Machine learning with H2O in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the notebook associated with the article titles [**Democratising Machine learning with H2O**](https://medium.com/p/7f2f79e10e3f/edit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is H2O\n",
    "\n",
    "The latest version called H2O-3 is the third incarnation of H2O. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Flow notebook/web interface, and works seamlessly with big data technologies like Hadoop and Spark. H2O can easily and quickly derive insights from the data through faster and better predictive modelling.\n",
    "![](https://cdn-images-1.medium.com/max/800/1*269jAbiB6QFXWjhCMzNURw.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H2O offers an R package that can be installed from CRAN and a python package that can be installed from PyPI.Also you may want to look at the [documentation](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html) for complete details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-requisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Python\n",
    "* Java 7 or later, which you can get at the [Java download page](https://www.oracle.com/technetwork/java/javase/downloads/index.html).To build H2O or run H2O tests, the 64-bit JDK is required. To run the H2O binary using either the command line, R or Python packages, only 64-bit JRE is required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependencies \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\npip install requests\\npip install tabulate\\npip install \"colorama>=0.3.8\"\\npip install future\\n\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "pip install requests\n",
    "pip install tabulate\n",
    "pip install \"colorama>=0.3.8\"\n",
    "pip install future\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finally Installing on the system with pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### or Conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c h2oai h2o=3.22.1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: When installing H2O from pip in OS X El Capitan, users must include the --user flag. For example -\n",
    "\n",
    "```\n",
    "pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o --user\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "; Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)\n",
      "  Starting server from c:\\users\\parul\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: C:\\Users\\Parul\\AppData\\Local\\Temp\\tmp2rkx4e8d\n",
      "  JVM stdout: C:\\Users\\Parul\\AppData\\Local\\Temp\\tmp2rkx4e8d\\h2o_Parul_started_from_python.out\n",
      "  JVM stderr: C:\\Users\\Parul\\AppData\\Local\\Temp\\tmp2rkx4e8d\\h2o_Parul_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>05 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>Asia/Kolkata</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.24.0.1</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>19 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_Parul_6tdu89</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.757 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.7.2 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------\n",
       "H2O cluster uptime:         05 secs\n",
       "H2O cluster timezone:       Asia/Kolkata\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.24.0.1\n",
       "H2O cluster version age:    19 days\n",
       "H2O cluster name:           H2O_from_python_Parul_6tdu89\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.757 Gb\n",
       "H2O cluster total cores:    4\n",
       "H2O cluster allowed cores:  4\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.7.2 final\n",
       "--------------------------  ------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data with H2O in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The data belongs to the white variants of the Portuguese \"Vinho Verde\" wine. \n",
    "* Source: https://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "* CSV FIle : (https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  fixed acidity</th><th style=\"text-align: right;\">  volatile acidity</th><th style=\"text-align: right;\">  citric acid</th><th style=\"text-align: right;\">  residual sugar</th><th style=\"text-align: right;\">  chlorides</th><th style=\"text-align: right;\">  free sulfur dioxide</th><th style=\"text-align: right;\">  total sulfur dioxide</th><th style=\"text-align: right;\">  density</th><th style=\"text-align: right;\">  pH</th><th style=\"text-align: right;\">  sulphates</th><th style=\"text-align: right;\">  alcohol</th><th style=\"text-align: right;\">  quality</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">            7  </td><td style=\"text-align: right;\">              0.27</td><td style=\"text-align: right;\">         0.36</td><td style=\"text-align: right;\">            20.7</td><td style=\"text-align: right;\">      0.045</td><td style=\"text-align: right;\">                   45</td><td style=\"text-align: right;\">                   170</td><td style=\"text-align: right;\">   1.001 </td><td style=\"text-align: right;\">3   </td><td style=\"text-align: right;\">       0.45</td><td style=\"text-align: right;\">      8.8</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            6.3</td><td style=\"text-align: right;\">              0.3 </td><td style=\"text-align: right;\">         0.34</td><td style=\"text-align: right;\">             1.6</td><td style=\"text-align: right;\">      0.049</td><td style=\"text-align: right;\">                   14</td><td style=\"text-align: right;\">                   132</td><td style=\"text-align: right;\">   0.994 </td><td style=\"text-align: right;\">3.3 </td><td style=\"text-align: right;\">       0.49</td><td style=\"text-align: right;\">      9.5</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            8.1</td><td style=\"text-align: right;\">              0.28</td><td style=\"text-align: right;\">         0.4 </td><td style=\"text-align: right;\">             6.9</td><td style=\"text-align: right;\">      0.05 </td><td style=\"text-align: right;\">                   30</td><td style=\"text-align: right;\">                    97</td><td style=\"text-align: right;\">   0.9951</td><td style=\"text-align: right;\">3.26</td><td style=\"text-align: right;\">       0.44</td><td style=\"text-align: right;\">     10.1</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            7.2</td><td style=\"text-align: right;\">              0.23</td><td style=\"text-align: right;\">         0.32</td><td style=\"text-align: right;\">             8.5</td><td style=\"text-align: right;\">      0.058</td><td style=\"text-align: right;\">                   47</td><td style=\"text-align: right;\">                   186</td><td style=\"text-align: right;\">   0.9956</td><td style=\"text-align: right;\">3.19</td><td style=\"text-align: right;\">       0.4 </td><td style=\"text-align: right;\">      9.9</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">            7.2</td><td style=\"text-align: right;\">              0.23</td><td style=\"text-align: right;\">         0.32</td><td style=\"text-align: right;\">             8.5</td><td style=\"text-align: right;\">      0.058</td><td style=\"text-align: right;\">                   47</td><td style=\"text-align: right;\">                   186</td><td style=\"text-align: right;\">   0.9956</td><td style=\"text-align: right;\">3.19</td><td style=\"text-align: right;\">       0.4 </td><td style=\"text-align: right;\">      9.9</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data = h2o.import_file(\"winequality-white.csv\")\n",
    "wine_data.head(5)# The default head() command displays the first 10 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:4898\n",
      "Cols:12\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>fixed acidity     </th><th>volatile acidity   </th><th>citric acid        </th><th>residual sugar   </th><th>chlorides           </th><th>free sulfur dioxide  </th><th>total sulfur dioxide  </th><th>density              </th><th>pH                 </th><th>sulphates         </th><th>alcohol           </th><th>quality           </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>real              </td><td>real               </td><td>real               </td><td>real             </td><td>real                </td><td>real                 </td><td>real                  </td><td>real                 </td><td>real               </td><td>real              </td><td>real              </td><td>int               </td></tr>\n",
       "<tr><td>mins   </td><td>3.8               </td><td>0.08               </td><td>0.0                </td><td>0.6              </td><td>0.009               </td><td>2.0                  </td><td>9.0                   </td><td>0.98711              </td><td>2.72               </td><td>0.22              </td><td>8.0               </td><td>3.0               </td></tr>\n",
       "<tr><td>mean   </td><td>6.854787668436097 </td><td>0.27824111882400976</td><td>0.33419150673744386</td><td>6.391414863209472</td><td>0.04577235606369949 </td><td>35.30808493262556    </td><td>138.3606574111883     </td><td>0.9940273764801961   </td><td>3.1882666394446715 </td><td>0.489846876276031 </td><td>10.514267047774602</td><td>5.87790935075541  </td></tr>\n",
       "<tr><td>maxs   </td><td>14.2              </td><td>1.1                </td><td>1.66               </td><td>65.8             </td><td>0.346               </td><td>289.0                </td><td>440.0                 </td><td>1.03898              </td><td>3.82               </td><td>1.08              </td><td>14.2              </td><td>9.0               </td></tr>\n",
       "<tr><td>sigma  </td><td>0.8438682276875128</td><td>0.10079454842486532</td><td>0.1210198042029825 </td><td>5.072057784014881</td><td>0.021847968093728794</td><td>17.007137325232588   </td><td>42.49806455414292     </td><td>0.0029909069169369337</td><td>0.15100059961506662</td><td>0.1141258339488323</td><td>1.2306205677573177</td><td>0.8856385749678312</td></tr>\n",
       "<tr><td>zeros  </td><td>0                 </td><td>0                  </td><td>19                 </td><td>0                </td><td>0                   </td><td>0                    </td><td>0                     </td><td>0                    </td><td>0                  </td><td>0                 </td><td>0                 </td><td>0                 </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0                  </td><td>0                  </td><td>0                </td><td>0                   </td><td>0                    </td><td>0                     </td><td>0                    </td><td>0                  </td><td>0                 </td><td>0                 </td><td>0                 </td></tr>\n",
       "<tr><td>0      </td><td>7.0               </td><td>0.27               </td><td>0.36               </td><td>20.7             </td><td>0.045               </td><td>45.0                 </td><td>170.0                 </td><td>1.001                </td><td>3.0                </td><td>0.45              </td><td>8.8               </td><td>6.0               </td></tr>\n",
       "<tr><td>1      </td><td>6.3               </td><td>0.3                </td><td>0.34               </td><td>1.6              </td><td>0.049               </td><td>14.0                 </td><td>132.0                 </td><td>0.994                </td><td>3.3                </td><td>0.49              </td><td>9.5               </td><td>6.0               </td></tr>\n",
       "<tr><td>2      </td><td>8.1               </td><td>0.28               </td><td>0.4                </td><td>6.9              </td><td>0.05                </td><td>30.0                 </td><td>97.0                  </td><td>0.9951               </td><td>3.26               </td><td>0.44              </td><td>10.1              </td><td>6.0               </td></tr>\n",
       "<tr><td>3      </td><td>7.2               </td><td>0.23               </td><td>0.32               </td><td>8.5              </td><td>0.058               </td><td>47.0                 </td><td>186.0                 </td><td>0.9956               </td><td>3.19               </td><td>0.4               </td><td>9.9               </td><td>6.0               </td></tr>\n",
       "<tr><td>4      </td><td>7.2               </td><td>0.23               </td><td>0.32               </td><td>8.5              </td><td>0.058               </td><td>47.0                 </td><td>186.0                 </td><td>0.9956               </td><td>3.19               </td><td>0.4               </td><td>9.9               </td><td>6.0               </td></tr>\n",
       "<tr><td>5      </td><td>8.1               </td><td>0.28               </td><td>0.4                </td><td>6.9              </td><td>0.05                </td><td>30.0                 </td><td>97.0                  </td><td>0.9951               </td><td>3.26               </td><td>0.44              </td><td>10.1              </td><td>6.0               </td></tr>\n",
       "<tr><td>6      </td><td>6.2               </td><td>0.32               </td><td>0.16               </td><td>7.0              </td><td>0.045               </td><td>30.0                 </td><td>136.0                 </td><td>0.9949               </td><td>3.18               </td><td>0.47              </td><td>9.6               </td><td>6.0               </td></tr>\n",
       "<tr><td>7      </td><td>7.0               </td><td>0.27               </td><td>0.36               </td><td>20.7             </td><td>0.045               </td><td>45.0                 </td><td>170.0                 </td><td>1.001                </td><td>3.0                </td><td>0.45              </td><td>8.8               </td><td>6.0               </td></tr>\n",
       "<tr><td>8      </td><td>6.3               </td><td>0.3                </td><td>0.34               </td><td>1.6              </td><td>0.049               </td><td>14.0                 </td><td>132.0                 </td><td>0.994                </td><td>3.3                </td><td>0.49              </td><td>9.5               </td><td>6.0               </td></tr>\n",
       "<tr><td>9      </td><td>8.1               </td><td>0.22               </td><td>0.43               </td><td>1.5              </td><td>0.044               </td><td>28.0                 </td><td>129.0                 </td><td>0.9938               </td><td>3.22               </td><td>0.45              </td><td>11.0              </td><td>6.0               </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pGAtnv8OoYU8y7JPQTMd0O69f6JzNAEyb25svJj3LF2OWkZqaCkDDRhX5fvrLmY4jI6kpqfww7g/eG92EUT+2Y314JCcizzu0uad8QYZMbs3wKW2p07gUMyb8BUCVWsEM+74Nw75vQ9/PmpHHy4PqdTKfON9IxMHzHDmbwLLXazC4fRkGLz6cYdsVe86SL4+7w7LnGxRl/qv3Me+V6jSqUJCv15y4tThu8xxVoXwxvvziJerULufQvkP7OoTO60vovL58MuoZihcvlOXkECBi/SGOHD/H8pmvMOSD1gwevSzddk0evpeZ3/Z0WV65QhCzv3+OBVNf5JEmlRjz9aosx5BhXMfOsnzWqwzp04bBn2QUVwVmfvecy/Kiwb6M+Kg97VpUy5Z4VPb7RySIwGtAG2NMd2PMAmPMyJwO6JrMxGOMOWmM6Wp7WhPI0QRx7cGtnL0Uf/OG2SB85XY6dayLiFCzRhniL1whJtbxwykm9jwXLyZwf82yiAidOtYlPNxKxB5uUBkPD+uDoWaNMkRFxQGQN2+etOVXrybd0k1ND+89S0CxAgQUK4CHpxt1mpZk6++OHzKV7g/Ey9sqtJetUohzsVcAOHUkngo1AnB3d8Mrrwclyvmxc1NUlmMA2L3zOCVKFaJ4iUJ4enrQolV1IlbtdWizdvUe2nSoCUCTFlX4c+PfGGMQgSuXE0lOTuHq1WQ8Pd3JX8CLyMOxVL2vBN62fqpVuzRrwndnOqYd2yMpWaoIJUsWIU8eD9q0qcWqldsd2qxcuZ2OHR8EoOUj97Nhwz6MMVSuUpLAQCv5urd8Ua5eTSIx0UqeCxTIC0BycipJSSlZft3CV+2iU/va1vvpvnuIv5BATKzjezkmNp6LlxK4v0Zp6/3UvjbhK3elrR8xOpT33mrncOxDf0dTr255AMqVCeTEyXOcPnMhUzHdzut3+FAstR8sC0ChwgXw8fFmz66TAFSrUZIiAT5Z6p/0HNpzhqDiPgQW88HD0516ze5h82/HHNpUqRWc9j6/t0oRzsZedtnPptVHue/BYmntbtfKvefoeF8RRIQaJXy4kJBC7IVEl3aXElP4Yf0pXg4p5rC8gNf1OK4kpnCr9Z7bPUeVKxdM2TJBNzzG4sWbadfmgVuLb+0BOraqZsVXrTjxF68Sc9q1tlGzWnECixRwWV7vgXvI6+0JQI2qxYiKyZ5zf3jEfjq2vs8urgRiTrv+zlhxub6PSxT1p+K9Qbm2UiductceuVWuTxBFZCJQFlggIm+JSE8RGW9bFyoiz9j+/7KITLP9v5yILBORzSKyVkQq2ZaXEZH1IvKHiAy9wTHn27bdJSK97Ja3EpEtIrJNRMJty+zjSXf/IlJaRHaKSB5gCPCEiGwVkSdE5ICIBNjauYnIQREpkr29mHOiY+IIDi6Y9jw4yJ/o6DjHNtFxBAf5O7aJcWwDMGfuekIaVkl7vm1bJG3bf0yHjsMZPLBbWsKYWXGnr1AoMF/a84IB+Yg7fSXD9r8tOUy1B62hnRLl/Ni5MYqrCclcOH+VfVtjOZfOh2pmxEZfIDDo+pBPYJAvsU4n8djoCwTZ2nh4uFOggBfn4y7TtEVV8ubLQ7tmo+nY8lO6P9sAP798lL03iK1bjnA+7jIJVxJZt3Y/0dGZ/2CIjjlPUbvXLSioINHRTh+a0ecJLlowLSYfn7zExV1yaBMWtpXKlUuSJ49n2rKXXhxPyMN9yJ/fi5aP3J/pmK7F5fhe8SM65nym24Sv3kVgoB+VKjomG5UqFGNF+A4Atu84yslT54hy+nkzcjuvX/mKwaxdtZfk5BROHj/H3j2niI7K3HEz65zT+7xQQL60LzrpWbP4EPc9WMxl+YbwIzzU/J5siyvmQiLBfl5pz4N88xCdToL45crj9HyoKHk9XX+/Pws/RtNxf7Foxxleb5L16hxk7zkqI0uWbaFt29q3Fl/sBYoG+V4/doAP0bGZ+/LibPaibYTUK3fzhrcUl+8tx6Vyp1w/B9EY84qItAKaGGNOi0hPu9W9gN9F5DDwDlDPtvwb4BVjzAEReRD4GmgKfA5MMMb8KCL/ucFhnzfGnBWRvMAfIjIHK5meDIQYYw6LSKF0trvh/o0xiSIyAKhtjOkNYEteuwOfAc2BbcaY05nrndzPGNdlzlWjzLSZMHEZ7u5udGhfJ21ZjRqlWbzwQw4diuKDflMJaVgFLy9P513dILZ0D5xu2w0rjhC57xzvfdYYgKp1goncd46RvVfi4+9F2SqFcXO7te9bhpvHkV4bEWHXzuO4ubmx6Nf3iI+/wis9v6NOvbKUKRtAj+ce5vVeP5AvXx7KVwzGIwtz/dJ7UZy7Jr3+s29y8MApxn0ayjffOv4qTP62N1evJvH+e1PYuGEf9Rtkfu5RRv1w07gErlxJZOLkX/l+Yi+X9b2eb8qwUfPp+PhYKtwbTOVKxTLdX7fz+rXrdD+Rf8fy3JOTCC7qT/UaJfHwyN7v7Rn1R3p+DzvM4X1n6P9FC4flcaevcPzvOKrXdU0cbzmudJaJUx1wT9Qljp5LoE+rezgRd9Wl/ZvNSvJms5J8s/YE0zZF31KSmF3nqIxs2xZJXm9PKpS/xb7L/GnqhhYs28muvVFM/Sq75p3f/Hfxnyw3V/bullyfIN6IMSbalnCtAjrbkroCQH1glt2b9drX1AbAo7b/TwVGZbDrN0Sks+3/JYHyQAAQYYw5bDv22XS2y+z+7X0PhGIliM8D/3NuYKtiWp9qIWWgSmAmdptzpk1fw8xZ6wCoXv0eoqLOpa2Lio4jMNBxgnRwsD9Rdt/Yo6LjCAy43mbe/A2sXrOTKd+/ke4JqFy5YPLmzcP+AyfTJohnRsGAfJyNuV71Oxd7Gf/C3i7tdm+OZvFPe3jvs8Z42s2Davt0Zdo+bSU3k4duIKiE6/BOZgQG+RJjV62KiY4nwGloMTDIl+jo8wQG+5GcnMLFi1fx9ctL2JIdPNTgXjw83SlUuAD33V+KPbtOUrxEITp0eYAOXaxhrQmfryAgKPMT04OC/Dll97pFR59zed2Cgv2JOnWO4OCCJCencOHCFfz8rYsMoqLO8cbr3zB8ZA9KlQpw2b+XlydNmlZn5codN00Qp834nZlzNwJQvWpJp/fKeQIDfB3aBwc5v5/OExjgx9HjZzh+4iwdHx+btrxLt3HMmvYGAUV8GTG0G2AlVM3aDKdE8fS+A7q6nddPRHjz/dZp7V7qMZmSpQpn6riZVcjpfX429jL+RfK6tNv55ykW/LiTfl+2cHifA2xcdYQHQm4/eZ2+KYpZW2IBqF4sP1HnrwJWX0XHJxLo4/gFb9uxi+w6eYnmn/1FSqrhzKVknp2ymx96VnFo17Z6EV6dvi/TCWJ2n6NuZPHSzbRtk7Xq4bQ5m5m1YKsVX6WinLKr/kfFXkh3yPZG1v1xmIk/rGPqV93Jk+fWP/anzf6TWQus+anVKxdziis+3SFu9c+V64eYM6E6cAa49vXMDYgzxtS0e9h/AqX3xTWNiDTGquQ9ZIypAfwFeGMVR264bWb279LYmGNAtIg0BR4ElqbT5htjTG1jTO3cnhwCdH+qUdrk7ObN7mN+6CaMMWzddhgfn7wuJ9bAAD/y5/di67bDGGOYH7qJZk2tKxUj1u5m8re/MuGrl8mb9/oViseOn067KOXEibMcPhxN8eJZ+2AtXakgMScuEnvqEslJqfyx8hg16jt+yz964Bw/jd1M72EN8C14PXlMTTFcPG9VNI4fiuP43+epUufG85AyUrlqcY4dOcvJ4+dISkpmxbIdNGzseDV1w8aVWGL7wFi1Yje165ZBRAgq6sefm6x+u3I5kZ3bj3NPGWuGwtkz1jylqFNxrA7fQ8s21TMdU7Xq93D0SCzHj58mMTGZJUu20KTJfQ5tmjSpTmiolbiFLf+LB+tVQESIj7/Mq69M5M23O1Cr1vXhrEuXrhJrG+pNTk5h7ZrdlCl78z7r3q0BoTPfJnTm2zRvUpX5C/+03k/bj+BTwNslQQwM8LXeT9uPWO+nhX/SrElVKpYvyvrVg1m5tD8rl/YnOMiPuTPeIqCIL/HxV0hMSgZg1tyN1K5VlgIFXL8spOd2Xr+EK4lcuWwNq25cb11FXKZc9v6Ol61UmKjjF4g5eZHkpBQ2hB+hVgPHRCpy/1n+N2YTb41ohF9B1597fXgkDzW7/eHlp+oGM++V6sx7pTrNKhUkdPtpjDFsO34BHy93ApyuQu5WJ4g179Ti1zfv56fnq1K6sHdachh5JiGt3ap95yhbJHOvF2TvOepGUlNTWbb8L9pmcf5h90cfYP4PLzD/hxdoFlKB0GU7rfh2nsAnv1eWErHd+6IYOGoZX3/SlcKFMr5KPFNxda3N/B9fYv6PL1lxLd3uFNftz5nNLXQO4j+8gigidYHWwP3AGhEJsw3/HhaRx4wxs8QqOd1njNkG/A50A37CGtZNjx9wzhhz2Tb8e23Yej3wlYiUuTbEnE4VMTP7v8C1r8zXfWvbZqoxJmuX4t6C6c8PoXGFWhQp4M+x4QsYuGgy369beEeO1SikKmsidtGi1WDyensyfNjTaes6dh5B6Ly+AAwa8AR9+/1EwtUkQhpWISTE+hAY+vFMEpOSee6F8YA1rDxk0JNs3vI3kyeH4eHhjpubMOijJyhUMGvfXt3d3Xjqjfv57P0ITKqhQesyFC/jR+j3O7mnYiFqNijG7InbSbiSzMRB6wEoHJSP3sMeJiUllU/+a10N6J3Pkxf6P5il27XY8/Bw591+bfnvqz+SmpJKu061KHtvIN98FU6lKsUJaVKJ9p1rMbjfXLq2/Qxfv7wM/eQxALp2q8vHH83nqS7jMQbadbyf8hWseZJ9357B+fNX8PBw491+bfH1da0a3Sim/h8+Tq8XvyI11dC5Sz3uLV+UL79YRNVqpWja9D4e7VqfPh/8SKtHBuHnl58xn1pXKk6fFsGxo7FMnLCMiROsKxsnf9sbYwz/+c8kkhKTSUlJ5cF6FXjiiYez1FeNGlZmzW97adFupPV+GvJE2rqOj48ldObbAAzq/6jtNjfJhDSoSMjDlTLaJQCHDkfzwYczcHMT7i0bxLDBj2epr2719Tt79hJvvvIj4iYEBPoycPijafv9cuxywpbsICEhifbNx9ChSy1eeq1pVroLAHcPN555szaj311JaqohpE05SpTxZ8532yhTsTC1Hi7BjAl/kXAlmS8H/gZA4cB8vD2yMQCxpy5yNuYylWre2hegjISU9yfiQBytvtyGt6cbwzqWTVvXeeIO5r1y4y8048KPcvh0Am4Cxfy9GNi2zC3FcbvnqBW/bmPosFmcPXuRl1+dSOVKxflucm8A/vjzIMFB/pQseevTyhvVL0fE+kO0fGwi3t6eDO/fNm1dp2e/Y/4PLwAw+quVLArbzZWEJBp1HE/X9jV4/cWGjP5qFZevJPLmh/MAKBrkywTb++92NKp/LxHrDtHysa/x9vJk+Iftrsf1zGTm//iSFdf4cBaF7bLi6vAFXTvU5PUXQ9ix+yS9+8wm/kICq347wPhvI1iUDVftq+wj6c7DymVEJBJr3t61OYi1seYcbgKeM8ZsEZEOwFtYcw1LAxOAooAnMMMYM0REygDTsRLjOcCHxpgCTsfyAuYDxYF9WEPLg4wxq0WkNTAcq0oZY4xpcS0eY0zvjPYvIqWBRcaYara5i8ttcY0wxvwiIp5YVdC6xhjHyx+d++LVernuBTPjM7zeJ8dERK/J6RBcVC+c+Qre3eTjmbmh1LvJIzHh5o3usnNyaxch3UkH4vbldAguHgg/mNMhpMv9iR45HYILE3drt+a5oyR3DixKoWfuaqnNZ1Dzu/ZZe2HQr7myjPiPqCAaY0rb/X8KMMX2tIbd8gXAAtvTw0CrdPZzGHjIbpHL7WmMMVexqpLpxbEUpyFg+3gy2r8xJhKoZvv/WaAOjmpgXZxyw+RQKaWUUupu+EckiP9mtptsv0rGQ9JKKaWUuov+TVdk36rcWUv+f8QYM9IYc48x5recjkUppZRSCrSCqJRSSinlIDdfXXy3aAVRKaWUUko50AqiUkoppZQdrSBqBVEppZRSSjnRBFEppZRSSjnQIWallFJKKTs6xKwVRKWUUkop5UT/GWeFAAAgAElEQVQriEoppZRSdrSCqBVEpZRSSinlRCuISimllFJ2tIKoFUSllFJKKeVEK4hKKaWUUna0gqgVRKWUUkop5UQriEoppZRSdrSCqBVEpZRSSinlRCuI/zBm/NCcDsGF9P4op0NwkTqwZ06H4OKiW56cDiFdl5PjczoEF75XE3M6BFfeue/79P0BD+Z0CC7cH6uT0yGkLzU5pyNwcdHXL6dDcFHg1OGcDiF9he7u4bSCqBVEpZRSSinlRCuISimllFJ2RLSCqBVEpZRSSinlQCuISimllFJ2dA6iVhCVUkoppZQTTRCVUkoppXIpEWklIvtE5KCI9ElnfSkRWSUif4nIdhFpkx3H1SFmpZRSSik7uWWIWUTcga+AFsBx4A8RWWCM2W3X7ENgpjFmgohUAZYApW/32FpBVEoppZTKneoCB40xfxtjEoEZQEenNgbwtf3fDziZHQfWCqJSSimllJ27WUEUkV5AL7tF3xhjvrH9vzhwzG7dccD5DvmDgDAReR3IDzTPjrg0QVRKKaWUyiG2ZPCbDFanl6kap+dPAlOMMZ+KyEPAVBGpZoxJvZ24NEFUSimllLLjlnsm4B0HSto9L4HrEPILQCsAY8x6EfEGigAxt3Pg3NMFSimllFLK3h9AeREpIyJ5gG7AAqc2R4FmACJSGfAGYm/3wFpBVEoppZSy455L/tSeMSZZRHoDywF34HtjzC4RGQL8aYxZALwDTBaRt7CGn3saY5yHobNME0SllFJKqVzKGLME69Y19ssG2P1/N9Agu4+rCaJSSimllB33XHIfxJykcxCVUkoppZQDrSAqpZRSStnJLXMQc5ImiP8SxhiGDZ/NmohdeOfNw8jhPahapaRLu527jtK331QSEpJoFFKV/v26IiKMGj2PVat34unpTqmSRRgx7Gl8ffOxfXskHw382ToG8Pp/2tCieY1sj/+7Hv1pV70BMRfOUX1o92zff0aMMQz7YjURGw/j7eXJiL4tqVohyKXduMm/E7p8N/EXr7JlWe+05XOX7mL0hLUEBRQAoHvnGjzWrnqW41j32z7GjFxIaoqh06N16PliY4f1iYnJDOw7kz27T+Dnn48RY56kWPFCABzYd4rhQ+Zx6WIC4ib8OKM3Xl6eadu+1fsHThw/y8z5b2Uxpv18OmoRqSmpdOxSh54vNnKNqd8s9tpiGj76SYoVL8jSRVuZOmVtWruD+6OYOvM/VKxUjK+/CGPxgr+4EH+FiE2DstZJ2F6vT5eyZt0BvL09GTmgE1UrFXNpt3PPSfoOmU/C1SQa1S9P/3daIyLs3R/FwJGLuHwlkeJF/RkzpAsFCnizYNl2vpv6e9r2+w5GM2/qy1SuUDRTca3/7QDjRi0hNdXQoUstnnkhxKWvBvefy77dJ/H1y8vHox+nWPGCJCelMHxQKPv2nCQ5JZU27Wvy7IvWtr/8tJ7QOZsxGDp2eYBuPepnqa9+W7ubkcPnkpKayqNdH+LFl1o4xZRE3w9+YvfuY/j752fM2J4UL16Ydb/v5bOxC0hKSsHT05133uvEg/UqcOlSAs88/Xna9tFRcbRrX5s+/R7NdEzGGIaNmMuatXus12/YUxmcp47R98Pp1nmqYWX69+2CiPDZl0sIX7kDNzehcCEfRgx7iqBAPxYs+pPJ34UDkD+fF4M+eoxKlYrnaEwAGzcdYPioeSQnp1KwYH5+mvJ6pmK6E+eDZUu28r/JqxCEgEBfho58Av+C+TMVT4b99vU6IjYdxdvLgxHvNaZq+QCXduO+30Tor/uJv3CVLQtfSFt+IvoC/ces5uz5BPx8vBjdpynBtvOoyh1y/RCziJQWkZ2ZaPOU3fPaIvKF7f89RWT8HYxviIi43LVcRBqLyCLb/ztc+wPbItLJ9rcSs1VExG4ij8QStmwgQwc/yaDBM9JtN2jILwwZ/CRhywYSeSSWiLXWn3NsUL8Si0L7sXB+P0qXDmTS5DAAypcvxpxZ7xM6ry/ffvMaAwb9THJySnaHz5T1i2n1ZdYSmOwQsTGSI8fjWD7tOYa825zBY1em265J/bLMnPRkuutaN63A/O+eZv53T99ScpiSksqoj0P5YsJzzFrwFsuXbOXvQ9EObULn/oGPb17mL32Pp3o8zJdjlwGQnJzCR31+oe9HnZgZ+jaT/tcLDw/3tO1WrthJvnx5bimmT4Yt4POvezIz9E3Clm5LJ6Y/8fXNy7wl7/JUjwZ8Oc6KqXW7mkyf/TrTZ7/OkOGPUbSYPxVtSVzDRpX44edXsxzPNRHrDhB57Cxhc95gaN/2DBq1ON12g0YtYkjf9oTNeYPIY2eJWH8QgP7DFvBO7+Ys/Pk1mjeuxLc/rQOgQ6v7CJ32KqHTXuWTwV0oXtQ/08lhSkoqY4YvYtyEHvw8vzdhS3dw+JDj7ccWzN2Cr683sxe/yZM96vPVZysACA/bRWJSMtPm9uaHGa8wb/afnDxxjkMHogmds5nvp/di6qzX+C1iP0ePnMl0P6WkpPLx0FlM+OYVFizsx5LFmzl08JRDm7mzN+Drl4+lywfQ45nGjB1j3TmjYMH8jJ/wMvMW9GXYiKfp+8FUAPLn92bOvA/SHsWKFaR5i6x9WYxYu4fIo7GELenP0EFPMGjorHTbDRo6iyEDHydsSX8ij8YS8dseAF58rikL531A6Jz3adyoCl9NWA5AieKF+WnK6yyc9wGvvtKSjwb/kuMxxcdfZvDHs5kw/iUWh/bh8097ZiqeO3E+SE5O4dORC5n0fS9mzHuTeysE88v0dZnuo/REbDrGkRPnWT6lG0PeDGHwF7+l265JvXuY+WVnl+WfTNpAxxYVWPDNY/zn6QcY+92m24onu7m73b1HbpWLQ8uS0kBagmiM+dMY88bdOLAxZoAx5tebtFlgjBlpe9oJyPYEMXzldjp1rIuIULNGGeIvXCEm9rxDm5jY81y8mMD9NcsiInTqWJfw8O0APNygclpiUbNGGaKi4gDImzdP2vKrV5OQO1R2X3twK2cvxd+Rfd9I+G+H6PhIZavfqhYl/uJVYs5cdGlXs2pRAgvfmW+3u3Yco2SpwpQoWRhPTw9atq7BmpW7HdqsWbmbdh1rAdCsZTU2bTyIMYYN6w5QvkIwFWwJmL9/ftxtZ5zLl68y7ce1vPBy01uI6bgtpkJ4enrQovV9rFm1x6FNxKo9tO1gxdS0RTX+2HgI5zsrLF+6jUfaXE8iqtcoRZEAX25VeMQ+OrWpYb1e1UsSfyGBmNMXHNrEnL7AxUtXuf++ktb7vE0NwtfsBeDw0dPUuf8eABo8WI6wVbtdjrE4bAftWmY+0d+98zglShWieAlbX7WqTsSqvQ5t1q7eQ5sONQFo0qIKf278G2MMInDlciLJySlcvZqMp6c7+Qt4EXk4lqr3lcDb9vtXq3Zp1oS7xpqRHduPUKpUACVLFsEzjwet29Ri5codDm1WrtxBx451AWj5SE02btiPMYbKVUoSaKuA3Vu+KFevJpGYmOSw7ZHIGM6cvcgDtctlOiaA8FU76NShju08VTrj89SlBO6vWcZ6/TrUIdwWe4EC3mntrlxJ5NrpqNb9ZfDzywdAzftKExXtuM+ciGnhki20aH4fxYoWBKBwYZ9MxXNHzgcGjLHiM8Zw6eJVAgJv/fcQIHx9JB2bV7D6rUqQ7dx5yaVdzSpBBBZ2rVQeOnqOh+63qrwP1ixG+PrI24pHZb+7niCKyCgRec3u+SAReUcso0Vkp4jsEJEn0tm2tIisFZEttse1MZeRQEMR2Soib9lX75y2DxCROSLyh+3hcln4DY6BiLxvi22biIy0LZsiIl1t/28lIntF5Degi912PUVkvG1fHYDRtljLicgWu3blRWTzLXQr0TFxBAcXTHseHORPdHScY5voOIKD/B3bxDi2AZgzdz0hDa/nsNu2RdK2/cd06DicwQO7OVSo/umiT1+kaOD1E3dwQAGiY10TxBtZseYAHZ6byhsDFnIq5sLNN3ASExNPULBf2vPAID9iYuLTaWO9dh4e7hQo4M35uMscPXIaROjd6zu6P/YFP3y/Jm2bCV+G8fSzDfH29iSrYmPOO8QUFORHbLRzTNfb2Mdkb8WyHbRsfV+Wj5+R6Jh4goOuf7AFB/oS7dRX0THxBAem36ZC2UDCI/YBsOzXXZyKdv1SsmTFLto+Ui3TMcVGXyAwyP718yXWKabY6AsEBdn3lRfn4y7TtEVV8ubLQ7tmo+nY8lO6P9sAP798lL03iK1bjnA+7jIJVxJZt3Y/0enEmpGYmDiCg6//rgcF+RPjlDTFRJ8nuKjde8rHm7g4xw/4FWFbqVy5BHnyOL6HlizeTKvWtbL8hTE6+nw656nzrm2cz1N2bcZ9vphGzQaxcPFm/tu7jcsxZs/dQMjDlXM8psjIGOLjr9Cj55d0eXwM80MzVyG7E+cDD093+nzUiW6dP6NVk+Ec/juajl3qZCqejESfvkTRwOuJX3CR/ESfvnyDLRxVLFuYsLWHAVjx22EuXU7iXHzCbcWUndxF7tojt8qJCuIMwD75exyYhZVQ1QRqYP2h6dEi4jzGEwO0MMbUsu3jC9vyPsBaY0xNY8y4Gxz7c2CcMaYO8CjwbTpt0j2GiLTGqv49aIypAXxiv5HtT9tMBtoDDYFg5x0bY9Zh3QH9PVush4DzIlLT1uQ5YMoN4s9QerfEdD55Z6bNhInLcHd3o0P76yePGjVKs3jhh8ye+T6TJodx9WqS827+uTLRJzfSpH5Zwn95gQX/60H9B0rRZ/jyW4jBNQiXENJ98SAlOZVtf0Xy8ahufPfjK6wO38WmDQfZt/ckx4+eoUnzzCc6Nz2c3LyNfaOd24/h7e3JveVdfhVuWfqHlEy3GfZRR6bP3kSXZyZx6XIieZy+7GzbeZy83p5UKOc6DzXjmG7eWem1ERF27TyOm5sbi359j7lL32L6D79z4vhZypQNoMdzD/N6rx9489WplK8YjEcWxqIydz5IJya7P/t68MApxn66gAGDXb6rs3TpFtq0rZXpeLIW143bvPXftqwJH0T7tg/w0/S1Du02bDrA7LkbePft9jkeU0pKKrt2H2PS1734dtIrfD0pjMORmfjLZ3fgfJCclMKcXzYwbdYbLFvVj3srFOV/3666eSw3jDOdELKQ67zfqx5/bD9J51dm88f2UwQVyY+He+5Nlv4/uusXqRhj/hKRQBEpBgQA54wxR213AP/ZGJMCRIvIGqAOsN1uc09gvC2hSgEqZPHwzYEqdr/YviLiY4yxL/tkdIzmwP+MMZdtP8dZp31XAg4bYw4AiMhPQK9MxPQt8JyIvI2VkNZ1biAiva7ta9KE/9LrpbYATJu+hpmzrHkk1avfQ1TUubRtoqLj0oaJrgkO9ifKrqoYFR1HYMD1NvPmb2D1mp1M+f6NdJOkcuWCyZs3D/sPnKR6tXsy8aPlTtPmbWXWImtaa/WKQQ5Vv6jYiwQWyfzE7YJ+edP+/1i76oyZlP48nBsJDPIjOup6NSIm+jwBTsOwVps4goL9SE5O4eLFBPz88hEY5Eet2mXSJps3aFiRvbtPkC+fF3t2n6B9y5GkpKRy9swlevWcxDdTXr6lmKKjz1PEaUgqyNbGMabr/RG2dLvD8PKtmjZrEzPnW4X16lWKE2VXSYuKiScwwHHoLjjQl6iY9NuUKx3A918+A8DhI6dZ/ft+h20Xh+2kbcusJdWBQb4O1bmY6HgCnGIKDPIlOvo8gWl9dRVfv7yELdnBQw3uxcPTnUKFC3Df/aXYs+skxUsUokOXB+jQ5QEAJny+goAgx9/nGwkK8k+bJgLW6IHzkGJQsD9Rp6yRh+TkFC5eSMDP3xqmjYo6x39f/5bhI3tQqpTjhQd7954gJTmVqlVLZSqWaT+vZebs9QBUr1YqnfOUY1zBwX6u56l0hkPbtX2Al1/7hjd6t7bi2neSDwfMYPLElynof+Pf4bsRU3CQPwX985Mvnxf58nlR+4Fy7N13kjKlA28Y2504H+TP7wVAiVKFAWjxSHWmfLeGrJoWupNZS6zpE9UrBnAq5nrFOer0JQIL58v0voKK5OfLQY8AcOlKEmG/HcbHFqfKHXJqDuJsoCtWQnTtaorMfHV4C4jGqjLWBrI6+94NeMhWvatpjCnulBze6BhC+sUJe7fyp23mAK2BdsBmY4zLTHRjzDfGmNrGmNrXkkOA7k81InReX0Ln9aV5s/uYH7oJYwxbtx3GxyevQ/IHEBjgR/78XmzddhhjDPNDN9GsqTX8F7F2N5O//ZUJX71M3rzXu/XY8dNpF6WcOHGWw4ejKV688C38mLlH98410y4qadawHKHL91j9tusUPvnzZGmuof18xZW//025ewplOZ4q1Upw7OgZThw/S1JSMmFLtxHSxHGaakiTKiwKtWYjhIftpM6D5RARHmpQngP7o0i4Ys1j2/LnYcqWC6Jrt3osW9WfhWF9+PbHVyhVukimk0MrpuIcPXI6LaYVS7cT0thx2K5h40osXmDFtHLFTurULZv2xSI1NZXwsB20aHX7w8vdH6ubdgFJ80aVmL9km/V67TiGTwEvAos4JWNFfMifz4utO45Z7/Ml22gWUhGAM2cvpsU34fsIunWpnbZdamoqy1buynKCWLlqcY4dOcvJ4+esvlq2g4aNKzm0adi4EksWbAVg1Yrd1K5rzWULKurHn5us38crlxPZuf0495QpAsBZ23sr6lQcq8P30LJN5udFVqteiqNHYjl+/AxJicksXbKFJk0ct2/SpBqhtmHPsOVbebBeeUSE+PjLvPbKJN58uz21apV12ffSxZtp3faBTMfS/cmGhM55n9A579O8aXXmL/jDdp6KxKdABuepfF5s3RZpvX4L/qCZLfbII9f/vOzKVTspW8aq9J48dY7X3/yeT0Y8fdME7G7F1KxJNf7c8jfJySlcuZLI9h1HKFf25pXpO3E+CAzy4+9DMZyzvf83rj9ImbKuVxzftN86VmP+pK7Mn9SVZg1KE/qrNW916+5o27kz81+uz52/Qmqq9ZH5zc9/8egjFbMcz52kQ8w5d5ubGVjDsUWAa/fOiABeFpEfgEJACPAe1h+dvsYPOG6MSRWRZ7H+LiHABSAzM4DDgN7AaAARqWmM2erUJqNjhAEDRGS6MeayiBRyqiLuBcqISDnb0HH6l7w6xWqMSRCR5cAE4IUMtrmpRiFVWROxixatBpPX25Phw55OW9ex8whC5/UFYNCAJ+jb7ycSriYR0rAKISHWiWfoxzNJTErmuResC75r1CjNkEFPsnnL30yeHIaHhztubsKgj56gUMHsv1hj+vNDaFyhFkUK+HNs+AIGLprM9+sWZvtxnDWqV4aIDZG0fOp/eHt5MLxPy7R1nV74ifnfWf04ekIEi8L3cSUhiUZdJ9O1bTVef+4hps7ZyqrfD+Hu7oafjzcj+jyS5Rg8PNx5r18HXn/5e1JSUunQuTbl7g1i4vgwKlctQaMmVejYpTYD+s6kU+vR+PrlZfho6+3l65eP7s805Jlu40GEBg0r8nCjSjc5YuZier9fB9545X+kpBg6dH7AFtMKW0yV6dilNgP7zqJzmzH4+uVj2Cfd0rb/a3MkgcF+lCjpmDB/MXYpyxdvIyEhibbNRtLx0dr0es3lJgAZatSgPGvWHaBFly+s9/lHHdPWdew+gdBp1hXSgz5oa7vNTTIh9e8lpH55ABaF7WT6LCspatGkMo+2vz9t+z/+OkJwoC8li2ctyffwcOfdfm3576s/kpqSSrtOtSh7byDffBVOpSrFCWlSifadazG431y6tv0MX7+8DP3kMQC6dqvLxx/N56ku4zEG2nW8n/IVrCH5vm/P4Pz5K3h4uPFuv7b4+ua9URguMfX7sCsvv/g1KampdO5Sj3vLF2X8F4upWq0UTZpWp0vXh+j7wVRaPzIEP798jLZdZfvztLUcO3qaiROWM9F2Re43376WdpHF8mV/8fWkV7LUR9c0CqnCmrV7aNH6Y/LmzcPwoddPkx0f/YTQOe8DMOijx9JuKRPSsDIhDa0vJ5+OW8jhyBhEhOLFCjF4gNWPX01YTtz5Swz+2LoC2d3dnbkz38nRmMqVC6Zhg8p06PIJbm5C10frUaH8za+Mv1Png5debcZLz07Cw8OdosX8GTjssUz1T4b9VrcUERuP0vLZGda5893Gaes6vTyb+ZO6AjB68gYWrTzIlavJNHryJ7q2rsTrz9Rm47ZTjPtuI4hQp3pRBrz+8G3Fo7KfZMPfc761A4vsAE4bY5rYngvWvL7WWJW4j40xv4hIaWCRMaaaiJTHqrhdBlYBrxtjCoiIJ7AMK+GcAvwFvGuMaSciPYHaxpjeIlIE+AqojJUcRxhjHM50GR3Dtq4P8AyQCCwxxvQTkSm2+GaLSCvgM+A08BtQLZ0YGmAlx1eBrsaYQyJSz3bMUrYh9oylrMiZF+wGpPdHOR2Ci9SBPXM6BBcXC2d+XtvdZExqTofgwvdKYk6H4OKcd+676UMBT/+bN7rLPFNy3/spt7pA7rko45oCpw7ndAjpklJv39VS2/0/PXHXPmv/evqXXFlGzLEEUV0nIu8CfsaYm2damiBmiiaImacJYuZogpg5miBmniaImacJ4t2nf0klh4nIPKAckPWb1SmllFIq2+kF1Zog5jhjjOst5pVSSimlcpAmiEoppZRSdtzdtISY+ybVKKWUUkqpHKUVRKWUUkopO7n5/oR3i1YQlVJKKaWUA60gKqWUUkrZ0TmIWkFUSimllFJOtIKolFJKKWVH74OoFUSllFJKKeVEK4hKKaWUUnZ0DqJWEJVSSimllBNNEJVSSimllAMdYlZKKaWUsqM3ytYKolJKKaWUcqIVRKWUUkopO1pB1AqiUkoppZRyohXEf5iI6DU5HYKL1IE9czoEF26Dp+R0CC5Oj/tvToeQrhSTlNMhuEjy8szpEFwkp1zJ6RBcRCWdy+kQXJS4kJrTIaTrcpHiOR2CC5+rubCvStTI6QhyBXctn2kFUSmllFJKOdIKolJKKaWUHZ2DqBVEpZRSSinlRCuISimllFJ29E/taQVRKaWUUko50QqiUkoppZQdnYOoFUSllFJKKeVEK4hKKaWUUnb0PohaQVRKKaWUUk60gqiUUkopZUfnIGoFUSmllFJKOdEKolJKKaWUHb0PolYQlVJKKaWUE00QlVJKKaWUAx1iVkoppZSyoxepaAVRKaWUUko50QqiUkoppZQdvVG2Joj/Sjs3RTFj/F+kphgati1L66cqOawPm7mf35b8jZu7Gz5+XvR8vzaFg/MDMHvSdnZsOAVAux5VqNO0ZLbFZYxh2Beridh4GG8vT0b0bUnVCkEu7cZN/p3Q5buJv3iVLct6py2fu3QXoyesJSigAADdO9fgsXbVsy0+Z9/16E+76g2IuXCO6kO737HjAGz4/QCfjVpGSmoq7TvX4pkXGjqsT0xMZmj/eezdcxI/v3wM/aQrRYsXJCkpmVFDFrF390nc3IQ3329FrTplHLZ9/43pnDh+jmlz/3PL8W38/RCffxJGaqqhXeeaPP18fYf1Wzcf5YvRYfx9IIaBIzvTpEXltHXvvPYzu7efoPr9JfnkyyduOQbI/f0EVl99MfpXUlNTadupJk8//5DD+q2bj/LlmF+tvhrRicYtrN/PA/uiGTtsGZcuJeLmLvR4oT7NHqlyW7Fcs2ldJF+PWU1qSiqtO1XjyefqOqzfvuU4X49Zw98HY/lweBtCmldIW9eyzmeUubcIAIHBPgwd1zFbYjLGMOzLNURsiMTb24MRfVpStUKgS7tx364jdPke4i9cZcuy1xzWLV21n/FTNiICFcsV4dOPWmc5jt9/28eYkaGkpBg6P1qX515s4rA+MTGZj/rOYM/uE/j752PkmO4UK14IgP37TjFsyBwuXbyKm5swdcbrpKYaPnj7J44fP4ObmxshjSvzxlttshSTMYZhoxex5vd9eHvnYeSgR6laubhLu517TtB34GwSribRqEFF+r/XDhHhzT4/c/jIaQAuXLiCj09eQn9+HYC9B04xcNh8Ll66ipsIs6e+hpeXZ8ZxDJ/FmohdeHt7MnL4M1StWso1jl1H6dv3RyuOkKr07/cYIkJc3CXeevs7Tpw4Q/Hihfls3Iv4+eXj/PnL9Os/laPHYvHy8mT4xz2oUKEYp06d5f0+P3D6dDxu4sbjjzfg2WeaZqnvVPb5f5UgisgrwGVjzI8i0hMIM8aczKDtECDCGPPrnYzDaXlpYJExptqt7js1xTD98y28NTqEggH5GPbKr9SoX4xipX3T2pQq70//ic3x8vZgdeghZk/azssDH2L7+lMcPXCOAd+2IDkxldFvrqbag8HkzZ/+ySOrIjZGcuR4HMunPce23VEMHruSmROfdGnXpH5ZunepQavuU1zWtW5agQFv3p0TxpT1ixm/ejY/9hxwR4+TkpLKmOFL+HxSDwKDfHnhqck0bFyRMuWuf1gunLcFH19vZi36LyuW7uDrz35l6OjHWDBnCwA/zXmNs2cu8s5/pvHd9Jdwc7O+/q7+dTd58+W57fjGjljGuIlPERDky0vdv6dBo/KUKReQ1iYo2Jd+Q9oz48eNLts/+Ww9riYkETr7r9uOIzf307UYx40MY+yEbgQE+dKr+xQeblSe0uWKpLUJKupLv8HtXPrK29uDfkPbU/KeQpyOucCL3f9H3fpl8fHxvu2Yvhy5klFfdyEgyIf/9JhO/UbluKds4bQ2gcE+vD+4JTOnbnbZPo+XB5N+fvq2YkjP9fPBs9b5YNxKZk7o5tKuyUNl6N65Bq26/+CwPPL4Ob6Z9ifTxz+Gn483Z85dznIMKSmpjPp4Hl9PfomgYD+efuJLGjWpQtly17+4zp+7CV/fvCxY+gHLl2zl87FLGPXp0yQnp/Bhn5/5eEQ3KlQqRlzcJTw83ElMTKbHcyHUqXsvSUnJvP810OkAACAASURBVPzCN/y+di8NGla6QSROffP7fiKPnSFs/jts23mMQSNCmfXjay7tBo0IZciHnalZvSQvvfEDEev206hBRT4bef28OnLsEgoU8AIgOTmF9z6cxeihj1Gpwv+xd9/hUVTdA8e/NwWSkEZINiEhdAgEkF6kF2mCNPG1IAoWfFUUuyIWREGs2AClKIigAgJBeif0KiWE3kNII42Qutn7+2NDsptsIAmB8Po7n+fhMdm5M3P2Znb2zLl3xsokJKbi4GBfeByhRzh3PoY1q8Zy8OA5xo77gwV/vlUwjo9+Z9xHj9GkSQ2efW4yoVvC6dSxAdOmr+bee4MY8WxPpk1fzbTpq3nzjYH8OG0V9etXYfIPz3H6TBTjPv6T2b+Mwt7ennfeepAGDaqSci2dBx+cSLu29aldu3KR+660yBzE/2dzELXWP1okZcMAf1vtlFL2WusPbkdyaCOOUnX2WDw+/q74+Lvi4GhHy66BHNh2yapNvaYGyjuZrw1qBnuREJsGwOXzydRt7IO9vR3lnR2oUsuDsN1RpRbb+q2n6d+zPkopmjSoTHJKBjFXUgq0a9KgMoZKrqW235LacuoA8deSb/t+wsMuUSXQi4AqXjg6OnBfr4Zs2XTcOpaNx+ndrwkAXboHs3f3GbTWnD0TS4vW5kqYVyVXXN2cOHbEfM2TmprBH3N2MOzZjrcU39GwSAICvfCvUhFHR3u69Qxm66YTVm0qB3hSu64vysZJtUXrGri4lL+lGODu7ye43lcVLfqqfsG+8vekVl0DKt9z1gKrVSKwmrky5W1wo2LFCiTGFz/pye/4kSj8Az3xr+KJo6M9nXsEsW3Taas2fv4e1Kzjg90d/FJcv+2MjfPBtQLtzOeDCgVeX7DsCI8NuAePnAS6UkWXYscQdvgiVap6UyWwEo6ODvTs3ZhNG45Ytdm0IZy+/VsA0K1HI/bsOoXWmp3bT1CnbmXq1jN/jXh6VsDe3g5n53K0bFUbAEdHB+rXDyA6OqlYca3fHM6APk3NfdOoKskp6cTEWp+LYmKTSUlJp+k9VVFKMaBPU9ZvCrdqo7Vm5brD9O3VGIBtO08RVMePenXNCVdFTxfsbzCWun7DIQb0b22Oo0kNkpNTiYmxfi8xMUnmOJrWNMfRvzXr1x+0WL8NAAP6t2FdzuunT12mTZsgAGrV9OPSpSvExSVjMHjkVihdKzhRs5Yf0dGJxeo7UXr+tQmiUuoJpdQhpdRBpdScnNfGKqXeUEoNBloAc5VSB5RSzkqpc0qpD5RSW4GHlFKzctqhlGqplNqes63dSim3fPtyVUqtV0rtV0odVkr1L0ocOT83z1m2A7i1sS0gMS4NL0PeibKijwuJcWmFtt+64iwNW/sBmBPCXVFkpBu5mpTB8QOxJMTe+hfUddFxKVQ25HWdn48r0bEFE8QbWbv5JP2Gz+HlD/7mcszVUoutLMXGJOPrl1fh9TG4ExudXGgbBwd7Krg6kZSYSu26vmzZdByjMZvIiASOH40kOmfd6ZM38ugTbXFyurUKcGzMVQx+eX83H1934sqg7+/2fgKIi0nB4GsRo68bsbHF76vwsEiyjNkEBFYspZgs/36uXCnG5y4z08gLj89l5JO/s23jqVuO57ro2BQq++RdCBb3fHDuYgLnIhJ5dOR8Hn7+T7bsOlfsGGJjkvDz88j93eDrQUxM/mMqr42Dgz2urk4kJqZy/nwcSileGDGDxx76hlk/byqw/avJaYRuPkqr1rWLFVd0TDJ+vnlx+Rncic6XIEbH5mvj6050vtj3/nOOSl6uVK9qrmCfvRCHUvD0i78w8LEfmD479MZxRCfi55d3DPr5VSQ6xjphi45JxM/X0yKOirlJ3ZUrVzEYzDEaDB7Ex5s/C/XqVWHt2gMAHDp0jsjIeKLyJYIRl65w9OhFGjeufsMYbxd7def+3a3+lUPMSqkGwBigndY6TinlZblca71QKTUSeENrvTdnHYB0rXX7nN975fy3HPAn8LDWeo9Syh3In3GlAwO11slKKW9gp1JqKRB8ozhy/AK8pLXerJT64lbfu9a64IuFVAV2rj3PueMJvPlNZwAatPTj3PEEJo7cgJtneWoGV8odgisVNkMr+qejS9ua9O0WRLlyDvwRcpB3Jqxm9jeDSy++slKUfimkTd8BTTl/No6nH5uGb2VPGjUOxN7ejhPHLhNxIZ5Rb/bi8qWEUo+vsGPqtrrb+wnQNgJQFK+v4mJTGP/e37w7ri92pfB/c7B1SijO32/e8mfw9nElMiKRN//7FzVqe+Mf6HnzFUugOIeVMdvE+YhEfv3mQaJjUxjy0kL+/uVx3N2KXq0uyumysDbZRhMH/jnLnD9exsnJkf8+M436wQG0blPHHJ8xm9FvzeORIe2oElip4EaKHZcqdptlqw7St+c9ub9nG03sO3Cehb++gLOTI8Oen0nD+v7c28p2Amvr+6RgHLba2NxcrhHP9mD8hAX0HziBunX8qV+/Cg4Wlcxr19J5+eVpvPvOYFxdnW+8MXHb/CsTRKArsFBrHQegtY4v4np/2ngtCListd6Tsy1bY44KmKCU6giYgADA92ZxKKU8AE+t9eacl+YABWZZK6VGACMAXv+sN/0eb1boG6jo40J8TF7VLyE2Fc9KBecwhe+LZvlvR3nzm844lsubg9Ln8fr0edx8g8H0j3fiW+XWhnrnLj7AgmVhADQK8rWq+kXFpmDwLjh0VJiKHnkniof6NuLLn7beUmx3Cx9fd6Kj8g6r2JhkvA1uNtsYfD0wGrO5lpKOu4czSilGvdkrt92IJ2YQWNWLf/ad5/jRSAb1nkS20URC/DVefPoXJs8cXoL43IiJyvu7xUYn4+1z56cA3O39BOBjcCPGoqoZG321WH11LSWDt1+ezzMvdqTBPQVvSihRTL6uxERb/v1SqFSMz931+P2reNK4eRVOHY8pcYI4d/HBvPNBPV8uW1QMzeeDoveVn48rjYMr4+hgT5XKHtSo6sn5Swk0qudX5G0YfD2IisobMo2JTsLHx91mG18/T4zGbFJS0vHwcMHX14PmLWpSsaK5L9t3qMex8Eu5CeInY/+ialVvhgy1vpGqMHPn72D+4r0ANAoOIMpiWDoqJhmDt/Wx7mdwt24TnYzBJ6+N0ZjN2o1HWPRb3o1+fr7utGpWA6+cmDu2C+LIsUirBHHu3M3MX7jNHEfDakRF5V04RUUlYPDJq1qat1nRqvoXFZ2AwWA+PipVciMmJgmDwYOYmCS8vMzxubo68+mEJwBzgtntvvepUsWcRGdlZfPyqOk88EArevRoWqS+ux3u5HSLu9W/dYhZYbvucTMFJ8AUbVtDAB+guda6CRANOBVh3SLFqbWeprVuobVucaPkEKB6vYrEXEoh9vI1jFkm9my4SOO21lMtL5xM4Lev9zFyfDvcK+Ylj6ZsTUpSBgARpxOJOJNEcMuCdxkXx5CBTVgy83GWzHycbh1qEbL6KFprDhy5jFuFcsWaa2g5X3HDtjPUqmarIPu/p34DfyIuXCEyIoGsLCPrVoXRvlOQVZsOnYNYudQ8JLNxbTjNW9VAKUV6WiZpqZkA7N5xGnt7O2rUMjDoPy1Zuu4NFq18lR9nPUVgtUolTnrqNfAn4kI8kZcSycrKZv3qcNp3qnvzFUvZ3d5PcL2vEiz66ijtOtcp0rpZWdmMef0vevZtaHUX+K0KCvbj0sUELl9KIisrm01rjtO2U80irXs1OZ3MTCMASQlpHDkYaXVzS3ENGdiYJTOHsGTmELq1z38+KG9zrmFh7mtfi10HIgBISEzj3MVEqlT2uMla1ho0rMLFC3FciognK8vI6pUH6dTF+s7xTl2CWRZiTtzWrzlMy9a1UUpxb7u6nDxxmbS0TIzGbPbtPZN7c8vk71aRkpLOG+88UORYhvznXkJ+f4mQ31/ivs7BLFn+j7lvDl/AzdUJQ/7E1cedChXKc+DwBbTWLFn+D9065cW+ffdpalb3sRqGbn9vXY6fjMqNec/+s9SuYX3n+JAhnQhZ/C4hi9/lvm73sCRklzmOA2dxc3POHTLOjcPgYY7jwFlzHCG76NbVXLXs2vUeloTsBGBJyM7c15OTU3OPqwULttGiRW1cXZ3RWjPmvTnUrOnH8GHditx34vb4t1YQ1wOLlVKTtNZXlFJeNqqIVwE3G+vmdwzwV0q1zBlidgPStNZGizYeQIzWOksp1QWoVpQ4tNaJSqkkpVR7rfVWzInmLbG3t+Oxl5vyzVuhaJOmXe8aBNTwIOTnMKoFedGknT8LfzxEepqRH8fuAKCSrwsjx7cnO9vE56M2AuDk4sjTY1rfcAJzcXVqU4PQnefo8dgvOJV3YMI7PXKXDXj6N5bMNN8p+cXUUJatP05aehadBk9ncJ+GvDT8Xub8dYCN28xf7h5uTnz6Ts9Si82WeU+No3PdZni7enJxwlI+XDadn7f/Xer7cXCw57XR9/Pq83PINmn6DmhKzdoGpk/eQL0G/nToXI++A5sybsxiHur7Le7uzoz73Dy0nhB/jVef/w1lp/AxuPHB+EG3IT47Xn2nJ68//7v50S39G1Ojtg8zpmymXnBl2neuy9GwSMa8tpCryelsDz3Jz1NDmbPoOQBeHP4r589dIS01k0E9vuPtsX1o3bZWCeK4u/vJHKMdr7zdnTde+AOTSXN//3uoUcuHmVNCCQquTPvOdTh6JJL3XluU11c/buHXv55l45qjHNx/keTENFYtPQzA6HF9qRN0axdp9g52vPRWV94ZuQhTtqZX/wZUr+XNrKnbqRvsS9tOtTh2JIqxb/xNSnI6O7acYfZPO5i54EkunI1n0vh12NkpTCbNI8Na3lKCaKlTm+qE7jpHjyGzzeeDt7vnLhvw9FyWzDSfDr/4cSvL1h0nLSOLToNnMrhPA14a3ob2raqxde8F+jw5Bzs7xZv/bW81ylAUDg72vP1uf158bgambBP9BrakVm0/pv6wmuAGVejUpQEDBrXk/dF/0K/3Z3h4uPDpF48B4O7hwpAnOjL0ke9RCtp1qEeHTvWJjkpk5rQNVK9h4LGHvgXg4UfbMnBw66L3TfsgNm87Tvf+X+Hs5MiEsQ/mLuv/6Pe5j6wZO7o/o8cuJD3dSMd2denYLu/CbcXqQ/Tp2dhqux7uzgx7vB2Dn5iCUuYKYucb3F3dqVNDNoceoXvPD3F2KseECUPz4hg4gZDF75rj+PDR3MfcdOzQgI4dGwAw4pkevPLaTBYu3E5lfy++nfQMAKdPR/H2O7Oxs7ejdi0/xn9i3u6+/acJWbqbunX96T9wAgCvvdKPTp1K/GCPErub5wbeKcrmnLV/AaXUk8CbQDbwj9Z6mFJqLJCitf5SKfUgMAHzfMJ7gaNAi+vDwUqpWZgfObNQKdUS+B5wzml/n9Y6xWJf3sDfgCNwAGgH9NZanytCHM2Bn4FUYDUw+EaPuQmNfO+u+4N1sKtS1iEUYPfRrLIOoYC4SaPKOgSbsnVWWYdQgL0qnUcrlSajKaOsQygg05Re1iEUUOWqqaxDsCnVu3SG7EtThQzjzRvdaS63Z37pLbPrdkdTtlGbn71j37Xfdpp+V6aj/9YKIlrr2cDsfK+Ntfj5L+Avi8XV87UdZvHzHqDNDfYVhznJLEkc+wDLy7yxCCGEEKLMlML9Yf/z/q1zEIUQQgghRAn9ayuIQgghhBAlIXMQpYIohBBCCCHykQRRCCGEEEJYkSFmIYQQQggLpfF/MfpfJxVEIYQQQghhRSqIQgghhBAW5CYVqSAKIYQQQoh8pIIohBBCCGFBpiBKBVEIIYQQQuQjFUQhhBBCCAsyB1EqiEIIIYQQIh+pIAohhBBCWLBTUkKUCqIQQgghhLAiFUQhhBBCCAsyB1EqiEIIIYQQIh+pIAohhBBCWJDnIEqC+D+nUaVGZR1CASl25co6hALiJo0q6xBs8n7127IOoYD4b14t6xAKOBh3qKxDKCAyJbGsQyhg1IrzZR2CTTEPdi3rEAqooJzKOoQCjPNnl3UIBTg8+EBZh2CbR1kH8P+PJIhC3CF3Y3IoRGm7G5NDIYrLXu5iljmIQgghhBDCmiSIQgghhBDCigwxCyGEEEJYkJtUpIIohBBCCCHykQqiEEIIIYQFeVC2VBCFEEIIIUQ+UkEUQgghhLBgJ+UzqSAKIYQQQghrUkEUQgghhLAgD8qWCqIQQgghhMhHKohCCCGEEBbkOYhSQRRCCCGEEPlIBVEIIYQQwoI8B1EqiEIIIYQQIh+pIAohhBBCWJA5iFJBFEIIIYQQ+UgFUQghhBDCgjwHURLEf40dW08y6bMVmEyafoOa8cTTHa2WZ2Ya+WjMIo6HR+Lu4cwnX/wH/4CKGLOymTA2hONHIzFmm7j/gSY8+Yx53T9/20HIX/vQaPoPas4jQ9sWO67tW4/z5cS/MWVrBjzYkmHPdC4Q14ej53M0/BIeni58+uWj+Ad4AXDy+GUmjFvMtZR0lJ3i1z9GUr68Y+66r46czaWIeOYvebVYMe3cdpJvPltFtsnEAwOb8cTTHQrE9PGYxRw7GomHhwsffz6YygEVycoy8tm4ZRwLj8TOTvHKW71o1rKG1bpvvTyPSxEJzF30YrFiKqqZQ8fQt1E7Yq4m0OjjIbdlH9fdrcdUYcJ2RzH/hwOYTJr299eg12P1rJZvXnqaTSGnsbNTlHd24PHXmuNf3b3U9m/LqX1xrJpxAlO2plmPANoPrm6zXfi2aBZ8dphnv2qFf53Sj6lrtWaM7zwCezs7fgtbw3d7FlotD3Dz4Yeer+JRvgJ2yo5Pts5m3bm9Vsu3PTGFz3fOY8q+xaUSk9aa8VO2E7r7Ak7lHfj0zc40qONToN2kn3cTsu4EyVcz2P/307mvX4q+ypgvNxGflI6HW3m+eKcrfj6uJYvj07/YHBqOk3M5Jo4fQoPgwALtwo5cYPSYuaSnZ9GpYzBjRj+IUorPvlzCxk1hODo6UDXQm08/eQx3dxeWLtvDzJ835K5//EQkixe8Sf36VUoU46frLxJ6OhlnRzvG31+dYD+XQtu/+NcpIhIzCHm6AQCTt0ay8GAcFV3MX/mvdAygYy2PEsUx/quVbN5+EicnRyZ+MIAG9fwLtAs7GsnocUtIz8iiU9s6jHm9N0opjp2I4sOJy0hNyySgsidfjhuEq6sTWcZs3vtkKeHHL2PMNjHg/sY8N6yDjQjEnfQ/O8SslPqvUuoJG69XV0qF3cJ2NymlWtxadHdWdraJLycsY9LUofy+ZCRrVh7m7OkYqzZLF+3H3d2Jhctf4dGhbZn8zVoA1q85QmaWkbmLRjL7j/+yeOFeIi8lcPpkNCF/7ePneSOYs+AFtoae4ML5K8WO67NPQvhu6nAWLH2V1SsOcOZ0tFWbkEV7cHN3ZsnKN3lsaHu+/3oVAEZjNu+/8yej3x/A/JDX+OmXETg42Oeut2FtGC4u5UrYVyv4asoQ5i1+kXWrwgr01d+L9+Pm7sSCZaN4+PE2TPlmnbkP/9oPwG9/vcA3Pw7l+6/WYDKZctfbtC4c5xLEVByzdiyn1/fFS4hL4m49pgpjytb8/u0/vDSxPWN/6cmeDReJPJds1aZVt6p8OLMH70/vTs+Hg1gw9WCp7PtGMa346ThDPmzCi5PvJSw0itgLKQXaZaQa2fX3RQLq3p5k1U7ZMbHr8zyy5EPazX6BgUGdqOtlnQC91vphQk5soevcUYxY8TmfdX3eavknnZ5h/bl9pRpX6O6LnL+UxOpZjzDulY589N1Wm+26tKnG/O8HFnj985920r97XZZOe4gXH2/O1zN3lyyOLeGcOx/LmpXv8/HYhxk7br7NdmPHzWfc2EdYs/J9zp2PJXTrUQDa3RvEsiWj+XvxO1Sv5sNP082fg359WxKy6G1CFr3N5xOHEhDgVaLkEGDLmWTOx2ewckQDxvasyrg15wttu/Z4Ai6OBb/an2hhYNHwYBYNDy5RcggQuv0k5y7Gs+avl/l49AOM/Wy5zXZjP1vGuNEPsOavlzl3MZ7QHacAGDN+Ka+PvI+/f3+B+zrXY8Zv2wFYtc58zvj79xdY9OsI/ly8l4jIhBLFKErPXZEgKrNixaK1/lFr/evtiqksKaWKVdkND4ugSlUvAqp44ejoQPdejQjdeMyqzZZNR7m/XxMAunQPZu+uM2itUQrSUjMxGrPJyDDi6GhPBdfynDsbS4N7quDkXA4HB3uatajO5vXhxXofRw5fJLBqJaoEVsLR0YEevRuzeYP1NjZvCKdv/2YAdOvRkN27TqG1Zuf2k9Sp60fdnKtTT88K2NubD5HU1Azm/rqFp5/rWqx4zH11iSqBeX11X6+GbNl03LqvNh6nt2Vf7Tb31dkzsbRoba4YelVyxdXNiWNHInNj+mPODoY9a11lK21bTh0g/lryzRveorv1mCrM2WPxGAJc8fF3xcHRjhZdAzm4PdKqjXOFvOpzRrqR2z2CdOlkEl6Vnano54K9ox0NOvhybFdsgXYb556m3YPVcCh3e07Hzfzqci7xMueToskyGVlyPJTetdpYtdFa41bOXJFyL1+BqGvxuct612rDuaQojl25UKpxrd9xjv731UUpRZNgX5JTMoi5cq1AuybBvhgqVSjw+ukLCdzbNACA1k38Wb/jXMni2HCYAf1ameNoXIPkq2nExCZZtYmJTSLlWjpNm9RAKcWAfq1Yv/4QAO3b1c+9eG3SuDpR0YkF9rF8xT763t+8RPEBbDiZSL+GlVBK0TjAlasZ2cSmZBVody0zm9l7onmubeUS7+tG1oceZ8D9jc191SiQ5KvpxMRdtWoTE3eVlGsZNL0n0NxX9zdm/WbzuePshThaNq0GQLvWtViz0fz5V0qRlpaF0ZhNeroRRwd7XCuUvy3voajs1J37d7cqswQxp9J3VCk1BdgPBCqleiildiil9iulFiilXHPaTlRKhSulDimlvsx5baxS6o2cn5srpQ4qpXYAL1rsY5hS6geL35cppTrn/DxVKbVXKXVEKfVREeK1FcMspdRgizYpOf+1U0pNydn2MqXUiuvtlFIfKKX2KKXClFLTlDJ/TeVULicopTYDo4rTl7HRVzH45l0RGnzdiY1JLtDGN6eNg4M9rq7lSUpMpWv3Bji7lKNvty/o3+MrhjzZDg8PF2rW9uXA/vMkJaaSnpbJ9i0niI4uXmISE5OMr59lXB7E5IvL3MbTIi4nkhJTuXA+DpRi5IiZDHnoO2b/vDl3nanfr+HxJzvg5ORIccXGJOPrl1ep8TG4E5vvfVm2cXCwp0JOTLXr+rJl03GMxmwiIxI4fjQyt0+mT97Io0+0LVFMd6O79ZgqTGJcGhUNzrm/V/R2JjE2rUC7jUtOMWbIShZNO8zDI5uUyr4Lc/VKBu7eTrm/u3s7cfVKhlWby6eTSY5Lp27LgkOrpaWyayUuXc1LTCNT4qjsWsmqzRc75zG4fhcOPjOL3weMZfTGHwFwcSjPSy0G8+XO30s9rui4a1Q25CV+ft4ViI5LLfL6QTUrsWbLWQDWbj3LtdQsEpLTix9HTBJ+OecgAD9fT6KjrRPE6Ogk/Hwt2vh5Eh1j3Qbgr0U76dghuMDrK1btp8/9zYod23UxKVn4ueeNTvi6lSP6amaBdt9viWRYK1+cbVQQ5+2PZeDP4by34hxJ6cYSxREdk4yfb97508/gTnS+80J0TDJ+Bttt6tY0sD7UfEG+at0RLud8/nt2C8bZ2ZH2939Fl36TeOrxtnh6FD6ELu6Msp6DGAQM11q/oJTyBt4D7tNaX1NKvQ28lpPgDQTqaa21UsrTxnZ+AV7SWm9WSn1RxH2P0VrHK6XsgfVKqXu01odsNVRKeRUhBkuDgOpAI8AAHAV+zln2g9Z6XM525wB9gb9zlnlqrTsVMf5cGm0r6Ju2UUpxJCwCOzs7lq17k+TkNP47bCYt29SkRk0fhg5vz0sjZuPiUo46QX442BfzekLb2ufN26Ag22ji4D/n+PWPkTg5OfL8MzOoHxyAh6cLEReu8PrbDxB5Kb7gujeNycbu8gdVSJu+A5py/mwcTz82Dd/KnjRqHIi9vR0njl0m4kI8o97sxeVL/45hkbv2mCo8YBvxFnypy4DadBlQm93rL7Dit2MMf6dl6ezfVkg3iUmbNKtnnmDAqAa3LYZ8u8zbd77gBgZ14o8j65m6fzEtKtdjSq/X6fDri7x17xB++mcJ17KKn3jd1M0PsRt6a0QbPvlhK4vXHKdFo8r4elfAoQRPN87fF7bisNkm3+9Tf1qNvYM9/fpaz1A6eOgczk7lqFun4Fy90ozxaHQqFxIyeKdbIJeSrC9EHm7qw3/bVkYpcxL5xYYIPrm/evHjsPFa/vPnjdqMf78/479ayZSZm+naIYhyOZXXQ0cuYWdnx5YVr5OcnMZjI36hbauaBObMRy8L8qDssk8Qz2utd+b83AYIBrblHEzlgB1AMpAOzFBKLQeWWW5AKeWBObG6XmKaA/Quwr7/o5QagbkPKufs22aCeLMYbGgPLNBam4AopdRGi2VdlFJvAS6AF3CEvATxT1sby4lzBMDXPzzDsGfus1pu8HUnxuKKNyY6GR8ftwJtoqOTMPh5YDRmk5KSgbuHM2tWHObedrVxcLTHq5Ir9zStytEjkQRU8aLfoOb0G2QeFpn67Vp8fIs3b8Xg60F0lGVcSfj4uNtok4hvblzpeHi4YPD1oFmLGnhWNFcY2nUI4lj4JVxcynM0/BIP9JhIdraJ+CvXGDHsJ6bNeq5IMfn4uhMdlXfFGxuTjLfBzWYbg685pmsp6bh7OKOUYtSbvXLbjXhiBoFVvfhn33mOH41kUO9JZBtNJMRf48Wnf2HyzOHF6q+7yd16TBXG08eZhJi8imFCXBqe3s6Ftm/RJZC53+wHbl+C6O5dnuS4vMQqOS4dN6+8YbOMtGxizl9j1hjz3L6UhEx+H3+A/9Qq3AAAIABJREFUR8c0KdUbVSJTrhDglleh9Hf1thpCBhjSsDsPL/oQgL2Xj1HeoRyVnN1pXjmIB+q044P2w/EoXwETmgxjFjMP3uwUaNvckDAWrDAPNzYK8uFyTN6QclTcNQyVil418vWuwPdjewJwLS2LNVvP4lbEYcm580KZv3CHOY6GVYmKyhsWjopOxGCwPi79/Dytho6joqzbLF6yi02bjzBr5sgCCdPyFfvpU4Lh5Xn7Y1h4MA6Ahn4ViErOqxhGX83E4Go93/lg5DXCo1PpPvUw2SbNlVQjw+YdZ9ZjQXhbTK8Y3NibFxaeKnIccxfsZv4S8zHaKDiAKIuqf1RMMoZ85wU/gztRMbbb1Kruw8/fm28dOHs+jk3bTgCwbPVhOtxbG0cHeyp5udKscSCHwyPLNEG8myilegHfAvbADK31xELaDQYWAC211ntttSmOsp6DaDnhRAFrtdZNcv4Fa62f1lobgVbAX8AAYFW+bShsX7QAGLF+j04ASqkawBtAN631PcDy68tsuUEMudvPGSq+/om1ee2hlHICpgCDtdaNgOn59ltwAo55/9O01i201i3yJ4cA9RsEcPF8PJERCWRlGVm76jAdOlvfwdmhcz1WLD0AwMa14bRoZZ5L41vZg727z6K1Ji01k7BDEVSr4Q1A/BXzhPqoy4lsWn+UHvc3KqyLbApuWIWLF65wKSKerCwja1YepGMX6+GXjl2CWRZivvlj/ZowWrauhVKKe9vV4eSJKNLTzHPZ9u89S81avgx+pA2rNo7h7zXvMOPX/1K1uneRk0NzX/kTceFKbl+tWxVG+05B+foqiJUWfdU8p6/S0zJJSzWfpHfvOI29vR01ahkY9J+WLF33BotWvsqPs54isFql/+nkEO7eY6ow1etVJOZSCnGXr2HMMrF3w0Ua32s9Dys6Im+u1OGdlzEEuOXfTKkKqOPOlcg0EqLSyM4ycWRLNEGt8xI1pwoOvDW3E6/MaM8rM9pTJci91JNDgH+iTlCjoj9V3X1xtHNgQFBHVp3ZZdXmUnIsHas2BqCOVxWc7B2JS0vigflv0/znp2n+89P89M9Svtk9v8TJIcCQ/g1Z8tNglvw0mG7tqhOy7gRaaw6ER+NWoZzNuYaFSUhKw2Qyn/qn/f4PD/YMuskaFnE81jH3BpL7ut3DkqW7zXEcPIubqxMGH+sE0eDjQQUXJw4cNB/XS5bupltX87EbuiWc6TPXMfWHZ3F2tk7aTCYTq9b8Q5/exR9efqxZ3k0l3ep6sjTsClprDl5KwbW8PT6u1tNZHmnqw6YX72Ht842Y83gQ1b3KM+sxc59YzldcdyKROje4eMpvyEOtCJn7PCFzn+e+TvVYsuKgua8OX8TNtTwG73wXjt5uVHApz4HDF819teIg3Tqa47gSn5LbL1N/DuWRQeZqa2VfD3btNfdtalomB8MiqFndu9h9VprslLpj/24kZ5RzMubCVzDwqFKqwDwGpZQb8DKwK/+ykirrCqKlncBkpVRtrfUppZQLUAWIBFy01iuUUjsBq0sfrXWiUipJKdVea70VsHz2xznghZwbYAIwJ3kA7piTsSSllC/mjt9UWGA5cyFtxXAOaA7MB/oD1z+xW4EnlVKzAR+gMzCPvGQwLmebgwHr502UgIODPW+824dRz/+KKdtE3wHNqFnbwLTJ66kXHEDHLvV4YGAzPnp3EYP7fIO7hzMff/4QAIMfacUn7y/hsUE/oDX07d+UOnX9ABj92h8kJaXh4GDHG+/2wd296CeV63G9+W4/XnruZ7KzTfQb2IJatX358Yc11G9QhU5dguk/qAUfjJ7PgN5f4O7hzIQvHgXA3cOFIU904IlHfgClaNchiPad6t1kj0WL6bXR9/Pq83PINmn6DmhKzdoGpk/eQL0G/nToXI++A5sybsxiHur7Le7uzoz73DzNNCH+Gq8+/xvKTuFjcOOD8YNuOZ7imvfUODrXbYa3qycXJyzlw2XT+Xn73zdfsZju1mOqMPb2djzyUhO+fXsLpmxNu97V8a/hwdJfjlCtbkUat/Nn05LTHN0Xg72DwsWtHMPfvr0PK7Czt+P+54L4bew/aJOmyX3+GKq6snHuafxru1sli7dTtjYxesOPzB80Djtlx+9H1nL8ygXevncIB6JPsvrMbj4Incmk7i/xXLMBoDUvrf7mtsfVqVVVQnddoMeTf+BU3oEJb3TOXTbguYUs+cn8ufti+k6WbThFWoaRTo/+xuDe9XjpiRbsOniZSTN3gVK0bFSZD15qX7I4OgazOfQI3XuPw9mpHBM+yfsK6T/oM0IWvQ3A2A/+Y37MTUYmHdsH5841/Hj8QjKzjAx/ZgoAjRtXZ9yHDwOwZ+9p/Hw9CQy8tWSnY013Qk8n0XtaGE4OdlbDw4N+CWfR8ILzHi19tSmCY9GpKKXw9yjH2J7VShRHp3Z12Lz9JN0HfYezkyMT3u+fu6z/kKmEzDXf/T727T45j7kx0rFtbTq2rQPAsjVhzFtgvtu8e5f6PPhAUwCGPNSS0eNC6PvIFDSaQX2bUq+OX4li/BdqBZzSWp8BUEr9gTnfyH+H38fA55iLX6VC2ZrbcCcopaoDy7TWDS1e6wp8BlwfJ3gP2AOEYE6uFPCl1nq2UmoskKK1/lIp1RzzHL9UYDXmCl3DnKreb0ATIAzwBcZqrTcppWYBrYEzQAawVGs9Sym1CXjDsjyrlKpcSAy+Oa/bAesxz4N0zUlIpwAdgRM57+drrfVapdQnwCOYk8uLmIfZx9rary0JGX+WzR/sBhzsbu+jXUoiM/s2zJm6Rd6vflvWIdgU/83tf2xOcR2MK2y2R9mJTCl4d2pZG7Wi8MedlJWYB4v/dIE7Qfnf3rmeJWH8dXZZh1CAw4MPlHUItnk8ekdnBc47/uId+659LGhyoe8tZ9i4l9b6mZzfhwKttdYjLdo0Bd7TWj9Y1FyiKMqsgqi1Pgc0zPfaBmxPCmqV/wWt9ViLn/cBjS0Wj815XWNdUbRcf1ghr3e28drlQmKIxjx38rrROa+blFJvaK1TlFKVgN3A4Zxl72FOfG+6XyGEEEL8u1neZ5BjmtZ62vXFNlbJTV5zClKTgGGlHdfdNMT8b7Ms527ncsDHWuuosg5ICCGEEDd3s7mBpSknGZxWyOIIwPLJ9ten3l3nhrnYtinnBik/YKlSqt+tVhElQbxNpCIohBBCiFu0B6iTc3PtJcxT1B67vlBrnQTkTnL9VwwxCyGEEELcje5kBfFGtNZGpdRIzPdX2AM/a62PKKXGAXu11ktv174lQRRCCCGEuEtprVcAK/K99kEhbTuX1n4lQRRCCCGEsHC3VBDLUlk/KFsIIYQQQtxlpIIohBBCCGHBTkn9THpACCGEEEJYkQRRCCGEEEJYkSFmIYQQQggLcpOKVBCFEEIIIUQ+UkEUQgghhLAgFUSpIAohhBBCiHykgiiEEEIIYUEqiFJBFEIIIYQQ+UgFUQghhBDCgp3Uz6QHhBBCCCGENakg/o9xc/Qq6xAKSDUml3UIBWTrrLIOoYD4b14t6xBs8nplUlmHUEDcpFFlHUIB/hUulXUIBawc4lfWIRSgnALKOgSbjHZ3Xz3Ezt+jrEMoQMdfLOsQbFJ3uKtkDqJUEIUQQgghRD5SQRRCCCGEsCAVRKkgCiGEEEKIfKSCKIQQQghhwU5J/Ux6QAghhBBCWJEKohBCCCGEBZmDKBVEIYQQQgiRjySIQgghhBDCigwxCyGEEEJYkCFmqSAKIYQQQoh8pIIohBBCCGFBKohSQRRCCCGEEPlIBVEIIYQQwoI8KFsqiEIIIYQQIh+pIAohhBBCWLBD5iBKBVEIIYQQQliRCqIQQgghhAW5i1kSxH+NLVvCmThhIdkmEw8Obsuzz/awWp6ZmcXot+dwJPwCnp4V+OrrpwgIqMT2bUeZ9PVSsrKMODo68PqbA2jTJgiAEc9OJjY2mWxjNs1b1OK99x/G3r7oReftW0/w1WfLMGWb6D+oJcOe6ZQvJiMfvruAY+GX8PB0YcIXj+IfUJGVyw4wZ9aW3HanTkQxZ/6LBNXzZ8p3a1i+9B+uJqcRuntsyTssx65tp/n28zWYTJq+A5vw+FNtrZYf2HeB775Yw5mTMXw4cSBdutfPXfb6C78TfugSjZoG8vn3D99SHDu2nmTSZyswmTT9BjXjiac7Wi3PzDTy0ZhFHA+PxN3DmU+++A/+ARUxZmUzYWwIx49GYsw2cf8DTXjyGfO6f/62g5C/9qHR9B/UnEeGtrW161Ixc+gY+jZqR8zVBBp9POS27edGdm47yTefrSLbZOKBgc144ukOVsv/2XeObz9fxemT0Xz02WC6dm9w22Pat+Mi07/egcmk6d4viIeebGK1fMm8Q6wJOY69gx3unk6Meq8jhspupR7HwV2RzPl2DyaTpnPf2vR7vKHV8hV/hLNx2Wns7RXunk48O7oNPn6uAMRFX2P6ZzuJj7kGKN76ogs+lV1vOSatNeO/WsHmbSdxcnJk4ocDaVDPv0C7sKORjP5oEekZRjq1q8OY1+9HKcWxE1F8OHEpqamZBFT25MuPB+Pq6lTsOEp67kxMSOGVV2YSFnaeAQPa8N77/wHg2rV0hj4+KXf96KhE+j7QktHvDi52bNdprZmw6CSh4VdwcrRjwpBgGgQWPE6enXqA2ORMjCZNi5oevP9QEPZ25kTnt9CLzN1yCXs7RafgSrzZv3aJ47ke0/ipOwndcxGn8g58+npHGtTxtmqTlm7klfHruXD5KvZ2ii5tqvL6Uy0B2HP4Mp/+uIvjZ+P5anQXenWocUvxiNL3/3KIWSk1SylV4NOqlKqulAor5rb8lVILC1m2SSnVoqRxFlV2tonxH8/nx2kvsPTv91ixfB+nTl22avPXwh24ezizavVYnniiC19/GQJAxYquTJ76HEuWjmHCp0MZ/favuet8PekpFi8ZTcjfY4iPT2H1qv3Fiunz8Uv5dsow5oe8wpqVBzlzOtqqTciivbi7O7N4xRs8NrQd309aBUDvvk2Yt/Al5i18iXETHqKyvydBOV8cHTrVY/bvz5eon2zF+PWnq/hy8iPMWfQc61Yd4ezpWKs2vn7uvDvuAe7r3bDA+o8+2Yb3xvcrlTi+nLCMSVOH8vuSkaxZeZizp2Os2ixdtB93dycWLn+FR4e2ZfI3awFYv+YImVlG5i4ayew//svihXuJvJTA6ZPRhPy1j5/njWDOghfYGnqCC+ev3HKshZm1Yzm9vn/1tm3/Zsx9uIKvpgxh3uIXWbcqrEAf+vl58N7HA+jeu9Edi+nHL7Yx9pteTP5jMKFrTnPhTIJVm5p1vfl69kC+n/sg7brW4Jcfdpd6HKZsE7O+3s1bX3bl8zkPsGPdOSLOJlq1qVbXi09m9Gbi7L606lyV36f+k7vsx0+20ffRYL74rR8fT+uFe8XiJ2G2hG4/ybkLV1izaBQfv9uPsRP/ttlu7MS/GfduP9YsGsW5C1cI3X4SgDGfLOH1F7vz9x8jua9LMDPmbCt2DLdy7ixX3pGXXu7Lm28OtGpfoYITixaPzv3n7+9F9+7WFwbFFRp+hfOxqax6rw0fPVKPcQuO22w3aXhDlrzdir/faUV8SharDpg/A7tOJrD+cBwhb7di2ejWPNW16i3FAxC6J4Lzkcms/vkhxo1qz0c/bLfZbvjgRqycMZhFkwew/0g0oXsuAlDZx5VPX+9I3y61bjmW28FO2d2xf3eruzey/wFKKQetdaTWuuSXhqXg8KFzBFb1JjDQm3LlHLj//mZs3HDIqs2GDYfo3781AD16NmXnzuNorakfHIjB4AlA7TqVycjIIjMzCwBXV2cAjEYTWVnZqGKU3I8cjiCwaiWqBHrh6OhA9973sHnjUas2oRuP0qdfMwC6dm/Inl2n0VpbtVm98iA972+c+3ujxlXx9nEvchw3cjQskoBAL/yrVMTR0Z5uPYPZuumEVZvKAZ7Urutr8723aF0DF5fytxxHeFgEVap6EVAlp696NSJ04zGrNls2HeX+fuYvmS7dg9m76wxaa5SCtNRMjMZsMjKMODraU8G1POfOxtLgnio4OZfDwcGeZi2qs3l9+C3HWpgtpw4Qfy35tm3/ZsLDLlElMK8P7+vVkC2brL9EKwdUpHZdP+zs7szQ0cnwWCpXcccvwB1HR3s6dq/FrtDzVm3uaeGPk5N5ICeooYErMddKPY7TR6/gG+CGwd8NB0d72nSrzr6tEVZtGjTzo3xOHLUbeBMfkwpAxNlEsrM1jVpWBsDJxTG33a1av/kYA/o0QSlFk0aBJF9NJybuqlWbmLirpFzLoOk9VVFKMaBPE9ZvNn82zl64Qstm1QFo16oWazYW//i+lXOni0t5mjevRbnyjoVu//y5GOLjr9K8xa0lQRvC4ujf0s/cV9U9SE4zEpOUUaCda87fxmjSZGWbcm+z+GPrJZ69rxrlHMxf+ZXcyt1SPADrd5ynf7fa5pjqG0hOySTmSqpVG2cnB9o0Nl/cl3O0J7h2JaLizMd4FT83gmp6Fet7RdxZ/y8SRKXUE0qpQ0qpg0qpOTkvd1RKbVdKnSmkmuiklPpFKXVYKfWPUqpLzuvDlFILlFJ/A2ssq45KKWel1B85+/oTcLbYXg+l1A6l1P6c9V1zXp+olArPWefLkry/6JgkKvtVzP3d17ci0dFJVm1iopPwq2xu4+Bgj5ubM4mJ1l9Ga9YcoH79QMqVyzvhPfvMD3Rs/w4VKpSnR8+mRY4pNiYJXz8Pi5g8iI22TiBiLNo4ONjj6upEUqL1CWbtqsP06H1PkfdbHLExVzH45Q3T+Pi6Exdz9QZr3B6x0Vcx+Ob1lcHXndiY5AJtfH0t+6o8SYmpdO3eAGeXcvTt9gX9e3zFkCfb4eHhQs3avhzYf56kxFTS0zLZvuUE0dFll8DdbrExyfj65V04+BjcCxxvd9qVmGt4++YNxVYyVOBKbOEJ4Nqlx2l+b5VSjyM+NpVKBpfc3718XEiISy20/ablp2jcxvylHnXxKi6u5Zg0ZjPvPrWceZP3Yco2lUpc0bHJ+Fkc934Gd6LzHffRMcn4Gdyt28Sa29StaWB9qDlZXLU+jMv5znlFiqGUzp2FWb58H716N7vlJCg6MQM/z7zKrZ9HeZsJIsAzUw/QfsxWKpR3oGcTAwDnYlPZdzqRh7/ey9Dv9nP4/K1/NqKvpFLZp0JeTD4uRF8pvF+SUzLYuOsi9zYpOI3gbmSn1B37d7f61yeISqkGwBigq9a6MTAqZ1FloD3QF5hoY9UXAbTWjYBHgdlKqeuf0HuBJ7XWXfOt8zyQqrW+BxgPNM+JwRt4D7hPa90M2Au8ppTyAgYCDXLW+aREbzJf1c28z/xNbLSx+PnUyctM+iqEDz96xKrN9Bkj2RQ6gcxMI7t22h7WKGJINmKysaJFo7BDF3FycqR2Hb8i77dYbrL/O0XbCiRfHLbaKKU4EhaBnZ0dy9a9yaKVrzJv9jYuRcRTo6YPQ4e356URs3nl+TnUCfLDoRjzR//n2OzCsj3xFufw2rjyJKeOxjHo8ca2G5QyVcgjPLauPsOZY/H0fTQYMA/BHj8Uw5AXm/HxtN7EXE4hdOWZUonB5jkp/3Fv6++a89/xHwxg3oLdDBo6lWupmZRztC9JEDZiKEKcRdz8ypX7uL/Prc8yKs6xNOP5JoR+3I5Mo4mdJ8xTGozZmuQ0I3+82pw3+9fm1VlhNt9X8YK6+d/vOmO2idcnbmJo/2ACK5fOCJC4/f4/3KTSFViotY4D0FrH5xzES7TWJiBcKeVrY732wPc56xxTSp0H6uYsW6u1jrexTkfgu5x1Dimlro9VtAGCgW05+y4H7ACSgXRghlJqObDM1htQSo0ARgBMmTqKZ0f0sVru6+vJ5ai8uU3R0QkYDB7Wbfw8ibqcgJ9fRYzGbK5eTcPD03z1FxWVwMsvTWPCxKFUrepTYP/lyzvSpWsjNmw4TNt29Qsst8Xg60F0VN6VeHR0Et4G6xODb04bXz8PjMZsUlLS8fDILbqyZuUhq+Hl0ubj60ZMVF7FMDY6GW+fW598X1wGX3diLKoWMdHJ+Pi4FWgTHZ2EIbevMnD3cGbNisPc2642Do72eFVy5Z6mVTl6JJKAKl70G9ScfoOaAzD127X4+FofE/8mPr7uREflVUViY5LxNpT+zR7F4W2oQFx0Su7vV2Ku4eVdoUC7A7svMX/WAT6d2hfHciVIcm7Cy8eFKzF5FcP42FQ8vZ0LtAvbe5mQOWG8932P3Di8DC5Ur1MRg7+5L5u3D+RUeBydSxjL3Pm7mL9kHwCNggOIsjjuo2KSMeQ77v183YmyqCqa25jPI7Wq+/DzD08CcPZ8HJu2Wk8PKYpbPXfeyLFjEWQbs2nQoGTz/eZuiWDhjkgAGlZ1IyoxPXdZVFIGPu6FT28p72hP10bebAiLpV09L/w8y9P9Hh+UUtxTzR07BQnXsvByLd5Q89yl4SxYZS4UNKrrzWWLinhUbCoGLxeb633w7Vaq+bvz5MCCc7nvVndzZe9O+ReXFHIpbF+AZeRrY2u9wtxofMHmxR7mpLJJzr9grfXTWmsj0Ar4CxgArLK5Qa2naa1baK1b5E8OARo2qsaF87FERMSRmWlkxYr9dOliPSzbpUsjQkJ2AbBm9T+0blMXpRTJyak8/98feeW1fjRrljdP5tq1DGJjzCdvozGbLZvDqVHTVh5tW3DDAC6cj+NSRDxZWUbWrjxEx87WyWWHzvVYvtR848uGtWG0bFUz9wrUZDKxfs1huve6PcPLAPUa+BNxIZ7IS4lkZWWzfnU47TvVvfmKpax+gwAuno8nMiLB3FerDtOhcz2rNh0612PF0gMAbFwbTotWNVBK4VvZg727z6K1Ji01k7BDEVSrYb6TMP6KOTmJupzIpvVH6XH/nbk5oyzUb+BPxIUruX24blUY7TsFlWlMder7EHkxmajIZLKysglde5pWHa2ThdPH45g8cQvvf9EDT6+CSVtpqFmvElERV4mJTMGYlc3O9edo3t56KPvciXhmfrGL1z/tjIfFTSi16lXi2tVMkhPMyUn4/igCqpf8QmPIf1oTMu8FQua9wH2d67Fk+QG01hw4fBE3VycM3vkujLzdqOBSjgOHL6K1ZsnyA3TrZP5sXIk3H98mk4mpP2/mkQdbFjueWzl33syK5bdWPRzSoQqL32rF4rda0a2RDyF7osx9dS4JNyd7DB7WCeK1jLx5icZsE5vDr1DTYE5kuzXyYedJcyJ8NiaVrGxNxQqFz50sNKZ+wSyZMpAlUwbS7d5qhKw/ZY7paAxuFRwxVCqYIH4zay9Xr2Xx7n/bFHt/omypWy4z3+VyhpgXA/dqra/kDOt+DSzTWi/MaZOitXZVSlXPeb2hUuo1zEO/Tyul6gJrMVcQHwVaaK1H5qybf51grfUzSqmGwAHM1cPzwD7Mw9ynlFIuQBUgEnDRWsfkxHVKa+11o/djNK21+QcL3XyEiZ8uxGTSDBzUhuf+24vvv1tGg4ZV6dr1HjIysnjn7V85evQiHh4V+PKr4QQGevPj1FXMmL6GqtXyKofTZ4xEa80Lz/9IVqaR7GwTrdvU5e13HsTBoWCFI9Voez7LttDjfP35MrKzNf0GNuepEV348Ye11G9QhU5d6pORkcWHoxdw/Fgk7h4ujP/8EaoEmt/+vj1n+OGb1fwy1/qO5e++Xsnq5QeJjb2Kj48b/R9swYgX7iuw7/Tsos0R2rHlFN99sRaTyUSf/o154tn2zJiymXrBlWnfuS5HwyIZ89pCrianU668A16VKjBn0XMAvDj8V86fu0JaaiYeHs68PbYPrdsWPhnd0a7wK/7tW04w6fOVmLJN9B3QjOEjOjFt8nrqBQfQsUs9MjKy+OjdRZw4dhl3D2c+/vwhAqp4kZqawSfvL+HsmRi0hr79m/L48PYAPPfkDJKS0nBwsGPUG71o2cZ2bF6vTLL5enHMe2ocnes2w9vVk+jkeD5cNp2ft9u+K7Uo4iaNunmjfLZvOcG3n68i26TpO6Apw57tyPTJG6jXwJ8OnesRHnaJ0a/+kfu3rFTJlbmLXyzy9q+kXyp2THu3XWD6JPNjbu57IIiHhzflt5/2Uqe+D607VuO9kcs5fyqBijkVPR8/V97/smeRt5+cmVakdgd2XGLOd3sxmTSd+tRiwBONWDjjIDXqedG8fSATXlnHxTOJeFYyx+Ht68LrE7sAcHjPZeb+sA8N1KjrxTNvtcbhBsO5LZyKdpGltWbc58vZsuMkzk6OTPhgII2CAwDo/9gUQua9YN5/+CVGf7SY9IwsOratw/tv9kEpxezfdzBvofmu7+6d6/P6yO43TNyMrrZPrSU9dwJ07/YBKdfSycoy4u7mwrQZL1K7tvmGnp7dP2TqT89Ts2bhU2Ts1iwqcl99vPAEW49ewamcPRMeq0/DquZK6sDPd7P4rVbEJWfy/LSDZBpNZGtoU6ci7wysjYO9HZlGE+/NO8rRSyk4Oije6l+bNnVt94cKKtrjZrTWfDx5B1v2ReBU3oEJr3WgUV3z98iAFxazZMpAomKv0XnoH9QM9MidAjDkgWAe6h3E4eOxjPx4HclXMylXzh6fis4sm/ZgoftTNd66oyW9/bGf3LHkqJnPe3dlufJfnyACKKWeBN4EsoHrz2+4WYLoBPyIeR6hEXhNa71RKTWMwhNEZ+AXzMPJB4DawMta671Kqa7AZ8D1LOE9YA8QAjhhrjJ+qbWefaP3UliCWJYKSxDLUlETxDvpRgliWSqNBLG0lSRBvN1KkiDebkVNEO+koiaId1phCWJZKmqCeCcVNUG80+50gnggbsId+65t4v3uXZkg/n+Yg0hO0lVo4qW1ds357zmgYc7P6cAwG21nAbMsfrdcJw14JP86Ocs2ALbGQFoV4S0IIYQQQtwx/y8SRCGEEEKIopKbVP6XJctFAAAgAElEQVR/3KQihBBCCCGKQSqIQgghhBAW7Ir8tMt/L6kgCiGEEEIIK1JBFEIIIYSwIHMQpYIohBBCCCHykQqiEEIIIYQFOyX1M+kBIYQQQghhRSqIQgghhBAWZA6iVBCFEEIIIUQ+UkEUQgghhLCgZA6iVBCFEEIIIYQ1qSAKIYQQQliwk/qZ9IAQQgghhLAmCaIQQgghhLAiQ8xCCCGEEBbkJhWpIAohhBBCiHykgvg/xiEzvaxDKMA9I7OsQyggq7xjWYdQwMG4Q2Udgk1xk/6PvfsOj6JqHz7+PbtJSEJ62TRC7x2kSe9IURCxi4qIPnZRHxCRLoiCYMGKBUUQpUgNvffeQm8BQkgjCel1z/vHxiSbDSRAIL7P7/5cVy52Z+6ZuXd2dvbMfc4sb5V1CjZ8hn5R1inYeL5/vbJOwcagegFlnYItV1NZZ1CkjOzEsk7BhnOQX1mnYENXvq+sUyjSvf7Zavmv9qSCKIQQQgghCpEKohBCCCFEAUrqZ7IHhBBCCCGENakgCiGEEEIUIGMQpYIohBBCCCEKkQqiEEIIIUQBMgZRKohCCCGEEKIQqSAKIYQQQhQgYxClgiiEEEIIIQqRCqIQQgghRAHyfzFLBVEIIYQQQhQiFUQhhBBCiAIMUj+TPSCEEEIIIaxJA1EIIYQQQliRLmYhhBBCiALkJhWpIAohhBBCiEKkgiiEEEIIUYD8ULY0EP9naK2Z+MkSNm87gaOjA5MnPE69OhVs4kKPhzNi1DzSM7Lo0LYOI4f3RSmVN/+nXzfx6bTl7Nw0Di/P8lxPTOWD0X9xKfwa5RzsmDTuMWrWCLi1vD5byeYdZ3B0tGfy6H7Uqx1om9eJCEaMX2zJq3UNRr7bE6UUJ09HMmbyclLTMgkK8GDq+P64uDiydNURfpq9PW/5U2ej+Hv2y9SpWXxuu7af4fNPVpFjNvPgw015dnA7q/mZmdlMGPk3J09E4O7uzIRPBxAQ5ElWVjafjF/OyeMRGAyKt4c9QNPmVayWHfbmXK6ExzNn0Wsl3kc3E7onkr9mHMJs1rTtVYUHnqptNX/z0nNsWnIOg0FRzsmOZ965j8DKbqWy7Rspbv8d3B/GF5+u4tyZKMZ9MoDO3erd1Xxu5KeBI+nToA3RSfE0mPD0Pdtufe/6PFXrSQxKseXKVkLCVtrENPdrRt+qfQHN5aTLfB86k2CXYJ6tMxAnO0fM2szyCyvYE7W3VHIK3RPJvBkHMedo2vWuSs9Cx9Gav06zLeQ8BqMBV/dyPD+sGd7+5QFY8P0Rju66CkCfgXVp3jm42O1prZk48S82bwm1nI8+fo569Sra5hV6kREjfrV87tvXZ+TIx1BKkZCQwtB3ZnLlyjWCgrz5fPoQ3N3L33S9ERFxfPjhbK5GxqMU/PD961So4MO77/1EaOgljHZQr34wI8c8gr29MS+H7dtOMXXyEnJyNA8/0oJBL3ayyjEzM5tRI+Zx4vgVPDycmTz1aQKDvIi4EscjD02lUmVfABo0rMjIMY8AsHrlIX76YYPlc9u+Nm+/27ukb1XR+/LnA2w5GIGjg5GPX29FvapeVjFpGdm8/dl2LkUmYTQoOjUL4t1nGgOwaON5psw+hJ+XEwBPP1CTR7tWK9G2t249xqSJ8zGbNQMGtGbISz0K7Zsshg//lePHLuPhUZ5p0wYTVMEbgB++X8XChTsxGBQjRz5G23Z1AZg1az0LFuxAKahZI4hJHw+kXDl7/vveL4SGXsTO3kjDBpUZO+4pq/dJ3Hu31ERWSr2plDqhlJpztxIqYR4dlVLLcx+XU0qtU0odUko9Xkrrn6WUGpD7+EelVN3bXM+O4tZfWrZsO0nYpRjWLHufCaMHMPajhUXGjf1oIeNHD2DNsvcJuxTDlu0n8+ZdjUxgx87TBAZ45E377sf11KkdyLIF7/LJxCeZ+OmSW8trxxnCLsexZuGbTBjxIGM/WVF0Xp8sZ/yIB1mz8E3CLsexZedZAEZOXMq7r3dl2R+v0rVjbX783bJLH3qgIUvmvMKSOa/w6bj+BAV4lKhxmJNjZuqkED775mnm/v0a61aFcuFctFXMsr8P4OrmyPzlb/H4M6345vN1ACxdeACA3xe+yuffDeSrz9ZgNpvzltu07jhOzg63tH9uxpyj+eOLg7wxuS1jf+nB3g2XiQhLtIpp0aUiY37qzqiZ3ejxeC3mf3u41LZflJLsP39/dz6c0I9uPRvc1VyKM2vnCh74aug93aZCMbD200w/OJ2RO0bR0r8lgeWtj0s/ZxO9K/dm0t6P+XDnaOaemgdApjmTH4/9yIc7RzPt4Oc8WesJnOyc7jgnc45m7hcHeGtyO8bPeoA96y/ZHEcVa3gw8ruujP2pO/d1qMCC748AcGTnVS6diWf0j9344JsurP7zFGkpWcVuc8uWUMIuRrNm9XgmjH+asePmFhk3dtxcxo9/hjWrxxN2MZotW48B8MPMVdzfqjZrVk/g/la1+WHm6mLXO3z4Lwwe3I2VIWOZ/9f7eHtbLpQeerAFq1aO5a+/3yEjI4vFC/fkLZOTY+aTj/7mq28Hs3Dpu6wKOcT5c1FWOS5etAc3NyeWrhzO0wPb8cW0kLx5FYK9mbdwKPMWDs1rHCYkpPDFZyF8/9NLLFjyLnHXktm960yx++yG+/LgVS5eTWL1V30Y/58WjPthX5Fxgx6qzcov+7BoygMcOBnLlgMRefN6tq7I4qk9WTy1Z4kbhzk5ZiaM/5MfZr7OsuWjWLFiH2fPXrWKWbBgB+5uzqxeM45nn+vM1M/+BuDs2auEhOxn2fIPmfnj64wfP4+cHDNRUQn8PnsTCxYMZ9myUZjNZkJWWF5PnwebE7JyDEuXfkh6ehYLFmy3yeleUhjv2d+/1a3WUF8FemmtrS7HlVJlWYlsAthrrRtrrf8syQK3kq/W+kWt9fHbSUxr3fp2lrsd6zceo9+DzVBK0bhhJRKT0omOsf4SiI5JJDklnSaNKqOUot+DzVi/4Vje/I+nLOG/Q/tYVRTPnY+iVYsaAFSrYuJKRDyx15JKnteWU/Tr1ciSV4NgS16x1stHxyaRnJJBk4bBlrx6NWL9ZkvD9cKlWJo3qQRAm5bVWLPR9q1YseYofbqXrDFyPPQKFYK9CKrghb29HV0fqM/WTaesYrZuPEXPhyxX35261WXfnvNorblwPoZmLS0VQy9vF1xcHTl5zHISTk3NYN7snTw/pH2J901xLpyMwxTkgm+gC3b2Bpp1DubwjgirGKfy9nmPM9KzKfDW3RUl2X8BQZ5Ur+mPwXCXkynG1rOHiEtJLD6wFFV1r0p0ajQxabHk6Bz2RO6hiW8Tq5j2Qe3ZEL6B1OxUAJKyLJ+HqNQoolItje2EjAQSM5Nwc3C945wunIzDNzD/OGreOZhD269YxdRuYqKco+W0WLWuF/ExaQBcvZhIzUa+GI0GyjnZUaGaO6F7Iovd5vr1R+jXt5Xlc9+4KomJaURHX7eKiY6+TnJyOk2aVLV87vu2Yv26w/nL97sfgH797mddwelFrPfs2Qiyc8y0aWO5li9f3hEnJ8vFWocODVBKoZSiXoNgoqLy8wg9epkKFX2oEOyNvb0dPXo2YlOBcyLApg3H6dO3GQBdujdg7+6zaK1v+NqvXI6jYmUfPL1cAGjRqjob1oYWu89uuC/3htO3o+Wc3bimD4mpmUTHp1nFOJWzo1V9PwAc7I3UrepJ5LXU294mwJEjYVSs6EtwsA8ODnb06nUfG9ZbX4BuWH+Evv1aAdCjRxN27TyF1poN6w/Tq9d9ODjYU6GCDxUr+nLkSBgAOTk5pKdnkZ2dQ1paJiaTOwAdOtTPe58aNKxEVGT8HeUv7lyJG4hKqe+AqsBSpdRQpdRYpdQPSqk1wG9KKaNSaopSaq9S6ohS6uUCy/63wPRxRazbmFtVC1VKHVVKDc2dvkkp1Sz3sY9SKqzQcibgd6BxbgWxmlIqTCnlkzu/mVJqU+5jq3wLrUcppWYopY4rpVYApgLzCubwZG5+oUqpT3KnVVJKncnNz6CU2qqU6p47L7kE679PKbVZKbVfKbVaKVXy/tsCoqKv4++XX/nz93MnqtAJ+WYx6zcdw2Ryp3Yt6+7f2jUDWbv+KABHjl4i4mo8kVHW6715Xon4++V3efqb3IiKTrSNMRUdU7OqifVbLA2QVeuOcTXK9gs/ZO0xeveoX6J8YqIT8fPP35avyY2YQussGGNnZ6S8iyPXE1KpXtOPrZtOkZ2dQ0R4PKdORBCVu+zMrzfy5LOtcXS0p7QkxKbhacqvIHn6OJEQk2YTt3HxWUY+vZJFPxzl8dcbl9r2i1KS/fd/mWc5D+Iy4vKex2XE41nOwyrG39kfP2c/Pmj+Ph82/4D63rbHbhW3KtgpI9GpMXecU0JsGl4m5/wcfZ1JiLU9jv6xLeQC9Vv6A1gahLsjyUjPJul6BqcOxRAfU3zDIyoqAf8Az7zn/v4eREUl2Mb4Fx1z7VpiXsPBZHInLi7ppusNC4vGzdWZ19/4jn4PT+STTxeSk2OmoKysHEKWHaB121p502Kir+Pv75733OTnTnR04fNBfoydnREXF0cSEiz74MqVOJ4c8DkvPv8tB/ZfACC4ojdhF2KIuBJHdnYOmzYcIzLS+rXfiqhraQR4l89/zV7ORN2k8ZeYksnGfVe4v6F/3rS1uy7z0DshvDl1G1djU0q03ehC+9rP39OqcQ0QFZ1AQG6MnZ0RV1cnEhJSiIq6XmhZD6KjEvDz82DQC13p0vlD2rcbgaurE23aWnfQZWXlsHTpHtq2K5uhKf8wKMM9+/u3KnFmWuv/ABFAJ6319NzJ9wF9tdZPAYOB61rr5kBzYIhSqkpuY6kG0AJoDNynlCpcZmkMBGmt62utGwC/lDCnaOBFYGtuBfFcMYsUzLegh4FaQANgCGBT+VNKBQKfAJ1z822ulOqntb6YO/074F3guNZ6TUnWr5SyB74CBmit7wN+BiaW5LUXprG9olWFyklFXfUqBWlpmXw3cx1vvdrDZv5LL3QmMTGVvo9NY/Yf26hTOxA7Y8kP6KKus23yuknMxFF9mbtgD/2f/Z6U1Ewc7KzL8YdDw3FytKdmNb/bTqhwPjeK6dOvCSY/NwY/9QOfT1lFg0bBGI0GTp+8SvilODp0qVOyHEqqyB1jO6lTv+pMnNOT/i81IOT3k7YBdzknm/33f5rtvii8ywzKgJ+zH5/sm8J3R39gUN3nrLqS3R3cGVL/RX46/kuRn+tbVWS16wbv2a61Fwk7FU+Pxy2NqHrN/WnQKoDJr29g5oRdVK3rjcFQ/Oe/6PPRrceUdL3Z2Tns23+G4cMeYcH89wm/HMuiv3daxU3+6G+a3FeVpvfljxsuya65UYyPrxshaz/gjwVv885/H2TksLkkJ6fj5u7MiFEP8/57cxj83LcEBnne0jmzJG70mcvOMfPu9B0M7FWTYD9LBbNTsyDWf/sQS6f1onUDP96fsatE2yj6vFwo5gaH542+j65fT2XD+iOsXTeezVs+Ji0tg6VLd1vFjR8/j2bNqtOsWfUS5SnunjvtGl6qtf7nUrQ70LDA2Dp3LA3D7rl/B3Onu+RO31JgPeeBqkqpr4AVQOEGVmkpmG9B7YE/tNY5QIRSakMRMc2BTVrrGIDccZjtgcVa6x+VUo8C/8HSeCzp+msB9YG1uR94I3C18MJKqZeAlwC+n/EqLw1+AIA587bz1yLLh6tBvWAiC1yhR0Zdx+RrfbOCv59HETHuXAq/RviVOPo+Ni1vev8npjN/zpv4+rjx8YQnAMsXTZdek6gQZD1AurA58/fw1+L9lrzqBhFZoMIUGZ2Iyde628zf5EZkdNEx1Sr78vNXzwJw4WIsm7aftlp2xZpQencvWfUQwNfPjajI/G3FRCfiY3ItMsbk5052dg4pyem4uTuhlOKt/z6QF/fSsz8SXNGLg/svcupEBP17Ticn20x8XAqvDf6Fr38aVOK8iuLh60R8dP7hGh+bhofPjcekNesUzJzPD2A5VO+Okuy//8viM+LxKpf/+fAq50lCRoJNzLmE8+ToHGLTY4lMicLf2Y8LiWE4Gh0Z2uQtFp39m/PXz5dKTp6+zsRF51ec4mNS8fB2tIk7vj+KFb+f4L+fd8TeIf9CrPczdej9jOXiZ+aEXfhVcClyOxv/PsuWFedxsd9JgwaViLya30UYGZmAyVSokurnSWRk0THe3m5ER1/HZHInOvo6Xl6u+csUsd7s7Bzq1gkmONhyw0iXro04fPgC0AaAGTOWEx+fwtQx/a1yMPm5ExmZXxWLjrqOb6Hz5j8xfv6W7SQnp+Pu7oxSCgcHy1do3XoVqBDszaWwGOrWD6ZDx7p06GipjC2cv6tEjeqC5qw8zfz1lnpHg2reXL2WAlheW2RcKiavos8Do7/bQ6UAV57rk38TkqdrubzHj3atxtQ5JRun7OfnYbWvoyLj86q6//D38+Dq1Xj8/T3Jzs4hKSkND4/ylu8aq2UT8DW5s3PnSYIqeOe9n127NebgwfM89FBLAL6esYL4uCTGffVSiXK8m5T8CuAd74GCtWoFvJFbyWusta6SW0lTwMcFplfXWv9UcCVa63igEbAJeA34MXdWdoEcbc9oRbvZMjerrRd3qX7Da1ullDPwzy3DRZ89b1wPOlZg3zTQWne3WVDrH7TWzbTWzf5pHAI8/UQblvz1Dkv+eoeuneqxeNk+tNYcOnIRVxdHmwaiydeN8uXLcejIRbTWLF62jy6d6lGrRgA7N41jw8qRbFg5En8/dxbNG4qvjxuJiWlkZmUDMH/Rbpo1rYqLy83fiqcfbZF3A0nXDrVZHHLYktfRy7i6lMPkY92gMPm4Ut65HIeOXrbkFXKYLu0tFYxrcckAmM1mvv15C0/0b5a3nNlsZtWGY7fUQKxTL5DwS9eICI8nKyubdatCaduhllVMu461WLn0EAAb1x7nvhZVUEqRnpZJWmomAHt2nsNoNFClmon+jzVn6br3WLRyKN/NeoHgSt533DgEqFzbk+grycReTSE7y8y+DZdpdL/1CISo8PzxnEd3XcUUdHcbayXZf/+XXUi8gMnZDx9HH4zKSAv/FhyMOWQVcyD6IHW8LPvMxd4F//J+RKfFYFRG3mj0Otuv7mBfdNE3ItyOf46jmNzjaO+GyzRqbT2U5NKZeH6ftp/XJ7bBzTP/823O0SRfzwAg/FwC4eevU7d50dX6Tg9XZ8yP3Vmy+EO6dmnM4iW7LJ/7Q+dxdXW0aVyYTO6UL+/IoUOWMb6Ll+yiS5eGAHTu3JDFiy0VwMWLd1pPL2K9DRpU5npial5X9O5dp6hezfJZmT9/G9u2HWfSp0/ZNNTq1a/A5UuxXAmPIysrm9UrD9Ohk3WXZ4dOdVm+xPJ+rF9zlOYtq6OUIj4uOa8bO/zyNS5diiUo2HIHb9w1y3kr8Xoq8+ft5OFHWtz8TSrk6Z41824q6dIiiCWbwiyv+XQsrs72mDxtG4if/3GEpNQsPhjU1Gp6wfGKG/ZdoVpQyX7loEGDSly8GE14eCyZmdmEhOynU+eGVjGdOjdkyWJLRXL16oO0alULpRSdOjckJGQ/mZlZhIfHcvFiNA0bViYgwJPDh8NIS8tEa82unaeoVtXSFT5//na2bTvO1M9euOUGtbg7SvPmktXAK0qpDVrrLKVUTeBK7vQJSqk5WutkpVQQkJXbPQxYxhcCmVrrhUqpc8Cs3FlhWLqF9wAlvev3n2VWAo+UcJktwMtKqd+wjA/sBBS+7W438EVurvHAk1i6h8HSxTwHuAjMBPqUcP2nAF+l1P1a6525Xc41tdbHuEUd2tVh87aTdOszGSdHeyaNz7+hu+9j01jy1zsAjB35SO7P3GTTvk0t2retfaNVAnDuQhTDP5yHwaCoXtWPieMeu7W82tRg844zdOv/pSWvUX3z83r6W5bMecWS1/DeuT9zk0371tVp39pyY8zyNaHMnW+567Bbpzo88mD+gP+9By/ib3IjuJiKZkF2dkbeGdGLoa/MJses6dOvCVWrm5j59QZq1wukXcfa9Hm4CeNH/s2jfb7Azc2J8Z9aDr34uBSGvvI7yqDwNbkyemL/YrZ2Z4xGA0+80Zgvhm/FnKNp07MygVXcWfrLMSrV9KRRm0A2LT7Hif3RGO0Uzq4ODBrerPgV34GS7L/joVcYMXQeSYnpbNt8mp++2cScv0vnZ39uxdwXxtOxZlN8XDy4PGkpY5bP5Ocdy+7qNs3azJxTc3i36VAMysDWiG1EpETQr1pfwhLDOBRzmNBrodT3rsdH909AazN/np5PSlYK9/u3oqZnDVwcytM20FL5+jH0Zy4nX76jnIxGA0+92YTPh21BmzVtelYhqIo7S34OpVItLxq3CWTBd0dIT8vmu7GWRpm3nzOvT2xLTo6ZT9/aCICjsz2DR7bEWILu0g4d6rN5Syjduo/CydGBSZOey5vXt99HLFn8IQBjxzzFiA9+JT09k/bt6tG+veVi76UhPXh76EwWLNxOQIAXX3z+0k3XazQaGD7sEZ57/nPQmnr1KvLoo20BGDN2LoGBXjz/9AwAOnetz0uvdAMsx/PwD/ry2ss/Ys4x89DDzalW3Z9vZ6ymbr0KdOhUj379mzNqxDwe6vkJ7u7OfDzFMkLpwP4LfDtjDUajAaNR8cHo/ri7W8Z6Tpm8hNOnLJ1BL/2na95P4dyODk0D2XLgKt1fX45jOSOTXm2ZN6/feytZPLUnkddS+W7hMaoGudF/2Cog/+dsZoecYuPeKxiNBtxdHPj49VYl2q6dnZEPRz3Oi4NnYDab6f/I/dSoEciXXy6jfv1KdO7ckAEDWjN82Cx6dB+Du7szn00bDECNGoE80LMpfXpPwGg0MGr0ExiNBho1qkKP7k14pP/HGO0M1KkTzGOPW96ncWP/IDDQiyefmApYqouvvdbrtvfbnfo3jw28V9TN7sayCbbcJNJMax2rlBoLJGutp+bOMwAfAQ9iqYzFAP201teVUm9hGSsIkAw8U3C8oFKqEZZxh/+8IyO01iuVUrWBv3KX2ZC7XGWlVEfgPa11n4KPc9fVDvgJiMLSqGumte5YON9Cr0thaex1Bv7pw/xda70g9yaX97TW+5RSTwEjcl9fiNZ6mFKqA5YGYhutdY5SahGwTGv9i1IqWWvtUsz6GwNfYumStwM+11rPvOGbkL7szgcllbaM5LLOwMa1csXH3GtHr93+nYx3U4MibpIoaz5DvyjrFGw8379sB80XZVC927qn7a5qH9ClrFMoUkr2v+9mKueTB4sPusd0/XbFB5UBg+pyTwc7J2YuvGfftW4Oj/wrB3LfUgNR/AtIA7FEpIFYctJALBlpIJaMNBBLThqIJXevG4hJWX/fs+9aV/uH/5UNRKmhCiGEEEIIK/Jf7QkhhBBCFGCQ+pnsASGEEEIIYU0aiEIIIYQQwop0MQshhBBCFKDkZ26kgiiEEEIIIaxJBVEIIYQQogD5oWypIAohhBBCiEKkgiiEEEIIUYCS+pnsASGEEEIIYU0qiEIIIYQQBcgYRKkgCiGEEEKIQqSCKIQQQghRgIxBlAqiEEIIIYQoRBqIQgghhBAFGJThnv0VRyn1gFLqlFLqrFLq/SLml1NK/Zk7f7dSqnKp7IPSWIkQQgghhChdSikj8DXQE6gLPKmUqlsobDAQr7WuDkwHPimNbUsDUQghhBCiAKUM9+yvGC2As1rr81rrTGAe0LdQTF/g19zHC4AuSil1p/tAblL5/0y8Si3rFGw5/vuuM7Jz0so6BRsRyQllnUKRAstfKesUbDzfv15Zp2Bj1qJjZZ2CjWBXY1mnYKNdYFlnULTrmTFlnYKNcntPl3UKNoxeXmWdQtGCupR1BmUlCLhc4Hk40PJGMVrrbKXUdcAbiL2TDUsDUQghhBCiAKXv4bYM6iXgpQKTftBa//DP7CIWKZxdSWJumTQQhRBCCCHKSG5j8IcbzA4Hggs8rwBE3CAmXCllB7gDcXea17+vb1AIIYQQoixp8737u7m9QA2lVBWllAPwBLC0UMxS4LncxwOADVprqSAKIYQQQvwvyh1T+DqwGjACP2utjymlxgP7tNZLgZ+A2Uqps1gqh0+UxralgSiEEEII8S+ltQ4BQgpNG13gcTrwaGlvVxqIQgghhBAFFd/1+z9PxiAKIYQQQggrUkEUQgghhChIKohSQRRCCCGEENakgiiEEEIIUZBUEKWCKIQQQgghrEkFUQghhBCiILNUEKWCKIQQQgghrEgFUQghhBCiIBmDKBVEIYQQQghhTSqIQgghhBAFSQVRKohCCCGEEMKaVBCFEEIIIQqSCqI0EP9X7Nx2humfhGA2ax7q35RnB7e3mp+Zmc24kYs4dTwCN3cnPpryGIFBnmRlZTN5/DJOHruCMiiGDu/Ffc2rAPDtl+tYuewQSYnpbNz94T3NKzsrh0ljl3DqRATZOWZ6PdiY5160LPvn7ztZsnA/Gk3f/vfxxMDWt5UbwO7t5/hyyjrMZjO9+zXmmRfut5p/aP8lvpq6jvNnohnzcT86dqsNwJlTUUybuIqUlEwMRsXAwa3p0qPubedxI2f3x7Lqx9OYczRNuwfRdkDlIuOOb49i/idHGfJZCwJruJV6HgXt33mZmdN2YjZruj1Ui0efa2w1f/HcI6xZcgqjnQE3D0fe+rA9pgDXUs+jvnd9nqr1JAal2HJlKyFhK21imvs1o2/VvoDmctJlvg+dSbBLMM/WGYiTnSNmbWb5hRXsidpb6vkV5aeBI+nToA3RSfE0mPD0PdkmwNUjcRyccxZt1lTtEECdPhWt5l/YGsnhP8/j5OkAQPUuQVTrGEBKbDrbvzyG1hpztqZGtyCqdw68pW1v3XKMiRP/wmw2M+DRNrz00gNW8zMzsxg+bBbHjl3Cw6M806a/SIUKPgB8//0qFi7YjsFgYOSHj82iOHgAACAASURBVNGuXT2uXo1j+LBZxMYmYjAoHnusLc8+1wWALz5fyvr1hzEYFF7ernz88XP4+XmUONc92y8wY+p6cnI0vR9uyFODWlrNP7z/Ml9/toFzZ2IY/fGDdOhaC4DIiOuMfm8JZrOZ7Gwz/Z9oykMDGhe1iduitebj9ZfZci4RJ3sDE3tVpq6/8w3jX1t4lvCEDJYMrgfA19siWHA4Fk9ny1f+2+2DaF/N/Y5zmjhjK1t2X8TR0Z6Ph3WhXk1fm7jpP+1iyZpTJCalcyDk5bzpH3+9jd2HwgFIy8gmLj6NvcuG3FFOonTdtIGolPIAntJaf1NMXGWgtdZ6bgnilmut699SlrbrGQska62nKqVqA/MADQzQWp+7k3Xnrj8MaKa1jlVK7dBa33ILRCnVDHhWa/3mzdZ/p7kC5OSYmTppOV/+8BwmPzcGPfk97TrWpko1U17M0kUHcHNzZMGKt1m78ihff76WiVMeY8nC/QDMWfQ6cdeSGfrqbH7542UMBgPtOtTi0Sdb8mifL+55XuvXHCMzK5s5i14nPS2TJx6eQbeeDUhLzWTJwv38PPcl7OyNvP3KbFq3r0XFSt63ld/0yWuY9u0T+Pq58dLTs2jboQaVq/nkxfgFuPHBuD7M+2231bKOjnZ8MOFBgit5ERudxItP/0KL1lVxdXW8rX1VFHOOJuT7Uwwc3wQ3b0dmvruHWi188K3oYhWXkZrN7mWXCap5dxuGYNln303ZzoSveuFtKs87zy+mZbtKVKzqmRdTtaYP036ti6OjHSELj/PLjD0Mn9ilVPNQKAbWfpqpBz4jLj2e0S1HcSjmEBEpV/Ni/JxN9K7cm0l7PyY1OxVXe0sjNdOcyY/HfiQqNRqPch6MaTmKo9dCSctOK9UcizJr5wpmbFrAb8+Pvuvb+ofZrNn/2xk6DmuIk1c51o49QGATb9yDylvFBbfw5b5na1hNc/RwoMuoJhjtDWSl57Bq5F6Cmnjj5FmuRNvOyTEzfvwf/PzLW/j5efLogI/p3Lkh1avnNzIXzN+Om5sza9ZOYMWKvXw29W+mfz6Es2cjCFmxl+UrRhMddZ1Bgz5n1erxGI1Ghr8/gHr1KpKcnM4jj0yidZs6VK8eyOAXu/HW2w8B8NtvG/jm6xWMG1+yhnhOjpkvPlnLlG8ew9fPlf88M5vWHapRuar1+WD42J78Odv6gsLb14UZs57CwcGOtNRMBj36C607VMfH16XwZm7L1vOJXIzLYOVL9TgSkcL4NReZ92ydImPXnorH2d529NizzUwMaulfKvkAbNl9kYtXrrN69jMcPhHFuM838dc3j9rEdbq/Mk/3a8ADA3+3mj7itbZ5j2cvOsKJszGlllupkN9BLHYMogfwagnWUxl46o6zuT39gCVa6yYlbRwqpUpcOb2dxmHucvuKahzeDcdDw6lQ0YugCl7Y29vR7YEGbNl40ipm66YT9HrIckXbqVtd9u0+j9aaC+diaNayKgBe3i64ujpy4lgEAPUbBePje/uVnzvJSylIS80kOzuHjIxs7O2NlHcpR9iFGOo1rICjkwN2dkaaNqvM5vXHbyu/E6ERBAV7EljBE3t7I1161GHbptNWMQGBHlSraUIZlNX04EreBFfyAsDH5IqnZ3kS4lJvK48buXLmOl4BTnj6O2O0N1CvnR8nd9ueRDfOOUebRyph53D3hxSfOR5DQAU3/IPcsLc30r5bNXZvuWgV07BZII6Olo9YrfomrkWnlHoeVd2rEp0aTUxaLDk6hz2Re2ji28Qqpn1QezaEbyA12/K+JGUlARCVGkVUajQACRkJJGYm4eZQ+hXOomw9e4i4lMR7sq1/xJ1PxNXPCReTE0Y7AxVbmrhy4FqJljXaGTDmNjbM2Wa4xe/MI0fCqFjJRHCwLw4OdvTq3Zz1649YxazfcIR+D1sq9z16NGXnzpNorVm//gi9ejfHwcGeCsE+VKxk4siRMEwmd+rVs1RAXVwcqVbVn6iohNznTnnrTUvLRCnrz+3NnAy9SmAFTwIreGBvb6Rzj9ps33TWKsY/0J1qNU0YCp0P7O2NODhYjvnMzBy01iXebklsOJPAQ/W9UUrRKMiFpIwcYpKzbOJSMnP4dW8UL7cOKNXtF2X9jgv07VYLpRSN6/qTmJxJ9DXbz3rjuv6YvMsXsYZ8KzacoXfnmncrVXGbivtGmQxUU0odUkpNURZTlFKhSqmjSqnHC8S1y40bqpSqrJTaqpQ6kPt300aWUipAKbUld/lQpVS73OnJBWIGKKVmFVquF/A28KJSamPudkMLzH8vt9qIUmqTUmqSUmoz8Fah9XgrpdYopQ4qpb4HVIF5ybn/FvnalVIPK6XW5c4PUEqdVkr5K6U6KqWWl2D9zyil9uS+9u+VUsZi3hMbMVFJmPzyuwtMfm7ERCfaxPjlxtjZGXFxKcf1hFRq1PJn68aTZGfnEBEez8kTV4mKvH6rKZR6Xp271cPJ2YE+XabQt/tnPP1cG9zdnala3Y9DBy5yPSGV9LRMdmw9TVTU7X3hxkYnY/LLr7r5+rkSE5N0y+s5HhpBVnYOQcGexQffgqRrGbj55Fck3XwcSbqWYRVz9VwiibHp1Gxu27VzN1yLTsHHL78q4m0qz7WYGzcA1y49xX33Vyj1PDzLeRCXEZf3PC4jHs9y1l2J/s7++Dn78UHz9/mw+QfU97btuKjiVgU7ZSQ69V9WvShFafGZOHnlV/ycvcqRFp9hExe+L5ZVI/ex/atjpF5Lz5ueei2dVSP3sWzoLmr3Di5x9RAgKiqeAP/8z4W/nwdRUfFWMdFRCQQEWGLs7Iy4ujqREJ9SomXDw2M5ceIyjRpVyZs2ffpiOnYYwfJle3jzrQdLnGtsTDIm//wLBV+TK7HRyTdZwlp0ZCKDH/uFx3t9xxPPtSi16iFAdHIW/m4Oec/9XB2ISsq0iftqawTPt/DDqYgK4twDMTz883E+DAnjenr2HecUFZtCgCn/Nfr7licq9tYvBq9EJnIlMpFWTYLuOCdRuoprIL4PnNNaN9Za/xfoDzQGGgFdgSlKqYDcuK25cdOBaKCb1rop8DjwZTHbeQpYrbX+Z92HSpK81joE+A6YrrXuVIJFPLTWHbTWnxWaPgbYprVuAiwFKtouWvRr11r/DUQCrwEzgTFa68iSrF8pVQfL/mmT+9pzgFsemKQp4mq10JVzUTFKKfr0a5LX/Tv905U0aBSMnV3pVKLuJK9joeEYDAaWr/svi1YOZe6v27kSHkeVqr4MHNSWN176lbdfmU2NWv7YGW8v3yK3TckrDmD5Upn44TJGjO1tU1W4U0UWIQpsQps1q386TfcX7t2Vd5Ep3eBlb1x5hrMnYun/TKO7kIntRgvnZlAG/Jz9+GTfFL47+gOD6j6Hk11+hcndwZ0h9V/kp+O/FH2s/q8o5jgCCGziTZ/PWvLAxGb41fNk98xTefOcvR15YGIzen/agrBtUaRft22Y3Mq2C1f1iqy2qeKXTUlJ5803f2DEB49ZVQ6HDu3Hps0f0+fBFvz++6aSp1r86eqmTP5u/PTXIH5fMoQ1y48RV0Q17XYVtY8K53YiKpVL8Rl0rWl7ofp4E19WvVyfhYPq4Otiz5QN4aWQlO2kW9lf/wjZeJbu7athvM3z+F2jzffu71/qVt+RtsAfWuscrXUUsBloXkScPTBTKXUUmA8UN3p/LzAot9rXQGt962WckvnzBtPbA78DaK1XAPFFxNzstb8BjAAytNZ/3ML6uwD3AXuVUodyn1ctvLBS6iWl1D6l1L5ZP66zWbnJz43oqPyqX3RUIr6FuoZNfm5E5cZkZ+eQnJyBm7sTdnZG3h7Wk9nzX2XKl0+RnJROcMVbH89XlDvJa03IUe5vUx07eyNe3i40bFIxr+v7of738dtfr/DdrMG4uTlR4Tbz9TW5El2g+hgTlXRLV/0pyRkMf/MvXnytPfUalv7Vr5tPORJj8ys5ibHpuBaoBGWk5RB9MYVZI/fz+YvbCD+VyB8TDxFx5u51YfqYyhMblV9VuRadgpePbffRoT1X+GvWIT6c2h17h1suihcrPiMer3Jeec+9ynmSkJFgE3Mw+hA5OofY9FgiU6Lwd/YDwNHoyNAmb7Ho7N+cv36+1PP7N3HyciAtLr9imBqXgZOHdRWwnIt9Xldy1Y4BxIfZnoKdPMvhFuRMzOmS9zD4+XtyNTL/dBoZlYDJ5GEbc9USk52dQ1JSGh4e5W+6bFZWDm+++QMPPtiC7t2thxb8o0+f5qxdc7DEufqaXIiOzH/dMdFJeN9GFdDH14XKVX04evDOGmFzD0TT/5fj9P/lOL4uDkQm5jfMo5IyMbk4WMUfjkjheFQq3b49ysDfTxEWl8Hzcy0NfZ/y9hgNCoNSDGjkw9Grt9d4nbP4KP2GzKPfkHmYvMtztUCFNTImpdiu5KKEbDxD7841ig8U99ytNhBLen0wFIjCUm1rBjjcLFhrvQVLI+oKMFsp9ew/swqElWT0fzbWr6nwMjf7VBRXQrjZaw/CMjrHTyl1o316o+v4X3Mrr4211rW01mNtFtT6B611M611s+df7Gqzkjr1grh8MY6I8HiysrJZu+oo7TrWtopp17E2IUsthdmNa4/TrEUVlFKkp2WSlmo58ezeeRaj0WB1E8mduJO8/ALc2bfnAlpr0lIzCT0STqUqlsHicdcsJ6XIqwlsWn+C7r0a3FZ+tesFEn4pnogrCWRl5bB+9QnadCzZiSorK4eR7y6kR5/6dOpW9GDxOxVUw41rEWnER6aRk2Xm2NYoarXM70p2LG/HsDkdePvHtrz9Y1sq1HLjyZGN7+pdzDXq+BJxOZHIiESysnLYsvYcLdpbF9zPnYrl68lbGTWlOx5eTjdY0525kHgBk7MfPo4+GJWRFv4tOBhj3fFwIPogdbwsd5m62LvgX96P6LQYjMrIG41eZ/vVHeyL3ndX8vs38ariRlJUGskxaeRkm7m0O5qgJtYXVWkJ+Q3IiAPXcA203CGbGpdBdmYOAJkpWcSeScTVv+TvaYMGlbgYFk345VgyM7MJWbGXzp0bWsV07tyQxX/vBGD16gO0amUZ19a5c0NCVuwlMzOL8MuxXAyLpmHDymit+XDkb1Sr6s+gQdbnw7CwqLzHGzYcoUpVvxLnWrteAFcux3M193ywYfVJWneoXqJlY6KSyEi3jAlMSkwn9PCVvDHKt+uppiYWDarLokF16VLTg6Wh19Bac/hKMi7ljPi62FvFP9HEl02vNWTtKw2Y/UwtKnuVY9ZTluO/4HjFdacTqOFze5/Lp/s1YPHMJ1g88wm6tK3CkrWn0Fpz6HgkruUdbrmBeP5SPNeTMmhSr/Runik1UkEs9mdukoCCJZ8twMtKqV8BLyyNuv9iaSAVjHMHwrXWZqXUc8BNSwhKqUrAFa31TKVUeaAp8BsQldsNewp4ODefm4kCTEopbyAZ6AOsKmaZf17X08BHSqmeQFGDyYp87bk3vPyCpZv8WeAdYGoJ178eWKKUmq61jlZKeQGuWuuL3AI7OyPvfdCbt175DXOOmT79mlK1uokfvl5P7bpBtO9Umwcfbsq4DxYxoPfnuLk7MeFTy91mcXEpvP2f31AGha/JjTGTHslb71fTVrMm5Cjp6Vk82HUqD/VvypBXO9+TvAY80YKPRi3mqf4z0Br69G1CjZqWk8iId+Zx/XoadnYG3vugN25ut3eys7Mz8Pbwbrz36jzMZk2vvg2pUs2Xn77ZQq26AbTtWIMTxyL48J1FJCWms2PLGX7+biu/LRzCxjUnOHzgMokJaaxaetSS1/g+1KhV8i+k4hiMBnq9XIvfxx5EmzWNuwZiqujCxjnnCKzuZtVYvFeMdgb+815rxry5ErNZ0/XBWlSq6sXv3++jRh1fWravxC9f7SY9NZvJH1iq3b7+Loya2qNU8zBrM3NOzeHdpkMxKANbI7YRkRJBv2p9CUsM41DMYUKvhVLfux4f3T8Brc38eXo+KVkp3O/fipqeNXBxKE/bwDYA/Bj6M5eTL5dqjkWZ+8J4OtZsio+LB5cnLWXM8pn8vGPZXd2mwahoOrA6m6cctfzMTXt/3CuU5+iiC3hVdiWoqQ9n1lzhysFrKKOiXHk7Wr5ouZBLjEjh0B/n87p8a/esgEdwyatqdnZGRo1+nMEvfok5x8wjj7SmRo1AvvxiKfXrV6Jzl0YMGNCGYf/9he7dRuHu7sy06S8CUKNGID173kfvXuMwGo2MHv0ERqOB/fvOsmTJbmrWDKJf348AGPpOXzp0aMBnny0m7EIUSikCg7wYN67k904a7Qy8Obwrw15bgNlspudDDahSzYefv91Grbr+tOlQnZPHrjLq3cUkJ2awc8s5fvluO7MWvMDFC9f4dtpGSx+r1jw2sDlVa5Te57N9VTe2nLtOzx9CcbQz8FGvynnz+v9ynEWDbt5J99mmcE5GpVr2i7sDY3tUuuOcOrSsxJbdF+n+zO84OtoxaVj+LxX0GzKPxTOfAGDK9ztYvv40aRnZdHhsFgN61eWN51sAuTendKpxSzcTiXtHFXe3lVJqLtAQWAkMAz4FemKpiH2ktf5TKWWPpSHmA8wClgMLgVRgI/CG1trlRj9zk9uI/C+QhaVh96zW+oJSagDwCXAZCAVctNbPF/qZm7zHuet6E3gTuIClIhmmtR6rlNoEvKe1tikZ5DYo/8jNfzOW8Yb35f7MTXJu7uoGr300lrGN7yilXLF0lz8M+OVur08x638cS/e0Iff1v6a13nWj9yM+48//4cFSpScz5+7/ZMmtWn/53vzW3q1q5lel+KB77OO9J8o6BRuzFh0r6xRsjHquYfFB99i4Vk+WdQpFuppyx7+AVupMf24o6xRsGHu0KusUiqSC3ry3rcjrf9y771r3J/+VLeRiG4ji30UaiCUjDcSSkwZiyUgDsWSkgVhy0kAsOWkg3nvyP6kIIYQQQhSgdc4929a/snXIrd+kIoQQQggh/sdJBVEIIYQQoiD5r/akgiiEEEIIIaxJBVEIIYQQoqB/8e8T3itSQRRCCCGEEFakgiiEEEIIUZBUEKWCKIQQQgghrEkFUQghhBCiIKkgSgVRCCGEEEJYkwaiEEIIIYSwIl3MQgghhBAFSRezVBCFEEIIIYQ1qSAKIYQQQhQk/9WeVBCFEEIIIYQ1qSAKIYQQQhQkYxClgiiEEEIIIaxJBfH/M2cSTpV1Cjaa+LYs6xRsRGbFl3UKNt4KuVjWKRRp5dP+ZZ2CjUH1Aso6BRvBrsayTsHGhF+PlHUKNsbX7VLWKRTJxdGzrFOwceH7w2Wdgo1qtYPKOoUiqXudllQQpYIohBBCCCGsSQVRCCGEEKIgqSBKBVEIIYQQQliTCqIQQgghREHyO4hSQRRCCCGEENakgiiEEEIIUZCMQZQKohBCCCGEsCYVRCGEEEKIgqSCKBVEIYQQQghhTRqIQgghhBDCinQxCyGEEEIUJD9zIxVEIYQQQghhTSqIQgghhBAFmXVZZ1DmpIIohBBCCCGsSAVRCCGEEKIgGYMoFUQhhBBCCGFNKohCCCGEEAVJBVEaiP+LjuyOYPaX+zCbNR17V+fBZ+pZzV/55wk2LT+L0WjA1aMcQ95vhY+/C8cPRDJnxoG8uKuXrvPqmLY0axd8W3ls23qcyZMWkWM288iA+3lxSDer+ZmZWYwY/jvHj1/Gw6M8U6c9T1CQNzu2n+TzaUvJysrB3t7Iu//tR8tWNUlJSefZZ77IWz4qMoE+Dzbj/Q8eua38APbsCOObqZsw55jp2a8+Tw5qYTX/yIFwvpm6mfNnY/hwUi/ad62ZN69788+pUt0HAJO/KxOm973tPArqXKkpEzu+hNFg4PfQNXy5d4HV/CBXX2b0GIp7ufIYlIGPtv3KurB9VvO3P/sNn+6ayzf7/y6VnA7vjmD2F3stx1Sf6jz0TH2r+SHzjrNx+TmMRoWbhyNDRrTC198FgNioFGZ+sou46BRAMWxKJ3wDXO44p9A9kcybcRBzjqZd76r0fKq21fw1f51mW8h5DEYDru7leH5YM7z9ywOw4PsjHN11FYA+A+vSvPPtHeNFuXokjoNzzqLNmqodAqjTp6LV/AtbIzn853mcPB0AqN4liGodA0iJTWf7l8fQWmPO1tToFkT1zoGllteN/DRwJH0atCE6KZ4GE56+q9vSWjPxsxA2bz+Do6M9k8c8TL3atq8x9EQEI8YtIj0jmw5tajDy3V4opTh5OpIxk5eSmppJUIAHUycMwMXFMW+5iMgEej82g9eHdGTwwLYlymnHttN89slyzDlm+vZvzvMvdrCan5mZzZgP5nPy+BXcPZyZNOVJAoM8Wbn8ELNnbc2LO3s6ktl/vUatAq/nnTd+40p4HH/+/fat7iobvu+MwLl1e3R6GlETRpJx6oTVfOXsTPD3s/Oe25n8SFy1nNjpk7HzC8BvzCQMLq4og4HYb6aTumNr4U3cEq01k+aGsuVIFI4ORiYNbkK9yh42cUM+20nM9QyyczTNanoxamBDjAbFqr0RzFh8ivNXk/hrVHvqV7FdVpQtaSAWQSk1FkjWWk8tpfWFAE/lPn1Ka/1Naay3KOYcM79O38vwaZ3x8nVm9EuraNq2AkGV3fNiKtXwZPzMnpRztGPd4tPM+/Ygr49rR92m/kz8uRcAyYkZvPfkUho0D7itPHJyzHw0YT4zf3oNfz8PHn9sKp061ada9fz1LVqwCzd3Z1auHk3Iiv1Mm7qUz6YPwtOzPDO+fRmTyZ0zpyN4eci3bNg8gfLlHVn49/C85R975FO6dmt0m3vKkuNXkzfwyTf98fVz5bWBc2ndoRqVqnrnxZj8XRk2rjt/zd5vs7xDOTu+/+OZ295+UQzKwOTOr/Doog+JSLrGmqems+rcbk7HXc6Leafl4yw5vZVZR1ZS0yuYP/qN5b6fB+fN/6jDi6wPs833dplzzMyatocR07vg5evMqCEradqmAhUKnNAr1fTiox9rWo6pv0/zx7cHeXNcOwC++2g7fZ9tQIPmAaSnZqEMqhRy0sz94gBDp7TH09eZif9ZR6PWgQRWdsuLqVjDg5HfdaWcox2blpxjwfdHeHnM/RzZeZVLZ+IZ/WM3sjPNTHl7E/Vb+uNU3v7O8zJr9v92ho7DGuLkVY61Yw8Q2MQb96DyVnHBLXy579kaVtMcPRzoMqoJRnsDWek5rBq5l6Am3jh5lrvjvG5m1s4VzNi0gN+eH31XtwOwZccZwi5dY82itzgcGs7YycuYP+tlm7ixk5cx/oOHaNwgmCFvzWbLjjN0aFOTkR8tZvhbPWhxXxUWLD3Aj7O38/YrXfKW+3jaKtq1rmGzvhvJyTHz6cSlzPjhBfz83XjuiW9o36k2Vav55cUsWbQPNzcn/g55jzUrD/PV9FV8PPVJevZpTM8+jQFL4/DdN2dbNQ43rAvF2cnhdnaTDefW7bAPrsTFAT1xrN8Q07DRXB78pFWMTk3l0sD8i+XgX/8ieeNaALxeeJnkdau4vuhPHKpUI3Dat4Q93P2OctpyJJqLUSmsmtyFw+fjGT/7CH+Oam8TN/3VZrg42aO15q2v97FqbwS9WwZRI8iVr15vzphfD99RHneN3MUsYxDvBa11L611AuABvHo3t3XuxDX8glwxBbpiZ2+kVZdK7N922SqmblN/yjlarg2q1/UhLibVZj17Nl2iYcvAvLhbdfTIRSpW9CU42Ad7Bzt69mrKhg1HrWI2bDhK376Wil33Ho3Zves0Wmvq1A3GZLI0aKvXCCAjI4vMzCyrZS+GRXMtLpn7mlW7rfwATh2LJDDYg8AKHtjbG+nYvRbbN52zivEPdKdqDV8M6s4bNSXR1L8mYQlXuXg9iixzNotPbaFntVZWMVprXB2cAXArV57IlLi8eT2rtSLseiQnr10qtZxsj6nK7N8WbhVTr+AxVc+HuGjLMRV+IYGcHJ13oeHobH/bx1RBF07G4Rvogm+gC3b2Bpp3DubQ9itWMbWbmPK2VbWuF/ExaQBcvZhIzUa+GI0GyjnZUaGaO6F7Iu84J4C484m4+jnhYnLCaGegYksTVw5cK9GyRjsDRnvLKdmcbYZ71MO19ewh4lIS78m21m8+Sb/ejVFK0bhBMIlJ6UTHJlnFRMcmkZySQZOGFVFK0a93Y9ZvPgnAhUvXaN60MgBtWlRjzcbjecut23SCCkGe1KjqW+J8jh0NJ7iiNxWCvbC3t6Nbz4Zs3mhdmduy8QS9H2oKQOdu9dm7+xxaWzceVq88TI9e+RerqakZzP1tOy+83KnEudyMS/vOJK5cCkB66BEMrq4YvX1uGG8fXBGjpxfph3IvFLXGUN5StTeUdyE7NvqOc9pwMJK+rStY3stqXiSmZhGdkG6bu5Plwis7R5OVbeafM2m1QFeqlEJPgrh7pIGYSyk1Uil1Sim1DqiVO62aUmqVUmq/UmqrUqp27vRZSqkvlVI7lFLnlVIDcqcHKKW2KKUOKaVClVLtcqeHKaV8gMlAtdz5U5RSs5VSfQvkMEcp9dCdvI742DS8TM55z718nfO+GIuyecU5Gra07eLZtf4i93etdNt5REcn4O+fX2Hy8/MgOuq6dUzUdfwDLDF2dkZcXB1JSEixilm75hB16lTAwcG6uhOyYj8P9GyKuoOGW2x0MiY/17znvn4uXItJLvHymZnZvPrMHF5/7g+2bzx723kUFODizZWkmLznEcmxBLh4W8VM2TWXAXU6cfjFWfzRbywjNn4HgLNdOd5oNoCpu/4olVz+EReTinfhYyrW9qLiH5tWnKVRK8sxFXk5CWcXB6aP3MwHL6xg7tf7MefcecsnodBx7unrTELsjY/zbSEXqN/SH8DSINwdSUZ6NknXMzh1KIb4Ii6SbkdafCZOXvkVP2evcqTFZ9jEhe+LZdXIfWz/6hip1/K/VFOvpbNq5D6WDd1F7d7Bd7166K6fWgAAIABJREFUeK9FxSTi75ffm+FvciMq2rpxGhWdiL/JzTomxhJTs6qJ9VssjcVV60O5mntOSU3LZOZvW3l9SMdbyicm+jp+/vn5/D/27ju+5ut/4PjrZJAhg0RuhNg7glpVI7H3LKWo0qnfVltd1KhZo0VpqVmqS1s7dpAgqlar9i4hQgZZEiLJvZ/fHzeNe3NvyEC0v/fz8ciDez/n8/m8cz7jnvs+53yi07kRG20eT4xJGTs7W4oVcyAxwfx82b71OO061s56vWDODgYMaoaDw8PJINqV9CIj+t6XmIyYaOxK6nIs79KuM8k7tma9vrn4a1w6dKH8hhB8Zs0nduaUAscUnZCKdwnHrNfexR2JibdsIAK8OmMfzd4NxtnBjvYNH/2wiYfCYHh8P08oaSACSqn6wPPAU8CzQMPMRYuAtzVNqw98CJh2DZcCmgFdMDb8wNiNHKxpWl2gDnAk264+Bv7WNK2upmkfAd8AL2XG4AY0ATYX5HfJ/s3WuG3rZfduu8Slszfp3K+m2fsJN+5w9WIC/o3yfyFbCcOiMWc1Vu6VuXD+Ol/MXM/YCX0tym3ZcphOnevlO76cYsyxsqxYvulV5v04gFGTOzJv5m6uRSQUKB4Aa3vPXk89qwXyy8kQ6nwzmH7rxjOvwwcoFMOfGcDCv9aRkm79Jv0wKauRwm/BF7l4Jo4umeeUXm/g7LEYBrxVj0mLOhJzPZmwLRcLvH9r505Ox27/9suEn42nfd9qAPg19Ma/cSmmDQ1l8aT9VKzpgY3NQ7oVWj2nzF/6POVBl5lP02FyA3R+xTmw+GzWMicPBzpMbkDnzxsR/ls0qYlpDyeuJ4T1+1P2+4Llev+UmDy2B8tXHuTZgfNJuZ1GEXtbAOYsDGVQvyY4O+WtQZ2b0+hB94kTxyJwcLCnchXjF5CzZ64REXGTlq39rKyYT1bP7Zy7QIu17citbfc+SlzadSZp0zrCu7bm2nv/Qzd+Wp7uddbk5bPmmw+fIWx2O9IyDOw/HWu9kHjiyBhEo+bAWk3TbgMopdYDDhgbbCtNbmCmd591mqYZgFNKqX++yh0Cliql7DOXZ28gmtE0bbdS6mullBfGhulqTdMyspdTSr0OvA7w8fSu9BzYIMdtlijplNW9B8bsj7uno0W5E39cZ/33Jxg1py32RWzNlh3YeZn6Ab7Y2eX/Q1Oncycq6l6DKTo6gZImWQEAnbc7UdcT8PYuTkaGnuRbqbi5G7NCUVHxvPv2N0yZNpCyZc27jM6ciUSfYcDPz3zwf16V1BUjJvpe91ZsdDIens73WcOcZ0lj94hPGXfq1C/DhbMx+PgWbKD1teSblHa59/v6FPM060IGGFCrLX3XjAPgj+tnKGpXBA9HV+qXqkbXKk0Z2+wl3Io6Y0DjbkY6S45uLFBMJUo6cTOX51TQDycYM6dd1jlVwsuJ8lWK4+VjzNTWb+bLhVM3aFGgiIwZQ9PzPD72Nu4eDhblTv0ZzaYfT/PR7BZm53nnF2rQ+YUaACyetB9dmYfT1eVYogh34u5lDG/H3cXR3bzRUrTYvWx4xRalOLbCssHsWLworqWdiD2XiG/D3HeZPol+WnGAFeuMXZ3+NUsTZdKTEBWThFdJF7Py3jpXokyyisYyxntHpfIlWTp3EACXLt9g12/nADh68irBoaeYMWcbSbdSsbFRFC1qzwt9nr5vbF46N6Kj7sUTHZ2IZ/b7VGYZnbeb8T6VnIqb273zf9uWY2bdy8ePXuHMqUi6tf8cfYaBuLgUhry0mIXfvvbgyjLh1rsfbt17A5B66gR2Ou+sZXZeOjJirXcTF6lSDWVry90z97rfXbs9y7V3jWM9U08cxaZIEWzdi6OPj7O6jZz8FHKJVbsvA1CrgjtRcfey9lHxdyjpbnkN/qOovS2t6uoIPRxFUz+vPO23UDzBmb3HRTKI92T/OmQDJGRm+/75qWGy3LTfSAFomhYGBACRwA9KqRdzsd8fgAEYM4nfWg1M0xZpmtZA07QG92scAlSs7kHU1VvEXEsmI13P/pDL1GtaxqxM+Lk4vp1xkPemBuJW3PKC3hcSzjOt89+9DFDLvyxXLsdy9epN0tMy2LL5MC1b+puVadmyFkFBBwHYFnyEpxtXQSlFUtJt3nxjIcPe70q9ehUttr1l05907Fy/QPEBVKvpTWREPNcjE0lP17Nr21maBFruz5pbSamkpRnb8onxdzh59JrZ5Jb8+ivqHBWK+1DWVYe9jR09qgWw9eIBszKRSbEElDV+IFUpUQYHW3tu3Emk64oR1F/6CvWXvsLCv9Yz++CKAjcOwdo5FU79Zpbn1JLpB/hgaguzc6pSdQ9SbqWRlNn1dOpwlNmEqfwqX704MZHJxF5PISPdwKHQCOo0Mc94Xzkfz49f/MnQyU1xNYnJoNdITjRevlf/TuDqxURqNsy5uy4vSlRw5Vb0HZJj76DPMHDlQAylnzI/L+4k3Lt1XDt8Excf45ei23F3yUjTA5CWks6N80m4eFs2xP9tBvR5mqDlbxK0/E3atKjOuk1H0DSNI8cjcCnmgJeneQPRy9MFZ6ciHDkegaZprNt0hNaBxhnqN+OMQ0AMBgPzl+7m+V7Gzp7li18ldP37hK5/n0H9GjNkcPMHNg4BatYqzZXLN4i8Gkd6egbbtxwjoEUNszLNW1Rn03rj0x1Ct5+gYaOKWVlPg8FAyLbjtO1wr3u5d9/GbAkdyfrg4Sz+fghly3vkuXEIkLjqZ64M7MWVgb1IDgvBtaNx9JFDrdoYkpPR37xhdT2Xtp3MsocAGVHXcWxoHMtsX74iqkjRPDcOAQa0rsDaiS1YO7EFreuVIuj3q8Zj+XccLo72eGVrIKakZmSNS8zQG9h9LIaKpVwsNyyeSJJBNAoDlimlpmGsk67AQuCSUuo5TdNWKuMdobamaTlOuVJKlQMiNU1brJRyBuoB35sUuQVkvzqWAQeBKE3TThb0F7G1s+HFYQ2Y/mEoBoNGQKdKlKngzuolR6lQzYN6zcrwy/y/SL2TwZxxvwHg4eXE+9NaABB7PZm4mNtUr1uwD0w7O1tGjenNkFfnoTcY6PlsYypXKcXcrzbhV6ssLVv582zvZxg54gc6tp+Im5sT02cOBuDnn/YQceUGC+YHs2B+MACLvnkTDw9j1QVv/Yt5C98oUHxgrKu3h7fi46FrMOg1OnT3o3wlT5bN/52qNXU0CazEmZNRjP9wA8lJqezbc5HvFu5jycpBXLkUx6zJO7CxURgMGs8PbvhQGoh6zcDI0AWseHYiNsqGn09u5+zNK4x4ZgBHos8TfPEgY8OWMKvt2wyp1wM0jbeDZxd4v/dja2fD4Pca8tkHIRgMGoGdjefUqm+OUqF6Ceo382X5vMOk3sngy7HGR2d46pz4YFpLbGxt6P9WfaYM24EGVKhaglZdKxc8Jlsb+r/zFLOHh6EZNJp2rEDpCm4ELT1BuWolqNvUh1ULjpF6J4MF4/cB4KFzYujkZsaZq+/uBIyTZl4Z/TS2tg/nu7KNraLewMrsnn7c+JibAG/cyjhzfM0lSpR3oXQ9T85viyTyr5soW0VRZzueftXY+Em6lsKRny8av25qUL1jGdx9H/0g/uUvT6RF1Xp4FnMnYsp6xm1czNLfNzySfQU2rcruvedp23M2jg72TBnbM2tZ9/7zCFpunMM3/uOujJywltS76QQ0qUJA5szkjcHHWb7K+KWybYsa9Or6VIHisbOzZfiobrzzxrfo9RrdetanUmUdC+Zup4ZfGQJb1qD7sw0YN3IlPTvNwNXNicmfP5+1/l9/huPl7UYZ3xIFiuNBbu8Nw7lJAOVWb0FLTSV60pisZWV/WG02e9mlTXsi3/uf2fo3vpqO18gJFO/3Imga0ZNGFzimwNpehB2Lpv2IkKzH3Pyj59hdrJ3Ygjt3M3jry4OkZejRG6BxDU/6tjQmH7b/eZ3JPx0n7lYab8zeT3VfN7758JkCx/XQyCxmlNWxPP8PKaVGAy8Cl4GrwClgNTAf43hDe+AXTdMmKqWWARs1TVuVuW6ypmnFlFKDgI+AdCAZeFHTtEtKqXCggaZpN5RSy4HawJbMcYgopbZi7JJe8KA4D0ZPfOIO2FMlH/xN/XGLun2psEOwUG9xwbN5j8KWAU/e8budYTmxo7DtuFLwmZ8P26TvjhV2CBa0z94r7BCsSnoIs+cftujmj/7RQnlVaVanwg7BKpsm0x/PoyQyaUfHPbbPWlVnwmP93XLrybtiCommaZOByVYWdbBSdnC218Uy//0O+M5K+fIm/+9vukwp5QRUAR7u1FMhhBBCiHySBmIhUkq1AZYCX2ialvig8kIIIYR4DGSSijQQC5OmaTuAgk3FFUIIIYR4yKSBKIQQQghhSiapyGNuhBBCCCGEOckgCiGEEEKYkjGIkkEUQgghhBDmJIMohBBCCGFKMoiSQRRCCCGEEOYkgyiEEEIIYeJx/pW5J/LPqCAZRCGEEEIIkY1kEIUQQgghTMkYRMkgCiGEEEIIc5JBFEIIIYQwJRlEySAKIYQQQghzkkEUQgghhDAlf4tZMohCCCGEEMKcNBCFEEIIIYQZ9TgfBikKTr/8xSfugNk+16+wQ7Cg3bhU2CFYSk8t7AisUu6lCzsESy5ehR2BBe0JfJqtSoop7BAsqBGzCjsEq65MH1zYIVgoYuNQ2CFYsLMpWtghWOXh0O+xXoGGXcMe22etTYvZT+DdRTKIQgghhBAiG5mkIoQQQghhSh5zIxlEIYQQQghhTjKIQgghhBCm5DE3kkEUQgghhBDmJIMohBBCCGFKxiBKBlEIIYQQQpiTDKIQQgghhCnJIEoGUQghhBBCmJMMohBCCCGEKZnFLBlEIYQQQghhTjKIQgghhBCmZAyiZBCFEEIIIYQ5ySAKIYQQQpiSDKJkEIUQQggh/o2UUiWUUtuVUucz/y1+n7KuSqlIpdTc3GxbGohCCCGEEP9OHwMhmqZVAUIyX+dkErA7txuWBqIQQgghhCmD9vh+CqY78F3m/78DelgrpJSqD+iAbbndsIxB/I/SNI0pWy8Tdj4BR3sbpvSoRM1SzjmWf+vns0TE32X9m7UB+Co0gtCz8Sil8HC2Y0qPSni5FMlXHJOnrmH3ntM4ONgzbXJ//Gr6WpQ7cTKCkWOWk5qaTmDzGowe+SxKKWbP2UxI6HFsbBQeJVyYOrk/Oi831m/8g8VLQgBwdirK+E+eo3r10vmLb85uwvaH4+Bgx9SP2+FX1cui3Kxvfico+DRJt+5yeOubZsu27DzH3GUHUAqqVfJk5icd8xyHRUzzfifs4BUcitox9aMW+FUpaRnT0oME7ThnjGnDK1nvR0bfYvSMXcQlpuLmUpTpH7fCu2Sxgsc0czO79543HsdxPfGr7mNR7sTpa4ycsIbUuxkENq3C6A86oZTizLkoxk1bz+3baZQu5c6MSb0pVszh/vubvILdYSdwcCjCtKmD8PMra7m/E5cZOfI7Uu+mExhQi9Gj+6CUIiEhhffeX0xk5E1Kl/Zg9qzXcHNzvu92r12LY8yYH7geFY9SsGjhUMqU8eSDD5dw4sQV7O1t8fcvz4SJA9i/7wyTJ6/AYDDQ+7mmvP56B7O40tLSGTF8GSdPXsHd3ZkvZr1KmTKeACxcuJXVq/ZiY2PD6DF9aN7cj+vX4xgxfBk3biRhY6Po06cZLw5qDcCXs9cTEnIUGxtFCQ8Xpk4dhE7nXujH7FpUAp37zGXoay14ZWCzB8aTF0sGjqaLf1NibsXjP2nAQ932/Rz8PZx5M3Zh0Bvo2KMW/V5qZLb82OGrzJuxm4sXYhkzpRMBbapmLYu+nsQXk7YTG50MCqZ81QNvH7cCx3Rg7998NX0HBoOBzj3q8sLLz5gtP/LnFebM2MHF8zGMm9qDFm2rA3D+bDRfTN5KSkoaNraKga80oXX7mgWKZf/e88z+bCt6g4GuPevx4ivNzZanpWUwafRazpy+hpubE5M+702p0sVJT8/gs4kbOXPqGjY2imHDO1CvYQUAdmw9wXffhGHQazQJqMJb77UrUIz/D+k0TbsOoGnadaWUxQeYUsoGmAkMBFrndsOSQSwkSqldSqkGJq/LK6VOPKzth11I5HJcKlvfrsOErhWYsOlSjmW3n47DqYit2XsvNy3Fuv/VZu0b/gRWLc683ZH5i2PPacKvxLJt82gmje/L+EkrrZYbP2klE8f1Ydvm0YRfiSXst9MAvPpSKzasHUHQ6uG0CKzJ1/ODAShT2oMfl73NhrUj+N8b7fhkwq/5i+9AOJevJhD80yAmftCaCbNCrZZr+UwFVix43uL98KvxLPrpD5bPfY6NywYyamhgvuIwi+lgBJcjEwle9jwThwUw4avfrMfUuBwr5vS0eP/zhfvp3rYq6xc9x1sv1OeLJQcLHtPv5wm/cpNta95l0qhujJ+2wWq58dM2MHFUN7ateZfwKzcJ+/08AKM/XccHb7Vlwy9DadOyJt/8sPf++ws7QfjlGLYFT2TSxAGMn7Dc+v4mLGfixBfYFjyR8MsxhO05CcCixVt5pnF1tgVP4pnG1Vm0OPiB2x0x4lteeaUtWzaPZ+WKj/HwcAWgW9dGbN0ynvUbPiH1bhorfg1j4sSfWfzNUDZuGsemjYe4cOGaWVyrVu7F1dWJbdsnMWhwa2bOWAvAhQvX2LzpEBs3jeWbb95m4oSf0esN2NraMuLj3mzeMp5ffh3BT8t3Z23zlVfbsn7DJ6wLGkOLFv7M+3rTfesuqw4f8TGb+sVWmjepkqtY8mrZvk10mPPeI9l2TvR6A3OmhTLlqx4sWTWIncFnuXzxplkZL28Xhk9oR6sO1S3W/2xcMH1ebMDS1YP4+vt+uBd3eigxzZq2jelz+/D96tcJ2XqK8L9vmJXRlXJl1IQutOngZ/a+g4MdoyZ15fvVrzFjbl/mzNjBrVupBYplxpTNzJw3gOVr32LH1hNc+jvGrMyGtYdxcXVg5cZ36ftCY+bN3gHA+tWHAfhx9ZvMXjCQOTO3YTAYSEy4zdeztvHVokH8tPYt4m6m8MeBi/mO8aEyGB7bj1LqdaXUHyY/r5uGopTaoZQ6YeWney5/mzeBzZqmReSlCqSB+B8Veiae7rU9UUpRp4wLt1L1xN5KsyiXkqbnu33XGRJgnlkoVvRecvlOmh6VzzhCdh6nR7eGKKWoW6c8SbfuEBObaFYmJjaR5JRUnqpbAaUUPbo1JCT0uDEOk4zFnTtpqMxA6j1VATc34w24bu3yREWbbzPX8e29SPf2NYzx+ZUiKfkuMTdTLMrV9SuFl4dlBnblxpP071EbNxdjnB4P4UMhZF843dtUNcZUU5dzTDV1VmP6+0o8zzxlzKY+XdeHkH3hBY9p9xl6dK5rjMnfl6RbqcTcuGVWJubGLZJT7vJU7bLG49i5LiG7zwBw6cpNGtYrD0DTRpXYtvPU/fcXcowe3Rsb91e3IklJd4iJyXbexCSSnJzKU09VNO6ve2NCdhy9t34PY6alR49n2GH6vpXtXrhwjQy9gaZNjRkWZ2cHHB2NGfPAQH+UUiilqF27PMeOh1O2nBe+viUpUsSOTp0bEhJyzDz+0GP06Gncf/v29di37wyaphEScoxOnRtSpIg9ZXw9KVvOi2PHwvHycsvKZBYr5kClit5ERydkvnbM2q7xGsjd1fgoj9mOXacpU7o4VSpaZrYfhj0XjhCXkvRItp2Tsyej8PF1x6eMO/b2trRoV429u/42K+Pt40bFKiWxyXYMLl+8iT7DQP3G5QBwdCqCg6N9gWM6feIapX2L41OmOPb2trRuX4Pfdp0zK1PKx51KVb1QNuYx+ZbzwLdcCQA8vVwoXtyZhLjb+Y7l1IlIyviWoHSZEtjb29GmQy327DprVmbPzrN07FYXgJZta/LHwYtomsali7E0eNqYMSzhUYxiLg6cOXmNyKvx+JbzoHgJ432swdMV2bnj/veG/yJN0xZpmtbA5GdRtuVtNE2rZeUnCIhWSpUCyPw3xsoungGGKqXCgRnAi0qpaQ+KSxqIj1hmZvCMUuo7pdQxpdQqpVTBWxEPEHMrDW+3olmvda5FiLbSQJwTepXBz5TC0d7WYtnskAhazfqLjcdv8nbLMvmKIzo6EW/ve5OqvHXuRGdrzEVHJ+Jt0mWWvcysLzcR2Ho8Gzb9ybtDO1nsY9Wa/QQ0q5G/+GKTKWXS/epdshjRscm5Xj88Ip7wqwn0G7qCvv/7lT0HwvMVh1lMN1Io5XWv4eft6Uz0jdzf2KtV9GDbHmPGePtvl0i5nU58Uv4zBwDRsUl46+51l3l7uRIdY/4BHh2ThLeXq3mZWGOZqhW9CAkzNjy2hpzg+gMa9NHRCXiXMjlvvN2zGkxmZbytl7l5MwkvL2O8Xl5uxMXduu92w8NjcHVxYujbC+jRczKffb4avd78MRfp6XrWBx2gXFkvSlmc0/FmZWOiEyiVuR87O1tcXBxJiE8hOjr+getevXqD06cjqFOnQtZ7s2ato0XgSDZuOMg773a9b91l1c8jOma376Sx+Ps9DH2tRa7i+Le4EZOMl84l63VJXTFu5vJecPVyPMVcijL+ww0M6f8jC2eHWZw/+Y/p3vEpqXMhNvbWfdaw7tSJa6Rn6Cntm+ME1weKjUlC520Si5crsdFJOZaxs7PFuZgDiQm3qVxVx55dZ8nI0HPtajxnT18jOjqJMmVLcPnSDa5HxpORoWfPzjPERD3eLwY50fTaY/spoPXAoMz/DwKCLH4XTRugaVpZTdPKAx8C32uadr/JLIA0EB+XasAiTdNqA0kY070APymljiiljgCbH+YOrZ1yKlse8HRUClfiU2lTo4TVbQxr7Uvoe0/Rxd+Dnw5G5y8OK4Fkz4A8qMx773Zmd8h4unauz4/L95iV23/wPKvW7OfD93P3oZkbuUzQAJChN3D5agLfz+7FzLEdGDM9hKRbdwsWgNX6yP3qw19vzKFj1+j5xioOHbuOztMZO9v85oAzQ7JykHJ1HDP/nTy2B8tXHuTZgfNJuZ1GEStfSMy2ZaUSstdBbsrkdrsZGXr++PM8I4b3YtXKj7kacYM1a/eZlZs4YTkNGlShYkVvK9vIXhc5VMYDzvWUlFTeeWcRI0f1McscvvdeD3btnkqXro348cdd9/8l7xPDwzhmcxaGMqhfE5ydilqu/C9mrS5ye+Hp9QaO/xXJ68OaM+/7/lyPTGTbhoJnwqyer3nsz7kRm8zkMRsYOb4zNjYFuA/k4l6eU5kuPZ7CS+fKK/0XMXv6Vvzr+GJra4OrqyMfje7CJ8NX8b+XvsXbxx1bW2mW5NE0oK1S6jzQNvM1SqkGSqlvCrJhmaTyeERomvbPAJ4fgXcy/z9A07Q/wJhpBDZaWzlzPMLrAPNfeZrXWlW1VozlB6NYeTgWAH8fZ6IS7wLGb8TRSWl4uZh3eRyNSObktRTazP4LvUHjZkoGg5ad4rvB5gOZO/t78r/lZ3OdRfzp5z2sWGX8cPWvVZaoqHsZkqjoBLxMMhYA3t5uRJlkh6yVAejSuT5D3lzEO0ONk0DOnL3GmLG/sHjBEIq75zwBxyK+tUdZudE43NO/uo7rJlmCqNhkvDxzP6HDu2Qx6tQshb2dLWVKuVGhrDuXI+Pxr27ZiLhvTEEnWLnZmK3xr1aS6zH3upSjbqTg5ZH7pLPO05k549sDkHInnW2/XcLFOe8f5j+tOMCKdX8aY6pZ2qwbPyomCa+SLmblvXWuRJlkqIxljMexUvmSLJ1r/JJ76fINdv1m3k1msb+6lYi6bnLeRCXg5WU+McNbV9z83DIp4+HhSkxMIl5ebsTEJFKihMu9daxsNyNDT80avvj6GrtMW7epw9Gjl4CmAMydu5G4uGTmzB3A0aPhXLc4p81j03kX5/r1eLy9i5ORoefWrTu4uzsb389h3fR0Pe+8s4iuXRvRrt1TFvUD0KVLQ94Y8jXvvGP9C9HjOGZHT14lOPQUM+ZsI+lWKjY2iqJF7Xmhz9NWY/q3KKkrRkz0vexcbHQyHp65u6946lyoXN0LnzLGY9m0RSVOH4+iYNPVoKSXCzEmWbrY6Ft45mHCWUryXUa8s4JX3wrAr3beJ/GZxaJzJdokuxcbk4Snl4vVMl46NzIy9KQkp+Lq5ohSinc/ujeR6/UXv8G3rDEx0axFNZq1qAbAulV/YFvAL7MPTcFnFz8WmqbdxMrEk8y2xatW3l8GLMvNtqWp/nhkP9PydOaZjk/IqXEI0L+RN2vf8GftG/60rl6coGM30DSNo1dv4VLUlpLZZiE/31DH7g/qsWPYU/z4sh/lPRyyGofhN+91Se48G09Fz5xnnGY3oF9zglYPJ2j1cNq08mfd+kNomsaRo+G4FHPEq6T5zD6vkm44OxXlyNFwNE1j3fpDtG7pb4zjcmxWudCdJ6hYQQfAtevxvD1sKZ9PfYEK5S1nHd83vp51WLdkAOuWDKB1s0oEBZ82xnfyOi7ORa2O68tJm2aVOHDkKgDxCXcIj0igTKm8z1wc0L0W6xb2Zt3C3rRuWp6gHeeMMZ2KxsW5SJ5iik+8gyHz5rbo57/o1b5anuMBGNDnaYKWv0nQ8jdp06I66zYdMcZ0PAKXYg54eZp/OHh5uuDsVIQjxyOMx3HTEVoHGgfz34wzNsINBgPzl+7m+V4N77+/1nVZF7TfuL8jF3FxccjqMs7an5cbzs4OHDliHOe0Lmg/rVsbZ+G3alWbdeuMX1LWrdtn/r6V7fr7lycx6XZWV/SB/WepXKkUACtX/sZvv51i5hevYGNjg79/OS6Hx3A14gZpaRls3nSIVq1qm8XWqlVt1mVmIIODD9O4cTWUUrRqVZvNmw6RlpbO1YgbXA6PoXbt8miaxpjR31OpojcvvdTGbFvh4fey96Ghx6hQUVeox2z54lcJXf8+oevfZ1C/xgwZ3Pxf3zgEqFbTm8iIeK5HJpJGxccKAAAgAElEQVSermfXtrM0CayYy3V1JCelkhBvHApy5FAE5Spa75nJi+p+Ply9Es+1yATS0/WEBJ+maYvcTQxKT9cz+oPVtO9Si5Zt8zcEx1QNPx+uXrnJtavxpKdnsGPrCZoFmt9bmreoxpb1RwDYuf0U9RsZx5Wn3knjzm3jEKeD+/7G1taGCpWM9+24m8bzLCnpDmtXHKJrz3oFjlU8HJJBfDzKKqWe0TRtH9AP+A14eH2iVgRUcSfsfAId5hzFwd6Gyd3v3eh6LjjO2jf877v+rJArXLqRio0CH/eijOtc4b7lcxIYUJPde07TtuOnODoWYcqkflnLuvf6nKDVwwEY/8lzWY+5CWheg4DmxhvazFkbuBQeg1KK0j4lmDD2OQC+nh9MQmIKEz41zoq2tbVlzYoP8h5f4/KEHQin3YDvcChqx5QRbbOW9XjlJ9YtMT5iY/qC39i44yx37qYT2HsJvTv78fZLjWnWqBy//XGFzoN+wMZG8dEbzSju5pjT7nIXU6OyhB24QrtBvxhj+rDFvZiGrGLdwt7GmBbvZ2PoBe7czSCw34/07lidt19swIGj15m15AAoRUP/Uox9u+CPIAlsWpXde8/TtudsHB3smTL23uzp7v3nEbTcOGpi/MddGTlhLal30wloUoWAzFmuG4OPs3yVcTZ12xY16NXVeoYsa3+BtdgddoK27T7B0aEIU6YMylrWvcenBK0bY9zfuP6MHPUdqalpBDT3IyCgFgCvv9aeYe8tZtXqvZQqVYIvZ79+3+3a2towYngvBg2eDZqGn19ZnnvOWG/jxi/Hx6cEz/f93Bh/26f4ZGxfXnn1Kwx6A716NaFKFR+++nI9tWqVo1XrOvTu3ZThH31Lu7af4ObmxBezjF/kq1TxoWPH+nTuNAFbW1vGjn0eW1sb/vzjAkFBB6hatTQ9un8KwHvvdycw0J+ZM9cRfikapRQ+pUswYUL/J/KYPUzLX55Ii6r18CzmTsSU9YzbuJilv1ufhf2w2NrZ8PbwVnw8dA0GvUaH7n6Ur+TJsvm/U7WmjiaBlThzMorxH24gOSmVfXsu8t3CfSxZOQhbWxuGDAvgozdWo2kaVWvo6NTz/vfY3LCzs2HYiLZ8+OYvGAwanbrXpkKlkiyZF0a1mqVo1qIKp09eY8z7a7iVlMrvYedZumAP369+jZ3bTnP0cARJCXfYut446W/kxC5UqZbzF4z7x2LL+yM78d7/fkBv0OjS4ykqVvZi8dehVPfzoXmL6nTp+RQTR6/luS5f4urqyMTPjfeq+LgU3vvfjygbRUkvF8ZOfjZru7M/38qFc1EAvPR6IGXLexaw1h6Sgo8N/NdTVsfKiIcms+t4MxAGNAHOY3wW0Wbgw+xdzJqm1brf9vTLX3ziDpjtc/0eXOgx027k/FifQpNesIkij4pyL1jX0yPhkres8OOgPSE9X6ZUkrUJi4VLjZhV2CFYdWX64MIOwUIRm9z3zDwudjZP5thSD4d+j/UKfJyftbb9v38C7y6SQXxcDJqmvZHtvRamLzRNCwfu2zgUQgghxKOn/UvGID5KMgZRCCGEEEKYkQziIyaZQSGEEOJfRsYgSgZRCCGEEEKYkwyiEEIIIYSph/CXcP7tJIMohBBCCCHMSANRCCGEEEKYkS5mIYQQQggT8pgbySAKIYQQQohsJIMohBBCCGFKHnMjGUQhhBBCCGFOMohCCCGEEKZkDKJkEIUQQgghhDnJIAohhBBCmNBkDKJkEIUQQgghhDnJIAohhBBCmDLIn9qTDKIQQgghhDAjGUQhhBBCCFMyBlEaiP82tn0HFnYIlgwZhR2BhduepQs7BAvOyqGwQ7Aqw+bJ60i4m5FU2CFYSEyLLewQLBRzKF7YIVi4Mn1wYYdgVdmPlhV2CBa0uZMKOwQLt7XUwg5BPCGkgSiEEEIIYUL+FrOMQRRCCCGEENlIBlEIIYQQwpSMQZQMohBCCCGEMCcNRCGEEEIIYUa6mIUQQgghTEkXs2QQhRBCCCGEOckgCiGEEEKYkMfcSAZRCCGEEEJkIxlEIYQQQghTekNhR1DoJIMohBBCCCHMSAZRCCGEEMKEjEGUDKIQQgghhMhGMohCCCGEEKbkOYiSQRRCCCGEEOYkgyiEEEIIYUrGIEoGUQghhBBCmJMMohBCCCGECU3GIEoD8b9C0zQmT1nF7rCTODgWYdqUgfjV9LUod+LkFUaO+oHU1HQCA/wYPao3Sim2bD3M3K838/fFaFb++iH+tcoBsH7DIZYs3ZG1/tlz11i7agQ1apTJfVxT17B7z2kcHOyZNrl/DnFFMHLMcmNczWsweuSzKKWYPWczIaHHsbFReJRwYerk/ui83AA4cPA8Uz5bS0aGgeLFnflx2du5imnvb2eZMS0IvV6jZ69GvPRqS7PlaWkZfDLyF06fisTd3YlpMwbgU7oEAOfOXmfyxNWkJN/Fxkbxwy9vYzBojHj/R65evYmNjQ0BLWrwznudchWLeT2tZnfYKePxmzwg5+M3+qfM41eT0SN7oZTisxnr2LnrBPb2dpT19WTqp/1xdXVi/cZDLFkamrX+2XPXWLvyo1wdvz17TjFtyir0BgO9ejfhtdfaZaundEaO+IGTp67g7u7MzC9epnRpDxLikxk2bAknTlymR4/GjPmkDwApKakMfGFW1vrRUQl06dqQkaN657qe8nvsrkXG0avbDMqVLwmAf+2yjB7XC4DgLUdYsigUg0GjWUB1hn3QOdfxWHNw7yXmzghBr9fo3LM2/V962mz50T8j+HpmKH+fj2Xs1K4EtqkGQNS1RMZ+GITBYCAjw8Czz9ejW++6+Y7j99/OMfOzjRj0Bro/25DBrwaaLU9Ly2DcqJWcORWJm7sTU6b3w6d0cbZsPMIPy/ZklbtwLoofVrxFteo+We+9//b3RF6N49e1w/Id38Hfw5k3YxcGvYGOPWrR76VGZsuPHb7KvBm7uXghljFTOhHQpmrWsujrSXwxaTux0cmgYMpXPfD2cct3LLm1ZOBouvg3JeZWPP6TBjyy/Typ9/O9e84wfVoQBr2BHr2e5uXXWpktN15/P3P65FXc3J34bObArOvv2a6fU668FwD+dcoyZpz5df/uW0uJvHqTVUEf5amuxKPz/76BqJQaDDTQNG1oQcpYWWcYsEjTtNsFDjIXwsJOEX45lm1bx3H0WDjjJ/zCyl8tL7TxE39l4oR+1K1TgdeGzCdszykCA/yoWsWHOV+9xrjxP5uV79a1Id26NgTg7LlI3hy6KNc3E4CwPacJvxLLts2jOXrsMuMnrWTlz+9bxjVpJRPH9aFunfK89r+FhP12msDmNXn1pVYMe9vY2Pr+x918PT+YieP6kJR0mwmfruKbhW/gU6o4N2/eylU8er2Bzz5dy7zFr6HzduOFvnMIbFmTipV0WWXWrTmIq6sj67eMIHjzEb78YjOfzXyBjAw9Yz7+mU+nPk/V6j4kJKRgZ2dLWloGA18KoGGjyqSnZzDklUXs3XOGps2r56GeMo/flk+Mx2/iClb+8oFlPU1cwcTxzxvr6Y0FWfXU9JlqfDCsK3Z2tkyfGcTCxdv56IPudOvSkG5d/jl+13jz7cW5On56vYHJk1aweMlQdDp3+vaZTsuW/lSuXCqrzOpV+3B1c2Rr8Hg2b/qDL2YEMXPWyxQpas/b73ThwvlrnD9/Pau8s7MDa9aOzHr9XK/PaNs29w2gghw7gDK+Hvyy+j2zbSYkpPDlzM38tOIdipcoxthRv3Jg/3meblwl13Flj/HLz7YzfV4fSupceOOFH2gSWInyFT2zyuhKuTJifEd+/eGQ2boeJYsxd1l/ihSx487tNF567luaBFbGs2SxfMXx+eT1zF30MjpvVwY9P4+AltXN6ipozR+4ujqydvOHbNtylDmztjJ1Rj86dqlLxy7G43LhXBQfvPODWeMwdMcJnByL5Dmm7PHNmRbKZ/OepaTOhbcGLqdJYCXKVfTIKuPl7cLwCe1Y8cOfFut/Ni6YAS83on7jcty5nYZSqkDx5NayfZuYu2sV3w8e+0j38yTez/V6A9Mmr2X+4tfR6dwY0PdLAlvWpFJl76wy61YfwMXVkfVbR7J18198+cUmPps5EDBef7+usbz3A4RsP46TU9FcxfHYyBhEGYP4CA0DnB7XzkJCj9GjeyOUUtStU4GkW3eIiU00KxMTm0hycipP1a2IUooe3RsREnIMgEqVvKlYQWdt01k2bfqTLp3q5y2uncfp0a1hZlzlc44rJZWn6lYwxtWtISGhxwEoVswhq9ydO2n88zmwYfNh2rapjU+p4gB4eLjkKp4TxyMoU9aTMr4e2Nvb0b5jHXaFnjQrsyv0FF26NwCgdTt/Dh24gKZp7P/9HFWqlqJq5oelu7sztrY2ODoWoWGjygDY29tRo0ZpoqPNf8cH1lPocXp0y8XxM6une8evWdMa2NnZAlC3TnmiohMs9rFpc+6P3/Fj4fiW9cTX15MiRezo1KkeO0OPmZUJDT1G9+7G7Fi79k+xf/9ZNE3Dyako9etXokhR+xy3fzk8hri4W9RvUClX8UDBjl1OIiPiKFvek+IljI2wRo0rE7r9RK5jyu7Miev4lCmOTxl37O1tadW+Ont3XTAr4+3jRqWqXtjYmDdq7O1tKVLE+J09LU1/37gf5OTxq/iW9aCMbwns7e1o27E2u3eeNisTtvM0nbvVA6BV21ocOvC3xT6Dtxylfac6Wa9v377L8u/38vIQ88xtXp09GYWPr3tWPbVoV429u/42K+Pt40bFKiWxydb4u3zxJvoMA/UbG7Nijk5FcHDM+Vx7mPZcOEJcStIj38+TeD8/cfwKvr4exuuviB3tO9Vl187s199JumZef23a1ebg/vMPPI9vp9zlx+928+qQ1rmORTwe/8kGolLKWSm1SSl1VCl1QinVVykVrpTyzFzeQCm1y8p6y5RSC5RSe5RS55RSXUwW+yiltiqlziulPjdZZ75S6g+l1Eml1ITM994BfICdSqmdme+1U0rtU0odVkqtVEoVy3x/mlLqlFLqmFJqRn5/5+iYBLy9i2e99ta5E52tkRAdnYC3zt28TIxlQyInm7cepnPnBnmLKzrRSlyJlmWyx2VSZtaXmwhsPZ4Nm/7k3aHGbGJ4eAxJSXcYOHgOz/aZwbqgg7mKJzYmEW/ve11RXjo3YmKScixjZ2dLsWIOJCTc5vLlGyilePP1b+j/3GyWLd1lsf1bSXcI232aRk9XzlU8/4iOScTbO+c6ACv15O1OdIxlQ3T1mv0ENK9p8f7mrYfp3KleruMpZXLcdLriFvHERCfindlAt7OzxcXFkYSElFxtf9OmP+nQsV6eMj8FOXYAkZFx9Os9m1cHz+fwn5cA8C3rQfilWK5FxpGRoWdX6EmionJ/TWR3IzYZL+97X1ZKerlwIyY51+vHRCXxSp9v6dtpAc8PapSv7CEY60FnUlc6nRux0eZ1FWNS5p+6Skww7/DYvvU47TrWznq9YM4OBgxqhoNDwTKIN2KS8dKZ1JOuGDdjc1dPVy/HU8ylKOM/3MCQ/j+ycHYY+v/Y3819Eu/nMdGJ6Erd259O505s9nuCyX3Mzs6WYi6OZtff872+4JVB8zj858WsdebN2crAwYE4FjArLR6+/2QDEegAXNM0rY6mabWArXlYtzwQCHQGFiil/klh1QX6Av5AX6XUPwNCRmua1gCoDQQqpWprmvYVcA1oqWlay8yG6RigjaZp9YA/gPeVUiWAnoCfpmm1gU+tBaSUej2zEfrHosWbrAZt7Uta9g/f3JTJydGj4Tg62FO1is+DCz/kuN57tzO7Q8bTtXN9flxuHBul1xs4eSqChfNe55uFbzBv4TYuhcfkM57cldFnGDjy1yUmf9aPJd+/yc6QExzYfz6rTEaGnpHDl/P8gKaU8fWw3Mh947LcqWVcVspkez1/YTC2drZ062J+4z96LBxHhyK5P34PKZ6cbNnyJ53y+GWjIMfOs6Qrm7eP4udVw3j/o66MHr6c5ORUXN2cGPlJTz7+8CdeGTQfn9LFsbPN/20xNzHej5e3K0tWvMSPQa+xbeNJ4m7mrsGdnzisJnZMCp04FoGDgz2Vqxi7EM+euUZExE1atvbLV0x52ff96PUGjv8VyevDmjPv+/5cj0xk24ZTBY7pSfKk3s+t7DAXMRmvvy07xvDL6vf5YHg3Rg3/ieTkVM6ejiTiyg1atfEvWByPgt7w+H6eUP/VMYjHgRlKqc+AjZqm7clDpmKFpmkG4LxS6iLwz0CyEE3TEgGUUqeAckAE0Ecp9TrGuiwF1ASOZdtm48z392bGUQTYByQBqcA3SqlNwEZrAWmatghYBIB+e9Yl+NPy3axY+TsA/v7liIqKz1onKjoBLy/zQdve3u5mXY9R0Ql4lczdwO5NW/6kc6fcfaD/9PMeVqzaZ4yrVlkrcblmi8vNMq5sZQC6dK7PkDcX8c7Qjnjr3Cnu7oyTU1GcnIrSoH4lzpy9RoXMQdA58dK5ERV171tvTHQiJUu6Wi2j83YnI0NPcnIqbm5O6HRu1G9QkeLFnQFo1rw6Z05FZo1X+3T8asqW9WTAwOa5qSZ+Wh6WrZ6y18EDjl+UeZm16w6wa/dJli0ZavFBsWnzYTrnoTtJp3Pnuslxi46Ot4hH5+1O1PV4vL2Lk5Gh59atO7i5Oz9w22fOXEWfocfPr2yu44GCHTulVFb3bU2/MpTx9eBKeCw1a/kS2KImgS2MGdfVK/djY5P/BmJJr2LERN0bDxsbcwuPfGQBPUsWo3xFT47/dTVrEkteeOnciDapq+joRDyzXVO6zDI6bzeTunLMWr5tyzGz7uXjR69w5lQk3dp/jj7DQFxcCkNeWszCb1/Lc3wldcWIiTapp+hkPDwffO4AeOpcqFzdC58yxkxV0xaVOH08io55juLJ8qTez//hpXMj+vq9/UVHJ1DSyjkVFZVw7/q7dSfH6+9yeCwnT0Rw6lQkndpORq83EHczmVcHz+ObZW/mKTbxaPwnM4iapp0D6mNsKE5VSo0FMrj3+zrktC6Q/TvQP6/vmrynB+yUUhWAD4HWmRnATTlsWwHbNU2rm/lTU9O0VzRNywAaAauBHuQt08mA/oEErR1J0NqRtGldm3VBB9E0jSNHL+Hi4mhxs/Aq6Yazc1GOHL2EpmmsCzpI61a1c9j6PQaDga3Bf+W6gTGgX3OCVg8naPVw2rTyZ936Q5lxheNSLIe4nIpy5Gi4Ma71h2jd0viNMvxybFa50J0nssbVtG5Ziz8OXyQjQ8+dO2kcO36ZShXvP+YGwK9WGSKu3CDyahzp6RkEbzlKYEvz7tjAljXZGPQHACHbjtPw6coopXimaVXOn7vOnTtpZGTo+fOPi1mD/r/+aivJyal8+HHXXNURwID+AQStGUHQmhHG47fe5PgVc8ihnhzuHb/1B2ndylhPYXtOsXjJDubPfc2iq8ZgMLB121907pi77mWAWv7luHI5lqtXb5CWlsHmzYdp2dL8XGnZ0p+goAMAbAv+i6cbV81VBmPzprxnD6Fgxy4+LjmrG/JqxE2uXLlB6cwsb9xNY9dmUuJtVv6yj569zGfT5kV1v1JERsRzPTKB9HQ9ocFnaBKYu+EGsdG3uJuaDsCtpFROHI3Et1yJfMVRs1Zprly+V1fbtxwjoEUNszLNW1Rn0/rDAIRuP0HDRhWzjp/BYCBk23Hadrh3zHv3bcyW0JGsDx7O4u+HULa8R74ahwDVanpn1lMi6el6dm07S5PAirlcV0dyUioJ8cauyyOHIihXMX/19CR5Uu/n//Cr5cuVKzeIvHqT9LQMgjcfoUVL82xyYEs/NmRefzu2Hcu6/uKyX3+Xb1CmjAd9nm/C9l1j2bx9NN/+8Bblyns+MY1DzaA9tp8n1X8yg6iU8gHiNE37USmVDAwGwjE2GrcAve6z+nNKqe+ACkBF4CzwVA5lXYEUIFEppQM6Arsyl90CXIAbwH7ga6VUZU3TLiilnIAyGLuhnTRN26yU2g9cyL6D3AoM8GN32EnadpiAo4M9Uya/kLWse8+pBGXOHh0/ti8jR/1I6t10AprXJCDA+AG7fcdRJk1eSVxcMkP+t4Aa1UuzZLFx0vahPy7grXPH19fTcscPjKsmu/ecpm3HT3F0LMKUSf3uxdXrc4JWDzfG9clzWY+5CWheg4Dmxg+zmbM2cCk8BqUUpX1KMGHsc4BxEHbzpjXo9uzn2NgoevdqTNUqpSwDyMbOzpYRo7rz1pBvMOgNdOvZkEqVvZk/N5iafmUIbOlHj2cb8snIX+jW8TPc3JyYOr0/AK5uTgx4MYCBz89BKWjavDrNA2sQHZXAkkWhlK/gRf/nvgSgb78m9Oz99P1CsaynsJO07TgRR4ciTPn03iM0uj/7GUFrRhjraWwf42Nu7qYR0Kxm1ljDSZNXkZaewUuvzgOgTp3yTBzXF4BDf/yd5+NnZ2fL6DF9eP3VrzEYNHo+25jKVUox56uN+NUqS6tWtenVuwkfj/ieDu3H4+bmzIyZL2Wt37b1WJJTUklPzyA05BiLvnkrawZ08NbDzF/4v1zHYhpTfo/d4T8vMX/uNmxtbbC1VYwa+yxubsY5ZNOnBXHurHG29etvtMl6FE5+2NrZ8M6INgx/axUGg4GO3fypUMmTpfN/o1pNb5oGVubMyet88sE6kpPusi/sb75dsJdlq17m8qWbzP9ip7FPTtPoM7AhFavkLxY7O1uGj+rGO298i16v0a1nfSpV1rFg7nZq+JUhsGUNuj/bgHEjV9Kz0wxc3ZyY/PnzWev/9Wc4Xt5ulPF9NA0vWzsb3h7eio+HrsGg1+jQ3Y/ylTxZNv93qtbU0SSwEmdORjH+ww0kJ6Wyb89Fvlu4jyUrB2Fra8OQYQF89MZqNE2jag0dnXo+ni7K5S9PpEXVengWcydiynrGbVzM0t83PPT9PIn3czs7W0aM7smbry/GYNDonnn9zZuzlZp+vrRo5UePXo0Y8/HPdOswFVc3J6bNMMZ9+I+LzJ8bnHn92TB6bC/c3B/bHE6RT6ogM+WeVEqp9sB0wACkA/8DHIElQDRwAONja1qYPsJGKbUMiAcaADrgfU3TNmZ/zI1SaiMwQ9O0XZnrPA1cxJhlXK9p2jKl1NvAW8D1zHGIrYDPgH/m8o8BDgFBGLOOKnOb3933lzPpYn5iGDIKOwILKaQVdggWnNX9EteFJ6MAXaqPyl39Y3k6VJ4kpsU+uNBjVsy++IMLPWZPYj0BlP1oWWGHYEGbO6mwQ7BwW0st7BCscrLr+nieZZQp5Z02j+2z1vmrHY/1d8ut/2QGUdO0YCDYyqKqVsouA5aZvLVX07T37ldG07QuJv8fnEMMc4A5Jq9DgYZWiua/P0sIIYQQ4hH4TzYQhRBCCCHy60keG/i4SAPRRE7ZQCGEEEKI/0+kgSiEEEIIYULTSwbxyRudLoQQQgghCpVkEIUQQgghTMgYRMkgCiGEEEKIbCSDKIQQQghhwiBjECWDKIQQQgghzEkGUQghhBDChIxBlAyiEEIIIYTIRjKIQgghhBAmNIOhsEModJJBFEIIIYQQZqSBKIQQQgghzEgXsxBCCCGECflTe5JBFEIIIYQQ2UgGUQghhBDChDzmRjKIQgghhBAiG8kg/stoCZGFHYKFZFe3wg7BgsvdJ+8RBRkrvivsEKyy8Xnyjp9TaV1hh2Ch6KFzhR2ChUsLjxZ2CBZcd35U2CFYpc2dVNghWFBDPynsECwsHlqvsEOw6lW/ro91fzIGUTKIQgghhBAiG8kgCiGEEEKYkDGIkkEUQgghhBDZSAZRCCGEEMKEQTKIkkEUQgghhBDmJIMohBBCCGFCZjFLBlEIIYQQQmQjGUQhhBBCCBMyi1kyiEIIIYQQIhvJIAohhBBCmJAMomQQhRBCCCFENtJAFEIIIYQQZqSLWQghhBDChDzmRjKIQgghhBAiG8kgCiGEEEKY0AyGwg6h0EkGUQghhBBCmJEMohBCCCGECRmDKA3E/yRN05g8azth+/7GwcGeqWO64FfN26LcrAW7Cdp6nKRbqRwO+TDr/W9/PsiqDUewtbWhhLsTk0d1pnQpt3zF8vtvZ5kxbQMGvUaPXg0Z/GoLs+VpaRmMG7mC06cicXN3YuqMfviULgHA+bPXmTJxLSnJqSgbxfe/DKVoUXu2bj7Ct4t3olCU9HJl0rS+uBd3zlv9TN/I7r1ncXAowrTxvfCrUdqi3InTkYwct4rUu+kENq3G6I+6oJRi2Mc/c+nyDQBu3bqDi4sjQT+/DcCZ89cZN3kdySl3sVGKVT+8SdGi9nmqM03TmBoSQdjfSTja2zC5U3lqejvlWP6t1Re4mnCXoFf8APj6t2usOnqD4k7Gy3tYQGkCKuXv+JnGNGXNecJO3cTB3oYpA2ri5+tiUe61+UeITUojw6DRoKIbnzxXDVsbBcCPYRH8tCcSWxtFYE0PPupeucAxTV56mLC/ruFQxJapQxvjV7GEWZk7dzMYNnMvV6JuYWujaNmgNB+8UBeANTsvMv2HI+hKOAIwoENVnmtTqUAx/RPXk3b8AEq+PxKnJgFoqXeInjSau2dPmy1XTk74Lvwh67Wdl46krRu5MWsadrpS6MZNwaaYC8rGhhvzZnH79z0FiufA3r/5avoODAYDnXvU5YWXnzFbfuTPK8yZsYOL52MYN7UHLdpWB+D82Wi+mLyVlJQ0bGwVA19pQuv2NfMdh6ZpTJ6yit1hJ3FwLMK0KQPxq+lrUe7EySuMHPUDqanpBAb4MXpUb5RSbNl6mLlfb+bvi9Gs/PVD/GuVA2D9hkMsWboja/2z566xdtUIatQok+9YrVkycDRd/JsScyse/0kDHuq27+fS4ZuELD2HZtCo3caHp58tb7Xc2d+jWT/jBAM/b4h3Zdes95NiU1n67n6a9KlAox7lHlPUIhnkqpEAACAASURBVC+kgfgASqlwoIGmaTfyuN4yYKOmaatyWb58ZvlaeQzRQti+v7l8NZ7gFW9w9OQ1JkzfyopvBluUa9msMgN616dD3wVm79eoqmPV0pdwdLDn5zWHmTFvJ7Mm9chzHHq9gc8+DeLrxa+g83bjxb5zCWhZg4qVdFllgtYcwsXVkXVbPiJ481HmfLGVqTP7k5Gh55OPf2Xi1D5Ure5DQkIKdna2ZGTomTltAyuD3se9uDNfztzMr8t/Z8hbbXNfP3vPER5xk23rPuDoiQjGT/0/9s47PIpy++OfkwIB0ihp9A7SQVCkhA6KdLFiL/izX73XgqAiKtj1KlZsqCgKSBUBqQHpoHRp0iEJhJIEEkKS8/tjJsludgMJSGa9vJ/nybM7776z883M7MyZ855z3qlM+PpBj37DR01lxLD+NGtchfseHUvc0m10aFuPd1+9ObfPq2/PJDi4JACZmVk8OWwCb7x0PfXrxnDs+CkCAvyLvN8W/5XMnqOn+WVwQ9YfPMmIOXsYf/tlXvv+uvUYpQM9I0VubxnJXVd6PhScL3Gbk9hz+BSzhrVm3Z5kRkzYyg9PtPTo985djQgOCkBVeeyLjcz6I5FrW0SxYvsx5m04wtSnr6BEgB9JKRkXrun3Q+w5lMLs93uxbnsSL366mh9f7e7R764+9WndKIqMM1nc9eIC4tYeJLZFRQCuaVOV5+/1/D8uBF88fqXbtCewSjX2DLyGoEZNiHzqefbdc7NbHz11ir23XZe7XGXsj6Qu+BWAcnffT+rcWZz46QdK1KhFxbc/Ynd/z31dWLKysnnn1Tm8/dFNRESFMnjQV7TrUIfqtSrk9omKCeXZF3sx/usVbusGBQXw7Eu9qVKtHEcSU7h30Jdc0aYmISFB56UlLm4zu/ccZs6sF1i3fjfDXxzPhB+e9Og3fMQPjHjxZpo1rcF9939E3OLNdIhtSN06FXn/vft4Yfj3bv379G5Fn96tANi67QAPPvzp324cAny17GdGL5zI13c+/7d/d0FkZym/jtnKDS80J6R8Sb55ahW1WlWgQpVgt34ZaZmsnbmfmDqhHt8x/8tt1GhevrgkFxlTKNvEIP5PMm/xdvpe3QgRoVmjSiSnnibxSKpHv2aNKhFZIdijvfXl1SgVZHm9mjasSHxi8nnp2LRhH1WqlqdylfIEBgbQ/ZqmLJq/2a3Povmb6dW3BQBdujdi5YodqCrLl26nTt1o6ta3buTh4WXw9/cDBVVIS8tAVTmZepqISM+Lz9mYt2gz/a5tbu2fxlVJTk0n8bD7/5h4OJnU1HSaN6mKiNDv2ubMW+iuXVX5Ze4Gel3dFIDflu+gXp1o6teNAaBseGlLcxGZv/04fRqVR0RoWimYlNNZHE4949HvZEYWY1clcH+bmCJvo8iaNh6hb6toa59VDyM5LZPEE6c9+gUHWc+cmdnKmaxsxG4fv+QA93WtRokAa3+UDylxwZrmrdpP347VLU11K5B8KoPEY2lufUqVDKB1I+uBpESgPw1qliU+6dQFb/ts+OLxC47tTPIv0wBI37gev5AQ/MtXKLB/YJWq+JctR/ofa6wGVfzKWNcKvzLBZB5JvCA9WzYepFKVslSsXJbAQH+69LiMJQu3ufWJqRhOrbqRiJ+4tVepVp4q1SxPcYXIEMqWLcPxo+d/TOfNX0+/vldY51HTGiSnpJF4+IRbn8TDJ6zrQbOa1vWg7xXMm7cegFq1oqlZI8rbV+fy889r6NXz8vPWeDYW7/iDoyfP7xp9vhzakUzZmFKER5fCP9CP+u2i2LHS04ey5Lu/uKJfNQJKuF8Ht684THhUKSpUKfzIj6H4MQaiCyIyRUTWiMgmERns5fPbRWS9iKwTkW/stmoiMs9unyciVV1WiRWRpSLyl4gMtPuLiLwhIhtFZIOI3Ph3/x8Jh1OIicozmqIjQkg4nHJe3zVxxjpiW5/fsFtiYjJR0XlDY5FRYSTmMzatPuEABAT4ExwcxInjp9i75wiI8PDgzxl0/XuM/WKR1SfQn2ee68dN/d/l6k4j2fVXAn0HtCqSroTEZKKj8nRFR4aSkM9ATDicr09UKAn5tK/+fTflywVTvap1o9219wgicM9DX9L/ltGMGRtXJF05JKaeITo0z4CKCilBgheP2/uLD3LnFVGU8uKB+m7tYfp/sZlhM3dzIj3zvHS4knD8NNHheR6a6LCSXg1EgHs/+oN2Q5dQpmQAPZpFArD78CnW7DzOjW+v5rb31rJhz4Xf0BKS0ogpn3eDiS5XmoSzGH/JJzNYsPoAVzXJ88z9unwffZ6YyaNvLuHQkZMXrAl88/gFRESSmRCfu5yZmEBARMFGTUj3a0mdOyt3OWnMB4Rc3Yvq0+dR8Z2POPzWyAvScyQxlUiXa1REVAiHz+MatXnjQc5kZlGpStnz1pKQeJzo6Lz1o6PCSUg47t4n4TjRUeHufRLd+5yNmbPWcu21f6+n2klSk9IJKZ93PQgpX5LUo+7Xg4S/UkhOSqdWS/cHkYz0LFZM3k2bG2oUi9bzJTtbi+3PVzEGojt3q+rlQEvgURHJ9X+LSENgKNBZVZsCj9kfjQa+VtUmwDjgPZfviwHaAb2AV+22AUAzoCnQFXhDRP5eF4KX803Es+1cTJu1kU1/xnPPoCvPU4enEA8dXvogkJWZzbrfd/Pyazfx+df/x8J5m1i5fAeZZ7KY9MNyxk14lFkLnqV23Ri+/GzBhcpC8gkrTJ8Zs9bRq0eT3OWszGzW/LGHN16+ge8+H8zcBZtYtnJHkbRZ2z73ftuScIq9x07Tta7njfHG5hHMur8Rk+66jIjgQN6Yv7/IGjw0eWkr6Jz67IFmxL3UlozMbJZvOwZAZpaSnJbJ+Mcv58m+tXn8q41e/88LJf8xyiEzK5t/v7OU23rWpUqU5Qnr1LIS8z7qw7S3e9KmcRTPjF7+t2jwxePn/WAVvP+Du11DypyZucsh3a8l+ecp7O7dhYOPP0DU8FfP76KSu2Uv+4iifd+Rw6m8Mmw6Q4Zfi5/fBWj5m64HBbFu3W5KBQVSt07F85H3j0SzlQVfbqPTnXU8Pvtt/F+07F2VEqVMhJuvY46QO4+KSH/7fRXA9ezuDEzMiUVU1aN2+1VYRh/AN8DrLutMUdVsYLOI5DyutwO+V9UsIEFEFgGtgPUFibK9mYMBPn7rDgbf0dGjz7hJa5gw7Q8AGteP4VBCnocm/nAKkRU8EwrOxtJVu/h47FK++WAQJUqc32kSGRVGQnzeUE1iwgkiIkK99DlOVHQYmZlZpKamExZWmsioMFq0rJGbfNK2fT3+3HyAMmWseL/KVS3bvVuPxnz1+aJzahn34zJ+nLwagMYNKhGfkKcrPjHZY/9ER4a690lIJjIir09mZha/LtjET98+nLdOVChXtKhBOVtzbNt6bPrzIFddce5kjO/WJjJxnTVE0yi6DPHJeR6nhJQMIoPdh2TXHTzJ5oRTdPtoA1nZStKpTO78bitf3VKPCmXykmIGNq3AgxOLbqQCjFu8n4nLDlqaqoYQfzw997P4E6eJCC1Z4LolA/3p3LgC8zcepm39ckSHl6RbkwhEhCbVQvETOHbyDOWCizbUPO6XbUyYtxOAxrXKcyjpJBBhaTp6ikg74SQ/z3+8kmoxIdzRq35uW9mQPP3Xd63Fm+PWFUmLK754/MIG3kxY34EApG/eSEBUnuc0IDKKzMPeh4lL1KmH+Ptz+s+8kIrQPgM4+Nj91ndtXIdfiRL4h5cl69hRr99xLiIiQ0h0uUYdTkihQoRnuEtBnEw9zdOP/si9D8XSsIlngtm5GPfdIn6csBSAxo2rER9/LPez+ITjREa6JwVFR4cT7+JVjE84TmRE4RKHfv5lDdf2/N/xHgIElw8iJSnvepCSdJrgcnm/p4y0LI7sPcn459YCcPJ4Bj+NWseAIU05tP0E25YlsujrHZw+mYn4QUAJP1r09EwMchKTxWwMxFxEpCOWR+8qVT0lIgsB16hn4WyP3Hm49nH1uUu+10Kjqp8CnwJo0ldeNQy67nIGXWfFuCz8bQfjJq3h2m4NWLfpICFlSnqNNSyIzVvjeeG1WYx550bKlzv/GJEGjSqzb28SB/YfJTIqlDm/rOPl190D42M7NWDG1LU0aVaNeXM20urKWogIV7Wtw9dfLiI9LYOAQH/Wrt7FLbe1IzIqjL92JnLsaCplywWzYtkOatSMOKeWQTdcxaAbrCzJhYv/5Nsfl3Ntjyas27iPkOAgIvMbrhGhlClTkj827KVpoypM+fl3brsxL8ty6cqd1Kwe4TYM3e6qunw2djFpaRkEBvqzau0u7rylbaH21S0tIrmlhTUcu2jnCb5bk0jPy8qy/uBJgkv6ExHsngl9U/MIbmpu/d8HTpzmwYk7+OqWegAcTj2T23/utuPUqeDdaDoXg9pXZlB7K6h+4aYjfLd4Pz1bRLFuTzIhQf5EhrkbiCdPZ3IyPYvIsJJkZmWzaHMSLWtaw3JdGkewfPsxrqhTll2JpziTpZQtU7TsboBB19Rl0DV1LU1rDjDul+1c27Ya67YnEVI6kMiynv/ru9+vJ+XUGV5+wN0TnngsLbf//NUHqFWpaLGsrvji8Tsx8XtOTLQSJ0q3jSV84C2kzplJUKMmZKemkpXkPe8upFtPN+8hQGb8IUq1ak3Kz1MIrF4TKVHyvI1DgPoNK7J/7zEOHjhORGQI82Zv4flRfQq17pkzWQz99yR69GpEp27ek3/OxaBbOjDolg4ALFy0kW/HxXFtz8tZt343ISGlPIy/yIgw63qwbhdNm1RnytSV3Daowzm3k52dzazZvzPu63+dl05fJaZ2CMcOneJ4Qhoh5Ury55IEej3eMPfzkmUCeHhsbO7y+OfW0PGOOkTXDuWWV/KM5d/G/0VgkL/PGYcGC2Mg5hEGHLONw/pA63yfzwMmi8g7qpokIuVsL+JS4CYs7+EgYMk5thMH3C8iY4FyQCzwJO7G6AXRoU0t4pbtpPv1HxMUFMjIodfmftbvjs+ZMvYeAN74YD4z5mwmLf0MHfqOZmDvpjxyb3ve+GABp9Iy+NewyQDERIXy0evXF1lHQIA/Tz7bh0fu/4KsrGz69G9JrdpRfDx6Dpc1rEyHTg3oO6Alzw/5kX7XvEFoWClGvmEZkKFhpRl0e3tuv2k0iNC2fT3adbC8P/c90IX77viEgAB/YiqG88IrRdPWoV09Fv22lW5936JUUCAjh+dlbva9+f3ckjXDh/RlyPCJpKdnEtu2LrFt6+b2mzl7Pdf2aOr2vWGhpbjz1rYMvP1DRCwPYsf29SkqsTVDidt5gms+3UhQgB8v96ye+9mALzfz011nL+nx1sL9/JlwChGhYlgJhve48BISHRqUJ25zEj1eWkZQCX9G3pJ3Y+7/+komP3UFaaezeWjMejIys8lSaF2nLDe2tYbVBrSOYdh3W+g9agWBAcKoQZcVeoiuQE0tKhK39hDdH55BUEl/Rj6YZwD2+88vTHnzGuKTTvHxpE3UrBTKgKesmLqccjbfzNzKglUH8Pf3Iyy4BKMezv+TPz988fid+i2OMm1iqTbpFzQ9nYSXhuV+VvWbSW7ZyyFde3Dg8Qfc1j/y3htEDnmRsjffDqokvDT0gvQEBPjxr6e78Z8Hx5OdrfTs24QatSL4/MM46jWIoV3HOmzZdJBhT/xESnI6S+O288XHi/l60n0smLOFdWv3kXw8jVnTNgAwZEQv6tQ7e6JIQXSIbciiuE10u/pF63rwyq25n/XtP4qpk4cAMPz5Gxny7Leknz5DbPsGxMZax/HXuet46ZUJHD2ayv0PfMxl9Svx+RhrZGHV6h1ER4VTpUrBCUEXynd3j6Bj3RZUCA5n38hpvDBjDF8snX7Rtgfg5+9H13vrMXHE72RnQ+MuMVSoGsyS73cSXSuU2lec+6Hd1zFZzCAXIw7on4iIlASmAJWArVjjVsOBr7DL3IjIHVjGXBbwu6reaZen+QKoABwG7lLVvfnL3IhIqqoGi3VXfB24Bsvb+LKq/lDYMjcFeRCdJDX0wmu0/d2EnPa9aZIyf5zktASv+FX0veMnlc7vZn8xyVq17dydipldn5z/sPjFInSBZ4kYXyCqZNGHoi828vBzTkvwYMzDLZyW4JV7G354YU+URWRnu8bFdq+ttWRDsf5vhcV4EG1U9TSW0Zaf6i59xgJj8623Gys+Mf/33ZlvOdh+VSwj88l8n+8GLrgGosFgMBgMhgvDxCCaLGaDwWAwGAwGQz6MgWgwGAwGg8FgcMMMMRsMBoPBYDC4YJJUjAfRYDAYDAaDwZAPYyAaDAaDwWAwuKDZWmx/F4KIlBORX0Vku/3qdd5JEXndnkZ4i4i8J4WoM2YMRIPBYDAYDIZ/Js8A81S1Dla95mfydxCRNkBboAlWtZRWwDkrvZsYRIPBYDAYDAYX/kFlbvoCHe33Y4GFwNP5+ijWZBwlsGZzCwQSzvXFxoNoMBgMBoPB8M8kSlUPAdivkfk7qOoyYAFwyP6brapbzvXFxoNoMBgMBoPB4EJ2MWYxi8hgYLBL06eq+qnL53OBaC+rFmrOSxGpDVwGVLabfhWRWFWNO9t6xkA0GAwGg8FgcAjbGPz0LJ93LegzEUkQkRhVPSQiMUCil279geWqmmqv8wvQGjirgWiGmA0Gg8FgMBhcyM4uvr8LZBpwh/3+DmCqlz57gQ4iEiAigVgJKuccYjYGosFgMBgMBsM/k1eBbiKyHehmLyMiLUXkM7vPRGAnsAFYB6xT1enn+mIzxGwwGAwGg8Hgwt/g2SsWVDUJ6OKlfTVwr/0+C7i/qN9tPIgGg8FgMBgMBjeMB9FgMBgMBoPBhX+KB/FiYjyIBoPBYDAYDAY3jAfRYDAYDAaDwYViLIPosxgD8Z+G+J7TN/jQLqcleFK5qdMKPAi4rrfTEryiR/c5LcEDrX650xI88C9XzmkJHtSqX8lpCR4c8yvptASvnNJ0pyV4MObhFk5L8OC+0WudluCVez9yWsGlh+9ZGwaDwWAwGAwGRzEeRIPBYDAYDAYXTJKK8SAaDAaDwWAwGPJhPIgGg8FgMBgMLhgPovEgGgwGg8FgMBjyYTyIBoPBYDAYDC4YD6LxIBoMBoPBYDAY8mE8iAaDwWAwGAwuGA+i8SAaDAaDwWAwGPJhPIgGg8FgMBgMLhgPovEgGgwGg8FgMBjyYTyIBoPBYDAYDC4YD6LxIBoMBoPBYDAY8mE8iAaDwWAwGAwuGA+i8SAaDAaDwWAwGPJhPIgGg8FgMBgMLhgPovEgGgwGg8FgMBjyYTyI/4OoKq+8M4e4pTsJCgpk1HO9aFgvxqPfOx8vYOovG0hOSWft/Kdy21f9vpdR785h685E3hrRn6s7X/b36fpwKXEr9xJUMoBRT3akYZ0IT11frGTq3G0kp5xm7fR7ctsPJKQw9M2FHD2RTlhISd54pjPREcGF3/bICSyK20RQUCCvjrydhg2revTbuGkvQ4Z8TfrpM3SIbcjQZ69HRDh+/CSPP/E5Bw4kUalSed59517Cwkpz4sQpnh36DXv3HaZkyUBGvnwbdetW5NChozz1zFiOHEnGT/y44Ya23NG/0dn1vfULi5Zut/Q934+G9St66ttykCEjplj62tRh6L+vQUT4c1s8L7w6g1NpGVSKCefNEQMIDg7iTGYWw16exuath8jMyqZfz6bcf2f7Qu0zD30fLSdu1T7r2P07loZ1Krj1SUvP5F+vzGPvoRT8/YROravy77tbAbBqwyFGfbyCrbuO8taQTlzdvkaht7148SZGvjKB7Gxl4MA23De4h9vnGRlnePrpsWzetI/w8DK8/fY9VKpcHoBPP5nFpEnL8PMThg69gXbtGwDw1VfzmDhxKSJQt04lRo66jZIlA3nyP1+yceMeAgL9adK4OsNfvIXAQP+i76vRi4lbscf6/T3VhYZ1vZznny9n6pyt1u9v5v257aM+WMKKP/Zb+/R0JkePpbFq+n1F0uBN08jvNhK3PoGgEv6MvKc5DauHe/S7761lHD5xmswspWXdcjx3WxP8/YRZqw4yespW/jqUwo/PxdKohue6hWH5b9t597VZZGVn07t/C26/x/1czMjI5KWhk/lzy0HCwkrz0usDialUljNnMnltxAz+3HwQPz/hX09dTYtW1jk0d9ZGxn4WR3aW0ia2Dg893r1Imn5b/CdvvDqV7Kxs+l13JXff19lD03NDvmfLpv2EhZfmtbduo2Klchw8cJQBvV+nWvVIABo3rcqwFwa6rfvYQ19wYH8SE6c+WdRd5cautUnM+2Ibmq006VqRKwdU99pv69IEpr25kdteb0V07dDc9uTD6Xzx2HLa3FCDK/pVuyAtheHz24bSq3FbElOO0filQRd9e4aLg/EgXgREpLqIbLTftxSR9+z3HUWkzcXeftyynezZd5TZEx5gxDM9efH1WV77dWpXlx8/v8ujPSY6lFHP9aZXt4INmvPStXIfew6cYPZXNzHiX7G8+N4S77paV+PH9/t7tL/+yXL6dqvLtE+v56FbL+ftz1cWfttxm9i9J5E5s4bz0ouDGD5ivNd+w1/8nhEv3sKcWcPZvSeRuMWbAfh0zGyuuqoec2a/yFVX1ePTMbMB+PjTWVx2WWWmTx3Ga6/ewSujJgDg7+/PM09dxy8/v8APPzzJd9/FseOvxIL1Ld3O7n1HmTPpUV4a0pvhr/3sXd9rMxgxpDdzJj3K7n1HiVu2A4Chr0zj3w93Zfr3D9K1Y30++3YpALPmbiLjTCbTv3+Qn74ezA+TV7P/4LFC77dcfav2s+dgMrO/uJ4Rj7XjxdFLvfa7a2BjfvlsID990I+1mxKIW7UPgJiIYEb9O5ZenWoVabtZWdm8NOIHPh3zMNNnPMfPP69mx45Dbn0mTlxKWGhpZs95kdvv6Mybb00GYMeOQ8ycuYbpM4Yx5rOHGTFiPFlZ2SQkHOfbbxYyceLTTJ/+HNnZ2cz8eTUAvXq3YuYvLzBt2jDS088wceJvRd1VxK3YY53n39zKiCc68uK7C73263RVdX78cKBH+5CH2jFlzE1MGXMTt/ZrQrf2NYuswUPT+kT2JJxk1qtdePHOpoz4Zr3Xfu882JIpIzoy/eWOHE3JYNaqgwDUqRTC+w+3omXd8uetISsrmzdHzuStDwfx3eSHmDtrI7t2uv8mpk9eS0hoEBNmPMaNt7bmw3fnAjBt0loAvp30IO9+fBvvvzWH7OxsThw/xQfvzOG9T+9g3OSHOJp0ktUr/iqSpldfmczoj+9l0rQnmTXzd3buiHfrM2XSCkJCSzFt1hAG3R7Lf9/O+21WrlKeH356gh9+esLDOJz36wZKly5ZpH3kjews5dcxWxk4rBl3/7c1WxYncGRfqke/jLRM1s7cT0ydUI/P5n+5jRrNz//YFZWvlv3M1e8/XmzbuxioarH9+SrGQLzIqOpqVX3UXuwIXHQDcV7cNvpe0wQRoVmjSiSnppN4JMWjX7NGlYisEOLRXjkmnHq1oxA/+Xt1LdtN3651LV0NokhOPU1i0klPXQ2iiCxfxqN9595jXNW8EgBXNqvIvGW7C7/t+evp1/dKa9vNapCcfIrExBNufRITT5Camk7z5jUREfr1vZJ589a5rN8agH59WzPXbt+54xCtW9cDoFbNaA4cSOLIkWQiI8NyPZTBZYKoWSuahMOexyBXX9xW+vVsaulrXIXkFM9jlngkhdSTp2nepIqlr2dT5i36E4Bde4/QqrnlGWh7ZS3mLLAMWxEhLe0MmZlZpKdnEhjgT3CZot+05i3bQ98utS19l0WSnJpBYtIptz6lggJo3dTyepYI9KdB7fLEH7GOb+XoEOrVLIdI0c6p9et3U7VqBFWqVKBEiQB69ryc+fa+z2H+vPX07Wcdmx49mrN82VZUlfnz1tGz5+WUKBFI5coVqFo1gvXrdwOQlZVFerq1X9LSMoiMDAOgQ4dGiAgiQuMm1UiIL7oxPW/pLvp2q2ef59H2vvJ2nkd7Pc9d+Xn+dq7tXLfIGvIz//d4+rapbGmqVY7kU2dIPJ7u0S+4VCAAmVnKmcxsco5WrYoh1IgpnLe+IDZvPEDlKuWoVLkcgYEBdL26EYsXbnXrs3jBVq7p0wyATt0asHrlX6gqu/46TMsrLY9hufLBBIcE8eemgxzYf4wq1cpTtpy1H1teWZMFczcXWtPGDXupUqU8lauUJ7BEAD16NmPhgk1ufRbO30Tvvi0B6Nq9CSuXbz/nTf3UydN8O3YR997fpdBaCuLQjmTKxpQiPLoU/oF+1G8XxY6VRzz6LfnuL67oV42AEu639e0rDhMeVYoKVc5+rv2dLN7xB0dPJhfb9gwXB2Mg5kNEhorIVhGZKyLfi8h/RGShiLS0P68gIrvt99VFZLGIrLX/PIw/22s4Q0SqA/8HPC4if4hIexHZJSKBdr9QEdmds3whJBxOISYq7ykyOiL0rMZJcZFw5CQxkXkXqegKZUg4cuosa7hTr2Z55izeBcCvS3Zx8tQZjiV73uS8bjvhONHRZfO2HV2WhMTj7n0SjxMdlTd0Fh1VloQEq09SUkquEREZGcbRo9b+rF+/Mr/++gdgGTMHDx4lPsH9e/cfSGLLln00bVipYH2JyUS7HrPIUBISkz37RHrvU7dmJPPirJvtrLmbOJRgtffo0oBSpQJp1/MtOvV5h7tvbUN4WOkCdRSoL+kUMREuxy6iNAlejJ4cklNPs2DFPq5q5jlMXhQSE44THZN33KKiy5KQ4G7YJyQeJ8buExDgT0hIKY4fP0lCwol864aTmHCcqKhw7rq7K106DyO2/RBCQkrRtl0Dt+88cyaLadNW0q59wyJrts7zPGMqOqIMCUcK3lcFcSA+mQPxybRuXvB5U2hNx9OJLlcqT1PZUiQe8/7buffNZbR7bDZlggLo0erCjp8rhxOTiYrOO0nQTAAAIABJREFUO38jIkM5nJBcYJ+AAH/KBAdx4vgpateNYvHCrWRmZnFw/zG2bjlIQkIylauWY8+uIxw6cIzMzCwWL/iTxPjCGyaJCSeIisn7zUdFhXM4wfPBMTo6PFdTcEgpjh+3rlsHDhzlpuve5p47PmTtmjzP5Yfvz+K2OztQqlSJQmspiNSkdELKB+Uuh5QvSerR0259Ev5KITkpnVot3cM+MtKzWDF5N21uKHxIh8EiO7v4/nwVYyC6ICKXAzcBzYEBQKtzrJIIdFPVFsCNwHsFdVTV3cDHwDuq2kxVFwMLgWvtLjcBk1T1zIX8D/bWPFqK6rm5KHh56C6KrKcGt2bV+oP0/7+JrFp/iKgKZQjwL9wXeHviz79PvPc5+/cOvq87ycmn6Nt/JN98u5DLLqtMgH/ez+rkyXQeffRTnn1mIMHBQQV+jzd/hIe+s/R55bm+fDdxJQNu/4STpzIoEWDFza3fdAA/Pz8Wz/w386Y8xhfjlrHvwNGz/1NeBRb+nMrMyubfry7ktr4NqBLjOdxVpM16acu/2YKcOVrA7+DEiVPMn7eeX+eOYFHcKNLSTjNt2gq3fiNGjKdly9q0bFn7bxF9Pj+/mQt20D22Fv7+F36ZLsq5/dl/riLu3e5kZGazfMvhC952ngjPJo9zqIA+vfo1JzIqlHtu+ZR335hF46ZV8Pf3IzS0FE8O7cVzT03kgbu+JLpi+IXvL4/rgvcuFSJC+WXuMMZPeoJ/P9WHZ58aR2pqOlu3HGDf3iN07tr4wnQUEs1WFny5jU531vH47Lfxf9Gyd1VKlDLpBoaiY84ad9oDk1X1FICITDtH/0BgtIg0A7KAoo4FfQY8BUwB7gK8RqKLyGBgMMDHb9/J4Ds6efQZN3E1E6b9DkDjyyrmepAA4g8nE1nhwoaHzpdxUzcyYaY1DNq4XgSHEvM8KfFHThJZvvDerKgKZXh/uJWgcDLtDHOW7CLkLMOl48Yt4kc7hqxxo2rEuwwXxscfIzIizK1/dFRZN+9ffMIxIiMtz0H58iEkJp4gMjKMxMQTlCtnDc0HB5di1MjbAesm3KXrc1S2EyTOnMni0cfG0Lv3FXTv3hxS3OOtxk1YyY9T1lj6GlQi3vWYJSYTGeE+/B8dGUp8ovc+tapH8MX7lo5de46w8LdtAMyYvYH2V9UmMMCf8uWCadG0Chs2H6RKpXIF7rdcfdM2M2GW5ZVsXLcChw67HLvDp4gs5/3YPf/fJVSrGHrWpJzCEhUVTvyhvOOWEH8s15ObQ3RUOIcOHSM6uiyZmVmkpKQRHl6GaI91jxMRGcayZX9SqXL53GPYtVszfv/9L/r0uRKAD0b/zLGjKbz4/uBC6xw3ZQMTfraGJhvXi+JQYl6MWPzhk+ccSvbGzAXbee7R2CKvl6tp3i4mLtoDQKMa4cQfTcvTdCyNiPCCH1hKBvrTuVkU89fG07Zh5HlrcCUiKpQEF+/e4cRkKkSGeO0TGRVGZmYWJ1PTCQ0rhYjw2JNX5/YbfPtnVKlqncPtOtajXUcrzGPKxNX4F/KhESAyKoyEQ3m/+YSE40REuj/UREWFER9/nKjocDIzs0hNSSMsrDQiQokS1i20QcPKVK5Snj27D7Np4z42bz5Az26vkJWVzdGkVO6980M+++rBQutyJbh8EClJed7elKTTBJfLu+5lpGVxZO9Jxj9nxWmePJ7BT6PWMWBIUw5tP8G2ZYks+noHp09mIn4QUMKPFj2rnJeWSwlf9uwVF8aD6Ik3f0QmefvK9ar6OJAANAVaAkUaT1DV34DqItIB8FfVjQX0+1RVW6pqS2/GIcCggS2Z8vV9TPn6PrrE1mXqL+tRVf7YeICQMiW9xhoWB4P6NmLKJwOZ8slAurStztS52yxdmxMIKVOiSDfOYyfSyM62Ds+n3//OdT3qnX3bgzowdfKzTJ38LF27NGHK1BXWtv/YRUhIKQ9DIzIyjDJlSvLHH7tQVaZMXUGXzk0A6Ny5CVOmLgdgytTlue3JyafIyMgEYMKE32jZsjbBwaVQVYYO+4aaNaO5607vcUiDrr+CqeMeYOq4B+jaoT5TZq6z9G3YR0iw5zGLrBBCmdIl+WPDPkvfzHV0ibX2QdJRyyDJzs7moy/iuGmAFTMVExXGitXW/3MqLYN1G/dTs7r7MFSB+69PA6Z82J8pH/any1XVmDpvh6VvSyIhZQK9GvfvfrWalJNnePb/WhdqG+eiceNq7NmTyP79R8jIyGTmzDV0svd9Dp06N2HqFOvYzJ79O61bW/F/nTo3YebMNWRknGH//iPs2ZNIkybViYkpy7p1u0lLy0BVWb5sK7VqRgPWMVyyZDNvvnU3fn6FvzwO6tc4N7GkS7saTP11q32exxf5PAf4a+8xTqScpnnD6CKt56apSw0mj+jI5BEd6dIihqlL91uadh4lpFQgkfkMxJPpmblxiZlZ2Sxan0jNmL/vunFZw4rs35vEwf3HOHMmk7mzNtKug/tvuH3HevwyzQrZWPDrZi6/ogYiQnpaBmmnMgBYuWwn/v5+1KhlGa5Hk6xzPzk5jck/rqJ3/xaF1tSwURX27j3Cgf1JnMnIZPbMP+jYyT2soEOnhkyfaiUxzZ2znlZXWrG4R4+mkpVlWRH79yWxd88RKlcuzw03teHXhc8z89ehfPnNQ1SrXuG8jUOAmNohHDt0iuMJaWSdyebPJQnUbpX3Gy5ZJoCHx8Zy/ydtuf+TtlSsG8qAIU2Jrh3KLa+0zG2/vFcVrhxQ3RiHhkJjPIjuxAFficirWPumN/AJsBu4HFgJuKaqhQH7VTVbRO4AzlUPIwXIP+b2NfA98NIFq7fp0KY2cUt30v36DwkqGcjIYb1yP+t3+ximfG05Kt8YPY8ZczaRln6GDn3eY2CfZjxybywbNh/k4WcmkpySzoIl2xn9WRwzvru/oM0VXtcVVYlbsZfud4wnqGQAI//TMU/X/ROZ8om1a98Ys5wZ83eQdjqTDjd/y8Br6vPI7S1Zse4Q73y+AkRo1TiG5x9pV/htd2jEorhNdOvxAqWCSjBy5G25n/XtP5Kpk58FYPgLN+eWuYlt35DYWOtmMfje7vzric+ZOHEpMRXL8d937gVg5854nn5mLH7+ftSuFc0rL1vfu2btTqZOW0nduhXp238kAE/cH0uHtt6dzB3a1mHR0u10G/AepYICGflc3zx9gz5i6rgHLH1PX2uXuckktk1tYttYw0oz5mzkuwlWVne3TpdxXe/mAAy6vhVDRkyl100foigDejWnfp2iGx0drqhC3Kr9dL97gnXsnsgrT9LvwclM+bA/8YdP8vH4ddSsEsaAh6dY2+/dgOuvqceGrYd5+KW5JKdksGDFXkZ/s5YZn153zu0GBPgz7Lkbufee0WRnZzPguquoU6ci7703nUaNqtG5cxMGDmzD0099RY/uLxAWVpq33rZKI9WpU5Grr2lBr2tfwt/fj+eevwl/fz+aNq1Bj+7NuW7AKPwD/LjssirccKN1Lr04/HsqVizHzTe9CVjexYce6lm0fXVlNeJW7KH7rd8SFBTAyKfyHhD63TeeKWNuAuCNT5YyY9426zy/4SsG9mzAI3deAdjJKZ3q/G2hIR2aRBK3PoEeT8/LLXOTQ//nFzJ5REfSTmfy0H9XkpGZRVY2tL6sAjd2shKffl1ziFfGbeBoSgb/9+5y6lcJ47P/XFUkDQEB/jwxpCePP/ANWdlKr37NqVk7kjEfzKd+w4q071ifXv2bM2LoZK7v9V9CQ0sx4nXrmnDs6Ekef+BbxE+IiAzh+VcG5H7vu6/PYsc2K/P4rsEdqFrIB6AcTU8P7c+Dg8eQna307d+KWrWj+fD9WTRoWIWOnRvS77orGPbM9/S5ehShYaV59c1bAVi7+i8+Gj0bf38//P39GPr8dYSFFz2+91z4+fvR9d56TBzxO9nZ0LhLDBWqBrPk+51E1wql9hWeJZSc5ru7R9CxbgsqBIezb+Q0Xpgxhi+WTndaVpEwHkQQX06xdgIRGQrcDuwB9gObgRnAj0AqMB+4VVWri0gdYBJwClgAPKKqwXZCygxVbSQiHYH/qGovEakLTASy7b6LRSQa2AXEqKp7doMX9OjXvnfAUj0z6pxGKjd1WoInKQWXuXESPbrPaQkeaPXLnZbggRzcdO5OxYzu8b1jd6xF4T14xUmpAGfCbM7Gd1t/cVqCB/eNXuu0BK/oR8uLNZB+Vrl6xXavvfroVh9IEvDEeBDzoaqvAK8AiMhwu+1PwHVca5jdvj1f+xC7fTfQyH6/ECsZBVXdlq8/QDtgYmGMQ4PBYDAYDBcf40E0BqKjiMj7wDVA0cawDAaDwWAwGC4ixkA8C6o6/CJ//yMX8/sNBoPBYDAUHeNBNFnMBoPBYDAYDIZ8GA+iwWAwGAwGgwvGg2g8iAaDwWAwGAyGfBgPosFgMBgMBoMLxoNoPIgGg8FgMBgMhnwYA9FgMBgMBoPB4IYZYjYYDAaDwWBwIdv35iwrdowH0WAwGAwGg8HghvEgGgwGg8FgMLhgklSMB9FgMBgMBoPBkA/jQTQYDAaDwWBwwXgQjQfRYDAYDAaDwZAP40E0GAwGg8FgcMF4EI0H0WAwGAwGg8GQD+NBNBgMBoPBYHDBeBCNB9FgMBgMBoPBkA9RNeXCL1VEZLCqfuq0DleMpsLji7qMpsJhNBUeX9RlNBUOX9RkKDzGg3hpM9hpAV4wmgqPL+oymgqH0VR4fFGX0VQ4fFGToZAYA9FgMBgMBoPB4IYxEA0Gg8FgMBgMbhgD8dLGF2NDjKbC44u6jKbCYTQVHl/UZTQVDl/UZCgkJknFYDAYDAaDweCG8SAaDAaDwWAwGNwwBuIlhIiUc1qD4fwQEX8RedxpHQaDwWC4NDAG4qXFChGZICI9RUScFgMgIqtF5CERKeu0Fl9GVbOAvk7r+CcgIlEi8rmI/GIvNxCRexzWJCJyq4g8by9XFZErnNRkMFwMRMTfaQ2GvwdjIF5a1MUKGr4N2CEiI0WkrsOabgIqAqtEZLyI9HDKeBWRDSKyvqA/JzTl4zcRGS0i7UWkRc6fk4JE5HURCRWRQBGZJyJHRORWJzUBXwGzsc4rgG3AvxxTY/EhcBVws72cAnzgnBwLEeklIuY+cA589Dz3VXaIyBsi0sBpIYYLwySpXKKISCfgW6AMsA54RlWXOajHD+gFfARkA18A/1XVo8WooZr99iH79Rv7dRBwSlVHFJcWb4jIAi/Nqqqdi12MjYj8oarNRKQ/0A94HFigqk0d1LRKVVuJyO+q2txVp4Oa1qpqi3ya1jm5n2wN32IZrpOAL1V1i4NaNgDebkiCdZ43KWZJeQJ87Dz38X0VgvXgfxeWE+oLYLyqJjulyXB+BDgtwFB8iEh54FYsD2IC8AgwDWgGTABqOKSrCdbFpCfWjWoc0A6Yb2srFlR1j62nraq2dfnoGRH5DXDUQFTVTk5uvwAC7deewPeqetQHohdO2ue6AohIa+CEs5I4Yw+95WiKwHoQchRVvVVEQrE8m1+KiAJfYh3LlGKW08t+FeBnrHPKV/C187zXubs4g33ejAHGiEgs8D3wjohMBF5S1R2OCjQUGmMgXlosw/KK9VPV/S7tq0XkYycEicga4DjwOZYX87T90QoRaVvwmheVMiLSTlWX2BrbYHlaHUdErgUaAkE5bQ57NqeLyJ9AGvCgbfikO6gH4AmsB59atmEfAQx0VhLvAZOBSBF5xdYzzFlJFqqaLCKTgFJYQ/H9gSdF5D1Vfb8YdezJeS8ip12XfQCfOs/z7asooJW9uFJVE51RlavHH7gW66G/OvAW1kN/e2AmVqiT4R+AGWK+hBCRG1T1x3xt16vqBAc11VTVv/K11VDVXQ5quhxrWCTMbjoO3K2qa53SBGAb8aWBTsBnWEbGSlV1OgGjLJCsqlkiUgYIUdV4hzUFAPWwvFFbVfWMk3oARKQ+0AVL0zwnh3NzEJE+WDfyWlgPj2NVNVFESgNbVLXaWb/g4ulaq6qOxtfmx0fP8xuAN4CFWOdVe+BJVZ3ooKa/gAXA56q6NN9n76nqo84oMxQVYyBeQni76Dp9IS5A0xpVvdwpTS46QrF+I04PTwIgIutVtYnLazDwk6p2d1BTaSyPXVVVHSwidYB6qjrDAS0Dzva5qv5UXFpykHOUlirOGFtviMjXwGeqGuflsy6qOq8YtbheB8Zhxf7m4uQDmi+d5/l0rQO65XgNbc/mXIdjgHNHX1za2qrqb05pMpwfZoj5EkBErsGKnakkIu+5fBQKZDqkqT7WUGlYvht7KC7Dp8Ws6VZV/VZEnsjXDoCqvu2ELhfS7NdTIlIRSMKhuFEXvgTWAG3s5f1Y8axO3Dh726+Rtp759nInLA9LsRuIWPtGsbw7VYFj9vtwYC/OH79D+Y1DEXlNVZ8uTuPQ5i2X9/HAmzmSsPahY8lY+NZ57opfviHlJJyvTvIekN/p8L6XNoOPYwzES4ODwGqgD9ZFLocUrGw8J6iHFWgdTt6NHSxN9zmiKC/OMMSh7Z+LGSISjjWktBbrpvmZs5Kopao3isjNAKqaJg5F76vqXQAiMgNooKqH7OUYHCopo6o1bA0fA9NUdaa9fA3Q1QlN+egGPJ2v7RovbRednCQsESkFPIiVqKbAYqzqBk7iM+d5PmaJyGysRBCAG7Hi/IodEbkKy4COyPeQHQqY2oj/QMwQ8yWEiASoqiMew4IQkaucLK/zT0VESgJBTg9/i8hSrLi63+wyLrWwsjwdKwItIhtVtZHLsh+w3rXNAU0eYRMislpVWzqk5wEsI6wW4JpVGoJ1LB2r8SciPwLJWMPMYGVYh6vqDQ5q8rnzPAcRuQ5oi+VpjVPVyQ7p6AB0BP4PcE16TAGmq+p2J3QZzh9jIF4CiMiPqnpDQbWznKiZJSJPqerrIvJ+AZocC2QWkbHAY6p63F4uC7ylqnc7pcnW4S3G7gSwwanMRRHphpWN2wCYg3WjulNVFzqhx9Y0GqiD5VVRrJpsO1T1EQc1zcbyhH1ra7oViFXVHg7pCQPKAqOAZ1w+SvGBuEiP+pBO14wUke7AUNzP87tU1Vtt0ksaEanmYxnohvPEGIiXACISo6qHJK8QtBtO/JhFpLeqTheROwrQNLa4NeUgLsWMz9ZW3IjIz1hFjXNuSh2B5VhlI0ao6jcFrHqxdZUHWmN5MJar6hEndLhiG9Pt7UXHvCouesoBLwCxdlMc8KJTxpiIhNrlbbwm0ThpJIrIV8DHqrrcXr4SuENVH3RKk63DV8/z17DiboW8QtmhDmh5V1X/JSLT8f7Q36e4NRkuDGMgGgz5sDMDO6rqMXu5HLBIVRs7rGs6cK+qJtjLUVixWfdiGUHFNoQq55jiz+mSQIazIyIzVLWXiOwiL4kmB1XVmg5JQ0S2YMUo77WbqgJbsAqLq0MjHvNUtcu52oobEdkB9PaRkkmXq+oae6jZA1VdVNyaDBeGSVK5BBCRFLxPywSAQ0+bXp8yc3D4afMtYKlYlf8BrgdecVBPDtVzjEObRKCuWrM6FHedv5yM0yCgJdZ0jQI0AVZgJRgUKyKyRFXbeTnfjVfFc7u97Fens6i9cbXTAnIQkSCs2qMV7FCTHEM6lLy5vp0kwReMQwBVXWO/GkPwfwRjIF4CqGoIgIiMwCof8Q3WhW4QzmXs5pSwGABEY8VmgRWQvtsJQTmo6tdizfDSCWs/DVDVzU5qsllsZ+jmFDa/Doizi/YeL04hLhmn44HBqrrBXm4E/Kc4tbhoame/+lIWes6w/5tn7eUQYs1W9IeqnhSRW7FKkbyrqnvPsepFw8fi1+7Hml2mIlYFiBwDMRmHMuPBLR55tYj8AEwBcmahcqrmZ0HzQwPOxLobLgwzxHwJISIrVPXKc7UVs6Y4VY09V5sTiEgk7lPaOXbTBLDLarhmLC4BJqmDP2IR+UNVm52rrZg1dVXVufna7nA4rjUyfyKRiNRT1a1OabI1rAeaYnl+v8Ga8nKAqnodJrxUEZFHtBinHTwXIvLlWT5WJxLqCopxz8HHDH9DITAG4iWEXarhA2A81pPezcBDqtrmrCteXE1bgGvVnm5PRGoAM1X1Mgc19cEaQq2INYxbDWvasYZOafJVROR74CTu2bnBqnqzg5rigE1YnsxgrFqRp1XVsfmYRWQr8JzaU12KyL+Be1S1gVOabB1r7bItzwMHVPVz8cFp7nwB2zveAPeHxq+dU2QwXFycrrhuKF5uAW4AEuy/6+02J3kcWCgiC0VkIVaG7r+clcRLWNmK2+wYrS6A49NEiUiKiCTbf+kikiUiyQ7LugvLGHsM67htttucpAOwE/gDy8v6nZPGoU1H4DYRmWAbsHUBx2voASkiMgTLsP9ZRPyBQIc1+Rwi8gLWbCDvY4WevI418YCjiEhlEZksIokikiAik0SkssOaWovIKhFJFZEMH7lOGc4DE4N4CaGqu4G+TutwRVVniTWvaX276U9VPX22dYqBM6qaJCJ+IuKnqgtE5DWHNXnE1olIPxw2MlQ1HXjH/vMVygJXYhmJlYFqIiJODsXbZaZmAUOwsnGHqGqqU3pcuBHrIfEeVY0XkapYM/UY3BmINRT/u6reZVcQcHoWI7CmAPwO62EfLEP/S6wZcpxiNFbt0QlYCWy3A7Ud1GM4T4yBeAkgPliUWkQ6q+p88Sz+XEtEHAmyduG4iARj1aobJyKJODRn9dlQ1Ski8sy5e/79iA8WX3dhOfCqqn4h1rRtr2F5gJ0MpfgVOAQ0wjJav7BjbR1J6MlBVeOBt12W9wJm2NSTNFXNFpFMEQnFCj1xrBSQCxGq6hqP+JWIOD0Cg6ruEBF/Vc0CvrTDmwz/MIyBeGmQUwZhtaMq3OkAzMd9HuYcFHDSQOwLpGENfw8CwoARDuoBPGZS8cN6OnfKK/aY/drLoe2fja45CUWqmgY8KiJOJz19oKpT7PfHRaQNljfRUXyp0LKPs1qsedDHYGUzpwIrnZUEwBE7+zxnLuabgSQH9QCcEpESwB8i8jrWg1GZc6xj8EFMkorB8A8hX+ZiJlY5oDH5s2OLUY8/MFtVuzqx/bNhJxrlGIWLVHW6k3ogt7B5K3txpVPHzRVfKrT8T0FEqgOhqrreYSnYIQGjsWZYUmAp1jShjmUM29nMiVixrI9jPWB/qKo7zrqiwecwBuIlhD3Mdb26zzE8Xh2aD9bWMBJ4PZ+mf6vqMKc0GQqPiEwDblPVE05ryUFEXsUyxMbZTTcDq1XVMY+diNyAFdu3EMtL1x54UlUnnm29YtD1m6q2dVLDPwURqYRV0SB35E1V45xTZDBcXIyBeAlRQM06R+cY9rZ9U2bDO/ZwzctYw9+zsILm/6Wq3551xYur6UesjO9fscrdAM7EtbpoWg80U9Vse9kfK7nAsbhIsaZv7JbjNRSRCGCuqjZ1SpOt479YheodL7Tsy9hJajdiZeln2c3q8IxPiMhYLI+h6wP2W07UQXTRlDN9oxvq4PSNhvPDxCBeWmSJSNWc+Cx7KMDpJwR/ESmZk7lsJxWUdFKQWDOTpLkYGH5AkKqeclIX0F1VnxKR/sB+rMzFBeTNQuMEP9t/vkY4cNR+H+akEBu/fEPKSfhGmbFQ4BTQ3aXN6RhgX6QfUM8HKizkp0mOcQigqsdExLEHfpuWLu+DsK5T5RzSYrgAjIF4aTEUWCIiOXNlxgKDHdQDlnEzz46vU+BuwLEZL2zmAV2xAtHBmot1Dg5mwdrk1KfrCXyv1hzMTupBVcfaAel17aatqlrc80LnZxTwu4gswBrOjcX5hJBZIjKbvGSCG4GZDuoBQFWdrln5T+EvrN+frxmIfiJSVlWPAYhIORy+r6tq/iSZd0VkCfC8E3oM548ZYr7EEJEKWEOCAixT1SMOS0JErsEqRi3AHFWd7bAen5s+ztbwKpYnIw2r/mE4MMPhqRI7Yhn0u7GOXxXgDqdjs0QkBisOUYAVdjkXRxER12kS41R1ssOSEJG6wEdAlKo2EpEmQB9VfdlhaT6BS2mwSlghHfNwH4p3LJQCQERux3r4yYllvR54RVW/KXiti67JNTwop9rCA06HUxiKjjEQLzHsGJU6uE8XZQKtXRCR34BHVHWtvXw5MFpVr3JWWe7xS1bVLHsoPMRJ40dE1gC35MwpbBsc36vq5Q5oqa+qf+a7QeWSczwNedijCU8Cn+TEAovIRlVt5Kwy30BE7jjb5+rg/N45iEgDoDPWg8c8Vd3ssJ4F5IUu5VRbeFNVtzkmynBeGAPxEkJE7sWqX1cZaxqy1lhexM4OamqNNX3VZUAJwB846WQdNhFphTVf9UG7KQa4UVXXOKXJVxGR9fmTP7y1FZOWMap6n32Dyo86cZ6LyBJVbSciKbjH+/pEvUERWaWqrVyTxXzBW+6L2KEU9bGO41ZVzXBQy1lj+lT16Nk+v5iINc+4Yp3jkC/OXVXf9ljJ4JOYGMRLi8ewht2Wq2onEakPvOiwJp+blklVV9n7ph7WRe5PH4ir81VWi8jnQM6Q1iCsQsLFjqreZ792cmL73lDVdvZryLn6OsQREamFfRMXkYFYhY0NLohIT+ATrOkbBaghIver6i8OSVqDdyNM7PdOZgxfjnWfmWrr6Y01K9U+BzUZzgNjIF5apKtquohgZw7/KSL1nBblK9MyScHT/9UR56f/81UeAB4CHsWOrQM+dEKIl+PmhhPHz5c9PTYPAZ8C9UXkALALy8g3uPM20Cmn2LNtVP8MOGIgqmqNnPf2OeYWNuQwFYAWqpoCICLDgQmqeq+jqgxFxhiIlxb77emipgC/isgx8oZRncKXpmXyyen/Coqpy8HJ2Dq77MfbuMzn6yA5xy0SK+N8vr3cCatAtRPHz9XTUxU4Zr8PB/YCNQpe9eIhIk+4LM7EKpfkh1XL8jp843j6Eon5ZgL5C2u2EEcpIGxoKVbSn1NUBVzeN8nQAAAOzUlEQVSH3zOA6s5IMVwIxkC8hFDV/vbb4XacVhhWwWUnuQ3rxvQw1rRMVbBuUMWOqr5g1zz8RVV/dEJDAbx1ls8UK0C9WBGRDZylhqYTMYg5JVtEZAbQQFUP2csxwAfFrcfWVMPW8DEwTVVn2svXYJVScoqcIe96uA8H3oblBTa4s0lEZgI/Yp331wOrcrzWDo4u+GLY0DfAShGZjLWv+uN86TLDeWCSVAyGfIhInKrGnrvnpYtdZL1AHJ4L1i0L1zb61zuZmSsia/JndovIalVtWdA6xYGIzAGucxkODMEaDrzaSV2+hrjPg54fdWrmEpckoz+AK1X1tC8kGdmjHu3txThV/d1JPYbzw3gQDQZPfhWR/wA/4D59nNPxYohII6AB7mWKvi5uHU4agIVgoUtRasVKgvKW2VycHBGRYViF4RW4FWs2Facxw4GFwIcLivti2FBO2IspK/UPx3gQDYZ82HOJ5kednktURF4AOmIZiDOBa4AlqjrQQU0DgNew4v4E3ynf0h9rBhXwgaLUdiLBC1iaFGsYd4TTDx0iMhS4AXAdDvxBVUc5qctXcCmU7RWnC2W7IiIdsMOGnCzBY/jfwRiIBp9ARMqo6slz97z4iEiQqqafq624seP+mgK/q2pTEYkCPlNVb0k1xaVpB9BbVbc4pcFwYZjhwIL5JxTKNhguFmaI+RLAS5FeNxwuSt0G+AwIBqqKSFPgflV90ClNWFmA+TOHvbUVN2mqmi0imSISipVF6ahXE0gwxuE/GzMcWDDGADRcyhgD8RIgp0iviIwA4rGyzASr3pnTBXzfAXoA0wBUdZ2IOJIgIiLRWHOulhKR5uQVoQ0FSjuhKR+r7XijMVjlU1KBlU4Icak5uFpEfsCKgXKdo9bUjDT8z5Bv+rhcnJyFymC42Jgh5ksIEVmhqleeq80JTfmm+lrnxMTu9nDSnVgzuqx2+SgF+MqXjB4RqQ6Equp6h7afk9XpOptDDk5mdfoDY1X1Vie27w1b06Oq+o7TWgznhz0few5BWKW4MlX1KYckGQwXHeNBvLTIEpFBWPMMK3AzkOWsJPbZw8xqF8x+FHBkyNIeThorItep6iQnNJwNb55VEYlV1WKvW+dSc3As8JiqHreXy/5/e/cfa3dd33H8+bqVDkFAcBUnIisg6qIF5Ic4qlmUmYFGfmxUZVAEIrHDKCzib4bij2lFApEN6hzdKAa1TdzQKUwWQCmboKTWiIkj0iYUFSliq6DY5uUf3++p51zvPReNve/vPef1SE7uOd9zk/vKbXPv+36+n8/7zfC+jTs713ZJCyTN78pG/TbTiTSr5TEHTTGHfa2k20rCRMySFIjj5TTgivZhYG17rdKbaPLsB9wP/DfN+K9ZJ+l029cBfzpp0gTQiSHzF/Y93xU4muZWc+VtrkW94hDA9k/a2/OVNtD8Ar+BwTZFlf9+ayVdyW+3Tsrevzlg0sjECZq7DM8oihMxK1IgjhHbG4ATq3P0s/0Q3Zn92hvx95TSFNOYfFpZ0v7A8qI4PROS9rb9kzbTPtT/XHmgfUxQv8e258/bj5f0XSuZghO/l/6Rib+i+SPknMpAETtb9iCOEUmHAFcB+9p+gaRFwGtsf7Agy5zpL9ZVkkQzIeSFhRmWAu8C1tD8ey4BPmR7VVWmiD80SUto+gtukXQRTUeDD2QFOEZZCsQx0u6ZuRBY0XcgZGAs2Sxm6Wx/sen21VUdvOjL1V9UTwCHARuqD2RI+jOalTAB/2P7nuI8nTtxKukfprpu+5Kprke3SFpve5GkxcCHafbZvrvygF/EzlZ9Kyhm126272wWnnbYVhGk4/3FurivDgZPVm8Drre9tipMT1sQlhaFk7yt7/mOE6dFWXr6m8DvCryaosNY8XvpHeZ7FXC17f+U9L7CPBE7XQrE8fKQpINoV1ck/Q3wg4ogki63fb6kLzD1as9rCmL1dHFfXdeL6s7o4olT2wMnuyVdStv7M+aETZJWAMcBH5X0RzSr+BEjq/yXXsyq84BPAs+TtAm4D6i6Pdnbo3Zp0dcf5uPAHZIG9tVVhWlH7A3br7loFuN03hQnTo+geydOd6N+Ck48cUuAvwIutf2IpD9hsKtAxMjJHsQxJGl3YML21g5keavtK2a6Ntu6tK9O0gHt0177n15x/bfAo9nHNkjSffzmxOk2mj+ELrF9e2Gm/iJ/HrCgzXRlVaaIiGFSII4RSauAN9v+afv6AOAa268ozHS37RdNurZjqkr8hqS1to+d6dq4knSq7dWSDrT9/eo8AJIW2r6vr8iHpmj9ke3qfZEREdPKHorxcjvwdUknSHoj8BXg8oogkl7f7j9cKOmGvsctwOaKTHPA7u0pSgDaCTS7D/n8cfOu9uOa0hSDelmusb2xfWxKcRgRXZc9iGPE9gpJ3wFuAR4CDrf9w6I4d9AckPljBkezbQVK5gvPAecA10jaq339CFDaeqdjNrd/YCxsp6gMKDr4NCHpYuCQjk7niYiYUgrEMSLpDOAiYCmwCPiSpLNsf2u2s9jeCGwEXjLbX3uuak/nHippT5rtIT+tztQxr6JpYLyKwnnQk7wOOInmZ21XprpERMwoexDHiKT/AM61/WD7+mjgk7YPK8x0DPAJ4PnAfJoN/D+3vWdVpq7pzYieagUKsgo1maQFtn9cnaOfpONtf7k6R0TEE5UVxDFi+6RJr+9si8RKV9KssqwGjqRZ3Ty4NFH39PYZZgVqiP6empOawQM1t5j7i3pJz5/8for7iOiqFIhjQNLbbS8fMv+4dO6x7XslzbO9HVgp6Y7KPF1je0X78f3VWTquiz01U9RHxJyUAnE89Hr4fWPoZ9V4VNJ8YJ2k5TQHV3Iydwrt9+eDwGPAjcChwPm2rysN1hG2S6elTCVFfUTMVdmDOAYkrbJ9RhcaUE/W9od7ENgFuADYC/hn2/eWBusgSetsHybpZJqDDxcAt9g+tDhap/Q1yh5gu2xyiaSVTJ0pp9AjopOygjgejmgLsbMlXUszYWIH2w/XxNpxmhmaVbGstgy3S/vxBOB62w9PtdcuOLLv+a7AqcA+03zubPli3/NdgZOBB4qyRETMKCuIY0DSW4BlNLNfNzFYILpiZSXzhX93kj5Cs3L4GHA08FTgi7ZfXBpsDpB0u+3FM3/m7JA0Adxs++XVWSIippICcYxIusr2suocMDBfeEp9K4vRR9LewBbb29uZ2nsUNjvvJEn9oxsnaFYUl3XpVryk5wL/ZTsn9iOik3KLeYx0pTiEwQJQ0r7AUe3LO3t9GmOQpN2A84BnA+cCzwSey+Dtyxhskr0N2AAsqYnSkLSVwRXzHwLvKIoTETGjrCBGKUlLgI8Bt9Lc+n4pcKHtLs3T7QRJnwW+CSy1/QJJTwb+t7LReUREjKaJ6gAx9t4DHGX7TNtLafbWXVScqasOsr0c+BWA7ceYdOAoQNJbJe2pxqck3S3plcWZjm23BCDpdEmXzbTNIiKiUgrEqDYx6ZbyZvL/cjqPt6uGvWkhBwG/rI3USWfb3gK8Eng6cBbwkdpIXEXT8/NQ4O00c8ivrY0UETG97EGMajdKugm4vn39WuBLhXm67GKaBtn7S/o0cCzwhtJE3dRbVT0BWGn7W6rvB7TNtiWdCFxh+18lnVmcKSJiWtmDGOUknQIspvnF/lXbny+O1DltgfMs4FHgGJrv1f/Zfqg0WAe1Tan3AxbSTJuZB9xq+4jCTLfRFPdnAS8Dfgyss/3CqkwREcOkQIxSki4AVtu+vzpL10n6ZmWRM1e0PQYPA75v+xFJTwP2s72+MNMzgNOAu2x/TdKzgb+wndvMEdFJKRCjlKSLaVqQPAx8Blhj+0e1qbpJ0j8B/2b7ruosEREx2lIgRidIWkSz//CvgfttH1ccqXMk3QMcQnPA4ec0t5mdqTMREfGHlkMq0RUP0jQP3kxz8jR+2/HVASIiYjxkBTFKSVpGs3K4AFgDfNb2PbWpYq6TtBh4ju2VkhYAT7F9X3WuiIi5IiuIUe0A4Hzb66qDxGho97UeSTOGcCWwC3AdTVug2c7ybQZH7O14i2wPiIgOywpiRIwUSeuAw4G7bR/eXltfUYzNNC2lfyZ5RESXZAUxIkbN421T6t7Emd2rgqQAjIi5KiPNImLUfE7SCuCpkt4I3Az8S2UgScdIukvSzyQ9Lmm7pC2VmSIihskt5ogYOZL+kmYWs4CbbH+lOM83gNcBq2n2Ry4FDrb9nspcERHTyS3miBhF36M5BHKzpN0k7WF7a2Ug2/dKmmd7O7BS0h2VeSIihkmBGBEjpb2tfC6wD3AQzVzmq4FXFMZ6VNJ8YJ2k5cAPgLK9kRERM8kexIgYNefRtLTZAmD7/6lvvn4Gzc/bN9NMwdkfOKU0UUTEECkQI2LU/NL2470Xkp7E1L0IZ9NJtn9he4vt99v+e+DVxZkiIqaVAjEiRs1tkt4NPLk9rLIa+EJxpjOnuPaG2Q4REfFE5RRzRIwUSRPAOfSdYgY+5YIfdpJeD5wGLAa+1vfWnsA228fNdqaIiCciBWJEjAxJ84B/t316dRbYMUllIfCPwDv73toKrLe9rSRYRMQMcoo5IkaG7e2SFkia378PsTDPRmAj8BJJ+wJHtW99N8VhRHRZCsSIGDUbgLWSbqA5MQyA7cuqAkk6FbgUuJXmtvcnJF1oe01VpoiIYVIgRsSoeaB9TAB7FGfpeS9wlO0HASQtoBkBmAIxIjopBWJEjARJq2yfATxi+4rqPJNM9IrD1mbSRSIiOiwFYkSMiiPaQyFnS7qW5lbuDrYfrokFwI2SbgKub1+/FvhyYZ6IiKFyijkiRoKktwDLgAOBTQwWiLZ9YEmwlqRTaNrdCPiq7c9X5omIGCYFYkSMFElX2V5WnaOfpI/afsdM1yIiuiIFYkTETibpbtsvmnRtve1FVZkiIobJHsSIiJ1E0jLg74ADJa3ve2sPYG1NqoiImWUFMSJiJ5G0F7A3U0xSKT40ExExVArEiIiIiBiQPlwRERERMSAFYkREREQMSIEYEREREQNSIEZERETEgBSIERERETHg1ywlfmwXkC1YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,10))\n",
    "corr = wine_data.cor().as_data_frame()\n",
    "corr.index = wine_data.columns\n",
    "sns.heatmap(corr, annot = True, cmap='RdYlGn', vmin=-1, vmax=1)\n",
    "plt.title(\"Correlation Heatmap\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling with H2O\n",
    "Let us now build a regression model to predict the Quality of the wine. There a lot of [Algorithms](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science.html) available in the H2O module both for Classification as well as Regression problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data into Test and Training sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3932, 12) (966, 12)\n"
     ]
    }
   ],
   "source": [
    "wine_split = wine_data.split_frame(ratios = [0.8], seed = 1234)\n",
    "wine_train = wine_split[0] # using 80% for training\n",
    "wine_test = wine_split[1] #rest 20% for testing\n",
    "print(wine_train.shape, wine_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Predictor Variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fixed acidity',\n",
       " 'volatile acidity',\n",
       " 'citric acid',\n",
       " 'residual sugar',\n",
       " 'chlorides',\n",
       " 'free sulfur dioxide',\n",
       " 'total sulfur dioxide',\n",
       " 'density',\n",
       " 'pH',\n",
       " 'sulphates',\n",
       " 'alcohol']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = list(wine_data.columns) \n",
    "predictors.remove('quality')  # Since we need to predict quality\n",
    "predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Linear Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall build a Generalized Linear Model (GLM) with default settings. Generalized Linear Models (GLM) estimate regression models for outcomes following exponential distributions. In addition to the Gaussian (i.e. normal) distribution, these include Poisson, binomial, and gamma distributions. You can read more about GLM in the [documentation](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/glm.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2OGeneralizedLinearEstimator :  Generalized Linear Modeling\n",
      "Model Key:  glm_default\n",
      "\n",
      "\n",
      "ModelMetricsRegressionGLM: glm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.566326060043901\n",
      "RMSE: 0.7525463839816792\n",
      "MAE: 0.5855739117180468\n",
      "RMSLE: 0.11135798916908833\n",
      "R^2: 0.2851690977880197\n",
      "Mean Residual Deviance: 0.566326060043901\n",
      "Null degrees of freedom: 3931\n",
      "Residual degrees of freedom: 3920\n",
      "Null deviance: 3115.13402848424\n",
      "Residual deviance: 2226.7940680926185\n",
      "AIC: 8948.855269434122\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>iterations</b></td>\n",
       "<td><b>negative_log_likelihood</b></td>\n",
       "<td><b>objective</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2019-04-20 12:25:35</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>0</td>\n",
       "<td>3115.1340285</td>\n",
       "<td>0.7922518</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    iterations    negative_log_likelihood    objective\n",
       "--  -------------------  ----------  ------------  -------------------------  -----------\n",
       "    2019-04-20 12:25:35  0.000 sec   0             3115.13                    0.792252"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the function for GLM\n",
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
    "# Set up GLM for regression\n",
    "glm = H2OGeneralizedLinearEstimator(family = 'gaussian', model_id = 'glm_default')\n",
    "# Use .train() to build the model\n",
    "glm.train(x = predictors, \n",
    "                  y = 'quality', \n",
    "                  training_frame = wine_train)\n",
    "print(glm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's check the model's performance on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsRegressionGLM: glm\n",
      "** Reported on test data. **\n",
      "\n",
      "MSE: 0.5546397919709453\n",
      "RMSE: 0.7447414262486982\n",
      "MAE: 0.5795791157106455\n",
      "RMSLE: 0.1107966197145175\n",
      "R^2: 0.26184927981796025\n",
      "Mean Residual Deviance: 0.5546397919709453\n",
      "Null degrees of freedom: 965\n",
      "Residual degrees of freedom: 954\n",
      "Null deviance: 725.8587305402418\n",
      "Residual deviance: 535.7820390439331\n",
      "AIC: 2197.993684313266\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm.model_performance(wine_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">  5.76109</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  5.76721</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  5.64325</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  5.85764</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  5.77967</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = glm.predict(wine_test)\n",
    "predictions.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## H2OAutoML: Automatic Machine Learning\n",
    "\n",
    "Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML makes machine learning available in a true sense, even to people with no major expertise in this field. H2O's AutoML tends to automate the training and the tuning part of the models.\n",
    "![](https://cdn-images-1.medium.com/max/800/1*st_Rwr7WbikMvS9wTprSWw.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the AutoML Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.automl import H2OAutoML\n",
    "aml = H2OAutoML(max_models = 20, max_runtime_secs=100, seed = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here AutoML will run for 10 base models for 100 seconds. The default runtime is 1 Hour."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "aml.train(x=predictors, y='quality', training_frame=wine_train, validation_frame=wine_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leaderboard\n",
    "Now let us look at the automl leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th><th style=\"text-align: right;\">     mae</th><th style=\"text-align: right;\">    rmsle</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>StackedEnsemble_AllModels_AutoML_20190420_122903   </td><td style=\"text-align: right;\">                0.392704</td><td style=\"text-align: right;\">0.626661</td><td style=\"text-align: right;\">0.392704</td><td style=\"text-align: right;\">0.454921</td><td style=\"text-align: right;\">0.0938982</td></tr>\n",
       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20190420_122903</td><td style=\"text-align: right;\">                0.392844</td><td style=\"text-align: right;\">0.626773</td><td style=\"text-align: right;\">0.392844</td><td style=\"text-align: right;\">0.454849</td><td style=\"text-align: right;\">0.0939121</td></tr>\n",
       "<tr><td>XRT_1_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.404961</td><td style=\"text-align: right;\">0.636366</td><td style=\"text-align: right;\">0.404961</td><td style=\"text-align: right;\">0.463282</td><td style=\"text-align: right;\">0.0953964</td></tr>\n",
       "<tr><td>DRF_1_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.413018</td><td style=\"text-align: right;\">0.642664</td><td style=\"text-align: right;\">0.413018</td><td style=\"text-align: right;\">0.466174</td><td style=\"text-align: right;\">0.0963974</td></tr>\n",
       "<tr><td>GBM_4_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.417493</td><td style=\"text-align: right;\">0.646137</td><td style=\"text-align: right;\">0.417493</td><td style=\"text-align: right;\">0.486555</td><td style=\"text-align: right;\">0.0966819</td></tr>\n",
       "<tr><td>GBM_3_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.426668</td><td style=\"text-align: right;\">0.653198</td><td style=\"text-align: right;\">0.426668</td><td style=\"text-align: right;\">0.499098</td><td style=\"text-align: right;\">0.097667 </td></tr>\n",
       "<tr><td>GBM_1_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.435121</td><td style=\"text-align: right;\">0.659637</td><td style=\"text-align: right;\">0.435121</td><td style=\"text-align: right;\">0.505029</td><td style=\"text-align: right;\">0.0984655</td></tr>\n",
       "<tr><td>GBM_2_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.440507</td><td style=\"text-align: right;\">0.663707</td><td style=\"text-align: right;\">0.440507</td><td style=\"text-align: right;\">0.512223</td><td style=\"text-align: right;\">0.0990434</td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_20190420_122903_model_1          </td><td style=\"text-align: right;\">                0.450951</td><td style=\"text-align: right;\">0.671529</td><td style=\"text-align: right;\">0.450951</td><td style=\"text-align: right;\">0.525711</td><td style=\"text-align: right;\">0.100326 </td></tr>\n",
       "<tr><td>GBM_5_AutoML_20190420_122903                       </td><td style=\"text-align: right;\">                0.473033</td><td style=\"text-align: right;\">0.687774</td><td style=\"text-align: right;\">0.473033</td><td style=\"text-align: right;\">0.533694</td><td style=\"text-align: right;\">0.102412 </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(aml.leaderboard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The leaderboard displays the top 10 models built by AutoML with their parameters. The best model is placed on the top is a Stacked Ensemble.\n",
    "The leader model is stored as ```aml.leader```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OStackedEnsembleEstimator :  Stacked Ensemble\n",
      "Model Key:  StackedEnsemble_AllModels_AutoML_20190420_122903\n",
      "No model summary for this model\n",
      "\n",
      "\n",
      "ModelMetricsRegressionGLM: stackedensemble\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.0692588342693806\n",
      "RMSE: 0.26317073216712494\n",
      "MAE: 0.1895322891944602\n",
      "RMSLE: 0.039487473668358815\n",
      "R^2: 0.9125797690060501\n",
      "Mean Residual Deviance: 0.0692588342693806\n",
      "Null degrees of freedom: 3931\n",
      "Residual degrees of freedom: 3924\n",
      "Null deviance: 3115.13402848424\n",
      "Residual deviance: 272.32573634720455\n",
      "AIC: 678.4678504783503\n",
      "\n",
      "ModelMetricsRegressionGLM: stackedensemble\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.3701741695966913\n",
      "RMSE: 0.6084194027122174\n",
      "MAE: 0.43325352090210517\n",
      "RMSLE: 0.09129212114028859\n",
      "R^2: 0.507348131460968\n",
      "Mean Residual Deviance: 0.3701741695966913\n",
      "Null degrees of freedom: 965\n",
      "Residual degrees of freedom: 958\n",
      "Null deviance: 725.8587305402418\n",
      "Residual deviance: 357.5882478304038\n",
      "AIC: 1799.39616697501\n",
      "\n",
      "ModelMetricsRegressionGLM: stackedensemble\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 0.3927036094767366\n",
      "RMSE: 0.6266606812915078\n",
      "MAE: 0.4549213744694069\n",
      "RMSLE: 0.09389823464709215\n",
      "R^2: 0.504319692718368\n",
      "Mean Residual Deviance: 0.3927036094767366\n",
      "Null degrees of freedom: 3931\n",
      "Residual degrees of freedom: 3922\n",
      "Null deviance: 3116.711515843068\n",
      "Residual deviance: 1544.1105924625283\n",
      "AIC: 7505.291729670382\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contribution of Individual Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\parul\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metalearner = h2o.get_model(aml.leader.metalearner()['name'])\n",
    "metalearner.std_coef_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XRT( Extremely Randomized Trees) has the maximum contribution followed by Distributed Random Forests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stackedensemble prediction progress: |████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "preds = aml.leader.predict(wine_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above is the quickest way to get started, however, to learn more about H2O AutoML it is worth taking a look at the in-depth [AutoML tutorial](https://github.com/h2oai/h2o-tutorials/tree/master/h2o-world-2017/automl) (available in R and Python)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shutting Down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WARNING] in <ipython-input-35-1edf85295eae> line 1:\n",
      "    >>> h2o.shutdown()\n",
      "        ^^^^ Deprecated, use ``h2o.cluster().shutdown()``.\n",
      "H2O session _sid_a3dd closed.\n"
     ]
    }
   ],
   "source": [
    "h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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