmjjGXE7H9kWACLO/zh2ctDtgUdYF74gbr169W5vc7wez3B8U5B2rFvDU3d8nV/ftICn7vg6O9atSfu5CoWi71AR7mlGzaOPUW6zsem8qXFRpxlNTU08+uijzJs3z7A4SkrJqlWruPvuuw2jr2BVFWdJyVmHDsUfj/RbG4mXV1p42fZtHrjgseixxKiy0ZbNM+HvEQw4mGN7B58vk4MHJlJbW5zW53A8GNlNQvoez6ki5PxOnttVa0iFQtF/UIJ7mqELIEDFOeW0Z2RAJ2YjenrZbIhBqhS1raSE4JHkPVlbxEnKTKQO+wJMeX9bdI+0LRhKEuaAxc4L4Vuxresw5M9wZfHsjzfQ2uAja6CTGdeMYNS04xdhPe192BcAKQ0/s2x/ej2cqSLkzgwbU1lDKsFVKPo3SnBPM74oL2frkDNpz8hIO7UMpJwYlMoEI/vieYzf8D5n7euwKBQuF0V33wVgOqBA0BFJphK5VmdH36TVYsNedyZLy6x8Mm8AUoBoreKadxu5Ke13mkxiRGp4gyIlU/d+DvNndHq9lFOQOjFaa6mv69JxhULRf1B7uKcRFRUVfDRuLO2ZmSCE9nsPkMoEo8XvZ/N5U/ninHNACGyDB1Pyn/9B7oIFgPGAAgEk2RObROFZXi9IsISc5LaN4p2ys/i4zIm0CBACaREsC3mo9Pm7/f6MIlIjpvhb07re8cyqzc43LgpzZWWpfV2Fop+jBPc0YtWqVQQTBxGk6V2dis5MMIJSsm36NMbu2E7FX15kXv7waLEQwK9Gn8kZTjsCOMNpTxZbnYS124OSi7eGKDx6Afm107A2FfDJSGfyexKCen/yYId0SWdvNtvnibvxqKp+zdQFy+gmI91ZtbNvugWbwxl3zGKz4Wtv1zyapYzu6yrRVSj6F0pwTxMqKip6xQbQao23MExlgpFYkRxbLBQ7Ru4Mk0jPFfCT5W0HKcltC3HlpjYmHIqPXGUvzB3pLPK0hUN8Lz8jeuOhz7CNtafcvv0eduz8f4DWOpR4k/Gr0Wem1VJkZA1pd7mRCZOiUo38UygUfYPaw+3nGJkdQPoWjvo19J7a7mA2jMBut5OdnZ10rpnRRapioVixeWB4CXduO0DQ0iHmtlCQmXsrmOJvZWDtebQ2xBcOATgzrQjZ86L7wPCS+D1cOtLeZzjtPDB8SNz69+/7VZI9JUiOHPkLeXmTKSm+huuKB3a7ZzfRGvLXNy0wPE/t6yoU/QsluP2YxHFxTU1NvPbaa0gpCYfD0WNGBhWxGKV5dex2OzabzdDqMXH83ZAhQ5KEvqGhIe6mwO12Y7Va42bz6iYY/1WfXjvNdcUDOXToEL+tb6fF6SbL52Ha/m2Ma6xh3oIFuDxFrFm8k6A/HH2OzWFhSlkbF398kLenlMenlaUk32E8TCAdumrfaO52JY/LetKIqurXGL9oH7YMH4FWG0c2FtG4TytiM9vvVSgUfYMS3H6MkVAaDZnvbExbqlTygkjxUjoewkZORytWrIh7rsfjQQBOfwCf3UaGz8cFQ4dSXl5O6fvbDCuSjVK2d583kXkxQi6EIBDp+Z03bx5zF43hg9f2xbX/iAcW8eMjR8hou5Xlsy8hbLFgCYe5+uMPKJ12tulnkA7pRKRRxyzxIvmylhtYzEzWx51zPNaTieipa3umFu07soMMuVC7fusXRYYj/xQKRd+hBLcf05U911TnpkrzlpeX07R8OVMrPou2C2U7nVxy1VVp2Qi2tLQkm2EA1oCfG1/RRhwL10qa8vN5YOrMpNRsqmIh/fUTo/zly5ezYMECbv3FzLjzd1RpYnPXkme5a8mzHQ8IwdFpv+30vaSDmYdyfOuQoE4U8bS8HSBOdF3Ozguj0sUodW2xS0pn1HPW5f/RY325Ru+5M4MOhUKRjBLcfoyZUJqda8a8efNYtmxZNA0NYLFYmDdvHh/+6U+8t3077WNGk9HezvQPPmRoTQ0lEyZAGoJrFHEDmqFGhINFRby+YQPtH3/MvLPK+Gj4eGrDRL+8y2oO8+hfnzXck+7KUPjOTDaOl1QOUUb7037hYqlcFBVcI0/o48EsWrZn+rsktrs3VidlC3SzELP3/Giw+1XfCsXpiqpS7scYDX+3Wq1YLPF/bemMaRMJrTJCCA4dOsTb+/ZF3abaMzPZdN5UDhYVUfPoY2mtMbFKWcfh87F8wVUsufEGPpwxnXaXC4Az/rGHG9e/ycp8C5vPH09ZzeEkf2Z9QL3+ZyOMjhfdfRci8jrR9xljsnG8pCr6MmsdqheFJHpC9xRm0XJXoujdG6tZs3hntAittcHHmsU72b2xGjB/z1Vp2lgqFIoOVITbjzEbF2d0rLy83HR826pVq5Ii0VAoxMcff4xMEMyQzUbFOeWc9cabaY2Dy87Oxm63x0WhllCIgN2OP0H8dGIj1M4iWCFEdIIRQGHhfoYO24rT2caGDasYPuLeqIjpZho1jz5GsKoKW0kJRXffpR1fuzbdjz3KjnVrWPfCc7TU15GdX0Dll3+AkRWUnmo13p92MO/8vV1+7XQYPuJedu78SVxauatR9Aev7YsrPgMI+sN88No+Rk0rNr2RCKRhBKJQKOJRgtvPMRvJlnjMqKJZr142ixJloglGhPaMDL4oL2eTyfViX9vtdrNgwYIOK0eHA29LCwGn0/DaOrERbarHE8W2bNSHWK3azYPXd4SdO38CECe6uvAake5MWaMhAdmtTTRn5SWdW2wLcl3wTzwpb8IvOm4y0jWz6C76e96/71d4fVW4nCVxNyDpYNReFXvc7EbCbumFhmeF4hRHCe5JQDoikSpSNNsLTowedTI8Hj47p5yAP95UwmzvNPGm4MEHH+z0PcWO8ks1oD728aHDtkbFVicc9qTdapPqpqS8vDyuOCinzcqsIaMZt7ci+vzZH77FW3O+RMDWkeZ3iTDXBn/PeXI1AY6xVC6iXhRQbAvz07LhvTofFzTRPZ40ddZAp6HoZg3UbpiMepDdFkFJGjaUCoUiHrWH288xmkEbu8epkypSNNsLNhJbSzjMBePG0eI39h5Op4grVQEXxO85G60t8XHC2o+p09lmeL10W21S3ZQkumA1Zeaw8sKFbB/ZcSMxbm8Fl659Nc4h6ruW5zhfrga0auTHuZ3n5fU81Hg7rf+zI7oX2l+Zcc0IbI74rwGbw8KMa0YA5q5Yebbu9zUrFKcrSnD7OalEIhYzkRNC8Morr2Cz2XA4HNHjZtXFzsxMpn/ta6bX60xMwVhEY5+vD6cHLbI0G16vP14QHocl6CQQcBhe06xIKNbPuK1tFw7HFsPzmpqaDIuDgnYHq2ZeEXds/P6D/MWaH7WhPC/4huE1bRkNSQVI/ZFR04qZu2hMNKLNGuhk7qIxcSMNryseGGe92dtRu0JxqqJSyv2cdKt0582bZzizVo9ijZykjNDPM7ue3++noqLCtEdXT38HAoFoylpPg+8pOoOH9lfxX/UBSt/fFu1h3VN0BounXRotPhpSVEI5mmDu3/crxl5URdCXicVmUMATBNcfj7LnX+d1FEjRYQqhFxSFwwFGjd4IQG3t8LhLuN1uDnv9hoMcvK5MDs4cydANewEbVtfMaEERaGKveSYnM+LKH1FT8SU+eM3eIzN5e4tR04r79foUilMFJbj9nFT7rw8++KBh36ruzGRWFNXZ64EWWR46dEirZI65jsfj4fVXX6X657/gzIoKfHffxYcHD/LBsWNJ65RSRtPDe4rOMOzn/KiplaXVx5KONzZ9zPCqDsG0OY1H3wkvZGyyEuQIVf+qDQfIXbDA2BTCEmTYsK1xgmu1WvH5fGT5PLS6MkhCCFaffQU/rHmE1srLsTnHUtd+iF888i6+Nh+5gwYzrqwauyWc+DTsmQ2UTP0zVZsAZiZf24TE6ujZN92ihssrFKcAKqXczzFLz+oiGLunW15ezt13382DDz7YLbEFKCsrA7RI9dNPPzW8TlBKtg45E6TEJwRv791rGonr6W+zfs5nK+sNjz96xGIwACAZGTPS97DLxuJnn+TXNy3A4zWOOp2u9rj0tcPhIBwOM23/tqTxfzr1FFA6rRGbcyxe11Facvfgb/MjEDQfHcvu3dNoDtgNn26x+dk3+QhT3t8WHUn4cnWD6fvxtrbw1lO/STlq7+XqhrSvdzws21LJzIdXM+z+N5n58GqWbanslddRKE4XVITbz0mMXI2I3dM93gh3z549gOaRbDbwADqcpDxuNyFb6h+jpqYm85myJvN46+SANFYLgRYbfysfjj0UJmARYLGAlARabTiyg0nnu5wlcQMZ9IrqstpKNowsx+tIbmcqoI6NGeP4y1W5NGUMIMt3FtP2b6OsVhOghpqRfFFfwvzzX0l67gZm8UfHzfgj7z/WncpoL7S1oT7aiqSjj9obO3uuofPTnRV7Wf2nVXx/5vQei4SXbankgVc+wxPQ9vorGz088MpnACycVNojr6FQnG4owT0J0NtuHn300ZR7urF7rt2NcJuamqioqEi557unsJSPho7ldxcu5MdtVfzugmuiE310EYolNzfXtJ/TjHzZYOQxEYeUYMsOMm7R3rgpOQBHNhYx5MIqLPaOzyEcttHWcBXP/nhD1MYwIyeLdq+Wrp65t4J1Y8vxiw7RdUgvE9nMM+J7+DO1ytxWVwbvjp4EEH2/lQNH8c/iSY5RQAF10cEFS1kU15sLxiMJdULB5JsE6Bi1Z1bc9fa46Qx/4nHe+sN28komxtkzdodHVu6Kim103YEQj6zc1auCu2xLJY+s3MWRRg+D89zcN3+0EnjFKYMS3JOIVC05QgjDiDS2cKmsrIw9e/akjIBzc3OTKqBj2VNYyrujJxG06j86AoQwFCHo8GweUlTCD3ccwthmIR5bKMjsqo2Eiq1xfbdC2LFaMgmGGpGyIzjelD2dJRcvov7iQnJaG5m98W3G7a0gmJHJGedW4nS2E5YWdu+aRm2Nj+z2Q7gYRGuDD7v/TKw5ewiFg5TVVpKdU8O60vOoJz8qnEvFzfiJj3yDVhsbh4+nrLYy+pmEhPaZ1NExuKBOFBq+R7OI32qSLcjOL2D3xmrT4q7mrDyQQYLe9bQ2jGXN4p0A3RbdI43GN1xmx3sCFVUrTnWU4J5EpBpmYBbRSinjjChiTTQS0QucXnklOTWqs3H4+BixjSdWhHScTqeWFq+o4MLdW3h/yGhanW6ElEhLcgmBCIe5cNcWimsD/MN7AePG7YpzUXqf2fzbjq3UkUcBdUxkM+9xUTSKbM4ewMoLFwIwpF1S9dF0AEaPHqAVS4kwbVkHcXkHAeBoLWSAy0og/wuampoYXZfBN0bvJBBYGqk+tvJbeadhtN3qdEc+k3GEEj4Tv3CxlFspsYWoCiZ/XvpIwsQCqZK5V2BzOOPSyjaHk5FTr2HN4p3kXppNU2ZyD2xOa6P2P+EW7e8ixp6xOwzOc1NpIK6D89zdul469FVUrVCcKFTR1EmEXtCUyLBhw5KGE+joxysqKvjlL3/JK6+8YurspPe/puq11UUm3cf11PT/fPxZVGyzfB7GHjmALRSfPrWFgly08+OoYFdXn0V29hPMu2gvM2eu431mc++uL6hjIAgLdaKId7gsKWUbtDtYN+0SpN24bzds9VFfsBGv6ygAom5gtNjs7rvv5rzz/pnhI+7FYnEDIQqoM7xOls9Dm7WNVqdBdTNQz0B+WjYcd4INom75qNtHxhZIeVqaGX/hPLILCkEIsgsKufQ73+eL3YUE/WHmVniwB+NvrmwBP7M3vq39wZIdPW5m25gO980fjdseL+xuu5X75o/u9jU7oy+iaoXiRKIE9yShoqKCjz/+2PCxhoaGlBGu7lZlti+bm5vL3XffHS3QmjdvnukUoCxf6i+/xMdzc3N5ubqBv585Wmu7iaSfd5ecxaiqf5DlbQcpyfK2c+GuLXHRcSgU4u033mDPRfPYMXYc//HB1qT9S4Txj3BzVh4ZbuPhCQgI23y05OzhkxEe/ufKbEpWb+HcVZujFb+xbUU3sBiH9MZdwh6SXLwlzNmehQyyGH9WpU67qVPTdcUDWffCc0kFUlJK9m/ZxPTv3o1l2kUcKTyLv2/eSl37IQAmHPJz5aY2clq9ICU5LceY/+6yiAWlDZtrVsffxcDUftapWDiplIeunUBpnhsBlOa5eejaCb0aaZpFz70ZVSsUJxKVUj4J0AXTTFSbmppwu92GgqrvyaaqODaKeM1ea9r+bQl7uB3YQkGm7d/GnsJSNg4fT6vTTZFVENhTmXR+0GrjUEEJN3/4VsriqBafj3eH3IavbCBHc9OrXAawCoH9osuxv/1m0nvXJw5tdk7ibTEfv9DSu0eEjXu2HwQgL8YuUp9nu1Quoo4CctvDzK3wMuGQn1Zg5kfw5tTMuP3pdAYX6IVQ20eWs27aJTRn5fHTlsNsKhlJ1cvLkELr7W1qakLkaqlil3cQEw75mXDIT9D3MWH/BsLBZoQlB6trJjbnWCDenrG7LJxUekJTuffNHx23hwu9H1UrFCcSJbgnAZ0JptvtxudLTh8KIfD7/Wm5TD366KNx4/xih9XHokeguqCC1CLUSJUyECfINWEgFDQs9OksPQ1gCTnxufIByG0PG+5fxlVQRQgBv24JcWFeEaPqOnpyYycOvciiuIpkAK+w8ND+Kh5PcJCayXpmsp5gax57//ZI3HPG7/dhd1nZMCU76palu2ilGlqfnV/AxrwSVl64kGAk/R222Fg1fT7BhGhfijBt2R17zwCu7PHMXXQdo6YVpxwif7Kgi7uqUlacqijB7edUVFSkrE7WTTGMBFJKmbalY1NTE6+88gorVqzo9DlltZVRMSgcPZrbPv4o+tjz0y5Njn5N9pdzPAEsISdhm8leowS7r6N1Zm6FhzenZhKwdVzPIb1cwGpWy/mERbwYBy3WpCKuqpFunrT+hjoKMAutK30Bho9NnjUrfHD0s+sMnzNqezuP/2B60vFUQ+v/fNMt/KpRRMU2um6D4jPQ9p716T66qG53hPj6w6s7BOr6vhWo423rOdFRtUJxIlGC24/RU8lmCCFYsGBByqpindjB7T5fJgcPTEzyFIb0PZfNMI1aE6JQe1By0ad+MluH0pKzBywGEbUAn7sGbyA3mkoFWFPupinDQj513MjzzGQ973BZp+vZU1jKOntykVUipU573KxZj/cIPq/2mdV7BPbs3QScxwhbfVhCTjJbh1KQMcTwWmbtP5W+AGPnzqVljfFABaPPMTc3lxnnj4hGsqtf3M3fhJdKq/Yafd1G89Nln7H4w0PotxeJ62nbUkPzyoOEGn1Y85zkzB9K5qSiE75OhaKvUILbj0mVSrbb7dGq4lQuVJA8uN3laqNs1IdAspG/Kfqerkm0qmPmSewM+LGHQ7Q63eT4PFy0t5kJhxx4XSCkBSkjghtz+dibhGD7QGoqvgRcgBDazUbYn0UwnIV0QD711Bv0vDoDHWMGNw4f36nYIiVfD2tiV1J8DRXvD+SDrWs6bghsPnzW6ug69eKrsyeeYXg5M8MPvS2o1OkwfDzLG3/jI6SFcWdNYc3inQT92lpCrUEuworfHWanU/u77as2mmVbKuPEVkdfzyXYaXxlDzIQWXujj8ZXNFczJbqK0wUluP2YVCIaO8LObLKPjtHgdqs1xNAEI/9E9L7f3Nxcxv797yBh03lT2VlyVnQP96etR9hTWBpNfxoVVdlCQWbtrYhLkVotNlrzilg5roAdpVcghUBIydgjB7hg32dJNwn2zAb2n7ePN8WlBCIV1Mccbp6T36Ry1SCms4a/zbkWmWAcEbDZ2FNYymjS2zMGyPpbFczSPpePPt2QHH0n3nNYwmz/x2bmMzvpWmYD3PWCKqPH7UHJhduOYQk6o1H0tHNnUb3ZQtAf/3dsR3CB1xYVXOibNppHVu5KEludI40emlcejIqtjgyEaV55MC3BVQ5UilMBJbj9GDOji9zc3LjxeImTfYQQDB06lMOHDxMIBEwHt292TuL5aZdGe2NjrRn1ViGdPcvfIHjkCO+Pm8i7oyYRjAhbWFjiHKb0568fWY4vsjdpM5i9GwoHWTm+gO2lw6JRsxSC7aWa0N2T/0rSTcJL1hsJiPgfWa+wsH7GpXznzw+zeuaVeBIENxzZx73q2EGy21toycwx/Cx0ctvDcf2rO4vyozcXqewrm5qa+NGuQzx/pIEQYAVuHjyQX47WUs0P7a9KKqiCDj/lh/ZXcdjrxxqWXLmpjQmH3MA0AJyZVuZfO5sn3lptuOYcGX8H0BdtNKlEfnCem1Cj8T692fFYlAOV4lRB9eH2QyoqKqipqUnpBpV4/ieffBJt5ZFScuDAAc44Q0tz+nyZSdfZwCyeEd+L6419d/Qk9hSWGr5G0d13IVwuXp5zWVRsdfQin1hCViuR3C9ehzN67Vh2DB6anKIWgh2DhxneJGiFTsk0Z7iwZVyCx2i8HlpkKwJ+Lvjg79iD5tXelpBkboUn2r/6cnUD746eZPgZJfLhuMk8GxFb0Kqknz3SwI92Hep0gLv++HuZJQxqDkf3qkFr77ngBq0txqyvtlnERM+92Eaze2M1z/54A0/ctpr6yjZ2b6yOPmYm8gKt3ceaZ7x2s+OxpHKgUihOJpTg9jP0QqmQQVQY6wYVy4oVKwyrlA8cOADAwQMTCYXiK3iXcHNSS0zQamPTyLNZsGABLk9R9Mv12R9v4GjRVBrvupP6nDzDdcema43sH4NWG6vGTuH5aZdGBUua7AdLIQxvErJoMTw/1xvG5hxLbrtxK1NOayMiFGTc3grG7/gYY5sKTRwsNhHtX31of5Xh+0i8ubDb7VQUnml4zeePpD86b9S0YrIHuqLCmjXQydxFY6LtPTOuGYHNkfxP1omFsT5rt80pjMb9VVW/xoYNs1m1eiQbNsxmy/rnWLN4ZzT6D4fCrFm8Myq6Rs5UAlg0fQgLJ5WSM38owh6/dmG3kDN/aKfrUw5UilMFlVLuZWK9ixOHxRthViiVmOKNpbPKYn2fNrZKud5lHC22ONy4PEVxxTmtDT7WLN5J0+A6sgYZF0XFOkyZ7pXGRIlWmw0Bpvt+Bw9MZFTZR1hs2mexgVl4SX5dOzBnaztg3DYUtT0cMZztI8vZNnYyybcyGiGrYMN52fw2InBmFcatTnfc/va8efP433pjsTd7LTOcmTZu/YXxsHpdeNct3Y23LUipXTDBbcUh4AZs0G5lIMmzk1Nh1Cd8z86DfFO+wflS60P2+o7QHnoI96Cv0vJFR+tTrF9zZz20+j5td6qUj8fXudETYGZs21Qk+n9k5S4qGz1YhSAkJaVqX/ikRgjRKqXMSvH4UOANKeXZXbjmnyLPeen4V6ihBLcX0aNVXUD1EXqAqeimGr8Xa07RVWprh8cVSOXOaKHJkbyfmRNo5q3lexH++LRn0B+m3dNqWhSlm16AeaVy9FpWG5tHTsAWDhEwcazy+ydRtWkMReWvYstoYAlfJWhJFpMsm5UZrYJWSGobymltYvbGtzTbwxHDWTf9UgK21IJUIzuE06zCOJ9axo78C837b2Di9H/isyIH1B8yvJ5ZNN1dRk0r5oPX9lHqDzEpw4o1NkvgD3Pspd1A+pW/Rn3CXmlhibye8+nYM7ZY/RSVvxonuBDv19xZD23mpKJuVSR314Fq2ZZKKo95qGzU/hYqGz3c9+KnICAQ0t5zKLINo/aFFScCJbi9iFG0qg+L10UzMQI2s2gEY8GuqKjo1rD561nMc/LrcW0yDunlermYBoaSHynYicUSciY5TVlkOMkDOZX9o06DsJpaOgatNv547lwufO8oY97UvuDrbzC2dTwWDPHfl2Yz8yPN8Um3PQQI+nbwycg8nrz1AX4QaqY503wog06xrWOgglEFsUN6mcRm7s38OXUTCshr2I/vmAucxrszNw9Onnl7vLQ2+JiRbYsXW52QTLvyF8yjeKP9cltGcnq8K37N3e3D7a4D1SMrd3HjcIH3gkHgsoI3RHB3M7Zq439fajLRyY8QIgt4DRiAlgD7qZTytcjDNiHEs8AkYDdwi5SyXQgxGfhvIAuoA74mpaxKvvrxowS3F0kVrYJxBGy1WrEYjK3TiRXszjyWUzHH9g4OvFF/YH326/m29ay3ftXwOQMpo55tho/FUlaTYP9oIAx6CtowEhaCmjC8fn4hoQ+aGX84jJAgTQT6qAzz5tRMLFbB2D0dQwYqxg1l1aRBmvC3NnfaQ+yQXq4NPkNVdSUlxdfEVRBX+vzky9qkcYCNroyOHmUD9CrlniRroBO3wR6/TjqVvzpmUbzRhKRge/xNT1f8mtu21BxXH253HKgOuQC3FdyRrzm3jeDZedraTURX7Quf9HiBL0kpm4UQBcCHQojXI4+NBr4ppdwghPgD8D0hxOPA/wLXSClrhRA3Aj8HvtEbi1NFU72I2Zg7/bhRBBwKhXA6U0cNumCvWLEipcdyKny+TGaynse5ncVcz+PczkzW4/NlkuHOSirOsTksTBh3NrsLzoyr3NXbgvRCKEtIW3tZbSU3b3yLeTs2G47hm7Z/G9P2b0t6LG6NFgvrz8vCFWg0FdvoucCHM3K55OvjolHXu2cPTBllA5pYyjAFsoZv8STny9Xs3/er6MN6BfHzUvuMtjIl2TzDRMitEJ0+1JPMuGYEnhT3WOlU/uo8MLwkaXygS4S5IfiXuGPCB/a/F2D3HUMisVgtcQVdnZGqD7e3kGNySUqjWC0ER5m3hqnJRCc9AviFEKICeAcoBXQD8i+klBsi//88MAtNhM8G3hZCbAV+Chi72PQASnB7kXnz5kW9jnViW27MIuDOiqCEEFRUVByXDaNR5XIoZOXggYmMO3sMcxeNiQqXNSfMh2Wv8sGHn7NxxBjTyt3Cwv1MnvFXZl3wZ6ae94pmXlFbyYW7tkTH8IlwOOpxDMQ9ZkSt3cY3n7mWEnvn5UeVvgCjphVz6y9mcsfvLqLVaeIqFRHZfFnD93gs7oYDwOM9Eq3O1qtwXU7NqMKsNclo/SHg3l1f9LjojppWjPW8YozKtMLAxsq2uLWnwmh84K/HDOXKP7+PtR6QYK2H3MVWBq/6BzM/+Cl/HSrIL83s0nCE4+nD7S5Bp8kOusv4uJpMdEqwCCgEJkspJwJHAf2LIPEfqUQT6G1SyomRXxOklJf21uJUSrkX0fdZzaqUUxlbmA2UB63PdtWqVWmtIXZUXqxxg1Hlsu6vXFu7mc1sRjgFsljSbmunWuxmsv9q0wrkVqfb1D6SWu232H1dvVr5wl1buHnjW5oBh0F6WbdAvEE+z5PyppTWjPq5OjkBL82O5PUWUMtj8nZCXoHNnSyUwXYtldza4GPVczsAGD7iXnZsf4AC6qgjOQXq9km8DpAJ0aI+qCCx9/Z4GfHlUbSNyKPx9b1ITwgJBKSkoj1EZUBCpLIc6FQYrysemLS+PZVnkvGvR5LOrcsYoIlS054urdea5zQU165E413lDKcdWpOPD7RYGRCpfFZVyqccuUCNlDIghJgLnBXz2BAhxAwp5QfAPwHrgV1AoX5cCGEHRkkpO9876wZKcHuZ8vJy06piI0tGPQLevn276TXNhDqRPYWlhiIHREU3lbWjvjecEcxgcv1kGrMOkOPJpjnDkXRuvqznQ+sMlhKzJ2xdzORhW6itHW7am6tPxZm2fxtrxpxLOGGY+2FfgCnvb+PqYCPf4snInnMhIOOHz0tJky/Ay9UNUfG4fYCbR1u9SYVhN7AYIcDqlIRDAou1Q3TDARHxbI78OSRZt3Q33/y1Nszgph1v8JT8Stw17UHJ5Z95eHVqcu8wmBcmxdK0fDk1jz6G97pr2fMf/0nR3XeRu2BByufEttoEG30EE0Le2LadrlJ0911U/ev/Q3o79sR9VjvBr9/GwkmlrF3bNcHNmT80bg8X0u/D7S4PDC/hSEN89bjbIvjP0Wdw3ZyuV/orOujHVpuLgeVCiM3AVmBnzGM7gFuFEL8H9gBPSin9QogvA/8jhMhF08THII1ilW6gBLcPSRUBb9tm/vddVlYWtXFMRWci1xVs0obffYCLt4zg9WlWgrYOYbSFgkwUH/E0t0eFqI4inpa3I51PAua9ua1Od9Re0ozDvgBPcQcu2mklmwK04qUP5SxayY46WrUAP9yhfcFeVzyQeS4LDcf+yKv26wBJgazhBhZHU8fCAjIoCXqsWF0hAq02jmwswtsQ3/ribdP2mUuKr+GnxdcwtrqB/9h5mKPhELntYS7bH+COC0fwUag+5aACM5qWL48Tt+CRI1T96/8DSCm6sYVIAsiwCiZmWEGPcolv2+mM+Jm6Aznntp+TufTXBKuqsJWUMPjuu5jYyU2AGcfTh9tdLqsKssIrKbaEOeoSlFhs/GR0aY9nG043+qPVpt6DK6WsA2aYnDbO5LlbgQsMjn+th5YXRQluH2MWAVutxvtMdrudLVu2pFWZnErkuoPFYuP6t/6AJfQV3pqST7PbHm0L2jrmvKR0r1+4WCq/yvWsT9mbm6pnVyco7LSiFZvVUcR78iLs+JMKlnzAjzdv50Gng1qrnVwW8RX5DMOYyuM8mvyebBD0CD5/amzkiBNXTDHuZ0McrCl387M1WykixKyNbzF86/vckV/A7JtuYexVc6PnPlDtSDmowIyaRx+LiyQBpNdLzaOPpRRco0IkmxCMc1mpDGg3Cc7M9DqBd2+sTjI72diawdyHFjO2hwbZZ04q4m0CkciomcErW7mP3omM9JuR3DFh3tis2YQKu4W83EHQM2/ntCWV1WY/iXL7LUpw+xl6X25xcfK3gt1ux2azpV0sZSZy2Z62pPm0RiTu/87e8QnFNUu5akMNQ1rLac/IoGDUKEZVH2LV2CmG16gX+YBJb24aazDDL1z4pfH+X1NGZvS6TY4cng7fwX+x3cCnSsOepVdKC2wZF0WPfzbEEedcdRQrr507j/ktLYzbW8FbT/0GgLGzNdGNbyNKHlRgRrDKuOXP7LiOWcGROybTLsyanRP44LV9UbGNvv5xpKSNOJGR0fFOJ1KYo6w2u48S3H5EbF+ukeB2RWzBfFTeD174Ixdvep/lVy+gPcNYhoz2f1eNP48zL/sCe5adgSWHGD9sK+HQd5gyYxm5gUsMnauyfB6QkrKawxAO8dGwcbRkZJPjCdDs7poNYdokiHjA4qCWInbJ2cwW65JOD7TayC4oZOTUa9i5KQcZuXlfU+6Os4kECNodrJt2CeP2VhD0+1j3wnNRwQXjAiQzoincC/4XZBiEhUGZzVQXTaG4ZjO2ktSRsVkhkidGZ/R0eGeYpZ67kpLuDD0ymla8iWvL3iDfdYx67wD+vulaFk76cY+9DvRNVfTpwvFYbZ7uKMHtR6QaOA+dtwslDolPdIWKVikfPQxAu9v8H4jR/q/f4eCvlyxgXsO7vFD6Veq5i//Xdpg617n8U/iPPBP+HoEY+0W933ZP0RnRNWS3t3DjzjVcetaz/AuPUG9Q8ZsuWbTgl874IQymUbPg/8T3ISyYbXkvejQcFBTlfo3Ln3gAgDPGduxjNmUYd801Z+Xx+0X30JyVR05rI58+/BDXFY/li92Fkf1PJzOuGZEyMoxL4QoBQkv9SouNnaO/AnY75971JdPng1aI1PDSbkSoI4UdlJLt3o50X7pOUFkDnYbi2hUnqc440uhhWvEmbh3/Ak6r9nNe4D7G1cOeo6p6PCXF1/TYa/VFVfTpQnetNhVKcPsEs4EG6VQemyFCIaZt2sw5M6ZT8m//xi9/+Us8Hk/cjNro659TzlmHDpHR3k57pnFlrdk+b4s7k2WlV0RFLYidp7mdb1me5Gu+p3iBr9PidJPlbWfaAa3SOjZSbsnM4dUxM8nnU25kMb+Vd8ZXG6eJQ3q5hWeQEv7s+270hsJjsxMy8UsOCRsvyFuY1vI+9qxgtEjqQNNuJs+JnOPfga/pObyNdeS230eTyfzc5uwB0d9fO3ce3neXU15VjM05NjrsAbSWnB3r1rDuhedoqa8jO7L3+9EKR1IKVydsdfKP8q8wd8HFKT+DSn+YXe0hRtsEbosW2W73dhRMdcUJasY1I+L2cLv6/HQYnOfm2rI3omKr47QG2L/vVz0quHpVdCy9XRV9utBdq02FEtwTTqqBBum2+wA4HA78fs0z2O12w7DR3DP1Yuqzcsha8T7TsgZS5jGuRNbTyOWfVrDpvKmEEubbvjdigvkLG0SPfuHiOflNvup4BtHmR7g67BwNI2XhZKlcxOPczm+5K633C2CRIcKIqA3lTNbj9WUiP3ores6fZlyeckJPozWPrW9Mw9EcY0YhNBvDHevW8NZTvyHo1yKjWR/8nb/NuRaZ8PkkfgZBu4N3Z17GmRvfQgSP4G4cSPEgHweO3sOhVY0EWu1YcwuhLpeWulpt79d2ETbn2Og19OKsO6SPJ67KZW6Fh6918nl88No+Wj0hjMYmpBNlx6Kf11Gl3LXnp8N980eT1XDM8DGvr2eta6P7tLs2A5yQqujTie5YbSqU4J5wUg00MOrLNcPtdvPjH2v7Xs/89mn+PW8wfrvWH5vYb2vGWYe0r+qN06dRMOggQ4dt5S/Om9guhne5mKmVbJ4S3yeUbY9bQ9BiXCVbRyEbmEUBtYZGEok4pI9vyt8yS6yPHgsFLRzcH1/h7bUn9wjHkk8tvhKtF14X3ex8zT1q3QvPRcUWYNzeClbPvBJPouAaoFtdSrudzLFb2T/azn9ZH9J6krPruH7eYqbP/IDDGwbRuC8XS3gDRAQ3rjirFZoyrbx5XmZcT7Hha6bYXzUb8ZeKUdOKe1RgE1k4qZS31w5ChI8mPaY7efUkmZOKsDdlcMbDs+OO9+MeUkU/QAhxGfA4mjvr01LKhxMeF5HHrwDa0YYdfJLOtZXgnmBSDTSI7csFOp0c9MYbb7CrooInJ82Jiq1OZ/22S264nhF79zLlky3su3pQ1CVqDfO7VzksBKGEWaxBq83c2F8Inpa3cwGreU9eFN9SJCW2UBBbOIzX7iDL52H63t0Max6Nf8Kn2DNboung5loLliI/YbsDS9CpFWMZGHMA2GSALwVeBksJn0+YwaahY2jOymOQCBNYvx9vXW3SczxptCxB/DzgqrIMnrN+Pa4n+RluR7hhxoWalWvjPi1lG/SHDYuzAlbRqUPVidh3TZd0RezsMT9i586fEA53fF4Wi5vhI+49YetMrJS+78VP+ffl22hsDygBPs0RQliBJ4BLgMPAJiHE61LKWCeiy4GyyK9pwJOR3ztFCe4JJpWdI3T05a5du5bx48fz6quvmvbcbt6spcu63G8bMYrYV1bGvrIypg5/JWrJGO5pe+0U4u0XLt6Rl5NJC9ZgGI813n4ylj2Fpfz8nPk0ZVxOTpuPCza/jRRe1l8zn+bMbEqdDmZubuGiT/28OdUeL2BSIgjzrfBvKdwb4v2ECuyjWHnY08SlZeeB9LJu2iXRgiiXtw2v23SuNZA8D/hV+3UmPcmLmGlfT/HMRhqPnsW0ce38Y5uLZpPirM4cqmZcM4J3ntseraoGrfaqJ/ddzYgd7J6XYafVGyQQ7ny2rL5Pu3/fr/D6qnA5Sxg+4t4e3b9NhVEPaSAsOdaufdb9wcRBkR5D73/zK8AvgCHAIeDHBx++8i+pn9Up5wF7pZT7AYQQLwDXALGCew3wnNS+mD8UQuQJIUrSGemnBPcEk8rOMRaPx5P26D2zfltnwB91cYoVssT+WuE4yCzWG1z5BCAEbeRgE0Hm7distQ8liHRHi5KWnm7OcrHyIs0QQv8UD/sCLBtr58qNXq7c1BYdQp9PLTfIxQxlKrlVVeyvnc7Gacn7ygGbYNXMywhatT1Z0AqirKEgIhRGWo1F0SJDfC30f5TgoRbNJrOeQsNz9cEHTlc7bSVDqV2/gnNKr2eQV1LtTr4xKUqjmEwgkDGe7On23R4PiYPddbGKJZURQknxNSdMYBNJp1dUmTj0fyJi+38Qba8/C/i/ofe/yXGKbinwRcyfD5McvRqdUwoowe0vxFYm2+32uKHx+h7uli1bOHjwIFJKRo8enfboPUNTiXAYn92BLyJera4M1ow5l7WjJhKy2qKi1urK4Bn5PQRELQ+7zXEYWegp8DG7PiGUVwAxe78bh41LFkiDa/hsVt4+186ijW9z54EvKBu9HostCALamUpx8X5amotMI3+v0560/pDVhtsbgAB4nCLucYf08i2eZKZ9PaFR2npra4eTG2imyZE8mlGfMevzZRK22dgyvISxfsEdu338/GwXXmt8VN7sC/LE+v3cMcvY7/qD1/YRDsXfkIVDssfMKl6ubjA08Xhk5S5uOrPzG8H+aIRg1kOaSH9cuyKOX0CSl01G5PjxCK7RF1jiD3s65xiixvOdAPTKZD2VHAgEkiLXpqYmDhw40K1h8okj8Fx+XzRtHEvYYtVaZhKO+4WTJdwMaJN0jofoqL1uvI9Wp5uMo1/grPoHwu/TUsF+X1rWjzotrgwQcNaITZrY6tcmix9af8N/jr0b0cW1eZw27n2tkavX7San5Vjc/Fz9JsVqDTF02FaE38f0Teuxh+L3VvWhCfoIRIB2oVWZX14d5Cefe8mJbRMSAo/TwsPeJtPxfmZFU7ktfqoe/ojD96+j6uGPaNtS06X3C5rY3r3jEId9ASRaBuHuHYd4ubohbTHqj0YI980fjdveud1lf1y7Io4hXTyeLoeBM2P+fAaQODYrnXMMURHuCaAzQ4ueINpvKyXPT7sUHF0rnKmnAK83k+udi3mG78WbSaRJls/DLete57nzr6Q1M9v8xBSR8CPf/U9c3jbmbfgb43Z+DEDO9Eujfa/prAHA6WyLHtvALAZQSJ3QqqGlEElrsAXD2AMePO7kvuR8ask+czPnfDGdMXvepvzrS+KWv4FZ2pQkVwE505uYvfFtLl3zWnQvOF/WcwN/ZrJvC3sOTI9OaMqQHcVdl1cHeWJU8meeqnjKqGiq1C6YlGmLmj6EGn3RftS/l9iiEWuxT/K9nV6u8loN22X+dedh/Amv548c18SoJWk9sehGCP2hIrhtS010aMK0PCe/nzyUB3Ye4Uijh1y3nTZ/kEBMpkCZOJwUHCJ+9F7s8eNhE1AmhBgGVAI3AV9JOOd14PuR/d1pQFM6+7egBPeEcDyGFl1GiC5FhLF83/47zt+5jdlUsH5kOT67I/0UsZS0Ot38+fwraM1IUWSUKrqMvJbXncXf5lwLaK05sze+zcoLF0b3Vs2ILV7y+TJxuTTRXcoivpPYnSsEIhxCCgsQJmi1YAsIbDJAUHRUWzuklxtZTFH5dlq+mI4z62ICbX/DkaUJzgZmxU1Jas4ewMoLFzL/3WV8d/GvtYtk5uE5q4xNcljH64dDDGhoJpgRwBZx5zrqEtBxnxCl0hdImOSj9cgamVWMc1tJjN9kIMwLmw/xs1H26GCFKqfg52e74HMvV0QEOVZ0G8Ihw7/7hnCIX8wfTeUO7WboYmzchosiBHVC8qT0sj3PHhWsdL2TY4U5L8OOlNDk6bxquDNBD3uCNK7qGAsYavQx4uN63rr27Oj77Q83BYou82Pi93BBa9E5Lo9QKWVQCPF9YCVaW9AfpJTbhBC3RR7/HfA3tJagvZHX/Hq611eCewLoiqFFKoQQBM6bxQu4kwqhYkk1mSfFxfHYMlg1dgrjKvdjD4ei+7+dEhMttmTmmItqF/Z4pc0W9Swet7cCgL9ddB3SqK9XSrK8HqYd2Mb5rKNqhpt/sf+K+shcXm1+bnXy04QFWzAQFXKvOwNkGJdsx4srzmBDZkD2mR/S8sV0aj+7gZKpf8Zi87OURUkVybF+ywC0NZLhGUsgYy+ekA9LwIe99gjNzQ1sygxRPuBCMmw5FHmMLTvyG5tZ9ddqwpZIn3XEyWruojHMXTQmTojdYWP3qv8ttcRNMQLwWgVPjHJy+Xttcab+bVtqGOSVHDUo5Cr0SqYs2Y/7HCsPuNqY58vHJbWdqSIpeNCeRd78MjInFTHz4dVpTZVJbNWJLcLqTKQ7E/RQkx8ZiM8cJA4xUCYOJx8HH77yL0PvfxN6vkoZKeXf0EQ19tjvYv5fAnd059pKcE8AXTG0SMXugsG8n1EQ/eI0M7iYtn+bNr2nm/2020vNh9KbPSfln6FbBVXNWXn4cwbiLyzlTJs9Eo0ac/PGtyks2kftaCvPWeJ7YJHGIiSkTI6ahQWvdPM9HosrIhMCSqY8q63rC61osaj8VeoyC0zXHsWSjbOxgMz2Ir60aAwh/w7N0Qo41LaDQ207AJhiK4dps+IvJCV1udk8dnUmcys8TDikJXr1ST63/mJmXIFU1cMfGXoIH3UZf/b68djnNK88yPddoaRCLldI8oPd2nmWsOQKb3FSVXSsmKU7VcaoVScWs6rhtMbEhYz/7tUQg5OfiLget8CeSFTR1AmgvLycBQsWRHttu8KewlKen3Ypv7vgGlaPmZwUpejVvbGU1VYyrnJ/twqXAMOCq74gu70FX8lZSIcThIgzl4gly+sFIRk6bCsvWf4pKeI082qWZu9RCJayKOmwxR6kaMLLCAQtX0xn35u/JLfd+DPOaW3suJxVSyXrIjl29lwu/c73yS6Ibx8at7eC3NZj2nP1wrPI30VTppU3p2by2ZCOGwSjoqk9Y3LwJhRMCruFEovxvfUgr3ZurKl/qNEXLeQq9oQRUlLsCfOTz71cXt1RiGbWgqSLmVnhUeLxdIqwjM5JS9BN2rnUEANFX6AEt5epqKjg0Ucf5ZVXXgEivsdpovefRm0DLcZ/XUZtLhfs+4x5OzYjTFKMPYEtFEy74tdC19YhQkHO+8euuPagafu3YQvFj5uzByXTDnwOaIVSeq9rei9iflNhdh17ZiNX59q4JNtGqV0wt8KDLRBfXmQL+Jm98e3on2VgO0GfFsXqIjl29ly+88Qfk0TX5fXw3ed/RW5LY/KYQZtgTXnH33Wio9SyLZV89+ODPIyHasKEkRwlzL7J+fxkTCn2hBYiV0hyx24fIYgz9dfF6PLqIG+818amt1p54722OLHVWVFs46oLMpl6aRZXXZDJimIbK0e4mfL+NvZNG4j/gkEEizvWbFSQlE5FsNE5Zs+73p0RrdJGSrDGf45qiIGir1Ap5V7EaFBBVzAy/jciNvJLNLUwjeKOFymxBvyGFb/J54YZSwXb5MT48xKfFxFvZ8DPrD1bKauLL/xLHDeY2x5mboWHwe11hG1aoVSBqy4tb+bOsCDZwKyk3mSbN5+/l9h5YpSToy7BgPYwZ+/8iP0Rm8ic1kZmb3y7Y/8WgCCDLdWckz0Bt1VQ9fBH5MwfSqU/TIuBnSRAU3ae8fGIK1XQ4sd1frzQ6ynWd4B3aI0eL90ZYsPCMbz9h+1RQ5BBHskde7xcWBlkizfEwpiCKX3STuIA90RWFNvi0s7VbsGDE1xYLIJAxCUr7LYhzx6AAIZ4MSxIum/+aO578dOoU1UiAm1/dubDq+OebzQm7gqLgzv8dkKeSPQf1jIFlgwb4fZgdIjB2wR4JOKUpQqlFCcKJbi9yPG2A5laM8YQW5lrNDS+22nlNPC5MqC1SRPNhFm88QgOyhFgSd7rFeEwMpIuvrhuDQvyl+B0tuEbnslBMTHaQqNzPuv4Svg3OGUrQTmQGvklKn0D8VmrOXhgIl8e/Vf+YPluXFrZIb1YUs4QSiYsrDwtbwdiDEHCNrbW3sGjMSLTkGmlecxk5r+7LEFkNbaPLOf96VfQmJnJIK8WUV5e7aPhpd3sag+BJRvCyS02A9vaaMhKrvYe2B6mxdHAxiFv0OY9wo1cFn3MLMVa2ejh2/ev5N6wm5+sa4sb4/c20GbVomNdcPRiIr2VxprnxDlmAJ6Pa+JE+DejHPFmHUDIIpI+aWkVDDpvEBvOH48RCyeV8u/Ltxk6VkGHo0BiUZTRmLh7/G6s7QmReBiEw8oZ/28GkF6xlULRGyjB7UWOtzLZrNo4VqRiq5QNI+J0ItCuYnS9WNFNWrCgDeO+XCkEt733GoWF+6MDFABcrjbKRn0IEBXdxHPsmQ2UnPcnCkM27HYfPl8m2VWDuaXoj7xqv456ChkQqOdGy/PkMweHLEje301B1P84GuVKnikZlSQySVXJEbaPLI9rZ6p2R1px0PZCR9sE+12zCLa/DXSIhM3h5Ae7fDw8MTO5aGl7A09M/ndtNa3ERX15GXZD0boYG/+CG3fkhifDChMzrNAe4mAgzGpHgN8u2cq/L9/Gvy0Yz8JJpWROKkrqzW07KzcqwtICR13p70h15gvdaCK2iSQWRSVWGB++f53h82KLpNIqtkoD1U6k6Cp9KrhCiD8AVwE1UsqzI8cGAkuAocBB4AYppfEQzX7O8bYDGVk22kJBLty1xXAKUDoR8XEhJc6AX+vP7Somgq+nw4cO2xoVUh3dvel9ZkfTyAXURlt1ACzWEA5rSDOfcC2irrSA3EArU3bsoqxmA9k7PyZvRBOZ06byTfkkS+XN1IuC5DWZ3JTE7eVaQtRajAVbq0oWxDq8rZt2SVIVdLQVpzqI20J0Jm7Qu16LdIVVK6h604r1c280da1Hx5fVO5na/J/8qeg1Vrv3xkVnifc7eo/sIERSgZNNCMa4rfzO5mOns6MdJ1Wkp4tw25Ya5PYPKTbxgDZigLBEhx0YiVO6louQHMkv21LJg69vo9ET4CWyKDYoTYktkkqVCUhMW5uhouRTEyNNSni826P5oO8j3D8BvwGeizl2P7BKSvmwEOL+yJ9/1AdrS4tYj+Tc3FzmzZsXHbN3vO1AiXuWZn23OrmBFpocOckP9FB0mylbCJtUu6b1OgmiJsKhaDo81hkqls3OSXE3HXUUJaV6/8g3eYfLo9ducuRo7VKhIOcCjftyyRrpJOsv9XyDxwEt+nxvxmW0ZGST5fMQtFjwOpLFNCvBUSnfZH5vbnsY14C78R777+ixuNagGPRWHE8kO2tzju0YRm9t44o323lFZHN5dTC5UEkIBgXzubPqZvzOvaykIzpr8nT8nF2MjR/hxp1imEGGhajY6nQW6bVtqaHupd1YxlkMPaBFMIjNbicQo/4OwPt5Q1RQjcTJaD/WjNhiqWVbKuP2f3+HN+l9JxZJpRL3dIWzp6Lkk43YKVGnaFT/J5I1KZZuj+aDPq5SllK+ByQaxV4DPBv5/2eBhSdyTV0h0SO5qamJ5cuXU1GhpRb1diBxHIJXVlvJzRvf4rb3XuPmjW8Zim1h4X6mT1/KV2zP4JDe+Ad7ag9XStpEJh5bhnmfbVcvieDdURP53QXXcLN4ie/yBzYQ34e6lK8mpcn9whVt29nALN7hsqQ1Ba02PhpuvGcIcJ77Ex6z3MHz8ss8Lm/jsppViHDyl72XjLg13eR/HVcw/r3ag5K5FZEvcEtH6jy2NSiWbK+XXZYqtnsTxsQhabZos3+ekJ6k9p5YXNLBt71jon/WvwB1bsOVUmxBi8dfIouLE+67U7XpVC/fhzVS7ZzYOpTTcoxrP36bx8acyRlOOwI4w2knb28r4cPxN1S6OLVtqaHq4Y+YsmQ/r4gsLukkBkiscn5k5a64Yqt3CPLLmCptrBbyri2LS4935qesry0V6fYYn0p0TInyIOm4OVm2xTgA6HUezP0KD+Ye5MHccOT3RAvGLmOiSbFER/NJKT8E8oQQJeleX3THLL8nEUIMBd6ISSk3SinzYh4/JqVMMtIVQnwH+A7AoEGDJr/wwgtpvV5raytZBsUo3aGmpoZQKPlL2mq1UlTU8Q/c4/HQ2NjYpWs7nU58vvg+S6/NTpvTTVhYsMgwmT4PWbTidLVGv1pbyeIYAwliw0aQMBbCSWZ/PUdxOEC1xd75iWkjKaSGLFqRwEHMZrtKhrGfLxhCEPPXHxS5QXHk5uFvagTA6gzhyAnEyZEE/sEwpME9qI0AZ3IIKcHfXkCbcNPithEWFqwEGUg9mWEPAc8Awh5BWLbgdblpzspFiiSjRbK97bgCASwhJzaLk3AoTBhotkhyMyRHI9/Zg7CQl2LgngR2RUqUHFYLg3JdVB7zEJaS0VjTHtQXBqoJ0xwReIfVwuji5D33sCdIqEG7ofO6Q7g8yT9X0gK2PBcWd4dwflZpvK2Sg2CwsMbdrCWuBTpGEOrvMc/d8fdtdm2dYblWw3/vjZ4AR5u8+E2MMQAmlJr3ze+qbjF8rtlnZ/S6se+nJ7+Xeotd1S0McISjP586nb3nzpgzZ07XIxJNXI2sHb/Ng03HZYaRqEkJj70BPCylXB/58yrgR1LKzelc2/R2Ugjxv6QYOSSl/EE6L9BbSCmfAp4CmDJlipwzZ05az1u7di3pntsZDz74oOljN9xwQ9yff/nLX+LxpH/3O3r0aHbt6rjLTqxABrBlBPla6P+Ya38neiwD4hKe98lfc0SU9pqRxY9bq/hFVto3eGmRL238l/deDu6bwG/OGm84uCBf1vDfnv/hP9wvmBpb5LQc47srl4HI5sz5V1K5YhnSAuO+sgdHZnJP6c95yTR6z5duLtr2JkP359OS8ymFg/YyJqaAC0C6bAx4Q/LCgCv464KvxO/fSokr4Gfm3opolsIqXfzrv98PwLD730QC90wI8uvPbGmlhKuFl98N/w+EvZE8UcQDo39IadNk3n99Nws8VmwGz5VIQwnPJ8yXacVtt/LQtROYk5AmbNtSE/Ek1vZCd0xoYuxnxoIk7PFR5U8eXm2Ywn1VZFMozdcSvR5w4OErDV/L7NoApXlufl5qSfnvfabJ80vz3PzzIvPnNSbs4QKmn53Osi2VPLDqMzwBC3py0W0P8dC148hjT499L/UWX7//TX44IcyvP4uXDe3vZ86JXk5vjefrjG6P5oPUe7hpKXYvcFQIUSKlrIqE6l2fLXaCMCuKMnKUuvzyy1m2bBnhbhpRGFUgB602nrbcxjN8FwdefLjIohWQtJKNEy8+4e4XrlFdoZ5Cvul6luxhLYw4sIfPx0wkaOuIpmwBP9PfXcvne0eRd2sjje7kSTpIyeyNa3AN+CEAwtqGNfMygt4N2LOSxRa0/dl6ox5eIagXhbw6ZhFzxDZG1ocNi7yEPUjjtU7eCl9pYBkpsIVDcVsCIdGR/k/cV+wsJewlxJ8GLcXiaASgKVDDg+8/yGOFP+POYBbSwGjEi8SB8TdGEYLSmD05vQK3stGDVQiWyEzDYiQjEr2KjfZn3XYrBQHj91eUsMJUxhj3zR/ND5duJbGF124VWuq5aU/c8cQhEN+bWMzPPjuUtLbOpgUZtSR1tp+Zat/359N7d3evJyqqzaZE9dEow94az9cZ3R7NByn2cKWUz6b6dRwL7ozXgVsj/38r8FovvtZxMW/ePOz2+HSm3W5n3rx5SeeWl5ezcOHCbr+WaQWyEEhhwScyQFhoFTm0ilyIHju5xBaI2hm2ZOawbewEphzdSX64DmSY/HAtX9r5F8btrcCfM5DJe3cluU8hJRM//4jyI4MA+GyIg5pcKw/dMpOnbr6X9/yXGbwoXC//krwHHkPQbufDkaMA8yIvmeujZqCxS1Wr083z0y5lT6H2RRd7Y5a4rzjIIPIDLUKtJsxjRS/x7oAP4x7zhrxkbvAbGlbIcIiGvFpqTW7G7XkuNtx/UVRsH3jls+gNQEjKJBGMXY8RsW04CyeV8tC1EyjN024hSvPcPHTtBGwm9oo1MddMR/ysBj/jN049M0lQdm+sZs3inVG3r9YGH63ra/jphCFJa0tHjBZOKmXD/Rdx4OEro59dKvpq3zf27/N49l7vmz8aS8Jn3YejDM3G8B3veL7OeB24RWhMpwuj+SCNKmUhRCFalfA4IFrGKaW8qBuLTbz2X4E5QIEQ4jDwb8DDwFIhxDfRPrzrj/d1egu9GtmsStno/FdffbVbQ+aFlL3nGtVXpNEfHLA62DO4kP/hu9oBAeHRgv3NI6nyn0VZw1HYtSVayZ3d3sLcDW8zvmoQCMEnBetZOeUyxvk6PIn/GPo6lpCX2da1ca81W6xDSMmLLNImDBmsTb/xiR3/F4vdO5BBwqRdJjI68d3Rk0BamFGdy+6N1YyaVszCSaVs/kcDtO1nzhcfE84vx5qRn3SJo0juzAvRnP+e4ec10G+y7ygsbN0FlQP9fNXuxJWiivffl29LisRqkBQbiG4YDCsEEr2KjSbytGFPcrQKWQVLHWHwaEIaW8BkJGiJRVM6a3YmO3h98Nq+uFGGoPlbB7ceY8MvjvvrrFPMqqN7O0LsqYrqhZNKWVa9ndI8a3+oUu6V8XwmmmSH4x/NB+m1BS1G64u9ErgNLeo09qPrIlLKfzJ5KDlE7KeUl5ebCmwsFRUVrFixoltiCymM9k9SHNLLbLmaDYF5eGPTrwbvs55C7uRJ6iLj9ibaN7NlxnnUi/xoq9TNG9/STpaSjKCXoKsJjm1i3Xl3aundmPqzgNXGC95vMku8h7DEfwHPEuuZxfrI6yWnl7PbWyAc4uCBiXEmHAAiaKdgz5e5g+R2mViCVhubhk3g/M9bWbN4JwCjphWzZmctN50JX9u+Av/AfbgmfRVh6xAuGfIz9isT2DCpiEtfKqaqLfnGusHRRIE/L+m4JwxFWQO50GLFKSEkJBYEtojVYexsWCPzDLN2m+zJRUkOVOl6FRs5Wg2YP5TzCbDcoMfV5nubAaFn8PqqcDlLGD7iXo40Gn+FGUWNRsMeUh3vKp2lbc1S60ap756kJyPrPLedDffPOc4V9QAPNv2FB3MhYTzf8RZMpdAk/fFuj+aD9AQ3X0r5jBDiTinlu8C7Qoh3u/uCpyMVFRW89tprhhXNOokeyD9pjd8W6NaM2+7Q065UBlhkiG/xJGEEm5mFFzsF1FFPAdIkdVkntC/nOop4R14etYlMGlEoBO2ZmeD24QzlmPbCNjrdpmP7pIQbxGKelrfHOVPZAn5G7N/G4vMuoSUjm9xAC9eHnsdu97GUm2mwFTBolGZQ8ZOIaUW1y3jyUnOk0lafIDRqWrH2BXgmFHoaCVZ+hBdwjv8Swj0Q6WkgWP0pzSuzObZkF4+7HuCJvL/EpZVl2M5nowLM+dSPsHbcxASlpDoQZmKGFVtkLVa0Pd2BCWJ7z9JPDT+TdwgCnuiw+QCwb3I+Fywsi3OgsiYIuH5dMyEycrR6xGCObnn+h1hbluC1av7RXt8Rdu78CfOHf4W/7z8nab161Bj2BKMjCy/Ns7OtLUhlIP6mN3EIRGcYvR+gUyOMVPu+a9f2nuD2VWTd62jielKN50tHcPXb3SohxJVoG8Rn9N6STl7MTDBWrFjRqdgmeiC3BDOie326EJ8IMQQQMqwJXy+8lkN6+RZPIiU8w/cIODRh0ObWmltDpvpz0Gpj9ZjJQMxcYIsFf2EpOa2NhlXOOa3NBMjGkZVcBBLyOZka+AiZRdSZKqe1keEHd/L5mMnRgqgmRw5/CH0HGQZp0/7udPvGn3zu5Y332rjqgkzD9HJuuyb2pXbBuECIw/ev4xWRTQWN1LrzGBQR3WDlRwDYSs/Dee4t0b3RbK+Nu6oXMbatjOmeURQGB1IjfCx12bhsjA9PRSvCNSDqmzzO1SG2Oi4EdUt20rBkF/4MG295WwmlyMC8QzA6FOEeQjzz6SG2LhxjKJo63XFkMoq8ri17A4c1flhDOOzhulFv8O4X5xpGjW1bagge8xJq1ATVTYelpS66NoeFGdeYtZ4li+vcMYW8/HFl0vtx2S1ppW37Yth9yshacUJJR3B/JoTIBe4B/hfIAe7u1VWdhFRUVMRVITc1NbFs2TIOHTrUaTuQ8VQgwYaR5QSt1vjHYr8Qe0N8hUAisMggYWntcQ/m2XI1M+QGfsDvCViTK3m7fWmLJS7Sjc0YJAq5Nj5vJUdkLkPmeLDYOoquwkEbh9cX0rgvmyx0ZyobCBu//8odSdXHYYNpTrH2jbft8vLzsx0EbB3n6UYZpXYRF3UWSsFgLIy49JcEfI2EPns5KriO8V/CkvB5ubBydcv0aJtPsXTxbY8k49xzcRRtZP2KFsoysykb/hFLx37BC46rqaeQwoCXf96hGVfo3brO9iD342Q0gkdJL8Xa6Al0mkbtzv6hUUSW7zJ2d7WEa3jo2gmGa9j3Hx8gErTUJgTjMqxUNgXJGujENnEAX1+znSOvfpy0fqObhcUfHkoqE/MEQqYOWf3BCKM7FdWK3qFTwZVSvhH53yZgbu8u5+RlxYoVSS0/4XCYzZs7764yq0D22h1dGxLQg4SFDZdsxy+dhLH0jPAKwbvhS8mukhwrTY46AYORfWHTPttEglYbGyPuUok9y/q1B7S1cf6Hf2Pc3goayQaLg9JpjdgyGgi2D+TNg3NZOf1ymi9OGLUnrabpaSOOugR7Z93NSPcxvu69kKWBm2l0DSCntYk5W44yoaqQcdm2pKgTQAiBwzWA0Lm3IIEjDbsZYdT6RPIQeDeC5pUHyZk/jfIBu2ku3MDLZ+/macu3o+nxGoebn03QBCLWOtKC4Es4+IxQJIXc4cdchKAGye/wRh8D4mwVjaLX7uwfGkVkDd4B5LuTRdflLGGeSdRoNxmI4BJwx+8u6jT6NrpZ6Oq/vP6Stu2LyFqRTDpVysPRzJpnoBUlfgDcLaXc38trO6noiqlFIl3enz1BBVReXCzmejYwK2k/s7sErTbeKb2AAozn1mbRgkt6qaOALFoIYMcnE3qJU6TWW51u0znCxV7JsnV+NtUFor0DjXuceOt+CWjtQ29OdUWj0ebsAbw151oAxu2tIKfNR3NWep9BPnWEMo4hgAvd7zI78B6H3imhcV+uZv+Y+23cndxHWK0O/FO/wddo5SWTKmEjQo0+mlceRIQkdWUvs9Tys6S/O5/FGo3CY7EguA0X79CaZL5RjOBHuAEP+nSjxArhxOg1nf3Dti01cXvAl8wfCglRa0b+97F4f0U43HEti8XNMes3Db19l22p5AwTeaxBMoTOo++uRKd5bju+YLjX07ZqQtHJTTqhw1+ApUAxMBh4Efhrby7qdGPa/m3JvaRok3n6FsEPeJJg0M7XQ08hTIqMukorWbSSlRSpO6SXr8pneJzbuZi/00p2fC+xlGTJZi5mBRZpviduljE46hLYLHbKB1zYcTDG+/i9cndc6hcgYLPx/vQrwJLNRZ8FsSf4KFtDQSzh+EjKIX3cwPNxxyx2yYjzPdww9F+4avAiSu0iOrwgFbkIvln8CUdm38uuS77Gvtn30FT8fsrnWPOc0b3eoKs+fuJRDPoQhUT0nlsj8w13RJBTEStURr7FbruVh8YMpurhjzh8/zqOLdkVXW+o0cexF3dx3vJDLGm0835eIW/NP5urpn+NMWN+jss5GBC4nINpc93LfW8WG/aXPrJyF7/Dm2T/4UGy1K0dTTXAAMyj08RPzW238uDV45N6ja+brEXJw+5/k5kPrz5uz+Ge6qdV9B3pCK6QUv5ZShmM/HqermdWTnnc7u6njspqK7lw1xayvO2aqHjbyfa2M2tvhYEQn0CEoF4U8Sfbt7BYQqYVxEDX0txC4E0S0ia+HvwdBZ8HWBO4ODL9x5L0vFay2coUSvjC+DWFebHXIK92foZNn6hkw+bqGExwLMP4n0NjZiY21ywmHPKzYEs72S2NoJv1b3+e74SfoEDWgAxTIGv4lvxtzAzdDkLuYwghyLTnMjnDittCp21izcUfcP74v5DpbgABQXc9R8f/yVR0PUitJSfyEdi8+RRQl/LzSKQGSZ7bbmp2kXj8Ymy8RBbvkc1LZHG9uyNbY2R68fvJQxnxcX2cOUYcYQhHhsiHGn00vrKHti01lBRfw8yZ65h30V4mZrxM6WvDWRnIiBvAoEeoRxo9vEOQasLRQQbVhPklHs6/elRKkdKNNO6bPxp7QmuX3SpYNN3YKCPWCOO++aN5+ePKHhXHVBG5omcQQpwphFgjhNghhNgmhLjT4BwhhPgfIcReIUSFEOLcdK+fTtHUmsiYvBfQhPZG4M3I3FqklKkmK5w2XH755Z22/hiR2A40b8dmymorcY0eHa24XTV2yolJI+tf/gmv5RcuXoxM50n5vO4iBEiYbVnH+oabeZ4rzN+vEB0VzV34TARhvl7zGTCc9mAzWLKxuWZ1jMVDEyCjiuJ86hgwsolwXZgbN7zAiM37o6897it7cFiDzCZm8LnJsmzeDiOL2AlSTcXvU1f2MkFXPb7W+2gq3kZu9fkA1JW9DAnVudLqp67s5eg5oDk+NSF52h7ksUlFHFuifQkX7LmOG85eEreHC2AJBzlvdzthtDSyTsgqGPvlsWydVETlv7+P9CT/POtOUHarYF7Ixj0xkXAxgjv8Vtq21EQrlxP3D6se/oiQgRuWGYlWkW1bamh8ZU/Uhzk21f0OwagdZUhKmpFxvsx5bjtPTypl5sOrTV8vrlI78UdbwpSzBvKzhRNSrrk3xvedjhOK+oAgcI+U8hMhRDbwsRDibSnl9phzuj2iLx3BvTHy+3cTjn8D7cdxeDovdKqT6DqVDkbtQKvHnMuGkeX80N/AH2dcnnrXrqfbhFJcq45CsmimFTMno+NbRys5IEAE/PFGGGak83oxX5wCSdMZ73HsWDX79p+HzTWLoHc9wfYVUfH99s5J/HKiFb/o6Mt0SC838DyDztnOWT99Een14ho7BK9D66M182VGEie8IuSgYM91Sac1Fb/P0fF/QkZEVVqCHB3/JwByq88n6Ko3vHzQVc++2feQv+c6mtH2ajNcDVxpHURVdSvWvBJCjT5yq8/nOsAy5vlolbIj4CG0w8tb1R7CCUVRf5B+LiXABcv2GIqtP1I4da5F8MiXz2HC8kM42+M/A2tIxglkIqaRbQpin9O88mCSfaU7Zu9ZgGF7k576hdQiVRpJJRu5WAXCMi3R7A1xPGX7abvJhGcnfIUE44vPbv3seI0vqoCqyP+3CCF2AKVArOBGR/QBHwoh8nT//86un06V8rDuLf30wKj3Nl3RNSrukRYrXocV/OBzpGjIP8FjFS2EuYU/8Ht5ByHRc+08cUgY3NyT70tG09JhrDxt+TbhMX9mQEUWwfa3yRtRz+BpNdizggRatxDYdjXfkkdZyqKoq9UNLNbSw26QXu19j65q4NMhRSAEgVYbjuxk0RWBTKwhF0FXvZbW3XNdXESqU1f2clRso6uOiWBt3nyCbgPR1dPLZ/+BAGEyI45ZInyUT7fdw9aSUXy56R5c0kFu9fl8rRpuRPJralllDRKIzLSN7a2NfFBkvb6bKR7jrwavgKtuOBuqt/PIyl38td2GUUifSlRj95jTJdYq0uy5eqrb6CfIKkScR7KZeAmIFjodj2j2hjiqftoOImIba+14FvB/E56dwPGKrk5kTN8kYGPCQ6XAFzF/Phw51qngmu7hCiEuivx+rdGvri7+VMRsAH26Ea7pQIJ0SLFX2RuEI19mNoLpiX0XbwiyaCHUmMUlGZf2TLGY7BBbHb9w8aLzGgZbqskbUc+QC6twZAcRAhzZAdyTX2GGfwuPczuLuZ7HuT26F2tt7vjCL21sxRbZOjiysYhwwtQbEXQwaMciRqz7NcWffQeA6glPGRY8pYpgQUsJi2CKiN8SxJ5gT2kXkhElu3m85HmO2hqi+5f/hYeC6SXcOPXMlJmTG7zmdQM5kTSuPoi8xqScI9FLOe4a84ci7PF/NxJJGEkjYfwJ10y0ijS7ttlaAMJSJtksJhZzCWDR9CFxomxEOqJpVix2POJoNgTiNK1STjWe77gRQmQBLwN3SSmbEx82eEpaX3ipItwLgdXAApOLv5LOC5zKrFq1ikAgvkI18c+pOLkGEgh+y11pi3wmLQSkI61WIpsMcAvPYBvQStvgTYw6OorPzhia+rXM0ulSYgv4CNqNv5TrKWB6wSXsnrEc7PH/Rix2SdAbQgQdCFuH6IuQg/wDN7PrvDr2eXfhCgQpOdbGFwU5WpsPxETKNgbtvZHc2vOT0sV6wRMQjXbNIlh9vze3+nxCEo6N0vZ4050qP8AqWZu7mbW5mwn782jbp83ddX9cictuift2GGapY7Ktkkzhp01Ai38cxdJ4xrE1z8kjK3dx05naFcw8llN5KRt5KP+/xmO8bdL7u9QW5MUlmxi8UmuDuWT+0KShB55IqtuMRJFMxwzieCNKp63DfWpAhp1/WzD+uMVR9dNG6bXxfEIIO5rYLpZSGulct0f0mQqulPLfIr93aRrC6US6kawZJ4/Y0rVoWoa5lWcAWCojk3dIiDhlGBAUUNuRthVaivWg5bG0Xq9A1lBHARYkYSzkU8eFu9fyzuCLaXYYC30+9QjhgoxkS0cAq9NL/qffoXn0y/Hp4JrzGVvYxL7Du/E67FTma+1EQzLHUu67kIz3c2gPNlNx7F3OLrwo+l5SpYullOTuXEDdhD8jbDFDEBL2ewdUz2Dg0fPZN/se4/SyARbvAPSJDcLeGD2e6Io0zFLHTPs/sAlNvLKA9fYdiACMDCeLbs78oRxZsin6dfMOQUo8B/iKvYAsazYe2YYY76bUZP9WJ9EOcvvDq6ExGL2mnuYWgIxkZqPGFNdO4JJry9ixZIepIUcic8cUJh3TxSvaB7xkP1UrK6Ne0N11aEo01ADwdqFITJEWh9DSyEbHu43QqhmfAXZIKf/b5LTXge8LIV5AK5ZKe0RfOsYXvwD+S0rZGPnzALQqrp+m8wKnMmYD6NPlhA0kOOGIaCpW/30DsyLim7A3mkDQVU+L6DxlZyEcvYaU4PNmUP3pVBo/reGV737Z+ElScoN8nn2zd5heN9Bqo+DoDAprkvdbO9qJIGyxcGbmWKYWXI7NohVQZdpzmVpwOb6wB5c1wzBdvIFZLHUvov7SLPLbvUz7wMP5awcxeFoNjqwgImxj0Lavxe336hXNBXuui4uYtYXYAAmWeMEu2nM9DNB6gWUgz/T9TrZVRsVWJyQkm+z7GOnrENwwkqzpJWROKmLwyo5B5GUtu8msf5e/yQ6xs1U5ubRUMnZ2+sZ0RtEkJOfpopW+91/EnSs/N+2lTeSvG79g8YeHkkRTr3jWo2W9DQmIim5XI8reqFBWJNEr4/mAmcBXgc+EEFtjXmsIHP+IvnSqlC+XUkbfhJTymBDiCuC0F9x58+axfPnyLqWRY5m2f5uxBeFJThaJWx6a8BoJbBLSwoBwI8dsJtaPEcLCytPydgCmtn7Izr+cBRYPWLJNBxZk0cIs1hM00fNwQHBk0xBGhCFQ2tGqo0e5ti/Gx51/zoALo2KrY7PYCYZCBMOBpHTxBmbxNB2OXXWZblZeuBDehcteC3Be4RXsLm/jfc7hiQucHHUJBnm16UOXVwfjWoWCrnoC7QMZtOfLOCwkrdVbN5o5lil8rWYhhcEBcVFgrCtSpjDeL2/DR4gQAgu1tgb+OPAdbuD7lD38ES802vnsTCsXY6P02EbsMj6yDPp9rHvhuS4Jrj4P2MirOJHKRg8zH16dtthCR9Vyon2jUcVzYhtSV1HtO73PZ7d+9pcJz06Anq9SXk8nGzfHM6IvnW96qxDCKaX0AQgh3EDX5lmdouitQN0dKq/32a4eMxlpSc8vuF9h8p69ZLCBWZ0KrOE2rCXMufIDVsnLElLQySf7hYsl8mZKN0ZcRsNa1DV749u8NfcaAtaOH1NBmFvkM4b/lKTUItsjG4to3J/LjnFPkD2uAmmL2Xsd9ydamsu13ZsIsRFvLE6Lkw9rlzNi5xU0li+JRqRLWZS0px20O1g37RL+5d02rMJGk13EzdLVpw8R2Z98YtQlHHVdSpFHMuOzdi6rCjAxwxoXEXuFj62OvdxT+fWoV7Peq+qw+Lj06nGAFom1tTvIMhDdLOnCihWv8PHHgjcJNE5h8Ic1hCLuzXbgftx84jqTL9qSMwYt9fFmG+lYEq7ZWZu2o05XxDaR2GjTrOK5O61LOqp958QQEddTbjzf88AqIcQf0TI83wCe7dVVnUSUl5dz6NChtIYU6CSaXZxUe7nQIbQmghsUdpbKRUmCu4FZcS0317OY88MbsFhk3DnrxEXJYpvY2BqhnoJo4ZLOuL0V2rVmXkyjK48C6iiglnHC+AZAAK2vXUdj2+eAF8c5e5C2+KhN2vw4ztkK28qix9qDzWTak/uS24PNHGrbwaGPIK++IFpQVSeMLRabs/LIsGkVrTUukTS43msV/GqsE5+147GjGYI3pjgJf1BJQ7OkLvMA7cKHGwdD8s9i6uFx2CzJtoz3uLIYETOftaKiIClLY5UWpgS19nqXdHLH4YVYpMRlTx7vN6NwAecMuJCKY+9yKEZ422xZDLv/zZQj7XR0IT6RjW56tGnWopSqyrozVPuOwox0+nD/SwjxGTAP7bvpP6WUK3t9ZScJFRUVfPrpp2mf/96ICWwvHR6N1lpdGSe8p/a40W8QUhpldIiLlPC+SEinUsQzfA+AWTHCbBQFIgQWGU7yxQXIaW00fP1xeyu4rCrA0fGX8fTYAmoJ8nOeZCKb2cqUuL3kC707OK/gEiblX4jT4mZ3lvGWTKLJRcWxdzmv8AqsouOfUUhKKpq0kXqTBl7MyNAkxPsCiaRgto+6zOQoJ6e1kfZgG5n2XIImn2mTPbkNLGi3s2ZyCYM/Wh1t8PPgZ1fdPoptdsoMip4STSriDFsam8iSLqYEh8cVTGVZszFDt6qcWnA5AIfadhAQNtblnhe1NDQbaffg69to8wWTzCUSsQpBWErTyDHVcxKnKeno0WbO/KHUvbQba6hjDSGrYECKKuvOUOPwFGaktXkopVwBrOjltZyUGLUGGbGnsJT1I8vxpRq5d6Ij3V58Td2/dwOzWCoilcpJKWEnS+VX4wTXzGg/jAV7OEAgZs/UFgoye+Pb5I1oimvLObKxiJzq6dRPuIZfTcjUosJWQZ0o4h15eXQddRTxtLyd3NpdjLDYsEb+OZi16gRa4/+5OIYe4uDkfyHoaojuneZUzWB6/sVMHnABdosjWvAkEPxgr+Rn40P4bR39mc6QZPLOZjY2beaCgXOxdfHmqyUjO7mb3hJmk20fZf5kwTWK3MrLyykvL+fAv67HHjB+fYm5eAHRoRCfe7/g/QHT2JM9Kua5xjR6Ov9347Zb43pNh97/ZqfPEcCvbzhHmxq04m3c9pBptPk2Ad6SHr6BI8lta2Gnr2SMmuijMCOdKuVrgV8CRWg/ywJt39h4A+s0w6xKOfYLKtHC0ZQTLbq99VpS4sXJH/km73FRyl7cepFPKGTFatW+EM3G9gFYg2Es+PDZHWT5PEzb8ynny3UMubCKD+wztXR1dgH5F9fxjYP/4M/FGUnpWaN94D8WTeC6nW3RY7HVwLFp8Dx7IzNHvsO4vRXkjWgie/pegjZNNBJ7bB3WZGG7ojoE+HhilJMalyVaEDWnYQC/OuMMPsr7K7MD83FIX5y9pCskcYQkzY7kv68sn3HE1y58BKWMm7cbBiz+EIfvX4c1z0nO/KG8TYBHVu6istHDG2SRZ+CFI4RAIJBSxnlAJ+K25/DskK+aPt4VBBiKVZ7bnlKoBXD+iIE8snIXdy/Zyv0Tw1w3eShrdtYaCuAjK3dRGfbzN2Irv+HTblYUdzZjN53npyPWStRPTtKJcP8LWCClNO+lOI1JpzXIbD5rHCdosPwJQQhayeWdxMInA7J8Hvbsn87QYVtxOttYGHyZZ23fJGBJto/0Opw4pI/b5WNMkZ9wuLmUwdNq+MA+My5dXU8Rjw/NxZ+mS0TimDq9AOnVMV/wtOPm6HUb3QO1qmIJ181YhrTFf/EbDRVIZKZcx2j5MkFZj03mU8B1uDmfnzQMYt+Rd2kdMZFvsTymhaqebx8+QkbjqLhiKtCE+IJ9ewxfxx520lT8AY2jOqqX8/deR16VtrZQo4+6l3bzlvRQGdbEJqeTz0sIQViGTSNdM6cnW84WnIUrEfZGZCAPX+18gs2TUr7WozdONBSQB68ezw+XbDXcXrAKwfThA/jkUFNU8PyhMC9/XGnqyNTTFcXH0xKUrlgfr6gr+o50BPeoEltzjFqDEoui0rZwPNmKpzqjE7FFSlqdbn4+9k7CWLSodd9nfK3sGV61X2eahn6RRcxyrWf41L0ImzTc9/ULZ9o3MEZj6nKrz+elUZn4E95D0O7gvfMv45/cSwyvFXTVx03/ifVRTuU8lVM1AzE7BMikFipbYT7Dd/4KieS3o1xx7UJlNW7W2S2EYnpphbQwvaCZ+nHL4l6rZtyfELLjhsIaknwDRzS6q0ljyL1A8JT3ALPIjztu5vRky9mCq+QVhEX79yEcjbhKXsELKUU3lYBYrYJwKPnvLCQlG/YlDy/zBELctWQrdy3ZmuT41N2KYrMI83gEvLPxe/rrWSKTkIzOU4J7fAghXMB7aJ04NuAl3QQq5hwBPI7Wi9sOfE1K+Uk6109HcDcLIZYAy9CtawATy6vTjsTWIKMJQKdM5NrTRMQ0jLan2erKYN24cxgl3+dxbmcRL2JUmazv81rsEhnGtPo3rb1xKfnurlZCMhRX/ATmA9pbMrLxeTNxudqSHhOBTFNRTeU8lb1/NKGBaP98Ewi66hEILq8OckV14muWQAA22/bTKrxYQk6mFzTD+KXIhPsdowh8EIKXyKIIQTMSPxJHCtGVGbB6wJmMpJV8wklOT4L4PVtn4cqo2EY/I0sAZ+FKsoPn0errGKSgczE2bgu4KFqyj8NL94MkmgJ/ZOWupPO7wrH2APe9pBU5LpxU2q2K4lQR5vG0BJmJsn59/fWMJiGler6iS/iAi6SUrRGLx/VCiBVSyg9jzun2eL50mj9z0L4GLkXzVV4AXJX++k99ysvLo324hunj/hy5duVmQMpev3nwCxcvCm32rtng9LjjQrNrNEUILDLyZZq4dim5uu4Ql1cF2df8aVIvtdmA9iyfh4MHJhIKxZvTh0JWba/TRFRTDiqwOuLm5caiHzdL5Y4Ml3CTfya3Np1Hma0F69kvm/7LNlpDMRYsiOj+rTRJDUsk/+nx0O4P0gJ8mVYuoIWf+jwMb7Jyb6OLbzc5GePr+FxibSVjEfZG/m3BeB758jlxxy/Gxo9wR9ekL0VPgY9r7J7JTCyBkOSuJVujM3G7OhAgVSRqNhTByFoyETNRtgqR9Hpdef6pyo4xY7+yY8zYgzvGjA1Hfv/K8V5Taujjs+yRX4n/IKLj+SJCnCeEMDYfT6BTwZVSft3g1ze68iZOB3JztX7M45oAdKKICGeWbCLNIRf6E3trRXHUUcgGZnEDi3HI+DSlNp92cfTPPl8msys/wiHNjQrCCIaxj4WVfyNf1oIMUyBr+B6PcdOA+2kbvImROZOShObLe49iC8W30dhCQWbu30lt7XD27J6O15uJlOD1ZvLFrgsI21sxQk8vG2Hz5mNxZkf8kxMmD5nM0U1ESoms3ETJ+CVxNo9GrxV3/YTXcyAM90cBmpC8TZBj7YHoMsf4rFzmsZMrLQgEudLCZR57VHTNbCXzHB22iaUxQnFbzDD7RKwhye1pDMNIl9jIdMP9F3Hg4SvZcP9FxzXrduGkUq6bXBr3DiTw8seVLNtSmfK6ZhOGzCLaxPNOpz7fiLj+H5qfsoj8/n89IbpCCGvE1rEGeFtKme54vk5JNZ7vXyK//68Q4n8Sf3XpHZwGzJs3D6vViqsnRsv1NpHRfq1kpTt8BoAsWjkhoisET6PZNn6LJ8mXNSAl+bKWb/EkAHfyJIt4kR9af0NLcxGzd1QgwsZSYUHSInNYkL+E/+G2uNF7evRpERZAxkW5rrpPuXDXFrK87doNiredC3dtYfTRSqzSQm3tcDZ9dC3r132VTzZ+mRFHLk0pqgV7rkOE4ovBYgU1t/p87J58bJ58kGDz5Cf5Kpt/ZILAkMkEB6Y4SZKWeBt9KfiRPBazRyulxCoEF3ht2BN+iuxoxwF8tfOR4Xj7S8J2Hpj+w+gf/yPzMM++9XPeXHYvg2Tqn8iCTh7vKrF7pOnS2di+NyuqTD2gU2E2fq80ReSrn3fd5FIeWbmLYfe/ycyHV3cq7su2VDLz4dVpn98P6bXxfFLKkJRyItoUoPOEEGcnnNIr4/n0Qqn0LZROQ2IH0O8pLMVnO4l8kYU1ffmUEg9O40KoXmhn8gsXS+UiHud2zpfrWb/uqxQW7qdqpJvn7F+PFkk1OXJYN7acKyrf4aKdH/PemHOSKpzDwkq9KGCPc1Jcz6+Onma1CKsWKUZEt1V4KautjFpwdqwN5gTGRfdNdbOI4aFBHK0pp3nIGhJDnIza8iQvZKPB9NZAFiPW/RopJSEZTPJqTkWGLQebZwChjGOGj1sCWemJt8H3SQCSpvGEpIzOx01EPx5snoQX4qqU/bXzCTRNBKBp+XJKn3kU6dXEXHoaEBnGNy2Qeuat227BH5KEOjHSSESPWNNttUm177tsS6WWATBA94BO1cJjNizB6PX01HdXq5ZPkSrnXhvPpyOlbBRCrAUuAz6PeahXxvMtF0JYgbOllPd1fbmnPhUVFSxbtoxwJLLaOHw80mLt5FknKUIQkiaD0HtpjzrWBGPW7D/j82XyP/zGsCJ5XelUfsR/MyK4maft3yEs4v8eJILfix/wJHdSQF2C49Qx7iy2cXl1ECFEVHAzpZM2kZyqzpIuRoZLGBljLLGi2MZdo5xUu+6ggBvipyEJaC+sgJ1aFKuLnl7NXD3hqaj4QseAhP0tFYzMmRSJvjtHCEHh3uupHv8MWBPSymEbRTu7n20zm2nVYpHkhJP//ptFh+gFmyclVSQ/+Po2Fk4qpebRx6JiC+Db9iquSV9F2JL7mM0qofXKY4D7XvyUznc74xmc5+6SCBk5Sc0dUxjtZ05Fd8StM+eqrrYinSLTjHprPF8hEIiIrRu4GM2HIpbeGc8npQwJISZ3Y92nJLHRbG5uLu3t7VGxhZNk//Z46ElhTSMq1ouj9NNcrjaayDI8t54CBg/eQyl7eIrvGp6ji3AdCY5TIp+fny0Bb1R0AXwFE3m+LDfa3jVt/zbG1FRFfYZ1VhTb4npkdQcr6BhPmFisZNYiFDr2b0AuQgjKcs6l2nOAQteZcZGuWdsRdLT8HB39PNKhlTxbAlkU7fxKWtFtKvRq5hok29B6z991BrnMY49LKxfbBTMz7NyMI1rB3FL8AdeWvUG+6xj13gG8sucqZj5s46kjR+JS2MHKj7SIePyXIGMgYcAKHE0x8zbDYWPhpFJmPrza1CbSbbdy3eTSOE9nALtF0O4PcteSrUnPSSVCsZGo0fzbVHRH3FKNCexqK9IpMs2ot8bzlQDPRoJNC7BUSvmGEOI2ODHj+bYIIV4HXgSiPQmnW1tQRUVFXL+tnkLW+22Fav1JG4sMcREr2Sq1CDOLFrxkEBQdopJYHKVj5kRVQJ2mn2ELBcLcrSpKgth7rYL/HQuXV2t/XlFs4+lxxVERbXVl8O7oSYwNncGIysy4lPETo5xJjlZ6SlwXXEsg/kbBrEUo6GxEr78QQlDsHka15wCD3MOwCJGylzdWdHOqZtDwt9sYMPcXWFKkaNNFolUzgzZ5qB4LF2PjHacmgBd4beRIwUCHYKLbijNmStEdxVupGb8EW2TNBe5j3Dr+BZ7dBrXuPAZ5GqkumsK+4Vfjcw7E6WugYPvfuWvKgrTWpgtFKsHwBEKs2VnLQ9dOiEaKuW47bf6gaQq4s2vqGEWM6a65J+hqK9KpMM1o7M4df9kxZiwkjOcbu3PH8Y7nqwCSGsQjQqv/f6+O5xsI1AMXxb4+cFoJbqJncmK/7Uk38aer9NA+rVX6+S5PRITomejxdAfU38Binpa3x6WV48RZhLkhvISnLd9OaSlpRK3dSVPxewA8PmEKXkv8F1DQauOlkYP42pH4Xlizft3YlHjY1sbuOXcg7W2aV7NJi5C0xEdwQggGuYfxSXuIiRnWlL28sRGs338MR0Ai3KmqqIxJtHAMI7UWnRgsaBXF79DKTmeInU5NcF4iC2fCuc1lL0fFVsdpDXBt2Rv8adzVfKVyP/vKbiQcscP0ufL5YsR1jPF1XDcVulB0NtxAryLWI8WZD6/u1M85VoS6anaR7nWPl672Ep8q04wi4npqjeeTUqYdLp/KJNo3pmXXeAphxU9I2jt3jzIiEv1n0cwt/CEqpBuYxRIWUd+JyMaiP24mzjZvPtftHoVl7PO84Lja0K3KjHzqqBnzF03AxCWG5xiJ6yCvpNqdfDyuX9gikQ5NqIPuetOaRhFO/pkSwL66pwjlXUVeql7eCBJJqEK7H+6sCMloKIGITtrpeH2juswig4NGx8xuLvJdx1h75mTKM6fgtMTv2VqElX9yZ3DrL2ZGhc5ITGOFwkhIYkkUuc72W2Ov3R2zCzNEZK36dR9ZuYubzmzhJ50UVJnR1elEappR35HO8ILhaDZW09G+Jj4A7pJSHujltfUrEj2TT/n92gRCmBRMpYMQOKQ3TmzXy1k8I+LH9f1W3sluRvP1mMg3kXDYwgw2MNOSLMwi5MBxdAzVI//KDHsLsz0fcZP798balhCx61Fy2N4Kwjx1bWSGccduHz8b78Rn67gZMUuJdyyWpBG/IuTA5sszPP2GIbfjC3moNJlkpLcjhZG8kfcuZ7d8RCGpi5C0ZRjfjAhIOagAoAWJRUDstqmRPaTZ9KV67wAAHBbjtbU2aAVruhAkiqkArpvcEbHqv//78m1JaWKjCM5qYJGoU9qFwqTOhD4WASyaPiS5uvjM46sWTrXH2xPnK3qGdMKVvwBL0TaTB6Pt5b7Qm4vqj8ybNw+7vWOP0WxKyylLpHfXlE72sP3CxVI0B6kNzOL34gcGc28tvMPlLOJF7uRJNjALKSHgd0bNJXbvmsHenbPAkwcSNsgLuJMnuZmX+OfwE6w80wKZLSC0SDLfxK0K0Iw/IiYY3+LJuOh6IpuT3pMrpPkXJ3LpES83fvA+OS3HEFJSEK5Pup4ZiT231kByUZgQAiEELltGyl5eicSLl6saL2Tg3IeQQ87TipC2/BkZ7toeY2diq5+TWKP0O7wkjpIv3H89FuL/rn0hO6/s0QzrYiuaY8ka2CHERoIngTU7a+OOLZxUypb/dymP3Tgx2r9q5h6VylBCN8DQ+1XNIlg9TZ2qX1anNM/NozdO5GcLJ5i+p+70BStOHtLJiQop5Z9j/vy8EOL7vbWg/krcoO6mJqbt32Y6ck+EQ0hh6d+Wjj2MEw8+6e5kKH1hdGRfYttOFKFNgNQi3rt4TnyLaft2cvbRo/hEkEzpZGpwBCMPf5MVxTb+cLYLr0V7zXrHQP4gb8MSGQAAcAPP81t5l+HruKSf33N99FAoZCUUsrLJcR7vcVH8e5GSqw4HuLw6uUo2IP0M2vZ3vrvt79ww9F9oLtlM9dkfdH47Ky1JlcZHCptYUWzjiVHOuCEF+ut21subgfalnyfy8U/6Ksec4NrzEa4p3+xkMbFvNfUYPp0sA73Sqog93IaLQViw5Tk5a9atDCgZxcefP4RL1EarlDdWTwXgPVdypbPNYWHGNSOifzYTvMpGD8u2VMZFtXluOw9ePZ4N91/E2rVrOXNcWXRcn97CkyjUsejCmU71sZ6m1iNGo+ckzvTV6Y/VwmrsX++SjuCuEULcjxbVSuBG4E0hxEAAKWXyeI5TFH1Q9y9/+cuoGUJslbIUIvJ7N/Y5T2b0/b7OvqSFiGvH6RQhaCWbd0dPYk5AE7tYMUinOngm6/ktdxpevo4CvN5MnM42fL5MDh6YCMCSsTcbRN+C9UU22Jkc4Tpjiqvag836E+JPSkgfIwGL1lIWW2ncZD87rsWo2i34+dku9JYliO/lTSSxZcg++GquG38D/1xi489lriQRT9zDlVISlH7swjjNG4uZCcU7BHmHVoSAR+dPZOGkIt7eMoW73/lXw2fohVEXeG3kSgtZA53MuGYEo6YVA6R0QRLAfS99GjfQoNET4K4lW/n35du4rzzEf66K33t9/kPzVs3Y1HNn1cdGaWqjtLbTZvx90N+qhU8RQ4x+TTqCe2Pk98Tmxm+gfW0M5zTD49H+kSSKLoC09GOx7aUB9+PZyjYmpndyN14/aLXxxChLXI8spFcdDJBvck+Y5fOw6aNrk47XjzWePmT2ehLJkMyxHGrbQcWxdym6aC0kVBsTaVlChEFaomIbvUak0rgmNCHpJsJrFTwxymkYXcdi1DIUGreYobmD+O8zSghZtZ9NXcQlHvKrvZyFlUFS0Cbhs7rlAEwtuDyu9ze5cpkkE4ppxZuSem3vWgJ3L92Ky2ZJ6WqmVzqX5rnZcP/M6HFdBMyQYDo96Fh7gPq2IJ5AesWNifu2qSLNxHMT8QY6/n4bPQFD4epv1cKniCFGjxDpw90MVEopr0p4rPfG80kph3V9uacmb7zxBh9//HH0z4mtQackaYj00fR8u48LI7ErDHipcSRHA5rns4aUcMmxd8B5Qdw5ZsPbCwv3k08d9SRPdxnklabp3ikFl0WHHwRdJkkfEWb0239i1yVfM3w46Kon2G78WZuJfSxmLUNHzxxGKOFG0GsVPFrm5Kx/NGsRppbJ51bvF+SEtM+vfMCFZNhyaA82c7R1N2e6RmJz5XFUSKoJx5lQTCvexK3jX8Bp1aI6vdcWYGP1VDwBs5EI8SSKXHd6XLuDQNu3jcUsAtVuCi5KOq6TrnDFFmRBS6ciboRRCli/ZlfTwv0xxd2H3Ilmb5xj8Fi3x/OdwkrRs7zxxhts3hxvK33StQZ1Mbq0yBBjqWCbPCdlO5BmXtFMK7ndW5cMEy3bNXmdTFq4Yq6dWruLzECIsCVAu9VleEPgwcUGZjGT9QgBV+S+QnXLRLK8eVHXqB/tEknD2/cUlvLimFnUk6+tScRWHfuYVSOS0r0/m+Dg6JjFzLCvoMgzkPw919KQopK4LdAE7dlaYZfB4zaTQh6zUYGxmLXfHBPGvbjH3Ba+67EzrmE/V1f8mUJPIzuLithfksuhNs1KXRfdnNxyfm4J8Q7auu9JiFevLXsjKrY6eq+tvlebDonp1FRf9m67FZfdktK4Il0sQvDTZZ+xZmdtnF1jojMVQLs/yLItlT3i/KTv/a5du5Z/XjSnS2s2SgHf99KnIIk6bnUlLdzfUtyd8cRtq79CgvHFHb+76Lj7coUQZwBXAj8HfmhwSnQ8H/ChECJPCFGSjr1jP85/9i9iI1uA90ZMOOVbg8IILXrtZE+6gDpu4Q9dn5UrZWRM3uMs5st8j8cjlcPx17HJAB5c1DjcSCFoddhot7lNK6dDwhGtiAawWkPk2hq4eeNb3Pbea3z3g3e5rCrAyHAJswNjyAq7otmKektB5LqWyBjDSBWz/C3ri2xJ6V6fxcoLjqtBQCijgdqznyOjtjypkjgcEOzd4OSNL57k4AfZhAMJva+RSuMir8SVkCI1q44GLY28b/Y9WtScOHE+wgCTlHpuexg7gnGiiEGeRizAuJoazv6ihtKscqYWXE6mXbOZLLA4+RFuLja5R893aQMTNjArOsnpTp5kl2u86XMSMUqnpvqyd9ktXFne+RjSdG4zQ1Ly/IeHqGzUaqwrGz28/HEl5w7JTfoRO9aupYj1veXEyTu5buOBEz0tXEaRdCAkk+wt0618NhsP2B8NMSJimzSeL3L8eHkM+BcwnVTZ8+P5FPHEjm17b8QEtpcOP+WrkLVeVOP9TB1BOGo8kUWz8UkmQpxFc3RMHmgFTr/nG3yPxyiQNVGxc9FOSHStD7iOwrj2IosIM3zEh1ikYGpwRHQ/Uh/evm3Y5ORshRAUUMfj3M6F3h1p7RlLq5/2wgoGbftaXMtP+8bJNO7LJSwEjftyOfRuieEYvtyA5Cefeyn2hBFSUuwJ85PPvYb7t/qebdBdr33lWMJJhhqBoJ1BXxyAUPx3hz0omVuhRTM+54C4x4Y2NDEta3rSpCI3gtswdu+q9w5gA7N4mtupE0UgLNSJIp4W32NScTYXY8MiiBs9d/P0IYaD32MFrM0XxG41/tyPtQdSFkDpLEp4nZunD8Gaxr9dTyDE+/saDH98dRHTo8xYoW7zB7Fb4q/fG8LVlVRvOueajQfsp/u3vTKeTwhxFVAjpfw41WkGx45vPJ8Q4txUT0x3k/hUIXaKzPbBw8zFVv/XebKLsZTcwGKWssjYlzjyPkXMz9l0NiRXIUuJnQABA+OMIHbu5Mkkx6jz5Xpmio4e1kW82PX1x7QXPS1v55fsZPDgPSBh7T7JZrmfKcHhjAxrEVIqMdWjz0Gj0nCUQkvtJlYSB8MB/JkrOORtIRw6TOO+XIp9t5FpT07DX14djBPYsNR2hxNbdYz2bGOLs2zefJ7acwUHqwdja2zEPTKXlgwLue1h5lZ4mHBIe67TlxwB21x5hp+HkZMUwCt7ruLz8pmGk5z+b1SYp6sDFE8bGO1BNSMxTdroCWC3CAZk2LuVOs5y2vhng9dcnIZQQ+pv0SONHtMoc0CGnQyHrVfba7ricJVudH0SGWL01ni+mcDVQogrABeQI4R4Xkp5c8w5PT+eD/h15HcXMAX4FO2fczmwEZiV1vJPESZPnszmzZvZU1iaUkytoSD2cBiv3XHSi64eeSZ6F8fum4ax8lvu4hm+ix9X8nsWgoA0TrF5ceMV2k2qLoyhkJURVUcoLjmA1aqJjpnrU7r4hYtjDKRIQMngvezfP51W4WWdfScEtCjX1J5RHotGn3fgi9vDBWNHKaMh9DaLnfIBF0LjRsoHLCDDmo0v7CEcDmHpZKSjAPwSnAnLM9uz1Yuzwkiy8QBBbNUeRvzDn9TvKkJ+Rux/PekSQW8jdveApOM1yGh0GCuCG6un4i03r+4uQrD4w0Os2VmbUnwefH1bsoCFJRkOG43tgfTnN0fwB42zgl21YzS7hlnk2NgeYMv/u/S4rt8ZRlXOdquI28OF/psWPk56ZTyflPIB4AEAIcQc4N4EsYXjGM9nmlKWUs6VUs4F/gGcK6WcIqWcjDZJYW9X38jJzpAh2o3ThpHlKYU0ZLXhdThPerHV08MzWc+3eDKa4rXIkKGo+kRG1/uPE67jFy5est7E4NI9hENW/H4HUsLCwMs4pPEeJmh7vLprlFn6Ohi5txQRV6PCwv2cO+0lgvMeYPecO7jO9nscMr7NxRWS3PlZRjRSvbw6GJfuLfJ7+Fb4/+IcpfRo2IgMWw5T8+eSacvRnKOsGUghCYYDcVsWifglfOYJJTkjGQl77HFLQgp4pzPEmuwgTSKMRNIkwuz17yev/tO453utdl5tP5LkGCWRuIA/ThvOmQMz4lpfANxeY3Eb5JXURGq49SIeo97aZVsqTYcJVDZ6yMswvnFLhT9kvCaj/cpEUv0L1kUs1USe3sYoBfzIl8/hkevPOVnSwsfDj9FacmLpifF8hgghbtNH9KGN59uPpoP/B3wv3eukU80wRkoZbYSTUn4uhJjYhbWeEqxatYo9haVa5JqKk1FoEyt9paSVHBbxUnTgwOPczgZm8Vvu6vLls2jGL51pTe+ppwAhwO7wEQpZ2bVzJiV4+OaY3/Iii6Lj/EDQSlbS8AItRZ0cDdsiLSxSCgoL91M26kOskSHt0tHG+byDxMtSqQ1TKAz4+OcdJO2dJqZ7m4pHUVf2maHrUyISmbQvahU2zXwixc+NQ8DkDCt+CUEpcQg4KiTv7VnA5PGL46qDEwU/MQX8qTXIp7lBLsbGbbgoyj2bpst/Rdu2V3EfWEutO48/jbuctflnsh0Pd+EiFxH9Lw9B9qY6vjjbiyfQcYN1MTYm7fbxyNnuuAyAKyT59m5vXM+uJxDi/dd3M21lJcFGH3VC8lvpYbUwb/8RgLcb7UEOq/FNoNkQeb1KOS/DjjcQMmxnynPbueqckuhAhUh9fZQTGVGapYBPQYGN447fXfSXJ25bDb1QpawjpVwLrI38/wkbz7dDCPE08Dzaz9XNaP1Jpw0vVzfwxJhpWlXyySioaZAlm2klC4iv/G0ll9/LO9jNaNZySZffvz60YDej03KZit0PtVpDVI1086rteurFQAqo43s8ntKj2Gx83wC04peqqpEMHbY1KraxzGR9x9ShYD4jqn+ddE4i+uzZkAwmieleSxWbbftpFV4ypZMpwRGUhZOrajuzUdQfdwpNcNd5gvw4wwPV5/I5Ia4te4MC1zFDwQ8D75EdHQT/DprY/hg3jogYD7C5CZ7zT7SecxPDEXwfiS1y7m1AXoJoW0OSAeH4Y7f9f/bOO06q+l7/7++ZM217LyyggDQRFMWKBSu2RBRbJCokJrZ7Q4x6gzeJ8aZcyc94E+6NJcYkaMQosaCxYSwoRVEQBOmCKCy7bO/Tzpzv748zZ3bOzDmzsxQFnOf1SnBP/c7M7jzn054HH1X1UTwELXPKN20OsqK+0zKzew4q3wu4iAZCCKBcCv4DP1IGbA3mwfjiyXSW14Tf7aKy0PkBOZl0zXQ3mEYJ1vsV57j5+TfGJOyPxtdmku6ezNJmsWeIkeuhZc+H4WZ/M8T18d7FGPT9WuDZ+hbu2LSDgC+5Ie4QghD4ZBAfQaPDNAlR4eFNeX6/U8aKjMZF/OczrW+ylpImypnJQ/G66OPuGRZHoUflzQCOpOtk35fH8USjKtu2nsSAAX+zPTcRjvVRG4SiAbwuawrxU6WOxe6N8RnfbhFiiXsjIlYz3lOoQnCk30iFDu/czKgvNrLjvSpC+RcwsfRsvAka1RKJGiPLKgQ/xg+xqNWTRKJqLHpNPtapSSr5i8M8LqXhC8nsJBK9CR/+pOuaHdBvJIiW7A1M4itqTxU3MeEkZehVFVuxjRyPypTxNUyc/ZatkUJfghhZZJGJ0lQQ+F3sf1873LutjkCyJcohiL7Gf2TailYqPDJocczp6/pAnJBNYvUQsul6tWol2yExUjXRw/GoLo083UcolIvPZ3jTLuXUWCe2tVParj5qJ+ovpcSluOLbE7WMj43pMzc2GuqnUaGzQt3GEeE9J1yAEiG4NtDENP9wcvOOo0frYE3rO3zY9BqjSs+hRPGgQ5xsTZikVpjBZ2kea2e3B6ABYwKfMqHxPfKjXXQPupl8NVWUx05v2YnEnbYnIjl963SMSXyLFjkTrpMilJOyldlklVVkymJPkYkf7kTgHoyOsPjxUsqvhYbyztDeq9gcHNjLVLmU5NFpW1eF/ncah4WPsLQX0G+iDF3v2zEwBcEirg5PpH2zYPeYuSxxncCj3JwaQesqU7eMAFLNAJJTtkIIeqpW0jTi2d6oWBjvps/XzfAR7wPESbdLWBuz9gQdSL7jHYQ7lsLOdRdyfNkFfNj0Ki/teJiIULlh0C0gSJGivGWz/UyvHSoQ/IIAP8ZviUgDSNr0EGc3vYOMGqNFn7QsStFfDsTS2MlwInEnM4REJKZvhbDvkcu0uaq/BCkwouKDTZEpiwMHmaSU/wzcBqwE9r+o6QGEZ+tbMnqiPiTQh9etizBRnB1kymhkDjc77j+GFf1zCkqDUpr6TbYS2PzZWET1K7iOeBOphJlPqitQWPh4JnoD36nXbM0Ado+ZS0/RFnrK16D5mhGRXKQaSjUriMHlinL4kNVxws2TfTeOpX0dUoIgTrYmzLGjz7s34MdwLXp3UKmN85CfoAhwWV3ff8oNSIvdXgUiXgs+W+tAar0zwKYU5NFlZ5Gj5NHpEdwf7rGtyT5M0JbE7cjZ9j3ASN92hzTbruZMBc/6OxokwdFw/hAdvcliHyMTwm2XUr6631dyAOLebXVfD7LtAz4CaDhHDS4ZTplFNSElLONU3hVn9Zts/dEAUZeS0gB1FfMyupT5xatFvCAFw0cvRgdk7Nwm7MdqGt0+oMvRDKBj0NvxhID0dPe5Dq/XOMYlFY7T9i4xJAUUOGQjctTePWta3+GBs69OlaJ0CR4c7eeyhm5wcNmB3hGgc1DjdnuJOFemEvYX3Rv4omcjtz/1T8O0PWz/EJJM4s0CHpABPu9cz/Wty8mPdtHpymNZ8YlsyR9he4100WlbIMLE2W8ZNVzHo+znWPuCaTgPqR3OiZ672capLOyQqR/ufcBzQHwY8lBXmnq2vuVrlE52hkcGUYnEBSrs4I9FJnaqUULAfKZlNBKUCKFHmaY/hscVTGmASle/tVxDQCTsRXFpCCFTSNopzV1KE+2VH6URlujXS0GLeDnxhOfxeLtQgqW0bZ5KYf3JsTX272KNahugU6mlPiz0evEa5Ffvs29ya1Nh2/Fl5C9voExi8cM1YY4Amc1TyZFqNNZAVzSsnQEnNuDO09C7/ZRvvZKdsxYzBxcPx8jaDnHPXOB3Vx7Djj//g7Ob38EtjeMLol2c3fwOgC3pFvrd5HrVtMb0dz7zMfed6vygmEicmUa6ihAMmfWyhVSzPrKHFoQQ24FOjIyuJqWckLRfsL/s+ei1HUq8qQQO2XY8szP5aw0pKaORK5nnaOBuoot8Yz43oenpQTmTzYxkBn/OrGEqCQJwq0HbBqj+QHWHHKNhpxGiK3mCphEbUB1cf/oDXVdwqWEUxXhW1fzNNBw1FyGMkSJT8CIT4g2KEHMrngdgZt00fAk1bk2PsKZ1meX4gq42OvJT1aL8QZ3r39+KBG6tKmbeiFRj+vixDt3D3a488o/oZPDpdShu4zW48gK0HvV3PMJLVf0pjmSdCJ9b4a7n1nJly/txsjXhlhqntC63JVwh+o5QI1FJXR9Eas6xTpz9Vkaka4qPJJLqV+Ujm2zNlzhH7BRl29n5ZR8KbHGmlLLJYd/+s+eLqU19rfB16Ux2QnKHsaOeciJS1KcU3pAXMIJNeyTNqCsu/qFfx6l7QbZ9wWmEaCJL0HxQtfb7lhou0Nu1kw7SOCwUykVRIng8qWnppuHPUlh/CkIIQrqGpnWTY9Pla1zO+F0MKUbGZVHhCmoKd3NCWQPS14orWErXJ5eyq7EGWA8xgjtt+b94fdJlRNTeP3NfVHLH5hCrUHmtys3vj/KlGNODtbHKrns46PJSc1J7nGztXlsmoz4hTUeXkB+1P8Zpe1tMUtLnth/hMaGl+TtOJJ+iHDduRVgkEd2KIM9nSEoqQqQofZmk+lV0LdtF1YlGDnZR9qEWid9/1cUp9ny3P/3SlzGXu3/s+YQQo4QQZwsh8pK2n7936z2wUft1TiVLaSFbMCLBtJ0oTtGZEMxnGlcyL0U2MRM0i+J4x1qy7dvSTKS89b7nhieyhDnczDyusDgXqcFSCutPSXH9KdhxZor1HhooPWr8mO0bzmTJ4mv58IPLcLvDNne1zvl6hIuXW7c6rtFUeSqM5jGzbhrTGMTRNZuQ/lbDFtDfTM74xzlnXBcXD/53BueNB+DIbZ9x2yc9Kc5Dl9RHuQkf2oiCONmaCLoED4ywNsc5dQ8X5KR6+ia/tr5GfUx+63TlpewbnDuaiwbdzLvk8wx5Fpu/ohw3dz23do+9cH+6YC23Pb067vLT2hMBYahIxWUSrziaVXefx2ezL0J3+P03z7fD/uxatouqk5Fsy5cuEj/YECPbFHu+2Pa9hQReF0KsFEJ832b/HtvzpXML+gGGfNUG4M9CiJlSyhdiu/8beC2TGxyMqPG6v7b1WyVmAZlcj80stEtFE+XMZxqn81a/u5RLaQJB3PYtEwGM3rnacsqUJq7kidSUdF8vRXfF5RGTXX8ActqG944KBYpZsqWDS+p/Q5Ew6qpve9+MXz9x5jcRiXO+3VJn8ZGrOKPNMnlnC5/0GpFt0rOEVMM0jXiWYbtP4fiyc3HnnMXGqM60erhyd+r9K6UAn72WcKJzUrru4eZgMWX+1rSvLZNRH4BlxSdaariDc0dbxowSxTiWuiVSklGzk0tJ/aAXrKq1tfWLRCW5XpXVP081HehvR/P+7lrONHpOPO4Qmx9OZ8+3t1HuRCnlLiFEBfAvIcRGKeW7Cfvtvj0y+kVPFwJ8DzhOSjkFmAT8TAhhFvMOTX3DGO4aWo3f5g/1kIeUjGZNqqcpN8f0i/cAQtAkKniXs+jPgFWiC49d01VY+Cwm80CSH6ugSZTzaEwD2rqmPpYiojQNf5b2KmtN9NUqlYtPz+WccecxUzzMtrVPMHTx/3Bd/aMUUhI/LnH0Z/tnxxCNWoktUe9YIslVFH7VfCXL89aiZ/AeLfGNsY32zchSFYLjclxckq/G09HJCGgdVLTYl6gqgzo6kt1qC7/1bXCswT635WJCUWtTUuJr68+oz5b8EbxZegYdrjwkcFTJJFs/3luFIcbf7mBykAi3ImyjzHteXOd4jhP53Dl5ZL++9Pa3YUCm0XPicV+l0cJ+wP6y50NKuSv2bwPwPHBC0iF7bM+XjnBdUsqu2I23Y5DuBUKI/+EQJ9ypVSX8duQgBnrdRio108G+QwC7qbElN3BQGcgQ8WvYQcpeJyIpKZMNGalUJW/PlJiB9KQremduTdJ9tUrl10f5qPcrSCGo9yv8+igfr1W72eqq52nvMh71vslTnqUM0ktwxULQxsahbNl8EsFgborZvHGrWLpY5nNh26l0uLrYrTajI4najL2/WqXyqLgl5YFoKadaIkshBPlCQbGR49T0CB+3LOJ7LzyFN2R1YfJFJbduDqPJMHz8HD946vfMXfgrJu1I9eNeXn88j627mqZAMVJCd6AE37rrya8/mXp0ftNHw1QytuSP4LHB1/KHITeTq+bbHlMqYcXnLSh9ZEpcQnDfFUdT5E/tUnZyJAJn8pkyvqZfI4L7uyaaidtRcpRtd85BPD/sZMO3V/Z8QohcIUS++d/AecAnSYe9CFwnDJxEP+z50uWv6oUQx0gpVwNIKbuEEBcDfwHSu0gfYvBGwoQOAX/bvlBKoyO5dZGPMHxt9vl9q4KSOfImx45g5/GdZjTNhcsVRYi+iHlzv9eV2AD0wAhvykxr0CWYM0LlqtZezeQuEaSlYhknDvkYxddBKJRL/bYTyF3y07iGcnvVMraednuKepWKSn40l+nDfwLApPYJKd3IfxjhISysX5qG3OW3mbql78kEXeps61zD0SVncJJaiHdNBw+OKWa332XtUhZeyodcSveWD6gMtDFz9TMALBp0nOV6y+uP56OGE8jzqQn11D3LhiT66zYiqXRQo7JLByfCrRhkO2V8TVppRzucOaqcibPfsu3irckwrfxlfE2Ya7rnxXXxB4hcjwu3S6E9ELHtQLabHz6Iu5T/E6OGm5hW3hf2fJXA87GpARV4Ukr5mmnNF3MNegVjJOjT2D1nZHrxdIR7HVgfT6WUGgaz/7E/r+BgQ9ywQDds60Ieb2+ke6iSrpSE8OIjSDClNAIIYZDt3rwHNud5ZIjTmhbxSccoRo5eyjKRqm3sOL4jn0DTfKiqUaN0IuZEByLreuiznmumaRPrmolo8qlxsgXDZ3dYgvWfz9fNkJFLKdeGQ321o3oVGPViF0r8LV5UuAKAb4mBRIYuRPM1s1v8w3YdzZQ52gJaX7JgeMGx8TGkC1t8XPBuj+1YkvD3psl90QjT17+aQrgAV50wiAmHlfRbRCIRxTlui7/uQ3uhRpXnU9OSSCKxJ8KrKjy7staxizdToQxTAvLLILKQ1vuedYej+N3wu6uOcby3k53fwYbbn37pyfuvuhj2cZeylHIbcLTN9n1iz5fOgH6nlLLeYd/SPbnZwYA1a9Zw18ebUseCTC3BQy29bL4eIegShQTpo56zDx84DDehBzm/bJ6lOSo5XQpwAw9RJhtA6vGU86nBT/B6u+MdzE2UGyb0CfDIEFdKexWsTGCmaUuD9qnRvJA14rGz/jMjZcBRvcrcD9Zfs47q92gf/aQR/Qvnh4eyYNSxXpsIIUQKuaabAc49717UGqOEVR5osz3m2ZWGmfy9l43FlcHvR/IRfrcrpQnqDTR+Q4B6jHpyf1LUbX10Lv/8G2NwJ2Ur3C6B3+1K28VrZ/jutvkG1SVfSufvodR1vCe4/emXnrz96ZcOv/3pl5TYvwe8VV8mwhdfG6xZs4Z//vOfdJx8ofNBh1qEmzI/u3evT0gNL2GDuPu4lo5gIkuQXhg+4n0eEn9wdAdKHNkBozlnbeMs/m/wUMPHN36vXrbKCwU4fesWZuz4LqsnJpdheg93inLNBqBPlTrGf9bJ2yOPRnP1/sl4ojqnb7WmLE0Zx2Rovmbaq5Y5qleZ281oWQB6uIjLK5vxJHypO0X7VZE/8ZsKPz9uuNJWOSoZKaYMm6dSuNsaIQshEDml+CZ8l0jJUL741H4wwfySf33yUbzuL8bdE7H47ybC73Yx9biaFIGGHz69OuW6dpKSmUBCWmnHZIUplxBEotKxtpvYSJUcIQ6Z9XKf5/QXmYpTHGJdx18LZAk3AW+++SaRSIS8UICug9X/9itOeytmn22Ga5jGMyAgz9VBF/bCD5barAQlksea+h/xu4HjCNrdRwgKwx18a/lbIEGIobiDJZCLY/pYDZQapCcVELqltvqqZynDGoPo6CwfOoYur5+8UICJW9dz1Q43H/iUOFE6jQEhYPeYuSiRPHRPKomowVIiIkKj2gIRKPRUMGb7leQM/X+W45zEOk5V13FEw/+gA+lbaXBOa8fUr1LfToF76Jl8VFTpeM3RbRGantmMN2o8vZhjPHnuCIvUKKMKl3HFyJcp9Lbi91Zz60l3UF11SZxc9jVq2wLc9vRqfjRW4yez37JVYco4RSxIkXM04TQuVJTjdqwFp0N/xCmyrkUHH7KEm4D29nYATty2jjdHTzg4o9n9vGZVRpBIosJjuz8qPERlZvZoJHTQdlEYSwenrr+MJqPDN4EEbzs9N6WJKRHtbqPL1RzRcUXyDGcfG7MBNVjKsMX3O17LtNQb3ljL8Mba3h1S4nrnFU499WZW5jTQJYLUbzuBw0cuAZeNi40rDFE3IuqxpJVF1IN722T+p/pvjHYdD0BEdvGD8GCabOQl7eQupQ+U2HtntLb1vjc6Mr4PHNLaam+DmB2EEJxeOpr/dYg4b8KHK8kMwY/gDs3D90qXsXvM0/F7BkO72LjxJ6zc3spdL1ftcd03vjbsG87NbXYqTHf+42M0KTOqEJnVJTvysyNtRRhCGmaduD+KTv2Ricy6Fh186FuK52uEwsJCtpTXsHzomK96KV8dkr6BXDJMnuyI106/zx+4kQdi9dR9XM8WSuo1paSSWoTusXjROjUxmcgLBVCkYEKCO0/lxmkpKlGJc6OO13Kw1Mvp6aGmrYsjXEdwdXgiN4TOZvLO66laN8Nx5Ei6uy3qVa5ACX/adhLfy1nI2wUr4sed0HwkBXouZVumpipb2cAyEoRAQ8ZnaV8qeoeg6B3/6Sut7YR0qlGO+6Q9wet6gFDrA32SbU2Rn5o+IrY9+S2M6JmRbTKSa6TJdd0ivxs7NclMa6tO6eDatgALVtVattnVlPf3/G8We4dshJuAdSecyZuhQ7gT2QmxyLKMRrz0UCsPi+8axTr+k19ZDs9IVnFPYVNTXiePZonrBCaOfYSm4c9StmUqlcFzqfc7z/UObqqjsnw7YsjLbPI1E+q6Exqhct30tIbydpigDWWxe6OlG1nognEfrwFAD7TgyuklvML6U4x72Iw5mZKR5j2llLSGe+DIXyPU3i/b6Q2XGPO5sePMNdv579o9NLgkbHvrZv5+pmRptZFkvrjtDBSEoylDImnbIZ1qlJOpPDgTeaGnhXNQU7x2zbqvGa2t+Lylz1GgLxPJpJhY1504+62MasFOSKdoZRclHypdx18XZCPcGJ6tb+HlMF9DspXcwhzmcTmV1FLLYb0d2UKwjmP4Dn+Lk2yympMt9vV7KBRDvCJBkGJGw1p8Tn6uQrBlwCDqRhHv7pWKFh+/Gbb4fkb+ay7DFt9vIT4TpqLU8eflcf65Yd49+2VOOeEFBpbtMGrImpe89hEc9oVBAp2bX0npELaLTE1iTLz+xafnUVmeg3C3AZAXlDzwgEZFpHckp7D+lPiaRyx6gKpPvmPRd04U0jAhAy1UdOrc+Ipk4rooJ3aNjaeV063NCX2N5DxMECdVYSci7wmW8GP8VKGgIKhC4cf4OQc1Hq1Bbxf0gYJ0NdJ0pJpJbTWdoMXXqQP5q0TMjOAZIcRGIcQGIcTJSfuFEOJ/hRCfCiHWCCGOzfTa2Qg3hq+z2fyDzORxvksX+bYRZogcHpH/xp+5kVAG3cd7DKlb6rqJSGyckq4wx5Q/wE8+mcMDI7zU+0TKmiKKh2fktziNdy3nbR8xjw0tkgnaUI7Qq5FSWkZlTEUpsz7cJEoN3Wb/Q5wyYhme9mvp3HEiSEl9xQSqGlbg+nwJHP1tS/k5OTI1o+llnG65/u4cwVvH5+NpOptzV71O+eFQ3mEQpsixJ6r8+pPTRuVSSkLrDBs/nwbXLJKUn2UlcLu12V1TItnt0HGciEUFH+Lz1TOj5RzKtRIkOq5Y+1bZlqkprkuhqJuyLVMtc7Zg1H1vET6+mHxE3DZvb2u8mUIRRlTdHXa+X181UqcIVUBGtVUzWrXr2oZsB/KXhDnAa1LKy4UQHlI1m/efPd9XhZgj0RyMpstHpZSz9+f9vrYOQUIAwrFD2IQm3Ghk0AzVV5e0036pcw6vORocJM+far5mLqjXuKBeY8J5qU4zYK885fV206UEWezeCBHi6k8m7BSlzNGkieoSDjtmAVWtR/NJdxtbh36TqoYViMJCAjrkJAUmhfWn4Ks9AXeoPS4i8cAZqdePqAKt6EquWfQ6Pdcb20Lrnsd37PUIl/U9DyP5J2Em4qYyJguZDKkF+bx7PZtGDyboVvFFNApFK6XSSrrJBJvcXBVA8huML/ib8HF3Qto3URNHDtlNz9CTeMZVynPRZnLbnuD8ukhcJcu8T2OM4JuDxTy35WLuc3hoKJOCqU+vZsXnLfuMYCYOK2F7cyCtUpRLiLRkW5NBt7FdI5MApp00OOPU75TxNfGRpWRkO5B7sXPW4hR7voGzT9urWVwhRAFwOjAdQEoZBpItv/aPPd9XBSGEC3gA40niSOBbQogj9+c9a7wZdtZmkR4ZiIMkW/V5ZJBzeI1VTDA2JJ2faGRgIjFNWUaj7X3sRCJCoVzAmHddoW5L2e+oKBUjb83XTK5awHH51RT7QuiApofZ0tmOlrRuTUo2dnbR/fpdRsQqhOP1pddPaUfv+VrtBwQ/egw91ImUEomkDZ3/JsD/FbzP9cN+SodiP/MbBdYOKifocYMQBD1uNjQsJpTyvdELHcnzhFOEJgDbtG9BjJi1Kj+hI8agq2UgBLpaRmfJd3mt2s2c6nlxXehg02ieWPxLbvjX//Ljxf/F8vrjHWvCDbEE/RPvf7HPVNu3NwdYOuustA1YEV06CneYZHvfwk0MmfUyE2e/ldLEBPaNTL+76hh+NaV/ariHmO7xPkeMbFPs+WLb9wZDgUbgr0KIVUKIR2OayonY9/Z8XzFOAD6NyWwhhHgK46li/b6+0bP1Ldy7rc6w4zuUpRu/TJika/NeltLIVcyzzJEewwre5Syr6EWMvBR0TuetFNELs97YXrWMK+VGHuXGFDGIY+QKZgrDZvBudtKgn075Z72Rx6rKUp4Ykstun4jrCFcGpW0zlkneJtGripuj8oqJAqKjh5z18/l47HWMzvHiVyCgw4aeEAXr5xtrjkW4Ttcv6GrjX2OGMCzBn1ar/QCt9gN2+4u44YqL8JYvRLjb8MXe4nw9+XvAgKr60BXrs3Rt10Yiqs6xJedQIPNSImMdWEuU32E1M3iGPNu0b3nsWV0bUQBK0teI4qW76EoW9dzGosIVSN1NsO4ytI7xlsMezkC+sa9OYpcQfOvEQby9sTFt9GpGyn1FzFEpUxSn/G4XZ44qTzsfm6lYRaY4xHSP9wf2lz2fChwL/LuUcrkQYg4wC/hZwjF2JJFRRVLIfT3asQ8ghLgcOF9KeUPs52uBE6WU/5ZwzPeB7wNUVlYe99RTT2V07a6uLvLyjBRkmxZlRzB8QKo1VukR6pUDP+ruzzoFOmU0kpc0y7mDwWnT1XbnCV1F0fxEPV2ApIs8WilBQ0VFw08PXRTEzRaq9Ai7FRdlNJIje2jXSmhzF5D4tyOAorCkzSMsfz299+/GHSjFFelNYYc6P0foRldwRM0h5C1CdankCBF/3tDUTqK+dqSi0SULaRKlFhMIgaSgsxVfMICnsAi1sRF3VEe4cxH+IoSiEhFRmtVWOl098fMODw3ALVOfmXUZpS1sH/U3F4YZGhyIyya5FQG2JjkUjcRl++0S8Ef5PACywHlkSQ1/hpQupFaAjNoLyRTEyFvFSFI3otPRz26KsTWFAGyq7yQctUp7VvphdwA8LoWRVfm2xyTDpQikJG4671KMxxPNZt7H41KoLPRR2xqwmNQrQlBT7Ld1K7JD4vfSgYz9sc5Jkyb1O8rZOWux/dA+yIGzT9vjzK0Qogp4X0p5eOzn04BZUsqLEo75I7BISvn32M+bgEmZpJQP1Ai3zycIKeUjwCMAEyZMkJMmTcrowosWLcI8dsKydey0ESg4EPCfXXX8d1513wfuDTKN6KVEEEUK66+LS4a5q7vBfp3JDVBS52z5GlfvXImvZg2K0vul9wv+4dgsZaJMupkT01XuvSbx35Qc6LUt0BVuiz5Og6c3fWi+n+Z1/pOHaRblKffJC/Zw+tYtfDD0SJp9KqWyhSt5nHHBDbHGonHxY3sibfyP6wFm/EtSEAuc1JoTLLXXZFWnHGCLPolnojfQ6PZRGZTcsKGVpg/+DsDAyVPY+foCJrdX4Tv2WhRXr1NQUOQyp3pe3NRgUvsEbq+dgZrwGWpS8mHTv9jZtSrltXX5NJ45q5aXNzxgqdXG3zYktyQ4/ZyDyhTpt9VaXjuunZWvfMSKyybSVJLa3KVoTeSsbUmJau3gVgSROJn1pZNlRU2Rn3+fNgmAtlW13PnMx0QSutdvH6vxv+vcXHXCIH7/5k4CEYV9WUkTwIAiF7VtqeuuKXKxdNakjK6T+L10IOMAWucXGGlku+17DCllvRBihxBipJRyE3A2qZnVF4F/i2VeT2Qf2fN9ldhjg9/+4GvbKNVPlNHIlczjcRnrZAby6OBa+RdKOROPLEtNBycTqFD4mAn8Z6uL3ZVbkO5Y/VE4u/wkwtZ6z+lZQeg0uu3FKszrNGPfAdzl9TO46TOGNH7OaZFRsaaqG1KO0/Qw70ReYuk4F//+Ym8DkXfMpZZGJzvRh4nKIs4IrWXY24a6lZRu5ifsD7lVOk++lBzNaznPJ71Mb7gkTriLCldwyedXM0L1x9WWvgjp7NKrMIgryuDc0YwrPoMctYAWpZXG1pdoECGqbMQ8kmuqMzWBUG2asqSkMxJk5upnWKJ8zm+nfZ+QN2Gtegj3ti8yItuaIj9njirnyfe/IH3cmQq3S9DSHeLwmJ5xcY6bq44fxMtr6uIqTy5FcNUJg/bo+pmg0O/Oahp/Ndhf9nwA/w7Mi3UobwNm7Ct7vgOyaQr4EBguhBgSe9FXYzxV7FN87RulMoxugxhfpn9kBvO4nFv4PT5CPCRm0koJw9kQN48X0vlrrYky6sc+YsgrGs3RgCHIn9xIlQxHiz0bqMFSKoP2aUnzOumut6W8hqjQ+VDdartfSsmuTfNZ6VsMQFdCH06ipR1kpurUo3UwOHc0Fw+8iRJvFRcNuolyrcT2vAqthEntRnPZpPYJDHG7UWJjTYoQDPYqDMwfwcYhfiqKRnB82QXkugsRQlAqS/hh/TQ+l66UuWGJZCnWB9Ail/1DC0BOTxO+aIRzPlzGHfMeobK5ESF1ylqaUdd2Iz5z1l0Goy76+6uOYemss3h7Y2NGZOh2CYr8bgQGuUajkkCCpV9rT4Qnl3/Bz78xhu2zL2L77Is4srog4+vvCYRw7hzOtKN4wapaNtV3pm3GysKKWDfy94DPMZ41Pwe+t7ddygBSytVSyglSynFSyilSylYp5cOmRZ80cKuUcpiUcqyUckVf1zRxQEa4UkpNCPFvwEKMR/W/SCnX7ev73DW0utf3Ngt7CEEXhcYsagyP0utWo+FmHcfEyTudQX0ZTbZRabIgP1jnau26lB2XG2uoupWQZd41+TpXMo8H5Q9t546XDx3D8MZauoW1gciEDLRQvOldrqmDpWOwFDuS52f7UnWKSo3ank85vuwCVMUNtJOvFjla7QkEM+umISVMb7wEb1LtWxWC4Xka94xcx01brkbVrPt9uDgOJaVpSiA4GzcTccdVn9CC4E4lDRloQdV7a73nfLiMcz5cBhjNVxdN+a3t2k0kj9dkGglGopJcr8rFR1fz9+U7bElUl3DXc2vizUazjtGpbdt/cUVbT4Sff2OMraZxOjN7E6ZZwS2jjKGs/uguf90RI9cD3pIvEQdqhIuU8hUp5YjYU8Sv98c9plaV8NuRgxjoNZ6aB3rdFIu+/Ta/jggLH/OZxnympVjoZRIp90WaE1nCHG5mHldwi/w9ZbLR4n17ilyCrvdxHwn5tRNpGv4sQ8d+mxu0P1MW7jZMBtC4gYfi5D4xutzxMl1eg2TsNJSllODyoNacQGmHkT3PSwjOQ+ueR0Z7I0VHVafNU+mRnTzmf5rq3GExsk04Js2Di096mV5/jWMUXKznIrRix/1OVdLC2NiPOf6DavP6oxEatixEU+yv0ugvclw3GGS7dNZZ8c7eibPf6leLlGlEEE3T6RiI6NS2GbpX4aiedrLIJQS/v+oYR3UnE07XGFDktx0FmnpcDc+urI2vwyTS5Oj1YPG0NT+rtbXt2Sh8L3BARrhfJqZWlTC1qveL6dk/XsMdw35AIE067euKJlKbjDKClBayS38sTJRLmSiSjhUQibhZKk7nefdUmhOs6czrKpE8OmuWxmumJ3te5ZTom7B2Ci0DRnG4axXSCyJYRNWWK6kaYT+ikxcK4JKKxfggvgwhEN58PBNmIMZfw6sb/IQvaEVf81x8jCcIeMddhfDkOao6FdSfzK2Fd/JZTQ/TN/R/dLBCemlU26jUilP27Uaibb/LUd84iv0ffkrUa/MgJbUgfyipZpKvkBqXG1/Cw0XQ5WbukRc4rjlxjjTZhi51LUbiwCVEWnLNBOnO/taJg5gyvoYVn7fw9+U7iEpjFvekocVsbw7Eo9MzR5Xz7MpaR2eeZE1jO4UsO9efg6H+a/msBvXP/SgLK772hGvBmvlMrXsZtCD/NfRWGrwl2bncROzhe5FHR2ZkC7FQwv4r8gP3CTwuvkNYGDXlJiriqe5Tox8gkam2c64w6hFvUtg2kdErH7Dss0s7q1GN07duSWiYsodLuHCpRr+G11uCfux1tOT6WOHaTtDdhG/7HL454ieAvapTt9bOaWtKkRIa1RYqtfTGAcloVFuZW7EgruZkQkdSgeDxSA5LiXAhnpQ511cIp2xPtvRzgvDksWjQcRzn0ZhzzOVMX/8qFYE2tLIKWq76Llu6ByISSCrZgzZxvtSJbItz3Pz8G2OYMr7G0eB9T+B3K/GaryLgmhMH86spY1mwqpZnV9bGiT0qJR990Z7ivDPhsJKM52IzJdKDwdO2P5aBWaRHlnAT8Ozyl7j3xKep9VZQFOn4qpdzCGHfPLT8Q0yLk62JsPAxX7+OqeuOpH7sI7bnOTUuXVCvAUEeGOFNEL+IcH7dENvoLh0Ul4ei4d8guPNhAEPlyQFSSmp7PkWRChPXlfDoyN3cHs2zEGc6AtSjIeYOWBDvVp7ecAkVWgmSXk/cKgQX4uGVmAxkshvPWqIWlx4fUJTB57Rb9D4MLRp0HMuHnmAhprNtzjFFIW57ejX3LdzEnZNHpo3gcjxq/Hrp3HP6AzOVbYdMCaU/zjxO61aEsJjZm1KQiVKZB5qi1MEQhR8sOGBruF82nq1v4Y4ht7DTV4UUCq2eomx0mw79SPN10b9BeRn/PytsR4OAZlFCYf0pjq40SzmdLQU6x5+Xx4Vnqrxa1fuceUG9xkvvdvPh61289G43F9RrRPSwxT0oU+SoVj3qUNT+C0kIwdD8cQzOHY1bU1h82B94Nfo3IgHjwSASaGabvp6ojBqyjlJHjwSQUhLtaeYF8TcA5m75Jf9RO4OSTpChrpTZWj+Cibi5nC5Op5PL6YobELyBZtn+exu3n+TGLTvHoL7qjWY6MrmWWZhGEGJXzPt14uy3qG0L9PkYYO43ZRntju8Ja451x74IxVxLf7qInVx/olJa3geAey8bi8elHLCetnvbhZ1FL7IRbgz3bqvL1m37AQUdPUORgv6M9EDSc06CuIXTvG6pbKa9aqWtK81SeSqPihu5g1akEDR4/PxqrBHNGBGuFVJK3IrHEuG2Vy1zdNZJ3ndE7hA+XWfMGK9qeYMTyi7ElSx9iCENOa74DBpa13Dq+iinvvIBQe0DoiP+jeDCP1Cgwr0XCpBwzTtQ2iFpLoAnJwncA0/sTSULcPtLHR8QKhDkjZqFWRWVkSJCjZNTZmTfQEMVQW6Q3njUu5QIp+KmAgU9FgXfhA+SzAvSRTpO0aOI9bPbrbrQ77bUd81fAUnv3O5LH9fFfWdzPC7Cmh4Xz7C7ZmtPxLHuWOh323rYSuCY/3qd7rAWF9PIVNIxuS5sB/NhZemss1jUvoXPZk+yPe6rhp0hw4EWhe8rCCFGAk8nbBoK3C2l/H3CMQLDWOdCjDnc6VLKjzK5fpZwY8iKYNggjbPPWSzkXWmfokuES4bpIo9pPAMY9dzr+AsTWcJSeSrzRa+mcmIDVBwJt7+SeTwqb07RTL5S/I36McupWjfDYjCPVJivpHZVhxQXc8b2cAorLLVV06rPxKtVKv83Ghrc51LGeGN9/iVxX13AQvCav5n8kzooCpbTttWQG4zICIp02aaoc9QCqttaGLtI0lYyga1Dv0lhvo+lJ/2CYdte5JpFK7jlphyWHmWtS8/dcokl/Qz2DU5g1IdFQl1ceNrwVT9nUGaMdA21JD/vhzReC1hlN9cSZRZ+fAmp6h/jZ1mC5vKAIr8j8TiRcU9EZ+KwEpZtbbEQpN/tQghSSFoCRX43S2edFa+5mkjn8JOIiH8Fd3/039y9po2q3CpmHjuTi4ZelDaRZUfEiVG9k74yYKkLO8GM5nfXdzIjIdV8IEW4iXV36MzINelgRUxd6hiIm+jUAs8nHXbo2fN92ajxug0DgywMSIklvLRAMIM/M4JNwFWO5+fRQQ+5BEWvGEwXhfxR3spmRvKu6DUsSGyAcmqwSp7XtZC0y+gETjSV33TudMc0dJMojhOneXwy2Vp8cRPX51pC0/BnjZeZ3KSlRhhwYgMF9SdxQvmFuETqn5glKj4dIq8MZ3P4GnSXF+gm5Ctl48hrGLkJUFYzqX0C0xsuoVwroVFtcRz3SX5gCIoQcyteSDlOKBG85QvROsZbapvJDUonVn3INcNf4fOkyD7RvMBJ2P+2p1c7erqaWF/Xye+uOiaFqG9LOu8cVKPeHBCsmPUOrxAiQGYka0ItWIWv+jmkYvyN13XXcc+yewBo60lzogN2tQX6HOnJxMfXjOYP9DlcM2pftGhRXErzq8Y999yTYs93zz337Mu53LOBrVLKz5O277E9X5ZwY7hraDV3rPuUgOIsxn7QYk9ckIRwnKdQYpIDE1nCOifCReIjRJcoTNkTFR7ekpPRhTUlHfedTdPRPJEljvs1XzPtVcviBKoGSynz26ehy2hCusI0DX/W1ng9rS8uSxwbsQA8eRonll+MYqMPnaytnOsH/ZLt5H64is4dJwFQ4xYcmZ+L/+Tv8fSmbnLw4pZGzbNSK0V3+GCkFgSXB4SCLnReL3wv3liVDOFuw+0S3Dl5JAtW1fJf/1xnueqJVR9y/ZinkDGtcc3fbHlAUTFqplOPq+HtjY22EWlfaO2J2DYiJXrBnoNqcRSqQnAnPnRkvB6dCbzlCxGK9YE6GA1y19u/QfLjjK9jYkCRf6+biZyi+XQdwPvalehgRYxsE6UdDwP+dM8997APSfdq4O82253s+Q5OP9yvAlOrSvjtpv/HwGB9vxqCDgrs4+YvPeHXRnEQzfMSdIwuk6+RiHTnJGIppzKTh5jGP5jJQyzlVBBQP/YR6kY9BkBO4ziulKmykYkiHE7E2ZcvrhoshWCR/eKCRbZkC/bayooaoXL8Uwy76Mf4ij+n+PwfE6l5DyEEhTIvTrbx4xEppCujEYSiIhQjfe3CxcXNJ/Ps48fywAMaE9dZv9RFtJj7Lj8agB/NXx3XHgaD5G4c/greJGMP8wEFjApuVEqe/mDHPukiTkRibfAmfLb2gEYtOXMId5vtdt3V2u/1mfXLdM1ETk1hpqKp2RzV1mOfVbMjbacGtK+pCEU6e769RkxS+JvAP+x222zLiDSyhJuAqQ3/YsXyq3hgwy/xR9Nr+34dIBx+h/Loe2QqhB8fzu+hE1EXRroIBnOR0vm5Zymn8ig30yQqQCg0iQoe5eY46XYMeptN506nY+A7TBRLuIGHUIlYlKvMKNmpszmdFrMpH7n9s/FEo9YoPRp1sf0zZ9F+J4JXvd24c1uMY/zNlgcHOwhgt9qMlJJIoBmpBS2mCQCKy4tvzKWUd8CNr8g46fpcPmafaUR1t81fTaKyqRlRyjQa0AEkpjpxZC9lUQ+f9TKHJ3X/ThlfQ3GO8VoqHHqUnbY7QUaK+rXdCS4h4l3E6UzinZ5xi3LcfDb7orjaVn86gL8MVao96cj+ijC4n9v7iwuAj6SUu2327bG5TpZwbTC18U1+u+n/pRXiP+SQzG5SptFF7t3uFKkiBEGnKCTWdJUceapRjQmfbuLDDy5j08aJSGl/bTt5SVN6Mr48ASi9qe9BfME8rmAON8fJVmhu8jdeaHuPWzeH8EWt74lHBrk89ALFa6dTUHcyOxoHsmXzSfEHhGAwly2bT2JH40D7140zwae81bEHh/aqZbaHN6gtXH/Ezziz8F3OrfwEvPajV6aZgn5MlCtODPG7gT389yCJbGjkrufWpnzsZkTptE4RLGZOxXw6Xd2Or3FPkByt/fwbY/C7XTQ46Fk7bXdCqHEyUrc+kEjdTahxcr+uYypTAbaSjiYZO0WuydvTkXYy9vc87EEWQTvZ8O2VPV8CvoV9OhkMI53rhIGTOATs+b5y/Cvn3LRC/IcSFBllNGtYJ4/utdVLk4ZOnKtV09bRnAnbbLoyG6BKZQtXir8xYcgqtnMMhw9ZbfHMTYRjI1SG6WhkrL67eSrr1zVRUhZJ0TJOFsUo6NE5c41G+RdT+dwtyPNGyZVeGhuH0tholYC002AGCBOxHV1K05tmW2PubYYS8U5jJ8s9pE7w3MNpv3AzeGPPItFWlM7ZjCu9muX1x1sONyNH23VG3TzW08VHpe9wpHsMasHWjCz4MkUgEuX2+R/zw6dXxyUd51a8xA8bp1i6soMixNzyF6DBKiM5cVgJH33RbtuspHWMNyQ3yxci3G2O41EmnEaW3t7YaPnZSQwjUwUp89zdmz6Kd4s71WX3tyrVQaYotd/s+YQQOcC5wI0J2/aJPV+WcBNROAjad7C55zReGHzSwS98kUGzlEcGuYGHjOiwDxN4E4lztcW0gBxkex+nWd0yjC8tswHKskwfDB/xPorS+4e/lFOZT29nch6ddJHajJXJvK+IeqhcN53C+lPQpY7n8AfZOv5HkNOZMmN7Qb3GBfUaUkpW9hjrGVug4hFGR/Px2jAWuzcSFb0PBk4azFGi/G7A37ip7goqmW6Z3dVdIXRPV8o5YKRww0QIuELkR3NpVFuYW/ECbxesINJ6Uvy4h6XOj5Ep9U6huOiY3ETSFBFeV4TLhr+UQrgmcdtpQJdumcpHxU/EjpTxTud9iUR5RYBFJe8g1G5Ll/bcihdYVLAC0XBBXG/5WycOiss0mk1XStKvpNYxPr7eIr8bXdPRErqdE2d9nerSTtFk4n3Nh4Vk0naKXKeMr8loDnd/z8MeTIpS99xzz5P33HMP7IcuZSllD1gNs01rvth/S+DWPbl2lnATcfbd8Nz3eK/r28hMuDYxH3cQkbMio+gI8ugEBA8yk0zlF5Ndf/Lo4hxe5Q15voWwPTLI6bzFu/Ks1LnZJNeg5LfO5YqCLkDIeL02cXzIrUtcUieqWO/n6EakG8epASuhdla/T/6YNZY52uRRIWN9gmNzjAcHJWGxR+jVEIEV6ja6RJBc6WF85HCG6VWWtzMoQsypNtaWgw93krZye9UyQ5bS4SOYP/q3zGNHwq+bQOpu3MXvo+ZtJNQ4mTc6xgMBfoofNelCmr/F9rqlvlZyh822RHxzffX8R8OVKIgUDWiJZG7TSCO6znNuRNqXkJEiFhWuSOm2luEiCv1uVv/8PMv25IjzyQWvprSY+d0u7vnmGADHjl9T5SoZdtFkshGD+bCQLNixtx3FifOw+6NL+WDQdU5EjFwPKnu+LOEmYtyV8Nz36NLLEJL0pCslR9ZuY3P1YWiuA+BttItmhUjdLiVnsZARbLIQWSbXL6PRVpwiOT2cOB/rtD3dsgGkkChRD/NdqfXaiCLIk534ZDC9aAaAhIKdZ6C7D2fY2vstu+w6hp1GhRSHB6oj9GqOCFcjpWT+9v/HxyyjNXc0R5WcTq5a2BuRFa5g7pZfpnQcAxTUn0xP0RY6Br2degMBw6o3o39ejdY1CrVgDcLVgxLrIE4UsnijYzx326zRyZO3NSpQPG2W6ywSEY4MV3Jx2xkpUpECQaVWysy6abxXtbPfDUd7glDjZHzVz1lGeszaa7eNKEUyBhT5+d1Vwx1JKpGszIahXW0BCv1u3C4RV5gC52gynRGDKdhx5+SRcT3pvSHK/ug59xdfJ0WprwoHAFMceMhTmjj20zxWDvfasoEa1Thj0yqWDx1zYJBtOtiQ8Go5gdVMyJxsMWq1jqRGgihFLPVrNjClm5tdSq/SlBltd5FHGU1M0Z7lWxuG0jzO3hKwizz+mEHpZKk4lfmDr+L73Rp3np7LrZtDcUlHp45hzdfMq1VqkqlByFYK0kSP1tu5vS2wjr+VvMtnNYaigpRGpOMkWAEgNl0FdoQLFLukLfHEz00QsrCz5CvbMpX6MXMh4eEiGhE0vFfBEE80vk6hRJBS8FD1fDbkbIubIiSbKPikl1KtuN8NR3uC/tZe7ZAJSS1YVcudz3wcJ9i2QARFGM5FbT2RtCTZV8q1LRDhzn98HO/oPpDFLWD/RdBZZAnXBoKT854guOoWoISPjvBaIl1fJExEUXhz9ISvbIW26EdKO62vrZm3TLqeJtw8Lr+bljyTU7/plKOSj0+syTZRwePuGZQXbaIyaO9Z61ivTWhAst6jjnq/wq+PMrSAL6jXHCO/9yIX8GiCylS9X/DLcQo9RduYujG1PiuR5KgFXDzwJj7oeot/HLY0TmLQ+1a2ilZKZSrpNsseLifK/wvnYpe8a4l4bYUbEmGmdx8maBGKAPDUn8wywowZ9iwFOZ1EulR2La8guLWQibHGtN71SqSuxNO4L294wDbTrUrXPq/fOiGx9pqMibPf2iek8F//XGeJZoH4uNRnsy9Ke66TFnMiksenDtRmpP0ZQWeRHQtKxYTvMCJnMWcWPMiVH2/np/9o5jfPbOLd137GAxt+SUR1EVXdxrfol1m3zUSMI1PBDuHUg2k2NNnv6yLfmHW1QZ+jOknLszs++dw/DR7AjIa1qFFrZOlYr5XWldvdI+gSPDDC6CAq2zIVEbUqi4moh/niGhuVKS9/GjzAdkxHIBBCkOsu5IziKZyvXGz7mta0vIOmW7+YNT3CmualADy76bKUdz6sw8vtSgb1UsOk4L1hv+K3vg3Uo6MjqUfnNwT4c/2xfPz3UXz8yGjWPzk8rvWs6grHbSruvYowrqVr/phXr/19M9d4MmQVc4fNJm/ULHKHzUYtWNXnOeafVk2Rnxy389dUJqMrmcyWtjqM8Thtt1trf3EgNiNlsX+RjXCTcfH/ADBixZ8ZkbPYsuuaoU8TtXF+2e/IUJoxjw58MmREsH0dL5SU65pEZqSFU+UQEcJRejGTUR2zpAzQJPoe4WmilKMG3sz3xETm69fSLEoppZGr0qS23QlRq9OaTBUp207c9RfReJx9k0gTpY5SkCYUBBe3ncGGnG0sKlxh0UEO5HWwrXMNNTlHkKMW0KN1sKb1HRq6N0DZSSyvP54LBn9BSBMUuyStUcFL7SofBc3BYgdJRwlC9JoTLD3sj7xZd1lKVJgfte+Ezg1aO8mFEoWol66NP+ch1JTu517hi77dokwNYzM6tzNPsIOC4P6rjs7IhD4QiXLPi+tsI7O2QIS73nQ2GDDTp3uKBatqMyJlOyQ2I7UFIvH6cTaVe+giS7h2iJEuK/5s2VzrtSGhPUGm2sYO6V07eGQw7sJzI3+xHZuxg9GxrKQ0RD0of2h73ybKbJflZJ2XnPo1z3E6PuVcRXIqSzg1Nj6UfN9EmGM95vyo0z0SVaSSO3EjwU5KRkdpzk390yijKa2GsgkFwfSGSwB6bfSAXHchwwqOJhK1Nmp1unrnmnuk5P46O8KXSN1tbR6SACJOtlaTgzYeQrXoDXe68iiwId1un03Dj9qGEPCG1ICAxaz+YYIcnZmSnW0qPLHm7ISolHFizMSEvi0QYcGq2hSS2t0eJBCxRsgmQYc0PSODASeY3cl7gsRmpAWraqltDVDbZjzAHKg13q8LhBC3ATdgPOGuBWZI2avSszf2fNmUshO2vJ6yqSbUsG+unQGBqkSYx+WZXU9Ki1zhdfwFlwz3cZKxDl248BCykO1EljjKN5bKFpYsvpZNGycSDsfSsdKwzlOl9YtVlRHHUZ0rSdU4ToTT+FBilGyBrsZHfirXTUcNlMZ0lK2KRL6o0QDlBNWbxy2bwynnmetxVIpKwKtVKt89ZTDPHPUDLj+txGJ47xIqPjUnnoI+vux89PJeAQdd5NM84Hc0Dnqc5gG/I5hzsvnqQRhNTVKCHi4iuOsqzKh3UvsEZtZNo1IrRUFQqRUzS6hcXP1MPI27rPhEIknuRRFFsnJkqp6wjBTF32fTrP6MJBN7O6T06DmkwjMZKTLrnE5m7smwkzgMR+3FU9oCkYzJ1ildna47ORnFOe64XCWAV+396r1v4Sb0pF/qfS3ZmEVmEELUAD8AJkgpj8JI41yddFiiPd/3Mez5MkKWcJ3QvjNl013bHkGJZvZkv1eQuiEogbPmcCKSjzGi3AcyrumGhY+H+YGlPnsdf7EV/T9t13IAGhuHsvz9qwz5xdiXrEwW1LeJgkzTgQeZiYcwebIdpE6ebCdPdtjqHSdDCJA6cb1lJZxH1SffiUeqhfWnMGzx/XznXzfwszU6VQHj/akK6Pzkk2DabuNQNMA3d2vc/sVGymSjZT2nRj+gbMtU47U5vLemrV+9X0EKEW/USiTdRKiKhyn5ReSNmoXr6H+hu0vR1TLjYUgtI5z/Xa5eegKnrtdiDxwSpDveqWuO5kxvSPXI9UkPMxovQYmlcT+r6ebN0jPocOUhAU8kynGfNXDHgs64zvLEdVEeeCDK/PubmLvwV0zasTJ+vb5+m/xuF9NOHGwhx73VMDat/nzu5CGlVNilhl3J6hd7gNueXp2i9+x0Pzv43S4uGldNMNL7d9oWiMRrz1+F4MRBpJnsiDffGnbNm28N2/7mW8P02L/X7KNLq4BfCKFiKFkl6yTH7fmklO8DRUKI6kwunCVcJxSm6uFObXyTKz7chT+Y8G2/ryEl5/AaeRipv7NY2Od9dOHij9zKjfw17p4DvYpOmUAXrl4DAAzSvoGHKJMNcdKZEf0jVVt7o1iXVBAxlaX5TCMqrA1IUeGxNE0lmw50iQLCeLmFOfyR7/BHZnALcwB4kJm9LkB2EPDxI6NpnXcZw97+X8e66gX1Gi+9283ojij/fKeLyXVhpJSEoyFb0vS6jHTu1I1DeXrNRzwduIk58mbOCGyg8hNDQzkY7aE+sANd6kgpLdexs/VLbNSyQ7lWbDxElFxGsvpFyOtl/nlXW8wHzJQsgNY1CimdR47M7eY5W/JH8Lk4krM/+YJz1m9nYFsX5R2SG1+RzHhN48ZXJOUdEgWoDLQxc/UzFtJ1gqkj/KspY+P6wrBvNIwlRvOS6hK40xConUDDvmhrND/d5AYtJ0GIIr87RV/ZzsLQjGL7Y2CwL3CQaSbbIkauf8Kw5ROxf/+0t6QrpawFfouhXFWHoZOcnO50sufrE1nCdcLZd2P39tza+hCzXqjnZ/NbKezJwNwgE2KOHaPIKOfwKjPorR2PYBNeAn1eJyo8dIkCi3vOMaxITdumMWRI7iqeyBLmcHNc9P90lnDqqU9w6ml/49RTn+CU8a/Gj82kaaqvTua0LkBJUAPFDM4dzfFlFzha4cVfcux9E0KgCIWo1FjZvJCQnhpBCCHiJu5mpDzyX3MNY/vdpyCEwCO8VPgHoAglfrxJvE62fk7bARrVFia1TyDgzrfd31BSik+Daxb1fv7C3YZasIoza9/jwQc16LFXk2pUWyznAExf/yq+qDX979PgvNUCX1Lw74tGmL6+93M+B5VnyGMkLp4hj3Nt2kBKPu/kD+0q75LPUx2nMfHzG9HDRb2pcJuGrkwQiUo8qn2k6yTQoO2lm1EyElO9TsYD93xzDEtnnWVxBUoXxd45eWSKsMr+FJz4MlyHvgTsF3s+IUQxRgQ7BBgA5Aohvp18mM2pGf2iZZumnDDuSuPfBbeC3lsPNTuX3+v6NmeuUXnxhDx0l/0XvqlTDPBneSMh/KlFroQGKh0X78qzGMEmxpM6q2ocr2PU89I/u4eFj9VygqGTnKD0dAwrUuQWE5HWAEDRe28rJOQ3GC0DZNY01RcpOxJycme07qJ8yxWUjZV8PmJWvMM4UbaxvWqZpfs42v0jXq0qTRCz+Ba3bA5yYX3/m2YUJbWeKIRAlzpF3d205qU691QGpYX4TYQIM7fiBaY3XMJiB0vAihajUas0oawuI0Wc3fwsNy6M4tMgtO55fOOvRai9kbRE4pNeJrVPYFHhingad0DJCHxjLkX4S5CBFkLrnker/QDF4YGuPNAGWM3gW4EqFP4DP5IAb7QFuPMfH7Nw/np+JL0Ww/g7gqOJbv1pvwzjndAdTv28inPc/PwbY2wbjDwOf5t7A5M8+yMUkU42ccr4GhbUr6emyPWldCkfTJrJabC/7PnOAT6TUjYCCCGeA04Bnkg4Zo/t+bKEmw7jrjT+d4/R8fts+dncO/T71HorqAk1cNe233PuJvjpET+g1W0cYw5vmF2/AI/z3VSyNYk2iTjDwseD8of8J/U8yESbLhQFIfWMnIyaKOdBfkgeHeTRRRNlrGYCp/MWb8nJ6MLOWCCNAUDyLRN+vpJ5PCpvTqub3BcpZ+oCpGh+ENAw5jGLDnL9UX+mYdST6O4uy/o0fzOtepBfjlMIx6Lher/g12O9NIz6Kye7X7UQdjJZJxJ5OihC4batCv89RhJUe98cb6xRyyRak3hD0QBSwJ27ZiAwLAGDw6zX9GmSG154CoDmAmLnG6nkaYuXxCNSrfYDQ5Fp3FUIT54ReSMojOYxs24aUsJr3cM4BxXfsdehuIz0v8gpxTf+WoJAaNeHuGxIt9FfBKQ3g3+DLiK65Lt40h6zP5DjUR3JqbLQh98dTdvc5DxwZY89SfX2JZtY5HezdNakfl93T3CwaSY74AuMNLLd9r297kkxx6AAcDawIumYF4F/E0I8BZxIP+z5sinlvrBmPmCQ7R0j/4OdviqkUNjpq+KOkf8BwIb3LuGBDb+kOmh8+1UFJdc3rQIJj3JzLNVro3PsBHOfwzEZ2wbGCL1LFFrSze9ylq0fbVoDgD5gV/NNbnyy60xOvKcT2Sdv191dtjrIKFHDdcccW01AKyWEhbWOGlJcPOX5Johe44K6UY+xe8xcY5Y3YbuTL20yLqyP8pN1QaoCOkJKKgNRfprUqCWEIBQNoCpu/K4cFEM6gwvqNaoDevzcqoDOnWtaCIfqeXP0YF6ckBc7H9xFH1giXjBIl2jYEkGDIcU4o3EKWsd4bsIXJ9v4elQvnjGX8vJhJxFMMrEPutzMPdLoos7EDH5fGcb3B7VtAcf6Y5Hfzb2XjaXIn6phbeKUYSWWmuu3Txocr0Enrzp5nCfTWmiyd26R343PrXDb06uZOPutPpWq9gROjVH98eA9gPGfxPNrcey1PZ+UcjnwDPARxkiQAjwihLjJtOjDsOfbhmHP9yfglkyvn41w02HNfPjnDwC4d+j3Cbis6c6Ay8evh/yAtsiN/HJkhUUK8AHPeaji5H7pFWeMvVS4Cgsfb8gLyKMDjwzH9YvTaSVngnS6yeZ+wGpmIOcxURjbM4mSAUTUm9E8bCI0h1/1xOhZusJ0DHwnblyfuL0vwYtEmLZ+YKR1k7WIwWjOSiZGgMKI5KV3DXP3qK7xQdNrfCEEIY+b8tZyhtQ281lND0LRaS6A8iTSNQ3nk1GuFQOdjsSn+Et46JipbCg9nOnrX6U80EanJwek5M6Vf2f6+ldpP/u/KFZTo6CGhPjQTss5+Zj9gb7mVkOac+/C9uYAS2edZbtvwapa7nlxXZwQfW7rOE9//GNN2cRkd6HatgC1rVHbOeI9hd09kt+jg1kz+eyztj755lvDIMme7+yztu4Le76fAz9P2py159vvePMXEDFSL06iF3W+Qv5vhLTtTEXaN8HsFTIVzegLQtBFIR4Z5Bbm7BHR7kmTtoWUE3SPzX1AWnchJEhXqN/tp6pD/TAlqhb2X8z9Jfj45RwWapKtXa0ZU7Qk6VRThtHUPX7yDMGNr0pLo5MeaMGVkzorbBKeEyHujolnLBp0HIsGHcekHSuZufoZfNEIas0JDB1zKcLlQ0ppeVAIxIQwTNhpOScfsz9gmtebbjxnjirn7Y2NXD2ok98v/DhumWeHvmqXiWTd2hOJE5fTebVtgbQaz3ZErUu5T7WV+3oYOBQ0k2PkmrXnO2SQMItbE2pgp68q5ZDKoHNnalrsK+LcS9g2Je0lkg3jHSNnm5c/kSVMlEuc+wAle1QIKaYFj6zoM3pGKo6ku/W02zOu51oumURSJtqrlsVVscBIX0f0ZtqrPqew/hRcQmVc8Rl80b0hfk6iDOPiUfkgO7nmXUlph1Hj/dj7PBcwA1+C7GIi4WVCiFqVnxXHT+TC715MRXsXt34GFzb0Hq/HJqzr0XmYYLwZyqUI3tDtlamcGqb6Wz9NB5NUa9sCPPF+rJQ3iLRkC+lrl+mIK50CVjq1qC+jaekQaYw65JAl3HQoHAjtxrjVXdse4Y6R/2FJK5uqRQ+M8No62hREIwRdelKXsSSPDg5jG+vkMf0iXY8MEsZ5nnNPkbYzOQ3slu7oGiSJp46BFFcfk6Dz6AIh6SI/haxFJBfp7rZfjASkCor9F3seXSkd28kPAiLqIb92Ip01S1PrwyLVoF4iCYgQPulBSGFLqsb7ZL/dtg6NtKSvc9QCy15fWGPiuihLRrvROsaxZMyHLB3bSwhS/5hPPt/E94KjbAnP+NeZELUqP9pRRTTFunt3FxXw3+MkIqEOrSDQgMsTmqDMTuH/+uc63uiJZNwg9SXIyKRFX7VLJ4Jq0JdRefjb5FU1OFoGOqWXv4ympUOkMeqQQ5Zw0+Hsu40abiTA1MY3ASxdyjdvEkyuN0bBfp1g5wYGGV8XfRzd1Wn7JT+ThzIn2xhJX8dfeJAfpj3O3s09fTSdtjO5n3Cetb2W08Kr0d1dKJE8dNVobkq16eslmESLv1OjH1C5YRr1Yx91jEALdp5GT/kaI/3rFD07mNQjFfJrJ1K98Xpy2obH07zJ15GuMLtHzSO//mQE4Ne9BqEKa3rYjJTVYCk5jePi60rsek7nx2si0WdX0XVG1bUwthbeKLoiplNsjb6EEmVJzRMs3DoLMEZ5bhIKd8t8GtUW/lryBq8Fq3jDYQ5WG1EASaM0pnBHYuOXCuQOm02ocTKi+9j4WI5JLof3YThwICB5nGjBqtqUuqYdcZmGDO2RiNGXmMaQobYtwJBZL1vqpHYdy4oQ+7RpKWsmf2AiS7jpYM7ivvkLaN/J1K5VTF31HQgbUVa3dgZt/DsX1Bseq+aMZ0VQ5982hxk61hAMML/kzUjuQWbSryKkEPhkiIks4XG+Y2tMIKROLp32pgVpyHZvOpPt4DzaU0LEFaCldjTl1Z/G08KZ2PTN169j6rojUXeOwT1gGJGyLbYjSj3laxi2+H42nTu9f4sWgNDprFlKTtvwuKGB03Wku5vOqveMKDS2juT0sPlQoPmb6Rj0tmVEyYySnfx4Tb1mPRpmQ+NbICW+iMbIuhZq2rrQMb7YfQOetpx3bE6Eiws1il09NA/4OZu3XMK36k/DJ430cqVWyg8bp0Dl33kNB7cen71mcXLZRBMaiqcNf/VzXH7YYfFmoPsWburTaOCrhksIolKS4+n9+nNqMpp6XA3Prqy1EJevYiH0w5AhsYMZ7JuWaoqj+7Smeig0Rh2KyBJuXzBncZPxi3Jy1XcA6NCu54L6Mi6u30WB+hgAbdq/8+nw3i9UWxGLfsAksuv4C3+Ut1pkFF0ybGgnQ0qXb7roVpHRtJrFe4J0s7YuV5TSARtB6U0kZpLObhYlFNSdTPOAd9BKPnd8VtF8zbRXLXMkMqQA6XJMO0tXmPqjHumd5XWq5wpSupbt08O9xyffp2n4s5RtmUr9mL+CK2I5uGzzVPSeZkLrnmdk7QeMBNSaE/CecDPCX4IWbOMcVN6LFCE8bYBBtlcXR/DEHmTK/K2UjXmSEB58Cev0SS8zmr7B20P+20IOasEqvOULCUd/bmg5JyHRYSkoQjSrbRABlAhLW/7GglVnpURUewJzHcLdZknVOm034Xe7Mrq3wFrrNUnQqVb79sZG7r1srKVTWTh4BPdlyJCuaWnRokV9rr2/OBQaow41ZOdw9xRTHgDhIld9h2rfdxjo+ybVvu+Qq75DrvoORer/UbHl3LjBeV+RXF8w076mMUHivOuNPBBPlybPwubR6XhNHbFXZLuUU9nB4Lh+81JO7XPW1rSSS35d6VAeCbLt9DtoPmauM6kBCNg9Zi45jeNSjOVBULX2e1R98h3UQKlz8VChd5ZX0R2PM9O+7VXL2Hra7f0fU/I1x5yNZtCiGQ5ALZqAFonyx7l0v36XMVeLQba+8dei5JQihMDtL+bH+Dm19ttxneLL85U42cYRI/ZklGslCFcAgZFW9Rau4ij3Z1y7dibf/NCLO2mExhWVTNscREeyW21mTvU8Ol29I5C7uur44dOr94psBb2pWsXThhDETRe8lQtst5tG9uasbeIsrROSP06TBPtqMkrsVNadDBm0Ior87rS5q2zT0oEPIcRMIcQnQoh1Qogf2uwXQoj/FUJ8KoRYI4Q4NtNrZyPcPYUZ9T5/E8jUL5pc9R1Gt73DsvrJ/G7ADTSJcvvrZNCtnJz2TTfvau4z09dd5Dneo4ymzDuKk2BG7HfQaghqxOqtN/BQ2uak5GXYzd5aX3uIy12PorkzIzTpCtNTvobKddMt4zbuQCmF9eMAo+Fp62m320fByRD2XddnBDekppH7ATNtHGoayS8S/G9/G8oj97x7LbKL3jGXWiQbwVBv+l5wFG/WXcb5vnpyBr6KHeweBBrVFgTw2eyLAJjxfy9z9IYrcOsexn4RAXp4e5yPjhwX5S3NVH38OQ9Eqpg7bDZKLKIezfHx62Xq/OMEv9vF1ONqeLH5N0ibVK2n+IOUTENiCrc7bGQszFnaBatque3p1Rk3ZJkpV6cmo+ToN9Q4GV/1c1ZvYt1NqGEyRV6V1T8/j4mz38o2LR2EEEIcBXwPOAEIA68JIV6WUm5JOCzRnu9EDHu+EzO5fpZw9wbjroTnvu+4+9nys/lV9Y8IKM6RbR6d+GSQJspQkOgo5NGJQtSIUvdAkMJeg9lKuh4Z5BhW2HcUQ5/3S6d7PIebM15v8uyt4ZKU0KUs5zFR6V8UbkaPiSnfDWPbLcckGtWng33X9S0UNm7kmOEPZEa2SfPGIuqhbMtUgiLE3IoXjEMknNkxAVFSipJTZBwXk13ElRytG6hAoHWMZ3pHHt3BD2wfIFxBqxCGeU+z+3nBqlpGbjkNt957j7FfhBn7RRhvsJmJ79/Nbn8R0yf/1JloYs4/faV97VCTUFt88bE2+4McmuTMFG4kap1hvW/hpn5LNaZrMrrt6dWW47WO8YaMps1r3YVBstmmpf2PqrdXX0OS8EX9mcfs7VzuaOB9KWUPgBDiHeBS4P8lHBO35wPeF0IUCSGqM5F3zBLu3iJhdCgZdupUFkjJSSyxuAOZ6OE25jFzj5Zkm75OcG43O54zNguwQaa6x8gY36QJ4vtSqMoU8UhUlFN2bgtX8jhnBDfEPGzHWI41ybj+qEdTlKUSYf8eeflL+Rj+xymNnPhtLxV8LSPRchssXcrsHsuc6nksKjRkWoUwPG1bSq1vlFC9SD0KNrrXQdnFY2/cTeU5v6PD5gFCRD2831ROvtpMuVZCo9rC3IoXWFy0mksH/Ijxv3id1p4Id4SLbV9GyGuQtWlekEg0YDj/JNZYE8k4XeeuCZcQnDmqnPsWbuK2p1eTP7wI1Fab99O+lp4YWSematOlbZNrvSYJpmsysmsE0zrG274uM4LNNi3tX8TI9k/0OgYdBvyp6u3V7CXpfgL8WghRiqGlfCGpWspO9nxZwt3vOPtueOFWiKZGOk7qVHEIwWo5AWwItz9ITnk6NiLFWC8cMyrPmDRtkIk7UPy2kRzwJMue7lukRKKi1IjW/Q8x8ai/EOyZyaZz77eM5RTUnUxED9N81BOg2jdSOb0XDX4X0S4/rjyHL3eTN4VOqHgrleum9zoZiU6uHvXjlFPKtRJa6EjZjlCIiAhuadUD9us+BhQdhQy0xK9tSaNvm8y8ggXMG/6z3pOkQmDnFexcX8Gf8FKBj54CycaATm3EGhd6Q4a1X6O/iOIcNzkeldo2g2iiORrdsdEjiEV7/ejcBaN5KS5QAfTsPs82go60HYe7aGXayDqn8nXGPXYXVblV5JedSUfTuJT7Ffnd3PPNMY4k6NRkdOfkkdz5zMdEounj5uQI1ul6duNHRWmvnIUN0tnz7THhSik3CCF+A/wL6AI+hhTVFidZnj6RJdy9hVnLffXHELB6kjqpUyViT0UnTNilPPvSXDSjWCfSVJBM4x9p09lm7TURtiNGwpA3lLpVlCJxiftCcCtttK4sMSKkmHhF/VijE7li4zX4d4xne9PrDDqpFVduABHJRSDQ3V2owVIq1BANntS6W2VQUtx8Eh25b1v//JLSx5CqxVyg53H5WzWsHNkal2kE07s29U+ywd2CL+qhECvhCpcb75hL49Z8iWn0oAgxpzp13EuiM6njeP4jQW0qTxEckyOgJxonXSUaYti2F+PmBVIaxOM09uPUodtX524izAg6p/J1UNtAKyKw+zyD4AOHOXYv+6qfi9d+67rroPQfqOFoCtELkb5z144IwYhSnchWxKSyMo1gncaP7j3FfhwrC0fsL3s+pJR/JhYFCSH+GyOCTUTWnu8rRfLo0Jr58PxNtupUydhb0Qnn9LEOaYzZmyjjFubYjhGZtn3parrmzypT+6w1S3e3MY6ThL6IVkQ9EHUjPQ7qUkmvJ+PtwuhE3j1mLpVyOm3vF9K2tZArD/+PFFWof6+CXx8lU0RNZjSspXPgUgu5mg8Rdi8rWcwiL6gyca3ROGWS7tyKF7iYyyznSS0E656n4Mjv2l5Y+Et6rfliHre7heTv1S/G09WW60WKbG32VCEY5RfsjOh4Qm0csXUBovNT5hxzOYsGHQeBSNqxH5kwopS8vT/QOsbT2TGez2ZfxJBZL8fDhuQUbk2Rn10EyKl8PaXRCofIurUn4qhxbEeEP0yq3dpBSvj9VcdknCp2Gj/a3b7v3YIOcewvez6EEBVSygYhxGDgMuDkpEP22J4vS7j7AzHynfrc9wCjlrvTWwFJxvG+qORq7UWw74npE0s5lSYcup8RlMkGY79Dh3Jyw5KCTPHITVfTncgSejieedzWx0oFJCsi9ZGUUYOllG2eikTScNRj1uYk3YWi+dHdXfH6cH9S3PHbucI0Dv8HvF/F+JJzbI8x1JWCCcb1hpzniOEPoCU1TBnPOQJEajRkdiVreoQ1rcb8drIZwaLCFZzgPoMaGaWQ3i7lnNoP0IdcamtKIGNZFa32A7TaD+INTp6uCjz5bts0rJNjUI6i8MeT/x09XER35ayU/YlEIVw95A57MB5xal2j0qZ9+wPTkcepc1hAnDTHPXaX7TWcImsnjWM7IswU/TEdcKovh6POfQRZ2OI/sdZwYR/Y88XwbKyGGwFulVK2mtZ8UsqHMez5LsSw5+sBZmR64ewc7v6CSbqNb7Ji+VXUv3smP1vdYvE6/cknQS7dOCh1ZjRdRji2z0wlO4WJZTQyh5u5hd+nnYudyBLmcDPzuALd4Yt4r9LekpjUfQYQIHQPVWu/z7DF91NQfzJFuydSuW56fHZWDZRS9cl3Gb7oD4z811xExEj39jX/6wTNbzTpHFEw3lHz+IJ6jZfe7ebD17t46d1uLqjXHOduhZCpn5+EnIZxdEfa+bDpVUczAoBOVw8vtP6Ti48PceKUwVx+87d54/hTCK97Hj0asl5WCxFa93z850Tv2nDHeIJ1l6GHi5ASdC0Hqav4BjxNo4Nwg5HS7jsNrBasQnG3WeZi3UUrEV0TKHQbDz1SChBGpGnOy2aKQETnpwvWcufkkY7FsvsWbgKgKte+ZJMusjZnbxOxN/Ox/TnXaSzI48p+FfcHscao7wGfY/xKfA58bx90KSOlPE1KeaSU8mgp5ZuxbQ/HyBZp4FYp5TAp5VgpZWoqyQHZT3l/onCQ5ceKT17ln+90Wb64DfGDXkJBV9KrPsb2pRPSSCbUvozhTThHgyIubNFf9NWhnHJ8gliDSYCF9acwbPH9jPzXXIYtvr+3+ahqGahGRJX8OktlI9+VD3Fi+EPQnetjkS419gr7V0g2I9bUF2Dz+QnoqVjDSzsftpAtQLcvyaZNyeWRy77P7tJypFDYXVrOb6d9n9cGqAQ/+hu71ea4AMUC5W/UdX6ADuz2F/Wmf2PQOsbTvXUWwV1XIZQIihpACJhb8TxBYY3OLSNKfaSBjS5l61OFUCJI/wbOrZqOKlSEkAkiFf+wJV1TqMIOf1++gynjaxwf1UySm3nsTHxJJZtMIutkktyb+dj+nOtk/l5ZuB98sw9x1J95zJP1Zx5zeP2Zxyixfw94q75sSnl/4uy7IZZWBmgNrSakn4vPZW2uK6w/BXXHGHLUAjafl1l2wjHqlDKFUJ3GbpK7m49hBe/Ks2xrwv2Z0U2HTIQ2TInGxI5bO1u8puHPWhqxzNcZCXvZunUChw9ZjcsbQonkxWQdSam5KqqkaFg7MmpvFG8HKaXtHK+mK6hp/HQvHng3a1rfiZOupuisHGkdg9HVEkJeq8hFyOvl0Uuu5uj/9wNuHf6RdS2jcpDRnFhq91+ojQpax3hOrPqQy4a/RKmvlVbNxUsdCh/1GE1XZm13esMUyrXi+LjQosIVTFwr+NbbPZR130Gjv4i5R15gIXFIEwGrbTyz/Q/gsjZ1CkXHW/VcSg126ayzHE0OTPnFmj5cby4aaoh3zPloDru66jKe/00mSbu52UzQ39lap3GhovYtfZyZxaGAbIS7PzHuSnDnxn88rWI7H7e8jqZbGyTMul6P1uEcOSXBKRoto7FfSlFNosJQihIVvMtZnM5bsSgxNbYwXH+mZbQ+ICW1anfPR7nZNnKuH/uIIeQgegX/26uWWY5xSuuq7hDDR7yPz9eNiDVIAeR/MQkRzo2vSwhQ/VGGnNlEa+USZB/d3SZC0QChaACibiNlLqErCvkbrnb8/NRgKbnuQo4vO59BuaMJunU0RXL6x2Vc/lYNQ2rNhzD7Z+CGklKenGR9IJASEGFLatdf/RwnD3mG68c8RZm/FSGgxB3l6uIIx+b0/t4tKlzB9Uf8lLMK3uW6w+7n7YIVnPKxjxtf1ano7kEBKgNtzFz9DJN2rIyfdw4qj336K44IDmbull8yqX1C73oiRUjFfvxLKBFLlGtGmE7ZD1dsh1NEmEhyFw29iNcvf53C+jl0b52V2p2cdG07kpwyvoZ7LxtLjjv1K9E8v6bIz7dPGmyRkJx6nDGnO2TWy0yc/RYLVtXav6Ckey2ddRafzb6IpbPOys7mfo2QJdz9jW/8Pq4UNLqwkSMLXuDDplfojrQjpbTU9da0LqJk8xQbHeBU7GnN0oTTGM1qJjCHm3EqJPernmvtEeNxvuNg3Tct5Tyn0ZpEOJGblAKXKylSEZJAxVpcUZ/ttdtGvoCUOrrU0xKvpkdYV/AXmo96wuiejr1Gj4CCaB5lW6amfH6mshSAqngYUTYZNQo+zYVAxDuWDdK1nwcub0l9uBDCphFdiXDZ4UvxuqwPdR4FLi60XltGiuJp566Ns/nW2yq+qPV980UjTF//KgoG2c7CS6VWgsBwH5pZN41J7ROQumJY9Tm8b0L0CmaAEWEuWFXrOMEWlZJhd73CD59ejVdVKM5xx0nu3svGOs7LJpOzIgTTkkjS6XwAmfQKBDDtpMFsj5Hjr6aMjZPlnZNH8uzKWmrbAhZHoExIN4uvJ7Ip5f2NJIu/0WU9LG5YzUs7N6YcurtnDbndBfijsXnLNCpNyR3G/ZWA7GuMZk+6ftNhKadavG4zWUsykiPanMZxFus7gGhURXFwA0pnMKD5mlEUF90RQwIy151qc6hLnQ+aXiF/4moUt5UpPAo0DP8HIxb/znhNadLhpcKDqluZ0uxYVka34AlXEPb0ppW9oRDfe+Epzv5Qcs0ijScnCZaOca5LF6v2adFiV++a7eqcpqJUMsoDbejALcKHT1rX7ZNepjdcwlu56xFdxyKj/0SoDlFuLBUtgDNHlXPPi+scXwP0ppXbAhH8bhe/62P8xsn27tYLxlqOW7Cqlomz37JVlEpOKUvg7Y2NtvdzGvHpT9dyprCbEc5GxgcfsoT7ZSBpTve0/72B15fVoiXMpqoiysnHbaXjGInuEkZDemyo3gkT5RLObm4mWLohfaOVDfoiVFtTASk5JkXlLDPMZ1qajurMSby9ahmF9afQXrWMjoGLU0QnimtjJvRpfGbT7ctRC3i/8Z+cUHYhLqX3zyOqa3zQ9Aqfd29gfJ49oeu+VsIylKLjnIxEU/lE5AZdKHo3tz/xCH+55GoaSkqpaGnmhhee4pwPjXR6eQfc+IoEoo6k26q5KHGnkm6r5kJKHOucjf4iKm1It9FfBECZtP/8yrUSFFcPeT6Vzt3fwDfgaduP2mzGksC897/ol95xpkTmZHuX6NWb+GeVOCbUl2NQMpx8f/fWESiZXM8cVW7x5HUabcriwEeWcL8CjP7Bo/DZESxuOJxOzUu+GuK0iu20jImiu6xfoqYEsi1XCQiWbOo32YI9oSZ3N29mJG/I83vzlkLwrjQcWVYzgSbKuJudNHHqHmsvI2XGaXDTfg+gYdSTqb62AjoHLEPobhvFJxFP69ppDpv7QtFA/FrJ10ZKKto76AlCrk1jamtU8LuaedxYe4kxRxvuArcPRelViNKkZE3bctuX1+2LkgOM37SMp366zPaY2qI8NlWXMHy7SvXuKGLI4VyknR/XSv5r+Qs8F4Rrh75nSSuHom6e2XA1XfXH214XYO6RFzBz9TP4osZ5as0JeMZcyjB/Cc+qbXRoEYpshsYb1Raq86rZ0hNBMp6I/3Pcxe9bfmeTI+r+kK2JPSWyZFELJ4u+dI5Bdtd0F6zCY6N+tTcdz3YCHHYPJ/srks5i/yJLuF8RRg/2MbrwQ8u2N72ZNUxZ4NAV2xcySUmvZkJKkTAsfLwhL4g/AWi4jXlg0ncvO0XUeXT2q+vZrOXq7i6H/SGkmjCvKkFEci32fOCc8vW4vJxYfjFK0ut2CZXxRacTee9l2ue7aJ8WRU386wnD5g0Ki0pWWBSeJrVP4Nad15MrFAI6rA9G2aVXYfzp9T4wmB3L5wMvTsjj8LoSQm4VX0RjZF0LNW1d1BblsXZQObpirO1I11iO7zkfVTFIsFIr5Yf132a21HgscBhTRz5Hiaeb1qjgn825rOxR0zr6mN3I09e/yoCSEXiPvRbF5Y1du5gIGmGieOh9KAyKEH8te5mZx87kvz81CCu0e4qjFOPeQBGCIbNe7ndKNRNRi11tAX531TEZO/z8+p15eG3MGkLAnZMz1kHIaK19jUZlcfAgS7hfFc6+G/75A4j0/tH4QjpBXz8lEPdCh7gvl56+TBBMZOIw5BRRX6/NQxeg9ENKNhKrxdq+dJvIVLq70bxttFd9Hk/3mmnppuHPUj/2EZqGP2s7epQIr6eICFC43IXmgsiFUfRicLVA/gsuLlztYseFvaneSe0TmN5wiYVsAS4oG4tfGUtPtJOPW95mQ/STuK5ycTiPG3J+Tu6IAnq0Dta0vsNa1ah1bqouiZMtwLjiM+Jka8InPdyEytU9KuvqVYRidj+H8VU/A0hEzB3JIIlnkJUvIlwBZKSIJQWTWTTopzyrRsnRrONJblTalW5apAsJ7Fab+WvJGyxnOH8YehGRyb3RmSnFKICzUbkJHxUIGpA8TJA3HJrD0sGs6fY3pZoJMQ0o8vfL4acn958odt695QuZMv6nfd5vb9aauOYsDi5kCferQlIzFf5ihm4PsnG436jhxpCBP/1+g1NUaocmytLO2KZE1LKVmWtzOEUOpX6sNdrrC0IH+kHQCJCKFk9Hm2SbmFo2R4/M/faQqDUnoNV+QMkyF2JZ6iKuWSRZOsYg25l10/BJLwjIccH4HON4c+QlVy3g2MrzWVzdzGeFtUxqn0BpaTF5qtGwZYwRGcpRm6rXEHRb/1xNT9tkVCAc3HtSozyhRBFKbEQnwVKvvON022vn6zlcRCe3E+V+zY27+ULuu/xowL5paXRbhB8nGCVUIfgxfiDQJ+m6hCAqZfzfRAQiUX749GruW7ipz2jXKVVsIjGKTWdukAjFYRbZaXumSCdnmfgOZL11D05kx4K+Soy7Em77BO5pgx9/RvWpcxi104MvFgn5NDfuyFensWo3eoS0X08eXX3O2CbKSM7h+5zCuzSNeDa1FpsOEuff2j4Kg4mjRU3Dn00xj7cbPUqEEAq+Y69HrTnB8ZjSWD/U9IZLDLJNgEuIONmaMLt8jXOmpIhvqIqbccVnEIyllxPh1HzVgOyXS08iTEs9Z/nHpO1J73nyjKmdUYIfwU04KyvVFPnZPvsitt57IdtnX4SeZkzLNBmY9qf3HI+5c/JI3C77p9bkMSGzg7mvudpCj/2DqNP2TOE0d9yf0aYsDlxkCfdAwrgrqZ6xkYkXbic//ygmnreRESVX92m3t79gJwt5Dq/Zzv+CtJ2xfdpJKCPWAJVuVCcOSe8Xu82MbuI1+4Lma0ZK6XjfvtYjXG48Yy6l04EvmmNBZ7lW0vdi6D1W1/yUa8W2+3PUAr4x6CYmRA9H0XsfeNa0vpMiohKIpWxlpIhjcyLcXR3gdwN7uLs6YBG+SAfhbuMRtc5G/jHMI6rVFCWiS3749GpHcnIySnDaDqREbpmkTpdubeGnC9Y6H5D0J+RWBL+/6hiL8ITZsJTJXO1dJ/0It0hKuQsvd530oz7Xmg6mAEcyuSbO/2bFMg5eZFPKBziqT7iX9W/O/8rub1fnHcGmeGpYJcINPMSDzLQ9v5kyx7S4dIUza1fdhyl1JZIHuoYaKEHLaUnZrwb7JkrFX8JfzxXc+KrEFws6zY7eIf4S5m5podPVTWE0L6M1NYgQ3Vt+ToPDmyGEIMddhH/EFZy45R+8m7sdNaqyPrqWT2Ql3+D4lProyfoRXF28C0/skbpElVxdHEFBYUVP+ny8iBazMDgQfJ/yfa2acq2IRtUg4YXBgbbnmOS04vMWXl5TR2uPQe7PkEeVzQfo9FohtS6bqezi35fv4FdTxqZsv2/hJiK69X4RXXLPi+v6dAxy6gZOlJSs766nKreKmcfOjG/fG2Sa1s7i4EOWcA8C+EJR22Yq6A1+9YhAKBLlS/hEE0m4h9sYyZJY7dZ+rnd/NX1ZvrOdbGWStuvuLj49+zZyd4ylc/BycPdGjCKsUPBaGbpoRvhLHN2DGtwtLDnCBSLKNYskVfknJHX0lhImQkREcMvekSAZG7cRrt5tQRHm4Via/mGCXJHm5QrVS8GoC3hi9M+QUT+h3d9E6xjNY6R2bF86eGWcbE14FLgwT+WDtkKjeziag1CC8SYqwDACaL2YTmBhcCCGNlQnaC7Q7MnWRCAS5Yn3rXakDxO01HChNwq3Q5HfnbItsTacrhZr1nkT51hnHaNT22afyGsLRPjpgrW8vbGRXbGo1g5OjUwXDb1onxBsFl8fZAn3IMDQ3T42DgzbNlOZnCAENG8oovDwLtwxYYY9bbbKxGAgGX3N9e4PKJE8KjZeQ/3YR+wPcJhd1j1ddB2+Ev9iSXgsREvMTmNB06dNLKt+iIqisZxQfpFF/AJixgU9Hm5cfyyPjPmIpWNg7pZLUzp6Pbhpp5Og2kG5VkJAa4HVzyOkYRSv+EtocBtzs2/UXQ7AG2ichk4pOpUIWzOFAlFifK5p6t7eygWUeLpt9xV7uuneem/8Z7VgFWc3vcC0JV2UdkC0QuV3g4K8nWB0ZTdKBKmRpB2MxqhAxl3K3WGNBatqUyI8M+pbsKrW0RjeJUTKHKvhM+tcOUt+QLBDf7uB+6sKtWBVLbvrO5mxByNPWRxcyBLugYpAK/zuKGjfSbW/GIJBPh6Qj5oXNWZLk75DFLekbEwbCIgGBS7fntV9TYMBkzgzdQnaW6nJPYHu7jLIdg8eLKQrTHgsVP7MGKtRa05AHXsZFTXFDI6N43zQ9ArjS87B6zK+cIUQCCFwefKZol0L6+CRMR851mvzZR5XHfFvBHddibd8IcrYttioEJQn8E3usNloXaNQ8zbS7fsW1w+7j8e23U2VTC0Uxz1rlQjeyn9yypDn+UZpO8UuSUs4l+e/OI613hW0RgUlaurvQEs41/Lzqet0blodwnQJVBramNn8DBJjLlctWIXPZt5UuIaBg1RnMt5A4w2bKNwOkaiMp3CdiOsfK75g6dbUcsC3Thy0V0byduhvN7CdcEW6ESbz+FtG6UiUrIrUIY5s09SBiDXzoX2H8T8kBFqoboxw2FI/6/40wpFghGI64Mg9jm6dTA0ycQmydiHfvF/JFkjfQJUBtBJ4ZdxQPjn5ItzHXY/bZ6SRE8dxXtjxfwS09pT0slC9TI5ezuE7c+IkmIxooIVT12loHeMJNU7mjNaTmFk3jUqtFAVBpVbKf9TN4Nbm8zhX0Xj889s5IjiYxz+/neX5y9N61gJMyG/nW+VtlKjG513q7ea6Ye9yXG6El9pVwkkN5WEdXmpzM2nHSuYu/BUvL7iDOz56Kq4sZcIbMywAHMaLIgjVvkN6X2BXWyBtA9O8753Mt08aHO/4dgnBt08azK+mjN1nYhB72g2crg68L47P4uBGlnAPRLz5i9TxGz3C6Ooo5936H2g9Xvvz9gH6MjU4qKCrac3nAYqO6GBcySRUxVo7NMdxAPxqqpGBuf3UT0p5WX2NoAhZ9kkthPbJ89z4qmRy+B4Apu++MmVUSEHwjbYz+FHdtVRqpXEXnsmdJ7GwYJnFcH5O9by4itWxORGmlUZs67QXF2p81OPmqVY3LZpASmjRBE+1usn5KMDM1c9QGWhDAVwOHfCmkYHTeJEQBkkkjzntCwwo8vdJRL+aMjY+NnT/lUfz9sZGhsx6eZ812O1pN3B/9Zj7uz2LgxvZlPKBiPadUGW/fXDeGNhwA7uGPbBPu3dN7GuXoDhsGpj2GyQQ9VK1/np0GaV+5OO2hwkBA05sIGeZfWrUFJYIaO3kuItS9vdoHai6QvCz7cw5bh4/2noJqq8EGWghtO55tNoP8AHTlnSx9JanqdhoLyYhEHiwEr5PejmpeyzTh/8s5fhjcyJcXRzBYbQ07gr0UY87bjpv4g/v6CkRrR06PTnMXfgryhdoNBeQ4lAkpQu/28XU42r4+/IdKcIUewozhXubQ502mYiSU7iZijQX57gJRqIEbObc7Rq3MkWmesxmutxpuVkVqUMT2Qj3QEShfTdot+dS2p7bQv42ZwH6vcXe+uw6IhOydfr26e93uQAhJKFogI1rW1kzd5jjoe48zVFAokfrQNF1grs/SfHIlVJS2/MpYLj8LCpcQc/Cu+h64Ua6X78LrfaD+LGlHQa5O6WeneAt25QyRyulEcEmR7aJaI3av9lSd1PW0beQSkS48EeC8SjYdCiauC4av46iFzH1uBqeXVm712TrEiIlhetEOMnb+1OzNe/x+6uOYdXd53HvZeNwK9b3yq0I7vnmmD15GYCzcEViHTgxXW6HrIrUoYtshHsg4uy7YX29dZvbT4d2HTL2RK4GS21t5uwgddA1geLuu7a7181PEpCqtYu2P9Gt3bF72BS1+4i/88UHw0EIhENqWQA7LpyJkAoFO8+geuP1xvlSUt+1mVG1jRRNGJ1awxWCmpwjkxeEDAAAMxFJREFUWNXyBr6wxsR1UZoLDHJKhimGMbfiBe7cNQMlgxdkSE/+lZJYtGrM0WqEW06m2PWu84lRD6PXT2du02j+Wv48iwpXAjLeXdzof9nWgi8qBEJKGv1F+LQQhRErGfg0Q7Zy2wnVTCy5FqUhJ6MOXxNul0BVREpE6Xe7bJWekm30zGPvnDzS0kyVKdXXFPlZOussy7b+aCdniuRrFuW4kRJuS5ChTPeQUJPtUj6kkSXcAxHjroTa56FwkJFeLhwIZ99N9Mnej6tsy1Tqj/oz2OjjpkCAUDJvpOrL1KDP22lepDtGuLorszUCSKVf7keJgZXda3Mn+NaqoWJEjscq5xgjdwEgdMPMHqjeeD1CCIbmj2PBqR9zlLDvQs5RC2KWfT1cs0jy5CTBja8YYhg9E6J0XhIlWgJKC/zvv6I8XbScl0qGcnHbGRbSjQgjck1MKzcOfwZc1tSvR5FMGbiWlnAupV6bsR9doWrd9LgW9My6GQTrrraM4Mw9UrdY8AEEVfjDSSfzZtllALy84A7b11vWAbcM+2usqzZEpgkylxBxveV7XlxHW8C4d3GOm59/Y0yK0lOijZ5JuiYRARmJYCTCrYiUCHN/mrknjjDZdSw7rV1AykNBFocWsinlAxX+4l6d5ds+oTs6ybK7sP4Uqj75LiKc2yt96GAQLgRfiiCGCenp7u0gVqKZR6hCN0i3H9jxRhU4CBtEuhJedNhP5brpKOE8q0yk5f7QMWhR74+ql1M8l9Lgtk8F92hGrri2NB9v1el8z/PflF30RyLfuZz2b0uipcY19VKQl0aZvltjTcvfuW/AXy3NUPdX/Y3/qfo7u9XmuAuP5rO/Z6mvlWc3XUYomhSxR91UfXKDxXjBTrN40aDj+MPJx9NYINCBxgL444WC5RNXoRasAnoN55MhhODdB//W77EbUwv5rufWxskWIJgU7TpZ0xX53fEGpj0Z+8nzqXsk37i3cGr8cmo0y9ZtD31kI9yDBB0Lt6dsK6w/hYK6k3m/6SWqC4dRPLgtxVz9S8fepoMVmwjXISUtBAya0EDx84KWa42UeXyf5mbwthkMHTiGj9sW0aMEKKg/2fDS9aSbCZW0Vy2LE5e3bBOfjXqMNnfI4p2r6RHWtL4DwMD8MVQOOR9V84CA1jELkR7r65BeCF4c5Zr7XNx6a69n7hmtJzF995VU6Lk0qi3sdjdx+/D7udthjrZDV/nWGd/j8XfgsgS/29HrZ9i6HNlpFr9/4mY+OM1K2AJjrlfrGM+TYy/ihx/MSz1TSr654gWem5yZ6IWJQr/bkXxun/8xYESFTp25bYFIXAxjT7p323p6Sb4/8o17C6e1RqXE73ZZ1qEIka3bfg2QJdyDBNG2kO12IQRX/Pn/AdCyYAvyE8nucX/68jqC9xAp+spOdV5doWDHGbQPfts2bSyKdHJWeFg9uIIBJzbiztNwB0so23I5hbtPATecWHIBG+hGIPo2SxCGk1Cvhd9fyYmldk0Lv1A0wNZ13XzRvQFI9aV1uke0BCo64Kl7jc7f1aefzoXqt/DFvAYrtVKaIy7mPnMseuF7dHw7Cgl2t2EdXmhTeOfN1cDxrM19E8VjkPLcJvsvazvNYsdRH1cP+SN+Sc15t8IHtofEx4X6g+6wZolsExGVMi70kM5GzyREp2NqYtFhXx3CX+YYTrq1mrVcM61dUxzN1m2/BvhKUspCiCuEEOuEELoQYkLSvruEEJ8KITYJISZ/Fes7EOEqsp+9TdxeMmU4rgknEu7e87GGLwtx8pSAThoHIJ2CT6ZY08MJcMUyr8ENuax/cjiDXpnDsMX/A8DW025n07nT2T7pLlB7aK9allHK2iRMuzpqvBkrRraQ6kurBkvtL6xDYEI03vl7SfiCONnGXy6C8iGXkrfCRdETLpQWLHO0K9p656ETiXNuxQsps8BOmsUyUmS7PCEAVzcv7JxDY26u7TF26Wa3Ihzt78BQj0o3r2tGmOkivNqYGEa6LmC7fcmRY6bdz/sC6daabGO4N6NIWRw8+KpquJ8AlwGWdkshxJHA1cAY4HzgQSFEf6zGD1kUTD4c4bZ+XNIlWVG3kPuv/gaP3DqDDYvfZvRpZ1JRcD26lvTR9ndy48tyBOxDLUoNlrCx8G8oqkxxKRQhyH/B+PUYWdeCouvkqAVxc3nN3wzCiEwj/ibqj/qLfco65Z6laeuoic1YkOpLW7ZlKiLqIQUuaJ8WpWeCkUpU/PbNWCK2PWeFi6qfevDc5eEXdX5aNpdyxTvl/NtnD3H9F39jyBfl8XMWFa5gTvW8eG24Hp3fOJi8hxonp3V8jMgQT54hCLqsJBB0uZl75AUpx+f5VK46flA8yrRDVMq0SZddbQGmjK+hOMeZeMxI2M6+zmxUSt5XU+y3RI6ZjO3sKzhZ7WUj2a8vvpKUspRyA2DnxnIJ8JSUMgR8JoT4FDgBcHaX/pogd7whRtGxcDvRthC6X/Jh7Wtsb18DQGdTI68/8gcAjpt0F9vfH8jnzf+H5ms2Rogy8Z1NggjnIt3dVkKUsezvnqSsndLGTteS0PV5ITknrrTUZ6UEuqFovoucFcaXZ02bUZcNDG63NZcHMjK6D+vweE8HHw3/mWMdtTOs0OXTOFI5iqNLzsCvFiCljP8+m7XU+qMeTSF46YXOSwzCbTjtR2j+VkttGEAGrERf2gGHf17OxPV5uKXxugqiXUxcnweuENsHGoS/qHAFb+d/TLDuMrSO8fHzT6z6kMuGv0Spr5XmYDHPbbmYT6J+hOqcRl06Lkho9xVMX/8q5YE2Gv1FzD3yAhYNOi7l2NaeCPPe/4JpJw3m7Y2NjmnhdM9wZoT582+McezkNSPhdApQydZ2ixYtSrsSRcDU4/afHV7ieszu6NueXp01KfiaQiQP9H+pNxdiEXCHlHJF7Oc/AO9LKZ+I/fxn4FUp5TM2534f+D5AZWXlcU899VRG9+zq6iIvLzOf0q8Sfa2z6YvtRLVUAnG5FMr8QSKRASQ+T4XydiIzIJw4pIKvczBRdxeatw2paAhdRQ0VEfY2IxTj90bXK1GU3Zlfdx9BaKDuSmXqqDcfvTJ1GDbTdbqaQA8JWvJBzzHmX5NLzS2awBXJpSpcmvbJI1iw3flGKQ8fAneglIjqx9PUioz0jv1oiosOnwdXstwnoAtBS76GEFGkdJHX7aO0J4CqR9EUF52FPnLzuxEJJGO+hh7dee1SutBDdnJnBir9sNuGV0tzPbT2ROKdyZlAIBhY4o+nVdsCEXa09DgeP7bGXmrTDol/R22BCDtbAsgk6hdCMLDYv1/Tum2BCLWtAcv7oghBTey+h8r30p5g0qRJB3jHyb7DfotwhRBvYC9Q+BMp5Qs228HZ1TR1o5SPAI8ATJgwQU6aNCmjdS1atIhMj/0q0dc677/6G9jnBSWXj17CzuCLJFYM2qvWZdzBrGtQuvZaKhrHAoVATeway2g84lFUfytgcE1P923k5P4OKUFGBYpNRLhfIMGlGSllM8oFeGv0YIbPqMWVZ2UDc53p4GqGyt8YqeAaFf54gSDvCDdnVgfx5mqEulXervOx0Kczd8svqdSKUpeVEOluPe1xe3GSKGBTKFFFMWLXvzH4f34T3xZ0uZlzzOUcpX3g+MfxhyGGm9OkHStTZmzrfxVBz039TEKa4P46f/xXKPG5QeruWJTs7H97+1iN+9emfn3UFHm5c/K4Pr1rE+F2Ce67/EgmJUR7pvhF6vX9/Pu0Sby87eWMzN8T/46Ma9pXqGqKXCydNSmj9e4JnO5t3vdQ+V7KIj32G+FKKc/Zg9N2AglOnAwEdu2bFR1ayC8to7OpMXW7ajTOuGgimqCJbKYrm4bPR/O12kZm5pdv1y4/beu6KSmLxEX960Y9ZghDCIessABcX2K2REC0FNqujdJ+RRSZZzRQ+VZ1s+P9YgafEbSkoZEQ1cHl0LWQWA8GQ1lp6uJcPtlezkal96RyRWfI2GZHSz6Abq0Zv1qCe9tkgqOfQk1MK4cBh0BK81sjWwnMOeZyFg06jsO+WE9BNHWcKaRGmb7zz+RFwngjUZrzvdS09RKuXmz/mZh6y0KArvmRutfid5uYkraDcPWQO+zBlHPMWuyU8TUcPuvltNcwkWjJZ+LOySNtU8vdIY3/eutvvLTrfwlGjYawuu467ll2D0BaQ/h0ncj72ywga1KQBRx4whcvAlcLIbxCiCHAcBwHFL7eOO3q61A91s5lVUQ5rWI7AAXqY4ikDtWi+uOY8F6NYzHNNLTPHxQgevRbfNj0Kt2Rdtorl8XJNh36rOvuDz52g8wnTsCDzzAkMb94p5pwp4qUEO5U0XpyyNtwDWqgFCQoPSp0GQ8ZSjMUzrNGygCbq0vQFeufiKorHLep2FEXWYa78IUxjOZ3H8eq2uEWx57HO920OGgdu5Iu2eAvitdMlxWfSESxvoFRdNxRhfxIGAGE3S7WDiqntqg35Zd8TROJesvCFaB76yy6Ns6me+sstI7xKGk+S7VgFYq7DcXTZoiqxDxy1YJVlm7f/qRok4nHbDhKbqJqC0T4x7ZH4mRrIhgNMuejOWnvka4TeX+LTnyZ3dFZHLj4qsaCLhVC7AROBl4WQiwEkFKuA+YD64HXgFullPvOTfoQwujTzuS87/8b+WXlIAT5ZeWMKe9mccPh3L/hVOZ91kNP5DFcNAASV5GXopN7yPV/kBFxlo1p44vuDby082G+GPqXvZ/rlUDUY1jmJW/fh0SsuCXVJzbQtrWQ9U8O5+NHRtP+4jfIiZYzuPY8hi2+n5H/msugpQ+wYPvp3LYzB9d9nhSyBQi67RNAuUGX7RiOjEYQqg9XTilCGH63l2/8Ee9uPI3bdubwizo/H/W4bb1qSYqwkzuCt+SPYOlRTXT5NCSSLp9GxC1xJamL6YrCpure6Dv/BZcRVScgrMNL7b2vTUaKUAtWkTtsNnmjZpE7bDanjf8ipZvXhLd8IckfmlAi+CoWWrp9TROA5GubilaJsCOeKeNr7Ksmapvtuuq76223m7hz8sgUswIwUtr7W3Tiy+yOzuLAxVfVpfw88LzDvl8Dv/5yV3RwYvRpZzL6tDMB2LD4bV5/+PdomvF80qn5eKW2mfMG/ozR191j6DMDDAni2/WfBD19sJyA466tJxqr1/aFFCELm+uhhkF3oYTz0N1d8e7c3aPmGXKQ+wiePI0un0Zu0EW3L8roitNoSHpi8EkvP9p6CRHvcosGsuWYiEbQkxqldfuicaWoOz+9BOE3LPlweVC8+Sn3md5wSfx4gJVdOYRbjmbKwLXxzuEN7x/FpHWb4GTY7dARvK3Cw/aBvRKE178y2Pb1Jz4oKKtdLB05gpHH1lPqa6U1KnipXY3b9kkJergUX/VzcaN54WljffgvXH3mD/jr66mpc0fhDHe7JS08ZXwNz295kZXd1mv7qp8jCPG0tZ0pwYAiP2eOKrcVzJCRIoQndQ1Vuc5NXuZ6IL2e8/7C/jBKyOLgQ1Zp6hDB4qcej5OtCU26WNw+ltEm2QKMu5KhFV42rv8xOs6+qAKI5mROtp07fOQPCvadVlaiKFEvR7z9f3RUvUfj8H+gu7vBTnkKDKGKDGZnE9EaFTxzVi8xzdhQTAOpncuqr4QbX5b88XwXfzjpRK5duZ6KQFucmkfWtbB2ULklrawpOitHGu/LosIVXPH++3GHoLxL/mi7nni9V4Ieq3e+1zGeD5uH4S1faNRBB2/liSsuYmbRQO6f/FPb64QaJ1uIsdsXJS+Y+ifs0TR0iPvYLq5qp3vxf5E7bDZKElEJAa7crSmfWzAa5JkvfkveKD2lrusonKEVxSUYTdQpz8fXGz9OieAtX4jWMR4hjLlaIEXof56DG1Hy+wDgc/mYeexM2+MTkTw29GXiq7x3FgcGsoR7iKCz2d4gvrPTOl6xYfHbLH5qAa6icmpOasKdEyaF7fppFh/pUvEVaxnP5mq+ZjafNyM+zxs/LUayiXOppohFissPILpB94FI+C2ORK3pUjB9aFN/1WWgBZ8G33rby/XnXsabky8zOn0//js+rXe2d2N1CUGPSrcvysqRrXxW0/ueJkbHMtCCyElVmWpUWyj0FLL7s/OJxEhLLViVElX6qp9DuIZxDiXchI8KBA0xtag30NA6xhOEOEmLIYdzUdfF5LoK6NE6WNP6DtsC63hnQhuPXNL7eoVsQy1Y5RiZOkLohi5JUlSqNU1GlFg/bKm7Cew+Ly5OYRJLnUOa11xLoc/NlPE1TJz9lq1xgR0S3wfF3UZ1XrVjl3JbIMLE2W9lo8osDghkCfcQgWPXcmmvFOCGxW/z+iN/QAuHoKmQtk8LUT1ejp9RRIB3kVJPjTT7goRdyys47Ox+NJMLyz+W7aLDR9Xiuw25RJHYXf1sXMQj9+9t5K5wUVuUx44zPVSd1IQ7TyPSpbLzwzJGDZtAXf4G6rrrAFiet5bhWLtupZRodYZwfll3D2rBKrSO8SwadBzeyn8ybUkXVfknMOyoSxnpK6HB3cLcihf4rNDqKrN0jAuIcs0iiWvd8/iOvRbF1dvMFhQh5la8QHu4HV/1c0gMwvCWL7SN/AqFxo/x44+9O1UIfowfYqpRWsd4tI7xXFz9DNeELsenGvfKdRcyoex8NuR08lnNDut1Bca9HcQuMvm8zai0UjmFOyfPQDSuQLQVo7taLRGwRpIRgFYEamqmxIyS22Op3f5265rvgwBen23fmbxgVS21rYH4OI7pDARkSTeLrwRZwj1EcNrV1/WSaQyqx8tpV18X/3nxU49b9gNo4RCrnuxBC42L7zvu2vqM0slSh1Cnm7athQycWIfq3/vuJ5kfZMfS/2LwxLvJcRcBBumaxLtbbeZPA/6Tawoku05XLWS7a3kFHVsLOSx3Cz8fESAQ6iEQEBy2O0yy5IUQArX6aEJrn6K5AHwDnibi/5zQ7im8WXYJnms+Z+buq3BLg9AqtVJm1k0DsNRjwSDdpWMAPmJSu8L0hkso10poVA2S7qh+j7sLNYpdPbTWzOW5z7ay1iHaLI3mx8nWhGmz9wZdqAWr8JYvZMbnt+OT1i51t+LhIu18/sHSlOsKJYLU3UjdbSH6Pmvviddwt8X9Whct2kLnlh/bRqEmeS5YVUtg93kp6V+puwk1GjLpZrOUk9C/6YfrhHRdvvct3MTVg6xn7y9noCyyyARZwj1EYDZPLX7qcTqbm8gvLeO0q6+LbwfntHOws9Pyc/tHw8ibuCJtWlnG0s6ePI2iYe04fjUmbsrki12H6rYuuja+ivuoK3An5IvNaHFpoYvA8RGmFTTiis3aevI1Bp9RR25lDyWj2gmGDIWoHL+kafwzRFvHYIh49KJzyCYafhUmWgw/j0Z4KWcxSwOH4Q5M4JbwRHzS+udh1wAFIHUFpApKmEWFKyz7j82JcHVxBE+sDFyiSq4b9i5/b8plVSj1/VKl/Z9kBcKShnaaA67QShxJVLh6CO66qrduHCkCJYxQnVWdLK9TK2LIrJeN1OzRUUeSNEnwvoWbUtLgidFwYpeu09ytz63gc7to7Ymk/Ib11eW7qy1gnepP3J5FFl8BsoR7CCGxa9kOTmnnZHy6rptxEzwoPmdVKvMLXSiSw87e5cyl/RwnkgpcfsXvuPrMRlo/+TvXNlxoiRZNMru4UMOVpGqluCVlY9oQyb4NrjCatw1TMQsM1azdR/4VqRpLLFElV5dEEEOfZ8rIGeQ/vc12fSbRJY6rSN2L1nE07uL3U46/uFCLk60JjwLfKA7w0a4cRFJDmCbs5TcbkJY0dKPaQqVmVy9udYxYZaQonoo1kVxLNl6PC5CWtUndTahhcty0fWdLlDNHDeHZlbUWkkwkQZPYku8Jqd3B5r//9c91tCb41wYihpXU7686Buhfl69B/J0O27PI4stHlnC/RnBKO2uRcIpM5BdLyhh8Rp1VrckBe2Rk4ID2cAn3XjaWt3dczqmjGmkd8ybbooLN6xSueMLFTR1G9234Xif1DvvNyTrSjcOfQarWGqpHgWvdbQy45lz0ybNRfMUp1+mQLcZbJYkTu1ADuIvft30fih3Ut4pVHan7EIo1umxW2wiKPEu62LTZS7bjm1k3zXJcUIT5a8UC2/slpnFNCHCMQIltO7PnCGY0Xkq5VmRp4JJIXvq4jnsvG+tIgk4RcJHfzaq7z0vZPmV8Dfct3GQhXMjMtMAOd04eyc4NKy3bvoyZ2yyycEKWcL9GcEo7v/KH+1OObdtqpF9HXQTB0JejrimEm1PG/xRYgbe5wZKGPWl0lNwRoKxwUd4B9S2g29nO6tjqFEtdoSfShl8tRA+1ofla7Lm5CJCS0Npn8Y2/FqH2EprUQoQ2PY84khRid3roaHVwHGrVXAhXaiq309XDnOp58TpwIsnlJsyfmpF+b724jb+WP887SelujJeT4iAERnq2yO+mu+tYum2kHCd1HM9MhwYuMEzl04262KWJ/W5XXBDDDvtcAjH5rf/qvFqyyCJLuF832KWdFz/1uG2qOdp+BBMn/pWlS0/7UkjXpeRSXXUJS5eelpKGxWPY2pmKUAUvuGifFiWxb0iEwP+eoHsilshcjwjCXS7+ueNhLtq8i+pf/oKQkotPpoptmFKIWu0HRuQ35tK4sEVo3fP4aj+gP382L7WrlhouGEpPz22fiFQ+tRVwMG32/O1Xo3UcQ2uPEZ2HGifjG/B0nNwT68VSGiljO+k4M5Vsh7ZAxPFh4SZ8jg1c79kY29vBqypxwjXTyIDjqE5fdeH+wK5pKqKn6jZnkcWXhSzhZtFnh/PQYXewceNP0PX922yiRdsBCIbqbPdHE/qETOLtvCRKtMQgStM56NNgEeUTWyzdy3lHuPDrkupf/oLCb3yDY+t11n9yJyi90VeygYFW+wFarVXKu7nAef0TP4lyzTuS0lja+8nTXSwZ4wNgak6EXB8oLeBd6MUbGEJozLCU+imAX8nn56f9hIuGXsSQBAMArWM8svJF29EeMxWcriPYCU5OehUO+fnE7UPvehldGi4+icS5YFVtSnQbjOis+LzFUvdNHtVxiorNNLCpRlXbFsAlBFEpU+5tIts0lcWBhizhZtFnh3N11SUAbNv6W4KhOlRXIQiBpmUwOhT7MndHdKKqQKZRxfd5q+P/OkXUu/4vjH+xoHi+m5wVqYYDAAPe1Vj72TCLQlTecDhz5p3sAp6+dQadTY1UHF5G9QmNiCLdQthOCKqG0MWk9gkpoz+RL1Zy42sCn2a84PIOuPFViLQdh1qwhkGvdyVIR0pmup4BLmcJl1nqp3qkmM7NdxM5ylBfSo74Qru/aUuqWtcoQ+NYRJDS6OfN1PnHCQ1IqmxItyEhL6vH/jOZOO9buCml4zgQifL35TuIJjF84qhOOgnEZBI3r+M0X5ttmsriQEOWcLMA+u5wrq66JE68JurqX2Dlir5TzWO+cLOtMogmFHrHh6xDHoriZ+iwO4A0EbUAXBA4QwIRiufbu9GUdnVRWw753aVxPeUuv8F2iZF8w/YSmrYVMXZHI5VtqdZ3YaEQcPvID/fQXACLvxXlotEqOe536Rq2Dt+WqVTWn8LMum/TvkHFpy2xnO+LRrl25XpATdVpjkaYvv5VFg36aZwQh3dupmRCgO9unsea+/MZeeP3OXNUOU8kSBzaNTlpXaNwF61MIGFpiWxzh83ul/WeiYcJWkQ4oLeB62ib4xOJ0ymKTCZbE4nHO9WF7Ujc7t4m7pw8ktqkpqmsYUAWXyWyhJvFHqGu/gU2bvwJUt6U9jghBBuHqOi6GTlKFMVPVdVlNOx+GS3aBoCuh1i//kds2/pbhg67g1Gjfh2LqG0IXUDgNEnxfONHzQU9bsgPQnOhYN4ZsHRMEOhVhfqB12Ur/GG665gyjqZ5UWOCgUBNkZ+fXvguxzY8gUcxztf8zeweMxcwhDk8wy6ie6uVcAHKA22O7015oI28UbOQkSIGbTqWSc2f45IXI4DcSCevP/IHtlSfBcrhlvOSx2zyh/8G7PSKK19EKFqSfOQ/kJX/RLh6+iTgN9CAgK3MpB3hQi9xOtVinZBJ1NlXKjj5flPG17Cgfj01Ra6stGMWBwSyhJvFHmHb1t9mWNOVKcfpeoCGhlfQZSL5GTOfwdAuNm78CVVVl6W/qgJvjR7MgPYWFk7ojkksgipUhBCgJxCQ7iYUyiPY1GhblUx012nMzeH6c38R/9mMiAo6f04wyY9WusI0DX+WwvpTEH57IYpGfxEAlTbE21wQ05L2tHFy4ybcSaIXWjjEqJ2LWT74cMf3we92OdrVCVcgpSFKKHp8FClRI9kdmMDU42p4e2OjhbjeQOMNUqN/J5jEaVeLdVKNErHjM7l2OhJ32XR/FfndLJ01qc9rZ5HFl4EDzYA+i4METo1NmULTWh0JW9cD7Nr1ZPrOaAlBj5vNleXsKuq1xNOkRo6aQ3VuNSCQkSICdZchozl0uvJsL+WLGPneoAovnOehpshIotYU+bn3srFMGV/j+Ho1XzMAbdEgQZc1xS18Pl6ccAlzj7wgZZ9ZDzaRG7SvHedHncmuyO/G51bQw4WOx/QFUyP53svG8qspY1k66yxq9rDGmZiuNQ3kE99Lp4kcGTt+wapaJs5+iyGzXmbi7LdYsMqqW23nKZsIp3R1FlkcKMgSbhZ7BLPBKR0UxY+qpopHZAbnL08poWldEQCqrnDcJus9OsIdvH756xTU/Z6uT2fFU6bLik8kIqxRpKLrjKhrobEA/nih4I1R3SyddRZ/+D7kHjGbu9dcwHnPnEcQ+/ZkNVhKAMlDHkHLTXegDhgAQqAOGEDtd2/jjZGSDyf9iz9eqNGQr6BD/F5mVA6G1Z4d3AUltsbl3z5pMCFNp7UnQqhxsiEvmfge6QoymuP4HlreA3dbSu3T6Z7FOdYHB7MHLvHhxMSU8TUsnXUWn82+KC2R1xT54w1RtW2BuJrVXc+ttZCuSeJ2kax5nSyyOJCRTSlnsUcwG5sSIYQbl5KLFm3H562ON0ElN0Apih9FeOP120xhKjw1rSuidlkv4SdHh6YReXLNb0v+CABOaV1OfrQzbrf3l4uDmH8K1blVvLztZe5Zdg/BqDFrWtddx/yo5KoSF15XQoo06sG97Xz+L28T5110IWePPxf+3RilWrCqlv98/TGUimf4/+3de3ScdZ3H8fc3l2ZiGpLSC51WUIIVEKwWK7tL0VPELS61p8B6Q10vu0fYs+uRdhd26SoSXBXOym7p6rq7uuvxQrmIgKWCKyJUkS6XEmoFWoRWqG2Sxi5N72mSyXf/eGbSuTzPZJJMZibJ53VOTydPZub55jnTfvP7Pb/f91tV1cdjZ1fz2NlBGcgpVXX0ZU3TPn36Ps5/dkbGsZopdSz52Cd53dQ35qzazV1AlJ2EjP4D87MWU4Wrr8o8b76Vwl+8JFg9vWHDBl7+yOK875st35afqFXN2QuhUo/zbR0SqVRKuDIiqRXLbU93AjaYYLNXMqekthTlS8THhd/t6ztUw/O3zcs5nj46TG9Enn3Pb97B3yST7SFqmxvZcupufhs/kvPaNW1rBpNtSluP4XsbeW9zHydOOcy+hPGj/U7bCfcSmxbjoqY5pNdq/spPXsCm/zi0BV9vfy1TauvoS7uHvevkPtpqjnByleM4tc2NLPnoleyYe4Svt32Sg/FO5r1hdrLv61xW3rl58LVBjeVE1nkS1EzdRk9H2rajRD1W1ZvxXHc46ntZ8oMlGT1lx6JZer5Env7zpAtbKJXvfUQqmRKujFh89nIaGjawePFLQz4vfyJuJ6jHmCBWN4cTp1/Arlduy6kW1f7ErJzXW20N2+f3YxizG2ZnJI30EVUscYwL/+/n1Hpwv7a/+xCLnp1OQ20Dm2e0Z7x21aOrMs6x6Lmg3+30A/3sbazi9gvqMqaDexI9rGlbk9EAvWtgI3VRDd+rj1D96p8x47UPBT173ehN9PHcSXt559Q+vnPxTmLVMQ7WPsa6jesyRtrX/vw6Vt2zBWd+2jUIP4/Vdoc2K0glYDhekrLjcAetG1sBQhu5F0tUIh9uhamx+IVAZKwp4cqY6+hclzPCTSXhqET8zL2bmPam7RnVorq3N1E3tZEpsVhGgY6/idg/nD4Sakjs4YBnbob1vn7O3xHnu3/7PxnHZzfMHmxev+i5BFc+4IP7aGcdHODKBwASGUm383Dn4OP7d9xPLH5PdCOFvmb2dp7Fe+fHuevgaqyqL+epPYke7vrNXQx4ZjchqvpIND0Ae+dnvF9YichUk/d0qQTccNpNVGW9JuwXh1IZqsKUyESghCtFs/XRR3KqVTXPO5AxdZza9gNEJluABedfE1pu8sJPXJG3QEe21Ejozm/9V+j3w3oEX3XOVYP3cD+8wXOLVvTDhzd4sul8IHXfGGBN25qcfbEpqYIUc5rrufu338Rqou+vDgwMhCbt7BFtWElHIko6DpYdiRgVp//iUErFmiZOlX4cfI+3hC9Gmyhyfl5NrVc0JVwZUlgizU56Wx99JCNBHtz7ex78xtd4yyd3MkDuPtwd22/Om3CHKjc5XNU14R/1xukzco6lRnhr2tYw/cDvQl83/cDxx+n3jSE6abkHpR5rjy7kmstO53O/2pe3XbBjWMi97OyRa3b1KUtM432nfoo7dsykn8wR45++bS63P/G7yFFx+i8OpTbaaeLs0o+7u4+ye1+CHz6ze0ImobCfN6zEpVQOJVzJKyqRAhnJL6yKU3/vMfr91dBuNIXs4x2q3ORwTD1xetD7N6JBQ7alLUtZ2rKUF79+If3tufuBu5uqQ+8bQ+aUdDozqDvhBb6wOFjp6/3NkSNNH6glceQUqhu2Z1w/d+g/dEbO81NTxbVVxtRYDd9+sY/m11RRV1PF/qN9GaOftY/vjGx0EB+4NDSe8SBspfOAT9zuQIWu7JbKoX24kldUIn30ju9mHAubmoVgZXEodzbc80Z2PHB9UeIcSmxqI0uu+DSNM2aCGY0zZrLkik8PmdBnrVyBxWIZxywW4+zP3ciWj2/hwfc9mHPPM320m6Omm02vvMrKOzdzrOsifCC3HvRAfz09HZdRFWvPrRRlUDN12+DXtVXGtNfUYgSFMDDYd6QPJ/j7WP8Aqz/41ozm7XOa64NRccdlDPQ24w4Dvc30dFzGL9pOySk4MV4UvZduhZtsP+9EoBGu5BWVSLOPN06fEdpTd9/zpxH/o52hjQgSzQle7r0VHoCWi28oWsxRRjJiblq2DICu1bfQ39FBTTzOrJUrBo+HWdqylBufuJH9vftzvndC7UzWPr4TJ6QRQX8z06bMpvn3N7GHjVh1+H+cVbXdGOTcs1t008N0H828JxxV1H/FnZtzVjCnjNcRUjF76Y4Hk+3nnQg0wpW8wu5xhh1/x4c+Rs2UuoxjNVPqWHD+NZxxxpeI1c0JLR7lU2Bnz+1Fi3csNC1bxryHf8aZW59n3sM/y5tsU1b9wSpi1Zkj41h1LBjVph3rP7CAw9uv5dC2mzj80rXU1zRyzUWnB0k44gZvfGp8sHpTemIsdMRzyYK5ORWj8j1/vAirkFVlNmFXOkdVBJuoP+9EoIQreUUl0ux7n2e+44LIKdv47OUsWvRo5DkSTRNvJenSlqW0ntdKvCGOYcQb4rSe18rezrPyvm73viDZZW/ZSRc1ZR01sgk7fv2ysyIXbI3XEVJY/ea50+rH5Wi9EGE/b3Z5TaksmlKWvIazWnioKdvq/dUkmnOTa/X+6IL041lq4VW6Lzc/nLfjTWqRT/wN8dCFV6+pPiFyn2z6Xtb0AhcHEtOY9+WNzKo6b3AK+pIFc9n0yquD09spqRHSeN1ukr3SecOGDeULpgRUAGR8UcKVIRVrtfApsct5ufdWfMrxY9YbHJ8swgo8ZGvvPsrXzrmK6355fUb5Rx+o5WDn0shtLqljX/r5Wo42pa1ArtlHXfwe9nTAqnt6B5/7xUvezMLXnZiTWIERbTcZr0lapFSUcKVkWi6+AR6And23k2hKUL2/mlNil5dkwVSlSC/wEDXSndNcz9KWd9F633Mca1gfLKgabBb/Fr7ykxeobdrMmrY1dB7uzNiadMmCuXx9+0P0HA5pSD/zJxzevoAb1j+XNzEuuunhYW830Z5QkaEp4UpJtVx8Ay1MngQbJjUNmJ2kIHORz97Os3By7/l2DWykNavGcnod5I6IwhupPb/7jvSx70iQkMMS40i2m2hPqMjQtGhKpEyGWuQTtXip/qQHc7oZpeogA1h/c+jrwmorw/HEmDKcxVcp2hMqMjQlXJEyym7S3lx/fLtO1LYPr+kOfa9UScmje5bkFNTwiNrKKemJcSTbTUaSpEUmGyVckQoVte0jHlHvOFUHeVbVeaFVpPzgORkJPV16YhzJdhPtCRUZmu7hilSwsG0ftU3HuxmlpDdQCFZC93J4+/EqUvW11dz4gaCGcyFt8Ia73URN4UWGpoQrMo4EW2/q6R5YTv1JD+I13cSzGigUkvzGIjFqT6hIfkq4IiXQ0bmOHdtvpudYB7G6OC2nXZ23PWGYzFXNC+g7sID62mr+6rI3s7QlM9HlS35KjCLloXu4ImOso3Md27Z9lp5j7YDTc6ydbds+S0fnumG9T76tNyJS+TTCFRljO7bfnNMtaWDgKDu23zysUe5ot96oEpRIeWmEKzLGeo7l1kTOdzzKaLbepKajd3cfxTle8GK89r4VGY+UcEXGWKwuPqzjUUaz9UbT0SLlp4QrMsZaTruaqqrMUWhVVT0tp109rPcZTTu2Qqej969fz4vvupCtZ76JF991IfvXrx9WjCISTfdwRcZY6j7taFcpw8hXGM9prg9tlpA+Hb1//Xo6rvs83hPs7+1vb6fjus8D0LRs2bDPKSKZlHBFSiA+e/mIEmyxhLUFzJ6O7lp9y2CyTfGeHrpW36KEK1IESrgik0AhxTD6O8IXcUUdF5HhUcIVmSSGmo6uicfpb28PPS4io6dFUyICwKyVK7BYLOOYxWLMWrmiPAGJTDAa4YoIcHxhVNfqW+jv6KAmHmfWyhW6fytSJEq4IjKoadkyJViRMaIpZRERkRJQwhURESkBJVwREZESUMIVEREpASVcERGRElDCFRERKQElXBERkRJQwhURESkBJVwREZESUMIVEREpASVcERGRElDCFRERKQFz93LHMGpm9nvglQKfPgPYO4bhFIviLC7FWVyKs7gmc5x73f09RX7PijQhEu5wmNkmd19Y7jiGojiLS3EWl+IsLsU5OWhKWUREpASUcEVEREpgMibcb5Q7gAIpzuJSnMWlOItLcU4Ck+4eroiISDlMxhGuiIhIySnhioiIlMCkSbhm9n4ze87MBsxsYdb3VpnZS2b2gpldVK4Ys5lZq5ntNrPNyT8XlzumdGb2nuQ1e8nMri13PFHM7GUz+3XyGm4qdzwpZvYtM+sys2fTjp1oZj81sxeTf08rZ4zJmMLirLjPppmdbGaPmNnW5L/1q5LHK+qa5omzYq6pmcXM7Ekz+1UyxhuSxyvqWo43k+YerpmdCQwA/wlc7e6bksffBNwOnAvMAR4C3ujuiXLFmmJmrcAhd7+53LFkM7Nq4DfAHwO7gKeAy939+bIGFsLMXgYWuntFFRYws3cCh4DvuvvZyWP/BLzq7jclf4mZ5u5/X4FxtlJhn00ziwNxd28zs0bgaeAS4BNU0DXNE+cHqJBramYGNLj7ITOrBX4JXAVcRgVdy/Fm0oxw3X2ru78Q8q3lwB3ufszdfwu8RJB8Jb9zgZfcfYe79wJ3EFxLKZC7/wJ4NevwcuA7ycffIfiPuKwi4qw47t7h7m3JxweBrcBcKuya5omzYnjgUPLL2uQfp8Ku5XgzaRJuHnOB36V9vYvK+vB/2sy2JKf1Kmn6ptKvWzoHHjSzp83sinIHM4ST3L0Dgv+YgVlljiefSv1sYmavBxYAT1DB1zQrTqiga2pm1Wa2GegCfuruFX0tx4MJlXDN7CEzezbkT76Rl4UcK9k8+xAx/ztwGvBWoAP451LFVYCyXrdhWuTu5wB/Avx1copURqdiP5tmNhW4G1jh7gfKHU+UkDgr6pq6e8Ld3wq8FjjXzM4uZzwTQU25Aygmd3/3CF62Czg57evXAu3FiWhohcZsZt8EfjTG4QxHWa/bcLh7e/LvLjO7l2A6/BfljSrSHjOLu3tH8l5fV7kDCuPue1KPK+mzmbzfeDew1t3vSR6uuGsaFmelXlN37zazDcB7qMBrOZ5MqBHuCN0HfMjM6szsVGAe8GSZYwIGF1ekXAo8G/XcMngKmGdmp5rZFOBDBNeyophZQ3JhCmbWACyhsq5jtvuAjycffxxYV8ZYIlXiZzO50Oe/ga3u/i9p36qoaxoVZyVdUzObaWbNycf1wLuBbVTYtRxvJtMq5UuBrwIzgW5gs7tflPzeZ4E/B/oJpnd+XK4405nZ9wimlxx4Gbgydf+kEiS3LdwCVAPfcvcvlTeiXGbWAtyb/LIGuK1S4jSz24HFBC3P9gDXAz8Evg+cAuwE3u/uZV2wFBHnYirss2lm5wOPAr8m2JEA8A8E90cr5prmifNyKuSamtl8gkVR1QQDs++7+xfMbDoVdC3Hm0mTcEVERMpJU8oiIiIloIQrIiJSAkq4IiIiJaCEKyIiUgJKuCIiIiWghCtSIAvpmpPnuYvN7LwinHPjCF+30Mz+dRTnPTT0s0RkOJRwRQr3bYJqO4VYDIw44Sa7MeHuI3oPd9/k7p8Z6flFpPiUcEUKFNU1x8w+Y2bPJ4vO35EsSP+XwMpkX9N3ZD2/1cy+Z2YPJ/uKfip5fLEFfVJvIyiKMDjSTH5vg5n9wMy2mdnaZMUizOztZrbRgt6lT5pZY/L5PxrifFPN7Gdm1mZBv2B1exIZQxOqlrJImVwLnOrux8ysOVl79j/I39t0PvCHQAPwjJndnzx+LnB2slVktgXAWQQ1qx8DFpnZk8CdwAfd/SkzOwE4WuD5uoBL3f2Amc0AHjez+1zVcETGhEa4IqO3BVhrZh8lKA9aiHXuftTd9wKPcLwH85MRyTb1vV3uPgBsBl4PnA50uPtTAO5+wN3DYgg7nwFfNrMtwEME7RVPKjB+ERkmJVyR0VsK/BvwNuBpMytk5ih7FJn6+nCe1xxLe5wgmKGykPcq9HwfIagt/rZkG7Y9QKyA9xKREVDCFRkFM6sCTnb3R4C/A5qBqcBBoDHPS5ebWSxZDH4xQfelkdgGzDGztyfjaYxI+GHnawK63L3PzC4AXjfCGESkAEq4IgVKds35X+B0M9tlZn9B0E3lVjP7NfAMsNrdu4H1wKVhi6aSngTuBx4H/jHVs3e43L0X+CDwVTP7FfBTwkepYedbCyw0s00Eo91tI4lBRAqjbkEiJWZmreRfUDWuzyci4TTCFRERKQGNcEVEREpAI1wREZESUMIVEREpASVcERGRElDCFRERKQElXBERkRL4fz5Acxm6lzsmAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 484x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.FacetGrid(pca_df, hue=\"label\", size=6).map(plt.scatter, '1st principal', '2nd principal').add_legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f0b5652",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69a55ece",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98d79fc6",
+   "metadata": {},
+   "source": [
+    "# t-SNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e4b4ee22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "fb29c193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_1000 = std_data[0:1000,:]\n",
+    "label_1000 = label[0:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "92050633",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "7df7fa56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      1\n",
+       "1      0\n",
+       "2      1\n",
+       "3      4\n",
+       "4      0\n",
+       "      ..\n",
+       "995    2\n",
+       "996    5\n",
+       "997    9\n",
+       "998    6\n",
+       "999    4\n",
+       "Name: label, Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "d68b5edb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TSNE(random_state=0)"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = TSNE(n_components=2, random_state=0)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "d3c29089",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[  8.527079 ,  23.172731 ],\n",
+       "       [-30.978794 , -20.935091 ],\n",
+       "       [ 20.953648 ,  10.85715  ],\n",
+       "       ...,\n",
+       "       [ 20.858547 , -24.118708 ],\n",
+       "       [-43.725403 ,  -2.9502473],\n",
+       "       [ 14.099981 ,  -8.939094 ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tsne_data = model.fit_transform(data_1000)\n",
+    "tsne_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "766a6cb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb088428940>,\n",
+       " <matplotlib.lines.Line2D at 0x7fb0884289a0>]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tsne_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "a15763ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tsne_data = np.vstack((tsne_data.T, labels_1000)).T\n",
+    "tsne_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "26437491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"Dim_1\", \"Dim_2\", \"label\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "dcde0ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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umebkxyfiN/d+ih2vH2d7bnzgIPGoP/15GQaNeR1L1tgVa09OmOVrZ430WkuwSylmCmzPXXjtPOxEvsfmZJbtMaU02G1YN2wE3DhIisgb1jVEkrvVW4Sv5qziEj1KgYmzVuCzX5bjje8W4pclWtiEvD6NO1sCyrZb9iBnEYhwzhuiakXz0VmnSeRjBPEYv0WyYrBpi9Zgkn4ng+XyL1AMyxQp7JyfGarkx7h/V9w092hHTsMyLFBJ3K9ZQSxOfvLcVejI5vHRT8tw0f++lSw1HMhrRdZTDFEWAffvggvjh+Lm3FHcNONSFwKPAcDTgcs3FvqI/HTg6wWgW/uHC3jow18AaLbg+2xWYT43XOVn3XAIP2M+B1zfD+hrmNVRHP+o9+Zkgb8gnp80Dxc+/y2ePX0nbD+4SaIcrb8xGQUplSPytqs3KXWZqLEVHHG/5aSyOZmF11MMkbfoZ6eAo0qQyGMQqBI0iAHfhNJdaVZnGBIxxSVytESRrpKE9RqiDKOuIOIkdxpvyyG2O4T4zTI+FPMExs8tI4YVtZPl5A+75xP8eqsSRfD0Qdlz8gbOiL8ikSr4NDFyPJAeA7x8htRiX6fLK09+fKI4nOr1A2x/rk3nQPO6bHCBbqNOKdozwY6szqXw1RyN85qxJJi4QCEynLwhk/fh5FX7MnGSEEO+60QM2vMcOv8Kwi9Tf8Zp7Y8CAEaSn9Ezpzm82XqaywCvnGWzhrLiyYfUEMeEM8aEB5u4Rm+pJa4hcN5zbZ1SXZVKNW0nZQoO/fFis6BxUy0TTRnimVMpDrrjI5dpp+lsTSnw8f8Ba0tgSixyclyzCLtm7AHPRF2JO067381bFU7bAmKDIfIy8JMd8+D8wBTApmQOjoiJNfztDNESEte0FcFx5rJ12OIfb+M5jixP2qJEQFXMBROQ6LgVrxyFqSwnTymsVaUKGUUnxxTXCZraBdO4mbTiyOxLAIBXUpfjriUnuhNNfx346glb8C7LGcrqZFhXz/XFMvxccRyOjL3PT0DcHHZWp+zxmOK68MLSu8q3z+gXIcDjiRuw6Yr3TL+Okx+37p71KtIYmdXtWUxd2Iq/PT/Z9t7YIPqsnQK8eyXw4un2KVOUEYSHuCaXBm7dBJesu9GmVFUp5X5Drw23M1GWRL7QT1zYgc+phKF4KzUGtyTEoVfZyy9WCczHWPyyVNsIxk93hjGgRVv1SGV/7FfAU7+3PZKyrgmgeJVpkPOVoRfImTJ5OaVfp8HHDDRs46ihygIAwK+VCai4tgkYb7/CgVed4byTiBFHDCEL8lOMmidD7S9m43al9C+06ZOrsIvynTtOlP5vTDedNS4b8fK1CA6OGGfFTOat1SqV2sdIpdq7ONz29F2BsiTyhaIQIu+UV8pYIrCcvEzIWIMY8Ai6vFWPPdSAASnCOOsj4Kd3gGXW1Xe8sXJzfHLiGkrZJU+lVZJxXWTUFZy8HNxKAh7BLGaftou5HCen8dfb0hJCXGTQCAKniWvcDIuWVq6BBBS36TdnEZ+YlzJ9bvzmATyVvN6lYOX5pthMKAsYUKtIQV2AMBifSu0jZOiInIwQL3ppZ6C7ro4uQWHiGj7R9MI6hsivkojZPXTyrRhEFrqJqG5uGAQuIh9EXHP3aDanf11m2T5E3tEgUVucpRicfJ52n2nM/RzETeQpqCk6uXe8T/z7D24CXvwLZlX8EQDw78S1mtWYoD5WKf7DAsfFLQ46mDWta3iKVw2ytIn9bBrRNQoqjMizZdnb5dBr6JtXmCSU8NrMNIRdR9SxDo0wHk5OniXynem4131WRzdAIZy8M4fMt8sxGi4ZW9+Npj6AxxI3cdpH5ReLGTTMWYL+OqCQQya1rOLV3gcvcY39XcwU18RsWW1EIZcBPrhZu4S6s8EjFMy7TRa9ZoZh9sT71wKTLcuvXWNTcHL8Tb0YRvyj/8sqxQ++8yPXexY507rGzcl7dEMv0F4ia1ZLwJywqFs27cUM+Qb74nznsAKEsRuHVhVDmDmRVgFdXMP+bXDyRMzJd6bvZETki0SQj9WeyePkx760cfKyczNBcqBO2SYNIpPXKnKeVkppdigrkwfsR23ZpsRkFK9fPgy8fw0w4S7JUr2xKZmDEQ5fA3+wnLw2FqPV77Dv9H+4vHULgeWA5BDXOFvBDKzbukZxceyWo5/g+zmeOzl5MzAdTyYfiJO3Sn5ywiw8oJsgKxBN3kJO5PzntjHh3BZ1iPIZqud/YpfJS3DynekhHxF5BnJenHa4jrge3+69aUswbprd2Un2WyugUFyJqfAo3ZHN4+0pizRHsCvrgbma27xTdFKostJFSPS2/Vr5FG8mx4BdaH6cvF2eqgq5MmcpBief95rGWd3RK2M5fH3441IMGvO67dIPL4yfvgTHP/oFAOCt1Bi8kbpEmJbLobLiGv3famjt6UVWSbWBhxYsR00ba3HlvanyLvI2rGsSMcKxrhF8t3FXcR/bOXniqXjlEbldY3ynRXY23MVcpGI+d17uEAKMOfvUZ7PNZ5Qh8sb7e5J3YsTYY8HOTqdMvj9ZghviD9pOAp0pnS97Z6ggKGSKNOaW4bjYO+bfYZnDOaFABeUce3mLJZNTsenlbwEAPtrtB7AW927irD9nCNG/PpmJjqyKv+w5VNgeEeG+K3k3J60/KOeXK43jVdxB5F2ip8/uB758xFXOAx9qMvAfFrRi12E9fdt22hOTkHEakQuQV6l2q9CSH4BUnUcfDKVm4YRpQsVZwCeAutMvtuciyyc2CqUBw7omrihu6xqDky9AYegrkw9cooaairgrloxRn1V44WvQ+T1aOxjCnBczBXZO3iDy2nq9NXE/dlCm4W26J97HcO2UE3HypYNXtL9COPlzl/0DVyceM/8u1bdTQN0rg/Jl8g99ZC36dofM321dYzy38M9Xf8CNb03zbI+bNDmP7tbfgcbVU/HqlMnznaEOWfsc8OhBwFt/B9YscLWP5vOIIS/ldUoplSbwevOBfx0IvH4++KNLef8UBfLyX2x/i8abN1dyKsXhyse4/+e9oQhuNJJto7vewsQ1zu/PWtewkTKd348l0K0dWbwR6EY2+4mWx8QQDidvz63BYDqczlA5vdMxQsz+x5HDc8krsaMifyNZUJQlkffijrwCQQV1Nb8k/hSGZH+yPRN9+s9+Wc4tI4h5mnux2GXybZkctrl6LF6dvIDJxymHLcGQ54Zw0vXrST3WAvfuBCz90dEmeynyMnm+CeWxrQ8Dcz51NI7i8U9nYfilb+K6pWfi54rjpJR1Py8N5gl87n+/cT/kWNeEqgJpWw6AmuMhIvIqo+8wzSMpxVnxFwEAVe32ewqomVZQ75ePaB6nOpx9MmXyah6vMHPSVrgEemEZMO11AMDJ6SdwduwFe4LZn6D38i9sj/727GT8NcDdys4+9snOw78T19rTqCyRF8Pot9MZyuAVFMUi8n3ICmyn/IhbEsFj4MuirIj8kjUdvjfcxzwoeVDrmtPir7ue2a0drN9HPfgZPvix8PtKFahuIu9QvM5ctg4r1mUwbdEaYTlC65qAVEckk+fVY8iI91W+0sQYH9/mKk/KxtnxyuUM5fP5/vHKFGTyKgbldMWdT59VlRZ304+nmML61YhWV7pAiCXwXPKfeCR5KwBvRbfzO+dVam6SquoOoau1VFBeZo3mcarDJpMnlkx+VVsaZ//na1vWIOKKhzIXA89o5qOHrvkvzk88r9XBpBm86C225Zi9PNi1g+aFXcwAOXUENBdMXOPk5FWGk7f6X3rXvbKSyf/tuW8x5ccZeCz5pTCNsbA7snn84UFHDO8QZC1sCQqobS/nyhIlP7K2cHkKLOs3fwMTm7kBLGfpztuRzaMiwY8LwzPnFMG0+hDYy4sPvt41OGXy3oSDc/z22dmGXPIGhveu8UzjDfdCdsrkD4p9iYNiX+LA9A2YRgcWVAuZMwHbMeyaiMizT412aEReV8YKFK+F2MlTapVLOPOWws4QeaE39FOw47L0vist7p3Csfb8d3BHeyR0JKolnxc5FQKs4jVvS2tw8jHFIvLW+iudjL6sOPmObB6PJW/ElsosYZo5K9rw+rcLMWPJWkx23B8axkCzzLZzsfFENrLiGgWU46DhkFFziBZ1PAvCye964/vC9riSuzh5ViZPHc/sua94eYrNEkNaBmx4vNLCQg3b6MCjB+HuxJ2uNGHEeZ++ZK0Zglf0vTcm84uux4BoHlPq9ibWvDUNixuLJTkn9j/0vmeYmU+uXmvybzL/BfQg2omy/r6tMJzY4y5RgT7J8ys6DA+2nvkQ887BzHjQ6kp0AD+/52iPOL2JvFifx+XkiZ3IG0kURilthTaOiLwUFAJsRLxFIg899wque/ptpOLurodC5D0Ujh1ZFQ1Yo02ygCACTp6FL/cCiT6qefM6vWWBlNR2xTBbj6tO3mZk/hA7eK3psDujOE0oPTl5zjsbJz/nU/wqVtjtTH51TlnQit/d96m9GaVb00LrGsr5rTnyaOPAWtGcl/gflIxGpGUPuOZoZjuw27Srbe/2VuziGifnbZXhUZkqiNjqhE+Db0g8DDz1O3vRpq8BH9uQH5F44WTzbwKKFlhMG1ul4YEt+g4xhZjMoDN+fSlQZkTen0y/mboYn1Scw03n57Tzx9g43Ja41zMNFRC642LvYCiZj28qTsfY1EWcnN5QoHJdrW1pOMRTXvGqp3xkP8yoON63PU5HD6+FFTRchIhYX/0a3wLB4kSDimsCNUu6XOc7HnEtLecmjsfPk8lb8fj5URNl5edGvavXueXhzjnAlrkpmWP+9vwkDJE/Jvau7ZW2aVi58yrFRmQRjo7ZL/kBgGEFnJp+H/vA9vfJsTc1E1azfqs/ImcoAzFFYcRDGko5H8pDJt+6APjqSbTkhknviFmOWZxf3usSbptrJ5wyeeMpa2bZn1hXjgVxhqKqDycv0XlfE8r5k6TaI5rAVj3uWkTiGhaqqgqJijOYmygOjyx4m+IuyncABJe28MqQWpxuj9dS+VMAHiaU7G/9Dza4Fm9+/e6+TzGixW3vz69XK+m3d42Hk7Ty/DMopahFGy5PPGVLJ5wdDJG/NvGoowK3meqLySvQRNbimfxe3m0BcM4z33jcNOXGzk6lLEdcY3DypkWTYXWjWHoOVlxDKZWy+AqK8uDkWxcC469DS36edBYjMBMLnoIoKGjakuEmkUV/shSJEOJKK6C+7eNOUpdM3t7vKfNX85L5wpA3mpgrvqHKLa7hpwKAJz6dKWSORZsYK3oQgrMD8HQYTyWvdz3zQiG+FaUGu/EYojfAkMnrv01loGVd4wqbAWDS7JV4b5qcVZhR9houJ+9UdGr/OWP3eHG02/zzLeE7Su2cPCEETWStrV322t3Q7hWW1T/Y033N6PcsO3mnTF4n8ox1DSuuKZ2PTTlA/4qsQsMPhXDyMuhx52Dz91WJx/Bx6hz0wkphenniSn1nQZob7EyseB0/fQlmLW9DDHls+9k5wIJvzHd7Kt/gnsTtwrpcnPwLp9rGnq949cfnM5cLifVTS3+L8+Ka+Vwd1uEf8Sds74N6EdbNGQu8cDqwtnDTVs+4+gGiL8oe13dVvvNNU0ssInuFHq3Sqsj+FdhL1EUnRdkQuQYTkiRu2bkzWNfC1R14dfICjjhRbPXkdQnHpNkrrD8otTEEzm8kmoeUI84Swfm9Ln/JukzeaV3jBGsnz5ZSKi/Y8hDXMLthceKacAd5T2UyAKCRiO3WZb+rNlG9Ey9ptRS6v1Y+1cVCw21pWI5qln4J+LGxd9Gy4B3ghVnmu8eSNwEArn9jKi4WtkcMp+KVQPUU1xiEZklrh3CyV9IOnBN/Af+XOwLnxZ9HBbGLb4KGb93oHV2R9u0zgfKxkJkzZt/WdFhimgKn2r8lThoGBwvA5klJAdw5TnPeM4kMtWTIPE4eAPqoC7EE9b71Gl81CXcgLycnf9SDE9CRVdHHRYDFA+M35yjziyXWzrqF1kegHu/scM7gYWQefqL99bR8O3mzPYQwQeWsNpUq3Hx5cPI6ghBpnrhmE8V9vV4xMNoTlrhGESxCALg1cR9GPbW5+fddybvx98QznuKaD3/SdAP/TDwOgH8SMCL+udsjfxKKQcXMimNxSVx8UbrRqryal/qKPJ1AZ4ZvNeAtk7e/2/7acVi+NqO/sb+7xiljlsDX6sa+aey261a7EnmN289TRlzD4eT7kyV4KXcG/hZ/1rcuY04k4ObkneNkXNbuZxjAIu5xrzB1SPOJ7be9TLGJqTwNcc7/Y2LvmieZvCGWcYhrzLyEcb5i9Hal4uSLJvKEkAGEkPcJIVMJIVMIIefoz5sIIWMJIT/p/zYW31xhIwAA3+vyZRlkOQTtieSNoTUJsCYCjyBtQuZgL+Vr7P/dBXJlESrktADgd7GPUEn9TTPZRdX/pydxa+I+8+8gk4xH3ES5jeN7nSFG8DgTK5KTnaeeCyqTDwMy4hq2akN57Fz49SSYhyZgefp6gSVGFBRDyAKcF38eZ362B7B6PmYsWWu2pDazBNVot+XvhVUAgB2Uqb51WZw8R1wjCpoWgDHzEtd4OTE556qXuMaJs2IvYBBxx8BxlnlU7H3MrDgWzVjJ2MnzFa/ab7sxQill8mGIa3IALqCUfkUIqQUwiRAyFsCJAMZRSm8ghIwBMAbA30Ooj4PgDgU5VcWV8cewjPofQwuF8UkTHBnl26kx2o9lAHCqZHnFK/nYMbpK5+AN5LmBuPhjGsQs0v1dxAtSm+z+ZatcIt/5rLzMN2EXNxsq14n9lIl4Tx2FPPhexl7lymDuinbMqvib9WDlLDw/aRV+n9SI0jFzr8TOyd7YK/N/ZpIga0qBiia04tXUZa53whj3HC5b1Cs/iy4TDpl8IHGN49UFiefxBzoeH+a3dKVmYYgOB5ClZvm8TSmJLHbPfQpV3UFvi9WmbsvJU0oXUkq/0n+vATAVQD8AhwEwqMjjAA4vti4hSHAin8lTnBh/B39LPOeZbtqi1oKv6rKOr+Hc2u50O5eBU4nlNUarO9yyVPHilJfJiyxQqtCBm+IPoB5rA1sZuO/ipHhryiJBai1HKeAtJw5W50PJ2/CX2CvS6WWIvKf1j24Xz5YzWLFfFu+0DPECIW57cqsdgnnkKJYVXzjhL5NnCmMKdnPyYnENDylk4GRMxDH7qfmOJ5M/O/4CrkrfhNSscba2EJcALzyEKpMnhAwCMArA5wB6U0oXAtpGAKCXIM9phJCJhJCJS5cuLbRm5v/l4AyYJMKBt39U8DGKmB/bz1OvMLMtQJuADyVu9cwl/svZDPfboIvWvwUwF+AJsXdwZPwDnMoEelMIRfWKKdiS2HUBo4g92qcTn8xYjte/DRJaVgyvQFROeI4DFRNIVWDJ0pfxoQgDnu3TA5KpnO9uwBI1+INAHJJCLK6Rs3wBAnDyjnJkmT8qqJ//TFymk5NnN8qe0ETKypqFtnfhW8dbCI3IE0JqAPwPwLmUUumwepTSBymloymlo5ubmwut3CitsPw+uPt98RHbC7KKV9lJWK26rXR2Un7AfjE5BybAm7PjteKmxEOcp/w2iyJwiuqsJpr8t52mwDqF7Dj2N7Yj/zbkR7yY+oejrfZlvHSttz4i7+NIZsP/TvZPo0PuakM3RHf70nD5Lu+5RY3rE/3l2VKcPMRXMYrmgEjxymu3FyfvTF2IuCYI+fCM9Kn/a21K1Pz/dqS0R/qNZbYrGburuAYACCEJaAT+KUqpEex5MSGkRX/fAqBwY2T/FpSuaAC3jf3RPxEHxkTgKaJ46fxw4zq3rNN/8dk/sVfqYPJX77RsPUIir8fwaUfKLI1XbgtZ4Xpm77c78JYTT3w60/M9CzJVXmTi7QzlTwic8CK4hUARRP7UKuPH42chihzKrcuDkw8ikxfBT1xjK6cQcQ0oXEJ5UfmC59pMNDh5d3vbdCJPjGspPTa1sBCGdQ0B8AiAqZRSNlD4KwBO0H+fAODlYuvybUuAtFsPaChVM0xYnLw3kS/lB3bXFY6HZhBPT/dk176UQeTXocJ8s6aDvZhBy8cbnc5XsfLhudm9eg4A+4Z0Tux/eDMptj8Im8h7giOTd0Ixv4EgTfNm5k8C8UlENE4iyxdebV7WNTY4OGLZueplQunsv/eaNcS07va2UX2uZ9bZyimlTD4M65pdABwH4DtCyDf6s0sA3ADgWULIyQDmAPh9CHXxUYDiNRkrvYtATOcKXCEAHCjlsnYrtsQIMsl4izZG+fG2XUReb1QV0Yk8rbApXg3Uog2tqOHLtJ0nlBIM4kCyGH2wAl/QzYRpZAgIO1LnJf4HQDxXvbhqJ2Tmu2f71Dy87VmAIbr5oDiNXSwXXCbP57J5fRM5FxntM9qYU+1bpbQJpbD0YCcOoz5j7Nn6nJw8u6l1WxNKSunHEI/bPsWWL4fgRN51aXEJEZSTX9zagd4h1b18XRrsddxhnRp45fy3nW8KKqqzSo9bkmWmIZvW0GXwCExnfL0PU+cBAC7Mnobn8nty0ygeDjpeCOM7yJTBjtwAYrec0Tj5uOfp4ZrEv7wrcKwj0SYlOkG6+yC+4zfmwyxZTXJ6vFL889UpzN8CfQiVN0r12jxNgwvTTt7SaxgyeZJrs70r5YwuD49X4uYC/dCZNtUpjps3C+cmsP//fRha3a98Yw+rGjTsrwi8cnrSleZvT05e/1IKZ4Kz+YzjbgNxX9wR1Ea8GNyceNAWDpdF0CiUfgi7X+x4vpK83P5SzaMea7F37Bv/dgktcFhOngo3DC+TQ166PfSQICz8rGuMNjpboEDFq59M9hX3iK1rqOu7iPqTJDmznc76KIgZ14Z7XWaJSFJ5EPkCOPlkXtbDsPiR9+PknfFInCF1i4GTwHpxnkH2PU+FHpyKV0dazm1VPHFNQufcbkg87FODf9sJKKamTsRbHvJwW3kv/tn2t2Yr7YaMMpBHuMNQvMqkZNdEo3OzpCr+kXgCMtgpxo/l76xLtEmx43SA8gV6Y4Wex13Gtur3eDR5i6uMICaU7HxoIGsxseIvuDT+FLdONs/ItNtSrQdZg2Pi9uDJIlrzTPIaDFEW+bdXtXP5pYxmWh5E3iaT91eIAMB/lx8hVfSsimMwgswqvG3wJwSjlBlowBrUo/ir5pyQNh8LiCAKXL87R9k2sr+93djtrfFTWxFQVJIMNpWMT0Qm/8eRX1xumAhfJu9lXZNHjSOMQWBQOycvIvIGU0Cg4oHk7Xgu+U/9bzdH21MPpeCE14nYaW3FoifRLLqN26m87ho+do3/nRGysOLJc06q1LChZ+vvxiaUXQ857qdQz9M9lG8LymdAhgP5puJ0TK44Tf8rvI/tXOSlFNew8LOT/0f8cTMgnKgs0bgdqnwaWKxRKuFOoXbycpeae0Nug/FIQ/PFb1JtlvOWFyfvdPoZqGiOjzzzRhFXWwnxdZSAfUzZUo2omDm/cBGBYjf5MzlWPHmrTeZvqmIYmecal1KgTIi8Bj/JPC8EqgykTbcECKqcC7LwnCnjyJledbyywnLDD7JZuBbEytk4Kf42+ur27wooI091K16duCLxhFtdV2IVi+ib3Jm4O9TyWBPENE0UVDYLP04+iO05F+2WHsZbJq+iFm2+J0sCMQF1hpZ2gi2rIWdtPoa4NKsTedlwwl6Qmf+8OPZm0MLZH2Fs6iIcFxtrtim6NMQLNo9X8Uj5KUBF8DOB9EPQhVTMrn5z4gFMrPiL+XepxDX+k5xRvDo3uY7VwrR2hS1/3LOIuzjGrggzDACbKXyFrB9Ec4Ilkq2o9CwjqEzeBZoP7WSnFygk8oPIYnxXcQpOiL1te87j5GOCU06FQC/iRHzeZ3hk+XHm34YzYs6HyAcxxpBZR86TKGusWpPVLgHfXJllvvvfV/I32wVBeRB5ScVroZy8ArUoxUhQIl9MXb+JfeIoyz4mhzveF44iFkTaHvVCsRF5CyJOPktjbiIftA0BUUx+nghDJIpiieRa6kfki5fJh6lT8JLJDyCaw/vBsS9sz3dziULF11zKEnknUsQQ12imuqLN8fv50tFYfA0PAHfsmueSV2EY4RNyAuD96aUJClAeRN5mQinmb1I+xz0RBpCl+KXi2ILyAp3LyTvh3DBGKx4hGoqMJ8+C7YOr/x1OIm850dhNKPlWSVkf225+e7qI1QefyJ8Qe4eb9sz4yxhKNLPXwcpiZKlc2GERvDn54pgXXl0iIm9w0c7T9JiE/VYuTaTBb3OKiIm8l47GYO6yfjL5kOGMJ58iWZwUf1uQunTzs0yu/9PgZ13jF0NGhEFOJ5KA6ExO3l1WEPm+POEMcv2fK23GbkV0Uty6oJltQZyo3M+pEQynuIYihjyaBZYZ3Q3DlPnCd7sr32IA1bi6hKeo0P/behJ5NVxxDatbcUJE5J0goCACZsOLk9dEIYLNwVC8Um9xTRAEUbx6wcvDNyyUGSfvPVAiW2c/pFGcAiyI4laBimeS1xRVn7280kyerRX5yJyu7+K44WpLZZa5wbBKatEi0WTyjiIB/D3+DD6rOEvQhuLht7C3I9O4z3dRvkdfyIcQPjL2AR5L3uybTqZP3qGQ1ZCJvCo8YalUIzXsGuSZDBNQIQPhJ67xI/KGZ3UYfZYb+wAB1QpvikQ7ygKlNaEs9sgchJO/OP40RgUgoH7YLzYxQGr5yb+t4h3f3ZOT53BqfLk1/+SVQ8zGMVIQ7PLl2TiNiUvvLr84EFCcGXvJM81zqatccfABYKiyEONSf+Pk4KNQZS4PvorXAsMy8FBBMkIiP0A3mTRMJwHg9sQ9rnQEYh8Mr/XrtcaSupi2ibRifPI89CKrhGllESSkhFfa7bzEpyGhzMQ1fu8LW+reR2Z/BOHkT42/EahsPxHLlrr2vivh5/Fqe2WTyfMXLs+6pt/i9zzbEEa4gC2Vmb5pagjfuajSQ55cKIpVvNKQFa9NWBNonPtwQkhrdvKiNonbenx8LI7HWO47Q0wb5lqQOQ0EiUujiZtKw8+XByevE41LEk95JitU1h3EnZqHWBcq/YIgzLgp9rAG8pEaCah5ctLG3T12GY64ptSQDQUbhm27LOS4SQ8iDxKquKaRBCPyvJRvpcagifHz8Esvg0J1cV6Q8/gOZoEWebx6Qvv8PYj75iQWhU5o/+v7fPIXeRLwQqgBrUI0NvfyeF2bFo8nAcwgTgnkuBtsDm4TyiDtKRQyIQeKdi4KgKLt5BGukr8HaQ1I5N1tqyNt2FudIJ1eBoXq4rwgN/bhllcoyoPISwYTl7Ft5cErjrVUvSEv/HU0FWp5pYbMSYa9/s8g8nHk+USeYyfvhyYfBsAP+8cmSZltJgs00y0VUiSHy+NPCt6SUOdmBbKhXHoijlhZGGQ33suE48Rri/wpSkpJSyJxjQ/kBqdQTqBYcU2x+Z0o1a4f5mGR2H77e92a1jUMkY8Rlats41nX+MHphBMUp8Vfl6qzFKIBEWTn88nxN7nPqaf8Ozg0f4fiOHmjnCDP/SBL5E8RjBO/LfL6ENmgeKVCeRB5Byf/597NeLi+zvbsxD698P0mT2LLwQPxdSqJA/v3xRvVVQCADIC9B/TFn3s34zf9+ph5nqyrxR9bepuxL3Ye2B9bDh6ILQcPxLRkAttv1B+n9OmFS3s2mXkuau6Ba3o0YnIqid0G9sNqRfGdnBkA2wwaYJb9VF2N+W5iRQq7DuyHqXp9s+JxV3lLYjEz7+IY3xLohqZGXNTcg/vuc72OGUmKPQf0w/y4VsbxLb2w5eCBeLGmGgCQB8xx22tAP7POK3tY/T+/V09sOXggcj20I/e8eAzPD5mEOXFNx39Kn154pKEal/dswmX6uLUTgtM2Ithy8EAs7PEtAIJFsRje3fgd/KbyBTih6jYYBn4Vs473L9dU49f9WpAmMNt3b0O9+f7xulpsOXggnq/V+vRDMoEdN+qPx+pqcWD/vsgA2GdAX4yrsrxNlysKthw8EJcMXeRqy2U9m3BbY4P5N42vwh4D+2FmQuvv6b2bcXdDPT6orEDtZmMwivnO7Lg5cU6vnrizsd71fLXeli0HD7SR09/064Nna7V5c1xLbzxTa82hb1NJ1GxyObYaNAATK+ynwMv65bHl4IF4p6oSPyfi2Hlgf5D4KmG7AOCZ2hpsOXggnqyrtT2fWbMSH2/yIg7t1yLM+2B9HU7q0wsA8M8WgvsatHX6Yk21ufZuaY7h1sYG3NdQhz/3bjbzGifxP/duNvMBwL/qa3FsS2+8XVWJw/q1IAfgxqYGXNTcA2f2bsbVw2aa35sLpQPVG1+PSSlrbE7p0wv3NdThyh5NtvVtIA8VB/VvwfjKSpzdqyfO7dUTKxQFO23UH98lkwDcwdbmx2PYeWB/PFFXiz0H9MNxLb3xZF0txlVV4oD+fUFLxCCUBZGfttA6iqsAPqmqxB1NDeazLIBJldY9ovc0NmB+Io5/6h9vaTyGpfE4PqmqxAz9AwHATT0a8V1FCkOVheggBGuYKwPvaahHu6Lg88oKvMIsqDdrqvHfulrc11CPVbEYvk0lfcU9y+IxZJmN6gZm8d/dUI/VsRhuaWpEu6LgldpqFwc0J2EZSb1eU8Wt46n6WrxZw5/odzU2YHUshjt7xrE8HsN7VVoZX1doY3aFvjmkCcH8RBxX9GzCsri1mfyP2ZTG6htnvtd47V1tDdrjWbym1/15ZQUeaqrBS7U1eFkft/nxOJbom8DCnlNAAYyrrkReySPV5JbPUtjd/+uJdTfAZc09MCuZwC8JSwF6H0Msb+nRCAD4Z0+tT/+uq8M6RcGtPRoxPxHHYr0t1+vpAOAzfe6oHCb15doa/IshOMsbfsaKWAxvVmv9/bSqEg801uNGvbwc853ZcXPiveoqPNTgJvJfVrhFdSqAGckkru7ZhDQBvqlI4VqGMD3YUA+iZKESgvsdZc7Ul8XfevXEC7U1WBNTEK+bLGwXALPsm5gxAoD3mrWrAmcmxcrnu5oaMFEfz2+rFNyrb5BXNPfAjKR2DhpXE8djDXW4t7EBnzCbrUEwP6mqNPMBwG1NjZhckcLfejfjl2QC6xQF/66vw5s11fhAz298bx6U1GIoidW4uYdV5ueVFbi3sQH/q6uxrW8DcxMxzEskcFtTA96vrsK46ip8UVmBtYqCx+q1zY/Arg94p7oKa2IKbu7RiOXxGL6pSOGmHo24ukcTFiTiyEI+rEIQlAWRZw/0yxlCnCbAaX2a8Y1jYRjpjZQ8x5o7GMJAAcyN261Ns47TwzEtvfFWtUVg2xXtfSWlaI1lcVKfXji+pRe+SSVt+RbEYzixxX3Z3xm9m/HHlt7m5mS0MUEp1inAsS29zTax+/+PySS2HDwQJ7T0cpUJAO9WVeKYlt5YxoyT0ZOVOt1uzNtFJAqluLGpAc/pkz2tuKfNrHgcR/e19+NPfXphkd7GOlW80TmPvme01COjj6/RkhubGkxubFpVGh+1TMe5vXrilqYGnNG7GZf2bMJRTP0rFfuJ5vtkEqf2abY9u6mpAa86OLyMPhiL45a6nS3pr72bMa6q0uSmDfySiOO4lt5oT2mWIS05O1c2N1Gc1c01PRrxUk01zmM4209qtHF7hdm8Rw/S2qRQilP69MIHlRUmoQOAuK5c/6Iiha1/ti5joYSgRv9GFb3fRFofh29TSZzSpxfmxWM4pqU3juxrnXQNLIrFcGxLb7TFrXnDftFnamtwVY9Gk4P3wiWc0+YqRcExLb2xOp7Fo/W1nFx27LpRf+7zB+vrQAGc3aunebq7qLkH4jXahShLBadgALhAb9fnFSkc0bcPjh+gbXB9me9szPB3aqpxfVMjCChSiSU4pU8v3NTUgJcFTJYhfKQlUBBr5ZcFmMh9Mev31GQSEyorMcOxwAxuShFYk6gAHmY4ng5CsMpx8XebYify31akcCeTpp0oeh3AJw1tJvdyVu9mfDTHcmn/V30dFsbdn+HDKn5wqjgFxldVYHJFCvc31IG2AXlmw3ldn0hfVVQgTYCUo4v3NNZjRjKJXxIJ9Mxr8bmNJHm9GOcGlqQU/3aIv5z4riKJ71P2zfTLygrU5rWpn+ZwwQayDjnu5MoEGvRb7Y2+GfUfsWYdHuu7EgAwB/xTCwCsYL5Fv2wOlzc32U5pAPAkp09ppu/LYjH0yecRY+bJR1WV+Ijzbe5obNCZCc3ZpyLksJj/ravFfx3P7uwNnDoTuJxDGHvk8/i8sgKfMydYQGMSAODcXs2uPNWq1ebpySRGpjP4R09t3K7p0YRvOacIAHiivhaTHe9UWJvjtRxxhwi80+bztTX4tiKFyoZWfN7QyMklh7uaGnDUmjV4v7oK71cDf121Gm/WVCNV8wEAoI3DvBh4p6YaWLoc5/RuxjomXU+GIWLZmKfra3H4MgrSaxz3OxjYOJMxaUVe4GNRLMqCyBN9Yc6Lx3B1s7XTv61z1qscO3RWX8cxaGKX+x2yz5yDIF3ZswmbZuy77FrOhGgj1rOpOsf+WH0t5ijWbr8qFsPiWAy99ckRk6QFBuGNg5rWKnlC0JFchT8LOKQ/9emNRxctRpKpwyB0GULwn9oaxABzgSb0dM/X1tg4MZlJ8lo1n0sxRFz/ravlcvNvVVehX9YtizQI6wu1NTiq1W4ZQygF9bGoWsF88woqb/NxByMGAIDbGhukrIOcPXNulCJ8WFmBGckE5sXjUIkmiBucsVvoeKntOwT1iDaZ1piC7Tfqj3bO/K1hvs81PZpw1+Kl5nz5RMB0iJAj3nOb7dMrAg7XgCF6/bxB9spOMXbZaID5e4xjc8yAYE48jscFpwUVGtPG4mVGlOMM23NJPyBXMd2zPRWUIqvrGvLo8Gl9YSgrIn9Fzx74KaVx7YThPp0LLmdYclC4CDz73sAbNdV4A/aJyBJ0A1nOenu/ugrOZXpTUwNuXarFk5a1bmjT+5Cg1vFOBfBLf7Gn57cVKbxbVYX917kXR5YQXOfgsIzN4NuKlI1rk1EHfepDBBbG47iKIxe9rGcT7lu81PU8zhCpk/vYxUAxiTaxJ682RTFPFH5g+5ElsMnbvZBxzTE5nCEhwmj14DBZESGLds78BLQTHg/9svZZPzWVxN8FinoZ5ECQ8pjba5mT8KUF1DMgW7yp6uuOzSVLgIubewhPLDl4mwM7WYkZFf4bfRtRzLmeR/GbGA9lReTZae1FCAyivzzOl8GNl+Ba5ifcQ9fqIdNjwXKhskoRQ8b/Wk0V1ukL+LPKCuRi3ke8Mb16opJDRGdx2r8owZ+UHR5EplikFcVl8QHY5eB5plmv1FTblJcisJtwGyFY6NCFyMDr+O6Es00ybZTBt8kUaqmYl39DoGjvUILVPz8Rd4kIJwlEDDJ4v6oSMYhPGs/V+svWeRiQzWJuIoE+uTwWxGO4salw8Y0TlBAhgQeAQ/v3dUkFWBRi4LlOIajPG0xbJK4RgrWxNqBQCA3KneIYJ8b06hlSy/zhPP6JYHBmUxi590rJTeWc3m75620hLo5ica9DRALYOWN2iC5t7oHN0hlTHCbCOobIrZYcJyfWCrhhHpycfJaE43dwTL/eeGb+QuH7CZV8hqStgE3mXcGpoBBc7LOGWOu3IDAYtBwBzuvVEz+kOs8xkMfYsZAV0bFoIwqa9O0hksl7wBhbVgaoEXz+oDvFMZ2NtQrBrY0N6J3P498S1gKAW9Fb7hhns1SyE1sZGflLHLO3oFgXYMydFlwv1tQgHpLuVaSE94Ia0kmisxCnFGesXO1L/BeZFmUEa0p4wiwETr8BGayJKVign1vzEScvBtE5LlaO6/X5ebLzzsSEykohByZC+3q2aEsJkQggbBRDRKankrgmJW9V4gXeSSdMxCi1WWh1BeKUmpY/MsgRgnwXM2tOzPLwD/CCcdIsFZHvXlthgSCEIAtgPMP9xajbSoGoWnc7AhzDuxJPz7c8LP2sSWRR7WGvvr7AaQoZFDxrHh7GSYgvymE8K7vqFnQGcSBQsIqpqaSv2HV9g1oicc36Qe18QAjBww4rCAXUxbEPW7p5J7YqOLZtt5tQeTkQFYpUFyzoim5GCGVPcmMliHxVN+tbIah09qGN70xUSsQoRTLg3FzC8S9Zn6GWyISyLIg8iGLK6gysjsVszypVFQNXDURDPm8LT9CdcNZKexztpnz4IYq7gmerUbueU2RRiIJMhPoyIPJOm3oy+wTpvE/V1WByCMrPGCw/jbAxtCNcll/2JBgEf2hdg43U00MvFygTIk8I32GENWOrU7X7LL1MoGRRCuILaArFPdqsI1shXPfJq1Z7vpe1yAkDho6klJcUdzXqJe3vuzOc8yzIJTc3eHjCBjnBxQLK5EX4Xav73tiqkD9RBQ3/m9epahRq2AuEEJsjQg1ncg3I5nBr8n5u/i3S6UD1fcCEJQgK0U30Bu5mbNqDHka/mjkH5zhOA+evWBmwlMJwROsaPLZgse3Ziau1gEvFKvV65vI4a8WqospgEeYmXSgnv3EmeJySN+YWPu+84PRM9fpaw9P8djtFjUAwWb8KYiPyT89fhI9mz5POb+DK5Stw3VL7pelsWIpD16zFZoI+yCIZEs8yLJMxYxxVlvC0Wx5EHnaHGV7Igb45/hGrJZcryNRtl7bClCRehNtoRo+cRoSCfpwY3At0645gG1hQDNOJ1dbpjMt7t6eAmCYDTug0IdjSsREP9SCSLAdpyJt3Z77X79asC1S/F7bvsBM3mTAVW3WksWcB8ycs4uIu116wlxe26HTZxNnseMyWCAevW2crWwFFZYEcs5NgsmfXHCHYl+MB7gUnU5AJieFWKMy4VVUlOB2Y9ZSsZB2EkAMJIdMJITMIIWNKUUeHmuF6TbIQyftenbfAZnrpxKmM+GOH9g58Pku7AOAuhuO+bfFS6YnjVZfh1PXWvAX4Qq9nwiz5Cwd4H3NUOoP35szDW3PnY+yc+Zg4a450eSLcs2gJPp49DxNmzcWzOsd16Np1rg2mQRdlsFEt71m0BBNmW316eOFiiGCcDDoUgp060vjbcutU8qu14vFmFdYfzpmPCbPm4nbmex3bugYfzp6HV+cu8O6oB46dPQQXzeyLzdJ2C64YJdjNh4D/a+FinLlyNT6ZPRefzZqLt+fOx4ez5+GmJcs887Gc7scFcLkA8NmsuZg00z4HnBEzve4vDWJWOpAju35x3kLswOH6L1ixynEqIrbgejLry1ib7PcfnMlCZTatPIBTVotD+g7KZPH5rLl4Zd4CfDZrLj6dNRfnOE6Rx6+2x1K6Uf9ufbM5fD5rLj6cPc91qjXQksvhFn0usiP567XhMR5OlJTIE0JiAO4BcBCAEQCOJoSMCLue2etm2jTt23MmkQHnsTJFgR06xOlZmeuO7R2o0hcaaxHbnM9zuRbn4gHEJwrA4uQrKDWPujVFyClH631tzqvol8ujTz7vikpZCPrk8qhXVdRQijiABpUfAGxj3YT1AGaBNuZVJGGNTX+P8Riixycx9BRsWi/5bT8mXQWlqKHU/F5xqm2ljaqKQbkc+haoREupcdzWcaLru2++NuUrwklA4y7rVIpqStE3l0ejqvrnY/pcr6rS8XhYVFMKpwHqSEZ8EaMUMagYJVgTorXC+xr7cQhzvZrnbiHaeFhvnGn6MN90JOd0OjiTNdcmu2b3bmvDZh0MkSfEk+hVUooqSjE4m0M1pail1CYlAIBRjlOlMQfqVBVVlKJRVYWnkIXxOPrpJ3VWV1VbQnFNqW2Qtgcwg1L6CwAQQp4BcBiAH8KspG/VANvfdy9eiokVKfyVE/zpvsVL8X0qiVpVRS99sE9b1Yp7dIeThxYuxqlMfPeVMQUfzZ6HOYk4tnDI8vpns5iXSCBD3CqTfy9YhBHpDBbHYzhoQD8AwBtzF+DBhjr8LLDzdkaxk8Uha9fhXIbbeG/OPLQqCvrm/GXPDyxagtMlgmSxdQ0XBIdyNn+TbBavz12A/rkcnq6rxapYzBXDX9TlN+fOR6Oq4p0589Gkav3Ys60dIzvSQkXfG3MXIEuAR+rr8DUn7MoHs+e5bLFfmL8QR/brgzmJBK5Ythy7tnXg21QSf+vdjCGZLB5YtARL4jG8ol8GY/aVAutQieHZLF6duwA98nnMSiQwu30kPu9tceQVqopnFizC4f37CnpqwdhwavMqnluwEFkQXJnYE5P6/AhA2xxYOfWb8+ZjQmUlLuzV0zUuL83TTinVqrap7Tuwn7De4ZkMXtdPNQoojiIqHli0FKsVBZWUop0QzI/H0SufQ59cHjkQ14UnrMVSfT6PBxYtxeaZDK50BB+LUyttUz5vixbawBBn5wUtrOPTw4uWYPtB9jX/nwWWTwm7WZ61cjV+VqvRgtW4tUejb+C43hymw6j7iNY1+NPqNRjApPlw9jwzrPUypi/seOze1o6Zibh5p4DRN3azKaXJf6nFNf0AsPKGefqzUFGfajB/V6oqKinFNvpun9I/+LCsRqArKcV2HWlsmsmackR2ELZ1cAm9cnk0qCpGpjOuwTI41FpVNblWA5tkskgA6K8T2uZcDgNyObNdPDgv6zCwu8/xf6NsFn2YvM15FUOzOaHii+UAd/I49QDA5g6uxUsX0cjhLAfmclBgHbfrdYK9lT4OVQwHM1DfPIZlMua4tTCnjxg0zgxwy3uTKsWAXA5DsjmMEMjrm1QVdQ6OqZpScwx2bE+jJZ/HEJ3Y7tbejj75PEamM9jSscETqph30Q7K5VBLKbbMZJACxSbMXNh/XZttsxUpLgHrBDIik0G/XB6Dcjn0aLdiDCUoRYOqokHve71KzRPRru327zI0m8PQbA598nkzrLUIlZRiYC6Hgbkc+ufy+Hv8P6ikFH3y2omtTz6PbdNp9MoqSADYUefmWdHjpky/jlizFpvr32CQY10kKTXDdv92jd0ShuXknVZLgxjGwpjXrCljNdMWo5w917UhBs1aaDu9zcN8olfuwFmfxhhvmsnaCPzGmQwaVRUDssZ7awzYvuzc3m47LRl927xIBbAsCC2hcwwh5PcADqCUnqL/fRyA7SmlZzFpTgNwGgAMHDhw29mzZweuZ9aCJcj/azPMjScwMJc1F+m0ZAIDszlMTyawFYdIs1io78It+TymJxPon83hx2QCI9MZiIwOcwC+TyWxdToDFdr1XglK0S+Xw6bM5P4uXov+6jo0qpp08OtUCn1zOcxOxNGY156lFWKbCCzaCcEviTgqKUWdquKzigq05PJYHlPQrijYb12beVSVwbKYgp8TCQzI5dA3l8f0RAIEWnjegdkcpqaSqFBVxAFskc7g/apK9MnlQUCxdTrjyXV8n0yidz6HNYpifgdAu8f2x2QSW+gLoZ0QzEzEMSKTxeJYDBmiiS8+q0hhp44OFzE20EYIPquswC7t7VitxJAmmgS5WlXRU19YFMDYqkps5liUIjjbBgCTU0lsns6YR10K4JtUEptlsvg5EccnbfvjutwfMaviGFd5VM+foMDG2QxSVJuLC+JxbJbOoMWD6E5PJNCSz5n9vzx7In5V+x9UqdomwsPXqSS2SmewJBbDnEQcg7NZNDuI5IH5MTg2PhbH0kkAtPtGH1b3xqbJ7/H7joVS3F4HTaCCZM2xGJHJ4P2qKhBKsV9bO6YlE1irKNimI22O22pFwaJYDK0xBVUqxeaZjDnem2cyGFdViWGZLDbSv9OUZBJrFIIddWK7KBZDngAtuTzerarEZpkMBuTy+CmRQK98Dq2KghQFejnG1Fj7VZRiltobg5TFtm86Lx5DnAKzYylU5lJoJq1YGI9jVDrNnd9f6+vceDcjkUBzPod6/TtNTybQL5uziVe/SybRoRBs25FGByH4sKoSozs60DOv4vtkEptkMlgVU9BOFAzM5XDcgHfw5Mk7SHwJNwghkyilo7nvSkzkdwJwJaX0AP3viwGAUno9L/3o0aPpxIkTA9cze8ESbPTgsGKaWlKspRWoIaXxZpPBc7nd8fv4h11WfzniwdwhuC53DGZV/LGk9VyWPQnXJP5VdDlnZM7GPck7bc/uyR2Kg5QvMERxX1DOg0HkZXBX7nCcFX8paDOFyFOCGCmMVs1VmzFAcYfbBrQ+rUY1epNVRbQuHJSKyJdaXPMlgGGEkMGEkCSAowC8EnYlpJtHaIx73u0TPhZQe2CsL+imnVr/hoDOcu+iIUlrnQQe0OTAYZXvxPP53UMtTy2CVCnEW0HdvalH8Sip4pVSmiOEnAngbWgi1UcppVPCrqdUnmJhQSnoOoHCkaYJ28zN07Jwh+hWKBVxdEL+4sLgCOqJHITQ5kPmH4vZVGMe66+zvmNXouQRfiilbwB4o5R1dH9OXkWaxpEi4ce84CHn+KzFcEERROgsIs//dmtoJWqLjFoYtAdBCK0aMmNBocD7tlsx/K7YLOewG0CZeLx2991YIbRT25h1EfnuPT7rIzpPXMNHON80WC+CzOGw53sx/e3sk3Sh2LI9uD5SBmVB5Ml6cKFG5xJ5uz1Qd98E10d01piK6glDHPLn+GsYqoivFnQiCKENexMshsivL+KaA9a+WJJyy4PId7NrwHjozMmUicQ1ZQPRvOmab9p1nHwx5XkR+VJBLcCzMUZLI84ti9XfffZiMapIaQOFGVhOa/G9Otj2LBLXhI9OE9cIiEVXEPlgnHz3Edd4ydyrSBrNxDs8dyEo5KQVK5EVXnkQ+fVAXNNZuDP3W9eziJMPH11tXdMVG3dXimvWO06+gDWn0IjIC0HWkztbuwoRJx8+uprIh22iKINgfQ6bky+8v11B5CNOPmREnLwFHgcVEfnw0XnWNfwlGraJogy6Ukm5vlnXFNLeSCbvgYjI2+FcjF3B9ZU7Os+6ho+u2LgjE0p5FHLyiDh5L0RE3gfR+ISPrnWG6oqNO2yZ/Mf5zQOUV4S4psCYN8WgIHFNJJMXI5LJeyMS16y/ECteu4LIy9cpw8kHmZXr2xwuTCYfiWuEiBh5C7zFFYlrwocXb/hCftcQ6ynMuuYbdUhobZCtk0V3cobqChQkrok4eTHWr89fWvCIQmdwfTmqYBmtK3k9YeLe3KEF5xXZrwPhjnd3cobqSsVrd/JMlUFkXRMyIsWrNzpjgXygboVHcweWvJ4wsY5y7giUhNeYhhn1U8TBdgXR89rYXGm7kcdrV6AQIh/ZyXtg/fr8pYfLuqYTzO0o5JRjP6sttr/H57cqUYv8UQw37CWOyIc4I0sZuyYoutLjNcgG0x1QSHsjTt4DESPvjc6QZ86hvaXksFfnjrX93ZWy1mKIsRcRC1dcw0f3J/L+CBLit7PnyXS1f1H5cwV8n7t7Xl5UnSKUBZEvFDckz/JPVAYIm6taTBtcz67P/VGqnnwBETILCfYkg+I4eQ9xTYjLStTGrhBfBBuv9Vtc87bKvUlPGoXMrVL1sSyIfKE3Q60fUaaLR2co6bKIS/FlOReR90eprJwLWVRtNOWbphSK1ynqRrbn3d1ian2ToTtRbPsjIh82Ch6b9Xsi8kBB8KuRLY5n4UI0anJcuX3KSeXxmabDOp7A2Pw2vuU44Ucop6oDXM8MC6LqlPhSNedGJou/Zs52PRONT6F1pGmioHyA93dw6lrCRmff3uR3m5QfCtmEi3H48kJZEPlCZfLrAyefoYUtZhbs4mylVUWXJ4IMwXbLKv3z+C23LOIFcU5+C5FXpkFcE3HxdymUI+tAklMWv8zClemFEy+v8fqR2mXYcjJ5eRRLdIOi2KBmhcxHtUTKxfIg8o6/b8n+Hvunbywgp4VSyYGDwinDLgTs8hiVfqDo8sT1BOfKZRRqMuUWQlj98vDaJsNBFxorhVefMV5OTtafU+T3rRiOOEhOuW8mD9LJoQmKjXdTmFI/IvJCOO3kv6eDMY82++ZT14P7ews6lntwBGFsGiJCIcfJF3I1YdcoXvmcvCGmEbepUK6Td1wXW9cU/x2Dwkuc4ByNsOXLnR1krFhOvhBxTaksiMqDyDv+ppL8yvqgHApDwRZ+P0VE3h/O/sjkkeP2g6MQcY1xfy712EgL5ZZ5/RTL5O1tezq3t1QdxcyEsE0oV6BWurwNQVwTyeQ94Fxv6wGDLg3npdx+4BOFzgqLK2NCKad4XVvLxF4pkazSj2jx3ltesuG3yas9znFyExH7rP9AHcktp1TimlQ8mEL9wuxpmEd7Sdfd2US+eHFNAUQ+ksmL4TSh7FVXJc29dhfZuwg5iK04flLcQagoSk/Si7GukebkqfUmUNgKRTxeTvhdvMEl8qjwbVPhnDxPXFOYdc3tud/hxMyFeDu5byhtE7XPQEKCF3mf8W6epg4MVHdnW9fEi/Q+jTj5kOFcbyftOliayHcHrv99D9d+L45gQnw7qfK9+riobkupMoqtx0rjx5HySgugeE3IWw/5c/LutrXBP95NZxAkPyJCoWC8OgqPpE60PS+GAfBaUzIy+ZWoRYduwhl0hDqbyBctrinA+qlUPSwLIu+EosiLOLqDXL6erBO+y3mYUMpvZOH08YLMn4uux21d40/k4zFxuVf3vMn+ICZvB+4rk+cs1HW6M1QQgicLXpmyMnnx6coOpQgrFa9NkffmpMyFjjQUGf1kKqs3M9DZ97QWr3gtZBZEnLwQTk6+rjKBrIeYwwAFQZzwP2Zn8g3bKDOE77wIkWjRBbF0cL55OHeQMO0cXYYqWp4yR1RnmymAX6WvcbeLyn2BHyu3duaUyqfV7Z2Wt1DTui27l7gmiDz38dx+Vns4okNRoCunGE9s8cRRHhfoexH00pD31VGu58/l9wQAtKJ0/hphIEY6X1wTWdd4wCmTb6pOSQ0ylSQkXQmvzUpkAupU4ATpZTvEbvvGhiMiKDJKYudEVqFgGa3npJSTyccUx7sA8nt/O3kPGbknkZcf8X/kTjJDFgRZ5LLOUDwHmx1yhflKePXK+Y1EY3t17lhs13EP5tLegeru7DAO8S5RvEacvBDOeVyZLNx1u7vBS8EmMrlzPl3j4eXqTOvFhfptnHkJDpFCwdj8tqYnLwXhlsty8jEPghrXify9uUOxnNYCg/fwbYPVFm+iWqiY6/n87gXl8yKizneyRGTXYW4LlhV58XyYu/GxwnfeJ0J7C3l9IbpZwFI0CssxsJLW+JZXKI7KXOabptiwv4X4t0QyeQ+4ph4h+M+pOxZUVuaU8cU2x4bVRYYR8ApZyrMM0hai9fya7DFYhRpXOhFiHlPN2FRES10mvCqlwKnZC/CqupNWJhULGmSg6BvA93QItk0/AFT3lMonU4OzZc/lLOLtdWD4ig7HQenrpdthtYf3PfmQIfIXHrAJztlvk2CN8OiXJ5GXkPUX4zDFEzuNyZ4inZ+FcwPhoVhOPg2L0ZzY83CpPJF1jQfcx3mCjXr4E1eutKZeM+0KS1lZbDleno0yHrsLaA/P9+0Vds9g4snJ+8mwZcQ12pQzlJoqFEE+OSLvEteEKJO/PvdHe8m25N55C+PkeGEN+Burs3xea5prU4gpwZY4QWFmxc7zo8y8fyW/s/Cd8+sbG+48qm3iN2X/gGfycg5g7rL5bWPjRBXLyWd0Ij9RHY6P+xxXVLuKRXkQedcDhbP43eANathXCfoRxqMzl3reNepFLPKCxSjrHLaS1iAfq7Q985In+4lrZAibMR4msQdBG0cPIKt4LZVM/uncXphKNxK+L4bI05o+3JKCWNc4OXmhrUpAOS8BdZd00M0AgM/UzTzy2cFdW46Sp9GBGNTxtCtdmsZd+Q0xohFFsxRKyi/VTTFVt99fSeW9cXmwRfuUnMsRJ+8BNyNPJNe6e/ANIt9ZnPwEdXN8pw62PVtELZml172pMgdKCoLrf8P3ftQkpPYx8CLy1OQq+WlkRAjG4jQsVyjsR1t76/wRdxH50k1pwhj+ib7qhdnTAHi3Pn/uD9znXnPFWZ70SaEAIu/azHc4DT8d/xVeyu/qmY9FMfLlQzNuaytjXhptK2Z9ivKybX4ivx83jSz4c9ob3dLjlRByMyFkGiHkW0LIi4SQBubdxYSQGYSQ6YSQA4puqXc7nE9MWW1XQ8523J7mhMzf8VRuH+D8qXhb3R4T8iP4ZXM4BB4JIgI783OyZ7oUrd6KV28iL0N4DNEMNRerwuVgbHXsdr6wPKUoTr4ICKoxTAS9WkEEIhRee9j584e0dT2cjImwXplcOiM5BNEwq7xDEMhY18haxk+nA135Dbv1PHMCZHFX7nBuWWnqHie5GEsxfJgv3FGQJfKy/e6u4pqxALaglI4E8COAiwGAEDICwFEANgdwIIB7CSGdFzaPKIgRgqMzl+LtfLBrvBSRDHO42H68WDg/7i+0Ly7NnQzU9dXaJLDll1dO8vv0kTrSxbkXZV0jMZ2MNKLFaoLdwHa7AI8I7PddljeBiJq77m8dpyphTp/NxGscRTn9FvnndDOsbNgCAJChcnbyztr87idY3Xc3bgsJEc+2Q9NXgydFDxPGeIo4+SWcKykB4Bu6cUH1EVDUVMiHyAAA9N7C/GkQ+WD32HZDcQ2l9B1KaU7/8zMAxs0BhwF4hlKappTOBDADwPbF1BUUCiGYoG6OD1RxyAAe2MX7dG4v60XvzQtqh8xH9ouxLiyDugkJBXGtL+JB+Ai1K5i8xTVGe/iQ4+TtxF0sW5U7/sdjwS8hwdC9hWW+njessrzL8atlqYDoAOLDhre4xn6KyjjEAcI9x/HtV3tYWm3a8S+sHHiA8Jt46QfC5kFdnrr6E1bMJwOe17LMOAPAFn3rJGsxKrPW0mpaDQBIIouuDp4S5tbxJwBv6r/7AZjLvJunP3OBEHIaIWQiIWTi0qVLQ2tMXaXHLqwHscpwjrwsQbwkdypuyf5e/6uwDyVH5Inn3yLCK7JacwmvPL6yW1zjL5N34ovhFwCQs5O3iLz9Xz+I6k44RVFSnLy7rHTgmCreuptWVOPd308DdjrTnZPwN3GpS0P0jZ03d/nNtJfpZT7YgZTOsfNELW58rw6Sa4NHGbKpneIap4hPrCcqbPshQCDRn9Yoay0Zm2kwM9EuEtcQQt4lhHzP+e8wJs2lAHIAnjIecYrifgVK6YOU0tGU0tHNzf4XfciCEIJhvQQTev9rgT3GYDzhHC4kPffCgGGq5nb1509wJyiHk3el8dFPOBeHlwmlCIubtEBpMgvKkMmrPkTSaV0jShd3itcKsK55IrcfjOnJ1lOddG5aTJuI64cLKkjR4iOR1kVeJm8v81s61LcFXEcmDvFn546EMZugZDkYJo2WuEYOxjh1VAbzsNUqsWq5OHuyRHpr7SSqNdNlCiJtXVMq+M5ASum+lNItOP+9DACEkBMA/ArAMdTSBM4DwN6C3B/AgrAb79FqALyjvI6KemCviwX22RIyYlj2un7wmvvU/Ndb5CAivESCyAPwJPKKowwvZyjhhkfkLR6cMnkjz8iOB23pVvYc5VsWACTibqW7LCgIBnU8jStyJ9meGb8ScfHyMN54uaJTIBCR91O8AlbvnESeJbgvCKxgLs3+CZdm/+TZBkL4HsjeZ1I5azbeKXFwz2puWme/jRhTzpOgHwxHwJ9GnCUs2+85APwnv49/ZYzo87qjd/Yt041uqHglhBwI4O8ADqWUtjGvXgFwFCEkRQgZDGAYgC+KqcsPm3b8y/XsgWO35Sf2mpGmCSVs/wIAzvgS2OZ4AMBTuX2lnEa8lobqkE+LIOLk48hxnzshUrwC7g1ELrgWtR/VA5idOo/bRt9bGVnxAekb8O12N7kzc5BwcfLhEdWkiElg0nmZvVEatD0SMnlDXOOyGrF6c372r85HAIC389uZAdZEEIprCLCOVnJyaJDpZbEx2gExJ8/qKN6vtMwfzSiuzHeQNVUN7DPDMEwkltTbG0D1WiKhQbEy+bsB1AIYSwj5hhByPwBQSqcAeBbADwDeAnAGpbT4L+wB286uc90DJbxe3fBQBDYPByobzSfvqtvYUizmKNu8PrHxxo/IsxzQK/mdzN8x0ZAyk5PCrXjdruNe7JW+VW+foy4P93TW+/KIzD+Y+oKLJAzRDq/v0+lAqHE7QRG1KuEkxEU6Q7HPEjEF+MsELNpE29j5JXtsBJRKjo1bVCROasjk3XbYrbQS7VRMxGUCyInENQCwAnU4OH0dcOki4MrVqGBvCpFoOm8tiIIEitrAekqzaGXChzxRdxpTjjFnOyFUse1UbNn154mcaK1UUp1irWs2ppQOoJRurf/3Z+bdtZTSoZTSTSilb3qVEwbsxMI/KokwnYNImDEsFGNRWe+d83qf9C0uEzXu3O83Wq9djgNmibx1/RwQE3DyLsLtUE4uRQNm0hbjpaMu8WJg29nBeqkW4ICkChRoTKHCulm4Ys0XyDkTk9BaSMUVoPcIrGveypXOLCMEcY3HbBSVyiXY26YfwMj0w8KcMtZPhACT1OHCNv5ABwEJYwNmWizReN6JVJRNNo6+gTWC0MUmXQjWVOlU9ixanj3TtwJNmu7j37l9sTrZB9jlHF8Rb7d0hupOCGRj6jmY9ndJktV+xJOOVBTOiZBF3CUrXcFzj04Zz+SIPEtY2H4mOESef9TW8nRQjheew33B67IEkccrCSCTNxBUgSZiFd0ileIWik1c4yGTN/DlqBvQ3rgpvywKYMe/YGx+G+77QmCZUNrnWYYmXPPPGaBA1kv2zOzZ+LUjxr+X6MK9PfJR6EUc9zBhP0R28mykVQJgdMd92J/cz+XkZU0o2R7VydjM6yaUOcSAmmY8vv9kvKTuqs2D/a7CtdljPLOXysSjjIh8cAWHzPEoBZ3Ix3TOlVh8l9uV292G47JjONXbRRX+nLw1Qdl+iiLluWyMFQWrdrmM6y7u5DS9TCgNM8NXGZERrwwZGPHQi7VekuXk/5vbE7NoH2CfKxhFsRsucQ3sPIGzvfNb9sXPv3ubWycFBaqacGr2bz69EMM1PtRuJ7+c1uKe3KGuYGo8yBF5gjZU4Ds6xPG0eChQ8dFFe9meyazBm3NHmb9F/hVLmTsJCCiWoR5LSQ9r/CQq8pqLR20vcSetLq4x9ACifXGqOhC7dNzhfhFx8n5gV2Jhwq2Lsqe6npnccszg5K16Xs7vYkvLi+U+j/LMQu3E3bj95xt1CEZ33OdKzXJArFepochaWWOZxfFuElII0LbdmfiRDnC9g0Nx6SW7zCCOrTsewGU5h4WGgLAekr5WWJZfiAQn5Pw5IVwof8+dhv3ztwO7XcCU5ha9sW9cNvjgiGsoBRHYD8pECQWAxio7l/iz2iJOrM/tLKN4vTl3FFrBt1Jh4eWRbFztKKIzvOe2sZBYc05OfqsBDcK0fheYuzZbWOvMaCshxMXJr6OpAIyFlU6V+ZjUbgFkPnboXHJQbO210kVEPjwobk5ObRyMK664AYglcF/u1/hN5ioAhscaLHENsZSPr6r2UKkqCH6k/W3PKAgmq0Nwc/ZIVzMsTl5DBgksQ70rnZ2TV3Be5i84JXMB4kTbgPJxyzLlOzrYQRE1O3mhGaWDQP/kaD+LPBSsQq3b9FQv+wcmauMKWoMpdLCwLMvJR9AsAmC7U4FqLWaK0J7eFdZARnkpVnQaFiS1pN2l1LUTeCuvaGxlbx7rXVthtue36Svxe0ap7fZj0P42iF0wixXx2PxP3d0zhZt9sSBLnJxK/Zt+N9IlUvKDURfv5G44eil6kYRJRyjFDh13Y+f0XVI1BglH8EJ+V8zZ/xGTyIvmtleJt2R/j1alQbrOICgrIj9JHab/8vlAlU2uRwpVUZOKA4TgxtzRJoFKEYOTNxSNYg6UguDEzEVY/ttnmacEh2WuwT35w20pjfSAP1fLilBUELyo7oZ31W2R0Be4EaTrjcQBXG6dELGzitPy5q7cb7jpltNam5kjC6rL9dNImrfueN1GZfRDzy1OdMgtwIU/2R7dlj0CT2z3kjhPAGsWHpagAQDQTFaZMnk/ebQorHUhB8qv6HD89ZAdOPXYx8uQyYd9wTXb17H5bfD60H94pHbjjtxv8Gw1X/asQLXtwc5hOyJ9BS7RnY5EQ+f0r2DhzMOagxJQLEaTZ1gHr3q9PuVPan+0Ddkfhp28y7nRzGw/wX/JKLjvzv8mEtfIQFr+pptB2jgtgWORxckbRN7bJHIVahHfeG+fhqpme+srE2a7RfJw9nmeo3g1FmaaOPUGen5CPFg0+xQQXfzxQn43QQF2CxNDXOQXrCzo0dQYgRwUtFaKTxtS0mOTk7cwg2pRNxZSjQGoQIbLyfM2YtEGGpRLNdBS77ZHt8IaGNY1GpFPeHDytRXBw92yXTk1+zdMaT5Ye84V17DQ2vWVOhzP1/EvyYgjb9tECLEv1Yl0UzytOx3JxtH3BuEyE4WIRfxICgFxiWsMcdRuw+yiGaP+YzKXBm5HISgzIu/GAurm2lHFeabyF0vSKZPPaD5fvAuvjY9XlfJRcCU0+ekHLSdh3AV74LY/bA1AzMmzV6uxRNhQvGZqNAK1QOHLchVCxI49ipzFxTpUCN+pHDtgWWunIMdiLT1chMKeoDC79GMyl+C4zBjMoP1wl3okzs6eycjkPcqkXuIa6/fIjock2uUPp3XN04LbkW46YiQO2qIP951n+UHoHyP2Yk1QXTH+dbjNc4MTW968SrusxjgEPeCxyi1qsef/Rh3KpKXauG17EgDLvHhk/wb8cNUBOFD/DkYJrHi2MxAwlub6h/HqKByVuQz3JW5HI1kL9NsWqNM4QdtnE3Dyy6keic7YGNJrAABrwPP+06aGy0HHiVgcuHI1fq3/2bNHDZObk1xfHB/kR+I+xpzM8HhdMeRw9N7tJLz2ahzAOnd+Bah0xWExmmy19Xdp/tF8Qn4EHsuJrwRgF54xpn4cl/Hey5qHhd+FJSZkqNSw/YFfxmMOtWKkr0AdPlK1y1UeJEdgDc1hm2TcVWcV0gCAjJLU20WFRJ7V1XkrRgNYfujEKg8FwzseFzo4HTmao2Rn8KPaD4glMZzOtD0Xyd79htWgoRREGE7EFdaaiGmvHydvvN+64wGT8XkuvwdOj7+ONKkAsE4rnzNvZOecgUuVc5BwZHFHigWw75XYeNw2yDFktSrJmrSWRhzjh7Li5EX4TB1h2Q//+k6XRQkAIZG/Oncszs/8GdhIt6RJrwbgL3MOBj+ZvNa2i7Kn2Zw+bsn9AVPVgUj33xHYeF9TbOJcOMSDkxddYMHi6OxlWAXxdWgqh9D4cfLGhJeVKfMWyPv5rdwJZTj5Hf+K8Yd+htmUz+nutalG/I2NMa9737bSajQRbZNvJZaCvFjFq5nemAdMcUbI4k9VI9S1IepTkEEChV4Zt3/mZpxV7Q4dIbaucb/IEst1n8WfdhnELaNXTQJ9660ToUzYAKcXuXNerUKtuSZuyB0NXLIAGaVCLx94TQ8dvaK3Fc9HLnQHYKzLNagGpRSHpK/FDwOOBuCej0Q7YtoIvBNWHivv6I77sGv6dsn2FIayIvIzDdOzlJsgmZylMJg3X1zTjgq8oO5u5TM5eZ3YbnuiLb1XvBMhGNt7HowNxWnnPIUOwkGZG5CsdFvkGKDQiJBwQYVwl4vKlOFl/cDC4uTlFlyH7q5viCmGdjyJP2Uv5NQiwS0RgmwFR2SnY5M+2vyp0t32WzfaH1dnj8X1uaMtIh/TxpxSi2dgj/CADH+uY6BGiFo5jMNC9MAuHXfg5twftKYbFhwecZNeO0t8TR+LRKy4b39N9RjclTvcZlHWszqFPTfh3yLVpyZhF7VJ1HGeEYdHx9YDjHHnKV4VIGk/MX1Fh+OSrT7Gunrr8hARJ78CtUJumwKYQgdjcaMWD8s9v+W5dLaOZajHPOp961axKCsif3nuJJyUuRDos4XrnSU+sAbYxmhJRnREk+YkYijo8Os7bK9TiUKGlDD/78YJmb/jn9njsJxjXsmr00nPnSLS/562o/k70+h2YZfBNgMbzN82xav+ryyRZzn5+4Y/gp077gTgHouH8wfjztzheDx/gJ4/pi3qACaURPCbxfaDmpDJaW2qSulcGVHwSP5gtKPC1PHkayz9h0IIdk3fgT86FWleVP6E13BE+grt90E34+5Nn8RiWBvPb9NX4rbsEQA0G3AnB+t19K9JObhJhc9dxjgMiWg4ebUtUXrh1tyRAAia+mlEdC3xOOE6GClN8cofJIOID+lZg6HN1ewL7R9xLVb5ItETJ/cl2ZNxGROhk8CKQnrefpuYtMKK/c+OHZWz3HX821koKyKfRhLvq6O47wwPS+fXuCyrKUugShL5A67DkenL8TPl3oEiNKfzhLmQ+J9/Pprxr7z4+kFnJEab0RDcNvI7DOlh/l4xUiJOto4LD9gETdUaR33HUdY4q5zTgJ9MXuXI5BdUbYoF4Mf3SCOJ23JHIo2k7RMeOrKvPaFfLBkjGecz/XjNQfjPaTuiLaPpOox48iz3OSZ7GsZu9yByNZZeRyEE82gz2hzKadVBwP6L/YG99XtaB++GiVQPhxBP4syjDrWl/YoOx53537ramItrRNRrfF1z8Pxp2DdvMSNb9a9Hr9oUtujnZhqchNEkbh7T+o2zdwM96Ab8OXMuflAcTMPpHwK7nKMX5oh4KkEZr/nNlhh3wZ6u596hCahnm52c/GHpq/B0fh+sRZWtXOPX4J7VnmXGJf3t/cOXlAZlReS9YIlrWHM/ineMO2Blg2QmKvEF3cz+7Iwv8Nb+7+pl8rP9bhsPsz9iOE+4M0+6bF/fJjkn3qpN/2C98+EyFCWGiZyAVDwkYnzeyK54Ja5nPBjLQl4+6sbpewxBfZXTQqHwpZKMK4gpBG0ZbS5UcZTVa1CF1j674Jx9h2HXjXvikJEtXBUP4ObYrsUpwO6FhzgAgA+3ux9fbHwulqBRmMZF5GuaMR+W/mH7wU344tJ9UVPhthATSvX8xjVeibdUziU8LVsBo3STSoEFGw8ibjemaibNXiEacnktt2EA4dJROUqfzNwDy908KXVz8sy1gnGSk9Iv+AUkLBWHX3ZEXmS+ZRIdxxc3nweYgC40b4J0VV+9eP6nuuJXIzwKMAielXe3YRpH26MmhR7V3jHAnYs63TAUHUMsaxiDYzp0q744cedBnnm9QClfWcZy8sYicC6W0R33YceOu8y/edY1suZ7ngTHUcjtud9iJ71e2Z6260S+0rSucVfRt6ES/z5lB9RVJDysa+xzYajopjJn+R7v2qr64duNTvDML/tNeZfWC8U1xbCZhpkuT1zjm9lecSqrGT6s9DAESOvitgpGjMm230vZf1bmLDyQO0TzHGdFu2ZrDHGN9S4R8JLBYHG2ikfZmVC+dS7facckOsxEo2CurJOVyQvgt7A8jT6YUAkAcPbeG+P8/TeRrptLZIxuMeKaO492i7IUQvCj2h+jlR+xWmDmV5uKY006B5UKFj3zh2IuAr3Df/4Eu94x0RWuwbSuIYJbr4IIOW0Z7QP9MxmEhejBSahh70174b1pS2zPnJy8X1NERH53hxPMoyds512QBGQcrHitYaensfconDkrlmN71Of3qQwmwMFIeYlrTG6XOIn8KgDuu2pvPmKk6XTUkdU36QSf2zfmaDtN4szsWbZ389GM63Nuj13TRFTvCxsFNIFcIJl8znXhi4ZIXCOJjXvxd3jTicgx0SziXySRd9wo5YS3/NEg8oUd2PwYN6/3MYXgn7nj8Yf05ZghiFtjNF2l1CzLRoQZjtUg2uYi7bMFN0iboSNhxTWyk9xzKEcchnzTxvhJ1XQm8bi3BQnv5NWWFYtrePXHHA+27FePWTccggFNdiVko8+JTAYyVpl5TiLeptlYVXx7DBhWZb3q3CIgi5O3rzGnxysfWrs/zmsmpAYnv8pB5HvWpNBHN880OPkUQ+TZ7hsx6SfToRinCm6Ps4HCWNnzeu8F7HQmrs5ZXr0xqP7iLAB/3GEjAIVfLl4oyo7Ii2AOrMrEYKesGMctrhHdP8kDjyuyvfek8cZnKJDI64XbzNMoKwZxV260J0YI0kjic0bPsJTWIUssWbdxSsmrvDibDoWmIa6h3lOLZ10TCqp7ov30zzGLahc380QSgPdG0ZbW5kiVKa4JdkorUQgSaTRUuok37+DlFN15gtMnlkD3qqvA//1hKzx0/Gh3wir9JLXTGY4iifBkkjRiRsU1wn1Cdgw26XgM3ww7A1DieHHMb9HC2Nyz68/g5CsEnPx0OgDXZY/GWZmzuO95aK7RNq/66mrggGttJwknJ99cy9noAOy/mTYnRaFDSoWyIfJ96ipwyq6Dhe/NgWW4CU1cI+bkT9hpI+n6TW5OQKc9OXnilsmz+MueQ7nPZcqmIFxRUlwxgm+58+yUvhsXb/qW+beRX6V8JS672A2i3VxXic371gnbpZaKyEP7FqabvcCefIu+mvjI4K5YeCleATfRd3LypYQfG6AQgXcz5+AlvOieA6+Nzuj+b0b1R88aDoFLVAJXrnYReUURc/LOmFF5xJBGEr/0Owy4Yjl6NdTZiDg7xU2ZPHPpy3aDmrCxqRMheDD/ayzVg9H5g+DMvYfh1t9vhYO37KM/YbrHxA/68tJ98d4Fe/CL0RlJmWsYw0TZEPnPLtkHl3koN01iznLy8N5Vg/DVBiEU5fGmA3ZxjbOMU3Ybglk3HCLM7aTh2sKhwveA1V7nCWRAUyUO2Xogjt6R3Vh0Iq9aLvwiBbOxUQ3vU4/XzxYHNTO+Bxu/PqijjFA0plj5RVtIr7oKzLrhEOw3orfrnWFCWahMvpQk30+8UZAJL4Mg8eSNk25VojDVXowQYX1mzCidk3/lzF2w5ybN+O0oy3SZzcp+Ax4nX1uRwLvn74GeNYWIqCiScQW/27Y/91QcJzlzHTXXpsSB4XTaw9Kcd8/fHWftvTE/fUgoGyLvhxxHJk8p9bQ3DuKVbhJ5EfGT4ORll+f+I3pj+8GW44y/qIjHyetEngDn7jsMF+ynmVFWJmK446hR2HajRia/9i8bi8Vmi8/8YcrYfQKfGZz8IZsH9/YT9fbry/cDYOfk8wVIwAwxRp96flA2X/+rIjn7YrKLiDz7tBChIK/UW47cCo+eOBoDexQW4sPrngMnJz+yfwMeO2l7VDsdvYz28Th5D8fE7Qc3cS+FERbqADuG0tY1OpFn76nduFctRjGOhaXABkPk38nrCpZ6u3LRk8gHKN+Pg/Im8nY7eb8J8+Dxo/Hs6dYVfPyy+eZj5luTyBOcu+9w0zGGVxZPXCPaAE3xi0+4BOuCZYoPLtwTb5+7u70cvwUI94ZqKDZjCnFb+QTAibsMxqwbDrEFl/KC89sXSqP/uudQW2wXHvysa0SiIylrpYCoScWx96buk5AsFEVM5M17HBK8QIBu8PptcfLWmBlT5rx9h+OkXcTiXT+wtWkhlCUy5d2cPNumUmGDIfKP5A/GC/t+DDRaMlg/khokwJQpxhC+BzD6T4K33uIa/7rZUgx4K14NTt584zFJjb7lWSLPLhwm7cPHbaOXJ6d4Bc1jox7VZrwYQDPZ3GMY79pEvWhfBoyYJwrZK/i8y/N+7xLXFEhPLzpwU3x68T6eaSj1JgqiU528D4LgeQk2iZgiFteYiPOVmABsjeX1m8d4mfbuRPMbCQMaJy8xPiYnH8nkQ0Wj6RFJkEnyY78AsCwACoSvnTwhwCCBjJrYiXxQ8Lgh44nQc9ChQzDS88oyDFQoZTYzUcGGlZLP6rV0JJb4zMhy7n7DfUVQnm0AMEgXIYRB5J1wErwixeC8GgrOKXIGtIlrChiTUugZFCKhQ4h7n2zYspzwUogTAFvopq7j/7anVB0iyNrJG2vDz/IsbJQ9kf/6iv3xB0FsbWOyrzr0X8Bp44uqJ8YQQiFEMyGhEaQ5tLCjL//I672SDWKQU42YHETYRGOxaCaUGlhvTm6gNx+Z/Eqqc+7Mycoqz7vtMlzTwCbtmF+IuCZofU6iX1LFK+fZlkwMGrHMuuv0BCIoROJLCoKrOcHrn6Uns54Zc4tNP6hntfdG7TMf48gFlMmL/C9KM3PKnsizEH2q7LBDgIaB7vQBOB4Xod3qj0DjYOEGY0N9P5ycuQBnZ88EEJxI+DFDPKLZ0lBpq8ubk2eIPEcsZSvd4Mx9ZPKT6cbA0f8F9r/WfCZ15BXV63ppiGuKZ+Vdilbf9HL9OHufYeY9stKg1CWXN75/S30F/n2y+35YwKl4ZfLXtgD7XGGlc/WV2P4NE4bidWTHQxjZ8SA/keRY8rh2r9OgMzl/lsjVfW/+MLmkeU2ZLNIDBr1/QBZlF9aAB5Gy0C9aXZA7Oi3xh57nN/cBAK5XKa46fHNRNhOs511wmXzwBfjAcdti/PSl6KsTe8WDkzfeiRSv3LtyZa4V3OTAwO0WtdEF4wYlpmmFMkpB88kmP3+/4Th/P//gcKfvMQQPfPCLR4VajX/dcygGCRz4hH24YJr27+uvezeiBEymIZPn3pp18lhgzmee+U/dbQgufuE7AAKDAc4zp3jSfB5w0T158vbAU8AKWoNP1S0kZfIaAxTJ5EsAX0Wd4HlhJpT254pCkPJxrS8WlserfJ6eNSkcsa1laWTk5RVhmVA67ORrOOIlg8hL3bVaHDy/z8gjAQA/U0u5ViijFFSxGvapu1etJZf26oKfkKsQFHoZuQwU4sGgDNge2OVsz/xHbz8Qtbp4ysuCjIdiv9HmujMd7zYvITh28vY2ReKakkE0uGE6Q3UmnPHkZSaPmcKRdq7abFnXqMCBm2sef43VSeDsr4Exc+wFmeKa4FPLzzwzEEYdB1yxEot4F7kHhOxJydooSymVd49PS52/clKefggUtyXoEiF8b+xCIMvJlwpSNVGDk48Ur50GK0a093sZ+IU16AyI5rSf7bWWWfuHXXM3bfY8Ds5cb+Pez99vOL66fD/NfT1ZDVTU28fJuPCco+NgwXN/D7okPblMQgBFsVnXFLrm3Xbw7oI+v2Qf/OvE7YwEoUJkGXPqboPx3ZX7m7FSSiHStWTypUGx3KvRZV6IIt7+YY1RuD2S6ocpk+9ccc0GIZM3ICIKYcjkZUz+Sg12kRvmmKm4IsnJuxdza6oP1iBjLqC8SqEoxLwdyqyXHafhBwJHPglsIr7J6smTt8cwQbRQGQQhDGEos2Q4wt51FZi5bF3RdfHAVk8pNS1oaisSqK1IMCcgcV9FG4V8G4LN79fP3hUdWf+4RMUuG6PPxSpefWpxP4ppptnTVc2wQqq4IXsB712NcflR9tJLzBhuIETeWxwjOl4XwsmHIcP0uyQkGIKJGrjWNabiVbKgEYd6JtnNw9EJkB9Dme8Thp28k0sUMgU+J0NZ+BGgo7YbgI5sHsfpAfRk6itWchE0uyGz9kMhRgMsLOcmjriG5wxlmFDKFO7Vtop64MQ3cPr9C3yTmui/LQZ1PC1Tc6jYQIi8BiFRCIEJl5ItGja/MbEX3//9YSscuhX//lg/7DeiN6YsaLXF9JblwIxUftY1xeDOo0eZijJuGyS/Q2efmWQJUf9GzVJpn82Cx+Pxgo0LhxY98pTdhrjSebpo+Izapn1qMaiHfGjtsFDs5mNegMIpx2S8OAMTipJz0C5YA80qqRA9jGHuWmrVwQZB5IWDSH3e69ifE6nQCUVgXWPDJgcDu54H7Cy2Gjh8634FT8Cz9x6G43bcCD1qUmjd/xa8cs+Z+Cy2pVReGY5IRORlab+sG3mpj69BIRubZkBTFb6+fD80uO6dDQ+FEiy/JG/psYNmLFlbUP5CUbxMXhsQL/8Oe3q9XpnCD7pR87gdspdv0kKMyXYdxr+0PmyEonglhPyNEEIJIT2ZZxcTQmYQQqYTQg7wyt/VECtetSmxscTdnH43QwHQbMf3vdJSTvLaUsSkVxSCHrpCU63rhwuyf0UOcgRH9TjGGmslL5B9+NHkC/Ybjp2H+oeN4Dla8dP5FhUqgphQNlYnQzeFky3Pa3OUbZGoqlJZDEnEofOE0WfeSZpbtiRjBwBoHAQc+TiQkLBekiiuq1A0kSeEDACwH4A5zLMRAI4CsDmAAwHcS4iPC2QnwLkGLGcob5m8aEKwHJtfqOHOhtV2yelnHnu9ZPKFcfJn7TMMT5+6o1w7AsAY6636y8l/C4WbgJR2SbtqYxWvhZbJFFLIHC3VxlqsTN4ANxyHl+K1K6xrGGzS2zI8WB8Ur/8H4CIALzPPDgPwDKU0DWAmIWQGgO0BTAihvsDwG34faY5wQrxznhUeNyx737BgmpYFo/HeMnmBsUQpHWZ4cDbx6VN3xMq2TMnqc1pudPZJgoUXgS7FVyh1X8NSvMqKa0qFoDW9fd7u/olCQlGcPCHkUADzKaWTHa/6AZjL/D1Pf8Yr4zRCyERCyMSlS5cW0xx/CBZIoR6M1Uy8cSlxTSfCFL8EPOqzyY3NbaTOKf9mm8IUwrIIulCMsa5OxdG/sbCLK2QguCa20+DLpMiYUDKFVHkov53wO80Wi6LLlTiBlqTeEMvrcsUrIeRdAH04ry4FcAmA/XnZOM+4M5BS+iCABwFg9OjRJaGR/iZvPuIaiXK7mhCIIDt/LJm8O0e/xkrP6wfDPm76lVcq928ROvN6Py58+isjejCKOGq7ATh772EeZXUuiufkdcUrZ/1JE6EQUIz4p8svDaGU7ksp3cL5H4BfAAwGMJkQMgtAfwBfEUL6QOPc2fCL/QEsCL/5wSC0oBRtAgItDc99XRS7pqsQlAPzEtf44aAteDxAAQhYd2eNdXcSxRXa5+G689n5+w3nX/Ttg5IpXjljG+Q6POrByZtpOCu/O3HypUbB/Cel9DtKaS9K6SBK6SBohH0bSukiAK8AOIoQkiKEDAYwDMAXobQ4RPitFxEnz7dA6V5f2Vg8LfVy16fxxDsHb9kCANhxiLdlzJBmf+ujMDGibx0AYPSgxk6pz21dU2LFq8fJ4ZCRLZz0/mXeftTWePLk7dFLIs4Nr+zOEtecuPMgvPjXXQKXw704h9Nmw0LMmf7CAzYJXOf6gpLYyVNKpxBCngXwA4AcgDMopXmfbF0GMSfPf08IAZiwu0D34vYAoKk6iTuO2ho7D5W0xeU4lew0tIenmKZU8FPkbjeoCZ9fsg96ByVYBXKj3eXbHr39QDM0NA9eXH5tRcLX0xgQb2ClGoHQYtdIFtOe1chQXaXdtPiMvTbGzW9PL7gdYWyCpRrj0Ii8zs2zf18L4Fp+6s6FWOYulkMHhaF47S4EAQAO21peUeplJ99ZML6DjEgiKIEHCrcCcn7SmgCKyzBgERCB4UCIdSUcxuWBTXEDIqzYNbz27blJL8QVguN3GuR61+jhsHbizoPw663cJyYvlDryaDHYIDxeDYiIh9gjlr8JcMU1CsE5+wzDfhLesd0RMrLNUqObSbxMsATktiO3wo5Dig9fHKh+yc0vDFPW/o1VuOmIkZgyfzUenzC76PL8MKRnDcZPL9yqzugxj7nqXVeBGdcdzM1XmRDrJa481P+SHye669wFNhAi7yuOEeQTi2v4z8+TuOWnu8KSyXddG07dbQhmLFmLY3fYqCTl7z+ieAXxb7fp75/IgVfO3AWLVncUXbcIYX+zI0cPwDe9a/H4hNnYa1MtDk+ppsWYgzbFbsN64qTHviwov1fsGi+E7pVcRN4KfcOpryxNOIwNgsgbcNoR+3GvYsUrQXewiN9qQAMmz10VSllWb7qOyjdVJ/HQ8aO57+orE1jdng1cpnF13mcX7+MKkdxZGNm/ASOD7w2BEaa10dYDGmz6mFJt/sm4Ym4kgFtWLgvZE+i75+9Rkg23mE1jl4174IpfjcDvR5dmkmwQRF40/IkYQTYvXhm+d8B2MZ1/6a87h9aGYp1ebv/D1pi6sDWcxnDw9rm7Y8frxwXOd/FBm+HigzYrQYtKh5ggtLFY3Fh6R7zO8k04Y6+hBeWTbd7GvWqkYlEFrr+YvITgT7sODq0tTmwQRP7Pew7F1IVrcPgouyLy5TN2xTs/LBK6P5cyNHEYIISEyGEVp3g9fFQ/1/iGiT71FRjaXI2fl4Z/MccDx22Ldelc6OUWit2HNeNPuwzGo5/MBGB9E+GlN53UrrDw2lm7mlYuBn669iAopPDrADvzqj8eIpl8F6OlvhLP/nkn1/MRfetMm2seROF3D9uqL56bNK9bWdIUC7UbKF798MY5uyHncfIqFAdsHpIzV0iIxxRc8esR6FGTxLYbNWL2cm1j81W8dr0EUQpb9HMHlEs4jy8B0dXzVvak8+Jfd8b8Ve0lbo0dGwSRLxSiRXP9b7fEJQdvVvTE7E4wTBI3bSn8Wr5SIxWPoZOtF7sUZ+y1MQCYRF6ErQY0AAA268bfrtToxryJDaMGNmLUwM5x4jOwAS2Z4BDJ5OMxBY1dpMQrFbbdqBEv/HVnbNW/oaubEiEgDt6yBR9dtBcGNJUuSFt3xZMnb4/nJs6zcdLjLtgDS1rTXdiq7oWIyHPwu236Y9natHXBwHon9SwM23QyhxFBDkZoikE9xdfzbYgEHtDuC3Z68g5trsHQAkJtNFYlsLItmAVXv4bKThe/BEVE5Dm49citAADXvzEVQPc5Cu4/ojfe+WFxVzcjQidj9+HNePqUHbCDTwyhzkAZqaFceO+CPdHaEYzIv/jXnfFDCa3KwkBE5D1QrQuAqwqI2lcKPCiwIY9Q/th54865D9QLVx+2ebfYaEqFxupkYDFsr7qKwEHfOhsRkffAabsPQSKm4OjtB5a8rslX7L/+2cJtQLjpiJEY1EMsLtkQcBwnBkyE7o+IyHugIhHDX/YszDkjKOo9AiZF6HocOXqAf6IIEbohyscGMEKECBEiuBAR+QgRIkQoY0REPkKECBHKGBGRjxAhQoQyRkTkI0SIEKGMERH5CBEiRChjREQ+QoQIEcoYEZGPECFChDIGcV6J15UghCwFUMztwT0BLAupOesDNrT+AlGfNxREfQ6GjSilzbwX3YrIFwtCyERK6QYT4GVD6y8Q9XlDQdTn8BCJayJEiBChjBER+QgRIkQoY5QbkX+wqxvQydjQ+gtEfd5QEPU5JJSVTD5ChAgRIthRbpx8hAgRIkRgEBH5CBEiRChjlAWRJ4QcSAiZTgiZQQgZ09XtCQuEkAGEkPcJIVMJIVMIIefoz5sIIWMJIT/p/zYyeS7Wx2E6IeSArmt94SCExAghXxNCXtP/Luv+AgAhpIEQ8jwhZJr+vXcq534TQs7T5/T3hJD/EEIqyrG/hJBHCSFLCCHfM88C95MQsi0h5Dv93Z2EBLh5mlK6Xv8HIAbgZwBDACQBTAYwoqvbFVLfWgBso/+uBfAjgBEAbgIwRn8+BsCN+u8Rev9TAAbr4xLr6n4U0O/zATwN4DX977Lur96XxwGcov9OAmgo134D6AdgJoBK/e9nAZxYjv0FsDuAbQB8zzwL3E8AXwDYCdoloW8COEi2DeXAyW8PYAal9BdKaQbAMwAO6+I2hQJK6UJK6Vf67zUApkJbIIdBIwrQ/z1c/30YgGcopWlK6UwAM6CNz3oDQkh/AIcAeJh5XLb9BQBCSB00YvAIAFBKM5TSVSjvfscBVBJC4gCqACxAGfaXUvohgBWOx4H6SQhpAVBHKZ1ANYr/BJPHF+VA5PsBmMv8PU9/VlYghAwCMArA5wB6U0oXAtpGAKCXnqwcxuJ2ABcBUJln5dxfQDuFLgXwL11M9TAhpBpl2m9K6XwAtwCYA2AhgNWU0ndQpv3lIGg/++m/nc+lUA5EniebKiu7UEJIDYD/ATiXUtrqlZTzbL0ZC0LIrwAsoZROks3Cebbe9JdBHNqR/j5K6SgA66Ad40VYr/uty6APgyaS6AugmhByrFcWzrP1pr8BIOpnUf0vByI/D8AA5u/+0I5+ZQFCSAIagX+KUvqC/nixfoSD/u8S/fn6Pha7ADiUEDILmthtb0LIv1G+/TUwD8A8Sunn+t/PQyP65drvfQHMpJQupZRmAbwAYGeUb3+dCNrPefpv53MplAOR/xLAMELIYEJIEsBRAF7p4jaFAl2D/giAqZTS25hXrwA4Qf99AoCXmedHEUJShJDBAIZBU9isF6CUXkwp7U8pHQTtO75HKT0WZdpfA5TSRQDmEkI20R/tA+AHlG+/5wDYkRBSpc/xfaDpm8q1v04E6qcu0llDCNlRH6/jmTz+6Grtc0ga7IOhWZ78DODSrm5PiP3aFdqx7FsA3+j/HQygB4BxAH7S/21i8lyqj8N0BNDAd7f/AOwJy7pmQ+jv1gAm6t/6JQCN5dxvAP8EMA3A9wCehGZRUnb9BfAfaHqHLDSO/ORC+glgtD5WPwO4G3q0Apn/orAGESJEiFDGKAdxTYQIESJEECAi8hEiRIhQxoiIfIQIESKUMSIiHyFChAhljIjIR4gQIUIZIyLyESJEiFDGiIh8hAgRIpQx/h9GjQMHrZ/lPQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(tsne_df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "29c27824",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 412x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.FacetGrid(tsne_df, hue=\"label\", size=5).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()\n",
+    "plt.title('With perplexity = 50')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "175b72e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yukqBwCGKi+YzE6R4iUY8Sy/gdeOa88YkxXjl7cpkIqMEJqWAqa1R9X2VpT9WUFrD0x5cj66eZGs3HKVYV9+MbffNSaxj8QufMl+3lW5V8j1FlrIgW8iohUspXUopHUspHQ9gAYANlNJ/BPAagJvjh90MYG2GligQ9Al3zp2MgDfZ+gt43bjxgnEYExcZpT3r97ow4/ThSdbjmMIAHrv67CRBZF1XiVLAWPW1alKtfVWWBfG2P/7WHqbYEgCzpoyyNMzgqmljUP+DS9B8xQy0zJ6GXV8vF2IryBiZtnB5LAfwIiHkXwAcBHBthtcjEKQVdSJSQcALQoDnNx9ESWEA/3jBuCRrVo7BPnb12di0ZLbhdatf24m2YLLYqa1hM2JqtR+yHXjroICmXlcMMxD0Jwh1aOB0ppkxYwbdunVrppchEKQMK9GI51oeUxhICK7R0Hej/Rcv36Drfva6CB6/9hxdcTO6x7kPrNcIv/K13Dl3Mje7mZfNrHwPBP2S1Icw9xOE4AoEWYaR8CkhAP62fB5TpANet8bFrIdRRvGwId5EnNXs+eo1rNl2GHe+tJ2ZOS0fz4tNs9Y1s+gjXD3xzxgZaEupVaYgowwawRXDCwSCLMNKnFR28Tox9F3OKObRxom/yphZw1XTxuDxa89JxKXVBMNRbNx9lBmbVp8zs+gj3Dx1FUYGTgCgiVaZzS0i5UOQnWRrDFcgGLSYzVhWxmCdGvouDx+wM8+Wdy/1teSs5TOWrGO6yZvagtw6X6UFffXEP8PnTn4IMGqVKRBkEmHhCgRZhlHGMisjmSeGdpKcWPf3ughO9UR0s4N59yKApeN529U1vSP87PbqvBaaAkGmERauQJBB9JKM9JKP1Bj1N7YCK2O6qyeSKN3hZQfPmjIKf9h8UHM9Gr+Wev121qy0fDdtWs5tlSkQZCNCcAWCDMHqn6wUMiulLvKxD9TsTAijz6PvwDLbuvHi5Rs0mcXqJhhrth3GKx/za2JZ7mY7DxZK9FplCgTZiBBcgSBDGA1yt0O3YrZtW5DfL9lI7JWYiQ8bNc3QcxPbfa1ynLZx/xPoDjWLLGVB1iMEVyDIEFYTnYxqXI2yhJXndoUizGNvf3E7Fr/wadL19ebfKuf18jDr2jZ6fSx4rTIFgmxECK5AkCEKh3iZrQ4Lh2gn2ZixSHmiJx+rPJeHeuYuwI+1zpoyynDurZsQU7XAVixugaC/IgRXIMgQvJ4zrO1mrFc9zA6DZ11f7uKktj6N3MhWGm/wXt/tL24HIERXMDAQgisQZIh2TotD1na9GlcjKzMV5PuyYq2LX/iUe94YiwlQvNcXpVRYuoIBg6jDFQj6mDXbDmPag+u5M19ZCUa8pCM3IYZiy5ubO2yIN1HTama2rtl9cm9jKwKpdx+rHbMEgmxFCK5A0Ies2XYYd768nTumjpdgxGuGwWrmrz7muzNPY557f9VUbFoyG39bPg8/v+4c5jF6yU68Ndmp/TUaI5jqWECBIBsQgisQ9CGPv7UH4ShbJPUSjNRdlnj9hZXIxzx81dmGc3N519ezUu2cY3QtO5a2QNBfENOCBII+hNc/GOid/GOFZWt24PnNBzXXHDbEi/urpva7uKcTU48E/Q4xLUggEDiPnqXGKgfSQ+7uxBJweUA9q4dxNuOk1SwQZBsiS1kg6EPunDsZd768nelW7uyOYM22w6bFxagsJ9WuVX2JnaYXAkF/QwiuQNCHyCKy+MVPNfW24Rg1JZBmujvJ9IdkI17Ti61ftGLj7qNChAUDBuFSFgj6mKumjQEvkHu4Lcgdfwf0ipMZsQX6R7IRr+nF85sP4nBbEBS9ItzfXOQCgRJh4QoEGYDXnxjQb2to5EZWoizRyaTL1ujePCtc/Uxi10Xete0ITr51ANG2ENyFPgydOx6500ZbfRkCQcoIC1cgyABGdae8Zg96lm1hwIthQ7yaZCOlVZwua3HNtsO4ePkGzYB6M/e2YoVbdZF3bTuCttX7EG0LAQCibSG0rd6Hrm1HLF1HIHACYeEKBA6xrnEdVn6yEi1dLSjKLcKi6Yswr5Rd5qOcBcsTUbW4rNl2GARsb7SbEFR/m10GlI4xgOp18QYP8O5d/drOpAH3XjdJSiTjvU6rLvKTbx0AVYwsBAAajuHkWweElSvoc4SFKxA4wMObH8aSuiVo7moGBUVzVzOq36/GusZ13HOumjYGm5bM5javUIvL42/t4dbwyj2HWVar1TGAVtETdN492oLhhNXbFgwDFEnW+Y0XjHOki5Vs2ZrdLhCkEyG4AkGKrGtchxf2vKDZ3h3txspPVhqeb7ZFopFA8tzQPKsw1YQq2Y2sZ6GbvUc4RjEkx4MV158LAHh+80H4vS4UBrQuciu4C32WtgsE6UQIrkCQInqi2tLVYni+UbMHWdjM9IRjibKTPY9lzGRLywlSZtsIya5o2fI9cSqMUCSGFdefmxgRyIoT6zF07ngQb/LHHPG6MHTueHRtO4Lm5R/i0JI6NC//UMR1BWlHxHAFghTRE9Wi3CJT12CNvwPYrQ71cBGiaZ6hjBc7laVslC3tdZHEPW7TGeOnhDX5SGm12xlQL8dp1VnKANC2el8ivhttC+HEC3vQXrMfBVUTRHxXkBaE4AoEKVKUW4TmrmbmvkXTF6V0bT1hYyUW8ebH8gTdLobxX4VZO0anBEom4HVzX2dTWzClxK/caaM1Atq8/ENNMhUAxE5F0LZ6X+I8JxHlSQLhUhYIUmTR9EXwu/2a7ddPvp6bpWwWnrARADdeMI65ry/mxxrFZsNRmlgDy6XtdZFEktSwIV74PPyPIhchpjO5zaKXNCVnMTtJ17YjOPHSnqTypBMv7RFu7EGGEFyBIEXmlc5D9UXVKM4tBgFBcW4xllcsx7ILlqV8bb2Ep427j3LPS3dLR6M6YuUaWDHqx689B9vum4MV15+L7nBMylTmoDfz127il1HSlNNZzG2vfQ6oDepYfLtg0CBcygKBA8wrnZeyNcvizrmTmePq7pw7GYt1YqMlhYG0dpcyU0fMWsOK689NWoOVzllqUkn8Gjp3fFIMV43TWcw0yH6NvO2CgYmwcAWCLEYvg5ln3REAs6aMSnt3KbmO+Mnrz2VmQZtZg11LPNWxfbnTRqPw6okgAa2VLmcx9xUiQ3rwICxcgSDL4SU8saxfOba7cffRtHaXUq8P0GZBm0l00uspzWNMYSBRJpQKcjJVXyQzuYZ4EDsVYe6T203KaxIMXITgCgT9FL1ynzOWsDtcWbUozbqlWQ8FPJf34bZgonSJ9dCgR6r1wyxYWcxOU1A1ASde3gsw5iADot3kYEEIrkDQj5GFThbGxS98isff2oOCgJeZiCS7oc1YdXo9ks1YyXrWq3wdAPB7XbqCK5c/jenHM3HV9cAsRLvJgQ+hOhmA/YkZM2bQrVu3ZnoZAkGfw2qO4XUTgEotE2UCXjceu/psfBNeTcIQ8bpQePXEJNHltW0sDHiR6/MYWr1GTTsKA16EIrHkdbsIcjwudPUknyOvvT+KrZrm5R8yxdVd6EPxkvMzsKKMY7YZWb9HJE0JBh3tNTXYN7sSDWVnYd/sSrTX1GR6SSnBipWGoxR5fg8z2Upvgo4SM4MHDrcFcdsLn+LcB9ZrErLkhC8ebcGwdt0xilMMgTZbW8wbE5hN6LWbFAxshEtZMKhor6lB8733gXZ3AwAiTU1ovvc+AEBBVVUml2YbrjCeCmPbfXM02826NK0kNLUFw0luYmXct5Dj3ubBc7oZxZ9TdYH3Fbx2kyJ+O/ARFq5gUHFkxZMJsZWh3d1ouvMn/dbatToNyOwEHTPNLZQEw1E8ULNTUwrU1ROB15XsNQx43Rg2xGv62oAUx9WzWvWyorON3GmjUbzkfIxdXoHiJecLsR0kCMEVDCoizeyex0CvtdvfRNfqNCCzLk1WDbCRSJ44xXATc9zb91dNtSTogH49cbrn/goEqSJcyoJBhae4GJGmJu5+2t2NIyuedMS93FC3EXWrnkPH8WPIHzESFQtuQlnFrJSvq8bqNCArLk11uY/V6UUyPPe2et1doYih+5lXT8xzgac699dJlNnhcvq1cCkPHkSWsmBQoY7hMiEEZQ27UrpPQ91GrH/6V4j09MZFPTk+zFl4a1pEty9Zs+0wHqjZiROnkoUx4HXD53ExBdNNCGKUoqQwgFlTRmHj7qPMhwOzgk4A/G15citN1rnZlN3cte0It50kK0t8EE0XElnKAsFApKCqCsUPPQhPSQn/IEpTjufWrXouSWwBINITQt2q52xfM1u4atoYbLtvDp68/lyNm7j622w3cZTSREz3D5sPcts9qt3YbsL+LGZZrXptMLMBVna4jDpLXBZn5XShttX7RAvIfo5wKQvSTrY9qRdUVaGgqkrX2k01e7nj+DFL2/sTRgMJAKD6tZ2mM5PVLmKlG5tntfLi007P/XUSo8YWyv16pVsD1ModFAjBFaSVfav+Cu+2GDxESrbJpr6xspAeWfEkM66bSjw3f8RIdBzTjs/LHzHS+kKzCLOlN6EI25LjwSs/shqfzjZYMVseyixx0Y1qYCIEV5A2Guo2wrX1FDyeoUnbs+lJXbZ2G8rOYhaA6mU161Gx4CZmDLdiwU2215oNGJXe6I3r04MAif7KaoysVic9KE5fKylmqyO26ixxd6GP241K0H8RgitIG3WrnsPled9n7su2J3Vl9vLhwjzsKR6Obq8HgRgF6jaaSnRSZyVPvawSjds+SnuWspKHNz+Ml/a+hBiNwUVcuHbStVh2wTLHrs8rsZEtXbuzbSlga5KRWtRS8aA4eS1AP2YLgJul3LXtCGiP9n0U3aj6P0JwBWmj4/gxnPKfRK63QLMv257URy++Dc333odDfg92nDYKMZeUTxh0E6x/+lcAoCuW6qzkjmNHsfOd2j7NSn5488N4Yc8Lie9jNJb4PlXRleO2PCPNTYgpsR2j073KTr2sk7FOp+Omeg+VvL7JvExmEnCj8NtnZoVXSGAfkaUsSBv5I0ai/sQ7iMSSk2ciNJx1T+py9vLesb1iK2MmuzgbspJf2vuSpe1mkeO2PKEMeN2IGpQXBrxuPHn9udi0ZDbGWOyMpYfdWGfXtiNoXv4hDi2pSwyAdzpuqvdQybsmzyp2+TxCbAcAQnAFaaNiwU1oCjfio2NvoCvcDkopuiInEZ7mysoPj4KqKgQ97M5HRtnFVrOS22tqUH/phdg1pQzvnl+GpcsuxrpG9gxbs8Qo233J226EPAjgthc+5VqvcukNT0SVx8juYrOdsV5pacWM93eieOOnmPH+TrzS0qq5ttk2lUp4JTckwP7Z2/XG6D1U8q4pkqUGNsKlLEgbsiu1btVz+PPh/07EMSdXfD0j6zHT+cludjHvvEAkioays+ApLsboxbclypEOLbsH3pBk+Y86CVy3phXPRO4BFgLzSudprmMGF3ExxdVFrD9Xm2lAQQBsWjI78b3ZphNmMo9faWnFHXu+RDA+XvBQKIw79nwJALimaHjiuKFzxzNHDeqJHc917PJ6AK/L0rX0yJ02GqEv2nFqc0vSdr1r8pKleA8Dgv6FENxByN4tLfhg7X50toaQN9yHC+dPwKSZRWm5V1nFrKzorMSKsbJis6zsYgAIh7rRoJM8xTrPHYth0qGjAKVJdb1HVjwJVyjZze6PAN/ZEMLDM1baFtxrJ12bFMNVbrcKKxtZjdIFbLV8xyjz+LHG5oTYygRjFI81NicJrp3JOzxrMXYqgmHXT7Z0LaOs5uFXTYTv9ALT1xw6dzxOvLQHUD030VAUXduOZKVnSGAeIbiDjL1bWrDx+d2I9Eh/0Z2tIWx8fjcApE10swG9GKtSROWva3//NEKdHYnt3R0duslTSmu+4/gxBCJRTDp0FGPaOhPHyHW9vFKjESeBlq4W5j4zyIlRTmQpGyUwsVzATjadOKx6INHbnjtttCUh0iu5sXIts1nNVq6ZO2002l7dB9qjUtwY0F6zXwhuP0cI7iDjg7X7E2IrE+mJ4YO1+we04BrFWNXuZlZHQZZAK1Fa83p1vbwBCseHAkW5qf0Mll2wzJEyIL1ZuGMU1qu665RTTSmGERdaGe7xMT5rI/1YsNzQABALRSxZkXayms3U+WrEVl7fqYipdQmyF5E0NcjobGW703jbBwq8GGz+iJEJd3PHMcn923HsKLo7OpjHm23N6Cku5m4fvfg2xFTC0e0BXp7tw6Lpi0xdP93wEpvkTGNZbNWzb3mj86ywZtthdH/WCkSThScHwNJS9vtqhdxpo1F49US4hiTbGzQYtdSv2GqCk+iPLBCCO8jIG87OjvSH2/rdHFgrVCy4CZ6c5Ncud35iuZt5mG3NOHrxbSB+f9I24vcnEqfGPvwIwqMLQQEcHQq8eNVwzF34iO34rdOYGQSQroHvj7+1B7FDXfB81gYEI5KnIBhB4eedSfHbVMidNhokR5uIRMMxnHhhT6JUSA+rGdJ6FrESXoKUSJzq/wiX8iDjwvkTkmK4AOCKhlC671U03/sZAHvN+rMddYxVmaX8+lO/MHUNK60Zk/o0x93IstjK+8sV7/OlVl5MH2EUk3WygQXrfE9LEJ6W3muxfQ720Su1MdNlymqGtFmLuPDbZ2oTp1zSdkH/RgjuIEOO09Y9/T66PQXwhVoxofE1FB3ZCgo4Nnw9G2FlTDfUbQQhBLy50MTlAqXUVmtGuU/zQGTNtsPcXvypDnznxY9dhHD7LduBlzwlYxSPtZohbbY/sp3Ma0H/QAjuIGTSzCJEv7fM0Wb9/RE5dktj/MYQlFLcvqp/udrXNa7Dyk9WoqWrBUW5RVg0fZHjrmpem0cCcEfnmWXWlFH4w+aDmu1RSjWTiVIZNsBLnkq6p0HDCSsZyFYsYquZ14L+gRDcAU57TQ3TrcnLlOUl+9jFTLMJJ86xg5nYrV7Mtq/WaYV1jeuw7L1liFApo7W5qxnL3pOylu2KrpyJfLgtCDchum0cKZCyBbpxt7aBiIxydm6qwwbUliQLtfWZisALy1UgBHcAox6wrmy+IDfrVw9fz7vMuWii2WYTqZ5jF6OMY72Y7V/+57+w/e3Xe6+VxnVa4bEtjyXEViZCI3hsy2O2BFfdccqoZ7Jei0ezGMWA5f1OleUULzmfOTRAbX06MU1Iz3J1cjSgIDsRgjuAObLiSY2gys0XJm6oxalPPkHbqheSXMvtr67BkOnTHYk9mm02keo5duG1YwSA/JGjEhar2pItnfa1JLFN9zqt0N7Tbmm7EWY6TsmwmmHYQa8GWN4P2C/L0RNMPcFzYpoQT1R5awt90Y7Q7hNChAcIQnAHMLx4rLy98513NXFcWZCdEFyrDf0b6jZyBdBs/asVeEPilSP1WBY3S2zTuc5MYibjmACONr24c+5kbh9npahbHdJuJJhGcdNUBwuwRPXEy3vR9trnoEHta6XhWFIf5mhbCCde2IO21z4Xo/r6KUJwBxDNLWvRuP8JdIea4fcVY8g38uF/+6TmODlOyxXkpiZNw307WBkEIAub3rWcRq9USMZKjW661mmFQl8h2kJtzO1GsGLSRtbmmMJA0gADJ1D2ZVbGjceoRN3Jspzm5R8aWpEk4GYKo9n6WObovShlXlMPuUEHYN6VLcgOhOAOEJpb1mL37nsQi0kfjt2hJvRc5cXQLjeGvK/4QIo3XwDATZwCoGm4b0d0eRYkKy6qJ2xW6l+tYjRcwarF6uQ67cT0lpy/BPduuhdhxQxir8uLJecv0T2PFzv/4dwb8WjXEENr02nM9GV2qiwH6BVjvbgsIYSdmc3qA6pzDyew6soWZAei09QAoXH/EwmxlYmRMDrm9SS+J0OGAH4/mn5yF/bNrkTeZZdquiGpkV3MZmmvqcG+2ZWShXz/w7jkaxcjf+QogBDkjxyV5K5VoidsvHP6AqsWa92q59BQtzHl+9ptAzivdB4euvghFOcWg4CgOLcYD138kGHCFC92HvygJmnerTsuLqzOU5kgd9poFC85H2OXV6B4yfm6AjR07ngQr/FHHqv7E8DvZWy2x7Hdubo8xIzc/oewcAcI3SG2ezha2Gvd0lOngFOnAEhu4/ZX16DgH65C5zvvSu5lTgaq2dpcVlZ07v/+H65/6EFDC1ntfi6c0I6SmUfgzYug1XUfmlvuQHHRfFPrcBKelT71sko0bvtI4zJ3Kls5lQSdeaXzLGck68XbF5qcAtQX9b+pwLKIrcRlrcaM1Zip+1Ve0zdlGIIfH+Ee77SAC9KPENwBgt9XjO6Q1j3sbuWfQ7u70fnOu5i4oRYAsG92ZUq1ubys6E/+6z+x783VzDhpIm6oEttxlzXD5ZUeALpDTdi9+x4AcFx0eXXKMkZx3qdv+b5GdJ3IVk41QccqVuLtLNY1rkP1+9Xojko//+auZlS/Xw3Afv1vOlAnRsmxWzUsMbMz7F59b6BX8F1DPIh1R5JaOBKvC4VXT0wc23V6Adpr9musaCv3FWQPQnAHCKUT7kiK4QIACQH5a/UTOpTWK6s2VxnzNYJlCR8uzMOOPA9i8Q9zpQUIgDnsvWTmkYTYysRiQTTuf8JRwdWrU1aLLk88rWZimyVVa8oqVuLtLFZ+sjIhtjLd0W6s/GSlY4KbjjpVq92fgNQaV6gF3+g1yceLGt2BgRDcfo4yM9njKYSL+BCJtsPd5kL+amDIVn3BVVqvRg33jWAlYe0pHo6YKzluJluA8tdqvHnsmBjPbW4XvTpls685VcuQR6rWlFXMZGzr0dLVYmm7VZxoOsHCqogqBbNr2xG01+zHiRek6Ugk4LZcriMf2/ba54myn/aa/SiommB7iL0gexGC249RZyZHIifgcgVw1lk/x5CPXGj+7D5QdHPPZ1mvqTTcZ1nI3V72r1jH8WPcmHG404OcfK3o+n3Otp00qlM2Q6qWIY9MtAE0ytjWoyi3CM1d2vetKLco1WUBcKbpBA87Yta17QhOvLwXiPb+DtNgVJryE7+mGau0a9sRzWSg2KmIdG2Isp+BhhDcfgwzMznuer24qg5AsrWad9mliQSpVGtsWSRZyE1NgNsNfziC7hyv5tj8ESPR2XqcOTig+aOv4IxvtCa9NpcrgNIJdzi2VoBfFmWln3SqlqEe/cmqWTR9UVIMFwD8bj8WTV/kyPX7OqZtxMm3DiSJbYIYEhnOZizyk28dSB7DJxOlOPHCHpx864BwHw8ghOD2Y3guVnl7JsbDyfeTLd3Jza3YcdqoJLeybAG+/qufM69xYt9QXH7r7UlNPEonOJ+lnGrMWiYVy3CgIMdp05Wl3NcxbSOMZumatciNHhiccp0LsgMhuP0YXmay065Xqyhjo2PaOgFIsdxurwf5o0YnLEB1drJM/shRKC6an/YyoFRj1oJk7JQjmYVZUuMCaE8Uh5bUpeRyt5OQpFdSZKXcyGgmLyAJddtrnwvBHQBkVHAJIacBeA5AESTHytOU0pWEkOEAXgAwHsABANdRSk9kap3ZCiszOR2uV6vIMdBTM6LomB8FGd6Kqa2tyH/NjfOeWpc4Lh3xT6MyHzU8L4DV62QTe7e04IO1+9HZGkLecB8unD8Bk2Y6E0vNFOqYNgm4QXtiiXIZu5ag3WSsoXPHa2K4AACXtI9VygMAriHJH7lD547XxHBZ0GAUXduO9NkDhSA9ZNrCjQC4nVL6CSEkH8DHhJC3AXwPQC2ldDkhZAmAJQDuyuA6sxLZAky369UqnuJinCz5Eu03RkHjHr/oCKD9xhiaW9Ym1ud0/NNsmU9fXScT7N3Sgo3P70akR/oE72wNYePzuwEga0TXboMMZUy7efmHiAaTLUM7SVR2k7HkfUphVWYpt732OfM8qkoUVGYpG/VUtpMgxhyY8NKexLqFAPctGRVcSmkzgOb41x2EkAYAYwDMB/D1+GHPAvgrhOAySYfrNdWOQaMX34bDocUJsZWhOVRTS+tk/NOJMh+96zQtWQrAWdF1ujvTB2v3J8RWJtITwwdr96ckuE6t06kGGU4lUaVyHb2kNp54srarS43kMiM7a1LDHJgQQ8qeAYE9sqaXMiFkPIBpALYA+EpcjGVRZv4mEEIWEkK2EkK2Hj3KHusmsIb8gdjc1QwKitIPD2Hod+/ErrIy7JtdifaaGsNrFFRVITqMXfLjdC2tEifKfHSPj0bRfO99Se/BusZ1mPPyHJQ/W445L8/BusZ17HMZqN9rWXysXENNZyv7Q5m33QzrGtfh3k33Jq3z3k332lqnXoMMK/CSpawmUTl1HaeumztttMbtnMqazIg0r3e0wHmyQnAJIXkAXgFwG6VUO0+OA6X0aUrpDErpjFGjRqVvgYMI5QfixTuj+LfXKUa2UxDa61o1I7p+fwl7exoTunjlPFbKfIyOVw5zSFUwnRKfvVta8Ozdm/DUDzdwj/Hlmhshx2L5h8uTpg8BQDgWxvIPl1u+Fq8RhlzDa/YBhjWIwE5jEKeu4+R1C6omOLYmsyItBiH0DRkXXEKIF5LYPk8pXR3f/HdCSHF8fzEA/REpAsdQfiDe8FcKvyrvw+z0oNIJd8DlCiRtS3dC1+jFt2mmH9kp82FdR4lsAacimO01NVj2sy+x6rEInnoqgot39roarXRnkmO2RhZs1ETDfB6s+bp62/XQa4Tx8OaHTT/A5E4bjcKrJyYExV3oS+pBbBanruPkde2c27XtCJqXf4hDS+rQvPzDxFSpoXPHA27j8YFiEELfkOksZQLgdwAaKKW/UOx6DcDNAJbH/1+bgeUNSpQdg0ZwfA2R5mbDLN5MJHQ5VeYjH9+0ZCkQ1cbcZAvYbjtDOSlrVFyrR50E/u11CiCKTVPdlrozsWK2LCI9bBd/X7No+iIsqWPP5n1p70uI0eTXoteP2anGIOlqMJLKdfXOVWcdq6cKKeOyALgd3WTEIIS+I9NZyhcD+CcAOwghn8a33Q1JaF8khPwLgIMArs3M8gYfyo5Bx4dKYqDGXVBgKovXakKXsi+0XYF2qtmHuoGHjNJittrOMPGQwuhu5Y9IHoWPy611Z0olNmuWgpwCtPe0M/eta1xnKdlpXuk8ruCqxVbGqX7MdsmmshpW1vGpzdr3R67ddfk8uiVHriEeTd9mQfrIdJbyewB4/o7KvlzLYINnoSo7Bv3p64fxwzcocsK9T8jE70cMMMwGtlrH2tyyFg0Nd4FSKVbYHWpCQ4OUmJ6pMieWxZx32aU4suJJNP3kLjwxqgB/PY3i3H1RjDgJHB8KvDzbh7kLtYKpLjViMfIkUH1RtSUByxvuMyW6/lz7f+pLZy7FsveWIUK1daVmM4zlLGfWA4qMi7iYoutUP2Y7pGtogl2YWcccaDCKqEGpEclxC7HtQzIewxU4S3tNDfbNrkRD2VncrGL5wz/S1ARQqkmGmlc6D+u/sx6//tlOjH/0p/CUlACEwFNSguKHHgRtZ1s7cmzT6Pos9u59KCG2MpSGsXfvQ3bfCkcoqKrCxA21KGvYhdGLb0P7q2sSr8t7pA3f+DiKUSelP6RRJ4F/eyOGS3ZqPxBZpUZqvCUllkttLpw/AZ4c/T9jl5ug4rpJlq6rZF7pPDx8ycNwEe19zMSslcllelw76Vr43cmxcyf7MdtBKXDtRe9jf8Xt2P31m7C1pQrNLX0f6bKc3GQQvh0oyVKEkE6D/eMJIZ9ZvObvCSHfSW1lyQjBHUCYFTq9elU1SsGZuKEWBVVVhtnAVq4vE4mwG4nxtqeT5gceQMPUr6JhShkapn4VzQ88AID9utSfZ65QmPk6jUqT7CR3AVIzi1k3TkHecCnpJW+4D1+9tCTp+8qbylJuejGvdJ6maYMMy+WrzDa++727NcllLKaNnobqi6pRnFsMAoLi3GLLFr/TyILUXvQ+/j7194gEjgMEiPiOYffue/pcdC0nN1FoMp5Tup4gJTIdwxUYYMU1a7bxQ6r1qkZN/52qh80EzQ88gLY/rerdEI0mvje7ftZxvMlEAOApKUmpdeSkmUUaQb3M1pX0MRuzVje34Am1mur3q1F9UTXWf2d96ovVYc22w3j8rT1oaguipDCAO+dOxlXTxjCPlXsdH5v4Cqi7J2mfPJmrL0MeQ+eO5zbGYCHHnFmdrAZislS8xHQtgGEAvACWUUrlpyIPIeRZSP0e9gK4iVJ6ihByHoBfAMgDcAzA9+Q+EE4jLNwsRs9iZbmOzQodt87U5TLd2KL4oQfhLizs3agoo7FTD+txF1rani7aXnyJu91sPS/rOF7JUsnjP0t4DrKdRdMXmXL5ssqlzGCnBtkqa7YdxtLVO3C4LYhKeLCyzY3pL+zH/gc/SJTSKJHraSP+4+w1p7GRCwsr8VZZUHOnjcaY+y/CsOsnO17+lIV0A/gHSul0ALMA/DxeDQMAkyH16y8HcBLAf8TLUv8TwHcopecB+F8Aj6RrccLCzWJ4FuvfH3kUse5uTZawu6AA0bY2zXXUAsCyUAEkOikB5toXxhTn07a2xLl2xt5Nmnwfdu26C4AyjuvFpMn3Ga7DURhlQPJ27vumgPc6B8JkIrMj+FLJKk53RvLjb+1BMBzFN+DBXQggEA8K+E5FmMlQ8teelpGI+I5prpeJyVzcCUNeAnduDjebuj/NV04BAuBRQsilkPKzxwD4Snzfl5TSTfGv/wDgRwDeBPBVAG/HddmNeLvhdCAEN4vhWawsUaXd3YgRAuL3GwqdXp2p2d7Deu7riRtqE8eYFZdM1O2y3PVwu9miSwg3a7nznXdNvc5MzCd2GjMj+HiuZxdxgVKKotwiBCNBZuOMdGckN7VJk7V+CH9CbGV4Qwtyp43GpJa7s2YyF3fCUJiC9kQx7PrJtoU1m0qgbHIjgFEAzqOUhgkhBwDIbhl1bINCEuidlNIL+2JxQnCzGL24HwsaDKLwuwtMCUBBVRWafsKeB2EmVmnkvrYjLnYHMdgZpcebCBQ4/2sIfrBZewIhaK+pSbyu3mb+r6BoahEWTX88o8k92YSyllvG7/YnJUCp47zyMenOSC4pDOBwWxCjOem74bZunLFknSa2K/9efr77Z+iJ/h2e7uEYfXgBhhZcKA0X7UNyp43mjv+LcSx1GT1BzbYSKJsUADgSF9tZAE5X7BtHCLmQUvoBgO8CeA/AHgCj5O1xF/MkSunOdCxOCG4Ww3PNwu8HZVi5AND5zrsJC9MInqCbiVXyziUFBczj9UQxldmzdkfp8Sz08BcHQQIB0GAw+YRYLGH5OzXxpr/DmyBkxvVs1j3tNHfOnYylq3fgSJiiiCG6R0BBARxuC2Lp6h0AkBDdoc0XorT2Z0l1sG1f6gtS17Yj3BF+qcASWxmepW4kqHZHFWYZzwOoIYRsBfApgN2KfQ0AbiaE/DeAfQB+TSntiZf+/JIQUgBJE58EkBbBJWYzCLOdGTNm0K1bt2Z6GY7DdHsCaLrzJ+wTCEFZwy7T12YJevFDDyaJFXcNS+8GIsl/+MTrRfGjj2jO590HYHdzUq+Bx77ZleyHhpIS3QePhrKz2C3v5PwKzr6yhl2Y8/Icpsu0OLc47Rm2qeDkKECehWq3jMfpMYV6rNl2GO+/thc/CLqT3MpBUPwUQfwFvb/TYwoD2LRkNoD4DF5G7NRd6EPxkvM127u2HeEOqR9yfhFCu0/Ydt3y1qJk7PIKU+fI6z+0pM70tRzGuNnzAEFkKWc5rDrYgqoqEGWGsAIrk3HkbGN1YwuWWKozpQHAnZenuSYNa+tQ9eK9dmp2lfBc7kaueL1MaqMsa7s9lDOJ0WQjq2MGnZp0ZGZtTnPVtDH42f2zUHL9lETWbgtiGrEFemO+DXUbETnBTpbjCd/Jtw5oxRYAYsCpzS2J82RLk5UlzYM1jUgJq77WaPZvukYVCnoRgttPKb7nbkcm47AEXYmeIEZ5HaeampLKi7jx3qYm3X1mumbBzRk7x9kuC8svZrSgx5v8YC2/f0ZTh3iJPZlsQWgETyCX1C1BxaoKLHtvmSXBc/Khw0nxtkLutNEoXnI+xi6vwKLCqEZsASnm21C3Eeuf/hVORdjTPHiCZKWLk9WZtPJEIRLQ/p7z6mt565Tn76ZrVKGgFyG4/RQz1qkT6CVH6VnTyg5X3OMIgZsT8wUkl7Vhe0idMh41Skvqvaku/Ppy4FgBASVIev+M3luz9ahGNNRtxNO3fB8/X1CFp2/5PhrqNlo63wp6QtgWatP0STYSPCcfOsyKt1Ur3Ap3zp2MgDdZvAJeN+6cOxl1q55DpCeE+hPvIKKaC6wnSFYtQ6ttFq3W1/JG9cVORdC6Zl/aRhUKehExXIEuejFSo7pUOY7aXlPDjTmTwkKpN7PJ30N1bNZKDNdK7NUopphqzFG2miI9yR+y53zzCnzjX/8jcUzdqufQcfwY8keMRMWCm1BWMcv0PZTwXrseBAT1N9cz9+nFcAFryVBmfi5Ox4xZ8DpQ/XxBVeL3c1xuGcqHXYYhnqE4FT2JsTeer5swxYzh8iAAKNJajnP4gfc1HadkUiknSpFBE8MVgivQxSixSk9MlQlcDVPKuMeYFVv1Nc2sT0n5s+XxHFTVJVXC0hcf7k/f8n10HDvK3HfFrbcDAN749ZOgCkuduN24/N9vsyW6rNdkhFESGOuhA4BlIdZbW3FuMRZNX8SdNNQXiWq8n1X+yFFY+NQzuueyspQD54xKml/LgnhdabEu9RKjeMlffcCgEVxRFiTQxahDUkFVFXfGq9KV7Ckp0T3GbL2x2j1tpYOT2V7AejFFpwS347i2a5FM3arn0NPdnSS2AECjUdT+/mlbgqsswzFj6ZpxkbOaYMx5eQ7zvVv+4XJ0R7p1S6lYa5OP4z0opJKotndLCz5Yux+drSHkDffhwvkTmEMeKhbcpPFGeHJ8qFhwE/fa6nrXYaqZs12nFyT2y5atknSV43C7VIHv0h4AzTCyBhHDFRhilFhllGRkdAxzn9cLeDzM462uT8Zs7LUvspDzR4zk7us4fgyhzg7mPt52M8hjF5dXLNe8D16XFwU5BSlP6eG9R22hNt3EKHltxbnaeH93tJs5GhCwn6i2d0sLNj6/OzFLuLM1hI3P78beLdr1l1XMwpyFtyJ/5CiAEOSPHIU5C2/lPvjI9a56WcjKhC2G0yVxntPoJUCxYs5mXstAgxDyLULIHkLI54SQJYz9hBDyy/j+ekLIdLPXFhbuYKb+RaD2QaD9EFAwFqi8Dyi/zvJleFYmEI+xxrcV/MNVul2wWOc72XtY3WxhaM5QEEKwtG4pVn6yMuHiNGsJp0LFgpvw+q9+ztyXP2Ik190sk0p8N11NJ9Y1rgMhxPR0IECyYMufLU+sgSfYMRqD3+13rDPVB2v3I9KT7NKN9MTwwdr9TCu3rGKW6ffXagMJPauza9sRR63J3GmjEfqiHac2J7/PvOSvAdIMwzSEEDeApwB8E8AhAB8RQl6jlCqbG1wOYGL830wAv47/b3x9EcMdpNS/CNT8CAgrOip5A0DVL22JrhorsdVMYJT0k0oM16wY/uV//gvb3349aZsnx4c5C2/FhmefRneH1pr15+dj9s0LmS5OPasr3ejFYf1uP3xuH9p72GVkyuP8Hj+zx7IylutEIttTP9zAXcctv5mtu04jrDaQ6Np2hDtyL11xVbNu4j5qhmErhjt+ybobADwKYByAgwDuPrB83h9TWgghFwKoppTOjX+/FAAopY8pjvlvAH+llP4p/v0eAF83M9JPWLiDldoHk8UWkL6vfdARwTU7m1cmlfaOVpA/lFkWrOzilJNwrFiACZFVWaYdx45i/dO/AgCNGH7jX/8DYyaXJc4jLhciPSHUrXoOky+owI6N6xFTdPJyeTyYffPCRJmKEvm8TAkubySfi7i4DzFquqPd8Ll9XEtWb3CC1XabecN9CXeyensq6LlaeWVCudNGcwU3HW5l+Z6s1o9qEeZZ35luhhEX298CGBLfdDqA345fsg4piu4YAF8qvj8ErfXKOmYMTEwZEjHcPqBr2xE0L/8Qh5bUoXn5h30W/9CtW2w/xD6Jt90iVobQ6839dRJlHS6Plq4WyyU/cokPzw0siyGLsopZqFhwEzw5PtCY5LrrOHYUO9+pxdmz5iTFDb/1w0Uoq5jFTbjSS8RKNzxXMKU0IZTVF1WjOLcYRMegOdlzMuk4s/Fkq80zLpw/AZ6c5I8/T44LF86foHsfI/SaV1iNn8r0xWcGK1Z74oU9TLHNkmYYj6JXbGWGxLenAuuXU+0GNnMME2HhphlWw/ATL+1JlAqkK+vP8Im/YCzQ/qX2xIKxjtzfymAEo/aOZi1fI6E0Mxi9wFdgeTABy+JUY5SVzLJYG7d9xCw74cV49RKx0o2ZuLfSQuXV3hblFmksWfnB0c4MXt52OU5rJkvZCnoWqd7f+NC545M+J9TXPPHSHsNrpAIrVssjcF5WzNUdZ3G7WQ4BOE3x/VgA6g8yM8cwERZummH+Isd6p32kK+vP8Im/8j4pZqvEG5C2p4DcjjHS1NQ7CCAOL8tYr72jWcvXTD9eoyxjr8sLSqnlNoNmLEujrGQr22WLWIlRmYqTsDwnrAxwADgVPsXsCGU2Y9xsn2U7na8mzSzCzY9ejFt+Mxs3P3pxymIL2O9HrO7ypCEGtL32earL42LFdR3afSJt67DAQYvbzfIRgImEkDMIITkAFgB4TXXMawBuimcrXwCg3Uz8FhCCm3bM/CJb7aNqBsMn/vLrpASpgtMAEOn/FBOmklzDQFJDC1JYCJffj6af3KXpi6zXItLsYAMzLkWjLGNKKTexR0+sjSxLIzHknc/bbrVMxUl4AggA1RdVo9BXmHR8e087c0jC0rqlSaU+LuLC/DPnayxXs65ip9ptpkoq/YjlUiEeNBhNm2vZSkw2XXFli9wN4JRq26n4dttQSiMAbgXwFqRxfi9SSncSQn5ICPlh/LDXATQC+BxSHPk/zF5fuJTTjF7KvxKnf4lNlbaUX6crsFZjmSzXMCCJLbq7EeXMrDVqEamGZRHz4rLK7YumL8Ky95Zp+gYnrksjcBEXYlTrWtMTa1ZjBJn8kaMMS3bsNFawUqbiJHoCuP4767Hyk5WaLGOlQCrd9acivZ+XMRrD2s/XYtroaUm/Y2ZdxZmarwtos9JnVdyMvAND0tIoIl0D4X1ThmlKhXhkOmEKAA4sn/fH8UvWAQ5nKQMApfR1SKKq3PYbxdcUwC12ri0EN83oxWaUOPVLrJeFa+WJ386QdZ5rmLa1abcpMpblmGzTkqX8YQQKWAMPeEKpbphACNFNb7BT7ykLn5W6WPWH9NTLKtG47SNH+iaraW5Zi8b9T6A71Ay/rxilE+5AcdF8W9cyEkC9/UYxdFY3Lys10XpZzKnCew/VPbE7jh3F6+v+MyWPg2uIhztg3ukaWHXrSSOyJGEKAORs5JQFti8Rgptm5D8MOd2eBNygPbGkhuZO/RKb6Ulr9gPJTntDXqIUD6VAF1RVoeknd5k6LxoKJTXUGL34NqbYAkjavvKTlQirpr2oMVPvycKKxcn6kN75Tq3mQ1opyv68PFAKhLo6LQlyc8ta7N59D2IxqQSsO9SE3bvvkV6rDdEt8BUw62RlARyaM5Tplh+aM9RUpy71MYumL2LWRPelq1jvPaxbtcbxEq2Cqgm6Qw+c8oapEzo1yC0n+2CowmBBCG4foK55S1dv0uUfLueKrdUG73baG7Jcw8Tvh8vvR5Rh5apjt6YFOxhEJCh9+Mnu6SuvHIo/T9TOK1W2CjT6wDdT72kVVhMMozrahrqNqP3900ltHJVNMPRqe9U07n8iIRQysVgQjfufsCy46xrXobOnU7Pd6/ImBJAQdskPIYRrrSpRW66ZchUrf25Tb9wPb27yz0t+DzuOFya2qScJ2e0SxesGJeOUN8wwM5k62txCACG4GYFVdJ4q6xrXMS0PwF4PYDvtDfVaPLKEWJ2xbDWWK0O7u/Hdd/34yxR9V7DeB75VD4AZWJYsL9YLSFnJvLF9asxaUd0h9uvlbVeGJGQ3vdLqZ8W/h3iGJN639hA76aw91I4l5y/RbX7Bs1zT6Spmof4ZeIawfxbdoWbkjzgTHceOYlxuGb428nJ4XF4AQK6nwHa8tWvbEQQ/ZidHOenSNbKUsyFWO9AQgjtAsDMsXA+7rjxlTFaNUT2t1ViuEu/RdlRf9LiuJcR7TU6O3VPCs2SJy5VocqEkf8RIUzW9MmbKkfy+YnSHtF4Dv0+bGa4OScjueKOJPSd7ej0Leg9q6qlABCQxLrHQV4gl5y/pU2Hlof4ZhDs9yMnXPmj4fcWJhLfyYZclxFbGbryVa3kSODqyTy+hUynsXduOoO21zxNzdF1DPChQTT8SmEMI7gBBz4pliaRRBrLTrjw9IVYfx52vC4AEAqDBoGa7p7jY0BLqa/ckTxBpLAZPjo+Zlfz6U78wfX0zjS5KJ9yRFH8EAJcrgNIJd2iO1Utqkif2GGVwL5q+CPduulcTK7907KUAen8GagHvjljzagDWs+jNov65NW0ZjXGXNcPl7Y2pyu9hcZHkYRjyZzfzWnbirdxzqLPZybyEThJwo/DbZyJ32mipz/NLewDFIbFTESnGjPQ14hioCMEdIPAsi4KcAs2HkNkMZDsdf1KlvaZGfyh9JCKN7VP0GOY11GDRl+5Jf16e7gACVlYzqx8zC7ONLuQ4rZksZaPQg9kM7khMaw2u3rc6UfLjxLxh1u/wkroleGzLY1g6c2lKP2N1J6+2/VJW/JgLj8Ob26N5D8sqZqF504dsoSTxAQAWEo/6qn+xOqGTtbaTbx1IEtsEUTogJwYRQv4XwJUAjlBKv8rYTwCsBHAFpLrf71FKPzF7fSG4AwSeu3TpzKWaY+184PE+4LYd2YZlFyxz7HUcWfEkX2wB0HAY7sJCkCFDbA86MDPNx4lhCryXQSk/q5lX0+v1++HyeC1nKQOS6JpJkDJKajKTwb3yk5UJN7GScCyc+P1yYt4wzxqXG20A/PI1I1g/g84vR+P0yx/kvufc8j+a/L/cWQ7gW4esa6WrHMcon0TPQs+SBhhO83sAvwLAbn6ewmg+QAjugMLn9iU+hHgxsXWN67gfqnofeLwPuBf2vKBpVqDGiuuPV8urJNrejrLNHxgex4KXyAT0ZvyqRwuqG3WYJdSlzejV265cg91Zt6nAemiTMZvBrfc7JO9zYt6w3n2sWstq1D+DSV85H+cMvwyudQTNmz5kWqhqazFRUsPAKLZrxvLsK/TivBlPqqou0IznQ3V7SnW5lNJ3CSHjdQ6ZD+C5ePOLzYSQQkJIsdnWjkJwBwCs+ltWTEw+jofeB56RGFu1jJfULWFmBpspDdJrBWmEmdF2VkcL8rA7ZMBKTa+TTS3USU3qLGUzAqZnJcu/X07U1hpZ43Yy85XIP4NErWqwd/gIz0JVWot6c2Tl6+iRjkoGOwydO14TwwUAuElmG2BIYqsZz4fqAqQqugbYHs0HiF7K/Qa9UXtm+83qJcUYfeClIsa8e7Ia0Y9efBuIX9sEX8ZKvJaFmUEBVkYL6pHuIQNyQwYpC5kmGjI0t6y1fc15pfOw/jvrsePmHdh+03bsuHkH1n9nvWlrcdH0RfAQ7XO8slZXParP7Ag+9X1YwxJk7GTms2BlDNNwDCde3KM7btPI+su4dWiS3GmjMezaySCB3qQw1xAPhn1nUqYfCNI1ns8I26P5AGHh9guMkpzMxsT0hNHoA2/R9EVYUreEuc+uGANa95+6ltddUIAYANreromlWo2zttfUIBCJIujWPmcqrU4rowVlWJZmWYVkaabLPexkUwunkH+Oj215LNFxihXeSDV5TT53+YfLNfXnTnai0ssYlverLd6ubUdAe/hlbdnUHtEM2WJtq0jXeD4jbI/mA4TgAgAe2PB/eOVvv0XMfQKu6DBcc8YPcP/sf8r0shIYJTmZjYnpueFka5j3ITivdB62HdmGF/a8kLTdjGVs1F1ILcpmSoisxlnl4yf5Pdhx2ijEXL2iq7Y6eR2zeJa1Xuu/sor5aYu/Wm1q0Vf0VSa4fJ90lQcB5oaPKGOy3HaJoj2i0xyE5EZmbU8nrwG4lRCyClKylOnRfIBwKeOBDf+Hl75YAeo5IVWjeE7gpS9W4IEN/5fppSUwsmDNjibTc8Px5owqWXbBMiyvWG7JFWjk+gOsu//aa2rQtGSp6dF9QG9cdkxbJ87+8ij8PWGAUgSiMU0P44KqKhQ/9CA8JSUAIfCUlKD4oQe5DwE8S3PProeT1rxvdiUays7SjCc0orllLTZtqkDthjOxaVNFwmXscWuHOOht512nvyO7wetvrrfk/jYDa9weC1mUeU0r3AU+jF1egeIl5wuxdYa0jOcjhPwJwAcAJhNCDhFC/sWp0XwAQKhOCUZ/YsaMGXTr1q2Wzyv/3aWgHu1AZRIZhvp/edeJpaXMnJfnMK1EZY9ks0/5etOE1Nd0CqMJRlbid2rLVgMhKGvYpXE3cxOx4senQu2GM8EK41AKjMn5H5S0dTItZlnE9RKf1NYzIDVdmDLlEezd+xAiEe3vrts9BF5PYdL1AHCvkyn3c39B2fucl33sLvSheMn5uslSwrrlwm7AbUQaspTTzaAX3K/+/myw+q1TCnz2vR0OrCx1WFnIqbYkLH+2nFkvSUBQf3O97bUC+uJv5sFA75h9syt1s5jdhYX4yj13a0WZ00zDU1KCiRtqNdutxIc3bapgtk/s6fDgy/UXYVbDQXZMuKQEeX/8kUYICfHC7cpFJNoOyQmljQf6fSVx17Hx36/LFYDL5WeKs99Xgosv1s+oFfTCchkTryvRcrF5OacBBuNYQQJ7gtsPGfQxXFd0GNPCdUWHZWA1bNLRktCJWkgWRgleRvE9o/ONMoVjYJf1sMSWF5e1Gh8unXAHPtt+e1Lrv1iYoGnLaHQcP6ab9cxyR1MaRiTaFv+OnXwjW68soVcTiwU191Bexw7pjJtmM0Y1skbzr52eZyvoXwz6GO41Z/wANKZqOh7z4pozftCn69Ar+wGcj1PxYqvBSFA3jmuE2RIlu+cb1eDS9nZdUTYTl9Wrw2VRXDQfRz+ZhJ4ODyiVLNuD7xSjbX8B8keM5K7ZU1xsW/BkV7HLFbB1vvI6VpEfipq7mkFBTcX/BxK500ajeMn5zJhs7rTRKLx6om7ZzwDt0CQwwaAX3Ptn/xOuPX0xSGQYKJVit9eevrhPs5Qz8QEm10IG3Mkf2G2htpTunWrbPqPzjep03QUFfIGLu4/LGnZh4oZarovYqA6XlQA17ZI7sfvFMmx/ugy7/jgRbfsL4PJ4ULHgJuaaZevajuD1Ns6fjylTHoHfVwKAwO8rgcddyH7t7kKNOPOGGBiR6kPVQEcWZJ7o9pcaXIHzDHqXMiCJ7v3IXBkQ7wNs+YfL0+6mC0a1rkblh6dVt2Gqrmr1+RfvjOKGv1KMPAnse7YSoxffhuKHHkTzI4+CMobaRzs7UXj5t9D+6hpuWY9RfJabZOVyoWFKWdIm2d186p//Cep8CPl73pzggqoqlLbENDFcNm4AMU1SlbpPMi/JatJkySXuRFcqJ3ohO4GTXbbSQV/2RBb0DwZ90lQ2wEtgAoDlFekTXV72swxrMoxRolaqCV7K8y/eGcW/vU7hVwyfUWb37r7gQqboekpKMHrxbUyBY2U5K68JmMiEZrDxq2ewG2qMHIWFTz2je65SODyeQkQinQB6x9tZzSZOtxCZyZpPN3rZ29kkusoMZ5GlzGXQJE0Jwc0C9ITPRVx49JJH0yK6ekLPm31q5kNVnVBz6dhL8e6hd01byvL5y372JUad1O6XXcMNZWexR/LolPrwspzdhYWYpBiIoLSC4XIBUX7nIAB4vbwUzHR3QnD7KvM1twBbMAFnrFMnSEfWvFV4meEi67pfkjWCSwg5DdKkoCJIOZhPU0pXqo6xPaJPCG4WsK5xHbdtohIrTeTNYGThsrBaNpTKh7ORoPLEk1fqo3tNACWP/4wZ11W7kVnwLNzGaRdjy6XfxuFQGGN8XiwtLcY1RcMNr6fEqjWnN37QKes301nKvNpngKBy9ud9tg6BI2ST4BYDKKaUfkIIyQfwMYCrKKW7FMdcAeD/QRLcmQBWUkpNjegb9ElT2cC80nkoyGF3B1LiVDKVnBHNE9vrJ1+P4lx2Mo/VsqFUEmzcBZxOSvGkKL1kJB56Wc6sLGQzHaGI34+Zl35DM6hgz5Tp+PPMb+FQKAwK4FAojDv2fIlXWloNr6lEr2eyGnn8YMexowClifGDDXUbHRl2IHer8h9YhPuLg3hr7kOOd3cyAy/ZzE4SmqB/cvazZ99w9rNnHzj72bNj8f9vSPWalNJm2VqllHYAaIA0DUhJYkQfpXQzgMK4UBsiBDdLWDpzqWELRCD1bFBlRrSa4txiLK9YjmUXLDPdLtIIXiKNkWXdXlODaKd2bizxehOCarUFIwBdMWZlJ/NKgWTke573ox9jzsJbkT9yFEAI8keOwpavz0dI9fAejFE81mjNq2ClZ7Le+EGecO/adacp0bUr2GZaShqVxalhlUTZzboW9D/i4vpbSP2USfz/3zohujLxubjTAGxR7eKN6DNEZClnCbKFcPd7dzNjp0pSyQbljctTx2adarahN7xgXeM67vWOrHgSiES0O3JzkwRVOehAjrs2/eQubneogqoqboYzXC6019QknWNU0xtpbk6IcllVVVJP5vs3fso873AozNzOg9fggmXN6Y0f5Nf8RrFr113xVpFtXFeznelEeoMd5HOMmp2wkM/Nlri2oM/RG8+XcntHQkgegFcA3EYpVWeS2B7RJyzcLGJe6Tw8esmjjjf7V2KlpMNOsw21pXLp2Eu5x+pZ6jyho+3tzO1yZnGkqQmgFJGmJjTd+RPsveBCjVu4+J672bW80Sia770v6XiuC5qQpHupzwOAMT4v81Tedh5WrDnecPv8ESMN3K3heOtHvuVqZzqRGXe43bBDcdF8XHxxHSpnf46LL64TYju4SNt4PkKIF5LYPk8pXc04xPaIPiG4WYZyODeLVGd98sTajoirxfXhzQ9rGnis/ZzvbtSz1PW6M7FgtnMEEG1r04ih7IqG2605Xt1RittoQ11zy+hEtbS0GAFX8sNwwEWwtNRanJHV4IKXMKU39N5KZypWjNhO3NSMSGdLXa+gX8Ebw5fSeL54BvLvADRQSn/BOew1ADcRiQtgYUSfcClnIcp+w05ng1469lLLM21ZsNyA6usCkqXCKzHSE3mrM2n1XL+yGKpd0U0/ucvwWqymFbzhCeo1yNnIjzU262YpJ7KKjx0FcblAYzHkjxyVlF2sbnDBQz5eb+j9rl13gtejWYlaLEsn3MHMltaLm/L7PbtQu+FM+H3FmDUsFxtOaOP1qfb1Fgxo7oYUw1W6lVMezwfgYgD/BGAHIeRTxb3GAQCl9DeQRvRdAWlE3ykA3zd7cVEWNIhglegAUlbysguWWbqW1ZIiO000rEzsMZoixKrNtVNWlMp5LOSsYnWiEyBZpupZvWrslPmwyoxYsGpa1fcbPmIWWo9v5N7fzL0o8eKF1hxs7uz9LEp3XW/zAw+g7cWXpPpqtxuF112L4vvvT8u9BIbYKguKJ0gljefbcfMOMZ6vLxCCa4yTHYL0mmawrr9o+qK01m0adodyu1Gy/LEkwTbTdcrsvVjnmRHDp2/5vlTCw0GvU1Uq3ZaaW9Zi754HFVOJkjFzHbP3730f+A9EMfcwPH50eJ/U9TY/8ADa/rRKs73wuwuE6GaGrKnDTTfCpTyIcDJWppd9rMTv9uPSsZfaElsr7nRZ6LgZyPGEKOWxej2O9TBznpnsXICfVWxmv52s4aRjqdqqlqar+30lpixls/eXv9azdF3RNqz/Tt88MLe9+BJ3uxBcQToRgjuIcHIG7qLpi5gdpOafOT+pjeOlYy/F2s/XWir5AOyVisglQu01NWhaslTTjpEXyzUSWL178TArRvkjRupbuJysY0AvIakp3vqQb1mz1ieLrdnWiFayltn368VKw4qUu2Xx2nQatO8UCFJFZCkPIpxqZgEkZ1MTEBTnFqP6omosu2BZUinRu4fetVXykUqHqoKqKiDGrmU2GmCvxkzTBhZmxYiVVSwjZxfz4IsUMWxOYafEx+z9Wdv1rmulYYUT3bJY2em62wUChxCCO4jgiaTdWJmZOl27buxU3d9Wy4pYpPLhblaMyipm9XaoAkBc0p9k/shRhglT7DIfyS2sJNUSH95Dh5X6YI+b17qUWJrwY6XNJY/C6661tD0VrHbQEgxsDF3KhBA3gH+FVNz7JqV0k2LfMkrpw2lcX7/g4c0P46W9LyFGY3ARF66ddK3lrN++Qlly1BfYdWOn6v62WlbEIpUY6fARs9DU9Dxzu5qyilm6wspD3W3J4y7gJkHZLfExE4s25d5lTVKSdhi9TN3XYbSdhRynTXeW8rrGdbhz2+toK/gJYsNH4Fj0OO7c9ioA/XCKYOBixsL9bwCXATgO4JeEEGUx8NVpWVU/4uHND+OFPS8k6kxjNIYX9ryAhzcP+ucQAPbd2Km6v+30WVaTyod76/GNlrbroefWlrstnXXWzxlJUL2oLdfkZhoA4E48TCivb2RRmu32FIm0cVYWs+QSdmpoQfH996Ns52co292Asp2fpSVZqvqzd9BaeBNinpEAIYh5RqK18CZUf/aO4/cSOAMhxE8I+ZAQsp0QspMQ8gDjGEII+SUh5HNCSD0hZLrZ65sR3PMppTdQSp+ENIoojxCymhDiwyBK5+bx0l52xiNve3/FrmvMrhvbCfd3QVUVJm6oRVnDLkzcUGs5OSqVD3deCYxeaQwLs25tvaQknpu3uGi+wi0cTaxv167b0bD7vvj3qVuUgP57ZsUl3J+GFhwOzAFcqvi8yydtF2QrIQCzKaXnADgXwLfi3aSUXA5gYvzfQgC/NntxM1nKOfIXlNIIgIWEkPsAbACQZ/ZGAxXeoAGjAQT9AbksR+3aNZtpLGPXjd3X7m81djor9eIGu5uTtcQc/oSfH6Nx/xMJF66eAOrFSHnZyk1Nf0Rh4Xk6naIoajdMgMddiEmT7zN0sbPeSyVmBbw/DS2IuUdY2i6wRsOUMk3ji7LdDSk1vqBSYwq57Zk3/k/dcCAxng/AZkJIISGk2Ex7RzOCu5UQ8i1K6ZuKRT1ICGmCBWUfqPDaFrpI9uejKQVVfh1ykwoAzK5UMnLGcH+ORRl1skrtw51XYmKt9ERPiJTxVP5EoRLd9fKvTxUtILWJWDKRaBt27ZJaZMr30Svb4bWVtOISNtvmMtOM8ERxPKr9iB3hEeVHqRIXW2Vrx9MB/LZhShlSFd143tLHAM4E8BSl1Ox4vtQFl1L6j5zt/wPgfxSL/Cal9G2j6w00rp10LbOH8LWTnM94NEJPQNXCqK5zlR8aZOvV5/ZxxVamPzeXV3cbkif+ANCIrtUPd8ndyxap3pipuXpSvoUpIbtj7Vrj+teXhYFyX49EOJFIZibJyr7XoH/x4KRSLN59AD209+E7h8Tw4KTSDK5qwJC28XyU0iiAcwkhhQBeJYR8lVL6meKQrBjP91MHr9VvWHbBMlw/+fqEResiLlu9ic3Ci6WqB8urBVQdc+XNxQUk67W9hz0GT0l/bS7fXlODtlXahyTWxB87SPFI1t8fSQiL2dismQk/3aFmSxOF1Nc3l4qh/3kiW8pmkqzsrLM/ck3RcKyYMh5jfV4QAGN9XqyYMl4zvEJgi7SN55OhlLYB+CuAb6l22R7P52SnqUGbQLXsgmVpLQN6paUVjzU241CoB+6ID0PoePjRnBRLNRJQtfs3Ves01TGBZnF6WhIQH27P6SFutTEGCz03rWwFslyrem0R9XoRe9wFSZ2lzjrr54n7GHWcKi6aj7a2j9HU9EeYfEhnIruEzSRZsbwGKXePylKuKRouBDY9HITkRmZttw0hZBSAMKW0jRASAPANaI3J1wDcSghZBSmR2PR4Pict3IExBSHLeKWlFXfs+RKHQmEABFHPSHQM/xd0D7kQQK+YWm0kYWSdFvoKNWU5Mqk2zDCL0mqX5+uyrHWr6ImqlcYYPPjZzSUJy5YXy2UJVm/pzy8Y1q4X0ViXxlJu2H2f6aYdZVMexFln/TxhdfISuzzuQkizudV4E5a7XmY3r7yJZe3v2vVjvPPOedY6SAkGE3dDGo2nxInxfMUANhJC6gF8BOBtSumfCSE/JIT8MH7M6wAaIY3n+y2A/zB7ccemBRFCPqGUmq5HcpqBOi1oxvs742KbjCtyDCOaFgMACIjhMAH1RCDeqD6gdzQagLRO+DHCyelGSvRG+ZU8/jNu+ZBZK0xvio7ehB4geSQe635AchJXONKGaFT9uQPwsqTN9ErWWz8A7N37ECKREwAAQgJwu/2IRNoS4/paWlZrzi0qupq5fcqUR3Std7PTjwT9Glve0XRkKacbJ13KBxy8liDOYYbYAsmlBbIY6gmo2v0rC6dRklUms5CdnG6khNWFCoSgcMH1umJrZvqP8nuWWOqJrTJ5iHe/KVMeSRLkXbt+zLmaeQtajdaN7U5Kzrrs0q1Ja5TFtzvUhJaW1SgqulozI1cvtqu3JivTjwSDi7i4ZrXAqjEtuPFU6XkAxivPo5T+Iv7/oO86lQ7G+LxsCzd6HECvmJoVUCWZrnM1wmx7R6txXjtj+T7/7CHEXObbPLLilJs2VXCvD7iTLDkzbSX1m0XwLNxkl6/Siva4CwBCuNaq+iGDt8bW4xs1VvSuXbczVymLsl4mttUmGwJBtmLFwq0B0A1gB4D+39Whn7C0tBh37PkSwViv65/QHuS2vcS0RrNZQK3CGwGotNZZY/yW1C3BtiPbdBPZrIzla6+pQc+QE8x9VsRA79izzno8SaDNJB/pXa+kZAHThassv1Fb0UrruzvUxEykUoq+lS5U/DrhYsOmGFbbNurR3LIWe3Y9jAhtRbjTgxO7JmDaJXfa6mUtEFjFiuCOpZSWp20lAiZyhuNjjc04HApjjM+LpaXjcE3Rf2d4ZelHabXzrFdedvYLe17AtNHTHHkAObLiSbj/HYgyGgRZEQO+JafNXdQTKNkq5eUpetyFKJvyIAoLz9ONORvNqOVdXxZUvTWq0asTltekjA2rj3GC5pa1aNi1BBQ9IATIyY9g1PS92PL2gwAgRFeQdkwnTRFCfgqgllJqP1vFAoSQbwFYCck39j+U0uV6xw/UpCmBPuXPloNyhCHV5CqZhrKzcOq8CNpvjIIqWuOSEFA27Rem44usZCQZdXIQL3GJlXykdx09ajecCTvFBXLilV5yFS+ZzCjpLJ3lQVKJlPYBoafDgy/XX4SFTz3jyH0Elhk0JaVWLNzNkLpuuABINSpS68mhTi8qHi9+CsA3IRUZf0QIeY1Susvpewn6N3rZ2U51wvIUF2PIVumDumN+FNHhgLsVKHh3JIovNy8Geu0N1fFZXuKVnlXq95VYEiij2KlEcocppcXZW8O7Kv563Cgqupp7fzNdu9LZtpHnAvfmRdBx/Fha7pkO6uvrUVtbi/b2dhQUFKCyshLl5cL52B+wUof7cwAXAhhCKR1KKc1Ph9jGOR/A55TSRkppD4BVkBpGCwRJ6DXfcKoT1ujFt4H4/Riy1Y2v3JuDkltyUPTIUJTOsd7sRBITdgqEWhBYo+/4cVuiOx6PhVEXK5crgMLCC9Fbl5ssqM0ta9HSshq9Dw9RtLSsztraWZ77P9zpQf6IkX28GnvU19ejpqYG7e1SJ7j29nbU1NSgvr4+wysbWBBC3ISQbYSQPzP2pXU8n8w+AJ9Rpwp39eE1h06CELKQELKVELL16NGjfbAsQbYxr3Qerp98vWa7k52wnJitm7S2FMb+mT1Xb4aujLrNosddCI9nGOSWi0VFV+PkyW3gCWrj7ge5mdRm7t/XlE64A6R3+BkAIBYm+PvHJahYcFOGVmWN2tpahMPJVQvhcBi1tbUZWtGAZRGABs6+tI7nk2kG8FdCyBuQZgYC6C0LchhTzaEppU8DeBqQYrhpWEcSXWtfw8ktMURjw+B2ncDQmS7kzv92um87KGluWZuURKM3Bm7ZBcswbfS0RHJVga8AlFIsrVuKlZ+sdKRhh5WsZiOGj5ilyQA2mxxkZkiB1ZphnlW8aVMFvzTpSAjd0RMA0f6pyvczc/++RL63Okt55jedz1Lu2nYEJ986gGhbCO5CH4bOHY/caaNTvq5s2ZrdPpB56ocbNI0vbvnN7JTrcgkhYyGVwD4CgFXontbxfDJ/i//LAVSPic5juzl0uuha+xraPgiAQmp3GI2NQNsH3QBeE6LrMM0ta9HQcBco7X2SZ42BUyKXRLHKhKzM7k03vW5Y5fMh0Y19KjEzMtBMDa8ZdMt+PnwQ/skxdPtZbSDdjtw/HWgeMNKwnK5tR9C2eh9oWAodRNtCaFu9DwBSFt2CggKmuBYUFKR03f5GXGw14/me+uEGOCC6TwL4CYB8zv70jeeToZQ+YPZYB/gIwERCyBkADgNYAOCGPry/hpNbYgmxlaHw4+SWLuSK6LKjNO5/IklsewkbfmizyoSyaXYvb+B76/GNpq9hlFhkpT5WD92yn/YdKP2bF7sn5SPm7rVyXVGKmNt+l6uBwMm3DiTEVoaGY2h77fOUBbeyshI1NTVJbmWv14vKysqUrtsPSct4PkLIlQCOUEo/JoR8nXcYY5sz4/kIIU/G/68hhLym/mfmJlahlEYA3ArgLUh+9BcppTvTcS+zRGPDLG0fNNS/CKz4KlBdKP1f/2LKl9Qfuq7/oe1kO0jeKEQlVmOVTomhHqnEiJWwkqoS7uuCsSg+2oMpezvg744ClMLfHcWUQzlJM39TuX9/JdoWYm6nwSi6th1J6drl5eWoqqpKWLQFBQWoqqoajFnK6RrPdzGAbxNCDkBK1p1NCPmD6pi0juf7v/j/en3kHIdS+jqkqQxZgdt1AtGYtvOB28XuQDQoqH8RqPkREI5bbO1fSt8DQPl1ti+rV65i9KFtth2kEWZc01Zipcr1m20WYRe7w+jV6LqvK0NAzY9QfDSI4qM90gneAFD1M2C0b9AMmWfhLvRxRffkWwdStnLLy8sHo8CqSct4PkrpUgBLASBu4d5BKf1H1WHpG89HKf04/v87AHYB2EUpfUf+Z/aF9HeGznSBINlVSdCNoTOdnHDYz6h9sFdsZcJBabsaC5Zw6YQ7DMfA8Vg0fZFmrKCdjGU917SM0bB1FrpWo0M4OeSdVZoEQHqgqvolUHAaACL9X/VLoPy6QTVknsXQueO5+3hCnG10bTuC5uUf4tCSOjQv/zBlyzwNpGs8H5M+G89HCCEA7ofk4iWQRDoC4D8ppYxP1szQF52m+jJLub2mxlJzfceof1ESzPZDQMFYoPI+vrVaXQh26IIA1W3J11RawkDcGvol99pWspTVcIcZWHhtvA5WBAT1N0s1j/xOTQSVsz/nrm+gDlsX9NL04AeInYpotrsLfShecn4GVmSerm1HcOLlvUBU8bvtJhj2nUmOZFozsNVpKl1ZyunEjOAuBnAFgIWU0r/Ft5VCqj16k1K6Iu2rNMFAau3YXlOjGR9H/P6Uaj9NYVUYV3xVciOrKTgNWPyZ8XHEDdCYsbAbrdmMiFp8bWZm8fJaBZqZOStgk2oXpYw9qKpQZyoDAPG6UHj1xHSJlmPwHhZcQzwoue/CdNxy0LR2NOMPvQnAd2WxBQBKaSOAf4zvEzjMkRVPJsT2cGEeNpSNw7pJJXj+2V+joc58NqtlrLiIAUncvKpORd6AtF1J+yH2+TQKgPbGfo0SrtRu6T//WDqv/Uvj61h0fy/6exP8seRMU7Vrui/cw4MJO12UXmlpxYz3d6J446eYXrsVz736OiJNTQCliDQ1ofne+9BeU9NXLyFB7rTRKLx6ItyFUvNtd6GvX4gtAKbY6m0XmMdM0pSXUqppNEopPUrYgTZBikSaJcvqcGEedpw2CjGX9FwUdBOsf/pXANI02YQnjO1fSiKntiDl/40szIKxbAtXSTgIvPpDYPVC9nVYCVpb/xcal65SRJXr4t1fvT1+n3nhIJA7BCuHFaLF40ZRTiEWXbA0qbTITE2swDxGXZTUlu++0WOTRlc2uTx44rrvAeEefOOj9wEAtLsbR1Y8mRErN3fa6H4hsIK+w4xL+RNKKbNXpN6+vmYguZT3za5EpKkJG8rGoTtH+0yTP3JUeiab8Fy/Sgxir0ku3kC8ZCrYCnUTfEPU9zGzNvX5SRYt7/4EuPpp4/sEhgM5ueZi2wJbVFdXc/d5vV6NGP9h5hx0+tWlmMBXjh/FqmU/6t1ACMoaxNwTsxx+4H3QoLaWmgTcGHP/Rem4pXApKziHEHKS8a8DwNnpXuBgRG6W3+1lOyAcm2yidtFOnKN1EavRczHLVqjs4g22xsUWsDwGTrZ45bVZEVvi1rqPufenya+HZ+UHW825rlOgvaYG+2ZXoqHsLOybXZkRV2gm4XVLIoRoxBYAOn3s39Ujw5PL9zzFg6P+1ykKv32mVhlc8e2ClDB0KVNKWb3bBGlEdn8Fnv01gm7tw58jk030XLTEHY+vcmj/UhJBtZXHipOmgryG9i9h3kIm+mtXUY/JqG2/BO3V1ZKrMnAhyoPvG58oP3joWbkWsqLViXJy/BFARtyhmYDXRYkltgCQFwoyLdzRrccTXxO/H6MX3+b4Wgcyshs8Hb2gBztWeikL+pCCqirMKszD+qd/hUhPb+2eJ8fnzGQTpjjGBY1GJUvXE1BYqCpYTS541qEjUJgTXZ39xCVlRcepx2TU4JsIQ3Lbt7e3o8Z9AeDqQHlsh/GS9F6vxaYgykS5xCvhxB8b6jaibtVz6Dh+DPkjRqJiwU3pien3MXI2sjpWK3+vZmbjTrwzeRoi7t6PMT+NYeE7b0lTnTKYpdzfGczx53iXqQ5IY7IilNIZqv0EwEpI1TunAHyPUvqJmWsLwc1i5A/RtHy4GoljOCgJriYWqjpGaeWZSY6yGstNgkolR1bcy0o8AQCxxOupxSUJsZUJRylqA99CeU5br2Xa08V+8AjotPXkZUW/cRdTcOVEOaPtDXUbkx7COo4dtZ1Il431wLwuSmrLFwAmHj0MANhSOhWdvgDG+nOwtLQY18x+An3cGE8w8JjFShaOoxzPNxNSiexMMxcVgpvllFXMSo/1YkYcgyekhKLaB3WyfBXCXXmfttZVzYx/Bvat1783z6Ut1/dyG24YEO6Skp/i62vnDANpD0aS52FN/Qfgk+eAmMq12dMpWbIsN7FeLJhxjqe4WCpnUaGOP9atei7J4wEAkZ4Q6lY9Z+n3pLllLRp2LQGF1JaxO9SEhl1LAGR2hB4LpeXb1t4GQGpAAkiie8axL/DJiE/w+q3p7QTb1w8oqdYkD3R+fv2VmsYXt7/w575ofGF7PN8g7ks4OEk02z8niE3nD0fzKJ1Ji4FhvTFIwgnlF4zt/Vpu98c7NjAcuPIX8aYYvMREAvzDbwCXKjvb5e2t71Xe0xIkyVItQCfzqAJ0JCdIbf8j4GG8T9EefgKZ3hoZ58iJckmrZcQfeQlzVhPp9ux6OCG2MhQ92LPrYUvXaajbiKdv+T5+vqAKT9/y/bTViZeXl2Px4sX44Ksf4MORH6LL3QUKii53Fz4e8TEiRemtEZX7ZkuNTmiib7bRsAq72KlJHkzExfa3kPopk/j/v41vTxUKYD0h5GNCyELGft54PkOE4A4ikj80gG6/C7snD42LrkoA3TlASCE8LIvTG5Aym396BlBdIP174y7gvO+xG2Jc/tPe73mCRFzAwc3awebK7yfO0a7XEK0ruxJ18CL5g9qLMCqh6hIVDkpuZRbth9h9otXNP9TnqCioqkLxQw/CU1IixR9LSpidxXgJc1YT6SKUHZvnbWchu7c7jh0FKE24t9PZnGXR9EU4VngMb457E6vPWI03x72JY4XHLPfKtoqdvtlmUTbvmPH+TrzS0mpYkyzQHc+XKhfHy10vB3ALIeRS1f70jecTDByYHxouoPHcKZLrWNmIPidP60IF4tZr/JhzbpBcrcr4ZrAV2PZ/wNjzey1d4paOVbpRWV2qAEnYt/5Osh6VyNZk/YuSxWnFpRwYzjy+HHtQhbdREPAAoCjASVThbZRjj4VrD2N3uwKkul0WnIeNgqoqTNxQi7KGXZi4oZaZ7FOx4CZ4cnxJ2+wk0oU72dEk3nYWeu7tdDGvdB6qL6pGcW4xCAiKc4tRfVF12mcdp2us4istrbhjz5c4FAqDAjgUCuOOPV9ia04e83hW8tggJV3j+UApbYr/fwTAqwDUza/TOp5PMEDQ/dC4+LpkQawuZF+ExnoHE6z4KluUoz3A395FUtbz9j8C4y7Qdql69Yfmy3jaD9krPQp1aDKUZcoLulCOVUDQqOFHLpQJV9K2+AMDLzkqonpoACTPgZ71a4BTiXQndk3AqOl74fL2PojEwgQndk2QIlQmcMq9bZV5pfPSLrBq0jVW8bHG5kSnLJlgjOKjM7+aSApTwqtVHoSkZTwfISQXgItS2hH/eg4AdQwofeP5BAMHS4PJeS5f5XbdTGedlosy5dcxRZALcdnLUI6F2feR+z6bKWfy+CQrXW21BznzkIOt7IcR2XJRu6AtjC8sq5iFhU89g9tX1WDhU8/YSqqbdsmdOLzpNPR0eEAp0NPhweFNp2HaJXeavgbPje3LZVtn/Zl09c0+HGLXGJ/0+vGHmXOwb1RvaNDr9aKysjKl+w0g0jWe7ysA3iOEbAfwIYB1lNI3+2w8X39hILV2TBfqgemA9KHBnFVqZrqO1Q5Q6rF9tq7hMAWn8ct+1KhLpIxqlXm4c5Jd5i6vFKNWbjNqoekAqdbzNtRtxBu/fhI0qvVQnPPNK/CNfzX9OdQvSEeW8oz3d+IQR3QBwBON4LI92zCjp3MgZynbau2YwSxl2wjBHWRY+tAw6pRU/yKw5j/YlhwL1jg+lrDbxmaNL0vwNJfmlCoFhgORoHb9HBe2JdRjDnlYmWHsME/963fR3dHB3HfFrbcPiIYc6USO4ardykqG9gSxd25axuJlC4Oml7KI4Q4yiovmm38qL79O/4Nb3vfGXeasPGWrRnXXJeXAg1CHeRFPvkH8f4vCGwvHE6vAfh1qi1RJsBW4+rfa9yBVsQXMubotdrRymu5OdmkVAMu1wYORa4qk37vHGptxqLtHm50Pyb0sGBiIGK4gNcqvA+76G1DdHs9yNkk4CKz+geRSBuINLdqka131X9aupUFuAwlJSHl1wUqCJ/hZxTl5OuuJ34d3birodbKSsTrD2GH0ypHSnTyVaVjlPKxtRlxTNBxbL5qKoeFu5n5/NGz5moLsRAiuwDl4A+n1YE3eKb/OoDkGDPYBiTaQd/0N8JvJ7KT8WHLwRDyzmFN+98Zd6YlDR0LGiVTcGcbp7Gst0VC3EeEQWyQAh4ZsZCmscp7bGg7itt3aEh+zAvnvwwLwRJPrwkksiognJ+matzYcxF17UkrGFWQIIbgC29TX12PFihWorq7GihUrUI8pUqKPsp438b0OPIuM2xzDLbWINBTzuOjwMonNUjA27p7luKn13OnEpe2aZZZwl/FIQL0GIg6PD1QiN73gxW8dG7KRRSit1x81HNTEXcMAwqqcmGCM4rFGc7W6i88/F7cP9WBoTxCgFEN7gsh3u1WtWaTfwueaWoWl2w8RMVwBl71bWvDB2v3obA0hb7gPF86fgEkziwD0tp6Tu+G0t7ejZu2rQM5mlAcZyTtGiVHtX0qWnPI8Vm9mZfbuuAv0+zzLYmRqqAIHuXQIkNzTVjOSaQxweeydq4Y1/IDXv5pGk2O5DidWsZpeyOSPHGU541nvdy0dvNLSiscam3E4FMYYn1caehCPp7KOXbbvME5EepPmzA+A5Jf+sFh8/rlYrPi+eOOnzOMopLgvb82C7ERYuFnOusZ1mPPyHJQ/W445L8/BusZ1fXLfvVtasPH53ehslT5UO1tD2Pj8buzd0gIA7NZzUYraYBk0FpncY9kwLqt3nsJiVjbPWPyZlLTEcmXLQll5n5T4ZAnV/epflIYV2CHaE4/xOpCMKQ8/kNHrXy17DuTEKiNr2QLc+CwhlmuDjX7XnIbX3YllMcrHKsXWKmN8Nj0cBudaEXKBeeLDCF4mhOwmhDQQQi5U7SeEkF8SQj4nhNQTQqabvbYQ3CxmXeM6VL9fjeauZlBQNHc1o/r96j4R3Q/W7kekJznTNtITwwdr9wPgt5hLmsCjdBXriaMa1nnVbdL/LKvMjDBzWuUxKThNe7/aB/XLhoyQLUsuLlXilY44W2kgwuvOlWJilVM9nQHj3zWn4XV3Yrl+Wcfy8ALwqrKMvVGKGX89gWfv3mTrAWJpaTH3NyEVIRfoshLAm5TSKQDOAdCg2q8cz7cQ0ng+UwjBTRNOWKYrP1mJ7mhyUkp3tBsrP1np1DK5yNYGbzuvxVwBVDE9dfKOWhx5WE36MRJms3FcpWWcynrUEJc0dIH3sEEIEFVaLDof8rL7XZlIpdcZLA2JVU71dAaMf9echmcZsrYbWZEuAKAUBV1RXL2jG0s8+Rjr84IAKDgVxbwPu3D2wR7bVvs1RcNxU8lwzV9KwEWwtDS1tpL9nUNL6m44tKTuwKEldbH4/ylPCiKEDAVwKYDfAQCltIdS2qY6LDGej1K6GUAhIcTUD0MIrsOsa1yHilUVWFK3JGXLtKWL/cfJ2+4kecN9utsrKyvh9SY/YUuTdt5LPoElBEpx5LmZbY/g46C6Xj0mYwX+BdW4DSvIQinhS20Z21kPcbHd13I/aWV7SPV+SxZ03DW8+gfStCaWmMsPD2badFqkrGIW5iy8FfkjRwGEIH/kKMxZeKutuluj3zWn4VmGrO16VqQPwFUfdeHeF0/gR39ux6Rdp+B58SD+6B6B5W934Uc17Tj7YO/P1K7V/tPJ4/CrsnEJIR/r8+KJyacN6vhtXFw14/kcEN1SAEcBPEMI2UYI+Z94T2UlYjxfNiC7gNtCbZp93dFuPLblMUvXK8plJ43wtjvJhfMnwJOT/OvhyXHhwvkTAEjzSauqqhKWbkHAgyrXO8mTdnjWohJeKVEKDf6N7lOPyajBN9GOoQAI2mkuarzfRn3lH/mJRBPnGN/DGwD+4b+B+U/xY6of/978sAazBFt7xZzlVjd4j/duacGzd2/CUz/cYMn16URPZ8D4d81plpYWI+BKthl5FiPrWAAY5nbhqh3dmNqonpYUSyR/sbBrtcu1us2zzsXWi6YOarGNk67xfB4A0wH8mlI6DUAXgCWqY2yP5xNZyg7CcgErae9px7rGdaYmnaxrXIdTYXVvbsDv9qd99ieARIaoXuZoeXk5ysvLUV9fj9raWqwOfgO15EJU0ndRXtBlLhNW3WkqXa0JFfepbb8EYSRbLvKsUW6v2n3r2dvldpVyk4rVC6XXwBNVp8VWJhyU1shqBanzHssJS3IMVXZ9AkhrlrASM79rTnJN0XC0fN6G/+xqQ5ufoLCb4v8NLWSKmLITlDqj+ak/bmBeX34NPHHdu6Wlz97bAUy6xvMdAnCIUrol/v3L0AquGM+XDZhx9crx15WfrERLVwuKcouwaPqiJBGWLWW1eBf6CrHk/CV9Npps0swiww8GTXlQ3FpEZZX5RutGLSSdIn6f9upq5m7dWaO8eCeNSbOE1e0V7fZ1TgW9mCznPdZLWOpLUTDzu8ZCr5yIt2/vlhZ4XjyI/6d43Z6ck9jrHcJcwzVFw5lizBNV+V5vP7OLuea+fm8HKGkZz0cpbSGEfEkImUwp3QOgEoD6B2l7PJ8QXAcpyi1Cc5f++y7Hc2Uxlb/v3OVG9/v56GwN4ZS/B2PHTsXnoz5OOjfgCfT5HFAjmOVBRtZihikoKGCKqzIRTDPk4fQiFH/B+NkWjOXM6JXbS/ah6NqIyfZ1wpKT6FnnALj7nHrIuHD+hKR7AL2u8Ekzi7iC2x/e237A3ZBiuEq3shPj+QDg/wF4nhCSA2kM3/fl0XyU0t9AGs93BaTxfKcAfN/shUUM10EWTV8Ev1u/0biLuDSW69iWqWh5nST+EId0F+CyxgU48+h5Scf1RbKUFdpratDe1sbep2ctZhhmwpdi1qg8xlAaOE7RHWrC7tOB5iJVaZHhPF3aG1M10885VczEmVX0dcKSk+gJp94+px4yJs0swqwbpyTeq7zhPsy6cUpCtPvze5vtjF1e8UcAPwDwBaSn2i8A/CC+PSUopZ9SSmdQSssppVdRSk9QSn8TF1vEs5NvoZROoJSeTSk1PaZOWLgOIlufKz9ZybR0/W4/M8Y78+CV8MRUAhDLwcyDVyZZuX2RLGWW9poaNN97H4Z88xs4latt3M8rG8oGZMu7trYW7e3tKCgoSJo12rj/iaSZwQAQQxiNU4pQHAxpY828blfK8XpOjSF0eQFfPrtrFS/OrIOelZYKclyf9f46hR3h1Iuv2hFCPVd4ut5bHn3xnmcTcXHN6vm3aoTgOsy80nkJ4V3XuE4Tq2WJcV4PeyqMcntfJUuZ5ciKJ0G7u1G+vR4fnf81RD29v0pKazFbkRO+WHSH2GGB7lg7sPhz7Q5eC0plprXVUYY8COGfb6OuNh0JS8y2nzU1AOCoABgJJ2ufL9eNSCg5cW3HuBxsPCeAk0Pc+M/3dya1ebTSAlJNXyaD9dV7LkgNMYC+j2ElRP3jJ9XIC2lF95S/Hf837X5mYlU6sPLh0lB2FhD/3fli3DjUn1OOU0OGYMipU/jWP/1T0h95f3vy3rSpIu5OTsbvK8Eoz0vsD1CzvYpXfDX1yULEzc52NjuwnoNT/YxXrFjBjZEvXryYcYY91DFcQLIgZ904BQA0+1jsGJeDdV/LRdjTW+kRcBE8MVlKQlUPhw+4CK4rGoba4x22RDhd8N7zQCCAnJycbP/bEwPoBfZgWbVKoVS6neVjxn8zF23rXZoPjvnfvRB3zqzvk3XLPWPlDxe5vywA5oeJp7gYkSZJlE4/eBCnH5SSAz0lJZj4+OOJ4/rjk3fphDuwe/c9SW5llyuAIfQHOiU0JjOtnRibR6OSBa1nUVvEyfIgbtvP9nZUV1cnffCn8jBmxoLUi9kCwMbyQJLYAlKbx1saDsIN7ZCCYIziuabWRCqc0d+JknQOaOC958FgEMFgMHFMtv/tDXSE4DqI2nqVM5Bl9IR476i+nZaiRq+/LOuDZPTi29B8732g3b2WOvH7MXrxbUnH9UUWc3tNDY6seBKR5mZ4iosxevFtKKiqsn294qL5AJCcpTzhDqz/5UjNhBzL2a3cyUUWMpoLTpPE1cHaZSfLg3hZ4DLyB//Bgwexffv2lB7G9GKo8r5n797EFd32Ify8UV7FtPqnpPd3IpPuemej91wm2ysIBjpCcB2E1/v4sS2PIRQNMYVYFl27dYhOYaW/LICEoBkJHe9DoK29DXNenpOyq1xO3pKFP9LUhOZ770taox2Ki+YnhFems5Xf6ICHxuMx7R8wb9NvtdbpOTeY60IlW7I2apf1QgZOlgdVVlYmeTVYhMNhfPzxx1CHtNIhCHqvoeBUDO25qWeQG/VcTne9s5n3XCabKwgGOqIsyEF4ZTvtPe0ZG0JgFiv9ZWUKqqowcUMtyhp2YeKGWqbA8bKVT7lPOTL9SE7eSlx3RhQt95zE1iG3YdOmCjS3rLV9bTVWyzyY054OvYl1F/9A24Lxyl8A//AbbQtGl1eapcuagmQBo5F0TpawqNt+8uDljzgtCHqvoXJXNyy9Qs6ajSb3pLveWdNqtaAAgQB7UAbv59JQtxFP3/J9/HxBFZ6+5ftoqNvoyNr6E4SQyYSQTxX/ThJCblMdY3s8n7BwHcRM4wslzV3Npls9ppulpcXMBJFUJ5KwnrwjJILPhknJPfKDh933INLc+36fmhFF+41R0PgnaHeoCbt33wMAGmvVDlbLPLjTno5twTyLLRhTxShkkGoJi9Z6HptIkOIl9BBCmKJrpqTMSjyU9dqkBQDXjx2ByrLReKyxGYc4VqobQAxAQTCGCV/2oL7UlxT39Uap4d+Jk6VIPNSZ9+r8CYBfQdBQtxHrn/5VImTScewo1j/9KwCw3SO7PxLvLnUuABBC3AAOA3hVdZhyPN9MSOP5Zpq5vhBcB1k0fZEmA9nv9sPv8TMHGgDQuJYzhV7P2FRQ1ry2tbfhlPsUPhv2GQ7l9yYPpdLQQ5m81TG/V2xlYrEgGvc/4YjgWi3zsDXtKU1tLo1CBqmUsBgl3LEeurxeL84555ykGK683aikjBUPffuZXWje34bLbpjCPMftJYiohzFRYPfmFsyaUIitF03VvA6gN2P5mqLheOqHUkjhtOMRbCwPoH2ICwWnYphVH8Q139D/O+nrmlzAuN5cSd2q5xj5CSHUrXouawW3urr6BkjDCsZBaul4d3V1tZN1uZUA9lNKv1BtT4znA7A5PrC+2Ex7RyG4DsLKQJZrZ1m9kYHULTwn4fWMTRX5yXvOy3M0HoDpQ8KYX0hRu+HMRHKSFXFUJm9FOUvn1dXawUqsnefxyEQDkzE+L9OCU7pC7eYRGFnP5eXlqO2O4dcngjjp9WNouBv/PiyAK88/F+PGjbOcpcyKhwLAZ+82oXhCYdJrYJUOKVHGUY0eOmUr9eyDPUlj98xYqVYeaFKp/VWjV2+upOP4MUvbM01cbJWtHU8H8Nvq6mo4KLoLAPyJsZ03nk8Ibl+jbHyhZkmdeuiERLa1bEwXag/A9CFhLBgWhjyZrTvUhO07b8enR7bh8vJqU9dUJm+5Ww8gOkJ7jN+XmUHdPI9HJhqYpCtkABhbz6+0tOKXQYJgjhRTPJkTwM+6gJ9u/BRjfV4s/e7NlgRFL+6pTkLiiTPvenoPnVasVJ7L2+iBxmp5XqrI6wTJA2iHZn/+iJGO39Mh9MbzpSy48T7K3wawlLWbsU2M58sm5pXO47Z8zKaWjelE7QGYX0ihGoMKL6E40fI81uV9zbTVX1BVhYKqKuTFeyCr62dLJ9zBPM+oZjpVeB6PTHgznA4ZyPWzW3PygLLzAKLNvyz0uDHj/Z1My9pOHauM3ug79XYzSUk8C5UlmrNunGJopaZSAmS1PC8VlOv0+C9B5NTbACKJ/Z4cHyoW3OToPR0kXeP5ZC4H8Aml9O+MfWI8X38gmyyeTKH0ANRuOJN5TKE7hp/ZcLPz6mdZLmq9mmmnRTcbwgWAJGgXoU56f7qb4d9fjGZYc+EDvck4uwpH453J00AZYuslBJ2RKE5EjOf/WhUUvdF3avHUE2dA30JlieasG6fg5kcv1l1fKiVAVsvzUkG5To+vDAAQ6X4PiHUgf+QoVCy4KWvjt0jTeD4F3wXbnQyI8Xz9g2yyeLIBv6+Y2ULxRJTYdrOz6mdZcDOIsySeng6aVR4Au1nccjOTLaVTEXFrP0LcALygOGVhbVYEZdLMIjTvb8Nn7yb/7rDEk5uhDOjGUVMRTT3r2yi72kys3SnU6/T4yhLCu/Cp2Y7fz2HSNp6PEDIEwDcB/JtimyPj+YTg9jHZZPFkmtIJd2D7ztvhJb0utJ4Y8Od2T9rd7LYyiNNMqh2zNDN8VdY9cwqSjSxuucSn08eu84wCOGWxRbtZQUkkFBWHMXrBSHy9/hQm7TrFFU+72dep1M3yrGpfrtvQ1ZzOWLvZdeYN96W1DaUTVFdX/7G6uhpIQ5YypfQUgBGqbb9RfE0B3GLn2kJwBRmjuGg+Pj2yDSdankehO4YTUYI/t3uwK5SP6ovS62ZPJYM4HbHfVDtmmbFeuVOQLGZxBwIBBINB5IWC6PSr81bA7EGsRN3E0qygqBOK/k5jeO2cAJ64bpKuO9pO9nUqdbM8qzrUpX1X1FZzusrzzK7Tk+PC+K+OSGsbSqeIi2u/Gs8nOk0JUiLV7jSXl1cjcOaT+NmJCXioeQiayThUX1Sddi/AoumL4Hf7k7aZiaczu0el2C0L0HbMAgDa3Y0jK540db6e9SrDy9a2ksVdX1+Pnh6pJGZm4054opGk/QEX0RXbp8rG4Vdl4zDW5wUBMNbnTdS5GqGXUOQ0F86fAI8qo89s3ax6ML0RamG/pmg4tl40Fc2zzsXWi6ambRKRep15w32YdeMUHPjsONedLkgNYeEKbONUd5pMuNntxtPTFftVdswys12NGeuVNwWJl8Wtpr6+Hq+++mqiO9TEo4cBAFtKp6LTF8BYfw6WlhZzuzYN87gT4mFHRKwkFKXqEk11lq2ZwQkyTnabsgrL+uclpDnVhnIwIwRXYJv+2J1GiR2hT1fsV9kxS73dDLwENKX1aiWLW42cmaxuxTjx6OGE8MZjagDYc2QfnjgmpaYOZhOKUinLcSJ2qbyGESyruS/nR7NeL8+dTlzAUz/ckJUx3f6CENxBTEPdRtT+/mmEOqWCd39+PmbfvNC0WGZjd5qGuo2oW/UcOo4fQ/6IkY6XNqSre5TZcYc8WNZrNOrG3r1TkZ9fn/jANpvFrYY1ZlGJsv8xLw4JIKWmDkYJRXpCF+mJ4d0X9+iKqRMj9Iw6W6nxeJPd1n05P5r3eqdcUITdm1s0r4HGv83WmG5/QMRwBykNdRvxxq+fTIgtAHR3dODN36w0HYfldaHJVHca2cXdcewoQGnCxe3k1BO7sV8jCqqqUPzQg/CUlACEwFNSguKHHjSdpVxcNB9TpjwCl2sUKAW6u3Oxb+8F+OKLItTU1KC+vj6l9elN8GH1P2bFIVONwV5TNBx3uPNRGIwBlKIwGMMd7nxcUzQ8IR56VmWoK5rYL4vG3i29ngm9UiCzmOlspaS7K5K0Dr350U7De70732vClAuKEq5uRpm1iOnaRFi4g5S6Vc+BRrXpLbFIxLRLuGLBTUkxXCCz3Wn6wsWdzlpquWOWXYqL5mPVnxo14mh3xqzStcmb7EMIQVVVlalrp9rUYe+WFnhePIj/l5RVexJ7vUMsCx2gzRB2YoSe0bHE1WspstbBe7DhbVda9fK1Uy19orH4UIcbp2DSzKLE0Aaz5/d3CCGLAfwrpGT6HQC+TyntVuwnAFZCqsU9BeB7lNJPzFxbCO4gRc/ta9YlLItYOl24VugrF3c211Jb/cDmoXZtssTW6/WaFlsg9aYOehao3Q//ztZQIrGJJYaAtaQmX66bWf6TN9yHmx+92FC8CgoKmD8r1shCtUvYqstXrwuX8iGgL0YLZguEkDEAfgTgLEppkBDyIqQhBr9XHGZ7PJ9wKQ9S9Ny+VlzCZRWzsPCpZ3D7qhosfOqZjCZLZZuLOxPwZsmamTGrhBezlR7upetZEVtAisEGXMl93600ddCzQPU+/POG++DP5dsW8nVZYmtlhN7eLS0Id2svQtxIXIO3Tnl7ZWUlvN7kBxDeyEI9q96My5dV+qREfl9SKZFKJ7UbJtxQu2HCgdoNE2Lx/29w6NIeAAFCiAdSJyt1NmJiPB+ldDOAQkKIqV9iIbiDlIoFN4G43ZrtLo8nmxuW61Kx4CZ4cpI/0PraxW2nLrm5ZS02bapA7YYzsWlTBZpb1tq+v5UPbD14FjGlFNXV1Vi8eLFGbOvr67FixQpUV1djxYoVmrjxNUXD8cTk02zV4AL6YsUThW9+/yzc/OjFqLhukq64KJFjlnJdqtnEoA/W7kcsqvUE+PyexDWMxKu8vBxVVVWJByS9Bxsjq95ov1yHy4rRyjx79yYAYNbrZjJhKi6uv4XUT5nE//9tqqJLKT0M4AlInauaIfVJXq86jDeezxDhUh6kyJZoKlnK2Ya87g3PPo3uDuk1uXNyLF0jlSxnO3XJTvU3ljEzdNxM2QnPtUkIQXV1teY8vezafaPHOtI5SW88Hq9uFkDCZezLdcPj9aC7K6LrTqUx4JbfWO8lzLted1dvcxAz9b1mZ9gaDWawMqeXl1ltZWhDH5OW8XyEkGGQLNgzALQBeIkQ8o+U0j8oD2OcKsbzDVTWbDuMx9/ag6a2IEoKA7hz7mRcNc3UA1YSZRWzMi6uRv1/7RAJ9Q4HD3V2mG7GkWojDztJW071N1bC+sBWiqwSXtlJZWVlkoDKyLFc9Xm87NpffrwDtaXUVCmQco1u6seQ9tMxcsg4zSxZnlipmzioY5yhrmjC6jVqSrF3S0va2kGy1imvxUqNq95gBisuX/X7qsbs0IY+Jl3j+b4B4G+U0qMAQAhZDeAiAErBFeP5Bgtrth3G0tU7EAxLiRmH24JYunoHANgS3Uxi1rqzYnWmkqmcapaznaQtJ/obG1msautTDSuLWW0ps7KUlefxXNB/LTqDWQr04O5D6PxlQ0JkimbEsLXh3cQao6QbHUP3ASeBjc9L22ShsuLi1Zv4ozfmz47AWBlQL5NK7a9aKK1mKauv1c8yktM1nu8ggAviE4OCACoBbFUdI8bzDRYef2tPQmxlguEoHn9rT58KrhMNJsxYd1atzlQylVPNcs4fMVKqAWZs52GmQxQLKxarUdMK+Tw1SktZ2UWKdR7PBc2bKPT3WHJN7JZtWxBzq9boiqEr7wD8x75iSwCNynwmzSzSbWNo1cq10w4ylTGA8j2dtDz7UUZyWsbzUUq3EEJeBvAJgAiAbQCeFuP5BilNbUFL29OBUz2UeVZcsLsJDXUbUVYxy7LVaVb0WA8MdgRTiZ26ZDv9ja1arGZKgoyymI3KVXguaN5EIQD45ZUFmFUfxNkHexBzscUx5u4VZauYEQ+9OKidbkpWBdBM7W9fjsqzY6VngsrZ+/9Yu2ECoBrPVzl7vxPj+e4HcL9qsxjPNxgpKQzgMENcSwrZloRd9CxYpxpM8Ky7cKcH61+WBNyq1WlG9HgPDFMvq8TOd2ptN/KwU5dsp7+xVYuVJ5YyZrKYWYKqPE/tgpaZ2bgT70yeph1UTwjac91Y97VcAEDJKR9iHq34uKK9mbF6sETJjHjoxUH7InZp9FDgRLtJK6Q6tKEviYtrvxrPJwS3n3Hn3MlJMVwACHjduHPuZMfuYWTBOtVggmXdxcIETVtGJwTcqtVZVjELh/c0oL72TdBYDMTlwtTLKpNEj/fA0LjtI8xZeGtKrnI7iWhW+xtbtVh51qd8nJnm+Gayn2UX9IoVKxJrVE8UAklO8Ax7CDaWB7DwL+PRWbAPlCiEL+ZCbud423HQWTdOwawbp+iKh/x1pibkGD0UpOpytoPTbmpBL0Jw+xlynNaJLGUetb9/WteCTcX1qracSy+ZhfCQ9fDmRRDu9KBpy2i07ZfEouP4MVxxy48tuWkb6jZi5zu1oDG5+04M299+Hdvffh35I0ehYsFNug8M2ZC5bYRVi9WMWJrBbLmKem3yRKHfXMp+qGgf4kJerBgnZ4zHn3KAk14/8kLduOyzVlwaKcSF8ydgx+k5uOH9nczSIj1RuvnRi00lH/EydNMduzSyKJ1oNynIHoTg9kOumjYmbQlSDXUbkwYaKJGFym4PZZblvH3NUUgd0rTkjxhp2U3Lsl4T649b6r7cPOZr7MuOVI2v34+D3X9CtCAKd7sb4/zfRekVD5g6147FalYsWZiNIcolXpdUNCEUysWBv52Lo0dLE/uHhrtxMkcb+ijspohcNw7PRTsSGc2d/gA2nD8WV0w+DTugP2XICVHKZOxSz6LsR0lMAhMIwRUkUbfqOe4+WZDs9lDWE0MWsoBbsTqN3NqRnhA8vhx4cnwZG7rQ+Pr9OOD6A2ih9H20MIoDPX8AXocp0XXKYjWD2RiissSLEMDv78LESZsBAEePlsLr9eLfhwXwyyBJKhMiAOZPGIlnjncgGNaWD/2o4SC0nYl7pwxdUzTcEVGyG7tUPoz4cz2goAh1RR2LffaXJCaBOYTgCpLQEyylINlxvVqJ8frz8y11eJLFnzfVRkl3ZyeuuOXHGRu6cLD7TwmxlaE5wMG2P6EU5qzcVCxWK5iNIbJKvNzuKMaf8Sl6eqYlHgha9hzEc02tibY8FMCLLSc0tboyLLGVkacMWRUlnsVuNXapfhhRdpQySm4y6zWw8yCwrnFdWqZZCVJHCO4Ap2vbEZx86wCibSG4C30YOnc8cqeNTjrGjGBZEUAevNivGk+OD7NvXshcn1oc1W5qI7GV15HJWG20gC0jvO2ZxKy7llfi5fefwuLFixPf1x7v0PTAC8Yo3NAXVxbylCErouRk1q/RSEBecpPVNVh5EFjXuA7V71ejOypNk2vuakb1+9UAIETXJISQRQB+AMkB81tK6ZOq/WI8n0BL17YjaFu9DzQs/WFH20JoW70PABKia0aw1AJoF17sd+pllWjc9pEpQVVnTPPc1MTlSiROqV9LpoczuNvdiBZq5cXdrh0m4TSvtLRa6mts1l1rtoEHb/ZtFNLkIJ6lq0Y5ZeiVllY8Fj2Ow9/MxRhfIZaWFmMS5zWZtdjNWKBmYsRyAw3ltSKhaNoyj1d+sjIhtjLd0W6s/GSlEFwTEEK+CklszwfQA+BNQsg6Suk+xWG2x/MJwR3AnHzrQEJsZWg4hpNvHUgIrq5gUeqou9VO7JdXwvP6r34uXYdjMVNKccWtt5sazuBE1ywrjPN/Fwd6/gCqmKtAeqTt6eSVllbd5CMWZt21vAYeu0feh0WK7OJCjxsnItqHjbFx8ZcfBlzgW7xjFQ8KVl+TnsWuFEb1PpYFajQ8AAD8uR6NNcvDiczjlq4WS9v7M0UbP70BqsYXLbPOTbUutwzAZkrpKQAghLwD4B8A/ExxTGI8H4DNhJBCQkixmfaOQnAHMNE29h+wcjsvrkopxe2rahxfk1VXrl7cV8897cvN01jT3R0dCaGWRdWprllWKL3iAeB1KWZrJ0vZLo81NjP7Gi/dvgc7fvMWM/nKrLuW1cBj98j78HDLaARjklV7KBSGF4CXEIQVnhTZWr2maHhCJNVCKh+nHufHe01yQpUankiqhVFNpCeGd1/ck/Q+jP/qCOze3MI9x5PjAgXVdTur15YqRblFaO7Sfu4X5Q6sutq42CpbO54O4LdFGz9FiqL7GYBHCCEjIPVSvgLaXsq88XxCcAcz7kIfU3Tdhb1/2Km2M0w3ZuO+Sjw5PhACw/IgwLmuWVYpveIB0wlSTsFz5570+gHwJweZjSGqG3gsen9nQmxlwgCGuQi8lOJIlCIvFMTXW/6GicMIoBBIWSyN3N+818TbzrPYzQhjqCuKUJdkd3e2hvDZu03w5BD4ct0IdUWZWcq8hhpqWF4DOy0dF01flBTDBQC/249F0xeZWkc/Ii3j+SilDYSQnwJ4G0AngO2Qeior6X/j+QghjwOoguQn3w/g+5TStvi+pQD+BZJX6UeU0rcytc7+zNC545NiuABAvC4MnTs+8b3dmtq+grU+FvkjRyW5hF9/6he6x0d6QpK7uauTud9q16z+wBifF4cYQpQX6nUDsyYHKWmvqcGRFU8i0twMT3ExRi++DQVVVcxjWfcCgLZoDLe+vy6pjrim6QCAXqE3G2vmvSY5oUoNz2I3K4xqIj0UAE2M/FOyd0tLYoKPGl+uG16fJzGnl4Dg7Wd24YO1+xPCaye5S47TDoIs5XSN5wOl9HcAfgcAhJBHIVmwSvrleL63ASyllEbiTxRLAdxFCDkLwAIAUwGUAPgLIWQSpTT7UjizHDlOq5elbLemtq9IWh/H0s0fOQoLn3omaZve8TKhzg748/MTw+qTrpklFr6TLC0t1rhpPdEIZjbuTDqO18WqvaYGzffeB9otWU+RpiY033sfAGhE95WWVhCwH/vze4LM2bmy0FuJy7JekzKhigXLYud1mjIDL+lq4/O7mWLryXHh0usmY9LMIkXGcq/lvPH53XB7ie3Eqnml8waiwKpJ13g+EEJGU0qPEELGAbgawIWqQ/rfeD5K6XrFt5sBfCf+9XwAqyilIQB/I4R8Dilj7IM+XuKAIHfaaE0ZkJpsb2cor08dbwX41rhZy5hSZLQJRl+idtPm9wTxtc8/S/Q7luFNDjqy4smE2MrQ7m4cWfGkRnAfa2zm+th64MJvLp2PvFAQMxt3Ju4vC72VuKxZ17MRekMMeFaqErVY80qGiAuYdeOUJEubJayRHnP3GcSkZTxfnFfiMdwwgFsopScG2ni+fwbwQvzrMZAEWEYOSGsghCwEsBAAxo1L2ZMgyHKsWONmLGMACHVltgmGXew2N1AmJtXX16Nm6xEobU29yUGRZvZDPGs7L4YKAN05Ug5Bp38I3pk8DYDUb1kWeqtxWeVrsgvL1WyUFKXEn+vBs3dvSpzLE0YaS3YJWxVQ0dJRomXWuX8s2vgp4HyWMiilFYxt2T+ejxDyFwAs/8c9lNK18WPugRSUfl4+jXE882GZUvo0gKcBYMaMGeYK+AT9GivWuCl3dIabYNjBqeYGVltEeoqLEWnShqo8xcWamGuh24UTUWOhirg92FI6FWe1HUkIvdW4rB5WEo/UruZn795kSmxdboJQdyTRaUpPRNWCaaa0SHmfcCiCp364wVLryL6cp9uXxMVVjOeToZR+Q28/IeRmAFcCqKS9HRdsB6QF+vR1vWmmYbmglbBcx/3hPXKyuYGVFpGjF9+WFMMFAOL3Y8udd+NeVczVSwi8APh2bi+dvgCqqqoS69CLy9bX15t+QEi1q5SRcMoCFg5FEtnLerAykfVc2Ur8uR6EuiNJWdJmXku65un2h7+TbCSTWcrfAnAXgMvkIuM4rwH4IyHkF5CSpiYC+DADSxxQmKk3feePu7HzvSbQmBRrmnpJCS67YUrG1pwqesMS5FF9yg8JKzW5mbQaMtXcQI7TqrOUnywoQlBlkYYpxTCPG7luV8Lq7YrGmE0vCoIx+IO9SU68uOzEI4eSpiTxyphkUp0la2R9ypnJT/1wg+E1WJnISotaL2lLtoqVvZrNvpZ0zNPNRO36QCGTMdxfAfABeFtqTYnNlNIfUkp3EkJeBLALkqv5FpGhbA9Nj+SY+g+vt970nT/uxmfv9joSaAyJ7/ur6HJLewjRZDUD5mty02U1mCXdzQ30SnIKqqo0CVKHpViahrZIFA0VZydd97bPvkDY3Rs18kYoZm0P4i+HduHtZ3YlHl6umVmU6CT1WGMzbm04KCV5FY5OSvLSK2NKdWyfnvWp/Jnrtb+8+dGLuZnIQK8bOzljWdvVi1e2ZPRazLwHe7e0oO7FvQlB9+W6E1nULDJVuz4QyGSW8pk6+x4B8EgfLmfAYbapvyxKO99je+13vtfUbwXXalMPvcH0StJhNVghnc0N7LR/NBtzvaZoON7+313YWB5A+xAXCk7FMKs+iLMP9iSSNJRitOP0nKS1nMwJJCVZybDKmF5pacV/frsQbX6SdB+AnXik57HgWZ/yz9yo/aXZ3xe9rl68NRglURn1wt67pQW1zzUgFu39fAh1RfGX53YlrUmJ2b8TgZZsyVIWOIzZ2bOy+PDKHozKIbIZq009zAq0EwPPlVjNOE5ncwOrrRIBa7WwF3YSnP1ndp2vjCxG/3lloWYtcpKVUnDVZUyJh4aACwDQnuvGuq/lAgCmtUSYHZ30PBZ6buPO1pBh+0srvy+8rl525+LyrPTO1pCUFBaKJomtDI2C+wCZ7d3pshkhuAMUM0+bSvHh1RoSl9Mr6zusNvUwI9DNLWtxZtUjcPuPI3JqOI7U/wM6vrwAgL2SDbsZx+lqbmC1JAewVgtrNkmoszXEvWenL5D4mlXGxHpoCHsI/nruEPx4RAmz6YWRBWpkKSqF8pWWVtzQ2IzDG1swxufFxWcNwaRdp7jnmsHOXFzWeUrsuqOzvTtdNiMEd4DCewrlTQGaeklJUgxXZuolJWlfazqxXUbEEOjmlrXYvfseeAJSK0RvbiuKZzyDcNcbaP/baSi74DrL68v0ODV11u+o6bNwhPXgBaDs3Xq0RWNMQTVbC6sWAN6DXt5wH9dVPTQsvV+8LGWeULcHXEyBMmOBmrUwWS75NWf7Ma87iqmNId1zjWBZv3qucPU+ueezWXgPBNnenS6bEYI7QOE9hc5ZeCvzD0OO0w6kLGU76Al04/4nksbPAYDLG0PJzCNo21+AHbX/h+IJBZY+eDI5Tq2+vl6T9Xvu7m14Z9I0qCUoBiTqauW47oftnag93mG5w5NSOPQShfJOz8Ftu79MmizkJoA7kIv/vuwqjPF5MdTXhI5NFYkJRaUT7sAY35mW6njNzPzlWZg7Ts/BDYrxg12RqMa6DgHYdH4+ZraBa51azXrfu6UF7764J0lAla5wQNuL2QrEDd0Hgv5Wu54tEF4yTX9jxowZdOtW9RSlwY2olXOW2g1ngtWDhVJg+9NlANh9nfWY8/IcZsZxcW4x1n9nPeOM1FFatSwOnT4Rb4yfyp1HK6Pulcwan2cGtdh0XlGMZ1xB7vADJTkI4V/pf+FivAdAmsHbWLwiPhZQf7SffG9lhq6MJ8eV1IKRBWuEIA8CoHnWucx9vIcO3v1ZxyuRHxTMiqxcsmQ2SzkNsJodDUiEhTuA6Y9PoX/5n/9Cfe2boLEYiMuF8spv4Rv/+h+ZXhYAwO8rRndI63YPd/b+GVnN1OzrcWpqq5bF2C/2ITZ+quG11DJjlFzFQx3/rN7zJYJhc4ZAD3x4ETcmBDcWC2LKsQfxxOS1hjFlnnCxBIdVKsWKFfMYeiqKvVtamCJmNeud16dZxoo1qxykIEg/QnAFWcNf/ue/sP3t1xPf01gs8X02iG7phDuwe/c9SW7lWJigaUvvcAirmZpOZRyb7cBUW1urK7aAFBvlxU+NUMZPzY7YU2JFxGSOIfk97w4168aU5XUd6u5BwZz8pJIhAPD6PBqxZZVKcddJKUC0tcYfdLIF1GrWu5Gg6lm4/lwPPD73gGvz2F8QgivIGupr3+Rud0pwG3bfh6amVZBGLbtRUrIAZVMeNHWuPFy9cf8T6A41oafTi6bNo9C2XypLsZupmWrGMSsWy+vAxHMjy8hZv+NGa0t9zCDHSe3U8wL62dA8RiLZq+D38UfzJa2LkKSSIVl01ULFK5VyA0y3eyBEkRONaWqN2ZOXzcWQzRwPJCdjsdzUFddNEgKbQYTgCrIGdScso+1WkcT2ecWWaOJ7K6IrC29D3UZ82f4cQDIbI2dZrbwOTAUFBVzRVVrG8lmPNTYzLV03AVw0uVeysvZ22d5Dlut5AWA0ovg73Nz9anIQwnXo/Zm+T2Zjdezf0LLxU6ZVzSsZ2lge4DbG4D0ERCG9ZnWs+Nv7eiyVAVmtseWVVvlzPRpBHYhDC/ozQnAFgwbJsmVvNyu4SrIlRs4dGM/YXllZqYnher3epOEBMrJbdsb7OzWiG6XA0Hiv5EOhMNzoFdQP2zu5k4KMLNhLtqzH2umViHhzejfGEzvH+nNQOSI/KTP630ccxZRjjegOEXzouRK/i92E7ohUPM6yqrklQ0Okc1hCx3Ovj40L+rJ9hxM9ov2EYNL00fB8ftC0gFqtsTV7PK+JhiBzCMEVZA1evx9h1YBzebsz8PJu+3erbp7VyhokzxvJBwArVqxgxoB5ItUWieLhiWM0ruPnmlq5a3VBcuvyrNzST9/H3I4O1M38Jk7mFWJoZxsqtryNs/bvwO2rahhnTAUmfxOvtLTiVw0HNT9JtVXNE8+CUzGucBl10upWeGBORGN4gnbgjuvGIe/1ZtPWpVoc925pSZqvqz5fiGn/RAiuIGsIh3rjUoUT2lEy8wi8eRGEOz1oblmbcOXahxd1M+/CzEZYVqvL5UJPTw+qq6s1AqoeyWcUA9brlcxy0epFfaOAbiw3f8RInPV5Pc76vD55+8hR3GvKcVneY9OhUBivtEgPAV0MyzvgInh0xhm45kr2Q4BeJ60Z7+9kus6f8Qax9dGLuWvWI9PDMQTpox837hMMNOQM38IJ7Rh3WTNy8iMgBMjJj2D37nvQ3LI2peuXlCywtL2/UF5ejqqqqoRFGwgEQAhBMChlU8sCWl+fLGL19fVYsWIFVq9ezY0BA5KFF3All0rKFp6dJCfZ6mRRseAmeHKSY51GyWhmMptvaziI23Z/qRkPOMztSqrPfaWlFTPe34nijZ9ixvs7E0J9TdFwbL1oKppnnYutF0klUyxXu4yd90VGr0xI0L8RFq4ga5C7Y5XMPAKXN/kDNBYLonH/EylZuXKc1m6WcjbBKgNavHgxAMk1LIutjDqJykw9ruym1rPweElV6qYYaniCZKdtoBlxCwOJWHDyQkli/F+h24XOGE10tuJlVptpeMHramUGp4djCLIHIbiCrEH+UD3c86/M/d0htlVk6R5THuyXAqvEyAVsJonKTD1uINA7JEBd11pfX48Vf3oWk3Py0DJlOiKuZLd8gBD43C7mwHlAX5CsJqPZrRkGgBORaGKNrEQvVma1kUXNm5Rkhr1b+C097QzHEGQXwqUsyCrKKmYh4GcPTNCrrxxM6JUBAexkKfV2o3pcAAiFQho3NNAr+O3t7Zh49DAu2/0JfKpRkKcoRXcshptLhnPd0amgdP12RWOwb08ao7ag9SzqsT6vrfaWMnpuY6vDDgTZhxBcQdZROuEOuFyBpG0uVwClE+7I0IqyCyMLduLEiZp9ckMLOW5rhlgslhBxJWrBn3j0MLwxrSUbjFG80dKKJyafhrE+LwgkQbquaBgea2zWxEn1UApsWd0O3NZwEIdCYVBIVioIwTC3CwTAMI9bI8BeAF5ir2Wv2hrnWedjfV5svWiqbbEF9N3GImGq/yNcyoKsI7mjU+8UmNSzlAcGemVA9fX12L59u2bfOeecAwCGcVs1rPuwtiln1Co5EqWYeOQQtl4kxY5ZHahubTiIWxoOJupa1YKlPoflpg5TilyPBw2X9t5HHXMGeht58PLV1bCscaMyoVTQ6zpldaKQIPsQgivISpQdnQTJ8JpXVFZWcmOz+/btw759+yyJLcB2T7MEPy8URKd/iObYvFAQtbVbEslaemVEvCQls/2Vla5evV7KeglPXgB5HjfaIlFu/2e9JLJU2LulBeFQRLPdk+PC+K+OEKVCAwAhuAJBP4PXvKK8vByrV69mnmMmZqtGFnE1LMG/6OAebJhwNiLu3o8UTzSCmY07k+5tlFHMSlIyW2JjJjNYT7x5FjYLPUG3A29ykdyu0epEIUF2IgRXIOiHqJtXyAQCAU1JENBrqZoVXr2JQyzBv+PC85Dz8Q78tegMdPoCyAsFMbNxJyYePZxkJZvJKFYLrJlzzLp0eeJNgER9bSbgjdzz+NyYNLMIbz+zi3key/0sXM/ZixBcgWCAUF9fj56eHs12l8uVsFTNxnDlml4eLMH/EYAzOK5uGVb8U43aUmWd4yUEeS6CtmjM0KWrjOe6wI7dplI3awYjETSqvTU7UWjvlhb85bldoNHe8//ynCTWQnQzjxBcgWCAUFtbi2hUKyc+ny9JHGXLlBACymgGwSsrMkK+xxtvvJGwsj2e5I8YZfzzUCisaZDBslRTiZmqE66Y4/Q41rFTlqKZVo1Ggmp2otC7L+5JiK0MjUrbheBmHiG4AsEAgecuDgaDms5UV199NQCtxcuL21ohEulN/AkGg4mmHPtGj00SzafKxgEwJ6R2Y6a8mK0bQAzg3tPJfsZm4q9Ggmp2QlCoi517zdsu6FuE4AoEAwReuVAgEGB2pqqqqkJVVRUz+couvKYcv/x4B2pLqWYg/ROTT0tr7JQXs40BaJ51Lvc8J5OUzLRqNCOoYkJQ/0cIrkCQQVg9ke0KHq9cCAC3M9XixYtTElg1PCv7r0Vn2BpInyp6k470cLKfsdn4qxOC6s/1oLtLW1rkzxUf9dmA6DQlEGQIZYtEQBKr1atX49lnn7V1PfXUoIKCAlRVVTGzluX7KdeyYsUKVFdXY8WKFcyWjmbgxX95jTFSmapjBr1JR0rUU4L2nqWtKQbs9TO+cP4EeHKSP2r1BtKnQsV1k+ByJ79el5ug4rpJjt9LYB3x2CMQZAhek4q//e1v+POf/4wrr7zS8jVZ2cOyBa1GFkejYQhm4WVJe71ejHYTHNFWvaSUHWzGO2Am4YrV/WrN2X7M645iamOvZWpXJM3GX53A6r32bmnBuy/uScR45bpf4bpOD0JwBYIMoVcT+/HHH9sSXBYTJ07E1q1bk7Ypk6P0hiGYFVzeuL9AIIDLL78cU0aPdbQdopWHBKOEK1ZiVQjApvPzMbMN+CCP4q/nDkF7wIXfRo9jaUuOZTd4X8Zfzd5LXUIEAN1dEdQ+15C4jsBZhOAKBBmCl+QEgFmuYwe93sqyMJkZ52d0j1dffZW55pycHMnqjn/vVDtEvYcEdTa00X14bu0jNIa8H5XhLZX1e8eeL/Fheydqj3c42tqxr/lg7X5NCREAxKJUdLBKE0JwBYIMUVlZyW3FSGxOtlGj11tZRm8YghGypcl7QFBe18l2iLyHga05efglQyDl+7PQS6xiWb/BGMVzTa2GPaCzHb0EMDHsPj2IpCmBIEOUl5fjjDPOYO4777zzHLmHGeu1srIykc0sY7Ye12iQvZ0mGg11G/H0Ld/HzxdU4elbvo+Guo2mr/vRmV/lZkPz0Eus4lm/6scLo3tkI3oJYGLYfXoQgisQZJCbb74ZM2bMSFi0hBDMmDHDsfitmWH0vOxmM/FbPbeznSYaDXUbsf7pX6Hj2FGAUnQcO4r1T/9KI7q8h4SOHHPZ0Mqs7IN/ehY/CtCkmb3yEHkrSV3pzrh2mgvnTwBxa7e73EQMu08TwqUsEGSYK6+80jGBVaM3yk8JbxiCETx3NCHEtGgrqVv1HCI9ye7MSE8IdaueQ1nFrKT1AtqJSS+ciDGzoUcpTAtWwpX37XX436oqlF90btJ5rD7O6naUMunux+w0coxWZCn3HUJwBYIBjN4oPyeabvAE3Y7YAkDH8WOmt7MeEs5/5nm8edpkzZjA8w/sAS47F4C1rGxWWVHliHy82HIiLQPo+xrRvapvEYIrEAxwWMLkVO2tnqDbIX/ESMmdzNhuhrFf7MNlp05hS+nUpDGBY48eThxjNStbLbq1xztwXdEw01nKymlFRsc62XlMkH0IwRUIBjisD3Gelffqq68CsC66TolCxYKbsP7pXyW5lT05PlQsuMnU+QUFBZh49DAmKgRW3q782kpWNqsxxostJxJxXuVxamEFoDmXl9HMeghavXo1Vq9enfi5WS15EmQXxKl6v0wzY8YMqi7uFwgGO6yGFF6vVzezOBWXsBM01G1E3arn0HH8GPJHjETFgpuS4rd6sF6v2+1GTk4OgsGgJMgTJ2L79u2m3eAz3t/JLBsa6/MmBi+oRRmQ3Mx+lwsnItpiV+W5MitWrNBNQmssPh3vTJoGZYQ74CIa4e+HOFMD1w8QFq5AMIDhWbK8WbjyfitdppymrGKWaYFVo3ZxBwIBhEKhRD/p9vZ2bN++Heeccw727dtnynXLyz4+FApjxvs7uYPtgzGKYIw9Fo91TaNGI++Pmwx1dWxfDIAQOIcQXIFgAKPXyUrP0jXbZSrT1NfXJw28l1tJLl68GIBkNaqHN4TDYezbty9xjBG8xhgESGy3Om2WldGs13kMyNwACIFziDpcgWAAo1eHW1VVxe1oZaVhhVOThqxSX1+PNWvWJAlqMBjE2rVrE2tItW0lwG6MwSsNUjPM7TI1rQhg1xYryQuxpz71t3KkwYywcAWCAYxeHW55eTkOHjyoGWwASAMPzMBK9FmzZk3C6pTdtYBzmcwytbW1iMW0RbfRaDThEk+lbaUMqzSIZfGqCbgIHp40VnMuL9FJ7Q5Xc9HBPcwYbn8sRxqsCMEVCAYwRmU7yp7KSnjb1bBixLFYLClmunbtWlBKE+KoV4JkpSxGz0qV95lt/GGEug80L5FKZqxKWM3GWJUZ3+r34o4Lz8N8kaXcrxGCKxAMcPTKdlJ1uZo5LhrVRjhZiVlWa4P1Yp6BQCDpPKcbfywtLcatDQeZbmVWBvLeLS2W5+Gyfm7l6F8DEgTJCMEVCAYxqbpcjRJ99FCfx8uofuONNzTCzHO7sjDb+GP16tV44403cPnllxsK7zVFw3FLw0HmvkPdPaiurk6IuD84Ghuf341Ij2Thd7aGsPH53QDEzNnBhhBcgWAQk6rLlXW+WQKBQKL2VE+4g8Eg6uvrE1ap3v32jRqT6DL10vs7uS5X3pSjYDBouuPWWE4sV05uki30guBkkJ7kNUR6YmLm7CBECK5AMIhJtTUjq+61p6cnyY3sdruTYrjyNnV9rB6y+1lvHOC+UWPwzuRpiT7Kel2d9O4nu7uVr4v1vrAGG3iiEcxs3Jl0rVbswwjM1NxHzJwdfAjBFQgGOU62ZszJycHUqVM1TSUOHjyIjz/+GJRSEELgcrksWcWyQOoJ5ZbSqUlDCwB+YwgjV7hsnerFk68pGo6DBw/i1yeCOOn1J/o2A8AfZs5J6uV8EWMmg5g5O/gQgisQCEyzrnEdVn6yEi1dLSjKLcKNhTfi0JZDScK0ffv2pDaJ9fX12L59e6KzFaXUsgtaOauXNw7QSmMII1c4IcRwolB9fT1Ovb0ONyiOU1vZnf4heGfydBR0dWNqo7I/tEvMnB2EiMYXAoHAFOsa16H6/Wo0dzWDgqK5qxk7N+/kCpOMnhtYTSAQ0DR/aCw+Hc9Mn4XijZ/imemz0Fh8etJ+r9eL8847D0PD3cxrjmJ8ypWXl6OqqiqRzay+Hq/tpVLsWa+LZWVH3G5sOj8/YdHmDfdh1o1TRPx2ECIsXIFAYIqVn6xEdzRZ1AIRtlWpFCaz2cRerxeXX345gN7Y6aHTJ+Kd8VMRiod/j8SAdyZNQ05ODsZ+sS9pGMHXCkcnWZeAFFM9d+8O1A9zaUts4q503jQlo+xt1n6elX2ExnDzoxebeh8EAxchuALBAIfXb9hq3Lalq0Wz7ZT7FHKjuZrtZsbhBQIB5OTkJIRu4sSJScJ39dVX45873QipXMIhAJ9OPhf/8/0bAUj9ksPhcGIkn3oWbunRw6itbeO+Xl4M2yh7mzUAwh/uQXeONjZLABRv/FQ0qxjkCMEVCAYw9fX1WLt2bVLWcDAYxJo1awBYm3tblFuE5q7mpG2fDfsMM47PgJu6E9vUwjRx4kRN+0jZmlXGQ1lJSocvmsdcizIuqxRz1ixc+Rgrg+DNZG+z3M68/spyfrYyc/qsfdttjyEU9E+E4AoEA5ja2lpmp6fY/2/v/mPkqMs4jr+fa6/HYfEQaW2lrbRyooCH0LVo1CApSLVX8EdMakwk0cQYNdYqUWsTOTWEPzBqEI0hSsSIEpP6AzRohUAgGkSKXkst9ZqipbVFSpW2abkf7eMfM9PO7c3c7t3uzezOfF7/sDuze/N9crRPvz+fkyenXIJv3eXrGPjTwLhh5YNnH+SC11/AoW2HEhNTtGCq2qWXXjru2WmHXpw1cpzDcyYO08YP7K/n8I29r+nlrjoLwUdqrd5Oeu5w55xJ2wHByumv7XiGj955O2MjwUKqIwefZ/MdtwMo6RaYEq5IgdVz3nC9Vi8LepvxVcrrLl8XXL86+TtpC6aqz2pOa8ubdz3Fny5ZMaGwe/zA/lorjjs7O3l82cXjfgac3jIE9RUXqJb03LOGj3PkjDNrfvc57ziVbCNjI8M8es+PlXALTAlXpMAm6/1NpWJOZPWy1acSbz3qPas5rZ2VkaO8/8LFkybEpMM3gHHVim5/YWJVITjd051KzzftuT09PXzylWdy23GbkNyrvfzo/xKvH3khYcOuFIYSrkiBrVy5csIcbmRkZOTUkYkzpd6zmictI1hVqSdJreHf81Kq+8yC1J5vPb3cpOcuic0Vnz2rg6MnnVEf30O/5u+PJf68s155bs1nSvtSwhUpsCgZxFcpR6ZybvB01XtWc6NHTFar3urz4bddxW2jNmFoOq0nmnRYRr2qS/klLda6qOMtbN7+xLhh5dlzunjH2o9M+7nS+ixtg3e7qVQqnlRIW0QCUaGAaj09Paxfv37GnttIGbzpPi8pyZ95zWruHutk3/Ao8zpgxe7tPLxgKUcT5lzjJfZmqv07Hn1Iq5QDlncDsqIerkhJNFr7drqaeVZzPdJWPPPHh3hi/fpxCfmKY8cmHJYRX5Q11Rq9U/GGd1xV1gRbWjraUaQk0hZJTWfxVCur9Q+LeELufX4fV+78K3NfOgbuLOrq5BsXLj41JJyWvONHV4rUSz1ckZJI2z5z+PBhBgYGMDOWL19Of39/Ti1sTDT0myb6h0V1Qo4fljEwMDDuXl6jAlJMSrgiJVG9MKmzs5PR0dFxVXyidRDtlnRrFaaPFmptOnCIn7712nHl9KJk293dfWqeO5qrrXeVtUg9NKQsUiJ9fX2sX7+egYEBxsbGEj+zZcuWjFvVuMkqEnV3d7NmzRqG5i/ixp3PBidXmYWl8y5jaN55dHR0MDIyMq7u7n333Udvb++E6kVJq6xF6qGEK1JSaTsU2nHnwmRDvHPmzKGvr49bdu+fsA1obNZs/nLBJXR1dU3Yqzw6OsrQ0BBr1qwZV483XutXZCo0pCxSUknVbqLr7WayE7Wi62l7a4/M6Z6wRzn+3b6+PobmLzq1l/buo7PYcOCQKv7IlKmHK1JSy5cvn9L1VjbZEG/UO40XPIh72UvHUv+R0dPTw6YDh7hx57PsHR7FOX3846YDhxput5SLEq5ISfX391OpVE4lGzOjUqm03YIpCOamK5XKhOvx+dYNyxbS3TE+sc4+McYVu7enDqO/+OKLbBjcOWnhA5F6aUhZpMT6+/vbMsEm6e/vZ8mSJamnQkVDwLfs3s/el0YmrFKG5GH2w51nJD6vkeMfpZyUcEWkMGqdahWdc1y93zbi7hPmg+cOH088/jEaop5KYXspNw0pi0jpTHbqVvXiqyt2b2f2ifFbqGafPMGGZQs1vytTooQrIqWzcuXK1P211cm4+vjHuS8d48qnn+QDC85J3Gqk+V1JoyFlESmdycoB7tmzh+rKY/HjH+F0DzltHlfzu5Ik94RrZjcCtwLz3P1geG0D8DHgBPAZd/99jk0UkQJKm+8dGhqq+d3e3l4gmMdNKmyftgVJyi3XIWUzWwxcA+yJXbsIWAtcDKwCvmdms/JpoYiUTT2FCQYHB9m6dWviVqN4eT+RuLzncL8FfAGIT4JcD9zj7sPu/gywC1iRR+NEpHzqKUwQlej7wIJz+MaFi1nU1YnBhPJ+InG5DSmb2XXAPncfrDrl5Tzgsdj7veG1pJ/xceDjAEuWLJmhlopImaSVMawW9YSjrUYitcxowjWzB4AFCbc2Al8G3pX0tYRricfAuPsdwB0AlUql/U5cF5GWEy2c2rJly6SFHFSiT6ZqRhOuu1+ddN3M3ggsBaLe7SLgSTNbQdCjXRz7+CLg3zPZThGRyNatWxkcHJw02apEn0xHLnO47r7N3ee7+/nufj5Bkr3c3Q8A9wJrzazLzJYCvcDjebRTRMonrbZuNPWlEn0yXblvC6rm7tvN7OfA34Ex4FPufqLG10REmiJtlbK7px4JKVKPlki4YS83/v5m4OZ8WiMiZZZWW1dzttKovLcFiYi0lMmOfRRpREv0cEVEWsVkxz6KNEIJV0SkSq0yfyLToSFlERGRDCjhioiIZEAJV0REJANKuCIiIhlQwhUREcmAEq6IiEgGlHBFREQyoIQrIiKSASVcERGRDCjhioiIZEAJV0REJANKuCIiIhkwd8+7DU1hZs8D/8q7HTHnAgfzbsQMKXJsUOz4ihwbKL52dNDdV+XdiCwUJuG2GjN7wt0rebdjJhQ5Nih2fEWODRSftDYNKYuIiGRACVdERCQDSrgz5468GzCDihwbFDu+IscGik9amOZwRUREMqAeroiISAaUcEVERDKghNtEZvZ1M9tqZn8zs81m9urYvQ1mtsvMdprZtXm2c7rM7FYzezqM8ZdmdnbsXlvHZ2YfNLPtZnbSzCpV99o6toiZrQpj2GVmX8q7PY0yszvN7D9m9lTs2jlm9gczGwr/+4o82zhdZrbYzB4ysx3h/5frwuuFiK+slHCb61Z373P3NwG/Ab4CYGYXAWuBi4FVwPfMbFZurZy+PwCXuHsf8A9gAxQmvqeA9wOPxC8WJDbCNn8XeDdwEfChMLZ29iOC30ncl4AH3b0XeDB8347GgM+7+xuAtwCfCn9fRYmvlJRwm8jdD8fevgyIVqRdD9zj7sPu/gywC1iRdfsa5e6b3X0sfPsYsCh83fbxufsOd9+ZcKvtYwutAHa5+253HwHuIYitbbn7I8ChqsvXA3eFr+8C3ptlm5rF3fe7+5Ph6yPADuA8ChJfWSnhNpmZ3WxmzwIfJuzhEvxBeTb2sb3htXb2UeD+8HUR44sUJbaixFHLq9x9PwRJC5ifc3saZmbnA5cBf6aA8ZXJ7Lwb0G7M7AFgQcKtje7+a3ffCGw0sw3Ap4GbAEv4fEvux6oVX/iZjQRDXndHX0v4fMvFV09sSV9LuNZysdWhKHGUipnNBTYBn3X3w2ZJv0ZpF0q4U+TuV9f50Z8CvyVIuHuBxbF7i4B/N7lpTVErPjO7AegHVvrpTdxtEd8UfndxbRFbHYoSRy3PmdlCd99vZguB/+TdoOkys06CZHu3u/8ivFyY+MpIQ8pNZGa9sbfXAU+Hr+8F1ppZl5ktBXqBx7NuX6PMbBXwReA6dz8Wu1WI+FIUJba/AL1mttTM5hAsBLs35zbNhHuBG8LXNwBpIxctzYKu7A+BHe7+zditQsRXVjppqonMbBNwIXCSoFTgJ9x9X3hvI8G85xjB8ND9qT+oRZnZLqALeCG89Ji7fyK819bxmdn7gO8A84D/AX9z92vDe20dW8TM3gN8G5gF3OnuN+fbosaY2c+AdxKUrHuOYDTpV8DPgSXAHuCD7l69sKrlmdnbgUeBbQR/nwB8mWAet+3jKyslXBERkQxoSFlERCQDSrgiIiIZUMIVERHJgBKuiIhIBpRwRUREMqCEKyIikgElXJGMmNmJsHTjdjMbNLPPmVlHeK9iZrc14RmfDsvvuZmd23irRaRZtA9XJCNmdtTd54av5xMc//lHd7+pic+4DPgv8DBQcfeDzfrZItIYJVyRjMQTbvh+GcGRi+cCVwI3unu/mQ0AS4GFwOuAzxHURH03sA9Y4+6jNZ71T5RwRVqKhpRFcuLuuwn+DCaVWHstsJqg/ulPgIfc/Y3A8fC6iLQZJVyRfKXVW7s/7MVuIzj7+Hfh9W3A+Rm0S0SaTAlXJCfhkPIJkkusDQO4+0lgNFYK8SQqqynSlpRwRXJgZvOA7wO3uxZSiJSCEq5IdrqjbUHAA8Bm4KvNfICZfcbM9hIUmN9qZj9o5s8XkenTKmUREZEMqIcrIiKSAS2+EGlDZvZLgr26cV9099/n0R4RqU1DyiIiIhnQkLKIiEgGlHBFREQyoIQrIiKSASVcERGRDPwf5NbiYZwxkkEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 484x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = TSNE(n_components=2, random_state=0, perplexity=50,  n_iter=5000)\n",
+    "tsne_data = model.fit_transform(data_1000) \n",
+    "tsne_data = np.vstack((tsne_data.T, labels_1000)).T\n",
+    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\"Dim_1\", \"Dim_2\", \"label\"))\n",
+    "sn.FacetGrid(tsne_df, hue=\"label\", size=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()\n",
+    "plt.title('With perplexity = 50, n_iter=5000')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2af0c6d3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05bd357b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}