search/retrieval-augmented-generation/examples/question_answering.ipynb

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   "source": [
    "# Question Answering Over Documents\n",
    "\n",
    "_Augmented with document retrieval from Vertex AI Search_\n",
    "\n",
    "<table align=\"left\">\n",
    "\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/examples/question_answering.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/examples/question_answering.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/examples/question_answering.ipynb\">\n",
    "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
    "      Open in Vertex AI Workbench\n",
    "    </a>\n",
    "  </td>\n",
    "</table>\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "31280da3e872"
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   "source": [
    "---\n",
    "\n",
    "\n",
    "* Authors: Ruchika Kharwar, Tomas Paquete, Tom Pakeman\n",
    "* Created: 06/06/2023\n",
    "\n",
    "---\n",
    "\n",
    "## Objective\n",
    "\n",
    "This notebook shows how to ask and answer questions about your data by combining an Vertex AI Search engine with LLMs. In particular, we focus on querying 'unstructured' data such as PDFs and HTML files.\n",
    "\n",
    "These patterns are useful if you have an Vertex AI Search Engine pointed at a store of documents, such as a Google Cloud Storage bucket containing PDFs.\n",
    "\n",
    "In order to run this notebook you must have created an unstructued search engine and ingested PDF or HTML documents into it.\n",
    "\n",
    "Users may wish to:\n",
    "\n",
    "1. Get an answer to a question from document(s)\n",
    "2. Get an answer to a question from document(s) along with the citation/sources\n",
    "3. Get an answer to a question from document(s) and follow it up with additional questions like in a conversation.\n",
    "4. Users might want to change the default prompt of LangChain while using the results of Vertex AI Search\n",
    "\n",
    "In each case we will use an example of the LangChain [retriever](https://python.langchain.com/docs/modules/data_connection/retrievers.html) interface to fetch contextual documents & segments from a Vertex AI Search engine, and the Vertex AI API to call Google's PaLM2 model for summarization & answering.\n",
    "\n",
    "Please note that the prompts and examples demonstrated here will need to be customised in order to work well with your own data.\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook the following examples will be elaborated:\n",
    "\n",
    "- ✅ Example of using `GoogleVertexAISearchRetriever` with LangChain `RetrievalQA`\n",
    "- ✅ Example of using `GoogleVertexAISearchRetriever` with LangChain `RetrievalQAWithSourcesChain`\n",
    "- ✅ Example of using `GoogleVertexAISearchRetriever` with LangChain `ConversationalRetrievalChain`\n",
    "- ✅ Example of using `GoogleVertexAIMultiTurnSearchRetriever` with LangChain `ConversationalRetrievalChain`\n",
    "- ✅ Examples of changing the default prompt with Vertex AI Search and `RetrievalQAWithSourcesChain`\n",
    "\n",
    "---\n",
    "\n",
    "**References:**\n",
    "\n",
    "- [LangChain Conceptual Documentation](https://docs.langchain.com/docs/)\n",
    "- [LangChain Python Documentation](https://python.langchain.com/en/latest/)\n",
    "- [LangChain Vertex AI Search Retriever Documentation](https://python.langchain.com/docs/integrations/retrievers/google_vertex_ai_search)\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "Mbpcf60y7cCo"
   },
   "source": [
    "## High Level Flow\n",
    "\n",
    "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLIAAAHECAYAAAAtXwOCAAAAAXNSR0IArs4c6QAAUs90RVh0bXhmaWxlACUzQ214ZmlsZSUyMGhvc3QlM0QlMjJkcmF3aW8taW50ZXJuYWwuZ29vZ2xlcGxleC5jb20lMjIlMjBtb2RpZmllZCUzRCUyMjIwMjMtMDYtMTZUMDMlM0ExOSUzQTIzLjU2NVolMjIlMjBhZ2VudCUzRCUyMjUuMCUyMChYMTElM0IlMjBDck9TJTIweDg2XzY0JTIwMTQ1NDEuMC4wKSUyMEFwcGxlV2ViS2l0JTJGNTM3LjM2JTIwKEtIVE1MJTJDJTIwbGlrZSUyMEdlY2tvKSUyMENocm9tZSUyRjExMy4wLjAuMCUyMFNhZmFyaSUyRjUzNy4zNiUyMiUyMHZlcnNpb24lM0QlMjIyMC43LjMlMjIlMjBldGFnJTNEJTIyVzR3NjRMZTFyeDk0ZDFpeTNWOFolMjIlMjB0eXBlJTNEJTIyZ29vZ2xlJTIyJTNFJTNDZGlhZ3JhbSUyMGlkJTNEJTIyYmZGSFhvSml4NmJIZXRuUFROWVQlMjIlMjBuYW1lJTNEJTIyUGFnZS0xJTIyJTNFN2IzWHNxdEdGeTc2Tkw0NVZYWVJCYm9rZzBRR0VYU3ppeVNFQkVJaXc5T2ZiczJ3WmxySjlyTDk3ejFubFpkRlE2Y1J2ekhvYm43RHVXcVNtdWg2MU9vMEszJTJGRGtIVDZEZWQlMkZ3ekFDb2NDJTJGc0dCJTJCS0NCcDhxRWdiNHIwb1FqOVV1QVVTJTJGWllpRHlXOWtXYXRhOGU3T3E2N0lycjY4S2t2bHl5cEh0VkZqVk5QYjUlMkI3RkNYcjN1OVJubjJyc0JKb3ZKOXFWJTJCazNmR3hkSTBoWDI3SVdaRWZuN3JHa01jN1ZmVDA5R05CZTR6U2VueFJoQXUlMkY0VnhUMTkzRHIycmlzaElTNzRrd0QlMkZYRXI5eDlIbG1UWGJvZnFmQiUyRmhMRTN5eFczUHhQSE9CNzklMkY3Tm0lMkZzJTJGdk9QN1F6QkNWJTJGZU9VSDBmYnpVODBhT3IlMkJrbWF3RmVRM25CMlBSWmM1MXlpQmQwZkFkRkIyN0tvU1hLSGdaeHExeCUyRnV6VHhkbTFIVlpjN21YWUFnc2JidW1QaiUyQlRFOUNCUFJSbHlkVmwzWURyUzMzSm5oOTZLdndOd3pFRUVKY0FkeDRIbkRWZE5uMlZGT2d6Z1lGa1puV1ZkYzBNSG5tc1FEN3g1RkVxNmNmTDhRdUw4YWRIamklMkI0U3hDUGhkR2pXT1hQVFg4aFBQanhTUHVmNFFQNmZUN2tnQkhYcjg3JTJCVVFPaSUyQk9seDVHZXBndEwlMkZOYXBnSHhCbFZYWlFZbW93SnpqcEolMkZsWTNYcW9TT3dYU1hsUmhLUlJGJTJGME82JTJGemVOc21yNTQ5ZEIwMEpBd2VLaWZDUjlvJTJCOHJ2TXlpNjVGJTJCMGRTVjZBNGFjRWo0aUdxaWhMU3hyaG1sOTh3MW9rdTdjdGVWdm5qJTJGJTJCOGpqSjhLbEV1ZXRWMVJYNTd1Z0FianQwOCUyRjl2MmwlMkJBM3ZBYmU3MTRvV2xVVU90U29CM00wQURWZ29Fd1d3WE16amphcElVMWlkYmJLMldCN2xBbXJ3dFM3QUxHRzdKUHNieWNPMiUyQnE1dUgyd3YlMkJrNzNIaFh5QXgzOWM3b09waWslMkJVZEt1NDdxckgwdWR1bSUyRnVWdVdCSnhnSnVVSkN2cEJmNHd6NXdCdnc2Qk4zd004dnJmNlFxZmlHUG43ZGdMelRET29EeGNCJTJGbFY3OGdMSDRYelRhUDhXSmY5RXFJZSUyQm9uYVVBTlR4ZTFrMTNyUFA2RXBYQ2wxTDJOVCUyQiUyQlBLUFc5ZldSOEtlczYlMkJaSENBUTE4aldQc3Fub0FsajlEJTJGTHhLbnh4aDU4ZVc3NWZ6RThYRnpEZkY1WGdaZmp5M3BkcTk2dW5ldiUyQmtqdDViZmJROSUyQk9QMUN3bEM3bjhmeVJhTjBDdWFmWll0eUlKdnd3SEFzYWZSZjFYWVBwYTFKaXVqcmhoZXQlMkYlMkJSWEQxV05hR0YlMkZXSXRjT0sxWXdXTzlnJTJGa3hkJTJCYkJydW95YlB1c1kwM3N2bzhxTDhBTmI1dlBBQlV2Y0tmUlhXSHg4JTJCdVJZM2lyRFNCcTdpN001d0hMT3dBaTNHMmhEZllLRG5uZHpsJTJGNGxLYUhhTCUyQjd0JTJGZU9xY09TajBidGRjSDBING9KcWdiN0wxRDVxa1VlU3FCVFFFUER0ejB3eVVtWGklMkY1YnhoWGVLeGhqOGhXeW1zRyUyRk9uTzdpanNjdkNMaDVmTXlERWglMkJCOGJNRU1sd3g5cnBCUXN6Njd6RE50cmdlcGFyQ01OYzlKY09LVTJYWkp4ZW10YkVHRXpNdVhNR2pOWEZqTFBac1clMkJEdWVrYUU0OHNLUG9maEJIeHEzYkZTb3hHcmt0Q1JFdDIyeHpIT2dtS2xPS09nQmtBRDA4bmdwYnBwYkJUd21YZ0w5Z3N4UEFKR3gwTnRic1VxNUZlaE9kdlhBV0c4TnJlOVdPVGZWOFRqZnJteU5FSUdZU3Q3aHlCRk5rZGZNY1VJZUk0azdISmxodjRzYSUyQkZpeVdIYWlZM08xUHllM2c5ZzQ1bmZzYkNoOFhkNG9wV2daJTJCQm85ekRkOGRVbWVWcU93SllVYUVQN0NXRGxva1lzbnRtTDA2c1J2JTJGZ3JxaGJkNVFEVlE5RXJsNVhGMEtkZTVOeDFxcHN3SWVUbmlEWXNMOHNJY3p3SWtwUHRCWDBRTlB0OVEySWpiS2pVeTJLMDl5ZUMlMkJzRHV1RHo1T0s0WnpLJTJGYmdhZkVtN2ViRiUyRk16ZVI2NDY5emQwY2pmRThhdGRUNTV2THJlSmhGMWdsZ1ZqWGtOaWVpYUVieHZXUnIwYlJvU01YRzRmakVUZVlncmVOcVNpWjBJJTJGY0lNNTRielRRNG5CTm5jdkZOY0tZbDAyMnRyYlpicGkwWUNZck5HY0JRaGJKbWxhRGMxOERxeU95MUdTeXQxREs1dFZ4bWNSVXgwZSUyQnRYZFhmbnU0c2E2TUNXc3B6Y3RydHhvU0ZjRmF5Q05nZmpkTHFCTmxBaTRNa1l5SW9EcVJoOVJjJTJCWGZtc2d6ZVZsd2VFMXQyeU04U0tHajM0QjlsMHZ3S005RlZjNk05d3BYb3VFRTU3Umd0dzN4TG9sVFBLWjlGRDV0Q1NBUUxrUGcyMTVUS0tYdGZYazRjbGhXWkVlVDhZaXVyT2FiZ3dCMDk0Nmk0eEthQ1BWTEFJZWlBaDZaJTJCRThaY2tNMnpPRVZZSVdTbm5VT09kMkYzZHA1aGIwa3VWQlNveEg5TG5JU2pxOWVHQzMwZkVoRDBCNzZYJTJGbFd1RjMxbnElMkY2anJ2ZiUyRlZoZjY0SnklMkJ3U0xxSDNHaDJGdDA5JTJCRGFmNW5UeFBEJTJGRmNGN2dma0k2alhxJTJCd1BCVnQ5QmZ2Y3JNMnNLUURBWVZmNWZMY3ZmaFlQVUQ4bzglMkJoVXolMkJzTkMlMkY1ZU1Jdlc5TE1rWG9YMUtWanlrUXU1a1ptQ0FSMTZuaDJURWw1VEpoNm1WdyUyRjN2NjBtUHY5TGhJVXBlMTNtVWlWJTJCWHgzbmZ3Y3VNRFNUNzVYNzdJWiUyRlRQTjBXWU1MbDJoUXRBS0tJazBWTmN2eHFHdWR0M1IlMkZOOTN3eEhlZyUyRkVMNER6cTVXYTE1Y2ZTMzU4emRnQ1l6QyUyRjZESTEwYjhnd3dqaW1OJTJGa084UnhlcFhCZlByZDhvVGwzVU1CQ2s1TnFDTDMzdGdFUHUyZyUyQlI5bEswcVM0dm85JTJCZ0tnV2FFWkZoQ0p2VHZNVWJGdnhOb25QNGVKVGo5TzQxakdZa25HWkY5RUYwOTV2SyUyQnc5U1hocEQ4cGhGJTJCd1VYeCUyRnZmM01JeEdYN3RjZlBXZVclMkJUcWc4ekxMJTJCTVYlMkJwNVo3NGo3cTVQazJCc2dnbFB2cWZMODl1cWZvY3BIMmNBJTJGa3lYJTJGazZhY2ZtZktmNkdaZkNoT2klMkJIRG9VSE4lMkJ2MHhNUTdIVm1hSDdzT3hQVFhVWHFQTEQwMFNmNXdrRWo4bk9YNSUyRkpDdHpkeFJGVjBUbE4lMkZ2Nk04ellYWUE1N3BPdWI0QUJ3aEFldUwlMkIycTV2c08lMkZTQjhmbDladSUyQks3NVQ3TzN6T1Z6SSUyQnJ4TEIlMkYxYmElMkZ5UFQlMkJaR1JmT01Dbjh0JTJGUlJyN3dYWjlNNDM5RDVzTiUyQnZ2RzlHMGU4SDE2N2wwRzc3dXB3bzh6Z2E5RHBmOWg0WG9PVUw0bVhIOHVvYW0wNEIlMkZPZmtobzhvc250Tnc5NnhQb2pvMG9UTk1TeWNwaUdFbmViMnhCM0dVaTFRVldod1NldndlTkQlMkJRU3J4dW4yJTJCNEclMkYzenRITFNaZW50aWJVJTJCa1R6UnQyNVVyME83NVJvaVdQODlrVFoyciUyQkV6Wk9MN0VPT0RjNVhyaTY1UGd4UWRlV0JrTVVSVG5UbVl4MlJJQ1daJTJGNVlzdlZydFVKeUVHWDBPaGNSR1IlMkZPTkF0dnA5M3lEWWlqZFY2b1NwemtnYyUyRkxGQ2Zoc2xOYkFEJTJGc3RTeWtQQnFYSFBycUZ4WnVzQmZoV093aWJvVkklMkZxakowZzVwUWczZjJPbm51TzMlMkJjM2ZNdjdPOURTcG9MYjFMYlIzcldlTmRiWml0bUxsQlo1Vk5lRzJMQ3o5ekhzeFUlMkZlcjY5Wm5QRlcweksxOEt6ZzdMVDB2VVdCbWplS1VJRzU5bjlCcDhUREFWN25ObmhEMnVjOXNjak5rVjB4djhTalRqU3BaYXJsUiUyQmF4NzRvN0ttZ2pCbyUyQnZvb0dGMlpZcEh3UmlRbVRjM3NoWG55UWc0ZEdHeWZOTnk1JTJGV29JNnpCeTRYSkx3NUh6ZXNzcms1V3dzJTJCYkZtdDRiYXNxNnI2JTJCNHRjdGFFNTJkV3NCWTFwYlhNY2VRWiUyRiUyQnFERzZaUkNLVkx2cmRSdmZJSW1hU2ExVnRkcEtsdVRIRVlVYkVIemFYS3ZFVHU0WVd5ZmI1aklCWkpjOUM0Z05iZ1g2Y2dnb1oxazF0cWNRY1NJVWNiTmVxODNWM05nWGdtVkV0ZWlkZzZDNURrSTdoSm9TdTM3ZUNhRyUyRjg3UU1aNUJPZGJyaXF2WXhBbFBRNTVieHltcDlYVWxKTGdnUzFUb09hdzhDdURQNEY5M0xtJTJCUEZqWExKaTI4OTRsd2dlOWUyYkUwYVFzYzlmVEIxVnB4cFUlMkJhM0VvaWxXR1J0SXdncGJhMXd5eWwyTUt5TnVHdXk3cWFhOEs2QXJtN2FocnhheUVaWVZEanozVnJlMFZ2cUdsNWNNVCUyRnZGQ0h4Mnl4SW9NM2p4Snc4eDRqZ0MwcXQ3WWx3TlhmSVdYVFBQcjh2cXh1RlZpQlFGUzhqcnpVWFN6NVd4ME01ZXNtUSUyQnROaDhvdEZaRVhQdHNOSmhkcG5Xa04zc2NBQVFMdHN2TEhzcFVxUHJJUVd5VDVzMkkzZUhJc3hGdm95Z0RPQTdKb244QThscmpaRGNUdTZCNXZheSUyRlVnSUNwU2x1elY4blllczQ4SWJIMWxhdkI4NXElMkJkaEl1TlRWJTJGSUxtaUJ0cGNZdmhqUURhYmp6c04lMkIzRyUyQlViRWhtSXJrVko0a3M2cDNHbkhLUkVPcFpMZzZNb0xKamVTMklabzhIbTR4YyUyQmVWZ1JibGhsUUF3aUtYbUd6RWtWT0VKYSUyQkZhTlp5bUZtUnViME4lMkZUZGY5cHBKZ21MTHFDcnpjYlE2S2paWnMySTNEbnViSEthWHRvRlVNcmRKd3RCUFd5STdvVDVuV0VuaUhvZTB3bjRPYUdpVmF3Mmt5MjFFMlczQ0JUOGQlMkJ0akhXd3o2SFdmJTJCQWRaWjVoREp5N1RvYThrUldQSVh5cmhLUE1JZnhjTEg1YkhPeWV6T0NzMjIwZkRuRVVqWUtHTE81OUJ0NnRPV0RjcXhqdmJwRlluRWxzNFF2dWlia096N1d6amJQTFBKSlVVN09qb2ExbHhXY1NIb2h5TEFzc1oyRnVKVVpiYSUyQkVxeCUyQmhqS0t5ZDc2dzJrVzF2S0lmNkNxb0xmRm9paTJmVnF4QjFyYlRGTnZHYnE0ZDFQWVFXRU9TTFFlSDBsYmptWXRybWxwWHFVZFpoZHZjN055SlQ5bDVuRnNPMDVpUkJtSWpwZ0pVJTJGWG1sR1pibklPR2NaY09zV0V0Nmw1a0RKd3k2U05pbncweTBZWFNkblR6MGlRQ3FLR015anBjWVZWMFlPWjQwVTBhbGJSVGtCUzlYNVhramgwU01tR1lQT002SDJzRWwyVmpxRVFHVGxkM1ZzNFdHT1FjR0RlaE9lcnBmYiUyRnZqekZReTMlMkZvSUdJUWQydU1BcFZGTUdIbEVXZU9vNFpOOTZTNlN1dG50V1N0Vk5SVTElMkJRdTdiZmVhczlNSXFVWDNEWG5lblAxRGpCOWN6emhBZHJIS0tKYWdmMDFkYjNWJTJCUjB0ZEFSVXIzMjY1WTdpbjJxTXdqTUQlMkZzV09tVTBmZEhKa3dtRnkyZFlqZ29tcUNBMmM0bkxoUTJMdjgxRFRsbUJjbVdTUmpONGtNQWx3b094bU1NUExiOWRnWGRvWG1ZME1BU2w4em5RV2wxN1Byd0tZRkZ6OGVMSlNsdDlOSTNPb1ZzNWtzUTYwbzRrQ29pZGRKN0p5WDQzbEMweFpMMVZHWmtIM2h1UTIlMkJiRnh1T2tKMmh5YVFVRzZueUQ1MTFvc0VpOG1vbkdvaEklMkZBendzVEh4ZE9jU1pOeksxb2R1U3Rhb25sJTJGcXF4ZE9KdUZFMU1WSVo1RmllbTU4aWdnUTB0d09qM3F3UWpZRjVCYWglMkJKdHdLd3pmMWlqN1FVaGEzRTVZY1hjTEJIYWVQbEtLTXBCV016elBtcXU1MGt5Z3YzaUV0UWsxcUZlR2x4Y3pnRnlZWkp4a21KZmt3MHQ2c3VURU5iUTJkUE1FSndSMlNvdUNRSmY5aDFINE5Qc3M3TyUyQnJnTTNkekk1QkFpRnZkVHpkaDFhc2NKUXg4eFFMWE1QdWJNViUyQkNuQzEzSExCTDVYcUxaY0ZtWTU0NW0lMkJJb0xhWk9SQ3ZiYXJPSFVwZGdsY2klMkI5bFFib0VyVkNSRFhQak91TTBuU0lIRmZDbThiQkRrVzhQNjkxaEszRjdyNzBkb29zSk9sQ0tZNzFiMm1FWnNzNDRYNHhobVdSM3lCeVpPaXMybHBrU2p0RmdFRG9NUG5CJTJCMm1zYjlBeE4wWG9XQzVxTWJ4ZUUyWlJyU29ESTQxWmR1eE44cGVhMndtcUZzd1IlMkZuUSUyRldoa2dMTlQwNFY2UU90Mkl2cmdLTDlXOGMxQmo1aUp5VSUyRmJXdGs2NVoxJTJCMEJDVlJrMXVWJTJCNk1nTE4wZjFKVTdwTElOR05Edm9HdlRkTjhScmVKdTJraFh0eDlBcFNjY2J0dVdiR1Zqak9oVEtOdEVwT0syZXVKSTg1eHdnOURDZzV3MmJoVCUyQktFWWx6R2s5dW12bmlrc0RTc0dudlduWGtpNW1ZRFg2ZzNZUWEzenA1V3kyRXliWE9JVTBGdXRwdEtIN0hobktsWUZDR1ZWTlpiNURUY2NNRnpWWlkxNkVvOWpwd3MxeXJxMXFiSTFTYWNMcDRHcmtXdjdqcXZEZ24wdHdmOUF2QXB0Mkt6am1PVFBwMWVLbnI3WEJETHEwNWp3bUdGYWk0SzAwUE9sUmlQNEVSaXpWVHNQaEtZdXpJeGdhcmgyOG5LWGxUYzl0OFdGJTJCS3lTQXdZciUyQmp4ek91NW5DUzRtYVk5NHZCMmZERmNHdXFVT0dGbnJyMXpXbGQlMkJPSDE0UGRHV1FVb2FSNlNsVFBRdTNDakRSY2R6SEpua0IwN294dDdIbnZqRExYY21ab0xscTlaYzF4UlhUR2d2aExaMDNMV0EyZUdiNGJkSTdIR1phNmhPTyUyQkdaZ2YzR3UwV00wQzAxWHBzcEFHJTJCZGJiRkllY1p6dUtXVk5xTm1XaHlrWCUyRnl5SDJ2bVNjMnRGVVZEWSUyRjhlbXE1WERiMlFwbnZkSjlwWmpKbFVWcGlxMFRpY29LeldyOXhDVlJUeFdLOGdUYWJJcDRaRyUyQmJrREowS2swM0dIdWpWdU5sc08zMVJiUkczUzBGbjBoUmFiaTViTmw2ZnlDeHhyYnN6SmtzaGVlcDhDS0l5SGd0WG9Ub2RKT2lhMldzdG5zTDlpam95akYlMkJQYU5XQWVNTlB0SzdVRGpuSmElMkZFNnEwU2pUdzQlMkI0Y1NGTnQ5Mkx0R2V5U3clMkYyek95b0htVklFZlZBYkN0dmNiUWtvNkdiRG9XNUJTTlNBY3BtMWhHVmZYdUVCTU1sTWxhYWhWaFB4RTFpVHVBRnR1Z1dSbUZjWXlPQWFlb0xKeFpZQjQ4ZW50cDROTUR6RUF5d1NVQWhuemxiVDExcGs5eUdOQ0twMllFeHRhQkF6d0hMbWNCVHpjbmxnTmdrYkdiNHNCTDVUcU1OanpRUTdFakJsbnZrSFlweXR0VmlQZllnSlBpJTJCcjVLNGJUclZwVGplOUo1N01FZkx1cGljemFxUkZtZzk5NjNvTmZDZHNLbzJFMXo1R2VWUmZ0eWtibEd1bXlncHE5SXppQWpxWkEyUjlnRWlOSVVtcUdRWWkwVE9MZU5KQ3F1JTJCYktRRDcyMUhiZTFISWxTUnBFcmdIRkJYWTlOcXRwdHFwdkdQJTJCUllxZUljbmZ3SGFGYVIyY0dFV3JDcXFtc2Y3aGxGUkc0S2FmYWJGVzEwdkhxcU9LU1k5JTJGRWFTVks4YXV2endjRTBTWXBYQmIlMkJrUERFZllwTTN5OHV1RXlUMVlNdzNMUmRPOWlSZE1XckFaU3JvRDQybFNVTFM0dnElMkJ5bVZuRVlBMTYlMkZycVNCelA2aUltdmo2alBITUpURWNtVHJMWmVkWiUyQjhrQ0lzdkdBajVhSTAwZ0FLMGpwZDN5UTAlMkZQeE1pcU91dTVORjUlMkZwOUREdGg0R3lBaEFoekVHZk5kaTVTdmVNRGRjUXBHN3AzN1lZNmtsN3RDYUNCa3lRMyUyRkdMamFRNE1GSkFrN2Y4WmwlMkZSJTJCMlJuN2JPZDN0S2t1eGJ5WHZiMVhjUjFuUWY4cVpGSmxVbkxlYlBxdGRCYWRBYjRqZHRpazVkejJuZXFFckpaYmtIUXRLNVBJcEpNJTJCdEdoeSUyRmgyS3paclJUR1AwU0dpVDVveWpWNFJzT2tLcnhTaFhCMVF6cHhRMThSdWJXa3dZYlBxSEklMkZjVGMyc2RvZ3k1MWVxckVKWldHUHBLV2V1JTJCT1prQWhPZVZaY2JzY28wS1c4eWIwY3laSFJPUjFxaDFrbXNZclRybnZVRXVvQ0VqQXBqM0lqVGtKdkI3Y3k2bmFLUkNDR2FrM2U0M1pRS05hJTJCNmNnajlXRWVaaDJVVzNwTHJWYzZvekNhbXIlMkJzJTJCUnd0SFRMbmlmRTBOSyUyQk52N2JZMXFmSUtRdUVHSlJtVHFHVzloOHMzVkJlYksyVzMwb0JxJTJCc1dNWTlwOG1OWHJIQ2hlMnpsWGl0WE9GT2FkYndhemhDb3hOSnFDRWZodXBHblcwcnZoc01hbTRXb2NRRVJzSExtZGJIaGhQZkhBUndFJTJCMFpHMU56YkhKZDZLckxaY2dldG1mV0Z5UlB1NFFYd0xYeEZNZVVFdlJPbVVTMjduYTFrcjAyaCUyRkxXOWI1UVN0WkdySSUyQmlrdzBnTU9aZjVjWGFuZHphZ3NyS2JQaEZCUkswM1JWSHU0NlhUY0RuQXUyUll1d2ZHQlBlc0lReXFnRWJtYXBHVDdSYk9Ob255bFdNbU1KMXM4Z2UlMkIyZkRhY1JtU3VHSDF1amJ4ZUkzTGNEQVVLN29SWHolMkZQYTlla2tiQ2s4R3JsaFp5ODhKMVpWUThkSWY5NGRWTzR5U0tEeFFFdVppME1mZlM5RTlyZ2dyMWJveWo5MnRUSmhtdHlKNXhOS2F1SVJSRDdzTnJDVW5JQ3V6MkhJcHNQbWliU3cxZnFtbWVKVkwzVGVDOVZxb1c3S0ZWWDY4NXBMbFdIamhvaHV1bUdla01iR3hwclZ2S0szMGtic2VNMkdUbnVnRG5oRTNKQmhzOGM5RkZnY3Rzc1IlMkIlMkJacDBDb2Y3QWFqQzU0TnlkQklPaWZYRDhhVW40U3JvSXFsZFZ2ZkxDZk9zOVhTaFVNOTBQZ3RvWXpTY3NiNDJPTnUweVc3UGk3bG5kVmV2QVpkblFWbmg0eWVVZHZ1ZWJxeXJrWTZ6c0hDYVQlMkJ0Uk5sbUt1ekNwUWVLSDlrQkpVdUxTSSUyRmJiTmpHbGdmc2UzYTZSbkN4VDJCdE9nY2xwNFVjbks2WHVISVgzZ1NjcGRlb3MxSmpPM2FTSFdVV2VRdGNHM1hibVZFQTZIZ0oxY3hvaWUxT0xJMW9VbVZKNUdwUzI4dkhXMjBHbU0wYVZ5TzFnS3NEemVlSEZic054WkZxdDlBVWhuMmc4ckxHV0lwQmRSQnlWWFI1dnBZTEs3YlRBVkhKTXhaY0pNJTJCOTdPY0czZXdUdlMlMkJaWUVzTGpvdEhETiUyQktZc1JRSU1ZY1laNUY5SnF0YUNhdGxVaHpzOUpQaUhsWWIlMkYxJTJCaCUyQnF4dnk3dW1SbDhPcld5UldNcFlWYk5EZVlTV093T2luQzFMT2ltRXJSMVphMjlBenB1SVA1RE4lMkJ4bDdlM25GVmZLY2NDZUI4NEY3bGJjRURETmNwV1lrN1JGSEJjdWglMkJzNDAxUlVwUm9tQk1EVXpWQlN6UTNFa1JtekgyOGptY0NRMnR1aVdXU0h5TVRqRnIzMlhMWSUyRkdmN2V3dnBiMUM1cU5MdGl1T3JRclVXZmQwMmc0WG8wbzJ0Qkp4cXBraGI2ZWtEckhQR0pTMmtqT1VGVVlkJTJCbWRMUmY5V2x5aFg3OVZKRVljNDZwMHIxWnh3TWUzMUF0eDBwRjVTV0piTlA1U01YNEdtQlZXVUJSUzZySjZiYTVHaGxYaXdGY0daZWNWanQxbm9MclBrQURDaG4zS3VLZjJScnBwUE9aOUMlMkJaZDhTbWZGekxod1oxTVNzN0MlMkJzOW1jQTFkV2RjcmxmbmdzQXJRa0w3UWJOOWlLWXVGdWFiSGQ1QzQ0d3Q0dUF2aFNmTFU5bGg1cm4zcjRmdDllSWZiQlNWMnlRc3p1dFpzJTJGQWJ3b3Z0akFYRmNsdkIxRU1BVUljYnJXZ1laSVhiQjZlNWlzcVRDY0o0M3dLUkdLVlNLSko1dDU2cXI1bTA3VEZmcDNzWWtkbEk0NjN3SXVLZFczdWhwMXNqRmZwSTFxdUYzMkNvbXBvQ2pRenJheTdFaDdUTHVYVk9xVXh0QXZBQVBiTUFVMHh6c0hYTk01ZDVqbHZiJTJCcG13ekxuckx2NHE2ZkV0dFBQdFFiZzVORTJwQzVTUTBkcVN0YnVvNHVqQ1pKSjRjdnV1VWdrNjNUZXlFTElhSWhlMTZiTjFoeHdOb3RLTms1ZVpxOUU5d1FWMVdRSEN5OW1kNWNWUFIlMkIxU2VGbnNvNWROUjZVem14MjB3T1EyYUx2eDE5QkUlMkJKQ2FiQ0pyTXBGWXdTNjh5S0hzZEJiT0lNTTJ3eUlpVlBtVnNCeERYd1RtWmVPNmluVTE5dEVrSGUxc0l4OGp2VTJ6Wm9iNHF0UmRoMjV2cm5NaVZMSU1ydHVWM1J3bmRYV2FKcTFmQjg0bUw3Vjl0eXZLTmJqdkg0TklXMndoSGQzcmdqbFkyNXl5YnVUclRUR3RiUkFyNnQ2TjU4aVN1TjdHRzFJZzFkcXIyJTJGWkd4Vmg1aEhLMUt1TlR3aFppTVd0cUw0ZFlKYkRYWEl1WEpYTzlhMEFQcFl3RkpWRHh5MkNJanVhcFRLb2QlMkZGb29LJTJGRjhYVzlnN0xNM0VwZTVTN2dmT3pmZzlkZ1JTVVNDN0E0aVhSNzhYQk52Sm5jZ0l2RWNHb3h0cWtZUGdvcVZpeWg1UmFCWG1vWCUyQkljSFo4T3FlandKMXhKallEZ0dpMEs2Nlh4N20yNlh5cWlIdU80Yk5sSHBONEd3OUhJZDhod1l5eFBTU2lNZGVKaXdJR25vSWZmT2dMRGJ4TlBTZXRhV1BCQUxVY1VmWmtRd2NRTHpsa1JaRHZDUTRGMUJXTDVzTGlEZW5mbEROaTlrUnlUN2I0TldSamNwbW1HcyUyQjA2Z3FLU0hxcEtQcm92RU1vb3VYVTBGcGZJN3BLclNDa3JjZlp1TVVzVHNubzY0VzN2QWR2M0YxNzI0d0Fjb2p1ZVZ3SzdjSEl1d3QyVTE4RVdOSFRrM1FodVpXUEFsbVV1R1IxRmhGRGN5Rko5U2thRXJJaGxpbFVVTmh2R21xZktDcnBXdVA0Y1RncVZKSnEyNGNVcHBIMU90SndsYiUyQmNJR1Nib0lZQlM3cDNYYXF1QjFIMyUyRmYzYk1lRjJHUkl4ZzZ6eCUyQnlDWUY0TFk1Y0hQSE5pUjVqRThodHJkemtEQktuTjNTWTgyTGQwMlFMbTJlUW1NTmYlMkJuVGdVVXlCdHZiUSUyQmVvTlJSTXJLM05EMU1oTXNKV0s3T1Jybnl4Rm1Yd3hGTiUyRmFYQmczUG85UG56RnEyQW9ZJTJCSjliZ25ZSUZjNCUyRkZjS3g4aURiUUM4TGFvY3JkVTd2ejJTUGdCcWFRdmJpdUNNT0NHNkpYSjEyOGFXcTNGJTJGaE5UeVZRTUNvM2xxTTZORUFEYkE4VWp2SlFtTEFCSGxjeGZacSUyRmdyNTc4N0wxSllzdXhGSXcyUlpYSlQzTFNZM1ZHaWJVeWlGbDEwZEMzS1Fvb3hNQlgyRHIyN3BzTnFTVDhwdDRxRlN4amNzRklxTW1hV3NhUmFMQ3hmYVg0RGF4aTdidCUyRkNtblBkWU1ZSFNScjRkOTB2TUx4bTBuU2dvSGhHWWhIaXFrQnZpTFp1ZGNFNSUyQjMxV3VDenBCZzBld3YzdW9VeVBYSnMzY1VXOXMwcDhORjJHSXJIYzRHN2xVT0ZWS25VNG1NQWlMS2NaSERGMm9pbjIzUzhyWSUyQkV5VzVIZHNCZDlVeTMlMkIlMkJ4RXFjUUN4ZWtXOGQwZTNNUCUyRk9JS0lmZGx0WThzZlNRSW9iMGdhNGVIbHElMkZadXh2QlVESjZBNVB3a2NST2dUdG8wcXlZVzMwTXo1MDIxQ3N1NWJkdWdIZ3BPOUwlMkJJSTZOUFNmTGVRY0ZnMmNoVjB6b2MyTnlMJTJCM0xmYUJGaEpsb2JOb3R1SHBIZVExY2dzekpwbnVDeUhwc2p2aVZzSzNCMmdGRm1wUkxMOUNaS1k2MmFyaTMlMkZjYWJCMEQlMkI1ZVNGbmlMeWVLZVZ5TEdKWmw1WUolMkY2NlZ5TWRHdlVhMjNlbEM2VGFTMTFlVnplVWNBcnF4TGc4ckdOdjFhRkxBaFU5ejRsQmNjbllZeE01akNoMnNtWm50dGNzZWNwTHVlc29tRnpJdGNqWWVzNUl3bmp5cWcwSDdFekEySkhiRkxzanp5Y2RkdW8ydTlzeHBDRjUyT1p5RHNtNTh6RlAzWk1reHlKdHBtMFRwZ0pCREQxanhVd0JUYXFzamJvTlpHVXJaajFwN09QVDJUb2paRlhzSFR5ZnFyaGt3clZneXlkeXBXNE9WMjA2akxLazFMSklhMVJ0JTJCSWFzR3F6dVR6amxyV29leGZOVUNwTDlGb3Z3VG5DM0VlWkl0NlhKNk1BV1FDVWN5JTJGY1hMYXVqWXR4Q0pUVHF3aUNnYkRvNnRtdm40N3E5c09oQk5kZW10OXRQMmZXc2wxQXd0Y0FKUmJZbHNQa2dMaVJSUzJYQkFjdWpISnBxSjhDODliMSUyRnZRd1lSNzJOQmxucVY3NXQ1ZERyMjMwZ0JaRVMxdEdXMjZUcm0zMkp5ZDJ0eHJWYmVkSmxveXp0S2RuZk5tZTZpMHRyc2M1UUxLUUJSUFZiSW1SclhnOGp1NFQyYnBjSXhXbFhJSm1ZemNlZzglMkZqOWphTXUlMkZLNURBYVlSb1Z5Ym83M2YyNzd0cUY0S2dDQzdxUDNRYlR1UHFEUTB6RmFCVVZHanh4V1NVVW9pYjQlMkJ1U2h2bjY2cHglMkJGQ3h4dHFlNGxUV2FaakR3RWVsYmV0Q3VZb1FjcERjSHA5NkxtbENLMHM3JTJCS0tSTlhnbE9UcENLVGl0NGVJcUlSc0JIZW5rSUNwOFQwJTJGcVlkV1FtOG9xZHZmTTc1emJWTWUzMTlQMkttWmVyWGc1aUZ6c1FGS015cnVhcTNrJTJCNURNdVExbXFGZ1VnbFZ0JTJGZjFmVUYwTFdVNHhLRjZvbXAzaUs0NGVHN0k2NDBFaWlSamw1N1VlJTJCbTZ6M1JGRnIxSTVnTGlDUTV3aU8wTmY2bUIlMkJ6czdHVGtOTU9pUHhxQnhPT2EzVVVkdjROcnJpSGhqS2RFcWlBN0ZCTjJJMk50WXdDOFNDY2VzQXVHJTJCZE1BWmxsUzNJVCUyQnhoRDJUNnkzbk9rcWhJRW8lMkZtN3hQU242NmwxeTV1JTJGSjRjbTlsbUlKNm8wWWZHa0dHZlA0aTgyTUc4R2ljdEdUQndWJTJGbnFhOGpDT3JHaG91NUdqaDZrZUhVVERtVzVoaCUyRlJxaHEwZ1pxYnRvdGglMkJmJTJCdHBJQiUyQkZXZWlMSXB0Y0ZJNjZzc25kemRaRFZLWmtkaTE4ZTZidG1nRWlhWVlqcmlUd0NJbXlaWEgwTmg1NjluTHBrTlBSeURlM2loWmtrN3d3Nml6bXNuZU12ZnJpb3pKR3JWdWNOR0UlMkJsWlJTYjJOdE5EJTJCUkhFZ1I5JTJCYWhvNlozN1Y1UVZ0ektPbyUyRm1zc0JYTXFoR1Q0TGQwb2tIZ1YzZU0xNXU4bzd2cmE0T29WRTJydTNOazVWYnh1eWZnJTJCTmdoQTZ6amR0d0F6UGJyakVwaDVIU3VvVnZ4RVhCTGtxMjNTJTJGREhsbDBZbEM3T01qbjI0SktOaFBwdmJPUlUyeTluV0ZNNkZ0bDBOWWtmRXRYS21XYkRwZUNOSmRMZ3FZN3l1Q0xlaGZwJTJCSWxGUlZaWXhOd0lOTE9JdlQ3dk5CMjdyT3QyZTJpcGpiTFo4c01xeXJJQ1JQN1hDOFQzTU1vWmU2Rmlibnl4UUVOT0ZsaDNUSSUyRklKb051c0Z4YlpYbHVjblJUNEZyS0g3d1VUVmtxNGlPMVlhNmR4V3JzYmlPZWpJYmZiRnlDRzYlMkJlRmM4dDQlMkI0NWpzazNoakNiZzR3UElrMTNEWnJOdFEwZTE4WCUyQlFoMkV5M0loaUoyN0lXMmVNRWMlMkJGV3QzS29aQUFRTUppdkdrYlVIQUdYUzhWNmtwa1pJVHFsUXI3VHpUUTVxN2xiUEcxQk1VVE5IQ1BJQWFyemthMmxkc2RZaTdvQ1E2UnMlMkJ6Z0QwVUZZdzNzQXNERUxPcjMxODVYakJ2TzU1SGhMN3M0WXZNMXVaUFdaTEolMkIzeERuMWFheGt1TDA2dDlYR3ZpZW5NMEJUbFA5WGxhMG8yMTkwajltRTNIYTRHRzA1bWE5Y3NoOFBGbFRnbWU3R3glMkZsakpDSkRJaUdsYmhzWkcyYW1seElYeGZyYUZyMHpyYjElMkZVd21wclpIbGd6bjNQSkZpbDZJNU9oRklnTG9TT3NsZzlIR2tXUGhGMHNpTzhibHNDMHRWNnhaN0dzejVVanhFZCUyRm1zbjhnaVo4djFXWnpaWmNkbUlCMzRBRldZME0yRTY2NTdlVEV3SzglMkIwMFNjM0s0S055JTJCamZpOW96QXppT0JVQmsybmZiRWlNeklNR25XMHhWUFoxZHZlNkhmY3NxRkxYOWtZWGhHTTVhaU5Yc3p1eG5ZRU9KMjlqYUxMaHFlalhTWWIzMmxCdWFkYzI0bGlUZzNEV0FBWUhQREFGREVZSXk4YjYlMkJZekcyMVglMkJCT2JKMXFIcnp1WWlFZGR2Rmw3bzdmWmFCTmxpUmVURVpsb3EyJTJCUmJWa3pyaWZCYmVSc3pkb01VbnZxWm9jaHM3eXZSeUpNQWJ3RnJ0d2hSYWVvdEMyRFdQcFdkR2JQME5vamNNQmthRHJzTHZmVWd0WGRXd1I4WFQzdWQ5dHJma3V0UGQ5VlBUbXlKJTJGNEt4aVJzczFLbyUyRmZYcTBzVFU4WHJMTlZ4TSUyQiUyQlpDRTdmZ0J1eTgzT2ZjTmJ3SkcydTlZM3kxdmZXM0kxeUJ3WDk3QlFaeG9IazBsNXN0TzZaWEklMkZ3TDI1QiUyQmFzblUwOWtpZjZEckYzJTJGMCUyQnRXNlRQVDlGbTJNJTJCdU9EdlVyWSUyQmhjdHNIcmFKUDVmV3EyS2ZyRGlHbnRhUVBxUExEdkRWdiUyRk4xYW9tTDhJRmxuWFNWeGs4SGVGenBlclBNWUlyNng0dVVYVzZ1cm12YVB0Y29QcSUyRnRVRDF3Vng5WjRIcVAya3BuazdQJTJCSmI5ek5MaWJoNjV0clg3OG41UTFCJTJGJTJGMzN1Q1BuZ0lTTGdmM05qJTJCZkhiS1Y2VG95MWFvYjV5N1VyNXB0SGtnMnZQSkxZODZEeFFoS1M2NWVyJTJGaXNmc0pMVkhUUFlyeCUyQjhOYWZuaVBmRHZBdk5NRU5BTGpURm5IOWpOTHhQN1VKd3RTUkxLTkpIdzlxQ0IlMkJTbWNTMTJaeVNLcGswRTdNcUhIckphMlNRcEdQWFN5UmkzRUoxMHAxUkZLWlhZd0N3TSUyRktPNldTMEVjJTJCUGNTVjJNZXprcWRZZVU2bGZLMmNsRkVwMkhydmw1ZEl0c0MxZ0lGckxQSTkzS3JXaE9tQSUyQnp5VGczNXl6UTFCUUJPZU1rbWdPQmRnVExsczl3NkxBT0JhbXM0RzJRZEhSTVgwSmJudlJOOFVPaEc2N1VhOTZJZ21kMVhrVDYxUmFJaDJ5dEg5eWFJT3ptTkxEcm1rdkVqdHE3S04lMkJmcWtuMEp5TDRXWWdTT1ZnaCUyQlB4bmp1RTl5YU0yeCUyRmphVnhwUUNBSDJLYktuUzc5YXMlMkI4S2MlMkI5R3BmaFYlMkZhZDhjaHdXMHlsbll2Um1YSzdCSE1QdyUyQng2WmpnMmxxQkxSUkt2dWVSd25YQlhLVjgxdVFRZDNoMnBSVDA5Mm82VHpVRlF1SFpXcnVFZU1RaGMxWW93d3VLaldGZzE0cWt0MkdnTDRCaVlONU1vVWlBNm1ka01qZ0dqM21sMHpFUSUyRjF6cVFsMklwJTJGblRpU1FpRWNlZVlrbGNvRFFBZWt5QVMyZlFocTV4ekJqeEcweEg4dkZOZjVmWGZXbGZ1R2M5dDNsdlElMkJlWUJRQnZUTnZseUtzMjRITlFlamdXU3NLa0FjbkpYS1NBbFBvaVFlQktPcGFSbjlZcG9BRjhKcm5BY3FIWG5aZmpHWWY5dCUyQmMwdjVnVEdmSEttSjVTUlpOJTJGZ2hadW5odCUyQlBvTjZ0ZXB1Zm9SejNFdk9DZGlmNUFENU4zQmcwcW9jUzAlMkJpb3IlMkJlTVJqM3BneHhLODhnZFYxQWJma01xUXY3eHlQZlJxSkhxa2VTZDkxalJ3UndhZ0gzNXZ2ekRvRyUyQm9RSzN2anlOd255d0hEQnolMkJIWkolMkY5OTF4TkRmNyUyQkNlam5UNmcxaVRjQnZ4dzclMkZVbTNDQ2ZvZWJjZVFQbE1Cb2tuciUyQjk0UGdndmhWenBING0yRDB2M28wbXQxZnVxSjZDUlklMkZEMGI3N3h5TTlyMHpGRjl2NXYwZ3N2eEh6MFREUHdxM3Y2OFJVSGNQSDhRemFuVEp1V05Vd0JnbHFtQnNjWW5iNjRQSzJHREFSUVk2QXI4dEdGUTlQZmduSmZaSHhlckhKZnVqMk9oJTJGYzR2VkIyZEElMkZLMG5nYTdmSkVpUTkyTDhuRFQ1UiUyQlNZcEQlMkJJY3Y3eko0M1E3MDRhb1g3N0V5ZU52QXRzVVlFV0lQSCUyQk5mbWp2eXQlMkZmOHRKSTA5NXV1OGVOZktBTXY2dG8wYUlqMkx5dnlrRFolMkZYdmprbjklMkI0N0klMkJCRno5NSUyQjFnZWczWlBCTmlvakhCRnprJTJGaWJiU0wyMmpjJTJGSGVyMjBqUiUyRnRXZjUxdG5IMTM3R056JTJCYndwMDdlUkw5akZmJTJGZk5ZQ3JIeldBZiUyRldBc2I5bUFMJTJGNyUyQnVMUEcwQlYxVDV0MzMlMkZFOWxFbyUyQmNmciUyQkFiJTJGNE96RGY5ajZVZjl6MXUlMkZaM0lXdlRlR245ZnZJcVAyQTljUCUyRlhldjNOeHcxOXpYcngxemFNV3MlMkJEZUIlMkZ4UUJpJTJGem53OXdQdkEzJTJGMWVncnl6U2N5ZnRncGtMJTJCS0trOVo1MSUyQmhrcThQN2ZxeTVnRkVhbkRrbjVyNkQybnFOOVRocTVMNk8lMkYxS1VKOVBpZnYzQlBYenZLblA4NlklMkJ6NXY2UEclMkZxODd5cHolMkZPbVBzJTJCYiUyQmp4djZ2TzhxYyUyRnpwajdQbSUyRm84YiUyQnJ6dktuUDg2WSUyQno1djZQRyUyRnE4N3lweiUyRk9tUHMlMkJiJTJCanh2NnZPOHFjJTJGenBqN1BtJTJGbzhiJTJCcnp2S25QODZZJTJCejV2NlBHJTJGcTg3eXB6JTJGT21QcyUyQmIlMkJqeHY2dk84cWMlMkZ6cGo3UG0lMkZvOGIlMkJxWG5UZjF2UlZWJTJCS3NWVmNUNzlWVHJQMURxJTJGWW9xN0pjdGlQem93NSUyRiUyRjlJTEkxV3Vxb1BoN3VtRFlCeCUyRjBSUkg2RjVFRiUyRjJpUjh2JTJGUVFyT1BsZyUyRiUyQmQ1YWdmYkFmRjI2aEZqOVk1UGdYbDZEeGQlMkYwZUg1YWdzUUV6VlBKSFM5QVlUTDh2UVlzRXM2dDR0QllGWVFjelZ1c2h2VzBuMFJFaWFvaWRZc3RLbHRYR3JJOGlPdG5rJTJCaXBoa1hTelBSOWN5VzBZeTJhY1RsUXdKVHd5USUyQjhpSjJCNlJFVVFoRExrcyUyQmxHTnl3elRBcVZVVEMzR0RuNWZRbFB0Y1pqeE9iREtEdm9EeGt4NkwwcEd0Y09Sd2FHVFZ0R3BJNzBkWlFuWEROUUJxc2xNTTRDMzJrS3R5bFglMkZXQnBjNDlyS214TkU0SWN6WGtyWjNQaExBVzVnQkR4SUxhSjAwc0Fvekhua3l2bGdCdmFKaDhESTJVOHhNSnk3aHpGdEl5d3VJMHhnNW1tZSUyQmtrSWZ0cjd1ZjlRUkdHbG1KRU9Bb3BPSXRLMlcxaUZraDRycUdvam96SWFwT3BPcWx6RyUyQmFneUdKZVRHbDJ6WWJkSnQwZWJtSiUyQm53MDc3MDhhMXFlTXhEUVh5WFNsTlRIRGVGemtpVEszQ2kzaGlwTzY2d1hlSWlFVTM1TGswalQlMkJzWEt1dzI1WEslMkJpUlp3WE0wd2FqeGMlMkJpSnF0JTJCVUcxWEJucndiaFFabTNxODNRa0hYNjVranZCcW1wNGglMkZmcUwzUVE1N0JzQ3dUVTlYWFIxSkdwRDE2ZmQ2cEo3Z2FGMG5jRlFyY0diakxRZjZqUmh0TWJuUVpEdGoxZDNtQSUyRkJjc2g3OFZvRmhweXJJQ0NIdHNTJTJCMUVadVh0RkJETTltcnFQSXNya1lQamQ3U3RickxZMnN6a2l3UnZ5dFo5bFhaand0RWw5U1p6YkhmWmlwb0ZieEZLaFh5Z2tERXB1eEdpQVhtSlIwTGZrNGI4RDl6TTdMSGEwdmhVVWlBSnladGM2bTRuYjBsUjNNVDRsU1pKUU0wUUZZSDVncnptTTNlY3FlNnFNbXlKdWNsSnFLODNmajNDN3QlMkJjaVdmSmVqdkx5ZXlSNFg1cFU5VXBhSiUyQkR3eVd5elIwZ1diNFdSb1QwZU5DNkolMkIzaHA4NnB0SFZwUUhBZWFFVG4yd0RvJTJCRGkyZ1VFMXZpMHNYRnVCT0dJOFpNRnlpViUyQiUyQk1DVUNGbTNJN0kzcExpam5MWWhGVzUyZCUyRlA3TmJhbW9MU21ibVYzOWl0WHJDQ1hKSndnY1ZRdWl5V1pKbWpBcVBMS21vZEh6YVdhayUyQjIxYklZVENoU0VWd21FSVYyVkJ2Y09jQ0pVYzUzaFhCbVpmUkVHVWVoMDNPNFdHcWYyUTQzRGRoTUlQWUdQUUtrdWQ3N2VtdXhZVXZ3dmw0VE1LRUtnRW1jSWxia055elBhME9KQ09ySlVzUVFnRXZoQmwlMkJxaXpLR0gxTXZQNThXTEJSN3pxRnBCMG5ZdFI0SGllTGlHOGRuZEFPbkdQTjZVVWs0cmdOY1BTbHB0M283OTJpb2VHWFM5QWxmbTZOWWo5Ym9tcTNPdU1CaE1lSkFyMWVlZk92b3phWWNLRDVuRVRRSU5HJTJCNGJpM3BIQVY2T3NwYXVnOUdCMFloUHBvRXlvMVBkamFDd1RXSnFGSzQyaFF4YXExUHFXaW43aWdYbm1xa21HYWxFWGVBSVI4Nm4zQkJCVDhRWktSNmI4dWt0MXVWSUhqRTdIRXZGcEtHMmpHVWx4b2tlY3cyWkdrek4wclVxaEVhTzNaajcwaWhPVyUyRnlQTCUyRmptNzhJY2I3dHpWSGs5WjYzRDc3NFRueHdoQ2J4eTF6NVJ4OTglMkY4bk5EZWk3elEwJTJGZmNMa1k0bCUyRmpHQ1hCWENVaU52MDhMUG1Mank5QmhkZjFYcCUyRlFPVGpQYmtlSVpIZzE3dVJyb2t1YlpSQURBR2JPMGFYdEx4JTJGNGh1ZUk0STQxd2dBcyUyQmI3TGYlMkZRRG96djdMbDR3a0xWbElON3h6JTJGaXFDMlNQJTJCNzR4QVlRaUl2S3N1NWh3JTJCbDAzJTJCVkElMkZZR3Q0QldVRVJSQ2tjY0RjdURpOVBnQjdhanhVJTJCTiUyRkNmN1FDTDJpMmY5NSUyQkhNb2k2djNTQUg0VzM2a3pkJTJCdnh0OEQzYiUyRnNyRVlDJTJGdyUyQkVJazg3ejU0Q3RBJTJCT2N2bG5QN0ZPJTJGRjJIZFAzTlo5MEs4UGlnYTFPMDBCSzkzdVQxenBaOG5uZjdNVE4yRjJDVSUyQnFUcm03dlolMkZpRXElMkZ2WjU2dTElMkY3ZFRiQjhQMW5WTnYlMkYxR2I4UU9uaHYlMkJYc3hmJTJGVWVINjNDYjN1VTN1YzV2YzV6YTV6MjF5bjl2a1ByZkpmVzZUJTJCOXdtOTdsTjduT2IzT2MydWM5dGNwJTJGYjVENjN5WDF1ayUyRnZjSnZlNVRlNXptOXpuTnJuUGJYS2YyJTJCUSUyQnQ4bDlicFA3M0NiM3VVM3VjNXZjNXphNXoyMXluOXZrUHJmSmZXNlQlMkI5d205N2xON25PYjNEZVhURDAyUSUyRnlCcmwlMkY4MFclMkIlMkJvMHElMkJYNU5KJTJGZkhCbW5KcyUyRmF0MnpiMWZUJTJGVlBma1hsdHhmZlVIbiUyQm9zclBmVVBxeTNkVFByJTJCaTh1SE92JTJCOSUyRlElMkJvck96MyUyRm1hJTJCb2tNUyUyRktvRiUyRjhndVBLJTJCS1ZEUDZCM0hjQmZIN2g4WWMlMkY4WVA5cUhBUyUyRjZwd2t2JTJCbWNLSyUyRiUyRmJoNSUyRkg5WGtvZ2ZsYVRWdnlwSjd6ZHZpVmtIRjhuREFXUkRkRjkwbjlaSlgyWHdzOXR2eGU3ek84ayUyRkkzdllkMlh2TDMwT2FvV3NYdUU0JTJGSU90Z1NqJTJCd1VMNVglMkZZNXFLY0JmSE9oJTJGUGt1Y0ElMkJjZkNGSUh5OTJMOSUyQnNqWDhuUGw5ZFBQOU80Rm9nVXNVbGQlMkIlMkZtNyUyRmZWRzE0OWZTam95YWglMkI4T0dnQiUyRkQ4MjVmZGVVOXIlMkZkJTJCdDdIJTJCJTJGJTJCUDhyUzluYkFlYTdKMEFFakRObEhkdlBMQkg3VTU4c1NCSEpOcEx3OWFEaUtaN09KSzdONUpCVXlhQ2RtRkhqMWt0YUpZVWlsV2ZUMmNEZnFGS3cyRDdZTEpHJTJGN2sxSG1kU1RVQ2p5JTJGcm9QVWk3Rzg3VnlZbktOWTJadE9mZjZ3aERhYVpkcnB3UlJUem1tTFFvQjZ0ZDd2N3hFc2dXZVZYQ2RJd2o5cE0yR2hIYmdIaDc1TmhMeFNLR2RORkk5SmFQQksyanNJNFZaaEtkTUVpaUZZeW9GUHg2Tm1jbE55UzczRnkyUGZOQldkZjg5S3dWOE83b3BkQ0owMjF6aHA0c2kyM1Bvazh1JTJCV3MlMkJ4QzBSTDNoemppOTZHZ1YxdU9RVFRDd0l6JTJCSERSVDBLcm41Z2VqSmpVbHhEYk9zcGp5OU14ODcxWmM1U1hMWE93JTJGeVJQY0p1TXBSMTRhajJHdm41TjVmTks0d2pTZUtwOVlZJTJCcGxJTTVuZ3QxSVo3SER1clRnQTlMS3BmdEhvd3FCbUlhWWlXU3VmV29uczRrb01WMUQlMkJpUVNDS2dSNDRaRHZKMmJqOCUyRmdnN1FaM2hCeVVmNjZFam1UeVhnOEFCNk8wV0JUUnFBRG9aN2Z1TDhDR2hWQXhxUnFpdjgxVmtRZjNFV2kxQzhtZ0dzTDklMkZ2dlh4dTBaWWREbnFIbyUyQjhVUHNHQm5BSUtKTGp1akpQbWd0bnglMkJheTdTbTY0Vmc5bERWQUhWZUVNZ2F6cTdsM09uMmNWWXVzMnhwV1ZzZ0NkNE9GJTJGWFJYNVUyc1VHdmlkd3pJS2pIJTJCT3NhNVVnJTJGUzZsd0cxVHNwazREcVNWT3RtNzZCdDVKTmxpSW1qY2RrTXFVJTJCZWpSbTl4bFczd0RKd0glMkZTMUdTSnN0NHFyZGI5MzNuSnBIUFolMkZudXI0QjF5JTJGYzFTUm9IUWp1ZWJ1RnZVRWROVlZXa0FiUUFObTBkMlFVQ1FONlBLSUFkck5HbSUyRmhDb2NBUFQ1am1odk9LaTlNOXpvbmhnVGxrJTJCWVFVRmRIQTlMMyUyQlRmVEFlMmF3RE9JUG8lMkZqcSUyQmU0c1FPNkQlMkJndVlGckJkSm9yOUlDV29KJTJCOEJWTDJwVzJlQWRkaEQ4WTI2bnlZZjZrenZxd3pLMSUyRkdRNmd2JTJCJTJCR1QlMkZNVVlRQjIyVk4wayUyRjFMMzNENjMlMkJhYjhSZnU0SXRkNTdEQWphSnZVQ2dMVlhHMHhlQ0VQWjFER1A0JTJCUCUyRkdrYThlZjh5JTJGd1NNRmNQakMlMkZNWDlBQXp1ZTVUWjM3S3EwSTQyRjhUJTJGekNkSWQ1cWplOXBvbUZmWjFlWjNEdjVYajJYNmZYaVhuWkR2bGl2b1FCNXBBVmJQZkFQMjBHNDVqQk9BaHRaayUyQnc3SmtuSnpoJTJGQzFpYmNOSlB5Z3VhbjBjdyUyRnU0ciUyRkNEZnlGYW4lMkJ6dHclMkZaWTJ6JTJCMHVodk1OdXJsbktPZGYyZ00lMkI2M21PemlzNVE3NUJ0eCUyQlhzNEw1cWl3QW11bWdENkFmQks3ekFnSDZ4MEc3dU9ZQ08lMkJQOEtPM3kxJTJGTDJobmJLQ3hrQiUyRlk5dm4zM1JadjVhdmw3UjRxMXVndXN2WTNramwyJTJGYWYzWHY5Vmo0ViUyRjA5eSUyRjJiOHJjMGV5Rm5RTVo0b0NmRko2MSUyQmdGWTRLRWQlMkJRaWZmenZGalczOENOT1JlNiUyQjZMOXFiM05QeXF2U2NNanZsaVQxJTJGWEclMkZYNUc3UnlHVng1WVpkVm5zbGY4a1A5UXY4UjROTTM5OTYwQTJ5JTJGQyUyRmlpODRBV1N3SndiVUpxcHpNTyUyRmFQcTdzRDFHZHFReHprenZYN25SVEtDM3c5ejVRaUFMUVRnbTNkNVdqQ3pBWEdOYTAwNjkwaDNoOEFOOTI3VG51czlQZiUyRlkzcjBOaFglMkZWMTBPN2Q5JTJGdzJOZERIVWlIcDc0NjdaUkRKSWRvcnZYZ3F6Z0VCYyUyQlRnRjZQdnVxeHY4ZHhQbyUyRjM2ZnJCNzNWd3pIc0hQUCUyRlFCOEE4R3E3UHJQJTJCQXJSUmNXMElTMkNkZ3U4N2dPc1EwWGtCMTRLTmZJdXElMkZUSzluZiUyRnZjQnJUVE9PZ0wyTVluR2ozUTRLRmQ1bm11T2pmT1lQeVk4anglMkJadFFmTU1oRGZTRGI5enJ1SGVNaFglMkJZUGZiRkNRRndGJTJCa2VlYlBDTGVVd2YwZkVCSjk1dHdxdiUyQkhzYkZyaFFKdWV1WXRrQiUyRmJ6Mk1GNDV6Q2U5OVB2T05GM0JGUUhKOUFmMmROQVRTRWRJRzlJRUFtUUUlMkJFb3lIUTRBdUp5Z1lIJTJCREJDUHF6QU1hMUNPWE9hMlhSVHNJRWRSWE1BWTVwZXJCcE9RRVFQUTV3RkJpN3NNRGZvQnpnUUFzQjJCakVaYkFjOE5IZDRRJTJGdEF6eHhZZ0QlMkJ1T3M4aU85QXhPUXdvRndnZFZlYlFEdEFubUVrdFVQQm1Gb2dCd2pzRyUyQmdtQWVvQWV3WDlXVGplN1J2d1o2QyUyRlIzcUVZRjRocHJ2UXRzQTZPemplNVVGMnpvQUdPJTJCS1I3eDJvQiUyRm9GVVlSNzEzVlFid2Y4WW5pWEtXQVRrVWY2Z3pGcEtLaTdnSEhqRCUyRnpQWjRnM3diZ0JEMEg3a1A3Znh2ZHZJeUZxWDVWdHpOZW44T1Nkd2lva0RXeCUyRmphV3hlNEc5WCUyQkw5eHppMkJwaW5lOEwyWFN5ViUyRmQ3WGp3bCUyRm5XUGM2JTJGZjgyM3ZYSWZLSkZXZ0RqU3VMVXYyeWozQkFuUUJnaWVDeGJmZmF4eGhadnNiNElMYmhnZjE5RnplOHhmbFFOelZndXhJb1E4aWRIb3VHdmZPVmdOWXZjUFlyRyUyRjh4SG51TlliOWglMkIxJTJGNkQlMkZLVlBYJTJCTlhmR3YlMkJFZnlGZDU4WGYlMkI1N210YiUyRndPNDYxRWZJUVkwWEFYb3RZTHFibzY5aVglMkJ3dTQlMkY3S2JxOHdnOWdYT0piYkUlMkIlMkJ4UFp2WW9SdjRJZmtLM0hRZWZvaCUyQm9EeVIxdjRBcnQlMkY0ZEhYZVB3YU8lMkJUQTNsaW93ZjBwN1BEYXg3N0c4cE1tQVN3JTJGJTJGd2taNHQlMkZpJTJCUFBYNkRScU0lMkZOVldvTyUyQlg5SUtBN1RDdjBLcjF4amtOZFo2aWhrbllGTkEyd29KJTJCbWhCN0FWJTJCTXhqQUVNaURQNFR5dGdQeEpJeTFvQTlnb085QUhtejNEalY0alZCaCUyRlFXMms0RG5JZjQ0ejZEJTJGOXE2JTJGSndYUUQ5aGJFSmZxOTVnQnhIcDhBbjNBQXRvRnZBVHR1NkJ2UUZkQUZ3TFlhaFRvd2RPejk3bnJDJTJCd1glMkJQTzdiN09nWFVWZ0xnRHdHWlJEMzUzbkFLT0FlRSUyQlpkT2ZCaDkzOVA1JTJGbndDYmZNZDFEbkFUcm51OSUyQiUyQnNFJTJCaHhQd0V5MmNvd1p0c3FzOSUyQkR6WG1vMTdiQW5hWDREUFdwSzclMkZRQTBBM2JNYWglMkJlVWRBbjNHVkFITGNJbyUyQjd1V2pqV3U5OXk4NyUyQk92M2p0VCUyQk9KQnh0ejclMkJzTGhuQ1kxOWhKREhPREIzSzFmQm52WFQ2JTJGNEwwWGRaJTJCd3dmTWNYbUVGNkhOZjQ2OThCdmdMNlBBVE5nSDR0RkJlNU5IMElaYlc4ME9PNnU3WHFMZDVva2NmQnZ4ZWdobSUyQlYlMkJudVExNUw5ViUyRms0Rjc0cnhRcno2bVVyNEIlMkYlMkZwTHY4cWRyWEFHZkdERFBmdXZOdlZ1TWxmMFA1cjFlNXZBQTM2MFJ5TWk3ak9QZDUzRlB1VDNnJTJCd3BpQVhJSzdLdUFhM2NNSW1CM2JBMzVVQkRqd3owRzZ0cDB0N1g4ZVZGNWlDMEVWRHRaYjhxZmZ5UFFya0g5QVhZQUJYcE9xbEMyWGVYTk0xJTJCckt3QWJ5Y0JjWW52UGg5OTFGZW9kOERIZ044QlZNSmZ6cWh6UUZkcVZFY2ptWFU1ZTlQMml6dXZuWG93ZFl0WVg3VDM3dE1jNEElMkJnbW41QlFoOEI0OE9jNlBOVHhIT2dwa1AzNWVTejRTeG9BR1FXNDg0emVaWE1HdUpNSGVORjU4Y3dkWnoyMWw3d1kyMU9mMWwwM245clhZSDdrdVozWDVWJTJGR2NNY0RRSTh0b0NzN29JZlA0MnloRFh6cTQlMkJPNllPNEFNd01NRGUwNDhLMGE0RCUyRkFQRUIzZ1U2TndHZXgzNHFSd1ZoaGJBM2ZXd0I5WjBEJTJGUU1jQUpnWSUyQkQ4UTVEUGsyN256anAyRjk0SWNWRE13UjJKbGtmdDMzRDlVRnRoaklMYlR6QUg5QWV3SHZhekJQOWJVNDFZRmpCdlVCSnIlMkZiU3hmYTNnVFl5eERhTnVpRENFVjh6bUV1WUd6QSUyRmdFN0QzeEU2UHpBbUw2UnYlMkZ5Qk1YMHJaJTJGa0Q5UGhLenUzbjZwS3Y4ckd2ODdrQUs0QjZ3QmI4SlZyOGJGM3VGU2I1RVRwJTJCcFc1TyUyRnBBOGZ3MmpQc2I0T3NRbU1QYUN1RzJCdUdHSEElMkY4TWNMRHdrenglMkJsNnY5V1pwMlAxMyUyRmE3ajNaJTJCbjZsWHpaUFM0QTJBblFhUG1yOVBnUlhuMFQ0JTJGN2NmUDY4akgwY0l5MUFYd2hRdGdEJTJGOU0lMkZxeTElMkJieTh1Nnk1JTJGWEYxRDNRViUyRnVOaFRveVhMM1o2NEdiUHdPMEFYRTB6TjdmSU1ieDhmNWRLJTJGeWVTJTJGelBBQ1BQc2pWeTN3Zjh6U1dsMjI5d3JNUDk3OWcwVWZNaU1HY3phdTgyVmZxUGVhbm5uT0dkM25uRU9JMTFreFF3SCUyRiUyRkVkT2dRQmRtNDU3ckEzTyUyRnY3JTJGZnpZQUdNTmVFZ0RnSFlESHJ1JTJGMDk1YnZBM0VtOWVJbGo3em0ycjlIdmRadmZvTlhUZkR3UTglMkJ2MzNCTmNrJTJGRDljVDM4MzdxUDY4czdMR0FYS3hDVGN2ZSUyQmtEdSUyRkZnWjdsNmY3a3prbDNRVnhUeFhPMzhrcHZYJTJCSCUyRk9aZVZLMnY4ZWtwdDlSMm9LeEpNUDJZZ0hqNkNaTWJGVG5FMWU3Vm0zMFFteUFhOSUyQjdOJTJGTXUzenNzNEpQaiUyQlluNDVpdnolMkZpdk9kWHg4SnlRZ2NLekpQNjJqWXVrbXpGNU40S0h5ZTRwY2JmOE1pSyUyQnJOQ2NiNEI2dmxVZXFQRmZiQmN2bWZYMllGTHBzYW5tbjdmRSUyQkM1MzlyZFpyQkolMkY1JTJGJTNDJTJGZGlhZ3JhbSUzRSUzQyUyRm14ZmlsZSUzRdlISPMAACAASURBVHhe7N0LfI7l/wfwz3U/2xy2hSiHHDZGoihURJoJ+RVFlFMKpaicj5vaFptjIaVIOSXnnM/MucMfJRVhJ0vo4LwNs+e+/q/rZrPNzu5new6f5/X6//7Z7vs6vK9rN76+1/cW4IcCFKAABShAAQpQgAIUoAAFKEABClCAAg4gIBxgjBwiBShAAQpQgAIUoAAFKEABClCAAhSgAAXAQBY3AQUoQAEKUIACFKAABShAAQpQgAIUoIBDCDCQ5RDLxEFSgAIUoAAFKEABClCAAhSgAAUoQAEKMJDFPUABClCAAhSgAAUoQAEKUIACFKAABSjgEAJZBrJ8fPz8ATwFYGdsbOQO/poe3A/8eXCV54HZT28+P/n85POTz09XeX5mfN6Z/TxlexSgAAUoQAEKUCDrQFZVvxBIBEMgNDY6MsSHv6YH9wN/HlzgeQAdO9IGHcz4bYLPT/5+wt9P+ecJV/zzlC2ep2Y8k9kGBShAAQpQgAKOLZB9RpYGf/WHkNSMLP6aHtwP/HlIydB00ucB1LzSBG3NeMQbGQpO6sXfH25mLHN9+fsjf3+87fdHWzxPzXgmsw0KUIACFKAABRxbINNAFtPCHXtROXoKUCD/AhmDTvlv6cadfJ7eqSDvpwAFHFXA7Oepozpw3BSgAAUoQAEKmCuQeSArwzFCc7tkaxSgAAVcRyDjsULXmTlnSgEKUIACFKAABShAAQpQwHyBrDOy0hyTML9btkgBClDAPgXMzqBiRoJ9rjNHRQEK2F7A7Oep7UfMHihAAQpQgAIUcASBLGtkOcLgOUYKUIACZgswg8psUbZHAQq4qgCfp6668pw3BShAAQpQwLYCrJFlW1+2TgEKOJiA2RlUzEhwsA3A4VKAAqYJmP08NW1gbIgCFKAABShAAYcWYI0sh14+Dp4CFLB3AWYk2PsKcXwUoAAFKEABClCAAhSggCMJsEaWI60Wx0oBCthcwOwMKmYk2HzJ2AEFKGCnAmY/T+10mhwWBShAAQpQgAIFLGCzGlnVqlW71ypFXynk5hNRUd8V8LyM7nx8fEpCc+uRdDVxxqlTpxILYwzskwIUcCwBe8+gqlLVr6cAKt9UvSohjpcpddfqAwcOXDdDms9NMxTZBgUoYPw5jG/B5kagAAUoQAEKUMAGAjarkVWtWrUHrbr4VUAOjYmJmmSDsWfapI+P3371jdjYyAa+vn4dJLBUE6JFdPTxrQU1BvZDAQo4roDZGVRmZyT4+vrtkUDjdMISvxcpYml09OjRy/mR53MzP2q8hwIUyEnA7OdpTv3x+xSgAAUoQAEKuIaAzWpkpQ1kaRrmGdlZEPuEDj8pZLK7BUsiIyP/VcxVq1YtoeuWzlLonhrECSnwoEXI6VFRUf8Y2QFwfxmAsFj0dVFRUX+qeypXrlxK09w7AloxQGyKjT32RxVfv0AB+Y76voA2XdflOqGJGRZN7xIVFRXp7+/vFhP31/MCsjp0eTA2NmqjutbX1+8lQJbVdZwUQqsuBH6OiTm+xTW2AGdJAQrYUsDsjAQjkCVRLjY20q9WrVoeV65ce1NCfAwp+kiLfkhI0RJ68pTY2NgLvr5+LaXAE8WLeoQnJl4fAOgnhBDeOmQJaREb4yIjf8/uuWm1ijLQ8IyQ+ElKPACIC0WKaN9cu2atD+BxAH/ExkauSvGrVq2aX7IUbTWJfxITi6/4++9DCba0ZdsUoAAFKEABClCAAhSggOsJ2KxGVoZA1kaVnZWOVyKqdOkSD1y8eFFLtuIggJppv2/R5ENCiAvJVqhjiZVufu+c1EUTIa6fh7D8BIjyACSAZF3orTWpzZQ3j9wI4C8BDFEZWZBo5uNTcU/siZMqOOV/8x4BIWfERke95evrt0wCL6btXwr0OhEd+ZXrbQnOmAKuLWB2BpXZGQlpA1k3AvH33y9h/QMSIULIBAkxEdLNNzb2j1hfX79JEhhcvJiHd+KVpFgApdOsbhKkVhdCX5fVcxPAwxCYnH5HyNM3n73GlwVEWEzM8VFG0AxYDcDD+DJw0M2ChpGRkddce0dx9hRwXQGzn6euK8mZU4ACFKAABSiQVsCWNbJSjxZqGlICWVsgk1+Swq2vAMKkjic0DbUl8AWk6J+c7PG1m0fSLEjZTgWyrFYxCAKdNaE/abW6nxWa9Wf1FyUhxXcQ8jNNiDaaJn9MtuIIpNwZGxv1YmZHZFQgSwpUE8AsCAyCnvwZNMsUSPGmJvRHpdRGSOAFKcTzRdzEj0nX9d/VX8JiYyJbcbtQgAKuJWB2BpXZejePFj4gICbr0D0ERAcANaSOxzRNBuQQyLpmtaCpu66X16W2GwKBsdGRY7N6bqYGsqQY4OYmZydbobKv/CFFjyJFtOXXrlm3AyipssN8fP2iAFxzs+Cp69dlE6GJb/kPAmavPtujgGMJ2Pvz1LE0OVoKUIACFKAABVIECqRGVmogS4o+sbHHP/fxqfYMhNgAqYJHeFYAr8bGRBYHYPX1rfaOhJhmBLJ0sU59TUB+BOCShBgIAQ8Nor8u5WYVbILEYgA73dwQpY4qZv0XMtkbQjwfGxN5l2rTx6emD0RyjKrhBYiGEmgSGxNZTsH4+Pr9AKBIbEzkI9wqFKCAawmYnUFldkZCmhpZVgCayn4SwMsxMZFLfH2rDckhkLU1Niayk4+PT1EItysQmBIbHTkw50CW9oA6vu1Ttdp4SDGsiIflLlWPy8e3+hwJ+SJ0Sx2hWaMl5FYhteVSwyUh5Wz1jwcxMZFvu9YO4mwpQIEUAbOfp5SlAAUoQAEKUIACSqCgamTdyMi6LZAl2wGiIwRax8ZE3q0GlCGQtVP9az+A1LdxSeDfEzGRlapUqdZO08RgCTQEoEuBHieiIxdkE8h6F0I8ERsTqY4j3qixZfE4ByEnCCmqZRLIKhobE/kwtwkFKECBOxEwOyMh7dHCqlWrP6dLuUZAvhsTE/VJ3gNZcmpsdNSA3Aey/MZBYrg6qnj48OF4FcgCZAdN6I10qR2SQLIAVIDtxkdieWxsZNc78eO9FKAABShAAQpQgAIUoAAF0goUbI2sTAJZQohHJRBo0WRlVcjd17f6UAk54UZGlvYVpNTUGwjVoCtWrHGfm1tSghDub+iQj56IiXzJz8/vnuRkfC8Fzp+IiXy0iq/fPiEh0r61UB0tBNAKAsPcLKgSGRl5smrV6k/rUm4RkL0B0YqBLP5gUIACSsDsDCqzMxIy1MgSPlX9foOUxX2qVKoeE3eyt5D4VLeidlxc5GFfX79PJdA3TY2sDBlZNwJZ2Tw3b9TIkikZWZkHsooX8yiXeCXpkgAmx8REDlaOVapUf+DEieNH1T80cGdRgAKuKWD289Q1FTlrClCAAhSgAAUyChRsjaxMAllSakeFJn+XwO+A2C4guwMooQJZyVI8JiS+BLAGEr9BoBuE2A8dOyHkFEi5UArtLwHZV11jHJnx9dsA4AlIfCkEvksp9i6l5YTQrH9I4BggVwohekiJItKaVMNi8fiCgSz+cFCAAkYgq6pfCCSCIRAaGx0ZYm8qGYu9+1St3gNSfiWAV3QdUULDdxDYLiUiBfAaAPecAllZPTdv1cjKPpAVGxPpVcXXbx6AzhowUwd0AfSCFCNiY49/bG+GHA8FKFAwAvb+PC0YBfZCAQpQgAIUoIDZAoVcI0u2i42NWunjU03VrxoDwBPAWfWWwpsZBUeqVPUbKCTeAVAWwBapu/c5ceLIP1V8q48DpPpLWlEBbBOwvhkTE/N35apVm2tS+0ZlAQjg3ZRAVmxs5I4qVas/K6ScpE4wAvhZ6nj7xInIn26+tTBjjSweLTR7t7E9CjiAgNkZVGZnJGQMZNWqVcsj8WpSNCTOxcZE1vXx8QuHwJvq2QiBc5C4L6dAVlbPzbwEsmrVquWVcCXpIwG8AMAigDnFinmMPHz4cJIDLDuHSAEK2EDA7OepDYbIJilAAQpQgAIUcEABm9XIyoOF8PWtHhQTczxMVVTx9fX7RAJva0IvGR0dfTEP7fBSClCAAnYnwIwEu1sSDogCFKAABShAAQpQgAIUcGABm9XIyq1J5WrVGmu62CMg1wFCHf17F8CO2JjIVrltg9dRgAIUMEvA7AwqZiSYtTJshwIUcDQBs5+njjZ/jpcCFKAABShAAdsI2KxGVl6G6+PjNwACA403FApEQE/uExsbeyYvbfBaClCAAmYIMIPKDEW2QQEKUMD+aw5yjShAAQpQgAIUcEwBm9XIckwOjpoCFHB1AbMzqJiR4Oo7ivOngOsKmP08dV1JzpwCFKAABShAgbQC9lAjiytCAQpQwGkFmOHltEvLiVGAAhSgAAUoQAEKUIAChSBQ6DWyCmHO7JICFKBAlgJmZ1AxI4GbjQIUcFUBs5+nrurIeVOAAhSgAAUokF7ALmpkcVEoQAEK2IsAM6jsZSU4DgpQwNEF+Dx19BXk+ClAAQpQgAL2KcAaWfa5LhwVBShQSAJmZ1AxI6GQFpLdUoAChS5g9vO00CfEAVCAAhSgAAUoYBcCrJFlF8vAQVCAAs4qwIwEZ11ZzosCFKAABShAAQpQgAIUKAwB1sgqDHX2SQEK2K2A2RlUzEiw26XmwChAARsLmP08tfFw2TwFKEABClCAAg4iwBpZDrJQHCYFKFAwAsygKhhn9kIBCji/AJ+nzr/GnCEFKEABClCgMARYI6sw1NknBShgtwJmZ1AxI8FulzrHgTUbEz9LCMRHBHkNyPFiXpAqEBCWsAdSXxQxyvuTwmZpHhbfTUoMiRjl9XBhj8UV+zf7eeqKhpwzBShAAQpQgAK3C7BGFncFBShAARsKMCMhd7gBYy6/A4jXIkZ5NcjdHeZcFRCW0BqQIQAeAvCvlFgkr3sG7wgVV/MbyGoWfqmxkJY5EUGe1fM7yoAx8QMgMPnm/TqAM4BcryclD90RWupCdu36T4gvZ0kWvbcFen6Q2XX+wbKo5pEQJYRssS3Q+3B+x5jVfdkFshqP/9fbI7noRCFEO0h4AvgBEMMigjx/ythes/D47UJHUsQor1bZjTG7NcxvIMvWRmabsz0KUIACFKAABSjgSgKskeVKq825UoACOQqYnUHFjIQcyY0LCiOQ1SwsvrkA1ggh3nW7lrzC6qaV1TUxB5A/RQR598lvIKtNiCwe73H5ke2Bd+3N3exvv8oIZAHd9OueDS9XgPA+e7maJrU5kDgcMcqrZ7aBrLD4hzVgf0SQl1vG64KDpRYaKvRm4ZefKnPN6/uloSIpv2PMcyBLShEQnrAdgC4tGGiBdl7q8jUp5TBdsz68Y2SJyJQ2A0ZfqAbNbQck3HSLpdGOkcViM+svpzXMbyBL9WVLI7PN7bU9s5+n9jpPjosCFKAABShAgYIVYI2sgvVmbxSggJ0LMIOqcBYou0BWxyXScu54wmgJvAHAAom1+nXP3ipryghUAO9A4HtIdAOQKIR4e1ug51o1k+bhCb2klO9DoCik/AIQ/XShP7Yj8K4/AsLjt0Hil4ggr0Eps24efulJKbVVRbw9y1+9lPCpEPCEhBcEnhLAUU23dtnyXonjN4Jv8Z0hEA6gNICfoCf3inivZFTz0fFPSw2LIoK8yviPT6yoJet/SinehJAjBFACEB9HBHmGZiedEshKm6EWEH65DyDejgj0etCY25j4QVJgMAB3AbHUw7v4oKT4RBUYGgegJICzkLK9EKKyBN4TQIwEHtOTPO/VPBKu65AP7Qjy/q3lROmZnBQ/BRAdAZyHkBMiAr0/CwiLny0ELm0L9Op/I7CT0FZIOS0i0NOnVcilUtc93L4AZAsBxOvA5O1BXhMNlyyOFt5wESuEsPpsC7zrbGrQKix+NSD/UsHD1K+NiR8rNHFF1/UimoB1W5D3+5l55bSGSZcTOkpgJAQ2Z7Y/mo2LryuS8SUEagKIExoGbRvptdE/WLqlNQoIS4iUkF8KoAMgagD6Fj3Jq4vag4XzE+MYvfJ56hjrxFFSgAIUoAAFHE2ANbIcbcUccLzuFf/3uLTIqZB4vECHL/CjsIr+10+u/7FA+2VnDi1gdgYVMxJytx2yC2QFhCeMgpRt3JOsrROLaBZ3KfZIYJYKnNwMZM2TEK9fLVZ8YfGriQOllG9GBHlVeXpswiO6Ln8QQrx4TVzf66G79wXkGF3oDxiBrLD4S1KKV7aP8lyV2ShvZmS1g9RaX3NPOFIkudgCAZzfFuT16tNjLjXRhbZWWmXjMlav42c9EuargFfEKK9nMwtkCciZV92uDimWVLS+roltQrNU3zayWHRWOukCWVKKFmMu+emaNg/AoW1B3m8GhMW/BiBY16wtilzFuese2hohtHXbAj3D/TNkZN00mi2l7ORexGtjUjyupQ3SNBsTP0cTKCMsSV2tSe4VhUXshNTbSqEVE8C8iEDPihBCquuEwJmIIK8RAWHxWwF5/GIZr34lzyU+LHW5V2ii8baRnvuyCmQ1C0t4D0IGbA/0apZ23s3D4vtLiVcjRnnVU19XQSThkRCdDPF4MSnckoW+u3R1z2pLXxLWjF45rWF2+8M/+B8vzaP4SSHxdpPrngt3uce/I4T4QE/yLKP6yRjIAuQ5SK2d5gaLbtV3QyAkItBrdu52uGteZfbz1DUVOWsKUIACFKAABTIKsEYW94RNBTwqt66lA7/btJMcGteA2klxG0yvA1OYc2LfjiPAjITcrVV2gSyV1WRJ1q9vC/L6W7XWLCzhEyFlcXXE7magYmhEkFdd43ujL90vNO0PPcmzmOYRP0rVvooI8n4+JUBiBCduBbKkJvUnt466a09WgSxNQ0JKRlLz8IQ3pZQ9IoK8GjYZe6FUERQplRKMah5+uYMuxYTtQV5VMwtkJWnJd+8ZWfK86idgTPxpYUEPlfmTbSDrVo0s4zIBMd2aVHywygIKCLusji0uigjynqa+p/qXuhihMrgyDWSlKXieNtvIw8MrJjkp4bxmRf2t73v9muoLefXJJM9huz0S/pRCf0le8/5R80j4WwDNtgV5HVLOMvnqXztC74035hQe/5uUUPOfl1UgKyA8fjIkKkQEeb2cdt4qs00KhCm7G3NJeEFK2SUiyOsl49dhl1fq0L7YHuS5LqNXQFh8tmuY3f64B9D/9YivsSPQ63cVqPMPuVRGc9f+1SxaleQrxU7dFsiSYmjEqOIrjPmGxS8EcEIF9XK3w3kVBShAAQpQgAIUoIBZAqyRZZYk28lUwK1ya/UXtWwL9RYA3abkuA3PFEA/7MIJBMzOoGJGQu42RbaBrODzJS0eHmFSyKcg4Q7gHimxevsor9cy1kDyH3vRT9Mtx/WkRG/No9hkQLsSEeTZT40iNYBzK5D1L6ToGTHKc01mo8xYIysgLOF1SPmWcdxPStEsPGGIkHgZAt6AUbg8OSLIyyezQJaedL1USpH2gLD4k0KIt1KOP2bWd8ajhQFh8aoo+oaUI4kBYfF/Aiir6k3dvF/9fv5vRJBXxbwEsjTdegWam6pNdS3NODQASyOCvLoGhMVPAuAmgHUS+CgiyEsVxYd/2OUHLdBCJGRtI8YG+AB4KyLIa06WRwvDE96XUvpHBHkFpJ1zszGX+4kbhf6NjKyAMfHrIPCkOiZ64zrhKaBv2xbk/UImgaxs1zC7/aGCcOq4JCAHCokKN+dRXdcsvrha9GTGQJYQGLIt0HOlGkOz8PivhTSy04bkboe75lVmP09dU5GzpgAFKEABClAgowBrZHFP2FTArXJrmdJBctyGAt1vhdm3TVHZuE0FmEFlU94sG8/2aGFY/HwpYXUv4vn25qEiISAsfoqUKJlTIMviUWyYBB6MCPJun1kgq1l4/Bbo+Gn7KK/hKQNrHp7YSJf64jJJnn7/uSdMFwLxEUFeqvC6qv2UGsgyam9B9r8uRavdQZ6nb2YRTbFdIMt4u+LCJC3ZV2V2NQ+7/D2E+HxboNfcjKh5CWRdK+YVVexKQjykpWrEqGInMrZ183jmaqHemAgRuz3Ia6wRyAmLjxNShJeuUfwLdeQvYEz8QQio+WcdyAqLbyGB5XrS9cpp37wYEHZ5hQT+3h7k/ZZRU+y6vk/3wCMpY3GXQrMmy1+uQ9RR1mnHmNManvNIeEmmzUZLE+i0FClaQUrtJ10K/x2jPPf7B58vqXm4n2cgy7xnAJ+n5lmyJQpQgAIUoAAFbgmwRhZ3g00FCjOYVJh92xSVjdtUwOwMKmYk5G65sg1kjYnfJAROCkvSIKtetKaQ+hwpcGB7oFe37DJuLB7Fq0rg/wDRTk9K+t5SxKOflDI05WjhjcLulvUSei+ZdGW9u1uR8lbNbbaA/ruqQ5VdRlazsPiRGvBSEsT/LFqyp6a7jQfk4yojyhYZWTcCaSorS26NCPIeFTAm4S0IOVDqetsyyd4x5zzi2wOi7LYgr6kpWWnQk/305BInLB4JndIFczIUMm8WHr9ISFkkSbP2LCp0XbcWGSCFvmN7oPfOm/2q4+HVdM1SU709UBXfP3s84ZIQop/1WtJyUcQtQEgxHxD9I4I8Z2WVkaXemLjbI2GHUJlWQg5Kktp5d8juAIJ0zVpPvbWweXjC+5CymqpDlnbnBITHfyMlfk0JpN0KPGa/htnujyJeD0mpb9U00UTq8pQU4g1IOVrXrNVx9a5YZmTl7mc3u6vMfp7e+YjYAgUoQAEKUIACziDAGlnOsIp2PIfCDCYVZt92vCQcWgELMCMhd+BGIEsIo95T2o96s56bVQhpwVcSqAVgvxA4CCnu2xbk2SGno2MB4fE9IBECoDgAlb00OCWQpfppFn65vZBCvUGwBoAzUmKRvO4ZrOpQZRfIah189q5rHh5zAdECgDoqqAItr+hJxR+yuCU0yfjWwjs9WqjG2nxs/DNSxxIhdN9tI73PNR+bMFRKqDf93atcpFX23f6+9+/q2GPz8PgVEqKl0NEWGsplF8h6ety5EtZkjw+FhjaQKCIF1spr199JyZpqHp4QKKX+bESQd+OUtWkWdvklATEewN2QWCWEvEsKsSci0GtSVoEsda9yS/LwmCgh2gHwUscWATErIsizrxp3QHhCNKQcFjHKe2nafdB8zOWXpdDCIgKLV1f1rNJ+L7s1zGl/NA+LD5fAm5BIgiY+hZTdNKm/nnzd+wcGsnL3s8urKEABClCAAhSgQEELsEZWQYs7aX9ZBY0KM5hUmH076TK7xLTMzqBiRkLhbhuVPZTytruAMYn3QegnhdDLbAu862zhjoy93wjOJTwqdfktBJZFBHoNpIpzCZj9PHUuHc6GAhSgAAUoQIH8ChRozaL8DpL32b8AA1n2v0YcYe4EmEGVOydHuMqot5QsF2kW0SX5SrF/NPf4jyFE3Yggr8cdYfyuMsaWIZfvtRZx80p5A6SrzNsV5snnqSusMudIAQpQgAIUKHgB1sgqeHOn7JGBLKdcVpeclNkZVMxIKMRtpI7YjU0cqUv5lgDukhLfS4ulj6rzVIijYtcUcBkBs5+nLgPHiVKAAhSgAAUokK0Aa2Rxg5giwECWKYxsxAkFmJHghIvKKVGAAhSgAAUoQAEKUIAChSbAGlmFRu9cHTOQ5Vzr6cqzMTuDihkJrrybOHcKuLaA2c9T19bk7ClAAQpQgAIUSBFgjSzuBVMEGMgyhZGN2IEAM6jsYBE4BApQwCkE+Dx1imXkJChAAQpQgAJ2J8AaWXa3JI45oLwGstwqPxMCKZ5KO1sh8O31uA3T1NfcKj7jDyGC02kIuTNZwxzEbjTq27hVar09o1bynxuapXyNby10zL1U2KM2O4OKGQmFvaLsnwIUKCwBs5+nhTUP9ksBClCAAhSggH0JsEaWfa2Hw44mX4EsZAhUARDAwOtxG6YYgSxN3BaokhBzrHHre6QGsgT8U9EkdjCQ5bBbyGkHzowEp11aTowCFKAABShAAQpQgAIUKAQB1sgqBHRn7DL/gSwZmhy3MSQ1cHUzGJXx10bgqnJrqf5/ctwGY98aGVkqkKXLZsknN+7I6MqMLGfcabafk9kZVMxIsP2asQcKUMA+Bcx+ntrnLDkqClCAAhSgAAUKWoA1sgpa3En7YyDLSRfWBafFDCoXXHROmQIUsIkAn6c2YWWjFKAABShAAZcXYI0sl98C5gDcSSDr1giMo4ZrkuM2tM1TRhZkaEobKrsr5b+ZkWXO2rpaK2ZnUDEjwdV2EOdLAQqkCJj9PKUsBShAAQpQgAIUUAKskcV9YIpA/gNZ6bo/JXTR/vrJ9T/mLZB1sw3WyDJlLdmIuQLMSDDXk61RgAIUoAAFKEABClCAAq4twBpZrr3+ps0+/4EsGQodRn2rtHWu8hTI0mXqmwrTtXGzppbR9s26WqZNmA05rYDZGVTMSHDarcKJUYACOQiY/TwlOAUoQAEKUIACFFACrJHFfWCKwJ0EstIeB0wZTF4DWSz2bsoyshEAzKC6tQ3CwiaXTxJ6fcjkBtwcFKCAjQQ08Z2H7vFLUNCAv23UQ6E1y+dpodGzYwpQgAIUoIBTC7BGllMvb8FNzpaBLAi588ZMjBpafGthwS2rS/ZkdgaVo2YkhIRPGAKIiS65CThpChSGgJQzQ4KGvVkYXduqT7Ofp7YaJ9ulAAUoQAEKUMCxBFgjy7HWy25Ha7NA1m0zlqEpGVxulVpvh4A/dNmMGVl2uzVcfmCOmJEQEj5RqoUrXryYtXzZspaKFSu4/DoSgAK2Ejh9+m+cOn3GGp+QYFF9hAQOZba8rbDZLgUoQAEKUIACTiHAGllOsYyFP4n8BbJujDvro4XwTzszCfxujdu4NOVrbpWfufGGQh07GMgq/D3gLCMwO4PK0TISQsImzIAQvWvVvB8d27VxlmXlPChg9wJLV6zB4T+OApBDQwKHTbL7AedigGY/T3PRJS+hAAUoQAEKUMAFBPivfi6wyAUxxbwGsgpzTAXRN/twXAFHzKAySzssbErZ6+L6GS9PT+vgfn2M7BB+KECByfW78wAAIABJREFUghOYOPVTa2LiFYu7dKsQFDTwdMH1bJueXPl5ahtRtkoBClCAAhSggBJgjSzuA1MEGMgyhZGN2IGA2RlUjpSREDx2QkshxaYaftXQuWM7O1gNDoECriXw9aJliIqJhYTWJjRw8FpHn73Zz1NH9+D4KUABClCAAhQwR4A1ssxxdPlWGMhy+S1AgCwEHCkjIThsfIgQWvBTTz4B/yZPcE0pQIECFtix5zvs3P0dpNRDQ4OG3zg+zw8FKEABClCAAhSgQDoB1sjihjBFgIEsUxjZiB0ImJ1B5UgZCQxk2cEG5BBcWsDZAllmP09denNw8hSgAAUoQAEKpAqwRhY3gykCDGSZwshG7EDAkTKozOZiIMtsUbZHgbwJOF0gq6pfCCSCIRAaGx3JDLO8bQdeTQEKUIACFKBAFgKskcWtYYoAA1mmMLIROxAwO4PKkTISGMiygw3IIbi0gNMFsnz8/KHBX71dODY2codLLy4nTwEKUIACFKCAaQKskWUapWs3xECWa68/Z5+1gCNleDGQxZ1MgcIVcLZAVuFqsncKUIACFKAABZxVgDWynHVlC3heDGQVMDi7s5mA2RlUZmd42WziABjIsqUu26ZAzgLOFsgy+3masyCvoAAFKEABClDAFQRYI8sVVrkA5phlIKtK6x8g8XgBDCHrLgR+TD6xoWGhjoGdO4yAI2VQmY3KQJbZomyPAnkTcLpAFmtk5W0D8GoKUIACFKAABXIlwBpZuWLiRTkJZBXIcq/4v8elJn/I6X5bfl/oouH1k+t/tGUfbNt5BMzOoHKkjAQGspxnH3MmjingdIEs1shyzI3IUVOAAhSgAAXsXIA1sux8gRxleFkFstT4PSq3rqUDHwFoVcDz2aQBg5LiNhwu4H7ZHQVSBRwpw8vVA1mdu3RDj9dexV9/ncJff/2F0mVKo89bbxprOWTocFSoUB6DBg4wfj14yDA82qA+zvz9t/HrAf375WrXX7hwEatXr0b37q+kuz4mJgYDBw3ByhXLc2xH9X1g/wFcS7qGc+fOo3y5csY9YWGj0bjxEznen9MF879egC9nfWVcdiIuDpUrV4KAQIeOL6Js2bI4+sdRjBoVmFMz2X7/119/xfARgbh48RJq1rwf0z6eip9//hk/HzyIvXu/w8Jvvr6j9h31ZmcLZDnqOnDcFKAABShAAQrYtwBrZNn3+jjM6LILZDnMJDhQCgAwO4PK7AwvWy6Sqweyer/ZBx1ebI9Lly7Dy9sLy5Ytx6wvZhjk/v7NjcDW8mVLjF83C2hhfG/N2rXGr3MbyDpx4gR69noD27Zuvm0pL168iBIlSuR6iffv348PPgjD6tUrbrtHSgkh7rx6gF/1mjj0y08oXry40UdycjKSkpJSf53rwWa4sEfP19Gtaxc0bx6Ad97tj4YNH4evjw+ioqKwZ+93mDnjs/w27dD3OVsgy+znqUMvLgdPAQpQgAIUoIBpAnf+p1zThsKGHFmAgSxHXj2OPa2AI2VQmb1yrh7Imjt3HurWrWsEgCpVqogWLVvjwP4fcfToUYwJG4uzZ89i0cIFsFp1PN2ilfG9KVM/xuHDR6CCULGxsejx2mt4663eOH/+AoJGvYeoqGhjmUaOGIZKlSqhX/8BxtcaNKiPr+fPTV1C9bW+fd/Bpk3rjXv7DxiIP/88CS8vT4SHjcZDDz1023JnDGT98MOPGPXe+yhSpAhatmhhBJ2KexZPzSqrU7ce9uzeAU9PT4SPHYddu3ZD0ywYNLA/WrVqmel2yhjIWrBgIY4dO4bQ0GB06tzVcDp06Ddcu3YNI0YMw4IF3xjzq3n//ZgxYzrc3d2xbNm3+GLWl5BSR6uWLTF48MB0fQUGvYcHa9dGvXoPG0HEI0eO4NVXu5u9vR2iPacLZLFGlkPsOw6SAhSgAAUo4GgCrJHlaCtmp+NlIMtOF4bDyrOA2RlUjpSR4OqBrIybJaB5S3w5awa2bt0GzWLB6VOnUb9BfRTx8MDiJUsx4/PpRiDr++9/MIJSFy5cwFP+zfHroZ+xZ89enL9wAS883xbHj0eiT9+3sXXLJmSVkZU2kDX9s89x/tx5BAWNNIJNGzdtNoJZGT+ZBbIGDhqM9evWoFSpUvjww8mZBrLWrFmHyKhIBL//nhGce67NC9gesQVFixa9rY+cAlktWjyNXj174NtvV+C994OxZfNGlCtXDl26voI3Xu+FypUrY8TIQHyzYD4sbm7o3Lkbhg8bjAYNGhh97dy5C6PHhGHVym+NAJurf5wukMUaWa6+pTl/ClCAAhSggE0EWCPLJqyu16ibPbydUB17idvALEPX2352PWNHyvBiICv9VhoxIhCPPtoAK1auwgehITh58iQ2bd6MUiVLoUTJEkagRgWy1CflaGG9+o9i29YtKF68GCZPmYrjx45DSuDgLwfx04F9uQpk/fLLIQwaPATPPfcsnmraFPXqPZLpHs8skKXGo7LG1CerQNagwUNx/Pjx1MCRygBb+M18+Pj45DmQNWTwQCMopbLShgwdZgTR1Edlo9WpUweXL1/G55/PxL333mN8PSEhAf3efQcvvtge//33H55r8zyWLF5k1OHiB3C2QBbXlAIUoAAFKEABCthCgDWybKHqgm3aw9sJFTsDWS64+UyestkZVGZneJk83XTNMZCVXlcdifvx/37Ezz8fNLKprl69iuefb4977r0HQ4cMRt26dbIIZG3G3HnzkZh4BYEjh+PSpUvwb9Y814EsNQp1VHH37j1YtHgJ6tapg6FDB9+29DkGsj6aYgTUUgrWpxwtVIGsdi88j2ef/V+O2ymnjKxsA1kPPYTL8fE4EXsCo0eH3tbXr7/9ZmS7DRzQP8dxuMoFzhbIMvt56ir7gPOkAAUoQAEKUCB7AWavcIeYJlCIbydMnQMDWaYtp8s25EgZVGYvEgNZ6UXVG/uefbYt2rd7AR98EGJ885Xur+Ho0WP47rvdcLNYsgxkTZj4Ie6rUAHvvNMXS5Yuw9ix4/DzT/uNtyF2fKkzvtu7K11naY8Whn4wGvfXqIFOnV6Gys5S2U1r16zKcyBr3vyvjfs/nDQBv/9+GC+0e9Go66UCdJu3bMG8ubOhisKro4wqS8pisdzWx50Gsh588EG8+lpPrF2z0njj4dy589GqVQuUL1/e6PvKlatGsI2fGwJOF8hijSxubQpQgAIUoAAFbCDAGlk2QGWTFKCA4wqYnUHlSBkJDGTdvm8fb/gExoaHISCgmfFNVbR8+/YdRs0n9cn8aOFmo16WehtfQkIimjR5wsiu2rhhnVGIvXOXbkZtKpXllfJJG8g6c+ZvDB02HP/88y903WrUsmrSpHGeA1kqq6vX673x77//onr16ti//wB27Yww3jg4eky4UZ9KFWB/tXt39Oz5WqY/tHcayHr55ZewePESzJg5C9evJ6FRw4YICxttFIHf+913WLhwMT6ZNtVxHxgmj9zpAlmskWXyDmFzFKAABShAAQooAdbI4j6gAAUoYEMBR8rwYiDLhhuBTVMgFwLOFsjKxZR5CQUoQAEKUIACFMizAGtk5ZmMN1CAAs4sYHYGldkZXra0ZyDLlrpsmwI5CzhbIMvs52nOgryCAhSgAAUoQAFXEGCNLFdYZc6RAhTItYAjZVDlelK5vJCBrFxC8TIK2EjA6QJZrJFlo53CZilAAQpQgAKuLcAaWa69/nY7e/UX6tCg4TeqK/NDgQIUMDuDypEyEhjIKsCNxq4okImA0wWyWCOL+5wCFKAABShAARsIsEaWDVDZ5J0LhIRPlCGBQ5kxeOeUbKGQBRwpw4uBrELeLOze5QWcLZDl8gtKAApQgAIUoAAFbCLAGlk2YWWjdyrAQNadCvL+/AqYnUFldoZXfueVm/sYyMqNEq+hgO0EnC2QZfbz1HbybJkCFKAABShAAUcSYMaLI62WC42VgSwXWmw7m6ojZVCZTcdAltmibI8CeRNwukAWa2TlbQPwagpQgAIUoAAFciXAGlm5YuJFBS3AQFZBi7O/FAGzM6gcKSOBgSz+HFCgcAWcLpDFGlmFu6HYOwUoQAEKUMBJBVgjy0kX1tGnxUCWo68gx58aGHOgjITsAlnJVitee60natSojo4dOiAyMhJr1q7DzBmfpS72smXfIiIiAtOnf2J3G2DWl1+hS+dOKF68uF2M7bfffsc///yDgIBmiImJwcBBQ7ByxXKbjK1T564Y0L8fGjZ83NT2W7X6H6ZNm4J9+w7g2LFjCA0NzlX7GzZsxJo1a2/bJ82fbonFi75BmTJlcmxH7cfRo8Owc8dOqP9+5JGHMfqDUJQsWSLHewviggsXLqJf/wGIjo5BqVIl8dGHk+Bb1Tfdz9ADD9S8bSjOFsgqCGv2QQEKUIACFKCA6wmwRpbrrblDzJiBLIdYJqccpNkZVGZneNkSPadAVr93+6Nc+XLo2aMHfv75Z7sIZEkpIUTOp+QbPdEEq1etwD333JNKmNt7bWG+ePESHPr1V4SNGW00f/HiRZQoYZsgjKMEsvJiMG/efOzavQeffz4dFk3DpEkf4czff+PDSRNssVx5alPtq88+n2Gs6cgRw/H1gm/www8/YsqUj5D2Z6hixfucPpBl9vM0TwvBiylAAQpQgAIUcFqBnP/077RT58TsWYCBLHteHeceG2tkacFPPfkE/Js8kW6hdV3Hgm8WwsPDA23bPIetW7dlG8j6999/MXTYCPz555/w8vLGB6HBqFu3Dp5s6o9vly81AkpvvtUXTZo0xivdumLFipX47vvvMXHC+NR+z5+/gKBR7yEqKtr42sgRw+Dv/xSmTP0Y33//A86cOWNk4Tz4YG0MHTocJ/86BS8vT4wbG47q1f1S2+nS9RXs338A999fA9M+noIRI4NQrFgxHDt2HBvWr8Hnn8/EtojtsFqT0b59O/Tt85YReJg46UPce889+O3331G7dm1M/3SaETT7YPQYbNsWYVioa9U9WY31+vXreD841BhvkSJFMHTIICQkJuKjjyYjISERHTq0NzLc+vZ9B5s2rTeyi4xMo527UMTDA2+88bpxTV7Hk3bxVCDLx8cHv/32mzHOCePHonHjJ3DkyB94//0QJCQmGB5TJn+ESpUqQl2vsoWU2blz51Ovv3btGoYNG4H9B34yfI8ePYa5c75Ml5F17ty5TNdi9+49eO+9YLi5u6Fq1apws1huy8h69LFG2LxpvdFuZvaapqVOa9q0TxH3Z1zqflH9/vTTz3j66eao/WAdtGzRwtgPmiYwdcpklCtXNs/zzcohqzlWr14TTZ9qCqvVijmzv0wd6+o1a42flymTP0z3M6TMM36cLSPLlZ+nzv07JWdHAQpQgAIUKFwB1sgqXH/2noUAA1ncGoUlYHYGlSNlJOSlRpY6GjZyZBDKliubulSXL8ejQf16RoBCBakaNKiPN17vhUOHDqHv2/2wbesmhH4wxjji1qplC6ijaVV8qmDunK8wfPhING36JJ599n+p7algzvkLF/DC821x/Hgk+vR9G1u3bDICWYd++RUzv/jcCIi8228Annv2f2jVqiV27dqNmTNn4euv56bbQmkzslSA4qmnmqLPW28iLu5PI4j27rtvGwGIpk8FYPWqb43g2dvv9MPaNStxz733om3bF4xgnKZZEBLyAVat+hZnz5415rlk8ULs2bM307F+MetLHP79MD76aBL+/vsfPP9CO+zetcPoMyUjS/WVEsiaPXsuvv/hB3w2/RNcunQZ7dp3MP5bZffkZTwWiyV1/mq+9R55BMOGDYE61rdgwULDZ86ceahX72HUqVMH879egKNHj2LM6A+MQFZm13/11Rz8+H//Z4zn9OnTePa557Fk8TfpAlmZrcVXX31hBDBnfTEDDz30EMLDx+HkyZPZBrIym2uDBg1S56SO7vV6/Q24u7ujXbsX0PqZVrjrrruM7/tWrY7tEVuM4J3aC8eOH8ekiePzPN+sHLLab6rf5cuWoF69R1LHefLkX+jWrTs+/XQaateuleNjzekCWayRleOa8wIKUIACFKAABfIuwBpZeTfjHQUgwEBWASCziwIRcKSMhLwGsrKrkfVArYewf98P8PT0NJzbv9gRoSHv49Sp09ixcxdatnwa+/cdwM5du4y6SG3atsOKb5emO16nMoAmT5mK48eOQ0rg4C8H8dOBfUYgS31U3Sf1efChh1GpUiWoE4bqOpU9tmnjumwDWWlrRi1atBibN2+Fxc1iZD4tX7bYyERS/SxauMBoRwWa2rRtg8ZPNDICPQ3q1zeCYer/3NzckNVYe/Z8A927dzMyydRHBb9KliyJZcuWZxrIeq1HLyNDrXnzAOP6CRMmGTWWVAAoL+NJO/m0RwtVLat3+w00fFSQRfmqINl///2HCuXLG8GlrK7v0+dtvPDC80bAUH0yq5GV2Vp8PPUjDBg4xMh+U5+samSlzcjKbK4qWJX2o47wHThwACtWrsKWLduMY4VPPtnECGTFRB83LlVzfPW1Hti2dXOe55uVQ1b7LW2/KeNs+3x79HmrN1q3fiZXzxtnC2TlatK8iAIUoAAFKEABCuRRgDWy8gjGywtGgIGsgnFmL7cLmJ1BZXaGly3XzMxAVp269fDD93tRvPiN41MvtHsRo0YF4oGaNY3/fuyxR/Fi+/aIiNiOUneXwsYNm7B06aJ001PBjMTEKwgcORyXLl2Cf7PmWQaydu7YhtKlS2fJkzEjKyWQpQJXH344GQsWzDOOCv7v2TbGEbBMA1ltnjMCEuq4oLpv46ZNxjG4ZUsXG0GmzMaqMra6dulsZJul/aStkZU2I+uN3m+hc+dOCGjmb1w+bvwEeHl5GYGz24I72YwnbV+3BWTeHWAcY1Rf7/3G60bB+c2bt2DlylWZB7JuXq+CeR06dkgdW1aBrIxroYJnQ4aOMDLd1Cdfgaybc02ZV8T2HXjowdqpNc/U0b1pn0zHqpXL0wWyYmNj0bNXb0Rs25zn+WblpgJZme23jIGs5ORkI4tuxufTc/1j62yBLLOfp7mG5IUUoAAFKEABCji1AGtkOfXyOu7kGMhy3LVz9JE7UgaV2dZmBrLUX+DVm+Re79UT6g19PXu9gd27tht1olSA4J9//sXWLRuNuk3dX+1pXPfOO33TTWlk4CjcV6GC8fUlS5dh7Nhx+Pmn/bdlZPV+sw/8qlUzjs6dOnUKW7Zsxauvdk/Xljra9vXX81ClcuV0GUerVq8xsqPmzZ1tHK3r0OFlLF++xKgllVngSAUn1DHCcePCkZSUhIaNGmPXzu0IHzs+07Gq43h//PEHJkwYZ2RjqbmvWrkCGzZswJ6932HyR5OMY4wpRwvVET/VvqrHFR8fj+efb2+8GVDV08rLeLy9vVPnn1VApllAC0ycMBb16tXD8BEjEX85Hp999untGVk3A1nq2OPRY8cwbmyY4fN0i1ZY+M38dEcLM1sLFZjzb/a0kd1WuXIlqPpWR44cyfZoYVZBu5RJqXpb6hMc8r5xvFQVf9++YydmfzXLCGStX7fGqPP10eQpRhagOlqY1/lm5ZbVfsssIysxMTFPb8p0ukCWA7211eznKdujAAUoQAEKUMB2AqyRZTtbtnwHAgxk3QEeb70jAbMzqBwpI8HMQJY6qqaKqp84ccLIdFLHClNqHKk3usVExxjBHfVp9MSTmDljunF8Lu0nJiYG77zb3wjiNGnyBFTB8I0b1mHGzC+My1KOFqraU6peV0xsLNzd3RAS8j6eaNQoXVvqiN7yb781AlbBIR8Y96paXepIoOrj8OEjuL9GDVy9dhX93n3HuDezYIo68hcSOtooLJ6YmIBXXulm1AHLaqyqVpXqb//+/dCEhsDAEcZxxL/++gsvvdzF+O9ePXukK/auAnaq1pfVqhvf69q184233mU86tjmOeMIYmbjSTv5rAIyKosp9IPRKFq0qJEhpxxVHausrk8p9h7355+4++67cfz4ceP6ffsOQGVdhYYGG21kthZqPmPHTUDJkiWM46Ye7u53FMhS2W+q6P7evXth0SyoXKWy8QZIVaxeBZRU3axff/0VJUuUxLRpU41i73mdb1YOWc0xYyBLjbHVM62NQGdu3qyp1szpAlmskXVHvx/xZgpQgAIUoAAFMhdgjSzuDLsUYCDLLpeFg8qHgCNleOUlkJUPCt5CgQIRyCwzqkA6NqETZwtkmUDCJihAAQpQgAIUoMBtAqyRxU1hFwLvh40frAltjJByRHDQsKkpgazQcZP6S12OE1KMCg4a8qFdDJaDcGoBszOozM7wsiU+A1m21GXbBSXAQFZBSefcj9nP05x75BUUoAAFKEABCriCAGtkucIqO8Acx48f733Vqp0FkAwgUQKlIXFOCBSTgFsxi156+PDhlx1gKhyigws4UgaV2dQMZJktyvYokDcBZ8vIcuXnad5WnldTgAIUoAAFKJAXAdbIyosWr7WpQEj4pLGAHATAI01HSYD4KCRwyEibds7GKXBTwOwMKkfKSGAgiz8GFChcAacLZLFGVuFuKPZOAQpQgAIUcFIB1shy0oV1xGndzMr6VwJFUsYvgGtFLfo9zMZyxBXlmJWAI2UkMJDFPUuBwhVwtkBW4WqydwpQgAIUoAAFnFWANbKcdWUddF4ZsrKYjeWg6+jIwzY7g8rsDC9b2jKQZUtdtk2BnAWcLZBl9vM0Z0FeQQEKUIACFKCAKwiwRpYrrLIDzTFtVhazsRxo4ZxoqI6UQWU2OwNZZouyPQrkTcDpAllV/UIgEQyB0NjoyJC8afBqClCAAhSgAAUokLkAa2RxZ9idgMrKEkIOllJ8yNpYdrc8Tj8gszOoHCkjIXjshJZCik01/Kqhc8d2Tr/WnCAF7E3g60XLEBUTCwmtTWjg4LX2Nr68jsfs52le++f1FKAABShAAQo4pwBrZOWwrlWrVi0hpdZUh6whpPByzm1gX7Ny83ArUs2vxvNRkcdWJSclX7Ov0XE0Ush4DeKYEPqu6OjoixTJXsCRMrzCwqaUvS6un/Hy9LQO7tfHwrWlAAUKVmDi1E+tiYlXLO7SrUJQ0MDTBds7e6MABShAAQpQgAKOIcAaWVmsU9WqVRvouhgEITo7xlJylBQoBAEpF2qa/Cg6Onp/IfRuky7NzqBytIyEkLAJMyBE71o170fHdm1sYsxGKUCB2wWWrliDw38cBSCHhgQOm+QMRmY/T53BhHOgAAUoQAEKUODOBVgjKxNDHx8/Vc8htZZDo4YN8cADNeF9l/edi7MFCji4wOVLl3HkyB/4/ocfbs1EIiQ2NjLUwadmDN+RMqhs5R0SPlGqtosXL2YtX7aspWLFCrbqiu3mQcC/yRM5Xp2QmIh9Px3M8TpeYD8Cp0//jVOnz1jjExKMLMiQwKFO82czPk/tZ59xJBSgAAUoQAFnEmCNrAyr6eNbfQYge6sv9+zxGnr16okKFco705pzLhQwReDUqdP48suv8NXsOTfbEzNjY46/aUrjhdiI2RlUjpqREBI+YQggJhbiUrDrDAL/a/U0Hq33cI4ucxcsRmzcnzlexwvsTEDKmSFBwxz+GZpW1eznqZ2tGIdDAQpQgAIUoEAhCbBGVhr4lEwsi8WCz6Z/ghYtni6kZWG3FHAcgS1btqJP33dgtVoBJ8rMMmsFHDkjISxscvkkodeHTG5glgfbyZ+AEFpw6btLoVf3rihWrGi2jWzftRe79n4PKXWnyJLMn5gD3aWJ7zx0j1+Cggb87UCj5lApQAEKUIACFKBAoQmwRtZNeqMmltT2qV/OnPEZg1iFtiXZsSMKqGBW7zf7GEPXhP6oI9fMMjuDihkJjrij7W/MIeETFwDo0rRxIzRr2jjbAUZGx2DB4uWQwIzQwKFv2d9sOCJXETD7eeoqbpwnBShAAQpQgALZCzhNHYY7XWgfn2rfqMLu6jjhe+8F3WlzvJ8CLicwenTYjWOGUi6MjY3q4qgAjpxB5ajmHHfOAiHhk2oC8oiHu7ve69Wu2r33lMnypoSEREz6eLr6/sGQwKGP5Nw6r6CAbQT4PLWNK1ulAAUoQAEKuLoAa2QBqFq1agldahfUZti7ZxdrYrn6TwXnny8BVTOrcZOmxr2a0EtGR0dfzFdDhXyT2RlUzEgo5AV1ou6Dx04MFxIja9bwQ9v/PZPtEcMvZs/HqTN/Q1r0SqHDh590IgZOxYEEzH6eOtDUOVQKUIACFKAABWwowBpZAHx9/dpIYLV6O+E338y3ITebpoBzC3Tp8orxNkMBtI2JiVzj3LPN3eyYkZA7J16Vs0DQmI/uc9es6wHUKXvvPWjTuiXuy+JlJGs3bsGBn3+BFKJP6Mghn+fcOq+gAAUoQAEKUIACFKCAYwiwRhaAKr7VBguISTxW6BiblqO0X4GU44UScsiJmKgP7XekWY/M7AwqZiQ44i6w3zEHhoWV9xAe6lWhLd3d3Y1g1kO1H7htwEePR2LRspXq678hKeHxkJCQRPudFUfmrAJmP0+d1YnzogAFKEABClAgbwKskQUg5W2F/fu/iwH9++VNkFdTgAKpAlOmfoypU6c59NsLmUHFDW3vAr1nzHCvcPbSlwBeUWN96sknUP/hOvD28ko39HkLlyAmNg5CYFjwyKET7X1eHJ/zCfB56nxryhlRgAIUoAAF7EGANbIYyLKHfcgxOImAUwSyfPz8ocEfOnbExkbuuNOlYUbCnQry/qwEgsdO+khIOVB9XwWx6j1SJ11A69ffj+Db1evUt+PcpftjQUED/qYmBQpSgBmpBanNvihAAQpQgAKuI8AaWQxkuc5u50xtLuAMgSyzkZiRYLYo20srEDx2Um9I2VsA9TMLaM2auwB/nToNCbkgNHBYN+pRgAIUoAAFKEABClDA0QVYI4uBLEffwxy/HQk4QyDL7AwqZiTY0QZ14qFkDGh5eHigQrmyKF+urCr6LpOuXxeQmB4SNPRtJ2bg1Oy+wsm2AAAgAElEQVRMwOznqZ1Nj8OhAAUoQAEKUKCQBFgji4GsQtp67NYZBZwikFXVLwQSwRAIjY2ODHHGdeKcnFcgY0DrtplKhIUEDR3lvAKcmT0JMCPVnlaDY6EABShAAQo4jwBrZDGQ5Ty7mTMpdAGnCGSxRlah7yMO4M4F3guf2MhNiIa6LhsJgUYAKt5qVay0WKwj3hs+/Oid98QWKJC1ADNSuTsoQAEKUIACFLCFAGtkOVggq2PHTth/4EC6vVCsWDFUr14dr3Trig4d2qd+b+/e79DtlVdTf61e1V6hfHk85f8UXu/VE5Uqpfl7DYAPP5yMTz6dnuU+GzP6A3Tt2vm27+e1H1tsZEdr899//8WCbxbiqaZN8cgjDzva8LMcrzMEssxeDGYkmC3K9vIjMHr8+PuTddFYSPEQIHwA6SOFnBY6cthX+WmP91CAAhSgAAUoQAEKUKCwBFgjywEDWYePHMbgQYNS98z5CxewefMWHDt2DG+++QZGDB9mfC8lwNT6mVZo0KABrNZknIiLw9q166HrOj7//FM80Uj9Q/2NT0og6+2+fXDvvffcticbNmqIGtWrZxnIym0/hbXZ7anfw4eP4Nnn2uK9UUHo2fM1exraHY3FGQJZZtd0YUbCHW0p3mxjgeDgySWFsLqFhAz5z8ZdsXkXFDD7eeqChJwyBShAAQpQgAKZCLBGlgMGsqKio/DTgX3pljM5ORmdOnfFzz8fxI7t24xsq5RAVmjI++je/ZXU68+ePYsuXbtDZQVt2bwBpUuXThfI2rRxHWrUqJHrH5i89pPrhp34Qgay7HdxmUFlv2vDkVGAAo4lwOepY60XR0sBClCAAhRwFAHWyMomkDVnVxK+j7Qaa1ntXg3Vy2k2W9fvj1txIOZGX37lNMzoWSzTvtTRwswCWerilStXYeCgIZg0cTxefLF9loEsde2vv/6Kts+3x4D+/dC//7s2CWRl1Y/6+pUrVzD142lYs2Yd/vvvP1SuXAndunYxAm5C3NqWVqsVX3zxJRYvWYozZ84YAbqXX34Jr73aHRaLBYmJiaj9YF10f6UbQkODU80yBoquXbuGmg88iN69X0fRokWxdOlyqIBe9ep+GBUUiJo1a2LcuPHYvGULrFYdDz9cFyHB78HX1zfdOixevARffjUbJ07EoVy5ssZY3nqzNzTtxt5Q63M9+TqGDxuKceMn4ujRoyhXrpyRdaXGqD716j+K8+cvpGv32+VLU48YqnX8YtaXiImJRalSpRDQzB+DBg0w/tveP06TkaXBHzp2xMZG7rhTc2Yk3Kkg76cABRxVgBmpjrpyHDcFKEABClDAvgVYI8sOA1kNq1sQ1rFongNZW7duwxu930JKLausMqVSGvZv9jS8vb2wZvVKmwWyVMMZ+0m2WvHyy53xyy+H0KnTS6hWtRp27tqFnTt3oUePV/H+e7deqDVo8FCsWLESz7dtg3r1HjEyzlauWp16XV4DWSqIVavWA2jZogX++fcfLFy4GG5ubkaArHix4mjePACnTp3CwkWLUdXXF+vXrzECZuqjjl5+Ov0ztGv3AurWrYMD+w9g9Zq1eOP1XggMHJEayPrj6FG4u7uhfbt2KHV3KSPAGBkZhQUL5hlHOXfv3oPo6GiEhI5Gp04vo8XTzdGgQX3cddddWLJ0GYYPH4mWLVvgySebGNd9/fU3qFq1KtauXQW3m2Ox18eKMwSyzLZlRoLZomyPAhSgAAUoQAEKUIACFHBlAdbIsrNAlkUDWjzkhqHPFslzIGvMmHAjW+ibBfPRqFHDbDOyVOM9e76Bg78cTD2mmFIja97c2bdlIqmAV4kSJTIdU04Bs4z9zJ07zwjijBsXjpdf6pja5sCBg40glQqsPfhgbaOovcpwSlv3S12s7lVt/N+P38HT0zNPGVl16tTB8uVLUgNC8+Z/jeDgUKivf7t8SWrQavyEifj885lIOWYZFRWNlq1ao2+ftzB48MDUMQ8bNgIrVq4yxqIyptR4Dx48iGXLlhjBLvX566+/0ORJf7z6ancjy0t9sjpaqO5XATZ1PDQlM+3jjz/B5ClTsWTxQjz6aAO7fl45QyDL7AwqZiTY9Zbl4ChAARsKmP08teFQ2TQFKEABClCAAg4kwBpZBRjIqnS3Bp97NBQvAvx9USL6Hx2Xrkhju6QcLfQuJtC4hiXbQNbxyEhs3LA2dZupY2oqG+vjj6eheo3qWLtmlXHULacAU9++72DLlq04fvwPo63s3lqYNuso4/7Oaz8dOr5sHM374fs9qYEj1WZ0dAyaP93SCBYNHToY48ZPwIwZX+C7vbtQvnz51G5PnDhhBI9UxlOZMqXzFMjq0qUTwsaMTm3r0KFDeP6FF43g1Dtv9039+po1a9Gv/0B8OWsmAgKaYerUaVBBmtWrV6Bc2bKp1+3atRtDhg7Hl19+YRwBVIEoVXtsx45t6Zjq1K1nBBdnfH7jrZBZBbJ69Hwd+/5vH+bPn5t61FBKCauuw6Jp6Y5d2uNzxikCWVX9QiARDIHQ2OjIEHt05pgoQAEKOIIAM1IdYZU4RgpQgAIUoIDjCbBGVj4CWTXKa7CkqeOU1bIfOXWj5lXKp3ENN9x71y1y9f0/TunGt1MCWWVLCjxSJftAlspUyuzTuPETRn0sVZNJfXIKML3WoxdUICelcHxKICv4/fdQocKtwJFqy8fXJ9M3FuannwaPNkS1alWxeNE36aahAjaqjtXTTzfHp598jHf7DcCmTZtx9I/fswzg5PVoYcZAVkpAKXDkcLzxxuup49mwYSP6vv2uEXhSx/xSssWyWuuJE8ajQ4f2WQayVF2sRx5+2Ah4qU9WgSyV+aWOh8bExMDHxwdNmz6JNm2eRYP69R3i6eIUgSwfP3+wRpZD7DcOkgIUsG8BZqTa9/pwdBSgAAUoQAFHFWCNrHwGsmrfd6NuUlafqH90HIpLH8gKqOWGEsVvkZ/4T8dPsTeuSQlk+d6r4f7yWrYZWaoG09QpH93oWggULVrEqOeUEsBKGVNOgayn/AOMuky2rpGVsZ969R/DAzVrGjWj0n50Xcf999dCq2da4ZNpU/HOu/2NjDEVyMrqU1CBLBXUUkG1WbNmQBO3F/2///4ahn9WGVm5DWSpeaoC97t274bK9tq+fSdUBlrbNs9h8uQPU4vK2+sDxxkCWWbbMiPBbFG2Z7aAW6Vnn5SQnQRkLQC1IaD+heUXCRwFxGZr3PpbKcBmd872KEABClCAAhSgAAUokEcB1sjKRyArj8apl9e6z2IEqdRHHSk8elrHyXO3MrIO/2XFfXdr8CubfSArq7cWZhxXdoEsVTS9/YsdjTcWqjcXqk9KRlZKXajczjOv/bRr3wGnT5/Gd3t3pwvMqGykp1u0Qp+33sSwYUMwdtx4zJw5C99/t8d4Q2DK5/Lly8bbAP38qsPTszhq3F8LnTt3QnjYrSODWb21ML8ZWWFhYzHry6+wadP6LDPT1PjuJJB1/fp1xMX9ibvvvhulSpU0pquy1MaOHW+8xVDVLlMF4O354wyBLLNrujAjwZ53LMfmVvmZEEDceuVr5iRrkuM2tKUWBfIqYPbzNK/983oKUIACFKAABZxTgDWyCjCQVcQdKOOlwbsYcDEROH3hRhBLfVRGVsy/Ou72EjYPZKkaTl26voKzZ89i08b1uOeee2wSyMqqny++mIXwseMxfvxYvNSxQ6rB0GHDsWzZt0YdqocefBD79u3HSy93Tq2ZlXJhSrBk29bNqFrVF48+1ggVKlTAqpXLU9tSRe9V8fv3RgWhZ8/XcO3aNePYYn4DWf/3f/vwcqcuaK2yxT75ODUA9+efJ423Kr79dh+j3lduA1nHj0caxeNHDB9mFLNXn4sXL6J+g8fRssXTmD79k9S5bNi4Caqe2bSPp+C555616yeRUwSyWCPLrvcYB2eegFvl1n8BqJDLFn9PjtvwYC6v5WUUMASYkcqNQAEKUIACFKCALQRYI6sAA1nZLaAKZP19SaKoO0wPZKngR8PHH4fK+ImKisKq1WuMoUz/dBqaNGmcOqw7zcjKbT9JSUno0OFlHD5yBJ06vQS/an7Ys3cvtm2LQPfuryA05P3UMak6WWvXrsMLLzxvFD8//PthLF22HB1ebG8EwtTng9FjMHv2XDzTqiXq1K1j1J/auHETkpOTTQtkqX5SAm2PP/4YWrZogYSEBMz/egGKFy+ONatXwNvbO9eBrMTEK6jf4DGULVsWL73UAW2eew6VKlVEcMgHmDdvPp599n9o2PBxnD93HnPnzTcys1TgrmTJzN8caYuHQ37adIpAFmtk5WfpeY+DCWTMxBKQ8yUwMzlJHIXmJt3cku8HMA5AahqoFKKr9cT69MUNHWzeHG7BCjAjtWC92RsFKEABClDAVQRYI8uOAlkXb77B0OyjhSmb2cPDw3j7n7//U+jVs4cROEn7udNAVm77Udep44GTp0zF+vUboN666FOlCrp264Lur3RLNyYVjJo+/XMsW74cZ878bWRedencCb169Uh946GqkxUWPhabN29FfHw86tatg1e7v2IUazcrI0sNStXwmj1nLhYuXGQcASxRogSaBzQzjkGq44Dqk9uMLHXtkqXLMGHCJFityZg75yvUqVPHqI/11ew5WLJkqdGHClypIOSgQQNQpUoVu38uOUMgy2xkZiSYLcr27ligYsdiblp8ZJpsrKXJcRteyqxdt0qtJ0JgiPqehIixJlsexKk1iXc8BjZAAQpQgAIUoAAFKECBfAqwRlYOgay0rg39si/wns81MG77dl8yKpS6tRyvNfW4k+Z4LwUKRcAZAllm13RhRkKhbEV2mo2AR8Vn6uia+OXmJb8mx22okx2YW+Vn9gPCeHWqEKh3/cSGnwlMgdwImP08zU2fvIYCFKAABShAAecXYI2sbAJZzr/8nCEFzBVwikAWa2SZuynYmt0JWKo8015IYRQVlBBzrHHre2Q3SPdKrRdIgS7G9VJ2tv65cZHdTYoDsksBZqTa5bJwUBSgAAUoQAGHF2CNLAayHH4TcwL2I+AUgSzWyLKfDcWR2EQgfX0sGZoctzEku47yer1NBs1GHVKAGakOuWwcNAUoQAEKUMDuBVgjy06OFi7/v2T8Fy9TN8zkbkXtfvNwgBTIKOAMgSyzV5UZCWaLsr07FWBG1p0K8n4KUIACFKAABShAgcIUYI0sOyr2fiDGauyFOpUtYCCrMH8s2Hd+BZwhkGV2TRdmJOR3N/E+WwmwRpatZNluRgGzn6cUpgAFKEABClCAAkqANbLsMJAVUNsNQc8X4Q6lgMMJOEUgizWyHG7fccB5FLjx1sJjAFJeXZvLtxYi0prsVpdvLcyjtwtfzoxUF158Tp0CFKAABShgQwHWyCrAQFYRd6CMlwbvYsDFROD0BT11ab8/boXKyCrqAfg/4IahzzKQZcN9z6ZtJOAUgSzWyLLR7mCz9iTgVqX1EEhMTBmTgJwvgZnJSeIoNDfp5pZ8P4BxAJqkXCMh2ljj1q+15TzuH9/Y+6q7pfeJQbs+tGU/bLtgBJiRWjDO7IUCFKAABSjgagKskVWAgaxa91lwf3nN2GOXrkgcPa3j5LkbwayUQNbdXgKPVbMwkOVqP4lOMl9nCGSZvRTMSDBblO2ZJeBW5ZkNkOKZXLUnxbfJf65/MVfX5uMiFcC64m4JBORgSGGNG7yrWD6a4S0UoAAFKEABClCAAi4gwBpZ+Qhk1SivofZ9lmy3R9Q/Og7F3ah5lfIJqOWGEsVvkZ/4T8dPsTeuSQlkVSyt4cGKWr4DWR07dkKbNs+ie/dXTN2+7dp3QIcXX0TXrp1NbZeNOZeAMwSyzK7pwowE59rjzjYbS5XWXYXE19nOS4jJySfWD7LF3G8FsDAQUgoIoX5THBk3aNdUW/THNgtWwOznacGOnr1RgAIUoAAFKGCvAqyRlc9AlkXkTHfkVPpAVuMabrj3rlv3qe//cepWRtYvJ6zwuVeDX9msA1kJCQmYMGES1m/YiCuJiXigVi2MGDEU9evVM/ZYQQeypJT4fMZMLFq0GGfO/I1KlSqiZ48e6NKlkzGen38+iJiYGLRv385efwY4LhMFnCKQxRpZJu4INuUIAu4V//e4LtBdQNYCUBsC6jemXyRwVECsSY5bv8XseWQSwPIw+hA4GzdwVxmz+2N7hSPAjNTCcWevFKAABShAAWcXYI2sfASy8rspKt2tweceDcWLAH9flIj+RzeOGKqPysg6dkZH2RIiy0CWChp16foKhBAIDQlG2bL3Ys2adRg7bjzWrVuNKpUrF3gga/bsuZg9Zw6mf/oJqlb1xS+/HMLb7/RDcPB7eL5tG3z11RxERGzH11/PzRObruvQtBvHMPlxHAGnCGSxRpbjbDgTR1r5w6ZbIdD4ZpOZ/0vFjYyhjL3e9gV580UqmTSS/kvqV6rNmxGcNA3fui7lv2Sal7NIhMYN3hVi4vQLvKnKHzVdBqAtAPdsOxe48Zvkjf9N+8nwlTS/FEJmcn2mrai7RGat3+rp9n6Mm8TtI8r0HrknbtDuFgUObCcdMiPVThaCw6AABShAAQo4mQBrZBVgICtl72g3/9Qs0/wxWAWyTp7X4V0060DWvn378Ur317Bn9w6UKXPrH6y7v9oDDRs+jr593koXyFKBr5kzZ2HO3Lm4evUqGjVqhNEfhKB06dJYvHgJFi1eghXfqr9LAFFR0Xi6RSscP/4H3CwWHDp0CCMDRyEu7k80bfokjh07htdeffW2o4UDBgyCl7cXxoz+IPVHY+HCRUhITMThw0ewadMmJCVdh7e3F/b93w9GcCqrMa1cuQofT/vUyOpSAbGDP+/HkSN/YPjwkYiKjkKFChUwKigQTz3V1Ojr4MFfEDTqPWOMTz7ZBGfPnkXbNm2MMSYmXsEHo8dg/foNKFHiLrz5Zm9069rFyX587W86zhDIMluVGQlmi9qmvcofNZXaRc2oy+RWuthtQQqPcl4Sv6vvGv+T+ilW4fZrvU95yx2ZDbP2PenbXQqgVq00Xwu9dVdImlDMrd+pswue2AbGRq2my8iCCuaJGxlZkOfiBu0ufbPbGzPPOOuQjG9cDr41ysOHb9zTMZOB//7vbX/m8b952eUKlzP989CVU1cy+XptlbeGpDPxWaZmJ5+9cZ9eQr8SN2hXzincNnJmsxSgAAUoQAEKUMAZBVgjqxACWZltJBXIOpcgYdGQZUbWrC+/wooVK7Fu7eos92Lao4XffLMIn3w6HXNmz8L/s3ce4FFUaxj+ZjabhN47hC4g7aIoIBBAROkCAkqvgg2khaomgFQlIIpKVUBA6SCIAiK9CIIUBREhJPRe07bMfc6EhARSNmF2d3b2m+e5z/XuzjnnP+//73D9+M4/RYsWxZix41TBatnSJakKWTarFYH1GuD119vjzd69sHfvPvTrPwAffjDqMSHrt61b0b//QAwdOkR1YGXPnj1JbI86slKLSQhZQUHDMP3zz1AvUIhVCmrWqoMxY0LQskVzLFjwHUKnTsPBg/thtVgQWO9FdOrYAT17dse+fb+j//sDMWrkCDXGIUHDcPPGDUybFoqLly7hjTc6YvbsmQlHMI34Y9bDnowgZGnd04WOBD1UZtoxCCGLgkPanLS+43FBCzZAMkyPLG+vK62fp1rXH+cjARIgARIgARLwTAL8W0IdCVm3HxwzTKlH1meffQ7hykrtmF5iIavlq23QutWr6NGjm1qdwqX0zLPPYe2alWrvqpQcWfv27sN7/fpj//59qjtLXKk1exfurVmz52L79h343/+qom/fN1H7hRfUcY8KWanFJBxcs2bPwU/rf1THWiwWtb9W2bJl1eOUN2/exDPPPo9dO7fhzJkwNcYD+/fB9CBGsfdWrV5F69atUPV/z2Ddj6tRrpx4gzwQHDwafn5+GDlyuGf+Uj0kakMIWeyR5SHVpm2Y3iY4+AY0edqmoLIkSwGK3V5ckqQASCgOBcVVshLOQsFZRVHCJVk+q9iVcJPJdjg2bOMJbcnHzZZI0BoMwBY+yBhvLfS2unq0NuhIdcavhXOSAAmQAAmQAAmwR1YqQpYry0OIWDkypa4rLlj4HZYtW44f165OMbTEQlbNWrUxfvw4vNgg/vAEUKdufYwZHYyrV6+mKGSt+3EdZs+Zm8T55chbC4VQJo7yjf14HCaM/xhNmzZ5TMhKLaY7d+4kEbLEJjdv/hVz532Dy5evQByVDAsLw47tW3Hgjz/w9dez8POGdQks4oWsOnVro379hvD1fXBSRRzvsNvRrGkT1aHFy3kEDCFksUeW8wpExzN7heBQonEJHzvegCK/AUmpmqF0KNgrQZpvUeQfcW7d+QzNkcogIWhFmk19IgZtn6L13O6YzyvqKhWwdKS6o+q4JgmQAAmQAAkYnwB7ZOlEyHKk1IR406VLd+zZvRM5c+ZIGNL3rXdQvfqz6jHAxELWa23bqw6lLp07qfdGRUXhf9WqY/nyH3Dyn5OYP38h1q5dpX6XuEfW77//jkGDhmDvnl0Ja6QkZIkjfF27dEKVKlUS7hVC1oXzF/DVVzMeE7JSi+m/U/8lEbKEG6t5i1fx/ZJFqFy5MoTQVfV/z6pCVnhEON57730c2B/Xd0tc8ULWa6+1RsVKVbF92xYUKVLEEbS8RyMCRhCyNEKRMA0dCVoTdc58hhUcCrfIa/axtlaAVgCaaklPgrLUDmm9zRa7Hud/va7l3EaZy7B1ZZQEcR8kQAIkQAIkQAIeSYA9sjxIyBIV1rlzN/j5+WL06BDkzp0b69avx5gxH6vH6EqUKJFEyFq2fAW++GIGvv1mrtooffz4iaqTad2Pa3DkyFG80aET1qxegVy5cuHTT0OxdNlytdm73WZDvfoNMWjQALRr+xqOHftLvXfE8GGP9cgSQtbp06dVB1bJkiVVx9Q77/ZTBbT33n1H7cUl3F2iqXy2bNmQWkxr1qxNImQdPHgInbt0w7Kl36tvaPz++6WYEjoVW3/bjEKFCql9vDp17KgendyzZ2+SPl6ip1dsbCwmT5qgCl3iiKNoiF+jxvMe+UP1lKCNIGRp3dOFjgTPqF4jCg4+xZq8DAlfACj7aBYU4JQEZZGkyJfssv2yBNNlk91+OSY66rK4188/UwGbLBdQYCsg2+UCimQvCEhtobY5T3qJuSDZB9jO/rLeM7LtuiiNWFfpoaf18zQ9a/NeEiABEiABEiAB4xJgjywPE7Ju3bqNj8eNx6ZNm2Gz2VCx4tMIChqM6s8+q1ZpYkeWOIonjuUtWLAQN27cRI3nn8O4cWNRsGBB9V4hbIk+WVmyZEbzZs0gmsnHv7Xw6LFjGD58JG7evIWyZcvg2rVr6Nihw2NClhCLpk//AmvWrlWP/+XPn19t+j5w0AC1v5Y4wtixUxdcvHgJ+3/fA39//xRjEs3eE/fIEjF+8skULF6yBGazL7p26YxVq1dj0sTxqF69esKbFc+eDUedOrVx5vQZdO3aRY3x7t27GDduAjb/ukUVtBq+2ACjRwc/1ozeuD9t9+zMEEIWe2S5p3jcvKrRBAdzsSZvKxK+fATrRUVRVkKW1tvObtiQEeQ+RRvXV0xyO0lR3gCQO/EcEpT3LeE/T8/IvEYdY7S6Sm+e6EhNLzHeTwIkQAIkQAIk4AgB9sjyMCHLkaR6yz1CyItv9C72XL/BSxg2LAhNGr/iLQh0t09DCFnskaW7unJFQEYSHEwBTZtLUOLemhF3/SRJykqL5L8CYatvacKzWMvCPrCKXlvdAVSOn1OB1MIW/tPD5oWaLOa5kxiprjKSBTpSM0KNY0iABEiABEiABNIiwB5ZFLLSqhHdfi+OOw4eNBDPPvsMVq5cjdFjxuK3LZuQN29e3cZs9MCMIGRpnSM6ErQm6pz5jCQ4+AQ0WQugxQNSy6zhG9o7hxqA4q8U8lHkmYnW+9EavqGl09bzsImNVFcehp7hkgAJkAAJkAAJGJgAe2RRyPLY8hZvNPx0ylSci4hA8RLFMWrUCLxQq5bH7scIgRtByNK6pwsdCZ5R2UYSHEwBTc5IQAmVvF1pYD3381ZnZiGxA0wCIizhGwKcuZ4nzW2kusoId62fpxmJgWNIgARIgARIgASMR4A9sihkGa+quSO3ETCEkMUeWW6rH3cubCTBwSegiRLP0hq+wSV/zrtjTXfWi6NrG6muHN1z4vvoSM0INY4hARIgARIgARJIiwB7ZFHISqtG+D0JOEzAEEIWe2Q5nG8j3WgkwcEdopI71vSE+jNSXWWENx2pGaHGMSRAAiRAAiRAAmkRYI8sCllp1Qi/JwGHCRhByHJ4sw7eSEeCg6DcfJuRBIfEopI7sLrKBeaOvaV3TSPVVXr3zvtJgARIgARIgARIwFkE2CNL9KotWXqwBOnTnj2648MPRzmLNeclAcMTGDt2HOZ98y0UKEPOnvlviiduWOueLnQkeEYVGElwoJCln5ozUl1lhKrWz9OMxMAxJEACJEACJEACxiPgkt4ZesdWsmSZFgqwtlbNmli8eKHew2V8JKBbAh07dsGevXshAS3PnDn1o24DTSUwOqg8MWtPHrORBAcKWU9eD1rNYKS6yggTPk8zQo1jSIAESIAESIAE0iLAHlkASpUqlcOuyLcErF07t6Nw4UJpceP3JEACjxC4cOEiatcJVD+VJXvO06dP3/ZESFo7qOhI8Iwq8HbBwTOy5HlRentdaf089bwKYMQkQAIkQAIkQALOIMAeWQ+olihRejEkqQOPFzqjzDinNxCIP1YIRVkSFvZfR2/YsyN7pCPBEUruv8fbBQf3Z8CYEbCujJlX7ooESIAESIAESMC9BNgj6wH/UqVKVbcr8n7xP2fN/AqNGr3k3sxwdRLwIAKbNm1Gn75vqxHLkv2506dPH/Cg8JOEqrWDio4Ez6gECg6ekSdPi9Lb60rr56mn5Z/xkgAJkAAJkAAJOIcAe2TWgJUAACAASURBVGQl4lqiRJlgSAgxmUz46ssvKGY5p+Y4q8EICBHr7Xfeg81mAxSEhIWdGu3JW6SDypOzl/HYvV1wyDg5jkyNgLfXFZ+n/H2QAAmQAAmQAAk4gwB7ZD1CtUTJsjMBpY/4WBwz7NWrJ3tmOaPyOKfHExA9sebOnae+pTDukmaFnfm3r6dvTGsHFR0JnlER3i44eEaWPC9Kb68rrZ+nnlcBjJgESIAESIAESMAZBNgjKxmq8c6s+K/E2wwrVCiPbNmzOSMHnJMEPIrA3Tt3cfz4CfXthAmXAZxYzkoCHQnOIqvtvN4uOGhLk7PFE2BdsRZIgARIgARIgARIQHsC7JGVAlO1Z5ZdGiQawGuPnTOSgEEIKMoSWVZCPbkn1qOZ0NpBRUeCPmu9eGjgYEXBx5AwPHzQ9s/iBYeA0MD3AUxUgA8iBm2fos/oGZWnEPB2IUvr56mn5J1xkgAJkAAJkAAJOJcAe2SlwbdUqVI5FEUOtEN5SlKkrM5NB2d/QED6X/Vng/888EcIieiPgCIp92RIJyXJvv306dO39Rfhk0VEB9WT8fOU0eUm1c4WZZavA5IVEiKhIA+g3ACQCZB8Mllsef4Ztuuup+yHceqTgNcLWaXKhECB6D86Ouz0Kf6Zrs8yZVQkQAIkQAIk4HEE2CPL41LmDQEr0ugJU2zBI4bI3rBb7lFfBLR2UNGRoK/8Jo4mIDRwAqAMAiTfh58rsYAUGj5o+wj9Rs7IPIWA1wtZJcrUh4z6sGNrWNiprZ6SN8ZJAiRAAiRAAiSgbwLskaXv/HhldIqiSGMmfmoLHhFEIcsrK8BYm6bDS7/5jHNlma4C8EsUZUwmiy0f3Vj6zZsnRebtQpYn5YqxkgAJkAAJkAAJeA4B9sjynFx5TaTBwcGy5JfVGjKSQpbXJF1HG9XaQaW1w0tHqAwRSlJXFt1Yhkiqjjbh7UKW1s9THaWWoZAACZAACZAACbiRAHtkuRE+l06ewAMhyxIyMshERiTgagJ0ULmauHvXe8SVRTeWe9NhuNW9XshijyzD1TQ3RAIkQAIkQAJ6IMAeWXrIAmNIQoBCFgvCnQS0dlDRkeDObDq2dpwrC4MBTGFvLMeY8S7HCHi9kMUeWY4VCu8iARIgARIgARJIFwH2yEoXLt7sCgLt2i01Vax2NpaOLFfQ5hrOJkCHl7MJP/n8D1xZ32Sy2HqwN9aT8+QMDwl4u5DFWiABEiABEiABEiABZxBgjyxnUOWcT0Rg6dKlpuP/nY0JHhHk80QTcTAJZICA1g4qrR1eGdiSU4YcO6c0sCv2GlLSRukprmW323mU3SmZeHxSSZajZUneV6mo9JuLltRsGaPV1a4L2+rVLlxvm2aA3DhRRupK6+epG7fPpUmABEiABEiABHREgP9ioaNkMJQ4AsHBwT7wyxo9eiSFLNaE6wnQQZU68xMnlGyWrLZFioIWrs8OV0wPAUnCj+Z7pk7ly0t30zPOHfeyrtxBPWNrpqeu+DzNGGOOIgESIAESIAESSJ0Ae2SxQnRHgEKW7lLiVQFp7aAymiPh6DnrWopYnvOTEKJD5aI+LfUeMetK7xlKGp+jdaX189SzKDFaEiABEiABEiABZxFgjyxnkeW8GSYghCzJL2tUyMggc4Yn4UAS0AkBIzkS4o592bboBC3DcJCALJle1PMxQ9aVg4nU2W16ryud4WI4JEACJEACJEACGhJgjywNYXIqbQj06TPTXLjEnUgKWdrw5CzpI6C1g8pIjoQjEbbhgCLe8MfLowhII6oUM03Ua8isK71mJq240q4rrZ+naUXE70mABEiABEiABLyDAHtkeUeePWqXM2fONF+6ced+8IggX48KnMEagoCRHFRaJ+RohC1YgRKi9bycz7kEFLsyumpxs27zxrpybv6dNbsjdcXnqbPoc14SIAESIAES8G4C7JHl3fnX5e5VIev6nfvBIylk6TJBBg9KaweVkRwJFBw8s/gdERzcuTPWlTvpZ3xtR+pK6+dpxqPlSBIgARIgARIgASMRYI8sI2XTIHsJDg72lfyy3guhkGWQjHr3NozkSDh81hIiyVKwd2fU83bviODgzl2xrtxJP+Nr672uMr4zjiQBEiABEiABEtA7AfbI0nuGvDC+B0LW3ZCRQX5euH1u2c0EtHZQGcmRQMHBzcWZweX1LjiwrjKYWDcPc6SutH6eunnLXJ4ESIAESIAESEAnBNgjSyeJYBgPCfTrN90vT6GYOxSyWBXuIGAkB5XW/JwlOFw4dxbLFn6Nwwf24M6dW8hXoBDqNGiC1m/0ROYsWbXeRrrnW75oFgoULIK6DZsljN2zfROmjhuGMVPmonylaume05UDHBEcXBnPo2s5q65S29PdO7fQrXVdlChdDqGzlrtz+x67tiN1xeepx6aXgZMACZAACZCArgmwR5au0+OdwU2fPt3vxr3oOyEjh9KR5Z0l4NZda+2gMpIjwRmCw99H/sCYYW/BbrfhhXovI3ee/Ag7fRJ/7N2GYiVKY9y0+ciRK49ba+KNJs+hcrXnMWr8jIQ4/ti7HV+FhmD42M9RplxFt8aX1uKOCA5pzeHM751RV2nFu27Fd5j12cfqbZ/P/xHFS5ZNawi/f4SAI3Wl9fOUSSABEiABEiABEiABQYA9slgHuiMghKyb92JuB48M8tddcAyIBNJJwEiOBK0Fh9iYaLzTpRlioqMw+avvUahIQALd33dtwbhR7yHwxaYY/NGn6aSu7e3JCVnaruDc2RwRHJwbQeqza11XjuxlYO82yJ4jF079cwwNm7RBz3eHOTLMI+6xWa0w+fg4PVa915XTAXABEiABEiABEiABtxFgjyy3oefCKRGYOHFijmjFZ3XIiCENSIkEXE1AaweVkRwJWgsO2zavw5QxQ9C73wi0bNftsVR/OmYwdmzZgPkrdyBn7jzYv3srxg5/C8PGfIba9V9JuH/tsvmY8/kETJu7CqXKVkj4fOO6ZVizdD4unQ9HnnwF8HLzdnitY29Ispxwz9aNa7H6h29wPuKMKmxUr1UfnXr1V/954of9sXvbxiRxde07CG079cGvG1bhswkjMO6zBapbK/76Ze1SrFuxEBfPhyNbjpyoXb+xOl+mzFnUW2w2G1o3qIg2HXurRyhFfDeuXUFAidLo8c6wJHNpVft6Fxy0rqu0uJ09fRL9urdU6+jooX3Yve0XfLNiO0wmU8LQ9OTp+tXLWDh7Kg7t34XoqEgUK1EG7Tr3QY06DdX5fpj/JRbNnY45S39F/oJF1M/u3L6JLq/WRuvXe6D720EJ64aODcLenb9iyfrfE8SotOp42LsdEXnvLuq82ATCaZYzVx7VZebsy5G60vp56uw9cX4SIAESIAESIAHPIMAeWZ6RJ6+Kst/06X5578fcDh5BR5ZXJV4nmzWSg0prpFoLDtPGD8eWn1djwepdqlD16LXrt58xKXiA6siq91LzdAlZ382ZhmULZ6LBK6+ibIXKOH70ILZvXo9Wr/dIcN9sXr8C0yeNQs26L6Hac7VxLvwMNqxZgiLFSqqi2JlTJ3Dz+lV8EjIQxUs/hfZd3kax4qVQsEhAskLWN19Oxqrv56liWLXnXkB42ClsXLccZctVwsQvFsHHbE4QsoRQJsStl5q2QUxMDDasXgJFUVSxI2u27JqmzhHBQdMF0zmZ1nWV1vJzv5iIXzesxPzVO9UcD+nbHh9M+ArP1374dyfxQlZaeYqJjsaAXq1w7+4dNG/bBZkzZ1XnPv3vcQQFT1H7qp08fkRd4/0RE9CwSWs1vN9+WaP2WCsaUApffvdTQsi92jVAyTIV8MGEL9XPHKljIWSdOHYIufPmx0tN2iB/oSJo1KxtWhie+HtH6orP0yfGzAlIgARIgARIgASSIcAeWSwL3RHgWwt1lxKvCkhrB5WRHAlaCw6j3u+Gf08cxdJfDiZbY6dOHMOgPm3R/a0hqoPJUUfWufDTeLdrc9UV07n3gIS5p08cid82rsX8VTtUx5UQAIRQNXPJRkhS3B+H3387A4vnfY4JX3yHilWqq58ld7TwUUdW2H//4P2erVCvUQsM+mBywpq//LgUMz75CL3eG45X23dPELLyFyyMaXNXJ4hWQtATwt6Ijz9HrcBGmv7mHBEcNF0wnZNpXVepLS8Equ5tAtV+bG8PClZvfbdLMxQtXkplH3/FC1lp5enwgd34cFBPDBg5ES82bqUOj4q8jx6v1Vd7p3087Vsodjs6t3wBz71QX71PXJODB+KvIwcS6k8cq71y6Tx6t2+ItwZ+hKatOyI9dfzfyb/x1XcbVJefqy5H6krr56mr9sZ1SIAESIAESIAE9E2APbL0nR+vjG7mzJnmi9fv3A8ZGeTrlQC4aUMRMJIjQWvBQYhUQkj6ZsW2ZHN+9sy/6NetBV7v+jY69X7fYSFryTdfQPwndPYK9Uhh/HXo952qWPTRpK9V19TooX3w1+EDGBv6DcpVrKreJlxRdrsdsiwniFuOCFniaJlwgH0xfx0CSpZJWFOIGD3bNUC+/IXUPmDxAolwzPQbFtdsXFzCwTOgV2u82X8kWrTtqulvwBHBQdMF0zmZ1nWV2vKi99rHI97Bp1//gKeejsu5eCulEC+FwJkte071M0fzFO+2EiLWW4OC4e+fSR0vXl4gLlmOO674Scgg/PP3n5izdIs6d+cWtSCOqc7/+lO80f1d1SkYL2bOWrJRdf05WseqIHvjGsQ4V156rytXsuBaJEACJEACJEACriXAHlmu5c3VHCDQp89Mc5GSd+8HjxhCIcsBXrxFWwJaO6iM5EjQWnAY9X5X9djVso1/JpvEjDqyRJ+hrZtS7hEUf8RLOF7GjXgH5yPCULhocVR7vg4CGzZDhcrPJInHESFLOGx2bfsFK389mqTXkpjoo0E91SNsC9fuTlEgiRfterwzFK3f6KlpUetdcNC6rlKDN37Ue/jv5F+Y9OWShNtuXL2MIW+9jjf7j0KLtl1SFbKSy5N6/O+7WTCbfVHpf8+pzqv6jVogS9aHR0Tjj7GKo6NXLl2AqP35q3Zi5mdjcefWDbXXmjjmeuzP/QmClKN1rGchS+vnqaY/DE5GAiRAAiRAAiTgsQTYI8tjU2fcwIODg30kv6xRISODzMbdJXemVwJGclBpzVhrwUH0CBK9ghau2YUcubTrkTXpo/exe/smfDjxK8jSw8bu8TwCSpVF3nwF1f8pnDMHf98J4dY6sGc7Lp4/i8CXmmHwB58kNIV3RMgSa4om3Ss3H0nSTF6sIY5Qin5ZYp8pOX0oZElx5/yceIkG691a14V4q19yV6myT2Pa3JXpFrLEgKuXL2Lvjk04tH83xHFD/0yZMWr8DDxd5Vl1PtEQvsdr9dQ+WeFn/lUFqymzlkG8bEC8NGDh2j3qMVrRqy3+yKOjdaxrIatUmRAoCIaE0WGnT4U4Mb2cmgRIgARIgARIwIsIsEeWFyXbU7a6dOlS01+nzsaMHhnk/PeHewoUxukyAlo7qIzkSNBayIpveJ3SWwtDPx4K8WbD+LcWirfCBQ/uhSEfTVHFpvjr0bcWzpsxSX0T4aPH/BIXkdViwaWLEciRM3fCcTJxrFA0bBdjR0+Zq4oK4nJEyJo/cwpWLJqt9ikqElAyYSlxtLBH2/pq76JPvvqBQlYyv2St6yqlh8WPyxdg9vTxav3Ev0Uy/t6/j/6h5m/6N2tQonQ5h/Mk3jgpemIlzvnli+cw6M3X1JcGiOOk8dd73ZqjTLlK+Pf4UdRt2FQ9UiiaxHdpWQvd3hoCUbdC/Ip/26EjdSzm1rWQVaJMfcioDzu2hoWd2uqyBzkXIgESIAESIAESMDQB9sgydHo9c3PBwcGy5JfVEjIy6OG70D1zK4yaBGAkh5fWgkN0dBTe69oMsbExmPzl9yhYuFhCxRzctwOjh/VVj2gNHDVJ/VwcBXync1O06dAL3d8OUj8T4tOED/ph747N6psGS5WtgL8O78eIfl3Uht7DRk9LcEgJgUGIZ+27voXI+/fVPkU16zbE8LHTE9bdvW0jJn7YH0Ehoaj7YlP18y4tX0CZ8pUQPHlWwn2PNnuPPwYpeiXFN/QWN8ff1/PdYWofJDqyHn8oaF1XKT12RDN+k48PQmctf+yWu3duo+urL6BZm84QwqqjeYoXmz6f/yOKlyybMG+/7i1V51fiNxKKezetX4H79+5g6pyVKP3U0+r9I/t3gXhZQFRUJBav25cgsjlSx6IHl56FLP4RQAIkQAIkQAIkQALOIMAeWc6gyjmfiACFrCfCx8FPSEBrB5XWDq8n3N4TDXeG4HDi2CGEBPWBothRp0ETtTn72dMn1WN6JUuXw9ip3yRxTA3s/RrOnjmJpq06IEfOPDiwdxtO/n1EPSIYL2SJTYrjWkJEEj2LatZ9SXXN/LRqMfwzZcHUOcuROUs2zJw2FutXLlLXrVzteYijZ+tWLhLqGL78bgOyZc+h8hKi2Kl/jqJNh97qfJWr1UgQqERvIzFWXDOnjsX6VYtQvWY9td/W+Ygz+GXtDyhZprwq1PmYzQ4LJE+UqEcGs0cW1B5lQsgSDdbbduqTLN6QIb0h3v737crtgCShdYOKeLQp/6NHQC+dD8eA3q8hU+bMaNzydfVtmMI5KITVTr364/Vu7ySsFe8ozJ03f9waDy7hABQiV8Wqz2HC5wuTxOZIHetZyNL6earl74JzkQAJkAAJkAAJeC4B9sjy3NwZOXJp9PhPbMEjgx5vbmPkXXNvuiBgJAeV1kCdIWSJGC+cO4tlC7/G4QN7VDEpW46cak+hZ2rUxdCQqcicJWvCVoRw8PXUMTjx15+QJBnP1qyLkqXLQxztSyxkiSN9a5cvwC9rl6pHCLNmy47najVQhQxxnFBcQvxau2wBNq1bjksXIpA1ew5VlOrU630UKhKQsOa/J45i6sfDcO3KRXTtOxjNX+ucrJAl3GHrVizEhtXfq2vmzJUXdRo0Roce7yW4bBx1+miZOwpZUI8UiqOFXy/+RW3sn9y1af1yfD7pA/V4n3irpSNClpgnIuwUvpvzGY79+bvqLixUpDiatu6IV1q0T3jzpbhPfNexWQ00eLkl3g0ak6Sm+3R4GZ17D1DdgokvR+pY10IWe2Rp+VPmXCRAAiRAAiRAAg8IsEcWS0GXBELGf6KEjAyi0KrL7Bg7KK0dVEZyJDhLyEquon5Y8BUWz52u9hn6YOJXKYoPxq5GbXZHIUsbjpwlKQFH6krr5ylzQAIkQAIkQAIkQAKCAHtksQ50SeCBkCUcWYouA2RQJOAgASM5vFwpZAm8+3b+iiXfzFAdMqJZOq+MEXBEcMjYzNqMcnVdaRM1Z9F7XTFDJEACJEACJEACxiXAHlnGza1H7Wz0uE/fh6xMUBQMDxkZND1k/Cf24BFDTCETPn1fAiYoUIJHjxw62aM2xWA9koDWDiojORIoOHhkSUOCFFK5mGm0XqNnXek1M6nH5Uhdaf089UxSjJoESIAESIAESEBrAjy6pTVRzpchAsHB07PLfjHXAFgVBVGQkEsCbgDIrACmSJM97+Rhw+5maHIOIoF0EDCSgyod23boVgoODmHS3U2OCA7uDJp15U76GV/bkbri8zTjfDmSBEiABEiABEggZQLskcXq0A2BkPGfTgCUQQB8EwUVqyiYOnpU0HDdBMpADE1AaweVkRwJRyJswwFlgqELwJCbk0ZUKWaaqNetsa70mpm04kq7rrR+nqYVEb8nARIgARIgARLwDgLskeUdefaIXQYHz8gq+UVeFi6sJEJWVr98o/v3v+MRm2CQJPAIASM5Eo6dUxrYFdsWJtmzCMiS6cVKRaXf9Bo160qvmUk9Lr3XlWdSZdQkQAIkQAIkQAKOEGCPLEco8R6XERg94ZMPFQVDAWQVbysHpGkhI4cMc1kAXMjrCWjtoDKaI+HoOetaRUELry8UDwEgSfixclGflnoPl3Wl9wwljc/RutL6eepZlBgtCZAACZAACZCAswiwR5azyHLeDBEY8sknWbJZcFkBsgCwKJl88o8eOPBWhibjIBLIAAEjOagysP00h5w4oWSzZLUtopiVJiq33yDEBvM9U6fy5SXd9xdkXbm9XBwOID11xeepw1h5IwmQAAmQAAmQQDoIsEdWOmDxVtcQGD3hk1GKgg8BfB4yMijINatyFRKII6C1g8qojoS442D2Gord7m+E2tl1YVu92oXrbdPzXmRZVhyJTwFiZEnep+fjhCntw2h15Ui+3H2PM+tK6+epu1lxfRIgARIgARIgAX0QYI8sfeSBUSQiEBoamulOtG2eEmN6d/ToQeLNhbxIwGMJ0JHgGakLCA20hN+WM2H0VqtnRMwo9U6g3KTa2aLMpm8yWWw9/hm2S/euOL3zZHwkQAIkQAIkQAIkEE+APbI8pBZ6z75XQJbkQEj20oDJz0PCZphpErDFQJH/syv27XPezCoa3fNyMwGtHVR0JLg5oQ4uHxAaGCXflnOFjd4a7eAQ3kYCqRIICA0Ub/gcDGBK+KDtI7wRl9bPU29kyD2TAAmQAAmQAAk8ToA9snReFX3m3X9GUuRhCpT2Og+V4T0hAQnSUkWyT5rVM8vBJ5yKw5+AAB1UTwDPg4cGhAbejfKTC119d+s9D94GQ9cJgQdurKsAxF88xWSy2PJ5oyuLz1OdFCTDIAESIAESIAGDEWCPLB0ntM/c6AGAfaqOQ2RoTiEgD5zVy3+aU6bmpGkS0NpBRUdCmsh1cUNAaOBNWZJLhg3cypdL6CIjnh2E6sZSlEGQJF8oSiwkKdQbXVlaP089uyoYPQmQAAmQAAmQgFYE2CNLK5Iaz9Nn7r3hgCyOJfDySgL2EbN6ZZ3olVs32KbpSPCMhAaEBl612uUKF4ZsveYZETNKvRJ4xI0VH6bXurL0mifGRQIkQAIkQAIk4LkE2CNLh7l7c9a9VyST/LMOQ2NILiSg2OyNZ/fJ+osLl+RS8W8tBOoB2BYWdmrrk0KhI+FJCbpmfEBo4EXZKlcLG7r1kmtW5CpGJZDEjRW/SS91ZdGRatQq575IgARIgARIwL0E2CPLvfyTXf3NOZF7JAk1dRgaQ3IhAUXB3tm9M9dy4ZJcSghZpcqEQEEwJIwOO30qhFC8g0BAaGCEzUeudb7/1nPesWPu0hkEHrixrkNRbJAQCUi5IeE6FGQGFJ9MFnseb+qVxeepM6qMc5IACZAACZAACbBHls5qoPfsu/Vl2fSbzsJiOG4iYLfbGsx5M9sTu4LcFL5HLqu1g4qOBM8og4DQwDOyJDcIG7g1zDMiZpR6JFAsNHCwBHwMYHj4oO2fBYQGKuGDtksBoYHvQ8FEScIHZwdtn6LH2J0Rk9bPU2fEyDlJgARIgARIgAQ8jwB7ZOksZ2/OuT9ekiSvfE23zlKhi3AURZkwu3eWkboIhkFkiAAdCRnC5vJBAaGB/9ohNzk3aOsply/OBQ1LIF7IMuwGuTESIAESIAESIAEScAMB9shyA/TUluwz9/4aQGqps7AYjtsIKGtn9cryqtuW98KFtXZQ0ZHgGUUUEBp4XLbLrcOGbD3hGREzSk8g4O1CltbPU0/IOWMkARIgARIgARJwPgH2yHI+43St8Oa8yN8kBfXTNYg3G5aAImHr7J6ZGxh2gzrcGB1UOkyKC0IKmBp4VLHKHSKCth5zwXJcwksIeL2QxZ6DXlLp3CYJkAAJkAAJuJYAe2S5lneaq1HIShORV91AIcv16dbaQUVHgutzmJEVA0IDD8mS3CNs4NY/MzKeY0ggOQJeL2SVKFMfMurDjq1avAWWVUYCJEACJEACJEACggB7ZOmsDihk6Swhbg6HQpabE6DB8nR4aQDRBVMEhAbuVyC/HTFo6wEXLMclvISAtwtZXpJmbpMESIAESIAESMDFBNgjy8XA01qOQlZahLzrewpZrs+31g4qrR1eridi3BUDQgN3mRT7O2cG7zxcLDRwj0mRB973x7FMsfa5is2+OmLIziXG3T135goC3i5kaf08dUXOuAYJkAAJkAAJkID+CbBHls5yRCFLZwlxczgUslyfADqoXM/cHSsWnF43n68F5yBJCoCNEpTiCqS9ALoAMCmQRkcM2jbeHbFxTeMQ8Hohiz2yjFPM3AkJkAAJkAAJ6IgAe2TpKBkiFApZOkuIm8OhkOX6BGjtoKIjwfU5dHTF4qH1ghQowQCyAIgEhKYlZQZwJXzQ9gKOzsP7SCAlAl4vZLFHFn8cJEACJEACJEACTiDAHllOgPokU1LIehJ6xhtLIcvzc0qHl75zGBAaeB1A7odRKpGQMDx84I7P9R05o/MEAt4uZHlCjhgjCZAACZAACZCA5xFgjyyd5czdQlZgeR90esGMvvOidEYm5XCqBpjQoZYZwSujEWPxmLAdCpRClkOYNL1JaweV1g4vTTfLyVA8tO57iiRNggLhxBKvQLkZPnB7ImGLkEgg4wS8XcjS+nma8UxwJAmQAAmQAAmQgJEIsEeWzrJJIevxhAxs7Ifr9xQs2BmbbLZyZZHwv+ImbP3bqh4MSusqU0BGUDM/DP8hGjfvOzIirRmd9z2FLOexTWlmOqhcz9zdKwaEBl4EUBDAfUjSmPCB2ya7OyaubwwCXi9ksUeWMQqZuyABEiABEiABnRFgjyydJYRCVvqFrPSmkEJWeol51/1aO6joSNB//RQPDexlh/SZBAW+PlnynOq/IUb/UTNCTyDg9UIWe2R5QpkyRhIgARIgARLwOALskaWzlOlNyGpcxQc1Spuw86QNL1f2gb9ZwvHzNszfaUFUbJybqWhuWT2OGJBXxvW7CrYet+L1mmZ8uDwaV+4okCWgWTUz6j5lgtkHOH3Fju/3WHD1btz4fNkk9f6yBU2w2BQcjbBj2T4L8mSV8EErvyQZCloSjTtRSV1UIr6e9XwTjkPO6JYJaw5aUKGwCWULyrgdqWDFfgsOhtnwzku+EEcRKUArfAAAIABJREFU46/tJ6xYtNsCkww0r2bGC2VNyOonIeKGXR3z7yW7WyuEjiy34tdkcW9xeB07oxS0+9helCSppN1mN2sCz4WTLP1n0bAcfrn+faVE05UuXFbzpWSTbFEU5YxsNW2pVFK6pPkCnDBdBLxdyEoXLN5MAiRAAiRAAiRAAg4SYI8sB0G56jY9ClmtnjXjSIQN6w5ZkTOzhF71fbHlL6sqFvnIwJi2/rhxT8GqPywwSUDb580onldOELLeqGlGhSImLN4di7vRQItqPiiSS0bIqmjY7VDFqlv348SmrP4SutbxxdlrdszdGgsfE9Dv5bijhWK8xfZ4JpITsqx2YNHuWFy4qaBRJR9UK27C0O+j1PFlC8gY2MQPHyyLxvX7ihpDlzpmVClmUkWtq3cUBJY3oc5TPghZGZ0guLmqBhKvQyHL9dS1dlBp7fByPZHUVxQCluJjH6dA6am32Lw9HgnSPMkqj6Kg5b5K8HYhS+vnqfsyyZVJgARIgARIgAT0RIA9svSUDQB6FLJequSj9pOyPhCRhPspqx8wfWMsni1pQu/6vhjxQzRuRcY5pYRQNKSZnypkRcYCn3Twx9SfY3DyYpy7KbOfhNBO/gj9KUZ1PH3RPRNW7bdg819W9XshghXKKWHvqbgF0+qRlZyQJUS1zcfi5sufXcLYtv6Y+GMMzly149GjhdkzSZj8hj/mbovF/tMPlbIRLf1w/kbKvblcUToUslxBOeka3uKg0oLskTBLLZikZQCKaDEf53AKgfOwKe2qlDDvccrsnDRVAl4vZLFHFn8hJEACJEACJEACTiDAHllOgPokU+pRyBJvMhy5NDphW+IYoXBUTV4fg6b/81GdS4m/L5VfxrDmcUJW7qySKkTFWpGkEbufDzB/Ryx2/2vDK5V98OqzZvx32Y6/z9tw8KwNl28/PD6YESFLuLHE3OISQpUQ06b9HIPjFx4XsioWNaH/y74Y+n20egwx/mpT3YzyhWWMX+u+djkUsp7k15SxsVo7qIzqSHhwlPAARayM1ZmLR52XrabqdGa5mDoArxey2CPL9UXHFUmABEiABEjACwiwR5bOkuxpQlaTqj6o/ZSPekwv/kpOyJq8LgZ3o5P2thK9rqItcaPEkcXKxUx4uoisHvFbe8iCX47EOaqcLWSVKyRjUBM/DFkcnSTG154zo1JRGaNXUcjS2c/Eo8IxqsPraIRtLo8Tek4pimOGlYuZenlOxMaI1NuFLGNkkbsgARIgARIgARLQGwH2yNJZRjxNyKpWwoQ+9X0x7IeHTdhFg/UhTeMcWTFWYNIb/vhmWyz2/ffw2J6/GaqIJRq91y3ngw1HrAnN40WD+aZVzei/MErNzoDGfmoPrgU7Y5PNVnJHC1NzZJUuIGNoMz/1uOTN+wqy+UuY3MEfs3+LVRvCx1/CVSYa0s/blvy6rigdOrJcQTnpGlo7qLR2eLmeyOMrPnBjXdRDLIzBcQKy1VSIrizHeWlxp7cLWVo/T7XICecgARIgARIgARLwfALskaWzHHqakCXe9jfmtbhm76v/sECWgfY1zAjI87DZe+faZrXZ+uI9FrWJu3ibYJvqPvhoRQxsCjCunR8On7VjwxHx9kAJ7Z83I5MvMOHHOCeU6MElembN22bB5Tv2hF5d8alLr5BVIIekxiwaux87Z1NjF29NfKaECQt3ircp2tXjkg0r+qgxRFx335sLKWS5/gdqVAeVliSPRFg7Alik5ZycyyUEOlUp5rPYJStxEZWA1wtZ7JHFXwIJkAAJkAAJkIATCLBHlhOgPsmUniZkib2KfllCrCqWR8bl23YcOmtDi2pmjFoWjWt3FZhNUHtgCcFJNHq/eNOOtQet6psQxSWau4s3HZbMJ8NiU9Sm8Ev3WdQ3FYpLfN6nga86NnjFw6byGRWyxLhudX1RvaQJO/6xqmsJQU7EWKuMCZl8JYRft2PVAYvajN6dF4Us19PX2kFlREfC0XO2UYqifOz67HjOijabDa0bVESjZm3Rb5g+UEmS9EHloqZxnkPR8yP1eiGLPbI8v4i5AxIgARIgARLQIQH2yNJZUtwtZGUEhxCqLA9P5KmCVfe6vui3IApW9+pAGdmOrsZQyNJVOjIUjBEdXofPWkIkWQrOEJAMDoq8fw+rvp+HPds34vLFc/D3z4ynqzyLtp37oGz5yhmc1XnD9ChkKXZldNXi5hDn7ZozP0rA24UsVgQJkAAJkAAJkAAJOIMAe2Q5g+oTzOlpQpZwMgW39seWv604GmFD/uwSutTxVV1V3+5wX2+pJ0iBroZSyHJ9OrR2UGnt8HI9kcdXdLWQdf/eHQzu004VsGo3aIwSpcrhzu2b2LllA25cv4rhYz9Dzbov6QFNQgwUsnSVDrcF4+1CltbPU7clkguTAAmQAAmQAAnoigB7ZOkqHYCnCVkCX/nCMlo+E9cX6160ojZMX3PQgpgHbyTUGWKPCodCluvTZUQHldYUXS1kLVs4EwtnT8WAkRPxYuNWCdsRLq0BvVojNjYGc5dugcnHR+utJplPiFOyLEOS0v6jk0KWU1PhMZN7vZDFHlkeU6sMlARIgARIgAQ8iQB7ZOksW54oZOkMoaHCoZDl+nRq7aAyoiPB1ULWjE8/wi9rl2Le8t+QN3+hJEWxfNEsbFy3HGOmzEXBwsXU765cuoD5M6fg0O87YLVaUbFqdfR4eygCSpZJGCtcXovnfYE92zaq7q58BQujccvX0bJtV0jirRUAvpszDUsXfI3+w8dDiGmXLoRj4drdyJ4jF+7euYWFs6Zi364tiIq8j4CSZdGh+7t4tmagOjaxkFXqqQpYs3Q+bly7goASpdHjnWGoXO15lxe33o4WHj+n5IlVLK1lmIo4CsNut6etIjo6mRPuW3J8gThyKxo8KpAkJYs5y/mWpdt844SlnDqlLMuKHbbzvpJ5VYWi0vWMLqb18zSjcXAcCZAACZAACZCAsQiwR5bO8kkhS2cJcXM4FLLcnAANljeiw8vVQtam9cvx+aQP0L7rW+jce0CqWbl29RIGvfkafH390bRVB9gVO35auRhRUZH4/Ns1qhAmRKagt9rj3NnTaPZaZxQsVAwH9m7Dvp2/ovtbQ9CmY+8kQpa/fyY0bNIG+QoUQvPXOsOuKBjU+zVcuXxBXSNfgcLY+dsGHD96ECPHfaEec4wXsrJmy44sWbPjpaZtEBMTgw2rl0BRFMxZ+ivEd6689CRkHY6I7S1B/hyAvysZcK10E4hWYO9XtZjvnHSP5AASIAESIAESIAEScBIB9shyEtiMTkshK6PkjDmOQpbr86q1g8qIjgRXC1mK3Y4xw9/CH3u3q43dhShUK/Bl5Myd57ECmTJmCA7+vgMzFvyU8P3F8+F4p0tTNG/TGb3eG44Txw7h0zGD0bFn/4SjikJc6t+9pSpAffndT0mErGGjp6m9ueIv4dISbq3gybOSOLCG9G2HmOhodXy8kJW/YGFMm7s6QbTa8vNqTBs/HCM+/hy1Ahu5tMD1ImQdjrA0liBtcOnmudgTEVCgNKlazPxzeifR+nma3vV5PwmQAAmQAAmQgDEJ6Nqib0zkqe+KQpY3Zj3lPVPIcn09GNFBpTVFVwtZIn673YYtG1bjxxULcebUCfX4X9VnauL17u+gYpXq6hZFr6yOzWqgRp2G6N1vRJJtfzSwB8x+fgidtTxFHJODB+L3XVuwfPPhJELWvBVbkTdfwYRxQ/q2V48jzvp+U5K5Dh/Yjb+PHsTr3d6GogCtG1REo2Zt0W/Yxwn3nf73uNrX683+I9GibVetU5PqfHoRso6EWzZCklyr4rmUtAEXU5RNVQLML6d3Z3yeppcY7ycBEiABEiABEnCEAHtkOULJhfekJWSJ1i1fdc+UbESHw234crP+3xQ4vIUfDoXZ8MtRq+ZkfWSgUWUf1Crjg9xZJdyJUvD7fzb8dNiCWO2Xczj+Gd0yYdHuWOz+1+bwGHEjhax04dLkZq0dVEZ0JLhDyEqcXOGw2rX1Z/WY3rUrl9BnwAdo1roTxOd9O6T879rCHTVn6RZ1qquXL2Ll4jk4tH+nOocQwcRlNvtixa9HUhWyuraqrb45cUzovBRrLqVm72fP/It+3VqgxztD0fqNnprUrKOT6EbIirBG8Uiho1nTzX3RVYr5JP9/PlIJUevnqW5oMBASIAESIAESIAG3EmCPLLfif3xxR4Ws1Qcs+CMsqSgSbYEq3DhyDWzsh+v3FCzY6Xrh6/nSJly8qSDiht2RUNN1T+faZlQNMGHpPgvO31RQIIeE154z48ptO6ZvdP1e44OnkJWuNBrqZiM6EtIjZIkje9FR9yCaq1sssfA1+yFLtpzw88/k0Nv/UiuG2JhoDH+vE8LPnML81Ttw/eoVvNetOeq/3BL1G7V4bKjZ109tsi6arvfv8ap63K91h14oWrwUMmXKggUzp+Dood/TFLK6vFobpcqUx+gpcylkZeDXeiTC6tgfVMnMfT9SwY07dhQraHJ45T//tWLnEQu6vOKPHFlpRHcY3CM3VinmQ3gZhcdxJEACJEACJEACmhJgjyxNcT75ZI4KWQt3WrDzZMYtRk8qZIm3z4ujM+m5ZAmwp3NMeuY3ycBnXTJh5X4Ltvz9kE35wjLEfseujsE5jcUzR/dEISs9mXTvvVo7qIzoSHBEyBIC1q0bV3Bg1y+4e/vxl57lzlsI1Wu/gqzZc6WacDHP250ao3S5SggKnvLYvT/M/xKL5k7Hp1//oApSHZrVwAuBjTBszGcpzrvq+3n45svJmDZ3FUqVrZBw3ychg7B3x+Y0hazBfdrh3t07mLnklyRrCJfX1csXUL7S/1I8WkhHFvAkQtadewpu30ufkHXmgg1/nbHhxWfNyOxPLSajT9iMCFlaP08zGjvHkQAJkAAJkAAJGIsA/x+dzvKphZDV/xVf3LovRCMF1UuaYLUDO/+xYfUfFhTLLeODVn5Jdh20JFp1chXLI6N9DTNK5JVxK1LB1uNWbPnLqr5HvHEVHwSW98Ff52x4rpQJS/ZYkCuLhJplfHD8gg3PlDAhk6+EI+E2fLcrFsIdJi4h4AjBrVwhE4TQ9OHyaHzczh/7Ttnw46G4myoUltG6uhmFcsq4G61gxz9W/Hw4bl1xpRZX4o0Ice2zznHrCUdW/OVjAkrll3H+hoL7MQqE+NSsmhl1nzLB7AOcvmLH93ssuHo3bsWcmSW0fd6MikVkyLKE/y7b1P3Gf5/cnswmqHsQbHx94sb8sM+Cy7fj5hRjxFHKUvlklCko43akghX7LTj4iKvu0XLk0ULX/0CN6KDSmqIjQtbvO37CubB/1KWFE6pIsbIoFFAG58+exIXwf2G1xv1Gq1SvjzIVqqUa4oeDeuL4kT8w6cslKP3U0wn3iibwHwzsjmN/7sfc5b+pfaxGvd8Nfx0+gCmzliW5d/UP36Dqs7VQskx5LP9uFhbMCsWkGYtRofIz6nyi59WgN9vixvUrWPnrUfUz0dBdNHZ/tEdWvHgmHFnVnqut3isEtw8GdMe58NOYv2pHQrP3R3tkUcjSVsgSwlb2ZFxWwoElrjpVzDh/zY7T522oW9Ws9U/Bq+bLkJBVqkwIFARDwuiw06dCvAoYN0sCJEACJEACJOA0AuyR5TS0GZtYKyHr6cIm/HTYij/O2FShqF0NM6b+HIN/LtghhJ1+L8cdLVy8OxYWG5Anq4SPWvvj17+s2PefFUVyyehe1xeL98Ri7ymbKmQJoUZ8v+dfG67fVxBYzoRW1c1YstuCXf/GjXm7oS+OnRNiVty/RAgB53aUWEcIQXZV2EksZGXPJGF8e39sOmrFnlNWFM8ro1tdX1WI2n7CmmZcj1J+pYoP2lQ3qz24dpy0qSKb/ZETjG/UNKNCEZO697vRQItqPmrsIaui1Xs/bOWnfr78dwtsdgWv1/RFZl9g/Nq4HjrJ7enNBr4IyCOrvG5HAs3/54OS+WSErIxGjDVujM0OfLc7FhduKmhUyQfVipsw9PuoBNEvuYqhkJWx39GTjNLaQWVER0JaQtbebT/iQvgp+GfKgrqN2iFbjqSuK0Wx4/qVC9ixabkqAP2vZkOUKlslxbSdjwjDyH6dVRdU7fqvoFiJMhDHCvfv2QrRPF30x+o78EN1/NnTJzHs3Y6w2+1o2qoDChQqij8P7Mae7ZvUe8S94ijiwN5t1KOFLzZpjdiYGOzYsh73795Vjz8u2/gn/Pz9UxSyoiLvq+OvX7uMpq06In/BIqqT6/AfezBw1CQ0eOVVClmp/AifxJEVHaMg1gJVvBLP1AtXbCicP+4vShJfy7fG4F6kgu5N/dVjheI/wztnfpJHg9ePzZCQVaJMfcioDzu2hoWd2ur1EAmABEiABEiABEhAEwLskaUJRu0mcVTIEoLLo8f0+i2IUj8TjiybDZiRqPH7pDf81eN2vxyJO3L36NHC12uaUTS3jCk/xYk14hLuLOHgmrIhRhWyXnzaB0O/j074Pt6lNXLpw89qP2VCpxd88f7CKFUgEwKOcCYJUSr+SixklS4gY2gzP3y8JgYR1+MUJ9HjKsaq4MQFO9KKKznyVQJMaryl88uqA2vbCRt++tOixpPVX8InHfxVUe/kxbj1MvtJCO3kj9CfYvDvJTtK5pdx5Y6Ce9FxbiohOPVt6Iv+C6LUhvGP7kk0lZ/Q3h+fb4xVRTxxCYfWkKZ+qpMr7JpdHbPqDws2H4vjkD+7hLFt/THxxxicuZpyrzAKWdr9ttw1kxEdXqkJWXduXcOv6xchc5bsqPdKe9y7cwu7fl2J3PkKQTb5IyryFqIj76Fh886qiLVx9Tfqf7/YrBNy5MqbYpru3rmFFYvn4PedW3Dl0nlkypwFRQNKoWmbjqjToEmSflsRZ//Dd7On4cjBfaowFVCyDNp06KXeF3+JNwwunD0NZ8+cRPYcuVRBSsz5y49L8f6ICeqRw5QcWWKOWzeuY8HsUOzf9Rsi799TnV7tu76F52u/qC7BZu8p/+KeRMgSs0ZFK8j04IhgrEWBrznu/8pExyrw9334f2ssVqiuW0eFrPicibdejho/I8UNiNoRLkHxZsyW7bqleJ8QVI8fPYiXmr2G/sPGpXjf+lWLMHPq2CQvGnDX8yq1dTMiZOlxH4yJBEiABEiABEjA8wmwR5bOcuiokLXmoAUHzyRt9n7pwTE2IWTdiQS+3fGwufmY1/zV5vBr/ohzSj0qZAU181OPvVkSaSrib7jFETghVCUnWiX3mRDDhKMpeEU0RDzJ9YZKLGSJY359XvRFxSIm9dji3xfsqotMCFDiSiuu1NKXL5uEGmVMaFjRR3VBffpTDMoViuuXJQSpxO26/HyA+Tvi3ioo3GnNq5nxVEFZFb7EkUXx/cDvohEZ+/ieKhU1od/Lvhi8ODpB/Ho0rkc5CCeaENSm/RyD4xcoZOnpZ6i1g0prh5ceWKUkZAkX1KY13+L+vdt4sWlH5MxTANcun8P2jcuQr2Ax+Jgz4+a187BaY9GoRVdkypINly+cxe4tq1CibGVUq9FQD9szbAw6emthhrslJm72HhOr4MoNO/LnluHnK2HR5hhULuWDKqVMWLwp7i9lOjbyg2j2/vO+2DQdWc4Ssvz9M2H+6p2qUJrcNaBXa9VZmPiNmXoswowIWVo/T/XIhTGRAAmQAAmQAAm4ngB7ZLmeeaorOipkpdbsPaNCluiLFS90xQcpjm6II4iOClkl8skY0cJPPVJ38VbaQlb8OkIAEz2pKgeYVBfYV5tjcOKiXRWyUosrMUwhDgmn03+X7UlEKnG0ckBjP0xeH6M6pYSQNXldjNqPK/El+oSJY4BC9BMuqVUHLLh5X0GVYia828g3RSErfv5Bi6ITBDgKWTr7YaUjHCM6qNKxfYduTUnIirx3Bz+vmotsOfKgUcsuAOL+iLHExqh9sqyWWHwzYxy6vjUMfv4Pj3ltWDlHvadZ274w+fg4FANvSj8BIwhZYtfCkSw/cpTwURqJhSzx3eUbdhTInfogZwhZZ/49jujoKLwzZDQat3z9saT9e+IoxMsDhMhltVgSXjSQ/uw6f0SGhCz2yHJ+YrgCCZAACZAACXghAfbI0lnSXSVkCWHnxj0FC3bGubY6vWBWG7ILASr+yKIQfcQ/CzErJSGrfgUfDP/h4dFC4X567TlzkqOFi3bHOZ3ir8SOrApFZFW42nj04dFD9WikHZixKTbNuBKnT7i6xNiPV8cgItHbCQvllBDSxh/Tf4nFuZt2iGOW32yLxb7/Hsbkb4baqyr+yJ/oh3X2WpxTSjRw710/ZSFLNIef+IaYPwZ/n48bI/4lq93zZuz4x4YLN+OOFibmQEeWzn54icLR2kFlREdCSkLW9asXse3n71HpmTp4quJzOHPyKCRZQoHCJdSeVTs3r0SBwqVQrtIzuHTuNHzMvihWsjx2/boKly+E4aUWXZE9Zx79FoeHR2YEISvxWwvFn0/XbtqRN5esvsRj/wkrShc2IXd2KYkjK/5oYcdG/ggokLKY5QwhS8x5/+5tZMqSFaGzlj9WQTM++Qi/bVyLGrVfTPLGTD2WWoaELPbI0mMqGRMJkAAJkAAJeDwB9sjSWQodFbJWH7CoRwUTX+K4nHAvOeLIEsKMEHjmbbPg8h07cmWWENzGX32L3s9HrGrjXCHECDFHvF0vJSFLNID/6U9rQrP3bnXN6hyJm72nJmTFH/Vb9rsFf561IXcWCb3q++L30zas3G+BOB6YWlyJ9y/+RUY4uMRxQOEsu3BLQY5MQMtnzMiRWVJFOsGoc22z2vdq8R6Lur8KhU1oU90HH62IQaxVweQOmdReV6KfmBC2RKN8IVbFv90xueOSPQJ91ebuopm7OI75SmUzKhWV8dGKaFUgo5Clsx+aC8MxosMrJSHryoWz2PnrSrzQsDVy5ymIdUu/Fu/zw/mIcyhQuBB8TCbcvHkTVosV+fLng59/JjRt2xd/7P4F4aePI/DldshboKgLs+NdSxlByEqcsUePFiYWsq7ftuPmXQVlippwN1JR/zk1EUvM6wwhKzoqUm3+P2/GJEybu0rtvRZ/ie+6ta6LWoGN1B5v2zevN5wjy7t+YdwtCZAACZAACZCAqwiwR5arSDu4jqNCVnLTiSN14vicI0KWEF36NPBVG52LflZCACtbQEbr58wonkdWm60LQWrFfiuiYlM+WtjgaR/1LYY1y5iQxU/C4XAhYsUmvIkvrR5ZYh81Ssc1Z8+fQ8b96Ph145qziyu1uB7l4GcGGlc2o3opE0QT9rtRitqD6sdDFtWBJi7hNHv1WbO6rtj/xZt2rD1oxZGIuAXLF4p7y2O+7DLO3bBjxz9WNKlixrztsQi7+ri7SowRPbTaPm/GsyVN8PWRcOqSHT/si1WPV4qLQpaDPwAd3Ka1g0prh5cOECElIevmtUv4bcMS1Y0lXFlCnBL/gl64WBms/WEWChYuimdqvaQ6sSLOnICvXyYULlZadWpduXiWjiwnJ9cIQpZo7n4/SkGu7DIeFbIirthRLH+c42rD3lhVvBI9sk6cteHgSSteq+8LvweN4ZND7Qwh687tm5g0YzF6tKmHRs3b4q2BHyUsvXHdMnwx+UNM/nIJ1q38Dnu2bTKckKX189TJPxFOTwIkQAIkQAIk4CEE2CNLZ4lKS8jSU7jJubT0FJ8RYuFbC12fRSM6qLSmmJKQFRV5DxtWzEbmLNnwSuteCW8SvHXzGr75fCzeGz4ZPmazeszQZHrYC0uMsVotaNq2T5LPtY7b2+czmpAl8hnf7F38c+Jm7+I44b0oBY1r+OLSDTt2H7WgTT2/VEvAGULW9auXMGfpFnw6ZjAO7NmOBat3wNfPX41jSN/2iI6OxBfz12HCB/1wYM824wlZ7JHl7Y8d7p8ESIAESIAEnEKAPbKcgjXjk1LIyjg7I46kkOX6rGrtoDKiIyElIUtRFGzdsBg3r19JOCZ49VIEdm5eoR7byp6zIBQlFrHRUWjYoosqeAnXljhaWLp8NVSpXs/1CfeiFT1dyBJN3m12BWafh//X5c59BdmzxP3vxEKWOFp48bodlUo9FEwv37SjQC7X9si6eP4sFqzehaOHfseo97tiwMiJeLFxK4T99w/693gVfd7/AM1f64wxQ/vi8B97jCdksUeWFz1huFUSIAESIAEScB0B9shyHWuHVqKQ5RAmr7mJQpbnp9qIDq+UhCyRrXt3b2Hz2gXqWwrrvdIeMTFR2L5xGQoUKgEf38y4e+sKoiLvoGHzLupbDDevW6gmuVGLrsiaPZfnJ1zHO/B0ISvikg0mE5Azm4zM/hIsVuDSNRsK5jXB7JNUyFq/OxbXbtvRrUmc++nwf1YcO2NDvapmFM2XvJjlDEfWubOnsWjdXjWGtzs3QY6cuTHxi0WYOW0sNq1fgfmrtiNL1uwIGdJbFbtW/HpEtxWUkWbvut0MAyMBEiABEiABEvBoAuyRpbP0eZKQpTN0hgyHQpbr06q1g0prh5friTy+YmpClrj7r0O7cPKv/aqY9VydJihQuDiAh3/cCMEg4sxxHNq7GcLFVa1mI5QsW0kPWzN0DJ4uZImeWDfvKLBYFWTPKiFH1qSCVHSsAn/fh3V2446ivkH2z1NWnL9mR/kAE5596qFD69FkO1vIWv3DNwlN30e93w016ryoOrTEZVQhS+vnqaF/oNwcCZAACZAACZCAwwTYI8thVK65kUKWazh7yioUslyfKSM6qLSmmJaQJdY7dnAH/vvnT9isVrXvVb6CxZA9Vx7cvHYF16+cU/tkSbKMC+fPQ5JkdH9nlHrUMD2XEMFEw+xfflwK4Xzx8/NHmfKV0PqNnqjyTM30TOUV93q6kCWSZFeAW3fsasN3fz9JbfruY4pLnzhaWKOCD8oUifvgzEWb6sISLxERn5cs9ODGFLLtbCHr7p1b6N46EPkKFMKFc2fVJvAVKj9jbCGLPbIzzTH0AAAgAElEQVS84tnCTZIACZAACZCAqwmwR5ariaexHoUsnSXEzeFQyHJ9ArR2UBnRkeCIkCUyFx0Vib1b1+Lm9ctQFPuDZEpqE/gChUugRr1m+GnlfJz865B6rLDHu6MSGmGnlXnFbsfk0YOw67efUaZcRVR5thZioqKwb+evuH7tstp7qFmbTmlN41XfG0HIik/YvUhFFbSE0S9PThmZ/CSEX7YjoIAM0UvryGkr/g6zIU8OGS9U9EG2zGn/vV28kFWidDm80qL9Y7WRO29+1ApshMMHduPDQT3xQr2XUblajcfuK1uhMp6qUAXD3u2oCqzxRwvFjaLp+/bN6xFQsoza5D3+MrQjS0Z92LE1LOzUVq/6wXGzJEACJEACJEACTiPAHllOQ5uxiSlkZYybUUdRyPL8zBrR4eWokKVmT1HUNxJaLDGw22yQTT7w9fWDycf84GsFv6xdhL8O7UWuPPnR5a3hMJt900z8htVL8FXoaLR6vQd6vjss4f7o6CiMDuqDE38dwvR5q1GsRJk053rSG4QzTAh1spy64+dJ13nS8UYSsgQLcdTw9j1F/e9sWeIE0hgLcPKcFWcv21GumAnVy6V8lPBRnvFCVkqcK/3vOYyfvjBByErpvg493oP4T3JC1rE/92Nk/y54s/9ItGjb1fBC1pPWLMeTAAmQAAmQAAmQQHIE2CNLZ3XRZ+79NYDUUmdhMRy3EVDWzuqV5VW3Le+FC2vtoNLa4aWHlKRLyHIgYCEE/frTUvj5ZUKdhi1UQSKt6+1OjRF5/x5m/7D5MRfX+fAzamPtZq07oe/AD9WpBr35GmJjY5K4YMTnbzR5DpWrPY9R42ckLHnl0gXMnzkFh37fAavViopVq6PH20NVF4244h05nXsPwB/7tuPk34cx5KMpmDtjEmRZwpylW5KE/07npoiNjX7s87T2qPX3RhOyBB+bDbh9z47jZ234K8wGf1/ApgAvPuOL4gVSfkOh1my9Yb6MNHvX+nnqDZy5RxIgARIgARIggbQJpP1vC2nPwTs0JPDmnPvjJUkaoeGUnMqDCSiKMmF27ywjPXgLHhe6ER1UWidBayFLxKf2zJLiXDVpXZcvnsObr7+EJq++gbcHhyR7+7tdm8NmteDrxb+o3zsqZF27ekm919fXH01bdYBdseOnlYsRFRWJz79dg7z5CyUIWSJWcbysbPnKeK52A2xatxyioXfo7BXqcUdxXTwfjr4dXlb7dvV4Z2haW3Pq90YUsuKBLfg5Gi8/54sL1+2qS6vBM3GOP17aEciQkMUeWdolgDORAAmQAAmQAAkkEGCPLJ0VQ+/Zd+vLsuk3nYXFcNxEwG63NZjzZjb2FXEhf60dVEZ0JDhDyEpPiv86vB8j+nVRjxSKo4XJXWOGvYWjh/Zi2cY/0yVkTRkzBAd/34EZC35Cztx5EsSod7o0RfM2ndHrveEJQlbT1h3x1sCPEpY/deIYBvVpi7ad+6Jrn4Hq52uWfou5X0zEpzOXqn2T3HkZWci6ctOOxZtiUDSfjLYN/NyJ2bBrZ0jIKlGmPtgjy7A1wY2RAAmQAAmQgLsIsEeWu8insu6bcyL3SBL4yi0d5saVISkK9s7unbmWK9fkWtoTMKLDy91C1oE9WyGEqveGjsXLzdslm7SJH/bH7m0bsXrrX2rvKkccWeLoYcdmNVCjTkP07pfUGPvRwB4w+/khdNbyBCFr0AeTUf/lpCfB3+r4ivo2xq++26DGNer9brh0IRxzl7n/7yeMLGRp/8vljI8SyIiQRYokQAIkQAIkQAIk4AwC7JHlDKpPOOebs+69Ipnkn59wGg73cAKKzd54dp+sceeieLmMgNYOKq0dXi4DkcpC7hay4htmd387CG069Eo2UiF0HTm4F8s3Oe7Iij8GmNLW8xcsrPa5iu+RlZyQtWjOZ/hhwVdqL648+fKjc4taalPvxA3p3ZVDHQlZUQD83cWB62aIQHSVYj6Z0jtS6+dpetfn/SRAAiRAAiRAAsYkkHYzEmPuW/e76jP33nBAnqD7QBmgkwjYR8zqlXWikybntKkQMKKDSuuEu1vIunQhAn3eaIQmrTrg7UHByW4vIz2yws+cwnvdmqsuq/qNWjw2r9nXT20Mn5qQFRF2CmLtjj37oXDREvh0zGB8+vUPeOrpqlqnId3z6UbICrdshCQ1SvcGOMB9BBRlU5UA88vpDYDP0/QS4/0kQAIkQAIkQAKOEGCPLEcouemePnOjBwD2qW5ansu6jYA8cFYv/2luW97LF9baQWVER4K7hSxRon06vIzoqPuqQ8rXN2lPJOGsEkf8hNAV38Nq6Ntv4O6dW/hqUVKza+K3Fkbev4sOzWrghcBGGDbmsxR/CakJWWJQv+4tIcsyipUojeNHD6oxOtLE3tk/Pb0IWYcjLI0lSHFnL3l5BAEFSpOqxczpdopr/Tz1CFgMkgRIgARIgARIwOkE2CPL6YifbIE+8+4/IynyMAVK+yebiaP1TkCCtFSR7JNm9cxyUO+xMj7HCRjRkaAHIWv9qkWYOXWserRQHDGMv0SfqzFD++KvIwcw/Zs1KFa8tPrVJyGDsHfHZny7ageyZc+hfiaasw9+qz2ef6EBRo2foX4melr9dfgApsxahtJPPZ0wr3gbYdVna6FkmfKpOrLEgGULZ2Lh7Knw8/dHk1c76OJYoYhLL0KWiOVwRGxvCfLnPGLo+LPETXdGK7D3q1rMd46b1ueyJEACJEACJEACJPAYAfbI8pCi6D37XgFZkgMh2UsDJsO/kilTzL/1o/zKesHb+mwxUOT/7Ip9+5w3s172kHI0dJhaO6iM6EjQg5ClKIoqTu38bYN6bK/qszURGxODPds34eqVi3hncAheafFQ/xci1vhR76FMuYp4od4ruH71Mn7buAZRUZFJhKyzp09i2LsdYbfb0bRVBxQoVBR/Htitztt34Ido1rpTmkJW/NFHVUD76geUq+j+Y4V6E7JEPMfPKXliFUtr2KWihn6o6GhzsiwrjoRjt9slyMo5X8m8qkJR6bojY5K7R+vnaUbj4DgSIAESIAESIAFjEWCPLGPl0xC7CQ4OliW/rJaQkUEmQ2yIm/AoAkZ0UGmdAD0IWaowoyjYuG4ZfvlxKc6dPa0eMRSOLEWxY/CHn6Jm3ZeSbH3N0m+xfuUiXLt6CYWKFEe7zn3w3ZzPVJdVvCNLDIg4+x++mz0NRw7ug8USi4CSZVTnV50GTdT50jpaKO4Z3Kcdbt64hrnL9HGsUI9CltZ16e75iofWnadIuBc+cEd/d8eil/X5PNVLJhgHCZAACZAACRiLAHtkGSufhthNcHCwj+SXNSpkZJDZEBviJjyKgNYOKiM6Eo6es41SFOVjPSZW9Mf6ePjbOBd+Wm24/nq3d1wephDYerd/EbXrN9bNsUIBQZKkDyoXNY1zORAvWLDUxJdyWH1jrwKwxdj8814O2njfC7ad5ha1fp6muSBvIAESIAESIAES8AoC7JHlFWn2rE0GBwf7Sn5Z74aMDDL8EUrPygyjzQgBIzoSjkRYOwJYlBEerhgTef8ePh0zBBUqVUO7Ln1dsWSSNeKPMYo+W2XLV3b5+qks2KlKMZ/FegrIKLEETKk7CcAARZJiZUmZfHbgjrFG2Rv3QQIkQAIkQAIkQAJ6I8AeWXrLCONBcHCwv+SX9WbIyKBMxEECriagtYPKiI6EY2eUgnYf20VX50bv6x37cz/27fwVm39aiTLlK2Fs6DxdhSxbTYUqlZQu6SooAwRTauZLOaz3Y68A8FW3IyEyxuqfn64sQOvnqQHKhVsgARIgARIgARLQgAB7ZGkAkVNoSyAkZGZm+N65GjIyKIu2M3M2EkibgBEdVGnvOv13HI2wzVWg9Ez/SOOOWLFoNpZ8OwOVqz2P94dPQM7ceXSzWQnSvMrFTL10E5CBAol3Y0GSVCFLAe7RlRWXYD5PDVTo3AoJkAAJkAAJ6IgAe2TpKBkMJY5AcPCMrJJf5MWQkUHZyIQEXE1AaweVUR0JD1xZBwAUcXWOuF66CZyXrabqdGOlm1uaAx5zYz0cERljoytL6+dpmgnhDSRAAiRAAiRAAl5BgD2yvCLNnrXJ4ODp2SW/mIiQkUE5PCtyRksCjxMwsiPhSJilFkzSMopZuq7887Ap7aqUMO/RdZQeGlxAaN3JgDQQEmKh4LokXqYJCCuejyQp49gry0MTy7BJgARIgARIgAR0TYA9snSdHu8MLjh4ak7Jz3omZGRQLu8kwF27k4DWDiqjOxKEM0vxsY/jMUN3Vm3ya4vjhJJVHkUnlnNyU2Jq/Zx22A7ALoUrijQMJrSQ7XaLZFI2KnZ5kiIhf4zV/zlv7pWl9fPUOZnkrCRAAiRAAiRAAp5GgD2yPC1jXhBvcGhobinK9m/IqCD9NJjxAu7cYhwBIzuonJnjB0cNX5QkqaTdZjc7cy1nzj359zH1hj7/0TZnruHMuWWTbFEU5YxsNW2hgOVM0nFzl5pWO+D0gF3h4p+LTa03RghZZwfHvbGw+NS6hc4O3OHVL0Xg89T5NcgVSIAESIAESMAbCbBHljdmXed7Dvn007yIUY6HjArKp/NQGZ4BCWjtoKIjwbOKJCA0UAkftJ1/yeNZadNFtI8KWboIys1BaP08dfN2uDwJkAAJkAAJkIBOCLBHlk4SwTAeEgj55JP8sOBoyMigAuRCAp5OgI4Ez8oghSzPypeeoqWQpadsMBYSIAESIAESIAEjE2CPLCNn10P3Fjx5ckHJKh0KGRlUyEO3wLA9mIDWDio6EjyrGChkeVa+9BQthazHs6H181RP+WYsJEACJEACJEAC7iPA4xPuY8+VUyAQPGlSYckm7w8ZGVSEkEjg/+xdCZiN1R9+z50Z+xItdrMiZUkIyWQnRfZ9SVKpZCdKRkoqplLSRqgU2lDJPrbIEkqyjJk7dlHZIszc83/e03fnf2fm3pk7XObO+J3n8eDe7zvLe8537j3vfX/v71ojIAqqa424f7UnRJZ/zUd26o0QWW6IrLCIKGiMgcJYe1xsVHaaT+mrICAICAKCgCAgCPgvAuKR5b9zc932bMwrr5RWSbb1UaOGlbluQZCBZxkCvlZQiSIhy6byshoWIuuyYJOb3Ji9CyiAr/dTwVQQEAQEAUFAEBAEBAEiIB5Zsg78DoExEyaUVY6ANVGjhgX7XeekQ4JAJhEQhVcmAcviy4XIyuIJyMbNiyIrG0+edF0QEAQEAUFAEBAEshUC4pGVrabr+ujsmJdfD1H60sqoUcNDr48Ryyj9CQFfK6hEkeBPs5txX4TIyhgjucI9AkJkpcXF1/uprD1BQBAQBAQBQUAQEASIgHhkyTrwOwTGvBwdpnTi0qhRw8P9rnPSoRyPgCiocvwUpztAIbKu7/m/ktELkeWGyBKPrCtZUnKvICAICAKCgCAgCHhAQDyyZGn4HQJjXo6OUDppUdSoYeX8rnPSoRyPgK8VVKJIyF5LRois7DVf/tRbIbLcEFkhEfVhQ304EGO3x8b403xJXwQBQUAQEAQEAUEg+yIgHlnZd+5ybM/HjJ9UXsGxMGrUsAo5dpAysOsGAVF4Za+pFiIre82XP/VWiCx/mg3piyAgCAgCgoAgIAjkZATEIysnz242HVvU+Im3AvrrqFHDKmbTIUi3szECvlZQ+VrhlY2hzRZdFyIrW0yTX3ZSiKy00+Lr/dQvJ146JQgIAoKAICAICALXHAHxyLrmkEuDGSEwZvzE2xT0vKhRw27P6Fp5XxDwNQKioPI1otmrPiGystd8+VNvhchyQ2SJR5Y/LVHpiyAgCAgCgoAgkGMQEI+sHDOVOWcgUS+9VgkKn0WNGlY554xKRpJdEPC1gkoUCdll5v/rZ1YRWaGhEV9orZbZ7Xvf9QVioaERb2uFE/a42Chf1Cd1ZIyAEFluiCzxyMp44cgVgoAgIAgIAoKAIJBpBMQjK9OQyQ1XA4Ho6Oi8Z//VTz4/asjEMS+9VkUpfBw1alhVtjVm/Ctvjh01YsDVaFfqFASuNgKi8LraCPu2/vSIrNDQci9q6GdTtfiTPT62tqdehIWFlXc4VH27fd/76fU0uxNZ3o7Tt7PlX7UJkeVf8yG9EQQEAUFAEBAEBIGci4B4ZOXcuc1WIxsz/tVPFFR7ALsdWk9XytbbpvSnAJ7VGgXhUA2inhu6OlsNSjqbLRHwtYLK1wqvbAlqNup0RkQWoIvHx8c+4u2QwsLKPeBw6Ci7PbaGj4ks5+e3dlfvtVZkeTtOb3HLjtcJkZV21ny9n2bHdSF9FgQEAUFAEBAEBAHfIyAeWb7HVGq8DATGjBmTy5a7wAkNFFTAPxoI0sAlBQRBq1lRzw7texnVyi2CQKYREAVVpiHLUTdcLpEVElKuFpR+D0AINL47fz7fo3nzn+sJjfEaKKCAQ/b42JDg4Aqhypb0EYAa0NilNR5NSIj9mYosAEe1Qh1oRAB6hj1+XxolakhoBPfJ7wG0CbTpaklJthtTt3vs2C//pCKyAkJCIl6DwsMA/tBKDUqI2/t9SFjEAZ2EVmwfgAoJjThiU44mNpttT2Ii3oFCWwCnFDAsPj52Xnh4eKUkh5oPjblQ4J58XCGpk1YBdVOPM0ctCi8HI0SWGyJLPLK8XD1ymSAgCAgCgoAgIAhkBgHxyMoMWnLtVUVgzPiJoxQwEtAFXBpyJCYh4sXRw+KvauNSuSBgIeBrBZUoErLX0rocIqtChQoFL1xMitVK9Uu6eH5RQFCemYDenRC/b3RqpVJIaMRPUFgUaMPLlxyqndJ6PAmu/0ILcWtgIJonJQXkdyBps05Crf37Y3e6IvgfkaVmJ14MGJs798VEh7btcdeuK5EVEhIxAgotoBMf1DqworJhoSPpYjkVkOsFpfCXPS52jEXEfWyPjy0fEhbxOqCK31ikUM+//z59r0PrBdCJRQMCAiKSHOpXBd3/woXz03PnzveaA7g5IT62oyiyACGy3BBZ4pGVvTZA6a0gIAgIAoKAIJBNEBCPrGwyUddDN6OiovIhV77jgMpnjfcCtP4o6tnh/a6H8csYcyYCovDKXvOaEZGloZ8BkOgclXbgbpsNYRoY6vTKCg4Pv1s51BR7fGw1V4KndOnypQKDHPvy5c1VaOfOnRdZR2hoOEmhablz55ulFZbb42Kn8vXg0IhNAUqNjYvb+21qIgvado/dvmdXaGhEe0/tpiKydgB6qN2+7wfWFRIasQQaUwCchMIb7GdwaPg4pVWg3R47smzZcmEOR66TBw/+9pd1/VmFgOo2W2JQkkMtt8fHFjN9DCt3v3LosQybFCJLiKzs9aRLbwUBQUAQEAQEAUEgOyMgHlnZefZyYN/HjJ/4vPrPTDkXh3fJYSv70nNDDuTAocqQ/BQBXyuofK3w8lPYcky3MiKy3HlkhYSUGwClXwNwzgKCn60n7fGxwa4ET2ho+apaOb6zx8WWTg1YarP3kNCIDdB6gt2+7xtPRFZ67aYgskIj/gYQAMBh1RWkoAfGx++bFhIacSjApu9KcqiFDpvus3/fvi3BwRVLKFviy4C+w7q+MrTt9oCApMAkh1pmj48tbgiukPDmUGqCPT72DiGyhMhytwn4ej/NMRuNDEQQEAQEAUFAEBAErggB8cjKAL6wsLDCWtsiHdDllVauIW9XBLzc7B6BwFy5cleqXHk43z1z5syWfXv20AtGip8goJU+a4Pao5RjdVxc3Ck/6ZZPuyEKKp/Cme0quxwiKzQ0oqMG+tjjY5ulHrArwVO+fPmbLl5yHLixaOFCW7ZsufQfGRRxb2AgNiQl4VOt1TK7fe+75nUviKz02k1FZO3UDvRJSIhdn7p/IWHhUwF1Chod7fGxYVbb8zXwU0J87CsAkkJCI05C22oLkZX+cpbQwrT4yH6a7bZA6bAgIAgIAoKAIJAtEBCPLA/TFBYWVsPhUIOhVJdsMZPSSUHgWiOg9Wc2m46Oi4vbfK2bvprt+VpBJYqEqzlbvq/7cois0qVvLxoQdGG30rpvSEiZb+32g42UUlXi4/e+Fhwc3kDZ1EzoxCp2u/1kSGhEjIJaFhxcakJCwsEWGng7JLh0WELCwc8zS2Sl124KIissIkprXTfxYlBnIhYUlPhsYCCiY2NjD5YNC2tk07bvFfBWfHzs0GQSTWE+HIlTgYCOUGqqIwBVgrRWnhRZqcfp+5nx/xqzE5Gltf4XwONKqRnukNVadwfwsVLKqx88tTYht6wvxLW+y91PPdXn/6vg+uuh1ro2AJLk5ZRSsekhoLWeDuBvpdQQXqe1DgawC0AZpdSJa4Fe6v5qrUnY366UeuBatH+lbWitewIYoJSq7k1dWut7ALwOoAoAhotzDkYrpZwK3XSr0VrfDmAigFoMlACwBMAQpdQf3rRvzfMqAJGprl+nlGLfMixa65oAJgGoBoDr5H0AE5RSbrP2ZrS/WX1qBeB5ALcycQmAWQDGKaUStdZUQj/ooWNjlVL8TGWo/lGl1EMZDsDNBVrrpwC8BaCHUuoT5yVaaxuARgD6WPUPdHnP+ayFKqXs3rZr4fGUUupDb+/JzHVa69sARAOoQx0CAOL3jFLqrIU1o2yiAHQDcAOAnwAMVEql8AB1bVNrXcJat435gxoA2iwMVUpRYc69owiAVwHwuc0NYK1VZ5wLXh7n2Jvxaa0bAFjh5tomSqllWmsq3bmXPQqASnWOZ6RSark39Wf2Gq01kwK1s/bLg97cn9Hzq7UOBDDYWm+MFtgH4CWl1Bxv6s/qa8Qjy80MhIREjIEyD5wpdWrXRsWKt6JgoYJZPV/SviCQpQicOX0Gv/++C+s3bPh/PzSi7PbYsVnaMT9uXBQJfjw5brp2OUQWqzG+WFq9BY0KVjbCvgkJsVtDQkLyQAXyS3xFe3xsofDw8DJJDvNlsjYUYpUO6Bofv3v35YQWpteuK5EVERGROzFJvwqoTgACNfT0hPh9I/hdkCGHzFaoHXjQqdgKCwu7x6HZR1VcAZ9qHh40RgUE6BOeiKzU48xes+6b3gqRlZbIulxkr3ciS2v9HICHlFIRl4uhN/f5oh1viSytTSKfI9z7lFK/WYdRfneoqJTq6E1/fXGNGyLrbgC3KKVShHH7oq2rUYfWmp8nc5RS72RUv9b6JutgykMpiQaSUsyaO0YpRXIr3aK1vtk6nJNEYPh8YYsIO6yUItniVdFa77aIiK9dbjillPo1owqsPpDsXADgZYvMmmmRGm+7uz8jIktr3RDAUouE4NgYRk9S/xWl1Dit9Y0A8qaquwyAdQBqKqW2XAmRpbUuC4BjL+RKZGmtScpwTMSd5M1spdTjzn64rF2/IbKsNbYDAIk9zg/7TrJ0pVLqMes5fwHAEwBIwjJ5F4nRSgBuU0r942EOVwOgZzJJIs4F53ybUqq9Vecyaz2yXtYxAQBV5XdYZGS6c5zRurPaoJiFz1nLVNf/qpQ6Ze2fJBp7W88JxzeMY1NKJRNq3rSV0TVa6zYAvmSGaW+JLG+eX60154bzxD9ck10BcF9uoJTiXuPXRTyyUk1PSGi59wDNhwYP934Iffo8jJIlSQpLEQQEAScChw8fwbRp0zH9I+eP+ep9e/xe84GV3YuvFVSXq0jI7jhm1/6nR2Rl1zFJv68NAkJkeVBkAfcCWGW3x8Z4OxOZJbL4y7hSige/HFEyIph8Nd6M2vEGzEwQWY/wV3+lFFUbVFRQecJD7aNKqcXetOWLa7ztry/a8nUdWmsSm78AKKmUOplR/VprkiBUnBVTSlGFSdypbGqqlKrsxf1PWkoatmfC4bXWJB1JjBXxpg/WPbSi6K2U+iqjNlO/r7XmDzCfAiiolDpv1TcNQIRSintLmuIFkTWP4mSlVGvnzVprEhZ1lFJUfbmrk+qfu50qsiskshZZa5/JrJIVWRa2JLeIL6/ZkQ2IrB6WQq6wUsoksdFa8zwwXilFQpD//x3A10qpUdb/SQruB9BQKbXSzZwzocxRAM2UUlQAsg62Q4IsP9ezdf+9SikSXny/gqXurEKCVGud6Tl2049B1rqlmtHdmqB6aaZSimSQKVprkvUcO9V2Pilaa6rYqPYi2c41QwVrhoosrXWGz6/WmmpEKrDecBkD21qslOL4/bp4JRn36xH4sHNOJVZAQACmvvM2mjShmlGKICAIeEJg6dJl6PfEU0hKSqK2I0cos0RBdX2vdyGyru/5v5LRZ0Mii5krGapSwzoUDFdKGcVE6tBCrXUQVRxUCQGgymMbAF7vPEQwtJBfsPmrOQ/PPGwsrH33PfHHjh57Bgpj4/ftbWspSV6y2qAq4U0eZgDwkM0v6ayTv3SbUEXrfYZu8HBHBcXD1vvOEJsXrV/Df1BKPaK15gGHypMWVB9a9zAMyySN0VrzV3weNDie+wHw4MVMofSm4y8zVOfwi/2LSqkPXL7Y83BLBQtDnA4BJuvnZHehTVprKvo7K6UYsuQ83LDu0kqpxi5ECg/oVF6Vs8gJkjw7tNYkKKh8cZYuSqnPrcM51ZwMd+EhvLzWmqpK8741PoZ2khyqp5Raq7WmooHqB2LPf1NOPYiKqHTaYbgMQ676WkqIHwE8rZTioY0YUknB0C6qWPYCYLgLD3ImtFBrTUUJ/53CM1BrzfDDac7wJq013yfGIQxzs0Lg1ljzzjln2CFJm17WGuGaIC68hlhxHtkfrkeuI6pZGMo031pHzrCmjPpLTEmK1LfqI9E23gqlc4ZJETNnMo8U24TWmmFTVDAwHJcHd6qHRiiluF7ZPypVTlvhTwyvIyE0RSlllOwu64FKDv6QzkP+FovgSxF6pbWm4oWHWLbFezNqm+QMFSpc1861yPAuPqd5LOWKx/m25pokWHKolNaa6hQ+qzcopYgP+8FnleFJDE3i+J9VSpGI4XtU0xA7kkQucv7/w6i1JvYcW1WLwHhHKcV1y/upduHzmZ/9tV7jfNdVSnHvSlOsZ6b3E74AACAASURBVCW9/Y3P0J9KKa4vJy4k+CKVUnzGU88x1YTcQx5TSs11mVfuW/zDtccxst/JET0e+kbVDtcXw/FI8KUILXTpD0PlPBFZtAEg5s610tcZpqe15jPynqUiYvgnn0fuuR5DC7XW6a55S0Hmaa/mXsr9KPn50Fo/bLXLOeM2lQDgQ6rdLOwYFsgw15ZKqRSZma33QwFQ0cT54PPOdUBFEolQfhZQgcX97B/nHqy1phqLexQVnz9ZYYHpzrEVGpjeXkfyksRYcw9zyc+5C851afWTBB2fbxLI7LfHzznr/Qw/W6yQbO5z/Gwj8ZdMZKU3hoyeXwtHfjYwzNfsp1afuP+sUEpxT/LrIh5Z1vQYTyxt28T/vv/eVCGx/HrZSuf8CQGSWY8+xvMLYFOOmtndM8vXCipfK7z8ae5zYl/KRkcm7h+8mgdgKYJAphDIhkQWvUYYlsFDBn9B5+GjvFIq3g2RRcKBhzVu9jws8BBFDxd6C+2ziCceRKmU4MGJhMrUhISEn+s3bLwJDsTEx+/lIXWzUmqd1poHAB4iGXJEMoeHXR5W9yil2ln18bDHcCKSJCUtVQbJo9Euh38SUzyI8JDJQ9vPlkcLD9X0AGI4FA8AVakosYgsHuo5bvo78nDE+g+TfLD6xF/++Us0sYjTWnMs7Ct/saYyhEoWhmc9oZT6OPUiyQSRRZKiv3WY4NjPKqXqaa3p9zKSB1wrFOyiRfTwwEyVAvseq5Ta4wWRRfKNPjGsi2QK54gEFMfEfc5dO1QS8JdctkNSjwdzkkH8w3PDHgBUA5BYYh08XN3pQmSRRLxZKUWyxHkw4qF9I4ASLuQHCYHflVIkSHngo18SD60kQPiac00wLIKHevaDB2CuMR6yelsEK/12OP+jrUMuD/EblFJ9rPcz6m8ykaW15jojOcf5IBHC/8+2CFi3hzqtNdcFD4MkKPgsETf+YfjXHxaRRXKE15DI4ZqjHxNDR2e7rGWqTUjg8dng+6Ws0CvjZWUdWDnObk4Vixdtc72ScCJp65yL+wAwkVJR+g1prT3Ot+sB3eV+klhUQJpwK4vEIunEtUyykuMjSUdC4WcXgoFhedwDSNSSeH1OKXVBa021C89fXEd8nipayhu+/5ZFTnOfIJFC4prvk1gj2czX0hSLyPK4v7l5ZklUkYB7XSlFQitF0Vo/bYWMcU6dZBoJSoZXcl3QR4nkGHGg8izZ88q1Iq31LZayhoT8Auv5vRwii+Qynz+uDZIt3OMYppekteackxjkGuR64V5I8pJkVxqPrIzWfEZ7tRusSMxzjSxTSg2w1gj3HYbpNbGeEWLMdVIhndBCriXOIe8jWUaC+g+lFO9LPT/cF/jMsu0a7vzfrNDmFHOc0drXWvM55L5EnLkP8XOA3l7cc1L3gaQy1wn3IZJfCRlh581ni9aaezFDChkWTEVmaiLrip5fN+PgXs7nsZZSyu89kMUjy5rBkJDw2TR2Zzjh6NHPutsX5TVBQBDwgMC4cS/9F2ao9Wd2+z7GV0tx7i1hEVHQoO/eWHtcbLq/1AloWY9A2ejIpP2nGgRh7FivjHCzvsfSA39BIBsSWVSZUI3kDPMiQfUZwz9ciSytNU1sqULiAdoof6x7SEzQK4SEAQ9V9AohUWEMmK1f5Xm4YAhSCh8Uy7CahEMppRR/mef1JJh4mKVqhYcv/uEvzxes93k4vFEpdZ/L4f9WpRQPuc6QJx7iwlwUWPRroSkyjeg/toisY0opHuyc4zhGVZFSimEYTpULQ5ioqpqnteZhmUSYUexY15AgotKFh9cUJRNEVnUe9K36GHZHko4qA/YhjUeWdTinuoy4OfuRkSKLqqBzSiljXG0ZKNMsmOEwZ1K3Y6mbSJY1VkqZUFDLVPlP66BOPLkGqKIyoS1a63oAqMzzaPZuhbNxHZAs5T1ULvF++mMZ02oXIosEIskkvsbDOAkleliRVONrJDPaKKVut1QaXBd836wxrTXHSpUYiTCGjqXbX621K5FFNQuJkp9dwqQ4/yRlaHaeplhmyuednjgWXlzTLahKcheCprUmYUMMSVymMfDWWoeTrLTmwaihLCUUSTOqx5zPGA+36bXNsRdwNbLXWlNdQrKQc8nicb5Te+RorUlkk6ypRiLV6heVM/OUUiSCneuSCkSSie9ah3X+n2pP7hkk9UhsvKWUesZSm5AQT8ZXa01SjGQAVYEcO8lYkiP8XKaCbL5rWGDqSbGeFY/7m5vriROJ2jud8+4yFobAcqzTlVIk151jJJFFny8qK52vkfjg/CQr4Fzb0lqTvNXOe66AyCJpxXA9YuNcK1Q4UilLZU3yXm0RoFRLPemByEp3zWe0V7s8J1RO8vOEzx0JW3osOcNZSURxj+BaZwg4/zAs0K1CzxoXFZlUBhW1MCRJzD3TfF5Y11CFRXKJ5C+TADD00+wdGc1xRnsd177WmkQ/1ys/x9hn5/+5R/Fzw9kPfgZxHsirdFRKkXji3JDcTu9zjj+0ePxssYgw+la9xs9qS7mYTGR5M4ZU6y/N85vqfa4FPqOrnc+eOyz96TXxyOI3nrCwwg5tM7Hm69auFk8sf1qh0pdsgQA9s+re819CGpty3BAXF0e5dLYsvlZQ+VrhlS1BzUadLhtd70KB0sUL7uw4z3g9SBEEvEUgGxJZKbIWaq355ZuHrPapiCznwZc+Oa7hBzTXJeFRw52nldaayqVf+vR59J0VK1bOc/XI0prJB8xBn4fHNMVDfTxM0ET3Hg+Hf/aHBAe9UpKL1pqHpbVKqaEWkXXQNdOY1pqEwSeuIUGuHjtaa6qEeAAzhJpVeDAjFgzdoiLMWXiwIvniTWhhsmGz1poZRT+zQnSYNc0TkZV6zjIisqgYoVcMFWdU4Sx0huqww26ILF5PlRsPvq4Z4XhQZIgXQ8eoOOGhzZSMPKcsVRSJUM4NVTm8h6Tn/UopKjSc9TgVWa64pMme6bo2tNYMVeKvz65hfyQeeJhmeBLvT7e/rkSW1TcSc/Ty4fpllicq5HYppe7QWi+0CD1eupuZA63QOV5PgpCqDZ6teCDkeL/xQGRRqcLsZsU9GXhrralG4TVc98SMRM4mpZQJzbVe4zjTa5tqMiqyqJJz3uNUZFHFwsyHHufbNaupFQrK8ZMk4Zpin0x4F4AOSimSh14Vy2CaxMqNWmsenDcqpZjFL02xiBoqYBjOSiKIzxhVKCTCJlghsk4VNcPXBrrzyHLd31wbsdYQSWwqUNKQIBZZShxTZNb0MK8k4Gikf1PqfllKGio5Sd4aEuQKiKwUZu9aaxLNVDGSyOJ+VdZJ5lvtkFAivkyyYEJerdKLpItFRnta8+nu1S7riiF//NGDqiHew2fGacxOApafI3zuqZylopfEG0ki7iUp+mQpcak6I8lK4prPIMlPqkr5ueEkcrkPk/Ci0o3KOKr7SLLyuuTibo611unude4y+lphmyTgR7l6YFlkLX2sqLqkqpIk3kYvPuc8frYwA7CluOQzyhBLLhf+mOJKZHk9BnfPbyqMiCXJWc4j2zN+dP5exCOLn3ShES01sIDZCWfPTqMS9/c5lP4JAn6BQNeuPUw2QwW0io+P5ZedbFnEIytbTpvPOl02ut4/F5Ly3nJs2BK3mXS8bSg8PLySa4Y/b++T67IvAjmAyKJiwmGF9iUTCC4pyOmVw0OIKa5f0j0QTwwj2tb8vgewe8/uFIpUS1XTSinlyUTXeGTxy7xLexkRWQxn4oGaYWyuh5jkg/IVEFkkYkguuRaGGPFHG2NobBX6UzG0xC+ILGueGDbFQyQPPVSPMEyvtRWumIIwcznc1bUOnK7j5dwzdKarK8ZeEFk8zI5TSlHp5Fw7VBnQYNhV4Xe5RBbXajIh5tJhKjioEkq3v6kUWeUtMoC40M+Lfm1UIJGwJZHFUEOjmqO/mlJqv9aaoY48bDJ8k+GiJJfoHZUekTWcB16lVLF0iCx+Bg21lBgkCKhcC1dKcS06ccyobarZqGIhEeu8h/0koZKHqhiLyHI7305CQGvNcFQqaqKUUlRNOutiOBUPvHzuvDZyd/E7IkFKM28q4Li20hTrME9/LVfFFr36qP4hYUifKOd5lgqp4x6IrOT9zaX/DI8mKdbESbKm7oBFZDPkmdcml3QIyuctgs6p0OE93CeorOPeZFRKVjEeSySYSIqmqj89j6zURBZJTxLqWy0iK/Ve7SSyqF4kGe0sJIq4pkmAeVrzJJI87tUe5oy+hxwvyWQ+C9w7HnDxTeN8UQFMpR5Jr9R9YjgdiVGqOJ0+bE7/v2SD91R4cT0TZ/qYOTNR8XPK7RxntNelJsNc1sx2ywidz7C79crnJI4/lnjxOUciy9NnCz3+SHjyc8YkWrDUiBwniXuG+/OsRSL6sp7fVPhRTUivNHrPJX/OuxujP70mHlmkckPDhyioiRJW6E9LU/qS3RBwhhdq6KEJ8fvSeAxkl/H4WkHla4VXdsExu/azbHTkycCLuYLjnll2RarC7ExkhYSEP2qz6Zi4uDgTOiLFOwSyIZE1WClFRZHTf4chQkz3PjKVIou/dlPR08kZMmHdQw+TfTSedgktpGrL6elD8/V3a9xV+5W/Tvy1NJUiiyHoDOlKzr5mZZ2iaoC/aDOsMLNEFn8Np6qJIVtOI3CGpdB8l74mH14mkcUDM0MIGf5kMiNqrXmYSHJmckt1IDD9V0rR48gUrTUVHQx/czV7T0+RRZURlURUNzjr4GE0tSKLhzy2ZYzptdYkYhjmQlUR1U8MhZqrlOIB11U9RR+ZLVrrFO1YoYc8WPV09fnRWhfiwU5rTdURD8PFSfJYdfLwQ/WE29BCrTXD2Ja7mHeTkKA6jHOfrHJzCS3MjCKLxBz7w7ZNiKLVJ2a4Y+hkhv1NRWQxlO0NpRQJBmddJFAZWkgyJ03RWhMveis5zclJOnH9uRJZ9PZhmJGzTpIqN6UKLSRJxWeQ82RIYGdoodaahBwN/OlTl1y8aJvm8SSeuPaMas0ihhoxa2FG821dT7NqPuufKqXSHN611vycYGiuyUhn3UOPuXhLkca+s72mLu/zNSZ2oCKLSqvKzmyW1v0cJ5VLr3EPsf6dnKFQa93Beta5LtOY8FtElsf9zWqD5C7ngaFgbn981VqTXKJPEMOISWC4Ym9M/JVSzOLoHDfXIufRZOZMdT1xJPHnWqgAo3E7QzO5T7nWnx6Rxb3IJAKwMlmyHo6Hocokp6haSs4EauHh1uxda53umtdaZ7RXMyy6e6qEApwrhiaToOKeyechRYZCrTXVYQv4eeMGKypUSazwOXaGljMUlgQLx8kwYxKnVA4Z3CzlJ8kf+o8xxJyveZzjjNa+5VXI55GqNZJFzn2fY+GPFfzs5ByQuDQJAKxrqJiih2FfL7Bj2LLbzxYrjJZEoGvh3sk1xv2d+zyViB73a6s/6T6/1jUkK0n4kcQye1B2KeKRxW88IRH0r4kaMKA/Bg5w+4NAdplP6acgkGUIvPHmZLz55ls5Jnuhr4AUhZevkLw29ZR9PfJEUlBQhUNPLadU/7JL9iayIjbbbCoqLm5vmmxClw3IdXBjNiSyaKnAMDiqiBjqwdAxmu+6M3vnF26SAjwY85d0Hl4YHsVwnK0uZu9Ueky2wj14AP2O2QStL8skqbYopdZb2dZ4kKHPC5UAPOBRGfEXjakvM7SQIWU8dPIwQyKMByhmjqIBMA/KZy+TyKKyYodluMvwRYZg8McajiWNAbiLZxTxoSqBhzqSCQxv9JbIovk+cWTMPg9Ff3pQmbB+hohxXkiuUZVGNYQza+EqS0HE0Cl6yNAjhf9mmBTrdNcOiQUaKjsN8amOII7MwkjlBw9QDNdkmBCxICHJA6MzayH/TbN3epLxIMvQzWCXcCrWT18nYwTtLJdJZLF9Ej4k+VgfD/H8Ik8FT03rcJtRf109spx+Xzxck6ig0ouHPJIyyVkoU/Wbh1xmyyReziyCVEg4s02yHqrNOEfEjR5eJL14+P/MRZFFMoXtUvFF8pR/M5SWGR05hmecWUVdMMuobaoF+bwyaQJDs+ipxWeUh2/6vPFwnt58k0QgIco9gv13DTflHFKxRt8z7g9cWyQ0GcbI56O+9ayzTXodMQyUCjz+n8b2VLyNsAhsKvS4xkhe8HllHydaRBbXH0kZPtPEiAosZ2II4/2WuljPSnr7GzPFUTlDTNgX10JVlwmrsgho+vglk2gu2DvN3knKMTSOhATH7dHs3U0/ieflmL1zHZFUdGf2TqKDSleSvCQ5qG7luvJk9p7umvdir2YILs3BuQZIqHM/4t5FxaLBzVK1MTyQzyZJKKoCSaLT08okWkv1TDlN8fkdhOuChA3XD4lFrh8qlLgnk0Tl2Dhf3Iu5HkjyMclChnOc3tq36mCYFuvhPsnvhGyLvnv8nDxqrY+7rPf5jDBslzg0Y6ZPL7DL7GdLitBCHz2//HzmZy/3fO6lruW488cbd8+ZP7wmHllCZPnDOpQ+5AAEcgqR5WsFla8VXjlgqfj1EMpGRx4NsAVVjR+4PNnI09sOBweH36dsihnb8kPpr6FVG3t8LH+dR3BoRB8FjNFAXqX0l4kXLww6ePDg+dKlS+cNzJX7HWhFkuBvrTAqIS7209DQ8KEatkr2+L0MoWAIvPm1Pz4+dmhIaMQ30EYhEwmFUPXfl/r1GnoyoHJr6PFOVWRISLlaUJpmoyHQ+O78+XyPHjv2yz8hoREntMJ4ZUx1VX6tMIjthoRG7NP012AGNejR8fH7poaERLwFhc6A/lcr9WJCXKxR8UhJiUA2JLJ4QGc2Mx4O+Ks2w5jow8ODRwpvIosQ4PVUldALhL/807+Hh0Fez8MSD07fWV/qeQhf0LJl64937PiNPiir4uP38tmY45KCnQdWeqfwyzkPzPyFneFWPBxnOrTQ6gc9iniQIJnCQyIP+lRjMXsX+8n/Z8ojy7qPhz2SWMSKRBn7OsypSnJzEGOYj5PY4C/0PHhRgeMtkUUlGVVLPJBSEUDTeXeKLJIrJGJ42OIYSYAQUyeRxQOh05+GB0kaI9PfhX0iHu7aISHGuaYhPt8n4UjiwxDbVkp3kh/MbsXDG8kJJjJxElncH/jvJlprKhfoWUO1nNNTiWTTPUop9iW5XA6RZdVJYoOkDBU/JDOpZOGcM0OZN/1NJrKs6+lzxEMx/bG4XkjCMHNazdRG4Nb1DHniHkuDbxIHVMGxDhrqv22FoJFUpXqOxBjJB/o7MVumq0qOh2Q+X1Rh8PnqwzFYxCjDrEqnVgBa3jwe27bqZ7+4BqjyIhHJcMTRLspJj/OtteYacJuBzxqf+XzSWpNkIknB54+4M+NgssrJyrzGNUXSg6oaKhRfcFHb0C/JSbTxs5dr6BUXLyQqsKj4ouKQY+A+xTaMv7Gb54/PSnr7GwkXQ7C7KSSiZlgkLNc3w4SNeXeq9Uoii5/DDN3lPkpl2NtKKZLJXpUr8Mhi37leuPZJ3pOkciq0GG7JNUGDfBqjc/44T8QrTdZCb9a81trjXm3dT/Kc65lrjPsj9y4Sr84EDdyHuH+S6OFzxT2FYapUa7otWmvuL7yHnx98ZkioUsXnfK5JApHkIllGoos+aiOcyjmttTdznNFex7nl88zvZ+w3la38nDTkm5UJkeNm+DRJYxLOzKaZHGbrBXZef7ak9siy+nBFz6/Wmmq+ZOVvqslIEcLq1aK+xheJR5YQWdd4yUlzORWBHENkSZbBnLpEvRpX2ejIgw4E1jo4eEWyD4k3N4aEhNwAFZiggEcuXgxcEZT70otOIis0tFykhp6rkNSUKZkdsH2mNHYaUios/BVoVcWmHPyyXCbJoVbYlKOu1urBdIks6KJJiYGdAwP1zRoOhn0sg07sCQTeCoWYxEu5S+bPn3jpwsWkWK1Uv6SL5xcFBOWZCejdCfH7RpPIUsAPuXIF9LtwyfEAtH7THh/LL5tUKScrsoLDIropjafOn8vXOHfuc8G2AKwPsOnq+/bto8pCigsC2YnIulYTJ4rUa4W0f7Zj+dNsdcnOyMPng64+S/7Zc9/1yp2XkmvtnjyynNdYXj6FM+NB5bveS02CgCAgCPgvAuKRJUSW/65O6Vm2QiDHEFkhEfVhQ304EOPq6XK5k+Frhdfl9kPu8w6BstH17DYVUN8+KCbZb8WbO4PDyrVVWo+wx8caQ1rX0MLg0Ij3lcYJuz3WeIiEhpav6oBjWUJ87M3BoRF7bVB94uP3GmVLSEh4a6UCfwcSW6avyFI/2O17TTYrEk9a65cSEvbR74PqrwNwoJXNhnANDLXHxxqj3+Dw8LuVQ02xx8dWM4osh6qXkLD392LFquTPm+/c2Xx5cxXcuXPn2ZRElhnXiwpJnePj438JDw+n18qp2NhY1wxu3kCU468RIivtFIsiNccvexlgBghcKZElAAsCgoAgIAi4R0A8soTIkmdDEPAJAjmFyPIJGC6ViCLB14he3frKRkfudcB238HBMZlSHIWERfTTGg0S4mON8asrkRUSGjEfWn1vt++l3B9hYWGFHdp2MvHSv/kCg/IcDrDpmqkVThmHFroQWaERG2xKvej0tAoJjbDblKO9wxFQF0q/5pKanp/5J+3xscEksqBt99jte3aFhITkgQo8D51YxG63n3Qlsixi7Eml0Q8KBaHxut0eyzAVKakQECJLloQgIAikRkCILFkTgoAgIAhcHQTEI0uIrKuzsqRWnyCwbt2P6N6jF16PnojWrd16WvqkHV9UklOILF8rqESR4IvVde3qKBsd+bvNYWtjHxpjfBi8LcHB4W1UgOpvj4ulV0QKIis0NOJtQJ2Mj99LU2ujyNJwrLbHxxYOCYnYoZStX3z8HqZhRmhoaBWt9R9AUCetdI2E+FiaorrxyMqYyNLaFqaBPvb4WPqPpCjeElllIiLCcwOnY2NjjwcHl6uobHqlw6bv379vH31jpLggIERW2uXg6/1UFpwgIAgIAoKAICAICAJEQDyy/JDISkxKQrlybpOjJK/aXb/vQO7c9O30rmzdug2rVq9Gt65dcPPNzGCa9eXo0aNoft8DePKJx9G3ryfPRd/2c//+A5j81tvYtGkTjh49hptuugkNG9RH//5P4ZZb/AMX1xELkeXb+femNlFQeYNSzr2m7OuRv2qbrcuBATHMiON1sVRWcTblaBYXF/dzSFjE89B4nGbvISERtaHwtSMJjQoUyLX/3PmLn9Cs3W6PfSIkLGKs0rp63ry5O//7778lHNoW40hCExWoQpXWkwNsuo7D4SiuEbBAAV/83+w9YyLr4sW8cQFBF3YrrfuGhJT51m4/yBToVeLj976WLpEVGrEKWn15442Fpv7556mxWqHypQvnOuXLly9/YhI2aZvumrBvH7NTSXFBQIistMtB9lN5RAQBQUAQEAQEAUHgaiAgHlk+JLKSHA7DDNpsTJpy+cVJZN12W0V06shEHWlLt25dERAQ4HUj06fPwLgXX8J33y4A6/WHcvz4cXTo2AV9H+mDbt2YEObqlu3bf0GXrt2RK1cuPPhgKxQvXgwktubPX4C8efPi66++QNmyTADiP0WIrGs/F75WUIki4drP4ZW0WDY6cqtN2XrbB8WkTkOcYbUuWQv5WbBSAw84sxaGhIT3AtRoKNwEYP75c/meMNkDGdZnC3gD2mT1+hdaP22372NGJltISMTHWqGVYvp6BbvS2JcZIisuLm6z8cXS6i1oVIDGLq3RNyEhdmt6RFZoaER3BzDVBvWC1pfegi1gqtW/89CYarfHep2VKUPQctAFQmSlnUxf76c5aLnIUAQBQUAQEAQEAUHgChAQj6wrILKSkhz4++RJHDx0GIePHMW5f89D0USkQAGULVMaJYsXQ4GCBWBTmRO+OYms+5o3wzvvMFv1lRdfElnsX4DNBpXJcTlHobWGw+HIFBF35QgAPXo+hA0bfsLSJYsQEsJsyf+V337bidZt2qFJk8Z4Z8pbvmgq3ToSExMRGMhMsRkXIbIyxsjfrxBFgr/PUMr+lY2O3KRh63dgcAxTWksRBLxGQIgsr6GSCwUBQUAQEAQEAUFAELgiBMQj6zKILK2BY3/8gSXLY3Dg0GFDypCc4R8WEjz8ExQYiNsqVsC9deugcOFCXk+Ut0SW87rHHuuLkiVLYvq0j0y/ykVEYNSoZ1C7di3s2bMHzZrfn6JtKpJ27/ot+bVNmzZjUvTr+PXXX5E3bz40btwII58ZjsKFC5trJk16HW9PeQevvjoBU6ZMxf79+7Fl808gOTblnal4/72pmPzWFOzevRsFCxZE2zatMWzYEAQFBZn7nWTMkCGDsCpmNbZt24bJk99A5cqVUS+yPgYM6I+BA5421164cAHvTH0X33wzH3/8cRylSpU0qrTeD/dGoIsC7dChQ3jl1YlYvXoNEi9dQs27amLkyBEoX66cR5wbNmqKpKRErIpZkeaaxx5/AlSIffXlPK9x4YX79sVh0qRorN+wAZcuJaJcuQgMeLo/6te/N7meDh064/SZM3jg/haYMXOWCWdc/MN35v3ff9+FiZOisWXLz0bJd+ed1TDymREIDw9Lgd34l8Zhb2ws5s9fiEuXLqFG9eoYNy4KpUqV8npdXe0LxSPLPcKiSLjaK8+39Zd9PfJHlaiGJAxbtd63NUttOR0BIbLSzrAoUnP6qpfxCQKCgCAgCAgCWYNA5qRCWdPHq95qSEjEGChEuRIq7holUXXy1Gls2LgZm7duNwQWCSuG+OXJnRt58+aBw6Hx77//mj8MNWTJnSsXmjaqj1srlEO+vHkzHE9miawiRYqgQIH86NC+vWn3k09nG1JtzeqVRvnz008bsXTZcnz++RxEjRmN4ODgZKIlJmYV+j76uCFQ7mveHIePHMGsWR/j1lsr4Msv5pqxOYmsfPnyon27doY069WrhyG1SHDRW2rQwAG4pVgxfP/9Inz5E0i67AAAIABJREFU5VeGfJowYXwKMoZYNW/eDFWrVEGjRg2RJ0+eNETWoMFD8e3Cb9GzVw+aHmPd2nX4YfESE3r44rgXTH301mrZqg3y5MmN7t26waEdmDXrE5w7dw4/LPoWJUqUcIsx616wYCE+mv4hIiPrpTsP3uBy4MBBtGzVGgUKFEDPHt1Nfz77fC727t2LOXNmo/qdd5o2SGRt+flnFCt2C9q3b4/SpUsZfHbt2oX27TvhhiJF0LlTR0P8zfr4E5w9e9aEgPI6JwlIUrF8+XJo1LAh4u12zJv3BapXvxNz53yW4Xq6VhfkGCIrLCIKGtwTxtrjYqOuFX7Sjn8gUDY6crWGevbA4FXGfF2KIOAtAkJkpUVKFKnerh65ThAQBAQBQUAQEAQyg4B4ZGVCkfXX3yexaMlyxNkTDIlFX6WK5cuhWLGbcUPhwiha5AYkJSXh75OnTMjhocNHsHdfPC5evGjUNjWr34G6tWuhYIH86c6Rk8hqUL8+xo8fl+baG2+80ZAezuuoyvnu2/nJCqqvvvoaQ4YOx9SpU9C8WVNzv7vQQva1XmQDQ5jM+Xx2cqig8/4P3n/XqLOcRNaUtyejRYv7kvvjfH3evM+NQshZ+vV7EouXLMXqVStTkDE9unfDCy/8nxc4ePBQCiKLaqxKlaqiTZvWRv3lLN269cSmzZuxc+evRpU1cOBgY1y/dMkPRt3EkpCQgMZNmqNXzx547rlRbvGlwqttuw44fPgwmjZtggdbtTSEHufRtXiLy4fTpmPGjFmYNfMjhIWFmipOnDiBWrXrokuXTsnEG4msHb/9huXLFhsS0FmYjXDbtu2IWbkseRxHjhxBo8bN0LZta3O/k8hi2OO7U6ck+68NH/4M5n3xJbb+vBk33PCfci6rS44hskIi6sOG+nAgxm6PjblSXEWRcKUIXtv7y0ZHrlBajUsYsmrltW1ZWsvuCAiRlXYGRZGa3Ve19F8QEAQEAUFAEPBPBMQjy0sii75GM2fPNeQU1U433XQj2rZsgaJFixjFlbty/vy/2Bdvx9IVq0xoWVBQIGrVqI56dWsjlxV25+6+jLIWMvytWrU7koksV/UT69u583fc/0ArPD/6OfTu3cs04Y7IolKrc5duRqXlSlD9888/aNCwickkOGrkiGQia/2Pa1C8ePHkLjuJrNSvr1q1Gg/17mPIqA7t2yWTMa9HT0Tr1g8m35+ayOK4q1WrYQzXp334fnJbJA0dWhsSi2TXHdVqGD+r0akIq+49HjKZHBfM/8rj03bmzBmQgJozZy6OHfvDkFgM+Xv66f6GdGPxFhdPjdSoWRuVKt2OGR9NM5eQyGLYYkzM8uRbiHHVO6qjXds2eOWVl1NUxb4pmw0dO7T3iJ1zPr//biEqVkw/w+W12npyCpHla7xEkeBrRK9ufWVfj1yCJPXa/qGrll7dlqT2nIaAEFk5bUZlPIKAICAICAKCgCDgrwiIR5YXRBaJq8XLVmLjlq1mHiPCQtGhTSswLdXaHzeYMMMHWzRHmTKlTNbCZStXY8fvu9Egsi7uql4Np06dxoczP8U/586ZUL0uHdogPPT/ZuOpF4eTyKLKqV+/x9KsnRo1qqNQoUIeiSynLxZJKJJRnogsEibPjHzW49ps164tJr72SqaJrPj4eNCPavCggejf/0mviSx2ZPHiJRg2/BmQ6KlSpTLuvTcSbdu0Sc4mSOVV/QaNPfaZ6rS1azIW0XBOqYZa+O23mDtnniGOPp41A3fcUdWQXN7gwk5s3boN773/AX79dQf+OHbMzAkLQxdnzpjukciit1bjJs0wdOhgPPlEP4/j8WT2zvDPMVEvGNKOXmP+UHIKkeVrBZUoEvxhdXrfhzLRkd/blHorYdCqRd7fJVcKAoAQWWlXga/3U1lngoAgIAgIAoKAICAIEAHxyPKCyEo4cBCfz/saFy5eRNnSpdCyRVPcWLSoyVQ489M5uHjpkgnLO3/2LHLnyQ1bYJBRbZUqURwd2rZC4UKFcPjoUcz5Yr5RZpUoXgy9unXyqOTKrEdWakWWt0QW/ZjGjBlrzMUrVCif5omgp9Ott96aaSKL3k/3tWiJEcOH4fHHH80UkcVO0CNq6dJlWLN2HWJiYnDm9Bk8++woPPRQT+zZuxfNmrUwyq7WD7ZK02cqsmhyn5lCcuyBlq0RERGOr7/6wvhUeYMLlWd9HnkUtWvVQteunU3YII30u/foacil9Iis2Nh9aNK0uTHFf6Lf40JkZWbCrvK1oqC6ygD7YfVloyMnOHBp7MHB68+XjY5cAKgP9g9etbB0dGTXXDa1N27gqk1+2G3pkp8hIESWGyJLPAf9bJVKdwQBQUAQEAQEgZyBgHhkZUBkXUpMxA9LlmPrLzuMoXuLZo1we8VbDXFFL6yvFnyLg4eOoEJEOIrdciMSE5Pwy2+/4+w/51CxQnm0atHUmJozPG7Dxi1YsXqt+XfbVvej0m3uQ8KuFZG1fPkKPNL3MYwdO8aYlXsqnkIIPb2+aNEPeOLJ/nj7rTdx//0tvCayTp8+jePHT5hMhcSM5dy58+j10MPYvn07tm3dbDzIGFrYrFlTvDPlLa+fQmZk7PdEf/R5uHdyuKXrzfThoiH7rt93wFtc+vTpi5+3bsWmTT+lyKhY8646uO22iukSWUZxVvVOdOjQHhNefinFOJj9MSnJYeoQRZbXU+yzC32toBJFgs+m5qpUVOGVugXPBwX8BY1EQE+Gwm1KY6NWqgGAutBYt3/I6oZXpXGpNEchIESWGyLLx56DOWrByGAEAUFAEBAEBAFB4LIREI+sDIisE3/+ia8XLjLqq7KlS6N7l/YICgw0gFN1debsPyYMrmiRIsiVKwg/rv8Jc774Gk8+3teEGrpmKTxy9Bi+Xvg9jp/4E8FlS6Nnl47J5t2uM3g1iKyPP/kUzz8fhW++/hJVq1YxzbHfde6uh8KFC4FeSwULFjSv04dq8uS3TVgijcQzIrKmTfsADRvUN/eSaKKJ+c8/b8Wa1TEmo6EnMia1R1YygRT1PHr27JEMCUPoGEq3aeN6Y4retVsPbNy4yYyFXlTOQu+runff7dYzihkN764bCWZ4nP/NlyY001n+/PNPNGl6nzHLX7liqde49OzV24Qnbli/DszoyMJQw44dO6PO3XWMCTyLO48svk7y7Jdff8WqmOUoWrSoufbvv0+iYaPGiKxXD2+++boQWZe9tfnPjaLw8p+58NSTMtH3jlJwPAcomwYCFHAJAB/qRMBRYf/gtXH+PwrpYVYjIERWVs+AtC8ICAKCgCAgCAgC1wsC4pGVAZG1NzYOC75fjLP//IN76tyFRvUjDYH1778XYAuwpQgPPH36DL78ZgGoLHr6yf/CxS5eZNghTMgZQxMXLV6O7Tt+Q/58+fDYwz1RsGCBNGvNSWRRkcOwQXelffv2yJU7F8qVu9VcM2HC+OTL3IUWrl+/wRBAdevebULvnnryCXP9F198heEjnkHZMmXQvkM75MubD19/8w327o0FsxFWrlQpQyIrf/786N69K4oXK44lS5eCbdFknmbzLN4SWRx369ZtQf8ohuqFh4Vjb+xefPLxp7jrrrvw6aezTH1ULLXv0Bna4UC37l1RpkwZrFu7Dj8sXpKuumzRD4vxdP8BKFS4MB544H6UKF7cZBmkT9aJE39i8puvGwWZt7jMmTsPzzwzCrfffhsaNWoIEnPff7/IkHkVK1Y0hBmLJyJrx47f0KFjZ5M4oFOnjsaofvbsz40xPEMcy5WLECIrC3ZiXyuofK3wygJIcn6TY+oHli3kOAEF1xSgF6DUZ/sHreqd8wGQEfoCASGy0qLo6/3UF/MkdQgCgoAgIAgIAoJA9kdAPLLSIbK0Brb/ugPfLV4GZi3s2rGtMXq3JxzA5p+3IV/+fLi9YgXkDgrCpcQkrN3wE37/fReaNmyAiPBQnDt/Hjt37TGqq3vr3Y38+fIjZs06rF3/k1Fvde/UHqVKlvBIZKW3vJgp8Kabb/aayGJdz41+HvPnL0SBAvmx/se1ydWvjInBlClT8dtvOxEYGAiayQ8dMtgQNCwZKbLemvwGZsycZQzPCxQoAJrEDx82xNSVGSKL1546dQrRr7+JJUuW4q+//kLx4sVw333NDfHGup2FHlOTJkXjx/UbcPHiRZQrVw6PPfpIMhHlCTuSfO++9wHWr1+PP//8yyi8aCr/+GOPGqN315IRLrx25sxZmDHzYxw+fBjh4WEYMniQ6Tu9vObNm2NCDj0RWbyfZNZrEydhy+YtIONZq9ZdGDFiGMqXK5cudmL2fvU2X1FQXT1s/bnmMtH1BingRUDlM/3U+iKCUG3/02t2+nO/pW/+g4AQWWnnQvZT/1mf0hNBQBAQBAQBQSAnISAeWekSWRqbf96OH5atML5WT/TtjSI3FDbhgU6Cygaqs/7FDUWKGtUWiyMxEY6kROTLXwCXkpKMgqt1y/tQtdLtWLdhI2LW/IiAAJvJfJhe9kJ/X2ieCC5/77f07+ogkKOyFtpQHw7E2O2xGafAzABOUSRcnfV2NWotEx35hwJuNjyWwlcHBq1udzXakTpzJgJCZLkhssQjK2cudhmVICAICAKCgCCQxQiIR1YGRNbW7b/i+yXLTbhYr64dUbZMaSxasgJbtm03YYU0bC96Q2Ec/eO4MXmnf9adVSujcKGC+PPvk8YkniFwvbp1Nr5Ya9ZtwKp1641aqXP71ggpWyaLl8DlNy9E1uVjlxPvzClElq/nxheKhLCwsMJa2yId0OWVVmnjkX3d6eu0Pn1v4Rq6Sv5m0Bq2H05+hNjzh69TKPx62FrpszaoPUo5VsfFxZ3yl84KkeUvMyH9EAQEAUFAEBAEBIGcjoB4ZGXgkbVrz14s+H4Jzp8/jyYN7sXdtWvi4qVL2L17rwktDAsJNiTX+FcmIXeePOjUvi3KlC6JgIAA449l378fgYFBCA0uY1RdS1eswsYtW5EnT2707t4ZN990U7ZdY0JkZdupuyodzylElq8VVFfikRUWFlbD4VCDoVSXqzJpUmkaBByPFgdOJsI294Sgkx0Q0Pozm01Hx8XFbc7q7gqRlXYGfL2fZvUcS/uCgCAgCAgCgoAg4B8IiEdWBkQWsxV+8+0ik2nw1vIRJhyQnlfOQnJq6/btmDL1Q5OpsPqdd5hQQnpqBQUFpZjlk6dOYf53i2FP2I+bbiyKR3p1Mwbf2bUIkZVdZ+7q9DvHEFlhEVHQGAOFsfa42Kirg1bGtYaERLAPye3XqV3bZOQsWOi/7KJSrg4Cf+AkLiERpZB9f2S4Osj4T61nTp8xfpTrN2z4f6c0ouz22LFZ2UshstwQWX6yn2blupC2BQFBQBAQBAQBQcD3CIhHVgZE1vnz/+Lrb78HsxcyXLBd65YoU6qkmQkqszZs3IKfNm7GX3//hTGjhkMphVVr12NfvB3V76iKGndWTSa+9sTuM/5azHh4T51aaFS/nu9nVGoUBLIIgRxDZPnY0+VyFAkhoeXeA/SjnMqHez+EPn0eRkk3iSGyaKqlWUHALxA4fPgIpk2bjukfzbD6o963x+99LKs6J0SWGyLLx/tpVs2ttCsICAKCgCAgCAgC/oWAeGRlQGRxujZv3Y7Fy1bCkZSE6tWqoknDe43a6tDho5jx6edGfUUgjxw6hHz586Ng4cKG0CpZojg6tmmFwoULGfJq3tcLEGdPMP5YTuN4/1oO0htB4PIRyClE1uUj4P7OzHpkOZVYDE+e+s7baNKksa+7JPUJAjkKgaVLl6HfE0+ZMH9koTJLiKwctaxkMIKAICAICAKCgCDgxwiIR5YXRNa58+fx+Rff4MDBQ8iTJw/q31MHNatXw18nT2Le1wtx4sSfKF2yBPLkCkJQUC4cOnoMp06fRvmIcLRq0RQBgYFYuXodNm7+2SyFu2vVROMGkX68LKRrgkDmEcgpRNblKKjSQyszHlnGE0vbNrG+99+bKiRW5peh3HGdIkAy69HH+pnR25SjZlZ4ZgmRlXbx+Xo/vU6XtwxbEBAEBAFBQBAQBFIhIB5ZXhBZxOzI0WOYPfcrnP3nH+TPlw9VKt+O+vXuxok//8TJk6dRotgtKFLkBuOPdeDQYZw9exY3Fi1q1Ffr1m/E9h2/GbP38NAQtGzRzIQpShEEchICOYbIykJPl5CQ8Nk0dmc44ejRz+ak5SFjEQSuOgLjxr30X5ih1p/Z7fu6XvUGUzUgRJYbIisL99NrPf/SniAgCAgCgoAgIAhcOwTEI8tLIotTsi/OjjlffYNLlxKN79XNN92I5o0bILhsGRNK6Fq0Bnbs/B3LVq7GmbNnDcHF0MQG9eoist7dKQzjr91054yW2rRtj/bt2qFbN0nk5k8zmmOILB97unirSAgLCyvs0LaTnNN1a1eLJ5Y/LW7pS7ZAgJ5Zde/5T+1sU44b4uLiTl3LjguR5YbI8vF+ei3nU9oSBAQBQUAQEAQEAf9FQDyyMkFkObTGzl27sWrNj/jzr78NOUUCK2+ePChRohhuufkmQ3L98cdxHDl2DImJSeaagAAbkhKTcPDgQQQGBKB3z66oUf1Oj2TWM8+MQkBgAF56cVzyyklISED9Bo3x85aNKFKkyGWvqIULv0WxYsVw1101L7sOdzceP34cn302B08//ZRP63VXmScia8+ePWjW/H5zC3EuU7YsOnfqiEceedgr4nDr1m2Ij49H27ZtrmgMvqrnijqRBTfnFCLL19B565EVGhrRUgMLmJ1w9uyPfd0NqU8QuC4Q6Nq1h8lmqIBW8fGxC6/loIXIupZoS1uCgCAgCAgCgoAgcD0jIB5ZmSCyuFBITB394w9s3b4DzEJ46tRpj+uHqq1iN9+McuXC4EhMxMLvFuHkyVPInz8fOrZvi7p1apnQw9TlahJZ9BCpUrkynnrqiTTtOom5y3kgdu78Ha0ebIPYvbsyfXtm282IyPpl+88GV6ZnHxM1FrffdhsmTBifYb+mT5+BFStW4pNPZmZ4bXoXXG49DD3lmsmuJacQWd4qqLydJ289soJDw4coqIkSVugtsnKdIJAWAWd4oYYemhC/b9K1xEiIrLRo+3o/vZbzKW0JAoKAICAICAKCgP8iIB5ZmSSynFNJ5dXxEydw+MhRxCccMH9fvHjRKLTy5c2L0JAyKF2qFEoUL4YiNxQ2t61b/xO++Go+Tp8+jQIF8qN1qwfQ4N56YHYy15IRkXXhwgXUubsexr80DlPffQ9nzpzBQ716YcCA/qaaPXv34tlnn8fOnb/h5ptvwZDBA9Gy5QO4t35DHDly1JA8dWrXwrRpH6B+/UaoVu0OrF33Ix7t2wckpIqXKI4Rw4eZuj799DN88818zJv3ufn/998vwqTo13H06FFUrlwZL457ARs3bsIrr75mxkWfsHenvmOUTZ/PmYuvv/rC3LdvXxwaN2mGvXt34cTx46b/Xbp0xoL5C/DZZ58gPDwCL4x70dTPLI+PPfYounf7z+Lkl19+wchRz2H//gOIjKwHKq843tShhU5F1m87tiNfvnzm3kOHDqFho6b4duF8lCsXYYjEkSNHYc3adYZQ7PPww3j00UcweMgwLF68GBcvXkLBggWwaeMGc3909Bv47PM5xt+sYcMGeHn8i8idO7fxQGOfVq9eg1y5cqFD+3YYNmwIhgwdnqYeklPvv/8hZsyciX///Rd16tTBuBeicOONNxpsJ781BWXKlMb27b9g29bNSNi/H8+OGo2t27YiODgYL4wdgxo1avjvLmL1LMcQWVnk6eLMVsjneOCAp/1+vqWDgoA/IpCV+5AQWWlXhLeKVH9cS9InQUAQEAQEAUFAEPBfBMQj6zKJrNRTSlWRSf2tFAJstjSeWbyeZEjM6rX44qtvcO7ceRQsUAA9unVGzRp3prjeWyKLRNCzo57Br7/uQLfuPbFyxTKULVsGVCzde28knu7/FNat+xEPP/wI1qxZheLFi5msTq6KLBJZIaEhhqC5+eabMWzYCI9E1q87dqBL52744IP3ULNmDbz73vv4+utvsHzZEkOAuSqy5syZmyGR9dSTT6BPn4dRqFBBDB8xEn//9RfeeCMaR44eRefOXU07lW6/HZH3NkCnTh3R95E+2LDhJ/R/eiBGP/esV0QWcX+g5YPo2LEDevboju7deyEkNBhRY0j07US79p3w5RdzUKVKFaRWUr399jtYtnw5Znw0HQ5HEtp36GxCFUl8kbjjmN+dOsVkrezQsROee3YUHnjg/jT1zJ79Od6e8g5mfPQhSpcujRfGvWSIvXlz/yMJifnkt97EvZGRyJUrCE2a3oc2rR9Ev36PYfHiJXh+TBTWrI5B/vz5/XcnAZCVB0hfAuOtgsrbNr1VJAiR5S2icp0g4BmBrNyHhMhyQ2SJR5Y8roKAICAICAKCgCBwFRAQjywfEVnezk2Sw4EVMauxYOF3KFmiBLp0ao+Q4LIpbveWyKJ6p3Dh/9Red9W6G6+9OsEQWF26dkdYWCgGDRyAm266CYcPHzZ/UznkjsgaNeoZNG3axNQzaNAQj0TWuBdfwp8n/jRkE0tiYiI+/HA6evfuZYiZzBJZ27dtQaFChQypV/WOO/Htwm9QoUIFU/eYMWON8onjear/09i06Sfje8WSUWihqyKL1/d++BFUu+MO498VFxdvCD2nYqtZsxZ47LG+xhcrNZFF1RnVa8TO2afz58/j1Vcn4M0338LqNWvwyoSXERERjj///NPgW7BgwTT1tHqwrSGmiBMLx3tn9ZpYMP8rQ4a9/8GH+P67/6xc1q5dhwEDB2PTxvXJYYbs44gRw4wizJ9LVh4g/RkXbxUJQmT58yxK37ILAlm5DwmRlV1WifRTEBAEBAFBQBAQBLI7AuKRdY2JLC6YpCQHtmzdhptvLIqQkOA06i1viSwnEcQ669x9jzGHJ9lx5MgRQ7SsWr0aRYsWNUqm1q0fNGv1SoisAQMGoWSpkslhh66L/3IUWc7+M5SOyjASQc5C9dr9Le5D/fr34oMPp+G7bxckv5dZIqvF/S2Nkqpnzx4mLPGNNyab8Euq6A4ePGRwa9++bRoCiqGSEydG46efNuJSYiL++usvNG7cCBNfewUM73xn6rsmFJLEVLt2bYwCjsRXakKsdp26GD/+JTRsUD95DPfUq29CBtmGK5E1Z+48jBz5LIKCgv6PRVISXnghyoRi+nPJygOkL3HxVkHlbZveKryEyPIWUblOEPCMQFbuQ0JkpZ0XX++nsvYFAUFAEBAEBAFBQBAgAuKRlQVEFoEniWImQKWdgtdem4R9cXEmbM1ZNm/ZAmZj2vnbLzhx4oTxmHJHZNWrdw927dqF22+/3Sh66F/Vs1dvfDFvDipVuj1DImvY8BEockMRUKXF4uqRNeGVV01GxuhJr5n3OIYf16/HXTVrYu/e2BSKrC+//AozZ36MBQu+Nte688hy9p++UbdXqorVq1agVKlSKZ5M1j948FBsWL8u+fXMEFkkyRo3bob533yF226riLvrRuLJJ/qhc+eOxpuMJNfDvXu7JbIGDR5qrnlhbBTy5ctrPLxOnz5jiKzdu3ejZMmSRoF14MBB9HmkrwlffKTPw2mIrHbtOxoisUf3bmYMVHXdUa0GvvhiDvbF7ktBZNFz69nnRptQwuxWsvIA6UusvFVQ+bJN1iVElq8RlfquRwSych8SIivtisuq/fR6XPsyZkFAEBAEBAFB4HpCQDyy/PAASXPzjp26YtLEV9GgQQMcP3EcI0aMRLFbbsGbb/5ntO6JyKpb927Uql0Xw4cNRZcunWC3241H1Nw5n+P2228zoYOBQYHGpJ2he1RCuYYWvvfeB1iwcCE++Xgm/v77JAYMHIQ8ufMYs3dmAezQsTNmzJiGO6tVw8cff4Jp0z9CzMrlOHDgAOo3aIyYmOWGjPptx2/o3KUb5n/zJYoUKWKUTXPnfZHC7N2ViKPvFc3yX33lZUPAUdVUu3YtY0R/b/1GGDx4oDFU32HVO/KZEel6ZLEOeoeNfj7KmLy/NfkN42FWuUo1jBkzGvc1b4Yf128wJNmY558zHlz09aL6iwb1JKhIAJYoXhzPPTfKEHFDhw1H5UqV8Prrk0y4Is3aX3rxBdPvbt17ofWDrfDwww+lqWfeF1/i7benYMZH0wz5NX78BJCYpAH9/PkLUhBZJPWaNG2Ozp074dG+j+D48eNG+TXg6f7Gw8yfS1YeIH2Ji7cKKm/b9FaRIESWt4jKddcaAXotdu/RC69HT0xW917rPnjbXlbuQ0JkuSGyxCPL26Ur1wkCgoAgIAgIAoJAJhAQjyw/JLI4f8uWLcfkyW9jb2ysIVVIvAwfPtQYfqdHZDG0cNOmzUY9FBcXhxtuKGJCCx96qKdZFlQ4PfXUANx6awXM/vTjNEQWMyAOGDAYG376CaVLl0LVqlVgj09Izlr4w+IliI5+3WQDpEE6yTaSM1RnPfb4E1izZi0+/OA9kFAjYcPMhcwO+MD99+PDadM9Ells96WXXsay5SsMMdSoYQOMHTvGeGjRZJ7hliTWSEpRkda1SxePRBbHyRA/Gqu3a9sGj/d7LNlf67vvvgeVZcxe2KRJY5N9sGaN6uhrkUZdu/UwmR3pUbV//34MGz4SsbGxqFy5klF0HTt6DO+887aZg2efe96EHTIkslmzphgb9bz5N8kn13ry5MljCL9Zsz7GX3/9jVp31cRLL41D8eLFjdm7a2gh+07SLGrsCyaLYd68ecxY6e/lTr2XiWf9ql+alQfIqz64K2jAW0WCvxFZiUlJKFfu1nRHvuv3HYYQ97Zs3brNhDx369rFb4hZPsvN73sATz7xuNkHrkVhBtbJb72NTZs24ejRY8aHj6HH/fs/hVtu8T/CWogs71aFEFne4SRXCQKCgCAgCAgCgoAgcKUIiEeWnxJZVzqxcr8gcK0RyClElrcKKm/x9Vbh5SsiiwkluLFTlXjsrFwzAAAgAElEQVQlxUlkkcDt1LGD26q6detqwm+9LVRaMmkEPe9Yrz8UEs8dOnYxhH+3bl2uepdIUDMhB0nvBx9sZZJPkNiiOjNv3rxGEcrss/5UhMjybjaEyEqLk6/3U+9mQq4SBAQBQUAQEAQEgZyOgHhkCZGV09e4jO8aIZBjiKywiChojIHCWHtcbNQ1gu+yPbKYPOLvkydx8NBhHD5yFOf+PQ8FhYIFCqBsmdIoWbwYChQsAJsbP770xuYksqgGpQrRF8WXRBb7F2CzXbZSkSpSJpXIDBHnCwx69HwIGzb8hKVLFiEkJCS5yt9+24nWbdoZpeg7U97yRVPp1sGss1SuelOEyPIGJUCILDdEVhbtp97NmFwlCAgCgoAgIAgIAtkVAfHIEiIru65d6befIZBjiCwfe7p4q0jIrCKL+SKO/fEHliyPwYFDhw0pQ3LGNZEEw1GDAgNxW8UKuLduHRQuXMjrVeMtkeW87rHH+pow4+nTPjL9KhcRYfz36HXHbKHNmt+fom0qknbv+i35NYZET4p+Hb/++ivy5s1nMoSOfGY4ChcubK6ZNOl1vD3lHbz66gRMmTLVhP5u2fyT8dOb8s5UvP/eVEx+a4pJxMBw7LZtWmPYsCHJGUCdZMyQIYOwKmY1tm3bhsmT30DlypVRL7I+Bgzoj4EDnjZtObOSMvSXCS5KlSppVGm9H+6dHKbM6xhi/cqrE8EkDYmXLqHmXTUxcuQIlC9XziPODRs1RVJSIlbFrEhzDcOzqRD76st5XuPCCxmOPGlSNNZv2IBLlxJNCDZ99Zj11Vk6dOiM02fO4IH7W2DGzFkmnHHxD9+Zt+l/OHFSNLZs+dko+e68sxroQxgeHmbed2I3/qVxJtx9/vyFuHTpEmpUr45x46LSJOnwepFdhQuzch8SIivthHqrSL0KS0GqFAQEAUFAEBAEBIEcjIB4ZAmRlYOXtwztWiKQlQfIaznOzLbla48sElUnT53Gho2bsXnrdkNgkbCisihP7tzGV83h0GDiAP5hqCFL7ly50LRRfdxaoRzy5c2b4TAyS2QxqUOBAvnRoX170+4nn842pNqa1SuN8od+ckuXLcfnn89B1JjRCA4OTiZaYmJWoe+jjxsC5b7mzXH4yBHjKUcvvy+/mGvG5iSymEG0fbt2hjTr1auHIbVIcNFbatDAAbilWDF8//0iMHMqyacJE8anIGOIVfPmzVC1ShU0atQQ9LBLTWQxY+m3C79Fz149EBoainVr14H+gAw9ZKIMFnprtWzVBnny5Eb3bt3g0A7MmvUJzp07hx8WfYsSJUq4xZh1L1iwEB9N/xCRkfXSnQdvcGHW1JatWqNAgQLo2aO76c9nn8/F3r17MWfObFS/807TBomsLT//jGLFbkH79u2NByLxYZbb9u074YYiRdC5U0dD/M36+BPjH8gQUF7nJLJIKpYvXw6NGjZEvN2OefO+QPXqd2LunM8yXE/X6oKs3IeEyLpWsyztCAKCgCAgCAgCgsD1joB4ZAmRdb0/AzJ+HyGQlQdIHw3BVOOtgsrbNr1VJHiryPrr75NYtGQ54uwJhsSir1LF8uVQrNjNuKFwYRQtcoPJ0Pn3yVMm5PDQ4SPYuy/eJFGg2qZm9TtQt3YtFCyQP90hOImsBvXrY/z4cWmuZdZOkh7O65it9Ltv5ycrqL766msMGTocU6dOQfNmTc397kIL2dd6kQ0MYTLn89nJoYLO+z94/12jznISWVPenowWLe5L7o/zdWZWpULIWfr1exKLlyzF6lUrU5AxPbp3wwsv/D9i9ODBQymILKqxKlWqijZtWhv1l7N069YTmzZvxs6dvxpV1sCBg41x/dIlPxh1E0tCQgIaN2mOXj17mGyn7goVXm3bdcDhw4fRtGkTPNiqpSH0OI+uxVtcmERjxoxZmDXzI4SFhZoqmBCD2WuZudZJvJHI2vHbb1i+bLEhAZ2F2Qi3bduOmJXLksdx5MgRNGrcDG3btjb3O4kshj2+O3VKsv/a8OHPgFlZt/68GTfc8J9yLqtLVu5DQmSlnX1f76dZvb6kfUFAEBAEBAFBQBDwDwTEI0uILP9YidKLbI9AVh4gfQmetwoqX7bJurwhsuhrNHP2XENOUe100003om3LFihatIhRXLkr58//i33xdixdscqElgUFBaJWjeqoV7c2cgUFeRxGRlkLGf5WrdodyUSWq/qJle7c+Tvuf6AVnh/9HHr37mXacUdkUanVuUs3o9JyJaj++ecfNGjYxGQSHDVyRDKRtf7HNSbjp7M4iazUr69atRoP9e5jyKgO7dslkzGvR09E69YPJt+fmsjiuKtVq2EM16d9+H5yWyQNHVobEotk1x3Vahg/q9GpCKvuPR4ymRwXzP/KI7bM0koCas6cuTh27A9DYjHk7+mn+xvSjcVbXDw1UqNmbVSqdDtmfDTNXEIii2GLMTHLk28hxlXvqG6yu77yysspqmLflM2Gjh3ae8TOOZ/ff7cQFSumn+HS18+Lp/qych8SIivtrGTVfnqt1pu0IwgIAoKAICAICAJZg4B4ZAEIDg0foqAmPtz7IYwe/WzWzIS0KghkcwTGjXsJ0z+aAQ09NCF+36TsOhxvFVTejs9bRUJGRBaJq8XLVmLjlq2m6YiwUHRo0wpMUbj2xw0mzPDBFs1Rpkwpk7Vw2crV2PH7bjSIrIu7qlfDqVOn8eHMT/HPuXMmVK9LhzYID/2/2Xjq8TiJLKqc+vV7LM1wa9SojkKFCnkkspy+WCShSEaxuCOySJg8M9LzvtuuXVtMfO2VTBNZ8fHxoB/V4EED0b//k14TWezn4sVLMGz4MyDRU6VKZdx7byTatmmTnE2Qyqv6DRp7XAJUp61dE5PhEuGcUg218NtvMXfOPEMcfTxrBu64o6ohubzBhY1s3boN773/AX79dQf+OHbMzAkLQxdnzphu/u2OyKK3VuMmzTB06GA8+UQ/j/31ZPbO8M8xUS8Y0o5eY/5QhMjyh1n4fx98vZ/61+ikN4KAICAICAKCgCCQVQiIRxaA0NCIlhpYUKd2bcye/XFWzYW0KwhkawS6du1hzKYV0Co+PnZhth6MDzvvrSIhIyIr4cBBfD7va1y4eBFlS5dCyxZNcWPRoiZT4cxP5+DipUsmLO/82bPInSc3bIFBRrVVqkRxdGjbCoULFcLho0cx54v5RplVongx9OrWyaOSK7MeWakVWd4SWfRjGjNmrDEXr1ChfBrk6el06623ZprIovfTfS1aYsTwYXj88UczRWSxE/SIWrp0GdasXYeYmBicOX0Gzz77v/buPE6n8v/j+HsGJTslSykGlVS+KYRslZRKKlmGCMU3KaWytSDtRWlX8SVliRBF1mQba1K2r2WMylKWbMUXM/fvcV2a+Q1mxmHOfd/n3Pfr/KMx577O53pexzV8+pzP6aX772+tdevXq0GDhrayq/GdjU6K2VRkmSb3p3OY5NjtdzRWuXJlNX7cWNunyomLqTxr/0AHXVetmuLjm9vHBk0j/Vb3tbbJpawSWRs2bFT9m2+xTfE7PfRvElmns2AZnEtFVjYB+TgCCCCAAAIIIOBQgB5ZkuLi4gqmBGL3GLP58+aoZMmMm/Q6NOU0BKJOYOvWbap5fW0779iYlEKJiYl7/YrgtILK6fycViRklcg6cvSovp02U8t/WmkbujdscKMqVrjMJq5ML6xxE7/Wb1u26dJyZVXs/HN19Giyflq1Rgf++lsVLr1EjRrebJuam8fjFi5epllz5tn/vrvRbbri8owfCQtVImvmzFl64MGO6tu3t21WntmR2SOEmf3+lCnfqtPDj+jddwbqttsaOk5k7du3Tzt27LRvKjRm5vj774Nqc387rVixQj8uX2p7kJlHCxs0uFnvv/eO01vBvpHxoU6PqH27tmmPW6b/sOnDZRqyr12zUk5d2rd/UD8sX64lSxYd90bFKlWr6/LLK2SZyLIVZ5Uq6957m+iVl188bh7m7Y/JySl2DCqynC0xiayTndzeT52tBGchgAACCCCAQKQL0CPrnxUuXbqs6TLcgscLI/2WZ37BEEh9rFCBwMikpI3xwbhGqMZ0WkHldjxZJbJ27tql8ZOm2Oqriy68UK1aNFGunDltCKbqav+Bv+xjcEUKF9ZZZ+XSgoRFGj12vB7+94P2UcP0bynctv13jZ80WTt27tLFF12o1i2apjXvTj+nYCSyhn/2uZ57ro8mjP9SlSpdZS9n4q5eo5YKFiwg02spf/789vdNH6q3337XPpZoGomfKpE1ePDHuqFeXftZk2gyTcx/+GG55s6Zbd9omFky5sQeWWkJpD7PqXXr+9JIzCN05lG6JYsTbFP0+Jb3afHiJXYuphdV6mF6X9WsUSPDnlHmjYY1ataWecPjVxO+tI9mph67du1S/Ztvtc3yv5s13bFL6zZt7eOJCxPmy7zR0RzmUcOmTZureo3qtgm8OTJ6tND8vkme/fTzz/p+9kwVKVLEnvvnn3t0w403qXatWho48E0SWQ7/sJPIOhkqXPupwyXjNAQQQAABBBDwqQA9sv5ZuLi4uGtTArFLzJcfDfrANvHlQACBUwuYx686dDzWXyc2JqVKYmLi0lN/yrtnOK2gcjoDpxUJWSWy1m9I1MTJU3Xgr790ffWqurFubZvAOnTof4rNEXvc44H79u3XlxMmylQWPfrwscfFDh82jx3KPnJmHk2cMnWmVqxcpbx58qhju9bKnz/fSdNJTWSZihzz2GBGR5MmTXTW2WepfPnL7DmvvPJS2mkZPVqYkLDQJoBq1qxhH73r/HAne/7YsePUrXsPXVSqlJrce4/ynJNH4ydM0Pr1G2TeRnjlFVecMpGVN29etWoVr+LFimva9Oky1zJN5k2zeXM4TWSZeTdufLdM/yjzqF7ZuLJav2G9Phv+uapWrarPP//Ujmcqlprc21yBlBS1bBWvUqVKaf68+fp26rQsq8umfDtVjz7SRQUKFtTtt9+mEsWL27cMmj5ZO3fu0tsD37QVZE5dRn8xRj169FLFipfrxhtvkEnMTZ48xSbzKlSoYBNm5sgskbVy5Srd27S5fXFAs2ZNbaP6ESNG2cbw5hHH8uXLkchy+IedRNbJUG7vpw6XgtMQQAABBBBAIMIF6JGVboFT/yFpGiF/8P67JLMi/OZnetkXMEmshzp1tv9oVkB9kpI29M3+qJE1gtOKhMwSWYGAtOLnlfpm6gyZtxbGN73bNnpP2vyrlv7wo/LkzaOKFS7V2bly6cjRZM1buEhr1qzVzTfUU7myZfT3wYNavXadrbqqU6uG8ubJq9lz52tewiJbvdWqWRNdkMHj1Kd6a6FZJfOmwPOKFnWcyDKfeebZ5/TVV5OUL19eJSyYl7bY382erffe+0CrVq1Wzpw5ZZrJP/lEV5ugMcepKrLeefstDR32qW14ni9fPpkm8d2eesKOdTqJLHPu3r17NeDNgZo2bbp2796t4sWL6dZbb7GJNzN26mF6TPXvP0ALEhbq8OHDKl++vDp2eCAtEZXZnWySfB8O+lgJCQnatWu3rfAyTeX/3bGDbfSe/jiVizl32LBPNXTYcG3dulVly8bpia6P29hNL68xY0bbRw4zS2SZz5tk1utv9NeypctkMp7VqlVV9+5P6ZLy5bO0o9n78StMIiuy9m5mgwACCCCAAALeFaBH1glrU7pM+UFSoIP5bfOYYfv27eiZ5d37l8jCJGB6Yg0ePMS+pfDYEfNR0qb1J7/aLkzxZeeyTiuonF7DaUVC5omsgJb+sELfzphl+1p1erCtChcqaB8PTE1QxcpUZx1SocJFbNWWOVKOHlVK8lHlyZtPR5KTbQVX4ztuVaUrKmr+wsWaPXeBcuSItW8+zOrthU7nGa7zMktwhSserhteAd5aGF7/E6/u9n7qrdkRDQIIIIAAAgiES4AeWRnIp/6DMvVb5m2GFSpcpvwFjvVu4UAgWgXMm9tMtY95O2HaEWGVWE4rqNy+B7JKZC1f8bMmT5tpK9/axDfVRaUu1JRps7TsxxX2sULTsL1IoYLa/scO2+Td9M+qXOlKFSyQX7v+3GObxJtH4Nq0bG77Ys2dv1Dfz0+w1UrNmzRW6YtKuT2dkI1HIitk1L64EIksby1TuPZTbykQDQIIIIAAAgi4LUCPrExEbc+slJiupgG82+iMh0BECAQCI2NjAwP83hPrxLVwWkHldA2dViRk1SNr7br1mjh5mg4ePKj69eqoxnVVdPjIEf33v+vto4VxpS+2Sa6XXu2vs3PnVrMmd6vUhSVlHpM2/bGSfvlFOXPmUpmLS9mqrumzvtfiZcuVO/fZatuquYqed57T6XjuPBJZnluSsAZkEllvJQxVoNo//+MpRgEFjgvpn68C5jHKTL5nnpSWYo79knr8/3+b533NZ48/zNc5YwKB5zc/MbdfWBE8dHG391MPTY1QEEAAAQQQQCCMAvTIOgV+XFxcwUAgtnaKApfEBGJO7ogcxsXj0giEWiAQEzgQq5h1MTEpcxITE/eG+vp+vJ7TioSsElnmbYUTvp5i3zR42SXl7OOApudV6mGSU8tXrNB7H3xi31R4TeV/2UcJTU+tXLlyHce2Z+9effXNVCVt/kXnnVtED7RpaRt8+/UgkeXXlQtO3CdUZD1vr5KacupjclPm6H3sl9WrY5T+PQardqT9nci8/3J/yf3H/R3p4NaD/3xdUaooHd5+4LjvH911MCap7+xDwZkZoyKAAAIIIIAAAgikCtAji3sBAQQQSCfgtILKKZrTioSsElkHDx7S+K8ny7y90DwueE/jO1TqgpI2BFOZtXDxMi1avFS7/9yt3r26KSYmRt/PS9DGTUm65l+VdG3lSmmJr3UbNtr+WuaNh9dXr6Yb69ZyOhXOQ8DzAuF8tNDzOGEI0O39NAxT4JIIIIAAAggg4EEBemR5cFEICQEEwifgtILK7QizSmSZay1dvkJTZ3ynlORkXXN1JdW/oY6tttqydbuGfj7KVl+ZDX3bli3Kkzev8hcsaBNaJUsUV9O7GqlgwQI2eTVm/EQlJm22/bFSG8e7PRfGQyBcAiSywiWf8XXDtZ96S4FoEEAAAQQQQMBtAXpkuS3KeAgg4GsBpxVUTifptCLhVImsvw8e1KixE/Trb1uUO3du1b2+uqpcc7V279mjMeMnaefOXbqwZAnlPiuXcuU6S1u2/669+/bpknJl1ajhzcqRM6e+mzNfi5f+YEOvUa2KbqpX2+k0OA8BXwiQyPLWMrm9n3prdkSDAAIIIIAAAuESoEdWuOS5LgIIRIWA04qEUyWyDNa27b9rxBfjdOCvv5Q3Tx5ddWVF1a1VQzt37dKePftUotj5Kly4kO2P9euWrTpw4IDOLVLEVl/NT1isFStX2WbvZcuU1h0NG9jHFDkQiCQBElmRtJrMBQEEEEAAAQQQyFiAHlncGQgggEA6AacVVE7RnFYkOElkmWtuTEzS6HETdOTIUdv3quh55+qWm+rp4otK2UcJ0x/m5WorV6/RjO/maP+BAzbBZR5NrFerpmrXqnFcw3in8+G8YwJ33d1ETe65Ry1beu/Ftj169LJvs3zu2WeibrlIZHlryd3eT701O6JBAAEEEEAAgXAJ0CMrXPJcFwEEPCngtILK7eCdJrJSAgGtXvtffT93gXbt/tMmp0wC65zcuVWiRDGdX/Q8m+T6448d2vb77zp6NNmekyNHrJKPJuu3335Tzhw51LZ1vK69pnKmySyTDMmRM4defKFf2lQ3b96suvVu0g/LFqtw4cJnTDBp0tcqVqyYqlatcsZjZPTBHTt2aOTI0Xr00c6ujpvRYFklsqZPn6G333lXGzZsVIEC+XVno0bq2vUx+0hoKA4SWe+YNxX2SUra0DcU3lwjc4Fw7aesCQIIIIAAAghEtgA9siJ7fZkdAgicpoDTCiqnwzqtSHCayDLXNYmp7X/8oeUrVsq8hXDv3n2ZhmOqtooVLary5eOUcvSoJn0zRXv27FXevHnUtMndqlm9mn308MQjmImsDh0f0lVXXqnOnTuddN3UxJxT3/TnrV69Ro3uvEsb1q897Y+f7nUzS2StXLlK9zZtrrfe7K9ata7Xli1b1b1HT1166aV6+aUXTjuujD5wqlhJZJHIcuVGc2EQt/dTF0JiCAQQQAABBBCIAAF6ZEXAIjIFBBDwroDTioTTSWSlztZUXu3YuVNbt23Xps2/2l8PHz5sK7TynHOOypQupQsvuEAlihdT4UIF7cfmJyzS2HFfad++fcqXL68aN7pd9erUUo4cOY5DPFUi63//+5+q16ill17spw8+HKT9+/fr/jZt1KXLI3acdevX6+mnn9Pq1atUtOj5eqLrY7rjjttVp+4N2rZtu02eVb+umgYP/lh1696oq6/+l+bNX6AOD7aXSUgVL1Fc3bs9Zcf6/PORmjDhK40ZM8p+PXnyFPUf8Ka2b9+uK6+8Ui/0e16LFy/Rq6+9budl+oR9+MH72rRpk0aN/kLjx421n9u4MVE31W+g9evXaueOHTb+Fi2aa+JXEzVy5GcqW7acnu/3gh3fvOWxY8cOatUy3n72p59+Us9ez+iXX35V7dq1tG7dOjvfEx8tHDVqtD4ZPEQzpk9N81z2ww8aPeoLvfbaK/r774OZXsMkGHv27KW58+bbRGP7du3UocMDdpwTjdq3b2ervozNkSNHVK9eXfV7vo/y5csns3bm5QB//fWXFi1arLi4OL09cIBKly7t3T8oLkXGo4UuQTIMAggggAACCCDgYQF6ZHl4cQgNAQRCL+C0gsppZE4rEs4kkXViDKZSJzk5WYqJUY7Y2JN6ZpnzTbP32XPmaey4CTapkj9fPt3XsrmqXFv5uPOdJrJMIujpXj30888r1bJVa303a4YuuqiU7SFVp05tPfpIZ82fv0Dt2j2guXO/V/HixXRiRZZJ0pQuU9pWLBUtWlRPPdU900TWzytXqkXzlvr440GqUuVafTjoI40fP0EzZ0yzCbD0FVmjR39xykRW54c7ySSFzCOA3br31J+7d+uttwZo2/btat483l7niooVVbtOPTVr1lQPPtBeCxcu0iOPPqZnn3n6pESWSa41vO0O3XVXY7W9/35deOEFxy3Tk091z/Aa11SurFat2qh0mYvVp7dJAK7WPU2a6cuxo3XVVVfZRFZ6o2GfDtfIkaM0ZPAnKlKksB7v+qTOP7+ofRTUrN3UadM09D9DVK5cWXV5rKsKFiyo/m+85vS29e15JLK8tXRu76femh3RIIAAAggggEC4BOiRFS55rosAAp4UcFpB5XbwbiSynMaUnJKiWbPnaOKkb1SyRAm1aNZEpS++6LiPO01k/bh8qU2SmKNqtRp6/bVXbAKrRXwrxcWV0eOPddF5552nrVu32l/POuusDBNZvXr10M0317fjPP74E5kmsvq98KJ27dxlk03mOHr0qD75ZIjatm1jK65ON5G14sdlKlCggE3qVfpXZX09aYJ9DNAcvXv31dlnn23n0/mRR7VkySLbX8wcWfXI+v33P2yV2jffTLbJpZbx8WrevKkOHfpfptcw809M3GQTfXny5LHXaNCgoTp2fFB3332XTWSlN7rt9ka6r1VLNW/ezJ67+ZdfbPVV03ub2ETWOXnOUe/nnrXfGzFilMaMHZtWmeb0PvHjeSSyvLVq4dpPvaVANAgggAACCCDgtgA9stwWZTwEEPC1gNMKKqeTdFqREMpElok9OTlFy5b/qKLnFlHp0hefVL3lNJGVmggyY1avcb2tCLrhhnratm2bBg58R9/PmaMiRYrYSqbGje+0bBlVZDlNZHXp8rhKXlAy7bHD9OtwJhVZqfGbRJBJFplEW+phqtdua3ir6tato48/Gaxvvp6Y9j0nby001XGmeuv5519QtWpV1f6BdplewyTmzOOKb731tn0s01TX/fbbFuvZpMndJyWyql1XQ6+9eixpeOJxYo8sU5lmHkGcOHG809vWt+eRyPLW0rm9n3prdkSDAAIIIIAAAuESoEdWuOS5LgIIRIWA04qEUCeyDL5JlpjD9NQ68Xj99f7amJioDz94L+1bS5ctU3z8fVq96ift3LnT9pjKKJFlmpyvXbtWFStWtG9FNP2rWrdpq7FjRuuKKyqeMpH1VLfuKlyosK1AMkf6HlmvvPqafSPjgP6v2++ZOSxISFDVKlW0fv2G4yqyvvxynIYNG56WwMmoR1Zq/IcOHVLFKyppzvezdMEFxz8OaMbv2vVJLUyYf8pE1kcfmUf9itjkU+ph3mLYrXsPJSyYl+k1zLk1atbWw50estVbpmeZeUSxXdu2GSayGt91j+LjW9gKLHOY/lqJiYmqXPlqW5GVJ28ePffsM/Z7JLKiYqthkggggAACCCCAQNQI0CMrapaaiSKAgBMBpxVUTsYy5zitSAhHIiurOZjm5k2bxdu+SvXq1dOOnTvUvXtPFTv/fA0ceKzRemaJrJo1a6jadTXV7akn1aJFMyUlJen2O+7UF6NHqWLFy+2jgzlz5bRN2s2jeyc+Njdo0MeaOGmSPhs+TH/+uUddHntcuc/ObZu9r1mz1r4VcOjQwap89dUaPvwzDR7yH83+bqZ+/fVX1a13k2bPnmmTUatWrlLzFi311YQvVbhwYb3xxgB9MWbscc3e0yfiTN8r0yz/tVdftgm4IUOG6rrrqtlG9HXq3qiuXR/TvU3ukXkzoRm3Z4/uJ/XIGjt2nF5+5VUNfGuArr32GvtGyb7P97NN6M18MruGOffKq65W797P6tZbGmhBwkKbPOv93DO2N9eJRsOGfaoRI0dr6H8+UaFChdSz59PKmSuX3nj9VRJZA3lrodP9Kdjnub2fBjtexkcAAQQQQAABfwjQI8sf60SUCCAQIgGnFVRuh+O1RJaZ34wZM/X22+9q/YYNyp8/v02wdOv2pPLmzZtlIss8WrhkyVL7dqs41kIAAA8ISURBVD5TJVSoUGH7aOH997e2bKbCqXPnLrrssks14vPhJyVpzBsQu3TpqoWLFtlm6ZUqXaWkTZvT3lr47dRpGjDgTW3ZssU2QjfJtpIlS9rqrI7/7qS5c+fpk48HySTUXnrpFdvw3bwF8PbbbrNvFEz/1sL0iSxz3RdffFkzZs6yCa0bb6invn172x5apsm8qXQyibXy5cvZirT4Fi1OSmSZ+ZlKMJNcMxVg5i2Cta6vqV69etp+WVldw/TUMhVnprqqfv2bdODAAVW59ho9+OADJxmZxxbfffd9jf5ijD2v4a232FhNYpCKLBJZbu9PZzpeuPbTM42XzyGAAAIIIICAPwTokeWPdSJKBBAIkYDTCiqn4TitSPBiIsvpHDkPAa8I0CPLKytxLA6391NvzY5oEEAAAQQQQCBcAvTICpc810UAgagQcFqRQCIrKm4HJhlkARJZQQZmeAQQQAABBBBAwAMC9MjywCIQAgIIeEfAaQWV04idViSQyHIqynkIZC5AIstbd4fb+6m3Zkc0CCCAAAIIIBAuAXpkhUue6yKAgCcFnFZQuR08iSy3RRkvGgVIZHlr1cO1n3pLgWgQQAABBBBAwG0BemS5Lcp4CCDgawGnFVROJ+m0IoFEllNRzkMgcwESWd66O9zeT701O6JBAAEEEEAAgXAJ0CMrXPJcFwEEokLAaUUCiayouB2YZJAFSGQFGZjhEUAAAQQQQAABDwjQI8sDi0AICCDgHQGnFVROI3ZakXBxmbJPxCjmjXZt79ezzz7tdHjOQwCBdAL9+r2oIf8ZqoACT27etLE/OOEVcHs/De9suDoCCCCAAAIIeEWAHlleWQniQAABTwg4raByO9gyZcrdEZAmVr/uOo0YMdzt4RkPgagQiI+/TwkLFypGarRp04ZJUTFpD08yXPuph0kIDQEEEEAAAQRcEKBHlguIDIEAApEj4LSCyumMnVYkxMXFFUwJxO4x486fN0clS5ZwegnOQwABSVu3blPN62tbi9iYlEKJiYl7gQmvgNv7aXhnw9URQAABBBBAwCsC9MjyykoQBwIIRKTA6VQklC5ddoRiYlrweGFE3gpMKsgCqY8VKhAYmZS0MT7Il2N4BBBAAAEEEEAAgTAJ0CMrTPBcFgEEvCngtILKafSnU5EQFxd3bUogdokZ+6NBH6h+/ZucXobzEIhqgenTZ6hDx4esQWxMSpXExMSlUQ3ikcm7vZ96ZFqEgQACCCCAAAJhFqBHVpgXgMsjgIC3BE6ngioYkae+vTBHjhz64P13SWYFA5kxI0rAJLEe6tRZycnJUkB9kpI29I2oCfp4MuHeT31MR+gIIIAAAgggkIUAPbK4PRBAAIF0AqdTQeUE7kwqEkqXKT9ICnQw45vHDNu3b0fPLCfYnBNVAqYn1uDBQ+xbCo8dMR8lbVrfMaoQPD5Zt/dTj0+X8BBAAAEEEEAgRAL0yAoRNJdBAIHoFDjTioTUyqxUNfM2wwoVLlP+AvmjE5JZI/CPwP59+7VmzVr7dsK0g0os7g8EEEAAAQQQQCBqBOiRFTVLzUQRQMCJwJlUUGU1bnYqEmzPrJSYrqYBvJPYOQeBqBMIBEbGxgYG0BPLmyvv9n7qzVkSFQIIIIAAAgiEWoAeWaEW53oIIOBpgTOtoArmpOLi4goGArG1UxS4JCYQky+Y12JsBLwuEIgJHIhVzLqYmJQ5iYmJe70ebzTH58X9NJrXg7kjgAACCCAQKQL0yIqUlWQeCCDgikB2KqgyCoCKBFeWhUEQQMCHAm7vpz4kIGQEEEAAAQQQCIIAPbKCgMqQCCCAQKoAFQncCwgggAACCCCAAAIIIICAewL0yHLPkpEQQCACBNyuoKIiIQJuCqaAAAJnJOD2fnpGQfAhBBBAAAEEEIg4AXpkRdySMiEEEMiOABVU2dHjswgggMD/C7CfcjcggAACCCCAQDAE6JEVDFXGRAAB3wq4XUFFRYJvbwUCRwCBbAq4vZ9mMxw+jgACCCCAAAIRIkCPrAhZSKaBAALeFKAiwZvrQlQIIIAAAggggAACCCDgTwF6ZPlz3YgaAQSCJOB2BRUVCUFaKIZFAAHPC7i9n3p+wgSIAAIIIIAAAiERoEdWSJi5CAII+EWACiq/rBRxIoCA1wXYT72+QsSHAAIIIICAPwXokeXPdSNqBBAIkoDbFVRUJARpoRgWAQQ8L+D2fur5CRMgAggggAACCIREgB5ZIWHmIgggEK0CVCRE68ozbwQQQAABBBBAAAEEEAiGAD2ygqHKmAgg4FsBtyuoqEjw7a1A4AggkE0Bt/fTbIbDxxFAAAEEEEAgQgTokRUhC8k0EEDAHQEqqNxxZBQEEECA/ZR7AAEEEEAAAQSCIUCPrGCoMiYCCPhWwO0KKioSfHsrEDgCCGRTwO39NJvh8HEEEEAAAQQQiBABemRFyEIyDQQQ8KYAFQneXBeiQgABBBBAAAEEEEAAAX8K0CPLn+tG1AggECQBtyuoqEgI0kIxLAIIeF7A7f3U8xMmQAQQQAABBBAIiQA9skLCzEUQQMAvAlRQ+WWliBMBBLwuwH7q9RUiPgQQQAABBPwpQI8sf64bUSOAQJAE3K6goiIhSAvFsAgg4HkBt/dTz0+YABFAAAEEEEAgJAL0yAoJMxdBAIFoFaAiIVpXnnkjgAACCCCAAAIIIIBAMATokRUMVcZEAAHfCrhdQUVFgm9vBQJHAIFsCri9n2YzHD6OAAIIIIAAAhEiQI+sCFlIpoEAAu4IUEHljiOjIIAAAuyn3AMIIIAAAgggEAwBemQFQ5UxEUDAtwJuV1BRkeDbW4HAEUAgmwJu76fZDIePI4AAAggggECECNAjK0IWkmkggIA3BahI8Oa6EBUCCCCAAAIIIIAAAgj4U4AeWf5cN6JGAIEgCbhdQUVFQpAWimERQMDzAm7vp56fMAEigAACCCCAQEgE6JEVEmYuggACfhGggsovK0WcCCDgdQH2U6+vEPEhgAACCCDgTwF6ZPlz3YgaAQSCJOB2BRUVCUFaKIZFAAHPC7i9n3p+wgSIAAIIIIAAAiERoEdWSJi5CAIIRKsAFQnRuvLMGwEEEEAAAQQQQAABBIIhQI+sYKgyJgII+FbA7QoqKhJ8eysQOAIIZFPA7f00m+HwcQQQQAABBBCIEAF6ZEXIQjINBBBwR4AKKnccGQUBBBBgP+UeQAABBBBAAIFgCNAjKxiqjIkAAr4VcLuCiooE394KBI4AAtkUcHs/zWY4fBwBBBBAAAEEIkSAHlkRspBMAwEEvClARYI314WoEEAAAQQQQAABBBBAwJ8C9Mjy57oRNQIIBEnA7QoqKhKCtFAMiwACnhdwez/1/IQJEAEEEEAAAQRCIkCPrJAwcxEEEPCLABVUflkp4kQAAa8LsJ96fYWIDwEEEEAAAX8K0CPLn+tG1AggECQBtyuoqEgI0kIxLAIIeF7A7f3U8xMmQAQQQAABBBAIiQA9skLCzEUQQCBaBahIiNaVZ94IIIAAAggggAACCCAQDAF6ZAVDlTERQMC3Am5XUFGR4NtbgcARQCCbAm7vp9kMh48jgAACCCCAQIQI0CMrQhaSaSCAgDsCVFC548goCCCAAPsp9wACCCCAAAIIBEOAHlnBUGVMBBDwrYDbFVRUJPj2ViBwBBDIpoDb+2k2w+HjCCCAAAIIIBAhAvTIipCFZBoIIOBNASoSvLkuRIUAAggggAACCCCAAAL+FKBHlj/XjagRQCBIAm5XUFGREKSFYlgEEPC8gNv7qecnTIAIIIAAAgggEBIBemSFhJmLIICAXwSooPLLShEnAgh4XYD91OsrRHwIIIAAAgj4U4AeWf5cN6JGAIEgCbhdQUVFQpAWimERQMDzAm7vp56fMAEigAACCCCAQEgE6JEVEmYuggAC0SpARUK0rjzzRgABBBBAAAEEEEAAgWAI0CMrGKqMiQACvhVwu4KKigTf3goEjgAC2RRwez/NZjh8HAEEEEAAAQQiRIAeWRGykEwDAQTcEaCCyh1HRkEAAQTYT7kHEEAAAQQQQCAYAvTICoYqYyKAgG8F3K6goiLBt7cCgSOAQDYF3N5PsxkOH0cAAQQQQACBCBGgR1aELCTTQAABbwpQkeDNdSEqBBBAAAEEEEAAAQQQ8KcAPbL8uW5EjQACQRJwu4KKioQgLRTDIoCA5wXc3k89P2ECRAABBBBAAIGQCNAjKyTMXAQBBPwiQAWVX1aKOBFAwOsC7KdeXyHiQwABBBBAwJ8C9Mjy57oRNQIIBEnA7QoqKhKCtFAMiwACnhdwez/1/IQJEAEEEEAAAQRCIkCPrJAwcxEEEIhWASoSonXlmTcCCCCAAAIIIIAAAggEQ4AeWcFQZUwEEPCtgNsVVFQk+PZWIHAEEMimgNv7aTbD4eMIIIAAAgggECEC9MiKkIVkGggg4I4AFVTuODIKAgggwH7KPYAAAggggAACwRCgR1YwVBkTAQR8K+B2BRUVCb69FQgcAQSyKeD2fprNcPg4AggggAACCESIAD2yImQhmQYCCHhTgIoEb64LUSGAAAIIIIAAAggggIA/BeiR5c91I2oEEAiSgNsVVFQkBGmhGBYBBDwv4PZ+6vkJEyACCCCAAAIIhESAHlkhYeYiCCDgFwEqqPyyUsSJAAJeF2A/9foKER8CCCCAAAL+FKBHlj/XjagRQCBIAm5XUFGREKSFYlgEEPC8gNv7qecnTIAIIIAAAgggEBKBU/bIUopmS6oj6fukpA2zT/xHGV+Xq4sP9wd/PiJzf3BjF05fkcB+yn7Jzwt+XkTrzws39lPGQAABBBBAAAEEjMApe2QpVnUVUG/FqG9S4oY+J5aJ83W5Pvhwf/DnIzL3Bzd+TKSvSGA/Zb/k5wU/L6L154Ub+yljIIAAAggggAACmSay0tOcWBbO1+Xq2n+Mpmh2WoUaX+PB/RCRfx7c/jHB/sn+yc8Pfn5G698f3N5PGQ8BBBBAAAEEoleAZu/Ru/bMHAEEEEAAAQQQQAABBBBAAAEEEPCVAIksXy0XwSKAAAIIIIAAAggggAACCCCAAALRK0AiK3rXnpkjgAACCCCAAAIIIIAAAggggAACvhIgkeWr5SJYBBBAAAEEEEAAAQQQQAABBBBAIHoF/g9RQ5PJaGoLagAAAABJRU5ErkJggg==)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZdBQ2O2rL9i5"
   },
   "source": [
    "The following is a high-level overview of the steps in the examples that are going to be demonstrated:\n",
    "\n",
    "- Users enter a prompt of a question\n",
    "- The datastore is then queried and relevant documents are returned\n",
    "- The documents are added as context into the user's prompt/question\n",
    "- The prompt is sent into the LLM\n",
    "- A response is returned using the relevant documents as sources\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zj-AnsYaOSNq"
   },
   "source": [
    "Services used in the notebook:\n",
    "\n",
    "- ✅ Vertex PaLM Text API for LLM\n",
    "- ✅ Vertex AI Search for document search and retrieval\n",
    "- ✅ Langchain in order to chain tasks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pQbOO-Lc-2-7"
   },
   "source": [
    "## Install pre-requisites\n",
    "\n",
    "If running in Colab install the pre-requisites into the runtime. Otherwise it is assumed that the notebook is running in Vertex AI Workbench. In that case it is recommended to install the pre-requistes from a terminal using the `--user` option.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ohPUPez8imvE"
   },
   "outputs": [],
   "source": [
    "%pip install -q google-cloud-aiplatform==1.36.0 google-cloud-discoveryengine==0.11.2 langchain==0.0.327 pydantic==1.10.8 typing-inspect==0.8.0 typing_extensions==4.5.0 --upgrade --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R5Xep4W9lq-Z"
   },
   "source": [
    "### Restart current runtime\n",
    "\n",
    "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which will restart the current kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XRvKdaPDTznN",
    "outputId": "154a71b5-f302-4f53-ed2f-b3e5fef9195b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'ok', 'restart': True}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restart kernel after installs so that your environment can access the new packages\n",
    "import IPython\n",
    "import time\n",
    "\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SbmM4z7FOBpM"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>⚠️ The kernel is going to restart. Please wait until it is finished before continuing to the next step. ⚠️</b>\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8vEczuYo_r-g"
   },
   "source": [
    "## Authenticate\n",
    "\n",
    "If running in Colab authenticate with `google.colab.google.auth` otherwise assume that running on Vertex AI Workbench.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "loTfn0KniwB2"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    from google.colab import auth as google_auth\n",
    "\n",
    "    google_auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_ARUa9gEK74l"
   },
   "source": [
    "## Configure notebook environment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "66ff7614"
   },
   "source": [
    "### Set the following constants to reflect your environment\n",
    "\n",
    "- The queries used in the examples here relate to a GCS bucket containing Alphabet investor PDFs, but these should be customised to your own data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "uVxFSrppK8Oy"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "PROJECT_ID = \"YOUR_PROJECT_ID\"  # @param {type:\"string\"}\n",
    "DATA_STORE_ID = \"YOUR_DATA_STORE_ID\"  # @param {type:\"string\"}\n",
    "DATA_STORE_LOCATION = \"global\"  # @param {type:\"string\"}\n",
    "\n",
    "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
    "MODEL = \"text-bison@001\"  # @param {type:\"string\"}\n",
    "\n",
    "os.environ[\"DATA_STORE_ID\"] = DATA_STORE_ID\n",
    "os.environ[\"PROJECT_ID\"] = PROJECT_ID\n",
    "os.environ[\"LOCATION_ID\"] = DATA_STORE_LOCATION\n",
    "os.environ[\"REGION\"] = REGION\n",
    "os.environ[\"MODEL\"] = MODEL\n",
    "\n",
    "if PROJECT_ID == \"YOUR_PROJECT_ID\" or DATA_STORE_ID == \"YOUR_DATA_STORE_ID\":\n",
    "    raise ValueError(\n",
    "        \"Please set the PROJECT_ID, DATA_STORE_ID, REGION and MODEL constants to reflect your environment.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I3tBINK7uUep"
   },
   "source": [
    "## LangChain retrieval Q&A chains\n",
    "\n",
    "We will demonstrate using three LangChain retrieval Q&A chains:\n",
    "\n",
    "- `RetrievalQA`\n",
    "- `RetrievalQAWithSourcesChain`\n",
    "- `ConversationalRetrievalChain`\n",
    "\n",
    "We begin by initializing a Vertex AI LLM and a LangChain 'retriever' to fetch documents from our Vertex AI Search engine.\n",
    "\n",
    "For Q&A chains our retriever is passed directly to the chain and can be used automatically without any further configuration.\n",
    "\n",
    "Behind the scenes, first the search query is passed to the retriever which runs a search and returns relevant document chunks. These chunks are then passed to the prompt used by the LLM to be used as context.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "fe224af7"
   },
   "outputs": [],
   "source": [
    "import vertexai\n",
    "from langchain.llms import VertexAI\n",
    "from langchain.retrievers import GoogleVertexAISearchRetriever\n",
    "\n",
    "vertexai.init(project=PROJECT_ID, location=REGION)\n",
    "llm = VertexAI(model_name=MODEL)\n",
    "\n",
    "retriever = GoogleVertexAISearchRetriever(\n",
    "    project_id=PROJECT_ID,\n",
    "    location_id=DATA_STORE_LOCATION,\n",
    "    data_store_id=DATA_STORE_ID,\n",
    "    get_extractive_answers=True,\n",
    "    max_documents=10,\n",
    "    max_extractive_segment_count=1,\n",
    "    max_extractive_answer_count=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBf9cgsb-ZvK"
   },
   "source": [
    "### [`RetrievalQA` chain](https://python.langchain.com/docs/modules/chains/popular/vector_db_qa)\n",
    "\n",
    "This is the simplest document Q&A chain offered by LangChain.\n",
    "\n",
    "There are several different chain types available, listed [here](https://docs.langchain.com/docs/components/chains/index_related_chains).\n",
    "\n",
    "- In these examples we use the `stuff` type, which simply inserts all of the document chunks into the prompt.\n",
    "- This has the advantage of only making a single LLM call, which is faster and more cost efficient\n",
    "- However, if we have a large number of search results we run the risk of exceeding the token limit in our prompt, or truncating useful information.\n",
    "- Other chain types such as `map_reduce` and `refine` use an iterative process which makes multiple LLM calls, taking individual document chunks at a time and refining the answer iteratively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "BwAUJxYS-e9g"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The CEO of DeepMind is Demis Hassabis.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "\n",
    "search_query = \"Who is the CEO of DeepMind?\"  # @param {type:\"string\"}\n",
    "\n",
    "retrieval_qa = RetrievalQA.from_chain_type(\n",
    "    llm=llm, chain_type=\"stuff\", retriever=retriever\n",
    ")\n",
    "retrieval_qa.run(search_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AUSB8Mn8Dvou"
   },
   "source": [
    "#### Inspecting the process\n",
    "\n",
    "If we add `return_source_documents=True` we can inspect the document chunks that were returned by the retriever.\n",
    "\n",
    "This is helpful for debugging, as these chunks may not always be relevant to the answer, or their relevance might not be obvious.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "P236bx-KA9TM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******************************************************************************\n",
      "The CEO of DeepMind is Demis Hassabis.\n",
      "*******************************************************************************\n",
      "-------------------------------------------------------------------------------\n",
      "PART I\n",
      "ITEM 1. BUSINESS\n",
      "Overview\n",
      "As our founders Larry and Sergey wrote in the original founders' letter, \"Google is not a conventional company.\n",
      "We do not intend to become one.\" That unconventional spirit has been a driving force throughout our history,\n",
      "inspiring us to tackle big problems, and invest in moonshots like artificial intelligence (\"AI\") research and quantum\n",
      "computing. We continue this work under the leadership of Sundar Pichai, who has served as CEO of Google since\n",
      "2015 and as CEO of Alphabet since 2019.\n",
      "Alphabet is a collection of businesses — the largest of which is Google — which we report as two segments:\n",
      "Google Services and Google Cloud. We report all non-Google businesses collectively as Other Bets. Our Other\n",
      "Bets include earlier stage technologies that are further afield from our core Google business. We take a long term\n",
      "view and manage the portfolio of Other Bets with the discipline and rigor needed to deliver long-term returns. Our\n",
      "Alphabet structure is about helping each of our businesses prosper through strong leaders and independence.\n",
      "Access and technology for everyone\n",
      "The Internet is one of the world’s most powerful equalizers, capable of propelling new ideas and people\n",
      "forward. Our mission to organize the world’s information and make it universally accessible and useful is as relevant\n",
      "today as it was when we were founded in 1998. Since then, we’ve evolved from a company that helps people find\n",
      "answers to a company that helps you get things done. We’re focused on building an even more helpful Google for\n",
      "everyone, and we aspire to give everyone the tools they need to increase their knowledge, health, happiness and\n",
      "\n",
      "success.\n",
      "-------------------------------------------------------------------------------\n",
      "Directors\n",
      "\n",
      "Larry Page\n",
      "Chief Executive Officer, Alphabet,\n",
      "and Co-Founder\n",
      "\n",
      "Sergey Brin\n",
      "President, Alphabet, and Co-Founder\n",
      "John L. Hennessy\n",
      "Chairman of the Board of Directors\n",
      "Former President, Stanford University\n",
      "\n",
      "L. John Doerr\n",
      "General Partner,\n",
      "Kleiner Perkins Caufield & Byers\n",
      "Roger W. Ferguson, Jr.\n",
      "President and Chief Executive Officer, TIAA\n",
      "\n",
      "Diane B. Greene\n",
      "Senior Vice President,\n",
      "Chief Executive Officer, Google Cloud\n",
      "Former Chief Executive Officer and President,\n",
      "VMware\n",
      "\n",
      "Ann Mather\n",
      "Former Chief Financial Officer, Pixar\n",
      "\n",
      "Alan R. Mulally\n",
      "Former Chief Executive Officer and President,\n",
      "Ford Motor Company\n",
      "\n",
      "Sundar Pichai\n",
      "Chief Executive Officer, Google\n",
      "\n",
      "Eric E. Schmidt\n",
      "Technical Advisor, Former Executive Chairman,\n",
      "Alphabet\n",
      "\n",
      "K. Ram Shriram\n",
      "Managing Partner, Sherpalo Ventures\n",
      "\n",
      "Executive Officers\n",
      "\n",
      "Larry Page\n",
      "Chief Executive Officer, Alphabet,\n",
      "and Co-Founder\n",
      "\n",
      "Sergey Brin\n",
      "President, Alphabet, and Co-Founder\n",
      "\n",
      "David C. Drummond\n",
      "Senior Vice President, Corporate Development,\n",
      "Chief Legal Officer, and Secretary,\n",
      "Alphabet\n",
      "\n",
      "Sundar Pichai\n",
      "Chief Executive Officer, Google\n",
      "\n",
      "Ruth M. Porat\n",
      "Senior Vice President and\n",
      "Chief Financial Officer,\n",
      "Alphabet and Google\n",
      "\n",
      "Directors and Officers\n",
      "as of April 2018\n",
      "\n",
      "Stockholder Information\n",
      "\n",
      "For further information about\n",
      "Alphabet, contact:\n",
      "\n",
      "Investor Relations\n",
      "Alphabet Inc.\n",
      "1600 Amphitheatre Parkway\n",
      "Mountain View, California 94043\n",
      "investor-relations@abc.xyz\n",
      "You may also reach us by visiting the\n",
      "investor relations portion of our website\n",
      "at: https://abc.xyz/investor/\n",
      "Alphabet’s stock trades on the NASDAQ\n",
      "Global Select Market.\n",
      "\n",
      "Transfer Agent and Registrar\n",
      "First Class/Registered/Certified Mail:\n",
      "Computershare Investor Services\n",
      "P.O. Box 505000\n",
      "Louisville, Kentucky 40233-5000\n",
      "\n",
      "Courier Services:\n",
      "Computershare Investor Services\n",
      "462 South 4th Street, Suite 1600\n",
      "Louisville, Kentucky 40202-3467\n",
      "\n",
      "Shareholder Services Numbers:\n",
      "(866) 298-8535 (toll free within the USA,\n",
      "US territories and Canada)\n",
      "(781) 575-2879\n",
      "Investor Centre™ portal:\n",
      "www.computershare.com/investor\n",
      "\n",
      "Independent Registered Public\n",
      "Accounting Firm:\n",
      "Ernst & Young LLP\n",
      "-------------------------------------------------------------------------------\n",
      "make things better.\n",
      "Ruth Porat, CFO Alphabet and Google: And then in terms of your question about Other Bet\n",
      "investment levels, so as we've talked about in prior calls, our goal for Other Bets is to use our\n",
      "deep technology investments to drive innovation with real potential for value creation.\n",
      "And at the same time, we are very focused on the overall pace of investment and the financial\n",
      "returns. And so what we're really looking at here are what are the opportunities for monetization\n",
      "and commercialization. As I've said on prior calls, there is no monolithic approach across the\n",
      "portfolio, but we are very focused on what's the pace of investment opportunities for\n",
      "monetization and commercialization.\n",
      "\n",
      "And just a bit more with respect to the DeepMind move. To be very clear, we consolidate an\n",
      "Other Bet into Google only when that Bet supports products and services within Google, or for\n",
      "Alphabet broadly. And you saw us do that some time ago when we moved Chronicle, as an\n",
      "example, into combining with our Cloud business and really the cybersecurity offering that is\n",
      "now in Cloud. And that was very effective.\n",
      "With AI, this is obviously an Alphabet strategic priority, and we see huge opportunity ahead.\n",
      "And DeepMind's research is core to that future across the product areas in Alphabet’s portfolio.\n",
      "And so this reporting change reflects the strategic focus in DeepMind, the support of each one\n",
      "of our segments. And that's why I indicated that beginning Q1, DeepMind financials will be\n",
      "reported within our corporate costs segment.\n",
      "Eric Sheridan (Goldman Sachs): Thanks so much.\n",
      "Operator: Our next question from Justin Post with Bank of America-Merrill Lynch. Your line is\n",
      "open.\n",
      "\n",
      "Justin Post (BoA-Merrill Lynch): Thank you for taking my question. Just digging into Search,\n",
      "kind of low single digit growth ex-FX, can you talk about the pressures there of volume versus\n",
      "pricing or CPCs? What's really driving the slowdown? It's kind of almost back to\n",
      "2009 recession levels. Just think about that. And then any signs that we're near a bottom? Any\n",
      "stabilization in growth rates you can talk about, or how your outlook is for '23 on that? Thank\n",
      "you.\n",
      "-------------------------------------------------------------------------------\n",
      "So in conclusion, Sergey and I are having a good\n",
      "time looking for new opportunities and managing\n",
      "and scaling our existing efforts. I still see amazing\n",
      "opportunities that just aren’t quite fully developed\n",
      "yet—and helping making them real is what I get\n",
      "excited about.\n",
      "\n",
      "Larry Page, CEO, Alphabet\n",
      "\n",
      "In June 2016, Marwan Fawaz became CEO of Nest.\n",
      "He has been doing great against their plan, and\n",
      "we have really been enjoying working with him!\n",
      "I recommend you buy all their excellent products\n",
      "including indoor and outdoor cameras, learning\n",
      "thermostats, and smoke alarms.\n",
      "Very recently Greg McCray started as CEO of\n",
      "Google Fiber. I’ve been enjoying working with\n",
      "him and his team and he has rapidly been getting\n",
      "up to speed. He visited all of our Fiber cities so\n",
      "quickly that I think he was still a bit out of breath\n",
      "when he got back to Mountain View! We have\n",
      "made significant investments in bringing gigabit\n",
      "fiber internet to lots of happy customers and I’m\n",
      "excited about our opportunities to do it better.\n",
      "We have many other efforts within Alphabet. Calico\n",
      "CEO Art Levinson, former CEO of Genentech, is\n",
      "building an amazing research and development\n",
      "company focused on aging. We also have newly\n",
      "branded investment arms, GV (formerly Google\n",
      "Ventures), and CapitalG (formerly Google Capital)\n",
      "which are doing well. Sergey is continuing to spend\n",
      "time working with the X moonshot factory. They\n",
      "have a number of efforts like Wing, which is doing\n",
      "drone delivery. I also can’t wait for them to launch!\n",
      "With the change to Alphabet, oversight has been\n",
      "easier because of increased visibility. We have\n",
      "streamlined efforts where it made sense and in\n",
      "other areas we have seen places to double down.\n",
      "I also think we have learned a lot about how\n",
      "to set up new companies with a structure for\n",
      "success. Our recent launch of Waymo was a\n",
      "great example of our learnings. In general we are\n",
      "taking a patient approach to investing our capital,\n",
      "especially significant uses. We’re not going to\n",
      "invest if we don’t see great opportunities and\n",
      "we feel like our track record for picking some\n",
      "important efforts long before others is pretty\n",
      "good. Machine learning and all the efforts around\n",
      "Google Brain and Deep Mind are good examples.\n",
      "Google Cloud led by Diane Greene is doing a\n",
      "fabulous job of getting our machine learning\n",
      "hardware and software out to everyone. We\n",
      "were early in machine learning and are already\n",
      "seeing significant dividends coming out. Many of\n",
      "the Alphabet companies are already using this\n",
      "technology and are planning to use it even more.\n",
      "-------------------------------------------------------------------------------\n",
      "2\n",
      "\n",
      "ALPHABET INC. ● ANNUAL REPORT\n",
      "\n",
      "PART I\n",
      "ITEM 1  BUSINESS\n",
      "\n",
      "DeepMind made a significant AI-powered breakthrough, solving a 50-year-old protein folding challenge, which will help us better\n",
      "understand one of life’s fundamental building blocks, and will enable researchers to tackle new and difficult problems, from fighting\n",
      "diseases to environmental sustainability.\n",
      "Google\n",
      "For reporting purposes, Google comprises two segments: Google Services and Google Cloud.\n",
      "Google Services\n",
      "\n",
      "SERVING OUR USERS\n"
     ]
    }
   ],
   "source": [
    "retrieval_qa = RetrievalQA.from_chain_type(\n",
    "    llm=llm, chain_type=\"stuff\", retriever=retriever, return_source_documents=True\n",
    ")\n",
    "\n",
    "results = retrieval_qa({\"query\": search_query})\n",
    "\n",
    "print(\"*\" * 79)\n",
    "print(results[\"result\"])\n",
    "print(\"*\" * 79)\n",
    "for doc in results[\"source_documents\"]:\n",
    "    print(\"-\" * 79)\n",
    "    print(doc.page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LxuDV3sdFhUk"
   },
   "source": [
    "### `RetrievalQAWithSourcesChain`\n",
    "\n",
    "This variant returns an answer to the question alongside the source documents that were used to generate the answer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "LlhQ-1JGFfMI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'answer': 'DeepMind is a subsidiary of Alphabet. The CEO of DeepMind is Demis Hassabis.\\n',\n",
       " 'sources': '2020_alphabet_annual_report.pdf'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chains import RetrievalQAWithSourcesChain\n",
    "\n",
    "retrieval_qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(\n",
    "    llm=llm, chain_type=\"stuff\", retriever=retriever\n",
    ")\n",
    "\n",
    "retrieval_qa_with_sources({\"question\": search_query}, return_only_outputs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NaxAUt9FhXR6"
   },
   "source": [
    "### [`ConversationalRetrievalChain`](https://python.langchain.com/docs/modules/chains/popular/chat_vector_db)\n",
    "\n",
    "`ConversationalRetrievalChain` remembers and uses previous questions so you can have a chat-like discovery process.\n",
    "\n",
    "To use this chain we must provide a memory class to store and pass the previous messages to the LLM as context. Here we use the `ConversationBufferMemory` class that comes with LangChain.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "mRdu6hK6F6UD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alphabet's revenues in 2022 were $282,836.\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "conversational_retrieval = ConversationalRetrievalChain.from_llm(\n",
    "    llm=llm, retriever=retriever, memory=memory\n",
    ")\n",
    "\n",
    "search_query = \"What were alphabet revenues in 2022?\"\n",
    "\n",
    "result = conversational_retrieval({\"question\": search_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ff9b72cd-da78-4690-9919-b74b2b460c44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alphabet's costs and expenses in 2022 were $100,301 million.\n"
     ]
    }
   ],
   "source": [
    "new_query = \"What about costs and expenses?\"\n",
    "result = conversational_retrieval({\"question\": new_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "o44PdiaGiM7m"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer is no.\n",
      "\n",
      "The question is \"Is this more than in 2021?\". The context is about the impact of COVID-19 on Google's financial results. The context says that \"For the quarter ended June 30, 2020, our advertising revenues declined due to the continued impacts of COVID-19 and the related reductions in global economic activity\". The context also says that \"During the course of the quarter ended June 30, 2020, we observed a gradual return in user search activity to more commercial topics, followed by increased spending by\n"
     ]
    }
   ],
   "source": [
    "new_query = \"Is this more than in 2021?\"\n",
    "\n",
    "result = conversational_retrieval({\"question\": new_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [`ConversationalRetrievalChain`](https://python.langchain.com/docs/modules/chains/popular/chat_vector_db) with `GoogleVertexAIMultiTurnSearchRetriever`\n",
    "\n",
    "`GoogleVertexAIMultiTurnSearchRetriever` uses [multi-turn search](https://cloud.google.com/generative-ai-app-builder/docs/multi-turn-search) (also called conversational search or search with followups) to preserve context between requests.\n",
    "\n",
    "Here is an example using the retriever with `ConversationalRetrievalChain`. This retriever can be substituted in any of the previous examples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alphabet revenues were $283 billion in 2022.\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.retrievers import GoogleVertexAIMultiTurnSearchRetriever\n",
    "\n",
    "multi_turn_retriever = GoogleVertexAIMultiTurnSearchRetriever(\n",
    "    project_id=PROJECT_ID, location_id=DATA_STORE_LOCATION, data_store_id=DATA_STORE_ID\n",
    ")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "conversational_retrieval = ConversationalRetrievalChain.from_llm(\n",
    "    llm=llm, retriever=multi_turn_retriever, memory=memory\n",
    ")\n",
    "\n",
    "search_query = \"What were alphabet revenues in 2022?\"\n",
    "\n",
    "result = conversational_retrieval({\"question\": search_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total costs and expenses for Alphabet in 2022 were $57888 million.\n"
     ]
    }
   ],
   "source": [
    "new_query = \"What about costs and expenses?\"\n",
    "result = conversational_retrieval({\"question\": new_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, Alphabet's costs and expenses in 2022 are more than in 2021. The increase was due to an increase in data center and other operations costs, partially offset by a reduction in depreciation expense due to the change in the estimated useful life of our servers and certain network equipment beginning in the first quarter of 2021.\n"
     ]
    }
   ],
   "source": [
    "new_query = \"Is this more than in 2021?\"\n",
    "\n",
    "result = conversational_retrieval({\"question\": new_query})\n",
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1eeba1fe-0a4d-4651-a4b1-b91dfc1254bc"
   },
   "source": [
    "## Advanced: Modifying the default langchain prompt\n",
    "\n",
    "In all of the previous examples we used the default prompt that comes with langchain.\n",
    "\n",
    "We can inspect our chain object to discover the wording of the prompt template being used.\n",
    "\n",
    "We may find that this is not suitable for our purposes, and we may wish to customise the prompt, for example to present our results in a different format, or to specify additional constraints.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "c1004f16-ed9d-4170-974e-6dd7528fe36b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
      "\n",
      "{context}\n",
      "\n",
      "Question: {question}\n",
      "Helpful Answer:\n"
     ]
    }
   ],
   "source": [
    "qa = RetrievalQA.from_chain_type(\n",
    "    llm=llm, chain_type=\"stuff\", retriever=retriever, return_source_documents=True\n",
    ")\n",
    "\n",
    "print(qa.combine_documents_chain.llm_chain.prompt.template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b30e2c43-b57b-43f1-9679-41c4a2a9e8ba"
   },
   "source": [
    "---\n",
    "\n",
    "Let's modify the prompt to return an answer in a single word (useful for yes/no questions). We will constrain the LLM to say 'I don't know' if it cannot answer.\n",
    "\n",
    "We create a new prompt_template and pass this in using the `template` argument.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "0ceca59d-f75a-4467-9d58-a15b3241ac22"
   },
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "prompt_template = \"\"\"Use the context to answer the question at the end.\n",
    "You must always use the context and context only to answer the question. Never try to make up an answer. If the context is empty or you do not know the answer, just say \"I don't know\".\n",
    "The answer should consist of only 1 word and not a sentence.\n",
    "\n",
    "Context: {context}\n",
    "\n",
    "Question: {question}\n",
    "Helpful Answer:\n",
    "\"\"\"\n",
    "prompt = PromptTemplate(\n",
    "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
    ")\n",
    "qa_chain = RetrievalQA.from_llm(\n",
    "    llm=llm, prompt=prompt, retriever=retriever, return_source_documents=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "f58539f7bec3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use the context to answer the question at the end.\n",
      "You must always use the context and context only to answer the question. Never try to make up an answer. If the context is empty or you do not know the answer, just say \"I don't know\".\n",
      "The answer should consist of only 1 word and not a sentence.\n",
      "\n",
      "Context: {context}\n",
      "\n",
      "Question: {question}\n",
      "Helpful Answer:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qa_chain.combine_documents_chain.llm_chain.prompt.template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "287d9859-453d-474a-a449-15252a14aa63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******************************************************************************\n",
      "Yes\n",
      "*******************************************************************************\n",
      "-------------------------------------------------------------------------------\n",
      "The following table presents the foreign exchange effect on our international revenues and total revenues (in\n",
      "millions, except percentages, unaudited):\n",
      "\n",
      "Three Months Ended\n",
      "\n",
      "Nine Months Ended\n",
      "\n",
      "September 30,\n",
      "\n",
      "September 30,\n",
      "\n",
      "2019\n",
      "\n",
      "2020\n",
      "\n",
      "2019\n",
      "\n",
      "2020\n",
      "\n",
      "EMEA revenues\n",
      "\n",
      "$ 12,565 $ 13,924 $ 36,546 $ 38,132\n",
      "\n",
      "Exclude foreign exchange effect on current period\n",
      "revenues using prior year rates\n",
      "\n",
      "456\n",
      "\n",
      "(250)\n",
      "\n",
      "2,034\n",
      "\n",
      "346\n",
      "\n",
      "EMEA constant currency revenues\n",
      "\n",
      "$ 13,021\n",
      "\n",
      "$ 13,674 $ 38,580 $ 38,478\n",
      "\n",
      "Prior period EMEA revenues\n",
      "\n",
      "$ 10,909 $ 12,565 $ 32,488 $ 36,546\n",
      "\n",
      "EMEA revenue percentage change\n",
      "\n",
      "15 %\n",
      "\n",
      "11 %\n",
      "\n",
      "12 %\n",
      "\n",
      "4 %\n",
      "\n",
      "EMEA constant currency revenue percentage change\n",
      "\n",
      "19 %\n",
      "\n",
      "9 %\n",
      "\n",
      "19 %\n",
      "\n",
      "5 %\n",
      "\n",
      "APAC revenues\n",
      "\n",
      "$ 6,814 $ 8,458 $ 19,446 $ 22,641\n",
      "\n",
      "Exclude foreign exchange effect on current period\n",
      "revenues using prior year rates\n",
      "\n",
      "17\n",
      "\n",
      "1\n",
      "\n",
      "433\n",
      "\n",
      "167\n",
      "\n",
      "APAC constant currency revenues\n",
      "\n",
      "$ 6,831\n",
      "\n",
      "$ 8,459\n",
      "$ 19,879\n",
      "$ 22,808\n",
      "\n",
      "Prior period APAC revenues\n",
      "\n",
      "$ 5,401\n",
      "\n",
      "$ 6,814\n",
      "$ 15,310\n",
      "$ 19,446\n",
      "\n",
      "APAC revenue percentage change\n",
      "\n",
      "26 %\n",
      "\n",
      "24 %\n",
      "\n",
      "27 %\n",
      "\n",
      "16 %\n",
      "\n",
      "APAC constant currency revenue percentage change\n",
      "\n",
      "26 %\n",
      "\n",
      "24 %\n",
      "\n",
      "30 %\n",
      "\n",
      "17 %\n",
      "\n",
      "Other Americas revenues\n",
      "\n",
      "$ 2,290\n",
      "$ 2,371\n",
      "\n",
      "$ 6,320\n",
      "$ 6,367\n",
      "\n",
      "Exclude foreign exchange effect on current period\n",
      "revenues using prior year rates\n",
      "\n",
      "66\n",
      "\n",
      "304\n",
      "\n",
      "442\n",
      "\n",
      "640\n",
      "\n",
      "Other Americas constant currency revenues\n",
      "\n",
      "$ 2,356\n",
      "$ 2,675\n",
      "$ 6,762\n",
      "$ 7,007\n",
      "\n",
      "Prior period Other Americas revenues\n",
      "\n",
      "$ 1,827\n",
      "$ 2,290\n",
      "$ 5,407\n",
      "$ 6,320\n",
      "\n",
      "Other Americas revenue percentage change\n",
      "\n",
      "25 %\n",
      "\n",
      "4 %\n",
      "\n",
      "17 %\n",
      "\n",
      "1 %\n",
      "\n",
      "Other Americas constant currency revenue percentage\n",
      "change\n",
      "\n",
      "29 %\n",
      "\n",
      "17 %\n",
      "\n",
      "25 %\n",
      "\n",
      "11 %\n",
      "\n",
      "United States revenues\n",
      "\n",
      "$ 18,711\n",
      "\n",
      "$ 21,442 $ 53,106 $ 58,311\n",
      "\n",
      "United States revenue percentage change\n",
      "\n",
      "21 %\n",
      "\n",
      "15 %\n",
      "\n",
      "19 %\n",
      "\n",
      "10 %\n",
      "\n",
      "Hedging gains (losses)\n",
      "\n",
      "$\n",
      "\n",
      "119\n",
      "\n",
      "$\n",
      "\n",
      "(22) $\n",
      "\n",
      "364 $\n",
      "\n",
      "178\n",
      "\n",
      "Total revenues\n",
      "\n",
      "$ 40,499 $ 46,173 $ 115,782 $ 125,629\n",
      "\n",
      "Total constant currency revenues\n",
      "\n",
      "$ 40,919 $ 46,250 $ 118,327 $ 126,604\n",
      "\n",
      "Prior period revenues, excluding hedging effect(1)\n",
      "\n",
      "$ 33,660 $ 40,380 $ 97,805 $ 115,418\n",
      "\n",
      "Total revenue percentage change\n",
      "\n",
      "20 %\n",
      "\n",
      "14 %\n",
      "\n",
      "19 %\n",
      "\n",
      "9 %\n",
      "\n",
      "Total constant currency revenue percentage change\n",
      "\n",
      "22 %\n",
      "\n",
      "15 %\n",
      "\n",
      "21 %\n",
      "\n",
      "10 %\n",
      "\n",
      "(1)\n",
      "-------------------------------------------------------------------------------\n",
      "Total revenues and hedging gains for the quarter ended June 30, 2019 were $38,944 million and $108 million, respectively.\n",
      "Total revenues and hedging gains for the quarter ended March 31, 2020 were $41,159 million and $49 million, respectively.\n",
      "Non-GAAP constant currency revenues and percentage change: We define non-GAAP constant currency revenues as total\n",
      "revenues excluding the effect of foreign exchange rate movements and hedging activities, and we use it to determine the\n",
      "constant currency revenue percentage change on year-on-year and quarter-on-quarter basis. Non-GAAP constant currency\n",
      "revenues are calculated by translating current quarter revenues using prior period exchange rates and excluding any hedging\n",
      "effect recognized in the current quarter. Constant currency revenue percentage change is calculated by determining the increase\n",
      "in current quarter non-GAAP constant currency revenues over prior period revenues, excluding any hedging effect recognized in\n",
      "the prior period.\n",
      "\n",
      "8\n",
      "-------------------------------------------------------------------------------\n",
      "The following table presents the foreign exchange effect on international revenues and total revenues (in millions,\n",
      "except percentages):\n",
      "\n",
      "Year Ended December 31,\n",
      "\n",
      "2020\n",
      "\n",
      "2021\n",
      "\n",
      "% Change from\n",
      "Prior Year\n",
      "\n",
      "EMEA revenues\n",
      "\n",
      "$\n",
      "\n",
      "55,370 $\n",
      "\n",
      "79,107\n",
      "\n",
      "43 %\n",
      "\n",
      "EMEA constant currency revenues\n",
      "\n",
      "76,321\n",
      "\n",
      "38 %\n",
      "\n",
      "APAC revenues\n",
      "\n",
      "32,550\n",
      "\n",
      "46,123\n",
      "\n",
      "42 %\n",
      "\n",
      "APAC constant currency revenues\n",
      "\n",
      "45,666\n",
      "\n",
      "40 %\n",
      "\n",
      "Other Americas revenues\n",
      "\n",
      "9,417\n",
      "\n",
      "14,404\n",
      "\n",
      "53 %\n",
      "\n",
      "Other Americas constant currency revenues\n",
      "\n",
      "14,317\n",
      "\n",
      "52 %\n",
      "\n",
      "United States revenues\n",
      "\n",
      "85,014\n",
      "\n",
      "117,854\n",
      "\n",
      "39 %\n",
      "\n",
      "Hedging gains (losses)\n",
      "\n",
      "176\n",
      "\n",
      "149\n",
      "\n",
      "Total revenues\n",
      "\n",
      "$\n",
      "\n",
      "182,527 $\n",
      "\n",
      "257,637\n",
      "\n",
      "41 %\n",
      "\n",
      "Revenues, excluding hedging effect\n",
      "\n",
      "$\n",
      "\n",
      "182,351 $\n",
      "\n",
      "257,488\n",
      "\n",
      "Exchange rate effect\n",
      "\n",
      "(3,330)\n",
      "\n",
      "Total constant currency revenues\n",
      "\n",
      "$\n",
      "\n",
      "254,158\n",
      "\n",
      "39 %\n",
      "\n",
      "EMEA revenue growth from 2020 to 2021 was favorably affected by foreign currency exchange rates, primarily\n",
      "due to the U.S. dollar weakening relative to the Euro and British pound.\n",
      "APAC revenue growth from 2020 to 2021 was favorably affected by foreign currency exchange rates, primarily\n",
      "due to the U.S. dollar weakening relative to the Australian dollar, partially offset by the U.S. dollar strengthening relative\n",
      "to the Japanese yen.\n",
      "Other Americas growth change from 2020 to 2021 was favorably affected by changes in foreign currency\n",
      "exchange rates, primarily due to the U.S. dollar weakening relative to the Canadian dollar, partially offset by the U.S.\n",
      "dollar strengthening relative to the Argentine peso and the Brazilian real.\n",
      "Costs and Expenses\n",
      "Cost of Revenues\n",
      "The following tables present cost of revenues, including TAC (in millions, except percentages):\n",
      "Year Ended December 31,\n",
      "2020\n",
      "\n",
      "2021\n",
      "\n",
      "TAC\n",
      "\n",
      "$\n",
      "\n",
      "32,778 $\n",
      "\n",
      "45,566\n",
      "\n",
      "Other cost of revenues\n",
      "\n",
      "51,954\n",
      "\n",
      "65,373\n",
      "\n",
      "Total cost of revenues\n",
      "\n",
      "$\n",
      "\n",
      "84,732 $\n",
      "\n",
      "110,939\n",
      "\n",
      "Total cost of revenues as a percentage of revenues\n",
      "\n",
      "46.4 %\n",
      "\n",
      "43.1 %\n",
      "Cost of revenues increased $26.2 billion from 2020 to 2021. The increase was due to an increase in other cost of\n",
      "revenues and TAC of $13.4 billion and $12.8 billion, respectively.\n",
      "The increase in TAC from 2020 to 2021 was due to an increase in TAC paid to distribution partners and to Google\n",
      "Network partners, primarily driven by growth in revenues subject to TAC. The TAC rate decreased from 22.3% to\n",
      "21.8% from 2020 to 2021 primarily due to a revenue mix shift from Google Network properties to Google Search &\n",
      "other properties. The TAC rate on Google Search & other properties revenues and the TAC rate on Google Network\n",
      "revenues were both substantially consistent from 2020 to 2021.\n",
      "The increase in other cost of revenues from 2020 to 2021 was driven by increases in content acquisition costs\n",
      "primarily for YouTube, data center and other operations costs, and hardware costs. The increase in data center and\n",
      "\n",
      "Table of Contents\n",
      "\n",
      "Alphabet Inc.\n",
      "\n",
      "36\n"
     ]
    }
   ],
   "source": [
    "search_query = \"Were 2020 EMEA revenues higher than 2020 APAC revenues?\"\n",
    "\n",
    "results = qa_chain({\"query\": search_query})\n",
    "\n",
    "print(\"*\" * 79)\n",
    "print(results[\"result\"])\n",
    "print(\"*\" * 79)\n",
    "for doc in results[\"source_documents\"]:\n",
    "    print(\"-\" * 79)\n",
    "    print(doc.page_content)"
   ]
  }
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