CenentTorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('concrete_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>blast_furnace_slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>concrete_compressive_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement  blast_furnace_slag  fly_ash  water  superplasticizer  \\\n",
       "0   540.0                 0.0      0.0  162.0               2.5   \n",
       "1   540.0                 0.0      0.0  162.0               2.5   \n",
       "2   332.5               142.5      0.0  228.0               0.0   \n",
       "3   332.5               142.5      0.0  228.0               0.0   \n",
       "4   198.6               132.4      0.0  192.0               0.0   \n",
       "\n",
       "   coarse_aggregate  fine_aggregate   age  concrete_compressive_strength  \n",
       "0            1040.0            676.0   28                          79.99  \n",
       "1            1055.0            676.0   28                          61.89  \n",
       "2             932.0            594.0  270                          40.27  \n",
       "3             932.0            594.0  365                          41.05  \n",
       "4             978.4            825.5  360                          44.30  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>blast_furnace_slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>concrete_compressive_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>281.167864</td>\n",
       "      <td>73.895825</td>\n",
       "      <td>54.188350</td>\n",
       "      <td>181.567282</td>\n",
       "      <td>6.204660</td>\n",
       "      <td>972.918932</td>\n",
       "      <td>773.580485</td>\n",
       "      <td>45.662136</td>\n",
       "      <td>35.817961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.506364</td>\n",
       "      <td>86.279342</td>\n",
       "      <td>63.997004</td>\n",
       "      <td>21.354219</td>\n",
       "      <td>5.973841</td>\n",
       "      <td>77.753954</td>\n",
       "      <td>80.175980</td>\n",
       "      <td>63.169912</td>\n",
       "      <td>16.705742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>192.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>164.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>730.950000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>272.900000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>779.500000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>34.445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>142.950000</td>\n",
       "      <td>118.300000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>1029.400000</td>\n",
       "      <td>824.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>46.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.000000</td>\n",
       "      <td>359.400000</td>\n",
       "      <td>200.100000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>32.200000</td>\n",
       "      <td>1145.000000</td>\n",
       "      <td>992.600000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>82.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cement  blast_furnace_slag      fly_ash        water  \\\n",
       "count  1030.000000         1030.000000  1030.000000  1030.000000   \n",
       "mean    281.167864           73.895825    54.188350   181.567282   \n",
       "std     104.506364           86.279342    63.997004    21.354219   \n",
       "min     102.000000            0.000000     0.000000   121.800000   \n",
       "25%     192.375000            0.000000     0.000000   164.900000   \n",
       "50%     272.900000           22.000000     0.000000   185.000000   \n",
       "75%     350.000000          142.950000   118.300000   192.000000   \n",
       "max     540.000000          359.400000   200.100000   247.000000   \n",
       "\n",
       "       superplasticizer  coarse_aggregate  fine_aggregate           age  \\\n",
       "count       1030.000000       1030.000000      1030.000000  1030.000000   \n",
       "mean           6.204660        972.918932       773.580485    45.662136   \n",
       "std            5.973841         77.753954        80.175980    63.169912   \n",
       "min            0.000000        801.000000       594.000000     1.000000   \n",
       "25%            0.000000        932.000000       730.950000     7.000000   \n",
       "50%            6.400000        968.000000       779.500000    28.000000   \n",
       "75%           10.200000       1029.400000       824.000000    56.000000   \n",
       "max           32.200000       1145.000000       992.600000   365.000000   \n",
       "\n",
       "       concrete_compressive_strength  \n",
       "count                    1030.000000  \n",
       "mean                       35.817961  \n",
       "std                        16.705742  \n",
       "min                         2.330000  \n",
       "25%                        23.710000  \n",
       "50%                        34.445000  \n",
       "75%                        46.135000  \n",
       "max                        82.600000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# given everything in the dataset is a continious feature we don't need to do any encoding\n",
    "# we can just split the data into training and testing sets\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffling the data\n",
    "data = data.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>blast_furnace_slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>concrete_compressive_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   cement  blast_furnace_slag  fly_ash  water  superplasticizer  \\\n",
       "0   540.0                 0.0      0.0  162.0               2.5   \n",
       "1   540.0                 0.0      0.0  162.0               2.5   \n",
       "2   332.5               142.5      0.0  228.0               0.0   \n",
       "3   332.5               142.5      0.0  228.0               0.0   \n",
       "4   198.6               132.4      0.0  192.0               0.0   \n",
       "\n",
       "   coarse_aggregate  fine_aggregate   age  concrete_compressive_strength  \n",
       "0            1040.0            676.0   28                          79.99  \n",
       "1            1055.0            676.0   28                          61.89  \n",
       "2             932.0            594.0  270                          40.27  \n",
       "3             932.0            594.0  365                          41.05  \n",
       "4             978.4            825.5  360                          44.30  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "!Feature Engineering\n",
    "From what I see, It'll be greatto have a column that is a sum of the features, not including age\n",
    "\n",
    "Let's convert everything into percent values, given the assumption that the items in the columns are \n",
    "the only things that make up the concrete, we can assume that the sum of the columns is 100% of the concrete\n",
    "\"\"\"\n",
    "# sum =data.iloc[:, 0:6].sum(axis=1) \n",
    "# for c_index in range(7):\n",
    "#     data.iloc[:, c_index] = (data.iloc[:, c_index] / sum) * 100\n",
    "    \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       79.99\n",
       " 1       61.89\n",
       " 2       40.27\n",
       " 3       41.05\n",
       " 4       44.30\n",
       "         ...  \n",
       " 1025    44.28\n",
       " 1026    31.18\n",
       " 1027    23.70\n",
       " 1028    32.77\n",
       " 1029    32.40\n",
       " Name: concrete_compressive_strength, Length: 1030, dtype: float64,\n",
       "       cement  blast_furnace_slag  fly_ash  water  superplasticizer  \\\n",
       " 0      540.0                 0.0      0.0  162.0               2.5   \n",
       " 1      540.0                 0.0      0.0  162.0               2.5   \n",
       " 2      332.5               142.5      0.0  228.0               0.0   \n",
       " 3      332.5               142.5      0.0  228.0               0.0   \n",
       " 4      198.6               132.4      0.0  192.0               0.0   \n",
       " ...      ...                 ...      ...    ...               ...   \n",
       " 1025   276.4               116.0     90.3  179.6               8.9   \n",
       " 1026   322.2                 0.0    115.6  196.0              10.4   \n",
       " 1027   148.5               139.4    108.6  192.7               6.1   \n",
       " 1028   159.1               186.7      0.0  175.6              11.3   \n",
       " 1029   260.9               100.5     78.3  200.6               8.6   \n",
       " \n",
       "       coarse_aggregate  fine_aggregate   age  \n",
       " 0               1040.0            676.0   28  \n",
       " 1               1055.0            676.0   28  \n",
       " 2                932.0            594.0  270  \n",
       " 3                932.0            594.0  365  \n",
       " 4                978.4            825.5  360  \n",
       " ...                ...              ...  ...  \n",
       " 1025             870.1            768.3   28  \n",
       " 1026             817.9            813.4   28  \n",
       " 1027             892.4            780.0   28  \n",
       " 1028             989.6            788.9   28  \n",
       " 1029             864.5            761.5   28  \n",
       " \n",
       " [1030 rows x 8 columns])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Honestly, that seems about all the engineering that we have to do.\n",
    "Thus, let's work on the model\n",
    "\n",
    "Now, we have to turn everything into a tensor, by first stacking into the right shape, shuffling, and then splitting\n",
    "\"\"\"\n",
    "import torch\n",
    "\n",
    "y = data.iloc[:, 8]\n",
    "cont = data.iloc[:, :8]\n",
    "y, cont\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cont = torch.tensor(np.stack([cont[col].values for col in cont.columns], 1), dtype = torch.float32) # for wierd sizing\n",
    "y = torch.tensor(np.stack([y], 1), dtype = torch.float32)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1030, 8])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 540.0000,    0.0000,    0.0000,  ..., 1040.0000,  676.0000,\n",
       "           28.0000],\n",
       "        [ 540.0000,    0.0000,    0.0000,  ..., 1055.0000,  676.0000,\n",
       "           28.0000],\n",
       "        [ 332.5000,  142.5000,    0.0000,  ...,  932.0000,  594.0000,\n",
       "          270.0000],\n",
       "        ...,\n",
       "        [ 148.5000,  139.4000,  108.6000,  ...,  892.4000,  780.0000,\n",
       "           28.0000],\n",
       "        [ 159.1000,  186.7000,    0.0000,  ...,  989.6000,  788.9000,\n",
       "           28.0000],\n",
       "        [ 260.9000,  100.5000,   78.3000,  ...,  864.5000,  761.5000,\n",
       "           28.0000]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "\n",
    "class CementModel(nn.Module):\n",
    "    def __init__(self, layers, dropout=0.25, in_szes = 8, out_sz = 1):\n",
    "        super().__init__()\n",
    "        layer_list = [] #don't immediately add the stuff to the item, just append it to the list\n",
    "        #allows for variable layers\n",
    "        self.bn = nn.BatchNorm1d(in_szes);\n",
    "        i = in_szes #\n",
    "        for layer in layers:\n",
    "            layer_list.append(nn.Linear(i, layer))\n",
    "            layer_list.append(nn.ReLU(inplace=True))\n",
    "            layer_list.append(nn.BatchNorm1d(layer))\n",
    "            layer_list.append(nn.Dropout(dropout))\n",
    "            i = layer\n",
    "        layer_list.append(nn.Linear(layers[-1], out_sz))\n",
    "        self.layers = nn.Sequential(*layer_list) # unpacks the list into the neural network\n",
    "    def forward(self, x):\n",
    "        x = self.bn(x)\n",
    "        return self.layers(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CementModel([100, 256, 256,512,1024, 512, 256, 256, 50, 25, 10, 5, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1030, 9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's have the testing data from the max\n",
    "import math\n",
    "batch_size = math.floor(data.shape[0] * .8) # ! unorthodox definition of a \"batch size\"\n",
    "test_size = int(batch_size * 0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(824, 164)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size, test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = cont[:batch_size]\n",
    "train_y = y[:batch_size]\n",
    "test = cont[batch_size:]\n",
    "test_y = y[batch_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([824, 8])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 540.0000,    0.0000,    0.0000,  162.0000,    2.5000, 1040.0000,\n",
       "          676.0000,   28.0000],\n",
       "        [ 540.0000,    0.0000,    0.0000,  162.0000,    2.5000, 1055.0000,\n",
       "          676.0000,   28.0000],\n",
       "        [ 332.5000,  142.5000,    0.0000,  228.0000,    0.0000,  932.0000,\n",
       "          594.0000,  270.0000],\n",
       "        [ 332.5000,  142.5000,    0.0000,  228.0000,    0.0000,  932.0000,\n",
       "          594.0000,  365.0000],\n",
       "        [ 198.6000,  132.4000,    0.0000,  192.0000,    0.0000,  978.4000,\n",
       "          825.5000,  360.0000]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[79.9900],\n",
       "        [61.8900],\n",
       "        [40.2700],\n",
       "        [41.0500],\n",
       "        [44.3000]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss 17.91734325, testing loss 17.79909035\n",
      "Epoch 100: loss 17.76049494, testing loss 18.59696253\n",
      "Duration: --- 13.326103210449219 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import math\n",
    "\n",
    "start = time.time()\n",
    "n_epochs = 200 \n",
    "losses = []\n",
    "test_losses = []\n",
    "for epoch in range(n_epochs):\n",
    "    y_pred = model(train)\n",
    "    loss = criterion(y_pred, train_y)\n",
    "    test_loss = 0\n",
    "    losses.append(loss.detach().numpy())\n",
    "    with torch.no_grad():\n",
    "            test_pred = model(test)\n",
    "            test_loss = criterion(test_pred, test_y)\n",
    "            test_losses.append(test_loss.detach().numpy())\n",
    "        \n",
    "    if epoch % 100 == 0:\n",
    "            print(f\"Epoch {epoch}: loss {math.sqrt(loss):.8f}, testing loss {math.sqrt(test_losses[-1]):.8f}\")\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "print(\"Duration: --- %s seconds ---\" % (end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  array(299.18646, dtype=float32),\n",
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       "  array(293.41913, dtype=float32),\n",
       "  array(296.2677, dtype=float32),\n",
       "  array(312.6287, dtype=float32),\n",
       "  array(297.53738, dtype=float32),\n",
       "  array(287.87656, dtype=float32),\n",
       "  array(333.8714, dtype=float32),\n",
       "  array(297.4097, dtype=float32),\n",
       "  array(281.4219, dtype=float32),\n",
       "  array(314.17825, dtype=float32),\n",
       "  array(290.0793, dtype=float32),\n",
       "  array(289.192, dtype=float32),\n",
       "  array(300.545, dtype=float32),\n",
       "  array(308.89374, dtype=float32),\n",
       "  array(307.75928, dtype=float32),\n",
       "  array(308.06567, dtype=float32),\n",
       "  array(318.8189, dtype=float32),\n",
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       "  array(303.4747, dtype=float32),\n",
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       "  array(276.8931, dtype=float32),\n",
       "  array(267.6873, dtype=float32),\n",
       "  array(266.981, dtype=float32),\n",
       "  array(298.8834, dtype=float32),\n",
       "  array(293.20474, dtype=float32),\n",
       "  array(319.66, dtype=float32),\n",
       "  array(295.21844, dtype=float32),\n",
       "  array(285.97052, dtype=float32),\n",
       "  array(302.2573, dtype=float32),\n",
       "  array(257.66776, dtype=float32),\n",
       "  array(293.6959, dtype=float32),\n",
       "  array(287.67142, dtype=float32),\n",
       "  array(283.69815, dtype=float32),\n",
       "  array(290.05606, dtype=float32),\n",
       "  array(305.50204, dtype=float32),\n",
       "  array(266.1709, dtype=float32),\n",
       "  array(278.87012, dtype=float32),\n",
       "  array(277.4698, dtype=float32),\n",
       "  array(242.75034, dtype=float32),\n",
       "  array(314.60077, dtype=float32),\n",
       "  array(294.9931, dtype=float32),\n",
       "  array(279.54926, dtype=float32),\n",
       "  array(266.73447, dtype=float32),\n",
       "  array(271.98993, dtype=float32),\n",
       "  array(295.43552, dtype=float32),\n",
       "  array(257.66077, dtype=float32),\n",
       "  array(255.47945, dtype=float32),\n",
       "  array(302.04703, dtype=float32),\n",
       "  array(299.97012, dtype=float32),\n",
       "  array(268.03833, dtype=float32),\n",
       "  array(308.69873, dtype=float32),\n",
       "  array(273.383, dtype=float32),\n",
       "  array(276.50958, dtype=float32),\n",
       "  array(245.94789, dtype=float32),\n",
       "  array(273.4777, dtype=float32),\n",
       "  array(278.15576, dtype=float32)])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_losses, losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(range(n_epochs), losses)\n",
    "plt.ylabel('Cross Entropy Loss')\n",
    "plt.xlabel('epoch');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Oh7HMAtM1kJWdDg6kjSjPQII3nUBkp9ObptFiBQCweajY0XEYTC0YstNGSBk4nQhjdXpLZiAgK24zg+3we97z5Ma4Bt0PEcbq8HWjeXTXeKmj4zCYWjBkp43oTJ0d9m+zuHQNHKnreQcH0kaoKfaGe08tuF1CdigEvHPMkB2D5DBkp43oTLsIYwrtRvDL3+nFo12oKDdgp8MhBulQEWGszo5DKDuG7BikgCE7bUQnigqa1PPuBCehKgmYrlBrS80Ukjdd4HSJskNz2k4TxjJIAUN22ohOZGPNRAVhKmAm1tlR73lzP04tdEvqOY1j93i5o+MwmFowZKeN6IRBWQpjmcWlazAzKyibMNZUhtslqed0HxnPjkEaGLLTRnCyUeqEQbnTs5SBwEzsjRUKY80QRWu6gMKtnU50oDltaKLctk2jwdSHITtthCstcG1KPTeena7ETCywV1bu+Zly3NMFdM92umQAVwh3T5hQlkEyGLLTRvAdUbnSrjAWX1Tb8pUGCeC4M4+EqspOpxUCg3Sg69Xp+5UroSYjyyApDNlpI7jiWm7ThDETF9WpgJnWxsPzvHCdnRlw3NMJjtMtnh1GdoxJ2SAhDNlpIzylzk475GCJ7JiddNdgpoUXdcc4Aw57WqHbKigDxqRskByG7LQRnGx4XnsWOf6dZifdPZhpjUB1JuyZcNzTCaLOTofnEVnZMWTHIBkM2WkjVHLTjiwc1yg7XYmZFl7kRTStjP93pxdNg3QQnp1Ot4swyo5BHTBkp41Q54h2dD7nE4Npvtg9mGmVrfkx5rP+tNPpcIhBOpBnp9OXzTUGZYM6YMhOGxFSdtpQa0ddUGbCwjoVMPOUneAYc5Y/7cyE455OCHpjdZGyY8JYBglhyE4bocq/7eiPpU5MnZagDXzMNLJDdaVydgZWNY5llJ2phW4JY/HnxbSMMEgKQ3baCNWj0Amy06pahk9vHcH9z+5ozYdPQ1RmmJeKVMysZcEWZKeTIzJIi24pKsgLshrPjkFSGLLTRqg72XY0A22XsvO+ax/Gu/77fmwbKbbk86cbeBXYmaHsENnJwMr4ZGcmHPd0ged54np1+rrxDZvJxjJICkN22gTP80I72bYoO23y7GwZnoTrmZ1WUsy0nmXUwyhrZ0Q21kw47umCbqrEbpQdg3pgyE6bwCeIvO2f9naQHTV01op0X8/zUKy2v2jHMU0HzLRu9KRiZu0gjDUDDnvaoFvqdambxpHJiplzDBLBkJ02ge+MClkiO+0tKqj7fzNQdjyxcBXb1PNrqoN7dtqRlddp0P2f42Esw3amDLqlca1ODTQmZYMkMGSnTeATRCFnAwik/VZCXUhbsSsrVhzxb7PLSga3SxaPdoE6ntt2BtXMcxPGmkKQDfXdMQ5SyI1vxyAJDNlpEySyU1V22lFUMFRnpwULK1dzDNlJhpnWCJRId86yYGcojNXdx/3nl4fw6ZvXY8eoMd13Cznnz8qCgTwA49sxSAZDdtoEKYyVa2MYS83GaomyY8hOWsy0RqBkKs2yOjvdftz//dtncd39L+L2Rzd3eigdByfnnfTs8HEsnFUAYKootxJf+eVfcPVdz3R6GE1BttMDmCng6ZKFbPvCWO2os1MsB2GsUqW7F7BuwYxrBFo9Xtuy4Hr+Tdjtxz1e8u/rYtkQ+G7x7LgasmOqKLcGI5NlfOWXTyGTAd77un3FujVVYZSdNoFPED259mVjtcOgbJSd9JjJFZSDMFYnR1QbdC/PBE9VLchh1+4Yx7x+P4xlDMqtAc3rnjc9Ek8M2WkTHI1npx1hLFXJaXUYqzQNHop2YKY1AhWp59bUCWORGmXITvd5drJWBnP7jWenleDP53SY1w3ZaRNosshkgFwb6+yEwlitUHbK0y8b69b1m/CPP/6jlGnWTMw0g3KwSFlBUcEuJxGUQDBNbumGUOkWsuNRODSDuX0+2TGendagYsiOQT2g+8bOZATZaUd9lYoxKNeFL9/5F9y49iU8/Pyulny+43TH4tEulFkF5aCoYHcfd8WEsQS6pb2J4wRkpy/ve0gmW7Qhmengc5QhOwaJQTsSy8ogZ/uTfUdSz1sdxpomBfK2Dk8CCEyqzQZXNVRCOh1RYRWUg95YnRxRbVDorZPZR90Cfo92kvuR98u2Msha7bMDzETwthzGs2OQGDRh2pkMstMtjDXNigqWKi6GJysA0LIw1ow1KFtTpzcW3cvdHm5rB7rlfuWenWx10zgd5pxuhPHsGNQFunGsTFD5sxlhrN89tR3X3f9C5O/VXWlLlB2WmlueBg/FjrGgiFyr0o6lVN4E1+SxjcP44PcfxlNbRloynlaDlAHbmjphLJGN1eWkrB3oltTzwLNjNXUeNQhD8uw4Uz9UaMhOm+CyMFbWak4Yy/M8nP8/j+DTN/8ZG3aOa19DN2w1ctAiZYeHsaY+2dk+EhgeWyXf8sUjSRjrx2s34I5Ht+B/121syXhaDVFBmYexupzs0HUxXKd7DMpBvSYYZafF4HOUCWMZJIbLsghy2ebsSF7ePSFqTAxP6mtN0IJCu6BWzAs81DMtyA5rD9C6bKx0Xc8nqxlvrRpPq8ENyoFnp7tZBKmU3U7K2gE59bxz4+BZfeTZmQmet06Ak0gTxmoQV199NVatWoXBwUEMDg5i9erVuO2228TvN2/ejPe85z1YsmQJ+vv7ceSRR+InP/mJ9Bk7d+7EWWedhcHBQcyZMwfve9/7MDo62u5DqQm6b+xMRhCPRnckT2wKQhpRNyNNUgHZaXE21jSooCyTndY85Lz+UZJrQuPo9MT+yIu78K7/vh+PbxpO9T5HG8Zq+vCaijIpO2Yx7ZpSCTz1PJ81yk4rYTw7TcTSpUtx2WWXYe3atXj44Ydx/PHH4/TTT8ejjz4KADj77LPx5JNP4mc/+xnWr1+Pt7/97TjzzDPxhz/8QXzGWWedhUcffRR33nknbrnlFtxzzz34wAc+0KlDioQjwklBGKvcYO+GJzYHC05URgJNDqQmtUKCnpxmdXa2jwZhLH5szYSs7NT2rwiy02F/wv/+4WXc+8wO3LZ+U6r30WKZs6wpVFTQpJ4T+LXqpNeKk2aTjdVaVEwYq3k49dRTccopp+DAAw/EQQcdhEsuuQQDAwO4//77AQD33nsvPvKRj+DVr3419t9/f3z605/GnDlzsHbtWgDA448/jttvvx3f+ta3cMwxx+B1r3sdvva1r+GGG27Axo3d5W0IwlgB8WhUBXl8c6Ds6EiG63pi90zp7qbOTm20Q9lJ26C1JJSdzp5fUjuKKa+zHMbyf9bt4SFaRKfBLd0wJGWnKzw7QTZWO3oMzkQYZadFcBwHN9xwA8bGxrB69WoAwLHHHosf/vCH2LlzJ1zXxQ033IDJyUkcd9xxAID77rsPc+bMwdFHHy0+58QTT4RlWXjggQciv6tYLGJ4eFj602oIspPJIGc1R359goUSdF4ZPinlqwSrJb2xWMbStPPstCobK2XPslKXKDsU0kk7Dm5QDnpjdTvZMcoOwZXCWJ0bB089b2cl+pkIORtr6p/jjnc9X79+PVavXo3JyUkMDAzgpptuwsqVKwEAP/rRj/A3f/M3mD9/PrLZLPr6+nDTTTdh+fLlAHxPz6JFi6TPy2azmDdvHjZv3hz5nZdeeik+97nPte6gNOBhLFFBuYFd+mTZwXPbx8T/dcybM3P6zlb4DySD8jTYAewY5dlYLQpjOemUHRpHpz07FUF20l1nnnoehLGaO7Zmw5CdAJVuCWPxRA/bhLFaCV41ezrM6x1XdlasWIF169bhgQcewLnnnotzzjkHjz32GADgM5/5DHbv3o1f/vKXePjhh/Hxj38cZ555JtavX9/Qd1500UUYGhoSfzZs2NCMQ4mFaBdhBUUFSw2EsZ7eOiplReh2N3wBbZtBudtXsARoi0E5ZWVrmmw6fX6JLJdT3keVKRbGclxPPF/d7i1qB7qmXYRUQbkaxupwaHcqYMPOcXzrt89irFhJ/J7KNGsX0XFlJ5/PC6XmqKOOwkMPPYSvfvWr+D//5//g61//Ov785z/j0EMPBQAcfvjh+O1vf4urrroK3/jGN7BkyRJs3bpV+rxKpYKdO3diyZIlkd9ZKBRQKBRad1AaSKnnduMPqZoNoyU7nkbZaXGdnemwy2oH2VEVmlq3QtCUsrPnl+6pepWdnGVNiaKC/Hkyyo6swnVHnR2j7KTB1379FH708EsY7M3hzKOXJXqPXGdnapa84Oi4sqPCdV0Ui0WMj/tF8ixLHqJt23CrK8Pq1auxe/duYVgGgF//+tdwXRfHHHNM+wadAFIF5WzjseYnNsuVdHXMm4eshGenwbVbt0BNp67njuth51jrs7FU0lKL+JJ3qNMTu1OvZ4ftyKdCnR2piN7UvqWbAjV7sFMgopVlm8apPue0A0MTfh22kckUys40Myh3VNm56KKLcPLJJ2PvvffGyMgIrr/+etx111244447cPDBB2P58uX44Ac/iC996UuYP38+br75ZpFiDgCHHHII3vKWt+D9738/vvGNb6BcLuP888/H3/7t32LPPffs5KGF4AqyE6RMNhLG4mnngL4BJ19MSPJtJHTw3/c8i//67bP44Qdeg/0XDoifSxWUp/hDsXOsJE3mbcvGqmVQdrojG4vGndawGBiUpwbZ4W1Pujnc1i50S7uICptHc6ZdRGLU09RWagQ6DQhlR5WdrVu34uyzz8aKFStwwgkn4KGHHsIdd9yBN73pTcjlcrj11luxcOFCnHrqqVi1ahW+973v4dprr8Upp5wiPuMHP/gBDj74YJxwwgk45ZRT8LrXvQ7/9V//1cGj0oN7dhoNY3meh8erBQX3W9APQN+TiqtJlKbZSOjgV09swbaRIn75+Bbp59OpESjviwXIqlUzUW/qeacJQr3KTpl1PZ8KRQXLKStcT3d0TVFBysayWeq5kd5qoh6z/Yz37ExMTMDzPPT19QEAXnjhBZFBddJJJ6X6rG9/+9uxvz/wwANDFZNVzJs3D9dff32q7+0EaHfIdyT1EoNto0XsHCvBygAr9xjEc9vHYlPPdaGDbSNFzOvPi4UnCUjleHLzqPbnwNRPUeR9sYD2eXZqZ2N1iUGZPDspFxh6fZbfi11MIni40FRQls9BJy9b4NmxJM+O53nIZJLPZTMNdN7SPHMzvs7O6aefju9973sAgN27d+OYY47Bf/zHf+D000/H1Vdf3fQBThfQZNEMY93G3ZMAgMWDPRjs9flqvLITlOh3XA+PbRzGq7/4S3z65nRZbZNV38iTW+QQmtz1fGovDGROpnmzde0iUhqUu0TZIZKW9t6l9/lkx/9Zq4/lqS0jeP/3HsajG4dSv5cbsE2UpIuUHe7ZYX7OTpdk6HbQZiNdGIsblGcg2XnkkUfw+te/HgBw4403YvHixXjhhRfwve99D1deeWXTBzhdoAsp1btLH62azAZ7crF9tmgBta2MKOTmeh6e3jYKzwOeVEzOtUDhqqe2jEY69TutPDQKIjuLZ/UAaGUj0HQG5SD1vEvCWGmVHZF63r5srJvXvYw7H9uCn6x9OfV702ZjeZ6Hh5/fKYyg0w3d4tnhGziaRwHj26mFUh3VwGe8sjM+Po5Zs2YBAH7xi1/g7W9/OyzLwmte8xq88MILTR/gdAFNEJYVNAKt9wEdqXY4n9WTFSqRzqAsZcCwQm6kAqUNOZGCU6y4eGFHUNBwWoWxqgUF95rbC6B1FZRDyk7MAuK6XtcYlGmcqZUdblBuU1FBunYTdfiu0oaxHnp+F97xjfvwLzc1VgOsW9EtZIcrhJzsTPV5p9WgzUaaMNZ0y8ZKTXaWL1+Om2++GRs2bMAdd9whfDpbt27F4OBg0wc4XcDbRTSq7IxUC0MN9GRFny1t6jnz7NjMJ0HfmzbkxFUOrgpNp6KCpOwsJbLTJmUn7rTxibzTO1hHhLHqraBsta2oIH1nPRM1P74kYZtNQxMAgI27J1J/11SATHY6Nw5hB7CVMNYUn3dajUod2VhSBeVpcH5Tk53Pfvaz+Md//Efsu+++OOaYY0Qfq1/84hd45Stf2fQBThfQvWIxz069NxDVSphVI4xFP7KZZ8d1A7JTr7IDAE9uYWSnPH3aRRDZ2WtOa5WdNHV2JLIzRbOx6PhydqZtvbHoPq+HsErKToJh0n0/HbwNOnSLZ0eQ5oyvENK81unnotX488tDeMfV9+Lh53fW9X7KLkyVjTXTlZ13vOMdePHFF/Hwww/j9ttvFz8/4YQTcMUVVzR1cNMJQTYWGq4PQZ6dgUI2tkChiG9LYSxPhLzS3sCTiZSdqT3pCLIjlJ0WkR2mugHxBmV+nTq9g02i7Dy3fQwfveEPUi0okXpuWdK92EpU6rzPgfSeHTq+6Up2+C6/k9lpDsvq439PdUW5Fm778yY8/MIu/OyPG+t6f7meMBaby2dsBeUlS5bgla98JSzLwvDwMG6++WbMmjULBx98cLPHN23gscWt0cqfo0Xu2fE/K64RaNbKgMLbrlefsuO4nkRkSNnxPG96eXaqqed7VpWdkuM2fXL3PE9cG9GzLGYS4ue30ztYugXixnHDgy/if9dtxPfvCzx8RNLsNqae0262nnuST/RJSJl4pqYt2Qn+3Q2eHdok5GdIywg6vvFSfaSjnjDWjFd2zjzzTHz9618H4NfcOfroo3HmmWdi1apVNWvizGTwLIJGU89FGKuQjQ2J8do+fDddrqNmi8rsn98+hsmyE/resuN2dc+jOHieJ4oKLq2SHaD5BE5q0CraeMSEsSRlp3nndu0LO/HijvFU73FFGCt6vC/t8n0rW0eCAo10zDk707aigu1VduoPmU0FSI1AO/h80/1HvkdRWHCKb7Jqge6viTrJTrmubKzgxdNBsUxNdu655x6Ren7TTTfB8zzs3r0bV155Jf7t3/6t6QOcLnDYjqRRZYcblJOEsaIMymkWAe5dmd2bg+v5ndfVh8DzOl8Lpl4MT1bEpLAnIzvN9u3wxSJJzzKJ7DQpG2vL8CTe+Y378L5rH0r1viR1dl6qmnS3MbLDKyi3q10Enat6JmoTxpIh9Qrr4OPN20UA/v0ETH9lpyKUneS9raT3N+rZmQZkMjXZGRoawrx58wAAt99+O8444wz09fVhzZo1eOqpp5o+wOkCusfkni6NG5TjVCKJ7DCDcj2eHfLr5OwMDtnDLz3w5OYRLRFoZOL50cMb8A/XPoSh8fbXKxmtkshC1kJ/ISvOWbN365KyQ2GsmBWEf3+zwljbRopwPWDz8GSq9wllJ4Z0bdSQHWFQbmNRwXJDyk59YSzd8+B5HjbsHJ+yiicgE/TOenaC0DyA2ASN6QR67uspowAE5UbSPHPONGsXkZrsLFu2DPfddx/GxsZw++23i9TzXbt2oaenp+kDnC7gIaVGdyOj1To7A4WseNjjPDuhOjuiZouXeOKiSbyQtbFicZXsbBkRCzFNPlFjSYrv/O45/PLxrfjhwy/W/Rn1YrxKdvoLflXqQlV1mWy2ssPOeSFbm+y0IoxFCkTaa0X3cdS9O1l2BMnZPloUC3xQ5j/TtqKClQZ8NGm7fMeFsW54aANef/lvcN39U7cOGV/4uqGooF1NO58p/bEqjYaxqufNZGOlwMc+9jGcddZZWLp0Kfbcc08cd9xxAPzw1mGHHdbs8U0bBA8pAlNxnf4WUiBmsTCWTmbktX10Yayo9+lAi2Mha2Hv+X7z0U1Dk+LnfXk79WfqMFndufzvuvqyDhrBWHUioWPpyfl/t1TZydY2KJekbLfmTDr0mWnvwVrZWJuGAqWoWHFFyFUUg+NhrDbV2ann+vFznmRDILpKe2HF9tltfi+5p7aOht7Hv+9Xj28Rz3a3QQ1jdUql4vMowLOxpq5qlgR0/us3KKcPY8lV8mcg2fnwhz+M++67D9/5znfwu9/9DlaVYe+///7GsxMDlyk7vBhWPVJ+EMbKxjYV5annujo7Ue/TgUhIT87G7N4cAGBooiwUn56c3RRJmWTaRzcO4+mt6dpZNAqh7ORlZafZDzpfOOj6xS2o/PubFfohQup56UJjtersqEX1tldVHppsczZvSptuzGnRSIaUVFcmhUEZCN8vtBCPFaMXqh+v3YD3Xfswvv7rp9MOtS1Q77tOiTsVRdlptKlyUjiuh0tvfRy/enxLS78nCo2EsRzXE+pkmvljxis7AHD00UfjbW97G/r7+wXDX7NmDV772tc2dXDTCS4jHlT1GKhPBeF1dpKmnlNjS8fzUKqkv4m5siPIznhJ7JoLOath4zUgh4x+1mZ1Ryg7BV/RCchOc5Ud3hQ2SVE0NfW8Gbtqft3TkLlavbFe3iWTHQpplaVj9n/X+jBW1bNTx/0oG5TTvV49n47YlUerNk9t8VUf7nPqJqiEr1MZWapnp9GaZUnxx5d245v3PIvLb3+ypd8ThUbCWHI18OTvm/EVlAHge9/7Hg477DD09vait7cXq1atwve///1mj21agZ5FO5NBDyM7aTN9XNfDaIlVUE5YVFA0Ag0pO8kmCVJ2Cjkbc/qYsiNIkC1IXGNkJ3iY//ePG9sql9NiRGGsQrYaxmqyZ4dXgbUTFNhTJ5pmmJQ52Umza+O9sXTX5iVF2dlWLdIYpJ63r6ggEaz6srHSGXJlsiMvSHS9xmIWqi1Vo3gn/TBxcJR5olPj5D5EoPGmyklBJGO83JkwI92P9Sg7cggyhbKjmPSnapYtIZv2DV/+8pfxmc98Bueff75Qcn73u9/hQx/6ELZv344LL7yw6YOcDuC7+axtIWtlUHE9qSpxEoyVKkJCntWTjS2qJar0ZiAtqpJnpxFlZ6IckKCsFdT8Sdlzi+C6QYFCKwO8sGMcf3xpCEcsm1PX56UFhRn6KIyVa00YS5slF1dUUJngHNdDzo54cUKUnPpafKhtA3gzRiA6jEX3XLaNRQVpN1zP9atIu+Hknh0gfD5phzwe48chstPpopFRUMfVKT+wWlSQLAGtPm+i5EKdc1ujoHtoouzA8zxkMpka7wiQ9l4W71NeW6q46M03OPF0EKnJzte+9jVcffXVOPvss8XPTjvtNBx66KG4+OKLDdmJAE3sdI/25GyMFiupM33IwJi1MgrB0BiUddlYdRuUA1ITRXbyDfb84ovScSsW4ddPbMX1D7zQNrJDyk5/Xg5jTdaZ7hkFXf2juMlaV7ixp0G2U7eyw8ZZdjxklWFQGGt2bw5DE2Wh7ARdzy1JZWwleFHBtAtEvUUFgTC5SqbskALWneEC9Rx0Lozlnx9BdrLtUXaIMHQqxZ3uIc/zQ/1pSEe5zkw6lRgVK86UJjupw1ibNm3CscceG/r5sccei02bNjVlUNMRPDMKAHpy9S2k3JycycQ3Fa1oFlVeZwdIvtBNMiMykR3XA3aMlcTP40Jqyb4jOBcfeuMBAICfPPIynt8+VtfnpYVQdkTqOWVjtSiMpRjHo6Beo2b4EySy4yS/B/kiV9YszBur3b9XLZ0NwPegeJ4nPD6ceLdaxODjS5utk3aBkMiOsoGp5dlxXQ9bRyal13YbQspOx8iO/3fQG6s9RQXp+DvlXeH3V9pQVlriTlB9eVPdpJya7Cxfvhw/+tGPQj//4Q9/iAMPPLApg5qOcBX5lRbSesnOQI+/IMcRDEGwVGWnjl5WXNnhxGZrdUfqq0zVXVadD8Ukq9nz6v3m4bgVC+G4Hr76q/YUq1SVnZ5ciwzKXmCyTOLZiVIKGkGxQYMyECZdruth025/0SY1bvtoCVuGiyg7Hmwrg3n9+aCoYJsMykD6RaqcUvrnodtIz05ENtau8RIr59+dZEdVnDpVWDCk7LSpXQTdS51Sdvh9kbaKcto+b1Gvnerp56nDWJ/73OfwN3/zN7jnnnuEZ+f3v/89fvWrX2lJkIEPekaIdNBCmpalj1QLCs4q+OqK8OzEZGNZvM6OC9SXeh4oOwAwpzeHrSNF0f+okLVjVaZ6vuMTb1qBu57chpvXvYwPH3cADqwWM2wVxoRBucXKjsOM40kMyiGy0/h4+DXS7diGJ8uYVciGQj8y2ZHft220iJLjwsoAr9grUHao+/l+C/rRk7MTqVnNQEVSWxwMFJJPd5xQJuFklZg+QmTujVqkeBXrbiU7IWLboWGGPDttSj2n69upej78e9NmZHGFM81pCnl2pnhGVmpl54wzzsADDzyABQsW4Oabb8bNN9+MBQsW4MEHH8Tb3va2VoxxWiCooOz/XxSsq9OzQ8oOxay1jUA16b5qNlZyg3Kg7AAQoayt1YnaTz1vTFIOavn4n3PY0tl486GL4XnAVb9pff2R8erOu19NPW9RBeWkZt2Wh7GUz//zy0NYdfEvcPHPHg29jy/GZWUyfLlqTl4y2IMlg3419e2jRTy52a+XtGKJT1bpmFsdCuHjSztR83OSts5OmJwS2XG0BI/UUf7abkPIs9MxZSd4doD29caqMOWtE6oWJ9NpN8iVJnl2ZlwYCwCOOuooXHfddVi7di3Wrl2L6667DnvttRe++MUvNnt80wZeyLNTXxhrlHU8ByBlY6mpwNKiysJYpTrkfVJdKENJkJ2RIIzVqGdnghUuJJx1zD4AgD9vHK7rM9MgpOy0KIzlsPBiNomy0+rUc+Xz1788BAC47oEX8dIuuSs6nyxVZYfMyXvN7cXCWQUAPtl5gshOVZkL1KyGDyMWlTpIvXivtBtOQHZiwlg8BKTLvuTKTremnqv3XOcrKFeLCoo6Va29mWSS3/5FnxOWtFWU04Zkdd8JTP0wVl1kR4dNmzbhM5/5TLM+btqB17wBgF4iOykXUm5QBiAVKFR3N7wfV1SdnbTKTk81tBOQnaqyk7Vj+3QlwaSG7FBNn7i03WaBJpFA2WlNGIv7DhJ5dhRC3Ax/QpyyQ9/nuB6+/bvn5O9WsrE4SNnZa04v5g/kxWsefG4nAK7s+K9v9cJeqcOIT+DkJckwOWGM81jpfDtbGNlpdXG8eqHen50uKijaRYg6O61WCes3uzcD/B5KHcYyBmUATSQ7BvFwIrOx0t1AI0oYiwgGEFZUdJV61dTzpCpMUVV2qiRk+6ifjcUNynWnngvPTnBM1JSzHT2DxoqqZ6c1qee8KWaSAnstUXZiCC9frH/40AbRgd7zPGnhV+8dqrGz55xeFLJB1h6RoIPbHsaqz4StvjdN13MgHPaUd+Xh+3jLFPDsqOPqvGen/e0iCPUmYDQCvsFJHcaqt6igCWMZ1AO6x2hxKyQIY1300/V43zUPSTFiEcbq8ReSHCM74WJmgZoU9CNSsrEibuDxUgXvveYh/M+DfvfxKGWHvkP27DSo7LDiLWQqHSs5LZfOhbIjwlgtUnYY8U0SxgopBU327Kifz/8/XnJw3QMvaMeojoOHsQCIUBbgK5nL5vYBQCI1qxng40tNdngGS4L7TvouR/8cAlHKTuDZ6ZRiUgvhooKdGSfPZATa1y6C3w+dyMgqdyCMFfLspChR0Y0wZKdN4JlRQLCgRyk7FcfF/zz4In71xFYppk/ZWEQCuGqjPoSi07RSqbeU4MF95IXd+PUTW0UYI6TsVMkOQWoXUSc5oB0LL1xFyo7DqiuL15cc3PSHl7CrWuunUQjPTpsMynZCg3KosWQzsrHiwlhVYrt40Ccr193/gnaM6ji2VwsILp7lm5MXDgRk56DFA4Lot0PZ8Wv71B/G4jvpJCRbVnbU1HNOHsPKzuahqajsdGacPJMRYF3PW+7Zifa4tQP8/Kevs8OUnTTZWAqBnOrKTuJczI9//OOxv9+2bVvDg5nOUGPNtYoK8rANn0jp5+TZAfxaE47rhR5CXsiQh0uSeHZoXKQkkbeI/DRhsmPFtq5IAmGCZspOH/PvjBYrkp/nxkdewmdu/jPe//r98C9rVtb1nRwiGyvU9bw1FZSzdpAllyb1vBkLIlcfVCWCyN1rly/ATx95WZjQayk7aiPVBUzZIb8OECxUrVyfVCUi7TVMXWcnQSNQQF9FmXxvSb+rE1DPZ7dkYwUbrHYqOx3OxkpdZyddZqF4bfVcZzJ+ZGKqG5QTk50//OEPNV/zhje8oaHBTGe4zCwMsGysiEl4eCK4oflipxqUAV/KnSy7mkXR/5s3AlXr7JQiHly6sYlcCWUnG6XsNN4uQk09p7H3522MlRyMFStYwNQC6hBNvqFGMaY2Am1VGMsNSCh5D9KQnWbI6HGhTDpeIn2O62f6hclOWGkDAs8TV3ZWLBkU/7bb0Bur0V2pFMZK69mJMSirRvuy40r3b7eSHTVs1TnPTmDuBzqUjTXlwljp7mUCndPenI3xkjNzyM5vfvObVo5j2iNMduJDJMPVcBUgT57CoFwIyEYha2EEmmys6s2a5XV21N5YETcw7YRHixWpQSepLpQlJcaQs4OaPw1WUFb7PvUXshgrOSGTMoULmqG8OK4nlKX+gqrsNPchl9tFBN8fhVBF3mZ4dmINyrJCA/hjVtcTtc4OyetEFrlnZwUrCCmysVq4YqphjUYqKCcqKij5g8KNWwmqskOqmficLiU7XdMuovq1RJjbV2cnfQZrM9FIGIsTwTS+R/rOvrxPdqZ6GMt4dtoEelZoR9JTo13E8ERAdqQwFlVQVpQd9XX8O7lBueJ6sR2aCXyBHytVQqqLTtlp2KBcXQh6FbIjTMqKuZPG2AxPDfdS9LW4EagrpHjWFDNFUcFm7P7jPDtE+gbywT3muF7Ys6O8j84hXb8F1fRzICKM1cXKTiWlQTmOPErKjhKC4JlYQOeMv7WgtovoXBirquzY7a6g7Gn/3S5IvbEaMSjXkY1FHspSxcXOsRL++55nhT9vKsGQnTZB7Y1Vq6ggV3Z0YSxe+p4e+JBXgHl26HtV82TUJMFfN1qshJQdnUE53yjZqX4HD2MBgdIypio71R10MwyDNIHY1W7yQOsbgVpWkEKbtut5o4hrBBooO8E9VnG9UKiA/99lylivouzM789LKk8Qxmr4MCKhhtjSXkN+zhsNY8VlY22pmpNtK9iMdCO6zaAcZGO1qTdWh8NYMmFuJIyV/H1C2cn580DJcXHNvc/jklsfx9fa1K+wmUjdG8ugPhDxoFZDPfn4bCzJs5PAoAwkq7MzqUzEUUSBT9ijk4GyQ9lYg7HKTr0GZX0Yi5QWNYxF564Zyo4w1+Zt0Q+qVY1AHa7ssDYeUQjVbWlTnZ1+lhXnOJowFu/Xw8gxXa9X7TsPr9l/Ho4/eJH0PivBMTcKNcTWSAXlJOs6Pxfq9eLPZZSys2SwBy/vnuhaz06I7HQoosGbGwM8G6vVYSxGdtoczlH9cg21i0hxnuiepnB2qeJi85BfXuJ3T29PNYZugCE7bQLPjAKAnmx8I1CdslNxXMHqqc4OAOSrCkRU6jlPcVaVnahFQFKTkig7uaBdROMG5agwll7ZaQYZoc/uZ6Eboey0KPXcSllUMJ+1UKq4zW8XEaqgLCs0gL/4qxK4nFIdXAMK0fYXsrjhA6tD392O1HN1p99IBeVa0r/ryotRnGdH3ZVvrtbY2XNOd5OdbvHsiDlN9ey0mIBUOph6rp77tGEsqfVJKs+OnKhQrDjYOeavS89sG8PW4UksqvbAmwowYaw2oVYYa+vwJL50x5Oi2qzOs8MlcB7GykcpO2wXJJQdleykVXZYiId7a6Su53W3i6AwVtigDITNnbQoNyPMNM6UHULQG6s1ZCfLemPFhrEUpaXp7SJCIZ+AdPICgI6i2Ok6MffmbEHgotCOooKqupjaoJyigrJqho7NxlKUHWqku+ccvxBjtxYV7LZ2EdQmgkLnrQ7/xbVJaTVU/5muVlMc+LOeRtnReXZ2jweZg/c9uyPVODqN1GRn3333xec//3m8+OKLrRjPtAXdr+HUc/9G/J8HN+Drv3ka36kW8RueDG5omjxHij4B4k03AUSSDF7IkL5XDZtF7Yj47pR7djgR4eoObxfReCPQpJ6d5pEdtaAg0MI6O5yEJlA56PgopbsZ2Vj8nEVVUOZkp6IxKFc0YSxOFqOQVNnZNVbCP934JzxQx6Sq+otURbMW1Hs4LoslRKxSeHYoG2uP2b2h13YT1HF1qhFo0GrFfzazDc45yb83+Px2e3bUe3kipdJcb7sIno0FVA3KjOzcP93Jzsc+9jH89Kc/xf777483velNuOGGG1AsTj1ndrsRpJ77/w9Sz/3Jb+eYfw6pWrIujKWrsQOAhY/U1HOaGMA8O/UpO7TgFxjJkshOrvGu57p2EQAwUCUgKtmh1zcjJZIKCvbpwlitSj1PWWeHGpQ227MTRXYKWUtqZxGqs6OpDNybiuzEv+7Ox7fghw9vwCW3Pl7zM1WEOjanvCfV98ddH3XDEC4VEO3ZGaoquJS51mqjbb0IKTud9uwoYaxWt4vopEFZPbZ2FRWsqGTHcaVq9fc/uzPVODqNusjOunXr8OCDD+KQQw7BRz7yEeyxxx44//zz8cgjj7RijNMCtcJYVD9nRzWljxuUaec4WgxnYgEs/TJiR2kzI6waxorOxgp+PjxZ1oaYZGWHh7Hqm3iKNcJYoTo7QtlpgmenRJ6dsLLT7NRzp3rO7ZQVlIWyU8Md6rgeLr31cdz52Jaan6n+G4BEbLmyo+4K+fsmIsoG6CBal9RgO0Tu//TSkCggmRTqfZ2+qKDyLMUpOzXCWHHKDm1q6FnqUmFHLHyUYNFxz45FYax2KTudC2Op91e72kUEyo4/70yUHOxm9ornto9JrU66HXV7do488khceeWV2LhxI/71X/8V3/rWt/CqV70KRxxxBL7zne90TObsVnBTKhDujUWqxc4qc5aVnSohEjV2ZHNwLqJyMU89FwZlNRsrQRhrF5MuCyzENBgKYzWaek69seTbsqZBuRl1dqjjOSOSPayCcjPvZ5p7sgkMyp4XtAEhZafWZPvIi7vwzXuexeW3PxH5miQG5ULWDjxFjhvaYerSYZOFsfy/a+0yOcm8+y/p2tE02rFZPcdxQ1VfG5c9pyo7ROjm9VeVnU6lOdUA3Z/0jHeqHpDq2clWldFWZ2O1soLyRMnBvc9sjzynYWWn9Y1AeQYYPdPbRoviOThkj0EAwH3PTp2srLrJTrlcxo9+9COcdtpp+MQnPoGjjz4a3/rWt3DGGWfgn//5n3HWWWc1c5xTHqFsLOqNxSoVA4zsMAZNi52uxg4A5LO1Us+DnZA6addqFwEAO1g5ex5i4lWUuY+obs9OSR/GCpSdiKKCTfHsUF+ssEHZ85q7mxOF0XjX84jVlB9bn2jfEH+8tNuKakUCJEs9L+QsqQ6QupuXwjOaJq5RSFpUkBOD3zy5tebnymNTCEijyk6KMFZo0xFRQdnzPLGBmdPnk50u5TriGMgQ3CkFylGUnWyb6uyUW+jZ+c7vn8O7/vsBfP03T2t/r957aevscLLN55kNO8cjiwPy76RnmuaVWT1ZvOHABQCA+56ZOr6d1KnnjzzyCL773e/if/7nf2BZFs4++2xcccUVOPjgg8Vr3va2t+FVr3pVUwc61UH3TsigTA03i+TdKcF1PUFsgGAx0tXYAYIJKMoYabNKvSpKEQsiXxyod08mE9T0AVTPji0k5UbbRRSisrGiPDuOr7xkIo4xCcZFXyzu2Qn2AsWKI5nCG4GuJEDUYsoXTiJitYgXhULV7CmC6r+JysbipnOdZ0fOxgqfvygIU3aN22SiFLzgnr9sQ8VxhUejFlSFpJEKykCNMFbE+QPC3dd5b6zJsivO4dzqxqHblZ181gKKnc/Gonuo0UKmab8XaH67iJd2jQMAvn//Czj3uAOEekZQj22i7KSa7/j7aQM8MlnGm664G3vO6cWvP3Fc6D38nqVmzFurZRLm9uXxqn3n4Zv3PIs/vTSUaAzdgNSz96te9So89dRTuPrqq/Hyyy/jS1/6kkR0AGC//fbD3/7t3zZtkNMBoTBWLghjeZ4n2kC4HrB7oqwoO1XPToSyE1XMjxuUo9KBk4SxdlTN04WsJT1g4WysRg3K5NlRw1hVg3JJ79lR/10PyEvRz7Kx8jYnO82b4HTFHiPJDvveXtaYMw5ETqOMzOo15//3vKASciFbIxtL6sScQtmpQfDEZ5aD6z0yWcHaF3bV/GwxtgbaRfDQofhZzNvjwljqIXJlh1QdKxOEpl2vc5lOcaBrTeS3U2EstRFouwzKPEzWbM8Ofd62kSJ+qfHZVTjRRPoO5FJRweq9tXOshMmyi5d2Tujfw8lOdb0hX+nc/jzmVsOuaVWmTiI12Xn22Wdx++23453vfCdyuZz2Nf39/fjud79b87OuvvpqrFq1CoODgxgcHMTq1atx2223Sa+57777cPzxx6O/vx+Dg4N4wxvegImJ4ALt3LkTZ511FgYHBzFnzhy8733vw+joaNrDajmCmjf+//mCXqy4knFx20hR3FhAMFEH4QV5QcllI5Qd1nzUjiA7UQ8un7BJ6lSNw0R2rIzvPwm8Q/VNBqTUqCZXUgvCjUCbR3Z0yk4mk2lJM1Cu7GRrhHToe/O2lbg0Pl2vesgOX+QLOZ6N5Wq6njPFglLPExiURQXlGos6ESji1795MrlvJ05tqQUdCUun7ESHPHhobph58Lhi2m3p557nCdImPDtdUmdHpJ63vOt568JY/Hm+/sFwSRd6zmaxTW4akqHz7NDcEHXeuCrcr6i18/pyYo5Oa5buJFKTnX322QcA8PDDD+P73/8+vv/97+Phhx+u68uXLl2Kyy67DGvXrsXDDz+M448/HqeffjoeffRRAD7Rectb3oKTTjoJDz74IB566CGcf/75sKxg2GeddRYeffRR3Hnnnbjllltwzz334AMf+EBd42kleM0bQCYOxbIrLeQv7hyX3huQnXD6NxAt5fL4thUZxopSdsKeHfV7ybNTyPotFhpVdqKysXQGZc/zJE9KoxlZOs8O0JqMrKARaGBQjtqZ0vXx08CTmTGFshNxPxSVXlhRaehSNpYTDmPx902kMCjbiT07/me+at95AIC7Uvh2QgblFPekjiTGenaqn03EkN+LutAfXdNhVkqCb0a6rT8WPwaaazrXCFQOY+XomagzAzQp+Kaw6WSHncvfPrUdz28fU34fzAH5GpX3dZCysar/pPPoefpryVVb9Zme258XCu7kFFJ2Unt2XnrpJfx//9//h9///veYM2cOAGD37t049thjccMNN2Dp0qWJP+vUU0+V/n/JJZfg6quvxv33349DDz0UF154IS644AJ86lOfEq9ZsWKF+Pfjjz+O22+/HQ899BCOPvpoAMDXvvY1nHLKKfjSl76EPffcM+3htQyuJ5OdnO0vJI7rYaLsSCEa9WanRo0iS0YJ80QZg3mlXlXZsTL+jR/14HISRIuOSkIoG4vG04hB2XGD0EFkBWWmfpUdTzJbN5qRpcvGAqoq2mSlqS0jgkagtYsK0nXIZy2xi60dxiqGXvdf9zyDr/3qafzwg6sx2CsfI7/W/DjzdkCwtBMiV3boHmlqGMv/zNctX4AHn9uJZ7eNxb6eo5HUcx0xigstBdlyWQxNlFGqBB4yHXEZL1WQz+ZFqHqwJyc9n51STaLAj4Ge8U5wHdcNFCY6X7kshVnb6NlpurLjf3Ym45OP/3nwRVx0yiHi90RWsraFvryNUsVNVWunoqkGrmaX2Zb83PKNsupVnNuXnxnKzj/8wz+gXC7j8ccfx86dO7Fz5048/vjjcF0X//AP/1D3QBzHwQ033ICxsTGsXr0aW7duxQMPPIBFixbh2GOPxeLFi/HGN74Rv/vd78R77rvvPsyZM0cQHQA48cQTYVkWHnjggcjvKhaLGB4elv60GnS/8UmN+mPtGCtKC/ezCtmhXQspGWq2EkngUfU9LCsDNYoV9Dup7dkhqMrO3GoGCd34+Yh6P0nAlZNwBeXAs0OLjjq+RiegQNmRiUArmoHqSGjUwk/fm2cF/mqRSfJY8UXq3md2YKRYwSMv7orMvuLfR/6spJ4dkXqeq71/SlpUkNQiMu+W3eQlABrx7PD31sqW468nBdL1gnPPryt9Fp2rkSmo7HQyjMWvAZFwoXa2uqgge+aa7Q+i5+h1y/0MJ7XJJp8vaK5NE8bi46VzyK+p7n6rxJCdef0B2am4XtMN261CarJz99134+qrr5YUlhUrVuBrX/sa7rnnntQDWL9+PQYGBlAoFPChD30IN910E1auXIlnn30WAHDxxRfj/e9/P26//XYceeSROOGEE/DUU357+c2bN2PRIrmjcjabxbx587B58+bI77z00ksxe/Zs8WfZsmWpx50WjqLsAIGCsZ2ldgM6ZacaxopQdqLCRzzdXQ1jkVoSWVRQcwMXFJJ12F6z8TdHL8NHTzjQH0d1l1UP8ZDITqiCsj9Wzwt2EqGqv40qO5p2EUBrqijLxR7je2NJYayEZsztIyXpe/i/x4qVyOwr/9+yusbVJHWM+mys5hUVpGtNCmKU5K4Dl/6BdNePnokkBnL+ep4lSd9H48hkgt/TvRaQnZyULdkp828UOMkQBuVOkB12XmybFPL2pJ63soIyPUcrq7VrVBsDHVvWzojwUZpaO/x5dzUkXHfuOMHK2xplhz3nU0XdSU12li1bhnK5HPq54zh1hY1WrFiBdevW4YEHHsC5556Lc845B4899hjc6iTxwQ9+EH//93+PV77ylbjiiiuwYsUKfOc730n9PRwXXXQRhoaGxJ8NGzY09HlJEHh2gp8JsqNUh31+h0J2VIOyQgZqhbEsTRiL1JIk7SKC8cq3i21l8H/fsQp/++q9AUT36EqCSRauUTPHenO2OG/kbVI9NI0qL9QuQlV2WmtQru1fkcJYCZqGjhUrYvKpuJ5QQkQz2ZITa1AOCgpa1TGyCsohg3JY2UmWjeX/XSt9mSZ0Xrwy6Q6eXkekPs09SecqZwebhPiigv7rOdGjNjB80SDzO4VjyaA82Nvlyg475zTXdMKzI5EdZgcA2qHstNKz43/efgv6AfgkeGicNYIW95AVKDspCAZ/TmmeqdXYlCs76uZ6Xr9vqI9qLt2tSE12/v3f/x0f+chHJFPyww8/jI9+9KP40pe+lHoA+Xwey5cvx1FHHYVLL70Uhx9+OL761a9ijz32AACsXLlSev0hhxwimpAuWbIEW7fKpsVKpYKdO3diyZIlkd9ZKBREBhj9aTVo0eGTGt1EFHYgbFJKcAtlp6ZBWQ0zRHt2hLITFcbS3MAqyVIRNY4kCAoKhm/JTCYjSAgtFFH9nOrFWIQyIchOEx9onbITHcYKe3bidrE7FJVQNSSOFyus/URAeNXwIN2bvIJyyGzL/t+KRqBC2WEVw5Nm3VQUApJO2fHHlUtwfYAg+zCftYKaV9XvDxpXZsRYxoSyE3h2Mpkg1Ezf9ckf/xGnff13be/FpIK3iqCwUSdsRXyBVosKtjobi4ds622HE/nZFAbtyWLhrAIAWd2hTLCsHdxDaZQduRGoXB3Z/71O2QlM96qyM6cvj0wmCKmlrejcKaQmO3/3d3+HdevW4ZhjjkGhUEChUMAxxxyDRx55BO9973sxb9488aceuK6LYrGIfffdF3vuuSeefPJJ6fd/+ctfREbY6tWrsXv3bqxdu1b8/te//jVc18UxxxxT1/e3CiKMJXl29GEsFWFlRx/GUnevLiNYoTBWlTw0ouyoaCQbSzQBjUhdVgsLqmGrhlPPRZ0dVdlpQRiLhRftGmZdrublWDXjKGxTKqLSREbv4coOhVU85jFR1UPJsxOj7NSVjVXjlJJaNMjCQ0n9YHQ8te5z7XtJ2claiVQo8XrbYuTY/5nDduVkfqd7jfrf0fGpZvBb12/Cn14aCoU12g0pHJ6A/LUKOv9TjpGvpGN6eusobl2/KVU9o1aGsSrsHtl7Xh8AmewIg7KVEbW20hCMsL1BDWOFz0NZkHRL69kBgrl6qtTaSZ2N9ZWvfKVpX37RRRfh5JNPxt57742RkRFcf/31uOuuu3DHHXcgk8ngk5/8JP71X/8Vhx9+OI444ghce+21eOKJJ3DjjTcC8FWet7zlLXj/+9+Pb3zjGyiXyzj//PPxt3/7t12ViQUEXYJ5bJ4k/6iS3bN6shiZDHbigWdHH8aKKlOvq7PD+yyp1TgrjqtdUGsqO1nWWsD1IgsZ6lCsxIdBaLwUxgoZlBsgI57naRuBAoHC0Uyp1hHZFbUXD7qmebu2vwcIqieL71Ji9OOliugAPlDIYgv815cqLnK2FSLUfAFWqxeUNdlYvQkqKItsrITKTl8hi6zlZzYlDfHQ2MiDlUaZo3OetTJwPQpj1fbs5G0LhZyFkSL37AQbjv4IZYcKCtpWBnCCa0XKWasL5tWCXPHb/1mnPTv03OTYQqzLKtLhkzf+EX94cTf+3wWvw6F7zk703S0NY7Gw6d7z+rD2hV0S2amwbKze6nyUJoylKzZbq9eX5NmJSEwRKtMUCWOlJjvnnHNO075869atOPvss7Fp0ybMnj0bq1atwh133IE3velNAPwO65OTk7jwwguxc+dOHH744bjzzjtxwAEHiM/4wQ9+gPPPPx8nnHACLMvCGWecgSuvvLJpY2wWPK1B2b+JSNlZMFCQiM/CgYJPdqo3Y5CNldCgzDLA1HYRXMEoOa5EZDhpmt2bw1A1RVaN3arghdHKrotCgomHIKonRxAqtdbOZEjZqf+BK1ZcEe5RU897WqnsWLw3VsTYykE2VhIzpqoSiuJhDhmUA2WHV+IuVVz0F4LvE2THDghWmOwwZSeiIKQOSYoKOizLozdnI2dbqLhhv1EU6BzVp+xUw1i2xchi9OtL7PUk+dP9yCsPk2eHiCHV2aFSAGrITJCeDoexHKYs1CqV0NJxsAWYwP9ddtxIZZiD+g9uHSni0ITfXWlhUcEyC3Uum9sLANiwi5EdFlKieyhNfRt1vnA9TzqeuGysrJ1BwZbPKdVX61VaHnU7UpMdwDcj33zzzXj88ccBAIceeihOO+002HbyxQ0Avv3tb9d8zac+9Smpzo6KefPm4frrr0/1vZ1AYBYOfkYLKe3G953fJ5GdBQMFPLt9TDxcUcpOLqInFS+tbik8pV9Z6DjZ4SGi+f35gOzUUHZ4Txf1M2shCGPpCZVaRVklN41kY/FihepiXRCp5y3IxpLCAvrPp0WaZ2PFeaJUlZAWKvr88VKgFPbkbFHrKfCFyWEsXkHZVm4iuc5OimwsZvqN6vHDd4t9edsnXeXkCw2pIsKIX0neP40blIuVBNlYlcBTQc+mUHbYQiZKKBRjlB0ExvJuITs0j1hWRpy/TgxJbRUByHNOUgWMXpeGMMh1dqK/Z/PQJGb1ZEPh8NjxCEJsYVk1jLUhStnJpw8d6ZQdTlb1yk5AsPgmd1ZPVpzznjr8Q51Eas/O008/jUMOOQRnn302fvrTn+KnP/0p3v3ud+PQQw/FM88804oxTgtwnwYhSD33FyiK1xLmD/hyYd0VlKv3s60LY7FFKdTbpxJM9oNK/6s48IknrUmZFjeVyInxFlpnUA6KJlqh8xRkYzXvgeZhAbvG4sGzsewaxAgIh7HKMZ4dyVBb/dmkuA7hbCx1MdHV2UnTG8s/Fv19QuQpk6lWjqW0+6Sp54qyw2vf1EKZKTV2AhWKzkOeeXbofEqeHUXZ4XV2AFnZ4WPtdHYWHbuf6CD/rK3jqN5uXM2xrYxQHJOalInY11uFOEpZ3T5axBv+/Td497eja7zpUGHKmc6zI8gQr7NTrq+oIFBVdqTj0Sg7jKRzgzL5dQCIkNpUCWOlJjsXXHABDjjgAGzYsAGPPPIIHnnkEbz44ovYb7/9cMEFF7RijNMCvPkjQWRjVUMPc/ryUq2OBQO+M7+WQTnw7Mg3ratZVMV3Z20xaYSLzDniNTzUUUsi5nVJ0u5GKSwVFQYZUHbFzUw9H48oKAgwg3ITKyi7TCLmyknFcfG/617Gxt1B7zdOTHIspBQFNYwleuGIMFZFUovyWVm5ivPsqAucXGcnRTaWVC1Y/5rJUnA/ZDIZEU5LHMZyZc9OmveK9g+2lShsw8mRSo7jPDu8gjK9BqiSnTo9IhMlB/9+xxNYt2F34vdwuK6H9S8NSd8ZHIOVOJOuFeAKE0fa9HMiK2nUkVoeFwB4bvsYShUXL+xIZygPQkYW9p7vk52Xd00wZS+YL/rqaNOgnhfXla+fPhsrIOkWC7eTXwfA9M/Guvvuu3H55ZdL2Vbz58/HZZddhrvvvrupg5tOoGclo1F26GYfKNiC4ACM7ESEGAjiYY+ooGyzHkz8PVH1efgCy8lOLWUHQEgpqIXNQ5OoOG7NMBYpO0EYSz/mejAWUVAQaFWdneqkrWS3/ObJbfjoDevwLzetF6/l1zyoFhvn2VGysUQYq2pQZqnneUZ2omo5yb2xIN7Hj6PMDO1JKignaY1AO1eaUNNm+pUVZQdIQ5RIqeFhm+iFlD43lw3MnOFsrEwoG0tVdrKSssPIRgqV9K4nt+Kq3zyD/3vbE4nfw/GTR17CqV//Hf7zN4FKz5UHul87UfhQ59kBfMUDSF5YUISxUik7wWdHhbHIC5R2o8cNyotn9SBvW6i4HjYN+ZseTjzqyYAKK/6yclirzg4QPPNc2SGlctoqO4VCASMjI6Gfj46OIp/Pa95hAMjEgxCqFNyTlW6mBbPkMFYUIQi6jUeTHVXZyWWDxp1RIaFC1sJAT3Jlxx9LcmXnTy/txmsu/RX++ab1NVPPiXRReKOpYayIgoIA9+y0tl2E6wGbh/36Sg88t1NMgHIj0NoLb8izQ8pOdfGUwlh2uC6MGirl30mhWPodtTHhE2+aooJxxzKhhMXSh7GCsdI5TmpSpjoqWZYBF/e1QSNQK1SqgPtM+pnfwnE9jBTJoOwrOzxLTQozpKghQ/46tTBpUtD7Xt4dqBO8hIUIu3YgssYVJo4kXjYOEcaqV9mJmGt2jVED3uC1Y8UK7n16eywRKysqytKqSZlCWYHSGCg76YoKxmdj6ci02l2eyA6Zk4Fgrp62ZOetb30rPvCBD+CBBx6A5/lGuvvvvx8f+tCHcNppp7VijNMCrtazo/aAksnO/P4IZSci9VzH4AFZQRDviVF2+IKXWtmJSIPX4cnNPml++IVdAdmJMDUHyg41RW1eGCuqoCAQKBxq9lcjkEio8MS4ohnpeMnBE9VzI1LPExuU9dlYPPWcf6ZaNFFtSSLV2VEatZJHghYNXZqqDtyzE6XsCLKjtK1IXmcnCEWJDKmE15BnvyTp0E7nmJ9P1bPDiwqOlypCoQSYsiNac7iS/yRNETtaeDYPT9b1TBBx1XmGeOp5mho1zUJwLuWfp1X96HiSLtJeSAnRf8/O8SrZYdfuy3f+Be/61gO49c/R7Yt4OwgAIZMyr8NTTxhL59mRyJuGTPOWKUAw98/jYax81bMzXcNYV155JQ444ACsXr0aPT096OnpwWtf+1osX74cX/3qV1sxxmkBurm4wKKqGAOFLOZzZYcZlD3Pk3b5HFENOF3NokrgabIhz45oGWBLHqJaqef0uf5Yak+G5L95aedETYNrqKhgE3tjBZlEYWWnFY1AdQZl1w2akQLA2hd2+d/LUsGDNHD9sZYqrtjZqyoQEaSy44mFVgpjKYSaSCev2kwci+4/2hHS+UuSdk7HTYgSLdSKzOK+Smky9jOk6BiTXcMyI4P0vCYKY7HvUj07WZZ6PlZyhF+nkA3UIG5Wr1XhNgp03jwP2LR7ssarNe8nssObRzIlshuKCmYVZScoyVB7TC5TNZKGgkKVwyPIzi4RxgratGyuVsPfMhR9LXiYEACWzaumn+9Uw1iZusJY6vweMsDHKTtKGGtuf9izMy1Tzz3Pw/DwMG644Qa8/PLLIvX8kEMOwfLly1sywOkCurekMJZCHgYKWZGBBQSenbLjSot7qIJyRANO3aIq3hOr7AS7e8mgnCCVPCqkpgMt7iXHFZJtFKFSDcqqstNI13MiSjq/UCsqKEthAbZ4jLPd/sMv7MI5x+4rFRUMWjfEewayVgZz+vLYPloUCyWfsHdX++7kbTukRKjtInS9sYTXTDF6JglhAUo2VpRnR2TIEenSE/ooCB+ExWvfJPX7sDBjIoMykZ1wGItS/23LEqnn48WK1ASUwFW+WtkyUeA7/g27xrFvtd9SUtB514U5LFaJvRMJYqqPhJCmZQR/TdJFOq4BLsfOsaCfleN6yNoZcW/EVuB2A4M7gFBGlhzGqobz04SxlPG7ntznThdiC3l27LBnp1fJLux2pCY7y5cvx6OPPooDDzzQEJwU4AscQd0JDxSymFcNXeWzllBVyo4nPZhRBmUdg6fvVOvs5OxMtLLDw1iplZ3knh0u5T+1ZRRAkjBW85WdKOO3/7N0IZAk4GmdnExQOA0A1j6/Uxqb3wg03rdCfp15/fmQssPVgd1VuT2JQZlnY9H3Cs+OEg5IkokFyJ6dyDCW8pn5FPcVH1vWzoSOseZ7GXlJ0tqCv14lVmIhszIYKPjEZvdEOeiL1Rs8X1Gp56mysdg8QcpAGpBKx+8XKfW8g9lYfBwcUQkaOnDimDSMFSY7EcrOeBBCrrgesnY4jKz/fDmMpZIdXuQy6I2VIvVck40l3V+asTkKAaP5YC7z7PROZ8+OZVk48MADsWPHjlaNZ9qCt24gqN6bfhbGGuzJSaXQaSeYyciVioHoBpzcJ6QqO/msFakI8QUvrbKTz1IbigTKDiM7z26vkp2oOjsiBCCTnV5RxK3+B06tLcPRPoOyJ2oIAcDGoUls3D0hmcVztkxgVBDZWTBQgK2kqfMJb5eO7KiFK3Vdz71ayk6yvRNvehmV1ROksvufmTaMFfggwun1td8bTPQZZhqu/XoWxhKd5wPvw34LfZXl+e1j2FFV4XTKjp96zrKxUsgo3Fv20q70PbXGNWGsimbT1IlsLK4wcSTpGUfg81LiMJaTjOyQssrHIpSdiLF5nseURP84yLND14+f/x4ROkp2L3ueF1K8HLURaAJl54yjluKwvWbjmP3mi9dM+zo7l112GT75yU/iz3/+cyvGM23hCrIT/Exd2Gf1ZLGo2vV2bl9OKuZEZKcna4eqwNbsjWWFpd9knh0rtWcnzQ6cKzv0wPfWSD0nQkAEhcbXSJgpXtlpZbuIoG6J43piV01Y+8IuliZuh2oYFSuOpPiROXn+QF5SZFSD5a6xwC+iKhGhbCxmmqX7SfXspKmxQ6jVH2ui3GgYK1BU6BqmV3aSFdErSWGs6vlUFrmsncGes3swqyeLiuuJOji8ySlX4/jGpW5lZ1c9yk7YoMyr6Sbta9YKRKWeZ1PMOfy8Jg1jqWQhKowlKTvVsdB9GEXEdM1Nl87xyc720RImy45kYE6rpvjPf/hntbOxgmsOAO973X74+UdeJ3t26jBLdxKp20WcffbZGB8fx+GHH458Po/e3l7p9zt37mza4KYTdGEstcdVfyGLV+03D+es3gfHLl+gkJ3o/lS066WbWO2xk7X8HWomA3Hj52wr0l/DfRskvQNJU8+Thwy4slPrO8KNQP3PH+zNYetIsSEyElfjR81WagZ4Vglf4IjIUY80mewE16vi+DH3U776W1RcD7/+xHGwrYyonrxwoIBNVUNkxfFC/gqh7NgxYSwiGTwbSyE7QSXa5K0iCJaVAdzw2AjjJX0YK6lZNwhjhY+x5ntZef7AQJ7Ss1OWFzu7+gwesmQQDz6/Ew8+58+Tg0zZ4eZfuYhdcmIhh7HSKzsTGs8OTQ+d9uw4mjkUCIhwEm8Tv3+SZhGpqkyUP5ArO2VBcoj06t/DSRCRNl7va7LssFCoJeaoevxG+ayFUsVNlI0V5Y/imGqp56nJzhVXXJGov4yBDJ4GTtBlY+VsC587/RXiZzk7g7LjCWVHl/7N0315599gcvB/Z2cyqHiB5F7ToKyEsZKkngcEqvbEw8M2hFp1dsaU3lizqzVKGikq2G5lhy+AnJhSiO4NBy7AT//wMh56fqdQrni9mIrrYbRUwTPb/JoowxNlzO3Pi3tksDfHSIobIgjCoJy1RNgxqkq3bclEGggXwxxX0sSToBaJoMm8V8nGSnJfAXKhtkIK0zwgk5ckRQVpYctrQmaqGnHwHrPw4PM78eeXhwBAUk4lZUcqKpj83uO77JfqUXaqxJXPCXyXH3iY0rOdjbsn8My2Ubz+wIWp38vHoS7AadRkniWadJFWP7fshPuslR1XPH8AL7hJnh39Z3MyQvc4ecUc18Nk2ZVCoUJNqWPshSrZ4TWzgFrZWNFzvtr+pNuRmuz83d/9XQuGMf3BO5AT+MKetTJaMpGzLZQdByPF6GacUrdx1vlX9QnRbhpQCspFFOjL20oYK0k2lig4ly6MRahVQXm85MCtTgJAEAZoimdHc/57WtAIlBstxW7e8wSRe+OKhbh53ct4dOOwuLZyuwhXFEIEgkVcbhoaECN1MuN1dtSigsG50Cg7SlFBCpHVF8aC+Awd1HT2esNYWctK7buqSGGsFEUF7eAZ1rWLAICDlwxKP+e952yNisZfmwQTUliziImSkzhLDtArO/wYRCp+HWGsj/9oHe5/diduveD1WLnnYOr3c2M/h6gsnsSz0wRlx/OCbCsCbSDUsdZUdhgZ4eG5QtbCeMlBseJInjB6HsqOh7LjSv0IdeCqYCFrYwSVsLKj8+xEnGuOqZZ6ntqzY9s2tm7dGvr5jh07Unc9n0lwtcpOcPoHerJaxYx2iqMxyk6OsW9OXNR+XNyknJPMqfLDzFPP+6XeWEk8O8kLfKUJY3GFaaxUEYsJLRbN8OzovjsISzS/zo6VyUi7edoh7b9gAP+yZiUAphrwbCxHztyikAmvySMUGceLXCxjw1iKQdlxwqnnNL60qedAELKJLiroSp+ZSx3GCghI2hYmJbG4JO2NFfbsBEUFZe/DwXvMkt47qxDOxnI9T3p+6vXsAOlNyqS2yp6dYB5Jmo3luB5uXb8JW4aD+jJbh/0w68u70ytOfBxRnp0kChg/l8mVnYBsqD8jcL8O/55KDWWHf45uIzxZdqWwKn/2kpAMOic8scV19QZ06X3KfauDKCo4XclOVOXMYrFo2kXEQISx2BnnN66uVQEQkIdhIjsawsEbtZU1N7EgO+zGlTw7Mann/Sx+nETZIUK0Y6xU45XpyA430w5PVsQCT56H5qSex2VjJfv8sWKlpsTPG4FygzIpXX0FG+973X64/IxVQgGZ3ZtTqi1zZceRxphnrSVUlYBDV/G3qGSmxXl2xFhEGCu5UFyrMrHqA8qJvmBJw1jhcG3i3lhMqaH9QeJGoDk57Kk+gysWy2RHVnYCMltvnR1VrUgTynJdTyxcnFQGZMdKHMa6+y9b8eEfPIIv3PKY+BkRUNWIHzWWRzcOSQQmyrOTZoNVT+o5HT8P06oh0Z1j+ga8QTZWlGcnUBGlvolMIeTEo8AKXSYZP6ldOZ4M4alhrAY9O9MtjHXllVcC8NNGv/Wtb2FgYED8znEc3HPPPTj44IObP8JpAL+thv9vqV0EIw88XMSREwt8OfQejnzWQqXkSA+8aorm922sZ4dVUC5kbbz50MXYPloSmWJxeM3+83HLnzbhtj9vwnl/FV+HSSzueTtURE5FJpPB7L4cto0UsWusxAzKzQtj6ZWd5GRnx2gRb/z3u/C65QvwjfccFfk6nbIDBOSPVKwzX7UMy+b14c8vD+GIpXOwdcTfGavKDoX0Ssx7FJXGzMFTz+n8lRT/klToLkLZmYhptxGFgOTpfz+h3A9UJiFtI9CslT71XFRQtmVPVc3XZzOh7DZVjegvZLHP/D7RGZs/9yQcqKnBaZQdupfn9+exY6yEDSmUnUn2DOl2/lkrWJBrRYw2Vqs3815t9Jm68LWKG9e+hP/zkz/hH086COcffyAAfX9BgGdjJQhj1ZF6Tu/pydli06k+U7vGVGWHwljy3yqiwkUFpuyo578358+XcRu8Z7aNYl5fXhuS9f13XDnUeHZYBfIoTLWu54nJzhVXXAHAX7i/8Y1vSCGrfD6PfffdF9/4xjeaP8JpAD5xSVJlnoWxCvpLQYvtSIyyAxApcqQJXUwOmbCyowthEFSl45vvOTrq0EI45bA9cPHPHsWfXx7GM9tGccDCAe3rPM8TFZRXLJmFP7y4G0B8qGxulewMTZTFpE7KTnMMyrpsrOR1fJ7bPobRYgV/eml37Ot05feBYAHhpGH1AfOx+gC/tgX34XBVTG31ICs7bnQYKxv2bQUhvbCyQ+RZNcRPlOsIY9UIh6jZWPX2P5J9NElDYIHfJ0lvLDp3kj+oLIeDssxbcfCSWYzshJUdP/U8fjGKAl2LAxcPYMezO1NlZPHFX87GYmEsK/x7Hej+5KSJjkOn6Kp4drtvvn9uezB+buznCLKxkoSxgvGQWTdOvQDkAnuUMKJek53jak86OYwVdf+IEKhyTHTPyqnn/s96qmQnStnZPDSJN19xDw7dcxD/cebh4r38XuanShca5v24ojBtu54/99xzeO655/DGN74Rf/zjH8X/n3vuOTz55JO44447cMwxx7RyrFMWfF7IRGRj9UeQnbBnR7+gqIuB5wVpvbowFl/oIhuBJvDoqJjXn8frD1wAAPjZuo2RrytWgrotZNoE4jN65lSb0O0a58pO456dJMpO2YkOBxFogpisMRZdI1AOXY8uQI6f88wP4dlhpE3exenHXbB1vbFkg7JNZQ3Y8UuVn5lnJ42yU2vRFNlYOZXsJA1jBT6aesNYuSxTMmLeysv9h9pFaHwm/H7ndXb4Oam7N1b1Why4yA+XpQlj8R261rOTCcKutRqB0kZG5z0a1WRhhsfi398UzgSCMJbqI4kqqqqDSogS+V6Y/yuKdEcpO6p3R4XaXZzQw8KhPCQL1FZUnt0+iorr4dltY5LfiCcFOFK2n0bZSRDG6mVZmWnUx04h9Wr2m9/8BnPnzm3FWKYtOKuX6+wEi8NARBgrL5SdoBCcDgUlJKVTk7g5Om4RiEvFToLTj9gLAPCzP26MnBS5lH0IM23G1fKhUuW7xsuBQbmneQZlfTaWzV4XPzHSOa81gfJ4uDpx9+bsyAmGqwMU1gTCREX17MQpO6rqoVZQ1nl2slZGagtSTzZWLaOranpO04bEf10w1nxVhU6eeh74HHhoKfr1QRhL9UCp3aMB+X7nyg4vBMkzi9IoOxTSPGixr6imCWPx0KjWs2MnLypI6o3kIaSK2wmUHSJLvDyFrgo9ENyjyXpjyeNOEsrioSb6rrBnR83Gkj1bUeeLm485JGWHeaaAQHWNUlTIPzRSrEiqI1dTKzXur4qGpKvgkYmpoO6kTj13HAfXXHMNfvWrX2Hr1q1wlRvs17/+ddMGN10gEQ+JcPhs2/WAgYjdPO0k4urs0GcBjOywh8vSKDskyQKaooLl6MU/Cd60cjF6chae2z6G9S8PYdXSOaHXjDG/zj7zg2aFcS0p5laVnd1N9uyoplwOHrIpll30xXjwK4zsqHU4OKTUc+U13BCugk88QyzVlcYve3aChTOJZydcVFAmO7yCsmVlkLMsTMIPkaVtFwGgpvdjQlF2RHpx4jBWWNlJamKXKyjXNuRSOrz/Xf7r6H6sqexoemNVlGuWtM5OxXHFs3xg1Qidpj+WFMaK8OwkLSoowljcG1J901gCgzIRaJ3aFM7GCozdtaCWLkim7ATkN8rnqGZjJW0XIe5T5ZgkZYeZmIFgA1CL7PBx5bL8XpbvZ51yKGoaxXh28rYl1q/JkiMVyOxGpCY7H/3oR3HNNddgzZo1eMUrXmEKDCaATDyCn2cyGRF/jVR2FLITpXzkFGMkv3+JYPGF1a+zoy+j30gYC/BDciceshi3/GkTfv7HjVqyQ8pOfyGLZXODKtxx3zmbKTuqZ6dcTY1W++YkQVzquV1VMcqOV1M9ognC9fzJTu1hRqiw3b6q4kSFsAB5ktcrOzrPTryyw8mO63qsVg/Vt2HKDuu1xtN9xxVikgS1jL9qbyyxyFRqL2j+uMKeHcpaq4Wgbo6VyJDLU9ULguzIO3vuM9l7Xh+Wzu3FeMnBQmb658Sq4obJRi3w8Cn1VxqaKKPiuCHlQAdOLLgCIha+DGufUcuzI8JYYaN1kjAWEaJxHsZy9Atwjt2LtaAu7EkUCa7EBk1HFc/OmJ7s1GoXQedHPSau7Ki9s0Q/wARkh/6tZmM1Q9nJZPwu7KPFypQoLJia7Nxwww340Y9+hFNOOaUV45mW8Njzpe7kiezU8uzUCmOpngZHEzqTlJ1sJmgE2uQwFgC84aCFuOVPm/BktZu5CpKnBwpZ7D2vD4cvnY2+fDZWTRLKjsazA/iLfo+Vfsy8Po0OhayNslOpqR7xneVk2Yks+CV1o1fmkrhQEL9+QxNc2dF4dqinleNGEgqpsCRTBegz/O8MFCJetynL7rd6srFqGX/VqsxCuUxaZ0eTjZXcs1M1YrM6O7GNQNnO2/MiPDtsMbOsDG776OtRcTzpGZOVHWakTajscLLClWLH8xJN9NEG5WB8mRrhR0IQxgrOA70liUGZxsJLLNApiep6nqS6tvqaJJlEPKMpqsVOSNkRYSzafEYoOxEG5aDOjhPyz9Rq06AjO1lFpazZCDTCDK6iJ2djtFiZnmGsfD6P5cvjU4oNZEjEQyU71Yl4Vk2yQ9lY0annQCDTchk6qs5OtEG5qhAk2A1Ggbq37x7X19sZE8qOjaxt4ebzXgsAsUoheXa2jRbFxMkNnsWyG+v5icJkjLID+Av/aLG2L4hPIJNlF7N6Il7HSgJkMhlRGh6IzsoD/HNDKhMnO6KCMvPs5NjCGRX6UZUdHubReXakHS7L9qonG0vUr9EsAp7nhT4zm7bOjjANh300tVASyk76MFYm40nfFZVaPEsj+XMfk2wgTTZu3uONNjJA7cwpAq9/I9W3ccPno9aQRpVsLH4PJiM7VWVHImCBwsSRpqig+ppEnh1J2dF/lyAVVqb6zHlSR/NajUDDBuVAqedky/8dGZT1x7tDR3YsK7L3mr6oYG1lB5hahQVTr2af+MQn8NWvfrWmG98gAL+x1DAL3biRyk6VcIyW4j07atl/HcHiX521MqFMHEJJ8W3UA8qcUuVdgghjVXegfqPS+AeLPnPzUFCVtb+QFcdVr2+ntrITSMpxqEhkJ/q16k6NT959MWSHv2d4gmdjyUUFeQXlNEUFqc4KV254uInXbcoyotyIQVmnmEwy0iUMyqLTejp1JmuHTdhJ3+uTF/9niYsKKnWL1ArKcbDFQuopxt5kcy33OXFylTQMFpWNxetCJa2grCo7EtlJ4NkRyo5kmtYTxxy712tBPZfJqhAH91JUViBlYy0YKFTHIiuqUWSZlzng4JXbVRNzrc7nu3RhrKyVqo5TVB8yFaJlxHQMY/3ud7/Db37zG9x222049NBDkcvJO5Sf/vSnTRvcdAERQ919Q0pNrWwsmlsiw1hK0TUdwaIQWr7qRQhqrMgPopp+XA9IhVF7xhDUAnrJPrNKdlgJ+kLWT/edKDt1Z2TVVHaUqrhR4Oc87rXaytbVuaK/BmEgY7Be2QnCj7wNBX0f73oPAAXblgivzpiuy8aymGeHt4voq6eCsuY08UlchLEs+f6uhTIzfqbveh42KEcRRs8LfE4524KHwLPjeV7kAq0DN4PrjL21MMFCf3zxTEqWxiLIjst2+UnIHxCQFV3qta4BsIoJQXaC1/LK4xxRoSXA97b9++1P4q9fuSeO2mde6DXJPDsBYdUZlCfLjjh3C2cVsHl40q+CncB3pao2BK7sqCb3Wj2p+AZzh/DsBJ/vhSooRys7Ub5DQi3i1U1ITXbmzJmDt73tba0Yy7RFVJlzAFg2txePbxrGPlVDoQo1lBRFQNTicLosEPp+uoGjJolGs7GAgJiMVtMf88pncYNyUsypEiiemZbJ+BOQT3bSP3AV5mmppezUyuZJouy4zLtACxK/L2qdD5ro4zw7eebZqbD6OLMKWVEBll5HJvVi2ZXahBCCejquCF346dxM2akrjBWtENBuPs/qBaXxZXB/SFZTS6gWRIn9BL2xOAnyS/4HmxNem6lWw0YAkQbSpGoWXYeevC1trJLW6ZlgKoq+EWiyitJA8HyLejNsDEkqKAfZXJ6YP7jCxBEXxvrNE1vx/ftfwMbdE/j2382rL4zFlBVd6jlt6Gwrg7nV8H3ZcbXV7FWUI8KcNM9Plp2gzxuRnRqdz7VhLDsjTPaOqyp3OmUnmWeHxjItDcrf/e53WzGOaY2o+hAA8O/vOBwf2j6Kw5fN0b5XJQlRFYajDMo8bEbfTyEB1edDUNOP68Fgb06kJe6eKGGRYmCh3V09ZIdAi3LaMAUHz2CJIpKBslOrzk7wWVGvrRVerKXs0OTDs7HouEssjMVVApp0B3tzYbLDlR2NMZ3XfqHjs6xA2RmdrAhiUVdRQW0YKxwWSxPG4ouM3wg0mTIn3k+1SRJkY/FwRo5VqfW/z6lL2VENykmVGV6IMVNtRRIXxlShGpSpfAL3lQRFBeM/S00958eQpM4OVwomSg7yWSuyEWiOEfuoY6LQmRp+SqJIyPWlwsoOEYq5fTnk7XTXMChYqJ/nJ8uuRLaAYF7Wmas9z5PCWPTvnG0FTUk9T/J01puNBUwtZSfxaqbrdM5RqVTw4IMPNjyg6QhaA3UT3uy+HI7ce27ke9UdYWQFZSHVO9XvrE6yjGCpu+SoHW8zwli2lcHs3uhQFk0+AzF1ZVTM6ZWL3BAJSdusk4Onb9ZUdmp8vqzs6F8r1VyqTox8oqvl2aGJfVRpF+F5nqTM8MweoexIrQn8EI1kUNaUHJA+pzp0O5MRJIhCioWsVV9RQc1CrGZiAenCWPw65Kw6PDssBFar0jN/drhnB/DPqSj1n8SzE5F6nlSRUqtO8yrXSaDuztW+TlYmI5lco+C6HgtjkVGXe3acWMN3qeJKCzDNFbo0fiA+jEXnn669ms2XxGtSrhHGokysuX158VxUXE/6rqjzFSh/ei9nseKEXhNHMIYnK9K9s4ORHSkbi4exdMpOhOKkopbK1E1ITHb22GMPifAcdthh2LBhg/j/jh07sHr16uaObprA9YLJIi1UZSeyNxZblPjfasorEIS8amVjNRLGAoJQls6kXE8YK5+1JI8P7X4CM18dZIdlnkXV6FFNp1GQPTsRyg57DS2A/L6oreyEd9bFapVV+ugoz05vzgpde964ks4fL+xI94+aek4T76aqWXzhrEKqmltxionwnnBlp86WANk6up4Lw3G2dpfvskR2MpIXzi8Il1zZiSwqmDQMpYQT6ZwlVXZUpYDeJ/VyS+DZGS+HSZN63dTX/OrxLfjFo5u14yDiFOXZiSsqSN+rZscRUik7tr7OjlB2+vNSSI1/V80KypG9sQJllu6POLKjzrUU7uZ9+NRsrEaUnanU+TzxaqZmXz3//PMol8uxrzHwIUJK6blOmOxEEBBbyUjQ7ShpjqCFSrcIeJ4nhUMawRxhUg6TnXoMyvwzgYDkJCUjOtCOJC5kl5RMyXV29K/luy4iOVy8q0X+dN6PkuNK1zCvZGNxGbyvqqTRtafjLlUcbfhS7noeTLo0Ob+826/Qy4vjJUGc92NcE8YKDNFJwlgyoUzbRLTMnp1aYSxxbtlruZIUFXrRIRuh7CTOxirJRntb2QDVgkpA1GQH20qWjaWmlvtmXTfyNaWKi3N/8AjOu/4RjJcqoWwtWkijPDu8DIIK+pnavoOQxGvCCYmu6nyg7OTEvVZxlDBWSoMyD53z3mtAvJoSlf3q+8/8f6vZWLrQcNJsrL4a1Zy7CY2tZgpMNWU93BS7OxUq4YgKLWWVxUPXbVkNY+nkXy71R9X0SYq5onGnJoxVh7IDqGRHCcfVE8ZKUECxIOLnyZWdOIMyga4Z39X112i5oFs0fXOxTHZkZSdYvPuVisS8ZIHOoExpvY7jSeFYIh8biewMpCQ7MYvmpCaMFaVC6sCPl2oTAcmNvkE2Vu2igvy1hCCsGu5rFAdLUnb4zjulsiNabCQzExNUL406l/hFMOPJHxAmOyXHDVUc5mHYkcmyCF3tGi+HCAiRnyjimI1R/YSyI1Lg5dckCb/w8gEBmQkbrgd7cmKOLbuuFMZKm3oeFBV0JfKt/k5FFNmR/VYp6uwkzcaaTsqOQf2Iy8aqhRDjj1BbsopJTzcxCIOy6tmpRJCdRsNY/UGXchX1hLGAgEABTTIos0JsUehROllHIYlnR1J2KIzFvrqvhodJdw8VmbKTrXpxuCLDd+a0EyPyUJA8O2HiJ3t2gp013UObdgdhrDSIS2HW9doKDMrh1+8cK8m9fhx5oo5bDHXgdXZqhbFKmp05nb9SncoOJ6hAcmUm0rOTMAwW5dnhm7Uknh01tbziuCGvDK+MzInP8ERZqq0DBAtpVCfuuCaxdC1JlVUJb5JFuszuJ12H9UkWduVm6TTKTmS7CObZIUIUFzraOVb0Py90jnhRQfme0IaxElZQnpYG5Uwmg5GREQwPD2NoaAiZTAajo6MYHh4Wfwz0oPuqHuUrlHoeobbw7BtAX4BLKDtZUnaqkqxEdpzqWJNN0HGIq7UTtItIpx7NYWSHHvpCQjKiQxJ/UlIDdKJsLF1JAO7ZqSOMpUsb13l2crYlDNCqKuZ6wTWR6uwwz46u6/nWkfrITlwYK1Ao2Dg0Kb8A8MTmYRz9b3fin29aL37GGzcC6Tum0/Pge3D8n0WnDlc9X+yc5Rn5TuPZsTTXDEgTxlKrTqc0KKteG6X6b1LPjpparipV6mvCZEfv2YksKhjj2RFhLKW4IYXP1WPWgadh69RwrqgJg7KSeu5EEE5hPlaOSRiUpWysJJ4df65dyvoNAtWaUUyl5MPRh7ESenbyU0fZSbyt9jwPBx10kPT/V77yldL/TRhLD1F5ti6DskwGanl2SBbVenaEQVn2FvCHktfYafR6xlVRHlMqKCfFXE0YqymenbgwVsLPT5SNpVH5pDo7tcJYGlmZe3aIDGc13gHbyggDdF4hOwDrvxaVjcUWG/p8OuR6yY5u0VSbgAKI9N08uXkErgc8tinYbIl7X6knlVQh4Y1ha4WxuApE4HWZeKuFWuAENcOOM3FvLKFSVpUdlgad6P0llaSQZyfwb9TqaQZowlgsK033mtFJHtKqhMhMEMbS+0jovOn6pqkGZZofZ/X4DSzT9sZyMvS5wXcRGevJ2eJaqQ14o5QwtToygSs7ZeUeiisqSMrOPvP78cKOcfHzrCWrlLWUQx4KjsNUUnYSrzS/+c1vWjmOaY0o+TUJktbZ4TtwQO/ZUcNYYpdSCU+sjaSdE3jjThX1hrHmSGEsSj1vPBsrLoyV1KCcxLOjS+nk/66Vvq337DhSVhl/nU9SgolL7SLOlcOgWCOvsxNWdiwrE9qJpvXsCOOv5pSqizYQvXuna8LVAHUBEYthAtLguEFF5B7WdqFWGCsrhbGYZydhCi8gFxUEu33SFhUMPDvpsrFURSXWsxMzJNVgXGHnVPca/u/hyXJozlMNyqE6O5pNm/hupbo41VAa7Mlh09BksnYRmuMv65SdvC3+rWbURV2D6ArKgWKiFiGN60dFqeb7zu/DPeznWVvJxmLD0beLSObZ6ZuOys4b3/jGVo5jWiMo8Jf+vWHPTnwYK86zI5Qdtaggu/ObUT2ZQCpMnEE5dTYW63JOCoTaFywNEhmU66mzU6OoIF/8rBRhLNXICCjm4uo5kdKYGfElsUSQItsSxR+pUKGugnLZcSWFUt2JplZ2WGaICtF+Qko914exaMLn5lpeJ8d/b3SYQ0VJ8azVLCrImoAG7wvCqqrfIg6coHrssU9cVFCEsdR7oD7PDs0LdN2zbLGP6wKvenbKShq2+pqRSTmMpdaaUlPPI3tjxaRQlxxXynIb7K2GsRL06VKzofxjCm9s+vI2hieC54W/JtLgHmlQ9v/PQ3xEPHpilB0qIrjP/H7p53mWjRVuNBt93pJ0PQemhrJjDMptgMeMnWkRzsaKUHYSeHYilZ1qUToAoUWzEUQZlF3XE71kUhuU+wOy0yOUnSBskASPbxrGbes3AUhmUObZNXGQ6uxEGZSVmhmAvHvqr+Fh0u20eDZWQGKC+4GnR9P51nlMhifCZEeuoBzcUyoJrzuMpWERqtEWiCYs9NqxOGXHTr7o8wXEL87o/7tWGIIrZEE6vxupRuhgU10cJVVbF57RoeFsrFJENhYzq9JhxhUFVMNYZccNnXv+GpX4qFlh46GigkoYK8aTVVbCgfR/KrA5kWDO4Oqczuc4wcJYUviYFxWMqqCsmQ+AgDDz80SkLi4DiiwDy+b2yo2fbZ5Jp5qn6/fsTKUwliE7bQA9b/V5dpIZlJN5dqqfacuufiBQLZIoHUkRhLFkZYebAtPX2WFhLFFUMJ1n54PfX4tzf/AInt46mlDZiU715OATSNRYSJngi6NkUK4j9Vxq9aAqO47cjFL17PCxiDAWuy8iPTvKjm9BvWEszRpAi5u+qKB8DeiaSB27ldASjZWq+cahyLLasgl6Y4mGo3b4fPqp5/psGx24P6iedhFq+C+bwrPjup44l1lFEQque3xPM0IojOV4oZ5mskE5mB+GJ8sh0/C4ko0VbhcRnW3Hzx0nO4PVxsvJwliBUqi7D2WDcnDukhQVjGq4SZsvfunoWYxLPacw1vyBgjS3Zi05G4tfv7iigkkrKE+FMJYhO20A9zqkRbhdRA3PjppBoa2z4//dnw8eTlJfmlU9GeDZWHJqMO1WrEy8oqL/TF3qefJsrK3Dk3hxp2/c2zI8KdpFxKaeJ1Z2gu+PIkaiOi/3UrH7ojeCzBJkDxbEuEoRnh3e00ry7LDPWVBVZe55ahsAfddzICBqtiWPf7AnG9kxPgpxxl8iXf2aMFbFlQkLhQv5QhaVjcV/F4VJhTDUWtyDPlrMs8Oy97iqVgu6DDoac5KCrVxhAIINUJSqIL2XLfqzqkRAzcayLV53KPqztMpOnEGZKTvDExUpLR0IyG9Uc8q4OkpcFSux6yGUnSQGZU02FicypA715mymMsktMqI9O1EG5fDzpLaLKGnOK4Wx5vfnMchC/jwby03QaDZpm5NaHdi7CYbstAFBGCv9e8Ndz+PDWHQT60zRahgrkwm69JL8WRTZSY3fGqTCcD8IIJuT02Z8zW2wgvL6l4fEv4cnyqmUnTSenaix0ASoIxT9ebsmIeYLN51fXY0cqYIyMxsOaMJYF596KApZSxAxTly4IkGEysrIYay0ISx/fNFhrA27fDK6dG6f+BlfDPhOlC9WQT8mRdlh762lkqhk32a7YR105JUb2tMkJ0SlnvPviYMa/lPnhDhwv84AkR2qs8M9O9XDjCNfoyHPTjj1nIcdpWysYpB6TnOfquyo1ReyiqrNIautrnhNKs8Ou590xS2lOjss9VwlrNrPVvxlBHXzZVtBhW6uePJGxpNlR5zXuf15qReeXGfHk5477XlLqexMha7nDa9ow8PDuPnmm/H444+nfu/VV1+NVatWYXBwEIODg1i9ejVuu+220Os8z8PJJ5+MTCaDm2++Wfrdiy++iDVr1qCvrw+LFi3CJz/5SVQqtW/gdsJpwLOjeiuiyEEwKcu7W51BOcc+c75KdpoYxspnLbE75ybles3JgNwMtJ4KyhLZmSwnbBeh9wQ9/PxOfPHWx0NFz4AYZUdjaKX7olYTUEDe1VI16WIlSD2ncyGpBKzc/QmHLMLR+8zFGUcuFZ/zhoMW4tr3vlpcD9rZA4E6ArDMI8uSlIx6yE5U/RrP80TK7N7zA7KTl8hOcG45qRTeDmW3zIlZLf+Lquwk7Y0leXYY+a7HoOwomTxAMr+R2hsrrpZR6L3MFK721OKeFWFQjvlMlUCUHTecjSV5drhBuSLev2AgX/08tc6OfC7z2WjPTkVRdng2FlA7NM2/1w9tho3y42V/vD1M2fH7gdVWdqJTz+X5l5MOvlGi+9XzPDGHZ60MBnuyIlRHn8/9Z7WUnaT37bT27Jx55pn4+te/DgCYmJjA0UcfjTPPPBOrVq3CT37yk1SftXTpUlx22WVYu3YtHn74YRx//PE4/fTT8eijj0qv+8pXvqJd5B3HwZo1a1AqlXDvvffi2muvxTXXXIPPfvazaQ+rpWhW6nmc2hLUmpA9O1KKc/Uc8olZbdap64/UCHQm5XrTzgF/IaZDCooKJsuWAoD1L3FlpyLVVIlClHJ0xS//gv+651n8thr+kevsxHt2ctkwCa3VBBSQd4DzJGVHVuTknlbB/bf/wgHceO6x+KuDF0mf+5r95+PHH1qNC45fjtMO3zM0NiAge5YlT4ILZ/XUHLeKqIV493hZhLH2nheQHSkUFdGDjIyuoWwsNta0yk7SooJ8fJx819MIlBPU4HuSh6J6QspO7eeCfDZ9LLRNcwifv+ppF+H7V+LCWHLqOZGb+VUfmBrGivTsaJ5/ft786yEblHWhIBWcPGs9OyUexmIGZe7ZqZF6riYeqEkA/LnPZDKSSfm/73kWr/7ir3BrNelibn8emUwmNozFx+N6YTKfVtmZlmGse+65B69//esBADfddBM8z8Pu3btx5ZVX4t/+7d9Sfdapp56KU045BQceeCAOOuggXHLJJRgYGMD9998vXrNu3Tr8x3/8B77zne+E3v+LX/wCjz32GK677jocccQROPnkk/GFL3wBV111FUolfY+QTsDVpBsnRV4jj+tAD5nq2dF5Q/hDNG9AJTuyhNwodLV2aFGqh+xYVkaEb4RBOWGdHc/z8CdF2UkStgua8smfTx2FaZFJUmenpFF26L7oS1BgMasJY/GO5TplJ2lhu0P2GMTHT1ohmcD1nh0ljJXSnAxE98Z6oeqnWjxYkMNpmnEA8nmeEGGsIGwH+PcMvb3WwhYofbY0zppdq7XKTrp2ERLZUQhKklo7fNHln5fE4DzOQjFqU2FeLqGW0gWEKyiXmUmezoPUG0vqkxWt7ER5H+MNyrKyQ0ZpCmMBtVUJfg217SJY6nlO2mQk8OzE3B89vN6VMh/3sH59tz+6GdtGirjkVj+6Qmo9V2izliX5z9TxqIqnqOycsDeW6lHqRqRe0YaGhjBv3jwAwO23344zzjgDfX19WLNmDZ566qm6B+I4Dm644QaMjY1h9erVAIDx8XG8613vwlVXXYUlS5aE3nPffffhsMMOw+LFi8XP3vzmN2N4eDikDnEUi0WpzUWrW1001C6CLcJxJtoknh1b8ewAgTqwS3h2SNlpPIwFBKEWKmMO8DBWfd9Bn5m2gvKW4SK2jRTF/4cnykIZSKLsqARGXVxlz05ygzJdoyRhPS7hz6um4TuuJybswLPD5fTkC64KywpaJvBJn0++9YWx9AoBmce5qkOv1/ol2DUZEynKYUIp+mPVCOlEeXbShbECcsw7xdeCTo0jJPHdFJUwVprUcxHGymXFAhcqUGrVbp8B6Ovs0D1Izy73eIxFtIugDD81TKzex0JJYyU0CPy8lRxHkJ/+QqAQ1yI73AOWU0JmnudJ4UPeh62cRNmJCRfxOVglHTx8RHMaHfq8KtkZ7FGUHeY/U8ejEuKolHgVkn+oy9Wd1GRn2bJluO+++zA2Nobbb78dJ510EgBg165d6OlJL2evX78eAwMDKBQK+NCHPoSbbroJK1euBABceOGFOPbYY3H66adr37t582aJ6AAQ/9+8eXPkd1566aWYPXu2+LNs2bLU404DsTOqx6CcMIylFhDTenZsDdmpPhg7Qp6d1ik7o3W2ilA/M20Y608v7Zb+PzJZ0Xb6VhH1+ZNKc0E5Gyu5QdkWnp0EYSx2E/HMtGGl1QP3XURlsSSFurhYmYz0s/oMyhDj43hxxxgAYO95/epbpCaLBL5QkSKgI3dix51Q2aGNRc2igtpGoGHPjq6nmQqe6RQOY6Xw7KgtQxIZlKthrEJQNbqs3NeWlampdAEB6cyzkA591uxqaCWqXcTwZFmYbClLUCWxUb2xdMeqtsKh85q3rSCTqBR/buWecLI/sOwEz1dPKPU8+NxajUB1CoquuCeB96TaPuqTHTqeuYLsyO1WeKsP9fqp91tSzw4n+d3u20m92nzsYx/DWWedhYGBAeyzzz447rjjAPjhrcMOOyz1AFasWIF169ZhaGgIN954I8455xzcfffdePrpp/HrX/8af/jDH1J/Zi1cdNFF+PjHPy7+Pzw83FLCE1X5MwmSh7GU3ZiGmb/l0CV45IVdOJ75NeYpnppS08kOVVHmYaz6DcoA8J7X7IOslcGxBywAIHeZjgOZk/O2hZLjYniyLB7mOCWrJyKMRQ934JOqbVAOumSHJ7Ik5C8rGZQDskM+F7pfggUrHEJIC9vKSLtU28pIJLyZ2VhkTt5nfl/oPf7i7ShhLGYUJSOrJrRE763lfQlltZGqVSOMJdXZqbMRqC2e4XARvlrjLrPsn3BRweREqS9vi+dINShnWRgrLhOenu/ZfTlsGylKqef+PTsW2Qi07HhCZQ4pO9XvDCk7inldIj88G4uVJ8haGfTmbYyVnNrKDlv41T5r/L1q6rlKcFzXC4XgogzKgKzkq6SDrvHOsZK477/5nqPwlV/+BWce7a9l3LPDiwo6rhcqR6CGscR9W2OHnqlmZuoy7roNqVebD3/4w3j1q1+NDRs24E1vehOs6kXYf//9U3t2ACCfz2P58uUAgKOOOgoPPfQQvvrVr6K3txfPPPMM5syZI73+jDPOwOtf/3rcddddWLJkCR588EHp91u2bAEAbdiLUCgUUCikn6DrRSPZWDxzKs40HHTblcNY/MF/w0EL8YaDFkrvE8rOqD/BUGZBM7KxAG5QDoex6vHsAMBfv3Iv/PUr9xL/T6rsENk5ap+5uO/ZHRieqIiKxYmUnYgwlmrk9MdSy7OjMSgnUHa472awNwvbysBxPVYQUPXspGtGqf1OywIQnFtf2WFkpw7PTpRiQp4dHdnRmUO5gjauGpS5wTNh53NV2Ukaxory7NRXZyc6rBAFvuj2hNpFpPDs5LJwXbliMfccZhJkY1EYa04vkR1PEAba/EQZlAFg8/AkAJ1nJ1CYOPh1LlVcsD2AFLYsVVzWVNMSm5ha6ef8+VFDqTQHkI+NVxxXCWrF9ZBXxh7XcJPPwVFhLCrT0Juz8foDF0jzu+rZicrGAuKUndr3bc62UHac6Ud2AODoo4/G0UcfDcD32qxfvx7HHnss5s6d2/CAXNdFsVjE5z73OfzDP/yD9LvDDjsMV1xxBU499VQAwOrVq3HJJZdg69atWLTIVyvuvPNODA4OilBYN4Duq7pSzzWTqA5qfD7pjlJVdrYM+RPN4sHmkEF9GKt+g7IOSTw7nueJTKzXHbjAJzuTZUEA0hqUXeaTUYuvAdFm6XKMspPIoMyuZ1/eRiFrYbzkBB3L7fBCl2bB1UFXnp8Tp0V13CtR4ZANVbKzbJ6O7ITDWPyahMNY4Z1x8myshEUFhWeHhbGYYT6VZ0cQibBnp5ayQ7VerIy+sGQtEPnoy9viOQqHxC3m+9B/pud5IuxE/pyKy1K+KYxVcuC6HjIZfZd0IFB2xquvjbqP7aqXyPPCvdNUgzIPY4kmljU9O8Hxi9Tz6hh56JBUDvpelaDq7qFAhdQYlHPhOSL4XZXs7JwAACyYlQ95QrlnJ59VsrE89f4Kxup5XipFMq7rfDehrjDWYYcdhve9731wHAdvfOMbce+996Kvrw+33HKLCGslwUUXXYSTTz4Ze++9N0ZGRnD99dfjrrvuwh133IElS5Zo1Zm9994b++23HwDgpJNOwsqVK/Ge97wHl19+OTZv3oxPf/rTOO+889qq3NRCO8JYtnLDJWXmaur5xt0+2dlzTm/qseoQGJQ52fEX5noNyipIzXhm2xjefMU9WH3AfPzrqSulh3/LcBE7xkrIWhm8ej/fYD88URYTXrLUc98AmclkpEVWPedAdCNQsThq4vGJlB2J7GQZ2ZFbPfCwZtKmfkm+E/AXZZrUbSsjeYeSQqeYTJYdsavfR0t2AiMqgRcVHFOUNr6ACGNp4jo7MmmMElZ0YUltnZ0k7SIkgqqGFZIpO7To+p+X3LPD6+wMT8qkkhfzo1shqqjgRDno0s2LXtIYeJ2sibID28qI383rz0vzBG9BMsnOpTqPknm9WHHDaopaVJDdG0mr/8o94fTKTo/ik+JqlhiL5joEyo4ujBVul6L+jgz9unYtvKhgrWwsPjb+u6TKDpC8rUmnkHr2u/HGG3H44YcDAH7+85/jueeewxNPPIELL7wQ//Iv/5Lqs7Zu3Yqzzz4bK1aswAknnICHHnoId9xxB970pjcler9t27jllltg2zZWr16Nd7/73Tj77LPx+c9/Pu1htRSNtIuwWJpvbDaW4tkpazw7OswfCMJMruvh5d3+TqFZZEfXH4sWZh5TbgQHLBzAvP48HNfDk1tGcM29z4tJgEBp4nP6cmJiGJ6sJDJk89/R4sZ3g0G6PyNATnhCoZ8DMomlySKJ0sXDJf35rCBNpOwEnp1gAkqaRhoF9R6yrECun9+fr4vE67J6Xto1Ds/zvVykOHLo2gJM6ooK0vGyBSSuMzZH2LMTr+zQ5/Fwc92eHUYA0yo7akFBgGVoJjA3jwvPTlYKxfC/bcuq2fWcQlKZDGs74QYGZV4na6xYkV6/eFBOcOH3wHjJYYke4XMpwktKKFttF8E9VkEYK57scBOxmubOvU4A5NRz5bzr2naI+6eGQVkly3SdKYylCyXz9PpszWwsvZk6kbKTMETcaaRWdrZv3y4Ul1tvvRXvfOc7cdBBB+G9730vvvrVr6b6rG9/+9upXq/bTeyzzz649dZbU31Ou9FINhYQxERjDcoJPDs6kPLiuB52jZfEznrp3OaSHW5QpswhLrM2gjl9edz7qeOxYec4zvzmfdg1Xg6lvwZZV7bIUhgtVsREF6/s8PRKF4WsLZGdsrIoBK91QgRGV2fnnUctxbaRIk55xR41j5VPen0FW4wt2rPjJSa+kd+pUXZoQU/bAJR/BiAvmmROXjavT1umQVdPRfLsRLSL4P+utfCrvdKSFxXUpJ4zlSNJBWVJ2YlZjHRQFQYgMJemVXbUAqVcJa5VVJB8U/1V1ZHGzhXN/nwWI1WiQ5/Xn89idq/8rAwUsujN+c/aeDFeJctlLaAYXnClRqAVR8qe60vYxJITVtWzo7bo0DXgJegIIjc/q5DrTKkGZf//L+2iMJaG7CjtItQsO//nmdg0+SRZhNkUCmInkVrZWbx4MR577DE4joPbb79dqDDj4+Ow7eaEJaYbvAYMykCwU2yFZ6eQtTGruiA/sXkEjushZ2fqMp3qMEdkY7HOxhOk7DTHswP4E8OBi2eJvj5qHJ4rOFzepbTNuHObszNS000AmGCmRlqI1IddZ5jWhT2O2X8+rn3vq7HvgnC6tQpOPGRlR5+NxbuV1+vZUTNFbDuDY/abh9ctX4D3vm6/uj5TF8YiNU4XwgLCBmXP8+RsLDIoazKk1P5JnucJtY8j3GMs3qMiyCs7t3Qv8f5PybwP/vtcRlB1Rex0UNPO/c9L7tnhneZFVphyX1uZ2kUFR5n3h46n5Mh+G9oAjBUd8fqBQlZanDMZn3ASIRkvV1jbivCzSsqI+sxJnh2HGcZtK3H1X34/qXV2BMnMK2Es1w0TL00oMqqCMiDPSWrvLNEMtHq8uvl6lpJ6TusPHxcVLuRjS6vs0BzU7cpOarLz93//9zjzzDPxile8AplMBieeeCIA4IEHHsDBBx/c9AFOB9A9UE8YCwgmvLhsrHo9O0CQMUXZSktm99Q9VhVEdkoVV0wqzVZ2OHrYrpqDVxjOZ4MaG0QS4pSdTCYjNXcEgmq1QLiQI0E3iYreWNk6iQeb6MmgDASLjC4bqxWenTl9eVz3D8fgHUctjXhXPHTZWHFp50BYLlcXNjLGljUZLmoI7JL/9ziO/MKdUvsQICYbK0LZITWJ9zWjazLOjLdJnkO6PJyg0jiSeo3kMFbyHfcYU3ZyjCgDkBQVOoyo88HLSgRejqABZ862RD2psVKFtY6xpU1IX9V7xBtN8oakKnSZeoCcjVUsB9lYOTuTPIzFTOZqnZ2AZFric/1jDqdi6y6hjpgT+JykkiF1vtIpO7JnJyOysXiJDvL4RSk7upChiqxGMepGpN5aX3zxxXjFK16BDRs24J3vfKcwAtu2jU996lNNH+B0QFysOQkCZSd5nZ00oYt5/Xm8uHMcf66SnT1nNyeEBfiTHqVHD02U0ZOzMVzdUTfLs8PRG5FhIcJY1Yd7sDcrvaZWXaFCzsJE2RELrBTGoh2wk4DsUFHBOttx8Nh+fyErGZ0BfQXlZnt26g2HyZ/h/+1olJ29I8iOGsaKqmgdXWcneO+jG4fhuB4e3zSMw5bOFq+LzMaKmMfHGUkg0D3GlZ0kBmWh7HjBQtmbtzE8WanpNSLyrVsg0zYCDbWL0KjE6meOFisYmSwLwtlfCCox8zo7OTsj6muNFSsizDfQk5OU3t5qZiLVnhovOpLCpCJQF6LDf9w8nbOYalSD7PDnRxRKpDo7JVlR4wQz1PJDcxOVY7L1pGupbFRUsrNwIOxxy2ct9OQsTJZdSdmRyA4LNarjtDLJNui8H1g3o644wjve8Y7Qz84555yGBzNdIcJY9a1v4gGLbxeh9+zoilWpICMgkZ29mmROBvzFYrAni13jZQxNlLFoVgHDZFBuobKjFvVTjciDPTlsGQ5aR9Rqj6Gmt3Oyo55zgq6wYClmJ5cEdoSyQwjSjgOTaaOenZaQHY3x93lRPVlPdoKFhvwSemVHhAbYONXdpwhDKIQppOzUMOSOswaa6ji5FySZZ8f/my+UtIgmrbPTK/k85KyqOJAi05vPhsJfguxkMqBPUk/H+655CH96aQgffOP+AKphLFZgj9cjIgLD6+sMFGxpPqDMxEDZqcR6dnStRNRj5ynuWTsjxpE09dy2wo1AVWM4VxBV4qUjzHEbkSQGZUJUYc/jDlqE9S8PYe95fQHZ4WGsXFj9S1o9mSCOuctTz+uace+++26ceuqpWL58OZYvX47TTjsNv/3tb5s9tmkDJ2ZHkgRJlB3VW5Cmai6ZiJ+vhhGalYlFmMMyssZLgdGwmZ4dQk9EHF5tC6GqSj21lB3W7whQPDvinKsEKzyJCo9HnRWqadKzrQwKWQt55Z4QYSxmTm3csxMOYzUKtX7N75/ejme3jSFrZbBiyazYcdA5jOxVplkUcwpR0mXVATrPjv/zKI9KoOywMFaOvCrB/ZDk1BORLVdcEd6jHXytnl7xnp3aixD5l+b25ZiZ2/9O4TPLWpHk77FNw5goO7jm3ucBKGEsV86C4p4dCiMPFLLSM0nHQaRnohz0tdKRbV1ZAkAO/42ypIWcbQmFaWRSrvOjQu4JFxA43hdLTT33w1hJPDvRxKIQo+z0qmGsCI/l1e8+Evf8n7+qNngNh5voPuc/C7xRyZ7zYCPR3cpO6hn3uuuuw4knnoi+vj5ccMEFuOCCC9Db24sTTjgB119/fSvGOOURVR8iKRIZlBV27aS4YecrEmizyQ5NYkMTZTGxZK1M6IFtBoi0hHfs8iLGzXtAcmWHFlhZ2QnX2eHfyaHL3kkD2kWRpyFa2Ql2mHHNBpNA9fo0w8+lpsH+2//zOza/+zX7YNEsfY+9UBhLIZNqnR1+jnO2PCELz0VJr+wUQr2x4pWdfh7GUq5JllUejgMRCe5F6kmo7FBRQb7jT1NBmbIl5/blpf5OnueJc5W3LaFO8/Phup5QaajERD8jO+VKoFT5YaxAreHV1PkzSYSoNxcQIzoMHdnOKUSYwJUdXik5Z1simUGt4KxC1MJhqeeAfy9NKGFMXmBPJaj6UhQJDcoRFZQJUWQnw0zllkJKrEyw6eLnKW17mWyEqtZtSL21vuSSS3D55ZfjwgsvFD+74IIL8OUvfxlf+MIX8K53vaupA5wOcBv07NADFp+NJcuROpNmFNSicHs1Ke2cMJuRHWFO7s3V1QW+FmhxCCk7yiKmhtBqKjs5CmNFG5TVRUVXWFBXVDANiHj0RbS5INIWGGuZf6vudhHB+5oRwuKf47oefrL2JTy+aRiDPVl89IQDI9+jyuUqUSFDsK7SruorSKzssOacOoxrSIaqwCY9Z7pU8d5ceOetw4QSfgOSZ2N5noddY/5zOa8/L3l2+G49n7XEOLhIMcK8N4T+gi0v/JWAcJOZe7QYhKZmKdlYRB76WBirlEDZiaoETN8H+Iu8bQXeodHJcFYeB1/81T5cauo5V8VCdXZ0FZRjSoT0aEKS4nfsfuvL24lqdNmKZ8e2MlJdIDFON918kZ8inp3UM+6zzz4r2jVwnHbaaXjuueeaMqjpBnre6l3cg2ysuDo71YmtDs/OfKWA215z0nevjwORnd3jpcCc3NP8EBYQTDpqpk7Is8NCaLaVqXmeetRsLI1Bmc65KCoXY1DO10k8aHIiv4FKmlTPDhAcezPaRTSL7NCj8NimYVx+xxMAgAtOOFBkBuqgZsKQckafRYXxypp7XyVKauowIW1vLCI7fLFRr0niHbLWpJou9VxXm6XWe8dKQXPVuX15ca4c15PCQoWs3DmbMKxJ4e/PZyUlrszUEZpvtgwXg9TznqxsUFbCWOs27Eap4qI3Z4eKDwL69Gd180HXiu6LWUmVHRZq4gpL2XHDnea5QTmmmjP/DCDKoMzvX8WgzO6xpLWu1GwsWwnLiXGmVnamRjZWarKzbNky/OpXvwr9/Je//GVLO4dPZfBy6/Vgj9n+w71kdjQJUVPPU3l2lAVmjyZmYwF+Q0DAnxS5stMK0AShLmLqjp3vImupOgBXdnR1dmRlh3aM+jo7jRmU6X2BsqP37PBdGY25GUUFm+HX4Z+z/uUhbB8t4YCF/XjP6n1i36N2nCbljJTJoM5OEDIhZJWFX00dJoSzsfyf10q15mEFVW1Let51nr6kYSw1KwgIe3Zc18PTW0dCxVmpy3hPzgp5O3hYKG9b4nxwlYKe6VmFrHj+eDZWxZF7Uh2wcAAA8MzWUYxOBmEs2aAsh7Hu+cs2AMDR+87VqqJ5FjIjqOSCrhVtGAYK/vfV8uzw1HPqwwX46qBaZ4ffc6qyq7uHhEFZ59nJhq8lgSuJUeZkFbTZDjq/WyEvGxDvI9JhqhQVTL29/sQnPoELLrgA69atw7HHHgsA+P3vf49rrrkmdQXlmYJGemMBwGfeuhKnv3IvvH75gsjXqGmmaTw7vCz7nL5c0xp0EqQwVrWgoOqZaRYiw1gVecfOyVYtvw6gMShzz45S26i/YGPnmD71vFQdR71k51X7zcPq/efjr1+5Z3Vc8Z4dIFCj6s8Aa76yQ8R9sCeLc47dF+997X6xBnyA+W6q14CUM+qpVKpW6tU2AlXq7Kjl/gnRvbH0CxXdD/2aOjvBdyddNORza2VYanCNhUQNp/jfK4fFvn//C/jXnz2KL5x+KN6zel/xOupHNa9KGnn4q8RUQcsKmkl6HkSfOHqmFw0W8PoDF+Kae5/HQYtniRYmcjZWBssX+WTn6W2jWDDL/041jNWrhLEog/M1+8/XHj/d20WliCAHZeuRTyWxZ4dtUDIZ32s4XnIwUXKEmtirGJSBsGdP2xsrYSPQUDYWu84LNGnnOthKNpaV0ZuL03pMdW1cuhGpV5xzzz0XS5YswX/8x3/gRz/6EQDgkEMOwQ9/+EOcfvrpTR/gdIDTYAXluf15vPGghbGvUdl1JYVnh5OdZtbYIWg9Oy1IOwcY2VF2VcVytLJTq8YOf02sZ6f6sFOISW9QbkzZmd2bw/984DWhcYn/K4u0P47mKTtN4jo47fA9se/8fhywaEAoYbUQlfar9lDihlLxXuX5EKGwGgpgXG8sbnjlqeeZajsNHi5IAtX4nbWtUH2gKOgUU9Wz89z2serfct+4nWROrp7HLFPQaJNAagqfw1zPb4HDv/szb12Jdx69FIcsGcRP//BydeyupCbsv9CvFL5zrCRqK/UX5DAWGb77lOa4r9l/nvb4icDw3ljq4kstZGiuHCjUJju6DuDz+vMYL01gx1gpMIaLMFZwflR1WWtQ1tyrhB5J2VHCWBLZSRrGUpQddn9pDcoJQ+1J79FOIxXZqVQq+OIXv4j3vve9+N3vfteqMU070D1eL9lJAnrIPE9uJJimzg7Q/EwsAJhdraK8e6LMPDutJTucjADxnp246smEguLDiauzQ7v82KKCdRqUVUQWFWT3WqOeHV1xvkaRyWRw+LI56cahtHwgMjnY49eGqbgexksVthPnBmXZnBllUFaVHV2lZwItZlyBIRRsS9tKIv74MqH/J901UxYUVSwHwl3Pg8rT8jFTGIvmgaA+TxDGEmRHKSxoWxnpmbatDA7dczYA2SdFY8hnM+jLZ7HXnF68vHsCT2waAeATD056qahgH3s2e3M2Dttrjvb4cxrfiKqkEKkhv5zw7ExWhEqlQu4T5f9+fn8eL+2awI7RkqbODld24smO43pSkUMVhThlp44wFl07uqZWJhPysvFxJlZ2pkgF5VQzVzabxeWXX45KJV72M5DRaBgrCbhHo+y6qWqrDPZkxdiabU4GVGWn+X2xOGiRCik7ooJyOBsrmbITXWdHVFBWyI7Ws6NpBNoIQossW5To0gdkp77vlJWd1t3DtSD6Eil1dgo5W6qGy1UE8V7mTeDp1JGeHTWrTcN2xliNHXWh5AtV0qwWdX7IWhnmNUpGdmbHKDsVxa9EoL51c5UwVoWF6Sg8yodIalfwTMsbGCn1XAktUiiL+9z8goNy+IrXL4ry6wDBfc/Pk3rOREjOlpUdfpwqdH2i5leVlJ1jxZBBmZ8f9d5SyQ4fn+4e6dH4r3S/S6zsKNlY8v3FlZ10myPVS9etSD37nXDCCbj77rtbMZZpi0bDWEmQVXZcaQpDZTIZMdE1O+0cUD07rVV2RDZWDeMp9wwl8ewQidIqO0qlWaojovXsaMyzjSAqGwsIk5uksrQK2bNT10c0BeGO0/7fvTlbLIq8rQA/x1LPIjYp81CDyzwqZFoXbS1iwlh9+fD9U4gJQUQhRHZsK8h0qbGQUFHAOayMhFpnh86b6mVRlR0iAzwbSxceJbIzFJFhyVPPS8yzAwRkh0D+GSJMujBWlF8H4EUFow3KYlxUqypvC7NxlEm5Iik7/nfQedo+WgqM4fmgvQjda2obCp2yIz5ba1COVlS5ZyepskMfUWZrg+pl8/9Nv0/oNZsi2Vipt9cnn3wyPvWpT2H9+vU46qij0N8vd2o+7bTTmja46YJA2Wndd/AJlZebT8rO5/fnsX202PRMLICRnfF2ZGNF9MYSnh2NQTlRNpZqUJa9ATy23xfr2amSnSaFsfiimrMzUpjBtjIAOw3N8OzUqw41A+EwVhBy4s0lVZ8JIPsKuLLBCamUZp1TemNpyU64L5Z4P/vuxHV2lM2QH8YiT0UtZccnLHO4siOSFmSSoyo7O1lBQfpe/zvlgoJA2LMDILLXHS9YR+Onz1HJDqmhgz05bBqaDMJYeU529H4dIFyWAIhuX0BjyGT8Wjsjk35DUh1pcBydsuOfp51jJb0x3LJQdpyayg4nY3qDMnu21WysOpSdjGJQ9uvshFWZtBXXcxrfTzciNdn58Ic/DAD48pe/HPpdJpOB48T3GZmJcNut7DjpPDsAcPax++Bn6zbiDQfGG6HrAfkIeDZW68JYUb2xlHYRPPU8jWdHY2wtO57k6SB5XFtUsBKk4DYDfFFVs5mydgZgJVDqr7MTfEcHuU5kGKsnawd9jkoONu2eBACpHgsvnsZ3oHxB4sRHKDsxjUB1rSIIEtFKYVDOZIK+Uznbglr5WYdSxRUhNdmzI783qls8KTtz+3PS+you9+xQmEZWkAFEJh3kWAhObc6qkp1Z1Wdm0WABT24ZEbV46NzG+XUAfVHBqHPGicUsIjsRyg5vN0HXcUG/Ty52jBa19Y3ouVNJZSiMpflsDm5QVlWWQtYS98rClGEs/p06VUY1ZNcCV/C6GalXHLfLD6gbQfdRM8rsR4FPlGk9OwBw1jH74Kxj4uuc1AtSdiquh83D/kLUOoOyPymEi8XJXgwpjJUqG8v/3PEy743lSrtIKoRWjFF26q2grIJ/Tq1idvWai1tRZ6ceqLvQSbbQUBhh20gRO6qL9zLWUDRK2eH3CZEAXmQyLvWcKjY3S9kB/HMthRnEMUfPuxRGymSAWT3Rnh21xhCBUs9J2ZGKCioGZX4sVK8nagPDF1IRxqq+f/lCvbLz6TUrce8z2/G6A/0yG6uWzsar9p2L1y1fGPvM5HWLdgTZ4X65gZ4sMASMFPVVlLlZl5QRCmPtGCtpK2irfryc7V9T1dPCK33rzNFxBmXLyuC9r90P20eLWDYvmRqva+irU2WclFGBad313CAdGm0XkRQ0Uab17LQavTlbPPAbqqmmrQ5jRRqUqxNmT87vGF6suOkMylRBuSR7dvhi2B+j7DTfoBxXzC6e/CQFN0+2krDXguh/pHp28rbweDy5xc/umduXkwg19ybwcBVXAAOlKDhvcUUFhbKjSZ2PKwgXB5uRHb8XUxBSisLQhE9WKBsq+F59NlbYoKxmYzFiSJ4djUFZVXZmRxmU2UJPP5vbn8f8/jx2jJWQyQSEccWSWVIj2L58Fj/+0LGRx07QVVCOUhq4PyZoGRGh7DjhhZ/CWDtGo8JYipk4a6PsVCINylFeuh4lRK3iM29dqX1fFNRn1yfTYVWGNgtcJYyDLhOuG5F4xv31r3+NlStXYnh4OPS7oaEhHHroobjnnnuaOrjpgqDreWu/R0xuTnrPTiuRyWQwu9efIGj33HqDcnzqORAQrkRhLKU31mRZ3kFKZKcqvevaRbTSoFxL2WmOZ6eT2ViU3VO9BozAEuF4YrM/P3FVB5BVIb7YlxxX+Eno2up6EsXV2enT3D9SNlYaspORz3VWGG+jFxJd2jkQkFTaqUelnu8c02dj6ZSdTCbDCKD/d1TSAV8ERaVgdt9TJeWBQjibLS0Cz07YoKyGjDm5GKiOOarWjk4hn18NY20dmRTElJMddSND94Jqcg8+OyLDTOpz1vjmSN1scwWTk+kNOycAAEvnys9QFNQGvd2KxGfwK1/5Ct7//vdjcHAw9LvZs2fjgx/8IK644oqmDm66gPd/aSX4JJU27tpqzFYk7lZ7dmq1AQCC7JE0Yayoruc6ZUeXztrsMJbs2VGVHfna6zI+koB/TkdTzxWlQpTqz9mCcDy52Vd2VLKTZaqQOinTtRSp7DwElQmeKRWBshMfxkqzUKnKDDcLR0GXdu6/X34vHTe/Lz3PE+bmIBtL59lhvi3FtD1SI/WcPys8lHrAooDsNIqchhSKIp/K9eFkZFaNwoJlxWsEBMrO9tGS+Jmu2zyB5hxHUZp0xS/l90WHseqBehtyZYcb4F/a5avvSxNm5qoZXVtHJnHxzx7FU1WVtVuQ+Cn84x//iLe85S2Rvz/ppJOwdu3apgxquoFi6upk1GzYbJJK0wi0HVCPvdWenVrtIoDA35BE2elRsrF49dyyK8fjSZLXFRWjlzXLoByr7CgTZDd1Pa8HwqBMYSymxBDBpAVo7xDZCbKa1DAOLcY6ZSeuqOBYTDZWvs6FSiI7NvNUxHh2dkfML6rfSJd6PlKsiHuXlCFOkkRmmx1NAKOa++pSsLmys7yZZEdTQZmy9lQDOR8DfXdU6rlO2Zmn9BLMZOKJCc05qjina2sif25GfG4zlB11oyI1AmU3+Eu7Uio7yibkf/+wEdfc+zy+8/vnGx1yU5H4DG7ZsgW5XPQClc1msW3btqYMarqhXWSHFwPj5rduAD9228poF4hmoDcXEA3e8FBtFwEEO9G0BmXX9ZQwlmwIDwiXPLvxmHYrigqq2Vi6InX1gBPmTpIdtcAe90v0KvfTMmWizjPvixoSmizJn8eJSlxRQSos2a/Jxipko3f6ceA+K556HhciEGnnffIiTOeL7k1RPZoRAsrE6s/bguTxystFjbLDfUyO62GkqFd26Pv59/H7/pV7zwEQVuHqgd6grFd2OHGo1R9L56vpydlyteecLYXhVAU1UtnRVPpWIchOE5Qd3XygK21AZCep8VnN6KJzyQuvdgMSU+q99toLf/7zn7F8+XLt7//0pz9hjz32aNrAphOGIupQNBs6z063hLH4RDzY03iMPgqUbeV6/g5WrXzM4+C0E03m2QnCY2p4iheqs62MMBaG1aXmk518TBhL136gHnSNsqMs/EVWZ6dfITshZYfV6IlUdsphZYcOV1dUcEyTiUOQw1jpsrHEv1lRwbg6O6SszIlQdoJ+eeEw1s6xMFHiTYVLmrBrQABlY6/a3FdXS4of35F7z8WNH1otvDuNgMbHiSzdJyFlh42rlkE5ylczfyAvFvVeZf6IUnbo/N/8h5cxtz8vzlcckenJ2RierDRl06oqO1YmEwp1TpQcbB8tAkiu7Ki+H7oGtQphthuJZ9xTTjkFn/nMZzA5ORn63cTEBP71X/8Vb33rW5s6uOmCtoWx2OQWhLG6g+zwY28l6eNhKq6sqNlYAHDYXrMBQMr+iAJ1hN7J+uEQKq4nio9lrUyoACFBVnaac124gqCGsdRsrHqJilRBuYOenbwSxuI1TtQFLRzGCkiDmjUiPDuae0T1p0jvq5IdvbLDyUG9nh3euyhG2ZnQG5RVP4YuG0vNxOLvK7OQX9Q5oUysnpwVUhbVQni6FOuj950nGpA2Al2dHdrw5bOWRBb4uGbVUHaifDX8fKmbJdU6QOfFdT3sGC3iwh+tw/k/eCTUQkMH+uxm2BHCFboDgzIRk5d3+36dWT3ZxOuVqqrRPVPWeBY7icTKzqc//Wn89Kc/xUEHHYTzzz8fK1asAAA88cQTuOqqq+A4Dv7lX/6lZQOdymhbGItlX1QidiSdAic4rfLrAL63wMr4yk6x7AC9OXiepzUof+AN++OMo5YmqkC6oFpddftoCWPKxMjr7NhSGEsmRcKcbFtNU7biDMqqIlPvd0q9sboijEV1dkiJsaSwqJUB9lB6vPF6IiFlpxSt7HAVQwXdBzplp56igvz7gKqyowkFqYg0KCtEqawJY4lMrP5wmwldNhYgq12jVGNH80yrC3Sz1EwdtBWUWZgon7VQqV5nqc5ODc9OVAkPysgCwtdfJXlc2RmZrMDzfK/USJUoxt0f9EwnbSYbB3WjIjUCrc5NG1L6dQBZNQXCvQK7BYnJzuLFi3Hvvffi3HPPxUUXXST8EJlMBm9+85tx1VVXYfHixS0b6FTFZNkRD2DbPDuOrDR0A7jErsrdzUQmk0FPzsZ4KSjXXnaC7sI8jJXJZBKXWl9QzcAoOS62jsjqJq8jkrWDHW6I7FRqx+jToiAZlKM9O42En1QfSaeg7t6D7ClbqnWz55ze0MLK64moys5kAmVHF8ai+0v1hNCYCGmM4Y0pOzU8OyIbK7gvRV8spgrRuYskO9UxekzZ0am16n3eSpU5r/E28Yaw+awljNJy6jkpO/qigqI+kBrGYuRQDWOpzxqRZx4WBPw6Pf54ais7zQgfq/teXSPQl6p10Jal6JGohlqFstNldXdSrTr77LMPbr31VuzatQtPP/00PM/DgQceiLlz57ZqfFMepOrYVqYpWQdx4JNbucs8O7PbpOwA/uQzXnLEzp8X90tiRtahkLUxuzeHoYkyXqxOCL05Wyx49IBLyo6yGy9VW6k0qy8WjSv4t7qT1kv3adEtnh01jCUMyqyoIBAOYQGKshMVxtJ5dqqnVBfGEspOLj6MVbeyE2EgVTGk6YvFP0stKuh6/udlbUuEsXTKDm/gWdBmY7HqyZoNTLiScOuUHV1RQd4Qll8PnbITHcaKUHYGGNlRlZ1QGCtMHgFgW9UbE7f5OfPopcjaGbxq33mRr0mKcDYW84RV14u0mVhAuDfWtCA7hLlz5+JVr3pVs8cyLcE7ArfKlEsIJqlu9+y0lvSptXZ4gcFGUr4XDOR9srPDnxAGerKhGi1+Npb//aWK3yBUNOATyk7zJv241PPmKTssjNVBzw434ANy6jlfcNRMLEBpXRAVxtIoO7Swex6kawkEKdVaZacJRQVztiW1uYhCLc8OzQWcMJUUsjOvL+zZcZx4ZcetqezEG+abibg6O6TsBK/VeHYiw1j6IqDzmSIcZ1DOZBgZcD2JAJAROO68vGf1vnjP6n0jf58G4XYRgFqhOyA7KZQdpQpzkcJYXVZksDsMHdMY7fLrAN3t2eETcauVnYLimeGLWCOEk7oik7LDw3GkCtiM7PjfzbNDAs9OsxCfjcVrf9T/nXzy7oZsrFI11Z8W4p6sJZmE956vITssbT2VsqPp8k2I63rOr3G9yo7flVreeesQWUFZeS8nTHTuRDaWZFAOFudYz47rRVZPpu/nj1trPTuy6uf/O9jwSddDUnbiKyhHKjsxYSz+3OWYeuK6nnQNkoSxmgl1o+IXrZQVsQ3VgoJpygFMFWWnO1bCaYyhCPNgK0CTaqkSeFS6xbPTrmwsQK61A+hbRdSDhbN80+sGQXaC4+DKDv8e7ttpdvVkQK6CGqfsNHIfNEshahTcTMl9J342FlN2NBM1hcAqrhfKEtGRYgI3ZKtVlOO6nhfYAphmMePEkmfLRO2SHddjvakUzw5LIfc8OXxHz8SuqkF5nib1vOJ6YpeuKyroecCwqJ6sV2u516WZXrXQ9+gMyq4rfpeX+kyF6+zUMiir1zA+jCVfQx5O5OPbMVZb2Wkm9I1A5VBnPcqO6qULyI5RdmYU2lVjBwh2FHwhqLdqbrMhe3baE8aaVMJYhQT1dOKwcEBRdpgHi3fMztmWmFh49dhm98Ui0OKspv42q6dV13Q9ZyEdXlagJycblHXmSh4CU5Wd8QTZWEDYtyN6YzWxzg7ffWdZhduo3lgjk2WxsYmqoFxxwx23hbIjPDu58PscV5wTThYyzLQdp+wA6sLfDs8ONyizchA8jMWuxwBr7aLLeIvqMZg09TxrZcQz43pKGGukvcqOtjeWeC5cjBUrQulLFcZSVDX1726BITstRjvJDk1SXE3oFmVnsI3KjlrBWNcqoh4smOVPcFuq2Vi9eVtM5oGy43/HXnP8yeKSWx8X1Xeb3fGckBdkJ8az0wDB4tlYnW0XEYSxRLXjKrHsr6Hs8Ak5qjeWVtlhh8vJjut64n1aZafOCspqUcGcFa/sUAirP29rGsH6//e8cM2nQNkJ19nJsUSHuKKCXFWKeqb5Qt5SgzIRYU3qeda2lPYdYYMygFBJCfUzOBbEeHY4mfI3Piws6ISVnWaklSeBtjeWeC48oerM6ctJqnUtqOoQzXPdlnpuyE6L0QnPDp/YusWz05OzBdloRzYWoAtjNUfZoTWvN2eL80sZX7QQXHbGYcjZGfy/P23C/73jCQDBTrPZkz4dV8izw3fVDdwHfHfeyTo7OaY4ENEgf9bs3hze9sq9cObRSyU/hXgvM4mqCz8ZlGt5dngYa7LiiPugqcqOUvwuJ8Jv+l1yVNo5IF//CaYwAkHbE5GNxd5vs8WrRL2xNATQ8zyWjRWl7LQpjKWpoCyKCoayseSwLF0/nW9H1xsLkM9Xbz4mfGxnQKfAVcJYpKK0K4kklI2VkXuvpW0ASlATB0pG2ZmZaCvZqT5kPPuoW5QdAJhT9RS0WtkpqNlYTfPsyDV5fLJDyo5cafXYAxbg/56xCgDwzbufxX3P7GiJZwcIFvywslPfgquiWd6fRkETs+vJHc8BP7Ryxd8cgcvfcbjWhM6rAkfV2RHKTkQmFecbPDyp7uwBVQlJ4dmR/FHcQBql7PgLpm5+4Z+l1nwqVVxMVhxhuuZme95jT1tBWavsRHh27PbcO/EGZUvyHKmbjbjCguWIMFY+a4lwvKrsyWEsWdnh46Nz37YwlurZseV2EcKvMyddrzI1jGUMyjMUw20kO/RQ0cSWyXR2J67i7GP3wbEHzMeqpbNb+j2BslMNY5XD4Yl6oBYg7M3b4kGnc84nlLcfuRQnHrIIAPDE5uGWZGPxzwuHMZpjLJYqKHcy9ZwtnLxNQRLoKijTeQtlY2WjsrECwjFeDJqQ6p6xZtTZydnhCrcqhiLSztXPUluclCqupPbwY+Yp67owVtAuAjU9O/yatSOMRTWEAJZ6Xq2grBsTwE3K4cKCcSU8aD5QPTtSfSum7DgK2RHjaVcYK5SNxdtFuCLxIq2yE2VQNqnnMwydUHYopNJNqg4AfPi45bj+/a9J1HizEajtGpoVxlqkKjt5W0wWxbL+nFNZ+bFiRYyj6QZloexEV3JtRCqXPTt1f0zD4Isl7cJ1qooOsmfHvw6kMIbq7OT4wh58Bq+iPF6udjzX1NjxP6PerucyQVV7F6mISjsH5NClGsYqsVBgT86SCBt/HylYuqKCLjMoR81vuTZ5dvhnk6LDqx9HFRUEgkQDXRgrMDmHx04+p7jU8yzz7PjkMXwd22U1UO9DuV2Ex7qdp1N21NRzmueiTPWdgiE7LUaQFtoGZYc8OxRS6RK/Truhdh3XdTyvB/P681LdkN6cLXwkPBuLo19MpI5YZJs96dNkG7fDbOReUEMrnYKO7CQlzrraMRR6UZWdgpJ5RIfvMsIxRsqOxq/jf0Z9yk7YoBwOz3AEfbHCnh3+tWoYq1gOlJ1QuwON1ye6qCClnkeQHWnhb93mi49P9Yyoyo662RiIaQbqRISxAL+JadbKYOWeg9LP1dCdLiwY9fpWQs3GUttFvLS7Xs9O9R515fPebcpOa3OADdqq7KgLb7cpO+0CLUCTSnXjRsNYWdvCvL48dlSNhb25QNlRs7EIA9Wd/1ixIjJFmu3Zef/r98eS2Zvw2uXzpZ+3ps5O3R/TMGwrI5q8UsihJ6Fax0OHpFbQMxnn2QH8HbDreVJRwbiO54Di2UmxmIVTz4OMKsf1QmR690S1KKBG2clk/IW2wjLHCFzZCSsTwXdQhpLOoFx2XEEQospJkMHa/9zW3Tx8zOpim7PVCsopPDtOdBjrn96yAuf91QGhzCU1dBc0k40IY7WJ7GQ02VhBJpWLDTvTt4oANEUFRSPQ4Fg37ByHbWWwcFahpQpfHAzZaTHaSXZUz0631NhpN3pUz06TwliAb1ImstMjeXbilZ2xYkVMnM327Jx06BKcdOiS0M+zUvipOZ6dTtbZAfyJtVhxxcKUVK3jCwqRHfKZxHl2gKqS4XpSGGusFN3xHIBS1yWFQVkpSMd3/WXHhW3J30dFS9W+WAS7SnZ0BmWh7OSjyQ6dG13qOc1tACJTlfk9mM+27t7JZPwqySUWpuTm4rzNPUkq2YmuokyeHZ2imclktMdtK2oWr3ekFrTUjadV0NbZqc5FI5MVcayplR2Wved5nlRUkFqsnPr132H3eBm//PgbsHzRrEYPpS7MzDhHG9EZz87MVnZooYmrn1IveEaWlI0V4ZPqZ36AUovCWFFonmeHGZQ7fE/RuRst1ufZAYJigLMTeHYA5lFh0k4tZafeOjt8ocxZlnSv6OqWRPXFItD9qCo7xYqD8bKe7NiSShIm6JTtRtWXe3PhGj/iGJoUSk0C+i7VIJu1LemaqmGjuP5YQT2n5NdQqrNjWaxxarhViW48rYJ6H/IQGxGdef15MWclBSfzaqhONKEVSQGt9WvGwZCdFqJYCTpvt6WCsvDs6EMqMwVqGCuooNwEssMysvryrM6OJhsLCCTysVIlKCrYwh0uh1xBuQHPTpvSh5OAFgYRxkpIdnIa0y15dugZjVR2yLOTRtnJybv7pOAv5a1AAH1GVpB6Hvbs+N9dJf4l+b2liovJCM8Ohb84JGWn+qvdE/Fp54BM7FsdrqFaO2UljJJTemNFhbF0ys5z28cApDPtZm29suN4etNuu3xwajaWZWVC5yKtqgPI81mx4kqkXGRoOe2d+3To6Gp49dVXY9WqVRgcHMTg4CBWr16N2267DQCwc+dOfOQjH8GKFSvQ29uLvffeGxdccAGGhoakz3jxxRexZs0a9PX1YdGiRfjkJz+JSkXf56TdIFUnk5FbC7QKos5OhFl2poAWq3CdncZ3FQsUZSenhLHUCV1nUG7X7qZ5np2wObVToMl5WBiUk01hlhUYjUPKjiDFEZ4dZfcLcGVHfy2lXlL1KjtsoQT0C2X9yg7PxgofgzpmXeo5Ea24IqF84W92+FYFfX6pUlUTWCZVstTz8Lrxly0jAIADFycPvagtMniD5nIlrM51VNlRvntZSr+O/zl8IyGfw7LjSQ1QW30PxKGjnp2lS5fisssuw4EHHgjP83Dttdfi9NNPxx/+8Ad4noeNGzfiS1/6ElauXIkXXngBH/rQh7Bx40bceOONAADHcbBmzRosWbIE9957LzZt2oSzzz4buVwOX/ziFzt5aADkGhTtWCRUz067jG/dBpq8i01uFwHIyk4PMygXRQVl+Tv6uUG5zbubVtTZ6QbPDhA8W2nKGGRtC6WKKzKpiOzQBE3hX/UzhcHU02VjRYWx6q2zw/9tIVNNDy47nja7hUIvAxGbKV0LGcAnTnFd27NWBkX2/wIj6DSXUSZYnGrNQzotV3aUei+8zg6/HuqCGyg7cp2dYsXB8zv8DKWDFg8kHge/3jkrI8ihn3ruaF7fLmVH/r9tWSE/WV3KDg8RF+Xjqziu8E4BgfrWCXSU7Jx66qnS/y+55BJcffXVuP/++/G+970PP/nJT8TvDjjgAFxyySV497vfjUqlgmw2i1/84hd47LHH8Mtf/hKLFy/GEUccgS984Qv4p3/6J1x88cXI5/XSbrvQTr8OEEwmkzNc2aHy7eSjabZBOfgeTQXlqDBWCw3KUZBVgukRxqKF+bdPbQeQ3LMD+AtPCYHKQYrEZNn1F6KIStu8iB5B1NmJUHYyGT/duVRxU4UpVHMr4C+GZcfRkp2JGMLivzcjvY5QLLuYtPVhLP+7LQDBe3TZWLuEspMwjNXiRT0fCmNRNpZMdtSKxbMiUs+f2z4Gx/Uwq5DFksGexONQw1jcF6NTdtqWjZXJIJMJ2t3YVvi76yE7mYyvQDquJ1UWB3xlh3t4OqnsdI2pw3Ec3HDDDRgbG8Pq1au1rxkaGsLg4CCyWf/mvO+++3DYYYdh8eLF4jVvfvObMTw8jEcffTTyu4rFIoaHh6U/rUDbyQ6FsaqTeZoskOkEEcZSulk3w6CsNgCkyTzKs0Ol5EelooLtuS58ImtaBeUOk51/PuUQaUKO8szoQIsQpVPz53KYZRapyg7fmRNoBxtFMoDgfqtX2aEds2iFoPTH8jxPmIx1zUiBICNTm3oekY2lG7MuGyuJssPvwWaXXFChGpRFnR01jKUcG5Ed6vNF+MuWUQDAgYsHtC1Iao0D8O85HgbVpZ63K4wFyMqsbVkaspM+jAUE5zQcxpIb787YMBYArF+/HqtXr8bk5CQGBgZw0003YeXKlaHXbd++HV/4whfwgQ98QPxs8+bNEtEBIP6/efPmyO+89NJL8bnPfa5JRxCNTpGd0gxXdnrIoCyUndZkY/Wx1POo2kZc2WlV1/MotMKz0+kw1l8dvAi/Xn4cblz7Eu75yzacdvieid+bEyHHcNLAbkZ2wj3G/L8lskOKSowXr5C1MYJKyq7nnFRY0rhVZafseGJMUaSPPk/XLoLQmwsfg3rvSBl55NmZqO3ZydvRJKPZoPNUCtXZkYsKqqSLmnpSY07CU1W/zkEp/DqAmlGnFBXsoEEZCMooAL7RXN0QL5uXXtkBgpIQqjpWdlxxr2WtTEc3Sx0nOytWrMC6deswNDSEG2+8Eeeccw7uvvtuifAMDw9jzZo1WLlyJS6++OKGv/Oiiy7Cxz/+cenzly1b1vDnqhgaby/ZMZ4dH0EFZaXOThPaVHCy0yM1AtUrO+TZ4YXwWr3DJbSkN1YXEOh81sK7jtkb7zpm71TvU3fQPTlLhJooJGNrMlSCMJaHC3+4DgtnFcQOtvnKjuz3AORWFxwTNZqR8s8LVVCuOIwohe9Hfg7U+1WQnbHa2VhZReVoJYIwVtWg7DKDMjs96vUgtXbHWFHUhQHqMycDYWXHZsoOLfxz+nJCGeuYslNVnRj/wV4pm4AS6BjUMBZvftqpYoKEjpOdfD6P5cuXAwCOOuooPPTQQ/jqV7+Kb37zmwCAkZERvOUtb8GsWbNw0003IZcLiMOSJUvw4IMPSp+3ZcsW8bsoFAoFFAqFyN83C0MT8aXUmw3j2fEhemOVVM9O4w/bnN4cli8aQLHiYG5fLpR6Hqqzw8ILNLmlqdnRCNTWA/WiW7qeNwqV/OdsC705G6WKKzYmunuEFvdnt43hpj+8DCAIfUSFj/hnpTn3am8sIFBnVLJDvqGslYkk0CoZJ5QqLiqZKtmpkY2lfjb9bkRUT06ajdU5gzJ3bKiLLvW4miz7pm3KoHyqGsZKY04GZHWOZ9TxMNbCgYKYD9pZIkT3LJNxf8FAPlVYmEMNERPKjiuen3Zt8qLQdaYO13VRLPp5AMPDwzjppJOQz+fxs5/9DD09skls9erVWL9+PbZu3Sp+duedd2JwcFAbCms3jGenM+hVwlhBu4jGlR3LyuDWC16POy98o5RWGlRQDqct0+6f1IO2hbGaFEJolkLUaajPQz5riYWerk1cGvYw64pNacpxyg5N7mkut9z1nMJYQRiEYzzGc0PIRuy4S5UgG0t3zPyaqz4L9RaI28xJYaw2pZ6nbRfRl7cFMaVQ1mTZwfM7/Bo7acNYsqHfkg3K1TFxhbidCjy/dqTykIJYr1+Hf4bOoGyUHfjhpJNPPhl77703RkZGcP311+Ouu+7CHXfcIYjO+Pg4rrvuOslIvHDhQti2jZNOOgkrV67Ee97zHlx++eXYvHkzPv3pT+O8885ri3JTC+0mOzRRmjo7/uTtp+u6TVV2AGgnTlFBWTNx9ReyGC852CVk66kVxtL5NaYitMpOlShQXyBdCjcdvq4OSxzZOfXwPVFyXsLhS+ckHqNsIA123kB0GCsuI43IdygbixV/06lTccZi9R6IVXbaqArmFP8cbxcRV008k8lgwUABL++ewI6xEpbN68Oz28bgen6m2aJZ6dYSOYzFUs9ZKwVOdtoZxrI0cwJl3tWTiUWgezQUxnJc0Dc2a/6tFx0lO1u3bsXZZ5+NTZs2Yfbs2Vi1ahXuuOMOvOlNb8Jdd92FBx54AABEmIvw3HPPYd9994Vt27jllltw7rnnYvXq1ejv78c555yDz3/+8504nBA6pezQJDZjPTts8p+suJHF4poBOudBOmf4nA8Ustg2UhRF2Nol58oqQSPKTjgTZypCDRfkbUvcK79/xk9lX7V0duh9lqLs9OVtVqMmego976+W47y/Wh75ex10i3KUQXlCZGLFKDsxYSwyy+o8O1yhrEl24iooxygqzUY4jBW0i+BD1o1jXn/eJzujflThqa2BOTlNJhaghrF4UUEPnuePjROotoaxNGSa5oZGlJ1AQZQ3BNyQ3U5Sp0NHyc63v/3tyN8dd9xx8LxwTQIV++yzD2699dZmDqtpGG63sqM8xFN5YWoEfAcxWXZY/ZTmVy5WpXnd7pVMykHdj04oOw14dpqUwt5pqJOtH8byz8sfXtwFADhqn7mh99ECQanJq5bOxhHL5uKRF3dpyVEjkArShcJYimdHhLGip/HAoOy/tz9vY6zkSEUFtXV2YsJY6j0Qp+zkNMfTKgiDckX27OSYukL/V0G+HWryW685GQirWfTdFcdDpuqT4iUs2umD0yo71bmh3kwsIAgRq9lYFccDqgSv056djhuUpzParezklIdmpvbGsiy/iFix2tm52WEsDnWi0pEBtVlkK7s/R43FeHbCxDRvW0KZIS+FjuzQYkXZdAOFLD518sEtGaN+MaJsLEXZEWQl+r5W20X0F7IYqz4TEzFkiavC6nOjCh3xdXbkAnutRODZ0WRjZfmzED5f8wfk9PO/1GlOBtRjtsT3uZ4HR+vZ6YyyQ/cGVXRvyLNT/YxQBWXXhevJCmWnMDNXwzahkLPQk7NiZd5mQtf7ZKZCtIyoOKxdRCuUndrnXPWBtE/ZaU74qZvaRTQCdUefy1rSPdGTs3DIHoOh9xEBIc9O2q7QaSC3GpCzuUKenTKZpGsrO0R26F4sVlwR2qqp7ERkYxHiNnP8Xm91GCNcZ4c3ArXZ68LjmE/KTjWM9Xy1AegBC+shO/waBn4hnobdKYOy5L+r/vsdRy7DK/eeg6M1RD8paK4ZU8NYlcCnZJSdaYyfnf86AEgUjmsG1IfGnqGeHcCfwIcmypgsu02toKxCJS66cJG6OHaiqGAjC40uHXoqQj3veWZQBoDDl87RXhv6UeDZad20qTvXIozl1JGNRWGsUqDsAL5nZyKG7PBxqOFf1bMzK7ZdRPvCWKQulCphz87s3hxsK4OBQlbrwZnXT7V2SvA8Dy/v9g3rdfWKsmRlR/RWY9lYgz05UeOpnVmz/Kvo3vjoiQfioyce2NDnRtfZcZFBdyg7huy0AWkNbvVCXWhntrJTzUIpszBWCw3KUf8HwmSnM0UF6/9O3vtmKpMd9drk7IwUAjoyYmcbhLGo6WbrutbrCGpUnZ1U2VgijOW/tlRxGFmqr6gg4Juj4xaxdvbGUg3KPBtrdl8O33j3UZHEjMJYO0ZL2DVeFudmzznpyY6k7NiyskOqUz7rE7BtI8U2p563ZuNC11bXLoIwo7OxDJoL49kJQOGJ8aoZE2iPQVmfjSV/b9sagTaxgSeRnW6ooFwv5AJ3fkdxThSO2rsW2Wm9sqMrBCmysVy9ZycuG0ut0cPDWELZ0RyPVFQwps5OnDnZP4bmqItJEGoE6sgJAW9auVj/RgRhrJ1jJby8y1d1Fs4q1BX6zinPna6Ccs62cPCSWdgxWmwo5TstdNlYzUBUPaey4wHV29YoOwZNg/HsBKBJajim51EzECKYEXV2pPd0IBur0d1j1vI7hk8Xzw79u4cRhShlR1QMFspO66ZNTibp+pFKqS4k4+XaYaxw+xJ/7JPloGdRI56dWn7EnJKG3Upwg7LnealKcMxjZOelXeMA6gthAfIm0zcoB2Sn7AQh9f96z9HYMVZsyBicFjoDfDNA11aXjeVUbRydbAIKGLIzrWA8OwFogYhr8NgMJFN2VLLTnusiTbpNUHb431MRWU3tGFro91/QLxY8FXTIQfPP1oWxdARV9G6qmmcJSZSdqMa0fBOgJzsxYSxOdmooO7ls8wh3LfBGr1wFS+KJoXO8fbSIl6rKzl51hLCAcChSZ1CmgpZL8+0jOoCi7DRx4yI8O0VV2XFFD7acCWMZNAtq2GomKzs0gQ+xBo+tSPHsas+OVB+nse/MTgeyozHLLh70W9CsPmB+5PtUQ25LlR1NmGFB1U+yPYLsJPHsEGjsvNhbj8bLxjdKYc9O8O9aff/UAnutBPfscDN3GmWnWHFFQcF6FRfVpxSQHVcYlDtVYM9qotrLIXpjaTw7RDyNsmPQNIQX3pnr2Znb509ez27zU0hbZY4LqWkJsrHa5tlp4sRGx9Xh+aoh5DRqxduP3AtzenM49oAFke8LhYJa6dmR0pb9MVKa8rYRmeyMx3huxOdFhLEIvTlbm0ARW1SQvX4wJhMLUBf+Vis7/ueXHVeYk4Fk9z71xypWXPzppSEAwF71hrGUdhG6zvOdSsPml7LRDRBHXG+sitMdqedTeOoyUBHy7MzgMNZR+/r+i98+7bcBaB3Zqa2mqQbljnh2Glxo6P1TuTcWD6nQAl7I2jj5sD0wuy9aoVCPuZVhLFvye8hhrO2jJem1ScJYaig7RHYi3ss3SuGigtyzUyOM1cbU80JWr+wkCWNRfywgqJ5cr2dH9SkROaQq1vTzTsBqURiL5kFHMdFXnKAtSau73teCITvTCGGVYeouTI3imP38sATthluRiQWEDcpJKii3K3bdzPo4amn5qYg4H0oc1Ay0Voax5Aq3/hhpEVaVHSoqGBfGCnt25NdGvTexQblmNlb7w1ilSqAmWJnw9YsChbJovV5ap2dH7nqe0T57XUF2mmpQ1n9WWUm37ySm7sxlEILx7AQ4YGG/1H+mFTV2gGTKTjgbqxMG5QY9O9UxT2GuU7fKoF6udhUVpHNOYawdY0WpQGmSooJR2ViEqPdKnp241PNa2Vh2+HhaBV5BmRbYND49qrVDaEYYizcCJdgRBKgdaGZomyPqeSo7rpRu30lM4anLQEU49XzmXt5MJoNj9p8n/t/TKmUngZqmKgFT0bMzp+qBmtOrz1iaCpDq7KRRdtpoUNb1M6NFuOx4ot8ekLTOjnycvTlb+o4oZYcrlupGIVU2llLbqJUgxbRcCcJYqvIaB56NN78/XzepzSqhSDVc1EmjLh9LM0PSURvtiuOKDDSj7Bg0DaE+TTPYswMAr9k/yLBplbKTxCfFd9M5O9O2itrN9Oz8+ztW4at/ewQO2SN9F+hugdyBO/n5UMMg7Ug9t63gPilkbdF/ioey4to9EML3pyUttlHv5d6hsLKT3LPTzFpPtRDU2XFFh/hUyg4jO/WqOgBdO//fPBuL0KlMLEDfLqIZUI+J5ryy4xllx6D5SNKBeybhNfsFyk7LDMrKjkaX4TAgkZ32PXLN9OwctHgWTj9ir7YRtVZAVnaSExZ1Z97KbCyLkR0OSj/fxtLP0/TGIuTsjLTDjjQoS6nn8mvkbKzkyk6rlWbqbF526kvxns/C3o1WNSaTMq+zE4yzg8pOi4oKqkS2v3pf8WvR6XYRhuxMIxjPjozliwbEbq1lBuUEXc97cpbwObST7OjqysxkSAt4KmUn+LcaBmo26P5Rwy+6jKxJEcaq3fWckLctmezUYVBO59lpYxhLeHa8oAloCoLFw1j1FhQk0L3GG4ESOhnGanVvLAIpOxWj7Bi0AqZdhAzu22lX6rluAslkMuLhb+eubrp0K28WdHV2koAvEP0tDGEBLOtNua/UWjue5wV1dlJkY+VsS3oWolPPY8hOKs9OBwzKFSdoAppG2WFkp9EWDnT+dNlYnawkbEnZfq0LY/UxZcdkYxk0HeF2Eebyvv7AhQAgZWY1E+Hmq/oJhEJZ7dzV5aRsLEN26s7GsjjZaW0dVpstkhy8nQHgZxxRTZP4bCz5OHOKshPV6DLOs2OnqrMTrhvUKgQVlL1QE9Ak4GGsRpWdWVUSOKsnq/HsdEcYq5lNfdVjIrWx7HaPsmMqKE8jGGUnjHcetRR9eRuvXR5dIbcRJFF2gGCRbKc5kacPG2VH8eykmHj5briVaecAV3bk66UqOxOsUm2a3lg5OyMde9R7+ferqihfJGfVqKCsqwjdKshFBavKTor7XlJ25jVGdi59+2F4bvsY9pnfHyq01y1hrGauD+o8KAzKle7JxjJkZxpBnUzMAuc/hKcfsVfLPj9pun9Adtro2bHqUzKmKyRlp84wllqUr9mgNif0N2GhouxQJlbOzsReW52qUGBqTn2eHf93/Xm75n01ty+PuX05zOnLN1VJ0IH3xiqLjufJr/OCgQLytgUPXsNhrDcctBBvOMhXldXD7mQYi5+O5io7ajaWf19V3KDOTqcrKBuyM42globvZIrjTEGSOjtAsEgaz07nkI0JzcSBv7TVys5+C/px1buOxH4L+qWfL5glNwOlTKyoMBRBvT9zWQsFnnoeVVQwgUG5VgiLxvfrTxzXlgWejrVUCZSdNHNgb97Gf551JDw0t5ZSJuP7dhzRELODqeetUnYiDMplxzPKjkHzEU49N7v5ViOUARcxkVG6cnuVHePZ4ZDTqetVdlo/Za5ZtUfoZwsH/O7sahgrLoQF6Dw7mUTZWHFZVESEapmTCXP721OIkldQLotsrHT3/YkrFzd9XIDvc3KQ3kfUbFgt2gDFpZ4Xu8SzY1bDaQSTet5+JFd22m9Q5kNJI+dPV9SbBs0XiFZnY0WBlJ0doyW4rifCWLWUJnUOyCfMxuL3sVqQk2ot1Uo7bzfywrPj1VVUsJWIU8raOo4WNQINZ2Ox1HPRCNSQHYMmwRQVbD+S9MYCmGcn275rkslkpBTYmY56PUx2Gw3KUZjf73t2Kq6H3RPlxGEsbQXltHV2QtlY/t9JlZ12gcbpSBlA3XHfd4t/rlWhbfWYaFNQYu0iOulVAgzZmVawWJlyoHse9OmMpASzEwZlAFgyuweFrIU5fd21MHUCkrKTKowV/LsdYSwd8uwabh8tYqLkdzyvFcZSwwtqGCuKLMX1ETts6Rz05Cwc26IMx3rBF1Mig50OnRCsGPLY1nFUFwjejqQZUKMKQtmRDMrGs2PQRGStjIhXG89O66FOplHZWMKg3OYH/ocfXI2xYkXU/ZjJ4Neq3t5YreyLVQsLBgrYPV7G9pEiC2OlU3ZylpUs9Twm7HLUPnOx/uI3dw2RIPBrSp6mbmmGHHc+2wm6ZM0MYQHh52mAVVCm9ajTBuXuuBMMmgZjSm0v1MUkStmZVw1DDNSoS9Js7DWnFwctnrrNO5uJeg3KdpsNylGg9PNto8XEYSy1GaxlZSQPTiLPjh1+TbcRHUAuvREoO90xB1pSGKvz2VjNtjio4Xwi0SXHKDsGLUK2RTFZAz2S9MYCgLcevge2jRRx6uHhTBuD9oBfq3oNyp3y7ADAAqWwIJAuG4sISt6uXWcnVycx7CQsy/eoVVwP49UwX7cYlLvFsxPVaLZRqJXkpd5YXeLZMWRnmoHX2umWXc10RqiCcsQ5H+zJ4aMnHtiOIRlEIKtZ+JOgnUUF47CQNQOl1N40nh36d5Ku53adfcQ6jXzWQqXkBMpOl2z4ui0bq+lkJ6tXdspdpOxMnbvYIBFkZcdc3lZDVXJM6LB7UXcYq41FBeNA6efbRoqiCWiaMBYtNoUU2Vi2ppFlN4NILJGdVvfjSgpbcx06OY5mz1P886xMcF+StwwwZMegyTCenfZCVQim0sIw0yAblOtTdlrdCDQOe872+zVt2DWeoqhgOHySJPWc3tfpBSot6Nh2jZcAdE8YS3cdOgG6l5vdukPNdKSownjRkX7eSXTHnWDQNJgWAe2FraT7d0v2h0EYnPyrzS3j0O4KylE4YOEAAOCZraOM7NQqKsgIXrXGU5KigrqQ11TAqr1mAwDue2YHgC4KY2W6g+zQVzdd2VH8cHTfkV8H6LytYmrdyQY1kTWenbZDlXANuhP1Kjt801BLSWklDljk98vaMVbCxqEJANHKDEGnKBDZyWSiSR8tVmlIYTfgpEP9dg8UPulGZaeTBFIoO01OPZf6zmXtkIfHynT+WnTHnWDQNNjGs9N20IOebXKhLoPmYioXFQR8FWevOX4o688vDwGIVmYIUhaQJYexenN25P3aU01P7yS5qwcnHLJYaZPSHc+jTDo7mHpOnp0mj4EfUyFrhRS1blAIOz8Cg6YiZzw7bQdNHCZs2N2oV/XslqKCALD/Ql/d2TVeBpDSs5OVQ1NxqtChe87G3x27L/7xzSsaGm+7sWCggKP3mSf+n+uSDV/XFBVsVTaWUrBTVU67oS5T50dg0FQYz077QQ+yIZfdjVyd6dS0QOTsDArZzpKd5YsGpP/XCmPJBI9CU/574lQh28rg4tMOxVtX7VnvUDsGCmUB3ansdLRdBGXZNTuMpWQ6que9G8KhnR+BQVOhq6th0FrwNF2D7oVqokwK8jd0Mu2cECI7NcNYuqKCtZWdqYyTVi4R/+4GRQHonmyslik7ykbCKDsGLQe/iU1mUHsglJ0ueKANolFvnR3aDXfSr0NYvlAmOzWLCmoUhf0W9iOTCROn6YK95/fh4CV+i5RuUVvlcGIXZGM1eSOsbiRUctMNnp3OP70GTQVn2EZpaA+MZ2dqIFdnBWWax/s77NcBdGGs+Clc2vxUD+SAhQP4/T8dj/kD+eYPsEvwoTcegC/f+Rcce0B3dGbvljAWGdKb3whUVnaoJIfnhX/fKRiyM80gKztm8W0HWlWV1KC5sKwMls7txdB4GfP6ky/0pOx0Qxhr/kABc/tywqBcM4yl8ewAwJ7VrK7pir9+5V7461fu1elhCMjtNzo3T9gtCrnLBmz/nszZVte0igAM2Zl2MJ6d9oMUA6PsdD/+97zXouS4NdsscJBnpxvCWICvzDz8wi4A6bKxumHBmanolkagrfLscCUnX113clYGpervO90EFOiwZ+fqq6/GqlWrMDg4iMHBQaxevRq33Xab+P3k5CTOO+88zJ8/HwMDAzjjjDOwZcsW6TNefPFFrFmzBn19fVi0aBE++clPolKptPtQugZZ49lpO4hUGmWn+zF/oIA9ZqdTNRZUG3DuOaenFUNKDR7KSmdQNvdnp2B1SQXlVnU9z2QyoTpO3MNY6AKi3dERLF26FJdddhnWrl2Lhx9+GMcffzxOP/10PProowCACy+8ED//+c/x4x//GHfffTc2btyIt7/97eL9juNgzZo1KJVKuPfee3HttdfimmuuwWc/+9lOHVLHweVSs/a2B/RQG2VneuLEQxbhO393NC46+ZBODwWAQnbqqKBs0H50T50d/+9WbIRFi5HqfSbV3ulg6I7QUV321FNPlf5/ySWX4Oqrr8b999+PpUuX4tvf/jauv/56HH/88QCA7373/2/v3oOiOs8/gH93l90FRNhwh3BVEwQFEtHg1lyaiCI/JzGNicTSitXExmBi1VgGUy/BmeLoGKe2xuQPI8mk1SZpTCZqa0CFNIo3lIm3EqBE2grSargocpF9f3+YPXIEAc3unrPH72dmZ+Ccd9fn5d3d8/jezlbEx8fj0KFDGD9+PL744gucOXMGxcXFCAkJwQMPPIDVq1cjNzcXq1atgsnU97h8R0cHOjo6pN9bWlqcV0kX8+gxf4S7+bqGUfqb82KiRR4GPZ4YGTJwQRcZ/n2y09fmbTeT9fQy2VGMWoYT7XE4+kagwI33mj2ZM97hVg/OonwE3+vu7sb27dtx5coVWK1WlJeXo6urC2lpaVKZkSNHIioqCmVlZQCAsrIyJCYmIiTkxhdReno6WlpapN6hvhQUFMDPz096REZGOq9iLmawD6mwy9pluBqLXGlUuC9MBj0i7vEesKx8STDfn0pRSw+b/T/Azhhyl/Zxkoax1FFnO8Vn3J08eRJWqxXt7e3w8fHBjh07kJCQgIqKCphMJlgsFln5kJAQNDQ0AAAaGhpkiY79vP3creTl5WHx4sXS7y0tLZpJeNjL4HrSvbF4MSEXCB7qic8WTICfl3HAsn1tKkiup5ZhrHu8r492WLwHfu/crhvDWDdWY9lxnx0AcXFxqKioQHNzMz7++GNkZ2ejtLTUqf+m2WyG2Wx26r+hFANXBrkce3bI1eLDfAdVrudbUg0rYu5WelnPjnLfE2kJwVj3bBIevs/x+w/Zkxv7/BzZrsoqSLQVT3ZMJhNGjBgBAEhJScHRo0fxu9/9DpmZmejs7ERTU5Osd+fChQsIDb2+HXhoaCiOHDkiez37ai17mbtNzzk75Bo973pOpCY6nQ4eeh2u2QR7dhTU107WSjB7GPDcWOeMYkj3Xrsp6QHU0bOjfAQ3sdls6OjoQEpKCoxGI/bu3Sudq6ysRF1dHaxWKwDAarXi5MmTaGxslMoUFRXB19cXCQkJLo9dDThnx/WM7NkhFbO/L418fypGr5JhLGe6eYKy2oZQFe3ZycvLQ0ZGBqKiotDa2oo//elPKCkpwZ49e+Dn54e5c+di8eLF8Pf3h6+vL1555RVYrVaMHz8eADB58mQkJCTg5z//OdauXYuGhgb85je/QU5OjmaHqQbCOTuuZ1/lwr85qZGHXocOcBhLSWrZVNCZPG6aoGy8w3vROYuiyU5jYyNmzZqF+vp6+Pn5ISkpCXv27MGkSZMAABs2bIBer8f06dPR0dGB9PR0vPXWW9LzDQYDdu7cifnz58NqtWLIkCHIzs5Gfn6+UlVSHOfsuJ49weTfnNRI6tnR6EXWHahlNZYzGfvbZ0cFdVY02dmyZUu/5z09PbFp0yZs2rTplmWio6Oxe/duR4fmtribr+vx3likZtL/uDm0rRiDTh0TlJ3pxgTl3jsoq6FnR/kIyKGkC69GP1BqxB2USc082LOjOEOPXg+tbvYqzdnpI7lWQ6LNd7/G3BhSYdO6ipGTwknFpBWaTHYUY+/Z0WqvDgCE+l2/d1y45fq95zz06urZUXzpOTmWgcugXc5Dmiel/Aea6Gb2XgUtX2jVTupdU8FF31nynxqN58dFYfwwfwDcQZmcjBvcuZ6R86RIxezJuBo2drtb6W8a4tEiP28jrMMDpN9NnLNDznRjfJ4XXldhgklqxtVYyrsb502prWdH+QjIoQxcBu1y3EGZ1OzmGzSS69mHuO+mNuiZ4JhVUG/lIyCH8uCmgi4XGzgEABAVMPBdqIlcbe7DsZiUEIJxMf5Kh3LXsl/3tTyMdTPus0NOZeAyaJeb9kA4kiL8EBMwROlQiHp5NiUCz6ZEKB3GXc3es9PzflFap5b7gdkx2dEYL6MBAOBpVP7NdbfQ6XQYFuSjdBhEpFL26Stq6OFwlZ4rz9QwfMdkR2PS4oMx86EoPJtyr9KhEBERbvS431XJjspukcFkR2Ms3iYUPJOodBhERPQ9+5COGibquoqRS8+JiIjuHj7m6/0Kvp5GhSNxHdm9sdizQ0REpG2TEkKw7P9GYmJ8iNKhuEzPvd7U0LPDZIeIiMiJPI0GzHt0uNJhuJR86bnyq9CUT7eIiIhIUzxU1rOjfARERESkKUaVzdlRPgIiIiLSFLXN2VE+AiIiItKUnrcsUsM+O8pHQERERJrCfXaIiIhI03oOY3mo4F6NTHaIiIjIoew9OyYPPXQ6JjtERESkMfal52pYiQUw2SEiIiIH69mzowbqiIKIiIg0wyjd6V35ISyAt4sgIiIiB4sLGYpR4b6YMCJQ6VAAMNkhIiIiB/MyGbDr1UeUDkPCYSwiIiLSNCY7REREpGlMdoiIiEjTmOwQERGRpjHZISIiIk1jskNERESaxmSHiIiINI3JDhEREWkakx0iIiLSNCY7REREpGlMdoiIiEjTmOwQERGRpjHZISIiIk1jskNERESa5qF0AGoghAAAtLS0KBwJERERDZb9um2/jt8Kkx0Ara2tAIDIyEiFIyEiIqLb1draCj8/v1ue14mB0qG7gM1mw/nz5zF06FDodDqHvW5LSwsiIyPxr3/9C76+vg57XTXReh21Xj+AddQCrdcPYB21wBn1E0KgtbUV4eHh0OtvPTOHPTsA9Ho9IiIinPb6vr6+mnzj9qT1Omq9fgDrqAVarx/AOmqBo+vXX4+OHScoExERkaYx2SEiIiJNY7LjRGazGStXroTZbFY6FKfReh21Xj+AddQCrdcPYB21QMn6cYIyERERaRp7doiIiEjTmOwQERGRpjHZISIiIk1jskNERESaxmTHiTZt2oSYmBh4enoiNTUVR44cUTqkO1JQUIBx48Zh6NChCA4OxtNPP43KykpZmR//+MfQ6XSyx0svvaRQxLdv1apVveIfOXKkdL69vR05OTkICAiAj48Ppk+fjgsXLigY8e2LiYnpVUedToecnBwA7teGX375JZ588kmEh4dDp9Ph008/lZ0XQmDFihUICwuDl5cX0tLSUFVVJStz6dIlZGVlwdfXFxaLBXPnzsXly5ddWIv+9VfHrq4u5ObmIjExEUOGDEF4eDhmzZqF8+fPy16jr3Zfs2aNi2vSt4HacPbs2b1inzJliqyMO7chgD4/kzqdDuvWrZPKqLkNB3N9GMz3Z11dHaZOnQpvb28EBwdj6dKluHbtmsPiZLLjJH/+85+xePFirFy5EsePH0dycjLS09PR2NiodGi3rbS0FDk5OTh06BCKiorQ1dWFyZMn48qVK7JyL774Iurr66XH2rVrFYr4zowaNUoW/1dffSWdW7RoET7//HN89NFHKC0txfnz5/HMM88oGO3tO3r0qKx+RUVFAIDnnntOKuNObXjlyhUkJydj06ZNfZ5fu3YtNm7ciLfffhuHDx/GkCFDkJ6ejvb2dqlMVlYWTp8+jaKiIuzcuRNffvkl5s2b56oqDKi/Ora1teH48eNYvnw5jh8/jk8++QSVlZV46qmnepXNz8+Xtesrr7ziivAHNFAbAsCUKVNksW/btk123p3bEICsbvX19Xj33Xeh0+kwffp0WTm1tuFgrg8DfX92d3dj6tSp6OzsxMGDB/Hee++hsLAQK1ascFyggpzioYceEjk5OdLv3d3dIjw8XBQUFCgYlWM0NjYKAKK0tFQ69thjj4mFCxcqF9QPtHLlSpGcnNznuaamJmE0GsVHH30kHTt79qwAIMrKylwUoeMtXLhQDB8+XNhsNiGEe7chALFjxw7pd5vNJkJDQ8W6deukY01NTcJsNott27YJIYQ4c+aMACCOHj0qlfnrX/8qdDqd+M9//uOy2Afr5jr25ciRIwKAOHfunHQsOjpabNiwwbnBOUBf9cvOzhbTpk275XO02IbTpk0TTzzxhOyYu7ShEL2vD4P5/ty9e7fQ6/WioaFBKrN582bh6+srOjo6HBIXe3acoLOzE+Xl5UhLS5OO6fV6pKWloaysTMHIHKO5uRkA4O/vLzv+xz/+EYGBgRg9ejTy8vLQ1tamRHh3rKqqCuHh4Rg2bBiysrJQV1cHACgvL0dXV5esPUeOHImoqCi3bc/Ozk588MEHmDNnjuzmt+7ehna1tbVoaGiQtZmfnx9SU1OlNisrK4PFYsHYsWOlMmlpadDr9Th8+LDLY3aE5uZm6HQ6WCwW2fE1a9YgICAADz74INatW+fQ4QFnKykpQXBwMOLi4jB//nxcvHhROqe1Nrxw4QJ27dqFuXPn9jrnLm148/VhMN+fZWVlSExMREhIiFQmPT0dLS0tOH36tEPi4o1AneB///sfuru7ZQ0HACEhIfjHP/6hUFSOYbPZ8Ktf/QoTJkzA6NGjpeM//elPER0djfDwcHz99dfIzc1FZWUlPvnkEwWjHbzU1FQUFhYiLi4O9fX1eOONN/DII4/g1KlTaGhogMlk6nUBCQkJQUNDgzIB/0CffvopmpqaMHv2bOmYu7dhT/Z26eszaD/X0NCA4OBg2XkPDw/4+/u7Zbu2t7cjNzcXM2fOlN1k8dVXX8WYMWPg7++PgwcPIi8vD/X19XjzzTcVjHZwpkyZgmeeeQaxsbGoqanBsmXLkJGRgbKyMhgMBs214XvvvYehQ4f2GiJ3lzbs6/owmO/PhoaGPj+r9nOOwGSHbktOTg5OnTolm88CQDZGnpiYiLCwMEycOBE1NTUYPny4q8O8bRkZGdLPSUlJSE1NRXR0ND788EN4eXkpGJlzbNmyBRkZGQgPD5eOuXsb3s26urowY8YMCCGwefNm2bnFixdLPyclJcFkMuGXv/wlCgoKVH9bgueff176OTExEUlJSRg+fDhKSkowceJEBSNzjnfffRdZWVnw9PSUHXeXNrzV9UENOIzlBIGBgTAYDL1mm1+4cAGhoaEKRfXDLViwADt37sT+/fsRERHRb9nU1FQAQHV1tStCcziLxYL7778f1dXVCA0NRWdnJ5qammRl3LU9z507h+LiYrzwwgv9lnPnNrS3S3+fwdDQ0F4LBq5du4ZLly65VbvaE51z586hqKhI1qvTl9TUVFy7dg3ffvutawJ0oGHDhiEwMFB6T2qlDQHg73//OyorKwf8XALqbMNbXR8G8/0ZGhra52fVfs4RmOw4gclkQkpKCvbu3Ssds9ls2Lt3L6xWq4KR3RkhBBYsWIAdO3Zg3759iI2NHfA5FRUVAICwsDAnR+ccly9fRk1NDcLCwpCSkgKj0Shrz8rKStTV1blle27duhXBwcGYOnVqv+XcuQ1jY2MRGhoqa7OWlhYcPnxYajOr1YqmpiaUl5dLZfbt2webzSYlempnT3SqqqpQXFyMgICAAZ9TUVEBvV7fa/jHHfz73//GxYsXpfekFtrQbsuWLUhJSUFycvKAZdXUhgNdHwbz/Wm1WnHy5ElZ4mpP3BMSEhwWKDnB9u3bhdlsFoWFheLMmTNi3rx5wmKxyGabu4v58+cLPz8/UVJSIurr66VHW1ubEEKI6upqkZ+fL44dOyZqa2vFZ599JoYNGyYeffRRhSMfvCVLloiSkhJRW1srDhw4INLS0kRgYKBobGwUQgjx0ksviaioKLFv3z5x7NgxYbVahdVqVTjq29fd3S2ioqJEbm6u7Lg7tmFra6s4ceKEOHHihAAg3nzzTXHixAlpJdKaNWuExWIRn332mfj666/FtGnTRGxsrLh69ar0GlOmTBEPPvigOHz4sPjqq6/EfffdJ2bOnKlUlXrpr46dnZ3iqaeeEhEREaKiokL22bSvYDl48KDYsGGDqKioEDU1NeKDDz4QQUFBYtasWQrX7Lr+6tfa2ipee+01UVZWJmpra0VxcbEYM2aMuO+++0R7e7v0Gu7chnbNzc3C29tbbN68udfz1d6GA10fhBj4+/PatWti9OjRYvLkyaKiokL87W9/E0FBQSIvL89hcTLZcaLf//73IioqSphMJvHQQw+JQ4cOKR3SHQHQ52Pr1q1CCCHq6urEo48+Kvz9/YXZbBYjRowQS5cuFc3NzcoGfhsyMzNFWFiYMJlM4t577xWZmZmiurpaOn/16lXx8ssvi3vuuUd4e3uLn/zkJ6K+vl7BiO/Mnj17BABRWVkpO+6Obbh///4+35fZ2dlCiOvLz5cvXy5CQkKE2WwWEydO7FXvixcvipkzZwofHx/h6+srfvGLX4jW1lYFatO3/upYW1t7y8/m/v37hRBClJeXi9TUVOHn5yc8PT1FfHy8+O1vfytLFpTUX/3a2trE5MmTRVBQkDAajSI6Olq8+OKLvf7D6M5taPfOO+8ILy8v0dTU1Ov5am/Dga4PQgzu+/Pbb78VGRkZwsvLSwQGBoolS5aIrq4uh8Wp+z5YIiIiIk3inB0iIiLSNCY7REREpGlMdoiIiEjTmOwQERGRpjHZISIiIk1jskNERESaxmSHiIiINI3JDhEREWkakx0iopuUlJRAp9P1unkhEbknJjtERESkaUx2iIiISNOY7BCR6thsNhQUFCA2NhZeXl5ITk7Gxx9/DODGENOuXbuQlJQET09PjB8/HqdOnZK9xl/+8heMGjUKZrMZMTExWL9+vex8R0cHcnNzERkZCbPZjBEjRmDLli2yMuXl5Rg7diy8vb3xox/9CJWVlc6tOBE5BZMdIlKdgoICvP/++3j77bdx+vRpLFq0CD/72c9QWloqlVm6dCnWr1+Po0ePIigoCE8++SS6uroAXE9SZsyYgeeffx4nT57EqlWrsHz5chQWFkrPnzVrFrZt24aNGzfi7NmzeOedd+Dj4yOL4/XXX8f69etx7NgxeHh4YM6cOS6pPxE5Fu96TkSq0tHRAX9/fxQXF8NqtUrHX3jhBbS1tWHevHl4/PHHsX37dmRmZgIALl26hIiICBQWFmLGjBnIysrCf//7X3zxxRfS83/9619j165dOH36NL755hvExcWhqKgIaWlpvWIoKSnB448/juLiYkycOBEAsHv3bkydOhVXr16Fp6enk/8KRORI7NkhIlWprq5GW1sbJk2aBB8fH+nx/vvvo6amRirXMxHy9/dHXFwczp49CwA4e/YsJkyYIHvdCRMmoKqqCt3d3aioqIDBYMBjjz3WbyxJSUnSz2FhYQCAxsbGH1xHInItD6UDICLq6fLlywCAXbt24d5775WdM5vNsoTnTnl5eQ2qnNFolH7W6XQArs8nIiL3wp4dIlKVhIQEmM1m1NXVYcSIEbJHZGSkVO7QoUPSz9999x2++eYbxMfHAwDi4+Nx4MAB2eseOHAA999/PwwGAxITE2Gz2WRzgIhIu9izQ0SqMnToULz22mtYtGgRbDYbHn74YTQ3N+PAgQPw9fVFdHQ0ACA/Px8BAQEICQnB66+/jsDAQDz99NMAgCVLlmDcuHFYvXo1MjMzUVZWhj/84Q946623AAAxMTHIzs7GnDlzsHHjRiQnJ+PcuXNobGzEjBkzlKo6ETkJkx0iUp3Vq1cjKCgIBQUF+Oc//wmLxYIxY8Zg2bJl0jDSmjVrsHDhQlRVVeGBBx7A559/DpPJBAAYM2YMPvzwQ6xYsQKrV69GWFgY8vPzMXv2bOnf2Lx5M5YtW4aXX34ZFy9eRFRUFJYtW6ZEdYnIybgai4jcin2l1HfffQeLxaJ0OETkBjhnh4iIiDSNyQ4RERFpGoexiIiISNPYs0NERESaxmSHiIiINI3JDhEREWkakx0iIiLSNCY7REREpGlMdoiIiEjTmOwQERGRpjHZISIiIk37f5Lqb5xIwiEYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(range(n_epochs), test_losses)\n",
    "plt.ylabel('Cross Entropy Loss')\n",
    "plt.xlabel('epoch');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "602.41284180\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_val = model(test)\n",
    "    loss = criterion(y_val, test_y)\n",
    "print(f'{loss:.8f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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