From ce8afde3801c8a4eb30ec1626dfba63d9896aa9b Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Sun, 12 Nov 2023 13:35:06 +0000 Subject: [PATCH 1/6] Initial commit From aae145c046a2e7e1fbbeb3dfb60e635a41f7165b Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Sun, 12 Nov 2023 14:08:56 +0000 Subject: [PATCH 2/6] Add code interpreter notebook. --- notebook/agentchat_oai_code_interpreter.ipynb | 161 ++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 notebook/agentchat_oai_code_interpreter.ipynb diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb new file mode 100644 index 00000000000..fbe3f2dfe0b --- /dev/null +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto Generated Agent Chat: GPTAssistant with Code Interpreter\n", + "The latest released Assistants API by OpenAI allows users to build AI assistants within their own applications. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In this notebook, we demonstrate how to enable `GPTAssistantAgent` to use external tools through function calling. A prompt describing the function and function configs must be passed to `AssistantAgent` to initialize the agent.\n", + "\n", + "## Requirements\n", + "\n", + "AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n", + "```bash\n", + "pip install pyautogen\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set your API Endpoint\n", + "\n", + "The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import autogen\n", + "\n", + "config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " file_location=\".\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-35-turbo-0613\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\"],\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n", + "\n", + "The config list looks like the following:\n", + "```python\n", + "config_list = [\n", + " {\n", + " \"model\": \"gpt-4\",\n", + " \"api_key\": \"\",\n", + " }, # OpenAI API endpoint for gpt-4\n", + " {\n", + " \"model\": \"gpt-35-turbo-0631\", # 0631 or newer is needed to use functions\n", + " \"base_url\": \"\", \n", + " \"api_type\": \"azure\", \n", + " \"api_version\": \"2023-08-01-preview\", # 2023-07-01-preview or newer is needed to use functions\n", + " \"api_key\": \"\"\n", + " }\n", + "]\n", + "```\n", + "\n", + "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform Tasks Using Code Interpreter\n", + "\n", + "In this example, we demonstrate task solving using `GPTAssistantAgent` with code interpreter. Pass `code_interpreter` in `tools` parameter to enable `GPTAssistantAgent` with code interpreter. It will write code and automatically execute it in a sandbox.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to Coder Assistant):\n", + "\n", + "If $725x + 727y = 1500$ and $729x+ 731y = 1508$, what is the value of $x - y$ ?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder Assistant\u001b[0m (to user_proxy):\n", + "\n", + "The value of \\( x - y \\) is \\( -48 \\).\n", + "\n", + "TERMINATE\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent\n", + "from autogen.agentchat import AssistantAgent, UserProxyAgent\n", + "\n", + "# Initiate an agent equipped with code interpreter\n", + "gpt_assistant = GPTAssistantAgent(\n", + " name=\"Coder Assistant\",\n", + " llm_config={\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"code_interpreter\"\n", + " }\n", + " ],\n", + " \"config_list\": config_list,\n", + " },\n", + " instructions=\"You are an expert at solving math questions. Write code and run it to solve math problems. Reply TERMINATE when the task is solved and there is no problem.\",\n", + ")\n", + "\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " is_termination_msg=lambda msg: \"TERMINATE\" in msg[\"content\"],\n", + " human_input_mode=\"NEVER\"\n", + ")\n", + "\n", + "# When all is set, initate the chat.\n", + "user_proxy.initiate_chat(gpt_assistant, message=\"If $725x + 727y = 1500$ and $729x+ 731y = 1508$, what is the value of $x - y$ ?\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 33affab92838d7444ffe447727604f50aa145dd4 Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Sun, 12 Nov 2023 14:32:10 +0000 Subject: [PATCH 3/6] Update code interpreter notebook. --- notebook/agentchat_oai_code_interpreter.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb index fbe3f2dfe0b..c1b1dd78487 100644 --- a/notebook/agentchat_oai_code_interpreter.ipynb +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Auto Generated Agent Chat: GPTAssistant with Code Interpreter\n", - "The latest released Assistants API by OpenAI allows users to build AI assistants within their own applications. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In this notebook, we demonstrate how to enable `GPTAssistantAgent` to use external tools through function calling. A prompt describing the function and function configs must be passed to `AssistantAgent` to initialize the agent.\n", + "The latest released Assistants API by OpenAI allows users to build AI assistants within their own applications. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In this notebook, we demonstrate how to enable `GPTAssistantAgent` to use code interpreter. \n", "\n", "## Requirements\n", "\n", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,12 +73,12 @@ "source": [ "## Perform Tasks Using Code Interpreter\n", "\n", - "In this example, we demonstrate task solving using `GPTAssistantAgent` with code interpreter. Pass `code_interpreter` in `tools` parameter to enable `GPTAssistantAgent` with code interpreter. It will write code and automatically execute it in a sandbox.\n" + "In this example, we demonstrate task solving using `GPTAssistantAgent` with code interpreter. Pass `code_interpreter` in `tools` parameter to enable `GPTAssistantAgent` with code interpreter. It will write code and automatically execute it in a sandbox. The agent will receive the results from the sandbox environment and act accordingly.\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -92,7 +92,7 @@ "--------------------------------------------------------------------------------\n", "\u001b[33mCoder Assistant\u001b[0m (to user_proxy):\n", "\n", - "The value of \\( x - y \\) is \\( -48 \\).\n", + "The value of \\( x - y \\) is \\(-48\\). \n", "\n", "TERMINATE\n", "\n", From 1c436456b6e83593520640c9f0fd29e3a15ea3b5 Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Sun, 12 Nov 2023 15:27:03 +0000 Subject: [PATCH 4/6] Update notebook. --- notebook/agentchat_oai_code_interpreter.ipynb | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb index c1b1dd78487..e5a53e51aed 100644 --- a/notebook/agentchat_oai_code_interpreter.ipynb +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -36,7 +36,7 @@ " \"OAI_CONFIG_LIST\",\n", " file_location=\".\",\n", " filter_dict={\n", - " \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-35-turbo-0613\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\"],\n", + " \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\"],\n", " },\n", ")" ] @@ -54,17 +54,10 @@ " \"model\": \"gpt-4\",\n", " \"api_key\": \"\",\n", " }, # OpenAI API endpoint for gpt-4\n", - " {\n", - " \"model\": \"gpt-35-turbo-0631\", # 0631 or newer is needed to use functions\n", - " \"base_url\": \"\", \n", - " \"api_type\": \"azure\", \n", - " \"api_version\": \"2023-08-01-preview\", # 2023-07-01-preview or newer is needed to use functions\n", - " \"api_key\": \"\"\n", - " }\n", "]\n", "```\n", "\n", - "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods." + "Currently AutoGen v0.2 only supports OpenAI endpoints. You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods." ] }, { @@ -122,6 +115,10 @@ "user_proxy = UserProxyAgent(\n", " name=\"user_proxy\",\n", " is_termination_msg=lambda msg: \"TERMINATE\" in msg[\"content\"],\n", + " code_execution_config={\n", + " \"work_dir\": \"coding\",\n", + " \"use_docker\": False, # set to True or image name like \"python:3\" to use docker\n", + " },\n", " human_input_mode=\"NEVER\"\n", ")\n", "\n", From 4216e0b87e2b9b1077b7c4a634b27944bdf82fff Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Mon, 13 Nov 2023 09:26:19 +0800 Subject: [PATCH 5/6] Update agentchat_oai_code_interpreter.ipynb --- notebook/agentchat_oai_code_interpreter.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb index e5a53e51aed..86a14339302 100644 --- a/notebook/agentchat_oai_code_interpreter.ipynb +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -36,7 +36,7 @@ " \"OAI_CONFIG_LIST\",\n", " file_location=\".\",\n", " filter_dict={\n", - " \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\"],\n", + " \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-turbo\"],\n", " },\n", ")" ] @@ -57,7 +57,7 @@ "]\n", "```\n", "\n", - "Currently AutoGen v0.2 only supports OpenAI endpoints. You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods." + "Currently Azure OpenAi does not support assistant api. You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods." ] }, { From 1b77e98162121f6467dd735b200cfba0d92ae0d3 Mon Sep 17 00:00:00 2001 From: LeoLjl <3110503618@qq.com> Date: Mon, 13 Nov 2023 11:00:01 +0800 Subject: [PATCH 6/6] Update agentchat_oai_code_interpreter.ipynb --- notebook/agentchat_oai_code_interpreter.ipynb | 106 +++++++++++++++++- 1 file changed, 105 insertions(+), 1 deletion(-) diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb index 86a14339302..dccf555a0eb 100644 --- a/notebook/agentchat_oai_code_interpreter.ipynb +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -66,7 +66,10 @@ "source": [ "## Perform Tasks Using Code Interpreter\n", "\n", - "In this example, we demonstrate task solving using `GPTAssistantAgent` with code interpreter. Pass `code_interpreter` in `tools` parameter to enable `GPTAssistantAgent` with code interpreter. It will write code and automatically execute it in a sandbox. The agent will receive the results from the sandbox environment and act accordingly.\n" + "We demonstrate task solving using `GPTAssistantAgent` with code interpreter. Pass `code_interpreter` in `tools` parameter to enable `GPTAssistantAgent` with code interpreter. It will write code and automatically execute it in a sandbox. The agent will receive the results from the sandbox environment and act accordingly.\n", + "\n", + "### Example 1: Math Problem Solving\n", + "In this example, we demonstrate how to use code interpreter to solve math problems.\n" ] }, { @@ -126,6 +129,107 @@ "user_proxy.initiate_chat(gpt_assistant, message=\"If $725x + 727y = 1500$ and $729x+ 731y = 1508$, what is the value of $x - y$ ?\")\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2: Plotting with Code Interpreter\n", + "\n", + "Code Interpreter can outputs files, such as generating image diagrams. In this example, we demonstrate how to draw figures and download it." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to Coder Assistant):\n", + "\n", + "Draw a line chart to show the population trend in US. Show how you solved it with code.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder Assistant\u001b[0m (to user_proxy):\n", + "\n", + "To create a line chart to show the population trend in the United States, we would need population data over a series of years. This data would generally come from sources like the United States Census Bureau or other governmental databases.\n", + "\n", + "Since we don't have internet access to fetch the latest data, I'll use a hypothetical set of data to demonstrate how you would create such a chart using Python and matplotlib, a common plotting library.\n", + "\n", + "First, I'll create some dummy data representing the US population from 1960 to 2020 at 10-year intervals. Then, I'll use this data to plot the line chart. Let's proceed with writing the code to create the line chart.\n", + "\n", + "\n", + "Recieved file id=file-TZbG4mSETWOeDqpidQsJCYVs\n", + "\n", + "Here's the line chart showing the hypothetical US population trend from 1960 to 2020 at 10-year intervals. The chart features markers at each data point and includes a grid for better readability.\n", + "\n", + "If you need the actual data or further detailed information in the chart, they'd typically be sourced from official databases or reputable statistical publications with access to the internet to fetch such data. \n", + "\n", + "If everything looks good and there are no further requests, please let me know.\n", + "\n", + "TERMINATE\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "gpt_assistant = GPTAssistantAgent(\n", + " name=\"Coder Assistant\",\n", + " llm_config={\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"code_interpreter\"\n", + " }\n", + " ],\n", + " \"config_list\": config_list,\n", + " },\n", + " instructions=\"You are an expert at writing python code to solve problems. Reply TERMINATE when the task is solved and there is no problem.\",\n", + ")\n", + "\n", + "user_proxy.initiate_chat(gpt_assistant, message=\"Draw a line chart to show the population trend in US. Show how you solved it with code.\", clear_history=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have the file id. We can download and display it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5CCCAAAIIIBA3Ab3hp9+etSZ9VkGkSb8ZbC5pFGkZsTpPvzFsfy6ILnVEQgABBBBIHIF33nnHp7I6MyKcpDfWL730UsnNzQ14Yzuc8gLl1ZlGuqyfNemzIUOZzas33HVZNGvSWdC6XFplSYMazzzzjE82ncXUtWtXn33ReqHBPuszmfT5YRq0qCxpEKdbt24+2d544w2f14n+Qv8meuSRR3wCj6G2SWdR6Swra9LgaijJ7ti9e/eQnummy2lq/5lJ+1X7N1jS4I32d6BnUgU6VwN5OovR/qyuUNqoy07qbH5ruuGGGyqdeagBKw1UW9OMGTOkoKDAuivoti6haE32zyPrMbYRQAABBBCIhwDBqngocw0EEEAAAQQQiIvAqlWrfK6jy/7pUkjJmPQbsaeeeqpP0zZv3uyzTJLPQV4ggAACCLhKQJcK0+ckmkmfW6jP/XFjmjNnjs+sKr0pH85zp3SWsi7dZiadDWyfLWMes/7UZc7UyUw6kySWz2e0f+nj3HPPFQ0KhJLs9dLAgbnEWijnJ3seDVhZk87isc7Usx4zt3Xpv5kzZ5ovjZ+6fF0oSfvN/h61928o5YSaR2cP6rKY1hTKTCX9DLA+i0v/vjvttNOsxQTc7tWrl/EMNWuGcNqoAVbrl7K++eYb3rNWTLYRQAABBOIuENpfXXGvFhdEAAEEEEAAAQTCF9CHXVuT/g9/Mif9Zrk+o8Ca9LkH+vD3ypI+U0OXKNqyZYvx4Hl9BkWjRo2MZ3Acd9xxossmxSKtX79e9Lknel29OaPPfND6alv0eRPJng4dOiRr1qwRfVi8zlTQmQV6Q+2www4zZiiovW4nQ9IljfQ9pu9JvdlcXl5uvMd0JobeIAu0vFFV267PjZk/f74RuNUbxeqpzx/Rb/XrsmXJnPbs2WOY6zjTGSn63tLxZb9J7GSgN431G/lr1641PhP0val2eiNTx2fjxo2dTqvyPl12bdGiRaLBdv1M0GW3tM46Uyhan0M63rRtOvNWx6BeQ28qd+rUyW8mRJUbFEYBX331lc9N6lD6KYzio5p18eLFPuXpc9bCeU/oe1GXbbMGqP773//6fenC5yKeF+PGjfPZpQEh/X0Vi7R06VLj88patv1LIdZj9m1dkvGhhx7y9qkG5DRgpYE6kvjNyNLxvmvXLmM8BvJRP3U0k76PTjrpJPNlpT+1//72t7958+myfPpZEKugsAasrDOb7H+Xeiti2bDPvjrhhBNC/v2oQWOdDTZ27FhviVqeLmUdStLz1fODDz4wsuvfhjoudTlSEgIIIIAAAtUhQLCqOtS5JgIIIIAAAgjERED/p9ua9Aa53jDXpWuSMWmgx56s30C3H9PXs2bNMp5tNW/ePJ+ljqx59Xkgffv2NZ73EM4zT/Th5X//+9+9RenzIswbJrp8lB776aefvMetG9oW/Qb7tddeG/JNmmDXs5YdaFtvkus1zaTPKpswYYL5Mmo/NSg1ceJEmTZtmhFEsT4k3n4RfQ/rc0B0WS+9yaY35oIlra/9WSBmfn3mmv4LlAK1V5/Pos/RMZPe6OvZs6f5stKfuhynPn8j2KwCHZNa5jXXXGMEriot9H8Z7O3Vh9vrM+k0aRBQn9ehN9qs31I3Dnr+oze49TkmN910kxxxxBHm7oT5GaztOq50fOl7zN527edgQRANbOnzg/Tb/Tt27HD00PelBnauuOIKn2ecOGa27NS+sb4HR44c6Z3poO+P1157LeBzfXRmrM6g0M+ESAO4GgzSm7grVqyw1OrXTQ1iXnbZZfLb3/620rH261nR29JZDNakn7tuTfab7qF8KcLeFp05Zg1W6e+jYGnZsmWi/8yk70MNCMUq6ewxazK/TGHdF2xbA/AaBLH+ntM2DiJYZbBZl1cM5mg9ps9Rsib1DeeLDvqe0y/i6FJ7ZtIyYxWssrfR/nepWQfrz/z8fOtLI1Dvs6OSFxrYtyYdM/q3oAblQ0n6TDEzWKX59VlbBKtCkSMPAggggEAsBJL/66uxUKNMBBBAAAEEEHClgP1b3nrTwLrEkisrXYVK6UwVewp0Y0RnWdx+++1y4403GgEr+w0Vazn6zdpJkyYZN3FHjRplzESwHg9nW2+c60PC9drWG3j2MvRGqN5UvuSSS5LqAd/ff/+9nHnmmfL000/L9OnTJVigSk10dosGex544AEjkKOzTRIl6XtKZxVoQOPbb78NupSQBpE1sHLdddfJPffcU6lLZQa6VJgGHfTmvz1YY56r+zWQpc8jsd4wN48n6s/XX39d9NlBGhAO1HantukMo9GjR8uFF14on3zyScBAlZ6r70sdv/qsPA1CVxYUd7qeuU/r+NRTTxnPMNKZd4GSjpW33nrLeD9pQC2cpDPq9DNnxIgRAQNVWp7O8Hzuueeq3KZw6mbm1c9ka/v1GTk6s9KtSWcsWlMkM5ft5+isGg3mB0o6Q9KaNEAWyjOyrOeEs21fSjiSZYR19pg16aw+0i8C69at86HQL2NYl4b0Ofi/F6tXr/bZbff1ORjghf0ce5kBTotot/2zyulLRdaCdeax/dlt9vpa8zttO+UP532nM52tsxU1aKu/o0kIIIAAAghUhwDBqupQ55oIIIAAAgggEBMBp/9h15uisbwxEZOGhFio0ywIpxs/ejNQgwJ6M9sp6Uwqp4eJazBMv22rD/CO9Lkb6v/RRx/5XFZvigRajm3Dhg2iDxYPNBPCp6AEeKE3oZyCilp1Ndf+crLX4xoc0CBEsJu5ms8NSdt58803Gw+Jd3oGibZR32dOSQOjOgMv0gCIztzRGTzWpaJ0SUmdjeM0M03fy3fddZesXLnSqToJtU9nJunMN+t7TAPW2nbrzUd7o7S/7rjjDnnzzTcdg9E6803fm05l6KzMq6++2li2z15uKK8fffRReffdd32y6nJ/WmenYLve/L3llltC/gzS/r3tttvC+rzTZQj1GvG8QauzO6zBxWOPPdbVs4DtM5QjsbKOUfMNEOymuv0LDu3atTNP8/7UgKNa6ueAzqTTmUy63GMkyV4XXdIt3NS6dWufU+xl+hxMsReTJ0/2abHOInYa89ZMdj+7rzVvoG37OfYyA50X7n4t1x7w1DYGS051sdc32Pl6TGdQ1atXzyebU7k+GSwvNIjcoUMH7x79ooA9UOw9yAYCCCCAAAIxFgi+rkiML07xCCCAAAIIIIBANAV0aS99+Lr1JrTe6NeZFLrcm/7TpaySJekNVnvKzs722aU3Q3U2hD34o1Z6w1mfjWAusaU3+PRm0j/+8Q+xLvmks1B0ZpDOUggn6Uwi8zknejNal9rSZduOPPJI4/lUeuNSbzL++9//FuvySxq00GCC3tAOZ7mfcOoW77wanNMlvvSfPitH/a1BKl2iSGdZjB8/XqzPr9A+0VlWL7/8smOVdSaG9q8mfT/oDCMz6bGzzjrLfOn3035zyy9DGDsee+wxnz7UU3WJNV3mT5fAMh/grn2rQVN9j1m/Za83pf/85z/L888/X+nNS2u19H2tSxZpgEzfKxdddJHxYHo11oCV7tfn0OgMnS+//NJ7qs4C0xl/OispUZM+++3zzz83qq+BBJ0hddpppxnLSGqQSce+Gutye9akJjqWdWabNekzvX7zm99IXl6et780CKbPX9HraNDZDFBouffdd5+xjJ9TQMtarnVbn7FnzibS94fOwtPPIPPmrJavnwUagLMGKvQ5WjrzUgPZlaVnn33W772oM3J0OUG9lhnQ1yCHBkr1vajjT6+nwb94Jfvnt/VmcbzqEM51zN8T5jnWZdXMfZX9dDpHb6oHWmbU+h7QsvU5WWbSWZTvv/++aPDUKUCugS2d1aq/d0L9PaLvM2tq0aKF9WVI2/Zz9H2mN/91WctUTvo5bP49YDroeAyWdJa3PfBo/xsn2PnmMXuf6JKxsUgffvihX7GVtdH+ntPfy4G+zONXuGWHttEaKAu3jfr5o31kJv2c7t27t/mSnwgggAACCMRNgGBV3Ki5EAIIIIAAAgjEQ0BvZmqgw5o0KKIzhPSf3jTXm/g6C0u/ya7ferUvTWQ9163buoSUPgfJmvRmhf05Ijpzwv4N2dNPP90IgNi/Ka/LK+kyfPqsAp2ZYN5U1mvoTWa96aLP/Ak1mTem9OawBlvsz4jQYM2AAQNEn5egz9ux3ijW2RQvvfSSMasr1Ou5MZ/2iQZh1NwanLLXVZ+pceKJJxr/dOlADUCZs9l0poDewHe6oatBL/2nScu3Bqv0xq4GMGKddDaD/rOmXr16yV//+le/saXfAD/nnHNkyJAhhosuy2cmDW6+9957xs1lc19lP82ba/q+1yXt9Pkk1qTf2tcA9SOPPGKMDesz1XRc6LM9NLCViMmcMarj9oUXXhD78+U0iGR9f5ht1M8EfY+ZSd83GrzSG/v2pAE/9dN/GvjUzwVzpt+CBQvk7bfflssvv9x+WsDX5meKjnkNFtpn2+lnUr9+/UTfP7qMnzXQpp9BugSh02w584L6rLWPP/7YfGn81M8sDabaAxYaLNNnw+nn3bBhw4wbteZnlk8BMXqhy31ak73/rMfcsK3PPrMmDWJqkKiymTH2c6yvddu+vKD1uH0ZVL2Jr0sH6ni2fnZYzzG39cb9iy++KO+8847obD6nz08zr/lzz5495qbx0wyy++ys5IXTOdrGVA5W6fvkmWee8ZHTL7BYnxnpc/B/L5zeG5Utq+dUjr1P7P3sdE64+/Tz2D6LXP++sQfK7OXa2xhJ+7RMbaM1WGUv135d+2v770F7oNien9cIIIAAAgjESoBlAGMlS7kIIIAAAgggUC0COotDZ1IFSnqjVW9y6c3d66+/Xk466STjWTe6XJ0GBZy+oR2orOrc/+STT4oGrKxJ225NGuzQG9PWpLMnHvQsmWYPVFnz6DfodYZLq1atrLsjmoWiNzJ1VpY9UGUtWPP88Y9/NIIY1v164yfSpeGs5VTntj4LYujQoUEDVfb66Q0uc7aUecz68HNzn1t+jhkzxqcqGiTTG5PBgsAaONAAggaMremNN95wXJbOmse+rd9E1/FsD1TZ82mgwz57RWdnJHLSgJQ+cynUQIfewLTPJtMb+U6BKruLBvb1c9I6k0qDVcGef2cvQ1/rZ4EGMu2BKmtevZGtMwqtn1O67Gl+fr41m9+2vhetn+HqokExe6DKeqIGUPX9Y3/moTVPLLatN5a1/EiWnItFvQKVqZ9l1qRLSVbWH9b8Gzdu9Jvhq8d19oxT0ll99mP63tMgZmWBKmt5OktYg5FffPGFdbfftv6+tC7LqBmCvW/8CvjfDqdzKntWYaCykmW/Bgw1kGxNOuOtsuePObk5+VrLddq2f1FE+9lpSUqnc0PZp8//GzlypM9nob5X9X1XWbK/xyNpn17D3kZ7uZXVw/75Y12hoLJzOY4AAggggEA0BQhWRVOTshBAAAEEEEDAFQL67X+dXWX/n3enyumNTZ1doUvO3XjjjUbARLf15oMbk954Gz58uHf5L7OO2tYrr7zSfGn81GXP9FvoZtKbJ7p0V7CZCWZeDTToM22sSb9pa86MsO4Ptq2zMew3OQPlv/XWW30CHLok2Lhx4wJlT+r9GjywfsM6nJvC8YTRAK/9pvs999wT0iwCDUjoe9k6M0OXfAo3gKTP9bLfaHMy0FlCGji0JnNmlnVfIm3rsn3BAsH2tugyVdabmKeeeqrYg9z2c6yvdUaqzkQyk/ZXoGfhmXnsP/X9YQ1C2Y+br3Xmk87Asib7bCTrMZ2Nqe9Hawr194DObNTP/3glvVFuf+ZgZTfu41W3QNfJzc31CzD+61//CpTdb78u92p9tpqZwfp+NPfpTw2GWQOPuk+D2dbfQTobVZd91KVAdWam/s4bNWqU3/Jl+vv84Ycf9lnmTMuzJnMmq3VfKO9Ta37ddvq7wynoYj8vWV/rcpcaDLYm/bzW5xRWlpzcnHwrK8fpHKeyKysn0HH9woB9JpLOOA3lSwT2ekTyntN62YNcTu/nQPXX/fbPH53V6DReg5XBMQQQQAABBKIhQLAqGoqUgQACCCCAAAKuE9Bv7epyUHozN5xn8xQWFhqzB3R5qHAeUB0NAJ31pTeT7f90aTRdIk+/Ua7L5tiX/9Nra2BJb+5akz5rypr69OnjXTLOuj/Qtt4ItM+uspcZ6Fxzvz5DKNSkMxz05rk1TZ061foyZbY1sGKddaQzzKzPeHILhP39oM+JCec5Fzpbp3v37j7NsZfpc9D2Qp3OP/98297AL+2B03iP8cA1i+zIBRdcENaJ9uUa9XMy3GQfo/YZE8HK06WmunbtGiyLzzENkFhTsOewaNDMGtzQ92IoS7+Z5WsQLthsQDNfNH5qkM9aVy3TvlRZNK4TzTJ0GTt7sFefe2Zf+szpmrqco9PzfDRvoBkuTkEsc6atfvFCg086a1eXp9VZcRr8VsOTTz7ZWHb2zjvv9AmE65cfHnzwQT93s75ON/e1zHCT0zmB2hhu2YmWXwMef/rTn3xmHOmXZXQZR3twxaltTm6hfNnGXpZTAMipbPt5obz+5JNPjC87WfN27tw5pGCcnmOvh9P7x1p2oG37efZyA51n7rd//miA1/rsUjMfPxFAAAEEEIi1QM1YX4DyEUAAAQQQQACB6hLQZwXozA0N8ugzWmbOnGksW2R/oLVT/fT5Azpj49VXXw1r5oJTWaHu03o98cQToWY38ulNO31Olwbl7Mn6DXQ9NnjwYHuWoK91xoueo9+IN5O9THO/00+9+WENuDjlse/TWR7WZ87ozBe9aRLJDSp72W55rd9W3rBhgxF80tkD+s9pJt/OnTt9qqw3/uzPJPPJUA0v7O+HcN9jWmW9uWwNeNjLDNas9u3biwY5Q02tW7f2yRqLZ5f4XCCGL/RZcKF8c9+sgt7ot86C08BMOIEjsxwNMFpTOP2ly5CGk+wz5oL1l/15UxpsDyfp7Iu+ffs6fhkgnHJCyWtfwlXPCeXmfShlxzKPzhb59NNPxdoPOpNJZ/BeccUVfp/TGpDT2bG67GOgWRqBloN0CjCYbdNZuNYZfuZ+60/9wokGBa2zv1asWGHMBHR69qLT7Jtwl7jU6zudE6wt1jon07YuOXrzzTd7n3Fntk1nO+oMzVCSk5vT78rKytJApT059bc9T2Wv9e9KXWbUmnRGtP4dZw8eWfNYt+31cHr/WPMH2raf52QX6Fzd7/RMNf3bxD7jKlgZHEMAAQQQQCAaAgSroqFIGQgggAACCCDgagG9CXjKKacY/7SieqNNHw6vgRBdXm3OnDliX4pF8+kNxbvvvlv0uSzhzM7Sc+OR9IaPLnlony2i19ZAh/1bsfpt33BTp06dfE5Zvny5z+tgL+w3tYPlNY/Zz9FvB+tsCg1KJHLSG2y6vJ3ObJk9e7Y4fYu/svZZbxBXljdex+3vB/v7JZR62N+XGsjT8eh088xeXsuWLe27gr623xjXm3GJmsIJVGkbNZhjnc2jwapAs12CmdiDDvbPmWDnxrK/7O/FcJZHNOusnz9OM1fN49H6aR//+qWDRAjI6+xdfTaPBhzMpO+Hl19+WfS5ejrLSQPqOuNx06ZNxpdEdHlGM+nvLJ0hal2eNtBstkDjX5+Jp4GoUJIuNTd+/HgxZ2TpOZ9//rk4BaucrucU5Kjsuk4zWuyfO5WVkejH9f2tf5tYg+PaJn1u4IUXXhhy85zcnHwrK9DpHKf+rqwc6/EFCxYYz5a0PudM38tOz/u0nmffttcjkveclmn/TLGXa7+u/bU9aOZUpv0cXiOAAAIIIBALAYJVsVClTAQQQAABBBBwtUCDBg2M5aF0iajf//73xjNc9FkXY8aMEZ29Yk1641xvwuksq+pKeiNTg2Va7yOOOMKYraQ32+yBHWv9rDcDzf32Jf3M/cF+Os1E0Rve1ucMBTo/3BvTWo4u56Q3Taw3l5zaEuiabtyvN7UeffRRvxt34dbVbYEVvalmv0Fmf7+E0kan96X2eSg323RMhJN0LFmTPfBiPeb2bZ1ZFU6yPyNJlzwNdyan0/XCGZ/h9pcGPazJemPYul+37fWI5PPH6b1ov06qv9bZrw888IA89thjPjNCdRaTdVas3UlnyT3zzDNy3nnn+RwK9J7QL5noeLX3uS6FG8rvH72I/i7RGVjvvPOO95rz5s3zbls3nK5n/3yz5g+07XROKJ9lgcqz79dnI9mfj2TPY32tMwzjOTtGZ/jo0n/2GZcXX3xxyEvjmfV3cnPyNfMH+mn9e0Lz6PvKKTgT6Hz7fn3OqQbjrHXR8vT5aeEGye0BOWuZ9usGe21vo73cYOdyDAEEEEAAATcJEKxyU29QFwQQQAABBBJcwH5jUZtjv9EUbhPtS77YbzaHW55Tfv2fen32iz6LRWdSzZo1yyebPpMjHsEqXSJLn00VjWSfhaN9E8nNC/u33rU/NWhi3+9U50hno2nZ1hsvupxQoiZ9Vos+O8XankjbYp0VE2kZ0TzPqV8i6XOn95KWnZ2dXWl1Q71pXWlBCZgh3PHs1F/RaHY4N1dj2V/2z7xovRejYWQvw77kn36u6u+6RJhdpW3RgJHOcNLg048//mhvnt9rDRjdc889xpcu7LOYdbneQEkDWdZZUZrP/oy7QOea+zW/NVilMwH1+ZD2Z/Rofv0ssgY9NV+4yemcQAG5cMvW/Ppstr///e8hn6pfcIlXsErfx/fff79Mnz7dp35nnnmmEcDy2RnCi8MOO8wvVzgzOc2T7X1Slf7Qmd7Dhg3zWQpTx60uhxnue1PrZ69LJO3TcuxtdLLTfIGS0+e4/XMq0LnsRwABBBBAIJoCBKuiqUlZCCCAAAIIpLiA081B+42pcInss0mcbmyHW2ag/Fr2k08+aTz/yXrDQJcz0qWM7M9PCVQO+xFQAb3pqTfu7IEqndHXv39/0SXz9EZto0aNRJ8vYX/GxIMPPmg8HwZNBKIhYH+mSTTKpIzIBJx+j+nvSvuN68hKj89Zubm58sYbbxjPgtRn9+iMJZ1dpUFRbZ9+tuln3VlnnSUdOnQwKqW/S+1Bd/syoNbaa0DMHqwKd/abU379bHYKVun1rDOC7DOtrXULtG0/RwNF4QaWA5Xt5v3ar4888ohMmjTJp5rmTLxIAtXqpv1kDcTojNBwk71PDj/88HCLMPLrtW+88UaxzlLVLwI9/PDDMmDAgIjK1PecNenfvBp4D/ezoKptdApWOX1OWevKNgIIIIAAArEQIFgVC1XKRAABBBBAIEUF9H9s9X/crUtrOT1IPhyeeAartF7aBv3W+NixY32qmWjBKvuNDu2Tffv2hf3sLXv/6cw2p6CkD9b/Xtj7zimP0z77NcP9hrBTmU77rO9Tp+NV3aff5rfeaNU++etf/2rcwA2lbO0vNyenfomkz+39rW12KtvNFolQN7vpscceawQbEqHuodRRx5f1SwbRei+Gcu1w8+izn/TmvTVwozfk7Z/b4ZZbHfmPP/540X+hpBUrVvhk00B9sOUa27VrJ7qMqjWF+vvHPMcpv9Nnjua3B6usz9syy6vs58aNG32y6MymVEhPPfWUTJgwwaep+r7Q5SKrMmNQ/azBKl2aOdxkPyeSPtHPFg1UWYNCOoZHjBghQ4YMCbdK3vxOddH6Blvm2Xvy/zY0+Gr/vHMq136e9bXVWPdnZGRIkyZNrFnYRgABBBBAIC4CBKviwsxFEEAAAQQQSA0B/R93fY7Kzp07vQ3WJVOqkuzn682tWCenb3pbgw6xvn40ys/KyvIrRr/VftRRR/ntD7bDfpNHb6aG+g1pvV64Sb+xbJ+JFOjZPPYlIcMNPgW6YRlunQPlnzx5ss+hO+64I+RAlZ7o9veczgTTZ4pYZ0/qjdpw32P2m7va9kB9rsdIkQnYPzuty51FVqK7ztL3jDVYpZ8/GpALJzm9F8M5P9S8OnZ0xojORDLTli1b5MgjjzRfJuVP+7OWdHZWsKTBKnvSIL7Ts4zs+czXTkF/pwCW5rdfz15fs8xgPxctWuRzONyggc/JDi/+7//+L+xnPzkUE9VdL730krz33ns+ZWrfPv30034zhn0yhfBCx8ScOXO8ORcvXuzdDnXDvlRluONMPytvuukmWbt2rc8l9Xf60KFDffaF+0JnIOoMMuv7VNsYTrDK/p7TOoTbRv38sSatV6h/61nPYxsBBBBAAIGqCqRXtQDORwABBBBAAAEErAK6tJk1LV++3PoyrG0NellvPurJToGksAoNIbPTOv1O+0IoqtqyaLDKvsxRJDfelixZ4tMGczknn50BXixdujTAkcC77efoQ8vty+SYZ9tvWFpv9ph5gv2MJJgWrDzrMX3+zOrVq7279Jvl+ky0UJM++6OgoCDU7NWWzx6Ysr9fQqmY/X3ZunXrsG5Gh3IN8ogcffTRPgz6/o91wNbngjF+Yf9simT82D9/Yllle2Bk3bp1sbycK8r++uuvfepx9tln+7y2v+jdu7d9l8/MFr+DDjuclo2zB27N03r16mVuGj/1749w+kWXUlu2bJlPGU5t8MmQ4C/++c9/+s1E10DL6NGjJRp/N9n7RMe105J1gRg1wGRdtk/z2csMdK7u1xlLN998s9hnBV5//fVy6aWXBjs1pGMaEMrLy/PJa59N6HPQ4YU9v34WOn1hyeFU7y77+7x9+/beY2wggAACCCAQTwGCVfHU5loIIIAAAgikgEDXrl19Wjl79myfb4z6HKzkxZQpU/xyHHfccX77or3DusyLWXYiLodit/rvf/9rNiekn7pElf0ce/8GK0iXlbF/ozlYfj327bff+mTRm16BlhCyL5kV7qyIuXPn+lwrmi90VpR1iS+9caSBt1CT1s2+rE9l59pnmmnAK9bJ/n6wv19Cub59Bpq9zFDKIE/lAvrMPQ0EmknfHz/88IP5MuF/dunSxacNTr8/fDLYXuiMzhkzZtj2xu6l/YsXK1eujN3FXFDy/PnzxTpTWn+nVvacH71hbn++kP3GfGVNsz6DSvPqdQP9PtffNzqjxJrsATbrMfu2fpbpFxXMpJ/5ffr0MV8m3c8PP/xQXnzxRZ92aRBW90XreUd9+/b1+d2pvuH8nrH3X3Z2dsizljQodvvtt4v9CxWXX365XHfddT7trsqLE0880ef07777LqyA3DfffONzvr08n4MBXtiDcfbPpwCnsRsBBBBAAIGoCxCsijopBSKAAAIIIJDaAvabT3oD8IsvvggbRW/0f/LJJz7n6cwA+40knwxReqEPi7cmXbvf/i1463G3btv7Ytq0aRLOMzj0hol9GUB7mZW1XW9mhZo0wGO/sXTCCScEPN2+vJIunRPqUoB6E+rzzz8PWHZVD+h7xpo08BRq3fS8N99803p6SNu6lJA1xWPWjP39oDe88vPzrdUIuq0zL+1BQ3uZQQvgYFgCJ598sk/+N954wyeo6nMwwV7oZ4V12apVq1aF9V7U31N79uyJW6vtwbVIZoLFrbJVvJAGRvV5fdZ08cUXB/wigjWf/T1rfy6SNa99WwMb9r8/KptVM2jQIJ9ixo8f7xOA8jloe/Gf//zHZ4/OqrLPAPbJkMAv9PfnqFGjfFqgwXBdEjDcWT0+hdheqJ99dtrHH39sy+X8Uvvf/n4JNZCj5957771+v59+85vfyK233up8wQj3Dhw4UKxfNtHf3V999VVIpekXwuyzouzv4VAKss8ItH/ZKZQyyIMAAggggEA0BAhWRUORMhBAAAEEEEDAK6DfTO7Ro4f3tW68/PLLfsv5+WRweDFu3DixP5vgsssuc8j5666JEyfKs88+67fky685Kt/SZyPYv5Gv7Qn0jIvKS6y+HKeddprPs3/0huHjjz8eUtBEb5bo8yasSb9pG+4NjE8//VT0G/WhJF02yBpg0YBPsOdBaPDSOutKl2yaOnVqKJeS559/XmL5zJ7DDjvMZwkkfa5TqEEcfe9rYDHcZF/28eeffw63iLDz641feyBXb2CGskyT3gx87LHHfIIlzZs3F/vN6bArxQkBBX7/+9/73DzXG5T2mREBT3b5AZ05Zg9EaIDkwIEDldZcA+X6eyqeqWfPnj6fX7qEZijjJp51jNa1nnvuOZ9lTfV5OldccUVIxf/2t7/1e8/aAxCBCvrXv/4l9mfxVLb0oP6dYf29ol/Y+Pe//x3oEt79GlyYN2+e97VuXHnllT6vk+WFzoD+y1/+4vPZrV8keuWVV6RZs2ZRb6bdUZ3tX2xxuqj2m/ULN9qvlf0dqeXoF0v+/Oc/i/2LS2eeeaYRwHK6VlX2NW7cWOzvS7WsLHiuv0Ptf6dpYE//Dg8n6d9d1iW7NUDYvXv3cIogLwIIIIAAAlETIFgVNUoKQgABBBBAAAFTQJdHsX7DXYMC+lDwUGf1aIBDgyrWpEsBafAlWNIbfW+99ZYR4HjyyScl3OePaKDjzjvv9LkBo9fTJV8SMenzIux1nzlzpjz88MNBvymuN0huu+02n5s82v4//OEPYTPoDLm77rrL7zke9oL+/ve/+30DWr/BHOjZInq+ts8+C+eZZ54RXX4wWHr99dfl/fffD5alysf0/W8P2uqN8927dwctW9/7GsCJJOnzo6zfztZvW0+fPj2SosI659prr/XJrzNa7r77btEAXaCkMy5HjBgh9gfD601J643iQOezPzIBvSl69dVX+5yss6v08zacQIn2n75Xf/e734kGetySrrnmGp+q6Ew/nR0RrG1a/2HDhlXpSw4+Fw3xhX4Bolu3bt7cBw8eFPuSdd6DLtrQpRJD7XO9Cf7II4/IO++8422Bfjbq2LfPPvVmsG3okn36PrMmDYhXtmSjfnnltddes55mfNmismX5dHaQ/UsS+vvJHriwFqxfrNG/OaypX79+Pv1rPZbI2zqT57777hPrMrP6uaIzqlq1ahWTpuk4UU9r0vdAsGWGdWa4vf/PO+88n6VQreVZt/XzUN8/1nTSSSfJyJEjff62tR6v6rb+fWUdE/p3jDoH+uzSQJWOLfvyfTfeeGPYVdHgn3Xmtwb9rXUJu0BOQAABBBBAoAoCNatwLqcigAACCCCAAAKOAvrNTg2S6LeazaSzPC666CLjJpD+T7/O0DGXLdOAhn77WWc16TI69pk4+tyHJ554IuT/edab5BqM0H96A19vcuhzcHRmkN5UMW+G6/+cawBNn4Hx2WefGdc362v+PPXUUxP6mRPaD3qTzWqq30rXZzBcddVVRrDHfPaT3hzRZ0GMGTPGL+Bz/vnnS7Al+Uwv689jjz3WuJmkNzY1CKEPI9dvD+u36vWGpd7w1htf+u1n7Xtr0huGN910k3WX4/Yll1zi85wrfW6VXuuGG24QXe7HbJsGTGfNmmUEM80Ze/qeiOXNYQ22WWdIaRBHb7r+8Y9/NCzNpZL27dtntF/fr+YNWH3P63s32M04O4gG73TsWQNU+rwN7Tf9prVapKf/+l01vVl+xhln2IsJ+7UGkTXQa122SNut412DB4M8y2rpuNOk/aA3Ef/xj3+IPvjemvTZJLo0GCm2Ahqs0kC+9VlhH330kTH2L7jgAtF+6Nixo8/MQH2P6vtXZ2Lp+0vfp8GCkbFtQeDSdbaSflZZl2PT96bOptD3oi63pbMeNW3dulUmTZpkfN7t3LnT2Ke/l+wBVONAjP5zyimn+Hz26TPE7EueVXZpbYd9NrB5jtPsUfUINOtS/ezLq5plmT8/+OAD4z2gQR/11OUM9Rzz96p+rq9evdqoky7XpjNerUmXULMG6azHAm3rLCx9Lo9Zb72Br+Vo8EH7W9+v+jtFAyj6+a7Lz3755Zc+XzzRzzsNNoSS9Ms1OnvIrLu2Sb/Iop9P+rnWtm1b43qFhYWiM2H1d5h1POjMlDvuuCOUSyVUHv17SR3ssxX1bzr9HW7/PR5K40J5z2k5+rtMl4w1gzf63r7++utFZ4vq+0BndunfdPolDe1//X1qDahp0FN/91aWNHhv/fzQ/Pr8rby8PL/9lZVlHr/wwgvNzYA/9Vla+hn16quvevPoZ63+naZfAOvfv78xw1Dbr8El/dKN/flt5557rjEevQWEuGH9O0VPGTJkSIhnkg0BBBBAAIHoCxCsir4pJSKAAAIIIICAR0C/3bljxw7j2/cmiH5zXG8imM8x0hvyeoNdv31tvalg5tefetNHZwLpkm+RJP3Wqf2bp3rjQW9s6Q3YQNfVa+nNgQcffDCSy7rmHJ1po4E+DfysXLnSWy/d1mVuNOlNPP2Wrt6Qc0oa7NMbVOEmPU8DVnrTSPteg5f6T29qaqAy0Cyjhg0bGs830b6vLOk3gM866ywj2Gjm3bx5s7fftK+1j603EjWfvp+GDx9uBNDM86L9U4NlGijS4IyZNm3aZCyfpK/VXZM+z8qe7rnnHuPGXDjBKi1DAxE6e878lrT2qwYgnR5I37Jly6gEq/S6+g1wDXZalzrUG7k6S0z/6TjXQJmOOaekgWQd5zouSbEVUOOHHnrI6A8NAJhJP6/1Bqj+06Tjr1atWsb7U99HiZI0SLBmzRqfZ81oYPTB/32WB/q80/egzm645ZZb4tZUvSmsy3jp56MmDZDoTflwkrZVP+NDTW+//XbArBrMqSxYpSdrsEKDXvpPk76nzC+fOH2emXnUVoML4SYtW2fNarDBnDmrn+saZNV/+ntOP+sD/S2hf2s8+uijIbVN66bBdZ0Jq783zd8dej2dIab/dFxom51+Z2pddHyF4hiuQ3Xn1890p89w7YNIU6jvOf2Si7rq723z7zbtG531pv/0d4zuN8eStT76Wab9aX5pwnrMvq2BVnvS99VTTz1l3x3y61CCVVqYzlLWv1c1iG4mfa2zQzXpe1zHl37By540AKwzmsNNWpb17wMdaxp8JCGAAAIIIFBdAunVdWGuiwACCCCAAALJLaABCb05qEvAmTfl7S3WGz367VjzxoP9uM4s0RkYgzwzM0JJenNIv2FdWdIbD7rUXaDr6o2Nm2++2biJqDelEj3ps4x0tlSgmVF688PpppsGF/Rb5HqTUG8ERZI0yKUzjKxJb3wHClTpMkL67JhwgpN68ypQ27SvzZuNZh30po5eI9D70swXjZ+6TI9+c9wpqbv9xq4uvXP//feLfkM6kqRLD+oSW5H2VyTX1HPU8oUXXjDq7RRw0m+DO93k1HP1GVX6bXJzppnuI8VWQN8fGuDQwEigcaDjRj+fgwWq9DPXbZ+R+vmtz0iyLxFqijp93uksS32OXbzbooF5DWqbSZ+vo7NeEy3pDW+nzzOzHTrrRT8f7MvSmsdD+alLAevvMafn8ejv8kB/S2hQXoMZgd4Pga6t7wl9bpDW3Z40WOf0O1Nn7WnwkZv9drHovB48eLDha86OtJaqv2OcAlXms7S0P92e9G8uDarq311OSf+ecQpUqYt+fkXye19n3ZsBYL2m/j6OpByn+rIPAQQQQACBSASYWRWJGucggAACCCCAQMgC+nB0nfmis2v0gdi6lJTT/2ybBer/JOfm5hr/s6438ZxufJt57T91CSd9ZpV++1eXvtOlUvR/xHWmTWVJbxJ06NBBTj/9dGO2iQZ4kinpN3KfffZZYym8f/7zn8asg0A3ofWbtboUmC49oyZVSfotcw0m6Y1CfX7EkiVLHIvTbzzrc0L0m8Xh3ijR/PqtZ126Z+zYsQH7W29a6c1SvRGk9QoUPHGsYIQ7NRCgz/LQ978uFRXovahBKr3BqTMHqvqNfHXUWYG6DJbOdNIxp0sx6s28QAHaCJvnc5re6H/ggQcMX52dozO89JpOSfPqskq67BEPcncSis8+XZZSA6Pvvfee9/M52JX181g/E44//njjpqYuAefGpJ9hGrDSMaBfeNAx4JSaNWtmLM2pv6fMZeyc8sVyny6Pap3hpsvK6Swvtyb9/NTPXF3CVWfjBUv6hRNzqT6d3VTVpMvD6jJtn3zyibz77rvGcoOByszJyTE+i3Rpy0iDkPr+1s9unRGsSxqay0Xar6mf87rErf7+CmX2jv18XocuoH9L6Ox8DVzqc/PsX/gwS9LnXWrf6xKSgQLyZl43/dTPIZ1ZrUEj/ezScRbob2adua7LBFYlOKqfN9akn0ckBBBAAAEEqlMgzfOLz38OcXXWiGsjgAACCCCAQFIL6E1zXdpMb3LpN6H128n6LB39pqzOqtGbdNG+aajX0aWS9HkLOqNHgxQanNIbmhrE0edP6A3YcIMkidxRaqDPO9BnhekNOA3e6M0d/Ra6fgM5kodr6+wY/Qa7mXRJLX32hzVpH+gzRfS6GjjRG3v6jXm9ptahqkn/tNXn6hQUFBjtMq+hM7U6deoUVvCzqnWxn69L82nd9HlBOg70tb73zfbrjJBkSjr7QJ8JpsFjfY9pe/U91rx5c+OZNak03hKlX/VzWWf26E99j2pAWz8n9fNZ36caSNXPzERLurSXfibo8520Tfo+1M8EnaWjvwuqO+lz9swlP/XGugbZEuHzQGeCqa0ub6pBA7XV+uvvEf28dZqVFE1rXd5RvwChv0/080Y/T/V3igaZ9BlA0Uz6u0Svpcuy6eeZ/q7RmXHt2rUzrhfJ78xo1i8Vy9KZVPqMOQ1G6992GkzXsa1BUv1b0g1ju6r9os9N0zbqWNPZrvp7U9/b0XiP60wt/YKW+aUSXVZZZxOSEEAAAQQQqE4BglXVqc+1EUAAAQQQQACBJBIIJViVRM2lKQgggEBUBPTZT/qsLTPps2cuvvhi8yU/EUAAgagLvPnmmzJ69GhvuX/7298CLlvszcQGAggggAACMRao/q+RxbiBFI8AAggggAACCCCAAAIIIICAWwUGDhxozJQw66fL2cZyyU7zOvxEAIHUFNBZaW+//ba38b179yZQ5dVgAwEEEECgOgUIVlWnPtdGAAEEEEAAAQQQQAABBBBIeYFhw4Z5DXTJr6+++sr7mg0EEEAgmgITJkwwlkXVMnX5xBtvvDGaxVMWAggggAACEQsQrIqYjhMRQAABBBBAAAEEEEAAAQQQqLpAz549ZfDgwd6CdFlVnf1AQgABBKIpoM+oev31171Fnn322T4zO70H2EAAAQQQQKAaBAhWVQM6l0QAAQQQQAABBBBAAAEEEEDAKqDPrapTp46xS2dXffTRR9bDbCOAAAJVFnj33Xdly5YtRjn169eXm2++ucplUgACCCCAAALREqgZrYIoBwEEEEAAAQQQQAABBBBAAAEEIhPIzs6W77//PrKTOQsBBBAIQeCqq64S/UdCAAEEEEDAjQLMrHJjr1AnBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBFBAhWpUhH00wEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwI0CaRWe5MaKUScEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHkF2BmVfL3MS1EAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFwrQLDKtV1DxRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB5BcgWJX8fUwLEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAHXChCscm3XUDEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIPkFCFYlfx/TQgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAtQI1XVszKuYagTPOOEO2bNkizZs3ly+++MI19aIiCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkPgCBKsSvw9j3gINVBUWFsb8Ool2gQULFhhVzs3NTbSqU18EUkqAsZpS3U1jE1iAsZrAnUfVU0qAsZpS3U1jE1iAsZrAnUfVU0qAsZpS3U1jE1SAcRq/jmMZwPhZcyUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGbAMEqGwgvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4idAsCp+1lwJAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAJkCwygbCSwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgJ1IzfpbgSAsklkJOTk1wNojUIJKkAYzVJO5ZmJZ0AYzXpupQGJakAYzVJO5ZmJZ0AYzXpupQGJakAYzVJO5ZmJZUA4zR+3UmwKn7WXCnJBLKyspKsRTQHgeQUYKwmZ7/SquQTYKwmX5/SouQUYKwmZ7/SquQTYKwmX5/SouQUYKwmZ7/SquQSYJzGrz9ZBjB+1lwJAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAJkCwygbCSwRCFViwYIHoPxICCLhbgLHq7v6hdgiYAoxVU4KfCLhbgLHq7v6hdgiYAoxVU4KfCLhbgLHq7v6hdgioAOM0fu8DglXxs+ZKCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACNgGCVTYQXiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMRPgGBV/Ky5EgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgE2AYJUNhJcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALxEyBYFT9rroQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGATSKvwJNs+XiLgI5CXlyeFhYWSnZ0t+fn5PsdS+UVJSYnR/Dp16qQyA21HwPUCjFXXdxEVRMAQYKzyRkAgMQQYq4nRT9QSAcYq7wEEEkOAsZoY/UQtU1uAcRq//q8Zv0txJQSSS4AgVXL1J61JXgHGavL2LS1LLgHGanL1J61JXgHGavL2LS1LLgHGanL1J61JXgHGavL2LS1LHgHGafz6kmUA42fNlZJMQKPqZmQ9yZpGcxBIKgHGalJ1J41JYgHGahJ3Lk1LKgHGalJ1J41JYgHGahJ3Lk1LKgHGalJ1J41JUgHGafw6lmBV/Ky5UpIJFBQUiP4jIYCAuwUYq+7uH2qHgCnAWDUl+ImAuwUYq+7uH2qHgCnAWDUl+ImAuwUYq+7uH2qHgAowTuP3PiBYFT9rroQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGATIFhlA+ElAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA/AQIVsXPmishgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjYBAhW2UB4iQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggED8BglXxs+ZKCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACNoG0Ck+y7eMlAj4CeXl5UlhYKNnZ2ZKfn+9zjBcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQFUEmFlVFT3ORQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqJIAwaoq8XFyKgsUFRWJ/iMhgIC7BRir7u4faoeAKcBYNSX4iYC7BRir7u4faoeAKcBYNSX4iYC7BRir7u4faoeACjBO4/c+qBm/S3ElBJJLYM2aNUaDsrKykqthtAaBJBNgrCZZh9KcpBVgrCZt19KwJBNgrCZZh9KcpBVgrCZt19KwJBNgrCZZh9KcpBRgnMavW5lZFT9rroQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGATIFhlA+ElAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA/AQIVsXPmishgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjYBAhW2UB4iQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggED+BmvG7FFdCILkE6tSpk1wNojUIJKkAYzVJO5ZmJZ0AYzXpupQGJakAYzVJO5ZmJZ0AYzXpupQGJakAYzVJO5ZmJZUA4zR+3ZlW4UnxuxxXSkSBvLw8KSwslOzsbMnPz0/EJlBnBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcKkAywC6tGOoFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgIsA5gKvUwbYyKgs8006YwzEgIIuFeAserevqFmCFgFGKtWDbYRcK8AY9W9fUPNELAKMFatGmwj4F4Bxqp7+4aaIbC0aIuMXT5H5hSuleKyg5KVWVdyG7eUqzv0lGOymgMUAwGCVTFApcjUENi8ebPRUIJVqdHftDJxBRiridt31Dy1BBirqdXftDZxBRiridt31Dy1BBirqdXftDZxBRiridt31Dx5BeZu2yAPzP1aftiyxreRe7fLzK3r5LWCWdK/eY481ONU6dG0tW8eXlVJgGUAq8THyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJDoAhM3LJdzJv7TP1Bla5gGsjSf5idFT4BgVfQsKQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSTEBnVF059T3Z51nyL5Sk+a6a+r7oeaToCBCsio4jpSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEACCujSf/vLDoVVcw1YjZw3MaxzyBxYgGBVYBuOIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQBILLC3aUunSf4Ga//3mn6Vg19ZAh9kfhkDNMPKSFQEELAJZWVmWV2wigIBbBRirbu0Z6oWArwBj1deDVwi4VYCx6taeoV4I+AowVn09eIWAWwUYq27tGeqVagJjl8+pUpP/sWy2jOp1ZpXK4GQRglW8CxCIUCAnJyfCMzkNAQTiKcBYjac210IgcgHGauR2nIlAPAUYq/HU5loIRC7AWI3cjjMRiKcAYzWe2lwLgcACC3ZsCnwwhCMLdxSGkIsslQmwDGBlQhxHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBpBTYe/BAldq191Bplc7n5F8ECFbxTkAgQoE1a9aI/iMhgIC7BRir7u4faoeAKcBYNSX4iYC7BRir7u4faoeAKcBYNSX4iYC7BRir7u4fapc6AnVqVG0Buvo1a6cOVgxbSrAqhrgUndwCRUVFov9ICCDgbgHGqrv7h9ohYAowVk0JfiLgbgHGqrv7h9ohYAowVk0JfiLgbgHGqrv7h9olv8Ceg6Xy5MJvZeHOqi0D2LVxdvJjxaGFVQsZxqGCXAIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiIZAadkhGbt8jjy9aKpsK91X5SKvObpXlcugABGCVbwLEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIKkFysrL5b3VC+Rxz2yq9cW7otLWAS2OkI4Nm0WlrFQvhGBVqr8DaD8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkkqUFFRIZ+tWyqPLJgkBbu2BWxljbQ0KfPkDTXVrZEhf+k+JNTs5KtEgGBVJUAcRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcQTmFq4Sh6aN0nyt28IWPkz2nSUEbmDZeO+3XLV1PdlX9nBgHnNAxqo+ufAi6VH09bmLn5WUYBgVRUBOT11BVq0aJG6jaflCCSQAGM1gTqLqqa0AGM1pbufxieQAGM1gTqLqqa0AGM1pbufxieQAGM1gTqLqiacwDxPcOrh+ZPkv5tWBax7v+Y5MrL7KdK7WVsjz7GNWsiEIVfJyHkT5fvNPwc8T5f+0xlVBKoCEkV0IM0zBS70eW0RXYKTEl0gLy9PCgsLJTs7W/Lz8xO9OdQfAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAkFlu3aKo8t+K+MW/tTwNZ1bZQtf+5+spzc8ihJ8yz955SWFm2RscvnyMIdhbL3UKnUr1lbujbOlmuO7sUzqpzAorCPmVVRQKQIBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqB6B9cW75MmF38pbq+ZLeYD5Oe0bNJb7PMv9nZfTWdLT0oNW9Jis5jKq15lB83AwugIEq6LrSWkpJFBQUGC0tmPHjinUapqKQOIJMFYTr8+ocWoKMFZTs99pdeIJMFYTr8+ocWoKMFZTs99pdeIJMFYTr8+osfsEtpcUy7M/fi+vF8yS0vIyxwq2zGwg93QdJJe17yYZ6TUc8wTayTgNJBP9/QSrom9KiSkiUFJSkiItpZkIJLYAYzWx+4/ap44AYzV1+pqWJrYAYzWx+4/ap44AYzV1+pqWJrYAYzWx+4/aV6/AnoOl8sqS6fLikmmy5+ABx8pk1aojtx97gvyhY2/JrJnhmKeynYzTyoSid5xgVfQsKQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiJFBadsh4ltTTi6bKttJ9jlepWyNDbujUR27u3E8a1sp0zMNO9wkQrHJfn1AjBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ+J9AWXm5vLd6gTzueS6VPp/KKWWkp8tVHXrKnV1OkBaepf9IiSVAsCqx+ovaIoAAAggggAACCCCAAAIIIIAAAggggAACCCCQEgIVFRXy2bql8siCSVKwa5tjm9M8ey8+sqsMzz1Jcuo3cszDTvcLEKxyfx9RQwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEUkpgauEqeWjeJMnfviFgu89o01FG5A6WYxu1CJiHA4khQLAqMfqJWrpQICcnx4W1okoIIGAXYKzaRXiNgDsFGKvu7BdqhYBdgLFqF+E1Au4UYKy6s1+oFQJ2AcaqXYTXCPwiMM8TnNIg1beeYFWg1L95jjzQ/RTp3axtoCxR2c84jQpjSIUQrAqJiUwI+AtkZWX572QPAgi4ToCx6rouoUIIOAowVh1Z2ImA6wQYq67rEiqEgKMAY9WRhZ0IuE6Aseq6LqFC1SywbNdWeXTBZBm/dknAmnRtlG0EqQa3bC9paboAYGwT4zS2vtbSCVZZNdhGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBuAmsL94lTy78Vt5aNV/KPc+ockrtGzSW+zzL/Z2X01nS09KdsrAvwQUIViV4B1L96hNYsGCBcfHc3NzqqwRXRgCBSgUYq5USkQEBVwgwVl3RDVQCgUoFGKuVEpEBAVcIMFZd0Q1UAoFKBRirlRKRIckFtpcUy7M/fi+vF8yS0vIyx9a2zGwg93QdJJe17yYZ6TUc88RyJ+M0lrq+ZROs8vXgFQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMRIYM/BUnllyXR5cck02XPwgONVsmrVkduPPUH+0LG3ZNbMcMzDzuQSIFiVXP1JaxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcJ1AadkhGbt8jjy9aKpsK93nWL+6NTLkhk595ObO/aRhrUzHPOxMTgGCVcnZr7QKAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFqFzjkWeLvvdUL5QnPc6n0+VROKSM9Xa7u0FPu7DJQmmfWd8rCviQXIFiV5B1M8xBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLdARUWFfLZuqTyyYJIU7NrmePk0z96Lj+wqw3NPkpz6jRzzsDM1BAhWpUY/00oEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOIiMLVwlTw0b5Lkb98Q8HpntOko93cbLJ2zWgTMw4HUEUjzRDcrUqe5tDQSgby8PCksLJTs7GzJz8+PpIikPKekpMRoV506dZKyfTQKgWQRYKwmS0/SjmQXYKwmew/TvmQRYKwmS0/SjmQXYKwmew/TvmQRYKwmS0/SDqvAPE9wSoNU33qCVYFS/+Y58kD3U6R3s7aBsrhmP+M0fl3BzKr4WXOlJBMgSJVkHUpzklaAsZq0XUvDkkyAsZpkHUpzklaAsZq0XUvDkkyAsZpkHUpzklaAsZq0XZuSDVu2a6s8umCyjF+7JGD7uzbKNoJUg1u2l7Q0XQDQ/YlxGr8+IlgVP2uulGQCRNWTrENpTtIKMFaTtmtpWJIJMFaTrENpTtIKMFaTtmtpWJIJMFaTrENpTtIKMFaTtmtTqmHri3fJkwu/lbdWzZfyAIu4tW/QWO7LHSzn5XSW9LT0hPJhnMavuwhWxc+aKyWZQEFBgdGi3NzcJGsZzUEguQQYq8nVn7QmeQUYq8nbt7QsuQQYq8nVn7QmeQUYq8nbt7QsuQQYq8nVn6nWmu0lxfLsj9/L6wWzpLS8zLH5LTMbyD1dB8ll7btJRnoNxzxu38k4jV8PEayKnzVXQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgYQX2HCyVl5dMl5eWTJM9Bw84tiOrVh25o8sJct3RvSWzZoZjHnYiYBcgWGUX4TUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4BUrLDsnY5XPkqUVTZXvpPu9+60bdGhlyQ6c+cnPnftKwVqb1ENsIVCpAsKpSIjIggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJB6Aoc8S/y9t3qhPOF5LpU+n8opZaSny9UdesqdXQZK88z6TlnYh0ClAgSrKiUiAwIIIIAAAggggAACCCCAAAIIIIAAAggggAACqSNQUVEhn61bKo8smCQFu7Y5NjzNs/fiI7vK8NyTJKd+I8c87EQgVAGCVaFKkQ8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSQXmFq4Sh6aN0nyt28I2NIz2nSU+7sNls5ZLQLm4QAC4QikeSKkFeGcQN7UE8jLy5PCwkLJzs6W/Pz81AOgxQgggAACCCCAAAIIIIAAAggggAACCCCAQJILzN22QR6eP0m+9QSrAqX+zXPkge6nSO9mbQNlYT8CEQkwsyoiNk5CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDxBZbt2iqPLpgs49cuCdiYro2yjSDV4JbtJS1NFwAkIRBdAYJV0fWktBQSKCoqMlqblZWVQq2mqQgkngBjNfH6jBqnpgBjNTX7nVYnngBjNfH6jBqnpgBjNTX7nVYnngBjNfH6LNlqvK64SJ5cOEXeXjVfygMswNa+QWMZ4Vnub+jhnSU9LT3ZCCptD+O0UqKoZSBYFTVKCko1gTVr1hhNJliVaj1PexNNgLGaaD1GfVNVgLGaqj1PuxNNgLGaaD1GfVNVgLGaqj1PuxNNgLGaaD2WPPXdXlIszyz+TsYsmy2l5WWODWuZ2UDu6TpILmvfTTLSazjmSYWdjNP49TLBqvhZcyUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKpFYM/BUnl5yXR5ack02XPwgGMdsmrVkTu6nCDXHd1bMmtmOOZhJwKxECBYFQtVykQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwAUCpWWHZOzyOfLUoqmyvXSfY43q1siQGzr1kZs795OGtTId87ATgVgKEKyKpS5lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQDQKHPEv8vbd6oTyx8FtZX7zLsQYZ6elydYeecmeXgdI8s75jHnYiEA8BglXxUOYaCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEQaCiokI+XbdEHl0wWQp2bXO8Yppn7yXtcuVez3Opcuo3cszDTgTiKUCwKp7aXCupBOrUqZNU7aExCCSrAGM1WXuWdiWbAGM12XqU9iSrAGM1WXuWdiWbAGM12XqU9iSrAGM1WXu2ets1tXCVPDRvkuRv3xCwIme26Sgjug2WzlktAubhwC8CjNP4vRPSPFHWivhdjislokBeXp4UFhZKdna25OfnJ2ITqDMCCCCAAAIIIIAAAggggAACCCCAAAIIIJC0AnO3bZCH50+Sbz3BqkCpf/MceaD7KdK7WdtAWdiPQLUJMLOq2ui5MAIIIIAAAggggAACCCCAAAIIIIAAAggggAACkQss27XVWO5v/NolAQvp2ijbCFINbtle0tJ0AUASAu4TIFjlvj6hRgkioLPNNOmMMxICCLhXgLHq3r6hZghYBRirVg22EXCvAGPVvX1DzRCwCjBWrRpsI+BeAcaqe/smEWq2rrhInlw4Rd5eNV/KAyye1r5BY2O5v6GHd5b0tPREaJbr6sg4jV+XEKyKnzVXSjKBzZs3Gy0iWJVkHUtzkk6AsZp0XUqDklSAsZqkHUuzkk6AsZp0XUqDklSAsZqkHUuzkk6AsZp0XRqXBm0rKZZnF38nY5bNltLyMsdrtsxsIPd0HSSXte8mGek1HPOwMzQBxmloTtHIRbAqGoqUgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAjAT2HCyVl5dMl5eWTJM9Bw84XqVRrUy5vcsAue7o3pJZM8MxDzsRcKsAwSq39gz1QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhpgZKygzJ22Rx52jObanvpPkeLujUy5MZOfWVY537SsFYdxzzsRMDtAgSr3N5D1A8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgpQQOeZb4e2/1Qnli4beyvniXY9sz0tPl6g495c4uA6V5Zn3HPOxEIFEECFbZemrHjh0ye/ZsmTdvnixdulTWrFkjui5lcXGx1KxZU7KysqRjx47St29fufDCC6Vly5a2EvxfFhUVyXfffSfTpk2TH3/8UX7++WfZvXu31K5dW5o0aSK5ubkyZMgQOeeccyQjI/zpmRs2bJB3331XJk6cKLq9b98+adGihRx77LFywQUXyOmnny5paWn+FWMPAggggAACCCCAAAIIIIAAAggggAACCCCAgGsEKioq5NN1S+TRBZOlYNc2x3rpnd5L2uXKvZ7nUuXUb+SYh50IJJpAmufNX5FolY5lfa+44gqZNGlSSJfQYNOwYcPktttuk3RPFNueNMB14403ytSpU+XAAed1RK3ntG3bVp577jnp06ePdXfQ7XfeeUceeOABI0AVKOMJJ5wgzz//vDRv3jxQlqD78/LypLCwULKzsyU/Pz9o3lQ6qIFMTTk5OanUbNqKQMIJMFYTrsuocIoKMFZTtONpdsIJMFYTrsuocIoKMFZTtONpdsIJMFYTrstiXuGphavkoXmTJH/7hoDXOrNNRxnRbbB0zmoRMA8HoifAOI2eZWUlEayyCVmDVY0bN5YOHTpI69atpV69erJ//35jVtT8+fPl0KFD3jN1htXo0aO9r82NLVu2SPfu3c2Xxs9mzZpJ165djcDRwYMHjZlWS5Ys8ebR2Vuvv/66MdPKuzPAhs6muvPOO71HGzZsKP3795cGDRpIQUGBaD3N1KlTJxk3bpzRDnNfqD8JVoUqRT4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA8gbnbNsjD8yfJt55gVaDUv3mOPND9FOndrG2gLOxHIKEFCFbZuu9vf/ubEdAZMGCAHHnkkbajv7zcunWrPPjgg/LJJ594j7/66qty9tlne1/rhhms0qUDf/Ob38gll1xiLM3nk8nzYtasWXLrrbfK2rVrjUMabNJlAzWwFSitWLFCTjnlFNGAlyZd7m/UqFFSt25d7ynff/+9/N///Z/oMoSa9PrPPPOMsR3OfwhWhaNFXgQQQAABBBBAAAEEEEAAAQQQQAABBBBAoHKBZbu2Gsv9jV/762QG+1ldG2UbQarBLdvzqBc7Dq+TSoBgVYTdqasnavDnhx9+MErQpfZ0ppM17dy5U8aMGWMEjDQAFSytW7fOmE21Z88eI5suLzh8+PCAp2gQ6tNPPzWO9+rVSz7++GPHpQgnT54sl19+uZGvRo0axhKHOlssnESwylmLKaDOLuxFwG0CjFW39Qj1QcBZgLHq7MJeBNwmwFh1W49QHwScBRirzi7sRcBtAoxVt/VI/OqzrrhInlw4Rd5eNV/KAzylp32DxsZyf0MP7yzpaf6PoIlfbVP7SozT+PU/7/IIrdPS0oxglXn64sWLzU3vz0aNGsldd91lLMvn3RlgQ59XZQaVNEuw52bpzK7PP//cW9KIESMcA1WaYfDgwaKBNE1lZWXy5ptvGtv8p+oCOmPNnLVW9dIoAQEEYiXAWI2VLOUiEF0Bxmp0PSkNgVgJMFZjJUu5CERXgLEaXU9KQyBWAozVWMm6t9xtJcUyYs6X0mvcC/LvlfMcA1UtMxvIc8efI9PPuUnOz+lCoKqau5NxGr8OIFhVBesmTZp4zy4uLvZuR7qhM6TMpDOtAqWvv/5aysvLjcPt2rUT63lO51x88cXe3V9++aV3mw0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2ArsOVgqoxZ+Kz3GjZaXl86Q0vIyvws2qpUpD/UYIvlDb5ErO+RJRnoNvzzsQCCZBWomc+Ni3bZly5Z5L9GmTRvvdjQ2dBZUoDRt2jTvob59+3q3A23069fPe2jDhg2yevXqgM/j8mZkAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBigZKygzJ22Rx5evF3sr10n2M5dWtkyI2d+sqwzv2kYa06jnnYiUAqCBCsirCXCwsL5dVXX/WefdZZZ3m3I91YunSp99RWrVp5t+0by5cv9+467rjjvNuBNrKzs6VZs2aiywdq0vOPPPLIQNnZjwACCCCAAAIIIIAAAggggAACCCCAAAIIIBChwCHPzKn3Vi+UJzyzqdYX73IsJSM9Xa7u0FPu7DJQmmfWd8zDTgRSSYBgVRi9vX//ftHl+SZPniyvvPKKbNu2zTi7Q4cOMmzYsDBK8s+qy/p99NFH3gPmc6a8OywbK1eu9L4KdUZX69atvcEq6/negthAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiFigoqJCPl23RB5dMFkKdv1y79heWJpnxyXtcuXeroMkp34j+2FeI5CyAgSrgnT9rFmz5Pzzzw+SQ2Tw4MHy4osvSv36VYt+v/HGG7JixQrjWumeqPoVV1zheF0NmJWUlHiP6YypUJI1nz4ULpKkAbWNGzcGPTXYjLCgJybgwRYtWiRgrakyAqknwFhNvT6nxYkpwFhNzH6j1qknwFhNvT6nxYkpwFhNzH6j1qknwFhNrj6fsmmVPDx/kuRv3xCwYWe26Sgjug2WzlncVwyI5LIDjNP4dQjBqgits7Ky5LHHHpOhQ4dGWMKvpxUUFMjjjz/u3XHppZdKx44dva+tG/v2+a5tWqdOaOuYWvMVFxdbiwx5e8uWLdKrV6+g+T///HO/47m5ucY+DZKtWbPG77jWzWyvLq+4efNmvzzqnZOTY+zXMpwCbvrBoUsealJTa1DP2On5j5ahZWlasGCB8dP+H62L1knP13KckrZJr6X1cConUdukbdU2JVM/0abEGE/x6Ce9Rjw/I+LRpnh/7tEmxpO+BzTF4r1n/v43f/5yJZFk/TtC28fvXLOXf/npxr/36Cc+98x3aSw+94L9vwbvPd570Xrvmb9XQ/3/XN57vPei9d4zy+G9F/wei/4/qt4Lc7q3xN9G8bm/F43Pve31ahpBqm8LV5lvfb+f/ZvnyAPdT5GGO/ZJyRpPn3v+WVM071lqufy/hlVXjPvBVb23rL9T6Sf/++Xm/7P7ikf2imBVEDf9H5KrrrrKyKFTOPfu3SurVq2SRYsWGQP+xhtvlH//+9/yxBNPSPv27YOUFPjQrl275JprrhEzgKTPkho5cmTAE+wBmIyMjIB5rQdq1arlfWkvw3uADQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFKBdaU7JFHfpor32z3/3K+eXKHOg3l7k4D5Dede0paWpoU7CgwD/ETAQRsAmmeIEyFbR8vKxHQbzyMGjVK3n//fSOnftPhgw8+kM6dO1dypu9hDRr97ne/kxkzZhgHGjRoIB9//HHQcrZv3y5du3b1FjRlyhQ56qijvK8DbVx//fUyYcIE4/DVV18tjzzySKCsfvvz8vKMb3k0b95cPvvsM7/j1h2ptAyg+U1Ic1aY1YFtBBBwjwBj1T19QU0QCCbAWA2mwzEE3CPAWHVPX1ATBIIJMFaD6XAMAfcIMFbd0xfh1GRdcZE8uXCKvL1qvpQHuLXevkFjY7m/oYd3lvS09HCKJ6/LBBin8esQZlZFYK1TdJ999lnR4NKYMWO8s6wmTZokNWrUCKnEQ4cOyQ033OANVOmycWPHjg0aqNKC69at61N+qLOkrPnq1avnU0aoL/RZWqkUjKrMxWpaWV6OI4BA9QkwVqvPnisjEI4AYzUcLfIiUH0CjNXqs+fKCIQjwFgNR4u8CFSfAGO1+uwjufK2kmJ5dvF3MmbZbCktL3MsomVmA7mn6yC5rH03yUgP7T6xY0HsdI0A4zR+XUGwqgrWw4cPN2ZX7dmzR5YvXy6TJ0+WIUOGVFpieXm53H777fL1118beWvWrCl/+9vfpG/fvpWem5mZ6X2ekmbeunVrpefY8+lMMBICCCCAAAIIIIAAAggggAACCCCAAAIIIIBAcIE9B0vl5SXT5cWfpsneQwccMzeqlSm3dxkg1x3dWzJrhvbYFseC2IlACgswB7EKna+Bo549e3pLmDNnjnc72Ma9995rLPeneXS20ujRo0MKcpllWp+PtX79enN30J8bNmzwHree793JBgIIIIAAAggggAACCCCAAAIIIIAAAggggIAhUFJ2UF7xBKm6fzJanlj4rWOgqp4nMHVXl4Ey77xb5ebO/QlU8d5BoAoCzKyqAp6e2rBhQ28JO3fu9G4H2hg5cqS89dZb3sP67KvzzjvP+zqUjQ4dOsiPP/5oZF28eHGlp+gztqwzsPR8EgIIIIAAAggggAACCCCAAAIIIIAAAggggICvwCHPEn/vrV5oBKjWF+/yPfi/VxmeCQhXd+gpd3oCVc0z6zvmYScCCIQnQLAqPC+/3Js3b/buq2x5vSeeeEJef/11b/4HH3xQLrvsMu/rUDf69esnn3zyiZF9+vTplZ42Y8YMb57WrVvLkUce6X3NBgIIIIAAAggggAACCCCAAAIIIIAAAgggkOoCFRUV8um6JfLogslSsGubI0eaZ+8l7XLlXs9zqXLqN3LMw04EEIhMgGBVZG7GWTt27JC5c+d6SzjqqKO82/YNXervhRde8O6+66675A9/+IP3dTgbp556quhSgvrsq5UrV0p+fr7k5eUFLOL999/3HjvttNO822xUTSAnJ6dqBXA2AgjERYCxGhdmLoJAlQUYq1UmpAAE4iLAWI0LMxdBoMoCjNUqE1IAAnERYKzGhTmki0zZtEoenj9J8rdvCJj/zDYdZUS3wdI5q0XAPBxIPgHGafz6lGCVxVqX8WvUKLSIuAaK7r//fiktLTVKqF27tpxyyimW0n7d1NlUTz75pHfHjTfeKLfffrv3dbgbzZo1kzPOOEM+++wz49RHH31UPvroI0lL09i+b5oyZYroP001atSQyy+/3DcDryIWqGwmXcQFcyICCERVgLEaVU4KQyBmAozVmNFSMAJRFWCsRpWTwhCImQBjNWa0FIxAVAUYq1HljKiwuds2GEGqbwtXBTy/f/MceaD7KdK7WduAeTiQvAKM0/j1bXr8LuX+K3344Ydy5plnygcffCB79uwJWOGffvrJCPqMGzfOm+f666+Xxo0be1+bG++++67ocn9muuqqq2TEiBHmy4h/3n333ZKRkWGcP3PmTLn11ltl3759PuX98MMPctNNN3n3XXjhhXL00Ud7X7OBAAIIIIAAAggggAACCCCAAAIIIIAAAgikmsCyXVvlyqnvyclf/l0CBaq6NsqWDwf/XiYMuYpAVaq9QWhvtQikedbirKiWK7vwon//+9+9gaWaNWuKLuvXrl070eipzlrSmVcaqPr55599aq8BrldeeUX0HGtasmSJ6JJ9OgtLU926deWiiy5ynAFlPc/cvvbaa43rm6/tP9955x3R5QTNpPXU51k1aNBAli1bJvPmzTMPSadOnYznXNWvH/4D/3SJwcLCQsnOzjaWHPQWmuIbCxYsMARyc3NTXILmI+BuAcaqu/uH2iFgCjBWTQl+IuBuAcaqu/uH2iFgCjBWTQl+IuBuAcZq/PtnXXGRPLlwiry9ar6UB7gt3r5BY2O5v6GHd5b0NOZ6xL+X3HVFxmn8+sM3uhK/67rySrVq1fLW69ChQ7J06VLjn3enbUMDP3fccYdcd911xhJ7tsNGcMsMVOkxnfn0xhtv2LMFfH3WWWcFDVZdeumlorHGkSNHGmUXFRXJ559/7lfegAED5Pnnn5dIAlV+hbEDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIIIFtJcXy7OLv5PVls+VAeZljzVvVbSB3HzdILmvfTTLSazjmYScCCMROgGCVxfbKK68UDex89913xqwknZ20YcMG2b17t5FLgz0tWrSQzp07ywknnCAaTKpXr56lhPhvXnbZZXLiiSeKzrKaOHGiUV8NijVv3lyOO+44ueCCC+T0008PeTZX/FvAFRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgegL7DlYKi8vmS4v/jRN9h464HiBRrUy5fYuA+S6o3tLZs1fHrvimJGdCCAQUwGCVTbe9u3bi/7TZ0tVNemSfBrsinVq3bq1sRygdUnAWF+T8hFAAAEEEEAAAQQQQAABBBBAAAEEEEAAATcKlJQdlLHL5sjTntlU20v3OVaxnicwdcMxfWVY537SsFYdxzzsRACB+AkQrIqfNVdCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJHAIc8Sf++tXihPLPxW1hfvcrxKRnq6XN2hp9zZZaA0z6zvmIedCCAQfwGCVfE354oIIIAAAggggAACCCCAAAIIIIAAAggggAACURKoqKiQT9ctkUfmT5Zlu7c5lprm2XtJu1y5t+sgyanfyDEPOxFAoPoE0jwDuaL6Ls+VE0EgLy9PCgsLJTs7W/Lz8xOhynGpY0lJiXGdOnWYJhwXcC6CQIQCjNUI4TgNgTgLMFbjDM7lEIhQgLEaIRynIRBnAcZqnMG5HAIRCjBWI4SznTZl0yp5aP43Mnf7RtuRX1+e2aajjOg2WDpntfh1J1sIhCDAOA0BKUpZmFkVJUiKST0BglSp1+e0ODEFGKuJ2W/UOvUEGKup1+e0ODEFGKuJ2W/UOvUEGKup1+e0ODEFGKtV67e52zYYQaophasDFtS/eY6M7H6K9GrWNmAeDiAQTIBxGkwnuscIVkXXk9JSSICoegp1Nk1NaAHGakJ3H5VPIQHGagp1Nk1NaAHGakJ3H5VPIQHGagp1Nk1NaAHGamTdt2zXVnl0wWQZv3ZJwAJyG7eUP3c7WQa3bC9paboAIAmByAQYp5G5RXIWwapI1DgHAY9AQUGB4ZCbm4sHAgi4WICx6uLOoWoIWAQYqxYMNhFwsQBj1cWdQ9UQsAgwVi0YbCLgYgHGanids664SJ5cOEXeXjVfygM82aZ9g8bGcn9DD+8s6Wnp4V2A3Ag4CDBOHVBitItgVYxgKRYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgagLbSorl2cXfyevLZsuB8jLHwlrVbSB3HzdILmvfTTLSazjmYScCCLhbgGCVu/uH2iGAAAIIIIAAAggggAACCCCAAAIIIIAAAiknsOdgqby8ZLq8+NM02XvogGP7G9XKlNu7DJDrju4tmTUzHPOwEwEEEkOAYFVi9BO1RAABBBBAAAEEEEAAAQQQQAABBBBAAAEEkl6gpOygjF02R572zKbaXrrPsb31PIGpG47pK8M695OGteo45mEnAggklgDBqsTqL2qLAAIIIIAAAggggAACCCCAAAIIIIAAAggkncAhzxJ/761eKI8v+K9s2LfbsX0Z6elydYeecmeXgdI8s75jHnYigEBiChCsSsx+o9YIIIAAAggggAACCCCAAAIIIIAAAggggEDCC1RUVMin65bII/Mny7Ld2xzbk+bZe0m7XBnedZAcXr+RYx52IoBAYgukeT4MKhK7CdQ+1gJ5eXlSWFgo2dnZkp+fH+vLUT4CCCCAAAIIIIAAAggggAACCCCAAAIIpIDAlE2r5KH538jc7RsDtvbMNh1lRLfB0jmrRcA8HEAAgcQXYGZV4vchLUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBJGYO62DUaQakrh6oB17t88R0Z2P0V6NWsbMA8HEEAgeQQIViVPX9KSOAsUFRUZV8zKyorzlbkcAgiEI8BYDUeLvAhUnwBjtfrsuTIC4QgwVsPRIi8C1SfAWK0+e66MQDgCqThWl+3aKo8umCzj1y4JSJXbuKX8udvJMrhle0lL0wUASQhUn0AqjtPq0iZYVV3yXDfhBdasWWO0gWBVwnclDUhyAcZqkncwzUsaAcZq0nQlDUlyAcZqkncwzUsaAcZq0nQlDUlygVQaq+uKi+TJhVPk7VXzpTzAU2naN2hsLPc39PDOkp6WnuS9T/MSRSCVxml19wnBquruAa6PAAIIIIAAAggggAACCCCAAAIIIIAAAggkocC2kmJ5ZvF3MmbZbDlQXubYwlZ1G8jdxw2Sy9p3k4z0Go552IkAAskvQLAq+fuYFiKAAAIIIIAAAggggAACCCCAAAIIIIAAAnET2HOwVF5eMl1e/Gma7D10wPG6jWplyu1dBsh1R/eWzJoZjnnYiQACqSNAsCp1+pqWIoAAAggggAACCCCAAAIIIIAAAggggAACMRMoKTsoY5fNkac9s6m2l+5zvE49T2DqhmP6yrDO/aRhrTqOediJAAKpJ0CwKvX6nBYjgAACCCCAAAIIIIAAAggggAACCCCAAAJREzjkWeLvvdUL5fEF/5UN+3Y7lpuRni5Xd+gpd3YZKM0z6zvmYScCCKSuAMGq1O17Wl5FgTp1+OZHFQk5HYG4CDBW48LMRRCosgBjtcqEFIBAXAQYq3Fh5iIIVFmAsVplQgpAIC4CyTBWKyoq5NN1S+SR+ZNl2e5tjm5pnr2XtMuV4V0HyeH1GznmYScCbhVIhnHqVlt7vdI8HygV9p28RsAqkJeXJ4WFhZKdnS35+fnWQ2wjgAACCCCAAAIIIIAAAggggAACCCCAQAoKTNm0Sh6a/43M3b4xYOvPbNNRRnQbLJ2zWgTMwwEEEEBABZhZxfsAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAICSBuds2GEGqKYWrA+Yf0OIIeaDbydKrWduAeTiAAAIIWAUIVlk12EYgDAGdbaZJZ5yREEDAvQKMVff2DTVDwCrAWLVqsI2AewUYq+7tG2qGgFWAsWrVYBsB9wok2lgt2LVVHvUs9zfBs+xfoJTbuKURpDqpZXtJS9MFAEkIJLZAoo3TRNYmWJXIvUfdq1Vg8+bNxvUJVlVrN3BxBCoVYKxWSkQGBFwhwFh1RTdQCQQqFWCsVkpEBgRcIcBYdUU3UAkEKhVIlLG6rrhIRi38Vt5ZtUDKAzxR5qgGTYzl/s49vJOkp6VX2nYyIJAoAokyThPFM1g9CVYF0+EYAggggAACCCCAAAIIIIAAAggggAACCCCQggLbSorlmcXfyZhls+VAeZmjQKu6DeSe4wbJZe27Sc30Go552IkAAgiEIkCwKhQl8iCAAAIIIIAAAggggAACCCCAAAIIIIAAAikgsOdgqby8ZLq8+NM02XvogGOLG9XKlNu7DJDrju4tmTUzHPOwEwEEEAhHgGBVOFrkRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEklCgpOygjF02R572zKbaXrrPsYX1PIGpG47pK8M695OGteo45mEnAgggEIkAwapI1DgHAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIAoFDniX+3vU8j+oJz3OpNuzb7diijPR0ubpDT7mzy0BpnlnfMQ87EUAAgaoIEKyqih7nprRAVlZWSrefxiOQKAKM1UTpKeqZ6gKM1VR/B9D+RBFgrCZKT1HPVBdgrKb6O4D2J4pAdY/ViooK+XTdEnlk/mRZtnubI1uaZ+8l7XJleNdBcnj9Ro552IlAMgtU9zhNZlt729I8H0oV9p28RsAqkJeXJ4WFhZKdnS35+fnWQ2wjgAACCCCAAAIIIIAAAggggAACCCCAQIIJTNm0Sh6a/43M3b4xYM3PbNNRRnQbLJ2zWgTMwwEEEEAgWgLMrIqWJOUggAACCCCAAAIIIIAAAggggAACCCCAAAIuFpi7bYMRpJpSuDpgLQe0OEIe6Hay9GrWNmAeDiCAAALRFiBYFW1RyksZgTVr1hhtzcnJSZk201AEElGAsZqIvUadU1GAsZqKvU6bE1GAsZqIvUadU1GAsZqKvU6bE1EgnmO1YNdWedSz3N8Ez7J/gVJu45ZGkOqklu0lLU0XACQhgEA8x2mqaxOsSvV3AO2PWKCoqMg4l2BVxISciEBcBBircWHmIghUWYCxWmVCCkAgLgKM1bgwcxEEqizAWK0yIQUgEBeBeIzVdcVFMmrht/LOqgVSHuBpMEc1aGIs93fu4Z0kPS09Lm3nIggkikA8xmmiWMS6ngSrYi1M+QgggAACCCCAAAIIIIAAAggggAACCCCAQBwFtpUUyzOLv5Mxy2bLgfIyxyu3qttA7jlukFzWvpvUTK/hmIedCCCAQLwECFbFS5rrIIAAAggggAACCCCAAAIIIIAAAggggAACMRTYfaBEXl4yXV7y/Nt76IDjlRrVypTbuwyQ647uLZk1MxzzsBMBBBCItwDBqniLcz0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCKAiVlB2XssjnytGc21fbSfY4l1/MEpm7s1Fdu6tRPGtaq45iHnQgggEB1CRCsqi55rosAAggggAACCCCAAAIIIIAAAggggAACCFRB4JBnib93Pc+jesLzXKoN+3Y7lpSRni7XdOgld3Q5QZpn1nfMw04EEECgugUIVlV3D3D9hBVo0aJFwtadiiOQSgKM1VTqbdqayAKM1UTuPeqeSgKM1VTqbdqayAKM1UTuPeqeSgJVGasVFRXy6bol8sj8ybJs9zZHtjTP3kva5crwroPk8PqNHPOwEwEEggtUZZwGL5mjdoE0zwdbhX0nrxGwCuTl5UlhYaFkZ2dLfn6+9RDbCCCAAAIIIIAAAggggAACCCCAAAIIIBBHgSmbVslD87+Ruds3BrzqmW06yohug6VzFl+2DojEAQQQcJUAM6tc1R1UBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8BeYu22DEaSaUrja/+D/9gxocYQ80O1k6dWsbcA8HEAAAQTcKECwyo29Qp0SQqCgoMCoZ8eOHROivlQSgVQVYKymas/T7kQTYKwmWo9R31QVYKymas/T7kQTYKwmWo9R31QTWFq0RcYunyMzNqySfWWHpEn9BpLbuKVc3aGnHJPV3I+jYNdWedSz3N8Ez7J/gZKer0Gqk1q2l7Q0XQCQhAAC0RDgd2o0FEMrg2BVaE7kQsBPoKSkxG8fOxBAwH0CjFX39Qk1QsBJgLHqpMI+BNwnwFh1X59QIwScBBirTirsQ6D6BXRm1ANzv5YftqzxqcyK/UUyc+s6ea1glvRvniMP9ThVejRtLeuKi2TUwm/lnVULpDzAk1yOatDEWO7v3MM7SXpauk+5vEAAgaoL8Du16oahlkCwKlQp8iGAAAIIIIAAAggggAACCCCAAAIIIIAAAhEITNywXK6a+r5nJtXBoGdrIOvsif/0zJBqJ99sXCEHyssc87eq20DuOW6QXNa+m9RMr+GYh50IIIBAIgkQrEqk3qKuCCCAAAIIIIAAAggggAACCCCAAAIIIJBQAjqj6sqp78l+z5J/oaT9noDW5+t/efyEPX+jWplyR5cT5Nqje0lmzQz7YV4jgAACCStAsCphu46KI4AAAggggAACCCCAAAIIIIAAAggggIDbBXTpv1ADVYHaUs8TmLqxU1+5qVM/aVirTqBs7EcAAQQSVoBgVcJ2HRVHAAEEEEAAAQQQQAABBBBAAAEEEEAAATcLLC3a4veMqnDqWzMtzTOLqrcxm6p5Zv1wTiUvAgggkFACBKsSqruorJsEcnJy3FQd6oIAAgEEGKsBYNiNgMsEGKsu6xCqg0AAAcZqABh2I+AyAcaqyzqE6qS0wNjlc6rU/ouO7CpP9DqjSmVwMgIIRC7A79TI7cI9k2BVuGLkR+B/AllZWVgggEACCDBWE6CTqCICHgHGKm8DBBJDgLGaGP1ELRFgrPIeQMA9Agt2bKpSZVbv2Vml8zkZAQSqJsDv1Kr5hXN2ejiZyYsAAggggAACCCCAAAIIIIAAAggggAACCCAQmsDegwdCyxgg195DpQGOsBsBBBBILgGCVcnVn7QmjgILFiwQ/UdCAAF3CzBW3d0/1A4BU4CxakrwEwF3CzBW3d0/1A4BU4CxakrwE4HqF6ifUatKlahfs3aVzudkBBComgC/U6vmF87ZBKvC0SIvAggggAACCCCAAAIIIIAAAggggAACCCAQgkB5RbnUSKva7deujbNDuBJZEEAAgcQX4JlVid+HtAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCSwbNdWuWXGeJm5dV2VanXN0b2qdD4nI4AAAokiQLAqUXqKeiKAAAIIIIAAAggggAACCCCAAAIIIICAqwUOlpfJ8z/9IE8unCIHPNtVSQNaHCEdGzarShGciwACCCSMAMGqhOkqKooAAggggAACCCCAAAIIIIAAAggggAACbhVYuGOTDJs+ThbtLKxyFevWyJC/dB9S5XIoAAEEEEgUgaotmpooraSeCCCAAAIIIIAAAggggAACCCCAAAIIIIBADARKyg7KQ/O+kcFfvOYYqDqyfiMZ2e1k0QBUKEnz/XPgxdKjaetQspMHAQQQSAoBZlYlRTfSiOoQ6NixY3VclmsigECYAozVMMHIjkA1CTBWqwmeyyIQpgBjNUwwsiNQTQKM1WqC57IpKTBjy1rPs6nGyfLd2/3an56WJjce01eG5w6SujVrycDsdjJy3kT5fvPPfnnNHbr0n86oIlBlivATgeoV4Hdq/PzTKjwpfpfjSokokJeXJ4WFhZKdnS35+fmJ2ATqjAACCCCAAAIIIIAAAggggAACCCCAQNQE9h4slYfmT5LXC2aJ083VTlnN5YU+50pe0zZ+11xatEXGLp8jC3cUyt5DpVK/Zm3p2jhbrjm6F8+o8tNiBwIIpIoAM6tSpadpZ9QFSkpKjDLr1KkT9bIpEAEEoifAWI2eJSUhEEsBxmosdSkbgegJMFajZ0lJCMRSgLEaS13KRkBk8sYVctvMCbKueJcfR0Z6utzZZaDcfuwAqVXD+dbrMZ5A1qheZwpj1Y+PHQi4ToBxGr8ucf7EjN/1uRICCStQUFBg1D03Nzdh20DFEUgFAcZqKvQybUwGAcZqMvQibUgFAcZqKvQybUwGAcZqMvQibXCjwM7SfXJ//tfy9qr5jtXLa9Janu97rnTOauF43L6TsWoX4TUC7hNgnMavTwhWxc+aKyGAAAIIIIAAAggggAACCCCAAAIIIIBAAgpMWPuT3DXrM9lSUuxX+0zPDKoRuYPl+mP6SA3PzCoSAggggED4AgSrwjfjDAQQQAABBBBAAAEEEEAAAQQQQAABBBBIAYHN+/fI3bM/l/Frlzi2dkCLI2R0n3OkXYMmjsfZiQACCCAQmgDBqtCcyIUAAggggAACCCCAAAIIIIAAAggggAACKSJQUVEh765aIPflfylFB355brm16Q0yastDPYbIFUf1kPQ0ZlNZbdhGAAEEIhEgWBWJGucggAACCCCAAAIIIIAAAggggAACCCCAQFIKrN1bJHfMnCCTNq10bN9prY+Wp3ufJa3rNXQ8zk4EEEAAgfAFCFaFb8YZCCCAAAIIIIAAAggggAACCCCAAAIIIJBkAuUV5TJm2Rx5aN43svfQAb/WNaldV57oeYb85ogukpaW5necHQgggAACkQukeaa0VkR+OmemgkBeXp4UFhZKdna25Ofnp0KTaSMCCCCAAAIIIIAAAggggAACCCCAQAoJLN+1TW6ZMV5mbF3r2GoNUGmgqmmdeo7H2YkAAgggUDUBZlZVzY+zEUAAAQQQQAABBBBAAAEEEEAAAQQQQCBBBQ6Vl8kLP02TUQu/lVLPtj21qttAnup9tpzRpqP9EK8RQAABBKIoQLAqipgUlVoCRUVFRoOzsrJSq+G0FoEEE2CsJliHUd2UFWCspmzX0/AEE2CsJliHUd2UFWCspmzX0/AwBRbt2CQ3e2ZTLfD8dEpXHtVD/tLjVGlYq47T4SrvY6xWmZACEIi5AOM05sTeCxCs8lKwgUB4AmvWrDFOIFgVnhu5EYi3AGM13uJcD4HIBBirkblxFgLxFmCsxluc6yEQmQBjNTI3zkodgZKyg/LXRVNl9I/fS5nDE1KOqN9IRvc5RwZmt4spCmM1prwUjkBUBBinUWEMqRCCVSExkQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEl1gpueZVLdMHy/Ldm/za0p6WprccEwfuS/3JKlbs5bfcXYggAACCMROgGBV7GwpGQEEEEAAAQQQQAABBBBAAAEEEEAAAQRcILD3YKk8Mn+yvFYwUyoc6nNMw2byQt+h0rNpG4ej7EIAAQQQiLUAwapYC1M+AggggAACCCCAAAIIIIAAAggggAACCFSbwH83rZTbZkyQtcVFfnWomZYud3Y5QW73/Ktdg1ulfkDsQAABBOIkwCdwnKC5DAIIIIAAAggggAACCCCAAAIIIIAAAgjET6CodL/cP/creWvlfMeLdm/cyphNdWyjFo7H2YkAAgggED8BglXxs+ZKSSZQp06dJGsRzUEgOQUYq8nZr7Qq+QQYq8nXp7QoOQUYq8nZr7Qq+QQYq8nXp7QofIFP1y6Ru2Z9JptL9vqdXMczg2pE7mC5/pjjpWZ6Db/j8drBWI2XNNdBIHIBxmnkduGemVbhSeGeRP7UEsjLy5PCwkLJzs6W/Pz81Go8rUUAAQQQQAABBBBAAAEEEEAAAQQQSBiBLfv3yj2zP5dP1v7kWOf+zXPk+b7nSrsGTRyPsxMBBBBAoHoEmFlVPe5cFQEEEEAAAQQQQAABBBBAAAEEEEAAAQSiJKDfx39v9QIZPudLKTpQ4ldqg4xa8pfup8qVHXpIuuc5VSQEEEAAAXcJEKxyV39QmwQS0NlmmnTGGQkBBNwrwFh1b99QMwSsAoxVqwbbCLhXgLHq3r6hZghYBRirVg22U0FgXXGR3D7zU5m0cYVjc09t3UGe6X22tK7X0PF4de1krFaXPNdFIHQBxmnoVlXNSbCqqoKcn7ICmzdvNtpOsCpl3wI0PEEEGKsJ0lFUM+UFGKsp/xYAIEEEGKsJ0lFUM+UFGKsp/xZIGYDyinL5x7I58pd538jeQwf82t24dqY80fMMufCI4yQtLc3veHXvYKxWdw9wfQQqF2CcVm4UrRwEq6IlSTkIIIAAAggggAACCCCAAAIIIIAAAgggEBeBFbu3yS0zxsv0LWsdr3dBThd5otfp0qxOfcfj7EQAAQQQcJcAwSp39Qe1QQABBBBAAAEEEEAAAQQQQAABBBBAAIEAAofKy+TFJdPliQX/lVLPtj21zGwgT/U+S85se4z9EK8RQAABBFwsQLDKxZ1D1RBAAAEEEEAAAQQQQAABBBBAAAEEEEDgF4FFOwvl5unjZMGOTY4kVxzVQx7qMUQa1sp0PM5OBBBAAAH3ChCscm/fUDMEEEAAAQQQQAABBBBAAAEEEEAAAQRSXqC07JA8tWiqPPfj93LI85wqe8qpnyWjjz9XTmzZzn6I1wgggAACCSJAsCpBOopquk8gKyvLfZWiRggg4CfAWPUjYQcCrhRgrLqyW6gUAn4CjFU/EnYg4EoBxqoru4VKRSgwa+s6z7OpxknBrm1+JaR59txwTB+5r9tgqVezlt9xt+9grLq9h6gfAiKM0/i9C9IqPCl+l+NKiSiQl5cnhYWFkp2dLfn5+YnYBOqMAAIIIIAAAggggAACCCCAAAIIIJBAAsWHDsgj8yfJq0tnitPNy2MaNpPn+5wrvZq1TaBWUVUEEEAAgUACzKwKJMN+BBBAAAEEEEAAAQQQQAABBBBAAAEEEIi7wLebVsqtMybI2uIiv2vXTEuX27sMkDu7DJTaNbi16QfEDgQQQCBBBfhET9COo9rVL7BmzRqjEjk5OdVfGWqAAAIBBRirAWk4gICrBBirruoOKoNAQAHGakAaDiDgKgHGqqu6g8qEIbDrwH65P/9r+ffKeY5ndWvcUl7oO1S6NMp2PJ5oOxmridZj1DcVBRin8ev19PhdiishkFwCRUVFov9ICCDgbgHGqrv7h9ohYAowVk0JfiLgbgHGqrv7h9ohYAowVk0JfiaSwGfrlkifCS85BqrqeGZQ/aX7EJl4+nVJE6jSvmGsJtI7lLqmqgDjNH49z8wqm/WOHTtk9uzZMm/ePFm6dKlo5HTz5s1SXFwsNWvWNB6o1rFjR+nbt69ceOGF0rJlS1sJwV9+99138sEHH8jcuXON50DVrl3bKOPEE0+USy+9VI466qjgBTgcXb58ubz77rsyZcoU2bRpk5SWlhrPl9JnTWkdTzjhBIez2IUAAggggAACCCCAAAIIIIAAAggggED1CmzZv1funfOF/GfNj44V6dc8R0b3OUeOOqyp43F2IoAAAggkhwDBKls/3nbbbTJp0iTb3l9eHjp0yAgwFRYWGoGhZ599VoYNGyZ6Tnp68Elqe/bskbvvvlvGjx/vU/b+/fuNb1EsWbJExowZI3feeafcfPPNPnmCvRg9erRoPQ4ePOiTbfXq1aL/PvzwQznvvPNk1KhRUr9+fZ88vEAAAQQQQAABBBBAAAEEEEAAAQQQQKA6BCoqKuT91Qtl+JwvZadn+T97apBRSx70zKa6qkOepHueU0VCAAEEEEhuAYJVQfq3cePG0qFDB2ndurXUq1dPNLD0888/y/z580UDVzqD6emnnzZmX2nQKFDSQNK1114rP/zwgzfLMcccI126dDHKmDVrljF7S/M98cQTRtm33367N2+gjb/+9a/y3HPPeQ+3aNFCevfuLTpba9GiRVJQUGAc++STT2Tnzp3yr3/9y5gd5j2BDQQQQAABBBBAAAEEEEAAAQQQQAABBOIssL54l9w+c4J8s3GF45VPaXWUPHv8OdKmXkPH4+xEAAEEEEg+AYJVtj7t16+fDBkyRAYMGCBHHnmk7egvL7du3SoPPvigaBBIk85e0nPOPvts47X9PxpQMgNVderUkWeeeUaGDh3qzXbgwAF58skn5ZVXXjH2aQCsT58+xlKD3ky2DV1O0BqouuGGG4yZW7Vq1fLm1PrpTK2SkhJjJtgLL7wgoQTBvAWwgQACCCCAAAIIIIAAAggggAACCCCAQJQEyivKZezyfPnLvImy5+ABv1Ib1cqUx3ueLhcf2VXS0tL8jrMDAQQQQCB5BdI8U24rkrd5sWuZsl1yySXeIJQ+F0qfG2VP27ZtM4JO+/btMw7pzKnLL7/cns14rQEnc5lAfd6Uue2U+ayzzjJmeOkxDXy9/PLLTtmM2VTDhw83jukygNOnTxedMRZO0rro0ofZ2dmSn58fzqlJnVdNNKkLCQEE3CvAWHVv31AzBKwCjFWrBtsIuFeAserevqFmCFgFGKtWDbbdIrBy93a5ZcZ4mbZljWOVzs85Vkb1OkOa1Umdx1gwVh3fCuxEwFUCjNP4dQcLvkZord/u0GCVmRYvXmxu+vz84IMPxAxUtWvXTn7/+9/7HLe+uP/++73PvtKgUKAydRlC/adJn5U1YsQIY9vpPxoYM2eI7d2715gF5pSPfeELaJCKQFX4bpyBQLwFGKvxFud6CEQmwFiNzI2zEIi3AGM13uJcD4HIBBirkblxVmwEDpWXyfM//iADPnvFMVCVnVlf/n3iJfKPEy5KqUCVajNWY/Oeo1QEoinAOI2mZvCyCFYF9wl6tEmTJt7jxcXF3m3rxpdfful9efHFFwedwqzPxurfv783/xdffOHdtm5Yy9QZXXpeoKRBtYsuush72HqudycbCCCAAAIIIIAAAggggAACCCCAAAIIRFlg8c5COfXLMTLSs+xfSdkhv9Ivb99dZpxzk5zVtpPfMXYggAACCKSWAMGqKvT3smXLvGe3adPGu21u6LOi5s6da74M+gwqM5M+M8tM5nOuzNfmz2nTppmbYZc5Z84cKS0t9Z7PRuQCBQUFov9ICCDgbgHGqrv7h9ohYAowVk0JfiLgbgHGqrv7h9ohYAowVk0JflaXQKknMPXYgsly0uevybwdG/2qcXi9LPnPyZfL832HSkPPc6pSNTFWU7XnaXciCTBO49dbNeN3qeS6kq5V+eqrr3obpc+QsqeVK1dKeXm5sVtnOHXp0sWexe/1cccd5923fPly77Z1Y8WKFd6X1vzenbYN63XLyspk1apV0qlTJ1suXoYroMFIEgIIuF+Aser+PqKGCKgAY5X3AQKJIcBYTYx+opYIMFZ5D1SnwOyt64xnUy3dtdWvGmmePdcf00dGdBss9WrW8jueajsYq6nW47Q3EQUYp/HrNYJVYVjv379f1q1bJ5MnT5ZXXnlFtm3bZpzdoUMHGTZsmF9JGqwyU9OmTaVOnTrmy4A/rUv6FRUVyfbt28W63KBec9euXd7znWZ0eQ/+byMzM9MoQ8vSpMGuSIJVGnjbuNH/2zD/u4zxo1WrVtaXbCOAAAIIIIAAAggggAACCCCAAAIIpIBA8aED8th8zz2zpTOkwqG9HRs2lef7DJXezdo6HGUXAggggECqCxCsCvIOmDVrlpx//vlBcogMHjxYXnzxRalfv75fvp07d3r3abAqlNSsWTOfbFqGNVhlLVMzhlpu8+bNjcCXnqNBsEjSli1bpFevXkFP/fzzz/2O5+bmGvv0umvWrPE7rkG8jh07Gvt1xtrmzZv98mRlZUlOTo6xX8twakOLFi2MB1NqJp2e6RT11jK0LE0LFiwwftr/o3XROun5Wo5TMtukSyo6lZPIbUrGfqJNvu9it46nWPWTGajXzxd9KKameH1GxKpNZo9Wx+cebTL1f/mZauPJbH0s3nvmWLX/XjV/5/LeM/V57/FZXvW/Yasynvbs2SMNGjQw3ohu+ru8Km3is5y/jXw/YcX4/9NQ/5/Qre89++/VcP4/161tSsZ+SqY2/eOHifLU+nmy6eA+e7OkZlq6DOvYR86s0VRqbdwhCzz/rCnV/97bt2+f470lfj/x+8k6TnSbz/Lqubds/Z0a7XvLyfA71/wMt79fI3lNsCoSNc85+gvjsccek6FDhwYsQX/ZmEn/0A0l2fNZy9Dzi4uLfYqx5/c5aHlhzWcvw5KNTQQQQAABBBBAAAEEEEAAAQQQQAABBEIS2HVgvzwwd6L8a/Wvz2y3nti1Uba82O886ZCZFfALwdb8bCOAAAIIpK5AWoUnpW7zg7dcvyn42muvGZmUae/evcbznhYtWiSHDh0y9vfr10+eeOIJad++vV9hzz77rDz11FPG/uOPP14+/vhjvzz2HbrUXtu2v06H/s9//iO9e/f2Zps5c6ZccMEF3te6LGF6err3daANPUfP1fSnP/1JbrvtNmM7lP/k5eWJzkjQ2VmfffZZ0FNSaRlA85vf0YweB8XlIAIIRCTAWI2IjZMQiLsAYzXu5FwQgYgEGKsRsXESAnEXYKzGnTwlL/j5uqVy16zPZNP+PX7tr51eQ4bnniQ3deorNT3bJGcBxqqzC3sRcJMA4zR+vcHMqiDWOq3v0Ucf9cuhgZtRo0bJ+++/L9OmTZNzzz1XPvjgA+ncubNPXutspgMHDvgcC/RCl5WzJmsZur927drWw6Ll2vP4ZPjfC+v1Q8nvVIYGxVIpGOVkYN1nLkto3cc2Agi4T4Cx6r4+oUYIOAkwVp1U2IeA+wQYq+7rE2qEgJMAY9VJhX3REthaslfumf2F/GfNj45F9m1+uOfZVOfKUYeF9kgMx0JSZCdjNUU6mmYmtADjNH7dR7AqAmtdJ19nTela7WPGjBFdW/LGG2+USZMmSY0av35bpG7dut7SnZ6f5D1o2bDns5ah2erVq2fJLcZzlUIJPlnLtZfhUyAvQhbQpSBJCCDgfgHGqvv7iBoioAKMVd4HCCSGAGM1MfqJWiLAWOU9EAsBXXXog58XyfA5X8iO0v1+l6hfs5aM7H6KXHN0T0n3PKeKVLkAY7VyI3IgUN0CjNP49QC/OapgPXz4cO/DhZcvXy6TJ0/2Ka1Ro0be19u2bfNuB9vYunWrz2FrGXrA/jrUcrds2eItlwHmpWADAQQQQAABBBBAAAEEEEAAAQQQQKASgfXFu+S3374t//fDx46BqpNbHSXTzrlRruvYm0BVJZYcRgABBBBwFiBY5ewS0t7MzEzp2bOnN++cOXO827phfY6VBpWss5t8MlpebNiwwftKg0pNmjTxvtaNpk2bSsOGDb371q9f790OtKHX3b59u/fwUUcd5d1mI3IBXa/UXLM08lI4EwEEYi3AWI21MOUjEB0Bxmp0HCkFgVgLMFZjLUz5CERHgLEaHUdKESmvKJexy+ZIv09fkq83LPcjaVQrU17pd758cNLvpG29LL/j7AguwFgN7sNRBNwgwDiNXy8QrKqitTVwtHPnTp/SNFilz3nSpFOlf/zReS1f60mLFi3yvuzQoYN327phDTYtXrzYeshx21qmLlPYrl07x3zsRAABBBBAAAEEEEAAAQQQQAABBBBAQAVW7t4u5058Q+6Y9ansOej/LPbzDu8sM865SX7bLlfS0tJAQwABBBBAoEoCBKuqxCeyefNmbwn25fX0WVI9evTwHp8+fbp3O9DGjBkzvIf69+/v3bZu9OvXz/sy3DJ1Jljt2rW957OBAAIIIIAAAggggAACCCCAAAIIIICAKXCovExe+OkHGfDZK/LDljXmbu/PFnXqy5sDL5GxAy+W5pn1vfvZQAABBBBAoCoCBKuqoLdjxw6ZO3eutwTrjCdz5+mnn25uyvvvv+/ddtrQJQC///577yHrud6dng3r/u+++042btxoPey3bb3uaaed5necHQgggAACCCCAAAIIIIAAAggggAACCPy4c7Oc9tUYeWDuRCkpO+QH8rv23YzZVGcf3snvGDsQQAABBBCoigDBKouefRk/yyG/zfLycrn//vultLTUOKazlU455RS/fBdddJHUrVvX2L9y5Up5++23/fKYOx577DEpKyszXubl5clxxx1nHvL52a1bN9F/mjT/448/bmw7/eff//63rFq1yjhUv3590fqQEEAAAQQQQAABBBBAAAEEEEAAAQQQMAVKPYGpxxf8VwZ9/qrM3e7/pejDPc+j+vjky+XFvudJVu1M8zR+IoAAAgggEDUBglUWyg8//FDOPPNM+eCDD2TPnj2WI76bP/30k1x++eUybtw474Hrr79eGjdu7H1tbjRt2lT++Mc/mi/lz3/+s4wfP977WjcOHjwoGqj65JNPvPuHDx/u3XbauPfee727P/74Y+N8Lcea9DojR4707gpUR28GNhBAAAEEEEAAAQQQQAABBBBAAAEEUkpgzrb1RpDqyUVT5FBFuU/b9UlU/9fxePnh7BvkpJbtfY7xAgEEEEAAgWgKpFV4UjQLTOSy/v73v8uDDz5oNKFmzZqiy/q1a9dO9FlU+qBInXmlgaqff/7Zp5ka4HrllVdEz3FKGkT63e9+Jz/88IP3cKdOnaRLly7GzKyZM2f6PPvqrrvukttvv92bN9DGk08+KaNHj/Yezs7Olt69exvPpFq0aJEsXbrUe2zgwIHy5ptvBqyjN6PDhs7yKiwsFC0/Pz/fIUdq7iopKTEars8mIyGAgHsFGKvu7RtqhoBVgLFq1WAbAfcKMFbd2zfUDAGrAGPVqsF2IIF9hw7IY57ZVK8snSHlDrcHjz6sqTzf91w5vtnhgYpgfxUFGKtVBOR0BOIgwDiNA/L/LkGwymL9xhtvyH333WfZE3xTl9W744475LrrrpMaNWoEzbx79265++67ZcKECQHzZWRkGOXdcsstAfNYD2icUYNVzz33nDE7y3rMuj106FAZNWqUNGjQwLo75G2CVSFTkREBBBBAAAEEEEAAAQQQQAABBBBwvcB3havllhnj5ee9O/3qWsPzhe3bjh0gdx03UOrUyPA7zg4EEEAAAQRiIUCwyqaqz5X67rvvZN68ebJs2TLZsGGDaKBJkwanWrRoIZ07d5YTTjhBzjrrLKlXr56thOAvp06daiwzOHfuXGM2lQaoWrVqJSeeeKJceuml0qFDh+AFOBxdvny58SwsLXvjxo1G4Err2aNHD+MZVTqrqiqJYJWzHlF1Zxf2IuA2Acaq23qE+iDgLMBYdXZhLwJuE2Csuq1HqA8CzgKMVWcX9orsOlAiI+d+LW+smOvI0bVRtue5VEPluMYtHY+zM7oCjNXoelIaArEQYJzGQtW5TIJVzi7stQgQrLJgWDYXLFhgvMrNzbXsZRMBBNwmwFh1W49QHwScBRirzi7sRcBtAoxVt/UI9UHAWYCx6uyS6nu/WF8gd878VDbt939Oe+30GnJP10Fyc+d+UtOzTYqPAGM1Ps5cBYGqCDBOq6IX3rnOD1kKrwxyI4AAAggggAACCCCAAAIIIIAAAggggIALBbaVFMu9c76Qj35e7Fi7Pp5nUj3f51zp0LCp43F2IoAAAgggEA8BglXxUOYaCCCAAAIIIIAAAggggAACCCCAAAIIxFFAn3WuASoNVG0v3ed35fo1a8kD3U+Ra4/uKelp6X7H2YEAAggggEA8BQhWxVObayGAAAIIIIAAAggggAACCCCAAAIIIBBjgQ3Fu+TOWZ/JVxuWOV7p5Jbt5Znjz5HD62c5HmcnAggggAAC8RYgWBVvca6HAAIIIIAAAggggAACCCCAAAIIIIBADATKK8rlXyvmygNzJ8qeg6V+V8iqVUceyztdftsuV9LS0vyOswMBBBBAAIHqEiBYVV3yXBcBBBBAAAEEEEAAAQQQQAABBBBAAIEoCazes0NumTFevt/8s2OJQw/vLKN6nSEtMhs4HmcnAggggAAC1SmQ5lm/tqI6K8C13S+Ql5cnhYWFkp2dLfn5+e6vMDVEAAEEEEAAAQQQQAABBBBAAAEEUkSgrLxc/rZ0hjy6YLLsLzvk1+rmderJU73PknM8wSoSAggggAACbhVgZpVbe4Z6IYAAAggggAACCCCAAAIIIIAAAgggEETgp6LNcsv08ZK/fYNjrsvadZNH8k6VRrXrOh5nJwIIIIAAAm4RIFjllp6gHgknUFRUZNQ5Kysr4epOhRFIJQHGair1Nm1NZAHGaiL3HnVPJQHGair1Nm1NZAHGaiL3Xmh1P+CZQfXsj9/L04unykHPzCp7aluvoTx3/DkyuNVR9kO8dpEAY9VFnUFVEAggwDgNABOD3QSrYoBKkakhsGbNGqOhBKtSo79pZeIKMFYTt++oeWoJMFZTq79pbeIKMFYTt++oeWoJMFaTu7/nbtsgw2aMkyVFW/wamubZ84eOveXP3U6W+hm1/Y6zw10CjFV39Qe1QcBJgHHqpBKbfQSrYuNKqQgggAACCCCAAAIIIIAAAggggAACCERNYN+hA/L4gv/Ky57nU5U7PIK+w2FN5Pk+Q6VP88Ojdk0KQgABBBBAIF4CBKviJc11EEAAAQQQQAABBBBAAAEEEEAAAQQQiEDg+8LVcsuM8bJ6706/s2ukpcktnfvL3V1PlDo1MvyOswMBBBBAAIFEECBYlQi9RB0RQAABBBBAAAEEEEAAAQQQQAABBFJOYNeBEhk5d6K8sSLfse3HNcqWF/sOla6NWzoeZycCCCCAAAKJIkCwKlF6inoigAACCCCAAAIIIIAAAggggAACCKSMwFfrl8kdsybIxn17/NpcO72GZybVILm5cz/J8GyTEEAAAQQQSHQBglWJ3oPUv9oE6tSpU23X5sIIIBC6AGM1dCtyIlCdAozV6tTn2giELsBYDd2KnAhUpwBjtTr1q37tbSXFMnzOl/Lhz4scC+vdrK280OdcObphM8fj7EwcAcZq4vQVNU1dAcZp/Po+rcKT4nc5rpSIAnl5eVJYWCjZ2dmSn+887TwR20WdEUAAAQQQQAABBBBAAAEEEEAAAbcI6C26j9cslntmfyHbS/f5VatezQx5oPspct3RvSQ9Ld3vODsQQAABBBBIZAFmViVy71F3BBBAAAEEEEAAAQQQQAABBBBAAIGEF9i4b7fcNesz+WJ9gWNbTmrZTp47/hw5vH4jx+PsRAABBBBAINEFCFYleg9S/2oT0NlmmnTGGQkBBNwrwFh1b99QMwSsAoxVqwbbCLhXgLHq3r6hZghYBRirVg13b+tsqn+tmCt/nvu17DlY6lfZhrXqyGN5p8ml7bpJWlqa33F2JLYAYzWx+4/ap4YA4zR+/UywKn7WXCnJBDZv3my0iGBVknUszUk6AcZq0nUpDUpSAcZqknYszUo6AcZq0nUpDUpSAcZqYnTs6j075NYZ4+W7zT87Vvictp3kr73PlBaZDRyPszPxBRirid+HtCD5BRin8etjglXxs+ZKCCCAAAIIIIAAAggggAACCCCAAAIpLlBWXi6vFsyUR+ZPkv1lh/w0mtep5wlSnSXnHt7Z7xg7EEAAAQQQSFYBglXJ2rO0CwEEEEAAAQQQQAABBBBAAAEEEEDAVQJLirbILTPGyZxtGxzrdWm7XHnUs+xfo9p1HY+zEwEEEEAAgWQVIFiVrD1LuxBAAAEEEEAAAQQQQAABBBBAAAEEXCFwwDOD6rkfv5enFk+Vg56ZVfbUpl5Dee74c+TkVkfZD/EaAQQQQACBlBAgWJUS3UwjEUAAAQQQQAABBBBAAAEEEEAAAQSqQ2CuZxbVzZ7ZVD95ZlU5peuO7iUPdD9FGmTUdjrMPgQQQAABBFJCgGBVSnQzjYyFQFZWViyKpUwEEIiyAGM1yqAUh0CMBBirMYKlWASiLMBYjTIoxSEQIwHGaoxgwyx236ED8sTCb+WlJdOlvKLC7+yjGjSR0X3PlX7Nc/yOsSM1BBirqdHPtDKxBRin8eu/tApPit/luFIiCuTl5UlhYaFkZ2dLfn5+IjaBOiOAAAIIIIAAAggggAACCCCAAAJxE/hh88+eZ1ONl1V7dvhds0Zamtzcub/c0/VEqVMjw+84OxBAAAEEEEhFAWZWpWKv02YEEEAAAQQQQAABBBBAAAEEEEAAgagL7D5QIg/O+0bGLp/jWPZxjbLlhT7nSm6TVo7H2YkAAggggECqChCsStWep91VFlizZo1RRk4O0/WrjEkBCMRQgLEaQ1yKRiCKAozVKGJSFAIxFGCsxhCXohGIogBjNYqYYRT19YZlcvvMT2Xjvt1+Z9VKryF3e2ZS3eKZUZXh2SYhoAKMVd4HCLhfgHEavz5Kj9+luBICySVQVFQk+o+EAALuFmCsurt/qB0CpgBj1ZTgJwLuFmCsurt/qB0CpgBj1ZSIz8/tJcXyx+8/kkv++7ZjoKpX0zYy9azr5c4uAwlUxadLEuYqjNWE6SoqmsICjNP4dT4zq+JnzZUQQAABBBBAAAEEEEAAAQQQQAABBJJEQB8D/581P8o9sz+XbaX7/FpVr2aGPNDtFLn26F5SI53vi/sBsQMBBBBAAAGLAMEqCwabCCCAAAIIIIAAAggggAACCCCAAAIIVCawybPU312zPpPP1xc4Zh2U3U5G9zlHDq/fyPE4OxFAAAEEEEDAV4Bgla8HrxBAAAEEEEAAAQQQQAABBBBAAAEEEHAU0NlU/145T+7P/0p2Hyz1y9OwVh15NO80uaxdN0lLS/M7zg4EEEAAAQQQcBYgWOXswl4EEEAAAQQQQAABBBBAAAEEEEAAAQS8Aj/v2SG3zZwgUwpXe/dZN85ue4z8tddZkl23gXU32wgggAACCCAQggDBqhCQyIKAk0CLFi2cdrMPAQRcJsBYdVmHUB0EAggwVgPAsBsBlwkwVl3WIVQHgQACjNUAMBHuLisvl9cKZsoj8yfLvrKDfqU0q1NPnux1pgw9vDOzqfx02BFMgLEaTIdjCLhDgHEav35I80xfrojf5bhSIgrk5eVJYWGhZGdnS35+fiI2gTojgAACCCCAAAIIIIAAAggggAACYQssLdoit8wYL7O3rXc897ftco1l/xrXrut4nJ0IIIAAAgggEJoAM6tCcyIXAggggAACCCCAAAIIIIAAAggggECKCBwsL5PnFn8vTy2eKgc82/bUuu5h8uzx58iQ1h3sh3iNAAIIIIAAAhEIEKyKAI1TEFCBgoICA6Jjx46AIICAiwUYqy7uHKqGgEWAsWrBYBMBFwswVl3cOVQNAYsAY9WCEcHm/O0bZdj0cfJj0WbHs689upeM7H6KNMio7XicnQiEKsBYDVWKfAhUnwDjNH72BKviZ82VkkygpKQkyVpEcxBITgHGanL2K61KPgHGavL1KS1KTgHGanL2K61KPgHGamR9uv/QQXli4bfy4pJpUu7w1Iz2DRrL6D7nSv8WR0R2Ac5CwCbAWLWB8BIBFwowTuPXKQSr4mfNlRBAAAEEEEAAAQQQQAABBBBAAAEEXCjww+af5VbPs6lW7tnhV7saaWkyrHM/uee4QZJZM8PvODsQQAABBBBAoOoCBKuqbkgJCCCAAAIIIIAAAggggAACCCCAAAIJKLD7QIk8NH+SjFk227H2x2a1kBf7DpVuTVo5HmcnAggggAACCERHgGBVdBwpBQEEEEAAAQQQQAABBBBAAAEEEEAggQQmblgut8+cIBv27farda30GvKn4wbKrccOkAzPNgkBBBBAAAEEYitAsCq2vpSOAAIIIIAAAggggAACCCCAAAIIIOAigR2l++S+OV/Ke6sXOtaqV9M28rzn2VTHZDV3PM5OBBBAAAEEEIi+AMGq6JtSYooI5OTkpEhLaSYCiS3AWE3s/qP2qSPAWE2dvqaliS3AWE3s/qP2qSPAWHXu64qKChm39ie5e/bnsrWk2C9T3RoZ8ufuJ8sfju4tNdLT/Y6zA4FoCzBWoy1KeQhEX4BxGn3TQCUSrAokw34EKhHIysqqJAeHEUDADQKMVTf0AnVAoHIBxmrlRuRAwA0CjFU39AJ1QKByAcaqv1Hhvj1y1+zP5LN1S/0PevacmH2kjPbMpsqp38jxODsRiIUAYzUWqpSJQHQFGKfR9QxWGsGqYDocQwABBBBAAAEEEEAAAQQQQAABBBBIWAGdTfXWynkyIv8r2X2w1K8dh2XUlkfzTpPfte8uaWlpfsfZgQACCCCAAALxESBYFR9nrpKEAgsWLDBalZubm4Sto0kIJI8AYzV5+pKWJLcAYzW5+5fWJY8AYzV5+pKWJLcAY/WX/l2zd6fcNmOCfFu4yrHDz2zTUZ7qfZa0rHuY43F2IhBrAcZqrIUpH4GqCzBOq24YagkEq0KVIh8CCCCAAAIIIIAAAggggAACCCCAgOsFysrL5fVls+SheZNkX9lBv/o2rV1Xnux9ppx3+LHMpvLTYQcCCCCAAALVIxD3YFW55w+GpUuXyk8//STr1q2TrVu3yr59+4zW161bV5o1ayZt27aVzp07yzHHHCPpPNCyet4ZXBUBBBBAAAEEEEAAAQQQQAABBBBIMIGCXVvl5unjZPa29Y41v/jIrvJ4z9OlsSdgRUIAAQQQQAAB9wjEJVhVVFQkEyZMkG+++UamT58u+/fvD0kgMzNT+vbtK6eccoqcc845wsPMQmIjEwIIIIAAAggggAACCCCAAAIIIJBSAgfLy2T0jz/IXxdNkQOebXtq5Vnq79njz5ZTWx9tP8RrBBBAAAEEEHCBQEyDVXPmzJHXX39dvv76azl48Jdp1/pgy1CTzriaPHmy8W/kyJFy6qmnyrXXXiu9evUKtQjyIYAAAggggAACCCCAAAIIIIAAAggkscCC7Rtl2IxxsnjnZsdWXnN0TxnZ7RQ5rFYdx+PsRAABBBBAAIHqF4hJsGrmzJkyatQomT17ttFCe4CqRYsWctRRR0l2drY0btxYdAaV5ikpKZHt27dLYWGhrFixQrZs2WLs10IOHDggn332mfFPg1X33HOPHH/88dUvSA0QQAABBBBAAAEEEEAAAQQQQAABBOIusP/QQRm16Ft58adpUubw5eh2DRrL833Olf4tjoh73bggAggggAACCIQnkOYJEoU+1amSsteuXSsPPvigTJw40chpFq3BqdNPP1369+9vBJiaNGlSSUm/HNbAlQa+fvjhB/nqq6+MIJYeSUtLMzIMGTLEuN7hhx/+ywn8NyYCeXl5hr0GF/Pz82NyjUQsVIOrmurU4ZtZidh/1Dl1BBirqdPXtDSxBRirid1/1D51BBirqdPXtDSxBVJhrE7bskZunT5eVuzZ7tdZ6Z77RsM69ZN7uw6SzJoZfsfZgYBbBFJhrLrFmnogEKkA4zRSufDPi2qwqn379sYMKA1S6Wyps88+Wy677LKoLdunM7Xefvtt+fTTT73PvdJAgc7CIsVOgGBV7GwpGQEEEEAAAQQQQAABBBBAAAEEQhfYc7BUHpr3jby+7JfVfOxnds5qLi/2HSrdm7S2H+I1AggggAACCLhYIKrBqjZt2kjDhg3lmmuuMf41atQoJk3fuXOn/OMf/zD+7d69W9atWxeT61DoLwIEq5zfCUTVnV3Yi4DbBBirbusR6oOAswBj1dmFvQi4TYCx6rYeoT4IOAsk61j9ZuNyuX3mp7K+eJdfwzPS0+VPXU6UW4/tL7VqxOSpF37XZAcCVRVI1rFaVRfOR8BNAozT+PVGVH9733TTTaL/DjvssJi2QINgd955p/zhD3+Ql156KabXonAEAgkUFBQYh3JzcwNlYT8CCLhAgLHqgk6gCgiEIMBYDQGJLAi4QICx6oJOoAoIhCCQbGN1Z+k+uS//K3l31QLH1vds2trzbKqh0skzq4qEQCIJJNtYTSR76opAqAKM01Clqp4vqsGq4cOHV71GYZSgQbF4XzOM6pEVAQQQQAABBBBAAAEEEEAAAQQQQKAKAuPW/Ch/mv25bC0p9isl0zOD6v5uJ8v/dTxeanhmVpEQQAABBBBAIHEFohqsSlwGao4AAggggAACCCCAAAIIIIAAAggg4BaBwn175G5PkGrCuiWOVRqYfaSMPv4cOaJBY8fj7EQAAQQQQACBxBIgWJVY/UVtEUAAAQQQQAABBBBAAAEEEEAAgaQVqKiokLdXzZcRnmX/dh0o8Wtng4za8kiPU+Xyo3pIWlqa33F2IIAAAggggEBiChCsSsx+o9YIIIAAAggggAACCCCAAAIIIIBAUgms3btTbps5Qf67aZVju85o01Ge6n2WtKob22elO16cnQgggAACCCAQUwHXBatKS0vln//8p0ycOFG2bt0qTZs2lcGDB8s111wjmZmZMcWgcAQQQAABBBBAAAEEEEAAAQQQQACB+AqUV5TL6wWz5aH530jxoYN+F29au66M6nWmnJ9zLLOp/HTYgQACCCCAQHIIpHmmV1fEoynr1q2TCy64wLjUKaecIo8//rjfZUtKSuTiiy+WefPm+R1r3769fPjhh0bwyu8gO2IqkJeXJ4WFhZKdnS35+fkxvRaFI4AAAggggAACCCCAAAIIIIBA6ggs27VVbp4xXmZtXefY6IuOOE4e73m6NKlTz/E4OxFAAAEEEEAgOQTiNrPqiy++kE2bNhnfgDn11FMd9Z5++mmZO3eukcceQ1uxYoXcdNNN8t577zmey04EEEAAAQQQQAABBBBAAAEEEEAAgcQQOFheJs//9IM8uXCKHPBs25Mu9fdM77PltDZH2w/xGgEEEEAAAQSSUCBuwao5c+YYfLqUX//+/f0oi4uLjeX/zIdjXnbZZTJkyBDZsGGDPPPMM7Jjxw6ZNm2afP/99zJgwAC/89mBQLwFioqKjEtmZWXF+9JcDwEEwhBgrIaBRVYEqlGAsVqN+FwagTAEGKthYJEVgWoUcPtYXbhjkwybPk4W7Sx0VLqqQ578pfsQOaxWHcfj7EQgWQTcPlaTxZl2IFAVAcZpVfTCOzduwSqdGaWBqGOOOUZq1arlV0t9RtX+/fuNPJdccok8+eST3jzt2rUTDV5pGj9+PMEqrwwb1SmwZs0a4/IEq6qzF7g2ApULMFYrNyIHAm4QYKy6oReoAwKVCzBWKzciBwJuEHDrWC0pOyijPDOpXvDMqCpzeCrFkfUbyfN9zpUB2Ue6gZE6IBBzAbeO1Zg3nAsgkEACjNP4dVbcglX6zCNNOTk5jq3TGVNmuvrqq81N4+fAgQON89auXSsLFizwOcYLBBBAAAEEEEAAAQQQQAABBBBAAAF3C0zfskZumT5eVuzZ7lfRdM+Xm288pq8Mzx0kdWv6f8HZ7wR2IIAAAggggEDSCcQtWLVv3z4Dr0GDBo6IM2fONPZnZ2fLscce65enU6dOolHM9evX+x1jBwIIIIAAAggggAACCCCAAAIIIICA+wT2HCyVh+dPktcLZkmFQ/U6ZTWXF/sMlR5NWzscZRcCCCCAAAIIpIpA3IJVNWrUkLKyMjlw4ICf7fbt22X16tXGEoDHH3+833Hd0aRJE2O/PtuKhAACCCCAAAIIIIAAAggggAACCCDgboFJG1fIbTMnyPriXX4VzUhPl7u6DJTbjh0gtWrE7faUXz3YgQACCCCAAALuEIjbXwP6XJ8tW7bIunXr/Fr+ww8/ePf17NnTu23dKC0tNV5mZGRYd7ONAAIIIIAAAggggAACCCCAAAIIIOAigZ2l+2RE/lfyzirnRznkNWktz/c9VzpntXBRrakKAggggAACCFSnQNyCVR06dJDNmzfL3LlzZffu3XLYYYd52z1hwgTvdqCZVeYzr8wZVt4T2ECgmgTq1KlTTVfmsgggEI4AYzUcLfIiUH0CjNXqs+fKCIQjwFgNR4u8CFSfQHWO1fFrf5I/zfpMtpT4r4yT6ZlBNSJ3sFx/TB+p4ZlZRUIg1QWqc6ymuj3tRyBUAcZpqFJVzxe3YNWgQYPk+++/l5KSErn77rvl2WeflczMTBk/frx8+eWXxhKAbdu2FX02lVNavHixkeeII45wOsw+BOIu0LFjx7hfkwsigED4AozV8M04A4HqEGCsVoc610QgfAHGavhmnIFAdQhUx1jdvH+P3D37cxm/doljk09ocYSM7nOuHNmgseNxdiKQigLVMVZT0Zk2I1AVAcZpVfTCOzduwaqLL75YXnjhBWNW1WeffSYTJ040glW7du2SiooKIxB15ZVXOtZ+4cKFUlRUZOTp2rWrYx52IoAAAggggAACCCCAAAIIIIAAAgjEV0Dv6bzrWe7vvvwvpehAid/FG2TUlod7nCpXHNXDuK/jl4EdCCCAAAIIIICARyBuc64bN24szzzzjNSoUcMITukzqDQApX/UaOrRo4dce+21xrb9P+PGjfPu6t27t3ebDQSqU0CXpjSXp6zOenBtBBAILsBYDe7DUQTcIsBYdUtPUA8EggswVoP7cBQBtwjEa6yu3VskF03+t9w4/RPHQNVprY+W6WffKFd2yCNQ5ZY3B/VwlUC8xqqrGk1lEEgwAcZp/DosbjOrtEmnnXaaaODp+eefl1mzZsnevXulVatWcvbZZ8ttt90mNWv6V2ffvn3y7rvvGiK1atWS/v37x0+HKyEQRECfwaYpOzs7SC4OIYBAdQswVqu7B7g+AqEJMFZDcyIXAtUtwFit7h7g+giEJhDrsVpeUS6vL5stD837RooPHfSrVJPadeWJnmfIb47oQpDKT4cdCPwqEOux+uuV2EIAgUgFGKeRyoV/nn90KPwywjojNzdXxowZE/I5GsD66quvjPy6rc+5IiGAAAIIIIAAAggggAACCCCAAAIIxF9g+a5tcvOMcTJz6zrHi194xHHyeM/TpWmdeo7H2YkAAggggAACCDgJxD1Y5VSJYPt0NlWbNm2CZeEYAggggAACCCCAAAIIIIAAAggggEAMBQ6Wl8mLP02TUQu/lVLPtj21qttAnup9tpzRpqP9EK8RQAABBBBAAIFKBVwfrKq0BWRAAAEEEEAAAQQQQAABBBBAAAEEEIiZwMIdm+Tm6eNk4c5Cx2tceVSe/KXHEGlYq47jcXYigAACCCCAAAKVCRCsqkyI4wgggAACCCCAAAIIIIAAAggggEAKCpSUHZS/Lpoqo3/8XsoqKvwEjqzfSEb3OVdOyD7S7xg7EEAAAQQQQACBcASqNVhVXFws69evlz179sihQ4dCrnefPn1CzktGBGIlkJWVFauiKRcBBKIowFiNIiZFIRBDAcZqDHEpGoEoCjBWo4hJUQjEUCAaY3Xm1rVyy/Txsmz3Nr+apqelyY3H9JHhuSdJ3Zq1/I6zAwEEQhOIxlgN7UrkQgCBSAUYp5HKhX9eWoUnhX9a5Gfs3btXXn/9dRk3bpysXLlSwr18mucPorVr10ZeAc4MWyAvL08KCwslOztb8vPzwz6fExBAAAEEEEAAAQQQQAABBBBAIDEE9h4slYfnT5K/F8wSpxtGxzRsJi/2HSp5TXm+eGL0KLVEAAEEEEAgMQTiOrNq0aJFctVVV8mWLVsMnXADVYlBSi0RQAABBBBAAAEEEEAAAQQQQACBxBP476aVcuuM8bKueJdf5TPS0+WOY0+QO7qcILVqxPV2kl9d2IEAAggggAACyScQt78uduzYIZdddpns3LnTq1izZk3JycmRRo0aiW6TEEgkgTVr1hjV1fcwCQEE3CvAWHVv31AzBKwCjFWrBtsIuFeAserevqFmCFgFwh2rRaX75f65X8lbK+dbi/Fu92jSSp7vM1SObdTCu48NBBCoukC4Y7XqV6QEBBAIV4BxGq5Y5PnjFiF69dVXjUCVLuNXv359ueeee+Siiy6SevXqRV57zkSgGgWKioqMqxOsqsZO4NIIhCDAWA0BiSwIuECAseqCTqAKCIQgwFgNAYksCLhAIJyx+unaJXLXrM9kc8lev5rX8cygGpE7WG7wPJ+qhmdmFQkBBKIrEM5Yje6VKQ0BBEIVYJyGKlX1fHELVk2ePNmorQar3njjDendu3fVa08JCCCAAAIIIIAAAggggAACCCCAAAJhC2zev0fumf2FjFv7k+O5/ZvnyPN9z5V2DZo4HmcnAggggAACCCAQTYG4BavWrVsnGqjq1asXgapo9iBlIYAAAggggAACCCCAAAIIIIAAAh6BpUVbZOzyOTJt3QrZX35ImhbOkdzGLeXqDj3lmKzmhpE+P/y91Qtk+JwvpehAiZ9bg4xa8lCPU+WKo3pIehqzqfyA2IEAAggggAACMRGIW7CqvLzcaED79u1j0hAKRQABBBBAAAEEEEAAAQQQQAABBFJRYO62DfLA3K/lhy2/PFvZNFhZsltmbl0nrxXMEp0pdVPnvjJm2RyZtHGFmcXn56mtO8gzvc+W1vUa+uznBQIIIIAAAgggEGuBuAWrWrVqJStXrpSSEv9v7cS6kZSPAAIIIIAAAggggAACCCCAAAIIJKPAxA3L5aqp78u+soNBm6eBLHswyzyhce1MeaLnGXLhEccZq+KY+/mJAAIIIIAAAgjESyBuwaqBAwfKihUrZOHChfFqG9dBIKYCLVq0iGn5FI4AAtERYKxGx5FSEIi1AGM11sKUj0B0BBir0XGkFASiJaAzqq6c+p7sLzsUcZEX5HSRUb3OkKZ16kVcBicigEBkAvxejcyNsxCIpwDjNH7aaZ61iivicTmdVXXqqafKgQMH5K233hINXpESQyAvL08KCwslOztb8vPzE6PS1BIBBBBAAAEEEEAAAQQQQACBJBc4++uxAWdLVdb0lpkN5KneZ8mZbY+pLCvHEUAAAQQQQACBmAvEbWaVPqtq5MiRct9998ktt9wi7733nnTs2DHmDYzkAuvWrZOpU6fKjBkzZMmSJbJx40YpLi6W+vXrS8uWLaVHjx5y/vnnS9++fcMqfsuWLfLBBx/I999/LwUFBVJUVGSc37BhQ+nQoYNR3sUXXyytW7cOq9zly5fLu+++K1OmTJFNmzZJaWmpEVjSINOFF14oJ5xwQljlkRkBBBBAAAEEEEAAAQQQQAABBNwtsLRoS8SBKm3ZmydeLHlN27q7kdQOAQQQQAABBFJGIG4zq0zR999/X4YPH268/P3vfy9nn322HHPMMdKgQQMzS7X9XLx4sdx7770yb968kOqgwarRo0eHFFwaM2aMPP7447J///6gZdeqVcsI5t1+++1B85kH9frPPvusHDwYeG3q8847T0aNGmUE28zzwvnJzCpnLQ04anJr0NW51uxFIPUEGKup1+e0ODEFGKuJ2W/UOvUEGKup1+e02L0C98z+XF4rmBVxBf/Ysbdn+b8zIz6fExFAoOoC/F6tuiElIBBrAcZprIV/LT9uM6vatvX9to6uPviPf/zD+PdrdSrfSktLk7Vr11aeMYIculShPVDVrl07I5jWqFEj2b17t8yZM8eYvaTFT58+Xc4991z5+OOPJScnJ+AVX3jhBXniiSe8x2vUqCHdunWTww8/3Ni3Zs0aWbBggZSVlRnLJD711FOya9cuefDBB73nOG389a9/leeee857SNfP7N27t9SuXVsWLVpkzN7Sg5988ons3LlT/vWvf0nNmnHrcm+9knWjpKQkWZtGuxBIKgHGalJ1J41JYgHGahJ3Lk1LKgHGalJ1J41JcIEFOzZVqQULdxRW6XxORgCBqgvwe7XqhpSAQKwFGKexFv61/LhFLjQ4pYEm86dumylOj80yL1fpzyOOOEIuu+wyueCCC4xl/6wnlJeXi84Ou//++41ZUvosp2HDhsn48eON9lnz6vaqVavk6aef9u4eMGCAPPbYY6LLIlqTLuWns7p06UFNr7/+unH9rl27WrN5t7/77jufQNUNN9wgd999t+jMLDNpkOrOO+8UHVC6RKAGzUKdsWWWwU8EEEAAAQQQQAABBBBAAAEEEHCfwN6DB6pUqb2HSqt0PicjgAACCCCAAALRFEiPZmGVlWUGpfSn9V9l58XrePPmzeWZZ54xnld10003+QWqtB7p6eny29/+1gj8mPWaO3euEQwyX1t//uc///Eu0ZednS1jx471C1Rpfn1m1RtvvCFaB03qo+cGStaZWkOHDjWCZ9ZAlZ6ny//pc8LM9Le//U127NhhvuQnAggggAACCCCAAAIIIIAAAggkqED9jF+/rBpJE+rXrB3JaZyDAAIIIIAAAgjERCBuM6vWr18fkwZEs1B9BpX+CyWdccYZ0r17d++ygZMmTZJBgwb5nbpkyRLvviFDhkjdunW9r+0b9evXF83z1ltvGYd0VpZTmj9/vug/TRo8GzFihLHt9J/LL79cXnvtNVm9erXs3btXPvzwQ/njH//olJV9CCCAAAIIIIAAAggggAACCCCQIAJHN2wqM7eui7i2XRtnR3wuJyKAAAIIIIAAAtEWiOvMqmhXvrrL69mzp7cK69Y5/4FYXFzszdOwYUPvdqANfTaWmXTJQaf05ZdfenefcMIJ0rp1a+9r+4Yut3jRRRd5d1vP9e5kAwEEEEAAAQQQQAABBBBAAAEEEkZg2pY18sW6girV95qje1XpfE5GAAEEEEAAAQSiKRC3mVXRrLRbyrI+d6usrMyxWtZA0rJlyxzzWHcuXbrU+7Jz587ebevGtGnTvC9DmQnWr18/b/45c+ZIaWmp1K7NdH8vSoQbOTk5EZ7JaQggEE8Bxmo8tbkWApELMFYjt+NMBOIpwFiNpzbXQsBfoLyiXJ7/aZo8Mn+SlHkeHxBpGtDiCOnYsFmkp3MeAghESYDfq1GCpBgEYijAOI0hrq1oZlbZQMJ5aQ0stWrVyvHUU0891btflwqcPXu297V944cffpDJkycbuzWYdOmll9qzGK9XrFjh3X/cccd5twNtdOnSxXtIg2qBlhf0ZmIjJIGsrCzRfyQEEHC3AGPV3f1D7RAwBRirpgQ/EXC3AGPV3f1D7ZJbYEfpPrn023fkL/O+qVKgqm6NDPlL9yHJjUXrEEgQAX6vJkhHUc2UFmCcxq/7XTOzSpfL02cq6XOb6tWrFz+BCK+0YcMG0eCSmXQ5Pqekz6AaOHCgTJ06VTRQ9Nvf/lauuOIKueCCC+Twww83Tlm7dq188MEH8u9//1t06T8NVD399NNyxBFH+BW5bds22bVrl3d/mzZtvNuBNjIzM6VJkyayfft2I4sGuzp16hQoe8D9WreNGzcGPK4HAgXtgp7EQQQQQAABBBBAAAEEEEAAAQQQCCgw2/Nsqmu+/1DWF/96P8DM3LVRtizfvU32lx0ydwX8qYGqfw68WHo0Dfw4gYAncwABBBBAAAEEEIihQLUFq9avX28EZ3RJux9//FEOHDjgbWatWrXk2GOPlf79+8vvf//7oM9k8p4U540HH3zQCD7pZXWpPw1KOSVdKnDs2LFy5513yieffCIlJSXy2muvGf/s+dPT043A1p/+9Cfp0aOH/bDxeufOnT77mzZt6vM60IvmzZt7g1VFRUWBsgXdv2XLFunVq1fQPJ9//rnf8dzcXGOfXnfNmjV+x+vUqSMdO3Y09hcWFsrmzZv98mgE25xyqWU4taFFixaSnf3LA2ILCgoMa3tBWoaWpWnBggXGT/t/tC5aJ+0rLccpaZv0/EBLKiZqm7StydZPtCkxxlMs+8kM1OvSqvH8jIhlm7RsTfH+3NNr8hmhCr8mN/5+0tolYj+ZyxzrF2ysKVn/jkjUfgr2txFtSo3fuXv27JEGDRqIjk23/F3Oey813nup+jtX/1/+k6Kf5YG5E+WQZwlAa0rzvLi6eSe5r9cQWVO2X0bOmyjfb/7ZmsVnW5f+G3HsiZK5dbcs2Ob//8P8zk2c+xHasYn49x5/R/w6JM37Rnpvad++fVK3bt1fD/5vK1U/9/j/3Krfs9S3EJ8RvkOqKuPJvK+k/68azXvLydJP5t8PvuKRvYp7sEqDUo888oi88cYbxiwirXaFbZ1lDQDMmzfP+Pfyyy/LVVddJSNGjBANYrkhvf/++2INygwfPjzoM6D0F9BLL70k1157rdxzzz3y008/OTbjqKOOkrPPPlusy/bZM+oMNGvSskNJ1nz2MkI5nzwIIIAAAggggAACCCCAAAIIIBA/gT1lnvsnC7+Wb7au9rtoo5q15f62PSWvfnOpkZZuzJSaMOQqGTfzOxm/Y7Us2eOZaVVeJg1q1ZEOmQ3l1rzB0rV5m1++lOkJVpEQQAABBBBAAAG3CaR5AkWRP5EzzNbs37/feA5Tfn6+X4AqWFE6OykvL0/effddY8ZLsLyxPqbfeNAl/HTWjabzzjvPCEQFu64Sv/nmmzJ69GjRmUMadNP26DJ/ujTgypUrZf78+d6ZWh06dJAxY8ZI+/bt/YqdOXOmcX3zwLp160RnZFWWtM56riaduXXbbbcZ26H8R+uq9dZvdH322WdBT0mlZQDNmVnRjB4HxeUgAghEJMBYjYiNkxCIuwBjNe7kXBCBiAQYqxGxcRICYQss2L5RrvruA/l57/+zdx/gUVRrA8ff9BASCD3U0EMPEHoTARF7xXrtXfR6Va7lCmLviF2uXsVybWBX0EuVXgOEHnqAQOgB0uu3Z/x23WRnk02yOzu7+5/nwcyeOXPmnN/JK+XNOVN2dxXV0ODG8fKfIVdKXFSM03aJVac0XEDAVALEqqmmg84goCtAnOqyeKTQ0JVVjzzyiKxZs0ZU8kkdaru1q6++WttarmXLltqSV7X0VSVgVL1vvvlGtm3bpiW2VIJLJVnefvttj0C40qh6t5Ra5WVNVKn3Pr300ksV3qre8/T3v/9dfvjhB63emDFj5IUXXtC2brK/cffu3VoCSY1zx44dmsvs2bOlfv369tUcVnCplWr2q6bKVLb7YL/Noiv17W61naqkWCAlo2wD5wQBBBBAAAEEEEAAAQQQQAABAwTUD7t+vGON/GvN71JgWRlV/ni421B5rMdwCQ0OKX+JzwgggAACCCCAgE8LVL4kx03DU9v6qYSNSlSppMekSZNk7ty5cuedd0qvXr1EvXtJ7c+qvqrPd9xxh8yZM0eefvppCQkJ0RJW6p1PagWSNw71HqVrr71W1Hub1KH2p/ziiy+0/dor6o/axtCaqFLv4FLvq1J7r5Y/2rZtK1999ZVtNdWhQ4fk1VdfLV9NateuXabMmjgrU6jzwb5e+TZ0qlOEAAIIIIAAAggggAACCCCAAAIGCpwpzJfbl3wn41fNdEhU1Y+oJdPPvl4m9BxJosrAOeFRCCCAAAIIIGCcgGHJqu+++842qgkTJmjJKOsKK9uFcifqunrP08SJE21Xvv32W9u5UScnTpzQElV79+7VHqmSTWpLQr2kk32fVIJIJaush1pZphJvzg6VRLLfnk+ZFRUVlaler169Mp+PHTtW5rOzD9Ykm7quXijHgQACCCCAAAIIIIAAAggggAAC5hDYdDJDRsz6QL5P2+TQoX6NWsrC8++Wc5p3cLhGAQIIIIAAAggg4C8Chm0DuHz5cs1MJXjUqqmqHCphNXXqVFGrm5YtW1aVW2tc98yZM3L99ddLamqq1pbalk8lqlq1alVp22o12alTp7R6tWrVkt69e1d6j1p9ZT2ys7O191mp7RKth1p5VrduXVu7Bw4ckPbt21sv635VSbPjx4/brlVW31aRkwoF7OelwopcRAABrwoQq17l5+EIuCxArLpMRUUEvCpArHqVn4f7oYDa9u+LXevkn6tnSV5x2R9WVcO9r/MgebLXSAmr4rZ/xKoffrMwJL8UIFb9cloZlJ8JEKfGTahhK6syMjK0LQD79+9f5dGpFVbqPvWHOJWwMupQ78+64YYbZMOGDdoj69Spo23917FjR5e6oMZsPVSCSW1/WNlR/h1Vp0+fdrjFPtm0aZPjT12Vv2Hjxo22IrWyS205yFFzAfXur+q+/6vmT6cFBBBwVYBYdVWKegh4V4BY9a4/T0fAVQFi1VUp6iFQuUB2UYGMW/6j3L/iZ4dEVd3wSPnirGvk2aTRVU5UqScTq5X7UwMBMwgQq2aYBfqAQMUCxGnFPu68Wnn2xE1Ps74zqbrvS7LeZ23HTd1y2ox6zs033yyrV6/W6qiVUZ999pn06NHD6T3lL6hvZOuhkk4q2VbZcfLkyTJVVJKr/DFo0CBbkXXFmq1A52TFihW20j59+khERITtMyfVF1DfI0Z9P1a/l9yJAALEKt8DCPiGALHqG/NELxEgVvkeQMA9Aqmnjsqo3z6Ur3anODTYu0Ezy7Z/d8n5LTs5XHO1gFh1VYp6CHhXgFj1rj9PR8AVAeLUFSX31DEsWWV919KePXuq1XPr+6Ks7VSrERdvKiws1LYqXLp0qXaHSu58/PHH0rdvXxdb+LNa8+bNbfXVKi21LWBlh/WZql5YWJjYt2G9d8yYMdZTWbx4sRw8eND2We9k+vTptuJzzz3Xds5JzQTU1pDW7SFr1hJ3I4CAJwWIVU/q0jYC7hMgVt1nSUsIeFKAWPWkLm0HisA3lgSVej/VNkvCqvxxZ0I/mTX6VomPLvu+6vL1KvtMrFYmxHUEzCFArJpjHugFAhUJEKcV6bj3mmHJKrW3o1pZpFYq7du3r0qjUPVXrVqlbSPo6T0ii4uLZdy4cTJ//nytj6Ghodr7soYNG1alPqvK3bp1E/vk2quvviolJSVO21EJrTfffNN2Xa2Csq4osxVaTnr27Kn9UmWqvy+++KL95TLn//3vf2X37t1aWXR0tIwdO7bMdT4ggAACCCCAAAIIIIAAAggggIDnBXKLCuUfli3/7l72g+QUF5Z5YExYuEwbOlZe7nu+RIQY9nrxMn3gAwIIIIAAAggg4E0Bw5JVI0aM0Mapkiv33XefZGVluTRulcC5//77pajozxeNjho1yqX7qlNJJdMefvhhmTlzpna7esfUW2+9JaNHj65Oc9o7qm677TbbvYsWLZK77rpLjh51/OkpteLsuuuukx07dtjq33vvvbbz8iePPfaYrej777+XF154QdSKMPvj559/lkmTJtmK7r77bin/TizbRU4QQAABBBBAAAEEEEAAAQQQQMAjArtOH5fR//uPfLpzrUP73evFyYLz7pJL47s6XKMAAQQQQAABBBAIFAHDflzn2muvlXfffVdL1Kjt8M477zyZOHGiqOSTSgqVP1TiaO7cufLcc89pK4OCgoKkUaNGcs0115Sv6rbPn376qcyYMcPWXnx8vLaiS63qcuV4/vnnHardc889opJU1jZmzZol8+bNE7VqqlWrVtpKq127dmlbBKpEnvW46aabxJrgs5bZfx06dKg88MADtpVYyva7776Tfv36ae+k2rhxo2zbts12i1oZppJ+HAgggAACCCCAAAIIIIAAAgggYJzAj2mb5e8rfpIzhQUOD72pfZK82GeM1AoNc7hGAQIIIIAAAgggEEgChiWroqKi5OWXX5bbb79dS9ColURq1ZFa6aO2tWvRooWoOmolVXp6uqxfv16OHz+uzYVKXKnt+NQ2erVq1fLY/FifZ32A6mNV3rGll6yKjIyUzz77TCZMmCDffvut1nR+fr6od1PZv5/K+kz1niqVhFK/Kjv++c9/Snh4uLzxxhvaqqqMjAxRq6nKH5dccolmrww5EEAAAQQQQAABBBBAAAEEEEDA8wL5xUUyce1s+TDV8Qdga1uSU6/3v0iuatPD8x3hCQgggAACCCCAgA8IGJq9OOecc7Rt9R555BHJzs7W3mGlEkTW90PZe6kElfVQ721SiaqRI0dai3zqa0xMjLYCSm3rp1Zuqfd27d27V06fPq2tKqtbt6506NBBBg4cqK0ci4uLc2l8arXZP/7xD7ngggvkyy+/1FZwHTx4UEtcNWnSRHr37q29o6o679tyqQNUQgABBBBAAAEEEEAAAQQQQAABB4G0rJNyy6IZsu7EQYdrneo2kk+HXSUdLV85EEAAAQQQQAABBP4UCLIkhf7KChmkkpaWJq+//rr8+uuvolYZOTsiIiLkoosukgcffFDUlnwc3hFISkoStWpLJdGSk5O90wmeigACCCCAAAIIIIAAAggggIAPCMzav03uXf6jnCrIc+jttW0T5dV+F0jt0HCHaxQggAACCCCAAAKBLOCVZJUVXK0sWrNmjWzatEnb8k9tAai2AmzQoIF069ZNe69TnTp1rNX56iUBklVeguexCCCAAAIIIIAAAggggAACPiNQWFIsT6+bK+9uXe7Q58iQUHnNkqS6vl0vh2sUIIAAAggggAACCIgYug1geXCViBoxYoT2q/w1PiNgdoHMzEyti7GxsWbvKv1DIKAFiNWAnn4G70MCxKoPTRZdDWgBYjWgp5/BVyBwIPuU3LrYsu3/sQMOtTrUaSDThl4lXes1cbjmqQJi1VOytIuAewWIVfd60hoCnhAgTj2hqt+mV5NV+l2iFAHfEFDbWaqDZJVvzBe9DFwBYjVw556R+5YAsepb80VvA1eAWA3cuWfkzgXmpO+Qu5d9Lyfycx0qXR7fTd4YcJHEhEU4XPNkAbHqSV3aRsB9AsSq+yxpCQFPCRCnnpJ1bJdklaMJJQgggAACCCCAAAIIIIAAAggggECFAkWWbf9eSFkgUzYvcagXHhwiL/YZI7d06CNBQUEO1ylAAAEEEEAAAQQQKCtAsqqsB58QQAABBBBAAAEEEEAAAQQQQACBCgUO5ZyW25d8J8uO/Lnjhn3l1tH15JOhYyWxQTP7Ys4RQAABBBBAAAEEKhBwa7JqxowZZR41duxY2+fy12wXqnFi3241bucWBBBAAAEEEEAAAQQQQAABBBBAoFoCCw/tljuWfidH87Id7r+oZWd5e+AlUjc80uEaBQgggAACCCCAAALOBdyarHrwwQdty9vVMnf7pJL9NefdqfxK+XYrv4MaCCCAAAIIIIAAAggggAACCCCAQM0EiktK5LVNi+TlDX9IabmmwoKD5Zneo+WuhP62fxcpV4WPCCCAAAIIIIAAAhUIuDVZpZ5TWlr+j2x/Pb2ia3/V4gwB3xCIjOQn5XxjpuhloAsQq4H+HcD4fUWAWPWVmaKfgS5ArAb6d0Dgjv9oXpbcueR7+SNjtwNCi9p1ZZpl278+DVs4XPNWAbHqLXmei0DVBIjVqnlRGwFvCBCnxqm7NVllv5Kq/BAqula+Lp8R8AWBhIQEX+gmfUQg4AWI1YD/FgDARwSIVR+ZKLoZ8ALEasB/CwQkwNLDey3vp/pWMnKzHMZ/bvOO8v6gS6VeRJTDNW8WEKve1OfZCLguQKy6bkVNBLwlQJwaJx9kWe3kfCmUcf3gSSYWSEpKkoyMDImLi5Pk5GQT95SuIYAAAggggAACCCCAAAIIIOAegZLSEnlz81J5PmW+FJf7p5MQy6sPJvYcKfd3GSTBQcHueSCtIIAAAggggAACASzg1pVVAezI0ANQQCXw1KGSeBwIIGBeAWLVvHNDzxCwFyBW7TU4R8C8AsSqeeeGnrlX4ER+jty99AeZc3CHQ8NNa8XIR0OvlIGN4x2umaWAWDXLTNAPBCoWIFYr9uEqAmYQIE6NmwWSVcZZ8yQ/Ezh8+LA2IpJVfjaxDMfvBIhVv5tSBuSnAsSqn04sw/I7AWLV76aUAekIrD66X25ZPEPSc047XB3RtJ38e/Dl0jCytsM1MxUQq2aaDfqCgHMBYtW5DVcQMIsAcWrcTJCsMs6aJyGAAAIIIIAAAggggAACCCCAgEkF1FsS3t+2QiatnSNFli0A7Y9gy7Z/j/cYLg91G8q2f/YwnCOAAAIIIIAAAm4SIFnlJkiaQQABBBBAAAEEEEAAAQQQQAAB3xQ4VZAr45b/JDP3b3MYQGPLKqoPh1whw+LaOlyjAAEEEEAAAQQQQMA9Am5NVqWnp7unV5W00rx580pqcBkBBBBAAAEEEEAAAQQQQAABBBCoXGDd8XRt27+0rEyHykOatJb/WBJVTSzvqeJAAAEEEEAAAQQQ8JyAW5NV/fv3lyDL0nhPHqr9ffv2efIRtI0AAggggAACCCCAAAIIIIAAAn4uoLb9+8/21TIh+X9SUFJcZrTqXzYe7jZMHu1xloQGh5S5xgcEEEAAAQQQQAAB9wu4NVll7Z76Ax8HAv4uEBsb6+9DZHwI+IUAseoX08ggAkCAWA2ASWaIfiFArPrFNDIIi8Dpgjz5x8pf5Ie0zQ4eDSKi5N+DL5eRzdo7XPOVAmLVV2aKfga6ALEa6N8BjN8XBIhT42bJrckqtT2fp1dWGUfDkxCoWCA+Pr7iClxFAAFTCBCrppgGOoFApQLEaqVEVEDAFALEqimmgU7UUGDjyQy5ZdF02XXmhENL/Ru1lI+GXCnNa9d1uOZLBcSqL80WfQ1kAWI1kGefsfuKAHFq3Ey5NVm1cuVK43rOkxBAAAEEEEAAAQQQQAABBBBAAAEXBdQuMJ/vXCuPrvlN8oqLHO76e5fBMqHnCAlj2z8HGwoQQAABBBBAAAFPC7g1WeXpztI+AmYSSEtL07pDdt1Ms0JfEHAUIFYdTShBwIwCxKoZZ4U+IeAoQKw6mlDiGwLZRQXy8Mpf5Zs9Gxw6HBseKe8NukzOa5HgcM1XC4hVX505+h1oAsRqoM044/VFAeLUuFkLNu5RPAkB/xLIzMwU9YsDAQTMLUCsmnt+6B0CVgFi1SrBVwTMLUCsmnt+6J2+wLbMIzLqtw91E1VJDZrLwvPv8qtElVIgVvW/FyhFwGwCxKrZZoT+IOAoQJw6mniqhJVVnpKlXQQQQAABBBBAAAEEEEAAAQQQ8KrA17tTtBVVOcWFDv24u1N/ebrXORIewj+NOOBQgAACCCCAAAIIGCzAn8gMBudxCCCAAAIIIIAAAggggAACCCDgWYHcokJ5zPJuqs8s76gqf8SERcg7Ay+Ri1t1KX+JzwgggAACCCCAAAJeEiBZ5SV4HosAAggggAACCCCAAAIIIIAAAu4X2Hn6mNy8aIZszjzs0Hj3enHyybCx0jamgcM1ChBAAAEEEEAAAQS8J+DWZNXAgQM9PpKgoCBZtmyZx5/DAxBAAAEEEEAAAQQQQAABBBBAwLcEvt+7SR5Y8bNkFRU4dPyWDn3khT7nSmRImMM1ChBAAAEEEEAAAQS8K+DWZNX+/ftFJZM8dZSWlnq0fU/1m3b9U6BJkyb+OTBGhYCfCRCrfjahDMdvBYhVv51aBuZnAsSqn02oHw0nv7hIJiT/T/6zfbXDqGqHhsmU/hfJ2DY9HK75awGx6q8zy7j8TYBY9bcZZTz+KECcGjerbk1WqW6rhBIHAoEgEBcXFwjDZIwI+LwAserzU8gAAkSAWA2QiWaYPi9ArPr8FPrlAPaeOSG3LJ4h608cchhf59jG8snQsdKxbiOHa/5cQKz68+wyNn8SIFb9aTYZi78KEKfGzaxbk1UrVqwwruc8CQEEEEAAAQQQQAABBBBAAAEEAlpg5v6tcu+yH+V0Yb6Dw/Xtesorfc+XqNBwh2sUIIAAAggggAACCJhLwK3JqhYtWphrdPQGAQ8KpKamaq0nJCR48Ck0jQACNRUgVmsqyP0IGCNArBrjzFMQqKkAsVpTQe53l0CBZdu/p9fNlfe2Of7QbK2QUHm13wVyfbte7nqcz7VDrPrclNHhABUgVgN04hm2TwkQp8ZNl1uTVcZ1mych4H2BvLw873eCHiCAQKUCxGqlRFRAwBQCxKoppoFOIFCpALFaKREVDBDYn50pty3+VlYfO+DwtA51Gsgnw66SLrGB/Y5hYtXhW4MCBEwpQKyaclroFAJlBIjTMhwe/UCyyqO8NI4AAggggAACCCCAAAIIIIAAAu4SmJ2+Xe5e+oOcLMh1aPLK1t1lSv8LJToswuEaBQgggAACCCCAAALmFiBZZe75oXcIIIAAAggggAACCCCAAAIIBLxAUUmxPJ+yQN7YvMTBIiI4RF7sc57c3CFJgoKCHK5TgAACCCCAAAIIIGB+AZJV5p8jeogAAggggAACCCCAAAIIIIBAwAocyjktty35VpYf2edg0Ca6nrbtX4/6TR2uUYAAAggggAACCCDgOwJuTVY99NBDtpGrn2aaPHmy7bP9NVthNU7Kt1uNJrgFAQQQQAABBBBAAAEEEEAAAQR8QGDBoV1y55Lv5Fh+jkNvL27VWd4acInUDY90uEYBAggggAACCCCAgG8JBJVaDnd1uUWLFmWW3O/fv9/WdPlrtgvVOLFvtxq3c0sVBZKSkiQjI0Pi4uIkOTm5inf7b/XMzExtcLGxsf47SEaGgB8IEKt+MIkMISAEiNWAmGYG6QcCxKofTKKPDKG4pERe3bhQXrH8Kv+PFmHBwfJc73PljoR+Zf4NwkeGZkg3iVVDmHkIAjUWIFZrTEgDCHhcgDj1OLHtAW5dWaVatea+9PaJtl6zPb0aJ3rtVqMZbkGgxgIkqWpMSAMIGCJArBrCzEMQqLEAsVpjQhpAwBABYtUQ5oB/yJHcLLlz6XeyMGOPg0XL2nVl2tCxktSwhcM1Cv4SIFb/suAMATMLEKtmnh36hsCfAsSpcd8Jbk1Wvf766057XtE1pzdxAQEEEEAAAQQQQAABBBBAAAEEAkZg6eG9ctvib+VwXpbDmMc07yjvDbpU6kVEOVyjAAEEEEAAAQQQQMC3Bdy6DaBvU9B7ZwJsA6gvk5KSol1ITEzUr0ApAgiYQoBYNcU00AkEKhUgVislogICphAgVk0xDX7ZiZLSEpmyeYm8kLJASsq9rSDE8k7sSb1GyX2dB7Htn4uzT6y6CEU1BLwsQKx6eQJ4PAIuCBCnLiC5qYpbV1a5qU80gwACCCCAAAIIIIAAAggggAACASJwPC9b7lr2g8w7uNNhxM2iYuSjIWNlQONWDtcoQAABBBBAAAEEEPAfAZJV/jOXjAQBBBBAAAEEEEAAAQQQQAABnxJYeXSf3GrZ9u9gzmmHfo9s2k6mDr5cGkbWdrhGAQIIIIAAAggggIB/CZCs8q/5ZDQIIIAAAggggAACCCCAAAIImF6g1LLV37tbl8vT6+ZKkWULQPsj2LLt3+M9zpaHug2R4KBg+0ucI4AAAggggAACCPipAMkqP51YhoUAAggggAACCCCAAAIIIICAGQUy83Nl3PIfZdaBVIfuNYmMlv8MuUKGxLVxuEYBAggggAACCCCAgP8KeC1ZpV5Mtm7dOtm/f7+cOXNGioqKXFIOsvyE1eTJk12qSyUEEEAAAQQQQAABBBBAAAEEEDCPwLrj6XLzohmyLzvToVNDm7SWDy2Jqia1YhyuUYAAAggggAACCCDg3wJBlqX3pUYO8eeff5aXX35Z9u3bV+3HqgQXh3ECSUlJkpGRIXFxcZKcnGzcg03+pLy8PK2HkZGRJu8p3UMgsAWI1cCef0bvOwLEqu/MFT0NbAFiNbDnvyajV//08OH2VTIxebYUlBSXaSrI8ml892HyaPfhEhLMtn9lcKr5gVitJhy3IWCwALFqMDiPQ6AaAsRpNdCqeYuhK6teeOEFef/997WuVjdHplZWcSBgBgGSVGaYBfqAQOUCxGrlRtRAwAwCxKoZZoE+IFC5ALFauRE1HAVOF+TJAyt+lh/3bXG42CAiSj4YfLmMaNbe4RoF1RcgVqtvx50IGClArBqpzbMQqJ4AcVo9t+rcZViyasGCBfLee+/Z+hgbGysjR46UTp06iToPCQmxXeMEAV8QIKvuC7NEHxEQIVb5LkDANwSIVd+YJ3qJALHK90BVBTaeOCQ3L54hu8+ccLh1QKNW8tHQK6VZVB2HaxTUTIBYrZkfdyNglACxapQ0z0Gg+gLEafXtqnqnYcmqadOmaX1TK6MuvPBCee2116R27dpV7S/1ETCNQGrqny8DTkxMNE2f6AgCCDgKEKuOJpQgYEYBYtWMs0KfEHAUIFYdTSjRF1C7qXy2c608unqW5Jfb9k/d8fcug2VCzxESFswPruoL1qyUWK2ZH3cjYJQAsWqUNM9BoPoCxGn17ap6p2HJqpSUFK1vjRs3ljfeeEMiIiKq2lfqI4AAAggggAACCCCAAAIIIICAyQWyCvPl4VUzZfqeDQ49jQ2PlPcHXSZjWiQ4XKMAAQQQQAABBBBAIHAFDEtWZWVliVpVNWjQIBJVgfv9xsgRQAABBBBAAAEEEEAAAQT8WGBr5hG5edF02X76mMMokxo0l4+HjpVW0bEO1yhAAAEEEEAAAQQQCGwBw5JVTZs2lbS0NKlVq1ZgizN6BBBAAAEEEEAAAQQQQAABBPxQ4Ktd6y0rqn6V3OIih9Hd02mAPNVrlISHGPbPEA59oAABBBBAAAEEEEDAvAKG/Smxa9eusnfvXtmzZ495NegZAggggAACCCCAAAIIIIAAAghUSSCnqMDybqrf5L+71jncFxMWIe8OvEQuatXF4RoFCCCAAAIIIIAAAghYBYKtJ57+etNNN2mPWLNmjaSnp3v6cbSPAAIIIIAAAggggAACCCCAAAIeFthx6pic8/t/dBNVPerFycLz7yJR5eE5oHkEEEAAAQQQQMAfBIJKLYdRA7n11ltl9uzZMmDAAPniiy94d5VR8DV8TlJSkmRkZEhcXJwkJyfXsDVuRwABBBBAAAEEEEAAAQQQ8AeB7/ZulH+s+EWyLCuryh+3duwjzyedK5EhYeUv8RkBBBBAAAEEEEAAAQcBw1ZWqSe/++67cvbZZ8uKFSvk/PPPlzlz5khxcbFDpyhAAAEEEEAAAQQQQAABBBBAAAFzCuQVF2rvprp9yXcOiaro0HD5z5ArZHK/C0lUmXP66BUCCCCAAAIIIGBKAUNXVikBtZDrjTfekMmTJ0tQUJBERkZK27ZtJSYmRvtcmZK6Z/r06ZVV47obBVhZpY+ZmZmpXYiNjdWvQCkCCJhCgFg1xTTQCQQqFSBWKyWiAgKmECBWTTENXu3EnjMn5JbFMyTlxCGHfnSJbSyfDL1KOtRt6HCNAmMFiFVjvXkaAtUVIFarK8d9CBgnQJwaZx1q3KP+fNJ7770nH330kZaYUomr3Nxc2bJli0vdUPVVsooDATMIpKWlad0gWWWG2aAPCDgXIFad23AFATMJEKtmmg36goBzAWLVuU0gXPll3xYZt/wnOVOY7zDcv7XrJS/3PU+iLCurOLwvQKx6fw7oAQKuCBCrrihRBwHvChCnxvkbmqwaP368fPPNNw6jM/C1WQ7PpgABBBBAAAEEEEAAAQQQQAABBJwLFBQXyVPr5sr721Y4VKoVEqpt+Xdtu54O1yhAAAEEEEAAAQQQQMBVAcOSVT/++KN8/fXXthVV7dq1k4suukg6deokdevWldBQw7riqg31EEAAAQQQQAABBBBAAAEEEAhogX1ZmXLbkhmy5li6g0PHOg1l2rCx0iW2icM1ChBAAAEEEEAAAQQQqIqAYRmi//73v7Z+3XHHHTJx4kQJDg62lXGCAAIIIIAAAggggAACCCCAAALmEfj9QKrcs+wHySzIc+jU2Nbd5fX+F0p0WITDNQoQQAABBBBAAAEEEKiqgGHJqs2bN2urqlq2bEmiqqqzRH0EEEAAAQQQQAABBBBAAAEEDBIoLCmW59bPl7e2LHV4YkRwiOXdVOfLje17805pBx0KEEAAAQQQQAABBKorYFiyqrCwUOtjv379WFFV3dniPlMJREZGmqo/dAYBBPQFiFV9F0oRMJsAsWq2GaE/COgLEKv6Lv5UejDntNy2+FtZcXSfw7DaxtSXT4aOle71mzpco8BcAsSqueaD3iDgTIBYdSZDOQLmESBOjZsLw5JVTZo0kX379vFuKuPmlid5WCAhIcHDT6B5BBBwhwCx6g5F2kDA8wLEqueNeQIC7hAgVt2haN425h/cKXcu/V6O5+c4dPLSVl3kzQEXS51wfmjPAceEBcSqCSeFLiGgI0Cs6qBQhIDJBIhT4ybEsGRVUlKSpKWlyfbt240bHU9CAAEEEEAAAQQQQAABBBBAAIEKBYpLSuTljX/IaxsXSWm5mmGWd00/l3Su3NGxH9v+lbPhIwIIIIAAAggggID7BILd11TFLf3tb3/TKqSkpMjWrVsrrsxVBHxAICMjQ9QvDgQQMLcAsWru+aF3CFgFiFWrBF8RMLcAsWru+alO7w7nnpHL5n0mr+okqlrVjpXfR98mdyb0J1FVHVwv3kOsehGfRyNQBQFitQpYVEXASwLEqXHwhiWr1LuqbrvtNikuLpZx48bJ8ePHjRslT0LAAwKHDx8W9YsDAQTMLUCsmnt+6B0CVgFi1SrBVwTMLUCsmnt+qtq7JRl75KyZ/5bFh/c63HpeiwRZeP5d0rthc4drFJhfgFg1/xzRQwSUALHK9wEC5hcgTo2bI8OSVWpIkyZN0hJVO3bskJEjR8rHH3/MyhTj5ponIYAAAggggAACCCCAAAIIICAlpSXaln+XWFZUHc7LKiMSEhQkz/YeLV+cdY3ERtQqc40PCCCAAAIIIIAAAgh4SsCwd1YNHDjQNobQ0FA5duyYlrxSCaw6depITEyMS9sKBFn+4Lxs2TJbW5wggAACCCCAAAIIIIAAAggggIBrAsfzsuWupd/LvEO7HG5oFlVHPh56pfRv1MrhGgUIIIAAAggggAACCHhSwLBk1f79+8sko1TSSR2lpaVy6tQpOX36dKXjVHWt91VamQoIIIAAAggggAACCCCAAAIIIGATWHFkn9y2ZIYczDljK7OejGzWXv496DJpEFnbWsRXBBBAAAEEEEAAAQQMEzAsWaVGpJJNzo6Krjm7h3IEEEAAAQQQQAABBBBAAAEEEKhYQP19+52ty+TpdXOluNzfy4MtP0j6ROII+UfXwRIcZOibAiruNFcRQAABBBBAAAEEAkrAsGTVihUrAgqWwfq/QGxsrP8PkhEi4AcCxKofTCJDCAgBYjUgpplB+oEAsep7k3gyP0fuXfaj/J6+3aHzTSKj5T9DrpAhcW0crlHg2wLEqm/PH70PHAFiNXDmmpH6rgBxatzcBVl+wsr5cifj+sGTTCyQlJQkGRkZEhcXJ8nJySbuKV1DAAEEEEAAAQQQQAABBBCwCiQfOyC3LJ4h+7NPWYtsX8+yJKg+GHyFNK4VbSvjBAEEEEAAAQQQQAABbwkYtrLKWwPkuQgggAACCCCAAAIIIIAAAggEkoD6mdQPUlfKxLWzpbCkpMzQ1dujH+l+lvzT8iskmG3/yuDwAQEEEEAAAQQQQMBrAiSrvEbPg31dIC0tTRtCfHy8rw+F/iPg1wLEql9PL4PzIwFi1Y8mk6H4tQCxav7pPVWQJ39f8ZP8vG+rQ2cbRkTJB5Zt/85u2s7hGgX+JUCs+td8Mhr/FSBW/XduGZn/CBCnxs0lP0ZlnDVP8jOBzMxMUb84EEDA3ALEqrnnh94hYBUgVq0SfEXA3ALEqrnnZ8OJQ3L2rH/rJqoGNm4liy64m0SVuafQbb0jVt1GSUMIeFSAWPUoL40j4BYB4tQtjC41wsoql5iohAACCCCAAAIIIIAAAggggIA5BdS2f5/sSJbH1/wm+SXFDp38R9ch8kTi2RIaHOJwjQIEEEAAAQQQQAABBMwgQLLKDLNAHxBAAAEEEEAAAQQQQAABBBCohkBWYb48uPJX+XbvRoe764XXkvcHXSbntujocI0CBBBAAAEEEEAAAQTMJECyykyzQV8QQAABBBBAAAEEEEAAAQQQcFFgS+ZhuWXRDNl++pjDHX0btpCPhl4pLWvHOlyjAAEEEEAAAQQQQAABswmQrDLbjNAfBBBAAAEEEEAAAQQQQAABBCoR+HLXOhm/aqbkFhc51Ly30wCZ1GuUhIfwV34HHAoQQAABBBBAAAEETCnAn1xNOS10yhcEmjRp4gvdpI8IBLwAsRrw3wIA+IgAseojE0U3A16AWPX+t0BOUYE8snqWfLFrvUNn6oRFyLsDL5ULW3V2uEZBYAkQq4E134zWdwWIVd+dO3oeOALEqXFzHWR5EWupcY/jSb4okJSUJBkZGRIXFyfJycm+OAT6jAACCCCAAAIIIIAAAgj4vMD2U0fl5sUzZGvmEYexJNZvKp8MHSutY+o7XKMAAQQQQAABBBBAAAGzC7CyyuwzRP8QQAABBBBAAAEEEEAAAQQCXmDGng3y4MpfJLuo0MHito595bmk0RIZEuZwjQIEEEAAAQQQQAABBHxBgGSVL8wSfTSlQGpqqtavhIQEU/aPTiGAwJ8CxCrfCQj4hgCx6hvzRC8RIFaN/x7IKy6Uf635n0zbscbh4dGh4fLmgIvl8tbdHK5RENgCxGpgzz+j9x0BYtV35oqeBq4AcWrc3JOsMs6aJ/mZQF5enp+NiOEg4J8CxKp/ziuj8j8BYtX/5pQR+acAsWrsvO4+c1xuWTRDNpzMcHhwl9jG8umwq6R9nYYO1yhAgFjlewAB3xAgVn1jnuhlYAsQp8bNP8kq46x5EgIIIIAAAggggAACCCCAAAIuCfy8b4vct/wnOVOY71D/xva95aU+50mtULb9c8ChAAEEEEAAAQQQQMAnBUhW+eS00WkEEEAAAQQQQAABBBBAAAF/FCgoLpJJ6+bI1G0rHYYXZXkn1eT+F8g1bXs6XKMAAQQQQAABBBBAAAFfFvBasqqkpER27Ngh+/fvl6ysLCksdHxJrDPYsWPHOrvklnLVp0WLFsmKFStk69atcvDgQcnOzpbo6Ghp2rSp9O7dWy677DIZOHBgtZ534sQJ+eWXX+R///uf7NmzR44cOSIRERHSqFEjadWqldbu4MGDJTEx0aX209PT5euvv5Y5c+aIOs/JyZEmTZpI165d5fLLL5cxY8ZIUFCQS21RCQEEEEAAAQQQQAABBBBAwDsC+7JOyq2Lv5Xk4+kOHUio21CmDb1KOlu2/+NAAAEEEEAAAQQQQMDfBAxPVh04cEDeeOMN+fXXX7UEUFVBVdLFU8mqTZs2yWOPPSbr1q3T7VZmZqaoXyqB9cUXX2hJpTfffFOaN2+uW798YWlpqXz22Wfy0ksvyenTp8tcVntfnjp1Snbu3Cnz58+XqKgoLZlXppLOh6+++kqefPJJLUFlfzktLU3Ur1mzZsnQoUPlrbfeksaN+UuNvRHnCCCAAAIIIIAAAggggIBZBH47kCr3LvtBMgsc3417dZse8lq/CyQ6LMIs3aUfCCCAAAIIIIAAAgi4VcDQZNW8efPknnvukdzcXFGJG7Mdu3btckhUtW3bVjp16iT16tXTEkxr1qyRQ4cOaV1fvny5XHzxxfL9999LfHx8hcNR433kkUfkyy+/tNWrW7euJCUl2ZJIaoXVli1bJCPD8eW5tpvsTtRqqvHjx9tKVHtqRVZMTIykpqbK+vXrtWuLFy+W6667Tn766SepXbu2rT4nNROobM5r1jp3I4CAuwSIVXdJ0g4CnhUgVj3rS+sIuEuAWHWX5F/tFJYUy7Pr58nbW5b9Vfj/Z5EhofKy5d1UN1jeUcVuGQ48FFQgQKxWgMMlBEwkQKyaaDLoCgJOBIhTJzAeKDYsWaW2p7vrrrtErSCyHmqlT5cuXbREUGioYV2xPt7p19atW2vJHbWFntr2z/5Q2xdOnz5dJkyYoCXdVGLpvvvuk59//rnCvzw8++yztkRVw4YNtftV+yEhIfbNa+dqddXMmTMdyu0LVB21Csx6qLZefvllbUWWtWzJkiWauXU12MSJE+X111+3XuZrDQViY2Nr2AK3I4CAEQLEqhHKPAOBmgsQqzU3pAUEjBAgVt2rnJ59Sm5b8q2sPLrfoeF2MfVl2rCrpHu9OIdrFCBQmQCxWpkQ1xEwhwCxao55oBcIVCRAnFak495rQZYVP4YscVKJkmnTpmkJHfU+JZVYGTlypHtHU8PW1Eqpffv2yZVXXqmbRLJv/rfffpPbb7/dVqS2BRw+fLjts/3JqlWrtHdHKWqVoPvuu+9ErdiqyaESf2orRXX07dtXW90VHBzs0KTaUvCGG27QylViTK1u69Chg0O9igrU6i+VlIuLi5Pk5OSKqnINAQQQQAABBBBAAAEEEEDABYF5B3fKXUu/l+P5OQ61L4vvKm/0v0jqhEc6XKMAAQQQQAABBBBAAAF/FHDMbnholIsWLdJaViuo1HuWzJaoUp0bOHCgXH311ZUmqlTd8847T3r16qVOtUMlgZwdahWWNSeo3ldV00TV0aNHtXdRWZ/3xBNPiF6iSl0fMWKE9s4qdV5cXCyff/65OuVwg0BKSoqoXxwIIGBuAWLV3PND7xCwChCrVgm+ImBuAWK15vNTbNmt4/n182Xs/P86JKrCg0Pk1b7ny0dDriRRVXPqgG6BWA3o6WfwPiRArPrQZNHVgBUgTo2besOSVQcPHtRWVamEUFVX9hjHUbUn9enTx3bD/v2O2zaoi2ol0ubNm7V67du3l3PPPdd2T3VPZs+eLWo7QnWoxJdaWVXRcdVVV9ku//7777ZzThBAAAEEEEAAAQQQQAABBIwTyMg5I5fO+0xe27RIym9x0qp2rPx+7q1ye0K/CreYN663PAkBBBBAAAEEEEAAAeMEDHtRVFhYmPa+qpYtWxo3Og8/yf4Ft2rVkt7x/fff24rPP/9823lNTpYt++vFuyr5V9kxaNAgWxX17rA9e/ZImzZtbGWcIIAAAggggAACCCCAAAIIeFZgccYeud3yfqojedkODzq/RYK8O/BSiY2o5XCNAgQQQAABBBBAAAEEAkHAsGRV8+bNZdu2bZKVleU3rmo81qNZs2bW0zJfV69ebfvco0cP7Xz9+vXadnzqHVmHDx+WyMhIUT5Dhw6VG2+8UeLj42336J3s2LHDVty9e3fbubMT9a6pRo0aido+UB3qfpJVzrQoRwABBBBAAAEEEEAAAQTcJ1BSWiKTNy6Wlzb+ISXlXhkdGhQsT/c+R+7pNIDVVO4jpyUEEEAAAQQQQAABHxQwLFl1zjnnyNatW7Vt8XzQyaHLaoXS0qVLbeUq0VT+yMvLk9TUVFuxSmi98MIL8v7779u28VMXVb3MzExtu8CPPvpIHnzwQXnggQds95U/2bVrl62oRYsWtvOKTlQyzJqssr+/onu4hgACCCCAAAIIIIAAAgggUH2BY5ZVVHct/V7mH/rr73DW1ppF1ZFpQ8dKv0YtrUV8RQABBBBAAAEEEEAgYAUMS1bdcMMNMm3aNFFJnh9//FEuvfRSn0Z/6qmnxLr1n0oEqWRc+ePIkSNSVFRkK546dar8/PPP2uc6deqI2p6vQYMGcujQIVFb+6mkVWFhobzyyivaCrQnnnjCdq/1JDc3V6tn/axWTLly2NdTibHqHOo9WerdYxUdzlaYVXQP1xBAAAEEEEAAAQQQQAABfxNYfiRN2/bvoOU9VeWPUc3ay9RBl0mDyNrlL/EZAQQQQAABBBBAAIGAFDAsWdW0aVOZMmWK3HXXXfLoo49K/fr1ZdiwYT6JPn36dJk1a5at748//rhERETYPltPTp8+bT3VvloTVddcc40888wzUrv2X38xOXbsmLaiav78+VpdtfpK+ZRfsZWTk1OmTbWFoCuHfb3sbMc90l1pQyXf+vbtW2FVexdrxcTERO1UJcnS0tKsxbavqm8JCQna54yMDG1rRNvF/z+JjY21bY+o2tBLuDVp0kTUlofqUCvaVPKv/KG2WFRtqSMlJUX7Wv4/qi+qT+VXxtnXU2NS9dQc67Xjq2NSY/S3eWJMh+2/dbVzM8aTJ+fJ+oMF6v8vRv4/wpNjsk6q0f/fY0zEkye/96yxWv73VX/9cwTxRDx5Mp7sd3ewPkd9dUc8qb/DWHd3MMufy9XYzPRnWLXt37/mfScfZmyREilV3bMdwRIkE3qOkLva9ZEdqdvlgO3KXyfumKdA+/OeVY8/G/3199zyv6+6+vdcZWmmeFL9Kf9nA1WmDsbkX//GouY0EL/31Pex2glJ7/uc/5cb8+97gfq9p8btS/8O6815sv891Z3/tuzNMalnW4+ajsn6Z1drezX5aliySq2oUu9Xeumll2TChAnyt7/9TUaNGiUXXnihdOrUSdRKo6CgIJfGolYyeetQv3mo5JT1UCvELrvsMuvHMl/LJ5bUxXPPPVcmT55cpp760LBhQ1FbAF5wwQWyZcsWKbXsZf7GG284JKvKJ2DCwsIc2tIrCA8PtxWXb8N2gZMqCVgTWlW6icoIIGC4QEhIiOHP5IEIIFB1AWK16mbcgYA3BNTfP9Sfgzn0BU7m58g9y36U/2Vsd6jQIDRSPhx8uZzdsqPuD9Y53EABAjUQ4PfVGuBxKwIGCqjfU139tz0Du8WjEEDAToDfU+0wPHwaZEmKlP1RLw89UP30nX0ySj3W/rOrj1X37Nu3z9Xqbq2nnnvJJZeIWmGkjs6dO8sPP/wgMTExus9Ria3zzz+/zLWFCxdK+/bty5TZf5g7d67cdNNNtqJ169ZJ48aNbZ+PHz8uPXr0sH2urD1rxbvvvlt++eUX7eMtt9wizz33nPVSpV+TkpK0TLvqx8yZMyusH0jbAFqTfvxlvcJvCS4i4HUBYtXrU0AHEHBJgFh1iYlKCHhdgFh1PgVrjh2QWxbPkAPZpxwqDY9rKx8MuVwaRUY7XKMAAU8IEKueUKVNBNwvQKy635QWEXC3AHHqblHn7Rm2ssraBWuSypqoMihXZn18tb8ePnxYrr32WluiSi2P++KLL5wmqtSD7Lf5U587duxYYaJK1Rk+fLhtCzr1ec2aNWUSXlFRUarYdliDxVbg5MS+Xvl+ObnFoTg4OFgCKRnlAFCuwLq9ijuXOpZ7BB8RQMANAsSqGxBpAgEDBIhVA5B5BAJuECBWHRHV32n/nbpSnlw7Wwot7/m1P9TeIY/2GC7juw2TEMvfpzgQMEqAWDVKmucgUDMBYrVmftyNgBECxKkRyn8+w7Bkldq6z5qgMm547nnSiRMntETV3r17tQbVPthff/21qK8VHfXq1StzuUOHDmU+630IDQ2VNm3ayNatW7XLau9Q+6NWrVplkllqX1tXDvt61nc2uXIfdRBAAAEEEEAAAQQQQAABBPQFThXkyf3Lf5Jf9v/59zf7Wo0ia8sHlm3/hjdtZ1/MOQIIIIAAAggggAACCOgIGJasWrlypc7jzV905swZuf7668WaQa1fv76WqGrVqlWlnW/QoIGoxJB6QaQ6XF3RFB3919YQWVlZ2r32/2nXrp1s3rxZKzpw4ID9Jafn6p1h1kPdz4EAAggggAACCCCAAAIIIFB9gZTjB+Vmy7Z/e7NOOjQyqHG8/GfIFdI0qo7DNQoQQAABBBBAAAEEEEDAUYB9CBxNbCU5OTlyww03yIYNG7SyOnXqaFv/qe38XD06depkq5qdnW07r+jEPkGlnln+sF+htWnTpvKXHT6r1Vn2K6vs73eoTAECCCCAAAIIIIAAAggggIBTAbXt38fbV8vo/32km6h6sOsQ+WnUjSSqnApyAQEEEEAAAQQQQAABRwGSVY4mWol6x9PNN98sq1ev1j6r7fc+++wz6dGjh5M79IuHDBliu7Bjxw7bubOToqIi2bNnj+2y3juiBg0aZLu+fPly27mzkxUrVtguqe0Y1TaDHAgggAACCCCAAAIIIIAAAlUTOFOYL3cs/U4eXjVTCkqKy9xcL7yWfHP2dfJkr1ESGhxS5hofEEAAAQQQQAABBBBAg4t/XAABAABJREFUoGIBklU6PoWFhXLHHXfI0qVLtasRERHy8ccfS9++fXVqV1x03nnn2Sps375ddu3aZfusd7Jw4UJRiTJ1BFtewNuvXz+HaqNHj9auqQuqveTkZIc69gXTp0+3fTz33HNt55wggAACCCCAAAIIIIAAAgi4JrD55GEZMesD+W6v4+4WfRu2kEUX3C2jm7u+C4drT6UWAggggAACCCCAAAKBIRBk2cKgNDCG6tooi4uL5Z577pGZM2dqN4SGhsqHH34oKkFU3eOqq66yJb5U8uo///mPblMqSXbhhReKdWu/MWPGyEcffaRb984777T1sX///vLdd99JUFCQQ12V/Lruuuu08pCQEJk7d65UZRtDdWNSUpKorQTj4uIqTYw5dIACBBBAAAEEEEAAAQQQQMDHBb7YtU7GW1ZT5RUXOYzkvs4DtdVUYaymcrChAAEEEEAAAQQQQAABVwVYWWUnpfJ2Dz/8sC0JpFY2vfXWWzVKVKnmJ0yYICpRpI7ffvtNHnnkEVHvw7I/jh8/LrfddpstURUeHi4PPfSQfZUy56qNsLAwrWzlypXywAMPOLSpVoaNGzfOdt+VV15Z5USV7WZOEEAAAQQQQAABBBBAAIEAE8guKpB7l/0g9y3/ySFRVScsQv571tXybNK5QqIqwL4xGC4CCCCAAAIIIICA2wXcurKqZcuWtg6qVT779u2zfba/Ziusxkn5dqvRhNNbPvnkE3niiSds19W7nc466yzb58pOnn/+eadVyrddt25dUe+eql+/vrZqadmyZZKbm6vdr8b48ssvy/XXX++0PXXhq6++kvHjx9vqxMbGam3GxMSI2nJw3bp1tmudO3eWH3/8UaKjo21lrp6wskpfKjMzU7ug3DkQQMC8AsSqeeeGniFgL0Cs2mtwjoB5BQIpVlNPHZWbF02XbZav5Y9e9ZvJtGFjJT66XvlLfEbAFAKBFKumAKcTCFRTgFitJhy3IWCgAHFqHHaoOx+lViapRIvezoIVXXNnH2rSllrdZH/s2bNH1C9Xj4qSVTfffLOod1899dRTkpWVJadOndJWWZVvWyWxXnzxRbnkkkvKX3L4fO2112rWkyZN0lZVqcCZNWuWQ70hQ4ZoK8Sqk6hyaIwCm0BaWpp2TrLKRsIJAqYUIFZNOS10CgEHAWLVgYQCBEwpECixOn3PBnlo5S+SXVToMA93JPSTZ3uPlogQt/512uE5FCBQE4FAidWaGHEvAmYQIFbNMAv0AYGKBYjTin3cedXtf7rWS1RZO1zRNWsdf/6qkkvDhw+X6dOny5w5c2T//v1a0kolqNq3by8jRozQVlNVJfmh3kelVn+pVVaqzfT0dC1x1bhxY+nevbtcfvnlot59pZKIHAgggAACCCCAAAIIIIAAAs4F8ooL5bHVv8unO5MdKsWEhcubAy6Wy+K7OVyjAAEEEEAAAQQQQAABBGom4NZk1YEDB5z2pqJrTm8y+IJ6X5X65cmjadOm2vul1Dum3HU0b95c2w7QfktAd7VNOwgggAACCCCAAAIIIIBAIAjsPnPcsu3fDNl4MsNhuN3qNZFPhl4l7eo0cLhGAQIIIIAAAggggAACCNRcwK3Jqpp3hxYQQAABBBBAAAEEEEAAAQQQMFbgx7TN8vcVP8mZwgKHB9/Uvre82Oc8qRUa5nCNAgQQQAABBBBAAAEEEHCPAMkq9zjSCgIIIIAAAggggAACCCCAgI8J5BcXyZNrZ8sHqasceh4VEiav979Qrm6b6HCNAgQQQAABBBBAAAEEEHCvAMkq93rSWgAJREZGBtBoGSoCvitArPru3NHzwBIgVgNrvhmt7wr4U6ymZZ2UWxfPkLXHDzpMSKe6jWTa0LHSKbaxwzUKEPAFAX+KVV/wpo8IVFeAWK2uHPchYJwAcWqcdVCp5TDucTzJFwWSkpIkIyND4uLiJDnZ8UXDvjgm+owAAggggAACCCCAAAKBKzBr/za5d/mPcqogzwHh6jY9ZLJlRVXt0HCHaxQggAACCCCAAAIIIICAZwTcurIqNzdXatWq5ZmeOmnVG8900hWKEUAAAQQQQAABBBBAAAEETCxQWFIsz6ybJ+9sXebQy8iQUHml7/nyt3a9JCgoyOE6BQgggAACCCCAAAIIIOA5gWB3Nj1kyBD58ssvpbi42J3N6ralnvHf//5X1DM5EPCGgFptpn5xIICAuQWIVXPPD71DwCpArFol+IqAuQV8OVYPZJ+SC+d8opuoahdTX+aMuV1uaN+bRJW5vwXpnYsCvhyrLg6Ragj4hQCx6hfTyCD8XIA4NW6C3ZqsOnz4sDz66KMyaNAgmTZtmmRnZ7t9JFlZWfLRRx9pz3j88cflyJEjbn8GDSLgioD6fle/OBBAwNwCxKq554feIWAVIFatEnxFwNwCvhqrcw/ukLNmTZVVR/c7AF8e300WnH+XdKsX53CNAgR8VcBXY9VXvek3AtUVIFarK8d9CBgnQJwaZ+3WbQBHjx4ts2fPlvT0dHnyySflxRdflAsvvFAuvvhibQVUaGj1HldUVCRLliyRn3/+WX799VdRW/9ZX7U1ZswY47R4EgIIIIAAAggggAACCCCAgM8IFFm2/Xtpwx8yedNihz6HB4fIC33GyK0d+rCaykGHAgQQQAABBBBAAAEEjBWoXvbISR8//vhjWbBggTz77LOyfft2ycnJkRkzZmi/oqOjpU+fPtK/f3/p3LmztG/fXpo2bSrh4WVfWpufny+HDh2SXbt2yZYtW2TVqlWyZs0aUSuq1GFNUiUkJMjEiRNl+PDhWjn/QQABBBBAAAEEEEAAAQQQQMAqkJFzRm5f8q0sPZJmLbJ9jY+OlU+GXiU9GzSzlXGCAAIIIIAAAggggAAC3hNwa7JKDePss8/WEkg//PCDvPfee7Jt2zZtdGfOnJE//vhD+2U/3Fq1aon6pZJQeXl52qop++vq3JqgUuedOnWS++67Ty655BJ++k2BcCCAAAIIIIAAAggggAACCJQRWJSx25Ko+k6O5jluTX9hy07yzsBLpG54rTL38AEBBBBAAAEEEEAAAQS8J+D2ZJUaSlBQkFx++eXaL7V939dffy2///67lowqP1S1+kr9quiIjIwUtd3fNddco20nWFFdriGAAAIIIIAAAggggAACCASmQHFJiWXLv0Xa1n+l5QhCg4Llmd7nyN2dBvCDj+Vs+IgAAggggAACCCCAgLcFPJKssh/UkCFDtASTes/U4sWLtV/r1q2TrVu3itryT+9QySm1gqpXr14ydOhQ7ZdafcWBgJkEYmNjzdQd+oIAAk4EiFUnMBQjYDIBYtVkE0J3EHAiYOZYPZqXJXct/V4WHNrt0PvmUXVk2tCx0rdRS4drFCDgjwJmjlV/9GZMCFRXgFitrhz3IWCcAHFqnHWQZYu98j9wZtjTjxw5IkePHrWtrIqKipLGjRtLo0aNDOsDD6pcICkpSTIyMiQuLk6Sk5Mrv4EaCCCAAAIIIIAAAggggICBAsss76W6ffG3cij3jMNTRzfvIO8PukzqR0Q5XKMAAQQQQAABBBBAAAEEzCHg8ZVVFQ1TJabULw4EEEAAAQQQQAABBBBAAAEEqipQUloib21ZJs+tnyfF5X4OM8SyPf3EniPl/i6DJNiyBSAHAggggAACCCCAAAIImFfAq8kq87LQMwQqF0hLS9MqxcfHV16ZGggg4DUBYtVr9DwYgSoJEKtV4qIyAl4TMFOsnsjPkbuX/iBzDu5w8GhaK0b+M/RKGdSYP6s74FAQEAJmitWAAGeQCFRTgFitJhy3IWCgAHFqHDY/XmacNU/yM4HMzExRvzgQQMDcAsSqueeH3iFgFSBWrRJ8RcDcAmaJ1dVH98uwmVN1E1VnN20rCy+4i0SVub+V6J2HBcwSqx4eJs0j4PMCxKrPTyEDCAAB4tS4SWZllXHWPAkBBBBAAAEEEEAAAQQQQKAGAuqVy+9vWyGT1s6RIssWgPZHkOXD4z3Oloe6DZWQYH4u096GcwQQQAABBBBAAAEEzC5AssrsM0T/EEAAAQQQQAABBBBAAAEE5FRBrty3/Cf5df82B41GkbXlw8FXyFmWVVUcCCCAAAIIIIAAAggg4HsCJKt8b87oMQIIIIAAAggggAACCCAQUALrjx+UmxdPl7SsTIdxD7a8l+o/Q66UuKgYh2sUIIAAAggggAACCCCAgG8IkKzyjXmilwgggAACCCCAAAIIIIBAwAmobf8+2r5ankj+nxSUFDuM/2HLln+P9RguocEhDtcoQAABBBBAAAEEEEAAAd8RIFnlO3NFT00m0KRJE5P1iO4ggICeALGqp0IZAuYTIFbNNyf0CAE9ASNj9Uxhvjyw4mf5IW2zQ1fqR9SSqYMul3Oad3C4RgECCIgYGat4I4BA9QWI1erbcScCRgkQp0ZJiwRZflKt1LjH8SRfFEhKSpKMjAyJi4uT5ORkXxwCfUYAAQQQQAABBBBAAAEfEth0MkNuWTRDdp457tDrfo1aykeWbf9a1K7rcI0CBBBAAAEEEEAAAQQQ8E0BVlb55rzRawQQQAABBBBAAAEEEEDA7wTUz1L+d9c6eWT1LMkrLnIY332dB8mTvUZKGNv+OdhQgAACCCCAAAIIIICALwuQrPLl2aPvXhVITU3Vnp+QkODVfvBwBBCoWIBYrdiHqwiYRYBYNctM0A8EKhbwZKxmFxXI+FUz5evdKQ6dqBseKe8NvFTOb9nJ4RoFCCDgKODJWHV8GiUIIFBdAWK1unLch4BxAsSpcdYkq4yz5kl+JpCXl+dnI2I4CPinALHqn/PKqPxPgFj1vzllRP4p4KlY3ZZ5RG5ZPEO2nTrqANe7QTP5eOhYiY+u53CNAgQQ0BfwVKzqP41SBBCorgCxWl057kPAOAHi1DhrklXGWfMkBBBAAAEEEEAAAQQQQACBcgLfWFZSPbTyV8kpLix3ReTOhH7yTO/REhHCX10dcChAAAEEEEAAAQQQQMCPBPgTvx9NJkNBAAEEEEAAAQQQQAABBHxFILeoUB5f85t8unOtQ5djwsLlrQGXyKXxXR2uUYAAAggggAACCCCAAAL+J0Cyyv/mlBEhgAACCCCAAAIIIIAAAqYW2Hn6mLbt36aThx362b1enHwybKy0jWngcI0CBBBAAAEEEEAAAQQQ8E8BryWrSkpKZMeOHbJ//37JysqSwkLHLR+ckY8dO9bZJcoRQAABBBBAAAEEEEAAAQRMLPBD2iZ5YMXPcqawwKGXN7VPkhf7jJFaoWEO1yhAAAEEEEAAAQQQQAAB/xUwPFl14MABeeONN+TXX3+V7OzsKssGBQUJyaoqs3GDBwTi4+M90CpNIoCAuwWIVXeL0h4CnhEgVj3jSqsIuFugJrGaX1wkE9fOlg9TVzl0q7YlOfV6/4vkqjY9HK5RgAACVReoSaxW/WncgQAC1RUgVqsrx30IGCdAnBpnbWiyat68eXLPPfdIbm6ulJaWGjdKnoSABwRiY2M90CpNIoCAuwWIVXeL0h4CnhEgVj3jSqsIuFugurGalnVSblk0Q9adOOjQpU51G8mnw66SjpavHAgg4B6B6saqe55OKwgg4KoAseqqFPUQ8J4AcWqcvWHJqvT0dLnrrrskLy/PNrrGjRtLly5dpF69ehIaalhXbM/nBAEEEEAAAQQQQAABBBBAwLMCs/Zvk3uW/SCnC/MdHnRt20R5td8FUjs03OEaBQgggAACCCCAAAIIIBA4AoZliKZOnaolqtQ2fk2aNJGXX35ZRo4cGTjSjNTvBFJSUrQxJSYm+t3YGBAC/iRArPrTbDIWfxYgVv15dhmbPwlUJVYLS4rl6XVz5d2tyx0IIkNC5TVLkur6dr0crlGAAAI1F6hKrNb8abSAAALVFSBWqyvHfQgYJ0CcGmdtWLJq0aJF2qjUCqqvvvpKOnToYNwoeRICCCCAAAIIIIAAAggggIBhAgeyT8mti2fI6mMHHJ7ZoU4DmTb0Kular4nDNQoQQAABBBBAAAEEEEAgMAUMS1YdPHhQ1KqqgQMHkqgKzO81Ro0AAggggAACCCCAAAIBIDA7fbvcvfQHOVmQ6zDay+O7yRsDLpKYsAiHaxQggAACCCCAAAIIIIBA4AoYlqwKCwvTtgFs2bJl4GozcgQQQAABBBBAAAEEEEDATwWKLNv+vZCyQKZsXuIwwvDgEHmxzxi5pUMf7YcYHSpQgAACCCCAAAIIIIAAAgEtYFiyqnnz5rJt2zbJysoKaHAGjwACCCCAAAIIIIAAAgj4msC2zCMybccaWbZ/p+SWFEnDjDWSWL+plnzqFNtYDuWcltuXfCfLjqQ5DK11dD35ZOhYSWzQzOEaBQgggAACCCCAAAIIIICAEjAsWXXOOefI1q1bJTk5GXkEEEAAAQQQQAABBBBAAAEfEFh7LF2eXDtblpZLQu3KOy0rj+6XD1JXSdfYJnIgO1NOFeY7jOiilp3l7YGXSN3wSIdrFCCAAAIIIIAAAggggAACVoGgUsth/eDJr4cOHZIRI0ZoK6vefvttufTSSz35ONp2o0BSUpJkZGRIXFwcyUY717y8PO1TZCR/8bZj4RQB0wkQq6abEjqEgK4AsarLQiECXhWYk75Dbl40XXKKC6vcj7DgYHmm92i5K6E/2/5VWY8bEKi5AL+v1tyQFhAwQoBYNUKZZyBQMwHitGZ+Vbk7uCqVa1K3adOmMmXKFAm2/KXl0UcflUWLFtWkOe5FwOsCKklFosrr00AHEKhUgFitlIgKCJhCgFg1xTTQCQRsAmpF1U2LvqlWoqpF7boya/StcnenASSqbKKcIGCsAL+vGuvN0xCorgCxWl057kPAOAHi1Dhrw1ZWpaena6NSSaoJEyZIYWGhjBo1Si688ELp1KmT1KlTx+W/yKj3X3EYJ8DKKn1rsur6LpQiYDYBYtVsM0J/ENAXIFb1XShFwFsCF86e5rD1nyt9qRdeS5IvuV/qRUS5Up06CCDgIQF+X/UQLM0i4GYBYtXNoDSHgAcEiFMPoDpp0rB3VvXvX3b7B7X74Jw5c7RfTvqmWxwUFCT79u3TvUYhAkYKpKamao9LTEw08rE8CwEEqihArFYRjOoIeEmAWPUSPI9FQEdgW+aRaiWqVFMnC3LlSF42ySodV4oQMFKA31eN1OZZCFRfgFitvh13ImCUAHFqlLSIYdsAWodkfUWWSjqpQ32u6i9rW3xFAAEEEEAAAQQQQAABBBBwr8C0HWtq1ODH21fX6H5uRgABBBBAAAEEEEAAgcATMGxlldq6z5qgCjxmRowAAggggAACCCCAAAII+IZAyolDNerohhMZNbqfmxFAAAEEEEAAAQQQQCDwBAxLVq1cuTLwdBkxAggggAACCCCAAAIIIOBjAlmFBTXqcVZRfo3u52YEEEAAAQQQQAABBBAIPAHDtwEMPGJGjAACCCCAAAIIIIAAAgj4hsAxy/umjudn16iz0aERNbqfmxFAAAEEEEAAAQQQQCDwBAxbWRV4tIwYAQQQQAABBBBAAAEEEPANgTOF+fLe1uXyzpZlklVUs5VVPerH+cag6SUCCCCAAAIIIIAAAgiYRiCo1HKYpjd0xJQCSUlJkpGRIXFxcZKcnGzKPtIpBBBAAAEEEEAAAQQQqLpAfnGRfLJjjby2cZEcy8+pegM6d6y4aJwk1G2kc4UiBBBAAAEEEEAAAQQQQEBfwBQrq44cOSInTpyQrKwsiY6Olvr160vjxo31e0wpAggggAACCCCAAAIIIIBAjQSKS0pkxt4N8kLKAtmffapGbdnfPKRJaxJV9iCcI4AAAggggAACCCCAgEsCXktWrVq1Sj777DNZvny5qGRV+UMlqwYNGiQ33HCD9OvXr/xlPiPgdYHMzEytD7GxsV7vCx1AAAHnAsSqcxuuIGAmAWLVTLNBX/xZQG2s8b/07fLM+nmyNdPx72Fq7EGWX6OadZDFh/dInmXllatHVEiYPN3rHFerUw8BBDwowO+rHsSlaQTcKECsuhGTphDwkABx6iFYnWYNT1apFVTjx4+XOXPmaN1xtgvh4cOH5ccff9R+jR49Wl599VVtxZXOGChCwCsCaWlp2nNJVnmFn4ci4LIAseoyFRUR8KoAsepVfh4eIALLj6TJ0+vmysqj+52OeEzzjjKh50jpWq+JzEnfITcvmi45xYVO61svqETVJ8Oukt4Nm1uL+IoAAl4U4PdVL+LzaASqIECsVgGLqgh4SYA4NQ7e0GSVSlRddtllsnv3bimfpIqIiJCoqCjJycmR/Px8TcBaZ/bs2bJz50754YcfSFgZ973BkxBAAAEEEEAAAQQQQMAPBDafPCzPWlZSqRVVzo4BjVrJpF6jZEDjVrYq5zTvIL+cc7NMWjdHlhzeaysvf6K2/lMrqkhUlZfhMwIIIIAAAggggAACCLgqYGiy6r777pNdu3ZJUJDaWELk7LPPluuuu0769u0rDRs2tPX5+PHjsnr1avnqq69k3rx5WrlKcN1///3yxRdf2OpxggACCCCAAAIIIIAAAgggoC+QlnVSXrS8k2r6ng1Sql9FusQ2lid7jpLRlsSU9e9p9lVVAkolrLZZtgyctmONLN+/S3JKiqRRTF3pUT9Obu3Yl3dU2YNxjgACCCCAAAIIIIAAAtUSMCxZtWjRIlG/1F+AIiMj5e2335YxY8bodrpBgwbaNXVdraoaN26c5ObmaverNoYNG6Z7H4UIIIAAAggggAACCCCAQKALHM3LkskbF8vHO1ZLYUmJLker2rHyr8Sz5crW3SUkOFi3jn1hJ0tS6+W+50tKeIpWnJiYaH+ZcwQQQAABBBBAAAEEEECgRgKV/62kRs3/dfPPP/9s+6DeP+UsUWWr9P8n6n1Vr732mq34p59+sp1zggACCCCAAAIIIIAAAggg8KfA6YI8bSVVrx/flH+nrtRNVDWMiJKX+5wnqy6+T65um+hSogpfBBBAAAEEEEAAAQQQQMDTAoatrFq1apU2lvj4eLn00kurNK5LLrlEVIJr7969Ym2nSg1QGQEPCKgVghwIIGB+AWLV/HNEDxFQAsQq3wcIVF8gv7hIPt6+WiZvWizH83N0G4oJC5f7Og+SezoPlJiwCN06rhQSq64oUQcB7wsQq96fA3qAgCsCxKorStRBwLsCxKlx/oYlq44cOaJtAdi7d+9qjU7dp5JVR48erdb93ISAuwUSEhLc3STtIYCABwSIVQ+g0iQCHhAgVj2ASpN+L1Bs2eJPvY/qhQ0L5ED2Kd3xhgeHyG2W90o91G2oNIysrVunKoXEalW0qIuA9wSIVe/Z82QEqiJArFZFi7oIeEeAODXO3bBkVWFhoTaq8PDwao3Oep+1nWo1wk0IIIAAAggggAACCCCAgI8LlJaWym8HUuXZ9fNk2yn9H+YLsozxGss2f4/1OFtaRcf6+IjpPgIIIIAAAggggAACCPi7gGHJqoYNG8rBgwdl+/bt1TK13qfa4UDADAIZGRlaN+Li4szQHfqAAAJOBIhVJzAUI2AyAWLVZBNCd0wrsOxImjy9bq6sOrrfaR/Pb5EgT/QcIV1imzitU90LxGp15bgPAWMFiFVjvXkaAtUVIFarK8d9CBgnQJwaZ21Ysqpbt26Snp4u69evl02bNon67OqxefNmWbdunbaNYNeuXV29jXoIeFTg8OHDWvskqzzKTOMI1FiAWK0xIQ0gYIgAsWoIMw/xYYFNJzPkmXXzZM7BHU5HMbBxK5nUa5T0b9TKaZ2aXiBWayrI/QgYI0CsGuPMUxCoqQCxWlNB7kfA8wLEqeeNrU8Itp54+uvo0aO1R6gtK8aNGyfWjGRlz1XfDPfee6+o+9Rx7rnnVnYL1xFAAAEEEEAAAQQQQAABvxDYe+aE3LnkOxk2c6rTRFVXywqqb86+Tmaec4tHE1V+AcogEEAAAQQQQAABBBBAwJQChiWrrrjiComPj9cQdu3aJaNGjZKPPvpIMjMzdWFOnTol06ZNk3POOUd2796trapq1aqVqHY4EEAAAQQQQAABBBBAAAF/FjiSmyWPrJ4l/X55R2bs3Sh//uhe2RHHW95F9e/Bl8uiC+6S0c07an9nKluDTwgggAACCCCAAAIIIICAbwgYtg1gaGiovP3223L11VdLXl6elqR66qmn5Nlnn5V27dpJ8+bNJSoqSnJycrR3W+3cuVOKi4ttK6rUtXfeeUdUOxwIIIAAAggggAACCCCAgD8KnC7Ik3e2LpP3ti6X7KJC3SE2iqwt47sPk5vbJ0l4CH8/0kWiEAEEEEAAAQQQQAABBHxKwNC/2fTu3Vs+//xzue+++7RtANXWfkVFRbJ9+3btl72cdds/VabeCfTuu+9Kr1697KtwjgACCCCAAAIIIIAAAgj4hUBecaF8vH2NTN60SE7k5+qOKSYsXO7vPFju6TxAosMidOtQiAACCCCAAAIIIIAAAgj4ooChySoFNGDAAJk3b558+OGH8uWXX8qRI0dsq6fKAzZu3Fiuv/56uf3226Vu3brlL/MZAa8KxMbGevX5PBwBBFwTIFZdc6IWAt4WIFa9PQM831sCxSUl8s2eFHlxwx9yIPuUbjfCg0PkjoR+8mDXIdLAsqrKmwex6k19no2A6wLEqutW1ETAmwLEqjf1eTYCrgkQp645uaNWkGUFk9725+5o26U2duzYIZs2bZLjx49Ldna21K5dWxo0aCDdunWTDh06uNQGlTwrkJSUpK2EUyvckpOTPfswWkcAAQQQQAABBBBAIAAE1F/DfjuQKs+unyfbTh3VHXFwUJBc2zZRHu0xXFrWjtWtQyECCCCAAAIIIIAAAggg4A8Chq+sKo+mElIkpcqr8BkBBBBAAAEEEEAAAQT8VWDp4b3y9Lq5svrYAadDvKBlJ3kicYR0jm3stA4XEEAAAQQQQAABBBBAAAF/EfB6sspfIBlH4AmkpaVpg46Pjw+8wTNiBHxIgFj1ocmiqwEtQKwG9PQHzOA3njgkz1hWUs09uNPpmAc1jpdJvUZJv0Ytndbx5gVi1Zv6PBsB1wWIVdetqImANwWIVW/q82wEXBMgTl1zckctklXuUKSNgBTIzMzUxk2yKiCnn0H7kACx6kOTRVcDWoBYDejp9/vB7zlzQl5IWSDf7t3odKzd6jXRklQjm7aXIMv2f2Y9iFWzzgz9QqCsALFa1oNPCJhVgFg168zQLwT+EiBO/7Lw9BnJKk8L0z4CCCCAAAIIIIAAAggEpMDh3DPy2sZF8smOZCkqLdE1aB1dT9vu7/LWXSU4KFi3DoUIIIAAAggggAACCCCAgL8LuDVZNWXKlDJeDz74oO1z+Wu2C9U4sW+3GrdzCwIIIIAAAggggAACCCDgMYFTBXnyzpZl8t7W5ZJTXKj7nMaRteWf3c+SG9v3lvAQt/61TPd5FCKAAAIIIIAAAggggAACZhZw69+KJk+eXGbLCvukUvlrNUGxb7cm7XAvAggggAACCCCAAAIIIOAugTxLYuo/qavl9U2L5WRBrm6zMWER8vcug+WezgOkdmi4bh0KEUAAAQQQQAABBBBAAIFAE3BrskrhlZaWaoZ6+6xbr9UEWa/dmrTHvQgggAACCCCAAAIIIIBATQSKSorl690p8uKGP+RgzmndpiKCQ+SOhH7yYLehUj8iSrcOhQgggAACCCCAAAIIIIBAoAq4NVn10EMPOXWs6JrTm7iAgIkFmjRpYuLe0TUEELAKEKtWCb4iYG4BYtXc80Pv9AXUD+PN3L9NnkuZJ6mnjulWCg4Kkuva9pRHewyXFrXr6tbxpUJi1Zdmi74GsgCxGsizz9h9SYBY9aXZoq+BKkCcGjfzQZa/YP25FMq4Z/IkHxNISkqSjIwMiYuLk+TkZB/rPd1FAAEEEEAAAQQQQMD9Aksy9sjT6+fKmmPpThu/sGUnmdBzpCTUbeS0DhcQQAABBBBAAAEEEEAAAQRE3LqyClAEEEAAAQQQQAABBBBAwJ8FNpw4JM+smyvzDu1yOswhTVrLpF6jpE/DFk7rcAEBBBBAAAEEEEAAAQQQQOAvAZJVf1lwhkCVBFJTU7X6CQkJVbqPygggYKwAsWqsN09DoLoCxGp15bjPKIHdZ47L8+sXyPdpm5w+ske9OHnSkqQa0bSd+Ou7dolVp9PPBQRMJUCsmmo66AwCTgWIVac0XEDANALEqXFTYViyyvrOqqFDh8pll11W5RH+/PPP8scff2h/6Zs8eXKV7+cGBNwtkJeX5+4maQ8BBDwgQKx6AJUmEfCAALHqAVSadItARs4ZeXXTQvlsx1opKi3RbbNNdD15oucIuSy+qwQHBevW8ZdCYtVfZpJx+LsAservM8z4/EWAWPWXmWQc/ixAnBo3u4Ylq6ZPn64lmmrXrl2tZFVKSopY2yBZZdw3CE9CAAEEEEAAAQQQQCAQBU4V5MlbW5bK1K0rJKe4UJegSWS0/LPHWXJj+94SFhyiW4dCBBBAAAEEEEAAAQQQQACBygUMS1ZV3hVqIIAAAggggAACCCCAAALeFcgtKpT/bF8lUzYtkZMFubqdiQmLkAe6Dpa7Ow2Q2qHhunUoRAABBBBAAAEEEEAAAQQQcF3AZ5JVpaWl2qj8de9316eMmggggAACCCCAAAIIIOBugaKSYvlqd4q8tOEPOZhzWrf5CMvqqTs79Zd/dB0i9SOidOtQiAACCCCAAAIIIIAAAgggUHUBn0lWnThxQhud2kaQAwEEEEAAAQQQQAABBBBwh4D6obhf92+V59bPl+2nj+k2GRwUJH9r10se6X6WNK9dV7cOhQgggAACCCCAAAIIIIAAAtUX8Ilk1ZkzZ2TRokXaO6+aN29e/dFyJwJuFIiPj3djazSFAAKeEiBWPSVLuwi4V4BYda8nrbkmsDhjjzy9bq4kH093esPFrTrLE4kjpGPdRk7rBNIFYjWQZpux+rIAserLs0ffA0mAWA2k2WasvipAnBo3cx5JVk2ZMsXpCNatWycVXbe/sbCwUDIyMmThwoVy5MgRLVnVt29f+yqcI+A1gdjYWK89mwcjgIDrAsSq61bURMCbAsSqN/UD79kpxw/KM+vnyfxDu5wOfmiT1jKp1yhJatjCaZ1AvECsBuKsM2ZfFCBWfXHW6HMgChCrgTjrjNnXBIhT42YsyLLtxZ8vg3LjM1u0aKElluybtD6muu+cUvdHRETIrFmzJCEhwb5pzj0skJSUpCUN4+LiJDk52cNPo3kEEEAAAQQQQAABBDwjsOv0cXk+Zb78kLbZ6QMS6zfVklTD49o6/J3G6U1cQAABBBBAAAEEEEAAAQQQqJGAR1ZWqR5Zk1Ple+esvHy98p+bNWsmL7zwAomq8jB89ppASkqK9uzExESv9YEHI4BA5QLEauVG1EDADALEqhlmwX/7kJFzRl7ZuFA+37lWikpLdAfaNqa+TOg5Qi5p1UWCg4J161AoQqzyXYCAbwgQq74xT/QSAWKV7wEEzC9AnBo3Rx5JVj300EMOI3j99de1n0xU/7A/YsQIh+vlC9QKLLWSql69elqCqlevXob9ZOP+/fu1d2StWLFCtm7dKgcPHpTs7GyJjo6Wpk2bSu/eveWyyy6TgQMHlu92lT9v3LhRLrjgAikuLrbdO2PGDBk0aJDtc2Un6enp8vXXX8ucOXNEnefk5EiTJk2ka9eucvnll8uYMWMMs6usr1xHAAEEEEAAAQQQQMAogVMFufLm5qUyddsKyS0u0n1sXK1oeaT7cPlb+14SFhyiW4dCBBBAAAEEEEAAAQQQQAABzwoYmqxSQ1FJJ71klmeH6VrrmzZtkscee0zUe7X0jszMTFG/VALriy++0JJVb775pjRv3lyveqVlRUVFMn78+DKJqkpvKlfhq6++kieffFJLUNlfSktLE/VLbZs4dOhQeeutt6Rx48b2VThHAAEEEEAAAQQQQMAvBXKLCuWD1JXyxuYlklmQpzvGOmER8o+uQ+SuTv0lKjRctw6FCCCAAAIIIIAAAggggAACxgh4JFml1/UBAwZoxW3atNG7bIqyXbt2OSSq2rZtK506ddJWeJ0+fVrWrFkjhw4d0vq7fPlyufjii+X777+X+Pj4Ko9h6tSpohJk1T3UaiqV7LIedevWlcGDB0tMTIykpqbK+vXrtUuLFy+W6667Tn766SepXbu2tTpfEUAAAQQQQAABBBDwK4GikmL5ctd6eXnjH3LQsvWf3hEZEip3JvS3JKoGS72IKL0qlCGAAAIIIIAAAggggAACCBgsYFiy6ttvvzV4aNV/XOvWrbXkjtpCT237Z3+UlJTI9OnTZcKECZKbmysZGRly3333yc8//1ylrfZ2794tU6ZM0ZpWWwr+8MMP9o+p9Hznzp3aKjBrRdXXl19+WaKi/voL95IlS+Suu+6yrQabOHGiqO0YORBAAAEEEEAAAQQQ8CcB9V7cn/dtkedT5suO08d1hxZi2Wb8b+16ySM9hkuzqDq6dShEAAEEEEAAAQQQQAABBBDwjgBvDrZzV9vkqWTOokWLZNy4cQ6JKlU1ODhYrrnmGnn77bdtd65du1YWLlxo+1zZifrL9COPPCJ5eXkSGxsrTz31VGW3OFx/9dVXpbCwUCvv27evqO0I7RNV6sKQIUPK9FMlDHfs2OHQFgUIIIAAAggggAACCPiqwMJDu2Xkbx/KzYtnOE1UXdqqiyy/cJy8MeBiElW+OtH0GwEEEEAAAQQQQAABBPxawLCVVb6gOHDgQO09VK709bzzztPev2V9v9W8efNk+PDhrtyqve9KbSGojieeeEIaNmzo0n3WSkePHtXeRWX9rNpQSTS9Y8SIEdo7q9RWgMXFxfL555/LM888o1eVsioKJCQkVPEOqiOAgDcEiFVvqPNMBKouQKxW3SzQ71h//KA8s36uLLAkq5wdZ8W1kSd7jpLeDav3jlln7QZyObEayLPP2H1JgFj1pdmir4EsQKwG8uwzdl8RIE6NmymvJquys7Nly5YtcuLECcnKyhK1xZ4rx9ixY12p5vE6ffr0sb3jav/+/S49T20b+Pzzz2t1+/fvL9dee61L99lXmj17ts1KvVNLrayq6LjqqqtEJavU8fvvv5OsqgirCtciIyOrUJuqCCDgLQFi1VvyPBeBqgkQq1XzCuTaO08fkxdSFsgPaZudMvSs31Qm9Rolw5u2c1qHC9UTIFar58ZdCBgtQKwaLc7zEKieALFaPTfuQsBIAeLUOG2vJKvU+5k+/vhjSUlJEbUlXlWOIMte82ZJVqm+WA+1asmVQ62COn36tISHh2vvmLJvw5X7VZ1ly5bZqqrVYJUdgwYNslVJT0+XPXv2SJs2bWxlnFRPQG3jqA7+h1U9P+5CwCgBYtUoaZ6DQM0EiNWa+QXC3YdyTsvLGxbKf3etlWInf4doF1NfJvQcKRe36izBQfo7DwSClSfHSKx6Upe2EXCfALHqPktaQsCTAsSqJ3VpGwH3CBCn7nF0pRVDk1W5ubly9913y/z587W+VZSoUkmciq67MjhP19m2bZvtEc2aNbOdOzv59ddftZVN6vq9994rHTp0cFa1wnL790517969wrrqYlxcnDRq1EjU9oHqUPeTrNIoavSf1NRU7f7ExMQatcPNCCDgWQFi1bO+tI6AuwSIVXdJ+l87mfm58saWJfLBtpWSW1ykO8CmtWLkkR5nyfXteklYcIhuHQrdI0CsuseRVhDwtACx6mlh2kfAPQLEqnscaQUBTwoQp57ULdu2ocmq8ePHi3q3kzoiIiJErfhR2+ft3LlTVHLqyiuv1LYDVKt/tm7dKoWFhVp5VFSUqHdEVWcVUtnhuu+T6uPSpUttDQ4dOtR2rndy6tQpmThxonZJJYr+/ve/61VzqWzXrl22ei1atLCdV3TSvHlzW7LK/v6K7uEaAggggAACCCCAAALeEsgpKpAPUlfJG5uXyKmCP1e0l+9L3fBI+UfXIXJnQj+JCg0vf5nPCCCAAAIIIIAAAggggAACPiJgWLJq7dq18tNPP2kJp9atW8tXX30lKtEyYcIELVmlvKZMmWJjO3PmjHzxxRfyxhtviHq31fHjx+X999+X6OhoWx1vnjz11FNi3fpPJYLOOeecCrvzzDPPyJEjR7Q6L730kpasq/AGJxfV6jTr0kNVRa2YcuWwr5eZmenKLQ511DvFDh486FBuX+DKCjP7+pwjgAACCCCAAAIIIGAvUFhSLF/sWievWLb8O5R7xv6S7bxWSKjc1WmAPNBlsMRG1LKVc4IAAggggAACCCCAAAIIIOCbAoYlq2bMmGETev3117VEla1A5yQmJkbbMnD06NHaO6r++OMPefDBB+XDDz/UqW1s0fTp02XWrFm2hz7++OMVJp+WLFkiX3/9tVZfrR4bMmSI7d6qnuTk5JS5xdX3JdnXU8m/6hwq2da3b98Kb7V3sVa0bpOnkmRpaWnWYttX1beEhATtc0ZGhhw+fNh2zXoSGxsr8fHx2kfVhl7CrUmTJtqWh6qSWp5pn9SztqPaUG2pQ70zTe9QfVF9Uvdbl3mWr2cdU35+vm47vjwmf5wnxlT2O9is8eSpeVI/7KAO9f8XtS2qOoz6f4SnxqQNwvIfb/x/jzFZ9f/8GmjxZB29J773rLFa/vdn6++5fO9Z9f33e69u3bqyPihbnl8/X3ae+fP/3WVHLRJi2Y3hhva95ZHuZ8np/Yckbdt2Kf+nS3f/eY/vvbKzoH6oUP1dTR1m+nM581R2nvj9iT/vlf99tSp/zyWeiCcl4Ik/71X2byyB+r2n/q2v/J+B1Rzw/3L+X66+D+wP/l/unX9btv89lb9rOP57ufXv7Pbfq9U9N+ytw6tXr9b6qCa0soSH/WDatm2rra5S76/6/fffZe7cufaXDT9Xv3mo5JT1uPTSS+Wyyy6zfnT4qlZCPfroo1p5vXr1ZNKkSQ51qlJQPgETFhbm0u3h4X9ti1K+DZcaoBICCCCAAAIIIIAAAh4SWJN1RK5f/4vcsniG00TV2XWbyy8DrpYp/S+SplF1PNQTmkUAAQQQQAABBBBAAAEEEPCGQJAlCVRqxIO7desm6r1NF1xwgUydOtX2SPUep2nTpmnbA6p3KdknVWyVLCcjR46U7du3y0UXXSTvvfee/SXDzvft2yeXXHKJbTu/zp07yw8//GD7yUK9jjz77LO28aoVZVdffbVeNVFbCVoPtQpNvc9L71CZ3B49etguLVy4UNq3b2/77Ozk7rvvll9++UW7fMstt8hzzz3nrKpDeVJSkrYioXHjxjJz5kyH6/YFgbQNoPWnXtyZPba35BwBBNwjQKy6x5FWEPC0ALHqaWFztr/ueLo8vW6uLMzY47SDZzdtKxN7jpReDf7687LTylzwuACx6nFiHoCAWwSIVbcw0ggCHhcgVj1OzAMQqLEAcVpjQpcbMGwbwKysLK1TanWR/WG/PZ2qU79+ffvLtnOV7FLLhTds2GArM/JEbU137bXX2hJVaoWYeqeWdQsMvb6ovlq3LVTJJ2eJKr17nZVFRUWVueTqKin7erVr1y7ThqsfgoODJZCSUZW5kKSqTIjrCJhDgFg1xzzQCwQqEyBWKxPyr+s7Th2T51Pmy0/7tjgdWO8GzeTJnqPkLEuyisM8AsSqeeaCniBQkQCxWpEO1xAwjwCxap65oCcIOBMgTp3JuL/csGRVrVq1RCWjioqKyoyiTp2/tvA4cOCA02SVdQGYem+S0ceJEye0RNXevXu1R6t9e9U7qNTXio7x48dLcXGx9j6rl156qaKqLl9Tjtb3Kambjh496tK99vXUnrccCCCAAAIIIIAAAggYLXAw57S8suEP+e+udVLsZIOH9jENZELPEXJxqy7a7gtG95HnIYAAAggggAACCCCAAAIIGC9gWLJKrchR2/idPHmyzCjVO6msR3Jycpkt7qzl6uuOHTvsPxp2rl4gfP3112urutRD1covlahq1apVpX3YvHmzVkdtbfjAAw9UWt9a4V//+pdER0drH7t37y4vvvii9ZL2tV27dmJtWyX4XDnS09Nt1dT9HDUXUC/+VAfJP42B/yBgWgFi1bRTQ8cQKCNArJbh8LsPJ/Nz5I3NS+WD1JWSV1z2h9esg20WFSOPdh8u17XrKaHBIdZivppMgFg12YTQHQScCBCrTmAoRsBkAsSqySaE7iCgI0Cc6qB4qMiwZFWnTp20hI96L5X90bNnT9tPTKpt9W644QYJDS3bLfVepo0bN2r1XEkS2bdfk/OcnBytP9atB9UqMNXHjh07VqlZlfBat26dy/fYJ+bst0m0NtChQwdbsmrTpk3WYqdfMzIyyqzAUvdz1FwgLS1Na4RkVc0taQEBTwoQq57UpW0E3CdArLrP0kwt5RQVyL+3rZQ3tyyVUwV5ul2LDY+UB7sOlTsS+kmt0DDdOhSaR4BYNc9c0BMEKhIgVivS4RoC5hEgVs0zF/QEAWcCxKkzGfeXB7u/Sf0W+/fvr11QySr71VXNmzeXfv36idrmT72T6pZbbhGVgCksLJTTp0/Lt99+K/fdd5+t0XPOOcd27skT9Y6nm2++WVavXq09Rm2/99lnnzld+eXJvpRvW73/ynosX77ceur064oVK2zXlHebNm1snzlBAAEEEEAAAQQQQMDdAoUlxTJt+xpJ+ukteWb9PN1EVa2QUEuSaoisv/QB+XvXwSSq3D0JtIcAAggggAACCCCAAAII+JBA2SVMHuz4yJEjtZVRKik1d+5cGTt2rO1pjz/+uFx22WXa5z/++EPUL71DbcF3xx136F1ya5lKlKnnLF26VGs3IiJCPv74Y+nbt2+VnmO/9V5lN6okkvWYMWOG2CekrOXWr6NHj5bHHntMSkpKRCX/1PaJSUlJ1ssOX6dPn24rO/fcc23nnCCAAAIIIIAAAggg4E6BktIS+TFtizyfMl92nzmh23RIUJDc2D5J/tl9mDSN+uv9tbqVKUQAAQQQQAABBBBAAAEEEAgIAcNWVqlkzJ133ikXXXSRHD9+vAxunz595NVXX5WQkBBthZVKaJX/1aBBAy1hpBJWnjyKi4tl3LhxMn/+fO0xakvCqVOnyrBhwzz52Cq13ahRIznvvPNs9zz//POal63A7kRtoah+qUP5qm0WORBAAAEEEEAAAQQQcKeA+rP7/IM7ZcRvH8ptS751mqi6LL6rrLzoPnm9/4Ukqtw5AbSFAAIIIIAAAggggAACCPi4gGErq5TTxIkTnXJdc8012uqgDz/8UFvRpN6zFBwcLPHx8TJq1ChtpZNKWHnyUH/Jfvjhh2XmzJnaY9Tz33rrLVErmcx2PPLIIzJ79mxtu8SVK1fKAw88IC+99JJERUXZuqpWhqnEm/W48sorq/y+Leu9fEUAAQQQQAABBBBAQE8g+dgBbau/RRl79C5rZSObtpOJPUdKYoNmTutwAQEEEEAAAQQQQAABBBBAIHAFDE1WVcbcoUMHeeWVVyqr5rHrn376qagt+KyHSpStWrVK+2Utq+irWuFk1NG+fXt58cUXZfz48dojv/vuO5k3b562fWBMTIxs375d1q1bZ+tO586d5ZlnnrF95qTmApGRkTVvhBYQQMDjAsSqx4l5AAJuESBW3cJoaCPbTx3Vtvv7ed9Wp89NatBcJvUaJUPjeGeqUyQfu0Cs+tiE0d2AFSBWA3bqGbiPCRCrPjZhdDcgBYhT46bdVMkq44at/6Ty2xPu2bNH1C9XDyOTVapP1157rbb936RJkyQnJ0cyMzNl1qxZDt0dMmSItkIsOjra4RoF1RdISEio/s3ciQAChgkQq4ZR8yAEaiRArNaIz9Cb07NPySsbF8p/d62TEsvOBHpHhzoNtJVUF7bsrL23Vq8OZb4pQKz65rzR68ATIFYDb84ZsW8KEKu+OW/0OrAEiFPj5ptklXHWHnnSddddJ2eddZZ89dVXMmfOHElPT9cSV40bN5bu3bvL5ZdfLmPGjOEfCTyiT6MIIIAAAggggEDgCJzMz5Epm5fIh6mrJK+4SHfgzaLqyGM9hsu1bRMlNDhEtw6FCCCAAAIIIIAAAggggAACCJQXCLK8p0n/xyHL1+RzwAokJSWJeodYXFycJCcnB6xD+YErE3UoFw4EEDCvALFq3rmhZwjYCxCr9hrmOs8uKpCp21bIW5uXyunCfN3OxYZHykPdhsrtHftJrdAw3ToU+ocAseof88go/F+AWPX/OWaE/iFArPrHPDIK/xYgTo2bX1ZWGWfNk/xM4PDhw9qISFb52cQyHL8TIFb9bkoZkJ8KEKvmm9jCkmL5bOdaeXXDQjmcl6XbwaiQMLm78wD5e5dBUje8lm4dCv1LgFj1r/lkNP4rQKz679wyMv8SIFb9az4ZjX8KEKfGzatbk1UDBw70eM+DgoJk2bJlHn8OD0AAAQQQQAABBBBAIBAFSkpL5Ie0zfL8+vmyJ+ukLkFoULDc1CFJxncbJnFRMbp1KEQAAQQQQAABBBBAAAEEEEDAVQG3Jqv279/v0XcjqR0LVbKKAwEEEEAAAQQQQAABBNwroP6sPf/QLnlm3VzZcPLP7Y71nnBF627yr8SzpW1MA73LlCGAAAIIIIAAAggggAACCCBQZQG3JqvU03kFVpXngBsQQAABBBBAAAEEEPCqwJpjB+RpS5JqyeG9Tvsxsll7ebLnSOlRv6nTOlxAAAEEEEAAAQQQQAABBBBAoDoCbk1WrVixojp94B4EEEAAAQQQQAABBBDwgkDqqaPy3Pp58uv+bU6f3qdhc5nUc5QMiWvjtA4XEEAAAQQQQAABBBBAAAEEEKiJgFuTVS1atKhJX7gXAZ8SiI2N9an+0lkEAlWAWA3UmWfcviZArBo7YweyT8nLG/6QL3evlxLL9n96R0LdhjLRspLq/Bad2IpbDyhAy4jVAJ14hu1zAsSqz00ZHQ5QAWI1QCeeYfuUAHFq3HQFWbbt0//bqXF94EkmF0hKSpKMjAyJi4uT5ORkk/eW7iGAAAIIIIAAAgg4EziRnyNTNi2WD1NXSX5JsW615lF15HHLO6mubtNDQoNDdOtQiAACCCCAAAIIIIAAAggggIA7Bdy6ssqdHaMtBBBAAAEEEEAAAQQQcI9AdlGBvL91hby1ZamcKczXbbReeC15uNtQuS2hr0SGhOnWoRABBBBAAAEEEEAAAQQQQAABTwiQrPKEKm0GhEBaWpo2zvj4+IAYL4NEwFcFiFVfnTn6HWgCxKpnZryguEg+27lWXt24UI7kZes+JMqSmLq380C5r8sgqRseqVuHQgSsAsSqVYKvCJhbgFg19/zQOwSsAsSqVYKvCJhXgDg1bm5IVhlnzZP8TCAzM1MbEckqP5tYhuN3AsSq300pA/JTAWLVvRNbUloi3+/dLM+nzJe9WSd1Gw8NCpabOyTJ+O7DpEmtGN06FCJQXoBYLS/CZwTMKUCsmnNe6BUC5QWI1fIifEbAfALEqXFzYliyauzYsW4ZVVBQkEyfPt0tbdEIAggggAACCCCAAAL+JKBeRzvv0E55Zt082Xgyw+nQrmzdXf5leS9Vm5j6TutwAQEEEEAAAQQQQAABBBBAAAGjBAxLVi1fvlxUoqkmh/rLd03bqMnzuRcBBBBAAAEEEEAAAbMKrD66X55eN1eWHvlzq2K9fo5q1l6e7DlSutdvqneZMgQQQAABBBBAAAEEEEAAAQS8ImBYskqNTiWbqnqo5FR17qvqc6iPAAIIIIAAAggggIAvCmzLPCLPWbb7m7l/m9Pu923YQib1GiWDm7R2WocLCCCAAAIIIIAAAggggAACCHhLwLBk1YwZM1waY0lJiZw5c0a2bt0qv/76q6SmpkpERIRMnDhREhISXGqDSggggAACCCCAAAII+LvA/uxMeXnDH/LV7hQpcfJDYZ3qNpKJlpVU57VIYIcCf/+GYHwIIIAAAggggAACCCCAgA8LBFlWLVV9uZOBA/7kk09k0qRJUqtWLfn888+lb9++Bj6dRymBpKQkycjIkLi4OElOTgbl/wWUiTqUCwcCCJhXgFg179zQMwTsBYhVe42Kz4/nZcvrmxbLf7avloKSYt3KzaPqaO+kurpNooQEB+vWoRCB6ggQq9VR4x4EjBcgVo0354kIVEeAWK2OGvcgYKwAcWqct+mTVYpi6tSp8txzz2lJgTlz5ki9evWME+JJJKv4HkAAAQQQQAABBEwgkFWYL1O3rZC3tiyTM5ZzvaN+RC15uNswubVjH4kMCdOrQhkCCCCAAAIIIIAAAggggAACphPwiR+zvOOOO6Rhw4Zy+PBh+eKLL0yHSIcQQAABBBBAAAEEEPCUQEFxkXyQulJ6//SWPJ+yQDdRVTs0TB7pfpasu+QBubfzQBJVnpoM2kUAAQQQQAABBBBAAAEEEPCIgE8kq0JCQqR///6idiycOXOmRyBoFIGqCqj3qalfHAggYG4BYtXc80PvELAKEKtWib++lpSWyPQ9G6TfL+/Io6t/k6OW7f/KH2GWLf7uTOgnay1JqscTz5Y64ZHlq/AZAbcKEKtu5aQxBDwmQKx6jJaGEXCrALHqVk4aQ8AjAsSpR1h1Gw3VLTVhYWxsrNarffv2mbB3dCkQBfLy8gJx2IwZAZ8TIFZ9bsrocIAKEKt/Tbz6Aa05B3fIM+vmyebMw39dsDsLspxf2bq79l6q1jH17a5wioBnBYhVz/rSOgLuEiBW3SVJOwh4VoBY9awvrSPgDgHi1B2KrrXhM8kq64vM8vP19+d3bbjUQgABBBBAAAEEEEDAvAIrj+7TklTLjqQ57eTo5h1kQs+R0r1enNM6XEAAAQQQQAABBBBAAAEEEEDAlwR8Ill16NAhWbp0qQQFBUmTJk18yZe+IoAAAggggAACCCBQqcDWzCPy7Pp58tsB51sM92vUUib1GiWDGsdX2h4VEEAAAQQQQAABBBBAAAEEEPAlAdMnq3bu3Cnjxo0TtdxOJasGDx7sS770FQEEEEAAAQQQQAABpwL7sjLlpQ0L5OvdKVLqpFanuo1komUl1XktErQ/DzupRjECCCCAAAIIIIAAAggggAACPitgWLJqypQpLiMVFRXJyZMnZfPmzbJu3TpR+/arIyQkRO68806X26EiAggggAACCCCAAAJmFDiWly2vb1osH21fLQUlxbpdbFm7rvZOqrGte0hIcLBuHQoRQAABBBBAAAEEEEAAAQQQ8AcBw5JVkydPrtZPgloTVcGWv6C//PLL0r59e39wZwx+IBAfzxY8fjCNDCEABIjVAJhkhugXAoESq2cK8+X9rcvlna3L5Exhge7cNYiIkvHdh8ktHfpIRIhhf1zX7QuFCJQXCJRYLT9uPiPgawLEqq/NGP0NVAFiNVBnnnH7kgBxatxsGfq3X2viqSrDs2799+ijj0qvXr2qcit1EfCoQGxsrEfbp3EEEHCPALHqHkdaQcDTAv4eqwXFRfLJjmR5deNCOZafo8sZHRou4zoPlHstv+qER+rWoRABbwv4e6x625fnI+AuAWLVXZK0g4BnBYhVz/rSOgLuECBO3aHoWhuGJaseeugh13pkqRUaGioxMTHSokUL6dmzpzRq1Mjle6mIAAIIIIAAAggggIBZBIpLSuTbvRvlhZQFsi87U7dbYZYdBG7t0Fce7j5UGkVG69ahEAEEEEAAAQQQQAABBBBAAAF/FjBlssqfwRmb/wikpKRog0lMTPSfQTESBPxQgFj1w0llSH4p4G+xqnYUmJ2+Q55ZP1e2ZB7RnbMgS+lVbXpo76VqFV1Ptw6FCJhNwN9i1Wy+9AcBdwkQq+6SpB0EPCtArHrWl9YRcIcAceoORdfaMCxZ5Vp3qIUAAggggAACCCCAgG8LrDiyT55eN1dWHN3ndCBjmneUCT1HStd6TZzW4QICCCCAAAIIIIAAAggggAACgSJAsipQZppxIoAAAggggAACCHhUYPPJw/Lc+nnye/p2p8/p36ilTOo1SgY2jndahwsIIIAAAggggAACCCCAAAIIBJoAyapAm3HGiwACCCCAAAIIIOBWgX1ZJ+XFDX/IN7tTpNRJy51jG8uTlpVU51pWVAUFqQ0AORBAAAEEEEAAAQQQQAABBBBAwCrg1WTV7t27ZdmyZbJp0yY5fvy4ZGdnS+3ataV+/frSvXt3GThwoLRr187aV74igAACCCCAAAIIIGAagWN52TJ50yL5ePsaKSgp1u1Xq9qx8nji2TK2dXcJCQ7WrUMhAggggAACCCCAAAIIIIAAAoEu4JVkVXJysrz44ouycuXKSv379+8vjz32mPTp06fSulRAAAEEEEAAAQQQQMDTAmcK8+W9rcvlnS3LJKuoQPdxDSOiZHz3YXJzhz4SEeKVP3Lr9otCBBBAAAEEEEAAAQQQQAABBMwoEFRqOYzs2OTJk+Wtt96SkpIScfXRwZafQr3//vvln//8p5Fd5Vn/L5CUlCQZGRkSFxcnKtHI8adAXl6edhIZGQkJAgiYWIBYNfHk0DUE7AR8IVbzi4tk2o418trGRXI8P8eu93+dRoeGy31dBsm9nQdKTFjEXxc4Q8BPBHwhVv2EmmEgUCMBYrVGfNyMgGECxKph1DwIgWoLEKfVpqvyjYb+mOfrr78uU6ZMKdPJbt26iUqGNG/eXKKioiQnJ0cOHjyoJUU2btyo1VWJLZXgUvv7jx8/vsz9fEDAWwIkqbwlz3MRqJoAsVo1L2oj4C0BM8dqseXPojP2bpAXUhbI/uxTukThwSFyW8e+8lC3odIwsrZuHQoR8AcBM8eqP/gyBgTcJUCsukuSdhDwrACx6llfWkfAHQLEqTsUXWvDsGSVei/Vm2++qSWc1Ioq9T6qZ599Vjp16uS0p6mpqTJx4kTtvVbqnnfeeUfGjBkjKsHFgYC3Bciqe3sGeD4CrgkQq645UQsBbwuYMVbVnz9/T98uz66fJ1szj+gSBVlKr2mbKI/1GC6touvp1qEQAX8SMGOs+pMvY0HAXQLEqrskaQcBzwoQq571pXUE3CFAnLpD0bU2DHvL82effSbFxX++ePr888+Xr7/+usJElep+QkKCVu+CCy7QRqPuV+1wIGAGAZVMVb84EEDA3ALEqrnnh94hYBUwW6wuP5Im583+WK774yuniarzWiTIkgvvkfcGXUaiyjqRfPV7AbPFqt+DM0AEqilArFYTjtsQMFiAWDUYnMchUA0B4rQaaNW8xbCVVUuXLtW6WKtWLXnttdckJCTEpS6r91W98sorsmDBAsnNzRVrOy7dTCUEEEAAAQQQQAABBKogsPnkYW0l1f8sK6qcHQMatZJJvUbJgMatnFWhHAEEEEAAAQQQQAABBBBAAAEEqiBgWLIqIyND2wJw0KBBUqdOnSp0UaRu3boyePBgmTNnjqh2OBBAAAEEEEAAAQQQcKdAWtZJedHyTqrpezZIqZOGu8Y2kSd7jZRzmnXQ/lzrpBrFCCCAAAIIIIAAAggggAACCCBQRQHDklW1a9eWgoICadKkSRW7+Gf1Ro0aaSeqHQ4EEEAAAQQQQAABBNwhcDQvS17buEim7VgjhSUluk22qh0r/0o8W8a26S7BQYbtoq3bFwoRQAABBBBAAAEEEEAAAQQQ8EcBw5JVrVq1kpMnT8qxY8eq5Xj8+HHtvpYtW1brfm5CAAEEEEAAAQQQQMAqcLogT97dutzya5lkFxVai8t8bRRZW8Z3HyY3t0+S8BDD/thcpg98QAABBBBAAAEEEEAAAQQQQCAQBAz7W/d5550n69ev1945lZ2dLVVZIaXqq3dVBQUFyZgxYwJhXhgjAggggAACCCCAgAcE8ouL5OPtq2XypsVyPD9H9wkxYeFyf+fBck/nARIdFqFbh0IEEEAAAQQQQAABBBBAAAEEEHCfQFCp5XBfc85bUquqRo0aJUeOHJHLL79c3nzzTeeVy135xz/+Id9++622haB6b1X9+vXL1eCjJwWSkpK0d4XFxcVJcnKyJx9F2wgggAACCCCAgEcEii1b/Kn3Ub2wYYEcyD6l+4zw4BC5vWNfebDbUGloWVXFgQACCCCAAAIIIIAAAggggAACxggYtrKqXr168sEHH8iNN94o33//vZw6dUqeffZZqWhbvwMHDsiTTz4ps2fPlrp162r3k6gy5huDpyCAAAIIIIAAAv4goH4u67cDqfLs+nmy7dRR3SEFW1bvX9MmUR7tMVxaRcfq1qEQAQQQQAABBBBAAAEEEEAAAQQ8J2DYyqoZM2Zoo1AJqLffflsKCwu1bf369OkjauVO8+bNpVatWpKbmyvp6emydu1aWb16tah/YAgPD5f77rtPWrRoUanE2LFjK61DhaoJsLJK3yszM1O7EBsbq1+BUgQQMIUAsWqKaaATCFQq4IlYXXYkTZ5eN1dWHd3v9Pnnt0iQCT1HSufYxk7rcAEBBP4S8ESs/tU6Zwgg4C4BYtVdkrSDgGcFiFXP+tI6Au4QIE7doehaG4atrHrwwQe15JR9t0os27GohJT6pXeoRJV6T1VBQYG8/vrrelXKlKm6JKvKkPDBgwJpaWla6ySrPIhM0wi4QYBYdQMiTSBggIA7Y3XTyQx5Zt08mXNwh9OeD2ocL5N6jZJ+jVo6rcMFBBBwFHBnrDq2TgkCCLhLgFh1lyTtIOBZAWLVs760joA7BIhTdyi61oZhySrVHb3XY+mV2Xe9suv2dTlHAAEEEEAAAQQQCFyBvWdOyAspC+TbvRvF2UtZu9VrIk/2HCWjmrV3+EGqwJVj5AgggAACCCCAAAIIIIAAAgh4V8CwZBUrnrw70TwdAQQQQAABBBDwV4EjuVny2qZF8smONVJoWbmvd7SOrif/SjxbrmjdTYKDgvWqUIYAAggggAACCCCAAAIIIIAAAl4SMCxZNWXKFC8NkccigAACCCCAAAII+KPA6YI8eWfrMnlv63LJLirUHWKjyNryz+5nyU3te0t4iGF/9NXtC4UIIIAAAggggAACCCCAAAIIIKAvwN/Y9V0oRQABBBBAAAEEEDCpQF5xoXy8fY1MtqymOpGfq9vLmLAI+XuXQXJ3pwESbTnnQAABBBBAAAEEEEAAAQQQQAAB8wqQrDLv3NAzkwtERkaavId0DwEElACxyvcBAuYW2JZ5RKZZtu9bkb5bcoqLpEHGGkms31Ru6dBHOsU2LtP5YssWf9/sSZEXN/whB7JPlblm/RARHCK3J/STB7sOkQaWVVUcCCDgXgF+X3WvJ60h4CkBYtVTsrSLgHsFiFX3etIaAp4QIE49oarfZlCp5dC/RCkCfwokJSVJRkaGxMXFSXJyMiwIIIAAAggggECNBdYeS5cn186WpUfSnLY1uHG8PNN7tPRq0ExmHdgmz62fL9tOHdWtHxwUJNe17SmP9DhLWtaO1a1DIQIIIIAAAggggAACCCCAAAIImFPA6yurdu3aJZs2bZITJ05IVlaWREdHS/369aVbt27Srl07c6rRKwQQQAABBBBAAIFqC8xJ3yE3L5puWUml/54pa8MqkXX+7GnSKrqu7Dh93Frs8PXClp3kicQRDiuxHCpSgAACCCCAAAIIIIAAAggggAACphTwSrLqzJkz8tFHH8nnn38uR44ccQrTpEkTueGGG+TWW2+VmJgYp/W4gIA3BNRqM3WoFWccCCBgXgFi1bxzQ88CU0CtqLpp0TeSa9nyz5Ujv6TIaaJKrbya1GuU9G3U0pWmqIMAAm4Q4PdVNyDSBAIGCBCrBiDzCATcIECsugGRJhDwsABx6mFgu+YNT1atWbNGxo0bJwcPHpTKdiBU3wivvfaafPXVV/Luu++K2o6OAwGzCBw+fFjrCskqs8wI/UBAX4BY1XehFAFvCait/1xNVDnrY/d6cVqSakTTdhJk2f6PAwEEjBPg91XjrHkSAjURIFZrose9CBgnQKwaZ82TEKiuAHFaXbmq32dosmrDhg1y/fXXS05Ojq2nwcHB0rZtW2nZsqXUqlVLcnNzZf/+/bJ7924psbxEWx0HDhyQ6667Tr799lvp3r277V5OEEAAAQQQQAABBHxHYFvmkQrfUVXZSJpH1ZGne58jl8V3leCg4Mqqcx0BBBBAAAEEEEAAAQQQQAABBHxEwLBkVVFRkbaiKjs7W6OpU6eO3H///XL11Vdr76gq76XeYTV9+nR5++235fTp06LuUyuyFixYICEhIeWr8xkBBBBAAAEEEEDA5ALTdqypUQ/HtEiQK1rzg0s1QuRmBBBAAAEEEEAAAQQQQAABBEwoYNiPpH7//feyZ88ebauW+Ph4mT17ttxzzz26iSrlVL9+fbn77rvlf//7n6j66lD3q3Y4EEAAAQQQQAABBHxPIOXEoRp1evPJP7fgrVEj3IwAAggggAACCCCAAAIIIIAAAqYTMCxZpZJT1mPq1KnSokUL68cKv6p67733nu19BL///nuF9bmIAAIIIIAAAgggYE6BrMKCGnUsqyi/RvdzMwIIIIAAAggggAACCCCAAAIImFPAsG0AN27cqCWcevXqVeX3TvXo0UPUfWvXrpVNmzaZU5JeBZxAbGxswI2ZASPgiwLEqi/OGn32R4GS0hLJKy6q0dCiQyNqdD83I4BAzQX4fbXmhrSAgBECxKoRyjwDgZoLEKs1N6QFBDwtQJx6Wviv9g1LVh07dkx7aseOHf96ehXO1H0qWWVtpwq3UhUBjwhYt6f0SOM0igACbhMgVt1GSUMIVEugtLRU/pe+XZ5dP092nTlerTasN/WoH2c95SsCCHhJgN9XvQTPYxGoogCxWkUwqiPgJQFi1UvwPBaBKggQp1XAqmFVw5JVYWFhUlBQIPn51du+Rd2rDtUOBwIIIIAAAggggID5BZYe3ivPWJJUq47ud0tnb+3Y1y3t0AgCCCCAAAIIIIAAAggggAACCJhLwLBkVaNGjSQrK0vWrVtXLQHrfaodDgTMIJCWlqZ1g+y6GWaDPiDgXIBYdW7DFQQ8JbDhxCEtSTXv4E63PWJIk9aSUJc/B7oNlIYQqKYAv69WE47bEDBYgFg1GJzHIVBNAWK1mnDchoCBAsSpcdjBRj2qX79+2qPU5P7yyy9Veuyvv/4qe/bs0d55ZW2nSg1QGQEPCGRmZor6xYEAAuYWIFbNPT/0zr8Edp4+JrcuniFnzfq3OEtUtY6uJxHBIVUaeFRImDzd65wq3UNlBBDwjAC/r3rGlVYRcLcAsepuUdpDwDMCxKpnXGkVAXcKEKfu1Ky4LcOSVRdffLGtJ+PHj5cFCxbYPld0smjRInn44YdtVezbsRVyggACCCCAAAIIIOA1gfTsU/LAip9lwC/vyg9pm3X7ER8dK1MHXSZrLr5fPj/rGlEJKFcOVe+TYVdJ74bNXalOHQQQQAABBBBAAAEEEEAAAQQQ8EEBw7YBPOuss2Tw4MGydOlSyc7OlhtvvFHOPfdcueqqq6RPnz5Sv359G9/JkydlzZo1Mn36dPn9999FvZg7KChIu1+1w4EAAggggAACCCDgfYHjedkyZfMS+U/qKskvKdbtUJPIaBnffZjc2L63hIf8+UfPc5p3kF/OuVkmrZsjSyzvtXJ2qK3/1IoqElXOhChHAAEEEEAAAQQQQAABBBBAwD8EDEtWKa733ntP1MootRWgSkD973//036pa5GRkRIVFSU5OTmSl5enirRD1VOHei/Qu+++q53zHwQQQAABBBBAAAHvCZwpzJf3ti6Xd7cukzOFBbodqRseKQ90GSx3duovtUPDHeqoBJRKWG3LPCLTdqyR5ft3SU5JkTSKqSs96sfJrR378o4qBzUKEEAAAQQQQAABBBBAAAEEEPBPAUOTVQ0aNJCffvpJHnzwQW0bQGsiStHm5uZqv/SYzz77bJkyZYqo+zkQQAABBBBAAAEEvCOQV1woH29fI69vWizH83N0O1HLsnrqrk4DtERVbEQt3Tr2hZ1iG8vLfc+XlPAUrTgxMdH+MucIIIAAAggggAACCCCAAAIIIBAAAoYmq5Rnw4YN5fPPP9e2A/ziiy9k2bJlcuzYMQdqVW/QoEHyt7/9TfvqUIECBLws0KRJEy/3gMcjgIArAsSqK0rUQaBigSLLFn9f7U6Rlzf8Iek5p3UrhwUHy03tk+ThbsMkLipGt05FhcRqRTpcQ8A8AsSqeeaCniBQkQCxWpEO1xAwjwCxap65oCcIOBMgTp3JuL88yLK66c999tzftsstZmRkyPHjx7UtANVWgGoFVVxcnMv3U9GzAklJSaLmSM1JcnKyZx9G6wgggAACCCBgGoGS0hL5ed9WeSFlvuw4fVy3X0GW0rFtesjjPYZL65i/3kGqW5lCBBBAAAEEEEAAAQQQQAABBBBAQEfA8JVVOn3QkiAkp/RkKEMAAQQQQAABBIwXUD/LNP/QLnl2/TxJOXHIaQfOa5EgTySOkK71WG3sFIkLCCCAAAIIIIAAAggggAACCCBQqYBHk1VqNc6nn34qixcvln379klWVpbExMRIy5YtZejQoXLjjTdK06ZNK+0kFRAwo0BqaqrWrYSEBDN2jz4hgMD/CxCrfCsgUDWB1Uf3yzOWJNWSw3ud3jikSWuZ2HOk9GvU0mmdql4gVqsqRn0EvCNArHrHnaciUFUBYrWqYtRHwDsCxKp33HkqAlURIE6rolWzuh5LVn355ZcyceJEKSgo0Hpo3W1Qbfd34sQJSUlJkQ8++ECeeuopueGGG2o2Cu5GwAsCeXl5Xngqj0QAgaoKEKtVFaN+oApsPnlYnrds9/fbgT9/GEPPoWf9plqS6uym7SQoSG0A6L6DWHWfJS0h4EkBYtWTurSNgPsEiFX3WdISAp4UIFY9qUvbCLhHgDh1j6MrrXgkWfXtt9/KI488oj1f/UOGNVFl7ZD6rMrz8/PlX//6l0RGRsrYsWOtl/mKAAIIIIAAAgggYJDA3jMnLO+kWiDf7t0ozl5k2qFOA227v4tbdXF7ksqgYfIYBBBAAAEEEEAAAQQQQAABBBAwsYDbk1VnzpzRVlSpMVsTVV26dJE+ffpI3bp15dSpU7J69WrZunWr7fqTTz4pY8aM0bYINLEVXUMAAQQQQAABBPxGICPnjLy2aZF8uiNZikpLdMfVPKqOPNZjuFzTNlFCg0N061CIAAIIIIAAAggggAACCCCAAAII1FTA7ckqtapKJaxUoqpOnTry1ltvyciRIx36OXfuXHnggQe05JV6l5W675ZbbnGoRwECCCCAAAIIIICA+wQy83PlzS1L5d/bVkhucZFuww0iouThbkPllo59JDIkTLcOhQgggAACCCCAAAIIIIAAAggggIC7BILd1ZC1ncWLF1tP5fXXX9dNVKkKo0aNksmTJ9vq2t9nK+QEAQQQQAABBBBAwC0C2UUF8vqmxdLzpzfljc1LdBNVMWHh8rhlJdW6Sx+QezoPJFHlFnkaQQABBBBAAAEEEEAAAQQQQACBygTcvrJqy5Yt2jPbtGkj5557boXPV1v/qXp79uzRtgWssDIXETCZQHx8vMl6RHcQQEBPgFjVU6EskAQKLKunPt25Vl7buFCO5GXrDj3CssXfHQn95B9dh0iDyNq6dTxdSKx6Wpj2EXCPALHqHkdaQcDTAsSqp4VpHwH3CBCr7nGkFQQ8KUCcelK3bNtuT1adPHlS2wIwMTGx7JOcfOrZs6eWrFL3cSDgSwKxsbG+1F36ikDAChCrATv1AT/w4pISmbF3o7y0YYGkZWXqeoRYtm3+W7te8s/uZ0nz2nV16xhVSKwaJc1zEKiZALFaMz/uRsAoAWLVKGmeg0DNBIjVmvlxNwJGCBCnRij/+Qy3J6uys7O1ZFXduq79g4e1nrqPAwEEEEAAAQQQQKBmAqWlpfLbgVR5LmW+bM084rSxy+K7yr8Sz5b2dRo6rcMFBBBAAAEEEEAAAQQQQAABBBBAwAgBtyerrJ0OsvykLgcC/iyQkpKiDc/VVYT+bMHYEDCzALFq5tmhb+4WWJyxR55dP09WHzvgtOmRzdrLkz1HSo/6TZ3W8cYFYtUb6jwTgaoLEKtVN+MOBLwhQKx6Q51nIlB1AWK16mbcgYDRAsSpceIeS1YZNwSehAACCCCAAAIIBLbAuuPpWpJqwaHdTiH6NWqpJakGN2nttA4XEEAAAQQQQAABBBBAAAEEEEAAAW8IkKzyhjrPRAABBBBAAAEE3CCw/dRRed6y3d/P+7Y6ba1rbBOZaFlJNbp5B22rZqcVuYAAAggggAACCCCAAAIIIIAAAgh4ScBjyap169bJlClTKh3W+vXrbXVcqa8qP/jgg7Z7OEEAAQQQQAABBAJNYH92pry84Q/5aneKlFjeUaV3tImuJ49b3kl1RetuEhwUrFeFMgQQQAABBBBAAAEEEEAAAQQQQMAUAh5LVqm9HK37OVY2Uuv7rV5//fXKqmrXSVa5xEQlBBBAAAEEEPAzgWN52fL6psXy0fbVUlBSrDu6uFrR8s/uZ8kN7XtLWHCIbh0KEUAAAQQQQAABBBBAAAEEEEAAATMJeCxZVerkp3xrOnhrYqum7XA/AggggAACCCDgKwKnCvLkva3LtV9ZRQW63Y4Nj5R/dB0idyT0k6jQcN06FCKAAAIIIIAAAggggAACCCCAAAJmFHB7smrAgAFmHCd9QsDtAgkJCW5vkwYRQMD9AsSq+01p0TiB3KJCbRWVWk11siBX98FRIWFyT+cBcn+XQVI3vJZuHV8oJFZ9YZboIwIixCrfBQj4hgCx6hvzRC8RIFb5HkDA/ALEqXFz5PZk1bfffmtc7z30pP3798uiRYtkxYoVsnXrVjl48KBkZ2dLdHS0NG3aVHr37i2XXXaZDBw40KUe5OTkyNKlS2XJkiWyadMm2b17t2RmZkpISIjUr19funXrJmefbXmnxBVXSFRUlEtt2lc6ceKETJ8+XWbNmiWq76dOnZKGDRtqf5G85JJLRP0KCwuzv4VzNwhERka6oRWaQAABTwsQq54Wpn1PCBRZtvj7Ytd6eWXjH3Iw54zuI8KCg+WWDn3k4W7DpLFl6z9fP4hVX59B+h8oAsRqoMw04/R1AWLV12eQ/geKALEaKDPNOH1ZgDg1bvaCLNv16b+V27g+mOZJKpH02GOPybp161zqk0pWvfnmm9K8eXOn9e+//3757bffJDdX/6eh7W9UiauXX35Zzj//fPviCs/nzJkjDz/8sBw/ftxpve7du8u7774r7dq1c1qnogtJSUmSkZEhcXFxkpycXFHVgLqWl5enjZf/YQXUtDNYHxQgVn1w0gK4yyWlJfJj2hZ5IWW+7DpzQlciOChIrm7TQx7rMVxaRdfTreOLhcSqL84afQ5EAWI1EGedMfuiALHqi7NGnwNRgFgNxFlnzL4mQJwaN2Mkq+ysf/rpJ7n33nvtSkTatm0rnTp1knr16snp06dlzZo1cujQIVsdlcD5/vvvJT4+3lZmf6Luz8/PtxXFxsZKz549pUmTJqLyhDt27JD169dr59ZKL774otx4443Wj06/Lly4UKtXVFSk1alVq5YMGTJEGjRoIGlpadrKMGsuUq0I+/XXX7WEk9MGnVwgWaUPk5KSol1ITEzUr0ApAgiYQoBYNcU00IlKBNTv13MP7pRn18+TjScznNa+sGUneSJxhHSKbey0jq9eIFZ9debod6AJEKuBNuOM11cFiFVfnTn6HWgCxGqgzTjj9UUB4tS4WXP7NoDGdd1zT2rdurVcd911cvnll2vb/tk/qaSkRNtyb8KECdpqKbXi6L777pOff/5Zgiw/6ax3qCTSpZdeKldffbWoxE+wZdse+yM1NVXUCqzNmzdrxartvn37SufOne2rlTlXW//dc889Yk1UqSTV+++/r20raK2o2rvlllskPT1dS7D9/e9/1/puvc5XBBBAAAEEEPC+wIoj++SZ9XNlueWrs+OsuDYysedISWrYwlkVyhFAAAEEEEAAAQQQQAABBBBAAAGfFSibNfHZYbin440bN5bXX39de1/VuHHjHBJV6ikq0XTNNdfI22+/bXvo2rVrRa1y0jtUsmj58uXy2muvaQmo8okqdY96SZt651SLFn/+A1RxcXGZ9vXafe+997R3U6lrKrn2ySeflElUqfKuXbtq5db3Van3Zjnrp6rPgQACCCCAAALGCagVVFcv+ELOm/2x00RV7wbN5MeRN8qPo24iUWXc1PAkBBBAAAEEEEAAAQQQQAABBBAwWIBklR24egeVWv0UEhJiV6p/et5550mvXr1sF+fNm2c7tz+ZOHGiNGrUyL5I91xtD2i/BeH8+fN166nCwsJC+fLLL23Xx48fL2r1lt7RpUsXGTt2rO2SSmpxIIAAAggggID3BHafOS63L/lWzpo5VWan79DtSELdhvL5sKtl7pg75KymbXXrUIgAAggggAACCCCAAAIIIIAAAgj4iwDJqhrMZJ8+fWx379+/33Ze3RO19Z/1OHPmjJw8edL6scxXtVLr1KlTWll0dLScf/75Za6X/3DVVVfZihYtWiQ5OTm2z5wggAACCCCAgDECh3JOy4Mrf5H+P78r3+3dJKU6j21Zu668N/BSWXrBvXJhq85OtxjWuZUiBBBAAAEEEEAAAQQQQAABBBBAwGcFeGdVDabO/h1Vauu+mh727am21Pux9A61nZ/1UO/AioiIsH7U/dqzZ09t5VVubq7k5eXJmjVrZNiwYbp1KUQAAQQQQAAB9wqczM+RKZuXyIepqySvuEi38UaRtWV8t2FyUwfL7+sh/PFMF4lCBBBAAAEEEEAAAQQQQAABBBDwWwH+NaQGU7tt2zbb3c2aNbOdV/fEvr3IyEiHd1BZ2925c6f1VLp37247d3ai3lnVqVMnWbdunVZF3U+yypmW6+WJiYmuV6YmAgh4TYBY9Rp9wD84qzBfpm5bIW9tWSZnLOd6R0xYhPy9y2C5u1N/ibacB/JBrAby7DN2XxIgVn1ptuhrIAsQq4E8+4zdlwSIVV+aLfoaqALEqXEzT7Kqmtbp6eliv8Jp6NCh1Wzpr9umT59u+zB48GCnW//s2rXLVq958+a284pOVD37ZFVFdZ1dUyu9Dh486OyyVu6OpF2FD+AiAggggAACJhfIt6ye+mTHGpm8abEczcvW7W2kZfXUnQn95YGug6V+RJRuHQoRQAABBBBAAAEEEEAAAQQQQACBQBEgWVXNmX7qqafEuvWfSgSdc8451Wzpz9vmzJkj6n1S1uPmm2+2njp8tX+XVaNGjRyu6xU0btzYVpyZmWk7r8rJkSNHxP69Wnr3zpo1y6HYmn1Wz01LS3O4rlaRJSQkaOUZGRly+PBhhzqxsbESHx+vlas29MbQpEkTiYuL0+qkpqZqWx6Wb0i1odpSR0pKiva1/H9UX1Sf1JaJqh29Q41J9UG9W+zEiRMOVXx1TGogalz+NE+MyTfiyZPzVFBQoJqXli1bGvr/CE+OSRuQ5T9G/3+PMVUcT7v37pEZezfKtMPb5HCh/vshQ4KC5IJ6reXGxgnSMKSW7N+2Q9RbL935+5OvztPGjRtV1yU8PFz7av2Pv/45Qo2P33Ots/znVzP+eY95cvz/norRpk2ban+mNsufy5knx3kinkT7u5z6O135I1B+z7X+Gdj6+6qrf88lnogna8zwd42a/7uRK/Gk/jx4/PhxycrKstLbvvL/cv5fbvtm+P8T/l/unX+ztP89NVD+HFGV7z3r39nL31OdzySrqqGmVkDZJ2Uef/zxSt8bVdFjVHLm0UcftVVRW/SNGDHC9rn8SXb2Xz+lrZIirhz29ezvd+Ve6ugLqL+c5+fn12ju9VumFAEE3CmgksocCHhSoLS0VH7dv1WeSv6f7M495fRRV7TuJtfEtJZGEua0TiBfsMZqgwYNApmBsSNgegH1D2rqL+zqH9A4EEDAvAL8vmreuaFnCNgLqH9bysnJkagodluwd+EcATMJ8HuqcbMRZPkHllLjHuf7T1KrcS6//HLbqp1LL71U3n333WoPTP1Fb+zYsbJmzRqtjXr16sncuXNtP/2v17BaHaC25FPHN998I0OGDNGrVqbs1VdflTfeeEMrU/XVfa4eSUlJolY8qdVZM2fOrPC2QNoG0Loyy53Z4wpxuYgAAtUSIFarxcZNLgosPLRbnlk/V9YeP+j0jtHNO8iEniOle70/V/86rRjgF4jVAP8GYPg+I0Cs+sxU0dEAFyBWA/wbgOH7jACx6jNTRUcDWIA4NW7yWVlVBet9+/aJ2p7PupVA586d5aWXXqpCC2WrqjzhP/7xD1uiKiwsTN57770KE1WqhYiICMnNzdUaKywsLNuok0/W5Yrqsv0qKyfVdYuDg4MlkJJRuggUIoAAAgggYBFIPnZAnl0/TxZm7HHqMbBxK5loSVINbPznNrZOK3IBAQQQQAABBBBAAAEEEEAAAQQQCHABklUufgOorfquvfZaUe9tUofan/KLL76QmJgYF1twrPbEE0/ITz/9pF1QiSC18kltAVjZUbt2bVuyypo4q+we+3rqfg4EEEAAAQQQqLrAtswj8nzKfMu2f9uc3qxWUKkk1ahm7SXI8o4qDgQQQAABBBBAAAEEEEAAAQQQQACBigVIVlXso109ceKElqjau3ev9lm9ZPLrr7/WXmzvwu26VV588UX59NNPbdeef/55UVsKunKorQKPHTumVT169Kgrt9iSbKoy+8u7REYlBBBAAAEEbAL7sk7KSxv+kG/2bJASJzsot4upL/9KHCGXxneR4KBg272cIIAAAggggAACCCCAAAIIIIAAAghULECyqmIfUS9Qu/766yU1NVWrWb9+fS1R1apVq0rudH75zTfflHfeecdWQa2wuvHGG22fKztp166d7NixQ6uWnp5eWXWHeu3bt3fpHipVLFDd7RQrbpWrCCDgbgFi1d2igdXekdwsmbxpkUzbsUYK//99keUFmkXFyCPdh8t17XpKWHBI+ct8dlGAWHURimoIeFmAWPXyBPB4BFwUIFZdhKIaAl4WIFa9PAE8HgEXBIhTF5DcVIVkVQWQOTk5csMNN8iGDRu0WnXq1NG2/uvYsWMFd1V86cMPP5RXXnnFVumBBx6Qe++91/bZlRP7ZNOmTZsqvaWoqEi2bftruyL7+yu9mQpOBRISEpxe4wICCJhHgFg1z1z4Uk9OFeTJ21uWytRtKyS7SP/9kPUjasmDXYfKbR37Sq3QMF8anin7SqyaclroFAIOAsSqAwkFCJhSgFg15bTQKQQcBIhVBxIKEDCdAHFq3JSQrHJird7xdPPNN8vq1au1GrVq1ZLPPvtMevTo4eSOyov/+9//ylNPPWWreNttt8kjjzxi++zqyeDBg20rs5KTk6WgoEDCw8Od3r5+/XrbO65UJrhPnz5O63IBAQQQQACBQBbIKSqQD1NXyRubl0imJWGld0SHhsu9nQfKOMuvOuGRelUoQwABBBBAAAEEEEAAAQQQQAABBBCoggDJKh2swsJCueOOO2Tp0qXa1YiICPn444+lb9++OrVdK/ruu+/kscces1W+9tpr5emnn7Z9rsrJwIGWfxyzrPI6ffq0tk3hb7/9JpdcconTJqZPn267NnToUImKirJ95qT6AhkZGdrNcXFx1W+EOxFAwOMCxKrHif3iAYUlxfL5zrXy6saFkmHZ+k/vCLds8adWUT3YbYg0iozWq0JZDQSI1RrgcSsCBgoQqwZi8ygEaiBArNYAj1sRMFCAWDUQm0chUE0B4rSacNW4jbd/l0MrLi6WcePGyfz587UroaGhMnXqVBk2bFi5mq5/nDVrljz44INS+v8vZL/00ku1rQCDgoJcb8SuZlhYmFx33XW2kldffdW2cspW+P8navu/GTNm2IrVajEO9wgcPnxY1C8OBBAwtwCxau758XbvSkpLZMaeDdL/53fk4VUzdRNVwZbfr//WrpesueR+eaHPGBJVHpo0YtVDsDSLgJsFiFU3g9IcAh4SIFY9BEuzCLhZgFh1MyjNIeABAeLUA6hOmiRZZQejkkkPP/ywzJw5UysNDg6Wt956S0aPHm1Xq2qnCxYs0JJfKgmmjnPPPVfefPNNUW3X5FAJtbp162pN7NmzR2699VY5ceJEmSa3bNmibWWotglUx6BBg2T48OHaOf9BAAEEEEAgkAXU7/m/H0iVYTP/LXcu/V72ZJ3U5bikVRdZfuG98vbAS6Rl7VjdOhQigAACCCCAAAIIIIAAAggggAACCNRMgG0A7fw+/fTTMquQ4uPjZdWqVdovu2pOT59//vky11Ty6Pbbb9feKaUuhISESIMGDWTSpEll6jn7cMUVV0jv3r11L9evX1/ee+89uemmm6SoqEgWLVok/fr1E7XNn3rG3r17ZcWKFbbVXGqrurffflu3LQoRQAABBBAIJIGlh/fKs+vnycqj+50Oe0TTdjKh5wjp1aC50zpcQAABBBBAAAEEEEAAAQQQQAABBBBwjwDJKjvH48eP230SUSuW1C9Xj/LJquzsbMnLy7PdrlZXffnll7bPlZ306NHDabJK3atWSX344YfaajCVGMvNzZXZs2c7NNutWzd59913hXcrOdBQgAACCCAQQAIbThySZyxJqnkHdzoddd+GLeTJniNlSFwbp3W4gAACCCCAAAIIIIAAAggggAACCCDgXgGSVe71NLw1tUXhH3/8Id9884389ttvsm/fPjl9+rQ0bNhQEhIS5JJLLhH1jiz1nisOBBBAAAEEAlFg5+lj8kLKAvkhbbPT4Xeq20gmWpJU57VIkOq+U9Jp41xAAAEEEEAAAQQQQAABBBBAAAEEEKhQIMjyzobSCmtwMeAFkpKSJCMjQ1uZlZycHPAeVoC0tDTtVG0XyYEAAuYVIFbNOzee7ll69il5ZeNC+WLXOil28sed+OhYeazH2TK2dXcJqeH7JD09Hn9vn1j19xlmfP4iQKz6y0wyDn8XIFb9fYYZn78IEKv+MpOMw58FiFPjZpdklXHWPvskklU+O3V0HAEEEAhIgeN52fLG5iXyYeoqyS8p1jVoHFlbxnc/S25q31vCQ1horotEIQIIIIAAAggggAACCCCAAAIIIGCQAP86YxA0j0EAAQQQQAABzwqcKcyX97cul3e2LpMzhQW6D6sbHil/7zJY7urUX2qHhuvWoRABBBBAAAEEEEAAAQQQQAABBBBAwFgBklXGevM0PxJgCagfTSZD8WsBYtWvp1cbXF5xoUzbvkYmb1osx/NzdAdcy7J66q5OA+QBS6IqNqKWbh0KvStArHrXn6cj4KoAseqqFPUQ8K4Asepdf56OgKsCxKqrUtRDwHsCxKlx9iSrjLPmSX4mkJmZqY2Id1b52cQyHL8TIFb9bkptAyqybPH39e4UeWnDH5Kec9pWbn8SGhQsN3VIkvHdhklcVIz9Jc5NJkCsmmxC6A4CTgSIVScwFCNgMgFi1WQTQncQcCJArDqBoRgBEwkQp8ZNBskq46x5EgIIIIAAAgi4QaC0tFR+3rdFXkhZINtPH9NtMchSOrZND3m8x3BpHVNftw6FCCCAAAIIIIAAAggggAACCCCAAALmECBZZY55oBcIIIAAAgggUImASlItOLRLnl0/T9afOOS09nktEuSJxBHStV4Tp3W4gAACCCCAAAIIIIAAAggggAACCCBgHgGSVeaZC3qCAAIIIIAAAk4EVh/dL89YklRLDu91UkNkSJPWMrHnSOnXqKXTOlxAAAEEEEAAAQQQQAABBBBAAAEEEDCfAMkq880JPUIAAQQQQACB/xfYknlYnls/X347kOrUpGf9plqS6uym7SQoSG0AyIEAAggggAACCCCAAAIIIIAAAggg4EsCJKt8abboq6kEmjRheylTTQidQcCJALHqBMbkxXvPnJAXN/whM/ZskFInfe1Qp4G23d/FrbqQpHJi5EvFxKovzRZ9DWQBYjWQZ5+x+5IAsepLs0VfA1mAWA3k2WfsviJAnBo3U0GW9z84+zcg43rBk0wtkJSUJBkZGRIXFyfJycmm7iudQwABBBDwbYGMnDPy2qZF8tnOZCksKdEdTPOoOvJYj+FyTdtECQ0O0a1DIQIIIIAAAggggAACCCCAAAIIIICA7wiwssp35oqeIoAAAggg4LcCmfm58taWpTJ12wrJLS7SHWeDiCh5uNtQuaVjH4kMCdOtQyECCCCAAAIIIIAAAggggAACCCCAgO8JkKzyvTmjxyYRSE398/0pCQkJJukR3UAAAT0BYlVPxTxl2UUF8sG2lfKmJVF1qiBPt2MxYeEyrvMgubfzQIkJi9CtQ6HvCxCrvj+HjCAwBIjVwJhnRun7AsSq788hIwgMAWI1MOaZUfq2AHFq3PyRrDLOmif5mUBenv4/qvrZMBkOAj4vQKyacwoLLKunPt25ViZvXCSH87J0Oxlh2eLv9oR+8mDXIdIgsrZuHQr9R4BY9Z+5ZCT+LUCs+vf8Mjr/ESBW/WcuGYl/CxCr/j2/jM4/BIhT4+aRZJVx1jwJAQQQQACBgBcotryH6tu9G+XFDQskLStT1yMkKEiub9dL/tn9LGlRu65uHQoRQAABBBBAAAEEEEAAAQQQQAABBPxHgGSV/8wlI0EAAQQQQMC0AqWlpfLbgVR5LmW+bM084rSfl8V3lX8lni3t6zR0WocLCCCAAAIIIIAAAggggAACCCCAAAL+JUCyyr/mk9EggAACCCBgOoElGXvkmfXzZPWxA077NrJZe5mYOEISGzRzWocLCCCAAAIIIIAAAggggAACCCCAAAL+KUCyyj/nlVEhgAACCCDgdYH1xw/Ks5Yk1fxDu5z2pV+jlvJkz5EyuElrp3W4gAACCCCAAAIIIIAAAggggAACCCDg3wIkq/x7fhmdBwXi4+M92DpNI4CAuwSIVXdJut7O9lNH5YWUBfLTvi1Ob+oa20Qm9Bwh5zbvKEGWd1RxIECs8j2AgG8IEKu+MU/0EgFile8BBHxDgFj1jXmil4EtQJwaN/8kq4yz5kl+JhAbG+tnI2I4CPinALFq3Lzuz86UVzYslC93r5cSyzuq9I420fXkccs7qa5o3U2Cg4L1qlAWoALEaoBOPMP2OQFi1eemjA4HqACxGqATz7B9ToBY9bkpo8MBKECcGjfpJKuMs+ZJCCCAAAII+KXAsbxseX3TYvlo+2opKCnWHWNcrWj5Z/ez5Ib2vSUsOES3DoUIIIAAAggggAACCCCAAAIIIIAAAoEpQLIqMOedUbtBICUlRWslMTHRDa3RBAIIeEqAWPWUrMjpgjx5d+tyec/yK6uoQPdBseGR8o+uQ+SOhH4SFRquW4dCBJQAscr3AQK+IUCs+sY80UsEiFW+BxDwDQFi1TfmiV4GtgBxatz8k6wyzponIYAAAggg4BcCuUWF2iqqKZsXy4n8XN0xRYWEyT2dB8j9XQZJ3fBaunUoRAABBBBAAAEEEEAAAQQQQAABBBBAQAmQrOL7AAEEEEAAAQRcEiiybPH3xa718srGP+Rgzhnde8KCg+WWDn3k4W7DpLFl6z8OBBBAAAEEEEAAAQQQQAABBBBAAAEEKhMgWVWZENcRQAABBBAIcIGS0hL5ad8WeX79fNl15oSuRpCl9Jq2ifJYj+HSKrqebh0KEUAAAQQQQAABBBBAAAEEEEAAAQQQ0BMgWaWnQhkCCCCAAAIISGlpqcw9uFOeWz9PNpzMcCpyYctO8q/EEdI5trHTOlxAAAEEEEAAAQQQQAABBBBAAAEEEEDAmQDJKmcylCOAAAIIIBDAAiuO7JNn1s+V5Zavzo6z4trIxJ4jJalhC2dVKEcAAQQQQAABBBBAAAEEEEAAAQQQQKBSgSDLT02XVlqLCgEtkJSUJBkZGRIXFyfJyckBbWE/+Ly8PO1jZGSkfTHnCCBgMgFitWoTssmyguo5y3Z//0vf7vTG3g2aaUmq4U3bOa3DBQSqKkCsVlWM+gh4R4BY9Y47T0WgqgLEalXFqI+AdwSIVe+481QEqiJAnFZFq2Z1WVlVMz/uDmABklQBPPkM3acEiFXXpmuP5V1UL6QskO/2bhRnP8WSULehPGHZ7u/Clp0lKEi9pYoDAfcJEKvus6QlBDwpQKx6Upe2EXCfALHqPktaQsCTAsSqJ3VpGwH3CBCn7nF0pRWSVa4oUQcBHQGy6jooFCFgQgFiteJJOZRzWl7duEg+37lWikpLdCu3qF1XHu8xXK5ukyghwcG6dShEoKYCxGpNBbkfAWMEiFVjnHkKAjUVIFZrKsj9CBgjQKwa48xTEKiJAHFaE72q3Uuyqmpe1EbAJpCamqqdJyYm2so4QQAB8wkQq/pzcjI/R97YvFQ+TF0pucVFupUaRkTJ+O7D5OYOfSQihD8y6CJR6DYBYtVtlDSEgEcFiFWP8tI4Am4TIFbdRklDCHhUgFj1KC+NI+AWAeLULYwuNcK/PLnERCUEEEAAAQT8QyCrMF+mblspb29ZKqct53pHTFiE/L3LILm70wCJtpxzIIAAAggggAACCCCAAAIIIIAAAggg4EkBklWe1KVtBBBAAAEETCKQb1k99emOZHlt0yI5mpet26tIy+qpOxL6yT+6DpH6llVVHAgggAACCCCAAAIIIIAAAggggAACCBghQLLKCGWegQACCCCAgJcEiktKZPqeDfLihgWyP/uUbi9CgoLkhva95Z/dz5JmUXV061CIAAIIIIAAAggggAACCCCAAAIIIICApwRIVnlKlnYRQAABBBDwokBpaanM3L9Nnk+ZL9tOHXXakytad5N/JZ4tbWMaOK3DBQQQQAABBBBAAAEEEEAAAQQQQAABBDwpQLLKk7q0jQACCCCAgBcEFh7aLc+unyfJx9OdPn108w4yoedI6V4vzmkdLiCAAAIIIIAAAggggAACCCCAAAIIIGCEQJDlJ69LjXgQz/BdgaSkJMnIyJC4uDhJTk723YHQcwQQQMDPBdYeS5dn1s+VhRl7nI50YONWMtGSpBrYON5pHS4ggAACCCCAAAIIIIAAAggggAACCCBgpAArq4zU5lkIIIAAAgh4QGBb5hFtu79fLdv+OTvUCiqVpBrVrL0EWd5RxYEAAggggAACCCCAAAIIIIAAAggggIBZBEhWmWUm6IfPCWRmZmp9jo2N9bm+02EEAknAn2N1X9ZJeXnDQvl6T4qUOFko3S6mvuWdVCPk0vguEhwUHEhTz1h9TMCfY9XHpoLuIlChALFaIQ8XETCNALFqmqmgIwhUKECsVsjDRQRMIUCcGjcNJKuMs+ZJfiaQlpamjYhklZ9NLMPxOwF/jNUjuVkyedMimbZjjRSWlOjOWbOoGHmk+3C5rl1PCQsO0a1DIQJmEvDHWDWTL31BwF0CxKq7JGkHAc8KEKue9aV1BNwlQKy6S5J2EPCcAHHqOdvyLZOsKi/CZwQQQAABBEwqcKogT97Zskze37ZcsosKdXtZL7yWPNhtiNzesZ/UCg3TrUMhAggggAACCCCAAAIIIIAAAggggAACZhIgWWWm2aAvCCCAAAII6AjkWhJTH6aukjc2L5GTBbk6NURqWxJT93YeKOM6D5K64ZG6dShEAAEEEEAAAQQQQAABBBBAAAEEEEDAjAIkq8w4K/QJAQQQQAABi0BhSbH8d+c6eXXjQjmUe0bXJNyyxd+tHfvIQ92GSqPIaN06FCKAAAIIIIAAAggggAACCCCAAAIIIGBmAZJVZp4d+oYAAgggEJACJaUl8v3ezfLihgWy+8wJXYPgoCC5tm2i9l6qVtGxunUoRAABBBBAAAEEEEAAAQQQQAABBBBAwBcESFb5wizRR1MKREayzZYpJ4ZOIVBOwJditbS0VGan75DnUubJppOHy43kr48Xt+os/0r8v/buAz6KMn3g+JOEEnrooQiIVCkBAipiRaV4Z+/tTr3z9OyenqIgcvbee+8Kh2cXBVERKQqh96J0Qu8hEJL9zzP+Z5xsZje7yZbZ3d/7+UBm3nnnnZnv5MnszjvvO/2lY73Gf2QyhUCCCyRSrCY4NbuPQKUEiNVK8bEyAjETIFZjRs2GEKiUALFaKT5WRiAmAsRpTJjNjaQZN8Z8sdscW0pEgdzcXMnPz5fs7GzJy8tLxENgnxFAAAHPC0zeuFLunvmt/LxpdcB9Pb5ZW7mzxwnSs2GLgGVYgAACCCCAAAIIIIAAAggggAACCCCAQKIJ0LMq0c4Y+4sAAgggkFQCc7eul7tnjZdv1y0LeFy9G7WQ4T1OlKOzDw5YhgUIIIAAAggggAACCCCAAAIIIIAAAggkqgCNVYl65tjvuAtobzNN2uOMhAAC3hXwaqwu37lF7pv9nXy8cn5AvE7GMH/ak2pwy46SZryjioRAMgt4NVaT2ZxjQ6AiAsRqRdRYB4HYCxCrsTdniwhURIBYrYga6yAQWwHiNHbeNFbFzpotJZnAhg2/v0+GxqokO7EcTtIJeC1W1+7ZIY/MnSDvLp8pxQFG4m1VK0tuzzlezmnTTTLS05PunHBACLgJeC1W3faRPAQQECFW+S1AIDEEiNXEOE/sJQLEKr8DCHhfgDiN3TmisSp21mwJAQQQQCCFBbbuK5An5k2UVxb/IvtKil0lmmTWklu6HSt/bddLqmVwiXZFIhMBBBBAAAEEEEAAAQQQQAABBBBAIOkEuBOWdKeUA0IAAQQQ8JLArqJ98sLCKfLswsmyq2i/667VrVpdbuhylFzZ6XCpVaWaaxkyEUAAAQQQQAABBBBAAAEEEEAAAQQQSFYBGquS9cxyXAgggAACcRUoLC6SN5ZMl8eN3lSbjV5VbqmG0Xvqyk5HyA2H9pOs6jXcipCHAAIIIIAAAggggAACCCCAAAIIIIBA0gvQWJX0p5gDRAABBBCIpcABY4i/D3+dLQ/O+UHWFux03XSVtHT5a/tcuaXrMZJds45rGTIRQAABBBBAAAEEEEAAAQQQQAABBBBIFQEaq1LlTHOcERfIysqKeJ1UiAACkReIVaz6fD75fPVCuW/Wd7Jk52bXA0kzcs9u001uzzleDq7TwLUMmQikqkCsYjVVfTluBCIlQKxGSpJ6EIiuALEaXV9qRyBSAsRqpCSpB4HoCRCn0bP1rznNuLnm889kHgGnQG5uruTn50t2drbk5eU5FzGNAAIIpLyAXkZ/yP9V7pk5XmZuXRfQY3DLjjI0p790qd80YBkWIIAAAggggAACCCCAAAIIIIAAAgggkIoC9KxKxbPOMSOAAAIIRERg2qbVcs+s8TJxw4qA9fVr0lqG9zxRDmt8UMAyLEAAAQQQQAABBBBAAAEEEEAAAQQQQCCVBWisSuWzz7FXSmDlypXm+q1bt65UPayMAALRFYhGrC7YvsEc7u+rNYsD7nxOg2ZyZ48TpH+zQyQtTQcAJCGAQDCBaMRqsO2xDAEEKiZArFbMjbUQiLUAsRprcbaHQMUEiNWKubEWArEUIE5jp01jVeys2VKSCWzfvt08IhqrkuzEcjhJJxDJWF25e5s8MPt7GfXbHAk0hm77ug3lDmO4v1NbdZb0tPSk8+SAEIiWQCRjNVr7SL0IICBCrPJbgEBiCBCriXGe2EsEiFV+BxDwvgBxGrtzRGNV7KzZEgIIIIBAggps2LtLHp37o7y1LE+KSkpcj6J5zboypPtxckHbHKmSnuFahkwEEEAAAQQQQAABBBBAAAEEEEAAAQQQKCtAY1VZE3IQQAABBBAwBXbs3ytPzZ8kLy36WQqKi1xVGlavKf/qerRc3qG3ZGZUdS1DJgIIIIAAAggggAACCCCAAAIIIIAAAggEFqCxKrANSxBAAAEEUlSg4MB+s4HqqQWTZMf+QleFOlWryTWdj5R/djpC6lbLdC1DJgIIIIAAAggggAACCCCAAAIIIIAAAgiUL0BjVflGlEAAAQQQSBGB/cUH5O1lM8wh/zYU7nY96urGEH9/73iY3NTlKGmYWcu1DJkIIIAAAggggAACCCCAAAIIIIAAAgggELoAjVWhW1ESgVICTZs2LTXPDAIIeEtg0faN8sbS6TI9f5XsMYbwy8qfLjkNmsll7XtLp6wmpXa22HgP1egVc+XBOT/Iit3bSi2zZjLS0uSiQ3rKv7sdKy1r1bOy+YkAAhES4LoaIUiqQSDKAsRqlIGpHoEICRCrEYKkGgSiLECsRhmY6hGIgABxGgHEEKtI8xkpxLIUS1GB3Nxcyc/Pl+zsbMnLy0tRBQ4bAQQSRWDG5rUyfMZYmbRxZcBd7tektdzda4D0bNhcvl67RO6ZNV4WGo1bgdIZrbvI7d2Pl/b1GgUqQj4CCCCAAAIIIIAAAggggAACCCCAAAIIVFCAnlUVhGM1BBBAAAHvCYxbu1Qu/XGUFBg9qYIlbcg6eewb0qp2PVm6c0vAoic0byd35vSXHKNRi4QAAggggAACCCCAAAIIIIAAAggggAAC0RGgsSo6rtSaAgKLFy82j7Jjx44pcLQcIgLeF9AeVX/9caTsNd47FUraV3IgYEPVYY0PkuE9TpB+TduEUhVlEEAgAgJcVyOASBUIxECAWI0BMptAIAICxGoEEKkCgRgIEKsxQGYTCFRSgDitJGAYq9NYFQYWRRFwChQWFjpnmUYAgTgL6NB/oTZUBdrVLllNZViP/jKwRQdJM95RRUIAgdgJcF2NnTVbQqAyAsRqZfRYF4HYCRCrsbNmSwhURoBYrYwe6yIQGwHiNDbOuhUaq2JnzZYQQAABBKIksMh431Swd1SVt9nmNevKiJ4nylltukp6Wnp5xVmOAAIIIIAAAggggAACCCCAAAIIIIAAAhEUoLEqgphUhQACCCAQH4E3lk6v1IZPbtlRzjm4e6XqYGUEEEAAAQQQQAABBBBAAAEEEEAAAQQQqJgAj49XzI21EEAAAQQ8JDB76/pK7c28bRsqtT4rI4AAAggggAACCCCAAAIIIIAAAggggEDFBWisqrgdayKAAAIIeERgd9H+Su3J7gP7KrU+KyOAAAIIIIAAAggggAACCCCAAAIIIIBAxQUYBrDidqyZ4gKtW7dOcQEOHwHvCFTPyKjUztSuUr1S67MyAghUXoDrauUNqQGBWAgQq7FQZhsIVF6AWK28ITUgEAsBYjUWymwDgcoJEKeV8wtnbRqrwtGiLAIOgaysLMcckwggEA+BfcUH5MVFU2Xu1vxKbb57g+xKrc/KCCBQeQGuq5U3pAYEYiFArMZCmW0gUHkBYrXyhtSAQCwEiNVYKLMNBConQJxWzi+ctWmsCkeLsggggAACnhDw+Xzy9dolMizvG/l119ZK79PlHfpUug4qQAABBBBAAAEEEEAAAQQQQAABBBBAAIGKCdBYVTE31kJAZs+ebSrk5OSggQACMRRYvGOT3DH9a/lu/fKIbPWopm2kY73GEamLShBAoOICXFcrbseaCMRSgFiNpTbbQqDiAsRqxe1YE4FYChCrsdRmWwhUTIA4rZhbRdaisaoiaqyDAAIIIBBzgR3798qDc36QVxb/IsVGzyq3lJGWbiwrcVvkmlczo6r8p+dJrsvIRAABBBBAAAEEEEAAAQQQQAABBBBAAIHYCNBYFRtntoIAAgggUEGB4pISeXvZDLlv9neyZV+Bay19GrWUh/oMls2FBXLpj6OkoLjItZwzUxuq3jzmXOnVqIUzm2kEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLEAjVUxBmdzCCCAAAKhC0zasEKGTB8j87ZtcF2pWY06MqLXSXJOm26SlpZmlvn8pEvlrpnj5Cdj3UBJh/7THlU0VAUSIh8BBBBAAAEEEEAAAQQQQAABBBBAAIHYCdBYFTtrtoQAAgggEKLAqt3bZYTR4PTxyvmua1RPz5BrDz1SbuxylNSuWr1UGW2A0garRds3yhtLp8uU1culoOSANK5TT7o3yJbLO/ThHVWlxJhBAAEEEEAAAQQQQAABBBBAAAEEEEAgvgI0VsXXn60jgAACCDgECg7sl6fnT5KnFkySwuIDjiV/TJ5yUGe5J3eAtK5d/49Ml6lOWU2MoQFPltnVZptLc3JyXEqRhQACCCCAAAIIIIAAAggggAACCCCAAALxFkjzGSneO8H2vS2Qm5sr+fn5kp2dLXl5ed7e2RjuXWFhobm1zMzMGG6VTSGQnAJ6KdJeVMNnjJW1BTtdD7Kz0fj0YO9Bckx2W9flgTKJ1UAy5CPgLQFi1Vvng71BIJAAsRpIhnwEvCVArHrrfLA3CAQSIFYDyZCPgHcEiNPYnQt6VsXOmi0lmQCNVEl2QjmcuAnM2brefC/VlI2rXPehfrUackfO8XJp+1ypYgz/F24iVsMVozwC8REgVuPjzlYRCFeAWA1XjPIIxEeAWI2PO1tFIFwBYjVcMcojEHsB4jR25jRWxc6aLSWZAK3qSXZCOZyYC2wu3CP3zhovby+bIW5dfDPS0sz3S93e/TipX71mhfePWK0wHSsiEFMBYjWm3GwMgQoLEKsVpmNFBGIqQKzGlJuNIVBhAWK1wnSsiEDMBIjTmFELjVWxs2ZLSSawePFi84h4D06SnVgOJ+oCRSXF8sriX+ShOT/IzqJ9rts7JvtgecAY8u/QrKauy8PJJFbD0aIsAvETIFbjZ8+WEQhHgFgNR4uyCMRPgFiNnz1bRiAcAWI1HC3KIhAfAeI0du40VsXOmi0hgAACKS/w7bqlMnT6N7Jk52ZXi9a1s+S+3IFycstOkmb0rCIhgAACCCCAAAIIIIAAAggggAACCCCAQPIL0FiV/OeYI0QAAQTiLrB85xYZmveNfLN2ieu+1KpSVW7qerRc07mvZGZUdS1DJgIIIIAAAggggAACCCCAAAIIIIAAAggkpwCNVcl5XjkqBBBAwBMCO/cXyqPzfpQXF02VopIS13069+DuclfPE6V5zbquy8lEAAEEEEAAAQQQQAABBBBAAAEEEEAAgeQWoLHK5fyuXr1afvzxR5k6daosXLhQ1q1bJ3v27JHatWtLs2bNpFevXnLGGWdI3759XdYOnjVr1iwZOXKkTJkyRdavX28W1jq1rvPOO0969OgRvAKXpWvXrpUPP/xQxo0bJzpdUFAgTZs2lS5dusiZZ54pgwYNYjgtFzeyEEAgegIlvhL54NfZcvfMb2Vj4R7XDfVq2Nx4L9VgOazxQa7LyUQAAQQQQAABBBBAAAEEEEAAAQQQQACB1BCgscpxnufNmydDhgyRmTNnOnL/mNy+fbvoP23Aeu+998wGpqeeekpatGjxR6EAU/v375d7771XXn/9dfH5fKVKLV26VPTfO++8I3/7299k2LBhUrVqaMNgffDBBzJ8+HCzgcpZ6cqVK0X/ffXVV3L00UfL008/LU2aNHEWYRoBBBCIisAvm1bLkGljZObWda71N8msZfakOr9tjqSnpbuWIRMBBBBAAAEEEEAAAQQQQAABBBBAAAEEUkcgzWg4Kd1ykjrHXuZIP/30U7n66qtL5bdt21Y6deok9evXl507d8r06dPtHlFaMDs7W/73v/9J69atS63nP3PDDTfI6NGj7Wwtrz20NM2YMcNsWLIWnn/++fLYY49ZswF/am+qm2++2V5er1496devn9SpU0cWL14s2ovLSp07dxY9vlq1allZIf/Mzc2V/Px881jz8vJCXo+CCCCQWgLrCnbKiBnj5L8r5roeeNX0dPlnp75ys/FuqrrVMl3LkIkAAggggAACCCCAAAIIIIAAAggggAACqSdAzyqXc96mTRu58MILzSH0dIg+Zyox3rkyatQos/fT3r17zUaca6+9Vj777LOAQ+1po5LVUJVu3KzVnlDag0qnNWmdr732mtx9993mtJY/4ogj5JxzznFuutT0smXLzF5gVqYO9/fQQw9JzZo1rSz56aef5Morr7R7g915553y+OOP28uZQAABBCIhUFhcJM8umCJPzJsoBca0WxrUooPcmztQDqnb0G0xeQgggAACCCCAAAIIIIAAAggggAACCCCQwgL0rHKcfH2P1KpVq+Tss8+WjIwMx5Kyk2PGjJG///3v9gIdFvC4446z562Jffv2yVFHHWW+90rztGHr9ttvtxaX+nn//ffLc889Z+a1bNlSJk6cKNWqVStVxprRRqgvvvjCnO3Tp4/Zu8tq/LLK6M/vvvtOLrnkEjNLj2n8+PHSvn17Z5Fyp+lZ5U6kQ0JqysrKMn/yHwKpJqAdc79YvVCG5Y2VVXu2ux5+h7qN5P7eg+SE5u1cl8cik1iNhTLbQKDyAsRq5Q2pAYFYCBCrsVBmGwhUXoBYrbwhNSAQCwFiNRbKbAOBygkQp5XzC2dtXhbi0Orbt6+cd9555TZU6SqDBw+Wnj172mtrI5BbGjt2rN1QVbduXbnxxhvdipl5N910kzmEn86sWbPGbFhyK7xp0ybzXVTWsqFDh9q9tKw862f//v3Nd1bpfHFxsfleLGsZPysnYL0XrHK1sDYCiSkwf9sGOX382/KXH0e5NlTVrVpd7jd6Uv3053/GtaFKdYnVxPwdY69TT4BYTb1zzhEnpgCxmpjnjb1OPQFiNfXOOUecmALEamKeN/Y6tQSI09idbxqrKmHdu3dve+3Vq1fb086Jb775xp495ZRTpEaNGva8/4Qu0zJW0t5bbkkbwHToQE36Ti3tWRUsnXvuufbir7/+2p5mAgEEEAhXYOu+Avn3L1/KMV+9KD/m/1Zm9TQj56/tciXvtOvln537StX04L1Uy1RABgIIIIAAAggggAACCCCAAAIIIIAAAgiknECVlDviCB5wWprelv09aa8ltzR58mQ7+8gjj7SnA01omffff99cPGnSJNdizjq1N1h5ybndtWvXym+//SYHH3xweauxHAEEELAFDpQUyxtL8+SB2d/Ltv177XznRN8mreSh3oOlW4NmzmymEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBoAI0VgXlCb5w0aJFdoHmzZvb09bEzp07ZcOGDdasdO3a1Z4ONNGtWzd7UX5+vuzatcseGtBasHTpUmtSnOXtTL+J7Oxsady4sejwgZp0fRqr/JCYRQCBgAI/5v8qQ6Z/LQu3b3Qt06JmXbknd4Cc3qqLOBvxXQuTiQACCCCAAAIIIIAAAggggAACCCCAAAII+AnQWOUHEuqs9lBy9nw6+uijy6y6fPnyUnktWrQoNe82419G6+jRo0epos56W7ZsWWpZoBmt12qscq4fqLxbvg49uG7dOrdFdp5bo529kAkEEEgogZW7t8mwvG/ki9V/NMw7D6BGRhW5octRct2hR0rNKtWci5hGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRCFqCxKmSq0gVHjBgh1tB/2hB00kknlS5gzG3bts3Oq1OnTtD3VVkF9b1VtWvXlt27d5tZ27dvtxaZP/fu3SuFhYV2nvaYCiU5y/nXGcr6Wmbjxo3lvh/rq6++KlNdTk6Omafb1RfS+afMzEzp2LGjma29yZy90ayyWVlZ0rp1a3NW63A7hqZNm4r2ItO0ePHiUk5mpvGf1qF1aZo9e7b50/8/3RfdJ3XWetySHpNVxq2eRD0mPdZkO08c0x+9O63f5fLiqaD4gHxasEbeXDVH9hnD/7ml4+u1kHt6D5QuzVqZi93iQBeEGk9aNlq/e1qvJv37Esu/EbrNaB2T1q0p1n/3dJsckyr8kcqLJy3JeQrtmqu/W5r8/54k6+cIPVbiSRX+SMRTbD7Dqnhlfvf0M7KeK01e+Vyu+1KZY+J3LzF+9zhP4Z0njQlN1nXVC5/LzR0y/uOz0R/nxTKxfnKekuu+kZ7X8q5Pev9I7wFasWr9LuhP/u6F93fPaafTxFPqxZOe92jcW3ZeUyN5b1n3t7y/EdE6Jq3XSpU9Jus7u1VfZX7SWFUBvVGjRomzUeb222+X6tWrl6lpz549dp5efEJN1oVKyzvr0PmCggL9YadQ63WW86/TroyJsAT0ohfoD0pYFVEYAY8I+Hw+Gbd9tbycP182H/ijUdy5e+0y68l1zbtLTq1G0qJGXeciz07rB3wSAgh4X4BY9f45Yg8RUAF98MN6iAwRBBDwrgDXVe+eG/YMAaeA3lsKdHPdWY5pBBCInwDX1NjZpxk3J32x21zib0mfdDjzzDPtXjunn366PPfcc64HNnr0aLnhhhvMZTo83rRp01zL+Wf26dPHHm7v6aeflrPOOssuosMPHnbYYfb85MmTQ/qyeP3118tHH31krnfBBRfIo48+atdR3kRubq554WzSpIl8+eWXQYszDGBQHhYi4FmBGZvXGu+lGiPTNq9x3ceG1WvKsB795ZJDeklGerprGTIRQAABBBBAAAEEEEAAAQQQQAABBBBAAIGKCNCzKgy1VatWyaWXXmo3VHXu3FkefPDBgDU4e1sVFRUFLOe/YP/+/XaWs0eUZvrPh1pvsDrtjZUzkW7coKYx6g8kffJFkzWs2B9LmEIgcQQ27N0l98waL+8tn+W601XS0uWKjofJbd2PlXrVariW8Xomser1M8T+IfC7ALHKbwICiSFArCbGeWIvESBW+R1AIDEEiNXEOE/sZWoLEKexO/80VoVore9R0h5J+t4mTTr0xXvvvSf6LqpAqVatWvYi53um7MwAE86yzjq0eM2aNUut5SxbaoHfjLOcf51+RZkNUcB6txaNVSGCUcxTAvuM91K9tOhneXTeBNlV9EcDuXMnT2h2iNzXe5B0rBfau/Gc63ppmlj10tlgXxAILECsBrZhCQJeEiBWvXQ22BcEAgsQq4FtWIKAlwSIVS+dDfYFAXcB4tTdJRq5NFaFoLp161azoWrFihVmaX0Z6Icffmi+FDTY6vXr17cX79q1y+yR5d8zyi7w/xN79+41X6xo5fuPiVmjRg2zd5XV+LRp0yaraNCfznL+dQZdkYUIIJBUAjry6zdrl8jQvG/k111bXY+tbZ0Gcl/uQBnYooOkpaW5liETAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIlACNVeVIaiPTRRddJIsXLzZLNmjQwGyoatWqVTlrihxyyCGlyqxZs0batWtXKs9/Rt9J5Uz+degyzZs/f75ZTOsMJTnrdaszlDoogwACiS2weMcmGTr9axm/frnrgdSpWk1u6XqsXNnpcKmeweXBFYlMBBBAAAEEEEAAAQQQQAABBBBAAAEEEIi4AHcjg5AWFBTIJZdcInPmzDFL1a1b1xz6r0OHDkHW+mORltdeWFZXQW1gKq+xau7cuXYFOryc2zCD7du3txur5s2bZ5cPNKHjajp7Vun6JAQQSB2BHfv3ykNzJsgri3+RA74S1wO/6JAecmePE6RpjcBDm7quSCYCCCCAAAIIIIAAAggggAACCCCAAAIIIFBJgfRKrp+0q+swe5deeqlMmzbNPEYdfu/tt9+W7t27h3XMRx55pF1+8uTJ9nSgiSlTptiL+vXrZ087J5x1Oss7yzinp06das+2aNFCDj74YHueCQQQSF6B4pISeXPpdMn99Bl5YdFU14aqPo1ayvhBV8izfU+noSp5fxU4MgQQQAABBBBAAAEEEEAAAQQQQAABBDwtQGOVy+kpKiqSK664QiZNmmQurV69urz++uvSp08fl9LBswYOHGgX+Pzzz0XfSRUo6bIvvvjCXjxo0CB72jkxYMAASU///dQtX75c8vLynIvLTI8aNcrOc+6PnclEhQT03V+8/6tCdKwUA4FJG1bI8WNelpt+/kK27Csos8VmRg+qF488Q74Z+Dfp1ahFmeXJlEGsJtPZ5FiSWYBYTeazy7ElkwCxmkxnk2NJZgFiNZnPLseWTALEajKdTY4lWQWI09id2TSfkWK3Oe9vqbi4WP75z3/Kl19+ae5slSpV5JVXXhFtIKpI2rdvn2gPqfXr15urX3/99XLbbbe5VvXAAw/Is88+ay5r2bKlTJw4UapVq+Za9h//+Ie9j4cffrh89NFHkpaWVqbshAkT5MILLzTzMzIy5Ntvv5VQhzG0KsvNzRUdSlCHJSyvYcxah58IIBAfgdV7tstdM8bJxyvnu+5A9fQMuebQI+WmLkdJ7arVXcuQiQACCCCAAAIIIIAAAggggAACCCCAAAIIxFKAnlUObW23u/nmm+1GIO299PTTT1e4oUqr1l5Zt9xyi70VbYx67bXXpMQYnstKOq15zz//vJVlrhOooUoL3XrrrVK1alWz/M8//yw33HCD6Du2nEl7hl1zzTV21tlnnx12Q5W9MhMIIOBpgYID+433Uv0gh3/2bMCGqlMO6ixTT7nGfDcVDVWePp3sHAIIIIAAAggggAACCCCAAAIIIIAAAiklQM8qx+l+8803ZejQoXaOvtvp2GOPtefLm7jvvvsCFtEeVdr7yUpt2rSRXr16mbMzZsyQFStWWIvkvPPOk8cff9yeDzTxwQcflGoI0y6J+j6rOnXqyJIlS2TmzJn2qp07d5ZPPvlEateubeeFOkHPKneplStXmgtat27tXoBcBGIgoI3sn6yaL8ON3lRr9uxw3WLnrCbyQO4gObZZW9flyZ5JrCb7Geb4kkWAWE2WM8lxJLsAsZrsZ5jjSxYBYjVZziTHkewCxGqyn2GOLxkEiNPYncUqsduU97e0ZcuWUjv522+/if4LNQVrrHr00Uelbt26og1ienNZG6ecDVS6DR3G7/LLL5c777wzpE1ecMEFZl133XWX2atq+/bt8tVXX5VZ96ijjjJ7iFWkoapMZWTYAuqticYqk4H/4iAwZ+t6GTJ9jEzZuMp161nVMuWOnP5yWftcqWIM/5eqiVhN1TPPcSeaALGaaGeM/U1VAWI1Vc88x51oAsRqop0x9jdVBYjVVD3zHHciCRCnsTtbNFbFyFqH9Lv33nvlrLPOkg8//FCmTJlivgdKN6/vgurbt69o41OPHj3C2iN9H5X2/tJeVuPGjZO1a9eaDVdNmjSRbt26yZlnnimDBg1yfZ9VWBuiMAIIeEZgc+EeuXfWd/L2sjzxuexVujZ8t+8tt+ccLw2q13QpQRYCCCCAAAIIIIAAAggggAACCCCAAAIIIOAdARqrHOdC31el/6KZevbsKfovkqlFixbmcIDOd2NFsn7qQgABbwgUlRTLq4t/kQeNd1PtLNrnulNHN20jD/QeLF3qN3VdTiYCCCCAAAIIIIAAAggggAACCCCAAAIIIOA1ARqrvHZG2B8EEEDARWD8umVyx/SvZcnOzS5LRVrVypJ7cwfInw/qTE9KVyEyEUAAAQQQQAABBBBAAAEEEEAAAQQQQMCrAjRWefXMsF8IIICAIbB85xYZlveNfL12iatHzYyqclPXo+XaQ/tKpjFNQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEk2AxqpEO2Psr2cEmjZlmDXPnIwk3JGd+wvlsXkT5YVFU6SopMT1CM9p001G9DpJmtes67qczN8FiFV+ExBIDAFiNTHOE3uJALHK7wACiSFArCbGeWIvESBW+R1AwPsCxGnszlGaz0ix2xxbSkSB3Nxcyc/Pl+zsbMnLy0vEQ2CfEUgYgRJfiXzw62y5e+a3srFwj+t+92zQXB7oM0gOb9zKdTmZCCCAAAIIIIAAAggggAACCCCAAAIIIIBAIgnQsyqRzhb7igACSS0wbdNqGTJ9jMzYss71OJtk1pLhPU+UC9rmSHpaumsZMhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUQToLEq0c4Y++sZgcWLF5v70rFjR8/sEzuSmALrCnbKf4yeVKN+m+N6AFXT0+WqTkfILV2PkbrVMl3LkBlYgFgNbMMSBLwkQKx66WywLwgEFiBWA9uwBAEvCRCrXjob7AsCgQWI1cA2LEHAKwLEaezOBI1VsbNmS0kmUFhYmGRHxOHEWqCwuEieWzhFnjDeTbXnQJHr5ge26CD35g6QdnUbuS4ns3wBYrV8I0og4AUBYtULZ4F9QKB8AWK1fCNKIOAFAWLVC2eBfUCgfAFitXwjSiAQbwHiNHZngMaq2FmzJQQQQMAU0FcFfrl6kQyb8Y2s3L3dVaV93YZyf+9BcmLz9q7LyUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBJJFgMaqZDmTHAcCCCSEwILtG+SO6V/LhPzfXPe3btXqclv34+SKjodJ1fQM1zJkIoAAAggggAACCCCAAAIIIIAAAggggAACySRAY1UynU2OBQEEPCuwbV+B3D/7e3l96XQpMXpW+ac0I+Mv7XrJ0B79pXFmbf/FzCOAAAIIIIAAAggggAACCCCAAAIIIIAAAkkrQGNV0p5aDgwBBLwgcKCkWN5cmmc2VG3bv9d1l45o3Eoe6jNYujdo5rqcTAQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJkFaKxK5rPLsUVVoHXr1lGtn8oTX+DH/F9liDHk38LtG10PpkXNunJ3rwFyRusukpamfatI0RAgVqOhSp0IRF6AWI28KTUiEA0BYjUaqtSJQOQFiNXIm1IjAtEQIFajoUqdCERWgDiNrGew2misCqbDMgSCCGRlZQVZyqJUFli5e5vcmTdWPl+90JUhM6OK3HBoP7m+Sz+pWaWaaxkyIydArEbOkpoQiKYAsRpNXepGIHICxGrkLKkJgWgKEKvR1KVuBCInQKxGzpKaEIiWAHEaLdmy9dJYVdaEHAQQQKBCAnsO7Jcn5k2UZxdMln3G8H9uSXtRjeh5krSqneW2mDwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDlBGisSrlTzgFHSmD27NlmVTk5OZGqknoSVMDn88l/V8yV/8wcJ+sKdrkeRdf6TeXB3oOlX9M2rsvJjJ4AsRo9W2pGIJICxGokNakLgegJEKvRs6VmBCIpQKxGUpO6EIieALEaPVtqRiBSAsRppCTLr4fGqvKNKIEAAggEFJi5Za3cNm2MTNu8xrVMg+o1ZFjOCfKXdr0kIz3dtQyZCCCAAAIIIIAAAggggAACCCCAAAIIIIBAKgvQWJXKZ59jRwCBCgts2LtL7pk1Xt5fPkt8LrVkpKXJFR0Pk9u6HSdZRoMVCQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBdgMYqdxdyEUAAAVeB/cUH5KXFP8sjcyfIrqL9rmX6NztE7ssdKJ2ymrguJxMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgDwEaq/6wYAoBBBAIKKDvpfpm7RIZlveNLN+11bXcwbXry329B8mgFh0kzehZRUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoX4DGqvKNKIEAAikusGTHJrnDaKQav26Zq0TtKtXklm7HyFWdjpDqGfxZdUUiEwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCCCQZvQWcHvdSoDiZKeiQG5uruTn50t2drbk5eWlIoHrMRcWFpr5mZmZrsvJTHyBHfv3ysNzJsjLi3+RA74S1wO6sG0PubPHCZJds47rcjLjL0Csxv8csAcIhCJArIaiRBkE4i9ArMb/HLAHCIQiQKyGokQZBOIvQKzG/xywBwiUJ0CclicUueV0AYicJTWlmACNVMl7wotLSuTd5TPl3lnjZfO+AtcD7dOopTzYe7D0atTCdTmZ3hEgVr1zLtgTBIIJEKvBdFiGgHcEiFXvnAv2BIFgAsRqMB2WIeAdAWLVO+eCPUEgkABxGkgm8vk0VkXelBpTRIBW9eQ80ZM3rpTbp42ROdvyXQ8wu0ZtuavnSXLuwd0kPS3dtQyZ3hIgVr11PtgbBAIJEKuBZMhHwFsCxKq3zgd7g0AgAWI1kAz5CHhLgFj11vlgbxBwEyBO3VSik0djVXRcqTUFBBYvXmweZU5OTgocbfIf4uo922XEjG/lfyvnuR5stfQMufbQI+WmLkdJ7arVXcuQ6U0BYtWb54W9QsBfgFj1F2EeAW8KEKvePC/sFQL+AsSqvwjzCHhTgFj15nlhrxBwChCnTo3oTtNYFV1fakcAAY8LFBzYL88smCxPzf9J9hYfcN3bPx3USe7tNUDa1GngupxMBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYoL0FhVcTvWRACBBBbw+Xzyyar5MnzGOFmzZ4frkXSq11ge6D1Ijmt2iOtyMhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQqL0BjVeUNqQEBBBJMYO7W9TJk+tei76dyS1nVMuX2nOPl8va9pYox/B8JAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIHoCNFZFz5aaEUDAYwKbC/fIfbO/k7eXzZASo2eVf0pPS5PLjAaq27sfJw0za/kvZh4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgCgI0VkUBlSoRQMBbAkUlxfLakmny4JwfZMf+QtedO6ppG3PIv671s12Xk4kAAggggAACCCCAAAIIIIAAAggggAACCCAQHYE0470tZbsXRGdb1JqgArm5uZKfny/Z2dmSl5eXoEfBbqeqwHfrlskdeV/L4h2bXQkOqlVP7s0dKKcc1FnSjJ5VJAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHYCtCzKrbebA0BBGIk8OuuLTJ0+jfy9dolrlusmVFVbup6lFzT+UipUaWqaxkyEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKIvQGNV9I3ZQpIKbN++3TyyrKysJD3CxDysXUX75NG5P8oLi6ZIUUmJ60Gc3aabjOh5orQwelWRkl+AWE3+c8wRJocAsZoc55GjSH4BYjX5zzFHmBwCxGpynEeOIvkFiNXkP8ccYeILEKexO4c0VsXOmi0lmcDKlSvNI6KxyhsntsRXIh/+OlvunjleNhTudt2pHg2aGe+lGixHNGnlupzM5BQgVpPzvHJUySdArCbfOeWIklOAWE3O88pRJZ8AsZp855QjSk4BYjU5zytHlVwCxGnszieNVbGzZksIIBAlgWmbVsuQ6WNkxpZ1rltonFlLhvc4QS48pIekp6W7liETAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAID4CNFbFx52tIoBABATWF+yU/8z8Vkb+Nse1tqrp6XJlxyPklm7HSL1qma5lyEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIrwCNVfH1Z+sIIFABgcLiInl+4VR5fN6PsudAkWsNA1q0l/tyB0q7uo1cl5OJAAIIIIAAAggggAACCCCAAAIIIIAAAggg4A0BGqu8cR7YCwQQCEHA5/PJV2sWybC8sbJi9zbXNdrXbWg0Ug2Sk4zGKhICCCCAAAIIIIAAAggggAACCCCAAAIIIICA9wVorPL+OWIPPSqQmcmwcrE8NQu3b5TbjfdSTcj/zXWzdapWl9u6HStXdDxMqmXwp80VKUUzidUUPfEcdsIJEKsJd8rY4RQVIFZT9MRz2AknQKwm3Cljh1NUgFhN0RPPYSeUAHEau9OVZvRU8MVuc2wpEQVyc3MlPz9fsrOzJS8vLxEPgX1OYIFt+wrkgTk/yOtLpkmxy5+rNOPYLmnXS4b16C+NM2sn8JGy6wgggAACCCCAAAIIIIAAAggggAACCCCAQGoK0P0gNc87R42A5wUOlBTLm0vz5P7Z38u2/Xtd9/eIxq3kwd6DJKdhc9flZCKAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4X4DGKu+fI/bQowLa20yT9jgjRVZgojHU3xBjyL8FxtB/bql5zbpyd6+T5MzWXSUtTftWkRAILECsBrZhCQJeEiBWvXQ22BcEAgsQq4FtWIKAlwSIVS+dDfYFgcACxGpgG5Yg4BUB4jR2Z4LGqthZs6UkE9iwYYN5RDRWRe7Erty9TYbPGCufrVroWmmm8S6q6w/tJ9d36Se1qlRzLUMmAv4CxKq/CPMIeFOAWPXmeWGvEPAXIFb9RZhHwJsCxKo3zwt7hYC/ALHqL8I8At4TIE5jd05orIqdNVtCAIEAAnsO7Jcn5/0kzyyYJPuM4f/c0umtDpX/GL2pWtWu77aYPAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEEFaCxKkFPHLuNQDII+Hw+Gb1iroyYOU7WFexyPaSu9Zsa76UaLP2atnFdTiYCCCCAAAIIIIAAAggggAACCCCAAAIIIIBAYgvQWJXY54+9RyBhBWZtWSe3Ge+l+mXTatdjaFC9hgzN6S9/bZcrGenprmXIRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEh8ARqrEv8ccgQIJJTAxr275Z5Z4+W95TPF57LnGWlpckXHw+S2bsdJltFgRUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIbgEaq5L7/HJ0URTIysqKYu3JV/X+4gPy0uKf5ZG5E2RX0X7XAzy+WVu5P3eQdMpq4rqcTAQqIkCsVkSNdRCIvQCxGntztohARQSI1YqosQ4CsRcgVmNvzhYRqIgAsVoRNdZBILYCxGnsvNOMd8a4dW6I3R6wJc8L5ObmSn5+vmRnZ0teXp7n95cd9J7AN2uWyNC8r2X5rq2uO3dw7fpyb+5AGdyyo6QZPatICCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqkjQM+q1DnXHCkCMRdYsmOT0Uj1jXy7bpnrtmtXqSY3dztG/tnpCKmewZ8jVyQyEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBJBfg7nCSn2AOL3oCK1euNCtv3bp19DaSoDXv2F9oDvf30qKf5YCvxPUoLmibI8N7nCjZNeu4LicTgUgJEKuRkqQeBKIrQKxG15faEYiUALEaKUnqQSC6AsRqdH2pHYFICRCrkZKkHgSiJ0CcRs/Wv2Yaq/xFmEcgRIHt27ebJWms+gOsuKRE3ls+U+6ZNV427yv4Y4FjKrdhC3mwz2Dp3ailI5dJBKInQKxGz5aaEYikALEaSU3qQiB6AsRq9GypGYFIChCrkdSkLgSiJ0CsRs+WmhGIlABxGinJ8uuhsap8I0oggEAIAlM2rpQh08bInG35rqWbZtaWEb1OlHMP7i7paemuZchEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSD0BGqtS75xzxAhEVGDNnh1y14xx8r+V81zrrZaeIVd37iv/6nq01Kla3bUMmQgggAACCCCAAAIIIIAAAggggAACCCCAAAKpK0BjVeqee44cgUoJ7D1QJM8smCRPzv9J9hYfcK3r5JYd5d7cgXJwnQauy8lEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIDGKn4HEEAgLAGfzyefrlogd84YK9qryi11qtdY7u89SI5vdojbYvIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEELAFaKyyKZhAIDyBpk2bhrdCEpSea7yP6nbjvVSTjPdTuaV61TLlju7Hy2UdektVY/g/EgJeEEjFWPWCO/uAQLgCxGq4YpRHID4CxGp83NkqAuEKEKvhilEegfgIEKvxcWerCIQjQJyGo1W5smlGLwlf5apg7WQXyM3Nlfz8fMnOzpa8vLxkP1yOz0VgS+EeuW/29/LWsjwpcfmTkZ6WJpe2zzUbqhpm1nKpgSwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNwF6Fnl7kIuAggYAkUlxfLakmny4JwfZMf+QleTfk1aywN9Bku3+tmuy8lEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCYAI1VwXRYhkAQgcWLF5tLO3bsGKRU4i76fv1yuWP617JoxybXgzioVj25p9cAObXVoZJm9KwiIeBVgWSPVa+6s18IhCtArIYrRnkE4iNArMbHna0iEK4AsRquGOURiI8AsRofd7aKQDgCxGk4WpUrS2NV5fxYO4UFCgvdexolOsmvu7bIsLyxMmbN741x/sdTI6OK3NTlaLn20COlRpWq/ouZR8BzAskaq56DZocQqKQAsVpJQFZHIEYCxGqMoNkMApUUIFYrCcjqCMRIgFiNETSbQaASAsRpJfDCXJXGqjDBKI5AsgrsKtonj837UV5YOFX2G8P/uaWz2nSVET1PkpZGryoSAggggAACCCCAAAIIIIAAAggggAACCCCAAAKREKCxKhKK1IFAAguU+Epk5K9z5D8zv5UNhbtdjySnQTN5oPcg6Wu8n4qEAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAkBWisiqQmdSGQYALTN6+RIdPGSN6Wta573jizltzZ4wS5sG0PyUhPdy1DJgIIIIAAAggggAACCCCAAAIIIIAAAggggAAClRGgsaoyeqyLQIIKrC/YafakGvnbHNcjqJKWLld1Olxu6Xas1KuW6VqGTAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEIiFAY1UkFKkjJQVat068IfEKi4vMd1Lpu6n2HChyPW8nNW8v9+UOlPb1GrkuJxOBRBNIxFhNNGP2F4FICBCrkVCkDgSiL0CsRt+YLSAQCQFiNRKK1IFA9AWI1egbswUEKitAnFZWMPT1aawK3YqSCJQSyMrKKjXv5Rmfzydj1iyWoXnfyIrd21x3tV2dhnJf74EyoEUH1+VkIpCoAokUq4lqzH4jEAkBYjUSitSBQPQFiNXoG7MFBCIhQKxGQpE6EIi+ALEafWO2gEBlBYjTygqGvj6NVaFbURKBhBRYuH2j3DH9a/kh/1fX/a9Ttbrcagz394+Oh0m1DP4kuCKRiQACCCCAAAIIIIAAAggggAACCCCAAAIIIBA1Ae5MR42WipNdYPbs2eYh5uTkePJQt+/bKw/M+V5eWzJNio2eVf4pzci4pF0vGZrTX5rUqO2/mHkEkkbA67GaNNAcCAKVFCBWKwnI6gjESIBYjRE0m0GgkgLEaiUBWR2BGAkQqzGCZjMIVEKAOK0EXpir0lgVJhjFEfC6wIGSYnlr2Qy5f/Z3stVosHJLhzc+SB7sPVh6NGzutpg8BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiJkBjVcyo2RAC0Rf4Kf83GWIM+Td/+wbXjTWvWUf+03OAnNWmq6Slad8qEgIIIIAAAggggAACCCCAAAIIIIAAAggggAAC8RWgsSq+/mwdgYgIrNq9TYbPGCefrlrgWl+m8S6q6w49Um7ocpTUqlLNtQyZCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEQ4DGqnios00EIiSw58B+eWr+T/LMgslSWHzAtdbTWh0qd/c6SVrVru+6nEwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOIpQGNVPPXZNgIVFPD5fDJ6xVwZMXOcrCvY5VrLoVlNzPdSHZ19sOtyMhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8IJAmnHT2+eFHWEfvCuQm5sr+fn5kp2dLXl5ed7d0RjvWWFhobnFzMzMmG551pZ1xnupxsjPm1a7brdB9RoyNKe//KVdL6mSnuFahkwEUkkgXrGaSsYcKwKRECBWI6FIHQhEX4BYjb4xW0AgEgLEaiQUqQOB6AsQq9E3ZgsIVFaAOK2sYOjr07MqdCtKIlBKINaNVBv37pZ7Z42Xd5fPFLcW5oy0NPlbhz4ypPtxUr96zVL7ygwCqSwQ61hNZWuOHYHKCBCrldFjXQRiJ0Csxs6aLSFQGQFitTJ6rItA7ASI1dhZsyUEKipAnFZULvz1aKwK34w1EDAFYtWqvt94F9XLi3+Rh+dOkF1F+1z1j8tuK/f3HiSdjaH/SAggUFogVrFaeqvMIYBAuALEarhilEcgPgLEanzc2SoC4QoQq+GKUR6B+AgQq/FxZ6sIhCNAnIajVbmyNFZVzo+1U1hg8eLF5tHn5ORETWHs2iUydPo3smzXFtdttKldX+7LHSiDW3aUNKNnFQkBBMoKxCJWy26VHAQQCFeAWA1XjPIIxEeAWI2PO1tFIFwBYjVcMcojEB8BYjU+7mwVgXAEiNNwtCpXlsaqyvmxNgJREVi6Y7MMzftGxq1b6lp/rSpV5eaux8jVnftK9QzC2BWJTAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGEEOAud0KcJnYyVQR27C+UR4zh/l5a9LMc8JW4Hvb5bXNkeI8TpFnNuq7LyUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJEEaKxKpLPFviatQHFJibz/6yy5Z9Z42VS4x/U4cxu2kAeM91L1aXyQ63IyEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIRAEaqxLxrLHPSSUwZeNKuX361zJ763rX42qaWVvu6nminNe2u6SnpbuWIRMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUQVoLEqUc8c+53wAmv27JARM8fJRyvmuR5LtfQM+WfnI8x3U9WpWt21DJkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCS6QJrPSIl+EOx/dAVyc3MlPz9fsrOzJS8vL7obS4Ha9x4okmcXTJYn5/8kBcVFrkc8uGVHuTd3gLSt09B1OZkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCSLAD2rkuVMchyeF9B24c9WLZA7Z4yV1UavKrfUsV4juT93kPRv3s5tMXkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCSdAI1VSXdKOaBYCWzfvt3cVFZWVrmbnLct33wv1U8bVriWrVctU27vfpxc3qGPVDWG/yMhgEDkBMKJ1chtlZoQQCBcAWI1XDHKIxAfAWI1Pu5sFYFwBYjVcMUoj0B8BIjV+LizVQTCESBOw9GqXFkaq1z8iouLZfHixTJ79mz738KFC6Wo6Pch2/r27SujR492WTN41sqVK2XUqFEydepUWbZsmezcuVPS09NFGzs6d+4sRx99tJx99tnSsGF4Q7/NmjVLRo4cKVOmTJH169ebO9GsWTPR/TzvvPOkR48ewXeMpWEJLNq+Ud5YOl0mr14me0sOSKM69SSnQTO5rH1v6ZTVpFRdWwr3yH2zv5e3luVJicuIm+lpafLXdrlyR87x0iizVql1mUEAgcgI6N9eTaE0LEdmi9SCAAIVESBWK6LGOgjEXoBYjb05W0SgIgLEakXUWAeB2AsQq7E3Z4sIhCtAnIYrVvHyNFb52X399ddy7bXXyt69e/2WVHy2pKREHn74YXnhhRfkwIEDZSrS90Hpv++//16eeOIJufPOO+Wiiy4qU84/Y//+/XLvvffK66+/Lv6vHlu6dKnov3feeUf+9re/ybBhw6Rq1ar+VTAfhsCMzWtluDGE36SNv9/4tlZdXrhTft60Wl5e/Iv0a9Ja7u41QLo1yJbXl0yTB+b8IDv2F1pFS/3Usg/0HmSUbVYqnxkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFJJgMYqv7O9Y8eOiDZUafVDhgyR9957z95SZmam5OTkyEEHHST79u2TFStWyNy5c83lu3btkltvvVUKCwvNRiZ7JZeJf//736V6eLVu3Vp69epllpwxY4Zoq682Yr366quye/dueeyxx1xqISsUgXFrl8qlP46SguLfe9cFWkcbsk4e+7o0NnpJrSnY6VqsZa16co/RoHVaq0MlzehZRUIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJUFaKwKcPYbN25sNijpEHrasPTDDz/Ia6+9FqB04OzJkyeXaqg69dRT5a677pLs7OxSK2njkjY+LVq0yMy///775eSTTxYdzs8tffjhh3ZDlQ4lOHz4cLNxS6c1aW8u3d+7777bnNbyRxxxhJxzzjlu1ZEXREB7VP31x5Gyt7hsrzi31faVFLs2VNXIqCI3djlKrju0n9SoQi83NzvyEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFJPgMYqv3N+/PHHyy+//CItWrQotWTmzJml5kOd0XdUWalr167y3HPPme+psvKsn9oj6u2335ZjjjnG7FWlPavGjBkjl19+uVXE/qm9sZy9pK6++mq54oor7OU6oY1Wmrdp0yZzm5r36KOPymmnnSbVqlXTWVKIAjr0X6gNVYGqPKtNVxnR8yTRXlUkBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ+EPg9244f8yn/FSTJk3KNFRVBmXhwoX26tqryur5ZGc6JrSB7PDDD7dzfv31V3vaOTF27FhZt26dmVW3bl258cYbnYtLTd90001Sp04dM2/NmjUyfvz4UsuZCS6waPvGMu+oCr5G6aUd6jaSrwZcJq8edTYNVaVpmEMgZgI69Kr+IyGAgLcFiFVvnx/2DgFLgFi1JPiJgLcFiFVvnx/2DgFLgFi1JPiJgHcFiNPYnRt6VkXZes+ePfYW6tUrv1dNVlaWXV6H8nNL33zzjZ19yimnSI0aNex5/wldpmXef/99c5H21ho8eLB/MeYDCLyxdHqAJaFlH5vdVvo2aR1aYUohgEBUBDp27BiVeqkUAQQiK0CsRtaT2hCIlgCxGi1Z6kUgsgLEamQ9qQ2BaAkQq9GSpV4EIidAnEbOsrya6FlVnlAllzuHE1yyZEm5tS1evNguc+ihh9rTzgl9D5aVjjzySGsy4E9nmUmTJgUsx4KyArO3ri+bGUbO3G35YZSmKAIIIIAAAggggAACCCCAAAIIIIAAAggggAACqSdAY1WUz/mAAQPsLYwcOVICDe2nhf773//KokWLzPL169cXHTbQP+3cuVM2bNhgZ+t7sMpL3bp1s4vk5+fLrl277HkmggvsLtofvEA5S3cf2FdOCRYjgEC0BfTvnv4jIYCAtwWIVW+fH/YOAUuAWLUk+ImAtwWIVW+fH/YOAUuAWLUk+ImAdwWI09idGxqromx90UUXSadOncyt7N69W/70pz/JE088IfouKx0icOvWrTJ9+nS5/vrr5V//+pdZTt9D9eKLL4r+9E/Lly8vleXsuVVqgWPGv4x/HY6iTPoJ1K5azS8nvNnaVaqHtwKlEUAg4gLawO9s5I/4BqgQAQQiIkCsRoSRShCIugCxGnViNoBARASI1YgwUgkCURcgVqNOzAYQqLQAcVppwpAr4J1VIVNVrKC+gO2jjz6Sf/zjH6JD8GnPqEcffdT8519j1apVZeDAgTJkyBBp166d/2Jzftu2bXZ+nTp1gr6vyiqo762qXbu2aGOZpu3bt5s/w/1P36G1bt26oKs1b9486PJEW5jToJn8vGl1hXe7e4PsCq/LiggggAACCCCAAAIIIIAAAggggAACCCCAAAIIpIIAjVUxOMtZWVmiQwBOmDBBbr/9dlm1apXrVnW4vlNOOUXatm3rulwztTeWlbQhLNSkZa3GKmcdoa6v5TZu3Ch9+vQJuspXX31VZnlOTo6Zp41kK1euLLNc9816UZ12q9TWav+khq1btzaztQ63BremTZtKdvbvjUP67q/CwkL/asw6tC5Ns2fPNn/6/6f7ovuk6x9ZUkde9i8QxvzlHfqYw4956Zic70VzHkoinyeO6Y8z6eV4itd52rJliwmkf18i/TciXsdknfFo/N3jmCxdMa8FXr0+JeN5smLV//rM9clbn42S8XePYwrv754OKa4PzWmK5edyzlN45yle3584T945T/7XVef3XM6Td87TH3vy+xTnKfHuG0UingoKClzvUfG3XER9I3F/LxLnyWv3LDmmP/6CRvtemPOaqvenw7m3nArnyfrO/scZqfgUwwBW3C7kNYuKiuTpp5+Wa665xmyoqlmzphx33HGiQwSeffbZYr13asaMGXL11VfLGWecIZs2bXKtf9++P96BpD2xQk3Vqv0xnJ3bH/lQ60m1cm0y60pOrUYVOuyjmraRjvUaV2hdVkIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFUE0nxGSpWDrcxxPvbYY/L444+bVfTt21dGjx4dUnV79+6Vv/zlLzJ58mSz/MUXXyx33HGH1KtXr9T6+hTxddddJ9b7pLp06SKff/65VK9e+p1HmnfVVVeZ6zZu3FhmzZpVqp5AM9rCuXnzZnPxyy+/bL47K1BZ//zc3Fyzd1CTJk3kyy+/9F9caj7ZhgHUg5uxea2cMu5NKSguKnWswWZqZlSVz0+6VHo1ahGsGMsQQCAGAlYvjUg+6RGD3WYTCKScALGacqecA05QAWI1QU8cu51yAsRqyp1yDjhBBYjVBD1x7HZKCRCnsTvdDAMYZesRI0bYDVXnnHOOPPTQQ65b1Juoo0aNkgEDBoh2LZw/f7689tprZk8r5wq1atWyZ8PpIeUs66zDriyEifT0dEnGxqjyDl0bnN485ly59MdRITVYaUOVlqehqjxZliMQGwGre3ZstsZWEECgogLEakXlWA+B2AoQq7H1ZmsIVFSAWK2oHOshEFsBYjW23mwNgYoIEKcVUavYOgwDWDG3kNZav369fPDBB2bZtLQ0ue2224Kup+9S+dvf/maX0fdc+af69evbWTpevLMRyl7gN6G9u6z3VekiAswPKITZk1q0N3tK6dB+wZIu1x5VWp6EAALeENDxhK133nljj9gLBBBwEyBW3VTIQ8B7AsSq984Je4SAmwCx6qZCHgLeEyBWvXdO2CME/AWIU3+R6M3Tsyp6tjJx4kQpLi42t3DIIYdIs2bNyt1av3797DLLli2TPXv2iLMnlNbjTGvWrJF27do5s8pMr127tlSefx2lFjITUEB7SmlD1KLtG+WNpdNlztZ82X1gn9SuUl26N8iWyzv04R1VAfVYgAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAuwCNVe4uEcnNz8+363H2iLIzXSYaNGhQKld7Tzkbq+rWrStNmzaVDRs2mOV0uMDyGqvmzp1r16m9t+rUqWPPMxG+QKesJvJQn5Nl5cqV5srauk5CAAHvChCr3j037BkCTgFi1anBNALeFSBWvXtu2DMEnALEqlODaQS8K0CsevfcsGcIWALEqSUR/Z8MAxhF48zMTLv27du329PBJrZt21Zqcb169UrN68yRRx5p502ePNmeDjQxZcoUe5Gz55adyUSFBPSchnpeK7QBVkIAgYgIEKsRYaQSBKIuQKxGnZgNIBARAWI1IoxUgkDUBYjVqBOzAQQiIkCsRoSRShCIqgBxGlXeUpXTWFWKI7IzLVq0sCvUIf2s3lB2psvEpEmT7FztQVWjRg173poYOHCgNSmff/656DupAiVd9sUXX9iLBw0aZE8zgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEW4DGqiieAe0BlZGRYW7B5/PJww8/HHRrGzdulNdee80uc+yxx9rTzokBAwbY77/asWOHPP30087FpaaffPJJ0TKaWrZsKSeeeGKp5cwggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvEUoLEqivr6nqpzzjnH3sKHH34od9xxh+zcudPOsyb0vVLnnXeebN682cxKT0+Xq666ylpc6mf16tXllltusfOeffZZs5GrpKTEztNpbfh6/vnn7Txdp1q1avY8EwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAvAWqxHsHvLj9Sy65RPLz80vt2qZNm+z52bNny0knnWTPWxPvvPOOZGdnW7Pmz6FDh8q0adNk+fLl5vxbb70lo0ePlsMOO0yaN28u+/fvl0WLFok2VjmTNmp17NjRmVVq+vzzzxd9X9VHH30k2jA1fPhwef3116VXr15muRkzZsiKFSvsdbQhzNlwZi9gAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIowCNVS74S5YskTVr1rgs+T2roKBAFixYUGZ5UVFRmbwGDRqYjVM333yzfPfdd+byPXv2yPfff1+mrGbUrFlT7rrrLrn44otdlzszH330Ualbt668+eabosMMauOUs4FKy6alpcnll18ud955p3NVpiMgoO8UIyGAgPcFiFXvnyP2EAEVIFb5PUAgMQSI1cQ4T+wlAsQqvwMIJIYAsZoY54m9TG0B4jR25z/NaOTwxW5zibGlww8/PGhjVaCjmDp1qhx00EGBFkteXp58/PHHor2eVq1aJbt375YqVaqIDhfYqVMnOfroo+Xcc8+VrKysgHW4LZg5c6boEINTpkyxe4RpD6++ffvKBRdcID169HBbLeS83Nxcs16tU4+BhAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghESoDGqkhJJnE9NFYl8cnl0BBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLNAepy3z+YRSFiBxYsXi/4jIYCAtwWIVW+fH/YOAUuAWLUk+ImAtwWIVW+fH/YOAUuAWLUk+ImAtwWIVW+fH/YOARUgTmP3e8A7q2JnzZaSTKCwsDDJjojDQSA5BYjV5DyvHFXyCRCryXdOOaLkFCBWk/O8clTJJ0CsJt855YiSU4BYTc7zylEllwBxGrvzSc+q2FmzJQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAT8BGqv8QJhFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCInQCNVbGzZksIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ+AjRW+YEwiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEDuBKrHbFFtCILkEWrdunVwHxNEgkKQCxGqSnlgOK+kEiNWkO6UcUJIKEKtJemI5rKQTIFaT7pRyQEkqQKwm6YnlsJJKgDiN3emksSp21mwpyQSysrKS7Ig4HASSU4BYTc7zylElnwCxmnznlCNKTgFiNTnPK0eVfALEavKdU44oOQWI1eQ8rxxVcgkQp7E7nwwDGDtrtoQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOAnQGOVHwizCIQqMHv2bNF/JAQQ8LYAsert88PeIWAJEKuWBD8R8LYAsert88PeIWAJEKuWBD8R8LYAsert88PeIaACxGnsfg9orIqdNVtCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwE6Cxyg+EWQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdgJ0FgVO2u2hAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4CdAY5UfCLMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKxE6gSu02xJQSSR2DdunVy8sknmwc0bdo0ad68efIcHEeCQBIJEKtJdDI5lKQWIFaT+vRycEkkQKwm0cnkUJJagFhN6tPLwSWRALGaRCeTQ0laAeI0tqeWnlWx9WZrCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACDgEaqxwYTCKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMRWgMaq2HqzNQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAYcAjVUODCYRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiK0BjVWy92RoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIBDgMYqBwaTCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsRWgsSq23mwNAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAIUBjlQODSQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdgKpPmMFNtNsrVEE2jdurUcOHBA0tPTpUmTJom2+1HZ35KSEtm4caNZt5qoDQkBBLwnQKx675ywRwi4CRCrbirkIeA9AWLVe+eEPULATYBYdVMhDwHvCRCr3jsn7BEC/gLEqb+I+7zeHx8zZoz7wjByaawKAytVix500EGigUlCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCyB7OxsycvLs2Yr/LNKhddkxZQRqF69uuzbt08yMjKkYcOGKXPcHCgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggEFojUaGz0rApszBIEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEoC/CinSgDUz0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBgARqrAtuwBAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIMoCNFZFGZjqEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEAgvQWBXYhiUIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJRFqCxKsrAVI8AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBYgMaqwDYsQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLIAjVVRBqZ6BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBwAI0VgW2YQkCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECUBapEuX6qRyAiAsXFxbJ48WKZPXu2/W/hwoVSVFRk1t+3b18ZPXp02NtasWKFvPfeezJp0iRZtWqV7NmzRxo0aCBt2rSRwYMHy1lnnSX169cPu15dYffu3fLVV1/JmDFjZMmSJbJx40ZJT0+Xxo0bS8uWLeXwww+XI488Uvr06WPml7eRrVu3yqhRo8w6V69eLTt27JBGjRpJx44d5bTTTjP/Va1atbxqWI5AVAW8Hqtnn322TJkypUIGGrc///xzuesSq+USUcADAl6PVSeRXu/+97//yffffy8LFiyQbdu2SUlJidSpU0fatm0rhx12mJx77rnSrl0752rlTq9du1Y+/PBDGTdunOh0QUGBNG3aVLp06SJnnnmmDBo0SNLS0sqthwIIRFMg0WL1gw8+MGNVP/tu375dateuLc2bN5fjjz9ezjnnHDnkkEPC5uK6GjYZK8RBQL+f/fjjjzJ16lTR76nr1q0zv1tqDDRr1kx69eolZ5xxhuj31nDTrFmzZOTIkeZn2PXr15ura51a13nnnSc9evQIt0rzuhfpa6B+l9bv5J9//rn89ttvorGr3631Wn3KKaeY361r1aoV9r6yAgKREkiUON25c6fMmTPHvvel98HWrFljMzz++ONm7NsZYUwQp2FgUTRuAokQq/p9NC8vTyZOnCgzZ8407/tu2bJF9LN7vXr1pH379uY93wsuuECys7PDtty/f7989tln8umnn5r3wzdv3mzWe9BBB8nJJ59sfv/Va2wypjSfkZLxwDim5BH4+uuv5dprr5W9e/cGPKhwG6v0j8cDDzwgL7/8svmHJFDF2rD0yCOPyEknnRSoiGu+fkC/6667ZMOGDa7LnZlz5841P8Q78/yn9UbazTffLPqHL1Dq1q2bPPfccxW6CRCoTvIRCEcgEWK1Mo1VPXv2lC+++CIoCbEalIeFHhFIhFi1qPTD+R133GHe9Lby3H7qwyB/+ctfZMSIERLKgxt6Q3348OFmA5VbfZp39NFHy9NPPy1NmjQJVIR8BKIqkEix+sknn8jtt98ueoMtUKpWrZrcdNNNct1114XcEMx1NZAm+V4RmDdvngwZMsS8URXKPun31qeeekpatGhRbnG9UXXvvffK66+/LoFu2+hDFX/7299k2LBhIV3/dKPRuAZOnz7djG19ADRQat26tTz77LNmw12gMuQjEA2BRIrTq6++2rxBHSjm1aeijVXEaTR+u6gzkgKJEqsvvfSSvPjii2anhPKOX7+bXn/99XLDDTdIRkZGecXN5cuWLRP9WzB//vyA5bXzgv4tOOGEEwKWSdQF9KxK1DOXQvutT1QHa6gKl0Iv+hr0zpvO2nvqiCOOMBuNtAeUPhG3a9cu2bRpk/nh/7XXXgu5weqJJ56QRx991N6tmjVrSu/evc2ntfWPlDY4aS8x7dUVSpowYYL8/e9/lwMHDpjFa9SoIUcddZQ0bNhQVq5cae6rHpM2eumTdXpcFWm1D2VfKINAMIFEiFXtKaG9EUNJesNNe3NYSXtaBkvEajAdlnlJIBFiVb0+/vhj88aX88t69+7dRW92Va9eXfSJO33afN++fWZPqzfffNP8wvDKK68E5dYnyW+55Ra7jD751q9fP7Onll6ftU5N+pTchRdeaD7NxpPgJgn/xVggUWJVY2/o0KG2jsanjiCgT37qSAO//PKLaG8QvfH+0EMPmZ+xneXtFf0muK76gTDrSYHly5eXaajSnkSdOnUyR+jQz5N6g9jqEaU9/E899VTzM6Zez4Klf//736VGD9Hy2kNL04wZM8zvgnqNfPXVV81Ye+yxx4JVZy6LxjVQb6bp9VJ7bGjS77x6XdXeX9rDTEcx0e+y+t1Vy2njtvqQEIiVQCLFqd6Pcn72jZQRcRopSeqJpkCixKr2pNZYtZJ+9s3JyTFH0dJ7tvo9VT//FhYWmiOC6fX5119/NR+E1IcsgyW9buq93fz8fLOYPpSi96v1M4DeT9bvqFqv9rTSh1Xeffdd8x5xsDoTbpnxR5CEgKcFjA/UPmP4EJ8R+D7jqWmf0XLsGz9+vO/OO+8083WZcRM55GMwPszb6+m6//nPf3zG0D+l1je+VPiMJz/tcsbQQj7jC0apMm4zxlNv9jpGl0+f0druM/6IuBX1GX+8fM8884zPaBRzXa6Zxh8iX+fOne06jWGOzDznCsaTBz5jKEG7jDHEinMx0wjETCCRYjUUlDfeeMOOK2No0DKx56yDWHVqMO11gUSIVWOoP9+hhx5qx6DRw9lnNCKVoTWGRPEZD3TY5fS6/uWXX5YpZ2UsXbrUZ3zQt8sbPbd9xs01a7H50/gCUGrb+nmAhEA8BBIhVo1hinytWrWyY8r4cl3mM7MxooFPP38bjVd2OaPXWFBSrqtBeVjoIQGj4cX8vTaGd/cZvYZ8xk2mMnunMWD0ZvIZw2DaMfDnP//ZZwwhVKaslaHl9Zqm/4yhqH3GiCA+rcdKOq15uswqZwwZby12/RmNa6DRCO3TY7f24cQTT/QZvatKbV/nNd8qYzx46TOG8y9VhhkEoimQSHGq97Y0ro3hc3033nijT7+TGkOM+YwHoO0Y0s8H4STiNBwtysZTIFFi1Xjwwmf0kPZdfPHFPuP1Lz6jg0UZNv0sa3SUsONWr4HGA15lyvlnGEMG2+sYw9379J6vM2m9em/Yuqbqd2Zj6G1nkYSf1hZ7EgKeFjCG0vPpzSj/ZPResoMz1MYq/QNiDJdnr2c8reZfban5yy67zC5rPIVdapn/jNFK7jOeojPLa+OW8QSdf5Gw5++55x57+/olwL9RzarQeEqm1M23H374wVrETwRiJpAosRoqiDEOsB1/xhMrQVcjVoPysNBjAokQq9ZNev0QrtfWYA+MGE9r+4zhD+x41carQOkf//iHXc5432OpG3/OdfShGOsLgN5gN96/41zMNAIxEUiEWNUHyaxY0TgM9JCWgulDXFbZY445JujNaq6rMfkVYyMREJg8ebJPr1l6LSov6Q0tKwb0p/EuRtdVNI6cN6bvv/9+13Kaed9999l16k0to7dxwLLRuAY6H+7SG2b6d8stGU+Il3oQ5J133nErRh4CURFIpDg1evn7jF7JZRw0vq2/H+E2VhGnZTjJ8KhAosTqCy+8UKYRyY1UH0px3lc2Xi0R9EGVb7/91o5zfWDaeF+zW7Xmw5bGsMJ22WCfE1wr8Hhm8L5nCddPjB1ORgF9V0QoY3qHcuw67ILRCm0W1eEJdGiFYEnfk2ElfVmsDscSKOl44toVU9Ntt90mubm5gYqGlG88bSbvv/++XVaHLNLupG7J+GJgvrTaWqbDsZAQiLVAosRqKC7Gk6f2UGBa3nhyJeBqxGpAGhZ4VCARYlVfTm8lfb9HsOFtdexvo+HJKm4OsWDPOCZ0aF/jRqGdo8OQBRqGoX///uY7q7Sw8fS6GDfV7PWYQCBWAl6PVR3yy3hAyubQ96vqMCiB0uWXX24OC6bLdSx+HebPLXFddVMhz6sCeo3S4XpCeQ/F4MGDRd+BaiXjwQhrstTPsWPHmsPnaWbdunXF6F1RarlzRt8DV6dOHTPLeMBTAtUZrWvgW2+9Ze/OlVdeGfA9j02bNhWjscwuy/dVm4KJGAgkSpwqRYcOHSTSw08TpzH4JWMTERFIlFi96qqrpEuXLuUesw7hp+90tZLxQIfoe7kCJee10Rg1S4yRtlyL6utmnMPa61CA1qtjXFdIsEwaqxLshLG7lRMwuk/bFRg9rKRx48b2vNuE0UNKjNZsc5GOs68veXZLOga5tUzffWF0BXUrFlaeNqxZjWO1a9cWo5dH0PWdN9N1/FSjF1bQ8ixEwMsC0YrVUI/5v//9r11U3w9nDMNgz/tPEKv+IsynkkC0YtV5DdPrankpKyvLLmI8wWZPOyf05p+1TN8nYgyh61xcZtp5XTWGLCuznAwEEkkgGrFqDAFofzHWG/XHHntsUJIqVarIcccdZ5dxNh7bmcYE11WnBtPJJqDvMraSvtPCLX3zzTd29imnnBLwgUUtpA8zahkrjRkzxpos9TMa18DffvtNjJ7H9nac10070zHhXK4Ppeg7rEgIeFEgXnEaDQviNBqq1OkVgUSIVeMVMeL8rhro2q8Pgek7Hq3kvGZaec6feo/Yatg2hgGUqVOnOhcn9DSNVQl9+tj5cAX0iTIrGeMAW5NBfzp7df3000+uZT/99FPzyWtdaAyBIpmZma7lwsl0/pHSXlrBnlTVenv06GF/kdEeXvoiXxICiSoQrVgNxUNvZv/vf/+zi55++unmi6LtDL8JYtUPhNmUEohWrBrDnNiOxnAo9nSgCWcZ7W3sloxhJexsfWqvvGQMv2sXWbt2reiXfRICiSoQjVh11tmgQQPRpzzLS6F8rua6Wp4iyxNZQJ+ytpL23HVLzuuV81rkVlbznGWc8eMs76wzUtdA57b0IZBgvaB1X5o1ayYHH3ywvVvO9e1MJhDwgEC84jQah+6MM+I0GsLUGU+BRIzVQNd+vYdrDOVrcupnar3HGyzpfWfniF7OWA+2XiIso7EqEc4S+xgxAWNYzkrV5XxyzFnRtGnT7FntsaVJhzcZPny4OYyQ9tDS7pvaO0OHHQrW7dOqSNe3klWnNe/2U4c17NSpk73Iub6dyQQCCSIQrVgN5fC1UVp7S1qpvCdanLFGrFpq/EwVgWjF6oABA2xCffpaHwoJlPTaPHLkSHOxfmEx3qHjWlSH97RSKLGqN92cPbCd61v18BOBRBGIRqxWtk5tBNanSP0T11V/EeaTSWDRokX24TgfzLAyd+7cKTpMkJW6du1qTQb86bymGe+Gkl27dpUp67yGOcuXKfj/GaFcA8OtU6t2btu5fqD9IB+BeAjEK06jcazOOHPGX7BtOcs51w+2DssQiIdAIsSqXpe155OV3K79uswZa3pvV0ckKC85Y9X5+bm89by+nMYqr58h9i+iAjqcl5X0C3IoyVnO+cfDue6sWbPsWX1i9LXXXhO90aY/f/31V9m7d6/oFw+9oaZjkA4aNEiGDRtmD51ir+yYWL58uT3nfArVznSZcJZLpj9ULodKVpILRCtWQ2FzDgGojczl3SQgVkNRpUyyCkQrVrV31IUXXmizXXfddfKvf/1L8vLyZOvWreYNbm3Eeuyxx8zhj3TYQG2o0gdCDj/8cHs954QzVivSu9q5vrNephFIBIFoxKqzzm3btpmfd8uzWLdunV1EG7vcPq86Y8352dZe0WXCWc6tTpdVyEIg5gL6vdL55PPRRx9dZh+cv/+60Pm7Xabw/2f4l/GvQ4s58yJ1DdTvuVaqSJ3EqqXHTy8JxDNOo+FAnEZDlTq9IJAosTpq1CibS4e3z8nJseedExW5TjsbvpLpmlp+M51TjmkEElyge/fu9hHMnTvXvOGlw5YESnphX7Fihb1Yb4bpu6uqVatm5+mQYc6n37744gv57LPPzOXaLVOHZdAhD7Zs2SI6/II2WumX8zfeeMPMe+GFF+y6nBP6pd9Kzie7rTy3n/oibis5W+6tPH4ikCgC0YjVUI599+7d4hzrX19qWV4iVssTYnkyC0QzVh966CHRdza+/PLL5lC72nvK6kHlb6pDIOgL6Pv37++/yJzXh0Z0iFwrhXpddZbjumrp8TMRBaIRq/owR3p6uvkuOH2p88SJE82HtQL56LAnEyZMKLXYLa64rpYiYiaJBEaMGGEPHa8NTCeddFKZo3P+/tepU8ce5r1MQUeGvrdKr5f6OVaTf1xF6xro3NdGjRo59ijwJNfVwDYs8YZAvOI0WkdPnEZLlnrjLZAIsaq9qp577jmb6qKLLgrYY8oZq85rpb2yy0Sy3gNOdzlWshBIWgFtONIP8pq00UmfyA6WHnzwwTKLrS8B1gKr8cmatxqq9N1VP//8s7zzzjvy8MMPm72sdLhA581vLfvhhx9aq5b66RwWJdR3YDnLOdcvVTEzCCSAQDRiNZTD/vLLL+0nw7Xb9VlnnVXuas5Yc8ZgsBWd5ZzrB1uHZQh4USCasao3we+66y754YcfSr2Pw99Bnyj705/+FLBHlZbXh02cyRmDznz/aWc5YtVfh/lEEohGrOrLop09GfVzdVFRUUAW/UzsHLFAC/p/rtY8Z6w5Y1CXBUrOcs71A5UnH4FYC+iT1V999ZW92dtvv931ncTO31/n77W9YoAJZ1lnHVo8WtdA53ac2w+wi2a2s5z/fgVbj2UIxEIgnnEareMjTqMlS73xFEiEWNWHtG644Qb7s642QF1zzTUB2Sobq871A24kQRbQWJUgJ4rdjIyAPp12+eWX25XpkHwPPPBAqaetdaF+cb711ltFb1z7J+eT2brM7UO2Pr2qQwD6P2GmDWVPPPFEqSe/n3rqKfsJO+e2rBfraZ6+jyqU5Ozx5b+foaxPGQS8IhCNWA3l2JxDAB533HFlYtitDmLVTYW8VBGIdqxqb2W9bmvP5IyMDPMlsvoeuQsuuED0BfF63dNhxe6++2455phjzGEC3ez9r4lcV92UyEtmgWjF6vXXX2+z6TtZNV43btxo5+mEjkLw9ttviz4B65/8Y1OXc131V2I+0QVmz54t2jhlpdNPP13OOOMMa7bUz4r8/msFwb4H+sdZpK6Bzn11br/UAfnNVK9e3c7x3y97ARMIxEEg3nEarUMmTqMlS73xEkiUWL3vvvtE34euSYerf/LJJ0Uf9AqUnLEaqet0oG15PZ9hAL1+hti/iAvcdNNN5k2v6dOnm3U/++yz8v7778sRRxwh9evXl82bN8uUKVPM4fq0wMknn1zqKTirZ5a1Y86nw6y8IUOGBGxg0j9S+r6q7777ziy+atUq0Xde6RBGzqQf5HXIBk3BnlJ1rqO9xazktl/WMn4ikAgCkY7V8o559erVMnXqVLuYsxeknekyQay6oJCVUgLRilX9gP/888+bln369DEf9jj44INL2erQCnrNHTdunOj0xRdfbD5o0rZt21Ll/K+JXFdL8TCTIgLRiFVtJL7yyivlpZdeMhX18602JOvnah3mTJ/y1JEFrB5V5X2u1kq4rqbIL2SKHKZ+17v00kvthyP1fahuo3dYHM7GnFCvVbpusO+B0boGOvfVuX3rWNx+Om/G+e+XW3nyEIiFgBfiNFrHSZxGS5Z64yGQKLH61ltv2Z+N1UnfvawPQwdLzlgN9frvvPYm0zWVxqpgvyksS0oBfepLG6f0j4U+sa1JX9buHJZB87Sc3gA76KCD7GU6JJE+mepMNWvWdM5KrVq1xO1luc5CHTt2FL3h9ttvv5nZ2nDm31il9ViNVaE+deYsp+uTEEhkgUjHankWo0ePNt8np+X0iZcBAwaUt4q5nFgNiYlCSSwQjVj99NNP7Yaq9u3bm9dt/+utkmZnZ8urr74q559/vv2giQ4dqMONOZP/us7rpbOc/7SzHNdVfx3mE00gGrGqBsOHDxd9YbSOHqBfrjVudPhO/3TmmWfK0KFD7c/Vurxu3br+xczP0nwGLsNCRgIK6HuNtSew1duwdevW8t5775X5Puk8NOe1xnkNcpZxm3aWddahZaN1DXRux7l9t/2z8pzl/PfLKsNPBGIp4JU4jdYxE6fRkqXeWAskSqzq91jtoGClv/zlL+b9Z2s+0M/Kxqpz/UDbSJR8hgFMlDPFfkZUQINYnwD95JNP5MILL5RDDjnEfJeVtkTr09iXXXaZfPPNN+aTotqQZSW9KaY9o5xJ19GX2lqpXbt25sumrflAP/Xmm5X0aXD/pL28rLRp0yZrMuhP64uQFgrWvTRoJSxEwEMCkYzV8g5LG6usdNppp5UaTsXKd/tJrLqpkJdqApGO1ccff9wmvPHGG8vcaLMXGhP6frnbbrvNzvr+++9Fv8w4k16nnU+bhXpddZbjuuoUZTpRBSIdq5aDjsk/ceJEcyx+HQ5b40Ubx/Sdcn/+85/NBudnnnmm1PuodF1d7p+4rvqLMJ+IAvodUhuqVqxYYe5+06ZNzXcV689gyfn7v2vXLrtHVrB1tHHX+f43/+tVtK6Bzn3V0UlCSVxXQ1GiTKwEvBSn0Tpm4jRastQbS4FEiVUd7UM/E+sQ2Jp0yF8dLSSU5IxV57Uy2LrJeg+YnlXBzjrLkl5AhxXSf8HSkiVL7MU9evSwp50T2kA1d+5cMyvUJ8Scwwm6vQhPG9CWLl1q1mkNneLcptu0s5zuEwmBZBGIVKwG8tAhiqybCVom1CEAtSyxqgokBH4XiESsrlmzRpYtW2aT9uvXz54ONKG9k7UxSp/Y9vl85jXZ/4agxur8+fPNKnQboSTndVXXJyGQLAKRiFV/Cx2N4I477vDPLjXv/FytX8q1p4l/4rrqL8J8ogloI9NFF10kixcvNne9QYMGZkNVq1atyj0U/2uNXq/K+17nvFbpBvzrsPIifQ3Uhzytoe0rcl0t77jKxaIAApUQ8GKcVuJwAq5KnAakYUGCCCRKrOr7qa666ir7NS46Uo++p0pH6AolOa/doV5T9d3NVkqma2poYtaR8xOBFBSw3m2lh+4/VJ/FocP6WamgoMCaDPrT+fSb/9CCuqLzD42+sLq8dODAAVm0aJFdzLm+nckEAkksEEqsBjr8//73v/Yi7fXYs2dPe768CWesEavlabEcAZHyYnX9+vWlmJxPmZVa4JjRLwHOJ8n1S41/cvZoDiVWtdez86k25/r+dTOPQDIKlBerFTnmUOrkuloRWdbxioB+F7zkkktkzpw55i7pUJc69F+HDh1C2kUt73zYwmpgCray9dCkltGRQNy+WzqvYZG6BoZbp+6fc1+d6+syEgKxEvBqnEbj+J1xFkrs6z4Qp9E4E9RZEYFEiVV9+FlH6LKGuj3qqKPkxRdfNEcACfW4nbGq93b1Hm95yRmrzs/P5a3n9eU0Vnn9DLF/cRXQnhazZ88290GHGTr99NNd90f/EFlp+fLldpdPK8/tp9VrSpe5DYHifJI8Ly+v1Etz3eqbNWuW/Y4rfbq8d+/ebsXIQyApBUKNVbeD1w8U1vvrdHk4vaq0PLGqCiQEQhMIJVadQ+tqrdu3by+3ch1qYceOHXY5t/fgHHnkkfbyKVOm2NOBJqZOnWovatGihfmuSTuDCQSSXCCUWA2XQOPUeb0966yzXKvguurKQmYCCOhnyksvvVT0ppUmvZ69/fbbokNjhpOc16vJkyeXu6rzmuaMH+eKzjqd5Z1lnNOhXAOddep3YP8heJ316bQ+BGK9s1nnA+2rLiMhEC0BL8dpNI6ZOI2GKnXGQiBRYlXvGetDKlbHBb0X+8Ybb0j16tXDYtL1rHW0LutedKBK9u3bJzNmzLAXJ9M1lcYq+7QygUBZgUcffdTO1C6c+qSaWzrppJOkatWq5iLtMaXdP4MlHQLF+UH9iCOOKFO8b9++9kun9QnxMWPGlCnjzBg1apQ9e/TRRwd9v4ddkAkEkkQg1Fh1O1x9P511k1t7Z+gL4MNJxGo4WpRNdYFQYlUf4HC+H3LSpEnlsulDHfrODiu1adPGmrR/6nXcGoZBb6rpOsGS87o6cODAYEVZhkDSCYQSq+Ee9AcffCDWsCZNmjSRQYMGuVbBddWVhUyPCxQVFckVV1wh1jVLbzi9/vrr5Q4573ZYzmvO559/Xur65l9er33ORuBAcRWNa6AOL+bsMeYcqcB/P3Xeubxz586uw4C6rUceApES8HqcRuo4nfUQp04NphNFIFFideHChXLhhReKNapHt27d5J133qnQ/Vh9r6yzwcn5XdTtvOk9YmvELh1hxO2+stt6iZBHY1UinCX2MS4CI0eOlI8//tjctj4VN3To0ID7oX8YnE+HPvTQQ0G7bN5///12XV26dBH955+08Uv/6FnpkUceCfhFRbuIOj/86xN9JARSRSCcWHUzGT16tJ19zDHHSLNmzez5UCaI1VCUKIOASKixqu/2OPTQQ22yp59+OuD1TwsVFxfLww8/bJfX9+Y4x/y2FjRu3FgGDx5szZovu9X3W7mlCRMmiP7TlJGRYT4t51aOPASSUSDUWA3n2HXoIecLpu+66y6pVq2aaxVcV11ZyPSwgF6HrrnmGvv9TToihw7/o58rK5K0Ycn6PKoPVOl1MFDS92FYD121bNlSTjzxRNei0boG/vWvf7W3p8fsHD7XXmBM6EvgdbmVnOtZefxEIJoCiRCn0Tp+Z7wRp9FSpt5ICSRKrOrDjxdccIE9Cog+vPH+++/bnQ4q4uGMVb3Ha7370r8ufVBF7xFb6eKLLw5ryEFrPa/+pLHKq2eG/YqawNatW+Xmm282u0u63aTSD/v33HOP3HLLLfY+aEOV21PadgFjQsvXrFnTzNIh+fTJOt2WM+3Zs8fc9rhx4+zsIUOG2NP+E/qlp169ema29sS6/PLLy9S5YMECc7iJ/fv3m+W0m/dxxx3nXxXzCCScQLRi1QmhX5ytG9KaH+4QgFZdxKolwc9UFIhGrP7jH/+wKfWBDP0AvnLlSjvPmtAhh7Ssc5ikf/7zn9biMj9vvfVWuyf0zz//LDfccIM9ZINVWJ+K15i20tlnn13qyXErn58IJJpANGJVDfRz8tixY12HrNYnY/WL+3nnnWffUP/Tn/4UcGhty5TrqiXBT68L6PdJ/W755ZdfmruqPXi1cUkbnCqatFeW87vos88+K6+99lqpoeZ1WE3Ne/755+3N6DqBGoG1UDSugRdddJH9PXnbtm3m9drqQWntmM7rEEnWsL7a00Nv8JEQiJVAIsVpNEyI02ioUmc0BBIlVteuXSvnn3++/YCG3i/+8MMPRR+6rEzSB04OP/xwswod5k8br/SerzPp53m9N6xDdmvSzhNXX321OZ0s/6UZvwjuj5QmyxFyHEkhoB9udYxrZ9Kntqwnt7SRyK0xSbtf+g/dpzene/bsaValQ5BoN019ia1+mdY/OPriZ6vhRwvph/6bbrrJuemA0/pF/e9//7v5lLcW0h5Z2nik+6B/UPRmmvXkmy6//vrr5bbbbtPJgOmHH34w/0BZL9fTOnWYv4YNG5p/nHQ8cSuMdTv6Rcn/mANWzgIEIiyQKLFqHbY+WaaN05r0HTc65q/GWEUSsVoRNdaJl4DXY1Wva1dddVWpoY20h5Nev7XXlN4M1MYrHcpPP8hbSYfl1Zt3WjZQ0qHInDcB9QO+Xqv1hfQ6TO/MmTPtVXWYok8++URq165t5zGBQCwFvB6raqG9R/TpUh2+RN/No70btWeJfubWd/c4P/v2799fXn31VXtM/mCWXFeD6bDMKwJvvvlmqRE4Dj74YDn22GND3j1nj0P/lfS74kcffWRn6/fdXr16mfP6mdW6UaUZ2iD8+OOP22UDTUTjGjh//nw544wzRB/M1KS9I/WdzvqddP369ebQiPpdW5Nea/W62qlTJ3Oe/xCIhUAixam+p8b5OdXy0XeeW3GkQ2br51dnysnJEecQvs5lOk2c+osw70WBRIlVffBKOylYSb+D6juOQ0l6HXeOzOW/zrp16+TPf/6z/R5IHR5fh/jTzwBbtmyRiRMn2qOO6Oftd99917xH7F9PIs/TWJXIZy+F9l1blv2f0Arl8LUhR78wO5OzscqZ7z+tDVgjRoyQU0891X9R0HltsNKn1qyGNLfCmZmZoj2qtPdVKEnr1Cf2tMErUOratas899xz0q5du0BFyEcg6gKJFKuKoU+u6DjDmvSJM+dQYmZmmP8Rq2GCUTxuAokQq/rgyIMPPiivvPJKqafJ3dD0Q7wOgTts2DDRa2x5SXt66DBk1otw3crrjTZ9Ol4/D5AQiJdAIsSq1VgVzEh7ilx77bXmv2A9P/zr4LrqL8K81wQee+yxkBqJAu23PiwZKOl18O677xa9eWc9nOhfVq9/+oT1nXfeafcc9i/jPx+Na6A+8HndddfJqlWr/Ddnz7du3VqeeeYZyc3NtfOYQCAWAokUp/qAc0VG+9D3PTqHt3dzJU7dVMjzkkCixGpFP5+rtca3DuEbLC1btszsLaWNzIGSdmDQh1QCDf8baL1EyK+SCDvJPiIQSYFGjRqZ73fSYX50CCD9grB582bzC4D2tGrfvr2cfPLJoi3lFXmSWod86NOnj/kU3FdffWU+8aaNTFqXtoTrk3b6lGw4vZ+0Tn26VN8hoC/R0y8BO3fuFD2Wjh07ymmnnWYOp6JPsZEQSBaBaMeqvj/DaqhSs4p8KfC3Jlb9RZhPBYFoxare0B4+fLhcdtll5vVPr9n6wd3qpaG9IfUJ9sMOO8wchkGnQ036Tki9HusT5jo0r34W0IYrq8f1mWeeKfqSer0JSEIgWQSiFasvv/yyOaSu3mDTYav1qU+NJ/0S3apVK/NLtH5WDfWJU6c311WnBtOpJqDXwXvvvdd8AluHF5oyZYo92oh+l9Sb0zqcXo8ePcKiicY1sHfv3vLtt9+a37O/+OIL+fXXX0WHBaxfv77osH/6lLh+1tYemCQEkkkgWnEaDSPiNBqq1JkoAokUq9oJQa+ln376qflP312l9631+69+ttZ71tqjurLDDnr13NGzyqtnhv1CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFJAID0FjpFDRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KgAjVUePTHsFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgI0VqXCWeYYEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGPCtBY5dETw24hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqkgQGNVKpxljhEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KgAjVUePTHsFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgI0VqXCWeYYEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGPCtBY5dETw24hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqkgQGNVKpxljhEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KgAjVUePTHsFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgI0VqXCWeYYEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGPCtBY5dETw24hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqkgQGNVKpxljhEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KgAjVUePTHsFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgI0VqXCWeYYEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGPCtBY5dETw24hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqkgQGNVKpxljhEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KgAjVUePTHsFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQCgI0VqXCWeYYEUAAAQQQQAABBBBAIGEFSkpK5LTTTpMWLVqY/8466yzx+XxhHc/DDz9sr9+lSxfZsGFDWOtTGAEEEEAAAQQQQAABBBCIpgCNVdHUpW4EEEAAAQQQQAABBBBAoJIC6enp8thjj0lmZqZZ09SpU+Wtt94KudYFCxbI888/b5e/6667pGnTpvY8EwgggAACCCCAAAIIIIBAvAVorIr3GWD7CCCAAAIIIIAAAggggEA5Au3atZObbrrJLvXAAw/I2rVr7flAE8XFxXLLLbdIUVGRWaR///5y7rnnBipOPgIIIIAAAggggAACCCAQFwEaq+LCzkYRQAABBBBAAAEEEEAAgfAE/vnPf0pOTo650u7du+XWW28tt4JXXnlFZs+ebZarU6eOPPjgg+WuQwEEEEAAAQQQQAABBBBAINYCNFbFWpztIYAAAggggAACCCCAAAIVEMjIyDCHA6xataq59g8//CAjR44MWNOKFSvkkUcesZcPGzbMfG+VncEEAggggAACCCCAAAIIIOARARqrPHIi2A0EEEAAAQQQQAABBBBAoDyBzp07y/XXX28Xu/vuu2Xjxo32vDXh8/nk3//+txQWFppZ/fr1k4svvthazE8EEEAAAQQQQAABBBBAwFMCacaXGJ+n9oidQQABBBBAAAEEEEAAAQQQCCig758aPHiwLFy40Cyj06+++mqp8u+++67cdtttZl7NmjVl/Pjx0qpVq1Jl9J1Xo0aNkh9//FFWrlwp27ZtEy3bokULOeqoo+Siiy6SQw45pNQ6bjN79+6VCRMmyKRJk2Tu3Lny22+/yY4dO0R7gDVo0EC6du0qJ554opx11llSrVo1tyrsvMmTJ8s555xjzvft21dGjx5tTuv+f/TRR+aQhto4V1BQICNGjJArrrjCXpcJBBBAAAEEEEAAAQQQSFyBKom76+w5AggggAACCCCAAAIIIJB6AtoI9MQTT8if//xnOXDggIwZM0Y+++wzOfXUU02M/Px8ue+++2yY22+/vVRDVUlJiTmc4Isvvmj3vLIK79+/X7Zv3y7z58+X1157TfQ9WdrolZaWZhUp9XPGjBly/vnny549e0rl64w2qmmj0po1a+Trr7+Wp556ymxU08arUNPOnTvlX//6l3mMoa5DOQQQQAABBBBAAAEEEEg8ARqrEu+csccIIIAAAggggAACCCCQ4gLdunWTq666Sp599llT4s477zR7Q2lPpjvuuEO0kUfTYYcdJpdddpk5rf8VFxeb63311Vd2XnZ2tvTs2dPsBaWNSzNnzhR935U2hD3zzDOydetWefjhh+3yzgntQWU1VDVq1Eg6dOggzZo1M3toaY8rrWfWrFlmXatXrzZ7V2nD1cEHH+ysxnVaBwG57rrr5NtvvzUby3JycqR9+/ai+YsXLw7YgOZaGZkIIIAAAggggAACCCDgaQGGAfT06WHnEEAAAQQQQAABBBBAAAF3gX379snAgQNl6dKlZoEzzzzTHG7v6quvNuczMzNl3Lhx0rZtW7uCRx55RJ588klzvkmTJmYPLB1G0L/n1Oeffy633nqr3ej1wgsv2D237MqMCe1ZNXbsWDn99NOlU6dOzkX29ObNm0XfraXD+GnSIQZHjhxpL3dOOIcBrFKlitnIpe/p0kYz/elMevzVq1d3ZjGNAAIIIIAAAggggAACCSpAY1WCnjh2GwEEEEAAAQQQQAABBBDQxqLTTjtNdGg/TbVr15bdu3eb09rbSntfWQ2Dy6QAAAnOSURBVEl7NvXr18/sXZWVlSVffvmltGnTxlpc5qe+g+rcc88187VH0/fff1+mUavMSkEyLrnkEvnuu+/MEj/88IPZS8q/uLOxSpdpg5q+r0p7jJEQQAABBBBAAAEEEEAgeQXSk/fQODIEEEAAAQQQQAABBBB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import io\n", + "from IPython.display import display\n", + "\n", + "api_response = gpt_assistant._openai_client.files.with_raw_response.retrieve_content(\"file-TZbG4mSETWOeDqpidQsJCYVs\")\n", + "\n", + "if api_response.status_code == 200:\n", + " content = api_response.content\n", + " image_data_bytes = io.BytesIO(content)\n", + " image = Image.open(image_data_bytes)\n", + " display(image)" + ] + }, { "cell_type": "code", "execution_count": null,