{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "edd718da-1295-49c4-b556-3cc7b718f93c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Preparation and Quality\n",
    "Lecture Data Engineering and Analytics<br>\n",
    "Eva Zangerle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b126eda-5b79-4531-b8ea-72898d09dc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required packages\n",
    "import json\n",
    "import os\n",
    "from pprint import pprint\n",
    "from sys import getsizeof\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import sklearn.datasets\n",
    "import sklearn.preprocessing as preproc\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap\n",
    "from scipy import stats\n",
    "from sklearn import linear_model, preprocessing\n",
    "from sklearn.cluster import DBSCAN, KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_extraction import FeatureHasher, text\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import pairwise_distances_argmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5406f6f3-1c06-4f3b-aaaf-9ac6f2967729",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a870c325-706d-4c07-bb57-e3b84322e6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Author: Eva Zangerle\n",
      "\n",
      "Last updated: 2021-11-29 09:33:54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Eva Zangerle\" -u -d -t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6ba96a-a20d-4c9e-bb38-46b80ab6ae1f",
   "metadata": {},
   "source": [
    "## Enhancing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05832b33-0bb8-4496-a2a1-f23f8c7927b1",
   "metadata": {},
   "source": [
    "### Scaling and Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b0f558-ce94-4349-a525-d65e09db72f8",
   "metadata": {},
   "source": [
    "The following example is based on the online news popularity dataset (taken from the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/online+news+popularity). The dataset provides set of features about articles published by Mashable in a period of two years and was originally used for predicting popularity of articles in social networks. In the following example, we are primarily interested in the word count for each article (`n_tokens_content`) and showcase the results of different scaling methods. This example is adapted from the FeatEng book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b966c0f-b892-408f-935f-d6fcb26db76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = pd.read_csv(\n",
    "    os.path.join(data_dir, \"OnlineNewsPopularity.csv\"),\n",
    "    delimiter=\", \",\n",
    "    engine=\"python\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071c9705-c8a4-486b-b124-0a55583d412f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> We use `, ` as a delimiter here. If we would use only the comma as a delimiter, we would be able to read the dataframe, but for instance, accessing a specific field would fails as the key is not recognized due to the trailing space. Furthermore, we specify the python parsing engine to allow separators of more than one character.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70e9a7e8-a3da-43be-b561-ac73ed655728",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.0</td>\n",
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       "      <td>0.013699</td>\n",
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       "      <th>1</th>\n",
       "      <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.913725</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.043137</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.286915</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>-0.125</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.575130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.393365</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.056872</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>1500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>0.503788</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.404896</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.500000</td>\n",
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       "    <tr>\n",
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       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.682836</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.074627</td>\n",
       "      <td>0.012127</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>0.139785</td>\n",
       "      <td>0.411127</td>\n",
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       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>39639</th>\n",
       "      <td>http://mashable.com/2014/12/27/samsung-app-aut...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.529052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684783</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.523121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037572</td>\n",
       "      <td>0.014451</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.333791</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1800</td>\n",
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       "    <tr>\n",
       "      <th>39640</th>\n",
       "      <td>http://mashable.com/2014/12/27/seth-rogen-jame...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.696296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.885057</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.405488</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.374825</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.211111</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900</td>\n",
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       "    <tr>\n",
       "      <th>39641</th>\n",
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       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.516355</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.644128</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.076923</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.024887</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.307273</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.356439</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>1900</td>\n",
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       "    <tr>\n",
       "      <th>39642</th>\n",
       "      <td>http://mashable.com/2014/12/27/ukraine-blasts/</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>0.539493</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.692661</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.975073</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.236851</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.205246</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.012500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1100</td>\n",
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       "    <tr>\n",
       "      <th>39643</th>\n",
       "      <td>http://mashable.com/2014/12/27/youtube-channel...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.701987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.471338</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063694</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.247338</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39644 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  timedelta  \\\n",
       "0      http://mashable.com/2013/01/07/amazon-instant-...      731.0   \n",
       "1      http://mashable.com/2013/01/07/ap-samsung-spon...      731.0   \n",
       "2      http://mashable.com/2013/01/07/apple-40-billio...      731.0   \n",
       "3      http://mashable.com/2013/01/07/astronaut-notre...      731.0   \n",
       "4       http://mashable.com/2013/01/07/att-u-verse-apps/      731.0   \n",
       "...                                                  ...        ...   \n",
       "39639  http://mashable.com/2014/12/27/samsung-app-aut...        8.0   \n",
       "39640  http://mashable.com/2014/12/27/seth-rogen-jame...        8.0   \n",
       "39641  http://mashable.com/2014/12/27/son-pays-off-mo...        8.0   \n",
       "39642     http://mashable.com/2014/12/27/ukraine-blasts/        8.0   \n",
       "39643  http://mashable.com/2014/12/27/youtube-channel...        8.0   \n",
       "\n",
       "       n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
       "0                12.0             219.0         0.663594               1.0   \n",
       "1                 9.0             255.0         0.604743               1.0   \n",
       "2                 9.0             211.0         0.575130               1.0   \n",
       "3                 9.0             531.0         0.503788               1.0   \n",
       "4                13.0            1072.0         0.415646               1.0   \n",
       "...               ...               ...              ...               ...   \n",
       "39639            11.0             346.0         0.529052               1.0   \n",
       "39640            12.0             328.0         0.696296               1.0   \n",
       "39641            10.0             442.0         0.516355               1.0   \n",
       "39642             6.0             682.0         0.539493               1.0   \n",
       "39643            10.0             157.0         0.701987               1.0   \n",
       "\n",
       "       n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  \\\n",
       "0                      0.815385        4.0             2.0       1.0   \n",
       "1                      0.791946        3.0             1.0       1.0   \n",
       "2                      0.663866        3.0             1.0       1.0   \n",
       "3                      0.665635        9.0             0.0       1.0   \n",
       "4                      0.540890       19.0            19.0      20.0   \n",
       "...                         ...        ...             ...       ...   \n",
       "39639                  0.684783        9.0             7.0       1.0   \n",
       "39640                  0.885057        9.0             7.0       3.0   \n",
       "39641                  0.644128       24.0             1.0      12.0   \n",
       "39642                  0.692661       10.0             1.0       1.0   \n",
       "39643                  0.846154        1.0             1.0       0.0   \n",
       "\n",
       "       num_videos  average_token_length  num_keywords  \\\n",
       "0             0.0              4.680365           5.0   \n",
       "1             0.0              4.913725           4.0   \n",
       "2             0.0              4.393365           6.0   \n",
       "3             0.0              4.404896           7.0   \n",
       "4             0.0              4.682836           7.0   \n",
       "...           ...                   ...           ...   \n",
       "39639         1.0              4.523121           8.0   \n",
       "39640        48.0              4.405488           7.0   \n",
       "39641         1.0              5.076923           8.0   \n",
       "39642         0.0              4.975073           5.0   \n",
       "39643         2.0              4.471338           4.0   \n",
       "\n",
       "       data_channel_is_lifestyle  data_channel_is_entertainment  ...  \\\n",
       "0                            0.0                            1.0  ...   \n",
       "1                            0.0                            0.0  ...   \n",
       "2                            0.0                            0.0  ...   \n",
       "3                            0.0                            1.0  ...   \n",
       "4                            0.0                            0.0  ...   \n",
       "...                          ...                            ...  ...   \n",
       "39639                        0.0                            0.0  ...   \n",
       "39640                        0.0                            0.0  ...   \n",
       "39641                        0.0                            0.0  ...   \n",
       "39642                        0.0                            0.0  ...   \n",
       "39643                        0.0                            1.0  ...   \n",
       "\n",
       "       global_rate_positive_words  global_rate_negative_words  \\\n",
       "0                        0.045662                    0.013699   \n",
       "1                        0.043137                    0.015686   \n",
       "2                        0.056872                    0.009479   \n",
       "3                        0.041431                    0.020716   \n",
       "4                        0.074627                    0.012127   \n",
       "...                           ...                         ...   \n",
       "39639                    0.037572                    0.014451   \n",
       "39640                    0.039634                    0.009146   \n",
       "39641                    0.033937                    0.024887   \n",
       "39642                    0.020528                    0.023460   \n",
       "39643                    0.063694                    0.012739   \n",
       "\n",
       "       rate_positive_words  rate_negative_words  avg_positive_polarity  \\\n",
       "0                 0.769231             0.230769               0.378636   \n",
       "1                 0.733333             0.266667               0.286915   \n",
       "2                 0.857143             0.142857               0.495833   \n",
       "3                 0.666667             0.333333               0.385965   \n",
       "4                 0.860215             0.139785               0.411127   \n",
       "...                    ...                  ...                    ...   \n",
       "39639             0.722222             0.277778               0.333791   \n",
       "39640             0.812500             0.187500               0.374825   \n",
       "39641             0.576923             0.423077               0.307273   \n",
       "39642             0.466667             0.533333               0.236851   \n",
       "39643             0.833333             0.166667               0.247338   \n",
       "\n",
       "       min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\n",
       "0                   0.100000                   0.70              -0.350000   \n",
       "1                   0.033333                   0.70              -0.118750   \n",
       "2                   0.100000                   1.00              -0.466667   \n",
       "3                   0.136364                   0.80              -0.369697   \n",
       "4                   0.033333                   1.00              -0.220192   \n",
       "...                      ...                    ...                    ...   \n",
       "39639               0.100000                   0.75              -0.260000   \n",
       "39640               0.136364                   0.70              -0.211111   \n",
       "39641               0.136364                   0.50              -0.356439   \n",
       "39642               0.062500                   0.50              -0.205246   \n",
       "39643               0.100000                   0.50              -0.200000   \n",
       "\n",
       "       min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
       "0                     -0.600              -0.200000            0.500000   \n",
       "1                     -0.125              -0.100000            0.000000   \n",
       "2                     -0.800              -0.133333            0.000000   \n",
       "3                     -0.600              -0.166667            0.000000   \n",
       "4                     -0.500              -0.050000            0.454545   \n",
       "...                      ...                    ...                 ...   \n",
       "39639                 -0.500              -0.125000            0.100000   \n",
       "39640                 -0.400              -0.100000            0.300000   \n",
       "39641                 -0.800              -0.166667            0.454545   \n",
       "39642                 -0.500              -0.012500            0.000000   \n",
       "39643                 -0.200              -0.200000            0.333333   \n",
       "\n",
       "       title_sentiment_polarity  abs_title_subjectivity  \\\n",
       "0                     -0.187500                0.000000   \n",
       "1                      0.000000                0.500000   \n",
       "2                      0.000000                0.500000   \n",
       "3                      0.000000                0.500000   \n",
       "4                      0.136364                0.045455   \n",
       "...                         ...                     ...   \n",
       "39639                  0.000000                0.400000   \n",
       "39640                  1.000000                0.200000   \n",
       "39641                  0.136364                0.045455   \n",
       "39642                  0.000000                0.500000   \n",
       "39643                  0.250000                0.166667   \n",
       "\n",
       "       abs_title_sentiment_polarity  shares  \n",
       "0                          0.187500     593  \n",
       "1                          0.000000     711  \n",
       "2                          0.000000    1500  \n",
       "3                          0.000000    1200  \n",
       "4                          0.136364     505  \n",
       "...                             ...     ...  \n",
       "39639                      0.000000    1800  \n",
       "39640                      1.000000    1900  \n",
       "39641                      0.136364    1900  \n",
       "39642                      0.000000    1100  \n",
       "39643                      0.250000    1300  \n",
       "\n",
       "[39644 rows x 61 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at data\n",
    "news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "280cf353-a469-4ea1-94f8-d74173600310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = news[\"n_tokens_content\"].hist()\n",
    "fig.set_xlabel(\"Word Count\")\n",
    "fig.set_ylabel(\"Articles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a88dfdf-9fae-44e9-8ba1-857f965b31c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# min max scaling\n",
    "news[\"minmax\"] = preproc.minmax_scale(news[\"n_tokens_content\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "975f64b2-33d6-4a3a-bbcf-b46c6c3c903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardization\n",
    "news[\"standardized\"] = preproc.StandardScaler().fit_transform(\n",
    "    news[[\"n_tokens_content\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9ce7284-8c2a-4d95-9962-c5eb867c3145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# l2 normalization\n",
    "news[\"normalized\"] = preproc.normalize(news[[\"n_tokens_content\"]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4bab441-0d1d-402c-8141-d81d49c66a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H13du8/rObV7fwZK31wBPrqqfbs4JkuwE/APwhqq6L8nD26qqkmyVeVKrahmwDGDx4sW1ZMmSrXHYKU1MTLDheY7r8b61mbLm2CUzHcLY6nVtNXd4fec2r+/c5vUd7LbpdcCCzTl4ku3pJG6fqqp/bMV3tNuhtJ93tvK1wN5du+/VyvqVS5IkzTuDJG8LgG8luTDJysnPdDul08V2FnBjVX2wa9NKYHLE6FLg813lr2ujTg8G7m23Vy8EDkuyaxuZelgrkyRJmncGuW162mYe+wV0npW7Nsk3WtlbgNOBc5KcANwKvLZtOx94ObAa+DFwPEBV3Z3knTwyJdc7quruzYxpXnHKLEmS5p5pk7eq+srmHLiqvgqkz+ZDe9Qv4KQ+xzobOHtz4pAkSZpLBplh4X46o0sBHgVsDzxQVY8dZmCSJEna2CA9bztPLrfn2I4GDh5mUJIkSeptkAELD2sv0P0cnRfnSpIkacQGuW36qq7VbehMU/WToUUkSZKkvgYZbfqKruX1dCapP3oo0UiSJGlKgzzzdvwoApEkSdL0+iZvSf58iv2qqt45hHg0ZL77TZKk8TZVz9sDPcp2BE4AdgNM3iRJkkasb/JWVR+YXE6yM3AynVkPVgAf6LefJEmShmfKZ96SPA54I3AssBw4sKruGUVg4+DatfdyXI/bkJIkScMy1TNv7wdeBSwD9q+qdSOLSpIkST1N9ZLeU4AnAm8F/j3Jfe1zf5L7RhOeJEmSuk31zNsmzb4gSZKk4TNBkyRJGiODzLCgOc53v0mSND7seZMkSRojJm+SJEljxORNkiRpjJi8SZIkjREHLKinXoMYwIEMkiTNNHveJEmSxsjQkrckZye5M8l1XWWPS7Iqyc3t566tPEnOSLI6yTVJDuzaZ2mrf3OSpcOKV5IkaRwMs+ft48DhG5SdClxUVfsCF7V1gCOAfdvnROBM6CR7wGnAQcBzgdMmEz5JkqT5aGjJW1VdAty9QfHRwPK2vBx4ZVf5J6rjUmBBkj2AlwGrquruqroHWMXGCaEkSdK8MeoBCwur6va2/H1gYVveE/heV73bWlm/8o0kOZFOrx0LFy5kYmJi60Xdx8LHwCn7rx/6eWaTUbTrbLBu3bp5813nI6/v3Ob1ndu8vjM42rSqKkltxeMtA5YBLF68uJYsWbK1Dt3XX37q83zg2nk2YPfaBzYqmosjUCcmJhjF75Bmhtd3bvP6zm1e39GPNr2j3Q6l/byzla8F9u6qt1cr61cuSZI0L406eVsJTI4YXQp8vqv8dW3U6cHAve326oXAYUl2bQMVDmtlkiRJ89LQ7vkl+TSwBNg9yW10Ro2eDpyT5ATgVuC1rfr5wMuB1cCPgeMBquruJO8ELm/13lFVGw6CkCRJmjeGlrxV1W/22XRoj7oFnNTnOGcDZ2/F0CRJksaWMyxIkiSNEZM3SZKkMWLyJkmSNEZM3iRJksbIPHvDrIZh0annbVQ2F1/cK0nSbGDPmyRJ0hgxeZMkSRoj3jbVUHgrVZKk4bDnTZIkaYyYvEmSJI0RkzdJkqQx4jNvGhmfg5MkacvZ8yZJkjRG7HnTjLI3TpKkTWPPmyRJ0hix502zjr1xkiT1Z8+bJEnSGLHnTWPB3jhJkjpM3jS2TOgkSfORyZvmlF4JXS8meZKkcTU2yVuSw4EPA9sCH6uq02c4JI0xe+0kSeNqLJK3JNsCfw28FLgNuDzJyqq6YWYj01zSndCdsv96jhuwF2+SyZ8kaRTGInkDngusrqpbAJKsAI4GTN40awx6y7YXEz9J0qDGJXnbE/he1/ptwEEzFIu01W1J4jeuBk1YR/Eco7fRJY2TcUneppXkRODEtrouyU0jOO3uwA9HcB6N2B96bYcu753R4017fbd2fBop/37ntrl4fZ+0KZXHJXlbC+zdtb5XK3tYVS0Dlo0yqCRXVNXiUZ5To+G1ndu8vnOb13du8/qOzwwLlwP7JtknyaOAY4CVMxyTJEnSyI1Fz1tVrU/y+8CFdF4VcnZVXT/DYUmSJI3cWCRvAFV1PnD+TMexgZHeptVIeW3nNq/v3Ob1ndvm/fVNVc10DJIkSRrQuDzzJkmSJEzeNkuSw5PclGR1klNnOh4NJsneSS5OckOS65Oc3Mofl2RVkpvbz11beZKc0a7zNUkO7DrW0lb/5iRLZ+o76ecl2TbJ1Um+0Nb3SXJZu4afaQOeSPLotr66bV/UdYw3t/Kbkrxshr6KNpBkQZJzk3wryY1Jnuff7tyR5I/av8vXJfl0kh38++3P5G0TdU3VdQTwDOA3kzxjZqPSgNYDp1TVM4CDgZPatTsVuKiq9gUuauvQucb7ts+JwJnQSfaA0+i8KPq5wGmT/9HQjDsZuLFr/b3Ah6rqqcA9wAmt/ATgnlb+oVaP9vtwDLAfcDjwv9rfvGbeh4EvVtXTgV+hc539250DkuwJ/CGwuKp+mc7AxGPw77cvk7dN9/BUXVX1U2Byqi7NclV1e1Vd1Zbvp/OP/550rt/yVm058Mq2fDTwieq4FFiQZA/gZcCqqrq7qu4BVtH5h0IzKMlewJHAx9p6gJcA57YqG17byWt+LnBoq380sKKqHqyq7wKr6fzNawYl2QX4VeAsgKr6aVX9CP9255LtgMck2Q74BeB2/Pvty+Rt0/WaqmvPGYpFm6l1sz8LuAxYWFW3t03fBxa25X7X2t+B2el/Av8deKit7wb8qKrWt/Xu6/TwNWzb7231vbaz0z7AD4C/abfFP5ZkR/zbnROqai3wF8C/0Una7gWuxL/fvkzeNO8k2Qn4B+ANVXVf97bqDL92CPaYSfLrwJ1VdeVMx6Kh2A44EDizqp4FPMAjt0gB/3bHWbt1fTSdJP2JwI7YIzolk7dNN+1UXZq9kmxPJ3H7VFX9Yyu+o91Sof28s5X3u9b+Dsw+LwCOSrKGzqMML6HzjNSCdhsGfv46PXwN2/ZdgLvw2s5WtwG3VdVlbf1cOsmcf7tzw68B362qH1TVfwL/SOdv2r/fPkzeNp1TdY2p9kzEWcCNVfXBrk0rgclRZ0uBz3eVv66NXDsYuLfdorkQOCzJru3/GA9rZZohVfXmqtqrqhbR+Zv8clUdC1wMvLpV2/DaTl7zV7f61cqPaaPZ9qHzwPvXR/Q11EdVfR/4XpJfakWHAjfg3+5c8W/AwUl+of07PXl9/fvtY2xmWJgtnKprrL0A+B3g2iTfaGVvAU4HzklyAnAr8Nq27Xzg5XQeev0xcDxAVd2d5J10EnmAd1TV3SP5BtpUbwJWJHkXcDXtgff285NJVgN300n4qKrrk5xD5z8c64GTqupnow9bPfwB8Kn2P8230Pl73Ab/dsdeVV2W5FzgKjp/d1fTmUXhPPz77ckZFiRJksaIt00lSZLGiMmbJEnSGDF5kyRJGiMmb5IkSWPE5E2SJGmMmLxJmlFJXpmkkjx9ijoLkvy3rvUntlcLTHXciSSLt2asmyPJuhk67xuS/MJMnFvScJm8SZppvwl8tf3cSHuD+gLg4eStqv69ql7dq/5M6nob/GzwBjoTfEuaY0zeJM2YNs/sC4ETaC/abOVLkvxzkpV0Xrh5OvCUJN9I8v4ki5Jc1+pum+QvklyX5Jokf9DjPIcl+dckVyX5+3be7u1PSHJlW/6V1hP4i239O+3N74uSfLmd46Ku7R9P8pEklwHva7Ov/GuSa9vLRft999e1Y30zySdb2VTneHXXvuu62mkiyblJvpXkU21WgT+kM0fkxUku3uQLI2lWM3mTNJOOBr5YVd8G7kry7K5tBwInV9XT6ExC/p2qOqCq/mSDY5wILAIOqKpnAp/q3phkd+CtwK9V1YHAFcAbu+tU1Z3ADkkeC7yo1XlRkifRmfD+x8BfAsu7znFG1yH2Ap5fVW+kM6fqmVW1P3B7ry+dZL8W00uq6leAk9umqc7Rz7Po9LI9A3gy8IKqOgP4d+CQqjpkgGNIGiMmb5Jm0m/SmUie9rP71unXq+q7Axzj14CPVtV66EyBtMH2g+kkNl9r06ItBZ7U4zj/QmcKtV8F/kf7+SLgn9v25wF/15Y/SafHcNLfd03D8wLg0131enlJ2+eHG8Q81Tn6+XpV3VZVDwHfoJPISprDZtPzGZLmkSSPo5PE7J+k6MwVXEkme9Ye2FqnAlZVVc9n6rpcQidZexKdCbDfBBSd+RWns2GsW3vewfW0/9lOsg3wqK5tD3Yt/wz/XZfmPHveJM2UVwOfrKonVdWiqtob+C6dBGpD9wM79znOKuD1k4MFWlLY7VLgBUme2rbvmORpPY7zz8BvAze3Xqy76Uxu/tW2/V945Lm8Y3mkR25DX9ugXi9fBl6TZLcNYu53jjXA5C3lo4Dt+xy321RtJmmMmbxJmim/CXx2g7J/oMeo06q6i85tz+uSvH+DzR8D/g24Jsk3gd/aYN8fAMcBn05yDfCvwEavJamqNXR66S5pRV8FflRV97T1PwCOb8f4HR55Tm1DJwMnJbkW2LNXhaq6Hng38JUW8wenOcf/Bl7c6j6PwXollwFfdMCCNPekamv37kuSJGlY7HmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxMidf5rj77rvXokWLhn6eBx54gB133HHo59HgvCazk9dl9vGazE5el9lnFNfkyiuv/GFVPX7Q+nMyeVu0aBFXXHHF0M8zMTHBkiVLhn4eDc5rMjt5XWYfr8ns5HWZfUZxTZLcuin1vW0qSZI0RkzeJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljxORNkiRpjJi8SZIkjZE5+Z63cbDo1PM2Kltz+pEzEIkkSRon9rxJkiSNEZM3SZKkMWLyJkmSNEZ85m0Eej3fJkmStDnseZMkSRojJm+SJEljxORNkiRpjJi8SZIkjRGTN0mSpDFi8iZJkjRGTN4kSZLGiMmbJEnSGDF5kyRJGiMmb5IkSWPE5E2SJGmMDDV5S7ImybVJvpHkilb2uCSrktzcfu7aypPkjCSrk1yT5MCu4yxt9W9OsnSYMUuSJM1mo+h5O6SqDqiqxW39VOCiqtoXuKitAxwB7Ns+JwJnQifZA04DDgKeC5w2mfBJkiTNNzNx2/RoYHlbXg68sqv8E9VxKbAgyR7Ay4BVVXV3Vd0DrAIOH3HMkiRJs0KqangHT74L3AMU8NGqWpbkR1W1oG0PcE9VLUjyBeD0qvpq23YR8CZgCbBDVb2rlf8Z8B9V9RcbnOtEOj12LFy48NkrVqwY2veatG7dOnbaaadp61279t6Bjrf/nrtsaUjz3qDXRKPldZl9vCazk9dl9hnFNTnkkEOu7LpDOa3thhkM8MKqWpvkCcCqJN/q3lhVlWSrZI9VtQxYBrB48eJasmTJ1jjslCYmJhjkPMedet5Ax1tz7PTH0tQGvSYaLa/L7OM1mZ28LrPPbLwmQ71tWlVr2887gc/SeWbtjnY7lPbzzlZ9LbB31+57tbJ+5ZIkSfPO0JK3JDsm2XlyGTgMuA5YCUyOGF0KfL4trwRe10adHgzcW1W3AxcChyXZtQ1UOKyVSZIkzTvDvG26EPhs57E2tgP+rqq+mORy4JwkJwC3Aq9t9c8HXg6sBn4MHA9QVXcneSdweav3jqq6e4hxS5IkzVpDS96q6hbgV3qU3wUc2qO8gJP6HOts4OytHaMkSdK4cYYFSZKkMWLyJkmSNEZM3iRJksaIyZskSdIYMXmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxYvImSZI0RjYpeUuyTZLHDisYSZIkTW3a5C3J3yV5bJIdgeuAG5L8yfBDkyRJ0oYG6Xl7RlXdB7wSuADYB/idYQYlSZKk3gZJ3rZPsj2d5G1lVf0nUEONSpIkST1tN0CdjwJrgG8ClyR5EnDfMIOarxadet5GZWtOP3IGIpEkSbPVtMlbVZ0BnNFVdGuSQ4YXkiRJkvoZZMDCwiRnJbmgrT8DWDr0yCRJkrSRQZ55+zhwIfDEtv5t4A1DikeSJElTGCR5272qzgEeAqiq9cDPhhqVJEmSehokeXsgyW60EaZJDgbuHWpUkiRJ6mmQ0aZvBFYCT0nyNeDxwKuHGpUkSZJ6mrbnraquAl4MPB94PbBfVV0z6AmSbJvk6iRfaOv7JLksyeokn0nyqFb+6La+um1f1HWMN7fym5K8bBO/oyRJ0pzRt+ctyav6bHpaEqrqHwc8x8nAjcDknKjvBT5UVSuSfAQ4ATiz/bynqp6a5JhW7zfa6NZjgP3oDJr4UpKnVZXP3UmSpHlnqtumr5hiWwHTJm9J9gKOBN4NvDFJgJcAv9WqLAfeRid5O7otA5wL/FWrfzSwoqoeBL6bZDXwXOBfpzu/JEnSXNM3eauq47fC8f8n8N+Bndv6bsCP2ohVgNuAPdvynsD32rnXJ7m31d8TuLTrmN37PCzJicCJAAsXLmRiYmIrhD+1devWDXSeU/ZfP22dfkbxPeaSQa+JRsvrMvt4TWYnr8vsMxuvybQDFpL8D+B9VfWjtr4rcEpVvXWa/X4duLOqrkyyZMtDnVpVLQOWASxevLiWLBn6KZmYmGCQ8xzXY9qrQa05dvrj6xGDXhONltdl9vGazE5el9lnNl6TQV4VcsRk4gZQVfcALx9gvxcARyVZA6ygc7v0w8CCJJNJ417A2ra8FtgboG3fBbiru7zHPpIkSfPKIMnbtkkePbmS5DHAo6eoD0BVvbmq9qqqRXQGHHy5qo4FLuaRV40sBT7fllfyyLRbr271q5Uf00aj7gPsC3x9gLglSZLmnEHe8/Yp4KIkf9PWj6cz0GBzvQlYkeRdwNXAWa38LOCTbUDC3XQSPqrq+iTnADcA64GTHGkqSZLmq2mTt6p6b5JrgENb0Tur6sJNOUlVTQATbfkWOqNFN6zzE+A1ffZ/N50Rq5IkSfPaID1vVNUFwAVDjkWSJEnTmOolvV+tqhcmuZ82r+nkJqCq6rF9dpUkSdKQTPWetxe2nzv3qyNJkqTRmna0aZJPDlImSZKk4RvkVSH7da+0d7A9ezjhSJIkaSp9k7ckb27Puz0zyX3tcz9wB4+8m02SJEkj1Dd5q6r30Jnl4BNV9dj22bmqdquqN48uREmSJE2a8rZpVT0EPGdEsUiSJGkagzzzdlUSEzhJkqRZYJCX9B4EHJvkVuABHnnP2zOHGpkkSZI2Mkjy9rKhRyFJkqSBDDK36a0ASZ4A7DD0iMbcolPPm+kQJEnSHDbIS3qPSnIz8F3gK8AanOdUkiRpRgxy2/SdwMHAl6rqWUkOAX57uGFpUq+evDWnHzkDkUiSpNlgkNGm/1lVdwHbJNmmqi4GFg85LkmSJPUwSM/bj5LsBFwCfCrJnXRGnUqSJGnEBul5Oxr4MfBHwBeB7wCvGGZQkiRJ6m2Q0aaTvWwPAcuHG44kSZKmMkjPmyRJkmYJkzdJkqQx0jd5S3JR+/ne0YUjSZKkqUzV87ZHkucDRyV5VpIDuz/THTjJDkm+nuSbSa5P8vZWvk+Sy5KsTvKZJI9q5Y9u66vb9kVdx3pzK78pidN1SZKkeWuqAQt/DvwZsBfwwQ22FfCSaY79IPCSqlqXZHvgq0kuAN4IfKiqViT5CHACcGb7eU9VPTXJMcB7gd9I8gzgGGA/4InAl5I8rap+tknfVJIkaQ7o2/NWVedW1RHA+6rqkA0+0yVuVMe6trp9+0wmfee28uXAK9vy0TwymvVc4NAkaeUrqurBqvousBp47iZ9S0mSpDkiVTV9peQo4Ffb6kRVfWGggyfbAlcCTwX+Gng/cGlVPbVt3xu4oKp+Ocl1wOFVdVvb9h3gIOBtbZ+/beVntX3O3eBcJwInAixcuPDZK1asGCTELbJu3Tp22mmnnyu7du29Qz/v/nvuMvRzjKte10Qzz+sy+3hNZievy+wzimtyyCGHXFlVA89eNe173pK8h05P16da0clJnl9Vb5lu33Zr84AkC4DPAk8fNLBNVVXLgGUAixcvriVLlgzrVA+bmJhgw/Mc12Mu0q1tzbFLpq0zX/W6Jpp5XpfZx2syO3ldZp/ZeE0GmR7rSOCAqnoIIMly4Gpg2uRtUlX9KMnFwPOABUm2q6r1dJ6nW9uqrQX2Bm5Lsh2wC3BXV/mk7n0kSZLmlUHf87aga3mge3ZJHt963EjyGOClwI3AxcCrW7WlwOfb8sq2Ttv+5erc010JHNNGo+4D7At8fcC4JUmS5pRBet7eA1zdes5C59m3UwfYbw9geXvubRvgnKr6QpIbgBVJ3kWnB++sVv8s4JNJVgN30xlhSlVdn+Qc4AZgPXCSI00lSdJ8Ncjcpp9OMgE8pxW9qaq+P8B+1wDP6lF+Cz1Gi1bVT4DX9DnWu4F3T3fO+WJRj+fq1px+5AxEIkmSRm2Qnjeq6nY6ty8lSZI0g5zbVJIkaYyYvEmSJI2RKZO3JNsm+daogpEkSdLUpkze2qjOm5L84ojikSRJ0hQGGbCwK3B9kq8DD0wWVtVRQ4tKkiRJPQ2SvP3Z0KOQJEnSQAZ5z9tXkjwJ2LeqvpTkF4Bthx+aJEmSNjTtaNMkvwucC3y0Fe0JfG6IMUmSJKmPQV4VchLwAuA+gKq6GXjCMIOSJElSb4Mkbw9W1U8nV5JsB9TwQpIkSVI/gyRvX0nyFuAxSV4K/D3wf4YbliRJknoZJHk7FfgBcC3weuB84K3DDEqSJEm9DTLa9KEky4HL6NwuvamqvG0qSZI0A6ZN3pIcCXwE+A4QYJ8kr6+qC4YdnCRJkn7eIC/p/QBwSFWtBkjyFOA8wORNkiRpxAZ55u3+ycStuQW4f0jxSJIkaQp9e96SvKotXpHkfOAcOs+8vQa4fASxaRMsOvW8jcrWnH7kDEQiSZKGaarbpq/oWr4DeHFb/gHwmKFFJEmSpL76Jm9VdfwoA5EkSdL0Bhltug/wB8Ci7vpVddTwwpIkSVIvgwxY+BywBvhLOiNPJz9TSrJ3kouT3JDk+iQnt/LHJVmV5Ob2c9dWniRnJFmd5JokB3Yda2mrf3OSpZv+NSVJkuaGQV4V8pOqOmMzjr0eOKWqrkqyM3BlklXAccBFVXV6klPpzODwJuAIYN/2OQg4EzgoyeOA04DFdAZMXJlkZVXdsxkxSZIkjbVBet4+nOS0JM9LcuDkZ7qdqur2qrqqLd8P3AjsCRwNLG/VlgOvbMtHA5+ojkuBBUn2AF4GrKqqu1vCtgo4fBO+oyRJ0pwxSM/b/sDvAC8BHmpl1dYHkmQR8Cw6U2wtrKrb26bvAwvb8p7A97p2u62V9Svf8BwnAicCLFy4kImJiUHD22zr1q3b6Dyn7L9+6Ocd1CjaYLbpdU0087wus4/XZHbyusw+s/GaDJK8vQZ4clX9dHNOkGQn4B+AN1TVfUke3lZVlWSrzJNaVcuAZQCLFy+uJUuWbI3DTmliYoINz3Ncj/etzZQ1xy6Z6RBGrtc10czzusw+XpPZyesy+8zGazLIbdPrgAWbc/Ak29NJ3D5VVf/Yiu9ot0NpP+9s5WuBvbt236uV9SuXJEmadwZJ3hYA30pyYZKVk5/pdkqni+0s4Maq+mDXppXA5IjRpcDnu8pf10adHgzc226vXggclmTXNjL1sFYmSZI07wxy2/S0zTz2C+g8K3dtkm+0srcApwPnJDkBuBV4bdt2PvByYDXwY+B4gKq6O8k7eWRKrndU1d2bGdO84pRZkiTNPdMmb1X1lc05cFV9FUifzYf2qF/ASX2OdTZw9ubEIUmSNJcMMsPC/XRGlwI8CtgeeKCqHjvMwCRJkrSxQXredp5cbs+xHQ0cPMygJEmS1NsgAxYe1l6g+zk6L86VJEnSiA1y2/RVXavb0Jmm6idDi0iSJEl9DTLa9BVdy+vpTFJ/9FCikSRJ0pQGeebt+FEEIkmSpOn1Td6S/PkU+1VVvXMI8WjIfPebJEnjbaqetwd6lO0InADsBpi8SZIkjVjf5K2qPjC5nGRn4GQ6sx6sAD7Qbz9JkiQNz5TPvCV5HPBG4FhgOXBgVd0zisDGwbVr7+W4HrchJUmShmWqZ97eD7wKWAbsX1XrRhaVJEmSeprqJb2nAE8E3gr8e5L72uf+JPeNJjxJkiR1m+qZt02afUGSJEnDN8hLejXH+foQSZLGh71rkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxYvImSZI0Rhxtqp56jUAFR6FKkjTThtbzluTsJHcmua6r7HFJViW5uf3ctZUnyRlJVie5JsmBXfssbfVvTrJ0WPFKkiSNg2HeNv04cPgGZacCF1XVvsBFbR3gCGDf9jkROBMenlv1NOAg4LnAaZMJnyRJ0nw0tOStqi4B7t6g+Gg6E9zTfr6yq/wT1XEpsCDJHsDLgFVVdXdV3QOsYuOEUJIkad4Y9YCFhVV1e1v+PrCwLe8JfK+r3m2trF+5JEnSvDRjAxaqqpLU1jpekhPp3HJl4cKFTExMbK1D97XwMXDK/uuHfp7ZZBTtuiXWrVs362Ocj7wus4/XZHbyusw+s/GajDp5uyPJHlV1e7stemcrXwvs3VVvr1a2FliyQflErwNX1TJgGcDixYtryZIlvaptVX/5qc/zgWvn2YDdax/YqGg2jUCdmJhgFNdem8brMvt4TWYnr8vsMxuvyahvm64EJkeMLgU+31X+ujbq9GDg3nZ79ULgsCS7toEKh7UySZKkeWlo3UZJPk2n12z3JLfRGTV6OnBOkhOAW4HXturnAy8HVgM/Bo4HqKq7k7wTuLzVe0dVbTgIQpIkad4YWvJWVb/ZZ9OhPeoWcFKf45wNnL0VQ5MkSRpbTo8lSZI0RkzeJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljZJ69YVbDsOjU8zYqm00v7pUkaS6x502SJGmMmLxJkiSNEW+baii8lSpJ0nDY8yZJkjRGTN4kSZLGiMmbJEnSGPGZN42Mz8FJkrTl7HmTJEkaI/a8aUbZGydJ0qax502SJGmM2POmWcfeOEmS+rPnTZIkaYzY86ax0Ks3rpePH77jkCORJGlmmbxpTrl27b0ct0Gi5y1XSdJcYvKmOc9n6CRJc8nYJG9JDgc+DGwLfKyqTp/hkDTGTOgkSeNqLJK3JNsCfw28FLgNuDzJyqq6YWYj01wy6HN1/Zj8SZJGYSySN+C5wOqqugUgyQrgaMDkTbPGliZ/G+qVDNpjKEkal+RtT+B7Xeu3AQd1V0hyInBiW12X5KYRxLU78MMRnEcD+sM5dE3y3q1bb4bNmesyh3hNZievy+wzimvypE2pPC7J27SqahmwbJTnTHJFVS0e5Tk1Na/J7OR1mX28JrOT12X2mY3XZFxe0rsW2Ltrfa9WJkmSNK+MS/J2ObBvkn2SPAo4Blg5wzFJkiSN3FjcNq2q9Ul+H7iQzqtCzq6q62c4LBjxbVoNxGsyO3ldZh+vyezkdZl9Zt01SVXNdAySJEka0LjcNpUkSRImb5IkSWPF5G0aSQ5PclOS1UlO7bH90Uk+07ZflmTRDIQ57wxwXd6Y5IYk1yS5KMkmvUNHm266a9JV7/9JUklm1dD7uWqQ65Lkte3v5fokfzfqGOebAf79+sUkFye5uv0b9vKZiHM+SXJ2kjuTXNdne5Kc0a7ZNUkOHHWM3UzeptA1LdcRwDOA30zyjA2qnQDcU1VPBT4EjMcrU8fYgNflamBxVT0TOBd432ijnF8GvCYk2Rk4GbhstBHOT4NclyT7Am8GXlBV+wFvGHWc88mAfytvBc6pqmfRebvC/xptlPPSx4HDp9h+BLBv+5wInDmCmPoyeZvaw9NyVdVPgclpubodDSxvy+cChybJCGOcj6a9LlV1cVX9uK1eSufdgBqeQf5WAN5J539wfjLK4OaxQa7L7wJ/XVX3AFTVnSOOcb4Z5JoU8Ni2vAvw7yOMb16qqkuAu6eocjTwieq4FFiQZI/RRLcxk7ep9ZqWa89+dapqPXAvsNtIopu/Brku3U4ALhhqRJr2mrTbDHtX1dadBFZTGeRv5WnA05J8LcmlSabqfdCWG+SavA347SS3AecDfzCa0DSFTf3vzlCNxXvepM2V5LeBxcCLZzqW+SzJNsAHgeNmOBRtbDs6t4KW0OmhviTJ/lX1o5kMap77TeDjVfWBJM8DPpnkl6vqoZkOTLODPW9TG2RarofrJNmOThf3XSOJbv4aaLq0JL8G/ClwVFU9OKLY5qvprsnOwC8DE0nWAAcDKx20MHSD/K3cBqysqv+squ8C36aTzGk4BrkmJwDnAFTVvwI70JkcXTNnVk3TafI2tUGm5VoJLG3Lrwa+XL75eNimvS5JngV8lE7i5jM8wzflNamqe6tq96paVFWL6DyHeFRVXTEz4c4bg/wb9jk6vW4k2Z3ObdRbRhjjfDPINfk34FCAJP+FTvL2g5FGqQ2tBF7XRp0eDNxbVbfPVDDeNp1Cv2m5krwDuKKqVgJn0enSXk3nYcdjZi7i+WHA6/J+YCfg79v4kX+rqqNmLOg5bsBrohEb8LpcCByW5AbgZ8CfVJV3D4ZkwGtyCvC/k/wRncELx9kpMFxJPk3nf2J2b88angZsD1BVH6Hz7OHLgdXAj4HjZybSDqfHkiRJGiPeNpUkSRojJm+SJEljxORNkiRpjJi8SZIkjRGTN0mSpDFi8iZpSkkqyd92rW+X5AdJvtDWj0py6sxFODxJ1rR3nw1a/7gkfzXMmDY438RMvOg4ySt7TKYuaURM3iRN5wHgl5M8pq2/lK43i1fVyqo6fUYim0faDC6zxSsBkzdphpi8SRrE+cCRbfk3gU9PbujubUry8SRnJPmXJLckeXWvg7V6Z7aJ0G9JsiTJ2UluTPLxrnpnJrkiyfVJ3t7KdklyU5JfauufTvK7Pc5xepIbklyT5C9a2cIkn03yzfZ5fiv/XJIr23lO7BPzbyf5epJvJPlokm1b+fFJvp3k68AL+ux7bZIF7e3sdyV5XSv/RJKXJtkhyd+0elcnOaSrbVcm+TJwUZLHJFnR2umzwGP6nO857Rp8s8W88zTn+Kuufb+QZElbXpfk3e04l7b2ez5wFPD+1hZP6RWDpOExeZM0iBXAMUl2AJ4JXDZF3T2AFwK/DkzVI7cr8Dzgj+hMPfMhYD9g/yQHtDp/WlWL2zlfnOSZVXUv8PvAx5McA+xaVf+7+8BJdgP+b2C/qnom8K626QzgK1X1K8CBwPWt/L9W1bOBxcAftv27j/dfgN8AXlBVB9CZieDYJHsAb6eTtL2Q/r1RX2t19qMz9dSLWvnzgH8BTgKqqvankxwvb21Ni/PVVfVi4P8DflxV/4XOG+CfveGJ0ply6TPAye17/hrwH9Oco58dgUvbcS4Bfreq/oXO9fqTqjqgqr4zzTEkbWUmb5KmVVXXAIvo/Ef//Gmqf66qHqqqG4CFU9T7P23Kn2uBO6rq2qp6iE5CtajVeW2Sq4Cr6SQ+z2jxrGr7/TXw//Y49r3AT4CzkryKznQ2AC8BzmzH+FlLBKGTsH2Tzpyre7PxxOyH0kmULk/yjbb+ZOAgYKKqflBVP6WTNPXyz8Cvts+ZdBLUPYF7quoBOonf37a4vgXcSmeOUYBVVXV3W/7VrnrXANf0ONcvAbdX1eWt3n1VtX6ac/TzU+ALbflKHrkukmaQyZukQa0E/oKuW6Z9PNi1HIB26+0bLfHZsN5DG+zzELBdkn2APwYObb1n59GZoJsk2wD/hU5StuuGAbRk5bnAuXR6AL/YL9h2i/DXgOe1HqarJ8+zwfdY3nqaDqiqX6qqt/U7Zg+X0OltexEwQWeS8VfTSeqm88AmnGdzrOfn/1vQ/d3/s2tOzZ/hfNjSrGDyJmlQZwNvr6prN3XHqvrTycRnE3Z7LJ3E5d4kC4Ejurb9EXAj8FvA3yTZvnvHJDsBu1TV+a3ur7RNF9G59UiSbZPsAuxCpwfsx0meDhzcI5aLgFcneULb93FJnkTn9vGLk+zWYnhNn+//PWB3YN+qugX4Kp3E9JJW5Z+BY9uxnwb8InBTj0Nd0r4zSX6Zzu3kDd0E7JHkOa3ezukMduh3jjXAAUm2SbI3naR3OvcDOw9QT9IQmLxJGkhV3VZVZ4zwfN+k0wv2LeDv6Dw3Rhuo8P8Cp1TVP9NJaN66we47A19Icg2dROmNrfxk4JAk19K5DfgMOr1y2yW5kc4zepf2iOWGdo5/asdcBexRVbcDbwP+tcV34xRf6TLg2235n4E9W2wA/wvYpsX1GeC4qnpw40NwJrBTi/Ud7TtsGOtP6Tyf95ftVvAqOr1p/c7xNeC7wA10ngm8aorvMGkF8Cdt4IMDFqQRyyM94pIkSZrt7HmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxMidfuLj77rvXokWLZjqMoXvggQfYcccdZzqMWcm26c126c+26c126c+26c126a9f21x55ZU/rKrHD3qcOZm8LVq0iCuuuGKmwxi6iYkJlixZMtNhzEq2TW+2S3+2TW+2S3+2TW+2S3/92ibJrZtyHG+bSpIkjRGTN0mSpDFi8iZJkjRGTN4kSZLGiMmbJEnSGDF5kyRJGiNz8lUh42DRqedtVLbm9CNnIBJJkjRO7HmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RByyMQK/BCZIkSZvDnjdJkqQxYvImSZI0RkzeJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljxORNkiRpjJi8SZIkjRGTN0mSpDFi8iZJkjRGTN4kSZLGyFCTtyRrklyb5BtJrmhlj0uyKsnN7eeurTxJzkiyOsk1SQ7sOs7SVv/mJEuHGbMkSdJsNoqet0Oq6oCqWtzWTwUuqqp9gYvaOsARwL7tcyJwJnSSPeA04CDgucBpkwmfJEnSfDMTt02PBpa35eXAK7vKP1EdlwILkuwBvAxYVVV3V9U9wCrg8BHHLEmSNCukqoZ38OS7wD1AAR+tqmVJflRVC9r2APdU1YIkXwBOr6qvtm0XAW8ClgA7VNW7WvmfAf9RVX+xwblOpNNjx8KFC5+9YsWKoX2vTXXt2nsHqrf/nrts0nHXrVvHTjvttDkhzXm2TW+2S3+2TW+2S3+2TW+2S3/92uaQQw65susO5bS226pRbeyFVbU2yROAVUm+1b2xqirJVskeq2oZsAxg8eLFtWTJkq1x2K3iuFPPG6jemmOXbNJxJyYmmE3fczaxbXqzXfqzbXqzXfqzbXqzXfrbWm0z1NumVbW2/bwT+CydZ9buaLdDaT/vbNXXAnt37b5XK+tXLkmSNO8MLXlLsmOSnSeXgcOA64CVwOSI0aXA59vySuB1bdTpwcC9VXU7cCFwWJJd20CFw1qZJEnSvDPM26YLgc92HmtjO+DvquqLSS4HzklyAnAr8NpW/3zg5cBq4MfA8QBVdXeSdwKXt3rvqKq7hxi3JEnSrDW05K2qbgF+pUf5XcChPcoLOKnPsc4Gzt7aMUqSJI0bZ1iQJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxYvImSZI0RkzeJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljZJOStyTbJHnssIKRJEnS1KZN3pL8XZLHJtkRuA64IcmfDD80SZIkbWiQnrdnVNV9wCuBC4B9gN8ZZlCSJEnqbZDkbfsk29NJ3lZW1X8CNdSoJEmS1NN2A9T5KLAG+CZwSZInAfcNM6j5atGp521Utub0I2cgEkmSNFtNm7xV1RnAGV1FtyY5ZHghSZIkqZ9BBiwsTHJWkgva+jOApUOPTJIkSRsZ5Jm3jwMXAk9s698G3jCkeCRJkjSFQZK33avqHOAhgKpaD/xsqFFJkiSpp0GStweS7EYbYZrkYODeoUYlSZKkngYZbfpGYCXwlCRfAx4PvHqoUUmSJKmnaXvequoq4MXA84HXA/tV1TWDniDJtkmuTvKFtr5PksuSrE7ymSSPauWPbuur2/ZFXcd4cyu/KcnLNvE7SpIkzRl9e96SvKrPpqcloar+ccBznAzcCEzOifpe4ENVtSLJR4ATgDPbz3uq6qlJjmn1fqONbj0G2I/OoIkvJXlaVfncnSRJmnemum36iim2FTBt8pZkL+BI4N3AG5MEeAnwW63KcuBtdJK3o9sywLnAX7X6RwMrqupB4LtJVgPPBf51uvNLkiTNNaka3kxXSc4F3gPsDPwxcBxwaVU9tW3fG7igqn45yXXA4VV1W9v2HeAgOgndpVX1t638rLbPuRuc60TgRICFCxc+e8WKFUP7Xpvq2rWbP75j/z136btt3bp17LTTTpt97LnMtunNdunPtunNdunPtunNdumvX9sccsghV1bV4kGPM+2AhST/A3hfVf2ore8KnFJVb51mv18H7qyqK5MsGTSgzVVVy4BlAIsXL64lS4Z+yoEd12Paq0GtOXZJ320TExPMpu85m9g2vdku/dk2vdku/dk2vdku/W2tthnkVSFHTCZuAFV1D/DyAfZ7AXBUkjXACjq3Sz8MLEgymTTuBaxty2uBvQHa9l2Au7rLe+wjSZI0rwySvG2b5NGTK0keAzx6ivoAVNWbq2qvqlpEZ8DBl6vqWOBiHnnVyFLg8215JY9Mu/XqVr9a+TFtNOo+wL7A1weIW5Ikac4Z5D1vnwIuSvI3bf14OgMNNtebgBVJ3gVcDZzVys8CPtkGJNxNJ+Gjqq5Pcg5wA7AeOMmRppIkab6aNnmrqvcmuQY4tBW9s6ou3JSTVNUEMNGWb6EzWnTDOj8BXtNn/3fTGbEqSZI0rw3S80ZVXQBcMORYJEmSNI2pXtL71ap6YZL7afOaTm4Cqqoe22dXSZIkDUnf5K2qXth+7jy6cCRJkjSVaUebJvnkIGWSJEkavkFeFbJf90p7B9uzhxOOJEmSptI3eUvy5va82zOT3Nc+9wN38Mi72SRJkjRCfZO3qnoPnVkOPlFVj22fnatqt6p68+hClCRJ0qQpb5tW1UPAc0YUiyRJkqYxyDNvVyUxgZMkSZoFBnlJ70HAsUluBR7gkfe8PXOokUmSJGkjgyRvLxt6FJIkSRrIIHOb3gqQ5AnADkOPaMwtOvW8mQ5BkiTNYdMmb0mOAj4APBG4E3gScCMbvP9Nw9ErGVxz+pEzEIkkSZoNBhmw8E7gYODbVbUPcChw6VCjkiRJUk+DJG//WVV3Adsk2aaqLgYWDzkuSZIk9TDIgIUfJdkJuAT4VJI76Yw6lSRJ0ogN0vN2NPBj4I+ALwLfAV4xzKAkSZLU2yCjTSd72R4Clg83HEmSJE1lkJ43SZIkzRImb5IkSWOkb/KW5KL2872jC0eSJElTmarnbY8kzweOSvKsJAd2f6Y7cJIdknw9yTeTXJ/k7a18nySXJVmd5DNJHtXKH93WV7fti7qO9eZWflMSp+uSJEnz1lQDFv4c+DNgL+CDG2wr4CXTHPtB4CVVtS7J9sBXk1wAvBH4UFWtSPIR4ATgzPbznqp6apJjgPcCv5HkGcAxdGZ0eCLwpSRPq6qfbdI3lSRJmgP69rxV1blVdQTwvqo6ZIPPdIkb1bGurW7fPpNJ37mtfDnwyrZ8NI+MZj0XODRJWvmKqnqwqr4LrAaeu0nfUpIkaY5IVU1fqTO/6a+21Ymq+sJAB0+2Ba4Engr8NfB+4NKqemrbvjdwQVX9cpLrgMOr6ra27TvAQcDb2j5/28rPavucu8G5TgROBFi4cOGzV6xYMUiIW921a+8d+jn233MXANatW8dOO+009PONI9umN9ulP9umN9ulP9umN9ulv35tc8ghh1xZVQPPXjXIxPTvodPT9alWdHKS51fVW6bbt93aPCDJAuCzwNMHDWxTVdUyYBnA4sWLa8mSJcM61ZSO6zGR/Na25tglAExMTDBT33O2s216s136s216s136s216s13621ptM8j0WEcCB1TVQwBJlgNXA9Mmb5Oq6kdJLgaeByxIsl1VrafzPN3aVm0tsDdwW5LtgF2Au7rKJ3XvI0mSNK8M+p63BV3LuwyyQ5LHtx43kjwGeClwI3Ax8OpWbSnw+ba8sq3Ttn+5Ovd0VwLHtNGo+wD7Al8fMG5JkqQ5ZZCet/cAV7ees9B59u3UAfbbA1jennvbBjinqr6Q5AZgRZJ30enBO6vVPwv4ZJLVwN10RphSVdcnOQe4AVgPnORIU0mSNF8NMrfpp5NMAM9pRW+qqu8PsN81wLN6lN9Cj9GiVfUT4DV9jvVu4N3TnXO+WNSeqztl//UPP2O35vQjZzIkSZI0IoP0vFFVt9O5fSlJkqQZ5NymkiRJY8TkTZIkaYxMmbwl2TbJt0YVjCRJkqY2ZfLWRnXelOQXRxSPJEmSpjDIgIVdgeuTfB14YLKwqo4aWlSSJEnqaZDk7c+GHoUkSZIGMsh73r6S5EnAvlX1pSS/AGw7/NAkSZK0oWlHmyb5XeBc4KOtaE/gc0OMSZIkSX0M8qqQk4AXAPcBVNXNwBOGGZQkSZJ6GyR5e7Cqfjq5kmQ7oIYXkiRJkvoZJHn7SpK3AI9J8lLg74H/M9ywJEmS1MsgydupwA+Aa4HXA+cDbx1mUJIkSeptkNGmDyVZDlxG53bpTVXlbVNJkqQZMG3yluRI4CPAd4AA+yR5fVVdMOzgJEmS9PMGeUnvB4BDqmo1QJKnAOcBJm+SJEkjNkjydv9k4tbcAtw/pHi0mRadet5GZWtOP3IGIpEkScPUN3lL8qq2eEWS84Fz6Dzz9hrg8hHEJkmSpA1M1fP2iq7lO4AXt+UfAI8ZWkSSJEnqq2/yVlXHjzIQSZIkTW+QuU33SfLBJP+YZOXkZ4D99k5ycZIbklyf5ORW/rgkq5Lc3H7u2sqT5Iwkq5Nck+TArmMtbfVvTrJ0S76wJEnSOBtkwMLngLPozKrw0CYcez1wSlVdlWRn4Mokq4DjgIuq6vQkp9J5CfCbgCOAfdvnIOBM4KAkjwNOAxbTeebuyiQrq+qeTYhFkiRpThgkeftJVZ2xqQeuqtuB29vy/UluBPYEjgaWtGrLgQk6ydvRwCfaC4AvTbIgyR6t7qqquhugJYCHA5/e1JgkSZLG3SDJ24eTnAb8E/DgZGFVXTXoSZIsAp5FZ5aGhS2xA/g+sLAt7wl8r2u321pZv3JJkqR5Z5DkbX/gd4CX8Mht02rr00qyE/APwBuq6r4kD2+rqkqyVabaSnIicCLAwoULmZiY2BqH3WSn7L9+ZOda+JipzzdTbTAbrFu3bl5//35sl/5sm95sl/5sm95sl/62VtsMkry9BnhyVf10Uw+eZHs6idunquofW/EdSfaoqtvbbdE7W/laYO+u3fdqZWt55DbrZPnEhueqqmXAMoDFixfXkiVLNqwyEsf1eFnusJyy/3o+cG3/S7jm2CUji2W2mZiYYKZ+B2Yz26U/26Y326U/26Y326W/rdU20442Ba4DFmzqgdPpYjsLuLGqPti1aSUwOWJ0KfD5rvLXtVGnBwP3tturFwKHJdm1jUw9rJVJkiTNO4P0vC0AvpXkcn7+mbejptnvBXRut16b5But7C3A6cA5SU4AbgVe27adD7wcWA38GDi+nefuJO/kkVkd3jE5eEGSJGm+GSR5O21zDlxVXwXSZ/OhPeoXcFKfY50NnL05ccxnzncqSdLcM23yVlVfGUUgkiRJmt60yVuS++mMLgV4FLA98EBVPXaYgUmSJGljg/S87Ty53AYhHA0cPMygJEmS1Nsgo00fVh2fA142nHAkSZI0lUFum76qa3UbOnOM/mRoEUmSJKmvQUabvqJreT2whs6tU0mSJI3YIM+8HT+KQCRJkjS9vslbkj+fYr+qqncOIR4Nme9+kyRpvE3V8/ZAj7IdgROA3QCTN0mSpBHrm7xV1Qcml5PsDJxMZ8qqFcAH+u03n/TqxZIkSRqmKZ95S/I44I3AscBy4MCqumcUgUmSJGljUz3z9n7gVcAyYP+qWjeyqCRJktTTVC/pPQV4IvBW4N+T3Nc+9ye5bzThSZIkqdtUz7xt0uwLkiRJGr5BXtKrOc7Xh0iSND7sXZMkSRojJm+SJEljxORNkiRpjJi8SZIkjRGTN0mSpDHiaFP11G/qL0ehSpI0s4bW85bk7CR3Jrmuq+xxSVYlubn93LWVJ8kZSVYnuSbJgV37LG31b06ydFjxSpIkjYNh3jb9OHD4BmWnAhdV1b7ARW0d4Ahg3/Y5ETgTHp5b9TTgIOC5wGmTCZ8kSdJ8NLTkraouAe7eoPhoOhPc036+sqv8E9VxKbAgyR7Ay4BVVXV3Vd0DrGLjhFCSJGneSFUN7+DJIuALVfXLbf1HVbWgLQe4p6oWJPkCcHpVfbVtuwh4E7AE2KGq3tXK/wz4j6r6ix7nOpFOrx0LFy589ooVK4b2vSZdu/beoZ9jKgsfA3f8x2jPuf+eu4z2hJtp3bp17LTTTjMdxqxju/Rn2/Rmu/Rn2/Rmu/TXr20OOeSQK6tq8aDHmbEBC1VVSbZa5lhVy4BlAIsXL64lS5ZsrUP3dVyfh/pH5ZT91/OBa0d8Ca99YKOi2TiIYWJiglH8Dowb26U/26Y326U/26Y326W/rdU2o35VyB3tdijt552tfC2wd1e9vVpZv3JJkqR5adTJ20pgcsToUuDzXeWva6NODwburarbgQuBw5Ls2gYqHNbKJEmS5qWh3XNL8mk6z6ztnuQ2OqNGTwfOSXICcCvw2lb9fODlwGrgx8DxAFV1d5J3Ape3eu+oqg0HQUiSJM0bQ0vequo3+2w6tEfdAk7qc5yzgbO3YmiSJEljy+mxJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljZMZe0qu5Y1GPlxXPxhf3SpI0F9jzJkmSNEZM3iRJksaIyZskSdIY8Zk3DYXPwUmSNBz2vEmSJI0RkzdJkqQxYvImSZI0RnzmTSPjc3CSJG05kzfNKBM6SZI2jbdNJUmSxojJmyRJ0hjxtqlmHW+lSpLUn8mbxoIJnSRJHSZvGlsmdJKk+cjkTXPKZEJ3yv7rOa4tm9BJkuYSkzfNefbQSZLmkrFJ3pIcDnwY2Bb4WFWdPsMhaYz1Sug2hcmfJGmmjEXylmRb4K+BlwK3AZcnWVlVN8xsZJqvBk3+TPIkSVvbWCRvwHOB1VV1C0CSFcDRgMmbZrUt7eHbXL2SxkWnnvdzzwL2qydJmt1SVTMdw7SSvBo4vKr+37b+O8BBVfX7XXVOBE5sq78E3DTyQEdvd+CHMx3ELGXb9Ga79Gfb9Ga79Gfb9Ga79NevbZ5UVY8f9CDj0vM2rapaBiyb6ThGKckVVbV4puOYjWyb3myX/myb3myX/myb3myX/rZW24zL9Fhrgb271vdqZZIkSfPKuCRvlwP7JtknyaOAY4CVMxyTJEnSyI3FbdOqWp/k94EL6bwq5Oyqun6Gw5oN5tVt4k1k2/Rmu/Rn2/Rmu/Rn2/Rmu/S3VdpmLAYsSJIkqWNcbptKkiQJkzdJkqSxYvI2BpIcnuSmJKuTnNpj+6OTfKZtvyzJohkIc+SS7J3k4iQ3JLk+yck96ixJcm+Sb7TPn89ErKOWZE2Sa9t3vqLH9iQ5o/3OXJPkwJmIc9SS/FLX78I3ktyX5A0b1JkXvzNJzk5yZ5Lrusoel2RVkpvbz1377Lu01bk5ydLRRT0afdrm/Um+1f5ePptkQZ99p/zbG2d92uVtSdZ2/b28vM++U/53bNz1aZvPdLXLmiTf6LPvpv/OVJWfWfyhM0DjO8CTgUcB3wSesUGd/wZ8pC0fA3xmpuMeUdvsARzYlncGvt2jbZYAX5jpWGegbdYAu0+x/eXABUCAg4HLZjrmGWijbYHv03k55rz7nQF+FTgQuK6r7H3AqW35VOC9PfZ7HHBL+7lrW951pr/PCNrmMGC7tvzeXm3Ttk35tzfOnz7t8jbgj6fZb9r/jo37p1fbbLD9A8Cfb63fGXveZr+Hpwarqp8Ck1ODdTsaWN6WzwUOTZIRxjgjqur2qrqqLd8P3AjsObNRjY2jgU9Ux6XAgiR7zHRQI3Yo8J2qunWmA5kJVXUJcPcGxd3/liwHXtlj15cBq6rq7qq6B1gFHD6sOGdCr7apqn+qqvVt9VI67xudV/r8zgxikP+OjbWp2qb99/i1wKe31vlM3ma/PYHvda3fxsYJysN12j8u9wK7jSS6WaLdKn4WcFmPzc9L8s0kFyTZb7SRzZgC/inJlW3quA0N8ns11x1D/39M5+PvDMDCqrq9LX8fWNijjr878F/p9Fz3Mt3f3lz0++128tl9brXP99+ZFwF3VNXNfbZv8u+MyZvGXpKdgH8A3lBV922w+So6t8V+BfhL4HMjDm+mvLCqDgSOAE5K8qszHdBs0l72fRTw9z02z9ffmZ9Tnfs5vktqA0n+FFgPfKpPlfn2t3cm8BTgAOB2OrcH9fN+k6l73Tb5d8bkbfYbZGqwh+sk2Q7YBbhrJNHNsCTb00ncPlVV/7jh9qq6r6rWteXzge2T7D7iMEeuqta2n3cCn6Vz26LbfJ9y7gjgqqq6Y8MN8/V3prlj8vZ5+3lnjzrz9ncnyXHArwPHtuR2IwP87c0pVXVHVf2sqh4C/je9v+98/p3ZDngV8Jl+dTbnd8bkbfYbZGqwlcDkiK9XA1/u9w/LXNKeIzgLuLGqPtinzv81+fxfkufS+Z2f04ltkh2T7Dy5TOdB6+s2qLYSeF0bdXowcG/X7bL5oO//Cc/H35ku3f+WLAU+36POhcBhSXZtt8gOa2VzWpLDgf8OHFVVP+5TZ5C/vTllg2dl/296f9/5PMXlrwHfqqrbem3c7N+ZmR6h4WegUSwvpzOS8jvAn7ayd9D5RwRgBzq3f1YDXweePNMxj6hdXkjnts41wDfa5+XA7wG/1+r8PnA9ndFNlwLPn+m4R9AuT27f95vtu0/+znS3S4C/br9T1wKLZzruEbbPjnSSsV26yubd7wyd5PV24D/pPIN0Ap1nZS8Cbga+BDyu1V0MfKxr3//a/r1ZDRw/099lRG2zms5zW5P/1kyO8H8icH5b7vm3N1c+fdrlk+3fkGvoJGR7bNgubX2j/47NpU+vtmnlH5/8t6Wr7hb/zjg9liRJ0hjxtqkkSdIYMXmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJmyXJnya5vk2L840kB7XyNyT5ha14njVb8pLcJMcl+au2/HtJXjfTMW3iuZYk+cIozrXBeRck+W+jPq+k6Zm8SdpkSZ5H503zB1bVM+m8iHJy7sI3AFstedtUSbbtt62qPlJVnxhlPJtqqvhHbAFg8ibNQiZvkjbHHsAPq+pBgKr6YVX9e5I/pPMCyouTXAyQ5MwkV7ReurdPHqD1Xr09yVVJrk3y9Fa+W5J/avU/RueFwpP7fK5N3nx99wTOSdYl+UCSb9KZVP74JN9O8nXgBV313pbkj5M8sfUWTn5+luRJSR6f5B+SXN4+L5gupq5jvybJB9vyyUluactPTvK1tnxokqvb9z07yaO72uK9Sa4CXpPk8CTfauuv6nUBkmyb5C+SXNd6P/9ggHPs3pYXJ5noapOzk0wkuaVdQ4DTgae09nn/9L8SkkbF5E3S5vgnYO+WIP2vJC8GqKozgH8HDqmqQ1rdP62qxcAzgRcneWbXcX5YnQmZzwT+uJWdBny1qvajM8/fL3bV/69V9Ww6b/z/wyS7tfIdgcuqM5n8d4C300naXgg8Y8Pgq+rfq+qAqjqAznyM/1BVtwIfBj5UVc8B/h/gYwPENOmfgRe15RcBdyXZsy1fkmQHOm9b/42q2h/YDvj/uva/q7XF51pMrwCeDfxfPc4FcCKwCDig9X5+aoBz9PN04GV05lQ8LZ05g08FvtPa6U8GOIakETF5k7TJqjNx+7PpJBA/AD6TzqTdvby29SBdDezHzydT/9h+XkknEQH4VeBv23nOA+7pqv+HrXftUjoTXe/byn8G/ENbPgiYqKofVNVPmWJC6Naz9rt0pnuCzu3fv0ryDTpT/Tw2yU7TxEQr/z6wU5uncG/g79p+L6KT2P0S8N2q+nbbZXnbPmkyzqe3ejdXZwqcv+0T/q8BH62q9e38dw9wjn7Oq6oHq+qHdCajXzjAPpJmyHYzHYCk8VRVPwMmgIkk19KZyPzj3XWS7EOnR+05VXVPko/TmYt30oPt58+Y5t+jJEvoJCzPq6oft9t+k8f6SYtnYOlMqH0WnTmC17XibYCDq+onG9Qd9LD/AhwP3EQnYfuvwPOAU3gkOe3ngUFPspnW88j/sO+wwbYHu5anvRaSZpY9b5I2WZJfSrJvV9EBwK1t+X5g57b8WDpJyb1JFgJHDHD4S4Dfauc5Ati1le8C3NMSt6cDB/fZ/zI6t2d3a7f/XtMj/u2Bvwfe1NVLBZ3bwX/QVe+AaWLa0D/TSVYvodPTeAjwYFXdSyehW5Tkqa3u7wBf6XGMb7V6T2nrv9nnXKuA1yfZrsX1uGnOsYZObyl0bglPp/s6SppFTN4kbY6dgOVJbkhyDZ1boW9r25YBX0xycVV9k04S8y06txG/NsCx3w78apLr6Tys/2+t/IvAdklupPMw/aW9dq6q21ss/9rOd2OPas+n89zc27sGLTwR+ENgcRsAcAPwe9PEtKF/pnPL9JLWE/g94Kstrp/Q6ZX7+9ZT+RDwkR7x/4TO7ejz2u3mO/uc62MtjmvareTfmuYcbwc+nOQKOr1rU6qqu4CvtQERDliQZpF0HqmQJEnSOLDnTZIkaYyYvEmSJI0RkzdJkqQxYvImSZI0RkzeJEmSxsicfBHj7rvvXosWLRr6eR544AF23HHHoZ9nvrFdh8N2HQ7bdThs1+GwXYdjS9v1yiuv/GFVPX7Q+nMyeVu0aBFXXHHF0M8zMTHBkiVLhn6e+cZ2HQ7bdThs1+GwXYfDdh2OLW3XJLdOX+sR3jaVJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI0RkzdJkqQxYvImSZI0Rubkq0LGwaJTz9uobM3pR85AJJIkaZzY8yZJkjRGTN4kSZLGiMmbJEnSGDF5kyRJGiMOWBiBXoMTJEmSNoc9b5IkSWPE5E2SJGmMmLxJkiSNEZM3SZKkMWLyJkmSNEZM3iRJksaIyZskSdIYMXmTJEkaIyZvkiRJY8TkTZIkaYyYvEmSJI2RoSZvSdYkuTbJN5Jc0coel2RVkpvbz11beZKckWR1kmuSHNh1nKWt/s1Jlg4zZkmSpNlsFD1vh1TVAVW1uK2fClxUVfsCF7V1gCOAfdvnROBM6CR7wGnAQcBzgdMmEz5JkqT5ZiZumx4NLG/Ly4FXdpV/ojouBRYk2QN4GbCqqu6uqnuAVcDhI45ZkiRpVkhVDe/gyXeBe4ACPlpVy5L8qKoWtO0B7qmqBUm+AJxeVV9t2y4C3gQsAXaoqne18j8D/qOq/mKDc51Ip8eOhQsXPnvFihVD+16T1q1bx0477TRtvWvX3jvQ8fbfc5ctDWlOGLRdtWls1+GwXYfDdh0O23U4trRdDznkkCu77lBOa7vNPtNgXlhVa5M8AViV5FvdG6uqkmyV7LGqlgHLABYvXlxLlizZGoed0sTEBIOc57hTzxvoeGuOnf5Y88Gg7apNY7sOh+06HLbrcNiuwzHqdh3qbdOqWtt+3gl8ls4za3e026G0n3e26muBvbt236uV9SuXJEmad4aWvCXZMcnOk8vAYcB1wEpgcsToUuDzbXkl8Lo26vRg4N6quh24EDgsya5toMJhrUySJGneGeZt04XAZzuPtbEd8HdV9cUklwPnJDkBuBV4bat/PvByYDXwY+B4gKq6O8k7gctbvXdU1d1DjFuSJGnWGlryVlW3AL/So/wu4NAe5QWc1OdYZwNnb+0YJUmSxo0zLEiSJI0RkzdJkqQxYvImSZI0RkzeJEmSxojJmyRJ0hgxeZMkSRojJm+SJEljxORNkiRpjJi8SZIkjZFNSt6SbJPkscMKRpIkSVObNnlL8ndJHtsml78OuCHJnww/NEmSJG1okJ63Z1TVfcArgQuAfYDfGWZQkiRJ6m2Q5G37JNvTSd5WVtV/AjXUqCRJktTTdgPU+SiwBvgmcEmSJwH3DTOo+WrRqedtVLbm9CNnIBJJkjRbTZu8VdUZwBldRbcmOWR4IUmSJKmfQQYsLExyVpIL2vozgKVDj0ySJEkbGeSZt48DFwJPbOvfBt4wpHgkSZI0hUGSt92r6hzgIYCqWg/8bKhRSZIkqadBkrcHkuxGG2Ga5GDg3qFGJUmSpJ4GGW36RmAl8JQkXwMeD7x6qFFJkiSpp2l73qrqKuDFwPOB1wP7VdU1g54gybZJrk7yhba+T5LLkqxO8pkkj2rlj27rq9v2RV3HeHMrvynJyzbxO0qSJM0ZfXvekryqz6anJaGq/nHAc5wM3AhMzon6XuBDVbUiyUeAE4Az2897quqpSY5p9X6jjW49BtiPzqCJLyV5WlX53J0kSZp3prpt+oopthUwbfKWZC/gSODdwBuTBHgJ8FutynLgbXSSt6PbMsC5wF+1+kcDK6rqQeC7SVYDzwX+dbrzS5IkzTV9k7eqOn4rHP9/Av8d2Lmt7wb8qI1YBbgN2LMt7wl8r517fZJ7W/09gUu7jtm9jyRJ0rwy7YCFJP8DeF9V/ait7wqcUlVvnWa/XwfurKorkyzZ8lCnjfNE4ESAhQsXMjExMexTsm7duoHOc8r+66et088ovsdsM2i7atPYrsNhuw6H7ToctutwjLpdBxltekRVvWVyparuSfJyYMrkDXgBcFSruwOdZ94+DCxIsl3rfdsLWNvqrwX2Bm5Lsh2wC3BXV/mk7n0eVlXLgGUAixcvriVLlgzw1bbMxMQEg5znuB5zlg5qzbHTH3+uGbRdtWls1+GwXYfDdh0O23U4Rt2ug7znbdskj55cSfIY4NFT1Aegqt5cVXtV1SI6Aw6+XFXHAhfzyKtGlgKfb8sreWTarVe3+tXKj2mjUfcB9gW+PkDckiRJc84gPW+fAi5K8jdt/Xg6Aw0215uAFUneBVwNnNXKzwI+2QYk3E0n4aOqrk9yDnADsB44yZGmkiRpvpo2eauq9ya5Bji0Fb2zqi7clJNU1QQw0ZZvoTNadMM6PwFe02f/d9MZsSpJkjSvDdLzRlVdAFww5FgkSZI0jale0vvVqnphkvtp85pObgKqqh7bZ1dJkiQNyVTveXth+7lzvzqSJEkarWlHmyb55CBlkiRJGr5BXhWyX/dKewfbs4cTjiRJkqbSN3lL8ub2vNszk9zXPvcDd/DIu9kkSZI0Qn2Tt6p6D51ZDj5RVY9tn52rareqevPoQpQkSdKkKW+bVtVDwHNGFIskSZKmMcgzb1clMYGTJEmaBQZ5Se9BwLFJbgUe4JH3vD1zqJFJkiRpI4Mkby8behSSJEkayCBzm94KkOQJwA5Dj0iSJEl9TZu8JTkK+ADwROBO4EnAjWzw/jd1LDr1vJkOQZIkzWGD3DZ9J3Aw8KWqelaSQ4DfHm5YmtQrGVxz+pEzEIkkSZoNBhlt+p9VdRewTZJtqupiYPGQ45IkSVIPg/S8/SjJTsAlwKeS3Eln1KkkSZJGbJCet6OBHwN/BHwR+A7wimEGJUmSpN4GGW062cv2ELB8uOFIkiRpKoP0vEmSJGmWMHmTJEkaI32TtyQXtZ/vHV04kiRJmspUPW97JHk+cFSSZyU5sPsz3YGT7JDk60m+meT6JG9v5fskuSzJ6iSfSfKoVv7otr66bV/Udaw3t/KbkjhdlyRJmremGrDw58CfAXsBH9xgWwEvmebYDwIvqap1SbYHvprkAuCNwIeqakWSjwAnAGe2n/dU1VOTHAO8F/iNJM8AjqEzo8MTgS8leVpV/WyTvqkkSdIc0LfnrarOraojgPdV1SEbfKZL3KiOdW11+/aZTPrObeXLgVe25aN5ZDTrucChSdLKV1TVg1X1XWA18NxN+paSJElzRKpq+kqd+U1/ta1OVNUXBjp4si1wJfBU4K+B9wOXVtVT2/a9gQuq6peTXAccXlW3tW3fAQ4C3tb2+dtWflbb59wNznUicCLAwoULn71ixYpBQtwi69atY6eddvq5smvX3jv08+6/5y5DP8dM6tWu2nK263DYrsNhuw6H7TocW9quhxxyyJVVNfDsVYNMTP8eOj1dn2pFJyd5flW9Zbp9263NA5IsAD4LPH3QwDZVVS0DlgEsXry4lixZMqxTPWxiYoINz3PcCCamX3PskmnrjLNe7aotZ7sOh+06HLbrcNiuwzHqdh1keqwjgQOq6iGAJMuBq4Fpk7dJVfWjJBcDzwMWJNmuqtbTeZ5ubau2FtgbuC3JdsAuwF1d5ZO695EkSZpXBn3P24Ku5YHu2SV5fOtxI8ljgJcCNwIXA69u1ZYCn2/LK9s6bfuXq3NPdyVwTBuNug+wL/D1AeOWJEmaUwbpeXsPcHXrOQudZ99OHWC/PYDl7bm3bYBzquoLSW4AViR5F50evLNa/bOATyZZDdxNZ4QpVXV9knOAG4D1wEmONJUkSfPVIHObfjrJBPCcVvSmqvr+APtdAzyrR/kt9BgtWlU/AV7T51jvBt493TklSZLmukF63qiq2+ncvtQssKjHoIg1px85A5FIkqRRc25TSZKkMWLyJkmSNEamTN6SbJvkW6MKRpIkSVObMnlrozpvSvKLI4pHkiRJUxhkwMKuwPVJvg48MFlYVUcNLSpJkiT1NEjy9mdDj0KSJEkDGeQ9b19J8iRg36r6UpJfALYdfmiSJEna0LSjTZP8LnAu8NFWtCfwuSHGJEmSpD4GeVXIScALgPsAqupm4AnDDEqSJEm9DZK8PVhVP51cSbIdUMMLSZIkSf0Mkrx9JclbgMckeSnw98D/GW5YkiRJ6mWQ5O1U4AfAtcDrgfOBtw4zKEmSJPU2yGjTh5IsBy6jc7v0pqrytqkkSdIMmDZ5S3Ik8BHgO0CAfZK8vqouGHZwkiRJ+nmDvKT3A8AhVbUaIMlTgPMAkzdJkqQRG+SZt/snE7fmFuD+IcUjSZKkKfTteUvyqrZ4RZLzgXPoPPP2GuDyEcSmTbDo1PM2Kltz+pEzEIkkSRqmqW6bvqJr+Q7gxW35B8BjhhaRJEmS+uqbvFXV8aMMRJIkSdMbZLTpPsAfAIu661fVUcMLS5IkSb0MMmDhc8Aa4C/pjDyd/Ewpyd5JLk5yQ5Lrk5zcyh+XZFWSm9vPXVt5kpyRZHWSa5Ic2HWspa3+zUmWbvrXlCRJmhsGeVXIT6rqjM049nrglKq6KsnOwJVJVgHHARdV1elJTqUzg8ObgCOAfdvnIOBM4KAkjwNOAxbTGTBxZZKVVXXPZsQkSZI01gbpeftwktOSPC/JgZOf6Xaqqtur6qq2fD9wI7AncDSwvFVbDryyLR8NfKI6LgUWJNkDeBmwqqrubgnbKuDwTfiOkiRJc0amm+kqyXuA36Ezw8JDrbiq6iUDnyRZBFwC/DLwb1W1oJUHuKeqFiT5AnB6VX21bbuITo/cEmCHqnpXK/8z4D+q6i82OMeJwIkACxcufPaKFSsGDW+zrVu3jp122unnyq5de+/Qzzuo/ffcZaZD2Cy92lVbznYdDtt1OGzX4bBdh2NL2/WQQw65sqoWD1p/kNumrwGeXFU/3ZyAkuwE/APwhqq6r5OvdVRVJdkq86RW1TJgGcDixYtryZIlW+OwU5qYmGDD8xzX431rM2XNsUtmOoTN0qtdteVs1+GwXYfDdh0O23U4Rt2ug9w2vQ5YsDkHT7I9ncTtU1X1j634jnY7lPbzzla+Fti7a/e9Wlm/ckmSpHlnkORtAfCtJBcmWTn5mW6ndkv0LODGqvpg16aVwOSI0aXA57vKX9dGnR4M3FtVtwMXAocl2bWNTD2slUmSJM07g9w2PW0zj/0COs/KXZvkG63sLcDpwDlJTgBuBV7btp0PvBxYDfwYOB6gqu5O8k4emZLrHVV192bGNK84ZZYkSXPPtMlbVX1lcw7cBh6kz+ZDe9Qv4KQ+xzobOHtz4pAkSZpLBplh4X4671cDeBSwPfBAVT12mIFJkiRpY4P0vO08udyeYzsaOHiYQUmSJKm3QQYsPKy9QPdzdF6cK0mSpBEb5Lbpq7pWt6EzTdVPhhaRJEmS+hpktOkrupbX05mk/uihRCNJkqQpDfLM2/GjCESSJEnT65u8JfnzKfarqnrnEOLRkPnuN0mSxttUPW8P9CjbETgB2A0weZMkSRqxvslbVX1gcjnJzsDJdGY9WAF8oN9+kiRJGp4pn3lL8jjgjcCxwHLgwKq6ZxSBjYNr197LcT1uQ0qSJA3LVM+8vR94FbAM2L+q1o0sKkmSJPU01Ut6TwGeCLwV+Pck97XP/UnuG014kiRJ6jbVM2+bNPuCJEmShs8ETZIkaYwMMsOC5jjf/SZJ0viw502SJGmMmLxJkiSNEZM3SZKkMWLyJkmSNEYcsKCeeg1iAAcySJI00+x5kyRJGiNDS96SnJ3kziTXdZU9LsmqJDe3n7u28iQ5I8nqJNckObBrn6Wt/s1Jlg4rXkmSpHEwzJ63jwOHb1B2KnBRVe0LXNTWAY4A9m2fE4EzoZPsAacBBwHPBU6bTPgkSZLmo6Elb1V1CXD3BsVHA8vb8nLglV3ln6iOS4EFSfYAXgasqqq7q+oeYBUbJ4SSJEnzxqgHLCysqtvb8veBhW15T+B7XfVua2X9yjeS5EQ6vXYsXLiQiYmJrRd1HwsfA6fsv37o55lNRtGu69atG8l55hvbdThs1+GwXYfDdh2OUbfrjI02rapKUlvxeMuAZQCLFy+uJUuWbK1D9/WXn/o8H7h2ng3YvfaBjYq29gjUiYkJRnH95hvbdThs1+GwXYfDdh2OUbfrqEeb3tFuh9J+3tnK1wJ7d9Xbq5X1K5ckSZqXRp28rQQmR4wuBT7fVf66Nur0YODednv1QuCwJLu2gQqHtTJJkqR5aWj3/JJ8GlgC7J7kNjqjRk8HzklyAnAr8NpW/Xzg5cBq4MfA8QBVdXeSdwKXt3rvqKoNB0FIkiTNG0NL3qrqN/tsOrRH3QJO6nOcs4Gzt2JokiRJY8sZFiRJksaIyZskSdIYMXmTJEkaIyZvkiRJY2SevWFWw7Do1PM2KtvaL+6VJEkd9rxJkiSNEZM3SZKkMWLyJkmSNEZ85k1D4XNwkiQNhz1vkiRJY8TkTZIkaYx421Qj461USZK2nD1vkiRJY8SeN82oXr1xHz98xxmIRJKk8WDPmyRJ0hix502zzrVr7+W4DXrkfDZOkqQOe94kSZLGiD1vGguOVJUkqcPkTWPLhE6SNB+ZvGlO6ZXQ9WKSJ0kaVz7zJkmSNEbGpuctyeHAh4FtgY9V1ekzHJLG2KA9dJvC3jxJ0iiMRfKWZFvgr4GXArcBlydZWVU3zGxk0iO2JCE08ZMkDWoskjfgucDqqroFIMkK4GjA5E1zwjB6Ajd0yv7rN3p/Xi8mkpI0u41L8rYn8L2u9duAg7orJDkROLGtrkty0wji2h344QjOM6/8oe06FIO2a947gmDmFn9fh8N2HQ7bdTi2tF2ftCmVxyV5m1ZVLQOWjfKcSa6oqsWjPOd8YLsOh+06HLbrcNiuw2G7Dseo23VcRpuuBfbuWt+rlUmSJM0r45K8XQ7sm2SfJI8CjgFWznBMkiRJIzcWt02ran2S3wcupPOqkLOr6voZDgtGfJt2HrFdh8N2HQ7bdThs1+GwXYdjtI9tVdUozydJkqQtMC63TSVJkoTJmyRJ0lgxeeshyeFJbkqyOsmpPbY/Osln2vbLkizq2vbmVn5TkpeNNPBZbnPbNcluSS5Osi7JX4088FluC9r1pUmuTHJt+/mSkQc/i21Buz43yTfa55tJ/u+RBz+Lbcm/r237L7Z/C/54ZEGPgS34fV2U5D+6fmc/MvLgZ7EtzAeemeRfk1zf/p3dYasFVlV+uj50BkR8B3gy8Cjgm8AzNqjz34CPtOVjgM+05We0+o8G9mnH2Xamv9Ns+Gxhu+4IvBD4PeCvZvq7zKbPFrbrs4AntuVfBtbO9PeZLZ8tbNdfALZry3sAd06uz/fPlrRr1/Zzgb8H/nimv89s+Wzh7+si4LqZ/g6z8bOF7bodcA3wK219t62ZD9jztrGHp+Kqqp8Ck1NxdTsaWN6WzwUOTZJWvqKqHqyq7wKr2/G0Be1aVQ9U1VeBn4wu3LGxJe16dVX9eyu/HnhMkkePJOrZb0va9cdVtb6V7wA4KuwRW/LvK0leCXyXzu+rHrFF7aq+tqRdDwOuqapvAlTVXVX1s60VmMnbxnpNxbVnvzrtH+l76WTVg+w7X21Ju6q/rdWu/w9wVVU9OKQ4x80WtWuSg5JcD1wL/F5XMjffbXa7JtkJeBPw9hHEOW629N+BfZJcneQrSV407GDHyJa069OASnJhkquS/PetGdhYvOdN0vAk2Q94L53/U9RWUFWXAfsl+S/A8iQXVJU9x1vmbcCHqmqdHUZb1e3AL1bVXUmeDXwuyX5Vdd9MBzbmtqPzuM9zgB8DFyW5sqou2hoHt+dtY4NMxfVwnSTbAbsAdw2473y1Je2q/raoXZPsBXwWeF1VfWfo0Y6PrfL7WlU3AuvoPFOoLWvXg4D3JVkDvAF4Szovb9cWtGt7zOcugKq6ks4zXk8besTjYUt+X28DLqmqH1bVj4HzgQO3VmAmbxsbZCqulcDStvxq4MvVeSJxJXBMG32yD7Av8PURxT3bbUm7qr/NbtckC4DzgFOr6mujCnhMbEm77tP+ESfJk4CnA2tGE/ast9ntWlUvqqpFVbUI+J/A/6gqR593bMnv6+OTbAuQ5Ml0/rt1y4jinu225L9bFwL7J/mF9u/Bi4EbtlpkMz2aYzZ+gJcD36bzfyB/2sreARzVlnegM9ppNZ3k7Mld+/5p2+8m4IiZ/i6z6bOF7boGuJtOL8ZtbDDiZz5/NrddgbcCDwDf6Po8Yaa/z2z5bEG7/g6dB+q/AVwFvHKmv8ts+mzJvwNdx3gbjjbdKu1K53nX7t/XV8z0d5lNny3879Zvt7a9Dnjf1ozL6bEkSZLGiLdNJUmSxojJmyRJ0hgxeZMkSRojJm+SJEljxORNkiRpjJi8SRpYknU9yt6Y5IYk1yS5qL3bbNZJclySv2rLv5fkdVvhmGuS7L7l0Q10riVJvjCKc21w3gVJ/tuozyupP5M3SVvqamBxVT2TzsTM79vaJ5h86e3WUlUfqapPbM1jbm2TL06dBRYAJm/SLGLyJmmLVNXF1Zn+BeBSOlPIbCTJx5OckeRfktyS5NWtPEnen+S6JNcm+Y1WviTJPydZCdzQ1r+S5PNt/9OTHJvk622/p7T9XpHksjbR9peSLOwRy9uS/HGSJyb5RtfnZ0me1N46/w9JLm+fF7T9dkvyT0muT/IxYKNJNpO8JskH2/LJSW5py09O8rW2fGiL79okZyd5dCtfk+S9Sa4CXpPk8CTfauuv6tOu2yb5i9Z+1yT5gwHOsXtbXpxkoqtNzk4y0dr3D9spTgee0trn/X1/ESSNjMmbpK3pBOCCKbbvQWey5l+nkxRAJyk5APgV4NeA9yfZo207EDi5qibnWvwV4PeA/0JnJoOnVdVzgY8Bf9DqfBU4uKqeBawA/nu/YKrq36vqgKo6APjfwD9U1a3Ah+lMgv4cOm+g/1jb5TTgq1W1H505YX+xx2H/GXhRW34RcFeSPdvyJUl2AD4O/EZV7U9nAuv/r2v/u6rqQOBzLaZXAM8G/q8+X+NEYBFwQOv9/NQA5+jn6cDLgOcCpyXZHjgV+E5rpz8Z4BiShszkTdJWkeS3gcXAVL0zn6uqh6rqBmCyR+yFwKer6mdVdQfwFeA5bdvXq+q7XftfXlW3V9WDdKar+adWfi2dBAY6PX8XJrkW+BNgvwFifwHwu8B/bUW/BvxVkm/QmbvwsUl2An4V+FuAqjoPuGfDY1XV94GdkuxMZ8Lqv2v7vYhOYvdLwHer6tttl+Vt+6TPtJ9Pb/Vurs5UOH/bJ/xfAz5aVevb+e8e4Bz9nFedicp/CNzJI9dI0ixi8iZpiyX5NTrz+h7VEiuSvHvydmRX1Qe7dxvg0A9ssN69/0Nd6w/R6V0C+Evgr1qP0+vpzD04Vex7AGcBr62qyQEZ29DpvTugffbs2jaIfwGOpzPH8WRP3POArw2w74bfeWtbzyP/9m/YNt3t+zMeaVNJs4jJm6QtkuRZwEfpJG53TpZX1Z923ZKcyj8Dv/H/t2/HKnFFQQCG/2lFEQM2qQR9gBSpLATLPIILYpdUppCQtNlHsLPIG2whBFIFUrjYBCXsgmIZEtMELcTGFPGkOCMsC7ubjRC88H9w4d7LcM4ph5kzeXdrkVoh+nyPI80DP/J9a1xgtgU7wJuBKhXUit72QNyTfD0AWvnvGbAwYuku8CrjvwDrwK9SyhU1oVuKiJWM3aRWG4edZdxyfm+M2Osj8OJuqCMiHk3Y4yu1DQu1JTzJNTD3F3GS/hOTN0nTmImI84Fnh9omnQU6WWl7P+Wa+0Af6AGfgNfZevxXb/Msx8DFhNhVaqu3PTC08Bh4CTzNAYBT6j07gDawFhEn1Lt630as26W2TA9KKb+B79S7eJRSbqhVuU62dm+BveEFMu458CEHFn4Ox6R3eY5+RPSA1oQ92sBuRBxRq2tjlVIugcMciHBgQXoAol6lkCRJUhNYeZMkSWoQkzdJkqQGMXmTJElqEJM3SZKkBjF5kyRJahCTN0mSpAYxeZMkSWqQP2KY19126huIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare different results\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 15))\n",
    "# fig.tight_layout();\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Article word count\")\n",
    "ax1.set_ylabel(\"Number of articles\")\n",
    "news[\"minmax\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Min-max scaled word count\")\n",
    "ax2.set_ylabel(\"Number of articles\")\n",
    "news[\"standardized\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_xlabel(\"Standardized word count\")\n",
    "ax3.set_ylabel(\"Number of articles\")\n",
    "news[\"normalized\"].hist(ax=ax4, bins=100)\n",
    "ax4.set_xlabel(\"L2-normalized word count\")\n",
    "ax4.set_ylabel(\"Number of articles\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fef108e-a831-4454-a8be-f9bb90bdd0d9",
   "metadata": {},
   "source": [
    "### Power Transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c154449a-6dc6-4707-b3fd-39ea0c2ae3e3",
   "metadata": {},
   "source": [
    "For investigating power transforms, we will look at an example we already discussed during our exploratory data analysis: the count of ratings per movie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2eba9b9-c768-46f7-9171-f94aa2c12896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 855598 entries, 0 to 855597\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   userID       855598 non-null  int64  \n",
      " 1   movieID      855598 non-null  int64  \n",
      " 2   rating       855598 non-null  float64\n",
      " 3   date_day     855598 non-null  int64  \n",
      " 4   date_month   855598 non-null  int64  \n",
      " 5   date_year    855598 non-null  int64  \n",
      " 6   date_hour    855598 non-null  int64  \n",
      " 7   date_minute  855598 non-null  int64  \n",
      " 8   date_second  855598 non-null  int64  \n",
      "dtypes: float64(1), int64(8)\n",
      "memory usage: 58.7 MB\n"
     ]
    }
   ],
   "source": [
    "# read in rating data\n",
    "ratings = pd.read_csv(\n",
    "    os.path.join(data_dir, \"hetrec/user_ratedmovies.dat\"), delimiter=\"\\t\"\n",
    ")\n",
    "ratings.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "513fc696-bc60-462c-9166-8a5f53f578b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# asymmetric, skewed distribution\n",
    "# number of ratings per movie\n",
    "rating_counts = ratings.groupby(\"movieID\")[\"rating\"].agg(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "061330ae-f08f-4b3f-8059-f7a575a2e3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of number of movies with given number of ratings\n",
    "# (binned value counts)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "axes[0].hist(rating_counts, bins=100)\n",
    "axes[0].set_ylabel(\"Number of Movies\")\n",
    "axes[0].set_xlabel(\"Number of Ratings\")\n",
    "axes[1].hist(rating_counts, log=True, bins=100)\n",
    "axes[1].set_ylabel(\"Number of Movies (log)\")\n",
    "axes[1].set_xlabel(\"Number of Ratings\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13a1272-f61c-486d-bd0a-7ad95dcd627e",
   "metadata": {},
   "source": [
    "Another example is the number of words in an article as already shown previously as part of the news popularity dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e89f79a5-ecfd-4d19-8499-9ed7b650c745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift + 1 due to log(0)\n",
    "news[\"log_n_tokens_content\"] = np.log10(news[\"n_tokens_content\"] + 1)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Number of Words in Article\")\n",
    "ax1.set_ylabel(\"Number of Articles\")\n",
    "news[\"log_n_tokens_content\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Log of Number of Words\")\n",
    "ax2.set_ylabel(\"Number of Articles\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8dfed8b5-cf59-4186-8f22-39b0013bf844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38045297261832045"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# box-cox transform\n",
    "# again, +1 as boxcox expects data to be positive\n",
    "# log transform\n",
    "news[\"n_tokens_content_lmbda0\"] = stats.boxcox(\n",
    "    news[\"n_tokens_content\"] + 1, lmbda=0\n",
    ")\n",
    "\n",
    "# as close to normal distribution as possible (optimal box-cox transform)\n",
    "# If the lmbda parameter is None, the second returned argument\n",
    "# is the lambda that maximizes the log-likelihood function.\n",
    "values, lambda_param = stats.boxcox(news[\"n_tokens_content\"] + 1)\n",
    "news[\"n_tokens_content_opt\"] = values\n",
    "lambda_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "792ae313-f920-4ddd-8f2d-d5fd6921ba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 15))\n",
    "\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_title(\"Article Length Histogram\")\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_lmbda0\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_title(\"Log Transformed Length Histogram\")\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_opt\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_yscale(\"log\")\n",
    "ax3.set_title(\"Box-Cox Transformed Length Histogram\")\n",
    "ax3.set_xlabel(\"\")\n",
    "ax3.set_ylabel(\"Occurrence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d978ee33-f2e4-4941-a369-f053466316de",
   "metadata": {},
   "source": [
    "### Discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be77ad3-8608-4577-a57c-6bf757a9e75a",
   "metadata": {},
   "source": [
    "In a first step, we will look at synthetic count data: we create uniformly distributed random counts (once small values, once with a wide range)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a72e595e-dbf2-4247-b9cc-4a258ea6e3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60, 85, 52, 68, 32, 46, 54, 98, 59, 91, 64, 12, 42, 94, 60, 64, 23,\n",
       "       65, 40, 37])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uniformly distributed, small values\n",
    "small_counts = np.random.randint(0, 100, 20)\n",
    "small_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82b66c99-f869-4330-a93e-522ec78ea7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 8, 5, 6, 3, 4, 5, 9, 5, 9, 6, 1, 4, 9, 6, 6, 2, 6, 4, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning by division\n",
    "np.floor_divide(small_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70968305-2047-46fe-a71c-ea4e87d2fe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counts spanning a wide value range\n",
    "large_counts = [\n",
    "    296,\n",
    "    8286,\n",
    "    64011,\n",
    "    80,\n",
    "    3,\n",
    "    725,\n",
    "    867,\n",
    "    2215,\n",
    "    7689,\n",
    "    11495,\n",
    "    91897,\n",
    "    44,\n",
    "    28,\n",
    "    7971,\n",
    "    926,\n",
    "    122,\n",
    "    22222,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03396b9f-c3bb-4f18-8930-30253bf4470b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 3., 4., 1., 0., 2., 2., 3., 3., 4., 4., 1., 1., 3., 2., 2., 4.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning via powers of 10 (0-9, 10-99, 100-999, 1000-999, etc.)\n",
    "np.floor(np.log10(large_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6e9522-b474-4d1e-8a90-3952acceae56",
   "metadata": {},
   "source": [
    "In the next step, we look at quantile binning to avoid empty bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "09f2515a-439a-40f9-a350-dffeda1b27a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1      2.0\n",
       "0.2      5.0\n",
       "0.3      8.0\n",
       "0.4     13.0\n",
       "0.5     21.0\n",
       "0.6     34.0\n",
       "0.7     56.0\n",
       "0.8    104.0\n",
       "0.9    230.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute 10 deciles\n",
    "deciles = rating_counts.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "deciles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c6a23d2-0fb2-4e87-b158-9833176856cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieID\n",
       "1        (230.0, 1670.0]\n",
       "2        (230.0, 1670.0]\n",
       "3        (230.0, 1670.0]\n",
       "4           (34.0, 56.0]\n",
       "5         (104.0, 230.0]\n",
       "              ...       \n",
       "65088       (0.999, 2.0]\n",
       "65091       (0.999, 2.0]\n",
       "65126       (0.999, 2.0]\n",
       "65130       (0.999, 2.0]\n",
       "65133         (2.0, 5.0]\n",
       "Name: rating, Length: 10109, dtype: category\n",
       "Categories (10, interval[float64, right]): [(0.999, 2.0] < (2.0, 5.0] < (5.0, 8.0] < (8.0, 13.0] ... (34.0, 56.0] < (56.0, 104.0] < (104.0, 230.0] < (230.0, 1670.0]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actually use deciles to bin data\n",
    "pd.qcut(rating_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe53b730-6fe8-41b4-b70d-0eec41627b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f518eadb580>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
     "execution_count": 22,
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     "output_type": "execute_result"
    },
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       "<matplotlib.legend.Legend at 0x7f518e4e7b50>"
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       "Text(0.5, 0, 'Review Count (log)')"
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     "execution_count": 22,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the deciles on the histogram\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots()\n",
    "rating_counts.hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color=\"r\")\n",
    "    ax.legend([handle], [\"deciles\"])\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xlabel(\"Review Count (log)\")\n",
    "    ax.set_ylabel(\"Occurrence (log)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "752c20ea-849a-4c99-886f-4c202214f0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f518e49ce20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5195343f40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5195343d30>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f519533ccd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f519535fc40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f519533ce20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f519533c700>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5193f06bb0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5193f06940>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the deciles on the histogram - now with x-axis not on log scale\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots()\n",
    "rating_counts.hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color=\"r\")\n",
    "    ax.legend([handle], [\"deciles\"])\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_xlabel(\"Review Count (log)\")\n",
    "    ax.set_ylabel(\"Occurrence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2154d441-f801-4276-ae79-d9b69b247ffa",
   "metadata": {},
   "source": [
    "### Discretization: Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaed8c0-d909-4bc4-992e-053d8af6cf65",
   "metadata": {},
   "source": [
    "In the following, we will dive into clustering algorithms based on a synthetic data set randomly created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "efc4e87c-3afb-40b5-bc6c-399b934c87c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y_true = sklearn.datasets.make_blobs(\n",
    "    n_samples=5000, centers=20, cluster_std=0.6, random_state=0\n",
    ")\n",
    "plt.scatter(X[:, 0], X[:, 1], s=50)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e4f7eb-8915-4dc5-a88c-a2b139938c50",
   "metadata": {},
   "source": [
    "Now we can compute k-means and also experiment with the number of clusters to be chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd875714-cc16-4f7b-8955-3cec153fbfb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=200, n_clusters=5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute kmeans, repeat 10 times\n",
    "# init could also be 'kmeans++'\n",
    "kmeans = KMeans(n_clusters=5, init=\"k-means++\", n_init=10, max_iter=200)\n",
    "kmeans.fit(X)\n",
    "y_kmeans = kmeans.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d0caecc-89cb-4541-9d8c-ed636943cdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize results\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap=\"Set1\")\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c=\"black\", s=200, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f30522f-a728-4278-bc9f-291c0d269ac8",
   "metadata": {},
   "source": [
    "For determining the number of clusters k, we can compute the sum of squared errors of each point to their closest cluster center for different k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e19fa6c-f455-4055-87af-9d2a4b5c37ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZOnUqAHFxcXz22WfdevyuyBBfqusbKSyrtXcpQghhMV265uLn58dzzz1nkQO+/fbbZGRkcPPNNzN//nz8/PwwGAwMHTrUvI5er8dgMADND3Be2p6fn09ZWRm+vr64uLiY12lZ32AwEBIS0vziXFzw8fGhrKyMwMBAi9RvaSVVzWOLRa36iFB/T+bFRZA4PNTOVQkhxLXpNFxefPFFFi5cyO9+97t2l7/22mtXdLAHHniAmTNnoigKf/rTn1ixYgUvvfTSFe3DkWQcLOLVj783/1xUXsuCtMMAEjBCiOtap+GSkJAAwCOPPGKRg/Xs2dP8fXJysjm09Ho9xcXF5mUGgwG9vnm8rfbaAwICqKyspLGxERcXF4qLi83r6/V6zp07R3BwMI2NjVRVVXXbMdFW7ThOXUNTq7baBhOrdhyXcBFCXNc6veZy8803A3D06FFGjBjR6uvo0aNXfLCSkhLz97t27TI/JxMdHU1WVhZGo5EzZ85QUFDALbfcwpAhQygoKODMmTMYjUaysrKIjo5GURRGjhzJjh07AEhPTyc6Otq8r/T0dAB27NjBqFGjUBTlimu1hbPl7V9n6ahdCCGuF1265pKRkcHDDz/cqi09Pb1N26Xmzp3L/v37KSsrIyoqiieeeIL9+/dz7NgxAEJDQ1myZAkA4eHh3HvvvUycOBGtVsuiRYvQarVA8zM2jz32GCaTifvuu88cSPPmzeOpp55izZo1REZGkpycDMC0adOYN28eMTEx+Pn58corr1zhW2I7vf09KWonSGQIfiHE9U5RO7mVatu2bWzbto0vvviC22+/3dxeU1ODRqNh06ZNNinSFpKSkkhLS7PpMTMOFrEg7TC1DSZzm4erhhVJt8hpMSHEdaGjz85Oey7Dhw+nV69elJWVtbru4uXlRUREhOWrdDItAbJqx3HOlteiAhN+oZNgEUJc9zoNl9DQUIKDg3Fzc2PEiBG2qsmpJA4PNYfJI28e4JPvS6mub8TbXUZJFkJcv372IUqtVotGo6GqSia1srYnJ4RTfqGBf3xWYO9ShBDimnTpz+MePXoQHx/PnXfeSY8ePcztlnqwUjQbGubP3RG9+FvuSR4efSNe0nsRQlynuvTpFRsbS2xsrLVrETT3Xqb+9VPe+vw//G7cTfYuRwghrkqXwmXq1KkYjUYKCgoA6NevH66urtasy2kN7xNA1MDm3stDo/vKDJVCiOtSlwau3LdvH3FxcSxZsoQXXniBuLg4Dhw4YO3anNaTEwZQWmPk/z7/j71LEUKIq9KlP4tXrlzJ66+/Tv/+/QE4deoUTz/9tM2fC3EWt/UNZOyAnmzIPcmDo27E001r75KEEOKKdKnn0tDQYA4WaD4t1tDQYLWiBDx5Tzg/Vht5e5/0XoQQ158u9VxuvvlmFi5cyJQpUwB4//33zeOOCeu448ZA7rwpiPW5J/ntqL54uErvRQhx/ehSz+WFF15gwIABvPXWW7z11lsMGDCAF154wdq1Ob0nJ4TzQ1U9/9x32t6lCCHEFelSz2Xz5s2kpKSQkpJibtu0aVOnA1eKazeyfxCj+gfy2p7v+c3IPtJ7EUJcN7rUc8nIyGjT1jKsvbCuJycMpKSqnnf2S+9FCHH96LTn0jIqcmFhYavZKGtqavDz87N6cQJG3xTEiH6BvLrne349QnovQojrg4yKfB2YMyGc32zcx5YvzvDQ6BvtXY4QQvysnx0VOTQ0lDfeeAMPDw80Gg2nTp3i5MmTDBw40FY1Or3RNwVxx40BvPrx9/zqjjDcXaT3IoTo3rp0zeW3v/0t9fX1GAwGHn30UTIzM5k/f761axMXKYrCkxMGcq6iji1fFNq7HCGE+FldChdVVfH09CQ7O5sHHniAP//5z5w4ccLatYlLjBkQxG19A3j1oxMYG5vsXY4QQnSqy+Fy8OBB3n//fe6++24AmprkA86WFEVh9oRwzlbUkfql9F6EEN1bl8Ll2WefZf369dxzzz2Eh4dz5swZRo4cae3axGWiwnsyLMyfv0jvRQjRzXXpIcoRI0a0muY4LCxMJgqzA0VRePKecFLeOEDaV4X8ekQfe5ckhBDt6jRcXnzxRRYuXNjqGZdLvfbaa1YpSnTs7oG9GHqDH+s+OsF9t92Aq7ZLnU8hhLCpTsMlISEBoNUzLsK+Wnovj7z5BelfFXH/HWH2LkkIIdroNFxaRj4eMWIE58+fByAwMND6VYlOjY/QccvF3svUW0Ol9yKE6HZ+9lNp7dq1jBw5kl/+8pfExcUxatQo1q1bZ4vaRAcURWF2dDinz18g42CRvcsRQog2Og2XN954g6+++orU1FT279/PgQMH2Lp1KwcPHuTNN9+0UYmiPRMiddwc6su6j07QaJI7x4QQ3Uun4ZKZmckf//hHwsJ+Oq8fFhbGqlWr2h0pWdhOS+/lP6UXyDx01t7lCCFEK52GS2NjY7vXWAIDA2lsbLRaUaJrYgbpGRTS3HsxNan2LkcIIcw6DRdXV9erWgawYMECRo8ezeTJk81t5eXlpKSkEBsbS0pKChUVFUDzCADLli0jJiaG+Ph4vv76a/M26enpxMbGEhsb22oOmSNHjhAfH09MTAzLli1DVdVOj+GIWp7aP/VjDbcv20m/+VmMWbFbrsMIIeyu03A5duwYt956a5uv4cOH8+2333a646SkJDZu3NiqbcOGDYwePZrs7GxGjx7Nhg0bAMjNzaWgoIDs7GyWLl3K4sWLgeagWLduHVu2bGHr1q2sW7fOHBaLFy9m6dKlZGdnU1BQQG5ubqfHcFS19Y0oQNmFBlSgqLyWBWmHJWCEEHbVabgcPXqUr776qs3XwYMHW/Uu2nPHHXe0mVAsJyeHxMREABITE9m1a1erdkVRGDZsGJWVlZSUlJCXl8eYMWPw9/fHz8+PMWPGsHfvXkpKSqiurmbYsGEoikJiYiI5OTmdHsNRrd75LZefEKttMLFqx3G71COEENDFscUspbS0FJ1OB0CvXr0oLS0FwGAwEBwcbF4vODgYg8HQpl2v17fb3rJ+Z8dwVGfLa6+oXQghbMFuT98pioKiKNf9Meytt7/nFbULIYQt2DRcgoKCKCkpAaCkpMR8J5per6e4uNi8XnFxMXq9vk27wWBot71l/c6O4ajmxUXg6dp6Zkp3Fw3z4mQaaiGE/dg0XKKjo83Px2RkZDBhwoRW7aqqcujQIXx8fNDpdIwdO5a8vDwqKiqoqKggLy+PsWPHotPp8Pb25tChQ6iq2u6+Lj+Go0ocHspLSUMI9fdEARQgItibxOGh9i5NCOHEujTk/tWYO3cu+/fvp6ysjKioKJ544glmzJjBnDlzSE1NpXfv3qxZswaAcePGsWfPHmJiYvD09GT58uUA+Pv7M3PmTKZNmwbArFmz8Pf3B+D5559nwYIF1NXVERUVRVRUFECHx3BkicNDzWHyl49OsGrHcfZ+9wN3hfeyc2VCCGelqC0PiDi5pKQk0tLS7F3GNatvNBH3Si4ajcKHT0bh5iKDWgohrKejz0755HEw7i5anp8ymJM/1PD3T07ZuxwhhJOScHFA4yN0xAzS8+ec7zhXIbckCyFsT8LFQS2aPAhTk8ry7cfsXYoQwglJuDiosMAe/P7um3j/32f59Psf7V2OEMLJSLg4sN+Nu4mwQE+ez/yaBpnzRQhhQxIuDszDVcuiyYP5rqSaTZ8W2LscIYQTkXBxcPdE6hgf0Ys1u76jpLLO3uUIIZyEhIuDUxSF5+MHY2xs4qUP5OK+EMI2JFycwI09vZgR1Z/0g0XsP3Xe3uUIIZyAhIuTmDn+Jnr7ebAo8wiNcnFfCGFlEi5OooebC3+YPIhjxVX83+f/sXc5QggHJ+HiRH55czBjB/Tkjzu/5cfqenuXI4RwYBIuTkRRFBZPGUxdg4mVcnFfCGFFEi5OZoDOm0fG9mPrl4V8dbrM3uUIIRyUhIsTeiI6HL2vO4syj2BqkhkXhBCWJ+HihLzdXVg4aRBHiirZvP+0vcsRQjggCRcnFX9LCCP7BbI6+zhlNUZ7lyOEcDASLk5KURSWJNxMVV0jL+84bu9yhBAORsLFiUUE+zD9zht558Bp8gvL7V2OEMKBSLg4uSfvCSfIy50/ZH5Nk1zcF0JYiIu9CxD25evhyrMTf8HcLf/m1qU7qahtoLe/J/PiIkgcHmrv8oQQ1ykJF4ECaBQor20AoKi8lgVphwEkYIQQV0VOiwlWZ3/L5WfEahtMrJIL/UKIqyThIjhbXntF7UII8XMkXAS9/T3bbQ/ydrNxJUIIRyHhIpgXF4Gnq7ZVmwKUVhvZ9GkBqip3kQkhroyEiyBxeCgvJQ0h1N8TBQj192R50s1MiNTx/L++5v+9l099o8neZQohriNyt5gAmgPm8jvDfnV7H17Z9S1rd5/gu5Jq1v/2NnS+HnaqUAhxPbFLzyU6Opr4+HgSEhJISkoCoLy8nJSUFGJjY0lJSaGiogIAVVVZtmwZMTExxMfH8/XXX5v3k56eTmxsLLGxsaSnp5vbjxw5Qnx8PDExMSxbtkxO61wljUbh6dgI/vpft3LsXBWT1+ZxUIbpF0J0gd1Oi23atInMzEzS0tIA2LBhA6NHjyY7O5vRo0ezYcMGAHJzcykoKCA7O5ulS5eyePFioDmM1q1bx5YtW9i6dSvr1q0zB9LixYtZunQp2dnZFBQUkJuba5fX6CgmDgkhbeaduLtq+NX6z9nyxRl7lySE6Oa6zTWXnJwcEhMTAUhMTGTXrl2t2hVFYdiwYVRWVlJSUkJeXh5jxozB398fPz8/xowZw969eykpKaG6upphw4ahKAqJiYnk5OTY8ZU5hsgQX/41ayy33xjAM6n5LP7X1zSYmuxdlhCim7JbuDz66KMkJSXx7rvvAlBaWopOpwOgV69elJaWAmAwGAgODjZvFxwcjMFgaNOu1+vbbW9ZX1y7AC83/vHICB4Z0483Py3godf3c16G6xdCtMMuF/Q3b96MXq+ntLSUlJQU+vfv32q5oigoimKP0sTPcNFqWBQ/iEG9fXk2/TBT1uWx4cHbGdTb196lCSG6Ebv0XPR6PQBBQUHExMSQn59PUFAQJSUlAJSUlBAYGGhet7i42LxtcXExer2+TbvBYGi3vWV9YVnTbruBLf89mgZTE/e9+inb8s/auyQhRDdi83C5cOEC1dXV5u8/+eQTwsPDiY6OJiMjA4CMjAwmTJgAYG5XVZVDhw7h4+ODTqdj7Nix5OXlUVFRQUVFBXl5eYwdOxadToe3tzeHDh1CVdVW+xKWNSzMn/cfH0tkiA+P//Mgj206wJ0rcug3P4sxK3aTcbDI3iUKIezE5qfFSktLmTVrFgAmk4nJkycTFRXFkCFDmDNnDqmpqfTu3Zs1a9YAMG7cOPbs2UNMTAyenp4sX74cAH9/f2bOnMm0adMAmDVrFv7+/gA8//zzLFiwgLq6OqKiooiKirL1y3QaOl8PNs8YxcOv72fX0RJzu4ysLIRzU1R5CASApKQk823R4sqNWZFDUXldm/ZQf08+mR9th4qEELbQ0Wdnt7kVWVzfzrYTLNDcgzE2yi3LQjgbCRdhER2NrAwQ/cePSf2yEJNMoyyE05BwERbR3sjKnq4aZkT1w8/Tlf/Z+m/i1uTyweFzMhyPEE5ABq4UFtFy0X7VjuOcLa+lt78n8+IiSBweSlOTyodfF/PH7OP8/u2vGBLqx//ERRAV3lOeZxLCQUm4CItpb2RlaB4Ac+KQEGIH6ck4dJZXdn7Lw3/fz4h+gcyLi+COGwPtUK0QwpokXITNuGg1TLvtBuKHhvDugTOs3X2C5Nc+4+6IXvxPbAQ3h/qRcbCo3d6PEOL6IuEibM7dRctDo28k+bYwNn1WwKsff8/ktXkMu8GPo8VV1F+8u0yelRHi+iUX9IXdeLpp+d24m8h9Zjyzowfw78IKc7C0qG0wsWrHcTtVKIS4WhIuwu78PF2ZGxvR4fKi8lrKL8joy0JcT+S0mOg2evt7UlRe2+6yW5fu5NY+AYz/hY7xEToiQ3zkTjMhujEJF9FtzIuLYEHaYWobTOY2T1cN/z3uJkxNKh8dL2HVjuOs2nEcva874yN0jP+FjjEDeuLt/tOvstwUIIT9SbiIbqOzZ2UAno6NoKSyjo+//YGPjpWwLf8c7xw4g6tWYUS/QMZH6DCpKmt2fkttg9wUIIQ9SbiIbqWjZ2Va6Hw9uP/2MO6/PYwGUxNfFJTx8fESPjpewrKso+1u03JTgISLELYj4SKuW65aDaNvCmL0TUEsmBjJmfMXuOvlj9pdt6i8lgdf30e4zodwvTfhOm8G6Lzx7+HW7vpyak2IayPhIhxGWGAPQju4KcDTVUvZBSOb959udU2nl487A3p5XxI4Ppz4oYrlWUfl1JoQ10DCRTiU9m8K0PJS0hDzOGdF5bWcKKnmu5IqvjNUc+KHatK/KqKqvrHD/dY2mFi+/Si/vDkYj8sG6BRCtCXhIhzKz90UoNEohAX2ICywB+N/oTNvp6oqhsp6viup4sHX97e775KqeiIXfUiovyf9enrRv6cX/Xp60a+XN/2CvAgN8ESrab49Wk6rCWcn4SIczs/dFNAeRVEI9vMg2M+jw1NrAT1cefjOGzn1Yw2nfqzhva+KqL6kt+Om1dAnqAceLhqOFVfReHH+mubTavnm2oRwBhIuQlymo1Nrz8cPbhUOqqryY7XxYthUc/LHGk79UMPuYyXmYGlR29DE/2z9N5mHiuhzsefUJ7AHfYO8CAv0pIdb2/8VpfcjrmcSLkJc5udOrbVQFIVePu708nFnRL+fpg3oNz+r3f02NjWfejtQUNaqxwPQ09udPoGe9AnsQZ8gL36oquO9r4rMU0TLTQXieiPhIkQ7rubUWouOhrEJ9fdk+5N3oaoq5RcaOH3+wk9fpc3/Higo41//Pkt7M0LXNphYkHaY44Yqevt5EOLnSbCfB739PQno4drucDjS+xH2IuEihIV1dFptXlzz4JyKohDg5UaAlxtDw/zbbG9sbCLiuQ9obzLo2gYTf8s92ea0m7uLhpCLgRPi70GInweGynr+degsRtO19X4koMTVkHARwsK6elqtI24umk57P3ufGc+P1fWcraijuKKWs+V1nKuo5VxFHecq6vj8+1IMVfWY2un+1DaYmJf6bz48Uoze1x2drwc6n5/+1ft6tOoFZRwsahWUcnpOdJWEixBWcC2n1aDz3o9GozSHga8HtNPzATA1qQx4dnu7vZ8Gk8qJH6r59Psfqaxr+2yPq1ZB5+NBLx93jhVXUtfQdo6dlz44yj2D9Hi5abs0OrUlej/Sg7q+SLgI0Q1da+9Hq1E67f3smjsOgLoGEyWV9ZRU1WG4+G9JVT2Gyjp+qKpvEywtDJX13Pz8DtxcNPT0ciPQ241AL/fm773cCPJ2J8jLjSBvNw4XVfDqx99f0wyjlupBSUDZjoSLEN2UNXs/LTxctfQJ6kGfoB7t7mPMit3tBpR/D1dm3n0TpdVGSmuMlFbXc77GyPcl1ZTWdBxKLWobTDy99d+szz1JDzctnq5aPN209Lj45eHa8r0LHq5a1uZ81+p1tOxjxQdHiY7U4eXmYn6AtSPdKaCcIeQkXIRwUNfa+4GOA2rxZc/8XO6CsdEcPIl/+aTddUxNKqH+ntQ2NFJjbOTH6npqG0xcMJqoM5q40GBq97rRpYor67llcba5Li93F7zdtXh7uODl5oK3uwteF7/e/3dRuwG1dNs3hAZ4tg44Vxc83DS4aTWtTvtZIqCcJeQkXIRwYNfa+7nagOrh5kKPQJdOBxMN9fdk48O3d7gPVVVpMKnUGk3ErdlDcWV9m3X8PV15PHoA1fWN1NQ3Ul1vosb8fSPFlXXm9up6UztHgdIaI8mvfdbuMq1GMYeOp6uWs+W17Twg23yL+OcnS3FzaQ4k14v/urlocHfR4HrxezethmVZ37QbcsuyvqFPUA+0ioJW0/zlolHQtPyrKLhoFbSKQvY3xSzLOmruIRaV1zI/LR9VVZl66w0dvqeXsvbNGoqqqp3/aeAkkpKSSEtLs3cZQjicyz/EoPVgorbaR0en+Hp6u/HKr4ZRazSZe04t39caL/7c0Eit0UTGobMd7l/n447R1ISxsYkGUxMNJvt8tCoK5jBqCSmtpjmULg2q4sq6dnuGof6efDI/usvH6+iz02F7Lrm5ubz44os0NTWRnJzMjBkz7F2SEE7JEqfnrHmK77lJg7grvFeX9nGgoKzDXtjlH8hNTSpGU3PQGBubmr9vVJn22qeUVLXthQV5ubH6/qE0NamYWr7Un75vbFJpuvjvcxlHOqzx8fEDftr+sn00qSqNpua2tK+K2t3+bDuv72o4ZLiYTCaWLFnCG2+8gV6vZ9q0aURHRzNgwAB7lyaEU7rW03OW2Ic1A+rSmyRaaDQKHhptmykanp0Y2e4+/jB5EOMjdJfvpl2vfvx9hyH3dGzbWtqz7+T5dvfR29+zS9v/HIcMl/z8fPr27UtYWBgAkyZNIicnR8JFCCfXHQLK1iFnzX10xiHDxWAwEBwcbP5Zr9eTn59vx4qEEI7CUXphlthHZxwyXIQQwtF1h5DrjMYqe7UzvV5PcXGx+WeDwYBer7djRUII4VwcMlyGDBlCQUEBZ86cwWg0kpWVRXR012+tE0IIcW0c8rSYi4sLixYt4rHHHsNkMnHfffcRHh5u77KEEMJpOGS4AIwbN45x48bZuwwhhHBKDnlaTAghhH05bM/lShUVFZGUlGTvMoQQ4rpSVNT+k/4ytpgQQgiLk9NiQgghLE7CRQghhMVJuAghhLA4CRchhBAWJ+EihBDC4iRchBBCWJw859KNnDt3jmeeeYbS0lIUReH+++/n4YcfbrXOvn37mDlzJjfc0DxPdkxMDI8//rg9yiU6OhovLy80Gg1arbbNVKeqqvLiiy+yZ88ePDw8WLFiBYMHD7ZpjSdPnuSpp54y/3zmzBlmz57N9OnTzW32ek8XLFjAxx9/TFBQENu2bQOgvLycp556iqKiIkJDQ1mzZg1+fn5ttk1PT+fVV18F4Pe//z1Tp061ea0rV67ko48+wtXVlT59+vDSSy/h6+vbZtuf+z2xdp1r165ly5YtBAYGAjB37tx2R++w5ey17dU5Z84cTp06BUBVVRU+Pj5kZma22daW7+c1UUW3YTAY1CNHjqiqqqpVVVVqbGys+t1337Va5/PPP1dnzJhhj/LaGD9+vFpaWtrh8o8//lh99NFH1aamJvXgwYPqtGnTbFhdW42Njeqdd96pFhYWtmq313u6f/9+9ciRI+qkSZPMbStXrlTXr1+vqqqqrl+/Xn355ZfbbFdWVqZGR0erZWVlanl5uRodHa2Wl5fbvNa9e/eqDQ0Nqqqq6ssvv9xurar6878n1q7zz3/+s7px48ZOt2tsbFQnTJignj59Wq2vr1fj4+Pb/L9n7Tov9dJLL6lr165td5kt389rIafFuhGdTmf+y97b25v+/ftjMBjsXNXVy8nJITExEUVRGDZsGJWVlZSUlNitns8++4ywsDBCQ60zf8WVuuOOO9r0SlreM4DExER27drVZru8vDzGjBmDv78/fn5+jBkzhr1799q81rFjx+Li0nzyY9iwYa2mubCX9ursiktnr3VzczPPXmstndWpqioffPABkydPttrxbUHCpZsqLCzk6NGjDB06tM2yQ4cOMWXKFB577DG+++47O1T3k0cffZSkpCTefffdNssunxE0ODjYrmGZlZXV4f+w3eU9LS0tRadrnke9V69elJaWtlmnvZlW7f1HyHvvvUdUVFSHyzv7PbGFt99+m/j4eBYsWEBFRUWb5d3pPf3iiy8ICgrixhtv7HAde7+fXSHXXLqhmpoaZs+ezbPPPou3t3erZYMHD2b37t14eXmxZ88eZs2aRXZ2tl3q3Lx5M3q9ntLSUlJSUujfvz933HGHXWr5OUajkd27d/P000+3Wdad3tNLKYqCoij2LuNnvfrqq2i1WqZMmdLucnv/njzwwAPMnDkTRVH405/+xIoVK3jppZdsdvwrtW3btk57LfZ+P7tKei7dTENDA7NnzyY+Pp7Y2Ng2y729vfHy8gKapxVobGzk/Pnzti4TwDy7Z1BQEDExMeTn57dZfumpkuLiYrvNCJqbm8vgwYPp2bNnm2Xd6T0NCgoynzosKSkxX4S+VHeaaTUtLY2PP/6Y1atXdxiEP/d7Ym09e/ZEq9Wi0WhITk7m8OHD7dbYHd7TxsZGdu7cycSJEztcx97vZ1dJuHQjqqqycOFC+vfvT0pKSrvr/PDDD6gXxxrNz8+nqamJgIAAW5YJwIULF6iurjZ//8knn7SZkC06OpqMjAxUVeXQoUP4+PiYT/nYWlZWFpMmTWp3WXd5T+Gn9wwgIyODCRMmtFln7Nix5OXlUVFRQUVFBXl5eYwdO9bGlTYH9saNG3n11Vfx9PRsd52u/J5Y26XX+Xbt2tXu8bvL7LWffvop/fv3b3WK7lLd4f3sKjkt1o18+eWXZGZmMnDgQBISEoDm2ybPnj0LNHfvd+zYwebNm9FqtXh4ePC///u/djl1UlpayqxZswAwmUxMnjyZqKgoNm/ebK513Lhx7Nmzh5iYGDw9PVm+fLnN64Tm/wk//fRTlixZYm67tE57vadz585l//79lJWVERUVxRNPPMGMGTOYM2cOqamp9O7dmzVr1gBw+PBh3nnnHV588UX8/f2ZOXMm06ZNA2DWrFn4+/vbvNYNGzZgNBrNfwgNHTqUJUuWYDAYeO655/jb3/7W4e+JLevcv38/x44dAyA0NNT8e3Bpnbaevba9OpOTk9m+fXubP4Ls+X5eCxlyXwghhMXJaTEhhBAWJ+EihBDC4iRchBBCWJyEixBCCIuTcBFCCGFxEi7CaURERLBixQrzz6+//jpr1661yL7nz5/Phx9+aJF9deaDDz7g3nvv5cEHH+zS+ldbV2FhIe+///4VbydECwkX4TTc3NzIzs6229P3HWlsbOzyuqmpqSxdupS33nrLihVBUVGReSj4rrqS1yEcn4SLcBouLi786le/YtOmTW2WXf4X/vDhw4HmuV5++9vf8vvf/54JEyawevVq/vWvfzFt2jTi4+M5ffq0eZtPP/2UpKQk4uLi+Oijj4DmB91WrlzJfffdR3x8PO+88455v7/5zW/43e9+1+7IAdu2bSM+Pp7JkyezatUqANatW8dXX33FwoULWblyZZttNmzYQHx8PFOmTGH16tVtlkdHR5uD9fDhw+bez/79+0lISCAhIYHExESqq6v54x//yBdffEFCQgJvvvlml1/HhQsXmDFjBlOmTGHy5Mls3769C/9lhCOSJ/SFU/mv//ov8+jHXXXs2DG2b9+Ov78/EyZMIDk5mdTUVDZt2sRbb73FwoULgea/9lNTUzl9+jQPPfQQd955JxkZGfj4+PDee+9hNBr59a9/zZgxYwD45ptveP/99wkLC2t1PIPBwOrVq0lLS8PX15dHHnmEXbt28fjjj7Nv3z6eeeYZhgwZ0mqbPXv2sHv3brZs2YKnpyfl5eVdfn1///vfWbRoEbfddhs1NTW4u7vz9NNP8/e//53169cD8O6773bpdezYsQOdTseGDRuA5kmvhHOScBFOxdvbm4SEBP7xj3/g4eHRpW2GDBliHhOtT58+5g/VgQMHsm/fPvN69957LxqNhhtvvJGwsDBOnjzJJ598wvHjx9mxYwfQ/GH7n//8B1dXV4YMGdImWKC5VzFixAjzoJXx8fEcOHCAe+65p8MaP/vsM5KSksxjfF3JcDC33norK1asMA+W2jKI56W6+joGDhzIypUrWbVqFePHj+f222/vch3CsUi4CKfz8MMPk5SURFJSkrlNq9XS1NQEQFNTEw0NDeZlbm5u5u81Go35Z41Gg8lkMi+7fDwyRVFQVZXnnnuOu+66q9Wyffv20aNHD8u9qC7QarXmATrr6+vN7TNmzDCPA/fAAw+wcePGNtt29XX069ePtLQ09uzZw5o1axg1apTdpuEW9iXXXITT8ff355e//CWpqanmttDQUL7++msAdu/e3SpcuurDDz+kqamJ06dPc+bMGfr168fYsWPZvHmzeX+nTp3iwoULne7nlltu4cCBA5w/fx6TyURWVtbPztdx5513kpaWRm1tLUC7p8VCQ0M5cuQIQKv5ak6fPk1ERAQzZsxgyJAhnDp1Ci8vL2pqaszrdPV1GAwGPD09SUhI4NFHH+Wbb77ptG7huKTnIpzSI488wttvv23++f7772fmzJlMmTKFu+6666p6FSEhIUybNo2amhpeeOEF3N3dSU5OpqioiKSkJFRVJSAggL/+9a+d7ken0/H000/z8MMPo6oq48aN6/SUGEBUVBTHjh3jvvvuw9XVlXHjxjF37txW6zz++OMsXLiQP/3pT4wcOdLcvmnTJvbt24eiKISHhxMVFYWiKGg0GqZMmUJSUhIPPfRQl17Ht99+y8svv4xGo8HFxYXFixd3/Q0UDkVGRRZCCGFxclpMCCGExUm4CCGEsDgJFyGEEBYn4SKEEMLiJFyEEEJYnISLEEIIi5NwEUIIYXH/HxgYlzfSt9JbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate sum of squared errors for different k\n",
    "k_candidates = range(1, 20)\n",
    "sse = []\n",
    "for i in k_candidates:\n",
    "    km = KMeans(n_clusters=i, init=\"random\", n_init=20, max_iter=200)\n",
    "    km.fit(X)\n",
    "    sse.append(km.inertia_)\n",
    "\n",
    "# plot\n",
    "plt.plot(k_candidates, sse, marker=\"o\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Distortion\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bf2b4f-cf67-493d-8ae3-c3e34825f172",
   "metadata": {},
   "source": [
    "The following example decomposes k-means into the expectation and the maximization steps (taken from DSHandbook):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6a3414b0-a14d-4279-bc21-06ffe74382d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.98, 0.98, 'Random Initialization')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.4967141530112327, 3.861735698828815, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.6476885381006925, 5.523029856408026, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-0.23415337472333597, 3.7658630430508193, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.5792128155073915, 4.7674347291529084, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.617884036481574, 1.7302164268618159, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.05544831815144, 7.31289000117005, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.5304909178700825, 2.8944167383856816, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.5802860667196599, 4.421012702817745, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(1.9825828101483296, 0.8677131436145118, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.3732439791827424, 7.75368870941779, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(-1.570847026766382, 2.8553540230789376, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.9700766645513808, 4.415327315087125, '')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'E-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.95, 0.95, 'M-Step')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.98, 0.98, 'Final Clustering')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y_true = sklearn.datasets.make_blobs(\n",
    "    n_samples=300, centers=4, cluster_std=0.60, random_state=0\n",
    ")\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "centers = [0, 4] + rng.randn(4, 2)\n",
    "\n",
    "\n",
    "def draw_points(ax, c, factor=1):\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=c, cmap=\"inferno\", s=50 * factor, alpha=1)\n",
    "\n",
    "\n",
    "def draw_centers(ax, centers, factor=1, alpha=1.0):\n",
    "    ax.scatter(\n",
    "        centers[:, 0],\n",
    "        centers[:, 1],\n",
    "        c=np.arange(4),\n",
    "        cmap=\"inferno\",\n",
    "        s=200 * factor,\n",
    "        alpha=alpha,\n",
    "    )\n",
    "    ax.scatter(\n",
    "        centers[:, 0], centers[:, 1], c=\"black\", s=50 * factor, alpha=alpha\n",
    "    )\n",
    "\n",
    "\n",
    "def make_ax(fig, gs):\n",
    "    ax = fig.add_subplot(gs)\n",
    "    ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "    ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "    return ax\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(15, 4))\n",
    "gs = plt.GridSpec(\n",
    "    4, 15, left=0.02, right=0.98, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2\n",
    ")\n",
    "ax0 = make_ax(fig, gs[:4, :4])\n",
    "ax0.text(\n",
    "    0.98,\n",
    "    0.98,\n",
    "    \"Random Initialization\",\n",
    "    transform=ax0.transAxes,\n",
    "    ha=\"right\",\n",
    "    va=\"top\",\n",
    "    size=16,\n",
    ")\n",
    "draw_points(ax0, \"gray\", factor=2)\n",
    "draw_centers(ax0, centers, factor=2)\n",
    "\n",
    "for i in range(3):\n",
    "    ax1 = make_ax(fig, gs[:2, 4 + 2 * i : 6 + 2 * i])\n",
    "    ax2 = make_ax(fig, gs[2:, 5 + 2 * i : 7 + 2 * i])\n",
    "\n",
    "    # E-step\n",
    "    y_pred = pairwise_distances_argmin(X, centers)\n",
    "    draw_points(ax1, y_pred)\n",
    "    draw_centers(ax1, centers)\n",
    "\n",
    "    # M-step\n",
    "    new_centers = np.array([X[y_pred == i].mean(0) for i in range(4)])\n",
    "    draw_points(ax2, y_pred)\n",
    "    draw_centers(ax2, centers, alpha=0.3)\n",
    "    draw_centers(ax2, new_centers)\n",
    "    for i in range(4):\n",
    "        ax2.annotate(\n",
    "            \"\",\n",
    "            new_centers[i],\n",
    "            centers[i],\n",
    "            arrowprops=dict(arrowstyle=\"->\", linewidth=1),\n",
    "        )\n",
    "\n",
    "    # Finish iteration\n",
    "    centers = new_centers\n",
    "    ax1.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        \"E-Step\",\n",
    "        transform=ax1.transAxes,\n",
    "        ha=\"right\",\n",
    "        va=\"top\",\n",
    "        size=14,\n",
    "    )\n",
    "    ax2.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        \"M-Step\",\n",
    "        transform=ax2.transAxes,\n",
    "        ha=\"right\",\n",
    "        va=\"top\",\n",
    "        size=14,\n",
    "    )\n",
    "\n",
    "\n",
    "# Final E-step\n",
    "y_pred = pairwise_distances_argmin(X, centers)\n",
    "axf = make_ax(fig, gs[:4, -4:])\n",
    "draw_points(axf, y_pred, factor=2)\n",
    "draw_centers(axf, centers, factor=2)\n",
    "axf.text(\n",
    "    0.98,\n",
    "    0.98,\n",
    "    \"Final Clustering\",\n",
    "    transform=axf.transAxes,\n",
    "    ha=\"right\",\n",
    "    va=\"top\",\n",
    "    size=16,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eecbf7a6-5cb8-4373-a6f2-ce9f744b38c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute kmeans, repeat 10 times\n",
    "# init could also be 'kmeans++'\n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
    "dbscan.fit(X)\n",
    "y_dbscan = dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b6bf319-81c4-40b1-b9a8-ebefd7bcc9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f518f4a2fa0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize results\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_dbscan, s=50, cmap=\"inferno\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c118aedd-677f-43f5-87f4-5dca871843d9",
   "metadata": {},
   "source": [
    "### Text Data Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5183ed93-0ca5-44ee-8928-8ce8d1abf5aa",
   "metadata": {},
   "source": [
    "In the following, we will look at different representations of text and feature extraction methods for text. We will make use of the Yelp Dataset (https://www.kaggle.com/yelp-dataset/yelp-dataset): \"This dataset is a subset of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the most recent dataset you'll find information about businesses across 8 metropolitan areas in the USA and Canada.\" This example is adapted from the FeatEng book."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78930164-4e89-426f-b153-efdc4a1cf7ef",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> As the dataset is simply too large to store it on gitlab, please download the dataset directly using the link above, unzip it and store it in the data directory..</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0218975-9c55-4953-bcc5-816ed530c252",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in review data\n",
    "# restrict to first 5000 reviews\n",
    "with open(os.path.join(data_dir, \"yelp_academic_dataset_review.json\")) as fh:\n",
    "    lines_read = 0\n",
    "    data = []\n",
    "    for line in fh:\n",
    "        data.append(json.loads(line))\n",
    "        lines_read += 1\n",
    "        if lines_read == 5000:\n",
    "            break\n",
    "reviews = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e6dede3-d6a1-4ad8-ab55-2f6dabae4456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   review_id    5000 non-null   object \n",
      " 1   user_id      5000 non-null   object \n",
      " 2   business_id  5000 non-null   object \n",
      " 3   stars        5000 non-null   float64\n",
      " 4   useful       5000 non-null   int64  \n",
      " 5   funny        5000 non-null   int64  \n",
      " 6   cool         5000 non-null   int64  \n",
      " 7   text         5000 non-null   object \n",
      " 8   date         5000 non-null   object \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 351.7+ KB\n"
     ]
    }
   ],
   "source": [
    "reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d5cb54db-4e22-42d5-822e-12a78cbdb53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2330    Really good. A kind of gourmet farm to table restaurant.  You could tell that all of their ingredients are fresh and local. Love it. Our waiter, I think his name was Kevin, was an older gentlemen that was really smooth and excellent. Everyone should come here.\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect single review\n",
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "reviews.sample(1)[\"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48fb9b9c-9281-4d23-950d-e7a006c5d3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5000x19525 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 371018 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract bag-of-words\n",
    "bow_converter = text.CountVectorizer(token_pattern=\"(?u)\\\\b\\\\w+\\\\b\")\n",
    "bow = bow_converter.fit_transform(reviews[\"text\"])\n",
    "bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e2f80ad-3cee-4b79-91af-a9556eb71456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '00', '000', '00am', '00pm', '01', '02', '05', '07', '08'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['harm', 'daily', 'filled', 'bicycle', 'putting', 'restocked',\n",
       "       'triple', 'tag', 'eat24', 'compote', 'wands', 'pow', 'unevenly',\n",
       "       'daydreaming', 'gunmetal', 'site', 'temple', 'ragu', 'phrase',\n",
       "       'breathing'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bow returns a sparse matrix, extract feature name mapping\n",
    "words = bow_converter.get_feature_names_out()\n",
    "words[:10]\n",
    "np.random.choice(words, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "133ff6b7-6dbc-4872-8aa9-c5da2b248d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19525"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of vocabulary\n",
    "len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5ab802f5-0881-4725-8c14-280d18369358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract bag-of-bigrams\n",
    "bigram_converter = text.CountVectorizer(\n",
    "    ngram_range=(2, 2), token_pattern=\"(?u)\\\\b\\\\w+\\\\b\"\n",
    ")\n",
    "bob = bigram_converter.fit_transform(reviews[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6b032c40-dde6-43eb-ae7c-06026f154dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0 1', '0 2', '0 25', '0 30', '0 4', '0 40', '0 50', '0 9mi',\n",
       "       '0 books', '0 environment'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['model which', 'decent value', 'food maybe', 'problems there',\n",
       "       'cart their', 'had coconut', 'much last', 'extreme professional',\n",
       "       'seating gets', 'best version', 'bucks from', 'minor work',\n",
       "       'i kid', 'the tenant', 'their 5', 'knows exactly', 'bready and',\n",
       "       'asian beers', 'dooo it', 'wall are'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract feature name mapping\n",
    "bigrams = bigram_converter.get_feature_names_out()\n",
    "bigrams[:10]\n",
    "np.random.choice(bigrams, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c28ea5e6-a868-45ac-a655-89972a8e0570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193577"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of bigram vocabulary\n",
    "len(bigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ac88c2a-293c-46fb-b774-3b9bb8881bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract bag-of-trirams\n",
    "trigram_converter = text.CountVectorizer(\n",
    "    ngram_range=(3, 3), token_pattern=\"(?u)\\\\b\\\\w+\\\\b\"\n",
    ")\n",
    "bot = trigram_converter.fit_transform(reviews[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1f59230-c2f2-4db6-a53e-44949f27fbbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0 1 stars', '0 25 oysters', '0 30 possible', '0 4 miles',\n",
       "       '0 40 oz', '0 50 for', '0 9mi 22min', '0 books on',\n",
       "       '0 environment 5', '0 i would'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['to get what', 'the salad was', 'in my rating', 'a second shot',\n",
       "       'medium well the', 'taco does a', 'priority over my',\n",
       "       'love the the', 'store ever the', 'i left somewhat',\n",
       "       'included nothing to', 'were fun and', 'have enough staff',\n",
       "       'do whatever i', 'i come all', 'great brews and',\n",
       "       'guacamole and more', 'cup of whatever', 'wasn t his',\n",
       "       'mind it too'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract feature name mapping\n",
    "trigrams = trigram_converter.get_feature_names_out()\n",
    "trigrams[:10]\n",
    "np.random.choice(trigrams, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ac01cd6-cd4f-4059-b5ed-77ebf93fa4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409449"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of trigram vocabulary\n",
    "len(trigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a1b15405-3461-4002-b7b8-443707e19739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare vocabulary size of uni, bi, and trigrams\n",
    "sns.set_style(\"whitegrid\")\n",
    "counts = [len(words), len(bigrams), len(trigrams)]\n",
    "plt.plot(counts)\n",
    "plt.plot(counts, \"bo\")\n",
    "plt.margins(0.1)\n",
    "plt.xticks(range(3), [\"unigram\", \"bigram\", \"trigram\"])\n",
    "plt.title(\"Number of ngrams in the first 5,000 reviews of the Yelp dataset\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c4e24b0-b2b7-4401-a3f3-bc22d7bc0c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0', 'x1', 'x2', ..., 'x193574', 'x193575', 'x193576'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 193577)\n"
     ]
    }
   ],
   "source": [
    "# Create the tf-idf representation using the bag-of-words matrix\n",
    "tfidf_transformer = text.TfidfTransformer(norm=None, use_idf=True)\n",
    "tf_idf = tfidf_transformer.fit_transform(bob)\n",
    "tfidf_transformer.get_feature_names_out()\n",
    "print(tf_idf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5dcf0a-c279-4295-b138-73b3857d4f95",
   "metadata": {},
   "source": [
    "### Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff4d805-993f-483a-a211-4befd6b2e1b3",
   "metadata": {},
   "source": [
    "The following example showcases encoding of categorical variables (dataset and code adapted from FeatEng)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bdf41196-1783-4f87-bb3c-551c4cb8369a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renting data per city\n",
    "rent_data = pd.DataFrame(\n",
    "    {\n",
    "        \"City\": [\n",
    "            \"SF\",\n",
    "            \"SF\",\n",
    "            \"SF\",\n",
    "            \"NYC\",\n",
    "            \"NYC\",\n",
    "            \"NYC\",\n",
    "            \"Seattle\",\n",
    "            \"Seattle\",\n",
    "            \"Seattle\",\n",
    "        ],\n",
    "        \"Rent\": [3999, 4000, 4001, 3499, 3500, 3501, 2499, 2500, 2501],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "50eb31c9-3d47-439f-b07e-1e37b0b4ddf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Rent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SF</td>\n",
       "      <td>3999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SF</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SF</td>\n",
       "      <td>4001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NYC</td>\n",
       "      <td>3501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      City  Rent\n",
       "0       SF  3999\n",
       "1       SF  4000\n",
       "2       SF  4001\n",
       "3      NYC  3499\n",
       "4      NYC  3500\n",
       "5      NYC  3501\n",
       "6  Seattle  2499\n",
       "7  Seattle  2500\n",
       "8  Seattle  2501"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect data\n",
    "rent_data\n",
    "rent_data[\"Rent\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ca30c5f-8169-4fbc-a5bd-3c62569428a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>city_NYC</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_NYC  city_SF  city_Seattle\n",
       "0  3999         0        1             0\n",
       "1  4000         0        1             0\n",
       "2  4001         0        1             0\n",
       "3  3499         1        0             0\n",
       "4  3500         1        0             0\n",
       "5  3501         1        0             0\n",
       "6  2499         0        0             1\n",
       "7  2500         0        0             1\n",
       "8  2501         0        0             1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to one-hot-encoding\n",
    "one_hot_rent = pd.get_dummies(rent_data, prefix=[\"city\"])\n",
    "one_hot_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc9043a5-24bd-453c-8914-5222729175b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 166.66666667,  666.66666667, -833.33333333])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit a linear regression model\n",
    "# y = w1x1 + ... + wnxn + b\n",
    "lin_reg = linear_model.LinearRegression()\n",
    "lin_reg.fit(\n",
    "    one_hot_rent[[\"city_NYC\", \"city_SF\", \"city_Seattle\"]], one_hot_rent[\"Rent\"]\n",
    ")\n",
    "\n",
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for one-hot encoding\n",
    "# coefficients: difference from global mean\n",
    "# intercept: global mean of rent\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "010243bb-4220-4fc3-b1cc-7e389fd749b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_SF  city_Seattle\n",
       "0  3999        1             0\n",
       "1  4000        1             0\n",
       "2  4001        1             0\n",
       "3  3499        0             0\n",
       "4  3500        0             0\n",
       "5  3501        0             0\n",
       "6  2499        0             1\n",
       "7  2500        0             1\n",
       "8  2501        0             1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to dummy coding\n",
    "# reference level not shown anymore\n",
    "dummy_rent = pd.get_dummies(rent_data, prefix=[\"city\"], drop_first=True)\n",
    "dummy_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b9cca547-09ea-4471-9757-65b37063d350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([  500., -1000.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3500.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for dummy coding:\n",
    "# coefficients: difference per category to mean of reference category\n",
    "# intercept: mean of reference category\n",
    "lin_reg.fit(dummy_rent[[\"city_SF\", \"city_Seattle\"]], dummy_rent[\"Rent\"])\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b470bd89-05b7-4bb7-878f-ed3b572f276b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>city_SF</th>\n",
       "      <th>city_Seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3499</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3500</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3501</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2499</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rent  city_SF  city_Seattle\n",
       "0  3999        1             0\n",
       "1  4000        1             0\n",
       "2  4001        1             0\n",
       "3  3499       -1            -1\n",
       "4  3500       -1            -1\n",
       "5  3501       -1            -1\n",
       "6  2499        0             1\n",
       "7  2500        0             1\n",
       "8  2501        0             1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert categorical variable (city) to effect coding\n",
    "effect_rent = dummy_rent.copy()\n",
    "effect_rent[\"city_Seattle\"] = effect_rent[\"city_Seattle\"].astype(\"int32\")\n",
    "effect_rent[\"city_SF\"] = effect_rent[\"city_SF\"].astype(\"int32\")\n",
    "effect_rent.loc[3:5, [\"city_SF\", \"city_Seattle\"]] = -1.0\n",
    "effect_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b8e650d-4ae4-4f0b-8afb-45b1b0094318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 666.66666667, -833.33333333])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3333.3333333333335"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect regression coefficients (y = w1x1 + ... + wnxn + b)\n",
    "# for dummy coding:\n",
    "# coefficients: difference per category to mean of reference category\n",
    "# intercept: mean of reference category\n",
    "lin_reg.fit(effect_rent[[\"city_SF\", \"city_Seattle\"]], effect_rent[\"Rent\"])\n",
    "lin_reg.coef_\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba0c1ce-3524-40a2-b783-c32185bb326a",
   "metadata": {},
   "source": [
    "In the following, we will briefly dive into feature hashing based on the Yelp review dataset used previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "80f35142-c195-4b67-89ff-faae20e6f55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2795\n"
     ]
    }
   ],
   "source": [
    "# we will define m as equal to the unique number of business_id\n",
    "m = len(reviews.business_id.unique())\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "640ab4fc-d38a-4208-ae7a-ca16c87886ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_hasher = FeatureHasher(n_features=m, input_type=\"string\")\n",
    "hashed = feature_hasher.transform(reviews[\"business_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ea541357-d52e-48c1-a9a4-cbc9be12ae1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our pandas Series, in bytes:  395144\n",
      "Our hashed numpy array, in bytes:  48\n"
     ]
    }
   ],
   "source": [
    "# We can see how this will make a difference in the future by\n",
    "# looking at the size of each\n",
    "print(\"Our pandas Series, in bytes: \", getsizeof(reviews[\"business_id\"]))\n",
    "print(\"Our hashed numpy array, in bytes: \", getsizeof(hashed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4c8d67cb-a29f-4136-855c-c4bd02a97010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['buF9druCkbuXLX526sGELQ',\n",
       " 'RA4V8pr014UyUbDvI-LW2A',\n",
       " '_sS2LBIGNT5NQb6PD1Vtjw',\n",
       " '0AzLzHfOJgL7ROwhdww2ew',\n",
       " '8zehGz9jnxPqXtOc7KaJxA']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare oriignal data to hashed data\n",
    "reviews[\"business_id\"].unique().tolist()[0:5]\n",
    "hashed.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef7c751-7be0-45e4-84cc-27e2bebb4d44",
   "metadata": {},
   "source": [
    "## Expanding Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb42d44-a081-46e1-97e7-beb4dd674586",
   "metadata": {},
   "source": [
    "### Value Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c497c77-a87a-44f8-89cf-46f326a35d52",
   "metadata": {},
   "source": [
    "Different imputation methods showcased, adapted from CleanData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8df460fc-829c-4592-ad72-80b5f43d4dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_derm_data():\n",
    "    np.random.seed(1)\n",
    "\n",
    "    # Histopathological Attributes: (values 0, 1, 2, 3)\n",
    "    # Clinical Attributes: (values 0, 1, 2, 3, unless indicated)\n",
    "    features = [\n",
    "        \"erythema\",\n",
    "        \"scaling\",\n",
    "        \"definite borders\",\n",
    "        \"itching\",\n",
    "        \"koebner phenomenon\",\n",
    "        \"polygonal papules\",\n",
    "        \"follicular papules\",\n",
    "        \"oral mucosal involvement\",\n",
    "        \"knee and elbow involvement\",\n",
    "        \"scalp involvement\",\n",
    "        \"family history\",  # 0 or 1\n",
    "        \"melanin incontinence\",\n",
    "        \"eosinophils in the infiltrate\",\n",
    "        \"PNL infiltrate\",\n",
    "        \"fibrosis of the papillary dermis\",\n",
    "        \"exocytosis\",\n",
    "        \"acanthosis\",\n",
    "        \"hyperkeratosis\",\n",
    "        \"parakeratosis\",\n",
    "        \"clubbing of the rete ridges\",\n",
    "        \"elongation of the rete ridges\",\n",
    "        \"thinning of the suprapapillary epidermis\",\n",
    "        \"spongiform pustule\",\n",
    "        \"munro microabcess\",\n",
    "        \"focal hypergranulosis\",\n",
    "        \"disappearance of the granular layer\",\n",
    "        \"vacuolisation and damage of basal layer\",\n",
    "        \"spongiosis\",\n",
    "        \"saw-tooth appearance of retes\",\n",
    "        \"follicular horn plug\",\n",
    "        \"perifollicular parakeratosis\",\n",
    "        \"inflammatory monoluclear inflitrate\",\n",
    "        \"band-like infiltrate\",\n",
    "        \"Age\",  # linear; missing marked '?'\n",
    "        \"TARGET\",  # See mapping\n",
    "    ]\n",
    "\n",
    "    targets = {\n",
    "        1: \"psoriasis\",  # 112 instances\n",
    "        2: \"seboreic dermatitis\",  # 61\n",
    "        3: \"lichen planus\",  # 72\n",
    "        4: \"pityriasis rosea\",  # 49\n",
    "        5: \"cronic dermatitis\",  # 52\n",
    "        6: \"pityriasis rubra pilaris\",  # 20\n",
    "    }\n",
    "\n",
    "    data = os.path.join(data_dir, \"dermatology.data\")\n",
    "    df = pd.read_csv(data, header=None, names=features, na_values=[\"?\"])\n",
    "    df[\"TARGET\"] = df.TARGET.map(targets)\n",
    "\n",
    "    derm = df.copy()\n",
    "    derm.loc[derm.Age == \"?\", \"Age\"] = None\n",
    "    derm[\"Age\"] = derm.Age.astype(float)\n",
    "    return derm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ef10fdef-711f-4546-a9bd-d54a31366cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "derm = read_derm_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "90fcb5e3-4e4c-466a-8f76-c9481ed04593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>erythema</th>\n",
       "      <th>scaling</th>\n",
       "      <th>definite borders</th>\n",
       "      <th>itching</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>psoriasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     erythema  scaling  definite borders  itching   Age               TARGET\n",
       "247         2        2                 2        0  62.0            psoriasis\n",
       "127         2        2                 2        2  44.0        lichen planus\n",
       "230         3        2                 0        1  30.0  seboreic dermatitis\n",
       "162         3        2                 2        2  22.0        lichen planus\n",
       "159         3        2                 2        1  47.0  seboreic dermatitis\n",
       "296         2        1                 1        3  19.0    cronic dermatitis"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use iloc on both axes and sample\n",
    "derm.iloc[:, [0, 1, 2, 3, -2, -1]].sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "43734f3f-a997-436c-93c9-04785c6a5c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean, suspicious = [], {}\n",
    "for col in derm.columns:\n",
    "    values = derm[col].unique()\n",
    "    if set(values) <= {0, 1, 2, 3}:\n",
    "        clean.append(col)\n",
    "    else:\n",
    "        suspicious[col] = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "46b6f7d4-77a0-4e7d-b70e-039caa83568d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No problem detected:\n",
      "['erythema',\n",
      " 'scaling',\n",
      " 'definite borders',\n",
      " 'itching',\n",
      " 'koebner phenomenon',\n",
      " 'polygonal papules',\n",
      " 'follicular papules',\n",
      " 'oral mucosal involvement']\n",
      "... 25 other fields\n"
     ]
    }
   ],
   "source": [
    "print(\"No problem detected:\")\n",
    "pprint(clean[:8])\n",
    "print(f\"... {len(clean) - 8} other fields\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d1180b53-e9b0-407d-bd1a-22474288e195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suspicious:\n",
      "{'Age': array([55.,  8., 26., 40., 45., 41., 18., 57., 22., 30., 20., 21., 10.,\n",
      "       65., 38., 23., 17., 51., 42., 44., 33., 43., 50., 34., nan, 15.,\n",
      "       46., 62., 35., 48., 12., 52., 60., 32., 19., 29., 25., 36., 13.,\n",
      "       27., 31., 28., 64., 39., 47., 16.,  0.,  7., 70., 37., 61., 67.,\n",
      "       56., 53., 24., 58., 49., 63., 68.,  9., 75.]),\n",
      " 'TARGET': array(['seboreic dermatitis', 'psoriasis', 'lichen planus',\n",
      "       'cronic dermatitis', 'pityriasis rosea',\n",
      "       'pityriasis rubra pilaris'], dtype=object)}\n"
     ]
    }
   ],
   "source": [
    "# Notice age has some expected ages and also a '?'\n",
    "print(\"Suspicious:\")\n",
    "pprint(suspicious)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "afb090dc-1a70-4aa3-945f-ac8331cc84db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inflammatory monoluclear inflitrate</th>\n",
       "      <th>band-like infiltrate</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>psoriasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pityriasis rosea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lichen planus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     inflammatory monoluclear inflitrate  band-like infiltrate  Age  \\\n",
       "33                                     0                     0  NaN   \n",
       "34                                     0                     0  NaN   \n",
       "35                                     0                     0  NaN   \n",
       "36                                     0                     3  NaN   \n",
       "262                                    3                     0  NaN   \n",
       "263                                    2                     0  NaN   \n",
       "264                                    3                     0  NaN   \n",
       "265                                    3                     0  NaN   \n",
       "\n",
       "                  TARGET  \n",
       "33             psoriasis  \n",
       "34      pityriasis rosea  \n",
       "35   seboreic dermatitis  \n",
       "36         lichen planus  \n",
       "262    cronic dermatitis  \n",
       "263    cronic dermatitis  \n",
       "264    cronic dermatitis  \n",
       "265    cronic dermatitis  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset encodes missing values as ?\n",
    "# --> added ? to na_values list to be recognized when reading\n",
    "# file in panda's read_csv\n",
    "derm.loc[derm.Age.isnull()].iloc[:, -4:]\n",
    "# alternatively:\n",
    "# Assign missing ages marked with '?' as None\n",
    "# derm.loc[derm.Age == '?', 'Age'] = None  # or NaN\n",
    "# Convert string/None ages to floating-point\n",
    "# derm['Age'] = derm.Age.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f8b344-39d7-4593-be81-ad90ad57830f",
   "metadata": {},
   "source": [
    "#### Typical-Value Imputation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2e990956-1d39-4894-ab5e-a3ac8712cfea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# detailed plot\n",
    "# count values\n",
    "age_stats = pd.DataFrame(\n",
    "    [\n",
    "        derm[derm.Age == x][\"Age\"].count()\n",
    "        for x in np.arange(derm[\"Age\"].min(), derm[\"Age\"].max())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "295def47-3520-42d2-b6df-3093585e4900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot\n",
    "plot = age_stats.plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Age\",\n",
    "    ylabel=\"Count\",\n",
    "    legend=False,\n",
    ")\n",
    "\n",
    "# adapt ticks on x-axis\n",
    "for ind, label in enumerate(plot.get_xticklabels()):\n",
    "    if ind % 10 == 0:  # every 10th label is kept\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "86677c98-7415-4126-840c-a5bc5fffec5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36.29608938547486, 35.0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean and median seem like best solution\n",
    "derm.Age.mean(), derm.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f153af7d-d05e-4902-82be-45be358be237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40.0\n",
       "1    50.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "derm.Age.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9888b03-63b3-466e-b8ac-8e862a8c5bb6",
   "metadata": {},
   "source": [
    "Alternatively, this dataset was created in Turkey - we might also want to use domain knowledge and use the mean/median age in Turkey at the time of dataset creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c3aa2477-b374-411a-bc44-fb84d48e9d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use sklearn's SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"mean\")\n",
    "# expects 2d array or dataframe\n",
    "derm[\"Age_imputed\"] = imputer.fit_transform(pd.DataFrame(derm[\"Age\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b9cbaf00-26a8-4763-af9f-d6146408ee51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band-like infiltrate</th>\n",
       "      <th>Age</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>Age_imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>psoriasis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pityriasis rosea</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>seboreic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lichen planus</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cronic dermatitis</td>\n",
       "      <td>36.296089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     band-like infiltrate  Age               TARGET  Age_imputed\n",
       "33                      0  NaN            psoriasis    36.296089\n",
       "34                      0  NaN     pityriasis rosea    36.296089\n",
       "35                      0  NaN  seboreic dermatitis    36.296089\n",
       "36                      3  NaN        lichen planus    36.296089\n",
       "262                     0  NaN    cronic dermatitis    36.296089\n",
       "263                     0  NaN    cronic dermatitis    36.296089\n",
       "264                     0  NaN    cronic dermatitis    36.296089\n",
       "265                     0  NaN    cronic dermatitis    36.296089"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "derm.loc[derm.Age.isnull()].iloc[:, -4:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be55127-f078-4a6f-a4d8-80e7c9c9d4ee",
   "metadata": {},
   "source": [
    "#### Locality Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c289112b-0a6b-438a-98f2-9c911f1dc9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shape: (50, 8, 8)\n"
     ]
    }
   ],
   "source": [
    "# load digits dataset (adopted, now missing a few pixels)\n",
    "digits = np.load(os.path.join(data_dir, \"digits.npy\"))\n",
    "print(\"Array shape:\", digits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e1526d7e-8c7b-48dd-953a-1db09d8d050d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# display digits\n",
    "def show_digits(digits=digits, x=3, y=3, title=\"Digits\"):\n",
    "    \"Display of 'corrupted numerals'\"\n",
    "    if digits.min() >= 0:\n",
    "        newcm = cm.get_cmap(\"Greys\", 17)\n",
    "    else:\n",
    "        gray = cm.get_cmap(\"Greys\", 18)\n",
    "        newcolors = gray(np.linspace(0, 1, 18))\n",
    "        newcolors[:1, :] = np.array([1.0, 0.9, 0.9, 1])\n",
    "        newcm = ListedColormap(newcolors)\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        x,\n",
    "        y,\n",
    "        figsize=(x * 2.5, y * 2.5),\n",
    "        subplot_kw={\"xticks\": (), \"yticks\": ()},\n",
    "    )\n",
    "\n",
    "    for ax, img in zip(axes.ravel(), digits):\n",
    "        ax.imshow(img, cmap=newcm)\n",
    "        for i in range(8):\n",
    "            for j in range(8):\n",
    "                if img[i, j] == -1:\n",
    "                    s = \"╳\"\n",
    "                    c = \"k\"\n",
    "                else:\n",
    "                    s = str(img[i, j])\n",
    "                    c = \"k\" if img[i, j] < 8 else \"w\"\n",
    "                _ = ax.text(j, i, s, color=c, ha=\"center\", va=\"center\")\n",
    "    fig.suptitle(title, y=0)\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6b4cf573-8f4d-4f8e-9c38-1528a875dce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_digits(digits, title=\"Digits with missing pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b81a57ad-a81f-445b-83b2-d2352e614167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coded for clarity, not for best vectorized speed\n",
    "# Function definition only; used in later cell\n",
    "def fill_missing(digit):\n",
    "    digit = digit.copy()\n",
    "    missing = np.where(digit == -1)\n",
    "    for y, x in zip(*missing):  # Pull off x/y position of pixel\n",
    "        # Do not want negative indices in slice\n",
    "        x_start = max(0, x - 1)\n",
    "        y_start = max(0, y - 1)\n",
    "        # No harm in index larger than size\n",
    "        x_end = x + 2\n",
    "        y_end = y + 2\n",
    "        # What if another -1 is in region? Remove all the -1s\n",
    "        region = digit[y_start:y_end, x_start:x_end].flatten()\n",
    "        region = region[region >= 0]\n",
    "        total = np.sum(region)\n",
    "        avg = total // region.size\n",
    "        digit[y, x] = avg\n",
    "    return digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a92431e8-ede0-4e84-ab13-3669b2f91d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new = np.empty_like(digits)\n",
    "for n in range(new.shape[0]):\n",
    "    new[n] = fill_missing(digits[n])\n",
    "\n",
    "show_digits(digits, title=\"Digits with missing pixels\"), show_digits(\n",
    "    new, title=\"Digits with imputed pixels\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41904e77-0f98-43c3-afd1-dcfd0c98200b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Reducing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731dd15c-6901-4a23-96cf-a42d103fbd16",
   "metadata": {},
   "source": [
    "### Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0aa854-2346-468c-b869-3cb615e5d6d0",
   "metadata": {},
   "source": [
    "The following initial examples are based on the iris datasets (directly loaded via scikit-learn). The dataset consists of 50 samples from three species of the iris flower and describes its sepals (Kelchblatt) and petals (Blütenblatt) (length and width). More information on the dataset can be found here: https://en.wikipedia.org/wiki/Iris_flower_data_set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a0a3fa-6e2d-47d1-8baf-eea4b8ffcbc6",
   "metadata": {},
   "source": [
    "#### Example 2D -> 1D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30aed5e-b026-44ce-9da4-ae698e02dfa9",
   "metadata": {},
   "source": [
    "Note that we could also load the iris dataset from scikit learn via the `load_iris` method and then convert it to a dataframe and reset the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "67acad73-88ee-445f-b303-af99f37af6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load iris dataset from seaborne\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "935e8acd-f27c-4cec-829f-36c0240da5d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y/1yTQU1rhyBCBKYuQxU4S1AmIBaJqLRW0epsHX+jBATOEfL0uWFt0/RFtDnbqLJWCyuSAiPC4/dQb6+n2laDw+ugSB9e/2OomiACAmOBEBMSIRpbuklOPHmQrkGnorvHgdfnRyoRtJ/A1CNNncoV5st4s/FtAgRQS1TcnHoD/+/Y72l3t6OSqPhKxpeZY5qNRDR0bJSAgMDpMdMwg52anZTaygFIU6eQpDLzwOGH8QV8ZGgy+GrGHSSpzRNsqcBkxeK28GbT23zU8gkBAhTpC1mffA2ZmgwqbVUAZGoymWuaPcGWCpwrCCIkQjS1WphZmHzS7yUSMXqtko4uK/Ex+nG0TEAgMqikKi5LvIS5pjlYvVYMUj2PVfyDdnc7AA6fg79VPM7/FD1Csjppgq0VEDi7iFPGst5wHYHUAP6AHzFifnnsNwQIAFBlq+Ltpne5I+M2pGLh0S4QTqm1jA9bPg5+PtJzlN1dGXwn5x6anM0ggkRlInqZbgKtFDiXEKbkI0RzW89J0/P2YzKohbgQgSmNQqIgXZPGNEMRnoCXRmdTyPe+gI8Od3jsiICAwJnj7fWQq8shX59Hlb0qKED62W85QK/gliVwEkp7y8LadnftRSqWkafPJU+XKwgQgXFFmC6JAC6XB5vdjV576tzaRr1KiAsROGvQSbWoJSrsPkewTYQIk8xEnb0ekUmML+Ab0jWr3dWBzWvDJDeil53eymCnq5Neby96mWHIeBUBgamK3eug3dWOXCwnThmLx++hzdWOOEZMo70JP36SlSlh22VrMtFIhNoh5yKBQIA2VxsOn5MoeRQ6WXjh5DR1KimqZGabZiFCRIW1khhFDIoJKirb7mrH5rVjlBsxnOZzQGBqI4iQCNDS3kuUUY1YLDplP71ORVuHMEslcHYQq4zlKxm381jFP/AFfIgQcXf2t/lv3csc7jmCRCThCt9lXBh3AdrjD0RfwMe+rgP8u+pJbD4bcYpYvpH1NbK0maM69mHLER6v/BcWTw8mmZGvZ32VAn3+WJymgMC40uRo4j9VT1NiLUUmknJl0hX4/D5ea3wDgEXRCxCLJAT8fm5OvZHnal8A+iYF1iVfhXyCXigFJg6P38P2jp08W/M8Tr+TZFUyX8+6k1R1qFDN0GaSo83m7aZ38QV8TNMXsSL2vHGP4et7Duzn31VPHX8OxPGNrK+O+jkgMPUR3LEiQFOrhehT1Ajpx6BV0iLUChE4i5hjms3/FD3C/bn38vOin3Ks9xiHe/pSifoCPjY2vE6FrTLYv9HRxGMVf8fmswHQ6mrj7xWP0+MZ+XXR4mzhz2WPYTm+TZenmz+XP0a7qz2CZyYgMP54/V7ebnqPEmspAJ6Al1fqNyARSwgc/29bxw6i5VHs7t5LIBDgocIH+F7ud3ik8EEyNOkTewICE0KtvY5/Vf0bp98JQL2jnmeqn8XhdYT0a3I08knbpmBa58M9R9jffXDc7W10NPHX8sHPgVb+XvHP4D1d4NxBECERoKmtB5NheBFiNKhpEWJCBM4ixCIxyeokZhpnYJDr2T1E8bQqW3Xw3+2u9rC6Bq2uNjrdXSM+ZrurI/iw7cfmtdHh7hyd8QICk4xeby97u/aFtdu8NmSiAceFWnsd8Yo4dnbtIkOTznTjNGKVseNpqsAkYqj06KXWcro9lpC2kt7SsH47OndhP0GsjDVtrnb8+EPaWl2tdI/iOSBwdiCIkAjQ3GrBoFMO28+gU9EmxIQInKWoJCoyNBlh7YnKhOC/h4r/0Eg0aCXDi/jB+xCfcOuSiaTopEJApcDURiVRka5JG7LdE/AGP8cr4uj0dJGnyxXSYQtgkBnC2uIUsailofFBKerwDJ452uwRx4Q4fU4a7A20OtsIBALDb3AShgp+10g1qKUjfw4InB0IIiQCNA1TLb0fo15FR5dtHCwSEBh/ZGIZaxPWoBn0IMnT5pKmTg1+TlYlcaX58uBnMWLuyLiNGGXMiI+ToIznptQbgp9FiLgl/WYSlPFneAYCAhOLUqLkmuR1qCUDSU6m6YuweQeeG4nKRGRiGSqxkqXRiyfCTIFJRqo6hZWxK4KfZSIpX06/NSzYO1+XT6ZmIO7CINOzOn7ViIRss6OZv5T/jQcOP8xDh3/KJ62bcHidw243FMnKIZ4D6bcRqxj5c0Dg7EAITI8ATW09FOYlDtvPoFPRabHh9weGDWIXEJhq+AN+DnYfYkXscqQiKWKRmBZnC22udhJUfashComCSxLWMtM4A4unh1hFDGbV8NfOYGRiGefHnke2LosudxdR8iiSVGbEImFORWDqk6XN5KdFD3HUUozF20OTo4kAAa4yX0GSyoxRbsDpc3FB3EqiFVETba7AJEAr03JdyjUsiVmE1WsjXhE35H01ThnLfbl3UW9vwBvwYlaaiR3BBFB/rNIhy2EAnH4nT9c8i1mVeFoJQZRSZchzIE4RQ+IonwMCZweCCIkALe09RI9gJUQmk6BUyLD02kcUQyIgMJWweq183vFFWHxHgjKB6cZpwc9KqfKMs6DIJXIyNOlCIK7AWYlWquPd5vdpcYX6+l8UeyE3Zdw4QVYJTGY0UjW5upxh+xlkBgyGcPetU9Hj7WVX1+6w9gZH42lnJYzEc0Bg6iNMHZ4hHo+P7h4HBt2pa4T0YzKohTS9AmclSrGSVFV47YI4RVxYW5vNRkVXBz0u13iYJiAQcbx+PzXd3dRauvH5/cNvMAoUYvmQsSHxcsHl8Fyk0+GgorODTsf4BpD3o5IoSVYlhbWbZMbxN0bgrGJSiJDKykquvPLK4P9z5szhySefnGizRkRrRy9GvQqJZGQ/pUGnoq1TECECZx9yiZwrk65ANcifPVubRY4uK/g5EAiwtbaadS89x+pnnuT2117laFt4ZhcBgclMq83K77Zt5aLnnmTNs0/yp53baLfbI7Z/qVjKJYlr0QxK2JCuTiVZHB5YLHB2s7epkZte/S+rn32SG159kT2NDeNug0qiYn3KdcjFAwHsRfoiYSVa4IyZFO5YmZmZvP766wD4fD6WL1/O6tWrJ9iqkdHS1kOMKbwy6cnQa5WCCBE4a8nUZvBI4YM0OpvwOb3kRudgHFTNvLyzkzvffA23ry9N776WJr7z/ju8cM31RKlGtpooIDDRbKqu4h97dgU//3nndjKNJq7ML4zYMdI16TxS9CANjgakIhkp6iSaK5pBcJ0/Z2jo7eHrb71Gx/EVkPLOTr721uu8fsPNJOtH51J1puTqsvlZ0UM0OZpRSpQkq5IxyIUq5wJnxqQQIYPZtm0bKSkpJCWFL/1NRpraLBgNI395EtL0CpztJKoSSFQlUFxcjDHRGPJdraU7KED6KevsoMnaK4gQgSlBIBBg47GjYe3vlJVGVIQAxCvjiFcOuDM20xzR/QtMbup7LEEB0k+X00F9T8+4ixAAs8qMWWUe9+MKnL1MOhHy9ttvc9lll52yj8vlori4eFT7dTqdo95mJBw6WoEo4KG2pnZE/X0eO6Xl3WNiy1id42Rjos6zoKDglN+fzrgcLVPpb9xvq1qtxmPwEyBAklqJQiLlyoIsUoxq9jd2squhGa/NRnH7xJ3XVPldT7RzMozJ4ZgKv+1obJTJZBRGx7KjoT6kfXZ8PMV1tXj9fpQeL36fD4lOgl8cQOwU4XF4gn3lWjk+mQ+xR4LH6h7Yt0qGXxlA7Bfh6/XhPyHWZKr8lsMxknE5Fc51OM70HMQKBRKRCN+gmhxikQiJ10txdTUeESj9fnzOsYutG3wOMoUMv9qPyC8mYPXjO2FCqR9VlAqP3IPUJ8Pd4Qobx+PNSMakwMQwqUSI2+3mk08+4bvf/e4p+ykUimEfvidSXFw86m1GwssfVJKRaiQ1LXX4zkCvS8bugzVjYstYneNkY7Ke5+mMy9EyWc99KIqLi4lOj2NP927erHgbp9/JspilbPjSWv5T/RQ7XS1kpefwjQVXMTMuvMjheNs6FX7X0do5HmNyOKbCbztaG68zGnizrIR2R18cyEWZ2WiUKm56503sHjd3LVjIoiwlz9c9T5e7m3mmOVyTfjWJqgQqrVU8XfMsVbZq0tVp3Jr+JbK0mTQ7W3i1biO7WndjlBm5Je1mZhqnIxUPPKanym85HCMZl1PhXIfjTM/B7fXyvSXn8ZvPtwTbfrLsfHxSKfd8+hEVXZ0sSk7hofNWUBAbngAkEvSfQ6uzjTca3+Tz2m1opVpuSruBucbZyE8odFjWW86zdS9QZi0nUZnAjSnXM9M0Y0xsGylTXcyezUwqEbJlyxaKioqIiZk6BWua23rISR/5xW/UC4HpAucOlbZKXqx7Kfh5c9sWtFINCrECgAp7Ga+1vEhe1PfQSkceWyUgMJHkx8Ty8nU3UtLRjkgEEpGIO998Lfi9XuvmrxX/JkDfDPaurj0A3JhyPY+W/RmLxwJAtb2GR0v/zMOFD7Cx/nV2dvXFmXR5uvhz+V95uPAnZGonVqALTBxyqZQvTZ/JPLOZxp5ezDodOoWCdf99DofXC8D2+jru/+Bdnrt6/Zi5tPoDfj5o+ZDP2j8HoMfbw98rHueBgh+Sp8sN9mt3dfBE1ZM0OZsAaHI289eKv/Oj/O8L41hgSCZFdqx+3n77bS699NKJNmNUtLT1YDKqR9y/r2q6IEIEzn7UajVl1rKw9t2de1gcszD4udZRR4erYzxNExA4Y9KMRtZkZbM6M5sDLaGxGhKZLShA+tndtZdOT1dQgPTT4+2hydnMjs6dIe0BAsGXOYFzF41cztzEJC7Py2euOYmGnp6gAOmnpKOdxt6eMbOh22Ph8/Yvwtob7KGZulqdrWFj1uV30ewUYpkEhmbSiBC73c4XX3zBmjVrJtqUEeP1+enotmEyjFyEqFVy3B4fDqd7+M4CApOQboeDhp4e3Cc8CPvxB/x0uDrwyf1Ey8MrOscr4pGLZFyWeAn5ujwUYgVKiRCULjB1mZ2QyB2z5pCkO54tyC8P62OUGVGIFYgQhbSLEKGWqIiRR4dtY5Ka6HB10u22hH0ncO5gc7up77FgdbvQKxRh3yulUjTy8DEXKZRixZD1nnQyXchnlUSFXCynUF/AZYmXMM80F4lIgkYqFGcWGJpJI0LUajU7duxAp9MN33mS0N7Ri16rQiaVjHgbkUiEyaCmvdM2hpYJCEQefyDA1toa1r/6IiuffoIfffwBlV2dIX06XJ28XLeBHx16kD81/4V0TRpxitjg91FyE6viL2BT2xbeaXoPiUjCXdnfDOkjIDBV6HE6eeHQAb734Xu8dPQwF2ZmcXluPmUtbop0A5myRIi4Lf1LJKoSWJd0Rcg+zo9dzua2raxPuS5EoFyTtI4jvUf50aGf8PCRn/J5+xdIlJPKg1pgHDjc2sJX33qNlU89wVffeA2JWMyt02eF9PnJeeeTbjCOmQ1qqZprk9chEQ2866Sr00hTh8bCpqiT+VbW1wF4p+k9Wl2t3JlxO+nq8MKbAgIwyWJCphpNbT1Ej8IVqx+TXk1bRy8pZtMYWCUgMDaUtLfxlTc24Dme6eS1kmLsHg9/uOgSVDIZAF90fME7ze8C4Pa7+V3Jo/ww//s0O5vxBLwkqcz8vvRRXP6+bC5Heo5i9znI0mYKs2UCU45djQ385NOPgp+fOrCPh5evZFlqGrHaZdTYa7F6rSQqE0lRJyMVS1kTv5psbTZl1nLEIjHFPcc42lNMta2aHxf8gA5XJwaZnhp7La82bAT6rqXHK5/g7tRvTdSpCkwALdZevv7W6zRZ+9L672is5xtvv84LV1/P6qxsWm1W0oxGCmLiEIlEw+zt9PEH/FRaq7jKfAWegAeJSILFY8Hi6SFuUAppp9/FhvrXqHXUAVBrr+Op6mf52bSHMTD+KYUFJj+CCDkDmlstRBlH/+Jk0KloFWqFCEwxKro6gwKknw8ry2m2Wskwmej19LKpdUvI9168lFiPcbm5L+32vq4DQQHST5WtinZXhyBCBKYcH1SGxzy9XlLMTdNnIpdImCGfHva9SqpCLVGzseH1kPZaRx0un4slMYuwe+08U/Nc2LZljnLmMTdyJyAwqamxWIICpJ9Wm41Wu5WlqeO3umDxWHiv5UPsPntIe4oqhRxddvBzu6s9KED6cfqdtDhbSFDGj4utAlOLiIqQqqoqnnjiCRobG/EO8hd/+umnI3mYSUNjiwXjKOJB+tHrlIIIEZgUuH1urF4rWqk2LNXiiQzli2xSqVBJ+24jcrGcWEUsIGKWcQZ2n4O9XfuIlkdTb23H6/eilobHfsjFchRiOY32JjRSNQa5MGMmMHnw+f202e2opVL0SmXId+enZSAWifmgopwuZ19RucKYWGxuN10+L7EaLWKRCIfXgcPnRC/TIRVLUUoUyERSPIG+52SaOpVcXS5R8mgaHU0oRAoSlAk0nRDQa5IJq+fnEjq5HBGEpDgQAVqZHJvbTa/bRZRShVx68lc5i9OJ0+sJjkWAHk8v/oAPo9w4IjsUYgUx8mhcfi3TjdOwem3s7dqH9oSJI4W4b1ynatLI0mTQ7GzhkOUwKokKl8+FzWsLedY4fU7sXkfwuhA494joX/3ee+/lhhtuYP369YjFkybcZMyob+4mJmr0s7dGvYqWtrHLZCEgMBJqbbW8Ur+BY72lFOjyuSZ5HamalJP2z4+JZVlKGlvraoJtjyxfScLxOC6FRMFV5ivZ0bWTbZ070Eg0XJt8NVLkPFrxfzh8Tq5OvIYFpvnBVKQA38z8Ghsb32B/1wESlPFck7yOGcbwGWQBgfGmrsfCUwf28vKRI5h1Oh4473wWJ6fS63LxdlkJf965HbFIxE3TZ3CwpZlOu51laelc/+qLtNps3DFrLqvyotjQ+Cr19gbmRc3lssRLiFfGc03y1bxY9xJXJV1Bvb0BhVjBO83vHhfuUaxLupJGeyMt7lYAouVRpEtHVo9K4OwgwxTF1+bM5x97B90v5y3E7fNx5xsbOdzWwgUZmdy9YDHZUaGJDdw+H1tra/j155tptdm4edpMbppRRJ27jJfrXsXld7E2YS3nxSwZduJHLVVzc9qNfNGxjS/at2OQ6VmffC0Z6vSQfvHKOL6d/S0+bdvElratJKmT+HrmnSjFKv5a/rfgs+a6lGtw+py8VPcyNfY6ZptmcYX5MsyqxIj9dgJTg4iKEKlUyk033RTJXU5qmlot5GWNfonRqFdzuKRxDCwSEBgZXe4uHi37Cx3uvtS4+y0HqHfU82DhA5hOMjsWp9Hyf6vXcri1hS6ng0xTFEWxoQHlhy0lfNL6KQA2r41na59nffK1tLnaAXi67im+knYn58UupdtjwaxM5LXGNzhkOQz01U34c/lj/DD/e2Rrs8bo7AUEhsfr9/Ovvbt55uB+AEo6XNzx+gY2rL+JWouFhzd9HOz71107+H8XXoRZp+OWja8EZ67lCiePlj+K29+XDXFr++d0u7u5O+dbrIhdTq42h40Nr9Pr7UUsErOzs+9ls97RwGMV/+DenLuosFYSp4gjV5dNR1UHJIznryAwkfQ4nbTarNy/aCkunxeFREqcWsPtb2ygx9Xn1vpmaQn1lh7+c+XVISt1R9pa+OqbG4Nj8W97djI3Q8FT9Y8H+7xc/wpqiZIL4lee0g5/wM8hy2E2t30GgN1n57naF8goTCeWgWeAw+fgjcY3qbRVAVBhraDJ0cTahDUcsBwC+p412dos3mp6B6e/r5L5to7tdLja+U7uvailo/cuEZi6RGS5oru7m+7ublauXMlzzz1Ha2trsK27uzsSh5iUNLVaiD6NmBCjQYgJEZhYWpytQQHST7u7g1Zn6ym3i9dqWZWZxbWF05iTaEYhlQ3s097Jts6tYdt0ejpRSwYeLG+3vEmcLInlscsQiURBAdKP2++m0SGIdIGJpdVm5b9HDoW0+QIBOux2XjxyMKz/J1WV9LjcIa4zKpUzKED6OdxzhHZXByqpCpVUyaGew0wzFLGna+8Jx/LR4mzh9cY3cfqdIQHAAucGNT3dbCwp5vfbP+evu3bw++2f02DtCQqQfva1NFHXE5rG+WhbW8hYTNTqKOkNrxz+ceunOHyOU9ph8Vj4tHVzSFuAALW20PiPNld7UID0Y/fZ8QV8IW3ugDsoQPoptZbTLtSLOueIyErI1VdfjUgkIhDoG/JPPPFE8DuRSMTHH398sk2nLC63F0uvA5Nh9PUN+rJjCQULBSYOpUQZ1iZCNGT7cHh8PrqdTmQSGXqZgXZ3e+ixxEo8g17E9BITSklffIlcJEMlUYU9BJXi0dshIBBJFBIpMWo1EpGYxSmpdNjtbK6pQiYRk2ow8Hno+xdpBiOyE9yQRQEZJ6IUK5GL+3ziZSI5SrESm9eGXqqjy9Md0ld2vN+JvvcCZy/991OtXI5a1hcTMjMhkfzoGEo7OpCIwueO5RJJMENhP4YTYvisbjc6abiQjVHEIBWFj9OQ/YvlGGUGbL7Q0gInJhNRiOVIRVK8gdAaUuITbB6c6rcfmUiGXHJqOwTOPiKyEvLJJ5/w8ccf8+677/LJJ5+E/P/OO+9E4hCTjubjqyCnE/ui1Shwujw4XZ4xsExAYHgSlQmsib8wpG11/KpRZzCp6OzkoU8/4pLnn+LBjz7lkrhLQx4wUXITCrEiGIArEUm4KH4tRmXfwytFk8I685Uh+8zRZoflnxcQGG+i1Wp+s+oilqWm8WlVJW02Kz89/wK8/gB50bGoB7306eRyLsvNIyc6mizTQIHOw412CnVFIftdn3ItsYoYAGKVMdyQup4dnbu4MP6CkH4pqmTEiIhXxJGhyRjDMxWYLFR2dfKzzZ9w6fNP8+133kQUCPCniy8jSqXiw8pyDEoFs+ITuCQ7N2S77yxcQtoJdUJmxCeQYRxIZGDzuJlmKEIv1QfbpCIplyZejGyYoHCNVMP1qetD6tgkKOLJ0KSH9ItXxrMu6aqQtvmmeWgkoWJFL9Uzxzg7pO2a5HVDFkQUOLuJaEzIDTfcwMaNG4dtOxtobLEQE6U9rW1FIhFRRjWt7b2kJoVXlBYQGGsUEgVXmC9jumE6ba42YhWxZGjSUEjCM2CdjB6nkx9+9B57m5sA2N3UwIKGJG5P/jZd3hZkIjkKfxRxSg03J+vw+D2YlckUmbJD9rMkZhFxylianM3oZXoy1OnEq4R0jgITi8/vZ1N1FS8c7nO9arPbONrexj0LFvPEvj18fe58PD4/CVot88xJ5Eb3CYsnrljHoZYWetwu8qNjSTEto9ZeQ5enmySVmTR1WkhNhyXRi0hUJtDp6uTu7G/1uWpJVMQqYuj1Wvle3ncEV6xzgF6Xi4c//Zgv6msB2FRTRZrByJbaaqq6uwD4tLqKis5O/n3F1azLL6TJ2kuGycT0uHgkJ0yIphqM/OfKq4NjsSAmliJTHA9ofkCVrQaP30O6JpXUEU74FOkLeKjwAWrtdWgkatI16cQpQ2MCxSIxF8SdT4YmjWZnC9GKKNLV6UhEYuKVcbS52olTxJKuSWe2aRbLbcvodHdhViWSrkkLWzEROPuJiAhpa2ujpaUFp9PJ0aNHg25ZVqsVh+PUvoZTlYaW7tOqEdJPlFFDS3uPIEIEJgydTMcM47TT3r62xxIUIABLU9P4z/591PVYkIvFuI/XFPnBkvP4xrzzT2nHbNMsZp+0h4DA+NNitfLsoQMhbV6/H4/fh8Xl5A/bv0AsEnHH7DncNH1msE+qwUjqCbPSMcqT3+cVEgX5+ryI2i4w9ajv6QkKkH70SkVQgPRT22Oh3WFnVebwiTuGGouJqkQSTyMLlVQsJUubSZY285T91FI1RYZCigyFIe1DZTw0yWeN2g6Bs4uIiJCtW7eyYcMGmpub+dWvfhVs12g03H///ZE4xKSjvqnrtILS+zHq1bS0CcHpAlMPl9eL3eNBIZEiFYvxHhcbvW4XJpWKKKWKu5fMxOLw8LNPt2JUhsd3ePxenD4HGqlGmP0SmJTIpRKiVKqwYnGDffL9gQBx6pM/B9w+Ny6/G6VEgdPnRCvVhqyC+AN+bF4bKokqrE6C1WtFJpKNanVSYOqikEqQSyS4fQNB3EPFfwCopFLcXi9WjwejUhms/zFSnD4nvoAfzTCZqDrd3aijTt0nEAhg9dpQiOXD1poSEDiRiIiQdevWsW7dOt5//30uuuiiSOxy0lPX1MX0vKTT3t6gV9HUZhm+o4DAJGJfUxOP7dpOSWc7d86ay9fnzuevu3YA8FlNNa/eeAXVrhLea38ejUTN49etJVEaep1U22p4u/FdKm2VzI+ax4q484VqugKTjhi1hgeWnc/d770VbMs0mrC6BzITGZVKlqQMXbm6rLecd5vfp0hfyMHuQ9Q7GlgYvYAVsecRp4yj2dHMx62fsrdrH9nabC5JXEuaJpVudzc7OnfxUcsnmORGrjJfQb4+TxDrZzlpBiP3LljM/20byDDY43Jy8/SZPDdoRe6GoulIxRK+9+F77Gtu4qKsHG6ePpMM0/CFLD1+D0d7jrKx/g2cficXJ6xljmk2Olmoa3mbs529Xfv4tG0TCrGCS6QXM8MwDdUJxWbbXO1sbfucre1fkKwyc3nSZUJqdYFREdGYkMbGRv7zn/+EtGm1WqZNm0ZBQUEkDzXhNDR3s3Lx6S+hmwwqGlsEESIwdSjr7OCW117G7ulLqPDTLZ/yoyXn8e8r1lHa0c6MuASqXId4pX5DcJvHKv7Od3LuIYW+QlptrjZ+W/J7er192eHebX6fekcD387+BirJ6DPNCQiMJasyMvnvNddzsKWZaLWaKJWao20tfHXOPGRiMTKJBJvHHbZdvb2e/1fyO9bEX8gr9a9iP5797e2md2h1tnBr2pf4T/XTHOstAaC9s4Pi3mIeLvwJ2zt28XL9KwC0ulr5v5Lf81DhA2RqheD0sxmJWMyN02dSFBfHsfY2UvRGZiUkIhOLWZGWQUVXB5mmKNKNRm569WXaHXYA/r1/DyUdbTx2yRXoFKdeNauwVvL70j8FP/+7+kkkIjHLYpeG9NvfvZ/n614Mfu6/j88yDbgdev1e3mp8m01tWwBod7dT3FvCI0UPkqQyn/HvIXBuEFERcvjwYQ4fPszKlX2Fbz799FPy8vJ48cUXWbt2LV/96lcjebgJw+fz09LWS4zpzGJCRlOwsNfl4g/bP2dfcxPzzUnctWAx+mFuOAICkaSsoz0oQPr5v21b+fCW21mRnkmLo4VXSz8L+T5AgDJrBTNNMwBodDQFBUg/hyyHaXO2n7Jau4DARKCUyZiflMz8pGTsHg/rX3mRo22tKKVS/IEAbp8Pp8fLfHNyyHb1jgbcfjcSkSQoQPrZ1bWHNQmrgwKkH4unhzp7PR+0fBDS7sdPla1aECHnAEalkuVpGSxPC/1br8rMYhV9Kwybq6uCAqSfz+tqqeuxUBh76gQGB0+oyQTwfsuHzIuaG0zP3uPpDRYlHMxhy5EQEdLh7mRLW2hdKJffRYO9QRAhAiMmoiKkubmZDRs2oNH0vZzffffdfP3rX+e5557j6quvPmtESGtHL1qNArn89H++KKNmxDEhLq+XL218mWiVmstz8/mirparXnyW569ZT4JWd9o2CAiMBLfPTYBAMCXpdYUFZMUYeO1IBQ29FhRiCb0uF1KRLKQoYT8qqRK3z42fAApxn3BOVacQp4ijylZNt6d72BSRAgITQa/LhVIqRSaRIBWLiFb1rdY5vQN1EIaKeZKJ+64V0RC++jKRDKlIihgxUfKovqxAiEhVp2KSGdFINFg8PSHbnE79HoGpSSAQwOp2o5LJkA5RAkAhDb9XSsVi5JLw2hsnopVqECMmX5+HQiznaM8xdFIdYsS4fH1uhlKRNKz+BwzUBHH4HEhFMiQiCQqJArlYRo42h053J5W2KmRiOYFAAIfPiUIiH7ImiIBAPxF98nd0dCCXDwQmyWQy2tvbUSqVIe1TnfqmLuJjzuzl36RX0Wmx4fX5kUpO7ev7553bUctkfHXOPEQiEYWxcbxeUsyXX9/AxvU3hRUpEhCIBG6fm+LeYt5sfAdvwMtlCZfz6pfWsLl9M1XOZm5fPodCzRKeP3KQ98rLWJ6SxiXT1/K3iscJHK/Vq5NqSVen8YfSP2H1WVkTfyH3ZH2LLzp30OBoZLphGvm6PGIVscNYIyAwfjT09PBm6TE2HDtCQUwct82cTU1XJ2uysvmirhbf8QyQOrmc5WnpYdunqVMxKxPpdHWSqk6l1j6Q9WhV/EqqrNV8LfMrFPeWUNpbRoYmDZVExdvN7/GltJv4R8W/sHj73HWNMiOZQp2Qc4La7m5ePXaEd8pKmZNo5ssz51AQG3pvzI2KZklyakgmrTtnzw2rEzIU0/XTkKRK2NW5G6vXxsWJF1GoK+BQzxHebuyr6Xap+RLWJqyhpLc0eB9XS1QU6gv4pOVTPm79lBh5NJeaL+YbmV/lcM9RjliOEKuM5ctptxIjj2ZDw2vs6txDri6b1fGrSFELq9wCQxNREXL55Zezfv16Vq1aBfQVMbzsssuw2+1kZZ09wUp1TV3EmE6vRkg/UqkEg1ZFa3sP5njjSfu12W08c3A/v7xgdcis2hW5+TT29vA/mz/hVxeeG8kABMaXMmt5iP+wDw//qvpXsLp5Y9PbdEV3UdcTS0VXJxVdnSjls7m34G7KrGWoJCoyNRn8sewvuPx9s2z/qvoPVyVdwZGeozh8DpqcTXS4uijUTcOgEFZDBCYet9fLY7t3BOuDlHd2sqm6ittmzuaN0mK+t3gZzTYrZp2e81LTyI8JF9Axihjuy72Hkt5SivSFuPwuqm01RCmiKO0tpcXZgtPvprinGIAmZxPFPSXMj5rLX8v/zn25d1Pccwy9TEeeLo9EVcK4/gYC44/N7eYXWzfxYWUFABVdnXxaXcmG9TeRrDcE+zm9XopiY5lnTqL1+Dh0+7y4fT5kw6yGOPwOXqj9b1Bc1NpriUo38Vzti8F79J/K/sIP877Hd3Pvo8RahkIkI1+fT5Wtiudq++JE6h0NWDy9pGtS+bRtMwCNziZKe8tYn3wtbzT2JXNocjZxsPswDxb+mBhFdGR/MIGzgoim2/j2t7/Nz372M3Q6HTqdjp/97GfcddddqNVqfve730XyUBNKTUPnaRcqHExMlJam1p5T9nnmwH4WJicTrQ51cxGJRNw2cw6f1lTxWU31GdsiIHAi2zt2hHy2enuDAiTYp3MHVxUNFLv62+59WHsNrE+5lkJHPuXWiuDDrZ9dnbsp0g/kkD/Uc5B6e/MYnIGAwOhp6O3lpSOHQtp63S7EIhG1Fgu/+eIzNh47iloqHVKA9BOvjGN57DIWxSxELVGzq2sPr9Zv5KDlMGnqtKAA6afL04Vaosbms9HqbOXKpMtZGbcC82nUdBCYetT1WIICpJ92u53yzs6QtvKuDv65bw9/272DDyvL+cP2z/nLrh3UWrqHPcbB7kNBAdLPR62fUqjPD2n7uPVTphuncW3yOnKtOcQqYnjz+EpJP0WGAra0h8aEOHwOLJ7QhDtdni6aHE0ICAxFxHP+FRUVsXbtWlavXk10dDSNjSMPvp4q1NR3EnuG7ljQFxfSdIoMWT6/n/8eOcSFGUOvIqllMr48cw4PfPIhrkE+ygIC/bh9PjyD8s4PRa/bPmS7RqpGjJg8XS4FunzEx317U1TJzDBMRyPVIBPJCCDigox0MoxGAGTivn5er3fIGgcKsQK3fyCjkEQkQSaWYHXb8foHbPX4vXj9wrgWGF+kYlGI371OLuf8tHRiNQMTQT0uFzJxX02HwXUdXF43Dq8TX8DXFwcV8OPxeVBIFPR6e4MvgAECiAiPF+m/XiRDxEh5/F4UKiEZydmKTCxGKhYTo1azIi2DDGNfyl3ZCe7a/bEfi5JTuHnGdPKiY5GIREjFEvyBQEi80okMlYFQJVbi8XvJ1+WRr8tDIpKExIT4/f7j8R+hLvXegBeZaAhX8CFKlkjFUgKBQDDuJLjvgB+3Lzy7nMC5Q0T9H5555hn+8pe/EBMTg3hQQNWbb74ZycNMOHWNnaw9v3D4jsMQZVRT39x10u93NtSjVyjCKp4OZlZCIpuqq/jn3t2s0hlO2k/g3MLmdrOtvpZ/79uLXCLhzjnzWGBOQj7o5arMUsu2jm2U2Y+Ro85ncfQicgwDNQ8WRi3EKDOxr3s/voCPeHkct6TdTElvCa3ONpbHLCNFnUKbtRlzxmFm56aSJDmPnKi+ZXe/30+hrgCVRBVcQREhYmn0kpD0j6tiV1Ftq+a5uqdJUCSxKm4lvf5uPmj+CJlIxsWJF5Gnyw0r5iYgMBYk6w3cu3Axv9q6hSvz8knS6dlaW8O2ujoePG8Ff9m1ncKYWPRKBbe//ioSkZh7Fy4CRRc7OrZTaMzjUPdhOj2dzDPNweLpIUYRw5XmK3i98Q2gLyPc8thlIVmIsjSZiBBxS9rNpKsHVhddPhdHeop5v/kDJEi4xLKWPF1uMPhd4Owg1WDk/114EQdamtnd2MCM+ATumD2H3OiYkH45UdE8v/5iDlp3Ue/8hJuXTCdbuQin18MDH3/A0bZWri0sYk1WTljimkJDAcpGJU6/E+i7H1+eeCn1znr2dffVIrku+Wpytbkh2+lkOq5LvobHKv4RbDtqOcaV5sv5b/3LwbZEZTxx8hNiWLQ5aCQaXq3fwAHLYWYZZ7AkegnegIfNbVso7S1jYdQCFkTNI1YpxAaea0T0qf7000/z3nvvYRpB0ZypisvtpbPbTvQZpOftJ8qkoa7h5CLkrbIS5puHL4h44/QZPLLpY2YvXHbGNgmcHWxvqONrb70e/Lyltprnr17PouS+AMEWewdP1f6HOkdfcGOtvZYy2zHuyrqbeHUUAE6/kxfq/hvcxzFrCR+1fILNZwOg2l7DBbErKLOWU+eop4ZaYuUlLJfmAH2zxqmaFB4o+AGHLUexe21MN0xHiYZrzTfQ6GwgX5uDxWvhufpngD5f41StmZfqXgke93DPEX6c/wPy9KEPRgGBsUAkErG+cDqFMbFsra3lsd07ATjY2sKmmip+v+ZiAL7x9hvBbb66KJt/V/yVdclX8mzN83gCfamsK21VXJxwEW83vcvi6EV8Nf0OSq3lZOsyydBkME1fxCHLEQwyPU6/kxfrXkIn1ZEzqOBbcc8x/lj25+DnIyVH+VH+9yk4wYVGYGrj9fv5uKqSt8v6UjcfaWtlZ0MdK9IzQ/rZfL38t+lJOj0dANTYa2nS1SPtmcVLR/tS8B7e3Ep9Tw8/WLo8JMOW3evgcvMldHssuP0eEpTxOPwOXqwbEBIlvaV8P+/+MPtmGmfwg7zvcthyBJPcRJGhAKPMSJI6kSOWYhJUCRTqC1BLVNyd/S1KektJVaeSrc3i7xWPU22vAfqeNUcsR0lXp/Fx26fBc6ix1/CVjNuHXD0XOHuJqAhJSEhApzu7U8bWN3URE6VFMkxGq5EQY9KybU/lkN8FAgE+rqrg+0vOG3Y/8Rot56Wm8VxFKUtnzTpjuwSmNl6/n6f27w1rf7P0WFCE1NubggKkn1pHLfX2xqAIOTEmRIQoKED62dz+GRcnXESdox6ANncbjY5GouQDExGp6lRSB83sAqTp+8R1maWWJ0qeCLbn6nLY17U/pG+AALu79ggiRGDcMCiVZJmi+eY7oav4do8Hh8fDW2WlwbYUvYF6VxUBAnj8nqAA6eeLjm3MMc7mo5aP+NWMX7AsbqAwXH8Fa4unJ7hdr7eXOnsDaZo0/AE/H7Z8HGbf9o4dggg5y6i1dAcFSD9NVivlHR0k6fTBtjp7Q1CA9HOo9wC3mZeHtD11YB83T59F2nE3WYD93Qf4oOXDoCttjcyASR4+aby5dQvTDEUhbUqJkiJDIUWGUC+QmcaZzDTODGmbFzWXeVFzASjtLQsKkH4qbJVMN0wLadvRuYsrzJeRrA6tuSNwdhNREZKSksItt9zCihUrQlLy3n777ZE8zIRSXd9BQqx++I4jIDZKS2OLhUAgEJZP/lhHOxKRGPMI64BckVvAd99/m+ruLtKNZ+9KlMCp8QcCiEUiVLLwlNgq2cDlLj5J7vbBOd3l4hNmpIbw9ZWIJPgD/pPuYzjEIjESkTi4D1/Ah0wsQ4yYLG0m3oCXKls1CvHZk+JbYGogEYtYnJRCaWcHNYOCfiViSci15PP7iJFFk6XNRDxEmKVMJMMb8CE5XhukH3/Aj0wkpdtjwRsI9eOXiAb6DTX2w65NgSmPRCxGIhKhUyjIj4mlzmKhobcHyQm1QqTi8PtrX3xR6A1aJpEgEYe2yY+78MXIo5GL5bj8bmSi8NdARQTq0vgD/uP395M8D054nogRIxJFPExZYJIT0b+42Wxm6dKleDwebDZb8P+ziZr6DmIjkBkLQKPue7hYeh1h331WU830uPghi10NhU6hYHFcAn/Y/sWwfQOBAEcsR9lY/zqftW0NCxYTmHq0WK28ePggN7zyX37x2SZunDY95B4vE4u5NDsv+DlVnUShNnQmqkg3gxT1QKXbhdHzQx4gbp+bWHmof/LFCWvY1bU7+DlTk4l5FNVyUzUJrIxZHfxc2lvG8uhlXJtyNXKxHL1Mz02p1zPXNHfE+xQQOFNarFY+qqyky+lkTqKZHyw5D6VUSoxajd3j5rqCacSq1WSYjNy3vIBi2yEUYgUp6mS00tDnw3mxy9jTtZerk64iWhFFi6OFNxvf5hfFv2Zf9wG+kh46SReniCP1eF0FsUjM6oQLQ4LYJSIJC6Pnj/2PIDCupOoN/PKCNVxTUITH52NpahqPLF9JbnRoatsUdRIpyrSQtiWmZeyoDl0duXvB4pAVFICZhhnclHoDUXITMrGMhVHzWBqzJEQcixGzPPb0XbvbXR180Pwhvyz+Da/UbUAmkjHXODukz1zTHHo9ocWaV8evIk6oF3XOEdGVkLvuugsAh8OBShWeheFsoLK2g5SkyKw0iEQi4mN01Dd1Y9SHpuDdWlvDvBHEgwxmSVw8fzx2hMquTjJNUUP28fq9PF75LyqsleTqcjnSc5TXG9/ku7nfEXLRT1G8fj//2b+Xx/fuAmB3UwP7mhp46qpr2VRdiUIiZXVWFjPiB1J9Rqv0XJ9yPUd7ZlJpKydTk02hPp9o1UByg0xNBg8U/JC9Xfvw+r3MNs5EJVFh9VrpdHeRok5GKVZznmEtlc4STJIEvPYonC4xhhHGzMokUi6IW4lZaeZo7xHMyiQkYmlITMjB7kM8kP/DyPxYAgLD4PX7+ff+Pfxzb5+43t3UQLRKxS8uWE19j4VHNn0CwBNXrMMlaeWp+r8Hs16VWyv4ZtbXqLJW0+XtJk+bQ4erk69m3kGuNher18q/q5/iWG9JsH+WJovv5NzDto4dmFWJTDdMI0k9cO/P0Wbz44IfsKdzLyK/iAWx88nQpI/vjyIw5viB7Q21bDzWl7p5T1MjKXoDa7KyQ/qJ/UpWGq+mO1BLo6ueTHUOMk8sFxaZidPqKevoYGVGJgvNyWGTmGKxmBdrX8JP38rz0Z5i7su5mwcKfsierr2AiLmm2WRpQ+NQRorL5+KlulfY0dkXR1VmLWdn527uzbmLmcaZlFnLyNPlUqgvwBvwkaQyU2WrDhatFZItnHtEVITs27ePn/zkJ9jtdjZt2sSxY8d48cUX+elPfxrJw0wo1fXtzJ+ZNnzHERIbpaO+qYtpeQOzx16/n33NjXxpxqxR7UspkXJRVg5/3LGNP669dMg+/6l+mk53F7el3xK84A92H+LXx/6PnxU9hFFuPN1TEZggGnp6ePJAaAzI/pYW7B43Dy5fedLt0nVm0nVm4IIhvxeLxGRrs8g+HiS7u3MPrzZsRCPRoJfp2du9DxEi5kiu550jClpsdVjdFRSY0ojXjny1MFETTaJmCSsTl+D1e/ltyR9Cvg8QYE/XXnL1OSPep4DA6dLQY+HJE2KqOhwO6nssISvNLdZe7OqykLoLLr+LD1s+5ru59yEZwm2mwloRFCDBNlsFGqmGb2Z/bUh7pGIpebpc8nS5lJWVnfYLosDkptbSzWvHQmvH1PVYKOvsIHHQikZ5Vydffe19ErVa8qNj+WfTYaxuN+/cdCtfmT3vlMc41H04KED6eaPxLX6U/31ydNkn2WrktLraggKknxZXC12eLs6PO4/z40JjXOOVQz97BM4dIuqO9ctf/pInnngC4/FAqPz8fHbv3n3qjaYQXq+PxhYL8RGoEdJPdJSG2sbQYkRH2lqJUWvQK0bv97s6M4vNNVXUdHeHfbejYxfHeku43HxpyIzDDON0phuK+FvF42H+/QKTH5GIIWsOjNSVb+TH6btd2Hw2mpxN+AI+xCIxHr+fiq5OrO6+fO/i48fVaE4vg5xkCL9g8RAvdAICkUKpHPCBF4lEiIcYg/5An9gQi0QUxcahUyjD/N0TlPGYlYlhBeEGGPqaHOm16hXqQZ21iBANOQ7EJ7T1f2yyWvm0pgqr2308ImT4MdR/b01UJpKuTkOECKlIOqJtR4Lo+H9DtQsIDEXEo4ASE0Oru4rFZ0+gUX1zN0aDGrk8cgtIcdE6qutCfTn3NDaE5QYfKWqZnFUZWfx9T+hshNPn5Lna51kbvwb5EIGOi6MXYfVa2dK2New7gclNst7AN+aF+oj3zZKNbgxZPVZ2duziDyV/4sXal6ixhWbPSlUnY5KFuiKeH7WKN48OZD65flo+fkUrvzv2J95zfcLBztBZ3+GQiqVcnHhRSJtEJGGuafZJthAQOH2qu7v4595d/OzAXp45uI+GHgtJOj1fnxt6PZm1OuweDzPjE/j+4mUkaLV8UFFGurIAiUiCQabnhpT1pGvS6XB3crjnKA6vM+x4icoEZhimh7QV6Quxeqz8ruRRNtS/ToPj7CvwKxBOh93G68eO8pU3NvB/X3yGiAA3FM0I6ZMbFU2MTswrtW/z6+Lf8nLNW0RpAxREh8ZOXF80nfRBWbBOxgzDDG5MuZ4UdTIGuYHrkq/hqqQrkEsik/gjThEbFk+SqkohaRRxggLnFhF1x0pMTGTv3r2IRCI8Hg9PP/00WVlDV/ueilTWtmOOj2xBwLgYHZu3l4a07WyoDxZ8Ox0uysrm+x++z/2LlhJ7fDb6veYPSFYlkaweOs5ELBJzYfwFvFq/gYXR84esrCowORGLRHxp+iyyTFG8U1ZKUVwcazKzSTlFkcuh2N65g2dqngdgv+UAm9u28GDBj4P+6W09AeYprsKpqafb10aiLAezJJ11+d3sbWqkIDaWuZliHq/+W3CfO7u3c5/ofqaZRp5eN0+Xxw/zvscXHdtQiJUsil5ApiZjVOciIDAcHXYb33n/HQ60NAN9tXQ+z6zlt2vW8qUZfdfTu+V919MF6Zm02qykGow89OlHwX3sbKjjf9d+C7/YxtM1T+M5nuVqb/c+vp39TRZEhbrHqKVqbk3/Eoe6D3G45yiF+gJUYiV/KPsTAActh9javpUH8n9IjPL0JqIEJj+BQIAXDh/i99s/B+DT6ipeOnKI36y6CKNSydG2VtKNRlZmpPJK44uU2fomc4p7iynuPcSfLr2DTyvr2NPUwAUZWZyXmoZCOvzrnA8fL9W/gi/gA+BA90Huyb4rYucll8i5ynwF2dps9ncfIFebwyzTTMHNW+CkRFSE/PSnP+UXv/gFLS0tLF++nKVLl/Lwww9H8hATSkVNG/HRkUnP209ctI7GFgs+nz9Ye2RfcxMXZ59+TQS9Qsni5BSePLCX7y85D4fPwQfNH3FT6g2n3C5BmUCqOpX3mz/kqqQrTvv4AuNPtFrNZbn5XJZ7erUDut3dvNbwRkib3eeg1l4XFCFH21p5+KMdxKo1xGmMlHUeA47x+9UX835FGSt1yXzYGlpXwRPwUGYtH5UIkYllFBoKKDQUnNa5CAiMhIquzqAA6eeDynKqu7uZFhfP5Xn5XJ43cD1lGE389fVXQ/rX9vSwqbSLtKTuoADp582Gt5iuL0IlDZ3QiVXEcEH8Si6IX0mLo5UfH34w5PsOdyf1jgZBhJzFNPb28rfdoXWYOhwODrW18J/9e8g0RXGwtZmlWYagAOmnwl5Oh6eNO+fM405OHQNyIoctR4ICpJ93mt5lmqEwYkUCoxRRLI9ddkYZtgTOHSIqQqKiovjd734XyV1OKsqrW8nJiI/oPhVyKTqNkua2HpISjDT19uLx+Yg7TX/6ftZm5/A/Wz7lrvmL+KxjK6nqFKIVQ2fMGsySmMU8V/MCFyWsFlZDziECx/8Pbx9o7ffqbbPbaLP3pd6WS/p84n2BAP5A3xYiRJhViTh8TjrdofFOAgKTh9H7qQf8Q8d6qCUqEpUJNDkHRM3JokJC9neSXiPZVmDyI5MNne0pcIqoIYfXy5G21mH2fHojJHA8rilGHo1MLKfJ2XQKSwQExp6IiJCf//znpwyse/DBB0/6HUBPTw8PPvggpaWliEQifvnLXzJ79uTzAa+sbef8RZGv2pwQp6emoYOkBCMHWprJjoo646DiBK2OnKhoNhw7wj7/x1wUf+GItouSm0jXpPFp62YuSVx7RjYIjB9Ntg6Ke45xqOcgCcpEZhtnkWtIH/H2JrmRK82X81ztC8E2lUSFXhzH/2z+BI/Pz6rMTOYmmNnTPOCz/sj5K6mxdJOk09Pt8HJZxpV0e9uptFWhkapJUCaQoRpd1hW318ve5ibeLD2GSibl0px8ZsYnhAVoCgicCZmmKKbFxnO4rSXYtio9c0jf+g67nZ0N9VyZXxAy/pP1OlbmRlFqP0iqOpXzY5fzWftWGhyNLI9dxra2gxxtcJNtjGdhUjImVWgq9hhFNBfGXcD7LR8G20wyE8mq0aVnF5g8uLwe9jYdv39JJFyu1TAzPiHkmW7W6fnG3AU8umMg21qUShVW16PVEiBbnUu5fcBlO0OdRYzCwMbad6hxVJOvLaDQUEiH1cfHlZVUdnWwIj2TxcmppBhC3cdnGmegkMhpcDTi8rtYHruMZFVyxFZBBARGS0REyLRp04bvdAp+8YtfcN555/GnP/0Jt9uN0xke0DfR2B1uOrvtEStUOJi4aB3V9Z0smZvF/uamiFU8X5OVw992b2PxHDFJo3iozTPN5Y3GN1kTfyFScUQXywTGAK/fx6a2TbzX+k5fgwW2dW7l3qzvkKEf+d99cfQiDDI9n7V/TqIykRm6Odz6ynt0H78enz98gMcvv4qttTWUdXRwfdF0ttRW82rxEQC+qKuhKGUuL9UP1PhQiBU8WPCjUZ3PjoZ6bhvk9vLMgf28eO31zE4QghsFIodYJGJdfgFzEs2UdrZTFBtHmsEwZCafjyor+PEnHzArIZEfLjmP7Q11RKlUXDcrmcdr/hJ0cdnZuYvb0m/B5rWxu2sPJb2lXBR9DT/+6CDfmr+Ar84JDXjvS8SwlmR1Etvbd5KlzWRh9AJiBVesKcv2+npuf2ND8PNzRw7x0rU3MCN+oA6XWCTipukzSDEY2Fh8hMLYOK7MK8CoVPK/Ky/knbJSFiQlsSAxg9miGzjUc5ByezFZqjxmmmbwz+p/0ehsAGCfZQ/nOc9jZ7GOjyqrAXivopxvzVvI/YuWhCQHChDg1fqNQdfBnZ27+U7OPePwqwgIDE1E3jDXrVs3on4///nPeeihh0Laent72bVrF7/+9a8BkMvlyOWRydQQSSpq2jDHG4JxG5EkLkZHZW0bAAdamlieFpkg3MKYWBw+Bxp3zqhWVhJVCehkOvZ172d+1Oh8TgXGn1prEx+3fRjS1uXpotZePyoRopNpWRi9gIXRCwD4n82fBAUI9DkAvHTkEH+79ErEIhHH2tu47/23g9+vykzjk/YPQvbp8rso6S0jVZM6Ihs8Ph9P7NsT2ub381FFhSBCBCJKRWcHP/9sEzFqNWkGI68WH6Hb6WR2gpmiuAG326be3mAh0P3NTRxsaWZaXDzXFkyj2nkgxMc+QIBdnbvx+D2UWssA2NX7KZfmreBPO7ZxaU4e5hNmu01yI8tjz2N5bGgNBYGph8vr5R97doW0uX0+Pq2qDBEhADFqDevyC1mXXxjSftP0mdw0feaglmjyTWnA5QBsa90XFCD9bO3YyuKUm4IiBOA/+/dwWW4e+TEDmbQOWw6HxS693fQuhfqCiGXIEhAYDeM6zb13796wtvr6eqKiovjxj3/MsWPHKCoq4ic/+QlqtXqIPfThcrkoLi4+6fdD4XQ6R73NYLbuqEGnkVBbUzt851Ei8jk4WlrP0aNHOdTSzIXRcdTU1Ay/4Qm43e6Q7dwBNzp9J7tKVCxSjm5/yd4kXqt8A23LmcWmjAVn+rc8XQoKTh0ofTrjcrQMee6xirACVAA+v/e07ZHL5diP1/0YjMvro7q6GpfTSa9CHuJNLJGI8eM7blIMTp+LXm8vLo+TkpIS/P4BG8ViMVKpFPcJx1ColLh84bUQbG4X5eXleDye0zqf4ZioMTVaTrRzMozJ4Zisv22vpG9ipt1up91uD7Zbenup6LXi9Xrx+/2I9Xq8g8auPxDgYEsz3U4HXqkrbL/egDdYZ0QtURMlN+GXiPH6A3R0dmKpbwjbZqRM1t9yMCPxZBjJuJwK53oiMpUSt88X1u5wuykrK4tInRevMXwfAQKcOM/oCwRwOgd+Z4lEglPed781yPTIRDLa3R14Ah7qG+px2cLHMkzNv8OJTEbvGoE+JtzXxuv1cvToUR566CFmzpzJ//7v//L4449z3333nXQbhUIx7MP3RIqLi0e9zWBe/7SW3MwkUtNGNqM7GmLi3Lz+SRmqxAQ0cgVF2adXubSmpoa0tIFq7vu79lMUMPDZETeKqFgSdCcXdieSHEjmYMXjGNINmCdZju8z/VuOFaczLkfLUOfu9LpZFrWczR2fBtu0Ui2pmlSyzSmnfax1SjkvFx8JERpfnjWbzPS+lTqb281FmTm8X9k34/tJRQ2/yV+LX2qjzl6PSqIkWh5Fkb6Q9JT04D5qLd1sra3hSEMrS5JTWZCUHEwlDfAVv48dDfXBz2KRiEvz8sk2J5/2uQzHZB1TJzJaO8djTA7HZP1tY2w28qNjONbRHmxblpKKTyrl3xVlFMXGsSw1jVSDkdtmzuZ/P9sU7CeXSMiKisKkX8in7ZtDgntnGWaysfF1rjJfgcPvxOaxYTTL+VnMcopS084otmmy/paDGckL60jG5VQ416H4qs/LN98eyDQoFolYnZ1DjjkycT61vc1Ey2PocA+M27nGuewr7w7pd0PRdIoSEpAlD9w3pVYZGrmadnc7Lr+bVaokEhUJZEZlnvR4U/XvMJipLqLOZiZchCQkJJCQkMDMmX3Lj2vXruXxxx+fYKvCKa1s4ZILziz25WSoVXIUcinbSmvINEUmHgTgsOUombp0rGYf7xyr4Y75I7+RSEQSZhim8UnrJr6UdlPEbBKIPEqpnNXxq4lVxLGvZzcJ8iSWRS8l23D6AgRgdnwiz6y7jqf278Xl9/HlmbNZmDSwT41czgPnnc9cs5l3yks5LzUNvVLOP6qeDPZRSVRkqgfSnLbZbNzz7tscbO3LIvTC4YN8ZfZcvr94GfLjee4XJ6fyr8uv4qn9+1DLZdw2cw6z4kOLoAoInCmxGg1/ufhyXi8p5rO6GlZnZJGg1YXEI82MT+Dxy67k4uwcNHI5G4qPEKNWc+O0GSwwJ1Pc3sIVMbdR7tqDL+AlTzmPNJWZL6XeyGuNbw7KDvc5N6bcICRXOAdYlpLK45ddydMH9qGRyfjy7LnMTIjc/Ust0XFd/G0cs++nzllFnmYa6YoCFs1UoZNpqOzq5KLsbC5Iz0R2Qu0Qh8fLa41v4Pb3rYiIEPGtzG9HzDYBgdEyriKkPz3cYGJjY0lISKCyspLMzEy2bds26Qocen1+aho6I16ocDDmeAO7SutITYjMMXo8vbS621iiXIwy2c0/dx3lljm5yI6nVB0J0w3TeLbmBa5PuQ6ZeOhUgwKTgxRtPCnaNazxrkAhjYxvr1wqZUlKKguT+mbSJOLweKgUg4E758zjtpmzsXrs/LbstyHfO3wOynrLKDD2zbSVdrYHBUg/T+7fy/VF08k+XqBTI5dzQUYWy9MyEItEwoubwJiRGRXFdxYv5crEJEQGPWuefTLk+wMtzZR1drAkJY3ri6ZzVW4+MokkGOz7Tlk5f9+zk5kJCcjEYv7TvI8VaV18dWlqWHrq1xpfZ37U3BGlSheYumjkCi7MzGZFeia11dVkJp3ZZNCJlHV0cPPGd1lgTmZWwlKe2FtFZfdbvHvTrfy/1Rfh8HhQnSQ18OGeI0EBAn1uXB+0vE+RvgCNXBlROwUERsK4ipBbb711yPaHHnqI733ve3g8HlJSUvjVr341nmYNS21DByaDGqVi7F7E42P07Ktu5ZL86RHZX0lvCcmqJCQiCTEaFYk6DZ9VN3FB1shdWkxyE3HKWPZ27Wdh9PzhNxCYcCIlQAYzlPg4EZlEgs/twxMIj9kY/NDz+IaIXQkEQnzu+5GO4LiR4kxTYgtMbXweDz6fjziNlg6HPcSvf/CYVZzwcmd1uwkA+5ubB7V58PnD4wK8fs+QsVsCZydSsXhMYtg8x8fWzsZ6djYOuK16j9ewOZkAAXD5hojz87vwCeNSYIKIiAj5xje+ccrv//73vwNw9dVXD/l9QUEBGzZsGPK7yUBJZSvJiZFzkxqKhFg97TvLyYhQet6jPcXkanOCn+cnx/FmcfWoRAjANH0Rm9o2CyLkHMDmdrO/uYkv6mpJ0Go5Py0dC40c6y3BF/CRr8snXhFLibWUFmcL+fp8srVZaKR9sUYxKiMrolfx34bng/uUiCTk6gbGYU5UNAkaLc02a7BtbVYOqfqxW2U8GZ3dNg4WN3CktJFUswFTdA/xsfrhNzwHaGjuZt+ROmoaOplVmMy0fDMG7dlTvLTOYmFnQx0B+jKylXd2kud28rU58+h0Omi12fjvkUMkaLTBFbqhuDgnh2cO7Q9p+/Ks2aSoNSjFSpz+gYDYVXGriJYLqyCRwGZ3cbi0kb2HakmMNzBnWiqp5snx23p8Pg62NLO1rgYZInxtrRTGxoX1a7Va2dXYwM6GepL0ehYnpzD9hAxaQ5EdFY1Zq6PR2htsW52ZRaph+HvodEMRn7R/EBLDtDJmFfWOGo60HAWgUF9AgS4/JLXvmdLp6qLUWkaVtYpMbSa5umxM8rF9pxKYGkREhNxxxx2R2M2k5VhF85i6YgFoDArE1gBG5ZkvifZ6eulwd5KgGkgzmR9r4u2Saio7e8iMGvmLVq4uh49aP6HT3UWUcNM4q/mwspz7P3g3+PnFGy/k8eq/BVc3Pmj5kJtTb+TpmmcBeKf5PW5OvZE1CQOFMOeaZiMTSdnauQWtRMequFUUGAYSLSTp9fz7yqt54dBBdjU1cFlOHpfl5qEe57TcLreXp17Zzqvv7gu2LZpTyUP3XIJBd/a8bJ8ObZ29PPjbNyir6qva/MLru/j6zedx81ULEIun/opRm83Kfe+/TZxGQ4/Lxbb6uuB3l+Xk0Wy1opbJeHj5ChYnp5KkP/n9ck6CmaevuobH9+zG4fVw55x5LElOpcPTzPqUazjSc5Q2VwdF+gLSNenBrFkCZ8bHX5Tw//42kA48KcHAHx5Zjzlu/CczTmR3YwO3vPYK/uPu53/bu4sXr7mBorhQIfJ6STG/+nxL8HOqwcDfL70yJKXuUJh1ep644mpeOHyAnY0NXJKdwxV5BWhGcA8tMORwV+Y9fNz2IQ6fg+XRK0lUR/Pb0j/gPZ66973mD/hO7j1MMxSN9tSHxOF18GLdS+zo3NnX0AJLo5dwS/pNqCTn9r1WIEIiZMGCBZHYzaTlWHkzKxZHvlL6YOxyH2J7AL8/gERyZg/6MmsZZmUiYgbiPyRiEfOS4niruJp7ls4Y8b5kYhn5ujw+b/uCy5MuPSO7BCYv7XYbv/n8s+Dn+eYkjloPhLhX+QI+DlkOka5Jo9rWl/L51fqNzDHNIkbRV1wtXh3FavX5LI6ZT2dbB6nR4f7Q+TGxPLLiApxeL+pTuA6MJfVNXWx4b19I2/a9VVTXdzCzYOyycE0FKmvagwKknydf3sbKJXkkJxgnxqgIUtrRwb7mJu5buCSkYjXA22Ul3L1gMX/auY17Fi4mb5gXQrlUyrLUdBYkpRAIBFAcDwTe21PH0zXPka5OwyQ3sqV9K5vbPsOsTBhxzRyBoWnrsPL3Z7aEtDU0Wyivap1wEeL2+fj7nl1BAQJg83jYVFMVIkLKOjr4y64dIdvWWiwcbWsdVoQA5MXE8PD5o7+HKqVy5sXMoMiQhz8QQCNX8o+KfwYFCPSlmP68fVvEREiTs3lAgBzn844vWJ2wigxNekSOITB1iei0THV1Nffccw+XXHIJq1atCv4/lfH5/FTWtpMyxu5YDdZe5CopHe3W4TsPQ3FPCcnq8HSA85Lj+Ky6CZt7dH6q0wyFbGn/bMjEAgJnB16/H5tnwF/YoFBi9znC+jl8ThRiRfCz2+/GFwj3J9bK1dh7bSc9nlgkmjABAuD2+BhqOHs84b785xruIX4Dt8eLz3t2/Db98R4njluxSIRRqQICZBhMeIeo93Ay5BJJUIBAX/wHQLW9hgPdB7F5bX2+90NcKwKjw+fz4XCGP8Nc7jOvwXGm+Px+LM6++6ZOLkd1fExYXKH3Uq/fh9Pbdw5GpRL58YQxrlHUETmTe6hKpggGotu99rDvB7edabycxz/0+4bXP/F/L4GJJ6KB6T/+8Y+55557+OUvf8nTTz/Nhg0bQgqUTUXqmrrQaZSoVWPrLlLZ3YU+Sk1To4W4+NP3S3f4HDQ5m1kUHb46pVfIyY0x8GFZHVcVnTwv+ImYlWb8AT/l1gpydKdXw0Rg8tHttlBuraDOUU+KKon/XbWc+979CIBPqiq4beGysBms6YZpbGh4Lfj5vJhlVLU7eKlhG5mmKGYmJNBitbKnqQGlSIyko52c6JjxPK0gpZUtHChuIBAIMKMgibzM+OADNTnByIyCZA4WDwR2JsTqSU0anV95r83JkdImjpU3kZRgZFpeEomTwCXkTEhPiUanVdJrHYhnWL2sgIQ4A909do6UNlFW1UJqUjT5WfE0tVo4XNJEXIyW6flJJCdMvNvmsfY2djU24PJ6mWdOYnpcfDDBQnZUNNEqFd1OJ8l6PfU9PVySk0tedAw2t5tErY6vzZ1HeVcnRqWS3JhY3F4vB1ub2d3YiFGpZL45iayTxIo0OZqJU8ahECtw+QcKwJ0XsxSzSkg1fabERuu45pLZvPjG7mCbUiEjIyWa7XsrOVbRTFKCien5SchlEo6UNlJW3UZmagzTc81ER2nHzDaVTMY35y+kqquLdrsNqViCRi5jcXLoinC60cTdCxYjFolosVnRKxT4/H4KY+J47dhRittaSTEYmRmfMKI4kTNhScxi9lsOhrQtjVlMeW8FZdYyRFoxWruWZPXprRAnqBIwK800OhuDbanqFOKV4XEyAuceERUhLpeLxYsXA5CUlMTdd9/N1Vdfzb333hvJw4wrJRUtpJjH/qFa093FtJgoGhu7mDn79FP6VVgrSFDEIxUNPUOyMCWe14uruaIwY8SpT0UiEUX6Qj5r2yqIkLMEh8/Jq/Ub2NK+Ndi2NHopP11xPk8fOES8RkPAFcXXM+7kg9aP8AV8rIq7ADVR5GuK6PC2kKOaSaqkiPs/eI9ma98K3vmpGWgUMt4pKwUges9OXrjm+lMG944FxeVN3PXQf4Ozo3KZhD//z/UU5fYV3tRplfz4W2t448ODfLarnBn5SVx/+TzionUjPobfH+DNjw7x2NObg23T88387/evINo4di86Y01KoolHH76W51/fTWllC6vPy2ftimmIRfDCG7t5bmOfMDUZ1NxwxTz+Nsg1Ji05it89eA0JsRMnxI62tXLjq/+l1923sicVi3l23XUsOJ5qOsVg4Mkrr+XJ/Xv4yqy5dDud1Fi6+cP2AdesC9Iz8QcCvFZSzO9Wr6W8s5M739wYDOeNVWt44er1ZEaFitZmZwv/V/I7pCIZX8+8k01tW2h1tTLfNI+5prkoJAoEzgyJRMx1l84hyqjhzY8Okp4UzZfXL2Lb3kr+8dzA/WxWYTIXLsvnt49/FGxbc14B3/3ahWjUY/d30MkV/G7bVnzHl1p1cgUXpIdO+qlkMkxKJQ9t+jjYdvO06bxbUcrjewfE1dxEM79eteakgjcSFOjy+WrGHXzQ8hEiRKyOvxCjzMAvin8dzOamblHzQMEPSTkNIWKQ6bkr+5t83Pophy2HmW6cxqq4lehlQhIQgQiLELlcjt/vJy0tjWeffZb4+HhstpO7ZEwFjh6f4RxLrG43do+HpDgD9fVdZ7Svkp4ykk5R4TzNqEMqErG3oY15ySOfiSgyFPJk9dN8yX8TcvH4BhELRJ5mZ3OIAAH4vONzLtRmkR8TQ4fdzm0b3+Bvl1zJPVn34Av42V3fyh3vvsXcxASSDOk8UdeM3bOJ9UXTeGLfHgA211Zx38IlvEOfCOlwONjd2DDuIuTDz4pD3DPcHh9vfnQoKEIAUsxRfPOW87n1mkW0NDeQlTa8L/ZgGlstPPHi5yFth441UlndTvSsqStCAPKyEnjonotxOD3otH1uG5W17bz4+q5gnwuW5PH8a7tCtqup76SsqnVCRcin1VVBAQJ9rob/2reb2QmJwTpJRXFx/HLVGhweD3U9Fq548dmQfXxSXRmMGSlub+NPO7Yx2HuvzW5jb3NjmAipslXTcbw+yJ/K/8qK2OXMMs5kUdRCNDL12JzwOUh8jJ6brpzPFaunI5dJaWnr5T8vbQvps/9oPXOmh8bffPBZMddeOofCnLFZkXJ7vTyxb3dQgAD0ul18VltDUdxAopim3h5+tz303rEgKZXvf/ReSNuepkZKOtrHVITo5XqWxS5llrGvYLRcLOePZX8JSSdt99k5YjlyWiIEIElt5ktpN+LwOVFJlEKCBoEgER0JDzzwAA6HgwcffJAjR47w+uuv85vf/CaShxh3isuax3wlpKa7m3itjugYHU0NltPejy/go9pec0oRIhKJWJQaz8YjlaPat16mJ0GZyN6ufcN3Fpj0uIfIFw99MR7vlJWyo6GeANDrdhKlNBCrMtHj6nMt2dvUzPtl1bTYrNg8bhSS0LmME/3eLS4n4017V/jkR1tnb1hck1gsQqdV4vEM/XucCo/HN6QfunMS+KZHAqlUEhQg0He+Pv/A76dSyrA5XGHbOV0Te/4d9nAf91arFd8JrsEyiQS9Uonb52OoaLf+cWz3eOhy9o1huUSC5PgKcv/1MBi3z4VMNHA9bGrbwoaG13AHRj++BIZHq1Yil0nxeL1DxjL5/eF/2bGMHfEFArQPMfHa4Qgdkx6/H6s7dEwECITUp+nHOYo4kTNBK9OilWnx48fi6XsPkYqkSER9wr3He2bxqmKRGI1ULQgQgRAiuhIyY0Zf1qVAIMBPfvITtNqpPRvYH5R+6zULx/Q41ZYu4rUa9AYVNpsLp9ODUjn6gLMWfwsmuRGF5NRpfmckxPBBWR213b2kGkfufjLNUMjmts9YFD22v4fA2JOgjCdBEU+zqyXYFq9IoKZzIIgwSqki1WDkpSOH8Pn9ZJqi+OaC6Zij/XiwI/Mbae6S8HltbXCbGLUah2fgoSkC5pnDkySMNWvPL+KTz0tC2q5cPTOiRQkT4/ScvzCHzTvKgm1atYL05PFd9RkvzPEGpueZOVTS59u9fV8Vq5bk8/6Wo8E+crmUzNSJiQHq58LMLJ48sDek7ZqCIko7OyiMjUMqFtNs7eVASzP1PT3Mik8gPzqGYx3twf5JOj3dTidRKhV50THcPX8RXS4HFqcLhVSCz+9nTkLobHqTowk/AVYnrEYjUfFp6xba3e2sOe7eIjB2JMYZWDI3ky/2DEyu6bThsZzmeMOYJplRyWR8edYcvvth6IrGyvRM9jY1cri1Bb1CwZyERK4vnM5zhw8E+1R3d7EoKYXtDQMpo01K1ZCrIC1WKwdamqjr6SE3KpoZ8QkYIpDeH0ApUXJ54qW0uduw+RxIRRJEiCjUF0Rk/wICg4moCDl06BAPPPBA0AVLq9Xyy1/+kmnTpkXyMONGbWMnet04BKV3dhKn1iIWi4iK1tDU2E1G5uhcQwDqvA2YdcMvM8skYhamxPPq4Uq+s2zmiPefo83mo5aP6XB1Eq2YHIWhBE4PGSouirqe/dYvqHVWkKrMYlnUCvZ4ejDrdCTrDNy9YBF3vrkx6NryrQUzsWl38kFXVXA/N6d9CYsjjmZrL/nRsdw5Zy6f19WSojdgUCi4uqAIhVhyMjPGjFmFSfzs/st48uVt+P0Bbr12EXOmnX6s1VAoFTK+ectyzPEGPv68hJyMOL583aJxiSGbCHRaJT/+9lpefmcvW3eWk5oUxbWXzsEcb+CdTw+TkmjijuuXjNqtLdLMTkjkH5deyR93foHd7eHS3Dw211bzsy2f8uSV11AYE8uPP/6AzTXVAMxJNPONeQv4sLKcPU2NzEpIZGFSMltra/jt6rXEa7XE67Q8uOmjYOpVs1bHVfmFwWM22Bv5Tcn/YfH0ACBGzM1pN+L2uVkUvTCi4lcgHJVSzj23ryTFbOKTz0vIz07gtmsXoVTIqG3oYMe+auZMT+WmK+cTM4aB6QAr0jP55QWreXzPLtQyGfcuWgLAdS+/EFxxS9br+fulVxClUvFq8REyTCYWJ6cyNzGJVIOBLbXV5EXHcOuM2RhOqP/R5XDwyKaP+aCyPNj23UVL+fq8BUgjVGBQJ9fxj6p/4Qv0rczopFrmR82NyL4FBAYTURHywAMP8MgjjzBv3jwAdu/ezY9//GPefPPNSB5m3DhW0TIuVVirLd1Bf9HoaC1NjZZRi5BAIECDv5FC9QUj6r8wJZ7fbz3AbXPyiFKPbAalr2ZIPp+1b+WqpCtGZZ/A5KKyu4s7N37E9Lg4iuJXsLmli7+0vsM7N93KDUUzUEqlPLZ7R4hvfbzJzyFLVch+NjZuYLbpOpZ605gZn8D/fbGV+h4Ly1LTsbrd/GrrZn6w9DymjXGGlxNRqxSsWprPwlnpBAKEuBVFkuREE9+85XxuumoBapUchTyit9RJR2pSFPfecQF3rF+CRi1HLpNSkJ3ANZfMRqmQoVRMXNrlflQyGauzshGLRLxZeoxnDu4Puk49uv1zvrN4aVCAACxLSeM777/Dg+etQCPrT6sqIl6j5Y43NvLsuut4dPsXIbUfGq29HGhuCsY6lVhLggIEwI+fXZ27uT/3XiEYfZxITjTx7VtX8KV1C0Ouxe99bTW9NhdajRyZdOyvT5NKxQ3TZnBxdi7Nzc0kJCZwwyv/DXH5q+/poayjk/sWLeG2WbNRSWUcbWvlK29uZElyCt+Yu4DSjna+8uZGHrvkctJNA+8hZZ0dIQIE4M+7trM2J4cs05mvwnr8Ht5tej8oQAB6vVaO9hwjRR3ZiRwBgYhekRKJJChAAObNm4d0HC76saK4rGnMi3O5vD7a7TZiNX1Bi6YoDY0Now9Ob3a1IEGCTjqyjBMauYzZ5hg2HKnizvkjX2adbpjGW01vc4X5MsG3cwpj9/S5XR1qbeVwa2vwAdnrdgWLZTX19oZuJAr3V3b4HHjEXl4tPkJhbCydDgcdDgevlxQH+3Q7xz8mpB+tZmzEx2DEYhEmw7kTdCyViMPO16iffOdf3tXBG6XHQtpabTZsbjciCI75wPF/dbucvHT0cNh+bG437fZwP//uQTEhPZ7esO+7PN14A14UCCJkvBjqWpRKJRNyfRqUShodDtxeXzAmZPC4s7iciEQiolR9trl8Xtw+H5tqqtk0SCSfWNer/949GLfPhzNCdXy8AS+dx5MrDKbbc/rxqgICJyOiCmH+/Pk8/PDDXHrppYhEIt555x0WLFjAkSNHACgqikwFzvGiuLyZC5flj+kxai3dxGm0weCvqGgN+/fWDrNVOGW9ZcSIRzcLsiwtkb9sP8T66VnolSNzOUtQxiMTyyjuOUaRoXD4DQQmjJL2No62tSIWi5kWG4fH5+dQWwu9LhfZUVH8YPFSfIDD40Ulk2J3uwgEArx05BB6hYI1WdlsODbg7+92qZCJZCFV1GfqZ7P5cDMALx85wlX5Bfx55/YQO+YknDxRwkhparFwrLIZm91NZmoMuRlxSKVj7+ZV29hJaWULbo+PnPRYstPjzinXms5uGyWVLbS09WAyqPEH/GhUSvKz4tHrVBNt3imZk9g37sQiEbfNnI1BoUSvUCAWifnJsvNx+X202+3Ea7TcvWAx6UYjcxPN7GkaqGegVyiIVqu4fdZcHF4PnQ4HTx/oS84xa9DqXr4uL+z4q+MuQCPVjPFZnpvY7S4OlTRSU9+JVqsgPythwmORBnO0rZXitlYCPh9z3E7unrcQq9eDw+NFKhZjdbuZlZDAnqYGyjs6MamUZJmimJdoZvcJ4y/LFOremWkyYVAoQxJ+zE00k6yPTMpblUTFhfEX8GT1MyHt0yNUQV1AYDARFSHHjvXNOv3lL38JaT969CgikYinn346kocbU7w+P1V17SQnGsf0OFXdXSQMCuCPidHS0mzB7w8gFo/8Zaest5xk8ejS5xlVCorioth4pIrb5oY/RIdCJBIx3TCNT1o3CSJkEnOguYmbNryE43hmld+vuZi/7tpBRVffDNfcRDOFMXE8c2g/AGqZjIeWr+TGV18KztRdm1/E/7vwIv6yaztenx+TNJb7c7/DxoYNNDmbWRy9mAXGJTTWHaG8s28cr0zLwB8I8NKRwxiUCu6av4gFZxiY3tjSzfd/uYGa+j7bxWIR/++Bq1k0O+OM9jsc1XUd3PvTl+jo7psFl8sk/PGn65meP/6B9hNBr83JX5/ezPubB4TodZfOYdeBGhbPyeArNyydFO5XJ2NmXAL/vPwqjra28mbZMco7+8aPCPjRsvPZ3diAUaHiwf0fBdv/cNElJOn0bKmtZlpsHHfOmcftr28IuiXmRkVzz8LFzIhLYPqglKtZ2kzuzb6Ll+s3YPfZuShhNfOj5o/3KZ8zbNpexi//OhD8nZMRy8P3XkpGysQLkb1Njdy84WVcvr57b4xKzaNrL+G2114Npu6dGZ9AQUws3/3w3eB28xLN3LNwMS8cPsi2+jpyoqK5oWgGcZpQIZtqMPLUVVfzh+1fcKClmQszsvn63PkYFJFb9Z1rmoPb5+Hd5vdQSBRcm3wNWZqRFzgWEBgpERUhzzzzzPCdpgi1DR0Y9WpUI1whOF3KOzuI1wyIEIVShlIpo7PDSkzsyDJX9Xh66PH2YpCMfibk/Awzj+04zLqijBGvhhTpC/l7xT+xeCwYhKwvk45AIMBzhw4EBQj0uZT0CxCA81LTeXTHQHG2tVk5/G33jhC/5VeOHWFNdjYb1t9MIBAgWt3nNpChvQ+Xz4VepkcsEvOLlWa+t9iJXqFAJZMxK9HMtQVFuGw2cpNOL6/8YI6VNwcFCPSl3Xzs6c0U5iSiH6NYD4BdB6uDAgT6ao0899pO/ue7lyOXTV0305FSXdcRIkAANr6/n5uvXMDTG7azenkBuRnxJ9l64pFLpazKyMLp8QYFCPS5w7xw6ABfmzufBz75MKT9ux+8y6vrb+THy85HBFz78gshcVGlnR3cGxXNyozQFzKZWMacqNnk6XPxBnwYhEJsY0Z9Uxf/eP6zkLayqjZKKlsmXIR4fD7+tXd3UIAAtDvsbKmpQStXBFcvZsQn8Kutm0O23d3UyL7mZmotFi7NyaPG0s33PnyXf1x2FWZ96HN2Rnwif734cnrdbqJUqmD9m0ihl+m5KHE1i2MW0tbSRlZUVkT3LyDQT0Sd+tvb23nggQe48847ASgvL+fll1+O5CHGjZKKljFN5ddPZVcXidpQsREdo6WhoXvE+yi3VmBWJY64AvpgotRKpsVH8cqhihFvo5QoydPlsqV16/CdBcYdn99PVVdoXJHjhFzz3hNqJphUKlqHyG/fdTxNab8Agb7leqPcGIwJkkulxGu1qGQDs+JpRhO+nnA/+dOh1xZej6Gt04rLHe4bHUla28Ptb2i24I2Q7/Vkx2YP/929Xj8isYhAAByOqVH7wu4NHyetdltIoHk/vkCALoeTeK0Wb8BP6xCxIPZT1G3QSDWCABljnC4P3ZbwWjB2+8SPR4/fT42lO6y9zW5DrxiIDdLJFXQ6HeHb+3wcaWvluUMH2Fpbc7xWU/h1CKCWy4nXaiMuQAajl+lx90787ypw9hJREfKjH/2IZcuW0draCkB6evqUcsEazLGKFszxYzvL7/L6aHfYiNOGLrdGRWtoHEXl9JLeMszK068AuzIziffKaum0jzyAeLZpFh+3for/hMJ0AhOPVCLhxukzQtoStdoQkWpxOUPE72c11VycnRuyjVgkImeUlXoDgQBH21p57dhRSv1e6npOL5jx0LEGXv/gAG99fJDEOD0n6usrLpxBtHFgBdHr9VFc3sy7m46wdVc5rR3hAqKmoYMPthzl1Xf2smt/NfYhCu0NZsGs9LC2dRfNRK06NwKNU8xRYVnFMlNjaGzpJj5Gh8frp76pi90Hanj1nb18sOUoNfUdE2TtycmJig6boLkoK4cup4NoVWhcS4bRRMZxH/xYtYb1haHp5SUiESalkoaeHgQmhqQEAysWh96rJBIx6Snh96qmli42bS/lpbf28MkXJdQ1jj7py2hQy2TcND087f3i5BTqB90LdzTUcUlO6DnIxGJi1KEB9Fq5nGxTeIbODoedz2qqefXoYXY11GNzC0JBYGoSUZ+Crq4uLrnkEh5//PG+nUuliCOUt3q8KS5v5oIlucN3PAOqLZ3EqTXBoPR+omN01FS3n2SrUFx+Fw2OBuab5tLZe3ovAAalgrlJsTy3v5S7l8wYfgP6AtS1Ug17u/YzL2rOaR1XYOw4Pz2DB89bwWO7dyAVi1FKpDx60SX8YfsXtNqsQIBHL7qEx3bvYEdDPYk6HbfNmIVJqeS/Rw6RoNXxk/POpyg2blTH3d3UwC0bXwlW/s0wmnjiinWkG0e+qrjnUC0/+vVGHM6+GeyZBUn87P7L+efzn9HRbeeqNTNZd9HMkJipnfur+dFvXgtWSJ6WZ+Z/7r+cuJg+oVXb0MlvHvuAg8caABCJ4IFvr+XilSevYVSUa+bh+y7lH89uwe70cOMV81i+KGdUv8dUxqBT8t2vruLlt/ZSXtPGnGkpzJmWyvZ9Vay/bC4/+NVGVi3No63Dyu6DNQAU5SbywF1rSUuaPAUbi2Lj+NflV/GLzzbTbO3lkpw8TEolbTYbf1p7GX/Y8QWHW1uYb07mnoWLSDUYgb6K6l+dMw+pWMzLRw+ToNVxw7Tp/HzLJuQSCY9fdhVpRuOEntu5iEqp4KqLZiKTSdm8vZSEWD1fvm4xcVGhk3m9Vgcb3jvAC2/sDrZduWYmd6xfTLRp7GqFrMnMotfl4vG9u1BJZXxv8VLmJCbx9bkLeO7QfqJVar42Zz5Zpij0cgWvlRSTqjfwk+Ur0MuVrM7M4rPaGnKjovnukmVMPyG9ucXp5Ddbt/BK8ZFg2yPnX8AtM2adljeEgMBEElERolar6erqCmaP2b9/PzrdyCtyTxb8/gCVte3ccvWCMT1OeWcniUP8PjExWrZ/Xj7EFuFUWauJVcQiE59ZgOjydDOPfn6Aq4sySTKM7AY92zSL95rfF0TIJCRapeaO2XO5NCcPsUhE7PHgxrkJZmxeD6l6A3KplL/GxtHtcmJUKFHL5RTFxfOV2fNQSqWYVKPLfmR3u3l0+7agAIG+xAt7GhtGLEI8Hi+vvb8/KEAADhQ3MG9GGn//1c24XF5iorQhAqTLYufRf38SFCAAh0saKa1qCYqQkqqWoAABCATg7899xrQ8MyknqQWkVslZc14B82ek4fX5iI2aeveyM6G6voOf//EdFs3J4KqLZjItz8zR0ibEYhF/eWoTgQC8++kRvnLD0qAIOVLaREll66QSITKJhBXpmcyKT8Tp9aKSSrF7vcSo1cgkEnKjY2js6iQ1OhqDMnTMpxiMfGfRUhK1Oo60tfK7bZ/jPO6Ota+5URAhE0BDcxc//+O7FOYk8L2vraajy8r//eND7r9zFcnmgXFXVt3Gf9/aE7Lt6x8c4PyF2WMqQmI1Wr4xbwFX5RfQ1d5BQXo6AN9bsoxbZ85CLpESdfze+sj5F/Dt+YtQy2TBiue/W30xTdZejAolsdpwO8s6O0IECMBvPt/C8tT04CqegMBUIaIi5Ec/+hHf/OY3qa2t5YYbbqCrq4s//vGPkTzEuFDf1IVOo0CjHlu3i7KODsy6cP9hnV6Jy+XFanWiHSbwttRaSpLq9F2x+tHIZSxLS+Q/e47x4AXzht8AyNPlsrntM6ps1WRo0s/YBoHIE3/CQyzxhDSOarkc9aCKvBKxeEhhPBKcPh81lnB3h+YhYk1Ohsfro7E13IWrrqkLg04FQ5jmcnloGSJ+Y3AsiW2IuJLObluI2DkZ51INkMFYbS58/gCf764E+irED55V7ufEGBmbbeLqwpwK4yBRPdjRNlqtprWmBsNJkii4fF7+tW8PbSfEh7Tbw+MSBMYep9NDe5eVTdvL2LS9LNhuPeEat9pcIRMT/QwVYzYWJGh1dNXVBz+LRSISToj/lEkkYfdbrUJBjuLk7x69rnD7nV4vNo/gkiUw9YioCKmtreVf//oXTU1NvP/++xw8eBCfb+oFcZZWtZJiHvsZhfKuDuYlhqf7FIlExMbpaKzvJjf/5JWm/QEfFdYq1iasjog9i9MS+MPWA5S0dZMXaxy2v0QkYZ5pDm83vstdOd+MiA0CU5colYrrC6fzh0FZtwDmJIxcJPdVOs+jpKIlpH3hEPEZ/URHabju0tnotSpcbi9SqYTmNgtpSQMrHKlJUUjEInyDXkqWzs3CPMbFSKcyyYkmNGo5tuMBv60dvaxYlEtuZhwulxe5XEJpVSs+30BcmFgsInUSrYJEgmiVmvVF0/nrrtD6N4NT9AqMHwlxBq64cDpxMfrg9d7Y3E1qUuiKZnKCkfgYXcgEhVGvJmmKX/PpRhMamQzboKKFBTGxJA0xoSkgMNmJaMDGY489hlarxWKxsGPHDm666SZ++tOfRvIQ40JJZQuJcWMblN7lcOD0eIPLsicSHa2lfpjg9Hp7AxqJGrUkMgWx5BIJF2Ql8e/dxQSGyBwzFDMMMzjac5RmZ8vwnQXOeq4pLOLO2XORSyTEqNX8fs3FzByFCAFYOi+bm66aj0opQ69V8s1bljNnWupJ+8ukUmYVpvDEi5/z1CvbeeLFz2lsthBtGrguinLN/PQ7l2GONyARi1ixKIfb1y9BO8arnVOZ5EQTv/3JNeRmxiESgVQkYnq+mcef38pTr27nny98TpRBg0ohQyIWYY438Mh9lzIt78yLU04mRCIRNxRN5/ZZs5FLJMRpNPxx7aWjHtcCkUGjVjB/ZnrI9d7W0Rs2cZiZFstP7r6YaXmJiESQnxXPI/ddQn7WySf2pgIZJhP/ufJqimLjEAEr0jL4/ZqLR+0+KyAwGYjoSojkeKq4zZs3s379elasWMGjjz4ayUOMCyUVzcyfmTamxyjr7CBZbyAs7c9xomO11NacOtC81FqGWRXZB/4ccxxbq5vZ39TObHPssP0VEjmzTbN5o+FNvpZ1Z0RtEZh6mHV6frh0ObfNnIOls5PC4/7QoyEtKYpv3HweF68oQiwWh6xoDEV3j4O/PLUpZJVj35E6yqvbiI/pmx1UyKWsXJJHfnYCdoebxDj9OZPl6kyYnp/EHx9Zj9Xuwun08JUfhNaC2vj+fv72ixtYtuBWVEr5mGcUnCiS9Hp+vGwFd8yah1wiCcZYCYw/XRZ72PW++1AtlTXtweu9nznTUvn5/ZfT2ePAoFWREHd2rBbMMyfz7Lrr6HG5iFapQlxqBQSmEhFdCYmPj+fhhx/mnXfe4fzzz8ftduP3T60UroFAgPLqtjFfsi1pbydJf/IbYmysjoa6k6+EBAIBSnvLSFZFtnqzRCzigqwkntxzbMSrIfNMc9jXfUBYDREA+uJKkvR6RI7wPPgjRSwWk5ESM6wAAXA43TS1hMeR9PSGxyYkxhnISosVBMgo0GmVJMYZcLg8uD3h7rU2u5ustNizVoD0Iz0+rgUBMrE4nG6a28JTJPdYh45Fio3Rk5cZf9YIkH4MSiUpBoMgQASmNBEVIY8++ijLli3jiSeeQK/X093dzQ9+8INIHmLM6ejqCz406MZ2abO4vY0U/ckf2gajGpvNjf0kQXRtrnb8AT9GuTHitk1PiMbm9rK7oW1E/ZUSJfNMc3mlbkPEbRGYWOp7LHxcVcGHFeXUdHeP33Gbutiys4wvdlfQ3Gaho8vKzn1VbNpWSmVtaPrqaJOGVUvzQ9pEIkhJGn1cV3V9B5u2l7J9bxXtnZEptng24HC6EYv6/OwHo1LKpqSPvcXpZEd9HW+XlnCopRmxLKJOAQIRZvB1KZOIWbEoNH2+SMS4xHFOBDa3m71NjbxVeoy9TQ1CTRCBs4qI3nlVKhVr1qwJfo6LiyMubnR1BiaasqpWkhONwTTDY4HD46HJ2oP5FFmIxOK+4PS6ui7yhghO78uKlYSIyNspFolYkZnEc/tKmZcUO6LfYl7UHP5V+W8qrJVkaTMjbpPA+FPR2cHtb2yg/nhhtmiViqevupaCUdYOGS1lVa3c97OXsfT2raQkJRhZf+kc/vDEJwDI5VL+8NC1zCzsy2Ykl0m5ff0SPF4fm7aXYjKo+c6dq8jLGJ2dh0saue9nL+N09QV8TstN5Kf3X0ZC7Nk9wz8cHo+Xje8d4K2PD/HNW5bz9IYdlFS0kJJo4vb1i9HrTp3Bb7LR63Ly++2f88zB/QCIgN9csJq8CbVK4GQMdV1+45bzsTnc7DpQTZRRwy1XL0SvmVrjcCR4fD5ePHyQX2zdHGz74dLl3D5zNnKpIJwFpj5Ts5LgGFJa1UpinHFMj1HS0UGiVo9ULDllv9g43UnjQkp6SklWR9YVazDT4qOwON0cbB5ZAUS5WM6ymGU8U/OcUEX9LOGjyoqgAAHocDjC8tNHGr8/wGvv7w8KEICG5m4aWy3B6t1ut5fHntmCzT6wSphiNvHg3Rfzwp/v5N+/vZWVi/OQjWJ2WypT8K8XtgZfdAAOlzZxpLQpAmc1talp7OIfz21h+cJs/vfP7xIfreO2axZRlJvIr//2AaWVrRNt4qgo6egIChCAAPC/n2+hrifcpU9gYnE63fxziOvyYHE9Xq+PW69exHkLsnnixc8pqTr73IEruzr59edbQtp++8VnVHaPbeV3AYHxQpDSJ1Ba1ULqSYqXRYrDrc2kGoafXY2N01FTFV453eKx0OPtJVYRMxbmAX2rIcvSE/nvwXJmJo7sONMNRRy0HGRz6xZWxq8YM9sExocjbeEvlweam/H6fEglpxbQp4vH6+VYRfjLREtbL1EGNb3H/b6r69uxOdwhtXwUChnJicbTOq4vIKKyLvxaaxnC9/xco7fXic8fQCIW43B62LKznC07B4qpdvdMrXoZXY5we3tcriHrLwhMLDanh6ohrssui51jFS3sPVwXbOu2nH4M2mSl2+nEd0Jspi8QoNs5OWvxCAiMFkGEnEB5VRuL52SM6TEOt7awIn14l6X4eD1ffFZGIBAIcYkq7S0jSWVGNMYLWbPNMXxcUU91Vy/ppuEL2IlEItbEX8h/615hpmkGUfKxFXMCZ4bD46Gss4PG3h7MOj05UdGoZLLg92uysnmrrCRkm3UFBREXID6fn6q6Dhqau4g2abl4RSFzpqeilEsRiUR0WexEmzR8tmvgxffCpflEnVBEsLq+g7rGTrRqJZlpMbS09fQJiwBkpMaQl3nyug4SfKxZXsiLJxTjyz3FNucKCXF6DDoVXT125s1IZVZhCnKZlMQ4PQ0t3WjUCnbtr0Yqk5CVGkOP1Ul1fSdymYSstOEz7I0UfyBAeWcH1d1dGBQq8mNiglWmR0Oa0YRMLMYzKGlKXnQMZu3pFekUGDtMejWrlxWEVT4vzElgen4S3RY7KpWc5lYLKWYT+4/W09DchcmgIS8zDqVCyrGKVppau4k/HqAulUqorGuno9NKYryB9ORo5OMQE1TZ1UnF/2fvvOOkrK4//EzvfXvv9N5VQFBRsWDvRmOPMZpf1FRjTI8aE6OJNfYUY++KBUQsoDRpC7ss23uZLdPr74+BgWEWtje4Dx/+mPu2887eed977j3ne9ra0CkVjLMlYtP2XAQ1w2jCptHQepDIh1mtJuMIojYCwVhCOCEH4fb4aG7ripP5G0w6vB6anK4jKmPtR6dXIZFIaGt1Yks4UPl6V+du8oYh70IulTI3I4k3duzlhydM69UxSeokZlpm8HjZP/nJ+DuQSkTE32jEHwzyvx3b+M1nq6Nt9yxeyqWTp6LY52TMz8jiB3Pn8/jGbwiFw1wxZRon5eYPui3rN5fzs/vejBa9u+/n5/L8a+tpa4/MWOek2/j+VYvRqBQ4XF7mTs/h5IUTkMsPOEObd1Rz++9execLRM/x4FOrqNunmpVkM/CrH54RzSM5lEDAz7mnTqeptYvVX+5Go1Zy4+ULmVAoakGYjRruuPFk3ly5hYVzC3n6pS+57Jy53PPXd6IyqaecMJ4Oh4dTF0/kwadWRVesJhWlcutVxw2KHV9UVXL9O2/g21cA9/zxk/jZwsWHrbV0OAqsNh4/8xx+vupDGhwOpiYlc9f842MqqgtGB1KphPNOn0Gz3RH9Xd5y9WJUSgX3/PWdqFrbwjkFzJqcxQ9+9b9on/z+lYtBAv94/kA+xXWXHIdep+LBpyLPPYkE7vrBcpYtmjCkeaCb6+u46s1XcexLKj8hM5t7Tz41rlr6oaQbjTx2xgp+8slK9trt5Jot3HvyqRF5f4HgKEA4IQdRXt1KSpIJmWzoBs7bGhrJtZiR9WJwLpFISEk1UVnREnVCnAEHTd5mjk8YnBd7T8zNSOavX3zLtXMmYFD1TgpwgW0eL1a9xFt173BO+tlDbKGgP+y1t/H7tZ/GtP1u7afMz8ikyBYJv0vQarl17gLOGz+JcDhMutEYdVAGi5a2Lu599MOoA5KTYeOjz4qjDghARW0rm3dWc+7p05FKJGzfXcdj/1rDn35yLhazjk6Hh7/+8+OoA5KXZePbnTVRBwQi1b5Xf1VyWCcEIgnwv/j+aVx38XEoFPIhL1g6ViitaOIPf/+Ae392Lj/+w+ucsXQy/31zQ0ydho8+38Vt1yzh47W7og4IwI6SevZUtTNpfHdn7j0tLie/WPVR1AEBeHXXDs4ZP4Hjs/pW00kqkXBiTi5vXHQ5nV4viXoddXvLB2agYMg48Ls8HoVCRiAY4s7fvxojF732mz2cMLcgpk8mJRj43cPvx5zr6Ze+4uoLFkQ/h8Pw5yc+YlJRKhmpQ6Ou5fT5+PNXn0cdEIDPqyv5trGhRycEYFZaOi9dcAmtLhdWjbZXKygCwVhBOCEHsaeieci17jfU15Jv6X2YUmKykb1lzcycnQNASdce0jRpyCRDE5N/KHqVgvGJZj4sreb8yb2bBZdKpJyVdiYvVP6LLG0mMy0zhthKQV+xdxNrHAiFaDuktodMKiXbbB4yO7qcXlrbndHPKUlGKmvb4vara+igvLoluk2nVdLh8GAx63C6vFTUHDimKDeFssr4OPI9FU0Eg8FoUdXuUKkUZKXbBnJLRx32dhduj5/OLg8erx+9VtVtHkggGKKyNl7IorahfcA2OHw+arri83NaXP3PR0nS60nSRyZ36vp9FsFwEPldRt6bu8oaqKlvj9vH4YzNk/B4/fgDsXVtQqFwdMJjP26P/7A1RgYDh89HcUu83H1dN/35cFg1Wqwa4XwIjj5GjROydOlSdDodUqkUmUzGa68Nf82J0oomUhOHLhTLHwyyvamRG2bN6fUxqakmPv+sNPp5V9cuMjSHn80dCuZkJPNmcTnnTcrr9ZK1QaHn3PQV/HPvM/ywSE+RoXCIrTy6aXO5KGlrxU4IfWc7mUZzj8fUd3Wyp60NqVRCodVGku5ASF+awYBRpaLzoGRck0pNumHg/b+usZ2qWjvBgIT2DhfmQ3I3PF4/5VUttHW4SLTqOW3xBFKTzIRCYTQaBQq5lJLy2KT4006cSJfTi9cXwKBVsXVXRB3n82/2kJ5s4vhZ+dGckXWb93LFufNYtzl2dnvBzLwjOiCCA7jcXsqrW3F5/Gg1Kn503UlotZEQNatJy8J5BaxdfyBHRyqVIJFImDUli7rGbTHnGp8/cEnnBK2W+RmZrKupjmnP2ecg13V1sqetFblURqEt4kSWtrYSCAXJt9oGpV8LRgeJVj2L5xeSkxH5OyvkMrbvriM5wcg1Fx9HIBBEpZRj0KkwGzW0dx6YWNFqlHFFeJMTDFhNWrbvrqOjy016ipnsdOuAwrNKWlsoaW1BKpOgDwY4vaCI/27fGrPP+IShE5YRCMYKo8YJAXjuueewWkcumbm0vInF84ZusLyzuZlEnQ69svfVmhMS9bTbXbicXiTqEHXueuZae+/EDAbZZj0SYFtDG1NTez9LnKpJ5cy00/lb6cPcWngL4wxFPR8kiKOuq5Offvwhn1dXApF6Hc+uOJ9JSYdPmi5tbeG6t9+Iyo5OSkji4eVnkmOOhBxkmcw8dsYK7vjwfeocXaTpDfx52elk9kK17Ujs3tvI7b99JfrinzMtm5/dfCpJ+/Ks3B4f/3t7I/988QsAjHo1d9xwMr97+P1oeMUFy2dw1fnzef61dchkUn79f2fyxspvo06FXqviN7efyfU/+Rf+QAipVML9vziPDoebrcW1+PxB8rMTOPOkKby/ejthYNnCCcye1rewnWMVh8vLC6+uY8O2KqaOT2fd5nKWLCjir099QjgciaO/9btL8PsCrNtcgcmg4dpLjkOplLFoXiGdDg9r1pWiVMi46oL5pCUOvH6DXqniV4uW8JOPV7K1qRGDUsU9Jy5lQkIiu1tauPat16hzRIpLTk1OYXlBUVTaNFWv5+mzz2NcwuAlyQtGDptFz5lLp3D3X97G7YlI9y5bOAGDXsnzr64jEIisdFx9wXx+evOpPPjUKhqaO0m06vnB1Sdis+h5d9V2WtudZKVZ+OVty/nfOxt5+d1NQKQO0b0/PYc503L6Zd/Gulp+8ME7NDgcAExOTOI3S06irquTNZUVqOVy7lhwAlOT4+t/CQTHGqPKCRlJwuEw5dUtXLpi9pBd48uaKsZZ+xbqIZVKSUkzsbesmWCWnRR1CnKJoucDBxGJRMKs9EQ+KKnqkxMCkKvL5czU5fyt5O9clXMl82zD60AdDWyoq406IBCp1/Hohq/5y7LTD1uw6tXiHTF1D3a0NLG6vJzvzjgQ9zw/I5PXLr6MVpcLm1Ybs1LSH3y+AC+8uj5m5vGbbyvZtruOk/Y5IXurWqIOCMCS48bx8LOfxsR3v/LeZn547VK+c/58wuEwnQ5PzKqGw+XlmZe/4pKzZ/PCa18TCoX5xb1v8tT9VxIIhtCoFaQlm5lQmMryJZMAyM20YdCLxOPesLeymX+/8Q3XXnI8T734BVdfuIB/vb6e/RPI4XAk2ff2609mXF4KLo+Pp//3FT/93jIWzMxjxqRMrru4A7lcRlqyiZKS3Ue+YC8Zl5DIc+dcQL2jC71SSYbRRCgc5sXt30YdEICtjQ3MT89Er1Ti8Pmodzj47/at3L14KdIhTD4WDA/l1S38/blPow4IwIdri8nNSog6IADPvrKO712xkDnTsrGadLR3ubn/8Y/4+28v4cn7rqCzy43NoqeiuiXqgEDkOfanR1byxL1XYDPr+mSbPxDgv9u3Rh0QgO3NTWyoq+Pvp59FbVcnKpmMLNPQFkQWCMYKo8oJufbaa5FIJFx88cVcfPHFh93P6/VSXFzcp3N7PJ4jHtPa7kYC2FuasMeHkw8YfyjIN7XVnJeVR2NjQ5+ONehlbNpYRlBdQYLEdtjj/YFAn8/dW9LkIZ4vbWBX2V408r6FtEiQcIL8OJ7b+wIbqjawWLVwQDktPf0th4oJEyYccXt/+mVPyGQySprj44m3NNRTVlMN7vhYZpVGw/ramrj2TfU1LDaZ8XZTD6GVVnpXlvLwSOUadpbGF/fbW9VEZqKUYDBIfXMgZpvZoKG5zRF3jL3dxXOvrkOplHPVBfPjtpdVtnD2yVOjnz2+ADX1zVj1YXwu6GiL2LHfXa+p7oo7x8GMVJ/qK4faORR9srYhMrgL7HMMw6FwzOAOIBAI0dDUyXOvrou2tbY7465V0tEwJN9tF1BcW4dCo+Hrutq47dWdHSRqddFk4K/raimrqCBwmPoKY/XvPxrx9KKGRW/65eHuNSwzdZs35jnIKdmPy+3n7Y9jwwNr61tI3Lfg2+Bupa7RF3dcY0sXtXVNNNX3LVdEYTR2W19pd0szLfX1BF0uXMCu+qF5Tw8FY6HP9URv+qRgZBg1Tsh///tfkpOTaW1t5bvf/S55eXnMmdP9rLlKperx5XsoxcXFRzzmiw1lZGfYyMrO6tN5e8u6mmrSjCbyMvqRzxHW8PnaEuThNhalnXDYlZDGxgaSh3CJt7DZSWVQzqn5ff+OsslmXKCI9xo+4JXw69yUdwMp6v7VYOjpbzlS9Kdf9oYZchls+iam7dSCQoqyspFJu1dZO6OthW8PcUhPzMmjQ6OmSyYlx2wmzaCn3tOA3WfHorSQqk5BLu3/IyEYDLFoXiGvvLcppn3m5GyaOwP4/WGSEi3IpJKoik1tYzt5WQnsrTrg+UskoFJF7PD5AiTb4hVkZk/NQqWUc9X585HJpHz+zR7yslP7rWg1WvvUofTVzv70SamyAYkkkuehkEfUiHRaJU7XgcGaTquMSfqVSCAt2cSECTkDtrmvLG9uiEv8LbBa+aS8LPr5jMIiCnMPX/+pNza2eFtp8DSgkqpI06Sik/dtlnwwGAv9tDcD1t70y4PvdW9VCxU1LWhUStJT1EyfmMGWnbETLUZDbNifVCpBrY59nmnUCnKzUshMO7AiHJbHT5yMz08mNzsVg67voYSLsnLY3Ro7kzk7PYPs7OEPB3UFXNS56/GEPCSrkklU9z0PZSz0uZ4Y607U0cyocUKSkyMDUpvNximnnMLWrVsP64QMBXsqmklJHDplrE/K9zLlCDH8RyIxyYDd7mJ8aPhDsQ5meloCH5VWc2pR/xw1rVzL+ennssm+md/s+D3nZ5zD0qQlYlm6B2ampvKDufN5dMPXBEIhTsjM4oop0w/rgACcVlDI1sYG3indjVQi4ZJJU3D4fZz/8n8BmJKUxG0nZvNC1QuECCGTyLg292oW2Ob3u7aLTCbllIXjKSlvZGtxLXK5lDtvXMYLr33Nhq2RcLIp49P4+S2n8+BTn9Dl9LK7rIEf37SM+x//iKo6O1qNklu/eyK79kQcKKlUQjAU5OoL5vOfN7/B5w8yoSCF80+fyY9+83KkkrdMyh03nDyk9X2OJXKzEvjlrct57tV1XHfp8bz98Vauueg4/vX619g7XFjNWn50/cn86/WvgYhD8r0rFlGUPzKFHRdl5bCxrpZPKyuQSiRcNHEySVodwX3FCJflFXBW0cA0giudVTxQ8iAd/kiI4xzLbC7PvgSLcmhkXQUH2LKzml898E5URe/4WXlcd+nxPPDEJ5RXt6BSyrnmouOYWJhKWrKJusYO9FoVd950CokWHTazjtZ2J2ajll/eejoZqeaY8xdkJ/DzW07jr//8BLfHT3aGlR/ftKxfDgjA6QWFlLS2sKaqAplEwoWTJjO1n+/+gdDh6+DF6pf5svUrAAxyA7eP+yG5upxht0UgOByjwglxuVyEQiH0ej0ul4svvviCm2++eVhtKClvJCttaJLiGx0OKtrtnFU0rl/HS6VS1IlBVA02GJqFml5RlGDm9R3lNHS5SDH0Ty5QIpEwyzqTHF0279S/z7ft27gh/1r08oHlIxzNWDRabpkzn7OLxtPS3s6k9Az0qiOLG2QYTdx78qncPGceUokEu9vNpa+9FN1++vh0/l39b0JEBmrBcJBnyp8jV5dDmiatX3b6/AFeemcTRr2aqy9cgFQiweP1Rx0QgG276sjLTOCJP12O0+0jyWrAatHxj99dSmNLJwadivQUCwvnFHDKwonIZBLyshKQy2UcNzsfj9ePxaThujv/HV1NCQZDPPjUKqZNyIjKeAr6j1IhZ9miiUwqSsPl8TFtQjqbtlVx4+ULaWzppMvh4e/PrOb6yxZSVdvGnOnZTJ+YOSK2hsNh3txdTCAc5ta5CwgTZk1FOSdkZvPeZVcRDIfINJrQKXtX46g7fEEfb9S+FXVAAL6xb2C+bS6zrbMG4zYEh8Hh8vLCa1/HyHh/sXEv0ydlctYpkzEbtEglEl7/cAuZaRYe+8NlNLV2YdSrSUs2A/DkfVfQ1u7EYtJ2O1GhVCo4/cRJTBufjsPtJTnBiNnYv/dbKBzmzd27mJCYyHkTJoEEVpWXsb2piQmJA1eJ6wvlroqoAwLQFejilerX+EHhzahlAxeLEAgGg1HhhLS2tvL9738fgGAwyJlnnsmiRYuG1YY9Fc3Mn3H45fqB8OHePUxNSkYu7V8ehCfoJmR10lWRAHMH2bg+IJdKmZJiZXVZLZdOH5iKmE1l44rsS1nTvJa7t/+G/yu6lUzt8EoPjyUUMhn5Vhu+xqYeHZD9aBQKxu9TBHrxEHlIuSJAwBebn+EPB+j0d5HWz/xtp8vHtl21NLV28fk3ZRTlJTOpm4rj32yt5KoL55N5kNNvMWmxHCTlazJqmTYxdiAwcd+5Nm6txOuPtd3rC2DvcAonZBBJTzEDsGZdCU/894u47ZW1rTz/6nqyMkbuO/cEAnxVU01xSzOfVx1wdtdWV7C8n5M+h+IOutnjKItrb/TEx/4LBpf2Dhclexvj2htbOnlv9Q5c7gMhgksWjMNq1mE9JJk8yWYgqZuQzoORSCSkD0KxQrffz7ra6rhwLL1SxYWTJg/4/H2hxRuf5Vfm3Isr6BZOiGDUMCqckMzMTN56660Ru77H66eptYvkhJ6rl/YVt9/PZ5XlXDOj/zNmVa5qkjL0VK/tJBwOj2j40rTUBN4qruCSaQUDtkMmkbE06USSVUn8add9/KDg+4w3Ds7AQRBL1iHSu26PDLVUjSd0IGFPLVVjUZr7fQ2DXs3iBYUUZCWCBBQKGWqlnNdXbonZb87UbKymI6981Ta0U17dglwmJTczgeSD6vck2gyolHK8BzlReq2KROvg/34FkJxoJDfTyuL54wiHwkikEj79ajehUBi5XEqCRY/T5UGnjQxsWtocVNfZUSplZA+xU6hRKDg1vzAuJ2R++uAtGevkOqaZp7K25fOY9gxt+qBdQ9A9NouemVOy+OTzXTHtqcmmGAcEICvNSk29nYbmTsxGDVnpVpSK4R3iaBUKTskriHNC5qYP/wRbdzmXU42TMYioA8EoYlQ4ISNNeXULqYlG5H1UfeoNH5eXkWe2YlL1Xx603FlBZkI6LbJ2WhpcJKYOf0LkfrJMenyBIGVtnRTYBieHZpJpIjq5jodK/8GthcIRGQomJyVz+/zj+dvXXxEIhfiqvJn/W3Idz1Q8gzPoRCfXcWPedST3UywAQC6TcuK8Qn738AfUN0VCV5YsKOK2a5by8LOrCYXCTCxM4bQTJyGXHz7vZGdpPX/8xweUV0dm8mZMyuC2a5dSkB0JZ8hMs/Cb28/kdw+9T5fTi8mg4e4fnkHavpl7weCSl5nAhWfM5q///AR/IIhCLuO2a5by4dqdXHvx8dz76EqWLZrIBcun09bu5uf3vRGtaL30uCIuOWNoZ4BXjBvPpvo6PquqQAJcNGky8zMGLzxMLpWzPPU0atw1lDsrkCLl9NTTyNPlDdo1BN2jUSs477TpVNW2UVrehEwqYcWyaYzPTyEj1UxNfTsKuYyrL1pAIBDku3c8j9vjRyqV8P3vLGbFKdNQq4cvj1IikXDehIlsrq/ji5oqJMClk6cybwSckFxdDueknc3b9e8SDAfJ0mZxdvpZKKQjl1cqEByKcEKA0vLmIRnA+IJB3i0p4ZLJU/p9DkfAQVegC5vKSnJGgMrd9hF1QiQSCVNTbKwqqx00JwQgR5fNWWln8PCeR/jZ+DvJEKFZg4pRpeaGWXNYVlCIy+8jy2jGotGQo8ukI9CBWW4ioR/KKQfj9nh5+d3NUQcEYPVXJUwsSOHqC+YTCkNlTSvvr97BhMJU5LJ4RyQUCrFyzc6oAwKweUcNG7dWRZ0QiUTC8bMLeOr+K2nrcJFg1pOSJJLSh4o9FU08+NQnUTUsfyDIQ8+u5uc3n8rfn19DS5uDZ1/+imkTMlj15a6oAwKw6ssS5s/IZuLA8sKPSLbZwsOnn0llRzsyiZRciwX1Yern9Jc0TSp3jPs/mj3NKKRKktVJYjA3DPh8AV59bxM56VaOn50PQLLNwMPPrGb6xExOOn48hCP73fvYh9HaIaFQmIef/ZSpE9KZUBAfEjqU5JgtPHLG2VS223E7nUzJyh70/tgbdHIdZ6WdwRzrbHwhH4mqBAwKsVosGF0IJ4RIUnpq4uAPYj7ZW0aa0TCgInDlznKSVElIkJKUoaO82M7sE0d2gD411cZzm3Zz7ewJyKSDFxqWo8tmSeJi/lLyEL+ZdDd6hVg2HkwUMhmFhxTLTFQnkMjAnI/92Nvd7Cipi2uvbepg1Re76XREQr/S9oVSGPXxcclOt4+tu+LrPnRXfyQt2RxNPhUMHc1tjphikhAZ9JVXt9JyUI2X5rYuNm6riju+vDq+psNgY1CpmDzECkR6uR69XjyThpMup5dN22to73RF2357+1nsKmtkV9mBXJGrzp9Pq90Zd3xzq4MJBcNiagwGlYrJySkUFxePiAOyH7lULsIGBaOa/mlxHmWU7G0alKS0g/EGgry5excnZA5AGzwcptxZQao6UvsjMVVHa4MLjzvQw4FDS7Jei04hZ3vjQMvbxTPJNJECfT6P7X2SUDjU8wGCUYPNomP6pPgwmASLHofrQIHEBTPzqG9sZ+36UkorYpN7DTo1MyfHx/NPGS9epCNBMBjCatahVsXO+qtViqgE7n6SEwzdinsUZA+Okys49jAaVMybkRPTVlHTxoSC2HpY7Z2uuJxOiQQh2y0QjHKOeSckFAqzt6qF9OTBrRHywZ4SMk1GUvT9X/5s9rUQDhNdQpXJpSSk6qgqaR8kK/vPlFQbq8viZ6wHg0WJJ9Dms/Nx4ydDcn7B0KBSKbhw+QxyMw+stixfOhmNRklon5xuUW4Sc6Zmc+PP/8PP7nuTW+/+H59/syfmPMsWjmdi4YFBxglz8rt1TARDi98fZOVnO3no6VX88NqlaPbF1mvUCn580yl8sSGiGKWQy7jp8oXkZSZw/ukzKMo7IEV61slTyEwZufBRwdhGIZdzxTlzyc2IPFMkEnC4fdx4+cKo4pVMKsFk0vCT752K2ajZd5yMO29cRm6mUMsTCEYzx3w4Vk2DHb1OhU7bO9nT3uDy+3h3z26umDJ9QOcpc5SRqk5GwoGQp+QMHXuL2yiaNrKzi1OTbfx93Ta+H5yMQja4Cf0yiYwzU0/nhcr/MNU0hRTN0FWBFwwuE4vSuPdn51FZ24pcJmF8fgpyuYwZkzLw+4MQDnPzL18kGIw4JV1OL/c//hGZqRay9w00xuWn8Ls7zqa8uhW5XEZulg2rSQxkh5vK2lbufWQlwVAY/3sb+fFNy/AHguSkW3n8P2uZPTWbRXMLQQLvrt7OzClZTCxM5S+/vICa+naUShlZaVbK9+7p+WICwWHIzUrgod9cTG1DOyqVHJNewz0Pvs3xs/MxGtRIJRLWfr2HKePS+ed9V9LY0onZoCEj1YKsm7wzgUAwejjmnZCSvU1kDnIo1tsluymw2kjQ9n/gFAj7qXbXMs8aWzU+OdPAp2/sJRQMI5WNnFSvWaMi1aDlm5pmjssefCfBorRwnG0+/yx/hp9P+Em/q3gLhp+0ZBNpySaKi4sx6CMzk0W5kXj991Zvjzog+2m1O2mxO6NOCEBSgpEkEUoxojS1OqIFIfdUtPDrB98F4OHfXMzGbdVs3FYdu39LJxMLUzEbtf0u9iYQdMfBdYRKK5rYWlzH1uLY/LO6hg4WzMwjZQjyOwUCwdBwzDshu8oaSBvEUKxOr5eP9+7hmukDq6Rb6azCLDehksau0Gj1CjR6BfVVXaTnjuzDdmpKAp/sqRkSJwRgpmUGxV27+LzlCxYlLhySawiGHq/XT1WdHX8gSEqikZREIyefMB6ZVIrL42P9pnJslliH3eH0UF3fjlwmJSPVjEYdX/G6tt5OW4cLm0UnEtQHiM8foLrOjsfrJz3FjNmoJcmmRyqVEAqFyc208Z3z5hMIBVEpZIzPT45JDAaE0ygYFqwmDZOLUsnPTsS8zzH5bP0ekhL0NLV00tjShWnfSoh0EIVTBALB4COckD0NzJ85eJXS39xdzMTEZEzq/tcFASjp2kPWYWRqUzINlBe3jbgTMjnZyvu7K+ny+jGoBl+uUiqRckryybxU/SqzLDPRyUVIzlij1e7g2VfW8cbKLYTDcPGZs7jk7Nk8+sJneH0BzEYtP715GTkHrYLU1Nu5//EPozPtpy6eyE2XLyRxXwx4KBTm82/28LuH38fl9mHQqfjV/53B/BmibkN/6Ohy8+JbG/j3G18TCoXJy7Rxz4/OIivdyp03LuP1lZu4cPks/vzkRzhdPnRaJXfccArP/O8LqurbkUkl3PydxeRligR0wdBjsxi4/rIT+NMjH1Lf1IFMJuXyc+aQYNVz3U/+RVu7C6VSzu3Xn8QpCycMe8FCgUDQe47pGJdQKExJeROZaYMTjmV3u1lTUc7xGQNLom3zteEJurGpbN1uT8nSs7fYPqBrDAYahZyiRDOfV8TLpw4WKepkCvUFvFrz+pBdQzB0bNtdx+sfRBwQAL1ezUPPrI5WO2/vdPHAEx/TfJDU60dri2NCfVau2cnG7QekX2vq2/j1g+9GKyZ3Ob386i/vUNvYPvQ3dBSyq6yBF15bHxUP2FvdyrMvfwVhOH3JJP7v2pP5yz8/wemKfN9Ol4/7HvuQ2647iasumM9Dv7mY806bPqxF4QTHLo0tnTzxn7XRekTBYIjnX11PTX07be0RKV+fL8CfHlkZU29IIBCMPo5pJ6Sm3o5Oo8Sgi69X0B9e37WTackp6FUDS3Lf3bWbdE1aTEL6wZgT1Pg8Aewt7gFdZzCYnprAh6XxtQEGkxMSjmNd63pqXEOjxiUYGuRyOcV7GmLa/P5AdLC7n+Y2B23tEY1/j9fP2q/jE5k3HVR/ornVEXVi9uN0+Whp7Ros048pquriJzS+3lJBh8ONXCalrd0ZLQK3H7fHT3Org+deWYfL7UMhZpsFw4S9w8WOkoa49v3PkP2Ew9DY3DlcZgkEgn5wTDshO0rryUofHAm/Npebr2qqmD/AVRBP0E2Nu5Y0Tdph95FIJKRkGdi7c+iLgPVEoc1MQ5eb6nZHzzv3E61cywLbfP5d9V/C4XDPBwgAcHl8lJY3UVbZjNfr7/mAQSYQCFCQnRjT1l1oRCSRORK+qFLKmTU1vrbOwXVCrBYdcnnso0utUmAxi3C9/pCWFJ8TN3l8WnRyxmrWoVTEKuApFTI0KgVSqYQEi/jeBcOHUa8mv5vaM2ajhhXLpvGd8+dx+TlzSbTqSbCK4pICwWjm2HZCdtcNWijW67t3Mi05FZ0yPoG2L+zuKiFZmYxCeuTQhpQsPXt3jLwTIpNKmJGWwMrS6p53HgAzLNNp8jSxtWPbkF7naKGusZ3fP/Q+373jea6+/TkefHoVzSOwUjBtQgYL5x4oWVy8p57vXbkomjCqUsr5xQ9OixYVk0gknLl0ckyOyKwpWcw+yDHJTLPy45uWReU3FXIZP/v+qYOucnesMKEgheVLJkU/28w6brj0BBRyGZ+uK+HPj3/ElefPjzp+crmU73/nRF77YDM/vPakGFUzgWCoSUs2c/OVizHoD0QwnH3KVJISjKzbXM7zr67ntQ82c+V588hOE3VCBILRzDG9hv5tcQ3nnDp9wOdpcblYV1PFjbPmDug8/rCPUsceZllm9rhvYqqODZ/W4XL40epHNhZ7Vnoi//xmJ1fNHIdiiHTZZRIZJyYt5t+VL/IdxWVDco2jiU/XlbJmfSkQCUt4++NtTB2fzulLJg+rHUkJBn7+/dOoWNGK3x8kM92CyaBh7rQc7B0uUhINZB4yUMjOsPG3ey6ksrYNmUxKTroV00GSr3KZlGULJzAuL5lWu5NEm57MNCsSiVDC6Q9Ws47brlnKimXTcHv8ZKZaSE40sreqhXv++g6BQAiPL8CV585DKpMwc1ImUqmEaRMzyE63iFAswbAzb0Yuf/nl+dTUt2PQq8lIMXP7716Jhl+5PX4efHoVk8enReXBBQLB6OOYfXs4XV7qGjvISDUP+Fyv79rJ9JQ0tIqBrYKUdJZiVVrRynrW2JfJpSRn6CgvbmPSnJF9yCbqNCTptXxV1cCi3MOHkQ2UfF0em+yb2eTbwmSGdzA9lggEgnz61e649vWbK4bdCQEw6NUx4VQAhblJh9k7gs2ix2Y5fCiFXC4jPzuR/EPCvQT9Q6dVMako9rfb2NJJIBACoLahnWde/gqAeX+6nImFqcNuo0BwMBMKUplQEOmHpRVN1DZ0xGwPhcI0NHUKJ0QgGMUcs+FY23bXkZNhQyEfWLXvZqeTr2uqmZ/evZxub/GFfBR37SJHGx8PfzhSswyUbhsd6h9zM5J4a2f5kF5DIpGwJGkxX/q+wuEfuhyUsY5cLmPG5PjcpMnjh8ZBbGt3UlLeSGNLZBbS5fZRVtlMADXBYKjbYxqaOykpb8Te4RoSmwQDx2qK5OpceMZMrjp/PmedPAWrWYtKeczOXQlGEXWN7XyztYKdpXWY9OpoMcODsYk8MYFgVHPMvk02basiL2vguvavFO9gZlo6mgGuguzs3EmCMqFPtTCSM/Vs+aIerzuASjOyf8qJSRbe211JWWsH+bbBK/54KImqRLJkmbxc8yrfzb1qyK4z1jn9xIl8tq6E6vp2AMbnJzN/xuDVw9nPjt11/Ppv71LX2IHZqOHXPzqL/775Des2l6OQy7j6wvmcc+p0TIZI4nkgGOLLDWX86ZGVdDo8ZKSaufu2M8TM+igkM83Kzd9ZzCPPr6G9001qkokf37SMh55ZxXfOX8CsKQMT4RAI+su3O2t48OlVlJY3oVEruO6S4/nlbcv56R9fx+cPAnDtxceROwjveIFAMHQcsyshG7ZWUpAzsFCOuq5ONtXXMW+AqyCOgIM9jjLy9H0bJCqUMhLTdZSNApUsmVTK/KxkXt2+d8ivNVkxiQ32TVQ4K4f8WmOVnIwEHvr1xTz4qwt56J6LuO9n55ExyInbLW0OfvnA29Q1RsIgjAYN76/ezrrNkRUxfyDIk//9gp2lB+rIVNa08ssH3qbT4QGgpr6dXz/4Tpy8pmDk2VvVzP2PfUR7Z0QKvL6pg788+QknnzCBX9z3JnWiLotgBGht6+KfL35BaXkTEMn/ePjZTwkEgjz95+9w38/P45/3XsGlZ89BqxnY5KBAIBhajkknpKPLTXW9nZzMgam6vLh9G/MzMlHLB5AYHg7zdds3ZGkyUUn7Xl8kPdfI7i3N/b/+IDI3I5kNtU00dg1tiI1SomRRwgk8Xf4coXD34T4CSLQZmD01m5lTsrAOgYxqc2sXTQcpbk0sTGXT9niVtF0H1Qqpb+qIC9GqbeiIKVYoGB3UNXbgDwRj2ppau5DLZThcXhpbRF0WwfDTbHeyeUf8c6auoYOcDBvHzcpjfEGKKJ4pEIwBjkknZP2WCorykgeUD1LS2kJpWytz0tJ73vkI7HGU4Qq4yNJl9uv4lCwDTTVOnJ2+AdkxGGgUcuZmJPO/rfHF5gabKabJQJgPGz4e8msJusdoUKM56EVf19jerWN/sAy21RTvDBl0KoyDVDBUMHAamjopr2rB2k08vUatQCaVIJdLo7VdBIKhxuP1U1HdSm1DO3qtsltpfbNJ9EeBYKxxTDohn60vZUJ+/xUzwmF4dstmTszJRS7tvyNj99nZ0vEtE40TkPTzTyGXS0nLMbBr8+hYDTk+O4UvKuup7xza8BqJRMKpKct4s+5tGjyNQ3otQfekp1j48feWRWt+bC2u5eIzZ2E8SL9/6oR0ivIO/NZyMm1cfcGC6GeZVMKPv7eM1OShyyMS9A6fz8/KNTv57h3PceX/Pcv2XbVcce4B2XGpVML3rljEy+9s5LbvLhV1WQTDQk29nd/87V2u+OEzXH37c3yztZKbr1wcI5CweH5htwUMBQLB6OaYS0z3ev18vaWCn99yWr/Psbq8jGA4xOTE/jsyroCTT5s+o0hf1Kdk9O7IKjKz5Yt60seNfHKvTqng+OxUntpQzF1LZw/ptaxKC8fbFvDonsf55cSfI5cec915xFkyv4jcDBt1TR2kJBhZs66Us0+ZilwuQyqVUNvQTkNTB1n7aoFoNUouO2cOC2bl0truJC3JPOCwSMHgUFrezG8fei/6+Z//+5Kbv7OYv/zyAprbukhONCIB7rhpGbkZVuQDVBYUCHoiGAzx6vub+Wx9ZHXd7fHzwBOf8NgfLuWvd19Adb0do05NblbCoOe8CQSCoeeYG7V9vqGM7AxrzGxtX2j3uHlx5zYunTSt38XRugKdrGr8lAxtOsnqI9dL6A3WJA1SqYTGKjcpKQM+3YA5PjuVv335LZtqm5mZPrR1HGZaZlDhquTF6pe4IlsUMRxu5HIZBTlJFOQkYe9w8sGaHXG5ApmpFuZOPyC6oNUo42pSCEae6np7XNsjz6/hpX9cx9zpOcNvkOCYp73LzSdf7Ipr31pcy2XnzGXqhIGJwggEgpHlmAvHeuujrcye2vtaHDGE4bENXzMjJY1k/eELqR3++DDlznJWNnxEljaTLG3/8kAORSKRkDfRSvE3HT3vPAwoZFLOGp/LQ19uxeXzD+m1JBIJZ6Sezkb7Zj5r/nxIryU4MlqNstvigekpItRqLNBdnYXMNAtarVAYEowMOo2yWyn9lETjCFgjEAgGm2PKCSmvbmFPRTPT+zl78ubuYuweDydk9c2J8YW8lDn28n7DSnZ07GS6aRppmsGdCc7MN+Fo91Nb3jmo5+0v4xLN5FmNPPTlVsLh8JBeSy1Tc176Ofyv+mW2tH87pNcSHB6VUsHVFy5Ad9CgdVJRKpPHDUy8QTA8FOYmsWRBUfSzQi7jR9edjNkY75wIBMOBWqXg+ktPiBHAmDYhnYliJVUgOCo4psKxnn7pSxbNLUCh6Hss89e1Nby/p5Srp89AJon33ULhII6AE0fAgSvgwhF04PA7aPd34Ay6sCqtZGozSVDZkNC/MK4jIZVJyJ6oZ+275Vx089RosvBIcsa4HJ78ZicvfruHS6cXDum1ElQ2zktfwRNlT3FN7lXMts4a0usJumdiYSpP/ukKKmpaCQX9TBmfhc3aj1VDwbBjNeu4/YaTOefU6TicHjLTrOSKfB3BCDOpKI1/3ncllTWtqFUK8rMTsFnEM0UgOBo4ZpyQ3eWtbNlRw8+/f2qfj11fU8NTWzZwyaSpGFVqCIfpCHTS7GmmyddMm7cNR8CJWqZCI9OglqpQylToFXpS1CnoFbp+q1/1heRMNa21ATZ9VsvsE0c+VlYhk3LFjCKe/GYnUqmEi6bk9zuPpjekadK4MPN8nqv4F9WuGlakn4W0G4dRMLRkpVvJSrdSXFwsHJAxhtmoFZXQBaOO7HQr2enWkTZDIBAMMseEE9LS5uCfL2/mojPnoFL1voCRPxjkteKdfFpZznkTxuOTtvFV63bq3Y1IAIvSjFFhZLxhHDq5bsQHvBKJhJkLU1nzdgW2ZC25E0b+oW1UKblu9kSe37yLSnsX35s/GUMf/gZ9JUWdzJXZl/Ne/ftsaf+WK3Mup0CfP2TXEwgEAoFAIBD0naPeCdm9t5Ff/vktphYlMXlc7+JIPYEAn1aW8HZJCUp5mKzULtZ1rMKisGBVWZhhmYZGphmSsKqBojUomX9KJh+/WsasxW6mHZeKTDayzpFJreTGOZNYWVrFDa+t5pxJuSwrzMKi6XuF+N5gUOi5KPMCtnVs56HSf5CiTubExMVMM09FJxfx7QKBQCAQCAQjzVHnhDS1drG1uJaK6ha+2lROSXkj5546HZslRGlrJaFwiEA4QDAUoMnlpMnhpMvnocPjoc3tpdUZxOWVoVL4STKFSTFoMEpT0cn1yJCAD9y+EG6Gthhff+h0uglJHaCEKYsS2bq+ga8+rCZroomEdA06k5KUXD1yxcg4JcfnJFKYoGdddQPPbyohUaeiMMFIhkmHVavCoFKgUcjJtxrQKg/fNT1hD66Aq8frFejzydXlsLurlHfq3uXxvU9iVpjI0maRok7BqrRgkOvRyrWopCpUMhV5utwRX9ESCAQCgUAgONqRhIdaumgI2LJlCypV97PoT7+6ha8210Y/K+RSlAoZfgIEwrFysYGgDMKHW80Yc19Lt0gDsffXOTGEZ+RrGhIOSQiGuh/s52W0kZ/RPujXDBLEz5Elgy/XXEKmvPt8GrlcTmHh4RPsj9QvBYKhQPRJwWhE9EvBaKOnPikYGcakEyIQCAQCgUAgEAjGLiLuRCAQCAQCgUAgEAwrwgkRCAQCgUAgEAgEw4pwQgQCgUAgEAgEAsGwIpwQgUAgEAgEAoFAMKwMixMSDAY555xzuPHGG+O2vfbaa8yfP58VK1awYsUKXn755eEwSSAQCAQCgUAgEIwQw1In5Pnnnyc/Px+Hw9Ht9uXLl3P33XcPhykCgUAgEAgEAoFghBnylZCGhgY+/fRTLrjggkE7Z2lpaZ+PqaioGLTrj1aOhXuE0Xuf/emXfWW03nt3CFsHn77aORx9sifGwnc7FmyEsWFnb2zsTb8cC/faE+IeRgdHwz0crQz5Ssgf/vAH7rzzTpzOw1cY//DDD/nmm2/Izc3lZz/7GampR66m53Q6KS4u7pMdHo8Ht9vdp2PGGsfCPcLI3eeECROOuL0//bKvjKW/sbB18DnUztHQJ3tiLHy3Y8FGGBt2ejwecnJyjrhPb/rlWLjXnhD3MDroTZ8UjAxD6oSsXr0aq9XK5MmTWb9+fbf7LFmyhDPPPBOlUsmLL77IT37yE55//vkjnlelUvX48j2U4uLiPh8z1jgW7hFG7332p1/2ldF6790hbB18+mrncPTJnhgL3+1YsBHGhp29cXp70y/Hwr32hLiH0cFIT8QIDs+QhmNt2rSJVatWsXTpUn70ox+xbt067rjjjph9LBYLSqUSgAsvvJAdO3YMpUkCgUAgEAgEAoFghBnSlZDbb7+d22+/HYD169fz9NNP8+c//zlmn6amJpKSkgBYtWoV+fn5Q2nSmKPF7uCbbytZs66ECQWpLJ5fSE6GbaTNEggExyBNLZ2s21zOFxv2Mn1iBgvnFpCRahlpswSCbqlv6mDdpnK+2lzO7ClZHD87n/QU80ibJRAI9jEs6liH8re//Y3Jkydz0kkn8cILL7Bq1SpkMhkmk4k//vGPI2HSqCQQCPLiWxt48a0NAHz+TRnvrtrGQ7++iJRE0whbJxAIjiU8Xj+P//tzVn62E4AvNpTx0dpi7r/rPGxm/QhbJxDE4nB6efCpT/hiw14AvtxQxmfrS/n9nWdjMmpH2DqBQADD6ITMmzePefPmAXDbbbdF2w9eLRHEUtfUwcvvbopta+ygvKpVOCECgWBYqam3Rx2Q/ZSUN1FZ0yacEMGoo6beHnVA9rNlZw1VdXamCCdEIBgVjMhKiKCXhMOR/4c2E98mEIwWSrv28EXrVyQHkpjA2E5oFBygm0fREdsFgpEkdJiOKbqrQDB6GJaK6YL+kZps4pxTp8e0Jdn05GYmjIxBAkEPlDn28teShwiGArzpfouSrpGvUyEYHDJSzZy4oCimLTfTRnaGdYQsEggOT2aahTnTsmPaJhamkJUmcpgEgtGCWAkZxSjkcq44dy752Ql8tLaYyePSOWXhBFKTRCiWYPQRCod4uvw5TkpewkTjBIIdQZ6v+Be/nXwPEolkpM0TDBCNWskt31nMtAnpfLqulNlTslh6/HgSLCIUSzD6MOjU3HHDKXz2dSmff1PG/Bm5LFlQhFmEYgkEowbhhIxyEm0Gzj5lGmedPFUM5ASjmi3t3xIKB5lgGA9ApiyD0tAednXtZoJx/AhbJxgMUpJMXHjGLC5YPlM8jwSjnvQUM5eePYdLzpot+qtAMAoR4VhjBPEAFYxWOr1efr/2U/7wxcdMN82I9lWJRMIU02Q+a147sgYKBh3xPBKMJUR/FQhGJ8IJEQgE/SYcDnPzu2+yvbmePU0+1u4KxmwfZxjHlvatBEKBEbJQIBAIBALBaEQ4IQKBoN98uHcP9Q4H8/IUnFCo4bPyeuq7XNHtRoUBs8LEHkfZCFopEAgEAoFgtCGcEIFA0G8e2/A1K8ZNoMRRSp4hg1npSby3qzJmn2xtFts7doyQhQKBQCAQCEYjwgkRCAT9oqytlerODiYmWWjyNpOiTmF6agJrymsJH6TRn63LYkfnziOcSSAQCAQCwbGGUMcaJfj9QYr31LNpexU6rYpZU7LIy0ocabMEgsPyVskuFmRkUu2uIkWVjEwiI1mvQSqRUG7vQrZvvzRNGjXuGnwhP0qpYkRtFvSP5jYH23bVsntvI+Pykpk6Pp0Eq5DmFYwMLreXHSX1bN5RQ0qigRmTssgU9T8EgjGHcEJGCZt3VHP7716JVh826FT8/beXkJ8tHBHB6OT9PSVcPmU6pY6vSdYkAREVmqIEMxtrmphrijgcSqmSBGUCFc4KigyFI2myoB84XV4e+9dnrFxzYDXrtMUT+b/rT0anUY6gZYJjlVVflvCnR1ZGP2ekmPnr3ReSmixqaAkEYwkRjjUK8Hj8PPPylxwUwUKX08vmHdUjZ5RAcATqujppdjopsFipcFaSokqObsuxGPm2oTVm/1RNKnsde4fbTMEgUFVnj3FAAD5Ys5PqurYRskhwLNPS5uCR59fEtNU0tFNa0TRCFgkEgv4inJBRQCAUpLPLE9fucHpHwBqBoGc+q6xgSlIKnYFOguEABoUxui3XYmBXk53QQV51siqZPU7hhIxFfP7u5ZV9vmC37QLBUOIPBHF7/HHtXp+QARcIxhrCCRkF6LVqLjl7dkybRAIzJ2eOkEUCwZFZW1XBxMQkqlzVJKmTkXCgGJhOqUCvUtDgOuBEp2pSqHBWjIClgoGSmWohN8MW05aXaSMj1TwyBgmOaRJtBs49bXpMm0opJy8rYWQMEggE/UbkhIwSFs4tAODFtzZgMmq4+oIFTChIHWGrBIJ4wuEw62pqOO3EIta17yRRZYvbJ8Okp6LTzYJ9n21KK+2+DtxBNxqZZngNFgwIq1nHb+44i1ff28zX31Ywd3oO558+A6tZN9KmCY5B5DIpF585C4tJwzufbCc73cqV588T+ZMCwRhEOCHDRE29nR0ldXQ6PIzPT2F8fjIKxYGv32zUctbJU1lyXBFyqQy1WqgICUYnZfY2lDIZiVodNfU1HGdbELdPmkFHZWdX9LNUIiVJnUi1q0Ykp49BcjMT+OF1J9Fqd1DX2MG6zRVU19mZWJgqVLIEQ0ogEGRXWSPFZQ3otSomFaWSlWblyvPms2LZdNRKOUqlGMoIBGMR8csdBmob7Nz+u1eobegAIqFWf/rpORw/uyBuX71WPdzmCQR94pu6WsYnJOAMOHAH3ZgU8Yo0aUYt2+tiE0WTVJHwLeGEjE3CoTBvf7yVZ19eF207cX4RP/7eMox68dwSDA0bt1Vx5x9eIxSK5Jgl2fQ8eM9FZKVZRb8TCMY4IidkGNhV1hh1QADCYfjH82vo6HKPoFUCQf9YX1NNodVGrbsOmzIhJh9kP6kGHfVOT0xyuk1po9olFN/GKjUNdl54dX1M26frSqioaRkhiwRHO06Xlyf+szbqgAA0tTrYvrtuBK0SCASDhXBChgGX2xfXZu9wH1Z1RiAYzWxqqKPIlkCtq44EpbXbfTQKOWq5jEbHAUc7UZVAtatmuMwUDDJeX4DgQYPB/Xg84jkmGBp8/iBt7a649i5HvJqkQCAYewgnZBjIz05EKo2dLT7vtOkkWEQstWBs0eJyYXd7SDMYqXHXYlPGJ6XvJ1GjpMLeeeCzKoFadx3hcPxAVjD6SUs2M74gOabNbNSKStWCIcNi0nLe6TNi2iQSmFgoRFsEgqMB4YQMA0V5yTxw1/mMz0/GZtZxzcXHsWLZVCSS+DAWgWA0s6WhjkKbDQjT6G3C1o0y1n7MKgVV9gPJ6Vq5FrlURru/fegNFQw6Rr2au36wnOVLJmMyaDh+dj4P/PJ8UpNElWrB0HHaiRO56YqF2Cw6CnMSue/n5zE+P2WkzRIIBIPAsCSmB4NBzj//fJKTk3n88cdjtvl8Pn784x+zY8cOzGYzf/3rX8nIyBgOs4YNuUzKnGk5TChIxesPYDVp++WAdDk9lJQ1UtfUQXKCgaK8FMxGIXcqGD42N9STZ7bQ6m1FK9OglCoPu69VJaey3RHTlqBMoM5dj0UpZs9HMy1tDnbvbcTe4SIzzcK43GTUagU5GTZ+fNMptHedgF6rQq0SKn6CoSXRauCKc+exfMlklAoZep0avz/AjpI6KmpaMejUjM9PISnBMNKmCgSCPjIsTsjzzz9Pfn4+DocjbtvLL7+M0Wjko48+4t133+XPf/4zDz744HCYNezodSr0qPp1rN8f4KW3N/LMy19F284/fQY3Xb4QjebwA0GBYDDZXF/Pwqxs6jz1WFXd54Psx6pWsr6lK7ZNaaXWXcsk08ShNFMwAOwdLu579EO+3HSgwv1Pbz6VM0+aAoBcLhOhpIJh5+C6NF9s3Msv//wW+yM7Jxel8pvbzxaOiEAwxhjycKyGhgY+/fRTLrjggm63r1q1inPPPReAU089la+++krEjHdDVZ2d515dF9P26vubqaxtGyGLBMcaoXCY7c2N5FutkdUMxZFXM6xqBfWdrpjfc8QJqR9qUwUDoKyyOcYBAXj42dXUN3Uc5giBYPhoszt58KlVHDxM2F5ST2lF48gZJRAI+sWQr4T84Q9/4M4778TpdHa7vbGxkdTUSJKZXC7HYDBgt9uxWg8/y+r1eikuLu6THR6Pp8/HjCY6XLIYmcL9NLXYCfvtwNi/x94yUvc5YcKEI27vT7/sKyP5N65xOlBJJNgbGqn0VJEny6HR1XDY/aXhEHIpfFtShmVf8c1A0M+e4F6KPaOrn46V386hdg5Fn2xti1e7crp8NDW30t7ad2nUsfDdjgUbYWzY6fH0rFzVm355uHsNSzW0tcePJ9rsXaPuuxkLf6+eOFruQTA6GVInZPXq1VitViZPnsz69et7PqCXqFSqHl++h1JcXNznY0YTre1O0lNMMfVGLCYtRfnppCQagbF/j71ltN5nf/plXxnJe9+9aydFiUlkZGbQVdJFfkoecsnhcwIaGxtIMeiQGM1kpyYAYPFb2FS5ZdT9/UZrnzqUvtrZnz6p1regVMrx+Q44I3OnZVOYn4FW3ffQz7Hw3Y4FG2Fs2NmbAWtv+uXh7tUfCHDqogm8/+nOaJtMKqEoL5VxoyxhfSz8vXriaLkHwehkSMOxNm3axKpVq1i6dCk/+tGPWLduHXfccUfMPsnJydTXR8IzAoEAXV1dWCwiafVQbGYdv79zBfNm5KCQy5g+KZP7f35e1AERCIaaLQ315JgttPha0Cv0R3RA9mPTqanpODBraZAb8IQ8uIOiUOdoJSfDxgN3nU9hbhJKhYxTFo7ntmtP6pcDIhAMNgq5nKsuWMDZp0xFqZSTk2Hjvl+cT0FO0kibJhAI+siQroTcfvvt3H777QCsX7+ep59+mj//+c8x+yxdupTXX3+dGTNmsHLlSubPn39MSdfuKmtgT0UzEgkUZCcecSanICeJ391xNh1dHow6FVpt/5LcBYL+8G1jA2cWjqPB04hFae7VMVaNmtrOA06IRCLBprTS4G4kV59zxGPrmzrYWlxLYW4ieVmJA7Bc0BckEgkzJmXy0D0X0d7pprGlk91lDXR2uSnMSUKtPuB8BoIhyiqaqKq1Y9CrKMxNwiaS1gVDTEaqhR9ddxJXXTAftUqBydC9SqS93UlxWQMNTZ0kJRgYn59MglUkrwsEo4VhUcc6lL/97W9MnjyZk046iQsuuIA777yTU045BZPJxF//+teRMGlE+HZnDT+/7006uiKzwhaTlt//eAVTx6cf9hiNWolGzEgKhplAKERJawu5c+eztvVbzHJzr45L0KrZ1WyPabMoLTR4Go7ohLz54bc89q/PKMpLpuzZZi46cxbfOX/+AO5A0FekUgmvvLeJV9/fHG370XUncc6p06PFV7/eXM5P730jmq82d3o2v7jldOGICIYcuVxGcsLhIwHcHj+vvL+Z5145IOhy4Rkz+e5Fx2HUq4fDRIFA0APD5oTMmzePefPmAXDbbbdF21UqFQ899NBwmTGq+HBtcdQBgYg05qdflRzRCREIRoI9ba1YNVq0CiUNniYmGMb16jibVk1dV2wSqVlhpt5z+IT2T78q4en/fckPrzuJRKuezi43Dz2zmux0K4vnFw3oPgS9p6KmNcYBAfj782uYPTWbrHQrbe1OHnjy4xjBjK+3VFKyt4kFs4QTIhhZSsub+Ndrsbmor7y3iYVzC5g5OWuErBIIBAcjKqaPEKFQiKq6eHnd7toEgpFmW1Mj+RYLoXCIZm9zr4sNWjUqmh0eQgfpaVqUh3dCWtoc3Pf4h3z3ogUkWiMDWaNBw2XnzOWBJz7G7fEN/GYEvaLLEa8o4/MFcLq9AHg8fppau+L26XCIfB/ByNPl9BA8RFEyHIbOLqGUJBCMFoQTMkJIpVIWzyuMa184p2AErBEIjsy3DfVkmy20+9tRSVVHrJR+MEq5DI1CTqvrwIvfui8cqzseemY1C2bkkZUeK9Gdl5VATlYCr32wpd/3IOgbaSlmNOpY8YGsdCspiSYAbBYdi+fFrkxJJJCdfuQilgLBcJCRYibBGrsiZ9SryUgVwjcCwWhBOCEjyLwZuVy2Yg4qpRy1SsF3zp/H7GnZI22WQBDH1sYGcs0Wmjy9XwXZT4JOTUOXK/rZooic59CipMV76tm8o5qTF47v9jwnHz+Ol9/ZSCAQ7PsNCPpMVpqV+39xHtkZEadi6oR0fv1/Z2AxaQFQqRTccPkJLJ5XiEQCiVY9f/jxORQKlSLBKCA7w8bdty2nMDfSH/OyEvj1j86kIEeIXAgEo4URSUw/Vqiua6OsspmOLg8ZqWYmFqbGJJVnplq44bKFnLp4IgC5mTak0li/MBAIsreqhdqGdswmLQXZiRiOwqQ6V8BFtauGDn8nSepE0jVpKKQK2n3tVLtq8IZ8pGlSSdOkjrSpxxy+YJA99jZyzBbWte3EpOibLLRVo6K+y8WUFBsAGpkGiQS6Ag6MigNKNY//ey3LFk1Apez+sZSRasFi1vHFxr3driIKBp/pEzP5x28vxeH0YDZpUcqlFO9pwN7uRKmU09Hp5oyTJnPGSZORSqWMy0umrqmDiupWlAo5+TkJI30LxwSd/k6qXbW4gi5S1MlkaNKRSCR4g16qXTW0+ezYVFYyNOmoZGNfVTEUClNe00J1rR2DXk1+dgIqpZzi0gZqG9uxmrQU5SczsSCV265ZQn1TJ8kJBsaPsjoio5kGTyN1rjr8Zj9tPjvWbiafQuEQNe5aGj2N6OV6MjQZGBQiH0zQe4QTMkRU1rbx5H/W8um6UiBSTOkXPzidZYsmxuwnl0vJzz78zMznG8q4+4G3o8mfK06Zyk1XLDqqHBF30M2bdW/zQcOHAEiQcEPedRQZCnms7AlKHXsAUElV/Hjc7SNp6jFJSWsLKTo9armcRm8T6Zq0Ph1vUitjVkIkEglWpZUmb1PUCSneU095VSuXrZhzxHPNmZbNO59sE07IMGI2ajAbNYTDYd5bvZ1HX/iMS86ezRP/+Tz6XFqyoAiPN8BJJ4zjwX+uwuGK5I2My0viR9csHEnzj3o6fB08W/ECm9ojIgJyiZzbi26j0FDIJ02r+V/1y9F9L8+6hKVJS5BLx/arf+O2Su78w2sEAiEAFs8v5PTFk/jZfW+wf4H1inPnYjPr+Nszq6PH3XT5Qi46cyZKZc81jo5lKpyV3LfrAZzBiKhIRks6txbeQrI6dpVzW8d2/lb6d4LhyOr0Aut8Ls++BINCyCALeocIxxoi9lY2Rx0QgGAozD+eX0NZZXOvz9HY0sn9j30Uoz7z5kdbKavq/TnGAjWu2qgDAhAmzL8q/8PurpKoAwLgDXl5rfYNlFohUTycbG1sIHdfAdEmTzNmhblPx1s1auo6YxWyLAoLjZ6m6OfnX1nHiccVIZfLjniu6RMz2LKjms5ukqYFQ0tNfTt/efITTlk4gRff2hDzXFr9VQlzp+fw8dpdUQcEYPfeJkqr7N2dTjBIVLgqow4IQCAc4PnKf1PvbuCl6ldi9n2x+mUaPY3DbeKg0tHp4oEnP446IABr1pWyt7qFgyM8ZTIp/3hhTcyxT/z3cyrrRH88EoFQgA/qP4w6IAA17lqKO2Orjrf7Onim/PmoAwLwVds6qlzVw2arYOwjnJAhor0zXiGm1e7E4fR2s3f3OF3eGAnf6Lk7XN3sPXZxBBxxbcFwkFZva1x7jbuGsCIc1y4YOjY31JNjsuAJuvGGvOjlfVtut2hVMSshACaFMToYqm1oZ/POGuZPz+nxXGqVgvH5Kaz9urTHfQWDS5fTg9cXQKtWdvt8CwSD3ar71TV2DId5xyxd/niFsgZPI96QhzCxz8pgONjt83Ys4XT7qKlvj2v3egOxDWFiHBWIhHF1dtN3BQfwhfxUuCrj2uvc9TGf3UE3dn+8Q9fp7xwy2wRHH8IJGSIyUs0cWvh9UlEKKUm9j6dPsBoYlx+7/CmTSo46dY8kdSJySWx4gEVpJlsXn6Q/zzqXsEs4IcPJt4315FmskaR0hRkJkp4POgirRkWTM/bFbz5IpveldzeyYGYeKlXvQiSmjE9j9VclfbJBMHBSEowkJxqprrfHJffK9hUvnDM1/jc7IT95WOw7VklRx3+/My0zMClMaGXamHaDXI9NNbbzdKxmHcfPzo9rNxljq6Z7vP6oiMJ+9FpVn97BxyJauYbjE+ILw04wxgqGWJTmuDYJElLUIu9G0HuEEzJETCpM4Wc3n4Z534NxQn4K37ty8RErvB6KUa/mZzefxviCyEvGYtLyuztXkJM5tl8ih5KqTuXWwluiYT5Z2ixuyr+BIkMBV2RfhlqqRoKEOZZZnJx8EgF/4MgnFAwaTp+P6o4Oss0mmnzNmBSmPp/DoFLi9AXwHqRqZVGYafI04XL7WLlmJ8fPzuv1+SYWpvLtzho8Xn+fbRH0H6tFx+/uOIvq2lZOO3FSVHXIYtJyw+UL+eiznSyaV8iyhROQSECllHPzlYtJTRz7idCjmSxtFjfkXRt1OCYaJ3BBxnkkqZP4YdEPSFJFHMZkVTK3Ft5Cgso2kuYOGLVKwfeuWMSMyZkAGPRq7r5tORMKUyjYl19pM+uYMj6d3995NllpkUm7tGQTf/zpOaSnHF2TeEPBfNt8Tkg4DgkSFBIFF2ScR6E+tnyAWqbmiqzLKNrXrpfr+V7+jWRoRbFlQe8Z29lpoxitVs2i+YVkZ1hxe3xYzXpyMyMP/za7k73VLQQCIZIS9LTYnUiAnAwbibbYhK6CnCT++ssLabE70GtVcduPBqQSKdPMU7hn0i9xBpyYlaZoyM/JSUuZbpqKPxwgQWlDKVPSRnyYlmBo2NHcRLbZjFwqo8ndhEnZ91lEqUSCVaOi0eEiyxzpv2almWZvCx+uLaYgOxGrWdfr82k1SrLSrWzYWsUJc+JnRAVDR3aahVu+u5SG5g6+d+UiLEYNOo0SfzDMycePJznRyPRJmVx1wQLkCilpSSZ27do10mYf1ShlSo5POI5xhnF4g15sKitqWUS4ZJyhiLsm/owuf0SJzthHZbvRSk6mje9dsYiaejsGnYrC3CQSrAbu+8V5NDZ3YtSryc6w4fcH+PWPzqKtw4nFoCE7c2w7YMNFoiqBq3O+w5mpy+ns6KIwuQCpJH7OOkObzo+Kfkibrw21TINNJWoECfqGcEKGiC6nh6f/9yUvv7sJAKVCxp9+ei4ZqWZ+87f32L67DojM2Fx01iwefeEzcjJs/OHHK+IKtRn06qNKDetwWJRmLEpzTJtEIiFRLXTdR4otDZFQLIAmXzOTjBN7OKJ7rFoVjQ531AnRyXT4Qn5efW8jpy+Z3Ofzjc9LZt2mvcIJGWY+/mI3DzzxcbQS9XfOn88Fy2eQdpATqVLKo7VFBMPH4VY4TApTv1YwRzNfbizj7gfeia6GLj1uHDddsZC0ZDNJ+ybqgsEQH64t5k+PrCQcjhTS/OG1Szn75KkoFGLo0xMKqYJUTSrtFe1IUw4fNKORa0iXi9UPQf8Q4VhDxJ6K5qgDAuDzB3nwqU/YsLUq6oAAtLY72V3WSEF2IhU1rXz29Z7uTicQjAgb62vJM1sJhUO0eFv7rIy1H5NaSZPjQF6IRCJB0ZxAh9NNUW7fcwbGF6SwbnN5v2wR9I8dJXU8/OynUQcE4IXX1rGn4uhS6xOMbhpbOvn7c2tiwjFXfbmb3WWxql819XYeeOLjqGJWOAwPPb2aKqGOJRCMGoQTMkS0tTvj2vyBEHsqmuLa91a1kJZiBmDrrtqhNk0g6DVbGhootNnoDHSilCpRSvsnj2xWR8KxDqZ9i5HCiSak0r4lukMkvtvj9VPb0N4vewR9p73LjdsTm4cTDkNr+9hWWxKMLbocHqpq41XY2toPeb50uvH5gzFtwVD4qFOXFAjGMsIJGSLSks1xbTaLlumTMuPap03MYPfeyCzOorkFcdsFgpGgrqsTfzBIklZHs6el36sgAOZ9VdP34/UGaNghIaXoyHVBDodEImFcXjLfbI2XkhQMDck2AwnWWHlmhVxGWpJ5ZAwSHJMk2vTMmJQR156eGhtylpRgwHhIGLNGrSA58ejIixEIjgaEEzJE5GcncNcPTkericwc52TY+NF1JzNjUiaXrZgTlbScNz0HnUZJU0snZ508hXm9qJUgEAwHm+vrKbQlIJFIaPI2YxpAFVzLISshmzc2YE1R4lHG1zjoLYW5SXwtQrKGjYKcJH7yvWUkJ0T6gcmg4Wc3n8qEgtQRtkxwLGEyaLnx8kXkZUVUItUqBd+/ajET8mP7YWqSid/deTY2SyRfab+6ZPq+qAOBQDDyiOysXtDc2kVVbRsKpZycdCtGQ6weeTAYorqujcZWB1aTBplUSrPdyaRxaTxz/5U43T6SEgyYjREJxesvPYHlSycTDIZItEbUsU5fMom0ZDMq5dj+k7T7Oqj31COVSElVp2IcwMBVMLJsqK8lf1+l9GZvE6YBroS0OA9UOf/i82oKxpuw+xr6fc7C3CTe+mgroVC4XyFdggM4XB4qa9pwuf1kplriail4vT5Kypvxev385vazcLp8mI0aivJEDZDhRG5QsLurhFA4RJo6FZPy6Eo4PxyNzZ1U19tRqeTkZtiYPC6Nu29bTmNLF1qNgrxMW1ydEICZk7P4571X0NbhwmzU9EkiXzC0+EN+6tz1dAY6SVDaSFGnIDm0uJrgqKdPI95NmzZRW1tLMHggzvKcc84ZbJtGFWVVzfzsT29Eq/4eNyuP2284OfowC4fDfPZ1Kb958D38gSAyqYTLz53LZ+v3UNfYzj3/dyaL5hXGnFOhkJGTcUDJ5FCnZqxS567nH3sepcYdyWsp0hdyfd61JAl1qzHJ17U1XDgxolzV7GkhWxtfiK63GFQKnL4AvkAQl8NPRUUHx59cxOf23f0+p8WkRaNWsLeqJa54nqD3tLU7efSFz3j/0x0AWM1a7v/F+Yzb52D4/AHe+3QnDz61imAwhEwm5YfXLmV6NyExgqGjydPEfzteoqyuDIBMTQbfL/geqZqjuzhcSXkTd/7+VVrtkTzLk08Yz4pl0/j5vW/Q5fQCcNbJU7nqgnmkJMY7ZYk2w1EpbT+W8Qa9rGr6lP9Vv0yYMEqpklsKbmaaecpImyYYZnodjnXnnXdy3333sXHjRrZt28a2bdvYvn37UNo24gSCIV59d3PUAQH4cuNethbXRD/XNrTzh79/gH9fIbZgKMy/X/+apceNw+cP8vu/v3/MJM9+1bIu6oAAlDhK2dqxbQQtEvQXp89HRbudfIuVYCiI3d8+oBoDUokEi0ZJs9PNhq/rKCiwYNTocQadBMPBnk9wGApzkti8o7rfxwugeE9D1AGBSILvk//9PKo+VFrexENPryYYDAGRld+Hn1lNaXm8yIZg6Pi2YxtlzrLo52p3Deta14+gRUOP1xfguZe/ijogAB9/voudJfU4XN5o29sfb41TxxKMXmrddbxY/RJhItJlvpCPJ/c+Ras3XnBAcHTT65WQ7du389577x1Ty2Vut49NO6ri2kv2NnHKwki9BHuHK04xJhgKE9i3WuR0+WjvdB31cajBUJAdnTvj2ku6Sjg5eekIWCQYCJvq68i1WFHIZLR4W9DJdcglAwsVtGgitULWr6tj5qxkZBIpWpmODn8nVmX/qhjnZSewcVslF54xc0C2HcvUNXXEtW3fVUeX04NapaClzRGdZNmPzx+kxS5UsYaT3Z0lcW3bO3dwdvhMZJL+CTyMdhwub7eKkc1tXWjUSlxu30Ftoj+OFdr98c+crkAXXYEuUfDwGKPXKyGFhYU0Nx9bevA6rYrjZ8cXQ5tUdCABLsGqj1PgUMhlSKWRr9Zs1JJgiVWUORqRSWXMtMyIa59knDQC1ggGyvraGsbZIomfzd4WzINQ7MykVlFR30Fjg4PsnMj5DAo9Hb72fp+zICeRb3fWEDqodoWgb2SlxTuAc2fkYNJHwkSTE4xo1IqY7Rq1QsTXDzNTTPHP0lmWmUetAwJg1KmZPzM3rj01yRTjgACkCtWrMYNNaUVC7IS2VWkdlPeMYGzRoxNy0003cdNNN2G32znjjDO49tpro2033XTTcNg4YkilElYsm8aUcWlApOLquadNZ8r4A9VBU5NM/PpHZ0YdEa1GyXWXHs/7n+7AZNBwz/+dccxIAs61zmaq6UD16/m2eUzu5sUpGP18WVPF+IOckIGEYu3HpFaye2sL+QUWZLLIo0cn12H3t/f7nGajFo1GSUVNy4DtO1YZX5DCVefPiyr25WclcPUFC1DuE8koyouoYum1KgAMOhU/vflUinKTRszmY5HJpknMMc+Ofp5qmsJsy6wRtGjoUShkXL5iDoX7+ppUKuGyFXOYWJhKWnJkwCqXS/nuRQsYX3h058YcTaRpUrk+75po3SmTwshN+ddjVppH1jDBsNNjfMU111zT75N7vV4uv/xyfD4fwWCQU089lVtvvTVmn9dee4377ruP5ORIEuQVV1zBhRde2O9rDjZZaVbu/fl51DbYUcjlZKaaUaliZwXnTMvhqT9fSUubkwSzhuY2J2lJJpITDMjlMj5bX0pmmgW/P4jb4yc9xRyntw+RBNGaejtKpZzMNCs6Tf8Kw40USeokvpd/I42eJqQSKcnqJNQydc8HCkYVLr+f3S3N3DZ3ARBJiB0McQGzWsmmkkZOPS4n2qaXabH7BlbBuCA7kc07asjLEsnp/cGk13D1hQs46YTx0efTfiU/nz9AaXkTKoWc+39xHk6XD71WiUQCpRXNOF1ezEYNmalWFIqjd0Z+NGBT2ThNdQpnTlpOOBwmRZ2MRn50iJociZzMBB781YXUNrSjUsrJTLOgVMj57R1nUdfQgUGvJj8rAYtZx56KJuoaO7CYtBTlJqJS9e4d6vcHqKqz0+XwkJJkIuUYmTgcKRRSBcfZFpCvy4+EYCmtWEUY1jFJj07I3LlzAbj//vu58847Y7bdf//90e3doVQqee6559DpdPj9fi677DIWLVrE9OnTY/Zbvnw5d999dz/MHx6MejXGHrTwUxNNWI0a3l21g78/9yk+fxCtRsnt15/M1l01fPb1Hlau2UE4DKlJRv74k3MoyDkwk1he3cKvHnibvdWtAJx+4iRuvPwEEqxjS9VDK9eSq88ZaTMEA+Cb2hryLFZU8sjjocXXQqF+4EU0dcjxtPiioVgAermeek//ZXoB8rIS2LStivNPjw8HFPQOhUIe58S5PT7eXbWdfzy3Bn8g8jz7yfeW8dTLX3LK8eN59uWv6HR4kMmkfP+qxZx90lTUh4RtCQYXv8tPge7YK2hrMmgwHaQiuWFrJb996D1a7c7o6sjUCen89qH36drXJ6+/9HhWLJuKQXdkR83l9vH6yi088e+1BENhzEYNf/zpudEICMHQIJFISNEkk4KQ+T6W6XVOyJdffhnX9tlnnx3xGIlEgk4XKRQUCAQIBAJHdWL77r1N/O3pVfj8kSROl9vHg0+v4sT5RXzwacQBAahv6uSJg9RnAsEQr7y3KeqAALz/6Q627aob9nsQCD6vqmRCQmRA6g8F6PR3YVQO3Bl21XkJGaQolQdmzPUKPR3dJCn2hfzsRLbuqiUcFnkhg8nuvY089MzqaFK6y+3jL09+wrUXLuDldzfR6YjUfQkGQzz09Gr2VouQOMHQU9/UwaMvfBZVzAqFwnQ5vdz/2Ed0HdQnH/vXWnaX9azgVlbZzKMvfEZwX15Ze6ebPz3yAe2drh6OFAgEA6VHJ+Q///kPZ511FuXl5Zx11lnR/0uXLmXcuHE9XiAYDLJixQqOO+44jjvuOKZNmxa3z4cffshZZ53FrbfeSn19ff/uZBTQ2NIVfZDtp8vhweH0xu27eXs1nV1uAJwuL19vqYzbp0RIYApGgE8ry5mSHImvbvO1YVAYkDLwUJumvV24daGY34hepo8opQzAf7CatUgkUF0/sLAuQSxNLV1xCf8dXW4CoTD13ShqNbV0DpdpgmOY9i43u/fGyvGaDZpu1bEaW7p6PF9jN/22sqaNjk53/40UCAS9osdwrLPOOotFixbxl7/8hdtvvz3artPpMJvNPV5AJpPx5ptv0tnZyfe//31KSkooKiqKbl+yZAlnnnkmSqWSF198kZ/85Cc8//zzRzyn1+uluLi4x2sfjMfj6fMxfSVSLV0SM8gy6NXodaq4fadNSKejvYXW5lrkcgWzpmTyziexL/bsNFOfbB6OexwNjNR9Tpgw4Yjb+9Mv+8pQ33uLx0N9Vyfyzi4quxxUBCpRBVU0NvY9ZMofCESPC4fDVJXYkeZJKKupw6g88OiRhCXsrtyNRtL/+PaUBC0ffPINC2dn9ev4sfLbOdTOoeyTVrMWqVQS44iYDBrkMimpSaY4R0SnkXV7rbHw3Y4FG2Fs2OnxeHrcpzf98nD3qtKYGZebxO6DJuk6HG4Srfo4RyTBounxOnpN/ARLVroFr7uT4uKBTQSOhb9XTxwt9yAYnfTohASDQfR6fbc5G+3t7b1yRACMRiPz5s1j7dq1MU6IxXJAHvLCCy/k/vvv7/FcKpWqx5fvoRQXF/f5mL7i9vi49Zql/P3ZT/EHgmjUCm67Zglrv97DaYsn8sGaSB2N5EQDN16+KKr4AXCx2syO3fWU10RCsk5dPJHZ0/K6TWA/HMNxj6OB0Xqf/emXfWWo7/1/O7YxIzWN3JwcAMqbKkgKJJBs6rvyTGNjA8n7VlSa65wolHKMZjkKvYEU64HET1ODEX2SnnRt+uFO1SOTx/tobHP3+7sZrX3qUPpq50D6pNPt5QdXn8gjz38WzQn50XUn8dT/vuDCM2byzMtfReLvpRJuvupEpozP7jYnZCx8t2PBRhgbdvZmwNqbfnmke73pykX87qH3aW2P5ITotUruuPEUfvfQe3Q5vchkUq675DgmFqX1KifkpisW8uR/PicYCmMyaPjZzadRVNj/51Fv7mGscLTcg2B00qMTct555yGRSAiHw9TX12M0RgYPnZ2dpKamsmrVqsMe29bWhlwux2g04vF4+PLLL7n++utj9mlqaiIpKTIYX7VqFfn58XU5Rpouh4eahnYUChmZqRZU+2Zxq2rbqGtsx2jQUJSbjEat5KyTp5CflUBruzOijiUFrVpJeoqZFcum4XR5yUi1kJEacb7sHS72VrUgkcC9Pz+XljYHCoWcrDQLOm38Ckp3BEMhKtrttMhlNDsdJOoijos36KXR00QwHMSitNDub0eCRKhWCQ7Lx3v3MDX5gMPR5G0mVT1w6cvqsg6S0nW0qULY3V4OVv7XyyMhWen0/6Wfn53I0/+Lz1sT9B+dRsXZp0xjQkEqrXYHFrMWwvDDa5bS2eXm/p+di73ThUGnRqdV0tDSSUaqBZfbR+1Bz8ujBbvbTVVnB2qZjFyzBaW8/8U7mz3NdPg7MSmMJKoTsfvaafW1opNpSVYnI5X0Ol3zmGTOtBzu+8V5NDZ3oteqyEozk2Az8tdfXUhdQzsWs44J+cmo1Ur2VDTR3OrAZtFRlBefBK3VKLn4zFksmJlHp8NDSpKR1MSxV69i/zig0+slw2iMjgNGglZvG3a/HYNcT5IqCYlEclCfN5GoThgx2wSjix6fovudjLvuuotTTjmFxYsXA7BmzRo++eSTIx7b1NTET3/6U4LBIOFwmNNOO40lS5bwt7/9jcmTJ3PSSSfxwgsvsGrVKmQyGSaTiT/+8Y+DcFuDR3VdG398ZCVbi2sjdUJOnc7VFy6gvLqF3z30Ps1tDpRKOTdedgKnLp7IFxv28uBTn5CVbmX+jFz+++Y3+PxBkhMMXHL2bP7x/Bqy0izc839nEQqFePSFz1i/pQKA42fnc8Nlx5Of3Xv9fbffzys7t/P7z9fgCwZJMxh4dPkKMiwqXq99kzXNEfGAIn0hRYZC3ql/j3nWuVyceQE2lW0ovjLBGMUbCLCuppoLJ06JtrX6Whlv6Dn3qyeqSttJzTbgk3toP6TImHaAtUIAUhKNuDx+mlq7SLKNLUW50YxKKWdiYQqrvyrhz49/xNUXHsdfnvyE9k4XGrWCW65azCvvbWbyuDS+3FjGtRcfz79e/5rtu+uQSOD802dy6gk5I30bA2ZPWys/Wvke25ubkEok3DBzNtfNnINV0/cQwm/bt/FY2RO4gi60Mg3fzbmat2vfocpTjUKi4IrsSznOtgClbGxJtA8nm7ZX8adHVlLX2IFcLuXK8+axeF4BP/nj67TanaiUcn5123JCwL2PfkiXw4NOq+SO609hyXHjkMtjnTyFQk5+9tiV+Hb5/by8czt/3DcOSDcYeeSMs5iSNPy1U3Z3lvD3PY/SGehEJVXx3dyrMMj0/KPsUVxBNzqZjhvzr2eaeUrPJxMc9fR6uuXbb7+NOiAAixcvZvPmzUc8Zvz48bzxxhu8/fbbvPPOO9xyyy0A3HbbbZx00kkA3H777bz77ru89dZbvPDCC6NqJSQUCvPWR1vZWlwLQDgMr32whbrGdv7y5CfR+FOfL8DDz37Knopm7n/8Q9weP4vmFvDcK+uiSlmNLV289dE2TpxfxN6qVv771jd8uXFv1AEB+GJDGes3VxxqxhHZ1dLMr9aswheMXKeuq4t71nzCzo5dUQcEoMRRiiPgwKa0sb7ta7Z1bB/ANyM4Gvm8upIcswWjKrIC5w/56fI7MCgGNqMWDIZoqOoiIVWLTimnzR0r1KCX62gbYK0QiURCQXakerpgcCktb+JPj6zkkrPn8Nd/fhJVDXJ7/Pzln6tYcco0nntlHaefOJlPvyph++6Iql84DK+8t4nqRudImj9g/MEgT2zcwPbmSH5AKBzmsY3f8G1j30VUGj1NPLLnMVzByHfoCrr5Z/nTTLVEBmT+sJ9nKp6nxl07eDdwlFFbb+eJ/6ylrjGSkxQIhHjmpa+oa+yMKmZ5fQE8vgB/+sfKqGKW0+XjT4+upLS88bDnHqvsbmnm1weNA2q7Ovnlqo/pGOZcCLvPziNlj9MZiCT7e0Nenij7J2XOvbiC+4R4gk4e2fMYjR4hvCPogxOSlJTEI488Qk1NDTU1NTz66KPRMKqjFafLy9pv9sS1dzq8VNa2xbU3tXQRCISAyEPwUMqrW6JVXlvaHHx9kAOyn43bqvpkY3VnvEqN3e2mpKskrn2Po4wsbSYAW4UTIjiE90tLmJl6QBu/zdeGUWEcsDJWc50TnUGJSi1Hr1Jgd8e+GPUyPe0DdEIAcjJtbNret9+PoGcamjtxeyJy4vaOWNnSYDBExz6Vv2AwxNZd8YPn0vKxLd3b7vHwacXeuPbdLX2/L7vPjicU2/+9IS8SYqXrW7xj+zsbSjq6PGzfHe8ANjTHqlz5/UEcrtgJD68vELff0UB344CtTY20uodXZrjd30H7IavaIUL4Q/6YNk/IM+AitYKjg147IQ888ABtbW3ccsst3HLLLbS2tvLAAw8MpW0jjkajZMakzLh2nVZJckJ8yIfNokMqjbxMlIr4SLe0ZBOt7ZGZGoNOxZTx8THwk4r6ViApuZu4T41CQY4uJ649S5tJw77CcOMNRXHbBccu/mCQj8vLmJuWEW1r9rZiUgy8cnBdRRfW5EjYikGljAvHGoxaIYBYCRkiEix6FHIZcqkEnTY2REgiAaMhkl8mk0ljCrDuJztjbOeFGFWqGOd8P7mWvt+XUWFEIYl9N8gl8rgcEItybH9nQ4lWoyA3Mz6nwGbRxXxWqxTR/M39yGTSPom9jBW6GwfkWSxY1P1XHOwPBrkBnSz27yBBglIaK1ihkCgwDsK7RTD26bUTYjabueuuu3jjjTd44403uOuuu3qtjDVWkcukXLh8JimJB34s82fmkpZk5IfXLkVzkBLMxWfNJjvTyg2XnYBUKmHzjmrOOvlAzKNGreCC5TNZ9cVuzEYNl50zl0XzCsjNPJCXUZCTyPFz+haONiExkZtmzYl+1iuV3L1oCVPME5loOKBokaRKJFWTSr2ngTxdHtPM8fVaBMcuX1RXkWYwYtNqo23N3maM8oHnV9Tu7cCWHDmvXqmg0+vj4LqCWpkGT9BLIBy/etgX0lNMtNidcbP1goFRmJfED757Iv99awM/uHoJCnlkZUwqlXDtxcez6svdnHvqdD5dV8JJx48jOfFAnzluVh656WN7sKGSy7l13gIStQcGV2cUFjEjObXP50pRJ3NN7tXIJJHvUCaRcXn2pWxo2xTd55y0s8nQDFyZ6WglJzOBm7+zCMNB0vdnLJ1MWrI56nRIJOD1+bntmiXR/A+ZVML3v7OYotyxm/txOCYkJHLDzNnRz3qlkj8sXYalHzlLAyFBZeP6vGuijrYECZdmXkSaOi2mz1+TexUpalEpXdCLxPTf//73/OIXv+Cmm27qdvtjjz026EaNJvKyE3nk95dSVduGQiEjJ8OGyaAh0Wbk4V9fTE2DHYtJS2FOEkaDhvNOnc60CRl0dLlJsuo5/cRJtLW7SEsxEwyG+MOPV5CRaiE1KRKW9cefnMPe6hYkSMjLTiA92dwn+4wqNbfMmc9pBUXU29soSkkl1xyZRbu54CbqPHUEQ0FMChMN7kZuK7iVbG0mNrX1iOetd9dj97djVphJ0/T9ZSsYW7y5u5i5abEDn+ZBUMYKh8M0VDsYNyPy4lfIpMikEpw+P3pVxImXSiTo5TrafR0kDEAsQSqVkpeVwNbiGhbPFyt9g4VSIeeMJZMYl5dMp8PDw7+5iMbmTkwGDSHCTBmfjlQCpy6eQF6WjSnj0qiqs6Pc97ysrakY6VsYMBMTk3jt4ssot9vRKOQUWG2oZXJKWiPP7iyTCVUv1LKkEilzrXPI0mZi97VjVppJUSUz0TCeZm8Lerkeb8hLo7eJLGkmUmlkAO0Nemn2toBFgj8UQCHtvzLX0cCCmXn89VcX0tLqQKtRkpxoIC3ZzDN/vpL6pk4sJi3ZGZFnSW5mAo0tnSTaDBTlJqFSKWlo7qDT4cFm0WMz63q42ujHqFbzg7nzWZKTR7vHQ67FTJFt8J0tV8BNi7cFpUxBkiqpWxW3aeap/GbyPbR6WzEqjKRpUpEg4deT7qbd145FaSZFnYJUIqXaVY0j4MSqtJGsPvqcQ0HP9PgkW7FiBQDXXHPNkBszWkmyGbpV3BlfkML4ggODtFAozJbiWv7w9/dp73STnGjk0rNn8/i/15KaZOSXty5nzrScmHMcLNfbX7RKJVOTU1C02aMOCIBBoWecooiKrjperX6LTR0bAFhgOZ7lqcvJ1Hc/E7HZ/i3PVTyP3d+OSWHkO9lXMNs6a0A2CkYvbr+fT8rLuO/kU2PaW7wDV8bqaPMglUrQ6g+sGhpVCuweb9QJgUhIVrvPPiAnBCAvK4FN26uFEzLIbNtdz28feo9WuxObRccvb13O7KnZlFU28/u/v0/J3iY0agU/uHoJyxZOYPbUA6sfR0uKdbrBSLohcl91XZ38ft2nvFq8A4lEwiWTpnDL3Pmk6HteOZRL5WRoM8jQHgh9TNGk4Ag4ebriWapc1SilSs5LP4cTEo7DG/Lxas1rfNW6HoCTPEs4M+0MLErzkNznWGBvVTP3PfbhQf3uRGxmPVnpNrLSY58hU8anM2Wf/HcoFOaLDXv4w99X0tHlJiXRyK/+70ymjOtbGPRowxvw88GePdyz5hOcfj/jbAn8ZdnpTEgcvLzdOnc9z5Y/x25HKQqJgvMzzuXExEVo5LGrLVKJlDRNatzkZaY2g8x9fd4X8vF58xf8p+p/OINOklSJfDfnKiaaxnY9EkHf6TEca/LkyUCkaOHUqVOZO3duzH/BAarq2rjr/jdp74wkajY2d/LCa+s5fclk9la1cv/jH9PlHP7KnZvtW9jY8Q3hff++tH/O9o6d3e5b7azmyfKnopKpHf5Ontz7NBWOiuEzWDCsfFxeRqHVhvmg+GF/yIcjMHBlrIYqRzQfZD96ZXxeiE6mwz4IeSH52QkiOX2QqW1o5xf3vxlVHmq1O7nr/reoqm3j4WdXU7I3onLj9vi577EPKa08+lVvPtlbxivFOwgTUcv6z/atrK2q7Pf5HH4Hr9S8RpWrGogM0l6sfom9zgo22jfyZeu66PP746ZV7Ow8douvuT0+Hn7200P63UeUVvTc76pqW7nr/rejYgoNzZ3c/ee3aDmk0vpYo7ilhTs//gCnP5IAvru1hV+tWYXD6+3hyN4RCAV4p+5ddjtKgYiK24vVL1HurOjX+cqdlTxV/izOYOSZ0uRt5pmK52j0NA+KvYKxQ69zQt544w1WrFjBRRddxL333suqVavo6Bj4oOFooqG5MyrJu59WuxPDvqKDO0vrabMPr1xlIBhkh+PbuPadXdu63b/Z14ozEGujJ+SJhAIIjkpe3rGdBRlZMW2tg6SM1VDVhdkW64ToVArsh8j06uQ67L54xbm+kplqob6pk07H8Dv7RyuNLZ04XbFOo8Plpa6xgw1b4x2+2ob2YbJsZAiHw7xTujuu/cOy0n6fs81np7hrV1x7k6eJ9a3fxLVvaY9/ph8r2DtcfPNtvMPXm35X39yJPxD7jm5uc9DU2jVY5o0IVR3tcW0b6mppdg1OflxnoItN7Vvi2us8fZepBmj2NBEiFNPW5G2m1SfGGccavXZC7r33XlauXMnDDz9Mamoqv/nNb1iwYMFQ2jbmsJi0SGKVFtGoFQRDkR9bcoIBva53VdAHC7lMRqY6N649S5PT7f4mhQH5IeotUqSYlGOvgqygZxodDr5trGfOIfkgLd4WTIqB/80bqruwJB3ihCjkcTK9Brl+wLVCAORyGXlZNrYWC5WswcJs1CKTxb4q5HIpFrOW7Iz43DLrURBjfyQkEgnz0uNVE2el9j+ZXCfXkqaOz70zKY2M60bJMF+f1+9rjXV0WhU5GfFhm73pd1aTNq5Nq1Fi2qfwNlY5WDRhP1lGU7Tm00DRyjRkaeL7vLWfKm7dvVt0ch0G2dGnXCY4Mr12Qt58803uvvtubrvtNr788ksuv/xy/v3vfw+lbWOO7HQrN16+MPpZKpVw+Tlz+fCzYhRyGT/+3qnYLMP/I5tvnYdNceChnaxKYbp5erf7ZmuzuSjjghjd+vMzziFTk9Ht/oKxzeu7djInPSMuqbbZ04JpgMpYwUAYe7MHc0LsC96gUtLmOrRgoZ72QQjHAsjLSmTT9upBOZcAstIs/N+1S6MTLBIJ/PCapeRnJXDH9afEyKAuXzKJotyju34UwFlF48k2HRhIFVitLMsv6Pf5bCobF2dehFJ6QAJ5jmU2OdpsTkg4DpvywPM7Q5POdNOxq25oMmi4/YaTY/rd6UsmUdiLfpedbuPGy0+IfpZKJdx50ymkp4xtSeQJiYlcNnlq9LNSJuP3S0+JUTscCGqZmosyL0AjOzChNNM8nVxd/ARnb8jRZnNq8inRz1KkXJZ5MZm6eEdHcHTTa4mNP/zhD2RlZXHJJZcwb948MjKOvkFpKBSmvqmdYDBESqIJ5b6HnM8XoKG5A5lchl6rpKa+HbVSTv4hmvhqlYILTp/J1PHpNLc6SEkyIpVG6hekp5rJTh9Y0u2hNDsdtHs8JGh1R5TiG2fO5baC/6PGVYtEIiVTm4476GZvZy2pGhv2QBtKqYoElQ25VM6ixBPI0mXS4m3FprSSqEygzWfHRBBv0Icn5MGqsNDi9OELBkkzGNAoIknGgVCAFm8LEomERFVit+oZgtFBOBzmpZ3buGrazLhtTb5m0jUDS9bsaPWhNymjEpn7MSgjiekHo5fr6fJ3EQ6HkRy6nNhHCnISeefj7sMNBb2nubULp9uHRALj85N5+DcX09rmIDnRRFFuIm6vH7NJw2N/vIzWtohKUW5mAgb92J5VPhxuv5+6ri6UMikFViv/OvdCdjQ3oVMosGgigz1fIIDyIIe+1duKJ+TFqrSgkWlo93XgDDgxKoxx+VbTLVP58bg7aPA0oJNrydZkY1QaaPG28MPCH+ANevEGvWTo0jEfw0npADMmZfL3315MdZ0dg07N5HFpvep3arWCC5fPYvaUbFrsTlKTjN2uqow2Wl0uWt0ubBpt1LFocjro8HhI1OkwqzX8+LiFnDN+Au0eL9kmMwXW7hUwA6EgVY56AuEg6dpkdIruvzd/yE+LtwWpWUYoHKLAkM89k35Jg7sBlUxFhiYdg6L7iapGTyN2Xzs6mY5MXfxY0aA0cGbqcqaYptDhbydJnUSOLruf345gLNNrJ2T9+vWUlpbyzTff8Ne//pXKykpyc3O5//77h9K+YaOzy8PbH2/l6Ze+xB8IctriiXz3ouMAePqlL1m5ZidKhZzzT59BWVUz23fVcd2lx7Ns4QSMhogDEA6H2V5Sxx8fWUljcyd5WTZ+9v3TOGFu/2fIuiMcDvNFdRU/+Xgl9Y4uxtkSuO/kU1EcYfCWbUgl25DK3s4aXq19jS0dG1FKlZySfDLlznIqnJVcnn0pcyyz0Mg1TDCOJxwOs6NjB7/fdS92v50sTSaLkxbxYtX/yNHlki9ZyF0frWNZfgE/OX4RRk2Yd+s/YHXzp0iQsDzlNE5JOUkUJRqlbG6oxx8MUWSNfwm3eFtj6sz0B3uTF7Mt/gWnVynocHsJh4nOrsulMlQyFZ2BzgGHgWWlWahpaKfL6cGgOzoHxENJIBjiq417efK/azlj6RTe/ngbK5ZN5ZX3NlPb0E5Gqpnbrz+Zf7/+NRu2VWE1a/nxTcuYWJSGXHZ0TjpUtrdz35dr+WBPCVqFgtsXnEAwFGJLQz1mjYaXd24nGApx6eSpfG/2XBJ0GjbaN/NC5b9xBBzMMs9kUdJCnqt4gTZfGxnqdK7N+y55+gMzyXXuel6vfYMdnTvRyXXclHc9r9Z9zZctX6GQKliSuJhGTxNZ2sxj/rm6tbiWx/69lq3FNRj1am64bCHHzc4lydbzd6JWK5hQOHZk5zfU1fKTjz6gvKOdbJOJvyxbTpfPx08/WUmDw8HExET+dNKpTE5KZnbakSeHm1x2Pm1ew0fNK/GH/Mwxz+OMlOXkGOPDcd+ofYvPW75EIVWwInwWJyYuIkWd3GN9j50dxfy76r/UuGuxKi1cmnkJs8wzkMli8wuNSiNTlJP696UIjhp6/cZwOBzU1dVRV1dHbW0tXV1dUQ3zo4Ftu2t59F+f4fUFCIXCvLd6B2u/3sPKNTt5f/UOQqEwHq+ff7/xNVPHp+N0e3nwqVXsKDmQmFVdZ+enf3qDxuZOAPZWtXLXn9+m1T64yht77XZueOcN6h2RZLrdrS384P13CKuPPOAKhUJ83vI5mzs2ECaMN+Tlnfp3mWAcjzPo5Im9/6TKdSDRtM5dz4OlD2P3R2L1q9zVrGz4iAW2+ZQ6SikLfcHi3CxWlu3hfzu2sal9Cx83fUIwHCQQDvBW/TsUd8YnWwpGBy/t2MYJWdlxKw/+kA9nwDlgZazWBi+mbpwQpUxKGHAfIuJglBsGJSRLLpeRm2kT1dP7SXlVC3fd/yYL5xbyxH8+Z/6MXJ556ato4m9NfTv3/PVd8nMiuv5t7S5+cd+bVFS3jqDVQ0cwFOKFrZt5f08JYcDp9/Obz1bjD4UosNn497Zv8QWDBMNh/rXtW1aW7aHKVc2jZY/jCESe/enaNP6x51Ha9okv1Hhq+ceeR2n3Rfq7L+jjlZrX2NEZUS1USVVsbt/CFy1fEiaML+RjZeNH5OvzeLv+3WP6udrU0smzr3wVzfvqdHj48xMfUVpx9Ckr1XZ2ctO7b1K+L/G8sqODL2uquOGdN2hwRPrWzuZmbvvgXVp7kYS+u6uUdxvfxhfyESbM1+3rWG+PFz74qnUda1u+iPa9l2tepdSxp8fz17nqeabieWrcEWHuNp+dJ/Y+yR5nWR/uWnAs0Wsv4tJLL2X16tWMGzeOBx98kJUrV3LvvfcOpW3DypYd8THktQ3trFwTL2VbU99Owr7cjuq6A8m0dU0deLz+mH0bmztp2OeUDBZVne14AoFD2jpoC/gPc0SEZk87Wzo3xbW3elsxyCP3U+9pOLC/rxn/IVWsm7xN0VCAUuduZu1byq7qaOPL1nVx595k39zzDQmGHbffz/t7SlmYFb8E3uJtw6QwIen946Fb7E3dOyESiQSjShmXnK6X67APQnI6QH52Ihu3Cane/lDTYCcYCkMYvL4AKqU8Tm2so8uNWnmgzkswFKamfuDqZqORNrebt0riB/3hcJhdLfED3zd27aTe3RDX7gvFKoy1+Fpp9UUctw5/R8yzskCfz45uZHibvE2YFWY2HsPP1eY2J19vqYhrr2s4+tQ6azo7aHO7Y9o8gQC+YOwETnm7nbqunscZux3xqm5bOjbQ5jlwrCvg5ouW+Hd5bxzfFl8LTd5YqWR/OBDXJhDsp9ejjLfffpt77rmHs846i5SU+CrKv/3tbwfVsOEmKz0+flKrUVKYF5/sZjVroy9lk/FALobZEJ+XoVLKBz1G2tpN/odGLkfXQ8VevUJLiip+GdqgMOAORu7HKD+wnG3oJjFZLVUTCEcegFallcauyHFKmZI8bU7c/lnarLg2wcjz4d49FFitWDXxiYst3mZMAwz1CAZDdNkDmKzd932DStmtTO9gKGRBJC9E1AvpH2ZjpE8oFJHwCalUgkwau1omk0qQHtJm7kZ56GhAp1AwzpYQ1y6VSkgzxP9OpiWnxv1+DlUcBFBKlWhlke9MLVOTrDrwrmn1tZGiig97MSlMOINOso/h56pGLSctOT5k0zjGFa66w6xWIz8k4kQli5dN1ykUvVLCSlHFj91SVOno5Ae+O6VUQY42fnIqVdNzCJtOrkUljbfDID92QwcFR2bQ4qk2bYqfYR9LzJicSUF2YvSzXqti8fxCLlsxB732wI8qJ8NGMBTG6wswfWIG4wsOvChyMm1cdcH8mPPeds1SMgZZeaPAYuOmWXNi2u5ZvBSdP3CYIyLoFGpOT16OWnrggZOpycAb9BIIB5humka27sDLLU2dxukpsVW0T0tdxrrW9cgkMk4wnsEbO8vQyOVcMWU6i5IWxjgxicpEZlimD+BOBUPFyzu2c3xm94mAzd6WmL9jf2hrcqPRyZArun/E6JTyOCdEP0gyvRDJC6lr7KCzy93zzoIY8rMTOevkKXy9pYIzlk5mzfpSzl8eK15wzcXHsWb9gboYZ588lfysxENPdVSgVSr50fzj0SoOrPzMSE6h3e1Bp1CSaTwwILZqNFwyeQpZ2izmWGZH27d37OD0lNNizntF1qUkqyOOh0Fh4MqcK5BJIgPMMkcZc61z0B6kRpSmTiUYDmKSm47p52peViI3X7k4RvBi3vQccrqZSBzr5Fms/OT4hTFtqXoD182YHdP26xNPIstk7vF8E4zjYyYitTItJyedjEp+QJVNLpVzWuqyqIMMkK5JZ6Kx5xzBHE0OF2aeH9N2YuJioa4pOCy9Tkw/2slIsXDfL86jrLIZvz9IbmYCmWkR5+HxP13GnvJmlEoZVrOOvdWt/PaOsyjMSUIikWDvcBEOh2mxO1i+dBLzpudElTfyshLiZgwHik6p5Huz57EkN48mp5NMo4lxNht7S3uO2ZxiHcdPFD+jK9COTCJDJ9fR4mtimnkqmdqMaLJjo9NOu7+ThdYTmWGeToe/A4vSghw5Ew0T0Mh01NmD3H9yOnkWK4W2SFjWsoGziAABAABJREFULyf+jBp3LVKJlAxNBgmq0a88cqzR5HSwtamB62fO7nZ7o7eZLO3AXhot9U705sM/XvRKBW2H1gpRGNjdVTKg6+4nUi8kgc07a1g8r3BQznmsYNSr+d6Vi9hT0YzH62fh3AJ8/iCzp2bjcnuxmnVoNUoWzMqjy+FBrZKTkWY9alWxvIEANo2WVy+8lJrOThQyKeMTEvEFg9R2drA4O4curxd/KESG0YhBqUIr03FVzhWcmnIKvpAPs8KEWWlhpmU6Hb4OktSJKKVKOv2deEM+lFIViSob90z6Jc2eZtQyNZnaDPL1d1HrrkMCaGQaOl2dnDzhpGPuuer3B2lu60Ihl5FoMzBvei4P3HUBNfV29DoV2WlWCo5CaWiFTMalk6cxLy2Ddq8Xk1pFntnKKfkhzi4ajzvgx6hUkWux9EpVMN+Yya35t9LqbyFMGJPMHJeUDpCjy+ZXE++i1l2L3+un0FqITWUlEArQ5rOjkCqwdKPQJpPJWGCdR6o6hVZvGyaFkTRNGja1FWfAhSPgQC/XoZMf3bWEBL1HOCEHkWQzkGSLDUFqaunkzQ+38toHm9Goldx0+UJOPmE8Ta1d/O3pVWzcXs2tV5/I+5/uZEdJHbmZCdxy9WKWHjduSG01qFTM6UEJoztavK180bqG1c1rUMvUXJJ5IXMsc9ActBy7tW03r9S+RKW7ggx1JhemX8wU01S2tG/hpZpXsPvsTDFN5uzUs5mfETvAS1InkaQ++l4GRxPvlOxmVmpaXG2Q/bR6W5liGphqSXOdE53x8I8Xg0pJZXtslWK9bF+tkDAwCH57QXYiG7ZWCiekHxj1GmZOjg/52byjmsf+9Rk7SurJybCxYtlUVq7ZwY2XL2LWlOxBn3AZaSra7Ty4/kveKdlNglbLPYuXsjArm06fl9eKd/LYxq+RSiR8f8488sxWLnvtZVx+H7fMncf8fDX/qf4Pdl87sy0zOT/jPIoMhex1lPNMxfOUOyvI1mZxXvo5rGz4iJ1dxSSpErkq50omGMcjlUgxKoykag6E0BTXFZOQemw5IPVNHfz79a95+5NtGHQqbrnqRDJSLbz0zka+3FhGgkXPjZcvJC3ZiFZ79DnCe9pauXv1x2xtamRKUjK/X3oKzU4nv1v7KeXtdmalpvGT4xcxO63nYpmugItdzh28UvMqvpCfk5OXYlCdgk0Vv4qUokkmRZNMcXExtlQrTZ5m3qp7my9avkIv13NZdkT1SilTxhxn99t5p+49irt2kaZO5aqcK3EFnTxT/jwVrkpydbl8J/vyGGU4wbHLoIVjhcPhwTrVqOKjz3fx0jsbCQRCdDk83P/4R+ypbOYvT37CV5vKOX3xRP71+tfsKKkDoLy6hXv+8g4l5Y0jbHn3fNHyBR83rSIYDuIMOHmq/Fn2OvdGt+/tqOPpyieodFcAUOOp5p+Vj1HmLOPxvU/S5msjTJitHdt4s+5NOrxdh7mSYLTy+q6dLMjoPqbcG/LiDrrRD3CmqrnOic6kOOx2g1IRl5iulCmQS+Q4goOjJleYmySS0weRytpW7n10ZVQRsKKmlSf/+wVzpuXy4z+8TkXN0aWO5QsEeOSb9by1exehcJgmp5Pvv/c2xa3NrKmo4MH1X+IJBHD5/dz/5efsam3GHfDjD4Uw6n38o+wR2nx2woT5xr6RV2teo9XTyoOlD1PurAAiFaffqn+XnV2RJPQmbzN/Kfkbte66Ebzz0UM4HOaND7/ljQ+/JRgM0d7pZtuuWv712nq+2FBGOAzNbQ5+//f32bq7vucTjjHquzq5/u032NoUGU9sa2pkTUU5t7z/NuXtkdDVjfV1/GLVR9R29pyYXuoo4/nKf+EKugmEA3zQ8CHftMWrYx1KKBziw8aPWNvyBSFCdAY6eazsCcpdFTH7dfm7eHTPExR3RZLY6zz1/KXkb3zbvo0KVyUA5c5y/lb6d9q8R6eQhaBvDJoT8p3vfGewTjVqcDg9vPNJfNGzppYuNu9T0zKbtHHqV11OLzV1gxPbPph0+bv4rPnzuPbSrgNhXI2eZuz+9tjjAg6aPI2EiXU0t3fuoMl39MkiHs1UdbRT29XJpMTuV6tavC2YB6iMFQ6HaW10oTcdYSVEraDd7ePQuQujwojd197vax9MRqqZ1jYHbe3OQTnfsU5tQwc19e0xbS63D6lUgj8QpLru6BpUNLtcvLk7VqEqDLS5XLy2a0fc/lsa6qMJ7DKFM+55ucG+iTa/nY6DZKgztZmUOWLlS4PhIA2eeHWtYxF7h4sPVsd+17OmZvPFxtjvLByGmvrR984dKNWdHTS7Yp9f3mAQ9yHqmKVtrVR29Hz/Ozri++1nLZ/jDhw5d67d38EXLV/Gtde6amM+t/paqfXEOtDeUCTnNPZ87TR7W3q0V3D002M41k033XTE7Y899hgA55133uBYNIpQKuXkZNhiZHghoppl1KujCllyuZRAIBSzj1F/+ArmI4VKqiJFk0KLL3bG8uClWJ1cixQpIQ7cjwQJWln8zLhJYepWCUMwenmvdDdz0jKQHabGT7OnBZNyYMUCnV0RqWil+vCOjFImQyaV4PT50asOrJjsl+nNHGBOCoBUKo2uhpyycGCFFwWg1ypRKmT4DqnvIpdHkqmPtpwQjUJBhsEYrdGwH6VMzviERL6qiZV1T9Ub2N26b2AVig1RATArzKikKiRIog6KJ+hBJ9PhDMYONHXdPG+PRdRqRWQy4aCJhM4uN0k2A40th4Rz6o6+d5FRpUYqkRA6aLbmULUsALVcjkHZ8/0ndxMqnaZORSE9/Ko1gFqqIkmVFF3N2M+hFdPVMg1KqTJOjnq/4MJ+JEjQyEbfGEkw/PQ43XnNNdcc8f/RjFIh54pz56I+aJCUkWImJ8PK/113EgBr1pVy/ukzYo5bsWwqBbmjTylGKVNyTtrZKKUHXpBp6lSKDEXRz7n6dJYlLf9/9s47PK7i7Nv39r7a1ar3bsm9d4NtMM0YMDUQEiCEFELnDaHkS3hJXggJCUkgBUJCCb0b04vBBffeZMnqfVfSSlppe/v+kL32aleWZEmWZJ+by9fFzs45M+fs6Jx5Zp7n94QdtyTuXJIVqRTpCkNlIkRclXYFGZq+/VAFRg8fHi5l1gl8h1s8LeijSDMPhNYmBzGxyj4DJfUKOdYoClmtnqFbUc/NjGP73uq+Kwr0SUFuAjdcOS+sbNnCQnbtr+HchYXkZo6+Z95giFWp+PXic5AcN45np3QLcVxRNAHDcclh41Rq8k2mUAK5w2YPE3TjQ9+LEHFD1vUkq5JYmXpJqHxz61YuO+4zwOzYmUNihJ8OqJVybrluIXLZsUnsN5tK+fF3F4XFH00uSg1TtzxdyDEYuXN2+N9cvFrNynHjw8runjO/193t4xmvH0+8/JjctEKs4MLkC5CKT7werZaquSbjqjBjIludRbYmPK4jQRHPtelXh5Utjj+bGGn4wtbK1EvDYp0Ezlz63AmZPXv2qejHiNLR6cTl9hJn1NLR6cTvDxAXq0UkEjGhIIVnf/ddKmqakcul5GclkJwQQ7xJx3+e+B7WdgdatZxZUzKpa2wnwaQjPyset8eHy+0NM2B6w9zVhUgECZpjGardPh+1HR1oZDJMajUtTgdauSJMC7zNZcPmtWNSGBCJA0gNsm71Clcnbr+bJHUcMkn4T5yvy+PX4x+izlmPTCwnQ5WG0+/E7DRjkJno9PhYZFrExJjxeAIu5CIlsXIjSpmE69Kvo85Vi91nJ1GRRJ4299g99HQQIIBRbqTN04YYcb9W1F1+F10+O1qphmAwiMPvQCfVRQS7CQye+k4b9bYOiuJ6f1mbXRZyNIMLGGw1O9AZ+16V0ylkWB0uMgzHxr1WqqXZPXQufgU5ifznjUg3AoHesXU6cbq9mAwapFIJdoebLocbsQjmTs9mclEqFdXNxMVqQdS9WFOYm0RMlDxJYwmn10ub04lCKsXqdGBQqliQnsFH134Pi8NOjEKFTCwmGAwSq1LzxhXXUN3ejkgkIjdOg0QkYkbKVXgCHmLlRuSyWdiD7fiCPgwyAwGC+AMBzktcxjjdOJrdLWilGsRBMfcU3InN24lRHkOGOgOVREW9owG5WI5ULEEikqCX6ZFIJLR7Oggeedae8Hp8Tux+B/ox/jydUpTGC3+8gXabA4lETEKcFq1aydOPXENnlxuJTExCrJacjHg8Hh9tNgcalWJM7Ix4/X6aHXY0MjkxR4xat89Hq9OBTq5Ap1Bw49TpLM3OxuH1oZFJSYsxcFZmFhfm59Nst5MRY2BCQgJisRi7z47L7yZGpg8ZFlanE7fPR4JGQ7IqiQcK78PqbSMQDBAjjSFJHZmPBrrdatu87ciOxPYV6sbxq6KHqHHWoBKryNXlECvvVszq8NpQShRopBrmx80jW5NNp68TpVhJsioZiUhMsiqJVk8rJrmJTE1Gn7svAmcG/VbHqqqq4k9/+hNlZWW43cdWL7/66qtej3G73Xz3u9/F4/Hg9/s5//zzueOOO8LqeDwe7rvvPg4cOIDBYODJJ58kLe3UrAJ5vX627a3mqefXEG/ScdbsfF5dtQ2ny8M1K2Zy8TmTiIvVkpMRR05GeLKqiuoWXn5vK5t2VpCebOQH18zn600lLF8ykd889QmHys3MmZrJLdcuJKcX/Xyr08kHJcX8bdtmRIi4Y848VhQU0tBp48U9u/jocAn3zF3AzqYGvqyooMBk4qFFi5mdmsbetkO8W/8udc4aJujHM9kwiTfr32aGYxppqjRWNaxmbuwClsQtJkufEtZumjqNNHUa5V0VrGpYzVbrdmLlRlamXsaHe6xcMjGdL1s+odxeSq42lwVx83i95C2K9OO4PHUlqepj53P6nWy37uCduvcYry8iQZnAF+avkIgkXJ56GbNiZ6KRRk9iVm2v4a3ad6h11HBF+hV8Y1lLjaOWqYYprEy7lFRVStTjBE6OLyvKmJaU0qsrFnQrY00fZA6C5gY7eoMCOLFYhU4ux+oI3wnRy7SUDpFML0BSvB63x0eDuZ2URMOQnfd0xO8PsGNfDU+98DWNFhuXLpvMwtl5vPDWJhbMymX9lsMcKG1kwrgUbrpqHp9+fYAvN5aQnWbi9psWYxzDyQrtKhW3f7KaWalpbKuv59vaGsaZTNw7fyEba2pw+X14/X58gQAauZx3ig+glsn4v6WLEanNPFm+Cm/Qy1lxC7F5bcQ4E0hQJPJ5y4e0eFqYbphKojKROmc9FydfRKF+HNJOKasaVlNsO0S6Op0r01YyIWY8tY463q//gG9bNqGX6bgk5WJKbCXMjJ1Js7aFpw/8A38wwCXJy5kfNy/CJQagrLOcN2rfpNJezRTDJFamXkaaemzuWlfWtPDBV/v4eM1+YnQqfnD1PDJTY/ngy318s6mUxDgdt1y7EICX3t7Cum1l5GbGcfuNi5lcOHp3lKra23h2xzZWlRSToY/hl2ctIUmn48lN3/JVZQUT4uN5cOFiAgT506Zv2dnUwLSkZO6ZuwC338fTWzdT0trK/LR07pgzlwZ1Fa9Wv47ZbWFu7BwuTLqAg01dPLp+Lc0OO9dOnMwNUwo55CxmdcNHuANuliSczezALLK04Tmj2j3trGtezydNn6MQK7hafiUpymS+aV7LptYt6GV6VqZeQrY6i4+bPmOzdQuJigSuy/wOcpGcDxo+5KCtmDR1KlelXc6EmAkU6odXMVRgbNLv6NMHHniAa6+9FolEwksvvcRll13GJZdccsJj5HI5L774Ih988AHvv/8+69evZ/fu3WF13nrrLfR6PV988QU33ngjTzzxxEldyMlQWmnmF4+9S21jO7OmZPHn/6zB0tpJp93Nc69/y9ebSqIe19Hp4B8vr+ObzaW4PT7Kqpt5+MkP+f7lc3n6xbXsLa7H4/Gxfms5Dz/5Ie02R9TzfFtTzSPrvqbV6aTF6eBX33zF7qZGnt+9kzcP7md2ahqflh/mo8OluP0+9lnM3LjqHYrbqvhHxd+odJTjDXrZ3bGHNZavmW6cyretmyjuLCZLk8k3LV+xtmUdvoA/om2n18mX5q9Y3/It7oCbRlcT/yx/lqunp/Faw4sc6jqIN+jjUGcJb9e9x/y4eWxv28mzFc/R5TumXlTaeZjnKp/H7ndgUph4r34VXb4uOrwdPF/1Yq95H9o8bfzl8NPss+1nUfwiXql+jXJ7Bd6gl21t2/l3xQvYfUJA8VDyadlhpiX1nvXW6XPiDXpRSQY3mWw1O9DH9m8npMURHhCpleiOyfQOASKRSFDJ6idlVRZ+/n/vUFnbisvtRSqV8ODj75OZZuK197ex60AdHq+fXftr+e1fP0GplOHx+CipMHPvb96hqnZsBpo22+38zzdfopTJ+Ki0hDVVFbj9PvZazPzs49XoFAp8gQAfl5ViVKl4fvdObG43FrudJm8Nz1e9SLu3HbvPzidNnxErj0WjkPLf+n/T4GrAE/Cw2bqVWkcd7d4OXq95E7PTwgtVL7G3Yx/eoJcKewVPl/2dWnsda5vX85Xla1wBFxZ3M/+ufIFJhknUOGt5ue5VOrw2unxdvFr7Bvs69kdcj9ll5o+lT1LaVYY36GV7207+VfHvsOf2WCEQCPDJNwd4+6OdOJweGi0d7Nhfw+urd/DpNwdwub1U11v5fN1BnnjmS7789hAej4/iw03c88g7VNeNTrEEt8/Hn7ds5PUD+3D6fJRYW7npg3fZWFPNx2Xd7/udTY18XFbCXZ99xOb6Wjx+P1vq67jzs49YV13FXosZt9/H19WVlNqqeKLkSWqddXgCHta1rOedund5fvc2qjrasXu9PLdrB3XeGl6qfpk2bxsOv4OPGj9hny1yDG237uCd+vdx+B20edt4puJfVDtq+Lp57ZFxaeHZin9T72pgXct6PAEPtc46auy1vFj9X/Z07MUb9FJpr+Kpsn9Q1VV16m+ywJig30aI2+1m3rxu38TU1FRuv/121q5de8JjRCIRGk13gJ3P58Pn80X4ia9Zs4aVK1cCcP7557Np06ZTJvdbUdtCMAjxsdqoyhrvf7aHLrs7oryuoT1iUuPx+jG32ELB6qE2alppNHfQk2AwyJsHI5W36m02PijtlrebEJ/ItoZw9QmXz0eT04zDH27Y1DnriVN079bs6zhAga47N8KW9o00OSKlM80eC1t6SPMFCGB2m2n1hE8mOrwdqCTdW8VVjmpa3MfOt6ttNwB52lyKbYci2tnWGl3+z+JqpvVIgLxIJMIVCL9v5fbysHYEBken281es5lJidG33gGa3c0Y5UZEg0jSEQgE6Wh1oTP0wwhRRu6EyCUyZGIZnUMk0wuQlxXPll1VQ3a+05WqOiv+wHEBsFIxnXY3Oo2CZmv472Fp7QwLRHd7fFSPQkXA/lDT0U6NrYMcQyz7my1h33V5PGhkMr6oKGNWShprqypD3+UYjTT5y3uejgO2g0C3ytXx7GrfzXhdISVdpbR6Wql11oV97/A7afe2s7l1c1h5kCAOn5OKrkp68o1lHYFguChKk8uMwx9u3Pd8bo8VGi02Pl8frlA2f0YOazeHL25lZ8Sx91CPd6XbS80oVWxr6urkw9LwRU5fIECzI/y9rpbJaegMD8Bv6upCKQ13ZXLRHjHedrTvZH72sef91eMnUGyLXFjd1LqFFtexd77T52RNc+Tc7nDXYWKPcwEMEqTJZQ6LMdXLdNQ4wgUbnH4nDYLam0Av9NsdSy6XEwgEyMzM5OWXXyYxMRG7ve+Var/fz+WXX05NTQ3XXXcdU6ZMCfvebDaTnNy9OiuVStHpdLS1tREbG5k85yhut5vi4uJev4+Gy+WKOEYu6Z5s2Z0e9FGUXZIT9DQ11eP1hE+UgmItWrWCrh4TKJUq0u9WKhXj9Tgj25bLydLH8G2P+jKxmESNllpbBy6fD61cTpcnXGniqEEQ1o7o2E+plWpwHnkJGaQG3F1OimvD21elqDHIDCFDIFQuVoaptxzlaECaTCTF1emkuKYYiUSCQWkAwOa1kayMXGWPlcVy+PBhfD0kBf2xgVA7PZUzAORieaidnkT7LU8FRUUnVlg6mXE5UE722jeYG8nUaDDX955/oNR3GKlfitl88i+MznYvUrmILnsHPr+ftrbeJwFBj4/mLgdNTeE5dZQBBcU1xSRJejeYBoJO4eeDvZUcOHCw12R6IzWmBkrPfg7lmJRKwv/mj6peSSRiRCLC5JRFIiLc+sT4o7Y12u9tQCE/okAUQCGR4vaHP6vEou5nstXpIEGr5dARBax2pwudODIuwyg3II6yvqeX6bH7HSjFSmSi7pw4PaVLpUgxyAx09ti1kIglxMgiY+wSFAlUVVaFuUgHjIGIet3PbVfU5+lQ43K5+qzTn3HpcrmQu7qIi9XS2nZsrtHl8GCIUYeV+XwBlAoZLrc37By9jclTRW9jP6BWEatS0dLD6JBLwt+FUrEYEeEbwyIiFbLERMZXaKVabM5j9+NQSwvzigwR9WJlRrwOH8WV3f2Uq+TEy0zUO8ONOr1Uj9Mf/tuqJMowJSwRYmQiKd4e41qBfMR/B4HRSb+NkAcffBCn08kvf/lL/vKXv7B582Yef/zxPo+TSCSsWrUKm83Gz372M0pLSykoKOjzuBOhUCj6fPn2pLi4OOIYg6mD3Mw4yqtbUCpkxMVqaTmy4ieXSfj+FXPJy43uU3rzd+bzl/98Hfo8dXwagUCAs+bks27L4VD5TVfNZ/y4zNAL/Xiu1mlZdbgkZGToFQomJiZx77wF3P3Zx3x0uIQbpkzjb9u2hI5ZkpVDmjqV6TGz2NlxbJdhacJitlt3ALAs8Vy+MH+FGDGXJq8kNz4ToghRXJF2Gf+q+E/I4EhXpaEXx3G26Ry+af0yVG++aS4HbcVHjrmcgsQCxEndD0G1Q8OXLWuoc9azMG4Bezv24Q50vxA1Ug1z4meToU6PaNvj93Ch53w+bvyUGkcNk2Mmsvc414Ir0y4nPzE/1M7xRPstRwMnMy4Hysle+3/qqpmdmUVmZmavdQ42FpMkSiJRd/KqJV2tVoxxaozGWNrarBiNvS8mxASCuGpbiYuPRyo59jubWk3IdTIyDNETKp4M2jVlKDTx5GVFj88arWOqJwPt50DGpKm1kwkFyaFkhAdLGzl3YSFbd1dxweIJfHJcvoaV509ly+5jK/Pzp+cwsTAjalzIaL+3bp+XH02dzseHS/n+lKn8a+f20Hfn5eaxqa6aS8cV8btv1/HAgrPYUleH2++jxelA7Z+ETqoNGQ1ysZwCXQFmp5kcdTYVjmP3aFniOXzS+BmXpV5CpjaD5ckXsqphdej7mcYZJKoSuCz1Uv5W9o+QTHqiIgGpSEKyMgmNVBNyU1WKlSxNXByRedruszPPPpdNx+2odD+3oz9Ph5r+TDb7My6Li4vJzUrjxivn8ssnVuP3d9+Pz745wI+vW8Rjf/80ZBhX1jZzy7ULeeqFY+/kRbPzmFCYjkE/crFKJxr7vzprKXd8+mHo88zkFPJiTWF1nF4v358yjRf37AqVXTdxCg5v+MJkrDSRQt04DnUe2+m4Ju1qnlp7zGPjQLOFcbpZGGQG2o/kApOJpCxLOodkQxLJscee+xd3LudA58GQMWGUGSjSF/Jx06ehOknKRFKU4XGbVV1VLE++iPcbPgiVTTdMI0uXhSmu93fBcDOaF0HOdETBAfo+dR2RINRqtX3UjOTpp59GpVJx8803h8puvvlmbrvtNqZNm4bP52PBggVs3rz5hPKeJ/NS6+2YRksHJRVmnE4PaSlGmiwduD1+xuUkkJeV0Gs/bJ1O9pc2UtfQRoxeRUpiDFV1raQlGXC4PFjbHaQkGhiXk4BW07t+fmlrCwebmxGJYHx8AvmxJuweDzsbGyiztpKs02FUqnB4vKjkUnIMsSRotTTYW6i0V9HubSdRGYdcLMXitJCgSsThdWP1tpOuTiVVmQaIiFWp6fR2IRKJ0Eo1tDociPBS666myWVGJVGSpExle3UnRXExuCRtWL2txCli0UiVtHhaSFQmkKXJjND3bnQ2Uu2oRRQEo9xIs6cFsUhMpjoDjVSDTCRFHSU4vcvbFXITSFIm4vA7sXltJCkTyVRnoJJGV9sZrZOaU9Gvk2kjGAwy99//5P4FZ5Os611+98WqlynSjSNRefI7ENvW1NFqcTBxdmKfRgjAW/vK+MHMIhKOy6tzsKMYmVjGOYlLT7ofEe18tJPJRalce8msqN+P1jHVk4H2c6D1zS02SsrN2LpcZKWZSIzXUVbVjMvlJRAM0tJmJ86oIS8rnrYOB7UNbcQaNIzLSexWyxqCPgw1Nrcbr9+PSd37ZPRgeTltMintLic6uQKX34dBoUSvUOANBKhqb0OnUNBit2NUqbHYu1DL5BTFxaFQumlw1+P1ezDKjdg8nShEBvw+CT5JO+6gnRhZDA6fE51MS642B51MR4eng/KuCizuZgzyGFJVaSQo4hCJRBzuLKPe2YBCrCBBGY/NayNLk0Vbh5U2STsBgmSq00lTp+H0OfEEvcTI9KHr6fDYqHJU0eZp7/W5PVz05/ceSB2P18f+kgYqaroXC8flJpIYq2X/4SbqGq1o1AryshJITzZSWmGmtrENk7F7TJqMA5+nDCUnuk63z8uB5mbKrK0YlEomJSSikyuoaGvD4fOglEhJ1cfgCfjY22SmodNGsk7H1MRkOj1uSltbaHU6SNXpGR+fgFLhp9peTaevi2RVElnqTMx2B/stFro8HgpiTUxISKSiq5w6Zz3+oI8UVQrjNAVIpeHr0cFgkBpHLbWOWgK+IAXGfAyyGEo7D9PobEIpUZKuTiNNlUqVo5pGZxM6qZYsTRYSkZhyewUWV/e4zlZnkagaml3tk2Wkn0ECvdPvnZB9+/bx4IMPhlywtFotjz76KBMnTuz1GKvVilQqRa/X43K52LhxI7fccktYnaVLl/Lee+8xbdo0PvvsM+bOndtnfoGhJDkhhuSEGNweL1t3V/HSu1twODx855JZmIxaYg3Rk0bpdSrmz8iBGcfKJhUOXH2kwBRHgSlceavD7WJ7Qz2vH9jHT2bOZkNNNZ+VH6YwLp575i0gQaslRRNHiqb7uO0N9Ty2cQPlbVaWF3QnzdpY18YFuXHcu+tNpibHsbhIzZqWzxEj5py4C/hwn5WyVhs/n7+QJelLaOjs5PkdO/m4rISiuHjumbeQ5anTonU58h6qkklWHXPFKiCfdk87G1s383nTl+ikWq5Mv5zx+qIwWT6tTMvEmAkDvmcCA6PU2opYJCbpBAsHwWCwO1t67OAkuVvMdvTG/iet0ysUtDpcYUaITqanrocrwGApyE5g6+6qXo0QgW4S4/QkxunDyuJjdZRXN/PSO5vZsa+GmZMyyUozMXV8OlPHR+5yjhY8Ph/f1tbwxKYNtLmc3DhlOpcVFoVJoR9F5PGwIDeXijYr/9m1g3i1hsauTtZUVpBpMHDH7HlUtFv55/ZtGJUqbpgylQ6Xk1s+XIVYJOLOOfM4NycXbT8Sxh0lRh7D9NhpmF0WvjB/xSvVr5OmTuWK1G6VrAkx4yOOsVa2Mq9oLgC+gI997ft5q/YdbD4b5yQuZYFpPrEKIzFyPVPkk0/+5o0i5DIp0ydmMH3isZ3RRksH+w7V89GafaQmGvjx9YtQKWVMnZDO1Amjd0wej0IqY3pyCtOTj+0m7Gio55X9e1hfXcU4Uzw/nTmbFK2OT8tL+ba2hnlpGeTFmvAE/LxfUsxecxPn5uSRazSRqo8NSzwMkK6Xk643hD7bvXaa3c18bv4Sd8DN0oTFxCviSJCG5xgRiURkajLI1GRQXFxMUnK3ETHZMInJhklhdQt0+aEY1KNMl/dv7iAg0O+92QcffJBf//rXrFmzhjVr1vCrX/2KBx544ITHWCwWvv/977NixQquvPJK5s+fz5IlS/jLX/4Skva98soraW9vZ9myZTz//PP8z//8z+Cu6CQ5UNrIA4+vorKmFXNLJ3/5zxrWby075f3wBwK8sHsnT23bzDhTHF9XVvDq/r20Op18W1vD9997m/Lj/OxLW1v4/vtvs6Whjhangxf37GJHYwNLs3J4cM0X1Ng6mJYl5436V2l2t2B2W3i1/iUW5uk5bG3lh6vfZ1dTI4+s/5r/7ttNq9PJhtoabnj/bSpP4M/fF5tbt/BG7Vu0eduocdbyZOlfqbRXDcEdEhgoG2qqmZSQeELjvtNnQyKSoIgSbzQQrGYn+n7kCDmKXimjtadMr1SL1TO0Qc752QnsL2nA4/X1XVkgjJa2Lh54/H2++raEdpuTL789xEN/WEVr2+hWW9prMfPD1e9R3NJMU1cXv/t2HR+URIpnHKXT7eaXa77ksLWV/c0W3jy4nxangx2NDfzko1W0u1yY7V0cam3mwTVf4PD5qLV1UN3Rzj2ff8KWurpez90bbr+bN2rf4gvzl9h8Ng7aivl9yR9pcDb2eWyVvZo/lv6ZamcNbd523q57lw2tPaMMTz98Pj+vvL+V/767BWu7g30lDdz9v29TVjV0+YVGgqq2Nn6/cT3vHyqm1elkY10Nt33yIc1OB6tLS7A6nXx0uIQvysu48f13+bqqklankzcO7OOBNZ9jc7v7bKOkq5RnK/9No6sRq8fK23XvsvOIuIyAwEjQbyNEIpEwc+bM0OeZM2dGbOH1pLCwkPfff5/Vq1fz4YcfcttttwFw5513cs453RnHFQoFf/3rX/niiy94++23SU8fmVWMbXsisyq//fFO7I6+/7CHErO9i5f37QFgalIyG2rD+2X3eim3HjMOyqytuHoEfZe0NnOopfuBPCUxiUrPnoh26r3FoR2YLfW17GoMf+l1ejyUt53cRLDT28kX5vD8MUGCHO469UadAKyrrmRCwomz6Ta7WzDKDYNqJxAI0mHtnzLWUXQKGc32HjK9Ui1d3s4ItZfBoFbJSYrXh+IdBPpPXWM7DT0U/uoa26lrah+ZDvWTHY31EUrPL+7ZhdXpjFq/ztbB5vpaZqemsaYyXPXK6fMROM5zOQi0Oh2oZcd2dt87dHDAfWz1WNnRtjOszB1w0+DsXUDiKFX2qggBkS/NX9HhiVRjPJ1otnax+stwZUmvz09V3diUiD5KXactQg2zw+2ipqM9rMwT8NPmCh/D2xrqqbX1/bvv7zgQUbah5Vts3s4otQUEhp9+GyGzZs3iV7/6FVu2bGHr1q08/PDDzJ49mwMHDnDgQOTAHmtEC6iMi9UiixJQPpwoJFJild2uKd6AH1UUQ08tO1amkkbPOmpUdZ+j0+1GLdZHfK8S67G5uxUjDEoV0RbJo7XdH2RiWVQlF60kumubwPDh8fvZ0djAhPg+jBBXc5hP+clgs7pQqmVIZf0PftUr5LT0MEIkYgkaqZr2IZ5M5WclRF1sEDgxKkX0Z4yyl/LRgkERuauXoNGgkER/piukUuQSSUiVsCcSUfi4VkqleP3HDOWUE8Rb9YZMJEUhjjTao5X1JFqcnU6qO+0zUctkEnRR4ixH+3jsC4VEEqGOBYQZuhA5DqFbVVPZy7g+Hn2UxJbdY+bk3vUCAoOl37OFQ4cOUVlZydNPP81TTz1FeXk5Bw8e5He/+12/VLJGOzMnZ2LQH/NLl0jEfP/yucjlp/aP06RW8+CiswH4+HAp10+eGvb9nNQ0xsUdU/gpio9nckK4mtElBUUYlEpiFEoOtbaQIp6ATHTsQaYUK9EHsmjo7CROrWZ+Wga3zpwTdo75aekUxoXHqvQXpUTJ5WmXheWbMMgM5PfwGxUYfvaam0jW6vr0VW9yW4iRRhqOA6HVMjBXLAB9lFwhADpZTIR89GApyE1k867IXAsCJyYjxcil54VLq192/hQyUiLlaUcTM1NSiVcfW/gQi0TcPXc+migGBkBGjIHbZ8/lo8MlfG9yuE/7pIREmrqOrRYnarRIRCK8gW7FJrVMxopxAw98jVPEcVX6FWFlBdp80qMoCvYkR5ODSX4sBkCEiKvSr4xqnJxOxBm13HHT4rCynMw4CrJHNvh5sIw3xXHz1BlhZXNT00hQhy/e6RUKzsnKCSu7bdZcMg19/z1O0E9AIz12PolIwvlJ550y0QIBgZ70e4b93//+dzj7MeLkZMTx9G++w/6SBpxuLxPykxmXMzIPtaVZObxz1bU0OzswKNSclZFJm9OFViGnMC4Otbw7EZZEJCFJq+Ppiy5mV1MjdW1tFCYkYlAocPsDPHfxpbR7HUjFIu6N+zkO2gExWrERuzvI21dlEKPQEa/RcHnheArj4jnYbCFNr2daUjKmIw8/n9+P3etFr1D0WzSgUDeOXxY9QIW9EpVESYE2H61Mhzfg7XWlzhfw4Q64UUvUp1Sc4HRmY20NhXHRZWmPp9nVQkbs4CRxrWYH2pjoE7ze0Mnl2NwefP4gUsmx31wv1WD1DG2isex0E7UNVmxdrqh5gQSio1LJufma+cyfkUN1XSuZqbEU5SejUg7stz7V5MaaePWKq9nV2ECnx8OUxCQmJUQ+032BAGKVimAwyOWFE5iVnEp1RztPXbCcivY24tRqpiUm4/D5yI01oZRKSdZqEQF/u3AFcrGYPFMMMSolHr8Hf9AfUvZz+pw4A25ie3F1FIlELIybT646G4ffiUwsw6QwYZBHXxAQiUS4/W6CBElSJfLzcfdSbq/A7rOTo8kmW5MVcYw/6Mfpd6KWqBFHWUUfi5w1O5+nHrmGQ+Vm4oxqJhSkkBg/uJ3ckcLmcqGUStEolVxSUMjUpCRsbjdqmYxknZ40nZ6XL7uKNpcDg1LJuLh4LsjL5/utLdh9DoxyHYXxCRG5Q6KRr8vjnoI7aXW1EggGMMgN5OvyAHD5uxeDlJJjC0kdng40seFGkMPnQCqSIpcc+/u3+xzIxfIT7qicjuNQYPD02whpaWnhT3/6ExaLheeee46ysjJ27drFVVddNZz9O6VkpZnISjP1XXGYcQS6aGIf33StJd6TwCz9Wby49xDfm17Ap80bOWDbz3h9EcsSzyVNnUqaPoY0fQzllZVUE+APm75Fo5Dy3RmpbLR/RZIykaAvnzWWrxEjZmnCEvba9tLu7eC8xGU8s85Mq9PNrTPn8KMZ4epBB5st/HvnDnY2NXB+bh7fmTCZLGPfKy5SsZQ8XS55ulysHiubWrewtnk9yYpEVqRcTJ4uN6x+tb2GT5s+o6yrglmxMzk7fuGgpGIFutlYW8NZmVknrOMP+Gnztg3aHavV7BhQPAiARCxCp5BhdUYqZDW7h9bHWyaVkJsZz4591SyZN25Iz326E2vQsGBmLgtm5vZdeRSRa4wl9wQy0SUtzby4Zye5RhMvlJWwqb6W+WmZ3Dh1WphqYYvDwWdlpbx/6CAxCgXXTZyMxeHAYu9gdq6K5+texxf0Ms80l0ZXI0W6QgxyAx83fkqzp4XZxpnMjZ1NmiYy75TF1cyWtu3sattFsiqZCxLPI04R+R5y+920Gdp5vfj3+IN+lqdcxBTDJBaq5vd6ffWOer4wr+GA7QCTYyZxTuISUlQpvdYfKyiVMqZNSGfaGFHCikZDp41Vh4p5u/gAucZYfjpzDmIRfFtbw9rqKori4vje5Gl0uF18Vn6YtTWVLMrIIlGjxSNpZ5frS2pd1YzXTiLOvYgYZd/3whvw4vA5+MLyFQ6/kwuTzifJm0iZvYLVDd05Sy5JuZgMdTp72vfxdfNaFGI5F0ovIFuTya72PayxfI1JbmJFynJM8lg2tW5mQ8tG0tRpLE++kFxtTkS7x4/DSTGTOPc0GYcCg6ffRsj999/P5Zdfzj//+U8AsrKyuPvuu08rI2Q0EAgG+NryDR80dj8QmlxmDnUWc/PsH7Kh4yNqXd3JhyzNzRy0HeLBol+EAoobREF+8uEH+AIBHrtwJs/XPotEJGGqcQrPV70YauPfVc9zdfqVvFX7DmVd5fxoyk+5/vVv2Fhby3vXXBdaOa+3dXDTqndpdnTLMj+7czvFLc387cIVaBX9m2wGggE+b/qST5o+A8DsMnOw8xC/Gv8Q6eruF3Kzu4UnSp7E5rMB8FHjx9Q76vhp3o9RDlKt6UzG7fOxz2LmRzNmnrCe1WNFK9UgFQ3O9dBqcZKSNXC/+BiFgpYeMr0xUh3V9poTHHVy5GfFs3VXlWCECNDU1cmPP1rF9OQU3jl0MCTmUdOxly31tbx2+dUkHJG1/rD0EI+s606EJxWLWZiRxe83ruf5q87mPzX/CJ2ztu5trkhdSYunlReq/xvKJr268SNsvk6+l3ld2E6ww+dgdcOHbGvrTjRrdlso7TzMfePuJVubFdbfw11l/LPmX6HP/yh/hjvzf8Z04/So19fh6eCpsn/Q6OoWY/jSsobSrsP8fNy9UWMDBE4dXr+f53Zu54UjSQgr29sQi0S4/T7WVlcBUN3RzvaGBn4wbQb/3bcbgFf27SHLpGCP/z06vN0xcxb3VzS5G/iJ8scYFSf+XSu6KvlT6V9Cggb/qXyBW3Ju5pmKY+PqqbK/86Ocm3m15vVQwszXa99kUdyCUBLCRlcTDr+DdHUaa5vXA91j90DHQX41/iFS1ccMjA5PB0+X/YOGI+PwK8saDncd5ufj7kE/yIUvgbFPv/fE2trauOiiixAf2fKTSqWh/xcYOto97Xxm/iKszBv0EZTaQgbIUSxuC02uptDn4tYWfIEAqTo9LYFu3/ccTTYHOiJVW8q7yklVpRAkSJ2rilSdHrffF3oRA5S3WUMGyFHW11RT0w8VjqNYPdYIpSxPwEOd45icZaOzMWSAHGV3x16aXWNbcnGk2WtpIkWnQy07sduMxd2MQWYYVFuBQJD2FueAd0IA9Co5LXZXeJkshjZPGxHyRoOkMC+JLXuqGGCOVoHTkIq2Nmo6OkjXx4Q996B7UljR3q0OaHU6eO64LOqTE5LYWFvDooxMDnbtjTjv3o59SETSkAFylA0t39LkbAora3I1sb2HOpbD76DOGSn3u6l1S0TZl+avCQQDUa+vyWUOGSBHqXHUYnaZo9YXOHU0dXXyyr5w1cpxcXEhA+QoLU4HTp83rEwic4YMkKMc6iqmwR4+tqKxr2NfmKJapiaDzVHG1ZbWbWRqjrnnTjFM4ivL12F1xuuLWN8cLgntCrio75HjqcllDhkgR+keh5Y++ytw+tNvK0KtVtPW1hby1d+9eze6k1ADETgxEpEkapCYpJdV6uNX1ZRHlLJcPi9yUfcOgjvgiXo+pUSFO9DtA6oQK3B6u1+YiuMUseSSyDYlIhGyfqhwHKsvibqbcXy/pVH8SCUiCRJBsWNQbK2rY5ypb3EBi6s5qprZQOhsd6NQSZHJB64mp5fLsHQ5wsoUEjkSsZhO/9DmokiK1+P1+qlrbB/S8wqMPRRHlA/FvcSfHVXRkool6I7b+XX4vGjlctpdLlSSyCBwlUQZ9ZwKsQKJKPzvQyKSRo2Rk4kjFw500kh1QZ1UGyYAcjzRnqvd5xaeqyONRCxB1UP1KhAMRo3riFTDiqwjRtzr7308qh6iBW6/O6qQgVqqDsWIHK3Xcx7hC/qQRxmnPcdzbzGg/emvwOlPv42Q+++/n5/+9KfU1NTwne98h1/84hf88pe/HM6+nZHEyGO4Jv3KsDKjzIi1Q8Z0fbiC1UzDdJKVx5SxxseaiFOraXU6UfiSUYqV1DhqGKcvCHv5yUQy0lVpNLtb0Eg0mCRpWF0u0vT6MCnXApOJuWnhfqY/nD6TrBhDv6/HKDdydQ/1lwR5ApnqY6ssacrUiIyrFyVdQIKi74Bqgd7ZVFcbpqTWG2a3uddA2P5iPQllrKMYVAosXZG5G2JkBlqGOC5EJBJRmJvE1t2CStaZTp4xlnOyc9lcV8uFeeHPnwty88mN7Y7L0CsU3DtvYei7Qy3NzElL51BLM3mq8WETMTFiJsZMoN3bQYIiXBb7kpSLSVGH+8GnqlK4MOn8HmWpZEZRx5odOztM5VAiknBu4tJeRTySlUnMiZ0dVrbQtIBEZVLU+gKnjhSdjvvmLwwrO9Rs4cYp4apsUxOT0fdQNhT5tORpwsfr2XFLydSk9tnupJiJKMXHFgUbXU0sMM0Lc8WViqTMi50T5mVx0HaIy1JXhPfXVsLK1EvDr0uZTEaPsRttHC4wzSdJGIcCDCAmpKamhueee47GxkY+++wz9u7di98/dMnEzgR8gQD+QCBstyEaM4zTuW/cvRy0FWOQGogRp/LpoQbmZc5nfvwsOn0dxEj1ZGmzQnJ7gWCQ2ECQf69YyYaaaiztHn5ccDtNnmpEQbin4E4q7JWIEZGpzuRwZxmXp64kT53HN4fb+e2Sc5mblk7GcQZGrErN78+9gG31dZS0NjM9OZUZySkD2gkBmG2chUlmorjzEHEKE4W6QuKVxybHermeH+X8kFJbKXXOevJ1eeRr84SVkkHgCwTYY27khh4vtWhY3M1Mipk4qPasFgda/ckbIS0OV0S5Xqqjxd0aVfFnMBTkJLBpZwVXXBTdl17gzEAjV/Drs5ZwoNmM1elgcWY2h62tTExIZHpScliukEUZGbx2+dVsrK1BK5eTqtPx1wuWU27u4seZt1PpKAORn1xtNl6/hxxjDvNiZ1PnqMcVcBMrN5KnzSUYDOINeEPKQlKxlLPiziJZmcThrnISlQkU6gpIViVH9DdHm81dGbdT7a8hgJ/x+vEn/NtQS9V8J/1qZhinUWWvJluTRb4uH5UQZ3dK8Pj9yJS93+sVBUVkGYzUtnegUyooik8gEPAzzhTPPouZbKORaUlJJGq6FSz3WZqYmJDIrJQ0HIEMSjpLaXQ1kKPJoUCXj1ImJxAM4vH7UfYyx8hQp/NQ0f0ctB3E6XcxIWY82eosHiq6P5QgM0WVQooihXsK7uSgrRiFWMF4fRHZmixiC2M5aDtIjMxAka4Qo9xAsiqZks5SEhXxFOoLMfUQVVBJVeHjUJtFvlYYhwLd9HuW9/e//50LL7yQjo4OtmzZws0338zDDz/MW2+9NZz9O23Y1djA87t3UmPr4LuTprA0KzskgdsTm8vH/lo/n5ZBotbHtRNj+NXZhZR0lLOz/QAHOw+Rp8lFI9VhUsRywGLmv3t3c7DZwhXjJ3BZ4XiSQ65yhbj8LoptJVR2VSFCRLwiAU/Ai8PbRqGugJ/Omt2rZF6aXk+afvygrl0lVTHRMIGJhgm91olXxBEff3J5SQQiKW5pxqRWh7mRRMPpc+IJeMK040+G1iYHOuPJSbaqpBL8gQB2jw/NcXl59DI9FvfQxwUV5iby5oc78Hr9yGSnNhmpwOhgR0M9bx7cz8FmC0uycliamcX62hpSdXoOW1v5187tzExJ5TsTJjMuLg6FVMactHTmpEVXIJoeLKS8q4KPGz/F5rNxQdJ5qCUa9tkOYHabmWmYSYy0Wy2ruPMQM4zTmWuaQ6IygThlLHHKucyLm9tnvyVWMcuLLuz3dcYqjMxRzGaOaXbflQWGhC6Pmw011Ty/eydamYwfSiXMTEmNWLxzeL2Ut7Xx9sH9jDPFkRFjIEahpMVhp7LdilQsZm5qGg6vl8PWVopbmpFJJIyPTyDHmEK2PnxX7fh5wJXjJ3Bebj5J2nCXeX/Qj91vp9xegcfvJVWdSoenA4vbwvqW7viOJQlnY5LFMtkwicmGSRw+fJj8tO6dlyJ9IUX6wrBzTjFMYoph0gnviTAOBXqj30aI5Mgf0Nq1a7n66qtZvHgxf/7zn4erX6cVB5stXPfuW7j9PqA7gdwvFy3mB9NmRNQNBoO8dXA/T2zaECr78HAJH1x3JW/Wv0FZVzkANY4aDnUe4pasn3D9e6voOJL9fP9aC7UdHdy/8OyQf2lJZyl/PvzX0Pl2tO/kO+lX80ntp2xo3cRDRfdHldUTGLtsq68LkxjtDYu7mVi5sVe/8v5itThITD9xVvbeEIlEGFRKmrucaGKPvTRjZDEcsEWKKgwWjVpBckIMew/VM2PS4HKjCIw9Slqaue2TDzHbu+ONDjRbKG1tIUWnY1dTI99UV4bKvygv482rvkOK7sQqPpX2Kh4tfjykJlTnqOcz8xc4/d1uhlX2ahbHn01JZwmNriaqHTWUdZVza+6PQzlFBE4P1tdU87OPV4c+r62p5vUrrmFmyjF3KX8gwIt7dvHPHVsB2Gcx0+Z0opRK+aisNHSeT8pKuGHKNP66dVOobF1VFc9fdjmxqmOxHBVtVr773lvY3N1xHPvXWqiz2bhvwVlhcSYVXZU8fuiJUHD6no69/DT3R/yj/NlQnUOdJdyW+1NmKbpVFX0+35DeHwGB4+l3TEhiYiK/+tWv+Pjjjzn77LPxeDwEAtFVOQTCOdBsCRkgR/nn9q1Y7JFBt2Z7F88ceTAdxeH1YvU2hwyQozS4GrG4m0IGyFH+u3c39bZutalgMMhX5jUR7ZQdUcfyB/2UdJae1HUJjF621NeSH9t3zhuL2zLooPRgIEhbs+uklLGOYlDKsdjD40IMshha3a1DrpAFMC4nkU07K4b+xAKjnpLWlpABcpQvKsqYlpQcMkCO0tDVyeHW1j7PWWw7FDJAjnLUADnKty3fMs14zD1yb8c+zG5BIeh0wuX1himpQber9JrKHu/uzk7+s3tHWNn4hISQAXKUxq4uHN7wucO+ZjMVbW1hZSWtLSED5Cgv7tkVmgccZW9PdSx1RlTVtW9bN0W7PAGBIaffRsif//xnFi5cyL///W/0ej3t7e3cd999w9m30wZZFMULuVQSRfWiW31KHiXmQiKO7jYSrVwqFiMRH1vZlosjJ4dysQxfoPvhJhtkfgiB0UUwGGRHYwOFpr6D0ptcZgyDVcbq8CCTi5ErTt61KUapwNwZqZAlFcvo8PVfErq/FOYlCUbIGUpUBSKxGEQiJFGCvPuTiTpCASjKxqJEJMUf9B9XRYS4/69ggTGAWCxCJY1Ug+oZoyERiSLmBcEgUcdfNN2BnmMy2hiVSSRh8wAgQs3KF/RFff8roqheCQgMB/1+AqpUKs477zyysrIASEhIYOHChSc+SACASQmJGJXhW+73zl2ASR0pjRev0YYpsQDEqdQYxPFMN4QHGRdo84mTJZKuD59E3j57HqlH3AdEIlG3gspxb0WJSEKGOgOz24JSrGScTkjcdjpR3dGOWCQiLsr46onFZQkluzxZrBYHeuPgggwNSjlNPWR6AYyymCHPnA6QmWqktc2OucXWd2WB04pCUzwFPXYJrxo/kS8rylkxLtzffWJCYr/cGov041Act9jj8Xswyoxhdc5NXMo267bQ57PiF5KkTDyZSxAYpcglUn48Y1ZYmUIiZUlWuLtzil7PXXPDM90fsDRx/eSpYWUT4xPQ9VDHOic7lxxjbFhZYVw8afpwl8Hj5wFHmRQzMcwQqXPWMz9uXpgxLEbcr/gkAYGhQFgCPwXkxpp45fKrWFNVQb3NxrKcXGYm9y6ntzx/HAkaLV9WlJERY2Bpdg7ZhjiWi1dQqCuk3F5GljqLPE0B2foUnr/kcr6pruRQs4VzcvOYl5oeJtuYr8vjwaJfsMO6E7FITJG+kBJbKZelXMJ04zQyNNGDLQXGJjsaGxhniutVuvMo/oAfq6eNmEEmKrRanOgMg1s5i1Up2BxVpjeGZlczedrcQZ2/J2KxmKK8JDbtrOSy86YM6bkFRjc5sbE8cd6FrKuu4lBLM0uyc5hkiqO4zUqz3c6M5FR2NTUwNTGZRRlZxGv6Fm3IUGfwYNEv2NW2m05fF5NjJjHNMI39Hfsxuy1MjJlAhioDk9xEhb2C8foiivSFIZUsgdOHOalpvHbF1ewzm5EAM1LTmJQYKUd7ZdFEcgyxtLucSMUSiuLjiVEomJaUzIaaaiYlJnJWRhYysYR4tZpam400nZ4ZqanoewiOpOtjQvOAw62tLMnOYU5KWsQ7IEuTyYOF97GzfTcOv4OZxhlkqjK4q+B2drXtQQRMNU6hSBdujAsIDBeCEXKKKIyLp/AEORu63G621Nfx2v69mFRqrpk4if9bugyL3c43VRX879o1XD1+Em3OWDodRZg1OsZndq+05cTGkhMbS2VlJcSLeL/pbSzuZs6KW8R4fRFamYYCXX5YLo7JfahZnGoanI1sad1KSWcps2JnMtUwOULqT6B/bK2vI68f8SAtnla0Mk2YRvzJ0NpkRzuIeBAArUKG0+fH6fWjOk6xyiCLoWmY/OaL8pLYsK1MMEJOM/yBADsbG3j9wD66PG6unTiF2alpqI9LDjcxIZGJCYlUtrXxSXkpbx3Yx7KcfJbl5JEeE8N3J3WPCavTwQclxbxTfIC82FguL5zAhITouxdZmkyyNJlhZQX6vLDPmdp0lrI4rKzN08be9n1sat1CrjaXeaY5pKn7zvlwMljdVvZ07GNL61bytXnMNc0htUfuEoHB4QsGcXq9bGuoQyOTMSkpGX8g0O3ydxztLie1tg4+Kz9MjjGWJK2WvFgTl4wr4pJxRaF6FpcFqb6GjsAeMvWTkMlMQKRQQm6sKZTb5kRka7PJ1maHlU0xTGaKYfLJXXAPWt2t7Grfw3brDgp145hjmhVVclpAAAQjZNSwtrqS2z/9KPR5VWkxr19xDasOFfPi3l2cm5PLGwf3sbmuNlTng8RinluxMuTW5VQ7+fOhp0OZ0Pd17OemrO+zOOHsU3sxA8TqtvLn0qcwu80AFHceotK+gO9nXi+sFJ4E2xvquWX6zD7rWdwWDD1cRk6GVrODohknp4x1FJFIRKy6Oy4k6ziFLIPcQEnX8AgnFOUl8dZHO3G7vcNyfoGRYY+5ievefRN/sDsA94uKcv614jLOyQ7fTWtxOLjz04/Y39z93NlcX8fG2mqePP8itEdWmt8rPsj/bVgLdCsTvX3wIG9fdS35pqFZIPEFfHzc+Cmfm78Eup9937Zs5KGiX4TlUhoKvAEfHzZ+zFeWr4+11bqJB4t+QZyw4DNkbKyt5kcfrgp9/vBwKW9ccQ3Tko8Ze75AgFf37+VfR4LYN9XV8klZKc9fcnnYrkmXr4v/VL5IcechoFu5anfHHu7Mvw2tVHuKrqj/uP1u3q57l42tm4HuMbbZupVfFN6LUT74d43A6YcQFTcK6HK7+fv2cEUsj9/Pt7XVrC7tfviMj0sIM0Cg+2Vb0WYNfW7wNYYMkKOsaliNzds5TD0fGhpcjSED5CgbWjYOS46I0502pxOLvSsiTigaja4mjEOkjHWy2dKPx6hSYu4RF6KX6enwdOINDL2hoFErSE82smN/zZCfW2Dk+KqyPGSAHOW5ndvx9FAoLLe2hgyQ0LFVFVS1dysPNXV18tS2zWHfd3rcFLcM3c5ci7s1ZBQcpc3bRp2zfsjaOEqzu5k1lm/Cylo9rdQ7hr6tMxWXzxcyLI7iCwRYUxUuglFmbeW/e3eHlVmdTopbwt95ZpclZIAcpbTzME3OJkYjFndzyAA5SqOrkfojiRAFBHoiGCGjAJEIxNFUMRCFVFZ6de8XHf+/0SoNNgPEyDFW+z2S7GisJ98UF7H1H40mp3nQq1O2djdypQSZfPBJ/wxKOQ09FLIkIjEx8phhM0jH5yezfmvZsJxbYGSI9sTrLgsv7zVmKlQuin6uPmKtBkRvXRiGp5+ol/MO6fWc4fR2j8VRx2SU4/v7W4zS36zXMSa8zQV6QTBCRgEauYJbZ80JK5NLJCzMyOSywm7f0H1mMwvSwxOrzUhOIdd4bBs9RZqMUhyuUrQy9RJ0svCsqaONVFUKKcpwn9Gz4xeRoByci8+ZyJb6OvJ6KKdEIxAM0OxuHrQRYjU7hmQXBMCkVtJos0eUx8oMmF3mKEcMnonjUtiwrZxAYBiSkQiMCOdk50RIlv5oxswI6fNcYyxTE8OfO+fn5JNt6P6bSNJquWPOvLDvYxRKxp8gtm+gxMlNnJd4bliZSR47LDEh8Yp4zk1cElaWIE8gVTU88SdnIgqplB/NCHeFlYnFLMkOV8cqiDVx49TpYWVxKjVFPcZWkiKRSfoJYWVFuqJRq6qWoEhgQVy46leqKpUUlRB3JBAdISZklHBWRhb/ueRy3jm4H5Naw2WFRUxKSCRJq2VifCK7mhqYn5bJ0qwc1tdUsyAjk3Ozc4hVHZP+FbeLeaDwPjZbN2N2N7PItIBx+tEvv2uUG7kj/2fsaNtFaWcpM4zTmRQzMVJ7X6BPttbXhQU19ka7px2FWB4mK3oytJgdg0pSeDyxKgXmLgfBYPhCn0FuoNHVCEzr9diTJSFOh0opo6q+nQkT+q4vMPqZkpTM61dcwzvFB+jyeLhy/ISoaoQmtZonz7+ILyvK2FBbzbnZuSzOykEjPxaHtnJcEUkaLe+XHCTPGMvlRRP6FfzbX6RiKRcknUeaKo2t1q3kaHOYFTuTOEXfssAn09ZFyReSoc5gm3U7edo8ZsbOwKToe9FCoP/MT8/kxcuu4K2D+9FKZVw5YRKTe6hjicVirhk/iTR9DJ8fCUy/KK+AiT1EDzQyDd/P+h57Ovayr30fE2MmMtUw5ZTGg8hk/X8PyyVyLk+9jAJtHjvadjJON47pxmmDloEXOH0ZViOksbGR++67j9bWVkQiEVdffTU33HBDWJ0tW7Zw6623kpaWBsCyZcu47bbbhrNbQ06r28oB2wH2dxygQFfA5JhJJCjjKbe28mVFOcUtzSzLyWNuWnrU3CDQ7Y4lFYvIizWhksrocLn4f19/xZyMeGKNdpKk5fg1Pi5IncJN02ZEPUcgECBLm0mWNlyhxen1UGwrZZt1O2KRmFnGmUwwFCCTRP/5W9ytHOg4wH7bQYp045gUM3HIgyR7kqxK5mJVMnDRsLZzOuP0ejlsbe3XTkiTu4lY+eAnHy2NdmJiB5cj5ChKmRSZREKb002s+phhEyuPZWfbziFpIxoTx6Ww80ATy88btiYEhgCr08mWulo+Kz9MQVwcy7LzogaIi0UipienMD2599XXyrY21lRVoJRIkErEjDcloJHJI7xcDCoVF+YXsCQ7g9Kuw6yxrmKX3cDM2BkRSlj7zE2sr6libo6WQ137sXramGGcRpG+EMMJJmEGuYGF8fNZGN+9guz0O9ndtoet1m2YFKZuGVVNRq/HDwSj3Mii+IUsihdyfA0XSqmURRlZLMrIory8nNzkFFrsXWyur+OzssMkarUsy8ljTlo6GQYD1048pkpl7upkY10tX1dWMDUpmaVZOWQZ41mmPIdlieeE6lW1t/FNVSU7GxtYnJXNgvRMErWRhkm1vYYdbTtpcbcwK3YW43T5NLnMbLVux+6zM880mxxNDjXOWja3dselzjXNJkOVToW9gk2tW1FKFIi7JGRrsvrlLmZSxHJ2wlmcnXDWENxNgdOdYTVCJBIJ999/PxMmTKCrq4srrriCBQsWkJcXLls4c+ZMnnnmmeHsyrDh9Lt4s/ZtNlu3ALDFuo0CbT7fT/8BN65aRX1ndzK0D0oPceecedw+e17U+I8NNdX8+KNjihpKqZT/t2gxdYE9vF3drc6y2bqVbyzr+EXh/wxo9epAxyGervgrQbpdTjZa13Nn7t1MNY2PqOvwOXi1+jV2tO8CYKt1GxP0Rdya+xO0stGnxiFwjN1NjWTGGFBI+/6zbnA2YRhkfhCA1iYn6bmDC24/nji1knqbPcwIMcpiaPN04A14h2V3bHJRKi+8+S3BYFDwjx+lBINB3jiwlz9s3NBdUAov79nN61deQ0aMYUDnaurq5KcfraIoPoHK9jb2mo8F+a4oGMejS88L2w0B2N2xl3+UH3tHfWH+il+Ov590dXeOpQMWM9e88wbPrDybf1U9g8PfHdu0rW0738u4jnOTzqG/7GzbzbMVz4U+f2n+ioeKHhg22V6B4cPj8QDweUU5v/z6y1D5Wwf3859LLmdGyrHf1OPz8Y/tW3npSMD6h4dLeOvgfl689AoSjjMwLPYubv/4Qw4cEUj48HAJ102czP87a0nYs7/eWc/vDv0hNBa/bd3EXfm387eyf+ANdos0rGtZz135t/OXw0+H5gdrLF9zV/5tPHn4qdC5vm5Zy0NF95OtzRrCuyMgMMwxIQkJCUw44uOg1WrJycnBbB4e3+6RwuwyhwyQo5R2HabO2RgyQI7yz+3bqLN1RJzD7vHwdA8VFpfPh1EXZFvHhvD23GbqnHX97l8gEGBdy9rQAwYgQIAt1q1R65vdlpABcpQDtmKaXKNTjUPgGNsa6sjvp6tIo7OR2EG6Yfh9AWxtriFzxwKIVSto6BEXIhFLMMpjMLuGJ19IerIRnz9AWbWgxjZaqe+08fTW8Gdkk72LQy0D/81KW1sptbaSZTCEGSAAq0tLQupYR+nydfFe/fthZa6Ai8Nd5aHPm+pq8fh82IPNoUnfUT5q+pRmV//62entjGjL4XdSYa/s1/ECo496m41nd24LK+v0eCLGXnVHBy/v2xNWVtLaQqm1NazssLU1ZIAc5fUD+6juaA8rq+yqChuLOqmWA7aDIQMEIF2VxrctG8PmB0GCbGjZRNpxsULeoJf9tv39uFoBgYFxymJC6urqKC4uZsqUyMRgu3fv5pJLLiEhIYFf/OIX5OfnRznDMdxuN8XFxQNq3+VyDfiY/uAz+qKW+/2R5YFgAGtbG/aGxrByiUqFx++PcpYggWAgotThdFDcGHkt0a5Rr9fjJ7IvvqCXmpoa7PbwCZ/HGF0KtbOrk+K6ob9/J8Nw/ZZ9UVR04liLkxmXA+VE1/5VaQnTYuOorq4+4TmCwQBNTjM5/izMopM3LtubPSjVEmyd7VG/9/n9tB0nId0fFAEfFZYumvThK9FKn4r9jQcISKP9nQye3HQDb7z/LVecP7ozBff8/UfDmOyLofh79apVEbK7AA6nc8Dnth+ZcEU5HQAdnZ0Utx4bt3KdHH8wcty5PS4OHTqESCTC5XEjFosJEHlSf9BPp72LlsqWPvsm0/feVnFx8Yg9+waCy+Xqs05/xuVYuNa+cLlc0GnD5498j/sCgbDrsysVBKMMyi67PbxelM3aQDBIR0cHxZZjxq5LHy7XLxaJ8Qf8EWW+KOPNF/QhEYULObi8bkpLS/FHnauMbvozJgVGhlNihNjtdu644w4efPBBtD38FidMmMCaNWvQaDSsXbuWn/3sZ3z++ecnPJ9Coejz5duT4uLiAR/THxw+J5PaJ7LvuFWCNFUqaZpUYlUqrE5nqPx7k6cyIT0DmSRSzvQnbid3f/Zx6LNULKbLIWGafg47bcdWAA0yA3nGXOKTI2M0ervGhZazwlYxRIiYEzuHjLhIP2O7z05h2zgOdZaEyrLUWeTG5aJPjszSOhIM1285WE5mXA6U3q7d6/dT9vUX3L7gLLTyEyd4bHY1o65Tk5aUPqi+tDU0Y4xXY+wlBqWtzdrrd70hU3vZa60iMTExzD/fZXdjcTeTkTY0vvE9Gd9q54uN1Tx052Wj2iVroGP/VIzJvhiKv1d/IMAPps7gnzuO7eAalEqmpKaTbRyYwpveZiNFq8Ns7yIvNpYy6zGDY1FGJhPT0tErwnf3Vsgv5j9VL4Q+y0RSioyFZKVlAeBq0vPU9q3EiBOQi+V4Ap5Q3fMSzyHHlA39jGdfIV3Oi9UvH9eWjAJDAZmpGaP22Xc8/TEc+jMux8K19kVxcTFFqWncMGUaj327LlSukEiZnJhEUdqxZ7DT6+WScUWsKjl2/9L0eianpZOiOy6Ba2cnGTEx1HQc86pYnl/AhLR01Mc9+zV2DfLGY2Oxw2tjQsx4vmleR4Buo6jGUcuKlOXsat8d1u8FcfP4W9k/Q5/FiJlmmkqeNjzh51hhrBuzpzPDboR4vV7uuOMOVqxYwXnnRUZ+Hm+UnH322fzv//4vVquV2Nixodihlqr4ftZ32WbdwY62nUyMmcBc0xxSVEn897IreevgfvaYm7i8aDznZOdGNUAAlmRl84+LLuGlPbuIVau5pKCQd4v3c07+NPLTstjevo08TQ6L4hcOOEh8kmE8P8m+lXUta5GIxJwdt4TxMQVR62qkGn6QfQNbrdvY1baHSTETmWuai142OgwQgejss5hJ1mn7NEAAGlxNmIYgKL25wT5k8rxH0cilBANBbC4PMapj1xKnMLGrfQ8EGZYEMgmx3YIRBw43MrFAkJMcbUjEYm6cMo2MmBjePniACQkJXDNh0oANEIBUvZ6XVl7JWwf2cd3EKTR02tje2MB5OXlclFcQYYAAzDBORylR8JXla0zyWM5JXEqm+lhg+qTEJF65/Go+Ky3lx+N/ytb2TVg9Vuab5jIpZtKA+jc7dhZqqZo15m+IU5hYmrhkyALTBU4t8iPP44vyx6GVy3m/pJgEjYZrJkxmTlr4IpBKJuPeeQuZlJDIh4dLmJOSxsqi8WEGCECyTse/Lr6M9w4dZHN9HRflFXBBXn6YAQKQocng/sKfs8byDRaXhaUJiynQ5nN/4c/5wvwVdr+dZYlLydfm8z/j7uGLpu6YlfOSziVTncEd+bfyhXkNarGK85KWkaPJHsY7JXCmMqxGSDAY5KGHHiInJ4ebbropap3m5mbi4uIQiUTs3buXQCCA8SReLCOKX43cmUeWLwGVU0fQpwGgKD6BX529FK/f36vxcRQJImRiMUuzc5FLxMQoFTx14QokYjEikYjzk5YiEZ9cQjitXMW8+BnMiu12hZP2oop1lERlIitSLuaipAtPuk2BU8vmulrGmfon61nvrB90fhDoNkIy8ocuKB26k3XFa1TUdnQRozpmKGmlGsSIsHqsg45l6a3dGZMy+PSbA4IRMkpJ0Gr5zsTJXFk0Aelxz1Ov388ecxPfVFWikkk5OzM7Quq0zNrK+upqam3tTE5MwmLvYmZqGtMSk4lVq6moqiInK6vXtrUyDXNMs5lpnIFYJI7YLROLRMxMSWV6cgrBYJDpsRPxBX3IJX0vCkS2pWWuaQ6zjDOjtiUwurF7PGxrqGNddRVKqZSzFHKmJCaRZTCyomAcKqmMeLUm6rFpej0/mDaD702eesI5Q74pjvsWnNXn3CJXm0OOJptAMBB6l+vlevJ1eQQJhlyuJsVMYIK+e9dJLOoOFZ5unM4UwxSqK6vJ0edEb0BAYJAMqxGyY8cOVq1aRUFBAZdeeikA99xzDw0NDQBce+21fPbZZ7z22mtIJBKUSiV/+tOfxtRD1+H18uTmb3njwDF3p7Mzs/jTeRdhPJLDoy8DBOCb6ipu//TD0GedXM6/Vqxkdmq3dPFQGAN9GR89EQyQscO3tdXM66erUr2jgRmxg8u5EQwGaWlyMGnu0CfNMmkU1HZ0MTEp3NhIUMZT46wdFiMEYObkDJ58bg133LQEuUxIoTRakfZ4nm5vqOd7779N4Ig//d+2beGNK65h0pHcDJVtbVz/7ltYHMfi3+6aM5//+fxTbp89lx9Mm4H7OLfZE9HXM1EsEoWS3MgZuAEykLYERiff1tbw049WhaKDXtyzi6cuvJgfrn4/VMeoVPHGldeQ14uQSH/mDP2tJxKJIuI7jhoafZVJRBLcbndEuYDAUDGsb9qZM2dSUlJywjrXX389119//XB2Y1ipam8LM0AA1lZXUd5mZWY/M9G2OZ38K4p6xtb62pARIiDQG26fj91NTdw0NXr+mLC6ATft3vZBJ4+y2zyIRKBUD/0jJEGj4lBLe0R5vCKBSns1Uw2R4hZDgcmoJTkxhm+3l7Nk3uhP8inQvQvyr53bQwYIdCsLrqmsCBkhB5rNYQYIwOsH9nJBXj5/3rKR83PDJeMFBE4Wh8fDC7t3hMkTOH0+1tdUE6dS0+LsVqtqcznZ09TUqxEiIHCmMKwSvWcCvkCk6gV0vxwHcg6HL1KVyuGNrlQlIHA8u5saSdPr+xcP4mzApDAhZnCrrM0NdgxxymHZtUzQqGmw2ekpKJOkTKDGUR1VQWaomD0li1Wf7+m7osCoIEgQW5SV2o7jyqIpD9o9XlRSKS6fD19g+MaTwJmFLxCI+t52er3Ie+xauKK88wUEzjQEI2SQZMUYmJWSGlGWMwBVoHiNhu9ODF/dFYtEzEkdnHqRwJnBhtpqiuL6J1ZQ66gfkqB0S70dvXFoMqX3RCGToJHLMHeF51vQSjXIRQrM7uHLNTRlfColFRYazO3D1obA0CGXSPnBtOkR5cfvbhTGxaPo4Yq6snA8X1SUc/2kKRGBvwICJ4teqQzLgA7dOhoLMzJp6OoMlcnEYiYf2akTEDiTERyfB4leqeR355zP2wf380VlGfPTMrhu0hQStQPLLn5uTh4SsZjXD+wjRqHgpqnTmZsmGCECfbOuuopLCvqX36LGXkOudvBBhk11XSSlD2yMD4RErZrqdhspenVYebIqibLOcpKUw/MCl8ukzJ6Sybuf7ua2GxYPSxsCQ8vCjEyeumA5L+/bg1Ii5aZpM5iWlBz6vigunlcuv4pnd2yjuqOdC/MKsHs8fH/KVJbnj0MmkSDppw++gEBfnJ2ZzaNLl/H+oYPIJRKumzSFKYlJPLp0GS/t3U2KVsePZ8xiQsLQx9MJCIw1BCOkF0rKzew51J2ZfGpRGgU5vT8wso1G/mf+Qn4yczadHje7mxr5rOww4+MTmJqcjEml7vXYoyRoNExOTCIrxoBYLCJerUEhHZqfJxgMst9iZmtDPVKRiFmpaYyPTxiScwuMLO0uJ+VtVvL7oYzlC/hocpuZY5o96Hab67somj4wqeiBEK9RUWntZF5GuLGRokxhX8d+FsYvGLa2F8zM5cnn1vCDq+ejVg0uuHisY2npZH9pA9V1rRTkJDKhIAWDXjXS3QpDI5OTpo9haVYOCqmUBI0G+XHPTpFIxPTkFP56wXK8AT8KSbcbllahwNzVxSeHSyluNjNJKmZ6UjKm45SLnF4ve8yN7GhsJEGtYWZqKtmGMabeeIbQ2eXiQGkDxWVNpCUbmVSYSlL8qZeWj9doKIyL71a6FIvINhhJ1un5zsTJrCgoRCYWh43P46nv7GBnQwP7my1kxhiYkZzCuH7ucgsIjEUEIyQKBw83ctuv3sDj6c40LpdLefqRaxifn9zrMSKRCH8gwP+uXcMXFeWh8h9Nn8k9cxf0+tA5ytb6Om5Y9U4owNKoVPHaFVdT0E/Z1ROxq6mR6959M+QbrZJKee2Ka4Tt4NOADTXVjI+Lj/A3jkaDs4EYqR65eHAT664ON4FAEJVm+B4fSToVexqaCQYJS1qYoIyns7WTdk8HBvnQygMfJS5WS25WPB9/vZ8rL4p09TlT6Oh08sSzX7BxR0Wo7PqVs7n5mgXIZKNn52BHYwPXv/dWKD5PJ5fz2hXXRCy0yKVS5EdeeVqJhA63i9+s+5qPy0pDdW6cMp37FixCeeR5/WVFGXcel0Q2K8bAC5ddQUaMYZivSmAg+P0B3v98N8+8siFUNnV8Go/cu4JYQ3Q53OFie0MD17/3Jv4j73KdXMHrV1xNUXwCmhPE7bl9Pl7es4dnjhOpmZGcwp/Ou5B0YbwJnKYIMSFR+HjN/pABAuDx+Pj0mwN9HlfWZg0zQACe27WDyvb2Ex7n8Hp5auvmMIWXNpeTzXW1A+t4L7y6b09YcKbT5+OjwydWLRMYG3xVWRGRE6E3qhw1JKgGvwPWVNtFbIJ6WKW0YxRy/EGwOsODjsUiEenqNA7YDg5b2wBL5hXw6vvb8Pn6LzBxulFZ2xpmgAC8tmob9U1tI9SjSHx+P//etT1MIKTT42FNZfkJjuqm3GoNM0AAXtyzk8q27utrttt5dMO6sO+rOto5YLEMQc8FhpIGcwfPv7kprGz3wToqalpOaT88Ph/P7twaMkAAOj1u1lZX9XnsoZZmnt+zM6xsR2MDxS3NQ91NAYFRg2CERKGlrSuirLk1sqwnLp8voiwQDOL2R5Yfjy/gD0n3HU+Hy9Vnm30RCAYx2yP7bolSJjC28AcCrK2uZFpS/5LrVdorSVQMgRFS04khbniC0o8iEolI1qupsNoivsvWZLG3fd+wqmRlp5uINWj4bN3wGjujmeMXYo7iDwRxe0ePYeYPBrHY7RHlLY7I52lPoj2vgxB6XnsDfjrckc9gQdVo9OH1+fBEGZfuKGN4OPEHg7REGY+tUd7vPXH7/VGV3JyCSqbAaYxghETh4nMm9ausJ9lGIwma8K3f6UnJZPaxlapXKLlparjbhwiYmz74wHSxSMR3J0XmVbh03PhBn1tgZNnV1IhBqSRe07e7gcvvotndQrxi8P7FTTVdGOOHPy4gWavmcJR8ISa5CYlYTJW9aljbP//sIv7zxia8o2jSfSpJTzUSawiPZ5tclEpK4vC4wZ0MCqmUG6ZEusydl5vf57E5RiMp2nBlrInxCaHndZJWxw1TwpN6yiWSIXGRFRhakhNimD8jXHBDp1WSmTo8iU17QyWTcePUyPG4NLtvMZBco5G5PRQxjUpVv+L9BATGKkJMSBSmTUjn13ct58W3NyESibjhyrlMndB30sBUnZ7/XHI5/9y+le2N9ZyTncuNU6YRo+x71fiC3HyCwSDP7dpOjELJ3XPnMyVhaGI25qdl8MdlF/K37VuQiUTcPmdehKywwNjjs7LDTO/3LkgVCYp4pKLB/cn7fQGaG+3MWNy/dgdDil7DzpKWiLgQkQgKtPlsbN1MtjZ72NrPzYwnLlbD+5/v5qrlfSeCPN1Ijo/hiV9eyX/f2cz+kgYWzc7jyoumo9MM7y7YQDk7M4vHzzmff+7YilIq5c4588PUsXojSavjuUtW8s/tW9nZ1MCijCxumjodo6rbwBaLRHx/8jS0MjmvHdhLuj6Gu+YsEEQ9RiEqpZw7blpCeoqRNd+WUJiXxA1XziUt+dSLCCzOyuHRpct4dsc21DIZd86dz7TEvsejSa3hgYVn8er+vaytrqTQFM+PZ8wSxpvAaY1ghERBo1awbFER86Z3T3C0A3jpjo9P4IllF9Dp8RCjVCIV92+zyaRWc/3kqVxcMA6pWIxWruh3m9WdjVTYq+jUdmBv8VGgy0Gv0FBubWWfxYzb52NyYhLvXHUtIpEIvaL/5xYYnQSDQT4pL+W2WXP7Vf9w12FSVH2/CPvC0mBHGyNHJh/+wGS9Uo5UJMLc5SBJF74in6nJ5KCtmEp7FdmarGHrw4pzJ/GP/67n/LPGo9eNLlWoU0FBdgL/786LsDs86LRKpJLRt3luVKm4asJEzsvNQywSoRvA8y1OrWHFuEImJySSZzIRrw7fVUzW6fjZ7LlcM2ESFoedveYmzPbObiXDXlSyLC4LlfYq7D4HGZp0stSZSMXCq3a4SUs28rPvL+b6lXNQq+Qo5CNzzw1KJVMTk/h/Zy0m4A+SbTCilMn6deykxCT+1xSH2W7HqFSi7WUs13WZqbBX0e5pI0GRSJ4uhzjV6NmhFBDoL8KT8QQMxPg4HrlUiukk5XUNyoFNdOq6LPyn6t9UOStDZdelfY9cxSS+8+4bWJ3O7j5JJLxy+VXMSBZ2QE4H9lrMiBD16eoH4A/4Ke+q5IKk8wbdbkOlDVNi35LTQ0VajIaS5vYII0QiEjPVMIVPmz7n5uwbB6341RspiQYmF6Xy7Ksb+J8fLxuWNkY7cpkUeczof1X0Z8f5eDrdbv6wcT1vHdwfKvvZzDncPntuhJrhodYWblr1TijgOE2v58VLryTbGG6IWFzN/LHkzzQdSagpQsTdBXcwxRCewE5geBCLRRhjTt3zKRo7Gxv47ntvheI7YlUqXll5NePi+udWJZdKSY/p3aCwOKy8VvsG+zv3hMqWJ17CyrTlyCSj/+9UQOB4Rt+ylsCAqLJXhxkgAKua3qXGbg4ZIAAev5/ndmzHGyXwTWDssepQMXNS0/qlUFXtqEEn1aKWDP7lXFvegSnp1L3kU/Vaii3R1ZjS1KnEyU180PARgWAgap2h4KIlE/hmcyn7SxuGrQ2BU095mzXMAAH4x46tVLaHj7cut5snNm0IUzyqs9nY1RQ5HirtVSEDBCBIkNdr3qLLJwiBnAl4/X6e27k9LMDc6nSyvqZqyNqocdSHGSAAn1k+pqqrfsjaEBA4VQhGyBjHHXBHlDl8DjwBT0R5bacNb2D4JmsCpwZfIMCHhw8xPz2jX/UP2A6Srh68yIHfH6CpppO45FNohMRoaOi04/BEN55nGqfj9Dt5veZN2j0dw9IHjVrBZedP5f/++glut6BUc7pg90Y+IwPBIM4eqlluvx9zV6QR0XbcIs9RHP4oKofeDrwBYdycCXgDAWo7IxX9Gjo7h6wNpz9Ssc0X9OHyR84FBARGO4IRMsZJViYj6xFsPEU/jXiFKaLu9yZNRd1P31SB0cv6mipMKjUpur6zAXsCHg53lZGp7p/BciLMdV1o9HIUylO35S8Vi0nWaShtib4bIhFLWBS3AKPcyPNVL/JazZtss+7A4m7u1lsdIqZPTCcxTsffXlo7dCcVGFGyYozEqcMN6vxYExn6cFcYk1rN9VEUBqckRQqHZKjTERG+O7kkYTExMsFf/0xALZNFHStLs4dOQCNFlRyxq52lziJlCHJACQicagQjZIxTGJPNrTm3k6HKRC1RMd+4kEtSLqHQlMifz19Oml5PnErNQ4vO5pyc3JHursAQ8Oq+PZyVkdWvusW2QyTI41FJBh9UXV3STkKqdtDnGSiZBh17m1p7/V4sEjEhpohLUi4mXZVKtaOGt2rf4R/lz7LDuhN/cGhcEK9cPp11W8tYu+XwkJxPYGRJ1XerGZ6VkYVWLueCvHyeuvBiYtWRO32XF03gzjnzMCpV5BiMPHvxZUyOol6Yqc7g7oI7SVWmoJFouDh5OUsTzkYsEl61Zwrn5uTx0KKziVOpSdPr+fP5y/utYtgfcvVp3JpzG3nqfJRiJdNiZvLd9O9hUhmGrA0BgVOFEMU0xhGLxUw3TSRLk0lLZytZsanIJd27HZeMK2RRRia+gJ94zamfPAoMPc0uJ9vq66PmfulJMBhku3Un4/WFQ9J2VUkbhdMGn2dkoGQatGypbcLt86OQ9q7KJRNLydCkk6FJJxiEFk8r+zsOsLt9L1emrxz0arRaJeeGK+bw+D8+Iys1lsy0yN1GgbHFxIRE/n7RCiobG8hLTUPRi6BIsk7HHbPn8Z0Jk1FIJb0KiEjFUqYYJpGnzcET8GCQGfoVtyVw+hCnVnPztJlcUlBIa3MLhVlZQ97GJOM4MtS3YfPaiVPEoJKNLtlsAYH+IizPnCbEKnX4W5whA+QoRpVKMEBOI1bXVLMgIwOltG+3umpHDe6Am+QhkObtbHdja3Of0qD0oyhlUhK16l4D1KMhEkG8wsTi+LPIUKfxUtXLtLh7303pL5lpJlacM4mfP/ou7ba+syALjH7Ucjlih7NXA+QoIpGIRK22XwqGGqkGo9woGCBnMPEaLcEocUNDRYxCS7o2UTBABMY0ghEiIDBG6PJ4+Ky+lvNy+s4GHQwG2dD8LUW6wggf9ZOhothKUoYOsXhkJlU5sXp21DcP+DiRCMbpC5gcM5nXa96kcwhUiuZMy2ZCQQr3PfoeLiFQXUBAQEBA4KQQjBABgTHCf/fuIk+vJ1Hb985WeVc5nf5OsrWZQ9J26e4WUrJ0Q3KukyHbqKe2vQubK1LRqF/HazPJ1WbzTu27QxIjsnzpRLRqOQ/9YRVeryB7LSAgICAgMFAEI0RAYAzQ6Xbz3M4dLO5HgKM34OFz81dMi5mKaAj+xG1tLtqanSSOQFD6UaQSMdmxerbXDXw35Cjj9eORiWWssXwz6P6IxSKuvXQWTpeXXz/5IT6/IH0tICAgICAwEAQjREBgDPDMjm1MSkwkUdV3TMbXlrUY5YYhiQUBOLDNQlpeDGLJyPq3F8Ub2VzbhD9wctq7IhHMjp3FIVsJFV2VfR/QBxKJmBuumEtrWxcP/2m1YIgICAgICAgMgGE1QhobG/ne977HRRddxPLly3nxxRcj6gSDQX7729+ybNkyVqxYwYEDB4azS8OOzeVia30dH5QcYndTI06v4DMuMDjqbB28vG83VxVN7LNuia2Eks5SZhpnDEnbPm+AA9stZI0zDMn5BoNJo0Qjl7PfbD3pcygkcubEzuKjxk9w+gcfNCqTSfjB1fNpsdr5f098ILhmjRLqbTa+rCjn07JSKtpOfrwICJwMFW1WPi0rpTwYoN4WmbxQQECgm2GV6JVIJNx///1MmDCBrq4urrjiChYsWEBeXl6ozrp166iqquLzzz9nz549PPzww7z11lvD2a1hw+7x8PftW3h25/ZQ2W8Wn8N3Jk5GIhY2nQQGTjAY5P99/SUX5hVgUqs5UVh1o6uJj5s+4+z4s5CL5UPS/qHdzcTEKtAbR4cCy6TEWL4ur2dykomTFR5KUiWSpkrls6YvuCz1kkH3SSaTcNM18/jvu1u479F3efQXl6JSDs39Fxg4FW1WfvjBe1R1tAOgVyh4eeVVTExIHNmOCZwR7LOYuf7dt+j0dGcwz4ox8O9LVpJtjB3hngkIjD6GdWackJDAhAkTANBqteTk5GA2m8PqfPXVV1x22WWIRCKmTp2KzWbDYrEMZ7eGjTJra5gBAvDb9WupPvIyFBAYKO8fKqa6vZ3l+QUnrNfibuGtmreZHTsTk3xoXnY+r5+tX9VRMCVuSM43FGQYtPiDgUHthgBMMUym0dnIQVvxkPRLJpVwwxVzEUtE3PHrN+noHD5pToETs76mKmSAANjcbl7aswt/QHCXExhefIEAL+7eETJAAKo62tlQUz2CvRIQGL2csmSFdXV1FBcXM2VKeJI1s9lMUtKxzLNJSUmYzWYSEhJ6PZfb7aa4eGCTB5fLNeBjBkoTkb7qbr+PhtZW3E3mKEcMLafiGkcDI3WdRUVFJ/z+ZMbliWh02Hl4y0ZuzB9HfW0dAB6Ph+rq8Bdae6CdNe615EqykdmkmG1NQ9L+3m+taPRixHIXbW2uAR/v8/tpGwZXmEK9itUHK4gNepAMIg9DgSifTxs+B2sQuVdOTXXNoPu2cGoi63fUctO9z3PXDXOIjx3avCo9x/6pHpMnw6n8e5XJZJRYIsULDrY0U1lTjdcZfRyPlWfnWOiny9X3s6I/43IsXGtPpCoVB5sjx19JSzNlZWV4x6B79lj8HXrSnzF5unHLLbfwxz/+Eb1eP9JdOSGnxAix2+3ccccdPPjgg2j7IS/aFwqFos+Xb0+Ki4sHfMxAUba1oZbJcBz3oEnT6xmXnEK8RjOsbcOpucbRwGi9zpMZl71h93i4+63XuGLCROblHssLUl1dTWbmMdndWkctX9etY4ZpOpnqjIjz+H0BPB4/MpkYqaz3bOM9qS3voOqgncWXZqPS9J0YMRptbVaMw+CCYDRCjdPHQbuPZXlpJ32eJBLxdfjZ6t7B2fKFZGRG3r+TITMrk/XbyvjDv7fwm3tXMHVC+pCcFwY+9odyTJ4sp/rvdYkIXi/eH1Z2eeF48rKyez1mtD5TejIW+tmfCWt/xuVYuNZoXG5t4dENa8PKFmfnkpeTO0I9Ghxj9Xc4nrFuRJ0M//rXv0a6C/1i2I0Qr9fLHXfcwYoVKzjvvPMivk9MTKSp6djKbVNTE4mJY9N3N9to5LkVK3nwq8+p6mhnUkIiv11y7ikxQAROH7x+Pz/7eDWpOh3LcvJ6rbe/fT9fWNYwzzSXZGX3bqLP66fyUDvlB1pprO7E0elFKhPj8wWQKyTEp2hIz4shu9CIMV4VNaNzzeF2PnvjMDMXp560ATLczM9IYlVxJYXxBtJjTn5ho1A/jpaWFrZ6dpAdzGYI8joCsGhWHvGxWh78wyq+f/lcrlkxQ8iefYqYmZLKLxacxVNbN+Hx+7l+0hQuzOs7waeAwFCwPL+AOpuNV/fvQS6RcOececxM6VtaXeDMw+FwcNddd9HU1EQgEODWW2/liSee4IILLmD9+vUoFAr++Mc/kpmZidVq5de//jUNDQ0APPjgg8yYMQO73c5vf/tb9u/vXni57bbbOP/881m6dClvv/02sbGxrFq1iv/+9794vV6mTJnCr3/9awAeeugh9u/fj0gk4oorruDGG2885fdgWI2QYDDIQw89RE5ODjfddFPUOkuXLuXll19m+fLl7NmzB51Od0JXrNHO3LR03rrqWjrcLuJUavTK0RHQKzA2cPt83P7Jhzi8Xu6YMy/qxNUf8LOm+WtKbIdZmrAYg8xAZ7ub3RsbKd5hwWBSkZylY+556Wh1ckRiEcFgEJfDh9XipLGmk90bGpHIxGTkx5CUrkOtleG0+6g42EpjTRezlqQSlzx6jWetQsb8jCRe3lnKz+ZNRH+SgeAiEcw1zeHTus9Z17KBs+IXDlkfC3OTuPvmpbz0zha27anigdsuIM44crlWzhSMKhU/mj6T5fkF+AMBUnR6ZJL+7wIKCAyGZJ2ehxadzY1Tp9HZ0cHEjExhAUIgKuvXrychIYFnn30WgM7OTp544gl0Oh2rV6/m/fff59FHH+WZZ57h//7v/7jhhhuYOXMmDQ0N3HzzzXzyySf8/e9/R6vVsnr1agA6OjrC2igvL+eTTz7htddeQyaT8fDDD7N69Wry8vIwm818+OGHANhGSMVtWI2QHTt2sGrVKgoKCrj00ksBuOeee0KW3LXXXsvZZ5/N2rVrWbZsGSqVikcffXQ4u3RKMKnVmNRD6wsucPpj7uriZx9/gFwq5fbZc5FGUVRrD7TzVfXXyEVyzks6F5vZx2frSqkqaScj38DZK7LR6CMn5CKRCJVGRmq2jNRsPcF5QWxWN5YGO2X7WnG7fMjkEkyJKsbPTEAmH/2TtuxYPR0uD//aepAfzhpPjOrkDBGZWMoU+ST22w7iC3pZEr94yCYNJqOW229czOfrivn+XS9w8zXzufS8KUilo//+jmVEIhFp+piR7obAGYpMIiHLYKS4sUkwQAR6paCggMcff5w//OEPLFmyhJkzZwJw8cUXA7B8+XIee+wxADZu3EhZWVno2K6uLux2O5s2beJPf/pTqDwmJvy5t2nTJvbv38+VV14JdMfHmEwmlixZQm1tLb/5zW84++yzWbhw6BbgBsKwGiEzZ86kpKTkhHVEIlFoa0hA4EzEHwjwdvEBfv/tes7NyeXScUWIe7y4Or2dbGzdzH7XASbrJhOsiWH1u4dpb3GSMz6WZVflIVf0f2IrEomIMSmJMY3tnbqpKXGIRSKe3rSPKyflMi7ecFLnkSPnnIQlfNuykdecb3BR8oUY5EMziZVKJVy0dCJTJ6Sx6vO9vL56B9+7fA7LFhUKUr4CAgICZyjZ2dm8++67rF27lj//+c/MnTu317qBQIA333wThUIxoDaCwSArV67k3nvvjfhu1apVbNiwgddff51PPvkkZPCcSk6ZOpaAgMAxgsEgFW1WPi8v47UDe9ErlNw7bwE5xwVyO31Oqh017G8/QHl9AzHWZCjPZk2dldgEJxn5Mcw+Jw2x+MxeaZucbMKkUfLugQpMaiXzMpLIj4tBOcDdBoVEzuKEszlkK+H5qhcp0OUzKWYiqaoUJKLB71ykJBr46ffO4nCVhU/XHuBvL33D3Gk5zJ+Rw6TCFJITYoRVUwEBAYEzBLPZjMFg4NJLL0Wv14dy5H3yySf86Ec/4uOPP2batGkALFy4kP/+97/88Ic/BI4JBsyfP59XXnmFhx56COh2xzp+N2TevHnceuut3HjjjZhMJtrb27Hb7ahUKuRyOeeffz7Z2dn8/Oc/P8VX341ghAgIDDMdLhe3f/IhG+tqCAQjZZznpqXR6a3j7898TLBZisgXbSIaTzM+xFofyjQJdrmL4kYXxY3DL/18svh8fqTSllPWniEIbeZO3imN9G3VKsSMS1Qgk0af5LtcLpQVx+6lIpDBDk8DmwPhcsgZ6gzG6QYf5FyQnUBSnJ59JfV89e2hqHUkEjEqhYwffXchl18wbdBtCggICAiMHkpLS/n973+PWCxGKpXy8MMPc+edd9LR0cGKFSuQy+UhV6uHHnqIRx55hBUrVuD3+5k5cyaPPPIIP/3pT3nkkUe4+OKLEYvF3HbbbWEiUHl5edx111384Ac/IBAIIJPJ+NWvfoVSqeSBBx4gcCR/0j333DMi90AUDEaZFY1ydu/ePeAtKQGBwSKVSsnP730C2tu47PC4+X87t1HeaUMtkRKnVJKq1mA8Utcf9HG4qwLnWgmijiPrAtIgYoUIsRrEGhiChfgziiDg9oHHB74AKKSQFStCLhn4ToMfP66gG0/QTZw4jhRp8tB3GLA7vbS0OeiwuelyePD6ul8Ol55TwMVLoo+7kx2TAgLDiTAuBUYbfY3J0cLxqlZnAmPSCBEQEBAQEBAQEBA4nRCMEAEBAQEBAQEBAQEBgWEkUgNUQEBAQEBAQEBAQEBgGBGMEAEBAQEBAQEBAQGBU4pghAgICAgICAgICAgInFIEI0RAQEBAQEBAQEBA4JQyJo2Qw4cPD/iYqqqqoe/IKONMuEYYvdd5MuNyoIzWa4+G0NehZ6D9PBVjsi/Gwr0dC32EsdHP/vSxP+NyLFxrXwjXMDo4Ha6hL959913M5tGbN6w3xqQR4vP5BnyM0+kchp6MLs6Ea4TRe50nMy4Hymi99mgIfR16BtrPUzEm+2Is3Nux0EcYG/3sTx/7My7HwrX2hXANo4PT4Rr64r333sNisYx0NwbMqMiYXlFRwd133x36XFtbyx133MGNN944cp0SEBAQEBAQEBAQ6IPP1x3kmVc2YGm1kWDS8+PvLuS8s8YP6pwOh4O77rqLpqYmAoEAt956KxkZGfzud7/D4XBgNBp57LHH2LlzJ/v37+d//ud/UCqVvPHGG+zatYvHH38cv9/PxIkT+d///V/kcjlPPPEEa9asQSKRsHDhQn7xi1+wZs0a/vGPf+D1ejEYDDzxxBPExcUN0Z05MaPCCMnJyWHVqlUA+P1+zjrrLJYtWzbCvRIQEBAQEBAQEBDonc/XHeTxf36O2929w2dusfH4Pz8HGJQhsn79ehISEnj22WcB6Ozs5JZbbuHvf/87sbGxfPzxxzz55JM89thjvPLKK9x3331MmjQJt9vN/fffzwsvvEB2djb33Xcfr776KpdeeilffPEFn376KSKRCJvNBsCMGTN48803EYlEvPXWWzz33HPcf//9g7wr/WNUGCHHs2nTJtLT00lNTR3prgicYto97Tj8TmKkejQyzUh354zE7Xdj9ViRixWYFGdGxlYBAQEBgaHD7rPT4bWhkaiJkceMdHeGnWde2RAyQI7idvt45pUNgzJCCgoKePzxx/nDH/7AkiVL0Ov1lJaWctNNNwEQCASIj4+POK6yspK0tDSys7MBWLlyJa+88grXX389CoWCBx98kCVLlrB48WIAmpqauPvuu2lubsbj8ZCWlnbSfR4oo84I+eijj7j44otPWMftdlNcXDyg87pcrgEfM9YYq9cokUiwGTp5peE1rJ42stSZfCfxKkStIoLBYET9kbrOoqKiE35/MuNyoAzntYtjJXzU9gl7bHvRSDRclXwFqa5k/G7/SZ1vLI3HsdLXnv0cDWOyL8bCvR0LfYSx0U+Xy9Vnnf6My7FwrX1xpl2DSCQiYAryuvktqh3VmOQmvptyLbp2DX7/yb1HhoL+jMnBYGm1Dai8v2RnZ/Puu++ydu1a/vznPzN37lzy8/N54403Tup8UqmUt99+m02bNvHpp5/y8ssv89JLL/Hb3/6WG2+8kXPOOYctW7bw9NNPD6rfA+rTKWupH3g8HtasWcO99957wnoKhaLPl29PiouLB3zMWGOsXmO9o4HfH/gj3mD3SkKVo5qXGl/lwaL70Ml0EfVH63WezLgcKMN17b6Aj/9UvsAe214A7H47L9S9xENF91OkO7n2RuvvFI2x0teB9vNUjMm+GAv3diz0EcZGP/szYe3PuBwL19oXZ9o12Lyd/N/Bx2hyd6s0tXpa+Uf1Mzwy8dekqJKHs5snZLgNwQSTHnNLpMGRYNIP6rxmsxmDwcCll16KXq/n1VdfxWq1smvXLqZNm4bX66Wqqor8/Hw0Gg12ux3oNl7q6+uprq4mMzOTVatWMWvWLOx2Oy6Xi7PPPpvp06dz7rnnAt1uXomJiQC8//77g+rzQBlVRsi6deuYMGHCKQuIERgdWNyWkAFylAZXA1aPNaoRIjD0dHhtbLVuiyhvdDZRoMsfgR4JCAgICIwlrJ7WkAFyFG/Qi8VtGVEjZLj58XcXhsWEACgUUn783YWDOm9paSm///3vEYvFSKVSHn74YaRSKb/97W/p7OzE7/dzww03kJ+fz8qVK/n1r38dCkx/7LHHuPPOO0OB6ddeey3t7e3ceuutuN1ugFDcx2233cadd95JTEwMc+bMoa6ublD9Hgijygj56KOPWL58+Uh3Q+AUo5VqI8qUYiUqiXoEenNmopQoSFImUesMf/gIRqBAT55+8Rs6u1w88LMLRrorAgICowiVRI1CrMAdcIeVayWR7/jTiaNxH0OtjrVo0SIWLVoUUf7KK69ElJ1//vmcf/75oc/z5s2L2NVISEjg7bffjjj23HPPDe2KnGpGjRHicDjYuHEjjzzyyEh3ReAUk6ZK5ZLki/EEPcjFcjq8Nop040hQRgZcCQwPGqmG6zKv5Y8lT+I7sis1QT+eLHVmWL1gMEizuxmH34lJHjuqjRSP34PF3QwEiVfEo5AoRrpLpwUffrWPLrtbMEIEBE5jXH4XFlczGEX4Aj6k4ujTxU5vJ60eK2qJinh5HN/LvI7nKp8PfX9+4jJS1SmnqtsjxnlnjR+00XEmMmqMELVazZYtW0a6GwIjgEQsIUmZyMs1r+HwO4iTxzE3dtZId+uMo0g3jocn/D8anY2oJCrS1ekYjlM28fg9bLFu5eXq13AFXKQoU/hJ7i1kajJGsNfRsbqtrGpYzdrm9QDMN83jirTLMClMI9yzsU0wGMTj8SESiei0u9BplCPdJQEBgSHG4rLwSvXr7O7YgxgxF3jO44Lk84mRhcc41Nhr+Ef5v2hwNaAQK7g+8zpmGqaTOj6VZnczMbIY0tXpqCSqEboSgdHOmMyYLnB6UWWv5t9VL+DwOwBo8bTw78oXMbvGXvbPsYxIJCJdncZs0ywmGSaGGSAAtc46nqt8HlegW2mkwdXAi1Uv4/BFz0YrlY7cGse+jgN807yO4JH/vm3dyK72PSPWn9OFdpsDuUxKSmIMTc2DU34REBAYfQSDQTa0bGR3R/fzMkCAj5s+5XDn4bB6Dp+TF6tepsHVAIA74Obflc/T6G4iR5vNHNNsCvXj0EgFt2qB3hk1OyECZx6t7lYO2IoJBP34g+HyfS2eFlrcrSQqE0aodwI9sbiaI8rK7eV0eDtQS7tXutx+N4e7ytjRtgu1XIXcrhiRnZJd7bsiyrZat3FOwhJEItEp78/pQkubnRi9ihidktY2O/lZI90jAQGBocTpd0UVKSnpLEEt0bDFuhWT3MSEmCLK7OUR9SwuC7nanFPRVYHTAMEIERgRHD4Hr1S/zo72nfw090cR32ukGvSy0zuYbaxhkEUmnYqXx4cMEID9HQf4a9nfQp+/bF7DQ0X3k6FJPyV9PEq+Nj9i56NQN04wQAZJe4cDvVaJVqOkrcMx0t0REBAYYhQSOfnafBpdTWHl8YoEnjz8FzwBDwDt3iXEy+Np9oQvThlkhlPVVYHTAMEdS2BEaHKZ2dG+E4ADHcVckHhe6DuJSMJ16deQrj61E1eBE5OuTuechKWhzzKRjJuyv0/MEePE6XPyXv0HYce4Ai5Ku8K38U8F043TSFUdC4ZMVCQw1zT7lPfjdKPd5kSjlqNRy2m3CUaIgMDphkQkYVniOaHnOkCeNo8ub2fIAAHY0LKRa9KvRCaShcrOSVgivLcFBoSwEyIwIhzvfrWuZT1nxS3ipzk/whv0kqhMJEuTeYKjBUYCrUzDVWmXM880hy6fnURlPMnKY9rvAQJ4g56I43wBX0TZcJOsSuLn4+6lwdlAkCApqhRi5cZT3o/TDVuXC5VSjlopx9Y5vFmIBQQERoYMTTq/Gv8gDc5GvC4PaTHp/N+hx8LquANuvAEfj0z8FWZXM1qphjRVKiqpEIQ+mvnLX/7CrFmzmD9//oCO27JlC//5z3945plnhrQ/ghEiMCIkKZPI1WRTbq8Eug2RGkct9467C/0oln0901FJVeTr8qJ+p5FquDj5ojB5RolIMmLJDo1yA0a5YUTaPl3p7HKhVspRq+R02KILEggICIx94hRxxCniqKioIDEpgeVJF/Fq7euh72UiGanqFFJU3f8ERg/BYJBgMIhYHOnsdOedd56SPvh8vn6J0whGiMCIoJNpuSXnFja2bmRP+14mx0xiQdz8CAOkvKuC9c0baPVYWRx/FoX6whHqsUB/mGaYxk9yZHxp/gq9VM8FKeeNyK6WL+DjcFcZX1vWEgj6WZKwmAJdPjKxrO+DBXrF1uVCqZSiVslpsXaNdHcEBASGAZfPxaGuEr6xrEMlVrG0M8Bc02zUUjVrLN8Qr4jjvKRzR6U8+0ixqqSYP2xcT2NnJ8k6HT+fv4hLxxUN6pxPPPEEycnJfPe73wXgqaeeQq1WEwwG+eSTT/B4PCxbtow77riDuro6br75ZqZMmcKBAwd49tlneeqpp9i/fz8ikYgrrriCG2+8kfvvv5/FixdzwQUXsHfvXh599FEcDgdyuZwXXngBmUzGww8/zP79+5FIJNx///3MnTs3rF/t7e08+OCD1NbWolKpeOSRRygsLOSpp56ipqaG2tpaUlJS+NOf/tTnNQpGiMCIkaxK5Iq0laxIXo5cIo/4vtpew+8O/SHkh7q3Yx+3ZP+AeHHcqe6qQD/RyjTMi5vDzNjp1FbXkqMbGZWUsq5yHj/0BEGCAGxr28F94+5lQoyQTGowdHa5iNGrUCll2LoEdywBgdORA50H+evhYwIjW9q28lDR/SyKX8Cc2FlIxBIkIskI9nB0saqkmAe/+hynr9v1uKGzkwe/+hxgUIbIRRddxKOPPhoyQj755BNuueUWdu7cydtvv00wGOSnP/0p27ZtIzk5merqah5//HGmTp3K/v37MZvNfPjhhwDYbOGS6h6Ph7vvvpsnn3ySyZMn09XVhVKp5KWXXgJg9erVlJeXc/PNN/PZZ5+FHfvUU08xfvx4/v73v7Np0yZ+8YtfsGrVKgDKy8t59dVXUSr7l0NKCEwXGDasbisHOg5yuLMMh6/3INZoBgh0TySPD4QDWNXwIRKNYDuPFgLBAA3ORva176fWUYs34AVAJpbhdrlHrF8bWr4NGSBHWWP5ZmQ6cxrRZXehVMhQKWV02Ufu9xUQEDg5urxdlHYe5mBHMW2etojv3X43HzV8ElbmD/rZ174f6H5fCwZIOH/YuD5kgBzF6fPxh43rB3Xe8ePH09raitls5tChQ+j1ekpLS/n222+57LLLWLlyJRUVFVRVVQGQkpLC1KlTAUhPT6e2tpbf/OY3rFu3Dq02XG20srKS+Ph4Jk+eDIBWq0UqlbJjxw4uueQSAHJzc0lJSaGysjLs2B07dnDppZcCMG/ePNrb2+nq6t4ZX7p0ab8NEBB2QgSGiRpHLX8u/SutHisAs2NncV3GNRgHGRwsOvJPYHSwu30vfy/7J96gFxEirs+8lrPjzxpxtydRlFEijJvBY3d6UCllKBUyHM5IEQIBAYHRS6u7lReq/svejn0AJCjiuTP/dtLUqWH1oj4/BXnzXmns7BxQ+UC44IIL+Oyzz2hpaeGiiy6ioaGBH/3oR3znO98Jq1dXV4dafSwxZExMDKtWrWLDhg28/vrrfPLJJzz22GM9Tz/kqFQDEyYQdkIEhhxvwMeHDR+FDBDoThRX1lUxoPPka3ORi8N3SS5LvQSf/dSrLQlE0uxq4V8V/8Yb7N79CBLk5erXaHA2jnDPYGH8/LAXqQgR5yQuPcERAv3B7vCgVEhRKmTYncJOiIDAWOJQZ0nIAAGwuJtZY/maQDAQKlNIFCxPuTDsOKlIyqSYiaesn2ONZF10MZ3eygfCRRddxMcff8xnn33GBRdcwMKFC3nnnXew2+0AmM1mWltbI46zWq0Eg0HOP/987rrrLg4ePBj2fXZ2Ns3NzezduxeArq4ufD4fM2fOZPXq1UD3bkljYyM5OeFu1TNnzuSDD7rl+Lds2YLRaIzYaekvwk6IwJDj8Dso7YzMDdHobABm9Ps8GZoMHiy8j42tW7B6rCyMm0ehrpCq5qqh66zASdPp68ThD3ezCxKk3dtOJiMbsJiryeWBwp+zvuVbAsEAi+IXkqfNHdE+nQ44nB4UchlKhRSnyzvS3REQEBgAVfbqiLKDtkO4/O6wpLPj9UXcW3A361s2oBIpOSthkSCbfwJ+Pn9RWEwIgEoq5efzFw363Pn5+djtdhISEkL/ysvLQzsharWaP/zhDxFKWBaLhQceeIBAoNvAvOeee8K+l8vlPPnkk/z2t7/F5XKhVCp5/vnnue6663j44YdZsWIFEomExx57DLk8fDH4tttu48EHH2TFihWoVCp+97vfnfT1CUaIwJCjkaiZbJjM2uZ1YeUnk8QoW5tNtjZ7qLomMIQYZDHEyPR0eI8FvElEEmLlsSPYq26kYinj9OMYpx830l05rXC6vSgVUhRyKS63j0AgiFgsuGkICIwF8rV5fG7+MqxsmnEqKkm4D79SomSyYSKTDRMpLy8nVycs4JyIo8HnQ62OdZSjOxNHueGGG7jhhhsi6h0NQgcoLCzkvffei6hzvMEwefJk3nzzzYg60dy25syZw5w5cwAwGAz8/e9/j6hz++23n+AqoiMYIQJDSou7lXpnPVMMkzDJjbxX/wExMj3fSb8aX9BHia2UVFUKWln41p3T56LeWU+Hz0a8PI4UVTJSsTA8RxsOn4M6Zz1dvi4SlQnclnsrfy17mk5fFwqxgh/m/ACZSMrutj34jD46PDZi5Poh74fH76HOWU+btw2T3ESqKmXE41DOBJwuDwq5FLFYjFwmweX2olZFF5YQEBAYXRTo8lkcfxZrm9cTJEiBNo+z4hbS5mmjztkAQJoqhVjFsYUkj2dgsV++gI8GZwPNnlYMUj2pqlSU0shA5bB3viKOVFXKmA54v3Rc0ZAZHWcSwixPYMioc9Tzp9I/h2JBUlUp/LLoARx+B389/LdQ7MC82Llcl3kNeln35NTlc/Fx08d80PAR0O2/f2veT5gdO3NkLkQgKl3eLt6qe5dvmtcC3X7Cd+ffzv9O+BVWTxs6mQ6nz8kjBx/F7u/2Vy1sG8ctOTcTpzANWT+8AS/fNK/llZruxFkiRNyY9T3Oil+EWCSEuQ0XwWAQl8uLQt792lApu4PTBSNEQGBsYJAbuC7jO5yTuBRfwEeiMoEOr43Hiv+AxWMBIEGewN3j7iBFlXxSbexo28k/yp8NqRNelnIJFyVfgEKiCNVx+lx81Pgxqxu73/lixNya92NmCe/8Mw7hjX0G0eXrwn4CqdzBsqFlA06/i3mmOcyOnUWLuxWrp43Xat4IGSAAm6ybqbbXhD7XuxpCBgh0xxU8X/kiza6WYeurwMCpcdSGDBAAX9DHf6peQiqWka/LI1ZmZHXjRyEDBLoDIcu6yiLO5fF76PB24A/6gW6p3w5vB25/38HOTS4zr9Uc20LuDoh/lSZX02AuT6APvD4/iERIpd2rlQq5VFDIEhAYYygkCuLkJuIV8WikGrZat4UMEACLx8I26/aTOrfF1czzVS+FyaO/3/AB9Ud2WY5S76wPGSAAAQI8X/kSLcI7/4xjVOyE2Gw2fvnLX1JaWopIJOLRRx9l2rRpI92t0wa7z86utt180PgREiRcmrqCKTGTUUXZIj1Z/AE/3oCPZYnnsLl1CxKRhItTLgICNLgi1ZKOjyOweW0R3zv8Dux+O/EIiQlHCx1RfqdWTysOn4MYmR5XwEWlvTKiTpPLHPa5squKVQ2rqbRXMcM4nbPiFrDZuoXNrVtJVaeyMvXSEwaR27w2AgTCyrxBH50+IYP3cOJydceDHEWQ6RUQGFt4/B722w7ybt17uAIurkm/OqqITLSy/mD323H6nRHlPd/xtv/P3nmHx1Fdffjd3qXVqvfeLPfee8MFgzHNhIQSQkhC+QIkkATSSCe0BEKA0Hs3mGJj4957lWT13vv2+v0he+W15C7bkrkvD8/jvXPvzJ3d0cyce875HXd36VqLx4LFYyFMPPO/U/QJT8if/vQnJk2axNdff82yZctITRVJUL3J4bZcXix5mTp7HdX2av5T9F+OmI/06jFkUhlx2liWVX9OnaOeansNH1V+glqqYXDwoG79I9UR/n+Hq8K6xYJGqCIwnWdNEUHvEqkO79aWoU/DqAgGQC/XMzpkVLc+ybouYYF6ez3/OPIEe1r30upqZXX9t7xd/j619jpaXK0cbDvEP/KeOKXMb5gqDLU00IDWy/WEKnsv5EvQHavdhUrZlXejUsqxOYRClkDQXyiyFPN0wb+osFXS4GjkpeKXGdyD9O65hkKbFCGEKwONCJlERrg6sC1Ibujxma+Xn7+kraB/ccmNkI6ODnbs2MGSJUuATtmwoKDeT2T9ruL1eVlzXAjNMbY0bj2j8WaXhdy2PLY0bu2sYO7pvvLZYG8krz2fLU3bum3b2ryN6+Ov9b+IqqVqbkv6AQnHKWXFaGL4Wepd6OWdyepRqkjuSv2RP2dE0DeI18bzo5QfopV1SjkmahP4ftL30ByVdpRKpEyLnOo3OhUSOVfHXkWqrktjvMZeS7o+jcWxV3FlzAKuibsam9dGgrZL0tfutVN9gvv+eCLVEdyT/lOMCiMAJqWJu9N+0qt5J4Lu2B0u1MouT4hKJccuZHoFgn7D/raDAZ/tXjtmt4Wp4VOQHP1vWviUHmuC+Hw+KqwVbG3azr7W/bQ4uldbD1YG89O0H3Nj/PVcGbOA6+KWcH/GfUSrA/NL7B4718Re7X/mR6oimBU5A+sFDBcX9E0kPp/Pd/puF47c3FweeeQR0tLSyMvLIycnh1//+tcBlR9PZO/evahUqpNu74ljOsiXMz2do0qlYqVnFVtaAg2E2eEzGeMZhct18pcIuVbOJvcW1jR1GTE3xy4l0ZqA+6getsQk5cWa/6GT6zApTexp3RuwjyvC5zDCPQyJWopD4UDukyM1S/zjjyGTycAATokLtUeFu8PNyS7NS/VbZmefWvniXK7Ls+VSX8dyuRyf3odL4kLtVuPsCDRK3WEeVresIUEbj8fnocpSxRXBc/G2duZ+eMK9rGtdz/bjYo6virkShUTBB1Uf+dt+Gv9jDK0nL34klUqRGqQ4pE5UHhVes8evh34uXOrv9Uw5cZ4X85osrWrl5Q/3cdPCzheU5WsLmDwqgVGDYk45rj98t/1hjtA/5mm3208bTn0m12V/ONfT0ZfOQSqVckRTwIe1gbKtOYYBXB+8BKu0M4xKYZPjOm5x4dg5OEKd/KvsOdy+zmd3qjaF603X4m3z+PvK5XJKteW8WfW2v21a6FQmyMfitnY9860hVl6pfp1xoWNRy9S0OFvY2byb++J+hq+l919Jz+Sa7C/U1dXxpz/9iWeeeeasxt1xxx3885//POUi/9NPP82oUaMYP378+U7zjLnkOSFut5vDhw/zyCOPMGTIEB577DFeeOEF7rvvvpOOUalUp334nkhubu5Zj+lvnOwcZ5pnsLN1F66jNw+VVMX48HEk65Nwe920udpQSlUYjpPNtXvslJhLWZMf6EV5r+ZDHhv4OyLVkQCsqP2GRmcTjc4mxoeNo9JWxVDjYDw+LwdaDzI2YkyvFznqq7/luVyXZ0tvn7vd07kSppfrUMvO72Hp9Dh5uvDfeL0+otRR2L12tjRtpTGskQnZnTe13Pa8AAME4OvalSyInuf/nKpLISM0HWO08bzmczb01WvqRM52nr15Tdq9FRj0BSQkdnqtQkLqCQ2LOO3++8N32x/mCP1jnrm5uaftcybXZX8419Nxsc/B7Dbj8DgIVgT3KHGvter4qmGlXzxEipSFsfNJDDr5Mzo3N5fE9CT+nv9PvwECUGQtpj2mg2FRQ2hztaGSqbC4Lbx/8MOA8Wua1jIxezzZiV3fQ7urg4TWhICaJdfEXkVGZAbSqN4P0DmTa7K/EBkZ2aMB4na7kctP/kr/4osvnnbf995773nN7Vy45EZIVFQUUVFRDBkyBIC5c+fywgsvXOJZXV6k6lL4zYCHKegoQiqRkq5PI0EXT729ni9rV7CpcTMmpYmlCTcwMHgAVdYq3qv4kFR9Srd9Ob1OrMclnh0fNlNlrWJC6Di+qVuNTCJjUcwCwlXd8wgEfYMySxnvVnxAfscRsgyZXB+/hMTzMBjtXgdjQkZTbi3ntdI30Mq1LIyeh4yu2F+7x97DODtJukSWxF1DuDKUNEMqRqXxnOchuDDY7S6Ux4VjKeUyUTVdIOgDeH1ectvzeKvsHeod9YwJHcPC6PlEaSID+oUpw7gz9YcUmAtxe90k65KJVkWddv92r526HtQHFRI5r5S+zrambUSqIrk+4Vqc3u4h28crJgIEKQzcnnwrReYi6hz1JGoTSNWn9GuJ9c2NW/iw8hOanE2EKkNZEnc148PGndc+H3/8caKjo7npppsA+Ne//oVWq+WTTz5h+fLlfPzxx6xcuRKr1YrX6+XFF1/koYceoqCggOTkZOrr63n00UcZNGgQ06dP58MPP8RqtXLHHXcwYsQI9uzZQ2RkJM899xxqtZqHHnqIqVOnMnfuXPbv38+f//xnrFYrSqWSV199ldbWVn7xi19gs3W+Az7yyCMMHz78vM7xkv/i4eHhREVFUVxcDMCWLVtEYnovI5FISNIlMStqBjMip5Ggi8ftdfNFzVesqV+L0+uk1l7Lk0eeptxSwTMFz3Kw/RA+fCilgTUAYtTRAQnAQ4yDgU7vilFp5NPqz7B4LLS723mj/G3yO3o3AV7QOzQ7W3jyyL843J6Lx+fhUPthnir4Fy3O7nG+Z4pBrqfOUcs39auxe+00O5t5rezNgCKCkeoIFJLAooJxmjiSdEksjJnH2LAxhKmEOkpfxO50oVR0GZQKhTBCBIK+QKW1kn8eeYoqezUun5uNjZv4sPKjbjmcRZYinjjyNF/XrODb+rU8V/Q8O1t2nXb/wYogxoWODWjLMmSyrmEDGxs34fK5qbRXsb15J9HqQKNGKVX2uBgZqjIxOnQUC2PmM9g4CJ1cdw5n3jfY3LiFV0pfp8nZBHSqRr5S+jqbG7ec137nzZvHV1995f/81Vdf+Rfsj3H48GGeeeYZ3nzzTd5++22Cg4P58ssvuffeezl06FCP+y0rK+Omm27iiy++wGAwsGLFioDtTqeT//u//+NXv/oVn332Ga+++ipqtZrQ0FBeeeUVPvnkE5588kkee+yx8zo/6AOeEOi0ph544AFcLhfx8fE9lowX9C6trlY2NG4KaPPho97RwGjTKGI00bS7O7gt+RYOt+WyvnEDafpUro69ir0t+4jWRJGkSyRDn871cdeS255Hbntet+PsaNrB8JChF+msBGdKvb2eFlegwdHsbKHe3kDIOaqSNTmb2Nq0vVt7saWEEabO1ZJodTT3pv+U18repMHRSJo+lZsTbupcEbPXo1foSdYlEqM5dZ6B4OJjt7tQnGCEiMR0geDSU22v8ddcOsbOlt0scbYEeEMOtnW+lLp8bjgaWvVtwzomhI33C4z0hEwiY07UbBK1iTQ6G1FIOmtD/T3vnwH9tjRt5ecZ9/JR1ScUmYuJUIXz/aSbeyx82Opso9RSSqurjUh1BEnapF4tG3Ax+bDyk24eIKfXyYeVn5yXN2TAgAE0NTVRV1dHS0sLQUFBREUFGnkTJkzAaDQCsGvXLr7//e8DkJGRQWZmZo/7jYuL84cJ5uTkUFVVFbC9pKSE8PBwBg/uXGTW6ztD9W02G3/4wx/Iy8tDKpVSWlp6zud2jD5hhGRnZ/Pxxx9f6ml8p1BIlRgVRr/lfgyTwsQR7xFeLHnZ3zYtfDK/y36E7S07+Gf+k/4aDXel3snY0NFcET2HsabRfFT1KQUnFKaLOMEdLOgbaGRqJEgCikpJkJxXXohaqiZEGULjCddU0FEJX+isWbOlaStZhizGhRqxu+0cMRfwVvk7/j6Z+kxuT76FSE0Egr6D3eFGIe8yQlQKOVa7qBMiEFxqtLLuQj56uR6lLDCSwaQ0desXoQpHLlV0az+Rens9r5S+5n9mxKijmRd9BcuPKzoYpgql2FyCURHMlTELaHG2sK1xG8naJPSKLk9Hh8vMW+VvB+QH3pRwIzMjp/fLkKwT36NO1342zJ07lxUrVtDY2Mi8efO6bddoTm48ngylsuu6kMlkOBynLxIM8OqrrxIWFsayZcvwer1+I+V86H+/tuC0NDmaqbXVBbhi3V43ZZZyyi0VWF1WWpzN/Dj1R6QeV8MhWZuEGxer69cE7G9Nw3raPe18Wft1QJG4t8reocXZgkQiwaQ2MSNyWkD9Br1cz8iQ84sXFFwYotRRzI2aHdB2RdQcwpSh1NpqaXY0n3Rsra2WEnMJHa7OglMWt4UaWy1efCyInheg/x6mDCXbkEGjo5E6ez119no2NW1lQ+NGPqteTpI+kU+qPg3Yf745n3JbBXX2ehocjSdVSRNcXDrDsY7LCVGKcCyB4GJgdpupsdXQ4eq5IGuCNoFsQ+Cq902JN3artTUwOIeQo9LmAAqJgvnRV6DoIYn9eCxuK+9UvE+6Po2F0fOZFTmzs5iwKoxBwQO5MmYB0yOmMTdyNsuqP2NXyx4+q17OhsZNbGjaRK29lkZ7E8XmYpocTVTbqrsJlHxQ+RH1joaz+Fb6DierUdUbtavmzZvHl19+yYoVK5g7d+4p+w4fPtwfvlVYWMiRI+cWDp+cnExDQwP79+8HwGw243a76ejoIDw8HKlUyrJly/B4PKfZ0+npE54QQe8gU8vZ0LCJd8rfw+qxMsY0isVxV+P1eVhV9y1rG9YjlUiZHj6VZmczB9oPsTB6PlfHXoXT6yRJl0iFtTJgdfwYPSUUd7g7cHq7XkJS9Mk8MuBhyq0VSCVSErUJRPfghhVcelQyFQui5zEwOIcmRzNhqlCC5cG8UvoaO1t2o5frWJpwIyNDhvtX0xxuJ7tad/NexQe0ulrJMmRyTdzVvFP+HsWWEiJUEdyZfAe/yLyfSmslcomCZH0S+eYCPqz4GKfXycSw8cyKnME3dasBkEnkAUIHx2hxtvCfov8ik8i4KuZKJodPClBvE1x8HA53QDiWUimnqcVyihECgeB8Kewo4pXS16m0VRKrjuHW5B+QbkgL6GNUBnNnyh2UWMswu81Eq6N6VKUMVgRxc+JNVNqq8Pq8RKgjCO3BO3IiTq+D0aaRFFtKWF7zJVq5llmRMwhTheL1efmsejlGhZFMQzomVSh19jr/2BBFCFaPlZdKXqHGXkOsJiZADbHrGE4cnjNbke9rLIm7mldKXw8IyVJKlSyJu/q8952eno7FYiEiIoKIiAgqKytP2nfp0qU89NBDzJs3j5SUFNLS0jAYzr4ApFKp9Od8HJNofuWVV1i6dCl33303n376KZMmTTplKY0zRRghlxEWrYWXjguj2tq8nRCFkRCliVX133Y2+uDrupUsiVvMgbZDvF/5IT9LvYtRoZ0VUq1uK2HKMBqdjf79BCuCCVN2VjU/Pu50jGlUt5WWOG0ccdq4C3iWgt5Cr9AzMDgH6PSUvVL6OjuOJil2uM38t/hFwrIfIsOQDnRW232h+CW/kZrXkc+HlR+THZRFsaWEekc9fzvyD/6Q8yhZUTPIz8+nTdXGm2VdmvHrGzcyI2IaIYoQWlwtlFnLGGDI5nBHl4SiQiLH6rHi8Xnw+Dy8X/khkeoIRppGXKyvRtADNrurWziW8IQIBBeOJkczTxf8m3Z3OwBV9mqeLvgXv8t5pJuAR4gqhBDVqfP5CsyFPFP4LHq5DjkKWt2tXBe/hPnRV5xynF6up8HRyL7WzpVxi9tCkbmYgo5CDrUfBjrzTJ8vepGlCTcEhNdeH7+E/xa/hNnd6cWpslXT4GhELVVj93YtbqbpU/ttwdljeR+9rY51jM8//9z/77i4OJYvXw7A4sWLWbx4sX+bSqXiH//4ByqVivLycm655RZiYjrzK7/9tvMd0GQy+ccD3H777f5///Wvf/X/e/Dgwbz//vsB89DpdAFzefDBB8/73IQRchlR6+ouoVdtr6XQUuz/HKWOZHLYJCweC7OjZiKRSCixlCFBQrwuHrvXwayoGexp2UuRuYgkXRKjTCOpd9Tzk9Qf80nVMuod9Yw1jWFBzBUBykeC/kubq51tPVS8r7HV+o2QOkdtNy9ZfscRZkRM83/O1GdQZ69nS9M2goODaGhr5EQOth1iQug4VtStpMHeyDVxV6Or1bK3dT9R6ihmRE7jk6plAWP2tR4QRsglxu7onpjucLpPMUIgEJwPjY5GvwFyjA63mQZH4zmpCBZ3lPDT1B9TZ6/H6XMSr4lnd8sebG7bKRPTO9xmdrfsDmhL1iXxWfXygDYfPjQyDSNDhrOvdT9JukRkUpnfADnGirpvuCv1Tj6r/pxyazlDjUO5Om5Rv1bIGh82rteMjnPFZrPx/e9/H7e7s9jzb3/724D8j76IMEIuIwyy7m43pVSJSWmiwFyITCJjesRU3il/3/8yaZAbuDXp+7xQ/D+CFcHcmfJDPqj4iExDBjMjZ1Blq+Ld8vdZFLuQ5dVfcGvSD8g0ZGBUGnsshiTon6hkSiJVkVTaA1Uyjg+B0sm6h0MZFUZ/qJ5aqmZC2DieKviXX9756thF3cbEaKKZFz2X6ZFTMcgNKGVKfqi5jSZnE1qZlk+rPqfN1RYwJlaoZV1y7A4Xel1XlWuVUnhCBIILiVauRYo0IBdTguScX9YzgtJ5tvB5rB4r0Fms8KdpPz7tYqJaqiZSFUmJtdTfZnabMSqMtLpaA/qGKI3cmXIHHe4OtDIt5daKbiIoNrcNjUzDA5k/x+axEqQIEguavYBer+93Ik8iMf0yIsIXTpq+K1Y0RZvEzMjpjDGNYoRxGAODc9jatD3gZtDh7qDeUY8XL3WOOiweC9fEXc2h9sN8VbuC/W0HmRoxmT2tezEoDFTaqmhyNge4UQX9H71cz01JNwYklQ8wZJGkTfJ/TtIlMCS4Sw1DgoTr4q7xr4bNjZrF13Ur/deXFClGRXCAbrxaqma4cRgKqYJQVag/30QtVxOrjSVEFcK0yCkMDR7CopiFXBmzgCFBgxlsHHQhT19wBtgdgYnpnZ4QYYQIBBeKKHUk18QtDmhbHHdVt1ocZ8qRjgK/AQLgxcuqum9x+U79d+zFw+TwSYwyjuDKmAUsiJ6H1W3jhvhrkSDx9xsZMoIEbTx2rx2z24LVYyNBG8+86MCE6gUx80jQxqGVawhVhQoD5DuMWMq+jPC0efhZ2l1HVx7gUNth/p7/Tzw+DyNDRjA3YiavlL7WbVyjswmFRIETJ2a3menhU0nXp1Flq6Ld3cGhtsN4fT4mh03i69qVfFn7NQnaBH6Uchvx2viLf6KCC0KWIZPf5TxCta0GrUxDgjYBo7JLXjdCHcH3Em9kkmUCZo+ZKHUUCZoEojSR1NsbiVZHsblpq7+/Uqqk3dXBFVFzaXG14PP5CFIEYfPYcPs8nMxJrJPrUMmULKv+HClSZkXOQNeDBKXg4uJwBiamqxRy7A4RjiUQXCgUUgUzIqaRaUin0dFEqMpEvCbunF/aTwyLOtbm8rrRyHoYcBSX143b56bV3caO6l0oJArmRc9BJVXzu5xHqLXXopfrSdDGU+9o4L9FL1LvaEAv13N78q3MDp9FpiGDJkczoSoTydqkU4Z/Cb47CCPkMiNEaSREaWR7806+rlvpb9/ZsotEbQJXRs/npdJX/O0SJJgUJiweC3KJnFhNLGq5mnRDGkZFML8//Cc63B1cE3c1H1V+4h9Xbi3nzbJ3uC/jbjQycTO5HJBKpCRo40k4hWEZoY4gQh1YvyNVkUqqPhWAyWET+bCq8zrpcHegkal5ufTVgP6zI2eiOUU9ku1NO9jWvAPoXKlbUfcNqfoUxoSOPpfTEvQSdkdgYrpCIcPuEJ4QgeBCojn6PD5REetcGBQ8sJsE/+TwiQQpTq2gpJfpKDAX+uuAuXwullUv5970u0nSJfqVuFqdrTxX+F9/fQyz28y/C5/jDzm/ZYjx/GtKCC4/hBFymXK4Lbdb27bm7dyXfjfXxV3DmoZ16GQ6ZkXOYEPjZrIMGSyOvTrgBTRcHc4Dmf/H59XL8Xi760HndeTT5mw/p2I5gr6Pz+ej2FLC3pZ9uHEzzDiUVH1KQMjWiYwyjcIHrGvYQJQ6gipbdbc+e1v3MSAoi72tBxgQlH00x6jT4+L0ONnW3L3q+oG2g8IIucR084Qo5Tid568TLxAILg5Zhkx+nHIHX9R8hcPrYFrEVIYGD6HQXMSelr0ADAsZSoouOaBoYIfHzIG2g9321+wMrCfV4mrtVqDP4/PQ6GgkThvb+yck6PcII+QyJVEXDyfU/UnXp1FpreKr2pUMCh6IzWPj69pvuC35+8RqYlHJVN32k6RL5MepPyK3Pa/btghVBFrhUr1sKbaU8Ofcv+H2dYbcfFWzgoeyHiQrKPOkY6I0kVwZu4DxYeOwtJqplFZ16xOrieWz6i8otpSwtmEdc6NmsyRuMQqpAoVUQYYhnTJrecCYnjTvBRcXh9ONsps6lvCECAT9BY1cw7iwsQwIysbt8xCqMlHQUchf8v7ul9//qnYFD2f9IsDzopVpSNDEk28OLH53okS/RqpBI9NgO6H2k3hPEJwMkZh+mZITNIBkbdeLm1FhZEr4JD6uXkaHu4PNTVs42HaQVH0KNfY6GhyNuL1d8d319gaOdBRQb69HIVWQrEtmStgk/3aFRM4tSTcTpAi6qOcluHhsa9ruN0CgU35xVd1qvD7vKUZ1ykrW2GroUJhJN6STfpxYgkFuINOQQbGlxN+2snaVv1KuRCJhSvhkQhRdD7cETTyDgkVi+qXG4XAHhmPJZbjcHrxeUdFeIOhPBCuDCVV1Filc37AhoP6Xx+dhfcMGzG4zxeZivCZvpwhJ/BLU0q4w2mHGoSTrkgL268XLguh5SI97tZwRMR2fT4JA0BPCE3KZEqGO4L6Me6iyVeP2uYlVxyCVynEdregpQcK18dfwTd1q1jasQ4qUq+MWMStiBkfMBTxf9AJWjw21VM2PUm5nWMhQbki4jonhEzC7zUSqIokR1dAva2ye7gpoPVU3P54iczGvlLxGha0SCRImhU3klsTv0+Zuw+11o5SpeOLIUwFjvHgDDJt4bRy/GfAwVbYqZBIZcZpYjEpjb5yS4DxwuALDsSQSCUqFHLvDhVbTt7XoBQJBz/R0T0/Tp/Jk/jMUWooAmGKdxOLYq/h9ziPU2GvRyDTEamIDJNwBPF4PBrmBa+KuxuF1oJQq0Mn0eH0ibFPQM8IIuYxQqwOTfY1KY7eXtytjFvLf4hcZFDyQHc27aHB0FpPz4uWjyk9I0ibyXOF//RK8dq+d54tf5A85jxKtifYXrhNc/owLHcP6xg3EaWKRSmRUWCuYFTkjIFb4eBxuJ6vqVlNlqyZZl4TNY2N94waygjKZcLSIU6OjCZ1Mh/OoMQwwKmQEEarwgH2FqUL7bfXcyxWnM9ATAqBUynE43cIIEQj6KVPDJ7OzZRfxmjgAnF4nlbYqvwECnTl+OUE5jAkdRZTm5PLAapmKj6o+CajzJEXKr7MfunAnIOjXCCOkH1FsLuFg2yHyOvKZEj6JGnstheYiBgYNxmsL5f39RSxy2JgYn0i4rquYkcvrosBcyPr6DUglUn6Wdhdmt4XXSt/odowGR2O3GiBOr5MWZytun4dtTdsos1YwLnQsA4MHiHCsy5h4TQL3pt/N5sYteH0eFkbPxyA38FbZu1TbqhkeMoycoAFEaSIBaHO34fA6uSbuKvI7CohSRzI1fAr19no+qvyEalsNk8MncnfaT1jTsI5SSylDjUMYbRrVYz6SoG/hdHoCckIAlAoZDqGQJRBcVGpsNexs2U1+xxFGhAxjcPBgGju8fH4kj8MN9SzMzGJSQiJh2sCihl6flyJzMRsbN2F2W5gSPokEdTx3p/2ULUfl1WdGTOe1su7vBoXmQsaEjjrlvMweS7dCs168tDhb+Lp2JQdaDzLEOIihxqFEqMNPshfBdwlhhPQTGh2NfFj5MYfaDzM1fDKfVn1Gtb0G6FQOGmQYjscXyv0rv+Lu0WO5d8x4pJLOOMwjHUf4e/4T/n1tbtrKI9m/IlmXFBCbD525I0qpMmClWiaRoZKp+HveP2l3twOwv+0AV8cu4sqYBSddGRf0bwrNhTxT+G9/8cFdrXtYmnAD39StwoePg+2HmBs5i2vjlyCXygmSG8jQp/NOxXv+fexs3s1P0u7k2YLnceNmZ8suro+/ljJLGaGqUDY2bqLN1c7NiUv9hQsFfROnq7snRKWQYxNGiEBw0WhxtvCvwuf8yoMH2g4yIXQ8u/JC+Kao83m+tqyEe0aP454x4/zvAQAlltKAJPSdLbu4M+UOXi551V+wsMZeQ4Y+gxp7bcBxYzUxp52bVqZFJ9dhcVv8bceqpb9T3vlcONh+iL2t+/lp6o/RKc6t8rvg8qHPvD1Onz6dhQsXsmjRIhYvXnz6Ad8h6ux1VFqrONR+GOgMszpmgBzjQMdufjQujXvHD+Hdg/upau80Fnw+H9/UfcuksAlcFXMlV8VeydjQ0Wxo3MDU8Mno5V0xneNDx6KSKrk9+Vbkkk77VCaRcWvSD+hwmf0GyDG+qPmKphMk+gSXjnp7A4faDlNmKcfpcZ62v9PrpLCjkG1NOyjsKAowPAF2tOzyGyDH2N92gFR9iv/z6vo1VB99GHrxsq5hfUB/l89FpbUKubRrvWNL0zZCVaHsbd1Hi6uVDY0baXCcIOUm6FP4fD6cLg/yE4wQhVLUChEILibVtppu0uebm7YwIt4Y0PbfXTsobW1hX20NmyvKqTWbOdB6MCAJHeCbulVcFXul//0gSZtIuiEtIEQ2J2hAQFL6yXB73SyMno9S2rmgJEHCFVFzaDvh3eFQ+2FqHXVnc9qCy5QL4gnZvXs3VVVVeDxdF/tVV1112nGvvfYaJpPpQkyp33K4PZenj/ybHyTdfNq+je4q8iSreGja9XDcy+Pg4EF8W7+GDY2bAEjRJTM1fAqbmzYzKWwCCqkCmUTG4fZczG4z5vZQxqpuQCp34POosbSFojZZTnJUQV8gv+MITx15BqvHhgQJi2IWMjd69kkLSbq9bjY1buGNsrfw+DzIJXJuTryJiWHjAwyGbvQghHQ6bSSL58Rrx4cEyQktgr7MsXwQqTTwd1Mq5DhE1XSB4KLhPcndUiHtXr9pd001v1i1AoCs0DBum9xT+LSEZmezv4hhojaBDH06MyKmY/PYkEgkuL3ukx43cG5eVtSuZG7UbKBzEXNH805Ghozo1tfnE3d9wQUwQh588EEqKirIyspCJuv8o5BIJGdkhAgCaXG08lX119i9dursteQYBnCo4zCtzjai1dHUHOcNGWocwhFzAVaPlUrPfqapOyVNJZLOG0yFrdLft9hSwijTCEaGjOTN8rf97VqZllBFFLd9/Sl2d9eLhVIm47ObFmOQG+hwd/jb50XPJVQpjMZLTburnZeLX/WrnPjw8Wn1Z52FAIMyehxTZi3nrbK3/atibp+bN8veJl4b5/d0jAoZweamzQHekMHGQX63OsC0iKl+N70cJeNDpvNhzVv+7QqJgiC5ISDPaFzoWD6pWub/PDFsQrfEdEHf4sQaIcdQKmTYncIIEQguFnqZjkh1JHX2Lk/C8JCh6D2BC063Dh3G87t2+D/nNTWi9WQjk8gCvCFTwyfzRtmb/s9l1nJaXa2sa9hIjCYap9dJgbmQW5K+f9q5RajCGRCUzWfVy/1t0epoFFJFQL8BQdlEq0+e4C747iDx9bI5esUVV/Dll18ikZydLvT06dMJDg5GIpFw/fXXc/3115+07969e1Gpzi6R1W63d1OP6qvIDDJKJeVsatlMiDKE7KBMPqv+ghvir6PR2ciR9gKmRkym2lbDkY4CknSJ2L0OdrXsZn70FRxqO0y7u53JxomkyVN5relNCs1FAccYbhzGVYaFlFHBtrbtRCujGK4fRmObjFu++rzbnF6aO58wk4SdbbtocNYwyDCMFHkSvhbHxfpa/Fyq3zI7O/uU28/lujxbejp3SYiEv5T/o1vf2+J+QERbzy/37WEdPFfy327tP03+MYbGzhA9jVFLs7qZzY1bcPs8jA8dR4jCyNbmbVTbaxgaPIRUdQqu2qNhXBoN65rLyYyVsrt1B3q5geHBo/B55exq24rFY2a0cRzRilD2m/dTaC1iqGEISZJEvO2XVsKxv9wfTpznxbomW9rt/PHZDdx5/fCA9s++PcL0cUmMyDm5XHd/+G77wxyhf8zTbrczbNiwU/Y5k+uyP5zr6bgQ59BqbKPGU4vZbabaVuNXIhyiG8GKvAZymxuZl5yKTqXinpVfBYxdkp3F0pFxbGnais1jZ0LYOPLbj7C6YU1AvwFB2YwPHcuahvWopEqGGgdjdloZ7MjB7T71ooM0SEapr4y9HftI16YxQJ0NXihwFXDYkkeOfgDp8jS8rRfvnn8m16Tg0tDrnpD09HQaGhqIiIg4q3HvvPMOkZGRNDU1ceutt5KSksKoUT0rMahUqtM+fE8kNzf3rMdcCnw+H59Xf8FHVZ8AUGot41D7YRZEz+OlkpeJUIXzi8z7CT+qLFFpqeRPeX/D6rGyJG4xH1R8iOtogbkSSym3Jv2AQUEDuxkhmYYMEqMTSSSRydET/e1lyhb0SiVmZ1d+gEomJzQomJs//IgglYpIXQyvNR7g5sEyHhw/Cbn04qYW9dXf8lyuy7Olp3M3u83ENcRReZy3CyDWEEtaTGqP+ynsKO4mQKCSqjCpQ0nL7vSEHG7L4+n8fxOviUMmkfFc0fNcF7+E/W0HCFIE8WHVx0wNn8z1mdcil8pxeTzkSwt4o/wDErUJNDia2Na8nVti72LZNhlqRRj/rd/IP2bN5ZqsqwMnFNsLX8550FevqRM523n21jVZWdOCRr2dhMSEgHajsZbw8KhTHqM/fLf9YY7QP+aZm5t72j5ncl32h3M9HRfiHHJbi/jvkZcIVgQTpgplbcM6lFIVsyPm8tDkHH+/DWWl3cYuGRbDUwX/IkYTjVKq5PWyt/yhU8eTHZTFO+Xv+0Npc9vzuCf9p6QnnJlEfyYZzGFWQFsWmSxkQVfDRSwzdibXpODS0GtGyI9//GMALBYL8+fPZ/DgwSgUXS64559//pTjIyM7ZT5DQ0OZNWsW+/fvP6kRcjnT4mzli5rA1Qun14nX50UukXNF1ByCFcFYXS4Kmxsxu1zcFH8jLe5mtDIt86PnsbZhPa2uVgA+qVrGj5JvJ8uQSV5HPgADg3OQ0j20AiDRGMJTc+bzfyu+oMPpRKdQ8Nj0WZS3tXH7sBF8kneY3bWdSXGv7dvD9wYNIT7YeMG+D8Hp0cv13Jb8A/5V8CwtrlZkEhnXx19LnPbkb/YOi4bvxX+Ptyvexu61o5aqWRq/FLtFA4bOPlubtgEEhPK1OduYHTmTdncHA4NzWF+/gVmRM4hQR+Dw2djZvom5UbORS+RIJVLq7HVUOcqoNnf4Ddu1pSXEBQXTarcTFxREmin0ohuygrPD4QwsVHgMpUIkpgsEFxKn201BcxPV5g6i9QZa7VLmhl7DquZltLpa0cl1TAlaRE2bi5jjxKYGRkTy4xGjeGH3Trw+HzNTUiiw5DMxbDyhqlDwgcPrQIKUIcGD2Nd2AIAhwYOR+CQBuXw+fBSbSxgeIrwJgt6l14yQ22677ZzHWq1WvF4ver0eq9XKpk2b+MlPftJbU+tXSCUSlFJlt1odYaow/jjwd0SpI3F6vLy6dxePb9nIrLREBqa2srNtCwAKiZxr46/ho8pPcXgdKKRyNjVvIUwZxl0pU6hx1FJsLqHUUtnT4QGYnpzC5zfcTI2lgyarlT+sX0O9xYJMIuHn4ybw6t49NFgtKKRSIc/bR0jVp/Bozm9ocjShlWmJ0kQik/RsaAK4gL+tKuL+yT9EIrPj9aj566p9/HVml+dEfULtjiHGwbS7O/i6biVw7Fpb4r8GZMiYGTGNDyo/xuw2A52VdzNDBuHxlgEwIjoGo1rNdR++2zlGIuFfVyxkbpoogtmXcbp6zglRyGU4RE6IQHBBcHu9fJR3mN98+w0+QAI8PHEK68o6GJN4LSqFlzarjz+tPsiLC9MCxoZoNNw7ZjxXZmZjdblICg4m33aAr2pW+EVqghVBLI69mtuSbqHF1QISCaHKUP5V+Gy3uahl/Ts0TtA36bU3yNGjRzN69GjWrVvn//fxbaeiqamJpUuXcuWVV3LttdcyZcoUJk+e3FtT61cYlUauiQsMVQlWBJGuTyNGE41UIqWguYnHt2wEYEJKiN8AAXD53KysXcUYU6cXaVLYRHa37GFj0yZ2t+5lR/MuDrUfZoxp9CnnEW0woJMr+O+uHdRbOldEPD4fz+/cwaLMTvfyPWPGoVcoTrUbwUXEpAwh3ZBGrDbmlAYIQEqIiYTgYO745Ft++OFmfvTJtyQZQ0gxGv19RoeO8ks1A6TqUtjcdOK19g0ul49mmxWfT8Le1v1+AwSg0FyEw2dncmISV2ZkMTs1jdf37/Vv9/h8PLx6JZXtbTTbrAGCCIK+g8PRsydEIReeEIHgXPH5fDTbbNhcPf8NlbS08Lu1q/3SID7g75s3MC0plbWF9eTXuFlxpIpZKalkmkKxuVw022x+5SmVXE5WWDjDo2MwaXWYXRbKrOX+/be52snvOIJapiFZn0yyLokghYGF0fMD5qGUKhkYnINA0Nv0ek7I5s2bu7WtX7+eBx988KRj4uPj+eyzz3p7Kv2WMaZRhCiN7GnZS4Q6gsHBg/xVqQEarcdJnkrt3cY3OptI16cTpY5iT+tebEdVk6psVUwJnUaYMoIUbdJJj1/Q1MjLe3exrqyUQRFRzEvP5IktG3F5vXQ4HaSGhPDk7Hnsr6vhyvfe4vuDh7IwM4sInf6k+xT0LYJUKh6dMo1ZKansqK5iVGws42ITMKi6VrtSdMn8KvuX7GnZi9vnxiDvLu/Y6Gyi3NzEz7/8hDtHDqHsqMfjeNpcLbTbnVSbzeRERHBFWjpfFRZ0bXfY2V5VyRNbNpEVFsbdo8cxJOoiBgwLTovD6UYh68EIUciERK9AcA5Utbfx/uGDfJR7iBRjCPeOHc+I6MAQ2mabFZfXG9CmkErJCA1la6WGLZUVTElM4saBg8lvbuKZbZspbG5mcXYO1+UMJC4oOGBshbV7BESJpZQOlwW1vMvznR2UxcNZv2BXy26UKBkZOoIkXWIvnr1A0EmvGSFvv/0277zzDhUVFSxcuNDfbrFYGD58+ClGCk5EI9cwxDiYIcbBPW6PMwSjlMlwejzg1nbbnqhJRubV80HlqwHyqumagXyws4VvS3fz8pWLmZqU3G1sk9XKPV8tJ7+5CYBacyF5jQ1clzOItw7sI8ZgYFBkJIvfexvn0Zvjnzauw+3zceeI714OT38mxhDENQMGcs2AgT1ul0qkpOpT/JK9uxsPd+uTpEmhtNlKjbmDN/Yd4urROWxzbgzo43Ya2Fq1Dx/wl43r+cnI0QSpVLQ7OpXV4oOC2V5dRbW5g2pzB9urq/j0uptIETWD+gxOl+eknhCr/fSFMQUCQRcuj4fnd+7grYP7AKju6GBnTTWfXLeUzLAuRUODShlwrwS4begI/m/FlzTZOhcX3z10gLK2VkxqDevLOxeB/r1jKw1WC7+fMh2l/Dhvtj6VdY2BBWUHBw8kXBN4r1VIFWQFZZIVlElBQQHJ+qRePX+B4Bi9ZoQsXLiQyZMn88QTT3D//ff723U6HcbjQjwE50+qycSzVyzkF6tW8P6+Mn4w9lpWNX2O3WsnRh3DnLCraOxwMSV0Ouub1uDFywD9IJpbQvi2dD8Ay/IPMzkxifymRoqbmwnTaJDLZDRYLX4D5BgV7W2YNBriDEE8PXc++2pr/QbIMV7Zu4trsgcQptUhuDyx2zVcF3MTn9V+hN1rJ1odzRURV7O5qBmAopYWlI6RDAhq5nD7YWQSGbMj5rL2UEtAmasVRYXMTE7l47zDxAcFc8vQ4fxj8wb/drPTSUFLkzBC+hAOp7tbtXTo9ISIcCyB4OyoMXfw3uEDAW12t5v8pkbsbg/lba2E63S4PB4enTyNRquVDqcDg1JFfFCQ3wA5xpbKCu4ePTag7cPDB7lzxCiSjCH+thRtKjMiprO2YR0en4dBQQMZbRpzyrmeTpJXIDgfes0IMRgMGAwGHn300W7bXC5XgFKW4PyQSiTMSEnlsxu/R4vNRrTewJTo4ZS2NbCptIHbPvoGl9fLsKgo7p/0fyhkEp7dsp/1Zfv9+4jQ6tlVXc33Pnkfo1rDzYOH8uTWTdwzZlyPxxwVE8fNg4cSptVR0trSbXuIWoOyh4qtgssHs8PNSztruHPMj5DLPNS0ubn383VceTRHSALIvAYqS1OYED0Ml9fH2gNm4oJCgK445GCVmsGRkUTpDSQbQ/jLpnXdckHUsl6PFBWcBw6nC7m8ewqhUimnpc16CWYkEPRfFFIZOoWSNkdgOLXT4+Hq97uKvT57xUION9Tz8t7d/rafjBzNgPAIDjfU+9vkUiknVnzTKpTdQijj9dEsUS5mtGk0Hq+HaHU0JnVgyJZAcDHp9Sf94sWLqampISioM368vb2dsLAwwsLC+OMf/8jAgT2HfvR3nB4nHW4zOrn2rFUk6i1mJEgI13V5ETxeLw0WC2qFHK1cQZPNhl6pxHBcgadYQxCxhs7vuc7sobpFwr+27fJv31Nby+Pr9/Kn6bMoa+1KFtbI5cxMSeXhb1fg8nq5MjOLl/bsxEfnisr89Ey+KMj3978qM4shkVH+Yw+NiiZSp6PO0pWb8sD4iQT188JS/R2Xx0Oj1YpWqSBYdWa/hcPjwOy2oJfrUMlOXTwsRK3B6nLxk89W+dvuHzuBA/W13D5sBHKJlNXFRWyuLOeLI13j7hk9DoVUisvrRSqRsGRADmvLSogPCmZ1SSE/GDyMJ7d15ZINjohiQHgE9RYzUolEeNf6ACfLCVEqZDiEJ0QgOCuiDQYemjiZh1ev9LdlmEI50tQY0K/BauG1fXsC2p7ftYNHJ0/jd+u+9bfdPGgoJS3NzEvPIFpvYEtlBUsHDibWEESHy4zL68KoDEYqkaJVashSCjVCQd+g142Q8ePHM2fOHCZNmgTAxo0bWblyJYsXL+b3v/89H3zwQW8f8pJTYa3k48pPOdh+iFR9CtfHX0uyLum041psNj4/kse/t28FCdw3Zjzz0zNpdzh4Ze8uPjh8kGiDgduHjuSVvbtQy+X8evJURsXE+ffh9XpZU1rC09s2MyG+e+JYcWszn+Yd5prsHJKMIdSZO4jSG9hZU01ZaysAarncH3O6vaqShRlZ3DdmPHa3i8GRUQyPjgkwflJCTLx59bXsqK6ixWZjZGwsQyKizu9LFJwXpS0t/GfXdpYfySMlxMSvJ01hTGw8EonkpGPKLOV8UPEReR35ZAVlcm3cNSTqEk7av8Vm4/6xEzjS3Ei9xcLI6BiUcjl762p468A+7hg2gtIevGQ+fDw2fSbNNhujYuLw+Xy8f+gg68tKmZqYzIyUFIZERbO3tpr4YCODwiP5NP8wz+/cgVIm44FxE5iTlo5eeWGr0QtOTmc4Vg+eEIUcu0hMFwjOmgXpGcQZgthdW02MPohUk4klH7wT0KfBasFzgovD6/MRpdfz4PiJVLW3k2YKZWhkFM12G49v2cSakmLmpWcwNj6WPS17ebf8fVpdbUyLmMLMyBmEqUIv5mkKBKek142Qffv28dhjj/k/T5w4kb/97W/84Q9/wOm8/BIY21ztPFv4H2rstUBnZdF/5j/F73J+Q5gq7JRjN1WUBaxm/GbNKkwaDdurKnn16OpHYXMzv17zDT8fO4HHt2zkB59+xLLrv0d6aOeNZFdtNT/96nOcHg9z0zK6HWNqYjLryko40tzEn6bNQiWXc/fXyxkRHcOUxGS+LS2mrLWVrLBw8hobAPj8SB4qmYzPb/geaaE9n0OqKZRUk7iZ9QVsLhd/27yeFUWFABxqqOeWZR/z6fU3kXVckuPxNDtbeKrgGZqdnUbDgbaDVNmqeXTArwhRhvQ4xqjR8P1PPwRAp1CSZgrl3V07yDu6erepopxZqWndVu4yQ8OYl54JdEpOLnrvTX/hwq+LCqi3WPjfoquZnJgEwPuHDvCXjV3Jkw+uWkGoVsvUpJRz+XoEvYDT6ekxJ0SpkGETnhCB4KzRKVVMSEhkQkLn4mF1RzspISEUNjf7+zjcbkI1moAckBC1hn21tby4ZycGpYpW+34emTyNv2xc51fS+ij3MNMzQnmv7gW/OM1XtSuQSqQsiVss6nsJ+gy9boSEh4fzwgsvMH9+p870l19+SVhYGB6PB+llWBW5wd7gN0CO0eHuoM5ef0ojxOfz8d6hrsS0CJ2OmwcPpaytjSi9gTtHjOLlPbtweb14fT5/zLzd7aa4tdlvhBQ2N3eqZAGrS4q4e/RYXtu3hw6Hg2lJKcQFBbOmtIT7xoyn1WFDJZNz9+ix/G/PLu4cMQqHx83XRQX8bcZszE4n9VYL4VodycYQdtfWUGuxkBMeQYhG09tfnaCXqDF3sPKoAXIMjVxBvcVCfmMjMqmEAeERpIR0JXrX2+v9Bsgxmp3NFLVXUlpfRZBKxcCISGIMXbK8dWaz/1pzemw026x+AwQg0RhCsErN3NR0VhYXolMouGnQUJqsVr4tKaLd4SBUo0UjV/iNEIDdtdVUtbcTHK7G4Xbz9oF93c7xm+IiYYRcQhxOF4oePSGiWKFAcC443W4ONdRT1NKMUa0mUqfnxyNG0+5w0GSzolcqSQgyMjkxiT+uX0tBcxOpISZ+OWES96/8CrfXS4u90zhpsdsCpHzlUimNrtoAdUyAtQ3rmRU546QLTQLBxabXjZDHH3+cZ599lp/+9KcADB8+nH/+8594PB6eeuqp3j7cJUctUyNFihdvt/ZTIZFISAkxsamiHKlEwp3DR/G3zRv8L3kxegN3DB/Fczu3AZ03lWPolcoe/72ntoaqjnZuGjSEyQlJPLltM8/t3MZDEybz7x1b/S9+RrWan4wcw+NbNjIhPoFXrlxMSWsLvz3OKzMxPhGDSslXhQXcNnQEPx83Aa0QF+iTqOVygtVqWu1dSY4/HTWGn375GZajRbBMGg1vXX2tX/6xp+tTgoR9NY38aU1nEuTA8Aj+M38RsUfzu4JUgeFQHp/Pn+sBneFax8KxfjxiNA6Pm2X5udwxfCT3rfgSs9OJUibjl+Mn8diGtf7Ho0omRyPvvLbkUilJxhD219cFHOvYHASXBrvDjaJHdSy5UMcSCM6BVSVF/Oyr5f7Po2JiuW7AIP6wfo2/bVZKKhPjkxgQHsGslDSqze1H63UFRpXITxCFcXu9KCXd7/EhCiNKqQhrFfQdet01YTKZeOSRR/j000/59NNPefTRRzGZTCiVShITL79iN5HqCK6MWRDQNiV8EjGa0xdbW5Kdg16pZGxsPCuLC/0GCEC1uQOPz4tGLifdFOpf8ZiamExWaFeITU5YBMOOK+xWb7EQozcQqdNhdTr5+dgJBKvUxB23ot1qt9Nss3LP6HG02x0Eq9X8bVOgdvjGijIyjx7n5b27KG5pRtA3iTEE8ZtJU/2fM0PD2F9X6zdAAJptNlYWd3lLotVRzImcFbCfscZJfJ1f7f98sKGegw1dxkB2WDhjYrvykVYUFfCTkaOZnZLGT0eNIVSj4aZBQzhQX8dzO7fxvz27kNCZhNnlQfGwuqSYMXHx/v08OH4iiUdlvGVSKT8YOgz1cdr2Jo2GGcILcklxOFw91gkRnhCB4OypM5sDQrEBdlRXUWvuYEZyKj8dNYYl2TlsLC+jxW5jWX4uz+3cxqd5ueQ3NjAjOfB+GB8UxOiYwEKHSdokYtRd7wYSJNwQfx06effaYgLBpaLXPSElJSW8/PLLVFVVBehLv/766719qD6BQqpgdtRMMgzp1DsaCFWaSNIlopGdPnxpUGQUH157IzUdHfz+hBsSdCadPz13PiFqNQXNzbywYBGDIqII1XbdRFJMJv46czYH6+postlIDTExPDoGgIUZWTyzfQtOj4crM7MZGhXNu0dDwErbWulwOFgyIAcJBLywHsN9nHu347hiSYK+x7z0TOKDgilqaSbVaOLvx9XdOEZJS1f4lUqmYmHMAgYZB9LoaCJYbuTVHcXsqi4OGHN8kawIvZ4n5sw7eq1ZyTCF4vH5WFdWxsriQpKDjUxOTObhCZNpsduRS6XY3C62VVagkSv8hkiNuYO/zJhFUUYWSUYjgyKikB6XQD8sKoaPrlvK4fp6FDIpOeGRpIqaIZcUh8uNVqPs1q5UyoU6lkBwltjdLpqsgdLWcqmUNFMoq0uLeXbHNhKCg3lg3EQcJ8iXP7N9K+8uvo55aZk0WC3EBQWTFRrKmDnzOFBfR7PNRprJxMCwSJJD7qXUWobNbSNeG0uiqHou6GP0uhFy7733csMNN3DttddeljkgPaGT68gJHkDOOYzNCA0jIzSMG5oG89cTvBHTklIYG9+pVjTiOEWsE0k3hZFuCiM3N5fsoyskq4qL+Mtx+/so9xC3DxuBSaOh2WZjSGQU/9q+lR3VVXyw5AZGxcSyo7rK318tl3PsvTBMq/WvVAv6Jmq5nFGxcYw66qm4fuAgdtVWB/S54gThAoNCz6DgTslst9dLiDJQHlICpJ8gPhCtNxCtNwBQ0dbGDz79kNK2VgBK2lp5+NuV3DJkOK/t243L68Xp8fCL8ZMC9PBvGDiI0bHxjI6N52Rkh4WTfZKkesHFx+Fwn8IT4ulhhEAgOBlRegPz0zNZXpCPXqnE7nazMCOLp7ZuJr+58z5c3tbG3zdv5Ik5VwSMjdTpMWk0jI7rfv+MNgSGraoV4USoxX1U0HfpdSNELpezdOnS3t7tZc/CjCzMTgcv793NiKgYlg4azP76OtxeH+1OO3mNDcQYghgWFU1mWDi15g721tZS2tpCVlg4QyIDJXI3lJV0O8aminImJSQRrTewt7bG7+nYXFHGn6bP4smtm1hVXES6KZQfDBnGE1s3MyYmjl9NmhKQoCzo+0xNSuGX4yfx3M7tKGRSfj52PKNjT27IyqVSbh8+Ah8+3j98kCidnt9Pm4FSJuOtA/vweL0Mj44hJzzCL/tb1tbqN0COYXW5SAkJ4caBg9EoFAyLisHj9RCp09PhdHDb0OEszMi6kKcuuADYnSc3QpxONz6f75Ry0AKBoAuVXM59Y8YzISGBI42NhGi0DImM4pO8wwH9HB43DrebUTGx7KmtYUhkFA+Mm4hBpeLrwiOUtraSHRbO0KhogkWdLkE/pNeNkGnTpvHWW28xa9YslMclTRvFSvopiTYYuG/sBJYOHML26iru+vJzRkXHYnO5eHr7Fn+/IZFR/H3mHB7fspFviov87T8bNZYF4ZH+z8crIR0jMzSUyQmJ/HXTBhqsXYUGYwzBpJlCeWL2PBqtVgxKJXKZlPHxiQSrVaI+Qz8kTKvlzpGjWZSVjQQJkXr9acckBBt5ZPI0fjxyNGq5nOqODq7/8F1/qJ5SJuPtxdf5w/2CVSpUMjkOT2C4gMPt5qU9u/zzeGHBVXx2w/dwe71E6vUBoVeC/oHT2XNiulQqRSaT4nS6UamEcIVAcKbsrq3m4dXf+D//Yep0dApFt9DoELWG5+ZdSZPVikmrQSqR8OA3K1hT2hU6e9+Ycfxk1NgAARuBoD/Q60bIJ598AsD//vc/f5tEImH16tW9fajLDqlEgkIm8yeJLx00hN+uXcWizGzigoKwuVx8lp9HXmMDkTo9BqXSr5Lx/K7tTJm/yL+vSYlJxAcFU9HeBoBBqeS2oSNwe710OLvi/DNNoYw6mtCmkssDVIi0iu4x4IL+RdTR0KkzRSGT+b1e/9uzK+CB6PR4+Cw/l3CtDovLiUau4I7hI/j3jm3+PosyswJWxButVnZWVzF0+OmFGgR9F8dJjBAApbIzOV0YIQJBJzaXi8r2NuxqNS6PB4Us8G+n1tzBnzeuC2gzO1zcMXwk5W1tRBsMWJxOzE4nMomEUK3Wnwu6rbIiwAABeHbHNuanZ4raXYJ+R68bId9+2z3BWnDmODxuvxKWTCrh3jHjeefQfpbl52JUq7lj+EjcXi/fFBdx75jxPL9zO402K26vF+dxieQpISbeXHwtuQ0NuL0eMkPD/DeoT6+/iSNNjWjkCrLDw0WolaBHaswdAZ8zQ8OINQSx4J036HA6yAkP53uDhvLAuAnY3G6UMhl7a2u6jTve6BX0T5wnCccCUCkV2BwuggyilpBAUNnexj82b2T5kTzkUik/Gj6KW4YODxCUcbo93cRenF4PkXoDn+Xn8XHeYUwaDfeNGY/ZFSjHa3N3V6Nzeb3YPSI3S9D/6HXfnc1m47nnnuORRx4BoLS0lDVr1pxmlOAYISo1i7MGAGBxunj/0AF/BdVWu51/btmESaOl1W7nya2buC6nM7F4QlwCEarAmND4oGBmp6Yx74QVkozQMBZkZDEjJVUYIIKTMiUxOeDzgoxM/rJpvd+oONTQwHuHDrC3rpZnd2zjya2b2V9X6y+sCZ3J7QOPCxMU9E+crlN4QhQyHA4h0ysQAHyWn8fnR/Lw0WkcPLtzW4DoC0CUXs+1AwYGtOkUCp7fuZ3io7WWmm02/rh+DSZ1oHGfGmIi5IS2MTFxJAQF9/7JCAQXmF73hDz88MPk5OSwZ88eACIjI7n33nuZNm3aKcd5PB6uueYaIiMj+e9//9vb0+rzWBwOtlZVsqmijMGRUegUShqtFvKbmwL6eX0+ilubuX3YCNocdlQyOX+fOZcOh523C/OZ7vVwqKGeeouFaUnJDIuOEUUGvwO02e3sqK5iU3kZKSYTE+ITaLXbWVNajFIqY0pSMoMiIgNCpWo6OthWVcGemmqGRccwIiqGkrYW1pWVEmsIIlqv5/6xE/gkPxe319NjeN7eulr+MXMue2trGBoZzZWZWRxuqCdGbyBIrebqrAHUdLRfzK9CcAFwOD0n9YQolXJsQqZXIMDidPL5kbxu7Zsrypiblu7/rJTLuX3YCHRKJZ8fySMhyEiaKZSyE4Q+XF4vVSfcP+ODg3l10TX8a/sW9tbVMDsljVuHjsCgErmbgv5Hrxsh5eXlPPXUU3zxxRcAaDQafD7faUZ11hFJTU3FbDb39pT6BWvLSrn7667qqekmE7+ZPI1QjYYmmy2gr9np5Lmd2xgbF881WQO4f+VXlLS1cvfosdy74guaj/Z/ee8u/jV3AfMzMi/quQguLj6fj/cPH+AvG7skmZONIUxNTOKVfZ2LAc/u3MZ711zPkKOFLTscdh7bsJavCo8A8HlBPj8eMTpAJjpcq2PpwMEMCAtHJpWiV3Y3QmL0BibGJzD5xu+jlst5YOVX7K2rZXJiEmang39u2cg/Zs65gGcvuBiczhNitwsjRCBQy+UMiogkvylQ7rwnoZiN5WWsKi5iamIydRYz++tqCVKpAmozAQFhXMcYFBnJM1csoMPhwKhWd8s5EQj6C70ejqVUKrHb7f4V1/Ly8gCVrJ6ora1l7dq1LFmypLen02dwut0caWpkb20NzbbAIkUtNhsv7t4R0FbQ3MyOqkp+NGI0suNWrxdlZvldu1srK2i22Shpa/UrDjWfYLD8Y8sGWk5oE1xeVLa38dTWzQFtJa0tBB0n2ej0ePjyqMEBUNzS4jdAoLOGyMt7dwXso8FqQa9SkWoykRhsxKRWMze1azVPIZVy27ARuH1ewnU6DCoVPxs9FofHzUe5h1hRVMiQyCiGRwdW8hX0P06ZmK6QYxeeEIEAj8/HyJhYwjRdhkOGKZRIrY5mm429tTUcaWykoq2VJ7ZuoqytlfcPH2RdWSlvHNjLL8ZPClAPvG7AQLw+HxVtreyuqab8OE+JWi4nXKcTBoigX9PrnpC7776bH/7wh9TU1HD//fezZ88e/vKXv5xyzJ///GcefPBBLBbLKfsdw+FwkJube1bzstvtZz2mt/AplXxVW8Xzu3fi8fnIMIXyp0lT0Vo7jQN5UNBJk810cgXPz19Em91GlbmDHVVVbKwo8/exuzsf/lKJBM9xienHsLnc1NbVUXsZGSKX6rfMzs4+5fZzuS7Plp7O3alR4+rhtz/RAdlms1FUVITT6aTthNAatVyOzdX9GvR4vTy9rVMielJCIjOSU8kKC8fl9SCXSnll7y5GhITSXtVZGFEplfLqnAVUWs0oJBLiNVraq6por6rqtu++xKW8P5wNJ87zYl2TDoeL2ppqlD2EZLndDgqLSghS23sY2T++2/4wR+gf87Tbe74OjudMrsv+cK4notBo+Dj3EIuzc1DJZUglEuotFrz4uPmTD8htbEAmkfDE7CtwnJBI3mi1Yne5uGf0OFxeD0qZjM0V5bTYbCxc8SbtDgcGpYq/Tp1OqlSO5yIlovfH3+FEzuSaFFwaet0ImTBhAgMGDGDfvn34fD5+/etfYzJ1d0UeY82aNZhMJgYOHMi2bdtO2u94VCrVaR++J5Kbm3vWY3qLjeVlPLury9NxpLmJlw/t54nZ81DJO3+CpYMG8/t1XQn8UokEo1rDb9auAuCT65by+v697K+v8/dJNoYQH2zEoFTR4XSgUypRymQ4j7s5/WjESLKTki7wGV5cLuVveSrO5bo8W3o6d5fHww05g3jzwD5/m0GpwnuCFbIoawCp8QkAyJsayTCFcuRoztE3xYUsGZDDK3t3+/urZPKAa2lDeRlpplA+z8+j8ag37/uDhzEgPr7batzQY3NNCkxu76v01WvqRM52nr1xTfp8PtyeL0hJSUTaQx0CY1AtoWGRJz1Of/hu+8McoX/M80xeWM/kuuwP59oT33PauefrL/yfh0RG0eFwcKSpkYHhEbQ5HOyureHqrAG8d+iAv59eqUSjVPLYcdK9ETodcqnMH6LV4XTw4JpVLL/hZpJCQi7K+fTX3+F4+rsRdTnTa0bIoUOHAj6Hh4cDUFNTQ01NDTk5OT2O2717N99++y3r16/H4XBgNpt54IEHePzxx3trapecsqNqF8ezvqyUZpuV6KPqVFOTkpFLpLx76ADBKjU3DBzE/3bv9Pcvb29jSlIymWHh7K2tIScighSjiTaHnTevXsLLe3ezsayMJ+fM4+Pcw1R3tHPz4KHMTEm7aOcpuDQoZDJ+PHI08cHBfHT4ENnhEfxgyDAarRaGRkWjksr46agx6JVK/rZxPaVtLcxOSeeWocPZVVPN/rpaBkdGMTA8kl9PnMJHuYdINBqZmZzmr1lzjL21NfxgyDCWF+SzOHsA89MzRTjAZY7T5UEmk/ZogMDRnBARjiUQADA5IYln5s7npd070SmV/N/YCby4ewf3jR3PnpoaMsPCGRcXz9rSEm4fNoKN5WXEBgUxPi6BWL2BHw4bwbqyUgaEh3PdgEHc9cVnAfu3ulzUmDsumhEiEFxIes0I+etf/3rSbRKJhNdff73Hbffffz/3338/ANu2bePll1++rAwQgJig7jK4Q6OiCTpOUje3oZG/blrPmLh4rC4Xv1y1krtHj2XfUc9HuFbHQ6tWEK7TkRUazv66OpYfyefzG28mMzSMf8ycQ0lFOelJycxISsHt84pig98hYgxB3DF8FEsHDkYlkyM/ahhMiE9EIpFQ2d7Gkg/e8a+orSgq5CcjR1Pc3ESyMYRd1VV8fiSPr5Z+nxsGDkYhlfLSnl1+j8cxhkfHcOeIUdwydBg6pVBj+S7gcLpRKk7+qFAoZdiFRK9AAECQWs2CjCymJ6dSVVlJXHgEw6Nj+NumDf4+Ugnsra2l2WZleHQsdRYzj21Yy9uLr+NXk6Zy69AOgtVqajo6uoVtKWUywnW6i3xWAsGFodeMkDfeeOOM+m3atIkJEyb01mH7BYMioliSncOHuZ3eolCNhl+Mn4TuaMK+xeng+Z3bWJSZTZhWh1QiQS2T02C1EKRSsSA9k8zQUP42cw73r/yK8rY2ZBIJj02fRYqxczVELpPhtnXGPSrlcoT58d3kRMNAc1Se+VBDPZmhYUyIT8Tt9WJ2OliWn8f0pGTeOLAPuVTK32bMJiHYiOzoiveUxCRWFhWyv74W6NSnX5CeiVwm8xs5gssfp9PdYy7IMZRykZguEBxPo9VCcUsLTo8bvc3GW8eFygIsy8/jmbnzefCbFXxTXAjAD4eNJDusM4Ik2mAAIMlo5C8zZvGLVStwe73IpVL+NH0WyUbhBRFcHvR6TsjpePzxx09qhIwZM4YxY8Zc5BldeMK0Wh6ZPI0bBw7G4nKSZAwh7rjCQj4fXJPdGY9felT9YmpiMgPDI7kqM5vkEBN6pZK5aRlkhIZRY+4gUqcnJcQkQmEEZ4ReoSDGYOCpbZ0qWhE6HT8ZOYZRsXFMT0klWm8g2RjiN0AAciIi+fe8+eQ1NuH1eckwhYkQgO8gjlNUSwdQKGRYbc6TbhcIvksUNjdx91fL/TK9M5JTmJeWwQvHhVc7PR7MTief3fA9yttaCVKpSDOF+hcmjyGTSlmQkcWA8AhqzB1E6Q2knHCfFgj6MxfdCDmTmiGXIwaVimHRMT1uUysU7Kur9RsgAGvLSpifnkFORKRfsk8ulZIRGkZGaNjFmLLgMsLj87Esv6uIVr3FwteFBVyTneNffeuJuCAjcUHG0+7f4XajlMkCiiEKLg8cThdK+ckfFSqlDJuoEyIQ4PX5eO/QgYA6IatLipmYkIRKJsfh6Qxb1MjlZIeFk2g0kmg0dtvP8fdT8dwXXM5cdCNEvKR0p8PhYHt1Zbf2vXW1HKyv56qsAQyJiroEMxNcLpxYPwZgT20NbQ57t9W3s6G8rZUvC47wVeERxsTFcW32QNLFw/Ky4nSeEKVCTku79aTbBYLvCjaXkw1lpd3aC5oa+c/8hbyydzdxQcFcN2Ag2eER3fpVtrXxZeERvijIZ1RMLNcOGERmmLifCi5fLroRIuhOkErF1MTkAIlV6Azj+vf2rXyQe5CPrr2RzFOsWAsEp+L48L9jjIuLx3icOMLZYnU5eWzDWlYVFwFwoL6Ob4qKeOea64jSG855v4K+hd1xGiNEKcduF4npAoFWoWR6copf+vwYo2JimZqUwpTE5JMuxNpcLv6xZSOfH+n0WB+or+PrwgLev/YGYgzdxW0EgsuBix5YGBt7eVdPtrvdHKyvY3VxEXmNDbjOoKCQTCrl+0OGMSC8y8i4Ii2d0tZWInR6fjhsJAfq69hbW4PFKWKvv4s0W61sq6ygDC+V7W1nPX5QRCR3jRztD+2LDwrigfET0Z7gBSltaWFNSTE7qippc5y6wFNpa6vfADlGWVsrhc3NZz0/Qd/lVNXSodMIsdnFfUkgkEgkLBkwkCGRXZELCzIyGRMX799+MsrbWv0GyDGqzR0UNDWdZIRA0P/pNU/IypUrT7l99uzZAPz73//urUP2OewuF28e2MufN3bWVpBJJDwxZx4L0jNPG4aWZgrl9auWcKSpkT21NWwoL+NwQz0/GzWWx7ds9BeNu2vEaO4aNRq9kEf9zlDd3s7Dq1eyoaIMgDCNlleuWkxOeOQZ7yNYreae0eNYmJGF2ekkITiYCJ0+oM/ummpuXfYRHUcN3UWZWfx60lTCtD3LQUolEiTAiVleMqkIubyccJxGHUulkGET6lgCAQAtNhuDIiKPej2g0WyhxWY7rXdYKpUik0jwnJA3KxdJ6ILLmF4zQtasWXPK7ceMkMuZopZm/rKxq7ibx+fjV6tXMjgiksQzkNQzabSMjUugpKWFrZUV3Dx4KC/u3hlQtfo/u7YzLTmFkTGXt0dJ0MWu2mq/AQLQaLPy/M7t/HPWFadMGD4RlVxO1klC+jocDv60fq3fAIFOGckrM7OZlpTS45hkYwjX5wzm3UP7/W1DI6NJN4We8ZwEfZ/TGSGd4VjCCBEIXG437x7cz0d5hwPak0whPeaAHE9iUDDfHzKUV/bu8bcNCAsnPVTcTwWXL71mhPzlL3/prV31Wxpt1m6rwhaXi2ab7YyMkGPMz8gkQqenyWbljf17u21vsFjOb6KCfkVxD+FNe2pqMLucmM7CCDkVHU4Hhxrru7Wf6lpTyeXcO2Yco2Ji2VRRytCoGCYlJJ3UcyLonzgdbuSnCsdSyIUnRCAAOlxODjZ0v4/mNTScdqxSLufOEaMZEhnN+rISBkVGMyUxqZvHWiC4nLggielr166loKAAx9HqzAA/+9nPLsSh+hSxhiCUMlmA5yJcqyP6LJN0g1RqZqSk0myzkbFnV0CSmwSID+6eZCy4fBkY0T3sanZqOsHnkVR+IiaNlmmJKawoLghoTwg2nnJcpF7P1dkDuDp7QK/NRdC3cDhdp8wJUQlPiEAAdN5HJyckBUj0AoyKjTuj8RE6PVdmZnNlZvaFmJ5A0OfodSPk0UcfxW63s23bNq699lpWrFjBoEGDevswfQq7201eYwONFgv/mbeQ/KYmLC4nBqWKUTGxRBnOTSnIpNHw91lzuffr5ZS1taFXKvnj1JlkiHCX7xTDo2P42agxPL9rB26vlwnxCdw8eMhpC1aVtbZypKkRqVRCVmg4SpmMvMYGOpxOUkNCyAgN8+cqqeVyfj5uAtXmdg7U16GUyfjF+EkMPE0IgeDyx346iV6lyAkRCI5xVVY2cUFBNNlsSCQQpTMwOubMjBCB4LtGrxshe/bs4fPPP2fhwoX87Gc/49Zbb+WOO+7o7cP0GTxeLx8ePsija1czPCqGjNBQ3j10wL/9T9NnMSQq2q9KdLYMjozig2tvpKajg2CVmoQeChsJLm9CNBruHj2OKzOyaWprZUBsHAbVqYUJchsb+P4nH9B0tD5IfFAwdwwfyaNrVwOglMl4ddE1jD2q2gKQHhrKa1ctobK9DZ1CQUKwUVTmFWB3uJDLTn4dqJRyHA4h0SsQQGdo6xNbN/vVBZODjQwQizkCQY/0+huGWt0ZIqLRaKirq0OhUNBwBvGQ/ZXS1hYe27AWgMmJSQEGCMAf16+hrLXlvI4RptUxKDJKGCDfYRQyGWmhoQQ5nKc1QADeP3jAb4AAVLS3UdzSjEmjAcDp8fDnDWu7yfAa1WoGRkSSHGISBogAOH2dEIVchtvjxe3xXsRZCQR9D4fbzRv79wbcV0vaWtl0nLCIQCDootc9IVOnTqW9vZ3bb7+dxYsXd+pmL1nS24fpM7Q5HP4cEJe3e00Qu9tN23G5MQLBhcbl8bCvvrZbe2VHOxFanb96emFLMxans1dzSwSXH3a765TqWBKJBLWqMy9ErxPS4YLvLhaXk6IehESKW0TtJIGgJ3rdCLnjjjtQKpXMmTOHadOm4XA4UJ3Bym1/JcZgIEqvp9ZsxucDrUKB1eUK2B57jjkhAsG5oJDJuDozm721NQHtOeERrC8r9X9ekJ5JmEZ7kWcn6G/YHS6MQZpT9lEp5dgdwggRfLcxabTMTk0n74TE9HHHhb0KBIIuej3e4vrrr/f/W6lUYjAYAtouN6L0Bp6bdyWZplDeP3SAX06YRJwhCIDM0DCem3cl4UJiT3CRmZmSxi1DhiGXSlHKZNw9eiw54RGoj0r6zkhK4ccjR59VnRHBdxO7w3XKcCwAtUqBVVRNFwi4Ii2dq7MGIJNIUMvl/GTkGEafoTqWQPBdo9feQBoaGqirq8Nut3P48GF8R6t+ms1mbMfFpl+ODI2K5u1rrqfRaiFEo2FuagatdhthWh0hmlOvIAoEF4Jog4GHJ07h5sHDkEokxAUFIZNK+eLG72N3u4nW69EqlZd6moJ+gN3hQqk49aNCqZRjEzK9AgGZYeH8cep0fjBkGB6Xi0ExsaLquUBwEnrNCNm4cSMff/wxtbW1AYUL9Xo9P//5z3vrMH2WEI0mwOAI13UWbGuyWrG4nIRrdWgUiks1PcF3EIVMRnJIYJHM2KCggM9tdjutdjtGjVrkhgh6xO5wo1Se2hMiaoUIBF1olSoGR0aRm5vrN0Ccbje1FjMquZxIER0hEAC9aIRcffXVXH311axYsYI5c+ac1ViHw8FNN92E0+nE4/EwZ84c7rnnnt6a2iXB4/WysbyMR9asorKjnZnJqfxiwiTSRI0PQR9hX20Nj65dzYH6OgZHRPL7aTMZEhl1qacl6GOciSdEpZSLcCyB4CRUtLXy3M7tfHj4IEa1ml9PmsrctAx/eKxA8F2l132Ew4cP51e/+hU//OEPASgsLOSDDz445RilUslrr73GZ599xqeffsqGDRvYu3dvb0/tonKkqZE7ln9KZUc7AKtKivjj+jVYnOJBLbj0VHe0c8fnn3Kgvg6A/fV13PH5J1QfvV4FgmOcSU6IUiHDahP3NoHgRLw+H2/s38t7hw7g8flostn4+cqv2F/XXcFQIPiu0etGyMMPP8zEiROpr68HICkpiddff/2UYyQSCbqj4Ututxu32+2v5NxfKWppxu0N1M3fUF5Grdl8iWYkEHRR0dZGo80a0NZotVLR1naJZiToqzic7lNK9EJnTohdVE0XCLrRaLHwcd7hbu2HG+ovwWwEgr5Fr/sCW1pamDdvHi+88ELnAeRypGeQlOXxeFi8eDHl5eUsXbqUIUOGnLSvw+EgNzf3rOZlt9vPesz5oJJ2N6JCNRpsHe3k1tVdkGNe7HO8VFyq88zOzj7l9nO5Ls+W3jp3iUqJBPAd1yaVSJC4XL12Dv3peuwvcz1xnhfjmjRb7DTW1+GwtJ60j9Nuo6S0gtzc7o+U/vDd9oc5Qv+Yp91uP22fM7ku+8O5ng673U5rQwMJQcH++kzHCJLL+8X5XS6/g6Bv0utGiFarpaWlxe/J2Lt3L4YzqJMhk8lYtmwZ7e3t/PSnP+XIkSNkZGT02FelUp324Xsiubm5Zz3mfIiwWpmdmsbKokIAJMDvpsxgYGLSBTvmxT7HS0VfPc9zuS7Plt46d4fbzT1jxvH0ti3+trtHj2VIfDwqee8IKPTV36kn+stcz3aevXFNerzfkJyciFZzcjW10II2goJNPR6rP3y3/WGO0D/meSYvrGdyXfaHcz0dubm5ZCQn80uFnB8s+8hf2HhgeASj4xOJCw6+xDM8PZfL7yDom/S6EfLQQw9x1113UVFRwQ033EBLSwtPP/30GY8PCgpizJgxbNiw4aRGSH8gVKvlj1NnsnTgYJptNpJDTGSHhl3qaQkEAKjkcm4bOoKxcfFUt3cQE2RgQFhErxkggssHh8MtckIEgvNgdGwcn1x/EwVNjegUSgaERxAtihgLBL1vhKSlpTFr1iw0Gg06nY6ZM2eSnJx8yjHNzc3I5XKCgoKw2+1s3ryZO+64o7endtEJ1+kI15363AWCS4VBpWJMbDzEXuqZCPoqbrcHr8+HXHbqkFqVUo7FKowQgaAnJBIJ2WHhZIeFX+qpCAR9il43Qn7xi1+g1+u58847AVi+fDkPPvggzzzzzEnH1NfX89BDD+HxePD5fMydO5dp06b19tQEAoFAcBbYHC5USvlphUJUKjlNLZaLNCuBQCAQXA70uhFSUFDAl19+6f88duxY5s2bd8oxWVlZfPrpp709FYFAIBCcB3Z7pxFyOlRKxWVdMd3j9fLWgX18UZBPkjGEB8dPIkyrvdTTEggEgn5Nr0v0DhgwIKDGx759+xg4cGBvH0YgEAgEF5hjnpDToVbJL9ucEIfbzY+Wf8r7hw8wLSkFq8vJDR+9i1nUfBIIBILzotc9IYcOHeKGG24gJiYGgOrqapKTk1m4cCEAn3/+eW8fUiAQCAQXAJvdhUp1erEClfLyNEI8Xi93f7Ucm9vNQxOmIJdKGRoVzX93beeJLZt4dIoIGxYIBIJzpdeNkJdeeqm3dykQCASCS4DN5jxDT4gC22VYrPCvm9ZTazHzi/ETkR9X7+qGnMH8YtUKfjRiJFF6oXIkEAgE50KvGyGxsUJqRyAQCC4HbHYXKsXpHxNKpRzbZeYJ+ST3EF8WHOH3U6cjlwZKFAer1YyPj+eN/Xt5cPykSzRDgUAg6N/0ek6IQCAQCC4PrHYnStUZeEKUcqy2y8cTcrihnj+sX8t9Y8ajV6p67DMjOZUPDh/E4/Ve5NkJBALB5YEwQgQCgUDQIzabC/WZqGOp5Ngcl4cnpMPh4MdfLOPmwUOJP0VF67igYIJVarZUVlzE2QkEAsHlgzBCBAKBQNAjVrvztNXSARRyGT6vD5fLcxFmdWF5dO1qMkPDGB+fcNq+o2Pj+KIg/yLMSiAQCC4/hBEiEAgEgh6x2s8sMV0ikaBWK7DYHBdhVheOtaUlbKuq4KZBQ86o/6iYWFaXFOH1+S7wzAQCgeDyQxghAoFAIOgRi/XMjBAAjVrZr2V6HW43j65dxQ+GDEctP70sMUCU3oBGruBAfd0Fnp1AIBBcfvS6OpZAIBAILg+sVseZGyEqBZZ+bIS8fXAfkTo9QyKjzmrc4MhI1pUWn9G4VmcbTc4mzG4LFreFdnc7ZpcZHz4i1REMNQ4lSCEkfwUCwXcDYYQIBAKBoEcsNidRhqAz6tufq6a7vB6e37md/xs74azHDoqI4qvCI9wzZnyP230+H5uatvBZ1ed0uDsIUYaglqpRy9RoZRrUMjUAJZZS3il/nytj5jM3ag4SieS8zkkgEAj6OsIIEQgEAkGPWKwO1MozC01SqRRYrf3TCFlTU01cUDBJxpCzHpsVFsbT2zbT4XBgUAXK+fp8Pl4ve5NDbYeZGTmDOE3sKY2LVmcrn1Z/TouzlaWJN5z1XAQCgaA/IXJCBAKBQNAjZqsDtfrMjBC1SoHZ2v8S030+H5+WlTAnNf2cxitlctJNYWyvquy27Zu61eS257E04QbitXGn9W4YlUZuiL+W3a17WFe/4ZzmIxAIBP0FYYQIBAKBoEcsVifqMyhWCJ3hWJZ+aITsq6vF6nYzMCLynPeRHRbGpoqygLZGRyOfVC3jypgFqGQ9FzzsCbVMzaKYhbxb8T4NjoZznpNAIBD0dYQRIhAIBIIesVgdaFRnGI6l7J+J6e8c3M/w0DCk55GDkR0eweaK8oC2Dys/ZrhxKCHKsw/xClOFMco0kjdK3zrnOQkEAkFf55IbITU1Ndx8883MmzeP+fPn89prr13qKQkEAoGAzsR0rUZ5Rn3VKjkdZvsFnlHv4nC7+bqwgGGhYee1n5QQE5Ud7bTabQDU2xvY13qAUaaR57zPUSEjqLBWcrg997zmJhAIBH2VS26EyGQyHnroIb788kvee+893n77bQoLCy/1tAQCgeA7jc/nw2pzoj5DT4hGrcBs6V/hWKtLikk2hhCsPPNwqZ6QS6VkmMLYUVUFwMq6bxhsHHRWYVjd9ylnYvgE3iv/AJ8ohigQCC5DLrkREhERQU5ODgB6vZ6UlBTq6vpH4Se320Njc8cpZSmtVgeNzWbcHu9FnJlA0P9pabXQ2m47r320tltpabP20oy+W1htTpQKOTLZmT0m1GpFv/OEfJp3mDFxcb2yr8zQULZWVuD0utjcuIWhwWdWdf1UZBuysHqsHGg72AszvLzwen00NptPm4fkdLppaO7A4XRdpJkJBIIzpU9J9FZWVpKbm8uQIae+eTscDnJzz85Fbbfbz3rMqfBINHyxJo+1WwtJijNx65LRGHVePB4P0OnhabFIeeX97ZRVNzN9fAZXTElH5rtwD+nePse+yqU6z+zs7FNuP5fr8mzpT7/xOc9VqmJvXgPvLd+DUiHjB9eMJi1eh9dz5i8REpmSvJI23vhkJz6vj5uuGkFOagg+b88LBv3lez1xnhfymmxqsaJSSCkvKz99Z8DS3kZtQ3O34/XV79bqdrGxvJTxQUacXidlZWWnH3QKjB4vK8sLGBDSSpAviLaaVtpoPe95pnpTeKfwfRQ6eZ/9Lo/Hbj/9M+5MrstTnatXombV5mJWrMslJiKY264fQ6gBvF5PQD+XT8N7y/ey60AFg7NjWHrlcNRyx0XzLPWH3+t0XC7nIOib9BkjxGKxcM899/CrX/0KvV5/yr4qleq0D98Tyc3NPesxJ8Nmd/KHp75kw47OsLEDedX86h9f8NLfv0dSXCgAxeWNPPzbN3E63QAsW7mf9g47v7nnClRnqLt/tvTmOfZl+up5nst1ebb01XPviXOd69drD/Hs613ypH957hv++ZtrGDMs7Yz3sXFHIY+/8K3/85P/W8sf71/ItPE9z6e/fK9nO8/zuSaPFNcRHKQlITHhjPq70JBX2tbteH31u/00L5eciEiyUlMpKysjMTHxvPYX4/HwelEBedISRkQOJzH4/PZ3jHhfPHnFLyOPU6CuPL3heak5kxfWM7kuT3bduFwenvzfaj77Zj8A7WY7v/rHcl786/fITI7w92tutfCzR96lvLoFgM27SigoaeC/f72JiNCLU5W+r177Z8Plcg6CvsklD8cCcLlc3HPPPSxcuJDZs2df6umclrrGDr8Bcgy7w0VZZZP/c1llk98AOcbarUeoa2i/KHMUCPojTqebj7/e0619w/azyxP7et3hbm2frdovYuvPgnaz/YyT0gG0GmW/ygn5/Eguo2J6JxQLQCGTkRJiZFdNNen6c6s50hNSiZSRphF8UfNVr+2zP9PYYuaLbwPD09xuLyUVjQFtVbWtfgPkGA3NZiprAtsEAsGl45IbIT6fj1//+tekpKRw6623XurpnBFKpbzHh7PmuDaNuvt2nUaF8gJ5QQSCywGpTEpUeHC39rNduYyOCOrWFhsZfNpicYIu2jpsaLVnnlit1SjpsPSPsId2h4PtVZWMjInp1f0a9T681ihUsjM33s6EQcE5HOk4QotXvEArFTKCDepu7Sc+kzVqBT39uWt7eDYLBIJLwyU3Qnbt2sWyZcvYunUrixYtYtGiRaxbt+5ST+uUxEQE85ObJwe0DcuJIy0h3P85PSmcwVmBD7if/mAKUeGBL0deb+cKzomrOMdjd7iorms97yRdgaCvcuwaN1vsXLdgOEqFzL/NoFczfmTqWe1v5oTsgJcStUrB/BmDaGmzUl3X2s1LKehOa7sN3Vm8sB3zhPQHb9M3xYUMCI9Aq+jdF1KfopHmtvNT2uoJpVTJEONgdjh39fq++xuhIXruuXVaQFt6cgTpyRHY7E6q61pp67ARH2Pi+oWBEskLZw4iPtZEu9lOdV0rVnv/q2sjEFxOXPKckJEjR5Kfn3+pp3HWzJ6cTUKMiaLyRsJD9QxIi8IUovNvDzHquGPpJA7lV9NmthMXZWTQCUZJVW0rX3x7gA++2A3ADVeOZN60gURHdK0El1Q08p8317N5ZzEJMSH8/EczGTEwQazoCi4bSiub+O9bG9i4o5DYKCMP3DGTR++bx5HiemRSKRkpkUSGn50nJCoiiF//bC4FJfX4gLSkcJxODz966E3qGjuYOi6DO26YQHyM6cKc1GVAa5sVnfbMX9IVchkymRSb3XVWYVyXgmV5uYyJje/Vfdo9dqyyKho64rG63GgVvft4HWYcyktNr2BxW9DJdacfcBmTmRLJL++aTUV1C3qdipyMaGw2F//47zds3V1CYpyJ+384k9mTsogI1dPQZCbUpGdodhxFpfU88dJqCksbGDEogbtvmUZaUvjpDyoQCHqdS26E9Fe0GhXDByUwfFDPSZtlFQ3c9/sP8Pm8aFRKLDYnY4Yl8dgDV/pDtbbtLeH1j7b5x7zy/hYiQg0snDkY6KxW/PgLq9h3uBKA8uoWHnzsY/73j5tJSTi/4loCQV/AanPy1Eur2XmgU4GpsqaVB/78MT9YMo73lu/C5/HidHv560NXMXHUmSem79pfxqNPLEcpl4JEgtPl4ZZrx9HSZsPr9fHtpny8Hh+P3HMFqjOsg/Fdo6XNiu4swrEA9DpVZxhXHzZCmqxW9tTWcNuwEb263yJLMdGaSDqC9Ryua2ZkXMTpB50FBoWBGFk0a+vXMz/mil7dd3/C6XTzv/c2s2pjHpFhBprbOvB4JNx72zS27i4BoKyymQf+/DG3XjuO/761AZ1WicXq5Pbrx/POZzv9svq7DpTzuyeX8+8/Xo8xSHspT0sg+E4ijJCzwOVyk1dUR0FJPRFhnSuzxeWNhIboGZAeRXJ8GEVl9RSUNFDb0M7fH76ausZ2KmpaCdKr2bSziPomM4mxJrxeL2u2HOl2jPXbCvxGSF1jh98A8c/B7aGiulkYIYI+icfj5UhxHfnF9chlPvTBzfh8kFtYg93hJjMlkjCTntzCGmobO4gJD4YTnHrBeg0JMSFcN38EUqkEuUxGh9nOqo25dJgdpCdHkJkSieK4kK0T2biziB/eMAGXq1OyU6WSc7ighoyUCPbndhaUW7ftCHe1TCI2KuSCfR/9mcYWM+nJZ/cirdepaG23Bnhz+xpfFOQzPDoatbx3H39H2guI0UThDFGxr6ap140QgAx5OivrVjEnahZy6Xfz8d3YYiYrNZIZEzIpqWgi2NB5vzi2kHEMp9NNsEHNj5ZOpMNsx6BXE2rUdqvrVVrZRHVdK3sPV9LYbCY1MZys1Mge8zoFAkHv8t28i50jW/eU8qu/f0pEqIHZkwfwxsddXoycjGh++ePZvPTuJjZsL2L+jIFs2FFIflFX4cVbrxuHTtO56iqVSomPMrLnYEXAMeKiu16INBoFeq0K8wnFmPS67kl5AkFfYM+hCu7/44d4vJ15AXd9bzLvLd9Jc2tnwUCZTMqDd87i78+vxHu0z/ULRlDX2EHFUSWbpVeN4k//+grnUQPi+0vG8uoHW6mqawVAIoG/PbyY8SNSTjqPiaPS+OtzK/wvHGqVggfvnMkrH2zx9wkN0Z9xNfDvIk0tFkYMOrt7jUGnprWtb+euvX/4IAszsnp1nx6vhxJrKfOi5iI1ufi2qPL0g84BkzQEo8zItuYdTAgbd0GO0dfRaxRo1Eoe/tsyf1tyfBgP3jmT1z7c6m/LSY+moKSeT1bs87fNmZzN8IHx7D7uubto9mD+9epaDuRV+9t+edccFs4cdIHPRCAQCCPkDHA43VTXtfHv19bg88H08Zl89FWXjKhSKSc+xkR1fRsbthcBEBkWxBerA2UEP1i+m7HDklEoZHRYnMyeMgCpVEqQQY3PBwfyKpk2LtPfPzo8mPtun85j/+qSZpw2LoPUROEFEfQ9rHYn/3tvk98A0agVtHbY0KiV3HTVQORyKXsOVfDV2kMMHxjPzv2dK5efrNjL9QtH8sbH28hOi2LPoQpmTMhi7LBkfD4fDS1mvwEC4PPBf95Yx8CMaIIMGn97Q1MHVXWtaDVKSssbA1Y87Q4Xuw9WIJN2anFIJPDzH84gNCSwJpHd4aK2oR2FXEbMGappdZjtNDab0elUF63+wMWgqdVC8HHf75mg16loarVcoBmdP7kN9TRYLAyK6F0vRZmtnCB5EBqZhgSjisp2C2aHC/0FMHJHmUbwRc2XjA8d+53JDWw326iobkEhlyGVSnjl/c1MGJlKWlI4NruL1RvzqKhpYcaETOKiQuiw2MlKi+KJF1cxb9pAwk16mlotrN6Ux323Tw8wQnIyolm2cn/A8Z59bS2jhyQSGd5dZU8gEPQewgg5DTV1bbz03iaCDRr/aq5SKfe/4GjUCn60dCIff703QPnK4/F225fF5qCorJE1m/P5eMU+3G4P08ZmoFTK+PjrvVw1eyghwYEP/fTkcO67fTotrVbUagXZaVHCEyLokzidHhqbu15ANWoFIcEaJoxM5cMvd+N0uZkwMpWs1CiKy7vU4JwuDyMHJ5IQayI2Mpj6xg427y7m909/gVIh45Zru6/4NrVYcLi6FK4O5lfxnzc3sO9wJQadipuvGUtmSiT5xV2eyPrGDn75kznU1LUSH2MiPSnwRbSypoX/vLGeddsK0KgV3Ll0IldMG3jKvIiC0nr+9twK8orqCAnW8osfz2bciBTksksuPHheeL0+ms/BCDFoVTS19F0j5PX9e5mSmIRU0ru/T0F7AbGaaADkUilJRgP7a5sYnxjVq8cBSNEls75hA/vbDjDEOLjX99/XyC+u46V3NrFldzEatYJH75vHkvkj2LSjkNc+3IpBr+b6BSNQKGSUV7ewelM+oUYdY4clc+dNk/joqz1U1rQSHRHMHTdOJDo8mCd/ey2NzWZiIoNpbDZ3O6bF5sAhFPQEggtO/35SXgS+XneIFesOU1HVzBXTcgA4dKSaUUMSkUjg2vkj+OCL3VTXtiIBjEFaFHIZBr0atTpwFWz8iBR8Ph/vfr4Lp9ON1+tj9eZ8PB4fxiAtH365m32Hq/z92812/vzs1zz1v2957aOt/PetDdz/xw8pPYWcr0BwqTAGaVg8dygAIcFaHE43wQYN7y/fhcPpxueDjTuK6DDbqalvI8ykRyqVMCQ7lsyUSK6YmsPg7DjKa5pZuT4Xr9eH3dE5TioNXPGdPTkb49EX5NYOK69/tM2fP9VhcfDc6+uYMjawYNz08ZkMzoplzpQcBqRHB+SUyOUKPv56D+u2FQBgs7t46uU15BbUnvR82812/vLs1+QdDblsabPym38suyz+PptazOg0qlPm3fREkEFDQ1PHBZrV+dFqt/FFQT7Tkk4exncu+Hw+jpgLidXE+ttSTEHsrm7o1eMcQyKRMCZ0NJ9WfdYv5JDPB6fLzacr9rJldzEmoxaJRILF4mD3wXIO5FcTGqLD5fLw0rub0GtVFJU1EGbS02GxI5VKeP2jbVTWtAJQU9/G/97bhEolZ9TgRK6YmsOQ7DiS48NQKgPXY6eOzSAy7PLxagoEfRXhCTmOwtJ6dh+swGJ1MGJwIgkxIdTUt/H7ny8gt6AGg17Do/fN48W3N3HHjROYPCad0oomFs4YREyUkd0HK/jtffPIL66jtKKJ+26bRkV1C5+tOsDYoUmkp0QE5IgcY/fBciaPTiMyPIjCsgbe/2IXIwYmIJVKyC+qD+jr8fqorGkhLan3kx4FgrOlpc3CgbxqDh2pJjUxnDHDkwkz6dmX2+mRUCrkaNQKbHaXf4xGrWDmxCyKKxrJSI5gSHYs+/Mq2ZdbRVyUEaVCjkTSGXYF8OW3B3nwzlm8v3w3LW0WpozNICUhjBXrD5NfVM+k0Wls3VPSbW5qlYKo8CB8Ph9zpgygrKoJn8/XYwiLBxmrNuYFtOm1Kiw2B+993qmmM3JwItlpUcjlnS/mDU0dHCnu6e+ztd//fVbXtxFmOnsZ2JBgLQfyq07f8RLw2r49jIyJJURzdt6d01Frr0MmkRKk6PKEp4UG8/6Bwl49zvFkGTLZ3LSVg22HGGQceMGOc6mpb+ygqraVu743meqjoZbRkcG4XB7uvGkSVbWtGIM0SCQS6hs7uP368VTXtREaosPr89HSZg3Yn8XqpKa+jZyMLrn8lIQwnnxkCc+/uZ7SqmZmTcziugUjhGqeQHAREEbIUQpL6/npI+9isXaGWb38/mae+B37wxsAAMRDSURBVM0Sxg5L5rdPLve/EOm1Kv760FXsOVTB/97b7B+fHB/KlbMG8+qHW/0rsstXH2D+9IHctGgUKzfkcriwhnnTuj8w4qNDyE6L4s/Pfu0/jkGn4p+/WYIxSNOtSKExWEgJCi49Lrebtz7dwbuf7QRAp1Xy8ztm8senv/T3CdKruXHRKF4++rcyd2oO2/eVcvioh2FPZDAtrVZePS6hNDk+jEWzh/Dp0YTSipoWWtqsDMmOBQm4XR42bC9ky1E5TrPVQVx0COVVzQHz02mVDB+YgEQKy1buZ8HMQSeNoZdJfKQmhtPcWuZvW3rVKP7076/9oZevfLCFJx5ZwsjBiUDnvaCnv8+Qy+Dvs7KmhbAT8mXOBJNRS019+wWY0fnRarfx2t49PDJl2uk7nyX5HfnEaWORHCfzFm3QYnN5qOmwEm3o/etBKpEyIXQc71d+SE7wgF4PL+srBOlUTBmTzpP/+9bflpUWybCcOP771gZ/W3REMPf/aAaPv7DK35adFsXCmYP4fNUBf5tUKiGkByneIQPi+OdvrsFqd2IM1vX7cEqBoL8g/tKOsmN/GRNGpvH7ny/gt/fN54E7Z2G2Ovjoqz0c7/E2Wx3sz68KUNwAKKlowqBTd5PU/WrtIexON2WVTSyaPRSXy0NibFeBNINezcTRaXyyYi+TRqVxy7XjuOXaccREGjlUUM0vfzInIBRl0ewhpCaIwkqC02O1OykoqaeorAGHw3X6AWdJRXUr7y/vquB8w5WjeO+oQXKMdrMdr8eH6mi4Q1yU0W+AAEwbl8m7nwdWgS6paCT8uATv2CgjQwfEMmJQPIOzYpk4Oo1te0v929dtK+Cq2UNQyLtCh0YOTsTl8vDlmoN8sfogCoWU2ZOyKa9uJq+wttsKqdvl4Ic3TEBzNIQySK+mw2IPSG73en28+fE2f7X1yPAgHvjRrIC/z6tmDyHlMhCOKK1sDvgNzpRwk57a+rYec+IuJU9t3czI2Fii9b0bYuPz+cjrOEKcJi6gXSKRkBEWzM7K+pOMPH+yDJl4fB62NG07fed+isvjYdk3gUnjHrePD7/cE9BWU98WkI8GkFtYy6Cs2IC27109mrTknp+fWq2KMJNBGCACwUVEeEKOEhdlpLyqhd8/9QVer49hOXHMnZpDh8XRra/F6kAi6R6L6/Z2f/B6vT7Sk8L5xV2zMVscSKQSbrl2HHWN7USHB6FWK7DZXEwbl8H6bYW8+sEWZFIJc6fmoNOqGDcsmZf+djNVtS2EGLWkJoRj0IvEdMGpqalv49+vrmXdtgIkElgwYxC3XTf+nF4sT4bb7fHL7AJo1QosJ2jwA8jlUn5z9xVYbU40GkW3bU5X9wTQsBAdD/xoJlKplKgwA5t3lfDu5ztxu71kpkTyo6UTef7NzpVQp9PN28t28PdfXU1NfTt6nZLkhDA0SiV/e/hqABJiTOw6UM4zr67B6XSTGGfid/ctCKiDkZMRw0t/+x5lVc0Y9KoeQ7zazDY8x/2dTxyV2v3v8zIQjigsrWfIgLjTdzwBlUqBMVhLeXUzyfF9wxjbV1fLZ0fy+OuMOb2+70ZHI06vE5PS1G1bRpiRreV1LMxO6vXjQqehMz1iKu9VfMDwkKFoZL0bZtYXcLq8WE6QqJfJpQHhncew97DQolTI+P3PF9DSZsUYpCE81ECQ/vL7ngSC/oowQo7S1mHj81VdKy57DlWSHB/G1HEZAUo+EgkMyoylqqaVtVsL/O16rYrIMAMRoQbqj0vMzEmPRqdV8dsnl+N2d7283HnTRJxOD7998guyUiLITIviQH6nTrnH6+OLbw8yYnACcrmMjJQIMlL6d4y54OKyZssRf5K1zwefrzrA4KxYrughHPBciY0yBmju79hXxrxpA3nxnY3+PlKphNSkcCYdrXZ+IK+SkGCt3xOx52AFk0ens3ZrV+FOvVZFdV2bX/P/13fP5c1Ptvu35xfXEWbSk5MRzaEjNQAYDRriYkyMGpIUMMeoiKCjx63i8Re+8beXVTbz5P++5R+/vhqdRoX0qHRvYlwoiXGhnXOXSHn70x0BntDrF4wMKGJ2Of59+nw+jhTXsWDGudVJSEkIY/eB8j5hhHQ4HNz79XK+P3gYQaqzq/5+JhxuzyVBGx8QinWM9LBgPj5UjNXlRqu4MI/aWE0sybok3i57l9tTbr0gx7iUREcEc+Wswbz4ziZ/m9PhYt70nABZXZVSHqBOCRBs0GAK1nHP7973t6lVCl5+/GYSYrobjQKB4OIjjJCj5BZ2V8HZsa+MOVMG8IMlY1m/rRCdVsk1Vwzj0xV7mTkxm8jwIDbtLCIlIYwZ47MoKm3gmnnDyC2opaC0nkFZsUSHB1FY2sCvf3YFX645SF1jB1fPGcK0cRn8/qkvAIiPDWXHvrJuxy8pD1TZqWtoZ8f+MnbsK2XogHjGDEsiJtJ4Qb4PQf/F7fawdkt+t/Zte0p71QjRaVU8eOdsPlu1n/XbChg7PJmi0gZuXjyGDdsLMehUTB2XQU1dm39MVV0bSxeNYl9uJSUVTcTHhjBiYAIJMSF8uyWf9KQIrlswgrLqZlITw9BplN3CLKCzKOLPfjCF1nYbw3LiuHLWYKJPoelfU9/WrW1/biX7c6tYse4wWSnh6INbiY0y+rcPSI/i8d9cw2sfbqXD7ODGRSMZN7J3lZX6IhU1LchkUoxB57ZiPHJQAm8v28m08ZmYjGef3N5beLxe7lvxBZmh4YyNi+/1/ft8Pg61H2Zs6Nget6vlcpJCDOyqamBSUnSvH/8YU8On8FrpG+xo3sko08gLdpxLxYwJWSgVcr749iDBQWriYkw43V5uXjyGtVuOEB0ZzFWzhyCTSlg4cxC7D1aQFBfKsIHxVNS0BOzL7nDR0NghjBCBoI8gjJCj9LRql5oYztqtR6ipb+P+O2bicLr4y3MrcDrdbN1TysDMGB69dz7JCSYUMgWHCqr52SPvkpYUQVZqJIfyq1m/rYBn/3g96cmRTB6Thsvl8df5GD4wgT2HKqmobiElIYzquraTzslic/Ls6+v4dnPny+XqTfkMHxjPHx+48qy1/AWXN3K5jGE58QG5FwADs2JOMuLciY8J4a7vTebmxWNQyKX8+d9fs3brEUYMSsRi65TK/e3/LfD3DzXqeOyZr8hMjSQrNZI9ByvYuL2I5x67gRsWjUKjlqOQyxmUFcv44SnIZNIew6LSkiKYMDKVscOSMQZrUZ5mpfnEooTH5r5qY57//7XbivjLLxcREtz54qxQyBkzNJkhWXF4vN5T1gu5nNi5v4yM5MhzLoSXkRLJ8IHxPPy3T3n+z0t7eXZnhs/n45E1q2iyWnlg/MQLcoxqWzUgwaQMOWmf7PAQNpRUX1AjRC1TsSh2Aa+Wvo5JaSJVf3kZynHRIdy4aBSzJ2ehVMppabXywRe7kcukLJg5iNqGdn7z+DLuuHESO/eXMyA9qrO48Ktr+e198wP2JZNJhbCLQNCH+M5nYNkdLgpK6slIiWDQcS9p4SY92elRFJY2EBkeRFZqFCkJ4aiO082PiQwmKjwIjUqFXC5FKpVww8KRFJbWs3pTPtX1bXzv6tF+SU+VUhFQaHD6hEyS4kLJK6plSHYcoSFdq4ajhiQyJLsrJruypsVvgBxj98EKyk5QBBIIAK6YlkN8tNH/OSs1krHDki/IsaRSCUF6NRq1stMYUchYu/UIO/aVMWpoEgOPk8OUyaRcc8Uw8ovqWL0pn4YmM0uvGgWSzmRwhbzLmDAZdQQbNAxIi2LiyFR/e5Beze3Xjyc0RE9EWNBpDRCAtORwFl8x1P9Zo1aweO5Q1mzpCgM7mF/d49+TWq34zhggAOu2FpCdfn5F9uZMGUBzq5X9eRdfrtfn8/G3TevZWV3FPWPGI5eeXa2TM2Vf636SdIk9hmIdY0Ckid1VDdh7yHvqTaLUUcyJnM0TR54itz3v9AP6GU2tFqpq26iua8ft8XHXzZOoqmvlP2+s55Ov9zJr0gBSE8NpajGzelM+uYW13LhoJMEGNbKjwhFSqYSf3z5deEEEgj7Ed9oT0tZh481PtvPuZ51x3w/8aBY3LByJ2+MlIcZEu9nG07+7jsQ4k1+u8qW/30xFdTM6rYrk+NAAo8LpdLPzQDm3Xz8Bp8uNXC5j+eoDJ335S4wN5anfXktpZRNSqZSnf3st9U1mlAoZSfGhGI+TEvSeRG3m+MRggeAYSXFhPPOH6ymtaEImlZIUH3pRQmMUChlL5g3H6fIglUow6FTIZF0vaS6Xh8MFNfzwhqN/IzIZH3+9l3HDT24gxceY+L87ZrBo9hDMVgdJcaEBCeVnQrBew51LJzFn0gDaLXYMOjUP/fUTv9LVMbye7/bfU1uHjcMFNdywcMR57UcqlTAsJ471WwuYObb3q4afiqe2bmZFUSG/mjgFreLC1HpweB3kmY9wRdTcU/bTKxUkhhjYWlHH1JTYU/Y9X9INaSikCv5d+B+mhk/mypgFqGT933guKmvgkcc/o7y6M7Tqn49cw1vLdvCnBxfR0GxGr1XS2GzG6XTxrz9cT2VNC6EhejKSI9BqlLz8+Pepa2wn3GQgMc501gU4BQLBheM7bYTkF9XxzrId/s+Pv/ANY4Ym8diDVwYkn0Lny75UKiE2yhgQN348ibGhOF1uXnq3K4lu/MgU4k7SHyDMpCfM1BUqknSSZM64mBBGDk5g5/5yf1t6cjgJMScPBRB8twk3GQg3Xbyqvy6Xh3c/28kX3x4MaI8MC2LymM7q5UnxobS2WwP+RmZNyjptblNkWBCRYd1zPjweL7IzlNTUaVXkZHZ6ZSxWB9lpUf5aIwBJcaEkxH23V0nXbysgOy26Vwq1ZaRE8vmq/RfVCPnPzm18kneYX0+aiuECJKIf42DbISJVEWjPQJFqSFQYKwsqLrgRApCkS+SWpJv5tn4tD+x7iLlRs5gWMRWtvH+GIDldbt74f/buO76t6m78+Ed7y1vy3nbiOHtBEkggiz2SEGYohbJXW/q0lA76tL+up4MOWgoFSgsUKHuPFDKAkL2HM733ki1be/3+UKxYkWfipeS8efXV6OqOc62jq/u955zveXNzKAABeOntLRTnp/DQ/3s9tEyvVfH7R1dQXJAaNhEhBLtV52WJtPaCMBaNiSDkkUceYd26dSQkJPD++++P2HHrmyIHq+48UE1HpzMUhNgcbrbvqeCtT3YRY9Cw/OKpTByXFjY3QJfEeD2/+O5VfLL+ANv3VnDBuYVccG4h2iHoymHQqfnunUv47KtDfLn1KLOn5rDk/KJRHfgpCN112l1s6TZ/R5djFU2hIMScaOQ3P1jOh2v3s/tAFYvmjWfBOQWoB3nTW1nbypoNh9i0s4zzZuVz4ZwC0pIHHpDrtCq+ffsipm48zPpNh5k6IZ1LF046pQn6ziSffnmQyUVDc7OckRpHdV0bTtfwdkXq8tyu7by4Zzc/PH8BMerhS5PsD/jZ0rqVmXEDay0qNsfz/qFyGjrsmIdh4sKT6eV6rky9nAZnI1tbt/J+3UdcaFrAZSmXoJNH1+9FR6cr7MEbwPa9VXxtxRwS4vSs3XiI9JQ4LrmwmOKCoR/zJgjC8BoTQcjy5ctZtWoVDz/88Ige19xDNp0pRWlh83Bs2VnGj3//Xuj12o2H+dsvbmBCQc8DDbPTE7jrpvPxeueGxoIMlbSUOL624lxuuGpW2MRsgjAWGHQqZk7J4qO1+8OWn/wUMicjkfu+tgCv13dK35E2q52f//nD0MD7fYdq2bq7jJ9/96pBzdGRao7lpqtnc93lMygvLyP7eGres1WnzTUkXbG6KOQy0pJjqahtZ9rUIdllr17au5untm3lh/MvIF4zvDf6BzsOopAoSFIN7Om6QiZlWmoSHxys4LZZRcNatu7MahOXp15Gm7udTa2b+d6eH3BD5nXMS5hzykkHRppBr2LGpEw+23BinEuqOYbt+yp4/YOdTChI5nBpA19tO8aTv7qRrLSz+zssCNFmTAxMnzVrFjExMSN+3PG5Zq67/MQPblK8nntWzQ+1gtid7rD5CSDY/WPr7vJ+9z3UAUh3IgARxiK5XMYNV84K6yJ4yYXFTCjsOWA/1e9IZU1rROav7XurqKq19LJF3+RyGR7P0M8oH2027yojLytpSLpidclIjaO8pm3I9teTV/bt4Y+bv+L7580nSTu8T/p9fh+fN21gYkxxnwPST3ZuhplPjlRhH+YB6j2JVcZwcfJSrklfzrs17/GXo3/D4XOMeDlOhVIh52vLzyHVfOL+4OvXzOHV93Zgd7jZtqeSsqoWOmwujpY3jWJJBUE4FWOiJWSwXC4XJSUlg9rG6XT2uM1F52Uyd3oGTpeX+Fg1freFkpJghhyFUh02qLZLIODn8OHD+Hy+UzuBYdLbOZ5pRus8i4r6fop5KvVysKLhM370wUW0tjmRySTEGhQ01VfRFDkNzymz23sOXmw22yn/baLh7wqR5RzKOvnBpzsxxyuprKjsf+UB0ir9HClvG7a/7bsVZbxaXsptBeNxtbRS0XJq2QLdbjcVFZFzNZ2sxHMQqU+KpE1Cg2RwlTpdp+SFr3ZycfapT2w50HL2ZoHkfHZYd/HDnY+yUrOCWOnQP/xzOp39rjOQetlV1yUSCf/vW0tobneiUsgx6FU9dof2eNxj7jscLdeVvpwp5yCMTVEZhKhUqn5/fE9WUlIy6G0AvrbCx/d//XbotVIpZ870PArzRjbjy0Cc6jlGm7F6nqdSLwdrrJ57T0pKShhXMPRzFrR3OMJmagc4f3Y+EwozTjmVbrT8XQdbzoHWSZ/Pz6Gyz/jWNxaFpQo/XSpdLJv+tW7I/7b+QIDfffUF79VV878XLiZJd3plrqioICsrq891LO5WSsoPsSR5EQb54BM+XKKL4ZmtB7jhnCkYT0p8MpTl7E9OIIftlp281PofHir8Jjn67NPa38kGcsM6kHp5cl0f1+29266dw1/+tT70Oi5GS3FhBhljLFFLtFxX+nKmnIMwNkVlEDKSZk7K4rEfX8Pqzw8QY9Cw6LzxFOaaR7tYgnDWijFoePjei9i0o4wdeyuZNTWLc6bmnFVzeQy1kqP16HXqIQ1AAEwJBlxuL82tnWFZAE9Hh8vFd1Z/RLW1nUfnX4hRNXyD0Lt4/G7erH6HiTHFpxSAAJj0GiYlx/OPbSV867wpQ1zCgZNIJMyMn45RYeC3hx7jjtzbmBY3ddTKcyouuWAi5iQja786RHZGAgvOKRxzAYggCP0TQUg/1GoFs6dmM3tq9mgXRRCE49LMsay4ZBorLpk22kU5I6zbeChsUsmhIpFISE82smN/FUvPP/2nqbvr63jw4w8Yn5jEI+ctQCEb/vFxHr+XN6rfxqAwUKDPP619LcnP4PGNe9lU2cC5maP7MKvQUIBerucfZf9kqWMJl6dcGjUD1mOMGi6cM44L54zrf2VBEMasMTEw/aGHHuL666+nrKyM+fPn89prr412kQRBEM4KPp+fT788yLSJGcOy/8wUI19tO3Za++h0u/nFF+u49d03WTZ+ArdOnT4iAYjVY+WlylcIALPiZwxqMHpP1Ao5103O548bdnO4uW1Iyng6UjUprMq6kY0tm/j94T/S5h79MgmCcPYYEy0hjz322GgXQRAE4ay0btNhYozasAxEQ6kgK57n391Hp82FXje4LnOdbjev7t/L37ZtYaLJxK8WLh3WOUC6uP1udlh2sbFlE+P0hUyIKTrtAKRLZqyBZRNy+fHqLdw3ZyLnZ6eMaguEUWHkxszr2dC8kR/sfZRLUi5isXkhmgFMxCgIgnA6xkQQIgiCIIy8mvo2Hn9uLddfOXPYjqHTKpk0Lo2/vfg537ljcY+Zjbp4/X4q29vY09DA2vJS1paXMslk5jtz5pEdO3x9/r0BDw3ORhqcDZTayii1lZGsMrPIdCExiqEPzopMcdyiGse/dhzi7QNlXFKYyeTkBEx6zagEJDKJjPlJ51FsLOKrlk18UPcRk2MmMSV2Etm6bEyqJBTSoUvdLAiCACAJBAKB0S7EYO3atQuVSgxCFUaWXC6noKCg1/dFvRRG2unUyX+/u5d1W4LpeDNSIiduHUoOp4dmi4MJ+Yl8++vnhJa/WnaMfx091Ot2iSo1RuWpZZLqTZu/DVfA1ec6MmRD1vLRlwASrPaenwVOzOpkXNrozOfhCDjpCHT0uc7XtTeTLOt5XIu4VgpjTX91UhgdURmECIIgCIIgCIIQvcbEwHRBEARBEARBEM4eIggRBEEQBEEQBGFEiSBEEARBEARBEIQRJYIQQRAEQRAEQRBGlAhCBEEQBEEQBEEYUSIIEQRBEARBEARhREVlEHLkyJFBb1NeXj70BRljzoZzhLF7nqdSLwdrrJ57T0RZh95gyzkSdbI/0fC3jYYyQnSUcyBlHEi9jIZz7Y84h7HhTDiHM1VUBiFer3fQ2zgcozPp00g6G84Rxu55nkq9HKyxeu49EWUdeoMt50jUyf5Ew982GsoI0VHOgZRxIPUyGs61P+IcxoYz4RzOVFEZhAiCIAiCIAiCEL1EECIIgiAIgiAIwoiSD+fO6+rq+N73vkdLSwsSiYRrr72WW265JWydzZs3c++995Keng7AkiVLuP/++4ezWGelRmcT1Y5qpEhJ16aTqErodV2Xz0W1o4ZmVwsJqjjS1emo5eohP44gALi8Lsrs5dQ76zHIjWTpMvutN06vk2pHNS1uC4mqeNI16ahkqhEqsSAMPX/AT42jljpnPTqZlgxtOkaFEThxXVWYlWxr3Y5UIiVdk45JnTTKpRYEQTh1wxqEyGQyvv/971NcXExnZycrVqxg3rx55Ofnh603c+ZMnnrqqeEsylmtyl7Nbw/9nnaPFYBEZQLfGfctUjWpEet6/V7WN33BvytfDi1bmb6Ci5KXoJAq+jxOpb2K3x58DKs3eJwkZRIPjfsmqZqUITwb4UyzrW0HT5c+S4AAAOfEz+K6jJUk9BKIePwePm38jNeq3wwtW5V5AxeaLkAuHdZLmiAMmwPWgzx2+I/4Aj4ApsdO4+vZN9PutfLbg4+x0HQBXzRvoMXdAkCMwsh3xz1EhjZjNIstCIJwyoa1O5bJZKK4uBgAvV5Pbm4uDQ0Nw3lIoQcbmjeEAhCAZncL2y07e1y3wdnAK1Wvhi17vfpN6pz1/R7ni6YvQgEIQJO7iZ2WXadWaOGsUOuo45XKV0MBCMDm1q2U2yp73abOUcfr1W+FLXu56lUanOLaIkSnDk8HL5S/GApAAHa07aTCXskXTV8QIECnrzMUgAC0e6x80bRhNIorCIIwJEbssWF1dTUlJSVMmTIl4r1du3Zx5ZVXYjKZePjhhykoKOhzXy6Xi5KSkkEd3+l0DnqbaNPTOWo0Go7aSyPWLe0oo6ysDKfTGbbcEesM+yEECBCgob0BW0Vnr8dWa9Ucs5VHLC/vLKe0tBSXyzWIM+nbaH2WRUVFfb5/KvVysKKpHg+krAEzYYFrF6u7vddtO2NtYUELgC/go6G9EWtF5L6GqqxjwcnlHAt1sj/R8Lcd7TLK4uQ0uBojlrc4WjlmKydOGUuzqyXi/VJbGeXl5WMq+8/Jvyk9GUi9HMhn4vX5abE4MCfqBlXGkTLa9WoonCnnIIxNIxKE2Gw2HnzwQX7wgx+g1+vD3isuLmbNmjXodDrWr1/Pfffdx+rVq/vcn0ql6vfH92QlJSWD3iba9HaOcxvncKTzaNiyWQkzyUnMiVi3xdWKsc4YdmOolWnIisvClNJ3/+O5DedyrPNY2LIZCTPITcgdzGn0a6x+lqdSLwdrrJ57TwZSVourjRxdNmXdAlgJElK0KYxPHtfjNo3ORrS1Guy+EzdeMQoj2fFZxKfED1tZx4LBlnMk6mR/ouFvO9pldPvcTO+YxnbLjrDlGYZ05krP5ZXK/zA9dhq72naHvT838VyyTdkjWNL+DeSGdSD1ciCfyRsf7eQPz6zj89e+g1QqGVQ5R8Jo16uhcKacgzA2DXt2LI/Hw4MPPsgVV1zB0qVLI97X6/XodMGnGAsWLMDr9dLa2jrcxTqrTI2ZwlLzYmQSGXKJnCtSLqPY2PNFJUEVz4MF95GsTgbApDLxzYIHBjQAcnrsVJaYFiGTyFBI5FyZcjkTjOOH9FyEM0ucKpabMq8nR5cNgFFu5I7cb5Cjze51G5P6eJ1UBetksjqZB/LvI151agGIIIw2pUzJNenLKTZOAEAn13FX7u1kaTOZHjuVC5IWUO+s54Kk+cglcmQSGUtMi5gaE9mz4GxSUR1sHWps6RjlkgiCcCqGtSUkEAjwwx/+kNzcXG699dYe12lqaiIxMRGJRMKePXvw+/3ExcUNZ7GilsfvodZRR4u7hThFLGmaNJQyZb/bxaviuMi8hPGG8UglEjK1mcQoY3pdP1uXxb15d2H1WDEoDKSqBzawPF4Vz/WZ17LYvBCJREKSKgmpRGSBPpPYvXZqHDV0em2Y1SZS1ClIJH0/gWz3WKmx1+AJeEjVpJKkSgx7v8BQwDfzH6DZ3YJOriVVk0Kzq4VDnYeRSWSkaVKJVcaGbTPeOI4fTXiETo8Ng8KAUWEY6lMVhNNi99qpdtRg89owq82kqJN7/K54/V7KbOU0OBu4KvUKrs24BoPcQMLxoLrrutrsasbWaeeS5IsACYmqhLM+EUN9U7DFvrGlg+Qk4yiXRhCEwRrWK9j27dt55513KCws5KqrrgLgoYceora2FoAbbriBTz75hJdffhmZTIZareaxxx7r96bmbOQP+NnUsplny/4Z6g9/Y8b1LDRf0G/WqhpHLX849Cea3M0ApGlSeSD/PlI0yT0e56uWjTxX9nzoOKuybuSCpPn9HgdALpWT3MN+hejX6enkjZq3WNO4DgCFRM63Cr/JxJgJvW7T7GrmqWPPcLjzCAAGuYHvjnuILF1m2HpxqljiVLFAMJvbY4f+RKsn2CKapc3kvvy7MavNYdvEKGKIUfQeTAvCaOnwdPBq1et83vwlAAqJgu+M+xZFPbQM77Ds5MnSp0Nj8S5InM9VaVeGrdN1XS0pLyEvaWi7t0az1jYbRr2aFkvv4xUFQRi7hjUImTlzJocOHepznVWrVrFq1arhLMYZocHZwL/KXwwbkPty1X8oihlPZj8pGjc2bwwFIBAMSna27SJFc3HEuvXOep4v/3fYcV6qeIXxhnFkaNOH4EyEaFVlrw4FIACegJd/lv2LHxf/oMdgQCKRcMh6OBSAAHR4O/iw7mPuyL2tx6e4gUCA9U2fhwIQgAp7JXvb90cEIYIwVlXYK0MBCIAn4OGf5S/wo6LvY+jWalfnqOPfla+EJQNZ1/w5U+OmEK8SPQL6097hJNlkpN06dgbmC4IwcKKvTJTo8HbiCXjClgUIYPX0nQ3IH/BzsONwxPKjJw0g734cb8Abvg/8dHhEn9uzXZunPWJZk7sZu7fnGwCZTEaNszZieamtFKe/52wlHr+Hwx1HIpaX95B5TRDGqvYersv1zvqwZAoAnV4bbZ62AW0vROqwOUmM02PtFNmPBCEaiSAkSsQr4zHIwzOLqaQqEpWJvWwRJJVIOSd+VsTyabFTe1w/QRGPTh6e7lAtVYf6Jwtnr56SExTo84jtpUuU1+slX58fsXxW/Ex0sp5TaiplSmb3UF8nxhQPsrSCMHrMqsjvygRjETGK8HEL8cp40jVpYcskSDCpTMNavjOB3x/AbneTEKejTbSECEJUEkFIlEhUJXB//r3EKYJN9Ea5kfvz78Ws7v/HanrcNOYlzEVy/L9Fpgt77cefqE7kgW7HiVEYeaDgXtEVRiBTm8EdOd9ALVUDkK5J52tZq9DINb1uU6DP48qUy5FJZABMjZnMgqTz+xz3dU78rFDgLEXKReYlFBlEljUhemRqM7kt+5bQdyVLm8mNmdejlqnD1ktQxXNz1iqSj19ftTINX8++mRxd1oiXOdo4XR4UChk6jZJOm2gJEYRodHan1ogy443j+Enxj7B62jHIDf2mJO3wdFLrrMXn97E8/SouTbkYCcGWjVpHHU2uZtLUqegU4U+lCw0FfLPgPppdLcQr41FIlext34dRbsTpddDmbcesMpGtzx6yc7N57FQ5qmjztJGkSiJLm3nWZ34ZDU3OJhpcjWhkGlLVKWEBhkKq4LykuRQa8nH4nMHWOYU+Yh819lpqHbXITDI8fg+LzYsoNBbg8XtI16ShlmrY176fdo+VJFUi2dqssCxvSeokrk1fyZyEc5FJZP1mcxOEsaarRc+kNuH1e1BIlVjcFtw+F96AD6lEisPnIE4ZR74+l/8p/DZN7mbUEjV+fNQ560nRJOPwOqhz1qOQKkhRpyA3yDloPUQgECBFkxyRNa4/gUCAemc9ze4WDPJg5sOBZFgcizrtLjRqBWq1ktrGyK6igiCMfeIuL8rEKWOJG8APT7OrmWdL/8mBjuAkPcH5Pu7Hj5+fl/w6NPB3euw0bs66MRTQ+AN+trRu4+nSZ/EFfEiRckXqZWxq3UKrq4Xl6cv4sO5jvAEv9+bdxeTYSad9TjaPjdUNn/JO7XsECKCQKLgj9zbOSZh92vsWBq60s4zfHf4DNq8NgAVJ53NN+nKMJ3UhMfXR+na44whPHH0Sy/F+7lNiJlNoKOC16jdCr/P0ubxV807os/5G7q3MSTgntI9aRy1/PvxX6lz1AIwzFHJHzm0kDWCuGkEYCzo9Nt6tfY9SWxmZ2gzWNK4jQIAUVTKLkxfx0vHB6DKJjNuyb2Fu4hzaPO389vBj2H12AO7JvYvXql+n2R2cC2NyzETydHm8dfAdANI1adyXfw+pmoGlUAfY376fPx39K26/GwkSlqdfzUXmJahkqqH/IwwzW1cQopJjs7tHuziCIJwC0R3rDHXAWhIKQAAaXY2sb1rPhqavwjIP7WjbGTZIvcHZyD/K/hnK1uLHz/t1H3Ju/Gw8AS/v133I3MQ5OHwOXqp8hWbXiaxbp6rcXsnbte+GMnJ5Ah5eqPg3Nfaa0963MDAOn4OXK18JBSAA65u+oNxWOeB9OL1OPq5bHQpAAHa378Eb8CI9fqnJ0+fyZs3bYZ/1vyteCg08DwQCfN60IRSAABzqOMw+64HTODtBGFmV9ko+afgvk2Mm8Vnj2lB9n5Uwk1cq/xO6vvoCPp4rf55qew0vVrwUCkAytRlss2wPBSAAe9r34cOHXBJ8dljtqGFzy5YBl8nitvB02XO4/cEb9gAB3qh+i2p79ZCc80izOzyoVQrUKgU2u2u0iyMIwikQQcgZqrSzPGLZAetBOny2iOXVjhMZjKxea+hHqosv4MMf8ANg89pQSoLzhdQ56+nwnH5+dovbErGsw9tJm8gQM2LsXjulPWSgsrhbI1fuhdXbQZm9LGJ5u6c91K3L4/dEvN/h7cR6PPuax+/hQA8Bx7FesrkJwljUFYifnNEwuCw8+6A34MXitlBurwgtS1GnUGmPfADQ6rZgkJ9I8bvPuj8svW9fOr2dPWbisvSwLBo4nG5USjkqpRyHM/LvLAjC2CeCkDPUOGNhxLLpcdOIV0SOI+k+CDJOEYdGFj7QWClVdns/NvS0LlubRawi9rTLmqRKREL4QOV4ZTwJSpGRa6To5XomGiMzUA2mC1ScMpYiQ1HkckUcdm+wziikih4/6/jjiRCUMiUz4qZH7KPIGLlfQRirklTBrIUKSfgEr/6APzRYvYtKqiJRnRg2kWG5vYICQ0GP+23vlip7ZtyMUNKH/hgVMZhV4QlGJEhIVPWdYXGscji7WkJEECII0UoEIWeo8YZxLEiaH7rhKzKO57zEucxJPIc8XXDGXQkSlpqXkKs/MQOvSZ3EvXl3hVKoamUalqVdxZfNGzDKjVyacgkbmjeSqEzkhszrQrNcn45sbRa3ZK8KBTsxCiO3Zn+NZI3IyDVSVDIV12QsJ1Ud7F8uk8hYkXY12dqBZ+lRSBUsMl9IljY4G7oECQuTLgwLamvstdySfXPYZ31L1s2k605MhDkn4ZywgOj8xPNEdiwhqmRqMrgx4zq2tW7nqtQrQvV9T9tevpa9Cq1MC4BWpuXevLtIVadwY8Z1oSCh2dXM9NhpFBxPcS1BwkLThShQ4CfYKj01ZgozewjYexOjMHJn7m2hB0dKqZJv5HydNE3qUJ32iLI73CgUMpSiJUQQopYYmH6GilPGclPm9Sw2LcQX8GFWm9DKgz98d+fdSa2jFqVMSaIigTpHHW3uNlLUyShlSibHTuJnxY/S5mnHqDAgQUKhvgCjwojN28n9+feQpEoasiBBJVcxL34u6Zo0OrydJCjjyRIpKkdchjadR4q+R5OrGbVUhVltjshQ5vF7qHPWY/faSVQlkqhKCHs/T5/LvXl3Ue9sQC6Rk6FLRyPVMN5QiDfgw6Q2oZaoSNek0+ntIEEZT6YuM2wfJrWJ+/LvocHZgEwixaw2n9LAWZvHRp2zPjggWJOMXh6ZyUsQhoNarmZx8iImxU7C7XMzM34Gbr8btVSNx+/hB0UP4/K5iFEYSVIn0eRswuazc3feHaEHR76Aj5uzbsLr96KUKjCrzVRUV/Kz4p8QIIBZZeozPTYEW14anA20edqJVcSSq8/lf4t/RIu7Fb1Mh0ltQiqJzmeRDpcHleJ4dyyXGJguCNFIBCFnMJVMRaYuI2xZtb2Gx48+Qb0zOPB3Rtx0VFIVG1s2cVnKJVyacjE6uY5EdSKJ6hPN9Ce65Qx9hiKP38OXrV/xYsVL+AI+NDIND+TfS3Evc5kIw8eoMEZkw+ri8Dr5tPFT3qgODiw3yPV8q+AB8g0nJiSstlfz+JEnqHc1AHB+4jyuSV9Bxkn1sMCQ12c5tHINOaeRArrR2cQ/y59n//HxJYWGAm7PuW1A8+oIwlCQSWQDylx1qOMwfzryF2xeG1KkrEhfxrGOY+xo3wXAItOFXJ12JUqZEq/dQ4EucgLQnvgDfra2bufp0mfxBDwopUruyr2dGXHTiVPGnc6pjQlOpweFUoZCLsPnC+D1+ZHLojOgEoSzlfjGnkW8fi+rG/4bCkAAtlt2YFabkCDh/boPqehhMORwq3XU8nz5i6EBlg6fg6dKn6G1hwHrwuipclTxevVboUw/Hd5Onit7PpScwOv38lH96lAAAvBF84ZRGVS+u31PKACBYOrgba3bR7wcgtCXdo+VZ0qfC2Wl8+Pnteo3yDHkhNb5rHEtZT0kjehPvbOBp0ufCQ2Od/vd/L30WRpcjUNS9tHmdHtQyuVIJBJUSjlO0SVLEKKOCELOInafg73t+yOWW9xt6I531Wp2tUS8P9xa3JbQjW2Xdk87Vo+YgGosaemhblQ7a+j0BoMQu8/O/vbIzFZVjpFPAbqvLbKe727fQyAQ6GFtQRgdHZ4OGnsICk7OIlfvbIhYpz9tnraITFwuv4s2d9ug9zUWORweFIrgLYxaJcfpEkGIIEQbEYScRbQyDZNiIjMgxSljsR3PXnRyH/+REK+Mi8iYFKuIxagQs2SPJQk91I10dVpo1nStTMvEHupXhiY9YtlwmxgbWY4pMZORSCQ9rC0Io8OgMGBSRXYRPDmrVrJ68OPvYhWxEftRSVXEKs+M66rT5UGhCPYoF4PTBSE6iSDkLCKXyrnIvCSUAQmCKR4bnA0ECHBFymWhzEYjKU2Tyi3ZN4dSTWplGu7MvZ34M6Df8pkkQ5PByvTloYDRIDdwa84toQHfcqmci5OXkqxODm0zP/F88vV9j/8YDlNjJjPReGJM0ThDIbPiZ4x4OQShLzEKI3fk3oZOHsxGKEXKtekrKO08Md/OYtNCcnU5ve2iV8lqM3fmfiOUmUspVXJn7u0RaXqjVXBgevA3Q6WQ43SLIEQQoo0YmH6WSdOm8VDhN6lx1KGSKUlUJtLqbuWSlItJVplRyoI/WE3OpmB2LLkRiQTaPR3EK+Nw+Z3YvQ4SVYmhJ2oun4sGZwO+gD8sC9dAdB2n2DiB/1f8Ezq8ncQr4zENYn4KYWRo5GqWmpdQaCik02vDrDZFpPdM16bxzfz7aHA1opAoSNOkopFpqLRX4fV7MamTesxSZXFbaHa1opdrMavNp52xJ0mdxL15d1PnrEciAbPaLLJjCWNG13VPJVWjkqj47riHsLgt6GU61FINxcYJXJF6KUgkyJBh8bShkg48Q1wgEKDR1USiMpEfFn0fp89JrCI2OP7vDGkNdDk9KGKDwZtSKcfl8vazhSAIY40IQs4ytY46ni59llJbGRIkLDEv4vLUy4jplhFpT9te/nbs79h9djQyDVemXs4h62FSNSmsbvgUb8BLojKBBwruwyg38kbNW3zZvAGACcYivp59M+YBdB/Y1baHp479HbvPcbz14w6mxoouM2OV0+fki+YNvFL5Kt6AlwRlAg/k30OO/sRT2sMdR3ih4iUq7ZVIkHBD5nXUOepZ17SeAAHydXncnnsbKZoTrSXHOkt5/MgTWDwWFBI5N2XdwLyEuaGA+FTpFDryFSPfCiMIfQle957G7rOjlWm4KfNGPqn/L5WOShQSBZekXMSetr3MjJ9Bs6uZdU2fI0HChaYLmGOY3e/+vX4vWy3b+UfZP3H73RjkBu7Pv+eMm3fJ6faiPN4SolTIxJgQQYhCojvWWcQX8PFpw2eU2oJN/QECrG74NCx7UaOziSeOPRWaFd3hc/BWzTvMTpjFh/Uf4z0+0LHZ3cK/yl+g2l4VCkAADlhL2Ni8ud+yNDgb+NvRp7D7HEBw0Pzfjj11xmRuORNVO6p5seKlUB1ocbfwXPnzocw+Lp+LtY3rqDyeYU2CBIfPwdqmdaHEA0dtx1jTuBZ/IDjhWoeng2dLn8PiCWZC8wS8/LP8BaodNSN9eoIw7E5c94LXV7vPwfMVLzI5diIAnoCHd2vfZ0bcdF6vfjM0DitAgDWNa2mUNfV7jFpnHX8/9gxuf3DujA5vB08ce+qMyzYYHBMSDEIUChlO0RIiCFFHBCFnEbvXzs623RHLy20n0vJaPBYcxwODLm6/m87jaVi7K7WV0e7tiFi+vW0HLp+rz7JY3G04/c6wZS6/C4vrzPqhPJM0OSOzY1XYK7F6gnWg1d3KwY5DofdiFMYes63tatsdugmzejqocdZGrNPsah6qYgvCmNHbde/kxBxdAYTbFz4JX4Wj/xTqre7W0KzqXdo97WdMVqwuLrc3FISIlhBBiE4iCDmLaGTBmatPlt6tX79RbgwNZOwik8hCKXy7S1WnoD8+oLK7YuOEiH2czKgwRmRukUvkvU6UJ4y+OGVsxLJklTk0qNYoN5KtzQ69Z/V29JhcoMg4HrVUDYBeriNRGZl1K7aHYwlCtOvtundyF9Tug8m7S1OHj8HqSawiNiKo0cl1GBSGUynymOVye1HKu1pC5LjEwHRBiDoiCDmLyKVyLkm+mARlfGjZzNjpYdmLzGoTt+V8PZSpSiaRcUXKZRywHuT8xHmh9bQyDbfm3EK6Np0iw/jQ8hRVMvOTzut3XEey2sxtObeEHee2nFvCxgoIY0umNoMrUi4NvdbINNyWcwvG4zc3OoWOi5KXhAIPX8CHSqpiSsyk0DZJykSWmBcjlwaHo8UoY/hG7q2hoESChKtTrxyVtL6CMNx6uu5dn3EtO1p3AsH6v9B0Afus+1lkWkiD80T31Kkxk0kJ9D8De6o6hRszrw8FIgqJgjtyvkGSKnEYzmj0dG8JkcukuNyiO5YgRJthHZheV1fH9773PVpaWpBIJFx77bXccsstYesEAgF+8YtfsH79etRqNb/+9a8pLo7M8X+2sXltNLtaUMmUaKU6Wj2t6GRakk4za1SmLoMfFT1CnbMepVSJUWGkwdWI1WMlXZuOXCpndvxMMrXptLosxChjkAYkWDxtJCjjWWi6EJvPjlmVhEkdzG9/X/491Dnr8Pl9pGiSB/QUWyqRck7CbDK1mVjcFhKUCcilMirtVcQp40ID5X0BH43ORjx+L0mqRDRyzWmdvxCu1W2h3dNOjCKm35TIWrmWy1IuY4JxAh3eDpLVyWTpwlM6jzeO4zsF36LZ3YJcIsesMnFB0nxqnXV4/B6SNcnEK+NodjbT6bMRp4xjgrGIn058lCZXEwa5gRR1MirZwDMB9cbj99DobCQAmFRJpz3QXRD64vV7aXQ14fV7UEiVuP1uVFIV3oAHiUSKx+8hXhkfuu61ulvRyrT4Aj5uy/06Nq8NtVSNnwBKqYJUTQoOn4N5iXOQS+WkqVOpPFoJaZHH9gf8NLmacPpcJKjiudC0gPGGQjq8nRjkBnwBH61uyxmV9tzt9qKQd++OJYIQQYg2wxqEyGQyvv/971NcXExnZycrVqxg3rx55Ofnh9b5/PPPKS8vZ/Xq1ezevZv//d//5bXXXhvOYo15NfZani77B2W2MpRSJUvNiznUcZhqRw23ZN/MrLgZoSfJpyJeFU+8Kp6jHcf4y9EnqLRXoZQquSr1ChYknY9BYSBNk0aa5sSvXTq9P5k2KPQYFAWDLodMIiNdm0aKJpmdll38o+xf2Hw2kpRJ3J1/BynqZD5rXMs7Ne/hDXgpMo7nlqybT+mchUj72w/wVOkzoSDkrtzbKY6Z0Ov6No+dja0bea3qTZx+J6nqFG7NvoVC44nPvt7RwEtV/2G/9UCoFW2JeREFhuB33hfwsa11e+izNqmSuDvvTvL0uac0IVtvWl2tvFP7PuubPgdgbsIcVqRf3eOEi4Jwujo8Hayu/4wP6j/EF/Ax3jCOAn0+9Y56imKKeL36Dew+B8kqM3fn30mOLpt0bQ/RxEnUMjVx/QQOLp+LL5u/4pWqV3H73WRrs7gj9zYydZnsa9/P7w4/RrvHSqwilrvybmeCsWioTntUhbWEyGW4xJgQQYg6w9ody2QyhVo19Ho9ubm5NDQ0hK3z2WefcfXVVyORSJg6dSpWq5XGxrM3Q5Lb5+b16jcpO57Byu13837dh0yOnYTD5+CpY09TMwSZgzo9nbxZ8zaV9qrQcV6rfoNjnaWnve/BqnXU8cSxp7D5glmWmtxNPHXsaY50HOWN6rdC2ZhKrAf5pP6/KJSKvnYnDECjs5HHjz5Bu6cdCA5c/cvRJ2h09p59p8xexgsVL4UG1tY66/h31cu0uYL78Af8fNa4hv3WA0Aw4Hi79l2O2U7UqVpHHX89+mTos250NfHksb/T7rEO6fnts+4PpQUOEGBDy1c9JmUQhKFwtPMY79a9hy/gA+BgxyHaPG3kGfJ4oeLfoSyA9a4Gnj72LB09JPo4VRX2Sp6veDE0mL3cXsF/ql6nwdHA40eeCH232jxtPH7kCZr6+I5HE1e3lhCFXCZmTBeEKDRiY0Kqq6spKSlhypQpYcsbGhpITj4xDiA5OTkiUDmbWL0d7G7fE7Hc4w9eYAMEaHSefuYgi6eNA9aSiOUNrpH/gWp2NYd+vLt0eDupsEdmgtlu2QGiR9Zpa3G3RmRBs/sctLpbe92mpwCl3FZBqye4Tae3k22W7RHrlNnKQ/9udjVHZO5pdDVhGeL0oTssOyOWbWndSiAQGNLjCAIQFmh3KbEGM8V1pafuUuOspc3TNmTHbnRGPrTb274Pq9cakYnL7rPT0sd3PJp4PL6wFL1iTIggRJ8RmazQZrPx4IMP8oMf/AC9/vRnLXa5XJSURN5A98XpdA56m9Gg1CtJ16RTYa8IWy6XnPioFB55j+cymHPUJGhJ06RGzMdglBlG/O8ki5dFLkNGgjw+Ynm2JhNPp2dUPsuior67MZxKvRysIavHccHucN2DP5lEht/up6Sm5/3rkyK/u4nKBHAFKKkqQaFWkK3JjpiPIF4SFypzT5+1TqbDbXVRUjk0fzuZTEa2OoedhLd85GvyOXr0KF5v5M1KtFwfTi7nWKiT/YmGv+3pljExJnLQd5o2FZkk8jlfjMKIs81BScXgj9dTOZVxkeOnUjWp4JZGfMflEjkBe6DX7/hQcDqd/a4zkHrZ12fi9wfwen3U1dQgkUjotLbjcHrHXD2LhrrfnzPlHISxadiDEI/Hw4MPPsgVV1zB0qVLI943m83U19eHXtfX12M29903XKVS9fvje7KSkpJBbzNaVsXewO8P/TH0FGtKzGRqHMG5FC5NvphxiYVokiObAwZ7jtcpV/KXo3/D5Q/O6TEzbgZ5hhxMSaYhOIuBc/lcXO25irdr3wGCN8O35nyNHF0Ok9smsad9LxBMM7k8cxnOSseY/CxPpV4O1lDVY6/fyyrpjTxf/iIBAkiQcHPWTYxLKkSWHBkoADQ7WzkvYS5ftnwFBLPu3Jh5PbnxuXB8qMVVtis4bDtCpzfY3aTYWERxwgQSUoMrBD/rK3m79l3gRFa0wvhC6D/xz4AZHEa2tG8JfW/MKhPnmecEb856EC3Xh8GWcyTqZH+i4W97umVMcpkY3z4uNE+OQW6gyDCe/e0HWGxaxKeNnwHH63v21ymIK4D+s+0OqJxWj5Xz7HP5sjn4vVRJVdySvYocXRY3Zd7ACxX/Dn3Hv5Z1E4VJBb1+x4fCQG5YB1Iv+/pMnC4PcoWMrOwsAOotfuoa28dcPYuGut+fM+UchLFpWIOQQCDAD3/4Q3Jzc7n11lt7XGfhwoW8+OKLXHbZZezevRuDwYDJNLI3wWNNoaGA/y3+MQ3OBjQyDTq5liZXMxeZl6CX66h21GBUGDGrB/538vq9NLuakUgkxMhjaPVYSFGn8Mj471HrrEMn05KpzSBeFdn6MBQcPgetLgtqmSpicLBKpuLSlIuYHDsRq8dKkiqRFE0KMomMO3O/QY2jFpffRYo6GZPaRAnignK65FI55yfOI0+Xg9XTgVFhJPX437w7l89Flb2aAAHS1Wlcm3EN5yedh9PvJFYeS6Y2I2z9bF0WP5nwI+qcdXidHgoSCkMpfKHrs76YybGTwj7roZaiSea74x4KBSGpmtQzKjOQMLYkqhK4L/9uahy1+Pw+VDI1Hr+bIsM4nD4X02On4g64iVXEopIq6fB0YlAMvleAQqmg3tlAIBAgSZWIXBqcW+nGzOtZkDQfm9dOstoU+k7NTzqPPH0uFreFeGV8j9/xaOTuNkcIiO5YghCthjUI2b59O++88w6FhYVcddVVADz00EPU1gZvDG644QYWLFjA+vXrWbJkCRqNhl/+8pfDWaSokaJJDpszI12bzr72/Tx1+GkaXI0kKhNYlXUT0+Km9LGXoFaXhQ/qPmJN41okEgkXJM3H5rOzw7KT5WlXMz/p/B4nIxwqtY46nq94kRLrQXQyHauybmBW/EwU0hMDzFUyFXn63IhtDQoD4xXjhq1sZ7NSWxnPlD5Ho6sRk8rEHbm3UWg4kemq2l7Duqb1rG1cjz/gZ17iXGbFz+Tvpc/Q6e2kUJ/PLdlfi8jyY1InYVInUVJSgjE5coK03j7roRanjOs3s5AgDBWjwohKquKrlk28UvkqTr+TCcYiCvUF7LPu55r05Tx+9K+0uFtJU6dye+5t5OpzBrz/dreVbdIdrN77KYFAgIWmC7gs5RLiVfHo5Lqw724XhVRBti6LbF3WUJ7qqOueGQuCKXpFECII0WdYg5CZM2dy6NChPteRSCT85Cc/Gc5inBFq7LX8vfTZUDajZncLT5U+zffHf7ffH5hd7btD3QEIwGeNa7kq9QoCgQCvVL1KuiaNSbETh6Xcbp+bV6tep8R6EACbz8ZTpc9gUpvCJkkURlaTs4k/HfkLNm9XlqpG/njkcX424VES1cH+7Yc6DvPfhs9C23zR/CUGuR5/IDiw/HDnUV6seIlvFt6PRiayBQhCma2cf5Y/H3p9wFqCTqbD4/fwds27JKuTaXG3UuOs5S9H/8ajE35IrDJmQPs+YC3ho6ZPQq8/bVxDmjaVhaYLh/w8xjqX24tS0W2cpFyGU6ToFYSoI2ZMjxJNrqZQANLF4XOEzajbE3/Az1fNGyOWl9sqSD3eZH+089jQFfQk7Z52dvWQGrXecfZmQBsLmt0toQCki81ro9ndEnq9r31/xHaHO4+EBb0lHQdpc7dHrCcIZ6Ou7n/d7bPup9BQwMGOQ2EtgC3uFlq6fd/6s8OyI2LZV82bIjILng3cnhPpeUF0xxKEaCWCkCihl+vDMmQBSJCEZhbvjVQi7bHFIUmViMXdBhCa+Xw4aGQaTKrI/XcfJyCMPL1Mh/Skr78UKXq5LvQ6rYdB3Ga1mRbXiRunOEUcWtEKIggAxCoiWzWS1WaaXS1h11wApVSJTjbwbrDZuuyIZXn63DNijMdgud2+sO5YCrkMtwhCBCHqiCAkSmTqMliednXYsstSLiFDk9HzBt2clziXWEVs6HWiMgG9Qo/VayVHm02hPr/3jU+TXqHna9mrwn4oZ8ROJ0ubOWzHFPqXrEnm2oxrwpZdl7GSZPWJcUiTYyeRqDyRejRGYWSCYTwNrmDrm0wi4+s5XyNmgN1JBOFMl6PLYYLhRCYhpVTJ3IQ57LceYFnaVWxp3Rp6b1XWjYN6ADQtbipJJ30fz0ucNzQFjzIujxeF/MTti1Ihx+U++1qEBCHajcg8IcLpU0qVXJA0nyxdJs2uFhKU8SQokmhwtBLn8yKVgSxOhsvnwu6z4/F7iVfG0e6xopKpeGT896iwVyCRSElWmen0dvLI+Icxq0x4Am7a3G3EKmNDx7N5bXR6O9HJdejlA8/i4g/4aXG3Bltp5EYsnjZSVSn8tPhR6p31aGU6MrXpGERLyKhSSBVcaFpAri6HFncLicpEMnUZyKUnLgmFhgIezL+PWmcdAfykqFNIVCXy3cKH6PB2kKJJIVObQbu7nTZPOzq5jkRVAja3jQZXI+o0dWhfra5WvAEf8co45FI5jXYLnV4bccoY4tSiLgjRy+fzUWmvAQmopSquz1hJu9eK0+8iVh6DK+DmB+O/R5LKxP+MS6LN3U6iKoEMbTodng7aPO1oZGoCgFqmJkZhxOv30uq2oJDKiVPG4Q/4UUgV3J15B50SG34CpGvSMKmTaHVb8B6/3nf//p7J3G4f8pO6Y7k9oiVEEKLN2XHFOkPoFDomxhQDsKNlH3+s+BMNrgbydXnMTTyXl6r+w4T2IiYYi3i16nXOiZ9FkiqJD+s/Zn7i+UAApVTJnrY9fNWyCbVUzSUpF7G3fR/1zga+lnUTU2InU2Wv5vmKFymzlZOpzeCWrJvJN/Q/iLzN3cZnDWv5qP4TpBIpS82LqXXUcbDzENdnrGR23CzUcnW/+xFGRo2jlpcqX6HcXkG2NouvZa8K67Pe4elgn3U/79S+hz/gZ3na1SRrzLxQ8RKt7lamxkzhspRLeKXqVY7ZSklWm7k9+zbWNq1jU+sW1FI1V6ZeTrLazFOlz+Dyu1iYdAHTYqfzXPm/aHI3kqct4Nr0lYyPFUkKhOhTY6tni2UzH9evxhfwMi9xLr6AH4NcT7YumyeOPUWbp43pcdNYmb4iLIPVYeuRsO/ONenLWVO/jqvSr+DL5g1saN6ITq7l9pxvcLTzKB/Xr0YqkXJV6pXMT5qHUqpkQ/NGXq58BZvXzrzEuVyZejkmddIo/kVGhst90pgQuRgTIgjRSHTHikJH2it5quxvNLiCg7uP2o6xuuFT5iScw572vexq280EYxEbWzfT7G7BpEpiTdNaYpWxuAMevmjegC/gw+az8Xr1m0yOmUi7p52/HP0bpZ2l/PnIXyizlQNQaa/iD0f+TLOzud9y7Wrbzbt17+MJeHD5XbxX9wGZugycPifPlv2TUlvpcP5ZhEFocbXwx8OPU26vAKDcXsGfjjweNt6jpOMQr1a/jsvvwhPw4A14efzIE7S6W4Fg1rU3a94OZcYq0o1jq2UbG1o2hurXy1X/we6zY/fZ8QV8/LfxM3a0bccbcANwzH6E5yv/RaPdgiBEm2O2o7xd+y5OvxNPwMu6ps8xKAzo5Xr+duwpLB4LAQJst+zg9eo3cPuC9b7R2cSz5c9x7Pg1sd7ZwNOl/2CR+UI+b/6SL5o34MePN+DjSOcR3qv7IHRdfbX6NQ52HKLMVs7fS5+hw9uJHz9fNH/JmsY1oex1ZzK3x4u8W3cshUKMCRGEaCSCkChU72wIzabefVnXnAgHOw6F8s/vbttDkXE8AE6fkz1teyL21+JuxSDXEyBAjaMOi6ct7P1ObyeN7qY+y+Tz+/iieUPE8nJbBanqYBauY7aygZ2gMOyaXM1YvdawZe0eK02uE8Hm9tbtYe97Ah78hN/glHQcDNW12Ymz2dZDBp9KezUm1Ymns3vb91FoKAy9rnHW0ODsu34Jwli0z7ovYtlB60ECx//rbrtlJ23Hr61NrkbqneEZAl1+F3a/g93drtE5uuzQLOzdbWvdTqs7MnD/svkrrB5rxPIzjdvtDe+OJZfi8foIBAJ9bCUIwlgjgpAo1NMYDYVEEXoCFqMwhtKvJqoSQj98UomUJFVkU71ersfpcx7/d2TWJAmSfrO4SCVSMrSRg+S7xqV0/VsYG7RyLRIkYcskSNB2m7QyTRM+CWFPWXi617VObyeJqoSIdeIUMVg9HaHXiarEUJ0EUElVaOUiw5YQfUwqc8SyRFVCxDUUIE4Zi0oW7I6qkWlRSBQR66ilKhKVJ75DFndb2OsuaZo0VFJlxHKz2oxKphrUOUQjt8cX1h1LKpUilUpxe8TgdEGIJiIIiULZujTmxp0ftmxp8mK2tG5DgoSl5iVsatmCXCLnvMR57Grbg1qqxqAwMD1uGspuP17JajNevxdPwEuRYRy5+lxWpF8dtu8rUi4j5XhrRm+6ZmLXyU6keI1VxBKjiMHqtZKuSaNgGLNwCYOTok7mqtQrw5YtS7uSlG7ZsWbETSOuW1a1FlcrU2OnhF5LkLAibRmbWrYA8NSxZ7gs+ZKwm6sUdTJxyrhQy51SqmRewlwOdRwOrXN1ygpy9OEBjyBEg4kxE4lTnHi4opVpydXlUu+sp9g4IbRcgoSvZa0KpVTP1GawLO2qsH2dlziXY52lLDEvCqVjr3PWMd4wLuy6GqeIZUbcNDJ1mWR3yzIol8hZmb7irJg49OTuWCBmTReEaCQGpg8Rl89Fp7cTrVw77D8CcWojy9KuYkrsVNo9bZjViShkMkyyJFL0KXj8Hr6WfRMJygTsXjt35d5BnCKWJlczMomU/xn3bSzuNhRSBXGKGBqcjTxU+E0ytRnEKeNYZFpIni6fRlcziap4cnRZKGWRT91Olq3L4scTHqHKXo1UIiVeGU+Tq4lvFjxAljaDhB6ekgujQyFVcHHyEsYZCmh2t5CoTCBHn41CeiKASNOm8YOih6m0V+EP+MnQpqOVaam0V9Hh7SRZbSZBGU+sMo4mVxOxihiyNVk8VPhN6pz1KKUKUjWpxCiMPFhwP16/hzRNGkq03J1zP1aPFZMqiXx9NlKpeB4ijG3+gD903eya52i8sYD78++jxlGNBIhVxtLuaWde4jx0khhakuuxe+0kq5PJ0KaH9iWXypmfdB4Z2nQaXU0YFUb0Mh3+gJ9MbQY/mfAjap21qKQqsnSZjDeOo8pejcftoSAuP5Ta98GCB6i0V+Lyu0jVpJKhSe+p6Gccl9uLXBbeMqtUyEWGLEGIMiIIGQKV9iper3qD/dYS8nS5XJ95baif/HAxaeMwacO7N5XUwrjUwl62gEIKelye2y0jEkCZpYP/27CXTdVVzEhN45Hz4phkGtikWimaFFI0J1pNhvvvIJy6Rlcj79V9wKGOw4w3jOO6jGvI6jYbOgQnsjx5LoNJJ80LMkU5KfTvuo4OPtzfzL927yVGpeb75yWxODeWGXGJYducXHcFYSxrcbXwacNaPmtcg16u54bM65gSMwmlTEm+IYd8Q/A65/Z6WV9Rzl1frKPJbuP6iZO5dcp00vWRc+kYFAYmx06KWA5gVBrJ1IV3b03RpFBSUoIp5cT3MUEVT4IqfuhONEoEx4T00BLiEkGIIEQT8fjxNLV72vnr0SfZ3b4Xb8DLoc7DPHb4jzQNIJvUWNRo6+Tu99/hi8oKPH4/m6qruP3dt6nt6Oh/YyFqtLot/OHw4xywluAL+NhvPcAfjzyOpYfBroPx9qEDPLFtCzaPh9rODh78+H1219cNUakFYeQFAgE+b9rAh/Uf4fK7aHG38JejT4QyCHa3r6mRuz94h0prOw6vl+d27eDFvbvwiwHTQyqYojf89kWhkOFye0apRIIgnIoBByGrV69m6dKlzJgxg+nTpzNt2jSmT58+nGWLCk3OZuqd9WHLOrydNB6fVTraVLa3U90Rnl2lyW6jok2kUD2TNDobsXjCP9NWt4XG08hS1epw8NLeyOxr2+pqT3mfgjDarF4r65rWRywv7SHb38HmJk4ON17Zv5dGW+cwle7s1Ft3LDEmRBCiy4C7Y/32t7/lySefJC9PTCrWnVqmRoo0InVptA4O1CuVSCWSiCd3etWZn3HlbKKRqZEgCUsjKkGCWnbqk0mq5XLSjUZqTgpiTVpdL1sIwtinlCpJUMaHZXQDQuNCuotRRX5/UvQGNPLITFjCqXO5vKhU4bcvCtEdSxCizoBbQhISEkQA0oNktZmr0sKzDF2YdAEpmuRethjbcmLjuHvG7LBlt06ZTl6s6MN/JklWJ3Nx8tKwZZckXxSWHWuwtAoF3zpnLopug8wzjDHMTBWZr4TopZFpuCZ9eViKarPKTH4P2f4mJ5sZF38iAYdMIuGR8xYQoz714F6I5OohO5aYNV0Qok+/LSGrV68GYOLEiXzrW99i8eLFKJUnMiUtXbq0t03PCnKpnCXmRRQa8mlyNhGviidbmzXiLSEWhx2/0YDb60Up7/ljtTgcAMRpei+bSi7njukzmZOeQbXVSqrRwKQkM1pl/9mxulg9HUglkh7nMxHGBpVMxeUplzIxppgWVwsJqgSytdk9ZkFrczjwEyBe039ygllp6bxz/SpaXW34fQGyjCYyYmP73c7n99PisKNXqtAqgk+NW+12pFIJserobFUUzhzjjeP4cdEPqLBXoZaqydNnk6ROirimZhhjeebKZextbMDmdlOUaCJeq8UfCCCVSPo6RIR2dzsKqVLModMDt9uLUhH+O6dUyHCKIEQQokq/QcjatWtD/9ZoNGzYED4r9tkehADo5FomGIvAWDQqx19fXsaft2yksr2NpXkF3DhpCsVJJzKotDudfHz0CH/dtgmZRMo3z5nD4tw89Mqeu1jFqNXMy8zq8b2+dHo62WbZznu1HyCTyFiWdhVTY6eikYungGORXqFnYkxxr+93ulysKS/lD5u+wuv3c++sc7gkv6DPoMDqaeeYexv/bfwUnVzHSsMKkv26sNS/Jytvs/Cv3Tt5//BBCuIT+d7c89nf1MDftm9BJZPz0LnzuCA7B90gAmFBGEpNNjsfH2zgX7v3k6DR8tC58Ti8Lfzmqy+QSiR8c/ZcFufmYVCpSDPGkGowsqW2mp+s+4yK9jZWFBVz46QpZMREZsk6WaurlS9bvuLThjXEymNYmbGCIuN45FKRzLKLq4fsWAoxT4ggRJ1+r2q/+tWvANi+fTszZswIe2/79u3DUyphwLbX1nD3B+/i8gUvvi/v24PF6eA3iy4KjeP4sqqCR9asDm3z0OqPeOaKZSzMye1xn6dqn3U/z5U/H3r9ZOnTPFT4TabETh7S4wgjY2tdDd/65MPQ6x+u+S8GpZLLC8f3us2mli28Wv06ABZPG3888jiPFH2PcYaeU0c7PB5+s+ELPj52JPi6sZ71lWX8afPG0DoPfPw+/7xqBfOzsofgrARh8N48uJ/ffvUlAI02G3e+/zbfnXs+1dbg+Kfv/Pcj/n751SzODXZZPtjSzC1vv4HbF5zB+6kdW2lzOfnZBYtQnDSgurtAIMDnzRt4q+ZtIJh98feH/8iPJjxCvl50h+4SzI4V/ndUyGW4xZgQQYgqAx4T8vOf/3xAy4SRdaS1JRSAdFl97Cjl7W0A+AMBXtq7O2K7dw6VDGk5fH4faxrWRSzf3Lp1SI8jjJwPDh+KWPbvvbvx+f09rA0dnk4+bfwsbFmAAEc6jvZ6jNqODj45HoAAzEhJZV15ZNah1aVHIpYJwkhosdt5Yc+usGUBoMVhD3UdBHj74IHQv4+0NIcCkC6vH9hHfWffqc7bPVY+a1hz0rECVNgqTq3wZ6jgjOnhQYhcLlL0CkK06bclZOfOnezcuZPW1laee+650PLOzk58J11khZHXU9YVvVKJ8vjTNqlEQrrBGLFOmiEys8vpkEqkwUmzTspEmaAUA9qjVbI+so6kGY299m1XSOXEKGJocoXPkdPX2CClTIZOqaTT7QbA6nIR38OYpWSdGF8kjA6lTEaSVkd9Z/jFTS2X4+n2G5hqPPF96R6cdIlRq/tsBQFQSBUYFAas3vAMc1rZwCaLPVu43b4eW0LEmBBBiC79toR4PB7sdjs+nw+bzRb6n16v589//vNIlFHoQ7HJzITEpLBlD86eQ2HCiRmqb5g0BZXsRLypVyq5tGDckJZDIpGwyLwQheTEj69GpmFm3MwhPY4wci7JL0DfbRyGSibnpklTkPQShKhlapanXY2EE+/HKmIpNBT0eoyMmBi+N/f80OvdDfUszc0PBdEARpWKRTmiK4owOgwqFd+ZMy8s+E7R65EiwXO8VVCnUHBFwYkxgROSTBQnmsL288PzFvQY2Henk2u5NuOasO9QgjKBXH3OUJzKGcPtiZysUC6X4nSJlhBBiCb9toTMnj2b2bNns2zZMtLSRKrN4WBzuwBJxMDbdqcTlVyOupdsVx6fj0Stht8tuZgqqwVXwE2MwsDEboPS7R4PeXHxvHPdjVjdNpBAjEKH2WDE7najVSppdzpRy+Wouh3H5/djdbkwqFTIu6VcdXm9OL3eHlNO5uly+fGEH3Cs8xhSiYx8fS7p2vTT/OsIg9HsaEMrV6NVDCwZgNfvp6OHzxmCAe5r19zArvo6vAE/U5NTKE4yYfc4sXudJGpigWCXP6vLiU6hDGYRmvADjnSUopIoGR9TQIomhTanA7fPh+l4i4bb68Xu9RCjUnP1uCJy4+IpaWokxWBgWnIKr6+8gd31dShkMqYmp4QF1YIwnLrqs1auCGUanJOeyWvXXM+OulqMKhVTzCl4/D4mmcxIJJBujGFcYlKonidq1fztykuobuukw+MgQRVDUVJSP0cOmmicwA+Lvk+ZrRydXEeePhez2jycpxx13J7IlhClQoZTjAkRhKjSbxBy99139/n+k08+2et7jzzyCOvWrSMhIYH3338/4v3Nmzdz7733kp4evFFdsmQJ999/f39FOmN0ulx8XlnOX7duRiaRcO+sczk/M4sOt4v3Dx/ipb27yYqJ5b7Z5zIjJTXsCXRJUxPP7txGtdXK/yyYxC73ahrdDUyLmUmG91y0XgUbq6t4fMsmFudmUZAaYG3Lf5EiZVHSUt7e4UQr0xAgwJslB8KOc8zSygu7d7Guoox5GVl8feo0ChMS2VlXx9+2beJIayvXFBVz9fgJpBlPdPWSSCRk6TLJ0mWOxp/zrFZmrWFjyyZ2WrdhUiZzsfliJsX33dp1pKWZf+3ZyRcVFczPyuaWydPIT0gIW2dcYiLjEk8EAHtbD/Fxw8c0uuuZFjOTGTGzeGN/KZ8cPcrMlDRunjyFLbXNvHOoFr1CwT2z0tnnO8qT27fQ4XZxffFkZqel8czO7eyqr+OSvEKumziJuRmZzM04UW9SDEYmmsSNlzCyytss/GffXj48ephpKSncMW0WxSYTh1qa2FxTxbuHD2FUKknW66myWvn33t3IJBLumXUODZ2dvLRvF5dPNHPUvYMpMRPZbttJg7OR+Unnk+U/Bw0J/ZZBLpVTYMinwBA5D4kQ5PH4UCgigxCXaAkRhKjSbxBy2223AcH5Qpqbm7nyyuDEfB988AEJCX1fUJcvX86qVat4+OGHe11n5syZPPXUU4Mp8xljU00V9390Iji798N3+ceVy9lRW8Nftm0GoLy9jY01Vbxx7Y2htLs1Viu3vvsGjTYbzy5bwtMVf8XpdwLwSeOHtHvaWBB7Gbe9+xZauZy7z8viheoT43meq3yG23Lu5pP9Fl7Zvzd0nE011bx57Y1899OPONDUBMAr+/ewuaaKv15yOaveehWHN/ik6febNtBg6+TH8y/st5+zMLwcHicf1n/IlrZNADS6GjlqO8xD0u8yLja7x22a7Dbu/+g9jrS2AsEB51trqnlx+UoSe5nh/FBbGX8p/XNYXbO4LWypUVFlbafT7SbFYOCJ43UXYGFLC7/Z8HloXvaff7GO78+bz7aaGmo7O3hqx1YOtTbz+MWXixS8wqiyud38dP1a1lcEEyNUWdv5qrKSD268mTVlZfxx81cAyKVS9jU1hrJlAdz34Xs8PPd85ubF8WbTP7ky9XJeqvoPbn9wrNPr1W/Q7m7jhqzrwiY9FE5NTwPTFQo51g7HKJVIEIRT0e+YkK7uWDt27OCPf/wjCxcuZOHChfz+979n27ZtfW47a9YsYgaQF/1s9fLePRHL3ijZx1c1VWHL3D4fh5qbQq9LLa002mwAOCSW0E1hly2WTdj8wYGNy8ZPYFv7Rk62u307nSdlEnH5vJS3t4YCkC5lbRZK2yyhAKTLK/v3UtfRd7YXYfjV2BvZ2rY5bJnT76TGUdPrNuUWSygA6XK4tYXytrZet6l21ETUtW1tm1lSEOymOT8riw+OnMiolaDRUG1tDwUgXV4v2cdNk6eEXq8rL6PS2t7rcQVhJFS2t4cCkC7NDjuNNhuvHn9YAzDZlMzGqqqTN2d7XS0qtR1fwIc/4A8FIF3WNK2j1d0asZ0weMGB6T3MEyK6YwlCVBnw7EcOh4OqqioyMjIAqKqqwuE4/acOu3bt4sorr8RkMvHwww9TUND7INYuLpeLkpLBpZh1Op2D3mY4qVQq4noYVxGn0kRkYQGQ+Hyh8ntlJy6+8h4+QqVUiSQQ7LrV7naRKI18sq2T63F5I5uuFZKe41JFD4ORNXIF1rY2SurqetxmuIzWZ1lU1PdklKdSLwerp3OXJKpQSVURAYIMea/l8Sh7njzQ43D0uo08tue65vQGMwTZPd6wgexun6/H8UxGpSqUDQtAIZXi6OykpKk5Yt2RMtauD705uZxjoU72Jxr+tk6nk0BnJ3KpFO9JKahlEklYvbZ73Jh0kddUg0qJNBCs79IerqNqqZr21naa2069nkfL37I/A6mXfZ2ry+OhvrYWWbffQqvFQlNL25j6+0TD59WfM+UchLFpwEHII488ws0330xGRgaBQIDa2lp++tOfntbBi4uLWbNmDTqdjvXr13PfffexevXqfrdTqVT9/vierKSkZNDbDLcbNWreP3o4lE9eKZOxfEIx5W0WttfVhtZLMxiZnp5BVmww3a3ZYWd+ZjafV5bjdGhJVqVQ7zoRCFxivpx4ZTx6pZL3Dx/ihamL2G3diS8QPI5CoqBIN5lOUyf/LTsW2i7DGEN+QiLXTpjIqwf2hZZfXjCOgsQk8uPjOdrt6fl3557HxOzsYfnb9GUsfpZwavVysHo6d7/fzyXOy3ir7o3QshRVKtn6TLJSUnrcT6fbxbJxRbzVbb6YZeOLmJyR2Wu3KG1HXURdW5p0KX/4LPj0eH1FGT+efwGPrv2MANDhdpOo1WJUqbC6XABIgK9Pnc73Pv0ktI+7ZsxiUkbmqHbrG6t16mSDLedI1Mn+RMPftqSkhPzMTO6aPou/dutOeE5aOkk6HXfPnM1Dqz8CghMRXlc8ic/KjoWyY6lkciYmmWlsaydGHovda8ekMtHoagzt69qMFeSb8qHnr+SAyxkNf8v+DKRe9nauPp8fv/8DsnOywsZJ2jxKqpucY+rvEw2fV3/OlHMQxqYBByHz589n9erVlJaWApCbm4vyNPtw6/Uncv8vWLCAn/70p7S2thIfH39a+x2rvH4/Xr8/9HR4WnIKr15zPV9VVSKVSJiTkcHEJDMF8Qm8uGwlm6qrSNHrOadbAAIQr9Hyy0VL2FpTw5HGZm4adxtVzjJa3E3k6wsoNOSjkCh4Y+UNfFFZQU2ThPtzvsVR22EkyCjQF7Cv2kF2TBxPX341uxvqSdEbmJeRQZJOz7fPnccF2Tnsaain2GRmekoqsSo1/7hiGV9VV1FlbWdWajpTTGacXm+v2buEkSGVSjk/cR4mlZkjnUdIUiYxzlBIlqH3ux29UsX35s1nUW4e+xsbmWgyMT0lFZ1SicfnQSqVhvqudwXJWYYU7sy5myOdR2hxNZGrzydDlclPFrSxs76WcQlJTE1O5pkrllFn60ApkZIdG8ezVyxja201HW43s1PTyY+N4xcLl3CkpZnpKWlMT0kV44qEEecPBHB5vWiOz+mhkMm4ddp0piSnsLOuloKEBGamppOg1XFOShpPXXYVW2qqSdRqmZmayvNXXcO2+hoUUhnnpmeglMr4orKMK5NuxiNr5pr0ZXR4O7C42ykyjiNPn9tneVw+FwqposdWFOGEYHpeWUSq8GB2LDEwXRCiSb93jxs3bmTOnDkRLRSVlZUALF269JQP3tTURGJiIhKJhD179uD3+4mLOzMnt9tRV8s/dm6nytrOjZOmsDgnjwStlsnmZCabk8PW1SmVEdmCTpZqMHLV+BOZqSaTS3V1NY2ydp6veJ52n4XZMfNYlDuRrJhglqHpiRNC60/tllNgUW4eNdZ2PjhymHcOlTDFnMz8zCwOt7YwMcnMk9s2s622lksLCrmicDwrjRPZUVfLI2v+S22HlZsnT+PCnBziNWJCrdGSoIlhrmYGc00zBryNWa/n0oJxoTlj2t3trGtcz9rG9ZjVZpaYF9PcpuCJbZvx+PzcOWMmCRodXxz0UNkuZVEOxKUHONzSzJ6GBrx+PwUJ8dR2dvDagX3EqdXcMmUacWoN+xobsXncjE9MosPjZm99PYdbWzCoVIxLTOxxgkJBGC4Hm5t4ae9uttfVckXheOYmBNPnxmu0LM7NY3FucF6aUksrT2/fynuHD5JmMHLPzNlUWa38ZN1adEoFV44rQi2V8tuvvsDt9fGN6TOYk5yJTqkacFlaXC1sad3GVy2byNVls8h0IZkiw2Cv3B4fSkXkrYtCpOgVhKjTbxCydetW5syZw9q1a3t8v68g5KGHHmLLli1YLBbmz5/PAw88gPf44OYbbriBTz75hJdffhmZTIZareaxxx7rdSK0aHagqZGb3nwNly947o98tprO8xfwjWlDO5Ffk7qTJ0r/jCcQPE65rQJX8nKyYi7rczun18tjGzeEuuaUNDfxadkxHjlvAb/8cj21nR2h5Xsa6vnWOfO46a3XQk/Iv/vpx/x0wSJunjJ1SM9HGFlfNH/Ja9VvAlBur2Bn2y7mqK9jQ1XwgcOstDRe2LMr1LVqQ1UF106YyJ7GBg42N1HR3oZJp+eXX64P7XNDVSV/uuhSPjx6GID1FeU8dO48XivZh83j4avqSvY3NvKbxRehFdmxhBFQbW3n1nfeoOF4co+S5iYuLyjk/9LTQ60iEOzm+PqBfTy5fSsARy2tTDSb+f3GDaF1NlRV8r2554e+I1s/qOGpy69iSe7A0ut6/F7erX2fdU2fA1Bpr2S7ZQc/nvBDzGpTP1ufnVxub0R6XgClQo7LLVpCBCGa9BuEPPjggwD86le/GvTOH3vssT7fX7VqFatWrRr0fqPN/qbGUADS5altW7micHxo8rahUO2oCgUgXda3rGFG7EyyjL3PuVBlbeftQ+F9JpvtdtxeXygA6fLf0mOsKCoOBSBdntq+hUsLCknQitaQaGRxW/ig7qOwZW6/G5XaHnotk0hDAUiXtw4e4I7pMznY3MQtU6by8r7wjG9ev5+S5ibMOl3opu+DI4c4PzObj48dAeDDo4e5f/a5jE8c2GRugnA6jra2hOpilw+OHOb+2XPCJsUstVh4ce/u0Os56RmsPnY0bDt/IEBZmwWzTk+DLZhQ5LldO7ggK2dAXQybXc2sb/oibFmHt5MaR40IQnrhdnsjJioEMVmhIESjAXfmX7x4MVOmTGHmzJnMnDlzQFmshCCFNLKPr1IuQz7EfX97yj+vkMqRSfv+MZRJJMil0tAgy9ByaWSrlFQiQdZDuVVyeY/rC9FBihSFVAm+kzPenfise2qkDGYTCibhdfv8qHoYH6SUyfF0C1pVMllYENtV/wRhJPRU12RSacT1Sy6VopLJ6MpV6PH5exz/ppTJ8PpP1GedQjHgFn2pRIJcIot4eCTmEuldby0hIkWvIESfAf/yf/jhh1x//fW0tbXxm9/8hsWLF3PfffcNZ9nOGJNM5og+7/8z5zzih7jVIFOTiU4enjryoqTLSNcn9rJFUIYxhttP6hqWFxeP2+djQlL407ibJ00lJzaWGFV4euGHzp1HrFr0649WMcoYrklfHrbMKDfS0Xmib3un202GMXzen1unTuej412tntm5jdumTg97X69UkhcfT2u3FInLior5sqoi9PqWqdPINIr5hISRURCfSHFi+HXtlklTyDTGhi3Ljovj7pnnhF5vqqni4vzwh29quZw0g5GW4+nqJcBtU2cMOKhOUiVxWeqlYctS1ClkaNIHeDZnH5fbi7KHIEQlumMJQtQZcEuIVCpFLpcjk8mQSqUkJCT0O2O6EJQXn8C/l61kbXkZNR1WFufmMTMltcd1/X4/0uM/YD6fD9nxJv3u/+6NssXPPdkPst+6l3ZPG5OMkyk0FBAIBCKezHVfFswKM4Nik4kvKisojE8gJy6OT44e4TvnzqPa2s7+pkbmZWZxTlo6Jp2el5YHz6ehs5PFuXlM7+V8hLGvqy7Mip9BjMLITssuklRJTImdTItVyk8XxOIN+JlsSua64knsqq+jxWEnPy6BgvgEzk1LR69UYvf4SDHoefKyK1lbXkqcWsMF2TkYVWq+dc4cLE4nS/PySdUZkJ8vZW9jPecdr1NKkWFNGEbdr3dmvZ6/XHo5X1RWsK+xgUsLCinQG1HIZKHrb9f/rxhXRF5sPHsb69GrlExPTuPl5dfyybEjqOVyihJNKGRS7pw+C3/Az5LcfKYkDzwHr1QiZbFpIemadPa27SNDl84k40TiVWdmhsih4OqlO5ZCIcPj9eHz+cPmDxEEYewa8C//jBkzKCws5NZbb2XlypVnbBar4TIuMYlxffR5b7J1sqGqkjdLDjA3IwOjSs0HRw5x86SpVFjbWFNWSlFiEpcWFDI7LaPX/QQ8eurrTaikqdhlMfxi55d0uF1cPX4Cexrqqe2wcuW48awpLcVPgBsmTmZqcgoen492pwunx4PV7SLNYORXiy/q9ThFSSaKkkSf5bGqzeFgY3UVr5XsQymVcW3xROakZaDpNvi7os3Cx0ePsKa8lEU5eVyUl8+U2MlMiZ0MgMfno9pby5aaatx+HwXxiXjtdnY31HOktQW5REq8RsPOhnq+rKxgsjmZpbn5BALg9QVwuD0opDLGJSRGjPfIiosDpiAIwyUQCLC7oZ6X9+6hwdbJ9RMnMTcjE6NKTVZsHFmxceysq2X1saMc0mjoPHyQTTVVzExNY0JiEtVWK+sqykL192BzEwkaLW6vF5vbjS8QICs2huIkMxfnF55yOQ0KA7PiZzArfuCZ7c5mvXXHkkgkxwene9FqRJILQYgGAw5Cfv/737N9+3ZeeuklXnvtNaZNm8asWbOYM2fOcJbvrLGmrIxH1qwmWa9nXGIiv/nqS64aN5415aW8XrIfgK21NawuPcpTl13NJHPkQPN2hZyvv/EfHF4v3583n3s+eBdfINhff015KQ/PPZ/n9+zko6OHQ5mv3j18kP+suI7XDuzjpW6Dil8/sJ9XrrmONIMx4jjC2PdVdSX3f/R+6PWnZcd45oqruSA7OFeBxeHg4U8/YUttDRCsW2vKS3nysquIVQe72u2sr2PVW6/hP16H8uMTeHX/3lDXky011dw8aQoba6o42toa3EdZKXfOmMUbB4N19uUDe3l95Q1MNPWeGEEQhsO+xgauf+M/ofFHn1eW89jSS7h6fDBV+dGWZn6w5r9kxcSyo76Wrd2+CxOTTBQmJLK5pprNx+cGuaZoIj9e+ym3TZvBa8evyS/v28N/VlzHNNESPGJ6awkBUKnkOJweEYQIQpQYcJvl4sWLefjhh/npT3/KggULeOutt7jrrruGs2xnjWabjX/t3gHAxXkFvHZ8tvJFOXm8dfBA2Lr1nZ0cbmnucT+H2tpweL0k6/WUtllCAUiX/5YeY3ZaOgFgd0N9aNzHppoq3j9yKGzdmg4rR3o5jjC2ebxeXt4bnqXKHwjw4ZEjodelltZQANJlS001ZZbW0Ov3Dh0MBSAACqksFIB0+c+BfSzOOZGOtKzNQveOf26fj6211adzOoJwSrbV1UZk8fvL1s1Yj49POtLayqGWZsYnJoUCkC77mhpJ7zZOqdluRyWX0eF20/2y6vX7WVdRNnwnIUTorSUEQKWUiwkLBSGKDLgl5IEHHuDgwYNkZmYyc+ZM/u///o8pU0R3iqEglRDqr+wPBDOmAAQ4vvykYELaS+aVrsX+QKDH6FIqkQR3evzfXXuVIkESiFz/TJyz5Wwh7SFTWffsP73XoW7rnLSPnjaRICFA3/VTOvBnHYIwZKT08B2QQNfirnra22VuoMtl4jo5otyu3ltC1EoFdqd7hEskCMKpGvDdwZ133snHH3/Ms88+yz333MPs2bNRqU5kztmwYUMfWwt9idfquG1qsD/wR0cPc13xJAA+OXqYlUUTw9ZNMxgp7CUhwLiYWPRKJY02G9mxcRGpgRfn5rG5pgqpRMJks5lSSytquZxz0zNYVjQhbN3smFgK40XigWikkMu5cVL4AwKZRMIl3TL75MbFMS8jfFbm8zOzyIk9MdbrysLxYTdYTq8Xky48+9pNkyaHzZ1QGJ8Qlo5XLZczKy3t9E5IEE7BrLQ0NCclPHhg9hyMxzP7FcTHMzHJxN7GhojvwrTkFMrbLKHXZp0eh8dLrFod1jqolMlYkJ0zjGchnMzl9iDvJQhRKmU4naIlRBCixYBbQiZNmtTn+7/73e+YN2/eaRfobHVhdg6PX3w52+tqyDQa+c3ii/j46BEuL8yiICGBzyvKGZeYyKKcPIp76V9vcHt4afm1rC8vw+Hx8ORlV7GmvJROt4uL8wrZ11TP8qJiFufm8UVFObdOnc6VheOZZE7GrNNTbDLxydGjTE1O4aK8fFLEeJCodV56Jn+//CreOViCQi7jqsIizu2W0CBGreGXC5eytryULyrLOS8zm4U5OcSoT6RenpKcwssrruPNkv24fT4WZGazMDuXT0uPcdTSyoXZOUw0mTDp9GysrmSSKZn5mVl0eNzcPm0GGpmchXl5EWmeBWEkTEgy8fKK63j3UAkNtk6WjS/mnG4BcW58Aj9fuISvKitI0GqZm57J9rpapiQnkxMbR5PdRofbTUFcAkVJSVS1t/GzBYvw+P1cXjCOOLWGa4snUZQkJtkcScExIT0/P1Up5dgdoiVEEKLFkOXFDAR66M8jDJhaocCgUmF1uyhvb+fygnFcM2EiTbZOttZUc0F2NgqZjFaHnbvef4cp5mQWZGVHBCRSiQSH10tNRzuTzMk8PG8++uMZkS7q9iR8UU5e2HYmvZ4VRRNZcVLLixCddCoVi3PzWZyb3+s6GTExfG3KNL42ZRoA9R0dvFmyn/+WHiXdGMPSvHyUUjkpBgN+fwAkEuLUGlINBuQyCQkaLRnGWKYmp3LnjFkA2NxuttXW0OpwEKdSi65YwqiabE5msjk5YnmppZU1ZaXBlhKJhHXlZSzKzeXR+Reyr7GRfU31ZBpj+PH5F1LW1spbB0vIjY0jPyGB8YlJXFY4jl31dby0bzcyiYQrxxUxNTlFTLo5Apxub68tISqlQowJEYQoMmRBiBg/cHq+rKzg7g/eCb1+Yc8u3rz2Rt45VMJT27eGlmcYY1iUk8vvNn7JmrJS/nTxpaQdH0DZoVLyjTdepcPtAuCdQwf53ZKLWV5UPLInI0Sl9w4f5FcbPg+9fqNkPz+/cDF/2PQVAJItG3l0wUJ+9vna0CiQh86dx72zzgn1r/+8spz7PnwvtI+X9u/htZU3iNYQYcyo7+zgng/eZUKSiTJLK3saGwCos3XyVVUVb3ZLBjIuPoEZqWm8d/ggAC/s3cV/rrmOJpudm958NfQ9eHnfHl5ecR0zU0XXw+HmdHp6bQlRKmTYRXcsQYga4rHNGGBzu/nLlo1hy5xeLxVtFv6xc3vY8ipre6jLzI76Wg61tITeO9xmCQUgXf6w6StaHfZhKrlwpihrs/D3HVvDlrU5nTTZT9SdALCuvDRsMrYntm2mxmoFwOpy8efjAUsXh9fLtpMyDwnCaDrc0sKR1hayY2NDAQgEx0S9fagkbN1DrS0kdRsH1epwUNLUxL/37ApLx+ALBHjzeFpqYXgFZ0zv+fmpUinHIQamC0LUGLIgJE0MPj1lgUAAj98fsdwfICLNbnD97usEevx3F1/Af3JyLUGIEPAH8PojK8rJdcrnDyCXSMPeD2XHCgTw9FDZvD3UbUEYLV1dh3u6Lg6kW7E/EMDt90Us9/gilwlDz+Hy9J6iVyHGhAhCNOk3CFm9enWf/+vyl7/8ZVgLeibTq1TcM3N22DKFVEp2TAzXTwxPCGDS6XB4g83N4xISwzJlFcbGoT4pG8x9M88hQasdppILZ4rc+Hi+PnVa2DKdQoH5pGxYC7Kz2VlfG3r99SnTSD2ewMCoVnPfzHPC1lfKZMwSXVSEMSQ/IYE0g5F6WycF3TIAbqmpZmle+BiqdIOR9uPzigDolUqKEpNYNXlqxH6XjxfdXkeCy+XpoyVEJoIQQYgi/Y4JWbt2bZ/vL126dMgKcza7MDuXv116JS/s3UWCRsvNk6cyLsnEfTPPZVxCEu8cLGGKOZkZqak8v3snd0ybyWWFhWTGxIb2Eevx8tLya3l5724qre3cOHEy52dmj9o5CdFl+fgJxKu1vHf4IGlGIysnTEQtk7E0Lx+X18stU6aTrNdx8+Sp7G1sYPn4Yhbl5IYNxl2Uk8tfL7mCF/fsIlGr4WtTp4vZ0oUxJc1g5Nkrl/F2yQFumDiZuo4OttXVcEFWDvOzspmdmsH7Rw5ybloGlxYUcrilmZkpaRQkJHB98SQKEhLJMMbwz6tW8K/dO5FLJHxtyjRmiFnTR4TT5UXZW0uISoHNLoIQQYgW/QYhv/rVr0aiHGekcouFL6sqONbawrzMbGamphKr1vS4rlImJU6tZmluPiqZDKfXy283fEmSTsuC7BxunDgZ2fGbvcW5+ShlkRdhn8/H1OQUpian4PP7Q+sLAsCRlmY+ryinpqODC7KzMev0bK2tZnd9PVOSkzknLYObp0zluomTwurX31JSCXBicrdHFyzstX4Z1WouKShkaV4+FeXl5KaIVhBhaLQ7nWyrq+HLinJy4uI5LzOL3Lj4Qe2j1NLKhsoKjrS2Mic9HX8gQF5cPNfl5pGblg5AUZKJr02ZGqrfxSYzV44rCqvvaoWC+VnZnJeZBfQ++acw9Fzu3rtjqVVyWtvEGEhBiBaDyo61bt06jhw5gst1YvDz/fffP+SFOhPUdli54/23OWZpBeBfe3bxvbnnc9eMWT1mEvuqqopvvPdW6LVeqeTO6bP4+RfreHrHVl5acV1oIrmeApCTiQBE6K7U0spNb71G8/GB5hXtFmQSKZ+WHQPgjYMHWJSdyy8XLSFJpw/bViKRRMw93V/9kkmlYdcJQThdb5Ts5+dfrAu9zoqJ4YVlK0k/nh2wPzVWK7e/+xbl7W0AvLh3F7dPm8lHRw9zWV4B30lOQXH82npy/e6tvovgY+QFW0J6vnVRqxTY7OK6IwjRYsB3qo8++igffvghL774IgCffPIJtbW1/Wx19ippbgoFIF0e37KRKmt7xLp2j4e/bN0UtqzT7abd5cSgVNJgs7G/sXFYyyuc2fY3NoYCEAjOn9AVgHT5rLw0LNuaIIwVNVYrf9i0IWxZRXs7Jc1NA97HgebGUADS5eV9u7kkv5Dn9uyksj3y2iyMPU6Xp9fuWGrRHUsQosqAg5CdO3fym9/8BqPRyP33388rr7xCeXn5MBYtunl9kRmB3D4fvh4yEPn8fjrdkRdOt8+HXBq82Hp7yMYiCAPlPilzT29JgESGH2Es8gX8EXUYBldfe1rX7fOhlEnx+P34AiKLWzRwubx9dMcSLSGCEE0GHISoj89NodFoaGhoQKFQ0NQ08KdQZ5vChESMKlXYsuuLJ5NmMESsa1CpuGP6zLBlUomEVL0Bi9OBWi5nfGLSsJZXOLMVJSWhkp3owlDbYWVcQmLYOuMSEilIGFwfe0EYCSl6A6smTwlbZlAqI+pwX8YlJGJQKsOWXVY4jrXlZVycl0/GALt1CaPL2Ud2LI1aBCGCEE0GPCbkggsuwGq18o1vfIPly5cjkUi45pprhrNsUS0nLo4Xl63kn7u2s7+pieVFE7i0YBxKec9/8sW5eTy29BJe3bcXvUrF1eOLeP3Afi7OL+COaTMHFITIBjBWRIhu0lMc6zMhycS/l6/kX7t2UG+zMTU5hRsmTubNgwfYUlPNOWnprCgqJt0YO7QFFoQhoJDJuH3aTFINMbx+YB/jE5O4bep08rql2O1PXnwCLy+/lqd3budgcxOLc/OQAHmx8cw3p6BRKIbvBIQh01d2LI1agU2k6BWEqDHgIOSOO+5AqVRy0UUXceGFF+JyuVCd9KT/ZI888gjr1q0jISGB999/P+L9QCDAL37xC9avX49arebXv/41xcXRl2u9xW5jZ309+xsbOCc9nYq2No5ZWpmQZOJb584jTq1Bd9ITuJPpFEoyY2I5Pzsbg1JFfnwCf7n0cpweL3ubGvjTpq9IN8aQoteztbaGwoQEpqekYdbrqWxvY1ttDeWtrcxWK5manIJe2fdnI0SXIy0tbKmtwmKz0VmnZ5IpOSxBQYfLxe6GOnbW1ZFmjGFWaioWp5MtNdUopFJmpaWjksmYlpJKs92OWacjNy6enyxYiNXpwKjW4PH52FhdyY7aWpJ0OmalpuPwethSU43X72d2WjqTTOYeEysIwnBLMRj5xrQZXF88CZVMhvx4/Xd7vWytq2FrTQ1KmYxz0tKRSiRsrK7C5fMyOzWNZL2B7XW1NNlsXFs8kfEJSRhVKuo7Ozjc0kxJRztHS/ZT1mahKNHE9JRUzHp9PyUSRoPL7UGp7KslRAQhghAtBhyEXHfddbz1VjB7k1KpRKlUsmzZstCynixfvpxVq1bx8MMP9/j+559/Tnl5OatXr2b37t387//+L6+99togT2F0ubxe/rp1C//cvYPvnDuPJ7Zu5suqytD7N06azCNz5/e7n801VXz9nTdDM1THqtW8vPxa1leU8+sNn4fWK04yUWwy86ctG7m8YBz/M/c87nn/XUpajneN276Fn12wqMfJtITodKSlmRvefJVWhwMAyZZNPHfVCuZnZYfWefvgAX6yfk3o9S8uXMz/rl+D5/hs5VqFgu+cO4//98W60Dr/t2gpK4snEa8NTkj48dEjPPjxiYcF6UYjVxSM52/btwDBCTRfWn4dM1LFfAjC6Dn5gc6Gqkru+uAdvMfrul6p5P5Z5/K7jV8CcNeMWbx/+CA1HR2hbR6/+HLOy8riF1+sp8Vhx6zT8/6RQ6H3rygYx88XLsagUo/AGQkD5fP58Xh9KOQ9t4Qo5DICBHC5PKhUomVLEMa6fvt2NDU1sW/fPpxOJwcOHGD//v3s37+fzZs34zh+U9SbWbNmERPTez/bzz77jKuvvhqJRMLUqVOxWq00RlkWqFKLhX/t3gFAqsEQFoAAvLJvLyUtzX3uw+7x8PjmTaEABKDN6WRDVSV/37E1bN39TY2k6oPjSt4/cogjLc0nApDjfvvVF9RYrad8TsLYsqW2OhSAAASAP23+CtvxZAY11nZ+e/yGC2CSycxn5aWhAASCdexQawsp+hNjkv68ZRPVxzMCtdjt/OrL9WHHrbZaUXXrPujx+3lx704CvY1qF4QR5vJ4eH73zlAAAsHMgtXWdhI1WgA0ckVYAALwqw3rqWxr4+NjR5ibkRkWgAC8d+QQRy2W4T8BYVBc7mB6Xqm059ZYiUSCTqOiQ4wLEYSo0G9LyJdffsmbb75JfX192MSFer2ehx566LQO3tDQQHJycuh1cnIyDQ0NmEymPrdzuVyUlJQM6lhOp3PQ2wxEi0JO1y1ZT9lb/IEANlffx5ZqNLQ4IwM6i6PnSZe6Z3Gx9ZBVy+7x0NzagrWmpp/SR6fh+iz7U1RU1Of7p1Iv+yOVSmnttEUsb3U4qKqtJeBw4NJosHs8ofd0SiUdPczR0eFyolOeeDpodTlp6+ygo7YWv1aD1eWM2ObkjEENnTbKystxOSPXPdlofU6nIlrKenI5R6NODtZw/m01MTFYeqi3HW4XWqUCHIQFKF2sLhd2Z/A74u8lqG5tb6fE0jak5T1d0VBPnQO4NgykXvZ0ru0dTuQyCZUVlb1sBQq5hN17DpBqikwCM9Ki4fPqz5lyDsLY1G8QsmzZMpYtW8Ynn3zCRRddNBJl6pdKper3x/dkJSUlg95mICwOO5NMZvY2NqCQyTDpdDTaTtw0TjaZGZdkIlnf9wXx1o7p/Hjtp6HXEuC8zGyOWSx8fOxIaLlBqQr9aKYbjBQkJKGRy3F4vaF1lhcVU5Se0esg+Gg3XJ/l6TqVejkQHbU6/rh1E91vlW6bOp3x2dlAsEvg8vETeL1kPwA76mp5YNa5bK0ND0Inm5L5+OiJunTthElMSElFmibFHwjw9SnT+eu2zaH3FVJpxMSYN0+eSm5OzoDKPVY/p55ES1kHW87hqpODMdx/25UTJrKnoT5s2fiEJN45dBAAlVyGQioNaxn8+pRpZMfHk6o3UGO1UhifwOHWE3PkZBiNTExLH3PjQqKhng7khnUg9bKnc62pb0Or3UxmVmav28XFHCMxKZWiovSBFXgYRcPn1Z8z5RyEsWnAd6nTp0/nBz/4AY2NjTzzzDMcPXqUnTt3snLlylM+uNlspr7+xI9HfX09ZrP5lPc3GuI0Wn6/9BKe372Tv2/fym8WX8zLe3ezt7GBuRmZrJo0pd8ABODivAIAnt2xjViNhm+eM4dpySkk6/VkxcTy3pGDTEg0ccW48fxl80aWjZvAHTOCWbNeXLaSx7du4khLC8vGT+Da4olnbAByNppsTuG5q1bwp81fYXE4uG3aDC7OLwy9r5LLeeCcOZj1Bt46uJ/ChETOy8wi1WDkiW2bUcpkPDB7DkaVghkpqdTbOrl63ASuKBwfyrYllUi4cdIUdEolL+3bTbohhgfPmYPd42Z8QiIev597Z53DvIzef/wFYTRckJ3NT+ZfyAt7d6OSybh7xizUMjkTkkw4PB4S1RqevXI5f926iZoOKzdNmsKVhUWYDQaeuWIZz+zcxsoJE9nf3MiW6mrOTc/g9ukzx1wAIoDd6Uat7Hush06ror2j767igiCMDQO+U33kkUdYvnw5Tz75JADZ2dl8+9vfPq0gZOHChbz44otcdtll7N69G4PB0G9XrLEoPz6BRxcsxOpyolcomZmSQqvTQZJWH9anvi8JWi03TZrC5QXjkEulocGXWbFxfHfe+dwxYyY6hRKpRMK8jEz0CmUo0JiWksoTl15JeXU147KyRPaiM4xSJmN+VjYzUlKpqq0NtYB0l2GM4aFz53Lr1OloFQrUcjlTklNYlJOLRCLBcDyT3YREMzaPhzSjMWIfKQYDd8+czbXFk1DLZGiP18FZqekECGAUg3SFMSjNEMMtU6dzUX4BcomURF0w0cK0lFS8AX/oIdD05BRcPh9xGk1o2/FJSfxy4RKOVVdx06Qp2L0eDEoVCpHufExyOnvPjNVFq1Fi7RDdbwQhGgw4CLFYLFx66aX8/e9/D24ol/c7Z8FDDz3Eli1bsFgszJ8/nwceeADv8W5DN9xwAwsWLGD9+vUsWbIEjUbDL3/5y9M4ldEll0qJPz4QUokc7SmmyI1RR97oSSWS0L6BsH93UcvlBBwOEYCcwXRKJYE+kkFIJBLiu91gARhPqk+xGg2xJ61zspP3YegnFbcgjAUntzh3BSNdtEolkVdOgg9zHE7UCgVqMVfImOZwelAp+w4QtRolFmvP4ykFQRhbBhyEaLVaLBZL6CZ3165dGHqY/bu7xx57rM/3JRIJP/nJTwZaBEEQBEEQzlIOpxtVv92xlFjaoz8IabLbeGXfHmJUalZOmCgm0xTOSAMOQr7//e9zzz33UFVVxfXXX4/FYuFPf/rTcJZNEARBEAQBALvDjbKflhCDTk1dY/sIlWh4NNvtLHvl3xSbTLQ7Xfx7725eXLaSpJNa9wQh2g04CMnPzw91m9LpdCxevJicAWbJEQRBEARBOB12R/8tIQa9mv2H60aoRMPj/61fw4zUNG6YOJlAIMCbBw/w9Xfe4PWVN4gWEeGM0u9khV2+973vUVpayl133cWqVasoLy/nu9/97nCWTRAEQRAEAQC704NS0XdLiFGvpqUtcm6laFFqaeWLygqWjQ+mxZVIJCwfP4EErZZH1302yqUThKE14JaQI0eO8OGHH4Zen3vuuVx66aXDUihBEARBEITubHYXqn6yY8UYNLRaojcIeWHPLi7IzkEtP9HiIZFIuG3qdH605lM+OXaEi46n9BeEaDfglpAJEyawa9eu0Ovdu3czceLE4SiTIAiCIAhCmE67C7Wq/4HpLrcXh9M9QqUaOl6/n3cPHWRBVmRXd7Vcwe3TZ/LjtZ9idYkUxMKZYcAtIfv37+f6668nNTUVgNraWnJycrjiiisAeO+994anhIIgCIIgnPVsdjemhL4nkZRIJMTHamlq6SQzLX6ESjY0NlZXkqTT9jpR5vjEJKaaU/j9xg389IJFI1w6QRh6Aw5CnnnmmeEshyAIgiAIQq867S4yU+P6XS8hTkdtY3vUBSGfHD3C9OTUPte5ZsJEvvfpx3x96nRyYvv/WwjCWDbgICQtLW04yyEIgiAIgtArm92FWt1/dqj4WB21DW3DX6AhFAgEWFNeykPnzutzPaNKxUV5Bfx580b+cJEYlytEtwGPCREEQRAEQRgtNrsbTT9jQiDYElJR0zoCJRo6R1tbCQQgzWDsd92lufmsKTtGTYd1BEomCMNHBCGCIAiCIIx5Nkf/A9MBzIlGyqtaRqBEQ+fzynImmcxIJJJ+19UplZyXmcVLe3aPQMkEYfiIIEQQBEEQhDHPbnej0fQfhKSaYzhW0TQCJRo668vLmGgyD3j9hdl5vHpgH16/fxhLJQjDSwQhgiAIgiCMaYFAAJvdNaDuWHExWvyBAPVN0dFdye3zsaO+lglJSQPeJs1oJFGr5cvKimEsmSAMLxGECIIgCIIwpjldHmQyKXJ53zOmQzBNb0G2ie17o+MGfVd9HWkGI3qlalDbnZuWzjuHSoapVIIw/EQQIgiCIAjCmNZhc6HTDvwmfeK4VD5au38YSzR0vqysoChx4K0gXWalpbOmrBSPzzcMpRKE4SeCEEEQBEEQxrSOTidajXLA608pSqOxpYMf/OYddh+oHsaSnb4vqyooTjINert4jZYUg54ttWP7/AShNyIIGWJ2h5t9h2tZv+kwh0obcHu8o10kQYh6lnYb2/ZU8MWWo1TURFfWmzOR1+fnaHkj6zcdZndJNR0252gXSTjDWQcZhMjlMh689ULMiQa+/+u3aGgem+NDOlwuDjU3UZiQeErbTzEl81npsSEulSCMjAFPVij0z+n08Or723jmla8AkEjgkfsu5pILigeUdk8QhEhNLR38+m+fsHlnOQAatYLHfnwNk8aLCVRHy1fbjvHj372Lzx8AYMUlU7n9hvMw6NSjXDLhTGXtdKIbRBACoNUoOX92Po0tHXy4Zh+3Xjt3mEp36rbUVFMQn4hKfmq3Y1OSU3hmxzYeXTDEBROEESBaQoZQeU1LKAABCATgsac/o7qubfQKJQhR7uCx+lAAAuBwevjbi59js7tGr1BnscZmK7958r+hAATgjY92UVrZPIqlEs50HR2DawnpbuK4VL7aXjrEJRoa6yvKB5UV62TZsXFYnA5qxcSFQhQSLSFDqM3qiFjmdHmwdjqAuJEvkCCcAZpbbRHLjlU0YXO4BzVQVRganXYXbVZ7xHJLW+TnJAhDpb3TgUbdf3renuSkJ1Ba2Yzb40WpGFu3Pesryrhn5uxT3l4qkTDRZObLygquLZ406O1dPhcf1X/CdssOnD4X6Zo05iaey4y46Ugl4jm1MLxEDRtCKUlGVMrwC1xSvB5zonGUSiQI0S8rPT5i2QXnFhJn1I5CaYTEeAMFOeGDaKVSCWnJ4kGLMHzarI5TbglRKuUkxuspG2OzqJdaWrF7PGTFxJ7WfooSk9hwCvOFtLot/GT/zzjccZj5iedxReplJKvNvF3zLo/s/RF72vaeVrkEoT8iCBlCmWnx/Orhq0mM1wdfp8bx8+9eGXotCMLgjc8z8+3bF4Wegp4zNZubrp6NQtH/fAHC0DPq1Txy30UUZAcDkVijhp995wpyMhJGuWTCmayt3X5aLZ+p5hjKxliXwf+WHmVaSsppjxmdkGRiU00VgUCg/5WPc/lc/O7gYxQaCrki9XLStekkqRKZHDuJmzJv4LzEeTxX/jxv2N+m1W05rfIJQm+GvV3y888/5xe/+AV+v5+VK1dy5513hr3/5ptv8pvf/Aaz2QzAqlWrWLly5XAXa1hIJBJmT83mmf+7ifYOJwadihaLja+2l5KWHEtmapwYoC4IJwkEAlTUtFLb0E6sQUN2RjxazYmbDa1GxfKLpzJnWg4ujxdzkhGt+tSeiApDozDHzJ/+dyVNrZ0EAsHsZQePNZCTkSC6yAnDos1qJy059pS3NyUYxlxLyIdHDnNZwbjT3o9ZF3zQWd7eRk7swFokX6l8lThlHOfGR3YFk0gk5OvzyNZm8XHpJ/xo76NclXoli5MXIpOIhz/C0BnWIMTn8/Gzn/2M5557DrPZzDXXXMPChQvJz88PW+/SSy/l0UcfHc6ijKjEeAMqlYJ/vraJ/7y3DQCVUs7/PbKMmZOzRrl0gjC2bN1dwff/723c7mA665uunsXNK85F3+1mViKRkHoaNyDC0DMaNJRWNfPdX7yJw+kB4KqlU7jzhvOIMWpGuXTCmabN6kCvO/UANynBwNHyxiEs0empbG+jqr2dCacwP8jJJBIJRYlJbKmpHlAQUtZZzlbLNr6Rc2ufD0blUjmTFBOZk3wunzWuZU3TOq7LuIZpsVPFA1VhSAxrd6w9e/aQlZVFRkYGSqWSyy67jM8++2w4DzlmHKtoCgUgAC63l18/8QktYvCmIIS0WDr59ROfhAIQgH+/vZVjFU2jWCphINo7HDz2909DAQjAO6t3c7isYRRLJZyp2qyOsAcTg5UUr6e6bux0K3rtwD7OzchALh2a27CChEQ2VlUOaN2Xq/7DeYnzUMsGllI7QZXAyvQVnJc4l/9UvcYP9z3K2sb12LzifkY4PcPaEtLQ0EBycnLotdlsZs+ePRHrrV69mq1bt5KTk8MjjzxCSkpKn/t1uVyUlJQMqixOp3PQ25yOmrrI9KH1TVZqahpprBueib1G+hxHy2idZ1FRUZ/vn0q9HKxo+owHUla3T0VjS0fE8pq6FpRELh8u0fJ3Pbmco1kn/RINpT10b6ltsFBSciJTYDT8baOhjBAd5XQ6+/99G0i9PPlcW9s6aWttwmk7tUDC5fZS29DOgQMHRuwpfm+fl9vn48XdO7ktfzwVFYMfUN4To9vD2xXl/f5dK71V1DrrmOqZTEV7/8d2u92hMiqQcwHzqfc1sKZqLf8ufxmTNIlMWSZp8hTSZKloJGOvFXQgdVIYHaOeq+7CCy/k8ssvR6lU8sorr/Dwww/z/PPP97mNSqXq98f3ZCUlJYPe5rQo6iMWjcszkZ2VglE/PBN6jfg5jpKxep6nUi8Ha6yee08GUlZrh4P87CSOloe3fORmmhmfn9zLVkMvWv6ugy3ncNZJu8PN9IkZ7NhXFbY8J9NEUbeJJKPhbxsNZYToKOdAgqSB1Mvu5+pye/H5P6KgIOe0AgilYi8paVnExehOeR+D0dvn9eyObRQmJjFr/PghO1ZmIMBzRw9jTE8jzdB7Rs73D37EeTFzyYnNGdB+KyoqyMoK70aeTTbncg5uv5sqezU1jlp2OHfxvv1DTGoz5yfOY0HS+ahkY2N82FgP3M9mw9ody2w2U19/4ma8oaEhNAC9S1xcHEplcJDpypUr2b9//3AWacTkZSbw/XsvCmX0yUiJ5Xt3XTRsAYggRCOjQcP3772ItOQYIDgb+g/uv5jcrMRRLpnQH61GyYO3LgxlxVIp5Xz79kUUZp9+H3dB6M7SZsOoV592C0ZivJ7ahtGd1K+qvZ2/bt3MygkTh3S/EomE8YlJbK2p6XWdGkctFfYKio0ThuSYSqmSPH0u85PO49qMa3ig4D7mJpzDdssOvrvnEXa17R6S4whnrmFtCZk0aRLl5eVUVVVhNpv54IMP+P3vfx+2TmNjIyZT8EdrzZo15OXlDWeRhp3H46OythVLu53igmR+/f1ltFvtpCbHMi7PHLF+RXUL5dUtSCQSstPiyUyPTHNZVWehoclKjEFDVlocSuWpTdgkCGPR+Lxk/vaLG2lo7sCgU5GWHEttQxullS3IZVJysxIHNNdOXVM7tfVt6LQqstLicbm8VNS2QiCYPlsmk3CkrAlrp4P0lLhQitnTdSZ/P7tfz5IS9JgTYjhc3kBTs5W4WB1I4H/uWoLP60eplJGXlUSH3cWBo3UoFXKye5jjRRAGq7XdjlF/+t184mO11De1U1zYd5fv4VLWZuG2d97k6vFFpBtjhnz/BQkJbKqu5OrxPbcyra7/L1NiJiOXDs+tn0wiI1uXTbYum0p7Ff8o+ycXmZdyWeolw3I8IfoNaxAil8t59NFHuf322/H5fKxYsYKCggL+9Kc/MXHiRBYtWsQLL7zAmjVrkMlkxMTE8Ktf/Wo4izSs3B4vH687wO///l8mjU8jOyOBd1bvJhAAvVbFT759GXOm54bW33eoht8//SlHyoJdUcbnm/n2NxZRXJgaWmf73koe+b+3sTvcSCRw103ns+KSaWhEilLhDBIfqyM+NthF4sCROn7x+EdU1LQCMGVCOt/+xkLy+wgaDhyp43u/fJM2a3Aswo8evIR3V+9hz8HgU8Hxeclcc9k0fv7njwAw6NX89NuXM3tq9mmVe/veSr7/67dwOD3Hv5/zWXHJ1DPi++n2ePlwzT7+8Mxn+PwBCnKSuOHKWfzf31bjcnuRSSXcePVsvtx6jBmTMig5Ws91V8zkqRe/oKahDQhOKnnjFUP7xFc4+7Qebwk5XbFGLfVNI98S0mK389etm3nz4H6uKSpmcW5+/xudgqLEJP6+fWuP7zl8Dja3buUbOV8flmOfLFObwarMG/lP1WtIJVIuSbloRI4rRJdhn6xwwYIFfPLJJ3z66afcc889AHzzm99k0aJFAHznO9/hgw8+4N133+WFF16I6paQyppWfvf3/+LzB5g5OYu3PwkGIACddhd/fHYNtcd/nAG+3HosFIAAHDzawOad5aHXLZZOfvH4R9gdbgACAXjyxS84NsYmXBKEoeL3+/l4/f5QAAKw+0A12/b0nvXFZnfxp3+sDQUgRr2aI2WNoQAE4OCxeg4cqSfheKDT0enk8X+uo6H51G9Iur6fXdmhgt/Pzyk9Q76fFdUtPHY8AAG4/opZ/OGZz3Adz2Tm8wf499tbWDhvHK9/uJNLLyxm7VeHQgEIwLpNhymvHbkEA8KZqdliwzAEQUhcjJba+rbTL9AgbKqu4qJ//5O6Tiu/Wrh02AIQgMyYGJrsdprt9oj3vmreSLY2C7185CZPNigMrMy4hg/rPmJ3W2RSIkEQM6YPoWaLDf/xH2y3xxvxfk19G5b24MXB5/Ox91BtxDr7Dp9Y1mZ19Jg5qLmHZYJwJrA53Ow9GPm9KDla1+s21k4nB46c2CbVHNvjpGTHKppITz2RQ7+sqpm2dkfEegPV2/ezqaXzlPc5lnS/ngH4/H46bOFZ//z+AF6PDwClQsGh0sj0vGVVrRHLBGEwWlo7MZzGHCFd4uN01DWOXEtIWYeVez98l3tmzOaWKdOJ0wxv5iipREpRYiJbaqrDlgcCAT5tWMOU2MnDevyexCiMXJF6OU+X/gOLmHldOIkIQoaQKcGATBb8k6qUkT3dstMTSIgLPomVyWRML86IWGdacXro3/GxutCA3e7MSf33jxeEaGTQqZk5KTNi+aRu2ZZOFmPQMK3bd6m6zkJeVlLEeoU5JiqqTwQn43JNJMZrT7msZ/r305RgQCY9MRBYKpEQe9IkhDKZFJk8eM1zujxhXUm75GeLJAPC6Wls6RiSlpD4GC11Te1DUKL+ef1+frt3F9cXT6bYFDkedLgUJiSysTq85fiYrRSX30WWNvLaOhIytOlMiZ3Ms2X/JBAI9L+BcNYQQcgQykyL50cPXoJapWDDtmPcdPVs5Md/oBPidHzrGwtJTjpx0zJnRi7TJp64eZo9JYtZk7NDr+NitPzogUuJiwneKCnkMv7nzsXkZoof9f/f3n2HR1GtDxz/bsmm97LpDRJKQgghCCItoSoKiIJXvdh/XCtXURGxo2K5dvTauFzrVUFQEARFqtIxSCihQ3o2yaaX7fP7I7AQkgCBTTabnM/z8Dzs7uzMu5MzZ+bMnPMeofMaNaRXo4vZYVfE0T+x5ZOnm6uKh+4YQai64diq1xnoERvE0CvOdO0c2DeahPgQ6538IH9PHrwjDX9fz0uOs+Xjs2lyCUcUFebHUw9dbb2h8t2KP3n8H2OsF4POKiV3TrmStX8cZNrkgaxYu5e0K+PpEXtm7M7EMUlEBrdPOlSh8yotq2nSAL4Ufj5ulGir2+VC+MeDB1DI5QyNjLrwwjbUKyCoyaSF6zTrSfLuY9dZzq/0H0ixrphtZTvsFoPQ8dh9npCOqrpWR25BOTIZ+Pu4U6ytwclJQWSIL66uDYNONSVVFJVU4eXpQkSIL0qlgvQrexCu9kFbUUtwoDdX9o+horKeELU3PWIb3w3pHRfCnAeuJju/4e5sTLgf6sDGd1b79Azj3eenUKCpxMfLjfiYIFTNPGURhHNV1ejIKyhDLpcTEeqL+2XMNtwa5ZV15BWWYzSq0OmNuDi3LltUj25qnv3nNWTnN2THio7wx81FxV8HcqmrNxAR6kdEiG+j78TFqPnw5Zsp0FTi7qYiIsSXhPhQJo7uC0BUuB8+Xu68P/cmqmt0hIf4EBl2+Y2FPj3DWPD639GUVOHl6UpEiK/1aaijUyoVjBjUg+BAL6rr9Hi6OVNdq+f1J6+nuqYeD3cXFAo5A/pGUa8zcmVKDHHRQST1Cie/qByVk5KIUF9OHD9q758iOLjS8hq8PS+/EeLqokKhkFNRVW+9edAWLJLEBzu3MzoktN0v/GN8fSiuq6WkrpZAN3dqTXVkVPzF/8Xc1a5xnEshUzAmeBTf5HxLsk8SroqON6mh0P7E1WwzCjSVvPnJGrb/dRKAfgkRxMUEsWjFn1yTlsj0W66isLiKJ19bRkVVHUqlnAdvG864tAS27DrOqx/+isFgwsPNmTunXsnn329DqZTzzIxrGNA3utG2QoK8CAlquftGZlY+c17/kYqqepRKOQ/dkcb49MRWX9gJXUteYTmv/vsX/jrQ0Dd4+MA4HrpzRKMncW3hRK6W595azvGchob1xDF9ufumwdbMVxcjt6CceR+sZu+pgeVPPjCWg0eL+PFUprlQtTfPzLimSRctf18P/H0bBl0ePVnC+5+vtw5o79s7nH/emXbebl2XSh3gdVEphB1Nvc7Aqg37+Wb5LiaN7stn32+lXmfE1cWJO268kh9//YvbbhzEqnX7yDxYgEIu466bruLGa/o12y1LEC6VrZ6EAAT4elBUUtmmjZA/crJRyGTEerR/vSCXyUkIDGJzTkOq3s2lm4l1j8FN2Xa/92KFuYYR5RbFsvyf+FvkVHuHI3QAneOWnY39sfOotQECsHt/LnKZDC8PF35ev4+TeWW88sFqKqoaBpmbTBbeWbieQ0c1vPz+agynssfU1On5Ysl2xo1IoKyijnf+s47C4ovvj1pWUctL83+2Zv0xmSy8vWBtp8m+I7SdtZsPWhsgABu3H2Hnnuw23abJZGbRil3WBgjAsl/3sP9w04Hm5/PLpgPWBgg0TML1w1mZ5go0lXz2/Taqa3QtrmNbxvFGGbX2HMjj9x3ijnxrHD5ezLv/WcfoIT1ZuGiLNQtYvc7IfxdvZdSQXrzx0RpST91YMVskPv3mD46cKLZj1EJnYzCaqK034OFum4l+/X3dKdC07biQ/+3bw4joy5vd/XL0DghiY/YJLJKF3zTrSPbpa5c4mjMscAgbS36nRF9y4YWFTk80QpqxZdexJu8dOVlMxKnMOmXlteQUNM3yUFhShdlsafReZXU9rqeeWmTnl1FWUXvRcZRX1jVbWRZfRlpRofMzmsz8sbNpGf7zPGlubaG6Vs+2jBNN3j968uJPNjq9kc1nHX8qlZKSZjJQ7T2Yj/Y8x9Lu/blN3svYn4vFYmlmaaE5mtIqzBYJGTJrWt7TdHojcpkMs0XCbGq8T+0xD4PQeZVoa/DxckMut80Fva+3W5s2Qip1OjbnZHNluH0GgQMkqYP5IyebvRX7QSYj3NX2T4AvlYfSg/6+/ViU+729QxE6ANEIacbAfjFN3ouNDCD/VH5xHx836yDYs6kDPJtUlJ7uztZ0veEhPvh6X3y3FB8vN9QBTQfOBvpf+mBaofNzUioY1EwZTk4Mb2Zp2/F0d6Z/M5mtYiObZqpqibNKyaCzJhA0GEwE+DXNa987Lhg/n5a7FyT1anrSTeoZhlwuqryLFeR/pj47dxyaSqVEQkIul6F0UjT+XjN1liBcKk1pFb426ooFDd2xcgvaLm30L8eO0CcoGHeV/SYsVXt44KJU8uWRX+jv08+uA9KbM8AvlayqQ5yoPWnvUAQ7E2fkZgy7onujvuO9ugejclJSUVVP2pXxxIT7M+fBcXicGugrl8u499ah9OwWzOP/GGPNiOXi7MS0GwaxasMBPD1cePiukc02Xlri7+vO0w9djbubyrqd+28bTrdWXNQJXdPoob0aJUJITYpiYHLThoktKZUKbp44gLBgH+t7Y4b1IjE+5KLXIZPJuDotkbiYM2VcAsaNSLC+DvDz4M6pg/HyaPnCZHD/biSeNS6hR2wQIwbFX3QcAsTHBnH/tOGs+SOLO24chOpUY0PlpOCOGwex5vcs/nlXOjt2Nzz9kslg2uSBxMW0PLO9ILRWcWk1PjYcvxHo79FsTwZbWX7oIFeEte0Nn4sRH+BFZn4Vvb162TuUJlRyFYP9B/FtziJ7hyLYmRiYfpbK6nqOZZdiNJp46oGxlFXWIZPJ8PRwprC4iiv6RqEO8uRknhYvDxfefX4K+UUV+Hi7EeDjxoGjhfTrHcZ7z0+loqqeoABPTCYzkfePQR3ohdFoZFdmNmHBPtTrDJhMFny9XckrqkQmaxjgWlBUgUqlpFtUAG6uzvRLjGThv6ZRVFKFt6cbHm4qjueW4uPtRmhQ2w4yFhxXZJgfbzw9mZyCchRyOZGhvnjZILtMc4q11ZSWNWSv6RYVyKuzJ5GTX46zSkH36EBUKgUZ+3IxGk1EhvkREuRN5sF8qqp1BPm7Ex8bjLa8Fk1pFZ7uLkSE+jJ35gQKiitQyOWEBXsxqG8Uo4f0pF5nICzYh7gYNUdPlqAtryHQ34PYyEAKiyvJKyzH3c2Z7tGBPP/IeLILykCC8FBfwtQ+TWMvrWqYlE/WfH9zg8FEXlE5RpOF8GCfdssw1t5MJjO5hRXoDUZCg7zxcHcmp6Cc1L6R9OoejMVs4b0XplqzCtXVG0hJjECnN3HvtGG4OivQGy10iwrEYrZw6JgGJyc54edkMROE1ioqrcLXy5aNEE/yCtumEVKhq+cvTSH3pPRvk/W3hsWlkMpcP5TyjnmZl+TTh13lGeyt3Ecf70R7hyPYSccsnXZwPKeExSszWLluHxaLRFKvcO6fNgyA1z9cQ+bBfORyGeOG98ZoNJPQI5Rvl++itt7AXVMH89xb2wgL9ubakUm8/9kGaur0+Pm4MW3yQP7z7Ra8PF2YcWcaG7YdBmT8uukAFotEcu9w+vQMIz4miI++2sS+Q4XI5TKuG5XELRNTCQv2tf7blZnNwy8spqKqDk8PF55+aByD+3frcI9ahY7B19u9Vd3/LsWeA3k8++ZPaCtqcXdT8eoT1/PhVxs5cKQIhVzGDdekoA705IPPN2KxSNw1dTABvu7M/3wD9TojIUFePH7vGP710RoKiytxcXbigduGceh4MSvW7kUmg7HDe9MvIZLXP/oVs9lC96hA/nl3OnNeX0Z1jQ5fb1eee/ha3l6wluz8MpyUCu6YMgij0cxn328DIO3KeO6/bTghZzXcd+/L4bm3V1BWUYeHmzNzHhzHVandrCl2yypq+WLJdpau3o3FIpGaFMlj00d3ugvr6hodS1bt5r+Lt2I2W0gbFE9KUiR7DuQyoG80y37dww3XpPDewvVUVtfj7enKtBsG8sPq3Vw3KolV6/dz/bhkamp0VFbV8+3yXfx1IA+5XMbUa/szclDTSVkF4WIVFFXYNJOVj5creoOJqhodXjaYAPFsvx0/Rp8gNS5K+2avLNIVUS8vRG8MI6+yhnDvpl1a7U0hUzA04Cq+y1lMQmJv5DLRMacrEn/1U/YfLuSn3/ZisTSk4MnMymNbxgnW/JFF5qlMPRaLxM/r99M7PoTlazIpKqni6hEJfLZ4K+WVdUwd3583P/mNmrqGCdHKKupOZcfqTYGmkoWLtpAYH8rqDfut2/nrQB61dXoOHtOw71ChdTvLft1D5sEzWYUKiyt59s2frBm5qmt0PPvmCnLb8LGyIJxPibaaZ9/6yTpAvF9CBD+v38eBI0VAQ7akRSv+RKczWicH6x0XzBuf/mbNtJTYI4z5/11vzRqn0xt589O11m6LkgSrNxygQFNhnb37aHYJi37axfCBcQB0jw7is8Vbyc5v6OdtNJn59JvNjcYxrN96mD92nsmOpSmt4pk3Gxog0JDJ7rm3VpB71h3SzKw8vv85w3qs7srM4ef1+zvdjL9Zx4pY8O1ma1KNuNgg3vzkN64ekci/Pl7DlPH9eePjNVRWN2Tpq6yuZ+F3W0i7sgcLvt3MyKt68u7CdfSKC2HDtiPWrGwWi8S3y3eRV1xvt98mOL4CTSV+vra7mSKTyQgJ8uZEru2zTP585DD9Q+w7CFySJH4rWkeiT2+SQgJYdyz/wl+ykx6e8ViwsE0rJjDsqkQj5JSDx4qavFddq2P77qbZfnLyy6zpQV2dnawn5+paPUaTudGy5ZV1uLs2dOGortFz5GTT9JUZ+3Kprm2abvTsmEq0NVSdk5JUbzChKW2aOUgQ2kNpWQ3a8jMZqlKTovhzb9MMXCVlNXieSq9ZUlZjvagHCA3y5kSutsl39PrG2ZhO5mkbPcX4c18ufU8NPu/ZLZg9WXmc69x1bNh2xNqAKNXWWBv0pxlNZjRnZXbKzGp68t6w7TC1dYYm7zuy0423007vt9KyGkwmCzq90dpoPK2u3oBCLsdksmA2W5CkhuyAmc38HQ4fF6k4hUtXUFxJgA0bIQAhQd6tytp3MWoMBnYU5JEScvFj4NrCvqr91Jnr6O4RS7+QAH47movZ0jFvnMhkMoYHDmNx3vcYLMYLf0HodEQj5JSY8IAm7zk7K+kd17RCCVX7oHJquMtqtlhwdWl49Orh7sy5PaPcXFXWhonKSUFUM7M09+ymxqWZWdBjws8s6+Pt2mSCQoVcdt4MQYLQlry9XK3JGaAhFW9zg5L9vN2pPfV08NxuFRVVdc1mgFOpGmdcClP7UFJWY30dFxPEyVMXz7kF5XSLapqs4dx1pPaJtHZd9PZytR63p8nlskaTKsbFNv0t/XqH4+LSuSYKDQlsPKHa6f3m4+2GTNYwy7STsvG+dFIqQNYwGP10dix/H3e6N/N3iArrXN3XhPZjNJopr6hr1WSnFyNU7U3W0UKbrnP9yeP0DAjEzcl+WbEqjJWs1axjgF8qMuSEernjrnJiV37Hnbsn0i2CQOdAfin61d6hCHYgGiGnJPYMbZTWM9DPgytTYpkwKonAs1KE9ukRSklZNZOvTsZZpeTXTVlMmzwQhULOus2HuGvqYGtDRKGQM23yQH7ZdAAXZyfumjoYbXkNCWc1bNQBnkRH+NMvMaJRKtJ+vcNJOitDV3iwL7PuHWPtkiKXy3jknpFEhvq11S4RhPMKVfvw5APjrNngVq7by43X9GvUMB7YLxqFXGa9E3cyT8u0yQOtn2/YdoQH7xiB81mN8L9NSG3UVaJX92CCA72oq294AuHl4cJtNwzku592ArA14xh333SVNYscwLgRvSk/60lHdLg/6Vf1tL4OC/bhyfvHWcd/yGQw4840osLPHE/JvSNISTwznkEd4MkNV/dDqehc1WbP7sGMGnJm3xw4UsTNE1LJ2JfLnVMHs2zNHv5x61Brul6FXMbfr7+C3/44yN+uS2XT9iNMHZ9CVU0d6Vf1JND/TD02MDma2HCRQEO4NIXFlfj6uFmPU1uJDvdn/yHbNkJWHj5IakjohRdsI3qLnu9zl9LLqxd+qjP12KCIYL7f23TeqI5keOAwVhWupsLQtpNICh2PTHLADs5ZWVn06tW6tHMtfUeSJAqKKtAZTCjkck7mazEaTUSHB1jv6h4+oSE7T4tK5URIoCc5BeX4ermiUinJK6ogOMALFxcntOW1BPq5U1Wjp0RbTViwD0aTmZKyWsKDfVDIZVRW6wj096DmVNctX2838jWVKGUyAvw9yD+VHat7dBDB59yhNJnMZOeXUVxaTYCfB1HhftYnMpe6XxxRR/2d7RFXR/vtZrOFnIIyioqr8Pd1JzrcjxO5WnLyy1CplMRE+oPU0PjQG0xEhfnj7enCibwyysprCFH70CNGTUl5NQWaSnw8XYmO8Cc7v4zcgnIUiobMXm6uKo7nlFJbZyAyzJeoMD+O55RSUVWPn48b3SIDyM4vJ6egDA93F+KiA0EmIztXi4REdLg//r6NB2eazBZy8svQlFTh7CSR2DMSk0WiqLgSlZOSULUPVTX1nMzVYjSZiQzzQx3g1cKeaB+t/ftf7PLVNTpO5Gmprzfg7+uOXC6ntLwGJ6Ucg9GC2WRG5aSktKKGAF8Pauv0uLmq0J+qN3193DCbLUSH+1NTqyf79N8/wp+CvJMdqsw2p6MdVy1xhDgvJsaLXUZbo+KrH7Yz/ZYhtgwRi8XCU68v53/z78bfBl29agwGrvzPR7w99ho8VGeeDmdnZxMVFXXZ678QvUXP4pwluChcSPXrj4wzXTLMFgvvbM7k0aHJJIU07YlxIe31GzYUb0Qmk3Nvt/+z+bod4bjpqrp0dqy6egOrN+7nwy83Ua8z0i8hgkenjyI6vPGBGh+jJj7mzJwL8bHBHDmh4eX5qygoruIftw7l2+W7KCyuxN/XnUfuGcm4EQnWidHq6vX8vH4/H331Ozq9kZTEhu2c7prVPfpMt48e3YJbjFepVNAtKrDZrieCYA8KhZyYiABiIhq6Mx46puGjr39n555sVE4K/jYhlR7d1Dz/1kqMJjPDB8Zx/23DmkymGOXm36irYs9uwfQ851gIPSvF7q7MbF6av4rSshrUgV48M+NqkntHEH/W3CjQ0EWoJUqFnNjIAGIjA8jKykKjreHdhevYlnEClUrJPTddxXWj+5Cc0PmzO3l6uBAfE8TazQd55s2fqKs3kBAfyhP3jSE2MoDNu44x74PVFGurCfL35G8TUvnx17+4529DGHZFrPVpGIC7mzPqs26gFDS3QUG4CNn52kY9EWxFLpfTo5uarRnHuXZkn8te36/HjtAzILBRA6S9lBvK+D73R/xUvvT3S2nUAAFQyOWM7B7OJzv28+51Q629KTqawQFX8p8Tn5FVdZBeXj0v/AWhU+hc/Qpa6dBxDW99utY66HL3/lw+W7zVOsN5S6pqdLz64S8czS7lmrQEPv9+qzW7j7a8lpfeW8Wh4xrr8gePaXjnP+vQ6Ru2k7Evl6+WbsdoOv92BMGRmEwWlv+2h517sgEwGM18sWQ7tWclbNi4/Qgr1u67rAxTeYXlzHl9GaWnxohoSqp4+l/LKTprUHlrKZVOfL8yg20ZDYkoDAYT//5yI1lHmias6KyOnCjmlQ9+sXZ723+4gH9/uZHDxzW8+O7PFGsbkmAUa6v576ItXNW/Gy+99zOHTnSdfSS0r6MnSxo1aG0pqVc4qzbst8m6Fh/Yx+CISJus62JZJAsZ5Rl8dvIrYjyimm2AnNY32B+FXM7S/R23W5ZKrmJUUDr/OfFf9Ga9vcMR2kmXboQ0l9729x1Hqaisa2bpM7TlNRw61jDQy93V2Zrm8zSd3khB0Zm+jdl5jbPPAGzcfpTyivNvRxAcSbG2iq1/Ns0mdzxX22iiwHVbDlFde+knGU1JlfVC+bSKqvpGma1ay4yC9VsPNXm/uWx2nVV+UUWT97ZlnKCopMqadvy06lo9KiclBqOZ/MKm3xMEWzh6spgwdduMKUrqGUpuQZk1Bf+lyq2sJKu0pF1T8xbWF/H5yS/ZXb6HkUFpxHnEtdgAgYYsVJMTYlmy7zh7i5pmI+wo4jy7o3YOEjOpdyFduhHi30xmqdhTM5Wfj4ebs7Wbh0WSGs1HAA2DXH3PWndzj5O7RwV02tmXha7Jy9OVmIimfY6DA70oKT+T2apnt2BcXS69J6i3l5t1kPRpTkoF3pcxI7xCJjXpygU0Sgvc2fk2Ux9Ghvnh4+3aZGCwQiFHdupvYOvMRYIADU9S84sqCGmjRohSqWDS2GSee+sndu/PRa+/tBSxX2buZmhkFCqF4sILX6Y6Ux0rC1axKHcx0W7RjFSn4+10cfvH19WZqX26M2/9nxwp7bgDwEepR5JR8Rd/lmfYOxShHXTpRkjP7sEMvaK79bWrixMP3T4CD/fzNw4C/T154r4xKBRy1m4+yK0TBzRKzXvbDYOIizkzbqNn92AG949ttJ37pg0XjRChU/Fwc+bW66/A86xZiHvHheDn7YbB0ND10NvTlVsmDsBJeemNkKgwXx64bbj1tUwGD9+dTkTopaeCNRn13DV1cKOUwymJESTG2zfnf3uKj1EzZtiZwZsqlZJH/28UcdFB3PO3wY2WvXlCKuu3HOKWiQOIbyYtsyBcrpyCSkLU3o2Sr9hacu9wrklL5LUPf2Xcbe8z5b5PWfN71kV/v0qvZ/GBfYyO7X7hhS+DRbLwV/lffHL8PxgsBq4JuZpYj5jzPv1oTnd/byb2juGZNdvZXdAx5+9xUbgwIfRaFp74jLy6jjvRomAbXXpgur+vB0/cN4Yp41OoqzcQEepHVNjFpbwd2C+GhW9MI7+wAj8fN/olRlCgqUQd4El8jBpP9zN3ZQP8PJjzwDiO55a2ejuC4EhSEiN557kpnMwtRamU0yM2GFdXJ95+9kYMRjPR4f6EBftc1jacnJRMHN2XpF5hlGhrCArwJDbC/7LTePaOC+HT1/9Odl4Zri5OxET4d6m7/L7ebjx8dzoTRiVRXasnIrQhC5lMJmPS2L706h5CsbYafx936nVGUhIjiY8NwusynkAJQksOHtcSG9l0/i5b698nkv59IpEkiRO5Wv79xUZq6/RMGpt8we9+8udOkoNDCHK3/eD504p1Jawq+gWDxcCIwKH4qi7v2qF3kB+uSiWvb/yLSQkxTOnTDfm5E5zZWahrCGmBI3jj0Ns81Xs2gc5tXw4E+2jzRsimTZt4+eWXsVgsTJkyhenTpzf63GAwMGvWLPbv34+Pjw9vv/024eHhbR2WlY+XGymJrR9QplDI6RYZSLfIM088UhLPsx1vN1K823fgmiDYQ49YNT1i1WRlZREe0vB0wt/HtidpFxcnenUPoZeNb0BGhPgSEdJ1J9fz8nBtNhuYp7srqUltn6ZTEE7LPFTM2BGXn7nqYslkMmIjA7h32jDeXbiO7tFBJPZoed6P4+VlfL33L15KG90m8ejM9fxeupl9lQfo451Id49urX7y0ZIYPy/uG5jA4n3H2J1fwsyhfQny6FgTHyd490Zn0fPygVd5rMcjhLu135gbof20aXcss9nM3LlzWbBgAStXrmTFihUcPXq00TKLFy/Gy8uLNWvWcMcdd/DGG2+0ZUiCIAiCIHRg+UUVaLS1dunqF+jnwU3X9ufZN3+ioqr55DE1BgP3//wTk3sm4O9m24t3nVnH5tItfHTsUyoNVYwPHkecR3ebNUBO83F15q7+vQjzcueh5X+wdN8xjGaLTbdxufr79mNIwGDmZb3GpuLfLyurotAxtWkjJDMzk6ioKCIiIlCpVIwfP561a9c2WmbdunVcf/31AIwdO5atW7eKgiYIgiAIXdSCb/4gqUcQSmXbD/ZuTp+eYSQnhDNr3g/UnJPJL6eygluWfEe0tw+jYrvZZHtmi5mTtdn8XLCKfx/7mLy6PEYGpTHALxVnhcuFV3CJFHIZw2PDmH5FbzZna/i/pRtYeTCbOsOlDdJvCwnevZkacSOrin7hhQMvsavsT0wWMb1BZ9Gm3bE0Gg3BwWcmHFOr1WRmZjZZJiSkYfCnUqnE09OT8vJy/Pxa7veo1+vJyrr4wWMAOp2u1d9xNF3hN4L9fueFZly9lHLZWo70Nxax2t65cXaEMnkhjrBvHSFGcIw4dTrdBZdpqVyaTBY+Xbyb/UdKmDq2B/sPHG3m2+0jOtiFkzl6xt/xPiOujKbUzcBJ5zqyqisYog4mxd2DvUfPH5/RaKDg8B7qLbVYsGDGggkTeozoJB11lloqLFXUSbWoZCoC5P50U/TCqU5Fbp0WaL90uqmBThTVSCzJPMS/t+1D7eZEtLcL/io5PkXFuCrluCjkRHk722UMSarUn1xDLp8d+4JqqYYgeSDBcjU+ch/c5e644IxaEYSP3KfJdy+mTAr20WUGpru4tN3dhI6iK/xGsN/vPHLkCHFxcXbZ9mmO9DcWsdreuXF2hDJ5IY6wbx0hRnCMOF1cXC65XJaU1ZKxvwilUs6y9R1jYj2zRWLt5ob5j7R9wMlfwf7yMvaXN53/qzm1Uh3n79vhBXid1dmq9JJjtRW5XI6mzoimrukTkQG9i/D3tu9FvQwZxZYSii2NM3xFyMO51f1vTZa/nDIptK02bYSo1WqKis7MpqvRaFCr1U2WKSwsJDg4GJPJRHV1Nb6+5x8Ympyc3BbhCsJlEeVS6GhEmRQ6opbKZa9ekD78ivYNRhAEu2nTMSF9+vTh5MmT5ObmYjAYWLlyJenp6Y2WSU9P54cffgDgl19+YdCgQcg6WLo4QRAEQRAEQRBsRya18SjwjRs3Mm/ePMxmMzfccAP33Xcf7777LomJiYwcORK9Xs/jjz9OVlYW3t7evP3220RENE0RKQiCIAiCIAhC59DmjRBBEARBEARBEISztWl3LEEQBEEQBEEQhHOJRoggCIIgCIIgCO1KNEIEQRAEQRAEQWhXXWKekNOD4tVqNR9//LG9w2kTVVVVPP300xw+fBiZTMa8efPo16+fvcOyuc8++4zFixcjk8mIj4/nlVdewdnZ2d5htbnCwkJmzZqFVqtFJpMxdepUbr/9dnuH1YRer+fWW2/FYDBgNpsZO3YsM2bMsHdY5+Uo9UN6ejru7u7I5XIUCgVLly61d0jn5ShlFhyjDDhCHW+L+nnTpk28/PLLWCwWpkyZwvTp09so2rbz5JNPsmHDBvz9/VmxYoW9w2k1Rzp2W+KI56IuSeoCFi5cKM2cOVOaPn26vUNpM7NmzZIWLVokSZIk6fV6qbKy0s4R2V5RUZGUlpYm1dfXS5IkSTNmzJCWLFli56jah0ajkfbt2ydJkiRVV1dLY8aMkY4cOWLnqJqyWCxSTU2NJEmSZDAYpBtvvFHavXu3fYO6AEepH9LS0iStVmvvMC6ao5RZSXKMMtDR63hb1M8mk0kaOXKklJOTI+n1eum6667rsGXmfHbs2CHt27dPGj9+vL1DuSSOdOy2xBHPRV1Rp++OVVRUxIYNG7jxxhvtHUqbqa6uZufOndbfqFKp8PLysnNUbcNsNqPT6TCZTOh0OoKCguwdUrsICgoiISEBAA8PD2JjY9FoNHaOqimZTIa7uzsAJpMJk8nUoef96Qr1g704Spl1hDLgKHX85dbPmZmZREVFERERgUqlYvz48axdu7aNom07AwYMwNvb295hXDJHOXbPx9HORV1Vp2+EzJs3j8cffxy5vPP+1Ly8PPz8/HjyySeZNGkSTz31FHV1dfYOy+bUajV33XUXaWlpDBkyBA8PD4YMGWLvsNpdXl4eWVlZ9O3b196hNMtsNjNx4kQGDx7M4MGDO2yc4Hj1w913383kyZP57rvv7B1Kq3TkMusIZcAR6nhb1M8ajYbg4OBG63S0i9/OpiMfuxfiSOeirqrj1ro2sH79evz8/EhMTLR3KG3KZDJx4MABbr75Zn788UdcXV355JNP7B2WzVVWVrJ27VrWrl3L77//Tn19PcuWLbN3WO2qtraWGTNmMGfOHDw8POwdTrMUCgXLli1j48aNZGZmcvjwYXuH1CxHqx+++eYbfvjhBz799FO+/vprdu7cae+QLkpHLrOOUgYcoY4X9XPn05GP3YvhKOeirqxTN0IyMjJYt24d6enpzJw5k23btvHYY4/ZOyybCw4OJjg42NrKHzduHAcOHLBzVLa3ZcsWwsPD8fPzw8nJiTFjxrB79257h9VujEYjM2bM4LrrrmPMmDH2DueCvLy8GDhwIL///ru9Q2mWo9UParUaAH9/f0aPHk1mZqadI7qwjl5mHaUMOEIdb4v6Wa1WU1RUZH2t0Wis5V5oXx392G2Njn4u6so6dSPk0UcfZdOmTaxbt4633nqLQYMG8cYbb9g7LJsLDAwkODiY48ePA7B161a6detm56hsLzQ0lD179lBfX48kSZ32dzZHkiSeeuopYmNjufPOO+0dTovKysqoqqoCQKfTsWXLFmJjY+0cVfMcqX6oq6ujpqbG+v/NmzcTFxdn56jOzxHKrKOUAUeo421RP/fp04eTJ0+Sm5uLwWBg5cqVpKent1HEQksc4di9EEc6F3VlXSJFb1fwzDPP8Nhjj2E0GomIiOCVV16xd0g217dvX8aOHcv111+PUqmkV69e3HTTTfYOq138+eefLFu2jPj4eCZOnAjAzJkzGT58uJ0ja6y4uJjZs2djNpuRJIlx48aRlpZm77Acnlar5YEHHgAa+jlfe+21DBs2zM5RnZ+jlFlH0dHreFvUz0qlkmeffZZ77rnHmja5oze2mzNz5kx27NhBeXk5w4YN46GHHmLKlCn2DuuidYZjV5yLHINMkiTJ3kEIgiAIgiAIgtB1dOruWIIgCIIgCIIgdDyiESIIgiAIgiAIQrsSjRBBEARBEARBENqVaIQIgiAIgiAIgtCuRCNEEARBEARBEIR2JRohgiAIgiAIgiC0K9EI6WS2b9/OP/7xjxY/X7p0KXPnzrX5dpcuXYpGo7G+Tk9Pp6yszObbERzXhcrmhezdu5eXXnqp2c9Ol7eqqiq+/vprm21TcDzn1kUtmT17NqtXr27x82nTprF3715bhibKZxdnq7J5Ie+++y5btmxp8v7Z5W379u1kZGTYbJuCcClEI0SwiR9++IHi4mJ7hyF0Yn369OHpp58+7zJVVVV888037RSR0BF15LpIlM+urb3K5j//+U8GDx583mV27NjB7t272zwWQTgfMWO6HdTV1fHwww9TVFSExWLh/vvvJzIykldffZW6ujp8fX155ZVXCAoKYtq0afTo0YOdO3diNpuZN28eSUlJZGZm8vLLL6PX63FxcWHevHnExsa2Ko6ysjKee+45CgoKAJgzZw79+/dn/vz5FBQUkJeXR0FBAbfffju33XYbAB988AHLly/Hz8+PkJAQEhISCAsLY9++fTz22GO4uLjw3XffAfDVV1+xfv16TCYT77zzDt26dbPtjhRszp5l87rrruPrr7/G09OTQYMG8eSTTzJp0iRmzZrFxIkTUSqVLFy4kI8//pjy8nIeffRRNBoNycnJnJ5z9c033yQnJ4eJEycyePBgRowYQV1dHTNmzODw4cMkJCTwxhtvIJPJ2npXCjaSl5fHPffcQ0JCAgcOHCAuLo7XXnuNY8eONSmXGRkZTeqiBQsWsH79evR6Pf369WPu3Lmt/vv/8ccfzJ8/H4PBYJ2t3N3dnfT0dCZNmtSknisrK+PRRx+luLiY5ORktmzZwpIlS0T57GTsUTYzMzP55JNPeP/99/ntt9+YOXMmu3btQpIkrrnmGtauXcvs2bMZMWIE48aNY9OmTcybNw9XV1f69+9vjfvbb79FLpezfPlynnnmGQB27drFZ599RklJCY8//jjjxo1r830odHGS0O5Wr14tPfXUU9bXVVVV0k033SRptVpJkiRp5cqV0uzZsyVJkqS///3v1mV37NghjR8/XpIkSaqurpaMRqMkSZK0efNm6cEHH5QkSZK2bdsmTZ8+vcVtL1myRHrhhRckSZKkmTNnSjt37pQkSZLy8/OlcePGSZIkSe+995500003SXq9XtJqtdIVV1whGQwGac+ePdKECRMknU4nVVdXS6NHj5YWLFhgjTMzM9O6nbS0NOmLL76QJEmSvvrqK2nOnDmXs8uEdmLPsvnMM89I69evlw4dOiRNnjzZuu7Ro0dLtbW1jb7/4osvSvPnz5ckSZLWr18vxcfHS1qtVsrNzbXGcXqbKSkpUmFhoWQ2m6WpU6day7zgGHJzc6X4+Hhp165dkiRJ0uzZs6VPP/30vOXy7LqovLzc+v/HHntMWrt2rSRJkvTEE09Iq1atanG7p9ej1WqlW265RaqtrZUkSZI+/vhja9lrqZ574YUXpI8++kiSJEnauHGjKJ+dlD3KptFolNLT0yVJkqRXX31Vmjx5srRr1y5p+/bt0iOPPNLo+zqdTho2bJh04sQJyWKxSDNmzLDWoe+99571/H36Ow899JBkNpulI0eOSKNGjbLFLhKE8xJPQuwgPj6e1157jX/961+kpaXh5eXF4cOHufPOOwGwWCwEBgZalx8/fjwAAwYMoKamhqqqKmpra3niiSfIzs5GJpNhNBpbHceWLVs4evSo9XVNTQ21tbUADB8+HJVKhZ+fH35+fmi1WjIyMhg5ciTOzs44OzuTlpZ23vWPGTMGgMTERNasWdPq+IT2Z8+ymZqays6dOwkNDeXmm29m0aJFaDQavLy8cHNza7Tszp07ef/99wEYMWIE3t7eLa43KSmJ4OBgAHr27El+fj6pqakXv1MEuwsJCbHexZ0wYQIff/zxecvl2bZv386CBQvQ6XRUVFQQFxdHenr6RW97z549HD16lJtvvhkAo9FIcnKy9fPm6rk///zTWj6HDRsmymcn1t5lU6lUEhkZybFjx8jMzOTOO+9k165dmM1maxynHT9+nPDwcKKjo63xLVq0qMV1jxo1CrlcTvfu3SktLb3YXSAIl0w0QuwgJiaGpUuXsnHjRt555x0GDRpEXFyctRvTuc59PCuTyXj33XcZOHAgH3zwAXl5edbuUq1hsVhYtGgRzs7OTT5TqVTW/ysUCkwmU6vX7+TkBIBcLsdsNrf6+0L7s2fZHDBgAP/73/8oLCzkkUce4bfffmP16tWXfUF2blkWZdHxnFvO3N3dz1suT9Pr9bzwwgssWbKEkJAQ5s+fj16vb9W2JUniqquu4q233mr288ut50T5dGz2KJupqals2rQJpVLJ4MGDmT17NmazmVmzZl3y74DGZVEQ2oMYmG4HGo0GV1dXJk6cyN13382ePXsoKyuzDhIzGo0cOXLEuvzPP/8MNPTX9PT0xNPTk+rqatRqNdAw2O1SDBkyhC+//NL6Oisr67zLp6SkWPuv1tbWsmHDButn7u7u1qcoguOyZ9kMCQmhvLyckydPEhERQUpKCgsXLmy2ETJgwAB++uknADZu3EhlZSUgymFnVVBQYC2DK1asoG/fvi2Wy7PLwOmLOl9fX2pra/nll19ave3k5GQyMjLIzs4GGsZNnThx4rzfSUlJYdWqVUDDeBJRPjsve5TN1NRUPv/8c5KTk/Hz86OiooITJ04QHx/faLnY2Fjy8/PJyckBYOXKldbPRFkUOgLxJMQODh8+zOuvv45cLkepVPL888+jVCp56aWXqK6uxmw2c/vttxMXFweAs7MzkyZNwmQyMW/ePADuueceZs+ezYcffsjw4cMvKY6nnnqKuXPnct1112E2m0lNTT1v+t6kpCTS09OZMGEC/v7+xMfH4+npCcD111/Pc88912hguuB47F02k5KSsFgsQMOJ9q233mrSxQDggQce4NFHH2X8+PH069eP0NBQoOGEnpKSwrXXXsvQoUMZMWLEZewNoaOIiYnh66+/Zs6cOXTv3p1p06YxdOjQZsvluXXRlClTuPbaawkICKBPnz6t3rafnx+vvPIKM2fOxGAwAPDwww8TExPT4ncefPBBZs6cyfLly0lOTiYwMBAPDw9UKpUon52MPcpm3759KS0tZcCAAQD06NEDPz+/Jk9lnJ2dmTt3LtOnT7cOTD/d8EhLS2PGjBmsXbvWOjBdENqbTJJOpZUROqRp06Yxa9asSzp5toXa2lrc3d2pr6/n1ltv5cUXXyQhIcHeYQl20NHKptA55eXlce+997JixQp7h3LRDAaDtSG/e/dunn/+eZYtW2bvsAQbc8SyKQgdiXgSIrTKs88+y9GjR9Hr9Vx//fWiASIIgnCOgoICHn74YSwWC05OTrz44ov2DkkQBKHDEU9COqklS5bwxRdfNHovJSWF5557zk4RCUIDUTaFjuSBBx4gLy+v0XuPPfYYQ4cOtVNEgtBAlE2hsxONEEEQBEEQBEEQ2pXIjiUIgiAIgiAIQrsSjRBBEARBEARBENqVaIQIgiAIgiAIgtCuRCNEEARBEARBEIR29f83FH6FxzQougAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first look at data\n",
    "sns.pairplot(iris, hue=\"species\", palette=\"viridis\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8c6e7a68-b433-4399-9c76-0507730015b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>-0.042434</td>\n",
       "      <td>1.274315</td>\n",
       "      <td>0.516271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.042434</td>\n",
       "      <td>0.189979</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>-0.121639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>1.274315</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>3.116278</td>\n",
       "      <td>1.295609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.516271</td>\n",
       "      <td>-0.121639</td>\n",
       "      <td>1.295609</td>\n",
       "      <td>0.581006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.685694    -0.042434      1.274315     0.516271\n",
       "sepal_width      -0.042434     0.189979     -0.329656    -0.121639\n",
       "petal_length      1.274315    -0.329656      3.116278     1.295609\n",
       "petal_width       0.516271    -0.121639      1.295609     0.581006"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b6ba2df8-f27c-40eb-909b-9d27c628f3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1a6df280-a126-41ee-8c5b-2f299471109f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iris_preprocessing(iris_data):\n",
    "    # encode species label as numeric value\n",
    "    label_encoder = preprocessing.LabelEncoder()\n",
    "    iris_data[\"species_id\"] = label_encoder.fit_transform(iris.species)\n",
    "\n",
    "    # scale values\n",
    "    scaler = preprocessing.MinMaxScaler()\n",
    "    iris_data[\n",
    "        [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "    ] = scaler.fit_transform(\n",
    "        iris_data[\n",
    "            [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]\n",
    "        ]\n",
    "    )\n",
    "    return iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e3222fe3-9f71-4fb9-a8a3-ef9dad329859",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>species_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  species_id\n",
       "0      0.222222     0.625000      0.067797     0.041667  setosa           0\n",
       "1      0.166667     0.416667      0.067797     0.041667  setosa           0\n",
       "2      0.111111     0.500000      0.050847     0.041667  setosa           0\n",
       "3      0.083333     0.458333      0.084746     0.041667  setosa           0\n",
       "4      0.194444     0.666667      0.067797     0.041667  setosa           0\n",
       "5      0.305556     0.791667      0.118644     0.125000  setosa           0\n",
       "6      0.083333     0.583333      0.067797     0.083333  setosa           0\n",
       "7      0.194444     0.583333      0.084746     0.041667  setosa           0\n",
       "8      0.027778     0.375000      0.067797     0.041667  setosa           0\n",
       "9      0.166667     0.458333      0.084746     0.000000  setosa           0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocessing\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "iris = iris_preprocessing(iris)\n",
    "iris[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "50e46151-194a-4ec9-b2cb-355e8c668b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ihmumNi0eImZC5GOgtQMRQ4E9BRE5u/J4hraIuHfA0ANEJJjPRcQuDOiYRsLYAxH7KhgSXNdhSXZdVzMmJZRLg9dXS6IeKylKEBFaOCLmRc+VIUkQPddzXcQtnsvhrhE+D2dl0UZ4/tkLUx9E21V+jrRuwjIc0XZto56zIaQfh+wOFgG/2o0bN5KamkpKSgrz58+vtv3gwYNcddVVXHrppVx88cVs2LAh0CEpiqLUW2PPId3UAnrn4HQ6efTRR1m4cCFWq5VJkyaRnJxMjx493GVeffVVxowZwxVXXMHu3bu56aabWLduXSDDUpRGI6UDyne6KlxNZ4MIB8f3oBeCqTdCi0WW/wJ6Dhh7IgztkY7fXfMaGLogjKc39SWcEinLXHNfSwnm/q4hMrzt49gNzoPun0NzIml9A+8FNDlkZmbSuXNnEhISABg3bhxr1671SA5CCIqKigDX1Hjt2rULZEiK0miktCGPXgnOPYAGIgSMZ0DZtorRUwXSnAT2z0CYQDqQoZdA6XIQRtdy5By08ClNfSn1IvVjyKOTK5rZAlocxC92ddqrhX78OSh+q/K6o59DC02ptXxTUGMr+VFubi7t21d+A7BarWRmZnqUmTFjBjfccAOLFi2itLSUhQsXej2u3W4nK6v6XLi1sdls9SrfHKlraHr1jT/O8iHxoT+jiYoOaXoxOL9ACOlugSptKxECdxNVWfKfimXXdr3wMXbv64JTxjTJNZyKdmGvEm3ejyZcU1/qDhvH991HTskdNZa3GPZyeuSbaMJeed0Ff+Pn7PeoHKm1UlP8P3JN9qOSQ6PKyMhgwoQJXH/99Wzfvp27776blStXomm1/yIsFgu9evWqdfvJsrKy6lW+OVLX0PTqG79ecBxsZe5lIar3STi5UdDJy5pmIbF7FMLkn59bY/wO9KNHodzhXtaEk+iIo8R2rvm80rYPeczk0ddBE4Ize1oRBmu18vW9Br8kEomqkPYnq9VKTk6Oezk3Nxer1fOX/dFHHzFmzBgABgwYgN1uJz8/P5BhKUrjMA0Aqs6DbMDzT66GiXeq0V1NPoOJaQCec0pYauhPUYWxJ0iH5zoRAlqbQER3SiT+6yE9Z84chg0bxkUXXVTj9q+++oqBAwcyfvx4xo8fz7x589zbvDXw8aeAJoe+ffuSnZ3Nvn37KCsrIyMjg+TkZI8yHTp04MsvvwRgz5492O124uJqfzapKMFChE1x9UnABFhc9Q1h11Qsh4LWASLuBMyuwfFEDETcVzEZTyiIMETMvGY16Y0vROTtYB6EqwOeGUwDEJF/q728sQtEPUzlzyEKEbsAUeOsdk3Fe0slX1srpaWlsWDBgjrLDBo0iOXLl7N8+XJmzJgBVDbwWbBgARkZGaxcuZLdu3c3+MpqE9DHSkajkblz5zJt2jScTicTJ04kMTGRl156iT59+jBy5EjuvfdeHnjgAd580zWZ+FNPPdVsJhVXlIYQQkPEPIfU73c9MtGsCKEhI6aDfhwMHRDCgAy7GvQ8MFgRwowMvxychyqWAzSERQAJYYbYf1VUSEvQ2nn9m9bC0pAhfwX9qPvn0Jy4Wiv557v04MGD2b9/f73386WBjz8FvM4hKSmJpKQkj3V33FFZMdWjRw8++OCDQIehKE3m5FY6QosGLbrKcjho4ZXLIgSCtAnrCUIIMNSv5aHQwkALC1BEDedLU9a8vDzS0tLcy+np6aSnp9f7XDt27OCSSy6hXbt23HPPPSQmJvrUwMefmrxCWlFaGymdQBlCuOojdF0HmY9miK99H73Y9ZipCe+qm0MMTUVKgcOHO4e4uDiWLl3aoHP17t2bdevWER4ezoYNG7jttttYs2ZNg455KlpX9buiNDG9+G1k7tnI3HPQj1yKXvAwHDoTDg9Dz+mFbt/kUV46dqMfugB5aBDy0ACkrfE7iErHXvRDF7piyO2PXvppo8fQHOhS8/ryh4iICMLDXXeSSUlJOBwO8vLyfGrg408qOShKI5FlX8PxZ3HNreB0zaNge69KCSfk3+S6kwCk1JF514L+p2ubLEEW3Il01P959SnHLCUy7zrQD7pioBSOzUY6/mi0GJqDEz2kG2NU1sOHDyOlq9lzZmYmuq4TGxvrUwMff1KPlRSlsZRtx5UYTtBrKOQE/QBoCa5Kav0YHnM2CCM4fgRjp8DGeoIsAP3ISTEYoPyHoK8XqS9/TeYza9Ysvv76a/Lz8xkxYgQzZ87E4XA15Z0yZQqrV6/m/fffx2AwEBISwvPPP48QotYGPoGikoOiNBZDWxDmitnZ6qBVPCrQoqg2mY90Vm5vDCKC6n0x9HpXNgc7if/uDJ5//vk6t0+dOpWpU6fWuK2mBj6Boh4rKUpjCbkYjGeBCHO9CAVxUkevkMvRNFczTiHMEPUIEFK5T+gYMPVrtJCFMEHU/1WJIRQsKWAa2GgxNBetbbIfdeegKI1ECBPEvQP2TSCPgWkgUusIJQvB+RuYL0ALHeWxjxY2EWnq43qUpHUE85BGby2khU2oiOEH0NqDeWira7EkJTj01vVdWiUHRWlEQhgh5MLKZYCIG+rex3QGmM4IbGBeCFMimAL3fDsY+KvOIVio5KAoiuKFP+scgoVKDoriR3t+m0ZHy2aQggPlI+kanwjFCwAnmP8CMfM9RhyWjr3IY3NdzVVNgxFRD7p6TPtI1x2QPw3KvwYMEHGrq8K6eAEgIHwaWliaxz6ybCfy+GOg57vmb468y2O4Cuk8gix8ABy/gqEHIvoxhKFtrTFIKZHFb0DpRyBCEBF/Q1S5O2opVHJQFOWU7MmeQZeQje5ht7toa5BFayqH4S7bCAU3QZxr0DWp5yGPpoMsBCQ4c5HOg4j4t30/af71UL61YsEBRS/i+rOuGOW08GF0YUELHQeASfsTmT8LZIlre8kHSFmIiH7KFZMsQ+ZNAecB1zGcfyLzLoc2/611vCNZ/AYUvQK4WmHJgjsg7l8I82DfryMItLbk0LpqWBQlgKzGDR7zMQhRfX4Gyr6o8v5rXB/iJ5qrlkH5N0i9yPeTln9Tw8qqw1/boPRD91KE6ZuThse2QemqKrvuqRgw70QZh6u/haOO0T9L/8OJxHDimLL0E1+vIChICU5d8/pqSdSdg6L4ibOG4ROkPDlBVC1Ty4irovrsZ7XTcPVcrkvl3Aq6NFHtO6Go8jEgLCBP6pwndVf/jFqdfB3CNR9Di+L7fA0tRctKdYrShPIMNyJPTP8pXa9qnyfh11e+t/ylokNbxQevCIXQKfUbpjv0yhpWVk0uoYiI6e6l4+XngRZB5ffCUAi/rbK4oWvFXAwnPtxDwDwADN1rDcE1V8OJ8sI1OF9YzZ24glVjDp/RXKg7B0Xxk66nzeD3nAgstgWARnnorXSMOR0KnwRsEDoVLeJad3khLBD/EbL4X+DcB+ZzEaGT6nVOLfo+dEM7KP0ACIGoBxBaJLLkfUAgwqYgTGe5y+syEhG/Alm8APQjiJBRiJDUKjEJiH0dWfw2OHaBsRci/Jo6+zWIkBSInY+0fQyEucobO9frOoKBbGEf/t6o5KAoftS5/bXAtZ4r266stbzQIhCRd9S63RdaxDSImOZ53OjHaj+nIR4RdU/t24UJ4aXvRbV9LEMRlqH12ifYtLQ7A29UclAURfHC9ZhQJQdFUXC138e2DFm2DQydEQxp8DHzSkt4c8d2jpaWkNKtBxd06eqHSJXAEzh1lRwURQFk4UNgW14xiqqFhMjTkXK5awiMU3DMZmPse2+TV1KKQ+os+2kX95+XxJVn9/dr3Ir/SVrf8BmqtZKi1EDqx109ft3Da9sxG/ZB2XenfMzlP2dxzGbHUdFU1OZw8NzWLX6IVmkMUgqvr5ZE3TkoSk2kjWrfnaSGZ2ev+rE5HDhP6kNQ5vDWR0FpLlpbhbS6c1CUmmhtwNiDyj4DAjCAacApH/LCLt0wVRlXKcRo5K89WvdIp0FDVvZdqevVkqjkoCg1EEIg4t4EywWujmqmAfx+/CmEFn3Kx0yMj2fh+ImcEd+G9hERTOrVm8eTU/wWsxI4Eu+PlNRjJUVpJYQWg4h9xb1cfiirwccc0rET/73ymgYfR2l8/ho7ac6cOaxfv574+HhWrqzeB+aTTz7hjTfeACA8PJyHH36YM888E4Dk5GTCw8PRNA2DwcDSpUv9ElNNVHJQFEXxQuK/x0ZpaWlMnTqVe+6puSNip06dWLRoEdHR0WzYsIEHH3yQxYsXu7e/9dZbxMXF+SeYOqjkoCh12JufR9bhw3SMiqpxmLwjJSV8e/AAoUYjf0k4HZPBUK/jSynZlnOQnONF9G7Xji4xsf4JvM5z6lD2VcVUpf0RhvYBP2fQ82MnuMGDB7N///5at59zzjnu9/379ycnJ8cv560vlRwUpRZLs37kgc8/wyg0nFJnVIeOvNSrl3v7T0cOk/7Rf1yT3SDpEhPL4smXE2L0bVRVKSX3rl3Dyl9+xiAEDqnzbMpfGZsYyClBncj8aVC+DfeIrrELWtzcC4HgS3LIy8sjLa1ycqX09HTS09NP+ZwfffQRI0aM8Fh3ww03IIRo8LG9UclBUWpgc5Rz37pPKXNWNjVdc2A/PxzKpU87KwD3fLaa42V29/bdeUd5Z+cObhzo2wftt38eYOUvP1PqKHevm73mv6R2T8SgBaatSKRpS0VfjcomubJgFqLdpoCcryXx5alSXFyc3+oBtm7dykcffcR7773nXvf+++9jtVo5evQo1113Hd26dWPw4MAkdp+Tw5o1a3j22Wc5evSo65uSlAgh2LZtW0ACU5SmlF9qQztpJFKDEOQUHXcnhz+Ljntstzud7Css9Pkcfx4/jnbSl1FdSorKyogOCcx8CEbtCFDuuVLPC8i5WhaBbMThM3766SceeOAB3njjDWJjKx81Wq2u/3vx8fGkpKSQmZnZ9MnhmWee4bXXXqN799rHdVeUlqJteDjhJhM2R+WsaU6pc2abyrmUB3Y4jXW/7aVcd3VsCzUaGdyxo8/n6NPOivOkWs640DCiLPWYz6GebM4z8JhGFA2MverYQ4ETFdKNkxwOHjzIzJkzefrpp+natXLsrZKSEnRdJyIigpKSErZs2cL06dPrOFLD+HzvGh8ff0qJYePGjaSmppKSksL8+fNrLLNq1SrGjh3LuHHjmD17dr3PoSj+ZtQ03r50EvGhYZg0AyFGI3f16U+nqMp+Dk+NTKV323aYNA2D0Liyb38uqkd9QbfYOP4xMhWLwYBJM2ANj+DtSyfVOXdCQ5U6ekPknbgShAkMXRCx8wJ2vhbDj53gZs2axeWXX85vv/3GiBEjWLx4Me+//z7vv/8+AK+88goFBQU88sgjjB8/3l2HcfToUa644gouueQSJk+eTFJSUrX6CH8SUtZ9SWvWrAHg66+/5siRI4waNQqzuXLKwNGjR9e6r9PpJDU1lYULF2K1Wpk0aRLPP/88PXr0cJfJzs7mzjvv5K233iI6OpqjR48SHx9fZ9BZWVn06uX7t536lm+O1DU0DSklBTYbURYLv/z8c43xF9ptWAxGLMZTq8Jz6DrH7XZiQkICmhig8ncgZRnIEhDRAT+nvzXF3/+HW9YxZ/+XXsutPDutWfwfv+WWW+rc/tprr3k9htf/zZ9//rn7fWhoKFu2eA4UVldyyMzMpHPnziQkJAAwbtw41q5d65EcPvzwQ6688kqio13fyLwlBkVpTEIIYkND6ywTZWlY/YBR07yew9+EMHuZF1qpJoh6QF9/vWs62jVr1nDkyBEuueQSADIyMnz+jPWaHJ588kkAvvvuOwYOHOix7bvv6h6hMjc3l/btK9tQW61WMjMzPcpkZ2cDcPnll6PrOjNmzPB6q2S328nK8r23qs1mq1f55khdg59icDoocTiJNZt9+sZcWF7OhoP7GdS2HbGagR937aKgzE6Y0USIwYCUkmPlZZg1A2G13DkcsZVS6nDQMczVs1VgQxOlOGU0oCEoQxPHccoYoH79JOqrOfwOGqqpriGYxk4aMsQ198hTTz3l0XoqOTnZo6ltXXy+D37sscdYtmyZ13X15XQ6+f3333nnnXfIyclh6tSprFixgqioqFr3sVgs6rFSEGrqa3hx6xf889uv0ISgU2QU76ZdhjUiotbyt2YsZ/We3a6FX7KIM5sJCwnhcHExTim5bdC5fPbbHn7NO4ouJVf06cdDSRe6k45D10ldtJDfCgoACDOZ2DzZQZRjAWAAQwcIvRyKngcEaBGI2DcRpsD1c2jq34E/nMpjpQaTNGprJX8pLS1l37597qc3+/bto7TUt5GFvSaH7du3s337dvLy8li4cKF7fVFREU5n3cMNW61Wj959ubm57qZYVcv069cPk8lEQkICXbp0ITs7m7PPPtunC1AUX6zP/o03tn2Do6Jl0e/HCpjx3xUsnjylxvJHSkoqE0OFvLIy8srK3Mv/75utCIR7GO7Fu76nf/sOXHqm64PrrjX/dScGgAFxv2GyrQajA3CA8w8o+gfuFvS6HZl/PbTdHHT1AK1CEN05nDBnzhyuuuoqEhISkFJy8OBBHnnkEZ/29ZocysvLKSkpwel0Ulxc7F4fERHByy+/XOe+ffv2JTs7m3379mG1WsnIyOC5557zKDNq1CgyMjKYOHEieXl5ZGdnu7OcovjL94dysHs0S5VkHT5ca/llWbu8HlOXkqqfGKUOB9v+POhODtv+POhRvm/sYUxa1S9UnnM7uFYVgCwEceqjvyr+d2JU1mAzYsQI1qxZw969ewHo1q2bR4OiunhNDkOGDGHIkCFMmDCBjvVoww1gNBqZO3cu06ZNw+l0MnHiRBITE3nppZfo06cPI0eO5Pzzz2fLli2MHTsWg8HA3Xff7dHpQ1H8oWNkFBajyaM3crvw8FrLJ3fpwpNbNtR5TE2IigThYjEY6Bxd+aHeKSqa/ccrO8UdKImgTDdg1BxVjiLw/EpqAFH7oy6lCQXRncOXX37JsGHD3K1NT/jjjz+AuhsSneA1OTS0SVRSUhJJSUke6+644w73eyEEc+bMYc6cOd5CUZRTdskZvfj4pyy25Rx093x+4a/jai3fPb4N7cPCySmpvFsWQKjJhEEInFIyoH0Hvj+Ui14xYkCPuHimVpkP+uUxF3H+wvnYKx6/rtrXnblDjhMmfsTVxUiC+S9g3wzCANIB0c8hRGArpZVTFTx3Dt988w3Dhg3zaG1alV+Sgz+aRClKUzNqGm9eOpFvDx6g0G6jX/sOtA2r/c4B4Itpt/D05o38b++v9G7bluldEolN6MT3ubnEhoYysMNpFNhsbPvzIKEmE0M6dsJYZUykNmFhfHPjdBZl7qDEUc5lZ/UhLjISyr8D/RiY+iEMbZHl34MzB4y9EMZOgf5RKKdCUuNTwObq9ttvBypbm9Zm2bJlTJgwocZtPj1WgoY1iVKU5kATgiEd6/fhe/d5I7j7PFfT6qysLNpHRNI+ItK9PTY0lJHdah85IMJs5pZBQzxXnjQCqjD1BVPfesWlNIEgrHPw5u233641Ofg8fMaJJlEn1KdJlKIoSrBriXNI1zVAhs/9HBrSJEpR/K3c6eTJzRtZ9evPhJpM3HdeEinde3jfsQ5b9//BTSuWU1xeRqTFwlvjJ9KvfQf3dpvTwaw1q9j8++/EhoYyN+lC/rF5I7sOH0YTcG3/c2gbHs6bO7YjgJsGDubqfgMaeKX1I6VElrwFxf8GBIRfhwi7RjWN9Ycg/PD3pq7/Fz4nh4Y0iVIUf3ty80Y++DHTPWrqHaszWDRhMud0OO2UjpdXUsLUpYvdj5UL7XYmLX6fnTfPIKzi//kz3+9ke94R7E4nR0pLuHrZR+7PC13Cgu3fYdI09yit/9iykSiLhUvPPKshl1ovsnQJHH8B93wNx19AikhE2MRGi6HFaoGPleq6c/D6WOnLL12DTa1Zs4b169fzxx9/8Mcff7B+/fpqzaQUpbGs/PUnj+G0bQ4H/9v9yykf79O9u6vVNzql5Iv9rkepUkq+OXLI3fIIav4ieSIxgKvfw1If+kv4Vekyqk7kA6VQ+nHjxtASSRA+vIJN1SlJT+b1zsEfTaIUxd9CT5qK06hpRDZgHoSoWibXiasYEE8IgUnTvI4KUJWABsV0SmrqI6FFVl+n1F8QDp/x1ltvMXHiRMLDw7n//vvJyspi9uzZnHfeeQDMnTu31n29Jgdfm0QpSmO67/wk/rZ6FTaHA6OmEWWxcHmfUx9yJbVbD+JDQzlapZFFx8hIj8dU1/Q4g7f2/ILN4cBcMcdDYZVpQgEsBiNlTtcdTajJxMwhQ085plMhIu9A5m0FaatYEYKImNGoMbRYQXhnsGTJEq655ho2bdpEYWEhTz/9NHfffbc7OdTF5zqHUaNG0a9fPwYNGsSgQYNITExsUNCK0hCp3RN5+9JJrN7zK5FmC1P6nO2130JdNE1jy3U3cfdnq/nl6BH6WNvzRHKKR5lLTu/CuWecyaY/smkbFsaVffuz4pef+OCHTMJMZuYmXYBJM7L0px8RCCad1ZtusXENvdR6EaazIH4JsuJRkgidgDCq2Rv9IgiTw4k6hQ0bNjB+/HgSExPrrGeoyufksGrVKnbu3Mm3337L008/zW+//cYZZ5zBK6+8cmpRK0oDDTqtI4NOq9+QLnUxG428WEevaYALunTlgi6VUzde0bcfV/Tt51HmnuGBm53LF8LYAxF5V5PG0OJ4DqMVNPr06cP111/P/v37mT17NkVFRWiabz0YfE4OmqZhNBoxGAxomkZ8fLzqIa0oSishgrK10uOPP05WVhYJCQmEhoaSn5/PE0884dO+PieHgQMH0rNnT6677jomT56sBsdT/MrmKOe1b7/hl6NH6Gdtz/UDBmIy1G+Mocc3ree973eiCcHNAwfTM74NT23ZSJlT5+qz+zH4tE7cu3YNx+12xvToybX9BzDzvyvJLS5iyGmdeGrkaOZv+5afjx6hbzsr1/U/h/d+yOTbgwfoGhvLhRHRLMn6kbW/7aF9eAS3Dj63QY+ylOAh8F9rpDlz5rB+/Xri4+NZuXJlte1SSh5//HE2bNhASEgITz31FL179wZcw128+uqrANx666219m52xy0Eu3fv5vPPP2fGjBmUlpZSVmXY+br4nByee+45vvvuO9577z0WL17MgAEDGDx4MMOGDfP1EIpSI6euc8WSxWRVNBVd//tvfHPwAG9cfKnPnbfuX/cp7/9QOcvg81u/8Nj+1JZNHssLd27jzZ3b3E8KVvz6M5/+tgcppSuG7N94a+d2Cu02bE4nZoOB9zSNMikpragEz/j1F9ZMvZboWlo6KS2Mn5JDWloaU6dO5Z577qlx+8aNG8nOzmbNmjXs3LmThx9+mMWLF1NQUMC8efNYsmQJQgjS0tJITk52T7Fck4cffhhN09i6dSszZswgPDycmTNnsmTJEq9x+jx8xqhRo7jnnnt45JFHSEpKYtmyZdx8882+7q4otfrx8CF+qehcBq4+C1v2/c7BouM+H+PDH7+v93lP/lu3ORyVMTgdHCopxlaxXOZ0cqy8nNKKvhUOXaeozM7/9vxa7/Mqwclf/RwGDx5c5wf62rVrufRS1xej/v37U1hYyKFDh9i8eTPDhw8nJiaG6Ohohg8fzqZNm2o9DkBmZiYPPfQQloom1dHR0ZSXl9e5zwk+3znMnDmTn376idNPP51Bgwbxj3/8g379+nnfUVG8KNedaCcNh6wJQXk9+hToTTCwjS6pV4xKEJP4VOeQl5fnMSBpeno66enp9TpVbm4u7du3dy+3b9+e3NzcauutViu5ubl1HstoNOJ0Ot134Hl5ef6vkL7ppps466yzMNTyHHjLli0MHz7c18Mpilvvtu2ICrFQ6ijHKSUmTaNzTCynR8f4fIwhp3Xiq4P7GxSHhispOSpiMGgaTqdOudQxCIEmBAahYavox2DUBBd27dagcypBxIfvH3FxcR6jVze1q666ittuu42jR4/ywgsv8L///Y8777zTp319fqzUt2/fWhMDwLPPPuvroRTFQ4jRxNLLriC5aze6xsTy1x49eT/tMvekPL54f1I6gyo6rAkguXNX7jh3GCFGI2aDgTE9EnkqOYVwkwmTpjGoQ0cWXHQp0RYLJk2jR2wcq668hgu7dqdrTCyjuyeyZuq1XHJmL7rFxHL+6V3459DzmXp2P7rHxjHktE58OHkKHSOjAvRTUZod6cPLD6xWKzk5Oe7lnJwcrFZrtfW5ublYrdY6j3XJJZfw97//nZtvvpm2bdvyz3/+kzFjxvgUh893Dt742rFCUWrSLjyC1y+6tEHH+HDylGrr7jj3Lx7Ll53Ui3r7zZ69h1+/aLzH8jMpf3W/z8rK4r5Bg7jv/AsaFKcSnEQjTfaTnJzMokWLGDduHDt37iQyMpJ27dpx3nnn8fzzz3Ps2DEANm/ezKxZs2o8RlFRERERERQUFBAfH8+4cZX9dwoKCoiJifEah9+SgxoSWFGUFsuPdwazZs3i66+/Jj8/nxEjRjBz5kwcFQ0dpkyZQlJSEhs2bCAlJYXQ0FB3v4SYmBimT5/OpEmTALjttttq/ZCfPXs2r7/+OmlpaR6fzVJKhBCsXbvWa5x+Sw6K0tgOFBby5f4/CDOZGNm1O0VlZWz8PRtNE1zYpStRFs8mpuVOJ2t/20uh3ca5HRPoXMMf1tcH9vNbfh6J8W18Gv774PFCvtj3B6FGEyO7dSPkpAEBlZbDX/0cnn/++brPIwQPPfRQjdsmTZrkTg51ef311wFYt25d/QOs4Lfk0LGj/4YxUBRvduT8ydRli13f5gS0Cwsn31bqHjI73GRm5ZSraBvu6qRmdzi47KMP2JOfVzFjl2TBxRMYlnC6+5iPbVxf0VfC9Slw88Ah3H5u7f14MnNzuGLph+4Y2n8VwfL0qYSreU5apiDsIf3pp58ydOhQIiNdI/MWFhby9ddfM2rUKK/7ek0O3uZsODFk97x583yJVVH84t7PVlNSpb32H8cKPO78yxwOnt+6hSdHuv5/fvzTLnbnHXX3UwC4+7PVbLruRgCyC/J59/ud2J2V2//57VdM6Vv7gH73rV3jEcP+wkLeztzOrYPO9dNVKs1KEFarzps3j5SUygEko6KimDdvnn+SQ23zOJyg5nNQmsKR0hKP5ZPrCh1ScvB4oXv5cEkJ9iqJASC/yvDcR0pKMBs07FW6LZg0A0dLSmpNDodLPGMoczr583hRPa5CCSbBOJmPrlevRfd1ThKvyUHN46A0R0M7JvDZ3t2UVfznNwqBEML9WCnUaCSpc+XoqYNO64jZaHTPHmfSNM45rbJOITEuvtoXQ6Om0bmOvhZDOyWwes+vlFX8sYUajQw//fRayyvBS8jGa63kT3369OHJJ5/kyiuvBODdd991j9PkTb3qHNavX8+vv/6K3V45wcmMGWoiEaXxPTlyNAU2G1sP7MMgBDMHD2V3fh4Zv/4MwMRevbm2f+UUiEM7JXDv8BE8uXkD5brO2db2vJRa2bwvOiSEhePTuGXlcvJKS2kXHsEbF19KqKn2CubHk1PILy3li/1/YBCCWwedS2p3Nc9JixWEdw4PPvgg//znP7nzzjsRQjB8+PA6Z3+ryufkMHfuXGw2G1999RWTJ09m9erV9O3b95SDVpSGiLRYWJQ2mXKnE4OmuTvMPTt6DAIw1DBEwNX9BnDV2f0p13XMNXToHNihI9/cOJ2yioH2vIkwm3l7wqRqMSgtVBAmh7CwMO666y5KSkoICwur174+95Devn07Tz/9NFFRUcyYMYMPPviA7Ozs+saqKH5lMhg8PpSNFcNe1EYI4fWD35fEUFcMSsvkr4H3GtO2bdsYO3YsY8eOBeCnn37i4Ycf9mlfn5NDSMWwxKGhoeTm5mIymTh8+HD9o1WCkt3hYG9+nkclbmMrczrZm59HXkVltEPXyS7I51CxqgRWlJo8+eST/Otf/3J3ljvzzDP59ttvfdrX58dKF1xwAYWFhdxwww3uXne+dMZQgt9PRw4zddlibA4HDl3n9iHDmD64cZtr/nr0KFcs/ZBSRzkOXeeas/uzZu8eDhUX45Q6l/Q8k3+MSlU99ZXACNJpQgE6dOjgsez3UVlvvPFGzGYzqampXHjhhdjtdvcY4UrLduOKj8mrcsfwyjdbGdopwacexH6LYeUyjlZpvvqvHdtASncT1oxff2ZYp9OZ0OusRotJaV2CsbVShw4d2LZtm6slX3k5b7/9Nt27d/dpX58fK1Udk9xsNhMZGVnvccqV4OPQdY/+AuD6AvXTkcZ7pKhLyb6Kwcaqrqv6t1rqcPD9oRwUJWAaaVRWf3r44Yd59913yc3N5fzzzycrK8t/rZUOHz5Mbm4uNpuNXbt2uUdfLSoqotSH588bN27k8ccfR9d1Jk+ezE033VRjudWrV3P77bfz0UcfqVZQzYhR04gLDeVold+1hqhxXKJA0YSgTVg4h0uKq8Tg2fEt1Gike1x8o8WktD7NscLZm7i4OJ577rlT2tdrcti8eTNLly4lJyfHo0NcRERErcPFnuB0Onn00UdZuHAhVquVSZMmkZycTI8ePTzKFRUV8fbbb6uZ5Zqpf467hOuXL0UTGuW6kwm9zuIvnRq3s9c/x13MtR8vdc0QpztJ7tKNL/f/Qbmuo+uSczqcRnpv9aVCCZBmemfgzb59+3j88cfZsWOHe9rR++67j4SEBK/7ek0OEyZMYMKECaxevZrU1NR6BZaZmUnnzp3dgYwbN461a9dWSw4vvfQSN954I//617/qdXylcQw+rRMbrp3GT0eO0CYsjJ7xbRo9hoEdOrLh2hv46cgR4sPCOCO+DUVlZfx4KJcws5nebdup5qRKQAXjncPs2bO54oor3GPfZWRkMGvWLBYvXux1X58rpM855xzuu+8+Dh06xIIFC9i9ezfbt29n8uTJte5T05ynmZmZHmV+/PFHcnJyuOCCC3xODna7naysLF9Dx2az1at8c9QcriEWcBYVk3Xo1Oob/HENsYBeJYYT87D9fDSvQcf1RXP4HTSUuoYGCMIK6dLSUi699FL38vjx433+nPU5OcyZM4e0tDRee+01ALp06cLf/va3OpODN7qu89RTT9V7/CaLxUKvXr18Lp+VlVWv8s2RuoamF+zxQ+u8Bn8kkubayc2bESNGMH/+fMaOHYsQglWrVpGUlERBQQFAnTPC+Zwc8vPzGTt2LPPnz3ftaDR6bS/rbc7T4uJifvnlF66++mrAVfl966238uqrr6pK6SB3y8qP+XTvHiRwVtu2LJ18hcf2t3Zs47FN63FKSYwlhI8uu5xusZUVyn8eP87M/63kpyOH6RARyYupY+ndrvb5cnUpefaLzbz3w04MQnDjOYMJNRn5f19vpdypM+HMs3hgxAUYfWzjrSjVBGFy+O9//wvABx984O4DJKUkIyPD64xwPieHsLAw8vPz3SfYsWOHewKJ2vTt25fs7Gz27duH1WolIyPDo+Y8MjKSr776yr181VVXcffdd6vEEOT+b8PnrNm7x7286/Bhrvp4CQ/3ds3f/M2B/TyysXIo+AK7jUs+eJcfbr0dAKeuM2XpfzhQWIhTSvbk53HF0g9Zf800YkNDazznG9u+4a2d29zzNby4dQsS3KO0Lt71PRFmE3f95fxAXLLSGgRhcrjrrrsYMWIEERERvPLKK+zatYvp06f7NDKrz1+j7r33Xm699Vb27dvH5Zdfzj333MMDDzxQ5z5Go5G5c+cybdo0xo4dy5gxY0hMTOSll17yaQ5TJTit2v1ztXU7cg663y/9aVe17SXl5RTabADkFBdV9Hz2/GvMzK29H8OqX3/xmMinTNfdiQFc/SD+u/tX3y9CUU7ir7GVNm7cSGpqKikpKe4nMVU98cQTjB8/nvHjx5OamsqgQYPc23r16uXedsstt3g916uvvkpERATffvstW7duZfLkyT6PreTznUOPHj3cE16Hh4czatQounbt6nW/pKQkkpKSPNbdcccdNZZ95513fA1HacYizSHkFhd7rKs6mF1cSM3f/sMqpteMNJtxnjRJiVNKIuvokR9TyzGrilI9+pWG8MOdgy/N+++77z73+3feeYdduyq/TIWEhLB8+XKfz2eo+LvbsGEDl112GRdccAEvvviiT/v6fOdw9913s3fvXm6++WamTp1KdnY2f//7330OUmk9nho1mpMblc4dcaH7/W2DzyXU6Pm9JKVbd3d9QJQlhOsHDCTUaEIAYUYTQ07rxID2nmPEVHXv8PMJM5kwahpGTSPcZCbKYsFsMGAQglCjkQerxKAo9VIx2Y+3lzdVm/ebzWZ38/7aZGRkcNFFF51y2Farlblz57orosvKymqcHa4mPt85/Prrr6xatcq9PHToUPcwsIpS1TkdTmPFlKm88OUXlOlOru03gAu7dne3Ggkzm9l6w808vH4dfxYd54IuXblp4BCPY9wzfASDOnTkh0O5JERHM/6MXnUOqterbTsyplzNqt0/YxAaF/c8E6OmsfznLMqcTkZ370EP1YNaaQgf7hzy8vJIS0tzL6enp3sMM+RL8/4TDhw4wP79+xk6dKh7nd1uJy0tDaPRyE033eR1LugXX3yRTZs2cf311xMVFcWhQ4e4++67vV8I9UgOZ511Fjt27KB///4A7Ny5kz59+vi6u9LKnNXWyhuXTKh1e6QlhOdS6/5yMbJbd0Z2822QMIDOMTHcOshztNhp5wyqpbSi1I8vXSzj4uJYunSpX86XkZFBamqq+9EQwOeff47VamXfvn1cc8019OzZk9PrmJo2NDSU0aNHu5fbtWtHu3btfDq/z8nhxx9/5PLLL+e0inl3Dx48SNeuXbn44osBWLFiha+HUhRFCT5+qHPw1ry/qlWrVlUbJO9E2YSEBIYMGcKuXbvqTA4N4XNyWLBgQUACUBRFae4E/ukE5615/wl79uyhsLCQAQMGuNcdO3aM0NBQzGYzeXl5bNu2jWnTpjU8qFr4nBw6duwYsCAURVGaNT8NvFe1eb/T6WTixInu5v19+vRh5MiRgOuu4USv5hP27NnDQw89hBACKSU33nhjtXHq/Mnn5KAoitKa+WuyH1+a98+cObPafuecc06jPr5XyUFRFMUXQdhDuiFUclAURfFBMA681xAqOSiKongTpJP9NIRKDoqiKD5Qdw6KoihKdUE42U9DqOSgKIriA3XnoCiKonhSdQ6KoijKyVw9pFtXdlDJQVEUxRetKzeo5KAoiuILVeegKIqieJL+Gz4jWKjkoCiK4gt156AoiqKcTD1WUhRFUapTyUFRFEU5mbpzUBRFUTwICUJvXdlBJQdFURRftK7coJKDoiiKL1pbU1atqQNQFEVp9qSPLx9s3LiR1NRUUlJSmD9/frXtS5cuZejQoYwfP57x48ezePFi97Zly5YxevRoRo8ezbJlyxp4UXVTdw6Koig+8EeFtNPp5NFHH2XhwoVYrVYmTZpEcnIyPXr08Cg3duxY5s6d67GuoKCAefPmsWTJEoQQpKWlkZycTHR0dMMDq4G6c1AURfFKgvTh5UVmZiadO3cmISEBs9nMuHHjWLt2rU8RbN68meHDhxMTE0N0dDTDhw9n06ZNDb2wWqk7B0VRFB/4UueQl5dHWlqaezk9PZ309HT3cm5uLu3bt3cvW61WMjMzqx1nzZo1fPPNN3Tt2pU5c+bQoUOHGvfNzc09xavxTiWHBigvK+eFm19n/QdbMBgNpN9zKVfePxEhRFOHpiiKP0nfHivFxcWxdOnSBp3qwgsv5KKLLsJsNvPBBx9wzz338PbbbzfomKci4I+VvFW+LFy4kLFjx3LxxRdzzTXXcODAgUCH5Df/vv99Nn74JeV2B7ZiOx889THr3tvc1GEpihIIfnisZLVaycnJcS/n5uZitVo9ysTGxmI2mwGYPHkyP/74o8/7+lNAk8OJypcFCxaQkZHBypUr2b17t0eZXr16sWTJElasWEFqairPPPNMIEPyq60rv8NeWuZetpfY+XLFt00YkaIogeCa7Mf7y5u+ffuSnZ3Nvn37KCsrIyMjg+TkZI8yhw4dcr9ft24d3bt3B+C8885j8+bNHDt2jGPHjrF582bOO+88f16mh4A+Vqpa+QK4K1+q1swPHTrU/b5///588skngQzJr2LaRbP/54PuZYPRQFz7mKYLSFGUwPFDayWj0cjcuXOZNm0aTqeTiRMnkpiYyEsvvUSfPn0YOXIk77zzDuvWrcNgMBAdHc2TTz4JQExMDNOnT2fSpEkA3HbbbcTExDQ8qNpiDdiR8b3y5YSPPvqIESNGeD2u3W4nKyvL5zhsNlu9yvtq7B0X8Ot3e9CdOghBSLiFgRP7BORcgbqGxhTs1xDs8YO6hobw19hKSUlJJCUleay744473O9nz57N7Nmza9x30qRJ7uQQaM2mQnr58uX88MMPLFq0yGtZi8VCr169fD52VlZWvcr7qlevXpw9sC9bV36HyWzkvInnEhUX6ffzQOCuoTEF+zUEe/zQOq/BL4lEAs7WNX5GQJODrxUoX3zxBa+99hqLFi1yV8QEC2vntoy/7a9NHYaiKAHW2kZlDWiFtC+VL7t27WLu3Lm8+uqrxMfHBzIcRVGUU+eH1krBJKB3Dr5Uvjz99NOUlJS4n7l16NCB1157LZBhBdS3a3by3hNLcDp00u4YR9LkYU0dkqIoDeVja6SWJOB1Dt4qX958881Ah9Botq/7nocnPO1u3rpnx29IXeeC9OFNHJmiKA3WypKDGlvJj5a/8r+T+j2UseTFjCaMSFEUfxFO6fXVkjSb1kotgWaonms1TQ2loSjBTkiJaGF1Ct6oOwc/mvS3i7CEVra2soSZmTInrY49FEUJGn6azyFYqDsHPzpr2Bk8teZBFj/7CU6Hk/EzxjA4tX9Th6Uoij+0sjsHlRz8rM/wM+kz/MymDkNRFD9TrZUURVGU6tSdQ+slpWTz0q/Y+/3vdEo8jQunDEfTPKtl/v3A+6x7dxPh0WHc9/6dAGxZ9hUms4nkK89Hd+qse28zulPn/ElD6ZTYwWN/e6mdNW+uJ//QMfol9abfBb0b6/IURTlVkhbXGskblRyqeHn6G3y2aCO2Yjsh4Ra2LP+aB/8zyz15z4Pjn2Lriu/c5af1/hvmEBOOcieaQePdx5eg6zrltnKkhPeeWMILG/+PHgO6AlBmL+f2YfdzYPeflJWW82Hocm5+9mouviW1Sa5XUZR6aF25QbVWOuHIgaOsfnM9tmI7ALZiO1+v2k72D3+4y1RNDCeU2crRnTqOMgfFx0ooPW7DUe7E6XBiK7Yz/+533GW/+PhrDu7NxV5ShpQSe0kZr81+G9nKblcVJRidaM5a16slUcmhQnFhKUaTwWOdwWSg+FhJg457PK+o8hzHSpC650S05fZydN2HyWkVRWk6voyrpJJDy3RadysRMeHuTmtCgNFooFu/Lu4y5rDqI8YajJUJxWg2YjRXPqmzhFm4IP0v7uV+F/bBNadUZfmzR5yFweCZlBRFaYZ0H14tiEoOFUxmE89teITEQd0JjQihS5/TeX7DI4RFhrrL/HvXi1jCLO7lYeMHM37GX4mIDSembRQ3P3s11z12OVFtIomMDefSmWOYfNcl7vKdEjvwf5/cQ/su7QiLDGVgytk8tOSuRr1ORVFOTWt7rKQqpKvo0NXKvK1P1rrdenpbVhZVn4zo1uev9Vi+7K7xtR5jQHJf3tn7yinHqChKE5BAK3v8q5KDoiiKL/yUGzZu3Mjjjz+OrutMnjyZm266yWP7woULWbx4MQaDgbi4OJ544gk6duwIuGaf7NmzJxD46Q1UcjhJ1le/kLlhF2cOSaTfBb3J3LiLte9upNvZnRl/2xiKCor5ddteImLC6TGgq7uZ6wmOcgc/f+OaV/qMwd0xhwTXzHaKolQnwC+PjZxOJ48++igLFy7EarUyadIkkpOT6dGjh7tMr169WLJkCaGhobz33ns888wzvPjiiwCEhISwfPnyBsfhC5UcqnjhltdZNf8z97K1c1tyfz/sXn5z7n8AkLrE6XByzihXncGJjnIlx0v52/kP8ufeXBCC6DaRvPzlE8S2i27cC1EUxc/80xopMzOTzp07k5CQAMC4ceNYu3atR3IYOnSo+33//v355JNPGnzeU6EqpCv8+VuuR2IAPBIDQFF+MUX5xRQfK8FWbGfbZ5mse2+ze/vbj3zIvp8PUlpko/R4KUf2H+Wfdy5slPgVRQkgiV+asubm5tK+fXv3stVqJTc3t9byH330ESNGjHAv2+120tLSuOyyy/jss89q3c8f1J1DhT07suu9j63EzoHdf7qXf/9xP+X2cveyo9zJH1n7/RGeoihNzYfhM/Ly8khLqxymPz09nfT09FM63fLly/nhhx9YtKiyEcznn3+O1Wpl3759XHPNNfTs2ZPTTz/9lI7vjUoOFc48N9G3ggJ3N/qQMAs9+nd1b+o1NJHvN+5yzwZnspg4c4iPx1UUpfmSvtU5xMXFsXTp0lq3W61WcnJy3Mu5ublYrdZq5b744gtee+01Fi1ahNls9tgfICEhgSFDhrBr166AJQf1WKlCm9PiuO7xKR7rzvrLGR7LHXt2wHp6W0LCLRjNRsbcMJK/jB/s3j5lzgTOTjoLc4gJS6iZ7v27cPOzVzdK/IqiBJJ/ekj37duX7Oxs9u3bR1lZGRkZGSQnJ3uU2bVrF3PnzuXVV18lPj7evf7YsWOUlbm+eObl5bFt2zaPugp/U3cOVVwxJ42xN47il2930+3sLrQ5LY6Cw8f48pNvSRzYjR79u+J0OMnJPkRYVFi1imaT2cTjGfdxeP9RdKeOtXPbaq2ZFEUJUnrDK6SNRiNz585l2rRpOJ1OJk6cSGJiIi+99BJ9+vRh5MiRPP3005SUlHDHHXcAlU1W9+zZw0MPPYQQAiklN954o0oOjSmmTRRD/npO5XLbaMbcMNK9bDAa6NijQ027AiCEoF1Cm4DGqChKE/BTD+ikpCSSkpI81p1IBABvvvlmjfudc845rFixwi8x+KLFP1bSddeIqXVtLy70HFzv8P4jHstZX/+CzWZzL2fv2kdhYaF7+cifRzh2rNi97HS6RmWtjZSSsioV14qiNHN+aq0UTFr0ncN7TyzhnUc/wulw0v/C3jy05O+ER4W5t3/w1DL+dd97AGgGjUF/7c/XGdvc2+M7xnL0QL57WWggq/aSrFI5DaAZNVKvv5A1//4cKWHEpKHc/dYMTGaTu8za9zbxwk2vUW4rp3PvBB7PuI+2neJRFKU5k+BsXcNntNg7hy+Wf8P7TyzDUeZA6pIfNv/ECzdWdjX/5dvd7sQAoDt1j8QAeCQGOCkxQLXJP3SHzn/nr8Xp0NGdOl9+8q274xzAnp3ZvHDja9hLytB1ye+79vPgJU817EIVRWkcUvf+akFabHLYvu57bCV293K53cHODbvcy1tXVp+4x9/spWV8t3qne3nXl794bNedOnt3/o7TWfsjKEVRmgH1WKnlaJsQjznERJmt8tl+bPsY9/vTerSvYS//0jRB24TKR0Zx7WMQBs98HBYVquZzUJRg4IfWSsGkxd45XHxrKu27WgmNCMEcZiI0IoTZb9zi3j5qahIdEz0ThMHU8A/piJhwQiNCCAkPITwmnFtfuNa9bejFA+k9rCchESGEhFuwhJm569/TG3xORVECrfXNBNdi7xxCw0N49bt/8FXGNvb8spexU0dXa2L676yXWPLCSn77/g8Gju7HyCvO5+nrXuGHTVl079+Fhz66i9uG3suebb8RGhHC+wdf58Ub57NxyVbMISbmffMkmZ//yFsPLcZoMnD/B3fSuVcnvlq1Hd2pMyi1HzFtK/tCGAwGnvjv/Xzz3+3k5x6j17CedO7VqbF/NIqinIoW9uHvTYtNDgDmEDPnTxxKm6zoGvseaJrG5NmXeKy7e+FtHsuvbPWsML73ndu5953b3cudup/G2GkpHmWSp5xXa0yapnHuuIE+X4OiKM2ABFpZ3WCLTg6Koij+0fIeG3kT8DqHjRs3kpqaSkpKCvPnz6+2vaysjDvvvJOUlBQmT57M/v2BG8V0b+bvzLpgLtedeTuv/m0h5WV1d0QrzDvODb3/xhjL5UyIv5YvVnzDnSMeZIzlci6JuopVCwI7ZK6iKM2IqnPwH19mPVq8eDFRUVF8+umnZGRk8Oyzz7pnPfKnQ/uO8LfzH6TkeGnF8lEKDhcyZ9Edte5zw1l3UnDI1RO6KL+Yh8Y/7d7mKHfywk2vE9s+lmEXqcdEitKiSVRrJX+qOuuR2Wx2z3pU1bp165gwYQIAqampfPnll8gAZOCvV233GNKirLSMDR/Wfq7iwhJ3YqjLxy9n+C1GRVGaLyl1r6+WJKB3DjXNepSZmVmtTIcOroHsjEYjkZGR5OfnExcXV+tx7XY7WVlZPsdhs9k4fPQQ8qQuzUITZGVl1ThyallJmU/HtpfXL5ZTZbPZGuU8gRTs1xDs8YO6hlMmW9/wGUFZIW2xWOjVq5fP5bOyspg8/VL+98oGjjkKcZY7CQm3MGn2xZx11lm17tetX2f27vy9csVJYykh4PaXbqRbry71vob6ysrKqtc1N0fBfg3BHj+0zmvwWyLRW1dyCOhjJV9mPbJarfz5p2uqTYfDwfHjx4mNjfV7LBEx4by2/RnG3/ZXki77CzNfmcbVD11W5z6vfvc0I688nzad4jhjcHfe3j2PSbMvpm1CPF36JPDyF4/T7ewufo9VUZRmSFVI+0/VWY+sVisZGRk899xzHmWSk5NZtmwZAwYMYPXq1QwdOjRgE+TEtovm1uev9bm8pmkefRoAbn7mam5+Rs3upiitiZQS2cruHAKaHHyZ9WjSpEn8/e9/JyUlhejoaF544YVAhqQoinJqWtidgTcBr3PwNuuRxWLh5ZdfDnQYiqIoDdPKmrIGZYW0oihKo5IS2cqGz2ixo7IqiqL4lZ8m+2nIqBGvv/46KSkppKamsmnTJr9dWk1UclAURfGB1KXXlzcnRo1YsGABGRkZrFy5kt27d3uUqTpqxLXXXsuzzz4LwO7du8nIyCAjI4MFCxbwyCOPBHSiMJUcFEVRvJHSL3cODRk1Yu3atYwbNw6z2UxCQgKdO3eu1qnYn4KyzqG+PaTBjx1hmpC6hqYX7PFD67sGu93uvZAXZw09g5d/fNRrucOHD3P//fe7l9PT00lPT3cvN2TUiNzcXPr16+exb25u7ilfkzdBmRz69+/f1CEoitKKJCYm+lSuV69ejBgxIsDRNA71WElRFKWRNGTUCF/29SeVHBRFURpJ1VEjysrKyMjIIDk52aPMiVEjAI9RI5KTk8nIyKCsrIx9+/aRnZ3N2WefHbBYg/KxkqIoSjBqyKgRiYmJjBkzhrFjx2IwGJg7dy4GgyFgsQoZiMkTFEVRlKCmHispiqIo1ajkoCiKolSjkoOiKIpSjUoOiqIoSjUqOSiKoijVqOSgKIqiVKOSg6IoilLN/wftZpZqJlwgpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = iris.plot.scatter(\n",
    "    x=\"petal_length\", y=\"petal_width\", c=\"species_id\", colormap=\"viridis\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fc13815a-80e4-48bd-813b-8a6129bd7dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=1)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply PCA, reduce to a single dimension\n",
    "petals = iris[[\"petal_length\", \"petal_width\"]]\n",
    "pca = PCA(n_components=1)\n",
    "pca.fit(petals)\n",
    "petals_1d = pca.transform(petals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9892b191-3eee-4d22-9d59-0be19aee77aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.57712403],\n",
       "       [-0.57712403],\n",
       "       [-0.58873212],\n",
       "       [-0.56551594],\n",
       "       [-0.57712403],\n",
       "       [-0.48157822],\n",
       "       [-0.54676326],\n",
       "       [-0.56551594],\n",
       "       [-0.57712403],\n",
       "       [-0.5958767 ],\n",
       "       [-0.56551594],\n",
       "       [-0.55390785],\n",
       "       [-0.60748479],\n",
       "       [-0.64230906],\n",
       "       [-0.6003402 ]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "petals_1d[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "567712a3-2554-4822-af66-01c2e0578d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.68487729, 0.72865842]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actual components of the PCA (principal axes)\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fe92f4e0-35ec-4970-bd29-92df0cb81616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98149993])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "56c720f9-a073-4eb1-ac33-6b29e0e4a918",
   "metadata": {},
   "outputs": [],
   "source": [
    "petals_inverse = pca.inverse_transform(petals_1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "07377fe7-7eb8-45b5-8d4a-33e44b9d3309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.677966</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.708333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width\n",
       "0        0.067797     0.041667\n",
       "1        0.067797     0.041667\n",
       "2        0.050847     0.041667\n",
       "3        0.084746     0.041667\n",
       "4        0.067797     0.041667\n",
       "..            ...          ...\n",
       "145      0.711864     0.916667\n",
       "146      0.677966     0.750000\n",
       "147      0.711864     0.791667\n",
       "148      0.745763     0.916667\n",
       "149      0.694915     0.708333\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "petals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b386e633-ccc3-43fd-bc81-98a4a5c83388",
   "metadata": {},
   "outputs": [
    {
     "data": {
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