{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agents\n",
    "\n",
    "AutoGen AgentChat provides a set of preset Agents, each with variations in how an agent might respond to messages.\n",
    "All agents share the following attributes and methods:\n",
    "\n",
    "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.name`: The unique name of the agent.\n",
    "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.description`: The description of the agent in text.\n",
    "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: Send the agent a sequence of {py:class}`~autogen_agentchat.messages.ChatMessage` get a {py:class}`~autogen_agentchat.base.Response`. **It is important to note that agents are expected to be stateful and this method is expected to be called with new messages, not the complete history**.\n",
    "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`: Same as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` but returns an iterator of {py:class}`~autogen_agentchat.messages.AgentEvent` or {py:class}`~autogen_agentchat.messages.ChatMessage` followed by a {py:class}`~autogen_agentchat.base.Response` as the last item.\n",
    "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: Reset the agent to its initial state.\n",
    "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`: convenience methods that call {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` respectively but offer the same interface as [Teams](./teams.ipynb).\n",
    "\n",
    "See {py:mod}`autogen_agentchat.messages` for more information on AgentChat message types.\n",
    "\n",
    "\n",
    "## Assistant Agent\n",
    "\n",
    "{py:class}`~autogen_agentchat.agents.AssistantAgent` is a built-in agent that\n",
    "uses a language model and has the ability to use tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogen_agentchat.agents import AssistantAgent\n",
    "from autogen_agentchat.messages import TextMessage\n",
    "from autogen_agentchat.ui import Console\n",
    "from autogen_core import CancellationToken\n",
    "from autogen_ext.models.openai import OpenAIChatCompletionClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a tool that searches the web for information.\n",
    "async def web_search(query: str) -> str:\n",
    "    \"\"\"Find information on the web\"\"\"\n",
    "    return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
    "\n",
    "\n",
    "# Create an agent that uses the OpenAI GPT-4o model.\n",
    "model_client = OpenAIChatCompletionClient(\n",
    "    model=\"gpt-4o\",\n",
    "    # api_key=\"YOUR_API_KEY\",\n",
    ")\n",
    "agent = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    model_client=model_client,\n",
    "    tools=[web_search],\n",
    "    system_message=\"Use tools to solve tasks.\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Getting Responses\n",
    "\n",
    "We can use the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method to get the agent response to a given message.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=598, completion_tokens=16), content=[FunctionCall(id='call_9UWYM1CgE3ZbnJcSJavNDB79', arguments='{\"query\":\"AutoGen\"}', name='web_search')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_9UWYM1CgE3ZbnJcSJavNDB79', is_error=False)], type='ToolCallExecutionEvent')]\n",
      "source='assistant' models_usage=None content='AutoGen is a programming framework for building multi-agent applications.' type='ToolCallSummaryMessage'\n"
     ]
    }
   ],
   "source": [
    "async def assistant_run() -> None:\n",
    "    response = await agent.on_messages(\n",
    "        [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
    "        cancellation_token=CancellationToken(),\n",
    "    )\n",
    "    print(response.inner_messages)\n",
    "    print(response.chat_message)\n",
    "\n",
    "\n",
    "# Use asyncio.run(assistant_run()) when running in a script.\n",
    "await assistant_run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The call to the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method\n",
    "returns a {py:class}`~autogen_agentchat.base.Response`\n",
    "that contains the agent's final response in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute,\n",
    "as well as a list of inner messages in the {py:attr}`~autogen_agentchat.base.Response.inner_messages` attribute,\n",
    "which stores the agent's \"thought process\" that led to the final response.\n",
    "\n",
    "```{note}\n",
    "It is important to note that {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`\n",
    "will update the internal state of the agent -- it will add the messages to the agent's\n",
    "history. So you should call this method with new messages.\n",
    "**You should not repeatedly call this method with the same messages or the complete history.**\n",
    "```\n",
    "\n",
    "```{note}\n",
    "Unlike in v0.2 AgentChat, the tools are executed by the same agent directly within\n",
    "the same call to {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`.\n",
    "By default, the agent will return the result of the tool call as the final response.\n",
    "```\n",
    "\n",
    "You can also call the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method, which is a convenience method that calls {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`. \n",
    "It follows the same interface as [Teams](./teams.ipynb) and returns a {py:class}`~autogen_agentchat.base.TaskResult` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Modal Input\n",
    "\n",
    "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can handle multi-modal input\n",
    "by providing the input as a {py:class}`~autogen_agentchat.messages.MultiModalMessage`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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6KjR6w067R6OI4piWO6//HD0h8LLP8Ej/VAAOYkzfzTafOzZfvBpP1b/MP3jSjEkIlzXpRBkAVgcQ7yKUOy7qR+MBr3AjzAM00fag3sPB4MRSdCf+MSr+VxxZWlVLVf7zdZXvvK/X7t6YzAYvfD8K3Gc+I63s7ndODoulSqnTp2BLN8P7m7ekWX56aef5Bl2f/9QlcUoCrwwKZVKzz3/7MzMVLtz3O932p1jTR+SJKrVymfOnMlq62h1dfmtt96iWcheoiQBU0nizYePOI7jOQp2dhqVSoXxUINcLldIUg6RQqc35DjBdT2e5y3PCIJgMLBPnlpPkghLoOg6Kc0HY4tncNd1JaWCiFgQOAjeEFwswzAcx+E4zrKNSqXS7XZnhZyiKJ63VyxRiqKMRqOvf/1rf/2v/1WGYTqdjmGNHddcURd4lsMx6IXQNF2pVGzbun796ur6WV6Y+BsUx36SRsPhoHG06/uumpOiOKBpmmGYfr9//sLZyxc/p2kGL0ieFxhGPwgChmEuXb44yd8IgkiSyHXtNI1xNGmUJCwLvROCICiSpkgoceI4nkJlDWNZnqKY7MukcYwgCZJleAIRrCg29vf39vb7/X6/NxyPdRwnZEkpFSqCIMiyyrDsf/FG+B+4fqi18O+qqf57ruTfnUEmSZI1owksq4UiHI+DwDZNLIrHI304HO3u7m5v7pXL5WefeYHjhPm5xUK56uj2l37n999449uO46yvn3zp5U9dvXYTx/Fms4kQeuqpZ6IoGgwGlUpFs+16vVqv19M0Pjo6SNNoZWUhTdObd27Xp2rj8bDVPhgOexgeFUu5hcXpQiFXqZYIgrh3715/kM7Ozi4vLwuSHEaRbbsbG1uNRqN13Hj55RerJ1cJMun3u8NRF4uJhflVWZabjYHreCTNFAtlqDFEEY7j0GPgKJ6XItft9/tYjKlKPgoTy7I4QTUMy/HcJPUtyzDH2srKWiVfvHjpPMdx+wdbpZwaBB5FUdjjpjZsfFHkYzMI/WAwGAShkxIRhkUURbEM3+l0i8WiKMgCx5tjE4uTbruTK1RC37Hs0c7uBsMiHE9xlM5NTxWKs9VquVwpEqokh261WpUkiSRJSZI03cSwx328D/L0ySUCq0tT8HVpmsQxhJRRFC3MzX+QsmJxnGZtRWgwdto9nudlWaVYNvL9wWDQarVHo9Gtm3eyTiYlCIKqFufmlkRB4hl4JlTgggBiVwz7825+H73z4Y8+3veCJBDRBA4ZOoJCNo4l6WgwPNjdI1Js2B/s7u5HUfTE5aer1WqpWJufX8Q44Ru//8U//MNvhGG4vnYyn883m60vfvErfhBIkjI7PSeKouNalmWpslQuFhCWDkeD8XDg2hZNk2fOnuUY9tqN645rUhRxfHzYaB6ePLX60kvPESS2tbVx3DjM5WWEYNuhbHGc0OsO9g72r9242e12a7Xa8tpqbXrKcuy9/Z1iIVcuV1lGxAloo+VzRZpUbt2+bxiGMOYJOhVFEZEJx0HT/Gh3V+UEWZAvXLhAkNxhe9hqdcIrNwiKyBXY+lR5fX19dnYWC2OagZigUMipqtLvdsIo8DynWCwsLM45Llh+lMSFoiqInIDRGIFFsReELoFDlXV9fT30/CCALrmiShzPEgTOsiTDIkTEssKura3NzEwVCvk08YPA63ZbzRuNbhdafK7rdrt9z/NwRFIUQ9P0JLzMDA+iSRzHYyjx+EmSUDTB83w+z4OhJikObUWKJB83HkKwo2hm9YQzHO7tQu++3x/ath2GYZIka8snaJqZdCl4QFWISYJ5jn90AE9rNBr9fv/PnRF+tIb5o+b3A6Y4iWx/TPRbOinBfDQqTuAVTX0oQZuYjjx7e3NrOBw6jmNq5vbDLc9xV1ZWV1bWcmph+dQpjObvvvPOv/nXv5XiaKo+kysUPMe9c/eBaRgMy6+tn7RtO/CDRvMoDMNarVKv15M0Pjw62NvbWZyfffZZiDzr9Xqn1W62DtM4vHXrxpNPXf7kJ19RVMG0jCQJLlw8t7W1qSiQ3szMzOiaubd3cOvWnes3bjquH2Ppiy+++MILL7AsTVL4aNCWZfHy5YuGoVMkL+SUiSsmCUlRFCHDZzU7h4kRKDkJw6h286jdbi8/+bRvh7KshBGyLKfTG9fnVp99/qlqXaboNPFj2zZjP+B5xrKMIPBuXt/SjbEoKHGE87w4Pz/baus4whbnFwSRCzzfdk2Wo0lmglaJjaFWzKsOBMADBpGapvm+3++2L1w8/fJrL585dVquVTDPG/W7x0fbpmnu7Gy7nh2HUT4PcKLZuWksScGAKbCQJEkm5ROADWYdhTD0SZIEhEHWfc9aC5HnWmkMZ1aGp+FphovC0HVMy7K+8fVvZ/lglKYpQ7OFfJnjOJKkRS7rQ7IcQsh1/Vajs7292zputNvtwAt83wewFPbnck1Ouz8hHP1Tc8s/5fUnfYqPvIakFALP6nWOu+3OoNszDKPb7e7vH06XZ5dOr8/PL548eZIvVTeuX//a177ZarVYhs8VSyjFd/YOTE1neKFUrqZpenR0tL66NtaGGJ6cOXU6jsOt7U2KIlgaFaH4eeqJJy72uu133v6OrusEiUGXPMVXV1cwPNK00crqRd0YHh8fLSwsOI6j6zpJMDTNuk7gexHLiI6fzs/NPvHks7l8EUtDSWYR7o+0VpzGQexjGGGNh4PRsNvvFXJQLLlz5051qoRhGEVRqqpSVBoEQT6fV/IlMxmnCe7YLkmwJMEW8tWlxbWj5n1JpiWOp2kGZ+h2p3G4t9tqHy7MTOfz6lR9Lo5QEAKYi+fZYjHfbjUWl5dKpRJr057vpFFEU0yhUCA++HIty5paXCYI4q/+1b88PTOHFAnD0sjzG1v39/b2ABuApwihldUlaF1y/MQp2Zbje14YhgzLT3bCxAeCSZCIpsEuoLoCC3K/7HEIUMVKPTWMwWC4t3c4GmqQ6PUGmqaVSzWCIOTsSJJlled5hJN4mgqceLB/eGPrVrvd7vV6EFcHAQ02zHEMLwnyn0cj/L75fcQOf/Q5P3Tnx3r9SUvi+zfZLXhCUZqenhn0+tevXz88PJyenj5/9sJTl54VeGVq/UQ0Hv+j/+EfvP/++6ViRS0Uz549e3hwtLG5NR6PVTUvyzKBKNM05+fnJVkwLd0wtPv37/ICt7g4/+yzT/uuffXqFURgDx7eOdjf1bTR/Pz8K6+8cPfORqffn5+fr0+V7969OR6PCRJfXFzc39/tdHokSRXypTCM0yR1HI8gqNnZeT8IoGLBs6oipCkYVZIkmj6SZZkBMIqWprGqypIseJ736NHDlz7xfKFQkHIcy9NpCg6E5zjftmmaNQxrOLQlUbXdNEnQzs4ezWE0A7uu2+04unn33s3FudkXX3yxVspzkoARjD00TTMeDAYJxgRBwPN8EoWWZeA4ls/no8g7bBxeuXLt/JnTt25fazWaAs/NT83QFDU7NY0x5MHWg2av0zxueB4Ujaemq4ooTdAtWS8hNE2TZdkJfoim6cFwPCnDkCQ5MUKKgvscJ3ywScANxgDyBCjMe9/4lqZpWcvHQQgJglQsVOq1mSzrk1QlD8ibBOv1evv7W91We2dr19KNJMFEkSdwkmcFkZdkSYrjmKOZSR37z50R/rF2+EM//aE7H8tiWXZijoVCYWVlRZKk+dm5lZUTC3Mro5H+T/9f/8vXvvY1SVJeeuVVVVV73f721k6z3fECP18o8bxog4VEvCgUi8XhECptcRwLIn/mzKmpqZrnOzgeCyLTah53O0eCwD/33LOra8txgntetLiy7HmOYWjFUkFVxeGo/6Uvfenu3bsURT3zzLNhCCiTmfk5XbOLpdrm7m6aYLVabWZmOgrd0aifIvz0ubOSQA+HQ88NcoXC2tpa8/g90zQLhUJ1CpAiw+F4bCb9YXdmpiLwEo1wx/FInNzd3dc0L4oSz404VlSVAs3TGOa5rs2y9NzU2sHh1vR0PV9QkiSydV3XbQLRGMa12g1ZqWJY4rouz89yHLex+Wh7dysM3UK58MKLz7Msa5omQujcuXMMS+3t7PY73cPWkRVatdmp1bVFVQXMUBRFKMU4VkgS6BkyDASfPM+7LrTvOp2OmisAUCmrmSGETQwSIZSm8CCOE2EY6prRanUajcZwOJQEOWsD0lAV4gHsJggSTbP12nSnA6Hm/v5+p9PVNM007MANFEGcvGoaYziBRF7iOE4WRZIkGQo6+39+WRR/rPn90BN+gpdFaRJjKYYnOFCWEMS5KYJ4N0UUIyS+h2hmZnaJepHRNK1UKBYqU1//vTe+9rVvdvv9s2cuqWq+1ezu7R4SBLG/f1ir12emZjvdbqvRLJSKc3PVQl7dP9iaqldt2xgMBq+9+omLl84eHe23Ot2bN2+eOXnq+RdesoF7QSwszNqOfuXKlTNnn2QYwfMthPLjwegPfvf3vNBrNI4WluYLhcKZs2c1zeh1hoLAlYt5nuebrRZ0vkiy1WqQRKqoAiJpzzf7oyFF4G7gHx8fBg6pqHIa0roxjrHgzTffDBN/ZrYy1HunTyyXS4Vhr4ulOMvzjMBPySXBjO7c2+o0W2vr80qBD+PQC12GpBiJBxw2wPYw34P+AQE9t4KhBUdHh4sLrCTzpqW9e+VtP3IxLLn0xPnzF84SBMrn1Sj2aIpkcvLUwvTew0d7+1sCLy0tLSysL1Is2J7v+wHkXD6RFT/TBIxKzN7R850wCoqlPMUAqyiDekZxEmEYpH9ZlYrUdXM0GjWOm+121zTtJMYosBkmcGJA/JTLqpqHsNZ2x+Nxxxj8m//f7+i6PhppnucRBMHQHEuzPMXhcaLkcpIAVkdRFM/zHENNKnNQkGXgwT93Rvjv2Vf8cdFtH6wES4IEi/EEZygeYUyAxZbtcbTgOLYs58zxmCLI6sySIun37937h//TP956cLyyfPLy+WWE0KN7WxClcFy9Xp+fmQ/juKAWHj7YKBUKWJKauoHFrqIwp8+snDixdufOvdpU+frN96/d+C7DUKfOnp6bncuptUKS3rt3u1ixWR5fOjET45YfRQIn37px7Xtvvpum6d3791557ROf+cLrnMAKItvotsrVnMCT5YJo6EMciziOty1NFHmSoYLYR1jYH/aSNHhw7x6BkU8+8ZyRRCSRSoIU+t7mo2NRFJ598UlF5c+IazRJm9q4XKuMe8MQT5VS4fCgZVuJIAhpkgRuEEeU6wa8INmW0e23l0+scDLvBF6aQgqNk1RGqjBkhR9rPRxzJZV97XMvP//88/V6DcdTP3BHo8H9+3tbWxtpGteq5X7rqD5bUXNcFCWswIdRFHoQPyNwZSTBoCSBHokkSaLEO55NUIhCJCewXuCSJJ6kgSjxiOAdy3Uc1zABwtbrjXrdfpoSaYITBKnK0NMTBImluelKPfRCXde3H+wfHh42Go1er+c4nuM4UPrkBZmD5gfPiRBqIkwWpRSLGRKKPTgOrCtB5GVZpijC933bth3X+nNnhP+xF02QOEYHyDcsjaRFlhZkKY9hhAsXOZSUEoao2++9/7u//TuH+wcYRr7y8qfGY/PevUcEgVMUMzVVd113MBjMzMzcvHXDd91CTqFpstk8rpaLJ0+unTu/dP/+3Vp1oVot//4f/B4voNde/6SiCBhGYgnh+YEs5+fml3J51Q06YeQEUUhgrMhL0IX3I1YQZ6YX6rVZXpApDnGioORkPEzD0HMs03PMKPS10YAgiEIh5/hWksR+4N6/f79SLSytLFcL1VK5FtgdAFpHQej5kqRMT8/M1GdMa2TpVrWc93z73u27UPTHdV7kpmdnjg67wCdkmSQO0xgNB4CMm56usQxRrVYZhrMsQ+B4imQQHpEEmyamokgn189UqtVCtcxyXJyEzdZhv981DN0ydds2L12+kKYxTZEplhiG5vtuEkVu4EpyDkvjNIEeA8JJhkEEAs+TJPAQIuB4/fCETZKUJGnoE3QHhmnpY2MwHEdBStGMwMkMK0iCLIgqzwoILiuEON/+ozebx43d3V3IrglCFMHYcgq7tLAIqWMcI4R4nhcESCl93+cFOg5CiibyeUWWxSAIhsN+r9fyfEgpCYKgJ1Wgn66PbyHLtUROpHGRFAU/woIgCQM38JNSYQrD8O0HG//mX/2b27duFdTc7MySJOXefvvtfB5A0pIkZNgLoJ4BwJAELkyn01IUiedZRZHm5mZq9crGxsN79+5uPNojCeb8+bP1qUKtlqMZNByYnmPTSl5V8ixLB9HYMCzDsAqFWugDuEdV1UKhQNKcHwDAkmGYMA6iOBYVOXWh1T4cDg8PD4vF4sb2TqPRYFk6TPxSWWE50vf9xcVFnucSPxn2ob0WhiHBEMVicWxZ0IxGVBJjpm1l6V/w6NHmiy++6NherVaTRL/f01Ispmio7GuaUS5WCQLFYXrYPJZkjmUEx3Epkr1398HxYeP8+Ysr5y/VpmZZXvFdZ2PnoQaIzbHnuRzN5Avq8uoK4DYRhhAGGHHPS+IQugiIjOMUAHQEQRIQ6SGcTNIo9AE/PUnLGYqjaTp7mm0Yhuu6ezu7mqYFbkCxgCotqDVZkOVcPi8XcJLCE1w37cbh8f2HG1sbG61WKy8pJCIoiqrVKgwDDcZJj9E0zVwuJ8v5NLuEJImgRKxKLANgWqD/jga7B5tJkuQL6vR8fWVlhWVZ8M+i+FMj/JiXyClRGuCA0KYQTpA0w9N8KuJHB8e/+Zv/+uo714yxuTA/Xy1X93f3LGtHgA4SkAxIiigUCrlcrtk89n3/6GjvwoVzo/EAGkqhy7CkaWl3797u9w/PnTvnOollOS+99FKSug83bqyuLQJWJqYpInf79u1r19/TjPaFS0snTp5w3CBAse+7hVz59Lnz2lhvd9thGJIkresaTiQcxyEipUUhiP3RaCThDEFQDM0JgpTiHEXSOIoKhZJp2LCPCa6QLxSk6Xt3dgaDgWmaUZRube7Mz8+JEh1HKVQjgP6fSpKEpci2guFwmCQxeFrHYlk6ChxWllMsJDBydmZhNOxtbu52Oi3fcTEMK5UqpVIFc5yjo0a/f6ff7/qxQ/N0uVwGe+aB0JghpEOaggYJjkPzAMe4jCwPIBuWlaMImn6BnxBETNMMK1EZl491XVfTtH5v0O/3h8OhadqB58uCrEhFLseRJCkIoqIoOE64jv/w/man09vZ3j06ati2zXFcsVC+cO78oNuRREEURYqioGWRJBQFed30TA2oTJlvzF4E13X98HCv128RBC6JYqVaPXHy6UqtosoKzTJ4CmfWYNDfO9j7qRF+vAtsDwecEwHgUpyK4/Thxv3vfffdr33lm/3eeHFmcfrkvO8GDx/s+K537ty50AdOQLfbxbCEJLEo8jpdqMI9//xzr7/++tbWxptvvomIxLK1nd2Hs3NTZ8+eWT+xGvqo1xsYhpZgLkIYRREURWztHuzvXrt+7Q7LUYWiUi5XcRxFUSRIUuKDsS0sLGwEW0EcA0o7W1l/jzH1QR4ryLI8PT3dGhgEonTd3N3Zx4gYw8NCUZ6fWxIEVuAFPMIMw8Ij6GHIbL5er2/tHXa7/eFwJIi1CWqZZflKuRaFCc8Lw6F2cLjHsQrNoMGw7Vj6iTNrd+7cvHf/TqGgvP7pTz24e1/XtcWlebFSr1QqaZq2O51vvfFtXbfn5xbPnj9TqOXC2JtgN03TdByH53klp0Z+4IL6DsYwgCD3fQ/HgTqUxhhNMjwrZC0HCAhNzfU8r9XqmKY5HA4Nw0qShOf5SnFKEgSOlvMQO7C2bQ+Hw9vbDzc2NvZ29+MY8jeapgtqoVwoZxjR1NT06ZkasHXxhOFIwCoIwgRqIwgCRVFxHGuadvzwIAgCRVEKxdzTz3+e4xhVVmRVYWnG9b1hf9DoNPZ3D7zAj4IQJ/789Qn/Yy/L1YFHQzBZXkKlGHr48OE/+kf/aHZq4eTJkzIn7+8ceY4/XZsmVfLoqCHwqFCUczkFR6nnO1Hs43hSLheeefYJHMX7B9v9QQta1RwhStyzzz4Zx26v18NTtlwuZ8w6bqQd67p+fevuO9+72WlZOMb+vb/3d1fX5zCkf/edN+r1erGY9y1ArhqGNhwOM0gH57kByoQhPNc5ODiQOEGWpbWTJ6JHh43WvatXr1Wrldn5KUnmMAwvFEquayYJ0LJ83y/IIJlBpdxgMECIFEU5E24BKDPLcrIslMuVIAgEAfgWR0eHszMLfuBsbT9Uc+I3/+gPK9XSdKU2PVcTSmWG4ep1/oknnuh0OgD1DoJCoXD58mVRFIMg8kPv6GiPExlFVjmOgWQPixEBqddjfhCGcxzYGwL0GSUKsj62kiQKvcgLAlMzO/1ev9MfG7qpGYIklQvlqfX5nJyjWIbA8CTBIivefLi7+Wij0Toej0aO6/Icp6hqGAQESUK4yTAcy9IMQ1MUTmAMR1A0zrLsJArNOh9BnER7+03fhx5ptVp9+pnL09PTuVyOYRgcJQmOWbpx98H9o/2jsa4TOE5Q1HRtikmgYCMq8k+N8GNePMcHYYDjXhgkKR7ztPzCC8/9nb/7t//Vv/itA3+PwmhVziuSGoYhzdGSJM3OFAiUHB3usRz95JNPnjy5vrgwFcfxcNDdeHT/6HDPc03f42amq/PzcwvzMzu7G6VSgSJFLEWaNopi13GcSqWk6zpN03NzC76XyLLiOp6k0uVyNdNxEXmGTIN0Z2dH0zSaYg8ODmZX5yb1iSAMh9p4wt+RJNheg8GoWK6eOXP21JkTiEgwPBwM2+VKvtE4GvcHgY+tLtDQ7C7miqWCZh4L2SYFuSoMTyLApufVHI5hvudiSWwaI98r16pFRc6xDHr9tZ9dPrWOMXR/b2fzyvuDXnduflobDVmaikMAjgIcHI9IBtmun6C4Wq9geJwksWWbURBSwEJhocbIS5Ko0BRFEjRgyrJlm07ghqOh1mw0ur2ea3sERfGsUFRLJ5dPszwncCKGcEMztzb3jg6O+v1hv9F1XZ/EkSALpXwpShKaRKzA52QlSqM0ilOEMxTJ8rzI8yToXOA4iUdRBE1NXU+SRJaBQv3sc88UCoWpqSlBVWPPg07uoJckybUbN4IojIKQ4bhKqbq0eoIiaD8MoAjUb/c6XS/wf2qEH+9KEEb5gc1SLMkwmm34PtQ2/tJf/tVf/oVf/PrX3vjS734F4aDA1+/269WpQlHpddsUjVm2kctPzc/PLizMGea41+tcv/6gPlX9zGdf3d/fbbdbKyuL9amqH9inTp0IgqjbGT98sPHw0d2nnj4/t1BXFLlaLXdaehRwSRywLJ9JiQFyn2IZN/B5kvKSpNvvQcFdVbf39j5Fvx7EcZLiE4SkklPHQewN9DCOLMvK5/Nzc3NxnJqmKcqM5/lXrlxrtY4q+fL8/HKhUND1MUcAniYMfdezfd9DCAIz07TZjLEuSpLnOdVaZXq6vrq2eOnyhScuP4UrCmZZh1uPHj16EMdhikWzM/XZ2WnHheoO+DMCY1jCNF3b1v3Yhwqta2IgC4hTBCko4PTCIA4cTxBAnQLDSUfXNc3Qdb3VanVandRHSQJhNsuy09OVYrGYzxV5XhwOh73e4Ob1O3t7e93ORACSZEgg/6qyDJYPMSqrqipNQyEqSSOR5yRpkv4BuDQIAj/yD4/aKZ5QFKOq8vnz52dnp2u1KV6RItcfDvt7e3vjMUS8rmtjGNB4S5UKHBY0hLuDvnZ47W6/DzA3x/FsG8j4UC37z71r/2tbQWLxAhNhHo6FvEAlMQpCG0eoVFZ/+Vd+7ptf/Vq70cwpRV6g4sTtdKxyQXzttc+cP3/WMLRev33jX76fpnG5Uvz8z3xaEPjt7e1ypXDh4mmO4/b2dnB8/ujoaH//+J23r46G2tlzJxcWFkZak6apcqX47HOFt9+6m8/ncRynadY0jEKhFGEexzGxF3uen1MLO3uHUZLW52r9ft90x5Vq3vdslmVxPPU859vffkPKTVcqlTAMDw8PT54+mbEruAcPHhBk8tprn8ajhGEEDEtUVbZskxdAKixNo3xBjeOw2+0yLL50Zu3e1XeHw+HJUydEUfxv/s7fRgjpmrG98wBAJ4MBzzD5fL5am5VUiSRBFhTDyTQr6IehZ4aeILIUjVSaA8IEQVAUBH4EhlM4xVIsR6GQZC3LGfTGg96g0+mMhtpE6ZBhOFVRobwpyzzP4xgxGmlX37u6t3fQ7/cdG5LDid6ZKqng21gmCkNFFHieB51JQG8jhiV5gZkI3tAMORgM2u0mTdOyLImKfPrsmaWVlVqtjhAeRbFpGu1u7/j69VarLYqCpumlUhGR1PLqesZ8SkZjc6N1uLO9lzXrudu3b3MsP6khyXIBx9PFpRM/NcKPd02YvgmGZRLAExIGAgKv4455Lv/5n3n1O996q3XcF3geEXESey+9/Lrvu4LANVtH+wc7i4vzBIn/9b/+V+/cuX3t+pXl5cXZ2elut3t4tMdyTLfX+b3f/f1+fxx4+FNPPfWzP/e5fEFIDx2aIU/Pn77yHiDRkjjJnBuDoQjRKCX47a1dPKbzShXQZ0r+69/8o86gG2DxyTMriifKknTq1KkJclLXdbUIYWqmjglsN0mSKIYrl8sUjeVyOWM4xrDEca1CMd8+7EMbI3QNc3TlyjtjbeS4Gs1cHhwf7+zsiBLYahj6x8f7R0dH4/E4lytIgvjsc0+gFKNpcL8Jnjiu7ThOGIa5AvCVZAI6aY7jWLbBUDTD8lhKsBRwiKD0Hyam7pimbZvWo0cblum4rs/RTKkIHo/jOBxHqQ+KoI3D9ub21u72zmA0DDyAxpYKRZxAOVXlBYGhaQzHCZwkKcRzUpJEBJnIOTUnKwRExb4XBJ1uw3IdChG5YuETn3hlcWWZpUkviJRq1dT0nZ29wWAwadWkaep53kRoo1SCv2U0Gl25cr3dbqcJ0WoPggjzXH9+fjFXz8liSRCgsgpwPE6I4wjHMijtT9fHuD5oBGch0eTfYIeY64CO7a/+pV998olL/+B/+I297b1Crlgo5HZ2djrtpiDw5XJ5eXHpU69+Ymtr4+7tOzRN1KsVikQ3b1yXZbFaKYAehOOraj7wcY9KlpZWamtr0biNEO55TrfbPj4+LJUKw4EVRUEU4bY31q2ubo8bjcZsbTmVQQGlWq0Cp8Zy5+fnn3rqKScwaTIl4ng8HiOEKQrIQ2BYEoReHMeWZfF83rUskEjTeqPRIPJBOimMQpalt7YfOi54+yhyR8Nefap85swLZ86edF1b17Tnnn0GS5KNRw8sy8jn1OWlBeBzxDHLQqsAECa+Q5Boog3BMJRtu64Nbwr+h2J4ToRCCCJoSgyDZNDVOp1Or9MHTKZpB66nqvm8Wiwtl1UZmgq6rh8ftE1N39sBDnunBblWTlGn6zMMRQdRCKQsAmNpjuFYiiBxAkGxBdJyGicgQzAs89H2fcuxRV5Q87n5pYXF5aXp+hRJU4HnD0bDVstIUuyrX/8WomhQ165UVAWQpcCKoOnt7e1mo9tqtfr9fq1WOzw8bDabEM9KJYogJZaslaanq3O7G0ckRjMEK6gKy9J+4HL0n3tm/ce9JtRs0I754E6Cw02qqCKJM3Fglcq5n/25z//Wb/7Wwd6BIkGQ+eILL5TLJVkWr169euXKe6+88pLrORiWGCZjmoYo8guLc4ahjUaD6an5paUVRdbv3tkAh9Du4igAggWZ6prTbrcFrr61tbm3t3fx0ilR5DUz9n3v9Jkzs9WlwMW2m8fDMQg6mL6rqrkJK59nieb+fkGRS0ru6WeeGhkRw9CZbYDMBMdxjqeXSqV8QSIpgkjpKA5EURZFnuOY6ZkZHMdPnjl56dlnMYZqbjx8663vIAI7cWLdNPV8QZ2bm8mItmwYQQnRcS3Pd+MkIRBiaNDqTdM0CuMkTjmWd10/jnwCwxmCpQgqiZIwSm7du2Y5gTHWTNPEcZTL5WbXF/JqgeO4OAi1sfHo3ubOzt7h4eFoMPY8j2c5hmFyudykjQ7KwywrUZCvsiwYzwTLkinBpCSFHR7vpyjCMUKSpPOXzy8tAW0KlHkJaAPubO82Gg3HcVzXJUHSgllZPxUGEWhVjQDVbZr6wcFRFAW6bqZpnKZ4GPpPXH7WdQJdc0D5Ti7GEZamOEmwHCslCcIxkmNFmH9AkWHkQ3j8n3nT/te3UgLHSAx/rEADSqIpoPRJnLEcC8UEjmONxtFw2J+dnRkMBr/6y7+yvLQUBD5FgeitaY1LlcqN61cFAWigGQhD7Pf7SRIBYzSKpuozFCHv7bTzuQKOEbwoBhEryYzrhJZtErgTRn6aJpzApVgAnDWPnZquh26YpqRhGEdHRwzDJEnS6XTQI4zliFqlfq3VKqpKmsa5fL4zaLierWnjCUXQsqwg8miaUlTZDxycwhuNhiK4x42Dmdn6L/7Sr+Air3WaN957SzfGAWBxiJxSmJ+f7Q+6JIkyJfzE821dH0/K+jQjhHGMJSlJM6DRggFimkDUsN8XBVnJF3zPa7fbBwcHnXbXsjxJzAuiXC1Nr6+oNA0q977v60P7K9/7+qDXb7c7ruuyLBRUZqdnMwVujKVonocIFo4/DCMg7gT8Shj5YRga2sh17SiKaHC4zNLqfLlWKZcqsizncgUMJ7Y2N2/futvt9iRJCYO4VqtNzSzFcey6frPR2tzc7/UGhwdHhqkjnDhxct2ygsGwz9Asxwv5XEE3NEkqkJTAsnJ9aj70k4RIQe4+BG+cxiBMS4EWW5jEIQhf+d5PjfDjXQis7jGTEATtP5QgjZLQ87xifgrDEEUTw+GwUqrmplRN0zY3N5955uksgwofbdz7Z//knzRbR088cWlpecF1nW6vnUm4l4+PD8EIp6a1sa8oOVGUeVmOgnGv14sTaZK/qQpoBA5H/fv37jBcQPMJQYDC0rBr18tzJEkN+qMkSUVBtl0njuNisaLkSoIgsCx0HfqDrijyxWJxUqdJ01g3xorKux6m6/rDR/f67RaBqJ//mV/QNbPf6t+5fX3/cDdfylMUNTVVnRTrAQ7m2dVqOQi8dqfFsnSxmK/VKyzLpgk+NuwkxhGicEQinE5TPI6yHDpCuztHx0dH3VbX931ZluuVmfxaiWOVIIJZD5bm9nrHe3t7+/v74+Eoq7tgAisXVOCwA0gtRUkSgbS2Y01EB3N5laKIIPD8wN0/2MXxlGEYSRJn56aKxWKtVskVc24SCIqIxVh3MGi2O2GcGmMjTNNKtS6rxdALE4SuXL3VbHdVSe71BkeHTZblJzgHUeRmphdBj9t0l5eXfd+nKCoMNMcJcIyOIlAAoCiaYABPh+M4z/MgooOlQeDTNIitIyLFsOinRvixLmAwfTCdIqPs45nqKI4RvucX8zVjPPJd/6/8zb8V++k3v/7GM08+Xa9UfceN/GBnd+vg4GBve0fTR9PTNYHjjLGWJHG5XFxYXGw1jz3HUVX14cODu3fu6bp++/bd8xdO+SGc7tkolfrp0ycH3aDROLp2DWc5fGW9AujFksxxom9DTJgd9jm71cJxvNPpnL1wCkP4cNgtlAsTcCl0t4bueDzs9oeCIIy0se3oly6fbbYO7ty9du786cuXL0McGIetVkvTRhzHnDl7Ip8HzSKWZT3PS6IgjUOEp6P+QFGlWrkCOE8SHwx6lq4nOO4FOAG0cjbFKdNyW412q9HWNMOzXTxFHMVN1xcqpSrMigHRt6hx2Dk4PN7Z3u52u54HwtWCIFTLUziOixzgpDNlaxB0YmnoV0qSUCoUEYF5njfo9Q1Do2hCkoSL58/xAlco5AqFHMNSvu97njsej4zQDvtthmIlWZmeLeim1e0M+sPRsKdZ9p3RYMzyQr8zJGnm3JmzjhfznFypwMfzfd9xHIpksZTgWDFNUJogjhWrlalioRIGyd7u4XCgIRwTOR7HURyECEtAMISiSJTQJEaSOEWkJEr/yzfCD4eNfRABfmQlP6h2/cGjfypLKYW28+Pbn+jzwEkJHjAFrdzMFQqcbFhjOVcw8TEWu5/9wqeLlaKtWTRLEwS6fuvm9773vU6no6jC6bPnX3v9k4uLs91ea3Z2utk6fvf99wRBeP7FTxCI6XavavpQlvONRoNkWVIuFZ3S3v5muTQ9NTXV7+w5jqMouRdeeIEvc4PmFkamo2FrPHCMkRslyclTq7pptPtdlmbOnDwTxU6/31yYXfIsnRSlVqeTpDTPs0mGnvPcdYYiZ1fX0sTv96bWVtZs22IZPkrSUrnQ7eZYgS7X8pZt6pbG8GWMSKLYS7FQkmReYAkCaRpIBk4w0yIvcrxse+lobB40D0eDoa6bjuWmEUbg5Or8miznVVlNYqzX7W7cu31wcDAcDgeDgQ+BHJkrFOZmpsGTZDoUkiRN5jEBxUgCncNJOXcwGnqeg+FpLqcsLs9Vqper1Woup2QQcM/3/XZngKMYIYykCETThXxVs0zXdcdHraPDxoMHG+1WN0kQgVhDM23Ly+UY2wrL5dziwqooqLeu33QcB4aI0CCWAQl5pm+fwdwommajyGg0WpZlEQQFMhwIFwQOmi9BhJPQ6E9RGsQRirEE4TCeKf0JFLh/eMLex7oeb/p//60Po60+vP+R3wXeSqaOndHUPiTL41j8AxpM3/9bJirlE54LMKoxIgssUZR6H9Q4v/9GH2EhfmByjxeUHJJsshL0JfCJ0HacpEkYhbIoYZjHShRGJLo36BnNnKyunloqKqWtzd3vvf1euVI3Tb1YqhWKJbVS3dx5NLzTK5fLq2trhUIJSwnP80SFJejEtMeVSn3UH+ZLQq8/niRaDx5u4ASvqHkMJ3p9bb6et8zw6HhfMw2BkWsnZitl+cH9zRQLsTRUOIEgGM8wyIgqlsuxmGsft0iK1XX7zOkTv/gLP3vi1MlCqWQZ2rXvfGtvb2dtfRnhtKqUTNM09CEr8M3ucUo9abmGFziiwkdpkKI4TFJBFmMscR0YQUOTFMeAhSQR1jxqtrsPBn3D9fwoSgDIUiiX5oqioFIEPeyPmoetd3bfPT5qDnoD1/VZihYlrpSTOJ4ClVHQKg8ZGkmqAnw8mo6iKF8qdrvdCHOPD/eiKCiVKrmievr089VaOY59RRW73W5v2P3uO9+lKV7XTUO3zl84e+nSuU632et3Yxzfbz5QcwWWZW/cuPndN986d+6CpQfTUwvzc6vfe/NtgeYjF1+aX3ZdO/RdjiJxLC4WALkqipxjOrZlloqFbrvDswBxs20bglKGUShKM6xSpeIFDsGSumWKopQgnBF4TTMKBd4NAxLDKY43HOfPmCf8yZzPD6/sFfAfuZ34qKx5kDmpH7jlSCYBI4UTCsNSUEhLoxR+MZnElSnAsiCEx4E7jxEY+f1XwBEQeQFZmQCdHocuVPYx8IkWKYFjINcH7SI/cJ2cmlML4pnzawvTi4EZb+1smrZ76szJo6OG6zucwLM8p4/6cwuzoghqX0mCtbudXncoSVKhkLtw4ez77925devG7Oz08sqMks9VqosMJ+7u/jZLl0VR7PX6zUZ7/uwyy/KD/uj5558vV6rDvtbpNgxzJCucoQ3xJNm+c9swtFOn1w53D/b2ty5ePP/q66/VKlWOE4Ig2NnZeeOP/tC27bn52aeferJcLQ0GAPUolIHq0eq20jSGgWccm2ApSdAMRYcIRP6SGGRpyqUaQsix3L29va1HW71ej6ZpRclxtJiXK4qiSKKcRGm327/23q1mo62PNE0zbNOhaVaWlEqxxHECyyCaTcPIJhGRLxYqlQrDsYZhgIZuxxAVud1vIoKo1Wqvf+7VqXqNodk0Ybrd7qPNh71eCxFps3lcKFRkSSEQhwhGFPPNVvftd/6x4xqra0sMy4lSUVVKHMdUKrWzZy889+yLHKuGAcZSHE2JSQQKqCTOkCjAU0QQOBASE5CiIbKLi1LQrsBw+JMzUQyYOTiZVZilf7A5sjEUKRCMOZakKS/wDctkGAq2U5y6f1YLMz+Os/2+pNmf5kUzU8SzJ3x/fNkHt37sTHQofsA48QREaDPDSyeCXJk5JvDPeHL/8W/hCXynMIkuxFCKMDDCJHnskwksJXGCxAiJETGGn3wM09Aax4e+Hli6u7K8hqNTh4cHo3E/SQIfkNyJIFJxEg67Pcdxjw4bhuFcOH9pZmaapvjr12CEC0VRMNuswjquNhodwfAT17Vtu1gs4yiNtCHgsIoqRiaGrZmubnvWo60HBEHxMvvulXev3briec7/+f/y3/UGXd3SKZYWRPbazSuaBhoN+Xz+8hMXqlUQenEcZzTokwSmQJjJpEnEUATH0tpIl2dyKIkgASYgx8FJHEKINPjOt94GysIYxjlUitVL5xcYhonjVODV4XC8u3XYbLT7fej7WYbtui7Hcfl8fm19WRJBxCWOIpZlRVFgadA+IxByPa/Z6ji+R5KIYuhyrfr8Cy9ALape6x0faoaxsbXZbnVMA7iFPE/jBJJV0QvcJEn3DvaPDju5XOH6tVtRFDz/wjMXT11K0jDB8CeffJIgqf6g5/suw9CiBKMFO+3Wwswyx3GeC7WWieAahmEMwxEEBf0GEpSC0wxZS1MMjiFoRWIMSYKhTkbB0DQ5abemTMIxLEPRaZzEYZBEIc9yrmsD7Z8AXeE/m0b4Y60PrS75U3UrMlP5QAXtB38FRjdM2uo/oJA/yR/BeUJQCwYJvLWPTF/KXg/ugiZXdmFSDOwNpRgCPlMmJEtgmOM5Ie4RGMwPoWnSNnTXMsdBemL51L1bDxrNo+Oj5sVLZ0+fWT11en1j82GhKEouZztGu91eW1t7/ed+pr13vLW1k6S1ra2tTLOdY1m2UqvZTvvKlSvnzp174YUXhn3/YL9JEHixmCdJIo6jqamK7eimqedy0P5qtg4EQcHxWFb4n/mZzymqPD1d3T/YXFqet2ztG298baY+tbKyUKvVYKavZWV0OILjOD9wc7kcQphnW5Nx2bVKFU8RS4s+ATRW147Hg3E7W71ut1adErnc1NoChI4EbZrm8VFvPNZ3dw41Te/3+7ZtZyqJyuzsrCjxmRqaBwIQTFLKFxiGgdqJ4xh2kFqPsaC16an69NTs7KxaUF3Pa3c733vn7QTHTNMgaZCHCaOoUC4HQTAa9Xr9lh844/GwVp0xDPP5l15aWFiyHH88Hj7z/HOra4uPHt2P0+R3fue3EAk998GgVy6VVldXREHudfV+W2MYxgV9LVCdyWwv5TgeISKJwfDSBGEpgWGIYbhJdjoRLgJKMYU4nqFownEcQYDxaVGYOI4dJ2GmNAWaOjRD5vIKLwA04s+eEf5YOSeeZBHjZCU/0jHHftTYPqoF+oE1TgZ6gqFNVpyl/qDRhMHYx8yeIdKYZIwQbIJQwuN3mVj7hzafjRgE2P+EAjfRUU8wjGfFDMtG0ExGDsrnc6oah8mdO7fzudzC/NK1a1fDMOr3+5cun33ttU9hEv3w2ruSJCwtPdtud//wd/8tQ/MXLlza3d2//+CuLMvDgbG3t2fq5x0fZtZmW9DneUCB3r5z89Of+SQmCfHIR0Rar1cIgvLcUNedqenqSy9+gqKoEydOwVjp7c179293us1qrRCG/uc+/5nsLIIgwI89gkIiJWQKgCSeJgFE0369WuVZFnQcUtRutEOHPD5q9Xo93/exBAYtVUuz8/VlWVaxBAjsO5uHO1vQ8rZtO+vXQPWiWprh51iOB3cBpFw8YXmqWi9IIADhjccDpw/DDFlGLNXLlUJtfn6+XK9hON7tth5sbjRaTYqlNEMvFos0w0wXc2a2mu32rdajbB5TWK0VkYe5vvNX/tpfrZ86jTmxM9SK5VIYBzt7u4WSyvJcd9AnGYhkCopSrqiFQskL7NF4MBiMY4wgaMBtQ5CJY4gg4ihhaJZATBzjiGTjx7sCp2g2U2YjfT+MsdgDCE0AfydFpimUQ+M4htg1TgSWKxVyx4f7pj6qVEtT1VKhJP+ZNMIfe/3x8ecHVjGpjn4wMjcFE8mGVKOPBJNYiqMkhPASOgw4jPmAxye+LoXHs7HWIL0MmgqAcICHsgTzcZoJOWSCJ2mKSDwJk6w4CoJCmQlD7JqEoMlCswTkmxgWOlHoAF9hfmZJFqDQZ5pmGEbHx8eNRmvt4hkM4qhkPDZ7vQFJ0C+88KKsFF3blmUZw7Bs5nND00DQkiKZWq3G8+BMxlYoiiAH2u4c9/YASTMzWzs43Hn4cCOnli5ffuIv/NovVav1rc2d7Z1Hjx49iqLw3Pkzr732CV5gNzc3T5xYH41GE1V8+NhZPB2lEQ6ZD5WTc47jBF7YbnZETsSi9HjveOPOIU5w06AMN83RTBzHjg0E9qvvf7vdbh8fHwMtneEVRZHlHMJJluUZhhUEANwwLAVugUhxApNlcawNtnaaJIlm56afWHmiVCoxJK2K5dEIMAbvXb+qaRpBkrlcrj4z7bguyTKe5924c1vXNdf3qtXq5qPNJy6/8LM/+7PlUk4uyN/6xle/9JUvkgyNeS7GKThtNVqt4Xj4tKqunz/r24breW98+9tRFBVyuVK5UCpWZFktl0sUKRzudybc+Qmdd6K/ms1CI+IICqGAuUlACQrKTlDuS2EUFKi3IZjVm4QETIsBbeKZmZnp6WnX8VmWwaGiENEM8n2bIDGWpZL0J+gTfgyFkz/VDf6YSmcfqbtkVje5M1HezYzuI6//uMiZoolL/OAWJ0j2g1wQQeXlg7wwzZRxkyiOkvixmWW+0LW9zPBAVCiNJ7dJgkFYko1NxjmOEwQeynowQzlELE9HFhZgfhBCmSFXfeZJheF5jKAxL/Td4JOfeC0M4xPrZ8qlWmJ6rmeWilVB5ODDJpCEdJo9YCFS7NzswngYaBrESxNxISxFnhvomrmxcTAajeI4evvt79578N5Tz5x1Q1MQ+NnZ6WKx3Ou1Nzd3Hj68n6a4KMpPPX25WMybpg50dYEtlUrtdjuXL2Sl/AD+BApEH5IoBYSkGxAJeXzc7rU7B3v7586dz8kljoo+8cIzpuUmcey47qPtPZhf3wCpaW08FgQhl8stzC1OmtRQzMKwSqmU2Th8bD+CSRKQI8BPkunZmWdefDajLyS6qe/u7Q0GY8eMsAggprwkTs8v2K511Gy2bl+DPgGBNrc3Xn755c987jNKBrL7n/7Hf1Cr15cvXsSwCEs8zdANyyzX6xiOReMRV6lU67WxOQ7jCAJdwyhVir/067/Wbx4bJqTBpqW32+3traNOe1wtzbIsDXsqG00DNhYGcFhjUH1CCILPJJOUoiigQYVxjCiyUiljeKlUzRMEnoCEB7JtR1Xl5eXFVqsDdQQsRChhOcpxzSB04kQOgj9rhZlJd+7H/ZXvDyPLLHDSMf/QE36/h5E9MaV+8C0e/zRwobI3QUX5WUgxmfY4mUUOD4KO5ePZqhlwHsCHE5WROJu3+qGOeqY7AjivUqmkqjJDUkkScRyHgXC1imGpl4Y0mfp+hMdhEDgMDa7i2Vc/j7keRhBYEiehxTIwhzL0neFwyDLCeGQ8eriNEHby1PqFC5euvHcP9CM4MRsMxABBQVL29vZ03axUKr1er1orX7y8fvHyias33puZncqphTCMBoPRzMy0LCsCSNAKuq73+10I3tIUhrkrim3bnhPCPC9BBC6FH+tjy4BOutM4ao76A9O0z5w6PT+zKvH5Ud+MfOzNb78F0pzd7mgE9U2CIABqp4iLC3OTUTMMAzrTAKBjOBgYqI0nexoncUEQytXq9PR0oZSHA4UAxcis3edZTtjtj/b3j3NSkab4wA92j+632+0Ew6anp9dPnDp1+oTn+//s//vPfvbnf375/NlA12A8qOeCpYfhuHOcm6uXyuUIZjxhse+TgpzoOiIhvz04OgyCQM3njo+Ps6ihaZo6QqhWqTIsWyjkPDemYBGIwCBahqIAaJN+MKQJEpPHqUt2xTPrikWRm1+Y5Xm6Pj01Gg3jJAhjjOGZIPAGg95g0FlamsvlZYJEw1FXVWWWoziOwlH4Exjhf0xP+FEn9kPvOpGse1w+gS8i2/QJRaAwCkExgGAxjPA8sBmG4RA0+rIh8JNMD4OBO3iKbDPGsznMvu8DnD/jmIVh6Lr+Y+ImYCmCSWN3wuiZiMOCWQZwJ/m+xT02QtB9yIaEZJPPEHBzIqiqsSybieqWSmUAghGZ7CRNk+AkeSirUJRD0gRJhAThpYmWZvVulgXuOM2JWBJRFF0ti3GMMVReeXI6TnxeoLKB0rHj+N/73rvfe/s7/9f/29+vTxfGY31mZpalrfW1s2fOnkpSb3f/zrvvvi2IrKUbJE6KgjRVrUUhFkWx51iOCTxaDENhHJIECVLQMS5L+TCAoeqWBSFlpwW0AFO3sDjJq/kzpy9KgjzsD/r9oSW61eLMN77xjfeuXJNlmaZpjmFKS0uTkdSKojge1F0KhYLrgoLTYNQTBIGkkJqT8gW1Xq8XikWeh8kQQRS6QRj73ljTQGmf50ha8Pz+4VG31ezf6mzAQRnDxVtfXx+OB3/77/w3cqWIRUG73YIajDaOLZPm+dGwJ8gSZJ4ZCwMGjwc+JwgPHj5YWV51dUBaszyvmcY8OZciPMbSdq8bhr5aUNdPrQG5luWxGKlyw3XuWaYXY1Ech3ES4ghxImfYZpTGkiIOh2M/9NW8Eva9KPYwkqRY0nYMSab7ww7vMTEWyrJI0njoR6al4Qjr9buKKhvmeKz1dWMoy8Lq2pIkcSxHy4rwZ8wTYslEtfpHfzCpiXxUoz6zRjxJQTAaT6AyRuIpyypZzgbhjueHgeu6gRsFge3ZjgnKn54ZpxHwR70PFuTTcWyZzsTUJ0b14ZQsqDd8n6A0sX84LMIgBuAxAjcSR4kfRXbiZ0xzQC0iRAVeYhnmCNnNo36SxDkFCKwAWaJQVurIBCeBQgpLEGSekxmah03MURSNx4lHEBgADxmKIjmSZAjEEIgYj4YnT5xeWbx8+uQT3W775u33ms2Opg9EUbh8+UkSSY3jzrvvvktSMSdEpVJpbm4mk53AoyQFZl2CEThBADmWzqIOUFajCDKIQlM3TJj2YkJ/uz8C6dFcfml+TeIlmoRf2NvZvfL2rW6365j2wsLCifX1Srk+PVPPxP8AyybLcqYNEzieWyoXLMvQjWEQhQtz8+unVhbnF1iWjoHyjzUard//0u8PBoPTp0+fOnOWIOmpqakoTtVC4Y+++a2vfv0bGIa12+3Tp0/ncunmJkzPffrpp5eWV7rv9YbjEclRfLGISPjwGIEIGC2I4cCTYOM0wRwH6BRI4Xk+m1s6KBXLNAW+fTJGeyK7NjNbX1lZylerWBpiEM74oWXGMeZ6AO8mSQIGy8DZ6yUJomiWYQlFlfzAZhhAZGj6YKz1w3AOEQJBpjSN+qNuggcrK0uFohxFkefbvhuUCiW1IFdKJZqjZZmfmZ9+hXp+anbqxOoKL/EMhRz/z1qz/t+ddiKEQxAOkcH3Z5sRGIrDkKJ5DFFE4scBSrHQNs3RWLcMx7JtE8DEXhSGtmsZmmZZrq3HeAwFK2jgPC54gs+cTOqbVDsfFzwTkAKKIW0G4BuEnZlLjCYd6TjOpoiAsGzmElMQ9MFw34sTCg9CbzQaZQPTOYaGcLTd6FEUWBQQgrJ3A0MkocNPEjClh6Y4igJiAST/KMnlJIJMaBBgYFhGhCIGyRFkynCxYTgcUzh37oJafv2VT7zI8vH+wUYcY+1WP01034s4VmD5NF+kJIXutkFAiaU5kmLIDEWURGmSJgxPwVeYYJ4djCy92+81jxuDoc7QYi5XOH8GfJo2HB3uHHbaXd8BJRttPMYSvFwuV6sKiMBnQ8XK5bIsi4IgEQQeJXF/2HMcJ9OzjQql/PLyMivwpUJ+MBodHO8dHR21Wo1G48gwLESRszPzUzMza6dOYry0dePWN9/49sLC0mAwqparFy9e/OY33sgrRdfyQd9+Zfnv/f2/z3Hclavvw5SYUgnISBkdCYReAB8Ne4OiqNFo1Gm3DXOkqiLPQ0vWMGCEMKgEZ0+YhN8gEkWS+ULBHo9JCmdYlqBpgiaoMKUYEvRf04QkkagI+ZKSRFEY+Zo2Gg67JAUocETEkecyAq0WZZ4HdkSYuPm8urA4W6mXEiwcaQOGIURRffHF5xcXF3OKStIUQ9G8wp45u1afqe9ub0V2EIeB5Zh/Bo1wYoEfYj4f3074QVD6gLgcHgF6EIZTBI3FaehZpukauqtrdrcz6veGUZQ6Nsh4+AEEqGEmIuT7UU6sJpAoEhi0TsEOJ9FvGIKuXprCe3xoh5lvjD68/0EWAMYWhonvR2HgZLkiJIuTESKeB5JbWWQLd3KquDA/Wy4X3/jmNzw3sJADcemHA2HTVJD4zKYJhE90vVjQaiPT7e1thBKSguIbQ/Msy9OUQJAJhmye56OAKeSrxWJRzXGLy9VqtV6ulI6PmoYWVCq1XE6OEtML+tlJXE1holAUgcIvqNbSHJRJwjAZ9AcHwHqH5oEgiLVabe7sKkmwjUbz/e+9227DME3HtCbM97wKmogCz8N0e9uqEeB2ojSZnZ3FcaD/+iHQOObmZqrwcWq5XA4nked57115/xvf/OrW9nYcx57nra6unzp38dVXX5s7c8br9TY3tr/4e188PDze2NopFouvvPJJHMfv33/gOA6oRkVRqVTZ3t49Pm66ro/jhGlY45Eejg3Hs0GrgkCtVtNybJ4HnhRJkibwLIGtPxgMJodsGIagT5rpHU967jArgmHiMBwO++Vy0fXsTrs9GPYs04mi+HC3efPGg8sXno3iwPc913XSJCKIlBekfEHxfBPhhGUjHGGlssrxpGEODXMkSNzs3InzF0/xPE9SaGFxZnllTlEUVSlAZcFxHM8wrdDxHN/12v1GFIYEhfA0iX8S7Oj37eQ/9vooAgbgYz/4CCBYMn4CZ3T7jUZn0B+PR/agP7ZMPwoxw7AhkshSuxDkaCd6E2jcO0oywtEPTJtIM8nKrCb/YSA6+YnnAeVngkX64Efwi5NU50PPCf1cRD7WgU5TjudzxTzEyhjmhwFJU4VyyfMc14WObYZmTSOoygRhHMDYZD+OQiBeZElviqM4l5dxHOT9SCCT8kACz4wwSnRVVfFEMrTw4YMtmk3LVSGXZ8+cPVWrTq2vL8URbpgjLzASzE8Tn0wjjhFlQSFJMgii0Wh02APrardASJsgyEqlVjszlab40VHjnYfvNw6Ox+OxpmkZVUdUFWV6aoplGUmSi8UiRVEwOcm2CqUcJ7JxGmNxMjVdm8/4e1NTU4VS3jTNTr+zf3SYLxZomuqPhqOxrhaKn/jEy5cuPUEg1jTc4bD/7v/2L+7duzdxU8PhkCTJ+fn5vR0Y010u5j7z+qcW52e2t3aHYwNkfyVpMmdzgtvGcTxDSAMTt9fr2TYYIcuB9lpEYdDFwSPDMB53+bK5n3Ag5nKqCux+24aA0/f9Xq/33pV3SQrRNKg/FnJ5KKCWZ2WpMBraULZFcApHMShkGNa4P2pLOS4M4pSIJEWs1Uq1el6QZgX515dXFrMBTcJ4PAYhjsiJ0qDda7a6HSCXhAFBUwLLMTxA5bzQ41IKACJpmrmXn8Qw/hPUSH/4dlKAylrhsIVx+G5jqHr77qA3bO43O72RNjS7/bFjhClOupYLiL0oDoCvFmEECGLjJMFzQjppSEPL5vvJp2WBsYXhY0HLD+wNHoOiDmy2ME0eJ6XZyQqXDTg5UF+BqeXZK8IcgslcHpqhbMjEh51OhyDwl156yffdIPCy8SAESUGNO4qgBpvVV0Ew37FBXgWwwpFrO0Yc+2HkW5YVBmYUwny/NA2LZb7d6uOpkM9VFUVFBNFudVtt79GjRwsLS6dOXqzXpmn4OIjnRYHPM1ze7Y/3dw4ajYY2NrIomiJJeqpaFwQpibHDw+Z3v/Vvd3f3gyBiaQrDE0mSluYXBEmAVicFezefV9tQAm0lScJyNMuRFI0TNCZK7PlzFyeAyRTHxppx2GwQBFGqFMuVWqPdsHXj3Nnzv/LLvxqnoIp77/6j3d12pz1wXbdYyC0snuj1OsdHBzwvfuELn+cF1tTGxaI6v1CPYidJPRyFRwcHDMMtL68WCiXd1NI0PTw8OnFyLQ6hUCfxEpixB6wiAKlQlBECAo6k8ro+nAyKoGm6VCoFPgylK5VKOI6Px2Nd18sVoOc/++yzkJRzNMSfCDKBYXe30+lEIQEoWCyO4gBI0jxFM1QUBbVauVKpwLjvuWmOA3q049kLizO5nNzttdtdKORwHEgiAJU4w2FxHBwcYeTDZnQDgIGkCaSvk8Qm+TPYrJ+0HH7kNkmjSSFmgpAG+BgO31LneDDsDfvdQbvZ1ce2afq2EXp+rEgqjPAgSJrmKHBQaZpEUJag6SCOPM9zXfexnwzA3h4bQwxm9zjozFwlL8D5Kogcz+c5Dgonk7mtLMNPBnpkkSe8IIjAQsMQbqIwSvAIUUhSJYalYmh8jXA8JUmwXkD30hAZEgTOUAQFGrM8wqk4AkHLOAkns5mj2MvCIdd1As+LAijWhvuHjyzLjgJsd3cvY9YTs/P56ZmS51vb27u9jnnixKnTZ1ZLFTEIR41G7/03v0iCJwAOX7lYY1mWJCgMQ48ebR7sH+3s7JmGI8vqVG2WodkkjStlNUlilqZFRWQZ3vOd0WjQbh3NL84xDDM1VVtYWIjTJE4AaTDSht3+IM5k5ykIBskwSfJ5tVqdxnG8koBxjvXRzt5+o3UMAUWIBLEoyUSK6QQhDAbGnbuPCnnlr/31v/HJT73SON5rHh9Wq1BM0rWRZQ5yeXllZWV/DxoJuq6Lsliv1ydjCcGx+y7oBcdQiPZcl2ZAruZwuxkHoarIFJEGQXbp4wQXRSpyMYLkWVZguRSL8CTlGRBQRoBLnSSYAcAHJDlfKtamp7Ye7kIhh6MlRSzklaWluVq9fOr0msgLMEoR5Lq7tmPBH86QkiR1+k3dGGed2zRKYz/2SzmVoqi9g31VVRFCQZQhvKH0iFiOCUMYHQWJFfpJ5hP+ac7wQybeB34myfgFP1xu+X5vHfuBO1B9mSBasil/H/wWRIqTFsXjKfNQKcESYjDQxiOn0x7ubB96TsRxCsOKGB5hOIRuIKSFYTFK4yCybdN0bMN0gMSVrcdhZAYOpGgK3BvULGmYMZ4tCAUZNHmUomCkQRxDojJJbyZZ3SRYhciHwAgSdz0QhAWgSQxVUIpGjmOOh53Z6TLHMizLgKpQHGJJjCChpaIonCSHSYJAbSWbZ0DRiGEoFhGSJGatbSKbbEFhePoy8bRhW0Qi7O4ctlqtRxv3NjY2Nzbvnz59slgsW5b7vbfe6XW6zz5/KYyM27eurS6swwQ0EV5nNBptbW3du3fv8OAYdNolVVFypVKZRBTPiZVSKZ9Xs4A5CgJPH40HyUAUxfX19amZOogCAhwh6Q66nU6HFdi8Av87ajUFQdyEtXHu4oVf+7VfLU5NDTtNTdP2D3Yd10UIFcogqqvpujZ2TD0mCV4fD/vdJkWinCqdO3fmk69+CkuD6Zkplia2tjZMQzMM7fDoQBTzg0HXDexiUS3UKxiG2a7jHR+TBM1xqeNa2fkFNuB6NkOx1Wo1Se7AASoIIoMiFwatjcdD2G0kwJTCyOEFiqY5QWRxlpYIPklxxJAYgTGuA0Gkru/t7dy5cyuOiEq9tLy2fObMqWIpxwv0eNi1HKPXb2aBPdSEeJFLkqzWaozJTDKYZZnxeAyXFqEoiRuHTV6UOEGENlgS8yyAK4DuGEIwhShQspmMy04+7mLmRz0Y5kcA4sJB7JFAwDN/7GTSOKJJFsMAcZckWdSeSSkjnPR9KwkTGFuMETApBYyAJVAMWJYYGnkEwgmKDl13MNJ8Fx0dD5vNsecTOM7ghIAjOkocClEpESdYGkSeoZu6DiRxhEjHiovFsu9ZcQTMVJ6HWSJRDHJJoHdCUAQB03wy0DV4z6zogodhlEGWHkewKRYDq5oCfBv0o1mk6zrMweJZDEUkTVAE6TgWgegg9At5pdezm43Dp5+8fO7sacsyRsOB7/uWrqVRwoA2YYphIY5SEmacIIIAWZTMsCfG+ZibD4UoPPVjh+FYmkjPP7F2Nll76VNPH+3vXb169db1R6XiqF6fqVemDna6gXvz/PlzdFqjyVyr0zk8vDUY9K5cuwrNOpJMCLw2PbW0MNfvDqbrlenqVPPokEitUddENJOmeLlSLZVK5XKREwXD0IIo3NnZE2UB+qVxEERxa/+QWmEtz3/73Xfml5bG49Gjg+2/9H/4Kwftg8PuXhD5Kysry8ISxNIhHFKlSlnJqTMzKRbiJKJI8nQQQAHZ9exisfjwzrXZuel+vwcD6KdmCoVcEAQsJx0dHYW4g1N+jLnDfkORc5NJLHwuR3terlIjiK94niUVq2jUx7PpTqalg3K25+IEHoZxoQAxJxzKmIdxjCThvj8+OmrvbJ8rFcQ4SUeG6XiuHzi9Xgdi1HJ1PDL2G/v12pyYF2HSvUTp7mhguSSRBgCVgboBmcFKvcCN0ggqhgj5UYSnuG9HiKYjDMMpemSZlMAzrGzZYRTFOKKCECquoOOIYYjAPpzo9LGHo+kP3fIkn2V10E4AjBUWhRiootIElaQADoH2NTgZynMD23RYmhRlGWMI1zScrI3e67aPjo5s256amjq5vq6Wy1jg2WMYpNrrjvs9c9DXHS9mGQkhmkA0IihBlAejsaLKg8HIsrVWp5HLKdV6JfCTpUW4rVbLak4Ow8CyDIqGDD+KoqzXAJgzOEKyGiwA53ni+2BUsMCscIrDmNU0jYIgRARk/wxLVqrFuYXpqakaJ/CFXH400ob9wfvvX/Ud98SJ1aO9/UePHrz04rMCz5w5ceK9997pNo8qtbIT+DgB3vgDaMGkdzLBhU/oGpm/TYKJghPFUpA0oiBJQ88LfC+Yn59fXz95/+6Dr3zlD2/fuGMuOXEQB26ytnRmffXSP/2n//Ng3MkXVD8Mz5y76Pv+1FT94sXzU7XKeDToHDe3tzYajYM4DmZmFlQ1J+aKCAQ/gcc4Gg/2Hu0jgpBykp8Eew8PoHlYLtTr9eNOU7etMA5e+9zrpWql3W4GmItRqRvbwB8hsHsP70BF0Auy7BoD2ewgxJOUTJAxHE94WAQBNOWD3Z2r7yWDYe/ChQsXL15UFFUs1zCSNA3v4cY2zKjpXGu0jjM1DQ4hcCBJNt0Nw2EEb6dj+ONx4HoChk9KOH7g4lQZwyEYxnF8b3/n+ne+I3AsjrDBYFCtlZ55+omcqliW1Wg1vSybrVYry8uLak7GWeH21RvvXrn6wkvPK0oBVDbSOIDhckFWGoCJ2hm5DfDCWXk9A5cmgKB/DM1CcMEQAiG5bOI9QqAtDu0gSBIhR5qUFdIEEkU43D92I/wBpCaU/rEQWmjZejwVEVhnLGDtEFyGzCgxmiY4TuW4vDEcbD7c3d3dfvTo0e1btzY2NmzbhMMsSWFUyBMXn3vuuScuXirPzycxfvP6o8PdXuO4ZduuLKk0zUdhNqCVJHmBy4oiLc+3L1++eNw4hOTKdT0HVJIKxYrn2ePxWJZllmV7vZ6i5CZmkEEcP+wZAgp0YgYAuME+QAtAIyPrHgFCAOAyLMvOzc09+eSl3b0tQRYkWXQcpz5VnZ6uB573C7/wC1//8h/ev39/a2urXq/ZnhuliRf4sDvDkExJAs9Y+JkIdApXF+M4DiaigA5tMGlwQTpKMTRDdvqa5YWJpILWEwPfTBwFly9fXlxc+tLvf/nWrTvr6+uWa/3Bl//g85//7Isvv/LWW9+an59jBZ4kkZJTn3nmqaWlhS9/5Uu2aSzMzFIUs7K0SOApQ9G6aSQURnBot7U/aW1jHDpuNt76ve/qui4p0qc//enVk2vz8/OP9jZiEqMFdn6hPjU7s7o2t7o+t7S0YFmGZZtxHNfqVQgiQqgDIUTGURrHCUpR7IS29bjtMenuEAjy1YODg8FQ/2f//F+FYXjx4sUXXniBZfmp+sK9B/ePj7o4Bv6Zl1Qcxx3HTmEDY3jgqqo4HDGMLDM5BUsAJuG69p07twCQSSLHtfOlYui7jUajXq/OzMwUC6VatS4KQjawLb186YkEEWESsCwTxzB/l7TcUqn0+c9/vl6fxnEiihLf96I0ytDYWfsKsDtwYmblQYjxJioKFE09puFk0H+oecK+wSH8gxY03BAkiDZkWyvJ5XJAcYrgiv9HLczA+yHgFdCIwAIighQZEkQSx8gkxsIgJBDBMhKG0aah37tzc2Nj48HdBzdvXt/d3aUZUhZBirxYqJVKRZqkbce6fevho4fb107f+uxnP02S5M72/uFedzw2sJSErySBgn6S4r7vDwaDXq/D8Yyi8r7vnz17emVluV6v7+/t3bh+8/BoW1FAchncr+2WSlXPBcgyZF9ZEvaYtQRfd+YXJ53DD+F0AOR+3JlACKaRhBm2AiAwWY/B9/3haJBX8zRND/t9hNDC8sp3v/vdb33rO3/jb/w1IE/Mz48Hw8FIY7iMHppdz8mbTFonmqZNCEocl4Fy4si2vcnk50KpOFOt+9lbIpwMw1AQyH6/S9Psq699Mkmjg/2jXCHPUOybb377tdc/KefyDx5t/dwv/OypU6eWVpY2Nh7+9//gH7q2efLEeoIIWhSjFHMC33DclMQ0vd/a6W5sbeZyua2trXffffcTn3j5F3/9l77+9a9funTpuZefhbidRacvnFlcXJzk1ZIkIYSpOZ6kCF4o1YgyAxEgkcAMUjDCNCGAUhfGKEHmUC8VwD77/f7R0ZGuOYArZeXPfPoLjUZjY2Njd3f37p3NVnP49NNPX7x8KV+oejBpkCjkyxgAUJnQDQmFxzwv8l2Gpbrd9o1331YUxXKd8Xis5ORG44jnWUHkRZH/5CdfoUlyenr68HDftu1uu5fP53vWiGW5e3c3p2cWCI71dN+ybJ7nFDkHdXKgO7C6DslLdmlCnAS8ISA3EqjP4xMDAxf32LxAPY6iwCAfb4uJSAqs2E8md0FwmJg0xmDzZMNk4PkfUzj6J0Kufd/nQGKYojA8ysgGQQjFQ46VWBqUqh7ef/SNr3/7u2+9fXzcgOaAF0qSsLp0kuUYWVIUVaYImHWKEDk/Jwoc+Ld2a/CVL39DluXR0Oz3xjhGsyyMLMexUBS5KE7H47Ftm2Hoz5Zq1Vqx0Tr+W3/r/yhKrGFozzxzYXau8t233tnbO6IoKms0DbOyZKZKkbWGEAHf7gQnRxOTuGJihI/7EziCJBbPuslpmsG7AwfC4x4M4vEcN03TUqlUyBWgktHpt5qdkydOP//SyzubW9s7e4sLCziiGE6MDZNAwMvOADeT5uTjeIHn2SAIXNcOQkDDTZCoFMXW6lVBEERe6Pb6YRL4se84HhgGinVjODe78NIrL9nu1zrdLktzgszeunPn8lPPXr16leal6vTMw62tt999z/WDc5curywvvvGNbzA0iWgi8P2b167uHe8vnl0+aBx6nvf8q8+//oVXX7j93Orq6smTJ0+cPzEzM4MQ8n0/iqJT504JghiHAeSsUQgdEUF0srolSgksjMbjIRz0AAjDAj92oLUepDEmMmKcVeZxjK6Up3kuB1XWoXn1yi1BEE6dvLi0ePLGjVu3b9/udb+xs9N48cUXn3/2k5ZtjEdmrhLgKD06Prj+1rcebTycmZnheLpcKfX7fd/3BVk5e/bs0sJ8FviRpglUZpohh5pue3ZBLdi2TdMshJQEHP47O0e9nlasFliWxxFMlTg6Ag4KIhmW5aOsGJHBa1CKQqCIZl7uMccXEeDjJuf0BAE1IX9lfb8Jch2eAEUNF6IcYGwREwZ4FrBibNb2RDjxn6BFgfAU+V6QgIBjDAKpnIRhRITCfk97++33//ArX79395Hj+DQF3TaGZhdmqtDnQYTjWv3+uNnsTkBeMH1OFOvVar0+Va2gZrO58WjX92KelaGRDV8ufEkg+QoVjYjjGIqGgRtHRwd/8S//OsNS165d0fTh/NzUhUsn5+Zm/sk/+eeH+82F+TWWAe0dUVA+gpKZINqgG4lhGWklMxIIQB5XaD84f7KfZQA0flI0o0jatgEXznOS50bd7jAME9v2Wq32+tqpzQdbX/3qN//KX/rLvV5nNDZlJR+DL4EO6AfQHNBMIIiUoghwsNlUkexv5/P5PAw7EsRGo2Fo4ziOJUnUNH047BcKBVVVisUCL7C1qfKFS+feef+9XrvHDBmoIDPU5acuX7x8IT9V/50/+J3b9+7uHmwVK8WVE8uGa7sjc79xwLJ0Lqd89qmfufj0RcQi27anp6cVTlk7vUYTnOXq03NTDCM4rinKcpLGCEf9bhdhOE9TfgCtAhYH0YCs8RN4np/EWBRh8EWCGwTl3DCAcvfIM3wXzDgbmMFjGEUgFksxArHa2B0ODoAeWZklLwr7+/tvf+/acASDqJI0fPM774zGAxhWk/gHh7uFglIqK+snV2q1iqrmNej86a5rUwTk81BkJkFii8rYWHEc9/t9y3SWFpY1zRQF5cr7ME2VF5SzF0+2e83hsEvRRLFYAIFGGupjDAhUwNDiJMHjbCgiRTCQnqTQoJqY4oTgTWRrcoZCnJdtjKxnnMGg4cn4hE3/4cqCagdBnAVfDvkfVA79Y3zgDzyCpxjP5bEk8CIP2MgUH3rh4cH+zs7e//M3/td+bzzSTJ6Tp+rztepUPg+w+l670213oihaXJr/1CunVFX1PM+2TTxNR6Ph8fFxr6utLq+gOmPpvpXaLEi2QLti4vQnEpQ0TevGeHZ2xg9sx7FKpeLv/u7vjMfD9ZNLceJ1e8dnTl/6+Z//wm/+q9/v9XoUKZZLVdeFVkFmV4/zQBwlk2zwQ7YENEY++C4/pFNAVZMEYKLjOMfHzdn5GQh7ukNdM8fD8f7eEYWou3c2bt68OTc3N9KcOCWarT5BkFCTQqzj2TiaUGPwOI5SYN9Drq9pY16AUfUVGWRzMQhxQ9vsbAz7SZJMT0+zNINDJOynWFQq5ca60eoOatUZRqAd36rWSzMztXfee9f19LE1+Jt/82+ajvGv/+X/Z3Pn4eqJpdVTC/OLs6LM/fyv/Fy5kkcIKYpMUQTNQ7UjwBwv8GRaBNZV6Pk+SFyncRLFbhqFtqmRJB0FIZak+WLBMXTLsD1v6MAkiSQMgMKSprhpOBM3mMRQpfS8IAqz0CslgiB1HN+2R74Phf5M2A72cRynruv6HmhAQCeVEUmC67TGHNsslpRGs+P59vLKzAsvPwPlaGPsuo6mjwgSWj4UTQqy4nlOEkGMatu2yMk+gnyCBuULUuClBNN0w8ZwiqH5g/1mGKCd7cNKvbK8svLpT7/GqEJsWyCOrukEIhFBT8opOLg0mqQwHEVJEmGZEU4s8MNTO86UwbJA9funM5b1mzMeeJa0ZFFo5iThFTiOA4EMDJHEj90n/LH6GbBfEwChYyRiEEG5tn/j5u1/+S9+89/+7tdq1Wq5VLt4/rmpmXkcJ5rN9ubGrjHWxqPRM8889cQTT6mqjCWpZRsPHz68ffu2a9nT0/VJUdvUbVWVoygFRBfFOI4XRzAOegIUxBAOUlaZ3qNhDv7CX/gLW9sbHMe5Lvutb33rN37j/769vQHahCTuODDwhOeURqMhCMoHp1QMsQYxQXh+nzb2YTj6IXx8ciUmfULbMYbDYbfb/vJXviqKEkUxo6EGAaEgrSwua2MzjrC93UPf9cul2te+8a1XX30V4bTnpa4XAdotK8HGMYogVgNTrFSqSQJ713F6GUAHqk0EiU1P16M4EETGsBxV5Aql/P7hwcPN+0uLK6snViCkj5K5xRnTM/XxeG5p+uTJkyRJlusFtSCsnpj/lV//wljTFFWiOM7SBivKEoZjgWvRHI9h8WDYIVgkCrxMiyO7H/txLpdL4th3vTQGfFKaJJ12r1QsWqZtmqY+HMchABVogvbcmCEZ29JdM7EcjyY4FyadRY4XOKanGWbohVCo86FJPUFEZA0hIo7TKMwUCtIMMxhjHhWSJB1HuCQWHj3aGAxGn/3cq7Mzc/uHG35g5Mui441rtaofuCyBJEkM/NBxHJLmICjlaEHgNG0M83pDP45DhAFS1DCMWrU+7I2Lhcq9u5vXr90pl6rPPfuyLMOACsDBDQb9fq/f75IM8JtdLyKynhkcEwSEJ2EEaCo+22Yf5ROG0PL9gY3x4cE90Z75gH/3OO3PnkbIkuI4gaHbjvPxkXp9PxRYyXItEQLO1LANWchHiZ8mWBxgDMeHfvA//8b/+odf/aY+Ns+dPvPyS68iggr8+M7Ne812l6aZwI9IhErFcvO4tbv9b7rdTj6f9zxHFkXbtgHcgEh9bEiyALO+UPrExSeAljocV8rVXg/oamHoE4SAQw0Z5N+DEOBjBIlsy8/l1FargRDq9XonTpxoNpoEQbz++utf/tI3WUYCyIXnZSgnpj/ocBwjiJyuj4rF4mR2uWWBrkQUJiBlXywapiYIgm0DOnFCNSyVSuvr6/V6/eq1m7s7h7ZhSoKappAO3bn3aDgcT1VrGbSKbncGkiwcHbeq5dLW1haZHYo4jmWUmYQEbdvED/wo1mQZNKYn4u2yIsIMiTjAMc/STTKgMJTaroUReL6ojPTBKrNqu0ahlGOAqcg/8+JTMHQPgD4sAJshJcdKVSlKHVllEAFwVYbDXE8HICCBB7GFYZik8DiWpIHvJS4LSD/S1c0oY1FGfgSnapKyiNI6I+hWR5EXR4EXBlFkBn6cYHYU+wEGw85c37UMxwMFCcfz4xDqvVAQSCCuy2INmECRUa5JAmGIhtqBLKmDwQC4oJB5hrKsel4wO7PQ7ba/+MUvvfzKM8VisdM9uHv3bqEoS5IoCIIFklMc8C0BsgbkI8d3nMAWZAEjkKIoE7BhgiWlUiWO0kKpEobpt998s1Kdbhy3TdPNl6W9vQNB5AFeEcdTU1Nu4Aehj0F07RMEyTB0ikc+/IERKFlkVoaywtzkIIbpItmdCbkmyxjh/x8wAXACJzKSdOpkslHgA1PsYPco6zBFMO7745K3EFjZdDSOE6I0dGxPFvMYhlumzWAcp+Z379z/n/7Bb3zzm28oueKli0+//trnNh5t37h5q9MeBGEsigoiSDv0EQXwmtFopKq5mZmZNE1tG0Dxi4uLBwcHUQQTVUmIE8goDnRdnzgihKBfBLFZGLuewwtCrV5pdI4MQ2MY4JXevH2DpsmTJ09atv7Wm9/7mS98rtlsv//ejYsXnnnmmWeuXrnFMjIep2HoplgAcBaoTYdhBDNoOVbIxrIDGw3mxWZEmFwuNx7D/BPg3Sf+0tLSJz/18srKiut4p09f/t733rlx7Uan3QuCaDIGKInxDCQH12BSO9U0jeOYKIl5ngf5JBdGhZE4ojjA1sdYCiPp85LIc37kZxjvOHacKHZ5geBFLl+CuqsgS6Iozi7MIZKYm50P4ownQdMTaTCcJDECBe44Tt04dLMIO5kcypkSAVCi8SzQzs71jC+SJKHrZDzyiWvCsQSPIpCJxkBN1A8y3V6AbUNoFgVREgap64e25bkuKBK4buDYnu9H45GZ4fdgGk7G1WIQgHdTEBrMEEUT5EMMCw4ynhPb7bYIB65JEJD/9/tdVc3xgtjptDKEPScrsmEOB4MRvEgaXbp0EcIr12U5FmqTOJYdVUQUh2kKuwKUoWHiBQCfcDCqUJbU+/fuHh22RCE3N7vUaHVTwiOZCMOSaq2QpGEch5woOI6n5oqZf4aXiT5ossE/H+93BG0IOEqySYd4ynEChE5gnkB2i+IAh8mnqShJ4/F42AcgqyTAca/rZrPRJkl6ZXGNpvnxSP/JPOGHchKPP9DkFsJOnHU8K2s/ItPWVEnFCOFbX/zqP/5f/vF33rr967/2Cz//C798sN94792r779/Nc1mRPmBDbBAAUKCDOcK5Add1yKYahAXcgqMjLSMqVoVuuopaGAmEcqmTx7Oz89P2kG2Y7EclfX6IIIVcR7H8V6v99xzz2xtbt+9c5/jmUajVSgot289ePnlTyGcOjpqLC9ZlUopjiNeIKMwAaXyCGc5kuNoisZZNssKsnoDXAkokADsqNFo0AyYJRR+OW5hcXVtbU0UxU6nMxyMBD63tLA0VZ3Z3Ny8evV6r9djGC6jDoWIQqLEJ2nkes5R85CgMZrBN3dAHUyRxFIlT1OUbo67vQ7gLXcfVuvVeq3CCTQDOCsO5GootLi2hBCWz+czNiNJcqxaKhAsi0Uph1HQ3yAJsC9w0D6OEpKFEB3ePWM/A2gpi5TiEPDtExnISVYDld4oJiMsnWw6EC/CkygFlF6UxgHExq7rwTQhMMjIsWzX9TXTcxzPggHXbuADwBUgRiEmCGKS4EkM1zTCfA/LoLlpJIggsjzhE31ULZfluRiLEIXzEigIRqD+4cSp4Pl+tVY5fWZ1dXU1jKzRuDvUeywbdjqbZ06fE2XJ8wA7SpEoSmNB4ODPAXgxfDkIQWRIAUAHomUlp0Z+cvXqdS+IUswNfPze3QeGVZBUCpQdybPFkoJhKZBUcNOyjCSGnBK+swzdgrLvm0DAtnmsSpvhiyfqDZZhMTQFcmwESVA4oOkytVJdN4v5iiiKlmXtbu8OBgOEw1hwVc3v7x48erR9/dqNjy0cDWJXFkG/DTrXNO94pmEYklB85xtv/MP/8R8OB9pLzz2xurS2s7V/4/rtK1ev+yEcHiwD8FCGhQHHwXik62Mp46/atk7TNChSESrL0ro+zqmy40J/1zR1URJohvJ8GyS6dGhFJEkEgasspynG89zc3Mz23nY+n7906ZLv+8ViybLMdqsbBEGxILebI5oSpuqzzWaTprhKtTAeARojjDwcx1mOxoEqHE0sECDCgYMQbpomgbxM1JWen589ODhgWXZtbe38hdOSJOn6eDweG4ZBEoNyqS6K4tR07Yx3otspTmDiosR7nothiR/EfhDqRrfZjgHEJKBer72x08tMHeQoFUVWVXlhca5aLc/MTpfLxXxeLRQKrCxAwS22IRiBmCGMoXTqg2Q4iFZEj9NU2PePR+3GWMjTZIKH0B4CiEec4QyAXDLhZD1mheEQA4MRhjGZIjxKQ6CbAsYlBN0J6Cn7LohnZviB0LFdw7C0sWHbrmE6fhT7Hhz/E91NLIXZDLblf1QS4XEWnaH8JuWLD5qxGElCQKfrWrlccl1AsaUpNJmWluZd179z5w4olNKJZY9n50DUzXKtVrPnB3a73V/Praiq+hgHAp075Hh+FrDQDAN8a/DWYRTHKUK0IAjv3Lj26OHG3Ny876XjvnHq3MnnXjpDswlJJTOzVZYjHMcyDMNxbFFSs6rSJMHLKATZV+SFUTYnAT41ScIcEchscUzkZQAFR2kUZhpFIZxWWWGGvnPj/u72TqfXDf2gNlU/sXYyV84d7x+PNWtnc0//ST3hH7MgqiHIOI1ZmnN9D8NQpVLb39n+3//FbzYOW88999KLL70iieq333x7e3sXx6lardSDoTkWy7IwtWfYM207n1ctY4iwyLGMysJCwxxv72xO1WoZDQwq9gxLcjxk3hiGjYb90bhPUnDc5XJKGPmj0YCiienZqSeevPQrv/Yr29vbUQS8sn6/v7u7+xf/4l+8efNmXi1sPNqHOZJq2XXdKLSKRXU47EZZCxN2apoCnDAbAOQ4TuO4gxA5PT0DHCUXFGhglCxNLy8vw+xAWd7f37csS1YERQE9BV0z9vY3YMD14bFhGBO8P0EQbjgGNCmJSZJQqPBxHB63NzVNg8RNFM9cWDlxYm1xcbFardTqlVypgKUxlsU38F/kR2FoGV0vcJW8CoAs389SKvAkJAEleIC7Zrsb5BcBxIPBPsEw17OgWg79ZBAfh8sUwUtSIOMPppmxtIDD5QdRGkaO6yc+AOImwh/ZnwwG5jhBmmB+kFiWo411mH/m+lBeAXoXqBAAVwxgtxO8EZpAnCcyrpN6HiIyz0FMlLceVzIImJUJHsNxHUQoceIPhh1oz9J0qax4XvBrv/5LOE7QDNreBpC4KLJ+7JVK1U6nubd7uLi8VC5Ve/0OjmMcR3teMIlWQBUWWnBREEBklab4dLW0u7v7h1/96ljXZKni2NFINwqFgmFYXITNzleyIcGgUmU6IJaVQOUfPSavgdZ2JreNYWquisUp5MkhCH+FoR+EIRZHrqsFngs1whDg0mHkey5AhrCU2Nja00ajQqmyvDhfnaqjFPU7o357iJNkXsm7rv+xGSFLCbZrcVBkI7rd7vzsEoah/+3//U/+9b/+5i987uWXX3xFEKT93YNb128eN7vT0/PGWM90xUGp0vWhJpFNHvZtW19bWfzc5z57/vzZ8RiG73U7nQcP7sHQPCcGqHXojDU47VzX7nbbM9OLrW6P54GfDll4EA6Gvbt37777/vuu68qyzPP8mTPnisUyRTHzc8uddnt3G+ZgTrrhQeDl8upnP/f6JCgKQ59hGMuybBtoaUCH1t12q6/rOsMwkxTUcZwbN26USqXhEIhqLEdqmnZ4tGfbMApzInvhOMDTLxaLMPMdGqDUSBsyHAf1utQiaX5uqra6+sLM3Oz8/DzHwVTlyYRAz3fTNPbcAYAMoyy6JEmMhEhI5EQRE7wkQIjwYhfEGEiguwH0LwLoTEbxAuGGxxJgEHZGoM0PGqskyuQIwEKBmIgAhR2ngR8GQCCPHB/cNUgQWk4ShGB+0F0PAz/x/TAKU9N24gj3IP60dM0GtF3GqJzgMyfCIzFUYGDHw9GQTeEF/R3w10GSRBAI4yDrRABqFNwgQeAczxeLBUWRpqefjuOQINH9+/d9311fXxFFsVKp5XOV7cz+bNtUZJVhScxHWEpjGNE47uiaVSoVss+Q4GHq+x5FC+COAA4WAmws6/hl85Lou3fvdrvds2fPakNvQjK0TBvheVnma9UpP7QQgg2pmRpFUTGIdxFpQoBibAot3AyFlxzu3UoiuD+RKYrABsM0TqBmE8LTWIYRBAG08VlE4HG/pyuiWi/Vq/WqJIpJkJqWYWrWdH06TjGtZzYOGz+xEf5QWjjJCROEIT90MlYVun3nxle+8pVf/LmXf/UXfz0K00az/fDhhu+Hqpp33axOkAmTeZ4nq1KhUGr3uq1247/7P/23BB5fvvRkp9uqT1Vfe/0Tw/5gY2PjzW9/m2cpGP5I4XEIBQ8Qe67XeU49bBy7LiCvWVYmSaLb7ezv7wmS3OnA+AHTNBmWz+eKd+/c/6Vf+pV/+S/+1ebGHkUTgsCOtT7DkrzAPPvck5mMeTJJ8xzHjSNAY3U6g8BP3/ijNzc2NsGbOb4gCLVardfvCIKwu7v18OFDWYGRkUHoSpJUqZSSNPI8FxFJpVI5efJkmibbuzutbnN1bXl1dWl5ZT6Xl2iWpGlSzcmSUtDHfYYDiRMMiwksEWBkPJlAWpb1IdM4yrzVYzwNQhQnEBiDYLQFkNO80EMk7gYuFAaAOwfHNoyBylYUBQTCIJ4M3DSKgRwWJLEHVAjXciDI9EDpLPBDDwwPRh1GjhOHgesHmSABJHgBUL9wy/WiGF4KCjBBpvyHcBTjXhBANJnhHzMk5UTOHI5IEgStEEuRGRYJoIs4QZGUSFIsRM3geRJFUer1KkhFsbTvuxRFyDI/HNljbaDpwyAE+QKOp2kGRqBhGDYe6ziJ+1BXhG/GsT1dNycICse1MqJJ5vOhBAVxL0Tc2fyW69evP3jwYDwe53NOt9vnWIWmhN3d3TMXFufmFvL5Yhiz4ERpYooBAamjw2OEANhs2YZpG65rWbbhWO6ob1AIOKXQzWfg0gmcSCEC6PzZG2VsBAT01MhO49gyQDBZYASBkyRe1oxxt93dPzhaXlwKMm2Farn67zLCD/VdftT6PtqO//79MLF4gYUM1XJqlQXPc3/7t77c79nll2fand54bDAs32y1HS+oVKb2Dw5LpUoQYYIosjwHc3aGQ4ZnL1++OB4MD/a3V5dXdrd3gIeehJ1mSxT5T7/+KYYBELPnOXqme1sulxmWPz7ui7cFhAjDsBw7yEbYSnGknVg/NxpaR0cNURTHGnT/T5w4cfbsWUn60sN791dXV/iCPIwCVVKjwBn1+0kKOZXv+4qcS1MY/cFQVByE77197ejoCItjtZC3jePR2KlPlUgqpZl0YXEKdH+z/1iWxVFqGHp9qixIHFSlZXGqVuUE7unnLi4tL4DSIU9l2JuAEzloR4ZeGFkKaN2SYQKDXDAMup2T2i/wCzMxKNAAj2HLAs+KpHu6JklpGAQMTcPojDThcBpjYs+ysiI9kYQZCR+oj3HoB1hCOqZr6kbgwmsmQeJaju8Ayz8MYApnpo+T+CApAB7E0MdxAJYJjhCsMg7CFETaMBRn+znDhcB1DwBJbXMMiMATWchNAIIy0+vM+p4ZkBbLPoXreW4Y+UmYlJUyIugwfKwh4Acuy9IUBUIVlWrZts2TZ04Pep2jRiOvKt3+wHYAhvHg0YZtg8RbbWqK45nhuBdFkazKBIl7nsPzHCIST7cEWUY4ndkC5IigZGl5rg0iQ5efepJieIoRet0xw+MUnSAUASKNSBRFGg77I62rG/12u+UGbr8/5FieZjkKsVF2AiZJ5Aeu6/iztXkANWWgmckiMQJwMjF8A5n/xxzb0zRtNBpZpuc6cavZ397Yffe961FG5J3o3Ny6cTcK4YyDaPyPt7QPpSV+xALThMqGn3wg9ALPiTA8CcJAYFQ/cjMrZbvt4Zvfun3ixNNvv3fdMJ2XXvzktWs3Gp2u+P9n7D+gJMvO80DweR/eps+sNOVdV1e1RTsAbKDhGiABUpRIjShRJMTVDFeitNLMama1Z88Od6UVZ6XVzJGGVhRJkQThgW50A2g00L6qy/tKbyLDR7x43u/57svKLgKkdgN9ClWVWZkZ77177/9//2fyhUa7TfOSYfvoD9AiIEQyTPDTZLVM6AeHFg8JLHfh3fc4ll5fXl46uDDot0WBm5ubPX7kSLvd3IwdLaPow8bmdvO98zcaux1Zys7NHNrabgu8wLECk+SvXVlpt3QB2Ve5QrE4GPaC0JNEvtdpyDJ99+6VwaB88tTRs+ceOnRoqVwukhSRvsBptmWtr220WuhXO+1+v2cmFKOpsirzp04dHplDijIePrNUqpa2tte63S5hLeNVq9XGJ+qiyKpZuZArZHIgx7J8OqtNFEUgho4JRfHwkyETW5qmbLAL0OtLWpbYgSdBlFC0SEhzGBYQOCCmKI5O2DhmClKG8oIsy1MOulM5oYxBNw5CJk4804p8dHcEsEF5H4Wx78emYRujkecRU5woxuJyXCwBG72f70WW5egjO/BDlmEi0stJqlrIl0p5leOETDafLeTjOHnlu69WSiXEcQ/7qqoyDOM4riyoUPcQ6D7tKYg7CNjwoZ9aSEIFHUZMGAI/Hw5Gs7NzaXgkkRpTpuEOBoai5Te3d5Eq5ZjtZuv6zRuN7R1ekEYju9PuCSJqB1nRDiwcmJyq2jdHTz37+Gd++hNDvZNQvqQKnm8tHT400oebjZ3xiZk4YvL5wg9/8ObW1tbU5Mzs3PTQGD507uTc/Mz77186/97FK5dv+V40OzYryslLL389CJ0wgjd2JqvyjHBk6agsqzHF9DuDbn8oCKqmKJGYJHISecz09DRHM71ej6XZcrEMiQ9FXbhwoUycVBuNxvvnL9y9e5eMsoquEzuu1+10EKgYAGY/fPiwIAijEXyxFBZA9/8/5eiDYWFEG0F6VrL89ldpwgtMTLnpyI6i6IvvXxsNg2Jec9zQ9UPHD4YjI2ZYH+pGDhABw6CjtWDHFMQBiwxlRVUzmpbptXa//tVv7DYax44cro9VXvz0Z3iBXr13b3nltj6cjkJvYWE6k4HA9PTp06fPPPrlr3znvXevrK1t6LrHMVo2U6pVy/fW7pbLVccbtVotmoGhcrPZXF6+uzA/szT/4cNHDs3OTk5MjtE0de3alS99/7uDPizQ0VhHKNLy+eL4+Hi9Ul1aOsRzIvDS0B0Mer4/9MNoZHJakR6byM8vjpdKxWK5rGkaUeKzsioIIux9UyX+Hi5Iwzsx5WfvlRCkWKIZVmFlwCUkSBSACo4VfATm74SvQxOhEzAMMidGHtAeRdNNE1pCD2eZY0Hpz1DQnPoOnGl03bBMz3UwGY/DyHat0VDXjaEDb+IQQ0GKFjk5ky1mtGKlMqVIqizLaRYaNFZkUGE7bqfTuX7tFo5FP9I0DfJ2ywpCT8NLjXw8GWkADpn5pRpkFIWpJITUxwzJlIOBTRwnrVZrMOi3Wi3CrqSCwBMEodUFCdu0UVtqGpgJ2Wx2bGxCVs3TZ87NzEx3u+3BsHP91k0lKxw7fuSVV7+lW5048T728Y+Oz0yH7nB7Y8Ny0ArZnivz2n/+w//SanXPnX307JlzV69dFEXBdd1KpfKxT33i8Sef+OY3vvX6a28Nhm0SBUWP1acEEf5AtmOZpolTnJNM0x7pIFzm1ZwqZQd9Y9DrJ1F09GA+DOOrV97odvtzczMMw62trVy6dIUc6aLnYVpDxOhCGI1SM29ZgSOr5wHGFwVAcZoGdYKmqI4D39G/SgNByAD3M4setKsg+DKdOpelHyVRuDjRuCAKeBbxbkkcvP76a9h66RzYgCTdu9vtI7nG9VlWYEiaJHGthrJYoAUyY3BbrRYTuaqItI1Dhw6xLPvWW2899NBDpXLO8x1FkeYXD1y/fq3b7Y5GQ5CG5cyBAwfK5TLmIjKvqLmMpmBrREi4n0ReJiMemphdWVsOguCll16aGBs/cuTI9MQkRVHf//4P7t27Nxz2bRRG5uFDR8ulsfrReq1WQ+gHyV62LGt7d0sQOJqhZJmfmaueOD2bL2Yq1XIQe9jJkLLASuQoTKvZbB7ciDTYKI6BkaTUUJbjUj3wHqcJXF+aisLRqM8KvCyIAsmUjMPAtSBlxlg/TdMgkzTAJJadmvOnTmFYfqkjOCB4zCkcx9F1wzRNmPyi44KzY78HmMl3XF4SqqXy9OySKosJTRdzxSCOKcQ206EbGbbjWZ7tDFfXV4LAMwyrO+g7ppPQLDYVgd/easigXRQkSRBYPqNoHMMlYSxJSuoJko5SyVDXTEFpYpxFYgX2QgRQjG2ub09MTCgagDQM0QVB1vKZTObso4+oqsqLiKTH4cBxIQSvPCdIzWZrc3ODZjDBm5mZ4Xk8HoIgnTv36OwsFo85GGoZWRBkz0+0TKFaHX/l5dduXr/7zNMfUeX82vKmImR7vY7ECfpgPQzDxYXDP/+Lf/9jH/7s+xcuubat9x06Ng1zWCgUut12FAccCuY1w7AaOx3XCYrFXY4VWq1up9MJXOfmzZtRGK+sLtuWc+XKFbgNOxZFBHQ0ZSVULImyViyyDFRmURBoKrxFBBFIleNAc1MoFMhthcxkbX3lrzsJ9w1g7lvF/Fg0fPrHB+pVgn7HRK+BgKHrN67KCsBi4vrI2pZD4n44N3BINs2e4IocF5DRhiFtO2av12MiXxX5IPDmDszkMpppGWGIQvGrX/6yIDCVaimTUUqlQiaD7KFmq0vzOaKpxVwooaiR0dttbpp2K4ysciW7tr7cam9WalXDGH3qU5949dXvyDyfwCdfrFQq9drYoYPHa7VaoQhPHl3XDQMpSMTOME5p9M9++NFiMSdKPMNQmSyyhGgOGSCSCjpY2nohTUYWKYZXKS2O8G/3KIIkjYa8V0zHyeKEQJHgh9CbU3GSK2QpkL88zzRSUji57rRj2o7jGIbhWrA2BXQM7QGRTiU4VgHQOa5pwjYHO52DzCD4d8syMRkgzVvETJ1YEkRVEvkoiS2Yt+gD3fB8H5Y8vudYvou+MAojEmzKJH5gxUnIc6KiKbKqeG7AMJyiahU3qNYxdCbyRRi4O44zsi2GsTw3MMnLcZzUL4u418GScB/AUBQEM7Ese+TQsUqlomjwpEuXKy/xKqJzIEPp9/tExg1iar/fd11/a2c3RVmXDi6MjdVmZhFvlMuVJsYnd7ZbuVyOpuNqrRx4SeDTqpKvVQ+899bbL33ju4888ngScGbfnTk6RzOJpmRsy7BtTHpHAzvyBpFHHVw4snJvtUMPzKF3/da9sWpFN3WB4/AcDA2K5SzdNmyn29TDmLENIFqCyLx/4aLvh6VSYXJyGi4HQTQ1OW1ZjqYpgiCRqTUlCBC4UhSHh0eEKZsPgh92JbgKCIiOxGcmYr1e/f9Zjv7kORneD8dMkZu9dRgnONnSkKCdBkJYFaXoOFaSULKsOo5nWQ7Lgl8f4xylOVZI41ZwHMNnmtAeOA6mdLCrklJXH1lRLly4oI+QNlIsZd98840XX/y047nLq/c8x3XceHL6sMjBLmmod2kKojtOCBjO+8QLz7z08jdsZzA+Xl9dubMwvyRJ4ud++rN5TTWGgyBIspkcMvQoCOR2GzB9iQBWASYMAqAILMsoKnflavOJJ88dXjiuKHJEAmMYjsOgkkWtyLKUwKMliAI7AaGcS9K3l1p6Ez5FSvsOA2TW0TQma1AbBH4QhHEYeZaNuYsXwmWWvFwH4ydjZKXMQ2CgMTirpmnZnhuzku1CKhVFkSxKuVyhUBqfUJScBikW0qNwbPpI6xuNHNtfW2uPDEsf9l0fJjwI00SrmXiuy+IPHMUgaTfE7CsI40DNCb4HkasdOIV8OVfOGyNnc2dbFmSG4XZ3W9vb266LEi5tAh0bQx1VVRGam88T0SPq8GKxuJ/c+KCfcqvVbXc76XtNqUgUtuCw2W6VSiWeRw2i5bKFQmFqdkYSlRc/9zNEhA6/wOXlu41Gg+OoyanxKKQvX7oGLltWmVs8RIV+Vot5XjB6xt1bG1MTc7Xy5KA/qpdKvgPvjFqtnJFUeRK607t3Vt9775t3b60MBsCrwtCfm5kdWfbDJ+YemZ5QFWVrZ/PLX/qKJIsir8YizwLsoRUuK0BgHdbrdTKCihRFy2azZF6KAh9TQbwV1CBQtyIlHR6KYQBXAcfxKYrhOVgEkHgc+JczTHz69Mm/EpjZ973+yRdoheQkpEnkZSqfT3NsGUyNqTCOqeXlZdd1RD40DDxJqqI5DiaYLKxshSgCWTG9AYTyQ+hR5Hb2qH5LEePQS6KwsbstivzMzHS/32+1G91u98jRpeGg2+21ibjJqlUr2WwZRaDvIOQN7h0RxyPJy7R6Fy7+qNXZKJQy5WqOYWc+/alPrKxurK7ezchSPpvhWHG32Vhb22BZhDR5HtiGgshFEaheWkYqlfL1sfLMbD3hnENHZopjBaBRIZcEyMRmJMKvD729lELypKUPGcuDgr3/5KW4RHrEpeUZsAfTsWzDsT1kMvV12FAShj74KC6BKQnZAlUyVEIuxwnw4c1kK4WCnM1zEs4NDKZCFB39/qDd1C9tXYcimizglA5KLBiAEvphLMmqqCosTXuh75iW5VqVYglhehSbMHRG0WpjYzNTU7li3nT6MR2vr29efP/SVmOjWKjwHPrb6lid5XjbcjlWKBVRh2ez2TiO5+fn9/PZ0dSZKePEvnHjxv3ZL+b+aVcSBEGlUgdTPKOlTBdOFCRJCILgsz/9uXq9rmmY9yQMSjvLslJJ9OamvrW90e93XdcuFPM0DaOTyckZVVUr5XoUBY2NpmUZpRJu9vL5S8bQeeLRp1eW14q5ck7Lb280VEWQeenu3dvvvvv2xfMXe71huVSXpWzgRLvbbUEQrKLLMjxL86Ddx6Fnh5qcS2hoAKiY5XkgrqDv+ZRu6oViRhIVojPWeYFlaI4X+InxKce1HNtLqJgIERFdCFkPMkvIvYjB40mZw0mSkB4HBtCKKsJb/scX2r7l7l/12tNQfRDBSZHQ4JQETIdhJPDq5uZmGqVCmvVEkhRS4AEcQ6667bMs73nwSCXD+pDhOYaBr1kcOX/n7/z3t29e+/73v9vrdSrlYn2sOhz2VzeWjx47fOjQ0o2bfqPZPLS0NDMzlSSx4+FfgcmdBJoidPo9y+7zAiPJaruz9eJnP/bMs08vzC/mi/W/89/8/X/6T/6Hnc2tTntnNOyvrmyurW2YppvR8tlMkcSsOqKUkxVJEKlyJVcsZWZnJw8fPVCfq8Ih2XcC2+NlmZYlOnASz6EFMc2LTEKXE3gO6jtcENeApXTaAqUvzLyDwDCMlPBNTNpd18WYjori0IG1qElKSoZhNU0rZAuqkuF5oTw1WS5WMIBKoLIdDkeWY6+tNkwbJpzg0OJ6ShI0zYKWKaU6b4aki5FvZKLN7nS1rCZJouWYlmEqmnrs5LG5+dlapYro6ckpRVNh9ec6ru24vjMxN5YpFULbffONd7/z0qs3b9wdWSNRAp3A9yAjmJmZ0xQ1SSiIGGzzxo1bpmMOh0MScopWNmX2RREBJGQ5W8hXyfGYmlkposrzfLlW4Xm+1Wo5jsNxjBf4b731Fs/zBMcdpSvQcRyWZQeDQa1Wy+fzosiPjdUI4iValvPwww+//PLLX/6Lb0xPT104f7XbbRcKhSeefKzT7Fw8f7GUr4ZeeO3KtTiMFxbmo8D5vd/+nWvXroyMYaVUPbh4iGVE30s0Wclnc2hlDVNUxE6zk8tlqTiMo0gWxDBJAsaHfpmCN5nveqSdwLiY58V8Do8NGZBoU1MTwyF+7DiG8IKiGMcGnYgEOotMas0OPAWlje+7ILvBb4bSNCUIwJh5cLWlOezxTyRvPrAIgdGlAYDk171/xdIsFYWwjZFURR8aLIvGWpIkiwTHjXQ75WdzrBjHLs9jzJJ2jLgNMZfJqLl8RpWFw0cPLc5P53JqY3d7d2eHPNCoWdPFViwWAcfAF2hV14dz84eiODSMkee5CRWGvlMoFz701GMfeurR0w8fp6iQ1zTfsLq95t/6hb/x9ttvHFpaMi39yNGDX/jCF3hOfu/dSy+/9L2VlY1UHhEEjuu50cgO41FM5UtlJaEmI89mE4Syk8KJcLJonmYpx3RhohxFuj7yA+iAdd2AIacguwScTB+jVGme/vBE9AQQJf12kqiIPJKPqpWJQj6vqVmWZT0PlnOO43VhH9+6evmerhuQXzBw4pBUJV8qF4vZUrFOGk0KgkMclm5zt4+V7WDhgZ5CJ/C9ZWleYhvtbSoKFw8d/NznPv3IE49O1MZpnuElmeLY2LS2GludZiuIo4yiSqoUxa416qlq/szDJ9vt9vLKmg56Vy3FQsbGJkRRXF9dv3HjBkNzlmtqmioqYi6XGxsb0zQtRWhSC/19VClF5tKbOIz0drsdEuV0GlsvyoJlWSlIQ3M0Gd+PFwpwN8zn88VisVAoiCK/s7PDMNTKyorv+xsbW72ePj09u7W18+47Fy9evDA9Pc1y9PvvXWRoanp6+vrVaydPno7KyfWrVy5eeNfzrcB3jh09XCjk2u1uY6udyymarJojK5/VyA6iU7S6trbiuJjloDsddEUBdpiYoUVBiFoplhQliChk2RGdgSCIcYyCZXe3petgnFIUTVJooGOmaUZVNNv2sVQYXIdU4CsIIMcROXjMC/ROY5PjKJ6cawncGvf6OtLt0DyqdZoBmxfe0mlsogjYGZUunDnJr3uZDuAcMbSqaBTFffazP/Of//BLCQGr08zhNJQjn883dtrp3yQJrAdc18ZXFdhmq8Hz3OL89NLJY5ff+mGj1YioeG5+NsXEiCNtrGUzfgBMEuYhpnHqzJlioXrx4t3VlXsmDEUyjz/+6N/5pV9YOrJEsaE+aOXymqP3cKRMjJ1jxSvvX37jjUartfGRDz+eUP72TuvZ55567mMfe+lr3/qzP/uzdrtp2q1SOVup5lWN1zL8xFR5bHaSAjcVelYmDAMgGpaLqiro60D/PNfHMMDAUTAajVzHH/b10Ntjn6QBLJhbMkyhUCW4X7lQKKSrlyxSKEgGI3d9a3kw0B3LpmlGkTVZVou5oqSo+fzYgijTyF30R6AjGMv31mwPeIxpmq7jx5hMAx8KImLwTISUsFuPApZmDFvPFtXTZ0787Oe/cPaRc4Igwq3IjwZ6tyyWraF1986dK9cuz07PLB06qA8GnBgHketbDs9yWiH3/Mc+OjEx/cbr77z37sVRb2hb/vTEbKVSPXTwyPgYMSOLA0FkYxogVqfTSbWzSZJ0uwC3UqUluF2E2pZWrdlMAaboApfP5zNZaNNoBorNSq2WzWaLJXSVHtmzRqNRt9dbX1/d3d3d3t429zzavKWlpSeffLLVbE9Nz5jmq9ls/pOffHFtbY3juIyqtNvbt2/clERVYEGpKxdLtWqxUMhaVl9RZJalFVkqZYuQfZhBtZy/N+i4npkR1Oc/9tzs7LRpjipVcO7DyEP4zMBwPcv3Q5qCFANUoziioLVHzHXgR5iQe56u60lMixIvCjKGB2GCypOCdYUEnwGsQI7jTGvI84xtwzFIlsWE8tNBPTc0wZQjIhdiiUjAOUSoE2YZUYLxipjdi+ajEolXKYoOQ0TVuk4atwL/faDrYajrehTSW5vNMKA4Fpa4POEHBOH96CJ0kjzEBJ4nyQJh0DOyKgpiNU5Cy9ZvX35/e2crX0Da48rqvVKpMFbHNiyK4vj4JIMw4pph6GP1iVs373S77zKMMhoNp6YmfvmX//5nPv95KnQojht0mrzAUopK+w6nZfTmlutE/+g3fv1f/6t/pcriO++8c+7soxzPfu/735mbXfz4ix8/8/Dx3/md3z5/4e2trfUoLgtSMr/woePHD++sLVMwkMSZg9g9yxsORv2hblnWQDds29b1kWEYnh+mkbSiIFQKNb7Ia0pGy2ZUWUFoA8ZoVK/T1Y3R3Vvr/eGlwAtZiJAkXhAkVRJlqVKdnZlVGIaFtNB0XMe/t7ru2u5oNDIMy3VQ0ML6hoLxJekxUocFSBagM6IiEQEMDPCe0Bkfr4+MgWmaP/e3Pv/sRx4/duyoVijgivuGIAisKuZozXaMV7/7ytra2qlTJw4eWjTMQUwHoqT2um1ZVpOI4k0rn6k8/szTU+Oz9fL4xQuXV1c27969u7KyWi5UZmZmWJbf2dy2favf7/Z6vXT4ia9PKlJRFNMRjoKsFkQmpY+ZZQLUEQj5KSCTDGM0CsNwfXMTaLAJYNIj6fDpF6Ghx61MTEzk8/mpqal0R7Zte2dnZ2Fh4cxDD5PgkOTi+5cMwzp8cMF1PEngHRc/1Vht/NTpEzzPdHvN8fEx1zNJU+CJEkvTmu+PhkO7WMwpKsi9mYycyythbFF0yAs0x1OCyAoCw/EwAYtjhGTvHThpBjvJTUcNSEcMzcEynuSBp8KL/Zx20mVA+YthTCxz8GkLeZ5VFHkwHDmOpWoyl9eq90tKWCliZAxDYU6AKpeybLPfM23b0XW93++PdLPd7sK3xw5NwwYoDfwcWhbPc2gmsSwro+V4Dv6fGTVjm54kQU95H3yHd11K3lVhIY5gZM93TJfVMgrLJq1W88KFd+u10iOPnjEMIwaGZ/Mid/zksYXFA8Tz2Ltz964Fy8Dp8fHJ06fOfe3r3z7z8KnHHn3yQx/60Le/+lWGo/P5zKNPnKMyXHv9nmGMJhNGVdUosi9deu9HP3p9fmb67p3lXLY0OTkdx+GduzeiKJiamvrvfv3Xvv718cGwc/LUkZnZCUGkYQ1k2iPTN0YYcvZ6vZEOBZ2PIVyiZLKyLE+O18h0S0jHPlEQ2bqFuJ9hb211O0K2L2n84iSfyYuKXC1OzEwvcTRnoQ8cjWyrj6/a7HcQhOQ4qFzJ9A+pLCy5qcRbjxElTUS+Hed6I4GFIVh6wGLURxCfOA4VVWR4LZdX1zfu5XKZf/4vfuPUQ8dzFQ17szdKsxTbnUZGyyHo790LKyv3ZmdneYFdXrk9OztTKGRhK85yvutRMR14kWeEpWI0tbD4i/OHDr/25s0bdzdWN5rNzsb61pVrVycmppaWllY2lmVVqtZri4uLcYwjsdfrAeqwrFQxmEYGDHSEwgdBIPAwoXAcez8LmSPchiiKstns0sGDExMTtVotk8lwAg8jKVlUFBQO/X6/0WhcvXp5OBxBecPypmlvbzf+8A//cHb2QKVS4jhpZ6cxVq+4thWGmEI5rnXv3h2Opyk66PeBvaUYGZsIHCdXq9VCobC7u+W44m5zZ2d39cDCeLGUgQuTFcZxQNExy2Hotuf8nMZx/WVx1j5K/GCG9v4fyXx4L7A9DUxIoFpH2iF4jjTcig/MT3OmBZATckWO9f0AUFurOxqZt2/fHQ6H7VZ3OBzaJCOS1AMuz4mQqflJ4IOleD8+JaKZOAVXJiamSNWKs9hzg1JOS92lweUl4XuoQADVAziUFVHLiCGFHsZ2zEGv+f3XXjlx/AgZFAGrWFtfuX3zerlcLhaL3W53cnJaUaRzD5+lKOrevXtXrlxpNpuPPnoum9U+/elPp9EutfHy008/eezU0pMfekySpN3dXaDwETUxWZ+dmbp48bIgSIO+9dBDDxUKxWaz3Wg0zp49wzD0iZPHJiZqly5f+MEPfiBJ/OracjaT1w2XUByypeL41ITGsnwIH7F4oOvERMwZDjouslqIdoGiM7Im8UKmnBuHRTdohAREATPL2Njt9XpD7H8OATCxyiKagvkDcXDBPE3OilmJZUklI0kZBQSc9O6CFgEpBDyhQzKWJa1/SpREk61m1YQKbt668pkXP/mPfuMfgrrJJ4FjRnFgGna5PsGwFM4lWWi1Wl//+jdffPHTponD4ezZM67rbm5u5nO5jKo5ppuV1UJlLHaTjY3tu997Y2N9W5Nyy3fv9Lq6LKvTM5ObG9s7O1tDY7i4dIBiIP/b3t5ut9ssy6aoJtzEHjAC3B/bKDL4iYVCIYMkMRUjJdChiVEd2h1gGP1+H/WnbXmes7MJ7+1WqwWyKEkCzueLY2Nju7u7b7/99sGDh//pP/1nxWLx/Pnz9+7dy+VAxE1iRhLlYrGMLBCeVzWpWMoEvh3HcChAMLNHJTFPU4C437/4Vn/Q7nTaZ8+dCiP3/PnzvV5vbGyMHLDY2n4ME3kwUW8/W5aw/7Hq0hnp/vslinuZdHPYuVGWgwkAoZkgcoPBQJT4kydPcpo6pg/7Vy/fWl5eXl/bXF1d3d5uEOdTFkJpAiQQTRyuDk3zFgyhYQ1C0yLH8OIeOSsSJQ4xboFXq052Oj2GTlFQtCukUMF3Ffe06sD0BYEzLTuMIl5g/chn2FgUhcnJ8dd+9P0LF3+0soLw6kfOTi0sLCzNL5w6dero0eNA1WbnwkF3dXX15Zdftixrampqdmbq3Xfe+vJffPuFj39iZna+Wq3rxnBtbfkHr3//29/85j/95/+E45DeKorie++8advmyvIOz0kLB476XrKz3er1O1CdM9STTz6x09jyfTuTyZaKtWq1fOjg8c3N7VyhrOtGp93b2W7Z1iZiadCDsdk8TMokURUFJYz3eCFRGGxv7ViG0e12B4MBMSDD1QNxDFra1KUSRWiabh9RkaDKAtaeLPDYGrE4yS2EwxoZCREdLSH+wrqSDRJkLQZkD0t3W6jdGErJqJtbq6Y1+L/9z/+Xj3/mBWfUc0wrV8zBSpLmJVWjArfX7ZdqY1TMfu/V7589c7bT6r3xxhuf/vSnPCdcvrf2yCNn27vNarFMFZhep/fK175++dJNy3TDgOkPRiKv7OIM3AmCqF4fK5Yghen2+7du3SpXSxRF3bp1y3GclO+Skq1Ta8B0ZE9idoCRxhGmU2CsRpHjeyOwxEaWZbm+t58V5xMjU2AFAlcuFM+e/ej4+DjHIXQpm80Oh8Mm0jW2hsPh0tKSKPKGYcBwjWeHg5E5wkZDx3RvMCoV8mPj06LEGmbf99zBsDMaGki0lPIsk9iWZVnmgcUDv/6ZX7t79+5759/52je/kc1qlXplZJmcKAQRRXNhwvjAVWI6gvY5gV0GQyjTafokuIepWCwd4O2VlamZSAL/DqxkQHECKwQwvGCIwmNsbOz8BSjycOb/zn/8ozt37ly/frPZbHouyLvEcF01dJuhBY5VOWI6hiysEN8+8AG80pQAG2KKj8MUUMWR6MGqKKISzhgBbadIHZh+lGzYhJVFJGcYkZPgTpohk+vQMYcGRcUZTSiX84cOzn3xi08cOnRorFZfWFjiMlljd/fq1evD4fDO7//B+vp6oVB4+OFz7Xb74vuXWSZ+880f/at/9T8PB1Zjt3vx/audXvvsudPlStF29F//9X/08z//hbPnHrp+/XqxlD92/Oj6vd742NzM9JI+tEUJQ8Ll5eXRaJjLZetj1d3d1pkzZzQ1/9JLL83NzZVKpa3NXYpiJEkdH9OiCCIaCxhqsLvbMchi63bbujH0fR/vkaZDx+MYTIQEQcjmNBiNY/IXYZMmDv3k8oqiIKfVmk/cUPD8AXEDfEViQjjXgIJ+z9A5hiNCug3qBnoMCjS41B+apLnF0YUL7zx05vj/+C//34ePzo36jWxO5mOBogOKkgLb4hXN9x3TtHlutL6+8d577z/7zIdbTZj/v/HG26ORdejQ0m6jK3HC22+8fefGzTu3VwzL1uR8oVQNWJpK9PHx8YyWTxJ6Y32r02kTv4LyiZPHf/DD12RVWlhYQO4IkpsSYg/TSUNUU5rBfpgxmQancxr4SmnwEMjzJKqR4N6CpoFYky8izZMkFAgSL8zNIfI2ZeTEcYzve+LEn/7pn/d6vQsXLtA03e/319fXdR0KVRmFgy+JnOuEG/o2aJJcTNEBy0bEEDGSZY2hA0mUVAUOPTduvTscDucOzBw9dlhRpEajQSyhc9/+1qsM7Tp2yLE4u4hLFR2EsSigjts/7vY91/5S5uz9YV4MxB4VMA4xzOEYF+JM2zCMVmsXvuxzcygS//2/+13LsqII56YkqmSwFYc+RSUqzq+Y5BNh9ZJXFImkOqITgaY5KuFgAQDXWhqTejaKKE8Q5CikNDUPMM+2S/lSeoqmXp24jql/mQV028dkKVlaWjx69PDxE0dPHDu4MF/j2VjJ5LbW199759233nrLMIyVlbV6Bd7pCwtLH/mp529cu/5Hf/QncQy2wR//0e/95m/+5vn3rt25vR4ENOSsNHf+ncs0F5bKmipq33npJdsxzjx8enNzs1wunz51Lp8dcx3v7t1rU9PjlWpBllRRlN94440vfOEL/d7o1Vdee+qppz76kRc2NrZ2G91WG4reVqu922j1evpIN2wH9Iggju7v7mB7YQ/ynDAIpqq1BDsTyxFfLVXVUnZluVxlGEKy5JG4ljpQBBGqHSKIRSVv29idPccNHNu2HODjewAM7fuRG/qwcoKUEHr9iIKxJfkiCIc58/Dpf/E//rMD85O2bWSLmm31YTDDa6E76rZ6mYyn66M4iBtbO2/84K3IS+7eWtZ1/cUXX7x9++bm6vaRpSNXr17d2V5fu3Wz025yrFSu1JEtZZjVyuTC/MFbt5fTJPBqtQo+l2U1O60ghkDM9f07JA270Wymyy99QPd0TaRW2jPMZVmC6ED5JEmSloMLVhhjTJU+tRyB72u1WpqmlDqgr66u9vv9tMS9cePGnTt3oEHNZj//+c8bhvHKK6+MRqMjR47Isry1tSVysutFKqYmpW63vd1oiwJVqxfanU56PT3f6vUcmhIySjGXU8vVmpoB3ddxXaQgcEIQRmvrm7KiuW5C0XyCGQFP8sRodA2EFXx/lSGwL20DoQD+oApNFyR0a1CUoJ70hYRP8UsYA8lyGIYTExMHDx5ESrHAZwKeCWF0wCOhzU94llfkDLEoIXUP0jn3ngXYFjgesWxA0AVNR0TFSVy6PJdKIpaheJaTeU7JaK5p9IyhJNYIxJQQ71Q7CI0EomX+2LEDH/nIh4+fODo+Xp9bmIHdw9aG45pf/epXLcsAyhpFE/UJRcswrPjh55b6/aEx1K9dvflb/+bfFnKwOTp69OjrP3jj//mvfuull14qFWrlSqXd0vO5kmmaQ72fy2nXr909cuSA446+/fVXx6rj5tBpbnd8L7p7d+X48WMTEzObG+uk2avfvnXr4MGFK1euL8wvbW1vbG02s9nsl/7sL27cvGnZRor7EZoyggtTh1LYYZCGgRcwYc8Vc6V8ARRIQZQE0LiIgSKCRFNfTeKzAHtMB763xBTeh/8JJqUEqUhxfNw/IkggvtH4aNqxp+UZzfIR2mlQUSMMi2KaiUAMpul/+T/9s3I1I2YEKvIdo8eyiWXq7e1tnslwlDhoD374xjuamlOV3M3rd03DzWfKhxaPXLpwZWy8Njs990d/+Me9fmeiVi2VqjPjM6Kimobd6+kszYSh395t0FQ8d2CG47h3z18wDVvN5lRJjuN4amr21p07g17v6PHjgQfPpW63Oz4+nj6Oe6bxLEY1IqiJIs9ggJwCM2kcsiyLsiyny4zHtB/W4/fu3dva2trdaZiGjvNf14dD2ExWq9VisZiybTKZTKfTeeaZZ44fPUpR1MsvvcIzUO1nMjAyJtl1/MTEmO9CklarjxnGyHUsjkWpDxoQy4aRl81q+XxWEMDaQQgXiV4WiDtwynAiOjKETSC3N4kZ4ke7b0Ob/pdKqfdl7ilZKbVsD0LP9m06SGSaT5gwl9cyWXlpafGppx+7evXy7MzCa699j7MBiHtxHGczCi+KHbPjB54qqUkYpVF94OMnxGIYVIxEwoCSikMnoV1AR7jE8C1JwpChonwhm5XZxDcH7eFUvTjsrnNM4Ht6HFkZjXru2bP/07/8RyxFc6JwYH4uioLRaLi5tfz+n7/S7/fhR60ospQplser9VnbtpuN5vr6jd2dpqmbgx5S1HmG8z1aH9pHjhzZXGt87PlPL9/bWt9orq7s1scmXd/Z2FrParkoTHq9YbU0aQ4j16Nqtanf/l//5Nd+7Ytv//Cm47C6ORpZ5sbWTj5Tau72isWiIpVaDT1wt2rlKZ6Rd7YaxROFT3z8BU3Tbt26RTz8oNwpFMCH5Hm+UqnUajDGhiIlAxETjJRxM1Bdg/BhOt3e0LabqVOGBw9fEFxSdC5FswjGTYcEUaM+yKK4H6iaAJ1L1Uwe8U0g1Dia4eQgCjKazDEBx9MMGzdbu//kN35talKLE9do3o1hkIhl7TqO78V0GHWaOsvzOyvN8QkhMPmcWuYS5/zbFz/y0eeefvLp8xfevnn18vzs5Hg11+v2J+rTUYQxOk3z9TGsJUEQ87mypCpBGBZLubF6+Ub3Tk2ujYZGTVVX7q1OjU8lQXzn5p2zDz8cBvFYdUxRFCilSDmaWpen61Dg+ASeUa5FqNthEIDS4FoomWzQOTzfMciHMplMuVzO5/ML8/P5jFYdq4+NjWXzoICnhKStra3V1fVPfOJT7739zu//7h/oui7xgiSKrmP4gU0XKwmVZVl6Y2ODYyjLMqiEHQy7DJ3Mz8/v7u7U63VJibq9VpnPra6sAL2jYp7nt7e3NS3LscLlS5cFXisWSr5DRb5XqdQcOxgMejBbjBNW4IcjQ9FUhhN6vUG5UnFJ2FsQeDRslljE3wQeL4kxnZQqecvWb9298tGPPvfzf+Pzjz/+eByGr33/9aFu/dt/+x8JWSVK8jlkMjqOR0VMMVd0XdcwLI6lA5DR0HcmsZ8itjTFEsc7vJDYuQcOITSzmMs2m92sygeOdfTIwd3mzhtvvV0uqsVS1jB0SeYff+Kxs+dOqbKA/KCAe/212/CvJJhNIadoCg8evu3durXa7euN7e2d7d3RaMSzQjFfymSyiqpltZzAcpIkLSwseY7r5T1Z1r7+za/V62Mch0xWnhcH/ZE+GFWr1W636ztBNqdxPNvvGaKQvXNjtV6ZfOPeJVmFayhZDAkbMf3eKJspNncB2Fy9cvPw4YM7WxulYhEgckLNTh0YmxivV8ckReQYZNDJopTNZ13bDRGOFtiO02o2R6ZuGbbreSNEzKMCIVahGDZgNw1Qu97PXdrzYCbOLDRJ2L7/gbTNABuJFkUycCM+LvtDtoSEWEVxLEoMWLLhqNPb/tmnPvNTzz/bam4kVAAftQi3H7x4E949GaEyGmB4a43sAT8KotGwZzAM99BD5zY3Gp1W2w8sVVaSKMwo6uzpmfauDkeoyLQtR5DilLZSKYNoGgJa9BQF9GPfgxQAPa1liQJMrIlJrCWJYozAgj4gX0LSQE8IL0nsIyzNmDpKoYQMq1KCGw1iD8B6RG5klGq1OjU9XavViM+dbwyGlmV0+r2333673e0A8cIsx7Eta3p68hMf+3ipXBBY7uknn5idnnvpO99udoaSIvd6ncFgcOLECUmSmo1GLpfb3m5qmiIKXL83JIrBxHHN3eYWRXuuO0dKDYaQc3LFYtnzAo4T79xGeL0oaprGNpu7SQys1TS78IaWIIwg5zziY4i/ScSTEIuECPspEhbEU2I+r/mhd+TIwcmp8s/93Gc/9PGPdlfXrl69ZhoGQ7HFfDHUYo5symDZmKadSOiqCT4eMITXAnaVzLGMEgRumi1arlQwdiQvlmF83019CrY3N0rlfCariBL7wic++eijj16+fLnX61y/cQXRQk4olCTPCfSBEfqJyHMsLQx67Z2dnV6vY1oGxiHtdn8wDCmW42VNUWdnMJkt5IrZTF4U5TnQI6wDs3M8z4PLv7E5Xz1w9erVRrNdglNG2OuPFEXNFUt6X3f9gBNwix3PlRmJjRPPsr/+zZd+7R/+tzdurd9b2ThwYJamE93U8/m8ZzrFYpFiaNO2Nje35ubmfD+8dfP2seNHJUkpFGvFUonjxaFuuI4TRpFtWcPRIPBS6mcUxhFyGuIQai4KaQppSEUKXSL3iqEYnjUdcJr2h0skCRgvSBl4QSLtpUIecEkAZp+iNVDLptJ08gqTBDICnvcDi6L9OLb9wDhx4pRlOobhUAm2YUxf4SVsGyPLdaLNfrfbG3luNBwOeU71QzhN1utj2Uzu1q1brmNMTFZr1UK/P6Th26n1ej0ltashhGxAKYRDx0uyJKL+HBsbG+nWYKATNzqw5LrdLsEno9u3b/u+b4Ica8Psk1BksH0I2EpwPzh+anwS1jPEcCCttAVwFoQROQAt27h27dobb755fyRmSxwyyWjEVCmFUrFSKS8szcuyvLSw2O/3ev1OoZD/6Z/+bLvTunjpgj4ayrLS7fWWFhYty7l3dwXcd1kLw7BahXeGbeO8kiRlMBg6EKWgBJ2fnx8bG/N9sMxN00SQiW4IAl+rVUa61WhsBz6d0Yo0xW9tbUkC5fmJGErE3peGL47AwrkjDkGW8GG0RZGWzzTNmI5jK8oVM/mMfPL4ubOnT1NJvLG68qMf/IDBaaqIvJhapHJEXimAhI6pFEAU2ELbpiSJtXqlXi+LAm9aQ89zGIZSM2rauqCyt1FsmVYfWK81ZFnaNKliMa+P+jduXl1YnGM56u5XllGWBFBUDHtDLad5jqWbo26rjbARx/KjkGdoJaPNTMweXFLGpqZlWc1AZ5Qlxu+g5oALTkXD4WBrh9P1UWu3Wa/XwyS+dvOG6wVXr92qVNAqCLwkiZhhbm1tqZKMpt+Hrorn6MNHDzV3d69eu/HYk49959Xvffazn8nn85ubm6k3QVptwhsvptfW1mvV0r17dw4cOJDNFXeb7fXtHWtkDY1hEkayprIUrZsGRzMRWnVU6gx2P3h1JKjeMT5IojhKYgxmQf9lE4oq5kssz0mCKCmyAv8SUeQRKswKIhzyUoKs4wwGg8ADSZBYzuzho3tFHaBnGl9DEkejgR+YPB89/sSZ+fnFlXsrgoDGHB5/vguKA3mIPTfxbMSSDQaGrhu+t2PCVg20RkgpwDKNUTkDo/ZZGH9v7O52a9U67L1VFZJaQSCjL7j0w7+diorFfK1eabfbioLsgFwulzI/Z2ZmWq0W0iA0CHwq9Vq646T4U1qB0wllG1ZIBaGJ9pj4JjpRAr6RCyqmQEL8YBcyMTFRKpVUTS7lyGyQ9IG8KPi+pxuGZZl3bt3e2Fw7evDQ5OT4H/+nP7hw4cLpk6dOnDixsbU10IcEiw1No6eq6rHDRxwXap7RaIimjmHyuaLjWr1eL6HCjY2Ne/fuIDiJgzsTw9JaBtPLGzdu7TS2EioURDqC5syxrWGtXmCoyDRHiAORZU2Ve4M+A5g6ZhmKTN/g+iqJPJVItoFBveN7tfJ0e3fn2acfVTLKzrXLdBSGrpPRigJDNpg4RrKpopTy+TxAG0GEhSoWoZzLIcagWMyrmuS5tq73ev2u77vDUc/zoSQCCBZh8KooiiiK8wvT1WqZY1hJZjUNw6l+v33z5vU8umTBtZ1Wp9Xc2uUEhIroxvDA7NxYtVgpl/KFQi6rwddCUSVFbnVbJP0y7HfaqbonLefuLK9Adbbb7vUGD59+aGZh7pXXXjVdZ2xiejQyw5jhBCVOGD9wx8Ynjxw7+vabb+3s7o6Pjx89cbLbajbbfc+PX/3ea1/84hcXDx1YXr1XrVd2drZMcyRJCjI9IOxB9bi5sV0qlfwguXUbw/rzF6/qxkjkRVnRDH3U7vTohELCoucj5ppmYCDGcgyHTEOapsrVGsUwIrZ/QYaARpRBf+Jtw0YhDw8lb6APraaD9RLG3W4fnJj7k1/CcEKvmMqRCNEdhwaJQ6QTNOGC6zs8x4iiRifuiaMnXNtrtbqiQAehA4tsF4twz//Ci7hYME3bGJn93pDnXJoTd5udKKSq9RrHItfS9wPPZXL5TKmQgR1rrprN5DzP6/f7NEuDD8TDtSGIElGUO91BOo8GNSqTSygQYhwf/hSuC5JdKsLhef78+fMfGM8QpgoZgaLbFThelHDyZzIZuHXJKLbr4+P4yX2Qp7O5nKoCqDetESZnnqubqJX6g0F/0O0PdMcLq6VsLpdZXr47PlF99tmnH3/80Wwmc/nKlV6vV6tVkQ8rq0ePHW40GleuXnJde2EBEbGlYn5ra7PVapXKhYcffnhqetxyunEc37x5k2YA2MiyaCFpOCH5iu7k1Fi/PzTtfqGYs+1RLl+KQ9+yYVMyPTOeyeUNyyBemzGNuGQvAnyNdOxsVksg446T2HEMXR91RJam4rDXbVuGrkriRL1GxSw0D0HE1erVQ4cO5XI5RIcGIcvSrmebVn/YHySIyAgIA92j6QRDG5mfnK4xiMgUFEUuFYv1OjrmfD4vsNzCwsLKveXf+Z3fiZOgP6i++uqr6ysbEq+KIgqtXCY7Xh8rV4qEl1Qh9hZ7XuhBCBHqyu6aYei8KBiGDqo7wbtHpuF58GEoloFZV6v1+cWFarVuWOba+rqiZDe3d1hGHAztKGaWlpYkSanVah/96Ec//vGPX7t27ctf/tLGhc3Dh5agsHBcRRSv37zxmc98+j/9pz/8uZ/9+UIx1+30WTa1akYcQrfTyeUy7Va3WCytrm4cP3Fi7sDiO+ff29rZSKlVUNZVq5qmlUql1Ng3faVTaWgpRn3oY93Asu1Op2FaFtxzEWzkkVMRYiOWxpghXWwZkkacLsL74cB7HEVSs+7j3SkojqgpyzEzmUwuL9umblnOe++e9wOHoQMftma241oBWBZ4wU3eHjoujpowDAt57cjxU+9fvMKxaChoOgkDqlQqlIoFUWQL+SzeEaGDj0YYoIdxZBgGTaLhFS1PRKt44EA8RLGM+WRgB6hUJWljY2NnZyeTyYRRNBgMJFUh03bS0MK7I7Vh4zKKFoeIn0ih0SiKwhjd840bNwAUuzif8XEyYHRcq5jNcBwrKnKxmD9x8hgWUqWsqrIsSrZt9lptmHZXiq+++sr25paWzYoSfBYFkS0UNc+zaToOAq9YykWxf/nKzYmxuqrJJ08drdfrAQos5/HHH2cwy0FNaNtmp9Mhbs52kkRzB6Zq1Ynr16+HkVcqa3ESGGafNAhBnPi1WsULQoaOXA9aNWJ7ZSG7g6Fs21ThEqsEoct7jO2MlhbmtIzkjwZ0Et9bvq2o0vbWJpXwDM3DwmOot3caYqcrDIfDyEcSqu9Dxs9znKKIksIqmlIsjB84MHtgfjafz2bygOGz2TxYR4SbKwrEpc9DObG+CsPJq1cv7+7uDofDjz73U4VcOZeBiUg2C/ce+DW4LnERSwbDQavVajZB5iIe8mi4dcMk9FYpV8gfXJyfnT8wf2CxUqsWyhXfCxaWDjqu/+qr33v77be9wO/tbE+OLwq8Ck2GIDRItmF/0Pv2yy9NTYz/6q/+6j//P/8PX/nKV7bWNzgOjn1eEFy48N7nP/95WZaC0JMVxQuaPM+j3MXS4IMwZlh+ODQLhVwcMZARBaEoKUePnTh16lQ2myXeM2G/3+90OpCYEXmxbaP8IxG2kGmzPMOzAlyM4IgIhJNi6FymAK+jlOeLVnEv3zUKiH39g6TENAuYvARCvNpj2Kd/IysCYRtzPFUqZAeDoWVSqiZZxiiJoBKG024Qw+AMMfGRImd5IdbUTHcI2eu5c+dWVtfb7Y4syzGEc6EkKUlMdTo9B3SzcNjXURxifhBIkB1g/CUIwnBkw4AsgfqM5HtKabMqS2oml6vV4GAty7KmaWk3LGvgaaRWPa4PbReO0Di51e0DviemUHuvBIckQuTwkGRLpZIoSaVSqVar5fKZDESnvCBLxOLeabebGxtr/X7XHBkX3j8/Xqk999yz28Ph6uoyQ9OqKqnZibX1TUlmTWtw796t2dnZTFb5xCc/+vbbb80dGP/ir/zq0sGF0Wi0vHzXMsxypXD16tViMXvo8MFKpeK6YNKlafJBEDV2dn0/ePrpp3q9vjFyNBQKbKfZyeY0iuF4gdnc2k4XSxj4BCKh8vmCIECxUK2Va+VKGDkMG6maMDZWHJuoJUyiZCSG4wRZareaLDxZaQ5FnMi22tspBiMLfEJFhaI2Nl6pVaqzc1NTUxOZrJrPaeVymSigPVRXElwkUApDkARsj6JiIZ9x+v3LVy4apl6tVs+eO3PmobNxEDMUJ4tKt9u+eXMdCqB+v7GLWOz19XViz87wPJvLFSYnx+fmnqjVavX6eC6XQT+QgSDNC3wM1GLgRqIsXb169cbN241me3l1DXutmLEtr2vBt9yynBs3btRq1VOnTnU6rUZz5x/+d/+Hz7744kMPPbS+vq6pWiaT297etKzR2vrKI488YpooJFKGB9AIyP9RC0FtLMadTk9VM9uNNsthyjkcGe+8d357qzEajURR9Dw/dY9MqRLYzAgMIUo8G4oCcAiRJTUmTDpJtm/K/4QXGYgUNEXSwaiElhSVQsDdB5mHaVB3yrCF8nMvYnKPJTwajVgWwvPGbmu8Xtne3s6o6u6OI3BMnCC4Ez8SecZTOIeOfRLYCKiA5/mpqYmUXU34dPg6u41WFLpDfVAu5CmKymWycPi1YbNPIFxsGIjUJQHuNEVb1tC2gS6m5ta9Xq+vIyKu0+kIgtButx3XhdzMhG/aHrmEnOWkVGDymZzIC5IMXDRFbgSw2XhF0wjpCkPTmNhdr62tgdwPJy6zrwPS9ALiKEcuSq1aee65Z2KkegwmJsY+8cmPj9fHPvr8R7Ya25ZjcwzUwEGAvNHf+Y+/ffDQzImTSzdu3CiW1XYHusTFpVnXdnq93uLivOvawyG+Pscx+XyeEFCpfn84Vh9fW9vgJ2XPCzqdrmU6P/zhG7Is1us1w4YTz+5uA9lpxKtM4HhFEqemJvIF7NRLS4snjh5j2DiiXUFKrl696IUeL7GMyFbrlfPvXa5NjMcUT4WsH8X0xz78DGyLJCkLs9ra0tLC0sHFer1cyOeRPs7RPIAs0kCSm2rakMPoQ/jw0IQE0+30IbAYDCVJ6rYhaUnpC/fu3Ws2Gjubu512GwnPo5EkwV6JcOTVkydPFotFBIDlYRaQWrX6Hkab3UGfoijd0FutVian7ezuhnHEcYKPEsX3/NC0vfMXLgqCpA9tiSsyNO/5oK3ms0qlUvYDu91ulor53d1dSZIOHFiYmpi+d++eKMhR7PUHrUxGfeKJp+/evTs3deCNN96SZTWfK9J7FHlqqA9q5UqxWAT2kNEogdvabezs7Ni2vZdXTwb3f5nUmz6sexY7ZMLOENccMUbdhcoNKFHaFxFi2h6PhIoZOgZDAuZoeychCXtO4W88b+nqTaOzoyhKzwQ6iXzfjsJAVvhapVwq5y1D53mWh2Cb4mhkHqoyenVAL47d7Qxu3r23sLD0v/3Jn73+zZd/67d+S8tkz5w58/Zbb1arZRoxNXa9Uh0Oh1ktg1IF5FUc5p7vMDQeDyWjweHvwLyiaN//3g/W1jaymYIXhKqScXyM+1IoFeuKrK58CY5pqRwJeoiUyR3FTII3GJAiYq9xDfEP290uodXiSqb4asr7K+WyiiJn8plKpTI2UUfugIJcpFZzd2Ji7I0fvD43N/PRZ5/78z//07fffOvAwoFnP/qhFz758Wy5ZPaQRhZFUbfVSafrOHVJe7o3hff3oshxbWHUD8wm7SxwZzAlEjlOIONBfTTCls0yfKPR3NrauXTx8tjYBEdzG+tbY2PjrMBvrG4cO3ZM13VZFqtjdcexx8bGFhane8Pddq/R7TWffe7JRx89t7Ozs7nRuH37XhKJhuWXclXDdjieZw4fWTxz5szS0kK9WlFV1DlJEhujYSr9gI+iafaBB/cMw7BI6ZVevtEQ5MkUx9ta33j++eefe+bDvu+/9trrV65cQRxKQs1MT09NTT799FMk7aSqKCCRxHHc7/cTCjKLjY0N24IkfKSbhmFREd3q9CRJMCyz3+8qmuKFCN8VeNElUgNZyXQHQ88DOyf1DnAdN4qh4uc4Fro4Iurp9duTU2OmYTca21EQDodDQbCJ4AUwQ7vdRrIVmbynEu9cJrdHTyEtYsp1DKOICnEDkXfFpuEKcHJKjVLvpyXDkofeC6yjFUUT4NaPlefAxTRlt6m93iCdQBDzGNfzQHQUWEbThJjkSu+XoOlp5nmEmXS/P9wHSInpA80jEogjrRZyL8Jml05CSRZi/IyRwHIZLTs+PjE2NnbmzEPtbufSxcshnaytbnzvK3/x4Z964Stf+cqtO7cTCmNA3/fH6tVGw24128ViMYLZNp1aobE8CFYMzwVBtL3dyGZyHCe0mp1ebwCCF8e5flAul5EVeH+Ish/1YtgAJAmbD6b16YcYivZsF2jWXnFN3h2xhKzX67lcDmx+35cVZWpqqlqt8gIrIYcDlqu+7xqWeePGjc3trVZrd9DvWZbhGObnP//Trmtj2efzE5Njb739o/pE8dTJ43B8s3AT/QDrJ2VNgrlMagQYr2IRovMkN3ovwIMlVhRpdUMyPXmBlwqFgqKo6Jh0s1iE2B8Vu+MFbjA2XlMVZWen4bsouYnJGqqVIPCbzWZMe6VqplAuaTll+sC8FwYRQ5Xq1SOC1O/ZOQdeMJ3lVe6Tn3phcXF+ZmaGopLt7c1ut93uNAdk5k1sTPHyiM4tVU+GEZUuPJT7LgjHaU715MTcgbmDly5e/cY3viGK4pNPPnnixIkDs9OyKMFIBZaY+r2V1XSXhZuIYaQ2qSkYFeNzPMu0A4/qg4JQYjluODRt1wMTiWc8NxzZjiSqqlbqdUehF/tQJiB8Dwp/judJNG8YxZmMmM2WBAHTXqoKqh0yMFx/bnouvQF+GHS73Vw2T7GMms1YI4tleJRaOGBwBEXIavf3VgIkyKnUi4N8FsAEqShTF1EQBFM+4d451t5FuvUerZThqYhCfp9tq6oSBaHhYbwGEy8pS1j3cDDcbwHTPThVg6aPQlqOPojNSHKqmoU0W8RyDxzb9GyLphLP9mM2wLrk8Silj92NGzfCOCqWClNTE7uN1rdf+tYzz334qaee/M6rL+/u7szOTiOrgwykPRsDEp6VRFGURM2n3bRg1oejwWAga+rkxEwUJhsbW4PBUOAh8oiiqNVq8RKydU2yU0NZT+5vemKnP3zaQ+JM5AVNVjmGBXeGNLosC+YtZN/Eh9LznXa73elg1A7zUmNIR6FlWb3BwLRMTmCASuSyaeQrw1DVYmFsbEySpKeffvrQwaVqvXR7+Uo2pwoiuG8jo0cExIR5i8UWR+iWMdvFwiO6TTij3T8eyX1I7yYjS2z6TwQBF4fnRFXJsAy/s9vI54uHDx9mGW57Y3vQu6YPh1ScnDvzcCafw5YU+WESCwKQZy0rj0+OeaHluEa1Pm4ZgxDAROT4QbPdCSM2ia2bt+5wFy9eXFtbo2kIage9Hok4paBAG8GwiJgURCzwMVxEjhUSii3k6nMzwFqwsctyq9W6du2aKGhf++q3ZqanP//5v5H6gvR6vf/wv//e5vpqoZAbq9Wz+RxD0UjysD1UIGESQbkS+TgHkphKfNezbZdOJN+nwpCZX1xY29igQirybF4EbdN3QwmljewYHk2JcYB6OV02DG5kzPOsKPEJsFlolPLZXK06dvLkQ1EUNHfalm2EEXq5fm/IJFy1MhbHVCFf2lzdnpnJeh6s+ygWw1bPA1MZ9AjLkllk9KZLIrVUS/Wd6dZOOiPcsz1ck06mZyZTJWRCAfNQVDEM4UUpSYKsoEK1bXQgvu+qqprVMvDsQd7rByOKtCckRubgDu97tN43UNtb84QCHgde6FquZdnFQg6ECorVNDi+sCyLymdzg4B4/sTEhOs6tXplefnu7/3e73z+8z/76vee2thYW1o4KIhcp9OxLKeUz+k6NkQCR4MNhEUI2ppUwJi8LIpyq9XZ3W3CMwUeRxiruq4vqRgMptormfioY6ZH3GX31iECzAkvj5SjYDhEhEZLnA8tx4Q/A0FEOR6iUxJV7aebda0EFX++WK7VKhNTExOTY7lCHjBb4OfyWYGhX3/9tZRpfOXKle2XNn79N75YrmDeBmDf8wSBk0TJI1lXZN9MgyX22BSky0UJk1Y0aWYw4rXj2HP3PJZ8zzYNpDghfBw8h/Fut93a7Y5V6gsH5gVO3NncWTZXTNO8c+dOsVj0I7hmyZocRKGWVcp3cxyfKJr4yCNnTdOWJIXnlCTmHvv55zw/Zhnp+LGT3Pe/96NMRoPjle+wFE1UnYg3m51ZTAOliCedDGtAFEUUy4kkA0zv9wY7Vi8MYQfS7YzGKlOf+uTnspnM8vJyc7en6/qVK5fW11e1jMpvN5fvbarZTD6TlTVV4kWaA32J5hg6pgiRGcVfhJ4w5iGdYjw/mpqaMw1XigH4irLEOtFoZEtS3sYJGvKMwNMyw2PDC32f5RLA1gobxW4Mt5Xo6JGDZ06dvn3rzrDfOnn8iMCwFy9ePnzk2Ex59vyF9wf6aKgbHCtVKrW+PqwF457np/kK4Gs5cPuWVKmr94RIRsYtDUyeikMapHrC7iRVF1mCML3bWz8M1dzdjmN0GqLACzwVRV6EngdhY1EEXyNIfmSOiFEi29E5Bn+zb6e1P6JIOSt7mrUHXukidG3KYk0mxXqgedn7nA+4YDQVwZnbb3eaML83dNsx8/nsbqv1pb/40+PHjz334Wd+93d/997ynYUFyNI9zxkOh0EQyUImCuFbI0tCEIWmAUkEUh8np9ut7u3bdwZ9XVG0JKERrMeLCNgia4BlwXBPO1iYVjQRn5ay0ikGEyAAyI5L4qRYSQbHPaW5Z3IaFgdZpRyPbpYl+sNCoVAsFscqVVWVwRdXESorCHxEhb7vWoZx5+7tbnN3bW1l6cBcsVikKUbTtH/9r//NkeNLn3zhE4ePHCyVKng4XK/XG6iqSvZQcAUJTZAlVpVRBBALYggSCQqnghg83mSkG8QdVKYSluSiWqbhwQUjr1YqFd+N2u22KtuTY+Nj5XqpUFLVjK4bSQKjJ9QhPE/RgchLd2/dm5kdn56YrE8fqDojRhAoQaEojnKi0cDiOXVqYoo7fOi4oiiZjKqoUlZFM5+WMYNeH+Ia0+tYPXNkGSNo3WH07CKHBBPVbJamkna7LUnCsaOn/0//5J9+4xtfa7cuptIv27bKpXq/D7QG2w/FxxHrBVRkeAPf9nwniVGlECoGemIib5FFmWUTdFQ8L5erYywvRVHCCwrPiS7oLAxDC72eHoUUTXHQMLB41GgGK1AUeccdcXzy8NlTTz/z5Icef6xULn/n5Vdu3rzNJFytXp6dnfY859jR4zuN1m6j1e8Pi/lKtVrNZpHInXJTyEMsWCZgHlnOMgySEtJuIX28yNm3J6NOvSceXCEUHfE8LZK5GQnuHoA6TFGixLs2GR6QIdv9dUIOtBhHIXy57zvkpqMLUQT8AAHNXxbIQAO5J5PBSAokLEXleMpzoWQn+HjsOLYkCeVyqVwuXr95LYz827dv5ysl37MFkdON0b/9d//LU08/Ozs7u766dv369XMPn52enm5sbaeHA6zroCcQaayr1C1Gsy1vY2NzY30LpZqcIf6UFCdzmUzGC6FFSp1Ifd9vtlqdTieTh5XgHuoOG12xVColUVwpljGgonGp0/oiiNC5ZWBjXOAFqB/iJEkTV6MoIiYjMk9stUzb6Pe7zXazP+hurmOqHPu+llEW5w585CPP1Wq1hInOPX5mYqo+OTlFU9ygr7uenc9kx8cnB30dBgYgDNPEjZeKIyQ1ppYIgKXTUgYeL6h1JInzvdA0hihZiecgub9hs90cn6jPTE61eWl7favb6o/Xx0+cOHX71l20G4ZRKJcEDgHUsijRNFMpVDRJo6LEbQ2C2DWMRhhQI8NZvrfR6fQZVul2e9zkxJxtWyPd6XQGjmnoOgyCIENkwdVG2GKMqRdEKLyiqVqlooZhAJBm5HEs7dh+vTbx3LPP/8Hv/xEGlGq+0+47LgwOXDfIaHkThh8hZceel/h+KhhnIpIG7gfpHAl1Hc/TIdSpnsSzhmFOzszynJjLFXZbjVwu67o+ywuyzCuS2m73WBpVMTq0MPRcN1/IiAIfBl5WU88+cuJnPvepU0+coxxr0G8vzM8wFH3jxu0kiQ7MTr97/lKn0xkfm2y3YLkNYTFFzx9YbDQaAscQTx5wRDwXlVIul9M0zXDchIxS9jPWQUfDWko9fz5wmCai+zBfyDj2sNsBr4immExWGx8fr9UAt6YTJH00aDQanU7bcewkZjhR2B8MpvjBg8UnS/rDB3vCGAbPJPnlvmg9rcbJIBG/xqC8GUHIaaqcJLnHH3/ctI0rV66NbGcw7AiCOD5ev3fvTr5QmJ2dDX1/eXn5nXffnp+fz+WzsqQNe7bvxamJaKFYHJ8ar1armYz68ivfbbfR7rIsIhaIGQcbxyBtQ+VITBzTw61cLkN2OAZTxr0uF3ZeZGuh6JyWhV01efTTfc0L4D2TzocSCl4yhmkS+Ap1+1ilnkr4+4NuEPmI+ctlJFl4+OGHq9VyXtOiONA0bWnpUK/TffN3f9Tq7i4cPCCLysGDB2VJgyWh5RFOCxYeQuYwPiWHHhYh8cmG6BxwWAyEKcWqEyphbdsfDkau6zJQSOFFMjW9mzdvVguVQr44OTnZ2GlvbW1Zhs1z3OTk9NbOdrqVUxzC3oLAy2ZFLqR3N5rvvfWupklrG6ujkdnr6tl8xbFDWeHskcG99oMfmqYZhFCCAeaLkTmqaTnbMAnewTG0wFBMGCWOk+JI3VKpEHioNDKVUqEAvBGTJS/c3V1L6ceuh8qbZvCbJGaxobICJpKsGBEPTz9M6CRAU4zCR1JEiRMRdOq6LrB31yrmNUUSxsZq29sboijqui7ATVqgaVYfmQwmyKSdh7dVwPCx6QyoxH/siad/6Zf+mwNHFr0BSOHbm1uNnd2Vu2uNrd3lu/fKpfrc3Mwbr79x8OgJloYIzzatQb9frZU3NtcETnJdRxD4fCFrGpxpjeIkVBSprw9ZDnqB9KEPIpBFA4gA9rKgU0oklSrK6ODOnQ1RYiYnJ8+effLxxx8/evRofQyYsFYuNlZXd5uNTAaP6fb29pe+9Gff+PrLCQU7PVgP7J2rFKhpLJt6mUZpyPueUC1mKEoUoO1I7UJkQrbE5Dv0s6oahF4azxPHlKGbgb3Z7XZPnDhWLJfOnX307sqyqqLUbLRampK58N77kqA8/PDZD33oma985SvvvgPrzsmJ2Xy2VK5MAEIBTx+ugWsbm2trK+vrm4KEbTEIAHLANFUULAeaPVEU01jMbjpm4PlSqRS4nqIoqqwgMBSpGfDtp2naGAJr8D3QYojcLvQDAvuRCOQwDAUoRyQifhUyajaMqXpt/PjJk9VauVKD2RDRGMAPod/rtDrt0bB//t33nvvIR3IFlGDFYr7b6L775oVuqzc2BktvMpCU4IAdI5Jgzwptz4mCSWdIoO8S63PPDTwvCvzQNF0qYWKkSXGBH7uQ0SI7LaY8RYGthGU5dMIVi6VO2Ov0ujPTc1Mz461+czTq60ZC83RElUd6TwQZFUZV77zzDixtLlwMMSvmXD+RRLVcqq6ub3BOFAB4YUSa4xAxjPlJGBgO3AmThIMfhs9xAuS8VOyFblaRe91OtVQejYbWSLdM/bnnnrl3755hGKzAu0FgOjaSYQmaKmfyPLJ1wOYJotDxAp6kpEQJJfJiFEIyzHNsmMSeY8PcIQmS2O21Vh3reBwahxanX/7211iO9vyQnIRiu9ui2cQJHEwIOMbzLCnLeKFeqRZKxfH/9v/4xakD0745NMyRaYKC5LsAxGempy5fvLJwbm5lZevOYL3X6U+NT61vbU6PTTjWiIoDKg44XokTP4xcls2IsuAGLMNhUkQFURzDQZSiKECmLMdKQugFQ88MQrder/quGQRuRpZ63WYS+Z/53HOPPXLmueeem1taoHieSDEjZFBYO5cuvXbw0GK9rlCUfeLs/ImH/9kTT5773/7dn50/f2thYcEwLJ7FAx0GseuAgYEEUOg40cQQTi8NLVUUixyf1q9UGHAcy/NcQglx4JcKOce0kjAROUxueIb3jPC1777OQnaczeWL5WwZCLdPi7S622xfv3pre6szNzt/6sQjjXKz0Whcu34vn+/s+W4QpX9qHwrnkVwejweeIAriHQrJ9mmLiyAL2K7QuFJJkoiwjvA9797duySrR9J1YMLZLNzc9NGIyAckgVg+UixDMZwgcTm4EKgk3lwiz9ueDAWNPja8sNfrrazc63bbzWaz3x/quk4sdMNDhxdt07i3cvfU6ePPv/B86ARxGB09eExRpQvvXp6emYShG8dUSmXi88fyLKKvYnCbUL94JDWA6N8RIkDyiVEAhkEcAzlESjEcvOCrxKABD/2QoVrNPscJCCPgJVaUPMvYaG7wMveRF566dfu6ILGHDi/uthqF/JLtjCBBjthyafKNH563fd73Es8D7T+OqB5taZkih7whFhsE2XHBkuJI3iq0dvD/hqg3dVSDU5OghY4l8vTI6PMCEqqmpyfDEFM4SAdw4sMqJQD8BXIIybgkBx7UrhH2onCPnZwy64FSeZCRwxY2n8vI3NJc7eyZgydPnthtbs/OzvzqF/9+6hT253/+JWLsILACT/t+TKG8jxOwDYuTFcMc/r/+zf9d01QoksxRQtPmyDBG5voKNL4r9zZOHj9RyOWnp6hbtzdtMOP4JApczxaEchj6KRkojiViywPfPoAfrisiBQURcaQiRaRpTPKXoyTOlfOCyN67faNazSeRc2+t8Xd+8bN/6+c+f/zYoaymUgLn6INms9Hr9WzHDMPg8uXLvu8uHpyWZNixrr1/R5Kk2enJFz7xsZ2dgWORaEdeQssd04VS2bWdhKJZBPsxdBIzqCfgISKxjETE1CRUJ0HCH2hfgcQLIsdGPLxIRE6kIo9n+ICiyuUqyTY2Bn1Ie1mGTyjASGNjE61Or9Nq97oDlhcipNkIWibX6XZTzieZoCSE0gJKyB4a/OOvFGMkZ8r9Epp4pvG+4xayCCRSoA8opDMb23OrtRpC4R4Yfkbk8UqpCL1eb3MTwBhZOXiZhgPejKn7ga0o0tjY2Pz84qlTmXKpPjExYZh6Qvm371wFwswzw2H/5a+98rEP/9TtG3cOHJg7MLu4AxN3l2XZ3e0u6hFNQ5odw9MUrG3iJIEgjcg/LVjYeHCiIDxCxPVETEg6JjQapEihmSj0IzdhAlgKRV6QyHKMHB2K6uqD6fzY8trdJ5565OOf+phtDQ1jWChqgkQP+kNFKnXbzivfeTMIWZrhK5WCrutBGLtekM1mOWM0wPek00wPgjWG0DijDaMwsiTdM7JyAs+PA79SgjFIr9fjebHV6s7OIgcjSQUrZAoH2l1IBK24tntBF/t+OIQthTZ3PwYspYOkt5Dn+Sc/9JRjDhJ4jiXlUunIkSOeG2j5EtLkowh5maQxi2I/VaaXSmWe5z/0xOOLi4tJHGzcu9vrtkejodEfNpvtq1evZzI5JhGWlg4N+qMoRJbIcITNiSGCa0HggoDPZrPdbhfkY6L0VdUMyTq2M2MFBGzAvYCiKXBtCaYAmmlzp8ky0USt7nvmzOTk//Vf/PfPPPmEbY0unr/oWHuG3DRD5YA3wOfzV/7er925c6vXMq5c/JbneYNBX9O08fH5jdWNQb8v82q+VFRJ3pg1suD7ikgDsE0pMGBAYUuHfxxQMRoWojTDseAOMnRChRQvMBi+hTiaQA+LiX96eppg3g1PQeD+SUgxbEwxrufzAl0q5xmWj2JMRD3PHRk43PY9gR7EnP6aRfiBqdGDxkdRBL/GlK+TLicivUO22+3bt+GG8oBIMkp9BshodJ8Kn2pWRVEcq08WS/larVwq5zQNp6LvY+x8987qjRs3bt66jvJv1F1YnHnyQ48dO3bi63/+LUXLqGqm1ermCgWHBEJbJpL5yDgUbrEih02WXN2El/ggwgHoualkBxbbSRIHRBUaoo1EliQxeWEZiNVYkrlCtg/0kmh3RVEemcZuo8VLzPUbtyenJ46fOIJDwHRYN9zZ2T12YlbTgOTHcWiZsOriBRywI2MANJFDgQNQ1vXQUZB2mWUSulaBcZ0HWCLVx7IxJ4YhNRoN4tCxHUPLSIViLpfP+Egkd6MkJhncJMWSqKoJEByzDIfHlvRU6c63D0Kkv0kpwukdcgzEuJZL2V5vUKuPDwbDoW6MT02/9PIrluPIbkBTPvHy9hP4kossS4POKmp/+2//wtra2vhYtd3udtrNnZ2dQWfAMkwuV7hze/nJx59Oi/hiqZbNaa1Om+OKsiybppk+W9lsFvyGlNQfhqBuIHXEwqqL6CAIcf1hcCZEQUDRtMBy1UIpCF2WTqrFyhd+5guPn3vk2pWrcehVyxUhJxaypQh+A6HtucNef3W4tXx7TTcNRRI6vd7k+PjC/NFirkAz8s3rtzKKqimZ0PMHVlcURKVclhXFMk2sP4qGlw9gYBYVCuxNPCgUyYvn4HADuJ1DJG5qaUeuKvHbo4KYigwLzBtJgpEL4WfFfhjTMSUrkgxvItpH4ZIIopKhGCgagiSduKS6+D17ir/+te8s+mD+WXpDRRHXcDgcptsrPsSSn4/DHIWIpLDFpxhpsVhM13+6QadKfLJUWMdBLmd/0B4O+5aNEZLr+sViWZG1bE575JFHgtAGkmmahWL57/3yL7//7oWZmZlioczzYjZbbDQanuvHEWtbkW3pSTJkEwIXocSnaQ4YGzl0BSrZM/pFLUqGFvtbzH33NNQl6UQpgTdXwDAYKVMUl8sVmq3t+cWZxs7WH/3n//KLf/tv1OvV4aCTyytUwrQbu4aOOB0AkwmXzWa2tjZOnjpu2+bt2zc5Lp2j4jQKmdQhi2NYhtaHgMI0RZBl0OGRJuf5ceJLMj/UO0h4HyTZbM6ydIqFl1FCMaTJBjWHRDrdd329D+ylG+p+alfq55MOJ1KUDNF8sff1r3/98z/z2XweW8BwqJfKVV3Xv/vd7+7fnpRQlv6Rpulut/PZz32yXq8vL99dX18fDAaXL11vNLZ5FNVifXxqeWVnc6uh654iqRyPtCDPc+A7LCM4YTQasiyfz+fTny2K4POJg0iQBgMdKDwvI3oNfGuaSTv1ACQ1RZZazf7QHk6dPP6NL3/1e9/65nPPfohJkltXbxLDfNAfMX6AZWXs+f5I1xOKOnvmbLU8VStN8jR/58bGe+cvrS2v8KIicFQQ+I5lq2wuif1R4OEkRBoJ0uN41KK8gGoyYWOf5Sj8xyYA9oDtYUMOEImG/9IrH2KkiZAsNHEYv+ydORQD1AfyPx8hXkEQWjbIT9hmJZFlecsyaQrnbEqI2Xe++ete+463+89rupdlMjAURKA2mb6m4A3NYfScMHvwb7rOQ3Ik3rp1a58lm06t0g3a9yJZERVFVDXxyJEjE5PjY/VxTcuCU07ko5al37p9dXl5eXu7MTs7+7/8P36r2WgpWjZA/YIACYGXbSuIIx4pAjh08XMQMobAUlS/1+J5cFwFAeREHOMhTdYDDkByPO2X3ISziIcWu0PaoyGHkIGwS5JEnpNXVjaWDs52Os0//S9/8eJPv+gH5vrGajabvXb1RhJLPC8SKbMZJ2G7sz02/uzExNFsnucGg05aK0PXTEp/ONs7JmmOYgxFRwOSe0gVCoVqZfLwwfl8Pnvo0BHTNAd9/e7d5Xa3l9arUdrmJVFCCugUtcf/SHx02lfsV6HEFQc3AEZ3pAghwQPs6trGtRvXz519VDcMTuBFRf76l77qeX6hhDCtdKSb3p50xlipVJ577rlGowEIIfBu3byTjmLHx6bu3VtJQraQL59/79Ij5x6Xpez16zdT3pxtg23oum6v1ytg0qrez2fGTNn3QayJgNc5sqxajh+jIEUkUxwQTzQmXru7fPjIgjmUrly+WMpp5VL+9e+/VimXSkXk109NzqbFmGEYnU7Pc3sCr66vbb4XXlYUtVQqWZa1tbV1584dWVZpOjGiIF8sZVWR40VzZKEZg8ic4hiKYVEHA4BBbxBzPOi+e9Y+5GqQKDRirUfspbFVMbzr+xzD+GEUYGsFvLNnF8xETIRoEMMwOFFgeU5RZLIKEAsR+BHLKA+GK94nlxDY/a8vRPc31vuyEjZNOEsPQMMwBgOogf0oNC2Lwj5AYhvuBxiGYTg+Pk4cIoknN3keyK9cpUwETRmFYWPk54yGBAW0dnZ2fS9sd5qdTosXqPpYaXtr5/iRo0ePH/v0pz5FJUw2m+dY8ebN21FI25ZnmV6aH07gWyaJucCn/SAUeI2iIMf1PSeF39JwT/Jk3i+z97LJUGsQL1EcJCl2SlzQwdbCVaIYY2TvNjocz+zsNF999fsnTh7WB4Nioeq6Js9SRHwLNqUgcA+fPZ3Ly1qWP/3QEe7Lf/HnGfJCqBXixXCsW4Z55cqVDby2HMuWJKlYLE5NTcHwaLxKCGvaq6++evPW1bW1DYYT5ubmd5sdYkD6gRqA5GPs7ZH7Jej+Itxj9N7fEdOWgIfYW33v3QvFQmVsYjwKk263v7y8TFLpcDsD4juWIgfkmfOmpuZzmQz8afpdKkmazc54fcwtBLKay+RK/Z7O8WpMsQwn6YbT6Q14Edx8x7XSjEtipIfckrRrIrRd2zRsRHzEtGEYxHKVCqMQSR4UtOGkemGf+tATm+v35qanJuuFnCovLc6tLN+5c+u265C4eXI707yktEon0QDMoGes3N2q1WrEDzc4uHiEogM/dBrbu0Fox2GsaJkoiFXkisUoQimWSgCxJgCDyFaw9+jD9ZCgaaTooAHseojdAarAslBOxiwXRrTrBMB3GI7gkFiwiN2Oo0wm5wbITiRYFMxjYeQmS0HEwNePPFQxTQHbJWQ9UBT24mAfPAZJXMZf7gnTmytJUhAEaRpMGoqWIq5TU1OsABA4bRr5+7mFzWYzVQDvh42SDiW6cf0WqiTH0Ec9xzE4npUlBTVOfbxeH3/ooYfyBa1YynV7jX4PHNeHz57+8pf+Ynl59R988R/OTNVZlnUMu1aurRvb2KqikEpij4sZJiDW5YmWEeIYT+b+W6AQyoCN7YFcwLR8Q4W35/0DPALzT+KUhwvh+eD0j49P9gbtTFZePHi4Ui4fmFuqPIyIgdEwdOyoXC7/zM/8DHpIiYsTP4rdMHJkJcc99dSHQCkyTRDw4ljVlGqlzDDMY0881mo2A88fHx9nFJWKwnvXbqyu3ev1OjxPS5JQKhfm5w8oinLtxq3r168WS1VY/uFtIP0wTfAlZz/wgXRvS0GwdE2mYEwqYkjhHEDh8G/gdnZ3txs7C4cOb2xsKWrm2LHjr37ve5IaR2mOaBQRZxzOh8lEkMtkEJ4+MnXdeu+dtw3DeOtH8FReWjo4Nzvve3G328lki9uNXSqmS5UyQC2/0Ol0UppZGrOe7vQpQEdMQBFPmR6YYcRCuBlHJO0PdaDI06LINbY3ioXM7ZvXNFWcOnO8UMw+9dSTN69f39hqBH5EcDafYTDWUxVYJDuOh2QLfcTzws/97N88evTo7m4rit1WdzmXV7a3Gr7v7+62giBstxC5Y5oGS/MRxVE0H9IsHfP4GUI2YqkQBRCKVVI0Qh1Mx2wcOfBKIIboiFOGGy2LUpQR/DAJiZE+ce4mZQTDmjaE5VEChh1DU2EUh5CHJ5gT3Qc892kD/5WKNN1b9/GYFGVB5oSCVPq0KHUcR9O0YrEYxCiWghhKIhJJj1cavLePlKYHI8vCHVgUJYbmcrnc1PR4Lq8WCplqrVIqlmF0T/O+h9N7c2v99u2bN29dOXX6+N/7pV80bGOnuT43P62P+i+9dGNycpajuU67k81qjhOEcG726SSGwQZxi7MtOJhxrMCCGMHGSUim+WkJep8X9QEjiriMEp4bmOeAychzTlOeG2Rzim72K5Vqs7XjuObDDz/c7fbeP//u9evXEUiqFsql8cdeeJIXOM+zbEenmVAQqYQKuR+9/nqn0xkO4erJ0tBT5XK5Uql06sRJ27QUUdreXL996+5OY8s0bIoONU25eOn9ubm5mZmJt95668iRw7utTr8/FAQhrYVEURzqo7GxsdSQC/eexLunuKgkSemSS0+2PW4hWMIkgDoJZUUOYWZRXV1dHxsbY1h+NBql1hKZTCZOADeDbkqUYDRNz0zPtZrtW7duF4vF1ZWNTCbT6+vFYkmRs7btm7YnKdndph5TerVU5ljBsvVyqbq91fDcYGpqptlsGobBMLDGePPNNzOZDAlamzJMXZT4Xq9bro7xEm3rVpw4ANwTFl6CWGPu1oauSPz4xOT09GSpVLAto1gp5otl07S6nX6z2RwMYFnL7lnfy65nFwp5hmFeeukly7IOHz46NjGda0eiFE/PIP4ttT4c6Ua/P2w0mq7j67oxGCAE1wusKBaoWIoY3vMoiTDjvCAcmbqiKIh/aPaiBHKQMEKGIpaN4ycxm8QcxioxqiygPBhPR5hpYDekE4rzkT9L05SQhL4deCwv7sXgfXAy7GmX91fmg4twH+5OP21f85He4larZZqmqqrNZvPOnTsqEiYDUKTJ8k6/hUCA0JSkJklSuYx6Pl3PRDIL/EaS0a8ZZv/Spfc7nR6h92Ko0G63a7WaqonVar3ZbK1urJ84dfS5jz578tipcrGyvbmVzSrj1VwYxLYNgErW5O2tBs8zisp1ep1CoUS0ZjLDcCAuw1UdQ7r9ZvhBJ8qU5w0ze+zHwMz2anXCpQCiK0m9jUEuryqq2unuOq4/Pz8ncnwuW+Q59eqVG4V88Vvf+la5UiqVclMz1UIx1+5sIan35o1baZIz0qe6PdM0Z2ZmTp8+/fu//we9Xtd3XKI58sbGayPdDCP3R2/8QFXlT37y0z//N/7mT//0Z7/+tW+XK0XH8XK5rK4bcRypqux63nA4tCx7bKxmGWY6QNq/6H9dd5G+YPcvKbbtzs7NN1ud2vjYkWPHrty4+deDBPTOTqvZ7Dab3SiiBwODZUTXCdutXuBTHCeIglwuV8Ay7w9Nx5YE1vN4cuYBwSNGBqB6zM/Pp/cesykTrI4g8NzQ1o02RXuqxkFZYvds2+R5LpdXREE7dOixDz3xyMNnHpqZGhNFPiJhJteu3CSsC0ycTNNq7DRXVtYajeZwoK+s3Mlm84VCabe5/Ud//J8OHjz06Rd/6uFHFjwf7VMYiuk5zHJKNidPz4xZlmOMsOCJEMxot9uDvsEjigfEMViAylqpWEmSpNPu4V17ERGH2SDfSrwsiBRLIVePYmA4RBBTuDjfBzyJjAhlCNkKgdZkMxrRAf4V04ifzFrYf+2zWx9csakMIsUDFUWZmJgoFAoUS2hFxAM/bQFoApwKgmAYRpo2MRqNCF+UcCc53nPBpLEdHTwKLlYUSDoKhcLBg/VKpSZLGfgVJQCrrly5dP36zYcfXpqenbp19+Z4beLw0UP5fNbS3cNHDl6/dqtQ0EamLam8osmtTqNSq+r6QBYyDsyvkcaJ/d2FdDzdDj4Ih997dykeQ9oofDJ+uV80UF4Y7TS2P/KRj7z51msHFmZf/OwnP/zh52xnFHherTohisqgbzZ2WlevXh3qA1Gi5xemDx858LM/97lr1y9x3/3uD1IKPBnQo0seDPTbt+96njcxMbazvV2r1bKFgqRoHCvuNDb/7i/9vV/94q8Qrkvy/GcPHz50/O/9yq9ks9nt7W1FUbmMkOYKlMvlJOnt7u7mMtm/RHH+CUhtn0OY+t6atiPIiu14DC+8/e57hw8fXlhYUhTFtlAT3scAUkUflCaO7a3cXWvutKGLE5ThcChJahAEO422aQWlUkkQmXyhsNtsh1Fi2UYxB2JxmpSUgggcBy2P62JE5nkonHQds1OE3bIJzfi8CBWYa5tB6BTL2pFDBw8fOXj8+NGx8erigblcvQRBjGUM+4gvHJuoWBbsBqMoqY0X5xdnzj32kOv69+4ub25u37599+7du9NTsy98+GOlUrnZ3pqcfHJkIhs0rYfTqYkoQCDG8QnLRbLLhaGcy2uFomaZbmtrkMRA7QeD4U5/B9AiKZB4TmR4ToRnqwqiD0L2DH2oS4KcmkoRWhzaGuSoA58kSCD4uilVMgbzhXiEpw/WT6y6v2IR7rGdf2KtpqDOPgZuGFAkwHhh0JcVxXcQb5zqKgyyxadhMulv0kYx9c4gWXFyvV4vFLVqrVgoapkMhBeqmqlWxhiG89yIouJev0NRoaZlNze2P/Nzn8hdeOff/7v/lWeEZ5/6qbmZJPLoOKCmpyY3NrckWaxUCxubm/1Bd3pufHx8cXN9l+EAoqYbBzA5ntNyIPc8uAKJFQsHZ19UdhQaZKI8hNMlRSGkleWDkH/nvbdPnj758Rc+evToIUaT3zv/1uLcAX1ovffupTfeeMuxfVGUJdEVBPr69evXb1zK5dXTp09wd++s5HK5FPGvVquTE+O7O41333l/ZnaqXK5ubjSOHjm5trbyJ3/8p1/4whf+5s//4qnTR//0v/zZ/Pz8I89+eOPWnUq59vRTz/7RH/1JoVjudFpUAnCf44H+UxQ1Pz/f7bR/7PR6kKz84FaaNvp4BCWB5titra1Lly51+71cAS4Y95ZX/8p5VafVD0O/38Nhks1mqQQTbBfwkut5XZ4XHdtXFMU0zXK5jAsdRbGbcKxIWEh4YvA5hK5dKhWWl5cTCm1JNouwSIYOEorq9nc8z5ubm33iiRcePvvQ1MS4Ak0NF8eR4fapnpfLZylVzEuVjOuyooL5ru3pxqjfHfT6fc/3GD48febImXMno+D56zdvNbZ31Cy/eHDq9Onj3X4viQP+PlbBE3EWy7KyMg0zXBPWg93OgKgl8+USPzc932sjsDWfz+7s7G5sbsUxlS8Vt7Z2YPwDNyQYlnEcIrEkSdRNc08DlTrcQC4ApY2mZTwX0nIUC6JIlIGub1ucyO0vwgdzMP/KtpBIuj74+70cxftmOelh63lAYtOvgMCJXg/BXcT5Is0REIhSQ5ZlklEHKRNR3qTLmCnka2itJVbLiJLMpr646bdbWV7b3Ny2LGtjY831zK3t9Yk75Z/9+U/6Uby4dPjI4tFSofreuxclVpoYn5mdPRBFodUzTc85e+4hQRX/4A//sFisUiGf00qpyS0sCwho3+v1sJE9sK3sXQQGByDLgEGTgK2XYPidMq6RPcyfOH34+ec/snjqKBV5b373lUql0ur07t66+/oP3tpttMvl6sLCwvb2ZhS7osQ0W1v/8T/89v/n3/9brj+0eTHD8wly4G3f8aI4iFQt12oNxscDmuF/9Mbb/X6vWKqxnLSxtXPx0vm3334TFMeQevjMudu37xw+fLhUKo0MEx6SgtpqdVLv9E6ni6Apjv3JKNO/rtEHZUxWgsCL4uTmrdthnOzs7F69cl3L5NIVuKdwvf+vGYbt94dwZfdjjpNM000S1vMjhhVUDROROKFM2+IE3g89kFAYNkFuBtpuj9jDBT5aGoZhOjA7zAkip6oKxm6BBxa/3uGV+JFHT7/wwgtnzjyUy2fCENMLWRaJjR/HINE+iUI3cizQjgTWdwYCDl+xUKgUSpnCMGMMDMt1R4Ph5vaWyPNzi+NT0+V2r3/52js7zXv1WiWXy2gZzEjIQ0DgKxuSX0VVs7lSsVyCq3i/b1lOFMTT4zMba5viNqNlZC0nV+pF1/EZXvjlX/67iOYJUcvAfN53RsZwNBoM9S6xe/RgYmDYMGVAUxaNkBsdJgkDg5+0HEngCIpy4z6t7MHz8K8b2SOo7y/nZj5weuwNLdIlJwhCsVgcn5hIe8L0uItIl3F/j9h7SNIjgaA2jmlsIV5W5gSR9nxzOESCbRBErWZXHyJNjDAr2InJsbnZAxNTZV13n3ji2db2cHenpYhhNlNQeFXXjcuXriwdOrizs90edD75mU86nv3N73xzYnzs5OGzvhO3yYu4fmUZhnddO0k+sBECBgykFKMplks43HTstFTCcBwIVNBD0WF9vPb88z9VrRWpJHr7jdd5iTfN0dZGY3V1zbbtSqXCMHwYxgzDqVreMONTp07fuHn1Oy9/j2M5cTA0MJ9nGGPkWOa6JHCSpHW7XV03zp19/OKlC7Oz888888z29vYf/8mfB64+Nl4LgvD3f+8/vf3We0eOHAuCqFSq3L23+g/+wT88cfzUb/7mb/7ozbcq5VqxWCIW2gDNH+Q0/WRr9+AfWZYdDMxmE3zicrns+/7N27eq1eoHliQPNCEczfheUK3UPBddDVj8jEDDFhWymhTLMQwj/SMgtYRKIlQTHotWSRRlEFopNpvNXr58+ey50/l8DkxLfUAzycLCwiPjJ37pV36OEzDL5sWYor0MdkyRF9iRMaIZSeDhi0GDB5aA9kwzAq9QnuOSrDyW5VSNp2iRMeOEko4WFhLiY08xycHD072eOujrO42mYwfKyI7gFZuoYP1jYtlqdchqV1VFIyOkHLxxHV8R+SNHF4ul7M2bt13fqtYKrh+NdPPazUtnz5790Iee1SbqVBBYva5h6kHg9Ie7loUxHRK/28Nebzjo67btdzsD34PvKJBb23JdXxAkNaP5XpAuwn24Zf+I+ysXIWEi/3g63/6RuD8HToG0UglACIX8Rhj2pQTdgADjoijC7ZfQ7tPFSXT3jqYWPc/xA5uiPY6nZZnP54uVSnFx4dD01IFyuUpRtGkatjNaW1u5cfPu9eu3Hn/8kZW17T/7oz//5POfLOWrgz4ep2q1vrm5LcviM888J4riy997+YUXPqbImW5jJIvZcqUUEHk6IWCJxSJgjgefz/0jhAbUjylmBHQfdV+MeTVmRnMHplmWzhWyF9956+2333zm2Q9duXhF4rVWqxUEgSSq0DobRjabVTWp093Vh8nC/MFvffM7nJopoIuj6Uw2l5ovBKFPwe4+9/6la4V85djx0wzDfPNbLwWePzU9Nordxg4YXhzH37lzFwNQhnv22WeffubZIAi+/OUvX716VdO0MPKHw0E+n0/TefYXz0+ehA/Ol4jUGY3v1nYDOQSSpICnOsAMc1+7fv+TGYLFjUYmw3ClUsX3QxgugMOFfVQU4HxDo6ujvTCQVEWQRTqmAicSwHdzE4RyAXRN09K73W6hUPAAKfXHJyB6+uxnP/vQw0cCqp/JIfCE50G7AxGPw86YhUEgmT9D4sAzFBckoevbfASpe0yDBc6KAjlB/CgJ1Gyl1eog7YgDw8/1DVFmFhZnW9sjhhUipCfRnucals0RfnYmk/ED37K9oW4qMkIOgV+IIs2E9VpJywjdXqvTa/leiJQaJrx+49L5C2/9zu//h9OnT3/sYz915twZtTJpDlq1cdFxTcOoGCNrNLJ73WG7NdSHpj4w+z1j0DdcN7ANpBf6Xug41p7OjzgJpLseobD/1Stw72bcNxn4sXWYnmaphZmu667r5kvFldXVkHjRpyeh53lpRtr+YYtSnIClpVKpXGY0tShJQr6gFYqKrAhkiAf+kGlgkVy4cOHWrdu6PiTVU+B4xvvnrx6YW3r+pz45XV84MHXg/fcu+p5xYO4QxzE3rt/J5pQXPvPJzc1NpDUJ7Hvvvr++0piozywsLNTr9SJfGPR1ONtDHk2SktNRIQ0lS/rIpVJSUWQpkUcqJ4vxchA6NMuoqnzg0OKNK+e/+rUvZ3PqN7/59cfOPbazCXw4k8nlsgXXRaNBUdTy8rLASzs7O7Nzk1EUcyPdxOnPMIYBeywyqMBaEESBppmbN2898si58fHxixcvzs8daLfaWVXz/bDVRIJfqVT+sz/98qOPPzEY6IVCaWQYO1uNbrc/NjZBQg5MhqY1zJ1//Lj7r7zIuZRPw+UMw0ophSn2/ZMnIU1TzWZzdnaW2ISZqViGFwRIZnSdF9jQBGeF5bCzEuoi5wc+yyQ+slUiimEc37U8XZQ4w+k7wShh3RNnFr/4D371wIHZSqUyGDYrpTzDIqQRshIeOw4IB55PihEiWuXYhAyxwajGpIWmYjGEmbTrjdqAW2l4q9q2rShCsZApFfIkn9DvdruOadVqFSreUwyPRjpUF5Ejy/Kg10AJBz8lt5fogiDlsoVMVqZjSI5NOGAFipyBy7ppR1EyOTEThmF/OPjBaz/64etvnDh96hOf+Pgjj572YWLAKLKmqfl6PXEm3G5nMByOOq3+6soGlfh0LLNj4nA4amw3OoNRQvMxvUdb2x8DPgCqfXA8kpua3gua9IYfnIfp6ZfSDFMuVIrEwI0Bg/CQZF4imkYmNIxUlJgOk/cyv/YEwaw+tIZDs9vb9QPbsoej0RBOzQntuWEuW2JZvlKpLi2dC0PEZWdzyvLy+qCrV0vVr9752ps/eGO8Njk3P3v9+k2Kog4ePLi5s7m9tXtv486Jo8dfeuVlnuN+7YtfvHXzXrfdY1m2VCiScolmeS4K0tlDqvPEPB5MDYaM8HGfCa+IdMRE8+V6gTs5OX31/fd/4zf+8fzCTBDkWToeDkemjlibaikXRYENq9uwXqsZ5pBl4omJiThMFhcPcnHoE14HwOK9ax0ToJymZyan+v3+q6++Ojc7W6vUt7cbosjbVmAZoSRmEixg5Fqs3F1TFO3ivctu4Ov9gSKpw/4wzVuUSY5P9MBdBFmc3K39IS+ox8T9I+UQqhnN1h1RhKs3y4rwukRMks+DieXPzE5duHAhFVPDFIdloyRst5vIdQRSEtK0aDm2H9ggHyeC5TqSJI10xJV5bmAGbhRQksTQDDsa6axAc2LU15szxfrI6V289tov/N0XP//5z4WRG8eDnfZutVZW82KaBJmOkveqa5YkpaRZrYCT9qgkSUw7EQkVpIFWUqyQkvNjGKVDDmwbdhJSAhjgDF+sh2FsjCwWZ6vI0Hw2k2FoejjQTd22LFeRuUjCVyVwv2fqniyLWQ2ekUEQUbFMU4plO77LCVzeNB3Po5g4Wy4qlmVdPr985/pvF0vaJz71rKrxxWIhl8vIErJdc1k1p4njldzcdGV7Y3v53lpje1dg3bmp3PRMrWMkzc5I1+HDnQK2FIVTa5+su9/Yp0MyjmFTn0PQx/GAEoccAGDkyBLYIA58C5AjJ3Kt1u7pUycuXbl4YHaeYhJBwNA45ccMBj1MaIgSdc/aD7TXWIWekLjXyUK5VDt08HC9Xs/n86KoioIsCFJA/Cx73QF4wAN7Z2335tUbzz33zK0bV06ePJnPKnfWbpTHi0jPbmwvHTxUH59YXV/Z2micOHys0WhevXwFjLYIDoiO6Viuk4SoYjiO9aMQHhgswzMM+K5JTLHwohvo9uTUYjabXV/fHAwHI2hXYSR17cqNu3dvDXqGXrSSmJ0Yr+t9q7Xbnhgbi0NK1zuCQEHiNBJyWaXX6cZUXCgWthtboJ7s86pTizGG1ISWaSKnKQOkyHVdTdPgU9YfFnLZaM+cgw6CJAyDJGapxAWTgITRUlEiiZDhpcHayDclPNIHy9F9qGafdrj/6vf7WIpqJh3rj0ajdLifolX7k6XUZxYQhuXpo2GzhdOSzLuxnuG/QAOYSfkA+4o1wplL7ty7rSiympFG5oCX2ExWWtu499nPPVerF4LQpFlv0GvOHZjRMjUY8sCIIXWyAHUL5DXylBGVJKEzQW0IpXJ6cOwd+EhQ+gDJSB2aaIw94KrIILoHHP6ESlRZxMHgu17kwHMhjCReYTWRTSTXdTvDboraC4LghLapG46mEdEZY1uB48a+lzh2REzvWc8FWY04e8ssTQcePRoGv/O//8nC4syhQ0vZnOx7JpUEpXK+VilxDC0JwtTUhMgLGUXe3m70O33D9BgKKUipJS4o9QBagV6ieSbvOyUkYjzNgDOS7P099tX7CdJ44wybTqH2BhXpACAIAts24zC6desGdhbiVEUT4nEW4jJwOXK5nJS+RJmDAhcjGI6DmY2qQgjS7/fX1jZM0x4MBu0WEsEYGpNGMkKISkX1h6/98BMfe/43fuMf37x50/O82nh5pNvIrY/8Yqmys73reUFzp5nPazubW5paaO3ufuKFT/X7/YsXL3/qU5+6d2950OtltBzeD/FiDmL0f3C5DBPLNqvVcqlUI5lw+O5JQguC1Ot1Lr5/OQi9sfoETfOW4YiCIvESy7Kaqva6fde2RFGKQr/TaUHTCBgcZMQoQCT93uCfjArxH0se39xYLSBZk6ldb7tNBnGke/ZD6AUT5DbjinsBiEgUy8RpcDZNpY4cNAkhIRobrLQH4bV0zf/YnDc9Hm3LQDiOKGUzWWwKLDc5juJ2dXU18HzICSmaYdnQD7BKyQO/D4LvJU6TlUColaBvRQlk1GBNEoUHS3NzsxPdbnukD2ZnK441cmzlC5//6Z/9wmdv37n6jW9+tVap5/CtWb2P/LZCrkh6SxTDPAdvaaiK0+zwJCFP0QNOTJjB0eR7E0VGEFFhREM3GuI9Y6mQ6DQQo3D18TPGdBgGnufv2XCjCcGxoao47gIfR/q+tIfgnCDu4ATw9/RiKdUrTkiErR/us5+RsGn4QUxdunzz+s27Y7XS9MxYIa+1Wr0LxqVypZjP5grZAsfw+Xw9SWSezTJD29dDCfzYlMGctuh7/N79HZOoIPZeYfQBgPEBpxyLMPVK3rMST9n2QRAIgnDw4MFGo6FpmqSgFiW5HKy/Z4yNKswPA8jEw35CxYaO+tN2MMdHDi45KojgEHzmY8eOIaBBVAktOU4itNX6sH3+/PmRMfjjP/7j2dnZmZnZfn+oKnmka5bLROpptpptxzEajcaxo7XTp0+22rvEx8xaXV32kctbNgwT+z65qvuWQiB2JH6qCBkOh8OBjp+HANqeB++ZYglk8zT0Nw2NKxaLlTLMgsMoKOSLQRDoOrCZarXueeRW8uIeQ+fHMBJydHDpKQSn+lyu0+nArkKWvQCcTZJzgksMDX1C0QEjCpIgigIJaoUgHMdpSHtQp0Ledr/t3o9V2CPCPnhW3Ndl0zRNREajdPGnWSKpTzasfsm2mhLB05Yj/TrpcYEbufe87k320zeYHp5JHNA8NRi2ZZkOwmh97d7Bhbm/+be+8OxzT1iGUSmVAtvv7sJ89caNa2BysNyoA1JbasK3t0EDcQF4QKdiDmLOieMwIcxCWK7FdBBSvh95XuS7UDdEUQymXey7fkQ8Z8kiDOOYsh2EBMbQd9NxRH5asnuEYSwKci6HELLUZgKrX2AtywmCSBRI5AQZD+AIAqN/TyG938Lh8I8jRcm5ARf6frtj2K6fy8girOaTVmfE0YzA8ZqWLecriqJl82MJ5xhB3wvBuaEoKpNBBgohee+Znd4HYj7YdlKk4f7a2wPh9hft/p67z8pIn0tidUsbFljapgkR+UDXU9ez/S0P/iI0LfAcUT+Pqxram2w2WygUyAysiPDjmO50equra2urG73eIPAsKvFzWenSpUtPPf3kE088Njk5GQRRRss1dxEuUioVdnd3U18p29H3H6Eg8KrV6srKyvr6+qlTp65fv5nN5sh+vvfuUoEYQm9U2bKslRWE+JI+X3EcB5IUDuFwZKdgFEXOZvOSpLR2dwLkCCSua6e1fWpdPxgMJicnUzP4vYRQ8s1SqwISJBKjTO+2myQlJ4OyQJYnJydpmm61WmpGCRFWSVI+CGqUnmKgF/OcrCop4Z+4qVMwEIgpPs0eI7eEI45G0QN8/LSFSJnqFEXNzs5yDHBI38eQnU6obCYLC8bBwDatwPM1RXWJcwyMFAk4jtwfVE1obfHz35eiwjVIEMIgoJJYFHga3zD23BGWbRw61mD+wOHP/8xnHn/srDnSJYE/ceREtVD+8n/58rGjR3bWdqvV6ghk3z1tDkHVBTijSBLLIzmE2GDLgiQS0R+5HJgwelRElH3E9jnyXai1ybmH+gHeqkEYm2ORhgAAoUJJREFUYI2l9gOu52O+kdAw5IMbH25IHFE+zjSBh7V94viBZ6PgCMgCJbolPNKpS2/qYQvrRzy8mMjDSQt6G5A8hrrNS6IgSq5rDvV+i6OKpVylWKAp1sURbLXb9q40ymbzIi+FFCPiHYJZuk/FTq/qg7j0/qqDooVovtKGGVZU91/pP0m3WuJSQ9iVgrCztSVwXEpSS/tMilgGp1FzNCmi0vE9x6PuUBXZ85ASg7xeOtF1fWNjQ9f1FNxyHT+OKUEQifGULGqaKNAsHa2vb/70zyD1ZHkZRKVqZUyWsoIgVKvVGzduFAoFYo/vZTI50zLGx8cLhdyVK9cyGbU+VpVluVwu4zwOQ3qP5QchYio3ZRmu3e4Ohxg2uA5RoiO/Ge0SoT0GToBE8TQCFbuYkHdti9TYij4EgyWfR/DO7m4rSaJCAdYNqBkI6ohrkQZlJMQjvF6vp2r3/bYK6A3PgwhPhq3kfAYXkUiraNtzcUQIPM2xxCCAlC7ked+/bQ8y1x4sYPZ7fSqhhsORppDpAk1ns1h+iqIEQZBScFLZu+M4JAIeEBzN4iEA9E9O0bR423fQSM/GVP1ETkI/iW2aof3QW1yY+bmf/dxjj5yhorBeriiKTMHQiH3/nYuLM4uhE/dbSOrRZAkDaXKGk+RPgRNRDjXFXUVBzKWiShxczwDEI9MTNytE5AkRp6bcVELMT3xsbzgXw5Ai1uuooGIKtyAMICYmFWbqmADtYuAT2VdCqwoa8lRwkKZZEMkpKfoYXuBR9DmQLOFFbhZRhsMTBTJExw70wIZnq6gJHKXrXq+7kc/nM4oscKoXBqOe0e6bipgRZYkngvdMJoP0ZULmRGUDYsgD4PZ9o0fsqvwHaYoEZdt7Jfc/P/3MFOYRBGHQ60RRBLfFMNQ0pOWFcaKqSHogwAzK0dRwEUqGJNGHAxJhDOUxoVIA70uTJ4DPF2uZTAb2mYg6DaLA63aarg0bqFartb6+PhqNjhw5Aj7jwFJVJLctLy+fPHUcSqvQVRRlZmbm6tWrJPqifOXKtSAIfuEXfsE0zWYTGbVRCJQhSdgwRC+TBjogpYMwgQIfx5Xv+/l8HggW0VdzrBDHSWOnOTk5+dCpYywTddpNz0XL0O12SSwSkNVcrrBPLYJbODkDU34Jnh4cMklSLpfW19dtO8jlcph8+R7DULk8Iq9StDNNG0LtRzF8jLgbLoH/PoiU6TGHphFV6/7a+6+sw/uLMOFZTpNhyQwSU8JEfuRRiBORBVlghdALM0pm09mEhsDzsFkytENiJaM45h+Yq4LUgMgxuG6gUU3i0Pf8wKVpd3W5/5HnTv/8z31hdnYa5lKyGHj+l/7sSzzLeqbnWN69W6tbW1v5XLFYyvfaOgyrcAKgMSAbNTquEIlIoqLJEib2kMWRIT4ngSK09/yR9BFcmfRkg945gMiIlJ04zoIoDgmfMwwoz4s8FH7Y0oCX0jSeAXKGkOKMYzmegd9JahGdAkMwmeE4lLKgcwDmhnYSfjTELzxVWCOjVVNBDkemCuIvWZpq7Lbz2Vwmk6PjJEDoTZwgWyRmPA80EFJPknGZkN40x3EfILJ9IJvYy8GGTpdYkhFjHMJQZYiTJ2n+wWdAh5K+Hdu2C7nc8vJyStnVdRT89yuiveY7jZpjGGZycgqpTCTCOjXJB0BDIq7QEVAQaty9u7y2tq4PDaAokT81UStXYC01PT3dbrdzudyFC+/PzizNzs6ORqN2uz0cDg8ePPj2O2+Jorizs5MGp8/MzDz66LkLFy6+/vpr4xN1Mli3/MDj2Pu0Ui+g6GQ0cv3QF0UJVmboPGI4ScBKPgl8nxfA0/XxV5QoqQkNL+l6vW5bIFq5TrC9vR0EUS5XyGQyRC2Ayws+fvrUQuZM4AOBvPmbN28mCbao1EuiXq/3+/0bt/CX++KxtORL0a0Hh3gozkgzBlSTON4+2Pv92G9+bDValp1R1LTTTZn4hFGJHM+UeCHLMnwvCXYqIlBx76ul46a0VwTvgQft6L5pBSE5B8RQMrSefGLxuWeeHB+vzkxPchzz2ve+v7W1M+j2iKCNGfXt1XtbxWIpDtjbN1Y1VdgzF0ULzpDsQYHjgFgHXuDZPiukodmMLEqixMpAGig42GExYBuKIypEQpOHsRKQZDaGCoYKA/Rsthch7Dxmo5iOItpF1RnBYg12+/AURf8IP68IfmK86MCadU9li5hDcsYGuEdCkgRYlSj5cW0hg/QCnheQjorcMiwAjhcTaK8TSRGCMB4MR5jj8RLHc24QuJYhSLAvSMMwCIjHwwTetvc9gfZ9UPc5MfvOyGlHc9/cCU8FLiiZy6cOQy55llzXJfUetEX5fL5chkw8XYSE18oycA/ggcqRA8d13eFwFAQd8oh6lmWkzO99HzRRxBE3MTmmSnIc+VlNGo2M0Wi0uLiISBk6qdVqNJ3Mz883Ww2KojY3N8+ePf39175H4joVgruqV65cWVxcnJ+fe+217/3jf/xPNjc3CfK5VyfuPeocYxkj8seEuBWDQ0IY6lbKfYWUjPisVyv1aqXe6fRu7m6Nj1VS25RKpaLrRq83IGaNe64CMHkgiXAUsa8EjSiVVjIMnSFi2aHeX1yat5H83BFFvlwurm5ukRKUjFyB7SEWLExgeiNIUgqZRvtUtSROPEw9UalyApK9XJ/E0CHnNR01pbxtbNwE2ojiuK/rqqqKitLX9SCKdNMkAV08zXHdwaBQLo9PTaFMYhjYTyhKGEZ41JDHhAz03Z3tNGmE45nQhxdTqVw09GEumx3q5vhE/fFHHv3QE0+OjaNhuHz5cr8L57jRyIatm6IdPf4QHbGjkY/4DlE0Rs7ezwkLVoojZScGe1SCnlARQXChaJpDTj3PszLP8AKLM5GE8qENBCiDg4tKWLSxMTnP4DfBxlRiwcw0irzEQxUShfg+cKmlY1xJuHwR3XsEpj4pQMlYfO8jkMiiNSYiAJ/YBnNAvVNsh6F5SQwjUP5ptNmpd/geCQRzVNzHVGJLkqGISt60AZOkDXYaOcgwgEZS+VzamOwrJFJVdOplgvsIsJj4XKY4OW4/ObQJQkBE6LhHvU43o2qLi4tBgGS1kYmQ49EIRvcBnKaw5m3bBpWE43xMC/FAph5WvMBmMurk5GSlUgkCKEJVVZUlNQzj69dvmPqIlwVd7+XyWhjEuVzu2rVrDMNoan5mej6b1b75zZcLBUi6m832wvxiq930vUAQkFlALPAGjz322IsvvnjgwKzneV/96td5noOZYAyMFLGc5ojhmIiKLdfBmesS3SbDmbbL8mIQoZ4UBMk0R7vtzoVLlxfnpx0HkwzisiNrqjYzMyMIGD6JgqypWX006Ha7CKBLkoRUBYj1QE9M8Mmt0QAxd5lMo9E4MDvXarVoOhFI1M5fop/dn9umLXhq6ROSS7n3l0QznR6V++Z2D4KxD6KjFJluoJLBnoczAIUyASc9z9MymQDxHlD99AeDXDZLiCrARdMgpMmJ8Ww222nhARIl3nEcXmBLpUKLWCdsbGwsLcx8+tMfPfPwiSShX/r2d5aXlwGOu0GjsYFlHCa+R490w3NCUZQZms0XMmS+l5ajoHKFUcKCg0v5gSvynOP43H2Pdwl6UFZE6vZe2gQmmdiV4DhAQbUdxxhYwEoI3V+UhBTtkcw8nIroZsn4FNoOmOrdv0qpum9vFJIiaFgSEVZURDCD1Al8/0qmifI0cDEYCJP7QhgHBL3cV3WSzyHoJa4j6u19w5H9eQ/RBKL8zufznuelkXj7bXw6rd2vS9OUxfu87Q/mwB8MhMmpmCTJ7u5urVa7c/cOcix1M1dEQjVZYyT8i+MKhQJRWUiymsHPz1JpZLggYkcg7qFQ7hcKJaDlFEsgdAwoaEpMEqDrOzs70zNjKQpSKpYqlVIcx7quw27DFQYDxEVizyJZPaoql8uAZBqNhh94fuDlc4Xjx4/+4LU38vliuwUjouFwhGYNl/HB/iklS6HuI958lItpLYivjZ2mIgqxb+WyGUXOEJ2aTdJjRRKMQS0sLBRL+fX1daAarU5L4ITZ2VlB4DudDsewEFrNzy0tLbEs+7WvfYXoR2jDMA3LZBkmJtbFRIGGDZkivSRGt3HMMYwqy2EQG8DfE45m3AS0AwoWVns3A33a3vT7/owtnWYTfBGOJiHJ6GCQoZWyUmBzS1ETU5PtZmtkGrl8Xh0OeEFYXl0BtbmIHlcnr7S0KBQK3XYz9PxsRkWw6cioHZjVZOmhU6cPLh7qNHuXd66urq5C9o7tHMQyy4aqYET5ve4gjul8Dn2MB3q3y5AJNcqqdEyZ/rA0HXCJ6yVkYoIpPgJXeZalIsLw8HiRVK0cHhF0SDSL1RjCWiQEyQGVREChUoVlR5gCKum0ey/57AFvBTimpf8P/0UsGxS06TokBpip6wxMtSiGhplMij/TKGwZzBJxkUNMMvaKlP3RQnoPyC8EwSKDgRSNu2/xhCNe07S09MJ5uOe5sjdSSjdZjDGQarCPjn4wCn6QlZFytdNBxfTkFHBdYgabMKleltTwe20OVmyuUCIgTZAi6BQN2Nw04zSaFuxIyxn04WCwvd0IXC8MAoGjRAlzvIfOHJ+fn+t0OrVabWZmZqgPBoPe+Pik7UBqR8D/PMcqnhdoSKlTOI7pdruDIRxKT5w4MT09efTYwbfefK9en4jjUNcHuWLB9S1yydPCG6OLVM5lWQ5R86AQ5DiEGpEo2ECg2ZFuIL8opkxzmGKWDMw4c9VqVcsoURRwrU7r+Y8+f/z48Z2dna2tzbm5uamJSbTjUVAsFhEEF0XEDQlKWccDH3d/CrTfHJI5Eu5Q2sqzDHJCHpxc7W+rf8kw4C8zZtKwdduNKDrmaIz+cRqCHOnYgSdy/L3VldFgWKnXluYXgHnY1vwSLJYJw0a1bbvb7ZZKpZRumtWUH/3odZbEKs3Nzbiec3Bx6ZFHHrl1c/nOnZuDwaBYLCpyvtPrBoGZzxdc1zNsLwop24+zmbySLw57o1avJQsJy2DMSBAZ1KIMWXU8ywVRzIZ48lgK9VvAx3xA8TBJw1CBdeI0XZ00b0BNQixC7GChB0oWLGEoAswQcS2x7iJ2JfcHNw8UCEDnSb2XUAHcvUh7CSyOjJT2LuT90RxDPhkhCgwT8iQLjKWpgHyFdLGRL0hOS3JPUqMU8u32aBvpLU5xFJ4P7lvRsTg9YEMI/PwnobUPglPJc0Z+s5dMuPetMcpKUv1uGIaLiyB/CaI8Go3WNjfSRZh6V6VfLY5j08SMFMxyGM56jmOFkU9RSNhN20WixhYLeYST5TNZFQbbdBQ7ogSMl6bpzc3NbBYhvhffv57q18npjQKrVKqIgmuaFil8uJQLwbC05zlRFGzvbD300Km1tTXLNFgWWypLaECYjJHA5tSHJr10KXAND/0YFjARl9KY4kq52Gru6EMnk8lgzGY7wKUz2UajMdT71Wq5VqtxpUKxUMiHYdBs7vZ6PUVR+gNk4HSarVdeeaVSwXHf6/WQVeQ7kiRbrsdioEwKy728zDRUg1C3WJZmOQGoJe9RCJAhmRupRcdesUSSbj9grt13E0pv5F7ng+gOGjg+cHsvdP+/rf0HmKXnfR+Gfr2dXudMn+3YxS4Wiw6CYAeLimmRlBnZshRbsX3tJHZccpWr2LnJtZ9cJbbjksg3sYolOZKoQgsiKYEgCBBEIzqwDdtmp7fTy9d7nt/7fuebM7MLyrr3ngdczs6eOXPO973v+2+/EoTtZitXyNcq1d29VrFQrlTrq7dXDMupVacAdMxgDAVnG9/PZzPD4TDwnOPHjrmWvbs7EIql6YXFY0eOXr92bWV13fP8mJG7PYBs3CA2TbfZ2RBF0XY9jpPciPVZ3otZwwsGps0QUgaSR+Jxw6OSw6V3WFiycQTUL0CbJ8J/PJ+FtxyFdxGKHun2kSPGQ40Eq20M0X1sOdIwxH/0s6OzTMJg0uE4kI6SMQkbhRLxMyQqhiT0of9JwbdEJJNMj2iiQZ6EMhGdUgw0iH50zAUM/AjIraHubvS+0P+D+ea+ISnZPx4k35GlUE17WZZtC2RLqtlF++EcyxMnUDLGpJKWksBxLDkZaD0SQNuMYVVViYKQjZnlm7dm5+euXbsmSsrGxsb8/DxEwLFwMdoBxckLGAyTMDlj2VhWRAXKqKxIutFHjhyhZmyzs7NTU9NRFG1t7qzdXltfWwsxijU3NjYoAlaSpLNnz+ZyudXV21TBhInxTdME39/UA1EAo41km24QuJIMLlE2m93YWAvD8Itf/PFf/dXfkMQwl9cMYwTIJrB6+JBJU5D8IUoyLwghNGZd3hVs24lju9XsTFdKRE/RJCbkbkRAUfTy7u3tBYE3OzstZDKZ119/narNcRzMbmwLS3lxbt5xnFarVa9juc/PzxPC4UDixYA7fAqS7YQwSGd3ApHuwRDT83lBTIryu/VC092YRlcKJMeZQxYByhryhKWjR1CyO3an39nc3jp//vxoNLIsi74O4NRRNOj3tre3o8AXJX7Q7z7wwAPtvV1VkwLPP3nyOMvFF9+/LADOJ4YBLLgc3wM5iOX8kCH+IKwiKZEfD0w7YAeW7yvZnB+5aFvCy4vxaF5KLjuthaidtcDHfAjxY56NsMGTmTU5bHF8olMaetAMJng20h2hwrIweEr3DAEWkR14kPxF/yTfBBDCBaaR7FWajsA/L45ZIldBq77xYYctQXqsLP4ZY0NodxNLxCgMJuBmY+IuiZAJbz0dSIyD3r4DKY6SsWgvtT0b71iiRDYWLE2FhfYTIgYlhmtjQEeZhFtbWxJYneHt27exsPnEWDufzyPtkOSp+jQhLsCYkZTZaNvKikghbLu7u8vLyyArGAYkKofDXCYfB76icgQZzy8uLlqW9cgjjxiG0Wq1yiWITVLfB9t2eQ79C1zJEFZTMQNKp+d5g0FPkoS5uZnXX//Bk09+/HOfe+rZZ59D6KNdMaJtnch5xkl9QpKFkKpAkuk6rv9A1/f2OjLx2PVcj+fkXAlUg+EQ+bMfoDW6u7srbGxtnDpxam4OJs/tdpthYJ+AIemwn81q1DF7OOrX3Eo2mzVNE1MylqNJFI2FOCSRHEdII9Az8gUFM1Vbloe6rkgyRTlNSnGlNcmdiLmIWJzQ44J44JK2hyAMDTBTp6env/zlnzxx4kS323Xff4+6ptEZBk1KO51O6HsPP/Lg6RPHFUUp5XPHw6Mba+vtVms4HObz+f7AtkzTdh1gqWHCGgqSlC9UTBuYlIjnYl5wYATneKGvqZJrmnwccgE1NMfGo5sQlwGIBXLmYC/SjNG3HR/hhugKkuE9YJgIQsTrnuYwNPOkYBefalgmI7JE4pKJSQMDeeOBB8vyvucR51CCBicuaFSjjfCtUKBTZT6KIUdlTZzbyGuipQvMNWB1UZRYLSZNbFqFQoRJSKRsJ89KMqbCMqWjC6pHSBHz1FJ7Ei5HHxRgSE+i9LRlGAb5HuIh6DWapp05cyYMcYZqmQxBJSeRnDrLMxzfarVcyAU4nu8QQDlKQVHit/a2GmD0QpxGltVSqTQ/P8/CN5UPXIfjA0nCe8jlcqIoWJaxtQn8Y7FQcxwP2mo+cmzLQirreza6WLihHHHmokZUzMzMTBQFf/RHf/hzf/Wvnzx5/IMProuSEvj7EYgmCGO18jgMcX2I3Tf8BVkW0JHt7e2ZxhQZ2KC6BvIelF1k41RIAcYm5WJ5bW1tZWUF+pikCcmEkSyLg2E/q2V5hq1UatVKvdPqBl4oSxnS8AbbnzTdmZCJeIZFLo+FgAlDIl5AelxJyXdYtYoc+BPE7cl9mHRQwygSeJqaEl8avpQv/NiP/ZjI8QN99OYPXs8W8guzc/1+39QNSRYGg0EUhXNzc91ue2QMFUU6cvTosaNLjUbjf/ln/3Rja3NhYUGUlN29tuOiWIoYPmCggsFJGC222r1ipWy6w8B24Q1M+sOYUsJIEgUYjXiorYjhA8ewOhleC1woihjXpqguTSJeLVzIx2i6gAQzNvOlqx5FHC3tMaWg3S3krERKKBmxJsdT4qU+VrVKjl5c7YSqgWdESY+OSJ7gfKYjjrGqGoI3tjc92pCQokNGINiJXRbiaYqACWRJCCMXbLp9f+IQrd0odBysTvQlNU3VZJ5lTVJc4HBJNi0lHuDFXRd6X0jVYamBKEyoqhhfaZmsF7jN9p6kiEePHt/YgI+inqj9UrABkL0UkMDymPRA2IAQ3mW5kctlcrncV48uQlxz0CuVStVqNQxDXdevXrmxsrwBRkpkWU73/INn6lOV+nRjamr65Zfe9NzQMr0oiDMZyfOdrJbp94eiqEKmPAYXQBDEwHNCghBSNXk07M/ONDbW17/xzT/6yl/4qW5/uLm1xwKLQTplBCFE7w+5a1CmJhgD3vOBB2aYSHcsPo6zhqkEmA7wHNsb9H3fLVeKMctYtqs4tqJqgm5aSEc4nvhgI1LHLOPZDi8qrhdUy5XR0OAYERrUdpjNZSBUJmqhhOIhCtEgwJEVhWhUBBFEXBjOdl1ZVaDM4LmGbVHiJk1gKBQixXCnLjEpq5qHIakXAIHJyqJUyucKubysKpZh/uDVl1t7zWw+1+t0Iyb+y3/pp7/xjW9Ua+VutwPwFB+HsVeuFrq3Wi+8+LyiyX/jb/2NX//1X792ezWrZnba3Ww2bwJ5xMFPAM17kO49LF2Gk+S+bvhB5LrEV5io9KuSLHL8cOjRg83znKn6tGnp2FcI7jwOTpZzg9ANUO6TPEqwHJveocRxhFrVkmiQZgEA/o2hJAyiK8pmGjBIwojnU8ODcZBJVjnpHE4WjSzORA4BM2RgEgj/yYQFTv8RgZCkSIQjT34lKIAg80JaG0w0ZC+YXCLwQsmbJMg8klhy0JNiHhNGcu9Ylnr5SaoK3QDMFT2BSFlzceghjaEIUlaV+cGgRxol6sjsCYKgacpw2FdksTPsNGpTR48f2d3drdZrtm3v7m6rGjEtU1SBmBZiYC9hWJ/L5CWUl2j70VsD1HAYX3z/ahQFlg0i4miE1I5g3/nZmTOOy5XKRV4NHd+6catZrdYjhrl86YYo5BSxGDKBZViZrGSb/WxG1A1LUjk25AzDFzWZY+RhdxAyrCSIszPTr37/+2fPnL5ybfl3vva1v/P3/5v/9y/+02G3b+p6o14f6rptmaUS/MLI1CKinhZA+0YKNVNQVGFgDAWJBVogYm3bFSQuithWr6uoSrVeUVV1e2ePDHnowGBczdO7jfhGqTbkdkdRDLl22+VkBQkO1piA/0dHnSAI0G0DeIfYKPp8ADNBWZZH6P1T/lPSIE1jYEpomiiBwsFwlM0AzpvL5egsuN1uWxZA6JRvxrNo70ZRdPPmzbm5uStXLi8sLAgi1+t1VlaW5+bmHn3s4c3Nze9///t/8z//W1evXotZRsvl9ZFpOYHl+AIvYVmRswwRF6d47BNTKtxkAvXkwI30IxcJVRhCRBzjWhedOssCsxaGKkSlW8DQi0wVyMb2fTEOfI6klGmvnjYb6Z+TUC/SaOGRWJCIN5mzpaPXSag0vU64zsTgj5Kh6Wbbb06SH5hMLwjCnv6XiKKTEjJkeQYQNwz0khEj4ij1HB7PI+l7pKcHz4k8YX8Te2Sg5OkLEdhGcoym0BmOYwPfPXr0qG4MbRuHl2Xq3XZPy0iFUkGQBMd3YatB+D5zM/MwwCNAWS+MbMc1LMdybHMExYqNtXV6ahPzNuovj99F3FwwH8rl1XPnzs0vzJVKBUHIrt0edPsDjtdHhnd77XaxlD1+/OiVD666jheHGseCrcdEQQSusUteC+MfqqRGrhEPOjbHBZ7fbTVnpxvNNlruO83Wb/327/wXf/u/+qV/8S8yqgqZdtM8dc89V65cqdcbRDiHtHOBrRgngMS5ImLY7eaeH4XT09OQj3EsUWBrlUa/23X9UBCIPyGamsn9IhtjvBXRcwfrhlqxQ6UzCDAt4GPGQ32DAy+ZRU100uiioSqadM+kmj+UnZ24T1KW+pjcRBli9Juzs7NB6Jm21R+Cnk9BT2pGsywL6uWiONRHgoDy/dqN65/4xCd2drZHo1HMhIVCPqsqrVaL4yFkIAni+++/b1nOsWPHCKsBDHEk4pxIQ0iS/RJJdi8EgRVvgDQbMF1jWZ9Fn5BuHvrQNEAraOZTKAJRZBiQ4ciQTB6UlsGwlM+NMUD7WAbaE564UGkFFVKO8KSY0mTMPKRPl0zA8e73E/jxYADzV7InJ/J8Ug9iXEjeEt4VCaHo4+Ce0ppTopO5ZOCEF+H31wFlI7Os6TjoiBAoGcP4AOoRSQqAgYRE/o62Uh3HJuMEzw890xjhqmazPKQ9RY4XNze3QD0RxEIhy7LmrVu3RF7a3d3tD0bkSiFRZgWk/7DCYZgMqL2wT6W0csC3CYD76LElsmpA3mYwzIKm0XDYvHZ107QcljNcv9Prrz/x0UdPnjz5zJ+80Gw2RSFfytcymRwWLtl8Pj51snTT2AAByBhzlFwuNz8/v727ByX/0HzmmWempmf/k7/41X/3a7+mZlUlo6ytr5w9d+bG9VuweSOAsJQbRVyBhSB0FEku5nNMzN6+fVvihfn5WTQm+n1a7/DQ+FCxa8cbkFz5fTXBhEBFbUfgRsmghRjaFuHN7Z/Z6bSQfIzEUouqa9HdlSLoSbUKbR9KgU/5DZQvn8qnI0cn7I30lVOdfPqgM5ler6fr+tLS0vXr1xVVgvRIEBYKMC5fX18/fvSY70P8F2iEPXipgxeTyfgYp5LDI44RuwLf8ROuA200Jf5+EyGL9uvSph/9IKZpUtlMykCndFViMkOgJ6i6iLcWF3CoKGOIJECvmUdWgT4baciMmxaTQTLtK3545byvfUYDYPr9AykNLdKIUgpJbumWppq9NAdBrTje4ZSDi+klfd3x69PhEgAULBPyKA4pRgcRUpKkRqNumuZQR6eaAmhyeZXnsywbd7tdImmLMl6W5VIJXD6KdnGICzfPi7btlhrlqampo8dOALZGwAWAbsMDnGjVkAUdhejoUH4t0SWLvvvd78Kr0bVHes809SgGRoJj5YWFM/mCygtMqVI5e/bo/RfOdrvd5557LmQiNoC2MvXeoK1BN4AXAD3pKKILMsoEgdzv9ymMTJIky/cbjYYgZX73d3/3y3/+x//RP/qHv/ALv6Cqai6Xbbfb5UrRttCqTaNLqodEB4mmaeZyuYX5JX3Y39zcBmZNAU2LbkI4gqVGP4ce0FwgXt7o6qA+FpB1MEg2E8D7wUM6COB/wnGYc9B9SJ8DRh/J8tJQSVd8qv97SAER1pykDU2TELqf6Z6kDumlUonYd2JXv/vuu08+8QRw8cNeNlscWVDdqtbQarJR6wL8TTnBVGsI6hsYHyMc4PBH5MN7gwLM+A3T60dyRbK4Q4iyZTKqYYw6nY7ruvWparlcbrfRN9Z1MI9TPrFlWSA1kbELdS+g0AaWZVRVJGnkuHtBSkqCgTm0wfa/ODivH3NQKL4svWI0Bh7OQg9uwqSFDSEOknAS+18qiUMs32hYJbcBRSqxeSL7LJHvwE9A0xlGbQIaDgDaAVYaRQGGuJHPsKGi8kxEbdhcSncMAz+AzD6YrDA6lzIiRH5zqGj9yDEdngWdtVAoVat1WHl6nuU6XgCdBj9CYwAKNDaYogyTGP3SvIll2UKhoKpKsZg/cfJIvV6dnZup16uamrt0aXl9ff3y1TdEOXfffWdPnDh2/cpNy7JmZmZdB10rOtgkg22wbFlOOrQJFRmbkPoChSEof9ut1dAJZmcXddN+7bXXTH30D//hL/ziL/7i3t7egw8+fPHixePHT2AmifQTvBjEc6IywjGx7bjFYoFn2V6nYxij2dnZ+87d2+22O502AhWP9CqJhMkNTW8d4I6IP1j+UIMFLQW8VSynpNw5KBK+n5Gmm5B+sEwmQ7nhFDNOVzl1/0sXWWpcTvmdIZJHeC3QUEDHU7SkpA5b9GIVi8VOp7O5ufnkk08+/fTThmHNzMxcvnyRHM+NjKplMhlSsYAh7vtQTICGjiADWRRhIUEYguxx+oKTgm505dOP4LouXYqlUolCNJaXl4vFIk29FEUB3FyG/yO0J3KFcTeY7pwEkjJOKSY3G40nB0Q7J8+1NB5OKFuT0pvqvOwnrpMvkhYXYyJ8MgTcRwjSfZhksGSkQfGeCJXoTUJGlLiyhdTcjv5ekmehB4gUEF3cCL1hPpYUXgWoHVwTz4GEjE2orpZlVYgFSK87yOaB/xzqo0ZjZntznWc5RQQpXpYlIhizqigKtM8TtT+OB+wI03+eZ6vVMpF7FAmCGOkoNTCs12tkqg/jWoaJVldX33rrjU673x9Ytm3GrHX+/scuXLhgmIO1tTUMVNBwZy1rBAUdnvF8D3ACHpQRik3nRSGg/JLx9EVRlJmZmfblK0jcRH5jY6NYLJqj7h8+/fXjJ47+9b/xn/3KL//a+vrq7Ox0v9+jUtSYGyCdSfQfqDtVrVZr7e10Op1Tp0787M/+bKVc/MVf/B/JDJkJQ6ztAzyjVNkabj2iADIVQdMnqRPD8gIfBkSLn9ScdBxM40cauOgdTTV/qPhWKkIxmX2lspZpzskwzMg0qH0xCmjygi7RpYShPMpfrk1gPTYpO0ul0rvvvvfII488/vjjL738osCCmE8OIZFmTaPhAGBFUXFdX5YxTkQ3FNxzIpwWBOG4q4BR3ngvjGFg+FPAWe75PoJzpVqi1EwynJRnZqbL5bLrunvNnWarpyhKo9FwLIc63CdCcgnfnvUI7HucE5LWB65syI9LuDvlyVNS38Gdye3fp6RyS7Cmk3sv3Y0J5DK9r2N1bfRmyAYm74R+6sT8i5j/JhJz4I7gOeidhlHMB5CBxzwY2iU+ywt+LBAbG991bTZiM1l1qlYrFAqzs/OlUnlja+vatRsjHQIQHkhaQaVcG+HqYQQt5SRgfVXYxSQIBwF9KhKCAS2gByG5m5g5GIY+GPSJdED46quwP3BAJXVkWSwU85kMzJXO3Xdqc3NDzXCf/vSnPc+7eXN5cfFIGAjPPfsKyygCqxRDh2EE23UEWeUE0Xd86rGIwIBOCWqWOAaxGDlUgOkoyo9srts3IG3GMmfvu/df/2//6h/9o3/0pa/8xC//8i/7oacoWkSGEAlLgUwNYMXNMdlsvrmzK0rCX//rf/2jH3m8XC5fuQrc8vT0dCarUE2m/U04mRixEM+VYh/MdOwu0kggCCjGA7EBNy5trtDTOuXykZCS3HWO40wTMZA2ZmiPK/W7nkidUxApEkhaiqZ8VqqrQ2Q8AK4fjUZUQqfX6zUajfn5+V/6pX/zN//m/41l2Wf+5JtnTt9DEmO0VfP5PGHlh44F9yVFgUm9KGnk05A4mMj2JVuC/P4JJZUkW46zWWhg9/v9Xq9Hqf2lUmlmZpp+/Ewmc+TIEfp+2u22KqssAUymG2kylKWfN/nUSdW3DxtKk/ZDrmPpN0kqMhkGSbI53k7jP+lPUVQHNU5I7y7p0exDUskuRcMGQZlLykeS7sZQhkuiKhvnM6rvu2gkx5AldBwnigNB52pT1Xq9trCwMDs7W6siUa+VK5l87vbyysgwcwAohy+/+loQeOVKxbKMgpYfDfuiCIl410WxYIx0UeR7nS4GKizjgSHkwEI1gla3MdIRCQVo7VH3HkEQWRaOQ5VKqVwpEjMzsVarLC4tTE3V+sPBN77xtOPqxAdlb2N96/zZC2+9eQmSZegHwc0C8t6eq3ioIGicoCkP6TlFhKoaHT16dHcbA8xarbax211bWytVGo5rSaLoeV65XP7617/+5Ec/Xq8DMECuRshCr5UAVgCGSK5xFEWlcuHo0aOPPfbI1NRUf9CN43hmZsbz3DiiIgmBoCro6pApRGIDAL4fUcVXC3nXdRUyrglDqdvvEUQsI8Hl0DcRslCYUby8T2BQVL90EgdDtxA1IUvX+iSK/5CrFnnapNos1iI1VUecJOB3wzAYhqFSv+U88D3PPvvsV77yFX3Yv3r18kc/+pFLly6VisXhEIQRwjbAb0F/dUKAiH5+nxQeIREFIyuU9GZoGQRGEPxTZEX0fGd6Zso0IRYSRWG9Xut2uzs7O5ZtUB9MYhJY1AfDIAiKxTIV44jjuFTEqeF6bgadBsJlopkD8X2NIzDQ6L6aTOlTIB4tnlMwJ70cVICI6gLRXmscsYRRiUVFEgEccETxFQc5GP8QJU6UY8haxu+Htp0F7axMJgNbDoiZIpWisziCiKMe6F4YhSsrq4LAaZoCuOZMZXZ2dnFxfmpq6vwD5wGkdjGIsnRjZfmD524t7+zs9XoD2/GK5crf/Xv/93fefTuKom63XatUMWEJvN2drWxWg/hKLlsplvLFfLGYx/uWRNuDOxrwmWB4m4oCyHgcA4myu9Os1Wr0O7IsQ0lVkx3Hmp2dFQQOYlAZtdnesx3jp776VZZIpTz77PNHj5w+cuzE5Ys3FxeP7O50RwbkyxRVdXzPcjzYxZFm1Gg0EiR4FQ8GA5b1NzY2stmsPhzFRHyQuirIsuy7EI/JZDKbW1sxG83MTb/+gx/UajW0tEJPYgSPaDoReC5J33zbMr1uLmsao8FQuX379re+9S1BEMplZFW97kBRNKFWKRNcOBHqIa0Ier/LhWK3261VSpIsXL58uVarTdXrpm1FUVCrTW9sbExNgSZM9xWUl8ZzsMnyhqyZ/XrvThe7OzClhzsTh0qmOx/NdmtxcXF3d+/pp5/+1Cc+Vi6XL158r1qtkkEWTasmGj+ofEj/ljpQ024EqZPu+uIRG0Gzljxov5c2nykcic7ih0PIZp88ebJQKNanG3sbe9TVlGRQqBJpoTsRypLtRGNgugPToEfzzzuHqIdKx4kvQAF2XRt24wKAizbBdhEVWSFf0KIo8AObIHUoqJVn4RTqCzz+lR5Mtm1HcZTNqP3BEMW7i+4lYNOynMFcVPnJL/3laq28tLA4NzdDlJedEVG9vf7BZVCuOa7f66ytQq1s2Bv6vn9kaand6Tqu02nvFgoZE8robLfXZpHycVlNrdfr5WKpUChoitTpdJQMoOGWYxfzuWq1Wq1Xj58Ck252Zu6dd95rtVrNJkwLDcOQJKXf7xeL+V6vI8kCxyEQdbvtl17+fr/fbbZ3ztx7mpQJfnOvo4+sSxevPPzwY5Ikwex+BOu7SrVsmY5JnIJ81yN+Z2T9B0wQ8DC1ZUE+jkOv3+15oIyLsQukq0e00IrFouM4VGmmVquFYVipVBDJ2VAGi5pTFPnzn//8d7/z3NbW5vHjJ02Taba2//iPvwmBOV3f2tqgKSHtNcL9OZ+FlgTI2CEYDAkelGEC31NkKQqDUj7/+COPOL7XarUA44+grQLP90LWsiCYlcnkkrnLRF9hXPezwLEd3GlpM/eQFzF12yBwj/EOpF98+A5Erx00Siefz6+vr3/wwQeaplGaEuX9jwk+NI8joMvx2CP1dh5rvZENcLcWI3VHIOefRije/Gg04gW20+kEQXD69Cmg50wonB87dnzUHXbanbm5Oco/Ivw9WFenPuGTfqm0JZDqkU9us0PV4MQ+pGN3Uvyh85kM5BUZzqcBsm6lVMzHcQzziWE3l9fi2IfsDFD+Mi8kvAbP9SxCyYsJWYwuKc/LyDJfKuVr5cXZ2dmlpaWlIwuz0zP5fFYQ+V6vs7259frrL+3t7XW7XULMse6/8GCpVMplNWs0cIwhHwe1EvwDWYHNaaJtmc29jZlGbXt7u1AsdJpdTZZqlQoTxaomeb5tGUwhNy2KQrlQyBbytuvki7liuZorZEvVymhk/Nq/+9WpeuOee+6J47jT6WxsbERRtLGxoWkQGlc1mbSCbM9zlo4s1moVJo5/8stfuXDhwZdffvmPv/WsJGYEXr3n1Jl2r68q2qkzpzwv2G02yShbLEhqwBIuCO1KcBROiMkRbQEGQUCNK+PYJql6NOgPpUbFdFw1mzt16tTi4uIf/oc/6HRavuvt7G4//MCFH/mRL1UrlR/90R9dufmBpoijUX+eCEFcfO9dlmXPnz9/5szpnZ0dx3ET+wnTFGQAcCNGENkYCl8oibEeIWpQP740Ghou9rwVxoHv2vlSXpQB5FtYWKBC91TyjWiKUIrNeD49oX55yDvy0OmeRgZapRAuwYGZ9Q97xEhKW3vNY8chlf/Ky68tLs0XCjAwSobSZJw15kITjUYyHgzjpDtKWzKIBpNok4mGsR9B54a2Z1O18i551Ov1OAaHutVq3XPPPa7rbm9vTs/M7OzskEELaTSIkFpxPYcgKNHgor1MQnfA17SVdQjdnsbDyQwieQKpGsYXbZ+zRDpySFFNc9jvw9Epny8u1eZNqw8uCq4AZJ8in3aMIl6IbAe5KFjzpdy9Z08sLCwVitkji7OlQn6qUS8VivSTbm2uDXqd119/zTTNfq9DawHAR1VV4vm333oD/WEF6ygKfCYMOF7wHcP1A0UQs5o0HHQXZqdeemlUqhRFgcnmlGIxy7Fsoz5FEbmN6aqswBhLy+UxV5SlQqEQMvG1K1fXtzaR1IT+s88+q6rqJz/58W9841uvvfr62bNni6TcKFegulapVOr1ajaX4TgmCKEWv7Gx2Wp2V1e2ms3mraXV0cg6fvx4p93lOKbf7+bzUGqzLKfdbudzmXTVjgGUWIe2bSsSGkIkn8eUmDa94f8nS67bdV376tVri4vzBDzAP3DhkUceeejHfuTzYei/8N3nb69cN8xht7cnSfLe3la32+U44cknn/zMZz576/btS5cu5fMFQZBCz9va3BGgL4h6GMrRPIZXMQfv+lgU5Mj3SoWcqIjDkXH2vntbrc7T33xaAOLZW1hYWl1dzeUzrusOBzqlF6S0znG3hkIzkoM/beSkLYq7bUXivJG0xQ+nXneNhFSjdnd3l2GYqakp2sgRRdGxkAdSkl66Ccn7G3dlUv9tSE9wNBVP4uF4hxOqAgROJHDY0ABAIuHZpqUXi3CtsW0TaVW5nMlker2OLMvT9XKukI2YSJEl07C9wA2BA6GXgsJdyFeI+RH+b2LHT5o93HkAJZcovVi0i5SYYABxSp+l8gpxL2Ki2B2NHF4M4EEOJ3vQOwmGU1M1ZTpfLRbvWVpamp6erlQqc3MzhULJ1Ieea7Wbe2+98frK8vL2NtBIuJsYi7uaphWzxVqpRtt1tGQVOBs31/NFUcioqkCkzhH2A0/W4IVl6P0TJ88Evh34jiSwpYJ2zz1Hjx05Oj8/LwvQ7xr0hhvrK1AFi8KpqVqr2xkO+4Is3bhxg2VZNcsNh8PpmQbPCc8999zp06cffODhd999N5vNkgwFNJq9vb3r19n1jTVdH/4P/8P/s1qtDfqjV155PQyYI0sn9ZH1b37p/zhx8tT2zov94UDRZEkRiZFTmM8XiFV9wpRPdHeDmCXhsVgsUvEXTdNIUAFSv9fv5HLZfKHEcaW1zY3Tp0/97M/+7NGjR/K5TBQF3/nOM7eXb779xpsczwyHw2xOY2Ou1+2eO3vmi1/84uzcwrvvvr+1tfXEE0+8/e67LCcEQbS+vikU8lmCBmZ5sv0IRBwbkmX4YrkqaxnLMT/44IP+oH3y1Om//w/+7te//nXPhw7iYDDIZvM04kE10Kchjh7e+5Hw0Nqa7IvcLRgSW4eJyvBPCYYxclG02gajWq1Wq1QgKSuK4IYlqsmYpZDhNroX6H2PUSnJ4UeQXeNAmW77NB7GHKbMQCCoqkqbwJaNUNDvI80QBG53d/fJJ5/8O3/n73zrW9/4jd/4jUI212g0bNuuVqumuQ7mDiQ/wNMhFwKnDBGd4SImiFGkJkigybLwUF6a5qsEiT+GSuwTDjHrohxiCloKQ0jgEMS86/lGsZSbJo968qgWCoVGoxHFUJoYDAbXr1+7dv1Su91eXb7d2t3h0V6nKCm06USiKVYtVamAHfBojh8TX8Y4JJQuDsx9jmUCxw0ijw6uJJFnQ48JvF5nr/bEE5VqsVIqHDuyOOx25hcaH/v4R3ieH3R7gs2trizv7e0oWuYI9GZjIlM/Ynjsvbm5ud1Ws1wuD4fDarV67733Gobx3Hef7feGnudls9l8ITsYDAxjVK2WBV782Mc+dvbsWVEQ3377/bXVzRMnTu9s721t7gmCNMdwlUqF53EZO50WKQ6xegEkILD4dFkSYIk7GAymp6r08xIrjhBg+JjNZvLoVYWR6/ovv/yq41jmcHDp8nvn7j3z9/7+f+UYo3/1r66N9J4g8A8//ECr1TqyeCyfR9D+3d/92pmzZx995PGZ+bkXnn8RowQRXcOtrR1BUyRSO+GA5iEmywroreG+q4psmYaiyktLC1evXb1x6+aFvQcffPBBj+QwhULO96EuTd8oeCr72dQ+pvHQSZ8usrTUmRQjJctoHzv3Q7wp000YRiCJZjKwMqcCsuVMsd/vqzLxl0v4comAEjpdZDA4menR2D3Bkj7woHg3qiPsui6dxIii2GjUO51WFEWNRuP8+XNUrosKQ1Yq5fX1Ua1W6ffhpUN6kmj5pjt8DI9DIzMgZiN3HjppOnooKRA4cTxCm8zwI9s2M1mVF1jThOZVsVh84IH7T5w8unRkVpbB8ACzjEngR0HojvT+yy+/fO3a1Xa7vbu7S4eugecV81me5SSoOSSQQ8qfxDwnwAIl8udgPDJRZNp2JqfxfMwjsw5gvAGOOTYvxuhhwDLRoNcTBa5aLp44fvSzn/3sH/ze72xsrF2+/L5pmvpAF0Xp9u1b6D97TrmCTjKFU1PDatd1X375+4899pEjR44ZhrG70xIE4fjx4+Bzm1BaESWemDTlq1Vs1C/8yOdFEaiJq1c+WF5ecexIFKV8sdRsdc7zUiaT2dnZxhiQZar1GscJq+trlVyOwA/A1idNZUBwoyjq9/s0QoIYqUDOEFawDFcoY9BijnRoTwlcvz+UOGZ7e/v+8/e+9dabN65e6g+6n/jkk1EUnLv3bBQxr7z02mgwzOSyU40aw8Sv/eCVkeGsrKwmYBWO63X7ggQBZwDr0E+DlhE9/vDwAh9sHtJOe/TRR7e2tl596ZW19fVGo0EBRCwlLzI8UaHfHzmkmxBnTECSLyYZ1qdHPtEC3Z9tUXYkqZn2fQsODTAmHnQCRnv06LgHfthqtfL5fLlQYGJGVTJpj2iiXURMjjGwSHpFBIVJcV0Tq//grkdk4WLfgQy7a6MTEIE2BsCCokiFbKE/6v/glVdvr9xavr5cLZU7nd7MzIxhwKWA8FzsbhcNDFlWSciiKTeZUOBPXATEnDELId2QVJUsff9JyhDFnAqYFYl79IkhAVMwpXJu0OuyLHvu3L1PPfXpCxcuiKI40vssF+rWiIQLCCYIUG3E/TX1/ttvvnLjxo1KpdKol1VNVmXF1C2FV0FLIg/fBVaTnll0+4JF4rr5TL5MVPkk6C+xPnhEARuHAs9JvAjR5yhmBVWWREZU+82WZbsxy9amak9+/rMc4/7hf/iDZ7/zx3HM5rIlSZLfu3iJaGEJw4Fx6dL1iIlFUW7udcvl8s7O3pe//OW333mTFmOVSmlvb69QAG6JSrHEMWcYo2Zz94MPkED+xJf+vCiKzz///JUrVx588EHXCTudbrFUIrT6zmhoeZ5/5szSreWVdrtdqdQgB9zts5zAiQJRO+UEgY9Dlwk9IkNeUDNZDvY8AhDinOeH8fL1ncXFhaMLc7Nz07mMFkaubZm7O+Gv/+qvvvvm6632ztL8/JOPP+q69o2b127fWnn4ocfz+QLDMJcuX2VjpLgZDfrc7XYv1EIBwjaBkNGUAFwsXHSsAJ4NIEcUCqwE4WiiCCfwYrvdyeXyX/nKl197840PblyneDRBhkxgzIHIC8RhIkvBQ5EWFDsM6AkcBjZEVKcVVRZEsYF8J6VuwiIhTUecp17gkzlAovlFYhG+QWndScwEoQ99UcwhJdGzPaDyiyXgs5EqhJwoBi5J/8YhBEuWo2NrFIQAxoI+B2znuAgcZ87J1qMQL/IGiKh0c2ebaDyhIatKYhj4ssg7tjk7PbW2ervTbT72yGPXblyHPq/ny2rGRG7GmFDzjZVM1vcwoEOqj5SfiBISLj46ccl8kpJrk1EqPSmTE4EAJJCsiGjAiDyL9YJQOS5rWSYOvWIhw3HxhfP33Hv66I2r7w6H/Vu3bnaG/dury1tbW8Vi/uGHHz55/ITtQH/FNg2OCc+cOEZMHVnTGAya1tzskt7zFCkzGg20jIIJdalQq1c2NzdlGfUwy0fFcqa5u6vmJZZTY5zCSqdjYExaKY5GIyeKZFZstlvVWm1juxVx/Mj0I05szM93h0PGdx57/GHHGfzrf/VvarVGpwvgt6DmAh8iqe9fupHLFZiYGxqOIuRCl6tVarapz81No7O/bX3k8Y/t7EKQTJZVajCm6zoMJBqNdrtdr9c7nc7s/IxpW8dPnuh0eoORnivkul04H1774ObC4jzHSW+9ebFQKJSLpX4XIKcgigVe1A2nXs2JHD8a9As5JYr4bncYMZClyhdLw5EFle6t5slTpz7x+AWGBYgiiqybH1y7detWMZ+9//7z5YwYOOZHH3tscXH+5o1r1Bb7/NlzjmXYhtnvDxZn56r1GccNXCdcmF3sdgxDR5Wez+Rg0ELm1NS9kwiV0zyHxyYhSwZLBB1Sy9pt7t177707zb3BYGCatgE1YlbTYJGjj4wxAiTpuGAU5ccQH5t4pL0FKqwABHGaWRKoP+V6U0wjzbVoxjvef7Qmwn4mqxc8N1GERTZH5ZAh2xBE4RhKcqCvg09ExhYMIbWHBzbeh6S9jmn5LqxpslpGlNDEo1ZkVGi53++fOnHyqU9/+s0333z33Xdc32+1Blo2z/P86so6VKfyRct0trdgL0Nao1Qehqo3kT9IyYerP06/x7OKpPdyIG0mSvoRxnzjBD4RnIkcx8pmtHK5wETe22+89rWv/bZt6Jwo9EZ6sVyYn51xbPul773w/ReeB05M01RZ5ngmo6heiP6QyvOG67R39xxTKBbFMCQpDH4RMKMEvw44IeUuF0p5TVNkWREg5u/PzM71ut3B0FhZWRsOh+fuuw+bkR1GnGTZdrnecFwvo2Vv3rxp6Pr26s0TJ0789E//9He+8/z6xvrC/LFieWo4sFhGjBiu39OR/nM8tJq8UJRYTsEUjuOEmzdvXbr0PpxuGw3TtE+ePJnNZgliCfLbjUbjyJEjDzzwwPdfeun5736v1WrX6+DgExuJhK5x7YObZPKpejDSdESgZ1DS2i6AabpuQvqS4x3L1VRQ8NA7LRa82yvb29sPP/jQ5z837brO1tZNQ4dppOs6Isf/lZ/5qXvuuafV2hOF+8+ePcsw8TvvvKUPdFjW+tHAHFXLFSCsCiWOl/d2m+12X5TUOGZ9hyiyk0IpsV8dNzBp+w5/pb0Nak9F0yRU5LYVbGyFTFyp1GpVodfrjUaG6/q2rQv8mN8KOR8qPwvSPS/IgCkSt0Uq1YdFgxYJXWM0+xqnoATlD1VA0mOgBQkAntDhg9MEJm9jregkTsLuCeKwBNE6ATqbSORoP4ZuyMlq8D+m9+NBOZ9IqgkCxCkQ0wFDCQLYgPR6vd3d3bm52ePHjxcKBd00NbUoCBCo3dnZoSZeuVwOI5OJN3agzJt4Jyl9fnITTv5rhAjpA71J6lt66Wgyz3Os41j9rr9881atXnVMS1GUSqUyNTVFuzs5UZwqFonQM/StWUEM/ciNQeOCDVaIalOV1V57KGHv2KIIpkKhkBd4UG8JSwvXnqi2yKT0ckzD7vaG9ekZTVa6vf7uXosIwMInrN3ulqsVy7DL1drG2ma93nj22ee+++x3p6qZjKacvvfe5dtbe3uDDz64LombpWJV07K+F+u6KcsyxhLwlfTjWNvZ2Tt28tixoydjkIAVjjVt271+/Sa9LERjGniXQqGwsLAUR8yLL77o+36xWIRqm6vncnB9ABSBnCZTU7UCpvy9MPQ5jjfBLXBs25mdnoPMIcvJmjoaDgQRHCVKT9MySqVSGumDjc1dx7Xm5ysZrX7mzJlTp07s7e299957q888k81qszONN954Y319fTDoH11cKleKruPLZZmJGN+LXC+CXRQve26wsnJrdX0jm8kDf0c4kwkLOJ0ipAuUkh4i8Mo5l8jgQUwpjnq9XsRwruMLgmTbmPpTlqDvERtyJEiIc2NuIT47rYXwxRjvT0F1McbniRAwlUqBmAmXIMvobyQ9un1rrvE63m+xWpbF8YwUC6k71+QGu6MyJOYQ+y3pA/t2YuulX+PMcBw0fmgApJkyEcIagm1MNJtffvnlE+Rxe3W1MbVw4xYkiTHFKpdpH2RmZqbbBWhw8oBIfvUdMKMUVppUrRN6rfTCEPlFohUzDqlcHGkydFN6nfb75uixhx9qNBqShJvrmiZDelGJegjPK6Ik5wFkA0DZDURO5BjWsgLfDvki+hO+7xM5QBGSuNmsbZsczxcKeXL0QMMyoxU8z2u3+rt7Td2y17a3P/2ppxzf07L5pYW5ZrM9NVUzLHhIcZxgm9bNm8uPP/GYZ3u/8eu/+bM//ZO3lm+EAet70V/7a3+j1ewv31p7/fU3eV5U1UwcgzCRR8ceDJ5MJnN25j7Hd7a3m1tbO8OB4XnevfeeO3ninosXLwsCVygA0WVZVi5XIHa5/ampRqkEuMzm5ubebgewcpbXdX1uboG6I7bbbZZjCgVC7BgOG1NTpmkzmPeaTBAysS9IvCRDg1SQhAHcsGGc6Ln2/NwM9N/9wac/+0nDMP7wD//QNM3RaDQzPfXjP/7jb/zg9V5vLwqZxcWlQqHYbvWiKFpcPKIPDNdxhiOz39/Ya3X6vRGUV9WsZTmcGMCFAvoMY7JCCiBOCQ1wz7MxRfEC2AMAMmTavKI4HqCgUP4ibVKQJEnexPHkxwnwmuCzASnCUU0EKYlK53i+jJ1GVxmaNhMBirSKyX5CLz9KPCjRuaWCgAmEeX/G6DiWIEAMBiQSIgRAXojUuPsNmWTHTX4z3YQ/PBiyDKRBPA+uOnTOVizliSwylji65CRE3Lx5a3Nzyw+iqSlpMBg0Go18Pk+3ItB/tVp6zB3a8InF+8Q4Z3I3HkQy0PdDMUVwUCfDFSK5FkfDgSVJQiGbi+KQguBHIysKw6wiSyKanEAtuy5KWfhNqoPBQBKkXIbocDKMIoWDSO90BgsLi1HMZ3JaNovhWBjFjucXCrnbK2uEUwsI3nRjhuXEMGJ1052anlnb3OiP9Chm643pSm1qfXPXdv1isby3t1NvTGtKZtgZ8BG3OLNw7er1b3/nxXIxb+iWYdif/MTnH3/sY6qS+5f/8l//7td+r1abgl2My8V7OHN932/19mRVcnxXkTO5bDEKRKisD8xutzs/v0C5NZIkDoVhNqMJPFLKl178PuHjYoChyFnf9aDFViys3L518uRJQZYd21YVxbEAEpJleXn5Zj6fZyI+l8tLLB9GQTan6YYRESKFllEKhdytmyuV2vQTH3306af/Q7e//cL3X+i224qiPPHEE4PB4NaNm6+88gqGWFp2bnaJ8ryL5TqcT/tD14tv3VwFwdV28sVyoVDqDYbDAQ5oj4isouNNSbepXCTOe6LIxAqAC6TJKp1Tgelo2DHH53I5QuxC6kxMcyDRTQOXy8LJDH59PB/FjAg9zERPfcKfHuk4qdDGfGRM87B5HEMnroZJgTpBKZhsru4zMIiBLv6jbCkyg0ejLB39HYqHk9rhKRPvYIp6IElUSXs6ikDtTejIrJDJQgXHsqzNTUwCp6amms3mzs7O4tJRHLTkQdyq4RxErRQPMZLSz3IgFE8gZijAgBIgJlhOUF3GcQQHtQR7QPrJcHsy9RGfz6uq3Gg0Vldvo42ZzwtR4Fk2jktJVGUZkzHLHgwGHCeUMjlR0oadDvDElSlGkP0AjRbXC3Kkcb+2hhk6OYAW1tc3KS6c541adUZVtWymqKrd+SNHGUlaW1/XVLVWK5q2U6/XicFrdne3OT+/WClW1rc293aaczOzvV7vu899b3FxMQ7i4VD/F//iXwq89Ff/6l997LFHvvWtb4WhDxtWSUF/UOIFWXBcd6iDwG34lu+Ftu0qSiaK4uHACLOYhu/uNFkOBkmlUnFmZm56ZqparRP9Tgh2hcB5gUFmWc7c3KwP6GVblqWjR4/0+/2NjQ1eAPeqUi05pl8qFQWWM3SdOJ2MstlyFAeGpZumWamW6tXyww8/uLQ0v9daLVfATrp06dLu7u7i4uKJY8eJEBHXbqE7bRjWaKiLojgYDN55573AQbO3Wpm2XMeynMHQZBl+cXEpn88PdX0wGIBURbMs13Un3cnBviWsrYRMJMiUFwtGoem7vu/ZrkdyUeqehQ6PwOczWVkGBoKmbaTxjpp4LMpMQmDywCSEfC+JD6gB0IsNQtdBIqtgMp7kjWSjJnrPQJpEYehBxh0wnUgUeQH8T4LcwrMTsDj1ZUjY8TTbJbjtA3GQ+WExkD5EQl2JY1ZVM7puUr0D0CMIqQpmsbD+hT489dPudptE0BKsgtFoRH1n6cDtTpPwQ1FxEuFAO1ZUmCb1rmNiUPI4GDbBgxDzOsgXoslVq1aazV3PsS1DJ2NbzoaMgCGzsSKLuUJOEIBhMG1XUrV8uQJaSxSPLNsFTZA1XT9mpUq1sLvbbDU7WgaKHmtrawQKx0GsXpSLxTLL8oP+iLhOwsZLEOWIYdRMdjTSY8cpBxEn8ZlM1vcHPAOVCmiVBEAlGCMjo2Z4lj918uzK2mroR5VKxXX8jebW+traT/0nf2mmMbW8vDIcDRpT06zAi6EIH0/fF+ARo+m6ibcbM6Ige27keYFhWBwn5HKZQgFGfzEDnI5je4ZhTE8fEwT46RKTalFWVMtiRqNBtVqdnm6MRoPLly+GYQjwzUMXpqfBPF5d3dzZ3hsaNsczfuTnCgXTBh4KXgkZZWqqvr629mu/8sunT5+69/zxi5fe3draXjx6ZH5u0XGc9l4bE38MS2ClFoZxu9lpNpu+7xfylVDjet1Rt6+ToiZbm6rDcTCHlpLrw98Xnn+UKk7xHNRvLQkyZNHQ2EL66gAxwwModjnX9wOQ/70ATQtIjzlOrVbNZDK5HIBstA+R9tknSs1UjYtzoLBIZDRAv8XMHQ3aKBLHajRU9o/Ga2xC+Bgl1lx0oELLIaKjKhz6RXflc9DAcujxpzZmiLQAGEDULYga7sAsoDt45513crnc3Nxcv9+lkjM3btxAxhclKjh0Nw4GA8/zCFV0nxqShsG0CjgESaNOtxDZmvSpZqMgCsAW5VkxEsEJAQcXejbDQS+jaZlsdnt7Gy6O2UzOzkW+F7Gh6diW6/AA93I+cKNh5PmlcmVjc4sThWK1HoXMyvparzc4efKkls3ba5uwE8tmK5VauVzkOK5WqziONzU1HUesadjU4ZQKGesjUzeN2tRUu912fW+mOLO7uxuSVTE/MwvDFsNgGSaraiNe0IeGAJxpvjhV7HXbTDiYnp4OgmB3b/v+C/dtb2/rpiFKEB9Dzg0jHpQktu0Oh1D34mCniXo4mwVTlEjaJMw427Gp/UmpVGq1m2ur68TeXNF1KJ4EIWy2Nrcg3La4uPiZpz718MMPC7x089YHm1sb3W6X8PkZLQPr5U57l2WjcinX6/UefuTBN9544+rVq7lcURA4yzJeeOGFhx95sFgs3byxDMIjJ3S7XVXNIHGP2fU12PKVCmVNze0N9wKBDQOeYfh8vpTNRoqq5rLY3nRUC6pwNosZe7KcSdDjidoIjT88CQI80SqG4S6RTkMklHJIDBzTki26e33X4xh2r7kbeL68NJ/RNI7kqJIEjML1GzfK5XK9XlcUxRvrdmCwocBqC3mawAde4Hguz3KqqvR6PRG1JcyBkfgpAL4AGwB4JDEbi2PDQIHKEFM0wxjyLFwCyYYmbB0Rn5A6HFF2Kv0zmcQdyAOTOEO/d9dN6PtBDja3sW271Jdc07K1Wo1azA2HQyJKW7Ftu9PpqloWJ269MRgMHMchXAqUx9QZk+522qyzLCuTycD3m9/nVU9OIwAHGHMp0+3KsaHAU/dVWIGKgiRLvAhN0VgWRJK/QcsoDGNF0SzbzWTV0LXKtVqv14s5lHNmf5TJF1hOeP+Dq7ppe17Ai1sPP/SoE0W73c5SdDynqAzPeWHAi6IfhvNLi7QMaXV6M3MLkqSYju0FAQimqtIfDfcuddqDwVOf+jjwuo5DMsPS7s6OqqpAewFTilyg3WoV8nnS7RYcz7JMt1SsZjV5NBrWahCqqNerHM/IshiG/siwBEnCJDcMHR9ZJfDAgkwk/6D+Qn2dSD5S2NnZkSSpWAK5tFaveI7VbO5M1aq6rluGXikVcTq4bv1o+fFHH3zyox+v1Wqu637ta197//1LfmDXauULFy54XgivodoUBL9dr1LNRwxje26r1ZJlpIGe52Yz6lSj5m5bL7/yGstCFFtTEaJdP+r2m2u317LZfOiHt2+v3XNKM0y7PzDyeZyWWiYXM5xMKiY0w9iQmmSgnBJ4LaMmMYSsWiLHTzZkKpEGDBu0D6GGRCE8sgAGGsPCVp520mhTR5bBCnvnvVZGzRw9ehSFgRsMBv1PfOITlJOu63o2kykUgB7wfb/Xw6iUTj5EnlFzQFfqw369Wgb/jdSltJvn+6Hr+Rl0GEDaME1T0+RqFY3HwWBQKhey2awkkw/iJ1pvqZrwnTUY82d80MqT3nuaTwZBMBoZe3s7BG+P69btdqnLGlUoSUVWqWd9qvxLwztt8KCpS3oPEp80qO9UOtxHsU68eeJAFtEjJtXgoQ01SpXmBHFkGtlc3o8iy/Hy2exetxszTDmX39tr9XqDxWyeicNWr2/Zbi5XGFl2ZzCQVU3L5llOGA51wrKBJhPLspbp8AJU6wuFwmg04jgQoxUVIFJQYxVldnpW3NuhWTptBVMWpWVZ9A7Kgmia5u7u7mOPPQbdk1gQRJUTJULgx8muqjLFaOXzWaftDvRRFLOSIEEB2vEVTNXwoFeV+I0lPfl+v++68GWI4qDf75XLhQKybtiKN1vbxNxQ6PbAsPnKV75y/NjJmzdvfvf5Z7vd7ltvvf2Rj3zkb/6tv/boo4+Uyrlarfb6D97Z3fnfTdOUREWWRV03hGKGWK+y09PT62ubnme8/fabl65crNSrZ++7d2nxaK/Xu3zx8u3bq+i+uO5RmJDm33rj3Z/9mZ+zLOff/tt/e+rkaV03VA3eJxBATBywyIA4Tqw3KcoanX0aHMlMApFEIRqEfkR6CVTglZQqdEuARomJDfBuCRCRZIztdptkXNAOuXH91tbmztRUo1IpX7t6PZfL1Sp1ShUndtycmMtP16f6faxdkcM9psT5aqWwtbWFtJa05mhjhpdERSqIotjpQF2CdiaHQz2KopnpOgTOJIGDCjwMMoAxQdUYxeyEqk1iLPZn34LjbUyFpwihG8UqsbXAJGplZYW01POSqGSyqmFaQOuSrgCNDLQxQ4Ue6elGI4OmaYoCu+LJdDSJyHQHJk3j/f4t/RtcBtArTqYs405V0t/ieYGXxFa7c/LMvX7IOqY9GPXX11Zm5+aqjdmdVlvXzdmFSFLkYrkkOd7S0tHr1250u31Rhqt2q9WaroAkQd+k53kbGxuiBOqqrIi7e9skIfSh9I72nS3LfLVaFSShAxRYxegP4QWBFJHJZjMSvIqh5qQosmHotamalsn0BiNR0WRFs8yh37fZ2Ef5Ypt7uzuFQj5mIekN9zji48RwvOXYgYd6D107AFjBaJHJoz5VJvP60DBGnu+M9N7v/M5vSZJw/tzZbDb71a9+lePZZrP5sY99bHNz83svvPj9739fkpS/8JNf+tVf+be25d6+ffuDK1evXb/80EMPcbxSLBab7VUbdWCRF+UgjDlegCo/D/poIVdcnF+cX1ySM1q33/v9N55eXl4mnoSqCG9QNIr3Wmuykh3p9qVLlwvFiigomsrwIlFzY5kAevXEPIT0KJJ0DGc10ZxLHDDR28SpTHU8bA+hD5ZwrgsECnG6gJ8R6XcIxLEk3YRRFJXLZSrga5rwEzcMM/B3+/3+sWPHqBM9Lasoudg0zXw+OzVVO3nimK4Pt7e3h4OOKIqapj3y8AOdbpfyZbPZrCzLjuMahtFsdWZnZ0URNqa6Du9EURR1HdAEaj9ChLcmiIuJf0MCDaVfEx3CP9smpGcBDfhU2tB1IVt05MgRloWiq+M4lGChadpgCLetdrdPSdP0zKasH7oPUZSTB+0k05xqPwYe/L1jtHfSlSHGfbQUZkM0O1L+W+IDEYEWKgiitLPXevgjT7KCOOoN280tfeRMhawfRI6LktC2XTUDN2mRE+emZzdW0eAtl8tIF2EEJyiyRt2CfT9st9uSDHV3KN4bBgTsOICNeEAj4H8Wh0G9Vtnd2cmqys76Zr/f9WwX4ogMY5ijOGKbnSYo0QYcOvPFXGSzI8vZ2W2Gvn1kfrpUyERRdPny5d3dbU3TOEEI46HKS2q2MBhZbBTr5kiVlXq9SqRVeNu2HQeXERyrWNb1AeWLqKooCMybb/0gr8n/5J/8d5VK5datGxKGMeKv/dq/vXjxIs+L/9lf+yvHj5+8fu3Ge++98+qrP1hbW6O6qe+9954i5zB6mZ7e2Nh0HC+MXFN3iZzXsUwul6XmnLbdbnUvX3u1N+g7jquqarmEis62fIbh9/bQjDl7+t7nvvPC6urquXPnTdPG0YYGRrhv85qIv0dQox0/EpYqvdO04opI6IeJHxkSEFWSpKvJsixM5wlFnaAxKVkPr1wulGhfNKtmMwqqneFAb9vtZqddzpepCJptmBzHVaqlY8eONXd3tre2+p328RPHTp38mD4Yrqys7LaaFy++Nz09ferkcc/zdnZ2dnfaqpqpVSvlImC4HBMfO7Lkuv7e3l4YCAvzs/gCXudwfIeFFs8DTELSM7KsKZlwYoT/Z46EbBhEPmY6SchyXV/XAb+sVqsLCwsUvDYcDklfKkcTV3oQ0AYVbJlI5kZ3C0prklXS6CooycyTmmPSx13TUWIRQzT5wUAGrCwIIo/YzUMADWY8GPNwgtTpDgVJldSs7exqWm5xYaFULLu2K/KipCmDXr9crBAHlFjmRZHlA8eD6kQUQQ/FtBkOSC7P92VVkSTMYwqlvOPZWlZtTE/t7e15oQsnLB+YR9s2IxYXCJy4OLx27SofcVONWhAHggxUtO2YlWo97sarG6uLR4/ceuntXL7amK1EgZPNSO3O7vdefP7WjetHjx6FiRXU5zgNY8q8CYWNAIy+KBiO+sNR34WkouV5gH8Ui8WR3gWQgENi3JiuPfHRTz/40Pnf+o3f+Of//H/6mZ/5GV5gBZEplQv9QedLX/7ikSNH8vnCb//W77z//iVF0ZYWjzBMVK2WmShYXV0XBY34L0KPT1ZUUcoMB612u0vKUfXI0rHbt29vb2+/+dZ7oppzQBaRPJ9zLOhRqaoGrPnWbr023euiH37q1Om9vVa5XMY+FPmQurJyACED2oV9ROR+cdPAPQLAihZg6XlM/RhsF2MGWslQhxG6aMjZTKQjiDBNGmRA5IMsJA421/FFIcrn2SjKRiy0k69evVYo5GYa9VwOeK6bN68vLsydPXvG0EfPPfcsG4ef/OTHn/joo6Zp3rhxo9PvbW1t0FNqZqah44HdW62VwjButvY4jpuZnQr8cHNznbx/zgsA56cjQCqmlqrBk/bMPlvsz7oJU/ZqKkNMk8zRaFQqlRoN+BDQoQ4tCAd96AiRPiqCM92E6aSRymZSIC5N71NVCyqrsM/zSt78gTcTAbVHCouYRdM2CARiu8aFrCBy0NrzYNgz1HUvCAvFqh8uV4uV40dwokG/KGTyuZxpmFzMZGUYKuj9ARtGmijHjgdznDB2LFtRNCrWnMlkyhWw3XO5zN7ebjabmZ5udDrt0agvCBVi3W13e+2wHQY+SF5TU7WV5WVZlMrlMsczRMiQQWkXuI7n7jZ3soUqLgFxDTBt1/fjfD5/4cKFE8eOvvfee5KCi8PzYq83GIwcLyBzIM+yLegs0yuZzYHRryjK3t5eNqudO3f60cdAac/lMqtrt1dWbt9zz/F/+b/+en/Q/W//21+Aolkc/pf/5X/uB+729vYzv/vHm1sbDzx4X0YrjEaGpimdTqtaLk9Vp7L5ynBk+e1+GMSO49lOIIryUDc9YrSEckOSNVE1Cr5u+YqcjWPsfFGQNS1nmub1a8v5XK5cqveBiwqmp2d7vQFPmO4BPGCxA8me2z9mKW6eiNwRT0Y6SyCPfZ1sukRoTUjp6DQro1aWdJJMGYAgPIRANluWBVkHTsxkcpRvApwNGrK+67mtXqvT2ytmC7MzjXq15trO955/oVLKP/XpT3JMfOXq5bffenNmdvapp57qdru7u3tEuAVN56ymNur1wWAAJITtqeQN99qtOGYLuWxArRGRbWM3gGBIdGVgYTVexHdOyf/jH2g+EZEfimSgG4xgKbNUlZ3n+Xq9ns/nadNoOIQmd2oLlww5qJISeWgaOltUKQO5CumCpufFZAV45yYkFT1xZInjkENPCEhropcfxTwsR32PuGL5umkXSiV8P4gdE++fkkYyatYeWaETRG4w7PRvXL7qjIzZo9NZVZsqVQRJ6hlGqVL2PBw0hK6Ncp2eOzETyooYRr5hQkKX+LFww0GfoD3L+miwMDejKZDUVVW53W6C4TkcYE4zsESRL5UKneEoiGE3r0mCrCr4qUH71q0bF99/9/ixk5bj8oT47wY28mZe4EWh0WhY5jAF33peQLxY9L/9t/+L+fnZcqVw7drV137wUq/XvXr1MsNGD953/z/7n3/h299+5r//7/+7UrkgScLCwsJLL700MzPjON7xYydXV1fiiBuNjKWlo/2e3emA4uRv7vUHRkYDlR50BdO0YyhZ6bo+OzNNj0io5hEDB1XiBZ6HXTEhlEP5H+QPRteNDPHBjlfXG1PTum6iZIs9KPbS7AxePkTvmXBl0JghwH0c2PRBLjTBtJC0k861oEoeBD5VTaIAGmq9RQ5kwNBI1znmgfsEs1bjUFL7gev6AicUi0VO5DBR9Qogm5ooFm8tr6yv84VCbmFxTpSlN956r5TP3Xv2PDnedn7lV37l2PEjZ86cOX7iyN5u69atW51Ou9frZjLZSqUUBMHQ0G3T4kQxo6qirLbaPYytQngwo5gmnyBkWDFhMJLWBcJ2DD2XFDd354OaAaJhNaGWS/qPYcwExFUX0Hae12Qhl8lKktBqtWisoyPB4XDoeYiTKnoSKAboVqTBTVVVMpF1NS1DHP4yhmF0B72ZKgDWkzI8yT4k0ZtYvxIwPJewUUKQsPBe+JgNYuy9GDsQ03Oikk2JGVBGyRARtMCLet2hpirEYwyAFAqhrFTLg2F/r7XHRLEo8aZjmo7JB/5wOCxVyqlCLGkDxbZpsUiRApJGAXzBc4wkisV81jT12blGtVzb3d09efLUwpGlwA0wL3V8OZPv9EaLR5Zklj1y7NRHP/6x7zz/ymj43sLiTL0xO+x3tnaacWBfeOChT3/6kzdu3HjnnXd6vWEmWzoze4oXMxtb7X6/O+xtDwYtx3Yr1fK999770EMP3X///Uvzc2+++eaL33t2c3N9bW11amoqX8iGvn/+/LnZ2dlmc69Wq3ue+yM/8iOLi/PPPffcyRNn8nkA61vNrqrmoAQtaW++8Q5Jo8J8rlir1xU1Z7vBYLPHclwur/EC7teNG7eyGc1yUDMPBiPF9kOWg7F2GObz+WKuTB3HqIauRwRvFxcXer0+9BRdS4wESI0Txzuq7EqyGOA6JV6hRIYoCgXHsUjzAPJNvk8KFQcHvNGBTz3PgSLth+igUh8fijVlIrBvyO2gHZ/Y8VwA1UDMh6qmh7DpxT58IWMOgvuqJvpFxG5I5bm21XIt269PVZHc2/F7l5dLpVKtWvzST37lxrXLzz//XL1ef/DBBz/3+U93B32CAHonsqJ8Pp8tKNMzVcO0b99ark/PNubmAW8PPUHLYpThoQPJ81JAwPVkS2G8BrQMFzIx64fIAMdRMRkcJlzKO4aFMcMYrpsrlLu9pmbmao16r98R4WYW+FGQK2q72yNN05ro0esLCwu9Hhx8GB5jDDLsiarVarPZJH9lNC3DxJxjezPTc7bluo6vipkgCAvEyAmDxHGCmiBmE3NmakpJT5MYvUdC0goZ1g0ZPogAAWQ5MGMYXuCFwHQkju3u7ExN1fk40kdeq9mrV8tTUzU/CEVZ8pnIDiDdyyrsdGPaMsz2qAWqR+TOTzU29nYyGXV7m4wiJPnWjZue5zXqVY5hC9nC+vJKrVjlOcY3TV5R5qfr5Vo+k8tijcTBEx/96Kuv/qA/MO45d2FzY7tYrT115v7Zubl7771X1tTnn3/uvfevynJJN8Jbt7aj0JWFMJvVNnb2XnvjtbffenVmfoZhoxs3LynS9ntXr588ep+u642p4uc++6lT95xsNOrFfPby5Yu/9e9/xXWs27dvl0qFBy9cePyRC5vrGxsbWwU1s3pzxdStSq36mae+cOrkadd133vvvXbb9DwxDBULGtByu90xDGDHCoUpTdPmlxY9H97pu62mrusK5Hki0xoqspDLljY2NhYW5gql8sraeiaf2Wv3TdMndlSQfjIdE0ck4czSG+QFnq+7giSYtinKYsRGXgxaHUddOkgCSbV5Qy+1VWVg6LUPECPdRTokJPkGvo+zMyRGBJTISzTJgaaHoTHRKyJmhugxgAlMbDElXpTwIgyLEp94ACJDpFrA2WzW83Ae65alr24UiyOazg11x7Z39/bWzp458alPfWpjY+PZZ591Xff+Bx84enTpJ37iiy+++GKr067VaqZtaBn1U099fKS765sd6KYR0JoXRK7v8TACACmNkhTpvgKZKRFFOsBvvONxqIMDY4YgCjlWsj3XsEwGDpV+yAJkRxrxGhiGPoZjiYXlGGtLJoFEPJ84Q8FpHONm1rKsbrdrWU4cswTkDigSLbknofOT8xQcHtTaHj5P0EOhPRye5byA4WGUTcGlkKx2HKyEQb+7dGSBj6MRik9BkJQI4taB7bm8xOeKuZE+EGTh5OmT29vbq6urARuKmiSoou0Ynge9YBUzFMU0cSi4rlsuFxVZoowWiHOjI+3CFlQWLl2+Vi5WZCWjZQpvv3dlb6+zttmSJW3u6D1zi8d7g/6v/eZvb21t7e5ut9rDmZkTMSt5tt3tdosFNV9Q2+1ub9D/6Z/9ac+znnv+hf6gwzHWJ594/CNPPHXixAmOD4fD7srKyjPPfHNna8OydUUS84Xsz//Xf3d1ddUnqlv9bq+5u3f06PGPffzjXhTZrrO+tnX1yk1IM3b6qqrW640rl6+6jg+RmGx2bvZouVxGthLHH1y7FURIAwEjQa3B8QIr86Lve2A8ef5ggNy7WEZ5JcoCK0TUze6uwrkhQL0sS03LiTIlCYMQek6QiGDSUlnp9Jwn6SgVGCca46hVRNLFlzUYuQREnd8PkcfTTRiHKHISkA2OYyR6LIvWUmLLhmQ3CS/E0kwk4oJUvx1TS0WBbM729rYg4Mm6Pur3e5IkQV+4VqrVMi+/+sb3X/7BJz7xif/XP/kft7a2vvnNbz799DfOnj17/vwF04RDwMbmenNvl4mjnd2W4wlM5PHEsVuFfR4LezuCUKHzCSJYEceMECENuLsF1Q950PkH9XsZjfR8PuM62G9shCEEdbFzbXAsaFpCSu5QlOWQ9CoC1xUBvhXiIJAI8sHUh5Yx8l07JlrXlHxIgL9Gqh5Ag+GdUG8iZJrcOQgUMIEH8Q0G/ZnxIUKaZ2yz2S7kSxyHRmUFGrs509T14WB7E1MHnkV30dJ1S9dtwyjlCwuzcysra6Y+LJRzMR+EMcZutmcoGbFQnmpM1YFPJBNn1KAw3aa9diEIhHP3feT0yXt6g9HO7jAIhTiWe31blpk/eeb53/v9P+r1+wyLFAagF0nZ2V03bbdaLqqawHKBbvQVRfr5n/+vp6dL/+7Xf8Vz7H/w9/9ONls9d/ahl15+69d/45cFgen2OhzgPkCuTDWqIs/1er3f//2vQ2G1VDp29MSXvvxVjuObzWZ/ONhttgejoa7raJWjWS72+8Pd3VYUMo3GzNzcXLFY9txge3t7bW1tr92qVGvwyAAEAvGDSCSjyyWJsm1bPMPv7jRPnizU643r168DYESEEQ5hfVPV+UnRysl5712+P/FAMkt5CXT/kItLciEB0yGOUGZpn4aiFljw2fZph7TfgRE/6byP6acJbwLHgACfvnQEQrPZOI6PHDmi6/pwCN14+rY6nU6/2xyOqhlNmp6efuvt9579znMX7r//M5/5bByH3/72ty9dunTkyOLHP/Gxc/edFQTOMa2v/d7X4zBkmdBzTd8L4Q3EwFsnivf7IhRYEpLWFPygP4RCfyfolOLaKK5FkUXDAOauVis7tmlbrioRLUqyRVMwa7FQcFx3oA+DIBOG4HmN9IEkSci0a1DOJ+NWKDKyrOX76N1RpyH622nXh17qH9JGov8WMpFPanjy8TiRT8QGgGSSpXano2nQtDZGw9zCtKKKrfaI5cJMRnXdjOPYUZQrFouaBnCF7/tU1X96elpRJZaLKpVirVYulfIBzGdDWVG4AGADjwgEKpKcLxSKxaKcyc3OnYw46dLFKzdu3PrWt17UsgU1Exum0x+YnIDuuqgoTBi6fsgAzCMemW3cvn2b531V4jRV9lzjxo3rfvCFq1evQo4xo/65P/cj80snv/71b/yHP/yd2dnF1dXVbBbGRqLIG6OBYeiBj2L15Il7PvWpT+XzxUuXLr355lumaesjk6ZdhFwn9fq94RDY+nKpWptrzM8vui5wWjeu3yZT6FDTtIWFRR9zHoQpqMugfwbvd9wmDcgnluVarc6JE6cW5pfW1zYFQRcENo4mK5oPXUVkeZA/J+QRDiGikk1IYmAq3UV9trCnh71eimULQgr/j3TdEjgq8ptYWBL6/L5sY/r9RCSGheQl8TTfp8bRZ5fyhYyi5jNZ0zQpBhWP0Ftb3SxXioOhVa/XGjPzly5fvbW8/PFPfOznf/7nr9/44Omv/8H/8k//2Wc/+5lz5+599523eDY6dvSoafnr65uddk+EbX3kuW4UwNSJkCdAyUjIhCQkMkwaNO6+vg/z98bzgghG4ATgwLK2bWcUlYm5IEC6YlmA5FITLIcYM/iuo8giy8SB54o8V69Wzp+/8OKLL0LxSRIkUYij0HMdScBfGLgdBOAbQAoOtEzMV2hXO72pSeShwnjQjIMkCcMG4F0GRJqJJ16EDCx0RRz/vh+qGaW1syGJMRO7tj0sFrSF+ZlsBn7GqiJtba4bup7RNM8ZSII8P7OQy+VOnDiqqFIQOro99FwMQDzH7Y56ggBN3vkjczMzM8UiyL9hEAUhf/X66ur63ltvvRUz3G6zOTczF/iRomWBNgG3m5EEOQ5hNkicgOyr1zY4jlHUvGfbccxPTdWgImlY29u7BMsRf/vZP37scX3pyOxf+xs/88y3v3vhwjnX9ZeXl3V9eGRxfmlpSdO0mZmZxtTUxStXb964NRzqOQ3aBd1un+O4VqdDnEWEcrl837n7p6amwhC0qUuXrpgGQN6g6CgqBeX5Xuh4nkPGS1TYgXIMBEF0nZDjJIFX+r2RodtLS0dqtcZwYNmOAc9y6itOlsxYqCxVRUjJ2OR8JIMI6niZWA+R70w6LwByObHmuPSpRCAM9DM0AFko6lL01tiiJCkdqe4S7QSSiVmyjhPsP+mewrBkjORM9ed7vV6xWKzX66PRaG9vj+IMNa3oBM52qydL4siwCt3BPSePSbLw+7/3B//nb/77v/DVL/3jf/yP33n37T/4vd/94OrlQiH3+KOPOQHjuZGlj4zRUOBFYmEJ3ZlgX8yN7D1MuIlF0YdkpB8WCSMC3wO1n4WtDZRIBIBmIgCaELsAmPZBwCuVSrOz04VSURAhJpnJqICDEaFBVVVPnjz++uuvWZZBzjXLcawwhAoD1bSk1KcU9TqZvUx+QU9WMiFi4fZLCFu0/RtEIR/TEzqSeM4cmJ1+r1QqbQmxrNChpaWqMi+gU0dn3IOBzrLxieMnl5dXatWpSrm2sbVequZHI6DPPWIUMz09m8tjy508eQpRMULxv73XXiaPdlsfGrFlBaViERRTDUbQHMe4hhfHoSQLcRSPeoORMcho6j33nDx56hjLeZ12k414Y2gNekPbGHiVchQyRIlnzzAs1w+mp6f+p3/2z2/eWn3wgcd++9//3vFj9ywszJ08+aknn3jCsoy33nqr2Wy99OIrlmVhTFUohF7caXc9LyiUS6VyrdFoTE1NQ9d0MLh69Uav17NMoJrCEJsTeQPg+KQVAhbOmJITottPOZMsyxNfQBjRDIf66up6sVh2HZ9KSaTFwSQF5+6K8gQlk+pTpijiQxxugfrajas4GtTw6tTDJPGsIN61tCZ0iDNwus0EgYtErAxFxoSXomzpVqRUXVGEUhSxaKQJKr6OmTCb02zHHI6A8KrVK/Wp6nA4bHf7LC9oWsG0jKA3ctywicN16qGHHgl894+/9cw3n/6jP/8TP/5Lv/RLGVV+9tln3r98JXQDSYAyI0ILcovYcX1VgFUTy6a64DgISDD8M6NHCdhFC3wAR03TbLVas9MzkgQmB7jnIePYniwhSuRyGTRvNXV2ZtrUjWKpYIz0er3e7/dhIWAZvueIQLmGlomciid1jqoqdMBIQW2JyNpYEv8Q2SphY5Hdhybp2NUMIyeIXOH6A/PBcH4Y93vDRqPxumkMhh1S88S5fCaOceUXFudiJvzxH//8ww8/2m71lpdX1tbWCoWSqmQMw8rk1OMnTldq1WKhnM8Xwpi1HPvW6sbO9t7ttTXqL58gaaXs7NxCb6B7jrO+ta5qMi/DmrLT3oNhiR8XitlHHjv32OMP3XffuWq1ygtxNqP8H//m//Pd575XKU6VSiXfcSVJ8T0mnyldvXStXpteX9v42td+79atWxwn7e5u/9zP/VytDNOVVqv19NNPb2xswBREyylqTssUYlgvDfaG3Xw+f2T+CHowjelmu3312o1EO5y0ItVs1jAMTc1mM7kwhCSK6+D9E54QJxIndtJwQusSwcbzia0DpBrimF1b26CIOdNw6fR2clMdPLUP7MMDcunj56Qs+f1NSM09J8fZFHSZyWZHowT8wRBqElWYVyAWfChc0J2+v27Sd8PFKJkwORzLZ6QrjKrokPMp+adCoSAp2l6nx8Rsqaj4PiCjMGFd31pfXz9z+sT58xeiwHvxe9///vde/Lm/8rM/9mM/wvPylQ+uZ7O50WjUbDZN27MtV+A4TRF008WWJ6lCymb8EE/pP2UTQoLVjcjp6PV6/fnZeZlXu602kQbFwJ0ashLPJnZtfZW61QEaoilz87Okaxqtra1RSy0ECjdpyQik8A5DjPVorks7zwlSYiISpreSFAsQqQuJbVsALX3kNiLHRxH4nbwo0ASEIkLVbAadYo7JgW2i2a4j8sJUo8HE8ee+8PnWXvf9SxfRwhFETdHOnD1Tn61xAjyJ+6PRzubOzeX1tfXNrd3drc0d1we5WVYVWcmJag7oUV5s9fZAm3LsYlnd2tqYatQKpcJ9D3zkvvNnT5w4fs+pY/liLgzcIPKJj6AYeE4hn1VkmYlj27BB7+bVfl8XeLVama41qi9877mzZ0df+om/UK7ULMttbrbfeeedzY1tXdepel25VDdN0/NMy3QURcvnynOzSzMzcyIvbG3vfu/7LxPanZrN5j3Pp+IGIqABYLoaBkYvwMcCRchg+MbEigguP12EoihSEo8kiH4AfJiqZBzbb+618/lCNpvrj4aU9zkpevThkXCfJXroSD0QCQOfNuIwcaBNOUqUpHbBPI/0I4pjBeYf6AsFcUCrc9JdACBrrN2QMC1o1kphIviVPASFRULYoSc9IcvHIg/v0ZiJeAzASZjG7RAXFwuWDYc6KB8jeUB1Kwr88vLKjevXT588/sCDF4zR8H/9pf/9j57+5he+8IX777+wt7d3z8lTmpZ98+13w7CfyeUt2wXkjuM9iEZ7HC+riuIHkWk6SA8nHndKDB8EuEE6wHVtCnAhhudxq9mZX5jNZLL93jCX1WZm5lzHIt7rBcqZ6HahdGSa+tTUFKy8ZNH1HIKw0/p9OAebJtYT6ZILuPIRYzme4wUgqjKc43gYCxFu5/79I+Ugw0ZCUhzij5DoN0Jai4v9EDiBQi7f7XclRZ2enb29unr23pMzc7OZfK7XaeWLRQFAUb6505yem/1P//J/Wq03drbbX/2pv9TcaWvZ3FR1ynKd3fZOb9hbfeHlK1evra6smJYjSbKsZnL5ouB7kP7huYgR/CAw4IXUlxWBE9z5xUq1Wv3cF544f/+958+fq1SLmqboRt+1rSDsSwIXeHa3O0RVpruL8wvmyCqoFTmXNYZ25DMCIxdL1eHA8oLW0SOnHn7oI5328NLl61HI9Zq94UB3XT+bzYsiRNAHfT0IgupUo95Qs0AggO1xe3W922oPAOiX/CB85PyFvb2dF1986e/9vb+3ubn51lvvjAhuGSoM5DZGBAlMgjntFILRTuoqeApIkuI4rigiT/R9QNg8MqsYU9tIpyBxGk/SyPHWSkRux/eN4KEIsYcOofHDUPZkeH5iE9LDnp4BBJ3sWoYN/X0NKBCofaD4w+FBRGiDVD8iXcRJnkncgmjzmpLc0rIw3fqU8EZ3Y6q5kmqf4U+Gi1mQFSAWmFFd27RBCrcsB1TgfEZa39zZ3t45ceL4xz/26SgKnv7GH9fr9QceeODMmTNT0zNhzL711tvNVgcXNAod1xUlTSvkHMfrdJqCIFcq5eGoO7kJ76y7Dj5ChiFSjhPCkL4fmoY9P7948+b1TqfXaNRBljXNnZ2dmZnG5uZmEHiFQsG2Qbagl1fTNGrhyPO8Sx4kpwCdkrZ5aFGdwgYoRO5QXppyneiD4hGjxDyH4OPC0AsDLZsLotiwrF6vhyMyYgzTjhhOFCXTdgRJPHn6zGeeeipAdcyfO3+hUCpzrHzx8pWXX/7B8u3b3VFvt7kzHNqKJpWLRQ2gEKD4+8MhYLQs49luGBr5YunM2Xvn5xpHj80szNdPnz597NhSLp/R9WGztdPtrO26dqVSzueV0WiwtdsEAjEOmFiqVWZzau0TT37i4vsf+DZvmvbSfIHnZIEX67UZwx419/rff/FVFp3S3KlTpz//1J/77rPPvf7664ZhZrQczMLqM/Pz8xHDjUajVqvT7XYNA5q3oijJikKM+pjXXnvNtu1PfvKT1Wr1xRdfGpI3f1ePnTvrOvoFxaNxFGxFKkaG2Vc8OvTkg3X7gb8S4d4JDfU7EtTESZKO1ESor6EdT3/AsgyFyO4Axk1MbCHk6AWkI8OnRwKJ41QhjS4a2INg+1PhBQzrkQAc/PxUwh0tBMrS2J+3AHAD0KCKpFf1NcUwJH3EOA4jCzzmP4ynSdyVD26trm6cOHHi3Ln7GCZ+65133373vY88/tEvfvGL2Wz2j//kGdLoCsq1qmm4WxA1kOZBche2d7coK+c//jGJOqWqOTiMBwORB5Vu0O81m835uZljx47ZNpxzZFWOIsayXcf1WUIQC8Ko0+0ZJkHSWubI0N0gKJYq+UIJ5RvD0ESLtr5oMkJBqqmM8qTCBfoxJOkYY3fJVJZoTMYcb7leTlNt2w5EsdsfOq5fKJUlOZPPF2fn52AvXavms7mFxSPvvv3u1evLO5vNG7durd3e3NzeUSQlm8sNzFHMcblCLoyjVqfn+cir8/l8hciXTE1NLRxZOn78+MLCQrVaVVRhZrogCmizbWzcBNrKNVRFPrI43e21JSlyzYFjDjKKWMxVTNPutPTnvv2cIpUKuYLn+MWKZumOY9mtZrNQPKoo2tbuFssKrhswPre6enNtdfc//P43FFHK58qVClR0RUF2g6DbH129epWKsEClIZOlk7I4Ym7dunX06HEqQmOa9h/+4R9dvnx5YWFB1/X91Z+MzakhMYUG7ieTyUyOMLChUUQ6eoEfQY8+Ttrjh5b0h3T1xpt87PKQnqqT2WwSCdNbTg9jwGIEQbcQ2ZOxHsFV0ncIxXqSr6a1KfDEY7fNOwdch76zT+0jD5q10sQAB48ggpmL8x3nD0f4viJQGoXABcTE8xBPbMc3zGEQrxqWd+TI/H3n7zdH+ne/9717YJD0sbn5hT/5k29f+eBaF9oEyrGjC54X9TpNx/EEMCwPTE7vfGOHNyFVT6QxCvqEvOv6w6GeUTLFUj6fg1BAt9Pv97uVSuXcuXPXblyjvWXXdan4Kvw6dT2OWWptCzqFDNFoGNwKomlDlI0w4tQUEU6vTLoJJ5JSYnJIBDvomUw63RQvAf8kH5YGkW1YxUJO1/V2u/v4Ex8/d+703MzMQB+sLt9GKrGxudtsd5qdq9dvWLrH8kwpX5mdXxj1R6wgmo5dLJUqlUqpVJpqwK1hfn6+UMhxAqi9lUpJEASXwLt5zmMi//svvhmF7sryrdGot3RkIZfLOra+tnyNYSMoHvCCZZnUBgOWFQ53+tSZbtcWOTSxaBU9HPUtaxTH4fT01O21WwzDtLvDXL6gasWpekOZkyulUhyxu7u7t26uhGFoWJhGlEolos5HsSkRvB7JJarVINxEhOG1VqvV7eK+pLOlO3okVMrnTp9MYg9IBn3pXI229iZvyuSW+xCXMQCWU/HYD9M0oso56PfQQR3COrE4z3Lwf6NGIBFJI0mtKAdeoi+Kwo6ogBBmHAB1kwuaWgUxMWC+B4QGx8udZtYQtaeOKAL4qLzAE5vrKIDyGvqoPMeKSgaNHEUpVqqjodHa3QsCPl8oGKb31nsXt3d2d/c6n/rkx3/sx39ifX31mW9/u1Qqfe5zT01PT1+99sHOzp7g8YqoZjRJFKAMqVv2D78id3ngDCIAhjErIggC4AxViSNFoywKrgchtps3b4ZhVCgVqXmGZVlhxOTyRVXLAv6PvC7O5gqqmlEz2Qgu8pFp2K4fAFwHoiCADSBegTlLEKKU/kL7S8DGEH1/2uMlQKhEx46NAs9F6R3hPgLSFEH08tKVqzMzjes3Vt54/d133393fW1tMBw6tt0begLDZHLy8ZMntExu+eZyJmYVLfuRJx7/2Kc/li1k6nXk2P1+t9/vliulpaUFlomGw6Fl9TQ4XZfCMLxx49q1D660dvdOnzo5NzPr16rzs9OZjDIcQN86juPtte3hYFAslOvVqsd4m7vb27udob4CehAr6sO+OdSLhXI2J1WqeU0TiiVks34Y5QuVpSPHGEbOZXKuaa2vbuw1W0EQ8RxKA16UIbaN9BuMc9d3wtBHlSWwDCdk1Gyr2ZFIw9NyzCDyS5Xi7u4uwCRcDIDlmOTNcgiJuMJkFJXIE+y3CZCpIQCB+Jd0QfGU/arvQOaZFg6H9/NBWaM7o2gyJ6QZGqlD/BSD70eYB9INFrMMyUvxZoPAY4lyBNEaSAIdaUgkr77fZR2/uYPKmck/0REInRmm6xsbL3CTFQc13yQNg8xuJh9FcTbLhjV4A/YH4OPltEoYcS+/9vrbb7/z1FOfufDA+UcffdzznOvXrz/yyEPVavndd9+/dXvF1A1ZVURZ0I0By4MJdacS9oftxsRDNyGg0A+CMngwGOVyOQXCRGG2VFyozOzt7d1cvlEslzCYdmGGzDI8VX8kxSFqGEqnKBQKdAbreF5/OKDXJ8WLThplj2XFU1I1j/4MBypTUvDTkXHMuq6D8kGWAtfjJdEL/P4AQmO/trPTbO3ZDvDCS7M1RcuVStWFI/K5e+9dXd/cWN0IGb4+Nf2ppz6riMp9F+7rjdq6NYiiIJ/PXrp88Qc/eHVubuZHf+zzjalasZSZns0HnjfSW83m7t72qql3Tp84fmR+KY7D3d2ddrMzEgWGjSReGA5HkcsIkdLcal95+4NWqyMKUrk+3ajNzCwciXz0t25cu8nxBcPsL9++dvrepVxeZSIIQ1WrUGnY2Wm9s/Gea1oCx4uyJEuqIEhBEFq2MxrqQRTCtlVGn5NhJEhl+k7guc2dVrEAz1ZQwMmAl/KtJyFmFIMxzon2d1Q6QKchksKd6e4aT79ZGFkfjJn08WGbkBMODCcOFRfJP/2Fz36BHhtwYINdhhyzgKeB2ELMYIihNDQayEeKPcensxQsGgIvoMqlogh0Mu3TgF06xiKnJieTSB+KNZ0wKE4SWkRWknaP4XlouuLXg+MfZjP5mEXnnaJ8Ws32bmszJ0sCzBYDx7UKxexHP/qRz37m08dPHLt48aJlWb7v6yPjgyvX3rt4yTadcq1m+oznA2CZ8ph/eDBMEEXk/UBDnPpaxHEhl43jsA5gZm7Y7+lGH54kx5ZG0DBzO51OsVjMZvOj0ci2beh3DNGkgR9QuaypkO0galf9nd2mKGJDUrEfWiGTRvm+8jLt6IxxuYSsBHI9MQInQ4uICtIEYT6j2YapyLLvuJ4Dl8L+oDs9Pb20MF8slxbm5y5fudLc21Ez2kMPPNhsddZW1hzPN3WrMT1rm5aaUQSN2dxaFUXh1D0nFEXqdZqlUuH8fWd+7Ee/kC9kNE0iLBmkP8NBr9vR+SA76Oq9fodAFyXftVdXVzbXN8Dacj2BFUrFSq1SLxYxR8/li11LL5YqWxvb3/zmty6+936xVBBF/oEH7/upv/TViIl/8zf+z9vr27YTGWagKMXAi3OKVsjnA8wJO7bt5LIFjYh2xixUaLzADUIPlubYG7ibIgtaMIXywtOC2LkTqS7wjChPnSZkk7sr2R0TY2QWcKgAtBXyI3QQhftCVF4nd1oabO46qBAOjqbvGgwFC2g5P/Ajx4WlXiaXZQXOj4gkkQyCJlRBye9IWb/ELYwgpsnbGju57/8aGtgPSGZMqBLRR+JcOxFwyPoLBIm0T4mDC/U5YUl3VitlMEyLolwGwh7Q/5Cle4+f3txa4TjWNJyAiX0vevXVH6yvrj300EM/8zM/bRjG8vKKba09+PAjn/rMU5sb2++8997yxu6HbDb27v6HycAHbFpaJCDtiZnOoCPCjy1HZSAyuVnXdb/73RfuOX065pKwT2WePc/r9/vZbJYgSKH0Tkj0sYuufcdyrJKWYfzA8YECjzk29ENRkWHrQdIgZJbgTRAbgeS9TvptkL0KewN1NIRHKiRPofjmZPO5hYWFT37yk9ls1jAxR3XcgOcF1wtHemdre6/Vau212oZuFQoFy7IUVYFKWt+UOC2rabbu+Jbnu2Frt/XqcPD8s9/++Cc++olPfrRWqegGamDHtSKPCyyhudPt9TrD4XBra8uyjHq9Pg3QCmRLa7WpnJqNIs7QrX5/sLZ9MxaYrb223jcy2eLxU2d7vY5hDte3Nk3bEBUx5kOWDbO5TBB6hULJ0jEn2NreDsO4UC5VKtWRgd+NdhcyNWQGACdwIq4QQQ8JvNzrDer1uiAIVA2RzmMP3uvkjzRgJZiWlNqAvYdhAE19SGEPqgohSRys0lMlhIkAwxHOLSVK0Jn65L/SH0wogfSbTz3xEQ3TCDEMEYJoauq60LEJYygouEFCssa25IUY4teIBh6RwSBcRAxhMSbGM4g/mQ89CJrIufAPxTMS3xgimOO6Cexm7BIOPCftzVDpPtpopeGXBkZK46ADScrxp7FiOOxzwNRGumHoxjCMQk1VMpnMufP3PfTQQ1/+8pc9z/uF/8c/ZFn2oYceYnhhZWvr1vLK7du3CRKANq8gwzoZuknFSnGwsF4c85sSERh67QVCbBd5FuC7KjqHOHQd0xwN0SREHADimZrGSJJUr4OIwDAw34aOlm03m812rx9zMpm6709y6O+mS2I/6UnueVQowG1i0saHdJh9ETozKB6YKC6Xy4tzs1CXjOPby6tf/HN/7sqVK61Wq1QqXbz4nmVZ1WpVkqT7779/e3v36tWrjUaD+hyLgiwLGmn/dh96+AFV4W7eulatFRRFaLZ2Ljxw7qGHHqiWC51Oa31jdW9vz9Qta2jKopzJZIrFYhliweV8vigpmiBInuePhlavPxwMhsAnECZdoVpkebHXNa9eubV8a9VxrazGy1nmr/zcTx0/sfDiiy+8+L1XPE8S2CIb54YDUybHEZWQhUrLuMFI2iQpcQEAotTvFdbvNIMgsZFeOd8lDBUycktE4FkO0iBQYpjQnTgYGNKrT2kOLItxQqI+iBxpv0MJIsH4jlAPMMB/ER6pXdi+NXUKl0lFugRZlog9C6nwQNsO0d2PYCqSaOBLKubpIgbu+PzwJgVFXOSB9iAFJHSpsvn8JDecDrto7Ud3WkpcTAXIDkWh5HiJEHJBVYzHKFX6r3Ec+j4sSYlvOyfCmR3OGyX4saRcPkALwsjQneee/97W5t7K7Q24lDVh2fXMd55zPFdSc3stjK1IlEZFF5HXmUDPJlj2FHg0NsUdK3qQv3iAU4CdaNr2YDSKQNqSFDUjkEluqoWRImPoHJ+eRDbQCANKp07u98SuS3HBk98nXRrULDh9qBPTuMfNk2d5ri3Lcq1SLZVKqiTCTHJ3F50hL/zN3/xNKh8eRVG93jh16tTOzg7ppwGzSvmNCalKzc5OLwZusCMofMSLnNSoTpfLOdsZfeTRJzgufvetd7vdtmGOVFWdmpqaPjZfJG3eXCanqBmG4UzHHuq21YL2Vbc3ME0nm4dLRD2bHQ70Vqd55QdvjHS71dRHQ1uWsrlczQtG7a3dF7734pMf/2/K1fLWzvb99z2xudZ3rDifKzvuiPijUsAs0dqi2QmyrTQcAfOetKtAcL1LnZ/MBkgTlaDj6aJKeX4/LDkarwRyCpIh0SEYWlrSU2Q2dnU6kyDnwOTGpo+Et01TVhHrAxtCxIQA8Av0QHkZtBhSpxGZYHxIaiEjIApiyaKlSeb7WMapqZNPesEgrUCBjypt0iBzqCacbDxMdilhnjqWGEyPDfoFBeLQFJf+FXxFgr2k/lCKqlL3KKDPBbndbv/RH/0R5NAzWjabhdywpu61eoYJXVC0mkjiAIYT2U4J5ZJoYCUXDH3K/Xo9NYZKP0UURYARkySQMINUEsBRe+zrpu673sM43sIYfdDvDzwPwD0vCcAf+jgoCowkBeIiY3ASrcKJsHeBwLIMhmFMngp+x4VC4cTRE9euXavVaoPBwLIsURRpy5446Q1UVT1x4oSmaZQYIXJiiHNK5GVmr70tDBiGDVVPGJmj5dXlcrnQmK4vHVvK51HTYrjl+3kt7zjeaGhs7XYhFhhHHAvtTJ6TT548rchaq9u7cuXK8u1V0wTLmcUaY+IYDRUJtQzPcFCD73b7vh9OTTUajRkdppcuy0Lbhu7AO+dJkzXY4S/2d+A+EiNZ9BzZjRN12p2jqTs38MFC7i64s3STU1/OyZ/ixg5iqUDM5L/SJwuarDi+xzMsIDIs5/iwguEl2E3DLpvAlBi0zmNUjr4vkiEhwEE+9Oeo4Gw2m3UTwxZSMdISlqxC20x8KdKamA7oKUAnZa8mGQLB1NIv0og6OahJWEXjR1oTozCQZYZMhxRFsSxLN4GiSFTD8pDVGAwGiucSVhZef2wxiQVNItVdxhUUU5DaCU/eLvoKcAwlHAh6mti2mstqlCFN3h6ZOOEzUQCUbxjWYNDr9RAGBegnKz48pA783jsxwZP70PcDepynfAsoNJPBCem7giUoCzCxQYEtCFc/+CCbyVBMD4HUdZ955plisUgFYFVVBYra90ejUfJBPKZSLkaRx/EML4jFYunosSM8v3Ti5FHMqKj7ObmJADSb9vrKHodJE2iQopQBzpBMp3ut1tvvX1pZWbNsF6jLfL4oSjgdXSOfKxXyBdPwhgPDskxJgUody7Krq+sZLXf82MlL799U1epo4ALdRWCGh4LJIYzhoTHd5Fxuf02ONyEdrt61G3coct51E3LwHjow90sW6sRz6GZLt+WdkJo0T0w2oSxCGBscaZZjohjGaK7HOhyBCEccAzI8TnZRgFkgy0ECPYwcCHGCiYWenhRhV5L2DBiR6I3yrJBcGsoWp+ueRjDKW6W79M6GTUD0TidrXxpmqZjnoe2XzFUpHJ7o0VJrJEjT93vUsM33/U6n4wUeT8wiJQWyHeM2Mc4WCrSnBt6o9PEgdS8m4/gIh7hPdEtiaj52NeaJTj6RPDYkYYZR8M+Y2tGryPJBzJi2azvmaGiYlh74IG8zHPwFDt3+wz6+B7Xxk2/CupECEnHw0ZKDVeChSwNaHOHgJ3seuozZbHZ1bY1IVJQb09NbW1t5omk/0nUTbKAYGnnEnyTw/ayqeYHNS0w2ly0UM8ViXs1AqZ6MUhDwKdY8qWqCuJCvM2EMywPd6HQ6zWa73W6PdCgJ5XI5iCEVAW/o9PoY/nKsKMKHyLYGHggJULPn+SCKgvW1zZs3lj/65Ed4KABqqqx1Wp1CoeR5qGLuDD6TTPb9/bCP7dyPWgcHUckm/DCAy+Q+mfzBFOnCk02YbKrU+ZLA0+4altMfTBlqk5swSUeN0cgLAo4cpSLPxyEkaQI/gKc5xvJkK8cx9gaZPMBawDWppIqiIL+KGOqdRHwawpDsFkxRYxbmRLQcSiHdqeNSOh5M4zLdXfQjprLT9GfpOHGfwUj3xjh4Ts5eqHENWFc52F+6LurVwHM5hiuXy7brDA2d5yUCDMAEh2DISbqC7J2k8+MSH8c+ngHv9gM7kNYG8BKgQBVYDdDgEATBzs6uosialpFliUPzhgZ+/GnbFlGPjkS0r2AD7gDMqXxYJLwzDFKGJsWQUqhg2tujjoLdbn847Ec+0Kq022QEeqvVMk3z8ccfLxaLs7Ozr7zyymuvvXby5Ml0dAYxGaAuoa8n8LxrmSE5Hei9297aBRgg8BqN+uzs7OxMnmE4wzB6vd7QMl955U3LwkXWdd1zA1bgoYKkZudLVXjLOr7nY0IgS6qcUyVJMMxBEKBvF0UEoSFRpBTfH+y9++7Fn/iJLy0sLN24tmVZ1tzcbKvV1jSVnnR3wjIPsdQnOgt3iWyHHoc2zGRX4jBK6W5z9uSm3Am6mniHh+jz6asdWsa4zo4FxeXEwF5Rs5rGMSxOaKCpcbuJKR78wSJow0a41p7DRrEqyaIiQ+nNDcI4cFzYMCGWgrsUEaV0Pg4DVcNmGLs+EVYN8nKGKnyPVxj2G92omqylOSd90/RMol3muyaoCTNjfBDSR384EEWxVIJlOSvw/T6Ii57j5tQM7O84njb9UWWHkR+FEIdBIk2ay8BWIPMiQJYDgTC9q8lpQX4jtRdgyIkAF3TLGvR1QeQEXoIRGWzufUlUEo1wEAHJKRtzPGQKPlT25u5hkI5u6Nf0ChFRZwyZGGD6oIFJDgi6+4v5EtozxKeJ58VcrvDEE0+2212OQ8ChpTXN/YmxppfNZGKW0bJ5XpRiVmQ4yfXdKA7m5haiOL5+c73dbjf32oPBwDRt1/UL+SoDMRFWELOiBKdIurx0g3jZhSGDNiTnBaHt6EHoQYUDMycxIvWt7QSSxCkqXypVbi+vv/Ly6/ecOvvsn4CwWyo2PM/NZJXJXO9ObYi7xp80Eh4+2iZ6KnfddYd24+TT0pbmoZ9NA8ChN5ZiMNIVewgsvn98/KXPfZ66DtGiAt6uZNCpE8vS9BekGoEjQyfOuFCw9TzPgrYyQlzEoBHKwP3cCojaNE04aYKXtlhScv0hmz4atcDeEMA0T5ON9E1TO6S0z0EFb0AtJb40yURk4g2zPEeBKbu7u5l8juq7oKsJmB4Fo3vU/XeMFphIb6iQEf3AhL901wZaehakvSVEYCXjwJXIDplQYGGETOeo+yncuJeLf5JE2/N/OMfxUKYURwF3sCaEf50g1CpVeMTzsMTgYqieY24B/l5YqVS63S6Vz1NVtdFovP322+Afw00F6CiELlVN8AAMiJ3FYlE3hqNRkoIyLBSAqBg0OStxFwBnx8ic53hREolRawRGFcXNkXyHvk/czzge3zic5dRrD9IqER4ew/pR7GayaLb/g3/wD37zN36n1Rz0uvri4hHax55Mdg61UiavDz2F4WpIf4TMlpKakCai5C0dGC3cwW/4sE1IH0EErfckNZ04HSLSMqSI00kgG1V2SSvMNH2b9A5j/+JnP0c9biiPiapXUPsESjklvEHqiQd4MMClEvaVBWsOhywE4qTt4JaLcEyRqMAMfUCPMlnxkzoaeDe0LzLmB6G8QWlHzMbSZZ1eqbQmTNHe9LA3bFjN0ERXISK89K1CuyU9k8j0nO6WsQfLhHUpKfqIVsK+RNV4OEH2IR1cHEpgSP+TvtUDjV8oE+7f4/SLg3PI/e/7UeIDfnizTSyOyS8iAi1M52NkqeGRh3FikoKGPq4V3fy0b0xPTKJchN4VfF3oMhqnRknjHLAT3HryXCeM4PpAfxcdLic1M9GyoTqLDMtDkRFjE+xI4qqcvv8E10qqa5CPSRlO622yYvFfjP84SH0WClnLsn70R3+U48RvP/PdXLY4GqGdezAZPBxtDrTcx7c7SYcouIk8ZJEwxch3IPoybrlPQAL3TVkOhcc0ezy0CcmOTp4pE38kSFCPn0+LEInfN95NJ4T0X9NPAXmi9IyhPUm6tuhmpZYJtKFCm5CpUIVMOBYUpBbEkSyRTItkIwmylJ4HxOQtbY1OGgnRSJ0OT+nTAjdR+z4kXJXaBtPCj+5qslmTAQlxZcJHSCLb2JWJ+AEkz0ntUJMFlLw+LiV1fk3HLZP7ik4mDjEPsV8nZq/pv4Zk5pBm2nd2wCdfB3Czie8forf9kMedz6GXhdbbTAQIIe0H0sZYOhCif1KvxUO5PSFbczwrgbMDd7lElChpJBC1/fEmpOwQNkL3fL/LR/83fm8J2iFBIOJPNJQSJfEkhqSXguU5OY5AYrp+/RbLgHWZzcSTzNdDV+/O4nn/raaRcEIsa4xhJmrmE7FtcmyQ7kzaOJn8Rft3jVzL8XGw/6shOjC+5uluQkjwCNJg/Grp66TPxCakvcQUSEn3IZ0sTxrFjLdWIMmC7+MWkgRGdIE+8wVW8FAZUSMy0hskZHliZ43giQOW4L8J3gXfT12KaJOGjuIJKgVpJ808Jzch3V37e2B8ZRURDvJjpknSLKWd0vRAwux/PM1HE5gMftMSnpYcUQjJspDFCIOkT+PYgO5HgpZOilX6V4q3Hp8v6ffp1R4vysO502RxkvyZ9GPvvs7u9qDbhl6cpJdLHYXJ7SKog+T9o3c9sedZUcBwTwDujvc9nOipE9T4GlJhaLr0cbXoW0RfgFDREzDP/h2A4QmpcCcaiYin43xn4n1T/O0dfc6I9JswyMFN5uTVFZh1ExM+JN4kFO83Hu+6A/czyf2Mknwi8k7o3+kCoPZc1EKPPlJE24dd9sO/Ov0FEz+FpIxAf6mlT4oAw6Ldh/Uc3MwTvw46XNQ9giYNlAjPQWdqjNAnZ1YQhZ7jYrolcrZtjscMbEBSSjAPoXFCFuzB82myXqLrlWaSdPOnzc8UiSYL6kHbveQdp13QyfoYp8Y47SR7LW3/CI4HHgOtJJEpjbOvmLg1HExjsJRtDylcsvTGHhKJxgxt/5AfQv48hkdN3PWEu0l1sSbvzeRs885TnNaxk9+8s81w6EfuPp5mUASS85u0AQhJLnm3E8zgNGjT5H+iMZY25aG9QBda+vZxqcmd3R9Vx/Q9cyETxrCXT7vt+1sheW8pL2HcVeaIVxE9OGiuSx88J4RBlM0UCKiYzWQU8HUQ1e+eFxyaDY4lc8mZOz77OAYfP71D5MeSnz009N//pOMEbTK0TuaulFub3IuJbUuFQyfPbvqnyNM0/vDWnVzeAobapK6gWuoMx0ElmxcApqa7Ci16/GaARcPAcb0o8FGAhlEAywkPdbkkqTxyYtsDO4E4phFQaBSiyXYwcadnjw3ZyaStT7PfZPwdJI5Fk55hk6SeyYONGnHSq5NuQvqcSe3A9AhAOs351NtoP36StzYe/+ynE8mfEU+3HEeeHIwzTCowQjuVyXUnaxHaOQRkRj/0+D9KnEl6m3TYQIPwncSWQ0stfT8HWwWT80zScPJsegEx8SNDP3ocQKcwvf4wt4lxGwlYgiIRxv+ePIsnBE/KFSYWV0iFEuor9dMjFSL5K+mdE+03AtlHpKNIaBKqE/ug5A3vp/Y0HaXBnLS8qEw6XE8CRVEJHQeWG4ZhAz4Fm4W7NEvu3g6928lFn0apQkQylIDLPnwwu+99NJ4W/sdUChQVSDuOdIUnGf4dJPJJ8EDysz7d9zS8kPwBE2sOErIxy3p011IAJEp/BX2wGJGXetkTgXsk0EjoxkGPDjTJkYwqhY75Jiu99DCmPQA6gUgwDQJxPaHCSuPyMoWATfA5kqDn+aCoJCnBmJKX/pa0hkztvjExIGtgIq3FVR4riOOiT0ICoLhM0cPkfiDWjJ2eiBb9fsQ7tHsnw+CH9V1IgwcGE3f5/odEQioje+cqTCXSkZqSZixtaXCkITFZu1JgA/36EJWMqBehnCDNknHGDPeEicVK5SGQNnEsE5HTYHym0NkqdjDKrv28NpWAPTARJScIi5MIrQNB8tyIyKgLDEenzbRlcgCOcuizH8odAD5ExpQ8B2F04qdomZiWoslhOp4/T3ZNJmNAepWS1yFg0OQFD56cZESAdUIXc3JVySE2+TbuPHCFDGFnTY4EsEPCQJQlkZVYUtqlmSTLCOV8DoNq2hiMI1mWMXv1vJAndVeEuMzLyAMBeouJeyaxR6SfCBrT48ZPok0ckNdDYEXoVETMhSapX7S/f1cogyAI0jglxIaZRAZNovXIbOpAmwQLK+lMJHshOZmSz5uuSzqMo+ch3YpJM4WGe/oryEKhP5/8KwXykjCY/L67aQSR5IYQ2T5kwvtDHjT9I1/g+aIg81yihBCQoSt9KQUiiwkjMc2sWJYDyT95//hvPwUFc41m7EkROMYG0MAVgqaalMB0yTKTw2tgNhKuLN2WdDOMzRuSb6eQaILDprrvEYSxSU0bQhMiCmUZwMZ0B94VLHoIjTwu0Q+MKJJfTUhCNGNO0E4TGXu6rtK9cFe5iuSjHuyg0j/TpkOauyWvSRHcEy9yl02Yy0GPkTav6Uolcwr41NA9jZ+MYvhR+7Ckjn0PLIEgDuNAEJDzsCy6bbQvF9DyfVy2hcQ7mnjrwSmBJ3sx7ezvz98RegGBpGuIZamzH5Oq99KGHh21pYKc9Gt1YhRBEyt6CTxI84ybnGT50efIaZTep0fg4SWNiiQCp417OhJImaAUxUZJtFEK2E9FmYl8ZXqz0hc/FBKTO0eWJlHUSYotiqPbR8QcpFaM32u6sMaLgLROiBMVS3wN8HTamwqIlBY9U2jJPTnCSt/ehPpQElE5JgrRJEM2xZOxC713+JNKmpOYhz5kcugQAz5CoiNXdSxNSzbqvgnYBNggmU8wSTCMwihDPBGIfBOcM1VVsSxnEpM5PnHAbIZt+PiOUDIEld9JHKgBsqB+7pRzElFYWQLgHkdCegaNeTPE35EUR9BVoUcE0ZLC/JkEhhiaEkgUOPpLxtLaxOqMmIGRAOi6mFrTv8qCCEo41lUKiiR3cfJQ/qtf/QlSySScOsMwPDhI4UFR0UAfswKOQXL/iIerH3ihH4JNT9H0dFY+Cbmmw3fH92Ba5HkhaYlGAWIsz/OKotgu7ZqSLgio2j6xheLopGX8RvcbMKAmui5RZ0+YivRolyRoUSY6qGT2mOhEcBiRJX5jZHBCL7RCCuW0P0HwIvgYYzbTPtEL/jxRBN2Wg4BVuoJo33iyBKcjB0ESKcR54kF/9eHdSBcowi05pOhCJ0RewpaBnCi+AycuYspK0sNowth38gEv+0M6s5TVRkPinekcBdDvtzRpNEA1gJneOCdN/iRwBfz+BJdJQX2kjXcI8bNfBCa/a0L0gVZ/ZGY4OedkknMgMXuiaXCqx0XgXOPzC+cyljJ5LRphyNog1weqaGQ/08w7udRsDBgBl8zlMMomAz2qMCjLShgQ/Cobm9bIsoxsVjt6bGlxcW751q3d3e3RCL5oGeh2h2EAVUXHjaEqRYa7lCVLNPjwu+nxMVGGEEg2BytrQKMAW0hajCmFgOZYgh9gBYc+S7cBMYRASeZ5ZFgvgNMQRQwd35I2ijde/QQ4SiAXlgVWfhpLaQhVFKXAMJbrJIZPHBcGsRAEQHNJEsNhKEy3GOmOYk5M7J1hUkWnSeOjDsGTvqCiaGnfJa0M0+yRmUwDiAp1koWOT6zJpmuKXaCPcVGUtCnS+vPgdpoo88Y65HRdpo4facdvIv04vAmJ8TwpqcZZH7XmwR0i9RZJHMZ3aT9oppHwrvnqRLBMPiFC6yGNvTvLqgNJF6ISlk4SYJNNRUJHGsBIBZoEf9qDvPvjjpo2KRAngdcJe3OcmpJnHTwvKG9rfF7QD04SY4oUJOdDcn2ANoxhL0h+mko1kTEFRCAoOyeFglG4AlG+iHuDLhuF0zP1Jz/20Ln7Ts/PTWuaYlqPrqzc/uDylZWVlVZrB87HmVwuW+d5KYzIyJFYQZM17CVnDWkgpxN1wklnPcfEUBlE3aTACUMUa2NmPfGSOHDZCDACxyF21/7i9n0S1hxYn9O0LrWpoJGQ8sfTRUZbKWnDcx8sQI43ejPohUacJ1GL/ojvh3EIVVv6zHHPFy9LmXKkZ5TMUulgQ1Vhr5O8pYOpfNJuJZzINMtHR/1gun/w5J4A09GfoqiUg7jBydxykvMSM4yHWzKRc46j67hfSj7P+EfIB9yP/OkO33cOvstW+9A+6qFCJR38TmI+Du3AQz9LmqIfOib5sB//sz7u+vrcxDWZ/CCToItDFzb9QJOf986YT6+q4wDbTJtSAamSCNbJ1TLs4w/e+/ijD88vzLBs7LiG63dih5cU4cSpxvx8ttM5vrW1s7q6urW50+2tR1E2jiRZljMZIOZdyKqEPC8qCvRmaQcx8KlIPfaIJkuUWp+uSQ783GTonXyQn/7SF8gxTOMjAQqQ85y+YuRTcd59DhEFMaRIMQqVokE2HfpNtgrT7hypr8jYkMQo6PwRvw7kRYRvQfqlaNBQFdeJpZPsZDJLxO9Nc9E0JNLkMLV/oqPO/byftgxojg1TniRY0eEkSYNTeeZ9mTOqJUdLivTjTHbVJr+Z/C4YrxEswoFH0vvZ34RjdhTJ2yd+fCK/nWxhT/wWQkG+2z68U6T0ruPsO2X/06MkwWHFpD96YKukULV0Me3b6f2pvquH2sIf9gU/xjwc2oSQe9hPR/cV02iknhSwGI9hkqp+skFCRe4Mw6DQKDoJPHPmzMOPnDt+qiFKkQBGgWPZehj6iiwoqjQc9kWRl4iaBMfBm2BnZ2drs72xMWruDtrtNhyRiV6Jpmn5XFHX8eKEzUxYdSQDwn4h73CSOUGz7rG4Hr4G45veW/J2SbZK0gKexz/RdgiMP5L0l/M8nCip+gvtC1HNmHTiN27SkA081hogzVQSYMgbojDxhFZLmiVUdT/w0P+ZVJ2hv4ceHmRguR8xUmB0wiEmG4OmqeJ48Hfo+elY7NBhOSby73elA7Lhx2rNSUZ36Kyd3Dn0pVDiHoxpE4VkGvH2vz4QSMnrpEStye2dbh7y/Ltswg+L7XdpCB3cFQe+c8dbvTPiHYoz/9897nz9+MN6wrQIvdvBkSbHk98JyAa7c9qeXr10+kV36c2b11neUSW5WCoUi1lJznmuZZp6johNGTrMJ2VZzueKc3MzpWLt+Am+3RrCFbjf7/X67VYXRO1Rs5AvAjqKRR9ERHt+7NUJ2ObkBDIM8CdFa6eVCHk3JJgIAqhlqqyQkE1qFUpEJ5pL4xk6GhgpJpsuX9/3s1lIkU/GqOSiEAGo8XUkTtYS5leiTOdaNgIgqaXJfkvCV3qGpaucAqxTNCOVFaPRmP5GKlKcEhFT2Rh8TSXJyP0IJ3Y43b00EloW5Prx3DHPg25CWHaO7zFNsOkBlJpqHILX4X5PfITJRbO/kg5sg/01PfmR0+cfXJ0/bP3fNRO785s/fG9MDrwPru+7pKZ3fn/yV3/Yr7jza/ZD3h7prEw84eDAjX55Z+Z5aMfSTTgajShtMpV+f+utt1559YUgGuSLcikP5cWYCWVFqFXKpVJh6chCNpvRZIWJFc9hDQYuPaIo+WGweKR+4tRcFEX6yGi3u61WRx+Zy8srjh16vhOEMc+IkqRAL4bjbcOm73q/JkvWIUWekA/yF//857C7CHuAcMwUSUBUoXIMgYuFTpM0CiWFLTt6pGBY054kHQrTSpQuTRoD6YXwQrBa6LaJIwImgK5UxPJQHKMmYSyPNJgEAVgkYhjiJ8iDNEmm65sORehnoIGUBt6EJUAap/QSp7UEaWvh62QLEbG2ceClMAN8n34cDFfHyFX6AoncONlpdAfSumIS+bU/dEIeScx0D+Su9FTm7kxH07bNoYDwYaN/9CSIAsAPWd8/PB39sB9Mgga4PtH/X9LRD0MpTNZ+k1/w4/T7UDoKgsbd0lGAeshI6NDnDUgvYzK/SJsI9K7ZNqBFmUyGGq5oGSGKfdhNx3CwA1qMYzgevFlNUwu5TCaTyWY1Su/SMhIvBI3pWqVSYWIsQkmSNTUvCNLq6po+srudYafT73UHvd6w3xuapj1Vn4Mh9xiRQhwRsYApGiFpzFCqCw2OVE3QMkzbtvP5IrZlgsHBz0CU0oIzpqIo+Xy+VCpRIBjdYO02/Prgoky2BxX2BkDUNOhyp/gSGmFQOhIaMaloFU4gDowwyrRlUQrJHDy9lLRyG9eyNFVOFGjorpgkf6QZCJ37pei5NBKKsKPAKXCI/aAoCsmN94dpSUgfj/72GzZjeYK7rH6GcUPINhw8rZOacHLZpz+V1oT7y24CMX8n+CY5Gz7kcShQHOpVHHrZO0Fbd33BP/U7/7882INbcXL7JVvrT/vxQ583jTZpIy39k1YuVJxS13XqcO7YDosEDEAU6v1CWm1srVKwbLPdNDqszhPUFE72yJpbKD38yH253P2qqrqeYw2NTqfteaEsqfmCVi6Xz5w5wzC8bbn9/sjQreVbm5bljEZIa5NIQ+am43E31sb/BbuofBwX+yWWAAAAAElFTkSuQmCC\"/>"
      ],
      "text/plain": [
       "<autogen_core._image.Image at 0x12a29f9b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "\n",
    "import PIL\n",
    "import requests\n",
    "from autogen_agentchat.messages import MultiModalMessage\n",
    "from autogen_core import Image\n",
    "\n",
    "# Create a multi-modal message with random image and text.\n",
    "pil_image = PIL.Image.open(BytesIO(requests.get(\"https://picsum.photos/300/200\").content))\n",
    "img = Image(pil_image)\n",
    "multi_modal_message = MultiModalMessage(content=[\"Can you describe the content of this image?\", img], source=\"user\")\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image depicts a vintage car, likely from the 1930s or 1940s, with a sleek, classic design. The car seems to be customized or well-maintained, as indicated by its shiny exterior and lowered stance. It has a prominent grille and round headlights. There's a license plate on the front with the text \"FARMER BOY.\" The setting appears to be a street with old-style buildings in the background, suggesting a historical or retro theme.\n"
     ]
    }
   ],
   "source": [
    "# Use asyncio.run(...) when running in a script.\n",
    "response = await agent.on_messages([multi_modal_message], CancellationToken())\n",
    "print(response.chat_message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use {py:class}`~autogen_agentchat.messages.MultiModalMessage` as a `task`\n",
    "input to the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Messages\n",
    "\n",
    "We can also stream each message as it is generated by the agent by using the\n",
    "{py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method,\n",
    "and use {py:class}`~autogen_agentchat.ui.Console` to print the messages\n",
    "as they appear to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- assistant ----------\n",
      "[FunctionCall(id='call_fSp5iTGVm2FKw5NIvfECSqNd', arguments='{\"query\":\"AutoGen information\"}', name='web_search')]\n",
      "[Prompt tokens: 61, Completion tokens: 16]\n",
      "---------- assistant ----------\n",
      "[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_fSp5iTGVm2FKw5NIvfECSqNd')]\n",
      "---------- assistant ----------\n",
      "AutoGen is a programming framework designed for building multi-agent applications. If you need more detailed information or specific aspects about AutoGen, feel free to ask!\n",
      "[Prompt tokens: 93, Completion tokens: 32]\n",
      "---------- Summary ----------\n",
      "Number of inner messages: 2\n",
      "Total prompt tokens: 154\n",
      "Total completion tokens: 48\n",
      "Duration: 4.30 seconds\n"
     ]
    }
   ],
   "source": [
    "async def assistant_run_stream() -> None:\n",
    "    # Option 1: read each message from the stream (as shown in the previous example).\n",
    "    # async for message in agent.on_messages_stream(\n",
    "    #     [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
    "    #     cancellation_token=CancellationToken(),\n",
    "    # ):\n",
    "    #     print(message)\n",
    "\n",
    "    # Option 2: use Console to print all messages as they appear.\n",
    "    await Console(\n",
    "        agent.on_messages_stream(\n",
    "            [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
    "            cancellation_token=CancellationToken(),\n",
    "        ),\n",
    "        output_stats=True,  # Enable stats printing.\n",
    "    )\n",
    "\n",
    "\n",
    "# Use asyncio.run(assistant_run_stream()) when running in a script.\n",
    "await assistant_run_stream()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method\n",
    "returns an asynchronous generator that yields each inner message generated by the agent,\n",
    "with the final item being the response message in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute.\n",
    "\n",
    "From the messages, you can observe that the assistant agent utilized the `web_search` tool to\n",
    "gather information and responded based on the search results.\n",
    "\n",
    "You can also use {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` to get the same streaming behavior as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`. It follows the same interface as [Teams](./teams.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Tools\n",
    "\n",
    "Large Language Models (LLMs) are typically limited to generating text or code responses. \n",
    "However, many complex tasks benefit from the ability to use external tools that perform specific actions,\n",
    "such as fetching data from APIs or databases.\n",
    "\n",
    "To address this limitation, modern LLMs can now accept a list of available tool schemas \n",
    "(descriptions of tools and their arguments) and generate a tool call message. \n",
    "This capability is known as **Tool Calling** or **Function Calling** and \n",
    "is becoming a popular pattern in building intelligent agent-based applications.\n",
    "Refer to the documentation from [OpenAI](https://platform.openai.com/docs/guides/function-calling) \n",
    "and [Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) for more information about tool calling in LLMs.\n",
    "\n",
    "In AgentChat, the {py:class}`~autogen_agentchat.agents.AssistantAgent` can use tools to perform specific actions.\n",
    "The `web_search` tool is one such tool that allows the assistant agent to search the web for information.\n",
    "A custom tool can be a Python function or a subclass of the {py:class}`~autogen_core.tools.BaseTool`.\n",
    "\n",
    "By default, when {py:class}`~autogen_agentchat.agents.AssistantAgent` executes a tool,\n",
    "it will return the tool's output as a string in {py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage` in its response.\n",
    "If your tool does not return a well-formed string in natural language, you\n",
    "can add a reflection step to have the model summarize the tool's output,\n",
    "by setting the `reflect_on_tool_use=True` parameter in the {py:class}`~autogen_agentchat.agents.AssistantAgent` constructor.\n",
    "\n",
    "### Built-in Tools\n",
    "\n",
    "AutoGen Extension provides a set of built-in tools that can be used with the Assistant Agent.\n",
    "Head over to the [API documentation](../../../reference/index.md) for all the available tools\n",
    "under the `autogen_ext.tools` namespace. For example, you can find the following tools:\n",
    "\n",
    "- {py:mod}`~autogen_ext.tools.graphrag`: Tools for using GraphRAG index.\n",
    "- {py:mod}`~autogen_ext.tools.http`: Tools for making HTTP requests.\n",
    "- {py:mod}`~autogen_ext.tools.langchain`: Adaptor for using LangChain tools.\n",
    "- {py:mod}`~autogen_ext.tools.mcp`: Tools for using Model Chat Protocol (MCP) servers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function Tool\n",
    "\n",
    "The {py:class}`~autogen_agentchat.agents.AssistantAgent` automatically\n",
    "converts a Python function into a {py:class}`~autogen_core.tools.FunctionTool`\n",
    "which can be used as a tool by the agent and automatically generates the tool schema\n",
    "from the function signature and docstring.\n",
    "\n",
    "The `web_search_func` tool is an example of a function tool.\n",
    "The schema is automatically generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'web_search_func',\n",
       " 'description': 'Find information on the web',\n",
       " 'parameters': {'type': 'object',\n",
       "  'properties': {'query': {'description': 'query',\n",
       "    'title': 'Query',\n",
       "    'type': 'string'}},\n",
       "  'required': ['query'],\n",
       "  'additionalProperties': False},\n",
       " 'strict': False}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autogen_core.tools import FunctionTool\n",
    "\n",
    "\n",
    "# Define a tool using a Python function.\n",
    "async def web_search_func(query: str) -> str:\n",
    "    \"\"\"Find information on the web\"\"\"\n",
    "    return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
    "\n",
    "\n",
    "# This step is automatically performed inside the AssistantAgent if the tool is a Python function.\n",
    "web_search_function_tool = FunctionTool(web_search_func, description=\"Find information on the web\")\n",
    "# The schema is provided to the model during AssistantAgent's on_messages call.\n",
    "web_search_function_tool.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Context Protocol Tools\n",
    "\n",
    "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can also use tools that are\n",
    "served from a Model Context Protocol (MCP) server\n",
    "using {py:func}`~autogen_ext.tools.mcp.mcp_server_tools`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seattle, located in Washington state, is the most populous city in the state and a major city in the Pacific Northwest region of the United States. It's known for its vibrant cultural scene, significant economic presence, and rich history. Here are some key points about Seattle from the Wikipedia page:\n",
      "\n",
      "1. **History and Geography**: Seattle is situated between Puget Sound and Lake Washington, with the Cascade Range to the east and the Olympic Mountains to the west. Its history is deeply rooted in Native American heritage and its development was accelerated with the arrival of settlers in the 19th century. The city was officially incorporated in 1869.\n",
      "\n",
      "2. **Economy**: Seattle is a major economic hub with a diverse economy anchored by sectors like aerospace, technology, and retail. It's home to influential companies such as Amazon and Starbucks, and has a significant impact on the tech industry due to companies like Microsoft and other technology enterprises in the surrounding area.\n",
      "\n",
      "3. **Cultural Significance**: Known for its music scene, Seattle was the birthplace of grunge music in the early 1990s. It also boasts significant attractions like the Space Needle, Pike Place Market, and the Seattle Art Museum. \n",
      "\n",
      "4. **Education and Innovation**: The city hosts important educational institutions, with the University of Washington being a leading research university. Seattle is recognized for fostering innovation and is a leader in environmental sustainability efforts.\n",
      "\n",
      "5. **Demographics and Diversity**: Seattle is noted for its diverse population, reflected in its rich cultural tapestry. It has seen a significant increase in population, leading to urban development and changes in its social landscape.\n",
      "\n",
      "These points highlight Seattle as a dynamic city with a significant cultural, economic, and educational influence within the United States and beyond.\n"
     ]
    }
   ],
   "source": [
    "from autogen_agentchat.agents import AssistantAgent\n",
    "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
    "from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools\n",
    "\n",
    "# Get the fetch tool from mcp-server-fetch.\n",
    "fetch_mcp_server = StdioServerParams(command=\"uvx\", args=[\"mcp-server-fetch\"])\n",
    "tools = await mcp_server_tools(fetch_mcp_server)\n",
    "\n",
    "# Create an agent that can use the fetch tool.\n",
    "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
    "agent = AssistantAgent(name=\"fetcher\", model_client=model_client, tools=tools, reflect_on_tool_use=True)  # type: ignore\n",
    "\n",
    "# Let the agent fetch the content of a URL and summarize it.\n",
    "result = await agent.run(task=\"Summarize the content of https://en.wikipedia.org/wiki/Seattle\")\n",
    "print(result.messages[-1].content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Langchain Tools\n",
    "\n",
    "You can also use tools from the Langchain library\n",
    "by wrapping them in {py:class}`~autogen_ext.tools.langchain.LangChainToolAdapter`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- assistant ----------\n",
      "[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')]\n",
      "[Prompt tokens: 111, Completion tokens: 22]\n",
      "---------- assistant ----------\n",
      "[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')]\n",
      "---------- assistant ----------\n",
      "29.69911764705882\n",
      "---------- Summary ----------\n",
      "Number of inner messages: 2\n",
      "Total prompt tokens: 111\n",
      "Total completion tokens: 22\n",
      "Duration: 0.62 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Response(chat_message=ToolCallSummaryMessage(source='assistant', models_usage=None, content='29.69911764705882', type='ToolCallSummaryMessage'), inner_messages=[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=111, completion_tokens=22), content=[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')], type='ToolCallExecutionEvent')])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from autogen_ext.tools.langchain import LangChainToolAdapter\n",
    "from langchain_experimental.tools.python.tool import PythonAstREPLTool\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv\")\n",
    "tool = LangChainToolAdapter(PythonAstREPLTool(locals={\"df\": df}))\n",
    "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
    "agent = AssistantAgent(\n",
    "    \"assistant\", tools=[tool], model_client=model_client, system_message=\"Use the `df` variable to access the dataset.\"\n",
    ")\n",
    "await Console(\n",
    "    agent.on_messages_stream(\n",
    "        [TextMessage(content=\"What's the average age of the passengers?\", source=\"user\")], CancellationToken()\n",
    "    ),\n",
    "    output_stats=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel Tool Calls\n",
    "\n",
    "Some models support parallel tool calls, which can be useful for tasks that require multiple tools to be called simultaneously.\n",
    "By default, if the model client produces multiple tool calls, {py:class}`~autogen_agentchat.agents.AssistantAgent`\n",
    "will call the tools in parallel.\n",
    "\n",
    "You may want to disable parallel tool calls when the tools have side effects that may interfere with each other, or,\n",
    "when agent behavior needs to be consistent across different models.\n",
    "This should be done at the model client level.\n",
    "\n",
    "For {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`,\n",
    "set `parallel_tool_calls=False` to disable parallel tool calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_client_no_parallel_tool_call = OpenAIChatCompletionClient(\n",
    "    model=\"gpt-4o\",\n",
    "    parallel_tool_calls=False,  # type: ignore\n",
    ")\n",
    "agent_no_parallel_tool_call = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    model_client=model_client_no_parallel_tool_call,\n",
    "    tools=[web_search],\n",
    "    system_message=\"Use tools to solve tasks.\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running an Agent in a Loop\n",
    "\n",
    "The {py:class}`~autogen_agentchat.agents.AssistantAgent` executes one\n",
    "step at a time: one model call, followed by one tool call (or parallel tool calls), and then\n",
    "an optional reflection.\n",
    "\n",
    "To run it in a loop, for example, running it until it stops producing\n",
    "tool calls, please refer to [Single-Agent Team](./teams.ipynb#single-agent-team)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structured Output\n",
    "\n",
    "Structured output allows models to return structured JSON text with pre-defined schema\n",
    "provided by the application. Different from JSON-mode, the schema can be provided\n",
    "as a [Pydantic BaseModel](https://docs.pydantic.dev/latest/concepts/models/)\n",
    "class, which can also be used to validate the output. \n",
    "\n",
    "```{note}\n",
    "Structured output is only available for models that support it. It also\n",
    "requires the model client to support structured output as well.\n",
    "Currently, the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
    "and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
    "support structured output.\n",
    "```\n",
    "\n",
    "Structured output is also useful for incorporating Chain-of-Thought\n",
    "reasoning in the agent's responses.\n",
    "See the example below for how to use structured output with the assistant agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- user ----------\n",
      "I am happy.\n",
      "---------- assistant ----------\n",
      "{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='I am happy.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=89, completion_tokens=18), content='{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}', type='TextMessage')], stop_reason=None)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import Literal\n",
    "\n",
    "from pydantic import BaseModel\n",
    "\n",
    "\n",
    "# The response format for the agent as a Pydantic base model.\n",
    "class AgentResponse(BaseModel):\n",
    "    thoughts: str\n",
    "    response: Literal[\"happy\", \"sad\", \"neutral\"]\n",
    "\n",
    "\n",
    "# Create an agent that uses the OpenAI GPT-4o model with the custom response format.\n",
    "model_client = OpenAIChatCompletionClient(\n",
    "    model=\"gpt-4o\",\n",
    "    response_format=AgentResponse,  # type: ignore\n",
    ")\n",
    "agent = AssistantAgent(\n",
    "    \"assistant\",\n",
    "    model_client=model_client,\n",
    "    system_message=\"Categorize the input as happy, sad, or neutral following the JSON format.\",\n",
    ")\n",
    "\n",
    "await Console(agent.run_stream(task=\"I am happy.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Tokens\n",
    "\n",
    "You can stream the tokens generated by the model client by setting `model_client_stream=True`.\n",
    "This will cause the agent to yield {py:class}`~autogen_agentchat.messages.ModelClientStreamingChunkEvent` messages\n",
    "in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`.\n",
    "\n",
    "The underlying model API must support streaming tokens for this to work.\n",
    "Please check with your model provider to see if this is supported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' South' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Buenos' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Aires' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Argentina' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' São' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Paulo' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Brazil' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
      "Response(chat_message=TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in South America are Buenos Aires in Argentina and São Paulo in Brazil.', type='TextMessage'), inner_messages=[])\n"
     ]
    }
   ],
   "source": [
    "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
    "\n",
    "streaming_assistant = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    model_client=model_client,\n",
    "    system_message=\"You are a helpful assistant.\",\n",
    "    model_client_stream=True,  # Enable streaming tokens.\n",
    ")\n",
    "\n",
    "# Use an async function and asyncio.run() in a script.\n",
    "async for message in streaming_assistant.on_messages_stream(  # type: ignore\n",
    "    [TextMessage(content=\"Name two cities in South America\", source=\"user\")],\n",
    "    cancellation_token=CancellationToken(),\n",
    "):\n",
    "    print(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the streaming chunks in the output above.\n",
    "The chunks are generated by the model client and are yielded by the agent as they are received.\n",
    "The final response, the concatenation of all the chunks, is yielded right after the last chunk.\n",
    "\n",
    "Similarly, {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` will also yield the same streaming chunks,\n",
    "followed by a full text message right after the last chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source='user' models_usage=None content='Name two cities in North America.' type='TextMessage'\n",
      "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' North' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' New' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' York' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' City' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' the' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' United' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' States' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Toronto' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content=' Canada' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
      "source='assistant' models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0) content='Two cities in North America are New York City in the United States and Toronto in Canada.' type='TextMessage'\n",
      "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Name two cities in North America.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in North America are New York City in the United States and Toronto in Canada.', type='TextMessage')], stop_reason=None)\n"
     ]
    }
   ],
   "source": [
    "async for message in streaming_assistant.run_stream(task=\"Name two cities in North America.\"):  # type: ignore\n",
    "    print(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Model Context\n",
    "\n",
    "{py:class}`~autogen_agentchat.agents.AssistantAgent` has a `model_context`\n",
    "parameter that can be used to pass in a {py:class}`~autogen_core.model_context.ChatCompletionContext`\n",
    "object. This allows the agent to use different model contexts, such as\n",
    "{py:class}`~autogen_core.model_context.BufferedChatCompletionContext` to\n",
    "limit the context sent to the model.\n",
    "\n",
    "By default, {py:class}`~autogen_agentchat.agents.AssistantAgent` uses\n",
    "the {py:class}`~autogen_core.model_context.UnboundedChatCompletionContext`\n",
    "which sends the full conversation history to the model. To limit the context\n",
    "to the last `n` messages, you can use the {py:class}`~autogen_core.model_context.BufferedChatCompletionContext`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogen_core.model_context import BufferedChatCompletionContext\n",
    "\n",
    "# Create an agent that uses only the last 5 messages in the context to generate responses.\n",
    "agent = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    model_client=model_client,\n",
    "    tools=[web_search],\n",
    "    system_message=\"Use tools to solve tasks.\",\n",
    "    model_context=BufferedChatCompletionContext(buffer_size=5),  # Only use the last 5 messages in the context.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Preset Agents\n",
    "\n",
    "The following preset agents are available:\n",
    "\n",
    "- {py:class}`~autogen_agentchat.agents.UserProxyAgent`: An agent that takes user input returns it as responses.\n",
    "- {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`: An agent that can execute code.\n",
    "- {py:class}`~autogen_ext.agents.openai.OpenAIAssistantAgent`: An agent that is backed by an OpenAI Assistant, with ability to use custom tools.\n",
    "- {py:class}`~autogen_ext.agents.web_surfer.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information.\n",
    "- {py:class}`~autogen_ext.agents.file_surfer.FileSurfer`: An agent that can search and browse local files for information.\n",
    "- {py:class}`~autogen_ext.agents.video_surfer.VideoSurfer`: An agent that can watch videos for information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Step\n",
    "\n",
    "Having explored the usage of the {py:class}`~autogen_agentchat.agents.AssistantAgent`, we can now proceed to the next section to learn about the teams feature in AgentChat.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ## CodingAssistantAgent\n",
    "\n",
    "Generates responses (text and code) using an LLM upon receipt of a message. It takes a `system_message` argument that defines or sets the tone for how the agent's LLM should respond. \n",
    "\n",
    "```python\n",
    "\n",
    "writing_assistant_agent = CodingAssistantAgent(\n",
    "    name=\"writing_assistant_agent\",\n",
    "    system_message=\"You are a helpful assistant that solve tasks by generating text responses and code.\",\n",
    "    model_client=model_client,\n",
    ")\n",
    "`\n",
    "\n",
    "We can explore or test the behavior of the agent by sending a message to it using the  {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`  method. \n",
    "\n",
    "```python\n",
    "result = await writing_assistant_agent.on_messages(\n",
    "    messages=[\n",
    "        TextMessage(content=\"What is the weather right now in France?\", source=\"user\"),\n",
    "    ],\n",
    "    cancellation_token=CancellationToken(),\n",
    ")\n",
    "print(result) -->"
   ]
  }
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