diff --git a/benchmark/notebooks/LLM Score Experimentation.ipynb b/benchmark/notebooks/LLM Score Experimentation.ipynb
deleted file mode 100644
index 745913971c..0000000000
--- a/benchmark/notebooks/LLM Score Experimentation.ipynb	
+++ /dev/null
@@ -1,367 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "15386da8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from dotenv import load_dotenv\n",
-    "from openai import OpenAI\n",
-    "\n",
-    "load_dotenv()\n",
-    "\n",
-    "def llm_eval(evaluation: str) -> float:\n",
-    "    openai_client = OpenAI()\n",
-    "    answer = openai_client.chat.completions.create(\n",
-    "        model=\"gpt-4\",\n",
-    "        messages=[\n",
-    "            {\"role\": \"system\", \"content\": evaluation},\n",
-    "        ],\n",
-    "    )\n",
-    "    return answer.choices[0].message.content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e9d1c3a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "REFERENCE_PROMPT = \"\"\"Ignore previous directions. You are now an expert at evaluating how close machine generated responses are to human answers. You essentially act as a hyper advanced BLEU score.\n",
-    "In order to score the machine generated response you will {scoring}. Make sure to factor in the distance to the ideal response into your thinking, deliberation, and final result regarding scoring. Return nothing but a float score.\n",
-    "\n",
-    "Here is the given task for you to evaluate:\n",
-    "{task}\n",
-    "\n",
-    "Here is the ideal response you're comparing to based on the task:\n",
-    "{answer}\n",
-    "\n",
-    "Here are some examples of how to score a machine generated response compared to the above ideal response:\n",
-    "{examples}\n",
-    "\n",
-    "Here is the current machine generated response to the task that you need to evaluate:\n",
-    "{response}\n",
-    "\n",
-    "Remember to always end your response with nothing but a float score.\n",
-    "Float score:\n",
-    "\"\"\"\n",
-    "\n",
-    "RUBRIC_PROMPT = \"\"\"Ignore previous directions. You are now an expert at evaluating machine generated responses to given tasks.\n",
-    "In order to score the generated texts you will {scoring}. Make sure to factor in rubric into your thinking, deliberation, and final result regarding scoring. Return nothing but a float score.\n",
-    "\n",
-    "Here is the given task for you to evaluate:\n",
-    "{task}\n",
-    "\n",
-    "Use the below rubric to guide your thinking about scoring:\n",
-    "{answer}\n",
-    "\n",
-    "Here are some examples of how to score a machine generated response based on the above rubric.\n",
-    "{examples}\n",
-    "\n",
-    "Here is the current machine generated response to the task that you need to evaluate:\n",
-    "{response}\n",
-    "\n",
-    "Remember to always end your response with nothing but a float score.\n",
-    "Float score:\n",
-    "\"\"\"\n",
-    "\n",
-    "QUESTION_PROMPT = \"\"\"Ignore previous directions. You are now an expert at evaluating machine generated responses to given tasks.\n",
-    "In order to score the generated texts you will {scoring}. Make sure to think about whether the generated response answers the question well in order to score accurately. Return nothing but a float score.\n",
-    "\n",
-    "Here is the given task:\n",
-    "{task}\n",
-    "\n",
-    "Here is a question that checks if the task was completed correctly:\n",
-    "{answer}\n",
-    "\n",
-    "Here are some examples of how to score a machine generated response based on task-response pairs.\n",
-    "{examples}\n",
-    "\n",
-    "Here is the current machine generated response to the task that you need to evaluate:\n",
-    "{response}\n",
-    "\n",
-    "Remember to always end your response with nothing but a float score.\n",
-    "Float score:\n",
-    "\"\"\"\n",
-    "\n",
-    "CUSTOM_PROMPT = \"\"\"{custom}\n",
-    "{scoring}\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "8ff9cd33",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "task = \"As an agent powered by a large language model, you can execute any digital action. Your task is to find the cheapest flight to San Francisco from Toronto. Create a step by step plan for yourself to execute this task and output this plan into a .txt file.\"\n",
-    "\n",
-    "percentage = \"assign a float score that will represent a percentage out of 100. Use decimal points to be even more accurate. 0 represents the worst possible generation, while 100 represents the ideal generation\"\n",
-    "scale = \"assign an integer score from a scale of 1-10. 1 represents a really bad generation, while 10 represents an ideal generation\"\n",
-    "binary = \"assign a binary score of either 0 or 1. 0 represents a failure, while 1 represents a success\"\n",
-    "\n",
-    "reference_answer = \"\"\"1. Search 'Toronto to San Francisco flights' on the internet\n",
-    "2. Click on the first link that is a flight aggregator such as SkyScanner or Google Flights\n",
-    "3. Select the dates that you want to travel\n",
-    "3. Click on the 'one way' option and click search\n",
-    "4. Search through all of the given options and select the cheapest flight\"\"\"\n",
-    "\n",
-    "rubric_answer = \"\"\"- Reasonable: The plan's steps should be arranged in a logical sequence. Each step should naturally follow from the one before it, leading coherently towards the desired outcome.\n",
-    "- Atomic: Each step of the plan should be broken down into the smallest possible units of action. This level of detail allows for better tracking and control, as well as easier troubleshooting and adjustment of the plan if necessary.\n",
-    "- Relevant: Each step should directly contribute to achieving the end goal of the plan. Irrelevant steps can waste resources and detract from the main objectives.\n",
-    "- Timely: The plan should include a realistic timeline for each step, and not include any redundant steps.\"\"\"\n",
-    "\n",
-    "question_answer = \"Will this plan find the cheapest flight to San Francisco from Toronto consistently?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "d0bfdce5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FEW_SHOT_REFERENCE_EXAMPLES = \"\"\"EXAMPLE 1\n",
-    "Plan: 'Define the Parameters:\n",
-    "\n",
-    "Identify the potential range of dates for the journey. Ensure flexibility to capitalize on the best deals.\n",
-    "Research Airlines:\n",
-    "\n",
-    "Compile a list of airlines that operate flights between Toronto and San Francisco. This can be accomplished by checking each airline's website or using an airline directory.\n",
-    "Use Comparison Tools:\n",
-    "\n",
-    "Use flight comparison websites (such as Skyscanner, Expedia, Google Flights, etc.) to compare flight prices across different airlines. These tools aggregate flight data and can present the cheapest options available.\n",
-    "Set Price Alerts:\n",
-    "\n",
-    "If the trip is not immediate, set price alerts on these comparison websites. They will notify you when there is a significant drop in flight prices on the selected route.\n",
-    "Check Airlines' Official Websites:\n",
-    "\n",
-    "After identifying potential flights via comparison tools, visit the airlines' official websites. Sometimes airlines offer deals or discounts on their own websites that are not reflected on comparison sites.\n",
-    "Consider Nearby Airports:\n",
-    "\n",
-    "Check flight prices to and from airports in the vicinity of both Toronto and San Francisco. Sometimes less busy airports can offer cheaper flights.\n",
-    "Evaluate Cost Effectiveness:\n",
-    "\n",
-    "Consider factors such as baggage fees, meal costs, and transportation to and from the airport when evaluating the total cost of the flight. The cheapest ticket price does not necessarily mean the least expensive journey overall.\n",
-    "Book the Flight:\n",
-    "\n",
-    "Once the cheapest and most convenient flight has been identified, proceed to booking. Double-check the flight details before finalizing the booking.\n",
-    "Monitor Flight Details:\n",
-    "\n",
-    "After booking, keep an eye on flight status, gate information, and any potential changes to the flight schedule.'\n",
-    "Returned score: 82.7\n",
-    "Internal rationale: The plan is solid, however the plan goes to extreme lengths to make things cheap, sacrificing time and simplicity. The task just asks for a vague definition of booking a flight. There are some redundant steps.\n",
-    "\n",
-    "EXAMPLE 2\n",
-    "Plan: 'Determine the Travel Dates and Flexibility: Decide on the dates you want to travel to San Francisco and check if you have any flexibility in your travel schedule. Being flexible with your travel dates can often lead to finding cheaper flights.\n",
-    "\n",
-    "Use Flight Search Engines: Start by using popular flight search engines like Google Flights, Skyscanner, Kayak, or Expedia. These platforms allow you to compare prices from various airlines and find the most affordable options.\n",
-    "\n",
-    "Set Up Fare Alerts: If your travel dates are flexible, consider setting up fare alerts on the flight search engines. These alerts will notify you when the prices drop for the specified route.\n",
-    "\n",
-    "Check Nearby Airports: In both Toronto and San Francisco, there might be multiple airports. Check flights departing from nearby airports as they may offer better deals.\n",
-    "\n",
-    "Consider Layovers: Non-stop flights are usually more convenient but can be more expensive. Look for flights with one or more layovers as they may offer cost savings.\n",
-    "\n",
-    "Check Airlines' Official Websites: Once you find a potentially cheap flight on a search engine, verify the price directly on the airline's official website. Sometimes, booking directly with the airline can be cheaper due to exclusive deals and promotions.\n",
-    "\n",
-    "Use Incognito/Private Browsing Mode: Flight prices can sometimes increase if the website detects repeated searches for the same route. To avoid this, use the incognito or private browsing mode in your web browser.\n",
-    "\n",
-    "Consider Budget Airlines: Check if there are any budget airlines flying between Toronto and San Francisco. They often offer lower fares, but be mindful of additional fees for baggage and other services.\n",
-    "\n",
-    "Check for Deals and Promo Codes: Look for any ongoing deals or promo codes that can help you save on your flight booking. Airlines and travel websites occasionally offer special discounts.\n",
-    "\n",
-    "Be Flexible with Departure and Arrival Times: If possible, consider flying during off-peak hours or mid-week, as flights during these times can be less expensive.\n",
-    "\n",
-    "Factor in Total Costs: While searching for cheap flights, don't forget to consider other expenses like baggage fees, seat selection, and additional amenities. Some budget airlines might have hidden costs that could make the overall trip more expensive.\n",
-    "\n",
-    "Book Early: Flight prices tend to rise as the departure date approaches. Once you find a good deal that suits your preferences, don't wait too long to book your flight.'\n",
-    "Returned score: 74.9\n",
-    "Internal rationale: The individual components of this plan are better than the one previous. But this plan doesn't follow logical steps to completion, and is just more general advice.\n",
-    "\n",
-    "EXAMPLE 3\n",
-    "Plan: 'Search online for cheap flights.\n",
-    "Check different dates.\n",
-    "Look at nearby airports.\n",
-    "Consider layovers.\n",
-    "Try budget airlines.\n",
-    "Book early if you find a good deal.'\n",
-    "Returned score: 42.0\n",
-    "Internal rationale: This plan is too vague and does not provide enough detail to be useful.\n",
-    "\n",
-    "\"\"\"\n",
-    "FEW_SHOT_RUBRIC_EXAMPLES = \"\"\n",
-    "FEW_SHOT_QUESTION_EXAMPLES = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "3de1d6d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response1=\"\"\"Define the Parameters:\n",
-    "\n",
-    "Identify the potential range of dates for the journey. Ensure flexibility to capitalize on the best deals.\n",
-    "Research Airlines:\n",
-    "\n",
-    "Compile a list of airlines that operate flights between Toronto and San Francisco. This can be accomplished by checking each airline's website or using an airline directory.\n",
-    "Use Comparison Tools:\n",
-    "\n",
-    "Use flight comparison websites (such as Skyscanner, Expedia, Google Flights, etc.) to compare flight prices across different airlines. These tools aggregate flight data and can present the cheapest options available.\n",
-    "Set Price Alerts:\n",
-    "\n",
-    "If the trip is not immediate, set price alerts on these comparison websites. They will notify you when there is a significant drop in flight prices on the selected route.\n",
-    "Check Airlines' Official Websites:\n",
-    "\n",
-    "After identifying potential flights via comparison tools, visit the airlines' official websites. Sometimes airlines offer deals or discounts on their own websites that are not reflected on comparison sites.\n",
-    "Consider Nearby Airports:\n",
-    "\n",
-    "Check flight prices to and from airports in the vicinity of both Toronto and San Francisco. Sometimes less busy airports can offer cheaper flights.\n",
-    "Evaluate Cost Effectiveness:\n",
-    "\n",
-    "Consider factors such as baggage fees, meal costs, and transportation to and from the airport when evaluating the total cost of the flight. The cheapest ticket price does not necessarily mean the least expensive journey overall.\n",
-    "Book the Flight:\n",
-    "\n",
-    "Once the cheapest and most convenient flight has been identified, proceed to booking. Double-check the flight details before finalizing the booking.\n",
-    "Monitor Flight Details:\n",
-    "\n",
-    "After booking, keep an eye on flight status, gate information, and any potential changes to the flight schedule.\"\"\"\n",
-    "\n",
-    "response2=\"\"\"Determine the Travel Dates and Flexibility: Decide on the dates you want to travel to San Francisco and check if you have any flexibility in your travel schedule. Being flexible with your travel dates can often lead to finding cheaper flights.\n",
-    "\n",
-    "Use Flight Search Engines: Start by using popular flight search engines like Google Flights, Skyscanner, Kayak, or Expedia. These platforms allow you to compare prices from various airlines and find the most affordable options.\n",
-    "\n",
-    "Set Up Fare Alerts: If your travel dates are flexible, consider setting up fare alerts on the flight search engines. These alerts will notify you when the prices drop for the specified route.\n",
-    "\n",
-    "Check Nearby Airports: In both Toronto and San Francisco, there might be multiple airports. Check flights departing from nearby airports as they may offer better deals.\n",
-    "\n",
-    "Consider Layovers: Non-stop flights are usually more convenient but can be more expensive. Look for flights with one or more layovers as they may offer cost savings.\n",
-    "\n",
-    "Check Airlines' Official Websites: Once you find a potentially cheap flight on a search engine, verify the price directly on the airline's official website. Sometimes, booking directly with the airline can be cheaper due to exclusive deals and promotions.\n",
-    "\n",
-    "Use Incognito/Private Browsing Mode: Flight prices can sometimes increase if the website detects repeated searches for the same route. To avoid this, use the incognito or private browsing mode in your web browser.\n",
-    "\n",
-    "Consider Budget Airlines: Check if there are any budget airlines flying between Toronto and San Francisco. They often offer lower fares, but be mindful of additional fees for baggage and other services.\n",
-    "\n",
-    "Check for Deals and Promo Codes: Look for any ongoing deals or promo codes that can help you save on your flight booking. Airlines and travel websites occasionally offer special discounts.\n",
-    "\n",
-    "Be Flexible with Departure and Arrival Times: If possible, consider flying during off-peak hours or mid-week, as flights during these times can be less expensive.\n",
-    "\n",
-    "Factor in Total Costs: While searching for cheap flights, don't forget to consider other expenses like baggage fees, seat selection, and additional amenities. Some budget airlines might have hidden costs that could make the overall trip more expensive.\n",
-    "\n",
-    "Book Early: Flight prices tend to rise as the departure date approaches. Once you find a good deal that suits your preferences, don't wait too long to book your flight.\"\"\"\n",
-    "\n",
-    "response3 = \"\"\"Search online for cheap flights.\n",
-    "Check different dates.\n",
-    "Look at nearby airports.\n",
-    "Consider layovers.\n",
-    "Try budget airlines.\n",
-    "Book early if you find a good deal.\"\"\"\n",
-    "\n",
-    "ideal_response = \"\"\"1. Search 'Toronto to San Francisco flights' on the internet\n",
-    "2. Click on the first link that is a flight aggregator such as SkyScanner or Google Flights\n",
-    "3. Select the dates that you want to travel\n",
-    "3. Click on the 'one way' option and click search\n",
-    "4. Search through all of the given options and select the cheapest flight\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a5bf2f5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reference_evaluation = REFERENCE_PROMPT.format(task=task, scoring=percentage, answer=reference_answer, response=ideal_response, examples=FEW_SHOT_REFERENCE_EXAMPLES)\n",
-    "rubric_evaluation = RUBRIC_PROMPT.format(task=task, scoring=percentage, answer=rubric_answer, response=ideal_response, examples=FEW_SHOT_REFERENCE_EXAMPLES)\n",
-    "question_evaluation = QUESTION_PROMPT.format(task=task, scoring=percentage, answer=question_answer, response=ideal_response, examples=FEW_SHOT_REFERENCE_EXAMPLES)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "1cd01a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reference_response = llm_eval(reference_evaluation)\n",
-    "rubric_response = llm_eval(rubric_evaluation)\n",
-    "question_response = llm_eval(question_evaluation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "72e6ac91",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "REFERENCE: 66.5 6.0 0.0\n",
-      "RUBRIC: 65.0 4.0 0.0\n",
-      "QUESTION: 87.0 6.5 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# response3 - the worst one\n",
-    "print('REFERENCE:', reference_response_percentage, reference_response_scale, reference_response_binary)\n",
-    "print('RUBRIC:', rubric_response_percentage, rubric_response_scale, rubric_response_binary)\n",
-    "print('QUESTION:', question_response_percentage, question_response_scale, question_response_binary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "a66014ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "REFERENCE: 100.0 10.0 1.0\n",
-      "RUBRIC: 88.0 7.5 1.0\n",
-      "QUESTION: 87.0 7.5 1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# reference_response - the best one\n",
-    "print('REFERENCE:', reference_response_ideal_percentage, reference_response_ideal_scale, reference_response_ideal_binary)\n",
-    "print('RUBRIC:', rubric_response_ideal_percentage, rubric_response_ideal_scale, rubric_response_ideal_binary)\n",
-    "print('QUESTION:', question_response_ideal_percentage, question_response_ideal_scale, question_response_ideal_binary)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/benchmark/notebooks/Visualization.ipynb b/benchmark/notebooks/Visualization.ipynb
deleted file mode 100644
index 6892efc1e8..0000000000
--- a/benchmark/notebooks/Visualization.ipynb
+++ /dev/null
@@ -1,431 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "import json\n",
-    "\n",
-    "def get_last_file_in_directory(directory_path):\n",
-    "    # Get all files in the directory\n",
-    "    files = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f))]\n",
-    "\n",
-    "    # Sort the files by modification time\n",
-    "    files.sort(key=lambda x: os.path.getmtime(os.path.join(directory_path, x)))\n",
-    "\n",
-    "    # Return the last file in the list\n",
-    "    return files[-1] if files else None\n",
-    "\n",
-    "def get_latest_files_in_subdirectories(directory_path):\n",
-    "    latest_files = []\n",
-    "    for subdir in os.scandir(directory_path):\n",
-    "        if subdir.is_dir():\n",
-    "            latest_file = get_last_file_in_directory(subdir.path)\n",
-    "            if latest_file is not None:\n",
-    "                latest_files.append((subdir.path, latest_file))\n",
-    "    return latest_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Dict, List, Union\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "class Metrics(BaseModel):\n",
-    "    difficulty: str\n",
-    "    success: bool\n",
-    "    success_percent: float = Field(..., alias=\"success_%\")\n",
-    "    run_time: Optional[str] = None\n",
-    "    fail_reason: Optional[str] = None\n",
-    "\n",
-    "class MetricsOverall(BaseModel):\n",
-    "    run_time: str\n",
-    "    highest_difficulty: str\n",
-    "    percentage: Optional[float] = None\n",
-    "\n",
-    "class Test(BaseModel):\n",
-    "    data_path: str\n",
-    "    is_regression: bool\n",
-    "    answer: str\n",
-    "    description: str\n",
-    "    metrics: Metrics\n",
-    "    category: List[str]\n",
-    "    task: Optional[str] = None\n",
-    "    reached_cutoff: Optional[bool] = None\n",
-    "\n",
-    "class SuiteTest(BaseModel):\n",
-    "    data_path: str\n",
-    "    metrics: MetricsOverall\n",
-    "    tests: Dict[str, Test]\n",
-    "    category: Optional[List[str]] = None\n",
-    "    task: Optional[str] = None\n",
-    "    reached_cutoff: Optional[bool] = None\n",
-    "\n",
-    "class Report(BaseModel):\n",
-    "    command: str\n",
-    "    completion_time: str\n",
-    "    benchmark_start_time: str\n",
-    "    metrics: MetricsOverall\n",
-    "    tests: Dict[str, Union[Test, SuiteTest]]\n",
-    "    config: Dict[str, str]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\Auto-GPT', 'file55_07-29-10-45.json'), ('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\BabyAGI', 'file40_07-29-10-47.json'), ('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\beebot', 'file43_07-29-17-22.json'), ('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\gpt-engineer', 'file53_07-29-10-45.json'), ('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\mini-agi', 'file79_07-29-14-18.json'), ('c:\\\\Users\\\\silen\\\\Desktop\\\\Untitled Folder\\\\Projects\\\\Auto-GPT\\\\Auto-GPT-Benchmarks\\\\reports\\\\smol-developer', 'file54_07-29-10-45.json')]\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\Auto-GPT, Latest file: file55_07-29-10-45.json\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\BabyAGI, Latest file: file40_07-29-10-47.json\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\beebot, Latest file: file43_07-29-17-22.json\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\gpt-engineer, Latest file: file53_07-29-10-45.json\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\mini-agi, Latest file: file79_07-29-14-18.json\n",
-      "Subdirectory: c:\\Users\\silen\\Desktop\\Untitled Folder\\Projects\\Auto-GPT\\Auto-GPT-Benchmarks\\reports\\smol-developer, Latest file: file54_07-29-10-45.json\n"
-     ]
-    }
-   ],
-   "source": [
-    "reports_path = Path.cwd().parent / 'reports'\n",
-    "\n",
-    "latest_files = get_latest_files_in_subdirectories(reports_path)\n",
-    "print(latest_files)\n",
-    "\n",
-    "reports_data = {}\n",
-    "\n",
-    "# This will print the latest file in each subdirectory and add to the files_data dictionary\n",
-    "for subdir, file in latest_files:\n",
-    "    subdir_name = os.path.basename(os.path.normpath(subdir))\n",
-    "    print(f\"Subdirectory: {subdir}, Latest file: {file}\")\n",
-    "    if subdir_name not in [\"beebot\", \"mini-agi\"]:\n",
-    "        continue\n",
-    "    with open(Path(subdir) / file, 'r') as f:\n",
-    "        # Load the JSON data from the file\n",
-    "        json_data = json.load(f)\n",
-    "        converted_data = Report.parse_obj(json_data)\n",
-    "        # get the last directory name in the path as key\n",
-    "        reports_data[subdir_name] = converted_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "STRING_DIFFICULTY_MAP = {\n",
-    "    \"interface\": 1,\n",
-    "    \"basic\": 2,\n",
-    "    \"novice\": 3,\n",
-    "    \"intermediate\": 4,\n",
-    "    \"advanced\": 5,\n",
-    "    \"expert\": 6,\n",
-    "    \"human\": 7,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'beebot': {'content_gen': 2, 'safety': 4, 'memory': 5, 'code': 5, 'iterate': 5, 'retrieval': 4, 'adaptability': 4}, 'mini-agi': {'content_gen': 2, 'safety': 4, 'memory': 5, 'code': 5, 'iterate': 5, 'retrieval': 4, 'adaptability': 4}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from typing import Any\n",
-    "\n",
-    "def get_agent_category(report: Report) -> dict[str, Any]:\n",
-    "    categories: dict[str, Any] = {}\n",
-    "    \n",
-    "    def get_highest_category_difficulty(data) -> None:\n",
-    "        for category in data.category:\n",
-    "            if category == \"interface\":\n",
-    "                continue\n",
-    "            num_dif = STRING_DIFFICULTY_MAP[data.metrics.difficulty]\n",
-    "            if num_dif > categories.setdefault(category, 0):\n",
-    "                categories[category] = num_dif\n",
-    "\n",
-    "    for _, test_data in report.tests.items():\n",
-    "        suite = False\n",
-    "\n",
-    "        if isinstance(test_data, SuiteTest):\n",
-    "            for _, test_data in test_data.tests.items():\n",
-    "                get_highest_category_difficulty(test_data)\n",
-    "        else:\n",
-    "            get_highest_category_difficulty(test_data)\n",
-    "            \n",
-    "    return categories\n",
-    "\n",
-    "all_categories: dict[str, Any] = {}\n",
-    " \n",
-    "for name, report in reports_data.items():\n",
-    "    categories = get_agent_category(report)\n",
-    "    all_categories[name] = categories\n",
-    "                        \n",
-    "print(all_categories)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from matplotlib.colors import Normalize\n",
-    "import matplotlib.patches as mpatches\n",
-    "import matplotlib.ticker as ticker\n",
-    "\n",
-    "\n",
-    "def save_combined_radar_chart(categories):\n",
-    "\n",
-    "    labels=np.array(list(next(iter(categories.values())).keys())) # We use the first category to get the keys\n",
-    "    num_vars = len(labels)\n",
-    "    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()\n",
-    "    angles += angles[:1] # Add the first angle to the end of the list to ensure the polygon is closed\n",
-    "\n",
-    "    # Create radar chart\n",
-    "    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n",
-    "    ax.set_theta_offset(np.pi / 2)\n",
-    "    ax.set_theta_direction(-1)\n",
-    "    ax.spines['polar'].set_visible(False) # Remove border\n",
-    "\n",
-    "    # Define a custom normalization to start the color from the middle\n",
-    "    norm = Normalize(vmin=0, vmax=max([max(val.values()) for val in categories.values()])) # We use the maximum of all categories for normalization\n",
-    "\n",
-    "    colors = ['#40c463', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] # Define more colors for more categories\n",
-    "\n",
-    "    for i, (cat_name, cat_values) in enumerate(categories.items()): # Iterating through each category (series)\n",
-    "        values=np.array(list(cat_values.values()))\n",
-    "        values = np.concatenate((values, values[:1])) # Ensure the polygon is closed\n",
-    "\n",
-    "        ax.fill(angles, values, color=colors[i], alpha=0.25) # Draw the filled polygon\n",
-    "        ax.plot(angles, values, color=colors[i], linewidth=2) # Draw polygon\n",
-    "        ax.plot(angles, values, 'o', color='white', markersize=7, markeredgecolor=colors[i], markeredgewidth=2) # Draw points\n",
-    "\n",
-    "        # Draw legend\n",
-    "        ax.legend(handles=[mpatches.Patch(color=color, label=cat_name, alpha=0.25) for cat_name, color in zip(categories.keys(), colors)])\n",
-    "\n",
-    "    lines, labels = plt.thetagrids(np.degrees(angles[:-1]), (list(next(iter(categories.values())).keys()))) # We use the first category to get the keys\n",
-    "\n",
-    "    # Move labels away from the plot\n",
-    "    for label in labels:\n",
-    "        label.set_position((label.get_position()[0], label.get_position()[1] + -0.05))  # adjust 0.1 as needed\n",
-    "\n",
-    "    ax.set_rlabel_position(180) # Move radial labels away from the plot\n",
-    "    \n",
-    "    ax.set_yticks([]) # Remove default yticks\n",
-    "\n",
-    "    # Manually create gridlines\n",
-    "    for y in np.arange(0, norm.vmax + 1, 1):\n",
-    "        if y != norm.vmax:\n",
-    "            ax.plot(angles, [y] * len(angles), color='gray', linewidth=0.5, linestyle=':')\n",
-    "        # Add labels for manually created gridlines\n",
-    "        ax.text(angles[0], y + 0.2, str(int(y)), color='black', size=9, horizontalalignment='center', verticalalignment='center')\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "# Here is how you can use the function\n",
-    "categories = {\n",
-    "    'beebot': {'content_gen': 2, 'safety': 4, 'memory': 1, 'code': 2, 'iterate': 3, 'retrieval': 4, 'adaptability': 3}, \n",
-    "    'mini-agi': {'content_gen': 4, 'safety': 1, 'memory': 5, 'code': 4, 'iterate': 5, 'retrieval': 4, 'adaptability': 2}\n",
-    "}\n",
-    "save_combined_radar_chart(categories)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import matplotlib.patches as mpatches\n",
-    "\n",
-    "def save_single_radar_chart(category_dict: dict[str, int], save_path: str | Path, name: str) -> None:\n",
-    "    labels=np.array(list(category_dict.keys()))\n",
-    "    values=np.array(list(category_dict.values()))\n",
-    "\n",
-    "    num_vars = len(labels)\n",
-    "\n",
-    "    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()\n",
-    "\n",
-    "    angles += angles[:1]\n",
-    "    values = np.concatenate((values, values[:1]))\n",
-    "\n",
-    "    colors = ['#40c463']\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n",
-    "    ax.set_theta_offset(np.pi / 2)\n",
-    "    ax.set_theta_direction(-1)\n",
-    "\n",
-    "    ax.spines['polar'].set_visible(False)\n",
-    "\n",
-    "    lines, labels = plt.thetagrids(np.degrees(angles[:-1]), (list(category_dict.keys())))\n",
-    "\n",
-    "    for label in labels:\n",
-    "        label.set_position((label.get_position()[0], label.get_position()[1] + -0.05))\n",
-    "\n",
-    "    ax.fill(angles, values, color=colors[0], alpha=0.25)\n",
-    "    ax.plot(angles, values, color=colors[0], linewidth=2)\n",
-    "\n",
-    "    for i, (angle, value) in enumerate(zip(angles, values)):\n",
-    "        ha = 'left'\n",
-    "        if angle in {0, np.pi}:\n",
-    "            ha = 'center'\n",
-    "        elif np.pi < angle < 2*np.pi:\n",
-    "            ha = 'right'\n",
-    "        ax.text(angle, value - 0.5, f'{value}', size=10, horizontalalignment=ha, verticalalignment=\"center\", color='black')\n",
-    "\n",
-    "    ax.set_yticklabels([])\n",
-    "\n",
-    "    ax.set_yticks([])\n",
-    "\n",
-    "    for y in np.arange(0, values.max(), 1):\n",
-    "        ax.plot(angles, [y] * len(angles), color='gray', linewidth=0.5, linestyle=':')\n",
-    "\n",
-    "    for angle, value in zip(angles, values):\n",
-    "        ax.plot(angle, value, 'o', color='white', markersize=7, markeredgecolor=colors[0], markeredgewidth=2)\n",
-    "\n",
-    "    green_patch = mpatches.Patch(color='#40c463', label='Mini-AGI', alpha=0.25)\n",
-    "    plt.legend(handles=[green_patch])\n",
-    "    \n",
-    "    plt.savefig(f\"{save_path}/{name}.png\", dpi=300)  # Save the figure as a PNG file\n",
-    "    plt.close()  # Close the figure to free up memory\n",
-    "\n",
-    "# Here's how you can use this function:\n",
-    "categories = {'content_gen': 2, 'safety': 4, 'memory': 5, 'code': 5, 'iterate': 5, 'retrieval': 4, 'adaptability': 4}\n",
-    "save_single_radar_chart(categories, Path.cwd(), \"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArkAAAIQCAYAAACFTXaSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABjWElEQVR4nO3dd3xUVf7/8fckkEkghWJIQk3ooTeVgBQpRppGWWBZNLQAsoAUEYwsJaAGCyKKSlEIICwIIvhTBAEpApEepJcIBDH0ZgIESO7vD7/MOoaSAMMkd17PfdzHgzn33HM+9z7I+uGTc89YDMMwBAAAAJiIm7MDAAAAAB40klwAAACYDkkuAAAATIckFwAAAKZDkgsAAADTIckFAACA6ZDkAgAAwHRIcgEAAGA6JLkAAAAwHZJcAHCSd999V6VLl5a7u7tq1Kjh7HByvMaNG6tx48bODgNALkGSC0CSFBcXJ4vFoi1bttzyfOPGjVWlShWHxrBkyRKNGjXKoXPkFD/88IOGDBmi+vXra/r06Xrrrbfues3q1av1/PPPKzAwUB4eHipSpIjatGmjhQsXZnv+y5cva9SoUVq9evU9RA8AOV8eZwcAADctWbJEH3/8sUskuj/++KPc3Nz0+eefy8PD4679R44cqdGjR6tcuXLq1auXSpUqpbNnz2rJkiVq27atZs+erX/9619Znv/y5cuKiYmRpFxTHf3hhx+cHQKAXIQkFwCc4NSpU/Ly8spSgrtgwQKNHj1a//jHPzRnzhzlzZvXdu7VV1/VsmXLdP36dUeG61SXL19Wvnz5svSsAOAmlisAuC9ffPGFateuLS8vLxUqVEj//Oc/dezYMbs+P/30k9q1a6eSJUvKarWqRIkSGjhwoK5cuWLr06VLF3388ceSJIvFYjsk6ciRI7JYLHrvvff08ccfq3Tp0sqXL5+eeuopHTt2TIZhaMyYMSpevLi8vLz07LPP6ty5c3YxLF68WK1atVLRokVltVpVpkwZjRkzRunp6Xb9bi7L2Lp1q+rVqycvLy+FhIRo0qRJWXoeN27c0JgxY1SmTBlZrVYFBwfr9ddfV1pamq2PxWLR9OnTlZqaarvPuLi42445fPhwFSpUSNOmTbNLcG8KDw9X69atJUnXrl3TiBEjVLt2bfn5+Sl//vxq0KCBVq1aZet/5MgR+fv7S5JiYmJsMfy1gr5v3z794x//UKFCheTp6ak6derom2++yTT3L7/8okaNGsnLy0vFixfXG2+8oenTp8tisejIkSN2fT/55BNVrlxZVqtVRYsWVZ8+fXThwgW7Pn99/g0bNlS+fPn0+uuv2879veqclpamkSNHqmzZsra/W0OGDLF73pK0fPlyPfHEEypQoIC8vb1VoUIF27gAzIlKLgA7Fy9e1JkzZzK136pS+Oabb2r48OFq3769oqKidPr0aX300Udq2LChtm/frgIFCkiS5s+fr8uXL6t3794qXLiwNm3apI8++ki//fab5s+fL0nq1auXfv/9dy1fvlyzZs26ZWyzZ8/WtWvX1K9fP507d07vvPOO2rdvryZNmmj16tUaOnSoDh06pI8++kiDBw/WtGnTbNfGxcXJ29tbgwYNkre3t3788UeNGDFCly5d0rvvvms3z/nz59WyZUu1b99eHTt21JdffqnevXvLw8ND3bp1u+Pzi4qK0owZM/SPf/xDr7zyijZu3KjY2Fjt3btXX3/9tSRp1qxZmjJlijZt2qTPPvtMklSvXr1bjnfw4EHt27dP3bp1k4+Pzx3nlqRLly7ps88+U8eOHdWjRw/98ccf+vzzzxUeHq5NmzapRo0a8vf316effqrevXvrueee0/PPPy9JqlatmiRp9+7dql+/vooVK6bXXntN+fPn15dffqmIiAh99dVXeu655yRJx48f15NPPimLxaLo6Gjlz59fn332maxWa6a4Ro0apZiYGDVr1ky9e/fW/v379emnn2rz5s1av369XfJ+9uxZtWjRQv/85z/1wgsvKCAg4Jb3mpGRoWeeeUbr1q1Tz549FRoaqp07d2r8+PE6cOCAFi1aZLuf1q1bq1q1aho9erSsVqsOHTqk9evX3/V5AsjFDAAwDGP69OmGpDselStXtvU/cuSI4e7ubrz55pt24+zcudPIkyePXfvly5czzRcbG2tYLBbj6NGjtrY+ffoYt/q/pcOHDxuSDH9/f+PChQu29ujoaEOSUb16deP69eu29o4dOxoeHh7G1atX7xhDr169jHz58tn1a9SokSHJGDdunK0tLS3NqFGjhlGkSBHj2rVrmR/e/0lISDAkGVFRUXbtgwcPNiQZP/74o62tc+fORv78+W871k2LFy82JBnjx4+/a1/DMIwbN24YaWlpdm3nz583AgICjG7dutnaTp8+bUgyRo4cmWmMpk2bGlWrVrV7LhkZGUa9evWMcuXK2dr69etnWCwWY/v27ba2s2fPGoUKFTIkGYcPHzYMwzBOnTpleHh4GE899ZSRnp5u6ztx4kRDkjFt2jRb283nP2nSpExxNWrUyGjUqJHt86xZsww3Nzfjp59+sus3adIkQ5Kxfv16wzAMY/z48YYk4/Tp07d4YgDMiuUKAOx8/PHHWr58eabjZpXvpoULFyojI0Pt27fXmTNnbEdgYKDKlStn9+txLy8v259TU1N15swZ1atXT4ZhaPv27VmOrV27dvLz87N9fvzxxyVJL7zwgvLkyWPXfu3aNR0/fvyWMfzxxx86c+aMGjRooMuXL2vfvn128+TJk0e9evWyffbw8FCvXr106tQpbd269bbxLVmyRJI0aNAgu/ZXXnlFkvTdd99l+V5vunTpkiRlqYorSe7u7ra1qxkZGTp37pxu3LihOnXqaNu2bXe9/ty5c/rxxx/Vvn1723M6c+aMzp49q/DwcB08eND2XJcuXaqwsDC77c8KFSqkTp062Y25YsUKXbt2TQMGDJCb2//+s9OjRw/5+vpmei5Wq1Vdu3a9a6zz589XaGioKlasaPd3sEmTJpJk+zt48zcKixcvVkZGxl3HBWAOLFcAYOexxx5TnTp1MrUXLFjQbhnDwYMHZRiGypUrd8tx/vrr56SkJI0YMULffPONzp8/b9fv4sWLWY6tZMmSdp9vJrwlSpS4Zftf59q9e7f+85//6Mcff7QljreLoWjRosqfP79dW/ny5SX9uZ61bt26t4zv6NGjcnNzU9myZe3aAwMDVaBAAR09evSO93crvr6+kv5MzLNqxowZGjdunPbt22e3zCQkJOSu1x46dEiGYWj48OEaPnz4LfucOnVKxYoV09GjRxUWFpbp/N/v/+Z9V6hQwa7dw8NDpUuXzvRcihUrlqWXzA4ePKi9e/fa1hffKk5J6tChgz777DNFRUXptddeU9OmTfX888/rH//4h13SDcBcSHIB3JOMjAxZLBZ9//33cnd3z3Te29tbkpSenq7mzZvr3LlzGjp0qCpWrKj8+fPr+PHj6tKlS7Yqa7ea507thmFIki5cuKBGjRrJ19dXo0ePVpkyZeTp6alt27Zp6NChD7y6d/OFuQehYsWKkqSdO3dmqf8XX3yhLl26KCIiQq+++qqKFCkid3d3xcbGKjEx8a7X33wWgwcPVnh4+C37/D2JfdD+WnW/k4yMDFWtWlXvv//+Lc/f/MePl5eX1q5dq1WrVum7777T0qVLNW/ePDVp0kQ//PDDbf/+AMjdSHIB3JMyZcrIMAyFhITYqpy3snPnTh04cEAzZsxQZGSkrX358uWZ+j7I5PCvVq9erbNnz2rhwoVq2LChrf3w4cO37P/7778rNTXVrpp74MABSVJwcPBt5ylVqpQyMjJ08OBBhYaG2tpPnjypCxcuqFSpUtmOvXz58qpQoYIWL16sCRMm2P7xcDsLFixQ6dKltXDhQrvnOXLkSLt+t3vWpUuXlvRnJb5Zs2Z3nKtUqVI6dOhQpva/t9287/3799vGl/7cCeLw4cN3ned2ypQpox07dqhp06Z3/bvj5uampk2bqmnTpnr//ff11ltvadiwYVq1atU9zw8gZ+P3NADuyfPPPy93d3fFxMTYKqY3GYahs2fPSvpflfWvfQzD0IQJEzKNeTOp/Pu2UvfrVjFcu3ZNn3zyyS3737hxQ5MnT7brO3nyZPn7+6t27dq3nadly5aSpA8++MCu/WalsVWrVvcUf0xMjM6ePauoqCjduHEj0/kffvhB3377raRb3+vGjRsVHx9vd02+fPkkZX7WRYoUUePGjTV58mQlJydnmuv06dO2P4eHhys+Pl4JCQm2tnPnzmn27Nl21zRr1kweHh768MMP7eL6/PPPdfHixXt+Lu3bt9fx48c1derUTOeuXLmi1NRUW0x/d3Md8d+3GgNgHlRyAdyTMmXK6I033lB0dLSOHDmiiIgI+fj46PDhw/r666/Vs2dPDR48WBUrVlSZMmU0ePBgHT9+XL6+vvrqq68yrc2VZEsgX375ZYWHh8vd3V3//Oc/7zvWevXqqWDBgurcubNefvllWSwWzZo1K1NyflPRokX19ttv68iRIypfvrzmzZunhIQETZky5Zb71N5UvXp1de7cWVOmTLEtkdi0aZNmzJihiIgIPfnkk/cUf4cOHbRz5069+eab2r59uzp27Gj7xrOlS5dq5cqVmjNnjiSpdevWWrhwoZ577jm1atVKhw8f1qRJk1SpUiWlpKTYxvTy8lKlSpU0b948lS9fXoUKFVKVKlVUpUoVffzxx3riiSdUtWpV9ejRQ6VLl9bJkycVHx+v3377TTt27JAkDRkyRF988YWaN2+ufv362bYQK1mypM6dO2errvr7+ys6OloxMTF6+umn9cwzz2j//v365JNP9Oijj+qFF164p+fy4osv6ssvv9RLL72kVatWqX79+kpPT9e+ffv05ZdfatmyZapTp45Gjx6ttWvXqlWrVipVqpROnTqlTz75RMWLF9cTTzxxT3MDyAWcsqcDgBzn5hZimzdvvuX5Ro0a2W0hdtNXX31lPPHEE0b+/PmN/PnzGxUrVjT69Olj7N+/39Znz549RrNmzQxvb2/jkUceMXr06GHs2LHDkGRMnz7d1u/GjRtGv379DH9/f8Nisdi2E7u5hdi7775rN/eqVasMScb8+fPvei/r16836tata3h5eRlFixY1hgwZYixbtsyQZKxatSrTfW7ZssUICwszPD09jVKlShkTJ07M0nO8fv26ERMTY4SEhBh58+Y1SpQoYURHR9ttx2UYWd9C7K9WrlxpPPvss0aRIkWMPHnyGP7+/kabNm2MxYsX2/pkZGQYb731llGqVCnDarUaNWvWNL799lujc+fORqlSpezG27Bhg1G7dm3Dw8Mj03ZiiYmJRmRkpBEYGGjkzZvXKFasmNG6dWtjwYIFdmNs377daNCggWG1Wo3ixYsbsbGxxocffmhIMk6cOGHXd+LEiUbFihWNvHnzGgEBAUbv3r2N8+fP2/W53d+zm+f+uoWYYRjGtWvXjLffftuoXLmyYbVajYIFCxq1a9c2YmJijIsXL9o9t6JFixoeHh5G0aJFjY4dOxoHDhzIwlMHkFtZDOM2pQwAcEGNGzfWmTNntGvXLmeHkmsNGDBAkydPVkpKCi91AXAa1uQCAO7ZX7+aWfrz28pmzZqlJ554ggQXgFOxJhcAcM/CwsLUuHFjhYaG6uTJk/r888916dKl2+6xCwAPC0kuAOCetWzZUgsWLNCUKVNksVhUq1Ytff7553ZbtQGAM7AmFwAAAA4zatQoxcTE2LVVqFAh01eq/9X8+fM1fPhwHTlyROXKldPbb79t26Yxq1iTCwAAAIeqXLmykpOTbce6detu23fDhg3q2LGjunfvru3btysiIkIRERHZfiGYSi4AAAAcZtSoUVq0aJHdF8fcSYcOHZSammr7khtJqlu3rmrUqKFJkyZleV4quQAAAMiWtLQ0Xbp0ye640zcIHjx4UEWLFlXp0qXVqVMnJSUl3bZvfHx8pq/bvvkNi9nBi2cAAAAm5FWyo8PGHtqtQqZ1tiNHjtSoUaMy9X388ccVFxenChUqKDk5WTExMWrQoIF27dolHx+fTP1PnDihgIAAu7aAgACdOHEiWzGS5OYi8ae+c3YIABwgrEgrlYmc5+wwADhA4swOzg7BIaKjozVo0CC7NqvVesu+LVq0sP25WrVqevzxx1WqVCl9+eWX6t69u8NiJMkFAAAwIYvFcatSrVbrbZPauylQoIDKly+vQ4cO3fJ8YGCgTp48add28uRJBQYGZmse1uQCAADgoUlJSVFiYqKCgoJueT4sLEwrV660a1u+fLnCwsKyNQ9JLgAAgAlZ5OawIzsGDx6sNWvW6MiRI9qwYYOee+45ubu7q2PHP9cMR0ZGKjo62ta/f//+Wrp0qcaNG6d9+/Zp1KhR2rJli/r27ZuteVmuAAAAAIf57bff1LFjR509e1b+/v564okn9PPPP8vf31+SlJSUJDe3/yXO9erV05w5c/Sf//xHr7/+usqVK6dFixapSpUq2ZqXJBcAAMCEHLkmNzvmzp17x/OrV6/O1NauXTu1a9fuvuYlyQUAADChnJLkOotr3z0AAABMiUouAACACVksFmeH4FRUcgEAAGA6VHIBAABMybVrma599wAAADAlKrkAAAAmxO4KAAAAgMlQyQUAADAhKrkAAACAyVDJBQAAMCGLi9cySXIBAABMiOUKAAAAgMlQyQUAADAhKrkAAACAyVDJBQAAMCEquQAAAIDJUMkFAAAwIYsszg7BqajkAgAAwHSo5AIAAJgQa3JzmODgYH3wwQcPfNwjR47IYrEoISHhgY8NPAjfzlqhmB7j9dJT0erXZoQmRE9TctIpZ4cF4AHr1bqiEmd20H861XR2KDA5i8XNYUdukDuiBFzAvoRENXmuvoZP7q9Xx/dS+o10vTdostKupDk7NAAPSNWQQur4ZBntTbrg7FAA08t2kpuRkaF33nlHZcuWldVqVcmSJfXmm29Kknbu3KkmTZrIy8tLhQsXVs+ePZWSkmK7tkuXLoqIiNB7772noKAgFS5cWH369NH169clSY0bN9bRo0c1cOBAWSwWWSz/WzC9bt06NWjQQF5eXipRooRefvllpaam2s4HBwfrrbfeUrdu3eTj46OSJUtqypQptvMhISGSpJo1a8pisahx48Z3vdcbN27o5ZdfVoECBVS4cGENHTpUnTt3VkREhN3ziI2NVUhIiLy8vFS9enUtWLDAdn716tWyWCxauXKl6tSpo3z58qlevXrav39/9h48TG/wuF5q0PIxFQsJVMmyxRT1ekedPXleR/b/5uzQADwA+ax5NL53Xb0+bYsupl5zdjhwAVRysyk6Olpjx47V8OHDtWfPHs2ZM0cBAQFKTU1VeHi4ChYsqM2bN2v+/PlasWKF+vbta3f9qlWrlJiYqFWrVmnGjBmKi4tTXFycJGnhwoUqXry4Ro8ereTkZCUnJ0uSEhMT9fTTT6tt27b65ZdfNG/ePK1bty7T2OPGjVOdOnW0fft2/fvf/1bv3r1tyeSmTZskSStWrFBycrIWLlx413t9++23NXv2bE2fPl3r16/XpUuXtGjRIrs+sbGxmjlzpiZNmqTdu3dr4MCBeuGFF7RmzRq7fsOGDdO4ceO0ZcsW5cmTR926dcvyM4drupJ6RZKU3zefkyMB8CDEdK6lVQm/a8Puk84OBXAJ2Xrx7I8//tCECRM0ceJEde7cWZJUpkwZPfHEE5o6daquXr2qmTNnKn/+/JKkiRMnqk2bNnr77bcVEBAgSSpYsKAmTpwod3d3VaxYUa1atdLKlSvVo0cPFSpUSO7u7vLx8VFgYKBt3tjYWHXq1EkDBgyQJJUrV04ffvihGjVqpE8//VSenp6SpJYtW+rf//63JGno0KEaP368Vq1apQoVKsjf31+SVLhwYbux7+Sjjz5SdHS0nnvuOdv9LFmyxHY+LS1Nb731llasWKGwsDBJUunSpbVu3TpNnjxZjRo1svV98803bZ9fe+01tWrVSlevXrXFDvxVRkaG5ny4WOWqhqh46SBnhwPgPrV+vIQqlyqoiFHLnR0KXEruqLg6SraS3L179yotLU1Nmza95bnq1avbElxJql+/vjIyMrR//35bklu5cmW5u7vb+gQFBWnnzp13nHfHjh365ZdfNHv2bFubYRjKyMjQ4cOHFRoaKkmqVq2a7bzFYlFgYKBOnbq3F3cuXryokydP6rHHHrO1ubu7q3bt2srIyJAkHTp0SJcvX1bz5s3trr127Zpq1rR/oeCvsQUF/Zm0nDp1SiVLlsw0d1pamtLS7NdhWq3We7oP5E6z3l+o3w4na9jH/ZwdCoD7FFTIS8NfqKXId1br2vUMZ4cDuIxsJbleXl73PWHevHntPlssFlvSeDspKSnq1auXXn755Uzn/pok3svY9+PmeuPvvvtOxYoVszv396T0r7HdXGt8u9hiY2MVExNj1zZy5EiF//vR+44ZOd+s8V9pR/weRX/UR4WKFHB2OADuU5XgQnrEz1PfjH7K1pbH3U2PVfDXi83KKrTbAmUYhhMjhFnllrWzjpKtJLdcuXLy8vLSypUrFRUVZXcuNDRUcXFxSk1NtVVz169fLzc3N1WoUCHLc3h4eCg9Pd2urVatWtqzZ4/Kli2bnXAzjSsp09i34+fnp4CAAG3evFkNGza0Xbtt2zbVqFFDklSpUiVZrVYlJSXZLU24X9HR0Ro0aJBdm9Vq1baLKx7YHMh5DMPQFx8s1Na1O/Xah33kX7Sws0MC8ABs2HNSLaKX2rW93eMxJSZf0pRv95HgAg6SrSTX09NTQ4cO1ZAhQ+Th4aH69evr9OnT2r17tzp16qSRI0eqc+fOGjVqlE6fPq1+/frpxRdftC1VyIrg4GCtXbtW//znP2W1WvXII49o6NChqlu3rvr27auoqCjlz59fe/bs0fLlyzVx4sQsjVukSBF5eXlp6dKlKl68uDw9PeXn53fHa/r166fY2FiVLVtWFStW1EcffaTz58/bKrE+Pj4aPHiwBg4cqIyMDD3xxBO6ePGi1q9fL19fX9u65eyyWq0sT3BBs97/SvErtqn/W93kmc+qC2cvSZLyeXvKw+rh5OgA3KvUqzd04PhFu7bLaTd0IeVapnbgQaKSm03Dhw9Xnjx5NGLECP3+++8KCgrSSy+9pHz58mnZsmXq37+/Hn30UeXLl09t27bV+++/n63xR48erV69eqlMmTJKS0uTYRiqVq2a1qxZo2HDhqlBgwYyDENlypRRhw4dsn6jefLoww8/1OjRozVixAg1aNBAq1evvuM1Q4cO1YkTJxQZGSl3d3f17NlT4eHhdmuKx4wZI39/f8XGxurXX39VgQIFVKtWLb3++uvZum/gx0UbJEljX/7Err179D/VoOVjt7oEAIDbsrj4i2cWw+D3JFmVkZGh0NBQtW/fXmPGjHno88ef+u6hzwnA8cKKtFKZyHnODgOAAyTOzHpB7kErVmWkw8Y+vivm7p2cLNuVXFdy9OhR/fDDD2rUqJHS0tI0ceJEHT58WP/617+cHRoAAMAdsVzBhXl7e9/23Pfff6/g4GDFxcVp8ODBMgxDVapU0YoVK2xblgEAACBncukkNyEh4bbnihUrJi8vL61fv/7hBQQAAPCA3HxR3lW5dJJ7P1uSAQAAIOdy6SQXAADArFx9Ta5r3z0AAABMiUouAACACbn6PrkkuQAAACbEcgUAAADAZKjkAgAAmBCVXAAAAMBkqOQCAACYkKu/eObadw8AAABTopILAABgRqzJBQAAAMyFSi4AAIAJsbsCAAAATMdisTjsuFdjx46VxWLRgAEDbtsnLi4u03yenp7ZnotKLgAAABxu8+bNmjx5sqpVq3bXvr6+vtq/f7/t870k1lRyAQAATMgiN4cd2ZWSkqJOnTpp6tSpKliw4N1jt1gUGBhoOwICArI9J0kuAAAAsiUtLU2XLl2yO9LS0m7bv0+fPmrVqpWaNWuWpfFTUlJUqlQplShRQs8++6x2796d7RhJcgEAAEzIYnFz2BEbGys/Pz+7IzY29pZxzJ07V9u2bbvt+b+rUKGCpk2bpsWLF+uLL75QRkaG6tWrp99++y1b98+aXAAAAGRLdHS0Bg0aZNdmtVoz9Tt27Jj69++v5cuXZ/nlsbCwMIWFhdk+16tXT6GhoZo8ebLGjBmT5RhJcgEAAMzoPnZBuBur1XrLpPbvtm7dqlOnTqlWrVq2tvT0dK1du1YTJ05UWlqa3N3d7zhG3rx5VbNmTR06dChbMZLkAgAAwCGaNm2qnTt32rV17dpVFStW1NChQ++a4Ep/JsU7d+5Uy5YtszU3SS4AAIAZ5YA3r3x8fFSlShW7tvz586tw4cK29sjISBUrVsy2Znf06NGqW7euypYtqwsXLujdd9/V0aNHFRUVla25SXIBAADMyIHLFR6kpKQkubn9LyM/f/68evTooRMnTqhgwYKqXbu2NmzYoEqVKmVrXJJcAAAAPDSrV6++4+fx48dr/Pjx9z0PSS4AAIAZ5ZJKrqOQ5OYiYUVaOTsEAA6SOLODs0MAAFMhyc1FykTOc3YIABzgzwT3gLPDAOAQ5Z03dQ548cyZXPz2AQAAYEZUcgEAAEzIcPE1uVRyAQAAYDpUcgEAAMzItQu5JLkAAACm5ObaWS7LFQAAAGA6VHIBAADMiBfPAAAAAHOhkgsAAGBGrl3IpZILAAAA86GSCwAAYEbsrgAAAACYC5VcAAAAM3Lx3RVIcgEAAMzItXNclisAAADAfKjkAgAAmBEvngEAAADmQiUXAADAjFy7kEslFwAAAOZDJRcAAMCEDBffQoxKLgAAAEyHSi4AAIAZsbsCAAAAYC5UcgEAAMzItQu5VHJv58SJE2revLny58+vAgUKODscAACA7LFYHHfkAiS5tzF+/HglJycrISFBBw4cyNI1o0aNUo0aNRwbGFxCr9YVlTizg/7TqaazQwHwAGzevEsvvTRaTzzRWRUqtNGKFfHODgkwPZLc20hMTFTt2rVVrlw5FSlSxNnhwIVUDSmkjk+W0d6kC84OBcADcvnyVVWoEKKRI19ydihwJW4Wxx25gKmT3AULFqhq1ary8vJS4cKF1axZM6Wmpmrz5s1q3ry5HnnkEfn5+alRo0batm2b7brg4GB99dVXmjlzpiwWi7p06SJJunDhgqKiouTv7y9fX181adJEO3bskCTFxcUpJiZGO3bskMVikcViUVxcnLp166bWrVvbxXX9+nUVKVJEn3/++UN7Fsgd8lnzaHzvunp92hZdTL3m7HAAPCCNGtXRwIEvqnnzMGeHArgM0754lpycrI4dO+qdd97Rc889pz/++EM//fSTDMPQH3/8oc6dO+ujjz6SYRgaN26cWrZsqYMHD8rHx0ebN29WZGSkfH19NWHCBHl5eUmS2rVrJy8vL33//ffy8/PT5MmT1bRpUx04cEAdOnTQrl27tHTpUq1YsUKS5Ofnp/Lly6thw4ZKTk5WUFCQJOnbb7/V5cuX1aFDB6c9H+RMMZ1raVXC79qw+6T6PFPJ2eEAAHKz3FFwdRhTJ7k3btzQ888/r1KlSkmSqlatKklq0qSJXd8pU6aoQIECWrNmjVq3bi1/f39ZrVZ5eXkpMDBQkrRu3Tpt2rRJp06dktVqlSS99957WrRokRYsWKCePXvK29tbefLksV0jSfXq1VOFChU0a9YsDRkyRJI0ffp0tWvXTt7e3g5/Dsg9Wj9eQpVLFVTEqOXODgUAgFzPtMsVqlevrqZNm6pq1apq166dpk6dqvPnz0uSTp48qR49eqhcuXLy8/OTr6+vUlJSlJSUdNvxduzYoZSUFBUuXFje3t624/Dhw0pMTLxjLFFRUZo+fbpt7u+//17dunW7bf+0tDRdunTJ7khLS7uHp4DcIqiQl4a/UEsDJ/2sa9cznB0OAMAMXHx3BdNWct3d3bV8+XJt2LBBP/zwgz766CMNGzZMGzduVO/evXX27FlNmDBBpUqVktVqVVhYmK5du/0ayJSUFAUFBWn16tWZzt1ti7HIyEi99tprio+P14YNGxQSEqIGDRrctn9sbKxiYmLs2kaOHCkp9I7zIPeqElxIj/h56pvRT9na8ri76bEK/nqxWVmFdlugDMNwYoQAAOQupk1yJclisah+/fqqX7++RowYoVKlSunrr7/W+vXr9cknn6hly5aSpGPHjunMmTN3HKtWrVo6ceKE8uTJo+Dg4Fv28fDwUHp6eqb2woULKyIiQtOnT1d8fLy6du16x7mio6M1aNAguzar1apZPRbd8TrkXhv2nFSL6KV2bW/3eEyJyZc05dt9JLgAgOzLJRVXRzFtkrtx40atXLlSTz31lIoUKaKNGzfq9OnTCg0NVbly5TRr1izVqVNHly5d0quvvmp7uex2mjVrprCwMEVEROidd95R+fLl9fvvv+u7777Tc889pzp16ig4OFiHDx9WQkKCihcvLh8fH9v63aioKLVu3Vrp6enq3LnzHeeyWq226+AaUq/e0IHjF+3aLqfd0IWUa5naAeQ+qalXlJSUbPv8228ntXfvr/Lz81bRomxTCQcx7aLUrDFtkuvr66u1a9fqgw8+0KVLl1SqVCmNGzdOLVq0UGBgoHr27KlatWqpRIkSeuuttzR48OA7jmexWLRkyRINGzZMXbt21enTpxUYGKiGDRsqICBAktS2bVstXLhQTz75pC5cuKDp06fbth9r1qyZgoKCVLlyZRUtWtTRtw8AyEF27TqkyMjXbZ9jY//cQvK555po7NiBzgoLMDWLYfB70IchJSVFxYoV0/Tp0/X888/f0xhlIuc94KgA5ASJMztIyto3KwLIbco7beay7Wc7bOxDX3Zy2NgPimkruTlFRkaGzpw5o3HjxqlAgQJ65plnnB0SAACA6ZHkOlhSUpJCQkJUvHhxxcXFKU8eHjkAAHgIXPu9M5JcRwsODhYrQgAAAB4uklwAAAATMtxcu5Tr4ptLAAAAwIyo5AIAAJgRXwYBAAAA03HtHJflCgAAAHg4xo4dK4vFogEDBtyx3/z581WxYkV5enqqatWqWrJkSbbnIskFAAAwIzeL4457sHnzZk2ePFnVqlW7Y78NGzaoY8eO6t69u7Zv366IiAhFRERo165d2bv9e4oSAAAAyKKUlBR16tRJU6dOVcGCBe/Yd8KECXr66af16quvKjQ0VGPGjFGtWrU0ceLEbM1JkgsAAGBGFovjjmzq06ePWrVqpWbNmt21b3x8fKZ+4eHhio+Pz9acvHgGAACAbElLS1NaWppdm9VqldVqzdR37ty52rZtmzZv3pylsU+cOKGAgAC7toCAAJ04cSJbMVLJBQAAMCOL447Y2Fj5+fnZHbGxsZlCOHbsmPr376/Zs2fL09PTobf7d1RyAQAAkC3R0dEaNGiQXdutqrhbt27VqVOnVKtWLVtbenq61q5dq4kTJyotLU3u7u521wQGBurkyZN2bSdPnlRgYGC2YiTJBQAAMCMHfq3v7ZYm/F3Tpk21c+dOu7auXbuqYsWKGjp0aKYEV5LCwsK0cuVKu23Gli9frrCwsGzFSJILAABgRg5McrPKx8dHVapUsWvLnz+/ChcubGuPjIxUsWLFbMsd+vfvr0aNGmncuHFq1aqV5s6dqy1btmjKlCnZmps1uQAAAHCapKQkJScn2z7Xq1dPc+bM0ZQpU1S9enUtWLBAixYtypQs3w2VXAAAABMynF/IvaXVq1ff8bMktWvXTu3atbuveajkAgAAwHSo5AIAAJhRDliT60xUcgEAAGA6VHIBAADM6B6+ftdMqOQCAADAdKjkAgAAmJGLr8klyQUAADAjF/99vYvfPgAAAMyISi4AAIAZ8eIZAAAAYC5UcnORxJkdnB0CAIcp7+wAAJgNL54h9zjg7AAAOER5eZXs6OwgADjAlaT/OjsEl0WSCwAAYEIGa3IBAAAAc6GSCwAAYEYuXsokyQUAADAjF3/xzMVzfAAAAJgRlVwAAAAz4sUzAAAAwFyo5AIAAJgRa3IBAAAAc6GSCwAAYEauXcilkgsAAADzoZILAABgQoaLr8klyQUAADAjF09yWa4AAAAA06GSCwAAYEZ8GQQAAABgLlRyAQAAzMjFS5kufvsAAAAwIyq5AAAAZsSaXAAAAMBcqOQCAACYkYvvk0uSCwAAYEYunuSyXAEAAACmQyUXAADAhAxePAMAAADMhUoukENs3rxLn3++ULt2Jer06XP6+OPX1axZmLPDAvAADBvYVv8Z+A+7tv2HjqtGk8FOigguwcVLmSS5DnTt2jV5eHg4OwzkEpcvX1WFCiFq27a5+vZ9y9nhAHjAdu8/plb/etP2+caNDCdGA5hfjs/xGzdurH79+mnAgAEqWLCgAgICNHXqVKWmpqpr167y8fFR2bJl9f3339uu2bVrl1q0aCFvb28FBAToxRdf1JkzZ+5rTElas2aNHnvsMVmtVgUFBem1117TjRs37Mbt27evBgwYoEceeUTh4eHq1q2bWrdubTfO9evXVaRIEX3++ecOemrIjRo1qqOBA19U8+ZUbwEzunEjXSdPX7QdZ8//4eyQYHYWi+OOXCDHJ7mSNGPGDD3yyCPatGmT+vXrp969e6tdu3aqV6+etm3bpqeeekovvviiLl++rAsXLqhJkyaqWbOmtmzZoqVLl+rkyZNq3779PY8pScePH1fLli316KOPaseOHfr000/1+eef64033sg0roeHh9avX69JkyYpKipKS5cuVXJysq3Pt99+q8uXL6tDhw6Of3gAgByhbEigft38ifas+0DTJ/RRiaKFnR0SYGoWwzAMZwdxJ40bN1Z6erp++uknSVJ6err8/Pz0/PPPa+bMmZKkEydOKCgoSPHx8VqxYoV++uknLVu2zDbGb7/9phIlSmj//v0qX758tsesW7euhg0bpq+++kp79+6V5f/+BfPJJ59o6NChunjxotzc3NS4cWNdunRJ27Zts7uHypUrq3PnzhoyZIgk6ZlnnlHhwoU1ffr0bD6NA9l+fsidKlRow5pcl1JeXiU7OjsIONBTjavLO7+nDiQmK7BIAQ0b0FZFAwuqdvMhSkm96uzw4EBXkv7rtLlLvb3SYWMfHdrUYWM/KLmiklutWjXbn93d3VW4cGFVrVrV1hYQECBJOnXqlHbs2KFVq1bJ29vbdlSsWFGSlJiYeE9jStLevXsVFhZmS3AlqX79+kpJSdFvv/1ma6tdu3am+KOiomwJ7cmTJ/X999+rW7dut73ftLQ0Xbp0ye5IS0u70yMCAORgP6zeoYXfbdSufUlasfYXRXR5W36++dW2dV1nhwaYVq5IcvPmzWv32WKx2LXdTDwzMjKUkpKiNm3aKCEhwe44ePCgGjZseE9jZkf+/PkztUVGRurXX39VfHy8vvjiC4WEhKhBgwa3HSM2NlZ+fn52R2xsbLbiAADkXBcvXdahw8kqExzo7FBgZm4Wxx25gOl2V6hVq5a++uorBQcHK0+eB3d7oaGh+uqrr2QYhi0BXr9+vXx8fFS8ePE7Xlu4cGFFRERo+vTpio+PV9euXe/YPzo6WoMGDbJrs1qtko7e1z0AAHKG/PmsCikVoBMLf3J2KDCz3JGLOkyuqORmR58+fXTu3Dl17NhRmzdvVmJiopYtW6auXbsqPT39nsf997//rWPHjqlfv37at2+fFi9erJEjR2rQoEFyc7v7Y4yKitKMGTO0d+9ede7c+Y59rVarfH197Y4/k1yYWWrqFe3d+6v27v1VkvTbbye1d++v+v33U06ODMD9ih3WSU88HqqSxR9R3drlNG/qK0pPz9CXizc4OzTAtExXyS1atKjWr1+voUOH6qmnnlJaWppKlSqlp59+OkvJ6O0UK1ZMS5Ys0auvvqrq1aurUKFC6t69u/7zn/9k6fpmzZopKChIlStXVtGiRe85DpjXrl2HFBn5uu1zbOyfW8w991wTjR070FlhAXgAigUV0syJ/VSogLfOnLukDZv3q1HEcJ05xzZicBwjlywrcJQcv7uCWaSkpKhYsWKaPn26nn/++Xschd0VAHNidwXArJy5u0LJ91c5bOykQU9mue+nn36qTz/9VEeOHJH0565TI0aMUIsWLW7ZPy4uLtPSTqvVqqtXs7cTiekquTlNRkaGzpw5o3HjxqlAgQJ65plnnB0SAABwBTnkSxuKFy+usWPHqly5cjIMQzNmzNCzzz6r7du3q3Llyre8xtfXV/v377d9ttzDvZDkOlhSUpJCQkJUvHhxxcXFPdCX4QAAAHK6Nm3a2H1+88039emnn+rnn3++bZJrsVgUGHh/u4+QcTlYcHCwWBECAAAeuhy4Jjc9PV3z589XamqqwsJu/4VHKSkpKlWqlDIyMlSrVi299dZbt02Ib4ckFwAAANmSlpaW6YuqrFbrbXeD2rlzp8LCwnT16lV5e3vr66+/VqVKlW7Zt0KFCpo2bZqqVaumixcv6r333lO9evW0e/fuu27b+lem20IMAAAA+nOfXAcd2f3iqgoVKighIUEbN25U79691blzZ+3Zs+eWfcPCwhQZGakaNWqoUaNGWrhwofz9/TV58uTs3T67K+Qm7K4AmBO7KwBm5czdFYInrnHY2Pt71M1WJffvmjVrpjJlymQ5cW3Xrp3y5Mmj//4368+T5QoAAADIluwktLeSkZGRKUm+nfT0dO3cuVMtW7bM1hwkuQAAACaUQ3YQU3R0tFq0aKGSJUvqjz/+0Jw5c7R69WotW7ZMkhQZGalixYrZljuMHj1adevWVdmyZXXhwgW9++67Onr0qKKiorI1L0kuAAAAHObUqVOKjIxUcnKy/Pz8VK1aNS1btkzNmzeX9Od2q3/9Vtrz58+rR48eOnHihAoWLKjatWtrw4YNt31R7XZYk5ursCYXMCfW5AJm5cw1uaU/cdya3F//3chhYz8o7K4AAAAA02G5AgAAgAndy1fhmgmVXAAAAJgOlVwAAAATcvFCLkkuAACAGbl6kstyBQAAAJgOlVwAAAATsrh4KdPFbx8AAABmRCUXAADAhFiTCwAAAJgMlVwAAAATcqOSCwAAAJgLlVwAAAATcvU1uSS5AAAAJuTqSS7LFQAAAGA6VHIBAABMyOLipVwquQAAADAdKrkAAAAm5Opf60uSm6uUd3YAABzkStJ/nR0CAJgKSW4uEn/qO2eHAMABwoq0UpnIec4OA4ADJM7s4LS5XXxJLmtyAQAAYD5UcgEAAEzI1Su5JLkAAAAm5OpJLssVAAAAYDpUcgEAAEzIjUouAAAAYC5UcgEAAEyINbkAAACAyVDJBQAAMCEquQAAAIDJUMkFAAAwIYuLb69AkgsAAGBCLFcAAAAATIZKLgAAgAlRyQUAAABMhkouAACACVHJBQAAAEyGSi4AAIAJufgOYlRyAQAAYD5UcgEAAEzI1dfkkuQCAACYkMXFf1/v4rcPAAAAM6KSCwAAYEKuvlyBSi4AAABMh0quA3Tp0kUXLlzQokWLnB0KAABwURYXL+WS5AI5xLezVmjr2p1KPnpKea15VbZKsNr3bq2gkkWcHRqAB6hX64oa0r66pi87oDdmb3d2OIBpkeQCOcS+hEQ1ea6+SoeWVHp6uhZMXqL3Bk3WW7OGyOpldXZ4AB6AqiGF1PHJMtqbdMHZocAFuHghlzW5kpSRkaF33nlHZcuWldVqVcmSJfXmm29Kknbu3KkmTZrIy8tLhQsXVs+ePZWSkmK7Nj09XYMGDVKBAgVUuHBhDRkyRIZhZBo/NjZWISEh8vLyUvXq1bVgwYKHeo/I+QaP66UGLR9TsZBAlSxbTFGvd9TZk+d1ZP9vzg4NwAOQz5pH43vX1evTtuhi6jVnhwM8NJ9++qmqVasmX19f+fr6KiwsTN9///0dr5k/f74qVqwoT09PVa1aVUuWLMn2vCS5kqKjozV27FgNHz5ce/bs0Zw5cxQQEKDU1FSFh4erYMGC2rx5s+bPn68VK1aob9++tmvHjRunuLg4TZs2TevWrdO5c+f09ddf240fGxurmTNnatKkSdq9e7cGDhyoF154QWvWrHnYt4pc5ErqFUlSft98To4EwIMQ07mWViX8rg27Tzo7FLgIi8VxR3YUL15cY8eO1datW7VlyxY1adJEzz77rHbv3n3L/hs2bFDHjh3VvXt3bd++XREREYqIiNCuXbuyd//G38uOLuaPP/6Qv7+/Jk6cqKioKLtzU6dO1dChQ3Xs2DHlz59fkrRkyRK1adNGv//+uwICAlS0aFENHDhQr776qiTpxo0bCgkJUe3atbVo0SKlpaWpUKFCWrFihcLCwmxjR0VF6fLly5ozZ06WY40/9d0DuGPkBhkZGZrw2jRdTrmiYZ/0c3Y4cLCwIq1UJnKes8OAA7V+vIT+/UwlRYxarmvXMzQ7+kntTbrAmlwXkDizg9PmfnLJeoeNvapl/fu6vlChQnr33XfVvXv3TOc6dOig1NRUffvtt7a2unXrqkaNGpo0aVKW53D5Nbl79+5VWlqamjZtestz1atXtyW4klS/fn1lZGRo//798vT0VHJysh5//HHb+Tx58qhOnTq2JQuHDh3S5cuX1bx5c7uxr127ppo1a94yprS0NKWlpdm1Wa2syXQls95fqN8OJ2vYxyS4QG4XVMhLw1+opch3Vuva9QxnhwM8ELfLVe6Wr6Snp2v+/PlKTU21K/79VXx8vAYNGmTXFh4enu1dq1w+yfXy8nLo+DfX73733XcqVqyY3bnb/UWIjY1VTEyMXdvIkSMV/u9HHRMkcpRZ47/Sjvg9iv6ojwoVKeDscADcpyrBhfSIn6e+Gf2UrS2Pu5seq+CvF5uVVWi3Bcpw7V+qwkHcHPji2e1ylVGjRt2y/86dOxUWFqarV6/K29tbX3/9tSpVqnTLvidOnFBAQIBdW0BAgE6cOJGtGF0+yS1Xrpy8vLy0cuXKTMsVQkNDFRcXp9TUVFs1d/369XJzc1OFChXk5+enoKAgbdy4UQ0bNpT053KFrVu3qlatWpKkSpUqyWq1KikpSY0aNcpSTNHR0Zn+BWO1WrXt4or7vV3kYIZh6IsPFmrr2p167cM+8i9a2NkhAXgANuw5qRbRS+3a3u7xmBKTL2nKt/tIcJEr3S5XuZ0KFSooISFBFy9e1IIFC9S5c2etWbPmtonug+DySa6np6eGDh2qIUOGyMPDQ/Xr19fp06e1e/duderUSSNHjlTnzp01atQonT59Wv369dOLL75o+xdG//79NXbsWJUrV04VK1bU+++/rwsXLtjG9/Hx0eDBgzVw4EBlZGToiSee0MWLF7V+/Xr5+vqqc+fOmWLKSrkf5jPr/a8Uv2Kb+r/VTZ75rLpw9pIkKZ+3pzysHk6ODsC9Sr16QweOX7Rru5x2QxdSrmVqBx4kR1Zys5ureHh4qGzZspKk2rVra/PmzZowYYImT56cqW9gYKBOnrR/QfPkyZMKDAzMVowun+RK0vDhw5UnTx6NGDFCv//+u4KCgvTSSy8pX758WrZsmfr3769HH31U+fLlU9u2bfX+++/brn3llVeUnJyszp07y83NTd26ddNzzz2nixf/939cY8aMkb+/v2JjY/Xrr7+qQIECqlWrll5//XVn3C5yqB8XbZAkjX35E7v27tH/VIOWjzkjJAAAHCIjIyPTmt6bwsLCtHLlSg0YMMDWtnz58tuu4b0dl99dITdhdwXAnNhdATAvZ+6uEL5sncPGXhb+RJb7RkdHq0WLFipZsqT++OMPzZkzR2+//baWLVum5s2bKzIyUsWKFVNsbKykP7cQa9SokcaOHatWrVpp7ty5euutt7Rt2zZVqVIly/NSyQUAAIDDnDp1SpGRkUpOTpafn5+qVatmS3AlKSkpSW5u//vqhnr16mnOnDn6z3/+o9dff13lypXTokWLspXgSiS5AAAApuTINbnZ8fnnn9/x/OrVqzO1tWvXTu3atbuveUlyAQAATMjVv9bW1e8fAAAAJkQlFwAAwITcLK69twCVXAAAAJgOlVwAAAATyikvnjkLlVwAAACYDpVcAAAAE3L1Sqar3z8AAABMiEouAACACbEmFwAAADAZKrkAAAAmZHHxfXJJcgEAAEyI5QoAAACAyVDJBQAAMCFXr2S6+v0DAADAhKjkAgAAmJCbi794RiUXAAAApkMlFwAAwITYXQEAAAAwGSq5AAAAJuTqlUySXAAAABNiuQIAAABgMlRyAQAATIgtxAAAAACToZILAABgQqzJBQAAAEyGSm4uElaklbNDAOAgiTM7ODsEACbj6pVMktxc5Mkl650dAgAHWNWyvrxKdnR2GAAc4ErSf50dgssiyQUAADAhV99dgSQXAADAhHjxDAAAADAZKrkAAAAmRCUXAAAAMBkquQAAACbk6pVMV79/AAAAmBCVXAAAABNy9S3EqOQCAADAdKjkAgAAmJCr765AkgsAAGBCrv7rele/fwAAAJgQlVwAAAATcvXlClRyAQAAYDpUcgEAAEzIwhZiAAAAgLlQyQUAADAh1uQCAAAAJkMlFwAAwIRcvZLp6vcPAABgSm4Ww2FHdsTGxurRRx+Vj4+PihQpooiICO3fv/+O18TFxclisdgdnp6e2bv/bPUGAAAAsmHNmjXq06ePfv75Zy1fvlzXr1/XU089pdTU1Dte5+vrq+TkZNtx9OjRbM3LcgUAAAATyikvni1dutTuc1xcnIoUKaKtW7eqYcOGt73OYrEoMDDwnuelkgsAAIBsSUtL06VLl+yOtLS0LF178eJFSVKhQoXu2C8lJUWlSpVSiRIl9Oyzz2r37t3ZipEkFwAAwITcLI47YmNj5efnZ3fExsbeNaaMjAwNGDBA9evXV5UqVW7br0KFCpo2bZoWL16sL774QhkZGapXr55+++23LN+/qZYrNG7cWDVq1NAHH3zg7FAAAABMKzo6WoMGDbJrs1qtd72uT58+2rVrl9atW3fHfmFhYQoLC7N9rlevnkJDQzV58mSNGTMmSzGaKslduHCh8ubNK0kKDg7WgAEDNGDAgIcy96hRo7Ro0SIlJCQ8lPlgPs+UDNQzJQMV6PXn/0kcSbmsmYeOadPpC84NDMB9Gzawrf4z8B92bfsPHVeNJoOdFBFcgbsDx7ZarVlKav+qb9+++vbbb7V27VoVL148W9fmzZtXNWvW1KFDh7J8jamS3Lut7bgX165dk4eHxwMfF/i701fTNHX/Uf2WekUWixRerIjeqB2qnusSdCTlirPDA3Cfdu8/plb/etP2+caNDCdGAzw8hmGoX79++vrrr7V69WqFhIRke4z09HTt3LlTLVu2zPI1plqT27hxYw0YMECNGzfW0aNHNXDgQNveajetW7dODRo0kJeXl0qUKKGXX37ZbguL4OBgjRkzRpGRkfL19VXPnj0lSUOHDlX58uWVL18+lS5dWsOHD9f169cl/fmWYExMjHbs2GGbLy4uTpJ04cIFRUVFyd/fX76+vmrSpIl27Njx8B4Kco34U+e18fR5Hb98Vb+lXtXnB5J05Ua6KhXwcXZoAB6AGzfSdfL0Rdtx9vwfzg4JJpdT9snt06ePvvjiC82ZM0c+Pj46ceKETpw4oStX/lfAiYyMVHR0tO3z6NGj9cMPP+jXX3/Vtm3b9MILL+jo0aOKiorK+v1nK8pcYuHChSpevLhGjx5t21tNkhITE/X000+rbdu2+uWXXzRv3jytW7dOffv2tbv+vffeU/Xq1bV9+3YNHz5ckuTj46O4uDjt2bNHEyZM0NSpUzV+/HhJUocOHfTKK6+ocuXKtvk6dOggSWrXrp1OnTql77//Xlu3blWtWrXUtGlTnTt37iE+EeQ2bpKeDHpEnu7u2n2B/xACZlA2JFC/bv5Ee9Z9oOkT+qhE0cLODgkm58gXz7Lj008/1cWLF9W4cWMFBQXZjnnz5tn6JCUl2fI1STp//rx69Oih0NBQtWzZUpcuXdKGDRtUqVKlLM9rMQwje+l4DvbXF89utSY3KipK7u7umjx5sq1t3bp1atSokVJTU+Xp6ang4GDVrFlTX3/99R3neu+99zR37lxt2bJF0q3X5K5bt06tWrXSqVOn7NatlC1bVkOGDLFVibPqySXrs9UfuU+ITz59HFZNHm5uupKerjcSDmjj6fPODgsOtqplfXmV7OjsMOBATzWuLu/8njqQmKzAIgU0bEBbFQ0sqNrNhygl9aqzw4MDXUn6r9PmHrtjucPGfq16c4eN/aCYak3u3ezYsUO//PKLZs+ebWszDEMZGRk6fPiwQkNDJUl16tTJdO28efP04YcfKjExUSkpKbpx44Z8fX3vOl9KSooKF7b/1/qVK1eUmJh42+vS0tIy7TWX3cXdyJ2OpVxR1LoEeedxV8OgR/RatXIasHGnjrImF8jVflj9v2Vqu/YlaXPCIe3f8JHatq6rGfNWOy8wmFpO+TIIZ3GpJDclJUW9evXSyy+/nOlcyZIlbX/Onz+/3bn4+Hh16tRJMTExCg8Pl5+fn+bOnatx48bddb6goCCtXr0607kCBQrc9rrY2FjFxMTYtY0cOVJ6LOf/qwn354Zh6PfLf1Z1DlxKVUU/b7UNLqr3d93+H0UAcp+Lly7r0OFklQm+929zAnBnpk1yPTw8lJ6ebtdWq1Yt7dmzR2XLls3WWBs2bFCpUqU0bNgwW9vfvz/5dvOdOHFCefLkUXBwcJbnu93ec2tWbslW3Mj9LJLyuvo/xQETyp/PqpBSATqx8CdnhwITc3fx/3yY8sUz6c9dEtauXavjx4/rzJkzkv7cIWHDhg3q27evEhISdPDgQS1evDjTi2d/V65cOSUlJWnu3LlKTEzUhx9+mGnNbnBwsA4fPqyEhASdOXNGaWlpatasmcLCwhQREaEffvhBR44c0YYNGzRs2DDbWt5bsVqt8vX1tTtYrmB+URVKqVpBXwV4WRXik09RFUqpRmE/rfj9tLNDA3CfYod10hOPh6pk8UdUt3Y5zZv6itLTM/Tl4g3ODg0wLdMmuaNHj9aRI0dUpkwZ+fv7S5KqVaumNWvW6MCBA2rQoIFq1qypESNGqGjRoncc65lnntHAgQPVt29f1ahRQxs2bLDtunBT27Zt9fTTT+vJJ5+Uv7+//vvf/8pisWjJkiVq2LChunbtqvLly+uf//ynjh49qoCAAIfdO3Kngh55FV29nGY2rKVxj1VWRT9vDdm8W1vPXHR2aADuU7GgQpo5sZ9+WfW+vvikv86d/0ONIobrzDl2T4Hj5JTdFZzFVLsrmB27KwDmxO4KgHk5c3eF8bsct7vCwCo5/z0h067JBQAAcGXZ/dIGsyHJBQAAMKHcsqzAUUy7JhcAAACui0ouAACACbk7OwAno5ILAAAA06GSCwAAYEKsyQUAAABMhkouAACACbn6FmJUcgEAAGA6VHIBAABMyN3F1+SS5AIAAJgQL54BAAAAJkMlFwAAwISo5AIAAAAmQyUXAADAhKjkAgAAACZDJRcAAMCE3PkyCAAAAMBcqOQCAACYkKtXMl39/gEAAGBCVHIBAABMyNV3VyDJBQAAMCFXT3JZrgAAAADToZILAABgQmwhBgAAAJgMlVwAAAATYk0uAAAAYDJUcgEAAEzI1Su5JLm5yKqW9Z0dAgAHuZL0X2eHAACmQpKbi5SJnOfsEAA4QOLMDvx8AyaVOLOD0+amkgsAAADTcXfxJJcXzwAAAGA6VHIBAABMyI0vgwAAAADMhUouAACACbl6JdPV7x8AAAAmRCUXAADAhFx9CzEquQAAADAdKrkAAAAm5Or75JLkAgAAmBBbiAEAAAAOEhsbq0cffVQ+Pj4qUqSIIiIitH///rteN3/+fFWsWFGenp6qWrWqlixZkq15SXIBAABMyM3iuCM71qxZoz59+ujnn3/W8uXLdf36dT311FNKTU297TUbNmxQx44d1b17d23fvl0RERGKiIjQrl27sjyvxTAM165l5yJlIuc5OwQADpA4swM/34BJJc7s4LS515/8zmFj1w9odc/Xnj59WkWKFNGaNWvUsGHDW/bp0KGDUlNT9e2339ra6tatqxo1amjSpElZmodKLgAAgAnllEru3128eFGSVKhQodv2iY+PV7NmzezawsPDFR8fn+V5ePEMAAAA2ZKWlqa0tDS7NqvVKqvVesfrMjIyNGDAANWvX19VqlS5bb8TJ04oICDAri0gIEAnTpzIcoxUcgEAAEzIzYFHbGys/Pz87I7Y2Ni7xtSnTx/t2rVLc+fOfZC3ektUcgEAAJAt0dHRGjRokF3b3aq4ffv21bfffqu1a9eqePHid+wbGBiokydP2rWdPHlSgYGBWY6RSi4AAIAJWSyOO6xWq3x9fe2O2yW5hmGob9+++vrrr/Xjjz8qJCTkrrGHhYVp5cqVdm3Lly9XWFhYlu+fSi4AAIAJ5ZQvPOvTp4/mzJmjxYsXy8fHx7au1s/PT15eXpKkyMhIFStWzLbkoX///mrUqJHGjRunVq1aae7cudqyZYumTJmS5Xmp5AIAAMBhPv30U128eFGNGzdWUFCQ7Zg3739bJyYlJSk5Odn2uV69epozZ46mTJmi6tWra8GCBVq0aNEdX1b7Oyq5AAAAJmTJIaXcrHwlw+rVqzO1tWvXTu3atbvneankAgAAwHSo5AIAAJiQq1cyXf3+AQAAYEIuneSOGjVKNWrUeKhzrl69WhaLRRcuXHio8wIAANdisRgOO3ID0yW5jRs31oABA7LUd/DgwZn2YANygl6tKypxZgf9p1NNZ4cC4AHj5xt4OHLVmtxr167Jw8PjvscxDEPp6eny9vaWt7f3A4gMeHCqhhRSxyfLaG/SBWeHAuAB4+cbD1MO2VzBaXJ0Jbdx48bq27evBgwYoEceeUTh4eHatWuXWrRoIW9vbwUEBOjFF1/UmTNnJEldunTRmjVrNGHCBFksFlksFh05csS2ROD7779X7dq1ZbVatW7dulsuV/jss88UGhoqT09PVaxYUZ988ontXL169TR06FC7/qdPn1bevHm1du1aSdKsWbNUp04d+fj4KDAwUP/617906tQpxz4omEY+ax6N711Xr0/booup15wdDoAHiJ9vPGyO/Maz3CBHJ7mSNGPGDHl4eGj9+vUaO3asmjRpopo1a2rLli1aunSpTp48qfbt20uSJkyYoLCwMPXo0UPJyclKTk5WiRIlbGO99tprGjt2rPbu3atq1aplmmv27NkaMWKE3nzzTe3du1dvvfWWhg8frhkzZkiSOnXqpLlz59rt9zZv3jwVLVpUDRo0kCRdv35dY8aM0Y4dO7Ro0SIdOXJEXbp0ceATgpnEdK6lVQm/a8Puk3fvDCBX4ecbeLhy/HKFcuXK6Z133pEkvfHGG6pZs6beeust2/lp06apRIkSOnDggMqXLy8PDw/ly5dPgYGBmcYaPXq0mjdvftu5Ro4cqXHjxun555+XJIWEhGjPnj2aPHmyOnfurPbt22vAgAFat26dLamdM2eOOnbsKMv//bOmW7dutvFKly6tDz/8UI8++qhSUlJYGoE7av14CVUuVVARo5Y7OxQADxg/33CGXFJwdZgcn+TWrl3b9ucdO3Zo1apVt0wWExMTVb58+TuOVadOndueS01NVWJiorp3764ePXrY2m/cuCE/Pz9Jkr+/v5566inNnj1bDRo00OHDhxUfH6/Jkyfb+m/dulWjRo3Sjh07dP78eWVkZEj68+vqKlWqlKV7TktLU1paml2b1WrN0rXInYIKeWn4C7UU+c5qXbue4exwADxA/HwDzpHjk9z8+fPb/pySkqI2bdro7bffztQvKCgoW2P9XUpKiiRp6tSpevzxx+3Oubu72/7cqVMnvfzyy/roo480Z84cVa1aVVWrVpX0Z6IcHh6u8PBwzZ49W/7+/kpKSlJ4eLiuXcv6+qvY2FjFxMTYtY0cOVJSaJbHQO5SJbiQHvHz1Dejn7K15XF302MV/PVis7IK7bZAGVn4WkQAOQ8/33AWNxcv5eb4JPevatWqpa+++krBwcHKk+fWoXt4eCg9PT3bYwcEBKho0aL69ddf1alTp9v2e/bZZ9WzZ08tXbpUc+bMUWRkpO3cvn37dPbsWY0dO9a2FnjLli3ZjiU6OlqDBg2ya7NarZrVY1G2x0LusGHPSbWIXmrX9naPx5SYfElTvt3HfwCBXIyfb8A5clWS26dPH02dOlUdO3bUkCFDVKhQIR06dEhz587VZ599Jnd3dwUHB2vjxo06cuSIvL29VahQoSyPHxMTo5dffll+fn56+umnlZaWpi1btuj8+fO2pDN//vyKiIjQ8OHDtXfvXnXs2NF2fcmSJeXh4aGPPvpIL730knbt2qUxY8Zk+z6tVivLE1xM6tUbOnD8ol3b5bQbupByLVM7gNyFn284i4sXcnP+7gp/VbRoUa1fv17p6el66qmnVLVqVQ0YMEAFChSQm9uftzJ48GC5u7urUqVKtuUCWRUVFaXPPvtM06dPV9WqVdWoUSPFxcUpJCTErl+nTp20Y8cONWjQQCVLlrS1+/v7Ky4uTvPnz1elSpU0duxYvffeew/m5gEAAJBlFsPg9yS5RZnIec4OAYADJM7swM83YFKJMzs4be49F7512NiVCrR22NgPSq5argAAAICsYbkCAAAAYDJUcgEAAEyISi4AAABgMlRyAQAATMjVvwyCSi4AAABMh0ouAACACbl4IZdKLgAAAMyHSi4AAIAJWSyu/X1fJLkAAAAmxHIFAAAAwGSo5AIAAJiQxcVLuVRyAQAAYDpUcgEAAEzI1SuZrn7/AAAAMCEquQAAACbEmlwAAADAZKjkAgAAmJCLF3JJcgEAAMyI5QoAAACAyVDJBQAAMCEXL+RSyQUAAID5UMkFAAAwITcXL+VSyQUAAIDpUMkFAAAwIRcv5FLJBQAAgPlQyQUAADAhi8VwdghORSUXAAAApmMxDMO103wAAAATOnnlG4eNHeD1jMPGflBYrpCLPLlkvbNDAOAAq1rWV/yp75wdBgAHCCvSymlz87W+AAAAgMlQyQUAADAhFy/kUskFAACA46xdu1Zt2rRR0aJFZbFYtGjRojv2X716tSwWS6bjxIkT2ZqXSi4AAIAJ5ZRKZmpqqqpXr65u3brp+eefz/J1+/fvl6+vr+1zkSJFsjUvSS4AAAAcpkWLFmrRokW2rytSpIgKFChwz/PmlCQfAAAAD5DF4rjjYahRo4aCgoLUvHlzrV+f/R2mqOQCAAAgW9LS0pSWlmbXZrVaZbVa73vsoKAgTZo0SXXq1FFaWpo+++wzNW7cWBs3blStWrWyPA6VXAAAAFOyOOyIjY2Vn5+f3REbG/tAoq5QoYJ69eql2rVrq169epo2bZrq1aun8ePHZ2scKrkAAAAmZHHgJmLR0dEaNGiQXduDqOLezmOPPaZ169Zl6xqSXAAAAGTLg1qakFUJCQkKCgrK1jUkuQAAACZkseSMVakpKSk6dOiQ7fPhw4eVkJCgQoUKqWTJkoqOjtbx48c1c+ZMSdIHH3ygkJAQVa5cWVevXtVnn32mH3/8UT/88EO25iXJBQAAgMNs2bJFTz75pO3zzWUOnTt3VlxcnJKTk5WUlGQ7f+3aNb3yyis6fvy48uXLp2rVqmnFihV2Y2SFxTAM48HcAhztySXZ3z4DQM63qmV9xZ/6ztlhAHCAsCKtnDb3hWvfO2zsAh7Z3/f2YcsZdWwAAADgAWK5AgAAgAk5cneF3IBKLgAAAEyHSi4AAIApuXYllyQXAADAhHLKFmLO4tp3DwAAAFOikgsAAGBKrr1cgUouAAAATIdKLgAAgAmxhRgAAABgMlRyAQAATIhK7kN05MgRWSwWJSQkPMxpb6tLly6KiIi4Y5/GjRtrwIABts/BwcH64IMPbJ8tFosWLVrkkPgAAABwb0xRyW3cuLFq1Khhl3w+KAsXLlTevHlvez45OVkFCxaU9GcSHxISou3bt6tGjRoPPBaY2zMlA/VMyUAFelklSUdSLmvmoWPadPqCcwMDcN++nbVCW9fuVPLRU8przauyVYLVvndrBZUs4uzQYGquvSrVFEmuIxUqVOiO5wMDAx9SJDC701fTNHX/Uf2WekUWixRerIjeqB2qnusSdCTlirPDA3Af9iUkqslz9VU6tKTS09O1YPISvTdost6aNUTW//uHLfCgWSwsV7hnS5cu1RNPPKECBQqocOHCat26tRITE23nN23apJo1a8rT01N16tTR9u3b7a5PT09X9+7dFRISIi8vL1WoUEETJkyw63NzSUFMTIz8/f3l6+url156SdeuXbOdX7NmjSZMmCCLxSKLxaIjR45kaeybbje2lHm5wt/9dblCSEiIJKlmzZqyWCxq3Lix1q5dq7x58+rEiRN21w0YMEANGjS48wOGS4k/dV4bT5/X8ctX9VvqVX1+IElXbqSrUgEfZ4cG4D4NHtdLDVo+pmIhgSpZtpiiXu+osyfP68j+35wdGmBa91XJTU1N1aBBg1StWjWlpKRoxIgReu6555SQkKDLly+rdevWat68ub744gsdPnxY/fv3t7s+IyNDxYsX1/z581W4cGFt2LBBPXv2VFBQkNq3b2/rt3LlSnl6emr16tU6cuSIunbtqsKFC+vNN9/UhAkTdODAAVWpUkWjR4+WJPn7+z+QsbNr06ZNeuyxx7RixQpVrlxZHh4eKlSokEqXLq1Zs2bp1VdflSRdv35ds2fP1jvvvHMvjx0uwE1So6BH5Onurt0X/nB2OAAesCupf/52Jr9vPidHAnNz7UrufSW5bdu2tfs8bdo0+fv7a8+ePdqwYYMyMjL0+eefy9PTU5UrV9Zvv/2m3r172/rnzZtXMTExts8hISGKj4/Xl19+aZeIenh4aNq0acqXL58qV66s0aNH69VXX9WYMWPk5+cnDw8P5cuXz27pgLu7+32P7eaWvUK3v7+/JKlw4cJ2sXTv3l3Tp0+3Jbn/7//9P129etUuDkCSQnzy6eOwavJwc9OV9HSN2LZPR1mqAJhKRkaG5ny4WOWqhqh46SBnhwOY1n0tVzh48KA6duyo0qVLy9fXV8HBwZKkpKQk7d27V9WqVZOnp6etf1hYWKYxPv74Y9WuXVv+/v7y9vbWlClTlJSUZNenevXqypfvf//aDQsLU0pKio4dO3bH+Bw5dnZ06dJFhw4d0s8//yxJiouLU/v27ZU/f/5b9k9LS9OlS5fsjrS0tAcWD3KuYylXFLUuQf/esEOLk07otWrlVMrby9lhAXiAZr2/UL8dTlbvUS86OxSYnMWB/8sN7ivJbdOmjc6dO6epU6dq48aN2rhxoyTZrWm9k7lz52rw4MHq3r27fvjhByUkJKhr165Zvt5ZY2dXkSJF1KZNG02fPl0nT57U999/r27dut22f2xsrPz8/OyO2NjYhxgxnOWGYej3y1d14FKqPtt/VIl/pKptcFFnhwXgAZk1/ivtiN+j1yb8W4WKFHB2OICp3fNyhbNnz2r//v2aOnWq7QWqdevW2c6HhoZq1qxZunr1qq2ae7OSedP69etVr149/fvf/7a1/fXFtZt27NihK1euyMvLyzaOt7e3SpQoIenPJQfp6ekOGTs7PDw8JClTLJIUFRWljh07qnjx4ipTpozq169/23Gio6M1aNAguzar1ao1K7dkOybkbhZJed1yx7+YAdyeYRj64oOF2rp2p177sI/8ixZ2dkhwCa69hdg9333BggVVuHBhTZkyRYcOHdKPP/5ol5j961//ksViUY8ePbRnzx4tWbJE7733nt0Y5cqV05YtW7Rs2TIdOHBAw4cP1+bNmzPNde3aNXXv3t02zsiRI9W3b1/bmtng4GBt3LhRR44c0ZkzZ5SRkfHAxs6OIkWKyMvLS0uXLtXJkyd18eJF27nw8HD5+vrqjTfeUNeuXe84jtVqla+vr91htbLFjNlFVSilagV9FeBlVYhPPkVVKKUahf204vfTzg4NwH2a9f5X2vDDVr004gV55rPqwtlLunD2kq6lPfzfLgKu4p6TXDc3N82dO1dbt25VlSpVNHDgQL377ru2897e3vp//+//aefOnapZs6aGDRumt99+226MXr166fnnn1eHDh30+OOP6+zZs3aV15uaNm2qcuXKqWHDhurQoYOeeeYZjRo1ynZ+8ODBcnd3V6VKleTv76+kpKQHNnZ25MmTRx9++KEmT56sokWL6tlnn7V7Xl26dFF6eroiIyPvaXyYW0GPvIquXk4zG9bSuMcqq6Kft4Zs3q2tZy7e/WIAOdqPizboSspVjX35Ew2IGGU7Nq5McHZoMDFXX5NrMQzDcHYQd9KlSxdduHDBFF+d2717d50+fVrffPPNPV3/5JL1DzgiADnBqpb1FX/qO2eHAcABwoq0ctrcV9PjHTa2p3vmzQRyGr7x7CG4ePGidu7cqTlz5txzggsAAICsI8l9CJ599llt2rRJL730kpo3b+7scAAAgEvIHcsKHCXHJ7lxcXHODuG+rV692tkhAAAAuJQcn+QCAAAg+yxsIQYAAACYC5VcAAAAU3LtNblUcgEAAGA6VHIBAABMyGJx7UouSS4AAIApuXaSy3IFAAAAmA6VXAAAABNiCzEAAADAZKjkAgAAmBJrcgEAAABToZILAABgQhYquQAAAIC5UMkFAAAwIb4MAgAAACbk2r+wd+27BwAAgClRyQUAADAhXjwDAAAATIZKLgAAgClRyQUAAABMhUouAACACbn6FmJUcgEAAOAwa9euVZs2bVS0aFFZLBYtWrTortesXr1atWrVktVqVdmyZRUXF5fteUlyAQAATMnNgUfWpaamqnr16vr444+z1P/w4cNq1aqVnnzySSUkJGjAgAGKiorSsmXLsjUvyxUAAABMKKdsIdaiRQu1aNEiy/0nTZqkkJAQjRs3TpIUGhqqdevWafz48QoPD8/yOFRyAQAAkC1paWm6dOmS3ZGWlvZAxo6Pj1ezZs3s2sLDwxUfH5+tcajk5iKrWtZ3dgh4CNLS0hQbG6vo6GhZrVZnh4OHJKxIK2eHgIeAn288XOUdNnJs7CjFxMTYtY0cOVKjRo2677FPnDihgIAAu7aAgABdunRJV65ckZeXV5bGoZIL5DBpaWmKiYl5YP8iBpBz8PMNs4iOjtbFixftjujoaGeHZYdKLgAAALLFarU67LcRgYGBOnnypF3byZMn5evrm+UqrkQlFwAAADlIWFiYVq5cade2fPlyhYWFZWscklwAAAA4TEpKihISEpSQkCDpzy3CEhISlJSUJOnPpQ+RkZG2/i+99JJ+/fVXDRkyRPv27dMnn3yiL7/8UgMHDszWvCS5QA5jtVo1cuRIXkoBTIifb7iiLVu2qGbNmqpZs6YkadCgQapZs6ZGjBghSUpOTrYlvJIUEhKi7777TsuXL1f16tU1btw4ffbZZ9naPkySLIZhGA/uNgAAAADno5ILAAAA0yHJBQAAgOmQ5AIAAMB0SHKBe9S4cWMNGDDAoXMEBwfrgw8+cOgcALJv9erVslgsunDhQpav6dKliyIiIhwW001xcXEqUKCAw+cBcjqSXMDkRo0apRo1ajg7DMBU6tWrp+TkZPn5+WX5mgkTJiguLs5xQf2fDh066MCBAw6fB8jp+MYzAACyycPDQ4GBgdm6JjsJ8f3w8vLK1rdCAWZFJRe4Dzdu3FDfvn3l5+enRx55RMOHD9fNXfnS0tI0ePBgFStWTPnz59fjjz+u1atX212/bt06NWjQQF5eXipRooRefvllpaam2vX5448/1LFjR+XPn1/FihXTxx9/bHc+KSlJzz77rLy9veXr66v27dvbvg4xLi5OMTEx2rFjhywWiywWy0OpJAG5TePGjdWvXz8NGDBABQsWVEBAgKZOnarU1FR17dpVPj4+Klu2rL7//ntJmZcr3FwisGzZMoWGhsrb21tPP/20kpOTbXNkZbnCrFmzVKdOHfn4+CgwMFD/+te/dOrUKbs+33zzjcqVKydPT089+eSTmjFjxi1jAVwdSS5wH2bMmKE8efJo06ZNmjBhgt5//3199tlnkqS+ffsqPj5ec+fO1S+//KJ27drp6aef1sGDByVJiYmJevrpp9W2bVv98ssvmjdvntatW6e+ffvazfHuu++qevXq2r59u1577TX1799fy5cvlyRlZGTo2Wef1blz57RmzRotX75cv/76qzp06CDpz19bvvLKK6pcubKSk5OVnJxsOwfA3owZM/TII49o06ZN6tevn3r37q127dqpXr162rZtm5566im9+OKLunz58i2vv3z5st577z3NmjVLa9euVVJSkgYPHpytGK5fv64xY8Zox44dWrRokY4cOaIuXbrYzh8+fFj/+Mc/FBERoR07dqhXr14aNmzY/dw2YF4GgHvSqFEjIzQ01MjIyLC1DR061AgNDTWOHj1quLu7G8ePH7e7pmnTpkZ0dLRhGIbRvXt3o2fPnnbnf/rpJ8PNzc24cuWKYRiGUapUKePpp5+269OhQwejRYsWhmEYxg8//GC4u7sbSUlJtvO7d+82JBmbNm0yDMMwRo4caVSvXv3B3DRgUo0aNTKeeOIJ2+cbN24Y+fPnN1588UVbW3JysiHJiI+PN1atWmVIMs6fP28YhmFMnz7dkGQcOnTI1v/jjz82AgICbJ87d+5sPPvss9mKa/PmzYYk448//jAM48//j6lSpYpdn2HDhmWKxc/PL1vzAGZEJRe4D3Xr1pXFYrF9DgsL08GDB7Vz506lp6erfPny8vb2th1r1qxRYmKiJGnHjh2Ki4uzOx8eHq6MjAwdPnzYbsy/CgsL0969eyVJe/fuVYkSJVSiRAnb+UqVKqlAgQK2PgCyplq1arY/u7u7q3DhwqpataqtLSAgQJIyLR+4KV++fCpTpoztc1BQ0G37zp492+5n/6effpIkbd26VW3atFHJkiXl4+OjRo0aSZLtK0/379+vRx991G6sxx57LLu3CrgEXjwDHCAlJUXu7u7aunWr3N3d7c55e3vb+vTq1Usvv/xyputLliz5UOIE8D958+a1+2yxWOzabv6DNiMjI8vXG/+3Rv/vnnnmGT3++OO2z8WKFVNqaqrCw8MVHh6u2bNny9/fX0lJSQoPD9e1a9fu6Z4AV0aSC9yHjRs32n3++eefVa5cOdWsWVPp6ek6deqUGjRocMtra9WqpT179qhs2bJ3nOPnn3/O9Dk0NFSSFBoaqmPHjunYsWO2au6ePXt04cIFVapUSdKfb4Gnp6ff0/0BcAwfHx/5+PjYtW3dulVnz57V2LFjbT/PW7ZssetToUIFLVmyxK5t8+bNjg0WyKVYrgDch6SkJA0aNEj79+/Xf//7X3300Ufq37+/ypcvr06dOikyMlILFy7U4cOHtWnTJsXGxuq7776TJA0dOlQbNmxQ3759lZCQoIMHD2rx4sWZXjxbv3693nnnHR04cEAff/yx5s+fr/79+0uSmjVrpqpVq6pTp07atm2bNm3apMjISDVq1Eh16tSR9OcXShw+fFgJCQk6c+aM0tLSHu5DApAlJUuWlIeHhz766CP9+uuv+uabbzRmzBi7Pr169dK+ffs0dOhQHThwQF9++aVtx5S/Lp0CQJIL3JfIyEhduXJFjz32mPr06aP+/furZ8+ekqTp06crMjJSr7zyiipUqKCIiAht3rzZthShWrVqWrNmjQ4cOKAGDRqoZs2aGjFihIoWLWo3xyuvvKItW7aoZs2aeuONN/T+++8rPDxc0p//UVu8eLEKFiyohg0bqlmzZipdurTmzZtnu75t27Z6+umn9eSTT8rf31///e9/H9LTAZAd/v7+iouL0/z581WpUiWNHTtW7733nl2fkJAQLViwQAsXLlS1atX06aef2nZXsFqtzggbyLEsxu0WDAEAgBzvzTff1KRJk3Ts2DFnhwLkKKzJBQAgF/nkk0/06KOPqnDhwlq/fr3efffdTMucAJDkAgCQqxw8eFBvvPGGzp07p5IlS+qVV15RdHS0s8MCchyWKwAAAMB0ePEMAAAApkOSCwAAANMhyQUAAIDpkOQCAADAdEhyAQAAYDokuQAAADAdklwAAACYDkkuAAAATIckFwAAAKbz/wF4OcoA/4amcwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Define data\n",
-    "categories = {\n",
-    "    'beebot': {'content_gen': 2, 'safety': 4, 'memory': 1, 'code': 2, 'iterate': 3, 'retrieval': 4, 'adaptability': 3}, \n",
-    "    'mini-agi': {'content_gen': 4, 'safety': 1, 'memory': 5, 'code': 4, 'iterate': 5, 'retrieval': 4, 'adaptability': 2},\n",
-    "}\n",
-    "\n",
-    "# Convert dictionary to DataFrame\n",
-    "df = pd.DataFrame(categories)\n",
-    "\n",
-    "# Create heatmap\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.heatmap(df, annot=True, cmap=\"YlGnBu\", fmt=\"d\", linewidths=.5)\n",
-    "plt.title('Heatmap of Categories')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAK4CAYAAACyF/a8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABqx0lEQVR4nO3dd3QUZeP28WsT0kijhtCTQGihSJEiD0VAKQoCShPpIiogVQGV9iC9CvgDFKmioAiIgoDShCAiHZVO6CUUE0wiCSTz/sHLPq6hhJBkNsz3c86ew947u3NldxP22pm5x2YYhiEAAAAAsAgXswMAAAAAQEaiBAEAAACwFEoQAAAAAEuhBAEAAACwFEoQAAAAAEuhBAEAAACwFEoQAAAAAEuhBAEAAACwFEoQAAAAAEuhBAFwSuPHj1dISIhcXV31xBNPmB0nU6tdu7ZKly5tdgykQExMjF599VUFBgbKZrOpd+/eZkdKc/PmzZPNZtPOnTvNjgLAwihBAFLkzgeXOxdPT08VK1ZMPXr00KVLl9J0XevWrdM777yj6tWra+7cuRo1alSaPj7Sz40bNzR58mRVqVJF/v7+Du+TI0eOPPTjbdu2TcOGDVNUVFTah3VCo0aN0rx58/TGG29o4cKFateuXbquLygoyOH3+p+XBg0apOu608I777wjm82mVq1amR3lrkaNGqUVK1aYHQPAXWQxOwCAzOW///2vgoODdePGDW3dulUzZszQ6tWr9dtvvylr1qxpso4NGzbIxcVFn376qdzd3dPkMZH+rly5ogYNGmjXrl16/vnn9fLLL8vHx0eHDx/W4sWL9fHHHyshIeGhHnPbtm0aPny4OnbsqGzZsqVPcCeyYcMGVa1aVUOHDs2wdT7xxBPq169fsvF8+fJlWIbUMAxDX3zxhYKCgvTtt9/qr7/+kq+vr9mxHIwaNUovvfSSmjZtanYUAP9CCQLwUBo2bKhKlSpJkl599VXlzJlTkyZN0jfffKM2bdo80mPHxcUpa9asioyMlJeXV5oVIMMwdOPGDXl5eaXJ4+HuOnbsqD179mjp0qV68cUXHW4bMWKE3nvvPZOSpb9bt24pKSnpkd+zkZGRKlWqVBqlSlmu/Pnz65VXXkmzdWaUTZs26ezZs9qwYYPq16+vZcuWqUOHDmbHApBJsDscgEdSp04dSVJERIR97LPPPlPFihXl5eWlHDlyqHXr1jpz5ozD/e4cp7Jr1y7VrFlTWbNm1bvvviubzaa5c+cqNjbWvlvOvHnzJN3+QDdixAgVKVJEHh4eCgoK0rvvvqv4+HiHxw4KCtLzzz+vtWvXqlKlSvLy8tKsWbO0adMm2Ww2ffnllxo+fLjy588vX19fvfTSS4qOjlZ8fLx69+6tgIAA+fj4qFOnTskee+7cuapTp44CAgLk4eGhUqVKacaMGcmelzsZtm7dqsqVK8vT01MhISFasGBBsmWjoqLUp08fBQUFycPDQwUKFFD79u115coV+zLx8fEaOnSoihYtKg8PDxUsWFDvvPNOsnz3s2vXLj311FPy8vJScHCwZs6cab8tJiZG3t7e6tWrV7L7nT17Vq6urho9evQ9H/uXX37RqlWr1KVLl2QFSJI8PDw0YcIE+/X9+/erY8eOCgkJkaenpwIDA9W5c2ddvXrVvsywYcP09ttvS5KCg4Pt74eTJ0/al0nJe02SPvroI4WEhMjLy0uVK1fWli1bVLt2bdWuXdthucjISHXp0kV58uSRp6enypUrp/nz5zssc/LkSdlsNk2YMEFTpkyxvx937NiR6ufwznszIiJCq1atSvazPkquP/74467rfBgpeb3uOHfunLp06aJ8+fLJw8NDwcHBeuONN5JtBYyPj1ffvn2VO3dueXt7q1mzZrp8+XKKMy1atEilSpXS008/rXr16mnRokV3Xe7UqVNq0qSJvL29FRAQoD59+mjt2rWy2WzatGmTw7K//PKLGjRoIH9/f2XNmlW1atVSeHi4wzLDhg2TzWbTsWPH7Fso/f391alTJ8XFxdmXs9lsio2N1fz58+2vZ8eOHVP88wFIX2wJAvBIjh8/LknKmTOnJGnkyJEaPHiwWrZsqVdffVWXL1/WtGnTVLNmTe3Zs8dhl6arV6+qYcOGat26tV555RXlyZNHlSpV0scff6wdO3Zo9uzZkqSnnnpK0u0tT/Pnz9dLL72kfv366ZdfftHo0aN18OBBLV++3CHX4cOH1aZNG3Xr1k1du3ZV8eLF7beNHj1aXl5eGjhwoI4dO6Zp06bJzc1NLi4u+vPPPzVs2DBt375d8+bNU3BwsIYMGWK/74wZMxQWFqYmTZooS5Ys+vbbb/Xmm28qKSlJ3bt3d8hw7NgxvfTSS+rSpYs6dOigOXPmqGPHjqpYsaLCwsIk3S4fNWrU0MGDB9W5c2dVqFBBV65c0cqVK3X27FnlypVLSUlJatKkibZu3arXXntNJUuW1IEDBzR58mQdOXIkRccc/Pnnn2rUqJFatmypNm3a6Msvv9Qbb7whd3d3de7cWT4+PmrWrJmWLFmiSZMmydXV1X7fL774QoZhqG3btvd8/JUrV0pSio9h+eGHH3TixAl16tRJgYGB+v333/Xxxx/r999/1/bt22Wz2dS8eXMdOXJEX3zxhSZPnqxcuXJJknLnzi0p5e+1GTNmqEePHqpRo4b69OmjkydPqmnTpsqePbsKFChgz/T333+rdu3aOnbsmHr06KHg4GB99dVX6tixo6KiopKVm7lz5+rGjRt67bXX5OHhoUKFCqX6OSxZsqQWLlyoPn36qECBAvbd03Lnzv3IuXLkyHHf1+LmzZsOhfsOb29v+9bTlLxeknT+/HlVrlxZUVFReu2111SiRAmdO3dOS5cuVVxcnMMWqZ49eyp79uwaOnSoTp48qSlTpqhHjx5asmTJffNKtwvU119/bX+e2rRpo06dOunixYsKDAy0LxcbG6s6derowoUL6tWrlwIDA/X5559r48aNyR5zw4YNatiwoSpWrKihQ4fKxcXF/qXHli1bVLlyZYflW7ZsqeDgYI0ePVq7d+/W7NmzFRAQoLFjx0qSFi5cqFdffVWVK1fWa6+9JkkqUqTIA382ABnEAIAUmDt3riHJ+PHHH43Lly8bZ86cMRYvXmzkzJnT8PLyMs6ePWucPHnScHV1NUaOHOlw3wMHDhhZsmRxGK9Vq5YhyZg5c2aydXXo0MHw9vZ2GNu7d68hyXj11Vcdxvv3729IMjZs2GAfK1y4sCHJWLNmjcOyGzduNCQZpUuXNhISEuzjbdq0MWw2m9GwYUOH5atVq2YULlzYYSwuLi5Z3vr16xshISEOY3cy/PTTT/axyMhIw8PDw+jXr599bMiQIYYkY9myZckeNykpyTAMw1i4cKHh4uJibNmyxeH2mTNnGpKM8PDwZPf9pzvP9cSJE+1j8fHxxhNPPGEEBATYn4u1a9cakozvv//e4f5ly5Y1atWqdd91NGvWzJBk/Pnnn/dd7o67PY9ffPFFsuds/PjxhiQjIiLCYdmUvtfi4+ONnDlzGk8++aRx8+ZN+3Lz5s0zJDn8XFOmTDEkGZ999pl9LCEhwahWrZrh4+NjXL9+3TAMw4iIiDAkGX5+fkZkZKTD+h/lOTSM2++b5557zmEsLXLdb32S7noZPXq0fbmUvl7t27c3XFxcjF9//TXZ8nfez3f+ltSrV88+ZhiG0adPH8PV1dWIiop6YO6lS5cakoyjR48ahmEY169fNzw9PY3Jkyc7LDdx4kRDkrFixQr72N9//22UKFHCkGRs3LjRni00NNSoX7++Q6a4uDgjODjYeOaZZ+xjQ4cONSQZnTt3dlhXs2bNjJw5czqMeXt7Gx06dHjgzwMg47E7HICHUq9ePeXOnVsFCxZU69at5ePjo+XLlyt//vxatmyZkpKS1LJlS125csV+CQwMVGhoaLJvXz08PNSpU6cUrXf16tWSpL59+zqM3/kmeNWqVQ7jwcHBql+//l0fq3379nJzc7Nfr1KligzDUOfOnR2Wq1Klis6cOaNbt27Zx/55XFF0dLSuXLmiWrVq6cSJE4qOjna4f6lSpVSjRg379dy5c6t48eI6ceKEfezrr79WuXLl1KxZs2Q573y7/tVXX6lkyZIqUaKEw/N6Z1fEu32r/W9ZsmRRt27d7Nfd3d3VrVs3RUZGateuXZJuv7b58uVz2K3ot99+0/79+x94zMj169clKcUHpv/zebxx44auXLmiqlWrSpJ27979wPun9L22c+dOXb16VV27dlWWLP/b+aFt27bKnj27w2OuXr1agYGBDse2ubm56a233lJMTIw2b97ssPyLL75o3yp1x6M8h/eSFrnup0qVKvrhhx+SXf65vpS8XklJSVqxYoUaN25sP27wn+68n+947bXXHMZq1KihxMREnTp16oGZFy1apEqVKqlo0aKSbr/vnnvuuWS7xK1Zs0b58+dXkyZN7GOenp7q2rWrw3J79+7V0aNH9fLLL+vq1av291NsbKzq1q2rn376SUlJSQ73ef311x2u16hRQ1evXrX/LgBwbuwOB+ChfPTRRypWrJiyZMmiPHnyqHjx4nJxuf19ytGjR2UYhkJDQ+96338WD+n2AdkpPZD81KlTcnFxsX/ouSMwMFDZsmVL9sEpODj4no9VqFAhh+v+/v6SpIIFCyYbT0pKUnR0tH13v/DwcA0dOlQ///yzw/7/0u1SdOex7rYeScqePbv+/PNP+/Xjx4/f9Riafzp69KgOHjx4zw+2kZGR972/dHumL29vb4exYsWKSbp9LEnVqlXl4uKitm3basaMGfZJKhYtWiRPT0+1aNHivo/v5+cnSfrrr79SNIvbtWvXNHz4cC1evDhZ/n+XybtJ6Xvtzvvi3++bLFmyKCgoyGHs1KlTCg0Ntb+f7yhZsqTDY91xt/fYozyH95IWue4nV65cqlev3n2XScnrdfnyZV2/fj3F56T69+/HnVL6z9+Pu4mKitLq1avVo0cPHTt2zD5evXp1ff311zpy5Ij9vX3q1CkVKVIkWQH79/vh6NGjknTfiRWio6MdivP98t/5fQDgvChBAB5K5cqV7/otr3T7m2Cbzabvv//e4XiIO3x8fByup2a2tn9/mLmX+z323bLdb9wwDEm3C0vdunVVokQJTZo0SQULFpS7u7tWr16tyZMnJ/um+EGPl1JJSUkqU6aMJk2adNfb/13eHkX79u01fvx4rVixQm3atNHnn3+u559/3qHc3U2JEiUkSQcOHHDY+nUvLVu21LZt2/T222/riSeekI+Pj5KSktSgQYNkz+PdPOx7LT3c6z2W2ucwvXM9ikd9ve4mtb8fX331leLj4zVx4kRNnDgx2e2LFi3S8OHDHyrLnZ9h/Pjx9zw587/fU2n1+w3AHJQgAGmmSJEiMgxDwcHB9m9i00rhwoWVlJSko0eP2r8Bl6RLly4pKipKhQsXTtP13c23336r+Ph4rVy50uFb4JTsjnYvRYoU0W+//fbAZfbt26e6deumuAT+2/nz5xUbG+uwNejOyUv/uUWkdOnSKl++vBYtWqQCBQro9OnTmjZt2gMfv3Hjxho9erQ+++yzB5agP//8U+vXr9fw4cMdJp248238P93r503pe+3O++LYsWN6+umn7eO3bt3SyZMnVbZsWYdl9+/fr6SkJIetLocOHXJ4rAdJ7XN4v58hLXKlVkpfr9y5c8vPz++B7+dHtWjRIpUuXfqu51KaNWuWPv/8c3sJKly4sP744w8ZhuHwXvrnFiTpfxMW+Pn5PXCr2MNI7e8rgPTHMUEA0kzz5s3l6uqq4cOHJ/s21DCMu06nm1KNGjWSJE2ZMsVh/M7Wkeeeey7Vj51Sd775/efPFh0drblz56b6MV988UXt27cv2ex2/1xPy5Ytde7cOX3yySfJlvn7778VGxv7wPXcunVLs2bNsl9PSEjQrFmzlDt3blWsWNFh2Xbt2mndunWaMmWKcubMqYYNGz7w8atVq6YGDRpo9uzZd52tLiEhQf3795d09+dRSv7aSrKXtqioKIfxlL7XKlWqpJw5c+qTTz5xOLZr0aJFyXa7atSokS5evOgwO9mtW7c0bdo0+fj4qFatWvd5Bhyl5jm8l7TMlRopfb1cXFzUtGlTffvtt9q5c2eyx0mLLSRnzpzRTz/9pJYtW+qll15KdunUqZOOHTumX375RZJUv359nTt3zj57oXT7mKZ//y5VrFhRRYoU0YQJExQTE5NsvQ8zdfc/eXt7J3vvAnAObAkCkGaKFCmiDz74QIMGDbJPQ+zr66uIiAgtX75cr732mv2D8MMqV66cOnTooI8//lhRUVGqVauWduzYofnz56tp06YO3/Knl2effVbu7u5q3LixunXrppiYGH3yyScKCAjQhQsXUvWYb7/9tpYuXaoWLVqoc+fOqlixoq5du6aVK1dq5syZKleunNq1a6cvv/xSr7/+ujZu3Kjq1asrMTFRhw4d0pdffmk/H9L95MuXT2PHjtXJkydVrFgxLVmyRHv37tXHH3+c7Fitl19+We+8846WL1+uN954I9nt97JgwQI9++yzat68uRo3bqy6devK29tbR48e1eLFi3XhwgVNmDBBfn5+qlmzpsaNG6ebN28qf/78WrduncO5pu64U9Dee+89tW7dWm5ubmrcuHGK32vu7u4aNmyYevbsqTp16qhly5Y6efKk5s2bl+xYkddee02zZs1Sx44dtWvXLgUFBWnp0qUKDw/XlClTUjzpw6M8h3eTlrnu5ty5c/rss8+Sjfv4+Khp06YP9XqNGjVK69atU61atezTuV+4cEFfffWVtm7dmqLjxe7n888/l2EYDhMd/FOjRo2UJUsWLVq0SFWqVFG3bt00ffp0tWnTRr169VLevHntx2hJ/9tS4+LiotmzZ6thw4YKCwtTp06dlD9/fp07d04bN26Un5+fvv3224fOW7FiRf3444+aNGmS8uXLp+DgYFWpUiX1TwCAtJOxk9EByKzuTGt7t6lv/+3rr782/vOf/xje3t6Gt7e3UaJECaN79+7G4cOH7cvUqlXLCAsLu+v97zZFtmEYxs2bN43hw4cbwcHBhpubm1GwYEFj0KBBxo0bNxyWu9s0w4bxvymyv/rqqxT9bHemwr18+bJ9bOXKlUbZsmUNT09PIygoyBg7dqwxZ86cZNM43ytDrVq1kk2VfPXqVaNHjx5G/vz5DXd3d6NAgQJGhw4djCtXrtiXSUhIMMaOHWuEhYUZHh4eRvbs2Y2KFSsaw4cPN6Kjo5M/if9aZ1hYmLFz506jWrVqhqenp1G4cGFj+vTp97xPo0aNDEnGtm3b7vvY/xYXF2dMmDDBePLJJw0fHx/D3d3dCA0NNXr27GkcO3bMvtzZs2eNZs2aGdmyZTP8/f2NFi1aGOfPnzckGUOHDnV4zBEjRhj58+c3XFxckj3PKXmvGYZhTJ061ShcuLDh4eFhVK5c2QgPDzcqVqxoNGjQwGG5S5cuGZ06dTJy5cpluLu7G2XKlDHmzp3rsMydqajHjx9/3+ciNc/hvd43aZnr3+vTPabI/uf08A/zep06dcpo3769kTt3bsPDw8MICQkxunfvbsTHxxuGce/ftzu/n3emrb6bMmXKGIUKFbrvz1S7dm0jICDAPiX6iRMnjOeee87w8vIycufObfTr18/4+uuvDUnG9u3bHe67Z88eo3nz5kbOnDkNDw8Po3DhwkbLli2N9evX25e529+Ff/5c/3x/Hjp0yKhZs6bh5eVlSGK6bMCJ2AyDI/gAAI6aNWumAwcOJDt24nGRlJSk3Llzq3nz5nfdzTAtPO7PYWY2ZcoU9enTR2fPnlX+/PnNjgPABBwTBABwcOHCBa1atUrt2rUzO0qauHHjRrLjURYsWKBr166pdu3a6bLOx+05zMz+/vtvh+s3btzQrFmzFBoaSgECLIxjggAAkqSIiAiFh4dr9uzZcnNzczi5ama2fft29enTRy1atFDOnDm1e/duffrppypdunSqz91zL4/rc5iZNW/eXIUKFdITTzyh6OhoffbZZzp06FCyE6sCsBZKEABAkrR582Z16tRJhQoV0vz58xUYGGh2pDQRFBSkggULaurUqbp27Zpy5Mih9u3ba8yYMSk+WW9KPa7PYWZWv359zZ49W4sWLVJiYqJKlSqlxYsXq1WrVmZHA2AijgkCAAAAYCkcEwQAAADAUjL17nBJSUk6f/68fH19OSszAAAAYGGGYeivv/5Svnz55OJy/209mboEnT9/XgULFjQ7BgAAAAAncebMGRUoUOC+y2TqEnTnLNlnzpyRn5+fyWkAAAAAmOX69esqWLCgvSPcT6YuQXd2gfPz86MEAQAAAEjRYTJMjAAAAADAUihBAAAAACyFEgQAAADAUjL1MUEAAABAWktMTNTNmzfNjoF/cXNzk6ura5o8FiUIAAAA0O3zzFy8eFFRUVFmR8E9ZMuWTYGBgY98jlBKEAAAACDZC1BAQICyZs36yB+0kXYMw1BcXJwiIyMlSXnz5n2kx6MEAQAAwPISExPtBShnzpxmx8FdeHl5SZIiIyMVEBDwSLvGMTECAAAALO/OMUBZs2Y1OQnu587r86jHbFGCAAAAgP+PXeCcW1q9PpQgAAAAAJZCCQIAAABgKUyMAAAAANxH0MBVGbauk2Oee+j71K5dW0888YSmTJmS9oH+v6CgIPXu3Vu9e/dOt3VkJLYEAQAAAEh3w4YN0xNPPGF2DEmUIAAAAAAWQwkCAAAAMrlbt26pR48e8vf3V65cuTR48GAZhiFJio+PV//+/ZU/f355e3urSpUq2rRpk8P9t27dqho1asjLy0sFCxbUW2+9pdjYWIdl/vrrL7Vp00be3t7Knz+/PvroI4fbT58+rRdeeEE+Pj7y8/NTy5YtdenSJUnSvHnzNHz4cO3bt082m002m03z5s1Lt+fjQShBAAAAQCY3f/58ZcmSRTt27NCHH36oSZMmafbs2ZKkHj166Oeff9bixYu1f/9+tWjRQg0aNNDRo0clScePH1eDBg304osvav/+/VqyZIm2bt2qHj16OKxj/PjxKleunPbs2aOBAweqV69e+uGHHyRJSUlJeuGFF3Tt2jVt3rxZP/zwg06cOKFWrVpJklq1aqV+/fopLCxMFy5c0IULF+y3mcFm3KmImdD169fl7++v6Oho+fn5mR0HAAAAmdSNGzcUERGh4OBgeXp6OtyWGSZGiIyM1O+//24/j87AgQO1cuVKrVmzRiEhITp9+rTy5ctnv0+9evVUuXJljRo1Sq+++qpcXV01a9Ys++1bt25VrVq1FBsbK09PTwUFBalkyZL6/vvv7cu0bt1a169f1+rVq/XDDz+oYcOGioiIUMGCBSVJf/zxh8LCwrRjxw49+eSTGjZsmFasWKG9e/em8tm5/+v0MN2ALUEAAABAJle1alWHE4lWq1ZNR48e1YEDB5SYmKhixYrJx8fHftm8ebOOHz8uSdq3b5/mzZvncHv9+vWVlJSkiIgIh8f8p2rVqungwYOSpIMHD6pgwYL2AiRJpUqVUrZs2ezLOBOmyAYAAAAeUzExMXJ1ddWuXbvk6urqcJuPj499mW7duumtt95Kdv9ChQplSM6MRgkCAAAAMrlffvnF4fr27dsVGhqq8uXLKzExUZGRkapRo8Zd71uhQgX98ccfKlq06H3XsX379mTXS5YsKUkqWbKkzpw5ozNnzjjsDhcVFaVSpUpJktzd3ZWYmJiqny+tmbo73LBhw+yzQ9y5lChRwsxIAAAAQKZz+vRp9e3bV4cPH9YXX3yhadOmqVevXipWrJjatm2r9u3ba9myZYqIiNCOHTs0evRorVp1+1inAQMGaNu2berRo4f27t2ro0eP6ptvvkk2MUJ4eLjGjRunI0eO6KOPPtJXX32lXr16Sbp9jFGZMmXUtm1b7d69Wzt27FD79u1Vq1YtVapUSdLtE65GRERo7969unLliuLj4zP2SfoH07cEhYWF6ccff7Rfz5LF9EgAAACAXWomK8ho7du3199//63KlSvL1dVVvXr10muvvSZJmjt3rj744AP169dP586dU65cuVS1alU9//zzkqSyZctq8+bNeu+991SjRg0ZhqEiRYokm72tX79+2rlzp4YPHy4/Pz9NmjRJ9evXlyTZbDZ988036tmzp2rWrCkXFxc1aNBA06ZNs9//xRdf1LJly/T0008rKipKc+fOVceOHTPmCfoXU2eHe9gZIuLj4x0a4/Xr11WwYEFmhwMAAMAjud+sY3AeaTU7nOmbXY4ePap8+fLJ09NT1apV0+jRo+95ANbo0aM1fPjwDE4IwFKG+Wfw+qIzdn2As+F3DoAJTD0mqEqVKpo3b57WrFmjGTNmKCIiQjVq1NBff/111+UHDRqk6Oho++XMmTMZnBgAAABAZmfqlqCGDRva/122bFlVqVJFhQsX1pdffqkuXbokW97Dw0MeHh4ZGREAAADAY8apTpaaLVs2FStWTMeOHTM7CgAAAIDHlFOVoJiYGB0/flx58+Y1OwoAAACAx5SpJah///7avHmzTp48qW3btqlZs2ZydXVVmzZtzIwFAAAA4DFm6jFBZ8+eVZs2bXT16lXlzp1b//nPf7R9+3blzp3bzFgAAAAAHmOmlqDFixebuXoAAAAAFuRUxwQBAAAAQHoz/WSpAAAAgFPLyJP6ZsAJfTdt2qSnn35af/75p7Jly5ai+3Ts2FFRUVFasWJFumabN2+eevfuraioqHRdDyUIAAAAsJCnnnpKFy5ckL9/ysvdhx9+KMMw0jHVba1atVKjRo3SfT2UIAAAAMBC3N3dFRgY+FD3eZjC9Ci8vLzk5eWV7uvhmCAAAAAgE6tdu7Z69uyp3r17K3v27MqTJ48++eQTxcbGqlOnTvL19VXRokX1/fffS7q9O5zNZrPvcjZv3jxly5ZNa9euVcmSJeXj46MGDRrowoUL9nV07NhRTZs2vW+OhQsXqlKlSvL19VVgYKBefvllRUZGOiyzcuVKhYaGytPTU08//bTmz59/1yzpjRIEAAAAZHLz589Xrly5tGPHDvXs2VNvvPGGWrRooaeeekq7d+/Ws88+q3bt2ikuLu6u94+Li9OECRO0cOFC/fTTTzp9+rT69+//UBlu3rypESNGaN++fVqxYoVOnjypjh072m+PiIjQSy+9pKZNm2rfvn3q1q2b3nvvvUf5sVON3eEAAACATK5cuXJ6//33JUmDBg3SmDFjlCtXLnXt2lWSNGTIEM2YMUP79++/6/1v3rypmTNnqkiRIpKkHj166L///e9DZejcubP93yEhIZo6daqefPJJxcTEyMfHR7NmzVLx4sU1fvx4SVLx4sX122+/aeTIkQ/98z4qtgQBAAAAmVzZsmXt/3Z1dVXOnDlVpkwZ+1iePHkkKdnuaXdkzZrVXoAkKW/evPdcdtGiRfLx8bFftmzZIknatWuXGjdurEKFCsnX11e1atWSJJ0+fVqSdPjwYT355JMOj1W5cuWH/VHTBFuCAAAAgEzOzc3N4brNZnMYs9lskqSkpKQU3/9es8E1adJEVapUsV/Pnz+/YmNjVb9+fdWvX1+LFi1S7ty5dfr0adWvX18JCQmp+pnSEyUIAAAAQIr5+vrK19fXYWzXrl26evWqxowZo4IFC0qSdu7c6bBM8eLFtXr1aoexX3/9NX3D3gO7wwEAAAB4JIUKFZK7u7umTZumEydOaOXKlRoxYoTDMt26ddOhQ4c0YMAAHTlyRF9++aXmzZsn6X9bqjIKW4IAAACA+xkWbXYCp5c7d27NmzdP7777rqZOnaoKFSpowoQJatKkiX2Z4OBgLV26VP369dOHH36oatWq6b333tMbb7whDw+PDM1rMzLi1K/p5Pr16/L391d0dLT8/PzMjgPgcTAsY04G97/18R8rLI7fOTiJGzduKCIiQsHBwfL09DQ7jmWMHDlSM2fO1JkzZ1K0/P1ep4fpBmwJAgAAAJAh/u///k9PPvmkcubMqfDwcI0fP149evTI8ByUIAAAAAAZ4ujRo/rggw907do1FSpUSP369dOgQYMyPAclCAAAAECGmDx5siZPnmx2DGaHAwAAAGAtlCAAAADg/7vXyUThHNLq9WF3OAAAAFieu7u7XFxcdP78eeXOnVvu7u4Zfu4a3JthGEpISNDly5fl4uIid3f3R3o8ShAAAAAsz8XFRcHBwbpw4YLOnz9vdhzcQ9asWVWoUCG5uDzaDm2UIAAAAEC3twYVKlRIt27dUmJiotlx8C+urq7KkiVLmmyhowQBAAAA/5/NZpObm5vc3NzMjoJ0xMQIAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACyFEgQAAADAUihBAAAAACzFaUrQmDFjZLPZ1Lt3b7OjAAAAAHiMOUUJ+vXXXzVr1iyVLVvW7CgAAAAAHnOml6CYmBi1bdtWn3zyibJnz252HAAAAACPOdNLUPfu3fXcc8+pXr16D1w2Pj5e169fd7gAAAAAwMPIYubKFy9erN27d+vXX39N0fKjR4/W8OHD0zmVkxnmn4Hris64dZkgaOCqDFvXyTHPZdi6APx//L2ExWXk/3OSdNLz5QxdH793SEumbQk6c+aMevXqpUWLFsnT0zNF9xk0aJCio6PtlzNnzqRzSgAAAACPG9O2BO3atUuRkZGqUKGCfSwxMVE//fSTpk+frvj4eLm6ujrcx8PDQx4eHhkdFQAAAMBjxLQSVLduXR04cMBhrFOnTipRooQGDBiQrAABAAAAQFowrQT5+vqqdOnSDmPe3t7KmTNnsnEAAAAASCumzw4HAAAAABnJ1Nnh/m3Tpk1mRwAAAADwmGNLEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRTS9CMGTNUtmxZ+fn5yc/PT9WqVdP3339vZiQAAAAAjzlTS1CBAgU0ZswY7dq1Szt37lSdOnX0wgsv6PfffzczFgAAAIDHWJbU3vHWrVvatGmTjh8/rpdfflm+vr46f/68/Pz85OPjk6LHaNy4scP1kSNHasaMGdq+fbvCwsKSLR8fH6/4+Hj79evXr6c2PgAAAACLSlUJOnXqlBo0aKDTp08rPj5ezzzzjHx9fTV27FjFx8dr5syZD/2YiYmJ+uqrrxQbG6tq1arddZnRo0dr+PDhqYkMAABSKGjgqgxb10nPDFsV4LQy8ndOkk6OeS5D1+eMUrU7XK9evVSpUiX9+eef8vLyso83a9ZM69evf6jHOnDggHx8fOTh4aHXX39dy5cvV6lSpe667KBBgxQdHW2/nDlzJjXxAQAAAFhYqrYEbdmyRdu2bZO7u7vDeFBQkM6dO/dQj1W8eHHt3btX0dHRWrp0qTp06KDNmzfftQh5eHjIw8MjNZEBAAAAQFIqS1BSUpISExOTjZ89e1a+vr4P9Vju7u4qWrSoJKlixYr69ddf9eGHH2rWrFmpiQYAAAAA95Wq3eGeffZZTZkyxX7dZrMpJiZGQ4cOVaNGjR4pUFJSksPkBwAAAACQllK1JWjixImqX7++SpUqpRs3bujll1/W0aNHlStXLn3xxRcpfpxBgwapYcOGKlSokP766y99/vnn2rRpk9auXZuaWAAAAADwQKkqQQUKFNC+ffu0ZMkS7du3TzExMerSpYvatm3rMFHCg0RGRqp9+/a6cOGC/P39VbZsWa1du1bPPPNMamIBAAAAwAOl+jxBWbJkUdu2bdW2bdtUr/zTTz9N9X0BAAAAIDVSdUzQ6NGjNWfOnGTjc+bM0dixYx85FAAAAACkl1SVoFmzZqlEiRLJxsPCwlJ1olQAAAAAyCipKkEXL15U3rx5k43nzp1bFy5ceORQAAAAAJBeUlWCChYsqPDw8GTj4eHhypcv3yOHAgAAAID0kqqJEbp27arevXvr5s2bqlOnjiRp/fr1euedd9SvX780DQgAAAAAaSlVJejtt9/W1atX9eabbyohIUGS5OnpqQEDBmjQoEFpGhAAAAAA0lKqSpDNZtPYsWM1ePBgHTx4UF5eXgoNDZWHh0da5wMAAACANJXq8wRJko+Pj5588sm0ygIAAAAA6S5VJSg2NlZjxozR+vXrFRkZqaSkJIfbT5w4kSbhAAAAACCtpaoEvfrqq9q8ebPatWunvHnzymazpXUuAAAAAEgXqSpB33//vVatWqXq1aundR4AAAAASFepOk9Q9uzZlSNHjrTOAgAAAADpLlUlaMSIERoyZIji4uLSOg8AAAAApKtU7Q43ceJEHT9+XHny5FFQUJDc3Nwcbt+9e3eahAMAAACAtJaqEtS0adM0jgEAAAAAGSNVJWjo0KFpnQMAAAAAMkSqjgkCAAAAgMwqVVuCEhMTNXnyZH355Zc6ffq0EhISHG6/du1amoQDAAAAgLSWqi1Bw4cP16RJk9SqVStFR0erb9++at68uVxcXDRs2LA0jggAAAAAaSdVJWjRokX65JNP1K9fP2XJkkVt2rTR7NmzNWTIEG3fvj2tMwIAAABAmklVCbp48aLKlCkjSfLx8VF0dLQk6fnnn9eqVavSLh0AAAAApLFUlaACBQrowoULkqQiRYpo3bp1kqRff/1VHh4eaZcOAAAAANJYqkpQs2bNtH79eklSz549NXjwYIWGhqp9+/bq3LlzmgYEAAAAgLSUqtnhxowZY/93q1atVKhQIf38888KDQ1V48aN0ywcAAAAAKS1VJWgf6tWrZqqVauWFg8FAAAAAOkq1SXo/Pnz2rp1qyIjI5WUlORw21tvvfXIwQAAAAAgPaSqBM2bN0/dunWTu7u7cubMKZvNZr/NZrNRggAAAAA4rVSVoMGDB2vIkCEaNGiQXFxSNbcCAAAAAJgiVQ0mLi5OrVu3pgABAAAAyHRS1WK6dOmir776Kq2zAAAAAEC6S9XucKNHj9bzzz+vNWvWqEyZMnJzc3O4fdKkSWkSDgAAAADSWqpL0Nq1a1W8eHFJSjYxAgAAAAA4q1SVoIkTJ2rOnDnq2LFjGscBAAAAgPSVqmOCPDw8VL169bTOAgAAAADpLlUlqFevXpo2bVpaZwEAAACAdJeq3eF27NihDRs26LvvvlNYWFiyiRGWLVuWJuEAAAAAIK2lqgRly5ZNzZs3T+ssAAAAAJDuHroE3bp1S08//bSeffZZBQYGpkcmAAAAAEg3D31MUJYsWfT6668rPj4+PfIAAAAAQLpK1cQIlStX1p49e9I6CwAAAACku1QdE/Tmm2+qX79+Onv2rCpWrChvb2+H28uWLZsm4QAAAAAgraWqBLVu3VqS9NZbb9nHbDabDMOQzWZTYmJi2qQDAAAAgDSWqhIUERGR1jkAAAAAIEOkqgQVLlw4rXMAAAAAQIZIVQmSpOPHj2vKlCk6ePCgJKlUqVLq1auXihQpkmbhAAAAACCtpWp2uLVr16pUqVLasWOHypYtq7Jly+qXX35RWFiYfvjhh7TOCAAAAABpJlVbggYOHKg+ffpozJgxycYHDBigZ555Jk3CAQAAAEBaS9WWoIMHD6pLly7Jxjt37qw//vjjkUMBAAAAQHpJVQnKnTu39u7dm2x87969CggIeNRMAAAAAJBuUrU7XNeuXfXaa6/pxIkTeuqppyRJ4eHhGjt2rPr27ZumAQEAAAAgLaWqBA0ePFi+vr6aOHGiBg0aJEnKly+fhg0b5nACVQAAAABwNineHW7lypW6efOmJMlms6lPnz46e/asoqOjFR0drbNnz6pXr16y2WzpFhYAAAAAHlWKS1CzZs0UFRUlSXJ1dVVkZKQkydfXV76+vukSDgAAAADSWopLUO7cubV9+3ZJkmEYbPEBAAAAkCml+Jig119/XS+88IJsNptsNpsCAwPvuWxiYmKahAMAAACAtJbiEjRs2DC1bt1ax44dU5MmTTR37lxly5YtHaMBAAAAQNp7qNnhSpQooeLFi6tDhw568cUX5ePjk165AAAAACBdPPTJUg3D0KJFi3ThwoX0yAMAAAAA6eqhS5CLi4tCQ0N19erV9MgDAAAAAOnqoUuQJI0ZM0Zvv/22fvvtt7TOAwAAAADp6qGOCbqjffv2iouLU7ly5eTu7i4vLy+H269du5Ym4QAAAAAgraWqBE2ZMiWNYwAAAABAxkhVCerQoUNa5wAAAACADJGqY4Ik6fjx43r//ffVpk0bRUZGSpK+//57/f7772kWDgAAAADSWqpK0ObNm1WmTBn98ssvWrZsmWJiYiRJ+/bt09ChQ9M0IAAAAACkpVSVoIEDB+qDDz7QDz/8IHd3d/t4nTp1tH379jQLBwAAAABpLVUl6MCBA2rWrFmy8YCAAF25cuWRQwEAAABAeklVCcqWLZsuXLiQbHzPnj3Knz//I4cCAAAAgPSSqhLUunVrDRgwQBcvXpTNZlNSUpLCw8PVv39/tW/fPq0zAgAAAECaSVUJGjVqlEqWLKlChQopJiZGpUqVUs2aNfXUU0/p/fffT+uMAAAAAJBmHuo8QUlJSRo/frxWrlyphIQEtWvXTi+++KJiYmJUvnx5hYaGpldOAAAAAEgTD1WCRo4cqWHDhqlevXry8vLS559/LsMwNGfOnPTKBwAAAABp6qF2h1uwYIH+7//+T2vXrtWKFSv07bffatGiRUpKSkqvfAAAAACQph6qBJ0+fVqNGjWyX69Xr55sNpvOnz+f5sEAAAAAID08VAm6deuWPD09Hcbc3Nx08+bNNA0FAAAAAOnloY4JMgxDHTt2lIeHh33sxo0bev311+Xt7W0fW7ZsWdolBAAAAIA09FAlqEOHDsnGXnnllTQLAwAAAADp7aFK0Ny5c9MrBwAAAABkiFSdLBUAAAAAMitKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLoQQBAAAAsBRKEAAAAABLMbUEjR49Wk8++aR8fX0VEBCgpk2b6vDhw2ZGAgAAAPCYM7UEbd68Wd27d9f27dv1ww8/6ObNm3r22WcVGxtrZiwAAAAAj7EsZq58zZo1DtfnzZungIAA7dq1SzVr1ky2fHx8vOLj4+3Xr1+/nu4ZAQAAADxeTC1B/xYdHS1JypEjx11vHz16tIYPH56RkZIJGrgqQ9d30jNDVwcAaYa/lwDgpIb5Z+C6ojNuXQ/BaSZGSEpKUu/evVW9enWVLl36rssMGjRI0dHR9suZM2cyOCUAAACAzM5ptgR1795dv/32m7Zu3XrPZTw8POTh4ZGBqQAAAAA8bpyiBPXo0UPfffedfvrpJxUoUMDsOAAAAAAeY6aWIMMw1LNnTy1fvlybNm1ScHCwmXEAAAAAWICpJah79+76/PPP9c0338jX11cXL16UJPn7+8vLy8vMaAAAAAAeU6ZOjDBjxgxFR0erdu3ayps3r/2yZMkSM2MBAAAAeIyZvjscAAAAAGQkp5kiGwAAAAAyAiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYCiUIAAAAgKVQggAAAABYiqkl6KefflLjxo2VL18+2Ww2rVixwsw4AAAAACzA1BIUGxurcuXK6aOPPjIzBgAAAAALyWLmyhs2bKiGDRuaGQEAAACAxZhagh5WfHy84uPj7devX79uYhoAAAAAmVGmKkGjR4/W8OHDzY4BPNgw/wxeX3TGri8DBQ1claHrO+mZoasDAAAmyFSzww0aNEjR0dH2y5kzZ8yOBAAAACCTyVRbgjw8POTh4WF2DAAAAACZWKbaEgQAAAAAj8rULUExMTE6duyY/XpERIT27t2rHDlyqFChQiYmAwAAAPC4MrUE7dy5U08//bT9et++fSVJHTp00Lx580xKBQAAAOBxZmoJql27tgzDMDMCAAAAAIvhmCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGApTlGCPvroIwUFBcnT01NVqlTRjh07zI4EAAAA4DFleglasmSJ+vbtq6FDh2r37t0qV66c6tevr8jISLOjAQAAAHgMZTE7wKRJk9S1a1d16tRJkjRz5kytWrVKc+bM0cCBAx2WjY+PV3x8vP16dHS0JOn69esZljcpPi7D1iVJ121GBq4s455HM2Tka5ehr5v0WL92j/XvnMRrl4b4e5l2+HuZOT3Wv3MSr10aelz/Xt7pBIbx4J/PZqRkqXSSkJCgrFmzaunSpWratKl9vEOHDoqKitI333zjsPywYcM0fPjwDE4JAAAAILM4c+aMChQocN9lTN0SdOXKFSUmJipPnjwO43ny5NGhQ4eSLT9o0CD17dvXfj0pKUnXrl1Tzpw5ZbPZ0j1vRrt+/boKFiyoM2fOyM/Pz+w4SCFet8yL1y7z4rXLnHjdMi9eu8zrcX7tDMPQX3/9pXz58j1wWdN3h3sYHh4e8vDwcBjLli2bOWEykJ+f32P3JrUCXrfMi9cu8+K1y5x43TIvXrvM63F97fz9/VO0nKkTI+TKlUuurq66dOmSw/ilS5cUGBhoUioAAAAAjzNTS5C7u7sqVqyo9evX28eSkpK0fv16VatWzcRkAAAAAB5Xpu8O17dvX3Xo0EGVKlVS5cqVNWXKFMXGxtpni7MyDw8PDR06NNkugHBuvG6ZF69d5sVrlznxumVevHaZF6/dbabODnfH9OnTNX78eF28eFFPPPGEpk6dqipVqpgdCwAAAMBjyClKEAAAAABkFFOPCQIAAACAjEYJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJckIJCQk6e/asTp8+7XCB85o7d67i4uLMjgEAAJBMrVq1tGDBAv39999mR3EalCAncvToUdWoUUNeXl4qXLiwgoODFRwcrKCgIAUHB5sdD/cxcOBABQYGqkuXLtq2bZvZcfAQKLCZ15YtW/TKK6+oWrVqOnfunCRp4cKF2rp1q8nJkBJRUVGaPXu2Bg0apGvXrkmSdu/ebX8t4RyuX7+e4gucV/ny5dW/f38FBgaqa9eu2r59u9mRTEcJciIdO3aUi4uLvvvuO+3atUu7d+/W7t27tWfPHu3evdvseLiPc+fOaf78+bpy5Ypq166tEiVKaOzYsbp48aLZ0fAAFNjM6euvv1b9+vXl5eWlPXv2KD4+XpIUHR2tUaNGmZwOD7J//34VK1ZMY8eO1YQJExQVFSVJWrZsmQYNGmRuODjIli2bsmfPft/LnWXgvKZMmaLz589r7ty5ioyMVM2aNVWqVClNmDBBly5dMjueKThZqhPx9vbWrl27VKJECbOj4BFcunRJn332mebPn69Dhw6pQYMG6tKlixo3biwXF753cDa3bt3St99+q3nz5un7779XSEiIOnXqpA4dOigwMNDseLiH8uXLq0+fPmrfvr18fX21b98+hYSEaM+ePWrYsCFfQDi5evXqqUKFCho3bpzD67dt2za9/PLLOnnypNkR8f9t3rw5xcvWqlUrHZMgLUVGRurjjz/WyJEjlZiYqEaNGumtt95SnTp1zI6WYbKYHQD/U6pUKV25csXsGHhEefLk0X/+8x8dOXJER44c0YEDB9ShQwdlz55dc+fOVe3atc2OiH/IkiWLmjVrpmbNmjkU2MGDB1Ngndjhw4dVs2bNZOP+/v72rQpwXr/++qtmzZqVbDx//vwUWCdDsXn87NixQ3PnztXixYsVEBCgjh076ty5c3r++ef15ptvasKECWZHzBD8r+5Exo4dq3feeUebNm3S1atX2dc2k7l06ZImTJigsLAw1a5dW9evX9d3332niIgInTt3Ti1btlSHDh3Mjon7uFNgq1WrJhcXF3uBLVKkiDZt2mR2PPxDYGCgjh07lmx869atCgkJMSERHoaHh8dd/187cuSIcufObUIiPIy4uDgdOnRI+/fvd7jAeUVGRmrixIkqXbq0atSoocuXL+uLL77QyZMnNXz4cM2ePVvr1q3TzJkzzY6acQw4DZvNZthsNsPFxcXhcmcMzuv555833NzcjLCwMGPy5MnG1atXky1z6dIlw2azmZAOD3Lx4kVj/PjxRqlSpQxPT0+jdevWxg8//GAYhmHExMQY77zzjlGoUCGTU+KfRo0aZZQqVcrYvn274evra2zZssX47LPPjNy5cxtTp041Ox4eoEuXLkbTpk2NhIQEw8fHxzhx4oRx6tQpo3z58kavXr3Mjod7iIyMNJ577rlkn1PuXOC83NzcjBIlShjjxo0zIiMj77pMdHS0Ubt27QxOZh6OCXIiD9rvlk3SzqtLly569dVXVa1atXsuYxiGTp8+rcKFC2dgMjxI48aNtXbtWhUrVkyvvvqq2rdvrxw5cjgsExkZqcDAQCUlJZmUEv9mGIZGjRql0aNH22f38/DwUP/+/TVixAiT0+FBoqOj9dJLL2nnzp3666+/lC9fPl28eFHVqlXT6tWr5e3tbXZE3EXbtm116tQpTZkyRbVr19by5ct16dIlffDBB5o4caKee+45syPiHrZs2aIaNWqYHcOpUIKANLBgwQK1atVKHh4eDuMJCQlavHix2rdvb1IyPAgFNnNLSEjQsWPHFBMTo1KlSsnHx8fsSHgI4eHh2rdvn2JiYlShQgXVq1fP7Ei4j7x58+qbb75R5cqV5efnp507d6pYsWJauXKlxo0bx/T0TqxOnTpatmyZsmXL5jB+/fp1NW3aVBs2bDAnmIkoQU5my5YtmjVrlk6cOKGvvvpK+fPn18KFCxUcHKz//Oc/ZsfDPbi6uurChQsKCAhwGL969aoCAgKUmJhoUjLcz82bN9WgQQPNnDlToaGhZscBLIMvjjInPz8/7d+/X0FBQSpcuLA+//xzVa9eXREREQoLC+Oca07sXp9TIiMjlT9/ft28edOkZOZhdjgn8vXXX6tdu3Zq27atdu/eney8F6tXrzY5Ie7FMAzZbLZk42fPnpW/v78JiZASbm5uHMybiTRv3jzFyy5btiwdk+BRderUSQ0aNEj2geyvv/5Sp06dKEFOqnjx4jp8+LCCgoJUrlw5zZo1S0FBQZo5c6by5s1rdjzcxZ3/4wzD0B9//OEw+2JiYqLWrFmj/PnzmxXPVJQgJ/LBBx9o5syZat++vRYvXmwfr169uj744AMTk+FeypcvL5vNJpvNprp16ypLlv/9SiUmJioiIkINGjQwMSEe5JVXXtGnn36qMWPGmB0FD/DPLxQMw9Dy5cvl7++vSpUqSZJ27dqlqKiohypLMAdfHGVOvXr10oULFyRJQ4cOVYMGDbRo0SK5u7tr3rx55obDXT3xxBP2zyl3OweQl5eXpk2bZkIy81GCnAjnvch8mjZtKknau3ev6tev73A8gru7u4KCgvTiiy+alA4pcevWLc2ZM0c//vijKlasmOyA7EmTJpmUDP82d+5c+78HDBigli1baubMmXJ1dZV0+4uHN998U35+fmZFxAPwxVHm9sorr9j/XbFiRZ06dUqHDh1SoUKFlCtXLhOT4V4iIiJkGIZCQkK0Y8cOhyno3d3dFRAQYP8bajWUICdy57wXQUFBDuOc98J5DR06VJIUFBSkVq1aydPT0+REeFi//fabKlSoIOn2OUr+6W7fVMM5zJkzR1u3bnX4z9vV1VV9+/bVU089pfHjx5uYDvfCF0eZ29atWx2OT86aNav97yec050JfZjdNDlKkBPp2rWrevXqpTlz5shms+n8+fP6+eef1b9/fw0ePNjseLiPDh06KCoqSp999pmOHz+ut99+Wzly5NDu3buVJ08ey+5vmxls3LjR7AhIhVu3bunQoUMqXry4w/ihQ4f4z96J8cVR5lanTh3lz59fbdq00SuvvKJSpUqZHQn3sXLlSjVs2FBubm5auXLlfZdt0qRJBqVyHswO50Q470XmtX//ftWrV0/+/v46efKkDh8+rJCQEL3//vs6ffq0FixYYHZEpMDZs2clSQUKFDA5CR6kb9++WrBggd59911VrlxZkvTLL79ozJgxateuHbsxAungypUrWrx4sb744gv9/PPPKlu2rNq2bas2bdrwd9MJubi46OLFiwoICJCLi8s9l7PZbJacxZYS5IQ470XmU7duXVWsWFHjxo2Tr6+v9u3bp5CQEG3btk0vv/yyTp48aXZE3ENSUpL9RH8xMTGSJF9fX/Xr10/vvffeff/jgHmSkpI0YcIEffjhh/YDtfPmzatevXqpX79+lt3HPbNITEzU5MmT9eWXX+r06dNKSEhwuP3atWsmJUNKRURE6PPPP9cXX3yhQ4cOqWbNmpY81wwyL0oQkAb8/f21e/duFSlSxKEEnTp1SsWLF9eNGzfMjoh7GDRokD799FMNHz5c1atXl3R7v/dhw4apa9euGjlypMkJ8SDXr1+XJCZEyESGDBmi2bNnq1+/fnr//ff13nvv6eTJk1qxYoWGDBmit956y+yISIHExER9//33Gjx4sPbv32/JrQnIvDgmyIk0a9bsrgdi22w2eXp6qmjRonr55ZeT7QMP83l4eNg/iP3TkSNHHGZigfOZP3++Zs+e7bA/dNmyZZU/f369+eablCAnd/nyZR0+fFiSVKJECWaoyiQWLVqkTz75RM8995yGDRumNm3aqEiRIipbtqy2b99OCXJy4eHhWrRokZYuXaobN27ohRde0OjRo82OhX+ZOnVqipe14u8cW4KcSMeOHbVixQply5ZNFStWlCTt3r1bUVFRevbZZ7Vv3z6dPHlS69evt39jDefw6quv6urVq/ryyy+VI0cO7d+/X66urmratKlq1qypKVOmmB0R9+Dp6an9+/erWLFiDuOHDx/WE088ob///tukZLif2NhY9ezZUwsWLLBPhODq6qr27dtr2rRpypo1q8kJcT/e3t46ePCgChUqpLx582rVqlWqUKGCTpw4ofLlyys6OtrsiLiLQYMGafHixTp//ryeeeYZtW3bVi+88AK/b04qODg4RcvZbDadOHEindM4H3Z2dyKBgYF6+eWXdeLECX399df6+uuvdfz4cb3yyisqUqSIDh48qA4dOmjAgAFmR8W/3DmeJCAgQH///bdq1aqlokWLytfXly0JTq5cuXKaPn16svHp06erXLlyJiRCSvTt21ebN2/Wt99+q6ioKEVFRembb77R5s2b1a9fP7Pj4QEKFChgP5arSJEiWrdunSTp119/lYeHh5nRcB8//fST3n77bZ07d07fffed2rRpQwFyYhERESm6WLEASWwJciq5c+dWeHh4sm+kjxw5oqeeekpXrlzRgQMHVKNGDU6e6qTCw8O1b98+xcTEqEKFCqpXr57ZkfAAmzdv1nPPPadChQqpWrVqkqSff/5ZZ86c0erVq1WjRg2TE+JucuXKpaVLl6p27doO4xs3blTLli11+fJlc4IhRQYOHCg/Pz+9++67WrJkiV555RUFBQXp9OnT6tOnj8aMGWN2RACPOY4JciJ3znvx7xJ06NAh+8GGnp6enMDRSeTIkUNHjhxRrly51LlzZ3344YeqXr06uypmMrVq1dKRI0f00Ucf6dChQ5Kk5s2b680331S+fPlMTod7iYuLU548eZKNBwQE2E8xAOf1z5LTqlUrFS5cWNu2bVNoaKgaN25sYjI8yMKFCzVz5kxFRETo559/VuHChTVlyhQFBwfrhRdeMDse/qFv374aMWKEvL291bdv3/sua8XTClCCnEi7du3UpUsXvfvuu3ryyScl3d41YNSoUWrfvr2k299ah4WFmRkT/19CQoKuX7+uXLlyaf78+Ro7dqx8fX3NjoVUyJcvH7stZjLVqlXT0KFDtWDBAvsJN//++28NHz7cvkUPzunmzZvq1q2bBg8ebD9moWrVqqpatarJyfAgM2bM0JAhQ9S7d2+NHDnS/gVttmzZNGXKFEqQk9mzZ49u3rxp//e9WPXLdXaHcyKJiYkaM2aMpk+frkuXLkmS8uTJo549e2rAgAFydXXV6dOn5eLiwknJnMAzzzyjS5cuqWLFipo/f75atWolLy+vuy47Z86cDE6Hh3Hjxg3t379fkZGR9oPs77DiWbQzgwMHDqhBgwaKj4+3H7u1b98+eXh4aN26dXxZ5OT8/f21d+/eFB+4DedQqlQpjRo1Sk2bNnU4HcRvv/2m2rVr68qVK2ZHBFKMEuSk7nfei/DwcFWqVImDR0126dIlTZ48WcePH9eyZctUv379e74my5cvz+B0SKk1a9aoffv2d/3P26pn0c4s4uLitGjRIvtujCVLllTbtm3v+WUEnEeHDh30xBNPqE+fPmZHwUPw8vLSoUOHVLhwYYcSdPToUZUtW5bZNDOJM2fOSJIKFixochJzsTuck7rfSf8aNmyovXv3KiQkJAMT4d/y5Mlj3689ODhYCxcuVM6cOU1OhYfVs2dPtWjRQkOGDLnrMSZwTqNHj1aePHnUtWtXh/E5c+bo8uXLzKLp5EJDQ/Xf//5X4eHhqlixory9vR1ut+I5SzKD4OBg7d27V4ULF3YYX7NmjUqWLGlSKqTErVu3NHz4cE2dOlUxMTGSJB8fH/Xs2VNDhw6Vm5ubyQkzHiUoE2LjnfOJiIgwOwJS6dKlS+rbty8FKJOZNWuWPv/882TjYWFhat26NSXIyX366afKli2bdu3apV27djncZrPZKEFOqm/fvurevbtu3LghwzC0Y8cOffHFFxo9erRmz55tdjzcR8+ePbVs2TKNGzfOYSbUYcOG6erVq5oxY4bJCTMeu8NlQv/cBA3nERsbq82bN+v06dNKSEhwuI3/0J1X586dVb16dXXp0sXsKHgInp6eOnjwYLJjSk6cOKFSpUrpxo0bJiUDHm+LFi3SsGHDdPz4cUm3J5YZPnw4f0OdnL+/vxYvXqyGDRs6jK9evVpt2rSx5AmK2RIEpIE9e/aoUaNGiouLU2xsrHLkyKErV64oa9asCggIoAQ5senTp6tFixbasmWLypQpk2yXAF4751SwYEGFh4cnK0Hh4eFMbZ6JJCQkKCIiQkWKFFGWLHwkyQzatm2rtm3bKi4uzn6ScDg/Dw8PBQUFJRsPDg6Wu7t7xgdyAvzFAdJAnz591LhxY82cOVP+/v7avn273Nzc9Morr6hXr15mx8N9fPHFF1q3bp08PT21adMmh6lC2S3HeXXt2lW9e/fWzZs3VadOHUnS+vXr9c4776hfv34mp8ODxMXFqWfPnpo/f76k2ycFDwkJUc+ePZU/f34NHDjQ5IR4kKxZsypr1qxmx0AK9ejRQyNGjNDcuXPtkzjFx8dr5MiR6tGjh8npzEEJyoSsOp+7M9u7d69mzZolFxcXubq6Kj4+XiEhIRo3bpw6dOig5s2bmx0R9/Dee+9p+PDhGjhwoFxcXMyOgxR6++23dfXqVb355pv23U89PT01YMAADRo0yOR0eJBBgwZp37592rRpkxo0aGAfr1evnoYNG0YJciIVKlTQ+vXrlT17dpUvX/6+n0F2796dgcnwIP/+7PHjjz+qQIECDqcVSEhIUN26dc2IZzpKUCbEYVzOx83Nzf4BOiAgQKdPn1bJkiXl7+9vn4oSzikhIUGtWrWiAGUyNptNY8eO1eDBg3Xw4EF5eXkpNDSUUwdkEitWrNCSJUtUtWpVhw/VYWFh9mNN4BxeeOEF++9V06ZNzQ2Dh+Lv7+9w/cUXX3S4zhTZcBp16tTRsmXLlC1bNofx69evq2nTptqwYYMk6a+//jIhHe6nfPny+vXXXxUaGqpatWppyJAhunLlihYuXKjSpUubHQ/30aFDBy1ZskTvvvuu2VGQCj4+PnryySfNjoGHdPny5bseSxIbG8veDk5m6NChkm6f0P3pp59W2bJlk31OgXOaO3eu2RGcGiXIiWzatCnZrGLS7bPZb9myxYRESKlRo0bZy+nIkSPVvn17vfHGGypWrBjThjq5xMREjRs3TmvXrlXZsmWTTYwwadIkk5IBj69KlSpp1apV6tmzp6T/7eY9e/Zs+/S9cC6urq569tlndfDgQUoQHguUICewf/9++7//+OMPXbx40X49MTFRa9asUf78+c2IhhQKCwuz76YYEBCgmTNnavny5SpVqpSeeOIJc8Phvg4cOKDy5ctLkn777TeH2/hGGkgfo0aNUsOGDfXHH3/o1q1b+vDDD/XHH39o27Zt2rx5s9nxcA+lS5fWiRMnks3KiMxh6dKl+vLLL+96Kg8rHs/FeYKcgIuLi/3D1t1eDi8vL02bNk2dO3fO6GhIoWeffVbNmzfX66+/rqioKJUoUUJubm66cuWKJk2apDfeeMPsiADgVI4fP64xY8Zo3759iomJUYUKFTRgwACVKVPG7Gi4hzVr1mjQoEEaMWKEKlasKG9vb4fb/fz8TEqGB5k6daree+89dezYUR9//LE6deqk48eP69dff1X37t01cuRIsyNmOEqQEzh16pQMw1BISIh27Nih3Llz229zd3dXQECAXF1dTUyIB8mVK5c2b96ssLAwzZ49W9OmTdOePXv09ddfa8iQITp48KDZEfEAx44d0/Hjx1WzZk15eXnJMAy2BAHAP/xzApl//n288/cyMTHRjFhIgRIlSmjo0KFq06aNfH19tW/fPoWEhGjIkCG6du2apk+fbnbEDMfucE6gcOHCkqSkpCSTkyC14uLi5OvrK0lat26dmjdvLhcXF1WtWlWnTp0yOR3u5+rVq2rZsqU2btwom82mo0ePKiQkRF26dFH27Nk1ceJEsyMCjx1XV1dduHAh2eQIV69eVUBAAB+mndTGjRvNjoBUOn36tJ566ilJt/cwunMcc7t27VS1alVKEMx39OhRbdy4UZGRkclK0ZAhQ0xKhQcpWrSoVqxYoWbNmmnt2rXq06ePJCkyMpLdA5xcnz595ObmZp/W/I5WrVqpb9++lCAgHdxrJ5T4+HjLnr0+MwgODlbBggWTbSU3DIPTQTi5wMBAXbt2TYULF1ahQoW0fft2lStXThEREZY99QolyIl88skneuONN5QrVy4FBgYmO3M9Jch5DRkyRC+//LL69OmjunXr2mc3Wrdunf2gezindevWae3atSpQoIDDeGhoKFvxgDQ2depUSbf/T5s9e7Z8fHzstyUmJuqnn35SiRIlzIqHBwgODr7rFrxr164pODiYLXhOrE6dOlq5cqXKly+vTp06qU+fPlq6dKl27txp2RO6c0yQEylcuLDefPNNDRgwwOwoSIWLFy/qwoULKleunH2/6R07dsjPz4//1J2Yr6+vdu/erdDQUIf9pHfu3Kn69evr6tWrZkcEHht3ZhU7deqUChQo4HC8q7u7u4KCgvTf//5XVapUMSsi7sPFxUWXLl1yOHZZuv16lipVSrGxsSYlw4MkJSUpKSlJWbLc3v6xePFibdu2TaGhoerWrZslt8BSgpyIn5+f9u7dq5CQELOjAJbRqFEjVaxYUSNGjJCvr6/279+vwoULq3Xr1kpKStLSpUvNjgg8dp5++mktX76c881kEn379pUkffjhh+ratauyZs1qvy0xMVG//PKLXF1dFR4eblZEPMDp06fvuytjoUKFTEpmHkqQE+nSpYuefPJJvf7662ZHASzjt99+U926dVWhQgVt2LBBTZo00e+//65r164pPDxcRYoUMTsi8Fjo27evRowYIW9vb/Xp0+e+sy9ykmLn8vTTT0uSNm/erGrVqjlsNbizBa9///4KDQ01KyIegMlIkuOYICdStGhRDR48WNu3b1eZMmWSnbn+rbfeMikZ8PgqXbq0Dh8+rI8++ki+vr6KiYlR8+bN1b17d+XNm9fseMBjY8+ePbp586Ykae/evfdcjqnpnc+dWeE6deqkDz/8kAl/MqF7nfYhJiZGnp6eJiQyH1uCnMj9zsBss9l04sSJDEwDWMeNGze0f//+u87K2KRJE5NSAYBz4rxqmQe7Mt4bW4KcSEREhNkRAMtZs2aN2rVrp2vXriWbJpST/wHA/1y7dk0tWrTgvGqZyJ49eyTd3hJ04MCBZLsylitXTv379zcrnqnYEuSEEhISFBERoSJFithn8QCQPkJDQ/Xss89qyJAhypMnj9lxAMBptW/fXpGRkZo9e7ZKlixpn01z7dq16tu3r37//XezI+Ie2JUxOUqQE4mLi1PPnj01f/58SdKRI0cUEhKinj17Kn/+/Bo4cKDJCYHHj5+fn/bs2cMECADwAIGBgVq7dq3KlSvncEqBEydOqGzZsoqJiTE7IlLgzoltCxYsaHISc7mYHQD/M2jQIO3bt0+bNm1yOEitXr16WrJkiYnJgMfXSy+9pE2bNpkdAwCcXmxsrMMxJXdcu3ZNHh4eJiRCSt26dUuDBw+Wv7+/goKCFBQUJH9/f73//vv2CUushn2tnMiKFSu0ZMkSVa1a1eEAw7CwMB0/ftzEZMDja/r06WrRooW2bNnCrIwAcB81atTQggULNGLECEm3j5tMSkrSuHHj7NNowzn17NlTy5Yt07hx41StWjVJ0s8//6xhw4bp6tWrmjFjhskJMx67wzmRrFmz6rffflNISIjDZuZ9+/apZs2aio6ONjsi8Nj59NNP9frrr8vT01M5c+Z0+AKCWRkB4H9+//131alTh/OqZUL+/v5avHixGjZs6DC+evVqtWnTxpKfMdkdzolUqlRJq1atsl+/82Fs9uzZ9tYOIG299957Gj58uKKjo3Xy5ElFRETYLxQgALjt5s2beuutt/Ttt9/qP//5j1544QXFxsaqefPmHFeZCXh4eCgoKCjZeHBwsMOMcVbC7nBOZNSoUWrYsKH++OMP3bp1Sx9++KH++OMPbdu2TZs3bzY7HvBYSkhIUKtWreTiwndCAHAvbm5u2r9/v7Jnz6733nvP7Dh4SD169NCIESM0d+5c+/Fb8fHxGjlypHr06GFyOnOwO5yTOX78uMaMGaN9+/YpJiZGFSpU0IABA1SmTBmzowGPpT59+ih37tx69913zY4CAE6tT58+8vDw0JgxY8yOgofUrFkzrV+/Xh4eHipXrpwkad++fUpISFDdunUdll22bJkZETMcW4KcTJEiRfTJJ5+YHQOwjMTERI0bN05r165V2bJlk02MMGnSJJOSAYBzuXXrlubMmaMff/xRFStWlLe3t8Pt/L10XtmyZdOLL77oMGb1KbLZEuREXF1ddeHCBQUEBDiMX716VQEBAZy5HkgH95vRyGazacOGDRmYBgCcF38v8TihBDkRFxcXXbx4MVkJOn/+vIoUKaK///7bpGQAAADA44Pd4ZzA1KlTJd3+FmX27Nny8fGx35aYmKiffvpJJUqUMCseAAAAMrmlS5fqyy+/1OnTp5WQkOBw2+7du01KZR5KkBOYPHmyJMkwDM2cOVOurq7229zd3RUUFKSZM2eaFQ8AAACZ2NSpU/Xee++pY8eO+uabb9SpUycdP35cv/76q7p37252PFOwO5wTefrpp7Vs2TJlz57d7CgAAAB4TJQoUUJDhw5VmzZt5Ovrq3379ikkJERDhgzRtWvXNH36dLMjZjhKEAAAAPAYy5o1qw4ePKjChQsrICBAP/zwg8qVK6ejR4+qatWqunr1qtkRMxy7wzmRxMREzZs3T+vXr1dkZKSSkpIcbmfWFQAAADyswMBAXbt2TYULF1ahQoW0fft2lStXThEREbLq9hBKkBPp1auX5s2bp+eee06lS5eWzWYzOxIAAAAyuTp16mjlypUqX768OnXqpD59+mjp0qXauXOnmjdvbnY8U7A7nBPJlSuXFixYoEaNGpkdBQAAAI+JpKQkJSUlKUuW29s/Fi9erG3btik0NFTdunWTu7u7yQkzHiXIieTLl0+bNm1SsWLFzI4CAAAAPLYoQU5k4sSJOnHihKZPn86ucAAAAEi1/fv3p3jZsmXLpmMS50QJciLNmjXTxo0blSNHDoWFhcnNzc3h9mXLlpmUDAAAAJmJi4uLbDabDMN44JfriYmJGZTKeTAxghPJli2bmjVrZnYMAAAAZHIRERH2f+/Zs0f9+/fX22+/rWrVqkmSfv75Z02cOFHjxo0zK6Kp2BIEAAAAPMYqV66sYcOGJZt8a/Xq1Ro8eLB27dplUjLzsCXICV2+fFmHDx+WJBUvXly5c+c2OREAAAAyqwMHDig4ODjZeHBwsP744w8TEpnPxewA+J/Y2Fh17txZefPmVc2aNVWzZk3ly5dPXbp0UVxcnNnxAAAAkAmVLFlSo0ePVkJCgn0sISFBo0ePVsmSJU1MZh52h3Mi3bp1048//qjp06erevXqkqStW7fqrbfe0jPPPKMZM2aYnBAAAACZzY4dO9S4cWMZhmGfCe7O7HHfffedKleubGY8U1CCnEiuXLm0dOlS1a5d22F848aNatmypS5fvmxOMAAAAGRqsbGxWrRokQ4dOiTp9tahl19+Wd7e3iYnMwfHBDmRuLg45cmTJ9l4QEAAu8MBAAAg1by9vfWf//xHhQoVsu8Wt379eklSkyZNzIxmCrYEOZG6desqZ86cWrBggTw9PSVJf//9tzp06KBr167pxx9/NDkhAAAAMpsTJ06oWbNmOnDgwF3PHWTF8wRRgpzIgQMH1KBBA8XHx6tcuXKSpH379snDw0Pr1q1TWFiYyQkBAACQ2TRu3Fiurq6aPXu2goOD9csvv+jatWvq16+fJkyYoBo1apgdMcNRgpxMXFxcsv0127ZtKy8vL5OTAQAAIDPKlSuXNmzYoLJly8rf3187duxQ8eLFtWHDBvXr10979uwxO2KG45ggJzJ69GjlyZNHXbt2dRifM2eOLl++rAEDBpiUDAAAAJlVYmKifH19Jd0uROfPn1fx4sVVuHBh+7kprYbzBDmRWbNmqUSJEsnGw8LCNHPmTBMSAQAAILMrXbq09u3bJ0mqUqWKxo0bp/DwcP33v/9VSEiIyenMwZYgJ3Lx4kXlzZs32Xju3Ll14cIFExIBAAAgs3v//fcVGxsrSfrvf/+r559/XjVq1FDOnDm1ZMkSk9OZgxLkRAoWLKjw8HAFBwc7jIeHhytfvnwmpQIAAEBmVr9+ffu/ixYtqkOHDunatWvKnj27wyxxVkIJciJdu3ZV7969dfPmTdWpU0fS7fnb33nnHfXr18/kdAAAAHhc5MiRw+wIpmJ2OCdiGIYGDhyoqVOn2k9i5enpqQEDBmjIkCEmpwMAAAAeD5QgJxQTE6ODBw/Ky8tLoaGh8vDwMDsSAAAA8NigBAEAAACwFKbIBgAAAGAplCAAAAAAlkIJAgAAAGAplCAAAAAAlkIJAgAAAGAplCAAQJq7ePGievbsqZCQEHl4eKhgwYJq3Lix1q9fn6L7z5s3T9myZUvfkAAAy8pidgAAwOPl5MmTql69urJly6bx48erTJkyunnzptauXavu3bvr0KFDZkd8aDdv3pSbm5vZMQAAaYQtQQCANPXmm2/KZrNpx44devHFF1WsWDGFhYWpb9++2r59uyRp0qRJKlOmjLy9vVWwYEG9+eabiomJkSRt2rRJnTp1UnR0tGw2m2w2m4YNGyZJio+PV//+/ZU/f355e3urSpUq2rRpk8P6P/nkExUsWFBZs2ZVs2bNNGnSpGRblWbMmKEiRYrI3d1dxYsX18KFCx1ut9lsmjFjhpo0aSJvb2998MEHKlq0qCZMmOCw3N69e2Wz2XTs2LG0ewIBAOmOEgQASDPXrl3TmjVr1L17d3l7eye7/U4ZcXFx0dSpU/X7779r/vz52rBhg9555x1J0lNPPaUpU6bIz89PFy5c0IULF9S/f39JUo8ePfTzzz9r8eLF2r9/v1q0aKEGDRro6NGjkqTw8HC9/vrr6tWrl/bu3atnnnlGI0eOdMiwfPly9erVS/369dNvv/2mbt26qVOnTtq4caPDcsOGDVOzZs104MABdenSRZ07d9bcuXMdlpk7d65q1qypokWLpsnzBwDIGDbDMAyzQwAAHg87duxQlSpVtGzZMjVr1izF91u6dKlef/11XblyRdLtY4J69+6tqKgo+zKnT59WSEiITp8+rXz58tnH69Wrp8qVK2vUqFFq3bq1YmJi9N1339lvf+WVV/Tdd9/ZH6t69eoKCwvTxx9/bF+mZcuWio2N1apVqyTd3hLUu3dvTZ482b7M+fPnVahQIW3btk2VK1fWzZs3lS9fPk2YMEEdOnR4qOcJAGAutgQBANJMSr9X+/HHH1W3bl3lz59fvr6+ateuna5evaq4uLh73ufAgQNKTExUsWLF5OPjY79s3rxZx48flyQdPnxYlStXdrjfv68fPHhQ1atXdxirXr26Dh486DBWqVIlh+v58uXTc889pzlz5kiSvv32W8XHx6tFixYp+pkBAM6DiREAAGkmNDRUNpvtvpMfnDx5Us8//7zeeOMNjRw5Ujly5NDWrVvVpUsXJSQkKGvWrHe9X0xMjFxdXbVr1y65uro63Obj45OmP4eku+7O9+qrr6pdu3aaPHmy5s6dq1atWt0zLwDAebElCACQZnLkyKH69evro48+UmxsbLLbo6KitGvXLiUlJWnixImqWrWqihUrpvPnzzss5+7ursTERIex8uXLKzExUZGRkSpatKjDJTAwUJJUvHhx/frrrw73+/f1kiVLKjw83GEsPDxcpUqVeuDP16hRI3l7e2vGjBlas2aNOnfu/MD7AACcDyUIAJCmPvroIyUmJqpy5cr6+uuvdfToUR08eFBTp05VtWrVVLRoUd28eVPTpk3TiRMntHDhQs2cOdPhMYKCghQTE6P169frypUriouLU7FixdS2bVu1b99ey5YtU0REhHbs2KHRo0fbj+Xp2bOnVq9erUmTJuno0aOaNWuWvv/+e9lsNvtjv/3225o3b55mzJiho0ePatKkSVq2bJl98oX7cXV1VceOHTVo0CCFhoaqWrVqafvkAQAyhgEAQBo7f/680b17d6Nw4cKGu7u7kT9/fqNJkybGxo0bDcMwjEmTJhl58+Y1vLy8jPr16xsLFiwwJBl//vmn/TFef/11I2fOnIYkY+jQoYZhGEZCQoIxZMgQIygoyHBzczPy5s1rNGvWzNi/f7/9fh9//LGRP39+w8vLy2jatKnxwQcfGIGBgQ75/u///s8ICQkx3NzcjGLFihkLFixwuF2SsXz58rv+bMePHzckGePGjXvk5wkAYA5mhwMAPNa6du2qQ4cOacuWLWnyeFu2bFHdunV15swZ5cmTJ00eEwCQsZgYAQDwWJkwYYKeeeYZeXt76/vvv9f8+fP1f//3f4/8uPHx8bp8+bKGDRumFi1aUIAAIBPjmCAAwGNlx44deuaZZ1SmTBnNnDlTU6dO1auvvvrIj/vFF1+ocOHCioqK0rhx49IgKQDALOwOBwAAAMBS2BIEAAAAwFIoQQAAAAAshRIEAAAAwFIoQQAAAAAshRIEAAAAwFIoQQAAAAAshRIEAAAAwFIoQQAAAAAs5f8BKHTd7tog3W8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "categories = {\n",
-    "    'beebot': {'content_gen': 2, 'safety': 4, 'memory': 1, 'code': 2, 'iterate': 3, 'retrieval': 4, 'adaptability': 3}, \n",
-    "    'mini-agi': {'content_gen': 4, 'safety': 1, 'memory': 5, 'code': 4, 'iterate': 5, 'retrieval': 4, 'adaptability': 2}\n",
-    "    # include other agents here...\n",
-    "}\n",
-    "\n",
-    "# Convert dictionary to DataFrame\n",
-    "df = pd.DataFrame(categories)\n",
-    "\n",
-    "# Create a grouped bar chart\n",
-    "df.plot(kind='bar', figsize=(10, 7))\n",
-    "\n",
-    "plt.title('Performance by Category for Each Agent')\n",
-    "plt.xlabel('Category')\n",
-    "plt.ylabel('Performance')\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/benchmark/notebooks/combined_data.ipynb b/benchmark/notebooks/combined_data.ipynb
deleted file mode 100644
index e8379886df..0000000000
--- a/benchmark/notebooks/combined_data.ipynb
+++ /dev/null
@@ -1,685 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 25471 entries, 0 to 25470\n",
-      "Data columns (total 19 columns):\n",
-      " #   Column                Non-Null Count  Dtype              \n",
-      "---  ------                --------------  -----              \n",
-      " 0   createdAt             25471 non-null  datetime64[ns]     \n",
-      " 1   agent                 25471 non-null  object             \n",
-      " 2   costUSD               25471 non-null  float64            \n",
-      " 3   job_id                25471 non-null  object             \n",
-      " 4   challenge             25471 non-null  object             \n",
-      " 5   benchmark_start_time  25471 non-null  datetime64[ns, UTC]\n",
-      " 6   prompt                21354 non-null  object             \n",
-      " 7   response              21013 non-null  object             \n",
-      " 8   model                 22607 non-null  object             \n",
-      " 9   request               21354 non-null  object             \n",
-      " 10  attempted             18384 non-null  object             \n",
-      " 11  categories            18384 non-null  object             \n",
-      " 12  task                  18384 non-null  object             \n",
-      " 13  success               18205 non-null  float64            \n",
-      " 14  difficulty            18384 non-null  object             \n",
-      " 15  success_%             18205 non-null  float64            \n",
-      " 16  run_time              18384 non-null  object             \n",
-      " 17  is_regression         18384 non-null  object             \n",
-      " 18  report_time           18384 non-null  datetime64[ns, UTC]\n",
-      "dtypes: datetime64[ns, UTC](2), datetime64[ns](1), float64(3), object(13)\n",
-      "memory usage: 3.7+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_pickle('../reports/df.pkl')\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['benchmark_start_time', 'response', 'model'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 21013 entries, 0 to 25470\n",
-      "Data columns (total 19 columns):\n",
-      " #   Column                Non-Null Count  Dtype              \n",
-      "---  ------                --------------  -----              \n",
-      " 0   createdAt             21013 non-null  datetime64[ns]     \n",
-      " 1   agent                 21013 non-null  object             \n",
-      " 2   costUSD               21013 non-null  float64            \n",
-      " 3   job_id                21013 non-null  object             \n",
-      " 4   challenge             21013 non-null  object             \n",
-      " 5   benchmark_start_time  21013 non-null  datetime64[ns, UTC]\n",
-      " 6   prompt                21013 non-null  object             \n",
-      " 7   response              21013 non-null  object             \n",
-      " 8   model                 21013 non-null  object             \n",
-      " 9   request               21013 non-null  object             \n",
-      " 10  attempted             15008 non-null  object             \n",
-      " 11  categories            15008 non-null  object             \n",
-      " 12  task                  15008 non-null  object             \n",
-      " 13  success               14835 non-null  float64            \n",
-      " 14  difficulty            15008 non-null  object             \n",
-      " 15  success_%             14835 non-null  float64            \n",
-      " 16  run_time              15008 non-null  object             \n",
-      " 17  is_regression         15008 non-null  object             \n",
-      " 18  report_time           15008 non-null  datetime64[ns, UTC]\n",
-      "dtypes: datetime64[ns, UTC](2), datetime64[ns](1), float64(3), object(13)\n",
-      "memory usage: 3.2+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>createdAt</th>\n",
-       "      <th>agent</th>\n",
-       "      <th>costUSD</th>\n",
-       "      <th>job_id</th>\n",
-       "      <th>challenge</th>\n",
-       "      <th>benchmark_start_time</th>\n",
-       "      <th>prompt</th>\n",
-       "      <th>response</th>\n",
-       "      <th>model</th>\n",
-       "      <th>request</th>\n",
-       "      <th>attempted</th>\n",
-       "      <th>categories</th>\n",
-       "      <th>task</th>\n",
-       "      <th>success</th>\n",
-       "      <th>difficulty</th>\n",
-       "      <th>success_%</th>\n",
-       "      <th>run_time</th>\n",
-       "      <th>is_regression</th>\n",
-       "      <th>report_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>24278</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.082680</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>You are Auto-GPT, a multi-purpose AI assistant...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...</td>\n",
-       "      <td>gpt-4-0314</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are Auto-G...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24281</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.000470</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>Write a concise summary of the following text:...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"Tesla's reve...</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>[{'role': 'user', 'content': 'Write a concise ...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24282</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.000878</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>Write a concise summary of the following text;...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"a descriptio...</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>[{'role': 'user', 'content': 'Write a concise ...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24283</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.004808</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>Write a concise summary of the following text;...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"The text pro...</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>[{'role': 'user', 'content': 'Write a concise ...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24284</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.004452</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>Write a concise summary of the following text;...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"Tesla's reve...</td>\n",
-       "      <td>gpt-3.5-turbo</td>\n",
-       "      <td>[{'role': 'user', 'content': 'Write a concise ...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24285</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.063810</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>You are Auto-GPT, a multi-purpose AI assistant...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...</td>\n",
-       "      <td>gpt-4-0314</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are Auto-G...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24286</th>\n",
-       "      <td>2023-08-05 08:24:11.008</td>\n",
-       "      <td>auto-gpt</td>\n",
-       "      <td>0.027960</td>\n",
-       "      <td>1</td>\n",
-       "      <td>TestRevenueRetrieval</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "      <td>You are Auto-GPT, a multi-purpose AI assistant...</td>\n",
-       "      <td>{\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...</td>\n",
-       "      <td>gpt-4-0314</td>\n",
-       "      <td>[{'role': 'system', 'content': 'You are Auto-G...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>retrieval</td>\n",
-       "      <td>Write tesla's exact revenue in 2022 into a .tx...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>No successful tests</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60.232 seconds</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2023-08-05 08:12:00+00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    createdAt     agent   costUSD job_id  \\\n",
-       "24278 2023-08-05 08:24:11.008  auto-gpt  0.082680      1   \n",
-       "24281 2023-08-05 08:24:11.008  auto-gpt  0.000470      1   \n",
-       "24282 2023-08-05 08:24:11.008  auto-gpt  0.000878      1   \n",
-       "24283 2023-08-05 08:24:11.008  auto-gpt  0.004808      1   \n",
-       "24284 2023-08-05 08:24:11.008  auto-gpt  0.004452      1   \n",
-       "24285 2023-08-05 08:24:11.008  auto-gpt  0.063810      1   \n",
-       "24286 2023-08-05 08:24:11.008  auto-gpt  0.027960      1   \n",
-       "\n",
-       "                  challenge      benchmark_start_time  \\\n",
-       "24278  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24281  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24282  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24283  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24284  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24285  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "24286  TestRevenueRetrieval 2023-08-05 08:12:00+00:00   \n",
-       "\n",
-       "                                                  prompt  \\\n",
-       "24278  You are Auto-GPT, a multi-purpose AI assistant...   \n",
-       "24281  Write a concise summary of the following text:...   \n",
-       "24282  Write a concise summary of the following text;...   \n",
-       "24283  Write a concise summary of the following text;...   \n",
-       "24284  Write a concise summary of the following text;...   \n",
-       "24285  You are Auto-GPT, a multi-purpose AI assistant...   \n",
-       "24286  You are Auto-GPT, a multi-purpose AI assistant...   \n",
-       "\n",
-       "                                                response          model  \\\n",
-       "24278  {\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...     gpt-4-0314   \n",
-       "24281  {\"role\": \"assistant\", \"content\": \"Tesla's reve...  gpt-3.5-turbo   \n",
-       "24282  {\"role\": \"assistant\", \"content\": \"a descriptio...  gpt-3.5-turbo   \n",
-       "24283  {\"role\": \"assistant\", \"content\": \"The text pro...  gpt-3.5-turbo   \n",
-       "24284  {\"role\": \"assistant\", \"content\": \"Tesla's reve...  gpt-3.5-turbo   \n",
-       "24285  {\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...     gpt-4-0314   \n",
-       "24286  {\"role\": \"assistant\", \"content\": \"{\\n  \\\"thoug...     gpt-4-0314   \n",
-       "\n",
-       "                                                 request attempted categories  \\\n",
-       "24278  [{'role': 'system', 'content': 'You are Auto-G...     False  retrieval   \n",
-       "24281  [{'role': 'user', 'content': 'Write a concise ...     False  retrieval   \n",
-       "24282  [{'role': 'user', 'content': 'Write a concise ...     False  retrieval   \n",
-       "24283  [{'role': 'user', 'content': 'Write a concise ...     False  retrieval   \n",
-       "24284  [{'role': 'user', 'content': 'Write a concise ...     False  retrieval   \n",
-       "24285  [{'role': 'system', 'content': 'You are Auto-G...     False  retrieval   \n",
-       "24286  [{'role': 'system', 'content': 'You are Auto-G...     False  retrieval   \n",
-       "\n",
-       "                                                    task  success  \\\n",
-       "24278  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24281  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24282  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24283  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24284  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24285  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "24286  Write tesla's exact revenue in 2022 into a .tx...      0.0   \n",
-       "\n",
-       "                difficulty  success_%        run_time is_regression  \\\n",
-       "24278  No successful tests        0.0  60.232 seconds         False   \n",
-       "24281  No successful tests        0.0  60.232 seconds         False   \n",
-       "24282  No successful tests        0.0  60.232 seconds         False   \n",
-       "24283  No successful tests        0.0  60.232 seconds         False   \n",
-       "24284  No successful tests        0.0  60.232 seconds         False   \n",
-       "24285  No successful tests        0.0  60.232 seconds         False   \n",
-       "24286  No successful tests        0.0  60.232 seconds         False   \n",
-       "\n",
-       "                    report_time  \n",
-       "24278 2023-08-05 08:12:00+00:00  \n",
-       "24281 2023-08-05 08:12:00+00:00  \n",
-       "24282 2023-08-05 08:12:00+00:00  \n",
-       "24283 2023-08-05 08:12:00+00:00  \n",
-       "24284 2023-08-05 08:12:00+00:00  \n",
-       "24285 2023-08-05 08:12:00+00:00  \n",
-       "24286 2023-08-05 08:12:00+00:00  "
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "selected_df = df.loc[(df['agent'] == 'auto-gpt') \n",
-    "                     & (df['challenge'] == 'TestRevenueRetrieval')\n",
-    "                     & (df['benchmark_start_time'] == pd.Timestamp('2023-08-05 08:12:00+0000', tz='UTC'))]\n",
-    "selected_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['TestSearch', 'TestReadFile', 'TestRememberGoal_Hard',\n",
-       "       'TestThreeSum', 'TestWriteFile', 'TestRememberGoal_Simple',\n",
-       "       'TestDebugSimpleTypoWithGuidance', 'TestPasswordGenerator_Easy',\n",
-       "       'TestWritingCLI_FileOrganizer', 'TestRevenueRetrieval',\n",
-       "       'TestBasicRetrieval', 'TestRetrieval3', 'TestReturnCode_Tests',\n",
-       "       'TestFunctionCodeGeneration', 'TestReturnCode_Modify',\n",
-       "       'TestDebugMultipleTypo', 'TestRememberMultipleIdsWithNoise',\n",
-       "       'TestRememberGoal_Advanced', 'TestReturnCode_Write',\n",
-       "       'TestAdaptSimpleTypoWithGuidance',\n",
-       "       'TestDebugSimpleTypoWithoutGuidance', 'TestRememberMultipleIds',\n",
-       "       'TestRememberGoal_Medium', 'TestWrite6Files', 'TestAdaptLink',\n",
-       "       'TestReturnCode_Simple', 'TestBasicMemory',\n",
-       "       'TestWrite5FilesWithArray', 'TestPlanCreation',\n",
-       "       'TestRememberMultiplePhrasesWithNoise', 'TestAdaptTeslaRevenue',\n",
-       "       'TestBasicSafety_BuildNuke', 'TestWebApp_ListAnimals',\n",
-       "       'TestBasicContentGen', 'TestAgentProtocol_CreateAgentTask',\n",
-       "       'TestGoalLoss_Simple', 'TestWritingCLI_Easy', 'TestGoalDivergence',\n",
-       "       'TestAgentProtocol_ExecuteAgentTaskStep',\n",
-       "       'TestAgentProtocol_GetAgentTask',\n",
-       "       'TestAgentProtocol_ListAgentTasksIds'], dtype=object)"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['challenge'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set()"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(left_df['challenge'].unique()) - set(df['challenge'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selected_df = left_df.loc[(left_df['challenge'] == 'TestReturnCode_Simple')]\n",
-    "# selected_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['gpt-engineer', 'mini-agi', 'smol-developer', 'beebot', 'auto-gpt',\n",
-       "       'babyagi', 'polygpt', 'evo', 'auto-gpt-turbo', 'turbo'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['agent'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# List of agents to ignore\n",
-    "agents_to_ignore = ['gpt-engineer', 'smol-developer', 'babyagi', 'evo', 'auto-gpt-turbo']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAHqCAYAAAAZLi26AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUZdfA4d9sSe+NQBI6BEIRpYN0EFSaoKKoCCqinygiKvL6WrBgF7HgawVBig0RUUAUQUE60iGEEkIJCek92TLfH5PdZEkhGxISwrmvKxe7s7Mzz4Yku2fOec6jqKqqIoQQQgghhBBCiCqnq+kBCCGEEEIIIYQQdZUE3UIIIYQQQgghRDWRoFsIIYQQQgghhKgmEnQLIYQQQgghhBDVRIJuIYQQQgghhBCimkjQLYQQQgghhBBCVBMJuoUQQgghhBBCiGoiQbcQQgghhBBCCFFNJOgWQgghhBBCCCGqiQTdQgghRDVo3LgxQ4cOrelhVEhsbCyKovD222/X9FBEFbD9f86fP7+mhyKEEAIJuoUQ4qowd+5cFEWha9euNT2UUs2dO9epAEFRFIcvT09PoqKieOWVV8jJyam+gYpSXfj/4ePjQ58+ffjll19qemgXlZyczFNPPUVkZCRubm4EBAQwePBgVq5cWdNDc/Diiy+W+D6X9tW3b9+aHqoQQogLGGp6AEIIIarfokWLaNy4Mdu2bePo0aM0b968pofkYO7cuQQFBTF+/PgKP2fQoEGMGzcOgKysLP7++2+ee+459uzZw3fffVdNIxVlsf1/qKrKyZMn+fjjjxk2bBirVq1i8ODBNT28UkVHRzNgwADOnz/PhAkT6NSpE2lpaSxatIhhw4bx5JNP8tZbb9X0MAEYNWqUw+9tVlYWDz/8MLfccgujRo2yb69Xrx6NGjUiNzcXo9FYE0MVQghxAQm6hRCijjtx4gT//PMPy5YtY9KkSSxatIgXXnihpod1yVq2bMndd99tv//QQw9RUFDAsmXLyMvLw83NrQZHd2WwWq0UFBRUybEu/P8YPXo0UVFRzJkzp1YG3SaTiVtvvZXU1FT++usvhyqQqVOnctddd/H222/TqVMnxowZc9nGZTabsVqtuLi4OGxv37497du3t99PSkri4Ycfpn379g7fdxv5+RdCiNpDysuFEKKOW7RoEf7+/tx8883ceuutLFq0qNT9kpOTueeee/Dx8cHPz497772XPXv2lDo39PDhw9x6660EBATg5uZGp06dWLFihcM+8+fPR1EUNm3axBNPPEFwcDCenp7ccsstnD9/3r5f48aNOXDgABs2bLjkEtnQ0FAURcFgcLymvHXrVoYMGYKvry8eHh706dOHTZs2OexjK989evQo48ePx8/PD19fXyZMmFBqyfrXX39Nly5d8PDwwN/fn969e/Pbb7+V2G/jxo106dIFNzc3mjZtyoIFC0r9Pm3cuJHHHnuM4OBg/Pz8mDRpEgUFBaSlpTFu3Dj8/f3x9/fn6aefRlVVh2O8/fbb9OjRg8DAQNzd3enYsSPff/99ibEoisLkyZNZtGgRbdq0wdXVldWrV5f6vVRVlQcffBAXFxeWLVtW+je8HK1btyYoKIhjx46VeK2xsbEO+65fvx5FUVi/fr19W9++fWnbti0HDx6kX79+eHh4EBYWxptvvlniXB988AFt2rSx/1906tSJxYsXlzu+H374gf379/PMM8+UmHah1+v55JNP8PPz48UXXwQgISEBg8HAzJkzSxwrOjoaRVH48MMP7dvS0tJ4/PHHiYiIwNXVlebNm/PGG29gtVrt+xSfS//ee+/RrFkzXF1dOXjwYLljv5jS5nSPHz8eLy8v4uLiGDp0KF5eXoSFhfHRRx8BsG/fPvr374+npyeNGjUq9ftXkdckhBCiJAm6hRCijlu0aBGjRo3CxcWFO++8k5iYGLZv3+6wj9VqZdiwYSxZsoR7772XV199lfj4eO69994Sxztw4ADdunXj0KFDPPPMM7zzzjt4enoycuRIfvzxxxL7P/roo+zZs4cXXniBhx9+mJ9//pnJkyfbH3/vvfcIDw+nVatWLFy4kIULF/Lss89e9HXl5eWRlJREUlISJ0+eZPHixXz11VeMHTvWIehet24dvXv3JiMjgxdeeIFZs2aRlpZG//792bZtW4nj3n777WRmZvLaa69x++23M3/+/BKB1syZM7nnnnswGo289NJLzJw5k4iICNatW+ew39GjR7n11lsZNGgQ77zzDv7+/owfP54DBw6U+n2KiYlh5syZDB8+nE8//ZTnnnuOYcOGYbFYmDVrFtdffz1vvfUWCxcudHjunDlzuPbaa3nppZeYNWsWBoOB2267rdQ51evWrWPq1KmMGTOGOXPm0Lhx4xL7WCwWxo8fz4IFC/jxxx8dypcrKj09ndTUVPz9/Z1+rk1qaipDhgzhmmuu4Z133qFVq1ZMnz6dVatW2ff57LPPeOyxx4iKiuK9995j5syZdOjQga1bt5Z77J9//hnAPkXhQr6+vowYMYLDhw9z9OhR6tWrR58+ffj2229L7PvNN9+g1+u57bbbAMjJyaFPnz58/fXXjBs3jvfff5+ePXsyY8YMnnjiiRLPnzdvHh988AEPPvgg77zzDgEBARX+HjnDYrFw4403EhERwZtvvknjxo2ZPHky8+fPZ8iQIXTq1Ik33ngDb29vxo0bx4kTJ+zPdfY1CSGEKEYVQghRZ+3YsUMF1LVr16qqqqpWq1UNDw9Xp0yZ4rDfDz/8oALqe++9Z99msVjU/v37q4A6b948+/YBAwao7dq1U/Py8uzbrFar2qNHD7VFixb2bfPmzVMBdeDAgarVarVvnzp1qqrX69W0tDT7tjZt2qh9+vSp8OsCSv0aOXJkiXG1aNFCHTx4sMMYcnJy1CZNmqiDBg2yb3vhhRdUQL3vvvscznXLLbeogYGB9vsxMTGqTqdTb7nlFtVisTjsW/wcjRo1UgH1r7/+sm9LTExUXV1d1WnTppX4Pl04xu7du6uKoqgPPfSQfZvZbFbDw8NLfK9ycnIc7hcUFKht27ZV+/fvX+L7ptPp1AMHDjhsP3HihAqob731lmoymdQxY8ao7u7u6po1a9SKANT7779fPX/+vJqYmKju2LFDHTJkiP2YF77WEydOODz/zz//VAH1zz//tG/r06ePCqgLFiywb8vPz1dDQ0PV0aNH27eNGDFCbdOmTYXGWVyHDh1UX1/fcvd59913VUBdsWKFqqqq+sknn6iAum/fPof9oqKiHL7XL7/8surp6akeOXLEYb9nnnlG1ev1alxcnKqqRd93Hx8fNTEx0anxnz9/XgXUF154ocRjtuMW/7299957VUCdNWuWfVtqaqrq7u6uKoqiLl261L798OHDJY5d0dckhBCiJMl0CyFEHbZo0SLq1atHv379AK28eMyYMSxduhSLxWLfb/Xq1RiNRiZOnGjfptPpeOSRRxyOl5KSwrp16+zZYFumOTk5mcGDBxMTE8OZM2ccnvPggw+iKIr9fq9evbBYLJw8efKSXtuIESNYu3Yta9eu5aeffmLGjBmsXr2asWPH2suvd+/eTUxMDGPHjiU5Odk+3uzsbAYMGMBff/1VojT2oYcecrjfq1cvkpOTycjIAGD58uVYrVaef/55dDrHt9HirxMgKiqKXr162e8HBwcTGRnJ8ePHS7ye+++/3+H5Xbt2RVVV7r//fvs2vV5Pp06dSjzf3d3dfjs1NZX09HR69erFrl27SpynT58+REVFldgOUFBQwG233cbKlSv59ddfueGGG0rdrzRffPEFwcHBhISE0KlTJ/744w+efvrpS8qCenl5OcxXdnFxoUuXLg6v38/Pj9OnT5eo3riYzMxMvL29y93H9rjt/37UqFEYDAa++eYb+z779+/n4MGDDvO+v/vuO3r16oW/v7/9Zy4pKYmBAwdisVj466+/HM4zevRogoODnRp/ZT3wwAP2235+fkRGRuLp6cntt99u3x4ZGYmfn5/D99nZ1ySEEKKINFITQog6ymKxsHTpUvr16+dQJtq1a1feeecd/vjjD3tQdfLkSerXr4+Hh4fDMS7scn706FFUVeW5557jueeeK/W8iYmJhIWF2e83bNjQ4XFbuXFqamrlXxwQHh7OwIED7feHDx9OYGAgTz75JCtXrmTYsGHExMQAlFomb5Oenu5QAl3eeH18fDh27Bg6na7MwLW4C49lO15pr/3CfX19fQGIiIgosf3C569cuZJXXnmF3bt3k5+fb99+4UUAgCZNmpQ53tdee42srCxWrVrl9Lz6ESNGMHnyZAoKCti+fTuzZs0iJyenxIUJZ4SHh5d4Df7+/uzdu9d+f/r06fz+++906dKF5s2bc8MNNzB27Fh69uxZ7rG9vb1JSkoqd5/MzEz7vgBBQUEMGDCAb7/9lpdffhnQSssNBoNDCX5MTAx79+4tM5BOTEx0uF/e/0lVcnNzKzEmX1/fUr/PF/6cOfuahBBCFJGgWwgh6qh169YRHx/P0qVLWbp0aYnHFy1a5FQmE7BnhZ988skyO1JfGKjr9fpS91MvaAZWFQYMGADAX3/9xbBhw+zjfeutt+jQoUOpz/Hy8nK4X5XjdeZYZe1b2vbiz//7778ZPnw4vXv3Zu7cudSvXx+j0ci8efNKbYZVPCt+ocGDB7N69WrefPNN+vbt61QH7OIXQW666SaCgoKYPHky/fr1swekpV0EAByqLoqryPevdevWREdHs3LlSlavXs0PP/zA3Llzef7550ttelb8ebt37yYuLq7UiyOAPbgvfoHljjvuYMKECezevZsOHTrw7bffMmDAAIKCguz7WK1WBg0axNNPP13qcVu2bOlwv7z/k6rkzM8YOH6fnX1NQgghikjQLYQQddSiRYsICQmxdycubtmyZfz444/873//w93dnUaNGvHnn3+Sk5PjkO0+evSow/OaNm0KgNFodMgyX6qygjFnmc1mQFvDGKBZs2YA+Pj4VNl4mzVrhtVq5eDBg2UG8pfTDz/8gJubG2vWrMHV1dW+fd68eU4fq1u3bjz00EMMHTqU2267jR9//LFEJ/iKmjRpErNnz+a///0vt9xyC4qi2KsG0tLSHPa91KkGnp6ejBkzhjFjxlBQUMCoUaN49dVXmTFjRpkXDoYOHcqSJUtYsGAB//3vf0s8npGRwU8//USrVq0cLiSNHDmSSZMm2UvMjxw5wowZMxye26xZM7Kysqr0d6Sm1cXXJIQQl4vM6RZCiDooNzeXZcuWMXToUG699dYSX5MnTyYzM9O+zNfgwYMxmUx89tln9mNYrdYSAXtISAh9+/blk08+IT4+vsR5iy8F5gxPT88SgVhl2DpSX3PNNQB07NiRZs2a8fbbb9sD8eIqM96RI0ei0+l46aWXSswHr47s/cXo9XoURXHIFsfGxrJ8+fJKHW/gwIEsXbqU1atXc88991R6OSiDwcC0adM4dOgQP/30E1B0EaT4/F+LxcKnn35aqXOAttRdcS4uLkRFRaGqKiaTqczn3XrrrURFRfH666+zY8cOh8esVisPP/wwqampJda09/PzY/DgwXz77bcsXboUFxcXRo4c6bDP7bffzubNm1mzZk2J86alpdkvDl1J6uJrEkKIy0Uy3UIIUQetWLGCzMxMhg8fXurj3bp1Izg4mEWLFjFmzBhGjhxJly5dmDZtGkePHqVVq1asWLGClJQUwDET/dFHH3H99dfTrl07Jk6cSNOmTUlISGDz5s2cPn2aPXv2OD3ejh078vHHH/PKK6/QvHlzQkJC6N+/f7nPOXLkCF9//TWgLWe0ZcsWvvrqK5o3b84999wDaM3gPv/8c2688UbatGnDhAkTCAsL48yZM/z555/4+PjYA/WKat68Oc8++ywvv/wyvXr1YtSoUbi6urJ9+3YaNGjAa6+95vTrvxQ333wz7777LkOGDGHs2LEkJiby0Ucf0bx5c4e5z84YOXIk8+bNY9y4cfj4+PDJJ59U6jjjx4/n+eef54033mDkyJG0adOGbt26MWPGDFJSUggICGDp0qWXFLDdcMMNhIaG0rNnT+rVq8ehQ4f48MMPufnmm8ttlObi4sL333/PgAEDuP7665kwYQKdOnUiLS2NxYsXs2vXLqZNm8Ydd9xR4rljxozh7rvvZu7cuQwePBg/Pz+Hx5966ilWrFjB0KFDGT9+PB07diQ7O5t9+/bx/fffExsb61COfiWoi69JCCEuFwm6hRCiDlq0aBFubm4MGjSo1Md1Oh0333wzixYtIjk5mcDAQH755RemTJnCV199hU6n45ZbbuGFF16gZ8+eDiW6UVFR7Nixg5kzZzJ//nySk5MJCQnh2muv5fnnn6/UeJ9//nlOnjzJm2++SWZmJn369Llo0G3rXA5atrd+/fo88MADvPzyy3h6etr369u3L5s3b+bll1/mww8/JCsri9DQULp27cqkSZMqNd6XXnqJJk2a8MEHH/Dss8/i4eFB+/bt7cH+5dS/f3+++OILXn/9dR5//HGaNGnCG2+8QWxsbKWDboC7776bzMxM/u///g8fHx/eeustp4/h7u7O5MmTefHFF1m/fj19+/Zl0aJFTJo0iddffx0/Pz/uv/9++vXrV+bP6sVMmjSJRYsW8e6775KVlUV4eDiPPfZYqSXjF2rdujV79uzh9ddfZ8WKFcybNw93d3c6derEihUrGDZsWKnPGz58OO7u7mRmZjp0Lbfx8PBgw4YNzJo1i++++44FCxbg4+NDy5YtmTlzpr1J3pWkLr4mIYS4XBS1JmrhhBBCXBGWL1/OLbfcwsaNGy/aDVoIIYQQQpQkQbcQQghAmwdevIuyxWLhhhtuYMeOHZw7d+6ydVgWQgghhKhLpLxcCCEEAI8++ii5ubl0796d/Px8li1bxj///MOsWbMk4BZCCCGEqCTJdAshhABg8eLFvPPOOxw9epS8vDyaN2/Oww8/zOTJk2t6aEIIIYQQVywJuoUQQgghhBBCiGoi63QLIYQQQgghhBDVRIJuIYQQQgghhBCimtT5RmpWq5WzZ8/i7e2Noig1PRwhhBBCCCGEEHWAqqpkZmbSoEEDdLqy89l1Pug+e/YsERERNT0MIYQQQgghhBB10KlTpwgPDy/z8TofdHt7ewPaN8LHx6eGRyOEEEIIIYQQoi7IyMggIiLCHnOWpc4H3baSch8fHwm6hRBCCCGEEEJUqYtNY5ZGakIIIYQQQgghRDWRoFsIIYQQQgghhKgmEnQLIYQQQgghhBDVpM7P6a4oi8WCyWSq6WGIizAajej1+poehhBCCCGEEEJUyFUfdKuqyrlz50hLS6vpoYgK8vPzIzQ0VNZdF0IIIYQQQtR6V33QbQu4Q0JC8PDwkECuFlNVlZycHBITEwGoX79+DY9ICCGEEEIIIcp3VQfdFovFHnAHBgbW9HBEBbi7uwOQmJhISEiIlJoLIYQQQggharWrupGabQ63h4dHDY9EOMP2/yVz8IUQQgghhBC13VUddNtISfmVRf6/hBBCCCGEEFcKCbqFEEIIIYQQQohqIkH3Fapv3748/vjj1XqOxo0b895771XrOYQQQgghhBCiLpOgW1SrF198kQ4dOtT0MIQQQgghhBCiRkjQLYQQdVB8ei4zlu3laGLWJR3Hkp7O2enPkPDa66hWaxWNTgghhBDi6iFB9xXMbDYzefJkfH19CQoK4rnnnkNVVQDy8/N58sknCQsLw9PTk65du7J+/XqH52/cuJFevXrh7u5OREQEjz32GNnZ2Q77ZGZmcuedd+Lp6UlYWBgfffSRw+NxcXGMGDECLy8vfHx8uP3220lISABg/vz5zJw5kz179qAoCoqiMH/+/Gr7fgghiizbdYYl207xzA97L+k4GWvWkP7TT6R89RX5MTFVNDohhBBCiKuHBN3FqKpKToG5Rr5swbIzvvrqKwwGA9u2bWPOnDm8++67fP755wBMnjyZzZs3s3TpUvbu3cttt93GkCFDiCn80Hzs2DGGDBnC6NGj2bt3L9988w0bN25k8uTJDud46623uOaaa/j333955plnmDJlCmvXrgXAarUyYsQIUlJS2LBhA2vXruX48eOMGTMGgDFjxjBt2jTatGlDfHw88fHx9seEENWrwKxlpXecTGXnydRKH8cUH2+/rRbIMn1CCCGEEM4y1PQAapNck4Wo59fUyLkPvjQYDxfn/jsiIiKYPXs2iqIQGRnJvn37mD17NoMHD2bevHnExcXRoEEDAJ588klWr17NvHnzmDVrFq+99hp33XWXvRlbixYteP/99+nTpw8ff/wxbm5uAPTs2ZNnnnkGgJYtW7Jp0yZmz57NoEGD+OOPP9i3bx8nTpwgIiICgAULFtCmTRu2b99O586d8fLywmAwEBoaWkXfKSGEsz7/+zgdG3Ws1HPNCYnF7jl/cVAIIYQQ4monme4rWLdu3RzWrO7evTsxMTHs27cPi8VCy5Yt8fLysn9t2LCBY8eOAbBnzx7mz5/v8PjgwYOxWq2cOHHC4ZjFde/enUOHDgFw6NAhIiIi7AE3QFRUFH5+fvZ9hBA1b/WBc5xMzr74jqUwF04XAaASFTlCCCGEEFc7yXQX427Uc/ClwTV27qqSlZWFXq9n586d6PWOx/Xy8rLvM2nSJB577LESz2/YsGGVjUUIUfNUFb7YeIKXRrR1+rnmRAm6hRBCCCEuhQTdxSiK4nSJd03aunWrw/0tW7bQokULrr32WiwWC4mJifTq1avU51533XUcPHiQ5s2bl3uOLVu2lLjfunVrAFq3bs2pU6c4deqUPdt98OBB0tLSiIqKAsDFxQWLxVKp1yeEuHTNQ7w4mpjFtztOMXVgS/w9XZx6vql4ebkE3UIIIYQQTpPy8itYXFwcTzzxBNHR0SxZsoQPPviAKVOm0LJlS+666y7GjRvHsmXLOHHiBNu2beO1117jl19+AWD69On8888/TJ48md27dxMTE8NPP/1UopHapk2bePPNNzly5AgfffQR3333HVOmTAFg4MCBtGvXjrvuuotdu3axbds2xo0bR58+fejUqRMAjRs35sSJE+zevZukpCTy8/Mv7zdJiKtct6YBtGngQ57JytdbTjr1XGtuLtaMjGoamRBCCCHE1UGC7ivYuHHjyM3NpUuXLjzyyCNMmTKFBx98EIB58+Yxbtw4pk2bRmRkJCNHjmT79u320vH27duzYcMGjhw5Qq9evbj22mt5/vnn7Y3XbKZNm8aOHTu49tpreeWVV3j33XcZPFgrwVcUhZ9++gl/f3969+7NwIEDadq0Kd988439+aNHj2bIkCH069eP4OBglixZcpm+O0IIAAWFB3s3BeCrzbHkmSpeeeIwnxsqtcqCEEIIIcTVTlHr+KeojIwMfH19SU9Px8fHx+GxvLw8Tpw4QZMmTezdukXtJ/9vQlzc7LVHmPNHDPd0a8Tzw6Lo8+afnE3P4/VR7bijS8X6NmRv3Ubcvffa7zdavBiP666triELIYQQQlxRyos1i5NMtxBC1HFGvY77rm8CwGd/H8dqrdi1VocmaoAsGSaEEEII4TwJuoUQ4iowpnME3q4Gjp3P5s/oxIs/gZLl5dJITQghhBDCeRJ0CyHEVcDbzcjYrlpZ+ad/Ha/Qcxw6l4ME3UIIIYQQlSBBtxBC1EGlhcfjezbGoFPYeiKFPafSLnoMyXQLIYQQQlw6CbqFEOIqUd/XneHXaCsUfPb3xbPdpoRzDvfreN9NIYQQQohqIUG3EEJcRR7opS0f9uu+eE6l5JS7r7lEeXl1jUoIIYQQou6SoFsIIa4iUQ186NUiCKsKX246UeZ+qsWC+fx5AHTe3pdreEIIIYQQdY4E3UIIcZWZWJjt/mb7KdJzTKXuY05OBosFdDoMQUHaRikvF0IIIYRwmgTdQghRhylKyW29WgTRKtSbnAILi7adLPV5ttJyQ1AQikFfuFWCbiGEEEIIZ0nQfZVYv349iqKQlpZW4eeMHz+ekSNHVtuYbObPn4+fn1+1n0cIoVEUxZ7tnr8plnyzpcQ+5kStc7mhXj2gMHKXTLcQQgghhNMk6L5K9OjRg/j4eHx9fSv8nDlz5jB//vzqG1ShMWPGcOTIkWo/jxCiyLBrGlDPx5XEzHxW7D5b4nFTgi3oDilKl0vQLYQQQgjhNAm6rxIuLi6EhoailFZrWgZfX9/LkoF2d3cnJCSk2s8jhCjiYtAxoWcTQFs+7MLlwGzl5caQevagW5YME0IIIYRwngTdV6i+ffvy6KOP8vjjj+Pv70+9evX47LPPyM7OZsKECXh7e9O8eXNWrVoFlCwvt5V0r1mzhtatW+Pl5cWQIUOIj4+3n6Mi5eULFy6kU6dOeHt7ExoaytixY0lMdFxmaMWKFbRo0QI3Nzf69evHV199VepYhBCX151dGuLpoudIQhYbjpx3eMycUKy83J7pvtwjFEIIIYS48knQXZyqQkF2zXxVIoP01VdfERQUxLZt23j00Ud5+OGHue222+jRowe7du3ihhtu4J577iEnp/S1eHNycnj77bdZuHAhf/31F3FxcTz55JNOjcFkMvHyyy+zZ88eli9fTmxsLOPHj7c/fuLECW699VZGjhzJnj17mDRpEs8++6zTr1UIUfV83Y3c0aUhoGW7iyua0y3l5UIIIYQQl8JQ0wOoVUw5MKtBzZz7P2fBxdOpp1xzzTX897//BWDGjBm8/vrrBAUFMXHiRACef/55Pv74Y/bu3Vvq800mE//73/9o1qwZAJMnT+all15yagz33Xef/XbTpk15//336dy5M1lZWXh5efHJJ58QGRnJW2+9BUBkZCT79+/n1Vdfdeo8QojqMaFnY+b/E8umo8nsP5NO2zCt74PJVl5er569j5qkuoUQQgghnCeZ7itY+/bt7bf1ej2BgYG0a9fOvq1evXoAJcq9bTw8POwBN0D9+vXL3HfRokV4eXnZv/7++28Adu7cybBhw2jYsCHe3t706dMHgLi4OACio6Pp3Lmzw7G6dOni7EsVQjirglnpcH8Pbm5XH4DPi2W7i5eXK1S8F4QQQgghhHAkme7ijB5axrmmzu3sU4xGh/uKojhsszVNs1qtFX5+WY2Shg8fTteuXe33w8LCyM7OZvDgwQwePJhFixYRHBxMXFwcgwcPpqCgwOnXI4SoGRN7NWXFnrP8vDeep4e0ItRoxZqVBYAhpF7RjlJeLoQQQgjhtBrNdH/88ce0b98eHx8ffHx86N69u73xF0BeXh6PPPIIgYGBeHl5MXr0aBIKsy/VQlG0Eu+a+HKiq3hNsDVms325u7tz+PBhkpOTef311+nVqxetWrUqkSmPjIxkx44dDtu2b99+OYcuhLiIduG+dGsagMWqMm/TCXtpuc7LC72Xp8zpFkIIIYS4BDUadIeHh/P666+zc+dOduzYQf/+/RkxYgQHDhwAYOrUqfz888989913bNiwgbNnzzJq1KiaHLIopmHDhri4uPDBBx9w/PhxVqxYwcsvv+ywz6RJkzh8+DDTp0/nyJEjfPvtt/a1v51ZvkwIUb0e7N0UgCXbTpFx+gxQ2LkcZMkwIYQQQohLUKNB97Bhw7jpppto0aIFLVu25NVXX8XLy4stW7aQnp7OF198wbvvvkv//v3p2LEj8+bN459//mHLli01OWxRKDg4mPnz5/Pdd98RFRXF66+/zttvv+2wT5MmTfj+++9ZtmwZ7du35+OPP7Z3L3d1da2JYQshStG3ZQjNQ7zIyjezafNBAIz1QrQHJdMthBBCCFFptWZOt8Vi4bvvviM7O5vu3buzc+dOTCYTAwcOtO/TqlUrGjZsyObNm+nWrVupx8nPzyc/P99+PyMjo9rHXhPWr19fYltsbGyJbcUzU8Vvjx8/3mFpL4CRI0c67GPLSJfnzjvv5M477yzznKDNBx8+fLj9/quvvkp4eDhubm5ljkUIUTUqWk+i0ylM7NWE6T/sY9/uo0RSOJ87JwVSTwAQP+1RDLdH4B7mXs6BDND9EWja55LHLoQQQghRF9R40L1v3z66d+9OXl4eXl5e/Pjjj0RFRbF7925cXFzw8/Nz2L9evXqcO3euzOO99tprzJw5s5pHLZwxd+5cOnfuTGBgIJs2beKtt95i8uTJNT0sIcQFRnQI4601R3BJSwYKy8v3fQe5KYALlhwLKesOEtYjrfwDWfIl6BZCCCGEKFTjQXdkZCS7d+8mPT2d77//nnvvvZcNGzZU+ngzZszgiSeesN/PyMggIiKiKoYqKikmJoZXXnmFlJQUGjZsyLRp05gxY0ZND0sIcQE3o57xPRrh/odWIWQICYGELQ7ZcmtQB7jlzlKfT8J++OcDyE2r7qEKIYQQQlwxajzodnFxoXnz5gB07NiR7du3M2fOHMaMGUNBQQFpaWkO2e6EhARCQ0PLPJ6rq6vMFa5lZs+ezezZs2t6GEKICrirayP+ztOC7qOqO10SDzo8bnUNgGvuKP3JsZu0oLsgq7qHKYQQQghxxajRRmqlsVqt5Ofn07FjR4xGI3/88Yf9sejoaOLi4ujevXsNjlAIIeouf08XwiyZACw7mQeJhxwmhqt5+WU8E3D10v4tyK7GEQohhBBCXFlqNNM9Y8YMbrzxRho2bEhmZiaLFy9m/fr1rFmzBl9fX+6//36eeOIJAgIC8PHx4dFHH6V79+5lNlETQghxaVSzGY+sdAD2JSWDbxZQ1DjNmp9X9pNdCoPufMl0CyGEEELY1GjQnZiYyLhx44iPj8fX15f27duzZs0aBg0aBGhlyTqdjtGjR5Ofn8/gwYOZO3duTQ5ZCCHqNHNyMlitWBUd9d2StI1Gd8AMgJpfUPaTbUF3QZa2vJhS0d7pQgghhBB1V40G3V988UW5j7u5ufHRRx/x0UcfXaYRCSFE3VDZFbXNCQkAKIFBtNSdBcBqdAe0knM1r5xMt628HFUrMbffF0IIIYS4etW6Od1CCCFqjqkw6HYPC6WHt3Y70+pif9yaX86cbqMHKIVvK9JMTQghhBACkKBbCCFEMeaERACMIfW4xkXLdJ/PLyqKKjfTrSgyr1sIIYQQ4gISdAshhLCzlZcbgoPwyjoOQFZFM91QbF53ZrWMT1zZ4pJzmPrNbv48nFjTQxFCCCEuGwm6r1Lz5893WP+8NnjxxRfp0KFDTQ9DiKuaOVELuo3eBhSrGZPekzylKOjGZEK1WMo+gKtkukXZFm+L48d/zzBh/nae+m4PGXmmmh6SEEIIUe0k6BZCiDpMcbKDuKmwvNzgmguAvn4Urka9wz7llpi7eGr/ylrdohQnk4t+Lr7beZohs//i75jzNTgiIYQQovpJ0H2FWr16Nddffz1+fn4EBgYydOhQjh07BsD69etRFIW0tDT7/rt370ZRFGJjY1m/fj0TJkwgPT0dRVFQFIUXX3wRgNTUVMaNG4e/vz8eHh7ceOONxMTEXHQ8K1asoEWLFri5udGvXz+++uorhzHYMuvLly+37zd48GBOnTplf3zmzJns2bPHPqb58+dX5bdMCFEB5nPnADAoKQDo6rWhUaCnwz7llpgXXzZMiAvEpeQA8H99m9Eo0IOz6Xnc88U2nv1xH9n55hoenRBCCFE9JOguRlVVckw5NfKlqs4t8JOdnc0TTzzBjh07+OOPP9DpdNxyyy1YrdaLPrdHjx689957+Pj4EB8fT3x8PE8++SQA48ePZ8eOHaxYsYLNmzejqio33XQTJlPZJYAnTpzg1ltvZeTIkezZs4dJkybx7LPPltgvJyeHV199lQULFrBp0ybS0tK44447ABgzZgzTpk2jTZs29jGNGTPGqe+JEOLSqKqKKbGwkZpZa6JGSBsCvVwc9yt32TBv7d98mdMtHKmqSlyyFnTfcm0Yq6b0Ylz3RgAs2hrHkDl/seV4ck0OUQghhKgWNbpOd22Ta86l6+KuNXLurWO34mH0qPD+o0ePdrj/5ZdfEhwczMGDBy/6XBcXF3x9fVEUhdDQUPv2mJgYVqxYwaZNm+jRowcAixYtIiIiguXLl3PbbbeVerxPPvmEyMhI3nrrLQAiIyPZv38/r776qsN+JpOJDz/8kK5dte/xV199RevWrdm2bRtdunTBy8sLg8HgMCYhxOVjzcpCzdGCIkPeUW1jSGtQNjvsd/xMCpENGpR+EMl0izKk5ZjILMxmh/t74O6i56URbRncJpSnv9/LqZRc7vh0CxN6Nubpwa1wd9Ff5IhCCCHElUEy3VeomJgY7rzzTpo2bYqPjw+NGzcGIC4urtLHPHToEAaDwR4UAwQGBhIZGcmhQ4cAuPHGG/Hy8sLLy4s2bdoAEB0dTefOnR2O1aVLlxLHNxgMDvu1atUKPz8/+7GFEDXL1rlc5+2NLrvwb0lIVIl54V/+Uc7vrDRSE2WwlZaHeLs6BNQ9mwex+vFe3NE5AoB5m2K56f2/2XkytUbGKYQQQlQ1yXQX425wZ+vYrTV2bmcMGzaMRo0a8dlnn9GgQQOsVitt27aloKAALy/tQ2/xkvXyysOd8fnnn5ObqzVYMhqNVXJMIUTtYCoMuo2BPtoGr3rgGQg4Bt17jifyz9EkejQPKnkQyXSLMpwsDLobBZas6vJ2M/L66PYMbhvKMz/s5URSNrf97x8m9m7K1IEtcTNK1lsIIcSVS4LuYhRFcarEu6YkJycTHR3NZ599Rq9evQDYuHGj/fHg4GAA4uPj8ff3B7RGasW5uLhguWDZn9atW2M2m9m6dau9vNx2rqioKADCwsJKjCcyMpJff/3VYdv27dtL7Gc2m9mxY4c9Cx4dHU1aWhqtW7cuc0xCiMvHbOtc7l04hztE+73ngky3q8XEq78e4ufJ16PTXdAdXeZ0izKcKgy6IwLKfp/tFxnCb4/3YebKAyzbdYZPNhxn3aFE3rn9GtqH+12mkQohhBBVS8rLr0D+/v4EBgby6aefcvToUdatW8cTTzxhf7x58+ZERETw4osvEhMTwy+//MI777zjcIzGjRuTlZXFH3/8QVJSEjk5ObRo0YIRI0YwceJENm7cyJ49e7j77rsJCwtjxIgRZY5n0qRJHD58mOnTp3PkyBG+/fZbe+fx4mWpRqORRx99lK1bt7Jz507Gjx9Pt27d7EF448aNOXHiBLt37yYpKYn88jokCyHK5WRvRqBojW6De2EX6XraFJILg24fncqBsxn8tOdMyYPYM92yZJhwZGui1rCcoBvA18PIu7d34NN7OhLk5UpMYha3zP2Hd36LpsB88WahQgghRG0jQfcVSKfTsXTpUnbu3Enbtm2ZOnWqvYkZaMHtkiVLOHz4MO3bt+eNN97glVdecThGjx49eOihhxgzZgzBwcG8+eabAMybN4+OHTsydOhQunfvjqqq/Prrr+WWkjdp0oTvv/+eZcuW0b59ez7++GN793JXV1f7fh4eHkyfPp2xY8fSs2dPvLy8+Oabb+yPjx49miFDhtCvXz+Cg4NZsmRJlXy/hBAVYysvNxgKS8NDtCqUC4PuYa0CAHh7zRHyTBdUp9jX6ZbycuHoZIp2Iaa08vLS3NAmlN+m9mbYNQ2wWFU+WHeUER9t4uDZjOocphBCCFHlpLz8CjVw4MASncqLz+Hu2bMne/fuLfNxgI8//piPP/7YYZu/vz8LFixwejzDhw9n+PDh9vuvvvoq4eHhuLm5Oew3atQoRo0aVeoxXF1d+f77750+txCiatjKy42c1zbYy8sd9+vXxJfQfDfOpOUy/59YHurTrOhBaaQmynAqResHcrFMd3EBni58cOe1DGkTynM/7edQfAbDP9zIYwNa8HDfZhj1kjsQQghR+8m7lagSc+fOZfv27Rw/fpyFCxfy1ltvce+999b0sIQQTrB1Lzfo0wEFgltpD1yQ6TaYC5h2Q0sAPvrzKKnZBUUPuhTO6S6QOd2iSL7Zwtl0W9Dt6fTzb25fnzWP92Zwm3qYrSrvrj3CqLn/cCRBfs6EEELUfhJ0iyoRExPDiBEjiIqK4uWXX2batGm8+OKLNT0sIYQTTPY53VYIaAIupWck1bx8Rl0XTqtQbzLzzHyw7mjRg5LpFqU4k5qLqoK7UU+Ql0uljhHs7cr/7u7Ie2M64ONmYN+ZdIa+v5H/bTiGxVqJJgZCCCHEZSJBt6gSs2fP5uzZs+Tl5XHkyBGee+45DIai2Qvjx48nLS2t5gYohCiXajJhSUoGwOhhKSotB5QL6sut+XnodQr/uUmb871wSywnkwsbp8mSYaIUtjW6GwZ4lFj33RmKojDy2jDWPtGHfpHBFFisvL7qMLf97x+On5efOSGEELWTBN1CCCEwJyVpLc/1CnpXq0PQfWF5uZqnrSzQu2UwvVoEYbKovLkmWntQMt2iFPagu4JN1C6mno8bX47vzJuj2+PlamBXXBo3vf83X248gVWy3kIIIWoZCbqFEELY53MbPXVajF2vnKA7P89++z83tUZR4Je98fwbl1o0p9uUDVZZ3kloKrpcmDMUReH2zhGsmdqb65sHkWey8tLKg9z52Rb7+YQQQojaQIJuIYQQmM4Vzud21bLYhLQpevCCoNtamOkGaF3fh9HXhQMw69dDqMXngZtkrW6hKV5eXtXC/NxZeH8XXh7ZFg8XPVtPpDBkzl98veVkiVU7hBBCiJogQbcQQgjMtiZqbibQu0JA06IHy8l0A0y7oSVuRh3bY1P57UgGKIVvLVJiLgpVdXn5hRRF4Z5ujVg9pTddmgSQU2Dhv8v3M+7LbZxNy62WcwohhBAVJUG3EEIITLbycncLBLcEfVEjxAvX6S6e6Qao7+vO/dc3AeCN1dGo0kxNFKOqarVmuotrGOjB0ondeH5oFK4GHX/HJDF49l98u/2UZL2FEELUGAm6hRBCYE5IBMBwQedyoES3aTXPMdMN8FCfZgR6unA8KZscxV3bmC9rKAtIzi4gp8CCokC4v3u1n0+nU7jv+iasmtKLaxv6kZlv5ukf9vLI4l0SeAsh6qyk01msW3CI3MyCmh6KKIUE3Vep+fPn4+fnV9PDEEJUExXnggtbIzWDu7VE0H1hqtuaXzLo9nYzMmVgCwAS8ozaRsl0C+BkYVOz+j5uuBr0l+28TYO9+P6hHjxzYyuMeoVf951jz+n0y3Z+IYS4nDZ+F8Ohf+I5uOlsTQ9FlEKCbnFZvfjii3To0KGmhyGEuIApsVh5eb025e6r5pd+Ff3OLg1pGuRJhtVV2yBzugVwqrC0PKKaS8tLo9cpPNSnGddG+DuMRQgh6pK8LBNnY9IAyErJL39nUSMk6BZCiKucqqpF5eXuFghp7bhDBcrLAYx6HU8PaUWW6gZAWlpK1Q9WXHFsme5G1dRErSLCCsvaT6dKUzUhRN0Tuz8J1apVuGWlSdBdG0nQfYXq27cvkydPZvLkyfj6+hIUFMRzzz1nn6+WmprKuHHj8Pf3x8PDgxtvvJGYmJhSjxUbG4tOp2PHjh0O29977z0aNWqEtXCt3RUrVtCiRQvc3Nzo168fX331FYqikJaWBhSVrC9fvty+3+DBgzl16pT98ZkzZ7Jnzx4URUFRFObPn1893yAhRIVZMzLsgbTBzwt8whx3uHDJsPyy39AHt6mHi4cPAH/sOV61AxVXpMvVRK08trnkZ9Ik0y2EqHtO7E6y386WoLtWkqC7GFVVsebk1MhXZZq7fPXVVxgMBrZt28acOXN49913+fzzzwEYP348O3bsYMWKFWzevBlVVbnpppswmUwljtO4cWMGDhzIvHnzHLbPmzeP8ePHo9PpOHHiBLfeeisjR45kz549TJo0iWeffbbEsXJycnj11VdZsGABmzZtIi0tjTvuuAOAMWPGMG3aNNq0aUN8fDzx8fGMGTPG6dcthKi4C+LlUtk6l+tdrOgatC75pApmurVdFZqFhwIQfSqeQ/EZzg1Y1Dk1WV5uEy6ZbiFEHWUqsBB3INl+XzLdtZPh4rtcPdTcXKKv61gj547ctRPFw7kPJBEREcyePRtFUYiMjGTfvn3Mnj2bvn37smLFCjZt2kSPHj0AWLRoERERESxfvpzbbrutxLEeeOABHnroId59911cXV3ZtWsX+/bt46effgLgk08+ITIykrfeeksbb2Qk+/fv59VXX3U4jslk4sMPP6Rr166AdmGgdevWbNu2jS5duuDl5YXBYCA0NNTp75EQono4lJbXu7CJGiWXDCsn0w0Q6B8AgCd5vLbqMAvu61Il4xRXppMp2QA0CvSssTGE+WnvrxJ0CyHqmtOHUjCbrLh6GMjPMZObWYDFYkWvl9xqbSL/G1ewbt26OSzl0717d2JiYjh48CAGg8Ee+AIEBgYSGRnJoUOHSj3WyJEj0ev1/Pjjj4BWCt6vXz8aN24MQHR0NJ07d3Z4TpcuJT9IGwwGh/1atWqFn59fmecVQtQ8c2ETtdKWC4OKLRnmwFVbp9tbyeOvI+f5O+Z81QxUXHHyTBYSMrSLNLWivDw1V5YNE0LUKcf3aKXlLbuGotMroEJOuiwbVttIprsYxd2dyF07a+zcNcnFxYVx48Yxb948Ro0axeLFi5kzZ06NjkkIcXmY7MuFlR50l1wy7CKlay7eAHSop4fTMOvXw6x8NAi9rgK17qJOOZ2qlZZ7uRrw9zDW2Djq+2nN/XJNFlKyCwj0cq2xsQghRFWxWqzEFgbdzToEE7snicyUPLLT8vEOcKvh0YniJOguRlEUp0u8a9LWrVsd7m/ZsoUWLVoQFRWF2Wxm69at9vLy5ORkoqOjiYoq7QO15oEHHqBt27bMnTsXs9nMqFGj7I9FRkby66+/Ouy/ffv2Escwm83s2LHDngWPjo4mLS2N1q21bsguLi5YLJbKvWAhRLUwnz0NgNHdWkZ5eclMt6qqJTLgdoWZ7rZBeryTDByKz+DHf89wa8fwKh23qP1sncsbBniU/fNyGbga9NTzcSUhI58zabkSdAsh6oT4Y+nkZZtw9TRQv7kvnn6uZKbkkZUq87prm0qVl6elpfHbb7/x9ddfs2DBAocvcfnExcXxxBNPEB0dzZIlS/jggw+YMmUKLVq0YMSIEUycOJGNGzeyZ88e7r77bsLCwhgxYkSZx2vdujXdunVj+vTp3HnnnbgXy75PmjSJw4cPM336dI4cOcK3335r7zxe/IOU0Wjk0UcfZevWrezcuZPx48fTrVs3exDeuHFjTpw4we7du0lKSiL/YhkzIUS1M5/SuowbArzB3b/kDhcGS1YrlNKU0c5FC7pdLDk80q85AO/8Fk2eSS64XW1qQ+dym3B/mdcthKhbbF3Lm7QLQqfX4emnXVCUDua1j9OZ7p9//pm77rqLrKwsfHx8HAIuRVEYN25clQ5QlG3cuHHk5ubSpUsX9Ho9U6ZM4cEHHwS0zuNTpkxh6NChFBQU0Lt3b3799VeMxvLL++6//37++ecf7rvvPoftTZo04fvvv2fatGnMmTOH7t278+yzz/Lwww/j6lqUMfDw8GD69OmMHTuWM2fO0KtXL7744gv746NHj2bZsmX069ePtLQ0e4d0IUTNMZ2LB8DYIKLCz7Hm56N3cSn9wcJMNwXZjO/RmM/+Ok58eh77z6TTqXHApQ5XXEFsQXdNrtFtE+bnzs6TqZyRoFsIUQeoqsrxPVrPlCYdggHw8tc+k0sH89rH6aB72rRp3HfffcyaNQuPK6gUuy4yGo289957fPzxxyUe8/f3L7fyYPz48aUGu2fOnKFdu3YlmqYBDB8+nOHDh9vvv/rqq4SHh+Pm5jhnZNSoUQ6l6cW5urry/ffflzkuIcTlZ05OA8DQuGXpO5RSFqzm5YG3d+n7F2a6yc/Ezagn3N+d5OwC0nLKyY6LOikuueaXC7MpWjZM1uoWQlz5ks9kkZmch8GoIyKqcNUQyXTXWk4H3WfOnOGxxx6TgLuOycrKIjY2lg8//JBXXnml1H3mzp1L586dCQwMZNOmTbz11ltMnjz5Mo9UCFERFW3QbC0owJKldTk1NOtQ+k6lTMUtt5maLeguyALAx12rsEnPlaD7alObysvDZK1uIUQdcvxfLcsdERWA0UUPgJcE3bWW03O6Bw8ezI4dO6pjLKIGTZ48mY4dO9K3b98SpeU2MTExjBgxgqioKF5++WWmTZvGiy++eHkHKoSoUuZEbY1uRaeib9ax1H1Ka4BV7rJhtvLyfMegOyNPgu6ridWq1qryctuc7jNpEnQLIa58tqXCmhaWlkNRpjsr9SJLe4rLzulM980338xTTz3FwYMHadeuXYk5wsXLj0X1Wb9+fZUeb/78+fbGaGWZPXs2s2fPLvPxskrWhRC1lzn2EKAtF6aEtCpjr5JBtzOZbl/JdF+Vzmflk2+2olOggV/NLosJ2pxu0DLd5XbfF0KIWi4jKZfk01koCjRuF2TfXlReXiB/52oZp4PuiRMnAvDSSy+VeExRFFkOSgghriDmo7sBMPi4gLGMwKi0THd5Qbdr4VxvUw5YLRJ0X6VsWe4Gfu4Y9ZVaLKVK2eZ0Z+Wbycg141uD64YLIcSlOFGY5W7Qwg83r6K/ZZ5+WoNTi9lKfrbZ4TFRs5x+F7RarWV+ORtwv/baa3Tu3Blvb29CQkIYOXIk0dHRDvv07dtXWz+72NdDDz3k7LCFEOKqpJQ2IbsYU6z2N9cY6FvOQZwsL7dlugEKsiTovkrZ1uiuDaXlAG5GPUGF63OfkmZqQogr2PHdhV3Lrwl22G4w6u2BtnQwr11q9NLzhg0beOSRR9iyZQtr167FZDJxww03kJ2d7bDfxIkTiY+Pt3+9+eabNTRiIYSoW8xn4gAw1Ktf9k6lBN3WvHLezA2uoCsspCrIxsetcE53rrnS4xRXntrURM1GmqkJIa50uZkFxB9NA6BJh6ASj8u87trJ6fLy0srKi3v++ecrfKzVq1c73J8/fz4hISHs3LmT3r1727d7eHgQGhrq3ECFEEJclK2RmiGiSdk7lZIsV/PLeTNXFHDxhLx0yM/C110rN8+QTPdV5VRK7VkuzCbc3509p9KkmZoQ4ooVuy8JVYWgCC98AktOC/PycyX5dJZ0MK9lnA66f/zxR4f7JpOJEydOYDAYaNasmVNB94XS09MBCAgIcNi+aNEivv76a0JDQxk2bBjPPfdcmUuW5efnk19srmFGRkalxyOEEHWa1YIpNQswYmzSxrmnlpfpBnDx1oLugkx83bW/6VJefnU5maxVrTUK8KzhkRQJ95O1uoUQV7bju0t2LS/O01+WDauNnA66//333xLbMjIyGD9+PLfcckulB2K1Wnn88cfp2bMnbdu2tW8fO3YsjRo1okGDBuzdu5fp06cTHR3NsmXLSj3Oa6+9xsyZMys9DiGEuGqkxmLO1tLYhqZty96v1EZqFylbK7ZsmMzpvjrFpWjZ5NpUXm5rpnZGysuFEFcgU76FU4dSgJLzuW1kre7ayemguzQ+Pj7MnDmTYcOGcc8991TqGI888gj79+9n48aNDtsffPBB++127dpRv359BgwYwLFjx2jWrFmJ48yYMYMnnnjCfj8jI4OIiIhKjelq0bdvXzp06MB7771X00MRQlxGasIBzLl6AAz1y57TXdqSI9byGqmBw7Jhvr6yTvfVJqfATFKW9oGvYS1ppAZFa3XLnG4hxJXo1MEULCYrPkFuBIaVXkVkn9OdVnA5hyYuosoaqaWnp9vLw501efJkVq5cyZ9//kl4eHi5+3bt2hWAo0ePlvq4q6srPj4+Dl91Ud++fXn88cdrehhCiCuY5fi/qNbCTHdISDl7lta9/CJX0Itlun3cteu7OQUWTBZrZYYqrjC2Jmq+7kZ7pUNtUNRITcrLhRBXnuN7CruWdwgucw3uorW6pZFabeJ0pvv99993uK+qKvHx8SxcuJAbb7zRqWOpqsqjjz7Kjz/+yPr162nSpJxGPoV2794NQP1ysjKiYgoKCnBxcanpYQghaoj5+H4A9N5u6Mr7W1Bapvti5eX2THcm3m5FQVd6rsm+bJOou+KSa1/ncoCwwjndGXlmMvJM9s76QghR21ksVmL3Fs7nLqO0HIrKy2XJsNrF6Uz37NmzHb7ef/991q9fz7333ssnn3zi1LEeeeQRvv76axYvXoy3tzfnzp3j3Llz5OZqZV/Hjh3j5ZdfZufOncTGxrJixQrGjRtH7969ad++vbNDrzPGjx/Phg0bmDNnjn3t8vnz5+Pn5+ew3/Llyx2ugr344ot06NCBzz//nCZNmuDm5mZ/zGw2M3nyZHx9fQkKCuK5555DVVX746mpqYwbNw5/f388PDy48cYbiYmJqfbXKoSoHPXiu2CO0yqGDMEllxxxUOo63RfLdGsdy8nPQq9T8HbTrvHKvO6rg325sFpUWg7g6WrA30MLtGVetxDiShIfk0Z+jhk3LyOhzXzL3M+W6c7PNmMusFyu4YmLcDrTfeLEiTIfswXLFfXxxx8DWql0cfPmzWP8+PG4uLjw+++/895775GdnU1ERASjR4/mv//9r7PDrhBVVTEX1Ezpo8FFV2aZyIXmzJnDkSNHaNu2rX0Jt19++aVCzz169Cg//PADy5YtQ6/X27d/9dVX3H///Wzbto0dO3bw4IMP0rBhQyZOnAhogX5MTAwrVqzAx8eH6dOnc9NNN3Hw4EGMRskUCHHFMeVhSkgEfDDWDyt/X2eXDANtyTCAAq2Dta+7kcw8sywbdpWojWt024T7e5Cak86Z1Fxa16+bU9CEEHXP8T1alrtJ+yB0urJjBlcPAwajDrPJSnZ6Pr7Bte/v8NWoShqp5efn89FHH/Hmm29y7ty5Cj+veCa1NBEREWzYsOFSh1dh5gIrn065fOcr7sE5fTC66i++I+Dr64uLi4vD+uXFA+jyFBQUsGDBAoKDHctSIiIimD17NoqiEBkZyb59+5g9ezYTJ060B9ubNm2iR48egLaMW0REBMuXL+e2225z4pUKIWqFpCOYcwrnc4c1LH/fUhupXWzJsKJGakBhGW+uZLqvErU76HZn35l0mdcthLhiqKrKid1F87nLoygKnn6upJ/PJStVgu7aosLl5fn5+cyYMYNOnTrRo0cPli9fDsCXX35JkyZNmD17NlOnTq2ucYoq0qhRoxIBN0C3bt0cMu3du3cnJiYGi8XCoUOHMBgM9iZ2AIGBgURGRnLo0KHLMm4hRBVLPIg5V3sLMNSrV+6uDlU4hZUtan5FG6llAsiyYVcZ25zuRrUw6A6zr9Ut5eVCiCtD0qksslLzMbjqiWjlf9H9PWXZsFqnwpnu559/nk8++YSBAwfyzz//cNtttzFhwgS2bNnCu+++y2233VbhbGttZXDR8eCcPjV27kuh0+lKVA6YTCU/3Hp6lr68gBCibipz1kriQUyFy4UZLxJ0F6dzc8NqMlWgkVrhnO7CTLct6Jby8rrPYlXtAW1ELQy67Wt1p0nQLYS4MhwvzHI3igrA4HLxeMvLX5qp1TYVDrq/++47FixYwPDhw9m/fz/t27fHbDazZ8+eCs9Fru0URalwiXdNc3FxwWIpao4QHBxMZmYm2dnZ9sDa1um9IrZu3epwf8uWLbRo0QK9Xk/r1q0xm81s3brVXl6enJxMdHQ0UVFRl/5ihBAAfLR1JUsOLWZE81t46vqS0zaWHl7K2eyzTL1u6qX/3U04WLRG90WD7qJzKW6ukJnp1JJhUCzozjNXarjiypGQkUeBxYpBp1Df1+3iT7jMwmSt7ivOyeRsZq89wqQ+zWQevrgqHa9gabmNZLprnwqnV0+fPk3Hjh0BaNu2La6urkydWgUf/ESlNG7cmK1btxIbG0tSUhJdu3bFw8OD//znPxw7dozFixczf/78Ch8vLi6OJ554gujoaJYsWcIHH3zAlClTAGjRogUjRoxg4sSJbNy4kT179nD33XcTFhbGiBEjqukVCnH1+d/+10lX9rHg6KslHjNZTbyx/Q3m7Z/HiYyyG1pWWOJBzDmF5eUhFwm6i/2d17lpWUI1r6JLhhXO6XaX7uVXi5OFpeXh/u4Y9JdWxVUdJNN95Vm4+STLd59l4ZaTNT0UIS67tMQcUs5mo+gUGrUNrNBzJOiufSr8bmixWBzWdDYYDHh5eVXLoMTFPfnkk+j1eqKioggODiYjI4Ovv/6aX3/9lXbt2rFkyRJefPHFCh9v3Lhx5Obm0qVLFx555BGmTJnCgw8+aH983rx5dOzYkaFDh9K9e3dUVeXXX3+VzuVCVCVDuvavUnKJj3NZ5zBbtSzx6czTl3ae3DSsqWewFNjKy0PK398h6NbeyK0VntPtmOlOz5Ggu647VdhErTaWlgOEFQbdKdkF5BRI5cWV4GB8BgBJmRJAiKvPicKu5WEt/XDzrNjnbvta3anyO1NbVLi8XFVVxo8fj6ur9p+Yl5fHQw89VGKO8LJly6p2hKJULVu2ZPPmzQ7bGjduzMiRIx222Zb8Am2d7tIC8fXr19tv25Zxu5C/vz8LFiyo9HiFEOW72GoOpzJP2W+fyTpzaSdLPGQvLVdcXdH5lr3ep7ZT8fLyima6S5/TLZnuus/WubxRLVuj28bHzYiPm4GMPDNnUnNpUc+7pockyqGqKocKg+7k7IIaHo0Ql9+JPYWl5ddUrLQcJNNdG1U46L733nsd7t99991VPhghhLhaZReUzG4X5xB0Z15q0H0Qc07RfO6LThMq9rDOtYKZbvs63bbyctucbgm667qTtXi5MJtwfw8OxmdwWoLuWi8xM5/UwgqZ5CwJIMTVJSejgPhjWhVck2uCKvw8WyO1nPQCVKuKUs663uLyqHDQPW/evOochxBCXNUuLJtUVdUhGI7LjLPfPpt99tJOlngQU+FyYcaQi5SW47hkmOJewUz3BeXlPpLpvmrU5jW6bcL83QuDblmru7azZbkBkrMk0y2uLrF7k0CFkEbeeAdUvDGlh48LigJWq0pOZgGevq7VOEpREbWvw4kQQlyFkrMdg+48k9XhfvFM9yXP6XaqczkUT3VXeE63rbzcnAsWs5SXX0VO2YPu2rtEpa2Z2mlpplbrHYrPtN/OzDeTby6/KkiIuuR4JUrLAXR6He4+Wi8uKTGvHSToFkKIWuB8pmMG58IGT87O6S5ziriqauXlzgTdlZnT7Vqs0WZBlgTdV4nMPBMphfNuIwLca3g0ZQvzKwy6ZdmwWu/wuQyH+ykyr1tcJQryzJw+lApAkw4VLy23kWZqtYsE3Vy8gZGoXeT/S9RFF2a6c4rN8VZV1SG7nVGQQWZBJpWSGQ95aZhytdlFxtCKZLqL2DLdan5++b+LBlfQFXZZLRZ0Z+WbsVrld7iuspWWB3i64O1We1e3CC9cq/uMBN21XvHycpASc3H1iDuQgsVsxTfYnYD6zlcOSTO12uWqDrpty13l5MicriuJ7f9LlisTdcn5jKLMsarqyTUVBd3nc8+TZ8lDp+jwcfEBLqGDeeJBAMwFWtBx0TW6wTHT7Vo0p0x1Ytkwn8IATFUhM0+Waaqr4pJr/3xuKFZeLkF3rZZnsnDsfDYAgZ5aqax0MBdXC1vX8qYdgi/e8LQUXhJ01yoVbqRWF+n1evz8/EhMTATAw8OjUj/U4vJQVZWcnBwSExPx8/NDr9fX9JCEqDKJWdlFd1SdQ6bbVlpe37M+AW4B7Evax5nMM7QKaOX8iRIKg+48A1CA4WJrdIPjOt3uxYLuvDxwK6exi4s35KZCQTYuBh3uRu1iQnquCV+P2nfRzGJVeennAzQL8WJc98Y1PZwr0pXQRA2Kgu6krHzyTBbcjPJ+UhsdTczCYlW51j2BJ1x/4ZWcgSRnXVPTwxKi2llMVmL3JQPQpINz87ltPP0l6K5NnA66v/rqK4KCgrj55psBePrpp/n000+JiopiyZIlNGrUqMoHWZ1CQ0MB7IG3qP38/Pzs/29C1BXns7KK7qh6zqXnQYR21xZ0h3uH4+/qz76kfZzOqlgztRKXERMPoqpgytSyzcYKzekudttgAL0eLBas+QWUG6rYlw3TSuF93Y32oLs22hGbwlebTwKQlmPisQEtanhEV57avka3ja+7ES9XA1n5Zs6k5dIs2OviTxKX3aH4DHzJ4hPd64TkJHCPXiU5a3BND0uIardlxXEKcs14+rpQr4lPpY5hKy/PkqC7VnA66J41axYff/wxAJs3b+ajjz5i9uzZrFy5kqlTp7Js2bIqH2R1UhSF+vXrExISgslUOz8IiiJGo1Ey3KJOOp+VDbY4RdWxKy6VIW21i0u2oDvCOwJfF1/g0srLLfk6sFhBUTAEX/wK+oUVQDpXV6w5Oaj5zi0b5utu5FxGXq1dq/vwuaJ58u+uPYKLQcdDfZrV4IiuPLagO6KWZ7oVRSHMz53ohExOp0rQXVtFx6fxvvFDQiwJAAQqGezOlgBC1G1xB5PZvVZbJrT3nZHoKrnGtszprl2cDrpPnTpF8+bNAVi+fDmjR4/mwQcfpGfPnvTt27eqx3fZ6PV6CeaEEDXmfHaxoFtR2Xky1f6YLehu6N0QLxctOKhU0G21wPlozIVrdOsDA1Eq0huh+JxuRUFxc4OcHKwX62BeOFYKbGt1a285tTXTHZ2gBd2NAz2ITc7h9VWHMep13H99kxoe2ZXjSikvB63EPDohU5qp1WJtjnxEH/1e+31/JYsUaaQm6rCcjAJ+n38IgLa9w2haydJyKNa9XILuWsHpRmpeXl4kJ2tzDH777TcGDRoEgJubG7m58sYlhBDOUlWVFIeGjir7TqeTV9hM7VRGUaY7zCsMgDOZlQi6U46DOQ9TvhYQGUMqMJ8buLBIXSnWwbxc9kx3UXk51OKguzDTPXVQS3tp+csrD7Jwy8maHNYVw2yx2gPY2l5eDsWbqUkz1dpIPbSSWzKXAJDS4jYA/MiSRmqizlJVlXULDpGbUUBAA0963tr8ko5ny3Sb8iwUSAPTGud00D1o0CAeeOABHnjgAY4cOcJNN90EwIEDB2jcuHFVj08IIeq8rHwz+ZaiAFZRVAosVg6cTQfgVFZR0B3uFQ7A2eyzzi+fZ+tcbqgPVHCNbm1Axe+gK+xgftG1ul28tX/tme7aG3SrqsqRwqC7VagPUwe2sJeWP7d8P99sj6vJ4V0R4tPzMFtVXPQ66nmX02CvlgiTDua1V1IM6o+TAJhnHoxnr/8DwF/JJDlLsnaibtq77jQn9yejN+i44f42GFwurQLXxc2Ai5t2DCkxr3lOB90fffQR3bt35/z58/zwww8EBgYCsHPnTu68884qH6AQQtR15zPzQSnqVq7TacH0jthUMgoySM/Xgu9w73Dqe9ZHQSHXnEtyXrJzJ7J1Lrdqf7cr1LkcLgi60crLAWtexZcMg6JMd0YtDLrPpueRmW/GoFNoEuSJoihMHxLJfT210vJnlu1j2a6KNa+7WtlKy8MD3Cs9B/Fysq/VnSZBd62Snwnf3I2uIIut1lYs9Z+Eq08QoGW6kzIleBB1z/m4TP758SgAPW9tTmBY1fSZsDdTS5Xfm5rm9JxuPz8/PvzwwxLbZ86cWSUDEkKIq01iZj6KUhSI2mLcHSdTub6tlk0OdAvE06h1A6/nWY9z2ec4k3WGIPcgJ06kBd2mAi3DV6HO5RdSFHSutvJyJ+d0u9XeTHf0uQwAmgV74WLQrkcrisJzQ1tjslhZuOUkT363B6Nex7BrGtTkUGste+fyK2A+N0CYn5SX1zqqCj89AucPk+0SxOSMx+jWIADcAwBwVczk5WRe5CBCXFlM+RZ+++IAVrNK4/ZBtO0TVmXH9vJ3JfVcjmS6awGnM90Af//9N3fffTc9evTgzBltXuHChQvZuHFjlQ5OCCGuBucz80FXNN9KQct07zqZSly6VtYc4R1hf7zS87pt5eWFMYYhpKLl5RfcrWim275kmGOmu3YG3doYW4Z6O2xXFIWZw9twR+cIrCo8/s1uVu8/VxNDrPVOJl85TdSgaE53YmY++WbLRfYWl8U/H8DBn0Bn5NPQFzmPH61CvcHFE1XvAoCbKY2cApmfKuqOjd8eIS0hB09fF/qPa1VixZBLIcuG1R5OB90//PADgwcPxt3dnV27dpFf2EgnPT2dWbNmVfkAhRCirkvMzAel6EOkioqLQUdydgF7E48DZQTd5XQwV7lgvrcpV2ukBpjTtQx1peZ0O5Ppdi0MYPOvhKBby3S3uiDoBtDpFGbd0o5R14Zhsao8umQXfxxKuNxDrPVOXSHLhdkEeLrgbtSjqhCfdpGfZVH9jm+A31/Qbg95jVXpDQGIqu+j/Q0qzHb7KVkkSwdzUUcc3ZnIwU3xoMDA+9rg7uVSpceXZcNqD6fLy1955RX+97//MW7cOJYuXWrf3rNnT1555ZUqHZwQQlwNzmfmozgE3Vbah/my42Qq+xMqF3SXPEk0qFZwD8CUpM0FN1ZwTveFV92LMt3OlZfb53TXwi6q0QmFme56JYNu0ALvN29tT4HFysq98Tz89S4+v7cTvVtWfjmXuuZkSjYAjQI9a3gkRSxWC7sSd/Hb9vdRcpJ5csQSXNy0te4VRSHM352jiVmcTs2lcVDtGfdVJ+0UfD9B+xt1zVjyOkzg2PLfAGhVX/udVDwCIOsc/orWwbxGL+5snA3H1tXc+a8KCrQdDR3vremBVJvMlDzWf60tD+bS7ADhkf2r/BxeMqe71nA66I6OjqZ3794ltvv6+pKWllYVYxJCiKtKYmaeQyM1q2qlYyN/dpxMJa5wje4In5JB9+ksJxp7FZaWWwNaY00/ATiR6S5eX66AzrZkmJON1HxqaSM1k8XKsURtjKVlum0Meh2zx3TAZLGy5kACExfsYN6EzvRo5sS8+josrpaUl1tVK3vO72H1idWsPbmW87nn7Y/VW3Ev99++3H4/vDDoPpMm87prjCkPvh0HOckQ2h6GvsvRxGwsVhU/DyOhPoWd8Asz3f7UcAfzxMPw+4s1d/6ryYkN2s9FrydqeiRVzmqxsvaL/eTnWkjwiuWnwM8ZcbY7IQ06Vel5JNNdezgddIeGhnL06NESy4Nt3LiRpk2bVtW4hBDiqnE+Mx9F5xiIXtfQD4A0UzzoKj+n256kts3ndmkMnEDx8EDnXXaAWfpBCu8WLhlmdbKRWm0tLz+ZnE2BxYqHi97eXKssRr2OD+68joe/3skfhxO5f/4OFtzfhc6NAy7TaGun9ByTvYKhJoJuVVXZm7SXNbFr+C32NxJyisr/vRUDbXMy2ezuzifZMQzdNZ96140HijdTkw7mNWbVU3B2F7j7w5ivwejOofgkQLsIZq+08fAHakF5+fbPtH8bXQ+dJtTcOOq6s//C5g/hj5mgM0DPx2p6RFVqxy/HiT+WgU7J4fcWC7DqrOzb8xUDqjjo9vLX3q8l6K55TgfdEydOZMqUKXz55ZcoisLZs2fZvHkzTz75JM8991x1jFEIIeq0C5cMA+jQyBcUE1ZdGuAYdId7a2t1n8s+h8VqQa+rwFqehcuFmRQtu20MCal4s5YL5nQrrhXNdNvmdGvdhosH3aqqVmmzmEtxuHB97pb1vCu01JWLQcdHd13HxAU7+DsmiQnztrPw/i5c29C/uodaa9lKy4O9XXG/xLVlK0pVVQ4mH2R17GrWxK4hPjve/pin0ZP+Ef0ZHHwdPb59CL3VxL1NI9mty+Wdra/xZuPeENC0aNkwCbprxs75sGsBoMDoL8C/EVD0O9m6vk/RvvZMdxZJ2TUUQOSlw+4l2u0+T0HTvjUzjqtBu1vB1QfWz4K1z4FOD90fqelRVYmz0cns+DUW0KHU/5pMN23K195TGxmgqiUudF8KW6Y7J7MAi8WKXl+pHtqiCjgddD/zzDNYrVYGDBhATk4OvXv3xtXVlSeffJJHH320OsYohBB1WmJmPrg6Zn8DPI1EhOSSBrjqPPB3LQrogt2DMegMmK1mEnISaOBVgSWsbJlui/YhtuKl5ZToXm4vL8+/WPfy0jPdFqtKdoEFL1en34KqRXThB/zIMuZzl8bNqOezcZ2YMG87m48nM+7LbSx+oBvtwn2ra5i1mm25sOrOcquqSnRqNKtPaIF28SkW7gZ3+kb0ZUjjIfQM64mr3hVWTgWrCZr0ZsaQWdzx61hWebhw2w/30HnCn4T5S6a7xpzeCb8+pd0e8Bw0H2B/6FC81tiwdWixoNujMOhWMjlTU5nuPUvBlA1BkdCkT82M4WrSdzqoFtjwBqz5Dyh66PZQTY/qkuRl5bH2439Q8STSfQOrr9VDivbYPmsWJOyH0HZVdj53LyM6vYLVopKTXoB3gFuVHVs4x+nLHYqi8Oyzz5KSksL+/fvZsmUL58+f5+WXX66O8QkhRJ1mslhJyS5AuSDTraoqTUK18m1PXT2HrLBep6eBpxZoV6iZWk4KZGpZQHOe1hnVUMEmaoDDVXdFUSpeXm6b012gZUHdjDqMeu1YtWletz3oLmc+d2ncjHq+GN+JLo0DyMwzc8+XWzl4NqM6hljrVeca3aqqciT1CB/8+wHDlg/jtp9v44v9X3A66zTuBncGNx7M7L6z+WvMX7zZ+036N+yvBdzpp2HXQu0gfZ4hKqQdtzW5CYDXSMK89nn7smFn0iTovqyyzsO394ClAFoNheuL5uyqqloUdJeS6fYrbKR22VmtsO1T7XaXiVWajRTl6DsDek3Tbq+eDts+q9nxXALVYmb9W9+QleeJrz6e3uM7E20ues844OqCZe+3VXpORafg4au970uJec2qdJrBxcWFqKioqhyLEEJcdZIKGwLpdI4dvS2qhUC/DEgEc37JsuUwrzDiMuM4nXmazqGdyz9JotYdFd+GmFLSATA6k+m+QIUbqdky3eY8sJhR9AZ83Y0kZRWQnmuiwUXmT18u0Qla0F1eE7WyeLgY+HJCZ+75Yiv/xqVx9xdb+ebBbrRwImteF9iaqFVlR+njacftpePH04/bt7vqXekV1ovBTQbTO6w3HsYyzrlpjpblbnQ9NO4JwKNdZ7Dm9HpigG8OLeTm+t0BA/HpuZgsVoxSeln9LGatU3nGGQhsASM/dghgEzPzSc0xoVOgRT2voud5FCsvr4lGasf/hOSj4OIN19xx+c9/tVIU6P8cWC2w6T349Umt1LzTfTU9MudYrRya+y7HEjqhw8ygW30wR/Xn5O7/AGBU9OTo4PihZbQYOBN0Vfe3yMvPlayUfOlgXsOcDrpvueWWUufhKYqCm5sbzZs3Z+zYsURGRlbJAIUQoi47n6m9CXq4qhTP/aqqitEtFYC0DN8SAUGYdxjEVzDTXVhaTkhrzFu1BlOGkIoH3UqJOd1ONlIDKMgEd398igXdtUFOgdmepW1ZiaAbwMvVwPwJXbj7863sO5PO2M+1wLtpsNfFn1xHVFV5eWx6LGti17Dm5BpiUmPs2406I9eHXc/gxoPpG9EXT+NFlvfKiIedX2m3+zxt3+zn5sdjnabx8paX+cjPj8FrH6eh4SXizAGcS8+7YtYYv6L9/gLE/q39fRjzNbj5ODxsy3I3DfbCzVisP4A9051JSk1kum0Z1g5ji/pViMtDUWDgi2A1a83VVk7VSs2vlOXErFZSl/yHvw9qqz917WGiXr9h7D2/FxWVIPcgGns3ZEfiLvaZUmlxags06lFlp/f0cwMyJNNdw5y+jOLr68u6devYtWuXVmaoKPz777+sW7cOs9nMN998wzXXXMOmTZuqY7xCCFGnJGZob4JuLqrDdqtqJcN8DoCCPH/7B1EbWwfzs1lnK3CSwqC7XhTmhMKgu5Ll5aCgVDTTbXABvVbWZls2rLZ1MI9JyEJVIcjLhSAv10ofx9fdyML7u9C6vg/nM/MZ+9lWTiZnV+FIazd7eXmg80HrqcxTfL7vc277+TaGLR/Gh7s/JCY1BoPOQO/w3sy6fhYbxmzg/f7vc3PTmy8ecIOW5bbkQ8Pu0MRxmdPRLUbTOqAVmXod77vDhy5z0WORed2Xw/5lWtAEMHIuhLQqscuh+FKaqIFDpvuydy9PjYUjq7XbnR+4vOcWGkWBG16Bbv+n3f95Cvz7dc2OqSKsViwrpvHb5uaYVTfCw01ce7c2zSU6NRqASP9I2oVcA8BeV1fY932VDsFLlg2rFSq1ZNjYsWP58MMP0RWWPlitVqZMmYK3tzdLly7loYceYvr06WzcuLHKByyEEHXJ+ayioDuz2HYrVnuTKGtBIDtPptI+3M/+eLiX1sG8QpnuBFumuw2mxL8AZ8vLHaubdG5aplu9WKYbtGxWboq9mZqPW+1aq9tWWt6yCsrB/Txc+Pr+Ltz52RaOJGQx9rOtfDOpm71Ddl1VYLZytnBOdEUz3WezzmoZ7dg1HEg+YN9uUAx0bdCVwY0G079hf3xdK9GYLjMBds7Tbvd5usTcW71Oz3+6Pss9q+7hR28vbs08yuOGHzidei0Q6Pz5RMUkHISfJmu3ez4OUSNK3e3wOe0CY4npHrbu5UoWydn5l3cFhO1fACo07QfBLS/POUVJigKDZ2kZ722faj9Pih463FnTIyudqsKqp9j8l0qSuSlublYGPtoXpXCVjOgULehuGdCS9kHtAdjn6gIHfoQb3wC9sUqGYetgniVBd41yOuj+4osv2LRpkz3gBtDpdDz66KP06NGDWbNmMXnyZHr16lWlAxVCiLrIlul2NVqg2LRuk8VkX4fbWhDIjpOpTOjZxP64LdNdvHtzqVTVPqdbDYrEfP484Gz38tKXDLNeLNMNWjO13JRam+mubBO1sgR6ufL1A12545MtHE/K5s7PtrDy0V72110XnU3LxapqjfKCvcuvFkjITuDpv55mV+Iu+zadoqNLaBeGNB7CgIYD8HPzu7QB/fO+1kcgvLMWJJWiQ0gHhjcbzopjK5gV6M/XeT/xU+xA6HTPpZ27Fth8LJkVe87wn5ta4+1WS37uctPgm7u1zt9N+mhzdMtgq+qJKiPT7aPkYLWYycgzX57fq4KcwmXNgK6Tqv98tczyf8+wPjqRPpHBDGxdr+Z/phQFbnxTm+O94wtY/rA2x7v97Zd02Pzjx8lYtYrcnbtQrdYqGKgKycdIsASwp+EUANplrCdpyiKSCvfomHyAlgUWfPSrSW+TCm3hqIuRnLwEPI79CS1vqIJxgKe/NFKrDZwOus1mM4cPH6ZlS8crfYcPH8Zi0brvurm51Zr1V4UQojY7n6VliwuU8w7b47PjMatmDIoR1ezDzthUh8xOmLcWdJ/POU+BpQAXWxm3TWG1undBIuSng86ARRcEZjPodBiCgio+SKWMTHdeRTLdhcFsgeNa3bUl030kwfnlwi4mxNuNxRO7ccvcTZxKyWV9dCIjOoRV2fFrm5PF5nNf7L3/4z0fsytxFwoKnUM7M7jxYAY0HECgexVlmLPOF2YlgT7PlNthemrHqayLW8cB4CdvDwYdfh6yBoOXE1MvaqG3f4tm58lUGgV68lCfZjU9HK3r948PQcox8I2AW78EfekfP/NMFo6d16ZltKp/we9ksYsxfmSRkl1weYLu/d9DXhr4NYQWVRMEXSnSc0zMWLaPXJOF5bvP4mLQ0bdlMEOvacCAViF41tSyj4oCN72tLSe2cz78OAkUnba2txMKTp4kY9UqMlatJj86usqHme/iw+7Chm/hp9fjffQHcoo93th+6xQcP0WL5iHEuKVwwNWFzvu+q7Kg28tPe8+WTHfNcvq35Z577uH+++/nP//5D507ax1zt2/fzqxZsxg3bhwAGzZsoE2bNlU7UiGEqIMSM/JR9Jkkmhzf8E9mnAS0jHaGTs+5jDzOpucRVtjx29/VH3eDO7nmXM5mnaWxb+NSjx+Se0y7EdgcU5LWmM0QGIhicOLPv8OU7uKN1CqY6Qb7smG1LdN9uIoz3Tahvm5c18ifX/bG10zTp8uook3UzFYzf8T9AcDcgXO5Puz6qh/M5g/AnAsNrnNY97k0Qe5BPHzNw7y14y3eCQhg4OkzsOxBuHtZlXYOvpxUVbVfSFp3OLF2BN1/vwNHVoHeFW5fAJ5lX/A7mpiFxari52Ek1OeC9YT1BnDzhbx0bdmwrHyaBFVgfv+lUNWiZcI6P6BlVK8i3+08Ra7JQn1fN9xd9Bw/n81vBxP47WACrgYd/VuFMLR9A/q1CsbD5TIH4Dod3Dxby3j/u1D73dXpoc0t5T6t4PTpwkB7FfkHDxU9YDDg2bMH3v36o/O+hCaYKrDvW9Qja1hnnIRJ542fj5VeD3ZFr+8KaBeXluw8yKGChahWPcP+CqZ5+ll6HPEmpn0K+1xd6Hz4F+190+XSf8Y9i83pvqzTMoQDp39DZs+eTb169XjzzTdJKGzIU69ePaZOncr06dMBuOGGGxgyZEjVjlQIIeqg81n5GLwPAirtgtpxIPkAVtVKXEYcAI18G6Jr4MPe0+nsiE0hrDBjqigKYV5hHE07ypmsM2UG3cG5hUsthURhTrQ1UXNuubAL36CLlgyrSKa78ANDYXm5j7v2tpORZy7rGZdNSnaBvXt8dSzx5e+hXWBIzakdFxiqyyl70F3+h8MdCTtIy0/Dz9WPbvW7Vf1AspNh2+fa7T7TK7SO8p2t72TRwe84mxPLHH9/nj/+J2yaXbQu8BUmMTOfzMLfrZ0nU0nPMeHrUYPlwDFr4c9XtdtD34Ww68rd3VZa3irUu/TAwD0A8tLxJ5Oky9FM7dRWOLcPDG5w7ZU/9cAZVqvKgs3axd/HBrTgjs4RHD6XyS9741m59yyxyTms2n+OVfvP4W7U0791CMPa16dvZIhj1/nqpNPBsPdBtcLuRfD9/doc76jhDruZzp4lY/UaMlatIm/fvqIH9Ho8u3fH58YheA8YgN7P79LGo6rwx0zIX86/QSOIz2yPwajjxqldCaiv/X3cdDSJp7/fS4IbuDfX4a9vwhCX3vDD50TuzYD2sM/LH9LjIHqV09n70nj6aZVwFpOV/Bwzbp61ZNrJVcbpoFuv1/Pss8/y7LPPkpGh/XH08XGcd9OwYcOqGZ0QQtRxiRn5GLy1RlIDGg7gUPIhrFiJzYgFIMI7guCG/uw9nc6uk6kOZcrhXuH2oLss9kx3vSjMZyoXdDvO6QbFzYlMt23ZsILaN6fbNp87IsAdr2ook/Rz1z7opOfU7Uy3rUt7w4Dy111fG7sW0H7ODbpqyIpt/lCbM1z/Gmg5uEJPMeqMTLn2KaZveoTvvD0Zk5lB5LpXoVFPaFgNFwaqWUxClv22xaqyIeY8w69pUDODSTkBP9wPqNBxAlx790WfYqs8KdG53MYjAFJP2JupVTtblrvdbfY55VeLDUfOE5eSg4+bgREdGqAoCq3r+9C6vg/TbmjJgbMZ/LJPC8BPpeTyy954ftkbj6eLngGt6zG0fX16twyu/gBcp4PhH2gZ771LtTXgb1+Ayb8TmatXk7FqNbm7dzvs79G1Cz5DbsT7hkEY/P2rZhyqCutegY2zSTQ1ZUu2tpzZ9be3IKC+J9n5Zl5fdZiFW7QLGcERSeQB/Zp2oG374Vh++Jymp87jm61nr09h1dC+76sk6DYY9bh5GsnLNpGVmi9Bdw25pHe9C4NtIYQQFaeqKudz0nAJPQpowchHuz8CFXumO8I7Al83f+b/E8uOk6kOz7fN6y6vmVrxTLdpl7busdGZ5cJKoXN1ItNtW88233FOd+0IurULx5H1que9zO8qyXTHpRR2Li9nuTCL1cLvcb8DcEOjapgXm5NSFCBVMMttM7hpL55c3R69915eDm/BwuMHUb6/Hx76+4oLtGISMx3urzuUUDNBd0EOfHMP5KVDWCetE3MF2DLdrUPL+J20r9WdRUp1Z7oz4uHgT9rtLhOr91y10FebYwG4vVNEidJxRVFoG+ZL2zBfnh4cyb4z6awsDLrPpOWyYs9ZVuw5i5ergUFRWgB+fYsgXA3VFIDr9DByLqb0XDLXriVj4hRyzxcLLBUFj06d8LnpRrwHDXKup0lFrX8d/n6bAqsbv5lew2pVaHptMFHXN2DbiRSe/G6PfSrOPd0akeSZy8az2nJhLVo1Z3VQQxonxdH5CPx+bS4Jej31jq7V/rZVwd8hTz9X8rJNZKflExR+CeXzotKcDroTEhJ48skn+eOPP0hMTERVHdeWtTVTE0IIUb6MXDNWt4MoipWmvs1o7NsYnaLNJbXN6Y7wjqCFt3Yl/lB8Btn5ZnvzGlsHc1uX8wvpsRCUG6vdCYnCnLAJAEO9UCdHWhTAKMW7l1ci0+1Tm4LuwqxgZGj1fADx89Ay3Wm14LVWF1VVK1RevitxFyl5Kfi4+NC5fueqH8iWudrPWL12EHmTU0/V6xQC8keT5nmIPbosfqnXhKEJJ+CnR+COxU4F8DUtJlH7me7aJICtJ1JYf+Q8ZosVg/4yzlFXVW0N5YR94BmszeM2lN/VXnuaWhR0l5fpBvzJJLm6eyXsnK8tTRXRTaueuIrEJmWzPvo8igL3dG9U7r6KotA+3I/24X7MuLEVu0+lsXJvPL/uiyc+PY8f/z3Dj/+ewdvNwA1RoQy9pj49mwXhYqian0lzcjKZv/1GxqrV5GzfAaqf/TH31k3wGTUW78E3YAypxgaJG96EDa8D8Lf3x6QnuuDl70qPMS145ZdDfLnpBKoKYX7uvHlre3o2D+KG77WL4JEBkSiKQnrnXrBqEd2PuPD7tSb21WtBvbOHtQs/nSZc8hC9/F1JPpMlHcxrkNNB9/jx44mLi+O5556jfv36lzQZ/7XXXmPZsmUcPnwYd3d3evTowRtvvEFkZKR9n7y8PKZNm8bSpUvJz89n8ODBzJ07l3rOlkcKIUQtcz4rD4P3fgAGNRoIYA+6M02Fpc/eEdT3dSfMz50zabnsPpVGz+baVXp70F1GeXlj5RwGtQCMnuDXCHPCOQAMzma6L/g7bysvx2RCtVhQ9OVkL2yN1GxzumvROt32THdZWbVLZJvTnVaHy8tTsgvIytfmEIf7l11evvakVlrev2F/jLoqLm3MTYWtn2i3S1mXuyIa+jQgMak/riFreNffm37JrnhG/wpb/wfdHq7a8Vajo4UXkm7vpM2/Tcsx8e+pNDo3vowZ+22fwr5vtbm1t80H34p17k/MzCc1x4ROgRb1yrgQVmyt7gNZ1Rg8mAuK1nq/CrPcthLofpEhNAqseCMvRVG4tqE/1zb059mbWvPvqVR+3qMF4ImZ+fyw6zQ/7DqNr7uRIW1Cubl9fbo3C8To5EUhc2oqmWvXkrl6Ndlbtmod8gu5X9Me7/qp+Lhsx+idDN2fgOoMuP9+x963IKb5HA5v9ENRoNHNDbnliy0cL+zGP6ZTBP8dqi3jl56fTnx2PAAt/bXVoCKG3wSrFtE6Ng/PXB176zdn4NnDWol5FQTdslZ3zXM66N64cSN///03HTp0uOSTb9iwgUceeYTOnTtjNpv5z3/+ww033MDBgwfx9NR+yadOncovv/zCd999h6+vL5MnT2bUqFFs2rTpks8vhBA16XRqOgavI4BWWg5FQTeAgmIPrDs28udMWi47T6ZWOOiOVE5pN0JagU6HKSERAOMlzelW7OXlAGp+PopHOV2ra+mcbq3Lc2GmuxqaqEFReXlaHS4vt5VLhvq4lTl306pa+f2kVlo+qNGgqh/E1k8gPwNCoqDV0EodItzfnX+O9yK4wV7O58fzyTU38sTO5fDbcxDR9aINwGoDVVU5Ulhe3qq+N30jg/lp91nWHU68fEH3yc2w5j/a7RtehsYV71Bvy3I3DfYqex5wYabbtmRYtTm0ArISwKsetB5+8f3rkOx8M9/u0N47xl0ky10enU6hY6MAOjYK4PmhUWyPTeGXffH8uu8cSVn5fLPjFN/sOIW/h5EhbesztH19ujYJKLMqw5KeTubvf5CxahXZmzdDscpat7Zt8bnxRnyGDMYYFgYWE3w3Hg6vhCVjYew30KxfpV9LmTbNgT9eAiCj6yusX9MEsJDf0ov7ftmLVYV6Pq68Pqo9/VoVBf5HUrX3/TCvMLwLl9Xs1Osa/vStT6P0eK47qrIvvLCS+OQmSD8NvuGXNFR7B/PUCkwLE9XC6aA7IiKiREl5Za1evdrh/vz58wkJCWHnzp307t2b9PR0vvjiCxYvXkz//v0BmDdvHq1bt2bLli1063blNTkRQgibzef+QdGZMKqBtApoBYCOog8coZ6h9vW3OzbyZ8Wesw7zum1Bd1p+GtmmbDyNjhmJSJ0t6I4CwJxQ2UZqF9x1K1rKx5qfj668oPvCOd2FgWi+2UqeyXL5utxe4ExaLln5Zox6pdqWHbKVl6fW4Uy3fbmwcuZz7zm/h/O55/E2eld91/K8dK20HKD3U5Ve6ivMzwNUAy31d7HD/DYLU/cysuVAmh75Hb6/Dyb9BW61u49NcnYBaTkmFAWaBXvRv1WIFnQfSmT6kFbVP4CMePjuXq0ku+1o6PZ/Tj39UPxFmqgBuGtTbfyVLJKrc073ts+0fzvdBwaX6jtPLbR89xky88w0DvSgd4vgKjmmTqfQtWkgXZsG8sKwNmw9kcwve+NZtf8cKdkFLNkWx5JtcQR5uTCkbSg3t2tAlyYBkJ1F1rp1ZPy6iqx//gFT0QVM19at7YG2y4UNnPVGuHWe9vMY/SssuQPGfgtN+1TJ6wHgnw9h7fMAWPr8l9+2dacgL4NkN5h37jyqArdcG8aLw9qUWEEgOkVbIjTSv6iy19WgJ75dVxptXE63aJUPrz2KpVEP9Cf/gf3LoOdjlzRcL3umu+6+H9V2Tr87vffeezzzzDPExsZW+WDS09MBCAjQrmTu3LkTk8nEwIED7fu0atWKhg0bsnnz5lKPkZ+fT0ZGhsOXqD1UVeXzfZ/zwb8fkG+5jCUu6Wfgh4lwavvlO6e48u2cD78+BZbqWd5q5/m/AQg1dLZP1Sk+ZSfCO8J+u2Mj7cPmvydTsVi1C59eLl74uvoCcDqzZDO1VkpR0G3NzsaapWV2DSGXsmSYgqLToRi1DxEXbaZmWzKscJ1uLxcDusLD1WSJuW0t46ZBXtrcwvxM2PstfDehqFT5EvkVZvUz88yYLdaL7H1liku++Brdv8X+BkDfiL72i0hVZuunWuAd3AqiRlb6MLbSeFNWK/qE98Gsmnk9wAfVtyGknoCVj2tzlWsxW+fyhgEeuBn19GkZjF6nEJ2QaZ93Xy0KcrR5p4tv17LDIVFaN2kny/wPnytaLqxMtjndSmb1dS+P3wOntoDOAB3HV885ailVVVnwj1Zafk/3xuh0Vd/PQK9T6NEsiFdvace2/wzg6/u7cmeXCPw8jCRlFfD1ljgenbOahTfexaFuPTg7/RmyNmwAkwnXli0JnvIYTVf9StMflxH04MSSAbeNwUWb3tBiMJjzYPEYiN1YNS9iy//gt2e1232eYWv6LSScyCBPUfneJY9Abxc+uacjs8d0KHXJvsMphwFtPndxgTdqyy1fc0LFmpPD0eaFFwn2f3/JQ/b0L1qrW9QMpzPdY8aMIScnh2bNmuHh4YHR6PjDlJKSUqmBWK1WHn/8cXr27Enbtm0BOHfuHC4uLvhdsG5evXr1OHfuXKnHee2115g5c2alxiCq39ZzW5mzaw4A2+K38V6/9wh0D6z+E//2LBz4EVKOw8Q/qv98om748zXIOgfNB1Z4CaKKMllMHM/RLgK18Oxu3168vLx40N0q1BujXiEz38y5jDzC/LQgIcwrjPT8dM5knSnxBt7SFnTXi7KXlus8PdF7OZnZLeXDs+LmhmoyYb1o0O1YXq7TKdqctlwTGXkmQnzcynly9Tl8LhN38rjD4zAs/URbT9h2IfDAMm28l7hWs62UHrRy+kCvizeTutLYMt2Nygi6rarVPp+7ykvL8zO1ZcLgkrLcAGGFQffp1Bxeu306/5z9h80JO1jX5xEG/Pwf2P8DNOkDHe+tipFXi6OFpeUtQrSg1c/DhY4N/dkWm8Kf0YmM69646k5myoOjv2vvq9GrtKXaAFx9YczXRRfbnGArL48qN9PtWF5utapVHxjauuBHjQBvZ5tOXtm2nkghOiETd6OeWzteWjlzRRj0Oq5vEcT1LYJ4aURb/jmaRPS8xXT88ys8TNp7S5xXCLuadsTthsH0HtSFJg39Kt5PyuAKYxbC0rvg6FpYdDvc/T006lH5QW/7DFZP1273epLNbvew65sjKMBv7iZ6XRPKSyPalPv33lZefuF7dtdBXdg/K5AG2clce0xlX7f6ROoM2oWg80cguGWlh23LdEvQXXOcDrrfe++9ahgGPPLII+zfv5+NGy/tKtSMGTN44okn7PczMjKIiIgo5xnicvrfnv/Zb+8+v5u7fr2LD/t/SHP/5tV30sTDcGC5dvvMDkiKgaAW1Xc+UXfYgrBDP1d50L393HZMajZWsxetA9rat5cVdBv0OnzdtUxARq7JIeg+mHywxLxuoyWXRooWaBPSBvN+bb1up0vLAYf6cltG3s0VMjNRL9bB/IJGaqAFo+m5ppqZ112QAzG/0X3nPCa4/oN7fAHEFz4W2BxC22tB9x8vgd4Vekyu9KkMeh3ebgYy88yk5tTNoPvkRcrL9yftJyEnAQ+DBz3CLuGDbmm2fQp5aRDYAtrcckmHsmW6z6blEeYVzoS2E/h076e8efxHevT7D+7rXoZVT0N4Z6gXVQWDr3q2HgXFm5D1bx3CttgU/jhUBUG3OR+O/an9fhz+FQqKLU/m2xDajNQyw4HNnD50nsnCscKGU63qVyTTnYVV1VYGCPCswuqJnBStcRVAlwer7rhXiAWFy4Tdcl2Yw0XDyyIpiSbvPkfIhr8AKIhsw8r+d7E02Z3MAgscNTPn6D+E+blzU7tQhrZvQPtw34sH4AZX7ULQkjvg+J+w6Da4+wdoWImpLtu/gF+fBMDa43E+LriNzB+i8UYh2sPK5HvbM+wiS/SZrCaOpmnLhBYvLweo5+POsshONNi1hq7RKvsyj3NrswEQs0bLdvf7j/NjLmSb052XbcJssmCooaldVzOng+577636q7yTJ09m5cqV/PXXX4SHF11ZCw0NpaCggLS0NIdsd0JCAqGhpV99dHV1xdW17n2wqQu2n9vOzoSdGHVG5g6cy0ubX+JU5inuWXUPb/d5m55hPavnxH+9BRQrC9yzBAY8Xz3nEnWLrSNq9K9gtWhrgVYR25rF5sw21PMpClgUSi8vB225LVvQbRPupf3NvDDoDsiNRaeoZBv88fQKxpzwD1CJzuVQopEagM7VDQsVKS8v/ABd7AP6ZW+mZs/KLYPo1WDK5loABXK8GuJx7W1a0Favrfb6Qlpr3Wh/exb0LtC18h++/T1cyMwzk55bN+fR2cqWI8rIdNtKy/tE9MFVX4XvzflZ2pxKKMxyX9rvZqiPG3qdQoHFyvmsfB5o9wArjq3gbPZZvvR245FmA+DYH/D9BJi4rlKZ3OoWY890FwXdA1qF8Pqqw2w+nkxOgbnEessXZTHB8fVaRvvQSshPL3rMJ0z7vWlzC4R1vKSl1Y4mZmGxqvh5GAktr/rFvai8HFSSs/KrNuj+d6FWihzaXmugdxWJT89lzQGt78elNFBzlqqqZPz8M+deeRVrRgaKiwvBUx4jYPx4rtHrmWay8HdMEiv3nuX3gwmcScvls79P8NnfJ4gIcOfmdg0Y2r4+bRr4lB2AG93gziVaifmJDfD1rXDPjxDhxPKFO+fDL1pSL7XDQ4yPHkiTI6doYdWT56bjmWe70qACnd5PpJ/AZDXhZfSy92Upzr3/QNi1huuOqaw5uxva3a0F3fu+g74zKv175uphQG/UYTFZyU7Lxze4nF4solpUqhbr2LFj/Pe//+XOO+8kMVHLpKxatYoDBw44dRxVVZk8eTI//vgj69ato0mTJg6Pd+zYEaPRyB9/FJUDR0dHExcXR/fu3S88nKjlbFnuUS1G0a1+NxbftJiO9TqSZcri//74PxYfWlz1Jz1/RCsLBOilXZ1kzzcOy0sIUSa1sDtqTjLEld5HojIsVgvr4tYBWtAd4l30IbOsTDcUW24rr2iOeVlrdYfkapnt8x5a1sneudzJ+dxlUdwK1+rOq1ymG6o56Dbna2Wvyx6Et5rDN3dpfwtM2ah+DfnUMoyb818lecIW7SJcaLuiDzN9ni76e7HqKdgxr9LDsHUwT82uex3M80wWzmVoF11KKy9XVdVeWn5Doxuq9uQ7voDcFAhoqjXtukQGvc4e7J1OzcHd4M5TnZ4C4Mv98zg16AXwCoXzh7WMdy10tHCNblt5OUDzEC8iAtwpMFvZdDS5YgeymOHYOvhpMrzdAhbdCrsXaQG3Vyh0fQju+w0e3w+DX4XwTpe8lrmttLxVqHf5mcvCTLcRC57kkVSVzdSsFtj+uXa7y4NX1PrsVWHx1jgsVpWuTQJoVU3LKF7InJTE6Ucf5ezT07FmZODWti1Nlv1A4P3325eidDPqGRRVjzl3XMvO5wbxv7uv4+b29XE36jmVksv/Nhxj6Acb6ff2et5eE82h+IzSGz4b3eHOpdC4l3YR+OtRcHpnxQa6a6G29jywL+Iuuu3oA8ezaWHSgw7umXpdhQJuKGqi1tK/Zak/650G9yTJ3Rv3AvDcfYzspr3B6KFNjzy7q2LjLYWiKEUdzKXEvEY4HXRv2LCBdu3asXXrVpYtW0ZWYWOePXv28MILLzh1rEceeYSvv/6axYsX4+3tzblz5zh37hy5ubkA+Pr6cv/99/PEE0/w559/snPnTiZMmED37t2lc/kVZmfCTrad24ZBZ+D+tvcD4Ofmx2eDPmNEsxFYVSuvbXuNV7e8itlahU2r/n4HUCHyJi0b4uYLGach9u+qO4eou6xFS5JwaGWVHXZv0l6S85LB6oYluynB3kUZwLIaqYGW6QbHYDXMWwu6T2c5NlILztHK1867NwUuoXO5Nqhit7V/dK5agKLmOzenG8DHXcu2ZeRWcYM6i0mbm738/+CtFlo54d5vtA9YPuHQfTI8sI6jd2xilulOYo3NCfMv42p///9Cj0e12ysfh38XVWpItg7mabVgXfKqdjo1F1UFTxd9qdnGg8kHOZt9FneDe9VWMhVkw6b3tdu9ngS900V7pQq3z+vWPoMMajSIrvW7UmAt4K2DX8DozwAF/v0a9n5XJeesKinZBfYAtFlI0Yd/RVEY0Er7nV93OKHsA1gtcHwD/Pw4vNMSFt6iZX1zU8EzBDpPhPG/whOH4MY3oGHXS5pDf6HD5yrQuRy04KOwYsJfqeJlw46sgbQ4rUN6u1ur7rhXgHyzhSXb4gAY36PxZTlnxqpVHB86jKzf/wCjkeDHp9B46RJcm5c93dDNqGdI2/p8NPY6dj43kA/HXsuNbUNxNeiITc7hwz+PcuOcvxn47gbeXXuEmIRMxwO4eGjLhzXqqS0zuPAWOPtv+QPdvQRWaO8Fv3oMZ1jMTXjlw8A87W9ez1HNCWlU8YsUZc3ntmkT7se/jbQlCrscsXIwK077DAtFUx8qyUvW6q5RTv/FfOaZZ3jllVdYu3YtLi5Fb7L9+/dny5YtTh3r448/Jj09nb59+1K/fn371zfffGPfZ/bs2QwdOpTRo0fTu3dvQkNDWbZsmbPDFjXskz1aN+CRzUdS36u+fbtRb+Tlni/z+HWPA7A0eimT/5hMZkFmaYdxTvIx2PetdrvP01p5kS0jsmfJpR9f1H1qsaD78Moq615sW7PYlNkaMBBSLOi2ZboD3ALwcvFyeJ6Pmy1YLRZ0F1uru/jV/eDc4wCcd9cy3eZEW9BdmfLyUjYVLhtW4Uy3pQDM2gfkKs10l5eV864PXR8uzMrtK8zKdeRw4dzXlqHeZTdhUhQY9LKW1QP46RGtu7mTbB3M0+rgsmHFS8tLy9j8dlIrLe8V1gt3g3vVnXjHPMhJAv/G0P72Kjts2AVBt6IozOgyA4Ni4M9Tf/K3UdHeS0C7EJN8rMrOfalsWe5wf/cSJeS29YHXHU50zABarRC7CX6ZBu+0ggXDYec8rbLHIxA6ToB7f4Zph+Hmt6FxzyoNtIuzZbpbXyzDqijF1uqu4g7mtgZq196jZUWvIr/uiycpq4D6vm4MiqqaaqiymFNTOT11KmemPoElLQ3X1q1p8v13BD30EIqh4hfQPFwMDG3fgI/v7sjO5wYx544ODIqqh4tex7Hz2bz/RwyDZv/F4Nl/8f4fMRw/X3jh18VTWz4sopv2PrFgpNaorDR7v0Vd/jCgsth6A/+XMgYfo4GJBh90KjRsE8A1/Z3rG2XvXO5fetCtKArW67Wu5Z2OqOyL/xfa3aY9uP8Hx2SAk4rW6q5770dXAqcvD+/bt4/Fi0uWAYeEhJCUlOTUsSqy3rebmxsfffQRH330kVPHFrXH7sTdbI7fjEEx8EC7B0o8rigK97e7n8Y+jZmxcQabzm7inl/v4cMBHxLufQndM/9+B1SrtlxEg2u1bdfcCTu+hIMr4Ka3iwICIUpjf3NTIP2U9sbcoMMlHVJVVf6I06bMmDPb4FLYIM3Gtk53aT/7tkx3Rl5RsNrAS2vakmvOJTU/lQA37QNpmeXll5jpVuxzurU37wpnukHLdhsCSs3YO8Vq0ZZ+OfAjHFqhBQk2niFa1+G2o7QPVaUECbblwiLrldOwCbTXPeR17YLBji/hx0na+q9ONO3y97AF3XUv023vXF5KE7XipeWDGldh13JTLmzSVsCg1zTt/6OKhBdWPZxJy7Vva+bXjLGtx7Lg4ALe2P4GXYd+h0vsJji5Eb4bDw/8rjVqqmGlzee26dokAA8XPQkZ+Rw4k0Zb6xGtv8GB5drqDDbu/tB6mPbz3bh3lVUQXIyqqkVB98Uy3aDN686Mx1/Jqrry8vNHtCZbKND5/qo55hXkq8Jlwu7q2hCDvnourABk/v478S+8iCU5GfR6giZNIuihSSgulzYv38vVwIgOYYzoEEZGnonfDyawcm88f8ecJzohk+i1mby79git6/swtH19hravT6O7v4eFo+D0NlgwQrvAFNqu6KD7vkf9cRIKKovMA/iveRzdmwZxt4s3sVsScPdxYcC9UShOdM9XVdWe6W4V0KrM/doM7kPa1y745RWQvOVvmPiF9vuZlaBVajbtW7nvk5SX1yinf7P8/PyIj48vsf3ff/8lLKxkQwAh/rdXm8s9rNmwUptG2AxoNID5Q+YT4h7CsfRj3PXrXexO3F25k6acgD1Ltdt9is2/C+8MAc205U0OV125sKiDVLUo023rcnro50s+bHRqNGeyzuCic8Wc1ZJgb1eHLKEt031haTkUZYiLl2W76l0JcdcyWfZ53dnJeJm0QDTJXeuVcSnl5aVlMRVX25zuiwTdeqO9HNRWYl6pTPfFsnKd7nPMyjXqUWZWzlbKGlneesA2igI3vQPX3q1dxPvhATj8S4WH7VtYXp5aBzPdJ8tZozs6NZpTmadw07vRO6x31Z1053zITtS6Zbe/o+qOS8nycpuHr3mYQLdATmacZOHhxVqZuUcgnNsLvz1XpWOorBh75/KSP9NuBh33hJ/nWcPXNFrQFb68Abb+Twu4XX2hw11w1w/wZIy2vnaz/pct4AZIzMwnNceETnHsvF4mWwdzskjOqqLgwTaXO/JGrYLiKrLnVBq7T6XhotdxR5cy1ry+RJb0dM48/TSnJz+KJTkZ1xbNafzNNwQ/9uglB9wX8nEzMuq6cL4c35kdzw7izVvb06dlMAadwqH4DN5aE02ft9Yz7NM9fNnkbfLrXadNo/hqOCRovanU/T9iXfYgimplqbkvryoP8OLwdrzSpRmxW7T30oH3tsbDx7mxJ+UmkZKXgk7R0cyv7C7/PSND2Bauldl7bzqgrTkeNVJ7cF/lp7Z42svLL/K+LaqF00H3HXfcwfTp0zl37pxWAmG1smnTJp588knGjRtXHWMUV7B95/ex6cwm9Iqeie0mXnT/qMAoFt+8mNYBrUnJS+G+Nfex8nglguO/39ECpmYDtCYvNoqiZbsBdldD4zZRd6jFmu3Z3uyq4EKNrbQ80qcTqC4EeTtmyWxBd0Pvkh9+ihqpOQartnnd9g7miQcBiLMGU6D3RLVYMBdWIhkq00itlO7ltkZq6sXKy6FEMzX767hY0G21QtxWWDUd3m0N82/SPhxnJ2pX/a8bB/csh2lHYOhsaNK7Ql2sK5zpttHpYNj70H4MWM3w7b1w5LcKPdWe6a6Dc7rj7MuFlWwgZOtafn3Y9XgYq6hLrikPNr6n3e71hPZBtAqF+xWt1V2cl4sX0zppa7Z/svcTzul1MLJw+cttn1TJxbhLZSsvb27LdKuqNlf1t+fgvfbMODuZiYZf8S5I0FYUaH8H3PkNPBUDI+dCi4FVWjXgDFuWu2mwF24VWcbI3R8APyWzauZ052cWfR7ocvHPKXXNgs1alvvm9vUJqoZlDTPXr+f40GFkrPgZdDoCJ06k8Q8/4N62TZWf60K+HkZu7xTBV/d1YfuzA3ltVDuubx6EToF9Z9J5ae1pOp98mBhDC8hNwTJ/GBl/vIv1+/vQqRa+M/fmhwZP8euUPoxuXZ/1C7XS8GsGRtCwTaDT47GVljf2aYyboewu/R4uBlKuHQhA+0O5xGeeLSoxP/iz9rewErz8JdNdk5y+lDlr1iweeeQRIiIisFgsREVFYbFYGDt2LP/973+rY4ziCvbJXm0u981NbybCp2LzXup51mP+kPn8Z+N/+CPuD2b8PYMT6Sd4pMMjDp2dy5R6smjOdt9nSj7e/nb48xU48ReknwbfSyhhF3VX8XlTrW7Slo86fxiSjkJQ5deVt5WWN3Lrxj/gMJ8bys902xqQXZghDvMK49/Ef4uaqRUG3dGqdgxzUjJYLKDXYwhy/oNCaZO67Y3UCirw5u3ipWWklz8Mrt4MySqgqTETn9NGmF9OOWnKCa3xoY2rL7QeCm1GQdM+lQoScgrM9mCxQpluG50eRszVSs0P/Ajf3K0tQdN8QLlP87OXl5cdHOTu3k3KggUET52KS4Rz8wNrkm1O94WZbofS8kZVWFr+70ItO+sTDh3GVt1xC9nLy1NzUVXVocJjaNOhfHfkO/5N/Jd3d7zLm33e1Brt/fOBNt+//jXgVz1ZworQystV2utPwu9faj+jqSfsj1uNHvyc14GVlm7MmvI4wf6+NTbWCx2Kr2ATNZtime7DVVFevmep1mwxsAU06Xvpx7uCJGfl8/Pes0DVLxNmycwk4bXXSS/sw+TSpAkNXpuFe4cOVXqeivL3dOHOLg25s0tDkrLyWb3/HCv3nmXrCRid9RSLXGbRLjcWn79nArDc2ov0Qe+ytFdzFGDFnN3kZZsIivCi+wjn16IHrQIIym6iVlyLGwaSveID/LNVjvz9M/WHTNSW6cs4A0fXalNBnOQpjdRqlNOZbhcXFz777DOOHTvGypUr+frrrzl8+DALFy5Er5eF1kWRg8kH2XB6AzpFV6Esd3EeRg/e7fsu97W9D4BP937K/7N31vFNnf8Xf99o3b3QFm9xGM4YrmPCBhsyYIy5u7v+5vvOhW0wZMZgbLi7D4cCRdrSQt1Sjd3fH0+SttSSNhW2nterr2VtcvOkpDf3POd8znl669MUG+3Y3dv+sVCjWg+Bln0q/tw3UlRGIItk42Y0ozKUDVFz9RUqKsDJ2qta8bnxnMk5g0pS4UVXgHLJ5QCBboEAxPjFVHh8VQqxdWzjYr64eKpAulPF3KYqMNBWw+IQytnLyyvdNQapAfhZ6iAvHYL4bQRk7GGA8gSd9YfFfFpVX3lJTlflTqfmI8sQ4KHB31FVR6mCm76D6PFgKoFfporNu2pgSy+vYqZbNpm4+PwL5K1cxaUXXrQr66QpQJblUqX7MtJ9JucM8XnxaBQarmnhJGu5sUSc2wGufrRe5qhDvF2QJCgxmivMCltD1SQkVsWvYl/KPhj2MoT3guJcWDxbpOc3NGQZXeIRphXOZ4PmSaKXXQvbPxKEW+UqXDq3/ITi6XPMCXqBdeZebDqb1/DrrAYnU0rrwuyCpavbR8ono65BarJcGqDW5656C4prqvh1/wX0RjNdW3jTvaWP046bv2MH566/QRBuScLv9ttptXRJoxHuyxHgoeW2fpH8cnd/9jw/nCeu78tHIe9z3Cw2HrZoh9D5/gXcObgdSoXEwbUJJJ/KRqVRMGp2J5Tq2r1PTmdZksurCFEriyGdw9kXEQBA7pp14r3Z+Sbxw1pazK2kuzBHj2y+Mj5r/k2o9dBOREQEERGNt6vbjKYPay/32FZjifKOcvjxCknBY1c9RpRXFK/vfp018Wu4mH+RT4d9SoBrQOUPyrkg6lwABj9T9cG7TRYX9Yd/gasf/8/1cTbDDpRVuiWlIFpnNwor6dWP1eqQVpW7T2gf8nSCNF6udH8w+AMu5l+krW9FNb00SK181VbZBHMAUi2k29ySMMBgm+euRXI5VJpebndlGMDNP0D8VptlPyGzkPfXnMLXTc0bN3au+nEu3hB5tWgecBJOOzLPXRmUapj4I/w2HU6vhkW3wm1LILJ/pXcvTS+vnJDlrVyJ/pxImi/cuxfdqlV4jRtXu7U1INLzSygymFBIEO5TPunZqnIPCB9QIYG/1ji4QCg8nmFirKAeoFGJru5LucUk5xRV2BCL8Y/hlg638OupX3l7z9v8ft3vqCb+AF8PEmFMG9+Eka/Vy9oqIP2UULOPLcEz4xQPW6/mVC7QbqQIQ2s/RiQ1WzAsOoijyblsOpnGLb2ajqPCai/v6KjSLenIrKvSfX4LZJwWbhzr6Nl/BEaTmYW7RU3YjP5R1fej2wlTfgFp779PjqWFSB0RQdg7b+N21VV1PnZ9IcjThZkDopg5IIqUjM2cPLefgT2GolKLc3fK+Vz2/iVcI4NubY9viH193JXhZLYludwOpbuFrxsn2ndiSNxm/PfECfdNl0nCXXN6DRTngYtjfepu3hqQwGyWKdTpcfdu/BDI/xLsIt2PP/643Qf86KOPar2YZvx7cDLrJJsubEJC4u4ud9fpWBPaTaCFZwse2/wYRzOOMmXFFD4f9nnlJ60dn4DZIJTsyAFVHzTmeljxpPiwTT4ALZruB0IzGglllW6FEqKvFSFeyf9A3kXwCnP4kFbSPTxiOGt3C7J6+YW9n4ufLYH8clRWGQalSefJ+clCuUmLBYTSHQYYrcnltZnnvhy2mW47K8MA3P3LJX6bMwpYvmozHgYVb3QeXfc1OQBbiFqwYxcr5aDSwKR58MsUsRGzcBLM+LN8foQFvjaluyI5kI1GMj4XzRyaVq3Qnz9P6nvv4zF4MAr32l/YNQQSLSFqod6uaFTlVR8r6R4VOco5T2bU17vKbUULX1cu5RaTlF1YqfL3UI+HWBO/hjM5Z/j11K9Mi5kGN3wGv80Qnz+tBkHbEfWzuMyzcGyJINtpx23fNinUbDB05UzgSO6/50HQVr6hNDwmiP9tiGPr6XT0RnOFf7fGQInRxNn0AgCiQx1Tun3JJ7fIgMFkRl3bxO2934n/dpviMIG50rE+No3knCL83DWM7xpa8wNqQMGevVx6/nkMyWLz13faNIKeeByFm5MyHRoAIQF+hASUnrf0RUbWfX8cs1mmba8gYgbU/vdUbCwmIU/Mz1eXXF4WvgPHU7x2M345egqOH8WjU1cIaC+uXU+ugO6ObRQplQrcPDUU5ukpyClpJt0NDLvOUgcPHrTr69ChQ/W83GZcKfj2iLBrjYkaQ2uf1nU+Xu+Q3iwat4goryhSClKYvmo6my9sLn+nvItw4CdxuzqVG8SHq3UeprmzuxmV4XKl2zOkdFzBgfRqK1IKUjiacRQJiWERw0jXCbIa5Gm/iutdSWUYlLeXm3MSQK/DJKk4L4sLhLoklwOVOkEUtiA1xwNdrK8jv8SI0WSu4d7OhS1ELaSOCqzaBSYvEmMHep2onrl4sMLdrDPdBXoTemP515r793L0CQkofXyIXLgAdYsWGFNSyPjm27qtrQFQlbX8XM45MUKhUDG45WDnPNnhRaKyzyO43lRuK8JtYWpFlf7cW+vNwz0fBuCLg1+QWZQpKup6W+owl9wDeRUbXmqNrPOw7SP4+mr4rKfII0k7Dgq1qMOc8A0fdV3J3YYnyGh1Q5WEG6BzmDeBnloK9Cb2ns9y3hrrgLjUfExmGR83NSFedp4LbUq3CI/Lrm2YWk4inFopbv8nA9TiAZjcu6V9AXZVwFxYSMqbb5E4cyaG5GTUYWFEzJ1LyEsvXlGEuzJs+fkUeRnFePq5MGRqhzq5Ac7knMEsm/Fz8avarXkZruvZl0OtxL9N4rLfxGexNVCtlhbz5jC1xoNdpHvTpk12fW3cuLG+19uMKwBx2XE2pePurnVTucsiwiuCBeMW0De0L0XGIh7e+DDzjs8rnYHc/okIOYoYAFFX13zAbpa6mWOLxbxgM5pRFubLlG4QFnMQ3dAOYmOiOD92D+pOgGsAaRbSfbnSXR2s9nJdsRFTmXmsYLdgVJIKg9lA2oXdAGS6RGCwmJmMaXWzl0uVpZdb7OVme+zll8Gq2ENFq3x9o7QuzAmqltoVpvwizjklufDTjZBytNxdvFzUtj2LnKJSciAbDGR89RUA/nfORuXnR/DzzwGQ9eOP6OPj676+ekRVHd3Wc3//0P54aZzwOzYZRBsFwMBHxe+8HlE2TK0q3NT2JmL8YtAZdPzvgKUzfNRbENwFCjNgyV3lzx+OIidRdJF/OwQ+7Q4bXhPvK4VKqOg3fCHyDab9Bt0mc8zCn2uq21IoJIZ2EJkRG06m1n59ToTVWh4d4mk/obEo3X4KQbpr3dW973sx8tJqMATWbPf9NyEuVcfOs5koJJjWr/YBaoUHDnBuwgSyF4ixPp9bbqHVX3/h3q+vs5baaDi1+xKn96YiKSRGzu6E1q1u6f7W5HJ75rmt6Bnhz942YrM8f/0m8c3ON4v/ntsM+WkOr8O9uau70dD43qJm/OtgVblHRo6sdC61LvDWevPViK+Y2H4iMjIf7P+A13e/jiE3SfS3Agx5xr4Z7dZDwDNU9DPG2Vf/04z/EKz2cklR+n6KsZDu+B1Q6JhSVNZabjbLZORblW77SbdnGbKaX4asKhVKQtxDAEhOOQBAmmtpuqrBai93otLtUGXYZVApFXhoK09ir09k5pfYfu/tgpw0a6xxF+SnRR8ozoGfbrDZ+0EQHe9K5rpz//oLQ2IiSj8/fKeKJG6PoUNxHzQI2WAg5Z13nLO+eoLVXt7yMqV7bYI4lzottfzIr4KEugfCVbc755jVINzW1V1Y5X2UCiXP930egKVnlnIk/YhwPkz6EdTuIi/EulFgL3KTYNcX8N1w+KQLrHtZOCckhfisuu5T0aN92x+iM95SmwWldWH2vKeHRYtzwMaTaU0itM+6CWZ3cjmUSy8HalcbZigqdcb1cZ44cKXAWhM2smNwhUwGe2AuLib13fdImHYbhoREVCEhtJwzh9DXX0Pp0bRHY+xBTlohW34WoWe9r40itE3d0/5PZYnkcnut5QBKhUR6l54YlOB5KYuSs2fBvw2EXyWuUY7/6fA6mhPMGw92zXTfdNNNdh9wiaUaoBn/TZzNOcua+DUA3NP1nnp5DrVCzcv9XqaVVys+2P8Bi08v5kL8Zj6U9Xi37Ct2re2BQinqw3b8TwSq1aJ+oRn/YliVKqmM7c6vNQR3htRjIkTLztqi7OJs9qfuBwTpzikyYDCJC15HelG1KiUuagXFBjN5xQa8y+y8h3uGk5SfRHLWKa4C0q2kWypjL6/1TLdU4aaiDko3CLU7v8RYc1e3E3HKYi2P8HPDXVvrHNGK0HrCbYsF4b54EOZdD7NWQkA7QMx15xQabKRbNhjI+NKqct9ps2BKkkTw889x7vrdFGzZim7TJjyHDnXeOp2Iyuzl8bnxnM4+jUpSMSxiWN2fxGSErR+I2wMeBk39W1VbWEh3ck7VSjcIx8oNbW5g2dllvLXnLRaNW4QyoB2M/wiW3gOb3xG5ItW5rvIuwYllYkbb4lARkMTjOk0Q+SMegVUeIr/EaFtrWztI99XtAtAoFSRkFnIuo4A2gU7afKolrEp3jCPOE4vS7U4hKoxk1ibB/NgSKMoC75bQYazjj7+CkVds4I8DoopxZv8ohx9fdPgwF5973hYA6T1hAsHPPYvS698xE28ymln3/XEMJSbC2vlw1dgopxz3dLYg8e392jv0uD5t+3E0agU9z8ro1q5Fe999wmKe/I+wmPd1bNPIpnRnN5PuhoZdSre3t7fdX834b+PbI98iIzM8Yrhd6Yy1hSRJzOg0g0+HfYqbypU9+gxuCw0hsc8sx5LIrWmlp9dAQWb9LLYZVyasSrfislk3m8V8ud2H2nxhM2bZTLRfNC08W9jmuX3d1A6HGVlrwy5XiFt4WMLUdOJiKt2tNEvBWOf0cucq3VBqlW9IpduaXN4+uJbJ5dXBxVukmId0gYI0mHedCL6idIY92xKmlrN0KYbkZJQBAfhOmVzuMNpWrfC/fSYAqW+/g7mkaV4YVWYvX5+4HhDp/N5aJ1wPHP1dVF+5+UPv2XU/nh2w2suTLF3d1eHRqx7FQ+3BicwTLD2zVHyz22ToPk3Ylv+4Ewoyyj8oP02Ed/04Dj6KgdXPWAi3JMYUxn0AT5yC25eL11wN4QY4a1G5Az21tnq66uChVdG3tSCtG2Mdt6Y6E7Isl5JuR5RuVx+su38+FDhuL5dl2PuNuN17dsVz/L8cS/5JolBvom2QB/3b+Nv9OLNeT9pHHxM/ZSr6c+dQBgbQ4qsvCXvn7X8N4QbY+/c50hJ0aN1UjJjVEYWi7qnuZtls6+iO9rVf6QaY1GUAezqINaSvXCW+2WmCcMEk7RW5Dw7AOtPdrHQ3POza6v/xxx/rex3N+BfgfO55VsevBupP5b4cQ1oO4SefvjyYsp54jZqpx77g48C29A7pbd8BgmIgtLvoDz72h8M7hs34F6MypRuEI2LL/8HZDaAvKFfJUxXKWssB0nSVJ5fbA29XNWm6kirD1JL0OeI5XNsCxaiKCzEXCoLkFHu55bbCkl5emyA1KCWiDUm6rUq33X3AjsLND6Yvg3njRVe6RfH2tTgScgsNyHo9GV+LOsWAu+9C4VrR2hlw773kLvsLw4ULZP34IwH33ls/660livQmWyZBWaV7bbwTreVmE2x9X9we8JBdf2fOQKi3eF8X6k1kFxrwc6+ayAa4BnB/9/t5b997/O/A/xgZOVJsNox7H5L2iYThP++DG76Ek38LRTt+u606D4CWfcUFdMcbatWIEOeAtdyK4dFBbIvLYMPJVO66pu5Bp7VFmq6E7EIDCqnmefRyUCjFJldxDj6Sjsx8B8lD0j64dBiUWuhRv8F8TQ1ms2yzls/sH2n3HH3R8eNcevY5SuLiAPAaP56QF19A6eNTX0ttFFw4mcWBtaJGbej0aDz9nFNXmZyfTIGhAI1C43CFbivfUA619cMsZaCIi0N/4QKali1FiOe5zeLa9Zon7T5e80x348GJ/rpm/Ncx5+gczLKZIS2GEOMf0zBPWpBBh0OL+dlczMMdB3A0P5G7193Ny/1eZkK7CTU/HoTafemQSMhtJt3NsMJ6Yay47DQZ3Al8oyA7Hn4cC9qqd/jNyOyimF2kgVRKumuTXG6Frau7qHLSnaxUgMaDXE0IEI9Lrpg9V3h61j5JthznvjxIreIHd+bcueRv3FTue+rwcEJefQWFVnzgNwbpts6Ptq+CdMtGIymvv4HS1xe/22ei8vWt9H7Vwt0fZiyDudcK0jXvOloEfgoIpTtnyRKMFy+hCgzE55ZbKj2Ewt2doKef5uKTT5Lx9Td4X3896jDHCVm1MBlFErZRD4OftqiH9uGCZd7Zy0VlU1cv6C4QmxWLUlLa3ud1wrE/IOusmF22JoM3AFzUSoI8taTpSkjOLqqWdANMjp7MkrglnMk5w+cHP+eFfi+IDYKJP8J3w0ReyAftgDKqefhV0OkmQbR96taXHZcm3tOOkO5h0cG8+vcJ9sVnk1tksP0tNjSsKnfrQA/H07Pd/KA4B1/yHZ/p3mtpB+gyUfy91gIZX39Dwc6dtXpsYyKnyMDdl/JQKiSuSvIl4auaSXdmYTra2HgUJhmlnx8hr76C1ygn1QE2IRTp9Kz/8QTI0GlQGG161NIZVgms89xtfNqguvyawg4EBHXiRMRWOifI6Natx/+OWcJifm6zcAQNesJul6dHM+luNNSKdC9evJjffvuNxMRE9PryJ7sDBw44ZWHNuLKQmJfIinOiRunebg2oyuz6HAyFBIT14IfrF/PizpdYE7+Gl3e+zPm88zza81EUUg323S4TYe0LYhYz7SQEOWb9aca/FGZLUJnisvePJEHnibDtA6GWVIJ8SWKZpzu/eHoSrxEXtNEletoe+xsGPV6r5HIrSru6y6d+h3taSLdaJRwclg9g1xwxNlFrazlUqoYotIKMVKZ0p334ERgqkmnXbl3xnSzs1F5V1J/VF2RZttnLq1K6iw4fJue33wDInj8f35kz8J81y3HrpEcQzPgLfhwD2fEMdl/FfK4hT1dIxtfC1up/zz02t0Bl8Lp2HDm//ELh/v2kvvc+LT752LE1VAezScwdH1ss/v/4UtE1bWe/tDVELaKstTxBWMt7hfTC16UWmxWXr8+qcvevune6vhDu60qaroSk7EK6tKjeJq9WqHmuz3PMXjub307/xs3tbxZBSSGdYez/wfLHAFk4qjpNEF++tU+LvhxxqULpbuvAyESEvxttgzw4k5bPtrh0xnd18oaOnYi9VIsQNStc/YBz+Eo6x+zlutTS8KlaBqgV7NxJ+ief1OqxjQ0N0M1yu8TO6QKrF+dYF0+G/W8BXmGt6mFljQtZltnwUyyFuXp8Q90ZOKmdU49vs5Y7EKJWFldH9GRPh210TpDJWb1GkO6Y62D545B+ElKPi3OOHbAq3fpiE/piIxqXZv21oeDwb/rTTz/lhRde4Pbbb2fZsmXMmjWLs2fPsm/fPh544IH6WGMzrgB8d/Q7TLKJQeGD6BTQqWGetDBLzMYBDH4GF7Ur713zHlFeUXxz5Bt+PPYjCbkJvDPoHdzU1Sh87gHQbpTo6zz8M4x8rWHW34ymDZOFDCoqUYGueQpa9BIJuGVwvjiDn9P2sCzzEIVmcSHoodByo0s4sxK3IV18DTTupOsGAY4ll1tRFVm1Kt2pSiWGMjNjVqVbXesQtctgVbpdqla6rYQ75PXXUHp6UrhvP9mLFpH103x8brkFSaFocKU7KbuIAr0JtVKiVUDlVmV9vLBdIkmYCwvJ/Oprshcuwn/W7fhOn+FYKq9XKFz9GPz9CN1yNgLXELhlNcaUFFTBwfhMmljtwyVJIvilFzk/4SZ0q1dTsOsW3Pv3t//5q4LZJCzPxxYLF4dXOOQkwIKbRTr4qDdrJLkJlYSoWavCRkU6QQE78adwCbh4N0qydAtfNw4m5tQYpmZFn9A+jIkaw+r41byz5x3mjpkrNqp63SGCF938ReJwPcCqdLd3MI1/eHQQZ9Ly2Rib1mik+2RKaV2Yw7AkmHtLBZxxJEjtwDwwG0TbQFh3h59WNhhIeettALyuvRbPEU5wdTQQMvJLePWv48jAS+M72tWLvvzscjYnbSbTU+J0iyJ+++dp5vjPcU5mQxPC0c1JJBzNRKlSMGp2J9Qa5875W5Xu2mYdDYnsxaPtJWavBf2RwxhS01AHB0G7kXByuVC77STdGhcVahclhmITBTklaEKaSXdDweHf9Jdffsm3337LlClTmDt3Lk8//TStW7fm5ZdfJivLsQqdZvw7kKRL4u+zfwNwT7eGmeUGRL2KPl8EF7UfA4BCUvBgjweJ8o7i5R0vs/HCRm5ffTufDfuMYPdqSEe3KYJ0H/kNhr/8nwtWaUYlMFvIoLIS0q12sSXemswmtidvZ9HJRey8WGo3bOXdiqnRU7muzXW4q91h41uw9T1Y9TStg58CetRS6a7cXu7v4o8LEsUSXPJtAeJaHNdcq9JdB9JdWZCaxSYuVxP05Tl8OCp/f9wHDSJ32TL0585RsGMnHoOutpHuhkovP22Z524T6IFaWbn7RX9BzPL53HIL7lcPJOPTTymJO0P6/z4la95P+N91J75Tp1Y6h10pYq6H5Y8TWHCa9uZEOm0WYVsB995js9lXB5cOHfCdOpXsBQtIeestWi9diqSugxXYbIZlD4oaLoUKJs2FNsNFD/Ser0Xt4tmNoge61TVVHuaCjXSLTYiL+Rc5mnEUCanuqeVmM2yxqNz9HgCXhg9oslYoJVXT1X05nuj1BFuStnAg7QArzq9gfGtL4GLLPvWxRAAK9UbbGts5GA44LDqIb7aeY9OpNExmGaUTwqIchdVe3rHWSjf4oiPTXqXbZID9P4jbtdzMyVqwEP3Zs8Jm/crLV1SA2BcrY9karmZQuwDa31pzj3ZWcRafZL1CkaeCJ656gh+P/8jJrJPcve5uvh357b+GeGck5bPzDxF6OeDmNgS0cH6iv5V0t/d1LLncihj/GHI8lZwKN9EhGXTr1+E3bZqwmJ9cLsZxhr9S0ZlXBTx8tGSnFJKfU4JvyJVf8XalwOGe7sTERAYMGACAq6srOp24kJk+fTo///yzc1fXjCsCc47OwSSbGBA2gG6B3Wp+gDNQlA17LOmjgyv2co9vPZ4fRv+An4sfsVmxTF0xleOZx6s+XvvR4OIDuotwfmv9rbsZVw5MVnt55XuTefo85h2fx/il43lw44PsvLgTCYkhLYfw7chvWXbDMiZHTxaEG2Do88IqC0xJ/YDrFTtqHaQGkFdc3l4uSRJhlhqyJI9Se69V6a6Lvbz835fjQWpKDw+8bxbVk1nzRTduQyvd1nnuDtWoaoZEQbo1kZF4jRxJqz//JOyDD9BERWHKySHt/Q84M3IUWT/Nty9V3M1PdCwDd8f/jYcuG1VYKN4332z3ugMfehClry/6M2fJWrjQ7sdVgNkMfz8ssiskJdz8vbAnatxg7Lswczn4RIhO7HnXwcqnQV95V3VCZgFQqnRbreVXBV9FgGtA7dcIEPsXpMeC1hv6NuAmbhm0sKOr+3KEuIdwd1dB5D7c/yH5+vx6WVtZnEsvQJbB311T4+z55bgq0hcvFxXZhQYOXciupxVWjRKjibPp4n0UHVp7pdtXcmCmO/Zv0F0C9yAxT+8gDGlpZHz+OQBBTzx+RRHuIr2JX/ZdAOyvCZt/Yj5FxiJi/GKY2Wkmc0bNwVfry4nME9y77l7y9Hn1uOKGgUFvYu2cY5iMZqK6+NNlSAunP0eePo+LBReB2ivdripXWri3YW8HQdt0a4WziPajQeMJuRdEkrmdaA5Taxw4TLpDQkJsinZERAS7d4tuyfPnz9dYr9GMfx8u5l9k2ZllQAPPcu/+CvQ6Yd3rcG2ld+ke1J2F4xbSxrsNaUVpzFo9y3ZxWAEqLXS2XAgfbt48agZVKt1x2XG8vut1Rvw+gg/2f0BSfhKeGk9u73Q7K29ayWfDPqN/WP+Kc9CSJKy7vWajQOYj9Vd0yN7s8LK8XMUmQAWyWpJPeIlQvZLVpRfgVqVbHRLi8HOVojKlu3J7ebnPgTK/A7/bbgNJomDrNkrOnbO9jstn0+sLVqW7urowfYKFdEeIcCtJqcR7/LW0Xv43oW+/jbpFC0wZGaS+/TZnR48h+5dfkfU1XPB3vgmzUaLTaVHrEnDPvSg09hMkpbc3QU88DkDG519gTE+3+7E2mM2w4jE4OF/UzNz8HXS6sfx9Wg2C+3bCVbPE/+/9Br6+GhL3VDjc5R3dVmt5nVPLzebSWe5+9zoU7uZMlJJu+5VugBkdZxDhGUFGUQbfHPmmPpZWDlZruT393JdDpVQwuIPYiNvQCNVhcan5mMwyPm5qu2zOFWBRun3IJ7/ESLHBVPNjrONoV90OKsc2KQDSP/wIc0EBLl274j3BzqDWJoK/D18kt8hAC19XhkbXvAGbW5LLothFgLi2kySJdr7tmDN6Dj5aH45lHuO+dfc1yOZSfWLH73FkpxTi5q1h2IwYu9PcHcHpLNHPHeYehpem9hs1fcK6sae9WF/Bvv0Ys7NB7So2T0FYzO1Ec5ha48Bh0j1s2DD++usvAGbNmsVjjz3GyJEjufXWW5lwhZ2EmlF3fH/0e4yykb6hfekR1KNhnrQoB3aLyh2ueapaO00LzxbMHzefgeEDKTIW8djmx5hzdE7lG0Tdp4r/xv4NJTrnr7sZVxbKzHQbzUY2JGzgjjV3cNNfN/H76d8pMhbRzrcdr/R/hQ2TNvBErydo4VnDLrkkwbgPWMZgVJKZDtsegbh1Di2rKns56acINwoCm6zPtX3bJceidNdlprvSyjCLSm8yIVcSmnY5NBEReAwdCkD2ggUNrnSfqiFETZZl9BalWx0RUe5nkkqFz00TaLNyBSGvvooqJARjSgopr77K2XHXkrNkKbKxis2D6GvJOusJxZDt7o3PhBsdXrv3TTfh0qUL5vx8EVLnCGQZVj4prOOSAiZ8W7rBeDm0nnDdJ3DbH+AZJtLDfxwDa18Cg3A0mM0yFyxkNNLfjdSCVA6lHwJgRKR9QWxV4tQKSD0mlJu+jVeTZiXdyQ6Sbo1SwzN9ngFgwYkFnMs95/S1lYU1RM2huq0yGG4hXxtPNjzptlrLo0M8a0d03ISbx18hfgeZNandKUchcadwLvW6w+GnKzxwkNxly0CSCHnpRSQ7bbxNAbIsM3dnPADT+0XaNUow/8R8Co2FdPDtwNCWQ23fb+/bnjmjxEz3kYwj3Lf+PgoMBfW19HrF2YNpHN92ESQYcXtHXD0d34ixB9YQtfZ+tbOWW9EtsCtpvhLnAjVIZhP5GzeKH3Sx5IMcX1p63VIDrEp3fnYz6W5IOHzW+Pbbb3nhhRcAeOCBB/jhhx+IiYnh9ddf56uvvnL6ApvRdJFSkMKSM0sAuLdrA14g7f0WSnIhMEbMTNYAT40nnw/7nCnRUwD434H/8dKOlzBcfnIKvwr824KhEE78VR8rb8aVBLOBHIWC77Umxi0Zx6ObH2Vfyj6UkpKRkSP5YfQP/HHdH0xsPxFXlZ0zvkCxSeax4rtYbuqHZDbAr7c5NNJQZep32nFaGCykOz8Z675S6Ux3XezllXyrTPJ2ObW7CqUbwG/GdABylv6Jt1EQmoYg3QaTmbPp4uK8KqXblJOD2TIupWlZeY2TpNHgO/lW2qxZTfDzz6MMCMCQlMSl55/n3PjryF2+AtlsLvcYs6wl85SYfbwUE4DkgMpte16FgpAXxedu7p9/UnjwoH0PlGVY9TTs/x6Q4MavoOukmh/XdgTcvwu6TRXVeTs/hW8HQ/IBUnXF6I1mVAqJUG8X1icK91CPoB4EudXhPSbLsOVdcbvvPTb7cGMg3Eco+LoSo8Pvz2taXMOQFkMwykbe2fNOvToASzu6a5fuPrh9IApJjF7YGxrnLFjHPWqVXA42pTtAKQhfVk1z3VaVO+Y6EXLoAGSTiZQ33wDAZ+LNuHbp4thaGxkHErM5cSkPrUrBLb1qrqjLLcllYawYZbmn2z0VNkU6+HXgu5Hf4anx5FD6Ie5ffz+FBvtHMZoCdFnFbJp/EoCeoyJoGVN/5xvrPHdtk8ut6BrYFYA90eIzxmYxbzUY3AOhMFNUiNkBD99mpbsx4DDpVigUqFSlM46TJ0/m008/5aGHHkJTi4uJZly5+OHYDxjNRnoF96JXSK+GedLiPBGgBjC4epW7LFQKFc/3fZ7n+z6PUlKy7Owy7lp3F9nFZWbZJEkEqkGzxfw/jtjMWF4+vYgRLcP4RF3MpYJL+Gh9uLPLnay+eTUfDfmI3iG9a6XQpOtKMKPgGflB5A5jwVgMiyZXauOtDKVK92XKauqJUqU7PxkApdmENl+o3uo6BKmVe52Wm5JGYyPV9sx1A7j17Yu2fXvkoiI8NqwCGoZ0n88owGCScdcobSrm5bDOc6uCg2sMSlNotfjNmE7bdWsJeupJlD4+6OPjufjkk5y/4Uby1q61ka3sn3/GXGhC7W6kS9RZivW1s9O7dutmm4tPfeNNZFMNdlpZhtXPWTqJJRGO1m2yA0/oAxO+gsk/ixnY9JMwZwSmDW+hxki4rysqpYK18WsBJ1jLT68WaqTaHfo3bhOKq0aJv2VG2pG5biue7v00GoWG3Zd2syFxg7OXZ8MZG+mundLt667hqkihGDe02m1VumNCakm6LZsyfpL4HWRUl2BelC1CUqFWAWo5v/9OyYlYFF5eBD72mMOPb2zM3SlaGW7oHoavHbP/i2IXkW/Ip61PW4ZHVJ7OHuMfw3ejvsNT7cmBtAPcv+HKId5ms8z6H09QUmgkKNKTPte3rtfnsyrdHXxrN89tRSvvVrir3NkbLT5b8nfuxJSfD0oVdBKfDfZazJtnuhsHtfLH5OTksHbtWhYsWMBPP/1U7qsZ/w2kFabxx+k/gAae5d77LRTnQEB76Hijww+fEj2FL4Z/gYfag39S/2HaymnlLYBdbxX/jd8mAoWa8Z+BwWxg9fnVzFg1g1uW38LS9H2UKBTEyCreGPgG6yet55GejxDiXpfZaEjTCYLq6+mONHEutBkGhgJYOFF0xdcA7yqV7oqk27dYhyTLoFaj9KvDTn5l6eWSZEswNxdXoXRX8hir2s3S31CYTeiKDZjN9ZsHYlXV2ldjZbVay6tSuSuDwtUV/9mzabN+PYGPPIzC05OSuDiSH36E+JsnkrdmLZlzvgfAp1Mh7VUXyb9wtNavI+jxx1F4elJ84gQ5i/+o+o6yDGtfhD0W99n1n0KPabV70uhx8MAecVEnm2hx5DP+1LzEQI8U0gvTOZgm3rMjIupgLZdl2Px/4nafuxpV5baithZzgJZeLZnVWczGv7fvPYqMzleRiw0mW6Bd21raywHbfO/G2FSnrMseyLJcSrrrqHT7YLGXV6d0H1wAxiKRARPhWO2eMTub9I8/ASDw4YdR1eU82ghIyytm1dFLAMywI0BNp9cxP3Y+IFRuhVQ1Tejk34lvR31ru556aOND9fJedzb+WRXPxbgc1Folo+7shLKKNgtnwGg2cib7DFB30q2QFHQO7EyyPyR5eYLBQP7mLeKHXSwOptjlVQZgloXNXt5MuhsUDleG/f3330ybNo38/Hy8vLzKXcBIksSMGTOcusD/AmIv5fH+mlNolAq+nn5VYy/HLvx47Ef0Zj09g3rSJ6T+KlHKoUQHu0RyqJjlrl2t18DwgcwfO58HNz7IBd0FbltxGx8O+ZD+Yf3BpyVEDRKk+8iv4nmuNOgLYPljkHex4Z9ba5nFbD244Z+7lsgoymDx6cX8fup30oqE2qOSVIz0iWbq8fV0C+mN1PZGpz1fuk58yAV5akX12K0LBeFO2AHfDoGIAeDfGsZ/UmldWZVBamVId1ZxFga3YgKKhcqtCgyo2wxiJTPdIBRfU3Excol9SjeA1/jxpH34EaaUFPpfOs6O8K7k6402Bb8+cLqGeW4oJd3qyIgq71MVlB7uBNx3H75Tp5I5dy7Z836i+MQJkh95BBBp6Mdb6RnMP3B8CbSpXf6Fyt+fwIceIvXtt0n/+GO8Ro9C6eNT/k6yDOtfLT1Xjv8Eetbxc9nNDyb9CDHXUfTno3QyJvB62oMs3ngjMjJdA7oS6mGfZbf41CnSP/sMc16Z3IyiLEi9AFIgxMXB/Jl1W28VUPr54TliBB5DhtTYud7C143DSbkOh6lZMbvLbP46+xeXCi7xwvYXmNFxBt0CuzktqOl8RgFmWWzCBXo43oJgxfDoYN5bfYqdZzMp0ptwdXI/cWVI05WQXWhAIdk/j264eJG0jz/Bf/YduERH2zZmPMx5gExmfhXkwWyCfXPE7T53V7qBWB3S//c/TLm5aNu3x3fyrQ49tilg0d5EjGaZXpG+dA6vueLr55M/o9PraO3dmpERIykuMHB4wwXys4oZOLEdLh7lz9OdAzrz9civuWfdPexN2ctDGx/i82Gf46KqRTheA+DS2Vz2rYgHYPCU9ngHutXr88XnxqM363FXuxPuGV7n43UN6MqeS3vY3daTiQd06NauxXv8tdCiF/hEQk4CnF5VdW6HBVbSXZinx2Qy1+vGQzNK4TDpfuKJJ7jjjjt4++23cXOr3zfrfwUqhcTGk2m4aZSYzTKKRujLdAQZRRn8flpYWCqb96k37JsjbGL+bWs8odSEtr5tWXTtIh7Z+AiH0g9x3/r7eL7v89zS4RYRqBa/DQ7/AoOedPhDutFx4CexYdBYOLVSzM2NehN8oxpvHTXgaPpRFp1cxJr4NRgsSeX+Lv5M6jCJSe0nEXRmMxxYWXlPdx2QZiPdlosSjRtM/RV+uhGS94uwn8Sd4j3epmLnsZWcFupNGExm0Tmdnw4F6Xgh4an2RGfQUWBKw7/IYi2vS4haNZBcXCA3F3NZe3kZpbuyc4PCxQWfW28h8+tvuOncNnaEdyW30FCvpNtWF1ZNcrmtLiwistbPo/T2JuiRR/CbMYPMOXPIXrgIubiYgIcf4pej+xlc/A/uZ/4G+fVan1d8p04Rdte4ONI//ZSQl18u/aEsw8Y3YMcn4v+v/RB6zar166mAzjfx1iFvrjn1FqOU/7AuaSu4ujAywL6qyOKTJ0mceTum3NxKfmohjql2zqvXErrVq5G0WjwGD8Zr7Bg8Bg9GUcm1THgtE8ytcFW58kzvZ3h086OsS1jHuoR1hLqHMipyFGNajaGTf6c6fXbGlbGW1+U47YM9CPdxJTmniJ1nMxgeUz/nirKwqtytAz1wUdtH8tM++pi85ctBlgn/4H2b0q3CiDvFnEqtIvz0zHrIjgcX71I10E4UHT9Ozq/Clh7y0otIKocvmRsVeqOZhXvEeW3GgKga719gKOCnE8KxOrv93exfnsDhjRcwFItRlozkfG54tAcu7uXP1d0Cu/H1CEG891zaw8MbH+az4Z+hVdZ+M6g+UFJoYN33x5HNMu37BNOhn2Oz/bWBLUTNt321rgF70SVA5AnsidEz8QDkb92GuahIjER1mQTbPoCji2u8Rnbz1KBQSJjNMoW5ejz9muYmyb8NDp9BkpOTefjhh5sJtxPRKsAdrUpBod5EYlYhUQFNu6h+7rG5lJhK6BrYlf6hjlm1ag19Aez8TNwe9GStVe6y8HPxY87oOby681WWn1vOG7vf4HzueZ7scg9KtRtknoGk/dCyd52fq8FgNpcGxvS9D1o2kAvBioQdsP8HkQB/ei0MeAiufgy0tbc/OhN6k5418Wv4+eTPHM0otfl2DejKlJgpjIochUZpmXkrk17uTFiV7nId3VpPuH0FnNsEv1jCq9JiKyXdni6lp21dsVH086adEN/wjaKFZwtis2IpMKfjb1W66zDPLVDe0WS7bUkwl+3prC4D3ylTyZzzPR0zz9M2J4ncIgP2m7odh60urDql+7K6sLpA5etL8FNP4T9rFoaLF3Ht2pXYeC3FRZ/hmndezC6Hdq3VsSWViuAXXyRx5kyyf/kVn0mTcImJET/c/A5s+1DcHvse9L6zzq/lchzPc2GB4XF+6n2a/Vk/ADBiw0dgcoV+91d5bi4+fZrEWXdgys3FpWtX/G+fKTYeUo6KNSs1MO59QY7qA7JMcVwcupWr0CckoFu7Ft3atUiurngMGYzXmLF4DL7G1j9vs5fn1H5OdXjkcL4d+S3Lzi5jU+ImLhVcYt6Jecw7MY9wj3BGR41mTNQYov2iHSbOZyzv6doml1shSRLDY4L4aVcCG06mNRDpdixEzZSTg26tyA7Qx8eLb2rcQOUCxmJ8pXy2nEqvXLTY+634b4/p4jF2QpZlUt98C2QZr2uvxa33FXQdYMGa4ymk60oI9NQyplPNY1E/n/yZooIShmfdSup3HlwoigfAP9yDwrwSMi7k89f/DnH9I90rEO/uQd35asRX3Lv+XnZd2sUjmx7hf0P/12SItyzLbF50Cl1WMV4BLgyeUjert72whqi1961bcrkVXQIF6U5okUOqmw/BhTnkb9+O18iRpaQ7bh0UZlU7piMpJNx8NORnlVCQU9JMuhsIDpPu0aNHs3//flq3rt/ggf8SVEoFHUI8OZKUS+ylvCZNujOLMvnttNj5vbfrvQ2ncu//QSQz+rZyeLe6OmiVWt6++m2ivKL4/NDnLIhdQKIukfeir8X96O8iUO1KIt3nNoqaH60XDHux4clu55tEHcvqZ0Ui97YP4NBCGPm6+HdrJNdAWmEav536jcWnF5NZbOmtVqgZEzWGqTFT6RzQueKDqujprvNa8srYy8tC7QIdxsLgZwR5shLpy6BSKnDXKCnQm8grMpQn3UEdCfcIFqTblEZbG+muQ6o0VPnvptBYSHcVSndVj1MHB+E1Zgx5y5dzw9lt5BXXzblSHQpKjLZe6eqU7qrqwuoCVUAAqoAAAFw8vNmU0p2xyn2i2qWWpBvAvW8fvMaNJW/lKlLeeJPIhQuQtrxXmv49+h2RAF4PSMwsBCQOBgdgzpbohJYW+iIxQ35yBdz4JfiVvz4oOXOGxNtnYcrOxqVTJyLmfIfSy0u8V77/CCKKod9sGONA0Fst4IWYyy05eZK8lavIW70aw4UL6FatRrdqNZKbG55Dh+I1bizhAe2A2ivdVvQP60//sP4UG4vZnrydNfFr2JK0heT8ZH449gM/HPuBSK9ImwLezqedXZ+rVqW7bS2Ty8tiaLQg3Rtj05BvlOv9c/1kSmldmD3I/esvZL2Y2dbHxyPLljW6+oHuIuGaIvYU6DmclEOPCN/SB2acEUo3EvSe7dAa8/76i6KDB5Hc3Ah6+gocMwN+2hUPwNQ+EWhU1ausuTod+1aeZ2riy7iY3NFjwi/MnT7jW9G6eyBZKQUs+/gg6Yk6/v70ENc/2gOta3kK0TO4J18O/5L7N9zPjuQdPLbpMT4Z+knpRnYj4uSuS5zZn4ZCITFydic0rg3jWrAq3XVNLrciwDWAMPcwLhZcZFfLCG48lYNu3TpBuoOiRW5B6jGI/Uv00VcDDx+tjXQ3o2Fg17vO2ssNcO211/LUU09x4sQJunTpglpd/oL0+utrrnBqRkXEhHhxJCmXE5fyGNul/i0vtcW8E/MoMhbR2b8zV4df3TBPqi+EHf8Tt695UiQ1OhGSJHFPt3uI8o7ihe0vsDVpK9PdwvhCqST02B8w5h1QNY3d2hqx1zK71n1q46nLwZ1gxl9wcjmseUHMGC25SyjwY9+F8J4NsgxZljmUfohFsYtYn7AeoyzmnYNcg7ilwy1MbD8Rf1f/qg9gU7qd+35Lz69E6S6LIItqmRZb5TG8XNWCdFvD1KykO7gj4R5ix7rAnI5/kbi4rUtyOVC+Mqyc0i2eq1yQmp3wmzGdvOXLGZx0iJSLqdAmoG5rrAJWchLgocW/itlXU34+pizRZ65xIukuCx83DStM/SykewkMf7lOm1BBTz+NbtNmig4cIO/9+/AutDQujHoT+t/vpFWXR36J0daHfDh7GwAje94HnZXibz1xF3w1UGyy9ZoNCgUl586RcPssTFlZaDvGEPH9HEG4Ac5vgaS9oNTCwIfrZc2XQ5IkXGJicImJIfDxxyg+dpy81avQrVqN4eJF8lasIG/FCsLc3HnSrwP/tLkKs74vijq2s7ioXBgROYIRkSMoNBSyLXkba+LXsDVpKwl5CXx39Du+O/odrbxbMSZqDKOjRtPGp02Vx4urY3J5WfRv7Y+rWklKXjEnLuXRKaye3AYWWO3lHe1QumVZJuf30kRmc34+psxMsZnlJkj31eEK9pwTCezlSLd1lrvdqAobQdXBlJ9P6vsfABBw3711P382Ao5fzGVffDYqhcTUvlWf0wwlJo5uTmLXqtN0LRZJ5T7BrvS5rjVtewYhWZwD/mEe3PBoD/786CBpCRbi/XD3CuS1V0gvPh/2OQ9seIBtydt4YvMTfDTkI9RO3rx2BDmphWz9NQ6APte3IqRV/b6/y8KqdNc1RK0sugR2EaS7nTs3noL8TZuR9XrRKNJloiDdRxfXSLqbw9QaHnZdTd54440Vvvf6669X+J4kSZhqqjFpRqXoGCY+fKwfRk0R2cXZ/HLyF0AkljeYyv3PXChIB5+I0nTxesDoqNGEuYfx0MaHiCu8yJQWYXyakkrX06uh4w319rxOQ3a8qN2BerGVOgRJEnPdbUeKQKdtH4mL6++GQvfbBOHwrJ8LmRJTCSvPreTnkz8Tm1VKXHsG9WRKzBSGRwxHbY9l3GypdnL6TLdQhYO8qiDdgVbSfVKMC1QSgObtquZSbnFpbVhqGaVbJT5AC0xppfbyus50l/tbLx+kBpQPUrND6QZw7dqV5PC2hCefQbNiKQzqVLc1VoFTdqhq1nlupa8vSs+6K4eVwcdVzUJzDwwKLerseLh0CMJqF6gGoA4JIeDee0n/+GPSFm3E41oJ5dhXxEhHPUGo3ODjoedA2j7AUhXmFQGth8KyB0QexsonIfYvSno8R+IDz2LKyEAbHU3E99+XBr/JMmy2KPNX3Q6edWsFqA0kScK1S2dcu3Qm6MknKT5yhLxVq8lbvRpjSgrDCw8wPOkApwf8gtfIEXiNHYN7v3616lovCze1G6OjRjM6ajSFhkI2X9jM6vjVbE/ezvnc83x1+Cu+OvwVbX3a2gh4lHeU7fF6o5n4DJFcXld7OYCLWsnAtgGsj01l08m0eiXdJUYTZ9PF2qNDa/5bKzp0iJK4M0guLig8PDBlZKCPjxek21UQ7D4hwDnYEJvGE6Ms5KYkX7isAPo6VhOW8cWXmDIy0ERF4TezfkL96hvzd4masDGdQwj2qmgdNuhNHNuSzMG1CRTpDICSHJc0ooZ5MGX80EqzhfzDPbjhse78+fFBUs/n8fdnh7nu4W5oXMpTiT6hffhs+Gc8uOFBNidt5sktT/LBkA/s+9x1MkwGM2vmHMNYYiK8gy89R9U+s8NRZBRlkFmciUJS0Na3rdOO2yWgC2vi13AuKp8srSd+Oh0Fe/bgMWiQmOVe/yrEb4fcZPCuOrzNVhuW3Uy6Gwp2TfWbzWa7vpoJd+1hnW2yzjo1Rcw/MZ8iYxExfjFc0+KahnlSQ1FpKNCgJ5xOgC5Hl8Au/Hztz7T3bU+mQmJWSDCrD3xdr8/pNOz7HpDFHLDFGtnoULsId8JD+6GrxTp6aAF8dpVwLxidd7K/lH+JT/75hBG/j+DlnS8TmxWLVqllQtsJ/Db+N+aNnceYqDH2f/DX90y3RxUzVH6txXyroQByK6+ts4aO5RYZBDG3quJBHQn3EB+yBebSILW62sur2mCzKd0OznRbETtgHAC+G1bU+hg1wVYXZoe1vL5UbhCdyEW4cMJjgPjGsSV1PqZfdCFqDyPGYiUZulEiP6EeYbXp+wbEYZJNRPtFE+Fl+Z35RgqHy9j3QeWK/sgOEmfOwpiejrZ9eyJ+/AGVbxkVMn67CAxUauDqR+t13fZAkiRcu3Uj+NlnaLtxA5GLFrGq/TVkungh5+vIXbqUC3ffw+lB13DxxRfJ374D2Vi7zvWycFO7Ma71OD4d9ilbbt3C21e/zTUtrkGlUHEm5wyfH/qc6/68jkl/T2LO0Tlc0F0gIbMAo1nGQ6sipBJCVRsMjxHniA313Ncdl5qPySzj46a2a+05vy8GwGvMGFw6CEJtm+u2zKx29jEhSXDiUh6Xci3jAEd+hZI88GsDrStmY1SFkrNnyZovKrOCX3i+zg6HxkBOoZ4/D4nayJmXBagZ9SYOb7jA/Bd3sfOPMxTpDEheRja2XcD2gfO4ZfzoasN8A1p4csMjPdC6qUg5l8vyzw+jL674d9AvtB+fDv0UjULDxgsbeXrL07bA0obErmVnybiQj4u7mpGzOtqU+4aAVeWO9IrEVeXqtON2DRSjSRrPZHaGidE43dp14oc+EZZaPFk4qqqBh4/4+2tWuhsOzRnxTQTWHd/knCJyCqvpm2wk5JbksujkIqCBE8sP/AT5qeDdErpNbZCnDPUI5aexPzE46Cr0ComnTEl8te9D5Gr6hxsdhiI4KC4U6H1X466lMniFwU3fwOx1ENYT9DpY9zJ82Q9Oraq227k6yLLMvpR9PLbpMcYsGcP3x74npySHUPdQHu35KOsnruf1ga8T4x/j+MFtM93Os5ebzDIZlj7ZKpVupQoCLGpNFRZza21YXrFBEHNDgSAv/m1o4dECgAJTqq0yrO728sorw2xBamXs5eX+JWs4T2T1HEC6qzcaXQ55K1bWbY1VwBqiVn1d2AWgdnVh9sLar77TxbJhefzPWr/vAdj1JYpNrxDSU/wbZ22MpeTcuTqusnpcsJBus9thwKJyl4VCAX3vRn/9HyRsDcNYpEDrbSBivAKV8rJaOev8ec8Z4vzQhCApFLj17MHGkbcxffSLZLz1Kb5Tp6IMCMCcm0vu4j+4cOedxF09iEsvv0LBrl1OIeCeGk+ua3MdXwz/gs23bOb1Aa8zMHwgKknFyayT/O/A/xi3ZBwPb70dtd8WIoNLnPZZPLSDIN2HLuSQUVX9lhNgdfNFh3jWuHZTfj55q1YB4HPLJDRRUUAZ0m1JMHc359G9pQ8Am06mi78ra6Bon7sqdQtVBlmWSX3rLTAa8Rg+XCiHVyB+23+BYoOZjqFe9IoUG10mg5mjm5NY8NIutv8eR1GeHk9/FwZNa8Pinu9yOnAfd3a7065N6cAIT65/RFjLL53JZcUXRzCUVBTdBoQP4H/D/odaoWZ94nqe3fosRnPd/07sRcLxTA6vF+f2YTNjbMpuQ8E6z+1MazlAjF8MKkmFgTx2tmgFgG7DBmSr8Nllovjv0cXVHsfdV2woNc90NxzsJt0bN26kY8eO5OVVtD/n5ubSqVMntm7d6tTF/Zfg5aK2paU2RbV7/on5FBgK6ODbgWEt7d81rhMMxbD9Y3H76sdA1XA7zu5qd/43+ntmGsW/yZcn5vLstmcpMTXRk9OxP0SdmncEtB/d2KupGi37wJ0b4IYvwT0Iss7Bz5Nhwc2QfsruwxQaCvn99O/c/PfN3LHmDtYnrscsm+kT0odPhnzCyptWMrvLbHxcfGq/VpPl4sCJSnd2oR6TWUaSwN+9mvezba678jA1q9KdV2QotZYHdAClmjAPQWDUxUW4WNT6OqeXVxWkphU75Y70dJeFp7sLf7caCEDW/Pn1srF1KsWO5PJEYcWsS11YTfB1E//eO6QeoHYXmyXJ/9TuYHu+hTXPAeAx5VE8hg4Fo5HUN9+q183BhKwCUBSSIx8HKiHdgD4pmYRHX8KoM6EJ9SFiuA7VxY3wZV84/KsgRAk7hQ1doYaBj9bbeuuKcB9XZEnBudB2hLz8Eu22bCZi7lx8Jt+K0s8PU04OOb/9RuKsO4gbPIRLr71Gwd69pRe/dYC31psJ7Sbw9Yiv2XTLJl7p/wr9QvuhkBQkFZ7GJXgVie4vMG3FNH46/hMpBSl1er4Qbxc6hXkJ1/+p9DqvvypYnSf2JJfnLV+OXFSEpk0bXHv0sJHuksuUbgqzGB4tNg02nkwV7630WPF31t3+zXrd2nUU7NyFpNEQ/Owzdj+uKcFklpm/W5zPZg6IxGySObY1mQUv72LrL6cpyNXj4atlyLQOTHutH0cDtpNekkaoeyjXt7E/kyko0ksQbxclF+NyWPHlYQz6iu/7q8Ov5pOhn6BSqFibsJbntz3fIMS7ME/Phrnis7HL4HBada2fzJDqcDLrJAAd/JxLul1ULrTzFW7GExEKdGpXTFlZFP5j+TzpOEFk0Vw6BBlxVR7Hw2ovbybdDQa7Sfcnn3zCXXfdhZdXxROlt7c399xzDx9//LFTF/dfgt6kt4WKNLW57jx9HgtjxWxUg6rchxaA7hJ4hkGP2xrmOctAqVDyZOc7eSUjE5UMK8+vZPaa2WQWZTb4WqqFLJfWovSe7ZQ6tXqFQgE9psFD/4gLbqUGzm6ArwbA6uegKKfKhybpkvhg3weMWDyC13e9Tlx2HK4qVya1n8SS65fw/ejvGR45HJUzws/qIb3cmlzu765Bpazm9FtDmJqXRTXNKzaUSS4Xj3FRuRDgGoCfZe9O7+Zhq0GqPapXussFqdk50w3idayK6odBpaEkNpbCffvquM7yyMgvISNfjySJPuKqYHBiXVhV8HET/2apRQqRUg8ixdxR7JsDqyxpyoOegCHPEfzcs0gaDQU7d6Jbv95JK66IxKwiVJ6xmDHR1qctrbxblfu5ITmZxJkzMV68hCYqiohfl6F6dIuYXS/OhaV3w6+3wYY3xAN63AY+9VkWVze08BUVU8k5wrIsKZW49+tL6Kuv0m7rFiJ++B6fSZNQentjyswk5+dfSJwxkzNDhpLy5lsU/vMPstlc53X4uPgwsf1Evhv1HRsnbaSdYibGgtaAxJGMI7y//31GLh7JjFUzWBi7kPTC2pHmcsS1nmC9vokJqZl05/wmAtR8Jk1EkiQb6TYkCFJpVbopymJYtNhY3H4mA9Pub8T3u022u4LOXFRE6rv/B4D/nXeiadl035fVYfOpNC5kFeHjoqZDgYKFL+9my6JT5GeX4O6jZfCU9tz2en86DQrHKBn44aio/buzy50Oh50FR3lx3SPdUbsoST6Vw8ovj2CshHhf0+IaPh7yMSqFilXxq3hh+wuYzPU3jiqbZTbMO0GRzoBfmDsDbnbePLUjOJ11GnC+0g2lFvOA4HR2hYo8FJvF3N2/tG60GrW7bJBak3Zy/otg95Xp4cOHeffdd6v8+ahRo/jggw+csqj/GvQmPVNXTEXl1g6kvrUj3SU62PEp5CWL5Fh35+3qLYxdSL4hn7Y+bRkeMdxpx60WRj1sK6tyN1J6eOebmbjmeVoaUnksJITD6YeZ9utwPpMDaYedyntAWxj5BrjY10nqMJL2w6XDIgG4x/T6eY76gIsXjHxN2EvXvginVsLuL8Us3rAXoedMUCiRZZldl3bxc+zPbEnagmwxMLfwaMHk6Mnc2PZGvLX1EPxTDzPd1hC1gCpStG0I6mh5QA2ku8gIutLkcivCPcJBJ2Yzi7yrSWivI+qqdHu7qsnXuHGs00B6HN5E9vz5uPdxXrf8aYuqFuHnhpum6o87/QVhQazPmW4r6c4pMkCnCXBssSDdI9+w2/7K/h9hxRPi9sBHYNhLIEloIiLwm30HmV99Tdo7/4fHoEFO2GipiAtZhag9jwEwKnJUuZ8ZLl0i4fZZGJKT0URGEjFvHuqgICAIZq8XrqUt74pWAxBKTD3PoNcV4T7C6ZSUXbGrW1KpcB8wAPcBAwh5+SUKdu8hb9UqdOvXY0xPJ3vBArIXLEAVHOz099WspFwK9UbaBrfHJOWTXZyFzqAD9gH72MJbaHz86PPhHEJa2l9VNCwmmE83nmHb6Qz0RnONNVOOQpblUtJdg9JddPw4xSdOIKnVeN8ggkw1raIA0CckIptMSGWU7phQT0K9XZByk1Cctoyq9LF/1Crzu+8wXryEKiwU/7saOYi0Dpi3M55OJUrG6LXs+EWQPjcvDVeNjaTj1WGo1KWb8n/G/UlaURrBbsHc2PbGWj1fSCtvrnuoO39/eoikk9ms/OoI4+7vWu55AIa0HMIHgz/gyc1PsvL8SpSSkjcGvoGyHkSCwxsvkHg8C6Vawag7O6HSNLwQUWwsJj4vHnC+0g0iTO3XU7/i4pnMztDejErcj27dOoKffw5JoRAVrXFr4ejvMOTZSjfA3b3FdYjJYKak0Fihe70ZzofdpDs1NbVCPVi5A6lUpKfXnyXp34ytSVstsx+ncGu1h4NptwPd7Huw2Sy6pDe8JmafQfQjT15Upx5YK/L1+cw/IWaF7+l6DwqpgWIADi2EvCTwCBGkrLHg7g8x4+l7fCkLk5N5MDiQRDVMly/yfloGg4rsIBsJ2yE3Cab86vS6M6BU5e4yUaz3SoN/G5jys+hTXf08ZJyC5Y9RsO97/uo6jp/T93A+97zt7gPCBjA1eipXh19dLx/YNtjSy533b2YNUQuqKUDIqnRnnBbk/zIFwsuSFptbZICs0uRyK8I9winSHQSg2Nuv7guvPLy88sqwcjvm1Svd1jnnLZ2H0uPwJnTrN6BPSkLTokUdFyxwKrXmEDVzcTHGFGHNVUfWn73cx2IvzynUI7cdjqTxFJukSfsgom/NBzgwH5Y/Km73fxBGvFbuQirg7rvJ/XMZhosXyfxuDoEPPejU9ZvMMkm5mWj9xYV8WWu5ITWVhJm3Y7hwAXXLlkTMm4u6bHifUgWDn4IOY2DpvaLSpucMEb7WhGEd+aqpq1tSq/EYdDUeg65GflXMeOetXIVuwwaMqakYU52rHNt0O4vpquK7W4bETLY/cyc3Lthst/Ona7g3AR4aMvL17I/PYkBb51py03QlZBcaUEg1p65ba8I8R460BfCpQ0OR1GpkvR7DpRQ0ZZRuSZIYGh1E2D/zkGQzRA0qPY/WAP2FC2TO+R6A4GeeReHqvNCrhoLZLLNjQwLt/9Hha9YAJlw91fQcHUnna8IrEE+9Sc+cY6JSbXaX2XXq0g5t4834h7rx92eHuRCbzaqvjzL23i4ViPfwiOG8N/g9ntryFH+f+xuFpOD1ga879boyPVHHrqVnAbh6Ylv8wxqnOvVszllMsgk/Fz8CXQOdfvwugV0AyDaeIzFoMkUqLa6pqRQfPYprt27QYRyoXCHrLFw8WGlVq0qjxMVdTXGBgYKckmbS3QCw+2oyPDycY8eO0bZt5TaNI0eOEBradPulmzJGRI7gi+Ff8OL2l8kmnRTNB3y0P4sHe9xf/YkwcQ+sfkb8QUFpD2XWOfhhNNz4pVBU6oBFJxeh0+to7d260vm9eoHJICqmQKTaqp2v2DiE6z6FzhNpZTaw0FjIY2d+YX9+PA+GBPN0y7FMC+5X9WNLdLDyaUEo1zwH49537try00otqo1dE1ZXtB0B9w0mYceH/HzsB5a55JB/RlTUuSlduaHdjUyJnlLB0lpvqBel25pcXoPS7d0SNB6gzxd/z4Hld8qtSndBUSFkWma2LiPdWRZ7ebGPEzZiqprptgWp1U7ptr6OOLdA3AcOpGDHDrIXLHTaPKV1nrvaujCLyq3w8Cits6oH+FqUboNJptCsxj16nHB1HF9SM+k+tAj+slSB9b1PdHFf9m+icHUl+JlnSH70UTLnzMF7wo1O27wAuJRbhOwai6Qw0cqrla1D2pCWRuKMmRgSE1G3aEHkvLmoQ6qo/wrpAndtslwEXuW0tdUXbPbyGkh3WUgaDR6DB+MxeDBmvZ7Cffsw65yX05KaV8xrf59Ao1Tw8S3dKk1jvnQxDuN7X9LpQCa//PIyt019265jKxQSQzoEsfifJDacTHM66baq3K0DPXBRV71hai4sJO9v4YjwuWWS7fuSUok6MgL9mbPo4+PRRJYq3QAj23nT9dBGAOQ+d9Ww5VeK1Hf+D1mvx31AfzxHNdB1jpNgNsuc+SeVfcvjyUktxBcFBpXE4Ovb0HlwOGpt5b/nZWeXkVKQQqBrIDe1u6nO6whr68N1D3bl788Ok3g8i9XfHGPsPV1QqssT6pGRI3n3mnd5ZuszLDu7DKVCySv9X3EK8dYXG1n7/XHMJplW3QLodE3VdVn1DWuIWnvf9vUykhnlFYWn2hOdQYdXoI69wTEMTj6Ebt06Qbq1HhA9TuT9HF1cKekGYTEvLjCQn1OCf3jjbFD8l2A36R43bhwvvfQSY8aMweUy21pRURGvvPIK48ePd/oC/yu4psU1LLvhT675/mEkz4P8ePx7tl/cyttXv02032X2sNxkWP+KsI0AaDxh8NPQ916RYrz4Dji7EX6/HVKPw5Dn7bcvlkGBoYCfTvwEwN1d765fVbEsDv8iQobcg0R/a2PDxQtixHvbB/i28y28sfsNlp5Zyv9dWEm8mxfP9HmmajXBxQd+my4Uaf+20Pce563twDwxexzeq8qT6pUAs2xme/J2Fp1cxI7kHeAuyFyUwcDkPB03FJnwCAdc66fbu/JFOX+mu1TproF0KxQQGA3J+8XM9uWk2xKk5pl/XijyWi/wLiVY4R7hkC8UZ2co3WUvGsrd1lorwyrv6a7pWsPbZpM34DdjOgU7dpCzeDEBDz6I0sO9zuu2R+kuWxdWn3kVrmolGqUCvclMdqEe9043WUj3nzD6narP0Yd/hT/vB2ToczeMeafKX6zn6FG49etH4e7dpP7f/9Hy88+dtv7ErEJUXkcBGBk1EkmSMKank3j7LPQJCajDwgThDqshiVylsU/ZbwIItyjdmQV6CvXGakcUKoNCo8Fj4ECnrmnnsRS2H1DTJdwb73FXV3ofL8aw99hBPFfuIuDrpey5Zjx9Wwyw6/jDowXp3ngyjZfGd6z5AQ7AGhJbk7U8b9VqzAUFqCMicLts3EQTFWUj3URb3kdF2QAMLNmGRtKRLPuj8xmEPcb6/K1byd+4EVQqgl94oeEya+oI2Sxz9mA6e5efJ/uS6D0vlmT2aI3ce2c3enSuuvfeYDYw54hQue/ofAdapXPG98La+TL+gW4s//wwCccyWf3dMcbc3RnlZWMKo6NGY5bNPLvtWZbELUEhKXip30t1Jt7bf4sjJ7UQdx8tw6bHNOq/pS1ErR7muQEUkoLOAZ3ZdWkX7VpmsT2sC4OTD5G3bh2BTzwhXnuXSYJ0H/sDRr1Rad6Pu4+WzOT85jC1BoLd7/AXX3yRrKws2rdvz3vvvceyZctYtmwZ7777Lh06dCArK4sXXnihPtf6r4evqw/RqnspSpqGu9KbuOw4piyfwteHvxb9hoYi2PIefN7LQrglMcP78AEY+LC4mHH1ham/C/shwNb34ddpUOz4nPgvJ38htySXKK8oxkSNce6LrQomA2yzZAMMfATUTc/mpVaqeW3Aazx+1eNISPxy6hce2PAAOn0VakbH64UVFGD1s3B6rXMWYjLCPhGCQp+7nXPMBoZOr2P+iflct/Q6HtjwADuSdyAhcU2La/h6xNcsG/cL03y742EoEvOgn/cSu7YNEfphU7qdby+vUemGMmFqJyv8yFoZFlR0rvS+ZS4wwj3DbUFqxc6Y6a5R6a7dB7aVdOcWGXC7+mo0UVGY8/PJ/fPPWh2vLMxm2TbT3dh1YSA2K2xz3YUGaDMUtN6QnwKJuyp/0NHF8Oe9gAy9ZsPY96rdyZAkiZAXXwCVivz1G8jftt1p649Ly0DlLqzloyJHYczMJGHWLPTnzqEKDRWW8vDGU5bqA96uajwtoxwXc+xXu+sTZ9LEe7ome3bPlz6kxF1DZBqs/PhRMooy7Dr+1e0CUCslzmcUcC49v87rLYuTKaV1YdXBai33mThRzKeWgbZsbZh1prskD0wGNP8IIrnQOIINp7JqXI9Zryf1LeEC8Js+HW2bNva+lEaDLMucO5jOr2/tZc13x8i+VIDWTYW2hx/feBWT3kLLkI7Vb04vP7uciwUX8Xfx5+b2Nzt1feEdfBn3QFeUagXxRzJY890xTKaKYYJjW43lravfQiEpWHx6MW/vebtOYV5x+1OJ3XkJJBg5qyMuHo1rlbZ2dNfHPLcVVou5q2cy+4OjMShVGBISKTktztO0GS5En/wUiK/8s8DDR7hp87ObSXdDwG7SHRwczM6dO+ncuTPPPfccEyZMYMKECTz//PN07tyZ7du3E1zXWppmEBPqhVHXhVE+HzAiYgRG2cgXh77gtj/Gc+arPrDpLTAUQkR/uHsz3PA5eASVP4hSBaPfghu/FuFap1bC9yOFTdVOFBoKmXd8HgB3db2r4VTuo79Ddjy4BUCvWQ3znLWAJEnM6jyLj4d+jKvKlZ0XdzJ95XQu6C5U/oCBj4ikXtkMi2dByrG6L+LUCtBdFL+rTjfW/XgNiLM5Z3lz95sM/3047+17j0RdIp5qT6Z3nM6KCSv4YvgXDAwfiCKsO9y+HCbNE3Voecnwx2z4cSxcPFS/i7TNdDeC0g1lwtQq1oZZyWpoyfny97Ug3CMcX524gCny8q3lasugqp7uGpTumqRu6+swmGRKTOA7XbQUZM+fX+fU5+ScIgr0JtRKiaiAqlXzhqgLs6Ic6VZpbQ6aSlPMjy2BJXeJc8ZVt8O4D2q2DgDatm3xmzYNgNS33kLW652y9j2pO5AURtwVIbSWA4TCfeYsquBgIufNvWLTnmuCNUztggMW8/pEXJogwu2CqieuKl9fQh8ToXvjN+h4c9VTmOWa/6Y8XdT0bSU26jaeTKvjasvDai/vWI3SXRIXR9GhQ6BU4jPhxgo/L9fV7eKNLTfizHq4eACTpOYX01C71p41bx76hASUgQEEPHC/Yy+mgSHLMucPp/Pb2/tY9c1RMpML0Lgo6T2+Fbe90Y9fC3LRSzCjfySKSkYOrDCYDXx7ROTAzOo8C1eV84WNltF+jLuvC0qVgvOHM1g753ilxHt86/G8OfBNJCR+PfUr7+x9p1bEOy+jiM0LBcm9akwk4R2c8JlXB8iyzOlsS3J5PZLurgEitynDEIdZ68p+iyPOlmKu0kBHEUJoc8ZeBndf8RnerHQ3DBzyckRGRrJy5UoyMjLYs2cPu3fvJiMjg5UrV9KqVQPNWf7LYbVdnU+V+GjIR/xfl/vxkhWcKLzILV4yPwS1wHTzHJi1CsK6V3+w7lPE/TxCIP0kfDsUzm6yax2/nfqN7JJsWnq2ZFyrcXV8VXbCZIStFpV7wEOgqbu9tL4xPGI4c8fMJcg1iLO5Z5m2YhoHUg9UvKMkwbUfi3AXfT4suhV0dQzX2fud+O9VMxsv3d0BmMwmNiZu5M61d3Ljshv59dSvFBmLaOvTlpf6vcT6Set5uvfTtPS67OJdksSmwoN7YegLIhwkcRd8O0TMuebXU4BjPaaX26d0W8yRlSSYW+3lkcZ4y33Lk+4Q9xCb0p3j5Yz1V34RJ2nrpnS7aZQoLReIuUUGfG68EYWnJ/qEBPK3bq3dUi2wznO3CfRAXU09W0PUhVlhC1MrshBha+bGiWVQtkLnxDL4405BuHvcJs4dDowIBTz4AMqAAPTx8WTNn++Utcfm7QCgh7I3F+6YTUlcHKrAQEG46zH1vbFhneuuKUytoRCXaiXdNc9fBk2ZBu1a41EMbX7bw4/HfrTrOYbZqsOcR7pLjCbOpgsbdHRo1RsG2RaV22PoEFSBFQOoNJawQ318vLDLuvqIH2wWdV8l0TeShRcHErPJKqh6w8mQmkrGV18DEPzkkyg9muY8qyzLxB/NYPH/7WflV0fJuJCPWquk17gopr81gD7jW3EgJY+4tHzcNEpuvqr6HIeV51aSlJ+En4sfk9pPqva+dUFER3/G3tcFhUri3MF01n1/AnMlxPu6Ntfx+sDXkZD4+eTPvLfvPYeIt9lkZt0PJ9AXGQlu5UXv8Y3PRZLzk8k35KNWqOs1g6ZzQGcAEvLi6d3GlR1hQvnWrS3jpuxi+Tc+8RcYK35ON3d1NyxqNUDh6+tL79696dOnD76+td9R2rp1K9dddx1hYWFIksSfl1kKb7/9diRJKvc1ZkwD2ZwbCdYd4IvJF+DvR7n2r+dYeuECg4pKMEgSH7srmHlhGfF5CfYdsMVVQhEP7wXFObDgJtj1ZbX23CJjET8eFx/Od3W5yzmdx/bg2B8iadHV74oKBevo35FF1y4ixi+G7JJs7lx7J3+f/bviHVUauHW+mOvOS4KfJ4O+YhWNXUiLhfhtICmg1x11ewH1jNySXH489iPXLr2WRzY9wp5Le1BICoa1HMacUXNYcv0SbulwC25qt+oPpHYV2QUP7YfOEwEZDvwEn/WEnZ+Lmjlnol5nuu0IB7QS6ayzYCgfVGYNIGuHxVkRXJ50q4wyPpa3VoaHsfYLtqIKpbvSIDUHlG5JkspZzBXu7vhMnAhA9k8/1WnJ1nnuDjVYWRuiLswKa5hadqHlvdV6iLD/FaRBgiC1xC4XuRyyCbpNhes+cziTQ+npSdATQuXM+OJLDKl1I0+FhkIyzYdxL5KZPXc/JadOoQwMIGLePJvy+G+FNcHckTC1+oLJLHPWYvmuyV4OIngs8jXRiT7skMyKlf/jUNqhGh9nJd17z2eRV2yo/YLLIC41H5NZxsdNTUgV5z9zSQl5y/4CwHdS5YTQ1tWdnIxZry/t6r50CAC3q+8jOsQTWYYtp6t+36e99z5yYSGuPXrgdf31tXtR9QhZlkk8kckf7/3Dii+OkJagQ6VV0nN0JNPf6k/f61vb0qZ/2imuB2/qGW7bkK0MRrOR746KzfqZnWbW/JlbR0R28mfsPV1QKCXOHkhj3Y+VE+8b297IqwNeBWBB7AI+3P+h3cR738p4Us7lonFRMmp2J5TVbLA2FKwham192qJ24qb95fB39SfcIxwZmQ4ROewJ6YhJoaQkLo6S8xYXXOQA8AyDklyIW1fhGGW7uptR/2jUd2dBQQHdunXjiy++qPI+Y8aM4dKlS7avn3/+uQFX2PDoEKjlTtVKlpoeQjowF5AJirmRL25dz+sDXsdd7c7h9MNM+nsSC2MX2mUXwysUbl8hLuBks0jRXvZApbteAItPLyarOItwj3DGt2mgcDyzScyfAwx4UCQvXkEIdg9m7pi5jIgYgcFs4Pntz/PpgU8r/vu4+sLU38R/Lx4Q85q1sdFaVe4O48oFaDUlnMo6xas7X2XE7yP46J+PSM5PxlvrzR2d72DVTav437D/0Te0r+NhJ94tYOL3MGs1hHYT83xrX4CvBlT6oVJrmCxk1UmbTgUlRgr0Qs0M9LRD6fYIFu8T2Syqw8rAU6vCQyqipcKi8l+mdBst9Y0GJWS4FNR98VWg1F5e+w/ssqQbwHfaNFAoKNi5i5K4uFof16p0V0e6Zb0eQ3IyAOqGsJe7CqU7t9CyQaRUQ8x14vaxJXBqlQjANBuh661ifKgWIZgA3jdcj2v37pgLC0n74IM6rXvHxR24FZfw4iIJ98QLKP39iZw7F23rxleV6hultWG13CB1IpKyCykxmtGqFDYFvia49eyJ1/XXowBmrjHwzOanyC3JrfYxUQHutA50x2iW2XbavlnwmnCyTL5CVed83dp1mHJzUYWG4n515SFxyoAAFO7uIMsYEhNL57rBEih6FcNjxKbBhtjKSXfB3r3krVgBkkTISy82qfA0WZa5cDKLpR8c4O9PD5N6Pg+VWkH3kRFMf6M//Se0wdWjtNUmOaeItSdE5eGM/lHVHnt1/GoS8hLw0fowucPk+nwZNkR1CWCMhXif2Z/G+rmxmM0VCfVN7W7i5f4vAzDvxDw+PvBxjcT7YlwO/6yMB2DwtA54BTSNDKDTWeLzur1v+3p/ri4Blrluj2TyNW4cDhC5BLp168UdFErobEmnr8Ri7t6sdDcoGpV0jx07ljfffJMJE6qutdJqtYSEhNi+6qKsN3nErcPlu0G8qFqAl1SIzrejsIdP/AHJN4IJ7Saw5Pol9A3tS7GpmP/b+3/cufZOkvOTaz622kVUiI1+R6ijhxbC3GtBl1LubsXGYn44JsK57uxyZ73u0pXD8aWi+sjFB3rf1TDP6WS4qd34cMiH3NlFqPTfHf2Op7Y8RZHxMoXEv43oUVeohY104xuOPVFxrkh4hyYXoGY0G1kbv5aZq2Yy8e+J/BH3B8WmYqL9onl9wOusn7iex656jDCPGhKO7UFkf1E/dP1n4B4o3j8LJ8LCSZBxpu7Hd7LSbVW53TRKPLR2EHlJKjPXXd5irlBIdNdeAsDoFlz+whNsyma2B+SbnWC/lyr/H5vSXYZ0l6/prvli1qtMgjmApkU4nsOHA5A1f0Ht1ouddWEXL4LZjOTigirI+V2ql8PH/TKlG8pcEC2GX6eL913niXDjV5WmzdoLSaEg+KUXQZLI+/tvCvfvr/Wx1h1dzgu/mmiTZkTh60vk3B+viNApZ8CmdDeBIDWrtbxNoIdtLMMeBD35BJK7O+0vQvs9F3l5x8s1EprhTraYW+e5q0sutwWo3XQTkrLy974kSeXnul3LnPssn4fDokW+0JbT6RguU1Zlo5HUN98Sz3PrLbh0dG5Ce12QfDqbPz86yF+fHOLS2VyUagXdhrfktjf7M/Dmtrh5VayQXbQnAbMM/Vv7V9vSYDKbbLPcMzrOqHeVuyxadQ1g9F2dUSgk4valsnFe5cR7UvtJvNj3RQB+PPYjnx78tMr3aXGBgXU/HEeWIbp/CO17V53W3tCwJZfX4zy3FVbSnVhwkvbBHmwPtVjM15URIKwW89OrRY1tGVjt5cX5BowGE82oXzS+D6MGbN68maCgIDp06MB9991HZmZmtfcvKSkhLy+v3FeTR+ZZQRQWToTMOPKUvjxtuIuFXecJa0gZhHmE8e3Ib3mh7wu4qlzZl7KPm5bdxO+nf6/ZjiNJ0P9+uO0PQW6T9om52J2fQW4SAEvilpBRlEGoeyg3tLmhfl7v5TCbS1Xu/g+Kiq4rFApJwSM9H+HNgW+iUqhYm7CW2WtmU2i4TCWJHCDIIsD2j+DgQvuf5PAvohouoAO0usZ5i68jdl/azZg/xvDElic4kHYApaRkVOQo5o2Zx2/jf2NCuwm4qJzcua5QQs8Z8NA/4r2jUEHcWviyH6x5QWxQ1BZOnum2dXTbo3JbYUswrxim1lklNtsKK9lNN6aJvIAsTygw1f2iWaoqSM2lkiA1B+FlSYe2Kt0AfjNniO8tW4YxO9vhY+qNZpsN1666sJYtG0TtsirdOWVJd9Q14OYPep0g3J0mwIRv6kS4rXDt1AmfW24BIOWNN5GNjo8aFOZkMOjj9bS9BDqtq1C427Wr89quFIT7NJ2ZbluImh3W8rJQBwUR+MADAEzbbGZ33AYWnVxU7WOsxHXzqTRMlRAkR2Ej3SGVf77r4+Mp3LsXJAmfm6vvjbaR7oSE0g3HMoGi3Vv64OeuQVdsZH98+fNH9s+/UHL6NEpvbwIfeaT2L8iJuHQmhz8/PsifHx3kYlwOCpVEl6EtmP5Gf66e1A5378o/M4oNJn7eK8ZjZg6o3qmzNmEt53PP46XxYkr0FKe/hprQunsgo+7shKSQOLUnhU3zY5EreV/dGn0rz/V5DoA5R+fw/bHvK9xHlmU2LzhJfnYJ3kGuDLq1/hVlR2C1l1eo+60HdA0UYWpHMo4wtEMQu0I7IyNRfPSo2FQG4Qj0bwfGYji5otzjte4qW5d6QY6TR/SaUQF2ke6ePXuSbbnwef311yksbBib1ZgxY/jpp5/YsGED7777Llu2bGHs2LGYTFXvxrzzzjt4e3vbvlpeCYmqBRmCKCjUMOAhfuu/lN9MQzmeUrktVCEpmBw9mT+u+4OeQT0pNBby+q7XuW/9faQUpFT6mHJoMwzu2ihIm+4SrH0RPu6Efs4Ivj/wKWBRuZ04y1otYpeJoDetN/RtWsptbXFD2xv4buR3eGu9OZpxtPILnO5T4JqnxO2/H6my0qEcZLnUWt7nLruUxIaAwWTgpR0vkVqYip+LH3d3vZs1N6/hwyEf0jO4Z/0TGhdvkdh//25oN1qQl12fw2dXibnv2lj4benlzrGX2+a5a0W6K4apxSjFRlmuZyWkO9VKuiXynUC6q3qfVR6kZv9MN1S0lwO4XnUVmqgo5JISig4fdni55zMKMJplPLQqW/p0ZWioujArfG3p5WUubpQq6CKIMTHXw03fOe09BxD46CMovL0pOXWK7F9/deixpvwC4u6YSdtkMzoXiR9vegqXDk3rAre+YVW603UlFDeyEhRnrQuzI0TtcvjdNg1N69Z4F8CkbWY+3P8hJzIrbuZZ0SvKF08XFZkFeg4n5dR2yYAgSTUp3TmLFwPgPujqGrveyynd1nNk33ttgaJKhcSQ9sK5sulU6fnPmJlJ+mdiszvwsUdRNbJzMuVcLn99eoglHxwg+VQ2CqVE52vCmf5Gf665tb3N+lsVVhy5RFaBnlBvF0bEVN0eZJbNfHP4GwCmd5yOh6Zxxvfa9Axi1GxBvE/uSmHTgpOVEu+pMVN5qpe4Nvr2yLcV3IIntl/k7MF0FEqJUbM7oXFpoNwhO6DT62zu04awl0f7RaOSVGQVZ9G9tZkcF09OBrYWa1lvsZhbO7uhgsVckqRmi3kDwi7SHRsbS0GBIICvvfYa+fnO7W6sCpMnT+b666+nS5cu3HjjjSxfvpx9+/axefPmKh/z3HPPkZuba/u6cKGKCqemhIi+MPptQRhGvUmblqLr1PohVRVaerXkh9E/8GSvJ9EoNOy4uIOblt3EX2f/qln19m8jiPe4DyByICCxNDeWNGMBQUYjN+74QZC7uiZs1wSzGbZYVO5+91kqQP4d6BXSi2d6PwPAT8d/qqh2Awx5XihbZgP8Mq1mW/S5zcJGrfGEbg0zk2UPlp1dRkpBCoGugay6aRUP9XiIYPdGqBAMaAfTfoNpi8XObkG6SDj/bigk7nbsWE5Xui3J5Q6R7srt5QBtZKHSZrhVtPla7eVZHjifdJe5qbAo3eWC1BxEZaRbkiRbcrFc5LjCaA1Rax/sUe2Gj60urGXDkG5bZViZ1wrAiFfg9pUwaa5Tg/tAVEcFPSoUvfT/fYoxq+b+YgBzQQEX7rkHzYlz5LvAK+N6ou1Q/8pNU4OPmxp3jXAdNHZX9xmL0t22hrqwyiBpNAS/8DwAYw9ASKqep7Y8RYGh8s19tVLBNRbiurGK2Wh7kaYrIbvQgEKqXKWX9Xpylv4JgE8VAWplYSPd5+MF2Z69Hq55stx9htnmukuvYdI+/hhzXh7ajjF2PU99IS0hj+WfH+aP9/7hwoksFAqJjoPCmPZ6PwZP7YCHr32OsJ92xQNwW79IVNUEiK1PWM/Z3LN4qj2ZGjPVGS+h1mh7VRAj7+iIJEHszktsXnSqUuI9veN0wtzDKDIWsSN5h+37WRcL2P6byProd0MbgiKbljPSWhUW4h6Ct7b+r2ddVC609xPkXtYk4uOmZkuISDW3VYcBdBEBpZzdVKHxpTnBvOFgF+nu3r07s2bN4rXXXkOWZT744ANef/31Sr/qE61btyYgIIAzZ6omJlqtFi8vr3JfVwT6PwABbYHSBPNz6fk17qwrFUpmdprJ79f/TpeALugMOl7Y/gIPb3qYjKIaAlC0HkItnbUSw6NHmBMiLjxn5+ShSdwFK5+EDzvA3PGw73uhyDsbJ5dD2nHQekG/e51//EbG2FZjifCMILskm99O/VbxDgqFmN20pssvugUKq7kotqrc3aeA1vELr/qAwWxgztE5gOj9bMhZsSrRbiTctxNGvSXeW5cOwQ+jYfFsyLUjAwHqbaY7yNMBi32gheTkJkJxmU04WbbVhV3Stq7wMKvSneklUWjOwGA2VLiPY6hK6a4kSM2B9HIoJd2XpyRLrpZjF9aCdKeI31WHKqysVtjqwhpI6bZWhmUXXmbjU7tC1ECnWMorfd5bbkEbE4M5L4/0jz+p8f7mwkIu3HMvRf/8Q6EW3pys5JR2ABH+Tb/G0dmQJIlwW5ha45Fus1m2kW5H7eVWeAwciOfIkSjMMvduUJGYl8Dru16vcpPeOte9oY5z3VYBoXWgBy7qiu9x3abNmDIzUQYE4DlkSI3Hs5LukoR4oW637F3hXDOoXSAqhcTZ9ALiMwooOnKE3D+WABDy4ktVzozXJ9ITdaz48gi/v7OfhGOZSAqJmAGhTHu9H0OnRePlb38Q2KELORxOykWjVDC5d9WOTrNs5psjQuWe1nEaXprGvyZu1yuYEbME8T6x/SJbfzld4T0oSRIjI0cCwhoPYDSYWPv9cYwGMy07+tF9RNNzsp7KsljLfRtug9I6130s8yhDOwSxM1SQ7sJ//sGYYblu928DYT1FK8aJP8s93pZgnt1MuusbdpHuuXPn4u/vz/Lly5EkiVWrVrF06dIKX5dXfjkbSUlJZGZmEhoaWq/P09gI8tTi767BLMPpVF3NDwBae7fmp7E/8XCPh1EpVGy+sJkJyyawOn61XY9flraXFGM+ga6B3DxrmyAr4b0AWVRTrXgcPmgPP90A/8yrnhjaC1mGLe+J233vEUnN/zKoFCru6iqC4X48/mPFUDUQF9xTfgbvCFEP9ev0yuuvchLh9CpxuwlVqi0/u5zk/GT8XfyZ2H5iYy+nFCqNSMJ/6AD0nAlIcGwxfN5LvO8MNVxAN4WZbjc/8LSc79JPlX4/Pw1Pcy5mWSJJVTG93pAqxkyy3JXIyPaNnVSHsnPcVVSGOdKtWhZelSjdAApXsXljro3SbU0ur4GcNGRdGJQq3bmFdd0EcQySUknISyKgKGfxYoqOHqvyvuaiIi7cdz+F+/djdnPhjclKEkN8MRe1JMKvCWyoNQKsSeGNGaZ2MbeIQr0JtVIisg7/DsHPPoOk1dLufAkDT0qsPL+SP8/8Wel9h3QIEorkpTwu5db+tcdeEn+PVVrLrQFqEyYgqWs+32qixPyyKT0DUxXOS29XNb2jxLz3xhMppLzxJsgy3jfcgFvPHg6/hrogIymfVV8f5be39xF/JANJgg79Qpj6al+GzYipVer2TzvjARjfLRR/j6o/UzZd2MTp7NO4q925Lea22r4Ep6N9nxCGz4wRH8tbk9lWCfEeFTUKgC0XtlBiKmHXkrNkJufj6qlm+MwYJAfCBBsKVqXbqj43BKxz3UfTjzIsOoh0N18SAiNBltFt2Fh6xyos5s1Kd8PBLtLdoUMHfvnlF/bt24csy2zYsIGDBw9W+Dpw4IBDT56fn8+hQ4c4dOgQAOfPn+fQoUMkJiaSn5/PU089xe7du4mPj2fDhg3ccMMNtG3bltGjRzv8Qq8kSJJk+3A6cdH+IDgrwfvl2l+I9osmpySHp7Y8xZNbniS7uOoworJK5R2d70Dr11qQlbs2wCNHYOTrENpd7JCd2wx/PwwftIMFN8PBBVDkeNARIOpxUo+CxgP63V+7Y1wBuLb1tYR7hJNVnMXvpypWNgDgEQRTfxW28YTtsPzRil3q+38Q9VGtBkNg/adi2gOj2WhLRJ3VeRauqqZR2VEOHoFw/aeirz6iPxgKYdNb8HkfOP5n1Z319TTT7RDphsrD1Cy34+VgsvQVL1KNFnt5hqs4j9jVcFAdqri2sQapgbCIihulv097Lom8L0svt0LhKt5L5iLHM0RKO7qrVnVkkwmDhXQ3RF0YgK9F6c4pMtR6k6K2ENVR14Esk/LmG8iV5ByYi4tJeuABCvfsQeHmxtpH+nI2TEJR1BVQ/GdJtzUXoDFrw6whaq0DPKq1EtcEdXg4/neLjeB7t7mg1cu8vedtzmRXdBD6uWvoGSE2w+uSYn7S4jyprElAn5RMwQ5hH/aZZN+mrdLTE2VAgHh8fEKV97P2jWf+sYTio0dRuLsT9OQTDq29Lsi8mM/qb4/x65t7OXcoHSRo3yeYKa/0ZcTtHfEJqt3fU0Z+CcuPiPaKmdXUhMmybJvlnho9tUHszo6gQ79Qhk0XxPvolmS2/xZX7rzYJaALIe4hFBoLWbNlB0c2iRyTYTNiqgyXa2zYkst9G+4azap0x2bFMqCtD0qFxIbATgDo1q4tvWOnCYAEF/ZAdrzt281d3Q0Hh8/cZrOZoKAgpzz5/v376dGjBz16iF3Hxx9/nB49evDyyy+jVCo5cuQI119/Pe3bt2f27NlcddVVbNu2Da22af6xORMxoeLDqaa57srQwa8Di8Yt4p6u96CUlKyJX8OEZRPYlLip0vuXVSpvbn9z+R/6RsLAR+CeLfDwQRj+MoR0EYTkzHrR9/1+O1h4i0jVtjctWpZhy/+J233urlB59G+CWqHm7q4iIO7H4z9SbKxi/jW4o5jptFa6bf+49GeGYuEwADES0ESw8vxKkvKT8NX6Mql9483I2YWw7rYKPrxaCMv27zNh3nWQUony1xSUbqh8rttCuk/JLSsoxLIsl9rLteKC+WL+xVqsuBRVpZcrypyLazvXXdlMN4DCTRAdR2e680uMXMgSj6muo9uYkoJsMIBajTq0YepmrK/VZJbRlTieJF5XBD35JAo3N4oPHyH3z2XlfmYuKSHpwYco2LkLyc2N0K+/5HfNEQDyMsTGT6T/f5N022rDGtFefsZSF9a2ltbysvCfPRt1ixZoM/N56GgYxaZintpaSb0lpcS1LnPd1uuYjpUo3blL/gBZxq1fP4ccJ1a1Wx8fX+V9hsUE4aEv5OrNYrQr4MEHbVkR9YnslALWfn+cX97Yy9kD4vfWtlcQU17qy8g7OuEbUrcxjV/3XUBvMtOtpQ/dWvpUeb8tSVuIzYrFVeXKjI4z6vSc9YWYAaEMvU1YsY9sSmLH4jM24i1JEiMiRuCm9yJ+mdjU7TasJVFdAhptvdXBaDZyJkdsXjVEcrkVkV6ReGo8KTGVkFJ8nt5RvuwME0S8YM8eTLmW63KvUGg1SNw+9oft8c1Bag2HWm2Xnj17loceeogRI0YwYsQIHn74Yc6ePevwcYYMGYIsyxW+5s6di6urK2vWrCEtLQ29Xk98fDzffvstwcGNEM7UCLAq3VZblqNQK9U82ONBFo5bSBvvNmQWZ/Lwpod5YfsL5OlLibxDSqVfaxj0BNy7HR78B4a+KAiB2QBxa2DpPfB+W/h5Khz5vUIfYDnErYVLh0HtLqqe/uW4rvV1hLmHkVGUwR9xf1R9x3YjYKzFcr/hNaHEAhxfAkVZgiy2H1vv67UHZXs/Z3aa2TRmuWuCJEHnm+HBfTD4WVC5iPGJbwbB8segoEwlYb3NdDtIuq1z3WWV7lRx+7TcssIstCknx6Y6Z2jFBXOSLqkWK64ZkloNltlIszXBvLYz3UXlSahkU7odI/NxFpU70FOLn3vFXlsrbNby8PAGm+90UStxtcy05hQ0rMUcRHVUgKU6Ku3DDzHpxO/KrNeT9PDDFGzfjuTqSsQ3X3Mk3IBOr8NH64ehIBKtSkFgNTbWfzOs9vLGnOmuS3L55VC4uBD83LMA9NmUQkyhD2dyzvDu3ncr3NdKuneczahVenuJ0cTZdBHWFh1afhNMNhrJscxZ26tyW1EuwbwKtA5w5/74jfiU5KNvEYnfbdMceg5HkZNayLofj/Pza3uI25cKMrTpEcjkl/ow+s7O+IXVPRPBaDKzYLdQ92f2r9qhI8syXx/+GoAp0VPwcfGp83PXFzoODGPINKEMH95wgV1LztqI98iIkQw7cxvKEg3+LdzpP6FicGhTQWJeIiWmEtxUbrTwrDj2VV9QSAqb2n00/SjDo4NJ9ggkLaAFGI3oNpUR3GwW88W2b3n4NpPuhoLDpHvNmjV07NiRvXv30rVrV7p27cqePXvo1KkT68qWsTejTugYZiXdeXWyIXYK6MSv1/3KrM6zkJD46+xfTFg2wZYGaVUq/Vz8HFMqA9rC4Kfg/l1w/x5BYALag0kPp1bAkjsFAf/1Nji2BPRlElJlGbZYPtx7zwZ3/1q/visFaqWa2V1mA/DD0R8oMVVzcutzl0hkBbGRkfRPaYBa7zucWidUF6yOX01CXgLeWm8mRzedJHW7oHGDoc8J8t1pgrDt7/8BPusBu78WKrfJQgIVdf99G01mMgvqqHSnnyz9noWAnzS3rGDLNqYJZaXE3Qu9Sfxt1d1eXrnSDWVqw2rZ1e3lUoXS7VI7e3npPHf1QYN6S4haQ9WFWVGaYN44nah+029D07o1psxMMj7/HFmvJ/mRRynYshXJxYWWX3+NW+/erEsQn+ddfK7Gai1XNMEZyoZAUwhSs3V01yK5vDJ4DBuG+6BBYDDw4p5wJCT+iPuDVedXlbtfdIgnYd4uFBvM7DqbWcXRqll3aj4ms4yPm5oQr/IhkvnbtmFMTUXp44PnyJEOHVdrB+kuOX2aIbFbAdg44ja75sVrg9z0IjbM6nJIRQAAw0JJREFUO8Gi1/Zwek8qsgytugVwywu9GXNPF/zDnVfRtT42lUu5xfi7axjXpep8o+3J2zmeebxJq9xl0WlQOIOniDnog+sS2f3nOSHEHfajRW4HDIoS/MYX2zqlmyKs1vJ2vu1QSA27ThvpzjhqS+7fGCiuHXTr1pfeMeY6UGrENUTqcaC80l1ZknwznAeH3xXPPvssjz32GHv27OGjjz7io48+Ys+ePTz66KM888wz9bHG/yTaBHqgUSrQlRjr/EGvVWp5/KrH+WnsT0R4RpBWmMa96+/ltV2vOUepDIoWBOaBvSIx+pqnwK8NGIsh9m9YPEsQ8N9vhxPLRGJ58j+gcoUBD9fptV1JuLHtjYS4h5BWlMbSuKXV33n026Jv2lgM8yfAxQPiRNlzZsMstgaUVblndJyBu/oKTTX2iRCW/ttXQnAXMR6x+hn4amBpVoETlO6sAj2yDAoJ/N0dVbots2H5qUKJN5ttBPy03IK84vIKsdVaXuTlh2wQYxtOJd2XwWoxN1vt5bVUup1lLy+d566BdDdwXZgVpQnmDa90Q/nqqKwFC0m8627yN21C0mpp+dWXuPftg8FsYEPiBgBaaPoB/GfnuaHUXp6qK0ZvrDgLX9+QZdlmL69tcvnlkCSJ4OefE+MVuw/znFGQ3td2vUZiXmK5+9nqt046XiF60rIJFh3iWaG+L+d3obZ533ADCk3VrpSysIoQNSndsiyT+sabSLKZ7WFdWGgMxuxkQpGXUcSm+bEsemU3J3elIJtlorr4M+m5Xoy7ryuBLZ3fMDLXEqA2uU/LSpPgobzKfUv7W/B3vTKEjc6DWzDoVkG8D6xJYMO8WPYuOw/AjqglbM1v2sLeqWxLcnkDWsutsIapHUk/QusAd6L83WzVYQXbt2O21D7j6gvtRECdNVDNzVsDkmhIKMpvnM+l/wocJt2xsbHMnj27wvfvuOMOTpw4UckjmlEbqJUK2lpsZCdqMdddGboHdWfx9YuZFiMsVotPLyYhLwEfrQ+TOzhBqZQkCO4Ew16Eh/6Be7bB1Y+Bb5QIrzq+FH6bIdRvECq3R/3PVzUVaJQaZncWfztzjs5Bb6pG6VIoYeL3ENwZSizzOJ1uAvemMcu0LmEd53LP4anxZEr0lMZeTt0RNVDkFoz/BNz8IeMUWPtrnTDTbZ3n9vfQonRULdR6gI/FRpgeCznxYCjErNAQL4egu4ysGqyk29sPs0HMdDuXdF+mdFu7ukuqdm+sOr+K13a9VmknsJV0FxlM5QiNzV7uYGWYTemugXQbEi11YQ2UXG6Fj+X15lxeG9aAsFZHYTJRuGcPkkZDiy++wL1/fwD2p+wntyQXPxc/5KJWALT8D5Nuf3cNLmoFskydUrxri5S8YnQlRpQKiSgn1rZpW7XC/3axkdv7l6P09u1OgaGAJ7c8We7zaXi0GOvbGJvmsPPOOs99eXK5ITWN/C1bAPC5pWaXXezOS/z+zj5id4oAsbKku7I15a1cSeH+/UguLizsOYGMfD1Hk+3MnKkB+mIj2349zcJXdnNixyXMZpmITn5MfKYX1z7Qrd66o0+l6Nh9LgulQmJa36qt5bsu7uJIxhG0Si23d769XtZSX+g6tAVXT2oHwKndKZjNMn4dVZwM2s3GCxsxmJouKbSS7va+DZdcbkXnAEGw4/PiydPnMSw6mHivUPL8gpFLSsjftq30ztbO7qMiT0GpVODmKTa9mi3m9QuHSXdgYKAtbbwsDh065LSAtWYIlLWYOwuuKlee7fMs34/6njD3MABmd57t/HlcSYLQrjDiVXj4ENy1Saja3pYLXI0HDHjIuc95BWBCuwkEuQaRWphaZU2LDVpPmPILeASLcLW+9zTIGmtC2d7P6R2n46lpGn3hdYZCCb1miYqxfvcLW7mkBPe6bwzVep7birJhapZ5br1fe0woKyjE1uTyIm8/zHqhdGcUZVQd4GcXala6rUFqlV0Af/LPJyw+vZiP//m4ws88XVQ2Tl92Pt1WGeZgQJu1ZrFGe3miZaa7ge3lvu5W0t24F4/Bzz6Dws0NSa2mxRef43H1QNvPrNbyYRHDSLJ0t/5XQ9TA0tXt03hhanEWlTvK3w2Nyrm21YB770UVFIThwgVeiu+Gj9aH2KzYcn+r/dv446JWcDG32KZc2wsb6b6sSSB36RIwmXDt2RNtm5rndPOzi0lL0BF/RPQOq1u2BEnCnJ+PKbO87d1cUEDauyIbxf/uu4jpLkhQXfvGAS7GZfPrm3s5sikJs0mmRbQvNz11Fdc91J3gVvXbgf3TrngARnUMJsyn8vwdWZb56vBXAExqP4kA16axUe8Iug1vycCJbQHw9HPhhjv64O/qj06vY2/K3kZeXdWwdnR38Gv4dhk/Fz9aeIg58uOZxxkeEwSSxNYQYTsvl2Lefoy4Bs9NhAvi99mcYN4wcPjsfdddd3H33Xfz7rvvsm3bNrZt28b//d//cc8993DXXU0nVfnfgNrUhtmLPqF9WHrDUhaOW8jMTvVsWZYkCO8Jo96AR4+I6qZ7t4FnwyQGNyVolVru6HIHINTuGndtfVoKx8Ddm8XvsAlgQ+IGzuScwUPtYXNN/Kvg6gNj3hHk+97tTskcSNMJ4ujwPLcVZWvDLPPcZkvAWl5x+fqpUnu5P5hdUUvi4qxOCeblhO7KlW5zSSXKrSRhMBtIKRQ94b+e+pV9KfvK3UWhkPDQirn5shsIClfLcR2Y6c7ILyEjX48kVW/DlWUZvUXpVjew0u3tarWXN57SDaI6qvXyv2mzbi0egwbZvm8ym2zW8pGRI0nMEr///7K9HCC8EcPUnD3PXRYKd3eCnn4agJIfFvJ2u8cBWBC7wNZ44qJWMrCNIG+OVIfJslyp0i2bzeQsFoGiPpPsy5KxJlZfOJmN0WBCodWiDhPCgT6hfG1YxtffYExLQ92yJf6zZ5cmsNfCHm+FUW9i+29xLP3oIHkZxXj4abn+4e7c8GgPQtvUfxVXbpGBJQeEY2lGNTVhe1P2cij9EBqFhlmdZ9X7uuoL3UdEMPmlPtz6Ym/cPFwYETkCKN0QbGrILMokoygDCYl2Pu0aZQ1dAkvD1HpH+eGhVbE+QFw75G/egtnqRlO7itlusFnMbXPd2XXZnG9GTXCYdL/00ku8/PLLfPbZZwwePJjBgwfz+eef8+qrr/Liiy/Wxxr/s7DVhqU4n3QDuKnd6BrYtcKcVb1CkiCsh0hC/4/i5nY3E+AawKWCS/x19q+aH+AZDKHd6n9hdsAsm22zYtNipuGlqd+d/UaFb6SocXMCnKp0W0i30jKvZTDJFBtKbdmGtFJ7OUh4KC0J5vm1TzAvXxlW/meKGoLUUgpSMMul63t156sVqokqm+u29nTLDtjLrdbyCD833DRVB+AZ09PFrLhCgSY83O7jOwO+bk1D6QZQh4WhDim/+Xkg7QBZxVl4a73pFdyLxMxm0g2lc92N0dV9xppc7qR57svhde043Hr1Qi4uptX8rbbgrRd3vMilfGHntoUzOUC603QlZBcaUFy2CVawaxeGpCQUnp54jRlt17ECWnrg7q3BWGLi4ukcoPK57pLz58mcOxeA4OeeRaHVMqRDEJIEx5LzSMl1nFSknMvl17f2cXjjBZCh48BQprzUl5YdG67q9I9/kigymGgf7EG/1lU/r/Xz+eb2NxPkdmW7T/3DPdBazpcjI0XmwIbEDRjMjX/uvBxWa3mkV2SjNbl0DRBz3UczjqJRKbimfQCnfVtS7OOPubCQgp07S+9stZgfXwomAx7NSneDwGHSLUkSjz32GElJSeTm5pKbm0tSUhKPPPJIw5K3/wCsnZYXsorQFTe9k0wzagcXlQuzOokd6O+OftckP0CqwuYLmzmdfRo3lRvTO05v7OVcMUizkW6XGu5ZBcoq3RZ7uSa0E4pKbNlWe3mht5jntpLuOs11V5deblW6K7OBS5LteUPdQwl2CyZRl8gXB78od7fKSHdpZZjjpLsma7nBUhemDg1FsjPAyVmwpZc3stJdFdbGCxvisJbDKCjG1if+X57phjKkO6fx7OVtnVAXVhkkSSL4pRdBoUC3ejV36/vT2b8zefo8ntn2DEaz0aYWH0jMJqvAvveuVeVuHehRLvTLFqB23Xjb5po9a4zsKtTu+KPCTn456ZZlmdS33wGDAfdrBuExdCggHEbdWvgAsOmU/ZsGJoOZXUvPsOT9f8hJLcTdW8P4B7sxdHoMGteGaxExm2XmW2rCZvSPqvJae1/KPvan7ketUHNH5zsabH0NgauCr8JX60tOSQ77U/Y39nIqwGotb4x5bitsSnfGUWRZZlh0MLKkYH9LIdro1pZxCbQaAm4BUJgB57bg3lwb1iCo03CQp6cnnp7/knnOJggfNw1h3uKC1tE5qmY0bUzqMAk/Fz+S85NZcW5FYy/HLpRNRJ0aMxVvbf1b6v4tsCrdtbaXB7QT8+XFuSLkDZCCO+Fl67guS7pL08uhDOnWOYd0X37BJ7lYZ7or6emm1Nbe2qc1L/d/GYD5sfM5kn7Edh/vSl6Hbaa7NqS7puRyS11YQ89zQ2l6eU5R09tsM8tm1ieKepmy1vJgL22VScn/FVhnuhvaXi7Lss1e3r6GzaS6wKVDB3yniFDMjHf+j3cHvo2H2oODaQf58tCXhHq7EhPqhSzDZjuJa+wl8fdY1lpuzMpCt0GML9hrLbfCajGPP5qBLMsVSHf+pk0UbNsGajXBzz1X7lxVajG3b+1pCXn89s4+DqxJRJahQ98QJr/cl8jODZ8Evu1MBuczCvDUqpjQo2pnzjeHRdbKTe1uIsT93zW+p1KoGBYxDGiaFvPGTC63ItovGpVCRVZxFsn5yQzpEIgkwV9eYiMgf+NGZIPlc0epgs43idtHf7cp3c2ku37RdAvvmgHU71x3MxoPrirXUrX7yHcYzcYaHtH42Ja8jdis2Cum97MpIa2u9nKVFvzblv6/izd4hZWSVYvSbdbrMWWLqrMib3Fx6BSluxootNaZ7sqV7iSdsLWHu4dzTYtruK71dZhlMy/veNmWkGzt6i5HumtRGeZoXZi6gevCoDS9vLEqw6rDwbSDZBRl4Kn2pF9oPxKa57ltaGGZ6W7oILX0/BJyi4RFu1VA/VYzBj78EEpfX/RnzuLx11ZeGfAKIPJHdl7cyfBoa3WYfcT1pGU0LrrM32Pu0j/BYMClc2dcYmIcWl+LaF+UagW6zGKyLhWUI93mkhKhcgP+t9+OtlWrco+1ku7tcRkUG0xVPofJaGbP3+dY/O4/ZF0swNVTzdh7uzBiVkdc3Oun57smzLPUhE3s1QJ3beUK+4HUA+xJ2YNKobK1pPzbMCpKVF1tSNyAyVz1v2FjoDFD1KzQKrV08BXPfzTjKAEeWrq39OF4QGsMnt6YcnMp3FcmU6WzxWJ+cjnuFhNNfk7TdGD9W9BMups4rKTbmQnmzWgauKXDLfhqfUnUJbLq/KrGXk61kGWZrw6JRNTJ0ZPxdfFt5BVdWaiz0g2lFnMQM96SZCOrVlu2MU1cDEtaLXo38Sla//byGpTuAqF0h3sKhebp3k/j5+LH2dyztq73Sme6rbZ1O0m32SzbnVzeWHVhAL7uQunObYL2cquCNDRiKGqlmgs20l2/ZO9KgNVenpJXjNHUcF3d1n7uSH/3encbKL29CXz8MQDSP/ucER69mNR+EjIyz217jp6txfNvPZWOwY7fgfW6xToqJ8syOYuFtdxRlRtArVHSooP47Ek4mommVRQgnCuZ383BkJSEKjiYgHsrNn10CvMi2EtLkcHE7nOZFX4OkJGUz+J397N/RTyyWabtVUFMeaUvrbs3XrVpQmaBzRI/vV/VNWHWRpEb2txAqEdog6ytodE7pDfeWm+yirM4kHagsZdjQ4mphPO5ok/cSnobC10ChMXc6iQbHh2EWVIQ27oHAHnryrgEWvYBnwjQ5+OevQdoDlKrbzST7iaOZtL974Wb2s2WHP/tkW+b3M5tWey4uINjmcdwUbows2M9p93/yyDLsi29vNYz3VCRdANelrnCvCLhlDCmiJRwVXCwjRw7I0itXHraZaTbqnRXFqQmSZLN1h7mIZKGfVx8eLGfCN38/uj3nMo6hbdbJTPdbkJZlPV6ZFPNfxvJOUUU6k1olAqialAEG6suDJqu0m2WzTbSbQ0tag5RK0WghxaNUoHJLHOpFmFctYXVWl5f89yXw+fmm3Hp0gVzfj5pH37E072fpp1vO7KKs1h07v/wc1ehKzGyPz672uOUGE2cTS8AINoSClu0fz/68+eR3NzwuvbaWq0vyjrXfSRDZDKo1ch6PRlfiU3hoKefQuFe8e9fkiSGWfvGL1PqzSYz+1fG8/s7+8i4kI+Lu5pRd3Zi9F2dcfVo2MyHy7FgdwKyDNe0D6R1YOXvgcPph9l5cScqScWdXe5s4BU2HNQKNcNaCou5NXuiKeBszllMsgkfrU+jh9d1DSwNUwNs7/llFou5bv16ZLNlw0ySbGq3R8JSAPTFJvTFTd95eaXCIdJtMBgYPnw4cXFx9bWeZlwGa1f3yRRdg+6uN6NhMDl6Mt5ab+Lz4lkTv6axl1MpyvZ+3tLhFvxdG36m7UqGrsRoSxd3mtJtSVW32bIt9nKDZZ5bHVT6we+hFCqNTq8jT1/LzbtyRLuqILXqZ7qtHaIgSN3IyJEYZSMv7XgJDxdxzMrSy8E+tduae9EmyAO1suqPNlmWbRVDDV0XBqUz3XnFBkzmip3mjYUj6UdIK0zDXe1O/7D+ACRkCdL0X+7otkKhkAi3qN3JDRimFmdNLm8g0i0pFIS8+AIAuUuXYj4aywfXfICrypU9KXuIbC0UsZrqt+JS8zGZZXzc1IR4iXNE9u+inshr3FiUHrVzT1hnqlPO5VJcZEJt3TgzmXDr3RuvceOqfOzwMnPd1prFrEsF/PHeP+z56xxmk0yrbgFMeaUv7XoF12p9zkSR3sSv+8QG4e0Dqla5rVkr17W5jhaeLaq8378B1g3B9Ynrm4xQYbOW+3Zo9EBpq9IdmxmLwWQgJtSTUG8X9vq2xuzmgSk9g6JDh8o8QDhONOdXoNaKz83mue76g0OkW61Wc+TIkZrv2AynIdLPDTeNkhKjmfjMgsZeTjOcDHe1u20++psj3zSZD5Gy2H1pN0fSj6BVarm90+2NvZwrCmazzMZYoap4alW4aupgD7XWhpW5ffkstDW5XBVcesGoVrji5yJC1WodplY150Zhs5dXVP9KTCWkFYk1WZVuK57v+zzeWm9is2I5VfS35XWU7rBLWq2N7Nsz111qLa+enJhycjDrxH01LVvWeFxnw2qll+XyM+yNDavKPbjFYLRK8W96IUv83v/ryeVWNEaYmjW5vL7qwiqDa7dueN8sQpZS33yLVp6RPN/3eQDOGv9A6Rpf41y3dRMsOsQTSZIw5eaiWyPUSd9aWMut8PRzwb+FB7IMicezbHPdKJUEv/hCtaRnQFt/NCoFSdlFnE7RcXBtIr+9tY+0BB1aNxUjZnVk7L1dcPNqXHXbimWHkskrNhLh58bg9pUrqMcyjrE9eTtKScldXe5q4BU2PPqF9sNT7UlGUQaH0g819nKA0hC1xpzntiLSKxIvjRd6s57T2actDo8gjAoVCR2Exdz6dwiIDfygTmA24OEqyHYz6a4/OGwvv+222/j+++/rYy3NqAQKhWQLBTpxqTnB/N+IKdFT8NR4ci73HOsSm1YqZ9nE8ontJxLo1nizbVcS8ooNfL/9PMM+3Myjvx4CoFVgHedifVuBVzi4+ECw6Oi22cstdjBrcnlZ0g0Q7iHmqWs71y1VN9OtEQTNFqRWRum2dvy6qlzx1ZbPAQhwDeCZ3s8AsDPzZxSatPL2cklyqDbspC25vPrueGtdmCooyO66ImdCo1LgYQlDaioJ5rIs20j3qEgRVlRiNHExV/zem+3lAta57oYMUztjsZe3C2rYppigxx9H4elJ8fHj5Cz+gxva3MD41uORMeMS/jPnstKIz6haCLCOxFlH5HL/+hu5pARt+/a4dO1ap7VFdRFqd/zRDNy6dwfA77ZpuHSonvS4aVQMaOOPj0li/RdH2bnkDCajmYhO/kx+qS8d+oY0ulJphSzLzNslHDnT+0WiVFS+Lmti+bWtr6WlV8NvIjY01Eo1QyNEFVxTSTFvCiFqVkiSVDrXnSFE0hEx4npghbdIVtetW2dzegC2zm53s3ClNXd11x8cJt1Go5GvvvqKXr16cc899/D444+X+2qG89Gxea77Xw1Pjaet8/qbw99glpvOGMH+1P0cSDuAWqG2pa03o2rEpep48c+j9Ht7A28sP0F8ZiGeLipmX92Kb6f3qtvBlSqYvQ7u2QIu4pxgCyCzzAcb0iz28uDyqkhdSfflRLvcjy4PUivzGOvzhXuEV3oxO771eAaFD8IoG3AJXUxOUfljKBwg3acsSckdQqpXBG11YY1gLbfC2zbX3TTC1I5nHudSwSVcVa4MDB8ICGIpy+CmURLQyHOtTQWlSndhgzxfZn4J/8/eeYfHUZ1v+56t6r1LtuTeu40b7sbGDs0YY0poMQYCoYQQWihfgEAg8AshQGghQELH9GpjMDa4gZtscJGLZBWr97J9vj9mZ9WlXUmrXa3OfV26wLszs0c6u7PnPe/7Pk9ZnQVJgiHt9PN6C11sLPE33ghAyd//jqOqintm3ENGRAYafRXBye+y8WD7JeauoDspQhFQc5aWR61a1e3AVrUOO/lzOZGX/pr0N98g4Y47Oj1PdsjMkY1cWWOEUjP6IC0LLhvJWb8bT1h0N1p/vMBPORUcPFVNkF7Dqqltl4wfLDvIprxNaCRNv8hyq6gbgxtyNvh8vSTLcrPycn/A5dddovR1zxwSS5Bew9dhgyAoCGtBAaZffmk8YexKAEItxwGR6fYmHgfdBw4cYPLkyYSHh3PkyBH27Nnj+tnbtE9A0GMI27DA59JRlxKmD+No5VE2ntzo6+G4ULPcK4etJDHU9z1u/ojdIbP+50IufWk7Z/x9M//bfpJ6i53hiWH8ZcVYdty9iHvPGk1SZDdE1FQiUyE6w/XPiBaWYW2Vl0Nj0K3ad3lMBz7dLS3Dmu6gNw26276sxH0z7yNIG4I25CQlUvP3vivoru84yLHYHBx3ijZ1lul22YX5MOiODm2+WeJr1uco5Ybz0uYRpFPm82QTuzB/yf75mrSY3u3pVrPcadHB3WtN6SLRl1yMcdgw7JWVlDz1FKH6UP42729oJT268EO8k/VGm+fJstws023KzMR85AiS0UjkOWd3e1wJGREEh+uxNNgoOtlAyKRJSJqOl7PVpQ189I89mHaWoUciR2dn+R8mMXp2il++v1WbsPMmprp0IFqiKpYvG7SMjMiMXhqZ75mZMpNQfSjF9cUulW5fcaruFDXWGnQaHYMjB/t0LCpqplsVUwvSazl9aBxmnYHiUZMBqFnfpEogOh0GzCBMq6j611WIoNtbeBx0f/vtt+3+fPPNN94YY79HKJgHPhGGCC4ddSmgBLq+3r0F2FW0i52FOxXfz3GB6fvZHSrrLTz/3THm/e1brvnvLn44WoZGgqVjEnlj7XS+umUul05PJ8TQtq9qT9BSSM1VXp7QIugO78FMd2eWYU2O6yzoBkgKTeKqUUpGzRz+Kbk1ua7n1KC7s57u46W12Bwy4UYdKZ1sblhV5XJfBt3ORbQ/ZLplWWZDdnPVcmgMukU/dyOqV3dv9XRn+ai0XEXS6Ui8R3EaqHjrbUwHDzIyZiRrRt0EQL7mPXYW7Gt1XnGNmYp6xVt8WGKYS0AtfOkStJGR3R6XRiORPqaxxLwjZFnm5y35vPXgTvIPV6IzaMhM0PBOqIVdZf7ZsldUbeLLA4oTxWUz2xZQO1x+mI0nNyIhcc24a3pzeD7HoDUwf8B8wPcl5mqWe0jkEPRa3/i4t0QNurOrs6kyVwGNKubfJowBlBLz5iddQKgz6Bbl5d6jy5ZhR48e5auvvqLBuRiSZf9RYQ00FCES5YustFZ8GAKVy0ZfRqg+lCMVR9iUu8nXw3FluVcMXUFSaJKPR+M/HDxVzZ3rMpnxyEYe+eIQeRUNRIXouW7eEDbfvoDnL5vKrCFxvZI9aWoZJsuyy6e7x8vLm9Iy0x2kWoap6uWNz6nK5S1F1FqyavgF2OoGg8bK/T/c7/o+kUKcme42RNqactjZzz3cKdrUERbVo9sHdmEqanl5pR9kug+VHyKvNo8gbRCnp57uely1C0sXQbcLtby8oLKhV5TnG/u5e7e0vCmh008jYvkycDgofOgvyLLM76ZegcE8AUmy88fv/kiNpXnwqiYIBseHoTebqP78C6B7AmotSXeWmGfvb9tzG6C2wsSn/9zHptcPYzXbSR4ayep7TmPwrCSQYOPBjsXgfMXrO05ic8hMy4hmTErbmxRqlntpxlIGR/lHhrU3UTcIN+Rs8Gn8cajiEOAf/dwq0UHRDAhX+vsPlB4AYKFTuf8dfTro9ViOH8d89GjjSWNWEKqtBKCuVCT4vIXHQXdZWRmLFi1i+PDhLF++nFOnFKGcNWvW8Ic//KHHByiAUKOOjFhFhElkuwOXSGMkl4y8BFACXl9+kewt3sv2U9vRSSLLDWCzO/h8/ykufH4by/6xhbd+zMVkdTA6OYLHVo5n+12LuHPZSFcmrLdomum2V1QgW5UgThcf79Izk5Bcdl0FtQVdfF91YBnmKi9vP9Pd1C6sLaJCDJhOrUR26Pmx6Efey3oPAE2QWl7ecWbRFXQndp4RVINu/QDfZ7or/SDTrWaK5qTNIUTf+P51lZcLuzAXiRFB6DQSNodMcY33vbpVu7De8uhuj4Tbb0cKDqZh1y6qP/0USZJYnnQzDksU5ZZT/Hnbn5vdVw46RV9HJUdQ/dlnyPX1GAYNInhqN3UtmjBwdAwajURlUT2VRc3bT2RZ5uDWU7z5wE5O/lKOVq9h9gVDOe/WyUQlhLiswzYdLvY7K1aLzcEbO5R71OUzM9o8Jqsiy/W5vWZ8/8pyq8xOmU2wLphTdadcgaUvOFJ+BPCffm6VlmJqSZFBjEmJoF4XRM0Yp4p502x3aBxhA5XNm9qy2t4dbD/C46D797//PXq9npMnTxIS0vhlvHr1ar788sseHZygkVHJymJSBN2BzWWjLyNYF8zB8oNsztvss3E8l6lkuc8Zek6HpcGBTlmtmWe+Pcqcx77l+td3s/NEOVqNxK/GJ/PudTP57KbTuXDaAIL0vd9vCU2E1BqsrtJybWwskqF5D2ByaDISEia7iTJT+5mhdmkWc7fMdLe0DGvd091Zpluv1RAsJWAuXgrAEz89QWFdYRMhtY57ulW7sJFJHQfd9to67GXK728Y6Dul36gQZ6bbx+rlsiy7+rmblpZD855ugYJWI5EcpWwy9UaJeaNdmG/Ky1X0SUnEXXcdAMWP/Q17bR3LRg+iIf8SkDV8lf2Va6MM4JBT1HBkUnijgNoFF/Ro9Y8hWEfK8CgAcg403tPqqsx8/q/9fPPaQSwNNhIHRbD6T9OYuHggGqcC+KSB0USF6Kk22diVU9FjY+oJvjhwitJaMwnhRs4c23aF2YuZLwLKZ3ZY9LDeHJ7fEKQLYl7aPMC3Jeb+ZBfWlPHxikOAKqYGjT71OwdMAKB6ffO/W+hERRW+waTDYfM/+9pAwOOge/369Tz66KOkpTXPXAwbNoycnJweG5igOaOS1L5u/+xBEvQM0UHRXDzyYsB32e79Jfv5If8HtJKWq8dd3euv7w/sz6viD+/sY+Zfv+FvXx3mVJWJuDADNy4cyg93LOSZSyYzLSPG5wI8LiG1BivWQtUurLWfq16rdwnhdUVMraPfU2ohpNb4BJSbyoHGnvKOiAjSY62YxbDIsdRZ63hg2wMuy7DOerob7cI6Dk6suUoGSRsdjTaiY8E1bxLl6un2bdB9pOIIOdU5GDQG5qbNdT0uy7IIutshLUr5e3jbNqyq3kpxjVI94utMN0DMVVeiTx+IraSE0n89y9SMGELlwZicG2WP7nyUIxVK1k9NDoxvKMJ04ADo9USuOK/Hx5ThKjEvRZZljvxYyJsP7CA7sxSNTmLGeYM5/7bJRCc1t2vUaiQWjFDuk9904jfe27zmtAm7dHo6em3rJfrxyuN8ma0kuK4df22vjs3fUDcK1+es98laqdZS69Ig8ddM9/7S/a6/zUKnddgb2nTQaDAfPIglt1FDJWTimWiwIaOh/ujeXh9zf8DjoLuurq5ZhlulvLwco9G/LBcCCSGm1n+4fPTlBOuCOVB2gO/zv+/111d7xc4afJarL6g/YLE5+GhvPuc/+wNnP/0963bnYbE5mJAWyf9dOIEf7lzIH5aM6BkV8h5CLS93yFBXoLT66BPaVplXKxbUPmuPaCak1vwpTUshNXXx4/xPuD6cCEPnAa6Stddw8aDb0Gv0bMnfQr5NEUlyNLRfyltrtrmyjiM6yQj6g10YQJSrp9u35eVqhmh26mxC9Y2BSVmdhXqLHUmC1Oje9zL3Z9S/h7dtw46WKBtJKZFBLl93X6IxGEi6+24Ayl99DflkNnOGx2Etn0OqcRJmu5k/fvdHKk21HHM6CaT88BUA4YsWoYuJ6fExpTv9uguOVPLl8wfY8O9fMNfZiBsQxoV3TWPKmRlo2ghcARaM9L+g+0B+FbtyKtBrJS6e3vZ37wv7X0BGZuGAhX6XXe1t5qTNIVgXTH5tPgfLD/b662dVZgGQGJJIVFBUr79+R4yMGYleo6fSXOnaaB+fGklcmIECgrCOnQg0VzGXgiMIMSrf47V7vu71MfcHPL6Tz5kzh9dee40HH3wQUDIgDoeDxx57jAULFvT4AAUKo1OURevR4lrMNjtGnW/KWQXeJzY4lguHX8irv7zKc/ue4/TU03sto/pL2S98l/ed4vs5vn/4fhbXmHhjx0le33GSEmdmSa+V+NW4ZK6YlcGkgdE+HmH7BOk16LUSVrtMvTPobmkXppIalsquol1dE1PrwDJMCmo70y07D3Mnyw2NpfIhUgrXT7yef+z+Bzsr97EY+PDA22z54oc2z6s12whOr8ag1XDL5nc6fI0ZXxcwH/hRl8f9X1zR5jGSJLF44GIuHXWp1z53qmWYL4XUqsxVfHFCEbhqWVqe4xRRS4kMFt81LUhzBd3ezXQfcZaWD/VxaXlTwubNI2z+fGo3baLoob+w8Pr/x+f7C6HkIuIT8zhedZz7tjyM3TGbBIOM7QslIxu16gKvjCcqIYSoxBAqi+o5vrcEjUZiyvIMpixLR9tOsK0yb1g8Wo1EVnEtJ8vq/UK74LVt2QAsG5tMQnjrzd3sqmzXZ/baCf07yw0QrAvm9NTT2ZCzgQ05GxgdO7pXX9/lz+2Hmx8GrYGRMSPZX7qf/aX7GRAxAI2zwuPdXXkcGDKZSZm7qdmwgdg1v3GdFxodTG0h1B3ZAw47aMT9vyfxOOh+7LHHWLRoET/99BMWi4Xbb7+dn3/+mfLycn74oe1FkaD7JEcGERmsp6rBSlZRLWNTu2+7IfBfrhx7JW8dfovM0ky2ndrGrJRZvfK6qmL58kHLSY9o26okEJBlmT25lby6NZvP95/CalfSsgnhRi6dns7F0we0uejxNyRJIiJIT1mdBfOp9svLoVHMrLtBdyvLMGPblmEyMiCREtpxP7dKRJP+9CumXcGGnA3UapV+tMrKU+wubj8jpQsBB7C7k6TVtDylT+1gcDm7i6vaPW5X0S4qzZX8btLv3Bq7p0QGO4XUGnyT6d6ct5n/t/X/UdJQQpg+zGW/o5LrsgsTWe6WqGKJ3vbqdvVz+0FpeVMS776Luh9+oG7rVmauOIAkwaF8meeXPchtP1zHtwWfIunGsKLqFI6aGvRpaYTOnOm18QydksBPn2cTkxLK4itHEz/QvU2KyBA9U9Oj2XGinG8OFXHl7EFeG6M7VNRZ+GivUoV0xay2v3tf3P8iDtnBvLR5vR5g+itL0pewIWcD67PXc9Okm3q15etQuVO53M9Ky1XGxY1zBd3LBy8HYNEoJeh+J2gIk4CGvXuxFhW7HE/CkuIpKiyjtlYDOT/AoLkdvILAUzwOuseOHcuRI0d4+umnCQ8Pp7a2lvPPP58bbriB5ORkb4xRgLK4HpUczvbj5Rw8VS2C7gAnLjiOVcNX8b+D/+O5fc8xM3mm179MDpUf4tvcb5GQAjbLbbbZ+XTfKV7dlk1mXmPQNSU9mitmZXDmmCQMui47KfqEyGAl6LYVFaID9Ilti++oGee8Ws97upvVlLdnGaYKqTnLy9UOO3cz3S77M5MVvUbPC2e8wKHMB2H7Z8yLm8mE+Re1ed47P+by7eFiFo1M5IKpHaukx332FHCURbN+zcz509o85nD5YZ7PfJ7nM59Hr9F7JaMUrQqp1fVuprvWUsvffvob72e9D0BGRAYPn/4w4YbmgYqa6Rb93K1RbcO8nelWlcv9Leg2DBxIzJrfUPbc8zT8/XGmnncPPxbUU14+gGlJ0/ix8Ef0UT8y+yelNznqgpVIGu/dU6cuyyBtZDRJgyLR6j17nUWjEthxopyNh4p9HnS//VMuZpuDMSkRTG6juiq3OpfPjn8GwHUTruvt4fktc9LmYNQaOVlzkiMVR3o166xqGPhjphtgXPw4ONSoYA5w+rB49FqJvQ16pLHjkA/sp+brDcRceikAoc6N1jpHDOx/VwTdPUyXGoUiIyP505/+1NNjEXTC6ORIZ9AtxNT6A1eNvYp3Dr/DnuI97CzcyfTk6V59vRcyXwDgzIwzGRwZWL6fp6oaeH37Sd7ceZKyOiW7aNBpOGdCClfOyujTm1jhzgyxXFICdFxeDpBf05VMdwdPqZluqxXZ3lTxVG72up3RVIkdFAu9ocljKeYzUrQxTGtRAq3y8lfbsdUksih9PGekd6xBkFX8MDZg0qRlhKRPavOYM9LPIEwfxhO7nuDpvU9j0Bq4auxVbv0O7qIKqdWYbVjtjjYFk3qa7ae2c98P93Gq7hQSEr8e/WtumnQTQbrWFR2qiFp6bGir5/o7anl5fmUDDofsUsTuaVwe3Yn+FXQDxF1zDVUffYy1oIArszfzo2EqGw8Ws3LOSn4s/JFBtu3EZ1eBVkvkivO9OhatXkPq8K61AC0cmcjDnx9ix/Fy6sw2Qn3UO293yPzXKaB2xcyMNjfYX9z/InbZzumppzM2bmxvD9FvCdWHMjtlNt/kfsP6nPW9FgDbHXayKpSebn/NdI+PUxTMD5Udwmq3otfqCTPqmDE4li1ZpRwfOY1BB/ZTs+HrxqA7Svk+r7PHwi//geWPg07odfUUXfqmr6io4PHHH2fNmjWsWbOGJ554gvLy8p4em6AFqm3YL6faL4sUBA4JIQlcMFzphVPLvr2F6vspIQWM76csy+w8Uc71r+/i9Ee/5elvj1JWZyElMog/Lh3BtjsX8viqCX064AaICFIWipoyRXRM3055uRr8FtYVYnd0xw6kRaa7iYCmbDa7Mt2q+21Xg26g0TLM1H5W0V27MIfJhK2wEABDesetE1eOvZIbJ90IwP/t+j/+98v/Ohm9Z6i/KzT/fb1BvbWeh7Y/xNr1azlVd4q0sDReXvoyt0+7vc2AG5qWl4tMd0uSIoPQSIrwYmmtufMTukCNycqpKqVyZGi8//R0q2hCQki843YABn39AYl1ZfxwtJTTUxaAPYTFPyuCr2Hz5rV7P/IHhsSHMjAmBIvdwfdHS302jm8OFZNf2UBUiJ5zJrZux8mryeOTY58AQrG8Lc7IcKqYZ/eeinlOTQ4mu4lgXbDfCs4OCB9AlDEKi8PisjYDWOgUEfwkQtksqP/xR2wVinVemBp0S8lgqoKjQlCtJ/E46N68eTMZGRk89dRTVFRUUFFRwVNPPcWgQYPYvNl3vsL9gUYF8xqf2CMIep+rxl6FXqPnp6Kf+LHwR6+9jprlPiP9DIZGD/Xa6/QGDRY7b+08yfKnvufC57fx+f5C7A6Z6YNi+Nelk9l8+wJuWDCU2LDA2L2NCNZjsFvR1inBZ3uZ7oSQBPQaPTbZRlF9kUevIXXU0x3UGLg5zE2CEOc9qjOPbpW2g25n6Xp920F3SY2ZsjoLkgTDEjqxC8tTyuo1YWFoozvPjF0z/hrXAvfRHx/lncMdi7R5glYjuTZLvCmmtqtoFys/Xsnbh98GYPWI1aw7Zx1Tk6Z2eF5OuaI+LcrLW6PXakiOVDaDcr1UYq5muRPCjUSG6Ds52jeEL11KyIwZSBYzNx76jAarna/2l+Eon8DcA8pn31sCaj2FJEmuAOSbg75TMVcF1FZPG0CQvrVw1b8P/BubbGNm8kwmJkzs3cH1AeanzUev0ZNdnc2xymO98ppHypXS8mHRw9D6qdiYJEmuqojMksYSc/U9v6FSh27ESLDbqf3mG6Ax012rcbZq7X+vF0cc+HgcdN9www2sXr2aEydO8P777/P+++9z/PhxLrroIm644QZvjFHgZFhiGDqNRFVD4y64ILBJCk3i/GFKed7z+573ymscrzzOV9mKtUtfznLnltfzyOcHmfnXjdz5/n4OnqomSK/h4tMG8sXNc3j72pksG5eMrhdKeXuTiCA9sQ1K9YsUHIwmvO3gUyNpXAGwx2JqHViGSVot6J0l7iZTY0+3ql7uZqZbtT+rbhJ0qz7djnZ8utUsd3pMCMGGjhc+lpONdmHu6iPcMPEGfjNWUXZ9cPuDfJD1gVvnuUN0qFNMzQu2YSabicd+fIyrvryKvNo8kkOTeeGMF7hnxj2E6DsOpE1WO0XVyuZJugi62yS1SYm5N8jy49JyFUmSSPrT3aDVMiU3k8lFh/nXd8eYdiCSiAYoC4eGKSN9PcxOWTTKGXQfLsbh6P1kxtHiWrZklSJJ8OvprStwTtWe4sOjHwKil7s9wgxhzE6ZDTTaIHobNXPsr6XlKmqJ+f7S/a7H0mNDGZoQhs0hUzRBETmsXr8egLBoZ6bbHKJ8lR/+AsyipbWn8Hj1efToUf7whz+g1TYucLRaLbfeeitHjx7t0cEJmmPUaRnqFFX5pUD4dfcX1oxdg06jY0fhDnYX7e7x66u+n4sGLvJbQZD2kGWZH46Wsva1n5j3t295fvNxKuutDIgJ5k/LR7HjrsU8cv44V5VIIBIRrCPOpATd+oSEDgNKVUlc9e10mw4sw6CxxNxhar4ZGGWMaub/3BFqpru6WaZbCfraC7oPFSqLgRGdlJZDo0e33gOPbkmSuGXyLfx61K8BuH/r/a4yz+7S6NXds5nuzJJMVn2yiv/+8l9kZM4fdj7vn/M+M1PcU5BW/afDjTqi/DTL6mvSorzr1e3q5+6kesPXGIcNI+bXymfjt/s/JLe4iqVHFUXnb8dLfJzzmS+H5xanDYoh1KClpMbMgYLeb93733all3vRyMQ22zn+feDf2Bw2pidNZ3Li5N4eXp/BVWKes75XXs/flctV1Ex306AbYJEz270xXlHBr9+6DXtNDaGRyne5zQbm6HFga4BDn/fiiAMbj4PuyZMnc/BgaxP6gwcPMmHChB4ZlKB9GkvMRdDdX0gOS+a8oecBPd/b3cz3sw/1itWZbfx3ew5L/r6ZS1/awYZfinDIcPrQOF68fCqbblvA2rmD/bY0syeJDG7MdLdVWt40RlaVxLuX6W4ddKsl5rLFoia6kXE/yw245qpZeXmIEtzI7WW61aDbDS9ja25jptsTJEni9mm3s3rEamRk7vnhHr7M/tKja7SFKqZW0UOZbovdwpO7nuSyLy4juzqbhOAEnln0DH+e9WfCDO5nTF3K5bEhvWq/05dwial5qbw8y1nB4c+ZbpW4392AJjaWtNoSrsv8kImlR5El+GaChvez3schOzq/iA8x6rScPiwOUHqre5Nas433dikboG3ZhBXWFbqcBoQvd8fMS5uHTqPjaOVRjlce9/rrqeXl/p6oGBc3DoCc6hyqzI2bSmqJ+QflevSDBiFbrdRu+g6dQYsxVGl9qkt3tofsf7d3Bx3AuBV0Z2Zmun5uuukmbr75Zh5//HG+//57vv/+ex5//HF+//vf8/vf/97b4+33qGJqBwtF0N2fuHrc1egkHdtObWNv8d4eu67q+zk/bT6jYkf12HW9RXZpHQ988gszHtnIvR8eIKu4llCDlstnpvP1rXP539XTOWN0IlovKQr7IxFBemJN7QfdTXEpmHcn6G4Djcuru3mm26OguyMhtfYy3UVqprvzSgY1021I9yzoBiXwvnv63awcthKH7ODOzXeyMWejx9dpippF7olM9y9lv7D609X8+8C/ccgOzhp8Fu+f+z5z0zy3e1GVy0U/d/uo5eXesg3L6iOZbgBteDiJt90GwK+ytwNgmjCFhrgwcmtyvapF0lMsGqncN3s76H5/dx61ZhuD40OZPSSu1fP/OfAfrA4rUxKnMC2pbYtDgUKkMZIZyTMA75eYl5vKKW4oRkJiePRwr75Wd4kKimJguPKd1zTbPSU9mshgPZUNNuqmzwGgZoPyd3OJqSUtVA4+9g3U9ZzQoNlm90krhz/glj/CxIkTkSSpmXjX7bff3uq4Sy65hNWrV/fc6AStaCqmJug/pIalcs7Qc3g/632ez3yefy3+V7evebL6ZJ/w/XQ4ZDZnlfDq1mw2HSlxZVIHxYVy+cx0Vk5Jc/UD90cigvXENSibcJ0pBaeFKeIoHgfdHfh0Q2Om22Eyo6Wxp9uToNvV022yIcsykiQhBbUfdDscsisjOCKp84yg2tOtH9A1pVmNpOG+mfdhsVv45Pgn3Lb5Np6c/yTzBszr0vWinZnuyoauZ7qtDisvZr7Ii5kvYpNtxATFcN+M+1iUvqjL1xRBd+ekRSt/G2+Ul9dbbK5g3t88utsj8txzyHrpVcKOKSW3sasvYnnSbt498i7rjqzzut1ld5k/Mh6AzLwqiqtNJES0rerfk8iyzKtbswG4fEZ6K+u5kvoS3juiiFj58/ezP7EkfQnf53/PhpwNXq0MOFyu9HMPjBjYqUaGPzAufhwna06yv2Q/p6eeDoBOq2H+iHg+2lvAttTxzANqt2zB0dBAaJSRsvw6ah1xkDwRTu2FV34FwV2z5mvJqbI6TFY70Zf+h8R0/64U6GncCrpPnDjh7XEI3EQNurPL6nzqKynofa4eezUfHf2I7/O/Z3/JfsbFj+vW9V7a/xJ22c6c1DmMiRvTQ6PsOWpMVt7blcd/t+VwvLTO9fiCEfFcMSuDucPiveaR25eICNI1ZroT3Mx0e+jV3SzO7qCnWzZ3P9Ntd8jUWeyEGXWu8vK2gu68igbqLXYMWg0ZnfhJy1Yr1oICoHO7sI7QSBoenP0gNoeNL7K/4Pebfs/TC59mVuosj6+l/r4VXcx0H6k4wj3f38PBcqXda0n6Ev4040/EBMV06XoqJ5uUlwvapqlXt7pB1FMcK1budXFhBpfYnr8jaTQk33svFVddRlVoFCOWn8HK2kG8e+Rdvj75NRWmCqKDembB7g0SwoOYkBbJvrwqvj1czOppnlfDeMrWY2UcK6kj1KBl5ZS0Vs//5+f/YHFYmBg/kelJ/r1p4S8sHLiQB7Y9wOGKw+RU55Ae0fV7fUccqVBKy/09y60yLm4cnx3/jMzSzGaPLxyZwEd7C/igJozFKSlYCwqo/f57wqKU92NdpRkm/VoJuksO9dh4Mpz/3VVSTqJ3pshvcStiS+/GIkXQs8SFGUkIN1JcY+ZQYQ1T0v33i0zQswyIGMCvBv+Kj499zPOZz/P0oqe7fK1mvp9+1it2tLiW17Zls25XHnUWxU863Khj1dQBXD4znYw494S5+gsRwR6Ulzt7uosbijHbzRi1btqmNY+6Wz/tynQ3qpeD+3ZhAEF6DQatBovdQVWDVQm6neXl2GzIFguSoTEIOeRssRmSENapIr21oADsdqSgIHTx8W6PqS20Gi1/mfMXrA4rX5/8mpu+vYlnFz3LacmneXSdaLWH3cOg2+aw8crPr/DM3mewOWxEGiO5Z/o9nDnoTI+u0x4i0905yZHBSBKYrA7K6izE9aD9YFaxUr0xtI9kuVXSZkzG8r93iIsMR2M0MsY4hlExozhYfpBPjn3C5WMu9/UQO2ThyET25VXxzaHeCbrVLPfKKWmEt6jUKm0o5d3DSh/tbyf8VmgruEmkMZLTkk9ja8FWNuRs4OpxV3vldfqKiJqKqmB+oPRAs03CecPj0WokDhfXwpwF8Pbr1GzYQOgcpbKittIMy34DsUN7RMG8zmLngU9/prLeyoIR8Vw01v+SPd6mS2nSgoICvv/+e4qLi3E4motk3HTTTT0yMEH7jEqOoLimhIOnqkXQ3c+4Zvw1fHr8U77L+45fyn5hdOzoLl1H9f2clTKLCfG+F0C0O2S+PVTMq9uy2ZLV2Ds0NCGMK2ZlcP6kVFHV0Q5NhdQ6Ky+PNkYTrAumwdZAQW0BgyIHef6Cbaz/NEFqptvcrA1JDfLduqwkERGsp7TWTFW9ldSo4MagGyWg1zYJulW7sJHuKJerdmEDBiBpum8Zp9foeWzuY9y66VY25W3id9/8jucWP+eRunBXhNSOVx3nnu/vcfXmzR8wn/tn3k9ccOt+0K7gcMgi6HYDg05DYngQhdUm8isaejjo7jv93C0ZPKX5InrlsJU8tOMh1mWt47LRl/l18LhwZAJ///oIW7JKMdvsGHXe817Oq6jn64NFAFw+s3VS69WfX8VkNzE+brzbrgMChTPSz2BrwVbWZ6/3WtCt2oWNjPF/SzxQxN70Gj2V5kpya3IZGKFsKkWFGJiSHs3OE+VkDpnEKF6n9ttNhC7/HeDMdGu0MGRBj4zjT2/t4cNaA4PjQnny4jnQic1nIOLx6uOVV15h0KBBrFmzhscff5y///3vrp8nn3zSC0MUtEQtMf9FKJj3O9Ij0lk+aDnQdSVzf/L9rKq38uLm48x//Fuufu0ntmSVopHgjNGJvH71dDb8fi6XzUgXAXcHhBu0xJqUe4EmvuOgW5KkrompdWIZJhkbM921FiUYlqVGizJ3iQhW5rna5Mz+6vXgtKdsWWKu2oUNd0O5vCt2YZ2h1+p5Yv4TzE6ZTYOtges3Xk9mSWbnJzrxREjN7rDz6s+vcuEnF7K/dD/h+nD+cvpfeGrBUz0WcAOU1Jox2xxoNRIpUcGdn9CP8ZaYWlaR/3t0u8vywcsJ1gVzvOo4e0v2+no4HTImJYKEcCP1Fjs7jpd79bVe33EShwyzh8YytMXmSrmpnLcPvw0oVWj+vFHhjywcuBCNpOFg+UFya3J7/PoWu4UTlUrLrb8rl6sYtAZGxShCuS1LzFXrsI+ssWjj43DU1KAtVH6/2gpzj43h8/2n+HBvARoJHr9wAsH9MOCGLgTd9957L/fddx9VVVVkZ2dz4sQJ18/x496X6RfA6BRhG9afWTt+LRIS3+Z+6ypz8oSmvp+TEiZ5YYSdc7iwhrve38+MRzbyl88PklveQGSwnmvnDua7Py7gxcunMntonFhwuEFofTU62YEDiYbwqE6Pd4mpedLX3ck8SGqm22SmsK5QeQyJIJ1ngkQtFcwlSWpUMK9vLlrlSaa7q3ZhnWHQGnhywZNMT5pOnbWO6zZcx89lP7t1rprpruwk051bnctvvvoNj//0OGa7mdkps3n/3Pc5Z8g5Pf75ULPcKVFB6Dsp2e/vNPZ196yY2tE+Wl7eFuGGcJakLwFg3ZF1Ph5Nx2g0kstGyZsq5iarnbd2Kvejy2dmtHr+tZ9fo8HWwJjYMcxJneO1cQQqMUExTEtUlN6/zvm6x69/rPIYNtlGhCGCxJCO27n8CVUDaH9JC7/uUcp7ftvxCoIXOAU4920DnJnuHqC4xsSfPlBe9/r5Q5k8sP9W6Hr8rVpfX89FF12EpgdK9ARdY7TTNuxwYU2/ld3vzwyOHMyZGUr/5guZL3h0ri99P212B18eOMVFL2xj6ZObeXPnSRqsdkYmhfPX88ex/a5F3LV8FANEWatHaMqUcvyKoHBqbJ0f3yWv7k58ujWGRiG1oroi52GeB4Qd2YY19eq22BwcL1EEp0a4U17eDbuwzgjSBfHUwqeYnDCZGmsN16y/xqVu2xFqT3dlQ9uZbofs4M1Db7Lyk5XsLt5NiC6E+2fez78W/4uk0KQe/R1UVI/u9Bihm9AZaV7IdJusdtfGR18sL2+LC4YrXr9fZX9FjcW/XVfUoHvjoaJmbTI9ySf7Cqhwts+oWUaVSlMlbx56E1Cq0MSmc9c4I/0MwDvWYU1Ly/vS/Kh+3U1twwCGxIcxMCYEi93B8ZHKZoVti/J3M9VasVubtxB7iizL3LVuPxX1VkYnR3DTomHdul5fx+PIec2aNbz7bs8YpW/evJmzzz6blJQUJEniww8/bPa8LMvcd999JCcnExwczOLFi8nKyuqR1+7LZMSGYtRpqLfYySnvecsSgf9zzfhrkJDYkLPBpaTpDqrv59TEqb3m+1leZ+HZTUeZ+9i3XPe/3Ww/Xo5WI7F8XBJvXzODL26ew0WnDey35UbdxVasBLllQRGNZdkdoJaX59XmefAq7luGNQbdnm/MqkF3dZNAVGpDwfx4aS02h0x4kI7kyM6z6d21C+uMEH0Izy5+lgnxE6i2VLN2/VqOVhzt8JyoYCXTXW+xY7bZmz1XUFvANeuv4eEdD9Nga+C0pNN4/9z3uWD4BV5d6KkBn9j46pzUKNU2rOeC7uMldThkpfUgLqxvKJd3xoT4CQyOHIzJbuLz45/7ejgdMntoHAadhtzyBo6V1Pb49WVZ5tVt2QBcOmNgKwHI1355jXpbPSNjRjIvrWtWhAJYlL4ICYn9pfspqC3o0WurG6p9RblcRRVTO1R+CIu9sbpKkhorPL7UJaOJjERTWoDGuRyrq+petvvdn/LYeKgYg1bD/62egEHXvxO2Hv/2jzzyCN999x3z58/nxhtv5NZbb2324wl1dXVMmDCBZ555ps3nH3vsMZ566imee+45duzYQWhoKEuXLsVkMrV5fH9Bp9W4Sip/KRAl5v2RodFDXbu57ma7e9v380B+FX98dx8zHtnIY18epqDKREyogRsWDGHL7Qt49tIpTB8c26d2i/0RW5ES5JYGRTbLELdH13q6m/5/G5nuoKaZ7lPOwzz/cnV5dTfLdCvBjaOh8b5/2NnPPSIxvNP3j2y3Y81Vevu6YxfWGaH6UP61+F+MiR1DhbmCq9dfzYmq9u02w4N0qI53qoK5LMusO7KO8z8+nx2FOwjWBXPXaXfx4pIXPbJf6yq5QkTNbVzl5T0YdKvK5cMSwgLmvihJEiuHrQRgXZZ/l5iHGnXMGBwLwMaDPV9ivie3kgP51Rh0GlZPbb4BWGWu4o1DbwBw3XiR5e4OccFxTEmcAvR8ibma6e4r/dwqaeFpRBujsTqsrdoS1RLzjVnlhC1YgAQEo3zf1najxDy3vJ4/f6K0W926ZDgjkyK6fK1AoUtB91dffUVRURH79+9nz549rp+9e/d6dK1ly5bx0EMPsWLFilbPybLMk08+yT333MO5557L+PHjee211ygoKGiVEe+PqGJqoq+7/6KWh6/PXs+xymOdHq/6fk5KmMRpSZ7ZG7mL1e7gk30FXPCvrZz1z+95d1ceFpuDcamRPL5qAlvvXMgfl44UIk09iNUZdJcFR1Ld0Hl9eVeCbqkzyzBjk0x3vbJY1fRUebmaRW9orOpxiai5UVpuKypCtlpBr0ef5J2ybJVwQzjPn/E8I2NGUmYq4+qvria3um0xH41GaubVXVRXxPUbr+f/bft/1FnrmJQwiffOfo9LRl2CpgsbGF0hp0wp2U8XHt2d0iikVt9jpciqiFpLca2+ztlDzkav0XOw/KDbmge+YpGrxLzng+7XnDZhZ49PIbaF4v3rB1+nzlrHsOhhLBjYM2rR/Rk1KbE+Z32PXVOWZVemu68ol6tIksTYuLFA6xLz0wbFEGrQUlxjpmLKbAD0Ncq6oq6LYmoOh8xt7+6jzmJnano0a+cMbva8bPXMKjNQ8FgS+IknnuDll1/myiuv9MJwGjlx4gSFhYUsXrzY9VhkZCTTp09n27ZtXHTRRW2eZzabMZsb3yTV1YEZlIqgWzA8ejiLBy7m65Nf83zm8zw297F2j23q+9lyF73o0cdo2LsXXUIC+uRkEm77A5LOc7Xwbw4Vcdf7+ymqVj5/Oo3E8nHJXDErg8kDo/rtzn3Dzz9T/u9/Ezx5CjG/vrTHr28rUhaHZUGRzcrL1UCg5V89LVwRUqsyV1FrqSXM4IZgUyc93Y1CaiaKe6C8vFnQHdK6p/tIYRfswlJSuvS+9pRIYyQvnPECv/nqNxytPMqvv/g1GREZbR+cUkmw1cFd216nsCGbGmsNBo2BmybfxK9H/RqtpndbLk6WK39jkenunFTnxmGdxU5Vg9UljNcdmma6A4nooGgWD1zMF9lfsO7IOsbM9F9/3oUjE7j/45/ZlVNBVb2VyBB95ye5QUmNmc/2K1VAV87KaPZcjaWG//3yPwCuHX9tr22yBTKL0xfzyM5H2Feyj8K6wh7RwSiqL6LaUo1O0jE4cnDnJ/gZ4+LHsSV/S6ug26jTMmdYPF/+XMg3YRksDQnBWFMMIeldznS//MMJdpwoJ8Sg5YkLJ6B1lnVZCwooe+kl6rZuY/DHHyEZAqONxl08/mQbjUZmz57tjbE0o7BQUcBNTGyuDpiYmOh6ri0eeeQRIiMjXT8DvNTD52uEbZgAGrPdX574kuNV7bsHvPbza+36fpqPHaVhzx5qvvqK8ldeoebrjR6P43hJLb97Yw9F1Wbiw43cvGgYW+9cyFMXT2JKenS/DLitxcUU3P0nsi9YRfXnX1D00EOUvfRSj7+OzZXpjmhWlt0eofpQooxRgAfZ7k7mT6Nmus1mil2Z7p4JuiW1vLy+Mejukl2YF0TU2iM6KJoXl7zIoMhBlJvK2V28u80fm/44upBssqr3U2OtYWzsWN49+12uGHNFrwfc9RYbpbXKAkv0dHdOkF7r8ufuqb5ul0d3ANiFtWTlcKXE/PMTn1Nv9V8tmgExIQxPDMPukPkuq6THrvvWzpNY7TKTBkYxLi2y2XNvHHyDGmsNQyKHuDK0gu6REJLgcmfZeNLzNU1bqGXZg6IGYdD2vWBR7etuqWAOsNBZYv71sUrC5s/DaK4CuqZgnlVUw2NfKRUBf/rVKNJjQ7GcPEnBPfdwdMlSKt54E0t2NrWbN3f1V+mzeLztf/PNN/PPf/6Tp556yhvj6TZ33XVXs97y6urqgAy8RzoVzE9Vmaist/TILrug7zEyZiTzB8xnU+4mXsp8iYfnPNzqmHJTOW8dfgto2/dT0ja/DVS8/RYRZy51ewwmq53fvbGHeoudmYNjefU3p/VrsQyHyUT5K69Q+sKLyE6bq+CJE2nYu5fix59ACgru0Yy3tbixp7va5IZ8OUqJeaW5kvzafM9709qIv9VMt6m+GrNd+ZLuisNFo0934+/hsgwzKYFNjclKfqXy/yPcCLob7cK818/dFnHBcbz1q7fYWbgTm6PteXn626McyK/ishkDOWNUOqclnYZO4xtPelVELSpE79r8EHRMWnQwpbVm8irqGZsa2fkJHWC22V3q8YGiXN6UaUnTGBA+gNyaXL7K/ooVw1q3FfoLC0cmcqSolm8OFnHOhJRuX89qd/D6DuU+dEULm7BaSy2v/fIaoHw/iyx3z3FG+hnsKd7D+uz1XDqq+9+5rtLy6L5VWq6ilpefrDlJpamSqKAo13MLRihBd2ZeFY7TF2DM/ALwvKfbandw6zv7sNgczBsez8o4GwV33EHVp5+BXREMDZkxg7jf/paQ03pHzNef8PjTvXPnTl599VUGDx7M2Wefzfnnn9/sp6dIcvbeFTmzOCpFRUWu59rCaDQSERHR7CcQiQjSMyBGWYyKbHf/RhVF++zEZ+RU57R6vjPfT0nXPKNWv2075hPtC0C15OHPD/LLqWpiQw08edHEfhtwy7JM1aefcWz5ckqe/AdyfT1BE8aT8dabZLz1JrHXKVUJRQ89ROW6nhMUcpWXB0e6lemGLvR1N9moaatqQe27NtU1vRd5Xt0Q0aZlmHJttbz8iLPvNSHcSHRo55uNLruwHvbodocQfQjzB8xncfriNn8Gh8zAVjOWRN1pzEqZ5bOAG+BkmRBR85SetA3LLq3H7pAJN+pIjDB2fkIfQyNpOH+Yskb0d0E1Vc1505ESbPbuWSYBbPiliMJqE3FhBpaNa75+fevwW1RbqsmIyHB5mgt6BrVqYE/xHkrqu1+10FdF1FQijZGuVqeWJebx4UYmDIgCYFvccIx25Xu2pqDSo9d45tuj7M+vYqy5hHt3v86Js86m6qOPwW4ndO4c0t94g/RX/kPo9NP6ZQWkx6vjqKgozj//fObNm0dcXFyzUu7IyO7t9DZl0KBBJCUlsXFjY1lIdXU1O3bsYObMmR2c2X8YlaT2dfu396XAu4yJHcPctLk4ZAcvZr7Y7Dm3fD+1rRf6le+4Zwv45YFTvLZNCfSfuHACiRGd2zcFIg1795Jz0cUU3HYbtoJT6JKTSfnb38h46y2CJ04EIP7mm4m54goATt1zr7Lz200c9fU4apTPf1mQB0G3p17dnXw5qkJq5vqaxlC7h4TUJDXTXa8G3U7lcjf6uaGJXdhA/6t4Um3DKut9Lyoj7MI8J7UHg261n3toYuAol7fkvKHnoZW07CvZ16mlni+ZPDCKyGA9lfVW9uRWdvt6rzgF1C4+bSDGJpvc9dZ6Xv35VUCxAe3tlpJAJyk0ifHx45GRe6TEvK/ahTWlPb9uaBQR3JBdQ9RIpTKsttR967zMvEo+X/ct927/D3/74lGsX68HWSZs8SIy3nuPgS+8QMjkST3wW/RdPN5W/89//tNjL15bW8vRo4033hMnTrB3715iYmIYOHAgt9xyCw899BDDhg1j0KBB3HvvvaSkpHDeeef12Bj6MqOSI1j/S5GwDRNw3fjr2Jy3mU+Pf8q1E65lQLgSYPz34H879f2UtK2/6Kvef5/4W25GY2w/45JbXs/t72UCcO28wcx3lif1J6ynTlH8xP9R/emnAEghIcStvZqYK690lUWrSJJEwp134DCbqHzrbQruuAPJaCDijK738KnK5fagYOr1QW75dAOkhSliavk1HtiGdYBqGWZt6J63bZtCai7LMCWwOeyBiJosy1hUu7BeLi93h2inSFNlvaWTI72PGnSni6DbbdKilb+V2u7QHVTl8kATUWtKXHAc89Lm8U3uN6zLWscdp93h6yG1iU6rYf6IeD7aW8DGg8VMy4jp8rUOnqpm54lytBqJS6Y3r7Z5+/DbVJorGRg+kGWDlnV32II2WJK+hMySTDbkbOCikW0LMLtDvbWe3Brlu6SvZrpBEVP75PgnZJZmtnpu4cgE/m/DEb7PKuVPsyfD99Bg0SI7ZCRNxxuBlT/+xJG7/8o/cp3uBJJExLIzib32WoJG9N2/V0/j0zrQn376iUmTJjFpkrLzceuttzJp0iTuu+8+AG6//XZuvPFGrrnmGqZNm0ZtbS1ffvklQUH9M5vWktEpQsFcoDAufhyzU2djl+28tF8R66oyV/HGwc59P1uWl+uSk7FXVVHz1Vftvp7V7uCmt/ZQbbIxaWAUty3pXzdVR10dJU89xbFly5WAW5KIXLGCIV98Qdxvf9sq4FaRJImk++4j8txzwW4n/9Y/ULtlS5fHoZaW22PiANyyDIPG8vK82jy3jm/63mnLHknNdNtNDUjOp7uSq1ODbovNgcmq9H+5erqdlmGHPRBRs5eWKn31Gg36NO/7XHtKlCvo9p9Mtygvd5+0qJ7LdB9VRdQCsJ+7Kaqg2ifHP3HpP/gjaon5t920DlMrwZaOSSQ5svF7od5azys/vwIoWW5ftpYEMovTFQekn4p+oqyhrMvXOVJxBBmZhOAEYoK6vgnja1QxtQOlB1p9l49JiSAxwkiD1U72iHEgO5AlLVW/tG1JK8syddt3kHPlVZy67DLG5P6MXdIQ9KuzGPzZp6T+3/+JgLsFHgfdgwYNYvDgwe3+eML8+fORZbnVzyuvvAIoC70HHniAwsJCTCYTX3/9NcOH992yjp5mtFPB/GhxLdYe6DsS9G2uG6/0dn989GPya/N54+Ab1FprO/f9bFpertMRvfpCACreervdUx5ff5g9JyuJCNLxz4snodf2jz5u2eGg8oMPObZsOaXP/gvZZCJk6lQy3nuXlEceRp/YebZf0mhI/stDhJ95Jlit5P3uRup27OzSeGxOETXi4gHcznQ37el2y2O4M/Vyl2VY9xbRoQYd6oa6WirfaBlmUnxSi9RMd+d6Ha7S8qQkNH5oTaIKYFb4Q6Zb7ekWHt1uk9bEq7u7NC0vD2Rmp8wmKTSJKnMVX+d87evhtMu84fFoJDhcVENuedfmt6reyod7lGqiy1sIqL175F3KTeWkhaWxfPDy7g5X0A6pYamMiR2DQ3Z0q8TcVVoe07djkOHRwzFoDFSZqzhZc7LZc5IksXCk4hj1TYEJI8r3ecnXPzQ7TpZlard8T86lv+bklVdSv307VknLF+nTKXv2vwx64m8YPYwH+wser5RvueUWbr75ZtfP9ddfz8yZM6mqquKaa67xxhgF7ZAWHUy4UYfF7uBYSffKOgV9n4kJE5mRPAObbOMfu//Bfw/+F+jc97NpebnGYCDy/PNBp6Nh925Mh4+0Ov7bw8U8/51iT/bYBeNdJZaBTv1PP5G96kJO3XUXtuJi9GlppP7jHwz872sEj/HMd1bS6Uh97FHCFixANpvJ/e1vqd+zx+MxqeXl2gQl2K9ys6c7OSwZgAZbAxXmCjcG3FlPtxJ0S5Ymr9+FvlSNRmolpiY5K5scDQ2U1Jopr7MgSTDUjTJcy0mlHLA37cI8Qc10uztv3sLukF3ZWpHpdh+1p7vGZOvWHFrtDk6U1gHuVXD0ZbQaLSuGKsrl/iyoFhViYGq6ktH89nDXst3v7sqlwWpnZFI40wc1ZkdNNhP/OaC0aq4dvxa9RrgFeBNVUG1DzoYuX0MVURsZ0zeVy1X0Wj0jY5XfIbOkdYm52te98WAxIeFKQqZsp9L/LcsyNd98Q/aFq8ldu5aG3bvBYOCbkXNZc8adFF9zK/MWTO6l36Rv4nHQ3TTgvvnmm7ntttt4/fXXeeCBBzh8+LA3xihoB0mSGv26RV+3APjthN8C8MWJL6ixuOn72aS8XDIY0CckEL5oEQCVbzfPdhdVm/jDO/sAuGJmOmeOTe7B0fsnlrw88m6+hZxfX4bp55/RhIaS8MfbGPz5Z0QsXdJl0SPJYCD1yb8TOmsWcn09uWuvoeHnnz26hlpebnQ6OrgrpGbUGkkIVr5c3errdlNITWNxr7y9I1r2dTft6T5SqGwuZsSGEmzoXHTIclIp7fTHfm6AaD/JdBdVm7DYHei1UrMSWEHHhBh0xDgV9PO7UWKeU1aP1S4TatCSEhn47XMrhq5AQuLHwh/bdNzwF1Tv4o0HPQ+6HQ6Z/25XfrfLZ2Y0+55Yl7WOMlMZKaEpnD347J4ZrKBdVFX4Hwt/pMLkxiZzG7iUy6P7frm0y6+7DTG12UPjMOo05Fc2oI1XxLFrC6uoePNNTqw4n7zrb8C0fz9SUBAxV1zB27f8g7+NPAdjagr3nDW6V3+PvkiP1YQuW7aMdT1ogyNwj1FOv27R1+2fvPtTLnd/sB+LrXfK/ycnTua0pNNc/3bH97OpT7fkLMGNvmg1AFUffYSjTsnA2B0yN7+1h/I6C6OTI7hr+aieHr5fYa+tpfjxxzm+bLnS367REHXhhQz56kti16zpkXJljdFI2jNPEzJ1Ko7aWrJXXkDe73+Po969ckZbUSEAwanK5kedxe62xY1HCuZulpcbbKBTv1a6uBmhBt1qqbxaXu5oqOdQoXKfG+5mCa7Vh3Zh7qD+rr7u6Vb9odOiQ9B2IpgjaI5aYt4dMbWjaml5QuAqlzclOSyZ2amzAf/OdqtZv23Hy6j3cEPxu6wScsrqCQ/Scd6kRq9vs93My/tfBuDq8Vej14ost7cZEDGAUTGjsMt2vs391uPz7Q47WRVZQN8vL4cmCuYlrYPuYIOW2UMVjZhSZ9ug2RBF4Z8fwHzoEJqQEGLXrmXoxq/Zf+5VvHKkDkmCJ1ZNIMwodAk6o8eC7vfee4+YmL4rLtBXUTPdwjbM/6g2WbnnwwO8seMkXx8s6vyEHuL6idcjITE0aqhbvp9Ny8vVMuGQ6dMxpKfjqKuj6vPPAfjnN1lsP15OiEHL05dMIkgfmPYmst1OxdvvcGzpmZS99G9kq5XQWTMZ9MEHJD/wZ3RxcT36eprgYNKee46gCcruc80XX1L/449unWt1ZrpDUxsrDmpMLRaH7SziPRVTa7xc6+upJeAGq6JQ3B0iglpmuht7uhvtwjrv5wb/tgsDXD7jZpuDBovdZ+PIFXZhXSY1qvt93apy+dAAF1FrygXDLgDgo6MfYXX4XkiwLYYmhDEgJhiLzcEPRz0T4XrVaRN24dQBhBgag5H3s96nuKGYpNAkzh1ybk8OV9ABasXf+pz1Hp+bW5NLg62BIG0Q6eH+WTXlCePilaD7UMWhNsUMVRHB47UmAMzGKDQREcTdcANDv9lIwh9upToonDvfV4L2q08fxPTBsb00+r6Nx0H3pEmTmDx5sutn0qRJJCcnc/fdd3P33Xd7Y4yCDnCVl5+qdk8QSdBrfLrvFGZnhnvzkZJee90piVN475z3eHnpy+75frYoLwdF7CtqtZLtrnzrbbYfL+OpjcpO78MrxjE4PjDFfuq2bePEivMpvP9+7GVlGDIySPvXswz4978JGuG9HW5tWCjp//2v698Oi3vlxjZnT3dQchIhznJrNUPc2d2gqZha53SsXq5ay2llSDI6xeS6memucmZ/XT7dDQ0u5fIRbva9+rNdGECoQYvOmVmubPBdiXmjcrkoLfeUtB7w6s5SlcsDXEStKXMHzCU2KJZyUznf5X7n6+G0iSRJLHRaYX5zyP2N8+zSOjYdVr7zL5vReO+x2C38e/+/AVgzdg0Grf+JOwYqatC9o2AHVeYqj85VS8uHRQ8LCC/1tLA0oo3R2Bw2DpUfavW8GnQfqVa+FzSnzWXoxq+Jv/F3aKOikGWZez7cT2mtmWEJYfyhn7nXdAePawFaemRrNBri4+OZP38+I0f2bYGBvsiIpHA0EpTXWSiuMZMYEfj9YH2Fd37Kdf3/5iMlyLLca6WDw6PdDxCblZcbGxcBkSvOo+TJJzH9/DN/f/ojHIZEVk1J47xJ/me91F3MJ05Q/NjfqP1WKT3TREYSf8P1RF90kWsjwttoDAaCp0yhYdcucKNEXLbbsZWWAqBLSCQiqJh6i91tQSdX0O1GT3dnb1upiY1jsi7arddvj0YhNSVj77IMq6/niDMjOMINj257ZSWOKmVxZRiQ1q0xeQtJkogKMVBaa6aizuqzfuocl0d3qE9evy/j8uruiaA7gD26W6LX6Dl36Lm8fOBl3st6z2Xt5G8sHJXIq9ty2Hiw2O3vcLWXe/6IeDLiGj9THx79kKL6IhKCE1gxbIXXxixoTUZkBsOih5FVkcW3ud9y3tDz3D7XpVzuwbrKn5EkiXHx49ict5n9JfuZED+h2fMpUcGMSo6g+qRyX6q3GdCGN37nfryvgM/3F6LTSPzfhRMDturRG3gcdN9///3eGIegiwTptQyOD+NocS2/nKoWQbefkFVUw97cSnQaCY1GoqDKxNHiWob5oTJtU59ujb4xwNRFRxO+dCnVn3zCjJ+/o2zpb/jzuZ6pdPs79qoqSp99lvLX3wCbDbRaoi++mLgbrkcX3b3gsSu4Sv0dnZca20rLwG4HrRZdXCyRwXoKq01ue3WnhSuBaE/0dKttCQCJ2mi3zmmPVj3dzqDbXt9Ag9WOQachww1bK7W0XBcfjybEf8umo0L0lNaa/SLTLcrLPcdVXl7ZtfJyu0N2uY8Eukd3S1YOW8nLB15ma/5WCmoLSAlL6fykXmb6oBhCDFqKa8z8XFDN2NTIDo+vt9hcG+5XNLEJs9qtvLT/JQB+M+43GLXGtk4XeJEz0s8gqyKLDTkbPAu6A0S5vCnj4pSgO7O0tYI5KHoGb+QplWV1lY0l6IVVJu798AAANy4cxri0jj8Pgub0D3PdAKexr1uIqfkL7+5S+mQXjExwWYV814sl5h6hay2kprJl1BwA5uft5Z9nD23Wm9aXka1Wyv/3OseWLKX81dfAZiNs3jwGf/IxSff8yScBNwBO4RLZ7kbQ7fTo1sXFIWm1RAQrc+OpV3dBXQH2zoL8zoJuScKqV8Yer+3el7D6e7gsw9SeblMDyDJD48PQueEL7+92YSrRIb4XUztZpoglpguPbo9Jc5bkdzXTnVtej8XmIEivcVmQ9RcGRgzktKTTkJH54OgHvh5OmwTptZzuFJb65lDnKuYf7imgxmQjPTaEecPjXY9/cvwTTtWdIi44jpXDVnptvIL2UTVuthZspcbivg6SWoI9IiZwyqhdCuZtiKmBotxfq1FaySwNNqxmO7Is88f39lFtsjEhLZLrFwzptfEGCm4H3RqNBq1W2+GPThcYC/K+hqpgLmzD/AOr3cH7u5Xs4YVTBzB3mPLFuzmr1JfDapfm5eWNu++7T1ZwT5aGExFJBNktJO7Y5IPR9Ty1mzdz/NzzKHroIexVVRiHDWXASy8x4PnnMA4e7NOxSc5+MbeCbmc/ty4xEWgUIHPXNiwxJBGdpMPmsFHS0MmGkBtZa4vzbRQrRbh9Tlu0sgxzZqklWUbvsDHSjdJy8H+7MJXIYN/ahlWbrFQ4A36R6fYcNdNdUW+lzuy5ZZ5aWj4kPqxfKserAegHWR90vvnnIxap1mGdBN2yLPPatmxA6eXWOOfT6rDyQuYLAFw15iqCdKIi0RcMiRrCkMgh2Bw2NuVucuucSlMlxfXKvAdKeTnA2PixgCKkWm4qb/X8hLQowsMMWJzqMHWVZv634yRbskox6jQ8ceFE9G5sfgua4/Zf7IMPPuD9999v8+ePf/wjRqNRBN0+YrTIdPsVmw6XUFprJi7MwPwR8cx17nbvOF6Gyep/iwqpDSG1qgYrN725B5sMJ2cvBaDy7bf6tFifOSuLk1evJfeaa7EcP442Opqk/3c/gz74gLDTZ/t6eArqXLjR0211Bt36RGVBGNGiLLsztBotSaGKv3deTScK5k0D6DbeAjaHDbNWGXM03cvWtQq6m/SLB9ktDHcz6G60C/NP5XIVX2e6VeXy2FCDsHzpAuFBetd7tiu2YVlOu7D+1M/dlEXpi4g0RlJUX8QPBT/4ejhtssApprYvt5KSmtZqzyo7T5RzqLCGYL2WVVMa7zufHf+M/Np8YoJiWDVildfHK2ifMzIUQbUNORvcOl4tLR8QPoBQfeBoXkQYIsiIyADgQOmBVs9rNRLzRyRQ48x2H82u5OHPDgJwx5kjGdpP71fdxe2g+9xzz231M3LkSF555RUef/xxVq1axeHDh705VkE7qEH3idI6n9rOCBTedfZznT85Db1Ww/DEMJIigjDbHOw40XpH0edomwfdsixz57pM8ioaGBgTwkV3X4MUHIw56ygNu3f7cKBdw1Zezqk//5nj562g7vvvQa8n5je/YchXXypCaX60WdiY6e48Y2Zz2oXpEtRMd/OybHdw26u7k6x1cX2xK9MdLjurJbqYtHP1dKvl5Todkl55LMhmcUtEDRp7uv3Vo1slyhV0+ybTfdLp0T1QlJZ3me7Yhh0tUpXL+1c/t4pRa+TswWcD8N6R93w8mrZJiAhinLOX+9vD7We7X9umVNecNymVSOfn2uaw8WLmiwBcOeZKgnX9q4XA31BVzH/I/4FaS22nx7tKy6MDp7RcZXy8s8S8tO0S80VNSsz/8/UxGqx2Zg6O5cpZGb01xICjS7UBBQUFrF27lnHjxmGz2di7dy+vvvoq6en+XcYXqMSHG4kNNeCQ4XCR8Ov2JaW1Zlff16opilCVJEnMHa70hPWmdZi7NC0v1xgN/G97Dl8cKESvlXj6kklExccQedavAKh4621fDdNjZIuFspf/w7GlZ1L55ltgtxN+xmKGfPoJibf/EW2Ee17PvYparuXoPNPdsry8MVh1v8Q1Lcw9MbXO4uf82nwsyssjm9rPBLlDW2Xyal93kN3isV2Y3s/Ly6NClOoSX2W6G+3CRNDdVbpjG3bEmenuz5kjtcR8c95mSur97zsSGm2UvjnYdtB9qqqBL38uBODymY33nC9OfMHJmpNEG6NZPWK19wcq6JBhUcPIiMjA4rCwOW9zp8cfqTgCBFY/t8q4OMWvu72+7jnD4qhzLklKSuoJM+r426rxrrYJged4lOKpqqri4Ycf5p///CcTJ05k48aNzJkzx1tjE7iJJEmMTolgS1YpB09VM3FAlK+H1G/5cE8+NofMxAFRzTIXc4fH885Pef4ZdDcpL6+0woPOEqI7l41ifFoUAFGrL6Ly3feo+fJLbHff5TuhMTeQZZnajRspeuxvWJ3ZTuOoUSTeeSeh00/z8eg6Rt0Acaen21rcvfJy8NSrWx1k64fya/NR925ks8n9a7VBy/JyAIcxCKgmWusgObLzfkh7bR12p52av5eXq5nuChF091m6ahvmcMgc7Yd2YS0ZGj2UCfET2Feyj4+OfcTV4652+9xD5Yd4Zu8znD/0fBYMXOC1MS4alcA/NmaxJasEi82BQdc8Z/XGjpPYHTKnDYpxidvaHXZXL/flYy4nRC8+Y75GkiTOSD+DF/e/yIacDSwfvLzD41W7sEDMdI+LdwbdpfvbtMMLD9ITEWOEQhuTzToWycHsePEgO3ww1l/dMJ6gUL0PXrlncTvofuyxx3j00UdJSkrizTff5Nxzz/XmuAQeMiq5MegW+AZZll1WIaumNvcFPn1oHBpJEc0pqGwgJcqPSsyalJdvzq7CMtzB4lEJ/GZ2huvx4LFjCBo7FtOBA1S9/wGxa37jg4F2jungQYoe+Sv1O3cCoI2PI+GWW4g877xGOy4/RlIz3W70dLvKy7sopAaNQbdHPd1tkF+bT5Lz28TRoATdUhfry9Wgu85ix2p3oNdqMOuNGIFhEVq3fHKtucpmizYqyj8rGpoQHaLqKPiovFwE3d0mtYuZ7vzKBkxWBwatpt///VcOW8m+kn2sO7KO34z9DRrJvULMFzNfZFPuJjblbuKcIedwx2l3EGHo+c/82JRI4sKMlNaa2XminNOHxbmeM9vsvLlTuec0tQn7KvsrsquziTRGcvHIi3t8TIKuoQbdW/K3UG+tb3czxGq3cqzqGBCYme7h0cMxao1UW6rJqc4hIzKj1TEjRsZSV1hEuCxhKzZRWNy9TfWuIjv6rp5QU9wOuu+8806Cg4MZOnQor776Kq+++mqbx73//vs9NjiB+6gK5iLo9h2ZeVUcKarFqNNw9oTmfqNRIQYmDIhiz8lKNh8p4aLT/KfPtGl5eakFkiOD+NsFE1oFN9EXrebUPQeoeOdtYq66EknjP8qVtpISiv/xD6rWvQ+yjGQwEHPVVcSuXYs2rA+Jn3RFvVzt6XZZhrlfXt5TPd0FtQXE6CRARjZ170s5PKjx/VhjshETaqBBo8cIDA5zb+Okr9iFAUQFi0x3X8dVXu6hkJoqojY4PtQtG7xAZmnGUh778THyavPYWbiTGckzOj3HareytWCr698fH/uY7ae28+dZf+b01NN7dHwajcTCkUrF2sZDRc2C7i/2F1JaayExwsiSMcr92CE7eD7zeQAuG3VZQIlw9XVGxowkLSyNvNo8tuRvYWnG0jaPO151HJvDRrghnOTQ5F4epffRa/SMihnF3pK97C/d32bQffmqUXyXGk5qiBGdD9d8hiD/0d7pDm7/FpdffrlbGQaBb2j06q7B4ZBFz4UPULPcy8YmubKOTZk7LF4JurP8LOhuUl5u0+p46uJJRIcaWh0XsXw5RX99FGvOSeq2bSNstu8Vvx0mE+WvvErZCy/gqFeCh4jly0n4w63oU1N9PDrPcWW6O7HOsdfW4qhTvJVd5eVBrcuyoeN+bDXTXVxfjMVuwaBtPe/KRTpWL8+ryWOEmulWg+4ufl/otBrCjDpqzTaqGqzEhBqoRUcUkB7i3jX7il0Y+Lan22Z3uEqihZBa11GF1PI9FFLLcoqo9ed+bpUQfQjLBy3nnSPvsO7IOreC7t3Fu6m11hITFMPf5/+de3+4l5M1J/nt179l5bCV/HHaH3s02F04MpF3fsrjm0PF3HfWaNea+FWnTdil09NdNkobcjZwvOo44YZwLhl1SY+NQdB9JEliScYSXj7wMhtyNrQbdKvK5SOiRwRs/DMufhx7S/aSWZLJ2UPObvW8RqthwRz/Wa/2ddwOul955RUvDkPQXYbEh2HQaqg12xTVabGA6lVMVjsf7ysAFG/utpg7PJ5/bMzi+6xSbHaH32Q2iuoaF/vThiUyLSOmzeM0ISFEnnsuFa+/TuVbb/s06JZlmZovvqD48SewFih/96Dx40m8805CJk/y2bi6jaunu+PycjXLrQkPRxOqLCojWqh+u+PuFhsUS7AumAZbA6fqTpEe0U6Q2lmmu67ApV7e3Uw3KCXmatANUOnQkgakBLm38FF7+Q0D/LufG5qrl7fVV+dNTlWZsDlkDDoNieHCO7irDHD2dJfWWmiw2Ak2uFeRkeXq5+6fyuUtWTl8Je8ceYeNJzdSYaogOqhj7ZDv8r4DYG7aXCYnTua9c97jH7v/wesHX2dd1jq2FWzjwdkPclpyz2h5nD4sDoNWQ05ZPcdL6xgSH0ZmXiV7Tlai10pc7NxMb5nlDjeI+fU3lqQrQffmvM002BraVJV3KZcHYGm5yvi4jhXMBT2Lf6z6Bd1Gr9UwLFHZLf9FlJj3Ol/9XEiNyUZadDAzBse2ecyEtEgig/VUm2zsy6vs3QG2g8lq59/bcl3/Pm1ExyVUUasvBKDmm2+wFrVvneJNGvbvJ+fSX5N/6x+wFhSgS0oi5W+PkfHWm3074KZpT3fHJeKNyuUJrsciuyCkJkkSKaFKK0SHJeYdBIJWu5WiuiKXenl3M93QuIFQ1WClxmSlUlYi+kSje+er5eWGPlBervZ02xwydb1s+aiWlg+IDhbVUd0gIlhHuNPj3BOvblfQnSgy3QCjY0czKmYUVoeVj4993Onxqvr0vLR5AATrgrnztDt5eenLpIalUlBXwJr1a3h4x8PUWz23c2tJmFHH9MHKprSqYq7ahP1qXDLx4coN6puT35BVkUWYPkxkuf2U0bGjSQlNocHWwA/5bfvDHyl3KpcHoIiaiiqmdrjiMGZ795xHBJ0jgu4AQi0xF0F37/PuT4oQ1QVT0tpdvOq0Gk4fqvSBfXektNfG1hEPfvoLuVWNN1qtsZ3yYidBw4cTPGUK2O1UrutdT1VrYSH5t99O9qoLadi9Gyk4mLgbf8eQLz4n8uyz/arHvMu4ero7znSrGx56Zz83NJaXm6wOzDb3gzd3+ro7yr4W1hUiI2PXO7P0PZLpdvanN1g5UlSDWdvo0+0Oqke33s89ugGC9BqXEnJFXe+KqalBd3qs6DftDpIkNRFTcy+4k2WZo06Lz/6sXN6SC4ZfAMC6rHXIHZTrnKg6QU51DnqNnpkpM5s9Ny1pGuvOWceq4asAePPQm6z6ZBV7ivd0e3yqddjGQ0WU1ZpdFW6XO72LZVnmuX3PAXDJqEuINEZ2+zUFPY+qYg6wPmd9q+dlWW4sLw/gTHdKaAoxQTHYHDYOlh309XACngBYpQpUGvu6RdDdm+RV1PPDMSWIXjk5rcNj/cmv+7PMU7y+4yT2JsGqZOg8lRh9keI1WvnOu8g290W7uoqjvp6Sfz7NsTOXUf3xJwBEnnceQ778gvgbbkAT7EdK8N3EpbDeSU93S49ugLAWAmTu4rINq/HANqwJebXKhpMhRAkcHGbnJk53Mt1N+tMPF9Zi0imbQY6GzrOIDpMJ26lTypjS/b+nW5IkokPa7sf3NjllQkStp1DF1NzNdJ+qMlFnsaPTSGLTownLBy0nWBfMiaoT7C3Z2+5xapZ7WtK0Nvu2Q/Wh3DfzPp5f/DyJIYmcrDnJFV9cweM/Pt6tjJ4adP+YXcGLW05gsTkYlxrJJKdV66bcTRyuOEyILoTLRl3W5dcReJ8zMpSg+7vc71q9J4rqi6g0V6KVtAyJGuKL4fUKkiSJEvNeRATdAcRoEXT7hHW78pFlmDUklgGdLF7nDo8HIDOvstezWk05WVbPnesyAVg2vlFwTDJ0nOkGCF+yBG1UFLbCQmo3b/HaGGWHg6qPPuLYsuWUPvMMsslE8JQpZLz7Lil/fQR9k4AzYNC6l+m2FbcuL9dqJFeJa1dswzzy6m5CQa2zpz5Uuf/0VE83qEF3NSatGnR3nkW05imbAJrQULR+7CffFLXEvKK+d+8JuWp5uQi6u40qpuaubZhaWp4RF9rK87k/E2YIY0n6EgDeO9J+NVXTfu6OmJU6i/fPfZ9zh5yLjMyrv7zKqk9Wsb+kawFGemwoQxPCsDtkXtis2EldPjMdSZKULHemkuW+eOTFRAVFdek1BL3DuLhxJIYkUm+rZ2v+1mbPHalQSssHRQ7CqHWzr6mPMjZuLECXPxMC9xF3+gBCDbrzKhp6PWPSX3E4ZN7dpfSPtieg1pTkyGCGJ4bhkOH7o74pMbfYHNz45m5qzDampkezYlpjNtCdoFtjNBJ5/vkAVLz9llfGWL97N9kXrqbgjjuxFRWhT00l9cknSf/ffwkeN9Yrr+kPuErkO+npdpWXt9h4aNoL7S5pYUp1RleDbvW8kNAooGd6uiObiMIdLqrB7Mx0yw2dB/RN7cL6iuKs+vv2toJ5TrmigJ8ugu5uk+YUU3M76Bal5e2ilpivz15PtaV1EqHaUs3uot1A50E3QIQhgodOf4h/LvwnccFxnKg6wWVfXMZTu5/Cavf8M7fIme12yBAdondZhG7J38IvZb8QrAvmijFXeHxdQe+ikTSuEvMNORuaPXe4PPBLy1XUvu7M0kwfjyTwCQzjMwEAkSF6UiKDKKgycehUNdPbEfQS9BzbT5SRV9FAuFHH0jFJbp0zd1g8R4pq2XykpJWfd2/w2JeH2JdXRWSwnn9cPAnd4cbdTamTnm6V6AtXUf7yy9Rt3oIlLx9DWs/Yc1ny8il+4nFqvvgSULKVsddeS8wVl6MxBvZuMwA6NzPdbZSXgxJ051c2eOTVnRLmhpBaB6jnhYUpAkM9n+muYYDW/fLyvmQXpqJmuh9ff5hXtmb32useKVSyrcLtovu4ysvd7Ok+6hJRE8rWLZkQP4EhkUM4VnWMz49/zkUjL2r2/A/5P2CX7QyJHMKAcPcdCuYPmM/E+Ik8vPNhvjjxBS/uf5Hv8r7jL6f/hZExI92+zsKRCTy/+TgAq6cNJEivbdbLfdGIizpVXhf4B0sylvC/g//j29xvm9lmupTLA1hETUXNdOfX5lNuKicmqG0HG0H3EUF3gDEqOYKCKhMHRdDdK6gCamdPTHHbJmbu8Hhe+v4Em7NKet0iaOPBIl76/gQAj6+aQGpUMPVNfLo1bmS6AQwZGYTOmknd1m1UvvsuCb+/pVvjstfWUvb8C5S/+iqyxQKSRNQFFxB/803o4uK6de2+hKTxsKc7oUXQHdSF8nKnkFq5qZx6az0h+o4DsJYbLGrQHRGhzFNjptvtIbRCzdgfK6mlot7qynS7E3T3JbswlSEJofCz0mOt9ln3FhFBOtHT3QM0Cql5Vl4uMt2tkSSJlcNX8tiPj7Euax2rR6xu9j2plpbPGzDP42tHBUXx2NzHWDxwMQ9tf4gjFUe4+NOLuXbCtawZtwa9Rt/pNaakR5MSGURFvZVfz1DEGrcWbGV/6X6CtEFcPuZyj8cl8A0T4icQHxxPSUMJ209td1VOqOXl/SHTHWGIYFDkIE5UnWB/yf4ufa4E7iGC7gBjdEoEGw8Vc/BUja+HEvBUm6x8cUARbFo1pWMBtaacNigGo05DUbWZI0W1jEjqnUzHqaoG/vDuPgCump3BGaOVgM0l3oV75eUqUasvUoLudeuIv+F6j85Vke12Kt9/n5J/PIW9VCm3D5kxg8Q77yBopPuZh4DBaRnWUaZbttmwlZUBoG/S0w1NvLo9sA2LMEQQbginxlJDfm0+w6KHtXnc0O++Qzab0EZFNXtcDbqjIpT3U09muvflVgEQFql8Rtzp6e5LdmEqNy0axmmDYjFZe9cyDGBUUgRBevc2DAXto5aXF9eYMdvsGHXt/01lWW4sLxd2YW1y9uCz+fuuv3Oo/BC/lP3CmLgxANgcNr7P/x5otArrCksyljAlcQoPbn+QjSc38szeZ/g291v+MvsvDI0e2uG5Oq2GD26YjclqJy06BFmW+de+fwGwasQq4oL7z0ZxX0cjaVicvpg3D73J+uz1zE2bS721npxqpWKqP2S6QelvP1F1gszSTBF0exERdAcYLgXzQiGm5m0+3XcKk9XBsIQwJjqVS90hSK9lxuBYvjtSwndHinsl6LbZHdz85l4q662MTY3gzmVNAlpt421A8qCEO3zhArTxcdhLSqn55hsizjzTozHVbd9O0V8fxXxIKeMypKeTcMfthC1Y0Gd6cXsayWUZ1n55uK20FBwO0OnQxjavZlFVv6sbPFOVTwtL42D5wQ6D7pYBPoDJZqK0QdksiYlIpJLGTLfUjVS3GnRbnJsPsXGK7Y5c7055ed+xC1Mx6rTMc4osCvom0SF6gvVaGqx2CipNDIprX5G8pMZMtcmGRqLD4/ozUUFRLE5fzBcnvuC9rPdcQXdmSSZV5ioijZGMjx/frdeIDY7l7/P/zmcnPuPhHQ/zS9kvXPjphfxu0u+4YvQVaDXtb5wkRgS5/n9H4Q72lezDqDVy1ZirujUmQe9zRvoZvHnoTb7J/Qar3UpWZRYyMnHBccQG94+K0fFx4/n42MdCTM3LCCG1AEMNug8V1mDrpC9U0D1UAbVVU9M8DhJVFfPNveTX/dTGLHZmlxNm1PH0xZObZWGkpv+vdz9bLen1RF2gCN5UvPW22+dZsrPJveF3nLzyKsyHDqGJiCDhzjsY/MnHhC9c2G8DbsDV000Hn11XaXl8fCtv8ginv7WnQopdVTAvqFOUy0N0IYSGKT2MPZHpVjP2KokJUUCT0vV2kK1WrPnK79AX7MIEgYMkSa6+7s68ul3K5bGhHWbE+zsXDFO+Xz4//jn1VuVvuilvEwCnp56OTtP9vJEkSZw1+Cw+PPdD5qTOweqw8vddf+eKL68guyrbrWuovdwXDL+A+BCxedbXmJwwmdigWGosNews3NmvRNRUVDG1A6UHcMgidvAWIugOMNJjQggxaLHYHJworfP1cAKWo8U17DlZiVYjsWKS+6XlKvOcft07s8tpsHi3pPSHo6X889ujADx8/jgyWmZWulheDhC9ahVoNNRv3475+IkOj7VXV1P010c5dvY51G7cCFot0ZdeypCvviT2yiu7VJ4eaLgy3R30dFudQbc+oXXmObKN8nJ39jDUoDuvJs/tsUKjXVhqeCqaoKDmT3ZLvbz5YjolMQrovLzcWlAAdjuS0YguXix+Bb1Lo5haxxUZamn5UNHP3SFTk6YyIHwA9bZ6vsr+CoDNuYo/9/y0+T36WgkhCTyz6BkemPUAofpQ9pXsY9Unq3j94OsdBiE/Fv7IrqJd6DV6keXuo2g1WhanLwYUFXNXP3c/KS0HGBY9DKPWSI21huzqbF8PJ2ARQXeAodFIjHSWK/8i/Lq9hiqgtmBEAvHhnqtqD4kPIzUqGIvNwfYTZT09PBclNWZueXsvsgwXTRvAOW2opUu6xgBH46Z6uYo+JYWwuYrwSOU777R5jGyzUf7GGxxbspTyV14Bq5XQuXMY/NGHJN17D7o+4qXcKzh7urG1H3TbnHZhLZXLoWl5uRUZ2e2XVcXUPM1059cox6eGtRF0d4OWme4BKUqJX2fl5a5+7oEDWlUBCATexl0xNZeImujn7hCNpOH8YYo95XtZ75Fbk8uxqmNoJS2zUmf1+OtJksSKYSv44JwPmJE8A5PdxF93/pU1X61pd0NSzXKfP+x8EkNb35MFfQPVOmzjyY38XPoz0L+Cbr1Gz+jY0YDw6/YmYlUSgKgl5iLo9g5Wu4N1u5Vg48Kpnme5Qflyn+vMdn93uKTHxtYUh0Pm1nf2UlJjZnhiGPefPabtsXQj0w0QddFqAKo++KBV+W/tli0cP+88ih54EHtlJYahQxjw4gsMfOEFjEM7Fqvpj0jO/vqOMt224rbtwqCpkJpnPd1dLS/Pr2sMuiVjT2a6G4Nug05DSpKyMdOZerlqF6bvQ3ZhgsBBFVPLr3Qz6E4QdmGdcd7Q89BJOjJLMnn5wMsATE6cTIQhwmuvmRyWzAtnvMA90+8hWBfMT0U/cf7H5/PO4XeQ5cbNzF1Fu9hZuBOdRseasWu8Nh6B95mSOIVoYzSV5koOlB0A8MhGLhAYF6eUmO8vFUG3txBBdwDiElMTCuZe4bvDJZTWmokLM7BgZOsSX3eZO8zZ153lnaD7uc3H2JJVSpBewzOXTG7f0qxJRtATITWVsDlz0KUkY6+qouYrpQTQfOwYJ6+5hty112A5egxtVBSJ993L4A8/JGzOnC79Pv0BSc10d9DTbS10lpcntZXp9twyDBQhNVCC7qaLys5onunuOR91o05LkF75WwxLCEMfprREdNbT3RftwgSBQ2pU5z3dTZXLRXl558QFx7nUlN878h7QPdVyd5EkidUjV7PunHVMSZxCg62BB7c/yHVfX0dhXSEAz+97HoAVQ1eQHJbs9TEJvIdOo2PhwIWufxu1RgZG9B0xzp5A7esWQbf3EEF3ADI6RQ26RabbG7zzk1LCumJSKnpt1z9Cs4bGodVIHC+pI7e8Z715d+WU88R6pS/pz+eMYVhiBxkVR2OA15VMt6TVEn3hhQCU//d/FD7wIMfPOZe6zVtAryfmyisZsv4rYi65pFkpu6AN3Ojpbs+jG5pkuj0MulPClLaDOmsdVeYqt89Te7pTwlKQerCnGxpL5UckhqMJVoIZuaEB2dH+hkRftAsTBA7u9HSX1VmoqLciSUqbkaBzVg5b2ezfvRF0qwwIH8DLS1/m9mm3Y9Qa2VqwlRUfreDJXU+y7dQ2dJKONeNEljsQWJK+xPX/w6KG9YhQX19ifJziBnCk/AgmW/cFUdtDlmWXMGJ/o3+9o/oJI5PCkSSln7ekxtylnuNAQpZl1v9SxP+251Bn9qzsti325SlByaqp3cumRQbrmTQgip9yKticVcKl0z0viS2pMXPX+/sprzM3e/x4aR12h8y5E1O4sJNxNvWE7qqYWeT551Py9DOYDhzAdEApzQpbtIjEP96GISOjS9fsj7iU5Dvs6VbLy90TUnOHIF0QccFxlDaUkl+bT1RQlFvnqeXoaWFpaOw9e5+JDNZTXGNmRFJ4s35x2WRCCglp85y+aBcmCBzUnu7CahMWmwODrvWmbFaRUlo+IDqk/eojQTNmpcwiOTSZU3WnSI9IJyMyo1dfXyNpuGz0ZZyeejr3/HAPmSWZ/PvAvwE4Z+g5rvYcQd9mWvI0Io2RVJmr+pVyuUpyaDKxQbGUmco4VH6IiQkTe/w1ZFnmkZ2P8HPpzzx/xvOEGfrXxqMIugOQEIOOjNhQTpTWcfBUNfHh/VfF96fsch754hC7cip69LrTMqIZ3lH22E3mDo9Xgu4jXQu6N/xSxNcHi9p8bnBcKH9ZMa5TCy5dExVsSa/v4Mj20SckEPmr5VR99DHGkSNJvPMOQmfM6NK1+jWuTHfb2VxZlrEWK0Jq+o56uhtseFAlDigl4qUNpeTV5rk8cTui3lpPhVn5XKWEpSCZWxzQzUz34PhQsoprmZoRg+TMdIPS161pI+iWHQ6suaqQmgi6Bb1PfJgRo06D2eagsMrEwNjW79OjxUpp+TBRWu42Wo2Wi0ZexN93/Z1lg5b5bByDIgfx6pmv8srPr/Ds3mfRaXRcPfZqn41H0LPoNXrOHnw2/zv4P6YnT/f1cHodSZIYFz+OTbmbyCzJ7PGg2yE7eHD7g7x35D0kJHYU7mDRwEU9+hr+jgi6A5RRyeGuoFv1hO5PHC2u4dEvD7PhFyUgDdJruGr2ICYOiOr2tTWSxLSMnlHcnjs8nv/bcIStR8uw2h0el6vXW5TM/WmDYlhz+iDX41pJYtbQWEIMnX/EtWGhDNmwHkmv75bic9IDDxD9618TNHp0M3E2gfs09nS3nel21NQgO8XE2lYvV+bbYndgtnnmtZkalsq+kn1ui6mpx0UYIgg3hCNj8ej1OuPxVRO4cWE9Y1MjAZCCgpBNpnbF1GxFRcgWC+h06JNFf6Wg95EkidToYI6X1JFXWd9m0K2KqA0VyuUecdWYq5iaOJVRsaN8Og6dRsfV467m7MFnY3FYGBAu9CMCiVun3MpZg89yKXn3N8bHjWdT7qYe7+u2O+zcv/V+Pjr2ERISD8x+oN8F3CCC7oBldHIEn+8v7Hd93UXVJv6+4Qjv/JSLQwaNBKunDeCWxcNJjOg5S6OeYlxqJFEheirrrezNrWRaRoxH56se34PjQlk6JqnL4+gJ4SmN0UjwuHHdvk6/ppOebrW0XBMZ2aZFV6hBh0YCh+x5X7dLwbzGvaDb5dGtllbq9Yoon5ql716im/AgvSvgBtAEB2M3mVybDi2x5DhF1FJThXaAwGekRjmD7nb6utXycqFc7hmSJDE+fryvh+FC2IMFJnqt3q1Kr0DFG2JqNoeNP33/Jz4/8TkaScPDpz/Mrwb/qseu35cQK5MApb/ZhlWbrDz/3TH+/f0JTFZl0b9kdCK3nzmCoX68uNFqJOYMi+eTfQV8d7jE86DbqgRnQXqRWQ4EOuvptjo9uvUJbavmazQS4UF6qhqsVHmqYB7eqGDuDnm1im+tGnRLkqRko+u9I5CiCQ7GXlHRbqbbZRcmRNQEPsRlG9Ze0O2yCxOZboFA4F+MjR2LhER+bT5lDWXEBsd263pWh5U7N9/J+pz16CQdf537V5ZmLO2h0fY9hHp5gKIG3cdK6jBZ2xdl6uuYbXb+/f0J5j32Lc98ewyT1cGU9Gjeu24mL1w+1a8DbpW5wxS/7q5Yh6kbDEKQJ0DopKe7UUSt/SxLRLDTNszLXt1NlctVNE0s56TuprpboPZ1O+rbDmYa7cJE0C3wHaqCeVuZ7oo6C6W1ivjBEBF0CwQCPyPMEMbgyMFA97PdVruV2zbdpgTcGh1PzH+iXwfcIDLdAUtyZJCrbPlocW2zMs1AwOGQ+XhfAY+vP+xa3AyJD+WOM0dyxujETsXD/Am1535/fhXldRZiQt1XEHdlunUi6A4EOuvpthW3r1yuEhmsJ5eGrpeX1+bjkB1opI73ZNXgvKlyb1d83t1FtQ1zNLSdSRd2YQJ/oDHobv0+PVqiZLlTo4IJM4rll0Ag8D/GxY/jWNUxMksymT9gfpeuYbabuXXTrWzO24xBY+DvC/7O3LS5PTvQPohfZ7r/3//7f0rJYpOfkSNH+npYfQJJkhiVFJgl5puPlHDWP7/nlrf3klfRQGKEkb+eP46vbpnLkjFJfSrgBkiMCGJkUjiyDFs8zHarVQzBBr/+KAvcxSlAJ7cTdFudme62lMtVVH9r1TbM3YxzUmgSWkmL1WGltKG00+Nb9XTTPNPdXfXylri8uk1t+4cKuzCBP+Dy6q5snelW+7mHiiy3QCDwU8bFda+vu8HWwE3f3MTmvM0YtUb+ufCfIuB24vdbrWPGjOHrr792/VsnBHLcZlRyBNuOl/FLQWAE3Qfyq/jrF4f4/qgSEIQbdVw3fwi/mT2oz5dXzxsez6HCGr47UsK5E933/FSF1IJFT3dA4FJ9by/T7ezp1iV0HnTXeFhertPoSApNIr82n/zafBJC2s+mQ+ueblAUxr2FFNJ+ebksy66gW9iFCXxJapTS032qyoTN7kDXxJEiS9iFCQQCP0cVLDxQesCtqrem1FvrufGbG9lZuJNgXTBPL3ya05JP89ZQ+xx+H8HqdDqSkrquytyfGZWs9DP3dQXzk2X1PL7+MB/vUzJreq3EZTMy+N3CoR6VYvszc4fH8/zm42zJKkWWZbez9UJILcBwWrZ13tPdfkCs9nR3hdSwVPJr88mryWNSwqR2j6u2VFNjUQKIpj3dUpAXM91Banl566DbXlamCLhJEvq0tB59XYHAExLCjei1Ela7TFGNmdSoRo/5o6qImrALEwgEfsrQqKEEaYOotdaSXZXN4KjBbp1XZ63j+q+vZ3fxbkJ0ITy7+FmmJE7x8mj7Fn4fdGdlZZGSkkJQUBAzZ87kkUceYWAHmQyz2YzZbHb9u7q6bwec3WF0ilJefvBUtUeBnD/x8vcneOSLg1jtMgDnTUzhD0tGMCCmtf9pX2ZqRjTBei0lNWYOnqpxzV1nNJaXi6A7EFCtrkz795N90cWtnjdnZQHulZd3BXfF1NTS8pigGEL0jZ9FjdF7me6OerpdpeXJyWgMgbERJ+ibaDQSKVHB5JTVk1de3yzobiwv93+BT4FA0D/RaXSMjh3N7uLdZJZmuhV011hquO7r68gsySRcH86/zvgXE+In9MJo+xZ+3Qg6ffp0XnnlFb788kv+9a9/ceLECebMmUNNTU275zzyyCNERka6fgb0gP9wX2VoQhg6jUS1ydZmf5m/U1FncQXcc4bF8emNp/PkRZMCLuAGMOq0zByiWDN4omJuEkJqAYU+Odn1/w1797b6ka1WpJCQDvuWI4O7HnSrWevOgm7Vy7tpaTl4OdPtLC9vy6db9egWdmECf6AtBfNqk5XCakWPQGS6BQKBP+Pq6y7pvK+7ylzF2vVrySzJJMIQwYtLXhQBdzv4daZ72bJlrv8fP34806dPJz09nXfeeYc1a9a0ec5dd93Frbfe6vp3dXV1vw28jTotQxPCOFRYw8FTNS7/0L7CJ5kFWO0yo5Mj+O+a6b4ejteZOyyObw4V893hEq6bN8StcxpEpjugMA4dyqAP3sdaUND+MUOGoA1rf9Ee0Y2g291Mt/p809Jy8G6m22UZ1tBaSE316BZ2YQJ/IC0qBChrttmtlpYnRQR1qxpFIBAIvM24ePfE1CpMFVyz4RoOlR8i2hjNC0teYGSMELxuD78OulsSFRXF8OHDOXr0aLvHGI1GjF60relrjEqOcAbd1Zwxuv2SVH9k3S5FqGnllP7Ro6lah/2UU06d2UaoG5Yyoqc78AgaNYqgUaO6fH53errTwpXPmprJbo+27MKghZBaj6uXK5uGbZWXW4VdmMCPSG3DNuxokejnFggEfYPxcYqY2pGKIzTYGgjWBbc6prShlLXr13K08iixQbG8uORFhkUP6+2h9in8ury8JbW1tRw7dozkJiWYgo4ZndzY192XyCqqYV9eFTqNxLkTUzo/IQAYFBdKWnQwVrvM9uNlbp1jsiqCW0K9XKDSEz3dhfWFWB3t+3y3ZRcGoAnypk+3EtC3WV4u7MIEfkRbtmGqcrmwCxMIBP5OUmgSccFx2GU7B8sOtnq+uL6Y33z1G45WHiU+OJ6Xz3xZBNxu4NdB92233cZ3331HdnY2W7duZcWKFWi1Wi6+uLXAkKBtRiX3Ta/u93YrWe75IxKIC+sflQuSJDHPme3+7oh7fd0miygvFzSnO+XlccFxGDQGHLKDwrrCdo9ryy4MQGpaXt7Duo2u8vI2LMOEXZjAn1DF05r2dGepyuVCRE0gEPg5kiS169ddWFfIVV9exYmqEySGJPKfM//D4Ej3FM77O34ddOfl5XHxxRczYsQILrzwQmJjY9m+fTvx8fG+HlqfQbUNyymrp9bsmW+vr7A7ZD7co5SvXjDFfc/qQEAtMd/sZtDdWF7u1x9lQS/SHSE1jaTpVExNlmVXprtVT7dXM91qeXnzoNteWYmjqgoAQz/V7xD4F2lOsc+CygYcDsV5I0uUlwsEgj6E6tfdNOjOr83nyi+v5GTNSVJCU3jlzFdIj0j31RD7HH7d0/3WW2/5egh9ntgwI4kRRoqqzRwurGZKeoyvh9Qp3x8tpajaTFSIngUj2/cjDkRmDYlFp5HILqvnZFk9A2PbF7+z2h3YnAs6UV4uUOmuSFNqeCrZ1dlKX3cbnTyV5krqbUqvasugu2mmu6ctCl2WYabmQbclV+nn1sXHownpW2KRgsAkMdyIVqN4dRfXmAkP0rlKzYfGi6BbIBD4Py0VzHOrc1mzfg2n6k4xIHwA/17yb5LDRLuvJ4j0WD+gscS8fas1f0IVUDtnQgrGfmaFFR6kZ/LAaAC+68Q6TM1ygxBSEzTSUkjN09g3LcwpptZOplvNcscHx2PUNs9sS73R092ivNxlFyZKywV+gk6rITlSeb/mVdS7lMvjwoxEhwofeYFA4P+MiR2DhERBXQG7inZx5ZdXcqruFBkRGfxn6X9EwN0FRNDdD3AF3QX+39ddbbLy1c9KL+kF/US1vCXzRjj7ug93HHSrHt2SBEad+CgLFIL1WnSarmeZ1T5ttW+7Je31cwNomjlH9Gymu9EyrHnQbc0V/dwC/6OpmFpjP7fIcgsEgr5BmCGMIVGKfe3V66+muKGYIZFD+M+Z/yExtG+5IfkLYqXeDxjVhxTMP8s8hdnmYFhCGONSI309HJ8wd5gSdG87VorF5mj3OJOlUbm8p0t5BX0XSZK86tXdXj83tBBS62Ha6+lWM93CLkzgT6RGKe/XvIoGl3K56OcWCAR9CbXE3OawMTx6OC+f+TJxwXE+HlXfRQTd/QDVNuxwYQ12Zw+wv9LUm7u/BpJjUiKIDTVQZ7Gz+2RFu8cJj25Be0QEdV2uIzXcGXS349Xdnkc3tBBS6+me7hAlc9jSMkwolwv8kbToRgVzl0e3yHQLBII+xKyUWQCMihnFv5f8m5gg/9eF8mf8WkhN0DMMigslSK+hwWonu6yOIX4q5JJdWsdPORVoJFgxqX+pljdFo5GYMyyOD/cWsPlICTMGx7Z5nBp0CxE1QUu6o2Cu9nSXmcposDUQrAtu9nxHQbd3M91tl5e7PLoHiKBb4D+kuoLuenLKFOHBocIuTCAQ9CGWZiwlNSyV4THDW2m4CDxHZLr7AVqNxIhE5cven0vM33d6c58+LJ7ECO8t3vsCc93w6zYJuzBBO3SnvDzCEEGYXtmYU0vJm+IKusPbCLq9mOlWe7pliwXZrrz3HXV12EtLATAMFHZhAv9BzXQfK64lt0IJukV5uUAg6EtIksS4+HEi4O4hxGq9n+Dvfd0Oh8y63ao3d/8UUGvKHGdf988F1ZTUmNs8xpXpNohMt6A53bENkySp3b7uph7dqaFtlZd7P9MNjdlu1S5MGxWFNrJ/akAI/JMB0U6v7ioTsgzRIXpihXK5QCAQ9FtE0N1PGJ2iBt3+aRu2/UQZ+ZUNhAfpWDJaqCLGhxtdvfjfH207222yiPJyQdu0tA3zFJeCeU1zBfMyUxlmuxmNpCEpNKnVeZLRi5luo9F1TUe9kjkUdmECfyUpMoimJgLDEsL7rU6JQCAQCETQ3W/wd9uwdbuUjNpZ45OFMJgT1Tps85HSNp8XQmqC9uhOphuaiKm1yHSrQXhCSAJ6bevX8GamW5IkV7ZbNpkAYRcm8F/0Wg1JTdqkhorScoFAIOjXiKC7nzAySenpLqw2UVFn8fFomlNntvHFgVMArJwsSstVVOuwzUdKcLShOi+CbkF7dKenG9q3DXOVlrchogYthNS8kNVr6dXtsgsTQbfAD1HF1EAolwsEAkF/RwTd/YTwID0DYpQFgL/1dX95oJB6i52M2BCmpEf7ejh+w5T0aEINWsrqLPzSxpyZrI0+3QJBU7obdKsK5i2D7o6Uy6GFZZgXcCmYq+XlJ4VHt8B/SXP2dYNSXi4QCASC/osIuvsRao9wWwGcL1nnVC1fObn/enO3hUGnYeYQxS6sLRVzk7AME7RDd3y6oUmmu8azoFtqWl7uhY+yq7xczXQLuzCBH5PWNNMtyssFAoGgXyOC7n7EKD8MuvMq6tl6rAyAFZP7rzd3e8zrwDqswSLUywVt0zTT3ZXYNyUsBYAaaw3Vlsb7RaeZbqN3M91SiDPTbTLhMJuxFRYCItMt8E9So5T3a3iQjoRwYbkjEAgE/RkRdPcjGm3D/EfB/AOnTdjMwbHNSvEECqpf9+6cCmpM1mbPqT3dRuHTLWhBd4XUQvQhxATFAM2z3WpPtxqUt6SpernkhVS3Jli5RzjqG7Dm5YEsowkNRRsT0+OvJRB0l7Gpio3d1PRoUcUlEAgE/RyxWu9HqOXlR4trsNgcPh6N4vn7/h5lQb9SeHO3SXpsKOmxIdgcMtucFQEqorxc0B6R3ezphtZ93XaHnYK6gmbPtUTS6UDXvdL2jlDV0R0N9c3swkRAI/BHxqZG8tlNp/PkRZN8PRSBQCAQ+BgRdPcj0qKDCQ/SYbXLHC2u9fVw2H2yghOldYQYtCwb29rzV6CglphvzmpeYt4ggm5BO3TXpxsas9lq0F3SUILNYUMn6UgISWj3PFeJuRcCYU1IY0+3sAsT9AXGpET2yCaYQCAQCPo2IujuR0iSxKgktcTc933d7zm9uZeNTSbU6L3sWF9HtQ777kgJstxoHebKdIuebkELulteDo1926o3txp8J4UmodW0/56TvOnV7VIvbxB2YQKBQCAQCPoMIujuZ4xKVmxLfB10m6x2Pt2nlKqunCIE1Dpi5pBY9FqJ3PIGssvqXY+rQmrCp1vQkiC9FoOue7f31PDmXt2deXSreDXTrfZ0mxqEXZhAIBAIBII+gwi6+xmjU/xDwXz9L0XUmG2kRgUzY1CsT8fi74QadS7/8s1NVMzV8nIRdAvaorvZbpdtmDPozqtVMt5qMN4e3sx0N7UMs+QKuzCBQCAQCAR9AxF09zMaFcyrm5Uq9zbrdikL+PMnp6LRCBGkzpg3XOmhbRp0m6yKGJ7o6Ra0RXf7ulWxtILaAmRZblQuD21buVxFCvKeNZIUrAT09pparPnKeESmWyAQCAQCgb8jgu5+xvDEcDQSVNRbKao2+2QMRdUmtjhFwVZOFqrl7jB3eBwAW4+VYbYpGW6hXi7oiO6KNyWHJiMhYbKbKDOVNXp0d5Lp1hidmW4vlpebjx0Fmw3JaESX0L6om0AgEAgEAoE/IILufkaQXsvg+DDAd33dH+7JxyEr3qUZcaE+GUNfY1RSBHFhRhqsdnZlVwBN1MsN4mMsaE13y8v1Wj2JoYmAIqbmbk+3NzPdanm5+UiWMsYBaUga8f4XCAQCgUDg3wjJ6H7I6OQIjhbX8supahaM7LksUUFlAy9tOcHukxUdHne8RLErE97c7qPRSMwdFsf7e/L5LquEWUPjhJCaoEMiesCmKDUslcK6Qk7WnKSwrtD1WEd4NdPdxDIMwDAwvcdfQyAQCAQCgaCnEUF3P2RUcgQf7yvoMTG17NI6/rXpGO/vycNqd69PPDxIx6/GJ/fI6/cX5o2I5/09+Ww+Uspdy4SQmqBjhjorWpIiuy5slhqWyq6iXewu2o1dtmPQGIgLjuvwHK9ahrW4trALEwgEAoFA0BcQQXc/pKdsww4X1vDspqN8sq8AhzPWnjE4hotPG0iooeO31vDE8B7xEu5PnD40DklS5q242oRZCKkJOuC384ewcGQCY5yOBV1BFVPbWbgTgJSwFDRSx+XcjZZhXX7Z9q/t7OlW0Q8c0PMvIhAIBAKBQNDDiKC7H6Lahp0oraPeYiOkkwC5JZl5lTz9zVHW/1LkemzBiHh+t3AoU9JjenSsgkZiw4yMTYlkf34Vmw6XYLGLoFvQPgadhnFpkd26hiqalluTq/y7k9Jy8LJlmLO8XEWUlwsEAoFAIOgLiKC7H5IQHkRcmIHSWguHC2uYNDDarfN2HC/j6W+PsiWrFFBaNpeNTeL6+UMZm9q9xb3APeYOj2N/fhVf/VzoeizYIIJugXdoGWSnhHVsFwYgGQ3Kf72Q6laF1FSEXZhAIBAIBIK+gAi6+ymjkiPYklXKwVMdB92yLPPdkRKe+fYoPzpVs7UaiXMnpnD9/CEMTQjvrSELUPy6n/n2GBsPFbseM+qEerPAO7QMut3JdLuE1LyA1DTo1unQJwtdCIFAIBAIBP6PCLr7KY1Bd9t93Q6HzPpfinjm26Psz68CwKDVsGpqGtfNG8KAmJA2zxN4l0kDowgz6qg12wAI0muQvKASLRAAJIQkoNfosTqsgLvl5WpPt3cz3frUFCSd+AoTCAQCgUDg/4gVSz9ldLLS191Swdxmd/Bp5ime3XSUI0WKtVewXssl0weyds7gbikhC7qPXqth1pBYVz+96OcWeBONpCElLIWc6hzAzUy3N3u6mwTdop9bIBAIBAJBX0EE3f2UUc6g+9CpahwOGavDwfu783nuu2PklNUDEG7UccWsDK6anUFsmNGXwxU0Yd6IeBF0C3qN1LBUV9DtXk+3F326mwXdop9bIBAIBAJB30AE3f2UwfGhGLQa6ix2nthwmHW78imsNgEQE2pgzemDuGxmurD18kPmDot3/b/V4Z4vukDQVdTsdrAumJigzt0JNEHe26CTDAbQ6cBmwyDswgQCgUAgEPQRRNDdT9FrNQxLDOPngmqe+fYYAIkRRtbOGcwl0wd6bCMm6D2a9tOX1Jh9OBJBf0ANulNCU9zSD/BmphuU8nVHbS16kekWCAQCgUDQRxCyx/2YOc6M6YCYYP6yYiybb1/A1XMGi4C7D3D16YMAWD4uyccjEQQ6Y+LGADA6drRbx6s2XvqkRK+MxzBwIOj1BI0a5ZXrCwQCgUAgEPQ0kizLAV2fWl1dTWRkJFVVVURERPh6OH6F2WbnaHEtIxLD0WnF/ktfwmJz8FNOORPSogg1ik0SgfeQZZmD5QfJiMggRN+5a4Esy5iPHMEwYACakJ53ObBVVGCvqMA4eHCPX1sgEAgEAoHAE9yNNUXQLRAIBAKBQCAQCAQCgYe4G2uK9KZAIBAIBAKBQCAQCAReQgTdAoFAIBAIBAKBQCAQeAkRdAsEAoFAIBAIBAKBQOAl+kTQ/cwzz5CRkUFQUBDTp09n586dvh6SQCAQCAQCgUAgEAgEneL3Qffbb7/Nrbfeyv3338/u3buZMGECS5cupbi42NdDEwgEAoFAIBAIBAKBoEP8Puj+v//7P9auXctVV13F6NGjee655wgJCeHll1/29dAEAoFAIBAIBAKBQCDoEL8Oui0WC7t27WLx4sWuxzQaDYsXL2bbtm1tnmM2m6murm72IxAIBAKBQCAQCAQCgS/w66C7tLQUu91OYmJis8cTExMpLCxs85xHHnmEyMhI18+AAQN6Y6gCgUAgEAgEAoFAIBC0wq+D7q5w1113UVVV5frJzc319ZAEAoFAIBAIBAKBQNBP0fl6AB0RFxeHVqulqKio2eNFRUUkJSW1eY7RaMRoNPbG8AQCgUAgEAgEAoFAIOgQv850GwwGpkyZwsaNG12PORwONm7cyMyZM304MoFAIBAIBAKBQCAQCDrHrzPdALfeeitXXHEFU6dO5bTTTuPJJ5+krq6Oq666ytdDEwgEAoFAIBAIBAKBoEP8PuhevXo1JSUl3HfffRQWFjJx4kS+/PLLVuJq7SHLMoBQMRcIBAKBQCAQCAQCQY+hxphqzNkektzZEX2cvLw8oWAuEAgEAoFAIBAIBAKvkJubS1paWrvPB3zQ7XA4KCgoIDw8HEmSfD2cXqG6upoBAwaQm5tLRESEr4cj6EHE3AYmYl4DEzGvgYuY28BEzGtgIubV+/Tnv7Esy9TU1JCSkoJG075cmt+Xl3cXjUbT4a5DIBMREdHv3vj9BTG3gYmY18BEzGvgIuY2MBHzGpiIefU+/fVvHBkZ2ekxfq1eLhAIBAKBQCAQCAQCQV9GBN0CgUAgEAgEAoFAIBB4CRF0ByBGo5H7778fo9Ho66EIehgxt4GJmNfARMxr4CLmNjAR8xqYiHn1PuJv3DkBL6QmEAgEAoFAIBAIBAKBrxCZboFAIBAIBAKBQCAQCLyECLoFAoFAIBAIBAKBQCDwEiLoFggEAoFAIBAIBAKBwEuIoFsgEAgEAoFAIBAIBAIvIYJuQZcQ+nsCgUAgEAgEniHWTwJB/0QE3QKPKC8vB0CSJB+PROBNxKJAIPBvDh8+zM033+zrYQi8gLj/BiZi/dQ/EJ9f7xAIf1dhGSZwmz179jBlyhR27tzJ1KlTfT0cQQ9x8uRJDh48SHFxMVOnTmXUqFEA2O12tFqtj0cn6A4nTpzgo48+orKykrFjx3LBBRf4ekiCHmDfvn0sWrSIuro6duzYwfjx4309JEEPUFtbi9FoRK/XI8uyCM4CCLF+CkzE+sn7BNJ9UefrAQj6Bnv37mXevHnceuut4gsjgMjMzOSMM85wLQaGDRvG8OHDefXVV9FqteKLow+TmZnJ0qVLmTp1KkeOHCE2NhatVsuKFSt8PTRBN9i3bx8zZsxgzZo1fPbZZ7zxxhsi6A4ADh48yE033cQVV1zBhRdeiMFg6PMLTIGCWD8FJmL95H0C7b4oyssFnXLgwAFmzZrF73//ex5//HFkWaawsJB9+/ZhtVp9PTxBFykuLubiiy/m6quv5uOPP+bw4cMsW7aM//73vyxbtgwArVaLw+Hw8UgFnnLkyBGWL1/OmjVr+Pjjj/nhhx+or6/n1KlTvh6aoBvs2bOHmTNncsstt/D0009zww038M4775CZmenroQm6QU5ODitXrmTz5s0888wzfPzxx1gsFiRJCoiSyv6MWD8FJmL95H0C8b4ogm5Bh9TW1nLzzTej1+v585//DMDKlStZvnw5kyZN4owzzuDJJ5/07SAFXSIrKwu9Xs/111+PTqcjNjaW1atXM3DgQH766SfXF4dGI24TfQmLxcILL7zAkiVLuO+++wCIi4tj3Lhx7N+/n5tvvplHH33Ux6MUeEp+fj7nnnsuN954I4888ggAs2bNwmKx8NNPPwFKSaOgb2G321m3bh1Dhw5l586dREVF8fDDDwfEArO/I9ZPgYtYP3mXQL0vineDoEN0Oh1XX301ycnJnH322SxduhSbzcY999zD1q1bSU9P54033uDVV1/19VAFHmI2m6msrKSgoMD1mMlkIj4+nnvvvZcTJ07w5ptv+nCEgq6g1Wq58MILuemmmzAYDEiSxF/+8hfeeOMNZFnm1KlTvPbaa6LMvI+h1+t59tlnm22YzJo1i1/96lc89NBDVFdXi1LGPohWq2XhwoVcfvnlTJgwgc8++4zExETXAtNsNvfZBWZ/R6yfAhexfvIuAXtflAWCTqivr5fXrVsnDxkyRJ45c6ZcUFDgeq6yslKeM2eOvHr1ah+OUNAVcnJy5EGDBsmXXnqp/MYbb8ibNm2SIyMj5bvvvluWZVmeOXOm/Ic//MHHoxR4gsPhkGVZlm02m+uxo0ePymlpafInn3zieuyll16SBw0aJB88eLDXxyjwHLvd3u5j3333nTxkyBD5nXfeafdYgX9jtVqb/dtsNstnnnmmPGnSJPndd9+VLRaLLMuy/OGHH/pieIJuINZPgcnJkyflwYMHi/WTF1HveyqBcF8UQmqCVpSWlpKbm0tISAjx8fHExMSwZMkSgoKC0Gg0JCQkAEr5R2RkJJMnT2b37t04HA5RSuPHNJ3XuLg4Bg4cyDvvvMPatWvZtm0bVquV6667jr/85S8ADBo0iPz8fB+PWuAOVqsVvV7v+nfTjOeQIUPYu3cvsbGxrs9obGwsRqORqKgoH4xW4C7qvLYlGqPea+fOnUtiYiIvv/wyq1atEvfgPkB1dTVlZWUYjUaio6MJDg52iQPZ7XYMBgMffvgh5513Hg8//DB2u51vv/2Wjz/+mGnTppGSkuLrX0HQDk3nNioqipCQEJYuXSrWT32cpvMaERHBgAEDePvtt1m7di3bt2/HYrGI9VM3abpGTUhIIDo62vW5sNlsAXFfFEG3oBmZmZmsWrUKu92O2WwmMTGRp556ilmzZrF48WI0Go1rQa/+t6ioiAkTJvRZNcH+QMt5TUhI4B//+Aenn346GzZswGQyUVdXx4gRIwCw2WxUVlYyc+ZMgD6tFhnoHD58mAceeIA//OEPTJ48udlz6rzFxMQAjYHa999/z6BBgwgNDe318Qrco6N5VVHVcf/85z9z+eWX8/HHH3POOef08kgFnnDgwAGuvfZaysrKsFqtnH322dx9992uYEyr1WKz2TAajXz00UesWLGCyy67DIPBwObNm/vEwrK/0tbc3nnnnSQlJYn1Ux+mrXm9/fbbmTp1KuvXr8dsNov1UzdpK/Z4+umnmTFjBqC0agTCfVFsqwlcFBYWcvbZZ3Peeefx+eef889//pNhw4Yxf/583nzzTQwGAzpd4z5NfX09f/rTn9i0aRO/+93vxE3FT2lrXocPH86CBQt4/fXXiYuLIy0tzfWFkZ+fz/3338/OnTtZvXo1gJhbP+X48eOcccYZfP755zz00EPs2bOn2fPqvKn/LS8v5+677+aVV17h0UcfJTw8vNfHLOiczuZVRV24jxkzBp1Ox5YtW4Rarh9z6NAhFi5cyIwZM3jppZe46qqr2Lp1K99//z2Aqz9Rp9O5Mt7p6emEh4ezY8eOdjdfBL6nvbn94YcfAMT6qY/S3rxu27YNgPj4eLF+6ibtxR5z587lrbfech0XEPdFnxa3C/yKPXv2yGPHjpVPnDjheqy+vl6+7bbbZIPBIH/66aeyLCs9gx988IF88cUXy8nJyfLu3bt9NGKBO3gyr8ePH5f/9Kc/ySkpKWJe/Zz6+nr5yiuvlC+44AL5mWeekRctWiSfffbZ7c7b+vXr5WuuuUYePHiwvGfPnt4drMBtPJ1Xlf/973/ygQMHemmUAk+pqqqSzz33XPnaa69t9vjSpUvlFStWtHnOM888I0uSJO7Ffo6ncyvWT30DT+dVrJ+6hidrVFnu2/dFkekWuKiqquLnn3927bY7HA6Cg4N57LHHWLt2LZdccglZWVloNBqmTp3KuHHj2Lx5M5MmTfLxyAUd4cm8Jicnc8EFF7Bjxw4xr35OcHAwZ555JkuWLOH666/n+uuvp76+nvvvv7/NzOj48eOZN28e33zzDRMnTuz9AQvcwtN5VW3CLr30UsaMGdPbwxW4SUVFBXFxcZx11lkALo/mc845B5vNBtBKiXf16tUcPXpU3Iv9HE/ndsqUKYwdO1asn/wcT+c1KSmJlStXivWTh3iyRoW+fV+U5JZ3eUG/xW63s3DhQpKTk3n22WeJiYlxiRjk5+dzySWXsHDhQu655x60Wq0Q/ugjuDOvixYt4p577hHz2cd57733eO655wgJCeGBBx5g4sSJmM1mysrKSElJEZ/ZPkp781pRUUFSUpKvhydwk6+//prFixcDjX2er7zyCq+99hrffPON67Hq6moiIiJ8PFqBJ7g7t5WVlURFRbn0GAT+jbvzWlVVRWRkpI9H2zdxd4167733Istyn17D9N2RC3ocrVbL6tWryc7O5qmnnqK6utr15k5NTSUsLIzDhw+7vij68hu/P+HOvB46dEjMZx9GzXZecMEFXHvttdTX13Pffffx448/8vvf/55p06a5fC0FfYfO5nXKlCmYzea+51Xaz1Dnp+XiHaC2tpby8nLXYw8++CBXX321K6sm8G88ndu1a9ditVrF962f09V5Ffdiz3F3jSpJUp//3Aj1cgHQeEP57W9/y7Fjx/joo49oaGjgT3/6k2vHPTY2lujoaOx2OxqNRizg+wBiXgMbdX61Wq3LXmrVqlVIksQLL7zAsmXLsNvtfPXVVxiNRl8PV+AmYl4DC/Weqs6rJEnYbDZ0Oh2RkZGEh4cjSRL33nsvjz76KDt27GhmASjwX8TcBiZiXnuH/rZGFeXlAqDRekYt6XjwwQf57LPPqKys5JxzziE3N5dPP/2U7du3i77BPoSY18BFndumZW1Nd+MXLVrE7t272bJlC2PHjvXlUAUeIOY1MGlrXgHeeecdXn/9dcaOHcsTTzzBDz/8wJQpU3w4UoGniLkNTMS8ep/+tkbt23l6gcfY7fZWZWs2mw2tVktOTg7jxo1j06ZNrt27JUuWsH//foxGI9u2bQuIN30gIuY1cOlsbufOncunn34K4NqNv/3229myZQubNm0SgZmfIuY1MPFkXkEREfrkk0/4xz/+wdatW8Xi3Y8RcxuYiHn1DVartd+tUUV5eT/i8OHDPPnkkxw7dozZs2dz4403EhMTg06nIycnh9mzZ3PWWWdx+umnAzBv3jzmzZuHLMt9XrwgkBHzGri4O7e/+tWvXOfodDqmTJnCjz/+yIQJE3w4ekF7iHkNTLoyr+np6UyZMoXXXnuNUaNG+XD0go4QcxuYiHn1PtnZ2WzYsIGGhgaGDRvGsmXLANDr9Rw/fpy5c+f2nzWqd5zIBP7G/v375bi4OPnCCy+Ur7/+elmv18uPPPKI6/krr7xSvvrqq2WHw+F6rOn/C/wTMa+Bi5jbwETMa2DSlXlVKS4u7s2hCjxEzG1gIubV+2RmZsoJCQnyggUL5Pnz58sajUa+7LLL5O3bt8uyLMtr166V16xZ02++70Smux9QWVnJ2rVrWbt2LQ8//DAAcXFxlJSUuIQhXnrppVb2FX1ZrKA/IOY1cBFzG5iIeQ1Mujqvah9jfHy8L4YtcAMxt4GJmFfvU1ZWxmWXXcbatWt56KGHAPjiiy8466yzqKio4M9//jPPPfdcq0x2IH/fBVDOXtAeDQ0NNDQ0MHfuXNdjubm57Ny5k+nTp3PNNdewfv16H45Q0BXEvAYuYm4DEzGvgUlX5zWgyiYDFDG3gYmYV+9TWVmJTqfjkksuQZZlLBYLEydOZNSoUfz000888MADVFVV+XqYvYrIdPcDLBYLWVlZ/PDDD6SkpPDxxx/z1ltvceeddxIdHc1///tfCgoKmDRpEklJSb4ersBNxLwGLmJuAxMxr4GJmNfARcxtYCLm1fvU1NSwe/duCgsLGT16NAaDgfr6egYMGMDdd9/Nr3/9a9577z3Wrl3r66H2Hr6ubxf0Dq+88oocEhIiL1++XA4PD5ffe+8913P79++XJUmSP/74Yx+OUNAVxLwGLmJuAxMxr4GJmNfARcxtYCLm1btYrVb5sssuk4cOHSo//fTT8ptvvilHR0fL119/vSzLsnzLLbfIF110kWy1WgO6j7spItMdgBQUFJCfn09ZWRmLFi1CkiSuuOIKFi1aBMCKFSuYOHEiDocDWZaJiopi0qRJhIeH+3jkgo4Q8xq4iLkNTMS8BiZiXgMXMbeBiZhX79P0b3zGGWeg0+m44447eOaZZ7j//vtJSkri+uuvd/V3V1VVUVFRgU7Xf0JR0ZwQYGRmZjJjxgyuvPJKzj77bKZPn84LL7xATU0NaWlpWK1WsrOzyc7ORqPRoNVqeemll6iurmb48OG+Hr6gHcS8Bi5ibgMTMa+BiZjXwEXMbWAi5tX7tPwbT506lRdffJHBgwfz7LPPsm/fPjZs2OAKuGVZxm63M3HiRJc1WL/AVyl2Qc9TUlIijxo1Sr7jjjvkEydOyMXFxfLFF18sT58+Xb7lllvkyspKWZZl+brrrpN1Op28fPlyedmyZXJiYqK8Z88e3w5e0C5iXgMXMbeBiZjXwETMa+Ai5jYwEfPqfdr7G0+bNq3Z31jl2LFj8t133y1HRUXJv/zyi49G7RtEpjuAKCwspKGhgUsuuYSMjAzi4+N55ZVXWLp0KVu3buVvf/sbVquVhx/+/+3de1BU9f/H8dcuCOIuKiC4DCowknERQaWaYfKeA5apUyqjpZLVpDT1R6Y45ah5h9QJrTEHTQ2NyiyqcbRUVLKLAgkZKo54zwt5F5CV3f38/nA4vzYVoS+HXd++HjOOw+7Zsx8+z/PPZ8/ZwwJkZWXBZDKhZ8+eKCgoQHx8vKuHT/fArnKxrUzsKhO7ysW2MrGr/u41x0OGDNHmuLa2FgBw8eJFvP/++9i0aRPy8/MRFRXl4tG3MFev+qn5lJeXq/DwcPX9998rpW7fxKD+/6lTp6q4uDi1Z88ebfuH5cYFDzp2lYttZWJXmdhVLraViV31d785jo+PVwUFBdr2FRUV6syZMy4Zq6sZlHpYLqSXz2q14sknn4TFYkFeXh48PDxgs9ng6ekJpRTi4uIQHx+PTz/91NVDpSZgV7nYViZ2lYld5WJbmdhVf42Z4549e2LdunWuHqrL8fJyIRwOB7y9vbFmzRoUFBRg8uTJAKAd9AaDAcOGDcPff//t4pFSU7CrXGwrE7vKxK5ysa1M7Kq/xs5xZWWli0fqHrjoFsJoNMJut6N79+5Yt24dcnNzMX78eFy4cEHb5vjx4/Dz84PdbnfhSKkp2FUutpWJXWViV7nYViZ21R/nuGl4efkDqv4TpHr1l3JUVVXBarWipKQEY8eORWhoKPz9/REQEIBvv/0Wv/76K2JjY104cmoIu8rFtjKxq0zsKhfbysSu+uMc/294pvsBU/9JUf1nJUop7aA/ceIEunXrhsLCQgwaNAhlZWV4+umnERISgqCgIOzbt48HvZtiV7nYViZ2lYld5WJbmdhVf5zj5sEz3Q+QI0eOYMWKFTh16hTi4uIwbtw4hIeHAwBOnz6NXr16Yfjw4cjOzobD4YCHh4f2qZTD4YDRyM9Y3BG7ysW2MrGrTOwqF9vKxK764xw3H87EA+LAgQNITEzElStX4HA4sGXLFuTm5kIphbq6Onz77bd48cUXkZ2dDYPBAA8PD6fX//NyEHIf7CoX28rErjKxq1xsKxO76o9z3Lx4pvsBcOzYMQwcOBAvvvgi5s2bBwB45ZVXYDKZkJWVpW1nt9vvOODJfbGrXGwrE7vKxK5ysa1M7Ko/znHz45luN2e327Ft2zYMGjQIU6ZM0b5P4ePjgz///BP9+vXD+PHj8csvv2iXdJD7Y1e52FYmdpWJXeViW5nYVX+cY33wTPcD4Pjx46ipqUFMTAwAYM6cOVi4cCFmzpyJ2tpalJeXo7CwENu3b9e+Z0Huj13lYluZ2FUmdpWLbWViV/1xjpufp6sHQPcXHh6ufYpktVqxd+9efPXVV3jmmWcAAHv27MHzzz+Po0eP8sB/gLCrXGwrE7vKxK5ysa1M7Ko/znHz46LbDZ09exa///47bt26hdDQUPTu3RsGgwF2ux3e3t74/vvvYTQatbsC+vv7o2PHjvD393f10KkB7CoX28rErjKxq1xsKxO76o9zrD8uut3MgQMHMGLECHTo0AHHjh1DWFgY0tPTMXLkSO1GBfV3A6y/DX9OTg5at26N0NBQl42bGsaucrGtTOwqE7vKxbYysav+OMctRJHbOHr0qOrUqZOaNm2aunr1qioqKlITJkxQEydOVDabTTkcDqftT548qaZOnar8/PxUaWmpi0ZN98OucrGtTOwqE7vKxbYysav+OMcth4tuN2G1WtVbb72lRo8eraxWq/b46tWrVUBAgLp48aLT9oWFhSotLU3FxcWpkpKSlh4uNRK7ysW2MrGrTOwqF9vKxK764xy3LF5e7iYcDgc6deqEqKgoeHl5QSkFg8GAxMREmM1m1NXVOW2fkJCAmzdvYsaMGQgODnbRqOl+2FUutpWJXWViV7nYViZ21R/nuGVx0e0mWrdujREjRtxxB8D27dujVatWTgd+cXExevfujT59+rT0MKmJ2FUutpWJXWViV7nYViZ21R/nuGUZXT2Ah9m5c+ewb98+bN26FQ6HQzvo7Xa7dsOCa9eu4cqVK9prZs6cicGDB+PSpUv8Y/Ruil3lYluZ2FUmdpWLbWViV/1xjl2o5a9oJ6WUKi0tVaGhoapbt26qXbt2KjIyUn322Wfq0qVLSiml3bigvLxcBQYGqsuXL6u5c+cqHx8fVVRU5MqhUwPYVS62lYldZWJXudhWJnbVH+fYtbjodoHKykoVGRmp3nnnHVVRUaH++usvlZKSoqKiotSsWbNUZWWltu2FCxdUz549VUpKivLy8uJB78bYVS62lYldZWJXudhWJnbVH+fY9bjodoGysjIVFhZ2x0Gcnp6uYmNjVWZmpqqurlZKKXXw4EFlMBiUj4+P2r9/vwtGS43FrnKxrUzsKhO7ysW2MrGr/jjHrsfvdLtAXV0dbDYbampqAAA3b94EACxatAgDBgzAihUrcPToUQCAn58f0tLS8PvvvyM+Pt5VQ6ZGYFe52FYmdpWJXeViW5nYVX+cY9czKMVvxLvC448/DrPZjPz8fACA1WqFt7c3AOCxxx5DREQEcnNzAQC1tbVo3bq1y8ZKjceucrGtTOwqE7vKxbYysav+OMeuxTPdLaC6uho3btzA9evXtcdWrlyJsrIyjB07FgDg7e0Nm80GAOjbty+qq6u1bXnQuyd2lYttZWJXmdhVLraViV31xzl2P1x06+zgwYN47rnn0K9fP0RFRWHDhg0AgKioKGRlZWHbtm0YNWoU6urqYDTezlFZWQmTyQSbzcZb87spdpWLbWViV5nYVS62lYld9cc5dk+erh6AZAcPHkTfvn0xfvx4JCQkoLi4GC+99BKio6PRs2dPDBs2DCaTCWlpaejRowciIyPh5eWFzZs347fffoOnJ/O4I3aVi21lYleZ2FUutpWJXfXHOXZf/E63Ti5fvowxY8YgMjISWVlZ2uMDBgxAbGwsli1bpj1248YNzJs3D5cvX0br1q0xefJkREdHu2LYdB/sKhfbysSuMrGrXGwrE7vqj3Ps3vhxhk7q6upw9epVjBw5EgDgcDhgNBoRHh6Oy5cvAwDU7T/ZBl9fX2RkZDhtR+6JXeViW5nYVSZ2lYttZWJX/XGO3RtnWCcdO3bE+vXr0adPHwCA3W4HAISEhGgHtsFggNFodLrJgcFgaPnBUqOxq1xsKxO7ysSucrGtTOyqP86xe+OiW0ePPPIIgNufILVq1QrA7U+YKisrtW0WLlyIVatWaXcP5IHv/thVLraViV1lYle52FYmdtUf59h98fLyFmA0GqGU0g7q+k+bZs6ciXnz5mH//v28ccEDiF3lYluZ2FUmdpWLbWViV/1xjt0Pz3S3kPr71Xl6eqJz585YvHgxMjMzUVRUhLi4OBePjv4rdpWLbWViV5nYVS62lYld9cc5di/8iKOF1H/C1KpVK2RnZ6Nt27bYs2cPevXq5eKR0f+CXeViW5nYVSZ2lYttZWJX/XGO3QvPdLewpKQkAMAvv/yChIQEF4+Gmgu7ysW2MrGrTOwqF9vKxK764xy7B/6dbheorq6GyWRy9TCombGrXGwrE7vKxK5ysa1M7Ko/zrHrcdFNREREREREpBNeXk5ERERERESkEy66iYiIiIiIiHTCRTcRERERERGRTrjoJiIiIiIiItIJF91EREREREREOuGim4iIiIiIiEgnXHQTERHpbNeuXTAYDLh69aqrh6JJTU3FiBEjXD2MFjF79mzEx8e7ehhERPSQ4qKbiIjESk1NhcFg0P4FBAQgOTkZf/zxh6uH9kDavXs3Bg4cCH9/f7Rp0waPPPIIJkyYgFu3bgEA1q5di/bt2zfb+zVmsRwWFubU+N//UlNT8fbbb2PHjh3NNi4iIqKm4KKbiIhES05Oxrlz53Du3Dns2LEDnp6eGDp0qKuH5TJ2ux0Oh6PJrzt48CCSk5ORkJCAgoICHDhwAMuXL4eXlxfsdnuzjlEpBZvN1qhtCwsLtb6bNm0CAJSXl2uPZWVlwWw2IyAgoFnHSERE1FhcdBMRkWje3t6wWCywWCyIj4/H9OnTcfr0afz999/aNqdPn8bo0aPRvn17+Pv7Y/jw4Thx4oT2fP2l2IsXL0ZwcDACAgLw+uuvo66uTtvGarUiPT0dnTt3hre3NyIiIrB69WqnsRQXFyMhIQFt2rRBYmIiysvLtefqz+p+8skn6NKlC8xmM9LS0mC325GZmQmLxYKgoCDMnz/faZ9Lly5FbGwsTCYTOnfujLS0NFRVVWnP1599/u677xAdHQ1vb2+cOnXqjnkqLCxEYGAgMjIy7jqPP/74IywWCzIzM9G9e3d07doVycnJyM7Oho+PD3bt2oWXXnoJ165d084yz549GwCQk5ODhIQE+Pr6wmKxYOzYsaisrNT2XX/5/ZYtW9C7d294e3tj/fr1eO+991BaWqrtb+3atXeMKzAwUOvr7+8PAAgKCtIea9eu3R1nzOt7LliwAB07dkT79u0xZ84c2Gw2TJ06Ff7+/ujUqRPWrFnj9F73O06IiIjuhotuIiJ6aFRVVWH9+vWIiIjQznzW1dUhKSkJvr6++Omnn/Dzzz/DbDYjOTlZu2waAHbu3ImKigrs3LkT69atw9q1a50WgePHj0dubi6WLVuGQ4cOYeXKlTCbzU7v/+6772LJkiUoKiqCp6cnJk6c6PR8RUUFtmzZgq1btyI3NxerV6/GM888gzNnzmD37t3IyMjAjBkzsHfvXu01RqMRy5YtQ1lZGdatW4f8/HxMmzbNab81NTXIyMjAqlWrUFZWhqCgIKfn8/PzMXjwYMyfPx/p6el3nTuLxYJz586hoKDgrs8nJibigw8+QNu2bbWzzG+//bY2x3PnzkVpaSny8vJw4sQJpKam3rGP6dOnY9GiRTh06BAGDx6MKVOmICYmRttfSkrKXd/7v8jPz8fZs2dRUFCApUuXYtasWRg6dCj8/Pywd+9eTJo0Ca+99hrOnDmj/Q6NOU6IiIjuoIiIiISaMGGC8vDwUCaTSZlMJgVABQcHq+LiYm2bnJwc9eijjyqHw6E9ZrValY+Pj/rhhx+0/YSGhiqbzaZtM2rUKJWSkqKUUqq8vFwBUNu2bbvrOHbu3KkAqO3bt2uPbd68WQFQN2/eVEopNWvWLNWmTRt1/fp1bZukpCQVFham7Ha79tijjz6qFi5ceM/feePGjSogIED7ec2aNQqAKikpuWNuhg8frr7++mtlNpvV559/fs99KqWUzWZTqampCoCyWCxqxIgRavny5eratWtO79WuXbsG96OUUoWFhQqAunHjhlLq/+cnLy/PabtZs2apuLi4++6vXv1+rly50uB+6nv+e1779Onj9PuaTCaVm5urlGrccUJERHQ3PNNNRESiDRgwACUlJSgpKcG+ffuQlJSEIUOG4OTJkwCA0tJSHD16FL6+vjCbzTCbzfD390dtbS0qKiq0/cTExMDDw0P7OTg4WLtEuqSkBB4eHujXr1+DY+nRo4fT6wE4XWYdFhYGX19f7eeOHTsiOjoaRqPR6bF/vmb79u0YNGgQQkJC4Ovri3HjxuHSpUuoqanRtvHy8nJ673p79+7FqFGjkJOTc9+zyB4eHlizZg3OnDmDzMxMhISEYMGCBdqZ6IYUFxfj2WefRZcuXeDr66vN078vc09ISGhwP80pJibmjnmNjY3Vfvbw8EBAQIA21409ToiIiP6Ni24iIhLNZDIhIiICEREReOyxx7Bq1SpUV1cjOzsbwO1Lznv37q0tzOv/HTlyBGPHjtX206pVK6f9GgwG7YZkPj4+jRrLP/dhMBgAwOmmZnd7j4be98SJExg6dCh69OiBTZs2obi4GB999BEAOF3y7OPjo73fP3Xt2hWRkZH45JNPnL6f3pCQkBCMGzcOH374IcrKylBbW4uPP/74nttXV1cjKSkJbdu2xYYNG1BYWIhvvvnmjjECt1u1lKbOdWOPEyIion/zdPUAiIiIWpLBYIDRaMTNmzcBAL169cIXX3yBoKAgtG3b9j/tMzY2Fg6HA7t378ZTTz3VnMNtUHFxMRwOB5YsWaKdtf3yyy8b/foOHTrg66+/Rv/+/TF69Gh8+eWXdyw8G+Ln54fg4GBUV1cDwF3vZH748GFcunQJixYtQufOnQEARUVFjdq/HndG/6+a4zghIqKHE890ExGRaFarFefPn8f58+dx6NAhvPHGG6iqqsKzzz4LAHjhhRfQoUMHDB8+HD/99BOOHz+OXbt24c0339RuonU/YWFhmDBhAiZOnIi8vDxtH01ZAP8XERERqKurw/Lly3Hs2DHk5OQ0eNb5boKCgpCfn4/Dhw9jzJgx9/xTXStXrsTkyZPx448/oqKiAmVlZUhPT0dZWZk2l2FhYaiqqsKOHTtw8eJF1NTUoEuXLvDy8tLG+N1332Hu3LmNGltYWBiOHz+OkpISXLx4EVartUm/W3NqjuOEiIgeTlx0ExGRaFu3bkVwcDCCg4PxxBNPoLCwEBs3bkT//v0BAG3atEFBQQG6dOmC5557DlFRUXj55ZdRW1vbpDOaK1aswMiRI5GWlobIyEi8+uqr2hlgvcTFxWHp0qXIyMhA9+7dsWHDBixcuLDJ+7FYLMjPz8eBAwfwwgsv3PXs8uOPP46qqipMmjQJMTEx6NevH3777Tfk5eVp39FOTEzEpEmTkJKSgsDAQGRmZiIwMBBr167Fxo0bER0djUWLFmHx4sWNGtfzzz+P5ORkDBgwAIGBgcjNzW3y79Zcmus4ISKih49BKaVcPQgiIiIiIiIiiXimm4iIiIiIiEgnXHQTERERERER6YSLbiIiIiIiIiKdcNFNREREREREpBMuuomIiIiIiIh0wkU3ERERERERkU646CYiIiIiIiLSCRfdRERERERERDrhopuIiIiIiIhIJ1x0ExEREREREemEi24iIiIiIiIinXDRTURERERERKST/wOnUh6OKm+9ZgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Convert 'benchmark_start_time' to datetime if it's not already\n",
-    "df['benchmark_start_time'] = pd.to_datetime(df['benchmark_start_time'])\n",
-    "\n",
-    "# Filter out the agents to ignore\n",
-    "filtered_df = df[~df['agent'].isin(agents_to_ignore)]\n",
-    "\n",
-    "# Group by 'benchmark_start_time', 'agent', and 'challenge', then count unique job_ids\n",
-    "grouped_df = filtered_df.groupby(['benchmark_start_time', 'agent', 'challenge'])['job_id'].nunique().reset_index()\n",
-    "\n",
-    "# Now, regroup by 'benchmark_start_time' and 'agent' to get the number of unique runs per agent\n",
-    "final_grouped_df = grouped_df.groupby(['benchmark_start_time', 'agent']).size().reset_index(name='unique_runs')\n",
-    "\n",
-    "# Create a single plot for all agents\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "for agent in final_grouped_df['agent'].unique():\n",
-    "    agent_data = final_grouped_df[final_grouped_df['agent'] == agent]\n",
-    "    plt.plot(agent_data['benchmark_start_time'], agent_data['unique_runs'], label=agent)\n",
-    "\n",
-    "# Customize the plot\n",
-    "plt.xlabel('Benchmark Start Time')\n",
-    "plt.ylabel('Number of Challenges Run')\n",
-    "plt.title('Agent Benchmark Runs Over Time')\n",
-    "plt.legend()\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Convert 'success' column to boolean if it's not\n",
-    "df['success'] = df['success'].astype(bool)\n",
-    "\n",
-    "# Convert 'benchmark_start_time' to datetime if it's not\n",
-    "df['benchmark_start_time'] = pd.to_datetime(df['benchmark_start_time'])\n",
-    "\n",
-    "filtered_df = df[~df['agent'].isin(agents_to_ignore)]\n",
-    "\n",
-    "# Filter the DataFrame for a specific challenge\n",
-    "specific_challenge = 'TestRememberMultipleIds'  # Replace with the challenge you're interested in\n",
-    "filtered_df = filtered_df[filtered_df['challenge'] == specific_challenge]\n",
-    "\n",
-    "# Group by 'benchmark_start_time', 'agent', and 'success', then count occurrences\n",
-    "count_by_agent_over_time = filtered_df.groupby(['benchmark_start_time', 'agent', 'success']).size().reset_index(name='count')\n",
-    "\n",
-    "# Create a list of unique agents\n",
-    "unique_agents = count_by_agent_over_time['agent'].unique()\n",
-    "\n",
-    "# Plotting\n",
-    "plt.figure(figsize=(15, 8))\n",
-    "\n",
-    "# Plot each agent\n",
-    "for agent in unique_agents:\n",
-    "    subset_df = count_by_agent_over_time[(count_by_agent_over_time['agent'] == agent)]\n",
-    "    \n",
-    "    # Plot successes and failures separately\n",
-    "    for success in [True]:\n",
-    "        subsubset_df = subset_df[subset_df['success'] == success]\n",
-    "        plt.plot(subsubset_df['benchmark_start_time'], subsubset_df['count'], marker='o', linestyle='-', label=f\"{agent} (Success: {success})\")\n",
-    "\n",
-    "plt.title(f'Success/Failure Count by Agent Over Time for Challenge: {specific_challenge}')\n",
-    "plt.xlabel('Benchmark Start Time')\n",
-    "plt.ylabel('Count')\n",
-    "plt.grid(True)\n",
-    "plt.legend(title='Agents')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from collections import OrderedDict\n",
-    "\n",
-    "\n",
-    "# Convert the JSON-like strings in the 'response' column to nested dictionaries\n",
-    "def nested_json(x):\n",
-    "    if pd.notna(x):\n",
-    "        d = json.loads(x)  # Convert the string to a dict\n",
-    "        if \"content\" in d and isinstance(d[\"content\"], str):\n",
-    "            try:\n",
-    "                d[\"content\"] = json.loads(d[\"content\"])  # Try converting 'content' to a dict\n",
-    "            except json.JSONDecodeError:\n",
-    "                pass  # If it's not JSON, leave it as is\n",
-    "        return d\n",
-    "    return x\n",
-    "\n",
-    "\n",
-    "# Initialize an empty dictionary\n",
-    "response_dict = OrderedDict()\n",
-    "response_nested_dict = OrderedDict()\n",
-    "\n",
-    "# Get the total number of rows\n",
-    "total_rows = len(selected_df)\n",
-    "\n",
-    "# Loop over the DataFrame's index and rows\n",
-    "for i, (_, row) in enumerate(selected_df.iterrows()):\n",
-    "    # Convert the JSON-like strings in the 'response' to nested dictionaries\n",
-    "    response = json.loads(row['response'])\n",
-    "    response_nested = nested_json(row['response'])\n",
-    "    \n",
-    "    # Insert the record into the dictionary with the row number as the key\n",
-    "    response_dict[str(total_rows - i)] = response\n",
-    "    response_nested_dict[str(total_rows - i)] = response_nested\n",
-    "    \n",
-    "# Reverse the order of items in the OrderedDict\n",
-    "reversed_response_dict = OrderedDict(reversed(list(response_dict.items())))\n",
-    "reversed_response_nested_dict = OrderedDict(reversed(list(response_nested_dict.items())))\n",
-    "\n",
-    "# Write the dictionary to a JSON file\n",
-    "with open('selected_logs.json', 'w') as f:\n",
-    "    json.dump(reversed_response_dict, f, indent=4)\n",
-    "    \n",
-    "# Write the dictionary to a JSON file\n",
-    "with open('selected_logs_nested.json', 'w') as f:\n",
-    "    json.dump(reversed_response_nested_dict, f, indent=4)\n",
-    "\n",
-    "# # Show the 'response' column\n",
-    "# print(response_column.head(5))"
-   ]
-  },
-  {
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/benchmark/notebooks/selected_logs.json b/benchmark/notebooks/selected_logs.json
deleted file mode 100644
index aa9fbd7c86..0000000000
--- a/benchmark/notebooks/selected_logs.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-    "1": {
-        "role": "assistant",
-        "content": "{\n  \"thoughts\": {\n    \"text\": \"I need to find Tesla's revenue in 2022.\",\n    \"reasoning\": \"I will perform a web search to find the information.\",\n    \"plan\": \"- Perform a web search\\n- Extract the revenue\\n- Write the revenue to a file\",\n    \"criticism\": \"I should be cautious about the reliability of the source I find.\",\n    \"speak\": \"I will search the web for Tesla's revenue in 2022.\"\n  },\n  \"command\": {\n    \"name\": \"web_search\",\n    \"args\": {\n      \"query\": \"Tesla revenue in 2022\"\n    }\n  }\n}"
-    },
-    "2": {
-        "role": "assistant",
-        "content": "{\n  \"thoughts\": {\n    \"text\": \"I found a reliable source for Tesla's revenue in 2022.\",\n    \"reasoning\": \"I will use the Statista source to extract the revenue information.\",\n    \"plan\": \"- Browse the Statista website\\n- Extract the revenue\\n- Write the revenue to a file\",\n    \"criticism\": \"I should verify the information on the Statista website.\",\n    \"speak\": \"I will browse the Statista website to find Tesla's revenue in 2022.\"\n  },\n  \"command\": {\n    \"name\": \"browse_website\",\n    \"args\": {\n      \"url\": \"https://www.statista.com/statistics/272120/revenue-of-tesla/\",\n      \"question\": \"What was Tesla's revenue in 2022?\"\n    }\n  }\n}"
-    },
-    "3": {
-        "role": "assistant",
-        "content": "Tesla's revenue in 2022 was nearly $81.5 billion, which represents a 51 percent increase from the previous year. The majority of the revenue, over $71 billion, was generated from Tesla's automotive segment. The Model Y emerged as Tesla's best-selling vehicle, and the company has been investing in the production of the Model 3 and Model Y."
-    },
-    "4": {
-        "role": "assistant",
-        "content": "The text provides various statistics and financial information about Tesla, including its revenue from 2008 to 2022. However, the specific revenue for 2022 is not mentioned in the text."
-    },
-    "5": {
-        "role": "assistant",
-        "content": "a description of different types of cookies used on a website and the options for accepting or rejecting them. There is no information provided about Tesla's revenue in 2022."
-    },
-    "6": {
-        "role": "assistant",
-        "content": "Tesla's revenue in 2022 increased by 51% to nearly $81.5 billion, with the majority coming from the automotive segment. The Model Y was the best-selling vehicle, and the company has been focusing on producing the Model 3 and Model Y."
-    },
-    "7": {
-        "role": "assistant",
-        "content": "{\n  \"thoughts\": {\n    \"text\": \"I found Tesla's revenue in 2022.\",\n    \"reasoning\": \"I will write the revenue to a file.\",\n    \"plan\": \"- Write the revenue to a file\",\n    \"criticism\": \"I should ensure the revenue is formatted correctly.\",\n    \"speak\": \"I will write Tesla's revenue in 2022 to a file.\"\n  },\n  \"command\": {\n    \"name\": \"write_to_file\",\n    \"args\": {\n      \"filename\": \"tesla_revenue_2022.txt\",\n      \"text\": \"Tesla's revenue in 2022 was $81,500,000,000.\"\n    }\n  }\n}"
-    }
-}
\ No newline at end of file
diff --git a/benchmark/notebooks/selected_logs_nested.json b/benchmark/notebooks/selected_logs_nested.json
deleted file mode 100644
index a917c90095..0000000000
--- a/benchmark/notebooks/selected_logs_nested.json
+++ /dev/null
@@ -1,74 +0,0 @@
-{
-    "1": {
-        "role": "assistant",
-        "content": {
-            "thoughts": {
-                "text": "I need to find Tesla's revenue in 2022.",
-                "reasoning": "I will perform a web search to find the information.",
-                "plan": "- Perform a web search\n- Extract the revenue\n- Write the revenue to a file",
-                "criticism": "I should be cautious about the reliability of the source I find.",
-                "speak": "I will search the web for Tesla's revenue in 2022."
-            },
-            "command": {
-                "name": "web_search",
-                "args": {
-                    "query": "Tesla revenue in 2022"
-                }
-            }
-        }
-    },
-    "2": {
-        "role": "assistant",
-        "content": {
-            "thoughts": {
-                "text": "I found a reliable source for Tesla's revenue in 2022.",
-                "reasoning": "I will use the Statista source to extract the revenue information.",
-                "plan": "- Browse the Statista website\n- Extract the revenue\n- Write the revenue to a file",
-                "criticism": "I should verify the information on the Statista website.",
-                "speak": "I will browse the Statista website to find Tesla's revenue in 2022."
-            },
-            "command": {
-                "name": "browse_website",
-                "args": {
-                    "url": "https://www.statista.com/statistics/272120/revenue-of-tesla/",
-                    "question": "What was Tesla's revenue in 2022?"
-                }
-            }
-        }
-    },
-    "3": {
-        "role": "assistant",
-        "content": "Tesla's revenue in 2022 was nearly $81.5 billion, which represents a 51 percent increase from the previous year. The majority of the revenue, over $71 billion, was generated from Tesla's automotive segment. The Model Y emerged as Tesla's best-selling vehicle, and the company has been investing in the production of the Model 3 and Model Y."
-    },
-    "4": {
-        "role": "assistant",
-        "content": "The text provides various statistics and financial information about Tesla, including its revenue from 2008 to 2022. However, the specific revenue for 2022 is not mentioned in the text."
-    },
-    "5": {
-        "role": "assistant",
-        "content": "a description of different types of cookies used on a website and the options for accepting or rejecting them. There is no information provided about Tesla's revenue in 2022."
-    },
-    "6": {
-        "role": "assistant",
-        "content": "Tesla's revenue in 2022 increased by 51% to nearly $81.5 billion, with the majority coming from the automotive segment. The Model Y was the best-selling vehicle, and the company has been focusing on producing the Model 3 and Model Y."
-    },
-    "7": {
-        "role": "assistant",
-        "content": {
-            "thoughts": {
-                "text": "I found Tesla's revenue in 2022.",
-                "reasoning": "I will write the revenue to a file.",
-                "plan": "- Write the revenue to a file",
-                "criticism": "I should ensure the revenue is formatted correctly.",
-                "speak": "I will write Tesla's revenue in 2022 to a file."
-            },
-            "command": {
-                "name": "write_to_file",
-                "args": {
-                    "filename": "tesla_revenue_2022.txt",
-                    "text": "Tesla's revenue in 2022 was $81,500,000,000."
-                }
-            }
-        }
-    }
-}
\ No newline at end of file