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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "-pftZ-ZF1_BA"
},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NPUGFpKP1_BH"
},
"source": [
"# Demonstrating the `AgentEval` framework using the task of solving math problems as an example\n",
"\n",
"This notebook aims to demonstrate how to `AgentEval` implemented through [AutoGen](https://github.com/microsoft/autogen) works in an offline scenario, where we use a math problem-solving task as an example. \n",
"`AgentEval` consists of two key steps:\n",
"\n",
"- `generate_criteria`: This is an LLM-based function that generates a list of criteria $(c_1, \\dots, c_n)$ to help to evaluate a utility given task.\n",
"\n",
"- `quantify_criteria`: This function quantifies the performance of any sample task based on the criteria generated in the `generate_criteria` step in the following way: $(c_1=a_1, \\dots, c_n=a_n)$\n",
"\n",
"![AgentEval](../website/blog/2023-11-20-AgentEval/img/agenteval-CQ.png)\n",
"\n",
"For more detailed explanations, please refer to the accompanying [blog post](https://microsoft.github.io/autogen/blog/2023/11/20/AgentEval)\n",
"\n",
"## Requirements\n",
"\n",
"AutoGen requires `Python>=3.8`. To run this notebook example, please install autogen-agentchat, Docker, and OpenAI:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2023-02-13T23:40:52.317406Z",
"iopub.status.busy": "2023-02-13T23:40:52.316561Z",
"iopub.status.idle": "2023-02-13T23:40:52.321193Z",
"shell.execute_reply": "2023-02-13T23:40:52.320628Z"
},
"id": "68lTZZyJ1_BI",
"outputId": "15a55fab-e13a-4654-b8cb-ae117478d6d8"
},
"outputs": [],
"source": [
"%pip install \"autogen-agentchat~=0.2\" docker\n",
"%pip install scipy\n",
"%pip install matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HxgqKJrd1_BJ"
},
"source": [
"## Set your API Endpoint\n",
"* The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file. It first looks for an environment variable with a specified name. The value of the environment variable needs to be a valid json string. If that variable is not found, it looks for a json file with the same name. It filters the configs by filter_dict.\n",
"\n",
"You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/oai_openai_utils.ipynb) for full code examples of the different methods.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "YRycFEDJ1_BJ"
},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"\n",
"import autogen\n",
"from autogen.agentchat.contrib.agent_eval.agent_eval import generate_criteria, quantify_criteria\n",
"from autogen.agentchat.contrib.agent_eval.criterion import Criterion\n",
"from autogen.agentchat.contrib.agent_eval.task import Task\n",
"\n",
"config_list = autogen.config_list_from_json(\"OAI_CONFIG_LIST\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6vPTtNkhk2V1"
},
"source": [
"# Run the Critic\n",
"\n",
"To run the critic, we need a couple of math problem examples. One of them failed to solve the problem successfully, given in `agenteval-in-out/response_failed.txt`, and the other one was solved successfully, i.e., `agenteval-in-out/response_successful.txt`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "5H1WRs_wkiK0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mcritic_user\u001b[0m (to chat_manager):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" \n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mcritic\u001b[0m (to chat_manager):\n",
"\n",
"[\n",
" {\n",
" \"name\": \"Accuracy\",\n",
" \"description\": \"The solution must be correct and adhere strictly to mathematical principles and techniques appropriate for the problem.\",\n",
" \"accepted_values\": [\"Correct\", \"Minor errors\", \"Major errors\", \"Incorrect\"]\n",
" },\n",
" {\n",
" \"name\": \"Conciseness\",\n",
" \"description\": \"The explanation and method provided should be direct and to the point, avoiding unnecessary steps or complexity.\",\n",
" \"accepted_values\": [\"Very concise\", \"Concise\", \"Somewhat verbose\", \"Verbose\"]\n",
" },\n",
" {\n",
" \"name\": \"Relevance\",\n",
" \"description\": \"The content of the response must be relevant to the question posed and should address the specific problem requirements.\",\n",
" \"accepted_values\": [\"Highly relevant\", \"Relevant\", \"Somewhat relevant\", \"Not relevant\"]\n",
" },\n",
" {\n",
" \"name\": \"Efficiency\",\n",
" \"description\": \"The solution should be derived in a time-effective manner, considering the complexity of the problem.\",\n",
" \"accepted_values\": [\"Highly efficient\", \"Efficient\", \"Inefficient\", \"Redundant\"]\n",
" },\n",
" {\n",
" \"name\": \"Logic and Structure\",\n",
" \"description\": \"The reasoning should be logical and the information structured in a clear and understandable sequence.\",\n",
" \"accepted_values\": [\"Exceptionally clear\", \"Clear\", \"Somewhat clear\", \"Confusing\"]\n",
" },\n",
" {\n",
" \"name\": \"Use of Resources\",\n",
" \"description\": \"The response should make appropriate and optimal use of external resources or tools (e.g., Python scripts) when necessary.\",\n",
" \"accepted_values\": [\"Optimal\", \"Appropriate\", \"Underutilized\", \"Overreliance\"]\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Notation\",\n",
" \"description\": \"The use of proper and standard mathematical notation in the solution and explanation.\",\n",
" \"accepted_values\": [\"Excellent\", \"Good\", \"Adequate\", \"Poor\"]\n",
" },\n",
" {\n",
" \"name\": \"Explanation and Justification\",\n",
" \"description\": \"There should be a clear explanation, rationale, or justification for each step taken towards the solution.\",\n",
" \"accepted_values\": [\"Thorough\", \"Adequate\", \"Insufficient\", \"Missing\"]\n",
" },\n",
" {\n",
" \"name\": \"Correctness of Answer Format\",\n",
" \"description\": \"The answer should be presented in the format requested in the problem (e.g., interval notation, simplified form).\",\n",
" \"accepted_values\": [\"Perfectly formatted\", \"Properly formatted\", \"Slightly incorrect format\", \"Improperly formatted\"]\n",
" },\n",
" {\n",
" \"name\": \"Handling of Edge Cases\",\n",
" \"description\": \"The solution should correctly handle any special or edge cases that may arise in the problem.\",\n",
" \"accepted_values\": [\"Complete\", \"Most cases\", \"Some cases\", \"No consideration\"]\n",
" }\n",
"]\n",
"\n",
"--------------------------------------------------------------------------------\n"
]
}
],
"source": [
"def remove_ground_truth(test_case):\n",
" test_details = json.loads(test_case)\n",
" # need to remove the ground truth from the test details\n",
" correctness = test_details.pop(\"is_correct\", None)\n",
" test_details.pop(\"correct_ans\", None)\n",
" test_details.pop(\"check_result\", None)\n",
" return str(test_details), correctness\n",
"\n",
"\n",
"# Reading one successful and one failed example of the task\n",
"success_str = open(\"../test/test_files/agenteval-in-out/samples/sample_math_response_successful.txt\", \"r\").read()\n",
"response_successful = remove_ground_truth(success_str)[0]\n",
"failed_str = open(\"../test/test_files/agenteval-in-out/samples/sample_math_response_failed.txt\", \"r\").read()\n",
"response_failed = remove_ground_truth(failed_str)[0]\n",
"\n",
"task = Task(\n",
" **{\n",
" \"name\": \"Math problem solving\",\n",
" \"description\": \"Given any question, the system needs to solve the problem as consisely and accurately as possible\",\n",
" \"successful_response\": response_successful,\n",
" \"failed_response\": response_failed,\n",
" }\n",
")\n",
"\n",
"criteria = generate_criteria(task=task, llm_config={\"config_list\": config_list}, max_round=8)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vu70o024lenI"
},
"source": [
"# The Criteria\n",
"Now, we print the designed criteria for assessing math problems. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k9DsDB5hqvtG",
"outputId": "0edd7a0c-b031-4f67-efc6-1a1e77066921"
},
"outputs": [],
"source": [
"current_task_name = \"_\".join(task.name.split()).lower()\n",
"cr_file = open(f\"../test/test_files/agenteval-in-out/{current_task_name}_criteria.json\", \"w\")\n",
"cr_file.write(Criterion.write_json(criteria))\n",
"cr_file.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PETPZluOEGCR"
},
"source": [
"*Note :* You can also define and use your own criteria in order to feed into the quantifier."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SmpUZv_ylo9U"
},
"source": [
"# The `QuantifierAgent`\n",
"\n",
"Once we have the criteria, we need to quantify a new sample based on the designed criteria and its accepted values. This will be done through `quantify_criteria` from agent_eval. \n",
"Again, you can use your own defined criteria in `criteria_file`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "4uUkZJh_subA"
},
"outputs": [],
"source": [
"criteria_file = f\"../test/test_files/agenteval-in-out/{current_task_name}_criteria.json\"\n",
"criteria = open(criteria_file, \"r\").read()\n",
"criteria = Criterion.parse_json_str(criteria)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "64rRJfB2l6lO"
},
"source": [
"## Running the quantifier on a single test case"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we run the quantifier on a single math problem test case, `sample_test_case.json`, for demonstration."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Pf623aNbHZTG",
"outputId": "0031871b-a438-43f5-d2b2-c99fa1ad0dbd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Accuracy\",\n",
" \"description\": \"The solution must be correct and adhere strictly to mathematical principles and techniques appropriate for the problem.\",\n",
" \"accepted_values\": [\n",
" \"Correct\",\n",
" \"Minor errors\",\n",
" \"Major errors\",\n",
" \"Incorrect\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Conciseness\",\n",
" \"description\": \"The explanation and method provided should be direct and to the point, avoiding unnecessary steps or complexity.\",\n",
" \"accepted_values\": [\n",
" \"Very concise\",\n",
" \"Concise\",\n",
" \"Somewhat verbose\",\n",
" \"Verbose\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Relevance\",\n",
" \"description\": \"The content of the response must be relevant to the question posed and should address the specific problem requirements.\",\n",
" \"accepted_values\": [\n",
" \"Highly relevant\",\n",
" \"Relevant\",\n",
" \"Somewhat relevant\",\n",
" \"Not relevant\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Efficiency\",\n",
" \"description\": \"The solution should be derived in a time-effective manner, considering the complexity of the problem.\",\n",
" \"accepted_values\": [\n",
" \"Highly efficient\",\n",
" \"Efficient\",\n",
" \"Inefficient\",\n",
" \"Redundant\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Logic and Structure\",\n",
" \"description\": \"The reasoning should be logical and the information structured in a clear and understandable sequence.\",\n",
" \"accepted_values\": [\n",
" \"Exceptionally clear\",\n",
" \"Clear\",\n",
" \"Somewhat clear\",\n",
" \"Confusing\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Use of Resources\",\n",
" \"description\": \"The response should make appropriate and optimal use of external resources or tools (e.g., Python scripts) when necessary.\",\n",
" \"accepted_values\": [\n",
" \"Optimal\",\n",
" \"Appropriate\",\n",
" \"Underutilized\",\n",
" \"Overreliance\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Notation\",\n",
" \"description\": \"The use of proper and standard mathematical notation in the solution and explanation.\",\n",
" \"accepted_values\": [\n",
" \"Excellent\",\n",
" \"Good\",\n",
" \"Adequate\",\n",
" \"Poor\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation and Justification\",\n",
" \"description\": \"There should be a clear explanation, rationale, or justification for each step taken towards the solution.\",\n",
" \"accepted_values\": [\n",
" \"Thorough\",\n",
" \"Adequate\",\n",
" \"Insufficient\",\n",
" \"Missing\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Correctness of Answer Format\",\n",
" \"description\": \"The answer should be presented in the format requested in the problem (e.g., interval notation, simplified form).\",\n",
" \"accepted_values\": [\n",
" \"Perfectly formatted\",\n",
" \"Properly formatted\",\n",
" \"Slightly incorrect format\",\n",
" \"Improperly formatted\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Handling of Edge Cases\",\n",
" \"description\": \"The solution should correctly handle any special or edge cases that may arise in the problem.\",\n",
" \"accepted_values\": [\n",
" \"Complete\",\n",
" \"Most cases\",\n",
" \"Some cases\",\n",
" \"No consideration\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Find $24^{-1} \\\\pmod{11^2}$. That is, find the residue $b$ for which $24b \\\\equiv 1\\\\pmod{11^2}$.\\n\\nExpress your answer as an integer from $0$ to $11^2-1$, inclusive.', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Since $5 \\\\times 24 = 120 = 121 - 1$, it follows that $-5 \\\\times 24 \\\\equiv 1 \\\\pmod{121}$. Adding 121 to $-5$ to make it positive, we find $(-5 + 121) \\\\times 24 \\\\equiv 116 \\\\times 24 \\\\equiv 1 \\\\pmod{121}$, so it follows that the modular inverse of $24$ is $\\\\boxed{116}$ when taken modulo $121$.', 'problem_id': '5', 'response_with_ans': 'To find the modular inverse of 24 modulo 11^2, we can use the Extended Euclidean Algorithm. Here is a Python function to compute the modular inverse using this algorithm:\\n\\n```python\\ndef mod_inverse(a, m):\\n g, x, _ = extended_gcd(a, m)\\n if g != 1:\\n raise Exception(f\"{a} and {m} are not coprime.\")\\n return x % m\\n\\ndef extended_gcd(a, b):\\n if a == 0:\\n return b, 0, 1\\n else:\\n g, x, y = extended_gcd(b % a, a)\\n return g, y - (b // a) * x, x\\n```\\n\\nLet\\'s use the above function to find the modular inverse of 24 modulo 11^2:\\n\\n```python\\na = 24\\nm = 11**2\\nmod_inverse(a, m)\\n```\\n\\nI will execute the above code to find the modular inverse of 24 modulo 11^2.', 'round': 0, 'messages': [{'content': 'Find $24^{-1} \\\\pmod{11^2}$. That is, find the residue $b$ for which $24b \\\\equiv 1\\\\pmod{11^2}$.\\n\\nExpress your answer as an integer from $0$ to $11^2-1$, inclusive.', 'role': 'user'}, {'content': 'To find the modular inverse of 24 modulo 11^2, we can use the Extended Euclidean Algorithm. Here is a Python function to compute the modular inverse using this algorithm:\\n\\n```python\\ndef mod_inverse(a, m):\\n g, x, _ = extended_gcd(a, m)\\n if g != 1:\\n raise Exception(f\"{a} and {m} are not coprime.\")\\n return x % m\\n\\ndef extended_gcd(a, b):\\n if a == 0:\\n return b, 0, 1\\n else:\\n g, x, y = extended_gcd(b % a, a)\\n return g, y - (b // a) * x, x\\n```\\n\\nLet\\'s use the above function to find the modular inverse of 24 modulo 11^2:\\n\\n```python\\na = 24\\nm = 11**2\\nmod_inverse(a, m)\\n```\\n\\nI will execute the above code to find the modular inverse of 24 modulo 11^2.', 'role': 'assistant'}], 'time': 13.481226921081543, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Accuracy\": \"Correct\",\n",
" \"Conciseness\": \"Concise\",\n",
" \"Relevance\": \"Highly relevant\",\n",
" \"Efficiency\": \"Efficient\",\n",
" \"Logic and Structure\": \"Clear\",\n",
" \"Use of Resources\": \"Optimal\",\n",
" \"Mathematical Notation\": \"Good\",\n",
" \"Explanation and Justification\": \"Adequate\",\n",
" \"Correctness of Answer Format\": \"Perfectly formatted\",\n",
" \"Handling of Edge Cases\": \"Complete\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"actual correctness: True\n",
"predicted correctness:\n",
" {\n",
" \"Accuracy\": \"Correct\",\n",
" \"Conciseness\": \"Concise\",\n",
" \"Relevance\": \"Highly relevant\",\n",
" \"Efficiency\": \"Efficient\",\n",
" \"Logic and Structure\": \"Clear\",\n",
" \"Use of Resources\": \"Optimal\",\n",
" \"Mathematical Notation\": \"Good\",\n",
" \"Explanation and Justification\": \"Adequate\",\n",
" \"Correctness of Answer Format\": \"Perfectly formatted\",\n",
" \"Handling of Edge Cases\": \"Complete\"\n",
"}\n"
]
}
],
"source": [
"test_case = open(\"../test/test_files/agenteval-in-out/samples/sample_test_case.json\", \"r\").read()\n",
"test_case, ground_truth = remove_ground_truth(test_case)\n",
"quantifier_output = quantify_criteria(\n",
" llm_config={\"config_list\": config_list},\n",
" criteria=criteria,\n",
" task=task,\n",
" test_case=test_case,\n",
" ground_truth=ground_truth,\n",
")\n",
"print(\"actual correctness:\", quantifier_output[\"actual_success\"])\n",
"print(\"predicted correctness:\\n\", quantifier_output[\"estimated_performance\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2VtdM44WEGCS"
},
"source": [
"# Run `AgentEval` on the logs\n",
"\n",
"In the example below, log_path points to the sample logs folder to run the quantifier. The current sample belongs to the prealgebra category which will be downloaded from [here](https://github.com/julianakiseleva/autogen/tree/agenteval/test/test_files/agenteval-in-out/samples).\n",
"In case you want to replicate the results described in the blog post, you can download all the logs for math problems using the following [link](https://github.com/julianakiseleva/autogen/tree/agenteval/model-logs/math-problems/agentchat). "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2024-05-08 17:42:25-- https://github.com/julianakiseleva/autogen/raw/ddabd4f0e7c13a50e33cf8462e79358666371477/test/test_files/agenteval-in-out/prealgebra.zip\n",
"Resolving github.com (github.com)... 140.82.116.3\n",
"Connecting to github.com (github.com)|140.82.116.3|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://raw.githubusercontent.com/julianakiseleva/autogen/ddabd4f0e7c13a50e33cf8462e79358666371477/test/test_files/agenteval-in-out/prealgebra.zip [following]\n",
"--2024-05-08 17:42:25-- https://raw.githubusercontent.com/julianakiseleva/autogen/ddabd4f0e7c13a50e33cf8462e79358666371477/test/test_files/agenteval-in-out/prealgebra.zip\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 28567 (28K) [application/zip]\n",
"Saving to: ‘prealgebra.zip’\n",
"\n",
"prealgebra.zip 100%[===================>] 27.90K --.-KB/s in 0s \n",
"\n",
"2024-05-08 17:42:25 (63.0 MB/s) - ‘prealgebra.zip’ saved [28567/28567]\n",
"\n",
"Archive: prealgebra.zip\n",
"warning: skipped \"../\" path component(s) in ../prealgebra/\n",
"warning: skipped \"../\" path component(s) in ../prealgebra/9.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/9.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/16.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/16.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/8.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/8.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/15.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/15.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/6.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/6.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/3.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/3.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/4.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/4.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/18.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/18.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/1.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/1.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/14.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/14.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/2.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/2.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/10.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/10.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/7.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/7.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/log.txt\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/log.txt \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/13.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/13.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/17.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/17.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/11.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/11.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/12.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/12.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/0.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/0.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/19.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/19.json \n",
"warning: skipped \"../\" path component(s) in ../prealgebra/5.json\n",
" inflating: ../test/test_files/agenteval-in-out/agentchat_results/prealgebra/5.json \n"
]
}
],
"source": [
"# You can set your own log path - we also limited the number of samples to avoid additional costs.\n",
"# By removing the condition about limitations on the number of samples per category, you can run it on all 120 problems\n",
"\n",
"log_path = \"../test/test_files/agenteval-in-out/agentchat_results/\"\n",
"\n",
"# The file is no longer in the repo, we can download it from an older commit\n",
"!wget https://github.com/julianakiseleva/autogen/raw/ddabd4f0e7c13a50e33cf8462e79358666371477/test/test_files/agenteval-in-out/prealgebra.zip\n",
"!unzip -o prealgebra.zip -d {log_path}\n",
"!rm prealgebra.zip\n",
"\n",
"assert Path(log_path).exists(), f\"The log path '{log_path}' does not exist.\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dZdIbHPFEGCS",
"outputId": "83c0a51b-f184-494b-81a0-d4b4a3667319"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Bekah has exactly three brass house number digits: 2, 3 and 5. How many distinct numbers can she form using one or more of the digits?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'If Bekah uses only one digit, she can form three numbers. If she uses two digits, she has three choices for the tens place and two for the units, so she can form six numbers. Finally, if Bekah uses all three digits, she has three choices for the hundreds place, two for the tens, and one for the units, so she can form six numbers. Thus, Bekah can form $3 + 6 + 6 = \\\\boxed{15}$ distinct numbers.', 'problem_id': '11', 'response_with_ans': 'To find the answer, we\\'ll consider all possible combinations that Bekah can make using the three digits she has:\\n1. Single-digit numbers: 2, 3, and 5\\n2. Two-digits numbers using the digits: 23, 25, 32, 35, 52, and 53\\n3. Three-digits number using all the digits: 235, 253, 325, 352, 523, and 532\\n\\nLet\\'s calculate the number of distinct numbers Bekah can form using one or more of the digits with Python code:\\n\\n```python\\nfrom itertools import permutations\\n\\ndigits = [2, 3, 5]\\n\\n# Single-digit numbers\\none_digit_numbers = set(digits)\\n\\n# Two-digits numbers\\ntwo_digit_numbers = set(int(\\'\\'.join(map(str, p))) for p in permutations(digits, 2))\\n\\n# Three-digits numbers\\nthree_digit_numbers = set(int(\\'\\'.join(map(str, p))) for p in permutations(digits, 3))\\n\\ndistinct_numbers = one_digit_numbers | two_digit_numbers | three_digit_numbers\\ntotal_distinct_numbers = len(distinct_numbers)\\n\\nprint(\"Distinct numbers:\", distinct_numbers)\\nprint(\"Total distinct numbers:\", total_distinct_numbers)\\n```\\n\\nPlease run the above Python code to calculate the total number of distinct numbers Bekah can form using one or more of the digits.', 'round': 0, 'messages': [{'content': 'Bekah has exactly three brass house number digits: 2, 3 and 5. How many distinct numbers can she form using one or more of the digits?', 'role': 'user'}, {'content': 'To find the answer, we\\'ll consider all possible combinations that Bekah can make using the three digits she has:\\n1. Single-digit numbers: 2, 3, and 5\\n2. Two-digits numbers using the digits: 23, 25, 32, 35, 52, and 53\\n3. Three-digits number using all the digits: 235, 253, 325, 352, 523, and 532\\n\\nLet\\'s calculate the number of distinct numbers Bekah can form using one or more of the digits with Python code:\\n\\n```python\\nfrom itertools import permutations\\n\\ndigits = [2, 3, 5]\\n\\n# Single-digit numbers\\none_digit_numbers = set(digits)\\n\\n# Two-digits numbers\\ntwo_digit_numbers = set(int(\\'\\'.join(map(str, p))) for p in permutations(digits, 2))\\n\\n# Three-digits numbers\\nthree_digit_numbers = set(int(\\'\\'.join(map(str, p))) for p in permutations(digits, 3))\\n\\ndistinct_numbers = one_digit_numbers | two_digit_numbers | three_digit_numbers\\ntotal_distinct_numbers = len(distinct_numbers)\\n\\nprint(\"Distinct numbers:\", distinct_numbers)\\nprint(\"Total distinct numbers:\", total_distinct_numbers)\\n```\\n\\nPlease run the above Python code to calculate the total number of distinct numbers Bekah can form using one or more of the digits.', 'role': 'assistant'}], 'time': 15.620970249176025, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"very efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'What is $.0\\\\overline{3} \\\\div .\\\\overline{03}$? Express your answer as a mixed number.', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'It is almost always easier to use fractions than decimals when dividing. So the first task is to convert these repeating decimals to fractions. First, $.0\\\\overline{3}$: \\\\[\\n10 \\\\cdot .0\\\\overline{3} = .\\\\overline{3} = \\\\frac{1}{3}\\\\\\\\\\n\\\\Rightarrow .0\\\\overline{3} = \\\\frac{1}{3} \\\\div 10 = \\\\frac{1}{3} \\\\cdot \\\\frac{1}{10} = \\\\frac{1}{30}.\\n\\\\]Next, $.\\\\overline{03}$: \\\\[\\n99 \\\\cdot .\\\\overline{03} = (100-1) \\\\cdot .\\\\overline{03} = 3.\\\\overline{03} - .\\\\overline{03} = 3\\\\\\\\\\n\\\\Rightarrow .\\\\overline{03} = \\\\frac{3}{99} = \\\\frac{3}{3 \\\\cdot 33} = \\\\frac{1}{33}.\\n\\\\]We now have the tools to make our calculation: \\\\begin{align*}\\n.0\\\\overline{3} \\\\div .\\\\overline{03} &= \\\\frac{1}{30} \\\\div \\\\frac{1}{33}= \\\\frac{1}{30} \\\\cdot \\\\frac{33}{1}\\\\\\\\\\n&= \\\\frac{33}{30} = \\\\frac{3 \\\\cdot 11}{3 \\\\cdot 10} = \\\\frac{11}{10}\\\\\\\\\\n&= \\\\frac{10+1}{10} = \\\\boxed{1\\\\frac{1}{10}}.\\n\\\\end{align*}', 'problem_id': '15', 'response_with_ans': \"Now, let's simplify the fraction:\\n\\n$\\\\frac{33}{3} = 11$\\n\\nSo, the result of the division is:\\n\\n$.0\\\\overline{3} \\\\div .\\\\overline{03} = 11$\\n\\nSince there is no remainder or mixed number component, the final answer is 11.\\n\\nTERMINATE\", 'round': 3, 'messages': [{'content': 'What is $.0\\\\overline{3} \\\\div .\\\\overline{03}$? Express your answer as a mixed number.', 'role': 'user'}, {'content': \"To solve the problem, let's first convert the repeating decimal numbers into fractions.\\n\\n1. Convert $0.\\\\overline{3}$ to a fraction:\\n\\nLet x = $0.\\\\overline{3}$\\nThen, 10x = $3.\\\\overline{3}$\\nSubtracting the two equations, we get:\\n9x = 3\\nx = $\\\\frac{1}{3}$\\n\\nSo, $0.\\\\overline{3}$ can be written as $\\\\frac{1}{3}$.\\n\\n2. Convert $.\\\\overline{03}$ to a fraction:\\n\\nLet y = $.\\\\overline{03}$\\nThen, 100y = $3.\\\\overline{03}$\\nSubtracting the two equations, we get:\\n99y = 3\\ny = $\\\\frac{1}{33}$\\n\\nSo, $.\\\\overline{03}$ can be written as $\\\\frac{1}{33}$.\\n\\nNow that we have the fractions, we can compute: \\n$(\\\\frac{1}{3}) \\\\div (\\\\frac{1}{33})$\\nTo divide fractions, we multiply the first one by the reciprocal of the second one:\\n\\n$(\\\\frac{1}{3}) \\\\times (\\\\frac{33}{1})$\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Let's compute the product of the fractions:\\n\\n$\\\\frac{1}{3} \\\\times \\\\frac{33}{1} = \\\\frac{1 \\\\times 33}{3 \\\\times 1}$\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Now, let's solve the multiplication in the numerator and the denominator:\\n\\n$\\\\frac{1 \\\\times 33}{3 \\\\times 1} = \\\\frac{33}{3}$\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Now, let's simplify the fraction:\\n\\n$\\\\frac{33}{3} = 11$\\n\\nSo, the result of the division is:\\n\\n$.0\\\\overline{3} \\\\div .\\\\overline{03} = 11$\\n\\nSince there is no remainder or mixed number component, the final answer is 11.\\n\\nTERMINATE\", 'role': 'assistant'}], 'time': 34.40860724449158, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely incorrect\",\n",
" \"Explanation Clarity\": \"moderately clear\",\n",
" \"Code Efficiency\": \"not applicable\",\n",
" \"Code Correctness\": \"not applicable\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'How many integers $n$ satisfy both of the inequalities $4n + 3 < 25$ and $-7n + 5 < 24$?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Subtract 3 and divide by 4 on both sides of the first inequality to obtain \\\\begin{align*}\\n4n + 3 &< 25 \\\\\\\\\\n\\\\Rightarrow\\\\qquad 4n &< 22 \\\\\\\\\\n\\\\Rightarrow\\\\qquad n &< 5.5.\\n\\\\end{align*}Similarly, the second inequality yields \\\\begin{align*}\\n-7n + 5 &< 24 \\\\\\\\\\n\\\\Rightarrow\\\\qquad -7n &< 19 \\\\\\\\\\n\\\\Rightarrow\\\\qquad n &> -\\\\frac{19}{7}.\\n\\\\end{align*}Therefore, we are looking for all the integers between $-\\\\frac{19}{7}$ and $5.5$. Since $-\\\\frac{19}{7}$ is between $-3$ and $-2$ and the largest integer less than $5.5$ is 5, we need to count the number of integers between $-2$ and $5$, inclusive. There are $5$ positive integers, $2$ negative integers, and zero, so there are $\\\\boxed{8}$ integers that satisfy both $4n + 3 < 25$ and $-7n + 5 < 24$.', 'problem_id': '10', 'response_with_ans': 'Here\\'s the code to solve the inequalities and find the intersection of their solutions:\\n\\n```python\\nfrom sympy import symbols, Eq, solve\\n\\nn = symbols(\"n\")\\ninequality_1 = 4 * n + 3 < 25\\ninequality_2 = -7 * n + 5 < 24\\n\\nsolution_1 = solve(inequality_1, n)\\nsolution_2 = solve(inequality_2, n)\\n\\nintersection = (max(solution_1[0], solution_2[0]), min(solution_1[1], solution_2[1]))\\n\\nprint(f\"Solution to inequality 1: {solution_1}\")\\nprint(f\"Solution to inequality 2: {solution_2}\")\\nprint(f\"Intersection of solutions: {intersection}\")\\n```\\n\\nExecute this code, and let\\'s see the solutions for both inequalities and their intersection.', 'round': 1, 'messages': [{'content': 'How many integers $n$ satisfy both of the inequalities $4n + 3 < 25$ and $-7n + 5 < 24$?', 'role': 'user'}, {'content': \"To find the number of integers $n$ that satisfy both inequalities, we'll first solve each inequality individually, and then find the intersection of the solutions.\\n\\nStep 1: Solve the inequalities\\n1. $4n + 3 < 25$\\n2. $-7n + 5 < 24$\\n\\nStep 2: Find the intersection of the solutions\\n\\nStep 3: Count the number of integers in the intersection\\n\\nFirst, let's solve the inequalities using the python code.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'Here\\'s the code to solve the inequalities and find the intersection of their solutions:\\n\\n```python\\nfrom sympy import symbols, Eq, solve\\n\\nn = symbols(\"n\")\\ninequality_1 = 4 * n + 3 < 25\\ninequality_2 = -7 * n + 5 < 24\\n\\nsolution_1 = solve(inequality_1, n)\\nsolution_2 = solve(inequality_2, n)\\n\\nintersection = (max(solution_1[0], solution_2[0]), min(solution_1[1], solution_2[1]))\\n\\nprint(f\"Solution to inequality 1: {solution_1}\")\\nprint(f\"Solution to inequality 2: {solution_2}\")\\nprint(f\"Intersection of solutions: {intersection}\")\\n```\\n\\nExecute this code, and let\\'s see the solutions for both inequalities and their intersection.', 'role': 'assistant'}], 'time': 19.949471950531006, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'What is the sum of the lengths, in centimeters, of the two legs of a 30-60-90 right triangle, if the length of the hypotenuse is $2\\\\sqrt{6}$ centimeters?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'We know that the ratio of the lengths of the sides of a 30-60-90 triangle is $1:\\\\sqrt{3}:2$. We know that the length of the hypotenuse is $2\\\\sqrt{6}$ and the ratio of the length shortest leg to that of the hypotenuse is $1:2$. Therefore, the length of the shorter leg is $\\\\sqrt{6}$. Since the ratio of the shorter leg to the longer leg is $1:\\\\sqrt{3}$, the length of the longer leg is $\\\\sqrt{6} \\\\cdot \\\\sqrt{3} = 3\\\\sqrt{2}$. The sum of the lengths of these two legs is $\\\\boxed{\\\\sqrt{6} + 3\\\\sqrt{2}}$ centimeters.', 'problem_id': '17', 'response_with_ans': \"Let's solve for x and find the lengths of the legs.\\n\\n```python\\nimport math\\n\\nhypotenuse = 2 * math.sqrt(6)\\nx = hypotenuse / 2\\n\\nleg_1 = x\\nleg_2 = x * math.sqrt(3)\\nsum_of_legs = leg_1 + leg_2\\n\\nleg_1, leg_2, sum_of_legs\\n```\\n\\nNow, execute the above code to find the sum of the lengths of the two legs.\", 'round': 1, 'messages': [{'content': 'What is the sum of the lengths, in centimeters, of the two legs of a 30-60-90 right triangle, if the length of the hypotenuse is $2\\\\sqrt{6}$ centimeters?', 'role': 'user'}, {'content': \"To find the sum of the lengths of the two legs, let's first analyze a 30-60-90 right triangle. In a 30-60-90 triangle, the ratios of the side lengths are:\\n\\n1. Opposite the 30-degree angle: $x$\\n2. Opposite the 60-degree angle: $x\\\\sqrt{3}$\\n3. Hypotenuse (opposite the 90-degree angle): $2x$\\n\\nNow, we are given that the hypotenuse is $2\\\\sqrt{6}$ centimeters. We can set up the following equation and solve for $x$:\\n\\n$2x = 2\\\\sqrt{6}$\\n\\nNext, we will find the lengths of the legs and sum them up.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Let's solve for x and find the lengths of the legs.\\n\\n```python\\nimport math\\n\\nhypotenuse = 2 * math.sqrt(6)\\nx = hypotenuse / 2\\n\\nleg_1 = x\\nleg_2 = x * math.sqrt(3)\\nsum_of_legs = leg_1 + leg_2\\n\\nleg_1, leg_2, sum_of_legs\\n```\\n\\nNow, execute the above code to find the sum of the lengths of the two legs.\", 'role': 'assistant'}], 'time': 18.742590188980103, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"very efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'How many positive and negative integers is $12$ a multiple of?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'The number $12$ is a multiple of $-12, -6, -4, -3, -2, -1, 1, 2, 3, 4, 6,$ and $12,$ for a total of $\\\\boxed{12}$ integers.', 'problem_id': '1', 'response_with_ans': '```python\\nnum = 12\\ndivisors = [i for i in range(1, num + 1) if num % i == 0]\\n\\npositive_divisors_count = len(divisors)\\nnegative_divisors_count = positive_divisors_count\\ntotal_divisors_count = positive_divisors_count + negative_divisors_count\\n\\nprint(f\"Positive divisors of 12: {divisors}\")\\ndivisors_negative = [-x for x in divisors]\\nprint(f\"Negative divisors of 12: {divisors_negative}\")\\nprint(f\"Total divisors count: {total_divisors_count}\")\\n```\\n\\nExecute this code snippet to find the divisors of 12 and count how many positive and negative integers 12 is a multiple of.', 'round': 1, 'messages': [{'content': 'How many positive and negative integers is $12$ a multiple of?', 'role': 'user'}, {'content': \"To determine how many positive and negative integers 12 is a multiple of, we need to find the divisors of 12. Then we will double the number of divisors, as each positive divisor has a corresponding negative divisor.\\n\\nLet's use a Python code snippet to find all the divisors of 12 and count them.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\nnum = 12\\ndivisors = [i for i in range(1, num + 1) if num % i == 0]\\n\\npositive_divisors_count = len(divisors)\\nnegative_divisors_count = positive_divisors_count\\ntotal_divisors_count = positive_divisors_count + negative_divisors_count\\n\\nprint(f\"Positive divisors of 12: {divisors}\")\\ndivisors_negative = [-x for x in divisors]\\nprint(f\"Negative divisors of 12: {divisors_negative}\")\\nprint(f\"Total divisors count: {total_divisors_count}\")\\n```\\n\\nExecute this code snippet to find the divisors of 12 and count how many positive and negative integers 12 is a multiple of.', 'role': 'assistant'}], 'time': 17.360238790512085, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': \"Amaretta's birthday is July 27, and her brother Enzo's birthday is September 3. Every year, Amaretta and Enzo celebrate by eating cake every day from Amaretta's birthday through Enzo's birthday (including both birthdays). If they did this for the first time in 2008, how many cake-eating days will they have observed by the end of 2016?\", 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'There are $39$ cake-eating days each year: the last $5$ days of July, all $31$ days of August, and the first $3$ days of September.\\n\\nThere are $9$ years in the list $$2008,2009,2010,2011,2012,2013,2014,2015,2016.$$ Besides listing them out, we can also see this by subtracting $2007$ from each year, which gives us the list $1,2,3,4,5,6,7,8,9$ (which clearly has $9$ entries).\\n\\n$39$ cake-eating days each year for $9$ years make $39\\\\cdot 9 = \\\\boxed{351}$ days in total.', 'problem_id': '3', 'response_with_ans': \"To calculate the total number of cake-eating days, we will first calculate the number of days between Amaretta's birthday and Enzo's birthday in a non-leap year and in a leap year. Then, we will count the number of leap years and non-leap years in the given range (2008-2016). Finally, we will sum the total number of days for each type of year for both non-leap and leap years.\\n\\nPlease run the following Python code:\\n\\n```python\\nfrom datetime import date\\n\\namaretta_birthday = date(2008, 7, 27)\\nenzo_birthday = date(2008, 9, 3)\\n\\nnormal_year_days = (enzo_birthday - amaretta_birthday).days + 1\\nleap_year_days = normal_year_days + 1\\n\\nleap_years = [year for year in range(2008, 2017) if year % 4 == 0 and (year % 100 != 0 or year % 400 == 0)]\\nnon_leap_years_count = 9 - len(leap_years)\\n\\ntotal_cake_eating_days = non_leap_years_count * normal_year_days + len(leap_years) * leap_year_days\\nprint(total_cake_eating_days)\\n```\\n\", 'round': 0, 'messages': [{'content': \"Amaretta's birthday is July 27, and her brother Enzo's birthday is September 3. Every year, Amaretta and Enzo celebrate by eating cake every day from Amaretta's birthday through Enzo's birthday (including both birthdays). If they did this for the first time in 2008, how many cake-eating days will they have observed by the end of 2016?\", 'role': 'user'}, {'content': \"To calculate the total number of cake-eating days, we will first calculate the number of days between Amaretta's birthday and Enzo's birthday in a non-leap year and in a leap year. Then, we will count the number of leap years and non-leap years in the given range (2008-2016). Finally, we will sum the total number of days for each type of year for both non-leap and leap years.\\n\\nPlease run the following Python code:\\n\\n```python\\nfrom datetime import date\\n\\namaretta_birthday = date(2008, 7, 27)\\nenzo_birthday = date(2008, 9, 3)\\n\\nnormal_year_days = (enzo_birthday - amaretta_birthday).days + 1\\nleap_year_days = normal_year_days + 1\\n\\nleap_years = [year for year in range(2008, 2017) if year % 4 == 0 and (year % 100 != 0 or year % 400 == 0)]\\nnon_leap_years_count = 9 - len(leap_years)\\n\\ntotal_cake_eating_days = non_leap_years_count * normal_year_days + len(leap_years) * leap_year_days\\nprint(total_cake_eating_days)\\n```\\n\", 'role': 'assistant'}], 'time': 15.208062410354614, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'In the diagram, $AB,$ $BC,$ $CD,$ $DE,$ $EF,$ $FG,$ $GH,$ and $HK$ all have length $4,$ and all angles are right angles, with the exception of the angles at $D$ and $F.$\\n\\n[asy]\\ndraw((0,0)--(0,4)--(4,4)--(4,8)--(6.8284,5.1716)--(9.6569,8)--(9.6569,4)--(13.6569,4)--(13.6569,0)--cycle,black+linewidth(1));\\ndraw((0,0)--(0.5,0)--(0.5,0.5)--(0,0.5)--cycle,black+linewidth(1));\\ndraw((0,4)--(0.5,4)--(0.5,3.5)--(0,3.5)--cycle,black+linewidth(1));\\ndraw((4,4)--(4,4.5)--(3.5,4.5)--(3.5,4)--cycle,black+linewidth(1));\\ndraw((6.8284,5.1716)--(7.0784,5.4216)--(6.8284,5.6716)--(6.5784,5.4216)--cycle,black+linewidth(1));\\ndraw((9.6569,4)--(10.1569,4)--(10.1569,4.5)--(9.6569,4.5)--cycle,black+linewidth(1));\\ndraw((13.6569,4)--(13.1569,4)--(13.1569,3.5)--(13.6569,3.5)--cycle,black+linewidth(1));\\ndraw((13.6569,0)--(13.1569,0)--(13.1569,0.5)--(13.6569,0.5)--cycle,black+linewidth(1));\\nlabel(\"$A$\",(0,0),W);\\nlabel(\"$B$\",(0,4),NW);\\nlabel(\"$C$\",(4,4),S);\\nlabel(\"$D$\",(4,8),N);\\nlabel(\"$E$\",(6.8284,5.1716),S);\\nlabel(\"$F$\",(9.6569,8),N);\\nlabel(\"$G$\",(9.6569,4),S);\\nlabel(\"$H$\",(13.6569,4),NE);\\nlabel(\"$K$\",(13.6569,0),E);\\n[/asy]\\n\\nDetermine the length of $DF.$\\n\\n[asy]\\ndraw((0,0)--(2.8284,-2.8284)--(5.6568,0),black+linewidth(1));\\ndraw((0,0)--(5.6568,0),black+linewidth(1)+dashed);\\ndraw((2.8284,-2.8284)--(3.0784,-2.5784)--(2.8284,-2.3284)--(2.5784,-2.5784)--cycle,black+linewidth(1));\\nlabel(\"$D$\",(0,0),N);\\nlabel(\"$E$\",(2.8284,-2.8284),S);\\nlabel(\"$F$\",(5.6568,0),N);\\n[/asy]', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Since $DE=EF=4$ and $\\\\angle DEF = 90^\\\\circ,$ by the Pythagorean Theorem, \\\\begin{align*}\\nDF^2 &= DE^2+EF^2 \\\\\\\\\\n&= 4^2+4^2 \\\\\\\\\\n&=32,\\n\\\\end{align*}so that $DF = \\\\sqrt{32}=\\\\boxed{4\\\\sqrt{2}}.$', 'problem_id': '16', 'response_with_ans': \"Now let's calculate the square of DF using Python.\\n\\n```python\\nDH = 9.6569\\nHG = 5.6569\\ncos_alpha_beta = 0\\n\\nDF_squared = DH**2 + HG**2 - 2 * DH * HG * cos_alpha_beta\\nDF_squared\\n```\", 'round': 2, 'messages': [{'content': 'In the diagram, $AB,$ $BC,$ $CD,$ $DE,$ $EF,$ $FG,$ $GH,$ and $HK$ all have length $4,$ and all angles are right angles, with the exception of the angles at $D$ and $F.$\\n\\n[asy]\\ndraw((0,0)--(0,4)--(4,4)--(4,8)--(6.8284,5.1716)--(9.6569,8)--(9.6569,4)--(13.6569,4)--(13.6569,0)--cycle,black+linewidth(1));\\ndraw((0,0)--(0.5,0)--(0.5,0.5)--(0,0.5)--cycle,black+linewidth(1));\\ndraw((0,4)--(0.5,4)--(0.5,3.5)--(0,3.5)--cycle,black+linewidth(1));\\ndraw((4,4)--(4,4.5)--(3.5,4.5)--(3.5,4)--cycle,black+linewidth(1));\\ndraw((6.8284,5.1716)--(7.0784,5.4216)--(6.8284,5.6716)--(6.5784,5.4216)--cycle,black+linewidth(1));\\ndraw((9.6569,4)--(10.1569,4)--(10.1569,4.5)--(9.6569,4.5)--cycle,black+linewidth(1));\\ndraw((13.6569,4)--(13.1569,4)--(13.1569,3.5)--(13.6569,3.5)--cycle,black+linewidth(1));\\ndraw((13.6569,0)--(13.1569,0)--(13.1569,0.5)--(13.6569,0.5)--cycle,black+linewidth(1));\\nlabel(\"$A$\",(0,0),W);\\nlabel(\"$B$\",(0,4),NW);\\nlabel(\"$C$\",(4,4),S);\\nlabel(\"$D$\",(4,8),N);\\nlabel(\"$E$\",(6.8284,5.1716),S);\\nlabel(\"$F$\",(9.6569,8),N);\\nlabel(\"$G$\",(9.6569,4),S);\\nlabel(\"$H$\",(13.6569,4),NE);\\nlabel(\"$K$\",(13.6569,0),E);\\n[/asy]\\n\\nDetermine the length of $DF.$\\n\\n[asy]\\ndraw((0,0)--(2.8284,-2.8284)--(5.6568,0),black+linewidth(1));\\ndraw((0,0)--(5.6568,0),black+linewidth(1)+dashed);\\ndraw((2.8284,-2.8284)--(3.0784,-2.5784)--(2.8284,-2.3284)--(2.5784,-2.5784)--cycle,black+linewidth(1));\\nlabel(\"$D$\",(0,0),N);\\nlabel(\"$E$\",(2.8284,-2.8284),S);\\nlabel(\"$F$\",(5.6568,0),N);\\n[/asy]', 'role': 'user'}, {'content': \"Let's use coordinate geometry to determine the coordinates of points D, E, and F, which will allow us to compute the Euclidean distance between points D and F.\\n\\n1. Assign coordinates to points A, B, C, D, E, F, G, H, and K.\\n2. Calculate the slope and equation of line DE and line FG.\\n3. Use the slopes and the coordinates of points E and G to compute the coordinates of point F.\\n4. Calculate the length of DF using the distance formula.\\n\\nLet's start by assigning coordinates:\\n\\n1. A(0, 0), B(0, 4), C(4, 4), D(4, 8), G(9.6569, 4), H(13.6569, 4), and K(13.6569, 0).\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Since the angles at points D and F are not right angles, we need to use some trigonometry to find points E and F. We already have the coordinates of points D and G.\\n\\nLet α be the angle between the horizontal line and the line DE, and β be the angle between the horizontal line and the line FG.\\n\\nLet P be the intersection point between the horizontal lines passing through points D and F. Therefore, the triangle DPF is a right triangle. \\n\\nSince we know the angles α, β, and the length DG, we can determine the length DF using the law of cosines:\\n\\nLet's calculate the length of DG first:\\n\\nDG = DH + HG = (13.6569 - 4) + (9.6569 - 4) = 9.6569 + 5.6569 = 15.3138\\n\\nNow, let α be the angle between line DH and DF, and β be the angle between line HG and DF. We can use the law of cosines to calculate the length of DF:\\n\\nDF^2 = DH^2 + HG^2 - 2 * DH * HG * cos(α + β)\\n\\nWe also know angle DAB = α, angle GHB = β and AB = FG = 4.\\n\\nWe can calculate cos(α + β) using the cosine formula for the addition of angles:\\n\\ncos(α + β) = cos(α) * cos(β) - sin(α) * sin(β)\\n\\nTo find sin(α) and cos(α), we can use right triangle with sides DA, AB, and DB. Since DB = BA = 4, we have:\\n\\ncos(α) = AD / DB = 4 / 4 = 1\\nsin(α) = BA / DB = 4 / 4 = 1\\n\\nSimilarly, for angle β, we have right triangle with sides GH, HB, and GB. Since GB = BH = 4, we have:\\n\\ncos(β) = HG / GB = 4 / 4 = 1\\nsin(β) = HB / GB = 4 / 4 = 1\\n\\nNow, let's calculate cos(α + β):\\n\\ncos(α + β) = cos(α) * cos(β) - sin(α) * sin(β) = 1 * 1 - 1 * 1 = 0\\n\\nFinally, let's calculate DF using the law of cosines:\\n\\nDF^2 = DH^2 + HG^2 - 2 * DH * HG * cos(α + β)\\nDF^2 = 9.6569^2 + 5.6569^2 - 2 * 9.6569 * 5.6569 * 0\\n\\nNow let's compute the square of DF.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Now let's calculate the square of DF using Python.\\n\\n```python\\nDH = 9.6569\\nHG = 5.6569\\ncos_alpha_beta = 0\\n\\nDF_squared = DH**2 + HG**2 - 2 * DH * HG * cos_alpha_beta\\nDF_squared\\n```\", 'role': 'assistant'}], 'time': 44.82826566696167, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"inappropriate\",\n",
" \"Calculation Correctness\": \"completely incorrect\",\n",
" \"Explanation Clarity\": \"moderately clear\",\n",
" \"Code Efficiency\": \"not at all efficient\",\n",
" \"Code Correctness\": \"completely incorrect\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'A $30^\\\\circ$-$60^\\\\circ$-$90^\\\\circ$ triangle is drawn on the exterior of an equilateral triangle so the hypotenuse of the right triangle is one side of the equilateral triangle. If the shorter leg of the right triangle is 6 units, what is the distance between the two vertices that the triangles do not have in common? Express your answer in simplest radical form. [asy]\\ndraw((2,0)--(0,0)--(1,1.732)--(2,1.732)--(2,0)--(1,1.732));\\ndraw((2,1.632)--(1.9,1.632)--(1.9,1.732));\\nlabel(\"$60^\\\\circ$\",(1,1.732),2SE+E);\\nlabel(\"$30^\\\\circ$\",(2,0),5NNW+4N);\\nlabel(\"6\",(1.5,1.732),N);\\n[/asy]', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Multiply the short leg of the right triangle by $\\\\sqrt{3}$ to find that the length of the longer leg is $6\\\\sqrt{3}$ units. Double the short leg of the right triangle to find that the length of the hypotenuse of the right triangle is 12 units. Since the hypotenuse of the right triangle is a side of the equilateral triangle, the side length of the equilateral triangle is also 12 units. By the Pythagorean theorem, the distance between the two vertices that the two triangles do not have in common is $\\\\sqrt{(6\\\\sqrt{3})^2+12^2}=\\\\sqrt{252}=\\\\boxed{6\\\\sqrt{7}}$ units. [asy]\\ndraw((2,0)--(0,0)--(1,sqrt(3))--(2,sqrt(3))--(2,0)--(1,sqrt(3)));\\ndraw((2,sqrt(3)-0.1)--(1.9,sqrt(3)-0.1)--(1.9,sqrt(3)));\\ndraw((0,0)--(2,sqrt(3)));\\nlabel(\"$60^\\\\circ$\",(1,sqrt(3)),2SE+E);\\nlabel(\"$30^\\\\circ$\",(2,0),5NNW+4N);\\nlabel(\"6\",(1.5,sqrt(3)),N);\\nlabel(\"$6\\\\sqrt{3}$\",(2,sqrt(3)/2),E);\\nlabel(\"12\",(1.5,sqrt(3)/2),SW);\\nlabel(\"12\",(1,0),S);\\n[/asy]', 'problem_id': '7', 'response_with_ans': 'We have found the distance between the two vertices that the triangles do not have in common (C and D):\\n\\nx = √(252 + 72√3)\\n\\nThis is the simplest radical form for the required distance. \\n\\nTERMINATE', 'round': 3, 'messages': [{'content': 'A $30^\\\\circ$-$60^\\\\circ$-$90^\\\\circ$ triangle is drawn on the exterior of an equilateral triangle so the hypotenuse of the right triangle is one side of the equilateral triangle. If the shorter leg of the right triangle is 6 units, what is the distance between the two vertices that the triangles do not have in common? Express your answer in simplest radical form. [asy]\\ndraw((2,0)--(0,0)--(1,1.732)--(2,1.732)--(2,0)--(1,1.732));\\ndraw((2,1.632)--(1.9,1.632)--(1.9,1.732));\\nlabel(\"$60^\\\\circ$\",(1,1.732),2SE+E);\\nlabel(\"$30^\\\\circ$\",(2,0),5NNW+4N);\\nlabel(\"6\",(1.5,1.732),N);\\n[/asy]', 'role': 'user'}, {'content': \"Let's denote the vertices of the equilateral triangle as A, B, and C, where A is the right angle of the $30^\\\\circ$-$60^\\\\circ$-$90^\\\\circ$ triangle, B is adjacent to the $30^\\\\circ$ angle, and C is adjacent to the $60^\\\\circ$ angle. Let D be the vertex of the right triangle that is not a part of the equilateral triangle (the $60^\\\\circ$ angle).\\n\\nSince the shorter leg of the right triangle is 6 units (AD) and it is a $30^\\\\circ$-$60^\\\\circ$-$90^\\\\circ$ triangle, we know that:\\n1. The longer leg (BD) has a length equal to the shorter leg times $\\\\sqrt{3}$ ($6\\\\sqrt{3}$ units).\\n2. The hypotenuse (AB) has a length twice the size of the shorter leg (12 units).\\n\\nNow, we are trying to find the distance between vertices C and D. Let's call this distance x.\\n\\nTo calculate x, we can use the law of cosines on triangle $\\\\bigtriangleup BCD$. Here, the angle at vertex B is $180^\\\\circ - 60^\\\\circ = 120^\\\\circ$. We have the lengths of sides BC and BD, so we can calculate x using the law of cosines and then simplify the result.\\n\\nThe law of cosines formula is:\\nx^2 = (BC^2) + (BD^2) - 2(BC)(BD)cos(120^\\\\circ)\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'First, we need the length of side BC, which is equal to the length of side AB (since it is an equilateral triangle). Thus, BC = 12 units. Now we can use the law of cosines formula to find the value of x^2:\\n\\nx^2 = (12^2) + (6√3)^2 - 2(12)(6√3)cos(120º)\\n\\nThe cosine of 120º is -1/2, so:\\n\\nx^2 = 144 + 108 - 2(12)(6√3)(-1/2)\\n', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Let's simplify the expression for x^2:\\n\\nx^2 = 144 + 108 + (12)(6√3)\\n\\nNow we will calculate the result:\\n\\nx^2 = 144 + 108 + 72√3\\n\\nx^2 = 252 + 72√3\\n\\nNow, to find the value of x, we'll take the square root of both sides:\\n\\nx = √(252 + 72√3)\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'We have found the distance between the two vertices that the triangles do not have in common (C and D):\\n\\nx = √(252 + 72√3)\\n\\nThis is the simplest radical form for the required distance. \\n\\nTERMINATE', 'role': 'assistant'}], 'time': 35.88237428665161, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"mostly correct\",\n",
" \"Explanation Clarity\": \"mostly clear\",\n",
" \"Code Efficiency\": \"N/A\",\n",
" \"Code Correctness\": \"N/A\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'The perfect squares from $1$ through $2500,$ inclusive, are printed in a sequence of digits $1491625\\\\ldots2500.$ How many digits are in the sequence?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': \"We consider it by four cases:\\n\\n$\\\\bullet$ Case 1: There are $3$ perfect squares that only have $1$ digit, $1^{2},$ $2^{2},$ and $3^{2}.$\\n\\n$\\\\bullet$ Case 2: The smallest perfect square that has $2$ digits is $4^{2},$ and the largest is $9^{2},$ so that's a total of $6$ perfect squares with $2$ digits.\\n\\n$\\\\bullet$ Case 3: The smallest perfect square with $3$ digits is $10^{2},$ and the largest is $31^{2},$ yielding a total of $22.$\\n\\n$\\\\bullet$ Case 4: The smallest perfect square with $4$ digits is $32^{2},$ and the last one that is no greater than $2500$ is $50^{2},$ giving a total of $19.$\\n\\nSo we have a total of $1\\\\times3+2\\\\times6+3\\\\times22+4\\\\times19=\\\\boxed{157}$ digits.\", 'problem_id': '5', 'response_with_ans': '```python\\ndef count_digits(n):\\n return len(str(n))\\n\\nperfect_squares = [i ** 2 for i in range(1, int(2500 ** 0.5) + 1)]\\ntotal_digits = sum(count_digits(square) for square in perfect_squares)\\n\\nprint(total_digits)\\n```\\n', 'round': 1, 'messages': [{'content': 'The perfect squares from $1$ through $2500,$ inclusive, are printed in a sequence of digits $1491625\\\\ldots2500.$ How many digits are in the sequence?', 'role': 'user'}, {'content': \"To determine the number of digits in the sequence, we can first find all perfect squares from 1 through 2500, inclusive. Then, we can count the number of digits in each perfect square and sum them up. Let's implement this in Python code:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\ndef count_digits(n):\\n return len(str(n))\\n\\nperfect_squares = [i ** 2 for i in range(1, int(2500 ** 0.5) + 1)]\\ntotal_digits = sum(count_digits(square) for square in perfect_squares)\\n\\nprint(total_digits)\\n```\\n', 'role': 'assistant'}], 'time': 9.788081884384155, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'In isosceles right triangle $ABC$, point $D$ is on hypotenuse $\\\\overline{BC}$ such that $\\\\overline{AD}$ is an altitude of $\\\\triangle ABC$ and $DC = 5$. What is the area of triangle $ABC$?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'In isosceles right triangle $\\\\triangle ABC$ below, $\\\\overline{AD}$ is the altitude to the hypotenuse.\\n\\n[asy]\\nimport olympiad;\\nunitsize(0.8inch);\\npair A,B,C,D;\\nA = (0,1);\\nB= (1,0);\\nC = -B;\\nD = (0,0);\\ndraw(A--B--C--A,linewidth(1));\\ndraw(A--D,linewidth(0.8));\\ndraw(rightanglemark(C,A,B,s=4));\\ndraw(rightanglemark(C,D,A,s=4));\\nlabel(\"$A$\",A,N);\\nlabel(\"$B$\",B,S);\\nlabel(\"$C$\",C,S);\\nlabel(\"$D$\",D,S);\\n[/asy]\\n\\nBecause $\\\\triangle ABC$ is an isosceles right triangle, $\\\\angle ABC = 45^\\\\circ$. Since $\\\\angle ADB = 90^\\\\circ$, we know that $\\\\angle DAB = 45^\\\\circ$, so $\\\\triangle ABD$ is also a 45-45-90 triangle. Similarly, $\\\\triangle ACD$ is a 45-45-90 triangle. Therefore, $DA=DB = DC = 5$, so $BC = BD+DC = 10$, and \\\\[[ABC] = \\\\frac{(AD)(BC)}{2} = \\\\frac{(5)({10})}{2} = \\\\boxed{25}.\\\\]', 'problem_id': '13', 'response_with_ans': '```python\\nfrom sympy import Eq, solve, symbols\\r\\n\\r\\n# Step 1: Find the length of side AC using the Pythagorean theorem\\r\\na, b, c = symbols(\"a b c\")\\r\\nPythagorean = Eq(a**2 + b**2, c**2)\\r\\n\\r\\n# We know BD = 5 and it\\'s an isosceles right triangle so:\\r\\nBD = 5\\r\\nBC = BD * 2\\r\\nside_A = BD # Side A is equal to BD\\r\\n\\r\\nisosceles_triangle = Pythagorean.subs({a: side_A, b: side_A, c: BC}) # Substitute values into the equation\\r\\nlength_AC = solve(isosceles_triangle, side_A)[0] # Solve for side_AC\\r\\n\\r\\n# Step 2: Find the length of side AD\\r\\nlength_AD = (BD * length_AC) / BC # Using the proportion\\r\\n\\r\\n# Step 3: Find the area of triangle ABC\\r\\narea_triangle_ABC = 0.5 * (length_AD * length_AC)\\r\\n\\r\\nlength_AC, length_AD, area_triangle_ABC\\r\\n```', 'round': 1, 'messages': [{'content': 'In isosceles right triangle $ABC$, point $D$ is on hypotenuse $\\\\overline{BC}$ such that $\\\\overline{AD}$ is an altitude of $\\\\triangle ABC$ and $DC = 5$. What is the area of triangle $ABC$?', 'role': 'user'}, {'content': \"Let's first analyze the problem and draw a plan to solve it.\\n\\nPlan:\\n1. Use the Pythagorean theorem to find the length of side AC.\\n2. Since right triangle ADC is similar to triangle ABC, we can set up a proportion to find the length of side AD.\\n3. Use the area formula of a triangle to find the area of triangle ABC.\\n\\nNow, let's execute the plan using code to calculate side AC, then side AD, and finally the area of triangle ABC.\\n\\n\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\nfrom sympy import Eq, solve, symbols\\r\\n\\r\\n# Step 1: Find the length of side AC using the Pythagorean theorem\\r\\na, b, c = symbols(\"a b c\")\\r\\nPythagorean = Eq(a**2 + b**2, c**2)\\r\\n\\r\\n# We know BD = 5 and it\\'s an isosceles right triangle so:\\r\\nBD = 5\\r\\nBC = BD * 2\\r\\nside_A = BD # Side A is equal to BD\\r\\n\\r\\nisosceles_triangle = Pythagorean.subs({a: side_A, b: side_A, c: BC}) # Substitute values into the equation\\r\\nlength_AC = solve(isosceles_triangle, side_A)[0] # Solve for side_AC\\r\\n\\r\\n# Step 2: Find the length of side AD\\r\\nlength_AD = (BD * length_AC) / BC # Using the proportion\\r\\n\\r\\n# Step 3: Find the area of triangle ABC\\r\\narea_triangle_ABC = 0.5 * (length_AD * length_AC)\\r\\n\\r\\nlength_AC, length_AD, area_triangle_ABC\\r\\n```', 'role': 'assistant'}], 'time': 22.85700249671936, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"mostly effective\",\n",
" \"Calculation Correctness\": \"mostly correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"mostly correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': \"Two numbers are said to be 'relatively prime' if their greatest common factor is 1. How many integers greater than 10 and less than 30 are relatively prime with 28?\", 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Since $28=2^2\\\\cdot 7$, a positive integer is relatively prime with $28$ if and only if it contains neither $2$ nor $7$ in its prime factorization. In other words, we want to count the number of integers between $11$ and $29$ inclusive which are divisible by neither $2$ nor $7$.\\n\\nAll of the odd numbers are not divisible by 2; there are 10 such numbers. The only one of these that is divisible by 7 is 21, so there are $10- 1 =\\\\boxed{9}$ numbers between 10 and 30 that are relatively prime with 28.', 'problem_id': '12', 'response_with_ans': 'I will find the relatively prime numbers with 28 in the range greater than 10 and less than 30 using Python code. I will use the math.gcd function and a loop to check each number in the specified range.\\n\\n```python\\nimport math\\n\\ncount = 0\\nrelatively_prime_numbers = []\\nfor number in range(11, 30):\\n if math.gcd(28, number) == 1:\\n relatively_prime_numbers.append(number)\\n count += 1\\n\\nprint(f\"Relatively prime numbers with 28: {relatively_prime_numbers}\")\\nprint(f\"Number of relatively prime numbers with 28: {count}\")\\n```\\nExecute the above code to find the integers relatively prime with 28 and their count.', 'round': 0, 'messages': [{'content': \"Two numbers are said to be 'relatively prime' if their greatest common factor is 1. How many integers greater than 10 and less than 30 are relatively prime with 28?\", 'role': 'user'}, {'content': 'I will find the relatively prime numbers with 28 in the range greater than 10 and less than 30 using Python code. I will use the math.gcd function and a loop to check each number in the specified range.\\n\\n```python\\nimport math\\n\\ncount = 0\\nrelatively_prime_numbers = []\\nfor number in range(11, 30):\\n if math.gcd(28, number) == 1:\\n relatively_prime_numbers.append(number)\\n count += 1\\n\\nprint(f\"Relatively prime numbers with 28: {relatively_prime_numbers}\")\\nprint(f\"Number of relatively prime numbers with 28: {count}\")\\n```\\nExecute the above code to find the integers relatively prime with 28 and their count.', 'role': 'assistant'}], 'time': 6.9820802211761475, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'What is the product of $0.\\\\overline{05}$ and $1.8$ expressed as a fraction in simplest form?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': \"First let's convert $0.\\\\overline{05}$ to a fraction. Let's define $x=0.\\\\overline{05}$. If we multiply both sides by $100$ we get $100x=5.\\\\overline{05}$ so $99x=5$ and $x=0.\\\\overline{05}=\\\\frac{5}{99}$. Since $1.8=\\\\frac{9}{5}$ we get $$\\\\frac{\\\\cancel{5}}{99}\\\\cdot\\\\frac{9}{\\\\cancel{5}}=\\\\frac{9}{99}=\\\\boxed{\\\\frac{1}{11}}.$$\", 'problem_id': '9', 'response_with_ans': '```python\\nfrom fractions import Fraction\\n\\nfraction_1 = Fraction(5, 99)\\ndecimal_2 = 1.8\\n\\nproduct = fraction_1 * decimal_2\\n\\nsimplified_fraction = Fraction(product).limit_denominator()\\n\\nprint(simplified_fraction)\\n```\\n\\nPlease run the above Python code to find the product of $\\\\frac{5}{99}$ and $1.8$ expressed as a fraction in simplest form.', 'round': 1, 'messages': [{'content': 'What is the product of $0.\\\\overline{05}$ and $1.8$ expressed as a fraction in simplest form?', 'role': 'user'}, {'content': \"To find the product of $0.\\\\overline{05}$ and $1.8$, first we need to convert the repeating decimal $0.\\\\overline{05}$ into a fraction.\\n\\nLet's call the repeating decimal $x$:\\n\\n$$x = 0.\\\\overline{05}$$\\n\\nTo convert the repeating decimal into a fraction, we can multiply both sides by $100$ to shift the decimal point two places to the right:\\n\\n$$100x = 5.\\\\overline{05}$$\\n\\nNow, subtract the first equation from the second equation:\\n\\n$$100x - x = 5.\\\\overline{05} - 0.\\\\overline{05}$$\\n$$99x = 5$$\\n\\nNow, we can solve for $x$:\\n\\n$$x = \\\\frac{5}{99}$$\\n\\nSo the repeating decimal $0.\\\\overline{05}$ is equivalent to the fraction $\\\\frac{5}{99}$.\\n\\nNow, we can find the product of $\\\\frac{5}{99}$ and $1.8$:\\n\\n$$\\\\frac{5}{99} \\\\times 1.8$$\\n\\nFinally, we will simplify the fraction. Let's write the code to compute the product and simplify the result as a fraction.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\nfrom fractions import Fraction\\n\\nfraction_1 = Fraction(5, 99)\\ndecimal_2 = 1.8\\n\\nproduct = fraction_1 * decimal_2\\n\\nsimplified_fraction = Fraction(product).limit_denominator()\\n\\nprint(simplified_fraction)\\n```\\n\\nPlease run the above Python code to find the product of $\\\\frac{5}{99}$ and $1.8$ expressed as a fraction in simplest form.', 'role': 'assistant'}], 'time': 18.04914665222168, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'All 50 states as well as the District of Columbia and Puerto Rico, have distinct two-letter postal abbreviations. If a two-letter sequence of letters (such as CO or EE) is chosen at random, what is the probability that it is a postal abbreviation for one of the 50 states, the District of Columbia, or Puerto Rico? Express your answer as a common fraction.', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'There are $26\\\\cdot 26$ possible two-letter sequences of letters, since we have 26 choices for the first and then 26 choices for the second. But only 52 of these possibilities are valid, so our answer is $\\\\frac{52}{26\\\\cdot 26} =\\\\boxed{ \\\\frac{1}{13}}$.', 'problem_id': '8', 'response_with_ans': 'We\\'ll use a reliable source like USPS to fetch the postal abbreviations, and then calculate the probability.\\n\\n```python\\nimport requests\\nfrom bs4 import BeautifulSoup\\nimport fractions\\n\\nurl = \\'https://pe.usps.com/text/pub28/28apb.htm\\'\\nresponse = requests.get(url)\\nsoup = BeautifulSoup(response.text, \\'html.parser\\')\\n\\n# Find the table containing the state info\\nstate_table = soup.find_all(\\'table\\')[0]\\n\\n# Extract state abbreviations from the table\\nstate_abbreviations = [row.find_all(\\'td\\')[-1].text.strip() for row in state_table.find_all(\\'tr\\')[3:-2]]\\n\\n# Add District of Columbia and Puerto Rico.\\nstate_abbreviations.extend([\"DC\", \"PR\"])\\n\\nnum_valid_abbreviations = len(state_abbreviations)\\ntotal_possible_combinations = 26 * 26\\nprobability = fractions.Fraction(num_valid_abbreviations, total_possible_combinations)\\n\\nprint(f\"Valid postal abbreviations: {state_abbreviations}\")\\nprint(f\"Total valid postal abbreviations: {num_valid_abbreviations}\")\\nprint(f\"Total possible two-letter sequences: {total_possible_combinations}\")\\nprint(f\"Probability: {probability}\")\\n```\\n\\nPlease run the above code, and the output will show you the list of valid postal abbreviations, the total number of valid abbreviations, the total possible two-letter sequences, and the probability.', 'round': 1, 'messages': [{'content': 'All 50 states as well as the District of Columbia and Puerto Rico, have distinct two-letter postal abbreviations. If a two-letter sequence of letters (such as CO or EE) is chosen at random, what is the probability that it is a postal abbreviation for one of the 50 states, the District of Columbia, or Puerto Rico? Express your answer as a common fraction.', 'role': 'user'}, {'content': \"To calculate the probability, we first need to know the total number of possible two-letter sequences and the number of valid postal abbreviations.\\n\\nTo get the total number of possible two-letter sequences, we'll calculate all possible combinations using 26 letters of the alphabet. There are 26 options for choosing the first letter and 26 options for choosing the second letter, so the possible sequences are 26 * 26.\\n\\nNext, let's find the valid postal abbreviations for all 50 states, District of Columbia, and Puerto Rico. To do this, we can create code to download the postal abbreviations.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'We\\'ll use a reliable source like USPS to fetch the postal abbreviations, and then calculate the probability.\\n\\n```python\\nimport requests\\nfrom bs4 import BeautifulSoup\\nimport fractions\\n\\nurl = \\'https://pe.usps.com/text/pub28/28apb.htm\\'\\nresponse = requests.get(url)\\nsoup = BeautifulSoup(response.text, \\'html.parser\\')\\n\\n# Find the table containing the state info\\nstate_table = soup.find_all(\\'table\\')[0]\\n\\n# Extract state abbreviations from the table\\nstate_abbreviations = [row.find_all(\\'td\\')[-1].text.strip() for row in state_table.find_all(\\'tr\\')[3:-2]]\\n\\n# Add District of Columbia and Puerto Rico.\\nstate_abbreviations.extend([\"DC\", \"PR\"])\\n\\nnum_valid_abbreviations = len(state_abbreviations)\\ntotal_possible_combinations = 26 * 26\\nprobability = fractions.Fraction(num_valid_abbreviations, total_possible_combinations)\\n\\nprint(f\"Valid postal abbreviations: {state_abbreviations}\")\\nprint(f\"Total valid postal abbreviations: {num_valid_abbreviations}\")\\nprint(f\"Total possible two-letter sequences: {total_possible_combinations}\")\\nprint(f\"Probability: {probability}\")\\n```\\n\\nPlease run the above code, and the output will show you the list of valid postal abbreviations, the total number of valid abbreviations, the total possible two-letter sequences, and the probability.', 'role': 'assistant'}], 'time': 27.01692771911621, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'On a number line, the coordinates of $P$ and $Q$ are 8 and 48, respectively. The midpoint of $\\\\overline{PQ}$ is $B$, the midpoint of $\\\\overline{BQ}$ is $C$, and the midpoint of $\\\\overline{PC}$ is $D$. What is the coordinate of $D$?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Since $B$ is midpoint of $\\\\overline{PQ}$, the coordinate of $B$ is $(8+48)/2 = 4+24 = 28$. Since $C$ is the midpoint of $\\\\overline{BQ}$, the coordinate of $C$ is $(28+48)/2 = 14+24=38$. Since $D$ is the midpoint of $\\\\overline{PC}$, the coordinate of $D$ is $(8+38)/2 = 4 + 19 = \\\\boxed{23}$.\\n\\n[asy]\\npair P, Q, B, C, D;\\n\\nP = (8,0);\\nQ = (48,0);\\nB = (P+Q)/2;\\nC = (B+Q)/2;\\nD = (P+C)/2;\\n\\ndot(P);\\ndot(Q);\\ndot(B);\\ndot(C);\\ndot(D);\\n\\ndraw(P--Q);\\nlabel(\"$P$\",P,S);\\nlabel(\"$Q$\",Q,S);\\nlabel(\"$B$\",B,S);\\nlabel(\"$C$\",C,S);\\nlabel(\"$D$\",D,S);\\n[/asy]', 'problem_id': '6', 'response_with_ans': \"Let's find the coordinates using Python code.\\n\\n```python\\nP = 8\\nQ = 48\\n\\n# Find the midpoint of PQ (B)\\nB = (P + Q) / 2\\n\\n# Find the midpoint of BQ (C)\\nC = (B + Q) / 2\\n\\n# Find the midpoint of PC (D)\\nD = (P + C) / 2\\n\\nD\\n```\\n\\nExecute the above Python code to find the coordinate of D.\", 'round': 1, 'messages': [{'content': 'On a number line, the coordinates of $P$ and $Q$ are 8 and 48, respectively. The midpoint of $\\\\overline{PQ}$ is $B$, the midpoint of $\\\\overline{BQ}$ is $C$, and the midpoint of $\\\\overline{PC}$ is $D$. What is the coordinate of $D$?', 'role': 'user'}, {'content': \"To find the coordinate of D, we need to follow these steps:\\n1. Find the coordinate of B (midpoint of PQ)\\n2. Find the coordinate of C (midpoint of BQ)\\n3. Find the coordinate of D (midpoint of PC)\\n\\nI'll use Python code to find the coordinates of B, C, and D.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Let's find the coordinates using Python code.\\n\\n```python\\nP = 8\\nQ = 48\\n\\n# Find the midpoint of PQ (B)\\nB = (P + Q) / 2\\n\\n# Find the midpoint of BQ (C)\\nC = (B + Q) / 2\\n\\n# Find the midpoint of PC (D)\\nD = (P + C) / 2\\n\\nD\\n```\\n\\nExecute the above Python code to find the coordinate of D.\", 'role': 'assistant'}], 'time': 9.041668176651001, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Triangle $ABC$ is a right triangle. If the measure of angle $PAB$ is $x^\\\\circ$ and the measure of angle $ACB$ is expressed in the form $(Mx+N)^\\\\circ$ with $M=1$, what is the value of $M+N$?\\n\\n[asy]\\ndraw((-10,0)--(20,0),linewidth(1),Arrows);\\ndraw((0,0)--(10,10/sqrt(3))--(10+10/3,0),linewidth(1));\\n\\ndraw((10,10/sqrt(3))+dir(-150)--(10,10/sqrt(3))+dir(-150)+dir(-60)--(10,10/sqrt(3))+dir(-60),linewidth(1));\\n\\ndot((-3,0));\\n\\ndraw(dir(180)..dir(105)..dir(30),linewidth(1));\\n\\nlabel(\"P\",(-3,0),NW);\\nlabel(\"A\",(0,0),S);\\nlabel(\"$x^\\\\circ$\",(-1,1),N);\\nlabel(\"B\",(10,10/sqrt(3)),N);\\nlabel(\"C\",(10+10/3,0),NE);\\n\\n[/asy]', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Since $\\\\angle PAB$ and $\\\\angle BAC$ are supplementary, $\\\\angle BAC = 180^{\\\\circ} - x^\\\\circ$. Since the three angles of a triangle add up to $ 180^{\\\\circ} $, we have $\\\\angle ACB = 180^{\\\\circ} - 90^{\\\\circ} - (180^{\\\\circ} - x^\\\\circ) = x^\\\\circ - 90^{\\\\circ}$. Thus, $M + N = \\\\boxed{-89}$.', 'problem_id': '0', 'response_with_ans': 'We know that $x + y = 180^\\\\circ$. From this equation, we can express $y$ in terms of $x$: $y = 180^\\\\circ - x$.\\n\\nNow we substitute the expression for $y$ in the expression of angle $ACB$:\\n$ACB = 90^\\\\circ - y = 90^\\\\circ - (180^\\\\circ - x) = x - 90^\\\\circ$.\\n\\nComparing this expression with the given form $(Mx + N)^\\\\circ$, we can see that $M = 1$ and $N = -90$. Therefore, $M + N = 1 + (-90) = -89$.\\n\\nThe value of $M + N$ is $-89$.', 'round': 2, 'messages': [{'content': 'Triangle $ABC$ is a right triangle. If the measure of angle $PAB$ is $x^\\\\circ$ and the measure of angle $ACB$ is expressed in the form $(Mx+N)^\\\\circ$ with $M=1$, what is the value of $M+N$?\\n\\n[asy]\\ndraw((-10,0)--(20,0),linewidth(1),Arrows);\\ndraw((0,0)--(10,10/sqrt(3))--(10+10/3,0),linewidth(1));\\n\\ndraw((10,10/sqrt(3))+dir(-150)--(10,10/sqrt(3))+dir(-150)+dir(-60)--(10,10/sqrt(3))+dir(-60),linewidth(1));\\n\\ndot((-3,0));\\n\\ndraw(dir(180)..dir(105)..dir(30),linewidth(1));\\n\\nlabel(\"P\",(-3,0),NW);\\nlabel(\"A\",(0,0),S);\\nlabel(\"$x^\\\\circ$\",(-1,1),N);\\nlabel(\"B\",(10,10/sqrt(3)),N);\\nlabel(\"C\",(10+10/3,0),NE);\\n\\n[/asy]', 'role': 'user'}, {'content': \"To solve this problem, we need to find the relationship between angle $x$ and angle $ACB$. We know that triangle $ABC$ is a right triangle, so the sum of angles $ACB$ and $ABC$ must be $90^\\\\circ$. Let's call the angle $ABC$ as $y$. Therefore, angle $ACB = 90^\\\\circ - y$.\\n\\nNow, we need to find the relationship between angles $x$ and $y$. Since $PAB$ and $ABC$ share the same vertex $A$ and angle $PAB$ is opposite angle $ABC$, they are explementary (their sum is $180^\\\\circ$). So, we have $x + y = 180^\\\\circ$.\\n\\nNow, we can express $y$ in terms of $x$ and then substitute the expression for $y$ in the expression of angle $ACB$. Finally, we can compare angle $ACB$'s expression with the given form $(Mx + N)^\\\\circ$ to find the values of $M$ and $N$.\\n\\nLet me do the calculations.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'We know that $x + y = 180^\\\\circ$. From this equation, we can express $y$ in terms of $x$: $y = 180^\\\\circ - x$.\\n\\nNow we substitute the expression for $y$ in the expression of angle $ACB$:\\n$ACB = 90^\\\\circ - y = 90^\\\\circ - (180^\\\\circ - x) = x - 90^\\\\circ$.\\n\\nComparing this expression with the given form $(Mx + N)^\\\\circ$, we can see that $M = 1$ and $N = -90$. Therefore, $M + N = 1 + (-90) = -89$.\\n\\nThe value of $M + N$ is $-89$.', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': 'TERMINATE', 'role': 'assistant'}], 'time': 28.305670976638794, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"not applicable\",\n",
" \"Code Correctness\": \"not applicable\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'John counts up from 1 to 13, and then immediately counts down again to 1, and then back up to 13, and so on, alternately counting up and down: \\\\begin{align*}\\n&(1, 2, 3,4,5,6,7,8,9,10,11,12,13,\\\\\\\\\\n&\\\\qquad\\\\qquad12,11,10,9,8,7,6,5,4,3,2,1,2,3,4,\\\\ldots ).\\n\\\\end{align*} What is the $5000^{\\\\text{th}}$ integer in his list?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'We can treat this list as a sequence with a repetitive pattern. We see the sequence repeats itself every 24 elements (from 1 to 13 then back to 2). When 5000 is divided by 24, its remainder is 8. Therefore we see the $5000^{\\\\text{th}}$ integer is the same as the $8^{\\\\text{th}}$ integer, which is $\\\\boxed{8}$.', 'problem_id': '4', 'response_with_ans': 'We can solve this problem by finding which \"group\" the 5000th number belongs to, and then determine the number within that group. Each group is either counting up from 1 to 13 or counting down from 13 to 1. There are 13 numbers in each direction, but since 1 is counted twice (once at the end of a down-count and once at the beginning of an up-count), counting up and down would consist of 25 numbers (13 up + 12 down).\\n\\nLet\\'s first find out which group the 5000th number belongs to and how many numbers in total are in the complete up and down groups before 5000th number using code:\\n\\n```python\\nnum_group = 5000 // 25\\nremainder = 5000 % 25\\ntotal_numbers_before = num_group * 25\\nprint(\\'Number of complete groups:\\', num_group)\\nprint(\\'Remainder:\\', remainder)\\nprint(\\'Numbers before 5000th number:\\', total_numbers_before)\\n```\\n\\nAfter knowing these values, I\\'ll determine if the 5000th number is in up-count or down-count and its value.\\n', 'round': 0, 'messages': [{'content': 'John counts up from 1 to 13, and then immediately counts down again to 1, and then back up to 13, and so on, alternately counting up and down: \\\\begin{align*}\\n&(1, 2, 3,4,5,6,7,8,9,10,11,12,13,\\\\\\\\\\n&\\\\qquad\\\\qquad12,11,10,9,8,7,6,5,4,3,2,1,2,3,4,\\\\ldots ).\\n\\\\end{align*} What is the $5000^{\\\\text{th}}$ integer in his list?', 'role': 'user'}, {'content': 'We can solve this problem by finding which \"group\" the 5000th number belongs to, and then determine the number within that group. Each group is either counting up from 1 to 13 or counting down from 13 to 1. There are 13 numbers in each direction, but since 1 is counted twice (once at the end of a down-count and once at the beginning of an up-count), counting up and down would consist of 25 numbers (13 up + 12 down).\\n\\nLet\\'s first find out which group the 5000th number belongs to and how many numbers in total are in the complete up and down groups before 5000th number using code:\\n\\n```python\\nnum_group = 5000 // 25\\nremainder = 5000 % 25\\ntotal_numbers_before = num_group * 25\\nprint(\\'Number of complete groups:\\', num_group)\\nprint(\\'Remainder:\\', remainder)\\nprint(\\'Numbers before 5000th number:\\', total_numbers_before)\\n```\\n\\nAfter knowing these values, I\\'ll determine if the 5000th number is in up-count or down-count and its value.\\n', 'role': 'assistant'}], 'time': 16.342331409454346, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"mostly effective\",\n",
" \"Calculation Correctness\": \"mostly correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"mostly correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Each interior angle of a polygon measures 170 degrees. How many sides does the polygon have?', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'The sum of the interior angles of a polygon is $180(n-2)$, where $n$ is the number of sides. That means each interior angle has a measure of $\\\\frac{180(n-2)}{n}$ degrees. We set this equal to 170 degrees and solve for $n$. \\\\begin{align*}\\n\\\\frac{180(n-2)}{n}&=170\\\\\\\\\\n\\\\Rightarrow \\\\qquad 180n-360&=170n\\\\\\\\\\\\Rightarrow \\\\qquad 10n&=360\\\\\\\\\\n\\\\Rightarrow\\\\qquad n&=36.\\n\\\\end{align*} The polygon has $\\\\boxed{36}$ sides.', 'problem_id': '14', 'response_with_ans': \"Quick note: A single backtick should be used for inline code, and triple backticks should be used for code blocks. It seems I accidentally used single backticks for the code block in my previous response. Please use the code below instead:\\n\\n```python\\nfrom sympy import symbols, Eq, solve\\n\\nn = symbols('n')\\nequation = Eq(n * 170, (n - 2) * 180)\\nsides = solve(equation, n)[0]\\nprint(sides)\\n```\\n\", 'round': 1, 'messages': [{'content': 'Each interior angle of a polygon measures 170 degrees. How many sides does the polygon have?', 'role': 'user'}, {'content': \"We can use the formula for the sum of the interior angles of a polygon to solve this problem. The formula is:\\n\\nsum_of_interior_angles = (n - 2) * 180\\n\\nwhere n is the number of sides.\\n\\nAs each interior angle measures 170 degrees, we can write the equation:\\n\\nn * 170 = (n - 2) * 180\\n\\nLet's solve for the number of sides (n) using Python code.\\n\\n```python\\nfrom sympy import symbols, Eq, solve\\n\\nn = symbols('n')\\nequation = Eq(n * 170, (n - 2) * 180)\\nsides = solve(equation, n)[0]\\nsides\\n\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"Quick note: A single backtick should be used for inline code, and triple backticks should be used for code blocks. It seems I accidentally used single backticks for the code block in my previous response. Please use the code below instead:\\n\\n```python\\nfrom sympy import symbols, Eq, solve\\n\\nn = symbols('n')\\nequation = Eq(n * 170, (n - 2) * 180)\\nsides = solve(equation, n)[0]\\nprint(sides)\\n```\\n\", 'role': 'assistant'}], 'time': 16.42014193534851, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'An equilateral triangle has a side of length 12 inches. What is the area of the triangle, in square inches? Express your answer in simplest radical form.', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'The area of an equilateral triangle with side length $s$ is $s^2\\\\sqrt{3}/4$. We have $s = 12$, so our area is $12^2\\\\sqrt{3}/4 = \\\\boxed{36\\\\sqrt{3}}$.', 'problem_id': '18', 'response_with_ans': '```python\\nimport math\\n\\nside_length = 12\\narea = (math.sqrt(3) * side_length**2) / 4\\nprint(area)\\n```', 'round': 1, 'messages': [{'content': 'An equilateral triangle has a side of length 12 inches. What is the area of the triangle, in square inches? Express your answer in simplest radical form.', 'role': 'user'}, {'content': \"To find the area of an equilateral triangle with a side of length s, we can use the formula:\\n\\narea = (sqrt(3) * s²) / 4\\n\\nIn this case, s = 12 inches. Let's calculate the area.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\nimport math\\n\\nside_length = 12\\narea = (math.sqrt(3) * side_length**2) / 4\\nprint(area)\\n```', 'role': 'assistant'}], 'time': 14.153439283370972, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"completely clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Simplify: $\\\\frac{\\\\sqrt{2.5^2-0.7^2}}{2.7-2.5}$.', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'We have \\\\begin{align*}\\n\\\\frac{\\\\sqrt{2.5^2 - 0.7^2}}{2.7-2.5} &= \\\\frac{\\\\sqrt{6.25 - 0.49}}{2.7-2.5} = \\\\frac{\\\\sqrt{5.76}}{0.2} = \\\\frac{\\\\sqrt{576/100}}{0.2}\\\\\\\\\\n&= \\\\frac{\\\\sqrt{576}/\\\\sqrt{100}}{0.2} = \\\\frac{24/10}{0.2} = \\\\frac{2.4}{0.2} = \\\\boxed{12}.\\\\end{align*}', 'problem_id': '2', 'response_with_ans': '```python\\nimport math\\n\\nnumerator = math.sqrt(2.5 ** 2 - 0.7 ** 2)\\ndenominator = 2.7 - 2.5\\n\\nresult = numerator / denominator\\nresult\\n```\\n', 'round': 1, 'messages': [{'content': 'Simplify: $\\\\frac{\\\\sqrt{2.5^2-0.7^2}}{2.7-2.5}$.', 'role': 'user'}, {'content': \"To simplify the given expression, let's break it down step by step and use Python to calculate the values:\\n\\nStep 1: Calculate the values inside the square root: $2.5^2 - 0.7^2$\\nStep 2: Take the square root of the result\\nStep 3: Calculate the denominator: $2.7 - 2.5$\\nStep 4: Divide the result of Step 2 by the result of Step 3\\n\\nNow, let's execute Python code to get the result.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '```python\\nimport math\\n\\nnumerator = math.sqrt(2.5 ** 2 - 0.7 ** 2)\\ndenominator = 2.7 - 2.5\\n\\nresult = numerator / denominator\\nresult\\n```\\n', 'role': 'assistant'}], 'time': 8.612567901611328, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"moderately efficient\",\n",
" \"Code Correctness\": \"completely correct\"\n",
"}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33mquantifier_user\u001b[0m (to quantifier):\n",
"\n",
"Task: Math problem solving.\n",
" Task description: Given any question, the system needs to solve the problem as consisely and accurately as possible\n",
" Task successful example: {'problem': 'What is the sum of all the distinct positive two-digit factors of 144?', 'level': 'Level 5', 'type': 'Number Theory', 'solution': 'Prime factorize $144=2^4\\\\cdot3^2$. The sum of the positive two-digit factors of 144 is $2^4+2\\\\cdot3^2+2^2\\\\cdot3+2^2\\\\cdot3^2+2^3\\\\cdot3+2^3\\\\cdot3^2+2^4\\\\cdot3=\\\\boxed{226}.$', 'problem_id': '0', 'response_with_ans': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'round': 0, 'messages': [{'content': 'What is the sum of all the distinct positive two-digit factors of 144?', 'role': 'user'}, {'content': 'To find the sum of all the distinct positive two-digit factors of 144, we need to first find all these factors. We can do this by iterating through the numbers from 10 to 99 and checking if they are factors of 144. Then, we can sum these factors and print their sum.\\n\\nHere\\'s a Python script to accomplish this:\\n\\n```python\\ntwo_digit_factors = []\\n\\nfor i in range(10, 100):\\n if 144 % i == 0:\\n two_digit_factors.append(i)\\n\\nsum_of_factors = sum(two_digit_factors)\\nprint(\"The sum of all the distinct positive two-digit factors of 144 is:\", sum_of_factors)\\n```\\n\\nPlease run this script to find the desired sum.', 'role': 'assistant'}], 'time': 11.140539407730103, 'trial': -1}\n",
" Task failed example: {'problem': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'level': 'Level 5', 'type': 'Algebra', 'solution': 'We have \\\\begin{align*} (2x+10)(x+3)&<(3x+9)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)&<3(x+3)(x+8) \\\\quad \\\\Rightarrow\\n\\\\\\\\ 2(x+5)(x+3)-3(x+3)(x+8)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (2x+10-(3x+24))(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (-x-14)(x+3)&<0 \\\\quad \\\\Rightarrow\\n\\\\\\\\ (x+14)(x+3)&>0.\\n\\\\end{align*} This inequality is satisfied if and only if $(x+14)$ and $(x+3)$ are either both positive or both negative. Both factors are positive for $x>-3$ and both factors are negative for $x<-14$. When $-14<x<-3$, one factor is positive and the other negative, so their product is negative. Therefore, the range of $x$ that satisfies the inequality is $ \\\\boxed{(-\\\\infty, -14)\\\\cup(-3,\\\\infty)} $.', 'problem_id': '0', 'response_with_ans': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'round': 1, 'messages': [{'content': 'Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.', 'role': 'user'}, {'content': \"First, let's simplify and isolate the inequality to solve for $x$. For this, we need to expand the expressions on both sides and then move everything to one side.\\n\\nStep 1: Expand the expressions on both sides\\n$(2x+10)(x+3) = 2x^2 + 6x + 10x + 30 = 2x^2 + 16x + 30$\\n$(3x+9)(x+8) = 3x^2 + 24x + 9x + 72 = 3x^2 + 33x + 72$\\n\\nStep 2: Move everything to one side\\n$0 < 3x^2 + 33x + 72 - (2x^2 + 16x + 30) = x^2 + 17x + 42$\\n\\nNow we need to find the factorized form of the quadratic inequality $x^2 + 17x + 42 > 0$ and then find intervals where the inequality holds true.\\n\\nLet's first find the factorized form of the quadratic:\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"I apologize for the confusion in my previous message. Let's find the factorized form of the quadratic using Python code.\\n\\n```python\\nfrom sympy import symbols, factor\\r\\nx = symbols('x')\\r\\nfactorized = factor(x**2 + 17*x + 42)\\r\\nfactorized\\r\\n```\\n\\nPlease run the Python code above to find the factorized form of the quadratic, and we can proceed to determine the intervals where the inequality holds true.\", 'role': 'assistant'}], 'time': 24.91333508491516, 'trial': -1}\n",
" Evaluation dictionary: [\n",
" {\n",
" \"name\": \"Problem Interpretation\",\n",
" \"description\": \"Ability to correctly interpret the problem.\",\n",
" \"accepted_values\": [\n",
" \"completely off\",\n",
" \"slightly relevant\",\n",
" \"relevant\",\n",
" \"mostly accurate\",\n",
" \"completely accurate\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Mathematical Methodology\",\n",
" \"description\": \"Adequacy of the chosen mathematical or algorithmic methodology for the question\",\n",
" \"accepted_values\": [\n",
" \"inappropriate\",\n",
" \"barely adequate\",\n",
" \"adequate\",\n",
" \"mostly effective\",\n",
" \"completely effective\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Calculation Correctness\",\n",
" \"description\": \"Accuracy of calculations made and solutions given\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"neither\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Explanation Clarity\",\n",
" \"description\": \"Clarity and comprehensibility of explanations, including language use and structure\",\n",
" \"accepted_values\": [\n",
" \"not at all clear\",\n",
" \"slightly clear\",\n",
" \"moderately clear\",\n",
" \"very clear\",\n",
" \"completely clear\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Efficiency\",\n",
" \"description\": \"Quality of code in terms of efficiency and elegance\",\n",
" \"accepted_values\": [\n",
" \"not at all efficient\",\n",
" \"slightly efficient\",\n",
" \"moderately efficient\",\n",
" \"very efficient\",\n",
" \"extremely efficient\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" },\n",
" {\n",
" \"name\": \"Code Correctness\",\n",
" \"description\": \"Correctness of the provided code\",\n",
" \"accepted_values\": [\n",
" \"completely incorrect\",\n",
" \"mostly incorrect\",\n",
" \"partly correct\",\n",
" \"mostly correct\",\n",
" \"completely correct\"\n",
" ],\n",
" \"sub_criteria\": []\n",
" }\n",
"]actual test case to evaluate: {'problem': 'Simplify $\\\\frac{2m+8}{3}-\\\\frac{2-m}{3}$.', 'level': 'Level 5', 'type': 'Prealgebra', 'solution': 'Both fractions have the same denominator, so we can subtract them: \\\\[\\\\frac{2m+8}{3}-\\\\frac{2-m}{3}=\\\\frac{(2m+8)-(2-m)}{3}\\\\] Distributing the negative sign across the parentheses, we get \\\\[\\\\frac{2m+8-2-(-m)}{3}=\\\\frac{2m+8-2+m}{3}=\\\\frac{3m+6}{3}\\\\] Notice that every number in the numerator has a common factor of 3. We can use the distributive law in reverse to get \\\\[\\\\frac{3m+6}{3}=\\\\frac{3(m+2)}{3}=\\\\frac{\\\\cancel{3}(m+2)}{\\\\cancel{3}}=\\\\boxed{m+2}.\\\\]', 'problem_id': '19', 'response_with_ans': '', 'round': 15, 'messages': [{'content': 'Simplify $\\\\frac{2m+8}{3}-\\\\frac{2-m}{3}$.', 'role': 'user'}, {'content': 'To simplify the expression, we can combine the fractions as they have the same denominator:\\n\\n$\\\\frac{2m + 8}{3} - \\\\frac{2 - m}{3} = \\\\frac{(2m + 8) - (2 - m)}{3}$\\n\\nNow, I will calculate the numerator and finish simplifying the expression.', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '\\n', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': \"My apologies for the confusion in the previous responses. Let me simplify the expression step by step now. \\n\\nWe have the expression:\\n\\n$\\\\frac{2m + 8}{3} - \\\\frac{2 - m}{3}$\\n\\nWe can combine the fractions as they have the same denominator:\\n\\n$\\\\frac{(2m + 8) - (2 - m)}{3}$\\n\\nLet's further simplify the expression using Python code.\", 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': '', 'role': 'user'}, {'content': '', 'role': 'assistant'}], 'time': 37.317880630493164, 'trial': -1}\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
"\u001b[33mquantifier\u001b[0m (to quantifier_user):\n",
"\n",
"```json\n",
"{\n",
" \"Problem Interpretation\": \"completely accurate\",\n",
" \"Mathematical Methodology\": \"completely effective\",\n",
" \"Calculation Correctness\": \"completely correct\",\n",
" \"Explanation Clarity\": \"very clear\",\n",
" \"Code Efficiency\": \"not applicable\",\n",
" \"Code Correctness\": \"not applicable\"\n",
"}\n",
"```\n",
"\n",
"--------------------------------------------------------------------------------\n"
]
}
],
"source": [
"criteria_file = \"../test/test_files/agenteval-in-out/samples/sample_math_criteria.json\"\n",
"criteria = Criterion.parse_json_str(open(criteria_file, \"r\").read())\n",
"outcome = {}\n",
"\n",
"for prefix in os.listdir(log_path):\n",
" for file_name in os.listdir(log_path + \"/\" + prefix):\n",
" gameid = prefix + \"_\" + file_name\n",
" if file_name.split(\".\")[-1] == \"json\":\n",
" test_case, ground_truth = remove_ground_truth(open(log_path + \"/\" + prefix + \"/\" + file_name, \"r\").read())\n",
" quantifier_output = quantify_criteria(\n",
" llm_config={\"config_list\": config_list},\n",
" criteria=criteria,\n",
" task=task,\n",
" test_case=test_case,\n",
" ground_truth=ground_truth,\n",
" )\n",
" outcome[gameid] = quantifier_output\n",
"\n",
"# store the evaluated problems\n",
"with open(\"../test/test_files/agenteval-in-out/evaluated_problems.json\", \"w\") as file:\n",
" json.dump(outcome, file, indent=2) # use `json.loads` to do the reverse"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qbrRRiP_EGCT"
},
"source": [
"## Plotting the estimated performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here you can find an example of how to visualize the obtained result in the histogram form (similar to the one in the blog post)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LKu2xZJcEGCT",
"outputId": "7780bc7c-382f-4ad3-b8c6-ac6051302303"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'completely off': 0, 'slightly relevant': 1, 'relevant': 2, 'mostly accurate': 3, 'completely accurate': 4, 'inappropriate': 0, 'barely adequate': 1, 'adequate': 2, 'mostly effective': 3, 'completely effective': 4, 'completely incorrect': 0, 'mostly incorrect': 1, 'neither': 2, 'mostly correct': 3, 'completely correct': 4, 'not at all clear': 0, 'slightly clear': 1, 'moderately clear': 2, 'very clear': 3, 'completely clear': 4, 'not at all efficient': 0, 'slightly efficient': 1, 'moderately efficient': 2, 'very efficient': 3, 'extremely efficient': 4, 'partly correct': 2}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vscode/.local/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/home/vscode/.local/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/home/vscode/.local/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:2244: RuntimeWarning: invalid value encountered in multiply\n",
" lower_bound = _a * scale + loc\n",
"/home/vscode/.local/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:2245: RuntimeWarning: invalid value encountered in multiply\n",
" upper_bound = _b * scale + loc\n",
"/home/vscode/.local/lib/python3.10/site-packages/numpy/core/_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
"/home/vscode/.local/lib/python3.10/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
" arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
"/home/vscode/.local/lib/python3.10/site-packages/numpy/core/_methods.py:198: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
}
],
"source": [
"# computing average and 95% interval for failed and successful cases on all criteria\n",
"try:\n",
" criteria = Criterion.parse_json_str(open(criteria_file, \"r\").read())\n",
"except: # noqa: E722\n",
" pass\n",
"\n",
"\n",
"nl2int = {}\n",
"for criterion in criteria:\n",
" score = 0\n",
" for v in criterion.accepted_values:\n",
" nl2int[v] = score\n",
" score += 1\n",
"print(nl2int)\n",
"\n",
"average_s = {}\n",
"average_f = {}\n",
"\n",
"conf_interval_s = {}\n",
"conf_interval_f = {}\n",
"\n",
"for criterion in criteria:\n",
" task = {\"s\": [], \"f\": []}\n",
"\n",
" for game in outcome:\n",
" try:\n",
" tmp_dic = eval(outcome[game][\"estimated_performance\"])\n",
" if outcome[game][\"actual_success\"] == \"false\":\n",
" task[\"f\"].append(nl2int[tmp_dic[criterion.name]])\n",
" else:\n",
" task[\"s\"].append(nl2int[tmp_dic[criterion.name]])\n",
" except: # noqa: E722\n",
" pass\n",
"\n",
" average_f[criterion.name] = np.mean(task[\"f\"])\n",
" average_s[criterion.name] = np.mean(task[\"s\"])\n",
"\n",
" conf_interval_s[criterion.name] = stats.norm.interval(0.95, loc=np.mean(task[\"s\"]), scale=stats.sem(task[\"s\"]))\n",
" conf_interval_f[criterion.name] = stats.norm.interval(0.95, loc=np.mean(task[\"f\"]), scale=stats.sem(task[\"f\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final plot would be saved in `../test/test_files/agenteval-in-out/estimated_performance.png`"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 695
},
"id": "zqa86vwgEGCT",
"outputId": "248cd0bc-0927-4d9f-b911-088bd76acf5d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_394256/2108490914.py:34: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations.\n",
" plt.tight_layout() # Adjust subplot parameters to fit the labels\n"
]
},
{
"data": {
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Qc1hU/fB6V17b/2UJrffeey8HHnhgNtxww5x44omu/S/wY7N6/i9LaE2bNi2bb755tthii/Tp0ycrrLDCz6pFeAcAAAAAgF9d1QfhkydPTqNGjYrb/pcAz7Rp0zJs2LDssssuWXnllRfcCQDMZ1X7ySlTpqRBgwbFbf9LgOezzz7LqFGjsvXWWy+Q4M4TTzyR8ePHZ9ttt80yyyxT3GdeAZ55DfgnyYwZM4qzRwALVtX2/Oyzz+a7777L73//+9SoUaPYXucV4Jk5c+ZcM+uUl5fnu+++M4PWT1T12r/99ttZeumls+yyyxb79h8L8MyaNas421Hy/XUfP3586tWrlyZNmpTqdBZ5Ve/Hq6++moqKiqy77rrF7W+++WY6deo0V4BnXm1h+vTp+fbbb7Psssv+7HqEdwAAAAAA+FVVfRD+yCOP5IEHHshuu+2WLbbYojiQ+2MBHoAlQdV+8qGHHsqoUaOy1157ZeONNy4GGX8swPPfjjc/a33kkUfSpUuXTJs2LU899VSaNm06x/aqAZ5LL700f/vb35IkY8aMyZZbbmlZFyixebXnZZddNnfeeWcaN248R0CwaoDnxhtvzNprr50k+fe//53VV1+9ZOewqKp67R9++OEcf/zx2XHHHTNgwIDiElo1atSYK8Bz6qmn/mjfz8/3w/tx0kknZc0118zf//731K1bN8n3S2n92Aw8yffh2aZNm/5qNc0ddQUAAAAAgJ/ph4NC3bp1y0UXXVR8sF35E92rrbZaLrroouy888659dZbs/feexeP8fe//z3jxo1b8MUDLAA/7Cd79OiRs88+O0svvXSSpEaNGikUCmnUqFFuueWWbL/99rnzzjvToUOHVP5M/nnnnZcPPvigeMwFEdwZNWpUevbsmQ8//DDDhw9P06ZNM3PmzMyYMaO4//7775/LLrss5eXl6dixY+6555506tQpW2+9dZ555pn5UiPw0/yw7+nVq1fee++9DBgwII0bN87s2bPn6EsGDx6czp0755VXXsm+++6bzz//PEceeWS22WabfPjhh6U6jUXSD699375989prr+XPf/5zcdbJGjVqpKKiIiussELGjh2bFi1a5Jxzzskpp5ySJDnggAPSvn37kp3D4uSH9+Okk07KM888k86dO6devXpzbG/dunUuvvji/OEPf8g111yTww8/PEly+OGHp2vXrvnyyy9/tbrmXkAYAAAAAAB+hh8+CO/Zs2feeeedPPLII1ljjTUybdq0TJo0qbisS2WAp1Ao5B//+EcOPPDANGjQIJdcckmuv/76tGnTZq7lYQAWZfPqJ99+++08+eSTadWqVaZNm5YpU6YUA4+VAZ727dvn7rvvzl//+tc0aNAgV155ZRo3bpyDDjponstSzY9ae/XqlVdeeSWjR4/OJptskkmTJuW2227LzJkzi3VVq1Yt+++/f5KkY8eO2W233VK/fv0cf/zxWWWVVeZLncB/N6++54ft+eGHH069evWy3XbbpVq1aqlevXoGDx6csrKyXHTRRVl99dUze/bsHHXUUVlqqaVKfEaLjh+79mPHji1e+3HjxqVVq1ZZccUVM3v27GKAZ/PNN8/AgQNz7733Zty4cenUqVMmTZqUxo0bl/akFmE/dj+efPLJbLLJJpk8eXL+9a9/pXHjxtlkk01SKBSKAZ7OnTvnyiuvzKhRo/L++++na9eucyxn9kuZeQcAAAAAgF/sxx6EP/zww9l0000zadKkXHXVVenevXteeumlJElFRUVWW221DBkyJLvsskuuv/76DBs2LD169Mg222wjuAMsVuY1i01lP/n73/8+kyZNyuWXX55u3brltddeS5KUl5cXAzw77rhjbrnlltx+++3p1atXdtxxxwUW3Kms9dFHHy0Obg4fPjyHHXZYHnjggTRq1CjVqlUrzgy0//77Z/DgwWnRokU6d+6cbt26ZcUVV5wvtQL/2U9pz8OGDUv79u1z1113pWbNmqlevXrKy8uTJBdeeGGOOOKI1K1bNyeccEJ69OiR5ZZbrpSntMj4Kdf+2muvzdZbb50RI0YkSXEJrRVWWCH//Oc/s8IKK2TcuHHp27dv+vTpI7jzC/yU+3HNNdekffv2ue+++5J8P7NdZYBnyJAhadu2bcrLy9O7d++ccMIJadSo0a9WX1mh8qsoAAAAAAD8DP/pQfjvf//74qDQ8ccfn0033TSPPfbYXO9955130rdv36y55po54ogjsvLKK5fqdAB+dT+1nzzhhBOy3nrr5amnniq+t7y8PNWrV8+kSZNyxBFHpGXLluncufN86yd/GDKqnHGnaq3XX399evfunQ033DCPPvroHO+rqKgohopefPHFLLfccvp0KJGf0vdcf/316dOnT9Zff/05vkdL/q//SZKRI0dm/fXXT7NmzRb4eSyKfuq179u3b9Zaa62MHTt2jvfPnj07NWrUyBdffJHrrrsuf/nLX9K8efNSnMpi4afej5NOOilt2rSZ635Uvv/NN9/MP//5z+ywww6/elsQ3gEAAAAA4Gf7qQ/Ce/XqlY022qg4yFt1cLfyzxMnTkxFRUWWXXbZkp0PwK/t1+gnKwfQZ8yYkSlTpsy3fvJ/qXWDDTYoDvRXDjLP6zhAafxa7blqgIef5te+9vrUX+bX/tr2w9d/LcI7AAAAAAD8LL/Wg3CAxdWv2U8uyMHbylpfffXVPPLII/p0WIT5Hq10XPuFy6hRo9K7d++MGzduobwfwjsAAAAAAPwio0aNSs+ePfP6669n1KhRC92DcIBSqxwwfOWVVxb6MMwzzzyTI444Im+//bY+HRZxY8aMSdeuXfPmm2/mkUceySabbKI9LyD33XdfTj755Lzxxht56KGH9KUlNmbMmHTr1i2vvvpqRo8evVC2BZ8AAAAAAAB+tvfeey9nnXVWXnnllYwePdrABMAPfPDBB+nTp0/GjRu30A4YVtWsWbPUrVs3Dz/8sD4dFnGFQiETJkzIqFGjFvq+Z3Hzyiuv5Nlnn82TTz6pL10IvPTSSxk/fnweffTRhbYtmHkHAAAAAIBf5NRTT82uu+6aDTbYIJMnT86wYcPSs2fPheZBOECpnXPOOWnXrt1CMWBYUVGRatWqzXNbZQ0zZ85MrVq1MmnSpAwfPlyfDgupH2vP5eXlqV69epJk0qRJady4cbE99+rVK7/73e+051/ox5YyrHrtP/jggzRv3jyTJ0/O8OHD06NHD33pfPJT7sdHH32UVVZZpfh1uHfv3gtVWxDeAQAAAADgR/2nQd6qD8OT5Jtvvsl1112Xnj17ZqONNsro0aOTlP5BOMD89J/6ycoQTKWqA4YbbLDBAu8nq/bbL7/8ct5///0UCoW0bNky66yzzhz7VatWLbfeemv233//bLbZZvp0WMj8MCQyYcKE1KxZM2uuuWbq169f7JvKy8tTKBRyzTXXpEuXLvn973+vPf9CVa/9d999l6+//joNGzZM/fr1kyQzZsxI7dq1UygUMmPGjFx44YU5/fTTs/HGG+eRRx5J4tr/mqrejy+++CJTp07N7Nmz06pVq7n2nT59eoYNG5bjjjsuG2644UIT3EksmwUAAAAAwI+o+iD8vvvuy4svvpjPP/88rVq1ypFHHll8wF05OPTGG2/knHPOKcmANEApVO0n77333rzwwgv54osv0rx583Tu3HmO4E6SvPvuuznppJPyu9/9boH3kxUVFcVa+/fvn0suuSRffvllkqRmzZo566yzcvjhh6d+/frF/TbddNMccMABGTp06AKtFfjPqrbnM888M1deeWXee++9JMmaa66ZoUOHZvPNN0+S4n4rrLBCtt1224wcOTKJ9vxzVe33L7rooowYMSL/+te/suaaa2bLLbfMkCFDUrt27SRJWVlZ6tSpk88//zzNmzcX3JkPqt6P888/PzfddFNee+211KpVK3vttVe6d++eli1bFkO2derUyQcffJCtt956oWsLZt4BAAAAAGAuVWeS6NWrVy644ILMmDEjNWrUyOzZs7PNNtvk5JNPTtu2bed42H3VVVflkEMOSbLwPAgHmB+q9pM9e/bM4MGDM3369GI/ueGGG+byyy/PeuutN8fMPMOHD8/f/va3JAuun6y6nEiPHj0yaNCg7LrrrjnooINSu3btDBo0KGPGjMlJJ52UY489Nssss8xcS5Do02HhULVtnnDCCTn//POz+eabp0OHDvn6669z2WWXZebMmRk8eHD+9Kc/FYMkVWnPP0/Vfr9bt2658MIL06pVq2y55ZZ5++23M2bMmGy33Xa5/vrr07Rp03nOzOba/3rm1RZat26dP/zhD5kwYULuvvvubLPNNjnxxBOz1VZbzXOWvIXpfgjvAAAAAADwo0466aQMGDAg+++/f4466qist956Oe+889K3b99sueWW6dOnT/7whz/M9dD7h0tqASxOqg4Y9u3bN2eccUYOOuigHHbYYWnbtm369++fU089Nb/73e9ywQUXZPPNN18oBnCvuOKK9OjRI3/729/SpUuXrLHGGikvL0/r1q3z73//O4VCISeeeGK6d++eZZZZZq5zBRYeQ4YMSe/evXPQQQelY8eOad26db755pust956+eCDD9K4ceNcdNFF+ctf/lIM8GjPv45Bgwbl5JNPzmGHHZYjjjgi66yzTt59991suumm+eqrr7LlllvmpptuyoorrjjH98T/aZlFfr7K/5scfvjhOeyww7LOOuvk3//+d9q2bZvPP/88bdu2zWmnnVYM8FS2g4WtPfhkAAAAAACQ5PsBnapGjBiRq666KkcccUROOumkbLrppqlRo0aGDRuWhg0b5l//+ld69eqVRx55JLNnz57jGII7wOKscrBvxIgRufrqq3P44YenT58+adu2bSoqKnLzzTencePGeeWVV3LUUUdl7NixKS8vn+MY8yu48+9//7u4HFZVn376aYYNG5YNNtgghx9+eNZYY41Mnjw566yzTr799tucfPLJ2XLLLXP22Wdn8ODB+eKLL+Y4V2DB++yzzzJjxoy5Xn/nnXdy/fXXp127djniiCPSunXrTJo0KZtuummmT5+eLl26pEGDBjn22GNz++2357vvvkuiPf8vZs6cOc/Xn3322Vx33XXZfffdc/TRR2edddbJpEmTsscee6R69erZfvvt8/jjj2e//fbLp59+Osf3xII7P9/kyZPn+jqaJC+//HKGDRuWHXbYIR07dsw666yTKVOmZJdddkm1atWy77775vnnn0+/fv0yevTolJeXF9vBwtYefDoAAAAAAJZgzz33XO67777MnDlzjgfY3377be6+++4ss8wyOfzww9OqVatMmTIlv/3tbzNp0qQMHDgwffv2zcsvv5zTTz89Dz/8cGbPnr3QPQQH+KWeeeaZjB49OhUVFXO8PnXq1Nx8881Zeuml07Fjx7Rs2TLffvtt1l577WI/2aNHj7z++uvp2rVrxo4dO9cxfm2vv/56WrVqleOOOy5TpkyZY1udOnXy73//O/vuu2/WXXfdTJs2LTvssEMmTpyYQYMGpV+/funRo0eS5LTTTsuFF144zxAQsGA8//zzWWONNXLZZZcVQ9KVpk2blqeffjqHHHJI1l577UybNi3bbrttvvrqq5x33nkZOHBgjjrqqEycODFdu3bNnXfeOc8QEPM2ZsyYbLXVVnn//ffn2vb222/n1VdfzVFHHZXWrVtn6tSpadeuXb788stcfPHFufzyy7P11lvn0UcfzQEHHJAJEyYs+BNYzIwdOzbbbrtt7r333rm+jr799tt55513cswxx+Q3v/lNpk6dmi222CITJ07MueeemzPOOCP77rtv/vnPf+bss8/OmDFj5hkCWhgI7wAAAAAALKE+++yz7LLLLmnfvn1effXVObbVr18/DRo0yNFHH50NNtgg3333XXbbbbd8+eWXGTBgQI488sgceeSRWX311fPEE0/kjDPOyH333bfQPgwH+DkmTJiQ3XffPdtvv31ef/31ObbVq1cva6yxRg488MCsv/76+e6777Lrrrvmyy+/zJlnnpnDDz88ffv2zVprrZXnnnsuxxxzTEaPHj3XLGe/phkzZmSLLbbId999N9fMPksvvXRefvnlHHzwwamoqMjJJ5+cV199Nd27d8+f//znJMmOO+6YTTbZJK1bt86ZZ56ZSy+9dL7WC/y4Tz75JDVq1MjTTz89Vzv87W9/m1deeSV77rlnZs+ena5du+btt99Onz59sscee6ROnTrZb7/90qxZsxQKhey777655557SnQmi5by8vKMHDky//rXv3LZZZfNde07dOiQkSNHZuutt87MmTNz8MEH5+OPP07//v2zyy67pHnz5unSpUtq166d0aNHZ5tttsnnn39eorNZ9JWXl2fcuHF5/vnn8/jjj891P/baa69cf/312WabbTJz5swcddRRef/999O/f//stddead68efbYY48kyaOPPpojjjgiTz/9dClO5b8qK/iKCwAAAACwRPr2229z1VVX5aWXXsr555+fhg0bpqKiYo4p/WfPnp0aNWpk8ODB6dWrV0444YT07NkzSy21VJLkmGOOyVNPPZVnn302f/jDH3LPPfekdu3apTolgF/VtGnTctFFF+Xtt9/Oeeedl4YNG/7ovhdffHF69OiRE044Ib179y72hUcffXSeffbZvPDCC9lss83y8MMPp1atWvOt5vfffz/LL7986tatm5EjR2aTTTbJ8ssvP8c+hUIhbdu2zdSpUzN27NjUr18/STJ9+vQ0b9487du3z6xZs9K7d++suuqq861W4D979tln85vf/CYNGjTIc889l7XXXjt16tSZY59vvvkmm2yySVZcccU8+OCDqVmzZpLvwz9t2rTJgQcemDfeeCNXXXVVmjVrVorTWOR8+eWXue+++7LbbrulcePG+eabb+b4Prny91dffTVbbbVVdtxxx1x99dXFvv3hhx9Ohw4d0q5duzzzzDN57rnn0rRp0xKf1aLr66+/zrPPPptNNtkkjRo1yrvvvpsWLVrMtUzvv//972y11VbZZJNN8o9//KP4+rhx47LLLrvkz3/+cx566KGMGjUqK6ywwoI+jf/KzDsAAAAAAEuo+vXr54gjjsill16ahg0b5vzzz89dd92VWbNmFfepnLnh6aefTu3atXPYYYcVgztJ8sQTT+Q3v/lNbr755lx77bWCO8Bio1AopG7duunatWsuueSSNGzYMIMGDcqDDz44z+Wvnn766dSsWTOHHHLIHH3hc889l1atWuWqq67KjTfeON+CO5U/r9+iRYvUrVs3w4YNy+67754zzzwzX3311Rz7fvXVV3nzzTfTtGnTYnAnSa655prUrVs3nTp1yiWXXJJVV13VjGpQApV9zEYbbZQGDRrkggsuyMYbb5wbb7xxruWvPvroo7z11ltZe+21i8GdJLnpppuy4oorpkePHhk5cmSaNWumPf8EFRUVWXbZZbP//vuncePG6dGjRzbccMN8/PHHxYB75e9vvfVWJk6cmD333HOOvv3+++/P2muvncsvvzyvvvpqmjZtOt+XTVycLb300tl+++3TqFGj9O/fP9tuu20effTR4ue58uvfa6+9lk8++SQbb7zxHO8fMWJE6tevn65du+aZZ57JCiussFDeD+EdAAAAAIAlVEVFRerUqZOaNWvmueeeS7du3dK7d++MGjWqGOCpfBg+efLkOX5PkuHDh2fixInZY489svfee2fllVc2KAQsNsrKylJeXp5atWqlVq1aefrpp9OjR4907Ngxjz/++FwDf7Nnz57j9yQZNmxYxo8fnz333DP777//fO0nq9ZTUVGRDTfcMHvuuWcuvvjinH766fnyyy+L2xs1apQddtgho0aNyvnnn5933303Q4YMyQUXXJBlllkmK6ywQnFw+oczGwDz3w8Xz1l99dXTpk2bHH/88XMFeFZeeeVsuOGGuf766zN27NhMnDgx1157bYYOHZqmTZumcePGxWCJ9vzfVb32M2fOzJQpU/Luu++mQ4cO+fjjj+fYZ6WVVkq1atUydOjQ4ntuueWW3HnnnVlxxRXTpEmTNGrUKIVCYY6ZLfnpfvg1s2HDhpk4cWJ69+6d0aNHp7y8PGVlZUmSddddN02bNs2DDz6Y8ePH57vvvsuNN96Ym266Ka1bt86KK66YunXrLrT3w7JZAAAAAABLoPLy8uIAzoQJE7LCCivk6quvTp8+fdKkSZMMGjQo2223XfEnuK+88socffTRadu2bQ499NC89tpruemmm1KrVq089thjlgIAFjs/7CeXX375XHHFFTnllFNSt27dDB06NO3atSsOAF5yySXp3Llz1ltvvfTu3TvPP/98br311tSoUWO+95OFQqE4eHnmmWemRo0aOf744/P666/n9NNPz6233ppjjz02ffr0ybLLLpskeeCBB4r7VGrVqlUeeuihNG/efK5lFIEFo2rbGzJkSJo1a5Y99tgj99xzT0466aS88847ueiii/LXv/61OMvXBRdckJNOOimzZ89OgwYNMmnSpKyyyip59NFH07x58zn6CH5c1Wt/zz33ZOONN85SSy2VM844I4MGDcrGG2+cESNGFJcfmzJlSv7617/m3nvvTZs2bdKoUaOMGzcuTZo0yeOPP55VVlmllKezyKt6P6677rpsuOGGad26da688sr07Nkzq622Ws4+++xsvfXWqV69er7++uucfPLJufjii7Pmmmumdu3aee+997LMMsvksccey6qrrrpQtwVfcQEAAAAAljCFQqE4IN2jR4/st99+efvtt7PPPvvkzDPPzBdffJHu3bvn4YcfLs7As/POO6dz58559dVXc8ABB+Sss85Kw4YN88ADD1gKAFjsVO0nTzjhhBx++OF5++23c8ABB6Rv376ZMmVKDj/88IwZM6Y4K8ABBxyQzp0758MPP0yHDh0ycODA1KtXb4H0k5UDkQMHDsxJJ52Uzz77LF9//XXWXXfd9O7dO3vvvXcuvPDCnH766fn888+TJDvuuGOuuuqqDB48OIceemjOPvvsjBkzJs2bN095ebngDpRIZdvr169funTpkmeeeSZTp07NLrvsklNPPTWtWrVKly5dcuONN2batGlJkuOOOy5Dhw7NgQcemPXXXz+dO3fOE088UWzPC2tYYWFTee27deuW3XbbLXfeeWcaNGiQE088Md26dcvTTz+dv/zlL8UZeBo0aJBrrrkmRxxxRCoqKjJhwoRst912xeCOGSl/mcr7ceKJJ+bwww/PiBEjkiTt27fPgAED8t577+XEE0/M6NGjM3v27Cy99NI54YQTctZZZ6V27dqZNWtWdtpppzz++OPFZSAX5rZg5h0AAAAAgCXU0KFD07Fjx3Ts2DHHH398VltttXz77bcZMWJEevTokeWWWy5nn312tt1229SuXTtff/11xo8fXxwM2mijjbLsssvOMTsFwKKu6k/lX3755Tn66KNz5JFH5sQTT0zz5s0zbdq0XH/99Tn55JPToEGDDB06NG3btk2tWrXy3Xff5Y033sjLL7+cFVdcMRtuuGGWWWaZ+dZPVj3urFmzsvnmm6dVq1Y544wzstpqqxX3e+mll3LmmWfm1ltvzTHHHJNevXr96ExA+nQojcq2VygU8sknn2T77bdP27Zt07dv37Ro0aK43913351+/foVZ+DZe++9U69eveL22bNnp1q1aqlWrZr2/BNVvU6PP/54/vrXv2bnnXdOz549s/rqqydJJk6cmLPOOivnnHNONtlkkzlm4Jk1a1amTJmS8vLyNGjQIHXq1HHtf4Gq1+7ZZ59Nhw4dsuOOO6Z79+7Fr22TJ0/OTTfdlF69emW11VbLwIEDs9VWWxVnDZ01a1bxODVr1lwk7ofwDgAAAADAEuKHS6Dsu++++eyzz3LFFVdktdVWKw5YzyvAs91226VWrVr/9ZgAi7If9mkHH3xw3n333Vx11VVp1apVcfu8Ajybb755cdDwPx1zfjj77LPTtm3bdO3aNQMGDMh2222XQqGQQqFQ/Ld/GODp06dPlltuuflaF/C/GzFiRNZbb73stNNOGTZsWDbddNMkc/YlPwzwdOjQIUsttVQpy14svPnmm/n8889z4IEH5p577slaa62V5P+u/X8K8FS1MC/NtCh544038vHHH6d9+/Z5+OGHs8EGG8yxfV4Bnm222WahD+n8mBqlLgAAAAAAgAWjcsCne/fuWWqppfL5559n3333zWqrrTbHgFD9+vXzl7/8Jcn3y2qdeOKJOeecc7LtttvONTAtuAMsTir7tM6dO6esrCxff/11DjjggLRq1aoYhCkUCqlbt27233//JMnJJ5+cww8/PFdccUW22GKLuQYN53c/+cADD6Rnz56pU6dO6tWrl4YNGyb5fimtsrKy4iDyeuutl969eydJhgwZkilTpmTQoEFp0qTJfK0P+Omuu+66HHTQQWnWrFnq1q2bNddcs7itsv8pKyvLbrvtluT7pbWOP/74fPfddzn00EPnGbTmpzn55JNz2mmn5Xe/+11WX331YnAn+b9r36RJk/Ts2TNJcu6552afffbJ8OHD07x58zmOJbjzy51//vnp1q1b2rVrl4022qgY3KkajGrUqFH22WefJEmvXr3Sp0+f9O/fPzvuuOMi+X+URa9iAAAAAAB+tnfeeSe33XZbTj/99DzyyCP58ssvk8w9uFwZ4Bk4cGAmTZqUgw8+OE888UQpSgZYoD744IPccccdGTJkSO69995MmDBhju2VgZjKAM8pp5yS6dOn509/+lOefvrpBV7vDjvskG7duqV+/fqZPHlyXnvttSTfLztStd4kWW+99dKnT59su+22uf/++xfJwU1YnG255ZbZeeed8/XXX+fTTz/Nq6++mmTe7Xm33XbL6aefngYNGuSCCy7IrFmzSlb34mCNNdZIo0aN8tprr+Xrr7/Od999l+T7WXeS/7v2lQGe7t27Z+zYsencuXNxH349q6yySjbYYIM8+eSTefHFFzNu3LgkcwejKgM8AwcOzDPPPJPBgwcvsm3BV2QAAAAAgCVIq1atMmTIkGyzzTapVq1aXnjhhUycOHGe+1YGePr06ZOmTZumdevWC7hagAWvefPmGT58eDbeeOPMnj0748aNS0VFRcrKyuYaQK8M8Bx//PFp0aLFXLMvzG+zZ89OWVlZzj777Bx88MEpLy9Pr1698uabb6Z69erzHPD/7W9/m/POOy8vv/xyGjduXHwdKK2Kioqsttpqufjii7P11ltnypQpOe2005Ik1atXz+zZs5PM2Z532WWXXHHFFXnsscdSr169ktW+ONhvv/1yxRVXpFatWnnhhRdyySWXJPk+4D6vAE/37t1z6qmnZsiQIYKQv6LKa/2Xv/wlffv2zYYbbpgvv/wyt912249+vWrUqFH23nvvXHfddRk6dGhq1669IEv+1ZQVfEUGAAAAAFgsVZ1WPskcS2Pdd999Oe200/LUU0/lzDPPLC4BMC9Tp05NktSrVy/l5eVzLQkDsKj6T/3kY489luOOOy4vvfRSevfundNPP32ufSrf/91336WiomK+9pNV/9151V8oFNKrV6+cffbZadasWf75z39mlVVWmaOe/3S+wILz39re+++/n2OOOSYjR47Mn/70p9x2221Jvg/s1ahRI8nc7dn3aD/Nf7v2t912Ww444IBMmzYtV1xxRQ455JC53lf558p74Nr/fP/tftx5553p379/XnrppVx66aU54ogjfnTfRf1+CO8AAAAAACyGqj60njlzZqZPn56Kioo0bty4uM/999+ffv365dlnn815552X4447rjTFApRA1X5y9uzZ+fbbb1OtWrU0bNiwuM9jjz2WY445JuPGjUv//v3Tr1+/JPMO8Pzwz/Or1n/84x954YUX8t5776VJkyY5/PDDs+qqqxZn0endu3cGDhyYlVdeOWPHjp0rwAOUVtX2+Mgjj+TNN9/Ml19+meWXXz777rtvateunVq1av2kAA//m6rX/o033sj48eNTKBSyzDLLZP311y/uN2LEiBx66KGZMWNGLrnkknkGePjlqt6PF154IR988EG++eabtGnTJuuss07q1KmT5PsAT79+/TJu3Lj/GuBZlAnvAAAAAAAsZqo+CL/00kvz4IMP5tVXX02tWrXyt7/9Ldtvv3023HDDJMkDDzyQvn375tlnn825556brl27lrJ0gAWiaj/597//PQ888EBeeOGF1K9fP3vuuWf23XfftGnTJkkyZsyYdOnSZa4Az4IKxFQdLO7evXsGDx6c6tWrp169evnqq6/SqFGjdOvWLXvttVfWWmutFAqF9OnTJ2eddVaaNWuWsWPHplmzZgI8sBCo2p579uyZiy++ONOmTStuX3/99XPCCSdkp512ytJLL50PP/wwXbp0yd133z1HgEd7/t9Vvfb9+/fP5ZdfngkTJhS3H3fccTnqqKOy5pprJvk+KHnIIYfMFeCZXyHNJU3V+9G3b99cdtll+fLLL4vb99133xx88MHZdtttkyQjR45Mnz59FusAj1gYAAAAAMBipFAoFAdzunXrls6dO+fFF19My5YtU7t27Zx00kk54YQTct111yVJdtxxx5xxxhnZaKON0q1btwwePLiU5QPMdz/sJyuDORtuuGGWWmqpDBo0KMccc0xuuOGGFAqFtGvXLpdccknWXXfd9O/fv7h81oIaOK8c3BwwYEDOPffcHHLIIRk7dmy++OKLPPDAA1lttdXSr1+/3HnnnZk1a1bKyspyxhlnpGfPnvn444+zxhprZPz48Qb6YSFQ2Z5POeWUnH322Wnfvn3Gjh2bCRMm5NJLL8348ePTuXPnjBw5MuXl5Vl11VVz0UUXZbfddssdd9xRDDJoz/+7qqGpU089NZtuumlGjBiRf/zjH9l7770zePDgdOnSJS+++GKSZK+99spVV12V2rVr59hjj82QIUOSRHDnV1J5P/r06ZMzzjgjW221Ve6999489NBDOf7443PrrbemS5cuefDBB5Mku+66a84444ysu+666dSpUy688MJSlj9fmE8LAAAAAGAxUjmgMGTIkFx44YU5+uijc/TRR6d169b5/PPP06dPn1x55ZVZfvnl0759+9SpUyfbb799kuTkk0/Occcdl7p16+awww4r5WkAzDeV/eQll1ySwYMHp2PHjunYsWPatGmTDz/8MKeffnquuOKKrLDCCtlrr71Su3btbL755hkyZEiOPfbY9OvXL/Xq1VugM5W99dZbufbaa7PtttumW7duadWqVZJk4sSJmTBhQpo1a5YjjjgiNWvWLC6pc8YZZ+Tbb7/NzTffHAtxwMLj6aefzhVXXJE99tgjvXv3Lrbn+vXrZ+bMmWnQoEF22223VK9ePRUVFcUAz6RJk/LUU0/lyy+/zLLLLlvis1g0jRw5MpdddlkOOuig9OnTJy1btkySTJgwIbfeemveeuut4v1Ivg/wVKtWLXvttVfOPffcHHTQQalXr16pyl/sPPzww/n73/+efffdNyeffHLWWGONVFRU5Isvvkh5eXmSZNNNNy3uv+uuu6asrCxHHnlkzj777Bx22GGL1f0w8w4AAAAAwGKkUChk8uTJ+cc//pG11lornTt3TuvWrVNRUZFHH300o0aNSvPmzXPJJZekTp06mTlzZpJk++23T+/evbPTTjsVwzwAi6NCoZCpU6fm1ltvzVprrZUuXbqkTZs2KS8vz9NPP50HHnggzZs3zwUXXJDatWsXBxC32GKLnHPOOdluu+2y5557LtCax48fn7feeivt27dPq1atMnv27Nx0003p0aNHateuneeffz5NmjTJrFmzikvwlJWV5cILL8xbb72VlVdeuXgeQGn9+9//zieffJIDDzxwjvbcu3fvLL300nnuuefSuHHjzJo1K7Nnz06SrLrqqrnhhhvy/vvvZ9lll01FRUWJz2LR9Nxzz2X69Ok59NBD07Jly8yaNSs333xzBg4cmJYtW+aZZ55J/fr1M2vWrOJ79txzz4wcOTJjxoxZrIIiC4MXX3wx3333XTp27Jg11lgjs2bNyq233poePXqkefPmGTNmTBo2bJhZs2Zl6tSpSZJddtkl11xzTZ577rnF7n4I7wAAAAAALEbKysry9ddf5+mnn87WW2+dNddcMzNnzsytt96aE088MYVCIc8880yWWWaZJMknn3ySyZMnJ/n+p1lvu+22NG/evDhYBLC4KSsry1dffZWxY8dm2223zZprrpkZM2bk1ltvTbdu3VKtWrU888wzxQHy9957r/jebbbZJiNHjkyLFi3mWz9Zedyqs+V8+eWXSZIWLVokSW655Zb06NEjZWVlefrpp4uzcHzyySfZZZdd8uGHHxbPtVGjRnMsFQYsOJWhuUKhUAzcvPHGG0mS9ddfP8mc7fmpp54qtufXX389Xbp0KQZJVl555Sy33HKpqKgoLjnEj6vah5aXl6dQKOSpp57KCiuskM033zwVFRUZMWJETjzxxJSVlWXs2LHFaz927NgMHz68+P6dd945zZo1E4L8BX54P5LkySefTP369bPhhhtm5syZxftRrVq1udrCTTfdlBkzZiRJtttuu6ywwgqL3f3QqgEAAAAAFiE/XPqk6k8GV6pWrVrKyspSp06dJMltt91WfBBedZB3ypQp2XHHHXPXXXcV31u7du0kSY0aNebXKQDMV/+tnywUCllqqaVSs2bN4r533nlnevToMVc/OXPmzLRr1y6XX3558f21atVK8uv0k1Vnz6icCa3yuE899VRx23LLLZckueGGGzJs2LD06tWrWGvltiQ599xz89xzz+Xzzz+f49+pXCoMmH/mNRtOZWjus88+KwZuVl999STJiBEjcvvtt/9oex40aFBuuummYhivkuDO3H547SsqKor9XmV4saysLCuuuGK++OKLPP7447nrrrvSs2fPeV77/v37p3///pk0adIcxxWC/Gl+eD/Ky8uL92PGjBnF67jeeutl8uTJefrpp/Poo4/+6P3o1q1bBg4cmG+//XaO4y5u90PLBgAAAABYRBQKheKD748//jhJUrNmzSTJ0KFDM3HixCTfB3BWWWWVDB8+POecc05xQPpf//rXHA/CzznnnEyYMCENGjRYwGcCMH9U7SfHjx+f5P/6yXPPPTfTp09PWVlZKioqsuKKK+aOO+7ImWeemRNOOGGufrJQKGTAgAGZOnXqHH3nr6lyEH6//fbL8OHDiwOeRx11VDbffPM8/fTTSZItt9wym266aa699tp069YtZWVleeWVV+aoddiwYbnzzjuz1157pU2bNvOlXuDHVbbnHj16ZPTo0cXXDznkkKy55pr57LPPkny/BF/Dhg1z9tlnp3PnzqlWrVpefPHFOfqZq666Ko8++mj+9re/pVmzZgv0PBZFldf+5ptvzsSJE4t/P/roo9OhQ4diUHPbbbfNtGnTcsopp+TYY48tzrRW9doPGTIkr7/+evbbbz/fI/9Mldf/oosuymuvvVYM2Rx88ME54IADMn369CTJWmutldmzZ6dTp0459NBDU7169bn+vzJkyJC89tpr+fOf/5yGDRsu+JNZgIR3AAAAAAAWEZUD0ttvv326dOlSXMrl2GOPzZFHHpm77747SdK0adPsv//+mTBhQvr375+Kioq88cYbWX755ZN8/9OwN998c6677rq0a9cu22yzTWlOCOBXVtlP7rDDDunSpUtxxorjjz8+3bt3z9ChQ5N8308ef/zx+fDDD3PqqaemUCjk5ZdfnqOfvPXWWzNs2LBsscUW+cMf/jDfan788cdz22235fTTT8/DDz+co48+OkOHDs2xxx6bFVdcMcn3A6EDBw7MmmuumS+++CKHHHJI6tWrVzzG0KFDc+qpp2appZbKwIEDU7du3blmIALmvxtvvDGDBg3KwIED8/bbb+eYY47Jddddl/32268Yzlt99dVz9tlnZ9q0afn0009zyimnpFGjRsVjXHvttRk4cGAaN26cvn37pnbt2trzT3DiiSfmr3/9a66++uok339/fOmll2aVVVYpBtx333337L333nnkkUfy1Vdf5ZZbbinOtJZ8P7vZhRdemFVWWSUdO3Zc7GZ2WZDOPPPMHHvssbnssssyadKknHDCCbn22muzzDLLZNq0aUmSvffeOwcddFBeffXVfP3117nmmmuKX4eT79vT4MGDs8IKK6Rr167FMO7iyrynAAAAAACLkK+++ipLLbVU7r777jRr1iyTJk3K8OHD071792y99dbF/U444YS8+eabue6667LJJpvk448/TosWLZIk559/fv7+97+nUCjk0ksvTaNGjVJRUWEZBmCR9cMZdxo0aFDsJ7/++usMGzYsPXr0yO677158T/v27fPSSy/l8ssvT5s2bfLuu+9mnXXWSXl5eS6++OJccsklKS8vz2WXXTZf+8nf/e53uemmm9K9e/e0b98+33zzTY477rj07t17jkHlDTbYIP369Uvfvn3Tv3//3Hfffdloo43y0ksv5YUXXkjTpk3z8MMPZ6WVVkp5eblBZyiBdu3aZeDAgenfv3+23377fPjhhznhhBNywgknzBFK2G233TJhwoQMGDAgPXv2zOjRo/Pb3/42o0ePzmOPPZbGjRvngQceyIorrqg9/xeVffO2226bl19+Ob169cptt92WJ598Mt27d8+xxx6bZZZZJknSoEGDHH744Zk8eXJGjRqVIUOGZKuttkrr1q0zbNiw/OMf/0itWrXy0EMPZfnll/f98S/Qvn37vP7667n44oszevTojBs3Lr17907Hjh3TpEmT4ue6f//+mTJlSv7xj3/kxBNPzAEHHJDll18+9957b+6+++4stdRSefDBB9O0adPF/n6UFcT0AAAAAAAWalOnTi3OsFAoFPLVV1/l5JNPzqWXXppCoZBDDjkkZ599dpo0aTLHAPYHH3yQk08+Odddd12qV6+eddddN1999VU+++yzrLnmmrnrrrvSokULg0LAIu/bb79N/fr1i33gBx98kPPPPz8XXXRRCoVCDj300Jx++ulzDf69+uqrGTx4cIYOHVrsJydOnJjPP/88a6yxRu666640b978V+0nR40alWHDhhVnh6i066675t57702jRo0yYMCAHHXUUUkyR73Tp0/PO++8k969e+f555/PhAkTsu6662arrbZKjx49DPTDAvboo4/m/fffz8EHH1x8bdasWdlyyy3z9NNPp3nz5hk8eHB22223JJmjfX755ZcZNWpUevbsmQkTJmTGjBlZbbXVsuWWW+aMM87IyiuvrD3/B88//3zq16+fNddcs/jauHHjsvPOO2f8+PHZYostcvnll+c3v/lNkjmv/eOPP54rr7wy1113XfG9jRs3zmabbVacrce1/98888wzqVu3btZee+3ia1OnTs0666yTjz/+OOuuu24uuuiibL755knm/No2YcKEnHrqqbnsssuSfP//neWXXz6bbbZZLrroojRr1myJuB9m3gEAAAAAWIj985//zN/+9rfccMMNadu2bcrKyrLsssumZs2axUHqqVOn5ttvv02TJk1SVlZWfL158+a55ppr0rZt24waNSpvvvlmNtxww7Rr1y5/+9vfstxyyy0RD8KBxdvjjz+eP/7xj7n//vuz5ZZbJkmaN2+e2bNnF/vDadOmZfbs2Um+X4KqctBw7bXXzpAhQ7LNNtvkxhtvzMcff5zf/e532WqrreZLPzl9+vScddZZGTVqVDbaaKN06tQpFRUVefPNN/PNN99kjz32yHPPPZeBAwemSZMm2XXXXYtLYJWVlaVOnTpZZ511ctddd2XixImZOHFiWrZsmdmzZ6dmzZr6dFiAJk6cmIMOOigfffRRmjdvXlxe78knn8zEiRPTtm3bPPvssxkyZEiWX375bLLJJqlevXqx/1l22WXToUOHbLPNNpk0aVI+/fTTrLvuuqlbt27q1KmjPf8H7777bjbaaKM0bNgwr7/+enGJwWeeeSaff/55Vl555fzzn//MPffckxVXXDENGzac49pvueWW2XLLLXPQQQfl008/zTfffJNNN900q6++eho0aODa/48++uijbL311pk1a1befffdrLLKKkm+D6tOnjw5zZs3z4svvphbbrklTZs2TatWrVKtWrXi17YVVlghl1xySfbff/9MmjQpX3zxRTbeeOOsuuqqqVev3hJzP8y8AwAAAACwEDvnnHNy4okn5ogjjsgll1ySJJkyZUoGDRqUmTNn5v3338+IESNy8MEHp3v37mndunWS739itaKiYo4H3TNnzkytWrWKf1/cp54Hlgznn39+unXrln322SfDhg1LRUVFZs6cmZNOOinJ9z/Rf/PNN+eQQw7JiSeemDXWWCPJ3H3gvPrE+dFPjhs3Lrfffns6d+6cJk2aFF9//fXX06BBg7z44os55phjUlFRkUGDBmXXXXfNUkstVRy8rFrT7NmzU6NGjTlmXQMWnOHDh+fxxx/PgAEDsvTSSxdf/+c//5nGjRvnvvvuS9++fbP55pvnjDPOyO9///sk/9d2q7bnqu1Ym/7vOnTokO+++y7Dhw9PgwYNknw/6+Tjjz+eunXr5rLLLsuoUaNy+umnp1OnTsV9Kq/9j3Ht/3fTp09Pnz598umnn+bSSy9Nw4YNkyTvv/9+Xn/99Sy33HK58MILM3z48Bx11FHp1q1bWrZsmST/NZizJN0P4R0AAAAAgIXYrFmzirNJNG7cOB9//HGaNWuWb775JmVlZZk9e3a6deuWa665Zq4AT/L9A+/y8vLUqFFjngO/AIuDkSNHZosttkjjxo3z0UcfZZVVVsm3336bJJk0aVJOOeWUXHXVVTnkkEPSo0ePtGrVqvjeQqGQQqGQatWqFUOO86ufrByErPy9c+fO+eijj3LnnXcW9/nuu+9yzz335MQTTywGeHbeeefUq1cvhUIhd911V2bPnp299trrV68P+GmqBgoqwyBdu3ZNixYtcuyxxxb3+/zzz3PFFVfktNNOyxZbbJHTTz89G220UfH7sYcffjgtW7YsBhn476qGPb777rsstdRSueCCC7Lttttm3XXXLfbfY8aMySmnnJLHHntsrgBPoVDIiy++mPXWW8/3xL9Q5fWeNWtWKioqUrt27QwaNCjbb7991l9//eL2Dz/8MD169MjNN9+cjh075vjjjy9+7ivvxxprrJH69euX+IxKxycRAAAAAGAhVVFRkZo1a2a33XZL48aN069fv7Rs2TLPPvtsGjZsmAYNGmTppZfOaaedloMOOihXX311Bg0alNdee614jDvuuCPHHXdcvv766+JAh0EKYHFRXl6eJNl1113TuHHj9O7dO82bN8+//vWv1K9fP/Xr10+zZs1y4okn5tBDD81VV12VgQMH5q233ioe4/bbb8++++6bGTNmFGcnm1/9ZEVFRZKkrKwsX331VcaOHZu77747hx56aHGfpZZaKrvuumsGDRqUatWqpXv37rn77rszceLEPPDAA+nWrVv222+/fPvtt/Ez+lAalQHqJKlRo0Zef/31XHLJJenatWuuvvrq4n7LL798DjvssPTr1y9PPPFE+vTpk2eeeSYVFRV56KGHcvDBB+ePf/xjcZk//rvq1asXr/1SSy2VO++8M8cff3z23XffvP3228X+u127dunfv3+23nrrnHTSSbn44oszderUJMn999+fffbZJ0cddVTJzmNxUa1atZSXl6dmzZqpXbt2Ro8enR49emSvvfbKa6+9Vrwfq666agYOHJgOHTrk73//e84999y8//77SZJ77703O++8c7p167ZEt4Mfnw8KAAAAAICS+uHD69q1a6dGjRrZe++9M2LEiGy44YZJkpVXXjmnn356kuTqq6/OrFmzctRRR2XChAnp27dvXn/99fTt23eB1w+woNWvXz9lZWXZfvvtM2rUqGy88cZJkjXWWCMnnnhiCoVCrrrqqpSXl+eQQw7JhAkTctppp2XcuHE577zzstJKK8232qouZfjiiy9m/fXXzy233JJOnTrl6quvTkVFRXHQv06dOtlll11SVlaWnj175qijjkrLli0zfvz41KxZM6+88soSPTsBlFpFRUVx6aXx48dnrbXWyogRI3LMMcfk0EMPTaFQyCGHHJLk+wDPoYcemrKyspx++uk57LDDsvrqq+fll19OtWrVct999/3HZZyYU+WMkpX22GOPdOzYMX//+9/zl7/8JbfeemvWXHPNJMmWW26Zk08+OWVlZTn55JPz0UcfpV69ehk5cmQmT56cnj17luo0FhtVZ0KaPHlyNt9885x++uk566yz8qc//Sm333571l577ST/F+ApKyvLpZdemvfffz+rr756Hn744ZSXl6d79+5LzBJZ82LZLAAAAACAhVDVJVtuuumm/O53v8saa6yRCy+8MP3790+jRo1y++23FwM8SfLpp5+mf//+GTp0aDHos+yyy+aRRx7JaqutZrksYLFStU8bNmxYtt566zRr1iwXXnhhTjzxxNSoUSOjR48uBniS5N133825556bSy+9NNWrV0+tWrWy7LLLZvTo0Qusn+zcuXOuvfba3Hbbbdl+++3z5ptvpnPnzhk1alQOPPDAOWbtmDFjRv71r3+lf//+ef/999OmTZtccsklWXXVVYtL9QCl07Fjxzz11FMZPnx42rRpkzvvvDNdunTJxx9/nCuuuKIY4EmSr776Krfffnt69OiRQqGQNm3aZPjw4WnevLn2/BNVXa6sY8eOqVatWoYMGZLk+771kksuybrrrjtHgCdJxo4dm8GDB+eWW25JtWrV0qZNm9x1111p0aKFa/8LVL0fxx9/fL766qsMGDAgyyyzTM4555yceeaZWXnllecI8CTf/5/ltNNOy6WXXpo6derkN7/5Te64444lvi0I7wAAAAAALMR69OiRQYMG5dxzz03Xrl0za9asDB48OKeeeuo8AzzTp0/PZZddllGjRqV58+bp2bNnVl555Tl+KhZgcdK7d++cddZZOfvss3PCCSckSc4///z07NlzngGeL774IiNHjsztt9+eVq1apVu3bvO1n6waCLrjjjty5JFHZqeddspJJ52UVq1aJcl/DPBU+uyzz9KoUaPUqVNHnw4lUjWscM0116Rr167Zeeedc/rpp2e11VZLkv8Y4EmSL7/8MhMnTsxKK62U+vXra88/w/nnn59u3bpl9913z5AhQ7Lyyisn+c8BnilTpuTZZ5/N1KlT07Zt2zRp0sS1/wWqtoUhQ4akS5cuOfTQQ9OvX7+sssoqmT59es4999wfDfAkyZgxY1KrVq2sueaa7keEdwAAAAAAFipVH1q//PLL2X333bPjjjumR48eWX311ZMks2fPzoUXXvijAZ7KfapVq5Zq1aot8Q/CgcVL1T7t9ddfz0477ZQ//vGPOf744+cYqJ1XgKdyWKysrCwzZ85MjRo15ms/WXVwc+LEibn33nszcODA3HHHHWnZsmUKhUIKhUKqVav2owGeGTNmpHbt2vM8JrDg/HBmrgEDBuTuu+/O9ddfn5YtW86x/ccCPLNmzUrNmjWLx9Cef5of9tH77rtvZs6cmYEDB6Zly5ZzzNbSpUuXDBkyZI4Az7yusxkpf74fXrv9998/n332WS655JK0atWqeL9+LMAzr2vvfiRL5nxDAAAAAAALqcqBibFjxxYHlbt06VIM7pSXl6dGjRo59thjkySnnnpq9txzz2KAp/LBd9Xp5gV3gMVJZZ82evTozJgxI9WqVcsxxxxTDO5UDhp27do1SdKzZ89svfXWxQBP5SBvrVq15jrmr61ysLhLly654447suyyy+a3v/1tMbhTVlaWsrKyFAqF/OY3v8mQIUPSqVOnXHvttalWrVquvPLKOYI7VY8JLFiVwYIuXbrktddey4QJE7LHHnukZcuWxe2V7XqPPfZIWVlZunTpksMOOyxlZWU5+OCD5wjuJNrzT1XZR59++umZPn16HnrooZx77rnFa1+jRo1i33/RRRelUCjkkksuyd57750RI0ZkjTXWmCsAtKQHRX6JymvXvXv3zJo1K2+//XY6duxYnE2uevXqqaioSJ06ddKtW7ckyZlnnpk999wzd9xxR9q0afOjx1ySuQIAAAAAAAuZs88+O1tssUUOO+ywNGnSJOuss05xW+XD8MoAT79+/TJ58uS0b98+Tz31lAffwBJhwIAB+cMf/pB+/fqlQYMGWWuttVJeXp7k//rJJOnatWvOOuuszJ49O9tvv30xGLmg1apVK5988kneeeedYm1J5pgJqFAoZM0118yQIUOy44475uqrry4GkICFxz//+c88+uij+eKLL4phkFmzZiX5v7acJLvvvnsuuuiirL766jn00ENz0003lazmxcG7776bfv365corr8xSSy2VFVZYIcn/Xfvq1asXvw5cfPHF6dSpU8aNG5dtttkm//73v4XZf2XvvfdeLrzwwgwdOjSvv/56pk2bluT72T+T78M4VQM8vXv3zmeffZYtttgib731VilLX2j5XxwAAAAAwEKmbdu2WXXVVfPGG29k0qRJ+eKLL5KkOCBR+TC8MsDTv3//vPfeeznmmGOKAxgAi7N99tknTZs2zTPPPJNvvvkmkydPnmPgtrKfTL4P8Jx99tn55ptvcuCBB2bWrFnFwfX5oWo4p9K5556bU089NVOnTs3NN9+cBx54YI5Zd5I5AzznnntuOnTokGOOOWa+1Qn8d/Nqz88//3x23HHHfPnllxk2bFjGjx+fmjVrFvf9YYBnwIAB2XjjjbPpppsu0NoXdT/sp1u2bJkHH3wwSfLxxx/nxhtvTJI5rn3VrwMXXXRR9t9//0yfPj1169ZdgJUvnn54P1ZbbbXcd999WW655TJlypT861//SvL9LEiV9+OHAZ5OnTplmWWWSf369Rd4/YuCssL8/O4EAAAAAID/qHKZqx96+umns88+++T999/PUUcdlUsuuSRJ5pjyv/K9s2bNylVXXZVddtklzZo1W6D1A8xvP9ZPfvTRR2nXrl0++OCDHHDAAbnqqqtSrVq1efaTSTJ06ND88Y9/zCqrrDLfaq36b//rX/9KoVCYY8B+wIAB6dOnT1q1apWrr746m2++eZIUl9qp+ufKY1Uu8wUsWFXb8+uvv54mTZqkadOmxe277LJL7rvvvuy2224ZOnRoll9++Tn6nKrtevr06alTp472/BNVvY4TJkwozrKTJI899ljat2+fL774IhdccEEx5Fj1PVXv3ddff52ll156rmWz+OmqXrtPPvkkTZo0yVJLLZUkGTNmTP7617/m008/Tf/+/dOvX78kc96Pyj/PmDEjM2bMSMOGDd2PeRDeAQAAAAAokaoPre+555688847ad++fZo2bZpq1arl2WefTfv27fP++++nb9++OeWUU+Z63w8HtT0IBxYnVfu0kSNH5uWXX85xxx1XnEXh448/zuabb56PPvoovXr1yumnnz5H8OWHx5jX338tVfvj008/PUOHDs1HH32UV199NWussUZxwP7UU09N//7989vf/jZDhgyZZ4AHKK2q/cTZZ5+doUOHZsaMGXnhhRfSsGHD1KxZM0myww475OGHH06HDh1y4YUX/scADz9N1Ws/ZMiQDB8+PC1atMgNN9xQ3OfRRx/NXnvtldq1a+eUU07JEUcckeTHAzzuw89X9ToOHjw4V199dX73u9/lvPPOS6NGjVJWVpbHH388++yzTz799NMMGDAgPXr0SJIfbQvux7xZNgsAAAAAoAQqKiqKD8JPOumkHHLIIenatWvGjRuX8vLyFAqFbLTRRrn55pvTokWLnHbaacWfZP3h0jBVCe4Ai4sf9pOHH354TjrppDzwwANJvh9QbNasWZ544omsvPLKGTBgQE466aQUCoVUr159jmVUqpof/WShUCj2xyeccEJOPfXUtGvXLvfdd1/WWmutOZYR6devX04++eS8/PLL6dSpU/75z38miYFMWEhU9iHJ9+25b9++WW+99XLOOedkmWWWSc2aNTN79uwkyYMPPphtt902N998c4455ph8/vnncyzbp13/b6r2+927d0+PHj1Ss2bNbLPNNnPst8022+TWW2/NjBkz0q9fv1x++eVJ5lwysWpf7z78PD9sC7169UqjRo2yzTbbpHHjxsUl4rbccsvccMMNWWmllfL/2LvvwJru/4/jz5sdkZAQM5LYq1rULEqtGjVqFlV7b2IFsfferb2J2Zq1YyulLS2t1dq1R4ise8/vD797v7m2lgR9Pf7BPecen3NOzvvcnM/rfj49e/Zk+PDhALbR8MD+HOh8PJlG3hERERERERERERFJQMHBwYwYMYKmTZvStGlT8uXLZ1tm/VbqwYMHqV279mMj8DxtKhkRkXdJz549GTlyJI0aNaJNmzbkzp3btsw6IsC5c+coUqQIFy9etBuBJ77r5Lx582jWrBktWrSgU6dOpE+f3m553BEM+vfvT//+/cmbNy+jRo2iRIkS8dZOEXm+r7/+mnbt2tG2bVvatWtHhgwZ7JbHnQKrTJkybN26lbp16zJq1Ci7aZ7k5Y0YMYKePXvSrl07WrduTZYsWZ643tatW6lZsyaurq4MHDiQpk2bxnNL/xvGjh1L165dadeu3WPXgjVuYjKZ2LFjB/Xq1ePSpUsMHz6crl27JlST30oK74iIiIiIiIiIiIgkkLVr11K3bl2qV69O3759CQwMfGydJwV4OnXqxOjRo+O/wSIi8Wzt2rXUq1ePzz//nH79+j2xTj4pwNOuXTvGjx8fb+201uratWuzc+dOduzY8dTO5riBooEDB9K3b19Kly7N2rVrcXFxibc2i8iTGYbBvXv3qFq1KufOnWPjxo2PBXes4gZ4ypUrx6ZNm2jZsiWTJ0/W6CL/0JkzZ6hYsSLJkydnwYIFBAQEPHN9a2jqxo0bzJ07l3r16sVTS/8bzp49S9WqVTGZTCxbtoyMGTM+tk7cabB27NhBgwYNOHfuHBMnTqRNmzbx3eS3lr6SISIiIiIiIiIiIpJAfvjhB2JiYmjbtu0TO6QB28gR+fPnJzQ0lMSJE7Nw4ULu3LkTv40VEUkABw4cIDY2llatWj21Tjo6OhIbG4u/vz979+7FxcWF0NDQeK+TN27cYPPmzWTPnp0sWbLYptWJyzq9lnUakT59+jBq1CimTZum4I7IG8JkMnHt2jV27drFRx99RIYMGWzXbFyGYeDk5GRb9v3331O7dm26deum4M6/cPHiRf744w8+//xzAgICeN5YJKVKlWLOnDlkzZpVI5i9BleuXOG3337js88+e2JwB/73+4phGBQvXpzp06eTN29eKlWqFM+tfbs5JXQDRERERERERERERP5rrJ08mzdvJlGiRAQEBNhNpWJlHZ0hMjISd3d38ufPz86dO0mRIgVJkiSx+5ariMi7xNoJuGXLFhInTkymTJmwWCwAdtNgWeuk9bV06dJx+vRpgHitkyaTiSRJkuDr68vdu3cBcHJysvv/rW29cuUKBw4csHVqdu7cGbAfwUNEEpaLiwtubm5ER0cDPPUz2tWrV7l3755tZJ7FixcDup7/jVu3bgGQKFEigMfquPUz89WrV0mRIgUA5cuXp2TJkri6uj7xM7X8c9evXyc2NtZ2DmJiYnB2drYtt14Ld+/eJSYmBl9fX8qUKUPx4sVxcXHR+XgJGnlHREREREREREREJJ45Ojri6OhIzpw5iYiI4PLlyzg6Otp9s9j6IDw8PJwBAwZw8+ZNAHLnzk2aNGkwm80K7ojIO8vBwQFHR0eyZMnCrVu3+P333+1COvC/Onnr1i1atmxJVFQUAGnTpiVt2rTxWicNwyAmJobUqVNz+PBhpkyZAtiPRmBte3BwMA0bNuTChQt221BHv8ibwfp5zNPTk1WrVrFt2za7ZXGv54YNG1KrVi0iIiLstqHr+Z+zhnaWLVvG33//bVf3DcOwBUGqVKlCx44dbctcXV2Bx4NW8u+kSZMGd3d3vvvuO27duoWzs7MtTGu9FgzD4LPPPmPYsGG2ZdaAj87Hi1N4R0RERERERERERCSBZMqUiaioKHr16sXVq1dtnbzWDmmAcePGMXr0aI4dO2b3Xj0IF5H/gty5cxMbG8uoUaM4e/as7fXY2FhbnZw2bRozZsxgz549du+N7zrp7u7OgAEDcHd3Z+bMmaxduxZ4GEQymUwYhkFoaCi7d++mTJky+Pj4xGv7ROTFmEwm/Pz86Nq1K9HR0Xz99df88ssvtmXWz2sLFy7kxIkTfPjhh/pc9gqVLl2aChUqsHfvXr799lvbaGYxMTG2Yz9z5kwuXLhAsmTJnjilmbw6uXPnpnjx4hw5coTBgwdz584dHBwciIqKsrsW/vzzT1xdXW3hHX3J4OWZjOdNEiciIiIiIiIiIiIir4VhGHz88cfs2bOHTp060bVrV1KlSmVbvnz5cvr06YOfnx/Lly8nSZIkCdhaEZH4Y50mJTY2lipVqrBt2zY6duxIkyZNyJQpk229FStWEBwcTLp06VixYkWC18kHDx4wduxYBg4ciL+/P19++SWNGjXC0dGRRYsWMW3aNGJjY9mxYwd+fn6a/lDkDWS9Lq9cuUKXLl1YtGgRZcuWpXXr1lSsWBGz2czs2bMZN24cFouF7du3kyZNGl3Pr4D1GK5bt4727dsTERFBx44dqVevHn5+fgAsWrSIQYMG4ezszKZNm0iZMmUCt/rdZZ3y6vTp01SpUoVTp07x1VdfMXjwYHx9fQGYN28eQ4cOxcHBga1bt9r9LiMvR+EdERERERERERERkQRgfRh+6NAhmjZtyi+//EKhQoXo0KED3t7ebNy4keXLlwOwa9cu/P397UbkERH5r9i1axfdu3fnhx9+IH/+/LRu3ZoUKVKwadMmW53cvXv3G1Mnr1y5wqJFiwgJCeH+/fskS5aMmJgYIiIiyJo1K2vWrCEwMNB2HxCRN9dvv/3G2LFjmTVrFgA5c+bk3r17XLx4kcDAQDZt2qTr+TWIjIxk8eLFjBw5kt9//51MmTJRrFgxTp06xc8//4y3tzdhYWEEBga+EXX/v2DPnj20adOGI0eOkCpVKrJnz86tW7c4duwYqVOnZvv27boW/iWFd0REREREREREREQS2PHjx+nYsSObN2+2vZYoUSLy5s3L/PnzCQgI0INwEfnPMpvN/PTTT4waNYqlS5faXk+cODF58uRh3rx5b2Sd/PXXX5k2bRoXL14kUaJEFC5cmJo1a+Lr6/vGtVVEni46OpqlS5eyaNEizp49i5+fH4ULF6ZVq1akTJlS1/MrZh19Jyoqil9++YUpU6bw3XffcefOHbJmzUqRIkUYMGAAadKk0bGPZzdv3qRbt2788ssv/P7777z33nsUKFCAHj16kDp1ap2Pf0nhHREREREREREREZE3xKpVq/j777+5ffs2+fPnJ1++fCRNmlQPwkVE/t93333HjRs3uHbtGgULFiR37txvZJ181kgQGiVC5O1ksVgwDMOu1rxptedd8egUZFeuXOHBgwekTZsWwzBwcXHRsY9nce9d0dHRXL58GX9/f2JjY3F2dtb5eAUU3hERERERERERERFJYM962K1OXhGRxzty43qT62Tcdj9rH0TkzWexWDCZTJhMJl3P8cRa3x893jr+CSPucX/auZF/TuEdEREREREREREREREREREREZEE8mbGkEVERERERERERETecvrepIjI28lisbzS7T16P9D9QST+vOrrWV7c0469zknC0Pl482nkHREREREREREREZGX8OjQ8HGnvDIMA8Mw/tX0LY9u/02eDkZE5EU8OjWgxWLBMIynThf4PK9r+pRH27VlyxZOnTpFREQEqVKlolq1ari4uLzUNCFx17tx4wbJkiX71+0UkeczDAOLxWK7ng8dOsSlS5cwDIN06dKRJ08eu3Vf9nqOiIggUaJEr6fxbzHrMYpb9y9fvszNmzfx9PQkefLk/+i4PWkKQk3X9HzW8xD3fJw4cYI7d+4QExND7ty5//X5sNLvLP+ewjsiIiIiIiIiIiIiLyHuQ3CTyWR7SD1v3jz27t3L33//jbe3Nw0bNiRLliykTp36hbcd90H4iRMnyJw5szolROStExkZiZub22NhmDlz5rB//37Onz9P0qRJadKkCe+99x4pUqQAXqwDPe46J0+eJFWqVHh6ev7jtu7cuZMjR47Qtm1bu9e7devG6NGj7UbJKVq0KE2aNKFWrVq4u7s/t71xl2/evJmlS5dSs2ZNypYt+4/bKyJPd+DAASIiIihRooTd9RccHMyECROIiIiwrdusWTPq1q1L8eLFgefXn7jLt23bxrZt22jQoAGZM2d+jXv09jh58iReXl6kTJmS2NhYnJycABgwYABz5szhr7/+wsnJiUyZMjFgwAA+/vhjW+1/nrjHft++fVy6dImKFSvi5ub22vbnbXf06FEyZsxIokSJiI6OxsXFBYB+/foxffp0Ll++DEDWrFlp0KABX3zxBYGBgS99Hw4LC+PYsWO0bt369e7Qf4TCOyIiIiIiIiIiIiIvaMuWLQwfPpzQ0FB8fHxsr3fp0oWxY8fi5OSEm5sb9+7dw9PTk0qVKtG9e3dy5cr13G3HfRC+adMmBg8eTJEiRRgyZMhr2x8RkVdt06ZNjBs3jvHjx9t1alvrpIuLC4kTJ+bmzZu4ublRp04dWrVqRb58+Z677bh1cuPGjYwePZqCBQvSv3//l/62v2EYhIeH4+vrS0xMDJMmTbJ1Po4YMYK+fftSq1Yt6tati6OjI4sXL+a7777D0dGRbt260aZNm2d2HD8a3OnSpQvnzp3j0KFDZMyY8aXaKiLPZhgG58+fJzAwkLRp07JgwQJbKGfAgAEMHDiQzz77jBo1avD333+zevVq9uzZQ548eejduzdVqlR57vbjXs+dO3fmxo0b7N+/H39//9e+f2+6n3/+mbx589K4cWMGDx5MypQpgYehqWHDhlGgQAGKFSvGH3/8webNmzGZTLRu3ZrWrVuTIUOGZ2770c/HHTt2xNPTkw0bNth9Fpf/OXLkCLlz56ZEiRKsXbvWNrJOnz59GDx4MB999BEVKlTgl19+4fDhw5w+fZoqVaowbNgwsmbN+sxtP3o+unfvzs2bN/nhhx9IlSrVa9+3d54hIiIiIiIiIiIiIs9lsViM6tWrGyaTyShXrpxx48YNwzAMY+bMmUaSJEmMjh07Gr/88osRHh5uLF682ChXrpxhMpmMMmXKGL/99ttzt221adMmI3fu3IaHh4dx9OjR17pPIiKvWt26dQ2TyWR89tlnxqlTpwzDMIzZs2cbXl5eRseOHY3Dhw8bUVFRxrx584wKFSrY1v3pp5+eud1H62SePHkMV1fX59bX5wkLCzM8PDwMd3d3Y8KECYZhGEbNmjWNqlWrGmfPnrWtd+PGDWPp0qWGv7+/4efnZ3z77beGYRiG2Wx+obYmSZLkufsoIv9Ov379DJPJZOTIkcPYunWrYRiGUahQIaNBgwZ21/OJEyeM/v37G87OzkaRIkWMH3/88anbfPR6zps3r+Hl5aXrOY6rV68a+fPnN5ycnIy2bdsaly9fNi5fvmwEBgYa7dq1M/766y/buqGhocYnn3xiODs7G7169TLCw8Ofut1Hj/2HH35oJE6c2Dh8+PBr3Z+33d27d42PP/7YMJlMRqVKlYzw8HDj5s2bRoYMGYz27dsbf/75p2EYhhEREWH8+OOPRuXKlQ2TyWR88cUXxoULF5663SddC4kTJzZ+/vnn171L/xkK74iIiIiIiIiIiIi8oMjISKNWrVqGyWQySpUqZcTGxhojR440PvzwQ+PMmTO29SwWi3HlyhWjXr16hslkMtq0afPUzokndfJ6eXnpQbiIvLXq169vCzpeuXLFGDNmjFGwYEFbh6HVyZMnjSZNmhgmk8no3LmzER0d/cJhmFdRJ63/1+7duw0XFxfD1dXV6N+/v5EpUyZjwYIFtv/b+v9HRkYas2bNMhInTmxUqVLlidt8Uls9PT1V00Veo7h1Y9iwYYbJZDKyZ89uzJ4920ifPr2xbds2wzAMIzY21rbejRs3jG7duhkODg5G3759n7hdXc8v7urVq8Ynn3ximEwmo0OHDsaSJUsMPz8/WxA9JibGtu6uXbuMggULGp6ensbOnTsNw7A/1o/+O+6x/+WXX+Jhb95e1p/xu3fvGuXLlzdMJpNRrVo1Y+/evUaWLFlsobO418zVq1eNTz/91HB3dzeWLFliGIbOR0LRtFkiIiIiIiIiIiIiLyA2NhYnJyeioqKoW7cuq1atonjx4sTGxlK0aFGGDh0K2A8n/+eff/LVV1/xxx9/cODAAQIDA+22aTwyDUP37t05deoUu3bt4oMPPojX/RMR+besdRKgbt26LFmyhDJlyvD3339TvHhxJkyYAIDFYrFNc3X06FFatmzJL7/8wqFDhx6bsuN110lrW/bs2UPJkiXx8vLCwcGBkSNH8tVXXxETE4Ozs7Nt/cuXL1OnTh127tzJoUOHyJMnT7y1VUSeLm5dGT58OD179iRr1qzcvn2b5cuXU6RIEcxmM46Ojrb3HD58mKpVqxIeHs7x48ftpv3R9fzyrl27Ru3atdmxYweffPIJV65cYd++fbi7u9vOjfWYfv3117Ru3Zry5cuzZs0au6kPdez/HevPeXh4OLVr1+b7778nS5Ys3Lt3jx9++IG0adM+9p5NmzZRuXJlihYtapvazErnI/683ASgIiIiIiIiIiIiIv9RTk5OmM1mXF1dWbRoEVWrVmX37t0cOnSIc+fOYbFYiImJsXvYnS5dOsqVK8f169dZt26d3fb0IFxE3jVOTk7ExsYCsGjRImrXrs3mzZs5f/48kZGRwMOAT9xO2ly5cvHZZ58RERFBWFiY3fZeZ520WCzA/zqSixQpwpYtW7h79y7Xrl1jy5YtADg7O9vWNZvNpE6dmqpVqwJw//59u21at7Vp0yZ69Oihmi4STwzDwMHBAbPZDED37t0ZPHgwf/zxB1euXOHQoUMAODo6Endci7x581KqVCnCw8OJiIiw26Y+o708X19fQkND+fjjj9m2bRsnTpzg1KlTtsCUyWSyHf9GjRqRLVs2Lly4YDtvoM/Hr4KjoyOxsbF4enoSGhpKmTJlOHHiBHfu3OHUqVMAtnu1VbFixciUKRNnzpzh2rVrdst0PuKPwjsiIiIiIiIiIiIiz2HtVLB2Pri6urJ48WIqVapEZGQku3fv5vLlyzg7O9vWtY5AUaFCBdu/47I+CN+4cSPBwcF6EC4ibzVr7bOOvAOwePFi6taty+3bt1myZAlHjx7FycnJ1nkbExMDwGeffQbAvXv37LZprZPff/89PXv2fGV10trRD3DhwgXb68WKFWPnzp24urqyYMEChg0bBoCDgwPR0dG2e8Bvv/2Gs7MzSZMmfWzbe/fupWPHjpw8eZLdu3erpou8ZhaLxVYr4taQnj17MmbMGACCgoL4/vvvgYd1Je5nsps3b+Lj44Obm9tj2w4LC6NDhw6cOXNGn9GewBpshP/dA6wBnooVKxITE0PXrl25cOGCLbjz6KRAcUc2A/ugSJcuXXTsX0LcEJT1Xuzp6cny5cspX7489+/fp2PHjkRERNjCttZz6O7ujpubG15eXk+8Fr7//nu6dOnCn3/+qfPxGim8IyIiIiIiIiIiIvIc1g7bXbt22TqbXV1dWbJkCTVq1OD8+fNUq1aNmzdv4ujoSExMjG2knrVr1wKQOnXqx7b722+/ERQUxNGjR9XJKyJvNWud3Lt3L3fv3rW9vmDBAho0aMC9e/do2bIlJ0+exGQyER0dbRvVZvXq1QCPTS0I8Ouvv9KnTx+OHDnyyuqktXO4R48efPHFF+zfv9+2rGDBgmzfvh0XFxd69epFv379AHBxcQFg5cqVbNiwgTx58uDv72+3XYvFwsWLF/Hw8GDHjh28//77/7qtIvJs1iBecHAw3bp149y5c7ZlHTt2ZPTo0cTGxtK6dWtbrbEGG1atWsXu3bvJnTs33t7edtuNjIzk559/5v79+2zfvl2f0Z7AeuzHjBnDt99+S3R0NAApUqRg5syZlC1blq1btzJgwADOnTuHyWSyvWfVqlWcOXOG999/3240NoDr16+zePFizp49S1hYmI79C7LehwcMGMCIESNsQSlPT0+WLFlCxYoV+eWXX2xTxTk5OdmO/bJlyzh69CjvvffeY+Gd8PBwduzYwe+//65r4TUzGY/G20RERERERERERETkMUOHDqVXr16MGDGCjh072jp+oqOjqVevHitWrCBnzpwsWbIEPz8/kiRJwuLFi+nfvz8mk4ldu3aRPHlyu22eOHGCMWPG0LJlS3Lnzp0AeyUi8uqMHTuWLl26MHbsWBo3boynp6dt2ZdffsmiRYvIlSsXM2bMIFeuXLi5ubFw4UIGDhwIwO7dux+rk7/99htjx46lffv2rzQMExkZSXBwMOPGjaNSpUoEBwdTsGBB2/K9e/dSsmRJoqOjKVmyJJkzZ7ZNv+Po6Mj27dsJCAjAYrHYdTxHRETw4MEDkiVL9sraKiLPdvPmTapUqcKePXsICgqiXbt2pEuXzrZ85MiRdO/eHYB27doREBDAH3/8wbZt24iKimL37t34+/vbTdkEcPbsWVxcXJ4YwJaHfvrpJ/Lly0emTJkYO3YspUuXtoUdr169Sp06ddi+fTu5c+emT58++Pr6snPnThYuXEh4eDh79+7Fz8/vse2uX7+eLFmykClTpvjepbfa2bNnSZ8+PUmTJmXQoEG0atXK9jMdHh7OF198wYYNG8iZMydNmzblvffeY9OmTaxfv57bt2+zb98+/Pz8HrsWDh8+TPLkyR8LrcqrpfCOiIiIiIiIiIiIyAvYv38/NWrU4MGDBwQHB9OhQwe7AM+XX37J8uXL8fHxIW3atCROnJjTp0/j6+vLmjVrCAwMfKyTFx52ID9peHoRkbfNjh07aNWqFTdu3KBXr140atTILsBTv359Fi5ciKOjI/nz5yciIoLr16/j7e39zDr54MED3N3dX3l7b9++zbhx4xgwYAAVKlSgT58+dgGeH374gRIlShAVFUWGDBkoW7Ys2bNnp3r16qRJkwaz2Wwb6QB4rLNTROLP6dOnCQoKYs2aNXTs2JEOHTrYBXjGjRtH586dAUiTJg2ffPIJyZIlIygoCD8/v8euZ3kx9+7dY+XKlQQHB+Pl5cWIESMoW7asXYCnYcOGfP/997i4uODh4UGOHDlImjQpkydPxt/f3+7Yq47+e7t376ZWrVpER0fTr18/2rRpYxfgqVu3LuvWrcPZ2ZkUKVLw/vvv4+HhwahRox47HxK/FN4REREREREREREReUGHDx+matWq3Llzh5CQkMcCPNYReFKnTk2zZs2oWLEi6dOnJ3ny5HoQLiLvPMMw+OGHH2jatCmXL1+mb9++jwV4GjVqxNy5c8mSJQulS5emWbNmpE2b9ol18lV14j66nbgBodu3bzNmzBgGDRr0xADP/v37KV26NBEREcyfP5969eoBqKaLJJBHr+e4/z59+jSdOnVi/fr1TwzwjBkzhqCgIFKlSsXq1avJly8foOv537p//z4rV66kW7dueHt7PzHA07hxY9avX0+FChX4+uuvSZkyJc7Ozjr2r8nu3bupXr06ZrP5iQGe2rVrs2PHDgIDAzl8+DBOTk44OjrqfCQwh+evIiIiIiIiIiIiIvLfYbFYnvrvvHnzsmrVKpIkScKAAQMYP348sbGxALi4uLBgwQKqVavG5cuXOXXqFPnz5yd58uRYLBY9CBeRd8ajddLKZDJRsGBBpk+fTurUqenfvz+zZ88mPDzcts7s2bOpW7cuJ06c4O7du3zwwQdPrZOvOrhz9epVABwcHGz7kDRpUjp37kzv3r1Zv349gwYNYv/+/bb3FypUyDYqULFixWyvq6aLxD+LxWK7nu/fvw88rBPWsSoyZszI2LFjqVChAuPGjWP8+PGcO3fO9v7OnTvTq1cvwsPDSZs2re11Xc/PZzabn7rMw8ODatWqMWLECG7dukW3bt3YtGkT0dHRAKRIkYLZs2eTK1cufvrpJxInToyzszOgY/9Pxb0PW/8e97WiRYuyYsUKHB0d6devH5MnT7ZdJ56enoSGhpIrVy4uXLjAvXv3bOdB5yNhaeQdERERERERERERkSf48ccfee+993Bzc3vsW97WEXjCw8Pp1asXHTp0sHVCREZG0r59e4KDgwkMDEyg1ouIvH4///wzmTJlInHixHavG4bB/v37adasGVeuXKF37940atQILy8v2zpt2rQhKCiI9OnTx0tbe/bsyZEjRxg5ciQ5cuQA7EfguXXrFoMHD2bMmDF8/vnndO7cmSJFitjeHx0djYuLi0YlEHkD9O7dG4vFQseOHUmRIgVgH9Q7deoUbdq0YceOHbRv357WrVvbfSYLDw/H09NT1/M/MG7cOD788EOKFi36WMDy/v37rFixgi5dupA6dWoGDx5M2bJlcXV1BeDGjRuYzWZSpEih6bFekREjRuDt7U3Dhg1xdnZ+bOrJXbt2Ua1aNRwdHQkODqZdu3a2437v3j0ePHiAr6/vE6eslPinMyAiIiIiIiIiIiLyiGHDhlGkSBEWLFhAVFSU3be64eEIPPPnz8fV1ZVx48YxZswYYmJiAHBzc2PatGkEBgbaRuUREXnXjB49mvz58/Ptt98SERFht8w6As+YMWPw8PBg3LhxzJo1i7t379rWmTx5MunTp4+XOnn79m2uXr3Khg0bGDp0KMeOHQPsR+Dx9vamadOmZM2ale+++44JEyawa9cu2zY0SoTIm+H8+fN8//33jBo1ilmzZtlG1Hp0BJ4ePXqQKFEi5s6dy9dff82ff/5p20bixIkxDEPX80vavXs3nTt3plWrVhw8eJBHxwjx8PCgcuXKtGnThl9//ZXhw4ezZcsWoqKiAEiWLBkpUqSwG0FJ/rnff/+d4OBgBg8ezNKlS4mJibG7rwEUK1aMcePGcfv2baZOncqkSZNs5y1x4sQK7rxhdBZERERERERERERE4jAMg/fee4+UKVMyaNAgFixYQGRk5GMBnkKFClG5cmUuXbrE9OnTGTJkyGOd0E5OTvHdfBGR185sNuPv709gYCDBwcGsWrXKNoWNlYODA0WKFKFEiRKcPXuWKVOmMHnyZO7du2e33uuok49O65U0aVJ69epFx44dWbRoEYMHD35igCdbtmzkz5+f9OnTs2zZMqZPn24LZqqjWSRhPBoQSZcuHZMmTaJkyZL079+fGTNmPBbgMZlMFCtWjPTp02MYBiNGjGDBggW2a91kMumafgGPHvucOXMyatQorl69StOmTTlw4MBj6yRNmpQaNWrg7OzMoUOHaNy4Mbt377ZbR0GRf+bRY50+fXpWrVqF2WymT58+hIaGPjHAky9fPhIlSsRff/1Fx44dmTt3rt12dD7eHDoTIiIiIiIiIiIi8p/2aCevyWSibNmyzJo1C4C+ffuycOFCW4DHYrFgsVhwdXWlcOHCfPDBB/z1118sXLiQBw8eJMQuiIi8VnHrpMViwdHRkUqVKjF27FgSJUpEt27d+Pbbb+0CPLGxsXh4eFCxYkXy5cvHzZs3mT9//mtvq9lstnVE/v777xw/fhyADBky0KFDB9q1a8eSJUsYNGiQXYDH6vfff6dmzZpMmjSJQYMG2UbcEZH4F3eElgsXLnDlyhXgYYB6wIABFCtWjIEDB9oFeKycnJyIiYmhc+fOtGjRggYNGiik8BLiHvvw8HCio6Px9vamSZMmBAcHc+HCBZo1a2YX4LGG2NOlS0e6dOlo164d6dKls01VKP9c3PNx/fp12+8i5cqVY8qUKURGRhISEmIX4ImOjgYga9asZMmShT59+pA7d25Kly6dkLsiz6AKJSIiIiIiIiIiIv9ZcTt5d+zYwbJly4iMjMTFxYVPPvmE6dOn4+TkZBfgcXBwsL1n/fr15M2blxMnThAWFoanp+dj34oVEXmbPVon16xZw/Xr13Fzc6Ns2bKMHDkST09PW4DHOrKOk5MThmGwdOlSUqdOzbZt29i2bZttuprX1VbrNDijRo2iWrVq9OrVi9OnTwMQEBBAp06daNeuHaGhoQwYMICDBw/a3h8aGsrNmzcpXLgwrVu3xt/fH7PZ/FraKiLPFrf2TJw4kbp16zJkyBCuXbsGQIECBRg0aJAtwPPNN99w7tw5W8Bh/vz5hIeH88knnzB16lRdzy8h7rGfM2cOHTp04NtvvyUqKookSZLQuHFjQkJCbAGeffv2ERkZaav78+bNw9nZmVatWrF7925Sp06tY/8vxD0f06dPp1WrVgwZMgTDMHB2dqZ8+fJ88803tgDPokWLbL/PmM1mJk6cyIULF2jQoAEHDx7Ez89P5+MNpTFbRURERERERERE5D/JOnoEwKBBg5gyZQo+Pj6kSZOGIkWK4OjoSMmSJZk+fTrNmjUjJCSEe/fu0bJlS1xdXVm6dClHjhzhyy+/JEOGDIB9x7GIyNsubk0bMGAAkyZNImXKlEyfPp1kyZLh4uLCp59+islkIigoiKCgIMLDw6lVqxY+Pj4sW7aMX3/9lQYNGvD+++8/ts1XKW5NDwoKYvLkyRQsWJAWLVqQMWNG23oBAQF07NgRBwcHJk2axJEjR6hUqRL3799n1apVeHh4UKhQIdv6quki8S/u9dy1a1emTJlC5syZKVeuHL6+vrapsQoUKMDAgQMJCQlh8ODB/Pjjj3zxxRf88ccfzJs3D09PT7JmzWrbrq7n54t77Hv06MHXX3+Nm5sbNWvWxMXFBcMw8PLyonHjxgAMHDiQpk2b0qpVK8qVK8fmzZuZMmUKadKkIXXq1Li5uQE69v9U3PPRvXt3pk6dip+fH/Xr17cF1ZycnGwBnhYtWtCzZ09+/fVXWrVqxerVq/n666/JmDEjSZIksYWAdD7eTCZDXwMRERERERERERGR/xhrpw887BQaP348VapUoUOHDhQtWtRu3djYWMLCwmjZsiVnzpzhgw8+IEWKFOzZs4dkyZKxb98+0qRJkxC7ISLy2jxaJ8eNG0eNGjVo2bIlxYsXt1snKiqKzZs306tXL44ePUqOHDlIlSoVe/fuxdfXl71795I2bdp4afewYcPo168frVq1om3btnbBnbjOnTvHypUr6d27NxERETg6OpIzZ06+++47AgICsFgsmmJHJIENGDCAwYMH07JlS9q0aUOWLFmeuN7hw4eZMGEC8+bNs72WJUsWvv/+ewIDA3U9/wO9evVixIgRtGjRgpYtW/Lee+8B9veG27dvs3jxYsaOHcupU6dwc3MjMjKSgIAAwsLCCAgIsFtf/rm+ffsyZMgQWrZsSYsWLZ54PqKioti9ezctW7a0jTgHD0Or27dvJzAwUOfjDafwjoiIiIiIiIiIiPxnTZ48mW7dutG8eXPat29P+vTpn7iexWLh1KlTtG/fnoMHD+Lm5ka2bNmYPXu2bRoGfYNVRN5FkydPpkuXLrRq1YoOHToQGBhot9zaKR4Z5rce5wABAABJREFUGcnJkycZMmQI69atw8fHh0yZMjFr1qx4q5N//PEHn332GX5+fkybNo3MmTPblm3bto07d+4QERFBvXr1bK///vvv/PbbbyRKlIgCBQqQLFky1XSRN8DBgwepXLkyBQsWZPTo0XZBvJ9++onY2FgMw6BAgQK211euXMn58+dxd3enatWqpEiRQtfzP7Bu3Trq1KlDtWrVCAkJsY0wCXDx4kVcXFxwcHAgWbJk3L9/n7Nnz/L1119z69YtUqZMSZcuXWxTZenY/3vbtm2jWrVqVK5cmQEDBtjdhy9cuEBUVBTp0qXDxcUFeBiqGjduHA8ePMDDw4PmzZuTKlUqnY+3gMI7IiIiIiIiIiIi8p90+/ZtqlWrxqVLl1i5ciU5cuSwLVuyZAm//vorf//9N0FBQWTLlg14+O3WkydP4uzsTPLkyfH09NSDcBF5Z92+fZsaNWpw7tw51qxZYzf9zOLFizly5AjXrl2jTZs25MmTx7bs+PHjJEmSBE9Pz3itk/v27aNYsWIMGTKEbt26ERMTw+nTp5kyZQqTJ0/GwcEBs9lM/fr1mTt37hO3oRE6RN4Mq1atonr16oSGhlKzZk2io6O5evUqkydPZsqUKURHRxMbG8vkyZNp3rz5E7eh6/mfGTp0KP3792fHjh0ULFiQ2NhYIiMjmTJlCnPnziUqKoq0adMyffr0p46GpM/Hr87EiRPp0KEDO3bsoFixYpjNZsLDw5kwYQJz5szh8uXL5MiRg+nTp5M3b16791pH2tH5eDs4JXQDRERERERERERERBLCgwcPOHbsGIUKFSJHjhyYzWYOHDjAlClTWLhwIS4uLkRHR7N582bWrl1Lrly5MJlMdp0UhmHoQbiIvLOioqI4ceIEOXLksAV39u7dy9SpU+3q5IIFC9izZw8ffvghANmzZ7dtIz7rZHh4OBaLhaVLl1K0aFE2bNjA8uXLuXDhAnXq1CF//vzMmDGD+fPnU7JkSRo0aPDYNtTRL/JmuHbtGgBbtmyhePHiLFiwgIULF/Lbb79RokQJMmbMyLRp02jZsiXvv/8+hQoVemwbup5fjmEYGIbB8ePHbWEpeBhqnzVrFmFhYWTJkgVvb2927dpFvXr12LRpE97e3nbbMJlM+nz8Ct2+fRuA69evAzB37lxmz57Nnj17KFiwIBkyZGDbtm00atSIPXv2kDhx4se2ofPxdlB4R0RERERERERERP6TDMPA39+f1atXM3DgQE6fPs2mTZuIioqiV69efPLJJ2zbto0hQ4YwdOhQFi1a9Ng2TCZTArRcRCR+GIZBkiRJ2LRpE926dePy5cts3ryZmJgYevfuTbly5di+fTt9+vRhzJgxzJ49G2dnZ7vaGJ91smzZstSvX5/58+dTtGhRTCYTuXPnZuvWrWTNmpUkSZKQM2dOypYtS0RERLy1S0ReXo0aNRg7dizTp09n7ty5REdHkzVrVjZs2ECuXLlInjw56dOnp1u3bly5ciWhm/tOMJlMmEwm6taty4IFC6hTpw6pUqXizz//xM/Pj2XLllGoUCHSpk1LuXLl2LNnD1euXLEL7+iz8atXqFAh3NzcqF69OsmTJ+f69ev4+/vz7bffUrhwYXx9falRowYrV67k8OHDfPzxx7b36ny8XRTeERERERERERERkf+kNGnS0L17d7p06ULfvn3x8vKiYMGCTJo0icDAQJydncmTJw/jxo3D09MzoZsrIvLKWEdGeJ5UqVIxefJkatWqxahRo0iaNCkFChRgwoQJpE+fHmdnZz788EOGDBmCl5cXLi4ur7Xdz5r2w7ps7ty5FCxYkAcPHpA6dWqqVKmCh4cH8HAKne3bt5MoUSLbKGoveixE5NV61rVnNpvx8fFh586dDBgwACcnJwIDA2nUqBFeXl629U6fPo2Pjw/p06ePr2a/9V5k+qRy5cqxfPlyQkJCSJs2LZUrV6ZXr14kS5bMto6DgwOpU6fG19f3dTf5P69MmTLMnz+fxYsXY7FY+OCDD2jXrh0+Pj62dQzDIFOmTGTOnDkBWyr/lsI7IiIiIiIiIiIi8k558OAB7u7uz1zH2mFUvXp1MmfOzLVr1/Dy8iJXrly4ubkBDzs35s2bh8ViIW/evHbvExF5m0VGRuLu7v7cTlyLxcLHH3/Mzp07OXv2LN7e3rz33nt2dfKbb77BbDbzwQcfAK++Tm7cuJGNGzcyZswYHB0dn9rmuMtat2792HLDMFi5ciUrV64kf/78tim+VNNF4s+2bdu4ffs21apVw2QyPbVeWK9nX19fxo0bh6Ojo926hmGwYsUKNm/eTKFChciQIUN878pb5/Dhw6RIkQI/P78XCvBUq1aNkiVLkjRpUmJjY3Fy+l+sIDQ0lF9++YWyZcs+cYomeb5Dhw7h7e393J9di8WCg4MD1atXp1y5cnh4eNheswoNDWXfvn2UKFGCpEmTvuaWy+tkMgzDSOhGiIiIiIiIiIiIiLwKmzZtIigoiEWLFvHee+89c91ndTAbhsGSJUsYNGgQ7u7ubNiwQd8sFpF3wqZNm6hXrx47duwgR44cz+3EfVqtNAyD0NBQBg4cSKJEiVi/fv0rr5Nbt26lTJkyAHTq1InRo0cDLzZyxKOGDh3K9OnTiY2NZffu3fj7+z/WASoir8/27dspVaoUACtWrODzzz8Hnh/4e9Ly0aNHM3XqVNv17Ofnp4D1M1iPffbs2dm0aRNp06Z9oTr6pGM6depUxo4di2EY7NixgzRp0ujYv6Rt27ZRunRpsmTJwoYNG547cpT1+D7tfIwZM8Z2PtKmTavz8RbTJxIRERERERERERF5J2zevJlKlSrx66+/UrduXY4fP/7M9Z/WGX3v3j169uxJjx49uH//PitWrMDX1xeLxfK6mi4iEi82btxItWrVuHHjBqVKleL333+3jXDxNE+qldevXyc4OJhu3bpx7949li9f/srr5LZt2yhTpgyFChUid+7cjB07lg4dOgA8t81x/fzzz/j7+zN8+HD8/PxswR2z2azgjkg8Wb9+PaVKlSJnzpz4+PhQvXp1li9fDmALJTxN3Bq0b98+cuXKxdChQ/Hx8WHHjh22kWQUVniyNWvWULp0aby9vTl+/Dg1atTgwoULL1RH4450dPToUSpWrMiAAQNwdXVl8+bNpEmTRsf+Ja1fv55y5cqROnVqTp8+zeeff86ZM2ee+R7r8bX+abFY2LlzJxUqVCAkJAR3d3e2bt1qC2XpfLy99KlERERERERERERE3npHjhzh008/5aOPPqJFixacOnWKzz///LkBnkedPXuWDz74gPHjx5MrVy52795NQECAOnlF5K13/PhxypcvT8GCBWnVqhVXrlyhaNGiLxTgievvv/+mUKFCjBw5kg8++IA9e/a8ljrp7e3NZ599xpQpU5g/fz4fffQREydOfOkAT86cOalQoQI9evRg1apVtuDOy47cIyL/3Pnz5ylYsCDz5s1j6tSp+Pr6UqtWrRcO8FglT54cT09Pmjdvzrp162y1R9fzk8XExDBv3jzy58/P2rVradSoET/88AM1a9Z84QAPQFRUFD/88AMHDhygcuXKbN68mcDAQB37f2D69OkULlyYuXPnEhwczLFjx6hWrdpzAzxxOTg4cOrUKX7++We+/PJLNm/erGvhHaFps0REREREREREROSdMGjQICpUqMAHH3zA8OHDGThwIAEBAaxatYrs2bO/8HZWrlxJREQEn332GUmTJtWDcBF5Z4wbN47ChQtTsGBBhgwZQu/evfHx8WH37t1ky5bthevd1q1buXz58murk9bprO7fv4+HhwcAP/30E23btmXfvn20a9eO8ePHA8+eQivu1CHW9TRVlkjCuHz5MqlTpwZg4cKFdO7cmWvXrrF06VJq1KgBPH9KU5PJREREBE5OTri4uOh6fgG3b9/m0qVL5MiRA4CGDRsyb948ChYsyLJly2wjFz2vhlssFs6ePUuqVKlwd3fX5+N/yGKx8Mcff5A9e3YiIiIYPHgwI0eOJEeOHKxcuZIMGTK88LbOnj1LypQpcXNz0/l4Ryi8IyIiIiIiIiIiIm+1Jz2svn//PmPHjmXIkCH4+/u/UIAnboeR9e/qFBKRd8HTatmgQYMICQn5RwGe5237VbUR/lfnf/75Z9q0afNSAR4RiX8vUhcWL15Mx44dXyrAI8/3tGMf97g2atSIuXPnvnSA50nbkmd73rUQHh7O8OHDGTFixAsHeB7dps7Hu0O/dYqIiIiIiIiIiMhbx2Kx2P4eN3ADDztxPTw86NKlC8HBwZw7d+6FptCK+9Db+ncFd0TkbRW3Tj5ay2JjYwHo3bs3AwcO5ObNm/9oCq0nbfufsm5nxowZ3Lx5026ZtUM5d+7cTJkyhcKFC/+jKbREJH5Yr+ft27cTHR1tt8xam+rUqcO4ceP+8RRa8jjDMGzH/vbt23bLTCaTrU7Onj2bBg0a/KMptKzbkhdjPR9Hjx61/exbf74tFguenp50796dbt26vfAUWo/ed3U+3h36zVNERERERERERETeOtaH1i1atOD777/HbDbbHlxbp0Vxd3d/6QCPiMi7wlonGzVqRFhYmF1nuJOTk60TsVevXgwYMOBfBXhelUmTJtG8eXNGjBjxWMez1QcffMDkyZMV4BF5w3Xp0oVSpUqxcuVKYmJibK87ODgowPOaWD8LV65cmUKFCnH16lW75XHr5L8N8MiLq1GjBpUrV2bPnj22UXKsQat/GuCRd5PCOyIiIiIiIiIiIvJW+vHHH5kxYwa9evUiLCzMrrPB+jBcAR4R+S/bt28fS5cupXXr1uzfv9+uMzxuB3rv3r3fiABP+fLladu2LaNGjWL48OHcunXrietZR+D56KOPmDhxIu3btwcU4BF5k3z++ed88skntGzZkhUrVtiNwPNogGf8+PG2AM+yZcsABXj+qYiICPLly8fff/9NjRo1FOBJYBaLhZo1a/LgwQO6du3Knj17sFgsdqN8KsAjVgrviIiIiIiIiIiIyFspT548bNiwgevXr9OtWze2bNmiAI+ISBx58+ZlyZIlREdH06xZM3bv3v1YgMdaN9+EAE/GjBnp0qULbdq0Yfjw4QwYMMA2xdejrCPwfPTRR0yaNEkBHpE3TNGiRRkyZAgffvghdevWJTQ09KkBwi+++MIW4Kldu7YCPP9CokSJ6NixI3379uXXX3+lfPnyXLlyxW4dBXjij4ODA9WrV2fatGmcPXuWli1bsmvXrsfWUYBHQOEdEREREREREREReUs5OjpSunRpZs2axenTp+nQoQM3b960W0cBHhH5L3N1deXTTz9l7NixXLt2ja+++uqx0WzidtLGV4Dn0c74uFPqBAQE0KVLF5o1a8bHH3+Mk5PTU7ejAI/ImyfutVewYEFGjRpFgQIFSJo0qW20ESsFeF4t67H38vKicePGdO/enSNHjvDHH388tq4CPK9e3J9TwzBsx8/JyYnKlSszceJEzp49+8SfZwV4BMBkqNqJiIiIiIiIiIjIW8xsNrNp0ybg4ZQrT2KxWHBwcODBgweMHj2awYMHExAQwKpVq8iePXt8NldEJN5FRUWxfv16EiVKxKeffvrEdax1EmDQoEGEhITg4+PD7t27yZYtG2azGUdHx3/dFsMwbB34p06dIlOmTLZlU6ZMoXjx4uTMmZMHDx7g7u7+Qts8fPgwHTt2ZPfu3bRo0YKpU6f+63aKyMuLe32PGDECLy8vWrZsyZ07d0iSJMlT3xe3/ixcuJCgoCCuXLnC4sWLqV27dry0/W0T95g96vjx42TPnp3bt29z/fp1uzr7qLi1vUGDBsyfP59ChQoRGhpKunTp7M6pPN2zzsfevXt577338PLy4u+//yZVqlTP3U54eDhDhw5l9OjR5MiRg+XLl5MxY8bX1Xx5Q2jkHREREREREREREXkrWb/N6ujoSLly5Z4a3AH7EXg6dOhA3759uXTpEhUrVuTkyZPx1WQRkXhl/f62q6srVatWtQV3nvWtf4BevXoxZMgQbt++zYcffsipU6deSXAHsHUCFy9enHr16vHbb78B0L59e9q2bcu+ffswm80vHNyJiYkhb968zJ8/nyRJkvDNN9+wbt26V9JWEXk51uu7f//+9OjRg99//50bN248M7gD9vWnXr16zJgxA4AWLVrYaoTYswZFWrRowcKFC22vN2nShIIFC/LHH3+QNGlSW3DnaeN5xB1hZ+7cubRo0YL9+/fTvHlz7t69q+DOC7Kej6pVqzJ27Fjb602bNqVWrVq2ET+twZ2nnY+4I/D069ePfv368csvv9CmTZvHRs6Td8/TxxoUEREREREREREReYM8+o3WuJ0JJpPpmd94hYcPw2NiYvD09KRHjx4sX76cw4cPs2HDBjJkyPDKOqZFRBLKo3Uw7ogJ1qlnTCbTUztjrXXS2dmZHj16sHLlSg4dOkRoaCg9evR4ZXXSMAwCAwOZP38+ISEhuLi4EBoaSlBQEGXLln2p/8fZ2Rl4OFpQbGwsPXv25L333nsl7RSRFxN39JbLly+zZs0aGjZsSKdOnUiWLNkLbSNu7Vq7di1JkyalSZMmeHl5vZY2vwu2bNnC9OnTOXjwIGnTpmXlypXMmTOHli1bPnbcnhXCiVtzDcPA29ubDz74gMjISB3/l3Do0CFWr17Nrl278PPz44cffmDWrFm0adOGgIAAu3WfdT6s14KLiwunT5/Gy8uLbNmy2U0xKe8mTZslIiIiIiIiIiIib7y4nUKhoaHs2bOHo0ePkitXLkqWLEn58uVxdXUlNjYWJ6fnf2exRYsWzJgxg+DgYJo1a4a/v//r3gURkdfqSXXy1KlTpE2bltq1a5M3b158fHyeG3S0+uqrr1iwYAEhISE0btz4tdTJ3r17M2TIEADq16/PxIkT8fLyeulpWs6fP8/7779PgwYN6NGjxzOnJBGR12fx4sWkT5+eGjVqsGDBAkqUKPHS29i5cyclSpSgS5cudO7cmdSpU7/6hr4j7t+/z5o1a2jdujXOzs5cu3aNoKAgunXrRvLkyV96e19//TWtW7emR48etG3bljRp0ryGVr+brPetsLAwSpYsiaurK1FRUQQHB9OhQwd8fX1feptTpkyhbdu2BAcH07p1a52P/wCNvCMiIiIiIiIiIiJvNMMwbB3SQUFBTJw4kcSJE+Pt7c2BAweYMmUK9evXZ9KkSXh4eLzQNu/evUvnzp1p06aNOnlF5K33aJ2cMGECHh4eeHt7s2PHDhYsWEC9evXo3bs3gYGBL7TNxIkT06ZNG1q0aBEvnecXLlzg0qVLeHl5vVRwxzAM0qVLx7Fjx3BwcCBlypSvsZUi8jSrVq2iXr16+Pn54ezsTNasWQFeOoz38ccfs3//fvz8/BTceQ4PDw+++OILpk2bRlhYGClSpCBr1qy24M6LhjWtqlWrhpeXFyVKlFBQ5B8qUaIEpUqVYtu2bbi6upI0aVJbcCduyPZFtGjRAi8vL0qVKqVr4T9CI++IiIiIiIiIiIjIW2H48OEEBwfTokUL2rVrR/bs2Tl37hzly5fn+PHjNG/enK+//vqZ24jbgXTnzh2SJEkSH00XEYkXI0aMoGfPnnZ18syZM9SsWZOffvqJVq1aMWnSpGd2pMetk3fv3n2lU6bE7UiOiopixowZXL9+nbNnzzJnzhzKly/PwIEDyZs37yv7P0Ukfpw7d46BAweyZs0arl69yuLFi6ldu/ZLbeNlgz4Cv/zyC61atcLT05O9e/eSMWNG+vXrR5UqVeymS3xROgcvz3rMDMPg0qVLfPnllyRKlIgNGzbg5eXFqFGjaNq0KfDigaqXDfrIu0HhHREREREREREREXnjnT17lgoVKpAsWTKmTZtGtmzZiIyMZPv27TRt2hRPT0/27NlDsmTJbO95WueDOiVE5F105swZqlSpQtKkSZkxYwZZs2YlNjaWtWvX0rFjR5ycnDhw4AA+Pj4J0r64HZE///wzrq6uZM+enYiICBIlSkTHjh2ZMGEC5cuXZ9CgQeTJk8euXt+7d4/EiRMnSNtF5Nms1+r58+cZNGgQs2fPpmDBgixZsoS0adMmdPPeaYZhcPToUXx8fNi7dy/NmjUjICCAgQMHUqVKFYAXnlZWXl7cMM61a9fw9fXl6tWrpEiRwjYFnKenJ2PGjKFJkyYAxMTE4OzsDOjciL0XHydLREREREREREREJIFcvHiR48ePU69ePbJly0ZsbCzfffcdLVu2xMXFhd27d5MsWTIsFgtnzpzBbDY/NaCj4I6IvIuuXLnC77//Ts2aNW3BnRUrVtC+fXtMJhP79u3Dx8cHi8XC+fPniYqKire2xQ3uDB06lBo1apA3b15OnjxJokSJABg9ejTt27dnw4YN9O7dm0OHDtnq9XfffUdQUBDHjx+PtzaLyJNZLJanLkuXLh19+vShUaNG7Nmzh2bNmnHr1q14bN27zWw22/07NjYWk8nE+++/j5+fH5UqVWLixImcPXuWkJAQvvvuO8xmM05OTpjNZjZv3sy+ffsSqPXvHrPZbAvuTJkyhQoVKtC+fXtSpEgBPJwCbsOGDYSHh9O5c2dmzpwJgLOzM2azmW3btvHtt99y586dBNsHebMovCMiIiIiIiIiIiIJKu7g4IZhPNYxAfD3338D4O3tDcDSpUvp1q0bDg4OHDhwgOTJkwNw69YtgoKC2Lt3bzy0XEQkYcStk9YaevnyZcxmM6lTpwYgNDSUbt264ejoyMGDB/H19QUejgzQunVrfv7553hpq2EYtuBOUFAQ/fr1I0+ePKxcuZLMmTPb1nN0dGTMmDG2AE/37t3Zvn07S5cupWfPnsyaNSvBRg0SkYfihhVOnDjBnj172L9/P5cvX7at4+fnR0hICM2aNeP777+nXr16CvC8AhaLxVZLZ82aRatWrahXrx6LFi2yhTHd3d2pWbMmkyZN4q+//iIkJITVq1djGAabNm2icePGfPXVV0RGRibkrrwT4p6Prl270r17d1xcXMiRI4fdOp9++inff/894eHhdOnShenTpwOwfv16mjRpwpAhQzQ9ltho2iwRERERERERERFJMHGHmn/Ujh07yJ49OylSpODEiRPkyZOHGjVqULt2bVq3bo3JZOLAgQO2DmmA9u3bM2fOHLZt20a+fPniazdEROJN3KmkTp8+TcaMGQE4fvw4hQsXpnjx4jRs2JBOnTo9sU527tyZadOmsXXrVgoWLBhv7Z4xYwZt2rShVatWtG/fngwZMjxxPYvFQvfu3Rk9ejQAbm5upE6dmi1btpA+ffpn3jdE5PWJe+0NGTKE6dOnc/bsWQDy5s1LixYtaNasmW39ixcvMmDAAKZPn065cuVYuHChLYQt/1zXrl0ZPXo0Tk5OxMbGAtCwYUM6d+7Me++9B8CDBw9YsWIF7dq1w9nZmRw5cnD69GkAtm/fTqZMmRKs/e+akSNH0qNHD9q2bUv79u1t92Qr6z178+bNVKxYkdjYWPLmzcu5c+dwcXFhx44dj71H/rv06UZEREREREREREQSjLUTqEKFCvTv39/2erNmzahevTonTpzAMAx8fX0pUqQI8+fPp2HDhjg6OvLLL7/YdUjPnTuXNWvWUKFCBbJnzx7v+yIiEh+swZ1u3bqRK1cuzp8/D0DatGnJmzcva9asoWXLlphMJn777Te7Ojlv3jxWrlxJxYoVbZ28r5t1RLVVq1aRJk0a2rRp89TgDjy8L4wcOZKpU6fSqFEjWrVqxa5du0ifPr3dqB8iEn8Mw7Bde926daN3796kT5+e0aNHM2vWLE6cOEHHjh0ZPHiw7T1p06YlJCSE5s2b8/3331OxYkVND/QPxB2HY/ny5XzzzTc0bdqUAwcOsGnTJurXr8/8+fMJCQmxjajm7u5OrVq1CA0NxcPDgzNnzpAlSxb27NlDpkyZbKEf+XdOnjzJnDlzKFSoEB07dnxiCMdkMmEYBmXKlGHbtm0UKlQIs9nMhx9+yL59+8iYMaPOh9g4JXQDRERERERERERE5L/tjz/+4PvvvycsLAw/Pz9+++03Zs6cSZs2bciYMSMmkwlvb29CQkLYtWsX169fp2HDhnh5edm2MWPGDIYPH46rqytjxozBw8PDbnQKEZF3zd27d4mMjGTHjh18+eWXeHl5MXXqVIoWLcq1a9do27YtiRIlsq3/zTffMGrUKFxcXBg7dmy81UmTycTVq1fZsmULn3/+OZkzZyY2NhYnp2d3UbVo0YIWLVrY2mg2mzW1iEgCsdaJyZMnM336dNq2bUu7du1sU98NGDCAS5cu0adPHxwcHOjZsyfwMMDTp08f7t69y/bt24mOjk6wfXgbxa3RkZGR/PTTT2TOnJlu3brZRs/JnDkzqVOnto1WFhISQu7cuXFxcaFs2bIcPXqUe/fu4eXlRaJEiTCbzc+tv/JiLl26xPHjxxk+fDjp06d/5rqGYVC0aFHWrl2Li4sLDg4OOh/yGE2bJSIiIiIiIiIiIgnG2inx888/U6xYMaKiooiNjaVHjx506dKFZMmS2b5xbDKZ2LRpE9WrV+f+/fvky5ePwMBA/vzzT44dO0aaNGnYvHkzgYGB6uQVkXeWtW6ePXuWjz/+mAwZMrB9+3bb8t27d1OtWjWuX7/Oe++9R5YsWfjzzz85fvw4fn5+bNq0Kd7r5O3bt8mQIQN58uRh69atjy23Tsdz4cIFfv/9d0qXLh0v7RKRF/fXX39Rq1YtUqZMyYgRI8iePTt3796lQIEC3L17lyZNmjB69GgiIyMZPHiwLcAD8Pfff+Pi4oKPj4+mvvsHunfvzrFjx3B2dubDDz+kV69ediHIixcvMmnSJEaNGkWlSpXo27cvH3zwwWPbUbD91Vq2bBm1a9dm4sSJtGnT5rFgqvVn/fbt2yRNmhSwPwc6H/IoVUYRERERERERERFJMNah5HPnzk2JEiVsD71dXFxIliwZAGaz2bZe2bJl2bp1K02bNiU8PJywsDAcHR1p164dO3fuVHBHRN551o6+5MmT8/HHH7Njxw5mzJhhW160aFH2799P/fr1cXR0JCwsDDc3Nzp06JBgddLNzY20adOyc+dOli1bZgtlGoZhNx1P7969adq0KVevXo23tonIi4mOjiY6Opr69euTPXt2IiIi+OSTT7h16xbDhg1j4MCBhIaGAg+v5UGDBtnemypVKnx8fOyud3kxN27c4Pz582zYsIFvv/2WU6dOYRgGTk5OtlqaNm1a2rZtS1BQEGvWrGHQoEEcOnTosW0pKPJq+fj4AA9DPHfu3LE7J9afdcMw+OSTT+jTpw9gfw50PuRRGoNJREREREREREREEpTJZOLvv//G0dGRatWqsW7dOoYOHUqSJEno1KkTTk5Otm+uWiwWChQoQO7cuYmKiuLq1aukS5cOR0dHHB0dFdwRkXfKozXN+m/DMPDw8KBbt26sWrWKTZs20aRJE0wmE7GxsWTIkIFp06YBD0dkCAwMxDCM11onnzWahpubG7169aJRo0bMnDmTwMBA8ufPbzf6wJIlS9i1axclSpSwmxZRRN4M/v7+zJ8/n1y5chEbG0v37t05ceIEgwcPplatWsDDKZx8fX1xdHQkJCQELy8v2rdvb9uGwgovL1myZPTr1w9fX1/mzJnDb7/9xq+//kquXLls4XaTyWQL8Dg6OjJkyBCSJEnC119/rSmZ/qVn3dtKlixJ0aJF2blzJzNnzqRZs2Z4enoSFRWFq6srFouF+fPnc/XqVZycnPR7ijyXps0SERERERERERGRN8KVK1dImTIlR48epWDBgpjNZoYPH07Hjh0BiImJwdnZGcD2UFxE5F1y7do1PDw8SJQokd3r3333HWXLlsXd3R14GHaxWCwAtGjRglmzZrFhwwY+/fRT2/L4nJYjbofkzp07OX36NDdu3CBlypR88cUXODs7c+3aNfr27cu0adMoXLgw9erVo3bt2hiGwcKFC5k0aRIWi4WwsDDSpk2r6UREEsizwgrWa/3q1auUKlWKpEmTsnXrVlxcXACIjIzkww8/pGLFioSFhbFs2TICAgLis/lvtWfVvePHjzNx4kS++eYbatWqxciRI/Hz83vsfefPn2fhwoXUrVsXf3//eGv7uyjuve3ixYtcuXIFk8lExowZbSHTnTt38tVXXxEVFUWLFi3o2LGjbYqsRYsWMXDgQFxcXNi0aRMpU6ZMqF2Rt4TCOyIiIiIiIiIiIhKvntYpZH1UaTKZOHDgAJ988gmxsbEMGzaMTp06ARAbG0tYWBiXL1+mQoUKtqm1RETedgcPHqR69er06dOHBg0a2DrDhwwZQu/evcmePTsNGzbks88+I3v27Lb3LVu2jNq1a1O5cmXmzJlj6zSML3Fres+ePZkyZQrh4eG25Xnz5mX48OF88sknnDt3jsmTJ/P1119z//59/P39iYqK4tatW2TIkIH169dr+kORBBT32jtz5gx37tzhzp07FChQAFdXV9uyEydOkD9/fsqXL8+SJUuAhyHrsWPHMnbsWE6cOEHixIlto4Fp9Jfni3vsr127RkREBFFRUWTJksW2zu+//8748eP55ptvqFu3LsOGDXtigMdal1VL/7m497bBgwczd+5cTp06BUDq1Knp3bs3VapUIU2aNISGhtKnTx9OnTpFlixZyJcvH+fOnePw4cMkS5aMnTt3EhAQ8MxgnAiAfjpEREREREREREQk3pjNZttD6+3bt/P111/Tp08ftm7dSkxMjK3ToUCBAoSFheHk5ETPnj0ZNWoUAOvXr6d58+YMHTrUNgqPiMjbzjAMLl68yOXLl/nxxx/tRl6oUaMGnTp1wsXFhe7du1O4cGGGDh3Knj17AKhZsyZ16tRh+/btXLp0CcA2Kk98sNb0kJAQhg8fTrVq1di+fTvnz59n8ODBnD59mi+++IKNGzcSGBhI7969Wb9+PdWqVSNDhgwULFiQgQMHEhYWpuCOSAKyWCy2a2/w4MFUrFiRggULUrJkSQoVKsTIkSO5efMmAF5eXiRPnpy1a9cyc+ZMLBYL8+bNY+7cuWTNmtUuSKLgzvPFPfajR4+mcuXK5M6dm/z581OzZk0WLVoEQLZs2ejQoQPNmzdn0aJF9OjRgwsXLgDYptCC/9Vl1dJ/znoMu3XrRp8+fUiXLh3ffPMNgwcPJkOGDHTv3p3g4GAuXrzI559/zurVq6lWrRoREREsWrSIW7duUadOHfbt20dAQIDd70AiT6ORd0RERERERERERCRexP22ae/evZkwYQL37t2zLW/UqBHNmzenYMGCttcOHjxImTJluHv3LtmzZ+fKlSskSpSI7du3kzFjRk2rIiLvjMjISI4cOUK2bNnw8vLil19+ITAwkCRJkhATE8O9e/eYMmUKS5cu5ejRoyRNmpTq1avTqVMn9u7dS+fOnSlbtiyhoaHx3mG7d+9eqlevzscff8yQIUPImDEjAAsWLKBNmzYkTpyYX3/9FW9v72duR6MSiCS8bt26MWrUKEqUKEHFihVtU9sdP36cUqVKMWfOHHx9fdm0aROVK1cmOjqapEmTcvv2bQICAggLCyMgIECf0V5Q3OMUFBTEmDFjyJs3L0WKFOHBgwcsXboUd3d36tSpw5gxYwA4deoUo0aNYtq0adSvX58BAwZoerLXYNmyZTRq1Ii6desSFBRElixZiI2NZcqUKXTs2JE8efKwc+dOPDw8bO+5efMm169fJ3369BiGgYuLi0Kp8sIUdRQREREREREREZHXzjAMu2lVRowYQbVq1WjdujVp06Zl0qRJTJ48mTt37tCxY0eKFi0KQP78+dm5cyddunQhPDycjBkzMnnyZNKlS6dpGETkneLm5kaBAgUAGDFiBH369GHOnDlUrlwZDw8PvL296dWrF/Xr12fv3r0MGTKEmTNnsnHjRnLmzImHhwf79u3jxx9/tAtBxodTp05x5coVGjRoQMaMGYmNjWXZsmX07t2b5MmT88MPP+Dt7U1kZCRubm7Awyl2rCOoWTuvFdwRSVjLly9n0qRJNG3alK5du5I5c2YAUqZMSYMGDbh8+bLtGi5btix79+5lxIgRODg44OfnR6dOnUidOrXCCi/BGtyZN28eEydOpFWrVnTs2NF27DNlykSPHj3Yv38/9+/fx8PDg0yZMtGlSxccHR2ZOnUqXl5ejB8/XjX0FduxYwfu7u60bt2aLFmyEB0dzXfffcfo0aPJkCEDGzduxMPDg9jYWBwdHTGZTPj4+ODt7W07r4Zh6FqQF6bfbEVEREREREREROS1sz7AXrRoETNnzqR169a0a9eOLFmycP/+fbZs2YKLiwurVq0iIiKCXr16UaRIEQDef/99Vq5ciYuLCxaLBXd3d8xms4I7IvLWijvSgmEYdlOmWCwWAgICSJs2LT179sRkMlG5cmUSJUoEgL+/P/7+/nz88cf8+OOPjBo1in379nH37l08PT3JlCnTa237kzrlf//9d+Bh4BIejlbQo0cPHBwc2L9/P8mTJwfg2LFjTJkyhRkzZthNfajROUTiX9xr2VqTduzYgaurK+3atSNz5szExsaydOlS+vXrR0BAAJs3b8bT05OoqChMJhN58+Zl9uzZts9mjo6OCu68gLijjFnvARs2bMDX15c2bdqQOXNmzGYzoaGhTJ8+nYCAANasWYOHhwdRUVG4urqSOXNm2rZti5eXF61atVJw51969Of2/v37bNu2jUyZMpE7d26ioqJYtWoV3bt3f+zedujQIRIlSkSuXLkA+3ua7m/yMnQVi4iIiIiIiIiISLy4c+cOq1evJlWqVDRp0oQsWbJw9+5d8uXLx61btxg3bhxt27Zl48aNjBo1ip07d9re6+HhgaurK+7u7voGq4i81SwWi11wx2Qy2Wranj17MJvNfP7554wdOxZnZ2e6du3K6tWriYiIsL3HMAzSpElD5cqV2blzJ1OmTCEoKIgjR46QLFkyLBbLa2u7ta0TJ05k0aJFAKRKlQqAlStX2gV3Dhw4gK+vr+39I0eOZNWqVfz111+vpX0i8mzbtm2jV69eALagjVVUVBT79+8nXbp05MqVi8jISFasWEHPnj2xWCwcOHCAZMmSAfDnn3+ye/duzGazbSQea3hEn9GebM+ePYSGhgLYBW1MJhPh4eH88MMP5MyZkxw5chAVFcXy5cvp2bMnsbGxdsf+r7/+4siRIwBkz56dQYMG4e/vb3cu5eXEvbetWbMGs9mMh4cHgYGBXLt2jUuXLrFp0yZbcOfAgQO24A5AkyZN6Ny5M9HR0Qm1C/KOUHhHREREREREREREXou4nQjWKa7MZjM9evQgd+7cPHjwgPLly3Pjxg2GDx9O06ZNadmyJalSpWL16tVMmDCBsLAw4PFODhGRt5W1nlWvXp0hQ4bYXm/WrBmlS5fm0KFDuLi4UK5cOUaOHImbm5tdgMdkMtnqoLXO1q1bl6FDhxIQEEBsbOxrG4HBut3evXvToUMHfvrpJ2JjY6lQoQLe3t4MHz6cTp06YTKZ+Omnn+yCOzNnzmTXrl18+eWXpEmT5rW0T0SezDAM7t27R8uWLRk6dCi9e/cGHgZtrIFCV1dXUqVKxf379wHYuHEj3bp1e2IQ74svvmDatGmYzWZbPdLnsyczDIOrV69SpkwZ6tSpw969ex9bx83NjUSJEtmCl999990Tj31MTAyff/45ixcvttV/a+hEoal/znpv69q1K7Vr12bKlCnAw3DUmTNn6NixI23atMHR0ZH9+/fbXQujR4/m2rVrlCtXTqOCyr+mnyARERERERERERF55eKOjtOzZ0/8/Pxo06YN06dPx8PDA8MwGDNmDIcPH6Z3795Ur14dR0dHsmfPzocffsgff/zBypUrcXV15aOPPsLFxSWB90hE5NU5duwYq1atYteuXaRNm5ZDhw4xc+ZM2rRpg7+/PwCurq6UL18eeNih2LVrVwC7KbTidtZa//46Og/jTidy5swZVq9eTZMmTWjbti1OTk5kzJiRbt26MWTIEMLDw1m5ciVJkya1vX/evHkMHz4cb29vevbsiYuLi93UYSLyeplMJhInTsyiRYv46quvGDJkCGazmaFDh+Lg4EB0dDTOzs7kzZuXdevWUaNGDQ4cOICTkxN79+61CyuMGTOGv//+m6+++kphhRdgMplIkSIFI0aMYO/eveTIkcNueUxMDA4ODuTJk4eFCxfSuXNnVqxYYZua6dFjf+XKFTJnzqxpsl6BuNOXhYWFMX/+fJo1a0bZsmUB6NOnD1u3bmX58uX4+Piwb98+UqZMaXv/0qVL+eabb8iUKRMNGjTQOZF/zWQYhpHQjRAREREREREREZF304gRI+jRoweNGzdm+PDhtiH/AapWrcrhw4f5/fffbR3RZrOZnDlz8vnnnxMYGEj58uVtHdkiIu+SH3/8kapVq3Lr1i0ePHhAly5dCA4OxtvbG/jflFpRUVFs2LCBrl27EhkZyciRI6lSpQru7u7x3uYNGzbg4+NDpUqV+Pbbb/noo49sy06dOsWkSZP45ptvyJIlC8WKFaNAgQKsW7eOrVu3kjhxYnbs2EFAQIBdGEhE4oc1qPDTTz9Ru3ZtTp06Rffu3Rk6dKhtndOnT1OoUCFu3LhB8uTJ+eOPP+xqknUqp1SpUrFq1Sq7YIk8WdyASExMDM7OzoSEhJAxY0YaNGhgW2/dunVUqlQJeDgV4dmzZ3F2drZtY8WKFfTo0YOAgABbmERejWvXrrF48WJGjhzJpk2byJ49u20UpNWrV9O7d2/Onz9Pq1atqFSpEkmSJGHevHksXLgQR0dHdu/ejb+/v925FvknFN4RERERERERERGRV+bRDtnixYuTNm1a+vfvT+bMmYGHHRBRUVHkzZuXe/fusW3bNtuy+fPn07t3b6ZMmULFihWfuE0RkXdF1apVWb16Ne7u7vTo0YM+ffoAj9e9uAEes9lMSEgIX3zxBW5ubq+1fXE7IufPn0+DBg3ImjUrjo6OHDx4EHd3d7u2njt3jnXr1jF8+HDOnTsHQNq0afn4448ZMWIEadOmVU0XSUAvEuAJCwujcuXK3Lt3jx49elCzZk3c3NyYNWsWoaGhWCwW9u7dq7DCS4g70tjhw4fJly8f7u7uzJ49m1q1atnWGzduHJ07d8bHx4fp06dTqFAhXF1dmThxInPmzCE2Npa9e/eSLl06Hft/Ie75CAkJYdq0aRQpUgRvb29mzJhhm0rOZDIRERFBWFgYffr04aeffsLJyQmLxYKHhwd58+Zl7ty5+Pv7694mr4TCOyIiIiIiIiIiIvLKjRw5Eg8PD0aNGsXUqVP59NNPbcusD8yto/I0adKESpUq8dtvvzFz5kxcXV3ZsWMHyZMnT8A9EBF59eJ2GJ47d4527drh6upKWFgYFouFkJAQ2rRpg6Ojo61jNu4IPBs3bqRBgwZkypSJHTt22EYtex3idgyfP38eNzc3WrZsybZt24iIiOC7776jXLlyj+0XwK1bt/jrr7+4ffs2uXLlwsPD47Ggj4jEn7jXqPXvzwrw7N69my+//JJz585hMpkwDAN3d3cKFCigsMJLetIUgXPnzqVt27YAzJgxg9q1a9uWTZw4kQ4dOgCQJEkSoqKiMJvN5M6dm6VLl2r0sn/p0WM3duxYhg8fztWrV8mePTvbt28nRYoUj70vOjqa2bNnc/36daKjoylatCj58+cnadKkOh/yyii8IyIiIiIiIiIiIq/Unj17KFasGOnTpycqKooVK1ZQsGDBxx5sHz9+nOHDh7No0SJiY2MByJkzJ2vXriUgIEDfKBaRd0rcGvj333+TKlUq7ty5g6OjIydPnqR8+fLExMTQr18/2rZti8lksk2xYhUdHc327dvJlSsXadKkiZd2d+rUiWnTpvHzzz9jNpvp27cvy5Yto2rVqsyePZskSZLYrf+02v2kDmwRef3iXpPW2mP1rADPxYsX2bVrF8eOHcPV1ZWCBQuSP39+kiRJorDCC4p77O/evYuXl5dt2bx582jZsiWOjo6PBXh2797N7t27OXLkCL6+vnz00UeUKVMGHx8fHft/Ie59qFmzZqRJk4b+/fszadIkJk2axJUrV5g8eTI1a9a0u/c+63cS/b4ir5LCOyIiIiIiIiIiIvKvPKlDdvTo0XTt2hV4+A3iNm3aPHHdy5cv88svv7Bnzx4yZsxIhQoVSJEihTomROSdEremTZo0yTZyxYoVK2zr7Nmzh+rVq9sCPC1btsTZ2Rmz2cyWLVu4c+cOn3/+ua1D8XXVybh1esmSJbRs2ZIaNWoQFBREtmzZ+O233+jXrx8rVqygUaNGTJ06FRcXl1feDhH59+LWicmTJzNr1ixy587NzJkzX2gKrSdRWOHFxD32s2bNYvHixVSrVo1WrVrZ1nlWgOdJdOxfjVGjRtG9e3dKlCjBt99+i8ViYd68eQwZMgQvLy9mzZrFRx99pMCpxDunhG6AiIiIiIiIiIiIvL3idiLcvn2bpEmTAtClSxfc3d1p27YtXbp0ITAwkIoVK9qmXrA+DE+dOjWpU6e2Tb1i3aaCOyLyrohb07p27cqUKVMoWLAgFSpUsFuvSJEirFy5kmrVqtG/f3/MZjNt27Zl48aNtgBk+fLlbeGd1x3cuXPnDj///DM5c+akd+/eBAYGYhgGOXPmpH///gDMnj0bQAEekTeQYRh2tWfq1Kl8+OGHFCxYEAAHBwcsFgt58uQhNDSU2rVrM3z4cEwmE0OGDAEejvZlvbat9UHhkeeLW/e7devGtGnTSJ06td1yBwcHvvrqKwBatmxJ06ZNAWwBntjYWBwdHe0+O+vY/zNxg1TR0dHs3buXOnXqMHDgQDw9PQFo1KgRJpOJ/v3706xZM6ZPn64Aj8Q7jbwjIiIiIiIiIiIi/1rPnj25evUqffr0ITAw0Pb6pEmTaN++PZkzZ2bSpEmUKVMG0PQpIvLfM2rUKHr06EHbtm1p06YNmTNnfuJ6+/bto1q1aly5coUcOXJw7do1XF1d2b59OxkzZoyX+tmzZ09Onz7NxYsXKVmyJAMHDsRsNuPg4GD7v48dO0bfvn1ZsWIFjRs3ZsqUKQrwiLyBrKOMtGvXjnbt2pExY0a75U8agSc4OJhBgwYlUIvfHYMHDyYkJITWrVvbPg9bxQ3Axx2BZ9asWdSsWTOhmvxOGzt2LLGxsfTv35/p06dTp04d4H/n4t69e8yZM4f+/fvj6+urAI/EO8XzRERERERERERE5F+5desWP/zwA7Nnz2bChAn89ddftmVt27ZlzJgxnDx5kjZt2rB582YA27eIRUT+C86ePcucOXMoUKAAnTp1empwB6Bw4cLs3LmTwoUL4+zsTP78+W1TC8bGxr72TsSbN29y5swZli9fzr59+7h16xaAbQQIqxw5ctC/f3+qV6/OrFmzqFevHjExMa+1bSLycs6cOcPcuXPJnz8/HTp0eCy4Aw9H4DEMwzYCT7Zs2RgyZAjDhg1LgBa/nSwWy2Ov/fTTT8yYMYNSpUrRpUuXx+q+deQjgK+++oqvv/4aBwcHateuzerVq+Ol3f8lR44coUuXLkyaNInkyZOTJUsW4OEIR9ZrIHHixDRs2JC+ffty7do1WrVqRVhYmH5nkXij8I6IiIiIiIiIiIj8K97e3sycOZPq1aszfvx4xo0bZxfg6dixI2PGjOHUqVO0bduWLVu2AOhbrCLyn3Hp0iWOHTtG1apVCQgIeGJHb1yZM2dm69at7N69m+XLl5MuXTrMZjNOTk6vva0+Pj4MHDiQjh07YjKZ2LJlC3v37n3iujly5GDAgAGUKlWKsLAwwsPDX3v7ROTFXb58md9++43KlSuTPn36Z65rDfDMmTOHIkWK2EYlkae7cuUK8L8AVFxnz57l3Llz1KlTx25UyrgcHBwwm83AwwDPiBEjyJAhA3ny5Hmt7f4vypYtG4sXL8YwDM6dO2eb9tHJyck2ol3cAE///v359ddfGTBggIKpEm8U3hEREREREREREZEX9mjHhPXf6dOnZ+TIkVStWpWJEyc+NcBz8uRJateuzc6dO+Oz2SIiCcoaannatFLWzltrRzCAq6srHh4euLm5YRgGjo6Or7xdTxtNIEuWLDRv3pxWrVpx8uRJpk6dyqlTp564bvbs2ZkyZQrHjx/Hx8fnucEkEYk/169fB8Dd3R14OMpIXNbr9fbt27ZQdYECBdi2bRsBAQGPrS//s3fvXvLkycPYsWOBx0PpJ06cwDAMfHx8gKcf+wcPHthea9GiBUeOHLEFNuXfsx5nFxcXqlSpwtixY0mVKhVLlixh/vz5dsGduAGe+vXrM3PmTObPn68pISXeKLwjIiIiIiIiIiIiL8Risdg6Ju7duwfYT38VGBjIqFGjnhngGTRoELGxsc+cMkZE5F2TKFEiAJYtW8b58+dxcPhf90zcYE61atVo3rw5YN8R/DpGKjObzbbtxsbGcu/ePbuRc7Jly0a7du1o2rQpixYtYvjw4Zw8efKJ28qcOTPJkyfHYrHY7ZuIJCxvb28Ali5dyo0bN2yjjMDD2mMdMaZ06dJ07drV9j5nZ2eAeBnt621kNpt58OABf//9N8eOHbMbmcUauvHz8wOwTYH1pGMfExNDsWLFWLhwoe391vvF6whs/hc8GiCNe09yc3OjfPnyjB8/HoChQ4eyYsWKJwZ4PD09adSoEX5+fgpSSbzRJygRERERERERERF5IdaH37169WLAgAH8/fffgH2AxzoCT7ly5Zg6dSoTJkzgzJkztm0EBwdz8eJFUqdOrQfhIvJOedaIM0WLFqV69eocPHiQFStWcOvWLeBhaMZkMmGxWJg1axbnz5+Pl/poNpttHcOzZ8+mQYMGFC1alE8++YSxY8eyf/9+ALJmzUrnzp1p0qQJs2bNYsSIEU8N8AAK7oi8YT7++GPKlCnDDz/8wPTp07lz5w4mk4moqChb7Zk3bx5XrlzB3d1dn81ewJYtWxg5ciRFixblp59+YuTIkTg7O/Pjjz8C/wvdlCxZkjRp0rBixQrbdE0mk4nIyEjbZ+d58+Zx9uxZrl279tSR0OTFmc1m231o06ZNjB49mubNmzNx4kTb+UmUKBEVK1Zk6tSpXLlyhT59+rB8+XLblxSs5ykuBakkvpgMVQIRERERERERERF5QRcvXqR69eocOnSIvn370rRpU1KlSgVg97B7x44dVK9eHZPJRMOGDWnZsiUZM2a0rQevZyQJEZGEEDcMc+7cOa5cuYKzszPJkiUjXbp0AGzdupU2bdpw/fp12rVrx5dffmmriwsWLGDIkCE4OTmxadMmW119HeLW6i5dujBx4kQ8PT1JnTo1J06cwGw2kytXLrp3706dOnWAh9O/jBo1ipkzZ9KsWTM6duxItmzZXlsbReTfs46EtW/fPho2bMitW7do2rQpnTp1wtfXF4CFCxcyePBgnJyc2Lx5MylTpkzgVr/ZwsLCKFmyJGXLlmXGjBm20XX69u3L4MGDmTp1Ks2aNbOtv2jRIlq1akVgYCBt27a1W7Z06VL69u1LokSJ+P77723nRP6ZuCO/9ejRg4kTJ9pNSZYmTRq+/PJLhg0bBkBERATr1q2jZcuWpEiRgkGDBvH5558rhCoJSuEdEREREREREREReaonTYHyww8/MHDgQDZv3kyvXr1o3rz5YwEei8VC4cKF+euvv7h27Rp9+vQhJCRE31wVkXdO3Do5aNAgZs6cydmzZwHw8vIiJCSEhg0b4u3tTWhoKMOGDePIkSP4+/tTtGhR/vzzT44ePYqPjw9hYWEEBgbGy/RTEyZMoGPHjnTu3JnGjRuTI0cO9u3bx+rVqxk+fDhp0qRh3Lhx1KhRA4BTp04xevRovvnmG7p06cKwYcNU00XeApGRkaxfv56QkBCOHTtGYGAg+fPn58KFC/z888/4+vrGa+15W/31119UqFABLy8vhgwZQsmSJW3Lvv32W+rXr4+3tzchISE0bdoUgKtXrzJ//nwGDRrEnTt3KF26NB988AHHjx9n9+7deHl5sWvXLgICAnTsX5H+/fszcOBAGjRoQJMmTYCHv7sMGzaMa9eu0axZM7755hvg4bWxZs0a2rVrZxsB77PPPkvI5st/nMI7IiIiIiIiIiIi8kRxR5I4duwYsbGxvP/++wAcOnSIPn36sHXrVrsAT9wRHd5//33q1KnDpUuX6Nq1K/7+/gm2LyIir9KTOlm7devGqFGjKFeuHLVr1yY8PJwVK1awZ88eateuzYgRI/D19eXo0aNMnz6dZcuWcefOHbJkyUKRIkXo27cvadKksau9r4NhGNy4cYOqVaty/fp1vv/+ewIDA+3WmTx5Mu3ataNYsWLMnj2bDBkyAPDHH38wa9Ys2rRpo5oukoDi1okXqRkxMTGcO3eOvn37snfvXs6dO0fOnDkpVKhQvNWet93OnTupWLEiPXr0oFevXsDDwGa1atXIkSMHmzdvpmbNmiRKlIgBAwbYAjy3bt3i0KFDdO3alT///JO7d++SPn16ChQowMiRI/Hz89Oxf0V++uknKlasSN68eZk0aZLdve348eOUKVOGS5cuMWDAAHr37g08vDaWLFnC8OHD2bx5M6lTp06g1osovCMiIiIiIiIiIiJPELcTYeTIkcybN48HDx6wdu1a21QpcQM8PXr0oFGjRraH5AsWLKBPnz4sWrSIwoULP7ZNEZG30a+//kry5Mkfm9ZqxYoVNGrUiC+++ILu3bvbpsMaM2YMQUFBZMmShYMHD+Lp6Wl7z/Xr14mKiiJ16tSYzWacnZ3jrU7+9ddf5MmTh9KlS7Ns2TLAfjqtBw8e0LlzZ7755htWrVpFlSpVbO+1tlE1XSThDR06lOzZs1O1atUXHrnlzp073Lhxg4CAAMxmMy4uLrqenyEyMhI3Nzf27dtH2bJlKVy4MJs2baJLly6MHTuWUaNG0b59e9u0h7Vq1SJRokT079/fbpqs8PBwbty4wcWLF8mePTvu7u64u7vr2L9C69ato1KlSsydO5f69evbrgnrMf7xxx8pV64cgYGBrF271nYvj4mJITY2VudDEpzG3hIRERERERERERE7hmHYHlp36dKFPn364O/vz+TJk8mWLRvW7wN++OGHDBgwgNKlSzNs2DDatm3LvHnzCAkJISQkhMSJE9uCPoAehIvIW23jxo0UK1aMefPmERMTY7dsz549ODg40KxZMzJmzEh0dDTLli1jwoQJZMyYkd27d+Pp6UlsbKztPd7e3qRNmxYHBwecnZ2B+K2TDg4OXL58mZiYGMxmsy24A+Du7k7p0qUB2LRpExaLBYvFYtdG1XSRhPXzzz/Tq1cvNmzYAPDc4I71Gk6SJAnp06fH0dERFxcXQNfz02zcuJGBAwdy+/Zt3nvvPapWrcqWLVvIkiULY8eOpXfv3lSrVg0nJycAypYty9KlS4mIiKBv377MmDHDti1PT08CAwP56KOP8PHxwd3d3e4zt/xz1p/t8+fP2/6MG0h1dHTEMAxy587N559/zuHDh/n5559t73d2dsbd3d22rkhCUXhHRERERERERERE7FgfdH/99ddMnDiRVq1aMWHCBD799FPbcmuAJ1++fAwbNoyvvvqK9evX07BhQwYNGoSLiwtr1qzB29vb9kBdRORtdfDgQerVq0fu3LkpWrSoLWwDEB0dzc6dO8mYMSP58+cnNjaWlStXEhQUhMlkYu/evSRPnhyA3bt3s2vXLiB+Ogjj1t8HDx7Y/p48eXI++ugj9u7dy7Zt22wdm4Zh2AJGxYsXBx4GeRwcHF5oRA8RiT+pUqUib968hIaGsn///ueuH/cajhvWkyfbsWMH5cuXZ9++fdy4cQNPT0/mz59P6tSp+fPPP8mcOTMNGzYkMDDQLtAZN8ATEhLCzJkzbcviBkpA5+GfevR3C+vP9kcffUSiRIk4cOAAFosFk8lkt66TkxMFChQAHo5AJfKm0SctEREREREREREReUxkZCRLly4lffr0tG7d2jYFjFXcAE+uXLmYPn063333HRMmTGDGjBns2rWLwMBAzGazOnxF5K1mNptZvHgxJpOJoKAgPvroI+DhN/utU86kSpWKK1eucOzYMdatW0f37t1xcHDgwIED+Pr62rYVFBRE586diYiIiJd2W+vv4sWL6dmzJ/PmzQMgceLEVK1aFYAvv/ySvXv3YjKZMJvNODk5ERsby4IFCwB47733AGw1X0TeDKlSpaJDhw7cvXuXffv2AY+HGuSfOXfuHO3bt+fDDz+kT58+ts/BS5Ys4fLly6RKlYqTJ0/Ss2dPoqKicHZ2thtZLW6AZ8CAAUycOBFQWOdViHtv27lzJ3PnzrX93KdNm5aSJUuyevVqBg8eDDwM9sQNTR05cgQPDw8CAgISZgdEnkG/NYuIiIiIiIiIiMhjrly5QlhYGCVLliRz5syYzebH1nm0A6JSpUq0bduWxo0b4+vri8Vi0dDzIvLWs1gsnDp1ioiICHLmzAlA48aNqV+/PleuXAEeftv/0qVL9OvXj/bt2+Po6PhYcGfcuHGcP3+eWrVq4ebm9trbbK2/PXv2pFWrVqxfv96ug7lJkyYEBQVx48YNypQpQ2hoKBcvXgRg4cKFTJ8+nRw5clCxYkVAnc4iCeXR4Fzcf5cqVYocOXIwZswYLl26pMD0K3Lt2jXOnj1LmTJl+OSTTwDo06cPhw4dYvny5WzYsIGyZcuybNky6tatS1RUlC34aFW2bFmWL1/O+fPnmTNnDvfv30+o3XlnxL23DRgwgHr16tGtWzfb1HHJkiWjc+fO+Pr60q9fP4KDg7l69art/rVq1SrWrl3Lhx9+SI4cORJsP0SeRhVcREREREREREREHuPg4IDJZOLChQtPDOFYv+F68eJFtmzZ8tRtiIi8rX755RdOnjyJs7MzX3zxBbGxsQwbNowmTZowZ84ccuXKZauNjRs3plixYixfvpzw8HDWrl1rF9xZsmQJU6ZMIUOGDDRs2PC110fr9vv27cvw4cP58ssvWbZsGXXq1MHJyckWyBwxYgS9evXiwYMH1KlTh7x585IuXTqaNm1KZGSkbT80modI/DObzbapfwDbiF1xpwJKkyYN5cuX5+LFi2zevBnQ6DuvgslkwsXFxXZMu3TpwuDBg/Hx8aFEiRK89957LFq0iJIlS7Jq1aqnBnhKly5NWFgY3333HR4eHgm1O+8M672tW7duDBw4kKJFi7J69WpbyBSgRIkSzJkzBz8/P4YNG0bJkiWpWbMmFStWpHnz5sTGxjJ37ly8vLx0rcgbx2RonEMREREREREREZH/LIvF8lgnsmEYREVFkStXLu7fv8/ChQspUaKErfMo7tDzjRs35vjx46xZs4bkyZPHe/tFRF6HTZs2Ub16dYKCgujWrRvh4eEMHTqU8ePHA9CqVSuCg4NJmzYt8LCWLl++nFGjRnH8+HHatm1LsWLFCAgIYPbs2SxatAgnJyd2796Nv7//E2vvq7Z3716qVq1KyZIlGTFiBP7+/nbLzWazLXz03XffsWvXLrZu3Uq6dOnIlSsX7dq1I1WqVHbricjrtX37dg4ePEi3bt2A/33m6tmzJ8ePH6dKlSo0atTI7j2XLl2iQIECZMmShW3btiVEs985UVFRBAUFMXnyZNKlS8f58+fp3bs3TZo0ISAggNjYWJycnLh16xa1atVi69atfP755yxatAhXV1fb8rhUS1+NJUuW0LRpU+rXr0/37t0JDAy0LYv7O8q+fftYvHgxq1ev5tKlS/j7+/Phhx8yatQo0qVLp/MhbySn568iIiIiIiIiIiIi76K4D61Xr17Njz/+SKdOnfD29sbNzY127doRFBTElClTSJ8+PQEBAZhMJttD8UWLFrFlyxYqVKiAp6dnQu6KiMgrc+jQIerVq0f+/PkpVaoU7u7uuLu7c/bsWUwmE4ZhcPXqVRIlSmR7j4ODA59//jlubm5MnDiR4cOHM3z4cAASJ05MgQIFmDVrFv7+/vHWYXjs2DGuX79Oo0aNHgvuADg6OtpCRFWqVKFKlSo8ePAAd3d32+vq3BSJH4ZhcPfuXb788ksuX76Mg4MDQUFBmEwmjh07xk8//cSmTZtYvXo1M2fOpEqVKnz55ZekTp2aFClSULJkSRYsWMDSpUupVatWQu/OW81sNuPq6srEiRNZt24d58+fJ126dNSsWdMuuGM2m/H29rYdc+sIPIsXL8bFxeWx+qla+mps374dZ2dnmjVrZhfcAWz3aJPJROHChcmXLx8hISGcO3eOtGnT4uXlhbu7u+5t8sbSyDsiIiIiIiIiIiL/QXFHfejduzczZszg6tWrhIaGUrNmTQBOnTpFcHAwy5cvp3z58jRs2JBy5crh6OjIvHnzGDt2LPDwIXqaNGnsvu0qIvI2MgyD3r17M2XKFObMmUOVKlUA2L9/P82bNydnzpw4ODiwePFi6tevz+DBg/Hz87PbRlRUFCtWrODKlStERkZStGhR3n//fZIkSRKvHYbdu3dn5MiR/Pbbb2TPnv2xGm1ty40bN0iWLJnda6rnIgljy5YtNG3alHPnzjFs2DDbCDzwcCq/uXPnsn79ek6cOEGqVKlo3LgxNWvWxN3dnRw5ctCqVSsmTpyYgHvw7pgzZw6NGzcmY8aMnD59mtKlS7No0SKSJ09uq5XWP+OOwFOyZEnWr1+Pi4tLQu/COyciIoLcuXOTJEkSDh48CPDY/ep59y/d3+RNpvCOiIiIiIiIiIjIf1hwcDAjRoygcePGtG3blvfff99u+ZEjRxg9ejQrVqwgIiKCjBkzEh0dzdWrV/H392fjxo0EBgbqG6wi8k4wDIP69euzePFifv31V7Jnz06jRo04fvw4kydPJmvWrNy+fZv+/fszc+ZM6tevz9ChQ0mTJo3t/U/rFIyPqbLiGjBgAP369WPChAm0bt3a7v+2ttMwDOrWrcsXX3xhCyqJSMKwXpc7duygbt26XL58mUGDBhEcHGxbJzY2lujoaMaPH09YWBibN28GoEaNGmzbto07d+6wa9cuChUqlFC78c7YuHEjN27coHDhwgQHBxMaGsonn3zC0qVLSZYs2RMDPGXKlOHMmTOcOHFC08m+BlFRURQoUIDz58+za9cucubMabfcep+9ceMGY8aMYdCgQQrqyFsl/j4lioiIiIiIiIiIyBtlw4YNTJ06lbp16xIcHGwX3LF+5+/9999n8ODBLF68mNKlS+Pt7U327Nnp06cPO3fuVHBHRN4pJpOJOnXq4O7uTt++fWnQoAFz584lX758+Pn5kThxYvz8/AgKCqJJkybMnz+fnj17cunSJdv7n/ad6fgM7gBUq1aNFClSMH/+fC5fvmx7PTY21tbOkSNHsm3bNmJiYuK1bSLyOOt1Wbx4cRYtWkTq1Knp3bu3bQo+eBhOSJQoET179mTjxo18++231K1bl127dnHz5k3MZjPr16+3rSsv5knH6tNPP6VatWqkT5+euXPnUr16dbZv306tWrW4ceOGXXDHOoXW1q1b+f3330mePLmO/2vg6upKmTJluH37NmvXruXBgwe2ZWaz2XafHTVqFAsWLODo0aMJ1VSRf0Qj74iIiIiIiIiIiPxHDRo0iMGDB7NlyxaKFCny3PUNw8BisdgFdRTcEZF3zZ07dxg7diyDBg3CYrHQrFkzQkJCSJs2rd3oOSdOnGDkyJFPHIHnTXDr1i26d+/OjBkz+PTTTxkzZgwZM2a0TeWyYsUK+vTpg4+PD6tXr8bHxyeBWywi8OQReIYOHUr37t0BiImJwdnZ2bZ+REQE165dY/z48axbt47o6GgOHz6Mt7d3Qu3CWyXuZ9kjR45w7do1bty4wQcffECGDBlsxzomJoa6deuyYsWKZ47AA/E/0tp/gfW62LdvH40aNeL27dtMnTqVkiVLkiRJEtt6y5cvp3v37uTKlYsFCxaQOHHiBGy1yMtReEdEREREREREROQ/xmKxYDKZKFmyJL/88gvHjh0jZcqUGIZh19Fg7Xh4UqfEs6aGERF529WoUYNVq1ZhMpkoVaoUc+bMIXXq1ABPDfA0atSIfv36kS5duoRsup2zZ8/SoUMHVq9eTY4cOShevDgff/wxGzduZOPGjZhMJvbs2UNAQIA6m0XeENbPac8K8DwpKBIdHc2QIUMYMGAAEyZMoG3btgm5G2+FuHWvf//+zJo1i/PnzwOQIkUKypYty9SpU/Hw8AAeD/AsW7YMHx8fhdnjUWxsLFOmTGHgwIG2qS4rVapEhgwZmD9/PnPmzMFsNrN79278/Pz0O4u8VRTeERERERERERER+Y9q2bIl06ZNY+PGjZQpU8ZumbUz4/bt27Rr144pU6bg6emZQC0VEYk/4eHhVK1alVSpUpE4cWJmzpxJxYoVbSPXwOMBnjFjxjBt2jTat2/PmDFjXmsI5kU7ia0dlufPn2fy5MmsXLmSU6dOAZA4cWKKFi3KN998Q7p06dTxLJJAHg3NRUdH20bHsgoLC6NevXqPBXjivtf69+vXr5MuXTpq1qzJvHnz4m9H3nI9evRgxIgRVKpUicqVK5M6dWpGjBjBzp078ff359ixY7i5ueHg4GAX4MmdOzdbt27VKEevwIsESK33tZiYGGbPns3MmTM5ePCgbbmTkxMffPABy5Yt09S+8lZySugGiIiIiIiIiIiISPx49JunefLkAWDMmDGkT5+eTJkyAfbTMcyYMYOFCxfy5Zdf8umnn8Z/o0VEXrNHa6OnpyffffcdAJGRkSROnJjx48cDMHbsWDJkyICDg4OtozFLliy0b98eLy8v2rZt+9pHr7F2RC5dupRatWo9cR8ATCYThmGQLl06+vfvT69evdi3bx9ms5ksWbKQKlUqPDw81LkpkkDiXnvz5s1j586dHDx4kJw5c/Lxxx/TsmVLAEqUKGH7LNazZ08Aunfvbjc6orXuREdHkyxZMm7dukVERASJEiVKmJ17i6xfv57JkyfTpEkTevbsSYYMGQC4ffs2u3btIjw8nJiYGBIlSoTFYsHZ2ZlFixZRsWJFfvzxR2JiYhJ4D95+cUf/PHPmDBkyZHjivclkMtnOQdOmTalQoQKrV6/mzz//xDAMPvroI4oXL243nZnI20Qj74iIiIiIiIiIiLyjnvcNVovFQs2aNVmzZg1t2rShefPmZM+e3bZ8xYoVBAcHkzZtWlauXEnSpEnjodUiIvHn0c69iIgIXFxccHL633ef//zzTyZNmsT48eOpWLGiLcAD9qGZJ00x+LpMmzaNli1bsnDhQurUqfPc9Z82bYimExFJGHGvvaCgICZNmkTSpEkJDAzkzz//5Nq1azRu3Jju3buTKVMm2xRa9erV49KlSwwfPpyuXbvabfP+/fvMmTOHdu3aERISQr9+/RJgz94+AwcOpH///uzfv598+fJhNptZsmQJffr0AeDgwYMkS5aMBw8e4OrqavtsHRMTw507d0iePLmmHXxF+vTpw+LFi22jxD3N8+5dOh/yttJPrYiIiIiIiIiIyDvIbDbbHlpv2bKFb775hq5du7Jr1y6uXr0KPPz2alBQEEWKFGH8+PHUqVOHb775htWrV9OhQwc6duzIgwcPmDNnDkmTJsVisSTkLomIvFJxQzYzZ86kXr165M2bl7JlyzJ69Gju3LkDQPr06WnXrh0dOnRg3bp1dOrUiTNnzgD/G90G/jciTnx80986fdfWrVsBnlufn9bJqeCOSMKwXnujRo1i3LhxNG3alI0bN7J//342bNjAJ598wqxZs5g2bRoWiwXDMChevDgLFy7E39+f7t27M3nyZLttRkVFsWbNGmrUqGEL7mgMh2ezWCz8+uuveHt7ky9fPiwWC8uXLyc4OBjDMPjhhx9IliwZAGfPnuXrr7/GbDbbRn9RcOfViYqK4sSJE5w5c4YlS5Y8c93n3bt0PuRtpZF3RERERERERERE3jFxOxF69+7NlClTuHv3LoZh4OTkRLVq1ejevTu5c+fGbDZz5MgRJk6cyJw5c2zb8PLyIk+ePMyZM4eAgAANPS8i75S439rv0qULkyZNwtfXl2zZsnHmzBn++usvatasSZs2bShWrBgmk4lz584xfvx4xo8fT5UqVRg2bBiZM2dOsH2oXbs269evZ//+/eTMmTPB2iEi/8y1a9coVaoUSZMmZebMmWTOnJnY2FjWr19Py5YtcXd354cffiB58uR279uyZQs9evRg5cqV+Pv72y27cOECfn5+gEYfeVGNGjUiNDSUX3/9lZ9++onOnTvj4ODAgQMH8PX15f/Yu++oKM63jePfXaoKSBM7Yu89GnvXWGLFHo29o2IDFARRsSuKvRdExYq9o9gQsXdjjb2Cnbq77x+enXcRzU8TkUjuzzk5UWZ2fGbXuWfd59r7gQ/3jJo1a2JiYsKGDRuwsrJK5VGnTefPn6dSpUq0bduWRYsWpfZwhPjupGILIYQQQgghhBBCCJGG6HQ6ZaLG09OTcePGUatWLUJCQnjw4AFt2rRhw4YNDBw4kFOnTmFkZETp0qVZsmQJO3bsYOXKlUyZMoXt27cTEhIiwR0hRJqkD+5Mnz6dGTNm0LNnT3bu3Mm+ffvYtm0bzZs3Z926dQQHB5OYmAiAo6MjAwcOZPDgwWzatInx48ej0WhS7Rxq1qzJu3fvmDdvHgkJCdJhQ4gfzKNHj7h48SJt2rQhf/78xMfHs379evr374+ZmZkS3ImPj+fmzZvK4+rUqcOxY8dwdHRMVoP0wR3D94Pi0/Q1s0mTJsTGxtKzZ0/c3NwwMjIiPDxcCe4AzJkzh6tXr1K1alUyZMiQWkNO07RaLXny5FG6ToWFhaX2kIT47oz/9y5CCCGEEEIIIYQQQogfhX5COigoiMWLF9OrVy9cXV0pWLAgsbGxnD17FmNjYw4fPsyAAQOYNWsWpUuXBqB+/frJjqfVaiW4I4RIc3Q6HS9fvmTVqlWULVuWAQMGkD9/fjQaDdeuXSMiIgInJyd8fX0xMTFRHufo6EifPn2wsLCgc+fOKVYfdTrdZ+tvYmIixsbG9O7dmxUrVrB3715iY2OxtLRM0lFICJF6Pr4WPw5C63Q6JXij3y8kJAR3d3el64u+405CQgLNmjVj7NixNG3aFECpS5+rQVIH/jf9c1SqVCkqVapEaGgoVlZW3L17N0lnneDgYKZPn07OnDnp3bu3vC/+m/SdoD71pQB92MzCwoK2bduyfft2du3aRfXq1eVLBOI/RSKXQgghhBBCCCGEEEKkMa9evSIkJAQ7Ozv69u1LwYIFefPmDaVKleL58+cEBgbSuXNnwsPD6d+/P6dOnQL4ZNcG+da2ECItUqlUPHv2jJMnT9K0aVOl68W6detwdXXFxMREmTxPSEjg1q1bymNz586Nl5fXJ7te/BMXL14kMjJSGZ9+snLMmDEEBwdz9+5dAIyNjdFoNGi1Wpo2bcoff/zBzJkzlccJIVKXVqv9bHBn9erVvH37FpVKhZWVFUZGRuzfv5+FCxcybNgw1Go1ERERSbq+eHl58fDhQ2xsbJSfybX+7eTOnZtJkybh5OTE69evGTZsGGvWrOHkyZO4uLgwdOhQYmJi2LBhAw4ODmi12tQe8g/l4cOHAMmCOwcOHOD9+/fK9aLvcteqVSuqVatGUFAQz58/l+CO+E+Rf3kLIYQQQgghhBBCCJHGaDQaMmXKxMiRIylevDgxMTHUr1+fqKgoxo4dS4sWLZg2bRoFCxYkMjKSIUOGEBkZKRNBQoj/FGPjD4sT6IOLf9X1olKlSqxZs0Z5rD7Y+K0mFW/fvk2JEiUYPnw4J0+eVH6+Y8cOfHx8aNeuHQ0aNMDLy4vHjx8TFxeHWq2mRYsW2NjYsG3bNt68efNNxiKE+Gf09aF27dqMGTNGqRP9+/enW7du7Ny5E61WS968eenVqxchISEMHToUlUrFyZMncXBwAD7UpqCgILZs2UKtWrUoU6ZMqp3Tj+jjcOXnlhbU6XRUqlSJ4OBg6tWrR2BgIO3bt6d8+fIsXbqUYsWKER4ergQ2Jdj+5Y4cOULp0qWZMWMG8P/3TD8/P2rXrk2VKlUYMmQIDx8+VF4vY2Njateuzf3795kzZw5arVaWhRT/GVJdhBBCCCGEEEIIIYRIY2xtbfH09KR169ZotVpmzJhBZGQkAwcOpH379qhUKjJkyECmTJmwtbXl0KFD+Pn5Kd94FUKI/wITExMyZszIjh07mD59+ie7Xuh0OsaMGUNsbCzW1tYpNhZra2tcXV05dOgQY8aMUTrwNGzYkIMHDxIQEMDr168ZN24cP//8M926deP06dPkz5+f4cOHc/z4cfbu3Zti4xNCfJ1Tp05x/fp1fHx8WLp0KYMGDWL27Nn069ePypUrKwGQ1q1bU7lyZd68eUPLli2V5bC0Wi3z58/Hx8cHtVrN9OnTsbCwkBDDV9AHRcLCwoDPdytSqVTodDrKlStHYGAgERERLFy4kKVLl3L8+HHWrl1Lzpw5Zfmmv0Gr1RIdHc2kSZOYO3eu8vMKFSowbtw4YmNjmTFjBmXKlKFfv37s3bsXtVrN4MGDKVq0KLt370atViuvkRBpnUonf9OFEEIIIYQQQgghhEjTnJ2diYiI4O7du0m+LVyhQgX69u3Lo0ePaN26Nblz507FUQohxPfn6+uLr68v6dOnx87OjnPnzikhHZ1Ox5o1a/Dy8qJ48eKsWLECKyurFBvLy5cvmTRpEhMmTKBx48Z4eHhQsWJFZfudO3cIDw9n8eLFhIaGki5dOurXr4+5uTk7duygSpUqrFixIsnSOkKI1HPgwAE8PT05fvw4AO7u7ri4uJA9e3a0Wq3ynmzDhg1MnTqViIgI8uTJQ/Hixfnzzz+5fv062bJlY9euXTg5OUl45G8YNWoUo0eP5ujRo0nq6dfS6XTSofJvOnz4MC1atEClUuHj40O/fv2UbW/fviU4OJgtW7awdetWABo3bky9evW4desW/v7+LFiwgO7du6fW8IX4roxTewBCCCGEEEIIIYQQQoiUodPpePXqFX/++SdxcXHcuHGDAgUKALB8+XJu3LiBnZ0dv//+OwCJiYnKMjJCCPFf0KpVK86dO0dISAjOzs68evUKa2trEhMTmTNnjrLUx6xZs7Cyskoy4f6tWVtbM2zYMAAmTJgAgKenJ+XLlwfAyckJJycn2rVrx9atW9m3bx9LliwhMTGRuLg4zpw5w5MnT7CxsUnRcQoh/po+6FGzZk1sbW2VriHp0qUje/bswIeltfTXqbOzM7lz52bXrl0sWLCAY8eOkSdPHgYOHIiLiwuZM2eW4M7flCVLFgDOnDlDxYoV/3ZtlODO31e1alU2bNiAs7Mzvr6+AEqAx8LCgm7dutGtWze2bdvG/v37Wbp0Kdu3b0elUqFWqzl48CC//fYbZmZmcl8TaZ503hFCCCGEEEIIIYQQ4gfztd/+1X/ruF27dnTp0oVTp06xaNEizM3NOXDgAPb29ik4WiGE+Hc7cOAA06dPZ+vWrdjZ2VGyZEkeP37Mn3/+iaOjI9u3b/+uXS+io6OZPHmy0oHHy8uLcuXKARAfH4+pqamyb0REBBEREaxcuZKTJ0/SqVMnlixZIhPNQqQyjUZDQkIC9evXx9bWlps3b3LhwgX8/f0ZOHCgst/HYZLXr1+jUqmwtLRU3u9JcOfve/jwIfXq1SMmJoaIiAh5z5uKDh06hLOzMyqVCl9fX/r06QNAQkKCslwcwM2bN1m9ejW7du3i2LFjmJubc/jwYcqWLZtaQxfiu5HwjhBCCCGEEEIIIYQQPxDDSZ6oqChsbW0/u69+0ufly5cMGDCAlStXKtuKFi3K1q1bcXJykg4NQog0xbCmvXnzBktLy0/uZxiEvHfvHlu3bmXx4sVER0eTO3duatWqRY8ePXBwcPguk+eGf8ZfBXi0Wi0qlSpJQOfNmzdUqVKF169fc+jQIXLmzJmiYxVCJPepcHVMTAxarZaTJ08yZMgQTp8+zfTp0xkwYACQtF7pOyDqa4Es1fRtDBo0iBkzZjBv3jx69uwp73tT0ecCPPq/+/rXRqfTodVqmTZtGu7u7nTo0IEFCxZgZmYm14RI0yS8I4QQQgghhBBCCCHED2jYsGHExMQwa9asL56EWL58Offv38fe3p4WLVqQKVMm+Ta3ECLN8vLyInPmzPTv3/+zte7jyfH4+HhUKlWSLgApNdH7v4774sULpk6d+skAj+G49ZOeS5cupVu3bixYsIDu3bt/8/EKIT7PsMbEx8cTGxtLYmJikpD1jh078Pb25vTp08k68OzevZsjR47g4eFBhgwZvvv4f2Qf13d9bdX//PHjx/z000+ULFmS7du3p+JI/xv+173tcwEew8cZ/rpWrVrcu3ePCxcuYG5unvInIEQqkgWshRBCCCGEEEIIIYT4wbx8+ZKNGzdiamqqLKHyJd/O7tSpU5Lfa7VaCe4IIdKk69evM336dHLnzk23bt1Inz79J/f7uG4aGxsrE4b6upoSwR3DyeZ9+/Zx7do1rl27RrVq1ShSpAhFihTBzs6OQYMGATBhwgQAJcCjUqmU8Rkbf5jqyZo1KwDPnz//5uMVQnye4fU8f/589uzZw6VLlzA1NaVDhw7UqVOHMmXK0LBhQ1QqFSNHjmTQoEHodDo6dOjA4cOH8fT0JCYmhoEDB0p453+4ffs2ZmZmZMuWDSDJc//TTz9RokQJ1Go1RkZGaDQaMmTIQNWqVQkODmb9+vW0bNkyNYefphleC9evXyc6OprY2Fhy586tdISrVq0aGzZswNnZGR8fHwD69OmDWq1WQjtqtVpZTqtixYocPHiQyMhIqlatmmrnJsT3ID3BhBBCCCGEEEIIIYT4wVhaWtKqVSuuXbvGzJkzgeQT0F9ClgwQQqRV+fPnp2nTply6dInQ0FDgQ2DxfzGsiym1NIdhcNLDw4OWLVsyaNAg5s+fT+vWrWnVqhUrVqwAIFOmTAwbNgwPDw+2bt3K2LFjOXnyZLLxvXr1ivDwcNRqNXZ2dikybiFEcjqdTrmehwwZQr9+/Thz5gy5c+fGxMSEESNGMHToUGXp0gYNGjB+/Hh+/vlnBg8eTNmyZencuTOvXr1i//792Nvbf1Gt+q+6ePEiBQsWZNq0aTx69Ej5+bJly+jTpw+VK1fm999/V2oofHjf3KtXL9RqNfv37wc+vG7i2zK8t40dO5ZGjRpRoUIFatSoQZ06dXB3d1f21Qd4dDodPj4+zJ07F0BZMgvAxMSE9+/fExUVhZmZmYTaxH+C/OtcCCGEEEIIIYQQQogfjJGREb1798ba2poDBw6k9nCEEOJfRT/x7ebmRoYMGVi9ejXw7wks6scxatQoJk2aRNOmTQkNDeXWrVvMmzePW7du0blzZ2Wy38bGRgnw7Ny5kyFDhnDu3Lkkx3zy5AmTJ0+mfv369OjR47ufkxD/VfoQ3ezZs5kxYwZ9+/Zlx44d7Ny5kx07dtC5c2cOHjzI1q1biY2NBaBu3bpMmzaNQYMGkS1bNho3bkxERAR58uRBo9H8a2rVv1FiYiLVqlVj7ty5zJ49m4cPHwIfuksuXryYjh07snHjRjp37kytWrWYOHEiT548oVq1akow8syZMykWzvwv0/+9dXNzw9vbmzx58jB16lQWLVqEWq1m8uTJ1KhRQ9nfMMAzduxYpk6dCiQNpq5bt4758+fTpk0bypQp813PR4jUoNJJtFAIIYQQQgghhBBCiH8lfev4j3+vb0nfr18/5s6dS0hICE2aNEnFkQohROr4uE4aevHiBe3atWP//v3s2bOH2rVrf+fRfd6pU6do1KgRFStWZOLEiRQoUICEhATCwsJwdnYmS5YshIeHY2trqzwmKiqKUaNGsXHjRs6cOUOmTJmSHPPIkSNUqVIF+OvnRQjx7eh0Ol6/fk2zZs14/vw5GzZsoECBAmi1WtatW8fw4cPR6XScPHkSOzs7ZSkg+HCdxsbGolarMTc3T7LkkPi8c+fO4e3tzY4dO3B3d6dXr17KkkwAERERbNy4kfXr13P79m1y5MhB7969uXDhAps3b6Z///6MGzcuxZZF/C/buHEjHTt25Pfff2fYsGHkyZMH+LCkWZ8+fbC2tubPP//E0tJSeczhw4epXr06xYsX5+jRo1hYWCjboqKi8PHxUTqNyr1NpHXyt1sIIYQQQgghhBBCiH+ZxMREdDqd8uH0rVu3knxYrZ/Y+fXXX4EP30qNjY2VZRaEEGleYmIi8GECz7BOvn37VqmBGo0GADs7O1xcXNDpdBw5cgT49yyVcu3aNZ4+fUrfvn0pUKAAiYmJrF+/nm7dumFra8vRo0extbUlLi6Op0+fAmBra8uYMWO4dOkSmTJlSlbzJbgjRMo5c+YM58+fT/ZzlUpFVFQUkZGR1KxZkwIFChAfH8+6detwc3NDp9MRGRmpLGd3//59Xr9+rTw2ffr0mJubJ1l+S3yavn6XLFkSX19fGjZsyMSJE5k/fz73799X9vv555+ZOHEi586dY+LEieTPnx8fHx927txJbGwsoaGhJCQkJFmiSXwbx48fB6Bnz57kyZOHxMREVq1axcSJE8mTJw/Xr1/H0tJS6UIFULVqVY4dO8b27duxsLBQXpPExERsbW0luCP+U+RvuBBCCCGEEEIIIYQQ/wJ79+6latWqaDQajI2NlUlZLy8vSpcuTceOHblw4QLR0dHKYxo0aEDz5s3Ztm0b9+/fl0kIIUSatnfvXrp06cLz58+TTOANGTKEkiVL4uPjw82bN5NMgFepUoXy5cszffp0bty4kSpLpXwqWHn9+nUAnJycSEhIYP369Xh4eKBWqzlx4gT29vYAPHv2jMmTJ3Pnzh0AMmbMSMaMGZMElz4mk5tCfFvXr1+nbNmy/P7771y4cCHZdrVajUqlIl26dMCH7iPu7u7Jruc3b97wyy+/sHXrViDp8kCyjNP/ZvgclSpVKkmAZ968ecoSWvAh+GFhYcGwYcPYv38/gYGBtGzZkixZsnDq1CkCAgKSHVN8nY//zREfH8/p06fJli0bpUuXRqPRsGHDBqX7VHh4uBJiu379urKkJUCFChXIkSMHGo1GeU2MjY2THF/ubeK/QP6WCyGEEEIIIYQQQgiRyjQaDStXruTo0aPUqVNHWTbh+fPnODg4kDt3blavXk3FihVp3749Gzdu5M2bNwC0bt2aV69eMXnyZOLj42USQgiRZvn5+REUFMTQoUN58eIFKpWK58+f8+7dO9KlS4efnx9lypRhwIABbNu2DfjQrUZfJ3fu3Al8OkzzrXx87MTERGXC8Y8//lC6AhUoUACAs2fPcvDgwSQT/YbLYbm6urJu3bpkk5ZS64X4fvLnz0+XLl04f/48ffv2VTrw6HQ6NBoN5ubm5MiRg5UrVzJ16lTc3d1RqVREREQkuZ6nTJnC48ePkywLJL6evo5+HOCZM2eOEuAxDMIDtG3blvnz53PgwAHs7OwIDQ2VjpVf4ePnKi4uTrkPvX37FvhwXzIzM+Pdu3c8efKEDRs24ObmluzeptPp6N27Nxs2bODdu3dJjivdp8R/nYR3hBBCCCGEEEIIIYRIZUZGRkybNo3OnTsTFhZGjRo10Gg02NvbM2DAACIjI9m6dStt2rThwIEDtGzZknr16jFlyhRq1apFwYIFOXPmDDExMcC/Z1kYIYT4lg4cOECtWrVYsWIFrq6uPHv2DHt7ewICAjh37hz+/v7UrFmTWbNm0aRJE5o1a8batWtp164dJUqUYPXq1Sm+7Ib+2C4uLhw6dEjpHNCzZ0969erFrVu3AChatCj29vZ0796dzp07Y2JiQnh4eJKJ/gULFhAZGUnDhg3JnDlzio1ZCPF5+qDI4sWLcXFx4ejRo/Tt25dz586hUqkwMjIic+bMdOzYkUePHuHj44NWq+Xq1as4ODgAH4IPwcHBrFixgmrVqlGjRo1UPKMfz8fBEcOAR6lSpfDx8flkgEdfjw3fFxcsWJC2bduyZ88eDh48mPKDTyP0z+WmTZsAMDMzA8Db25tp06bx5s0bTExMaNKkCY8fP8bV1ZURI0agVqs5fvx4knvbzJkz+eOPP6hQoYLSrUoI8YGEd4QQQgghhBBCCCGESGVarRY7OzumTp1Kx44dOXr0KNWqVVMmjExMTGjUqBGLFy9m7969TJgwgTt37uDm5kbVqlV5/fo1J0+eZNGiRYB0ZBBCpD2JiYmoVCr27dtHtWrVCAoKYtCgQTx//hxTU1OMjIwYOHAgISEh7N69m+7duxMeHk7btm2pUqUKT58+5fjx4yxcuDDFx7po0SLmzJmDh4cHN2/exMPDg0WLFlG8eHFsbGwAKFGiBL169eLNmzc8e/aM2bNnkyVLFuUYQUFBTJkyBWtra0aOHImZmZkEM4VIBUZGRsr7sYCAAFxcXDh27Bj9+vXj3Llzyn5DhgyhY8eOvH//ngIFCvDgwQO0Wi0ajQZ/f39GjBiBTqdj3rx5ZMyYUbq+fCGNRqMER06ePMmmTZuYN28e586dU5aSLVOmDN7e3p8M8MD/vy/Wh35KliwJwIsXL77nqfzwWrVqhbOzM3PmzAFg0KBBjB07FiMjI+U5Ll++PAULFiQ4OJjo6GiuXr2aJHy6du1aAgICyJs3L506dZKlsIT4iEon7/aEEEIIIYQQQgghhEh1+m4Q0dHRuLq6EhgYSKVKlQgLC8PIyIi4uDjlW64ADx48YN++fQQFBREWFkZCQgKVK1dmw4YNZMqUSQI8Qog0JzExUelkU6NGDQ4dOkT79u2ZMWMGdnZ2ypKDAAkJCTx79gx/f39OnjxJWFgYAB07dmT58uUpPlYvLy/GjRtH5syZefLkCd7e3nTv3p0cOXIkGWfv3r1ZsGABVlZWdOvWjWzZsnH06FEOHTpEhgwZCAsLw8nJKcljhBDfn+E1OGDAAGbNmkWlSpWYPXu2Ega5ffs2o0aNIjAwEGNjY4oVK8aLFy948uQJBQoUYMuWLXI9fwXDTmkjR45k4cKFPH36FPjQCcbZ2Zl+/fpRrVo14MMyhD4+PuzYsQN3d3f69etH1qxZkxzz1atXjBkzhoCAAFatWkXLli2/70n9wDZt2kS/fv14+vQplStX5vDhw7i7u9O7d29y5cql7LdhwwZ69epFVFQUY8eOpWzZsuTNm5d58+axbt06tFotR48exdHRMcW74Qnxo5HwjhBCCCGEEEIIIYQQ/xL/K8CTkJCAiYlJsg+6N23axObNm1m5ciU7duygXr16qXgWQgiRcv5XgEe/XV8ntVotiYmJrF69moULF3Ls2DH27dtHrVq1UmR8hpPyRYoU4caNG9jb27Nw4UIaNWqETqdT/tPvN2HCBNauXcvZs2cByJkzJ9WrV2f8+PFkz55dJvqF+JfQvw+DpAGeWbNmUapUKWW/+fPnExoaytWrV8mbNy9Vq1alQ4cOZMqUSa7nv8HT05Px48fTokULOnXqRIYMGdi8eTMzZ84ke/bsBAYGKkuR6QM8e/fupWfPngwfPjxJ55eIiAgqVapE8+bNWb9+fSqd0Y/r5MmT1KhRg7i4OKpVq8bWrVtJnz49Go0GlUql/Ptk69at+Pr6cvr0aeWxpqamVK5cmaVLl+Lo6CjXghCfIOEdIYQQQgghhBBCCCFSiU6nS9YhRz/hHBUVxaBBg5IFeAwnrg1DPHv37qV+/fo0atSI4OBg0qVL993PRwghvjV9ndRPZahUKuLj4zE1NQU+HeDR18aPa+zatWtp27Yt7u7ujB8//pM1+FvQarVcvnyZOnXq4ODgwMWLFylfvjwzZ86kXLlyyn6GE5fPnj3j/v37vH79mkKFCmFtbY2ZmZlMbgqRSr6kI8hfBXiAJLXqS48pktq/fz+tWrWiQYMGjBo1ivz586PRaNi5cyfNmjWjYMGCHDt2jIwZMyqPOXv2LAMHDuTPP//k3LlzSbY9f/6cPXv20L59e0Beky+lv18uW7aMrl27kj59emJiYpg1axZ9+vQBPjyXKpVKua/evn2b27dvExkZibm5OeXLl6dIkSJkzJhR7m1CfIaEd4QQQgghhBBCCCGESAWGH1rHxcURHR1N+vTpsbKyUvaJiorC1dWVlStXfjbAYzj5XLlyZaKiooiMjMTCwuL7n5QQQnxDH0/uvXz5Emtr6yQ/1+l01KhRg8OHD38ywANJJ2dLlixJfHw8Z86cwdzcPMXGHhMTw+3bt8mXLx9jxozBz8+Pn376idmzZysBno/P4+MgUUqFi4QQf83w2tyzZw8PHjzg7t27VKtWjfz585MjRw5l3/79+zN79uxkS2gZdiKRgMjfN2XKFEaMGEFYWBgVK1YkMTGRdevWMXz4cFQqFZGRkdjb2xMfH09cXByWlpYAXLp0icyZM2Nvb//ZWiqvy9d78uQJ+/fvR6PRMHToUJ4/f87UqVNxdXUFPjynhp3lPkWedyE+T64MIYQQQgghhBBCCCG+M8NJoRkzZvDLL7+QJ08eihYtirOzM1euXCEmJgZbW1v8/f3p2LEjx44do3r16mg0GoyNjUlMTARQJiNevXqFkZERWq2WFy9epNq5CSHEt2BYJ+fNm0fDhg3JkycPP//8My4uLkqdU6lUHDx4kGrVqrFq1SoGDhzIixcvUKvVaDQaAGWS8OnTpxgbG5MhQwZiY2O/2Vi1Wm2yn6VLl44iRYpgamrKmDFjGDp0KCdPnqRfv35ERkYCYGRkhE6nIyIiguPHjyc7jgR3hPj+DIMHHh4eNG3alB49euDr68svv/xCw4YNOXr0qLL/zJkzcXFx4dixY/Tr14/z588DH65vfe2RoMLX09fvyMhIMmTIQKlSpYiNjWX9+vV4eHigUqk4ceIE9vb2ANy5c4fVq1fz+vVrAIoWLYq9vb3SDeZT5HX5a5+6t2XOnJl27drRsWNHli9fjp2dHYMHD2bGjBnAh+dUf/3cuHGD58+fJzuGPO9CfJ5cHUIIIYQQQgghhBBCfEeGk0JDhgxhyJAhREVF0a1bN4oXL87evXtp1aoVa9eu5c2bN9jZ2TF9+nQlwFO7dm0lwKMXFxfHli1bOHLkCHXq1CFXrlypdXpCCPGPfVwn+/fvz61bt6hXrx4qlYr58+dTuXJlTp06BSQP8AwZMoRnz54l+eb/+/fvWbNmDefOnaNKlSpYW1t/k7FqNBplInLTpk2MGjWKVq1a4evry/Hjx5X9Jk2alCTAc+bMGQC2bdtG27ZtmTJlCvHx8d9kTEKIv08f9Jg4cSLTpk2jUaNGrFmzhsDAQFq2bMnFixepUaMG27dvVx4TEBCgBHjatm3L5cuXU2v4aYa+fhcpUoQ3b95w/fp1wsLCcHd3R61Wc+LECTJlyqTs36dPH+bMmaOE2/UkKPL3GN7bLly4wK5du9i2bRvh4eHKNVK/fn1WrFhBpkyZGDx4MP7+/srjt27dSs+ePVmxYsUnQ0BCiE+TZbOEEEIIIYQQQgghhEgFCxcuxMXFhR49etC/f38KFixIVFQUvr6+zJw5k4YNG7Jx40ZMTExQqVRER0czZMgQli1bRtOmTdm0aZNyLI1Gw8SJE7lz5w4LFiwAZLkVIcSPb/bs2QwaNIhevXoxYMAA8ufPT2xsLF26dCE4OJjatWuza9cutFotJiYmANSuXZsDBw7g4uLCjBkzktTB/v37c/fuXTZv3gz88zppuPSHm5sbc+bMQavVYmVlxbNnz9DpdMyaNYtWrVopk8weHh5MmjQJBwcHqlWrxvHjx4mLi+PYsWPkzZv3b49FCPHPGF7Pb9++pXXr1jg4ODB69GgcHR2V/SZOnIinpydqtZpjx47x008/Kdu6dOnC2rVruXXrFpkzZ/7u5/Cj+lQt1r8e69evp3Xr1hQpUoSXL19iZmbG0aNHyZIli7LvrFmzmDBhAh07dmT06NHK/UD8PYbXgre3N/Pnz+fZs2fK9m7duuHq6krhwoVRq9Xs2bOH33//nadPn+Lh4YGtrS0LFy7k4cOHXLp0Kcn1I4T4axLeEUIIIYQQQgghhBDiO9LpdOh0On799Vdu3rxJSEgIhQsXJj4+nm3btuHq6oqZmRnHjh1L8o1igBcvXjBmzBhcXV1xcnJKsi0hIUGZrDD80F0IIX5EsbGxNGjQgOfPnxMcHEyRIkWIjY1lz5499OvXjwwZMnD48OFkdVKn09GqVSumTJmi1Mm/mhj+Fvz8/Bg5ciQ9e/akS5cu/Pzzz6xdu5aRI0dy/fp15s+fT5cuXZSOaRMmTGD69OmoVCry5s3LqlWrcHR0JDExMUlXNSHE9zd58mSyZMnCqFGjmDhxIi1btkSn05GYmKi8z/L19cXX15cGDRoQFBSEhYWFcu1GR0djY2Mj78W+kOESidHR0VhZWaHT6ZLUwubNm7N582YyZMjAoUOHKF26tLJtzZo1eHt7Y2Vlxfbt2yU09Q0NHz5cuQY6dOhAunTpWLVqFStXrqRChQr4+/sr4bX9+/czYMAArly5gpGREYUKFWLr1q04OTkleY2FEH9NwjtCCCGEEEIIIYQQQnxHOp2OZ8+ekTt3bjp27Mi8efOIi4sjJCQENzc31Go1kZGR2Nvbo9PpOH36NGXLlk3yeJVKlWSS13BiWjruCCHSgrt375IvXz4GDx7MhAkTSEhIYOPGjcnqJMCpU6coVKgQGTJkSHIMwzppOJH+LevkH3/8wS+//ELRokWZMWOG0j0nJCSEnj17YmZmxtmzZ7Gzs0syhitXrmBiYkKmTJnImDGjTG4K8S9w6tQpKleujImJCVqtlvXr19OgQQOlZhhew9WrV+f27dtERkaSOXPmFKsxaZnhczZ16lS2bNmCsbExRYoUYfjw4WTLlg2AN2/e0KJFC/bv30+5cuXo1asX1tbWbNu2je3bt2NqasqRI0fIlSuXhKa+kV27dtG+fXuaNGnCyJEjlXvbggUL6NOnDzly5ODixYtYWloqj7l48SKXLl3i7du3NG7cGAcHB7m3CfGVpHoJIYQQQgghhBBCCPENffxducTExGT7ZMiQgfTp0xMVFUViYiLbt29XJqRPnDihTEirVCoaN27MsGHDlMfqJ4MMv5FsOEEkk0VCiH87wzqp1WrRaDTJ9klMTESn0/HmzRtiY2OTBHcM62RiYiJ9+/Zl3rx5yY5hWCcNJ3O/tk4+evSIN2/efHLbvXv3+PPPP/n999/JmzcvCQkJrF69GldXVzJkyMCZM2ews7MjNjaW6Oho5XGFCxcmX758ZMyYEa1WK5ObQvwLFCtWjNmzZ1OoUCFiYmLYuHEjUVFRSs1Qq9XEx8cDULx4ce7fv8/58+eVbXryXuzL6J8zDw8Phg0bxqVLl7hy5QqzZ8/m559/JjIyEgBLS0u2bt1K+/btuXjxIt27d6dly5Zs27aNihUrcuzYMXLlyoVGo5Hgzld4//79Z7edPXuWd+/e0a1bN+XeFhwczLhx48idOzenTp3C0tKShIQE5THFihWjTZs2dOvWDQcHB7m3CfE3SAUTQgghhBBCCCGEEOIbMfym9cOHD5N0ffD19eXEiROoVCpMTU3JkycPhw8fxtfXF1dXV4yMjIiIiFCWgNHpdIwePZq4uLgkywMIIcSP7OOOFGq1WpncW7BgAeHh4QDkzp2bwoULc/ToUZYuXYq7u7sS3DFcKmv06NFcvXqVAgUKpMh4IyMjKVGiBIGBgZ8M8Lx69QpAWaplw4YNeHh4oFKpkoSMXr58SfPmzbly5UqyY8hksxDf38dha61Wi5mZGR06dKB3794UKFCATZs2sX//fiWwo9FoMDU1BSAqKgp7e/tky5iKr3PhwgXWr1/PgAEDOHbsGA8fPsTHx4c3b97QqFEjwsLCADA3N2flypUcPHiQTZs2sWLFCg4cOEBQUBA5c+aUDi9f6cCBA/Ts2ZOzZ89+cntkZCR2dnZUrVoVgPXr1+Pm5oZKpSI8PFy5tx0/fvyT4VmQe5sQf4dcNUIIIYQQQgghhBBCfCP6Cen69evTvHlzHj58CICrqyu+vr6EhoYSGxuLiYkJXl5evH37lnHjxqHRaAgPD8fBwUE5VnBwMIGBgZQtW5YGDRqkyvkIIcS3pq+TFSpUoGLFisrP+/TpQ79+/bh69SqxsbGoVCo6derEpUuXGDx4MDqdjpMnTyYJ7qxatYqgoCAqVapEtWrVUmS80dHRWFlZMWbMGIKDg5MFeCwsLADYvXs3K1eu/GzIaOTIkVy7di1JlwIhROrQaDRKLYqLiwP+P2igD/C4ublhYWHBoEGDCAoK4tGjR0o4ZNOmTezZs4ciRYoowT3xZfShKf3/Hz16xJMnT+jUqZMSwvTx8WHcuHEAODs7KwEegHLlytG0aVM6dOhA0aJFyZAhAzqdToI7XyExMZGNGzeyatUqpk2bxoULF5RtWq0WgKxZs/LixQv27t1LSEgIHh4en7y3jR07lgkTJvDixYvvfh5CpEXG/3sXIYQQQgghhBBCCCHEl3r+/DmOjo4cPHiQQYMGYWFhQWBgIG5ubnTo0AFzc3Pgw8R1nz59mD9/Po6Ojpw/f55SpUphamrK3LlzlW+xLl68GBsbG7RarXyDVQiRJjx//hxTU1OOHDlCq1atyJEjB/Pnz2fgwIH88ssvSp1s2bIlYWFh7N69m4IFCxIXF6eEaWbOnMnMmTPR6XQsXLhQWX7qW9fJWrVqMX/+fIYOHYqHhwcArVu3xsrKCoA6depQo0YNAgIClCURT58+jY2NDfBhgnrFihXs3buXhg0bki9fvm86PiHE1zFcymfOnDkcOHAACwsLihcvzuDBg4EPAZ7ffvsNlUrF6NGjGThwIMWLF6dly5YcOHCAmzdvkiFDBlasWIGVlZW8R/tCht1xHjx4gIWFBc+fP6d+/fpKl8mEhARMTEzo27cvKpUKHx8fnJ2d2bBhA9WrV0/WvQ1kmbKvZWxsjJeXF2q1mpkzZ5KYmMjw4cMpXry48ve4bt26zJkzh9GjR/Pnn39iZGTEyZMnsbOzU44zd+5czp8/T/fu3cmYMWNqnY4QaYpK93FfOCGEEEIIIYQQQgghxD/y5MkT5s2bx+jRowHo2rUro0aNInv27EkmHa5fv86SJUuYNWsW7969w9HRkbi4OF6/fk2hQoXYuHEjuXLlkqUAhBBphr4GPnv2jIEDB7JmzRoA3N3dGT58uBKK0e935coVRo4cSUhICOnSpSNbtmy8e/eOFy9eUKBAATZv3oyTk1OK1En9GBITEwkNDcXNzY379+8zYcIE2rRpg6WlJVqtlqCgIMaMGcONGzeYN28ePXv2VI6xZMkSxo8fj7GxMfv37ydbtmyfnHwWQnxfbm5uTJkyBVNTUxISEtDpdNSsWZPg4GBlSaC4uDiCgoLw9/fn0qVLFClShMKFC1OuXDl+++03smfPLu/RvpBhwGn8+PGsWrUKnU6HVqvl6dOnhIaGUqJECSBpyGfu3LmMGjUKtVrNihUrqFu3bqqdQ1rz7NkzfH19mTNnDm3btlUCPABv376lf//+LF++HHNzc/bt20elSpWUxwYFBeHr64ulpSU7d+5M0j1UCPH3SecdIYQQQgghhBBCCCG+scyZMxMVFaVM0N6+fVvZplKplJ/nz5+fESNG0LRpU2bNmkV0dDTW1tbUrFmTZs2aYW9vL5NCQog0RaVSodVqyZQpU5IlpE6ePKkEd/SdF3Q6HYULFyYgIIB27doRGBjIixcvyJQpE7Vq1aJNmzZkypQpxeqkPmBjbGxMrVq1mDRpEm5ubkoHnlatWpExY0ZatWrFgwcPmDVrFsOHD2fDhg389NNPnDhxglOnTmFjY8POnTvJli2b1HQh/gWOHj3KqlWr6N+/Pz179iRjxox4enoSGBhIw4YNWb9+PY6OjpiZmdG+fXu0Wi3Tp08nKioKFxcXKleujLGxsVzPX0Ef3PH09GT8+PHkyZMHW1tbLl++TFxcHJs2bcLJyQkrKyuMjIyU57ZPnz6o1Wr69OnD4MGDOXXqFKampql8NmlDpkyZ8PHxAT50oQKUAI+FhQWdO3cmOjqarVu34u/vz+nTpylWrBirV68mJCQEMzMz9u7di4ODg3SfEuIbkc47QgghhBBCCCGEEEKkAD8/P16+fMnbt29ZtGgR9erVY/r06eTPnx/40NEB/rrVv3wQLoRIy7y9vbl9+zbPnz9n9+7dNGrUiK1btwKQmJiIsbFxsjoYFxeHmZmZ8vvvUSf/qgOPs7MzNjY2xMXFsWvXLlatWsX69evR6XTkz5+fOnXq4OnpKcEdIVKR/hrW/z8wMJDBgwcTFhZGkSJFAIiKimLixIlMmTKFMmXKsGHDBhwdHQGIjY1l1apVeHt7Y2RkxLx586hbty7GxtIj4X8xrHuPHj3i119/pVKlSgwZMgQnJyfWrFlDQEAAp06dYvLkyXTv3p306dMne+yKFSuoXbs22bNnT7VzSas+7sDj7u5OyZIlATh+/DiBgYHMnTtX2d/GxobKlSsze/ZscubMKfc2Ib4hCe8IIYQQQgghhBBCCPEPfW4JFI1GQ1RUFJMnT8bf35969eoxY8YM8uXLl2S/ly9fYm1trUxCy5IqQoi05nN1TafT8fLlS7p27crmzZv59ddf2bJlC5A0qBMVFYWtre3/PN638FeBoISEBEJDQ3F3d08W4NG7e/cuGo2GnDlzAkiHDiFSkeG19/LlSywsLFi6dCknT55k/vz56HQ6NBoNxsbGvHz5kgkTJjB58uRkAZ64uDhWrVqFj4+PEuCpU6eOXNdfaOvWrbx69YpevXqxb98+KlasqGzbt28fo0aNIjIykkmTJtGjR49PBng+9Xvx5T4OsRl68uQJY8aMUQI8bm5ulCpVStkeGRnJgwcPePXqFeXLlydHjhxYWlrK6yHENybhHSGEEEIIIYQQQggh/gHDD60fPnzI27dvefv2LaVLlwZQls2aO3euEuDx9/enQIECAGzbto2ZM2cyYcIE5TFCCJGWGNbJt2/fEh0dDaCEW+BD4MXV1ZWQkJAkAR6dTsfu3bvZunUrXbp04aeffvpuY71x4wavX78mISGB0qVLY2JigkqlIj4+ngMHDiQJ8OiX0NKPWQKYQqQ+wyDetGnT2Lx5M8bGxjx58kQJ4uk7uej31Qd4pkyZQrly5Vi1ahW5c+cGPgR4Vq9ejY+PD48ePeLUqVMUL1481c7vR7Fx40ZatmxJlSpVSEhIIDw8HPj/JRIB9u/fj4+PzycDPOKfM7y3vXv3Dp1Oh5GREenSpVP2efz4MWPHjv1kB55PkXudEN+e9HMTQgghhBBCCCGEEOJv0mq1ygfhkyZNYs2aNfzxxx8kJCRQtWpVWrduTefOncmdOzf9+vVDpVLh7+/PwIEDGTJkCA8ePGDKlCncv38fe3v7VD4bIYT49gwnDKdNm8aGDRs4c+YM5ubmVKlShcGDB1O2bFkcHR2ZMWMGACEhITRp0oR58+Zx4MABxowZw7t37/D29k7RsRrWdF9fXxYvXsz9+/cBKFGiBB07dqRVq1Y4OjpSs2ZNJk6ciLu7Ox4eHgC0adMGS0tLmcwU4l9CH9zx8PBg0qRJZMqUCbVazbNnz7C0tCQ0NJQ2bdpgamqKWq1Gq9VibW2Nh4cHRkZGjB8/HhcXF2U5PzMzM9q1a8e7d+/4448/JLjzhYoWLUrPnj1ZunQpCQkJbN26lcaNG2NiYqLcI2rXrg3AqFGj8PT05P3797i6uiYJl4i/x/DeNnv2bLZv3869e/dInz49bdu2pUqVKpQrV44sWbLg5eUFwJw5cwAYPnz4Z/+ey71OiG9POu8IIYQQQgghhBBCCPEPDRs2jKlTp1KhQgVq1apFfHw8q1at4t27dzRv3px58+ZhamrKgwcPmDt3LgEBAbx9+xYTExOyZctGaGgouXPnltbzQog0xfBb+UOHDsXf358SJUpQu3Zt7t27R1hYGBkzZqRr16707t2bjBkz8uDBAwYPHsy6deswMzNDp9ORJUsWQkNDyZMnz18uafWteHl5MW7cOCpUqEC9evU4d+4cZ8+e5d69ezg7O+Pn50e+fPmSdOB58uQJI0aMoEuXLlhYWKTo+IQQf83w/dT169dp2LAhDRs2xNXVlVy5cjFlyhTmzJlDfHw8CxcupH79+sr++hoTFRXFvHnz6NChg7J0lr6mJSYmYmxsnGR/kZzh63Dt2jXmzZtHQEAAzZo1Y+zYsRQuXDjZfqGhofTv35/3799z4cIFqaff0NChQ5k2bRp2dnZky5aNW7du8e7dOwoVKsS4ceNo1qwZkLQDT4cOHRg0aJB0BxXiO5HwjhBCCCGEEEIIIYQQ/8CqVavo2rUrXbt2ZdCgQeTPnx/48M3W/v3789NPPxEaGqpMPjx//pzTp0+zYcMGsmbNSq9evciaNasEd4QQadbSpUvp3bs33bt3p3///hQqVIiXL18ydepU/Pz8qFevHiEhIZibmwPw5MkTgoKCOH/+POnTp8fLy4ts2bIlmTD/lgzr771796hWrRpNmzbF1dUVJycn3r17x8WLF/H29mbv3r107NiRCRMmkDVrVhITEzlw4ABdu3bF2tqa48ePkyFDhm8+RiHE1zt27BjXr1/H1dWVAwcOUKpUKeDD8ldr1qxh1KhRxMfHs2DBAn755ZdkgRx9WEfeo32Z/xVkunr1KjNmzGDBggV06dIFd3d35X2z4XN85MgR8ufPT+bMmWVppn/A8Dndtm0bXbt2pUuXLnTv3p38+fNz4cIFgoKCmDRpEra2tixbtoxff/0VgKdPn+Ln58fMmTPp27cv06dPT5H7rxAiKQnvCCGEEEIIIYQQQgjxhQwnJfSTCV26dGHHjh2EhoZStGhRNBoNwcHBeHt7o9FoOHnyJHZ2dsTFxWFqappkAkJ/PJkUEkKkFZ+qky1atODs2bNs376dwoULk5CQwObNmxk6dChGRkZERERgb2+PRqNBrVajUqmU4yQkJCRZWiUlrVu3jpw5c9KpUyfWrVtHiRIlkpzPo0ePaN++PSdPnmT58uW0aNECgMTERI4dO0aBAgXIkiVLio5RCPFlFi5cSK9evWjcuDEajYZt27YBKCHAhIQE1qxZg7e3txLgMezAI76OYY3evXs3ly9f5urVq1StWpXixYtTsmRJ4EOAx9/fn0WLFv1lgAekq9E/YRh6iomJITQ0lL59+7Jv3z7l+dabNm0aQ4cOpUKFCqxYsYJ8+fIBH4K0M2fOpGfPnkr3KSFEypLwjhBCCCGEEEIIIYQQf+HgwYNcvnyZvn37AkknEt6+fUu5cuWwt7fn8OHDxMXFERISgpubG2q1mhMnTpApUybgw3IBr1+/ply5cvItYiFEmnL8+HFu375Nu3btgKRdK6Kjo8mXLx/169dn1apVxMbGsnnzZqVORkZGYm9vD8D58+fJly8f6dOn/+6TtvqJ/vz58xMTE8PJkydxcHBItt+OHTto2rQpv/zyixIGMCRhTCH+HY4cOcKYMWPYu3cvpqamhIeHK0v/GIYD9QEerVaLv78/TZs2lWv4KxnWaw8PD2VJMiMjI2JiYsiTJw9DhgyhT58+ANy4cYPJkyezcOFCunbtioeHhxIYEd+Wu7s7kydPplGjRtja2rJ8+XLgQ7gHUP490rdvX+bNm8eWLVuU7jv6/aT7lBDfj8QVhRBCCCGEEEIIIYT4BJ1OR1RUFC1btsTFxYV58+YBoFar0Wq1AFhYWJAxY0bi4+OBDy3pPxXc0Wg0tGjRguXLl6PRaCS4I4RIM54/f07NmjUZMGAAwcHBAEmWm7GxscHW1pbXr18DsHPnziR1Uh/cAWjYsCEjR45UjvE9NWvWjKpVq3Lz5k1ev37NjRs3gA9dOgxVrVqV3Llzc/36dZ4/f57sODK5KUTq0ocSqlSpwqhRo2jevDnx8fEEBQXx6NEj4P/fy5mYmNC2bVvGjBlDVFQUfn5+JCQkpObwf0j6eu3r68ukSZNwdnbm4MGD3Llzh5UrV/LkyRP69evHggULAMiXLx9ubm706NGDlStXMmLECG7fvp2ap5BmmZmZkSFDBnbt2sWZM2d4/Pgx8CG0o//3iE6no379+gBs3rwZQPm3jn4fubcJ8X1IeEcIIYQQQgghhBBCiE9QqVTY2tqyaNEiMmfOjIuLC3PmzAE+TFLEx8ej0WgoW7YskZGR9OzZk0GDBmFkZER4eLgS3IEP7eifPn1K0aJFpf2/ECJNsbe3Z+nSpcTGxjJixAhWr14NfKihiYmJxMXFkStXLvbt28fgwYMZOHAgRkZGHD9+XKmTOp0OPz8/YmNjKV68+Hc/h4SEBDJlysSmTZuoWbMmr1+/ZvDgwcTHx2NsbExiYqIykWlpaUn69OmxsLDA3Nz8u49VCJGU/trUMwxIV6xYkaFDh1KvXj1mzZrF0qVLefr0KZA0wNOmTRuWLVvG1q1b5br+m86ePcv8+fP59ddfGT58OBUqVMDa2prMmTNjbGxMgQIFcHZ2VvbPmzcvQ4cOpXnz5oSHh5MxY8ZUHH3aow+xjR49mhEjRmBtbc3169cJDw8H/v+6SUxMRKVSUblyZYyNjUmXLh3w/QO0QogP5MoTQgghhBBCCCGEEOIT9B96N2vWjEWLFmFtbc2AAQOUAI+pqSlGRkb89ttvqFQqFi1aREJCApcuXSJLlizAhw/G161bx/z58ylSpAitW7eWrjtCiDSnbdu2LFu2jAcPHuDl5aUEeIyNjTE3N2fUqFEYGRkxffp0EhMTiYiIIHPmzMCHWrt+/XqWLVtGyZIladKkSYqOVaPRJPuZiYkJALa2tqxdu5a6dety4sQJnJ2diYuLw9jYWOkmtHbtWi5fvkyxYsUwNTVN0bEKIf6aRqNRQgY3b97k6NGj7Ny5k0ePHhEXFwd8CPCMGjWKqlWrMmbMGBYtWpQkwKPRaDAxMcHZ2Zls2bJ9skaI/+369es8fvyYPn36UKBAARITE1m/fj1du3bF2tqaI0eOYGdnR3x8vNIBKX/+/IwfP55z585ha2ubLIglvtynQmz6znHDhw9nyJAhaLVaOnXqxIkTJ5TrRh9QXbVqFYmJieTMmRP4/38HCSG+L5VOrj4hhBBCCCGEEEIIIT5Jv+wLwPbt2+nUqRMvX74kICCAvn37KvutXLmS33//HTMzM6ZPn069evUwNTVl3rx5BAYGkpiYSHh4ODlz5kSr1cq3WYUQadKGDRv47bffyJ49O2PHjqVdu3YAvHv3jlmzZjF27Fhy5cqFt7c31atXR61WM3/+fBYtWoROp+PIkSMpWicNj7tx40bu3r2LjY0NlStXJl++fMp+UVFRtGnThv3791O6dGm6detGsWLF2LlzJ9u3byc6Oprw8HBy5MjxzccohPgyhtezn58fS5YsUZZesrOzo3379vz++++ULVsWgOPHj+Pt7c3hw4cZOXIkPXr0SNIlUXw5jUaTbBmlCRMmMGLECC5cuECBAgVYv349Hh4eyZaSffDgAZMmTcLFxYX8+fMrjzd8zy2+juHrcfnyZd68eUORIkUwNTXFzMxM2W/ixIl4enqSLl06AgICKFOmDCVLlmThwoUEBATw/v17jh07poRrhRDfn4R3hBBCCCGEEEIIIYT4C18a4FmzZg09evTg3bt3mJubk5CQgJGREWXLlmXVqlXkypXrk5MdQgiRlhgGeMaMGUP79u0BuHv3LmvXrmXkyJHExcWRNWtWYmNjiY2NpWjRoqxbt+671clhw4YxdepU5fdFixalb9++9OnTR/lZdHQ0bdq0Yd++fZiYmODg4EDhwoWxtLTE398fR0dHqelC/AsMGzYMf39/qlevTuPGjfnjjz84ceIEp0+f5ueff2bKlClUrlwZgIiICEaOHMnx48fp378/gwcPxs7OLpXP4MdiGJoaNWoUNWrUoEaNGoSEhNCiRQuWLFlC9uzZ6d69e7LgDnzo1HbkyBEOHz5M7ty5U+s00gzDf6eMGDGCmTNn8u7dO/LkyUOjRo3w8vJK8vxPnDiRUaNGodFoSJcuHUWLFuXmzZvkyZOH1atX4+TkJPc2IVKRhHeEEEIIIYQQQgghhPgLOp0OnU6nTFT8VYDn/PnzHDlyhAsXLpAxY0YqVqxItWrVsLGxkQ/ChRBplr6+6ScRDQM8o0eP5rfffgM+1NMLFy6waNEiHj16hJWVFdWrV6dRo0bY2dl9lzq5fPlyBgwYQIsWLahbty5RUVGMGjWKV69e4enpyahRo5R9o6KiaN26NYcOHaJixYpKkMfwnIUQqSckJIS2bdvSpUsX3N3dleDB7du3mTJlCgsWLKB69erMnDmTYsWKARAZGUnfvn2JiorizJkzWFlZpfJZ/JjGjh2Lt7c3Xbp0ISAggLt371K3bl2io6OxtrYmXbp0HD16NEkXl4ULFzJ27FgaNGjA9OnTMTc3T8UzSFsmTZrEiBEjqF69OsWKFePo0aOcPn2amjVrsmLFCrJnz67sO378eGbMmMHz588ZN24cPXv2RKPRfLf7sBDi8yS8I4QQQgghhBBCCCGEgS/50Hrbtm107tz5kwGeT5GlsoQQacmX1LTPBXj+yTG/haFDh3LixAmWLl1K3rx5Abhy5Qo1a9bk6dOnjBw5El9fX2X/Fy9e0KpVKw4ePEjLli1ZsWIF5ubmMsEpxL/AiBEjmDZtGhEREZQsWTLJdZmQkECPHj0IDAxk6tSpuLq6Ko87e/Ys2bJlw8HBQZZr+kKGz+2bN2+oXr06ZcuWZejQoRQsWBD4EArx9PRUQpzNmjVTHr9y5UpGjx6Nubk5u3fvJmvWrPLcfwNarZaYmBgaN25MtmzZmDBhgrKkY9u2bVm7di0VKlRg3bp1SQI8fn5++Pn5YWZmxtGjRylSpAjx8fGYmpqm1qkIIZDwjhBCCCGEEEIIIYQQCsOJiaCgIM6ePcv58+epVq0aZcqUoUGDBsq++gDPq1evmDFjhhLgSUhIwNjYGJVKJZMSQog0x7BObty4kcuXL/PHH39QvXp1ihcvTvny5ZV9P7eEVkJCgtLBJiXrpOFY9eGgX375hcqVK+Pt7Q1AYmIixsbG3Lx5k8qVK38ywKPvwBMaGkrLli1ZuXIlpqamEswUIpVoNBpUKhUNGjQgNDSUK1eukC9fvmT7nThxgoYNG5I1a1YiIiJInz59ku1yDX+9wMBAEhIScHd3JyQkhMqVKyeptUOHDmXatGmYm5vTuXNnsmfPTkREBEeOHMHKyoqDBw/K0kz/0Md/b1+9ekX58uWZM2cOtWvXThLC6dy5MytWrPhkgGfSpEkMHz4cCwsLDh8+TIkSJeR1ESKVGaf2AIQQQgghhBBCCCGE+LfQf1g9bNgwpk6dSrp06VCpVOzduxcAT09PhgwZgrW1Nb/++ivLly+nU6dODBw4ELVaTe/evZUJaUCCO0KINEWn0yWrk/qg4sqVK8mcOTMuLi54enoC4OzsDMBvv/3GyJEjMTIyok2bNt+lTmq1WmWsgYGBXLhwATs7O0xMTNBqtcD/h4g0Gg158+bl6NGjVK5cmTFjxmBkZKQEfGxtbVm7di1t27Zl/fr1PH/+nL1798oEpxDfycchP/21V7ZsWfbu3cvVq1fJly9fsuBB+fLlKVKkCJcvX+bZs2fkypUryXEluPN1Nm3aRKdOnfj555+xs7Mjb968ymujf+6nTJmCo6Mjq1evZtGiRSQmJuLk5ETTpk0ZO3Ys2bNnl4DIP2D43B0/fpznz59jY2ODWq3GzMwMQLmvGRkZsWzZMgBWrFhBq1atWL9+PdmyZQPAzc0NAC8vL6pVq8b+/fspW7bs9z8pIYRCOu8IIYQQQgghhBBCiP88w0mhJUuW0L9/fzp06EC3bt2ws7Pj0KFDjBo1inv37tGnTx+mTJlCunTpANi+fTvdunXj6dOnLF26lE6dOqXmqQghRIqbM2cOQ4cOpW3btvTs2ZP4+HhOnz7N0KFD0Wq1DBo0iKlTpyr7b9iwgc6dO6NWq1m+fHmSpVRSmj5kZKhUqVIcPXqUdOnSKROc+v/fvHmTGjVq8ODBAyZPnsyQIUOU7jxRUVHUqlWLAgUKsHbt2u92DkL8lxmGFW7fvg1A7ty5gQ+1pVWrVuTJk4cjR46QJUsWpSuPPphTuXJlXr9+zZEjR8iYMWPqnMQPwrCji1arTRLY1OvduzcLFiwAPoR5mjZtqmwzfK2ioqJ49OgRr169okCBAlhZWWFqairBnX/A8N8rHh4eTJs2jcTEROUetnDhQrp166a8jobPtb4DT4ECBTh48CBZsmRRjjtp0iQ8PDzo3r278toKIVKHhHeEEEIIIYQQQgghxH+a4Qfhb968wcvLiz/++IP58+fj6Oio7Hf48GG8vLw4fPgwEyZMUL6tCh+WjvHx8WH79u1JHiOEEGnBx0t0tG3bltevXzN37twknSyOHTtGixYtePr0KdOmTcPV1VXZFhQUhLe3N4cPH1a+9Z/SY924cSM9evSgbdu2NGvWDEtLS1xcXDh9+jQNGjRg3bp1pE+fPlmA548//qBNmzZs3rxZqen6be/evSNDhgxAyi75JYRIGgbx9/dn9erVWFhYsHjxYiXA89tvv7F69WoqV67MmjVrkiwLtH79enr06EGLFi2YO3euspSQSM6wnj1+/DhZuMPBwYHOnTuTmJjIiBEjmDJlCsWKFWPJkiX89NNPnzzOX/0Z4u+bNWsWQ4cOpX79+tStW5fjx48TFBRE+vTp2bdvHxUqVEh2XwNo3rw5hw4d4vLly2TOnDnJ/XL79u00atQoNU9LCIGEd4QQQgghhBBCCCGEAGDEiBE8e/aMs2fP0rRpU7y8vNBoNMD/L8+wf/9+mjZtilar5ciRI5QuXVqZhIiNjcXc3Fy+USyESLOGDRuGubk5ERERtG3blq5du6LRaFCr1eh0OtRqNWFhYdSuXZvSpUuzadMmcuTIoUzYxsTEJOl28619PDEcHBzMqFGj2Lx5MwUKFAA+hDRbt27N7t27adKkCatXr04yJn2XnY9///HxZRJaiJRleI0NGTKEuXPnUq5cOdzd3WnYsGGS4MEvv/zC3r17yZ49O/369aNw4cKcOHGCNWvWkJCQQHh4eJJQj/i82rVrc+DAAe7evUuOHDno27cv8+bNY9q0afTs2ZP06dOTmJjI0KFDCQgIoHHjxowZM4YSJUqk9tDTrI8DtC1btiQhIYGZM2cqAdMJEybg4+ODsbExYWFh/PTTT5/swBMVFYWtre0nt33qzxJCfF9y9QkhhBBCCCGEEEKI/7wnT55w6tQpFi9ezKlTp3jz5g3wIbRjZGSE/vtvtWvXZujQocTHx/P06VNUKpWyzczMTHmMEEKkNTdv3iQkJAQ/Pz/27dvHnTt3gA81T79EjVarpXr16nTr1o3Tp09z8+ZNAGUCXr/cYErVSf2fM3jwYNKlS8e6deto3LixEtxJSEjA0tKSNWvWUK9ePbZs2UK7du2IiYlJFtTRT17qf294/I9/LYT49vTXWEBAADNnzqRXr14sXryYhg0bAh+u0cTERAB2795Nr169SEhIYMSIETRv3pzp06fj4ODA4cOHyZ49uxLIFn+taNGiAFSsWJFOnToxb948XF1dadWqFenTpwc+1MWpU6fSt29ftm7dipeXF+fOnVOOIX0jvi39/cjDw4NJkybx6tUrfvvtNxwdHYmPj1e2jR8/noSEBKpXr87JkyeV+7K+Aw+QJLgDye/HEtwRInXJFSiEEEIIIYQQQggh/vMyZ87M5MmT6datGwAhISGcOHFC2a5SqUhISACgYMGCaLVaLl++rGwz/L8QQqRFefPmZfbs2dSuXRsjIyNOnz7Nw4cPP7lvsWLF0Ol0/Pnnn995lB/ExcWh1WrZvHkzN27cQKPRoNFoMDExQaPRkDFjRtauXasEeDp06MD79+8/G9QRQqSOly9fEhQURJEiRejXrx/58uVLst3Y2FgJ8MydO5ft27ezfPlypk2bxoYNG9ixYwe5cuWSrohfQB+4CQgIYNKkSTx69IjAwEC6d+/OpEmTknUuMjIyYsaMGfTt25dt27bh7e3N+fPnAamfKeHq1asEBgbi4eHB4cOHefv2LQCmpqZKMGfw4MFMnDiRxMTETwZ49CSgI8S/l1ydQgghhBBCCCGEEOI/RavVJvm9frKiRIkSuLi40LFjR65fv86SJUuUrhEAJiYmAFy4cAFjY2MKFy78/QYthBDf0cddE/QTg/Xq1WPYsGH8/PPP7Nixg0WLFiWpqfoJwRs3bmBmZkaOHDm+36D5/3HPnDmTgQMHYmZmxtGjR7ly5YrSWUffgcDKyoq1a9fSsGFDNm3ahIuLy3cdqxAi+Xuyjz169IjIyEiaNGlCvnz5Prm/sbGx8vOyZcvSsWNHXF1dqV+/PjY2NsmCC+LTVCqVUuvv3r2LVqtFpVIREhLCs2fPAJSglJ4+wNOvXz+2bt2Ki4uLEm4X31ahQoWYPXs2VapUISEhgbNnzxIXFweQpLPOoEGDmDBhAiqVivLly3Pu3DkJ6wjxAzH+37sIIYQQQgghhBBCCJE2GH7z+uHDh1hZWaFWq5VlAEqWLKksi7Vw4ULevXtH165dqVmzJlqtlo0bN7Jq1SoKFy5M+fLlU/NUhBAiRRjWyZcvX5IuXTolvAgfAjxqtZqRI0cyatQoYmJi+P3335VAY0hICBs3bqRo0aKULl06Rceq0+mSLWWlH79+8nLy5Mk0bNiQ8PBwsmfPriyNpQ/wrFy5EhcXF7y9vVN0rEKI5PShgsmTJ9O+fXulu4v+2o6NjQXgwYMH6HS6ZCEE/fX+6NEj7t+/z88///zZP0N8nv551Nd+Z2dnzM3NiY+PJyAggNKlS3PkyBHy5cun7Kt/jYyMjAgICODly5fs2rWLTJkypfLZpD3657xZs2YAvH37llmzZlG4cGH69OkD/H+Ax8jIiEGDBvH+/XsWLVqEra1tKo5cCPG1VDpZeFAIIYQQQgghhBBC/AdotVplAmf8+PEEBQVhZGRE1qxZmThxIiVLllT2vXDhAuPHj2fNmjWYmZnx66+/cvXqVRISEtBqtezdu5dcuXIlOaYQQvzoDGvaxIkT2bp1K7GxsVSpUoWOHTtStmxZZd/9+/fj5eVFREQEBQsWpFSpUjx69Ih79+4BEBoamqJ10jBklJCQwIsXL8iYMSOmpqbKz7VaLcOHD2fy5MnkzJmTY8eOJQnw6P+v9/HvhRApb8WKFXTu3JkuXbrg5+dHlixZlG3Pnz+nSJEi5MyZk+Dg4CTLZhmG9zp27Eh8fDyLFi3C0tLyu59DWuHh4UHevHnp0aOH8jMfHx/GjBmDg4MDR48eJW/evMTHx2Nqagp8CFbpQ1fR0dFKtyN5f/z3fPzc6TsgGQZVN2/ejLe3NxcuXGD+/PlJXi/De+ObN2+wtLSUZeOE+IFI5RRCCCGEEEIIIYQQ/wn6D8K9vLzw9PTk7du3qFQq9uzZQ40aNVi9ejUxMTEAFC9eHA8PDzp37kxcXBxnzpyhWrVqTJ8+nWPHjpErVy40Go1MTAgh0hR9TRs+fDjDhw/n5s2bREdHExAQQJMmTdizZ4+yb+3atRkzZgxVq1bl5s2bhIWFUatWLTw8PDh69GiK1knDicjZs2fToEEDcuXKRbFixfj11185d+4c79+/R61WM378eNzc3Lh37x6VKlXiwYMHnwzuABLcESIV1KlTBx8fHwIDA/Hy8uLRo0fAh3COvb09v/32G2fOnCEgIEBZGghQwgwrV65k79695MyZE3Nz81Q5h7Tg8OHDTJo0ievXrwMoXY98fX0ZOXIkT58+pXLlyly/fl0J7uzYsYMBAwYQHBwMgI2NzSc7JIkvY3jPXL9+PR4eHjRu3JjOnTuzd+9eoqOjAWjatCmjR4+mWLFi9OrVi4ULFyrHMDIyUpaRs7S0RKfTSXBHiB+IVE8hhBBCCCGEEEII8Z9x9+5dtmzZQv/+/Tl48CBnz55lyZIl5MyZkx49erB69Wrev38PQIkSJRgwYAAdOnTgzp07WFtbU6xYMezt7eWDcCFEmnX+/HnWr19P//79CQ0N5ebNm0yYMIHXr19Tv359du7cqexbp04dRowYQaVKlXj8+DFZsmShR48eZMmSJcW+6W9YfwcPHszAgQO5f/8+derUIUOGDOzevZv69esTGBjI8+fPUavVjBs3TgnwVK9enbt370pQR4h/iWzZstG7d2+8vLxYsmQJw4YNIy4uTgnn/Pbbb5QtW5ZZs2bRq1cvjh8/jkajQafTsXTpUsaOHYutrS1DhgxJssSf+DoFCxbEwcGBx48fA2Bubq6EQAwDPNWqVWP//v3Mnz+fIUOGcOjQIapUqaIcx7BDjPhyWq1WubcNGzaM33//HX9/f8LDwwkMDKRhw4Z4enpy8uRJ4EOAZ+zYsUqAZ/HixcqxDMNT8noI8WORd6dCCCGEEEIIIYQQIs36uPX8kydPuHLlCgsXLsTJyQmAzp07Y2dnx6hRoxgwYAAAbdu2JX369JQqVYphw4YRGxvLxIkTiYmJwdXVFUdHx9Q4HSGE+OY+rpNRUVG8evWKzp07U7hwYQDc3Nywt7dnyJAhNGrUiO3bt9OgQQMAfvnlF4yMjPD09KR37968fPkSNze3FAs46ici582bx8yZM3FxcWHgwIHkzp2bmJgYpk2bxtKlSxk+fDjm5uZ07NhRCfAYGRkxfvx4WrVqRXh4eLKlSIQQqSNz5sz06NGD+Ph4fvrpJ8zMzJRtP/30E9OmTcPT05Nly5axfv168uXLR0xMDHfu3CFr1qzs27ePrFmzyvJAX8hwyTH4/44vVlZWXLp0CZ1OB3wIgejvEb6+vpiYmDB16lTq1q2LSqUiT548nDhxguzZs8tz/w8ZLu07depU+vbtS4cOHShSpAjBwcGsWrWK+fPn8+zZM7y8vChZsiRNmjRBrVbj7e1Njx49SJcuHe3bt0/lMxFC/BMqnb4CCyGEEEIIIYQQQgiRhhhOIly/fh0zMzMluLN+/XoAEhISlG9pb9u2DR8fH65du0ZAQADt2rUjXbp0AFy8eJGxY8eyceNGBgwYQL9+/cidO3fqnJgQQnwjhnXy3r17pEuXjq1btxIUFMS+ffuApHVy6dKlDB48mFevXiUJ8ADs378fT09PTpw4weTJkxkyZEiKjVun09G4cWOuXLnC7t27yZcvn7IMVnx8POvWrcPNzQ21Ws3Ro0eVwKVWq2XixIm0b9+eXLlypdj4hBB/T3x8vLIkEyQNmVy6dImwsDAWL17Mq1evyJo1K1WqVGHAgAES3PkKhs/Tx8+Zs7MzYWFhnDt3juzZs3/yMZs3b+bSpUskJCTQq1evFO209l9z69YtGjZsSObMmVm5ciU5c+ZUtp0/fx5/f39WrFiBu7s7fn5+yrWxYcMG5s2bx+LFi+ULBkL84CS8I4QQQgghhBBCCCHSHMPJntGjR7Nw4UISEhLQ6XQ8e/aM3bt3U7duXSDphMT27dvx9vbm5s2bjB07lm7duikBnkuXLjF27FiCg4NZuXKlfLNVCPFDM+y4M3bsWFatWkVcXBw2NjY8fvyYvXv3Kp13DOukPsDz5s0b1q9fT7NmzZRjhoaG4unpSUREBNu2baNhw4YpMvYXL16QJ08eypYtS2hoqBLc0df++Ph4PDw8mD59Ov3792fGjBlJQkiA8hghxL/bx11i4uPjiYmJwdLSUumeJeGRr+fr68utW7coWLAg2bJlo3Tp0koAMzw8nLx58ybZ/+MubfrXRZ77byciIoKKFSvi4eHBuHHj0Ol0SZbTOnHiBN27d+fatWucPHmS4sWLK4+NjY3F3NxcXg8hfnDyzlQIIYQQQgghhBBCpDn6SZ5Ro0YxevRoSpYsSc6cObl8+TLPnj0jMDCQPHnykDdvXoyMjJQPuhs1aoRKpaJfv37MmDGDLl26KMcsWrQo7u7uVKlSRYI7Qogfnn4S1svLi3HjxlGgQAGcnJw4dOgQGo2GlStX4ufnB5CkTnbp0gW1Wk2XLl3o27cv9evXx9jYGGNjY2rVqsXIkSM5ceLENwvufDxxD2BlZUWuXLm4c+cOz58/x97eXtlPp9NhamqKq6srgYGB3L17FyBJcAeQ4I4QPwjD619/fRt25wEkrPCVtm3bxowZM3j58qXys4wZM6JSqXj58iWDBg0if/78lCtXjqxZs1KyZEmsra15+/YtFhYWwP+/LvLcfztarRaAuLg44MPfd8Pnt3z58rRs2ZJRo0Zx6tQpihcvroSqzM3NAXk9hPjRSecdIYQQQgghhBBCCJFmGH7b9N27dzRq1IiCBQvi6emJo6Mjhw4dYt68eQQHB+Pi4sKgQYNwcnJK9tj9+/dTtGhRsmTJ8tk/6+NvIAshxI/g46WymjVrRsWKFRk0aBB58+YlJCSE8ePHExkZiZ+fH8OHD//kY9euXUulSpXIkSMH8OmQzT+tk4aPf/PmDenSpVNCN+3bt2fNmjW4u7vj6emJhYUFOp0OnU6HWq3m8ePHFC1alKpVqxISEvK3xyCEEGnVgwcPiIqKIjIyklu3bhEREcHBgweVWq/RaABIly4dJiYmtGvXjhkzZiQLT4mvY3hvM/z15cuXqVy5MgAHDhygVKlSymP03eOOHj1K1apVmT59OgMGDPjuYxdCpCyJlgshhBBCCCGEEEKINEM/qbxkyRLi4uI4efIkI0eOxNHREYBq1aphb2+PWq1m1qxZ6HQ6Bg8ejJOTU5LOErVr1wb+euJZgjtCiB+Rvk5u3rwZExMTbt26xcyZM5UlUpo1a4a1tTWenp54enoCKAEewzrZunVr4P8DPR8Hd+Cf1cmPl+rasWMHDRo0oGXLllhZWTF27FgiIyNZsWIFTk5OtG/fXllGBz4sgxgTE0P58uWBT4eLhBAirfur97JZs2Yle/bsyvJLBw4cYP/+/YwePZqGDRty9+5dbt26xZkzZ4iKisLDw0OCO/+Q4b1tx44d3Lt3j3LlylGmTBmKFClCjx49mDJlCn5+fvj5+VGgQIEkyz7u2bMHIyMjihUrlpqnIYRIIdJ5RwghhBBCCCGEEEKkKWFhYdSsWZPSpUuTkJDA4cOHyZgxY5IPvq9cuYKfnx+rV6/GxcWFwYMHkytXrlQeuRBCfB+bNm3C2dmZEiVKoFKpOHXqFGq1msTERKWzTVhYGCNGjCA8PDxZB56UZjjZ7ObmxoIFC7CysmLq1Km0bNkSlUpFYmIia9euxc3NjZiYGJo1a8aQIUOwtLRkx44dTJ06FYDDhw+TOXPm7zZ2IYT4tzAMily5coUHDx6g1Wqxt7enTJkyyn7698hRUVFky5aN1q1bs3Tp0iRLMMXHx2NqaprkmOLrGN7bPD09mT9/PiqViqCgIKpVq4a5uTmvX7/m999/Z8uWLfzyyy/4+voqIdTg4GBGjhxJxowZ2b17N7a2tql5OkKIFCDhHSGEEEIIIYQQQgiRprx+/Zq5c+fi6+tLbGwsc+fOpVevXkDySQw/Pz/Wr19P+/btGT16tLL8ixBCpGVXr15lxowZrF27lujoaDZv3kzjxo2BpJOLYWFheHp6cuzYMUaMGMHYsWO/6zjHjh3LqFGj6NOnDy4uLhQsWDDJ9rdv37Jnzx68vb25fPkyGTJkQKfTodVqcXR0ZNeuXTg5OclksxDfiXS4+vcwrOU+Pj4sWLCAJ0+eKNtdXFzo378/+fPnT/KYggULkj17dg4ePAjIa5oShg8fzqRJk+jRowedO3emQoUKSbbfvHkTDw8PNmzYgImJCVWrVuXt27dcvXoVKysrDh06hJOTkyzhK0QaJOEdIYQQQgghhBBCCJEmGH6A/erVK5YtW8bQoUMpXrw4U6ZMoVatWkDSAM/Vq1dxc3Pj7NmznDt3Dhsbm1QbvxBCpDTDSdhr164xa9Ys5s+fT4MGDZgxYwZOTk5A0np66NAhevfuzYsXL7h+/TpWVlbfZaxnz56lSZMmFClShLlz55I7d+7P7hsdHc306dO5e/cucXFx/Pzzz7Rt25bMmTNLcEeI78Swvjx58kQ6Xv1LDB8+nIkTJ9K8eXM6dOiAsbExq1evZu3atdSoUYNp06ZRokQJpfNaw4YNOX78OBcvXiRLliwSDvnGduzYQZs2bWjTpg3e3t7K0r6fMn78eLZv3865c+fIkycPP/30E6NHjyZ79uxybxMijTJO7QEIIYQQQgghhBBCCPF3fPxtU8NfZ8yYkd9//534+HiGDx+On58fxsbGVKtWDSMjI+UD70KFCjFt2jRsbGywsbGRbxcLIdKUj+ukYX0rWLAgLi4uJCYmMn/+fCwtLZkwYQI5cuRArVYrj61WrRqLFy8mT548WFlZfbM6+fHYPv79rVu3uH//PlOmTPnL4I5Go8HGxgZfX99P/hkyuSnE96GvC3Xq1CFr1qyMGTNGCQSK1LF9+3bmzZtHp06d8PLyIm/evAA8ePCANWvWcOPGDfLkyQOgLJlYqVIldu3axfv37yW48zcY3iM/1RknIiKCd+/e0adPn78M7sCH4NWgQYN49eoVDg4OJCYmYmJiIsEdIdIwCe8IIYQQQgghhBBCiB+O4YfWR44c4caNG9y8eZNy5cpRpEgR8uXLh42NDT169ECr1eLp6Ymvry8+Pj7JAjz58uUDPv0BuxBC/KgM6+S5c+e4c+cOr169IkeOHFSrVg1jY2MKFiyIq6srOp2OBQsWoNPpmDhxohLg0R+jYsWKwLetk/rjhIaGUqtWLdRqNTqdDp1Oh1qt5ty5c8D/BwI+nqzU//7Vq1eoVKpPdk6Tmi7E96XT6ciUKRNBQUHY2NgwePBgCfCkoI/DlB///vTp08TGxtK9e3fy5s1LQkICGzduZMKECeTJk4fjx49jYWFBQkICJiYmwIfaamxsjKWl5Xc/nx/dx8+/4X1Up9Oh0Wg4ceIEFhYWSZYrM6TfPzo6GhsbG8zNzTEzM0OlUikBKwnuCJF2yTtXIYQQQgghhBBCCPFDMeyk4OXlRePGjenatSt+fn40a9aMVq1aMX/+fACsra3p1asXfn5+hIWF4evry+HDh4HkH3zLJK8QIq0wrJM+Pj7UrVuX5s2b07lzZ+rUqUPr1q3ZuHEj8KEDz6BBg+jZsyerV6/Gw8ODBw8eAClfJ7t27UqdOnVYvXo18CGoo9PpAChdujQAZ86cUcai36bT6ZQQZteuXdm3b983HZcQ4u9RqVQEBQXRp08f5s6dy7hx47h9+/Zn99df03parTalh5hmGAZF7t27B5CsK1pERAQODg5UrlwZnU7H+vXrcXNzQ6VSER4ejr29PQDh4eEsXboUgO7du/Pnn38qyw6KL6d//qtXr06TJk0AlHuVPnxjb2/Pu3fvuHPnDgCJiYnK4/X3toSEBKZPn87Vq1eTHFe6gwqR9sknEkIIIYQQQgghhBDih6KfPPby8mLChAnUr1+fHTt2EBQUxMCBAzl37hx9+vRhypQpwIcAT+/evfHz8+PYsWMMHDiQiIiI1DwFIYRIUfo6OWLECMaOHUu1atVYtWoVCxYsoFGjRuzcuRM3NzcWL14MfAjwDB48mJ49e7Ju3Tp69+7No0ePUnycDRo0oHDhwri4uLBmzRrg/wNDefPmxcbGhgkTJrB+/Xrgw8RlQkKCMoG5dOlS9u7dy/Pnz5OFAIQQ35c+hKBWq5k9ezZt27Zl0aJFvHnz5rOPUalUHDp0iBkzZihdt8SX0dfB2rVr079/f65du6Zs04egsmbNyvPnzwkLC2Pz5s14eHigVqs5ceIEmTJlUvb39fXFz8+PqKgosmfPTtasWWXZwb/p6dOn2NnZsW3bNjp37gx8uK/Fx8cDKEGq8ePHAx+WK0tMTESr1SqvqY+PD7NnzyY6OjpVzkEIkXpk2SwhhBBCCCGEEEII8cM5evQoc+bMwdnZmQkTJpArVy4A2rVrR40aNWjevDnDhw8nc+bMdOzYkYwZM9KvXz/evXvH8uXLyZ07dyqfgRBCpKy9e/cyZ84c2rdvj5+fH46OjgDUr1+frVu34ubmxtSpU8mZMyf16tWjQIECDBs2jDdv3hAWFoapqWmKj7FVq1akS5cODw8POnbsSEJCAh07dgSgePHiTJo0iR49ejBkyBDi4uL47bfflKVd1q5dy5QpUyhUqBAtW7aUjgRCpCKtVqss6bN582aaNm1KYGAgQ4YMoUSJEp993O3bt2nYsCE6nY48efLQuHHjZEsPib9WqFAh5s6di62tLe7u7hQsWFAJQdWuXZvFixczevRobty4gVqt5uTJk9jZ2SmPnz17NpcvX6ZXr15YWVkpP5cg1d/j4ODA9OnTsbW1ZcmSJcTExBAcHKzcUxs0aEDBggUJDg4mW7ZsTJ06Vbl2dDodGzduZNOmTZQpU4YiRYqk5qkIIVKBVF4hhBBCCCGEEEII8cO5efMmL1++pFOnTuTKlUtp66/T6WjatCmrVq1Co9Gwbt065RvfFhYWuLu7c/HiRRwcHGRpBiFEmnbt2jXev39Pp06dcHR0VGpejhw56Ny5Mz4+Pty4cYNNmzYpj8mbNy9+fn5cuHABOzu7FK2T+k45v/76K2PGjCFHjhzY2Ngk2datWzcmTJjAvXv36NixIy1atGDgwIE0btyY3r178/79ezZu3EimTJmkpguRivRBDw8PD5o3b86sWbMAKFWqFJB8eSy99OnT4+7uDsD27dsBWRroa82ePRsPDw+WL1/OqFGjuHLlirLt119/pU2bNhw4cIAXL14QHBycJLizatUqZsyYQfbs2enbt68SIhH/jKOjI97e3rRr145169Zx6tQpdDodOp2OXLlyERwcjLW1Nf7+/jRp0oQVK1Zw4sQJ3N3dGTZsGG/fvmXRokVkzJhR7m1C/MeodNJLUgghhBBCCCGEEEL8ILRaLWq1mlGjRjF69GhWrlxJ+/btk31L+/379zg7O7N//35OnjyZ7Fvf8q1uIURapa+T3bp1Y+nSpezbt49atWqh0WiSLIFy7do1WrRowZUrV7h+/Tp58+ZNcpzvUScN/4zHjx+TJUuWT+63Zs0apkyZwu3bt4mOjsbJyYmff/6ZyZMnkyNHjmTnJoT4PvT1BuDIkSO0atWK1q1b07t3bwoXLvxFx7h79y6urq4cO3aMsLAwChYsmJJDTrMGDx7M9OnTOXnyJGXKlFF+HhoaypQpU9izZw/t2rWjevXqFClShJUrV7Jx40ZMTU05fPgwuXLlSvJ6in/uzz//5MWLF0leD/1zfOnSJXr27MnJkydJSEgAPiyhVb58eYKCgpQvJ8i9TYj/FolQCiGEEEIIIYQQQoh/rY8nEfS/Ll++PAARERG0b98e+P9JYJ1OR/r06SldujS7d+/myZMnyY4rwR0hRFrxuTpZtWpVli5dyuHDh6lVqxZGRkZJ6mTBggX55ZdfuHLlCjExMcmO+z3qpH4sKpVKCe4YBnr0v27bti3Vq1cnJiaGP//8k0KFCmFtbU26dOlkclOIVKSvN3fu3OHkyZOYmprSt2/frwrgODo60rNnT0JCQvjjjz8kvPM3TZs2je7duytLLenrZ61atTAzMyNLliwsW7aMoKAgAKytralUqRJz584lZ86cUku/MX2XHf3SvvrXQ61Wo9VqKVq0KJs2beLq1atERkZibGxM8eLFKVu2LBkzZpTXQ4j/KOm8I4QQQgghhBBCCCH+lQw/tI6MjCQqKopffvkF+DBJ1LJlS06fPs26detwdnYGkk76/v777+zevZuwsDAKFSqUOichhBApyLBOXrhwgdjYWMqVKwfAyZMnqVmzJu/fv2fdunW0aNECgISEBExMTABwdnbm6NGjnDlzhqxZs6bOSfwPf9UBSLqoCZH6xowZw9y5cylfvjyWlpYEBgZ+cfDA8BpesGABXbp0UeqT+PsMg5qGNfLw4cM8fPiQ169fU6FCBZycnLC0tJSgSCr4q/uXdEAS4r9LrnwhhBBCCCGEEEII8a+j1WqVSYRx48bRrl07OnfuzNGjRwFwcnKiX79+ALRq1YrAwEDev3+vfAgeEhLC/v37KVu2LDly5EidkxBCiBRkONk6ceJEWrRogaurK5GRkQD89NNPTJs2DZ1Ox+DBg1m9ejWAMjG+adMmwsPDqVChAtbW1qlyDl/ir8I5EtwRInXFx8fj4OAAwJYtWzh9+jQvX7784iCIPmAC0LNnT0xMTEhMTEyx8f5X6GvjxzWyatWqtGnThh49elC8eHEsLS3R6XQS3EkFhh3m9PS/luCOEP9d0nlHCCGEEEIIIYQQQvyrGH4TdejQoQQEBNC4cWNcXV2pWrVqku0BAQEMGzaMhIQE6tSpQ6lSpXj06BGhoaGoVCqOHTuGo6OjdGcQQqQphjVtyJAhzJo1i7p16+Lq6kqdOnWSbJ88eTLu7u4AdOzYkUKFCnHnzh22b98OQHh4ODlz5pQ6KYT4W968eUNISAjjxo3j9u3b+Pv706VLF8zNzVN7aEIIIcQPRcI7QgghhBBCCCGEEOJfadmyZfTp04fu3bszePBgcufOrWwz7DgRHBxMcHAwO3fuJC4uDgcHB8qWLcvcuXNxdHSUpQCEEGnWwoUL6d+/P71798bV1RUnJ6dP7rdq1SpGjx7NgwcPePfuHfb29pQsWZLFixdLnRRCfJG/Cvi9efOGTZs2MWLECDJkyIC/vz9169aVJbCEEEKIryDhHSGEEEIIIYQQQgjxr9SqVSuOHTvGnj17KFq0aLLtWq1WaSv/7t07/vzzTx49ekTOnDnJnj07GTJkkAlpIUSaFRcXh7OzM+fPn2fXrl0UKVIk2T6Gk+23bt3i9evXXL16laJFi5IrVy6srKykTgoh/ifDOqHVann9+jUZMmRIEs7RB3jc3Nyws7Nj8uTJEuARQgghvoKEd4QQQgghhBBCCCHEd2cYvElMTMTY2DjJ9hcvXpA/f34qV67M1q1bk+z/pWQJGCHEj+x/1b0HDx5QvHhx6tWrx5o1az6731/Vwr9TW1PyOEKIfx/D4M78+fPZvn07kZGRFC9enPr16zN48GBl37dv37Jx40YJ8Ig0Qe5tQojvTSqOEEIIIYQQQgghhPju9B+Ed+rUiZCQEBISEpJsNzY2xtTUlFu3bvH06dNkH5xrtVoAHj9+zLx58z75Z0hwRwjxI9PXvW7dunHlypVk22NjY4mPj+fu3bu8fPky2XZ9nYyKiiI4OPgv/4xvNdZ27dpx/Pjxb3JMIUTq02q1SnBnyJAhuLi4cObMGcqUKcOff/7J0KFD6devH7GxsQBYWFjQokULJk2axIsXLxg+fDjbtm0jMTExNU/jh6Cv2XoajSaVRiL09Pe2VatW8fr161QejRDiv0DCO0IIIYQQQgghhBAiVVy6dIlNmzbh6enJ7t27kwR4MmbMSPny5bl58yZ79uxJMulj+C3YCRMmMH36dG7fvv3dxy+EEClt165dLF26lJYtW3L9+vUk2/LkyUOlSpW4ceMGN2/eBFBqpWGdHDx4MEuWLOHJkycpOtawsDC2bNlC69atOXXqVIr+WUKI78Pw/dbs2bPp3bs3W7duZfv27SxfvpwMGTIwd+5c+vbtmyzAM3nyZC5evMjMmTMlvPMF9M/15MmTiYqKwsjIKFmgR3x/kyZNokOHDsyePZu3b9+m9nCEEGmchHeEEEIIIYQQQgghRKooUqQIO3fuBGDYsGFs376d+Ph4ZXu3bt2wsLBg6tSpnDp1iri4OOD/JzfWrl3L5s2bKVOmDFmyZPn+JyCEECmsRo0aLF68mOjoaFq0aMG1a9eUbSqVinr16vH8+XM6d+5MdHS0sgShvk6uW7eOAwcOkD17djJmzJiiY61SpQpLly7F1NQUZ2dnTpw48dl9dTpdio5FCPHtHDlyhOXLl+Ps7IyLiwulSpXi9evXdO/enQwZMlC2bFmWLVvGgAEDiImJAT4EeJo2bUpwcDCBgYGYm5un8ln8GEaPHo27uztDhgzh5cuXqNVq6cCTyn799Vc6deqEt7c3s2bN4s2bN6k9JCFEGqbSybtkIYQQQgghhBBCCJFKdDod4eHhtGzZEiMjIyIjI5UgzsuXLxk3bhzTp08nX758dO3alcaNG2NnZ8eKFSuYO3cuWq2WQ4cOkT17dnQ6nSyVJYRIM/Q1LS4ujtWrV9O/f38yZcrEhQsXSJ8+PSqVipiYGDp37sy6desoWLAg06ZNo0iRIuTMmZM5c+YoHS8OHz5MtmzZUqxOajQajIyM0Gg0bNiwgf79+6PVarlx48YnQ0P6cVy5cgVLS0ty5MjxzcckhPg2pk+fzuDBgzl69CgVK1bk3bt3/Pzzzzx//pzZs2fj6OhI+/btuXnzJj179sTf35906dIlOYa+Roi/lpCQQPv27dm2bRvt27dnypQp2NjY/OVj9PXUsOOa+LauX7/OuHHjWL58OT4+Pnh5eX3y77P+tXj+/DmWlpaYmZmlwmiFED8yCe8IIYQQQgghhBBCiFSl0+k4cuQIsbGx1K1bV/mZSqXi2bNnzJ49m2XLlnH37l3MzMwwMjIiISGBggULsmXLFpycnGRSSAiRJulrYWxsLIGBgWTPnp2GDRsC/z8Z/u7dO/r27cuaNWtISEggQ4YMmJiY8OrVKwoWLMiOHTu+aZ38eII4Pj4eU1NT5fcajYbg4GDs7e2pV6/eZ49z9OhRqlatStu2bVm+fDkmJib/eGxCiG/vxYsX7Nmzh3bt2hEXF0ebNm04fPgw48ePp1OnTpiZmREQEICrqysAjRs3Zu3atRJc+EoJCQmYmJiQmJhI+/btWb9+Pe3atWP58uVKVzVD+vvDo0ePyJo1a5Kfia/38XOnfz30rl+/jre3N61bt6Z58+afPc7evXsZNGgQnp6etGvXTkJVQoivIuEdIYQQQgghhBBCCPGvov/wXP//t2/fcuvWLZYuXcrjx48xNTWlatWqNG3alEyZMklwRwiRpn1cEw1/pq9/sbGx7Ny5k/3793P16lXs7e2pVKkSbdu2xcHBIUXq5NatW6latSrW1tYA9OvXj0KFCtG/f/9Pjv9ju3fvpkePHty/f59169bh7Oz8TccnhPg6hrVGp9OhVqtJTEzE2NhYCSCEh4dTv3592rRpw+zZs5VwQ1BQEEOHDqVw4cJcuHCBa9euYWtrm8pn9O/1cV38uEbHx8fj7OxM9+7dadq06WePs3PnTho1asTs2bPp06dPio45LTN8Pe7fv5+kG9ySJUuoXr06efPm5d27d2TIkOGzx4mLi6N///4sWrSIGjVqEBoamuJjF0KkLcmjmkIIIYQQQgghhBBCpCL9h+f6/1tYWFCiRAn8/f2T7avVaiW4I4RI0z6uiYa/NjIyQqvVYm5uTvPmzWnevDkxMTFJlqxJiTrZpUsXli9fzoIFC+jevTvDhg1j7ty5DBo0KNnk5ue6QPzyyy/Mnj2bpk2bsnPnTpydnaVDgRCpxDA8olKpeP/+PRkyZEhWOy5evMibN2/47bffknQlOXLkCKVKlWLOnDlYWVlha2sr1/NnGAZF3r59i4WFhfI8T5kyhdKlS1O7dm22bt36P4917tw5APz9/albty758uVLuYGnYfrXo0aNGhgbG7Nw4UJy586Ni4sLc+bMYfny5eTOnfsvgzsAZmZmeHl58fbtW9asWcO6deto1arV9zgFIUQaIXdNIYQQQgghhBBCCPGv97nm0TIpJIT4r/u4DhoGdz61/Z/S6XS0bt2aQoUK4ePjQ82aNZk6dSqenp4MHDjwf05u6o8BH5bXcXNzY+nSpTx79kxquhCpwDC4s3jxYlq3bk3x4sVp0KABfn5+vHjxArVajU6nI1u2bAAEBwcrj1+3bh27d+/G0dGR3LlzY2dnJ8Gdv6APitSqVYtatWrx7t07AFxcXHBzc+PSpUvExcV99r2vIQ8PDzw9Pblx4wa3bt1K0XGndQkJCVhYWBAaGoqvry8dO3Zkzpw5DB48mOrVq3/R32edToejoyOenp5YWVlx4MCB7zByIURaIstmCSGEEEIIIYQQQgghhBDiq5w/f54qVaoQExNDzZo1mT17Nvnz5//iSXt994lz586xcOFCZsyYIZ3UhPjODLvADBkyhICAAHLkyEGePHl48OABf/zxB2XLlmXjxo3kzJmT27dv07x5c86fP0/9+vVRq9UcP34cS0tLDh8+nGS5IfF5z58/p0+fPmzYsIFmzZqRKVMmFi5cyODBgxk0aBDZs2f/4mNdvXqVDh064ODgwLp1674oQCk+TavV4uLiwrx58wDo2rUr/v7+WFpafnYJyE8dQ61WM27cOPbt2ydLZwkhvorEXoUQQgghhBBCCCGEEEII8VUiIiJ4+/Yt6dOn58yZMxw+fJh3794pHTr+F/0kaMmSJZk1axZGRkZoNJqUHrYQwoD+Opw+fToBAQH07t2bPXv2sH//fiWgc+rUKTp16oRGoyF37tysWLGCRo0aERERQUREBGXKlCEsLIwcOXLINfyF7O3tmTNnDgMHDiQkJISFCxcqnXe+JrgDUKhQIWrWrIm9vT3p06dPoRH/N6jV6iThp6dPnxIdHf3VxwDo27cv+/btAz7fQVQIIT4mnXeEEEIIIYQQQgghhBBCCPGXPu6oExkZyfnz58mYMSNeXl5ER0czatQoOnXqRPr06ZXJyi/pVCCESD0vX76kQYMGxMXFERQUROHChYmJiWHfvn307dsXS0tLwsLCyJQpk7LEVnR0NG/evEGn05EpUybSp0+fZPkt8df0XVy6devG0qVLAahRowY7d+7EzMzsi7u8GO6nr9Ff+ljxgeG97f3798ycOZPXr19z7do1Nm7cSKtWrfD19aVQoUJ/6/jyegghvoZxag9ACCGEEEIIIYQQQvz4Pp7U/dJlU4QQ4r/i4wm8H2lCz3BSfs+ePVy/fp0OHTpQrlw5ADJlykTPnj0ZNWoUOp2OTp06JelecPfuXRwdHVNl7EIIuHTpErly5cLCwiLZtidPnnDixAm8vb0pXLgwiYmJbNmyBTc3N4yNjZXgjv44Tk5O2NjYYGNjoxxDp9NJcOcr6Gt/7ty56dOnD69evWLVqlU0b96clStXYmtrq+z7V/cKlUqlvOdWq9Xy/vsrGd7bLl68iJWVFe7u7rx//5706dPTqVMnAgMDARg9ejQFCxZMFvb5X92OfpT7vBDi30EquBBCCCGEEEIIIYT4x/QfYh8/flz5vTR8FkKIDwwnX2/cuAH8OBN6Wq1WmdzUd9YZPXq0shwIQJUqVVi4cCHW1tb4+vqyfPlyEhMTAdiyZQt16tRRuksIIb6vq1evUrx4cVq1asXbt2+TbX///j06nQ6tVotGo2HdunW4u7ujVqs5ceKEEtyJiYlhwIABbNq0KdkxfpR6lpq0Wm2yn3l5eTFz5kwCAgLo0aMHu3btokOHDspSTRqNRnluP7d8k2FYR4I7X84wuDNx4kRatGhByZIluXfvnhLImT9/Ph07dmTdunV4e3tz+fJl5d84mzdvZuTIkdy6dSs1T0MIkcZIFRdCCCGEEEIIIYQQ30TdunX59ddf2bZtG/D/3wYWQoj/Ov3k66+//krbtm05c+aMsu3fHnTUTwa7u7szduxY6taty/bt23F2dlb2MTIyUgI8NjY2jBw5khEjRjBp0iRGjBjBw4cPqV69emqdghD/aWZmZrRs2ZLdu3fTuXPnZAGe/PnzkydPHvbv38/69esZMWIEKpUqSXAHYMyYMZw8eZJs2bJ971P44Wk0GqWWPnr0iLt37/LixQvgQ421tbVl5MiRSoDnt99+48WLF0q4ZMeOHfTq1UsJyYt/xrBT1NChQ/H29qZYsWIsX76cnDlzKvuZm5uzYMECJcDj6elJZGQkGzduxMPDg9mzZ3+ym5UQQvxdKt2//V8GQgghhBBCCCGEEOKHEBISQsuWLSlUqBATJkzg119/Te0hCSHEv0ZCQgLz589n+PDhVKlShXHjxlG6dOnUHtYX2bhxI507d6Zt27Z4enqSK1euT+6n1Wo5ceIEffv25ezZsxgZGZE/f3527NiBk5MTiYmJGBsbf+fRCyHu3LnDqFGjWLFiBS1atGDZsmVYWFgo3XY8PDzw9/cnQ4YM2Nvbc+HChSShhKCgIEaOHEnRokUJCgrCysoqFc/mx2K4zNLYsWMJCgri3r17lCpVCmdnZwYNGqTs++DBA0aPHs3ChQupX78+Y8eO5cKFC0yaNImoqChOnTol4alvaP78+fTv359+/frh4uJC3rx5P7lfXFwc/fr1Y8mSJZiammJsbIyDgwP79+8nd+7cslyZEOKbkfCOEEIIIYQQQgghhPjH9B9a79q1iyZNmpAtWzamT59Os2bNPrm/fgmZuLg4zMzM5ENvIcR/QlxcHKtWrWLQoEGUKFECf39/ypYt+8l9P66TqWno0KHMnTuX0NBQfv755/+5//v371mxYgU2NjbUrFkTBweHJEuUCCG+v9u3b+Pr65sswAMflvNr06YNZ86coXHjxmzevJn379+TPn16Zs2axfTp09HpdISFhZEjRw553/Y3jBgxggkTJlCgQAGcnJw4cuQI79+/p3///syYMUPZ78GDB4wfP57FixcTFxeHsbEx2bJl48CBAxIU+UZ0Oh3x8fE0atSIP//8k507d5IvX77/+bgJEyZw/vx5bGxsGDFiBNmzZ5d7mxDim5LwjhBCCCGEEEIIIYT4JvQTzdu2baNJkybs2bOHOnXqfHb/Xbt2MXfuXGbOnImjo+N3HKkQQqSeuLg4li1bRt++fTl8+DCVKlX67L779+9n9+7d9O3bFycnp+83SAPx8fFUrVqVqKgorl+/DpBs8lhf/z83qSyTzUL8O9y6dYvRo0crAZ6lS5diaWkJwMWLF+nZsyfHjx/HwcGBHDlyEBUVxcOHD8mbNy/bt2/HyclJwgpfSP886XQ6rl+/Tt26dWncuDGDBg0ib968HD9+nP79+3Pq1Cn69u3LrFmzlMc+ffqU0NBQDhw4gLW1Na6urmTNmlWe+2/o7t275M6dm99//52lS5d+VWc4/T1NXg8hxLcm4R0hhBBCCCGEEEII8c3oP8x++vQpDg4On93v1atX1K5dm9OnTzNkyBDGjRuHsbExKpXqO45WCCFSR1xcHI8fP/7s8lMADx8+pHr16ty8eZPFixfTpUuXVAnBJCYmUqdOHY4cOcLBgwepUqVKku36Mb18+ZLhw4czYcIEMmbM+F3HKIRIzrBeJCYmYmRkhEql+ssAz507d9i6dStbtmzh2bNnZMuWjZo1a9KpUyfpoPU3hYaGYmtrS5s2bVi/fj3FixdXXpvz58/Tr18/jh49mizAo6d/zuW5/7aePHlCnjx5qFu3LiEhIcm261+j+/fv8+jRI8qVK/f9BymE+M+RqLsQQgghhBBCCCGE+Fu0Wm2yn+m/J2ZjY5Pk9x+ztLQkICCA4sWLs3PnTmVCSQgh0hJ9Dfy4FpqZmSnBnc/VSXt7e9zc3MiWLRuzZs0iLi4uVbrXGBsb06RJE7RaLVu2bOH169fKtsTERGVM/v7+bN26latXr373MQohktJoNMq1uWnTJsaPH09kZCQajYY8efLg4+PD77//zsaNG+nSpQtv3rwBwMnJif79+7Nnzx7CwsLYsWMHw4YNw8HBAa1WK+GRrzRt2jTq1KlD586dyfh/7N1lYBTX+/bx78YJDknwENwdWtyhWKE4tFhxtwQIEAoEKBoguBaX4Fbc3d1KcXeHENnd5wXPzj9BKr8CgXB93iTZkZzZzZzZ7LnmPnHjGsEdm+zZszNu3DgKFSrEuHHjaNu2rbEsLCwMwHjO9dx/WM7Oznh4eLBx40ZWr15tPG61WrFarcb506VLFxo0aMCjR4+iqqki8hVReEdERERERERERP61iINCly9f5tChQxw9epTr168D4OjoaEyj8i52dnbkzZuXZs2acfHiRWbOnPnJ2i4i8imYzWajD7xx4wZ//PEHJ06c4MWLF+9cJyKr1YqTkxP169enevXqnDt3jiNHjny0tr4ZxgwPD4/0c6lSpcibNy9jx45l/vz53L17F8CYYmTx4sXMnTuX7Nmzkzlz5o/WThH5exFDNt27d6d58+YMGjSI+/fvG+d6qlSp3hvgsVgsmEymtypoaeq7f69UqVKkT5+eU6dO8eDBA548eYKdnV2kYGe2bNkYN24chQsXZty4cTRo0AB4/V5a/pt33WhgEy9ePHr06MGrV6/47bffOHHiBAAmkwmTyYTVamXOnDns27ePwoUL4+rq+qmaLSJfMU2bJSIiIiIiIiIi/0rEaRgGDBjAjBkzOH/+PACenp40bdoUPz+/f7SvGzdukD9/fho3bkzfvn0/WptFRD6liP3kkCFDmDdvHufOncNqtfLtt99Ss2ZNWrdu/da6EdkCkE+ePCFBggT4+/vTs2fPD97WiFOxzJ07lz179nDo0CGyZs1KiRIlqFu3rrHMz8+PW7duUbNmTSpVqkTWrFmZM2cOc+fOxWKxsHPnTlKkSPGX4U0R+TT8/PwYOHAgLVu2pGnTpuTKleutdS5dukTfvn2NKbSmT59OrFixoqC10Y+tbz116hS1atXizJkzNG7cmClTpkRabusvT548SZ06dbh48SI3btwwqljK/ybitW3Pnj1cvnyZp0+f4uHhQdWqVYHX/4d0796d2bNnU7ZsWerVq0eNGjUICwtj9uzZBAYGYrVa2bJlC0mTJtW1TUQ+OoV3RERERERERETkf9K1a1eGDRtG2bJlKV++PE5OTowdO5bTp09Tt25dpk2bhpOT03u3jzhYkTFjRqOCg4hIdNGlSxcCAgIoWLAgRYsWxd7entGjR/Pq1Sujn/wrtsHHffv2kSdPng/eT0YMDvn4+DB27FhcXV1JliwZV65c4dmzZ3To0IEePXrg7u7OwoULmTp1KuvXrzf24ejoSO7cuZk/fz4pU6aMNGAqIlFj06ZN1KhRg0qVKtGvXz+8vLzeu27EAE+pUqVYtmwZMWPG/HSNjQbeF8K0OX36NDVr1uTMmTN06tSJgIAAgLf6y7NnzxI/fnwSJUqkoMh/EPH16NmzJ+PGjePJkyfG8uLFizNgwAAKFCjA2bNnGTlyJL/99hvh4eFkyJCBly9fcvfuXVKmTMnatWvx8vLStU1EPgmFd0RERERERERE5F9btGgRjRo1okGDBnTp0oVUqVIBMHz4cHx8fPDw8ODChQt/O/gTcWBCH4qLSHQyd+5cGjduTOPGjfH29iZNmjQABAYG0qlTJ9KlS8fhw4f/1SB5eHj4Rwk6DhkyhO7du0eq0LFjxw66devG3r178fPzw9/fH4CHDx+ybds2zp07R1hYGN9++y158+Ylfvz46sdFPhNDhw6lW7du7Nq1iwIFCrx3Pdv7sIsXL+Lj48OuXbs4deoUbm5un7C1X7aI/d6+ffuM4KO7uzuVK1c21jt58iS1atXi7NmzdOzYkeHDh7+1vc3fhYHkn+nVqxcDBgygXr16NGzYkOTJkzNz5kwCAwNxc3Nj+vTpFC9enAcPHrB3715GjRrFo0ePSJgwIYUKFaJZs2YkSpRI1zYR+WR0O5OIiIiIiIiIiPxru3fvxmQy0aRJE1KlSkVoaChLlixh9OjRpEmThj179hAzZkxCQkJwdnYGeOcdxBF/1ofiIhId2Pq6rVu3Ejt2bFq1akWaNGkIDw8nKCiI0aNHkypVKnbv3v1WP/l3PkZw5+bNm0yfPp1ixYrRsWNH0qVLh9ls5tGjR1y6dImUKVPSvn17Y/0ECRIYU45EZLFY1I+LRDFbyGDnzp3Y2dnh4eGB7R7+iO+5bOs9efKE2LFjkzp1akaOHEmsWLFIkCCBwiP/UMR+r2fPnowfP57Hjx8by8uWLUvv3r3JkSMHWbNmZeHChdSqVYuRI0diMpkICAjA3t7+rXCInvv/bvv27UyaNIk6derQu3dvI0CbPn16AMLCwsiRIwcACRMmpGLFipQvX/6t517BHRH5lNT7i4iIiIiIiIjIX3qzcHNoaCgHDhzAy8uLPHnyYLFYWLJkCd26dQNeB3tsd2wfO3aMxYsXA6j0v4h8FUwmE8+fP+fAgQOkSZOGbNmyERYWxuLFi+nRowdms5m9e/eSMGFCAC5cuMDWrVs/Wfve7NOvX7/O2bNn+fHHH0mXLh1hYWEsWLCA9u3b4+LiwoEDB3BzcyMkJISrV6++d78abBaJeraQQebMmbFYLFy/fh2TyRTpvLdardjb2xMaGkqfPn04c+YMAJ6engru/Eu258nPz4+BAwdSsWJF1q9fz4kTJ+jWrRs7duygQYMG7Nu3D7PZTJYsWVi4cCGZM2dmxIgRNGvWDFCA/WP4448/uHfvHo0aNTICtPPnz6d3794kSpSIY8eOET9+fEJCQoxtzGaz8b3tnNFrIyKfkq6+IiIiIiIiIiJisFgskX4ODQ01QjehoaHGOo6Ojjx69IiLFy+yaNEiunXrhp2dHfv378fd3d3Y3sfHh5kzZ/LixYtPdxAiIp9QxME+GwcHB5ycnIx+c+nSpXTt2vWtfjI8PJx69eqxcOFCwsLCPklbbX36kydPAIzf6+LiAsCSJUvw9fXFZDKxf/9+I4z56tUrqlSpwqZNmz56O0Xkv8mUKRPw+n3YrVu3sLOzw2w2Y7FYjD5gxIgRjB8/nhs3bkTaVsGd93sz/AiwdetWJk2aRN26denbty+lS5cmS5YsZMiQAYDg4GBy586Nvb09FouFzJkzs3DhQjw8PFi2bFmkSj3yv3nXdfjEiRM4OTlRsGBBABYuXEi3bt0wmUzs3bs30o0G3t7eADg6Ohrb66YDEYkKugKLiIiIiIiIiIjBNmCzZcsWAJycnAD45Zdf6Nu3LyEhIbi4uFC1alVu3bqFv78/vr6+7wzujBo1itOnT1O8eHFixIjx6Q9GROQTsN2VHxgYyPXr14HXQZgSJUpw9OhRWrdubQQc9+zZE6mfDAwM5OrVq2TNmvWjTIkVka3aBrzu0/v06cOtW7eIHz8+ABs2bOC3334zQkb79u2L1NZevXpx7do1XF1dP2o7ReS/a9CgAeXLl+fQoUN06NCBGzduYG9vb7zPW7RoETNmzKBAgQJ8++23Udzaz9/Tp0+Bdwc6zp49y/3792nUqBGpU6c2Krz06dOHxIkTc/ToUeLGjUtISIjx/GfKlIkdO3Zw/Phx4sWL985QkPwzEacuGz9+PCtWrAAgceLEhIaGsnTpUhYvXvze/1eGDh3KrFmzjOu3iEhUUnhHREREREREREQiqV27NqVKlWLRokUAtGvXjv79+xMzZkyjtHzBggXJnj07s2bN4smTJ28N8gYFBTFq1CjSpk1L/fr1dRe3iERrK1eupFOnTowePdp4rGzZssSIEYMJEybw6tUrLl26ROLEiYHXQZqFCxcyfvx4MmfOTK1atT76Xf4Rq230798fq9VqVIGoX78+c+bMwcfHBzs7Ow4dOoSHh4fR1tmzZ7Nq1SpKly5NtmzZPmo7ReS/sVUhmTVrFoUKFWLRokWULFmSUaNGsWDBAlq3bk2nTp14/vw5M2fOJF68eG9VXpT/s2PHDqpVq8a6deveufzkyZO4uLhQuHBh4P0VXo4ePYqvr6+xXbp06UiSJEmkimjy79n+x+jRowdt27Zl9+7dWK1WKlSoQIwYMRg4cCAdO3bEzs6Ow4cPR/p/ZfLkyezevZv69esb1zwRkaikT01ERERERERERCSScuXKkSxZMmrVqkW5cuUYO3Ys3t7e1KtXjzhx4gCQJ08e2rVrh6enJ48ePWLOnDmsWbOGq1ev0q1bN7p27UpoaCgLFizAzc1Ng0IiEq3lypWLRIkSsXbtWq5duwZAyZIlGTFiBAB37txh5syZnD59mjt37tCrVy+6du1KSEgIc+bMIWHChB+tn3xzOpFVq1ZRuXJl2rdvT7JkyQD48ccfyZ07N48fP6Zhw4bEjBkTeB3cmTx5Mn369MHBwYGAgABixYqlKhEiUeTN8/ld/Ya9vT1Wq5UECRKwdu1aateuzbVr1+jYsSN16tRhzpw5ZMiQgZ07d+Lp6YnZbFbI+j0sFgu7d+9m8+bN7N27N9Lzb/vew8ODV69esWzZsr+s8DJkyBCmTZv21jRltqox8u9EfC3Onj3LsmXLaNq0KS1btsRkMpEtWzY6d+7M5cuXuXXrFuPHjzcqzcHrcNvQoUNxc3OjS5cuODk56domIlHOZFVPJCIiIiIiIiIivB6ktd35u3HjRqpXr87z58+pUKECy5cvx87ODovFEmnqlblz5zJ69Gj27dtn7MfJyYkCBQowY8YMY1BIAxMiEl1ZLBbs7OwYNWoUHTt2ZPz48bRo0cJYPm3aNLp06cLDhw+NxxwcHMiXLx9z584lZcqUn6Sf7N69O9myZWPy5Ml07NiRKlWqRPq9M2bMYPjw4Zw5c4ZMmTKRM2dOzp07x+nTp0mUKBHr16/Hy8tLfbrIZ2Dx4sVUqlQJZ2fn965j65sA9uzZw9WrV3ny5AnZs2cnc+bMxIkTR+fzP/DkyRM2b95MiRIliBcvHpcvX8bLy8tYfujQIQoXLkyaNGl48uQJDg4OHD58OFJQZNKkSfTt25cff/yRX3/9FUdHxyg4kuhp27ZtODk5UalSJVavXh1pGrhTp04RGBjItGnTyJcvH8WKFSNPnjwsW7aMdevW4erqyvbt2z/ZdVhE5O8ovCMiIiIiIiIiIgbbQM/w4cPx8fEhduzYPHv2jAULFlCjRg3jjlSr1WoMCF27do2TJ09y/Phx7O3tKVCgAFmzZiVu3Lj6IFxEvhoHDhzgu+++I1GiRKxatYo0adIYy44cOcLx48c5dOgQ8eLFo0CBAuTPn5/48eN/tH4y4n6PHz9Ozpw5AYgZMyYzZsygWrVqQOQB/l27drFixQp+++03QkNDSZs2LaVLl6ZTp04kTpxYfbrIZ8D2Hm3nzp0ULFgw0jn8pr86Z/9qO3m37t27s27dOkaOHEnRokUBCA0NpXfv3owYMQKz2czvv/9O2bJljW1mzZpFv379cHV1Zd26dSRKlChSYF7+nYh/t1OmTKF58+Zkz54de3t7Dh06BET+u//zzz9ZunQpQ4cO5cGDBwAkSpSIokWLEhAQQPLkyXVtE5HPhsI7IiIiIiIiIiLylsePHzNnzhxMJhMDBgzg1q1bzJo1i59++gl4Hd4xm804ODgY27w5EKFBIRGJLt6sOhZRxEG/Hj16MGjQICPwGBoaipOT01/u92P0kxH3u3jxYn744QdmzZpF7969uX79On369KFXr15Gv/1m//3o0SMsFosxnZednZ0GN0U+E+vWraNixYr4+vrSv3//qG7OVyMkJIT+/fszYMAAypQpQ8+ePY0Az9GjRxk9ejQzZsygYMGCFCtWjNy5c7N06VLWrFkTqcKL3h//7yI+d/fu3ePFixe0bNmS3bt3YzabWb16NcWKFQPe/r/kxo0bXLlyhUePHpErVy7ixYuHq6urrm0i8llReEdERERERERE5Cv35uDtmx92L1y4kA4dOnD79m0jwBPxw/Nz586RPHlyXF1do+oQREQ+uMOHD3Pr1i0qVqwY6XFvb2+cnJyoU6cO2bJlw87OjvDwcBwcHDhx4gRly5YladKk7Ny5kxgxYkTqU9/Xz34sXbt2ZdiwYQQEBNCpUyfGjBnDL7/8wpMnT1i1ahXly5d/50Dyu9osIp+Hy5cvU7ZsWe7fv8/mzZuNqlryYb2rb3z8+DGTJk2iR48eFC9enF69ehlhkdOnT7Ns2TKGDRvG48ePAfDw8KBo0aIMHz5cFV4+oI4dOzJnzhyOHTvG7du36dOnD6tWraJBgwaMGzfurf9J3heY0vVNRD43Dn+/ioiIiIiIiIiIRFcRBxFevnzJs2fPSJgwIfb29saH3DVr1sRkMtG+fXvq16+P1WqlXr16APz+++8EBARQpkwZunfvHmXHISLyId25c4dixYrh4uLC2rVryZMnD1arlWPHjjF27FhCQ0OZMWMGZcuWpXfv3ri5uRE7dmwyZsxI8eLFCQoKYunSpfz444+R9msbJPxYg4URByj37NnD3LlzadeuHRUqVACgbdu2ODg40KVLF77//ns2btxI8eLF3xrYjNg+DWyKfF68vLzo3LkzrVu35siRI+TMmVPVXD6wiO+PT58+zYsXL8iXLx/x4sWjVatWWCwW/Pz8jPWLFStG5syZyZw5M/Xq1TMqvOTOnZv48eMTM2ZMBXf+g4ghm1mzZjFt2jSqVq3K8+fPyZ07N7169SIsLIyZM2fi5ubGsGHDIm3/vnND1zcR+dyo8o6IiIiIiIiIyFcq4iDC2LFjWbBgAceOHSNFihTUrFmTunXrki5dOmP9RYsW0bFjR27evMmgQYOwWCxMnz6dO3fucPz4cVKkSBFVhyIi8kG9evWK8ePHc/DgQSZMmEDs2LGNZadOneKPP/6gb9++nDhxAnd3d0qVKkWTJk0oVaoU586do1ixYnzzzTcsX748Stp//fp11qxZQ8+ePdm8eTNZs2aN1OdPmDCBLl26EBwczIYNGyhRooQG/0U+I+8KelgsFuB1EOHcuXOUKVPGmI7J3d09KpoZLUV87ocMGcL06dOJESMGU6ZMIVeuXAA8e/aMsWPH4ufn91YFnndRhZf/XcTn7tmzZ/Tu3ZvDhw8zY8YMUqZMaax38OBB/Pz8WL9+PZ07d2bo0KF6zkXki6PwjoiIiIiIiIjIVyjiIK2Pjw/Dhw8nXbp05MqViwcPHrBp0ybKlCnDwIEDyZ07t7Hd0qVL6du3L8ePH8dkMpExY0ZWrVpFqlSpdEexiEQrYWFhADg6OjJq1CgSJ05MrVq1jOVPnz5l8eLFLF++nBUrVgBQv3598ufPz4EDB5g5cyazZ8+mTp06n7TdAwcOpE+fPlSoUIEYMWIwd+5cbMMAVqvV6PsjBng2bdpEsWLFNMAs8pmZMmUKsWLFemc/0rJlSyZNmkRQUBA1a9bU+7APIGIf6O3tzdixYylRogQ+Pj6UKlUq0rpvBnh69+5NkSJF3tqPfBjdu3fn7t27HD9+nCpVquDn54fZbMZkMhnXtYMHD9KrVy/WrVtH586d36rAIyLyuVN4R0RERERERETkKzZ48GD69u1LkyZNaNOmDRkzZiQkJITEiRPz/Plzvv32W8aMGUPOnDmNbY4cOcK5c+d4/Pgx1apVw93dXQNGIhJtHThwgG+//ZYcOXLQv39/Klas+NY68+fPZ8WKFSxZsoTw8HDixYvHw4cP6dixI0OGDMHBweGTtNVisTB58mQCAgI4f/48yZMnZ/v27Xh5eUVaJ2KAp3v37jx58oSdO3dSsGDBT9JOEXm3iO+ntm7dSsmSJQEoV64clSpVombNmkaVncuXL5M3b14KFSoUZVW+oquJEyfSoUMHWrZsSfv27UmdOvU714sY4CldujRdunR5K+Qj/93du3dp0KAB69evB14HeQYMGGAsjxiWihjgad68ORMmTIiSNouI/C8U3hERERERERER+UodPHiQevXqkS9fPvz8/MiQIQOPHz+mYMGCPH36lEyZMrFp0yYKFizI6NGjjakC3qSpVkTkS/ZmHxYaGoqTkxMA4eHh2NnZ8dtvv+Hj40OaNGno27cvlSpVMpbbgjmhoaEcPXqUoUOHsmvXLu7du8fFixc/2ZSCtsHL0NBQ5s2bx6hRozhz5gzDhg2jcePGuLi4vPOYR44cyfjx49mwYQOenp6fpK0i8raIAYRffvkFT09Pvv32WwIDA1m/fj3Xr1/H09OTDh06kD9/fgoUKMD333/P6tWrWbp0KZUrV47iI4geQkJCqFy5MhcuXGDVqlVkzJjxL9d/9uwZEyZMoFu3btSqVYuZM2ca1xD5cE6cOMHo0aOZMmUKOXLk4Lfffov0v0nE8+fQoUO0adOGCxcu8OeffxIvXrwoarWIyL+j8I6IiIiIiIiIyFfizQHqBQsW0KpVK4KCgihdujQvXrygQIEC3Llzh8DAQKpVq0bDhg0JCgqiUKFCjBo1ily5cmkqABGJNiL2Z7t3745UeWbgwIGkTp2amjVrEhYWxpw5c2jfvj0ZMmSIFOCxVcqw9bFPnz7l5s2bJEyY8KNWJvurvtgW4Onfvz/Pnz9nypQplCtXLlI7Il4TXrx4QcyYMVVFTeQz0KdPH/z9/alZsyZBQUEEBwcTHBzM8OHD2bBhAwcOHMDBwYFevXrx7Nkzhg0bhp+fH/7+/lHd9C/G8ePHcXBwIHPmzG8tu3btGunSpaNevXpMmTLlH4XUHz16xNy5c/n+++8VgvzAIl7rjh07xvDhw5k1axYtW7bE19c30vMdcd3jx4+TJEkS3N3d9b+LiHwxFN4REREREREREfkKRBx42L59Ozly5ODBgwecOXOGihUrEhoaSv369dmwYQMDBw6kYcOGuLi4MH78eNq0aYOjoyNp0qRh0aJF7xzoEBH5khUpUoR9+/axbNkyKlSoQMeOHRk1ahRDhw6lTZs2uLi48OrVK+bOnfvOAM/7BgY/VmWyiCGbhw8fcv/+fSwWC+nTpzd+X0hICPPnz6dPnz6EhoYyadIkvvvuu0hTeEVsnwY3RaJGxPP57t27VK5cmZw5c9KlSxfSpEkT6Tx9/vw5K1euZMaMGWzbto2wsDAsFguxYsVi586dZM+ePSoP5Ytw5coV0qdPT4oUKdi6dSvJkyePtPzSpUukS5eOYsWKsWzZMmLHjh1pue31uH37Nrt27aJ69eqRlisE+b+L+LdusVgICQnBZDJFqhx3/PhxBg0aRFBQEB06dKBjx47vDfC8uU8Rkc+deisRERERERERka+A7UPrDh06ULduXVatWkXq1KkpXbo0AEePHmXLli2UL1+eBg0aGB+Sf/PNN+TNm5eiRYty8eJFEiZMGGXHICLysdSoUYPw8HDatm1LpUqVGDVqFF27dqVGjRpGf+ji4sKPP/7IqFGj+OOPP+jduzerVq0CwGQy8a77ZD/GgKHFYjEGhocNG0aZMmXImDEjmTNnJl++fEydOpVr167h7OxMnTp16NOnD05OTjRv3px169ZhNpvf2T4Fd0Sihu18Xrx4MevWrePw4cPUqlWLNGnSAK/PU1v/EitWLOrWrcuCBQs4fPgwdevWpUCBAjx//pzVq1cDRDrHJbK7d++SMmVKmjdvTr58+d45nZKXlxe5c+fm3LlznDp1KtKyiEEQPz8/xo8fz507dyKto+DO/8ZsNhvP7axZs2jWrBklS5akXLlyTJ06lWPHjgGQPXt2unfvTq1atQgMDGTkyJFcvXrV2M+b1zIFd0TkS6LKOyIiIiIiIiIi0VjEQYZVq1bRpEkTqlatSrdu3UiVKpWx3pIlS6hRowbTp0+nQYMGwOsP0du1a8fBgwfZv38/T58+JU6cOLqDVUSijYh36Nv6QZPJROXKlZk1axaxYsV6q897swJPv379qFChwidvr7e3N4GBgeTIkYMiRYpw4sQJjh07RnBwMPXq1aNr166kSZOG0NBQ5s+fT+/evbFYLIwYMYIqVapogFnkM7Jy5UqqVKlC3rx5CQ4OZu/eve+cys7WB9i+hoSEcOnSJX788UdCQ0M5evRopOpa8n/WrFlDxYoV2bNnD9mzZzcqukycOJH8+fOTI0cOY92AgAC6dOlCmTJlmDNnDm5ubpH2NX/+fHx8fChfvjxjxozB2dn5Ux9OtPLmtW306NG4uLiQMGFCrly5gp2dHd988w09evQwKt6dOHGCX3/9lYULF9KpUydat24d6X8bEZEvkT5lERERERERERGJpqxWa6TS86GhocSIEYP27du/9eG2rbLEsmXLuHDhAgCLFi1iy5YtpE+fnrCwMGLFihVpnyIiXzqTyYTFYgHg9u3bwOu+c//+/ezcuROIXPUCIlfguXjxIq1bt2bjxo2frL3wuirBqFGjaNu2LQsXLmTkyJEsX76c6dOn88033zB58mRGjRrFnTt3cHJyom7duvTr14+HDx8yYMAAwsLCPkl7ReSfyZIlCx07duT06dOcOnWKBQsWAG9XcbH1AbYAj7OzM6lTp6ZYsWKcPn2alStXfvK2fwn27NnDjz/+SKFChYz3wy4uLmzcuJFWrVrRrl07Tp8+baxfq1YtfvjhBzZs2ECNGjVYvnw5d+/eJTQ0lPHjx9O7d29ixIhB3759cXZ2fmflNfnnbH/XI0aMYMSIEbRr1449e/Zw6dIlNm3aRIsWLdi7dy+dO3dm7dq1AGTLlg0/Pz/q1KlDQEAAM2fONK7nIiJfKlXeERERERERERGJ5nx8fBg+fDiVKlUiSZIkTJw48Z3rNWrUiJkzZ5I2bVoSJUrEkSNHSJAgAbt27SJFihSfuNUiIh/XmxV1duzYwdmzZ3n8+DHdunUjefLkjBo1ih9++AF4HeqJGGAMDQ1l0qRJBAYGsmPHDhInTvzJ2t64cWNWrFjBnj17SJcunVGdw2KxcObMGZo1a8a5c+eYP3++MT1iaGgoK1eupECBAiRNmvSTtVVE3i9iZZ2LFy8yefJkAgICKFSoEAEBAeTOnfsvt7f1Y5s2baJMmTJMnTqVn3/++VM0/YsRHBxMrVq1OHfuHFOnTqVw4cIAPH78GDs7OwICAhg+fDh58uRh7NixZMqUCTs7Oy5evEivXr1YsWIFL168wMPDA5PJxIMHD0iTJg1r1qzBy8vrrepI8u9ZrVZu375NlSpVCA4OZuXKlXh5eRnLw8PDGTx4ML169aJcuXJMnDjR+N/k+PHjTJw4kW7duuHp6RlFRyAi8mHoNikRERERERERkWjOYrHg4uLC6tWr+eOPP3j27FmkO4Rt3w8bNgwfHx8eP37Mw4cPKVeuHLt37yZFihSYzeaoar6IyAdnNpuNEM6OHTtYu3YtRYoUoVGjRnTp0oUpU6Zw/fp12rdvz/Lly4HXlQFs29iq9LRp04ajR4+SOHHiT3LHv8Vi4eXLl+zYsYNYsWKRLFkyLBaLMXBsZ2dHxowZadGiBQ8fPmT06NHA637eycmJ6tWrkzRpUvXpIlHkzX4iYugjderUNG3alHbt2rFjxw6GDRsWqRrMu9jZ2fH48WNWr14NgJOT04dv9BfK9v42PDycy5cv4+TkRL58+QDo3LkzPXv2xNHRkbZt2+Lt7c2+ffto06YNZ86cwWKxkDp1agICApgzZw41a9Ykffr0fPvttwwYMICtW7cquPMBmUwmnj9/ztmzZ8mTJ48R3LG9hg4ODrRr144ff/yRdevWcf78eWPb7NmzExgYiKenp65tIvLF08SXIiIiIiIiIiLRyLsGEYYPH07MmDEZPXo0R48e5fDhwxQrVsxY11aq3s3NjSFDhtCqVSsSJEiAo6Mjrq6uGpgQkWglYtjF39+fyZMnc+PGDfbt22dUuWjcuDGAMZBusVioWrUqACtWrGDq1Kk0btyYKlWqEDNmTICPMqWg1Wo1+mjb73B1dSVHjhxs2LCBW7dukSZNmkj9tL29PZUrVyZ58uRcvXqVFy9eGG20UZ8u8ulFPE9Xr17N8ePHOXPmDIUKFSJXrlzky5ePNGnS0Lp1aywWC6NGjQLAz8+PzJkzA2/3CVarlbNnz7JkyRIqV67MTz/99OkP7DN17do1PD09iR07NhkyZDCmFjx06BBTpkyhR48ehISE4O7uTqtWrQAYPHgwbdq0MSrwJE6cmMqVK1O5cmVCQkJwdnY29q/3xx9WWFgYZrOZmzdvYjabsVqtODj83zB2nDhxKFu2LHPnzmXt2rWUKFHCqDxlW0+vh4h86VR5R0REREREREQkGrF9aL148WKePHliPN6vXz/at2/P06dPqVWrFhcvXsTe3j7SHaq2u1u9vLyIGzcurq6uWK1WfRAuItFGxGmvunTpQr9+/ShVqhRbtmwhX7582NvbG31h48aNjQo8LVu2ZMqUKUydOhVfX1/Wr19P3rx5P2pbLRaLMUh/7949Hj16ZBxDtmzZePbsGd7e3rx8+dLoz21tjxs3Ls7OzsZXEYlaEUOD3bp1o1atWvj7+7Ns2TJatmxJtWrVGDZsGABp0qShY8eOtG/fnqCgIPr378+ZM2cAIgV3bD+nTJmSgQMHsmzZMuN3fe3WrVuHl5cXCxYsAGDMmDHkyZOHTp06MWXKFNq1a0fnzp2JFy8eVquVRIkS0apVK7p16xapAk9Eb/alen/8v4n49xkSEmJ8nzRpUr799lu2b9/OwYMHcXBwMNYNDw8HoGTJksD/VZj6GKFZEZGopF5NRERERERERCSaGTNmDDVr1qRz5848e/bMeNzf359evXpx7949ChYsyIULFyIFeGwDQhEHht4cJBIR+ZLZ+rR58+YxZswYmjdvTu/evSlWrFikdSIGeKZPn87jx49p3rw5rVq1wmKxcPr0aZIlS/bRpuiIOK3X+PHjqV+/Pn5+fty7dw+TyYS3tzfZs2dnxYoVdO3a1QjwRDy+mzdv8u2332pwU+QzYDsP+/Xrx9ChQ6lduzbbt2/n6tWrLF++nBcvXtC1a1dGjhwJQMqUKY0Az5IlS/Dx8eHcuXPv3HeSJEmoU6cOgFGJ5Gu2d+9e6tatS9GiRUmSJAkAiRMnJmXKlLx69QpnZ2fixIlDrFixgNfP2fsCPH83bZn8OxGvbQsXLqR3794sXboUgHjx4vH9998TGhpK3bp1OXnyJHZ2doSHh+Pg4IDZbGb+/PkAkSpRiYhEJyarejYRERERERERkWglODiYwoULc+TIEZo0acLw4cOJHTu2sbx3797069cPDw8Pdu3aRZo0aYwPxkVEojPblDONGzdmxYoV7Nixg0yZMv3lugCbNm3i0KFDADRo0IDEiRN/tClTIg6++/j4MHHiRFKlSkX//v35/vvvjQoe586do1KlSpw/f55ixYrRpUsXEidOzKZNm5gyZQqhoaHs2rWLpEmTfvA2isi/d+LECSpUqED27NkZMWIE6dOnJzQ0lO3bt1O7dm3c3d3ZtWsXCRMmNLa5fPky/fv3Z/369Rw5ciTSMnlbaGgoP/30E4cPH2batGkULVoUgFmzZtGqVSuqVavGkSNHuHbtGn5+fjRp0oT48eMbIRCTycSdO3cYP348w4cPJ1WqVCxcuJD06dNH5WFFCxGvbT169GD8+PG4ubnh7+9PzZo1jf9DmjZtym+//Ya7uzszZ84kR44cJE6cmFmzZjFw4EAcHBzYvHkzbm5uUXk4IiIfhcI7IiIiIiIiIiLRiC2E8+rVK4oUKcKhQ4f+MsCTLFkyNm3apEEJEfkqWCwWXrx4QYYMGUiaNCkHDx58Z3jRFtx5XxWLjxXciWjIkCH06NGDdu3a0apVq0j9tK19V69e5ccff2T37t3GMmdnZzJkyMCyZcvw8vL6JG0Vkb+3fPlyqlatyrJly6hcuTLh4eEsWrSIbt26YWdnx4EDB3BzcyM0NJR79+6RLFkyAK5fv06sWLGIFy+eKuv8jZCQEAoXLsyjR484ePAg8eLFo3PnztjZ2fHNN99QqFAhHj16RIMGDbh48SJ+fn40bdrUmD4LXgd47t69y9ChQ1myZAl79uzBw8Mjio8s+kqEurkAAQAASURBVOjVqxcDBgygZcuWNG/enJw5cwKRr6tt27Zl3LhxwOvKUi4uLly/fp2kSZOyZcsWvLy8dC6ISLSk8I6IiIiIiIiIyBfqzQFZ24fY/zTA4+/vT58+fciRIwcHDx7Ezs5O02SJyFfh22+/5f79+xw7doxYsWJFqrJj60vv3r3LihUraNq06Sdv35UrV/juu+9IlCgR06dPJ1WqVADvbOerV69YtmwZp06d4vnz5+TMmZOKFSvi5uam4I5IFIkYLLCdtyNGjMDb25sDBw6QI0cOFi5ciK+vL3Z2duzfvx93d3cAbt26hb+/P+3bt49UGSzi+S/v16pVKyZOnMjAgQM5d+4c06ZNo0ePHvj4+BghnYMHD9KyZUvOnz+Pn58fzZo1eyvAc//+fZycnIgTJ46CIh/Itm3bqF69Ot999x0DBw7E09Mz0vKIYdq5c+eyY8cOtmzZQsqUKcmRIwedOnUiSZIkuraJSLSl8I6IiIiIiIiIyBdu9erVfPPNN7i5uRkDO7YPv213IB86dIjGjRszfPhw4sSJY2wbEBBAzZo13/rwXEQkOrJYLADUrl2bxYsX07t3b3x9fXF2do40aAvQsWNHJk6cyLFjxz55dbIdO3ZQrFgxxo8fT4sWLf6nCkAabBaJGhFDNk+ePCFu3LgArFu3jvLlyxMYGEjGjBlp2rTpW8EdgPr167NhwwZ27txJ2rRpo+QYvkS2Pu/Zs2fUrl2bTZs2ERYWRuvWrfH39ydBggTGa2O1Wjl06BAtWrR4K8DzZt+p0NSHM3bsWNq1a8eGDRsoVarUO9d58/l/+vQpceLEMa53Cu6ISHSmd+4iIiIiIiIiIl+wcePGUalSJQYOHMjDhw+NAQkHBwfCw8NxdnZmzZo1ZMmShd9++42OHTvy9OlTY3tvb288PT0JDw+PwqMQEfmwbCGdN9nZ2WFnZ4e/vz+JEiVi5syZLFy4kJCQEEwmkzFAu2DBAtasWUP58uWNqWs+pdu3bwOvB/5t7Y7IbDYDcOfOHS5duhRpmS2EpOCOSNSw9SOdO3emZs2axuOpU6cmVapUdO7cmYYNG+Lo6Mi+ffsiBXemTJnCjh07qFSpEsmTJ//kbf+S2fq82LFjEy9ePMLDw7G3t8fR0THS1Ii2ME6ePHmYOHEiadOmpX///vz22288fPjwrb5TwZ0P58yZMwBGKO3Na7XZbMbOzo5Hjx4Zj8WMGRP4v9dXwR0Ric707l1ERERERERE5AtWqVIl8uXLx+jRo98Z4LFYLLi5udG4cWMAFi1aRJMmTXj+/Hmk/UQc1BAR+ZLZBv8AVqxYQf/+/WnevDndu3fn4sWLvHz5kkyZMtGnTx8ePXpEly5d8Pb25uTJk5w7d45ff/2Vnj17EhoayqhRo4gZMyafuoC9bWDz6NGjxuCmrQ1Wq9UYvOzcuTPTp08nNDTU2FYDzSJR78mTJ+zYsYONGzeyceNGANKlS0fHjh0xm83cvn0bf39/PDw8jG1mzZrFkCFDiB07Nv369cPFxeWT9z1fOtuUWEuXLqVBgwZ88803TJgwgWHDhnH79m0jpPlmgCdjxoz4+PiwYMECPecfUYwYMQDYvHkzwFsVjmyVddq1a2ecN7brna5tIvI1UHhHREREREREROQLEfHuVFulHE9PT5YsWUKePHkICAiIFOCJuH7WrFnJkycPzs7OrF27NtJAr4hIdGGxWIyBvq5du1KrVi38/f2ZN28egwcPplChQkyYMIHbt2/TqFEjAgMDcXZ2Zty4ceTPn5/s2bPj7+9P7Nix2bJlC8mTJ8dsNn/yQUNPT09KlizJ/PnzmTJlCvB/A5e2r9OmTWPdunW4uLioEoHIZyZu3Lj069cPOzs7Nm3aZDzerl07evfuDUC9evVo1KgRfn5+lC9fnvbt2xMWFsbKlStJkiRJlPQ9XzqTyUTevHnZsWMHAwYMYPbs2RQoUIAhQ4Ywfvx47ty5Y6xnC/DkzZuXwMBAvvvuOypVqqTn/COqUaMGcePGZc6cOTx48MB4PDw83HhNBg8ezMaNGwkODo7CloqIRA2TVRFSEREREREREZHPntlsNgZnFyxYwMaNG+nQoQNZsmQB4MaNG9SoUYN9+/bh7e2Nr68vCRMmBF7fydqyZUtevHjB7NmzuXv3Lh4eHlgsFk2rIiLRwpv92YABA+jVqxfNmjWjRYsW5M6dmxUrVtCtWzf++OMPhg8fTseOHYHXU1RNnDiR27dvY7VaKViwIBUrViRhwoSR+t5PbcmSJfz000+EhIQwfPhw6tWrh5ubGwBz586lX79+ODo6smHDBhIlShQlbRSRd7NarTx8+JBq1aqxd+9e9uzZQ+7cuY3lU6dOZfbs2ezfv5+QkBBSp05NsWLF6Nu3L0mTJo3SvudL8mbf/+bzZrFYOHPmDO3atWP37t1069aN1q1bv7PPDAsLw9HRUc/9R3Tnzh06derE/PnzqV27Nr/++iuenp7G87148WL8/Pxwd3dn2bJlJEiQIIpbLCLyaSm8IyIiIiIiIiLymYs4MNG9e3emTJlCSEgIgYGB/Pzzz8Z6N2/epEaNGuzdu5emTZvSp08fkiZNyvz58+nTpw/FihVjwoQJwOtBJQV3RORLtnHjRrZu3Ur//v0jPX7q1CmqVKlCunTpGD16tDEF1fz58+nYsSNOTk4cPXqUBAkS/OUgbVQFHG3VIABmzpxJx44defz4MdmzZyd9+vTcuXOHo0ePEj9+fLZu3YqXl5fCmCJRxNaHRDxvI34/ZswY2rdvT5cuXRgwYAAWiwUnJyfg9dRa9+7d49GjR6RNm5aYMWPi5OSk8Mg/FPF5Wrp0KYcOHeLKlSu4ubnx888/kzJlSuLGjYvFYuHs2bO0bdv2bwM88vGdPn2a9u3bs3nzZvLly0fx4sUpWrQoK1eu5Pfff8dqtbJ79248PT11bRORr47COyIiIiIiIiIiX4hffvmFAQMG0KxZM9q3b0/mzJnfWufmzZvUqVOHnTt3kjBhQpImTcrp06dJnDgxe/bsIXny5FHQchGRD2vLli2ULl2a7777jgkTJuDp6WksW7duHeXLl2f69Ok0aNAAs9nMwoUL8fX1xc7Ojv379+Pm5kZYWBihoaHEjBkz0iBwxIH3D+3NQfn3/a6IA5Zr1qxh+fLlLF26lBcvXpAyZUoKFy7ML7/8QrJkyTTQL/KJ/FWVl5MnT5I1a1ZjWcQqLsWKFeP27dscOnSIuHHjEh4ejoODwzt/x8fsf6KTiK9Fly5dGDVqFPb29sSMGZMHDx6QMGFCmjdvTrNmzfDy8gLgzJkztGnThj179uDr60uzZs1ImjRpFB5F9PFvr0N//PEHY8aMYenSpdy8eROAGDFiUKhQIaZMmYKnp6eubSLyVVJcUURERERERETkC7BlyxYmTJhA9erV8fX1fWdwByBp0qRs2bKFDh064OnpSXh4OJUrV2b37t0kT54cs9n8iVsuIvJhXbhwgebNm1OkSBG6d+8eKbgDr6cRBEiXLh0AQUFBdOvWDZPJxL59+4ypp65cuUL16tW5d+9epAHCjzlwbvs9PXr0YOPGje/9XXZ2dlgsFgDKly/PhAkTOHHiBEePHmXv3r2MGTNGwR2RT8wWFjl+/Djwf+ezt7c32bNnp379+sydOxcAR0dH4HXIpHz58ly8eJEBAwZgtVrfG9yBj9v/RCe212LgwIEEBATQuHFj9u7dy71799ixYweJEydm4MCBTJs2zehLM2XKxJgxYyhSpAh9+/Zl7ty5xjL5b2znwsqVK//R+hkyZGDw4MEcPnyYJUuWsGjRIg4ePMjixYsV3BGRr5rCOyIiIiIiIiIiX4BTp07x+PFjmjRpYtxB/C7h4eHY29szYsQINmzYwI4dO5gzZw4pUqTQB+EiEi1cv36dK1euULJkSYoUKQLArFmzOHr0KADJkiUDYO7cucyfP5/u3bsbFXfc3d2N/QQEBLBjxw7jrv9PZfv27QwaNIj58+cTFhbG+4rjR6zwYbVa8fDwIG3atMSOHdsIBqhPF/m0ypcvT+3atdm2bZvxWPr06alWrRqLFy+mXr16lCpViqVLl3Ljxg0cHR1p0aIFXl5erF27ltu3bwO897yXf+7s2bNMnz6dkiVL0rlzZ7Jnzw68rkL58OFDUqZMSceOHbGzszOe78yZMzNs2DBq1KhB7dq1NSXTBzR69GiqVKnCqlWr/tH6MWLEwMPDgx9++IFq1aqRKVMmYseOjdVq1bVNRL5auiqJiIiIiIiIiHzGbIMNu3fvxmQykSJFCoC3KujY7hy2fbVarSRIkIAECRLg4uKiD8JFJNoIDg4mPDychw8fYjabad26NQ0bNuTcuXMAFClShMyZMzNp0iQ6deqEyWTixIkTRnDHarUyc+ZMfv/9d2rWrGlU6PlUihYtSvHixdm0aROPHj3CZDL97UC+qnGIfB5q167N5cuX+eWXX9i6dSsALVq0YM6cOWzfvp2qVaty4sQJqlevTrly5ZgzZw5WqxVfX19OnTrFokWLAJ3TH8LNmze5cOECtWvXJl26dISHhzN//nx8fHxwdnbmwIEDxI8fH7PZzIMHD4z3yNmzZ2fu3LlGsF0+DFtwdtOmTcDb/6u86X3ngM4NEfmaKbwjIiIiIiIiIvIZeXMA1/YBdrp06QgLC2P37t3A62oLtnWtVit2dnY8f/6cSpUq8eeff771wbc+CBeRL9mGDRto06YNAOXKlaNRo0aMGzeOggULMmHCBLy9vSlUqBAArq6uDBs2jMSJE3Pnzh3atm1LrFixjH1NnTqV/v374+rqyq+//oqrq+snq4JhGzyuUqUKV65cYeTIkYD6aJEvRaNGjZgxYwYHDx7Ex8eHjRs3Aq+nycqbNy8zZsxg27ZtNGvWjJs3b1K/fn3Kly/PunXrSJQoEWPHjjWChvLfXL9+HYvFQqpUqYD/myLRzs4u0hSJ169fp0mTJly6dMnY1jZ1mYLtH07VqlUpV64cs2fP5urVq3puRUT+BwrviIiIiIiIiIh8JsxmszGAe+XKFe7du2csK1CgAADjxo3jwIEDwOvB3tDQUGOb6dOns3XrVo4cOfKJWy4i8vFs2bKF8uXLc/78ec6fPw9Av3798PT05MCBA+TPn5+6desad/0DFCxYkF69epE4cWJ69OhB6dKl6datG6VLl6Zz586YzWbWrl1L0qRJI/W9H1LEQJDte9sULbVq1SJx4sRs2LCBp0+fvrW+iHy+atWqxeTJkzl8+DBOTk4AxtRMsWLFIlOmTEycOJHVq1fz66+/cuHCBTZs2MDt27exs7MjUaJEUXwE0UPatGlxcHBg9uzZLF68mB49erxzisShQ4eyceNGnjx5EoWtjf5MJhMlSpTgwYMHjB8/HrPZrOuaiMi/ZLKq5xQRERERERERiXJms9m4Q3XEiBFMmzaNrFmzMnjwYGOqrM6dOxMYGEjNmjVp06YNRYoUMba3DVq4u7uzYsUKEiRIECXHISLyIV25coWyZcuSKFEiBgwYYPR7HTp0YPTo0aRNm5bLly/Tq1cvmjRpQtKkSY1tX7x4wYkTJ/D19eX06dPcv3+fLFmyULRoUfz8/EiSJEmkvve/slgsRjjnr/YbHh6Og4MDgwYNokePHsyYMYP69et/kDaIyKdz48aNSKFBm4h9AcDJkyfZuHEja9euZdy4caROnfqtdeTfe/bsGYUKFeLkyZN4eHjg4uLCqVOniBkzprHOzJkz8fPzo1ixYkycOBFXV9cobPGXzWq1YrFYIl3brFYrJpPJuOaFhoaSJ08enJ2d2bNnD46OjsY6IiLy9xTeERERERERERGJYhE/1Pbx8WHcuHEUKFCAli1bUrNmTWO9s2fP0r9/f+bOnUuiRIn4+eefSZ06NXv27OH333/H0dGRXbt24enpqUEhEYkW9u3bR9GiRenUqRODBg0CYMGCBVy4cIHHjx9TvHhxJk2axO+//063bt1o3bo1SZIkibSP8PBwHj58yL1790ifPj1WqxUnJ6cPGtzZuXMnf/zxBzVr1iROnDjG4+3bt+f48eP88ssvpE+fnuTJkxvLtm3bRpkyZShSpAgLFiwgYcKEH6QtIvJp/dNwgi24Z/sq/zvb+9w9e/ZQrVo17ty5g5+fH/7+/sY6kydPZujQodjZ2bF582aSJk2qIMm/dObMGcLDw8mWLVukxwcPHkzOnDnJkSMHiRMnBjAq7fj7+9O/f39GjRpF27Zto6LZIiJfLIV3REREREREREQ+E6NGjcLHx4fWrVvToUMHUqVK9dY6ly9fZvbs2fj7+xMeHg5AwoQJyZs3LxMnTsTT0/ODDkiLiESlffv2UaBAARo2bMjYsWPx8fFhwoQJLF26lO+++w4XFxcOHz5M7969WbduHb6+vrRq1coI8ETsDyMO2n7IAdxHjx5RokQJTp06xaRJk6hRowaxY8fm4cOHFC1alEuXLhEaGkrmzJlp3749JUqUIHXq1AC0bt2aKVOmsGvXLvLly6fgpYjIv2A2m1m+fDmtWrXi3r17FCxYkKxZs3Lq1CkOHz5MkiRJ2LhxI15eXnp//C/9+eefZMiQgSpVqtCvXz+yZs0KvK72WbNmTUwmE3ny5KFWrVo0bdoUV1dXnJycOHLkCAULFqRIkSIsX74cFxcXBaZERP4hhXdERERERERERD4Dt27domrVqjx//pylS5eSLl26v1z/yJEjPH78mGvXrpEnTx48PT2JHTu2BiZEJNqxTRmYPXt2jh07hre3Nx07dow0Xc2RI0fo3bs3a9eufSvA87GFh4ezfv16fvnlFy5fvsygQYOoWbMmcePGJTg4mNOnTzN//nymT5/OgwcPSJUqFRUqVMDHx4d9+/bRunVrChcuTFBQEM7Ozp+kzSIin6v/5b3syZMn8fb25vz589y4ccOYIrFr164ffIrEr8Xdu3fx9fVl9uzZ1KxZk+7duxsBnrVr13Lo0CGGDx/Oo0ePyJAhA0WKFKFr166kTZuWPn360L9/f9avX0/JkiWj+EhERL4cCu+IiIiIiIiIiHwGTpw4wbfffkvr1q0ZNmzYO9exWq1Yrdb3VmXQVAAiEh09fPiQIkWKcObMGXLlysXYsWPJnz8/EHmQN6oDPJs3b6Zr165cv36dQYMGUb16deLHj2+sc+zYMY4ePcrAgQM5d+4cSZIkIU+ePGzduhVPT08juKnqOyIiMHDgQDJlysQPP/zwl+vZ3v8GBwcTFhbG9evXjSkSHR0dFdz5D+7du0efPn0YP348devWxcfHh1y5chnLz5w5w86dO5k8eTIHDx4kXrx4VKpUCZPJxNKlSylXrhxTp04lduzYUXgUIiJfDv0HICIiIiIiIiLyGbh//z6vXr3i5cuXAISFhUVabrFYMJlMPH36lNOnT79zHwruiEh0YrvvdMiQIZw5c4asWbNy9OhRFi5cyKVLlwCwt7fHYrEAkCtXLvr27Uu5cuUICAhg6NCh3L1796O302Kx4ODgQMmSJRkyZAjJkiXD19eXxYsX8/TpU2O9HDly0LBhQ7Zv386aNWvIly8f+/fv5/nz55w+fZrZs2cDKLgjIl+9Q4cO0bNnTw4cOPC365pMJqxWKzFixCBOnDhkzJgRBwcHHB0dARTc+Q/c3d3p3bs3LVu2ZN68eQwbNozjx48byzNlykSzZs3Yt28fc+fO5fvvv2fhwoXMnz+f58+fc/ToUR49egRgXKtFROT9VHlHREREREREROQzcOnSJYoWLYqrqyt79+4lfvz4RvUF2x3FVquVcuXKkTp1akaOHKnpVUTkq3DixAkWLVpEsWLFmDlzJrNmzaJNmzZ07twZLy8vgEjVao4ePUr79u25evUqx44dI27cuB+sLW9WxQkJCTH6YtvA5MaNGyNV4Kldu7ZRdeDN7Tds2GBM95IqVSrWr19PmjRpPlh7RUS+RLdv36ZSpUpcuXKFrVu3kiVLlqhuUrT2ZvXO8PBwHBwcjJ8fPXpEz549mTBhAnXr1sXX15ds2bIBr284sAWlADZt2sS+ffuYNWsWf/zxB+3atSMwMPDTHYyIyBdMEX4RERERERERkU/kr+6h8vT0pHDhwvz555906NCBZ8+eYWdnR2hoqBHcmT9/PidPnsTV1VWVGUQkWnrzznyLxUK2bNno1asXJUuWpEuXLtStW5exY8cyYsQILl++DLyuVmPbNmfOnIwbN44DBw4QN27cv+x7/y1b3zt//nyePXtmBHc6duxoTHloq8CTPHlyfH19CQoKMirw2LY3m80AlClThk6dOhEYGMilS5c4cuTIB2uriLzfm32N7nP/vCROnJiaNWvy4MEDtm7dCvxfvykfVsTgzo0bNwCM4M6UKVOMmwr69u1Lq1atmDdvHoMGDeLEiRMAODo6GlP7ApQqVYoePXqwefNmvLy82LhxI3fu3ImCIxMR+fLoUx4RERERERERkU/AbDYbH4yHhoZy69YtLBaL8UG3vb0948aNI2vWrMyePZsGDRpw8+ZNY6B3+vTp+Pv7EydOHHx8fCLd4SoiEh2YzWajz7t8+TJ79uxh79693Lp1yxhIzJIlC927d6du3bqMGTPmrQCPbXA3a9asuLu7G1MOfkgNGzbkxx9/ZOLEiQB07dqVUaNG8fDhQ168eBFpCi1bgGfBggWRptCyTeNiG/AsWrQoLi4uTJ8+nfDwcAUJRD4yW1/j7e3NwYMHjaC0fFpvPucRQyBNmzYlQ4YMTJo0iZCQEE1/9ZHYrpHffvst33//PVeuXAGgTZs2NG/enD/++AOz2WxMofWuAI/JZHqrck+SJEno0KEDZ86cYfv27Z/+wEREvkAK74iIiIiIiIiIfGRms9kYcBgzZgxly5YlZcqU5MuXD29vb169egVA/PjxWbp0KTly5GD58uVky5aNb775hkyZMtG8eXNCQ0NZs2YNSZIk0d3HIhKtWCwWo5/s168fxYsXp1ChQhQuXJiMGTMyePBg/vzzT+B1gKdnz56RAjy2wcY3B3c/RpWytm3bkj59evz9/SlUqBDDhg3Dz8+PVq1aETt2bKxW63sDPM+ePYu0L9uAZ9KkSfHw8MDe3h4HB4cPHjgSkbfNnDmTESNG0KVLF44dO6YAzycWseKLrW+09YlWq5U4ceJQpEgRTpw4wbx584xt5ONImDAhR48epV27djRo0IDx48fTqVMnSpYsaVxbPTw83hvgASLdlACvK4sC3Lp16xMfjYjIl0nhHRERERERERGRjyjigLS3tzcdOnTg9u3b1KtXD2dnZ0aOHEmZMmV4+fIlAGnSpGHnzp106dKFPHnycPv2bZIkSULnzp3ZuXMnXl5ekcJAIiLRgS1k061bN3r37k3GjBmZOXMmo0ePplChQvTt25cePXpw6tQpADJlymQEeCZOnEjfvn25fv36R2+n1WolX758bN68GavVyoEDByhcuDANGzYkZcqUkaqsvSvAs3DhQp48eRJpn8+fP2fy5MlcvXqVHDlyfPRjEJHXGjRoQO/evdm3bx+dO3fmyJEj7w3OvRkaUYj6v4lYFc3Pz49GjRqxcuVK43k2mUw4Ojri4+ND7NixWbNmjfG4fFi2KeRWr15N8+bNWbVqFbNnz6ZJkyb069ePFClSRKqI9K4Az8mTJ4H/e31MJhNPnjzh8OHD2NnZES9evCg5NhGRL41DVDdARERERERERCQ6sw1IDxkyhPHjx9O6dWtatmxJlixZuHXrFtmyZWPXrl0UKVKEHTt24OrqSsyYMRk0aBAmk4mbN2+SNGlSwsPDcXBwUHBHRKKtpUuXMn78eH7++Wd8fX1Jly6dMcC7du1azp07h5eXl7F+pkyZ8PPz4+nTp2zcuJGAgICP3kbbwOS2bdt48eIFsWLF4tChQyxbtow2bdrg4uKCxWIx+v6IAZ4ePXrQtGlTYsSIQd26dY19hoeH88cff1C2bFn8/f2ByBUpROTDCw0NxcnJid69e2MymejTpw+tW7dm3bp1xI4d+73n37lz50ifPj329vaRznX55yK+l925cyenT59m2bJlLF26lHLlylG2bFnatGmDvb096dOnp0KFCgQFBdGwYUMqVKgQxa2PfmxTTtrb25MwYULj8XPnzvHo0SNcXV2xWq2R/tZtAR6ASZMm8eTJE4YNG0bGjBmNda5evcqAAQP4/vvvadCgwac7IBGRL5jJqhpzIiIiIiIiIiIf1f79+2nUqBG5c+fGz8+PjBkz8ujRI4oUKcK9e/fInDkz27ZtI1++fGzdupUYMWIQFhaGo6OjMTCkgVwRie58fHyYOnUqGzduJE+ePISFhbF06VK6dOmCo6Mje/fuxc3NzQgz2pw/f5748eOTMGHCj9ZXvjlIf/r0aU6ePEmCBAlo3bo1N27coGfPnnh7e+Ps7GyEjmxtMZvNrFmzhsDAQKZNm0by5Mkj7f/27dskTpz4nb9LRD6sdwWhe/bsyTfffEOVKlXeu92mTZsoU6YMzZo1Y+LEiR+7mdFSxD66R48ebN++nRUrVnD06FF+//13Zs6cyYMHD8iYMSO1atWiUaNGXLhwgTJlytC5c2eGDRumPvIDivh6hIWF8dtvv3Hnzh2OHTvG0qVLKV26NGPGjCF9+vTGuhGf//v379OpUyc2b97M8ePHI4V/ANavX0/ZsmUBXdtERP4JhXdERERERERERD6yoKAg6taty6ZNmyhRogQvXrygQIEC3L17l7Fjx1KsWDFq1KjB9u3byZs3L1u3bsXV1VVVdkTkq2CxWLBYLOTPn5+wsDCOHTtGaGgoS5YsoVu3btjZ2bF//37c3d0B2Lt3L2azmUKFCr21n48xMBixL96xYwdPnjyhQoUKxu86ceIEVatW5datW/j5+dG5c2ecnZ2N7S9cuECKFClwcHAgLCwMZ2fn9/bvCmqKfDotWrTg/v37BAUFRQoEvs+qVauoXLmyMY1TwYIFdc7+jyZMmED79u1p1KgRPXr0MKqqXbt2jWnTprFu3Tr27NlDrFixaNSoEdOnT8fJyYmDBw9GqsAm/7uI16Hz58+TIEECEiRIYPxNN2jQgNmzZ1O6dGnGjh1LunTpIoVnnz59Spw4cXjy5AkWi4X48eO/9zqs4I6IyD+jnlJERERERERE5CMrUaIEa9eupUSJEoSGhtKwYUNu3LhB3759KV++PG5ubrRt2xZXV1cOHjxIxowZCQ4OVnBHRL4KdnZ2ODg4kDZtWu7du8eff/7J+vXr3xncAWjTpg3e3t4EBwe/tZ8PLeLg5oABA6hTpw7du3dn//79xjrZsmVj2bJlJEmShP79+0eavmvFihX88MMPTJ48GTs7OyPU877+XSEAkY8n4r3so0ePZubMmZjNZm7fvv2Ptq9UqRKzZ8/mxYsX7N69G9A5+09ZLBbj+5CQEHbs2EGZMmXo2bOnEcaxWCykSJECPz8/du3axeTJk6lQoQLjxo3j+fPnPHz4kKlTp2I2m1Fdgv8m4rVt5MiR1K1bl7p16/L06VPMZjMAM2fOpH79+mzcuJE2bdpw9uxZHBwcsFgsrFy5kp49e3Lo0CHixo1L/Pjx35paKyIFd0RE/hn1liIiIiIiIiIiH5mHhwclSpQAXk+1snnzZipUqEDDhg1xdXUFIF68eMSIEYOSJUvy5MkTnjx5EpVNFhH55LJmzcrt27fp1KkTrVu3xt7enn379kUK7owYMYIbN25QpUoVnJycPmp7rFarMbjp4+ND3759yZ8/P+PGjSN//vyR1suaNSvLli0jadKk9O7dm5YtWzJo0CB8fX25dOkSFSpU+KhtFZG/ZpvKzubkyZOUKVOG4cOHvzWN3V8pVaoU1apVY8aMGdy8efNjNDVasoU3fvnlF0aMGMGZM2eoWrUqKVOmNII9bwY8mjRpwvz589m5cycdOnTA09OTtWvXvvVayr9jsVgiXdt69OiBq6srbdu2JU6cODg4OBgBnhkzZhgBnubNm3PkyBEWLlxI165dmT59OilSpDD2q9dEROS/+/s6gCIiIiIiIiIi8p85OjoCcPHiRR4/fkzNmjVxcXExlq9atYrs2bOzcuVKQkJCiBcvnqbNEpGvgm2Kji5durBhwwZWr15NvHjx2L59Ox4eHsZ6QUFBTJgwAS8vL5o2bfrR+0fbQOTo0aMZP348bdu2pV27dqRKleqd62XNmpXff/+d6tWrM2nSJBwcHEifPj0nT57Ey8tLfbpIFIoYHnny5AnLly+nV69epE6d+l/tJ1GiRHz33XfY29uTOHHij9HUaOvcuXPMmTOHS5cuARAeHg68HdqJ+LPVaiV//vxkyZKF2LFj079/f2bMmEHTpk0/XcOjGdvz++uvvzJq1Chat25Nq1atyJAhg7FOxGvVjBkzsLe3Z/r06XzzzTc4ODiQNGlSjh07hoeHh6bEEhH5gExW1ZYTEREREREREflkNm7cSNmyZalYsSLz588nZsyYLFy4kJ49e5InTx5mz56Nvb29MZgtIvI1sPV569ev55dffuHYsWM0bNiQH374gYQJEzJ37lyCgoJwcHBgx44dRrWGjz1g+PDhQ3744QcePHjAokWLyJQpk7FswYIFnD17lrt37+Lj42NM/fL8+XMWL15MzJgxKVasGO7u7gruiHwGbt26Rbp06bC3tydWrFiMGDGCWrVqERYWZoSs/0rE92a27xVc+Hd+//13xowZw7p166hZsybDhw8nWbJk/2jbs2fPkjlzZlq3bs2YMWM+ckujt1OnTlGuXDly5cpFYGBgpFDq4cOHefjwIR4eHmTIkMGY7jEgIIDz58/j6OiIr68vSZMm1bVNROQDU3hHREREREREROQ/ePND67/7ENtsNvPdd9+xefNmMmfOTIIECThy5AgJEiRg586dkcrPi4hEB28Obv9VODE0NJQ9e/bQr18/Nm/ejMlkwmq1EjduXPLmzcvUqVPx9PT8ZAOG169fJ0eOHJQuXZqgoCDCw8M5dOgQ48aNY9asWTg4OBAeHk7atGn5/fffSZcu3Vv70OC+yOfj2LFj1K1bl7Nnz1K+fHmWLVuGo6PjPz5P3xXgkbfZnps3v8LrapNDhgxh3759DB48mBYtWhAjRoy/3de9e/fIli0bhQoVYv78+f8ocCXvtnbtWipUqMBvv/1Go0aNMJvNXLt2jdGjRzNhwgSCg4NxdHTE39+fVq1aESdOHGNb27mi4I6IyIen/xhERERERERERP4D24fWs2fP/kfBHXt7e9auXUvDhg0JDQ3l1q1blC1b1gjumM3mT9V0EZFPwjYgvmPHDkJCQv5ysNvJyYlixYqxceNG5syZQ2BgIL/++isrVqxg0aJFnzS4A68HjZMnT87ChQsJCAigZcuWVK9enZUrV+Lr68vKlSvp1KkT58+fZ9CgQcY2ESm4I/J5sFqt5MiRg6CgIDJkyMCaNWv49ddfCQ0Nxc7ODovF8rf7iNh/Kbjzbmaz2XhuwsLCeP78Oa9evTKWV6pUiW7dupErVy66d+/OzJkzCQ4Ofu/+TCYTL168YP78+dy9e5fcuXMruPMfOTg4ALBr1y6uXbvGsGHDqFKlCuPGjaNSpUp4e3uTNm1a+vbty9mzZyNta7umKbgjIvLhqfKOiIiIiIiIiMh/FBgYSKdOnVi6dClVqlT5yzuxw8PDcXBwwGq1cuvWLezt7YkTJw4xYsTQHawiEm0NHTqUwYMHc+XKFWLGjPmX/eRfVcD4WFVs3lUdwmbGjBl0796d27dvEzNmTL799lvGjh2Ll5cXzs7O3LlzBy8vL5o0aaKpXEQ+A3/VT9jO8ePHj1O1alXu3r2Ln58fnTp1wsnJSZWy/qOI72UnT57MunXrOHv2LDFixOCnn36iaNGi5M6dG4DVq1fTt29fjh8/zogRI2jYsOF7K/DcuHGDVq1a4ezszMKFCwFVPvo3rFYrVqvV+Nu+du0aTZo0YePGjcY6GTNmZNKkSWTLlo24ceMa/9+MGzeOli1bRlXTRUS+Kg5R3QARERERERERkS9dgQIFcHFxYcuWLVSpUuUvBxIcHByMgaGkSZMaj1utVgV3RCRaCg8PJzQ0lIcPHzJ58mQ6duz4l/3kXw2cf4xB9YiDzY8ePeLly5eEhoaSMmVK7O3tadiwIRkyZODx48fEjRuXXLly4eLiYmw7d+5cTCYTuXLlAjSgLBKVIp7PW7Zs4dSpUzx69IgsWbJQrVo149zMnj07S5YsoWrVqvTv3x9AAZ7/KOJ7WW9vb0aOHImnpyepU6fmwYMHdO7cmYIFC9K5c2eqVatGhQoVsLOzo3fv3nTu3Bl7e3t++uknXF1d39p3smTJGDx4MJkyZQI0HeE/EfE5MplMhIeHY2dnh9VqJUWKFIwePZoNGzZw8+ZNMmbMSNWqVYkdO7ax/eXLl3F3dydv3rxRdQgiIl8dVd4REREREREREfkPrFYrd+/epXr16uzfv5+tW7dSsGDBqG6WiMhn5fTp0xQoUICcOXOybNky4sWL91kEXCIO9I8ePZrFixdz5swZQkNDqVChAqVKlaJx48bv3NZqtRIUFES/fv1wdXVlzZo1uLm5fcrmi0gEEcMKPXv2JDAwkJcvXxrL69ati4+PjxG0Azh27BhVq1bl3r179O7dm3bt2uHs7PzJ2x6djBkzho4dO9K6dWvatm1L+vTpCQ4OplmzZsydO5dKlSqxcOFC43leu3Yt/fr1Y8+ePcycOZN69epF2t+bgUgFJP9exGtbUFAQO3fu5NChQ2TOnJlvv/2WWrVqETduXMLCwt45BdmCBQvw9fUlXbp0LFy4kDhx4nzqQxAR+SoplioiIiIiIiIi8h+YTCYSJUpE06ZNCQ8PZ/v27cDrD81FROT1QGvmzJlp3bo1O3fu5ODBg5/FwOubVSI6derEjRs3KFSoEMmTJ2fx4sU0a9YMX1/fSNtYrVaCg4Pp2bMnvr6+PH/+nEWLFuHm5obFYomqwxH5qkWcEqhHjx4MGjSIypUrs3XrVq5cuUKHDh2YN28e/fr1Y+/evcZ2OXLkYOnSpSRJkoSuXbsyderUqDqEL57VauXhw4csWrSITJky0bp1a9KnT094eDgrVqxg3759eHl5MW3aNJydnQkNDQWgXLlydO3alQoVKlCsWLG39vvm9eJzuH58ziJe23x8fKhfvz6//fYbJ0+eZNq0abRo0YIffviB+/fv4+jo+Nb/LAEBAXTv3h2r1cqUKVOIEyeOrm0iIp+IwjsiIiIiIiIiIv/A+8I4tserVq1Knjx5mDhxIg8ePNAUWCLy1bD1g+/qJy0WizHQWqRIEaxWKwEBATx+/PhTNvGdbO367bffCAwMpGPHjqxdu5YlS5awceNGgoKCiBUrFkOGDKF3797GNufPnydz5syMHDmSzJkzs3PnTlKmTInZbNY0LiJRxHY+z5s3j99++42WLVvi7+9P0aJFSZw4MWvXrsXV1ZXly5fTu3fvtwI88+bNI3/+/Hz//fdRdQhfPJPJxKNHjzh8+DAlS5YkY8aMhIeHs3jxYrp27Up4eDj79u0jYcKEAFy7do3bt28DUKVKFRYtWkSKFCkUgP+PbOfC4MGDGTlyJC1btuTAgQOcP3+e7du3U6BAAbZt20bx4sV5+PAh9vb2hIWFsW3bNgoVKkTfvn1xc3Nj69atxuuha5uIyKeh3lZERERERERE5B1Onz7NtWvXjJ9tYZyRI0cyefJkrFarUZLeYrEQN25cihUrxpUrV5g7dy6A7lIVkWjt0qVLwOv+MTw83OgnV61axYEDBwAiDfhVqFCB6tWrc/DgQe7fvw9EbT9ptVoJDQ1lzZo1xI8fn+bNm5MmTRoAPDw8qFKlClu3biVWrFiMGjWKdevWAZAuXTr69+/PuHHjmDt3rjG4qdCmSNR68uQJy5YtI1myZDRr1oy0adPy9OlTsmfPzuPHjxk8eDDt27dnw4YNDB48mN27dxvb5smTh+3btys88i+863kKDg7GbDYTO3Zs4PX0S127dsXOzo79+/fj7u4OvH6tateuzdatW7FarQC4uLgAqC/9AK5fv86sWbP45ptv6NSpE5kzZ8bDw4NChQqxdetWqlevzunTp2ncuDEvX77E0dGRuHHjkjBhQnx9fVm1apURStXrISLy6Si8IyIiIiIiIiLyhj///JNs2bLRpk2bSAGejRs30rlzZ1q0aEHRokXx9/c3BqABunfvTvLkyVm+fDmA7lIVkWhr3759pEmThs6dOwPg4OAAwOTJk6lcuTKlS5emXr16HDx4kAcPHhjb1alTh4cPHzJ48GAgavtJk8lESEgIp0+fJmnSpKRPnx54HeoxmUxYLBZy5crF6NGjefLkCTt37jS2/emnn2jYsCHx4sXDYrFocFPkMxArVizMZjMtW7YkZ86cBAcHU7FiRR48eMCgQYNo06YNbdq0wcPDg+XLlzN48GB27NhhbG/rx3Q+/zO252njxo3GY4kSJSJt2rTMmDGDCRMm0KNHj7eCOwDDhw/n7NmzxI4dW9NgfQT379/n9OnTlC1bllSpUhlB2fDwcBwdHZk9eza5cuVi06ZN7N+/H4CcOXMyd+5cunTpgru7u65tIiJRQJ8giYiIiIiIiIi8wc7OjgYNGrBu3Tq6du1qBHhKly7N9u3b8fPz4/bt2/Tr14/cuXPTrVs3Nm/eTMKECSlXrhybN29m9uzZUXwUIiIfj6OjI+7u7owcOZKePXsaj+fNm5dFixaRLVs2FixYQPHixalYsSIrVqzg7t27fP/99+TIkYP169fzxx9/ABhVF6KCvb09Li4uXL16lbNnz0ZaZmdnh9VqJV++fDg5ObF582ZevHhhVJuwDTgrqCkS9WxBg6CgIBo3bgxAYGAghw8fpn379tSsWROAtGnTUqpUKXLmzMnKlSsZM2YMYWFhUdn0L9qgQYMoW7YsmzZtAsDd3Z3vvvuOGzdu0K1bN6xWK3/++acR3LFarcybN4+ZM2dSvHhxChcuHJXNj7aeP38OvK4kGhwcbFynHBwcCA8Px9nZmebNm/PixQsOHjxobBcrViwcHR0BXdtERKKCel4RERERERERkTekSZOGXr160bRpU4KCgujSpQtXr14FoHDhwvj7+3P06FGGDBlC5syZCQgIoHTp0nTs2BGTyYTJZGLPnj1A1A5Ki4h8DBaLhdy5c7N27VrSpEnDwIED6d69OwC5cuWiWrVqrFixwpia4+zZs/zwww+UKFGCyZMnU6dOHa5du2ZUa/gUVRci9sW2781mM66urlStWpUnT54wf/58oz22qRFNJhOZM2cmXrx4JEmShJgxY6oSgUgUe9d0e7aggclkMs7Rffv2ETduXFq2bImrqyvw+vw/ePAg+fLlY/z48QwbNswIK8i/lyxZMgAjvAMwZMgQvvvuO549e0bq1KkJDg4mPDwceD39bK9evQCYMGECcePG1TSzH0HOnDnJkCEDR48e5c8//4y0zHauZM6cGXg91ZmIiHweFN4REREREREREXmH1KlT07lzZ1q2bMmCBQvo1q0bV65cAV4P/MSMGRNvb2/Wrl3LokWLaNCgAb/99htz587FarUybdo0jh07pqkARCTasbOzM6aUWrBgAWnSpGHw4MH06NHDWCdBggQULFiQGTNmsGXLFoYPH05wcDDt2rUzgj7jxo3j0qVLH729thCOje172wB/iRIlSJEiBf7+/owbN85Yz97envDwcKZNm8a9e/fIli0bFotFoUyRKGQ2m43wwfbt25kxYwZDhw5l0aJFkSqMmM1mrl+/jqOjY6RwwqxZswgJCaFatWq0aNGCFClSGNW05N+rVasWpUuXZsqUKZGmmp0zZw7ly5dn27ZteHh4kC9fPjw9PenWrRsxYsRgy5YtJE+ePNLrKf/O+0JPFouFGDFiULduXc6fP0+vXr148uSJsdxWVW7r1q04OjqSKVMmQDcciIh8DkxW9cYiIiIiIiIi8hWzWq2RBnVDQ0NxcnIyfj5//jzDhw9nwoQJ1K5dmyFDhpAiRQrg9YfjtgEHi8XChQsXGDt2LCdOnGDLli20b9+egIAATCaTBiZE5Iv1Zj9pNpsjVZ/Zv38/P/30ExcuXKB79+4MGDAAgJCQEJydnY31Ll68yPHjxwkMDOTMmTM8efKE5cuXU7Zs2Uj96YcUcb/jx49n3759mM1mKlasSK1atYxltql2goOD6dChA2XLliV//vzMnj2bCRMmEBISws6dO0mcOPEHb6OI/DMRz+cePXowYcIEHj9+bCzPkiUL06ZNI1u2bDg7OzN06FC6detGnTp1+PHHHzl69CjTp0/HycmJbdu2GVM5yX9je57bt2/P4MGDcXJyMq4Zw4cPZ//+/Zw5c4Y0adJQpEgR6tWrh7u7+1vXEvnnIj53K1as4NmzZwBUr14dZ2dnTCYTFy9epH379qxevZqyZcvStWtX8ufPj6urK0FBQfTq1YvYsWOzYcMGEiRIEJWHIyIi/5/COyIiIiIiIiLy1Yo4IP3nn3+SLl06Y1lAQADVq1fHy8uLCxcuMHz4cMaPH//eAI/ta1hYGC9evOD777/nzp07HDlyhJgxY0bJ8YmI/FdvhmoiDhjOmDGDChUq4O7uzuHDh6ldu/ZbAZ53Dc5arVYWLlxI06ZNyZUrF+vXr48U8vkYunXrxtChQ3FxceHVq1cAtGzZkrZt2xpThyxevBh/f39OnDgBgIODA+Hh4WTMmJHVq1fj5eWlwWaRz8Avv/xC//79adCgAT/99BOZMmVi/PjxjBw5EkdHR1auXEmRIkX4888/6d+/v1GVB14HfFatWkXKlCk/Wmgwunmz33vzva/ZbCZv3ryEhoaybds23Nzc3grDP3v2jNixY7+1D/lvbNc2m5IlS+Lt7U3x4sWJESMGZ8+exc/Pj1WrVmEymUidOjWOjo6cP38eNzc3tm7dipeXl14PEZHPhHpiEREREREREflq2YI7ZcqUoXHjxhw/fhyATp060aVLF1auXInFYiFNmjR07tyZVq1aERQURNeuXY2pAWwfdNu+Ojg4EDt2bL777jvOnz/PvHnzouDIREQ+DFvfVqJECbp3724M4LZr144WLVqwceNGwsPDyZ07N0FBQaRJk4aBAwcaU2jZ29tHmpLGNoVV9erVKV26NAcOHIg01crHsHnzZmbMmEGLFi04ePAgmzdvpkGDBkyaNIlevXpx5MgR4HXFgrlz5zJv3jwaNGhAq1atGDduHNu2bVNwRyQKvOve8927dzNx4kTq1KnDL7/8QpkyZUiePDlZsmTBzs4OFxcXsmbNCkC6dOn49ddfWbt2Lf3792fevHls3ryZlClTarqm93jXVEy2fm/MmDHs37/f6NPt7OwIDQ3F3t6ehg0bcubMGWPqQScnp0ivny3IbntMz/1/N3HiRMaNG0f16tWZNGkSrVq14vjx47Rp04a5c+fy8uVLMmbMyPDhw5k8eTKFChUiODiYGDFi0LhxY3bv3m1c2/R6iIh8HhyiugEiIiIiIiIiIlHp2bNnpE6dmqlTpzJw4ECsVisLFiygW7duVK5c2fgw2xbggddTrwCRKvBEZG9vT9GiRbGzszPu9BYR+VIdO3aM69evM3jwYJInT86ff/7J2LFj8fb2plixYjg4OGCxWIwAT+3atRk0aBAmk4kBAwYYAR57e3vs7e0JDw/HwcGBatWqsWzZMs6ePUvatGk/WHttVdVsX8+fP4+rqyve3t6kTZuWzJkzkypVKhIlSsTw4cOxWq306tWLXLlykSVLFrJkyULt2rUj7dNisSi4I/KJPH36lNixY0ears/m3Llz3Lt3j59//pnUqVNjNptZsGABPXv2xN3dnf379xM/fnxevnyJi4sLyZIlI1myZBQpUsTYh87n97O97/3tt98oW7YsyZMnB2DWrFm0b98eR0dH6tSpY0w9aKuuU7ZsWeLHj8/cuXOpV68eqVOnjvT62fb7rtdU/jdHjhyhZMmSDBkyhFSpUvHs2TMqVKhAmzZt6N27N1arlR9//BFPT0/q169PvXr1uHv3LnHjxsXBwQEHBweFUkVEPjOKUoqIiIiIiIjIVy127NgEBATQp08fgoKCWLBgAfXq1cPb29uYUsF2l/BfVeCxMZlMPH78mN9//x2LxYKLi0tUHJaIyAeTI0cOZsyYQbFixWjXrh2jRo2ia9eudOzYkaRJkwKv+76IAR5bBZ6ePXsCr0ONtooODg4OPH36lL1792IymXB1df1gbbVV9oHXIR6z2UyCBAkoVaoUadOmJTQ0FICUKVPSvn17fHx8WLlyJf369ePYsWOR9mPbB6hKhMinsn37dipWrMi2bdveWXnn4sWLAOTJkweABQsW4OvrC8C+fftwc3MD4PLly7Ro0YKwsLC39qHz+a+NHDmSpk2bMmHCBG7dugVA/fr1mTJlCrVq1WLWrFnUqVOH6tWrM3v2bJ48eULmzJnp1asX586dY+fOncC7KyfJ/yZiBTubU6dOkT9/flKlSoXVaiV27NiUK1eOyZMn4+DgQJ8+fZg3b54xVaTJZCJRokS4uLjg4PC6toOCOyIinxdV3hERERERERGRr16sWLF48eKF8fO9e/e4c+cObm5u2NnZRRp8iFiBZ8qUKTx58oQpU6YYA9gA165dY9GiRVSsWJFmzZp9ugMREfnAbNVrChYsSJw4cSJVtEmWLJmxnslkeivAU7t2bQYOHMizZ88YNWqUMWButVrZsmULs2fPpmbNmpQsWfKDtDViNY0RI0awdu1abt++TUhICO7u7litVpycnIxKA8mSJaNt27YADBs2DAcHB3x9fcmdO7exH1WJEPl0wsPDOXz4MLt27aJPnz7079+fQoUKGX2LnZ0dsWLFAmDNmjXGOWtnZ8f+/ftxd3c39tW7d28OHTrErVu38PT0jKpD+iLVqVOHs2fPMnjwYEwmE02bNiVlypQ0btyYxo0b8/PPPzNhwgQ2bdrE0qVLyZ49O3369MHd3Z306dMzaNAgihUrRsqUKaP6UKKFiNe2efPmcenSJcLDw0mYMKHx/4fZbMbBwQF7e3tKlizJ5MmTadasGb1798bOzo66devqhgIRkS+Ayaroq4iIiIiIiIh8hWyDzwAhISGMHj2ap0+fcu/ePSZOnEiVKlXo2bMnefPmNda3Wq3G4POFCxfo06cPW7du5ejRoyRMmNDY98OHD9m/fz/lypUDMAacRES+JLZ+0mw2Y7FY+P7774kVKxaXL1/m8OHD/Prrr0bFCxuLxWIEeQ4fPkypUqVwcnLi3LlzxI0b11jvypUrzJkzhx49ehjbfah+snv37gwePBgPDw9MJhNPnz7FYrEwevRoGjdujJ2dXaSpQm7cuMG4ceMYOHAgTZo0Yfz48UZVAhH5tB48eMCcOXPo2bMnOXLkYNCgQUaAB+DSpUsUKFCAuHHj8vLlSxwcHDh06BAJEiQw9jF16lR++eUXatWqxeDBg42pneSfu3v3Ln369GHChAn4+PjQt29fYsSIYSx/9uwZN2/epH///qxbt45Hjx6RM2dOrl+/zp07d1iwYAE1atSIwiOIfrp27cqwYcMiPVagQAGWLVuGu7t7pOuo2Wxm8+bNtG7dmmvXrjF9+nTq1KkTFc0WEZF/QeEdEREREREREfnqRBy0vXDhAnHjxsXNzY3g4GAA+vTpw9ChQ98K8MDrweyQkBBcXFy4fPkycePGJX78+O8deFZwR0S+RBH7rjt37pAoUSLCw8Mxm80cPXqUzp07s2fPHgYMGED37t3f2iYkJARnZ2eOHTuGu7s7SZMmNcJAb/aL/7WfjNinnzx5krJly1K1alV8fHwwm838/vvv+Pv74+HhwYABA6hataoRSrJtd/XqVebMmcNPP/2kKh0iUezhw4fMmDGDX3755a0Az7Nnzxg4cCDjxo3jxYsXrFu3LlL1rtmzZ+Pv74+Liwvr168nceLEkQLb8s/duXOH7t27U7lyZX744Yd3rmO1Wjl58iRz585l9uzZ3LhxA3t7e65du0bixIk/bYOjmYjXxlmzZtG2bVuqVatG1apVuX37NjNmzGDPnj20a9eOXr164ebm9laAZ+3atfTr14/FixdHqpYnIiKfJ4V3REREREREROSrEnGwdsSIEcyaNYusWbMyaNAgo/T8gwcPGDp0KEOGDKFKlSr06NGDfPnyAbB06VKCgoIYOnQoKVKkANCgkIhEKxH7yfHjxzNjxgzq1q1Lu3btjEHBrVu30rNnz7cCPABr165l06ZN+Pr6GlXJIu7zY9m5cyc3btzAz8+PlStXkjFjRgBevnzJsmXLaNOmDUmTJsXf359q1aq9FeCxDXp+iraKyF978OABM2fONAI8AwcOpFChQtjZ2fHHH38wcOBA5syZQ758+ShQoAD58+dn6dKlbNq0CRcXF7Zv307KlCl1Pv9HoaGh761c9Gbw8uDBgxw9epTy5cuTLFkyPff/wZsVQqdOncqcOXOYNWsWqVOnxmq1cu3aNX766Sd27dpFhw4d6NGjx1sVeCwWC2FhYTg7O+v1EBH5Aii8IyIiIiIiIiJfjYgfZvv4+DBu3DiyZ89Onz59jCmubB4+fMiQIUMYMmQIFSpUoE2bNjx9+pT+/ftz6tQprl+/boR9RESii4j9pK+vL+PHjydFihT4+vpSr169SAOKEQM8/fv3p0mTJuzcuZNevXrx4sULDh48iLu7+0dp25uGDRtG165dKVmyJHZ2dqxfvz7SoHNoaCiLFy+mdevWJE2alH79+hkVeFQhTSTqves8vHfvHrNnzzYCPL/++iuFCxfGzs6OCxcusHDhQiZPnsylS5cAcHd3p0SJEgwbNozkyZMrrPCJvCvEruf+n/ura1Dbtm3ZtWsXVquVMmXKMHTo0EjTU96+fZtatWqxc+fO9wZ4RETky6HwjoiIiIiIiIh8dQYMGIC/vz+tWrWibdu2pE2b9p3r3b9/n8DAQAYMGACAi4sLSZIkYePGjaRKlUofjItItPXLL78wYMAAWrRoQceOHUmfPv0719uyZQt9+/Zl+/btJEqUiBcvXhAnThy2b99O6tSpP0o/6e/vT4MGDfDy8jIeO378ONWqVePixYukSpWKY8eOEStWrEi/P2KAJ2XKlHTt2pUff/zxg7ZNRP69iEGP48eP8/LlS7799ltMJtNbU2hFDPCYzWZevXrF0aNHCQ0NJUuWLMSOHZsYMWIoPCJfnB07dpA3b15ixIhhBKKqVavGsmXLiB8/Ps2bN2fgwIGEh4fj4OBgbBcxwNO5c2e6du2Kh4dHFB6JiIj8rxTeEREREREREZGvyvHjx/n+++/JlCkTY8aMiRTc2b17N8HBwYSFhRmVeMLCwliyZAkrVqzA09OTdu3akTRpUg0KiUi0tXXrVurWrUuJEiXo378/qVOnNpadO3cOs9mMs7Oz8fj+/ftZvnw5O3fuJFWqVPTv3/+jVb1YsGABderUoUaNGgQEBBjTFwKcOXOGunXrcvz4cTp06ED//v2JGTNmpHaEhoaydOlS6tatS/Hixfn999+JESPGB22jiPxzEc/PQYMGMWXKFNzd3QkMDOSbb74BeCvAE3EKrXfRdKbypWnevDnTpk0jKCiIChUq4OLiYixr0aIFkydPJn78+Gzfvp0sWbK89Td++/ZtfvzxR7Zu3covv/xC7969dQ6IiHyBFN4RERERERERka/Kxo0bKVu2LBMnTqRZs2aEhoZy/fp1xo4dy9ixY7FYLISHh9OjRw/69+9vbBexRL2COyISnU2ZMoUWLVowf/58atasidls5tGjR4waNYrJkyfz6NEjsmbNStOmTWnZsiXwerA8NDQUAGdn54/WT967d88YxK9cuTKDBw8mZcqUxvIzZ85QrVo1Ll26RK9evejcufNbVThCQ0NZs2YNefLkIXny5B+8jSLyz0QMIHh7ezNmzBjKli1Lhw4dKF26dKR13wzwDBo0iEKFCmEymRTWkS/e7t27ad26NY8ePWLEiBFvBXhatmzJpEmTKFq0KBMnTiRDhgxv/d3fvHmTtm3bMnLkSDw9PaPiMERE5D9SXWcRERERERER+ao8evQIgOXLl/PHH38wdOhQfvjhByZMmECFChXo3r07Hh4e/Prrr6xatcrYzs7OzviAXMEdEYnObty4gdVq5caNG1y+fJkpU6ZQuXJlBg0aRJo0aahRowZ//PEHo0aN4tKlS8Z2zs7OODs7Y7VaP1o/6e7uzs8//0y/fv1YsGAB7du3Jzg42FieKVMmFi9eTMqUKRkwYAABAQEEBwdjb2+P2WwGwMnJiSpVqhjVgUQkatjeV02ZMoWxY8fSsmVLRo8e/VZwByBBggQ0bNgQf39/jh07hp+fH1u2bFFwR6KFggULMmXKFBImTEjTpk1ZsWIFVquV8PBwACZMmMDPP//M9u3badOmDWfPnjWCazZJkyZl0aJFeHp66tomIvKFUuUdEREREREREfmqWCwWypQpw5YtW4zHsmbNyvjx48mUKRMJEiRgzpw51K9fn5kzZ1KvXr0obK2IyKdjGwT/888/qV27NqdOnSJOnDg8evSIVKlSMXLkSPLkyUPixInp2bMnAwcOZMeOHRQqVOiTt/X+/ftMnjyZzJkzU6VKlbeWnz59mqpVq3Lt2jV69uyJt7c3Li4uWCyW9061IyKfXkhICDVr1uTIkSOsW7eOzJkz/+X6Dx48YM6cOXTs2JEqVaoQFBSEk5PTJ2rtlydiuElBp8/fvn37aNCgAYGBgcYUvhErxzVu3Jjp06dTsmRJxowZQ8aMGfW6iohEIwrviIiIiIiIiEi08+bgbFhYGHZ2dsYH3xaLhX79+mG1WkmePDl16tQhVqxYxvre3t5MmTKFtWvXUqBAgU/efhGRj+2vQiwhISEcPXqUwMBAQkJCyJkzJx06dCBOnDjGOj/99BObNm3i8OHDJE2a9FM1O5KwsDAcHR3fu9wW4Ll9+zZt2rThl19+iTQNiYhEvZs3b5I9e3ZKlizJggUL/tE2d+7cYcWKFZQrV44UKVJ85BZ+uSL28y9fvsTV1VUBxi/A48ePiRcvXqTH3hXgKVu2LAEBAWTJkiUKWikiIh+DwjsiIiIiIiIiEq1E/HA7KCiInTt38scffxAzZkwaNGhA5syZyZAhwzu3tVqtLFmyhB49epAyZUoWLVoUabBaRCQ6iNhPrl69mlOnTnH69GnSpElDpUqVyJkz53u3tVgsLF68mK5du5IvXz6mT5+Oq6vrJ2r5v3fmzBkKFy5M/PjxOXz4sPp0kc/MxYsXyZEjB1myZGHNmjXEjx8/0nJb2OTBgwesX7+eOnXqGNMFmUymSP2ZvFuZMmXImDEjAwYMIE6cOArwfCHerKgT8W+9WbNmTJ06lVq1ajF79mwcHByiqpkiIvIBKbwjIiIiIiIiItFGxA+5fXx8GD16NCaTiVixYvHw4UOcnJwoUaIEfn5+75zmZciQIUycOJGwsDB27dpFihQpNMAhItFKxD7N19eX8ePH8/z5c0wmExaLBRcXF/r370/NmjWNihYR+9YxY8YQGBhIaGgou3btInny5J/9lB3nzp0jduzYJEmS5LNvq8jXqHz58hw8eJDVq1eTL18+wsPDcXBwiNRfNWzYkFu3bjFjxgySJEkSxS3+smTIkIELFy7g6+tL165dFeD5gkUM8Hh7e9O2bVtSpUoVxa0SEZEPRVdmEREREREREYk2bAOyQ4cOZfjw4bRu3Zr9+/dz69Ytli1bxg8//MC6devo2LEj+/fvN7Y7cOAAadOmZdCgQSRKlIgdO3aQIkUKzGazBjZEJFqx9Wl9+/ZlyJAh1KhRg7Vr13L8+HH8/Pxwd3ene/fujBs3jnv37gGvp6c6d+4cRYoUoW/fvsSOHZvt27eTPHlyzGbzZx+GSZ8+PUmSJPki2iryNSpTpgwPHjygYcOG3L9/36giYuuvFi5cyJYtW0iWLNlblXnk/SwWCwBnz56lQIECDBkyhH79+vHkyZN3vr+1Wq28eb+/bR/yebC3tyc8PByAgIAAUqVKZfwsIiJfPlXeEREREREREZFo5cqVK1SuXBlHR0cWLVqEl5eXsezFixd0796dMWPGULt2bcaMGUPChAl59OgRTZo0IUeOHLRp0wY3NzdNwyAi0dbJkycpX748uXLlYuzYsUaFHYA1a9YwaNAg9u7dy6RJk2jYsCGvXr1i+fLlTJgwgdy5c9O1a1cSJUqkflJEPoiQkBB+/vln5s+fT9q0aRk+fDhZsmTB09OTcePGMWbMGMLDw9m+fTvJkiVTBa1/yBa8sbOzw2q1kitXLo4fP87JkyfJnDnze7fbsWMHt27dolatWp+qqSIiIoLCOyIiIiIiIiISzRw/fpz8+fPTvHlzRo4cCUSe8uXevXs0atSIbdu2sXPnTnLmzAm8LkNvtVrfmqZBRCS6WbduHeXLlycwMJB27dphtVoxm81GtYslS5ZQv359YsaMyf79+/Hy8iI0NJRHjx4RP358nJyc1E+KyN+K2E+8L3BjCwEGBwfTpk0b5s6dS2hoKK6urjg6OvL06VMyZMjA6tWr8fLyUmjwPd7sk4ODg4kRIwYAFy5cIE2aNFitVrZv306xYsXeu59z586RMWNGYsaMycqVKylevPjHbrqIiIj8f/rvSkRERERERESilRcvXvDq1Stu3rwJQHh4eKTBooQJE1K2bFlevnzJ6tWrgdcDHvb29m9N0yAiEh29evUKINKguoODgzFdSrVq1fjpp5+4f/8+ly9fBsDJyYlEiRLh5OQUadv/QveVikRfEcMktvdi75qCyd7eHrPZTIwYMZgwYQJBQUG0b9+eQoUKUb58eUaOHMnWrVsV3Pkbtufa398fwAjueHt706lTJy5duoTJZDKCO+/rf+PFi0e7du0IDg5mx44dgKbO+i90nRMRkX9Dn0SJiIiIiIiISLSSLl06cufOzZYtW7hw4QIODg6YzWbg9eCRnZ0dZcuWBVBYR0S+SvHjxwdg0qRJXL582egDTSYTISEhABQuXBiA69evf5Q2RKzC8bF+h4hEHVu/0qpVK6pWrRrpsTfZ29tjsVhwcnKiSpUqjBw5kmXLljF37lzatWuHh4eHEbSW9+vVqxd9+vShbt26AHTp0oURI0aQLVs24saNG2nd90075uHhQceOHSlevDhTpkzh/v37ep/8L7wZdHpfaE1ERORddMUVERERERERkS/O+z4Et1gsuLm58d133/HgwQNq167NzZs3sbe3JywszAjyLFmyBID06dMDuitWRKKfvxosLFq0KHXq1OHEiRNMnjyZ27dvAxAWFoazszMAhw8fJmbMmGTJkuWjtM82cFyqVCnat2/PuXPnPsrvEZGoYbVaCQ4OZvXq1Rw7dsyo4vW+91xvBkRslWPet1zeVq9ePapUqUJQUBDp0qUjICCAnj170qxZMxIkSPCP95MqVSrq16/PrVu3OHny5EdscfRj+ztt2LAhs2bNMh5TgEdERP4JvdsRERERERERkS+K2Ww2PhhfsWIF06ZNY968eTx+/Nh4fMCAAVSoUIHDhw9TqlQpDh06xMuXLwFYsGABs2fPJmfOnBQtWhR4/93HIiJfooj95JEjR9i8eTN37tzh2bNnxjpdu3Ylb968jBo1il9//ZXz58/j6OgIwKJFi1i2bBm5cuUiderUH7Wt6dKlY9myZQwZMkQBHpFoJkaMGPTu3Zvr16+zfPlyQO+5Phar1UqGDBmYM2cOKVOm5OrVq6RPn55OnTrh5eVFWFjYP94PvA6fdOrUyajCJv/ciRMnmDVrFv369WPRokWAAjwiIvLPmKy6tUxEREREREREvkDdunVj6NChxs9p06ZlxowZ5MmTBycnJwCqV6/O0qVLcXZ2xtPTEycnJ86fP4+Hhwfbtm3Dy8sLi8Wiu7lFJNqIOB1V9+7dGT58OGFhYbi5uVGuXDl69epFunTpCAkJYdu2bfTt25c9e/aQJEkSKlSowPXr1zl8+DCOjo7s2rWLlClTfvR+0tfXl6FDh1KnTh169epFxowZ//LYbF/Vf4t8/o4fP06ZMmWIFy8ea9as+eiBwK9ZeHg4W7Zs4bvvvsPLy4vLly9Tq1Yt5s+fbyy3TRn7V8xmc6Qpyt78Wd7Pdn3auXMnpUuXxsvLi969extTmb1v/ff9LCIiXxf9ZyMiIiIiIiIiX5wpU6YwduxYatSowaJFi2jZsiWPHz+mSpUqLF++nBcvXgCwePFiRo0aRZUqVXj+/Dnx48enWbNm7NmzBy8vr0jVKUREogPboN+IESMICAigVKlS9OjRg1y5cjF79mxq167NsWPHcHZ2pnjx4sybN49WrVphtVqZOnUqZ86coVixYuzdu5eUKVN+tH4yPDzc+H7QoEG0bNmSefPmGVXS3ndsW7duZdCgQYSEhKj/FolitnvDzWZzpJ8tFovxffbs2WnevDnnz583qmupAsnH4eDgQJkyZdi0aROzZ8+mcuXKLFiwgBo1ahjLw8PD/3a62DeDOgru/HNWqxWLxULhwoVZv349586do2vXrty8efMvtzty5AgXLlwwAqoiIvJ1UuUdEREREREREfnitGzZkqtXrzJu3Di8vLwIDw9nwYIF+Pv7c+/ePSZMmEDFihVxdXU1trlz5w6JEiUiLCwMR0dH3UUsItGSxWLBbDZTpUoVYsaMyYgRI0iePDkAHTp0YPTo0aRPn56goCBy5MhhbHf37l2ePHlCkiRJcHBwwMXF5aP1kxEr5gQFBVG7dm0ATp48SdasWd+73dWrV8mZMyfPnj1j7ty51KxZU1UKRKLA8+fPiRUrFhD5fH7zHA4NDcXJyYkDBw5QqlQpsmfPzvr16yO9P5P/3d9VH7t8+TLt2rXj999/p1q1asYUTjaHDx/G2dmZLFmyfOymRktvPv+2v3eAFy9eEDNmTLZs2cKTJ0/44Ycf3rufAwcO8O2331KgQAEWLVpEkiRJPnbTRUTkM6VbE0RERERERETks2a7mzuip0+fGlMChIWF4eDgQJ06dejXrx8eHh60bNmS33//nZCQEGMbNzc3AGO6AAV3RCS6iFjFws7OjvDwcK5du8bPP/9M8uTJCQ0NBSAwMBBvb2/OnTtH7dq1OX78uLGdm5sb6dKlI1asWLi4uGC1Wj9aP2kb7PT19aVu3brGFIi2Qf/33W8aM2ZMevbsiaurK8uXLwdQcEfkE9u2bRs1atRg7969wP+dzx07diR79uw0atSIJUuWRAoy5MuXjxIlSnDw4EEOHjwIqPrOfxWxKtrq1asZO3YsM2bM4NChQ8Y6Xl5ejBkzhooVK7JkyRKqV69uLFu9ejVNmzZlyJAhxjVC/h3b879+/XoA4++9V69e9P1/7N1nQBTX28bh3y5VsCBFxQLYI/Zu7L33buwVFbAiTRRFURREFEWNlVgi9t47VuwlxliiUWPvCihseT/47mSxJCZ/EctzfUncnRnOzO6cmd1z73NGjyY+Pp4aNWoowZ33XdvUajWlSpXixIkTyuv3rs8/Qgghvn4S3hFCCCGEEEIIIcRny7jqw5IlSxgzZgze3t78+uuvyoCtoYqOWq2mVatWBAUFKQGe9evXKwMShu3IQK8Q4mtiPIC7d+9eli5dypEjR5Tn4K9+EiA0NDRFgOfcuXPv3G5q9JXGg/VHjhxh4cKFeHp60qRJkw/623Z2dvzwww80adKELVu2pAgfCSFSX3JyMrGxsWzbto2xY8cqQRyAQoUKUa9ePVatWkXr1q2pWLEiixcv5pdffgFeh/VMTExYtGgRgEx79z8y3Nd6e3vTuHFjPD096d69O82aNWPSpEnKcs7OzkybNo3GjRuzevVqateujYeHBx4eHly7do3Ro0croRPx7/3www/Ur1+fJUuWADBgwACCg4PJlCnTWwGc913bSpUqRWhoKJkzZ2b+/PmA/MhACCG+VXJ3JIQQQgghhBBCiM+W8cBEp06dCAwMJCwsjNOnT7N582YuXbqkLPdmgCd79uy0bdtW+TWsEEJ8bYyr4/j4+FCjRg1++OEHatasyS+//MKvv/6qLKtWq1MEeLy8vPjtt9+oUaMGFy5c+CQD6Ya/cfnyZY4ePQq8ngbxu+++++BtODo64ubmxqNHj1LsnxAi9ZmZmdGrVy/Gjh3Ljh07CAgIUM5lNzc3YmJiOHToEF26dOHx48d07tyZevXqMXHiRB49ekTp0qVZu3YtsbGxabwnXy7j6i1TpkxhypQpdOjQgblz5zJlyhQePnzIsGHDGDlypLKcIcDTrVs3Tpw4wbx587C3t+fkyZO4uLhIlZf/QYMGDXBycqJTp07UqVOHadOm4eXlRefOncmYMeMHbUOlUlG2bFnat2/P4cOHlc83Qgghvj0q/fvqtAkhhBBCCCGEEEKkEb1er/w6dfny5fTs2ZNWrVrRo0cPTp48ydq1a9m3bx9Dhw5lwIABZM+eHfirUo9Op2Px4sXMnTuXRYsWkTNnzrTcHSGESFWzZs1i8ODBNGnShIYNG3L27FnCw8MBWLVqFc2bN1cGfHU6nRL46devHytXruT06dM4Ojp+kraOHz+eiRMnUr16dczNzYmJiUlRZe1DLVmyhDZt2mBmZpZKLRVCvM/du3f58ccfCQ4Opnr16gQFBVGuXDnlea1Wy/3794mOjmb58uWcOHGCvHnzcv/+fRISEpg6dSp9+/ZNcb8n/tmbfWWfPn24efMmUVFRuLi4AHD06FHat2/P1atXCQgIICgoSFn+8ePH/PHHHzx69IgSJUpga2v7n/pfkfKzyt69e2nWrBkvXrygbt26bNiwAbVajU6n+1fB2PPnz+Pn58fq1aulMpUQQnyjJLwjhBBCCCGEEEKIz4rxl+H3799n3rx5bNy4kejoaHLnzg1AXFwcwcHBbNq0CR8fH/r37/9WgEev1/Py5UvSpUsnAxNCiK/KmwOCXbp04c6dO8yePRtnZ2cA5syZg4+PD48fP2bdunU0btz4nQGeJ0+eYGNj80n6SY1Gw5IlSxg1ahTXrl3DxcWFQ4cOkTVr1g/expuD/RqNBlNT09RorhDib7wZ4BkzZgxly5Z953KnTp0iNDSUa9eu8fvvv5M9e3YOHTpErly50qDlXz4fHx+SkpLYvn07Xl5edOvWDb1er/Tt7wvwvNl//ttwiUjJcPwiIiIYMmQImTJl4unTpyxevJgOHToAbx/zf9rW+/4thBDi2yCfaoQQQgghhBBCCJFm3vXFtOELbl9fX44cOUJiYiI1atQgd+7cJCUlYW5uTrly5Rg9ejQAEyZMAFACPIbKO2q1mnTp0gFIcEcI8cV6Vz9p+PfQoUOB19UUOnbsiLOzs9JP9urVC3NzcwYPHkzTpk1Zu3YtTZo0Qa/Xp6gIYGNjk2L6rdRkampK69atSZcuHePGjePs2bMsXrwYNzc3rK2tP2gbbw6CSnBHiLSRNWtW+vTpA0BwcDAjRoxIEeAxBAKzZs1KvXr1KF++PPfu3SM4OJiFCxeyb98+OnbsKCGFf0Gv13P37l1CQ0OxtrbGxsaGDBkyAK+DjGZmZuj1esqWLcvSpUtp3749Y8eORa1WM2rUqLf6Tznu/43hPWs4fr169SJdunTo9XrGjRtHx44defXqFd26dUOlUinBWePj/2ao533XeSGEEN8W6f2FEEIIIYQQQgiRZgxfTB85ciTF44mJicTHx7N3717i4uJ4/vw5AObm5mi1WgBKlCjB6NGjadiwIRMnTmTWrFncvHkzxXaFEOJLZgjaAFy4cCHF49euXWPLli1MnjyZLVu2cP/+fSBlP9mlSxcmT55M5syZadasGRs3blQGC437ydSYtkan073zcSsrKxo0aICfnx/Ozs5MmTKFrVu38vLly4/eBiHEx/G+CRyyZs1Kr169GD58OHv27GHEiBEcPXoUQKmCaFjfxsaGAgUKEBISgoODA4sXLwbknu3fypYtG6dOncLS0pI///yTtWvXAmBmZoZOp1PCImXLliUmJoYCBQoQFBREaGhoGrf866DVapX37LZt25gzZw7x8fG4ubnRt29fpkyZQs6cOenRowfz588HUgZ1Ll68yIsXL2S6OCGEEO8kd0VCCCGEEEIIIYRIUx4eHnz//fds3LhReSxdunSMHj1aKfO/du1adu3aBbweDHozwNOkSRPGjBnDkiVL3jtgLIQQXxrD4F6jRo1o1KgRcXFxyuMuLi5MmzaNevXqAXDq1ClevHgBpOwnDQGeLFmy0KRJE7Zv357qg4bGg5tnz55lz549rFixguvXr5OQkED69Olp0KAB48ePB8DLy4uNGzdKgEeIz5BWq1X6jAcPHnDjxg2uXLmiPO/o6EjPnj3fGeAxrGf4r16vx9HRkaJFixIXF8eNGzc+8d58Wd4MTalUKrRaLcWKFWPv3r1kzpyZRYsWMXbsWAClqpohwFOmTBkWLFjA999/T9u2bdNiF74qxlNOjhgxgs6dOzNw4EB+/fVX5frVokULpk6dSs6cOenZsyfz5s1TrocbN27Ezc2NiIiI9wbihBBCfNukpqgQQgghhBBCCCHSVN26dTl27Bjt27cnJiaGhg0bAmBra0u/fv1ITk5mzJgxhIeHkyFDBsqWLasMTJuYmFCiRAl8fX3JlCkT7du3l19wCyG+OqVKlWLv3r0MGjSIiIgIypUrB0CNGjVQqVQkJiayZMkS8uXLx6hRowBS9JNdunQhMTGRyMhIChQokKptNR7cHD16NAsWLODGjRvodDocHR1p2rQpI0aMIHv27Ep/P2zYMIYNG4ZKpaJRo0ZYWFikahuFEB/G0IcAhIeHs3LlSi5cuIBOp6NBgwY0b96ctm3bkj17dnr27An8NYXW2LFjKVOmTIrtqVQqXr16hbW1NRYWFhJg+BvGxx7gxYsXpE+fXnnM1dWVvXv3UrlyZQIDAzE1NcXX1zfFtIg6nY4KFSqwZ88ezMzM0Gg0MtXg/8DwGcPX15fQ0FB69epF165dqVixIvBXhZ3mzZsDMHDgQHr16sXt27dRq9VER0dz9+5doqOjpfKOEEKId1Lp5e5ICCGEEEIIIYQQaWzbtm0EBgZy5MgR1qxZQ9OmTZXnHj9+TFhYGOPHj6dJkyYEBARQtmxZIOXARnJyMmZmZm8NdgghxJfKeKqN0NBQxo4di7OzMz/++CMVKlRQltu3bx8BAQHs37+foKAgAgIClOcMg7gACQkJWFlZfZIBXF9fXyZOnEjDhg1p3rw5mTNnZurUqcTGxlKwYEF27dqFo6MjL1++ZN26dQwbNgxLS0sCAwNp1aqVBHiESGPG/Y+Xlxfh4eGUKFGC77//nvj4eFauXEnGjBnp3LkzISEhANy7d49Zs2YRHBxM7dq1GT58ON9//72yTY1Gw08//USvXr3o27cvUVFRabJvnzvje9nZs2ezbds2Dh06RMGCBSlfvjxjx45FpVKhUqk4e/YsVapU4fnz5wQHB+Pr6/vWNoxfS/G/Wbt2LR07duSHH35g+PDhODs7p3je+Jq7fv16Ro0axcmTJ1GpVBQqVIgNGzbg4uIin1eEEEK8k0RshRBCCCGEEEIIkWYMgwl169ZFo9Hg5+f31jKZM2fGy8sLvV6vDA4ZAjwmJibKl+RmZmYA8kW4EOKroVKplD5u2LBhvHr1ipEjR/Lq1Svgrz60atWqBAcHM3z4cEaOHAmgBHiMqzBYWVkBpHpwZ+PGjUybNo3u3bvj5+dHvnz5lOdiY2NJTk5W2mJpaUnjxo1Rq9V07dqVadOm0aJFi1RtnxDinxnCHgsWLCAyMpL+/fszcOBA8ufPD0CBAgUICAggNjZWCQZmyZKF3r17Y2JiQkBAAJkyZaJMmTLKPZpKpcLe3p6ePXsqwR0JlqSk1+uVe9mhQ4cSGRlJjhw5KFmyJJcuXSIkJIQTJ04wduxYihcvTtGiRdm/fz+VK1dm+PDhqNVqvL29U9wPy/H93xnep/v37yc5ORl3d/e3gjvw+pprWLZJkybkypWLy5cv8+TJE5o1a4aDg4MEd4QQQryXVN4RQgghhBBCCCFEmjIetLl37x5ZsmR553KPHz8mNDSUkJAQWrRowdChQ5Uy9UII8TUz/iX/pUuXlMFzSNmHxsbGMnz4cPbv38/YsWPx9/dPk/YGBgYyYcIEDh48SKlSpdBoNMTExDBixAj0ej3Hjh3Dzs6OxMRETExMMDc3Jz4+nt27d1OqVCmyZ8+eJu0WQvxFr9ej1Wrp2LEjBw8eZOvWrbi6uqLVaomJiSEwMBCNRqOcz69evVIqZt26dYvly5fTsmVLcuXKlWK7xpW/jPs2kdKUKVPw8vKib9++9OvXD1dXV65du8bQoUNZvXo1Xbp0Ye7cucDr4Pq5c+eoUaMGDx8+JDIyEnd39zTeg6+LTqdDo9FQtWpVrl69yrVr17C0tHwrGGUI5jx79oyMGTO+czvynhdCCPE+coUQQgghhBBCCCFEmlKpVBh+W/S+4A68rsAzbNgwhg8fzurVq5kzZw7JycmfqplCCJFmDNVzACW4Y+g3jfvQKlWqEBwcTLVq1QgICGDKlCmftJ2Gwf6TJ09ia2tLqVKlSEpKYuXKlfj7+6PX64mLi8POzg6AGzdusHTpUl6+fIm1tTWNGzcme/bsaLXaT9puIcTbVCoVz54948iRIxQrVgxXV1devXrFihUr8PPzQ6PRpDifr127xi+//AJA9uzZ8fT0JFeuXG+dz8aVvyTE8Nqbv7F//vw5P//8MyVKlMDDwwNXV1eSkpI4c+YMR44cIV++fISGhmJiYqJUcClSpAjbtm3ju+++o0mTJmmxG181tVqNubk5uXLlIiEhgQcPHqBSqVK8v3U6HSYmJrx8+ZKZM2dy+/btd25HCCGEeB+5SgghhBBCCCGEECLNfWg5/8yZMzN48GDGjx/PqFGjlGkYhBDia/fmgJ9xv/lmgCcgIIDmzZvTvHnzT9lEVCoVJiYm2NvbEx8fz6NHj9i8eTPe3t6o1Wri4uJwcHBQlu/WrRs///wzGo0mxXZkOhEhUp8hEGiQlJSk/L+hP7GwsMDS0lL599q1a995PiclJdGiRQtiYmKU7Rr6LDmf3+3ChQucP38eePs++O7du8TFxdGiRQsKFixIUlISq1atwtPTEzMzMw4ePKhMv3Tu3DllvZIlS3LmzBmcnJwkBPkRGIeqNBoNOp2OwoULEx8fr0xRaWJiglarTVFRJzAwkODgYG7dupUm7RZCCPHlkvCOEEIIIYQQQgghvii2trZ4e3vLwIQQQhgxDvDUqlWLpUuX4uzs/FYw5mMx/K03BzcBqlevzvPnz2nTpg0DBgzAxMREGWw2iIyM5Nq1a1SqVIl06dKlShuFEO+m1+uVoMGJEycAMDc3B2DixInExsai0WgwMzOjRIkSbNmyBW9vb4YNG4Zarebw4cMpzufw8HDu3LlDnjx5pLLIB/j9998pUqQIHh4e3Lx5863nDYGnhIQE9Ho9a9euxcfHRwlN2dvbA68DWG3btmXWrFlvrSuhqf/GONRmXFnH1NQUtVpNz549yZEjB9HR0SkCPGq1Gr1ez4oVK1i3bh0VK1ZMMcWlEEII8SHkLkoIIYQQQgghhBBfHMMvlGVgQggh/mIc4DEMxBtPUfOxaLVapR82Du8Y/lbNmjUpUqQIu3fv5sWLF5w6dQpHR0dl+eXLlzN16lScnJzo06eP9OVCfGKG87dWrVq0aNGCvXv3AjB48GB8fX05efIker0ec3Nz2rVrB0BYWBgajYaLFy+SNWtW4HXQYfny5cyePZuSJUvStGnTtNmhL4yJiQnt2rXDzs4OW1vbt563sbHBwcGBvXv3Mn36dIYOHYparebIkSMpQlMjRozgzp07uLi4KI99aDVL8TatVquEz37++Wfc3d1p0KABXbt2ZfPmzdy+fZtcuXIRExODnZ0dY8eOpWXLlmzcuJFjx44xfPhwvL29SUhI4McffyRjxoxvVbgSQggh/o5K/+ZkmkIIIYQQQgghhBBCCCHE/0tOTlamKdRqtUrYZvbs2ezevZukpCRKlCiBn5+f8tzZs2epXr06jx8/pmvXrrRt2xY7OzsWLlzImjVr0Ov1HDx4ECcnpxTTjQghPp3Ro0czevRoKlasiIODg1LhpW/fvjg7OyvLTZgwAT8/P7JkycLcuXMpW7Ys5ubmTJ06lfnz56PRaDh48CC5cuWS8/kDPXjwAGtra9KlS8fSpUtxdXWlWLFiyvPBwcEEBgZibm6Og4MDx44dSxHcWbJkCSNHjsTV1ZVFixaRMWPGtNiNr4Zer1eCT15eXkyZMoWMGTNia2vLvXv3ePnyJe3bt8ff35+CBQty+vRpOnbsqEx9BmBmZkapUqWUynfG10shhBDiQ0h4RwghhBBCCCGEEEIIIcQ77d69mxEjRhATE0OOHDmUx728vAgPD8fU1FSZLqt27dpMnjyZggULYmpqyi+//IKbmxuHDh1SKvRYWVlRqVIlZs+erUx/KIObQqSdefPm0atXL1QqFa1ateLHH3/ExsZGqRhiCOKEhYXh7e0NQObMmXn58iUajYbixYuzfPlyCSv8R+vXr6dZs2a0a9eOkSNHUqhQIQBOnz7NiBEj2LJlC23btmX8+PFky5YNgJkzZxIREQHA3r17yZkzp4SmPpLQ0FB8fX3p27cvHh4eFCpUiOvXr9O2bVvi4uLo1asX06dPx8zMjAcPHrB3717Onz+PSqWiRIkSVK5cGRsbGzkXhBBC/CcS3hFCCCGEEEIIIcRHYTxoIF9YCyHE294cXNVoNKkyrdXHotFo8PHxYfLkyVStWpUlS5aQPXt21q5dS/fu3fnhhx/o27cv5ubmTJ48mQULFlCqVCmioqIoXLgwpqam3L9/n0uXLnHq1ClMTEwoU6YMBQoUIEOGDHKtEOIzMHHiRHx9fQEoVqwYkZGRVKlSBXi7z9q9ezcHDhzgzJkzZMmShUqVKlGvXj1sbW3lfP6PLl++TGRkJDNnzqR169b4+/tTuHBh4PXxnjx5Mhs3bsTW1pYCBQrw8OFDbt68iYuLCxs2bMDFxUWO/Udy7do1mjVrRvr06Zk3bx4FCxZEo9Gwfv16hgwZgomJCXFxcdja2v7t9VuCVEIIIf4rCe8IIYQQQgghhBDio+rWrRtNmjShadOmyjQrQggh/jJgwAAmT56MiYnJZx/gefDgARMnTiQsLIwKFSqwZcsW5s2bR1RUFOvXr6dgwYIA3Lt3j9mzZzN+/HiKFStGVFQURYsWfe+AsvEUJUKItJGcnMzq1au5ffs2jx49YsyYMZQuXZrg4GDq1q0LvA4i6PX6vw2HSFjhwxgfp6SkJMzNzYHXoZGIiAgiIyNp3749fn5+FClSBICLFy9y8OBBoqKiSEhIIHv27NSsWZMePXqQJUsWCe78D968DsXFxVG5cmVCQkIYMmQIGo2GlStXMmzYMCW44+DggF6v58aNGzg4OJAuXbr3bk8IIYT4tz7fT4VCCCGEEEIIIYT44pw7d44VK1Zw+PBh0qVLR506dSTAI4QQRqKjo5k2bRrHjx9n3759yrRTn2OAR6fTYW9vj4+PD1qtlsmTJ9OgQQPy5MlD69atKViwoDK1TpYsWejbty8qlYpx48bRv39/ZsyYQfHixYG3BzVlgFOIT+/N89DMzIxmzZqh0+lIly4dmTNnZsiQIQwfPhyAunXrpgjlPH78mPTp0yv3dobtSXDnnxmHbFauXMkvv/xCyZIladKkCS4uLgwePBiAyMhIACXAU6BAAQoUKEDHjh0BUtxX63Q6Ce78R+8KPf35559oNBplerKYmBj8/f0xMTHh6NGj2NvbA3D//n3c3NwYMWIEFStWVNaX65oQQoj/ldxRCSGEEEIIIYQQ4qMpXLgwW7ZsQavV4uXlxaZNm0hOTv7g9aVAsBDia9eiRQtGjBjBkSNHqF69Olqt9r3BnbTuEw1/387ODj8/PwYPHsyZM2dYvHgxZ86cITExEbVarQzc29nZ4ebmhr+/P2fOnMHDw4Pjx48DMqgpRFrTarXKeajRaHj58iWJiYlYWFgo1UMGDRrE5MmTOX78OMOHD2fr1q3K+mvWrKFXr17KOQ1yXn8o45CNv78/vXv3Zs6cOWi1WuU+2dnZmcGDB+Pp6cnSpUsZP348v/zyi7INExOTFKEpQEJT/5FxJanevXvj5uYGQJEiRcicOTMLFy5k5cqVDB8+HLVaTVxcnBLcAZgwYQL79u2T978QQoiP7vP7OYcQQgghhBBCCCG+SIZfX1esWJH58+fTqlUrBgwYQIUKFciaNet7lz979ixJSUmULl1avgQXQnzVdDodGTNmxMvLC5VKRVBQEGXLluXEiRN/u96NGzfIlStXqrftzYFgw+Dm2bNnKVq0KL6+vuh0OhYtWsTFixe5fPkyRYsWTbGOIcCjVqvx9/cnKCiIlStXfpaVhYT4VhhXGYmOjmb79u1cuXKFzJkz4+3tTbly5bCysgJg4MCBAAwePJhhw4bx4MEDdDodISEhXLx4kalTp6bZfnypDH1rQEAAISEh9O3bl549e1K6dOkUyxkCPPBXBZ6AgAAKFSqUon+W++X/jeH4TZo0iZ9++olq1apx9+5dHB0dKVu2LFu3buX48eNYWVlx/vz5FFNjGYI9jRo1euv6J4QQQvyvVPq0/vmGEEIIIYQQQgghvjhvDvK+evUKCwsLABISErCysmLv3r28evWKunXrvnc7x44do1y5ctSvX585c+aQPXv2VG+7EEKkJUP/+fz5c8aNG0e5cuVo0aLFe5ffunUrDRo0ICQkBG9v71RvX69evahSpQpdu3YFoGfPnvz8888cO3YMV1dXHjx4wPjx45kyZQply5Zl5cqV7+y779+/z9KlS2nevHmqB4+EEO9nfM82bNgwJk+eTIYMGXBxceHPP//kxYsXjB49mg4dOpAzZ05lvaioKDw8PIDXUzU5OTmxbds2cufO/c4ph8Tf27NnDy1btqRx48aMHTsWJycn4O2pzAD++OMPJk+ezMyZM6lduzaTJ08mf/78adHsr4rxuZCcnEynTp0wMTFh3LhxuLi4APD7779TuXJl7ty5g5ubGzNmzFDWnzlzJpMmTQJev545cuR45+snhBBC/FfycwchhBBCCCGEEEL8a4YvvmNiYmjXrp0S3PH19SVz5swMGDCAatWqKcu/74vtx48fU758eXbu3MmpU6ckvCOE+Oqp1Wp0Oh0ZMmRg7Nix/zgAfvnyZQBGjx5N7dq1KVWqVKq1bc+ePcybN4+4uDhy5MjB2rVrWbBgAf369cPGxgYAe3t7/P39AZg8eTJt2rRh+fLlKfpvvV6Pg4MDHh4eqFQqGegXIg0Z7tnGjh3LlClT6Nu3L71796Z48eJs27aNhg0bEhQUxIsXL+jTpw85cuQAoH///uTJk4edO3eSKVMmevbsiaOjo5zPf+Pvjs3p06d58uQJvXv3VoI78O4qOs7OzgwcOJDnz5+zfft2bG1tU63N3xLDuRAaGopGo2Hz5s1MmzZNCe5oNBry5MnDmjVraNGiBbNmzWLPnj0ULFiQP/74gwsXLuDk5MTWrVvJkSOHnAtCCCE+OpkQUwghhBBCCCGEEP9J8+bN6dChA+Hh4QB4eXkxceJEEhIS0Gq1KZZ93y9S69Spg5+fH+nTp+fHH38kOTk51dsthBCfkk6ne+sxwwDih/xa393dnbCwMBITEzl8+PB7t/kxlCtXjuXLl3P79m06dOjA9OnTGTJkCKNGjVLCOTqdDjs7O/z9/Rk8eDCHDh2iTZs23Lp1S9mOYb8M/5XBTSHS1q5du4iOjqZDhw4MGjSI4sWL8/LlSwYOHIi9vT3Ozs6EhITw448/cvPmTWW9+vXrExoaSkBAgAR3/sGePXto0aIFjx49eus5vV7P2bNngdfBHMNjxgz3zg8fPgQgd+7cjB49mjNnzmBnZ5dq/f63wPhzya+//oqvry+RkZHY2NiQL18+4HVwxzC9Y7ly5Th8+DA9evQgffr0HDp0iAwZMjB06FD27t2Li4uLnAtCCCFShYR3hBBCCCGEEEII8Z/4+/uTOXNmfHx8qFSpEuHh4fj7+9O9e3fSp0//j+sbBi2aNm3KDz/8wKZNm3j+/HlqN1sIIT4ZrVarBHU2b97MpEmTGDp0KCEhIdy9exeNRvO36xsGa4cMGUKDBg2IjIx8a9rC/8XevXsJCgpS/m1lZUWrVq0oUaIEDx8+xN7envz582Nvb6+0R61Wo9fr3wrwdOjQIcWgvxAi7RgHPTQaDfv27ePFixf079+ffPny8eLFC0qXLs3jx48JDQ0lPDycPHnyEB4ezvz58/nzzz/fuV0JK7zfkiVL2LBhA7NmzXrrOZVKRaZMmQA4efIkkPI10uv1mJiYoNPpGDFiBHv37gUgZ86c2NjYoNfrP1q//63R6XTK+3b79u2kT5+eNWvWYG1tzc2bN5k7d64S3DG8Jlqtlly5chEVFcXBgwc5cuQIe/fuZfTo0WTLlk2CO0IIIVKNXO2FEEIIIYQQQgjxr2k0GsqVK8fZs2dRq9XExcVRpUoVPD09lV+j/hOVSqV8ST527Fjatm2rDGwIIcSXznjA0MfHh1atWjFs2DCmTp2Kv78/lSpVYsGCBdy7d++92zAEZQDat29P+fLlP1r7nj17RmBgIKNGjWL58uXK42fOnCExMZH69euTlJTE1KlTWbFiRYrgjqH/NgR4vLy8iI2NxcPDQ6pDCJHGjAN+d+7cwdTUlOrVqzNixAjKly/Py5cv6dChA3fu3GHs2LF07tyZunXrUr16deLj4wkLCyMsLIy7d++m8Z58Gfbu3cvOnTsZP348UVFR9O3bF4CXL18Cf4V06tevj4WFBZGRkUpYR6/Xo9VqlSplwcHBLFu2TFnX4EOqtIl3M5wLQ4cOpV69euzfv58aNWowefJknJycWLFiBdHR0Wg0GuUaZ7h2m5ubY2ZmhrOzMyqVSnlcgjtCCCFSi4R3hBBCCCGEEEII8a8ZysofPHiQ5ORk1Go1+/fvZ8mSJQDKr4f/ieFL8kyZMrFo0SJMTEw+KPgjhBCfO8OA4ZgxYwgNDaVjx47ExcXx7NkzlixZglarxdPTk9WrV//tdgyDtu3bt2f+/Pmo1eqPEpDJmDEjw4cPJyAggNq1ayuPFytWjFmzZjF79mzmz5/PrVu3CAwMZPXq1UpwxzDIaQjweHl5MWrUKKZOnSrVIYRIY4ZzsG/fvpQuXZqLFy9SvXp1JVSya9cuYmNj6dChA127dlXWq1KlCsWKFaNw4cKsWrUKS0vLNGn/l2Tnzp3UqFGDmTNnYm1tjZubG5kzZ8bLy4vatWvz5MkT5fUoXrw4NWvWZNeuXbRp00apNmkIgqxcuZKff/6ZIkWKUK5cuTTbp6+F8eeJAwcOsHTpUnr27EmZMmVInz499erVY+rUqVhZWTFu3DhiYmLQaDSoVKq3pjST8JQQQohPxTStGyCEEEIIIYQQQogvV/HixVmzZg2Wlpa0bdsWLy8vXr16ha+vrzKwq1Kp/vZL7zefk1+zCiG+FqdOnWLevHk0atQIHx8f8uXLB7yeIuXFixdkzZqVtm3b/uN29Ho9ZmZmyv//rwEZQwinTp061KxZExMTE7y8vNDr9UyaNInChQsD0LBhQ6ZPn467uzsjR44EoFmzZpiamqLVatmxYwcWFhZKVQ+VSiXTiQiRRgznNcBPP/3EsmXLqFOnjtJfGP578uRJnj17RrNmzZR+BWD9+vWkT5+eGTNm4OjoSKZMmVJsU6T0yy+/0L17d2rUqIGHh4cSdoqPj+f69escPHiQbt26sWDBAmxsbMiSJQtTp06lffv2rFq1ilu3btGgQQPKlCnDhg0bWL9+PXq9ni1btpA5c+aPOkXit8hwHTp//jxPnz7FysoKb29v8ufPD4CZmRkNGzZk1qxZ9OnTR7nGtWvXDlNTU3nvCyGESBNy5RdCCCGEEEIIIcQHeVelh3z58lG/fn3q1q3Lvn37yJAhA8OHD2fChAnA64EiwxffV69e5eHDh5+0zUIIkZauX7/OH3/8QceOHcmXLx8ajYalS5fi5+dHhgwZOHHiBJkzZyY5OVmZJuXNX/xDypDjxxhMNN6GiYkJ165dY8uWLUyePJkxY8Yoz1lYWNCqVSuioqK4ffs2I0eOZPXq1Wi1WrZt20avXr3o3r07iYmJyjYluCPEp2ccNEhMTOTWrVsULlyYCRMmKKFBg2zZsgGvQw0GMTExxMXFUb16dYoWLYq9vb0SwBbvdvLkSe7du0e7du2oVq0aAFu3bkWtVhMREUGfPn1Yt24dnTt35smTJwDkzZuXZcuW0bFjR65evUpgYCCNGjVi/vz5FChQgAMHDuDk5IRWq5Xgzkfg6+tLkSJFGDVqFDly5CB//vwpKvKYmprSsGFDfvzxR+Lj4xk5ciTLly8nOTlZ3vtCCCHShFz9hRBCCCGEEEII8Y+MBxEOHjzIpk2b2LdvH/Hx8ZibmwOvp1qJjY0lQ4YM+Pv7ExISoqy/bt06OnbsyNKlSz/KdC9CCPG5MQwI6vV6JYBz7do1AFxdXQFYtmwZPj4+qFQqjhw5gr29PQCXL1+mc+fOPH/+PE0GDF1cXJgxYwbVq1cnMDCQoKAg5Tlzc3NatmzJjBkzuHv3Ln379qV69er07t0bgO3bt5MuXbpP3mYhxF8M/YaXlxctW7Zk/vz5lCtXDhcXl7cCgSVLlqR48eL4+PjQu3dv2rZty+DBg9Hr9crUWoCER/7BvXv3SEpKUo69m5sbbdq04dChQ2TPnp3hw4fTs2dPNm7cmCLAkzt3bqKiojh06BBLlixhyZIlHDx4kNWrVyvBHQlBfhyVK1cG4NixY0pA9s2pfQ0BntmzZ5OcnEzfvn1Zv359mrRXCCGEUOnf9VMOIYQQQgghhBBCiP9n/Gvu4cOHExERQWJiIgAtW7akZ8+eNGjQQFn+7NmzVKlShWfPnuHu7o6zszNz587l+vXrnD9/Hmdn5zTZDyGE+FiMpzN5c2qTy5cvK5UuVq9eTatWrfD19aVkyZJ4eXmhVquJi4vDwcFBWadXr14sXbqUuLg4JeiTWt6cCsS4/bGxsQQEBBAbG8uoUaOUaUQANBoN+/btw83NjaSkJPLnz8+8efNwcnJCo9Fgamqaqu0WQvw9jUZDkyZN2Lp1KzY2NvTv35+xY8e+c/ql1atXM2fOHDZv3kz69OkpUaIEixYtkvDIP9i9ezdXrlyhV69e3L9/nwYNGnDv3j1cXV3Ztm0bgwYNYujQoeTIkQOAmzdvEhQUxJw5c2jYsCELFy4kc+bM792+TNX08Rje9zt37qROnToABAcH4+fnl+J5A41Gw5o1axg3bhwbNmwge/bsadJuIYQQ3zYJ7wghhBBCCCGEEOKDjB07lsDAQGrWrEmFChX47bffWL16NQUKFGD06NG0bt1aWfbcuXO0bNmSy5cvY2pqSoECBdiwYQMuLi4yKCSE+Gq4u7vToEEDGjduDLwO4cybN49Lly6RN29eHjx4QOnSpXn06BEZMmTA3Nycs2fPkiFDBmUbCxYsYMSIEdSrV4/IyMhUrWJj3P/q9XoSEhJQqVRYWVkpy+zbt48RI0a8M8ADkJCQwPPnz8mQIQNWVlbSpwvxGUlISMDT05P58+eTJUsWtm/fTtGiRZXn35xe6+LFi2TKlAk7OzsyZMgg5/PfMIRAmjdvzrRp08iePTsHDhygadOmPH78mEaNGhEeHk7+/PmVyi5qtfqtAM+iRYuwsbGRoM5HZDiWxsfU+L28e/duatWqhampKVFRUfTq1Qt4O8Cj1WpJTk7G0tJSzgUhhBBpQsI7QgghhBBCCCGEeCfjL601Gg3NmzfHzs6OoKAgnJ2diY+PZ+XKlfTs2ZPcuXMTHBxMmzZtlPWvX79ObGwser2eunXrkiVLFvkiXAjx1Vi/fj3NmjWjXLlyzJgxg3nz5jFjxgz69euHr6+vUnlhxYoV9O/fnwcPHhAZGYm7u7uyjXnz5jFhwgRMTEzYuXMnjo6OqTaga9z/zpkzh127dnHmzBkyZsxIhw4daNy4Mblz5wbeH+B5sw+XwWch0sa7wgqGIMKLFy8YOHAg8+fPp0WLFkyYMEGpBma87vu2Kd524cIF6tevT548eRg1ahRVq1YFoEePHixYsABbW1vMzMyYNm0a9evXx9raWpmuTKVSpQjwNG3alPnz5/9tBR7x4YyvS3fu3OHJkyeYmJiQKVMmsmTJoiy3fft26tWrR4YMGZg0adJ7AzxCCCFEWpLwjhBCCCGEEEIIIf5WVFQUOp0Of39/Fi9eTJMmTYC/Bnl+/vlnunTp8s4AjzH5clwI8TV58OABa9aswdfXFzMzM+7evcuQIUPw8fFJMSXWw4cPWbRoEaNHj0aj0VCrVi1KlizJoUOHOHjwIHZ2duzatStVK5MZD8oPHTqUqVOnkjVrVvLnz8/Nmzf5/fffad68Od26dVP6+P379zN8+HBiY2MJCgoiICDgo7dLCPHvGfcT8fHxPH78GDs7O7RaLenTp1ce79OnDz///DM//PADo0aNShHgEf/OqlWr6NChA6GhoQwYMACAXbt2sXDhQtKlS0eZMmWYOHEiL168YOLEiTRv3hwrK6u3AjzBwcHMmjWLzp07s2DBAglL/Y+MP1uEh4ezZMkSzp07h5mZGQUKFKBz584MGjRIWV4CPEIIIT53MhGxEEIIIYQQQggh3uvIkSN4eHjg6upK9uzZlakXNBoNpqavv1bo0KEDAF26dCEgIAC1Wk2rVq3e2pZ8KS6E+JrY29vTq1cvFi1axL59+8iWLRtly5ZVgjuGAXY7Ozu6d++Oq6srPj4+7N69m7Vr11KwYEHatWvHqFGjyJ49e6pWJjMMEE+dOpUpU6bQr18/+vbtS+HChbl48SKBgYHExMRgb29P3bp1sbCwoHLlygQHBxMYGMjIkSOxtrZm8ODBqdI+IcSHMe4nIiIiiImJ4dixY2TJkoVixYoxcuRIvv/+e6ytrZk9ezZ6vZ4lS5ag1+sZPXq0BHj+o2fPnpGcnExycjIAvXv3ZvHixWzbto1SpUphZWWFnZ0dw4YNw9vbGyBFgEev15MzZ058fX3JkCED7u7uEtz5CAyfLYYOHcrkyZMpWrQo7du35/Hjx6xfv56TJ09y6dIlpkyZgqmpKXXq1GHr1q3Uq1cPX19fEhMT8fT0lM8oQgghPhtSeUcIIYQQQgghhBDvlZCQwMKFC/H39+fx48cEBwfj5+cHvP0r1Z9//pmePXuSLl06oqOjady4cVo1WwghPonjx4/To0cPsmTJQlxcHN999x3jxo2jWrVqmJqavtVPxsfH8+jRI+7evYurqysmJiZYWFik+pSCer2ex48f07hxY5KSkvj555/Jnz8/Op2OlStXMnToUNRqNcePH1cqeBjas2vXLqKiopg0aRLOzs6p1kYhxN97s4JWREQEpUqVonLlyty/f5+VK1eiVquZOXMmnTt3BiAxMZGePXuydOlSOnfujJ+fH999911a7sYX6dmzZ7Ro0YLTp09TvHhxdu/ezcCBAxk2bBjZs2cH4OXLl2zdupVhw4aRkJDwVgUevV6PWq1WrgvGQXjx3y1cuJBevXrh4eHBwIEDcXJyAl5fuwYNGsS5c+cYOHAgkydPVtbZsWMHdevWpUCBAhw/fhxra+u0ar4QQgiRgoR3hBBCCCGEEEII8bfi4+NZtmwZAwcOJFu2bISHhyvBnDcHpufPn8/o0aM5ePCgMpghhBBfs1OnTpE+fXr27dvH0KFDyZcvHxMmTKBatWqYmJgoA7TG/aVxOMZ4QD41Xbx4kUKFCuHv78+YMWNISkpi1apV+Pr6olKpOHr0KPb29mi1Wv78809lABQgKSkJc3NzGWwW4jMwd+5cPDw86NWrF4MGDSJv3rwADBw4kMjISFxdXTlx4gTm5ubA6yC2m5sbixcvxt3dnYiIiFQNC35tDH306dOnqV+/Pnfv3qVOnTpERERQqFAh4K/74aSkJDZv3vzeAI9U2/n4+vTpQ0xMDLGxsRQrVixFUCouLo727dtz7do1FixYQJcuXZTXat++fbi4uKS41gkhhBBpTWrBCSGEEEIIIYQQAp1O997nrK2tadeuHZMmTeLmzZsEBQWxefNmAOUXxAbdu3fn/PnzyhQwQgjxtXhfP1miRAny5ctHy5YtCQ4O5vLly/j4+LB3716SkpKU4M6WLVuIi4sDSDFw/qkGcw1/08LCAoDVq1fj4+ODSqUiLi4Oe3t74HVgs1KlSqxevVpZ1xACkOCOEJ/G392Xbdq0iSxZstCnTx/y5s3Ly5cvWblyJWvWrKFAgQLs2bMHc3Nz5T7MysqKmTNn0r9/f7y8vCS48x9NnTqVu3fv4ujoSFxcHEeOHOHp06fA6/thvV6Pubk5DRo0ICwsDCsrK/z9/Vm6dCmJiYkS3EkFL1++5MyZM9jb21OsWDEAJbgDUK5cOaZOnQq8rrZjeF6v11O1alWcnJzk84oQQojPinzaEkIIIYQQQgghvnHGFSDOnDnDtWvXAMiTJw9FihQBXg/8dOzYEb1ez6BBgxg5ciQADRo0SDEFgGFZQAaHhBBfDeN+8tSpU/zxxx+oVCpy5MhB6dKlAbCxseGHH35ApVLh7++Pt7c3ISEhVKlShT179tC3b19evXrF9evXMTc3/6QDuXq9HlNTUxwcHFi+fDnW1tZERESgVqs5cuQIDg4OynITJkzgyZMnSshHCPFpHThwgCtXrtCiRQsyZMiQ4rmHDx+yf/9+atWqRdGiRdFoNKxduxZvb2/UajWxsbFKEO/o0aPY2dmRP39+rK2tmTZtGoBU0PqXDH11mTJlsLS0pHLlykyaNIkhQ4bw6tUr2rdvT6ZMmVCpVEqAp379+qhUKrp160ZUVBTt27dP47348r1ZucgQcEuXLh1Xr15ly5Yt1K9fX/k8otfr0Wq1VK1aldy5c7N//35evHiBlZVViu3I5xUhhBCfE7lDE0IIIYQQQgghvmE6nU750nrUqFFERUXx4MEDAMzMzJgwYQK9evUiffr0WFlZ0alTJwAGDRpEYGAgKpUqxRflQgjxtfm7ftLKygofHx9GjBgBvA7wdOjQAZVKRUBAAD179iRfvnxcvHgRgIMHD6ZqKObNqQwNVCoVzs7O9OnTh7FjxzJy5EgyZ87MuXPnsLa2VtZdtmwZP//8MzVr1qRy5cqp1k4hxLs9evSIPn368Pvvv6NWq2nevDnp06dPsYxarebJkyc8efKEbdu2KcGduLi4FEG8gQMHUqlSJSZOnJgirCPBnX/2rimu+vXrR2JiIunSpcPe3h4fHx/8/PwA3hngqVu3Lj///DNFihRRgu3ivzEO0N65c4ds2bKhVquxtLSka9euxMbGsmrVKsqUKaOE17RaLaampmTMmBETExNy5Mjx1rkkhBBCfG7kmzUhhBBCCCGEEOIbZVxW3sfHh6CgIMqXL8+KFStYt24dFSpUYMiQIYSGhqYYqO7UqRMRERH89ttvuLu7s3v37rTcDSGESDXG/aSvry9BQUF8//33xMTEsGPHDooVK0ZgYCDDhg1Do9EAf1XgmTVrFubm5pw7d478+fOzf/9+8ubNqyz3sWm1WqWtR48eZf369Rw/fpxbt24py/Tu3ZvWrVsTHx/P999/z8OHD4HXwZ3IyEgCAgIAmD59OhkzZvzbqXuEEB9fhgwZGDduHAULFsTLy4tVq1bx4sUL5Xk7OzsqVarE8ePHiYqKwsfH563gDkBISAgXLlygdOnSEtb5l7RarRLcefz4MZcvX+by5cvA6yovANWqVWPChAnkzp0bPz8/li5dqkyhZQjwWFhYULduXZlK9n9kHNyZPn06jRs3pk+fPsrzlSpVolatWsyZM4eoqChu3rwJoExZuWjRIm7cuEHJkiXRarXo9fo02Q8hhBDiQ6j0cqUSQgghhBBCCCG+aXPmzMHHx4eOHTvi6elJ/vz50Wg0FCpUiCtXrgDg7e2Nl5eX8mvWxMREfvzxRyIjI9m/fz/ZsmVLy10QQohUNWfOHHx9fWnfvj2DBg0iX758ABQoUIA//viD5ORkBgwYQFhYWIqB8sTERO7evYuDgwPW1tYpBiE/JuMqEX5+fkyePJmkpCRMTU35/vvvGTVqFDVq1ECv13PkyBHCwsJYtWoVGTNmpESJEty5c4fr16+TJ08eNmzYgIuLS6q1VQjx95KSkti+fTvDhg3j4cOHTJw4kZYtWypTaC1dupT+/fvz/PlzsmTJwtmzZ7G1tVXWX7p0KQEBAeTKlYtVq1aROXPmtNqVL45x9bKJEycSExPDqVOnMDMzo2TJkvj7+1OpUiVsbW3RaDTs2bMHHx8frl69yvjx4+nQoQMZM2ZM4734ehi/HsOGDWPGjBmUKlWKjh074ubmpiy3efNmAgICOHnyJC1atKBx48bUrl2bpUuXMm/ePF6+fMnBgwdxdHRMq10RQgghPoiEd4QQQgghhBBCiG/YrVu36NChA+bm5oSHh1O0aFGePn1KhQoVePLkCf369WPnzp3ExsYycuRI+vfvT5YsWQB4+fIlWq0Wa2vr907VIoQQX7o7d+7Qpk0bLC0t39lPuru7s2HDBuLi4hg2bBhjx47FzMzsrfDLu6Zh+djGjRvHiBEjqF+/PuXKleP3339n4cKFZMmShTlz5tC4cWMAnj17xvz581mzZg3Xr18nf/78VKtWjZ49e5IlSxYJ7giRRjQaDaampmi1Wnbv3s3QoUN5+PAhwcHBNG/enEyZMvHixQu8vb1ZtGgRWbNmZdGiRdjZ2ZEjRw4iIiKYPXs2Op2O2NhYcuXKJfdoH8i4jx42bBjh4eGULVuWRo0a8eeff7Jjxw5evnxJ165dGTRoEA4ODsrr5OPjw82bN/Hz86Nnz55K0Ep8HGFhYfj4+ODp6Ymnpyd58+YFUr5m27dvZ86cOSxfvjzFukWLFmXt2rUSShVCCPFFkPCOEEIIIYQQQgjxDXlzAOfRo0eUKFGC0aNH0717dxISEqhRowbXrl1j0qRJdOrUiY0bN9KkSRMAhg8fzoABA1JMzSCEEF+z58+fU6VKFdzd3endu7fST169epWwsDC6dOnC/v37qVq1KgCDBg0iNDT0kwwQvjkQWatWLRwcHAgNDSVXrlwATJ06laFDh5IpUybmz5+v9OeG9TUaDRYWFspjMtAvRNowPp/37dvHsWPHiI2NZe3atRQsWBBfX1+aNWuGjY0NT58+JSgoiOjoaB49ekT69OmV87lYsWKsWLECZ2dnCSv8BwsXLqRPnz5069aNQYMGUbBgQeLj45kyZQoBAQFUrVqVzZs3K1NoabVa9u7dS48ePUifPj1HjhzB2to6jffi63HlyhWaNWtGhgwZWLJkCblz507xvHGA58mTJ5w4cYLY2Fh0Oh1FixalevXq2Nvby7kghBDiiyDhHSGEEEIIIYQQ4ht0+PBhihUrhpWVFU+ePMHGxgadToe3tzczZ85k9OjR9OvXDysrKzQaDZUqVeLZs2f89ttvjBkzBn9//1SvICGEEJ/a+wb3jPtJPz8/oqKiGDVqFP379yddunQkJSVRuXJlEhMT+eWXXxg+fDhjxoz5qG37u8o9EyZMIGPGjEyZMoWpU6dSt25dpYIHQFRUFAMGDMDGxobo6GgaNWoEvK7yYWJigkqlktCOEGnozaov0dHRZMqUiXLlyhEXF8eVK1fInj07wcHBSoAnMTGRkydPsn79eq5fv0769OmpXr06devWxc7OTsIK/5LhNejYsSP79u1jy5YtFC5cmOTkZNasWYOXlxdmZmYcPnz4rTCIRqPh8OHD5MuXj2zZsn2SSmvfitjYWKpVq8a4cePw9fV95zL/dP2S65sQQogvhek/LyKEEEIIIYQQQoivSWhoKOPHj+f69esAZMqUSXlu//795M2bFzc3N6ysrABITk7m2rVrtG3blqpVq9K5c2cZkBBCfJUMA7ELFy7k+++/J1++fMBf/aRGo2Hfvn3kzp2bPn36KJUXTE1NuX37Nk2bNqVMmTL06tXro7bLeCD4+fPnypQsOp2OI0eO4OfnR548edBqtUplNONATv/+/QEYMGAAXbt25aeffqJhw4ZKuAeQgU0h0pDh/J42bRqTJk1i4MCBeHh4kDdvXh49ekR0dDRRUVH4+PgAKAGeihUrUrFixbfCIjqdToI7/5Jer+fFixfs2rWLsmXLUrhwYV6+fMnatWvx9vZGrVYrwR2A06dP4+TkhL29PaamplSuXBl4fwhU/DcPHjwAUD6XGAdT4a9gzuPHj8mcOTPwdthVrm9CCCG+FHLFEkIIIYQQQgghviHJyckkJiby5MkTIiIigL8GjB49esTFixfJmjUr6dOnV9aJjo7GysoKd3d3ZsyYgZOTE1qtNi2aL4QQqW7Dhg107doVLy8vrl27BvzVT7548YLr169jZ2eXIkAzc+ZM9Ho9Q4YMYd68eTg7O6PRaD5amwx/v1GjRkyaNIlHjx4Brwckv//+eyIiIvj999/5448/iIuLA0hRUQegf//+TJ06lfj4eBo3bszOnTs/WvuEEP87vV7Pli1byJo1K56enuTNmxeNRoOtrS19+/ZlwoQJWFhYMHLkSNauXcuzZ8+Udd8MVUtY4d9Tq9VkyJCBbNmy8ejRI5KSktiyZYsS3ImLi1OCOwAtW7Zk2LBhb21HgjsflyGQs2zZMh49eoSpqSmGCUX0ej1qtRq9Xk+tWrWUyjzyIwMhhBBfKrmDE0IIIYQQQgghviFmZmZ07dqVLFmysHr1av7880/g9eCzjY0NderUYceOHcpAcFRUFBEREdjZ2ZEtWzZlMEgGJoQQXytXV1c8PT3ZvHkzQ4YM4erVq8pzlpaWlCtXjr179zJq1Cju3btHVFQUkZGRODo6YmtrqwwaGlcG+Bju3btHfHw8oaGhzJ8/XwnwwOuKOjNnzgTA29ubDRs2AK8HMN8M8IwZM4ZcuXJRsGDBj9o+IcR/p9PpeP78OSdPniRz5szkypULrVar9CPp0qWjfv36dOrUiRs3bhASEvJWgEf8b/R6PVqtlrx583Lw4EEGDBjAgAEDMDEx4fDhw0pVM71eT0hICPHx8VSoUEEJkojUUb16dWrWrMnBgweZM2cOz549Q6VS8erVK+X69tNPP3Hv3j0sLS3lBwZCCCG+aCq93FkIIYQQQgghhBDfDENp+YkTJ+Lr68uCBQvo0qWL8vy2bdsYPHgwv/76q/JYvnz52L59O87Ozsr6QgjxNfvjjz+IiIhg6tSpNGvWjEmTJpE7d24ADhw4QNeuXfn999+V5fPmzcuOHTtSvZ+8fv06Q4YMYdu2bfj6+uLm5oadnZ3y/IwZM3B3dydPnjxMmzaN+vXrA68Hmw0VCuB1BaH06dPL9C5CfGaaNGnCoUOHOHHihFLp0MTERJkG6I8//qB06dK8evWKhIQEli5dSps2bdK62V8FwzE+f/48NWrU4P79+2TJkoUTJ06QPXt2Zblly5YxfPhwcuTIwcqVK1P0weLjMlxPDxw4QLdu3Xj27Bm9e/dm8ODBynFfvHgxY8eOxczMjO3bt5M1a9Y0brUQQgjx30l4RwghhBBCCCGE+Aq9a0DWeED54MGD1K5dmzx58rBx40acnZ2V5Y4cOUJcXBynTp3iu+++o3PnzmTLlk0GeYUQX5V39WmGwVv4K8ATGRlJ06ZNCQsLI3fu3KhUKk6fPs2uXbu4cuUKuXPnpmPHjp+sn7xx4wZubm5s2bKFixcvki9fvhT9+/Tp0/H09PzHAI/xvgoh0pZWq0WtVjNy5EiCg4Np2bIlixYtUiqJqFQq1Go1T58+pUiRInTo0IHTp08zd+5ccubMmdbN/2ro9Xo0Gg1z584lMDCQdOnS4ePjQ926dTE3N2fOnDnMnz8fgP379+Pk5CTB9k/g5cuXbNiwgZEjR3LhwgXy5MlD2bJluXHjBidPnsTBwYE9e/bg4uIir4cQQogvmoR3hBBCCCGEEEKIr9iGDRvInz+/Mj2K8RfaQ4YMYerUqaxZs4bGjRuTlJSEubn5O7cjwR0hxNdq8eLFNGvWjPTp0wNvB3gmT57M1KlTad26NePGjSNv3rzvDL18yn7yjz/+4M6dO5QvX1557H0BnunTp1OvXr1P0i4hxN/7p2DB06dPqVSpEufPn8fT05MJEyZgaWmpPL9gwQLGjRvHihUrKFSoEGZmZnKP9oHePPZ/F2B88OABa9aswd/fnwcPHmBjY0NycjJ6vZ5ixYrx888/4+zsLMf+f2B87D7kOCYlJfHHH38wcuRIDh8+zPXr1ylSpAjly5dn1KhRZM+eXV4PIYQQXzwJ7wghhBBCCCGEEF+pqKgoPDw8cHJyok6dOowcORJbW1usra0BOHbsGPXq1cPV1ZU9e/akmJZBCCG+BcuWLaN9+/a0bNmS6OhopX807gsvXbrEoEGD2Lx5M507dyYwMJA8efKkZbNTMG7ruwI8GTNmZPXq1dSoUSMtmynEN884WLBp0ybOnDnD1atXyZw5M926dcPBwQE7Ozt+/fVXGjduzNWrV6lVqxa+vr5kzZqVXbt2MWPGDNKnT8+uXbvIkCFDGu/Rl8O4nzx//jyurq4ftN6NGzeYPXs2d+/exdzcnKpVq1KrVi1sbW0lKPKRhISE4OrqStOmTT+4as6TJ094+PChMlWlubm5vB5CCCG+ChLeEUIIIYQQQgghvlLHjh3j9OnTRERE8Msvv+Do6EiDBg3o0aMHFStWRKPR0KlTJ5YtW8bSpUtp27athHeEEF+1N/u4S5cuERQUxOLFi2ndujXz589XAjzG1qxZQ8uWLQGoVq0aP/30E7ly5fpk7f43jAc/Q0NDCQ4O5vz582TPnj2NWybEt8v4vPTx8WHatGkkJiYqzzs6OtKpUyf69OlD3rx5+f333/nhhx+Ii4tDrVajVqvRaDTkyZOHHTt24OLiIvds/0FAQAD79u1j3759/7js3wVJZGqmj+PkyZOULl2avn37EhUV9Y/LGx934/e/nAtCCCG+FhLeEUIIIYQQQgghvgJ/96X1kydPWL58OStWrGD79u0AdO/enU6dOpEjRw4qVqxIo0aNiI6O/pRNFkKIT+p9g61Xr14lMDCQRYsWvRXgSU5OxszMjPPnz9O4cWPKli3L4cOHOX78OPb29p96Fz6Y8b6+ePGC9OnTS1UCIT4DY8aMITAwEDc3Nzp27EjOnDlZunQpy5Yt49SpU3Tv3p2AgABy585NfHw8q1at4tSpUzx9+pQCBQrQtWtXsmbNKufzfxAfH0/Pnj1ZtmwZu3fvplq1ah+0noREUs+tW7eUKlNbt26lXLlyad0kIYQQIk1JeEcIIYQQQgghhPjCGQ/g/Pnnnzx8+JBXr15RokQJTExMUKvVymBDdHQ069atY/Xq1QBUqlSJW7ducfXqVdatW0fjxo3TcleEECLVeXp6otVqU/zK/+rVq4waNYqFCxfSqlUr5syZQ6ZMmZTng4KC2Lx5M4sXL8bOzo5MmTJ99pUXDO0zfP0rA85CpK0zZ87QrFkz8uXLx9y5c3FycgJAo9Fw7tw5fHx82LVrF0FBQQwePBhLS8t3bkeCO/+dYarE3r17ExkZiamp6Wfdj38LFixYQI8ePZg8eTIDBw787K+tQgghRGqSK6AQQgghhBBCCPEF0+l0ygDOuHHjqFu3LiVKlKB8+fKULFmSWbNmcefOHWXQtmvXrixdupQ9e/bQrFkzrl+/ztWrV3F2dqZMmTJpuStCCJEqdDqd8v9Pnjxh06ZNzJw5k4CAAOXx3LlzM2rUKDp37szKlSvp3LkzsbGxPHnyhOjoaJYsWUKuXLlwdnYmU6ZM6PX6z35w0dA+lUolwR0hPgP37t3jxo0b1K9fXwnu6HQ6TE1NKVGiBCNHjiR37tzMmTOHpKQk5XkDQxBPgjv/nuHYtW3blnr16rFhwwYePXqUIuAoUs+bx9j433Xq1KFQoUKEh4dz+/btz/7aKoQQQqQmuQoKIYQQQgghhBBfMMMX3N7e3owYMQJ7e3vCwsIIDAzEwsKC4cOH4+Xlxe3bt5V1TE1NqVq1KvPmzWPTpk24ubmxe/dusmXLhlarTatdEUKIj06r1Sr9ZExMDKtWrSJjxoxkzJiRcePGERgYqCxrCPD06dOHnTt3Ur9+fYoWLUr37t1JSEggLCxMGTSXMIwQ4t+6f/8+Op2OxMRE4PW0fMZBhTJlylCzZk2uXr3Kjh07AFI8L/3OuxmCIIb/vu9e1vB8/fr1uX37NqGhoWi1WjmuqUir1aLT6ZRjnJCQALx+LxuCaTly5KB+/frcuHFDmd7XOLQmhBBCfEtk2iwhhBBCCCGEEOILFxMTQ/fu3enSpQtDhgyhQIECJCUlMXfuXNzd3Slfvjw7duzA2tr6b7ej0WgwNTX9RK0WQohPx8vLi9mzZ5M/f34KFSrEq1evWLFiBQC+vr6MGzdOWfbOnTvs3buXWbNmkZSURO7cuRk/fjw5c+b8aNPVvDktiEwTIsTX79SpU9SoUQNnZ2f2799P+vTplXPfcA+2efNmGjVqxOLFi+nQoUNaN/mL8ODBA+zt7YGUU4odO3aMbNmykTNnzhTLx8fHU6pUKTJmzMi+fftIly6dMr2s+N/t3r2b48eP4+XlBaAcW39/fy5cuECzZs3o2rVrinX+/PNPypYti6urqxJcE0IIIb5F8olQCCGEEEIIIYT4ghj/mtjwe5zdu3eTPn16PDw8KFCgAMnJyaxevZqJEyfi4uLC+vXrsba2Jjk5+W9/ySrBHSHE12j27NmEh4fTs2dPVq5cycKFC1m2bBlbt24lX758hISEpJhCK1u2bLRr145du3axf/9+5s+f/1GDO/BXNY3ly5eTlJSEWq2WSgNCfAX+7jwuXLgwVatW5cyZMwwePJiEhATUajWvXr3C1NQUvV7Pzp07MTU1JW/evJ+w1V+uvXv3kj9/fpYvXw78NaXYhAkTKFeuHI0aNWLq1KkpKlBaW1vTtWtXjh8/TnR0NCBVjT4GvV7PkydP6NixI97e3oSHhwOvj+0vv/zCsWPHWLNmDd27d6dq1aqEhYVx584dALJkyULNmjXZtWuXEqwVQgghvkUS3hFCCCGEEEIIIT5zu3fvJigoCHg9KGEI8Oj1ehITE9m7dy958+alSJEiJCYmsmrVKry9vQE4cuSI8mvk3377jbNnz6bNTgghRBo5ePAgVlZWdOnSBWdnZ/R6PTqdjjp16hAdHU22bNnemkIrKSlJ+X9DsPFjBXcMunfvTrt27YiMjFSmz5EAjxBfLuNp+g4cOMCOHTs4fvy4ct9mZmbG7NmzKViwIHPnzqVXr148evQICwsLAFauXMmaNWsoW7YsBQsWTLP9+JJcvHiRp0+f4u7uzpo1a5THXV1d8fHx4e7duwwaNIhq1arRu3dvfv/9d5KTk2nXrh3m5uYsW7aMBw8eIBNU/O9UKhU2NjYsWLCAnDlz4uXlRVhYGPA6uLZt2zaOHz/OwIEDuXv3Lt7e3pQuXZqRI0fy66+/EhAQgEqlIjY2No33RAghhEg7Mm2WEEIIIYQQQgjxmdLr9bx8+RIXFxfu37/PmDFjGD58OJByipV69epx8+ZNTp06xebNm/H09EStVhMXF4eDg4OyPVdXV4oWLcrixYulyo4Q4pug0WioVasWly9f5vr165iYmKDT6VCpVEqlhXXr1tG8eXMAhg0bxoQJEwA+aqWdd3nw4AFVqlThxo0bBAUF4enpiZmZ2QetK1O8CPF5MT4n/fz8CA8PJzk5GQsLC+rUqcPPP/+sTF964cIFWrduzfnz58mZMyfFixcnISGBEydOkD59evbv34+zs7NMp/eB5s6dS//+/bGysmLu3Lm0bNlSee7s2bMcP36c8PBwzp07h42NDTVq1MDb25vo6Giio6PZuXMnFSpUSMM9+DoYnwO7d++mY8eO3Llzh/Hjx+Pj46Msp9FoePXqFZMnT2bPnj3s2rULlUpFmzZt2L59Oy9evCA2NpZy5cql1a4IIYQQaUbu/IQQQgghhBBCiM+USqUiXbp07N27F0dHR0aMGKFU4FGr1SQnJ6PVasmfPz+//vornTt3xt3dHRMTEw4dOqQEd/R6PaGhoTx8+JDvv/9eBoKEEN8MlUpF1qxZuX37NkuXLgVe958qlQq9Xo9Go6FKlSoUK1YMJycnQkND8fX1BVJWOvvYkpOTsbe358CBAzg5OeHl5cWkSZPeW/3B8Pjjx4+V/RJCfD4M5+TkyZMJDw+nZs2ahIWFUb58eTZs2MD333/PxYsXAfjuu+/Yv38/vXv3xsHBgY0bN3Lnzh0aNWrEoUOHcHZ2TlHFR7yboVJZz549mTZtGgkJCfTs2ZPVq1cryxQtWpRu3bpx8OBB1qxZQ/369Vm/fj3ff/89ixYtIjExkeDgYJ4/f55Wu/HVUKlUyjWzRo0a/PTTT2TLlg0/Pz+lAg+8ft2sra0JCAhgx44drFy5krZt27Jnzx4eP35McnIymzZtUpYVQgghviVSeUcIIYQQQgghhPiMaTQaTE1NuXz5MpUqVeL+/fsEBgammN7l9u3blC1bllu3bmFvb8/p06dxdHQEXg/4rlixAn9/f7Jly8bq1auVabSEEOJr8XeVaDZs2ECzZs2oV68eYWFhuLq6Aq8DNIZKN4UKFaJWrVocOXKE48eP4+3tTUhICPC/V+AxbptOp0Ov16fY3v379ylTpgyRkZE0bdr0vdvZvHkzQ4YMITw8nAYNGvzn9gghPj69Xk9SUhLt2rXDwsKCiRMn4uzszLNnz5gwYQKTJk0ib968rF69mgIFCgCv7/E0Gg1XrlzBxcUFExMTLC0tU73q19fEuDrR7Nmz8fDwwMrKivnz5ysV1Yz7enhdFebIkSOEh4eTkJCAra0te/fuJXfu3FLt6F9481gZv2+TkpIwNzdnx44ddOnShTt37jBx4kS8vLyAt1+T+Ph47t69y5QpU9i0aRM6nY4TJ06QKVOmT7tTQgghRBqTuxAhhBBCCCGEEOIzZmpqilarJV++fBw4cAAHBwdGjx7N6NGjlWUcHR2ZOXMm2bJlIyEhgaioKM6cOcOFCxfw8/NjyJAhvHz5ksWLF2Nvby+/YhVCfFW0Wm2K4M7Tp09JSkpS/l25cmW6devG1q1biYiI4MSJEwCYmZmh1+v56aefSEhIYOjQocyaNYvSpUszceJE/Pz8AP6nQXTDFF0GarVa2d7ChQs5d+4cDg4OXL58+W+DOy9fvmTZsmX89ttvzJo1i6dPn763So8Q4tMwvp8ynOf379+ndevWODs7k5ycTMaMGfH398fPz48rV67QokULpQKPqakplpaWFCpUCGtraywtLd8K94m/p1arldehd+/eSgWe7t27s2bNGuB1X6/T6ZTlatSoga+vL0ePHmX48OHcvHmTKVOmKNsTH8ZwrDZu3Aj8da309vbGw8OD5ORkateuzeLFi8mWLRve3t5KBR7Da2KQLl068uTJw8SJE+nQoQNXr15l8eLFn3iPhBBCiLQndyJCCCGEEEIIIcRnzjB1y98FeGrVqsW8efOwt7cnODiY8uXLU6xYMaZPn06ePHmUqVlkGgYhxNfE+Jf+M2fOpHnz5ri6ulKxYkV8fHy4f/8+NjY2DBgwgEaNGjF37lz69u3LpEmTOHfuHBMmTGD8+PFkzpyZDBkyUKpUKaKioihTpgwTJkxg2bJl/1P7DP1t+fLl6dSpk/J4v3796NevH+fOnSM5OVnZh/cFciwtLRk9ejRt2rRh165d3Lt3T6bOEiINGd9Pbd68mYULFzJ//nwePXqElZUV8FdAwdraGi8vr3cGeCBlYETO63/2ZghdrVYr0zW9L8BjmC7RQK/X4+zsTOfOncmfPz/btm1TpiUUH87NzY0mTZowf/58AAYMGEBYWBhZs2blxYsXwOuw1KJFi94K8BgHrwz/b2Fhgbu7O2ZmZhw9ejRtdkoIIYRIQzJtlhBCCCGEEEII8ZkzlKU3lJg3nkJr5MiRjBo1Sln28ePHxMTEcP36ddRqNRUrVqRixYrY2NjINAxCiK+K8XRUQ4cOJTIykly5clGiRAmuXLnCmTNnqFmzJt7e3tStW5fTp0+zaNEiwsPDU4Rk8ufPz9atW3FxcVH6yQMHDrB161aCgoL+53b+/vvv1K9fn8uXL+Pt7Y1eryc0NJQBAwbg4+OjTHP4ITZt2kTv3r3p2LEj48ePlz5diDTm4+NDaGgo8FfY2svLi4CAADJmzAj8dR8XHx9PWFgYYWFhWFtbc/DgQfLkyZOWzf/iGN/L3r59m/v372NtbU26dOnInj27styPP/6Ip6fnW1NoGV83DNvy8/NjwoQJnD59mqJFi37yffqSxcTEMHHiRE6ePEmNGjXYvXs3Xl5eDBw4kBw5cqQ43rt27aJTp05vTaH15ueTW7duUbZsWcqUKcPSpUtJly5dmuybEEIIkRYkvCOEEEIIIYQQQnxmDIM8Bq9evcLCwiLFMpcuXaJy5crvDPB8yDaFEOJrMW3aNAYPHkzfvn3p168frq6uXLt2jcDAQBYuXEjHjh356aeflAHE48ePc+jQIR4/fky+fPmoXbs2Dg4OSiWNNytffIz+88KFC/Tv3589e/YAMGLECDw9PbG3t/9X29FqtTRo0IDs2bOzYMGC/6lNQoh/zziMMHv2bAYMGECTJk1o27Yt27ZtY+vWrTx//pzp06fTunVrzMzMgL/6kYSEBAIDA1m5ciUHDhz4V+G9b51xXzx+/Hiio6OVCkY5c+Zk9OjR/PDDD8o98+zZs/Hw8PjbAM/Tp09xc3Njy5Yt7N27l+LFi3/6HfsCGR/D/fv306JFCx4+fEidOnVYuHAhWbJkeeeyxgGesLAwhgwZkmK78fHxLFiwAE9PTwIDAwkMDPx0OyWEEEJ8BiS8I4QQQgghhBBCfEaMf30aHR3Nrl27OHz4MKVKlaJMmTIMHTpUWdY4wGP8BbehQg+k/MJcCCG+dMaDt3q9noSEBOrXr8+LFy9YunQpBQsWJDk5mQ0bNjBgwADMzc05cuTIP4ZkUjPgaOiHGzZsyJYtWwDo1q0b8+bNA0Cj0WBqavqP2zGuwqZWq6XqjhCfmHE/8eLFC/z8/Lh27RqRkZG4uLig0WiIiYlhzJgxPHjwgBkzZtC8efO3AjyJiYkkJyeTMWNGqYr4HximXqpVqxZNmzbl+fPnLF68mF9//ZVBgwbh7++v9PmGAE+mTJmIjIykXbt2ynYMr1fv3r1p3bo1P/30U1rt0hfH+H0bHh7OsGHDsLW15eHDh8ybN49u3bqlWP7NAE+3bt24efMmUVFR9O3bV1nu4cOHdOjQARsbG2XaSvksI4QQ4lsi4R0hhBBCCCGEEOIz8eYUMNOnT8fOzo48efJw9epVbt26RbNmzZg9e7YyKGE8hVZQUBABAQFpuQtCCPHRHT16lLt379K4cWMgZV957do18uTJg7+/P2PHjuXVq1esXr0aHx8f1Go1R48exd7eHq1Wy8WLFylUqNBb20gtb/6NLl268OzZM27dusWxY8dwd3cnMjISeHvakPcxDg/IoL8Qqcf4XHsz3Dd8+HCeP3/O/v376dWrF/3791eC0zqdjhUrVjBy5Mi/DfCAhBL+i6VLl9KrVy86d+7MsGHDlGnHxo0bR0BAAHny5OHs2bMpplqaO3cuvXv35rvvvuP48eNYWloqx33r1q1s3ryZiIgIQF6TD2F8jKZPn87169fJnj07mTNnZvLkyZw+fZoZM2bg5uamrPPmObRx40ZGjBjBmjVrcHJySrH969evK49J5VAhhBDfmn/+SYcQQgghhBBCCCE+CcMX4ZMmTWLKlCnKFDCFCxfmypUr9O7dm7Vr1+Lg4MCsWbPQarXky5ePAwcOUK1aNUaOHIm1tTWDBw9O4z0RQoiP4/bt25QvXx4XFxfUajUNGzZEpVIpg4eWlpaYmJjw8uVLANatW6cEd+Li4pSgo06no1q1agQGBuLu7p7qg7PGwZqLFy+SNWtWparD5cuX6d69O9OnTwcgMjISExMTkpKSMDc3ByAhIQErK6u3tms8iCnBHSFSh16vV861Bw8eYG9vr1TIun37NrGxsezfvx+VSkVycjIAZmZmynnfunVrVCoVI0eOpG/fvqhUKpo2bYq5uXmKc1hCIv/evn37sLS0pFevXuTJk4ekpCTWrl3Ljz/+SL58+Th48CDp0qVLUdGsZ8+epEuXjipVqqQI9QDUq1ePevXqARIU+VCG960hMBUYGEjHjh2xt7fH1taW4cOH069fP1QqFX369ElxXM+ePUv+/Plp1KgRtWvXxsLC4q0gqiG4Y3weCiGEEN8KufIJIYQQQgghhBCfkYcPH7Jo0SLKlSuHp6cnhQsX5tWrV1y4cIHffvuN/PnzExwcjEqlUgYl8uXLx86dOylWrBitWrVK4z0QQoiPx9HRkSlTpnDz5k38/PzYsGED8HrwUKvVYmZmRrZs2di2bRsTJ05k6NChqNVqjhw5goODA/B6AHD06NEkJyeTK1euVG+z8UDkpEmTaN68OR07duT69evodDry5cvHtGnTqFSpEtOnT8fT0xNACe5s3bqVoKAgLl++nOptFUK8zRBOaN68OY6Ojvz555+Ympqi1+txdHQkIiKCTp06odfr2bx5s3KumpiYoNVqUavVtGrViqCgILJnz07btm3Ztm1bWu7SF8l40gi9Xs/Lly/Zt28f3333HaVLl0aj0bBq1Sq8vLxQqVQcOHBACWweOnSI2NhYZf0ffviBXLlyodVq3/v3JCjy94yPXXx8PCtWrKBjx4506dJFOe6NGzcmODiYokWL0rdvX2bOnKkc182bN9OzZ0+lSqihGtX7gqgSbhNCCPEtkso7QgghhBBCCCFEGnqzPP+ff/7J6dOniYiIoGDBgiQnJytTwJibmysDExqNhosXL+Lq6gqgTAVgGDiSigxCiK+Fp6cn5ubm9OvXD39/f+D1AKGJiQl2dnb4+PgwYMAARo4cSZYsWTh+/Di2trbA6z42JiaGn3/+mUqVKlGtWrVUbater1f6Xy8vL6ZPn061atXo3r17iqlBihcvzvTp03F3d2f69Ono9XpCQkLYtGkTI0eOJDk5maFDh6ZqW4UQ76fX66lQoQKxsbFUrFiRgwcPkiNHDgBKlSrFwIEDSUxMZNWqVcyZM4cBAwaQPXv2FPdhrVq14uXLl8ydO5cSJUqk7Q595t6sevPmvaxKpUKtVpMhQwbu3bvHlStXOHXqVIpKa4bAJoC3tze2traULVsWS0tL5XG5P/7vDMdu6tSpVKpUCZ1OR8+ePcmdOzfw12tomOIyICCA/v378+eff6JWq4mJieHhw4csX74ckLCUEEII8S5ydRRCCCGEEEIIIdKIVqtVgjsPHz5UHgPQaDRotVpWrVr1zilgkpOT6d27NzExMcr2DF+Cy8CEEOJrYegT3dzcWLJkCefOnSMoKIg1a9Yoy9SrV4/OnTuj0WgoXbo0t27dIjk5mZcvXxIREYG/vz96vZ6ZM2eSKVMmdDpdqrXX0KdPmzaNqVOn4ubmxowZM95ZFa1YsWJMnz6d6tWrExUVRe7cuenevTsJCQls374dBweHVG2rEOLdDMFqHx8fRo8eTUJCAkWLFuXmzZvKMqVLl8bf358mTZowadIkoqKiuHXrFpCyAk+nTp3YvHkzOXPm/NuqL98y4+mRjh07Bvx1L+vv709QUBDwujpZ7dq1uXr1KiEhIUqltTeDO5MnT+bSpUvUr18fCwuLT7w3X7f169czaNAgatasyb1795TPJfD6c4ihWlLjxo0ZP348NWvWJDg4mLFjx2JqasrRo0dxdnaWc0EIIYR4D6m8I4QQQgghhBBCpIE3qzO8fPkSb29vcuTIgbW1Nfv378fBwYGAgIB3DkwEBARw/vx5HB0dlcekvLwQ4mtiXHnh8OHD2NraUqNGDfbs2UNoaChqtZqmTZuSP39++vbti16vZ9GiRWzfvh1XV1ceP37M7du3yZMnD+vXr1cGz1Mz4KjX67l//z5Lly4lT548uLu7K1UJ3qVYsWLMmDGDn376ibNnz2JnZ8fYsWPJkSMHGo1GmR5RCPHpqFQqpYqIu7s7SUlJ+Pv7c+7cOXLmzKksV7JkSUaNGoVer2fChAkA9O/fX6nAY9hGunTpAAlXv4/h/rVDhw7ExMSwY8cOatasiaenJ9OnT2fUqFE8ffqUTJky0apVKzZs2MDcuXOxtbXl2LFjKe6Ply5dSlRUFAULFqRDhw5yb/yR1a5dm759+xITE8OjR484f/48RYoUUa6tKpVKCb81aNCAggULcv78eZ4+fUq9evWwt7eXCqFCCCHE31DpjScOFUIIIYQQQgghxCcVGRnJwIED6d27NyNGjCBnzpx4eHgQFRVFhgwZsLW1fSu4s2jRIkaOHEmJEiWIjo4mQ4YMabgHQgjx8RlPKejt7U1MTAzx8fGUKFGCw4cPk5CQQIkSJRg1ahRNmzYF4Pbt2+zbt4+ZM2fy+PFjcuTIQc2aNenSpQsODg6fbMDwwoULlC1bli5dujB9+vS/3UdDxYmkpCTMzc159eoVFhYWMrgpxGfAEL7R6/VcvXqVPHnyKM8Z91GnTp0iMDCQzZs34+fnR+/evVOEfMSHmTJlCuHh4Tx79oxq1aqxbt06hg4dyqBBg5Qpy/R6PTNmzGDKlCncvXuX4OBgChcuTL58+YiKimLhwoWoVCr279+Pk5PTW9Nxif/OECh9+fIlQ4cOZcaMGeTJk4e9e/e+FTh9c1pgA3k9hBBCiL8n4R0hhBBCCCGEEOITenNAtnXr1iQmJjJlyhTy5csHwIEDBxg4cCAnT56kf//+TJ48GbVajVqtZubMmYSFhaFSqdi9ezc5c+Z87xfkQgjxpQsLC8Pb25thw4bRuXNnihQpwuHDh1m3bh0hISEUK1aMoKAgJcADb/ez8GkHDI8cOUKlSpXo1KkTCxYsIDk5GTMzs7fa8uTJE65du0bx4sUBqZ4mxOfozb7D+J7rzQDPmDFjWL16NSEhIXh5eUlI4QMZH8eVK1fSvXt34uPjadmyJTNnzsTOzg74q2/XarUsWLCAWbNmKdNsAVhaWlK2bFkWLlyIk5OThCD/B++7ZhpeK0PF0GnTpvHdd99x8OBBbGxspGKcEEII8T+S8I4QQgghhBBCCJFK/i5U4+vri5mZGdu2bWPw4MG0b98+xRflGzduJDAwkBMnTpAnTx7y5s3L7du3+f3338mVKxebN2/GxcVFBiaEEF8lvV7PgwcPaNy4Mffu3WPXrl0ppp/SaDRERUUxaNAgSpYsyciRI2nWrBlAirDMpwo3Gv+dmzdvUqNGDRITEzly5Ag5cuRIUcHDsFyDBg1wcHBg1qxZyrQ6Qogvi/E5ffToUWbMmMGoUaNwcnJK45Z9WQx95JgxYwgMDCR9+vTo9XrWr19P9erVleNs3JfeuXOHzZs3c/36dQCqVKlC6dKlsbGxkfvj/4HxsTtz5gwPHjzg+fPnlC9fnqxZsyrvdwnwCCGEEB+fXEGFEEIIIYQQQohU8OZArmH6BJ1Ox8WLF5k4cSLp06dHpVLx6tUr5TmVSoVKpaJRo0Y4Ojqyc+dO5s2bx8WLF8mZMydeXl7069ePrFmzysCEEOKrpVKp0Gq13Lx5kyJFiijBHcPArampKd27d+e3335LUZGsadOmKarcpFZw582qBMZ/J2fOnFSvXp25c+fi6enJrFmzcHBwUKbG0ul0LFu2jF9++YUWLVpIPy7EF8z43C9btiwlSpTAzMxM7tH+JUN/2rBhQywtLVGpVERERNCsWTNiYmKoX7++spwhHOLo6EiPHj3e2pZOp5Nj/x8ZH7uxY8cye/Zsbty4AUCRIkXo0aMHnp6emJiYYGlpycSJEwGYNm0aFStWVAI88v4XQggh/hupvCOEEEIIIYQQQqSiWrVqkSlTJkJCQihQoIDy+Jo1a+jXrx93797F3d2dyMhIIGWAxyAxMZGkpCQyZcqkPCZfigshvnZ3796lbNmymJmZsW/fPrJnz/5WGGfZsmW0b98ec3NzsmbNyrx586hVq1aqtsu4/z1x4gQ3b97k7t27VKxYkezZs5M5c2ZevXpFtWrViIuLo1atWkRFReHk5ISFhQVz5sxh0qRJ6PV6du/ejaOjY6q2VwghPkfvm4LMYO7cuYwcOZIXL14oAR7j4OSlS5fInz//J2/318r4NfDx8SE0NJSaNWvSrFkzcubMiaenJ69evaJr165MmDBBuQ4aV+BxcHDg4sWLKT6zCCGEEOLDyaSrQgghhBBCCCFEKsqfPz9r1qxh4sSJXLx4UXm8efPmzJkzh0yZMjF9+nSmTJkCoEwFYKDX60mXLt1bX4JLcEcI8TXT6/VkzZqVFi1acPXqVTZs2IBKpVL6R41GA0DdunUpUaIEXbt2RaPR4OrqmqrtMq5KMHLkSBo1akTz5s1xc3OjVKlSeHh4EBsbi4WFBatXr6ZKlSrs3LmTEiVKUKJECfLmzUvfvn1JTk5my5YtODo6otVqU7XNQgjxudFqtUpQRKPR8ODBA+B1H2vQs2dPxowZQ/r06WnXrh2bN29WgjubNm2iR48ehIeHf/rGf6UMr8fMmTOZPXs27u7uTJ8+HU9PT1q0aIGVlRWPHj1i8uTJ+Pr6KtcuS0tLQkNDletwQkJCWu6GEEII8UWTyjtCCCGEEEIIIUQq8/X1JTQ0lPbt2zNixAi+++475blNmzbRqVMnXrx4wZQpU+jXrx/w9pQsQgjxNXpfX2eoAGDoI588ecKqVato3ry5so5Go2HKlClMnTqVkydPYm1tjYWFxSepTDZ8+HDGjx9PixYt6Ny5M9bW1qxevZqZM2fi5OTEokWLqFy5MjqdjuDgYI4dO8aZM2coUKAA5cqVw93dnWzZskkVNSHEN8e434uKimLVqlUcPnyY4sWLU69ePTw8PLC1tVWWnzdvHoGBgTx//pyQkBCeP3/O/PnzefDgAceOHcPJySmtduWrc/PmTTp06ICFhQWRkZEUKlSI58+fU7ZsWeLj4xk6dCjh4eHcvHmTwYMHM2HCBExNTQF49eoVCQkJZM6cWa5tQgghxH8k4R0hhBBCCCGEEOITGDRoEFOnTuXYsWOUKlUqRWn6DRs20LVrV549e8bUqVMlwCOE+CYYD+6dP3+e69evo1aryZs3L3nz5lWWM/zyH2D8+PFUr16d0qVLEx0dzdSpU8mWLRurVq3C2tr6k7R7x44dtGnThkaNGhEYGEj+/PnRarVs3ryZZs2a8d1333Hw4MG3KqY9ePAAe3t7Zb9lcFOIT0Pupz4fxq/F0KFDiYiIIE+ePJQqVYorV65w4sQJ6tatS3R0NFmzZlXW++mnn5g0aRJnz54FoECBAmzevJncuXNLX/oRXbx4kT59+uDp6UmrVq1ISEigWrVq/PHHH0yYMIHu3buzb98+GjZsiLm5OT169CAkJEQJ8MC7p0ATQgghxIeR8I4QQgghhBBCCPGJ/PLLLxQuXFj59/sCPNOmTcPNzS2tmimEEKnOeAA3KCiIGTNmcPfuXQDSp09PREQEbdu2JX369ADMmjWLkJAQ/vjjDwBsbW159OgRTk5O7NmzBxcXl082YDhx4kRGjBjBvn37KF++PBqNhuXLl+Pn54dKpeLo0aPY29vz6tUrkpKSyJAhQ4p9loFNIdKGv78/3bp1o0CBAmndlG9eSEgIQUFB9OjRg/79++Pq6sqzZ8/IlSsXz58/p3LlyixfvjxFgOfMmTMcP36cly9f0qpVK7JkySLBnY9Mp9Nx5swZSpQogUajwdvbm5kzZzJu3Dj69++Pubk5ly5domLFiiQmJpKQkEBQUBABAQFp3XQhhBDiqyDhHSGEEEIIIYQQ4hMzHrh9M8DTq1cv7t27x4IFC+jSpUtaNlMIIVKFcb/n7e1NWFgY9evXV/q88PBwjh07RkhICD179sTOzg6AI0eOcPToUdauXUvmzJnJnTs3gwYNwtHRMdUGcN/cbnJyMh06dGD37t3cunULvV7PmjVr8PHxQa1WExcXh4ODA/C6gkFsbCzt27f/ZFWBhBDvtnbtWlq0aEGDBg2IjIwkT548ad2kb9bhw4fp2rUrFSpUwN/fn4IFC/Lo0SOqVavG3bt3yZMnD3FxcVStWpWYmJgUAR5jUlHpv/uQY/f06VNq166NSqUiLi5OeVyr1VKhQgV69erF4sWL+emnn3BxcUnlFgshhBDfBrmzEUIIIYQQQgghPjHjigsqlQrD72oaN27M9OnTcXV1pXr16mnUOiGESF2GPvDHH39kzpw5eHh4EBERQfv27WnZsiWPHj0CwM/PjxkzZij/Ll++PB4eHmzcuJFly5Yxfvz4VA3u6HQ6ZbszZsxAr9djZmbGd999x7Nnz7hy5Qp79+59Z3AHwM3NjenTp5OUlPTR2yaE+HeqVq3KtGnTiI2NZeDAgVy+fDmtm/TN0Ol0Kf596dIlrl27Rrdu3ShYsCDx8fFUq1aN+/fvM3PmTPbs2UPVqlXZt28fbdq04c6dOwBoNJoU25Hgzn+j1WqVY3fjxg3Onj3Lr7/+SnJycorl7t69y++//46NjY3ymEajYdq0aVy5coWmTZuyb98+XFxc3npthBBCCPHfyN2NEEIIIYQQQgiRxowDPK1ateLYsWM4OTmh1WrTuGVCCPHfvTlga+zmzZssXLiQsmXL0qdPHwoUKMDTp08pXrw48fHxDB8+nNKlSxMYGMicOXOUAA+gBGoMIaDUmjLFMLg5YsQI3N3dGTZsGABFihRBq9XSvn17evXqhampKYcOHUoR3ImMjOTSpUvUr19fmfpLCJE2dDodmTNnpkOHDoSEhLBx40b69u1LQkLCO5c33JNdu3aN27dvf8qmfnWMK7zs3bsXrVZLlSpVWLNmDTVq1CApKYkuXbpw69YtgoKCqFevHpaWlvzwww+YmZlx4sQJatWqxf379zE1NU3jvfnyGYdSg4KCqFKlCsWLF6dw4cKULFmSefPmcfPmTQCcnZ3Jly8f+/fvZ+rUqTx69Ij58+cza9YsChcujJWVlbJdeW2EEEKIj0PCO0IIIYQQQgghxGfAOMBjYWEBpN6AtBBCfAqGAdv58+fz+PHjFM+Zmpry66+/0qlTJ4oUKUJCQgK1a9fm0aNHhIWFMWbMGLy8vNDr9fj5+fHjjz/y5MkT4O3wzsdmHJy8ePEiS5YswdPTk27dugHQvn17GjZsyLlz53j69CmrV68mW7ZsyjoxMTFERkbi6OjIwIEDMTMzS5V2CiH+mXGVEZVKRf/+/QkPD2fAgAEpwgfGVCoVe/fuxdXVlcjISJKSkv42jCjez3DshwwZQseOHYmJicHFxYUaNWoAcPz4cXbv3k2TJk3o3LmzMsVgnjx5cHR0xNXVlT///FOO/0dieD0CAgIYNWoUjo6ODB8+nEaNGnH//n08PDwICgriwoULWFhYEBERQZYsWRg0aBC5c+fGzc2NpKQkFi1aRKZMmeR1EUIIIT4yicMKIYQQQgghhBCfCcNAdGoNSAshxKcWERHBkCFD+P333/Hy8iJTpkwAZMuWjfPnz5MlSxa0Wi3Dhw/nwoULjB49mhYtWgDQpEkTihcvzosXL/D398fS0pJBgwalepsN4aADBw7w22+/8ezZM3r16kWRIkXQaDSYmpqyaNEimjdvzr59++jXrx9ubm5kypSJ9evXs2HDBkxNTdm+fTtZs2ZNUXlCCPHp6PV65Xz29fXll19+ISoqioEDB/7jvdbZs2exsLBgzpw59OjRg3z58n2KJn81jPu9DRs2EB0dzQ8//ECFChUAsLS0BF5P2/TkyRNat25NunTpgNev24oVKyhQoADbtm3j8ePHZM6cWfrS/4Hx9JI3btxg0aJFeHp6MnjwYFxcXHj16hW7du0iIiKCOXPmYGlpyYgRI/j+++/ZvXs3QUFB6HQ6smfPzsCBA8mWLVuqTVkphBBCfMskvCOEEEIIIYQQQgghhEgVjRo14rfffmP8+PGoVCqGDBmCjY0NAFmyZAFeD9TGxsZSoEAB+vXrpwzgajQabty4wQ8//MCTJ09o2bJlqrZVr9crA/rTp0/H09OTBg0aULhwYYoWLaoMVOr1emxsbNi4cSM9evRg3bp1SlUeOzs7KlasyLRp08iVK5cMbgqRhgzn84wZMwgNDaV///7odLoPCkl7eHiQmJiIj48P0dHRjBkzJkUfId5Pr9crIRudTselS5ewsbHB3d2dPHnypFjWMN3SwoULqVixIra2tqxYsYJdu3ZRoUIFtFotNjY2Etz5HxmuQ2vWrMHc3BwTExN69+6Ni4sLWq0WCwsL6tWrR65cuXB3d2fhwoU0adKEOnXqkDt3bubPnw/8FcqSa5sQQgiROlR6Q01uIYQQQgghhBBCvNebgwbypbUQQnyYq1evEhYWxowZMxg2bBjBwcHKgC3ArVu3yJcvH3Xq1GHt2rXK41FRUUyaNIlt27bh4uKCiYlJqvW9xtt9+PAhV65cYeDAgRw5coQMGTJw/PjxFJU3jK8JR44c4ebNm8THx1OqVCly586NtbW1XCeESCNv3rO1atWK+Ph4ZsyYQe7cuf/V+jVq1ODPP//k4sWLqdber5WPjw/Lly+nVKlSODk5ER4e/s4AVL169di+fTtFixbFzs6Oo0ePkjlzZg4ePEjOnDnTqPVfJuP3ruFYG/4bFRWFh4cHZcuW5cGDBxw9ehQbG5sU54pOp+Pnn3+mc+fO1KtXj02bNgF/BeEkwCaEEEKkLokqCyGEEEIIIYQQH8DwxXZYWBiPHz/GxMQEnU6Xxq0SQojPX+7cuRkyZAjdu3enQoUKKYI7APb29tSuXZv169czf/58bty4waxZs4iMjMTOzg57e3slBJNaYRjDdvv160ffvn2xs7MjKiqKWrVq8fz5c0JDQ7l3756yvKHyAED58uVp1aoVXbp0oUiRIlhbW6eYrkcI8WkZ7tl8fX2ZNGkSDx48oGPHjuTOnfuD7t2Mz+/g4GAKFiwo93z/0suXL3nx4gUPHjxg1apVnDx5kqdPn6YIfhiO8apVq+jQoQP37t3jxo0b1K5dmwMHDpAzZ05lGfFhDO99T09Pdu3alSJs07t3b0qXLs3Ro0e5f/8+N27cSPFeN6zfokUL8uXLx40bN4iPj0/xmklwRwghhEhdUnlHCCGEEEIIIYT4QKNGjSIoKIju3bsTHh5OpkyZpLKCEEJ8oISEBKysrN753Jo1axgyZAjXrl1DrVaj0+nImzcvO3bswNnZOdWmTDEe2Jw5cyaDBw+mefPmTJ48mWzZsnHy5Ek8PDw4efIkI0eOpE+fPtja2n70dgghPq7z589Ts2ZN7t27h1qtZurUqfTv3/9fbyc5ORlTU1NUKpVM3fSBDP3q48ePCQkJITo6GnNzc5YsWULlypXfuSzAlStXsLa2JmPGjFhZWck99n908OBBKleuTPny5QkNDaVSpUrKMU5OTqZ27drExsZSuXJl1qxZg62tLVqtFpVKhVqtRq/XU6xYMczNzdm/f78ylaUQQgghUp/caQohhBBCCCGEEB/I39+fli1bsmTJEoYOHcqTJ0/+cVDB8JsZ+cW2EOJb967gjqGPbN68OfPnz2fcuHG0a9eOoKAg9u3bh7OzM1qtNlUGzHU6XYoqAr/++iuVKlVi7NixZMuWDYASJUowbdo0ihYtSnBwMD/++COPHj366G0RQnxcrq6uTJ8+nYoVK6LX6zlx4gTPnj3719sxMzNT+gkJ7rzbm78PNxyvzJkz4+PjQ9euXbl79y4DBgzg+vXrby1ruEfOmzcv2bJlw8rKSqqX/Q/KlSvH6tWruXLlCj4+Puzduxe9Xo9Op8PMzIydO3dSuXJl9u/fj4eHBw8fPsTExER5fy9btoyLFy9SvHhxzMzM0nhvhBBCiG+LVN4RQgghhBBCCCE+QHJyMmZmZiQnJ9OhQwdWrVrFDz/8wIIFC96aAgb++iXx7du3cXR0TPGYEEKIv/xdNYtPUXlhyJAhPHz4kEOHDtG/f38GDRoEpOyzT548Sd++fTl//jzDhw+XCjxCfMaM+42VK1cyduxYTp8+zfTp0+nXr18at+7rYnysb9++zcOHD0lKSqJEiRKoVCpUKhUPHz5k4sSJhIWFUapUKVauXImTk1Mat/zrptFo2LRpE+3ateO7775jx44d2NnZodFoMDU1JTk5mWrVqnH48GFKlCiBt7c3jo6O7N69mxUrVvD06VMOHTpEzpw503pXhBBCiG+KhHeEEEIIIYQQQog3vBmyeXPw+NWrV7Rs2ZI+ffrQrFmz925n06ZNNG7cmBkzZuDm5paqbRZCCPHvPX/+nKxZs5KUlIS9vT0jR46kf//+SmDTmCHAc+nSJdzd3Rk6dCg2NjZp03AhBPD34T+DNWvWEBAQwPnz55k7dy7du3f/RK37uhkf+/Hjx7NkyRLOnz+PXq+nePHi9O/fn6ZNm5I1a1YePXrExIkTCQ0NlQDPJ6LRaFi3bh0WFhY0atQoxeOmpqZoNBpq1apFbGwsVlZWWFpaUqxYMTJkyEBkZCROTk4ydZkQQgjxiUl4RwghhBBCCCGEMGIc3ImPj8fa2lp5zvCL4Zo1a35QFZ1x48YREBBA/vz52bRpE3nz5k3VtgshhPhwhn781q1bVK9encuXL1O9enV27NiBWq1+56Dl6dOnadOmDSqViqNHj5IxY8Y0ar0Qwvgc3bp1KxcvXuTKlSvUrVuXkiVLKpUPAdauXcvw4cM5f/48c+bMoUePHmnV7K/OsGHDCA8Pp1KlSjRu3Jhnz56xbt06bt26RePGjRk/fjyOjo48efKEkJAQwsLCKFu2LEuWLCF37txp3fxvknGAp0aNGhw4cIBGjRoRHR2tVJWT4I4QQgjx6Ul4RwghhBBCCCGEeIeaNWuSkJDArl27sLKywsPDg6ioKKZMmUKfPn0wNzf/oCmwAgICGDduHNu2baN27dqfoOVCCCH+iWFQMikpCXNzc+7cuUPVqlW5fPkyHh4eTJ48GRMTk3cOXv7yyy/Y29uTNWtWmQ5RiDRiXPXF39+fqKgonj9/rjxWq1YtwsPDcXV1VdYxDvDMmzePbt26pUXTvypLly6le/fudOvWjcGDB1OgQAFevXrFnDlz8PT0pEKFCmzfvl0Jwz9+/JjQ0FBCQkJo0KAB69ev/8fKSSJ1GE+hVblyZY4ePUr37t0JDQ3F1tZWwjtCCCFEGpC7IiGEEEIIIYQQ4g0PHjzA1taWuLg4unTpgpubG1FRUQwZMoSWLVtiYWHxwYO1nTp1okSJEkyePJmEhIRUbrkQQqSeL/k3gDqd7p3/Njc3ByBbtmzs27cPFxcXpk2bhr+/vzJwqdVqU6xbuHBhsmbNik6nk+COEGlAr9crgY+AgIAUQZAXL17QsWNHtm3bRps2bTh79qyyXrNmzQgODqZYsWL06NGDpUuXptUufJGM+0LD9WDnzp1kzJgRDw8PChQogEajYc2aNYSFheHi4sK6deuwtrYmOTkZjUZD5syZ8fLyYuzYscyYMUOCO2nIUHnHzMyM/fv3U758eebPn8+wYcN48uQJJiYmX/R1XwghhPgSSeUdIYQQQgghhBDiHe7du8e4ceOYOnUqAB4eHgQEBJAlS5Z/va2hQ4dy//59oqOjZaBXCPFFMq4w8+zZsy9quijj6gExMTEcOHCA/fv3U65cOYoWLYq7u7uy7K1bt6hUqRJ//PEHw4YNY9y4ce+twCOESFtLlizBy8uLpk2bMnToUPLnz49Op6NkyZJcu3aN58+f891337F8+XIKFy6srLds2TJmzZrF/PnzcXJySsM9+Pzt3LmTlStXEhUVBfzVn+p0Ol6+fEnJkiVxcHBg//79JCYmsm7dOry9vVGr1Rw9ehR7e3sAzp07B7wOP6pUKuWaIn1r2jOuwFO9enUOHTpEs2bNWLx4MVZWVmndPCGEEOKbYprWDRBCCCGEEEIIIT43er2eLFmy8Pz5c+Wx8+fPY2Njozz/ISEcw3KTJk1SKjTIFCtCiC+Rod9q2LAhRYoUwdvbWxmU/ZzpdDplYNjLy4vIyEgsLS1Rq9WcO3cOjUbDxo0bWbhwIXZ2dmTPnp2DBw9SsWJFQkNDUavVjB07VgaXhfjMPHv2jFWrVpEpUybc3d3Jnz8/L168oEyZMjx79ow5c+awZcsW5s+fT5s2bYiJiaFo0aIAtG3blqZNm2JpaSnhkffQ6/W8evWKYcOGcerUKUxMTIiMjEwRZrSysiJHjhw8fPgQvV7P9u3b3xncAWjdujVlypQhOjoaExMT5Zoixz7tGVfg2bNnD4UKFUKtVktwRwghhEgDUpNQCCGEEEIIIYR4g2FAwdnZmb59+9KhQwd27dpFy5Ytefz4cYrwzd8VtFWpVMrULGq1WqZYEUJ80RISEnj06BHTpk1j1qxZPHjwIK2b9I8MU7KEhIQQERFB7969OXjwIL/++it79+6lbNmybNmyhebNm/PixQsAHB0dOXjw/9i764Cq7v+P4897KYMSQcEA7Nbpps6u2R1Ym92KBSKICBYYKHaLgRggds/Czul0OrsDRWfS3Pj94e+egbG570SUvR//KPcEn3Mu53PPve/X/XyOUrBgQSZNmsTEiRPT8xCEEO9hZGREoUKF8PLyolSpUsTHx9OgQQP++OMPxo8fj4uLC8HBwZQuXZrLly/TsWNHzpw5o2yfKVMmZT/iXSqVikyZMhEWFsZ3333HnDlz6N+/P/DmnCUlJaHRaChcuDC//fYbnTp1YsCAARgZGXHs2DEluKPX6wkMDOT58+dUqFBB7oO/UCkDPFevXmXdunXA1z1dphBCCPE1kmmzhBBCCCGEEEIIPjyajk6n4/nz54wYMYLFixfTqFEjQkNDsba2TvVt7efPn5MtW7bP3WwhhPisXr16RceOHYmMjGTo0KEMGjQIOzu79677dr+q0+mUMM3nFBUVRZ06dciePTsrVqzA2dlZaUtUVBSDBw8mIiKCNm3aEB4ermx3//59XFxcWLNmDU5OTp+93UKId6XsR6Kjo5X+Z9q0aYwYMYJRo0bh4eGBmZkZWq2W+vXrc/XqVe7fv0/16tXZs2cPxsYyIcHHMNznXr9+HRcXF86dO0fv3r2ZP3++ss6dO3eoUKECT548wc7OjvPnz5MzZ07gzWtAREQE3t7eODg4sH79+q9ixLYvyduvm2k9gmfK3yejhQohhBCfn4R3hBBCCCGEEEL856UM4URFRaHRaMiaNSs2NjbKOvfu3WPcuHFKgCckJERZvn37dpYvX46bmxsVK1ZMl2MQQojP5dWrV7Ro0YLIyEguXbpEkSJFPrju0aNHuXPnDh06dPiMLUzt119/pVy5cowZM4ZRo0Ypfb6hSPngwQN++OEHrly5wvbt22nQoAFJSUmYmpoq62g0Gin4C5EOPjb0165dO/bv38+9e/cwMzNTHq9atSrNmjVDrVbTpk0bnJ2d07C1GUvK++OnT5/yww8/cPHiRXr06JEqwLNhwwb69OmjTLPVqlUrTExMWLJkCaGhoQAcOXIER0fHdAtxfo1ShmcuXrxIiRIl3nlcCCGEEBmL3CUJIYQQQgghhPhP0+l0SmFi/Pjx1K5dm+LFi9O8eXNmzJihrJc3b158fX3p2bMn27dvp3Pnzpw9e5aQkBA8PDw4ePAgefPmTa/DEEKIz8bS0pINGzZw5MiRvwzuXLlyhapVq+Lu7s7hw4c/YwtT02q1AFy+fDlVMdownWHu3LkZPnw4ANevXwfA1NQU+HMaRQnuCPH5abVaJehx8OBBlixZwtixY9mxYwdRUVHKei9fvuTZs2ckJiZy69Yt4E3AISQkhMuXL1OsWDGGDRuGs7MzGo0mXY7la5OyrwwNDWXDhg1kzZoVExMTFi5ciKurq7JuvXr1CA4OJmvWrPj6+lK+fHlKlizJrFmzyJcvnxLcSfl8ir9neP2pUaMGLi4uHD16VHlcvpMvhBBCZEzyrlMIIYQQQgghxH+aoYjg7e3NxIkTKVSoEJUrV+bw4cMcOXKE27dvM23aNADy5MmDr68vJiYmBAcHs337dkxMTHBwcODo0aPkypVLvlEshMhQDN/wNxQKVSoVWq0WKysrKlWqlGqdt5mYmNCvXz8WLVrE/v37qVq1apq29UP9b8GCBcmVKxfHjh3jxIkTVK5c+Z118uTJA7yZhiclGd1AiPSRMlzt7e3NvHnzePnypbK8atWqDBw4EBcXF6ysrKhatSp79+7Fzc2NoUOHcuLECZYvX469vT3ff/+9sp0E8T6O4dy7u7uzZMkSnJycqFKlCjY2Nhw8eJC5c+ei1+uZM2cOWbNmpWnTplSsWJGwsDDu3LmDiYkJVapUoWrVqu9MNSv+maZNmzJ8+HC8vb0JCAigcuXK8tokhBBCZFAybZYQQgghhBBCiP8kQxFBr9dz9epV6tWrR9OmTRk6dCgFChTg6NGjDBo0iDNnzuDq6srMmTOVbaOjo9mzZw+RkZFYW1szZMgQcuXKJYUJIUSGkrJP02q1vH79OlUR9mPCir///jt9+/YlKiqKQ4cOYW9vn+Zt3bx5M6dPn8bNzQ1ra2sAAgMD8fb2pnXr1kyePBlHR8dU248bN44JEyawYsUKWrdunSZtFEL8c35+fowbN46OHTvSq1cvbG1t2bNnD8OHD8fY2JjFixcr0/J169aN5cuXK9uWKFGCLVu24OzsLOHq/8HSpUvp0aMHQ4YMYeDAgeTLl4/k5GQuXLhA69atuX37Nv3792f27Nl/uR859/+blMHYBQsW0K9fP7799luCgoKoVq3aX25jOOfy3kQIIYT4ukjMXAghhBBCCCHEf5Lhg+wDBw5gbW1NpkyZ6NOnDwUKFECn01G5cmUWL16Mq6urUpQwBHhy5MhBx44d6dixIxqNBmNjY/lwXAiRoaTs0+bPn8/WrVs5ffo0Tk5OVK1alb59+1KoUKG/7fuKFy9O9+7d6d69O7du3UqT8E7KETp8fHxYvHgx0dHRlCpVChcXFwCaN2/OiRMnCA8PJz4+nh49etC4cWOMjIwIDw8nJCSE4sWLU6tWrU/ePiHEh31o5C6Ao0ePMn/+fFq1aoWvry+FCxdGo9Fw9+5dTE1NyZs3Lw0bNlTWX7p0KXXr1uXZs2dYWFjQuHFjbG1t5R7tf6DX6zly5AgmJiZ06dKFfPnyAW9GIytbtiy7du2ievXqzJ07F0C5V05KSlKmHTQ8txLc+d+kvDb69OlDXFwc7u7uf7mNSqVi165drFu3jqlTp2JhYfE5miqEEEKIT0RG3hFCCCGEEEII8Z81depUPDw8KFOmDKamppw4cQKdTgf8OZ3WuXPnGDBgAEePHk01Ak9ycjImJibp1nYhhEgrKQuG7u7uzJgxg4IFC1KsWDEeP37M8ePHcXZ2Zs2aNVSoUOGj9jN69Gh8fHzSdMoab29vJk+eTPfu3RkwYABlypRJtfzcuXNMnjyZjRs3kpSUROnSpdHr9dy4cQNra2sOHDggI3QI8Zm9fv0aCwuL94Z4Fi9eTJ8+ffj555+pU6cOGo2GiIgIPD09UavVnDp1CltbW5KSknj58iV2dnbv7F+u5/+NVquladOmnDhxgqioKExNTd8ZzSUyMpKGDRuSmJhInz59mDdvXno3+6v19t9pYmIiZmZmAOzfv18Jlt66dUsJUr3P06dP+e6777h79y4BAQF4enoCMgWkEEII8bWQu1YhhBBCCCGEEP9ZtWvXpmDBgly4cIE//viDV69eoVarSfk9lzJlyjBnzhwqV67M7Nmz6datG4AEd4QQGZahyDd79mxmzZpF//792bZtGxs2bODo0aO0a9eO27dv07dvX7RaLR/6bqBh6g54E94xNjZGo9GkSZt37NjBvHnz6NixI97e3qmCO4b2lSlThoCAAJYsWUK5cuV49uwZarWaTp06cfToUZydndFqtVLoF+Iz2bdvH9988w3Hjh1DpVIp16qh3/j1118xNjamePHiJCcns3btWiW4c/LkSWxtbQG4d+8eCxcu5MmTJ8q+DfuS6/l/ly1bNp4/f86aNWuAP8+lkZERGo2GokWLkjdvXuzt7VmwYAEjRoxIz+Z+1QzntnHjxkRGRirBnQEDBtCnTx8OHToEgJOTE8AHX3etra2ZPXs2efPmZefOnahUKgnuCCGEEF8RuXMVQgghhBBCCPGfpNPpKFu2LOvWraNQoULcvHmTYcOGAW+KElqtVlm3TJkyzJ07l6JFixIeHs7z58/Tq9lCCPFZxMbGsmHDBgoWLEjfvn0pUKAACQkJbNy4kaNHj1K0aFF27dqVaiqa9xUT3y6cp9XIO6dPnyYhIYG+ffvi7OycalnKwqWTkxPt2rXj0KFDnDlzhsOHDzNjxgxy584tU+sI8ZkdOHCAW7du0bVrV06dOqUEeAzX7DfffENycjInT54kMjISLy8vJbiTcpQdd3d3Fi1aRFJSkvKYBBb+OcO9r2Eqwt69e5M1a1bWrFnDrVu3lPWSk5MxNjbG3t4ea2trunbtSoMGDejTp096NT1D2LBhAzt27KBJkyZcu3aNkSNHMm/ePJo2bUqRIkWAP19TP/T3bWxsTO3atenevTvHjx9n27Ztn639QgghhPj3JLwjhBBCCCGEECLDMxSU3y4s63Q6SpUqxdq1aylatCiLFy/Gw8MDeDfAU7p0aSIiIrh58ybZsmX74DdehRAiI3j69CnHjx+nfv36FC9eHI1Gw6ZNmxg8eDBqtZoDBw4oxfNz587x+PHjdCmW63Q69Ho9e/fuJUuWLOTPnx+9Xq+M3JFyPfizOG1iYkK2bNnIlCmTEtiR4I4Qn9eYMWPw8/Pj2rVrtGvXLlWAB6Bw4cIYGRnRt29funXrhrGxMSdOnEgV3Jk/fz5nzpyhefPm7502S3zYh/pJQ0CkQIECtG3blp07dzJmzBguXryIXq/HxMQEnU7HihUruHfvHl26dGH79u04Ozun2ehq/wUtW7Zk9uzZaLVaihcvzoQJExg7diyDBw8mR44cH72fLFmy4OLiQqZMmbh27VoatlgIIYQQn5qEd4QQQgghhBBCZGharVYpKD9+/Jhbt25x69Yt1Gq1UpwoUaIE4eHhFClShKlTp35wBJ7ixYuTM2dOdDqdfKNbCPHVShk+1Ol0qfo5g/j4eLRaLRqNBo1GQ3h4OMOHD39n1IvExERcXV1Zu3ZtuoQa1Wo1KpWKIkWK8Pz5cy5cuIBKpUo14o9Op0OtVvPixQu6devG69evpQ8XIp0Z+h0/Pz9GjRrF7du3lQCP4fqtXr06Q4cO5fHjxzx58oQ5c+akCjGsWrWKoKAgbGxsGDFiBKamphKu/kgppwgMCwtj0KBBVKxYkb59+zJnzhwA8uTJQ//+/WncuDEhISH07t0bf39/fvvtNyZMmEBAQAA5c+ZUpi+DtBtdLaMzBKf69+9P8eLFMTIywtjYmDJlyuDo6PiXU1S+T/HixVm3bh2urq5p1WQhhBBCpAGVXu5mhRBCCCGEEEJkUCmnQJk2bRqrVq3i2rVrqNVqfvrpJ9q3b0/lypWV9X/77Tfatm3LlStXcHd3JzAwEPiz8CuEEF+7lFPSJCQkkClTJmXZsmXLKFy4MJUrV0ar1VKpUiViYmIYPnw4Y8eORa/XvzNdzejRo5kyZQqrV6+madOmn63tb5s/fz79+/enQYMGzJo1iwIFCgBvpncxMTEBYMqUKQwfPpydO3dSr169NG2rEOLvpbxP8/X1Zfz48Tg7OxMWFkb58uWV9fr06cOiRYtwcHCgZ8+e5MyZkwMHDigjbh08eBBnZ2eZ+u4jpbyvHTZsGLNmzSJTpkyo1WpiY2PRaDTUq1ePVatWYWNjw9mzZwkNDWXJkiW8fPlS2U+hQoXYtWsXzs7Ocq/8Lxhe25KTk3n+/DkFChSgePHinDt3DrVazb59+/j+++8/+hy//Vqp0WgkVCWEEEJ8JSS8I4QQQgghhBAiQ3q7MBEUFESJEiWoWrUqCQkJhISEUK1aNQYPHkzLli2V7VIGeHr37s38+fPT6xCEECLNVKtWDY1Gw8GDBzExMaFv374sXLiQ0NBQWrdujYmJCf7+/vj5+ZEpUybs7Oy4fPkymTNnVvaxatUqfHx8KFy4MGFhYVhZWaVZe1MW5XU6HTqdLlUxUqvV0rZtW7Zs2YKrqyu9evWiWLFiyvJ169bh7e1N7ty5Wb9+PdbW1mnWViHEX/tQCCFlgGfNmjVUqFBBWTZ69GiCg4N5/PgxGo2GPHnyULVqVSZPnkyePHkkuPM/mDhxIj4+PvTt25d+/fqRPXt2bt26xZAhQzh16hSVKlXi559/JmvWrLx+/Zro6Gi2b99OYmIiefLkoU6dOtjZ2cm5/xdSXgt//PEH2bNn58WLF+h0OsLCwhg6dCgqlYrIyEgqVqz4ztRm79uPEEIIIb5eEt4RQgghhBBCCJGhTZ06FT8/P3r06EG/fv0oWrQoL168oEiRIjx9+pTSpUvj5+dHixYtlG0uXrxI7dq10ev1XL16VYq8QogMJT4+nho1anD69Glat26Nra0tCxYsYNCgQQwfPpxcuXIB8PTpUzp06MDevXupWLEie/bsITExERsbG6ZNm8bs2bPR6/UcPHiQPHnypFnxMOV+58yZw8GDB3n06BGNGjWiSZMmlChRAr1ez7Fjxxg5ciQHDhygTJky9OvXD3t7e/bu3cv69etRqVQcPnwYR0dHKXQKkU5SBj3Onj1L5syZKVq0qLLcz8+PcePGvTfAc+3aNZ49e8aTJ08oXbo0dnZ2ZM6cWcIj/4OoqCjq1KlD9uzZWbFiRarRc6Kiohg8eDARERG0atWKiIiID+5H+tL/Xcq/20WLFrFu3Trq1KlD7969lTDs9OnT8fLyShXgMThw4AAPHjygY8eO6dJ+IYQQQnx6Et4RQgghhBBCCJFhXbhwgY4dO1KkSBH8/f0pXLgwr1+/pnz58sTExFCvXj2WLVtGqVKl8PPzo1WrVsq2V65cwdLSEgcHh7+cqkUIIb4mhkJrUlISHTp0YMOGDQAMGjQIf39/smbNCvxZVIyKiqJ3795s27aNrFmzYm9vT0xMDM+ePaNo0aJs2rTps01XYxhFLUuWLKjVamJiYihRogTz5s2jatWqaDQazp8/z6xZs1i+fLmynYWFBeXKlWPZsmU4OTlJoV+IdJLy2pswYQILFy4kOjqaS5cu4eDgoExx96EAz/vux+Qe7X/z66+/Uq5cOcaMGcOoUaOU58bwGvHgwQN++OEHrly5wrZt22jYsKGc608oZejJw8ODBQsW4ODgwLhx42jbtm2q5TNmzMDT0xOVSsX+/fv5/vvv2bJlCx4eHmTPnp0dO3ZgaWmZnocjhBBCiE9EJroUQgghhBBCCJFhpPygW6/Xc/fuXWJiYujZsyeFCxcmNjaWqlWr8vz5c6ZMmULz5s1xcHBgwoQJTJkyBZVKpUyhVaRIEQAp8gohMhS1Wo1Wq8XU1DTVNFcXLlwgS5YsAGg0GoyNjdFqtTg4OBASEsLWrVvZsWMH9+/fJ2fOnNSsWZO2bdum6ZQpKfv0TZs2ERwcTP/+/enatSu5cuVi6tSpTJs2jY4dO7JixQpq1KhBuXLlWLp0KW3btuXZs2c8fvyYChUqUKpUKaysrKRPFyKd6PV65dobNmwYM2fOpHXr1nTo0AFHR0fgz3uuMWPGADBu3Djat29PWFgY5cuXf294RMIk/xutVgvA5cuXU/WLarUanU5H7ty5GT58OD169ODGjRuAnOtPyfDaNmHCBKZPn06/fv0YOHAghQoVUpYbnpfBgwejUqkYOXIkNWrUoFKlSly6dAmA7du3Y2lpKcEqIYQQIoOQkXeEEEIIIYQQQmQIKYu8r169wtLSkqioKM6ePUujRo1ISkqiV69ebNiwgUmTJtGzZ09MTExYu3Yt7dq1w9TUlOzZs7NkyRLq16+fzkcjhBBpLzAwkFu3bnH58mUiIyNp3Lgxq1evxtzcXCkaGoI8BjExMZibmys/p9WUKSkLkc+ePWPPnj0EBAQQERFBwYIFlfUCAgLw8fEhd+7crFy5kurVq39wnzK9ixDpLzg4mH79+tG/f38GDx5Mvnz5Ui1PGSQxjMBTqFAhli5dSuXKldOjyRnSy5cvKVGiBKampoSGhqY6t4a+cvfu3dSvXx8fHx/Gjh2bjq3NmC5evEjz5s3JmzcvwcHB5M+f/511Ur5uLVu2jJCQEO7cuUOhQoVYuHAhjo6O77xOCyGEEOLrJe9WhRBCCCGEEEJkCIYPtl1dXenUqROxsbE4ODjQoEEDAG7evElkZCS1a9emT58+ytQM33//PaVLl6Zv376o1WpKly6dbscghBCfg06nA8DNzY25c+eyZ88eGjZsyLZt2+jQoQOxsbEYGRmRlJSkFASfPXsGkCq4A3yyMMyjR494+fKl8rMhuDNw4EBKly5NUFAQZcuWpWDBguh0OjQaDQDe3t74+/vz4MEDfvzxRw4dOgS8Cf+8TYI7QqQfvV5PcnIyGzduxN7eHldX13eCO4AydRPAmDFj8PPz49q1awwbNozk5OT3Xtvi/Qzn8W16vR4rKysGDx7MvXv3mDlzJnfv3lWWG/rK48ePkylTJsqUKfNZ2vtfc+/ePW7evEmnTp3eG9yBN8+F4fWua9euhIeHc/DgQSIiInB0dESr1UpwRwghhMhA5B2rEEIIIYQQQogMQ6fTsX//fs6dO0dcXFyqZbdv3+bevXuUKVNGKUpotVrmzZvH8+fPGT9+PNevX8fBwUGZSkAIITKCtwu4huJ3ymlSVq9erQR42rdvz6tXrzA1NUWr1bJjxw4CAgI4ffp0mrTv7NmzlChRgvnz57/Td2fOnJmHDx9y8eJF5ThUKhVGRkZKXz1ixAglwNO5c2f27dsn04cI8YVRqVQ8ffqUffv2KUE8QyghJb1er0zdBG9G35k8eTKrV6/GxMREru2PpNVqlfvd7du3ExoaysaNG3nx4oVyDps0aULz5s0JDw9n4MCBbN68WelXw8PDCQkJoXjx4tSqVSvdjiMjMrwGG6a+Mpzzt68Hw+OvXr3i8ePHANja2pI7d27Mzc1TTUUnhBBCiIxBwjtCCCGEEEIIITIEQ7FnyJAh3L17l8DAQODPbw87OzuTNWtW9u3bx6lTpwBYs2YNGzdupHz58hgbG2NmZgYgH4QLITKMlAXciIgIhg4dSsOGDRk7diy//fabsp6lpSVhYWFKgKdt27Y8fvyYtWvXMmTIEFauXImTk1OatPHZs2eo1Wp+//33d0YQmDx5Mv7+/sTGxrJixQr27NmDSqVCpVKhVqtTBXgmTJjAnTt38PT0JCkpSUboEOILY25ujrW1NbGxsQAYGxunuk51Oh0qlYrHjx9z4sQJ5fFhw4bh5OT03rCPeD/Dvezw4cNp0qQJnTt3plWrVrRt25YNGzYAUKxYMUaMGEGHDh3Ys2cPrVu3pkKFCpQrV45evXqRlJREREQENjY2HxzFR/xzhvBUmTJlUKlUyt+6sbGxcp4NwRydTsdPP/3Eli1bPrgfIYQQQmQcKr28ixVCCCGEEEIIkYHcvn2bGjVqYGRkxJYtWyhRooRSvB43bhzjxo3Dzs4OW1tbrl+/To4cOTh48CCOjo7o9Xr5IFwIkWHodDoluDN8+HBmzJiBiYkJVlZWREVFUaxYMQYOHEifPn2Uvi8mJoZOnTqxadMmMmXKhF6vx8HBgT179pA/f/5U+/xUtFotly5dwsnJCQsLC06cOEHhwoXJli2bss7kyZPx8vKiaNGiLFiwgGrVqgFvCpw6nU4pVM+ZM4emTZvi6Oj4SdsohPh39Ho9L1++pHr16ly4cIFVq1bRvn17ZRn8GUZo3749x44d45dffsHW1jbd2vy1W7BgAe7u7jRu3JiaNWty/fp1Fi9ejJWVFSNGjKBfv37Am6llT506RVBQENHR0WTPnp3vv/+eESNGkDt3brRarQTb/0d/9d7i5s2b/PDDD9y+fZt58+bRp08fAJKSkjA1NQVg2bJlDBo0CD8/P4YOHSrTPwohhBAZnIR3hBBCCCGEEEJ8dd4uHhuKCoYPyBcsWEC/fv1SfRAO8PjxY7Zs2cKkSZOwsrKiSJEiTJ48WQoTQogMzc/Pj4CAADp16oSrqyvlypVj3bp1uLi4UKhQIQYMGMDAgQOVAmNcXBxTp07l1q1bmJiY4OfnR65cudBoNO+MjPNvvV3YnDVrFoMHD2bOnDl07NgRKysrZZm/vz+jRo2iZMmSzJkz54MBHkD6dCG+UIa+p2bNmowePZrq1asry3Q6HWFhYfj5+VGpUiUWLFhApkyZ0rG1X7fevXvz4MEDZs+eTb58+dBoNOzbt48OHTpgbGyMn58f/fv3V9ZPSkoiNjaWzJkzY2JiokxPKH3p/yblubt//z6vX78mJiaGsmXLolarUavVbNq0iZYtW2JtbY2vry9DhgxRtl+zZg2jR48mU6ZM/Pzzz+TIkSOdjkQIIYQQn4uEd4QQQgghhBBCfLUuXLhAyZIllZ8NReALFy7QoEEDAA4cOECBAgVSbffq1SvMzc1JTk7GzMxMChNCiAxr69at9OvXjyZNmuDm5kahQoV4/fo1lSpVIioqiuTkZDJlysSoUaNwdXVVgjSG/tQQ2Pkc/aRer2fnzp2MHDmSe/fuMW7cODp06JAqwDN+/Hh8fX3fCfAIIb4OL1++ZOzYscyYMYOyZcvSo0cPZQSeFStWMGfOHLRaLZGRkeTOnVtGRfxI7+ujv/32Wzp06MCwYcOAP/v1yMhI2rRpg5GREaNHj1ZG4EmLkdX+q1Key4kTJ7JmzRquXr1KcnIy33//PW3btqV79+5kzZqVJUuW0LNnTwBq1KhB3rx5efjwISdPniRbtmwcPHgQJycneX6EEEKI/wB5pRdCCCGEEEII8VUaOXIkpUuX5qeffuL8+fO8ePFCKe6ULFmSH3/8kYcPH3Lo0CEANBoN8KZwYWFhgVqtxszMDL1eL8EdIUSGlJiYyKFDh9DpdHTv3l0J7lSsWJGnT58yZ84cli5dSnJyMtOmTWPGjBnK9DU6nQ5AGWnnc/STKpWKevXqMXXqVJycnPD29mb16tW8fPlSWcfHx4exY8dy4cIFBg8ezN69e9O8XUKIv/ex3xG2srJi0KBBeHl58csvv9C/f3/KlClDkSJFcHd3R61Ws3v3bmVURAnu/L2UI4+Fh4czYcIE5s2bh6OjIzY2NsCbUXUM57JmzZpERESg1WoZPXo0CxcuBJBgyCdkOJceHh54e3tjbm6Op6cnXl5ePHjwgJEjR9K7d2/i4uLo3r07e/bsoVatWty4cYOVK1fy8OFDWrVqxdGjR3FyclKmABZCCCFExiYj7wghhBBCCCGE+OokJSURGhrKtGnT+P3337GysqJ69eoMGzaMsmXLkjVrVu7fv0+VKlXImTMnJ06ckOKPECLDe3uECr1ez7Rp07C3t6djx44kJCTQpEkTzp07x+TJk+nUqRM6nY7mzZuza9cuChcuTNeuXfH09EzXPlOr1XLgwAGGDx/OzZs3CQgIeGcEngkTJjBy5Ehq167N9u3bMTU1Tbf2CvFfl7LvuX//Pnny5Pmo7SIjIwkODub+/fvY2NhQqVIlOnfuTI4cOWRUxP/B8OHDmTJlSqrHatSowc8//4yJick75zQyMpL27dsTHR3N8uXL6dSp0+ducoa2Zs0aunXrRteuXZWR7wAWLlxI3759KVeuHJGRkZibmwPw7NkzNBoNt27donDhwmTOnJlMmTLJtSCEEEL8h0h4RwghhBBCCCHEF89QFDK8hTUUiJ49e8bhw4dZtGgR27ZtQ61W06xZM1q1akWHDh3o1asXy5YtY+bMmbi6uqbnIQghRJpKOZ3G2/9PSkoiU6ZMLF26lP79+zNo0CD8/PzIkiULAGPGjGHdunVcuHCBYsWKceLECaWYmF4M0+Z4enp+MMAzc+ZMmjVrhrOzc/o1VAihGDFiBNHR0QQHB//tFD9/NR2WTA/0cVKepxUrVjBw4EDatGlDw4YNSUpKYty4cVy+fJl+/foxderU9wZBfv75Z9zd3dm5cye5c+dOr0PJUAx/27169WLjxo3s37+fkiVLotFoCA8Px9fXF61Wy+nTp8mePTsJCQmYmpq+929epo0TQggh/lskvCOEEEIIIYQQ4ouWssig1+t59eoVVlZWJCUlpRppYe3atezevZvFixcD0LZtW5ycnJgxYwZNmzYlPDxcPvwWQmR47u7uxMTEMGfOHIyNjVMV/oYNG8acOXO4cuUKjo6Oyja1a9emcOHCDB48GEtLS3Lnzv1ZCoZ/9ztSjsBz48YNJkyY8E6AB95Mi2iY3ksIkT6ePXtG/fr1uXLlChcuXEjVx/yVlAEUCSp8vLfP1dy5c1m2bBmrV6+mQIECAERHR1OzZk0uX76Mq6srkydPfm+AJzExETMzMxnh5V94+9y9fv2aSpUqYW5uzvHjx0lISGDTpk0MHz4ctVrNyZMnsbOzA+Dy5cu8ePGC77//Pr2aL4QQQogvhMTXhRBCCCGEEEJ8sVJ+EL5gwQJatGhB8eLFqVq1Kr6+vty+fVtZ18XFhYULF3Lo0CF69OjB/v37CQwMJCkpiZ9//pmYmJh0OgohhPg8YmNjWbx4MYsWLcLb2xuNRoNKpSI5ORmAP/74g8TERI4ePapsEx4ezp07dyhcuDDFihUjd+7caLXaNCmga7XaVD//3e8wMjKiRo0aTJo0iQIFCuDr60twcDCvX79OtZ4Ed4RIfzY2NrRs2ZKYmBhCQ0PR6/XodLq/3S7laCMS3Hm/t89jyuDO4MGDKVasGGvWrKFBgwZKcCc5OZkcOXJw5MgRihYtyuzZsxk+fDgJCQkYGRml6o/NzMwAJLjzP9LpdMq5O3nyJAAWFhZky5aNxMREALZv3/7e4I5Op6NNmzYsWbIEjUaTPgcghBBCiC+GhHeEEEIIIYQQQnyR9Hq98kG4u7s7AwYM4NKlS5QrV46EhAQmT55M/fr1OX/+fKrtqlSpwowZM/jll1/o3Lmzso6FhQUy+KwQIqPS6/VkzZqVq1evUqhQIaZMmYKXlxcajQYTExMAevfujY2NDePHj2fkyJEMHDgQNzc3ADp27KjsK60KuIb9enp6MmPGjI/epmbNmgQGBmJubk5wcLAU+IX4whjurwYOHEjhwoXZvHkzKpVKpr76RAznce/evSQnJ6NSqZRAz7Vr17hy5Qrnz59XgurJycmYmJig0WjIli1bqgDPiBEjlACP+DQMz8/QoUP5/vvv2bp1KwDffvst586do1u3bgwZMgQjIyOOHTumBHcAgoKCiI6OpnTp0nK9CCGEEELCO0IIIYQQQgghvkyG4uysWbOYOXMm/fv3Z/v27WzZsoXTp0/TqlUrrl27Rs+ePdHpdKmCOZkzZyZPnjwsWbKEdevW4eTkpIxAIYQQGcHbYUSVSoVGoyFnzpwcOnSI/PnzExQUpAR4AIoXL05gYCCxsbFMmDCBJUuWkDdvXvbt24e9vf07I+OkhZs3bxIYGKiMTvAxjIyMqF69OitWrGDv3r2Ym5tLGFOIz+Tta+19o+moVCr0ej2ZM2emUaNGnDx5knnz5n2uJv4ndOrUiYYNG7J27Vo0Go0S9Ni6dSsdO3bk1atXREREEBUVhYmJCVqtFmNjYyXAc/ToUUqVKsWMGTMICAhI56PJGFJeC7t27WL16tX06NEDJycn4M1zZmJiwvLly0lKSuK3337D3t5e2Xbt2rXMnz+fokWL0r59ewnvCCGEEAKVXt7pCiGEEEIIIYT4QsXExNCkSRP++OMPwsLCKF68OPHx8fz8888MHDgQc3NzDhw4gJ2dnTKFwNv/QurpBYQQ4mun0+mUIp9hhAXDYxqNBmNjYx4/fkyVKlW4efMmQ4cOZeLEiZiYmJCcnMwff/zBgQMHyJUrFyVKlMDGxibVNIVpSavV0qFDB9avX09kZCRVq1b9223e7sNTHr8QIm3Fx8eTOXNmdDodKpVKuRZv3LiBnZ0dlpaWqa7RixcvUqFCBRo1asTatWvTs+kZypkzZ2jbti0ajYaAgABcXFyUUdV0Oh1du3YlNDSUChUqsHHjRiWQaWRkpLwuPHv2jNatW7Ns2TIlYCL+vXv37rF+/XoWL17M5s2byZcvn/I6FRERQdu2bTEyMiIwMJDGjRtjZmbGggULCA0NRavVcvToURwdHeW1TQghhBAy8o4QQgghhBBCiPR17tw5nj59+t5lT5484fjx4zRs2JDixYuj0WjYvHkzgwYNwsjISAnuAJw/f54//vhDKR6lLPRKcEcIkZEYins+Pj6MHDmSuLg41Go1Op1OGWkhZ86cHDlyBGdnZ6ZNm4aHh4cS9LG3t6ddu3ZUq1YNGxsbdDrdZ5tCxcjIiCZNmqDT6Vi/fj16vf69I3mk9HYfLsVNIT6PvXv3UqFCBS5evIharVZG4fHz86Nw4cJ06tSJPXv2EBsbq2xTokQJ2rdvz7p164iMjEynlmc85cqVY8OGDZiamjJw4EBWr14NoIzCs2zZMjp27MjJkydp27Ytjx8/xsjIKNUIPDY2Nuzbt08ZkVL8eyNHjqRhw4asX7+esmXLki9fvlSjfbZp04b169djYWGBm5sbpUqVUkbGc3R05MiRIzg6OqLVauW1TQghhBAS3hFCCCGEEEIIkX6uXbtG2bJladKkyXsDPPHx8eh0OjQaDVqtlvDwcDw9PVGr1Zw8eVIJ7iQkJODq6irf8BZC/Gc8ffqUhQsXMn36dIKCgj4Y4AkPD8fKyoqlS5fi6elJcnLyO/tKi4Lh24EcvV6vFP47d+5MjRo1CAsL4+nTp6lCAUKIL8eWLVu4ePEiP/30E5cvX0atVhMbG4uDgwONGzdm27Zt1KtXj2bNmhEUFKSEeNq1awdASEgIcXFxfxvQEx+nVKlSrF27FjMzM3LkyAGAsbGxEvwICQmhQ4cOHD58GBcXl3cCPCm9/bP4OCmnl4yNjcXa2pqrV69y6NAh5b1MynOr1+tp0aIFx44dY+HChfTp0wcvLy8iIiLYsmULTk5On23kOyGEEEJ8+WTaLCGEEEIIIYQQ6ebRo0d4e3sTEhJCzZo1WbNmDba2tsrypKQkKlSogF6vZ9iwYfj5+aHX61MFdwB8fX0JCgoiPDycRo0apcehCCFEmnrfdBpXrlyhTZs2XLt2DW9vb4YNG0aWLFmUdbVaLS9evKBq1arcu3ePuLg4Ro8eja+v72dra3R0tFJkBpQi8+TJkxkxYgSjRo1i9OjRMkKaEF8oNzc3pk+fTokSJZQpTA127drFwYMHmTNnDq9evaJYsWI0a9aMgQMH0r17d65evcqxY8fImTOnTGH6Cb1+/RoLC4tUjxkCIHq9nk6dOrFq1Spq1KjBypUryZUrVzq1NGNJ+Tc8YsQInJ2dadq0KVu3bsXNzY34+HiWLVtaJ9tCAAEAAElEQVRGp06dUq3/V3/7MlWWEEIIIVKSuwIhhBBCCCGEEOlCr9djb29PQEAAffv2Zd++fbRv31751qphGpdmzZrx22+/0bdvX3Q6HRcvXkwV3Fm1ahWhoaFUq1aNKlWqpNfhCCFEmjIU986dO6dMd1KkSBEiIiLInz8/AQEBBAYGEhsbi1qtJikpCSMjI7Jnz469vT2BgYFUr16dzp07f/K27du3j4kTJ77TVk9PT+rVq8eiRYuIjo4G3kybpVKp6NGjB7ly5WLPnj3K8ch3DIX4chhGGAkKCmLQoEFcvHiRdu3acenSJWWd+vXr4+/vz/Hjx5k1axampqZMmjSJIkWKcPbsWW7fvs2ECRMkuPOJGYI7KftMwwg7KpWKFStW0KlTJw4cOICrq6uMfPSJGP6GJ0+ezKRJkzh9+jRZsmThxx9/JDAwEFNTU2bOnMmePXuU9VP+7ad8vgz/l+COEEIIIVKSOwMhhBBCCCGEEOnC8IG2vb09Pj4+9O/fP1WAR61WY2RkRL9+/ahWrRrx8fE4OTlhbGzMixcvAJgxYwajRo0CYNGiRVhZWUmBQgiRoaQs9gUEBCjTTaUM8GzYsIH8+fMzceJEJk+eTExMDKampuh0OhYtWsTvv/9O48aNiYyMxNnZWdn2U3j58iWjRo3C29ubwMBA5fHo6Gju37/PpUuX6NOnD7Vq1cLNzY0HDx7w+vVrbG1t6datG8eOHWPFihUAUtwX4gtiuE8DmD59OgMGDODixYu0bduWy5cvA28CPjqdjqJFizJgwABOnTrF8uXLadeuHU+ePMHExITjx4/z+vVrQAJ6n9rbfWbKAM/SpUsZMGAA06ZNk4DIv5RyqiyAbdu20b59ezw8PLC2tiZr1qx07tyZwMBAzp8/j5+fH7t37wZSX0cpny95vRNCCCHE+8i0WUIIIYQQQggh0pXhG6mPHj1i/PjxzJ07l9q1a6eaQuvOnTv07t2b3bt3Y2lpSa5cuXj58iVPnz6lSJEibN68GWdnZ2XKACGEyAhS9mmxsbGsWrWKQYMGUa5cOfr370/79u2V5VeuXKFVq1ZcunSJVq1aMXToUPbv38/y5cuxtrbm559/Jlu2bGnSzj179jBu3DgOHTpEQEAAXl5eyrIzZ85w4sQJpk6dys2bN7Gzs6NevXoMHDiQhIQEGjRoQKNGjVi6dCnm5uZS0BQiHbw9dY9Go8HY2BiAX375hW+//RaAwYMHM2vWLEqUKMHatWspWrSo0k+9fQ928OBBVq9ezYIFC1iwYAG9evX6vAf1H5by+Xvfz+J/ExgYSNasWZkyZQrz5s2jfv36qUbWiYuLIzg4mGHDhvHdd98xZswYfvjhBwAZfUoIIYQQH0XCO0IIIYQQQggh0t2HAjyrV69WpsiKjo5m69atbN++nYcPH5IzZ05q1apF+/btyZEjhwR3hBAZSspi+pQpUzhw4ADXrl3j1q1bJCcnU7p0aTw9PWnbtq3S992/f5+2bdty/PhxZT/58+dnz549ODs7v1Og/7dSFiMjIyMZOXIkx44dw9/fnxEjRqRa948//mDXrl1ERESwceNGVCoV7dq1Y8eOHSQnJ3Pw4EHKlSsnBU4h0lH//v0ZPHgwRYoUAd6EdbZv3054eDhly5ZVHns7wJOyb0l5DZ84cYIffviBqlWrsnHjRkxNTeX6Fl8dvV7P0aNHqVatGvny5SMxMZF169ZRsWLFd95/pAzwfP/99wwfPpzGjRunY+uFEEII8TWR8I4QQgghhBBCiM/q74rHDx8+JCAg4L0BHoOYmBjMzc0/ep9CCPG18vLyIigoiPbt29O4cWOMjIzYsGEDW7ZswcHBgdGjR6cK8CQnJxMWFkZ0dDRZs2alZcuWaRpwTDmiw969e/H19eXYsWOpRuB5+3dv376dgwcPMnfuXLRaLfHx8bRv357g4GAyZ878ydsohPh7S5YsoWfPnnz33Xfs27cPX19fpk+fjoeHB+7u7uTIkUNZ958EeFq3bs2+ffu4dOkS9vb26XJsQnwKU6dOxcPDA4BZs2YxYMAA4N1RdeLi4li6dCkDBw6kWbNmhIWFYWZmli5tFkIIIcTXRcI7QgghhBBCCCE+m5QF3P3793PlyhWePn1Kjhw56NixI2ZmZpiYmKQK8NSqVYs1a9ZgZ2cnIR0hxH/K7t27adq0KU2aNCEoKAhHR0fgzUhk27dvZ9iwYdjZ2eHr65sqwPO2TxXcebsPfl+f/KEAz/vacenSJfbt20dwcDDPnj3j2LFjODg4yOg7QqSDp0+fMmvWLCZMmEC2bNl48uQJXl5e9O/fnzx58gCpr+G/C/AAPHv2jPbt23PlyhUiIyPJly9fuhzbl0z6uy+bXq9Hq9UqIdW5c+fi6uqKqakp69atU0bVeft5jI2NJTw8nB9++IG8efOmS9uFEEII8fWRiU6FEEIIIYQQQnwWOp1OKfiMGDGCWbNmERcXpyxfsGABw4YNo1GjRuTKlQsfHx9UKhVz5syhffv2hIWFYWtrm17NF0KIz+7BgwckJyfToUMHHB0dlcJ4jhw5aNeuHbGxsXh4eDB16lS0Wi0dOnTAyMhIKbAbiomfasQdQ1E+NDSU6tWrK2Gizp07k5iYSFhYGHXq1MHIyIiRI0fi7e0NoAR4UrZDr9dTrFgxChcujLm5Od26dWPhwoX4+flJIVuIz0yv12Nra8uYMWPYtm0bv/76Kzlz5qRZs2bkyZMnVV9i6F9mzJiBSqVi5syZdOjQgRUrVlCyZElln4mJiWzatInIyEi6dOkiwZ3/93bASaVSSYDnC/K+58cQ3IE3U8vpdDoGDRqEm5sbpqam1K1b953nMWvWrHTr1g34dAFaIYQQQmR88nVFIYQQQgghhBCfheGD8NGjRzNp0iRcXFw4evQoDx8+ZN68edy/fx9XV1e2bNmCTqfD3t6ekSNHMmDAAA4cOEC9evV49uxZOh+FEEKkPcNA2Xfu3EGv1/PHH38Ab4qKBpkzZ6ZJkyYULVqUM2fOMG3aNMLDw98J7nxq3t7edO7cmdDQULRaLW5uboSGhmJtba20s2bNmvj7+1OpUiW8vb2ZOHHiO/sxtM3IyIgGDRpgYWHBmTNn0Gq1n7zNQoi/plKp0Gq1nD17losXL/Ltt9/y5MkTBg4cyK1bt1L1JUZGRmg0GgCmT5/OkCFDOHfuHIMHD1YeBzAzMyMuLo7OnTuzaNEi4M++7b/McD/cpUsXQkNDgT8DPCJ9abVa5fnZs2cP8+bNo2/fvoSGhnL27FllPVdXV4KCgrh27RoDBgxg9+7dwIefRwnuCCGEEOJjybRZQgghhBBCCCE+mxMnTtC6dWsqVKjA5MmTKViwIACrVq1iwIABWFpacu7cOaytrZVvvj569AhPT092797Nr7/+So4cOdL5KIQQIm0ZgjeHDx+mfv36NG3alDVr1gB/foPf0EeOHz+elStXEh0djbW1Nf7+/ri4uKRZgOfw4cNMnjyZ3bt3880333DixAm8vLxwdXUlV65cqUYtiIyMZOTIke+dQiulmJgYypcvT44cOdixYwdZsmT5pG0WQny8c+fOYWZmxurVq/H396dMmTKsW7cOZ2dn4M/+KeVoIj4+PvTq1QsnJ6cP7lemPv3T4cOHqV69OgULFmTChAm0bt0akHOUnlKee29vb+bOnUtcXJwSSLO0tGTGjBl06dJF2Wb69Om4ublRqFAh5syZww8//JAubRdCCCFExiF3gkIIIYQQQgghPpubN28SFRVF586dKViwIBqNhjVr1uDt7Y2NjQ2//PIL1tbWJCcnK6Mv2NvbM2XKFC5evEiOHDlSjTwhhBBfu/f1aYbAjaOjI2XKlCE8PJyAgADgzTf4k5OTlSLj0aNHKVeuHCtXriQmJgZfX18iIiLQarVpMvJO1apVmTZtGra2tpw6dYrKlSvj4uJCrly5lLYbviv49gg8gYGB7+wvPj6ekJAQrly5QtWqVSW4I8Rn8qFRrsqUKUPRokUZOnQow4cP5/z587Ru3Zrbt28Df/ZPhw8fZvv27QCMHz8eJyenVCPvpPzOsF6vl1BKClWrVmXdunW8ePGCkSNHKuHM950jvV6f6lzK6GRpw3DufX19mTRpEo0bN2bz5s1s27YNLy8vXr16Rbdu3Zg+fbqyzZAhQ5QReNq1a8fBgwfTqfVCCCGEyCjkjlkIIYQQQgghRJp4X3Hh4sWL6PV6ypYtC0B4eDienp6oVCqOHz+Ora0tAJcuXWLQoEFKEcjOzo5s2bJJ8UcIkaGknKLj6tWrnDx5kp9//pno6GiSk5NxdHTE398fIyMjfHx88PX1BcDExASAjRs3cvfuXb777jvq1KnDnDlzeP36Nb6+vixfvvyTT8Ni2N+xY8d48eIFuXPn5tSpU+zcuZNHjx4Bbwr77wvwVKtWDU9PT+bMmZNqn3FxcVy6dIl27drh7++f6vcIIT49jUaTatScNWvW4OXlhY+PD2vXrlXWs7a2xtPTEw8PD86fP0+rVq24desWWq2WHTt2MGDAAMaPH09cXJxyzRobGyvbpwwPpkWQ8GtluLdt2bIl8+bN486dO3h4ePDw4cMPbqNSqThz5gzPnz9XRl4Tn97Zs2eZN28erVu3ZsKECTRo0ICGDRsSEBDAqlWryJUrF25ubqmukyFDhjB+/Hg0Gg2FChVKx9YLIYQQIiOQabOEEEIIIYQQQnxyKadqmThxIg4ODnTp0oWQkBC6du1KYGAg+fPnZ8iQIajVak6ePImdnZ2yfadOndi8eTNnzpyhQIEC6XUYQgiRZlJO0TF+/HhCQkK4ffu2UgBs0aIFPj4+WFhYsG3bNtq3b09sbCyVKlWiWLFixMTEsHv3bszNzTl27Bi5cuUiOTmZrVu30rp1a9zc3JgyZconbyvArVu3OHXqFJaWlsydO5ddu3YxYsQI+vbti729PUCqcADAjh07WLBgATNmzHhnap379++TJ0+e9/4uIcS/t3fvXhYvXkxoaGiq63LYsGEEBQWlWvenn34iJCRE+fnVq1dMnjyZqVOnkidPHooVK8bZs2dJTEzkxIkT5MuX77Mdx9fm7f7s7X4RICwsDHNzcxo3bvzB/Rw7dowqVapQoUIFdu/ejYWFRZq1+b9s/fr1tGnThrVr19K6dWt0Oh06nU4JpRnexxQrVozt27eTJ08e5fmMiYnB3Nz8vc+xEEIIIcTHMv77VYQQQgghhBBCiH/GENzx9fXF39+fXr160a5dOypVqoSFhQWTJ0/G2NgYU1NTfv31V6ysrJRtlyxZwv79+/nxxx+VYq4QQmQ0hoKup6cngYGB/PDDD/Tv3x9zc3OmT59OYGAghw4dYteuXTRu3Jg9e/YQFBTE8ePHOXbsGHZ2dpQqVYqQkBBy5cqFVqvFxMSEJk2acPLkSb777rtP0s6Uhcg9e/bw6tUratasSdu2bQGwtbVFo9EwYcIEVCoVffr0wd7eHiMjI/R6PWfOnOHbb7+lYcOG1K5dGzMzMzQaTaoROgx9vYyuJsSnpdfr0Wq1jBs3joMHD2JsbMzSpUsxNjZm8eLFLFiwgJ49e+Li4oKxsTEeHh6Ehoby4sULNm/eDIClpSXDhw8ne/bsLF26lKNHj1KqVCmWLVuGk5OThBX+gqE/i4yMpGbNmsp58vT0JCkpiWnTptGuXTtl/ZTh95RsbGwoUKAAJ0+eZNeuXbRp00aCjmng5cuXwJsR4eDN86dWq5Vz3blzZ3bu3Mm2bdt49eqV8jqnUqnImjUrer1ergUhhBBC/CtydyeEEEIIIYQQ4pNJOVXWvXv32LFjBz179sTLy4tMmTJRqFAhJk+eTGxsLFFRUYwdOzZVcGf58uVMmjQJa2trRo0ahZmZmUyfIoTIsDZs2MDs2bPp2bMnc+fOZciQIUqfCRAdHa1Mj1KxYkUWLlyoTK118OBBNm/ejKOjY6riuYmJiRLc+bdTq6Tc75gxY2jfvj1Dhw7lxo0bytQv3333HePGjaNOnToEBAQwf/58njx5AsDOnTtp0KABvXv3BsDU1BRIPbVOSjK1jhCflkqlwtjYmLCwMGrXrs3KlSv56aefAIiKiqJEiRJ4enpSt25datWqxcaNG2nRogVbt26lWbNmyn4sLS1xdXVl3759HDhwgM2bN0tw5yN17dqV2rVrs27dOgAGDhxIYGAglpaWSljE4EN9YJEiRQgNDcXW1pZNmzYBSHDnX/jQa2OuXLkA2Lx5szIVJLw518nJyQAULlyY169fc+nSJeDP58wwZaQQQgghxL8hI+8IIYQQQgghhPhkDAWcLVu2oFKp+P3335kyZQrOzs7KOk2bNiUqKoqJEyfi5eXFgQMHKFWqFJGRkURGRmJlZcWuXbtwcHCQopAQ4qv3oZEU4M1UKCqViv79+1OwYEG0Wi1r1qzBz88PZ2dnTpw4gZWVFfHx8ZiammJlZYWVlRU5c+ZU9qHT6T7YT/6b4m7KEQSGDx/OtGnT6NixI927d6d8+fLK71ar1Xz77beMGTMGlUrFhAkTuHPnDtmzZ2f79u0YGRnh4eEBSDhHiPSg1WrJmTMnq1atwsXFhfDwcDQaDc+fP6d169YUKFBAGaEnT548zJo1C4CNGzfSrFkzZQQelUqFra0ttra2ADLKyEeqXr06J06c4Mcff2Tx4sXs2rULd3d3evfunSrA/ndKlixJjx49WL58Ob/99hulSpVKw1ZnXCnfWxw5coS4uDjq1q0LQJ06dWjSpAnbt2+nQYMG/Pjjj2TKlAm9Xo+JiQnwZppHOzs7SpQokW7HIIQQQoiMS8I7QgghhBBCCCE+qXXr1uHi4kKpUqVwdnamYsWKwJ8flufKlQtXV1eKFCmCl5cXK1euJDExkXz58tGsWTMCAgLInTu3BHeEEF+9ffv2ERoayrx58zAzM0u1LCkpiV9++QV7e3u++eYbkpKS2LBhA97e3qjVak6ePEn27NkBuHPnDhcvXqRly5bvBHLSavQFQ9Bm/vz5zJgxgwEDBjBkyJBUYUy1Wq2Ek8qXL8+4ceOwsbFh+fLlGBsbU6RIEY4fP46zs/M7U2UJIT4PIyMjtFotOXLkYO3atbi4uLB582YyZcpEtWrVANBoNJiYmKDT6cidO3eqAE/Lli3ZsGHDO9evhPH+mqFv7N69O3ny5KF9+/b8/PPPNGnSBE9PTyUE9bGyZs1KkyZNuH//vgRH/kcpw65jx45l8eLF3L9/n9OnT1O2bFmMjY3p1KkT58+fx93dnZiYGJo0aUKBAgWAN6Pl7dixgzJlypA3b970PBQhhBBCZFAqvYw/LoQQQgghhBDiE/r999+ZPn06ERERvHjxglWrVtG+fXvg3REoHj9+zIsXL4iKiqJ06dJkyZKFTJkySXBHCPHVi4+Pp0uXLkRERNCjRw9mz579ToCnZcuWnDx5khs3brB9+3aGDh2qBHfs7OyAN/1mxYoVcXJyIiQkhMyZM3+W9uv1euLi4mjbti1Xrlxh586dFCxYUFm+atUqTpw4QXx8PM2aNaNJkybKsj179gBQrlw5bGxspE8XIp0YRsdK6fHjx7i4uHD48GHKly/P0aNHlYCPkZGRss2DBw8YMmQI69ato0uXLixdujSdjuLrZTiXU6ZMYfjw4WTLlo1Xr14RHh5Oy5Yt/3Jkto/dt/g4Kc+1h4cH06dPp2PHjnTt2pVatWqlWr5w4UKmTJnC3bt3KVq0KA0aNODmzZscOXIElUrF0aNHcXR0/FfPnxBCCCHE+0h4RwghhBBCCCHEJ3fp0iVmz57N4sWLqVOnDrNmzVK+tWr4oPtDH3jLB+FCiIzi4sWL+Pv7s2bNGrp06cL8+fMxMzNTiq6zZs1i8ODBNG/enLNnz6JWqzly5AgODg7KPmbMmMGECRNwd3fH3d39sxZro6OjKV++PI6Ojhw6dAiAQ4cOMXfuXMLCwjA2Nkaj0QCwfv16WrRo8c4+pMAsRPo7ffo03333nfLzkydPaNeuHZGRkbRq1YqwsLD3Bnju3r3L2LFj8fHxSTXqlvhnrl+/zo4dOzA2Nmb69OncunWL5cuX06FDB2Wdt+9/pe9MGytXrqRHjx707NkTd3d38uXLpyxLec537drF2rVrWbJkCQB2dnZ8//33zJ49m7x580ooVQghhBBpQsI7QgghhBBCCCHSxJUrV5g6dSqLFy+mU6dOTJgwgVy5cqV3s4QQ4rO6dOkSfn5+RERE0LlzZxYsWKCMwHPp0iUaNGjAvXv3sLW15d69e8oyvV5PREQE3t7e2NnZsWnTJmU0ns8lPj6eWrVqcfLkSXr16sWLFy/Yt28fOp0ONzc3GjZsyG+//Ua3bt3o2LEjixcvxszMTAKYQqSzlEGQMWPGMH78eGbPnk2fPn2UdZ48eYKLiwsHDx6kXbt2hIaGvjfAY/hXpr77OH8XugkPD8fHx4fbt28TEhJC+/btU21z+fJlihYt+rma+59huCY6d+7Mzp07OXjw4HvP89vP39WrV4mPj8fW1hYbGxsyZ84swR0hhBBCpBm52xZCCCGEEEIIkSaKFCnCsGHD0Ov1BAcHA0iARwjxn2EodBcrVozJkyej1+sJCQnBzMyM6dOnkzlzZooVK8ayZcuoX78+T58+ZfTo0TRq1AhbW1sWL17M2rVr0el0rFmzBjs7uzQbieFDI55lzpyZkJAQmjRpwqJFi7Czs6NixYpMnz6dfPnyYWRkRNmyZRk4cCBGRkZkypTpk7dNCPHPpAwWJCUlUahQIQDmz5+PSqWid+/ewJuRRMLDw2nbti1hYWEASoAnZVDH0OdIcOfvpTz3v/zyC0+ePMHOzo58+fJhY2MDQNu2bQHw8fGhc+fO6HQ6OnbsCMD27dsZN24cDRo0wM/PL30OIgN7+fIlu3btIn/+/BQtWvS9gTTDa6HhdbFw4cKpluv1egnuCCGEECLNyB23EEIIIYQQQoh/5J8UjwsXLoyHhweABHiEEP8ZWq1WKQguWbKEx48fc+HCBSwsLFi0aBHGxsZMmTKFzJkzU6tWLfbu3Uu/fv2YNGkSkyZNAsDMzIxKlSqxbNkyHB0d0+yb/in3++zZM54+fYpOp6NIkSJK4fL48eP88ssv5M6dm/z58yshHb1ez9y5c9FoNJQvX/6Tt00I8c/odDrleg4MDGTnzp3ExMSgVqs5d+4ckyZNQq1W07NnTwBy5MhBeHg4Li4uytRZy5cvl6DO/yBlqMPb25vp06eTkJCAqakpFSpUYNGiRRQpUgT4M8AzatQounXrxr1790hOTmblypU8ffqUrl27ptdhZFgqlQorKyty587NixcvSExMxMzMLFV41fAe5/Hjx/z888906tTpvfsRQgghhEgrMm2WEEIIIYQQQoiPlvID7nv37pE3b96P2u7q1asEBgayfPlyGjduzLx587C3t0/LpgohRLobNmwY8+fPp0KFCpQsWRIjIyMWLlxIfHw83bp1Y/bs2WTOnBmAu3fvcvnyZX755RcyZcpEhQoVKFWqFJaWlmkW3EkZxpwyZQqrV6/m7NmzAJQtW5YBAwZQt27dD/b1q1atYty4cWTNmpUdO3Z89mm9hBDvN2LECIKCgmjXrh0uLi4kJCRw8OBBli5dirW1NWPGjKFHjx7K+tHR0XTo0IH9+/fTr18/5syZk46t/7oFBAQwatQofvjhB6pXr87JkyfZsmUL2bNnZ//+/ZQsWVJZd/369UyZMoXjx4+jUqkoUqQI27ZtI1++fDI10yem1+vRarW0atWKrVu3EhAQgLu7OyYmJuh0OlQqlfIex9XVlWXLlnHu3DkKFCiQzi0XQgghxH+JhHeEEEIIIYQQQvxjgwYN4tdff2Xr1q1YWFh81LdQr127ho+PD0eOHOHcuXNkz579M7RUCCHSx4oVK+jSpQv9+vXD09MTR0dHAE6cOIGfnx8///wzPXr0YObMmUqA530+x1RZ7u7uzJgxgzJlylC1alV+++03zp8/T0JCAj/99BNeXl44Ozsr2z569Ihp06axatUqjI2NiYyMxMnJKc3aKoT4eHv37qVx48Y0btyYoKAgnJycAHj9+jW7du2iR48eWFlZ4evrq4zAA2+ua1dXV6ZMmZLqehd/7e2QTeXKlcmXLx8TJkxQ+v2xY8cyZswYLC0tOXToUKoAz5UrVzh37hwxMTE0bdoUOzs7Ce78C3937s6dO8cPP/xA9uzZGTNmDK1bt0410lR4eDijRo2iVKlSLF++nKxZs36OZgshhBBCADJtlhBCCCGEEEKI/8Hly5f5/fffef36NZaWlqmKwB9SqFAhJkyYgLW1NTY2NlLkFUJkaOfOncPExIQff/wRR0dH9Ho9er2eihUrMmXKFPr160dwcDCmpqbKFFopv/1v6FfTqp809NkrVqxg5syZuLq6MmjQIPLnz09MTAyRkZFMnTqVhQsXYmZmho+PD3Z2djx69AgXFxeOHDlCkyZNmDt3Lnny5JFisxCf0Z07d5RQztsePHhAUlISP/30E05OTsq1aWFhQZs2bYiNjaVfv35MmTIFrVZLnz59ALC3t2ft2rWoVCo0Go1MnfWRUk5TVqBAAZKTk+nVqxeOjo4kJydjYmKCr68vpqamjBw5kmrVqqUK8BQpUkSZTgtST30m/pmUr0MRERFcvnwZtVpNxYoVqVOnDgAFCxbEz88PHx8fhg0bxsmTJ+nduzcmJiaEh4cTHByMVqtl+vTpZM2a9aPe4wghhBBCfCryKakQQgghhBBCiH+sffv2PHv2jHHjxinF5o+RP39+Ce4IIf4Trl+/jqmpKfny5QNSj6BTsmRJAgICAJg3bx6DBg0iISEBtVqt9Kefq1i4b98+rKysGDBgAPnz50er1WJubk6jRo2YPXs2FStWZOXKlZw/fx4AW1tbpkyZwtq1a1mxYoUEd4T4zM6fP0++fPkYOHBgqsd1Oh0A9+/fB95Mbwq8c7/VoEEDKleuzNWrV5kxYwbLli1TlhkG6Zfgzj+ze/duPD09GTRoEA8fPsTKygq9Xo+RkZHyvHh5eREQEMCrV6+UEc7gTeAkJbk//t8ZXoc8PDxo27Ytvr6++Pj40KJFCwYPHgxA1qxZadu2LUFBQeh0OqZNm0b58uUpWbIkfn5+mJubs2/fPuW1TYI7QgghhPic5E5QCCGEEEIIIcQ/5uLiQpEiRTh06BBPnz4F/iz4fAwpTAghMrqiRYsSGxvL6tWrgTdFRUM/qdFoqFatGvXq1aNkyZIEBwczYsSIz9o+nU5HXFwchw8fxtzcnDx58qQa8UGtVlO0aFH69OnDs2fPmDlzJvCmqF+xYkVat26NlZWVjBIhxGf2+vVr8ufPz927d0lISFAeN9xb1alTh0yZMnH06FHgTRDQECDR6/XkzJmT2rVrU7BgQe7cucOkSZM4cOBAqn2If6Zu3bp4enry9OlToqKiuHHjhhL6UKvVyvn39PQkICCAhIQEypQpw6VLl6T//ARSvgeZP38+c+bMoWvXrkRERLB+/XpsbW2ZNWsWnTt3BiBHjhx069aN48eP4+3tjYuLCz/++CPz5s1j9+7dODs7SyhVCCGEEOlC7saFEEIIIYQQQrzX298ENhQeNBoNFhYWDBgwgEuXLhEWFgZ8vlEihBDiS2ToIw2aN2+OSqVi8eLF7N+/H3jTTyYlJSmjWty9e5dvv/2Wvn37MmTIkM/aXrVaTZYsWShdujQvXrzg4cOHqNXqVH2/kZERzZo1I0+ePNy9e5eYmJj37kcI8flUqVKFbdu2sXLlSjJlysSePXtSLXdycqJatWqEh4czadIk4M11mnL6n19++YVatWqxcuVKbt++zdq1az/7cWQUycnJAEyYMAFPT08AevbsycmTJ5Xz/naAZ/jw4eTJkwcLC4t0a/fXzhDYeXsE0Hv37vH999/j4+NDq1ataNGiBUePHqVSpUqEhobSqVMn4M3rcd68eRk/fjzBwcEsWrSI7t27Y2trK6FUIYQQQqQbeXcthBBCCCGEEAKAuLi4VD8bPrQ+deoUcXFxSoHWUHSuUqUKlpaWLFu2jAcPHnzexgohRDp7O6zzdoCxUqVKjB07lqtXrzJp0iR27NgBgKmpKQBr1qwhMTGRfv36MXfuXJycnNBoNGnS1veNjKbVatHr9ZQuXZpXr14xbNgw4uPjMTIyQqvVKsdnbW2NqakpVlZWZMqUKU3aJ4T4axcvXuT3339Xfi5SpAjm5uYsXLiQevXq4evrqyyzt7fH3d2dbNmyMWLECMaMGcOLFy+UPmrTpk1cvnwZR0dHKlWqRPHixVm6dClXr1797Mf1tXm73wcwMTFR/j9mzBhGjRrFq1ev6NixI6dOnUKlUr0T4Bk7diwXL15UpmYSH88QIk05shHAkCFDaN++PVu3bqVJkybkz58feBOucnBwYO3atVSqVImVK1cqI/AAJCUlKf83vFZKKFUIIYQQ6UXuQoQQQgghhBBCcPDgQcqWLcv58+dTPT5u3DgqVqxIrVq12LhxI9evX1eWlS1blj59+vDbb79x7do14J9NnSWEEF8rrVarFPfWr1/PiBEjaNSoEXPnzuXXX39V1uvQoQMDBgxgz549dO7cGQ8PD7Zt24aPjw8jR47E1NSUfPnyKesbwpGfuq2GImd8fLzyuJGRESqVCjc3N0qVKsWmTZsYPnw4cXFxGBkZKce3evVqHj58SMWKFaWgKUQ6uHLlCqVKlcLd3Z1Lly6lWla4cGGqVq3K+PHjGT16tPJ4vXr1WLJkCfb29owZM4Z69erRvXt32rVrR48ePXj16hVdunQhZ86cFC9eXOkPxIel7PcjIiLw9vamVatWDBw4kGPHjinhyzFjxuDt7c3Nmzdp3779BwM8FhYW6PV6GeHlHzh8+DCtWrXi4MGDqR5PSEhg1apVhIeH8+TJEzJnzgy8Ce6YmJig1WrJlSuXEuAJDQ2lW7duwJ+BWpBRRIUQQgiR/lR6+WRVCCGEEEIIIf7T9Ho9EyZMwMfHh2LFihEeHk6JEiVISkoiMjKSefPmcezYMaKjo8mfPz/9+vWjUaNGFCtWjDNnzvD9999TuXJlNm/ejKWlZXofjhBCpCmdTqcUcD09PZk1axZqtRpzc3Oio6MpVaoUw4YNU6bmuH//Pps3b8bT05PY2FhlP8WLF2fr1q04Ozun2mdatXX27Nns2bOHLFmyULJkSTw9PZWi8ZUrV2jSpAk3btygZs2aeHh4kDNnTvbu3cvixYtJSkri6NGjODg4fPI2CiH+WnJyMl27dmX16tW0atWKsWPHUrx4cWX50aNH8fb25uDBg/j6+qYK8Rw4cICVK1eydetWHj16hK2tLcWLF2fZsmU4Oztz8+ZN6tevj7m5OTt27MDe3j4djvDLl7Iv9fDwYPbs2ajVaqytrYmOjsbIyIjRo0fTqlUrChcuDICvry/jx48nX758hIeH8+2336aaukz8MxqNhsmTJ+Pj48Po0aPx8fFJ9br54sULatasyfnz56lWrRq7d+/G1NRUee60Wi1GRkZERUXRvn17Dh06xMCBA5kxY0Y6HpUQQgghRGoS3hFCCCGEEEIIQXx8PDNmzGDcuHHkzp2bDRs2UKJECWX5pUuXWLduHcuWLePmzZvY29vTsGFDPDw86NGjBw8ePGDjxo188803aVaEFkKIL4mfnx/+/v78+OOP9O3bl0qVKrF48WL69OmDk5MTPj4+dO/eXVn/ypUrXL16lTt37lCoUCG+++47smfPrhQU05KnpyeBgYFkypQJrVZLcnIy33//PREREeTKlQuAO3fu0LFjR44dO6ZsZ2pqStGiRdm4cSPOzs6fpa1CiHclJyfTr18/lixZ8t4Az5EjRxg5cuR7Azzx8fE8e/aMK1euYG9vj4ODA9myZePhw4fMnTuXgICAd7YR7zdhwgRGjhxJ37596dGjB99++y3btm1j1KhR/Prrr0yZMoWBAwcqU2kZAjwWFhYcPHiQMmXKpPMRfN2ioqI4duwYtWvXxtramvv375MnTx5l+cuXL6lRowbnz5+nb9++BAUFKa97hikhjYyMuH//Pq6ursyYMQMnJ6d0PCIhhBBCiNQkvCOEEEIIIYQQ/3GGsE1CQgJBQUH4+/uTK1cuNmzYQMmSJVOte+PGDS5cuMCkSZM4efIk2bNnx9jYmKioKPn2qhDiP2PTpk24urrSqFEj3N3dKVy4MC9fvqR69ercvXuXly9fkj17dgIDA+natesH9/M5wo7Hjx/HxcWFVq1a0adPH+zs7Bg5ciSLFy+mZMmSrF+/noIFCwJvivwbN27k4sWLxMbG8s0339C4cWNsbW0luCNEOvtfAzzv62fOnz/P7NmzWbFiBc2bN2fNmjUAMjLMX7hx4wYNGzYkX758zJkzR+k3IyIicHV1xcTEhF9//fWdUKabmxurVq3izJkzSlhS/Hs+Pj7s2LGDuXPnUrFiReXxly9fUqVKFX7//XcGDBiQKriaMsBjuC40Gk2aTFkphBBCCPG/kPCOEEIIIYQQQvzHpSwwxMfHM3XqVCZMmJAqwPN20TYmJobffvuNxYsXs2fPHu7fv4+trS07d+6kbNmy6XUoQgiR5hISEvD09CQ8PJxt27ZRrlw5YmJiqFChAs+fP2fRokVER0fTs2dPcuXKxZgxY+jRowfwecI6huK74d81a9YwYMAADh48qIyo9vr1awIDA5k0aRKFCxdmw4YNSiH6fWRENSG+DElJSQwYMIDg4OB/PAKPwblz5+jbty+///47HTt2ZN68eYBc5/BmmsMsWbJgY2PzzrJDhw5Ro0YNgoOD6datG8nJyURERDBixAhUKhWnTp3C1taWxMREEhISsLKyUrZ9+fIlVlZWEoL8RGJjY/H392fKlCnUrl2bsWPHUqFCBWX5ixcvqFKlCpcuXXpvgEcIIYQQ4kv1374bF0IIIYQQQoj/GJ1O987/DR9iX7x4kcyZM+Pu7s7IkSN5+PAhLVu25MKFC6k+6NbpdJibm1OpUiWCg4PZsGED48eP5+nTp5w8efLzHpAQQnxmarUaZ2dnpk2bRrly5UhISKBZs2Y8ffoUf39/6tWrR/fu3alfvz5RUVEEBAQoxfG0LoxrtVpl1Iznz5+TlJREXFwcLVq0UII7Go0GCwsLhg8fjpeXF1evXqVly5Zcv34dSP06YfjO33+9oC9Eekh5LWq1WuDNVHZz5syhe/furF+/Hl9fX37//XdlvSpVquDv70+NGjUYO3Ysw4YNe2e/hQoVYtiwYaxatUqCOymcPXuW4sWLM2nSJGJiYt5Z/vr1awAcHByANyPueHl5oVKpOHnyJLa2tgA8efKEKlWqcO3aNWVbKysr9Hq9BEc+kaxZszJkyBDGjh3LgQMH8PHxSfUexNramiNHjlCsWDHmzJmDl5cX8fHxcv6FEEII8cX7b9+RCyGEEEIIIcR/jKEw07dvXzZv3qw83qtXL0qVKsWlS5fInDkzbm5u7wR43t6HoahUrlw5WrZsiYODA9OnTyc6OvozHpEQQnxepqam9OrVixYtWgAQGhrKkSNH6NmzJ+3bt8fU1BSA/Pnzkz9/fm7dusXs2bOJi4tL03bpdDqlMDl16lSaNm3KDz/8QFBQEPv37+fOnTsAGBsbKyFMDw8PJcDTtm1brly5kqqAL9PnCJE+tFqtci3u3r2bsLAw7t69C7zpg+bNm/e3AZ5SpUoREhLCq1evlGV6vZ4sWbLQunVrGjdurDz2Xw/uGGTNmpUHDx5gYmKiPGYIMWbNmhWA9evXs2zZMry8vFCr1Zw8eRI7Oztl/YkTJ3L37l1evHiRat/Sn35aOXLkoHv37owaNYpDhw59MMBTunRpZs6cSUBAQDq2VgghhBDi48hknkIIIYQQQgjxH7Njxw4WLlzI6dOnyZ07N8uXL2fp0qUMGDAAS0tLADJlyoSbmxsA/v7+tGzZUplCyyBloado0aLUqFGD3bt3k5CQ8HkPSAghPjNzc3Pl/4bRyYYPH06WLFmUx3///Xd69epFjRo1sLe3T7UsLRj6ZC8vLyZPnoy9vT0mJiY8fPiQzJkzs3v3bjp16oSZmRlqtTpVgEetVjNmzBgGDBjArl27ZHQCIdJRyiDe6NGjmT17NtmzZyc4OBhHR0cATExMlFFzlixZApBqCq3KlSsr61taWirT6L0vQCKhkje++eYbjh49iq2tLWZmZkRGRlKyZEllRJ0aNWpQv359li1bxsaNG8mSJQvnzp1T7p31ej2hoaFs2bKF5s2bK6OdibSTI0cOevbsCcC4cePw8fFh/PjxyhRa1tbW7N+/n9atWyvrCSGEEEJ8ySRSL4QQQgghhBD/MRUqVGDp0qXcu3ePJk2aMHfuXIYMGYKvry+5c+cG3hSODAGeD43Ak9Lr16+Ji4tDrVbz/Pnzz3k4QgiRLvR6PcnJyTx69IiEhAQOHDigLAsLC+PatWuYmZlRsWJFnJyc0Gg0adIOw3Q6ADdv3mTjxo0MHDiQI0eOcOfOHQIDA8mZMycjRoxg27ZtJCcnA6QK8Li5uTF58mSWLl0qwR0h0lHKUXCGDx+Ov78/jRo1Ijg4mKpVqyrr6XQ6TExM3plC69KlS8o63333HTly5ECn00lA5yOoVCry5cuHhYUFs2fPpnbt2ixatCjVfe2gQYMoVqwYT58+pV+/fkpwB2DRokWMHTuWTJkyMWnSJLJkyaKM2iPSjiHA86EReLJly8bevXvT9HVYCCGEEOJTUenlDlIIIYQQQggh/pPq1KnD/v37yZEjB0FBQXTs2BF4UxAyFHXVajUJCQkEBQXh7++Pk5MTq1at4ptvvlH2k5yczObNm3FxcaFbt24EBwen0xEJIcS/Z+j7DAwjVnzI7t27qV+/PmXKlKFDhw5ER0ezdu1aTE1NOXToEPb29p+j2Rw8eJDbt28zcOBA9u/fT7ly5QDQaDRERETg5+fHs2fPmD9/Ps2aNVOmhdFqtakCO2//LIT4/BYuXMigQYPo168fgwcPxtnZOdXylNdpYmIirq6uBAcHU6tWLebNm0fhwoXTodUZx8mTJ+nfvz83btzAy8uLnj17kj17dmJjY1m1ahVTpkzh7t27fPPNN1SsWJFff/2Vs2fPkiNHDnbv3o2zs7P0pf/C/3LuoqOjWbx4MePGjaNmzZr4+PhQpUqVNGqhEEIIIUTakPCOEEIIIYQQQvwHnT17lh49emBnZ8epU6dwdnZm4sSJ1KpVSynowp8fnickJDB9+nS8vb2pXLkykZGRGBv/ORNzeHg4Z86cYeLEicDfF7uFEOJLlLLvunz5MkWLFv3b9QHmzZuHm5sbSUlJmJmZUbp0acLDw3FycnonDPS/ens/Go1G6YeXLl1Kjx49aNiwIRqNhl27dgFvwpUmJibodDrWrl2Lr6/vewM8QojP60PXs16vJy4ujg4dOnD+/Hl27dpFkSJFlPVCQkI4ceIE0dHR9OrVi3r16gGQlJRE586d2bt3L5cuXVKmehJ/L+VzYXgN0Gq1nD9/nj59+nD58mVGjhxJjx49sLW1JT4+nl9//ZU5c+awbds2Xr16RcmSJalVqxaenp44ODhIcOcTmT59OjVr1kz1pYG/Eh0dzZIlS/D29qZdu3aEhITI65wQQgghvioS3hFCCCGEEEKI/6hz585hbm7OkSNHcHNzI0+ePAQGBlKrVi2MjY2VwoOhqBEbG8vixYtp1aoVefPmTbWvlAXvT1WoFkKI9OLp6cnRo0fZs2cPJiYmH9Wn/fbbb5w/fx4bGxsqVqyIjY1NmhRwf/rpJyZNmkTu3LmVgv+JEyfw9/dn69atqNVqDh8+zPfffw+Qqi83BHhev35NYGAg7dq1SxXEFEJ8XuPGjWPUqFHAn9fq8+fPqVSpEtbW1hw/fhyAAwcOMG/ePMLDw8mSJQtxcXEAbN26lUaNGgFvAkCvXr3CxsZG7sU+Uso+et++fdy+fZv69esr08iePXtWCfB4e3vTs2fPVMGoJ0+ekJCQQN68eZX+WII7n8bmzZtp0aIFCxcupGfPnh99Xh89esTatWtp0aLFO+9XhBBCCCG+dBLeEUIIIYQQQogM7u0Pu98eFefly5eEh4czYsQI8uTJw+TJk6lRowZmZmbodDp27NiBubk5NWrU+OA+hRAio9BoNPz444+sX7+eX375hdKlS//tNu8bbSwtiucbNmygdevW5MuXjyNHjqSakuv06dMEBQWxZs0a+vfvj5eXF3ny5AFSB3jWrVtH3759yZ07NydOnCBz5syftI1CiI8zc+ZMhgwZgouLC2FhYcCb/icuLo6mTZty6NAhunTpwuvXr4mMjATAzc2Nhg0bcvbsWXr27EnDhg0JDw8nc+bMSn8jwZ2Pk/I8jR49mvnz55M1a1aCgoJo1qyZ0qe/HeDp3bs3NjY2yj5UKhUqlUpGnfzEHj58SOXKlcmdOzeHDh36R3/TKUdQkvcrQgghhPiayF28EEIIIYQQQmQwOp1O+X/KD62XL19Ov3796NmzJ4sXL1bWsbKyom3btkycOJH79+8zfPhw9u/fT3x8PLt376Z///60bt2a+Ph4Zd/yQbgQIqMyNjamSZMmaLVagoKCiI+P/9tt3lewTYviebNmzZgxYwZ//PEH1apV4+HDh8qy7777Dnd3d5o2bcrChQtZtGgRUVFRwJs+W6vVolaradWqFUuXLmXXrl0S3BEiHbVq1Yr+/fuzdu1a2rdvD7zpfywtLVm4cCFFihRh1apVHD58mCpVqnDixAm8vLwoW7Ys3bt3J1u2bNjY2JA1a9ZU/Y0Edz6O4Tx5enoyfvx46taty8qVK2nevHmqPr1s2bIsWLCAokWLEhAQwKJFi3j+/LmyD8O6Etz5tLJnz06lSpU4duwYa9euBf6cqvLvGJ4Leb8ihBBCiK+NjLwjhBBCCCGEEBnIvn37mD9/PkuWLMHc3Fx5fNiwYQQFBaVa96effmL8+PE4OjoCb0bgiYiIwNvbGxMTEwoWLMj169dRq9Xs37+fAgUKfNZjEUKI9FS9enUePnzIyZMnsbGxSfdRFQyjROh0OubOncugQYPImTMnt2/fxtjYWClSnjlzBj8/P3bt2oWXlxf9+vXDwcEBeHfUNBmVQIj0YehPoqKiCAgIYM6cOTRr1oyNGzcq0y89evSIGzdukC1bNgoUKICZmRnwpi9YsGABbm5u+Pv74+bmlu7909cqIiKCbt268eOPP+Lt7a3cE7+PYQSe69ev4+rqipubG9bW1p+vsRnU+/52DY9dvHiRypUr07JlS5YtW5Y+DRRCCCGE+Iwkhi+EEEIIIYQQGURiYiIhISFERETQq1cvYmJiAAgLC2PRokX07t2bX375hQMHDtCmTRtCQ0MZPHgwN2/eBN6MwOPi4sLChQuxtLTkypUrFClShMOHD1OgQAE0Gk16Hp4QQvwrb39/TavVvnc9w+Pt2rXj5s2bSvAxvQvjhuCOWq2mX79+TJkyhYULF2JmZpYqgFOuXDnGjBlD/fr1mThxIvPmzePRo0fAu6MQSHBHiPRhmGbJwcEBLy8vevTowU8//QS8GX1Hp9Nhb29PlSpVKF68uBLcAVizZg2zZ8+mWLFiyjbp3T99yf7qu8uHDx9Gp9PRs2fPvwzuAHzzzTcsWLAAGxsbwsLCpP/8BLRarfK3a3ifYZiKTKvVkjt3bqpXr05ISAj79u1Lz6YKIYQQQnwWMvKOEEIIIYQQQmQg169fZ8KECSxdupRWrVoRHh7O6NGj2bRpE+vWraNgwYIA3Lx5k1mzZjFr1iyaNm3K1KlTyZ8/v7KfxMREHj9+jK2tLVmyZJHRGYQQX73Xr19jYWGhTB9lKBieOnWKPHnyKKPTGNy7d4/vv/8eBwcHtm/fTo4cOb6I0S0MAZ6UDO1K2T7DCDz79u2jb9++eHp6kiNHjvRoshDiAwzXbGJiYqqAzvvWe/jwIdOnT2fNmjUYGRlx4MABnJyc3tsniDcOHz7M2bNn6datW6oRKQESEhKoUaMGL1684MqVK8C7o8AYzm1SUhKmpqZotVouXbqEra0t9vb2X8RrwtfkxIkTZM2alZIlS6Z6PCAggOjoaDw8PMiZMyfGxsbKsrCwMDp06ICXlxcBAQHynkQIIYQQGZrc1QshhBBCCCFEBlKwYEF8fHzo1KkT69evp127dty4cYMOHTpQsGBBtFoter2e/PnzM2TIEAYNGsSWLVtwd3dXRuABMDMzw9HRkSxZsqDX6+VDciHEV+3AgQPY29tz7NgxjIyMlJEYJk+eTMWKFalbty4hISFcu3ZN2SZv3rwMGDCAM2fOEBkZCXwZo1u8r0hvaJchwANvRuAZO3Ys5cqVY/369X8ZDBBCpA/Dtft31+fz589p3LgxU6dO5bvvvuPw4cM4OTkpYUTxrtjYWNzd3Rk8eDCHDh16Z7ler0etVnPnzh1Onz4N8N7gzuvXr/Hx8SE6OhojIyNKliyJvb29MkKM+Dh3796lTp061KlTh4sXLyqPnzt3jilTpjBz5ky+/fZb+vXrx65du5Tl7dq1o0qVKgQHB/Pw4UN5TyKEEEKIDE3u7IUQQgghhBAig8mXLx+jR4+mU6dObNq0idWrV3PlyhU0Gk2qD7ydnJwYPHiwEuAZPnw4169ff2d/UpgQQnztzp49S3x8PA0aNODkyZOo1WoSEhL45ptv6N27N3/88Qddu3alQYMGSpgxMTGRli1bYmRkxMyZM3n8+HF6H8ZHSRngKVu2LPPmzePkyZNYWVn95fQxQogvl42NDcuXL2ft2rUsW7aMPHnyyAgkf+HMmTOYmZkxYcIEvLy8qFSpEgDJycnKOpkzZ6Zly5YkJyenCovAmymcDKGocePGsWHDBh48eJBqHQlN/TM5cuTAzc2NxMREWrRowYULFwAoVaoU9+/fZ9asWVSqVIng4GAaNmxImzZtmD9/Pnq9ngYNGvDkyROWLVuGTqeT1zIhhBBCZFgybZYQQgghhBBCZACGAk7K4ftv3LjBuHHjWL9+PcWLFyc8PBxHR8d3tr1z5w6zZs0iKCiIbt26sXDhQikGCSEynJkzZ+Lh4YGRkRH79++nYsWKyrLffvuN48ePExQUxJUrV7Czs6NatWr4+/vj7e3N4cOH2b17N6VLl/5qpqj50PQvQoivj1zPH2/37t3Ur19fmWZJo9FgbGzMyJEjsbS0ZODAgWTJkgV4M61Wz549uXHjBsuXL6d169apRkFav349np6eFC1alFWrVmFhYZFeh/VVM/y9JiQkEBQUhL+/P7ly5WLDhg3KFFqGv/EtW7awc+dOwsPD+eOPP6hatSo1atRgwoQJ1K5dm127dr0zTaQQQgghREYh4R0hhBBCCCGEyECOHDlClSpVlJ9v3LjB2LFjWbFiBS1btiQkJISsWbO+s93NmzdZsWIF3bp1e2/ARwghvlYpR6eYPn06np6eGBkZERkZSYUKFVKt++DBA86fP8/MmTPZs2cPKpUKS0tLnj17Rps2bVi9erWEG4UQ4gt15swZGjVqRKlSpRgxYgS1a9cG4Pbt29StW5fbt28zffp0unbtqtwPL1y4EC8vL169eoWrqys1atSgXLlyLF26lBUrVpCcnMyRI0fImzevBEb+BcNrcUJCAlOnTiUgICBVgOftQNqdO3dYsGABO3fu5Ndff1UeX7RoET169EiHIxBCCCGESHsS3hFCCCGEEEKIr1jKIsK4cePw8/Nj0qRJeHh4KOvcvHmTMWPGsGLFClq3bs3SpUsxNzd/Z1+GD81lGgYhREaTsig4Y8YMPD09UalUREZGUrFiRWWKlJSFQ8NoOwsWLCA+Ph4LCwt27NhBqVKlZNQLIYT4Anl7e7NgwQJCQkJo3Lgx8GbaxLJly7J//378/Pw4ceIEU6ZMoWvXrspIOsuXL2f+/PmcOHFC2Zdarebbb78lPDwcJycnuT/+B95+jUxOTsbExET5OTY2lunTp38wwGM414Z/Z8+ezdmzZ1m2bBmtW7cmPDxcglRCCCGEyJCM07sBQgghhBBCCCH+N28XERwcHMiRIwdjxozB1NSUwYMHA5A/f35Gjx4NwIoVKwDeG+AxfMguhQkhREaTshho6Bs9PT2pWbOmEuDRarXAn31r1apVqVq1Ki4uLuzatQsPDw/Wr19PqVKlPklwRwqPQmQccj2nv+TkZO7fv8+LFy+UUE6/fv04deoUISEh1KpVC61Wy+jRoxk2bBh6vZ6uXbtiaWlJly5dqF69OufOnePs2bMYGRlRvnx5KlasiI2NjQR3/gG9Xq+8Rv7yyy8UL16czJkzA+Dq6kqDBg1o0qQJ7u7uqNVqxo8fT4sWLdi4caMyhZbhXBv24+rqyosXL1Cr1QQHB3Pq1CnKly+fDkcnhBBCCJG25CtCQgghhBBCCPEV0ul0ygfb/v7+VKlShfnz55OYmEhcXBxDhw5l1qxZyvr58uVj9OjRdOrUiXXr1tGrVy9ev36dXs0XQog09/Zg00ZGRspjgwcPZuLEiej1emrWrMmJEycwMjJK1bca1i1ZsiRNmzbF2dmZFStWEBUV9UnaZij0P3r06F/vTwiRflJez3Fxcencmv+eY8eO8eTJE0xMTGjTpg16vZ7AwEBcXFxYsGABNWrUIFu2bAD88MMPjB49mvLly+Ph4cGyZcuU++F8+fLRokULxowZg6+vLw0bNsTGxibV64L4e4ZroXXr1jRp0oTjx48DMGzYMObOnUtkZCRxcXFkypQJNzc3fHx8iIqKokWLFly4cOGdfRlei62tralbty4Au3btAt59nRdCCCGE+NpJeEcIIYQQQgghvkKGb6J6e3szZswYChQowOTJkwkNDSUgIAB4U5yeMWOGso0hwNO1a1fCwsIYNmxYurRdCCHSmlarVQqI0dHRXLx4kZs3b/Lq1StlnSFDhjBhwoRUAR61Wo1OpwNINYpG4cKFadq0KQ8fPiQmJuZft8+w76ZNm9KyZUtu3br1r/cphEgfhuu5Xr16eHt7p+pnRNras2cPVapUYeTIkcTHx9OsWTNmzJjB9u3bWbduHR07dsTT0xMHBwc0Gg3wboBn6dKlxMbGKvt8OxAiUyT+c3FxcVStWhW9Xs/w4cNp0aIFQUFBeHt7M2jQILJkyYJOp8PMzEwJ8Dx69OiDAR7D63KlSpXIkiULf/zxh7JMCCGEECIjkTtPIYQQQgghhPhKnTx5kunTp9OoUSPGjx9P7dq1ady4MV5eXmzZsgULC4v3jsBj+ODc29s7HVsvhBBpI+UoCRMmTKBGjRqUKlWKggUL8s033xAWFsbTp08BGDp0KJMmTXonwGOYQsvg+fPnPHjwgCxZsnyywnxMTAyVKlXi4sWLDBo0iNu3b3+S/QohPr+oqCisrKyYM2cOU6ZMkQDPZ3D27Fk6d+5MnTp16NChgzI108WLF5XRkO7fv8/9+/cBMDY2/qgAjwRC/r0sWbLQt29fZs6cyW+//cbWrVtp06YNvXr1wtHRUZlaK2WAZ+TIkURFRdGyZct3AjxqtZrXr1+zbNky4uLicHJySqcjE0IIIYRIWxLeEUIIIYQQQogv2I0bNz5Y0L1//z4JCQm0a9cOR0dHtFqt8s3Uxo0bs3z5cuDNCDzTp09XtitYsCBTp07FyclJKWIIIURGYRglwcPDg5EjR+Lg4MCMGTPw9vbGwsKCnj17MmnSJO7duwe86SMNAZ66dety+PDhVFOkJCcns3fvXrZs2ULjxo359ttvP0k7zc3N6d+/P/7+/hw6dIgePXpw8+bND65vGA0iOTn5k/x+IcSn4+DgwKRJk+jevTuTJk3C39+fly9ffnB9w/Us0/7877Zv305sbCwDBgygVq1aAGzbto3Dhw8zaNAg3NzcOHToECNGjODw4cPA+wM8lSpVYtCgQYSFhaXbsWREmTNn5tq1ayQlJWFiYsLFixe5c+cOycnJSkDq7QCPj48PT548oVq1aly+fDnV/h4+fMi+ffto0qQJQ4YMSYcjEkIIIYRIe8bp3QAhhBBCCCGEEO935coVSpQogYuLCzNnzsTOzg5A+TaxoYB79+5dAKXYbFjeokULunXrxtKlS/Hx8UGtVjNo0CDgz28VGxvL20IhRMazevVq5s6dS+/evRk2bBgFCxZEr9eTJUsWfHx82LNnD2PHjlXWHzx4MGq1msGDB9O1a1cuXbqEsbExKpUKExMTjIyMGDJkCJMmTQL+7Gf/F3q9XhkdyNrams6dO5OYmIinpyePHz8mf/78791OpVKxb98+jh8/TpcuXcidO/f/9PuFEJ+WRqPB2NiY/Pnz4+vry+vXrwkMDKRr165YWVm9dxuVSsXRo0e5f/8+jRs3JmvWrJ+51V+/Bw8e8Pr1a0xMTADo3bs3165dY9q0aZQtWxZra2ssLS0ZPXo0AL6+vlSpUkUJ8BgbG/PDDz+QlJREtmzZqFu3bjoeTcag0+lSTTNWoEABJkyYgFqtZvLkyQwaNIhJkyZRp04d5T2I4bXUzMyMoUOH8vr1a9auXYulpWWqfRcuXJjx48dTpUqV9/4uIYQQQoiMQKWXeL8QQgghhBBCfJEuXbrEwIEDyZYtGyEhIcp0AAbnzp2jbNmy1KhRg4ULF1KoUCFlmVarxcjIiIkTJzJz5kySkpJITExkxYoVtGjR4jMfiRBCfF69evVi8+bN7Nq1i2+++YakpCQ2b96Mu7s7ZmZmHD16FFtb23eKf8HBwdSrV4+8efN+cN//tGD4V+tfvnyZokWL8uLFC/744w8KFCjwwf3cvXuXihUr8vjxY8LCwnBxcZHipRDpLOU1uHv3bqpWrcqLFy949OgRZcuW/eB2ly9fplSpUuTIkYOtW7dStmxZuZ7/oSNHjlC3bl2+++47smfPzqZNmxg8eDBeXl7kzJkTgJcvXzJr1iz8/Pz44YcflAAP/HmvDJCYmIiZmVmqx8Q/k/Lc/frrr5ibm1OwYEEAkpKSCAkJwcvLizx58jB58mRq166d6ksEjx49wt7envj4eJKTk7G0tFT2+XZg9t8EaIUQQgghvmTybkAIIYQQQgghvlDFihVj+fLlLF++nMyZM7N582Zu3LihLC9Tpgy9e/fm4MGDhISE8OLFC2WZ4cPz69ev07p1a1avXk1sbCyhoaHExMTINA1CiAzDMF2gQUxMDPv376dUqVJKcGf9+vW4u7ujVqs5cuQItra2ABw6dIjr168r2/bo0YO8efOi1WqVx97uL/9pcd2wfq9evVi1apXyePfu3WnSpAlXr17F2tpaCe58qH/OmzcvHh4eWFpaMnHiRGJiYqTQL0Q6M1yDbm5udOjQgdDQUBwcHJTgzoeuZ2tra7p160ZUVBSzZ89OtS/x9zQaDVWqVGHRokUcO3aMTZs24eLigqenJzlz5lT6cCsrKwYNGsSYMWOUEdeOHDkCvLlXNqxnZmamPCb+uZTBnUmTJtG8eXP69++vvL6amprStm1bJk6cyP379xk+fDh79+5Vtt+0aRM1a9Zk9+7dZM6cGUtLS/R6vbLPt4M6EtwRQgghREYl46MLIYQQQgghxBfk9OnT2NjYKNOmGKZFCQsLo0OHDvTv359hw4bh7OwMQPv27Tlz5gz+/v7ExcXRsWNHvv32WwA2btzIoUOHaNKkCXXr1qVRo0bs3r2bqKioVKP0CCHE1yrlSBV79+6lUqVKmJubY2dnR1RUFPfv3+f06dN4enqiVqs5efKkMgUhQL9+/ciTJw/btm1Tpl6B1AXcT1Ek3LlzJ8HBwZw6dYrcuXOzefNmli1bRr9+/d6ZVud9v89wnEOHDuXSpUusWrWK+/fvU7Ro0X/dNiHEP5ey7/n5559ZtWoVHTp0oE6dOqnWe9/1rNfrsbe3x8/Pj/Pnz3PgwAGeP39OtmzZPkvbMwLDiC2RkZFKcOTGjRtcu3YNe3t7jIyMlOfI0tJSmTbWz88PY2NjPDw8qFmzpoR1PoGUIRsPDw9mz55NnTp16N+/vzLyDoClpSVt27YFYMSIEXh4eHDnzh20Wi3z58/nzp07qUafk4COEEIIIf6LJM4vhBBCCCGEEF+Iq1evUqFCBerXr8/NmzcBlG8ElyhRgg4dOrBo0SKCgoKU5TVr1mTUqFFUqVKFGTNm0LJlSzp37kzz5s3p0aMH8fHxSsHC3t6euLg4Xr16lT4HKIQQn1jKUS9++uknVq9eDUC5cuW4dOkSfn5+DB48GCMjI06cOJEquDNlyhSePn1K06ZNU03dkRYaNGjA8uXL+e2332jTpg3Tpk1jxIgR+Pn5KdO7/BW1Wo1Op0OlUjFjxgy0Wi0RERFp2mYhxPvp9Xql74mLi+PevXuYmJjQt29fJXz9V1QqFXq9nty5czNjxgxu3rzJoUOH0rrZGYperycxMZHbt2/j6uqKn58fFy5cwNPTk59//hn4s98ElADPuHHj2LFjB4sWLSIpKSk9DyHDMIRsAgMDmTlzJn379mXmzJk0atTonXUtLS1p1aoVU6ZM4cWLF/Tt25dBgwYRGxvLpUuXyJ8/f6qR74QQQggh/mtUehkrXQghhBBCCCG+GF26dGHFihV88803REREpCoCXb58GX9/f1atWsWAAQMYPHiw8g3VU6dOsXPnTqZNm8arV6/Ili0bZcqUYfHixTg7O3Pv3j0aNmyIXq9nx44dODo6ptchCiHEv5Zy1IsdO3bQpUsX2rVrx6BBgyhUqBDXrl2jVatWXLx4EVtbW44dO5bqG/1r1qzB19eXnDlzsnHjRrJnz/5Z2l21alWOHTtG9uzZmThxIt27dwdSTznyVwzr7d+/n2rVqqV56EgI8WEjR45k1qxZ1KlTBzs7OxYuXAi8CZZ8zKghhkBecHAwXbt2lev5b7zvvCYkJBAXF4eNjQ2zZ89m2LBhlCtXjjFjxlC3bl0g9evFy5cvWbZsGS1btpR74U/o1q1bNG/eHHNzc5YvX55qhM9du3bx6NEj4uPj+fHHH7GwsCA2NpaoqChCQkLImTMnrVu3xt7e/qNfC4UQQgghMip5RyCEEEIIIYQQXwDDh9XLly8nS5YsLFiwgDZt2qQK8BQtWpSRI0cCMGfOHAAlwFO+fHnKly9Pjx49ePHiBZkzZ8bW1hYLCwuioqJYsGABv//+OyNHjpRihRDiq5Zy1AvDyAuZM2emf//+SsHQ0dGRQYMGMXXqVJ4/f87mzZupXLkyOXLkYPHixSxfvhwjIyNWrlxJ9uzZUxV3P3VbDcXmCxcuEBcXR/Xq1Tlw4ADTp08ne/bsNG/eHCMjo48q+BuKmrVq1QI+PvQjhPi0tFotarWaTJkysWnTJsqWLUt0dDQ5cuT46Ol+DH1Oz549AdBoNBLg+YCUfV1iYiIJCQloNBqyZ89OpkyZAOjcuTMAw4YNw8/PD4C6desqI/Co1WqsrKwYPHjwO/sU/87Lly+5fv268jqs0+n4/fffmTNnDgsWLFDWCw0NZceOHVhYWFCwYEHGjh2rLJPnQwghhBBCwjtCCCGEEEII8UUwMjJSPrSeN28ewEcHeIYOHUq+fPkAyJUrF7ly5VL2e/LkSZYuXcry5ctp27Yt48aNAz7+W+FCCPGlMfRdI0aMIDg4mGrVqtGkSROKFSuGXq9Hr9djZmZG+/btMTMzY9q0abi7uytFwUyZMlG2bFlCQ0NxdHRMs4JhykDQq1evKFmyJOHh4WTPnp3t27fTqVMnpT9v3rw5KpXqHxfvpdApRPowMjLCx8dHCVzfvn2bkydP0qRJk/95nxLceb+UffS8efPYtWsXFy9exNTUlI4dO9KgQQO+/fZbLC0t6dq1K/DXAR4D6T8/ncTERBITE9m4cSMVKlTg9OnTbNiwgcePH9OjRw/q1KnDkiVL2LNnD8uXL8fV1fWd9yLyfAghhBBCSHhHCCGEEEIIIb4YRkZGSuH2nwZ43N3dcXJyUval1+u5ffs2AwcO5MKFC3Tt2pW5c+cCpNkIE0II8bkkJyej0WjQaDRs2LCBihUr8scff5A9e3ZUKhV6vR4LCwt+/PFHmjRpwvLly3ny5Ak6nY4aNWrw/fffky1btjQL7qTc76JFi1i9ejXVq1dn9OjRAPz4448kJibSs2fPVAEeY2NjtFothw8fxtjYmIoVK0pBX4h0ljJkYPi/RqPBzMwMNzc3VCoVEydOxNXVlbx581KmTJl0bnHGodfrlb7U3d2dGTNm4OTkRLFixYiOjmb06NH8/PPPdO/enS5dumBubk6PHj2ANwGecePGkZSUROPGjeXe9xP4UPi/YsWKjB8/npEjR9K+fXtMTEwoV64ca9asoUiRIpibm1OmTBlKlChBQkICgHyJQAghhBDiPeTdvxBCCCGEEEKks5RhmpSFhY8N8CxcuJDXr18zduxY8ubNC7z5QNzBwYGZM2fy6tUr6tat+87vEkKIr5WJiQmjR48ma9asLF68mP9j764Dqrr/P44/Lw2CioSICohizC5m6+xu3eyeogIWIGKAimKh2IGt2MHsRKyJTqfYPWMmdlA3fn/4u2dgbG5fgcnej3+Ue4LPuZfzuefez+u8Pzdu3ODUqVNKX6cfFDQ0NCRHjhwMHDjwg31otdo0q7ij36+vry9z586lUKFC2Nvbp1qve/fuqFQqevTokSrAs3PnTvr06UOePHmIioqS8I4QGej9gN+rV6/ImjWrcl6mDPCMHTuWFi1asGnTJgnwfCH6vnz27NmEhYXRt29f+vbtS+HChXn06BEjRoxgwYIF5MyZU6m2Zm5uTvfu3TEwMKB///5YW1tTu3ZtTE1NM/hovm4pzwWNRsObN28AyJo1K/CuGl7BggW5f/8+efLkoW7dulhYWADv3hfXrVuHiYkJRYsWzZgDEEIIIYT4Cqh0Op0uoxshhBBCCCGEEP9VKb8I37ZtGxcuXKBLly6pBnk9PDyYN28epUqVShXgAbh8+TI+Pj6cPn2aM2fOYG1t/cnfJVNlCSG+Rp+qemFkZMSbN2+YPHkykyZNIk+ePERGRlKoUKEMbvE748aNY8SIEfTr149+/fp9sl1Lliyhe/fuWFlZUa5cOa5cuYJWqyU6OpoCBQqkc6uFEHopr9HCw8PZunUrx44do1KlStSpU4cff/xRWZ6UlMTUqVMZO3YsdnZ2EuD5QnQ6Ha9evaJFixY8fPiQjRs3UrBgQSUM4u/vj06n45dffsHGxibV1IOvX79m/fr11KpVSwm3i3/m/XNh9+7dnDp1ClNTU9q2bUuDBg1wd3f/6LY6nY7169czcuRI7O3t2bx5859+XhFCCCGE+C+T8I4QQgghhBBCZJCUVXACAwOZPXs25ubmzJkzh4YNG6b6ovzPAjzXrl3D2toaGxsbCegIITKV96tevH79GktLy1TrvH37lkmTJjF+/HhcXV3ZtGlThgd4zp07R9OmTSlQoAALFixINa3hx2zZsoU+ffpgYmKCi4sLS5cuxcnJKdVAtBAi/aS8RhsyZAhhYWE4ODhQpEgRLl26xN27d+ncuTPh4eHKOZoywJMrVy5WrVpF2bJlM/IwMoVbt25RrFgxOnfuzKxZs0hKSmLjxo34+flhYGDAiRMnsLW1BeDmzZvY2Ngo1WD00mqKxP+ClJ8tBg8ezIwZM7C2tsbFxYWzZ8+SkJBA6dKl8fT0pGvXrqm2SUxMZPLkyYSHh6NWqzly5AhOTk5SCVQIIYQQ4hPkCkkIIYQQQgghMoBOp1O+tPb19WXs2LHUrVuX9evX07BhQ+DddC9qtRp4N4XWjz/+yOnTp2ndujU3btxQ9lWgQAFsbGzQarUS3BFCZBopB1sXLVpEmzZtKFy4MO3bt2fRokXKehYWFvj4+DBs2DBu3LhBixYtuHz5ckY1G3g32Pzbb7/Rtm3bvwzuADRp0oRffvmFY8eOsWXLFpycnNBoNBLcESKD6K/RgoODmTlzJr169WL79u3s3r2bqKgozMzMWLZsGR07dkSj0QBgYmLCwIEDGTVqFNeuXcPT01O5jhP/nKGhIQYGBpiZmQGkCu4cP35cCe68fv2aunXrsmnTpo/uQ/wz+s8WM2bMYNq0aXh5eREVFUVMTAwHDhxg2LBhXLlyhaCgIFatWqVsc/XqVYoWLcq4ceNwcXHh6NGjynubBHeEEEIIIT5OvgEQQgghhBBCiAyg/yJ80aJFzJkzh379+jFgwADy5cv3yW3mzp0LwPz586lZsybR0dGpBoXli3AhRGah1WqVwVZ91Qt7e3vc3NzYv38/69at45dffmH27NnAuwDPkCFDABg/fjxt2rRh1apVFC1aNEPaf/fuXQBlUPn9qg/6qgPPnj1Tpg/JlSuXslyn08lgsxAZ7ODBgyxZsoR27drh7e1NoUKFePr0KS1atCBLlizkzZuXtWvXYmhoyLJlyzA0NMTExARPT08sLCxo0qSJBPD+RzqdDhMTE/LmzcvKlSvJmTMns2bNwsDAgJiYGOzs7JR1J02axMOHDz+ouiP+NzqdjpcvX7J+/XoKFCiAl5cXTk5OALi7u1OkSBHy5cuHl5cX8+fPp3r16jg6OmJnZ0fPnj0xMzOjU6dO2NjYSAUkIYQQQoi/IN/sCiGEEEIIIUQa0mq1n1ymVqvZtWsX2bJlo3v37qmCO+Hh4XTt2pW6desSGRnJkydPgHcBnnbt2vH69WtMTU3TvP1CCJERUla9mDFjBj179mTHjh0cOHCAffv2YWRkxNy5c+nWrZuyjT7AExAQwLlz5+jfv79SESO96QeP161bx9u3b1MNVuorr2m1WurUqcPUqVM/2F6qqAmR8c6dO8etW7fo2bMnhQoV4vXr11SrVo1Hjx4RHh7O3r17cXV1ZdWqVXTo0EGpsmNqakrfvn3JmzdvhvVBmYVKpcLe3p7OnTvz6NEjgoKC0Gq1XLp0CXt7e+DdtfaaNWtYtmwZVatWpWbNmhnc6sxFpVLx4sULTp48SaFChXByckKn06HT6QCwsrKiefPmdOrUiejoaHbv3g1A9uzZGTp0KF5eXkqFUAnuCCGEEEL8OQnvCCGEEEIIIUQa0g9Ajx07llu3bqVaFh8fT2xsLDY2NpQsWRK1Ws2+ffto3bo1P/74I5s3byY6OprOnTsrX4QDrFy5kqtXr+Lg4CCDQkKITCsqKorFixfToUMHBgwYQIkSJXj69Cnt27fH0tISZ2dnli5dSq9evZRtLCwsGDhwIFOmTFEqYaQV/cDlxzRt2pSSJUuye/dutm/fTmJiIgBJSUmoVCq0Wi3h4eH8/vvvwJ8HPYUQGaNcuXKsW7eOypUrk5iYSPv27Xnw4AFjxoyhTp065M2bl+7du2NoaMhPP/1Eo0aNPrguk7DC/0bfNw4aNIhu3boRHx9P/vz5uXXrFhqNBrVaTWhoKMOGDUOn0zFv3jyyZcsmfeoXZmVlRfbs2Xn69CnwYcDUxsaGZs2aAXD8+PFUz7/+s5BUCBVCCCGE+GtyxSSEEEIIIYQQaWzNmjWMHDkSPz8/ZSoVeDegU6lSJc6ePUvHjh1p0aIFbdq04eDBg0yaNIl9+/YRERGBWq1mypQpJCcnk5ycDIC1tbVMqyKEyLR0Oh1nzpzhzp07H616sXDhQnbv3o2joyMLFy5MVYEnS5YsDBw4ME2rXmg0GmXw8tmzZ1y5coW7d++SkJCQqg0ajYbAwEBWrlzJmzdvMDExAd5V5Jk6dSqOjo506NBBBjWFyED6IN77gTx3d3fq1asHwOHDh4mKiqJ169Z07NgRCwsLAJydnXF0dMTR0ZGYmBieP3+erm3/2v1ZCBL+CHwYGRkxatQounbtysGDB/nmm28oW7Ysrq6uDB8+HEtLSw4cOECePHnQaDTSp35B+qmuihYtytGjR1myZAmAEkTVv8+6u7sD714zef6FEEIIIf4ZuYoSQgghhBBCiDT23XffERISQmRkJL6+vty+fRt4VyGid+/eNGjQgA0bNnDq1Clq1qzJyZMnGTx4MGXLlqV169bY2tri4OCAsbExxsbGyn5lWhUhRGalUqkoX748q1evplKlSiQmJvLDDz/w4MEDRo8eTe3atXFzc6NHjx4YGBiwZs0aWrRo8cF+0iLgmHLqj4kTJ1KjRg0KFy5MgQIFKF++PNu3bycpKYmWLVsydOhQ4uLi6N27N/Xr12fYsGE0atQIDw8P3rx5w8aNG7G3t5cqEUJkkJRBPIDExMRUgRIzMzMArl+/zps3b+jWrRvm5ubK8t27d1OhQgXOnDnD1atXlemBxF/TarXKc6+v6PJnnJycWLRoEQsWLKBNmzYYGBjg7u7OhAkT2Lt3Ly4uLkrQRPx9nwq7GhoakjVrVjw8PAAYNmwYkZGRwLugjqGhIVqtloiICABKly6dPg0WQgghhMiEjDK6AUIIIYQQQgiR2dnb29O9e3dUKhV+fn7Ex8ezcuVKLCwscHd3Z/HixcTFxWFhYYGjo6NSmUGj0bBo0SKePXtGxYoVlcEkCe0IIf4L9KEdgAMHDnDgwAG6dOlCx44dlcFzJycncufOjU6n48CBAzx9+pQcOXKkWZt0Op1SUWDIkCFMnTqVihUrEhISwp07d9ixYwedOnXC09MTX19fevfuTfHixZk0aRIHDx7kyJEjODk5Ub9+fSZOnKhUiZDBZiHSX8pzb8mSJezfv58rV66QPXt2OnToQOXKlXF1dUWn0ynXYBEREbi7u6NSqVi/fj1Hjhyhbt26ZMmShSxZsqDVaqXqyGfSP08+Pj4kJiYyffr0P33+dDodKpWKHj160KNHD5KTk1OF2lMGK8Xfk/K5W716NRcvXsTOzo5SpUpRpUoVAJo3b87EiRPx9fWlc+fOBAcH06pVK3LkyEFERASzZ8+mcOHCNG3aNCMPRQghhBDiq6bS/VVtSiGEEEIIIYQQX8Tjx4+ZP38+xYoVo1mzZn+6rk6nY+XKlYwbNw5DQ0P27t1Lzpw506mlQgiRPt4PrugHZ983a9YsPD09OXnyZKq7+jt27AjAzJkzSUxMJGfOnOkyeL58+XJ69+5Nly5dGDRoEG5ubiQnJxMcHMzo0aMpX748Bw4cSFWh4/r167x69QpXV1dMTEwwMzOT4I4QGSRlXzN48GDCwsKwt7fH0dGRx48fc+fOHRo2bMjQoUOpUqUKb968wd3dnYsXL1KnTh3Mzc05dOgQlpaWHD16lNy5c2fwEX2dnj9/TpkyZTA3N+fMmTMYGRl98n0A/njd9IEqAwMDCUx9QT4+PkyZMkX5WaVSMW3aNDw9PZXHpk+fzoABA4B3NygYGhoSFxdH3rx5lQpI8poIIYQQQvwzUnlHCCGEEEIIIdKJnZ0dvr6+qe4Sfp9arebly5cMHz6crVu3YmxszP79+8mZM6cM8gohvnr6AT39VDX6Pm3FihX8/PPPPHz4EBsbG7p06UKBAgWwt7dHp9Mp09CsXr1aCe+sX7+en3/+mYYNG5I9e/ZU+09ru3fvxtraGg8PD9zc3EhKSmLLli0sXryYAgUKsHXrVszNzVO1J3/+/Kn2odPppE8XIoPowyHTpk0jLCwMDw8P+vfvT6FChXj69CmdOnVi+/btWFpaUq5cObJkycL27dvp2LEjR48excrKCnd3d+bNm0fu3LnlGu0fsrKyonXr1kyePJmZM2cyYMCAP60wqV+mUqmU/0tI5MvYsGED4eHhdO/enS5dunDz5k1CQkLw9vbm4cOHjB07FgAvLy+KFSvGtm3bOHr0KLa2tpQuXZq+ffvi4OAg54IQQgghxP9AKu8IIYQQQgghxL/I/fv3adeuHb/++it16tQhLCxMBoWEEJnC3r17mTp1KqtXr8bKykp5fPDgwUydOhUDAwNMTExISEggW7ZsNG/eHF9fX4oUKcKTJ08oX748v/32G02aNMHExISoqCgsLS05cuRImla9eD8Q9Pz5c4oVK0aZMmX46aefSEpKYtOmTfj6+mJgYMDx48exs7MD4NChQxQsWFAqpwmRwT52HXX//n1atGhBcnIyK1eupHDhwqjVajZv3szQoUPR6XScOHGCHDlyKFM0JScnc+HCBbJkyYKDgwOWlpZyjfY/unnzJqVLl6Zq1aps2bIlo5vzn6GvYqR/jxsxYgQ//fQTGzduVMKmR44cYeTIkURFReHv78/YsWM/Gq7S70vOBSGEEEKI/43E0oUQQgghhBDiXyRXrlxMnDiRNWvWsHjxYgnuCCEyBa1WS1hYGDt27KBjx468evUKgPDwcMLDw/Hy8uLkyZPcu3ePZcuWUaZMGZYuXcqQIUO4cOECNjY27NixgzJlyrBnzx4OHjxImTJlOHjwoNJPplW79cGdQ4cOodFoyJ49Ozly5ODBgwc8fvyY7du3fzS4A9C9e3c8PDzSrH1CiD939uxZ3r59i6Gh4Qfn4ZMnT4iNjaVRo0YULlwYjUbDhg0bGDx4MBqNhmPHjpEjRw4A7t69y9OnTzE2NqZkyZIUKFAAS0tLqaD1mfTV097/WaPRkC9fPtq3b8+2bdskvJNO9NXv4I/KRRqNhh9++IH8+fOTnJwMQOXKlQkODua7775j/PjxjBgxItU+3n9d5VwQQgghhPjfSHhHCCGEEEIIIf5l3N3dqV+/PlZWVjIoJITIFAwMDFi1ahXNmjVjy5YtfP/99wA8fvyYQoUK4e3tTcmSJcmePTsdOnRgxYoVtGrVih07drBgwQJevXpFoUKFOHToEIcPHyY6OpoNGzbg5OSUpgFH/aDmoEGD6NChA6tWrQKgRIkSnD59msDAQLy9vTE0NCQmJiZVcGfChAk8f/6cevXqybQuQmSA2NhYSpYsSZMmTT4a4Hnx4gVJSUnY2NgA76bl+1gQ7+nTp3Tu3JmYmJgPfsefTfEk3k0Hq9PplD7wxo0bqX7W992NGjUC3k2HmJiY+EEoRHw5Wq1Wed4XLlxIr1696N+/P5s2beLs2bOo1WqMjY2V16BChQpKgGfcuHGMHDkSePfa6V9HOQ+EEEIIIb4M+eZACCGEEEIIIf7F5MtwIcTX6v3BV0tLS1asWEGjRo3YuXMn9erVY/v27dSrV498+fIBf0y9kStXLsaNG4e7uztr1qzhxYsXAJibm1OmTBkKFy6cpgHHlG3fsmULS5cupVmzZnz77bcA+Pr6kjdvXubMmUNCQgJRUVHY29sr26xevZqFCxfyzTff0Lp1a+nLhcgALi4uVKlShaioKNq1a6cEeNRqtbI8T548LF68mOXLlxMQEPDRClqTJ0/m1KlTmJiYZNShfFX27NlD9erV0el0GBkZKf3p8OHDKVOmDJ07d+bcuXM8e/ZM2aZRo0ZKuPP333/HwMAAnU6XUYeQqekDN76+vvTq1YvFixcze/ZsLl++zJUrV4iNjVXWez/AU6dOHcaOHcvEiRMzrP1CCCGEEJmZhHeEEEIIIYQQQgghxBenHyA8fPiwMgibJUsWVq9eTcOGDdm/fz8nT57k9u3b6HQ6kpOTU4VcXFxcqFevHg8ePGD79u0f/R1pEYpJWRVCq9Vy5coVrK2t6devH25ubgDkz58fLy8vnJycMDMzY/fu3Zw9e5bff/+dUaNG4ePjQ2JiIsuXL8fGxkaqSAiRzjQaDVmzZmXbtm3Uq1ePLVu2KAEefaDE0dGRGjVqEBsby4ABAwC4fPmyEtzR6XREREQQERFBnTp1cHd3z8Aj+jqo1WqWL1/OoUOHqF27thKwjIuLw9bWFmdnZ1auXEnFihXp2LEjmzdvVqZRbNu2Lc+fP2fixIkfvB+I/13K96EDBw6wePFi+vTpw5kzZzh8+DDff/89Z86cISgoiN9++w34MMAzfPhw2rRpo1TPE0IIIYQQX5ZKJxF2IYQQQgghhPhs+qoQQggh/lqbNm3Yu3cvixYtonnz5kr/+fr1a77//nt27NiBq6srR48exd7eXpkCS61WY2RkxPHjx6lQoQIzZsygX79+6dr2IUOGEBkZScmSJXFxcWHy5MnodDol3PPkyRPWrl1LaGgo169fx9zcXFlWsmRJIiIicHZ2TtNpvYQQn6Y/916+fMn333/Prl27aNKkCatWrcLCwgKAV69eUb16dU6fPk2LFi3YsGGDsv2MGTOYPn06Wq2WgwcPkjt3brRarUyD9xfi4uLw8fFh6dKlVK1alQMHDih9f1JSErt372bDhg1ERESQnJxMxYoVadWqFZ06daJq1apkz56dXbt2kS1bNrnuTgO3bt1i48aNzJkzh23btimh1OvXrzNjxgxmzJhBs2bNmDJlilIVL+XffVJSEiYmJsr7tBBCCCGE+HIkvCOEEEIIIYQQnynlAMLvv/9O7ty5M7hFQgjx73bgwAHatWtH9uzZGT9+PM2aNVP60Tdv3tC+fXu2bNlC5cqV2b59O1ZWViQnJ2NsbIxWq2XMmDEEBQWxbt06WrVqlaZtfT9k4+npycKFC0lISOC7775j06ZNZM2aNdU2ycnJPHr0iPDwcOLi4jA0NKR69epUr16dHDlySHBHiAz2OQGemzdv0rp1a3799VdsbW0pUqQIDx484LfffiN//vxs374dFxcXOZ8/gz7k8eTJEwYMGMDKlSupXLkyBw8e/CCEc/DgQY4ePcq0adN49OgRhQoV4uXLl9y/f5/JkyczaNCgDDqKr9v7AbOUf7ejR48mPDwcd3d3cubMyaxZs1Cr1RgaGqJSqbh16xbTpk1j+vTpfxrgEUIIIYQQaUPCO0IIIYQQQgjxmfThnZo1a2JtbU1ISIhyt6oQQoiPO3r0KG3atMHAwICpU6fSunVr5Y79N2/e0K5dO7Zu3UqZMmWIiIjA0dERS0tLVq9ezahRozA0NOTQoUPY2NikWRtThjN9fHzIly8fvXr1IiAggKVLl2Jubk5ERASVKlX67H3KQKcQGeNT596fBXji4+MZM2YMJ0+e5Nq1a7i5uVGtWjV69OhBzpw5JbjzmbRaLSqVCpVKRVxcHAMGDCAiIiJVBZ7ExERMTU2Vbe7evcuePXuIiIjg4MGDJCcnU7VqVdavX4+tra1U3vmHFixYQK9evYB3AR6NRsOCBQsYM2YMjx49onz58uzbtw9LS8tU54w+wDNjxgxatGjB+PHjKVCgQEYeihBCCCHEf4aEd4QQQgghhBDiM/z88888e/aMOnXq0L9/fxYsWECPHj3w9fWVAI8QQvyFQ4cO0bhxY9avX0+dOnWAP6oBpKzAY29vj6OjIxYWFly7dg1bW1u2bt2Ki4tLuoRhgoODGTlyJM2bNyciIoJXr14xYcIEwsLCKFmyJJs2bSJPnjx/ug+Z5kWIjJMyZBMZGcnx48cZNGiQEv5LGeBp3Lgxq1evVgI8eo8fP8bOzk45lyW483Hv98kp+77bt2/j5OREXFwcAwcOZOXKlVSpUoXo6GhUKlWqCmsp97Fx40YiIyOJiIhgx44d1K5dO92PKzMYPXo0gYGB+Pj4MGHCBOXxly9fsnHjRoKDg4mLi2PWrFm0bt0aExOTDwI8M2bMIDQ0lO7duzNv3jw5B4QQQggh0oHc/iOEEEIIIYQQf2Hfvn1UrlyZjRs38vbtW+bNm8eQIUNYvHgxQUFBXL58+ZPbvn+/hFarTevmCiHEv07VqlW5efOmEtwBMDQ0RKPRkCVLFiIiImjSpAmPHj3i4cOH1K1bl40bNxIVFaVMV5MWwR2NRqP8//nz50RFRdGlSxemTJmCqakptra2+Pv74+3tzcmTJ2ndujV37979031KcEeIjKHVapWAQUBAAL1792b8+PHs27cPeHe+Z82alTVr1lCvXj22bt1Ku3btePv2LQBqtRrggypfElr4OH2fHBgYyKVLl5S+r2/fvtSsWZM7d+5ga2tLWFgYHTp04PDhw9SoUQOdToexsTFqtVrZh/76uGXLlnTo0AGtVsvMmTNJSEjImIP7yjVv3pzWrVszadIk/P39lcezZs1Ky5YtGTZsGKampowbN46oqCjltdC/Ds7OzvTt25eRI0cycuRIOQeEEEIIIdKJhHeEEEIIIYQQ4k9cunSJbt26UbVqVdq3b0+2bNkAmDhxIr169SIiIkIZ9PkYlUrFgQMHmDBhAklJSTKFihDiPytHjhxA6lDj+wGexo0bc//+fW7dukWlSpWws7NLNSD/pen3u3jxYjZv3syRI0do2bIlLi4uwLvB/hw5cuDv78+QIUM4fvz4ZwV4hBDpT3+NFRAQwIQJE2jatCmnT5+mbdu2wB/9TcoAz5YtW5QAj5GRUarqIxLE+2tz5sxh9OjRjBw5klevXuHl5cXcuXNp2rQpJiYmwLu+f9q0aXTo0IFDhw4pAR4jIyMlMGVgYKC8N9StWxd3d3euXLmiLBd/T4kSJQgKCqJt27ZMmDCBgQMHKsuyZs1K69atGT9+PI8ePcLHx4e9e/d+EOBxdXVlxIgRODk5pQq6CiGEEEKItCPTZgkhhBBCCCHEn1i8eDF9+/Zl/vz5dOrUCYCVK1fSoUMHAM6dO0exYsU+uf2tW7coVaoUb968YdWqVbRq1UqmVBFCiPfop6V59eoVnp6eBAYGKgGatBYdHc13332Hm5sbarWaqKgoZbDS0NBQ6bOfPn1KSEgIU6ZMoWLFikRERODk5JQubRRCfJ6dO3fSrl07GjduzJgxYz7aj+jP7ZRTaFWtWpWdO3dibm6e/o3+il25coUFCxYwbdo0XFxcuH79OkOGDGHgwIHkypUL+GN6rSdPnjBgwIAPptBSq9UYGRkp+3zx4gWNGzfm8ePH7Nq1C2dn54w6vK/e+fPnGTZsGB07dqRNmzaplr169Yr169fj5+eHg4MDEydOpHbt2hgZGclnFSGEEEKIDCK3fAohhBBCCCHER+jvMDUyMiIxMZHHjx8D4OXlRadOnVi/fj2AEtz51H0RWbJkYdiwYZibmxMZGQnIndxCCPE+Q0ND1Go1VlZWLFmyBBcXl3SruFCkSBFCQkJ49eoVN2/eZOXKlUqb9AOYOp2OHDlyMHToUHx8fDh69Cj9+vWTqRCF+Jc5efIkCQkJ9O7d+5MBQH1/o6/A8+2333L27Flev36dvo3NBAoWLMjIkSMpVqwY169fx83NjRYtWijBHZ1Op1RzsbGxUSrwHD58mFq1aikVePQSExOJjIzkyJEj1K5dW4I7/6OiRYuycuXKD4I7AFZWVrRu3ZoJEybw4MEDhg0bxvbt21Gr1fJZRQghhBAig0h4RwghhBBCCCHec+jQITZu3IhWq6VkyZIULlwYHx8fatSowcyZM/H19cXd3T3VNp/6ktvW1pYOHTrQqFEjduzYwdmzZ9PjEIQQ4quTcgD3Yz9/afrQpb29PV27dsXHx4fs2bOzcuVK9u/fD6AEd1IGeHx8fBgzZgyzZs2SqRCF+JfQarXodDqioqIwMzPD1dUVnU73QcBO/7P+ui1r1qzs2bOHy5cvK9P0ib8nJiaG27dvU6ZMGa5du0ZoaCg3btwA/nieDQwM0Gg0SoCnc+fOHDhwgNatW6fal5GREXfu3KFnz57MnDkT+HRAXnweS0vLTy7TB3gmTZrE6dOnmT59ukxVJoQQQgiRgWTaLCGEEEIIIYRIYf/+/dSuXZsffviBsLAw7OzsOH78ON999x0JCQnUrFmTyMhILCwslGkXPod+WpY1a9Z89O5XIYQQaUs/dcunPHjwgBUrVhAYGEi5cuUYN24clSpVAlACPPp96H/+O+8DQoi017dvX+bOncv27dupX79+qmX68/fFixd4eHgwe/ZssmfP/sFy8fcdOHCALFmysG7dOqZMmULTpk0JDQ0lX758wB/Prf7fhw8fEhwczKBBgz6okJSUlISJiUmq7UTaevnyJTt27KBy5crkyZMno5sjhBBCCPGflba3MAkhhBBCCCHEV+TKlSv06NGDKlWq0KtXL+zs7AA4duwY8fHxmJubs3//flasWMGPP/6IoaHhZw8qVK9enWXLltG8efM0PgohhBDvSxmyOXLkCJcvX+bevXvkz5+fatWqkTt3bhwcHOjYsSM6nY7AwECGDRumBHj0lXf0/b2+moQEd4T4dylbtiwAU6dOxdXVlYIFCwKQnJyMsbExAOHh4axZs4ZOnTrRoEEDZVsJify1TwUWa9SoAYCdnR1JSUnMmDEDlUrFlClTcHZ2VkKPhw4dwtbWlqJFixIWFoZKpUKtViuV1nQ6nRLcSdnnirSVNWtWvv/+e+DTr7EQQgghhEh7UnlHCCGEEEIIIf7fmjVr6Nq1KzNmzKBnz57Au8GfFy9e4OjoiKmpKT4+Pjx9+pSpU6fi6ekJ/PVdwfoKDXopBymEEOJr8LF+7v2+7d8qZdsDAgKYNWsWL1++VJaXLVuWjh074u3tDcDDhw9ZtmwZgYGBlC9fnvHjx1OhQoWv4liFyMw+t8/5/vvv2bhxI3379uXHH3+kaNGiyrINGzYwbNgwHB0d2bRpU6rKO+LTNBoN8EdgcePGjVy4cAETExPKlClD7dq1lXV/++03wsLCmD59Ok2bNmXKlCm4uLiwZ88ePD09yZEjBwcPHsTIyEjCOUIIIYQQQqQg3xYLIYQQQgghxP8zNjYmMTGR69evA+Dl5cWsWbOYOXMm7dq1w8rKCjs7O7p06cLAgQNRqVT0798/1TQAH/P+QJMEd4QQXxt9/9arVy+aNGlC06ZNlWo0//ZQi77tI0aMICQkhPbt29OpUyeSkpLYu3cvS5cuxc/Pj2fPnhEYGEjOnDnp0qULAMHBwfTp04dFixYpFT2EEOnn7NmzqNVqSpcu/dl9zuDBg3ny5AkzZsxg//799OnTB0dHR/bv38+mTZswMDBg6dKlZM+eXaZl+hN79uwhKiqKcePGparE4uPjw5QpU1KtO2bMGAICAgBwcXHB29sblUrFrFmzePToEU5OTsTExBAfH8/OnTuV6jpCCCGEEEKIP0jlHSGEEEIIIYT4f1evXqVjx46cOHECd3d3jh8/jp+fHx4eHjg5OSnr7dixg86dO/Ps2TOmTZtG//79gb+uwCOEEF+znTt30rBhQ4oXL86kSZOoW7cu8HVU4Dly5AjNmzenWrVqhIaG4uzsDMDbt285fPgwHTt2JCEhgalTp9KjRw8Anjx5wsyZM1mxYgVHjhzB3t4+Iw9BiP+c3377jUKFClGpUiVCQ0MpXbo08Nd9jk6n49y5c8yYMYPw8HDl8axZs1KmTBmWLFmCk5OTTA/0J169ekXNmjU5efIkAQEBjBkzBoC5c+cyaNAg2rZtS4cOHbh//z4TJkzg4sWL+Pj4EBISorw2t2/fZuXKlUyaNImkpCRKlCjBqlWrcHZ2liqUf8PX8B4rhBBCCCG+DAnvCCGEEEIIIQR/BG+uXr1KuXLlePPmDRUqVGD79u1kzZoVtVqNgYGBEs6RAI8Q4r9o8eLFDBw4kHz58hEcHEzDhg0/ut77g40ZPUi+cuVKOnXqxIYNG2jRosUHffXGjRvp1q0b1apVIzIyUln27NkzjIyMsLKykv5diHR2//59pk6dyqxZs6hZsyZBQUGUKVMG+PxAw549e3j69CkPHjzA3d2db775hmzZsmV4n/Q1OHr0KIMHDyYmJgZfX19CQkLo27cvsbGxLFu2DFdXVwCOHTvGqFGj2LNnD0OGDGHChAnKa6PRaHjw4AEPHjzAzc2NrFmzynP/N6T8O09OTsbY2DiDWySEEEIIIdKSxNuFEEIIIYQQgj+mVdm6dSuvXr0ia9asHD16lFmzZuHv74+RkREajUb5Er1BgwYsW7aMzp074+Pjw9u3b/H19ZWBXSFEpqQPrnTr1g2AHj16MGDAAMqVK/enFWliY2MpVKgQpqamGRJ+0f/Oy5cvA/D8+fNUj+tVrVqVSpUqsW3bNn755Rfc3d0BsLa2Bt4NoEr/LkT6ypUrF4MGDcLMzIyQkBAAJcDzV1No6ZfVqVPng2VarVbCI5+hUqVKhIWF0bdvXyZOnIharebZs2d069YNV1dXJYRToUIFxo4dC8DkyZMBlACPgYEBuXPnJnfu3IA893+X/u+7YcOG1KpVi379+mFmZpbBrRJCCCGEEGlFvnUQQgghhBBCiP+XmJiIoaEhM2fOZOnSpTg7OxMQEEBQUBAAhoaGaLVa9AVMGzRowPLly0lMTGTmzJm8fv06I5svhBBpxsDAAK1WC0C3bt2YO3cuU6ZM+WRwR6VScejQIUqVKkWzZs1ITk5Ol/CLvo0p2w3vwjmGhob8/PPPABgZGSnr6nQ67OzsqFGjBsBH+3KZskSI9HHkyBF2796t/Ozg4ICHhwdDhw5l165djBo1ilOnTgEoAZ6P+bNzVoJ4n8/d3Z3Zs2dTpkwZQkND2bhxI48ePQJSP//ly5dn7Nix1KlTh8mTJzNs2DBlnZTkuf/7bty4wYsXLxg5ciRLly4lISEho5skhBBCCCHSiFTeEUIIIYQQQvxn6e/K1v9rampKr169UKvVWFlZYW9vT7t27QgKCkKlUjFy5EgMDQ3RaDQYGBigUqmoX78++/btw83NDUtLy8+exkEIIb42BgYGSqWFH3/8UXn8U/2ehYUFdnZ27N27lz179tCwYcM07SNTTsVy/Phx4uLiaNCgASqVCldXV4oUKUJ4eDilS5fGw8MDAwMD1Go1Rkbvvh67dOkS2bJlw9HRMU3aJ4T4c3fv3qVatWoYGxuzZcsWpWpOrly58PDwAPjbFXjE36evTKb/193dnRkzZjBgwABOnDhBbGws8Md7gv6aWB/gMTIyYsKECVhaWhIQEJDBR/P1c3V1Zc6cOYwYMYIhQ4aQlJREr169PlmB5/3zQc4PIYQQQoivh0TdhRBCCCGEEP9JGo1G+SI7Pj6eJ0+eEBcXh7m5OVZWVgBUrFiR1atX4+TkRGBgIKNHjwY+rMDz3XffkSdPnlT7FEKIr937VWx0Ot1Hpzv5VL9XtmxZVq1ahYWFBTt37vzTdf9XKadiCQ4Opm3btgwaNIgDBw4AkD9/fmVal379+jFt2jQAJbizefNm9u3bh7u7O3nz5k2TNgoh/lyePHkYP348AB07dkxVgUcf4Pm7FXjE36evjnPlyhXlsYoVKzJt2jTKli3LmjVr8PPzAz68Ji5fvjwBAQG0a9eOjh07pn/jMxmNRgNAiRIlGDt2LO7u7nh7e3P//v1PbqNSqThx4gR79uxBrVbLZxMhhBBCiK+ISiefbIQQQgghhBD/MSmrM8ybN4+tW7dy/vx5DA0NadasGbVr16Z+/frK+seOHeOHH37g9u3bBAYGMnLkSOCPO5OFECKzSdlPrl27lhMnTnDmzBmKFy9OrVq1aNiw4Wft5+XLlwQEBLBlyxZ2795NwYIFv3hbU1YV8PHxISwsjKZNm+Lt7U3VqlVTLV+1ahUdOnQAoGbNmhQuXJgnT56wf/9+jI2NOXr0KE5OTlKpQIh0lvKaKiwsjEGDBmFra8vy5cupW7eust79+/eZM2cOISEh1KtXT6nAA1Jh5H+V8vkbMGAA4eHhLF++nBYtWijrxMTE0K9fP06dOsXQoUMZN24cQKoKPADJyckYGxunqm4m/tz7nyv0zyG8m87R0tKSM2fO8PbtWypWrPjJ/cTGxlKqVCmKFSvG5s2bcXV1lXNDCCGEEOIrId8yCyGEEEIIIf5TUlaOGDx4sDIAkStXLt68eUNoaCgdOnRg7ty5yjYVKlRQKvAEBQXh6+sLIMEdIUSmlLKKjY+PD127dmX+/Plcv36dWbNm0bhxY8aMGcOjR4/+cl9Zs2alUaNGVK5cmQIFCqRJe/UDksuWLWPWrFn07t2byZMnU7VqVWW5vopQu3bt2LlzJw0bNuTs2bPMnj2bw4cP8+233/Lzzz/j5OQkVdSEyAD6aZoAvL29CQ0NJS4ujk6dOkkFnnTwfr9nb2/P27dvGTlyJJs3b1Ye//bbb5k9ezZlypQhJCSEYcOGAX9U4NHTh04kuPP59J8rpk6dyvXr15XnsHfv3gwYMIA3b95QsmRJJbjzqb91U1NTmjdvzrlz51iyZAmQdlXvhBBCCCHElyWVd4QQQgghhBD/SXPmzMHT05OBAwfy448/4ubmxo0bN9i2bRve3t4AzJ8/n549eyrbxMTEUKtWLaysrLh8+TJZs2bNqOYLIUSaCwkJISAggN69e9O9e3fKlSvHzp078ff358yZM0yaNInBgwd/1r70d/2nZcWyDh06sHfvXvbs2UOJEiU+2QaAJ0+e8ObNGy5fvkyBAgWwt7cnS5YsqSoOCSHS3z+twDN69GhKly6dUc3+qqV8zkNDQzlw4AA6nY5du3ahVqvJly8fU6dOpWnTpso2x48fp1+/fpw8eRJ/f3+Cg4MzqvmZyvDhwxk3bhx9+vRhypQpjBo1ismTJ+Pp6UlQUBDZs2f/rP2cP3+e5s2bkz17dg4ePIiZmZkEeIQQQgghvgIS3hFCCCGEEEL8p+h0Ol69ekWbNm24ePEi+/fv/6AaxIYNG2jTpg358uVjzZo1lCtXThnYOHXqFA4ODjg6OkoJeiFEpnXr1i3q1KlDvnz5mDVrFgUKFECn0/HTTz/Ru3dvTE1NOXnyJLa2tso2GdknPnv2jOLFi1OkSBH27Nnz0fb8VfukTxfi3yFliO5zAjxTpkyhTJkyzJkzh2LFimVUs796fn5+TJ8+nfr169OhQwfu3LnD2bNnWbJkCU5OTkyfPv2DAI+3tzcxMTEEBwfj7++fga3PHB49esSPP/7ITz/9RNGiRTl//jwjRoygZ8+e5M2b92/ta/369bRt25Zjx47h7u6eRi0WQgghhBBfktR4F0IIIYQQQvynqFQqEhISuHDhAoULF1aCOzqdTik/36pVK4KCgrh58yYnT55UttXpdJQpUwZHR0eZVkUIkandvXuXa9eu0bFjRwoUKEBycjKrV6/G29sbc3NzJbiTmJjI/fv3gYydlkM/Xcv169e5ffv2B+3RarWoVCqePHnCtGnTPjrdiPTpQqS/lFMt6RkaGpKcnAz8MYXW48ePPzqFVt++fenTpw93794lZ86c6dbuzGbXrl1MnjyZli1bMm3aNFq3bs3AgQNZsGAB06ZN4/bt23h7e/PTTz8p27i7uzNlyhQaNGhAu3btMrD1mYe9vT2bN28mV65cXL58meLFi9O4cWPy5s2LVqv9W9PCNWnShHHjxlGmTJk0bLEQQgghhPiSJLwjhBBCCCGE+M/RaDTAu0He33//Xam2oB+41el01KhRA4CdO3cq66cc2JVpVYQQmYW+j0vp7du3AMr0gBs3bmTo0KGoVCqOHz+uVNx5/fo1zZo1IyYmJv0a/P9SDmLa2NhQq1YtHj58yLFjx1Ktp9FolClhRo0axbJly7hx40a6tlUI8aGU52ZsbCz79u1j7dq1vHjxAmNjY2U9b29vpk6dSlxc3AcBHgcHB/z8/Dh9+jR2dnYfDQOJdx48eEB8fPxHl127dg2dTkfXrl1xdnZW3hcMDQ3x8vJi/Pjx3Lp1i8GDB7Np0yZlu0qVKrF582ZcXFxQq9XpchyZ3c6dO3ny5AkODg6cPXuWxYsXc+fOnb895aSpqSlDhw7FyMhIXhshhBBCiK+EhHeEEEIIIYQQ/zm5cuWiSZMm/Pbbb+zduxeVSqUMAqvValQqFeXLl8fc3BwbGxsMDQ3/9hfmQgjxb/T+wLZarVbCiNu3b1cet7S0BGD37t0sXboUX19fDAwMOH78OHZ2dsp6I0aM4ObNm+kSaHy/7fpApf7xRo0aYWJigp+fH6dOnVL6dX3b1q9fz7Zt2yhYsCC5cuVK8/YKIT5Nq9Uq5+aYMWNo1KgRderU4YcffqB06dKEh4fz6NEjZX19BR59gEc/PR68q1aSLVs2dDqdXK99wq+//oqrqyvTp09XqhrBH/1nXFwcAE+fPgX+6Df1/eiPP/5I1apVuXHjBv7+/mzdulXZh35dIyOjtD+QTCjle5tOp6NixYpERkaya9cumjZtyty5cxk9ejR3795VPrP83ZCavDZCCCGEEF8H+TQjhBBCCCGE+E+qW7cu5ubmdOvWjT179ihfhuvvTl2yZAnx8fEULVoU4G+VqRdCiH8r/cB248aNiYiIUAb0PD096dixo1LRomLFijRr1ozw8HAGDx6MgYEBJ0+eVII7Op2O5cuXs2PHDurVq0eRIkXStN0pK3SsWbOGSZMm4ePjw+XLl0lISACgefPm9OzZk1u3btGsWTNmzpzJr7/+yosXL5g8eTL+/v4ATJkyBQsLC+nXhcggKUM2vr6+jBo1Cjc3N8LDw1m8eDE5cuRg1KhRzJkzhwcPHijb6QM8L168oF69ekRHR6far0x992kPHz4kW7ZsnDlzJlXfp38dypYtC0BUVBRv3rxRluuvj62trSlSpAgODg5cuXKFkSNHcuLEiVT7EH9fyve2devW4enpyb1795T31cWLF9OwYUMWLlyYKsCj3+bcuXOpzhEhhBBCCPF1k8i1EEIIIYQQ4j+pefPmBAUF4ePjQ7169ZgyZQpVqlShTJkyLF68mJkzZ1KwYEE6dOgAyICQECLz2Lt3L3v27OHgwYPkzp2byMhIZs2axeDBgylevLiyXufOnbl+/Trnzp3D398fa2tr4N3A+8KFC5kwYQImJiZMmDCBLFmyKFMQpgV9ZQdfX18mT56MgYEBWq2WiIgIvLy8aN++PXnz5mXMmDGYmZmxfPlyvL29ATA2Nkaj0VC0aFEiIyPJnTs3Go1Gpj8UIoPo+4l58+Yxf/58PD098fDwoHDhwjx//pyxY8dy//59QkND0Wq1eHh44ODgALwL8Lx9+5bZs2dToECBjDyMr0r9+vXZsWMH+fPnx8TEhJiYGIoXL46FhQUA7u7uVK9encWLF1O9enXatWunbKt/vR4+fEi/fv2wtramX79+bNu2jfLly6dp35+Zpaw+FRAQwLx58wAoWbKkEoi1trZmxYoVdOzYkfDwcADGjh2Lvb09W7ZswcvLi9atWxMSEiLvaUIIIYQQmYBKJ7cZCSGEEEIIITIRrVb7l3cAp1xn+vTpTJw4kXv37gGQNWtWXr58SYECBdi9ezcuLi4yyCuEyHRWr17N8OHDuX37Nmq1mqFDh9K3b1/y5MmTqo+cPXs206ZN4/fff6dkyZKUKVOG2NhYzp49i42NDXv37k23fnLu3LkMGDCAFi1a0KFDBy5evMiGDRs4ffo0gwYNonfv3jg7O5OcnMzp06fZuXMnV69excLCggoVKtCoUSPs7OykTxfiX+Du3bt06dKF5ORk5syZQ9GiRXn58iXu7u68fPkST09PNm3axKVLlxg4cCC9e/fG0dFR2f7169dYWlrK+fwnLl26RM6cOZXgpd7MmTPx8vJi1qxZdO3aFXNzcwCWLl3KgAEDiI+PZ9asWTRq1EgJTf30008MHDgQDw8PevXqhbu7O0lJSZw4cQJbW9t0P7bMZMSIEYwfP55u3brh7e1NsWLFPljn+fPndOrUiW3btlGrVi2KFy/Otm3bePz4MSdOnCB//vwZ0HIhhBBCCPGlSXhHCCGEEEIIkWmkvPP3999/J3fu3J+8GzjlYM/Ro0c5fvw4u3btws7OjmLFitGlSxdy5swpg0JCiEwlZTCnVq1aHDhwAENDQ+bOnUv37t0/ut6+fftYt24dK1asIDk5mUKFClGrVi18fX3JlSvXF+sn3++vU7YhISGBIUOGcPv2bcLCwsiXLx9qtZrLly8zePBg9u/fz6BBg+jbty9OTk6ftU8hRMa5ePEi7dq1Y/To0TRt2pS3b99SvXp1bt++zZQpU2jZsiU7duygbdu2ODo60rNnT3r16pUqwCMVXz7t119/pWzZsrRq1Yr58+djbW2t9NWRkZGMHTuWS5cuMXnyZDp27EiWLFmAd6H2cePG8fz5cypWrEjVqlW5e/cuO3fuxMTEhKNHj+Lo6Ei9evU4deoUv/zyC87Ozhl8tF+vvXv38sMPP1C3bl3Gjx//p89lfHw8PXr0YPXq1Zibm1OoUCE2bdqEs7MzarVamQZTCCGEEEJ8vSS8I4QQQgghhMh0vL29Wb9+PWfOnPnTu4HfH8RNSkrCxMREGQyS4I4QIjNSq9U8e/aMKlWqkC9fPs6dO8ezZ8+IiIigWbNmynrv95EPHjxAp9Ph4OCgTPfxJftJ/e9LTk7G2NhYeXzkyJFoNBoiIyPp168fHh4eqX7v1atX8fLyYt++fQwaNAhPT09y584NyOC+EP9msbGxlChRArVajY+PD/PmzWPs2LF4eHhgbm7O/fv3KVeuHAkJCTx79oyJEycyaNAgCeB9Bo1GQ9myZYmNjaVTp05MmzYtVQWeXbt2MXLkSGJjY5k2bRodOnTA0tISgPXr17Np0yZWrVoFgLm5OSVKlCAiIoJ8+fJx69YtqlatSt68edmyZQs5cuTIkGPMDKZOnYqvry/bt2+nTp06n1wv5XvZ3r17MTc3p3DhwtjY2MjnFSGEEEKITETi2EIIIYQQQohM586dOzx+/JjLly9ja2v7yUoL7z/2/h2r8kW4ECIzMjQ0xM7OjiNHjqBSqdi3bx8+Pj60b9+eVatW0bRpUwBloFA/aOjg4IBGo0GlUin955fqJ6Oiohg8eDDR0dFYWVkpj9++fZvNmzdz8eJFjI2NMTU1VX6vvl1ubm5Mnz4dLy8vQkNDMTQ0pE+fPuTNm1eCO0L8C+nP3RIlSgDvgntHjhyhWLFiDBo0SFnPysoKIyMjhg4dSmxsLG3btpXgzmfQV2E5deoU1apVY/ny5QCpAjz16tVDp9MxatQoBgwYAKAEeFq3bk2rVq0YMmQIcXFxZMuWDTc3N6ytrbl//z5z587l7t279OnTR4I7/6Njx45hampKkSJFAD4I4ug/w6jVaiXUWrt27VTL5fOKEEIIIUTmIZ92hBBCCCGEEJmGvrDokCFDMDIyYs6cOcCHIZ1P0a8ng71CiMxEq9Wm+lnfx9na2mJjY0Pbtm0ZM2YMdnZ2tGvXjp9++inVenv37mXGjBnAH2GdL9lParVa1q5dy+nTpwkKCkq1zMnJiTlz5tC4cWMSEhLYsmULd+7cUdqg7/f1AR791CMrVqz44LiFEP8O709ld+vWLU6dOkWOHDlITExUli1fvhyNRkPdunVZtmwZTk5OaDSajGjyV8XIyAi1Wo2BgQHR0dFUqlSJ5cuXM2DAAJ49e6asV79+fYKCgihRogQDBgxg5cqVvHnzBnj3GpUuXZo6derg7u6OtbU1sbGxTJgwgWnTpvH9998zbNgw4I/rb/H32dvb8/btW3bv3g2kDsTqdDoMDAx4/fo1DRs25MqVKx9sL2E2IYQQQojMRSrvCCGEEEIIITIN/WBQwYIFKVWqFBs2bKB3795UrVo1g1smhBAZI+Vd/FFRUdy8eZO7d+9So0YNSpYsSbZs2QDo3LkzKpWKESNG0K5dO1auXEnt2rWJiopi6NChaDQaOnXqRPbs2b94Gw0MDJg4cSKlS5emTZs2AKmmzqpUqRKDBw8mPj6e7du34+7uTq9evbC1tVUCPPoKPJMmTSJ79ux06NBBBjWFyECfO2WdgYEBbm5uNGrUiAMHDrBmzRoqVqzIgQMHCAsLI0+ePOTLl0/Zl1QZ+Tz6AI+RkRHR0dFUr179oxV46tevD5CqAk/nzp0xNzdX9qXVajl37hydOnXi6tWrdOrUifnz5yvLpK/9c392LjRu3JhZs2YRERFBhQoV+Oabb4A/pvIFWLBgATExMZw/f56CBQumW7uFEEIIIUT6U+kkGi+EEEIIIYT4Cr1fVl7/s/4L8p9++okWLVowbtw4/Pz8MrClQgiRMVIOqg4fPpw5c+YoVRfMzMxo0qQJU6dOxdHRUdlmxYoVBAYGcufOHYoUKcLdu3cxNjbm6NGj5MuX77MH5P+O9/vzgQMHsm7dOi5duoSlpaXy+M8//0xAQADHjh0jMDCQ7t27Y2trC6QeHNXv7/39CiHSxvsBjoSEBMzMzAB4+vTpn06tlPK6LSAggAsXLmBhYUF8fDyurq7s2bMHZ2fnNOl7MqNPhWk0Gg3Vq1fn6NGjdOrUKVWAB2Dnzp2MGjWKixcvEhQUhIeHh/IaAjx48IADBw5gYmJCy5Yt//R3iT+8/z708uVLzMzMlGDOmzdv8Pb2ZsmSJfTs2ZOePXtSrlw5Zf0NGzbg7++Pg4MDkZGRqV4zIYQQQgiR+Uh4RwghhBBCCPFViIuLw9LSEjMzs1QDOLt27aJWrVoYGb0rLKrValGpVNy+fZuGDRvy8OFDjhw5QqFChTKy+UIIka5S9pMBAQGMHz+eVq1a0b59e6pUqUK3bt3Yvn07FSpUYO3ateTJk0fZdsuWLaxcuZIzZ85QuHBhpk+fTt68edMlDJOcnEyXLl1YvXo1pUuXJjo6+h8FeIQQ6W/hwoX06NFD+dnPz49Hjx4RGhr6l6EDjUbDyZMnWbduHTdv3qRgwYJ4enqSK1cuCeJ9ppTPU3R0NA8fPqRJkyZKFZ2/CvDs2rWL/v37o9PpOHPmDFmyZEm1/5RhHQnu/LWUr8e8efPYtWsXJ06cIFeuXNSuXZtBgwZha2vL6dOnGTFiBNu3b6dIkSK0bt2a4sWLs2fPHrZs2YKBgQFHjhzByclJnnchhBBCiExOwjtCCCGEEEKIf72ff/6ZDh06MG7cOJo3b67cCRwYGMjo0aMpWbIk7dq1o0mTJhQpUkTZLigoiKCgIBYtWkTXrl1l8EcIkal9rI9btmwZvr6+tGzZkoEDB+Lm5gZA8eLFuXXrFq9fv6ZChQqsW7eO3LlzK9slJSXx4sULsmTJgoWFRbr0n/rwzZs3b/D19WXOnDmUKFGCw4cPfzLAM3r0aDp37oy9vX2atk0I8ef69+/P7NmzCQgIYMyYMQwcOJCwsDBGjBjBwIED/3TKvT8L3sm12+dJ+TyNHTuWWbNmkSNHDubPn0/lypWVKbT+KsBz4MABihQpQs6cOSUQ+T9I+dwNHjyYGTNmkCdPHkqWLMn169c5d+4cderUwcfHh9q1a3PmzBlWr17NlClTUKvVAGTNmhV3d3fCw8NxcnKSc0EIIYQQ4j9AwjtCCCGEEEKIfzWdTsf69evp3bs3dnZ2jBs3jkaNGmFmZsapU6dYunQpBw4c4OzZs2TJkoX+/ftTuXJlGjduzIMHD/juu+/IkiULMTEx8oW3ECJT+vXXX7G2tsbFxSXVgOGzZ8/o3Lkzd+/eZfny5RQrVozXr19Trlw5Xr58ycSJE1m7di1bt25NVYHn/QHC9BzA1VcVePPmDUOGDGHevHmfDPCMHDmSffv2MXPmTDw8PGSQWYgMFB0dzdChQ4mJiaFMmTKcOnUKPz8/+vTpg7Oz82ftQ8Ii/0zK583Hx4ewsDCaN2+Ol5cXVapUUdZLTk7G2Nj4LwM8IJV1vpSZM2cycOBA+vTpQ58+fShatCg3btxg1KhRrFy5ko4dO7J06VLl9YuNjeXWrVs8efKEMmXK4OLiQtasWSW4I4QQQgjxHyHhHSGEEEIIIcS/Xnx8PNu3b2fw4MEYGBgwadIk6tevT5YsWVCr1SQmJrJgwQK2b9/O3r17AWjVqhWtWrUiIiKC/fv3M3v2bDp37iwDQ0KITOXq1asUKlQIOzs7zp07h52dnbLsxYsXDB06lAoVKtClSxfi4+OpVasWV69eZcKECXTv3p2EhASKFSvGjRs3qFixIhEREZ890J5WPjfAc/DgQWbOnMmUKVPImzdvBrZYCAFw48YNKleuTFxcHGXLliU6OhpTU1Ol6otIW7NmzcLX15cff/wRLy8v8uXL98l1UwZ4mjZtytKlS8mWLVs6tjZz0+l0vHnzhvr16/P27VtWr15NwYIFUavVbNmyBS8vL0xMTIiJiVGmfvwUCVIJIYQQQvx3SHhHCCGEEEII8VVISEhgy5Yt+Pj4KAGeBg0aYGFhkWqdo0ePsnDhQnbu3IlGowHg5cuXeHh4MGvWrIxqvhBCpJlmzZqh1WqJiIjAysoK+GOw7969ezg6OqLVapkwYQJBQUEEBgYyaNAgTExMiI+P57vvvuPevXvcvXuXxo0bs3nz5gwfKPzcAE9SUhImJiZSlUCIDKT/ennVqlV07NgRBwcHHjx4oEyhBTL91ZfwZyGO58+f06pVK+7evcumTZv45ptvlGWrVq3i7NmzPHjwgCFDhlCkSBFUKhUajYYSJUpw584dbt68iY2NTXodSqb0/utz/fp13NzclPMgMTGRTZs24efnh4GBASdOnMDW1ha1Ws21a9coXLhwBrZeCCGEEEL8G0h4RwghhBBCCPHViI+PZ+vWrakCPPoptOCPaQPi4+N58uQJEydO5MyZMxw6dAiAvXv3UrNmzYw8BCGE+GJSDobHx8djbm7OjBkzaNiwIfnz509VaUyr1dKsWTPOnDnD9evXMTY2VvZToUIFWrVqxZMnT+jTpw8uLi7p0ua/8rEAT5kyZYiKilJCSkKIf5f169djbGzMuHHjOHHiBL6+voSEhADvzn8DAwOpgPg/Wrp0KV26dEn12IMHDyhdujTlypVjy5YtaDQaYmJimDNnDitXrsTExISkpCRy587N1q1bKVmyJPCun338+DE5c+aUCi//g5Tvbbdv38bJyYn79++TJ08eBg8ezMSJE1m3bh1DhgzBwMCA48ePK5XykpKSyJMnD6NHj6ZPnz4ZeRhCCCGEECKDydW4EEIIIYQQ4qug0+kwNzenSZMmTJo0Ca1Wi4+PD9u2bSMhISHVuubm5uTJk4fp06ezefNm5syZA0BUVBTwbqBCCCG+doaGhqjVauBdv7dhwwa8vb1p27Ytt27dUiorALx69Yq4uDg0Gg2PHz8G3g02Llq0iKtXr+Lu7k5ISAguLi7KPtOqzfBu8Pm3334D/qja8T4DAwO0Wi1ZsmRh8uTJ9O3bl1OnTtGqVas0a58Q4vPoz9v3z9/WrVvTrFkzZsyYQdmyZZk4cSJDhw4F3p3/+uDO2bNn+f3339O30ZnAhAkT6NatG6NGjUr1uE6nw9nZmW3bthEYGEj37t1p1aoV27dvZ/jw4ezYsYMRI0bw+++/M378eACSk5MxMDCQ4M7/SKfTKe9tvr6+dOnShV9//RVTU1McHBzYvXs3kyZNUqb/jYmJUYI7Op2O0aNHk5ycTO7cuTPyMIQQQgghxL+AXJELIYQQQggh/pXeD9joB3vMzMxo1KjRRwM8Ke/k1g8mWVtb0717dypUqMCyZcuIi4uTwQkhRKag0WgwMjJSfm7VqhXdu3fn119/pXXr1vz2228YGhqi1WrJli0b5cqV4/79+wwZMoRffvmFKVOmMGHCBPLkyUPRokWV/aTcZ1rYvHkz3bp14/DhwwB/WoUjZYAnJCQEPz8/5s+fn6btE0L8OY1Go5y3jx494tq1a9y7d4+kpCRlHXd3d2bPnq0EePz8/JRl27dvp1evXkyaNEkJGIrP06BBAzp37syYMWMIDAxUHs+VKxdDhw7FxcWF0aNHExkZScmSJTl27BgjRozgu+++Y+DAgWTJkoWsWbMCpKrAJtfG/5z+XFi4cCFTp06lWLFiZMuWjRw5cuDr60tsbCwjR44E4OTJk9jb2wPvPqusWbOGVatWUaVKFapVq5ZhxyCEEEIIIf4d0vbbGCGEEEIIIYT4B1KWnr9x4wbPnz/n5cuXlC9fHlNTUywsLGjQoAEAPj4++Pj4AKSaQkv/RbpOp8PY2JjSpUtz/Phxbt26ha2tbQYclRBCfDkp7/Tv27cv5ubmTJkyhfDwcAwMDAgPD6dNmzasW7dOmQZr8uTJ3Lhxg9WrV7N69WoAChcuTGRkJLa2tulWecHGxgaABQsWULduXWUg81MMDAzQaDRYWloqFSPUanWah4yEEB9KeY0WGhrKihUruHDhApaWljRv3pwuXbpQtWpVAMqXL8+sWbPo378/kyZNIi4ujpw5c7J+/XoeP37MmjVrPnsaPfFOiRIl8PPzQ6VSMXr0aBITE5V+sXnz5hQoUIBHjx6RNWtWihUrplwXazQali1bhkajoUyZMgCpplYUf9/775kHDhygSpUqDBw4EFdXVwAaN27ML7/8wqpVqyhfvjwPHjwga9asaDQaZs+ezYwZMwCYM2cO2bJlkwpIQgghhBD/cfIthxBCCCGEEOJfRavVKgM5wcHBrFixguvXr6NWqylatCjt27enV69e2Nra/mWAB96FeOLj40lMTMTCwkLu8BZCZAr6AdfQ0FDmzp1Ls2bNuHfvHo6OjkplmvcDPKampmzbto05c+bw4sULsmfPTqtWrbCzs0s1IJ/Wqlatyo8//siyZcu4ffs29vb2fzlg+X7bJLgjRPpLeY3m4+PDlClTKFmyJL169UKtVjNv3jxOnjzJqFGjaN68OfBHBZ5Ro0axbNkyVCoVRYsWZffu3Tg7O6dr35NZFClShMGDB5OQkED58uWBP4I4xYoV+2B9nU7H2rVrmTdvHkWKFFGmHpTgzv9G/541ZMgQjIyMePjwIe3atcPV1VX5u86fPz/9+vVDp9MRERHB7t27KVq0KE+fPuXevXu4urqyZcsW8uTJI+eCEEIIIYRApfvUxOJCCCGEEEIIkYH8/PyYNGkSNWrUoHHjxuh0OlasWMHFixepWbMmS5cuxc7OjoSEBLZs2YKPjw8mJiaMGjWK1q1bY2pqCrwbaNqyZQstWrSgS5cuLF68OIOPTAgh/rn3B/fatm0LwPjx48mfP3+qijS9e/dmwYIFlClThnXr1pEvX76P7jM97/TX/67t27fTuHFjWrRowZo1aySMI8RXZMqUKQQGBtKtWzf69etHoUKFAHBwcODx48e4uLgwdepUmjZtqmxz+/Ztbty4wZMnT6hRowY2NjYSVvgfvX79GktLy08u1+l0vH79mrFjx7J69WpUKhXR0dE4OztLhZf/Qcq/20ePHlGxYkVu3rwJQFBQECNGjABSVzZ6+PAh+/fvZ+7cuTx79ow8efJQs2ZNOnfujL29vZwLQgghhBACkPCOEEIIIYQQ4l9ow4YNdOrUiY4dO+Lj44ObmxsAy5cvp0uXLpQqVYro6GisrKwAiI+PZ/v27XTp0oXixYuzf/9+zM3Nlf3FxsayceNGAgMDgfQdqBZCiLQwbtw4EhMTmTVrFmFhYXTo0EFZlnIQMGWAZ/369bi4uCjL02rKFJ1Ol6pCR0r6/jchIYFatWpx6dIloqOjKVasmPTNQnwFzp8/T7t27ShSpAijR4+mUKFCvHjxgm+//ZY3b95Qu3Ztli5diouLC6GhoUoFnvfJ+Z72bt26Rc2aNbl37x61atVi7ty5UuHlf5Ty7zY0NJQffviB+/fv4+/vT1RUFE2bNmXu3LnY2dkBH05N9rHnXs4FIYQQQgihJ7c1CSGEEEIIIf419F9wR0dHY2pqiqenJ25ubqjVatauXUtgYCDOzs7s3r0bKysrEhMTMTQ0xNzcnPr167N69WrKlCmTKrgDUKJECUqUKAHIF+RCiK/flStXGD58OLly5SJLlizkypULgOTkZIyNjTE0NFQGCOfNmwfAggULqFmzJtHR0eTNmxf4slOmnDhxgrdv31K9enVUKpUyODlmzBisra2pWLEiZcuWVfpfMzMz/P39adq0KWvWrKFYsWLSNwvxFXj48CHPnz+nS5cuFCpUiDdv3lC9enWePXvG5MmT6dSpE05OTowZMwZ/f3+AjwZ45HxPe87OzoSGhvL69WsaN25MtmzZJLjzP9L/3Q4fPpxx48Zx48YNZs6ciY+PDwkJCWzatIkSJUowatQo4MP32ZQ/6z/3yLkghBBCCCH0pPKOEEIIIYQQ4l9Dp9ORmJhI1apVSUxMJDY2loSEBCIjI/H19cXAwIDjx48rd7NeunSJe/fuUa1atVRTrsjAhBAis9NXG3vy5Am9evVSQjopA4op+8J27dqxe/duzp8/j4ODwxdrh06n486dO7i4uJA3b16WL19OtWrVANi7dy8tW7ZUpnbx8PCgQYMG1KhRA4CbN29So0YN1Go1e/fupUiRIl+sXUKItHHv3j0uXbpEzZo1SU5OpmfPnmzevJmQkBC6d++OqakpmzdvpmXLlhgZGWFmZsaGDRuoU6dORjf9PyVlxRf9/yXA/s+lfD+9ceMGjRo1ok6dOqmmjYuKimL48OH8/PPPjBs3jqFDh2Zkk4UQQgghxFdIrtaFEEIIIYQQ6Uar1ab6OTExUfl/cnIyKpUKMzMzHBwcePPmDQC7du36aHAH4IcffmD+/PloNJpU+5XgjhAiM9L3dTqdjoYNG7Jq1SqyZ89OeHg4c+bMAd5VBdD3tfoKPACrVq3i6tWrODg4fNAX/y9UKhVOTk6MGDGCO3fu0LdvX6KjowGoXbs2Z8+eJTw8nIIFCzJ9+nTq1q1LgwYN2L9/P3ny5GHSpEncv3+fM2fOAB++Twgh/l0cHR2VgN7169c5cOAANWrUoEePHpiamgJQrlw5ypUrR7du3TAwMOCbb77JyCb/J6Ws8KL/vwR3/jn9Z4vDhw9z6NAhbt++TadOnShUqJDyvvXdd98RHBxMxYoVGTZsGCEhIRnZZCGEEEII8RWSK3YhhBBCCCFEutEPGvz0008kJycrgzw+Pj4sXbqU+Ph4dDodZcqU4ebNm7Ru3RpPT08MDQ35+eefUwV3QkNDefDgAe7u7hgbG2fI8QghRFp6P5ioH4DV/1u7dm1WrVqFpaUlwcHBLF68GPh0gCdHjhxfvPKC/vcEBQURHBzMhQsX6NevH/v37wfeTdvSvXt3tmzZwoEDB6hbty4nTpygdu3alCpVimPHjmFqasr48eOJi4uTwWUhvgL6aocPHjzgzp07lC1bFhMTEwDUajUzZ84kLi6OefPm8fDhQ3Lnzv1BfybE12bRokVUq1aN1atXU65cOcqXL68s009uUKNGjVQBnkmTJmVUc4UQQgghxFdIvhERQgghhBBCpKs2bdrQvHlzZZDZx8eHKVOmcOvWLaWsf+fOnbGxsWHjxo0kJCRw8uRJZZoXnU7HunXrmD17NgUKFKBTp04y2CuEyHRSTtGxbNky+vfvT7Vq1QgICGDt2rXKevXq1WP16tW8fPmSESNGfDLAo/el+8uUv8ff318J8Hh5eSkVeADs7e359ttvWbduHYcPH8bPz4+EhARmzJhBYmIi9+7d48SJE8qxCyH+/aytrVGpVGzZsoUjR44AsHbtWn766SdKlChBfHy8EuqRqogf0gc+xNehSJEiNGzYkF27dnHo0CEOHjwIvHsfVKlUHwR4qlatip+fH7NmzcrIZgshhBBCiK+ISiefEoQQQgghhBDpaNeuXXTr1g2tVkvhwoU5ePAgAQEB9OzZE2dnZ6UqxIEDB2jatCmvX79m6NChtGnTBjMzMxYtWsSaNWvQarUcPXoUJyenL15JQggh/i2GDBlCaGgoFhYWqFQqZUrBzp07s3jxYqUKz86dO2nbti1Zs2YlODiYLl26pGs7U/bD48ePJyAggG+++YZZs2ZRvXp14F1FDn3FDoBLly5x/vx5QkNDiYmJoUWLFqxbty5d2y3Ef9n710/vn6OfY9y4cQwfPhwrKyvy5MnDtWvXcHBw4NChQzg5OSnBbJFayufl9evXWFpaZnCLxKekfK2OHz9OaGgoa9eupVu3bgQGBpI3b96Prrt7925mz55NWFgYzs7OGdJ2IYQQQgjxdZHwjhBCCCGEECLd6L/QvnDhAmXLlkWtVlO9enUWL15M3rx5lUoT+vWOHDlCx44duXXrlnJHq7m5Oe7u7ixduhQnJ6dU1SmEEOJrl3Lgb8WKFXh4eNCpUyc8PDywsrJSqtrcuHGD5s2bs3HjRmXbnTt30qFDB968ecPSpUv5/vvv07XdGo1GGfj/VIBHp9Oh0+lSBQbu3btHx44dOXLkCAcOHKBixYrp1m4h/qtS9jXnz5/Hzc1NqZLj4+ND8eLF6dy581/uJyEhgYiICCZPnoy1tTVubm4EBwcrU2XJNdqfa9y4Md9++y39+/fH2to6o5sj+DDU9r6YmBjGjh3Lrl27GD58OL179yZnzpzK8pTnVmJiIqampv8oGCeEEEIIIf575NZUIYQQQgghRLrRf5F94sQJEhMTMTY25tdff2Xnzp1otVoMDQ3RarVKUKdy5cocOXKEiIgIhg8fzpgxY/jpp5/YvHmzBHeEEJlOygG/hIQEYmJiKF26NL6+vhQvXhxnZ2caNmzI9u3bqV69Ops3b8bT01PZvn79+ixatAgXFxeqVKmSpm3VT5Wlp1KpUg1M+vv7M3bsWC5cuEC/fv2UKbRUKlWqKhxarRZHR0d8fHxITk7mzJkzadpuIcQ7+vOwVatWVKtWTTn3BgwYwJQpU7h69Srx8fF/uR8zMzO6d++uTCM0b948Ce58pocPH/L8+XMmTpzI0qVLefbsWUY36T9Po9EowZ1r165x6NAhtm7dyt27d0lMTATg22+/ZcSIEdSqVYvg4GDmzp3LgwcPlH2knELL1NQUQII7QgghhBDis0jlHSGEEEIIIUS6O3r0KL/99htmZmb07dsXtVpNYGAg/fr1Q6VSKYPCf3bXq0yVJYTIrHx9fbl79y5Pnz6lWrVqDBs2TBlQ1FetuXjxIt999x2JiYns2LGDChUqKOGfhIQEzMzM0mzwPOV+t27dyrlz5zh//jwVKlTg22+/pVy5csq6+il1PlaBRz/AqVKpOHHiBN9++y2DBw9m4sSJMs2OEOkgPj6e0NBQZsyYgYODA25ubmzYsAFfX1/69+9Pnjx5PntfKcOHMlXW57t16xbe3t5ERUUp08jmyJHjo+u+/7zK8/xlpfxsMXbsWBYuXMitW7cAyJ49O+3bt6dz5864u7sD76bQCgwMZN++fQQEBHxQgUcIIYQQQoi/SyLfQgghhBBCiDT1sZBNpUqVqFChAgYGBtjb29OqVSsCAwMB6NevX6r179y5g7W1NZaWlsAfAxUS3BFCZEaPHj3i8uXLbNmyBQB7e3sAJSyjDzgWKVIEPz8/Bg8ezI0bN6hQoYIyiGtmZpZqmy9JXyUNwM/Pj7lz55KYmIiFhQUrV67E3NycuXPn0qlTJwCGDRuGSqUiICCAfv36MXv2bKpVq6a0VaVS8erVK6KiogBwdXWVwWgh0om5uTlDhgwhd+7ceHh4cPbsWdq1a0e/fv3+VnAHSHXeyjn81/QhSGdnZ2bMmEG3bt0YOnQorVq1+mR4R6VSERMTw++//07Lli3lef7C9J8tfH19CQ0NpUaNGgwaNIirV69y/PhxZs+ezfHjx5k0aRLVq1fH3d2doKAgACZOnMjr16/x8fHBzs4uIw9DCCGEEEJ8xeTbbiGEEEIIIUSaSVl6PjY2lj179rBx40Z+/fVX5fEqVaqwYcMGDA0NCQwMZObMmcr2kZGRtGzZksjISOUxGagQQmRm9vb2jB07lh9//BFzc3NOnjxJTExMqnX0RZTz5csHoFQGSA/6vnvMmDFMmjSJtm3bsnfvXh4/fkx4eDhZs2alS5cuREREKFXU/P39CQ4O5sKFC3z//fccO3Ys1T7v3bvH5MmTad68OR4eHul2LEL81+l0OkxNTblx4waJiYmYmJhw4sQJHj9+/MHUeOKfe/+5TExMVEKQDx8+JG/evCxcuJCjR4+SP3/+T+7nwoULVK5cGX9/f2WaMymq/2VFRkYyffp0evbsSXh4OJ6enkybNo2IiAj69evHL7/8wvDhw5Xnv3z58owZM4bixYuzfv16ZZosIYQQQggh/gmpvCOEEEIIIYRIEymrMwQGBrJw4UJ+//13AKysrGjQoAGLFy/G3NxcCfC0bt2agIAAfv/9dxwcHFiwYAE3btygcuXKGXkoQgiRJt6f8kRfqax48eL07duXpKQkli5dyqxZs8iVKxdOTk7AHxV1fv31VwwNDSlQoEC6tvvChQvMmzePpk2b4ufnp/z+nDlzkpycTJ48eahbt64yzZdKpcLf3583b94QFhaGs7Nzqv0VKlSIjRs3UqVKFUCmRRQivej7n6JFizJ69Gh0Oh3Tp0+nY8eOLFiwgEqVKn1yKiyZsunz6fuzkJAQunTpQq5cuQDo378/8fHxhISE4OzsrPSNn3puVSoVHTp0YPny5fz000+ULFlSXoMv7JdffkGn09G7d29cXFyUCkmurq5MnTqV+Ph4Fi1axJ49eyhZsiQAZcuWZd68eeTKlYusWbPKuSGEEEIIIf4xlU7i+UIIIYQQQog05O/vz4QJE2jRogXNmzcnV65chIaGsnPnTooVK8bevXuVaWFOnDhBq1atuHv3LoaGhri5ubF9+/ZUX54LIURmkLJP0+l0vH37Fp1Op0wRCO8qlk2ZMoXly5fTqlUrevbsSb169QBYv349fn5+WFhYEBUVha2tbbq1fdu2bTRp0oTIyEiaNGmCWq1m3bp1+Pv7o1KpOHHiBLa2tsTHx/P69etUU4i8fPmSrFmzKsf/qQCTECJtfCpYoNFo0Ol0zJ49m9GjR5MzZ07mz5+fKsAD7yplOTo6pmeTM4XBgwczdepUunTpwuLFi/Hz82PSpEkMGjSI4cOHkz179s/az5kzZ+jatStarZb9+/djY2OTtg3/j9BXC23YsCG7d+/m/PnzFC5c+IP1Tp48ScOGDcmRIwcnTpxI9Z4N8h4mhBBCCCH+N1J5RwghhBBCCJFmduzYwezZs+nevTvDhg3D1dUVgNu3b7N7927u37+PmZmZsn758uWJiYlh06ZNWFpaUr9+fezt7SW4I4TIVFL2aYsXL2b//v38+uuvmJmZ0b59e2rWrEmpUqUoUaIEgwcPBmDlypXs3LmTMmXK8Pz5c16/fo2RkRFbtmzB1tY2zQYMP7bfuLg4ABwcHADYsGEDQ4cOxcDAgOPHjytBohcvXtC3b1/GjRunDIJaWVmh0+mU438/RCCDnkKknZR9z6tXr3j69ClarZZ8+fIpj3fp0gWVSsXo0aPp1asX8+bNo2rVqsC74F5gYCA+Pj60bds2w47jaxQSEsKZM2dYunQpx44d4/Lly4wcOZJu3bp9dnAHoGTJkgwaNIguXbrw22+/SXjnH3o/xKb/+y9fvjy7du3i0qVLFC5c+IPPIGXLlqVo0aKcPn2ax48ffxDekfcwIYQQQgjxv5DwjhBCCCGEEOJ/kpycjLGx8UeXnT59mjdv3tCzZ09cXV1Rq9WsXbuWMWPG4OzszPHjx8maNSvx8fGYm5sDkCtXLvr27avsQ4I7QojMJGVwZfDgwcyYMQMrKyty587Nr7/+yqlTp6hYsSKenp788MMPSoDH2NiY1atXc+nSJTw8PChbtiyVKlXCxsbmi/WT7wd1EhISlIDlzz//TMWKFQGwtrYG4PDhw/z222/4+voqwZ2UVXZ8fX05ceJEqt8hU4kIkTFS9hPTpk1j/fr1nDhxAgMDA6pVq0bPnj2pWbMmNjY2dO7cGYAxY8bQu3dv/P39efHiBfPmzeP+/fu4u7tn5KF8dfTXyvpqkzdu3KBIkSK0atUKZ2fnz+7D9YGTTp06cevWLWXaJvH3pHy+b9y4gU6nI3/+/MC7cI5KpcLb25ty5cqRJ08eNBoNKpVKeX9MTk4mV65cynuhEEIIIYQQX4pEwYUQQgghhBD/WFRUFP379+f+/fsfLNPpdJw9e5bs2bNToUIF4N00L/7+/gAcO3ZMuVv4ypUrhIaGfvR3SHBHCJGZ6MMrYWFhTJs2DU9PT6Kjo4mNjSUqKooBAwZw8uRJAgMD2bhxIwAlSpTAy8uL77//nsePH/Py5UsqVKiAjY1NqjDQ/0o/MNmyZUtOnjypBHf69+9Pz549iYmJAaB69eqULVuWwMBAvLy8MDQ0TBXc0el0LFq0iOjoaOrVq4ezs/MXaZ8Q4p95PzTo4+PDs2fPaN++PUWKFOHIkSN4eHgwceJEHj58SLZs2ejSpQujR48mMTGRLl26MGDAAOLj4zlx4oQynan4PMbGxmg0Gg4ePEhcXBzW1tZcvHiRKVOmEBcXh6GhIVqt9i/3kzL8OHz4cIyMjFCr1WnZ9EwnZXAnNDSUtm3b0qNHD65fvw5As2bN6NSpE3fu3KFNmzbcunULQ0ND5f1xw4YNnDt3Dnd3dywsLDLsOIQQQgghROYklXeEEEIIIYQQ/8jbt29ZsmQJy5cvx9DQkFGjRpEzZ07g3SARgJmZGS9fvuTkyZNcv34dPz+/j1Zn8PHxIT4+nq5du5IjR44MOR4hhEgPOp2Op0+fsnbtWgoXLoy3tzdOTk4AVKtWjRIlSuDi4sKQIUMIDw+ncuXK5MyZkxIlSjBw4ECSkpKYNm0ar1+/ZsyYMUq/+6WsWrWKzZs3c+jQIc6ePcv06dOZPXs2gwYNIl++fABky5aNDh06MG7cOB49esTatWtT9enLly9n/PjxWFpaMnr0aMzNzT+YokQIkX70597ixYsJCwvDy8uLfv36kT9/fh49esTevXuZOHEiU6dOxdTUlMGDB5MtWza6du1K1apViYyMxMrKirZt25IzZ06pivgPGBoaUrRoUQ4fPkzevHnp2rUry5YtQ61WExYWho2NDVqtFpVK9bf6SiMj+Xr/c70fYps9ezYVKlTA19eX/PnzK9XnlixZQlxcHNu3b6dChQr069ePIkWK8Msvv7B27VqsrKwYM2YMJiYm8t4mhBBCCCG+KJVO/626EEIIIYQQQvxNZ8+eZdq0aSxevJhevXoxevToVAPJO3fupGnTplSvXp1r164BfBDcmTNnDkFBQXh4eBAQECCDEEKITOf9ge5r165RokQJGjduzNq1a9HpdOh0OuXO/ocPH+Lr68vy5ctZt24drVq1UrY9f/48EyZMYOXKlfTq1YuRI0fi6Oj4Rds7adIkhg4diomJCYmJiQQFBdG5c2ecnZ2VwU2NRsOwYcOYOXMmlpaWfPfddxQvXpyjR48SExODlZUVUVFRSoUOGegXIuPoAwY//PAD0dHRHDlyBFdXV+V8VqvVnDx5km7duvH69WvWr1//wdRY+n3I+fx53p+GUE+tVmNkZIROp6NGjRocOnSI9u3bKwEevXPnzmFmZkaBAgXSs9n/CTNmzGDw4MH069eP/v37K1NmwR+vD4CXlxcbNmxQKoxaWFhQokQJVq1a9bemOxNCCCGEEOJzSXhHCCGEEEII8T+5cOECEyZMYPny5R8EeG7evEn//v3ZtWsX5ubmnDt3LtX0KatXr2bEiBFky5aN7du3Y29vn1GHIYQQaSLlAO6dO3fImzcvd+/epXTp0pQqVYo9e/YAfHD3/oYNG2jTpg2DBg1i8uTJqfaTst9duHAh3bp1++JtLVWqFJcuXcLAwICtW7dSs2ZN1Go1hoaGStBIq9WyePFiNm3axPbt2wFwcXGhVq1aBAUF4ejoKIObQvwLaLVaXr9+TdGiRcmaNSvnz5//IFySlJTE3LlzGTBgAN27dyc8PDwDW/x1S9nvbdmyhfv375OYmEizZs1wdHRMFVSvUaMGBw8epH379sybN48sWbKwZcsWfH19adiwISEhIRgbG2fUoWQ6L168oH79+rx9+5YNGzZ8NByVMsBz+vRpLl68SFxcHIUKFcLd3Z3s2bPLe5sQQgghhEgTEt4RQgghhBBC/M/eD/AEBQXh4OAAwP79+/H19eXUqVP06tULd3d3SpUqxaJFi4iMjESlUnH48OFUFR2EECKz6devH3PmzOHs2bM4OTnRqFEjDh8+nKqyjk6nQ6PRYGRkxIMHD3B0dFTCO+87c+YMx44do3fv3l+0ncnJyTx8+BAXFxdKlSrFqVOnsLa25uDBgxQtWlQZsHy/v758+TJarRYnJydMTEwwNjaWwU0h/mVq1arFhQsXiI2Nxc7O7oNz9Pbt25QoUYJSpUqxc+dOzMzMMrC1Xz8/Pz8mTZqk/FyiRAl69uxJz549Uz23+gBPpUqV+Pbbb4mMjCQuLo5Tp07h6uqaEU3PtC5dusQ333yDv78/wcHBn/zs8WfTYcnnFSGEEEIIkVbkKlMIIYQQQgjxP/vmm2/w8/OjU6dOLFiwgFGjRikl5mvWrMnUqVNp3bq1Eu4pX748S5cupUSJEhw9elQpPS9fhAshMouU90qtWbOGtWvX8sMPP2BgYICVlRW9evUCwNfXl7179yrbGBkZodVqWb16NQAlS5b86P5LliypBHe0Wu0Xa7exsTF58uTh1q1bbNu2jYkTJ/Ls2TOqVq3KhQsXMDQ0RKPRfLBdoUKFKFKkCFmyZMHY2BidTifBHSH+JdRqNRqNhhIlSvDw4UOGDRsGoJzP+j4kV65cWFhYkCVLFkxNTTOyyV+90NBQpk2bRosWLQgPD2f48OE8ffqUYcOGERoaSnx8vLLugQMHaNWqFTExMcybN4/s2bNz5swZXF1dP9rfin8uMTERgLt37370s4f++b537x5Hjx796D7k84oQQgghhEgrRn+9ihBCCCGEEEL84f07UfV3n37zzTf4+voCsGDBAgBGjRqFo6MjVatWpWjRojx69IhffvkFgDJlypA3b16srKykOoMQIlNJ2U8+ffqUa9eu4ebmRkhICE5OTgB06tSJa9euMWbMGDp06MCkSZNo1KgR1tbWLF++nHnz5vHNN99Qv379v/x9/8tA4vsVBDQaDTqdjty5cwMwZMgQEhISGDlyJFWqVOHQoUMULVpUWf/QoUM8fPiQ1q1bp9rvpyoWCCHSTsrrqZTntn4KoMGDB7Nu3ToWLlyIo6MjQUFByvo6nY6IiAiePn2Ku7u7nMN/U8rnW6vVcvHiRZo2bcrEiRPJly8fGo2Gli1b0rZtW8aOHYtOp2PQoEGYm5sDsG7dOqKjo1GpVBQtWhQbGxu5Pk4DTk5O5MyZkzNnznD9+nUKFiyoLEsZOvX39+ft27cULVqUbNmyZVRzhRBCCCHEf4xMmyWEEEIIIYT4bCkHEZKTk0lKSkKlUmFhYaGsc/bsWSZPnqxU2QkMDCRXrlyf3OeflaUXQoiv2dChQ4mNjeX+/ftUr16dadOmKXf16/vS4OBgRowYAUDu3LkxNDTkwYMH5M6dm3379uHi4pJmU3Sk7NPXrFnDL7/8wrVr18iePTu9evWiUKFC2NjYpGpn9uzZlQDP1q1b8fHxwd7enm3btmFpafnF2yiE+Dwp+4klS5Zw9OhRdDodxYsXx8vLS1nv559/pmnTpjx58oQ2bdrg7e2NnZ0d27dvZ86cOSQnJ3P48OE/vXYTnzZ27FiSk5OZN28ewcHB9OjRA/jjevfChQu0aNGCO3fuEBAQkCrAk5JMzfTPfeq501eY8vPzY8qUKfTu3Zvp06djbGycar0VK1bg4+NDu3btmDBhwgfLhRBCCCGESCsS3hFCCCGEEEJ8lpRfhM+cOZO9e/dy7do1rKys6NChA7Vr16Zw4cLAhwGeoKAgHBwcMrL5QgiRrt68eYOHhwcrVqzA0NAQDw8Ppk+fDnwYWtyyZQs7duzgyJEj5MqVi9KlS+Pt7Y2Dg0OaVV5I2af7+Pgwffp0TE1NyZ49Ow8fPsTY2BhPT0++//57SpUqBcD48eMJDAzE1NSUihUrEhsbi0aj4dixY7i6un7xNgoh/r4hQ4YQGhqa6rEGDRqwYsUKrK2tATh16hTt27fnypUrGBoaYmhoiE6nw83Nja1bt+Li4iJVX/6B3377jTJlyqBSqTA1NWXZsmXUrl0btVqtVD8CUgV4RowYwYABAz4a4BF/X8q/20OHDvH27Vt0Oh1169ZV3vNOnjxJ//79iYmJoVOnTvTu3Zvy5ctjaGjIkiVLmDhxIiqViv3790uITQghhBBCpCsJ7wghhBBCCCH+Fv2gkI2NDXny5CE2NhadTkeNGjXo378/LVu2BODcuXNMmjSJ5cuX06dPH4YPH46jo2MGt14IIdLPo0ePmDp1KhMmTMDW1pa1a9dSo0YN4F2AR6fTpaoOoB/g1Qdr0mPwfMKECfj7+9OnTx969+5NyZIlOXjwIEOHDuXYsWOMGjUKf39/TExMAJgzZw7Lly/n7t27FCpUiIULF+Lk5CQD/UJkkJRBvNWrV9OnTx/at29Pt27dMDc3Z9SoUWzatInq1auzevVqcubMCbzrnzZv3szp06fRaDSUKlWK1q1bY2dnJ+fzP6TVajl8+DCDBw/m5MmTtG/fnrlz52JpaflBaPPChQu0adOGixcvMm3atFTVkcQ/k/I5DggIYNq0aSQkJKDT6WjTpg29evWidu3aABw+fJiRI0dy8OBBLCwscHV1JTExkVu3bpE7d2727NkjITYhhBBCCJHuJLwjhBBCCCGE+FMpB4UiIyPp2rUrPXv2pFu3bnzzzTdER0ezfv165s+fT6FChRg3bhyNGzcG3g1MTJ48mSVLluDj48P48eNlCgAhRKajHzD82DSA9+/fJzQ0lClTptCsWTOCgoIoUaJEqu30/wc+uZ8v1caUbty4QZMmTciZMyfz58+nQIECwLsptAYNGoShoSGnT58mR44cqSpHPHz4kPj4eGxsbLCyspLBTSEyyPvn9axZs5g1axaRkZG4ubkB787X4OBgZs6cSbVq1VizZo0S4PkYma7p86Ts91MGMdVqNT///DP9+/fn+vXrjBs3jh49epAlS5YPXq/Y2Fj69+/PypUryZs3b0YdSqYzduxYRo0aRY0aNahevTonTpxg//79uLm5MXLkSFq0aIFKpeL8+fMcOXKERYsW8ezZMxwcHKhSpQqenp5pWvlOCCGEEEKIT5HwjhBCCCGEEOKzPHjwgOjoaIKDg4mMjCRfvnzKsri4OBYvXkxAQACNGjVi0aJFytQMsbGxLFq0iEGDBuHk5JRRzRdCiDSRcnDv2bNnJCcnY2ZmRpYsWZTH79+/T0hICDNmzOD777/H39//owGetHDjxg1y5MhB9uzZP/hdR48epUqVKsyYMYN+/fqhVqtZv349Q4cORaVSceLECWxtbUlOTub169dKv55SWrdfCPHO+6GalD97enqyd+9eypUrR8GCBRkxYgQ6nQ6tVouhoSGPHj0iODiYGTNmpArwpOy/5Fz+fO+HOt6+fYuFhYXymiQlJRETE8OPP/7IkydPGDVqFF27dv1ogEcfjJSgyD+X8rl7+/Ytbdq0IWfOnIwcORIXFxceP37Mtm3bGDBgAHnz5iUwMJCWLVsqr0NycjLx8fFYWlqiUqlQqVTyegghhBBCiAwht1EIIYQQQggh/pK3tzfFixdn0qRJ5M+fn3z58imDQgC2tra0b9+e1q1bExkZycGDB5VtS5QoweTJk5VpVYQQIrPQD4wDhIaG0rhxY0qVKkW5cuXw9vZm165dAOTKlQt/f3/69+/PmjVrGD9+PLGxsQBpOlh+8uRJChYsSGBgIC9evEClUin9NsCLFy8AyJ07NwBr167Fz88PlUrF8ePHsbW1Bd5V7qhTpw6XLl364HfIYL8Q6UMf1Nm9e7fys0ajIT4+nlu3bnH58mVWr17N9evX0Wq1Sv+k0+mwt7cnICAAT09PDh48SIcOHbh//36qcIKcy58nZahj4cKFfP/99xQvXpxGjRoxfvx4njx5gomJCe7u7syfP58cOXIQFBTEkiVLePPmjVKtR09f0UyCIn+f/nOF/rmbN28ea9eu5dChQ7Ro0QIXFxcA7OzsaNeuHbNmzeLOnTsEBgayadMmZT9GRkZkzZoVAwMD5TyQ10MIIYQQQmQECe8IIYQQQggh/pKtrS1Pnjzh4sWLJCYmAih3purlzp2bli1bAnDs2DEAZZBYBiaEEJmRfjDdx8eHIUOGcPfuXQoXLgzA7Nmzad68OeHh4QA4ODgwbNgwJcAzceJETp06labts7Ozo0CBAsybN4+QkBCeP3+OgYGB0jfnyJEDAwMDIiIiWLJkCf7+/hgYGHD8+HHs7OyU/UyePJkLFy7w/PnzNG2vEOLj9GGP5s2bU79+fZYuXQq8u64yNzdn8eLFdOzYEYBffvmFp0+fYmhoiFqtVsIi+gDPgAED2L9/P15eXkhB9r9Hp9Mp17KDBw+mT58+HD9+HGdnZ65du8aIESNo0KABd+/exdTUlMqVKxMeHq4EeJYtW8arV68kKPU/2L9/P5MmTQJSf674+eef8fDwIDg4GDs7O6W6nVqtBsDU1JQ2bdooAZ5Ro0axefNmQIJrQgghhBDi30PCO0IIIYQQQoi/NGLECKZNm0Z8fDw7d+5kzZo1AMqAkP6L8apVqwIQHx8PkGp6ByGEyCxSVhE7dOgQq1atwsfHh+joaPbv309MTAxhYWEkJiby448/KgPtDg4ODB8+HG9vbyIiIli4cKHSf6YFJycndu/eTdGiRZk0aRITJkxQAjwA3377LbVr12b9+vX4+vqiUqk4d+6cEtzR6XSsXLmSTZs20bx5c4oXL55mbRVCfJo+XDBq1Cisra0ZNmwYy5YtU5bb2NgwdepUfvjhBy5cuEDz5s3RarUYGRl9EODx9fVl+PDhTJkyRUILf5P++Zo2bRrTp0+nT58+7N69m/379xMbG0vdunX55Zdf6Nq1KzqdDgMDAypVqkR4eDg5c+akX79+bNy4MYOP4uv14sULPD098fPzIzIyMtWyQoUKERYWRnJyMjdv3mTNmjVoNBqMjIyUkJqJiQlt2rRh9uzZPHjwgL59+7Jjx46MOBQhhBBCCCE+Sr5JF0IIIYQQQihSTqeil5SUBICXlxczZswAYMCAAcqX3TqdDiMjI7RarRLqKViwYDq1WAgh0p/+bv+7d+/y9OlTkpKS6NKlizJFh7W1NZ6enixfvhyAoUOHcvz4cQDs7e0ZOnQow4cPx8/PT6lMllacnJzYuHEjpUuXThXg0Rs7diwlS5YkLi6Odu3akSVLFmXZvHnzCAoKwtTUlMmTJ5MlSxap1CFEOrp69Sr37t0D3oUGS5cuTXR0NElJSfTv35+FCxcqy2xsbAgLC6Njx44cPXqU6tWrfzTA4+DgQFBQkExn+g89f/6cNWvWULx4cfr27Yubmxvx8fHs2bOH8+fPU7hwYVatWqU83wYGBlSoUIGwsDCqVq1KrVq1MvoQvlrZsmVj4sSJeHl5KTcM6OXIkYMOHTrg6+uLra0ty5YtIyoqCq1Wm2qqMhMTE1q3bk1ISAjZs2enVKlSGXAkQgghhBBCfJxKJ9+6CCGEEEIIIXg38KMfkL506RIJCQm4ubmlGsgFCAsLY+DAgVhaWhIWFkb9+vWxt7dn8eLFTJ06lYSEBI4ePUrOnDkz4jCEECJdjB8/noCAABo0aICJiQmbNm0C3gUa9QO2AOPGjWP48OFMnz6d/v37K9vrdDpUKlWqvjct3b59m5YtW3L69Gl8fHzw8/Mje/bsvHnzhl27djFy5EguXLhA8eLFKVeuHBcvXiQ2NhZHR0d2796Ni4tLurVVCAE7duygUaNGzJs3j65du2JsbKycg7GxsXz77bds3LiRBg0aAO8C2AYGBjx9+pQBAwawYsUKKleuTHR0NAYGBqjV6jQPC2YWFy9exMnJ6YNrYHh3jfzNN98QFBTEiBEjUKvVbNiwAV9fXwwMDDhx4gS2trYAnD17lnz58mFpaYlWq0WtVmNiYiJ96T+gf8+EPz6zeHt7kz17doKCgpT1nj17xsqVKxk+fDiFCxcmJCSEatWqYWBgkGofycnJJCcnY2FhIa+HEEIIIYT415DKO0IIIYQQQgh0Op3ypXVAQAAVKlSgTJkyfPPNN4SEhHDz5k1lXW9vb8LCwnj9+jU9evSgZs2auLm5ERwcjLm5Ofv27SNnzpxyN7cQIlOzsbHBzc2NnTt3cuTIEc6fPw+8m1bFwMAArVaLVqulXr16GBgYsG3bNuCPKbf0A4jpMWCo0+lwcnJiw4YNSgWekJAQnj17RpYsWWjYsCGbN2+mffv2vH79moiICNRqNR4eHhw8eFCCO0Kks+joaJo2bUqtWrUoVqwYxsbGwLv+QqPRUKJECR49eqQEdwCl38mRIwfTpk2jY8eOHDlyhFq1aikVeMRfu3z5MkWLFqVBgwYkJiZ+sPzt27fAu35Vo9Gwdu1aJbhz/PhxJbgTHx+Pl5eXMk2WgYEBJiYmQPr0+5lNyineDA0NuXHjBhEREYwZM4bJkycry6ytrenQoQNjx47l4sWLDB06lIMHDyoVePRVRo2NjbGwsFD2J4QQQgghxL+BhHeEEEIIIYQQyhfiISEhhISEULJkSTp37oytrS3Dhg1j2LBhnD17Vlnf09OTmTNnAvDy5Uvq16/P8ePH2bt3rwzyCiEyNX0B4x9//JFhw4ZRvHhxnj17xq5du1Ktp6+CUbRoUbJly0a2bNmAtB8k/Nj0h/o+3tnZmfXr11O6dGkmT57MhAkTePbsGWZmZhQoUIAVK1Zw+vRprly5wvHjxxk/fjwODg7SpwuRjp4+fYqXlxcajYZixYpRsWJF4I/gn/5ctLS0BEg1ld37AZ4uXboQHR1N69at0/kovl7GxsbUrl2bHDlyoFarP1heoEABXFxc2LdvHxs2bCAgIEAJ7tjZ2SnrjR49mhMnTpA7d+70bP5/hqurK2vWrKFo0aL4+voyceJEZZk+wBMcHKwEeA4fPqy8LwshhBBCCPFvJVerQgghhBBCCAASExPZtWsXHTt2ZOXKlSxZsoSffvqJHj16sGbNGkaMGJEqwNO3b1+mTJnC/fv32bt3L+fOnSN79uypposRQoiv3fthmJR3/3fp0oWBAwfi7OxMQEAAa9euVSo1GBkZoVarWb58Oc+ePSNfvnxotVrScvZyjUaj9L+nT5/m8OHDbN26lQcPHpCUlAT8EeApVaqUEuB5/vw58C4EYGVlpQw260MCEtwRIv3kyJGDcePGUaxYMcLCwpg2bRrw7jxM2R/p+6KUfRKkDvBMmjQJT09PQkND0639XztXV1eWL19OREQEWbJkISIigtevXwPv3g/Mzc1p2rQphw4domfPngDExsamCu6sWLGCNWvW8N1331G+fPkMOY7MTP8+WrNmTaZPn07hwoUZOnToBwGejh07EhwczLVr1+jZsycxMTEZ1WQhhBBCCCE+i0qXlt8aCSGEEEIIIf613r/7NC4ujgIFCrB+/Xpq166tPJ6cnIy3tzdz586ladOmjBkzhuLFiyvLp02bxqBBg3Bzc2PmzJnUqVMnXY9DCCHSSsqKM1evXiUuLg5DQ0NcXV2VqVEAli5dSmBgIA8ePMDHx4dq1apRpUoVFixYwIIFC3j58iU///wzuXLlSrO2puzTR40axbx583j69ClqtZpChQpRs2ZNxo4di7W1NQC3bt2iVatWnD59Gh8fH4YOHUq2bNnQ6XQfhAGEEOkj5fm3d+9e+vfvz5UrV5g8eTKDBg0CPrx++xR9/6Xfp1qtlqmz/qY1a9bQrl07WrVqxZIlS8iSJQsAV65c4fvvv+fMmTM0b96cjRs38vbtWywsLJg1axZTp04F4MCBA+TJk0cqvqSBlOdKVFQU/fr149KlS4SEhODr66us9/z5c+bPn8/ixYs5cOAAOXPmzKgmCyGEEEII8ZckvCOEEEIIIcR/UMoB6ePHj/Ps2TOcnJzo168fM2bMoGjRoqkGedRqNV5eXkqAZ+zYsRQrVkzZX8oAz+TJk2nSpEmGHJcQQnwpKQdbR44cybx583j8+DEANjY2DBkyhEaNGil94dKlSxk7dizXr18HoFSpUjx8+JDChQuzcOHCdJtScOTIkQQHB1O5cmXq16/P7du3iYqK4urVq5QvX55t27YpwaNbt27RunXr/2PvLgOiSt8+jn9pUUBRVFQEVKy1de11rVXXXrsVW+ygQUFQsVHEwk7ETgwkXGxdu9ZV11rXBFEUiZl5XvjM+Q/GpoCy1+eNMie4zwxznzNz/851c+nSJfr378+UKVOwsLDI0PYJIf7YpwzwiH/n559/xt/fn/Xr19OpUyeWLVumTFd2/vx5Bg0axKlTp7C2tsbGxoZnz57x4MEDihcvzp49e2Qq2Qz2VwM8CQkJGBgYYGZmJu8dIYQQQgjxWZPwjhBCCCGEEP8xul90e3p6Mnv2bGU6FYCFCxcyePBgIP3gkG6A59tvv2XRokWUKVNG2W7u3LmMGTOGcePGMWPGjEw8IiGEyDjaMEyDBg1o1aoVV69eJSYmhlu3btGmTRtcXV2pVasWACtXriQoKIhz584xfvx4hg4dSs6cOTE3N8+wqhe6A8O//fYbzZs3p2HDhowdOxY7Ozs0Gg0JCQl07dqVAwcOUK9ePXbu3Enu3LkBuHv3Lg0aNEClUnHhwgXlcSFE1pEAT+bTfT61UxDq6elx8+ZNJk6cyNq1a98L8Pz666/s3LmTnTt38uTJEwoXLkyjRo1wdHSkQIECEtz5F/7qc/exAM+MGTMYN27cR9cVQgghhBDicyThHSGEEEIIIf6jAgMDcXd35/vvv1cqRISEhGBubs66deto1aoV8H6Ax9HRkf3793P16lWsrKzSLd+/fz/NmjXLsmMSQoh/S3fAMD4+ntq1a9OoUSPc3Nyws7MD4NixYyxcuJC1a9fStWtX/P39KVGiBPA2wDNp0iQePnxIWFgYLVu2zJRB9rCwMOzt7WnXrh2bNm2ibt26AEpoKCUlhSZNmhAbG8uUKVOUqgT6+vr89ttvGBgYYG1tLYObQnwmdN+LkZGRDB8+nJ9//jldKEECPJ+Gbr+/bds2rly5QtOmTalWrRr6+vp/GOABlJBknjx5lMfktfk0fvzxR6pXr46JiclHn893AzyjRo3i0qVLzJ8/Hycnp8xsrhBCCCGEEP+KhHeEEEIIIYT4j3j3DtauXbvy+vVr5s+fT9GiRQEIDg5m5MiRFC9enHnz5tG8eXPg/buRX7x4gaWlpfL4uwMUMmAhhPjSrVq1CjMzM1xcXNi8eTNVq1ZN14/euXMHZ2dndu3axbJly+jRo4ey7dq1a/H29ubx48ds2LCBNm3aZGhbt27dSseOHbGzs0OtVnPixIl0QRxtu2/fvk316tWpVq0a+/bte28/0ncLkfU+FqCTAE/G0H3+PD09WbJkCa9fv2bz5s00btwYY2NjgPcCPMuXLydXrlzy/GeguXPn4uzszIMHD8ifP/8fhkt1l+3fv5+pU6eyYsUK7O3tM7HFQgghhBBC/DvyyUIIIYQQQoj/CO2As6urK25ubty/f5/evXtTtGhRtJn+4cOHExwczK1btxg+fDh79+4FUAI62v1YWlqi0WiUwYp3By1kEEMI8SVbv349ffv2ZcqUKaSlpZEnT573Bmjt7Ozo27cvADNmzCApKUnpJ3v27MmkSZMoVKgQ3bp1Y8OGDRna3mrVqtGvXz+SkpK4d+8e4eHhqNVqZSDTwMCAtLQ0ChUqRLFixTh27Bg3b95U2qslfbcQWUs3gPDLL79w9+5dZVnjxo0JDg6mdOnSuLi4MGvWLODt+zYtLS1L2psdaPs9b29vpk2bRufOnTl8+DDNmzdXgjsAJUqUwMfHh549e7Jp0yb69evH69evpd/MIGq1mqSkJFQqFUFBQenOaR+ip6enfJ5p1qwZ+/btw97eXt4bQgghhBDiiyKfLoQQQgghhPgPuXHjBjNnzmTBggX8/PPPyuMqlUoZxB06dCjBwcH8+uuvDB8+XKnO8O7ghEyrIoTIrrp37067du04e/Ys9+/f55dffkFfX593ixe3aNGC6tWr8/jxY2UQVzfAM3HiRNRqNRERERnWVo1Gg52dHRMmTKB169YYGRmxbNky7t+/r6yjUqkwNDTExMQEIyMjbG1tKViwoAw6C/EZ0Q3uREREMGjQIIKCgkhOTkalUgEfD/AYGhoCkJCQkDWN/wL8UfH5gwcPEhwcTPfu3XF1daVKlSofXO/dAE/79u1JSkrKqCb/p+nr6+Pk5ESxYsU4ePAgKSkpwB+/jroBHhMTE+B/7w0hhBBCCCG+BPItjRBCCCGEEP8hDg4O7Nmzh9y5c/Ps2TMOHz4M/O+L7Q8FeLp06UJ0dHSWtVkIITJTamoqAFu2bKFnz57A26pk2gCPWq1WBtLhbb9pbm6u9KPvBngOHjzIsmXLPknbdH+vlnaw39bWlgkTJtCnTx+OHTvGoEGDuHr1KikpKRgYGKBWq9m0aROnT5+mTJky6aZRFEJkLd3gTlRUFOPHj+fQoUN07doVExOTdEG7dwM8c+fOBWDw4MEEBATw8uXLLDmGz522X/5QJZYzZ87w4sULhg0bhp2d3R/uRxvgad26NWfOnOH169cZ0t7/OpVKRe7cuenZsycnTpxg/fr1wJ/fPCA3FwghhBBCiC+ZhHeEEEIIIYT4D9BoNMqgRfPmzVm5ciVWVlYEBQUxc+ZM4H+VdXQDPDNmzEBPT4/SpUtnTcOFECIDvTttFICRkZFyh//q1avp0aMHN2/eZMiQIVy7dg19fX0l+LJ582bOnz9PpUqVMDU1VfahG+CpW7fuR3/X322r9veGhYUxYcIEFi5cyI8//qisU7RoUcaPH8+AAQM4cOAAPXr0wN3dnUOHDjFixAgmTZpEgQIFmD17Nqampn9YwUAIkTneDe64u7tz8eJFjh8/ztdff82LFy84fPgwN27cULbRDfCMGTOGGjVqsGTJEkDCCx8SFRVFs2bNePLkSbpKLCqVCo1Gw+HDhzEwMKBAgQIf7Be1wcnnz5+jUqkoUaIEQUFBXL16lXz58v3r/v2/SPucfSiUqtFolPPd999/j56eHhs2bOD58+dy3hJCCCGEENmankaueIUQQgghhMh21Gp1uru0dQeGtA4ePEiPHj14+vQps2bNYvTo0cq28L8wT2JiImZmZqhUKqnUIITINnT7tOvXr5OQkEBCQgKNGzd+r7/s1q0bYWFhFCxYEA8PD4oUKcKJEyfYvXs3L1++5OjRoxQtWvSDfe2npjtVDkCuXLlwc3PD29tbeezevXv4+/sTGhrKq1evqFixIubm5pQpU4bx48dja2srfboQn4EPBXcuXbpEdHQ0NWvWJCEhgRUrVjB9+nTc3d0ZOXJkumu8qKgoBg0axJs3b3B0dGTYsGEUKlQoKw/ps9SjRw9CQ0Px8fHBx8cHSP/cu7m5MWPGDA4dOkS9evXSPcfa9VJSUnBzc2PgwIF89dVXyr7fveYWf+yXX37BxsYGU1PTdOehDRs2UKZMGUqWLEmuXLnSvT4jRoxg2bJlxMbGUq1atUw51wohhBBCCJEVJLwjhBBCCCFENqP7RfiWLVs4c+YMx44do0aNGnz99dd07NhRWXf//v307t2bJ0+eMHv2bCXAo1Kp0NfXR09PT7nDVb4kF0JkF7qDrQEBASxfvpzbt2+jUqmoVasWrq6ufPfdd5iZmSnb9O7dm7Vr12JoaEiePHmoUaMGFhYWTJ06NdPCMJs3b2bgwIF06tSJTp06kZCQgKurK7dv32b48OEEBQUp6969exd/f382btxI+fLl2bt3LxYWFsDbaWN0q08IITLfXwnurFmzBi8vL8qWLcvx48c/uu2VK1f44YcfsLGxyZJj+VydOHGCly9f0qBBA4KCgujbty+WlpYkJSVhampKamoqRkZGrFy5kn79+lG9enV27txJwYIFUalU6OnpKeeKqVOn4uPjw65du2jatGkWH9mX6dSpU9SsWRNHR0fmz5+vVKxbtmwZAwcOxMLCgurVq+Pj40OxYsUoUqQIAFu3bqVjx460b9+eVatWkStXrqw8DCGEEEIIITKMhHeEEEIIIYTIRnQHpF1dXQkODsbAwACNRsPr168BGDVqFC4uLhQuXBiAAwcO0KtXL548ecKcOXMYOXJklrVfCCEymu6gt7aKTa1atejQoQNpaWksXLgQU1NThg0bRp8+fTA3N1e27dmzJ+vXr6dBgwasWLECOzs7IOPDMNo2T5gwge3bt7N161YcHBwAOHv2LCNGjODo0aMfDPBMmjSJpUuX0qpVK1auXEnevHkzrJ1CiL/mrwZ3PDw8qFq1KocOHQLS9zW6+0hOTsbExCRrDuYzFRERQbNmzRg0aBBz585Vnp8xY8Zw6NAhDh48mK4//P777zlw4ACdO3cmMDAwXQWjzZs3M2HCBAoWLMi2bdvIkydPZh9OtvDixQsqVqzI3bt3lel5TU1N+fnnn7l16xaLFi1i3759AJQvX54BAwbQo0cPLCws6NChA0ePHuX48ePY2dlJxSMhhBBCCJEtSXhHCCGEEEKIbGjy5MlMmDCBAQMG0L9/f6ysrDh+/Dh+fn5cv36dXr16MXv2bPLlywe8HeDo27cvDx48ICQkhAEDBmTxEQghRMaaP38+Hh4e9OvXj8GDB1O2bFmePXtG9erVuX37NoULF8bNzQ1HR8d0AZ5OnTqxZcsWmjdvTlBQECVKlMiQQUTdSj7aAXvtlDje3t7pqkKcP3+eYcOGfTDAc//+ffz8/JQAz+rVq2XgWYjPRFRUFB4eHly8ePFvBXfEH7ty5QpNmjShVKlSTJw4kW+//RZ42682bdqU6OhomjRpQmhoqBLgefr0KW3atOH48eOULl1a6W+joqLYuXMnenp6HD58GFtbWwmO/APav98XL15Qt25dLl++jJOTE9OnT09XSefw4cPs3buXxYsXExcXh4ODA61atcLa2hp3d3ecnZ2ZPn16Fh6JEEIIIYQQGUfCO0IIIYQQQmQzt2/fplGjRpQsWZIlS5Zga2urLDt58iR+fn6Eh4fj5eWFv7+/smz37t14e3uzc+fOdNsIIUR28+uvv9KzZ09y5sxJUFAQZcuW5cWLF9SoUYMXL17Qt29fQkNDefPmDe7u7jg6OipTTgF07dqVjRs30rJlS4KCgihWrNgnbZ/uwPDy5cs5fvw4efPmJTIyEgcHB1auXImJiUm6aQ11AzyjRo0iMDBQ2d/9+/eZNGkSISEh1KxZk8jISHLmzPlJ2yyE+HtiYmJwdnbmypUrxMTEUKNGDQnu/EvaakRLly5l6NChLFy4kP79+wMQGRlJ48aNSUlJoVevXmzatIlGjRqxceNGJcDz8uVL+vfvz65du0hOTgbAzMyM6tWrs2LFikybIjG7+liAZ+bMmcoUWloXL17kzJkzzJ49m8uXL6NWqwGoXLkymzdvpnjx4llxCEIIIYQQQmQo+dQnhBBCCCFENvPgwQNu377NsGHDlEEGbXWGGjVq4OHhwfHjx5k8eTLff/89devWBaBVq1Y0adIEExMTGZgQQmRrT5484cmTJ8ydO5eyZcvy+vVrGjRoQHx8PLNmzaJNmzbY2tri5OTE4sWL0dfXp3fv3kqAZ8OGDejr67NhwwaePHnCrl27yJ8//ydrnza4o53WS5darebevXs4ODigp6eHRqNBo9FQqVIl5s+fz6hRo5g7dy5mZmb4+/uj0WiwsbHB29ubuLg4DA0NJbgjRCb5UIUWtVqNWq0mIiKCX3/9lejoaAnufCLaacTS0tJIS0tTKkwOHDiQZcuWERUVRYMGDVizZg0qlYqtW7fSuXNnJcBjbm7Oxo0bOXLkCHfu3OHly5dUrFiRcuXKYWFhIdfH/5KhoSFpaWlYWFhw5MgR6taty8KFC9HT01Om0EpNTcXIyIgKFSpQoUIF2rVrR2RkJNu2bWPXrl2cO3eOEydOSHhHCCGEEEJkS1J5RwghhBBCiGzm6NGjfPPNN7i6ujJ16tQPrjN16lQ8PT3ZvHkz7du3V+5U1v4rhBDZ3dGjR6lTpw6pqamMGjWKVatWERAQwODBgzExMeHy5cvUqFEDfX19Xr16xdKlS+nXr58ysAjQrFkzTE1N2b59+ydpk+5Af1RUFF27dqVLly4MGDAAfX19xo8fz86dO2ncuDFhYWFKtQjdvvunn35i0qRJzJ49W6kIpF0eFxf3wW2EEJ+ebtDj5MmTvHz5ksaNGyvLnz59yvPnz3FwcOD58+esXbsWT09PqlSpIsGdf+DYsWNcu3aNvn37cvnyZbp168a9e/f49ttv2bVrFyNHjsTZ2RkbGxsAUlJS6N69O1u3bn2vAs+HyFRZn472PKpbgWfo0KFKgEf73nn3Od+5cyc9evSgZMmS7N+//5OGZoUQQgghhPgcyCcOIYQQQgghvlDa8vHvypcvH3p6emzdupVTp06lW5aamgpAiRIlALh16xbwvzuVZSBXCJGdfKifTEtLA6BOnToAvHr1isOHD1O9enVGjhyJiYkJAIUKFaJAgQJ4e3vTpEkTmjZtCoCRkZGyj/379yvBnU9xb5R2kPLx48dcuXKFXLlyMXz4cCpVqkSFChUIDg5m8ODBREZG0q1bN549ewaQrgJPtWrV2LRpE8WKFVPaqe3bJbgjRObQDe4EBATQuXNnXF1dOXv2LPC2b7KyssLBwYHXr1+zYcMG3NzcJLjzDx08eJC6dety7Ngxnj59Srly5fD19UWtVrNr1y46dOjA+PHjleBOWloaxsbGrF+/nvbt2xMVFUXnzp2Ji4sD/ne9rEuCO//Mh87D2gCshYUFR48epXz58ixYsABnZ2dev379XnBHu482bdrQp08fLl++THx8fOYdhBBCCCGEEJlEPnUIIYQQQgjxBVKpVMoX2vv27SMkJEQZxC1dujQeHh7cuHGDVatWcfv2bWU77ZflZ8+eJVeuXFStWjXT2y6EEJlBt5+8d+8ely5dIikpSQnZqNVqNBoNt27d4tKlSxQpUkTZVqPRsGrVKtLS0mjXrh3h4eHY2NigUqmAt1N/6A5IfsowzMSJEylXrhyHDx+mVatWlC5dGrVajUqlwsbGhvHjxzNkyBAiIiLo3r27Mtisp6entEE74P+xgX8J7giRcTQajRLccXZ2xtfXlwoVKjBr1iyqVKkCpA+C6Ovrc/z4cerWrSvBnX/g559/pm/fvtSvX5+ePXtiZWUFQGxsLC9fvsTc3Jx9+/Zx8eJF4O3ro52+ydjYmNDQUDp06EBUVBTdunXjyZMnyvWy+Hd0z8OnT59m165drFq1imPHjinnUHNzc44cOUL58uVZuHAhLi4uvH79Gn19fWUdfX19JYxau3ZtUlNTOX36dNYclBBCCCGEEBlIwjtCCCGEEEJ8YdRqtTIo5O3tjaOjI0OGDOH8+fPKF9udO3emWbNmLFq0iIkTJxIVFaVsu3nzZjZs2ED58uWVQSQhhMhOdPtJX19f6tSpQ8WKFalevTp+fn78/vvv6Ovro6enR8WKFalZsyaxsbHs3buXhw8fsmrVKhYvXkyJEiUoUqSIsi/tv5B+8P1ThWFSU1PJlSsXJiYmbNy4kWPHjvHs2TP09fUxMDBAo9FQuHBhvL29lQBPr169ePr06Sf5/UKIf0/bH8yZM4cFCxYwbNgw5s6dS4MGDT64fo4cOZgzZw4HDhwAJLjzd/300088fPiQzp078+233wIQFhaGtbU1bm5ueHt7kzt3btq2bcu+ffvShRzT0tIwMjJi/fr1dOnShYiICIYPH/5JKqn91+mehydMmEDr1q1p27Ytffv2pVGjRjRv3pwnT54AHw7wJCUlpQvwGBoakpiYyIULF4C3lUaFEEIIIYTIbvQ08mlECCGEEEKIL5KHhwczZsygf//+9O/fnxo1aqRb/uOPPzJ79mx27tyJqakp3333HfHx8Vy9ehVTU1NiY2Oxs7NLV5ZeCCGyk/HjxzN58mSqVatG0aJFuXbtGteuXaNDhw4EBgZiY2NDSkoK69atY/z48cTHx2Nubk58fDw2NjZERUVhZ2eXqdNMJSYmsmrVKubNm8ezZ89YsmQJrVq1UgbztW158OABAQEBzJ8/n27durF27VqpqCPEZ+LRo0e0atUKlUrF+vXrKVOmjLJs+/bt/PrrryQkJDBixIh0IQS5Jvv7wsLC6NatG0uWLKF///4MGTKEkJAQtmzZQuvWrTE0NGTZsmVMmDCBxMRENm7cSLNmzZTttWGplJQURowYgYeHB/b29ll3QNmMp6cnU6dOpVOnTnTo0AF7e3tmz57Nxo0bKVKkCLGxsRQtWhQDAwNevnxJ3bp1uXTpEj169GDJkiXkyJFD2Vd4eDht2rShU6dOhIaGZuFRCSGEEEIIkTEkvCOEEEIIIcQXaOfOnXTv3p2uXbvi7e2dbpBBd5D5119/ZdeuXcyePZv4+HgKFSpErVq1mDRpkjIFjG4lCSGE+JLp9mlPnz6lXr161KtXjwkTJmBjY8OzZ89wdHRkz549tGnThqCgIGxtbXnx4gXHjh1jxYoVxMXFUapUKTw9PSlcuHCG9ZN/FAh6+fIlq1evZtKkSZibm7Ns2TLq1q2rDOprt71//z4LFixg8ODB2NnZffI2CiH+mRs3blCpUiV69+7NwoULSU1N5cKFCyxYsIAVK1agp6eHRqOhVq1abNu2jYIFC2Z1k79Yv/zyC46Ojpw+fZqGDRty4MABxowZw5gxY7CxsVHWW758OePHj//DAM/Hfhb/zMGDB+nUqRPt2rXD29ub4sWLA7B27Vr69++PhYUFN27cIHfu3Mq59uXLl5QpU4akpCRu3ryJpaWlsj+1Ws2cOXMYO3as8rOE3YQQQgghRHYi4R0hhBBCCCE+Q7qDuh8a4HVzc2PevHkcPXqUypUr/+H2APHx8aSlpWFmZoahoSFGRkYS3BFCZFtbt24lNTWVcePGsXPnTqpWrZquX/zhhx/YuXMnbdq0Ye7cue8FX7QDghnVT+ru98WLF8TFxaHRaChWrJiyjjbAM3HiRPLmzcuSJUvSBXi0bdT+K4PNQnw+rl+/TrNmzXjy5AkLFizg5MmT7N69m4SEBPr27UudOnXYtWsXa9aswdnZmenTp2d1k79okZGRdOnShbi4OFq2bMmKFSuwsrIC0ve3fxTgEZ+eNmhz+PBh6tSpg0qlIiwsDC8vL/T19Tl58iT58uUjKSmJHDlyKOfoxMREXr58SaFChZRz97vnYwnuCCGEEEKI7EiucIUQQgghhPjM6A4wv3nzBj09PdRqNfB2ACI1NZWIiAgsLCwoXrw4KpXqg/sAeP36NQCWlpbkz58fU1NTjIyMACS4I4T4op05c4ZTp07x7j1Ja9asoWPHjoSEhGBtbU25cuWUfjItLQ14O21NmzZt2LlzJ6NGjeLevXsASl+rHRDM6OBOYGAg33//PaVKlaJMmTJ89913hIWF8fTpU8zNzenduzc+Pj7ExcUxcOBAjhw58l4btf9KcEeIzKd9P77bD5UqVYqhQ4eir6+Po6Mjy5Ytw9bWlsOHDzN58mQ6duyoBBjEP6d93iMiIoiLiyNfvnzs27ePH3/8UVluYGCgvE79+vXD398fc3NzevTowc6dO7Os7dnNu+8BjUajTNVbu3ZtADZt2oSHhwcAx48fV6aMu3XrFn5+fqjVatRqNWZmZhQqVAiVSqV8Jnr3fCzvHSGEEEIIkR3JVa4QQgghhBCfGe2X1C1atKB37968evVKqa5gYGCAkZERZcuW5dWrVzx+/DjdoAT8707UFy9eMH36dF68eJFVhyKEEBnit99+o3HjxjRr1oybN2+mW9amTRtatWpFdHQ058+f5+rVqxgYGKCnp4ehoeEHAzy9evXi999/z/DBQO1AMsC4ceNwdXXlxYsX9OrViwoVKnDixAmcnJyYNm0av//+uxLg8fX1JS4uDicnJ6Kjo98bJBVCZD6VSqX0Gb/99htXrlzhypUrPHr0CAAXFxc2b97Mxo0bCQ8PZ//+/ZQrVw5TU1NUKhXbt2/HxMREqaAo7+u/T09Pj+TkZEqUKMGECRPw8/OjRIkSdOnShfXr1yvX1NrraHgb4Jk0aRIvX77E1dWVN2/eZOUhfJF0P3fA/242APj999+Bt6+NqakpSUlJHDlyhE2bNuHm5qZU3MmfP7+yvbOzM3v27OHZs2fpzsNyo4EQQgghhPivkfCOEEIIIYQQnxmNRkNycjLx8fHs2bOHsWPHKgGe1NRUAEqUKMGrV6/w9vbm+fPnyqCEbgn5WbNmMWXKFK5cuZKVhyOEEJ+cmZkZw4cPp1WrVhQsWDDdsty5c7N27Vq6d++OSqXC29ubW7duKcvfDfB8++23nDp1SqlKlpG0g5srVqxg7ty5jBgxgh07drBs2TL27dvHkiVLKFasGHPmzGH+/Pk8f/4cc3Nz+vTpg5+fH1euXMHPz085FwghsoY2UA0wbdo0mjdvTqVKlahSpQpt2rRh1qxZADRt2pSOHTvSsGFDTE1NgbfXeZs3b2blypVUqFBBmbrp3SlSxYe9G3IyMTGhV69eeHh44OTkhJ+fH8WKFaNPnz6EhoYq6+kGeBwdHVm1ahWRkZHkyJEjU9ufHeh+1gCU59DFxYWRI0cqAZ7OnTtjamqKq6srLi4uHwzuLFq0iHPnztG0aVPy5MmTuQcihBBCCCHEZ0ZPI7d1CCGEEEII8VmIi4vD0tJSGbxJSkqic+fOxMTE0K5dOxYsWICZmRkAycnJ1KlTh4sXL+Li4oKzszOWlpbKvjZv3sz48eOxtbVl06ZNWFhYZMkxCSHEp6KdUlD77+vXrzEwMMDExIS5c+fy9ddfU6dOHaUPffHiBf369WPr1q307duXCRMmYGdnp+wvLS1NmWrq6dOnWFlZpQtAZuQxdOvWjejoaI4cOUKJEiWU35uWlsbZs2fp168f8fHxbN68mVq1agGQmJjIli1b+O677yhSpEiGtVEI8de5uLgwa9Ys6tevT/369TEyMiIoKIgnT57Qp08fVqxYkW79tLQ0/Pz8WL16NRqNhtjYWGxtbTO878kudKcdTEhI4OnTp+TNmxdDQ0PMzc2V9TZu3Ii3tze//vorq1evplu3bh/cx4d+Fn+Nq6srM2fOZMCAAYSEhDBu3DgCAwPx9PRk3LhxWFpa8vvvvzNq1Ci2b9+OkZER586do2TJkso+NmzYwPjx4zE3N2fv3r3vhXGFEEIIIYT4r5HwjhBCCCGEEJ+BvXv3Mm/ePCZNmkTVqlWVgYSkpCRl+peffvqJKlWqKMuOHj1K//79+fnnn2nUqBEjR44kd+7chIeHExYWhkaj4fDhwxQtWlQGhYQQX7QP9WHaIExsbCz169enQoUKLF26lK+//jpdgKdPnz7s2LEDR0dHfHx80gV4dAdtM6OfVKvVJCYmUr58eXLlysWVK1fQaDTpfm9KSgohISGMHDlSGfzXHuuH2i2EyBqhoaH069eP/v37M27cOIoVKwbAzJkzcXV1xdLSknv37pEzZ04ALl26RMuWLXn27Bl16tRh2bJlFC1aVN7Pf5FuHz1z5kxCQ0M5e/Ys5ubmlCpVitmzZ/PNN98ofeWmTZvw8vL6YIBH/Hvnzp3D3d2dAwcOULZsWa5evYqnpycDBgzA3t5eWe/06dOMHTuWw4cP06lTJ+rUqUOVKlVYu3Yte/bsQV9fn8OHD2NnZyefV4QQQgghxH+ehHeEEEIIIYTIYpGRkTRp0oS6deuyePFivvrqK+B/g7OvX7/m/Pnz1K5dO912Go2G8+fPM3z4cI4ePao8bmpqSuXKlVm/fj12dnYyKCSEyDY6depE1apV8fDwUB6Lj49n2bJlTJ06laJFixISEvK3AjwZ6d3QjVbTpk25cOEC58+fp2DBgu/10/fv36dixYqUL1+eAwcOyLQuQnyGnJycCAsLIzo6mkqVKpGamsrmzZvx8vLC0NCQY8eOkS9fPpKTkzExMQHeTjNkZmZG586dsbS0lGu0v0i3L3V2diYwMJCaNWvSrFkzfvvtN7Zv386bN2+YNGkSffv2VarwbNq0CW9vb+7du8f8+fPp27dvVh5GtqF9PZ49e0aVKlV4+PAhZcqU4ciRI5ibm5OWloaBgYHymp0/f56goCC2b99OfHw8AObm5tSvX5/58+dLiE0IIYQQQoj/J+EdIYQQQgghstC1a9eoU6cOr1+/ZteuXTRp0iTd8ne/yP7YQPCGDRt48OABiYmJVK9endq1a5MnTx75IlwIkW1cv36dqlWrYm5ujpeXF8OHD1eWJSQksGzZMvz9/bG3t//DAE/79u2ZM2cONjY2Gdpe3QoCcXFxGBsbY2ZmRlpaGm5ubgQGBuLo6Mjy5cuBt/29np6eMn2Wvb09FSpUUCoTCCE+H69fv+bbb79FX1+fkydPolKp2Lx5M66urspj+fPnB95WKLl//z6tWrUC/tc3SJWRv2/lypUMHTqUfv36MXr0aBwcHABwd3dn+vTpVK5cmWPHjmFsbKz0/1u2bGHQoEGYm5tz5coVpRKS+HdUKhXh4eG0bduWokWLcu/ePWUKLe1y3c8gL1++JCEhgZMnT6LRaKhcuTLW1tbkypVLPq8IIYQQQgjx/wyzugFCCCGEEEL8lxkbG9OkSRO2bdvG4sWL+eabbzA1NVWWv/tF9rvBHe2X3V27dn1v32q1Wr4IF0J80V69ekWuXLkAKFWqFDExMXTr1o2AgACSk5MZN24cALlz56Z///4A+Pv7M2jQoHQBHgsLC1avXk2bNm2IjY3N8MFb3YHIkJAQwsPDKVWqFJ6enuTJkwdnZ2e2bNnCypUrKVSoEJMnT1bW12g0rF+/nqdPn1KjRo0PBjaFEJnnY8FpQ0NDJZCwd+9e3Nzc0NfX59SpU1hZWQFvr8WcnJwoW7YsjRs3xtTUVAnsSHDn79u/fz/58uVj0KBBODg4kJyczO7duwkNDaVkyZLs378fExMT1Gq18pp16NABExMTqlatKsGdT8jAwIDWrVuzZ88eTExMmDZtGkuXLiUtLY3ly5djYGCQLpRqZmaGubn5e8FZjUYjn1eEEEIIIYT4f/IpUQghhBBCiCxUvHhxpk2bRs+ePdm6dSvdu3fn5cuXf3l73cFeLe3/ZVBICPElO3DgAB4eHty5cwd427d9/fXXhIaGolar8fPz49atW8oybYBn/Pjx3L59m0GDBnH69GmlTzQ3N2fXrl1cvnyZvHnzolarM6TdusFJZ2dnnJ2duX79OnXr1sXCwgKVSkWhQoXYtGkTBQoUICAggA4dOhAbG8vPP//M3LlzmTJlCkWKFGHIkCES3hEiC+mGQJKTk5XHc+bMScuWLbl69SpeXl54e3ujr6/PiRMnlOAOwNy5c/n555+pVq2aTH/3LyUkJBATE0PNmjWpWLEiqampbN++nbFjx6Kvr8/hw4eVakdHjx5Vzg8ArVq1onDhwqhUqqxq/hfv3XOm9tzavHlzGjVqRGBgIA0bNmTlypX069cPePs5Rft55Pz58/zyyy/v7VfOcUIIIYQQQvyPTJslhBBCCCHEZ+D27dv4+fmxcuVKfvjhB1atWoW5uXlWN0sIIbLEwYMHadq0KY0bNyYkJIRixYoB/6uAceLECRISEmjatKmyjXbZu1NoLVmyhKpVq6YLNGbGdDUBAQGMHz+e4cOHM2TIEMqUKfNeWy9evEi3bt24cuWKMsipp6dHyZIl2bVrF/b29jKdiBCfAXd3d9RqNV5eXuTOnRuAqKgoevbsycOHD8mfPz8PHjxI917duHEjHh4eWFtbs2PHjnShHvH3JSUlUb58eYoUKcKWLVv48ccfleCO7jRlAFWqVKFSpUosXboUQ0MpPP9v6Z6HYmNj+eWXX4iPj6dgwYJ06tQJAwMDDA0NuXLlCiNHjiQqKoo+ffqwYsUKAMLDw5kwYQJVqlRhwYIFGBkZZeXhCCGEEEII8dmS8I4QQgghhBCfCQnwCCHE27vzq1evTv78+dm8eTO1a9dOt/zd4I3ulDbvBngCAgIwNTVl586dVK5cOdOO4caNGzRr1oxixYqxdOlS7O3t32ur9jiePXvGtm3bOH/+PGlpaVSuXJn27duTP39+Ce4I8Rm4e/cunTt35uTJk/j5+TF8+HDy5MkDwLx583Bzc+PNmzesWrUKBwcHihcvTlBQEOvWrUOlUnHkyBFsbW0zJTSY3XXv3p3w8HAGDx5MWFgYenp67wV3pk2bxrRp05g5c6ZSAUb8c7p/t56engQHB5OYmKgsr1OnDmPHjqVZs2bkypWLa9euMWLECCIjI2ndujXFixdn165dPHv2jLNnzyrnQyGEEEIIIcT7JLwjhBBCCCHEZ0QCPEKI/7qIiAg8PDw4d+4cU6dOxdnZOV3o5c/oBniCgoIIDQ0lKioKa2vrT9bGPxuE11YOWrlyJb179/5o+/8onCMD/UJ8Po4ePcqUKVMIDw/H19eX4cOHkzdvXgBCQkKYOnUqt2/fVtY3NDSkZs2arF27Fjs7Owni/Uva/jAqKgpHR0fu379PwYIFOX36NEWKFFHW27BhA+PHj6dIkSJs3bpVeY3Ev+fj44O/vz+9evVi0KBBFC5cmB07duDv74+JiQlz5syhY8eO6Ovrc/36dXx8fNi9ezepqamUL1+ezZs3SzU5IYQQQggh/oTUDRVCCCGEEOIzYm9vz4QJEwBYuXIlffr0kQCPEOI/pUmTJhgZGeHp6YmrqytpaWm4u7v/5e319PTQaDTkzp2b0aNHM3r0aMzNzT/pgKE2VBMREUGTJk2Ux7Uhnfv37wNvp3nRtkmXti2PHj1CX18/XbBIuw8J7giR+d4N2mlDI3Xq1MHT0xOVSoWvry+AEuAZNGgQtWvX5sqVK5w9e5acOXNSp04dvv76a/LkySNhhT/w8OHDvxSs1PaHNWrUoG/fvixZsgRTU1NOnTpFQkIC1tbWzJ8/n6VLlwKwZs0a8ubNKyHIf+BDYdOjR4+yZMkS2rdvz/jx43FwcAAgf/78pKWlYWZmRrNmzdDX10ej0VCqVCnmzp3LmDFjePbsGTVr1iRv3rzyXhBCCCGEEOJPSHhHCCGEEEKILKL75fizZ8/Ily8fIAEeIcR/l7ZfbNCgAZMnT8bLywtPT0/09PRwc3P7y/vR9q3aflOj0XzyAUMXFxdmzZrFqlWr6NWrV7rfW7x4cQDOnTun/H7tct22jBs3jipVqjB27FgMDQ3T7UMIkbE+FtTRpa+vny7AM378eAB8fX3R09Nj2LBh5M2blwoVKlChQgW6dOmSbnu1Wi1hhY84deoUNWvWZP78+Tg5Of3p+hqNBjMzM0aPHo2JiQkhISG0b9+eHDlyKMsrVapEWFgYRYsWlaDI36T9LKI9T+m+N3755RceP35M3759cXBwIC0tjc2bN+Pl5YWVlRUnTpwgd+7cpKamoqenh6GhIQUKFKBAgQLKPuS9IIQQQgghxJ+TWw+EEEIIIYTIArpfikdFReHh4cHevXuVZdoAj6OjI9u3b6dPnz68fPkyK5sshBAZTjtoCNCwYUMmT55MrVq18PDwYNq0af9qv5+KWq0GoHnz5jRu3Jj+/fuzdu3adOuUKlWKmjVrsnjxYrZu3Yqent57A6JLliwhPDwcIyMjqQwhRBbQXldp+xzt+9DFxYWJEycq62kDPIAS4KlXrx4+Pj6EhIQQFxenrKvdl+624sN+//13bG1tGT16NMuWLfvT9bV9qKWlJWPGjGHfvn14eXnRs2dP+vfvz5o1awgPD5dpyv6B6OhoGjVqxLZt24D052KAixcvoq+vT7Vq1QDYuHEjbm5u6Onpcfz4caysrAC4dOnSRyvlyXtBCCGEEEKIPydXzUIIIYQQQmSyd4M7zs7OrF+/Hnt7e+B/g8zvBnh69+5NSkoKAEFBQURGRmZJ+4UQIiNlVIDn3zp9+jS3bt1SBiAbNWqEr68vDRs2pHfv3qxYsUJZ19ramoEDBwLQsWNHVq9eTUJCgtK/b9iwgcDAQIoXL06PHj1kUFOITBYdHU358uWJiYlR+hyNRsPNmzfZvn07EydOZPbs2cr6xHtzYwABAABJREFU7wZ4Ro8eDYCnpyeLFy/m+fPngFTO+jvatGlDcHAwpUqVYuDAgX8rwGNqakqZMmXw9/cnJCSEefPm0bFjR2WqLAnu/D03btzg4sWLTJo0id27dwNvn2uVSgVAyZIlSUtLY9u2bezYsQMPDw/09fU5efIk+fPnV/Yzbdo0li9fzp07d7LkOIQQQgghhPjSybRZQgghhBBCZKJ3gzvu7u5cv36dgwcPUrZsWV69esXTp0+xs7MD3p9Cy9HRERMTE1atWsXy5ctJTU3FyMgoy45HCCEygm6VGm2Ax8vLCw8PD4C/NYXWpxAREUHLli0ZOXIkkyZNwsTEBD09PerWrYunpyevX7/G0tIS+F8/369fP168eIG7uzuOjo7UqVOHsmXLcufOHU6fPo25uTnh4eEUKFDgg9P1CCEyzrFjx7h//z79+vVj9erVfPPNNwCUKFGCRYsWMWHCBJydnVGr1Tg7OwPpp9Bq164dDRs25Pbt23h5eZEjRw7GjBmTlYf0Rbh+/TpmZmYULlwYgFatWqFWq/Hy8lICj/379//DfWivo9+d2klL+tK/b+DAgejr6zNw4EA8PT2Bt6+NNgRVp04djIyMmDJlChqNBiMjI86ePUuePHmUfSxdupTDhw/To0cPrK2ts+IwhBBCCCGE+OLpad6t5yqEEEIIIYTIEB8K7ly6dIno6Ghq1qzJ8+fPWbduHTt37sTX15fatWsr29y+fRt/f39WrFhBzpw5GTFiBCNHjqRQoUJZfFRCCPHp6PaTSUlJmJqaKsuio6Px8vLi+PHjBAQEZFqAJyIigmbNmlGrVi0WLFhA5cqV32trXFwcefPmVbbRDeOEhYWxc+dOduzYwZs3b7C3t+ebb75h8uTJFClSRKZ3ESKLTJ06FU9PT4oUKUJoaKgS4AGIjIzEy8uLkydPMn36dCXAo33fp6SkUKZMGb7//ntevXqFv78/tra2WXUoX4QLFy5QuXJlevXqRUBAgBLgAdi5cydeXl5cvnyZJUuW/GmAR2SMJUuWMHjwYMqXL8+UKVNo1aqVsmz27Nl4eHiQmprKhg0b6Ny5s7Js9erVTJ48GWNjYyIiIrC2tv5ouEoIIYQQQgjxcVJ5RwghhBBCiEzwZ8GdhIQE1q1bh7OzM7Vq1aJ27dpA+im0PD09MTMzI3/+/PTt21eCO0KIbEW3nzx06BCRkZH06NGD0qVLA3y0Ak9Ghl+SkpIICQkB3k4boh1s1rZV+69ucAfSV+jo0qULnTt35u7duyQmJlK4cGFy5cqFsbGxBHeEyALa9527uztqtRpvb2+6deuWLsDTuHFjALy8vHB1dUWtVjNq1ChMTEwACA0NxcTEhNGjR1OiRAkMDAzk/fwnbGxsqFOnDmvXriVnzpyMHz9e6VPbtGkD8Lcq8IhPT/vcDx48GE9PTzQaDa1btwagXbt2PH78mFmzZuHh4cHhw4cpV64cBw8eJDIyEgsLC/bv34+1tbW8F4QQQgghhPiHpPKOEEIIIYQQGeyvBHfWrFmDh4cHX3/9NdHR0QDpvvjW7iM+Ph5AmZ5FCCGyg3f7yXHjxvHbb79x7NgxSpQokW7dmJgYvLy8OHbsGJMmTVKm+NiwYQMVK1bkq6+++qRtu3btGh4eHuzZs4dx48bh5OT0lyts/FHlAalKIETW0b3GmjJlCt7e3h+twOPt7c2JEyfo3bs3DRo04O7du6xYsQILCwtiYmLkmuxviIuLo3v37hw4cIDBgwenC/CAVOD5XOhW4Jk0aZISrvrtt98IDw/H29ub+Ph40tLSsLW15ZtvvmHatGlSTU4IIYQQQoh/ScI7QgghhBBCZKC/E9ypWrUqhw4dAiAtLQ1DQymUKYTI/j7UT165coWDBw9Sq1YtkpKSSE5OJk+ePMo2ugGeoKAgzp8/z7Jly9ixYwctWrT45AOHN27cYOTIkURERDB27FhGjBiBjY3NJ/0dQojMpTu93R8FeH788UeCg4PZuXMnKSkpADg4OBAREYGdnV26/Yg/Fx8fT/fu3dm/fz+DBg1i/PjxFClSRFm+a9cuvLy8uHTpEiEhIQwYMCALW5t9vVtBDtK/J5YuXcqgQYMoX748/v7+tG3bVtn23r17PH/+nAcPHlC5cmUsLCwwNTWV4I4QQgghhBD/koR3hBBCCCGEyAQHDx7Ew8ODq1evEhkZKcEdIYTg4wHHmJgYatSowfPnzwkLC+PEiRP4+Phga2urrK8b4DExMWHkyJGMGTMGa2vrDGnrjRs3GDVqFAcOHJAAjxBfMN1+JzU1FSMjI+CPAzwPHz7k8uXLxMbGYmdnR4sWLShYsKCEFf4hCfBkLd2/28ePH5OSkoKhoSFWVlbpPoPoBnh0K/B8iFSTE0IIIYQQ4t+T8I4QQgghhBCfyLtfWmvvXr137x5jxoxh586dxMbGSnBHCCH4a5XJ1q5dy9ixY2nUqBF79+59b7uIiAhCQkIoW7YsQ4YMSTf9SkaQAI8QX6Z3q+PoXm/p9ikfC/B8qLqOBHf+nb8a4Ll27RqzZs1ixIgRWdja7EP3b3nGjBls2LCBu3fvYmxszHfffUfr1q3p2LGjsr5ugGfy5Mm0bt0akLCOEEIIIYQQGUHCO0IIIYQQQnwCul+E37t3j4IFC2JsbAy8/XI7ODiY+vXrU7FiRZ4/f866detwd3eX4I4Q4j/pr04p6OnpSdWqVYmJiQH+19fqbn/z5k0sLCzInz9/prRdAjxCfFl0Qzbr168nOjqaW7duUbRoUcaMGYODgwO5cuVS1v9QgEemxvpnPhbw0F7zxsXF0aNHj48GeHbv3s3gwYPR09Pj559/Tvc6iX/H1dWVmTNnUqJECb766ivu3r3L+fPnAQgICMDNzU1ZVxvgqVy5Mp6enunCPUIIIYQQQohPR8I7QgghhBBC/Eu6g0Lz589n06ZN2NnZsWzZMvT19dMN9rx8+ZIVK1bg6enJ119/rQxIS3BHCPFf8VeDO39WmSwz7vp/93dof5YAjxBfBt3QjYuLC4GBgZiammJtbc2DBw/InTs3rq6u9OzZEysrK2U7bYDH3t6eZcuW0bBhw6w6hC+W7vXx8+fPSUhIwMjISKmQpu1P/yzAExERQfny5SlUqFCWHEd2oft6HD9+nC5dutC1a1ecnJywt7cnMTGRXbt20adPH9LS0pg1axZjxoxRtl++fDkDBgygUaNG7N69mxw5cmTVoQghhBBCCJFtyS0jQgghhBBC/AtqtVr5ItzFxQU3NzeSk5P54YcfMDQ0fO8u7Vu3brFixQoqV64swR0hxH+SNgxz8OBB3N3duXz58t8O7ujuJ6PoBnfOnj2b7nc6ODgwd+5cmjZtyuzZs5k3bx73798H3g6QCiE+D9rrsIkTJzJnzhwGDBjAkSNH+OWXX9iyZQsPHz5k9uzZhISE8OzZM2U7T09PpkyZwu3bt3FzcyMlJQW5//Gv0w2KBAYG8v3331OqVClq1KiBo6Mjz549U/rTvHnzsm7dOpo1a0ZISAj+/v48ePBA2VeTJk0oVKiQ9K3/kvb1uH79Oi9evCAxMZGePXtib28PQK5cuejWrRs7d+4EwMfHh+joaGX7fv36sW7dOpYvXy7BHSGEEEIIITKIVN4RQgghhBDiE/D398fPzw8nJyecnJwoW7bsB9fTaDRs2rSJzp07AxLcEUJkbx+rXHP9+nWGDRtGdHQ0R48epUaNGn8ruJOZ7Y6KiqJ///6ULVuW8PDwdJU83q3Ao61gAPDkyZNMm8pLCPFxBw4cYODAgTRp0gR3d3ccHBxISkqiatWqxMXFYWpqSnx8PG5ubgwaNChdBZ558+bRtm1bbG1ts/AIviy6feS4ceOYO3cupUuXpnHjxty5c4ddu3bRuHFjZsyYQaVKlZS+VluB58CBA3Tt2pVZs2ZhbW2dlYeS7cyaNQsXFxcaN26Mvr4++/fvB1CCadrXYtGiRQwdOpRJkybh6en53rlcN5wlhBBCCCGE+HSk8o4QQgghhBD/0qlTpwgODqZdu3aMHTs2XXDn559/5uTJkzx8+BC1Wo2enp4S3FGpVBLcEUJkW9o+D94OygLpKi2UK1eOkydPKsGdtWvXfpbBHQ8PD54+fYqnpyfwv0oeGo3mvQo8S5YsISkpiVGjRtG1a1fi4+Mzte1CiPRSUlI4cuQIGo2GAQMG4ODgQGJiItWqVSM+Pp4ZM2awaNEirKysWLBgASEhITx9+lTZfsSIEdja2pKWlpaFR/Fl0faRs2bNYuHChQwfPpywsDCCgoIIDAykQIECREZGMmzYMM6dO6cER/Lmzcv69eupUaMGUVFRmJiYZOVhZEs2NjZUqlSJQ4cOcfz4cU6fPg28PTfr6emh0WhQqVTUr18fU1NT9uzZQ2pq6ntVpyS4I4QQQgghRMaQ8I4QQgghhBD/0q1bt3jy5AldunTB3t4etVrNw4cP8fDwoG7dutSqVYtq1aqxdu1a0tLSUKvVgHzxLYTIvlQqlTKAu2zZMgYMGMCMGTOAt6EXKysrZsyYQdWqVXnx4gUrV67Ew8ODatWqfVbBHXd3dy5evEhkZCTffPMN8fHxbN++HUg/hdacOXOUAE+1atUIDg6mdu3aMs2OEJns3amVjI2NKVasGL6+vtSqVYs3b97QqVMnHj9+zOTJk+nVqxfff/891apV48GDByxatIjAwMD3gncStv57zp07x9q1a2nZsiVDhgyhfPnyJCQk0KpVK/T09OjQoQMnT55k1KhRnD17Vrk2trS0ZO/evVy4cAFLS0vpQz8R7fPYpUsXxo8fT9WqVXn58iW7du1K957RVtQpU6YMFhYWWFlZYWRk9N40wEIIIYQQQoiMIVfeQgghhBBC/EvaAYcbN27w/Plz5s+fT+vWrZk5cyY1a9akd+/eqNVqPD09efDggXwBLoTI1tRqtRJOdHd3Z8yYMdy6dQs7Ozvgf6EX7WD42bNnCQwMpHz58sTExABZH9yJjIzE3d2dS5cuER0drVQHWr9+Pe3bt2fMmDHKNgAlS5Zk7ty5NGjQgJs3b+Lm5sawYcPImzdvph6DEP912r5nypQpbNu2DQBHR0d69eoFwI4dOzhy5Ai9evWie/fuynu+WbNmVKtWDX19fcLCwiRg/S/duHGD8+fPM2zYMMqUKcOrV6/49ttviYuLY+7cucycOZMffviBw4cP4+bmxoULF5T+NE+ePOTPnz9d9Tbx92g/m2jpPo/t27fHzc2N8uXL4+/vz5IlS3j16hXw9ryclpbGypUrefToESVKlEClUkmISgghhBBCiEyip5GrbyGEEEIIIf4S3YFdtVqthHAuXLhAr169uHjxIvr6+qjVakqUKMHChQupVq0alpaWuLm5MWPGDEJDQ+nSpUtWHoYQQmSKCRMmMHnyZAYPHszQoUMpX778B9d78OABYWFhSiAmq4M72oo72uBOzZo1SUhIYM2aNXh5eVGtWjWioqI+uJ+bN29y8uRJ6tati62tbWYeghDi/506dYqaNWvi4uLCtGnT0r2/vb29CQgI4NixY9SoUUPZplWrVqSmphIUFISlpSUFChRIt534+/bs2UPLli1JSUnB0dGR8PBwAgIC6Nu3Lzly5GDfvn20adMGfX19bGxs2LFjB+XKlcvqZn/xtNVzAGJjY3ny5AnFixfHxsYGKysrZb3t27fj7e3NlStXGDFiBPXr16dx48YsWbKENWvW8Pz5c44dO0bhwoWz6lCEEEIIIYT4z5Gar0IIIYQQQvwFul+EA7x584acOXMCULFiRYKDgzlw4ACPHj2iUqVK9OzZk9y5cyvrx8XFUaRIEapUqZLpbRdCiMwWERFBcHAwffr0wc3NTam6A/Do0SNev35NsWLFAChcuLAS3FGpVJ9tcMfDw4OqVasqwZ0PhYxKlCiBvb29VO0QIgs5ODhQr149Fi9eTJ8+ffjqq6+UZQYGBmg0Gi5cuEC1atUwMDBg48aN/Pzzz/Tt25fSpUsD6UPa4u/RXjO3bNkSgHv37hETE8N3333HoEGDlP7R0tKS3Llz07ZtWw4fPky+fPmystnZgkajUZ5f7Y0D8Pbvvk6dOgQHB1OhQgUAfvjhBwB8fX2ZN28e8+bNo3Llyjx9+pSvvvqKHTt2ULhw4fc+AwkhhBBCCCEyjoR3hBBCCCGE+BO6X1ovX76cAwcOcPz4cSpWrEiVKlWYOHEi9erVo3bt2h8cdN64cSP79++natWqcveqEOI/4dKlSzx//pyePXtiZ2eHRqPh1atXBAYGsnbtWm7evEnjxo2ZMGECdevWVQbKs2KAUBvciY6O/tPgzqFDh4A/rg4kg5xCZC1LS0vatGlDbGws69atw8/PD3j73mzTpg1hYWFMmDCBCxcu8OLFCyIiIjAxMcHR0VHZhwR3/rl3+8Br167x8OFDGjZsmG7Z7t27sbW1xdfXlzx58mBmZiahqX9Jez5bsGABQUFB/PDDDzRp0oTDhw8TGhpK/fr12bdvn1J16ocffkCtVjNz5kxOnDhBo0aN8PLywtjYmFy5cmVJJTwhhBBCCCH+y+TTkBBCCCGEEH9A9w7WcePG4eTkxLFjxyhZsiSXL1/G39+fxo0b89tvv33wy+2goCA8PT0xNDQkODgYMzMzZOZaIUR29/z5cwAMDQ1JTU1lzZo1tGzZEh8fH/LkycM333xDVFQUPj4+n8VgbVRUFCNGjODatWv/KrgjhMg8H7qe0j42atQoqlevzsaNG3n58qVyLVexYkV8fX0pUaIEwcHBbNmyhWLFihETE6NUGRGfVsmSJcmbNy9hYWEkJSWRkpJCWFgYYWFh2NnZUbBgQeX6OKvPBV86tVqNWq0mJiaGxo0bM2fOHJycnFi3bh0BAQG8evWKBg0acPLkSWWb9u3bM27cOMqXL8/s2bPZvXs3uXLlQq1Wy7lOCCGEEEKITCafiIQQQgghhPgD2jtYAwMDCQoKYuDAgezbt4+IiAhiY2Np1qwZ0dHRuLq6KgNGKpWK2NhYGjVqxIQJE8idOzfR0dEULVoUlUql7FMIIbIT3YH0atWqYW5uTuPGjbGzs6Nv3778+uuvbN68mV27dhEREUHz5s2JiorixIkTmdI+tVr9wcdVKhUPHz7k7t277N+/X4I7QnwB1Gq1cj2VmJioPK6np6dca7Vr146bN28qUwcBGBkZ0blzZw4ePMjBgwc5ceIEe/bswdbWVqYH+oh3+86UlBTl/38l7GRtbU3r1q05fPgwVapUoVatWgwcOJDU1FTmzJmDkZERgFwf/0O6r4++vj56enrExcXRq1cvbG1tldfLzc2NadOmoVKp3gvwdOjQAT8/P8qWLUufPn1YuXKlBKmEEEIIIYTIAnoaue1XCCGEEEKIP5SQkMB3332Hnp4eq1evpkyZMrx584bIyEgGDx6MmZkZsbGx5M+fX9lm165dTJs2jYYNGzJixAgKFCggg0JCiGzl3Yo5Go0m3eDrsmXL2Lt3L69evaJ69eqMHj2avHnzKsvbtm3L9evXOXLkSLrHM4Ju/xsTE4O+vj7ffvutsjwhIYGkpCSsra15/vw569evx83NTYI7QnzmfHx8uH79OkOGDKF+/frplv3+++/KlKX79+/Hysrqo9din0MFsM+Rbr9+7tw5KleurCybPXs2Dg4OtGzZ8qPXt9rtHz9+zLx58wgPDycxMZFKlSoxe/ZsbGxs5Pr4X9B97vbs2cOdO3cwNzdn8uTJjBs3joEDB7633pw5c3Bzc8PAwIBDhw5RvXp1ZX87duxg/PjxXL58maCgIIYNG5b5ByWEEEIIIcR/mIR3hBBCCCGE+BPXrl3jq6++Ytq0abi4uJCamsrWrVtxdXVFX1+fU6dOKQNCly9fpmLFigA8efKE3LlzY2xsLINCQohsRXcgcOPGjZw8eZKffvqJWrVqUbVqVTp16gTA69evMTIywtDQUBkA1mg0hIWFMXr0aJo2bUpISAg5cuTIlLb6+/sTHBzMy5cvOXv2LCVLlkzXN6ekpLBkyRJGjBhBgwYNiIqKAiS4I8Tn6Nq1a/Tq1YuffvoJPT09+vTpQ7NmzejSpYuyztSpU/H09GThwoUMHjw4C1v7ZatduzY///wzO3bsoF69eowcOZLg4GClKqWJiclHt9VeA6vValJSUkhNTcXExARjY2MJ7nwirq6uzJw5M91jI0aMYPr06cpr826Ax9vbm9evX3PmzJl0oawdO3YwYMAAmjdvzurVqzPtGIQQQgghhBAybZYQQgghhBB/KjU1FYA3b96QlpaWLrhz8uRJrKysgLeDu4MHD2b58uUA5M+fH2NjYwAJ7gghsg21Wq0MALq4uNC3b1+WLl3K/fv3mTt3Ll26dMHDwwONRkPOnDkxMjJKV5EnJCSECRMmYGZmRkBAADly5CCj7ivSaDRKW52dnZk0aRJNmzZl586dlC5d+r2+WaPRoNFoJLgjxBegdOnSnDp1itDQUNq3b09oaCjdunWjRYsWbNu2jWfPntG1a1fMzc1ZuHAhN27cyOomf7EaN27M8+fPGTZsGO3btyc4OBhnZ2fatGnzh8Ed+N81sL6+Pjly5MDc3BxjY+N0/bP4e3TPmWvWrGH+/Pn07NmTPXv24OfnR5EiRZg3bx7Lli1T1jMwMFCmORs9ejSenp7Y29uTL1++dPts27Yt+/fvl+COEEIIIYQQWUAq7wghhBBCCPH/Pnb37/PnzylVqhRVqlShW7du+Pj4KMEd3amyXF1dCQkJYcuWLTRu3Dgzmy6EEJkuICAAb29vBg0axIABA6hWrRqRkZGMGjWKK1euMHnyZDw8PIC3Icjz58/j7+/P0aNHKViwIHv27MHOzi5TKi/Mnz+fMWPG4OTkxMiRIylRosRH133x4gUWFhaABHeE+JzpTun06tUrLl26xOTJkzl+/DhPnz6lYsWKBAYGsmjRIvbt28e6deto1apVFrf6y6L7HIeEhDBkyBD09PTo2LEjK1asIGfOnFJdMpPpvibJyck4Oztz4cIFVq5cSbFixQDYtm0bvr6+XLx4kUWLFjFo0CBle91z7suXLzE3N1cee/e1lNdWCCGEEEKIzCXfQAkhhBBCCPH/tF9kr1ixgrJly1KrVi0A8uTJg6OjIzNnzuTo0aNYWVkpU2VprV27li1btlC/fn1q1KiRJe0XQojMcufOHZYvX07jxo0ZO3YsJUuWJC0tjeTkZJ4+fUqxYsUYMGCAsr6RkREHDhzg6tWrdO/eHQ8PD6ytrTMluJOYmMimTZsoV64co0ePVgY3AbZu3cqZM2cwNTWldu3aNGrUSAnuaDQaCe4I8RnT09NTggw5c+akZs2arF27lnv37hEYGMiGDRto2rQp+fPn5+XLl8yZM4eWLVsq24o/p6enpwQ4EhMTgbd945kzZzh37hx16tRBX18/XaBEZCzt8+zq6srDhw95+vQpHTt2pFixYqSkpGBsbEy7du0wMjLCy8uLIUOGACgBHt2Qjrm5eboKSO8GdSS4I4QQQgghROaSyjtCCCGEEELoiIyMpEmTJjRt2pTJkydTrVo1AE6fPs2oUaM4ceIEvXr1Yv78+RgaGmJkZMTChQuZPXs2ADExMdjY2MgghhAiWzty5Aj16tVj7dq1dO/endTUVDZv3oy7uzv6+vpKwDE5OZkHDx4ogZkbN25gY2NDjhw5MiW4A3D79m0cHBxo1aoV27dvB+D48eMsWLCAtWvXKuvZ29szb948ZXBfCPFlO3r0KIcOHcLX1xd7e3v279+Pvb19Vjfri6RSqYiIiODWrVvcv3+fqVOnUrp0aYKDg5Vqk9qvmOX6N+M9evSIfv36sXfvXuDttJDTp08H0lfW2b17N15eXly8eJGQkJB0oVohhBBCCCHE50fCO0IIIYQQQuh48OABISEhzJgxg/r16zNx4kSqV68OQFRUFD4+Phw5coTChQtTrFgxnj59yt27d7GzsyM8PBx7e/tMG5AWQoissm/fPlq0aMGWLVto164dYWFhuLq6vjel4PPnz+nUqROenp40bNhQ2T4zA44ajYZatWpx9+5dnJycuH37Nvv37ycpKYmRI0dSq1Ytbt26xfDhw3FzcyMgICBT2iWEeN+n6BvevQ67cOEChQsXxsrKSqbC+4u0r4Pu65GSkoK+vj6GhobMmDEDNzc3Spcuzfz582nUqFG6dZ88eUKePHkwMjLKysPI1s6dO0dwcDDr1q2jTp06LFq0iJIlSwLvB3h8fHw4e/Ysa9asoUePHlnZbCGEEEIIIcQfkE+rQgghhBBC6ChcuDBOTk7o6+szefJkAHx9falRowaNGjUif/78xMbGsnz5cp4+fUrhwoXp1asX/fv3p2DBghLcEUJkKx/r0woUKADA4cOHSUlJ+WBwB8DNzY3z58+TO3fudNtnRHBHOw3Ihx739PTE09MTX19fcufOTZUqVVi4cCElSpTA0NCQX3/9FUNDQ37//fdP3i4hxF9z6NAhrly5Qp8+fciZM+c/3s+7fVbFihWBt32BBHf+nG6/r1KpSE1NxcjICGNjY6W6jouLC/C2jx82bBjz5s3ju+++A2Dnzp2Ehobi6OhIs2bNsuYgsjHtua5y5coMGzaM5ORk1q1bx+zZs/Hz8yN//vwYGBgor2OrVq1ITk5m0aJF1KtXL6ubL4QQQgghhPgDUnlHCCGEEEL8J31oQFr3juGHDx+yePFipkyZQuPGjZUAj1ZqairJycmYmZn94T6FECI7CA8P56uvvlKmnElJSaFjx46Eh4eTJ08eLCwsOHHiRLrgzqpVq/D19aVOnTqEhISQK1euDGufbv979+5dHj16BECxYsWwsrICIC4ujujoaEqXLk2JEiUwNTVVtp87dy4TJkwgMDCQfv36ZVg7hRAfFh8fT4sWLThx4gQLFy6kd+/e6d6jInPo9qUrV65k37593Lx5EysrK1xcXKhcuTJ58+ZV1p85cyaurq7Y29szbdo0Xr9+zbRp0/j111+5efMmhQsXzqpDyRbe/Wzx+vVrNBpNuvPpuXPnmDFjBqGhoQwdOpQJEyYoAVvd7d+8eZOpU1YKIYQQQggh/r73b0kTQgghhBAiG1Or1cD/7speu3YtN2/eBFCmBwCwtrZmyJAheHp6cvDgQfz9/Tlx4oSyH0NDw3TBHd19CiFEdrJu3TpatWpFcHAw9+/fB8DY2Jju3btja2tLfHw8o0aNShfcWb58OZMmTcLExITp06eTK1cuMureIbVarfS/kyZNomHDhtSsWZOaNWtSpkwZAgMDuXXrFnnz5qVDhw6UL18+XShg/fr1LFiwgNKlS9OmTZsMaaMQ4o9ZWlri7e1N3bp1GT16NCtXriQpKSmrm/WfotuXuri40L9/fyIiIlCr1Zw7d44ffviBuXPncvv2bWUbZ2dnZs2axe3bt+nSpQsDBw4kLS2NK1euULhwYeW6W/x97wap+vTpQ+3atWncuDHz5s3j4sWLAFSuXBkXFxe6du3KggUL8PPz48mTJ8Dbzyba1yBHjhzKY0IIIYQQQojPk1TeEUIIIYQQ2d7Bgwe5dOkSo0ePBv5Xbn7Xrl20bduWtm3bEhgYqFSU0K3Ac/fuXSZMmMDq1avp0qULY8eOpXr16ll0JEIIkfnOnTvH5MmT2bVrF8OHD2fEiBHY2dkBMG/ePKZNm8bjx4+pVasWZcuW5erVq1y4cAErKysOHjyIvb19ptzp7+bmxowZM/juu+/o3r07cXFxhIeHc/z4cdq2bYuPjw+lSpVS1n/58iVTp05l7dq16OvrExMTg52d3Uen3xJCZAzd6679+/fj4+PDuXPnmD17No6Ojv94Ci3d/Yq/zt/fH39/fwYOHIiTkxPly5fn6NGjNGjQAHNzc/r27cvIkSOxtbVVttm2bRvnz59HT0+PQYMGUahQIanw8i/o/u2OGzeOefPmYWZmRqFChbh69SoA9erVY8SIEXTs2BF4e66ePn06GzZsYMSIEXh6elKwYMEsOwYhhBBCCCHE3ycTPQshhBBCiGwtPj6eYcOG8csvv2BgYMCIESOUQdmvvvqKESNGsGjRIvT19Zk1axb29vZKBR49PT1sbW1p3749q1evZtOmTdy+fZvFixdTsWLFLD4yIYTIHJUrV8bX1xd9fX3mzJmDnp4eQ4cOpVixYowYMYJixYoRHh7O2rVrOXPmDMWLF6dv3764urpm2gDuli1bWLBgAf3798fV1ZWSJUuiUqkwNjYmKiqKK1euUKRIEWX9K1euMGbMGCIiImjatClLly7FxsZGBpuFyAJ6enqkpqZiZGREs2bNMDY2Zvz48YwbNw4jIyO6dev2XrXDP6MbfkhKSsLU1FTe339BTEwMK1asoHv37owZMwYHBwdev35N//79yZcvH/nz5ycoKAiNRsOIESOU4Hu7du1o166dsh95rv8d7d/unDlzmDNnDqNHj8bR0ZEKFSoQHR3N9u3bWbhwIU+fPsXIyIi2bdtSuXJlPDw8MDIyUsI+kyZNkgCbEEIIIYQQXxAJ7wghhBBCiGzN0tKSwMBAXFxcGDVqFCqVSqnAU6JECcaOHYuenh7z5s0DSBfg0Q4kVatWjdKlS1OnTh1iYmIoVKhQFh6REEJkjA8Ntmqr0JQrV47x48ej0WgIDAwEUAI8rVq1olWrVvj4+JCWlkbBggXR09PDwMAg0wZwY2NjMTY2xsnJiZIlS5KSksL27duZMWMGxYsXJyIigly5cpGWloahoSGFCxdm4MCB9O/fn6ZNm5InTx4ZbBYii6hUKoyMjADYt28fV69exdjYmOTkZJydnQHo0aPHX67Aoxvc2bNnD7NmzSIkJAQHB4eMOYBsQqVScfLkSVJSUhg0aBAODg4kJiZSs2ZN4uPjmTFjBiVKlGDkyJEsX74cAwMDhg4dqgR4dElf+u9oNBqePXtGWFgYZcuWZdSoUUqlo4YNG1K5cmXs7e1xdXVl6dKl1K5dmwIFClChQgVGjx6NhYUFgwcPluCOEEIIIYQQXxgJ7wghhBBCiGxLO3jTokULjIyMGD58OGPHjgVQAjx2dnbK/+fNm4dGo2HGjBnY2dlhZGSEWq1mxYoVGBoa4u3tzZw5czA3N5dpVYQQ2YJuX6YdbI2MjKRatWrkyZMHfX19ZZ3y5cszYcIEAAIDA9HT08PJyYlixYoBfHB6jowewFWr1ahUKqKjoylWrBhVq1YlOTmZbdu24ebmhr6+PseOHcPKygqA06dPY2BgQPXq1Wnfvj16enro6emhVqtlsFmILKDRaJT3nouLC0uWLKFEiRKUKlWK+vXrc+jQIUaPHk1aWhqOjo6Ympr+6f60gYWIiAgmTpzI6dOnSU5OzvBj+dIZGBhQoUIF/P39qVOnDsnJyXTr1o3ff/+d6dOn07NnT/T09KhRowZnzpxh+fLlPH/+HB8fn3SVzcS/p6enx/Pnzzl37hw//PADtra2aDQaNBoN+vr6WFpa0rVrV3766SfWr1/PsWPHaNu2LQBVqlQhMDAQQ0NDCaUKIYQQQgjxhZHRBiGEEEIIkW1pp78CaNKkCcHBwZQsWZKxY8cyZ84cZT17e3tGjx7NiBEj2LlzJyNHjmTPnj0kJiayatUq1q9fT+nSpSlatCjm5ubKF+dCCPGl+vHHHzl16pQSztFavnw5TZo0Ye7cubx48QIg3Trly5fH2dmZmjVrEhQUxPLly7lz506mtVvbp2vp6+tjZGRE8eLFefLkCXfu3CEiIkIJ7pw8eZL8+fMr6w8ePJhx48bx5s0b9PX1lUF+6dOFyBra9+DChQuZNWsWffr0YevWrYSGhhIdHc2CBQuUSomrVq3i9evXH93Xu8EdNzc3rl27xrlz5yhXrlymHM+Xrnnz5vTq1QuAgwcPcvjwYbp160avXr2U57Zp06ZUrVqV4sWLs3fvXnLlypWVTc62cuTIQc6cOYmPj1ce062kU6hQIdq0aQPAkSNHAJRztaHh2/t1JbgjhBBCCCHEl0W+nRJCCCGEENna3wnwjBkzBhcXF2JjY2nXrh0lS5akf//+vHr1ilmzZilfhEsJeiHEl+z+/fs0adKEmjVrcvr0afT19VGpVAAUL16c7777jsmTJ380wFOjRg1atmxJSkoKgYGBBAQE8ODBgwxvt1qtVvrfxMREAKXd5cqV4969e4wePZqhQ4diYGDAiRMn0gV3Zs+ezcOHD2nVqhXGxsYZ3l4hxF8XHR2NlZUVQ4YMwc7OjrS0NACGDBnCpEmTsLGxwdnZmfXr1yvvf10fCu7cuHGD2NhYKlasmKnH8qXTXu9eunSJhIQE2rZti4mJibJ8w4YNGBsbs2nTJs6dO0eePHneC1aKv043QKtbISpHjhyULVuWAwcOsGPHDqVSnEajUd4f9erVS7cvCaIKIYQQQgjxZZMreiGEEEII8Z+g/WL8jwI8dnZ2uLi4sHnzZr777jsqVapEnz59OHr0KHZ2dsogsRBCfMlsbGyYOHEiOXPmpH79+pw6dUq5O79BgwZMnDiRRo0a4evr+16AJyUlBYBmzZpRuXJlqlevTnh4ODlz5szwdmsHJUeOHEmHDh14+PCh0m53d3dq1KjBjh07ePXqFeHh4RQoUEDZNiwsjEWLFlGiRAn69u0rA5xCfCY0Gg1JSUn89NNPWFhYYGtrq0z1ow2EtGnThgEDBvD69WvGjRtHaGgor169SrePjwV3KlWqlCXHlR3kzp0bgJ9++kmpeLRx40bOnDlD/fr1sbW1JV++fOmCleLvUalUyvlo48aN+Pr6EhYWBoCVlRX9+/cH3k4pFx0dDbz9TGNoaIharSY0NBRA/s6FEEIIIYTIJvQ0cmuEEEIIIYTIZtRq9QcHZnUfj4iIYPjw4fzyyy/Mnj2b0aNHf3BfaWlpGBoaKgNJQgjxJdPtBwMDA3F3d8fQ0JCYmBiqV6+urHf06FH8/PyIiIjAx8eHkSNHkidPHuDtQPmoUaM4ceIEy5cvp1ChQuTNmzfdAHpGqlGjBqdPn6ZLly4EBgZibW1NWloa+/fvZ/z48Vy/fp0BAwbQtm1b8uTJw7p161i/fj2GhoYcPnwYW1vbj54nhBBZo0ePHmzdupVDhw5Ro0YNpT/RvldfvXpFrVq1iIuL4/fff2fFihX06dMn3T4OHjyIq6urBHf+n26frFar0Wg0f+ta9vr16zg6OnLx4kXatGlDcnIysbGx5MyZk9jYWGxsbDKq6f8JuuchDw8PFi1ahLm5OXPnzqVly5ZKhThPT0+mTp2KtbU1M2fOpHnz5uTJk4c1a9YQEBCAoaGhUrlKCCGEEEII8WWT8I4QQgghhMhWdEM2hw4d4tatW9y+fZu2bdvi4OCAhYWFsu7BgwcZNmwYv/zyC7NmzWLMmDEApKamYmRkBJBpg9FCCJFZ/kmAx8XFhe7du1OxYkXWrVvH5MmTadq0KYGBgekG2DOSbn/cokUL9u3bR6dOnZg7dy7W1tYkJiZy7NgxfH19OXbsmDLVl4WFBdWqVWPFihXpqnoIITLXh66ptI/NnDkTV1dXGjVqxJIlSyhWrBgajQa1Wo2BgQEJCQmUKlWKdu3a8ejRI4KCgihatKiyn6ioKAYNGkRcXBzR0dH/+eDOH/XJq1at4uuvv6ZcuXJ/up99+/axePFiduzYgaWlJRUrVmTVqlXSl35CEyZMYNKkSQwdOpT+/ftTpUoVIP37xdfXFz8/PwCKFi2Kvr4+v//+O0WKFCEyMhJ7e3sJpQohhBBCCJENSHhHCCGEEEJkG7pfWo8fP54FCxYQHx8PgImJCf369WPw4MFUrFhR2UY3wBMYGMioUaOypO1CCJGZ/mqA59ixY0ybNo2dO3diaWmJjY0Nly5dwsbGhsOHD6cbPP/UPjTQr62GBm+n7oqIiKBjx47MnTuXQoUKKce2evVqnj17xuvXr/nmm2+oWrUquXPnlsFmIbKI7ntPpVKRnJwMkG7KvdatW7Nnzx769++Pu7s7JUqUUJZpq4xs2bIFBwcHjIyM0u3z6NGjdOrUiV27dlG1atVMPLLPW82aNbG1tWXTpk0AODk5sWrVKpYuXUqXLl0+2h++2/+eOXMGKysrLC0tMTc3l770Ezly5Aht27aladOmTJ06FVtbW+DD579t27axb98+jhw5QuHChalSpQpjxozB2tpaXg8hhBBCCCGyCQnvCCGEEEKIbEH3S24PDw+mTZtGmzZt6N+/P/b29kybNo1t27bx/fff4+3trdzVCm8DPKNHj+bKlSssXryYgQMHZtVhCCFEptEd7PujAM8vv/zC7t27CQwMJF++fNja2hIcHEzRokUzbMBQN1z04sULLCwslH7+YwGeoKAgrK2t/9I+hRCZR7efWLZsGVFRUVy9epUcOXLQt29f6tSpQ7ly5bhx4wb9+/cnNjaWihUr4u3tTZEiRTh69CghISHkyJGDH3/8kdy5c3/w9yQmJmJmZpaZh/ZZu3PnDi1atODq1auMGjUKIyMjZs6cyciRI3Fzc1MCj3+XVKX86/7svLN48WKcnJyIjIykYcOGH1zn3ec7JSUFY2NjZd8S3BFCCCGEECL7kPCOEEIIIYTIVlavXs24cePo2rUrw4cPp3Tp0iQmJlKzZk1u3LhBamoqrVq1ws/Pj8qVKyvb7dmzh2nTprF69Wrs7e2zrP1CCJERPjaA+FcDPABxcXFKxYUcOXJ80gHDZ8+eYWZmhomJSbrHXV1duX//PjNmzKBIkSIfDPA0btyY6OjodBV4dJcLIbKObvBg3LhxyjR39vb2PH78mJs3b9KiRQucnJxo0aIFd+7cwd3dnbCwMPT09NB+beng4MCBAwdkeqC/6caNGwwbNoyIiAgAvL29GT16NHnz5s3ilmV/un/7cXFxH3zOR44cSXBwMJcvX6Zs2bLvLdeeZx8/fkyBAgWA/53PJUQlhBBCCCFE9iOfdIUQQgghRLYRFxfHjh07KFy4MIMGDaJ06dK8fPmS6tWrExcXx6JFi+jZsye7d+/G19eXn376Sdm2ZcuWREREYG9vT1paWhYehRBCfFoqlUoZ6D579iwRERGsX7+euLg4dO/nGTNmDFOnTiUtLY0GDRpw6tQp4O1UVWq1GktLS4yMjMiRIwcajeaTBXeOHTtG7dq12blzpzKVjkajISkpiR9//JENGzYwefJkfvvtN2Uw39DQUOmrV61ahb29Pbt27WLMmDE8ePBAgjtCfCa04YKgoCDmzp3L0KFDiYiI4PDhw0RFRdGpUyf27NnDhg0bSEtLw87OjtDQUDZu3Mj06dMZPnw4CxYsIDY2Fnt7+3T9mfhjGo0GBwcHjI2Nlcdu3bqlhEhSU1Ozqmn/Cdq//bZt21KjRg0ePnz43jr58uUD3k5LBm/P11ra86xGo8Hb25v9+/cDKH//EtwRQgghhBAi+5FPu0IIIYQQ4oul+wU3QFJSEkZGRnh5eVGhQgWSkpJo2rQpz549Y+rUqfTs2VOZgiEyMpKAgABlcBpQKj7IoK8QIrtQq9VKyMbf35+2bdvSvHlzevbsSd26dVmwYAFxcXHK+u8GeE6fPq30iboDhZ9q0FCj0XDnzh0ePXqEj48Pe/fuJTk5GT09PUxNTQkPD6dFixYsWrQIf3//9wI8KpWKAgUKULhwYUxNTdm4cSP+/v6o1epP0j4hxL+j0Wh48uQJYWFhVKxYkeHDh1O2bFnUajVHjhzhxIkT2NraEhgYiKGhISkpKQB07NiRcePGERQUxJAhQyhYsKBMD/QPFSxYkPbt21OrVi3Wr1/PoEGDADAyMnrvWlp8Omq1mqSkJGrWrElcXBzt27dXAjzac9R3332Hubk5c+bM4dWrV0pYR6VSKefZqVOnsmnTJiXcKoQQQgghhMi+JLwjhBBCCCG+WNoBnJ07dwJQpEgRpk+fTocOHVCr1cyYMYMzZ84wZswYOnfujJGREcWLF8fGxgZzc3O2bt3K/Pnz5c5jIUS2pNFolDv03dzc8PHxoWTJksyfP5+wsDCMjIyYOnUqAQEBPHv2TNlOG+DR09OjRo0anD9/PsMqXejp6dGmTRuWLFlCYmIizs7O7N27lzdv3gCQN29e1qxZw/fff09ISAh+fn7cv38fPT09UlNTMTAwwNjYGBMTEwIDA+nXrx+urq5SmUOIz4Senh7x8fGcOXOGJk2aUKpUKVJTU9m4cSOurq4AnD59mnz58qHRaHj48CGvX7/+4L4kuPP3aAMiS5cuZdOmTaxfv55vv/2WpUuXMnjwYODtc6oNTAG8evUqS9qanfz666/A2wo5pqamjBgxAl9fX65fv06zZs34/ffflXNUhQoVaN68OT/99BOtWrXi4cOH6SrbbdmyhTVr1lC+fHm++eabLDsmIYQQQgghROaQb7OEEEIIIcQXberUqfzwww9ERUUBYGtri4GBAfr6+pw6dYrChQvj4eGBqakp8LaqTmJiIh4eHkyZMgVfX1+MjIyy8hCEECJDaO/aX7x4MSEhIYwYMYL58+czePBgWrRowZs3b3j48CELFixg6tSp71Xg8fDwwM7OTpliJaPkzJmT1q1bM336dFJSUt4L8FhaWrJu3Tq+//57lixZwsSJE7lz5w5GRkZoNBpWr17NpUuXKFu2LEuXLqVYsWIy/aEQmUR36j348FRMqampqNVqLCwsANi8eTNubm7o6+tz8uRJrKysAHj+/DnNmzfn4MGDGd/wbOjdimO6r4Wenh729vbMmzeP+vXrs2TJEiXAo51Wa//+/UrIRPwzBw4coHr16qxbtw54+/4wNzenb9++uLu7c+3aNeX51Wg0WFhYEBQURKNGjTh06BBNmjTB3d2djRs3MnDgQEaNGsXLly9Zu3YtefPmlapyQgghhBBCZHMS3hFCCCGEEF+0ypUrY2BgQHR0tPKYSqXi2bNnXL16FY1Go9wBC7Bq1SqePn1KyZIlcXd3x97eXgZ5hRDZ1m+//caGDRuoUKECQ4YMoUyZMrx8+ZJq1arx6tUrZs2aRcmSJZk3bx5Tpkzh6dOnyrZeXl5cuHCBokWLZvjUKqamprRt21YJ8Li4uHwwwNO8eXOWLVtG586dCQ0NxcPDA19fXwoUKECJEiWU/cn0h0JkPI1Go4QEHz9+DKAEotevX69U0LGwsKBQoUKsXr2a4OBg3N3d0dfX58SJE+TPn1/Z36xZs5Rgnvh7VCqVUs1l9erVDBgwgMqVK9OmTRumT59OUlISarWaChUqpAvwDBo0iISEBMLCwhg1ahSbN2/G0tIyi4/myxQREUGrVq2oWLEi5cqVA1CmeTQ3N2fQoEFcuXKF+vXrK8u0Uz+GhoYyZMgQXr9+zcyZM+natSthYWGUL1+eI0eOYGdnl+41FkIIIYQQQmRPepp3b5ERQgghhBDiC3L37l3atWvHlStXiImJoWbNmsoyLy8vAgICGD58OG3btuXs2bMsXryYnDlzEhUVRb58+bKw5UII8empVKp0U8tcunQJJycnxo0bxw8//MDr16/59ttvuXfvHjNmzKBbt27ExMTQrl07cufOTY8ePXB1dVUqYUD6AfqMlpSUxI4dO3B1dcXY2JgZM2bQvHlzcuTIAUBycjIDBw5k7dq1yjalS5dm79692Nvbo1arZXBTiEzWrFkzChcuTEBAANbW1kqVr7CwMDp16gTAuHHjCAwMxMLCAgsLC3755RdMTEyAtxVjNm7ciLu7O5UqVWLNmjVKlR7x53T7aGdnZ4KDgzE3NydPnjzcvXuX1NRUWrRogZubG7Vr18bQ0JDLly8zZswYDh48iKWlJUlJSeTPn5/o6GiKFy8ufenf9OTJEypWrMijR48ICAjAzc0NQHke3z2P6v6sXefNmzc8f/6cU6dOoVarKVWqFEWLFsXMzOy9c7sQQgghhBAie5LwjhBCCCGE+OItXrwYJycnfH19mTBhAqmpqRgZGXH9+nW8vb3ZvHkz8PYO1zJlyhAeHo6dnZ0MTAghsq2ffvqJatWqAXD27FmqVKlCWloabm5uzJ8/n4CAAIYOHYqJiQl37tyhevXqJCcn8/LlS/z8/PDy8srQwM4fBYL+LMADb6cmefz4MUZGRjRu3BgrKysZ3BQiCzx8+JCOHTty6tQpXF1duX//PqtWrWLcuHGMGDECW1tbABITE+nRowe7du2idevWrFixQpmSb86cOQQHB6NWq/nxxx+xsbGRa7R/YObMmbi6ujJs2DAGDRpEqVKlOH36NLNmzSI8PJyvv/6a2bNnU6NGDQBu3LjBmjVruHjxInny5MHf358iRYqQlpYm1cv+pjdv3rBp0yacnZ0pUKAAkyZNolWrVhgYGPylAOwfrZOZAVohhBBCCCFE1pLwjhBCCCGE+Ox9aEBWrVYDoK+vz8uXL2nWrBn379/nzJkz6SpGxMXFsWPHDq5evUrRokXp0qULBQoUkEFeIUS25eLiwpIlSzh27Bhly5ZVHk9KSqJhw4akpaVx+vRp5fGUlBSqVKnC8OHDiYqKYubMmdjZ2WVY+97tfxMTEzEzM0u3zrsBnpkzZ9K8eXOlUse7ZKBfiKyhVqt58OABLi4ubNq0CbVazaBBgwgICMDS0jJd8ODnn3/GxcWF3bt3Y25uToUKFXj06BH37t3DwcGB3bt3Y29vL9do/8CzZ89o0qQJRkZGbNq0CVtbW+W5v337NgsXLmTmzJl06tSJDRs2KNtpgzopKSkYGxvLc/8vpKSksHnzZoYNG0bhwoXx9/enXbt2ytRZEsARQgghhBBC/Bn5ZksIIYQQQnz2tIMIq1atYteuXcDb0I6+vj5qtZpcuXLRuHFj7t+/z7x581CpVGgz6nnz5qVv375Mnz6dESNGUKBAAdRqtQxMCCGyLTMzM168eMH169eBt4PrGo2G+/fvc/nyZSwsLEhOTlaWLVmyhKdPn1KvXj02bdqEnZ0dKpUqQ9qmOzC8fPlyunTpwldffUX37t1ZunSpsp6pqSlt27Zl+vTppKSk4OzszN69e5V2v3sfkgR3hMga+vr62NjYkDt3biVEp1arSUtLA1CCC/B2irudO3cyefJkvvnmGx4+fEipUqWYOHEiUVFREtz5F548ecK5c+eoVq0atra2pKWlKc+7vb09AwcOpFatWmzcuJHQ0FBlO+1zbWxsnO5n8fcZGxvTsWNH5s+fz4MHD5gwYQLbtm0D0r8PhBBCCCGEEOJjpPKOEEIIIYT4ImzdupWOHTsC0Lt3b9q2bUuLFi2UKgwvXrygatWqWFpaEh0djZmZmVRiEEL8J506dYrmzZtTsGBBoqKiKFiwoLKsRYsWxMbGMn36dJo3b87+/fsJDAwkb9687Nu3DwsLiwxrl26fPG7cOObNm4eVlRWlSpXi2rVrPH78mNGjRzN79mxlG90KPLly5cLHx4f27dsrA81CiKyl0WiIj4/Hz88PjUbD1atXiYqKYuTIkYwaNUqp4qXRaN4LT798+RJzc3OlKolct/1z9+7do0yZMrRo0YJNmzYB70+3tHnzZjp37kxAQABubm5Z1dRsT7cCT5EiRfDz85MKPEIIIYQQQoi/RD4RCyGEEEKIL0KTJk3YvHkzzZs3Z8uWLXTo0IEmTZpw8OBBfv31VywsLOjTpw8//fQTixcvBqQSgxDiv6l69er88MMPXL16lZiYGODtYCK8nVLL3t6eYcOGUalSJYYNG4ZKpSI0NBQLCwtlSsKMoO2Tp0yZQnBwMAMGDGDPnj3ExMSwadMmcuTIwZw5cxg6dKiyjbYCz8yZM7l9+zbBwcEZVhVICPHXaO8D1AYR8ubNi5eXF35+fqxcuZJWrVoRFBTE3LlzuXv3rrKugYGBEuIByJkzZ7r9yXXbP5cjRw4KFCjAli1b2LJlC/C/ai/aKkj29vYAPH36NKua+Z+gW4Hnt99+UyrwaN8vch+tEEIIIYQQ4mOk8o4QQgghhPiixMXFcfv2bWWKhZSUFCpUqICXlxcWFha0bduWqlWrEhoaSpEiRbK6uUIIkSHenVpGOyioffz69et8++23VK1alfDwcGW91NRUbty4QXBwMPHx8djZ2TFq1Cisra0zZbqa6OhoBg8eTO3atfH09KR06dIkJCRQr149Hj16RO7cublx4wYjRoxg7ty5ynavX7/m4MGDVKtWTfp2IbKQbj+RnJzMmzdvyJ07t7Jco9Fw8+ZNnJ2d2b17NyNHjmTEiBEUK1YMgO3bt/PTTz/h6emJqalplhzDl+rPKhOtX7+enj170qhRI3x8fKhXr1665TNmzGD8+PGsWrWKLl26ZHRz/1M+VFHnQxV42rdv/9H1hRBCCCGEEELCO0IIIYQQ4ovw7pfcqampnD59mtDQUJYsWUJycjKtW7fm7Nmz3L9/n/DwcL7//vssbLEQQmS88PBwSpYsScmSJZXH1Go1iYmJ9O3bl23btrF27Vq6d+/+h/vJjOAOwPTp0/Hw8ODo0aPUrFmTxMREatWqxdOnT1m0aBHW1ta0atWKuLg4hg0bxrx587KsrUKI9HTDI8HBwezZs4ebN29ibW1N06ZNadOmDRUrVnwvwOPk5MSgQYO4desW7u7uXL9+nUePHmFlZZXFR/Tl0O33Dhw4wJ07d8idOzdly5alQoUKADx58oQpU6YQFBREjRo1GDhwII6OjiQnJ7Nt2zZ8fHzIlSsXBw8elOf+E9L9jPLw4UOsra2VZX8U4NF9TSXMI4QQQgghhAAJ7wghhBBCiC/Qu3ceHz58mMjISIKCgoiPj8fExITr169TtGjRLGylEEJkrAULFjB8+HDy5s3L+PHj+eabb6hWrZqy/Pjx49SvX58uXbqwevVq5XHdQcKMHDD8UMjmwoULXLt2jc6dO5OcnEz79u05ceIEAQEB9OrVixw5cjB58mTGjx8PQJcuXQgNDc2Q9gkh/hlnZ2dmz55N/vz5KVCgAA8fPuTZs2cULVqUFStW0KhRIzQaDbdu3cLNzY2tW7eSJ08e1Go1lpaWREZGUrx48T+tJCPe0u2n3dzcmDFjhrLM3t4eFxcXnJycALhx4wbLli1j2rRpAFSqVImkpCR+//138ubNS3R0NPb29vLcfyK6r01ERASjR4/G3d2dXr16Ket8KMDTunVrjIyMProvIYQQQgghxH+ThHeEEEIIIUSW+6dfVr878HDr1i3CwsJwdHSkUKFCUp1BCJGt3bx5k2XLlrF161auX79OgQIF6NatG8OGDcPa2hozMzO6devGxo0biYyMpEGDBlnSTm9vb+rVq0ezZs1IS0tDo9FgZGREZGQkbdq0wdHRkVmzZpEjRw4AFi5ciL+/P3ny5OHXX3/lwYMHWFpaZknbhRDpr7f27dtHnz596N27N05OThQvXpy7d+8yc+ZMgoODyZEjB3v37qV+/foAJCQkMH36dI4fP469vT0TJ07ExsZGrtH+gdmzZ+Pu7k779u1p2LAhz58/x8PDA4ApU6bg7u4OwJs3b4iJiWHOnDncvXuXPHnyULNmTVxcXChcuLA895+I7ueXqKgovL29OX78OIcPH6ZOnTrp1k1JSWHLli0MHTqUQoUKMW3aNFq3bo2rqyu5c+fGy8srKw5BCCGEEEII8ZmR8I4QQgghhMgSV69eBaBs2bKfZH/agSXtF+kyMCGE+K+4ceMGsbGxBAQEcOPGDQoXLkzDhg3x8fHh8OHD9OvXj+7du7No0SLMzMwytW0nTpygdu3aNGvWjC1btpAzZ05lWVBQEKNHj+b8+fPKtC+AMs3L1KlTMTU1pUCBAlKRQIgsovveS0xMZOPGjfj6+nLw4EFKlSqVbl1/f398fHyoWLEiW7ZsoUSJEsqylJQUDAwMMDAwkGu0v+jd56lPnz7ExcURHByMnZ0dAIcOHaJHjx48ePCASZMm4enpqayfmpoKgJGRkXKdLM/9p/FucMfd3Z1Lly5x6NAhqlevzvPnz/n999/Tfc7RBniGDRuGnZ0dVlZWREZG4ufnx5gxY8iVK1dWHY4QQgghhBDiMyH1UYUQQgghRKa7ceMGFSpUwMvLi2vXrn2SfWrvCNd+kS4DE0KI7E6tVgPg4OBA3759iYiIYN26ddja2rJu3Tq+/vprTp06Rb58+di7dy+PHz/O9DbWrFmT7777jhMnTiihzbS0NAAlSBQWFqasv3nzZmJiYsifPz92dnYUKFAAtVotwR0hsoj2vTd+/HgKFixIeHg4TZo0UYI7Go0GlUqlrNO3b18uXLjA2bNngf+9342NjZVrM7lGe9+H7q3UPk8eHh64u7tz+vRpunbtip2dHWq1GrVaTf369Vm/fj2FCxfG29ubgICAdPvQTs0k18efzseCO9HR0VSvXp2EhARWrlxJuXLl2Lt3r7KdsbExHTp0YMGCBfzyyy9ER0fj6elJ//79JbgjhBBCCCGEAKTyjhBCCCGEyAK3b99m8uTJrFmzho4dO+Ll5fXJKvAIIcR/nUajYfXq1ezcuZNt27YBbwOOv/32GwULFsy0dmgrPBw4cICWLVvSu3dvli1bpiy/efMmderU4cmTJ3Tq1AmNRsOhQ4cwNTXlyJEjFClSJNPaKoT4OLVajZ+fHwsXLuTJkyc4ODgQGRlJ0aJFlXV03+/ff/89ffv2ZdmyZVI16y/QfY5+//13ChUqBLx93n/77Tfs7OzInTs3ZmZmLFiwgNatW5OamoqRkZGy7Y8//kj37t158OABU6dOxdXVNSsPKdv6o+BOzZo1SUhIYM2aNUyYMIFSpUpx/Pjx9/aRnJzM/v37uXLlCj169Ej3PhJCCCGEEEL8t0nlHSGEEEIIkens7e3x8vJi0KBBhIaGMmnSJKUigxBCiH9OpVKhp6dHnz592LJlC1u3bmXo0KHcvn2bggULKhUyPjXd+4K0/9dWeKhQoQKVK1dmw4YNHDp0SGlniRIlOHDgANWqVSM8PJyDBw9Svnx5fvzxR4oUKZJhbRVC/HXa6Za8vLwYO3Ys9vb2PHr0iKNHj35w/QoVKmBiYvJexRfxcdrnqGnTpowcOZJbt24Bb0OXRYsW5fDhw6SlpfHbb78RHh4O/G8qLD09PTQaDd9++y3r16/Hzs4Od3d35s+fn2XHk1391eCOh4cHFSpUUII72upTWiYmJrRs2ZJRo0ZJcEcIIYQQQgiRjoR3hBBCCCFElrC3t2fs2LGMHTuWDRs2MHXqVC5duvSP9iXFJIUQ2cHdu3d58uTJv9rHu1Oi/PDDD8yZMwcbGxulMsan9u60Vtr/azQaNBoNhQoVwsPDg6SkJKKjo5V1VCoVlSpVYu/evRw9epTDhw+zY8cObG1tM6ytQog/pp2OT0s7LamRkRFjxoxh8ODBGBoa4uzszLFjx5T1DQwMUKlUbN68meTkZOzt7QG5Rvsjus/N69ev+eqrr9i+fTvTpk3j119/VZbVqVOHqKgocuTIweLFi5k9ezbw9rV5N8CzdOlSqlatSuvWrTP9eLK7vxrcqVq1qhJUTUtLw9DQ8L19GRgYYGpqmqntF0IIIYQQQnz+JLwjhBBCCCEylW4lBQMDAypXrkyDBg1Ys2YN8+fP5/Lly39rf7p3wR44cIAdO3Z80vYKIURmOH/+PGXKlGH+/Pn/OsDzLu3AYUaFYbSD+0OHDqVBgwbs3r2bR48epQv01KpVi+rVqzNz5kwuXbqEvr4+BgYGaDQarKysqFChAl999RVmZmZoNBoJ7giRBVQqlfJ+vn37NidPnuTHH3/kxYsXqNVqjI2NGTNmDG5ubrx48YKOHTuyePFiLl68CMDKlStZvHgxxYoVo2/fvoBU3vkjL168UP6fM2dO/Pz88PDwYOXKlfj4+HDz5k3gbaCqevXqxMTEYGJigpeXF0FBQcD7AZ7GjRtz9OhRJQQpPq39+/fj7u7O1atX/3FwRwghhBBCCCE+RsI7QgghhBAi06jVamVA1t3dnRYtWjBw4EBl8GLx4sXMmDHjL0+hpRvcOXjwICNHjqR79+7Ex8dnzAEIIUQGefPmDRUrVmT27NksXbr0kwd4MtrTp085evQo586d44cffqBVq1bs2rWL3377DYDChQvToUMHXr9+zdatW1Gr1e9V7NGSwX4hMp/uNdqkSZNo0qQJtWrVokGDBnz11VdMnz6dGzduYGxszOjRo/H29iYlJYWRI0dSs2ZNqlatire3N+bm5kRGRmboNH3Zwe7duylatChXrlxRHrOwsMDZ2ZnRo0ezdu1a5fpYG9CpUaOGUr3M1dU1XYBHO2UioExZJiHIf+bd6lNar169Yu/evZw+fZqDBw9KcEcIIYQQQgjxyelppH6tEEIIIYTIZJMnT2b8+PGMHj2arl27Ur16dTZu3Mjq1avZu3cv3bt3x9PTk6+++uqj+9AN7kRERODm5sbt27eJjIykSpUqmXUoQgjxyZw8eRJPT0+OHDnChAkT6N+/PwUKFMjqZv0pbX/8/Plz7t27x8yZM9m0aRPJyclUrVqVrl27MnbsWF6+fEnbtm25d+8e586dU6rsSFhHiM+Hm5sbM2bMoHHjxnTq1ImkpCR27tzJiRMnaNSoETNnzqRUqVKkpqYqYcNff/2V6dOn07p1a/Lnz0+ePHkkvPAHoqKiaNOmDZUqVWLWrFnUqlUr3fKEhAQePHhA2bJl0z2uVqvR19fn+PHjNGzYEI1Gw8yZMxk+fHhmNj9b052y8cqVKxQuXJg8efIoyy9fvoyhoSGlS5cmISGBtWvX4u7uLsEdIYQQQgghxCch4R0hhBBCCJFpNBoN9+7do3Hjxpibm7Njxw6KFi2qLL969Spz584lJCSEPn36MG7cOMqXL69sqx3g/VBw58aNG8TGxlKpUqXMPzAhhPgXdPu048ePM3HiRKKjo5k4cSJ9+vTB2tr6X+0zM7z7+2JiYoiJiWHGjBkkJSVRrVo1vvvuOxISEggJCcHV1ZUpU6ZkWvuEEH9u69at9OrVi+7du+Pi4qKEdNasWcOAAQOoWrUqMTExmJmZAZCSksLs2bOZMWMGlpaWbNu2jQoVKqQLQIj/0Wg03L59mxIlSgCwfPlyHB0dlWUf6rPffVw3wNO0aVMSExNZsmQJ/fv3z5RjyM50/25nzJjBvHnzKFGiBDt27MDc3Dzd6/Dq1SsWLlzIxIkT+frrr5WKSBLcEUIIIYQQQvwbMm2WEEIIIYTINHp6erx8+ZJbt25RuXJlihYtikajUcrTly1blsGDB1OjRg1WrVpFcHAwly9fVrYFCe4IIbIf3Sk6HBwc6NmzJw4ODsyYMYO1a9fy6NGjv7U/3X4yNjb2L09F+G9of5/2WBo0aICvry9HjhzB19eX58+fM23aNNauXYtarebWrVskJydneLuEEH9dbGwsJiYmDB8+XAnubN26FT8/P4oVK8a+ffswMzMjNTWV5ORkjI2NGTt2LO7u7jx+/JhWrVpx9uxZCe58hJ6eHsWKFWPkyJEALFy4kDNnzijLPraNLu0UWrVq1WLPnj0UK1aMpk2bZmzD/wN0p41zcXHBx8eHypUr4+TkhIWFxXuvQ3JyMseOHaNkyZIS3BFCCCGEEEJ8MlJ5RwghhBBC/B979x1Y0/3/cfx5s2SQHSskMYKgZmPPWlVBUVF7r1gpGYKQEDv23jMIYsTeIUHsotSsGqV2IoIkd/z+8Lvne4O2tJKUvh//lHvOuf2cc53POfe+X+fzyVQ3b96kdOnS1KxZk82bNwNvP1U8ZcoUBg0ahImJCQ0aNGDy5Mm4u7tLcEcI8dnRj6IAMHz4cA4ePMjx48fJmzcvN27cwMbGhsGDB9O5c+f3mkLLsJ/cu3cv3bt3x8rKipMnT2Jubp6h+/Jnnj9/ztixY4mNjeXHH3/k/PnzuLq6Zll7hBD/o9FolECIWq3m7NmzynRZAQEBGBkZcfz4cZycnAA4d+4cSUlJVK5cGSMjI9LS0pg6dSpjxozB1taWNWvWULFixSzeq38fw5Fd9NOTlS9fntmzZ+Pp6fm33is1NRUzMzMZ7egjGT9+PEOHDqVPnz7069ePwoULv3M9jUbDzZs3KViwICDBHSGEEEIIIcTHISPvCCGEEEKIDKcfiUGj0WBlZUXx4sXZsmWLEt5RqVTodDrS0tIAqF27Nu7u7jRu3JizZ89ib2+vrAewZcsWhg4dyvXr1yW4I4T4pBkGd0aPHs0XX3zBxo0bOXDgAMOGDSN37tyEhoayePFiHjx48Kfv9WbAMSAggMTERJYtW/a3gjv6Z300Gs0Hb2tIo9GQPXt2wsLC2LhxI7dv38bV1RW1Wv2P3lcI8fe8+RyfsbExpqamFCpUiISEBJ4+fcr+/fvfGdwB6NChA2FhYaSlpaHT6TA1NcXX15ehQ4dy8+ZN1qxZk9m79EkwNjZW+tPx48fj7+/PqVOn6NOnDydOnPjg9wIwNTVN93fx9129epWlS5dSp04dfH190wV34uLiWLt2Lbt37+b+/fsYGxtToEAB4PX3HAnuCCGEEEIIIT4GCe8IIYQQQoiPznAKGPhfcdrY2BhHR0e6dOkCvB6WXj/UvFarVQoQmzZtwszMjLCwMM6fP4+Dg4PynlqtloiICE6ePCnBHSHEZ+HMmTPMmTOHmjVrEhAQwNdff42LiwsjR45kxowZfPnll4SFhbFkyZI/nELrj0YmO3DgAOXLl//gNu3du5c+ffqQnJyMsbHxW/36hzA2Nlba5+DggI2NDTqdToqdQmQBrVar9BUJCQkkJycDr/uQIkWKcPv2bTp06ECvXr0wNjbm6NGj6YI7kyZN4v79+9SuXRsTExNUKpVyD9e/f3+2bt3KlClTsmTfPgXvCvCcPHnybwV44I+n2xIf7vfff+fy5ctUqlRJCeZcuXKFfv36UaNGDb7//nu8vLwYMGAAd+/eVY69/nuOEEIIIYQQQvxTMm2WEEIIIYT4qAyH7d+7dy8XL17k1q1blC5dmipVqlCoUCEAAgICCA8PJ0+ePISHh9OoUSOsra2JiIhg3LhxlCxZkuXLl2NqavrWtFrJycn89ttvFClSJEv2UQghPqYDBw5Qp04dRo4cybBhw4D/9aU6nY4tW7bQq1cvUlNTGTx4MO3btydXrlzK9h9zSkGdTodOp6N8+fKcPXsWHx8fxo8fj5WVVbopvoQQnx7De7QFCxawbds2KlSoQK9evbC3t+fFixdUrFiRCxcuYG9vz4kTJ5QQA8DatWsZNmwYjo6ObN68OV2o583+QfqLP/euKbS+/PJLZs2a9cFTaImP4+TJk1SoUIFvvvmG3r17c/z4cdatW8etW7f47rvvKF++PNu3b2fv3r1ER0fTsGHDrG6yEEIIIYQQ4jMjj7kJIYQQQoiPRqvVKoWIwYMHM2vWLF6+fImRkRFqtZqSJUvSq1cvfHx8mDBhApaWlowcOZK2bdtSqFAhjI2NuXbtGnnz5mXcuHHKSDyGwR21Wo2VlZUEd4QQn43U1FQAkpKSgNf9nImJiRLKadKkCevWrSMiIoLx48eTmJiIr68vDg4OHzW4A6/7W5VKRWxsLLVr12b27NloNBrCw8MlwCPEJ8zwHs3Pz4/58+eTO3duOnXqhK2tLRqNBktLS2bPnk337t25du0aEydOpE2bNlhYWBAREUFkZCRGRkasWbMGJyendP3Bm/2C9BPvpu+z9SOaGRkZMX78eAAmTpxInz59JMCTwd58KEDPw8ODgQMHMmXKFLZv346JiQklS5Zk7969lCxZkuzZs+Pu7s6uXbu4cuWKhHeEEEIIIYQQH52MvCOEEEIIIT660NBQQkND6dChA71798bZ2ZnY2Fh69uzJ8+fPmT17Nr169QJgw4YNbN++nZiYGJycnChatCijR4/G2dk53VPJQgjxOTEsHl69epXatWuj1Wo5cuQIbm5uSlE3NTUVMzMzoqOjCQ4ORqfT8fz5c3788Uesra2V99u+fTshISFcunTpH08pqA8PPX/+nKpVq3L+/Hl69OjBpEmT/naA581i6R8VT4UQGWvs2LEEBwfTt29ffHx83gpDazQaTpw4Qe/evTl79qwyzVOOHDn48ssvWbJkCS4uLnKP9jcY9nsJCQnkyJEj3TH8oxF4pL/8eAz/3b548YJnz55hZGREzpw5AUhMTGTv3r2cPXuWUqVK0bBhQ6ysrJTtg4KCmD17Ntu2baNatWpZsg9CCCGEEEKIz5eEd4QQQgghxEd18uRJvv32W8qVK0d4eDhFihRBrVazZ88e2rVrh5OTE0ePHsXOzi7ddi9fvsTCwkIpGktRSAjxOfmrwEvPnj1ZsGABXl5ezJkzB2dnZyW4A9CjRw/OnTvHzJkzcXV1xcnJSSnoJiQkULduXU6fPs2PP/5IqVKlPmpbS5Uqxc8//0ynTp2YOnXqBwd4DAvP9+7dI2fOnNK/C5EFLl26xDfffEPBggVZsGCBMiXWu8IhaWlprF27lt9++w21Wk3lypUpV64cNjY2co/2Nxge4127drF06VJ69+5NjRo1PmgKLRn97O8zPM5Tp05l8+bNnDlzBgsLC2rXro2vry+lS5cmW7Zs79xeP22ci4sL69evx9bWNhNbL4QQQgghhPgvkGmzhBBCCCHER3X16lXu3r3L3LlzleDO+vXrCQwMxNramri4OOzs7EhLS+Pp06fKk67m5uYAyo/qUhQSQnwuDAuGJ06c4NatW/z+++9UrlwZV1dXHBwcmD17NpcvX2br1q0kJycze/ZsihYtCrwuGB45coSGDRtSrlw5jIyM0hVwbW1tmTZtGjly5PjHwR3Dtq5YsYJnz55RtGhRfvrpJ1atWoWxsfEHjcBjWLDeuXMnK1eupFGjRrRu3foftVMI8eF+/fVXfv31V0JDQylQoIByfr4Z3FGr1ZiamtK2bdu33sNw+i3xfgz7wf379zNixAiOHz/OkCFDAP5wCi0fHx8WLlxI6dKl6d+/P23atKFSpUpZth+fMp1Ol27auKlTp+Lh4UGbNm24desWO3bs4MKFC3Tt2pUePXoo30vgdZBtypQpzJkzB41Gw+LFi7G1tZUglRBCCCGEEOKjk/COEEIIIYT42971o/WFCxcAKFKkCBqNhrVr1xIUFISRkREnTpzA0dERgDt37jBnzhwGDBiAs7OzUtSQaQGEEJ8Tw0J3cHAw8+fP5+HDh8Dr/q5Zs2b07t2bOnXqsH79etq2bcuePXvw9PTE09MTtVqt9J39+/dX+lzDvler1VK1atWP0l59WwcNGsSSJUvImzcvjRo1onbt2ly+fJn58+ejUqkIDw//ywCPYcF6z549BAUF8dNPPzFixIiP0lYhxPvRn6fXrl0DXo92qH/dMIijD+89fvwYrVZLnjx5lGX681nCCh/mzeDO4MGDuXjxIvHx8XzxxRc8f/6cZ8+ekStXLmVdwwBPnz59yJs3L+vXr8fe3p4vv/wSExP5OfdD6T+DhQsXMn36dHr16kWfPn3w8PDg0aNHTJgwgfDwcPbu3UvPnj2B1+fDzz//TJcuXTh//jzly5dn1apVMm2cEEIIIYQQIsPIN24hhBBCCPFetFrtW6/pCzi//vorGo0GgLJlywJw4MAB9u/frwR3jh8/rgR3AIYMGcLy5ctJTU3NhNYLIUTW0PeTw4YNY/To0dSoUYMtW7Zw4MABfH192bBhA+3bt2f//v04Ojqya9cugoODKV++PLGxsdy7d4969epx+PBh8ufPr/S17/p/fCzLli1jypQpdOzYkejoaMaPH8/GjRuJjo6mRIkSzJs3Dz8/P5KTk5VRgN70ZnAnMDCQGzducPLkSdzd3T9qe4UQ6b15Tur7iJIlSwJw/vx54H8jvkD6kUn69evHpk2b0vU3Eq7+cO8K7vz000/s27ePChUqkJiYyNy5c2nevDnnz59HpVIpx3z8+PEEBARw5MgRdu/eTXBwMD169JDgzt+k0+nQaDRs2rQJNzc3+vbti4eHB2q1mpiYGCIjIylQoACLFy8mW7ZsSrBNo9FQp04dwsLC2LRpkwR3hBBCCCGEEBlKvvEJIYQQQoj3oi/8DBgwgPr169OoUSMAunTpwrlz54iKisLV1ZVChQphY2ODn58f1tbWmJubc+zYsXTBnQULFnD48GG+/fbbdE91CyHE52j//v3Mnj2b77//ntDQUNzd3dFoNDx//pzp06djZ2dHuXLllPVDQ0NRq9Xcvn2b3LlzA2BhYZEpBUOdTkdsbCwmJiZ07dqVggULAmBtbU25cuXYv38/NWvWZN68eWi1WiZNmkT27NnTte1dwZ1r164RGxtL6dKlM7T9QvzXGZ6Lp0+fJiEhga+++goAV1dXPDw8mDVrFpUrV6ZNmzYYGRml22bRokXs2LGDypUrS2DnH/ij4M6BAweoWLEiiYmJrFy5krCwMAoUKECZMmUAlMCIsbEx48aNI0eOHDx48IDu3buTN2/eLNyjT5tKpeLx48ccOnSIFi1aUKxYMV69esXmzZsJCAjAyMgo3feVixcvUrx4cUqXLk2RIkUwMzNTwm4S3BFCCCGEEEJkFBl5RwghhBBCvLfIyEhmzJjB+PHj+fHHH/Hz82Pp0qVUrFgRCwsLAMqUKcOIESNITk7m3r17hIWFpQvurFixgokTJ2Jra8uIESMwNzdHp9Nl1S4JIUSG+/HHH3n+/Dl9+/bF3d0dtVrN2rVr6dOnD/ny5ePgwYPY2tqSmppKYmIiACYmJri6umJhYYGFhUW6UTEykk6n4/fff8fc3BwXFxflNXgdCnBycmL69OnY2NiwZs0a/P39ef78uQR3hPgXMAwWjBkzhpYtW9KvXz/i4uIAKFCgAEOGDAGgc+fOLF68GPjfdHlr1qwhPDycQoUK0bZtW5ki6296n+DOihUrGDx4MGXKlOHMmTMAyqg7+gAPwNChQxk1ahT58uXLmp35jNjY2JAjRw5evHgBwLZt25TgjuEIoVqtlvr16xMSEgK8Ds/qzxE5J4QQQgghhBAZSUbeEUIIIYQQ761Vq1b89ttv+Pn50ahRI+7du8fQoUPp06cPOXPmRKvVYmRkhK+vL0+fPmXUqFF069aN2NhYnJ2dOXbsGLGxsdjY2LB7927y5MkjQ88LIT4rhkVb/Z9PnjyJpaUlpUuXJiUlhY0bNzJ48OC3CoY3b95k3759tG3blhw5cqQrEmbWCBhGRkbkzJmT58+fs3PnTlq2bKn8v/V9dfHixbG2tubx48fMmzcPW1tbxo4dm27fd+/ezZAhQyS4I0Qm0el0Sp/h5+fH9OnT+eabb/jhhx+oVq2acn62bduWxMRE+vbtS7du3Vi5ciVubm7cuXOHEydOYG1tzY4dO9Ld14n3977BnaCgIMqVK0dMTAwAarU63ZRYxsbGyntZW1tnxa58st713SItLY20tDRcXFzYuXMnQUFBREREYGJiwtGjR3FycgJef36jR4/m1atXFClSJCuaL4QQQgghhPgPU+nkMWchhBBCCPGBypcvz9mzZ8mdOzeTJ0/G29sb4K0iz7x585g+fTq3bt0iOTmZggULUr16dcLCwnB2dpbgjhDik/Zmn5eWloapqSkAT58+xc7ODoCwsDCGDx/OyZMnefToEd27d1eCO/qCIUC9evW4f/8+Bw4cwMHBIUPbri8K638SUKlUSp984MABvv32WypUqMCaNWuUthgWl2vUqEHr1q3ZunUrs2bNws3NTXnvY8eO0atXLy5fvkx8fDylSpXK0H0RQvzPkiVL6NOnD926dWPgwIHpzk3DPmvDhg0sW7aMI0eOkJCQQIECBahcuTJjxoyRe7SPYNeuXYSGhvLjjz8SExNDhQoV3gruHDx4EHg7uCM+jhEjRmBvb8+AAQOU17Zv307Tpk3RarXkzp2b8+fPY29vD7y+Lq5bt44hQ4ZQsGBBIiMjleu4EEIIIYQQQmQG+WYohBBCCCE+yKlTp0hOTqZ8+fKcOHGC6dOn4+TkRO3atTEyMkKr1aJSqVCpVPTs2ZNGjRrx/Plz7t27R4kSJbC2tsbc3FyKQkKIT56+CB4eHo6XlxfFihUDoEePHqSmpjJ58mTs7e0pUaIEAG3btiUpKYls2bJx+PDhdMGdmTNn8vPPP9O2bdsMH2Xhzf43ISEBOzs7Jcjj7u6Ol5cXq1evpnfv3oSHh5MnTx5MTU3RaDRERkZy6dIlqlatSu/evYH0xeeEhASsra05evSoBHeEyEQ6nY5t27ZhZ2dHjx490gV3AOU+zcjIiObNm1OvXj0SEhK4f/8+bm5u5MiRg2zZssk92nsyHGXH8LWkpCRCQ0OJj4/n2LFjeHp6SnAnE+l0Oq5fv86oUaOwtLTEysqKbt26AVCtWjWCgoIYP348efLkIT4+nipVqgAwZ84c5s+fj1arZdGiRdjZ2cnoU0IIIYQQQohMJSPvCCGEEEKID5KUlMT169dxdnYmIiKCgQMHUqlSJUaPHk3t2rWB9IWIPypsZNYUMEIIkZFCQ0MJDQ2la9euTJ48mVGjRhEeHk7fvn0JDQ1Vntpv1aoV69atw9LSkpiYGL788kvlPdasWcPw4cOxtrZm27Zt5MqVK8Paa1iUnzNnDtHR0cTHx1O4cGHKli1LWFgYOXPm5Oeff2bAgAHs3buXypUr88033/D111+zY8cOIiIiyJYtG/v27fvDEYISExOxsbHJsP0QQrzt4cOHFC9enKpVq7Jp06Z0I2u9L7lHez+GfenNmzexsLAgZ86cyvL4+HiMjIxkxJ0stG/fPho2bIi5uTnh4eH06NEDgOvXr7Ny5UpCQ0MBcHV15cWLFyQlJVG8eHGioqJwdXWVEJsQQgghhBAi00l4RwghhBBC/KH3edp03LhxDBky5K0Aj1ar5ejRo1hZWVGqVCl5alUI8Vl68uQJvXv3Zt26dRQrVoxLly4RHBxMt27dyJ8/v1L8S05OpmXLluzcuZMyZcrQs2dP7Ozs2LFjB9u3b8fMzIy4uDhcXV0z7El/w6L8oEGDmDZtGu7u7pQtW5abN29y9OhRChUqxIoVK6hUqRJXr15lypQpbNy4kfv37yvvU6RIEXbu3Imbm9tbbZXCvxBZ5/79+xQvXpy8efOyZ88ecufOnW65/nx9+PAhGzduVMIM4sMYhjqmTZvGsmXLqFatGsOGDSNnzpzp+sXExEQWLlzI6NGj+eKLLyS4k0n0n8G+ffuoX78+VlZWTJw4kZ49ewKvP8Njx46xZMkS7t69i4ODAzVq1KBZs2Y4ODhIcEcIIYQQQgiRJSS8I4QQQggh3snwR+sNGzZw6dIlLly4QN26dfHw8KBSpUrKuuPHjycoKIhKlSoxduxYatasybZt2+jZsyclSpRgy5YtmJmZZdWuCCFEhnNxceH333+naNGizJ8/n8qVK2P4dVulUpGWlkbPnj1Zv349z58/B8DJyYmqVasyffp08uXLlykFw1mzZjFw4EB69OhB//79cXd3JzU1lU6dOrFmzRpq1qzJ7t27MTU1JSkpiUePHrFp0ya0Wi25c+emQYMGODo6SnFTiH+hZs2aceDAASIiImjUqJHyumGgxMfHh9OnT7N27VpcXFyyqqmfJMPj6O/vz5w5cyhevDjBwcE0btz4rfXv37+Ph4cHbm5unD59GpDgzsf0ruuQ/jN6V4BnwoQJ9OrV60/fU6bKEkIIIYQQQmQVCe8IIYQQQoi3GI6cEBAQwKRJkzAxMSEtLQ2AHDlyEBYWRr9+/ZRtJkyYwODBg8mTJw+enp78+OOPpKamcujQIQoXLpwl+yGEEJlhz549fPvttzg5OXHr1i26du3KkCFDKFCggLKOYTHw9OnT3L17l2fPnlG2bFlcXFywsrLKlDBMSkoKDRs25MGDB6xdu5bixYvz6tUrdu/eTZ8+fbCysiI2NhYnJ6c/LWBKcVOIrPFH515aWhqmpqYsW7YMHx8fChQoQFRUFEWLFk233rp16/Dz86N69eosWLAACwuLzGr6Z2XUqFGMGjWK3r17069fvz+9171w4QIlSpQAJLjzT718+fKd/2bHjRuHvb29MprUmwGevXv30qBBA3LkyMG4ceOUAE9qaqrygIGMHCeEEEIIIYTIahLeEUIIIYQQf2jWrFn4+fnRqlUrunTpgpGREYcPH2bIkCHodDpCQ0MJDg5W1l+4cCGDBg3C0tKSQoUKsWrVKlxcXKRQIYT4rN2+fZtr167h7OxMcHAw69ato0OHDoSEhODm5gb8ddgls4qGt2/fpnDhwgwYMIAJEyaQlpbGhg0bCAgIwMjIiBMnTuDo6AjAyZMnKVq0KDly5FDaJ8VNIbKOYcDv119/5cmTJ6SmpqYbDTEpKYmAgADmzZtHkSJFCAgIoEqVKuTPn58FCxYwe/ZsNBoNhw4dwtnZWc7pv+H06dN8++23fPHFF0ybNi1dcOfYsWOkpaWRmprKV199Bfzvc5P74X8mJiaG5cuX4+/vj4eHB/D62nnp0iVKlCiBs7MzY8eOpV27dsDbAZ4VK1bQqVMn8ubNy9ChQ/9yBB4hhBBCCCGEyGzyjVEIIYQQQigMi8spKSkcOXKEr7/+mpEjRyrTKlSrVo3ixYvTt29fRowYgbOzM126dAGgW7du1K5dGxMTE+zs7LC2tkaj0UihQgjx2XhXCCd//vzkz58fgAULFqBWq1m+fDkqlYoRI0bg5uaGkZEROp2O8+fPY29vT758+dK9x8conhsW4bVaLTqd7q2RfNRqNTqdjmfPnvHq1Ss2b96sBHeOHz+uBHc0Gg0+Pj60bNkSf39/5X2lyC9E1tBqtcr5PG7cOJYsWcLVq1cBqFChAuPHj6dcuXLkyJGDsWPHYmFhwapVq+jWrRsmJiZky5aNly9f4uHhwZYtW3B2dpap7/6m33//nbt37zJ8+HAKFy5MWload+/eZdasWcyaNQuNRoNGoyEwMJCwsDDlGMv98N/36tUr1q5dy9KlSzE2Nsbf358iRYqgUqnw8PBg48aNtG/fnmHDhqHT6Wjfvn264I5Go8HT0xNHR0cePnyIj48POXLkoG3btlm9a0IIIYQQQgihkG+NQgghhBBCoS9IDxs2jGzZsnH16lW6deuGi4sLGo0GAGNjYxo3bkxaWhrfffcdkyZNolatWkpxulChQsr7vatwLIQQnyrDQvfevXu5e/cut2/fpnr16ri7u5MnTx6sra1ZunQpAMuWLQMgLCwMZ2dntm7dSr9+/WjatCmTJ0/+qP3jm6NnGAaM5s2bR8mSJalatSoFChSgePHiHD16lMWLFzNhwgQluOPk5KRsM3LkSC5fvvzWlDtCiMyn0+mUc9rPz48pU6ZQoUIFJkyYQEJCAkuXLqV3794MHTqURo0aYWtry6hRo2jWrBnR0dH88ssvZM+enSpVqtC8eXOcnJwkuPMP3L9/H61Wy549e6hatSrR0dGsWbOGy5cv89VXX1G2bFmmT5/OmDFjqF27NnXq1MnqJn/yzM3NGThwIMbGxsyePRu1Ws3gwYOVa1TTpk1ZuXIlrVu3VkYF1Qd49FNjFSlShGLFilG9enWWLVtGjRo1snKXhBBCCCGEEOItMm2WEEIIIYRI5/r16zRu3JhLly4Br5/uDggIUJYbFoh79uzJypUriYmJwdPTM0vaK4QQmcGw7wsKCmLatGmkpqai1WoxMzOjRIkSzJ07V+kLnz17RpcuXdiwYQM1atSgVKlS7Ny5k8ePH3P8+PF0QcePqVKlSuh0Oo4dOwZA7969WbhwIfPnz6d169aYm5szefJkBg8ejLGxMU5OTpw5cwYHBwflPVatWkVwcDDu7u5ERkZiY2OTIW0VQnyYmTNnMmTIELp06ULPnj3x8PDgwYMHVKhQgVu3bpE/f37CwsJo2rQp1tbWynZvBnX+aho/8dofTReo1WqpV68eBw4cwMzMjNTUVEqUKMGsWbMoXrw4jo6OrFy5kg4dOrB+/XqaN2+ehXvxefnll18IDw9n3rx5dOjQIV2AByA6OprWrVvj5OREaGgoHTt2BF5/ZlOnTmXq1KncuHEDlUqljMgjITYhhBBCCCHEv4V8UxdCCCGEEOkUKlSIqVOn4uXlBbweXeL69evKcpVKhVqtBqBYsWK8fPmSCxcuZElbhRAis+gLt+PGjSM8PJyvv/6aiIgIlixZgpeXF2fOnKFGjRrs3r0bAGtra1atWkWnTp04dOgQixcvJkeOHJw+fZpChQop/ejH9PjxY8zNzTlx4gQtWrTA19eXefPm0bdvXxo0aIC5uTkA3333HQ0bNkSr1VKkSBFevXrFkydP0Gg0TJ06leDgYHQ6HQsXLsTGxgatVvvR2yqE+DDXrl1j9erVVK1aVQnuJCYmUrNmTVJTUxkwYAA6nY7g4GA2b95MUlKSsu2b4QQJ7vw1jUaj9PupqakkJSWRnJwMvD5+u3fvZtCgQfTr148ZM2Zw9OhRatSogaOjIzqdjhMnTpAjRw4KFCiQlbvx2SlYsCB+fn707NmT5cuXM27cOC5fvqwsb9KkCatXr+bhw4cMHDiQ4cOHc/v2bWbOnMmCBQsoWLAgL168UM4BCe4IIYQQQggh/k1k5B0hhBBCiP+wP3qiGGDnzp2Eh4cTExNDaGgoffr0wdbWNt06/fv3Z/HixezYsYPq1atnYsuFECJzGI5Q8ezZM7y9vcmbNy8hISG4uLgo64WFhTF8+HDMzc05evQopUuXVpbFxMRgYWGBu7s79vb2GfKkv74ff/z4MQMGDGDVqlUABAQEMHToUHLkyJFufy5fvsywYcPYtGkT2bJlI2/evCQnJ/PkyROKFCnC5s2bcXNzk1EJhPiXOHLkCF5eXkRERNCwYUOSk5OpXr06v/32G1OnTqVhw4bMnj2bYcOG4eHhwZAhQ2jSpIly7ov3Z9jvzZs3j507d3Lp0iXMzc1p3bo1tWrVokKFCu/cVqfTERUVxZAhQyhUqBBr166VzyAD/NUIPHv37qVVq1Y8ffpUec3FxYWYmBjc3Nze+d1HCCGEEEIIIbKahHeEEEIIIf6jDAsTr169Ii0tDZVKRfbs2ZV1du3axejRo4mPj8fPz49WrVopBemNGzfSr18/8uTJw+7du7Gzs8uS/RBCiMwwefJkcufOzZAhQ5g8eTLNmzdHp9OhVqsxNTUFYOjQoYwdO5bGjRuzYsUKLC0tMTExSfc+GTldjf69vb29Wb9+PQC1a9dm3759AKSlpWFqaqoULe/du8fRo0dZuXIljx8/xtHRka+++gpvb2+cnJwkuCPEv8yRI0eoUqUKaWlp9OnTh9WrVzNmzBi6deuGhYUFFy9epEqVKgAkJycTGRkpUzZ9IMNQx6BBg5g6dSqurq64u7vz+PFjTp8+TcWKFfH19aVVq1ZvbR8eHs6cOXNIS0vj8OHD5M+fX4IiH5HhNfSvAjzXr19nzZo1PHr0CAcHB7p160bu3Lnl2iaEEEIIIYT415LwjhBCCCHEf9CbTxTv2LEj3RPFtWvXVp4o3r17N6NHjyYuLo78+fNTr149Ll68yOPHj0lNTeXAgQO4urpmaEFaCCGy0okTJ6hcuTJWVlZotVo2bdpEnTp1lIKsYZ9apUoV7t27x4kTJ3B0dMyS9oaGhnL9+nUePXrEzp07adiwIdu2bQNArVZjYmLyVp+dkpJCtmzZlL9Lny7Ev8eb52NCQgKVKlXC2dlZCecB3Llzh/Lly9O9e3eOHj3K8uXLcXZ2zoomf/JmzZqFr68vvXv3pl+/fri7u/Pq1St69+7NsmXLaNiwIZs2bcLU1BStVsvFixfx9vbm/v37FClShDVr1uDq6ipBkX/gfa5DfxXgeXOUUfk8hBBCCCGEEP9m8kucEEIIIcR/jE6nU360HjRoED4+Ppw/fx43NzfMzMwICgqiX79+rF69GoD69esTHBxMvXr1uHXrFvv27aNcuXIMHjyYo0ePKoUJKfIKIT5XJUuWZNasWRQqVIjk5GTWr19PQkKCMpKCsbExqampAJQqVYqbN29y/vz5TGnbu57HGTFiBMuXL2fVqlU0a9aMHTt20KhRIwBMTExISUlR+uwnT54AKMEd/ftJny5Exnvf5+nePB+vXbvGlStX0oUUACIjI7G2tqZLly7s2LEDZ2dnNBrNR2vvf4FOp+Pp06dERUVRpEgRfHx8cHd3R61Ws3nzZuLi4nBzc2P58uWYmpoq98DZsmXDw8ODPn36EB0dLcGdf8jwu8XmzZsJDQ2lbdu2zJ07l2vXrinrFSxYED8/P3r27Mny5csZN24cV65cUZa/eY7J5yGEEEIIIYT4NzP561WEEEIIIcTnRF9snj17NtOnT6dPnz7KE8UpKSn07t2bpUuXsnLlSpo1a4a5uTl169ZFrVaj0+k4cOAApUuXxtvbG0tLS7RarfwQLoT47Bg+rW9hYUH79u2B11OibNiwgQYNGtCoUSOleGtmZga8HhEjZ86cuLq6Zngb35z+MCkpCQsLC2X6Q1tbW6ZOnQq8nuqwUaNGbNu2jWzZsqHT6di5cydbtmyhc+fOeHp6AsjULkJkkn8ylVLx4sUpW7Ys27dv59ixYxQoUICdO3eyYMEC8ufPT+7cuZU+Se7RPoxKpSIhIYHTp0/Trl07ihUrRlpaGhs2bCAgIAAjIyOOHz+Og4MDADdu3MDa2hp3d3ciIiIwNjZWRuORY//3GB67wMBAZs2aRWpqKhYWFqxevZpChQoRERGhjBKqD/DA6xFFjY2NGThwIMWLF1cCQHJtE0IIIYQQQnwK5FE6IYQQQoj/GJ1OR0JCwjufKN64cSOxsbHKE8Xm5ubKaBJff/01fn5+eHp60qdPH+bNm0dycrKMziCE+GxotVrlzyqVCrVarRT8LC0tadeuHf7+/piamuLr60tkZCQPHjxQiowbN25kz549eHh4ZPiUWYbFzfDwcOrWrUvhwoUpV64cXbt25eLFi7x48YL8+fMzffp0mjdvzo4dO/jmm2+4c+cOq1at4ocffiA6OhoXF5cMbasQ4m36vqVOnToMGTLkg7Y1MzOjXbt2JCUl0aBBA8qWLUuPHj1IS0tjyZIlWFpavveoPuJtKSkpaDQarKysAFi3bl264I6TkxMAz549w9vbm127dgFgbm6OqakpIKOX/RP6YzdixAgmTpxIy5Yt2b17N4mJiQwdOpTr169Tq1Ytjh49qmyjD/D4+PiwePFiFi5cKKNOCSGEEEIIIT45MvKOEEIIIcR/jP6J4lOnTtG2bdt0TxQHBga+9UTxrVu3sLS0JG/evNSrVw8jIyNCQkIYMmQIxsbGdOnSRRnlQQghPlWGo9isWrWKQ4cOcfXqVXLlykXr1q0pX748efPmpW3btgCEhYXRr18/SpUqRcuWLdm7dy/Xrl0je/bsLFu2DGtr6380ssZf0Rc3AwICCA8Pp2DBgtSoUYNz586xZMkS4uPj8fPzo3nz5jg7OzN9+nRMTU2JjIykcOHC6HQ68ubNy6FDh8iVKxdarVaKzUJkshs3bqBSqRg3bhzW1tYMHjz4L7fR6XSYmJjQu3dv8ubNy8aNG7l37x5NmzZl6NChylRZMurLX3vzOKnVakxMTMiZMydFihRh2bJluLi4MHHixLeCOwCTJ0/m6tWr2NraZkHrP29RUVEsWbKELl26EBgYqDxosGnTJhwcHHj8+DG1a9fm4MGDVKxYEXgd4Onfvz85cuSgV69ecg4IIYQQQgghPjkqnTyKI4QQQgjxn3P58mU8PT3p1asXEyZMYNWqVQQFBWFkZMSJEyeUESOSkpKoWbMmvr6+tG/fXilC79u3j5CQEA4fPszOnTupX79+Vu6OEEL8I4YhGz8/P6ZNm4aNjQ1OTk7cv3+fpKQkvL29CQoKomTJkrx8+ZKVK1cyZcoULl26RIkSJXB3d8fT05MOHTpkaPHcMGRz+PBhmjZtSocOHfDx8aFw4cLcvHmTxYsXs2jRIlQqFRMmTKBFixaYmZnx8OFDIiIiOHfuHJaWlgwZMoS8efNKoV+ILHTu3DnGjBnD+vXrCQ4OZsSIEX+5zZthO/2oYUZGRnI+/w0jR46kRYsWlChRQjm2w4YNY8yYMeTIkQMbGxtu3LihHFedTkdkZCRBQUEUL16cVatWYWNjk8V78fl4+fIlPXv25ODBg0RHR1O6dGmeP3+Op6cnCQkJTJ06lZ9//pmRI0eSLVs2Dhw4QKVKlZTt9eeAnAtCCCGEEEKIT42Ed4QQQgghPmOGxR3908QAT548oX79+ty+fZvg4GDCw8NRqVRvPVEcEhLCpEmTiIiIoEmTJukK3Nu3b+fUqVMEBwdn/o4JIUQGCA8PJzAwkN69e9O3b1+KFSvGb7/9RqtWrThy5AidO3dm3rx5mJiY8PLlS5YvX87UqVN5/vw5a9asoWLFipiYmGRKwfDs2bNcuHCBUaNGsW3bNgoWLKj8f589e8aGDRsICAjAxcWFnTt3KqFMfT+uvyZIcVOIrGF4T3X+/HmGDh3K1q1buXTpEkWKFPng9xB/z/79+6lbty62trYcPXqUokWLKsuaNm3Kli1bqFKlCtHR0eTIkQNTU1MmT57M7NmzUavVxMXFkS9fPhm97B948zqk0+mYM2cOpqamdO/enZcvX9KwYUMuXLjAuHHj6Nq1KwB169Zl//79WFpasmXLFmrXrp1VuyCEEEIIIYQQH4WEd4QQQgghPlOGP4Rv2bKFa9eu4enpSbVq1QAYPnw4YWFhZM+eHVtb2/d+ovhdhSIpWAghPnW3bt2iadOmmJubs2TJEooVK4ZarWbLli0MGjRImTLF3t5e6V9fvHjBypUrGT58OFZWVsydO5evvvoqw8MwISEhjBs3Tpkq5ODBg6jVaoyNjZX++enTpwwdOpS5c+cSEhLC8OHDASn2C/Fv9eOPP5KamkqFChWyuin/OSNHjmTkyJHY2tpy+PBhJcCTkJBA586d2bx5MyYmJhQtWpSnT5/y4MEDihUrRnR0NG5ubhKC/EiWLFnCd999R44cOUhOTsbS0hKVSsXChQvp27cvAQEBDB06lGzZsgHQoUMH4uPjuXbtGm5ubly6dAlTU1O5xgkhhBBCCCE+WVJhEUIIIYT4DGm1WqWIMGzYMDp16sSUKVN48uQJqampwOtCRdOmTXn+/Dn58+fn6dOnyrLJkyczbNgwdDod8+bNw8bGRpmS4V0/iEtwRwjxqXvw4AEXL16kefPmSnAnKioKX19fdDodR48exd7eHp1Ox507d3j+/DmWlpa0b9+eUaNG8erVK3x8fIiJiUGj0WRoWz09PTEzMyM2NpYnT56QlpaGiYkJhs/m2NnZ0bNnT0xMTLh+/bryuhQ1hchabz5Dp/97mTJllOCOPGeXOfR99fDhwwkJCeHJkydUrVqVS5cuAWBra8vGjRuZNm0arVq1wtTUlEqVKhEeHs6+ffskuPMRrVmzhq5du7Jw4UIArKyslOvVsWPHUKlU/PDDD0pwB+Du3bt4eXmxcuVKYmJiMDMzk2ucEEIIIYQQ4pMmVRYhhBBCiM+QPkwzZMgQxowZw3fffUdUVBRNmjTBzMxMCeIsW7aMZs2acfToUfLkyUO5cuVwdnYmKCgIS0tLYmJiyJcvHxqNRgI6QojP2oMHD0hLSyNXrlwAREZGEhAQgJGRESdOnFCmFHz48CE9evTgzJkzAFhYWNC+fXtCQ0N5+fIlTZo04fLlyxna1kaNGrF582acnJy4cOECI0aMAF73/VqtVunjnZ2dMTc3Jzk5OUPbI4R4PxqNRgkXpKSkkJiYSEJCQrp1tFqtBBAywJuhSn3QXd9fDhs2jNDQUJ48eUK1atXS9eP9+vVjxYoVxMbGsn79evr374+Tk1O6sLz4Z4oWLYqpqSlbtmzh8ePHwOsQm0ajQafTkZKSooSqdDodq1at4vz583zxxRe0adMGFxeXDA/OCiGEEEIIIURGkwqMEEIIIcRnateuXcyaNYvOnTszdOhQPD09lWVGRkbodDqsra2JiopixowZtG7dGnNzc6pUqSJPFAshPlv6Qi2kH92iSJEiODg4sHz5ctatW8fQoUOVqbIcHR2V9SZOnEhcXJwSaNTpdJibm9OuXTv8/f3p0qULxYsX/+htfVPt2rWJjIzE3t6e2bNnM3PmTOB1/65v26ZNm3j+/DklS5b8KO0RQvx9hvdTc+bMwdvbm/Lly1OlShVGjRrFqVOngP/do4l/Rq1WA//r5/XHPjw8nJMnTyphRyMjIyX0ERwcrAR4atSowdWrV9O9l4WFRbr3lGD7x6HT6ShbtiyDBg0iJiaGY8eOAa9HijM2NlZGpOratSsREREEBQURHByMra0tDRs2VN5Hvq8IIYQQQgghPnUqnfwiIIQQQgjxWRo9ejTBwcEcOXKESpUqvXMdfdFCLzk5GSsrqz9cLoQQnzLD4vnBgwe5f/8+lStXJn/+/Lx69YqWLVuybds2HBwcsLKy4tKlS5ibmyvbr1y5kmHDhuHp6cmSJUvInj078LrwqFKpSEtLw9TUFPjn/adhWy9cuMCDBw949eoV+fLl44svvlDW27t3L97e3iQnJ9OtWzcGDhxItmzZ2LJlCzNnzuTFixccOXKEPHny/O22CCH+GX0fATBo0CCmTZuGm5sbxYsX58GDB5w6dYrKlSvTtWtXOnbsmMWt/fQdOHCA6OhogoKCyJkzp3L8d+zYQaNGjShUqBBRUVGUKlVK6asN++xevXoxf/58nJycOHToEEWLFpV74kywa9cuGjVqROXKlYmKiiJnzpzKsqCgICZOnKiEWkuXLs2mTZtwdXWVBw2EEEIIIYQQnw0J7wghhBBCfIY0Gg2tWrVi+/bt3Lt3Dxsbm3SFI/06xsbGJCQkYGtrm+61N9cVQohPnWHhNTQ0lDlz5mBtbc3MmTP56quvMDEx4fbt21SrVo3bt2/TpUsXFi5cqGw/Z84cJk+eDEBMTAzOzs5/2Ff+0z70zbYuWrSIO3fuAGBra0vTpk2ZP3++EhTat28f33//PY8fPyZfvnw8f/6cfPnyYWVlxZo1a6S4KcS/xOzZs+nfvz8+Pj74+PhQrFgxHj58SHBwMPPnz6dFixZERERgZmaW1U39ZKWlpVGnTh3i4uIYOHAggYGByrSHAAEBAYSHh+Pu7s7atWspXbp0uhF4jI2NuXLlCnXq1OHx48e8evWKq1evUqhQoSzcq8/DH12HDF9v374969atY+fOndSqVYuUlBSyZcsGvA7dPnjwAAsLC6pUqYK9vb1c24QQQgghhBCfFXlkRAghhBDiE/dmFlun02FsbIylpSWvXr3i9OnTb62nX0etVjNy5Eh+/PFH4H/DzUtwRwjxudGHYQICAhg1ahT169dn8eLF1K9fHxMTEzQaDfnz52fTpk24uLiwePFi3N3d8fLyonTp0gwcOBAjIyP27NmDs7MzGo3mD/vKf9qH6ts6ePBgQkNDKV++PIsWLSI6OpqyZcuybNkyqlWrxpMnTwCoU6cO69atw9HRkcTERL777jv27NlDbGysBHeE+BfQ6XQ8e/aMqKgoihYtSt++fSlWrBgajYb9+/eze/duXF1dmTt3LmZmZso0TuLDmZqasmbNGmrVqsXkyZMZM2YMDx8+VJZPmDABf39/rl69ire3N2fPnlVG3tHTh0UaNGhA7ty5lb+Lf0Z/HRo6dChz587ll19+UV7XT03Wu3dvTExMmDRpEvD6s9Avq1mzJi1btsTLywt7e3u0Wq1c24QQQgghhBCfFQnvCCGEEEJ8wrRarVIkvnfvHikpKcrfGzdujEqlUkaOMDIyQqfTpSs4h4SEsHr1ap49e5Y1OyCEEJlo3bp1zJo1ix49ehAWFka1atWUZfoCYNmyZYmPj6dnz544OTlx+vRp7Ozs8Pf35+DBg7i5uWVKGGbbtm3MmTOHbt26MXnyZDp37oyXlxfff/89AL/88gsmJibK+rVq1WL16tWoVCq2bt3K1q1bleUSyBQi8xmGQVQqFU+fPuX48ePUrFmTIkWKkJqayvr16wkICECn03HixAkcHBwAuHnzptyb/QN58+Zl1apVVKtWjWnTpr0V4Bk/fjwBAQFKgOfMmTMYGRkp/fqqVavIly8f69at4+rVq+TLl08CVR/JoUOHGDt2LD4+Pnh5eTFkyBASExOV4+vh4YGnpyfbtm1jx44dAH94vZVpzIQQQgghhBCfG/mWI4QQQgjxidJoNMqP1rNmzaJt27a0adOG1NRUAMqXL0+FChVYvXo1AwYMUII++h/AN2zYQFRUFCVKlKB06dJZth9CCJFZDh48iE6no3v37ri4uLxzHY1GQ+7cuZkxYwaHDh3i2LFjxMTEMGLECHLnzp1po9icPn2aly9f0rVrVwoUKIBarWbVqlWMGTOGggULcunSJaytrXn58qWyTZ06dVi/fj0vX75k2LBhb4U3hRCZw/Ae7dGjR8DrAI+RkZEyisuGDRsICAjAyMiI48eP4+joCMDz589p0KABUVFRWdP4z0Tu3LlZu3btHwZ4xo0bp4zA06BBA7Zv3861a9dYtGgRy5YtI0eOHKjVaqysrJQRK8U/V6NGDY4ePcrMmTNJSEhg3LhxVKhQAX9/f3766Sfs7OwYM2YM5ubmbN68GZAAqhBCCCGEEOK/Q6WTX/CEEEIIIT45Wq1WKQr5+fkxd+5cypQpQ58+fWjdurWy3vHjx2ndujU3btzAy8uL+vXr4+npydq1a4mKikKj0XD48GFcXFzSvacQQnxukpOTqVWrFklJSVy6dAl4PZWNYVFQ3w++fPkSCwuLdK+9uW5G0mq1eHt7c/jwYe7duwfAmjVrCAwMVAr9Tk5OAJw5c4bDhw/Tt29fZfu9e/fy/fffY2ZmRkBAAL6+vpnSbiFEer6+vmzatIkDBw5ga2tLjRo1ePDgAQMHDmT27NkYGRlx7NgxcubMqWwTEhLClClTWLJkCc2bN8/C1n8efv/9d1q1akVsbCz9+/dn6NChSv8JMHLkSMaNG0dKSgomJiakpaWRL18+ZdrBzOz7P3dvfte4efMm69evZ926dRw/fhxLS0t8fHz44osv2Lp1K9HR0ezYsYNatWplXaOFEEIIIYQQIhNJeEcIIYQQ4hM2ceJEBg8eTP/+/enVqxdFixZVlumLDadOnWL48OEcOnSI5ORkAExNTalYsSIrVqzA1dU100aSEEKIzPBmsVWn0/HixQtq167N5cuXOXbsGMWKFUu3nr6o+OLFC6ZOnUrXrl3JlStXprdVP7Vh+/bt2bRpE0eOHOHy5cv4+/u/FdwBqFu3LhqNhg0bNmBnZ6e8vm/fPurVq4ePjw8zZ87M8P0QQqQ/nxctWsQPP/xAs2bNCA4OpnDhwkyaNAl/f38sLCywt7fn2rVrykg8Wq2WdevWERQUhIeHBxEREdja2mbh3nz69J/HXwV4Nm/ezJEjR7hw4QLFihXjhx9+wNnZWe6PM9CbQZ7p06ezZcsW9u3bh6WlJVZWVjx8+JDhw4cTHBwsn4MQQgghhBDiP0HCO0IIIYQQn6hbt27RoEEDnJycWL58OW5ubm+toy9aPHz4kHv37nHkyBGMjY0pWbIkxYsXx8bGRgoTQojPimFBMDExERsbG2XZyJEjCQkJITw8nIEDByqvG/aD/fv3Z8+ePaxfv54SJUpkWlvv3btHnjx5lGWbN2+mRYsWNGjQgIsXLwK8FdyZPXs2oaGh9OvXj6CgoLf68gsXLij7IKNHCJGxDM/n5ORkAgMDuXLlCgsXLlSm6dNoNPTq1YtFixZRvXp15s2bR+HChQGYOnUqc+bMQaPREBcXR758+WRUxPf0Z8dJ37//VYBHv66RkREqlUrujzPJm+fN7t27mTVrFocPHyYtLY2bN2/i7Oycxa0UQgghhBBCiMxhktUNEEIIIYQQf8+dO3e4fPkyvXv3xs3N7Z2FC32h1snJCScnJ0qVKpVuuVarlcKEEOKzYVhsXbJkCdu3b6dkyZKMGDECgKpVq+Lo6Iifnx/58uXD29sbQNlmw4YN7Ny5Ew8PD1xdXTOtrbNmzWLlypW4uLgQGRkJQIkSJahduza7du3C0tKSCxcupCs0r1mzhilTpuDi4kKPHj3e2ZfrgzsSABAi4+nPsWHDhpGUlMShQ4do3769MjWpSqXC2NiY4cOHo9PpWLx4MSVKlKBkyZI8efKEhw8fUrRoUTZv3ky+fPkkPPKeDI/TgQMHuHjxIs+ePcPNzY3WrVsry3Lnzk1kZCStWrVi+vTpAEqAR99HGh5vOfYf7u9cawynpbSysqJZs2ZUrVqVGzdu4OrqSu7cueVcEEIIIYQQQvxnSHhHCCGEEOITlZSUBLz+oRx468dy/Q/dd+/excbGBisrq7dGXpBirhDic2EYRgwMDGTu3LkUKlSIRo0aKevUqVOHsLAwevXqxffff8+5c+eoWrUqZcqUYcGCBSxfvpy0tDRmzpxJ9uzZM2y0GsO2+vv7M3v2bDw9PalXr56yTuHChRk0aBAPHjzg/PnzTJo0ifLly1O6dGkWLlzIxo0bAYiKiiJnzpx/WjSVvl6IzHH37l32799PfHy8EkoA0v05f/78LFy4kKpVq7J3714uXbpExYoVqVGjBq1bt8bJyUnCCu/JsC8dOnQo06ZN48WLF8rydevWMWnSJNzc3FCpVG8FeIyNjQkICMiUKRI/V0eOHOH333+nefPmGBkZ/a0Az5vX2Zw5c5IzZ04AOReEEEIIIYQQ/ykybZYQQgghxCfq6NGjVK1alQYNGrBw4cJ0Q8obFpwbN25Mvnz5mDVrlhRwhRCfvbCwMIYPH07fvn3p1asXxYsXB9KPCLBkyRLGjx/PlStX0m1brlw5oqKicHV1zZSC4cSJEwkKCqJv3774+PhQpEgRIH0fvn//fqZPn8727dtRq9UAWFhYUL16debPn4+Li4sUN4XIIu8KKpw4cYKpU6eyevVqKleuzMKFC/Hw8ABQAjyGYQW1Wo2Jicmfvqd4m2E/OXToUMaOHUubNm3o1q0bxYsXJzQ0lDlz5tCwYUMmTJiQbhrE33//nTZt2hATE8Pw4cMZMWKETCv4gXQ6HQ8ePCBPnjxYWVmxfPlymjVrBsi/YSGEEEIIIYT4uyS8I4QQQgjxL6YvTBgWKAx/EPf29iY6Oppp06bRoUMHLCws0m2zaNEiRowYQe/evQkKCpLirhDis3b27FkaN25MqVKlmD17Ni4uLumWG/afFy5c4OLFi8THx2NlZUX58uWpXr069vb2mRKGuXHjBo0aNcLe3p6IiIi3puky7PcfP37MnTt3OHXqFACenp64ubmRI0cOCe4I8S9w+/Zt8ufPr/z9xIkTjBs3jk2bNuHn58cPP/xA7ty5leWG92o6ne5vj1giYNWqVQwaNIhmzZoxcOBAChcujE6nw8PDg3v37pGUlETt2rWZPn06xYsXV/rVu3fv0q9fP2X6QfH3LFq0iO7du5M3b16mT59O8+bNAQnwCCGEEEIIIcTfIeEdIYQQQoh/KcOCbGpqKmq1GmNjY7Jly6ass2HDBvz8/Hj06BHBwcE0adKEokWLotPpWLVqFaNHj8bMzIxdu3bJlABCiM/epk2baN68OREREbRu3fqd6/xVQTGzCo5xcXHUqFGDiRMnMmjQoHdO0fVX03Zl1LReQoj3FxISwpgxY4iNjaVixYrK66dOnSIkJIRdu3YRFBREr169yJMnTxa29POTkJBAu3btePjwIfPnz6d06dIkJSVRoUIFEhISGDp0KBcuXGDevHk0atSIsWPHUqJEibcC8RKC/HCG15/ly5fTqVMn8uTJw/Tp02nRogUgAR4hhBBCCCGE+FAmf72KEEIIIYTIbIZFhEWLFrF9+3auXbuGra0t/v7+eHp6kitXLpo3b86TJ0+YPHkyQUFBzJs3j6pVq3Ljxg3OnTuHra0t27dvJ1euXPIDuhDis3fnzh0AHBwcgLcLh/q+9dGjRzg6Or5znczqJxMTEwEwNzcH3g7i6Nv64MEDcubM+c51JLgjRNbS6XSYmppiYWGBt7c369ato0KFCgCUL1+e0NBQdDodY8eOBZAAzz/0ZsjG1tYWKysr6tatS+nSpXn58iXffPMNjx8/ZsKECXTq1Il79+4RFRXFtm3bUKvVjBs3jtKlSwP/6+8luPPhVCoVaWlpmJqa0qFDB2xsbGjWrBn+/v6o1WpatWr1wSNKGV7jnj9/Tvbs2TNyF4QQQgghhBDiX0eqN0IIIYQQ/zJarVYpIvj5+dG9e3diY2OxsrLi1q1beHt7M3nyZC5dugRAt27dmDlzJn369OH+/fusXr2ahw8f0qpVK44cOYKbmxsajUaCO0KIz56VlRUAW7Zs4cWLF+n6PZ1Op/Stbdu2ZcaMGUDGh3W0Wu07X7e0tARg3bp13L179w/b+u2339KvXz9AwjpC/NuoVCoGDx7MqFGjePbsGc2aNeP48ePK8nLlyjFy5Ejq16/PuHHjWLBgAb/99lsWtvjTpu8XN23axL179wCIjIzE19cXgGnTpnH69Gn69+9Pq1atAMiTJw9fffUVX375Jbt27WLUqFGo1eosaf/nRKPRYGpqCsDBgwfJmzcv1apV4+bNmwwbNoz169cDKAGev2IY3Nm3bx+TJ0/m/PnzGbcDQgghhBBCCPEvJBUcIYQQQoh/GX0BNywsjBkzZuDj48OBAwc4cuQIa9euRavVMnfuXGbMmMHly5cB+Oqrr5g2bRrXrl3j119/5ezZs8yaNYu8efPKVABCiM+S4QzQ+sLgt99+yxdffEF0dDTHjx9XXk9LS0OlUqHT6Zg5cybnzp3DzMyMjJ5F2jA4eeTIEWJiYpSice3atWncuDHx8fFs3LhRGYlH31atVsuiRYu4ffs2Tk5OaDSaDG2rEOLP6c9BnU6n9B36wHWfPn0YMWIEL168+MMAT8OGDQkJCWHNmjXvFWYQ77Z8+XKaN2/OzZs3gfTXgvj4eGxsbOjevTsWFhbK6+fPn6dkyZJMnjyZKVOmYGIiA5H/E4YBU39/f77//nuaNWuGhYUFefLk4fr16wwcOJCoqCjgrwM8hsGdPXv20L9/f2bMmIGdnV3G74wQQgghhBBC/ItIeEcIIYQQ4l8oNjaWpUuX8v333+Pr60uJEiV48eIFHTp0wM7OjiJFijBv3jymT5+uBHgAnJycyJs3L2ZmZkphQoI7QojPhWHxTx9wgf+FHi0sLOjQoQMPHz7E19eXrVu3kpiYqIwOsG7dOmbPnk3+/Plp3rx5ho5kYziKWmhoKC1btuSbb77hwoULpKWlAdC7d2/y58/PyJEjmTVrFjdv3lTaunr1aiZNmoS9vT09e/aUvlyILGQYVnj8+DEqlUoJ5+nP9X79+ikBnubNm3PixAll+3LlyhEUFET79u3x9vaW0RD/Af1USmPHjiUpKUnpx3U6HU+fPlWmMdNbtmwZz549o23btvj6+pI/f34JQ/5D+mM+efJkJk2aRMeOHdm7dy+7du1i3759hIWFcefOHQYMGPCXAZ43gzuBgYH89ttv7Nmzh3z58mXeTgkhhBBCCCHEv4BKl9GPGgohhBBCiA+i1WqZNm0a4eHhrF27lqpVq/L8+XMqVarEo0ePmDhxIh4eHvj6+nL27Fm6dOlC3759cXd3z+qmCyFEhjEcRWzt2rUcOnSIGzduUKhQIfr27Uv+/PmxsLDg4cOHhIeHM2/ePExMTPD09KRWrVocO3aMuLg4smXLRlxcHK6urmi12gwpohsWIwMCApgyZQpt27alXbt21K1bV1kvJSWFNWvWMHHiRC5evEjBggWpXr06169f58cff8Te3p6YmBjc3NwyrK1CiPcXGBionK/FihVT+iX9+alWqxk/fjzBwcG4ubmxdu1avvzyS2X7tLQ0TE1NZVTEf+jrr78mPj6e3bt3U6FCBeW4jhw5kpCQELy8vBg4cCBxcXEsX74cU1NTYmJicHJyyuqmfzaePHlC06ZN+fXXX9m/f/9b30MWLlxIjx49yJ8/P+Hh4bRs2RIg3bXsXcGda9euERsbS+nSpTN3h4QQQgghhBDiX0DCO0IIIYQQ/0IxMTH8+uuvdOrUiZSUFLy9vYmNjWX8+PF07doVIyMjBg0axJQpU7CxscHLy4uwsDBcXV2zuulCCPHRGRb79GEYU1NTHBwcuHfvHq6urgQEBODt7Y2dnR2PHz9my5YtLFmyhNjYWABy5cpF9erVmTx5Mvny5cuU4vmqVavo0qUL3bp1w8/PDzc3N2WZvmiZmprKuXPnmDt3Lhs3biQhIYGiRYtStWpVQkNDZfpDITKRYZgAQK1Wp5tiafDgwUyYMAEnJycOHjz4zgAPQN26ddm/fz8uLi6sWLGC6tWrZ/q+fI70x3r37t00btyY9u3bs3DhQmX5nTt3GDRoEJs3byY1NRUADw8Ptm/fnqGBzf+ix48f8+WXX1KgQAH2798P8NbxDQoKYvz48bi7uxMWFqYEeECCO0IIIYQQQgjxLhLeEUIIIYT4l9IXjHbv3s3333+Pt7c306dPx8zMDICdO3cydOhQsmfPzrVr1/jpp5+ws7PL4lYLIUTGGTFiBGPGjKFz58706dOH0qVLs3HjRlq0aEGhQoXo06cPnTp1wtbWVikinj9/npSUFAoUKICVlRXm5uYZHobRFyU7dOjAtm3bOHz4MMWKFfvD9fQePHjAq1evyJs3LzqdTkboECITGQYPnj17hrW1tbJs69ateHl5ATBmzBiGDRuGvb09sbGxeHh4KOfpq1evMDc3JywsjPXr13PhwgVKlSpFfHy8MiWe+Gvv6vcM+8t79+7RsGFDLl++zK5du6hRo4ayzcOHD/npp584ffo0+fPnp3bt2jg5OUlf+pE9ePCAypUrk5SUxIEDByhRokS65Tqdjh07dtCkSROMjY0xMTFh/fr1NGzYMN16e/bsYfDgwVy9elWCO0IIIYQQQoj/PHncRAghhBDiX0r/pPeFCxdISEjgm2++UYI7ABERERgZGREREaEEdySXLYT4XO3YsYMlS5bQuXNnAgICKF26NC9fvmTo0KE4ODiQlJTEmDFjWLp0KQkJCUoR/osvvuDLL7/EwcEBc3NzdDpdphRwExMT2b9/P25ubsroHG/S99kvX74EwMnJCRcXF0xMTJRCvxSbhcgc+j6jSZMmjB49mqSkJAB8fHxo0qQJ27dvB2DIkCGMHDmSJ0+eUL16dX7++WeMjY1JS0vD3NwcgOPHj1OqVCkWLFjA5s2bJbjzgfT93pw5c9i+fTu///47KpVK6TPz5MnD8OHDSUlJ4cCBAwCoVCq0Wi1OTk7Url2bQYMG4e3tjZOTE1qtVvrSj0Cr1Sp/zpkzJ23btuXJkyfs2LEj3XpqtRqVSkXNmjUpX748HTt2JFu2bJQqVSrdejExMQwcOJCrV68SFxcnwR0hhBBCCCHEf56Ed4QQQgghMoHhj90fytHREYCTJ08qhaS1a9dy7NgxatasSd68ebGzs0Or1aYbwUEIIT4XqampHDp0CCMjI7p160bhwoV5/vw55cuX58mTJ4SHhzNnzhxMTU2ZMWMGS5Ys4dmzZwBvhRozo59UqVTY2NiQO3duXrx4oQSGDK8F+lE+EhISiI6OfmsUHiFE5vvll1/46aefmDt3LkuXLqVHjx7MnTsXX19fvvjiC2W9YcOGMWrUKJ48eUK1atU4ceKEEtBZs2YN58+fp06dOnTq1EmZpk98mKioKPr06UOTJk345ptviI6O5rffflOWV6hQAU9PT6ZMmcLFixcxMjL6wymxZKqsv+evvr/UqFGDAgUKEBQUxOrVq1Gr1cDrBxDUajXz58/n/v37zJ8/n99//x1nZ2flXEhLS+PEiRMkJydz6NCht4I9QgghhBBCCPFfJNNmCSGEEEJkouDgYDp27EjhwoXfe5sbN27QtWtX4uPjadiwIWq1mqNHj2JpaUlcXBz58uXLwBYLIcS/w6xZs7CwsKBLly68evWKJk2acPr0aSZMmECXLl1Qq9U0bdqUHTt2UKhQIbp06UK/fv3Inj17prdVq9WSmppK69at2bx5M2PHjiUwMFBZplKplKBOjx492Lx5M/Hx8RQoUCDT2yqE+B+NRsPly5fx8fEhPj6e1NRU+vTpw8iRI5WpSQ2nXxozZgyjRo3C1NSUpk2bKiPBWFtbExcXR548ebJydz5pjx8/5urVqyxcuJAVK1ag0Wj48ssv8fb2pn///piYmDBv3jx69+5NSEgIwcHB6HQ6Cep8JIb/ziMjI4mPj+fEiRNUqlSJL7/8ku+//x6AefPmMXjwYJ4/f05AQAB169alSpUqLFu2jFmzZmFvb8+WLVveeS1++PAhaWlp5M2bN1P3TQghhBBCCCH+rSS8I4QQQgiRSTZu3EiLFi1o3Lgx06ZNw83N7b233b9/PwsXLmTNmjU4ODhQunRplixZQv78+dP9uC6EEJ+z1NRUzMzMWLFiBb1796Z3796MHDkSCwsLAKZPn86KFSv49ddfcXJy4vjx4xka3tGPnvNHTp48SfXq1cmZMyejR4+mXbt26bZdv349w4cPp2TJkixbtgwrK6sMa6sQ4v21b9+eiIgIzMzM8PX1ZcSIEVhYWCjnvOG914IFC1izZg0HDhzAwsKCEiVKsG7dOlxdXeUe7W96cySymJgYDhw4wJQpU3j+/DnlypWjRYsWtGrVivbt25OQkEB8fDw5cuTIwlZ/PgyvbX5+fsyePRtzc3Ny5crF7du3efHiBb6+vkyePBmApUuXMnPmTE6fPg2AlZUVycnJuLi4EBMTg5ub21ufqYw2J4QQQgghhBBvk/COEEIIIUQmefz4MREREQwZMoR69eoxadIkChYs+KfbvPnD9oULF7C3tydHjhxkz55dikJCiP8kf39/pk+fzsmTJ9NNZVOvXj0sLS0JCwvDycmJ3LlzZ1iB0LD/vXTpEg8ePEClUlGgQIF0I6LNmzePAQMGYGlpSa9evejYsSOWlpZERESwePFi0tLSiI2NJV++fFLMFCKLGJ57v//+O/7+/piZmXHmzBmuXr1KUFAQPXr0UKYyBVCr1ZiYmADw8uVLzp8/j62tLbly5cLGxkbu0T6CN4/h2bNn2bhxIxEREVy/fh1HR0dsbGy4fv06o0aNYujQoVnY2s/PuHHjGDp0KD169KBHjx6ULVuWQ4cO0a9fP86fP09wcDChoaEA/Pzzz8THx7N9+3YsLS0pUKAAPXv2JE+ePHIuCCGEEEIIIcR7kvCOEEIIIUQmevr0KStXrmTAgAHUq1ePjRs3Ymlp+bfeS4q8QohP2V+NWvNnfvjhB6ZNm8aqVauUqTsiIyMZOnQoPj4+DBw4EHi78PuxGLZ99OjRLFiwgFu3bgFQqlQpevTogY+PDwDPnz9nw4YN+Pj48OLFC2X0DrVaTcmSJdm4cSNubm5S3BQiixiee7du3cLR0REzMzMSExN59OgRHTt25KeffmLIkCH07NkTBwcHZZs/uhf7J/2b+GtJSUmEh4dz6NAhDh48SK5cuTh58iTOzs5Z3bTPxu3bt6lTpw4uLi7MmTMHd3d30tLS2Lt3L507dyZ79uwcPXoUJyendNvpR8h71yhVQgghhBBCCCH+nIR3hBBCCCEy2ZMnT1i6dCmFCxemSZMmWd0cIYTIUsuXL6dp06bY2Nj85br6Qvnhw4dp06YNpqamtGzZkidPnrBt2zayZctGbGwsefPmzbD2GhbrAwMDmThxIl999RWNGzcmZ86c+Pj48OrVK/z9/Rk5cqSy3eXLl1m7di1Xr17F0tKSChUq0LRp03RBACFE5jI89/SBQDMzM6Kjo7Gzs0On03H27Fl69+7N+fPnGTJkCN26dSNnzpzodDqio6O5ffs2ffr0kUD1ezIcsejvMAxGJSYmsmnTJmrXro2Li4v0pR/R0aNHqVq1KsuWLaN9+/akpaWxfv16Bg8ejJGRESdOnMDR0ZHU1FTu3LmjjCaq/3zkIQMhhBBCCCGE+HAS3hFCCCGEyAL/tHAhhBCfg8mTJ+Pn58eIESP44YcfsLa2fq/tXr58yerVq5kxYwZnz57F3NycMmXKsGbNmkwr4M6aNYvg4GDatm1L//79cXd3B6BQoUL89ttvpKamMnz4cEJCQv70fWSEDiGyhmG4YNCgQcyZM4dKlSrRp08fWrRokW7ds2fP0qtXL3766Sf8/f3p3Lkz586d44cffuD27dvcu3cPW1vbLNiLT8fx48cpXLgw9vb2//g++F3BELm3/vvedR3as2cPDRo0YM2aNXh7exMZGUlAQABGRkYcP35cGXEnMTGR5s2bM2zYMGrXrp0VzRdCCCGEEEKIz4Z8qxVCCCGEyAJSXBBCCGjUqBGXLl1i1KhRqFQqBgwY8Jcj8Oh0OiwsLGjfvj3fffcdhw4dIm/evBQoUAA7O7tMCe7cvHmT5cuXU6VKFXx8fHB3d+fZs2dUqFCBly9fMnjwYCZNmsTIkSNRqVSMGDEC+N90Ivr9UKlUEtwRIovowx9Tp05l2rRp9O/fn969eytBPEOlS5dm7ty59OnTh5CQEBYtWkRycjLZs2fnwoUL2Nraykgjf+LatWtUqlSJPHnycP78+X8c4HnXcZZ76/fzZlDH8HPYvn07NWvWxMrKily5cgEQFxeHVqslMDDwreAOwODBgzl37hw5cuTI3B0RQgghhBBCiM+Q/EoohBBCCCGEECJLFC1alMGDB9OtWzdCQkIYP348arX6T7fRF21NTU2xtrbGy8uLcuXKYWdnh1arzZQpU9RqNRqNhvbt2+Ph4cGLFy/46quvePr0KWPHjiUkJIT169cDEBoayvDhwwGU4I7hfgghss7Dhw9ZtmwZZcuWpV+/fu8M7uiVLl2ayMhI2rZtS65cuahbty6xsbEULFgQtVot5/SfyJ8/P927d+fevXtUqVKFJ0+eYGJi8pf9vfj49MGdWrVqsWjRIiW44+PjQ/fu3Tl8+DBarZbixYvz7bffMmvWLPr27YuxsfFbwZ1ly5axc+dO6tWrh4eHR5bsjxBCCCGEEEJ8TuSxFCGEEEIIIYQQWaZgwYL4+fnx6tUrPD09/9HoCZk1ik2+fPlYtmwZJUqUQK1WExQUxKVLlxg9ejStWrUCoECBAuTKlQuVSkVYWBjW1tb4+fllSvuEEO/n7t27nD17lpEjR1KgQIG/HLnL2dmZFStW8OLFC0xNTTE1NUWj0cioL39Co9GQLVs2pk+fjoWFBdOnT6dKlSocOXLko0yhJT7cwYMHOXTokDJyXVxcHHPnzsXX15dSpUphZGSEkZER7dq14/z58/zyyy8EBASkC+4sWrSIcePGkS1bNiZOnIiVlZWMPiWEEEIIIYQQ/5BKp9PpsroRQgghhBBCCCH+ewyn73jx4gWWlpZZ3KL3py9SPnr0iDp16mBlZcXBgwcxNTUFXk+RVa5cORo3bszBgwdZvXo1rq6uWdxqIYShkydPUqFCBfz8/JgwYcJby/Vhnt9++w21Wi3n8N+knzIwJSWF4OBgwsPDKVSoEPHx8Tg4OHxwgMcwJKK/jrw5HZT4c5s2baJv377cvXsXgJEjR9KpUyfy5cuX7ljOnz+fMWPGcOfOHTw9PfHw8ODy5ctcuHABJycn9uzZg5ubW6ZMWSmEEEIIIYQQnzv5ViuEEEII8ZFotdp3vi5ZaSGEeO3NflJfHNTpdFke3PnQvlpfOE5MTOTWrVvkyZNHCe6kpaUxffp0Hj58SGBgIIcPH8bV1VWmiBEii2g0mne+bmtri0ql4sCBA1y6dCndMp1Op4QRBgwYwKBBg3j16lWGt/Vzo9FolCkD7927x1dffUXFihW5fv06NWvW/OAptAyDO/v372fs2LG8fPlSgjvvSX8d/vbbb3F1dVWuW3Z2duTLl0+5FurX69GjB/PmzaNv375cvnyZqKgokpOT6dq1K4cOHZLgjhBCCCGEEEJ8RPLNVgghhBDiI9BoNErR4OrVq/z444+kpKSgVqtRqVR/WDQyJCEfIcTnzLCf3LdvH8uXL2ft2rXcvHkTlUqVpX2gYTH40aNHymvvw87Ojly5crF9+3bmzp1LWloay5YtY/HixRQtWhQjIyPlvWVqGCGyhj5YsHLlSo4dO6a8XrhwYXr37s3p06dZs2YNiYmJyjJ9v7R06VLi4+Nxd3eXc/gDGQaggoKCqFq1Kn379uXp06c4OTlx8eJFqlevzuPHj98rwGPYV+/evZuBAwcya9YsHj9+nOH78rkwMjJCrVbz6tUrHj9+TJUqVXBycqJfv36sXr0alUqlXLf0AZ4GDRowdepULl++zKVLlzh16hQTJ04kT548EtwRQgghhBBCiI9Ips0SQgghhPiHDAsJQ4cOZc6cOSQkJFCyZEm8vLwYMmQI2bNnf+eP27du3SIxMZEvvvgiK5ouhBCZwrCfDAwMZOLEicqy3Llzs3z5curWrZtVzVPUrFmTXLlyMWXKFJydndO1+130y2NiYmjUqBEvX77E2tqaZ8+e4ebmxoEDB3B1dZXpXIT4F9i+fTteXl58++23BAcHU7ZsWQAOHDiAv78/Z86cwdfXF29vbypWrIhGo2HlypWMGzcOY2Nj9u7dS+7cubN4Lz5NY8aMYdiwYfj6+tKzZ09cXFy4d+8evr6+bN26lSJFinDkyBHs7e3/cAotw/54z549DB48mCtXrhAXF0fp0qUze5c+afpjmZKSgkajYe/evfTs2ZP79++zatUqvv/+e4B0n4X+z391XRRCCCGEEEII8fdJeEcIIYQQ4iMZO3Ysw4YNo2rVqhQtWpTY2FiuXLlCkyZNWLp0Kba2tukCPHfu3KFFixY8fvyYyMhIypcvn8V7IIQQGWvy5MkMHjwYLy8vmjdvzokTJ1i4cCFqtZqVK1fSsmXLLG3f119/ze7du+nRowfBwcEfFOD56aefGD9+PAD58uWjf//+MiqBEP8ijx49YuzYsUybNo1vv/2WwYMH8+WXXwKwadMmwsPDOXr0KHZ2dnz55Zc8fPiQK1eu4OTkxP79+2V6oL/p8ePH1KxZE2NjYzZt2kSBAgWUQGNKSgq+vr7MmzeP4sWLc+jQoXcGeN4M7gQGBnLt2jViY2MluPMe3vx3qx8R1PC1NWvW8MMPP7wV4IHX05NduHCBrl27ZvkUl0IIIYQQQgjxOZPwjhBCCCHEP6TT6UhMTMTLy4tChQoxevRo8uXLR3JyMt999x27du2iXr16REZGpgvw/Pzzz7Rp04ZHjx5x+vRpnJycsnpXhBDio3qzYPjNN99gamrKtGnTcHNzA2DFihWEhoZy+/btLAvwGI6M07ZtW1avXk379u2V/vx9t09NTcXMzEzZbyn0C/Hv8uTJE8aOHcukSZNo3rw5gYGBeHp6AnDq1Cn27t3L7NmzefnyJS4uLlSvXp2AgAAJ4v0DN27coFChQnTp0oWFCxe+1T++evWKb775hpiYGDw8PDh06BAODg7Kcgnu/DOG17fFixcTHx/P1atXyZ8/P97e3tSsWZMcOXIA6QM8ERERtG7dmk2bNhEYGIiFhQUxMTHY2tpm4d4IIYQQQgghxOdNwjtCCCGEEH/Dm1OgPHr0iEqVKrFo0SJq1qypFHBfvnxJ27Zt2bRpE3Xr1iUyMhI7Oztlu3PnzuHs7IyDg4NMqyKE+GwFBARQokQJ1q5dS48ePWjatClpaWmYmpoCrwuGw4YNy9IAj2FhvlmzZmzevJnTp09TpkyZ934PfZFZphURIuu8K2RjeI/1ZwEegKSkJLRarRJoMDIykuDOP/D7779TrFgxChYsyN69e7G3t1eW6Y/rtm3baNeuHUlJSVhbW3P9+vV098sgwZ1/ys/Pj8mTJ2NhYYGNjQ2///47AN27d6dr165UqFABgLVr1+Ln58edO3f44osvuH37Nubm5sTFxVGwYEG5vgkhhBBCCCFEBpLqkBBCCCHEB9JoNEoBSP+U9uXLlzExMVEKPfqRFywsLFi9ejXffvste/fupVWrViQkJCjvVapUKQnuCCE+a6dOnSI8PJzOnTtz5MgRtFotACYmJsqfv//+e8LCwsifPz/t2rUjKioq09upHwkCYOPGjRw7duyDgjuAUtCUwqYQWUcfslm/fj0///wz8DqAo+9v7O3tCQoKYuDAgWzYsIEJEyZw4sQJZXtLS0tsbGwwMjJS7s0kuPP35c6dm0qVKvHjjz8SGRlJSkoKkH4qrDx58mBkZMRXX32FSqXi1atX6d5j9+7d+Pr6cuPGDQnuvCf9v3d4PcLdvHnz6NevH7Gxsfz666+sWLGCunXrsmjRIsaNG8ePP/4IgLe3N/PmzaNly5YYGRlRs2ZNjh07RsGCBVGr1XJ9E0IIIYQQQogMZPLXqwghhBBCCD2dTqcUcIKCgpg6dSopKSmYm5uTmprK1atXKVeuXLopAbJly8bq1auVoefr1avH3r17sbGxUd5XgjtCiM9V+fLlmT9/PmPHjuXGjRtcunRJWaZSqZTw4vfffw9ASEgILVu2JDo6Gi8vrwxtm2HxWN+/v3r1CnNzc2UkDhllQIhPg2EQOioqCm9vbzp27MiQIUNwd3dXAjxGRkbY29vj6+vLvXv3WLt2LRYWFvTv358vv/xSgjp/w1+F0AcPHsy5c+eYNWsWLi4ufPXVV1hYWCh96+7du/Hw8CAqKgqtVouNjY1yL/3ixQtOnTrFzZs3iYuLk+DOe9DpdMrnkZiYyIMHDyhbtiwDBw7E1dUVeD1FZKlSpZg2bRpLlizB3d1dCaw2bNiQ6tWro9VqMTExwdLSEo1Gg4mJ/IwshBBCCCGEEBlJps0SQgghhPgbpk2bRkBAAPXq1aN27drExsYSHR2Ng4MDBw8epHjx4ukCPMbGxqSkpPDNN99w5swZLl26RM6cObN6N4QQIkOp1Wql2Ldo0SKCgoJ49OgRGzdupGnTpui/jhoWGpcuXcqCBQtYs2YN+fPnz7C2GU6Dk5SURGpqKg4ODhn2/xNCZBzD8/nVq1e8ePECf39/VqxYQfv27Rk8eDDu7u5A+qDJkiVL6Nq1K+bm5lStWpUpU6ZQsmTJLNuPT5Hhsd+6dSsXLlzg119/JW/evLRr144CBQrw8uVLZsyYwejRo8mZMyft27enS5cuODg4EBUVxfjx43F2dmbz5s1ky5btrdDk5cuXyZ49O87Ozlm1m5+kQYMGceLECZ48eUK1atWYO3cuWq023cMIJ0+epHv37pw9e5Zjx469M7gqIVYhhBBCCCGEyBwS3hFCCCGEeA9vPlHcpEkTzMzMmDx5Mi4uLgAMGzaMMWPG4OjoyOHDh3F3d38rwJOamsqzZ89wdHSUqbKEEJ+V9+nTFi9ejJ+fHwkJCWzZsoVGjRq9M8Dz4sUL5Un/jBgFw7CtEydOZP369dy5c4dKlSrRr18/PD09sbKykoKlEJ+YAQMGcPToUQ4cOMCjR48YO3YsCxYsoFOnTukCPKmpqZiZmXHr1i1atGiBq6srR48e5ezZszg6OmbxXnw6DPvSgIAAZs2aRUpKCtmyZePly5c4OjoyduxYWrZsiU6nY9myZUybNo0bN25gZ2eHpaUld+/exdnZmbi4OFxcXKTf/Uh0Oh3t2rVj9erVWFtb07p1a+bMmfPO4ztp0iT8/f1ZsGABXbt2zaIWCyGEEEIIIYSQapEQQgghxHvQFyaGDRvGnDlzePbsGW3btsXFxYXU1FQAwsLCCAkJ4dGjR1StWpWrV6+mC+5oNBrMzMwkuCOE+OxoNBqlT9uyZQtjxoyhTZs2DB8+nOPHjyvrdenShfDwcGxtbWncuDHbtm1TiogqlUoJ8lhaWgJk2PQ1+rYGBgYSGBjI3bt3sbKyYtOmTbRv354lS5bw7NmzdG0SQvy7zZ49m0WLFlG4cGEePnyIq6sr/v7+dO/enaVLlzJu3Dhl2j4zMzM0Gg1z5sxRRoW5dOmSco8m3o++Lx01ahTh4eG0b9+ew4cPk5yczLJly7C2tqZ79+6sW7cOGxsbunfvzvbt2+nevTtlypTB3d2dfv36cfToUVxcXNBoNBLc+UhUKhXLli3D19eXZ8+esXDhQg4dOpRuNJ20tDQAatWqBcCDBw+yqrlCCCGEEEIIIQCZrFgIIYQQ4j2dO3eOmTNn8uzZMywtLVGr1cD/CkDGxsYMHz4cgJCQEKpWrfrWCDx6EtwRQnwutFqt0r8FBgYqxXAjIyPS0tIYM2YMo0aN4vvvv6dAgQJ06dIFlUrFoEGDlABPw4YNM2W0BcPg5Llz51i1ahV9+/Zl0KBB5MyZk+3btzNixAhGjBiBWq2mS5cuWFtby0gQQvwLGZ7PL1684PTp09SuXZtx48YpoyIWKlSIgIAAVCoV8+fPJzk5mc6dO9OgQQNWrlxJdHQ05cuXx9HREVNT03QjgIn389NPP7Fo0SK8vLzw9/enUKFCwOv748TERPLkyUOzZs0AsLCwoGjRosybNw+NRqMcbyMjowwbae2/QH+NMrxWabVaTExMGDt2LCqViilTphASEkJ4eDjlypVDq9ViamoKwM6dOwGUkamEEEIIIYQQQmQN+UVCCCGEEOI9lSpVilmzZuHp6cmLFy84c+aMEuDRj6wDMHz4cEJCQnj69ClFixblxo0bUowQQny29IXukSNHMnHiRFq1asXu3bv56aefGD16NG5ubgwfPpzp06dz584dADp37szkyZNxcnKiUaNG7Nu3L1PCMfq2XrhwgZMnT5KamkrPnj1xdXXFwsKCpk2bsnDhQvLly0doaCiLFi2SEXiE+JfSn88hISGMHz+emJgYGjRooEy9pB9Bp2DBgvj7+9O/f3/Wrl1Lw4YNcXFxoXPnziQlJREWFqaEGCSk9+Fu377NrVu3aNu2LYUKFUKtVrN69WoCAwPJnj07Z8+exd7enlevXimjVeqDOiYmJsrnKPfKf4/haEWJiYncuHGDp0+f8uzZMwCyZcvG2LFj8fHxISYmhl69erF7927leK9atYrly5dTtGhRatasmWX7IYQQQgghhBBCwjtCCCGEEO/0ZpFWXwBq27Yt/fv3p2jRooSHh7N27VplnTcDPAMHDiRPnjxKQUgIIT5XP//8M4sWLaJhw4YMHz6c2rVr4+7uTlBQEHPnzqV27drMmDGDrVu3Ktt06tSJkJAQPDw8MvVp/0mTJlG2bFl2795NrVq1KFGiBFqtVhmloGLFisyfPx8XFxdGjhyZLsAjhMh6hvdoN2/eJCIigrFjx/Lbb78pU+5B+lEOCxYsyKhRo1i/fj2VKlWiaNGitG3bNt10TeKvves4/f7778D/Rm1Zt24dgwcPRqVScfz4cRwdHYHXIZ969erx9OlTCep8JIYj302aNIm6detSqFAhChYsSOPGjdmxYwfwehSkqVOn0rdvX06ePMnXX3/NV199RdGiRQkODsbExIQdO3bg5OQk08YJIYQQQgghRBaS8I4QQgghxBsMn2DVarW8evWKlJQUZXnbtm0JDg7G1dWVzp07/2GAZ/z48fz888/ky5dPikJCiE/elStXuHnz5juX3b17l9u3b1OnTh3y58+PVqtVRiarW7cu/fv3x8rKisDAQK5evaps17t3b44fP46Li4uyfkbSarUUKFCAIkWKsHbtWuLi4rh165YybQu8HnmjQoUKSoBn7NixzJgxg6SkpAxvnxAivTeDBCkpKco92oMHD3B1dWXRokXUr1+flJQU1q9fz61bt94ZtsuePTvNmzdn9+7d7Nmzh0WLFuHs7CzTNf0Bw2OvD0zpj9PJkyeVZTY2NgBERUURERHB4MGDMTIy4vjx4zg5OSnrTZgwgTNnzvDbb79lRvM/e4ZTvPn5+eHv74+ZmRmBgYG0bt2a8+fP4+XlxbRp0wAwMTFh8uTJDBo0CHh93a5ZsyYbN27k4MGDuLm5odFoZNo4IYQQQgghhMhC8o1MCCGEEMKAYQFn8eLFdOrUiRo1avD111+zbNkyLl26BECbNm0YOXIkLi4utG/f/g8DPNbW1uh0OikKCSE+aRcvXqRYsWL4+Pjw8OHDt5anpaW99ZqJiYlS8PXy8qJjx44kJSVx48YN4H+FYSsrK2X9jKQvdHp5eTFu3DgqV67MgwcPWL9+vTK9iJ4+wLNgwQLMzMxYtWpVhrZNCPFu+iDB+PHjuXfvHtmyZQOgX79+BAUF8eTJE6pXr46fnx+1a9dm3759rFq1iidPnvzhe+r7HP3IiHKP9m76Y9+tWzf279+vvN63b19q1qzJmTNnAPjmm28oXbo0M2fOxM/PD5VKxdmzZ5Xgjk6nY9myZezatQtvb28KFSqU+TvzGdIH1JYuXcrs2bPp06cPK1euZOzYscyePZuePXui0+mYOXMmr169Al5fZ0ePHo2Pjw9XrlzhwYMHmJiY4OjoKMEdIYQQQgghhPgXkG9lQgghhBD/zzBk4+fnR48ePdi3bx9GRkbcvn2bLl26MGTIELZt2wZA69atCQsLUwI869evV97LsBAkU60IIT51+fPnx9PTE1NTU6XwDf8bjUE/8sL8+fO5evVqulFsUlNTAahSpQoAd+7cAcjwIuGbI3bo+2IzMzPq1q1LUFAQxYoVY/z48WzevJnk5OS31vf09GTLli3s27ePHDlyvDWlohAi4w0cOJCgoCCGDBkCQGBgILNmzcLe3h6VSoVKpaJGjRqMGDECT09PxowZw4oVK/4wwKPvC+T+7K9t2rSJxYsXM3DgQC5dukS/fv2YPXs2vXv3Jnfu3ACYm5sTEBCAvb099+/fZ8iQIVhbWyvvsWTJEkaPHk327NkZNWoUFhYW0pd+RHv27MHOzo5u3bpRsGBBZQSqyMhI3N3dOXr0KObm5srodmZmZkyZMoVevXoRHR1NUFAQ586dw9jYWM4JIYQQQgghhMhiKp18YxZCCCGESGfq1Kn4+fnh4+ND79698fDw4ObNmwwaNIgNGzbQsWNH5s6dqzz9HRkZSUhICJcvX2bz5s00btw4i/dACCE+vpcvX6LVarGysmL9+vWUK1eOggULKsu7du3KkiVLGDhwIL6+vuTLlw+1Wq2MqDNo0CDmzZvHgQMH8PT0zNC2Go6idubMGe7fv8+9e/eoWrUqOXPmxNbWlpcvX7Jv3z4CAgJ4+vQp48ePp3nz5mTPnv0v31MIkXlSU1Np2LAhBw4coGjRoly+fJnhw4fTuXNnXF1d0el0qFQqdDodcXFxDB48mPPnzzNq1Cjat2+Pvb19Vu/CJ+vJkyfKfa5Wq+Xx48cMGjSIQYMGKeEdgMePH7No0SImT56MVqulYsWKVKpUicOHD3P06FHs7OzYv3+/MjWT9KUfR2JiIqVKlaJMmTJs3rwZtVpNVFQUAQEBb01ddvToUSwsLChTpgzw+prWv39/5syZQ5MmTQgODqZ8+fJZuDdCCCGEEEIIIWTkHSGEEEL8Z705KoNOp+PBgwdERkZSpkwZ+vbti4eHB1qtlvj4eE6dOoWLiwvh4eFky5ZNmSamVatWBAYGUqVKFeUHcSGE+NxYWFhgZWXFsmXL8Pb2ZtSoUdy8eVNZPnDgQKpUqcLs2bMJCwvjwoULSnBn/fr1bNiwgTJlyuDu7p6h7dRqtUpheMSIETRp0oRGjRrRtWtXKlWqRN++fbly5QoWFhbUrVuXiRMnYmdnR2BgIBs2bHhrBB49KTYLkfnS0tIwMzNj3759ODo68ssvv+Dh4UGLFi1wdXVFo9EowR2VSkW1atUYN24cX3zxBcHBwURERPD48eOs3o1Pkk6nw97eXgmyP378GEdHR6pVq6YEd/TPAzo4ONCzZ08WL15MyZIl2blzJ8HBwVy7dg1vb2/i4uIkuPMPvfnspf7vxsbGPHnyhMePH7Nhw4Z3BncAfvjhB+bNm6eMhmdsbMz06dPp3bs30dHRHDp0KPN2RgghhBBCCCHEO8nIO0IIIYT4z/npp5+wsbEhf/78aLXadFO3/Pzzz5QtW5ZBgwYxevRo0tLSiIqKIjAwECMjI06cOIGjoyM6nY47d+7g4OCApaUl8HpUCgsLCylMCCE+afoiOLwOwhhOKQjw/Plz2rRpw9atW+nUqRPDhw/Hzc0NtVpNbGwso0aNIiYmBgcHB7755hvu3r3L2bNnMTMz4/Dhw7i6ur7V92aEIUOGMG7cOJo0aULLli1xcnJi9uzZREdHkydPHg4cOECRIkVIS0tj9+7d+Pv7k5SURHBwMO3atVP6diFE1tJoNMTFxVG7dm1y5szJgwcP6NixI+Hh4Tg4OCj9yZsj8AwdOpS4uDgWLFhAly5dZEqgv0Gr1XLhwgW+/fZbChQowOnTp3F2dmbu3LlUrlz5D/vxX3/9leTkZAoXLoxKpcLMzEzuj/8Bw2vmixcv0l2funXrxrp16+jfvz8rV64EeCu4M2HCBMaMGcOUKVPo3LlzuvdWq9VERETQsWPHTNgTIYQQQgghhBB/RkbeEUIIIcR/yk8//USpUqXo3Lkzt2/fxsjIKN0IPCkpKWg0GmxtbQFYt26dEtw5fvw4jo6OwOtpBBo1asTBgweVbS0sLAAZnUEI8ekyDO6kpqZiZGSk9GnLli3j6NGjZM+enYiICJo2bcrSpUsZOXIkN27cwMTEhBo1arBy5UoGDhyIhYUFK1as4Pr169SpU4f4+HhlpIyMDu7s3r2bmTNn0qFDB8LDw2nbti3169ena9euwOv+OmfOnACYmppSr149wsPDSU1NZc6cORnaNiHEXzO8NzM2NqZixYrExMRw4sQJatasybJly/D19eXx48dv3cupVCqqV69OaGgoXl5e1K9fX4I7f5ORkRFffPEFmzdvZt68eYwZM4Z79+7Ro0cP4uPjldFf9Mdfo9EA4ObmRrFixciWLRtmZmZvhUDF+zO8Zq5cuZLOnTuzceNGZXmTJk2wsLBg7NixvHr1ilOnTqUL7qxZs4b58+dTunRpmjZt+tb7m5iYKMGdN0clFUIIIYQQQgiRuUyyugFCCCGEEJmpZMmS1K1bl71799K3b19mzpypjMCjUqmws7MjV65cLFu2jOzZszNu3Lh3Dj0/adIkfvnllwwvQAshRGbSF7irV68OwMGDBzEyMqJXr17Mnz+fiIgIypcvT44cOVi+fDkdOnRg6dKlAAQHB1OgQAHy5s1LeHg4gwcPJikpiVy5cmFsbEy2bNkybeSFU6dO8fLlS/r27UvhwoVRq9VERkYSHByMq6srx44dw9bWllevXqHT6bCwsKBOnTqsWrWK4sWLy6g7QmQhw35ix44dXLt2jU6dOlGjRg0A9u3bR61atYiIiABg6tSpODg4KNufO3cOW1tbateuTdWqVWXUlw/w5nHSBzpLliwJvJ4eKyUlhVGjRtG9e3cWLFhAhQoVMDExQafTsW/fPlJTU/Hy8kr3PhKe+nsMp4EMCgpi7ty52NvbU7NmTWWdb775ho4dO7JgwQJUKhWnT58md+7cuLq6MnXqVBYvXoxWq2XlypXY29v/6ch38r1GCCGEEEIIIbKWTJslhBBCiP8MtVqNicnr7HKzZs3YvHkzjRs3VgI8en369GHOnDnY2NiQI0cOrl69SrZs2YDXP6JHRkYSFBREmTJlWL58OdbW1lmyP0IIkRGSk5OpXr06P/74I61atcLOzo65c+fSv39/AgICyJs3r7JuUlISHTp0YPPmzXTq1IkRI0bg6uoK8FaB0HBUn4yi0+nQ6XS0adOGPXv28OjRI9LS0ti4cSMBAQFvhTGvXbvGvn37aNu2LdmzZ1feRwr9QmQNw34jNDSUOXPmYGZmxsKFC6lfv75yL6fT6ahVqxaxsbG0bduWefPmYWlpyZYtWwgICKBhw4ZMmDBBue8Tf82w31u3bh2nT5/m0aNH5M6dm9atW+Ps7IyNjQ0JCQksX76ckSNHkitXLubNm0fZsmWJjY3lhx9+ICUlhfPnz2NpaSmhnY8kJCSEUaNG0aNHD/r374+Hhwfwv/MlLS2NsLAwFi1axN27d9P9uy9XrhyRkZHKyHdybRNCCCGEEEKIfy8J7wghhBDiP+V9AjwJCQm0bt2aXbt20bx5c5YuXaoUdadNm8aMGTPQarXExsbi7Oz8p0+wCiHEp0Tfn7169Yrvv/+e6OhoAAYMGEBYWBhWVlZvbZOUlETHjh3ZtGnTWwGejPZHhUhfX1/mzJnD9evX+fHHH+nTpw9GRkacOHFCmf4QoEaNGqhUKqKjo7GxscmUNgsh3s0w4Ofv78+UKVPw9vbG19eXChUqKOsZBnhq1qxJXFwcVatWpUKFCmzevJlHjx5x+vRpChYsmFW78knz8/Nj8uTJ6V5zdnbG29ubgQMH4uzsTEJCAitWrGD06NGYmJjg7u7OlStX0Ol0xMXFybH/iA4fPsx3331H5cqVmTJlylvXV/11UKPRcPbsWXbv3s0vv/yCpaUlNWrUoFatWtjb20twRwghhBBCCCE+AfIIkhBCCCH+U0xMTJSiz8aNG5UAD6AEeKytrZk4cSJarZYNGzawf/9+Spcuzd27d7l58yaFChVi27ZtODs7yw/hQojPipGRERqNBnNzc+zs7JTXL168qAR3DEOQADly5GDZsmV07NiRpUuXkpiYyLRp08iXL1+Gt1ff/wYGBlK6dGnatGkDQLVq1Zg+fTotWrTg999/x8TEhLi4uHTBnZkzZ3L9+nW6dev2zlCSECJz6YM7CxYsYO7cufTp0wdfX18KFCiQbj19YFqlUnHw4EG+++47Nm/ezJkzZyhatCj79u2TUUY+gGFoau7cucybN4/evXvTuXNnHBwc2Lp1K4sWLWLKlCk8fPiQ8ePHkydPHjp16oSjoyNTp07l559/pnTp0ixYsAAXFxc59h/RtWvXuH//Pu3atXtnMNbY2FiZXqtcuXKUK1furXUMp98SQgghhBBCCPHvJSPvCCGEEOI/KS0tDVNTUyD9CDwzZszAxcUFnU6HVqtl1KhRHDlyhKtXr1K0aFFq1KhBt27dyJkzpxQmhBCflTentQoLC+O3337j4sWLxMbG0rhxY1avXo2lpaXS/xlu8/z5cxo3bszFixf5+eefsbe3z5R2nz17lrJly1KxYkW2bt2Kg4MDT548oUmTJhw5cgQbGxsuX75Mzpw5lf1ct24dQ4cOxc7Oji1btpArV65MaasQ4s+lpqbSqlUrTp8+zZYtWyhVqpSybMmSJcTHx/Pbb78xYMAAvvzySyVkGBMTg5GRESVKlMDBwUHu0d7Tm6NHBgQEcPToUVasWIGbmxvwOrD5yy+/0K1bN+Li4ggJCSEgIABzc3MAUlJS+O2338iVKxdWVlZy7D8yf39/Jk2axKlTpyhbtuxbAVr9Z5icnCxBVCGEEEIIIYT4xEl4RwghhBCfvT+a1sqw6PyuAI+hxMREbGxslG1kqiwhxOfEsE+7ceMGbm5uSv+oVqvx8vJi9+7dNGnShNWrV2NhYUFqaipmZmbA6+kGbW1tefnyJcnJyTg6OmZqP9mmTRs2bdrEjh07qFmzJgB37tyhRo0a/Prrr3h5edG+fXscHR2JjIxky5YtqFQqDh8+jKurq/TpQvxLJCQkUK5cOXLmzEl8fDxqtZrY2Fjmzp3LunXrsLKyIjk5GQcHBxYsWMC333771nvI+fzhBgwYwPXr10lMTKRp06b4+fmh/7lQfy04ffo0bdq0wcjIiPj4eKytrd8Kfb75d/H+3vx3qz+WY8eOZejQoQQHBxMaGppuG/06KSkp1KhRg1GjRlG/fv3MbroQQgghhBBCiI9Efs0QQgghxGdNo9EoP4QfOnSIqKgoZs+ezalTp3j27Jmy3saNG2natClbtmyhX79+3L59G3j9Q7pOpyNHjhwASiFDikJCiM+FYT85ffp0mjdvTosWLYD/TZG1Zs0a6tevT3R0NK1bt+b58+eYmZmh0WjYsWMHo0eP5tSpU1hYWGRqcEej0QDQr18/jIyMmDRpkrIsX758xMbG8vXXX7N3715atWpFnTp1WL16NWXKlOHo0aPK1DrSpwvx72BmZkalSpU4fvw4PXv2xNvbm5YtW7J//37GjRvHnj17WLRoEYmJiUyePBmdTsebz6TJ+fxhEhMT2b59O7t27eLMmTPKPbBarU4XxPHw8KBevXpcunSJnTt3ArwV1JHgzt9jeB26cuUKDx8+TPeAgZ2dHVFRURw+fFjZJiUlBZVKhU6nY+HChVy9epW7d+9mSfuFEEIIIYQQQnwc8ouGEEIIIT4rJ0+e5Pjx40ohRz9s/9ChQ/Hy8sLb25u+fftSu3ZtmjVrxvXr15Vt3xXg0f+Q/uZ/hRDic6DVapV+0t/fnyFDhmBra4u3tzcAJiYmaDQabG1tWbt2rRLg8fb25tGjR6xbtw5fX19WrlyZbsSyjOgrtVqt8uc3+3gPDw8qVarEtm3b2LZtG/B6ekRnZ2fWrl3L4cOHWbp0KUuXLiUuLo7IyEjy588v07sI8S9jaWlJz549qVWrFgsXLuTIkSNUq1aNU6dO4e/vT6VKlWjdujWOjo44ODigUqkkMPIP6HQ6bGxs2LdvH56enrx48YL4+Hhlell9QFKj0WBhYUGjRo2A18ER8XEYXocmTpyIl5cX3377Lffv30ej0VCoUCEGDBjAlStXGDNmDLt27QIgW7ZsAERFRTFjxgyKFi1K48aNs2w/hBBCCCGEEEL8cyZ/vYoQQgghxKfhzp071K9fHzs7O9asWYOnpycA/8fefcZFcb1tHP/RixQRRAEFFAULduy99yj+7S2xNxQVQVBREexiwYYNu6go9hJb7AbsXWOJvSuKKFJ293nhZ+cBNYlJFJTc3zcmO4Uzs+yZZc419wkJCWHixIk0bNgQT09P3rx5Q3R0NPv376dSpUps376dMmXKAO8DPNoptF6+fMmqVauwt7fPzMMSQoivRhuyGTt2LNOmTaNv377069cPNzc3ZR3toKKFhQVr1qyhY8eObNu2TZluys7OjiNHjpAzZ84vWnFHW/UH0k8n8uG0LNpwUWBgIAcOHODnn3+mcePGysCzmZkZpUqVolSpUun2r9FoJLgjRAZ5+/Ytpqamn7Vu9erVWbFiBU+fPiVbtmw4OjoqU/SpVCqWLl1KQkICFStWBGSqpr/jw3OlnQrW0dGRtWvX0qpVK2JiYmjfvj1RUVHo6ekpQR61Ws2BAwcAyJkzZ2YdQpaS9jo0ZMgQZs2aRf369Wnfvj25cuUC3l+D27dvz8OHD1m8eDEnTpygYcOGlC9fnsOHD7Nnzx4MDQ35+eefsba2lmnjhBBCCCGEEOI7pqP5sL6wEEIIIcR36tWrV8yZM4dp06Zhb29PeHg4rq6utGzZkoIFCzJ8+PB0lSEGDx7M9OnTyZUrFzExMemW1axZk9jYWG7fvo2NjU1mHI4QQmSIU6dO8cMPP1ChQgWmTJmCs7OzsuzSpUs8f/4cZ2dnrKysMDMz4/Xr10ydOpXff/8dQ0NDRo8ejb29fbqwzb915MgR9u/fT/fu3ZUBTIA+ffpw6NAhhg0bRsWKFcmXL5+y7OnTp7Rv3569e/dy6NAhKleu/EXaIoT4dw4ePEi3bt1Yt24dJUqU+NN1/yyIo9FoWLlyJePGjUNfX589e/Zga2v7NZqcJaWt8JKSkkJcXBympqaYmZkp69y9e5fWrVsTExND48aNiYiIwMrKCn19faKioggICMDU1JT9+/eTI0eOzDqULGfOnDl4e3vj5eXFgAEDlGtb2s/D7du32bZtG+PHj+f+/fvA+xBV+fLlmT17tlSTE0IIIYQQQogsQMI7QgghhMgStDe34+PjWbRoEcHBweTPn59Ro0bRq1cv5s+fT5MmTdBoNKSmpmJgYAC8HwieN28eHTt2ZN68eejr6yvLnj17ho2NjTzBKoTI0qKjo2nZsiURERH89NNPANy/f58ZM2Ywf/584uPjyZ07N0OGDKFLly5YWVkp/aI2sPMlBwzfvn1LvXr1OHr0KFFRUfzvf/9Do9Hw5s0bmjZtypUrV3j8+DEFChQgICCAatWq4eLiAkBERATdu3dn0KBBTJw4EV1dXem/hchEGo2GCRMmMHz4cNzc3IiKisLd3f1v7UOlUpGQkMCIESPYvHkzenp6/PLLL0r1L/mM/7W0fXRYWBibNm0iNjaWnDlzUqFCBYKCgsibNy/GxsbcuXOHtm3b8uuvv+Li4oK9vT36+vpcvnwZCwsLdu7cibOzs5z7L0Cj0RAXF0eLFi24f/8+O3fuVK5nf+TZs2dcv36dFy9e4O7ujrW1NdmyZZPgjhBCCCGEEEJkARLeEUIIIUSWoQ3wvHr1ioiICMaMGYOxsTHJycns2rWLMmXKKAMN2hvcKSkplCpVCo1GQ0xMDGZmZulufsvAhBAiq9u8eTPNmzdn5MiRdOzYkS1btrBixQouXLhAw4YNyZMnD3v37uX169fs37+fAgUKfPU2HT9+nI0bN+Lr60v27Nl5/fo15ubmvH37lgcPHjB37lyWLVvG8+fPcXV1pUWLFvj7+2NgYECHDh04cuQIZ86cwc7OTqbUESKTvXv3jhkzZjBmzBjs7OzYsGEDxYoV++zt7969S7t27Th79ix169YlLCyMPHnySFjhM6XtA318fJgxYwaFChWiSpUqXL9+nWPHjuHq6sqQIUNo1qwZZmZm3L9/n5YtWxITE4OtrS1eXl6ULl2aEiVK4ODgIOf+C/rtt98oVaoUbdq0ISIi4pPXLO3fI9opzD4k1zkhhBBCCCGEyBpkJEoIIYQQWYaOjg4ajQZLS0u6du1KYGAg5ubmxMXFsXr1at68eaMEcfT09EhKSsLAwIASJUpw+fJlLl68iEajSTcYIcEdIURW8UfPbbi5uVG9enXGjBmDq6srPj4+vHnzhh07drB06VJmzZpFx44defDgAb/++muGtLVs2bIEBweTPXt2AgMDGTt2LI8fP8bU1JQCBQoQGhrK9u3bmTFjBnFxcUyYMIGyZcvi5+dH7ty5efnyJUFBQaSkpMiAphCZSK1WY2xszMCBAxk+fDgPHz6kefPmnD9//rP3kTdvXsaOHUtkZCRLliyR4M7fpO0D582bx8yZM+nduzfr1q0jPDyc1atX06VLF86ePcvatWsxNDRErVbj4ODA2rVr8fDw4MmTJ9y7d49GjRrh4OBAUlKSnPsvSKVSKZVB4eNrtTa4ExcXx61btz65D7nOCSGEEEIIIUTWIJV3hBBCCJHlaJ8+ffnyJREREUydOhV9fX0WLlxIzZo10dPTU6Z6AWjevDknT54kNjYWOzu7TG69EEJ8eR8OdGsr2WjFxsZy6NAhrl27RunSpWnbti0WFhbK8r59+7Jhwwb27t1LkSJFMqzdjx49okaNGty+fZvAwEB69uyJjY1NunVu3rzJli1bWL58OadOncLU1JS3b9/StGlTVq9ejYmJSYa1VwiRXtq+JzU1lcDAQMLDw8mdOzfr1q2jaNGif7r9n1UhEZ9Ho9GgVqtp1KgRd+7cITo6msKFC5OcnMzWrVsZOHAgRkZGHD16lJw5c6Y753fv3qVVq1bExsbSuXNnlixZApDue7T45zQaDXfv3qVKlSq8fPmSAwcOUKpUqXTLte9FvXr1MDQ0ZN26dRgbG2dWk4UQQgghhBBCfEVyt0MIIYQQ3zW1Wv3Ra9qb3NmzZ6dLly4MHjyY169fM3DgQLZs2cKrV6+UAYfo6Gj2799P4cKF0w1kCyFEVpF28HzBggW0bNmSQoUKUbNmTfz8/NBoNJQrVw4fHx/Cw8Pp2bNnuuDO2rVr2b59Ox4eHuTNm/ertvXDPj1XrlysXbuWsmXLMnbsWMLDw3n27Bnw/wPS+fPnx9vbmxMnTjBx4kRq1qyJtbU1M2bMkOCOEJlIrVYrfc/kyZPp3bs3S5YswcTEhKtXr9KmTRsuXLjwp/v4VEURCe78PTo6Ojx9+pQDBw5Qp04dChcuTFJSEhs2bGDQoEHo6elx7NgxJbhz+vRpZdu8efMSFRVFuXLlWLZsGV27dgWQ4M7fpH1uMu3zk2q1Gh0dHRwdHenQoQMJCQmEhITw22+/ASiV41QqFZGRkVy/fh0nJyf5/RdCCCGEEEKILEwq7wghhBDiu5V2QHr//v1cvnyZhw8fYmNjQ9u2bTE3N8fExIS4uDgWL17M+PHj0dPTw8PDgzZt2rB9+3bOnz9PfHw8hw8fxtHR8ZNPeAshxPcqbZ/m4+PDzJkzyZMnD8WKFePatWtcuXKF2rVrM378eEqXLv3RoGBYWBgzZsxArVZz8OBB8ubN+9X6ybT7ffv2Laampsqyc+fO0adPH86cOUNAQAC9e/fGxsZG2Sbt9eD169cAmJubS3UIIb4Bfn5+zJw5k0aNGlGrVi3Mzc1ZtGgRhw4dokCBAkRHR+Pu7p7ZzczSXr9+jZOTE40aNWLZsmVs2LCBwYMHo6ury/Hjx9NVNLOzs6Nbt26EhIQofeu9e/do1aoVMTExzJs3jx49emTi0Xxf0l6f4uPjiYuLI1euXOjo6GBkZKS83rlzZzZv3kzt2rUJDg7Gw8MDfX195s+fz7Rp01Cr1fzyyy/Y29tn5uEIIYQQQgghhPiKJLwjhBBCiO9S2ikTAgICmD17NgkJCcryokWL8uOPP9KlSxesra2VAM+sWbO4desWxYsXJ3fu3JQrV46ePXuSJ0+ej6aVEUKIrCIsLAwfHx969+5Nnz59KFKkCLdv38bHx4fo6Gg6depERESEMq3gyZMn8ff35+TJk7i6urJ+/XqcnJwypJ/08/Nj165d7N69m5w5cyqvnz9/nj59+nD69GkCAgLo1atXuuUfhookjClE5tu5cydNmzalffv2BAcH4+joCMC7d+8YM2YMoaGhODk5SYDnC0n7/Thtf/3u3TsqVqzIs2fP6NatGxEREejr6/Prr79ia2sLvO8zx4wZQ1hYGHPmzKFNmzbp9nPr1i2WLFnC6NGjM+XYvkdp34MZM2awatUqTpw4gZOTE/Xr12fAgAEULlwYjUbDjRs3GDZsGOvWrQOgcOHCJCYmcufOHfLnz8+uXbtwdnaWv1eEEEIIIYQQIguT8I4QQgghvmujRo0iJCSEH3/8kXbt2mFhYcGaNWtYv349z549Y8CAAfj6+pIjRw7i4uKIiIhg7ty5xMfHs2/fPooWLfpR1QYhhMhKEhISqFu3LikpKaxcuRI3NzeSk5PZuXMnffv2xdTUlCNHjqQLwmzZsoUFCxZQpkwZ+vTpg62t7VfrJ9PuNyUlhc6dO7N27VoaNmxIRESEMrAMfx3gEUJ8W8LDw+nbty/r16/H09MTeP85NzAw4N27dwwdOpSZM2dStGhRIiMjJcDzL6TtS9evX8+JEyfo3Lkzrq6u6OnpsX79ejp16kRycjK5c+fm1KlT6YI7a9asYcSIEbi4uLBmzRqyZ8/+yX1D+pCQ+LS052jIkCFMnToVd3d3qlSpwuPHj9mwYQPFihVj0aJFeHh4KNtNmzaN/fv3c+HCBQoWLEi5cuXo168fuXLlkr9XhBBCCCGEECKLk/COEEIIIb5bMTExeHp64uHhwaxZs5Snud+8eUNMTAwDBw7kzp07TJkyhS5duqCnp8erV6+YOXMmK1as4MCBA+TKlSuTj0IIIb6uGzduULBgQcaMGcOIESNITk4mOjqaoUOHppsyRaVScfHiRYoXLw7As2fPsLS0xMDA4KsN1KYdiJw9ezb37t1j9erVJCYm8uTJExo3bkxERMQfVuAZPnw43bt3TxfwEUJ8OyZMmMCwYcPYvXs3tWvXVqay0/YpiYmJeHh4cPnyZdzc3Fi5ciWlS5fO7GZ/dz6sSLlw4UJev37N6tWradq0KXp6ejx69IiJEyeyYMECSpUqRXBwMCVKlEBfX5+5c+cyZ84cdHR0lCkSJaDzZUyePJmgoCC6du1Kv379cHNzAyBnzpw8f/6c/Pnzs3bt2o9+7xMSEjAzM1PeBwnuCCGEEEIIIUTWJ3+FCyGEEOK78WHm+N69ezx58oQWLVrg6OiIRqNBo9GQLVs2atSoQUhICEZGRixevFi52W1paYm3tzfHjx8nV65cqNXqzDgUIYTIMNrB18TERDQaDZs2bVKCO7GxsdjY2ADv+9i2bdsyZ84cAGxsbDAwMEi3jy9N2zf7+vri5+fH+fPn6dKlCz4+Pjg7O7Nt2zbat2/P06dPlW2KFSvG3LlzKVu2LCNGjCAyMvKj64MQ4tuQN29eAObMmcPbt2/R19dHo9Ggq6tLUlISJiYm1KhRA3d3d65evYqXlxepqamZ3Orvj7aPDgwMZPLkybRq1YrY2FiaN2+u9LO5c+emV69e9O7dm5MnT1KrVi3Kli1LoUKFCAoKIkeOHOzfv5+8efOiUqkkuPMFnDlzhiVLltCsWTP69u2Lm5sbcXFxFC1aFD09PRo3bszNmzdp06YNp0+fBt4HsdRqNaampgDK9I8S3BFCCCGEEEKIrE/+EhdCCCHEd0GlUik3r7Vu3LiBWq0mOTkZgNTUVGUdXV1dqlatSsWKFTl27BgHDhwA3g9Om5ubY25urgweCSFEVqBSqT75upWVFba2thw4cIBZs2YxZMgQdHV1iYmJSVfRJjAwkEePHpE/f/6MajIAUVFRhIaG0qFDB+bMmcPIkSPx9fXll19+oVOnTuzdu5eOHTt+FOCZNm0azZo1o0WLFh9dH4QQGefPgtAtWrSgVKlSbN++nSVLlpCYmIiOjg7JyckYGRkB77/Pubu7M3v2bNasWYO+vn5GNT1L2bNnD7Nnz6ZVq1b4+fkpVdTSKlSoEIGBgezcuZOWLVuSL18+qlatSmhoKLt27cLJyUkqvHxBjx8/5s6dO3Tq1IlChQrx5s0batSowfPnzwkLC2PLli106dKFGzdu0KpVK06cOIGuri46OjrK3yhyfRNCCCGEEEKI/w4ZrRJCCCHEN0+j0SiDCL169cLPzw+A8uXLY2BgwObNmwGUqV3gfZDHysqKZs2aAfDu3Tsg/Q1wuRkuhMhKtP1keHg4O3bsUF7Pnj073t7e/PrrrwwdOhSA2NjYdFNNrVq1iqioKKpUqUKlSpUytN2XLl0CoE2bNsr0h6mpqTg5OTF+/Hg8PT3ZvXs33bp148mTJ8p2pUqVIioqSqkSIYTIeGkrtNy9e5cLFy7w6tUrpXqOiYkJ48ePx9ramokTJzJr1izevn2LoaEhAOvWreO3336jQYMG9OnTRz7P/8LZs2dJSEigb9++ODs7/+F6lpaWVKtWjbVr17J7925Wr15N7969sbGxQa1WS3DnCypatChbtmyhQYMGpKSk0KNHD+7cucPo0aNp0qQJAE2aNMHY2JinT59Sp04dzp8/L3+jCCGEEEIIIcR/lIR3hBBCCPHN097AnjJlCgsXLuTMmTO8ePGCwoUL4+7uzvbt2xk9ejSAMg2D9qntmJgYTExMlGkbhBAiKzty5Ah9+/YlJCSEffv2Ka83atSIxo0bk5qaSuXKlUlMTCQ5OZmUlBRmzpxJYGAgGo2GOXPmYGFhkaFTCt6/fx8Ae3t74H1wRzu1jr29PZMnT8bS0pKtW7fSoUMH4uPjgfehAW1fL4PNQmS8tEGP0aNHU7FiRYoXL06VKlUICAggISEBgHLlyjFu3Dh0dHQYOnQodevWZdKkSXTt2hVvb290dHSoU6eOsl/5PP892rDTL7/8go6OjjIt7If9uPb/37x5o0w1q6X9b6lI+WXlyZNHCcReuXKFffv2UbduXX788UdlWqwCBQpgZ2dH1apVMTAwwNraOjObLIQQQgghhBAiE8lf5UIIIYT4ZqV98jo5OZkjR47Qvn175s2bR44cObC1tWXRokVYWFgQEhKiVOTRTsMQHR3Nzz//TPny5SW8I4T4TyhRogShoaGcPn2aUaNGsWfPHuX1QYMG0bBhQ9auXUvp0qWpXr067u7uBAQEYGJiwt69e8mTJ0+6ShoZwcXFBYBFixYBKMEd7dQ6+fPnp0mTJhQrVox9+/bRsWNH4P0Af9rBZyFExtL2E4GBgYwZMwY7OztatmxJYmIioaGhtG/fnlevXpE9e3Y8PT1Zt24dNWrU4Pjx4/j7+xMZGUnevHnZt28f9vb2UnHnM33Y72nDTiVLliQlJYVr166hq6ubrh9Xq9Xo6ury5s0bvLy8uHXrllSj/AI+5xqkrTL16NEjnjx5Qu3atTExMQHe/62zZMkSrKys2Lp1K7///rt8FoQQQgghhBDiP0zCO0IIIYT4ZmkHI6ZPn05YWBi7d++mcePG5MuXD3hfnaFkyZKsX78eCwsLpkyZgoeHB127dsXT05NevXqRkpLCkiVLMDc3l0FeIUSWZ2ZmRq9evRg3bhwxMTEEBQWxe/duAGrUqMHkyZOJiIjAxcWFhIQEnJ2dCQwMZN++fTg7O6NSqTK86kXr1q2xsbFh1apVrF+/Hng/kJyUlKQMet68eZMSJUrQsmVLtm7dyrhx45T1hBAZK22w4MmTJ6xZs4YePXoQHR3N2rVrOX36NHXr1mXr1q20adOGV69eYW5ujoeHB3v37uXs2bPs2bOH06dPs2vXLpycnDKl7/keqVQqpd+7f/8+jx8/VpYVKlQIgFGjRnH58mXl9dTUVCXIM3/+fJYvX87FixczsNVZx4fVjHR0dD67Up2xsTEAK1as4MqVKwBERUWxfft2nJycSEpKIlu2bOmmCxZCCCGEEEII8d+io5FRLCGEEEJ8w86ePUupUqVwdHQE3lfTKV26tDKtitaVK1fw9fXl4sWL3Lp1C2dnZ0qVKsX06dPJmzevDAoJIbKUT/Vp2soK8H5alAULFuDn50f58uUZOXIkdevWVdZNTU1Fo9FgYGDwye0zivZnLlq0CG9vbwoVKsTAgQOV6jrwfnBz2LBhzJ8/n/z581OmTBnKly/Ptm3bMrStQoj0oqKiMDc3Z8CAAaxZs4ZSpUqRkpKCgYEBb968oX379mzZsoX69euzZs0aLCwsPrmfzOh7vkdp+/2wsDBWrVqFu7s7gYGBODk5AdC5c2dWrFhB27Zt8ff3p3jx4sr269atY8SIEdja2rJp0yasrKwy5Tiygnbt2lGjRg169eoFfP7vcN++fQkPD8fW1hZ7e3suXbqEra0thw8fxtHRUak6J4QQQgghhBDiv0nCO0IIIYT4piUnJxMdHY2vry/379/Hy8uLsLAw4P9vlGv/ff36NcnJyVy+fJmCBQtibm6OqampBHeEEFnW4sWLKVy4MBUqVADSDyC+ffuWefPmMXToUCpVqsSwYcOoV68ekH4Q+FsYLHz69CkLFixg/PjxJCcn07p1axo1asTx48eJjo7GwMCAvXv34ujoSNmyZfntt9+4efMmOXLkyPS2C/FftGDBAnr16kW5cuV48+YNx44dw9jYGH19faV/efv2Le3atVMCPGvXrsXc3Fy+l/0DaftpHx8f5s6dS/HixQkICKBZs2ZK33/r1i369+/Ptm3byJs3L/3798fW1pYjR46wdetW9PT0lKCIhKb+mRs3blCwYEHy5s3L2LFjlbDpn53PtMtGjRrF2rVrMTU1pXDhwkyYMEGZslI+F0IIIYQQQgjx3ybhHSGEEEJ8k9LewE5MTGTr1q30798flUrF9OnTad++PTo6OspgRtpBjT/6byGEyEr27NlDvXr1qF+/PmPHjqV06dJA+kHC58+fM27cOKZNm0ajRo0YOHAgderUycxm/6G4uDj279/P4MGDuX37tvJ6sWLF2LhxI/ny5eP333+natWqlChRQirvCJHJqlatypEjRzA3N+fUqVO4uLgo/c+nAjzly5dn9+7dmJmZZXbTv1vTpk3Dz8+Pfv36MWDAAPLnz//ROo8fP2bMmDHMnTtX+Y5sZWVFmTJlWLhwIY6OjhIU+Ye0v99nzpyhevXq5MiRg8DAQLp27fqX26b9m+Thw4eYm5ujr6+PsbGxvB9CCCGEEEIIIQAJ7wghhBDiG/FXN63fvHnD1q1b6dOnD3Z2dowZM4YWLVp8FNwRQois6sO+7v79+yxYsIDJkydTvXp1xowZg4eHB5A+wLNjxw4aN26Mvr4+bm5uzJ07lypVqnzVtqb9+UlJSRgZGX32ts+ePePChQvcvXsXZ2dnChcujI2NDffu3SM0NJQZM2YwadIkhgwZ8rWaL4T4E9qpsQAaNGjArl27qFOnDitWrMDW1vaTAZ6GDRty/vx5rl69Ss6cOTP5CL5PDx48oEmTJujo6LBmzRoKFCjwp+sfOnSIly9fcv/+fcqUKYObmxsWFhYSFPmXtOdPO7WvtbU1p06dIm/evH+57aeq88jfMUIIIYQQQgghtCS8I4QQQohMl3YQITIykpMnT3L16lXKly9P2bJlqV+/PvA+wLNlyxb69u1L7ty5CQ4OlgCPEOI/IW0f9+bNG7JlywbAo0ePmD9/PmPHjqVWrVoEBwcrAZ7k5GQMDQ2Ji4ujdu3aVKhQgZ07d3Ls2DFy5cr11dqatk9ft24dx44do3Xr1pQvX/4vt/2jaUd+++03Fi5cSFhYGC1atGDVqlWADHoK8bX90WdM278A1KlTh3379tGuXTtmzJiBjY3NRwGexMREEhISyJkzp0zX9A+dOnWKcuXKMWrUKAIDA//wvUlNTUVfX/+T+5Bz/2Vof69Pnz7NvXv3aNq0aWY3SQghhBBCCCFEFvDpv+aFEEIIITKQdpB3yJAhTJ06FQMDA1QqFdu2bcPY2JjOnTsTHh5OtmzZ+OGHHwDo27cvI0eORFdXl+bNm8vgrRAiS9P2cf7+/jx69IiJEyeSK1cucufOTY8ePQAYO3YsAEFBQXh4eGBoaIhKpWLOnDkkJCQwYsQIpkyZgqmp6VcbwFWr1UqfHhgYyOzZs7GysqJSpUqfFd75VEWCkydPUrFiRczMzOjYsSMLFy5UfpYMQgvx9aQN4t26dYuEhARev35NxYoVleAOvJ/Cr1atWkRGRgKkC/Do6emhUqkwMTHBxMREPrf/wqNHj1Cr1Tx//hx4//6kDelo36+4uDgePHhAiRIlPtqHnPt/T/t7nZqaSqlSpShVqhQgYVIhhBBCCCGEEP+ehHeEEEIIkWnS3uSOiIhg3rx59OrVi969e2NoaMj58+cZOHAg8+fP59mzZ6xbtw5TU1OaNWsGwIABA+jTpw9GRkY0atQoMw9FCCG+irSD58+fP+fcuXPs3LkTOzs7vL29yZ07N3Z2dkqAZ9y4cbx7944uXbrQsmVLVq1axapVqyhatCjW1tYYGRmh0Wi+2gCudr/+/v5MmjSJn376if79+yuDm3+Xjo4OHh4eBAYGYm9vT/fu3QEJ7gjxtaXteyZNmsTSpUu5efMmycnJNGrUCG9vb6pVq6aEePbt2/enAR4t+dz+c0WKFCFXrlycPHkSAH19feV90mg0ynnu2bMnLi4uuLm5YWxsnJlNzhI+vN6kpqZiaGioBKe074EEd4QQQgghhBBC/FsybZYQQgghMsWHN8J79erFpUuXWL58Oc7Ozsrrt2/f5ocffuD8+fP4+PgwefJk4P10DatWrWLy5Mns2bMHOzu7jD4EIYT4qtIOnq9YsYKbN2+yceNGLl68SEpKCr6+vgwePFiZAuvRo0csW7aMMWPG8PbtW6ysrIiLiyNv3rwcPHgQJyenDKkMEBkZSa9evejcuTNDhgxJ16cnJyeTlJSEubn5Z+3rU+2V4I4QX1faz52vry+hoaGULl2aJk2akJiYSEREBE5OTnh5edG+fft0VXhq1arF/v37adSoEcuWLSNHjhyZdRjfpT/qozUaDQkJCXTu3JlNmzbRt29fZs2aBaSfJmvlypUMHDiQ7t27Exwc/IfTZ4nPk/Y6vHz5cg4cOMCvv/6Kh4cHZcuWpV+/fpncQiGEEEIIIYQQWYmEd4QQQgiRqQYPHszz5895+fIltWrVwtvbW7lRrv33xo0bVKxYEQsLC7Zu3UqhQoUASElJISUlBVNT03Q314UQIivx9fVl7ty5lC9fniJFipCcnMzy5ct59+4dAwYMYNiwYdja2gLw7t07zp07x/jx49HX18fa2pqRI0dib2+fYf1kv379WLlyJXv27MHDw0MZjA4LC2P79u1cv36dnj170qFDBxwcHL56e4QQ/0xYWBjDhw+nW7du9OzZkyJFivDkyRM8PDy4d+8eBQoUYNiwYR8FeEqVKsX169e5desW1tbWmXgE35e0fXRKSgqvXr3C3NwcIyMjZZ3Lly9TvXp1nj17xk8//cTUqVMxNzdHT0+PpUuXMn78eHR1ddm3bx+5c+fOrEPJEtIGqXx8fJRpIJ2cnPj99995+vQpbdu2JTw8HAsLi0xurRBCCCGEEEKIrEAewRFCCCFEpnn48CExMTEcO3YMAFtb23TTK2gDPC4uLvTq1YuxY8dy/vx5JbxjYGCAgYGBsq4QQmQ1S5cuJTQ0lN69exMQEEDevHkB+N///kdYWBhhYWHo6uoydOhQcuXKhbGxMeXKlWPt2rUYGBgoFRkyKriTmprKjRs30NfXp0yZMqjVarZt28a8efPYvn07efLkISkpCX9/f969e8fIkSO/epuEEH/fb7/9xooVK6hatSq9e/emUKFCvH79mmrVqqFSqRg4cCArV65k7NixaDQa2rdvr4RMTp8+zZMnT7C2tpZKWZ8pbR89Z84cNm3aRGxsLEWLFqV27doEBQUBULhwYXbt2kWzZs1YsmQJe/bswd7ensTERH777Tfs7e3Zu3cvuXPnlmD7v6QN7kyZMoUZM2bQq1cv+vTpg7u7O5cuXaJXr16sXr0aa2trZs6cmcmtFUIIIYQQQgiRFcgdFCGEEEJkGjs7O8LCwmjRogXGxsZcvHiRGzdupFtHe+O8WLFiwPtpYYQQ4r/i5MmTmJqa0qVLF/LmzYtKpQKgXr16jB49mmrVqjF9+nSmT5/O48ePle3ShiDT/vs1aTQadHV1qVy5Mi9evKBs2bJUrVqVtm3bcvLkScLDw9m3bx9bt27F0dGR2bNn8+TJE6QYrBDfngcPHvDs2TO8vb0pVKgQb968oVq1asTFxTFp0iRGjBiBj48PN27cYM6cOURGRpKUlKRsrw1kS3Dnr6UNrvv4+ODt7c21a9eoW7cub968ITg4mA4dOhAfHw9AyZIl2bNnDz4+Pjg4OHD79m3MzMzo27cvhw8fxtnZWYI7/9CH16OnT5+ybNkyKlasiLe3N+7u7iQlJXHr1i1u3LhBwYIFPwqhqtXqjGyyEEIIIYQQQogsRCrvCCGEECJTaEvRlylThoCAAFJSUtiyZQuTJk1ixowZmJqaAiiDPmfOnEFXV1epOiGEEFmdWq3m/Pnz6Ovrp5v+RNt/enh44O3tzcGDB5k4cSLJycn4+/uTM2dOpe/UBiC/Rts+HJTX0dFBR0eH7t278+jRI3bs2IGxsTFt27Zl7Nix6Y4he/bs5M2bV5nuSwjxbalRowYLFiygdu3apKSkMHDgQK5du8aECRNo1aoVhoaGVKlSBYALFy7Qv39/smXLRqtWrZR9SHDn82jP08SJE5k9eza9e/emR48eFC9enHPnzlG9enUiIyN59+4dERERWFpaUrBgQcaPH49arebhw4fY2dmhq6uboZXWsorLly8D76safXjNvH//PhcuXGD27Nm4urqSkpLChg0bGDp0KEZGRhw5cgQbGxtSUlK4ceMGhQoVkt97IYQQQgghhBD/mPxFKYQQQohMkfbmeJkyZQgKCqJx48YsWrSI3r17Exsbqyxfv349q1evpkiRIlStWjUzmiuEEBlOV1eXSpUqER8fz759+4D/r6CjrQ7g6elJ3bp1cXFxYdq0aQQHB/Ps2bOv2i6VSqUMTm7evJlFixYxbdo0nj59SlJSEnZ2dkyaNInDhw9z8OBB5s2bpwR3VCoVS5cu5datW1SoUIHU1FSpvCPEN0ZbOaR27doAPH/+nIMHD1K5cmW8vLwwNDQEIH/+/OTLl49hw4ZRtGhRKlWqlGlt/t4dO3aMZcuW0aJFC7y8vChevDivXr1SpiOrXLkyGzZsoGfPnrx8+RJ4f40wNDTEyckJQ0ND9PXfP58nwZ3Pd/36dYoVK8awYcO4cuWK8rr2uqStJqXRaFCr1URHRzN06FB0dXWJjY3FxsYGgJSUFHr06MGGDRsy/iCEEEIIIYQQQmQZUnlHCCGEEN+EkiVLEhwcjI6ODitWrGDfvn0UKFCApKQkHjx4gLGxMZs3b8ba2lqmYRBC/GdUrFgRgJCQEFxcXKhSpQo6OjqkpKRgYGBAamoq169fp06dOpQvX55Zs2aho6NDYGCgMqj4JWk0GmVg2N/fn0mTJqGjo4NGo2Hp0qV4e3vj6elJ9uzZyZYt20fbL1++nIkTJ2JnZ8fgwYOVwWYhxLfjw+9Y169f59q1a9SqVUt5TfuZT0lJoWPHjvj7+2NgYCBVX/6h8+fPc/nyZebNm4ebmxtv3ryhSpUqPHv2jNmzZ1OoUCE6duxIVFQUurq6zJs3DwsLi8xu9nfPwMCArl27smzZMrJly8bw4cPTVeCxt7fH0NCQQ4cOYWlpybBhw5TgTs6cOZX9DBs2jIsXL0o1OSGEEEIIIYQQ/4rcKRVCCCHEN6NkyZIEBQWhq6vL3r17OX/+PMOGDcPJyYlq1apha2srg0JCiP+UJk2a4O/vz4QJExgzZgwBAQHUrFkTAwMD4H1lMu3gY7Zs2UhJSVECPAEBAeTKleuLtkc7oDlt2jSmTp1Ks2bN8PT05MyZM2zatAlfX19evXpF165dlYHl5ORkHjx4wOjRo9m9ezfZsmVj9+7d2NnZSZ8uxHegRIkSFCpUiIMHD3L48GGKFSvG5s2bWbJkCYULFyZ37txKnySf53+mQ4cOWFtbU6VKFZKTk+nUqRP3799n/PjxNG7cGGNjYwYMGECPHj1Ys2YN9+/fZ/v27ZiZmWV2079rTk5ODB8+HBMTE2bNmoVGo2HEiBEULlwYAAcHBzp16sSiRYvYsWMHVlZWHwV3li9fzubNm6lVqxYlSpTIrEMRQgghhBBCCJEFSHhHCCGEEN+UUqVKERgYiI6ODps2beL58+f4+PgAyCCvEOI/RaPRoKOjw+DBg3nx4gXz58/n+PHjDBkyhCJFinDmzBmWL1+OoaEhBQsWJEeOHEpVgLCwMDw8POjYseMXaUvaimdJSUmcOXMGT09PJkyYQL58+WjVqhUtWrSgX79+jBkzBo1GQ7du3bCwsCApKQlfX19++eUXGjVqxIQJE7C3t5c+XYjvgEajwcjIiF69ehEUFESzZs2wsLDg0aNH2NnZMX/+fExMTJT+Svx9Go2GbNmy4enpCbyvwvPLL7/QvHlzunTpokxTlj17dnLkyEGZMmU4deoU7969k/DOF+Dk5MSgQYMwMjIiNDQUfX19fH19cXd3R1dXl7Zt23L8+HHOnz9Pjx490lW1mz9/PpMmTUJfX58ZM2ZgZmYmnwUhhBBCCCGEEP+YjkY7kbMQQgghxBfyZ9Nafe6UV6dPnyYwMJDt27fTt29fZsyYIYO8Qoj/rNevXzNr1iwCAwNRq9XA+yo4hQsXZuvWrTg7OyvrnjhxgpiYGPr16/fF2zFu3DiMjY2ZMWMGISEhdOrUSenXNRoNsbGx9OzZk7t37xIYGEiXLl3Inj079+/f59atW5QsWZJs2bJJcEeIb8DfCRnExcWxd+9ewsPDeffuHW5ubgQHB0sQ7ytYsWIFnTt3ZseOHdSvX195fcCAAVy6dImlS5diamqKlZWVTCX7L6T9vb179y4HDx5k8eLF7Nu3j549e9KvXz+KFSsGwLp16xgzZgwXLlygUKFCFChQgHv37nHt2jXs7e35+eefcXZ2ls+CEEIIIYQQQoh/RcI7QgghhPii0t603rFjB5cvX+bu3bvY2dnRqVMn7OzsPntfZ86cITAwkG3btuHl5cW0adPkhrgQ4j8tJiaGhw8fcvXqVQoVKkTlypWxsbFBpVKhq6v70UD8lxzYvXr1KlWrViUpKQl9fX0iIyOpV69eup+h0Wg4fvw4PXv25Pbt24waNYqOHTumq1QgVQmEyBwf9gd/9f9/Rvt9T8IKn/Zvzu2+ffuoU6cO7dq1Y+XKlQBERUXh7+9P1apVWbx4MTo6OhLc+RfSnjt/f3+2bdvGjRs3KFq0KCdPngSgU6dOSgUegKNHj7Jv3z4iIiJITk7G0dGR2rVr069fP3Lnzi2fBSGEEEIIIYQQ/5qEd4QQQgjxxaS9ET506FBmz57N27dv0dfXJzU1FXt7eyZNmkS9evXSDeT+mbNnzxIYGMjWrVtZunQpnTp1+pqHIIQQX9U/rUz2Z4GXjBrATU5OZteuXUycOJEjR47Qo0cPJkyYgJWV1UdtPX78OH379uXUqVOEh4fTo0cPCewIkYnSBgvWrFlDbGwsx48fp2LFipQtW5aWLVv+5T4kLPL3/fTTT/Tq1YuKFSt+9vl79OgRLVu25OjRo1SuXBljY2NOnjyJhYUFhw8fJk+ePBnQ8v+GsWPHEhgYyMCBA2nbti1ly5Zl7dq1LFu2jB07dtC+fXuGDh2qVOABSEhIICUlBSsrK+XaLMEdIYQQQgghhBBfgoR3hBBCCPHFjRkzhtGjR9O9e3e6du1KmTJlWLZsGWPGjOHevXssW7aMDh06fPb+jh8/zqFDhxg8ePBXbLUQQnxdaQf3du3axdWrV3nw4AG5c+emY8eOWFtbZ3IL/5h2gFIb4Bk5ciS///47oaGhtG7dGjMzs4/WP3r0KEFBQURERMhgsxCZKG1oZMiQIcydOxdDQ0NsbW25d+8eiYmJ+Pn5MW7cOAnnfEEbNmzgf//7H66urqxcuZIyZcr8ZYBH29fevHmT4cOHc+jQITQaDSVLliQ8PJy8efNKUOQL0Gg03L17l9q1a2Nubs6mTZvImzevsvzy5cvMmDGD+fPn8+OPPzJ48GAlwCPV44QQQgghhBBCfC0S3hFCCCHEF3X27FmaNm1K6dKlCQ0NxcXFBYDVq1fj5eWFkZERFy5c+KhSw+eSp76FEN+jDyuTzZkzhzdv3ijL8+XLx6RJk6hVq9Y/7h+/lA/72Q8HKpOSktizZw9+fn48f/6cSZMm0aJFi08GeFJTUzEwMJDBZiG+ARMmTGD48OH07NmTHj16ULp0afbv38+AAQO4cOECwcHBDB8+PLObmaWEhYUxduxYrK2tWbJkCeXKlfvLbbR98Nu3b4mPj0elUpEjRw5MTEykL/2CLl68SPHixfnxxx+JiIhAo9Gg0WiU69/p06fp06cPsbGx9OzZk/79+1O0aNFMbrUQQgghhBBCiKxMRr6EEEII8UXdvn2be/fu0alTJ1xcXEhNTSUyMhJ/f38sLCw4e/YsVlZWvHv3jtTUVOB9NYrPJcEdIcT3SNt3BQUFMXnyZNq2bcvhw4d5/fo1s2fP5t27d3To0IHdu3dnajtVKpXS1i1btjB69Gil6kBMTAzPnj3DyMiI2rVrM2nSJKytrfHz8yM6OpqEhIR0+9LR0cHAwABABpuFyGR37txh0aJF1KpVi8GDB1O6dGlSUlJITEzkyZMn5M+fn549e6bbRp71+ue0320HDBjAiBEjuHXrFi1btuTVq1d/eV61fbCpqSm5c+fGwcEBExMTNBqN9KVfkJmZGebm5jx//hx4f81KG1QtVaoUbdq0ASAiIgJ/f3+uXbuWKW0VQgghhBBCCPHfoJ/ZDRBCCCFE1nL//n0AChcuDEBUVBT+/v7o6uoSExODjY0NALdu3cLb2zutvEoAAQAASURBVJvo6GiyZcuWae0VQoiMcurUKRYuXEjz5s3x9/dXKpNlz56dd+/eYW1tTd26dTOtfWq1WhkY9vPzY+bMmSQlJSnLly9fTrNmzRgxYgTOzs5KgMfPzw8/Pz90dHRo3rw55ubmmXUIQog/cO/ePW7cuMHo0aMpWLAgKSkprFu3Dn9/f0xMTPj111+xsbEhKSmJhw8f4uzsLFMD/Qt6enpKBZ3+/fuTkpKCm5sblpaW/3if8n58OWq1GjMzM4oUKcKWLVvYtGkTzZo1Q0dHJ13VuJo1a1KwYEHc3d05fvw4OXLkyOymCyGEEEIIIYTIwuTRdSGEEEJ8UdmzZwdg3bp1SsUdXV1dYmNjyZkzp7Le5MmT+fXXX7lz504mtVQIITLWrVu3uH//vlKZTKVSERkZSUBAAJaWlukqk2krM/ydymT/lrbaw9ixYwkNDaVr167ExMRw+/ZtwsPDcXFxISIigkGDBnH79m2MjY2pU6cOkydPJleuXPz000/8/PPPGdZeIcTn01bGMjY2BiA6OjrddzRtuPrdu3d0796dAwcOZFpbswpdXV2lyuTgwYNp3LgxIBWNMpJarf7k67q6ulhbW9OtWzcAfH19+eWXX5RttFXjNm7ciKGhISEhIZw/fx5ra+s/3KcQQgghhBBCCPFvSeUdIYQQQvxt2ieJ09JoNOjo6NCkSRPc3NyYMWMGRkZGGBoacubMGeVJY41Gw9KlS9m1axdt27YlX758mXEIQgiR4bRhxWLFigGwdu3aT1Ymu3HjBsOGDWPdunXKAOLXkJqair5++j8JL168SEREBFWrVsXX1xdnZ2cAunfvTuPGjenWrRubNm3CxcWFoKAgsmXLRt26dUlKSmLmzJlUrFjxq7VXCPHXPvUdDcDW1haAQ4cOkZKSwtChQz8Zrh46dCjnzp3DzMwsw9qcVXzq3Gv7WO0y7fdl8fWpVCqlmtyFCxe4c+cOCQkJODo6UrZsWfT09OjWrRu//fYbkydPpmPHjkyZMoXGjRtjYWHBypUrWb9+Pe7u7hQoUAADAwM0Go1M4SuEEEIIIYQQ4qvR0cgjP0IIIYT4DNrBhrQ3wp89e8br16/Jly+fslytVrNs2TJGjBjBgwcPiIiI4KefflL2s3jxYsaPH4+BgQF79uzBzs5OBjKEEP8JS5cupUuXLowbNw5nZ+c/HDz/6aef2LhxIzExMbi5uX3xdly4cAF3d3fg48HmgwcPUqdOHUJCQvDz8wNI10dfvXqV1q1bEx8fz7Fjx8idOzfwPgiUmpqKsbFxuuuEECLjpP3snT9/HpVKRcmSJYH3n9GWLVuydetWsmfPjoWFBTExMen6nqVLlzJ69GgqVarE/PnzZVrTvyHtuV+/fj2nT5/m6tWr5MmTh/bt21O2bNlMbuF/S9pr2+jRowkPD+fJkyfK8nbt2tGuXTuaNGkCQGBgIGPHjgXAxcUFPT09rl+/jr29PYcOHcLJySnjD0IIIYQQQgghxH+OPC4ihBBCiD918+ZNXr58iY6ODikpKcrAREhICFWqVMHFxYVatWoRFhbG27dv0dXVpV69evTu3ZscOXIwfPhwWrZsyZQpU2jatCk+Pj6kpqayfft27OzsUKlUEtwRQmQZn5pOQ/u8RJMmTXB2dmbatGn4+Pigo6PDmTNnlMFzjUbD4sWL2bt3L+3atfsqg4WXLl2iePHilCpVCuCjCgKPHz8mNTWVR48eAZCSkpKuj3Z2dqZevXrcvn2bXbt2Ke3W19dXpuOR4I4QGS9teGTy5Mm0bt0ab29vrl27BryvANOhQwecnZ2Ji4vDy8srXXAnIiKCkJAQjIyMmDRpEtmyZZPpnT6TWq1Wzr2vry+dOnVi6tSpHD58mLCwMKpVq8bkyZO5detW5jb0P0R7bQsICCA4OJgKFSqwdOlSpk2bRu3atYmKisLPz49Vq1YBEBwcTFRUFF27dkWj0WBlZUXHjh05duwYTk5OGTqFpRBCCCGEEEKI/y6pvCOEEEKIP3Ty5EnKly9Pv379CAoKInv27AAMHz6c8ePHU6RIEWxsbLh69SqPHz+mb9++TJgwATMzMx4/fszRo0eZNGkSMTExAOTPn58aNWowZswY7O3tpTqDECJL0D7hn7ZPi4uLIyEhgbx58yrrqVQqFi1axMiRI3ny5AnLly+nQ4cOyvKMqEz24MEDWrZsiYWFBevXr1cqa6SkpGBgYMCNGzeoWbMmOXLkIDY2FkNDQ+W4tP/u2bOHevXqsWjRIrp06fLF2iaE+GfSVhnx8fFhzpw5VK5cGX9/f+rUqZOuH5k9ezYTJ07k0aNHlCtXjsKFC3PlyhXOnz9Pzpw52b17N87OzvId7R8YP348w4cPp3fv3nTr1o0yZcqwZcsWAgMDOXfuHNOmTWPAgAESWs8gO3fupHXr1rRo0YIxY8bg6OgIwK1bt1i/fj3Dhw+ncOHCTJ06lZo1ayrbJSYmYmJiokwtKZ8FIYQQQgghhBAZRT+zGyCEEEKIb1fOnDkpWLAg8+fPx8TEhKFDh5KYmMjKlSvp1asXAQEB5M2bl99++43WrVszZ84cUlJSCA0NJVeuXHh6euLp6cmNGzd4/fo1rq6u6OnpYWRkJDfChRDfvVu3bmFjY4OZmZkSfgEYO3YskZGRXL58mXr16vHDDz/QrVs3DA0NadCgAffu3SMsLIwRI0awfft2KlSowN69ezl48CCWlpbs3r1bqUz2JftJtVqNvb0927Ztw9DQkGzZsrFkyRJ++uknDAwMUKvV2NnZUa1aNVatWsWPP/7IqlWr0NPTU45PrVazZ88e9PT0yJcv3xdrmxDin9MGdyZPnsycOXPo3bs3Xl5euLi4AKQLi/Tr1w9nZ2e2b9/OihUrOHv2LPnz56dr1674+vp+lb7nv+D69etERERQr149fHx8lHP/7t07Hj16hIODAx07dpTgTga6ePEiSUlJdOnSBUdHR+X32tnZmV69evHmzRuCg4PZtm2bEt5Rq9UfVZGTz4IQQgghhBBCiIwilXeEEEII8afu3r1LixYtOHXqFAEBAZQpU4ZBgwaxdetW3N3dlae9ExISqFq1KmfPnqVnz55MmTIFMzOzT+7zS1eSEEKIjHb8+HGqVKmCt7c3gYGBmJubAzBs2DAmTJiAm5sbOXLk4LfffuPly5f079+fcePGYWxszIMHDzh8+DATJkzgzJkzwPvpqGrUqEFwcDAODg5fbfA8bf+7fPlyfvzxRxo0aMD27duVdR49ekT16tW5du0aTZo0YeHChdjY2KCrq8u6desYNmwY1tbW7NixQ6nIJoTIXL///juNGjUiZ86cLFq0iIIFCyrLdu/ezZMnT3j37h3dunVTXn/y5AmpqanY2tqio6OTrsKW+HsOHDhAzZo1Wbx4MT/++COpqalERUUREBCAjo4Ox48fx8bGhqSkJJKTkzE3N09XMUl8Odrz2r59e1avXs3hw4epVKnSR7/bFy5coFmzZty/f5/ffvtNqcwjhBBCCCGEEEJkFrlLIIQQQog/pNFoyJs3L+vXr6dUqVJMnjyZ8PBwHBwccHd3JzU1FV1dXVJTUzEzM+Pw4cOUKFGC+fPnM2TIEN68eQO8v4melgR3hBDfuxw5cmBvb8+cOXOYPHky8fHx3LlzhxUrVtC7d29+/vlnDh48yL59+3BxcWH69On4+fnx7t077O3tad26NbGxsVy5coXY2FjOnTvH3Llzv2hw58PnNFJTU9P1v1WqVKFZs2bs3LmTxo0bK6/nzp2bvXv34u7uztatWylVqhQ1a9akWrVqdO3alcTERFavXk327Nk/6t+FEJnjxYsXXL16lfr161OwYEGSk5O5ePEi/fr1o379+nTq1IkePXrQoUMHkpKSALC1tcXe3h59fX2pMvIvxcfHA2Bvbw9AVFQU/v7+6OjoEBsbi42NDfA+MFW5cmVu3LghwZ0v5MPrkPa8VqtWDYCjR48C73+3014X3d3dqVu3LsnJySQmJmZQa4UQQgghhBBCiD8mdwqEEEIIAaQf5FWr1ahUKmWQ19HRkejoaNzd3dm9ezfXrl3j4cOH6Ovro9Fo0NfXJzU1lWzZsqUL8PTs2ZO3b9/K4IQQIstxcXFhz5495M+fnwkTJjBjxgxOnTqFnp4e/fr1w9HRER0dHYoVK8aRI0dwc3Nj1qxZ+Pn5KQPn+vr6uLq64uHhgZmZGUZGRmg0mi8W3NH24c+ePUOlUqGv/37W5AkTJnD58mXy5cvH7NmzadGiBTt27EgX4MmTJw979uzBz8+PfPnyceLECeLj4/nf//7Hr7/+ipOTEyqVSvp3ITKBSqX66DVtkHrFihUcPnyYUaNG0aJFC5YvX063bt2YOXMm1apVIzIykoiIiExoddbwR4HFbNmyAbB+/XqWLFmCv78/urq6xMbGkjNnTmW9iRMncuvWLeLi4jKkvVld2uvQpUuXOH36tLLM3d0dAwMDRo4cyY4dO4D3DxCkpKQo6zx48AB7e3usrKwytuFCCCGEEEIIIcQnyLRZQgghhPjTaazmz59PsWLFqFixInfu3KFz584cPHiQLl26MGnSJKytrZXtU1NT0dfX582bNxQpUoT4+Hhu3rwpN8SFEFmOtt+7ceMGzZs358aNG1StWpXExEQOHjxISkoKBgYGSr8YFxdHpUqVuHr1Kv3792fSpEkYGRl99WlT6tati4GBAUuWLMHW1paBAwcSFhZGaGgo/fr1w9DQkAcPHtC/f382bNhAw4YN2bZtm7K9tlrP9evXyZs3L7q6uhgbG8vUOkJ8A86ePUuhQoUwMjICoE+fPsybNw94H1IoXbo08+fPp0CBApibm7Nv3z7q1KnD2LFjCQgIyMymf5fS9nu//vorKpWKChUqKK/Vq1ePAwcOYGlpiampKefOncPCwgJ4f81YsWIFI0aMoHr16oSHh2Nqapppx5IVpH0/Jk2aREREBLly5WLatGmULl0agGnTpuHj44OrqysTJ06kWbNmyvbR0dH07duXKlWqsGLFCoyNjTPlOIQQQgghhBBCCC39zG6AEEIIITKfNrhToUIF9PX1OXz4MPB+EGjhwoUsWLCAMmXK4OjoyLJly2jdujWLFy8mZ86c+Pv7kz179o8q8Fy5coVXr15hZWX1p+EgIYT41qXtw9RqdbrqOC4uLmzYsIHmzZuze/du7OzseP78uRJs1PaLVlZWHD16lEqVKjFz5kxev35NeHg4hoaGX63d9+/fx8rKiujoaIYPH45KpWLJkiX4+fnRsmVLDA0NUavV2NvbM3PmTAA2bNhA48aNlQCPWq3G0NAQV1dX5Rx8qepAQoh/burUqQwZMoSVK1fSvHlzTExMmDt3LlWrVuXVq1fY2dlRt25dpSKMRqPh4MGDmJmZUaJECeU1+X72edRqtdLvjRkzhvnz5/Pq1SuOHTuGm5sbBgYGDBw4kEePHnHhwgUmTpyoBHcAFi5cyKRJkzAxMWHChAmYmprK+f8X0l6HfHx8mD17NjVq1MDHx4fSpUsr53bQoEHExcUREhKCp6cnvXv3pmDBgty4cYPNmzejr6/PtGnTMDY2lvdDCCGEEEIIIUSmk8o7QgghhADg6dOneHp6cvToUVq3bo2dnR0zZszA29sbX19f7O3tlXXv3LnD//73P06dOoWvr2+6AI+Ojk66J2GlOoMQ4nv2Z4N5CxcupGTJknh4eHDjxg06dOhAbGwsvXr1YsKECVhaWiqVddJW4HFzc0OlUnH9+vWvXpns9u3bzJgxg+nTpwPQq1cvRowYgYODg3Js2jY+ePCAAQMGEB0dTaNGjdi6dSsg/bgQ36KVK1cSEhLCs2fPmDlzJj/88MMfVnLRaDSsW7eOwMBAbG1t2bRpk1RF/BvSXgd8fX2ZPn067du3p0OHDtSrV09Z7+3bt6xYsYIpU6Zw7949SpcuTbly5Th9+jSnT5/GxsaGPXv24OzsLP3qFzJv3jy8vb3p3bs33t7e5MuXT1mW9n2bP38+48aN4/HjxyQlJZEjRw6KFy/OkiVLcHR0lPdDCCGEEEIIIcQ3QcI7QgghhFBubj958gRvb2/WrFkDgL+/P/7+/umeHNb6qwCPEEJkJeXLl8fS0pJdu3YB7yuTRUREsGDBAtq1a4eBgQE3btygZcuWXLhwgYCAAPz8/DAzM/sowPPq1Svevn2LnZ1dhvSZvXv3Zv78+QB4enqyYMECcuTIkW6dTwV4KlasyJEjR75q24QQ/4xGo2HDhg0EBgby8OFD5syZg6enpzKFVlohISFERESgUqk4dOgQjo6OX33KvqxoxYoVdOvWjZ49e+Lj44Ozs7OyTNuXv3v3jlOnTjFz5ky2b9/O69evcXd3p2bNmvj7+2NnZydBkS/k7du3eHp6cu3aNbZt20bhwoU/WiftNfbq1avExcVx9epV3N3dKVCgAJaWlvJ+CCGEEEIIIYT4Zsi0WUIIIYRQKi/Y2tqSkpKivH7y5EkluJOSkoKBgYGyzNHRkfXr19OyZUumT59OQkIC48aN+2TQRwghvmcPHz5ErVazZ88eunbtipWVFfPmzWPAgAHUqVNH6RtdXFxYt24dnp6eTJgwASBdgEdfXx+VSoWlpeVXHTDU7lf7r6mpKV5eXrx48YJVq1ZhYmLC+PHjyZs3r7KNjo4OGo0Ge3t7ZsyYQVxcHDExMTx9+pScOXN+8TYKIT7Pp/oJbfDG09MTjUbDyJEj6du3LwAtWrRQpuOLjY2lXbt2PH36lFKlSrF8+XKpMvIPaAMgO3bswNzcHC8vr3TBHfj/PtTY2JhKlSpRqVIlpcqLo6OjEt6Uc//lPHv2jGPHjtGiRQsKFy78yTBs2upybm5uwPtpgrXSTocmhBBCCCGEEEJkNqm8I4QQQoh0AgMD+f3333n27Bm7du2iadOmbNq0CUAZeEh7c/zu3bvUqFGDlJQUzp07R/bs2TOx9UII8WVp+7v79+/Tv39/Nm7cCEBAQAC+vr6f7PNu3LiBp6cnV65cwd/fXwnwZHRlspiYGMqXLw9AUlIST58+JTg4mAULFtChQwfGjx9Pnjx50m3z+vVrzM3NuX//PkZGRtjY2EiFDiG+Ab/99huurq7K/2s/lxqNhujoaAIDA3n8+DFz5syhefPmSgWefv36UaBAATp37oy1tbWER/4BjUbDy5cvKVq0KI6Ojvz6669/Gqp69+4dxsbGaG+3aYM9Upnyy7py5QrFixenatWqbNy4EXNz83TLte/Hs2fPOHToEJ6enpnUUiGEEEIIIYQQ4vPIHVghhBBCACgDDMHBwaxYsYLIyEiaNWvGli1baNasGQD6+vokJycrgw8vX74kb968HDp0iNjYWGXaLCGEyCq0T+07ODik698uXLigBHfSViyD9xV4NmzYQKFChQgNDWXkyJG8efMmQwduJ0yYQMWKFZXpsoyMjMiTJw8+Pj707NmTlStX4u/vz507d5RttmzZQufOnbl27RoODg4S3BHiGzFu3DgKFSrEjh07lNd0dXVRq9Xo6Ojg6emJv78/arWaQYMGsWXLFt68eQPA7NmzGTBgANbW1lJl5B/S0dHB0tKSnDlz8vbtWwD09PRQq9XKOtq+Mi4uTnmfdHR0lH5fgjtfXoECBShXrhxXr17l7t27wPsHDYB0165BgwYRERHBs2fPMq2tQgghhBBCCCHE55C7sEIIIYQA/v+pYHg/RYOVlRVhYWEfBXgMDQ3RaDTs2LGD4cOHc+LECezt7cmdO7cyiCSEEFmJdgAwX758tGnThho1arBlyxbatWsHgIGBgTJgqO1HtQEeS0tLNmzYoCzPKPnz58fBwYHevXuzaNEi5XVXV1clwLNq1SoCAgI4duwYq1atYtiwYfzyyy+Ympoq60twR4jMZ2lpSe7cuenQoQM7d+5UXtcGeHR1dencuTMNGzbk0aNHDBgwgKioKJKSkgCUwI58nv8ZlUrFu3fvcHBw4MKFC4SFhQH/f/41Go1ybn18fPDy8lLCJOLv0V5DP+dhAF1dXerXr8+DBw/o1q0b7969Q19fX1kGsHbtWn755Rfs7Ow+qswjhBBCCCGEEEJ8a2TaLCGEEOI/7o/K+GsHg+7du0f//v3ZtGkTTZs2JTw8nH379hEcHMybN284ceIEuXLlyoSWCyFExviwn3z06BFdu3Zl586dtGnThsjISACSk5MxNDQE4NWrV1haWnLr1i2MjIyws7PL8GlTtmzZogwiL1iwgG7duinLrl+/zvTp01mwYAEpKSkYGRlha2vL/v37yZcvn0ytI8Q3ZsmSJYwYMYL4+HjWrFlDw4YNlWVJSUkYGRmxYsUKpk6dypMnT7CwsODEiRPpwnjiz/1Vv3f48GFq1KiBs7Mz48ePp1WrVsoytVrNunXrGDlyJMWLF2fJkiVy7v+BZ8+eYWNjA/zx3yhpvXz5ko4dO7J9+3ZKly7N5MmTKVSoEHZ2dsydO5fp06ejVqs5cOAA9vb2GXEIQgghhBBCCCHEPybhHSGEECKL+3DKk78zIJs2wDN48GDWrVuHsbExarWa3Llzs2/fPvLnzy/TqgghsjztIKK2v7tx4wb9+/f/KMADsHPnTnbs2EHXrl0pUaIE8Pf63r/jw/1qK6BpBzw3b95M//79PxngefDgAYcPHyY6OhonJye8vb2xt7eX4I4Q35C037EWL15MYGBgugBP2uVt27bl9evX9O/fnxIlSmBnZ5eZTf+upO33Nm7cyJUrVzA1NcXd3Z1atWop682YMYMhQ4aQI0cOvLy86NChA6ampixbtoyFCxeSmprKwYMHyZMnT4YHNr93R44coWrVqqxcuVKpbPdn51D7u//8+XN69+5NdHQ0Ojo6ZM+eHT09PZ4/f06BAgXYuXMnzs7Ocm0TQgghhBBCCPHNk/COEEIIkYWlveF9/fp1ChQooCwbNWoUdnZ29O7d+0/3ob0x/vjxY1asWMH58+cxNTVlxIgR2Nvbk5qaqpSoF0KI793nDLZq17l58yZeXl5KgGfmzJns3r2boKAgEhMTOX78OLa2thnSzhMnTuDh4QF8OsDj5eXFvXv3WLx4MT/++GO6fWmnfdHT05PBTSG+QX8U4ImIiKBu3bpYWlqyevVqRo4cSZ8+fRg0aBDw9UKDWZmvry+hoaHK/xsYGODn50dwcDDwvqramjVr8PLyIjU1FTMzM1QqFUlJSRQtWpRNmzZJUOQfioyMpEuXLiQnJ7N27VpatmwJ/Pl1WXue4+Pj2bZtG7t27eLatWvY2tpSqVIlOnXqRK5cueT9EEIIIYQQQgjxXZDwjhBCCPEf0KxZM3bu3MnJkydxd3enf//+zJ49m5CQEAYOHPiXZf0/rDiRkpKCgYGB3AgXQmQpafu0v+rf0gZ4Bg0axJYtW8iWLRupqanY2tryyy+/fPHKZOfOnSM5OVkJ6WjbMHr0aMaMGcO8efPo0aMH8HGAJyoqijZt2gAQERHBTz/99FnHKYT4NqTtS5YuXUpISAh37tyhXLlymJqacvToUWxsbDh8+DAODg6Z3NrvR9pgyNy5c/Hx8aF169Y0b96chIQEhg0bxr179/Dy8iIsLEzZ7ty5c0RFRfHbb79hbm5OuXLlaNGiBTY2NtKv/gtr1qxhwIABPH369LMDPB9eZxMSEjAzM1P+X94PIYQQQgghhBDfCwnvCCGEEFmcSqVi9OjRzJo1C2tra8qWLcuaNWvw8fFh4MCBf2uAR8r/CyGyqrT9W69evUhMTCQ8PPxPw43abe7du8fy5cs5d+4cFhYWjBw5EgcHhy9amezGjRsULFiQSpUqMWPGDMqUKaMsi4yMpFevXqhUKmbMmEH37t2BjwM8Q4YMYerUqQBMmzYNb2/vL9I2IUTGSBtS2LFjB6tXr2b58uXkypWLggULsmLFChwdHSWs8Jk+DH14e3tz9uxZFi9eTL58+QA4ffo0Xl5eHDt27KMAz+fsU3yetOdt5cqVDBw4kOfPn7N69Wpat24N/PXfIfJ3ihBCCCGEEEKI752Ed4QQQogsLO1N7PDwcAYOHEhKSgodO3Zk6tSpWFtbZ3ILhRAi86XtK8PCwvD19aVhw4bMmTMHe3v7z9pWO1j+NSuTeXl5MWfOHOrVq0dISIhSgQdg48aN/PTTTyQmJjJ79ux0AZ7U1FQMDQ2ZNWsW06dP58WLF2g0Gu7du4epqakMdgqRSf4obPBnAZAPl125coXs2bNjZmamTOEkwZ2/Z9CgQcTFxXHnzh3atWtHjx49UKvVAOjq6nLmzBn69evHsWPH6N+/PzNmzAAgKSkJIyMjQIIj/9aHv7fTpk1j2rRp3Lt3jw0bNtCsWTNAzrMQQgghhBBCiKxNHgcSQgghsjDtVFcAv//+O8nJyRgYGLBv3z6ePHkCvL8JLoQQ/1Xa6jRaZ8+epUGDBkydOvUvgzuAsq12MN3AwADgqwyez5o1Cx8fH3bt2sWIESM4ceKEsqx58+YsXrwYExMT+vXrx8KFC5V2GRoaAnDy5Elq1arF5s2buXDhAtmyZZNBUCEyiUqlUj5/SUlJPH36lOfPnwP/35986jvah6GeQoUKkTt3bszMzNBoNBLc+ZsePXrE8uXLWb16NWfOnCEpKQl4//5oz3XJkiWZPXs2FStWZObMmQwePBhACe4A0pf+C2q1Wvm9HTt2LA0aNGDWrFnKe+Hp6cnGjRuB9+dZ/nYRQgghhBBCCJFVSXhHCCGEyOK0Aw/VqlUjMDCQIUOGEB8fT8OGDTl16lS6wYYPb4bLzXEhRFan7SNHjRqFj48PW7dupWHDhuTPn/9v7SejBm4nT578hwEeT09PJcDTp08fZs2aBbwfGF27di379+/H3t6eKlWq4ODggEqlypA2CyHSS1tlZObMmfzwww8UKVKEMmXK0Lt3b06fPq2Ee/7OdzEJkPx9uXPnZs+ePbi4uPDy5Ut++eUXAKWCmlbJkiWZM2cOVatWZfr06YwcOTKzmpzlaK/DAQEBjBkzhhw5cjBt2jTmz5+vBKVatGjB+vXrAQnwCCGEEEIIIYTIumTaLCGEECIL+rPpFgAmTZpESEgIOXLkYMOGDZQqVSrdQNLdu3fJmzdvRjVXCCEy1f3798mfPz+mpqZky5aNmTNn4unpqUyB9S3y9fUlNDT0D6fQ6t27N0+ePKF27dqoVCpOnz6NpaUlR44cwcHBIRNbLsR/W9ppf4YMGcL06dMpWLAgFSpU4Pbt28TGxlK0aFG6d+/OTz/99M32Qd877Xdl7fffCxcu0KpVK65evYq3tzfTpk0DPp7O6cSJE4SEhDBt2jTy5cuXWc3Pco4dO0bNmjVp2rQpoaGhODo6KssWLVrE4MGDef36tUyhJYQQQgghhBAiS5PKO0IIIUQWk7bM/61bt4iNjWX//v28evVKeYLY29ubESNG8OLFCzw9PTl58qQyMLF9+3a6dOnCsmXLMu0YhBAiIzk4OHD48GFsbGx48OABS5cuRa1WY2BgoEw9+C1I+9zFpEmT/nQKrcjISFq1akVMTAxnzpyhePHiHDx4UCruCJHJtGGDefPmERYWRu/evYmOjmbx4sVERkbSvXt3jh8/zs6dO6W6yBf0YV+u/a6s/f7r7u5OVFQUbm5uzJgxAz8/P2V52j7Tw8ODqKgo8uXLR2pqaga1Puu7d+8eycnJtGjRAkdHR9RqtXJ+u3Xrxvjx44H3FeY2bNgASAUeIYQQQgghhBBZj1TeEUIIIbKQtBV3QkJCWLp0KTdu3ADA3t4eLy8vWrZsSYECBUhOTmbGjBmEhIRgZWVFaGgoDx48IDw8nGfPnnHixAmpviOEyNK0FRW0feepU6do3bo1N2/eJDg4mKFDh6Kvr/+X1cy+lr/6uWq1mqFDh/5hBZ7ExETi4uJQq9XkyJEDU1PTj6pICCEylkajISUlhR9++IHbt2+zceNG3NzcSE1NZePGjfj4+KCnp0dsbCw2NjaZ1v9kJWn7vW3btnHu3DmuXLlCo0aNlGkEtc6fP0+rVq347bff8PHxYfLkyR/tQ3x5a9eupW3btsybN48ePXoor6f9/W/Tpg1RUVEALF26lE6dOmVKW4UQQgghhBBCiK9FwjtCCCFEFjR06FAmT55M7dq1adWqFYmJiWzevJmYmBhq1arFlClTcHV1JSUlhdmzZzNt2jTu3r2Ljo4O+fPnZ9euXeTLl08GKoQQWcqHg+CfGhQ/efIknp6exMfHM3LkSAYMGJApAZ60/e/Zs2d5+PAh9+7do2LFiuTOnRtra2tlPX9//08GeD5ss0wxIsS34eHDh7i4uNC5c2fCw8NJSkpiw4YNDB06FF1dXY4fP46NjQ0ajYarV69SqFChzG7ydyttPxgQEMDMmTNJTk5GX1+fd+/eUa9ePfr370/jxo2VbdIGePz8/JgwYUJmNf8/49ChQ1SvXp3KlSuzePFiChQooCzTTmEZEBBAZGQkT548wczMjNu3b2NsbCzXNSGEEEIIIYQQWYZ+ZjdACCGEEF9WdHQ0s2bNolu3bvj6+iohHXNzc3755RcePHiAvb09AAYGBnh5eeHh4cGxY8cA6Ny5M7ly5ZLgjhAiS0nbp23cuJHjx49z8uRJypQpQ5kyZWjRogUAZcqUITo6Gk9PT4KCggAyPMCjVquVtgYFBTF//nwePnwIgJmZGbVq1cLHx4eqVauip6fHxIkTAQgNDQVg7NixlClT5qO2ygCnEN8Gc3NzsmXLRlJSEgBbtmxRgjvaijvwvt+qWbMmgwYNUqZxEn+Pth8cMWIEkyZNok2bNvTt25dixYoxZ84cAgMDef36NSkpKTRv3hyAYsWKERUVRfv27Zk0aRJmZmaMGDEiE48ia/iza2jVqlXp2LEjkZGRrF69mp49e2Jra6tMYQlw//596tSpQ4sWLShevDgmJiYZ2XwhhBBCCCGEEOKrk/COEEIIkcUcOnQIIyMjvLy8lOBOdHQ0Y8aMIV++fOzcuRMzMzNSUlLQaDQYGhpSpUoVqlSpolRlkOCOECIr0PZpacMwvr6+zJw5E41GQ2pqKrt27QKgXbt2rFy5EgAPDw82bNiAp6cnY8aMQVdXFy8vL/T1M+bPJ+3gpr+/P1OmTFEq6ty6dYvjx4+zefNmjh8/zuLFi6lXrx66urrpAjxxcXHMnz+fEiVKZEh7hRCf9qnvUykpKaSmpuLg4MDWrVsZOXIkS5YsQU9Pj19//ZWcOXMC7/uvsWPH8u7dOxwdHTOj+VlGVFQUixYtonfv3nh7e+Pq6srr169ZuXIlJiYmHDt2jFGjRqGjo0OzZs2A9wGepUuXMmjQIJme6QtI+1m4fv06Dx8+RFdXF2tra6WyVK9evbhw4QITJkzgzZs3tG/fnmLFigGwYcMGjhw5QufOnWnUqNFH+xRCCCGEEEIIIbICmTZLCCGE+I59eNP67du3VK1aldTUVM6ePatMl+Xn56c8za0dFDp37hyvX7+mcuXKMpWKECJLiY+Px8LC4qPXx44dy6hRo+jRowe9evXC1NSUy5cvM3jwYH7//Xfq1KnDzz//rPSHJ0+epHXr1vz+++/MnTuXXr16Zdgx/Pzzz7Ro0YJ27doxYsQInJ2dUavVpKSkEBwczLhx43B2diYyMpLy5csr2/Xp04d169Zx6dIlpb8XQmSuoKAgHBwc6N69u/La2rVradu2LXp6euTKlYuLFy9iaWkJvK9Qsm7dOoYNG0aBAgVYvXo12bNnz6TWf9/evHnDwIED2bdvH+vWraNUqVK8fv2acuXK8fLlS0JDQ7lz5w7Dhg3Dw8MDf39/pRIbQGpqKvr6+sq/4u9LW3EnKCiIhQsXcv/+feB9FVBfX1/69euHnZ0dW7ZsYfz48fz66684OjrSrFkz7t27x6FDhzA1NeXIkSM4ODhk5uEIIYQQQgghhBBfzdev+S6EEEKIryJtJYmNGzcCYGpqSv78+Xn58iVxcXHs27fvk8EdeD89VkhICElJSRLcEUJkGT///DN9+vTh8uXL6V6/ceMGERERVK9enaFDh1KyZElcXV1p1qwZR44coXLlyuzZs4c+ffoo25QpU4aVK1fi4eFB48aNM/Q4tAHM7t27K8EdHR0djIyMCAkJYfDgwdy6dYvIyEhSUlJQq9UAzJ07l2vXrpEzZ07lNSFExkr72bt37x5BQUGMGjWKZcuWKa/XrVsXHx8f1Go1zs7OnD59mjdv3pCQkMDkyZMZOnQoqampLFiwgOzZs8vn+R/S0dHBwsKCkJAQSpUqRWJiIo0bN+b58+eMHz+eVq1a0aNHD0qWLMnJkycJDQ1l7dq1yvbawI4Ed/65tNXkgoKCKF26NJGRkSxfvpwGDRowYcIEevXqxc2bN2natCnh4eEMHjyYp0+fMnPmTPbt24e7uzsHDx7EwcEBlUqVyUckhBBCCCGEEEJ8HRLeEUIIIb5TaW+Et2jRgunTpwPg5ubG3bt36dy5M71790ZPT49jx46lC+6Ehoby+PFjatasKYMRQogs4+DBgzRs2JAHDx4ofaTW48eP+f333/nhhx9wdnZWBv9UKhW5c+dm9erVODg4sGnTJk6dOgW8H4CvUKECR44cIU+ePBkyYKgdoL969Srw/wPGurq6yhRgAD4+Pri5ubFlyxZev36d7nizZ8+ORqP56BwIIb4+lUqlfPb27t3L/fv3qVGjBs+ePWPkyJEsX74cACsrK7p3746Pjw9Hjx6lVq1alClTBjc3N4KCgrCysuLAgQPkzZs33T7FH/tUH21qasqoUaNo0aIFGo2G+fPnExMTQ58+fWjdujUGBgZYW1vj7u5O/vz5OXbsGHPnzuXdu3eZcARZ1+bNmwkPD6dLly5MnTqVNm3a0KFDB+rWrYtarebixYvY2NgAULx4caZMmcLly5e5ePEi586dY8uWLTg6OspUWUIIIYQQQgghsjS5+yOEEEJ8Z9I+eX306FGWLl1K//79qVevHgABAQEUKVKEbdu2kZiYyN69e8mdO7eyzdq1a5k3bx758uWjS5cucgNcCJElPH78mKFDh1KsWDFGjRqFm5tbuuUvX74E4Nq1a+kG//T09FCpVDg4ONCnTx8eP37MhQsXgP8PSRoYGCjrfmkfzmKs/ZlFixYFIDY2Fvj/QWldXV00Gg12dna4ublx79497ty589F+paKaEBlPo9Eo/YSvry8dOnSgVatWmJubkydPHu7cuYOvr68S4HFzc2PcuHHs3LmT9u3bkzt3bqpWrUpoaCi7du3CyclJwgp/g/Y8jRgxgtDQUOV1c3NzjIyM0NHR4cyZM5iZmeHr64upqamyzuXLl+nQoQNbtmxh6dKlGBsbZ3j7v3dv3rz5w2UxMTGkpqbSvXt3XFxcSE5OZu3atUyZMgUXFxdiYmKwsLAgJSVF2SZPnjwULlyYvHnzki1btnSfLyGEEEIIIYQQIiuS8I4QQgjxndEO7P7+++8cP34cAwMD+vbtS5EiRUhNTSVbtmzMmTOHggUL8vLlS6ZMmcKRI0c4efIkPj4+DBo0iMTERFavXi3TqgghsoyEhASuX79OqVKlqFGjBgB9+vRhy5YtAJQsWRIbGxtiY2M/Crtogy6FCxcG4MWLFxnSZpVKpfzsu3fv8uTJE2VZlSpVyJUrFyEhIVy/fh09PT00Gg0ajUbZJikpCScnJ+zt7TOkvUKIP6f9bE6fPp3Q0FB+/PFHdu/ezaZNm9izZw/Tpk3jyZMn+Pj4KAEefX196tWrx4oVK9i/fz+rV6+mT58+2NjYpJsiVfyxtN9lX7x4wbhx4xg/fjyzZs0C3r8vycnJpKSkcOvWLd6+fcuZM2eUbSIjI3n8+DH58uWjcePGSoUX8fn27t1Lu3btlMp1aanVao4ePUqePHmoWLEiKpWK6OhofH190dHR4ejRo0rVnSNHjijTAX9YbUpCqUIIIYQQQgghsjoJ7wghhBDfoZCQECpUqMD+/fupWrUqbm5uqFQqZXqVypUrs2TJEtzd3Zk7dy41atSgbNmyLFy4kEKFCnHkyBFlYEKmYRBCZAUmJibkyZOHXbt2kZiYyLBhw5g3bx779+/n7du32NjY0L59e06cOMHEiRPTPd2v7QePHz+OiYkJhQoV+urtTVtNIywsjHbt2hEcHMzTp08BKFeuHB06dODRo0fUqVOHc+fOoVarlcHLDRs2EBsbS5kyZTA3N//q7RVC/DWNRsPLly/ZuHEjuXLlonv37koVsHz58uHt7c2qVat49uwZfn5+LFu2TNk2bZ+kDaPId7S/lva77M6dOzl79iz16tUjPj6ecePGMWfOHAAMDQ0xMDCge/fuJCcnM27cOBYsWEBgYCABAQGYmJjQsGFDZb8Smvp7Nm7cyNatWwkJCeHs2bPK62q1Gl1dXezs7Hjy5AnHjx9n69at+Pv7o6urS2xsbLqpfUeMGMHw4cOJj4/PjMMQQgghhBBCCCEylX5mN0AIIYQQf09KSgq5c+fGwMCATZs24erqyosXL8iRI4dSkUFPT4+KFSty/Phx1qxZw4MHD0hJSaFSpUqULl0aS0tLmYZBCJGl2Nra0qVLFwYOHIitrS1v3rxhzJgxdOjQQZka5aeffiImJob58+fz7t07vLy88PDwAGDdunWsXbuWYsWKUb58+a/a1rTVNHx9fZkzZw6urq40atRIqYimq6vL5MmTiYuLY/HixdStW5f//e9/lCtXjjNnzrB582ZMTU2ZPHkyJiYm6SryCCEyh46ODhqNhnv37pE/f34KFiwI/H+AAaBt27Zcv36dkSNHMnr0aAA6d+6MgYGB8jmW0M7nSTuN0pAhQ1i+fDkmJiZUqFCBAgUKcPXqVQICAtDR0aFPnz4A1KpVi2HDhjFlyhR27tyJvr4+7u7ubNiwAVtb23Tvlfh806dPR09Pj7CwMFJTUwkODqZEiRLKuaxevTqrV69m9OjRXLhwAV1dXY4fP65U3AGYOXMm169fp3///mTLli2zDkUIIYQQQgghhMg0OhqNRpPZjRBCCCHE35OQkMCmTZsYN24c169fJzQ0lG7duqUbwP2zcI4MTAghspK0fVqpUqW4ePEixsbG7N69m/Lly5OcnIyBgQE6OjocO3aMgIAADh06RI4cOShSpAgqlYqLFy9ibm7OoUOHcHJyypB+Mjg4mJCQEHr37k2/fv1wdXX9aB2NRsPo0aPZvHmzUs0gW7ZslC5dmmXLluHk5CRhTCG+Ic+ePaNy5co8fvyYAwcOUKJEiXTLNRoNBw4coGHDhiQlJZE3b17CwsJo1qxZJrX4+zdz5ky8vb3x9fWlW7duuLq6cu/ePfbu3at8P544cSJ9+/YF4O3bt5w/f55ff/2VPHnyUL16dWxsbKQv/ZdUKhUDBgxg7ty5NGnShKCgIEqVKgXA8+fP6dKlC1u3bsXc3JwDBw5QsmRJZdvIyEhGjRpF9uzZ2bZtW7pqPEIIIYQQQgghxH+FhHeEEEKIb9ifDR7Hx8ezefNmhg0bhqGhIdOmTaN+/foYGhqmWy9tNQapzCCEyMo2btxIixYtKFOmDCdPnsTBwYFDhw7h7OxMSkoKBgYGAFy6dIldu3axaNEiHj16hJ2dHeXKlSMoKAgHB4cMGcA9fvw4P/zwA+XKlWPq1Km4uLgoy06dOkVqaiqpqalUqlQJgPv373P69Glev36Ns7MzRYsWxcLCQgabhfiGaL9njR07lpEjRxIUFMSIESOU5ampqejr65OamkqNGjXw8PAgLCyM6tWrs3TpUhwdHTOx9d8fjUZDQkICLVu25NSpUxw5cuSjEOTPP/9M48aNMTMzIzg4mP79+39yXxJs/+c+vA7169eP8PBwmjVrxogRIyhdujQAW7ZsYerUqRw9epRu3bpRrVo1ChYsyNKlS4mKisLAwCBDA7RCCCGEEEIIIcS3RqbNEkIIIb5RaW+EX716lcTERBISEvDw8EBfXx8LCwuaNm2Kjo4Ofn5++Pr6otFoaNCgQboAT9qwjgR3hBBZmaurK1u2bKFEiRIsWrSIoKAgqlSpwpEjR3ByclICPEWKFKFIkSL07NmTp0+fYmVlhbGxMYaGhhkWhrl//z6PHz+mQ4cOuLi4kJyczOPHj5k9ezZz5swhJSWF5ORkZs6cSd++fXFwcMDBwSHdPtJOvyWEyDh/FCzQhneqV6+Om5sbI0eOxM7Ojk6dOmFoaKgEd8LDw7lz5w6bN28mZ86cBAYGcvLkSQnv/E3a77X379/HwcFBCe5o3x+NRkP9+vWZOnUqgwYNYuLEiQBKgCft+yhBkX8m7TVz0aJFxMTE8PjxYzQaDTt37kStVjNy5EhKly5N06ZNMTY2Zv78+YSHhxMeHg6AhYUFFSpUYMGCBeTNm1dCqUIIIYQQQggh/rMkvCOEEEJ8g9IOyI4ZM4aIiAgePXpEcnIyFSpUoHHjxvTt2xcrKyuaNGkCgJ+fH35+fujo6HyyAo8QQmR1RYoUwcXFBSMjI0aOHElycjLjx4+ncuXKSoBHW/lCo9FgamqKk5NTun1k1IDhs2fPANi1axfVqlVj5cqVrFy5kkuXLlGzZk0KFizIvHnz8PLyomTJkkoFnrRksFmIjJc2WHDy5Elu3bpFQkICZcuWpUiRIgBUqVKFoUOH4ufnR48ePbh8+TJ169alWrVqLFu2jAULFpAvXz709PQoX748ADt27MDT01MqjvxNGo0GQ0NDLl26RExMDOXLl1fOn46ODhqNhgoVKpAtWzYePnxISEgIOXLkoEOHDnKevwDtZ8HX15fw8HBKly5NixYtsLa25tKlS2zevBm1Ws3o0aMpXbo0devWpVatWvz88888evSIhIQEKlWqhKurq1STE0IIIYQQQgjxnyfhHSGEEOIbpB1MGD58OOPHj8fDw4P27dtz6dIlTp06pTyhPX/+fGxsbGjatCnwPsATEBBAUlISzZo1U6aIEUKI/wojIyNl8G/s2LHo6Ogwbty4dAGeb2FwsGXLlkyfPp2IiAiWL19OSkoKhQoVYufOnbi7u2NjY0O+fPnw8fHh8ePHmdpWIcR7acPVo0aNYvbs2bx48QJ4H2IIDg6mbdu2ODs78+OPP2JgYMDMmTOZOnUqU6dOxcTEhMTERBwdHYmOjsbS0hITExMAZeo8CZT8NW3ASa1WY2FhQZcuXRg4cCDr1q2jXLlySkUebVizXLlylChRgqpVqzJ58mRmzJhBuXLlKFiwYCYfSdYQFRVFaGgoPXr0ICAgAGdnZ1QqFTdu3GDSpElEREQAKAEePT09GjVq9NF+pJqcEEIIIYQQQoj/OgnvCCGEEN8Q7YCyRqPh1q1bREZGMnDgQPr370++fPl48+YNv//+O927d2fjxo0YGRkRHh6OpaUlzZo1Q1dXlx9//JGZM2fSpEkTCe8IIbIc7bQ0f0ZPT0/pT0NCQgCUAM/Ro0dxdHT86gGeP2unSqUie/bsHDhwgODgYHR1dXF2dqZLly6Ym5sr6127do0cOXKQP3/+r9ZOIcTn0Wg0SrBm6NChTJkyhcaNG9O9e3eMjY0JCwtj5MiRPH/+nB49euDq6kr79u0pU6YMJ06cYOPGjZiZmeHk5ESPHj1wcHDg3r17zJo1C319fdzc3DL5CL9dH1YjSk1NxdDQUHmtXLlylCpVitDQUHLnzk2fPn0wNTVVpilbtGgRN2/eJCoqCgcHB/r378+xY8ckvPM3JCQkYGZm9sllp0+fBqBnz55KcEdPTw9XV1dmzJjBu3fvWLVqFXp6egQGBlK6dOlP7keCa0IIIYQQQggh/ut0NBqNJrMbIYQQQoj0Nm3ahLu7O/Xq1SMqKorSpUunGwh+/fo1NWrU4Ny5cyxevJiOHTsC72+s7969m3LlyuHg4JCZhyCEEF9c2gFcbZ/4VyEZbUBnxIgRjBs3DgMDA27evPlV+sgjR46QkJBA/fr107Xxz9qmPaa062o0GqKjoxk6dChubm6sXr06XahHCJF5Fi9ezJAhQ+jQoQNeXl64urqSmJhIyZIluX79Ojo6OvTt25f+/funC4ekpKRgYGCgVIO5ffs2ixcvZuzYsfz4448sXLgwE4/q25W2H1+2bBkHDhzg119/xcPDAw8PD/r37w9AdHQ0gwcP5s6dO/To0YP69etTq1YtVq5cybx58zA3N2fbtm1cuXKFSpUq0a5dO1asWJEulCU+7ejRo7Ru3ZqVK1dSvXp15XXt9atDhw5ERkYSGxuLh4fHR9vHxMTQokUL4uPjadCgAX5+fpQtWzYjD0EIIYQQQgghhPguyB0KIYQQ4huQNku7ePFiPD09qVevHnp6ehQoUCDduiqVCnNzc6ZOnYqenh5bt25VlpmZmeHp6YmDgwMqlSrD2i+EEBlBO8A6YMAA/P39Af60Co+2Ag9ASEgIAwYMwMrK6qu07f79+1SvXp0ffviBXbt2KW37o2cltIPR2vanPY6pU6cydOhQUlNTlUFneeZCiMz3+PFjoqKiKFy4MN27d8fV1ZX4+HhKly5NfHw848aNo0GDBsyZM4d58+Zx7do1ZVtt/6Wrq8uxY8f44YcfCA0NpXXr1kpwR61WZ8pxfas0Go3SV/r4+NCzZ0+2b9+Oubk5O3fuxNvbmzZt2pCQkECLFi2YNWsWtWrVYsGCBbRs2RJbW1v69+/P69evWbZsGdmzZ1f2V7BgQXR0dCS48xliY2N58OABK1euTPc7qj132sBObGwswEd/g5QvX54CBQpgbGzM+vXrmT17NqmpqRnUeiGEEEIIIYQQ4vshlXeEEEKITJb2iWKVSsW9e/fo2bMnR44cAWDDhg3UrVv3oykDXrx4gYeHBzo6Ohw5coRcuXL95VQyQgjxvbtz5w6FCxfGwcGB3bt34+Tk9JfbpO0/X716haWl5VeZNmvKlCkEBgZiZmbG8uXLadCgAfB5U30BHDt2jB49evDo0SNcXFxYu3YtTk5OX32KLyHE53ny5Al169Zl6NChtG/fnrdv31KrVi1+//13pkyZQocOHdi7dy/169dHX1+f/v3707t373QVeBITE1m8eDHR0dFUq1aNkSNHAh9PDSX+35QpU/D396dXr1706dMHd3d3Ll++rHxf7tWrF3PnzgXg9u3bnDt3jujoaExMTMiTJw8//fQT9vb23L9/H39/fyIjI1m9ejUtW7bM5CP7PqhUKnbu3EmVKlWwtLTk7t275M2bV/mdPX78OM2bNyclJYWDBw9SqFAhJXCqvfZVrlwZT09PUlNTadu2Lc7Ozpl4REIIIYQQQgghxLdJwjtCCCFEJko7oBsQEICuri4jR47k3r179OvXj127dtGgQQO2bdumVHBQq9XKIG7x4sUxNTXlwIEDGBkZZeahCCFEhhkzZgxBQUFs3LiRpk2bftag96em3PpS0u47LCyMwYMHY2VlxbJly2jYsOFn/8ybN2/y448/Uq1aNQYOHEjOnDkluCNEJvmjz97jx4+xtbVFo9EwfPhwwsLCGD16NP369cPU1BS1Wk2VKlV4+vQpN27cwN/fn+Dg4HT7evv2LfHx8eTOnRuQ4M6fefr0KbVr18bS0pJFixbh6upKUlISe/fupXv37pibm3P48GFy5syZbjvt+6f999atWyxZsoRx48bRqVMnFi1alElH9H358Hdz9OjRjB07lsOHD1O+fHnl9YCAACZOnIi9vT2bN2+mZMmSynbR0dEMGjSIyZMn07p1awBl+jghhBBCCCGEEEL8P7k7JIQQQmQi7UDutGnTmDhxInFxcbx48QIXFxdmz55N/fr12blzJ23btiUhIQH4/2lgVq9ezaVLlyhUqFBmHoIQQmQY7XMHDRs2JFu2bISEhBAfH/9Zg95p1/nSVcp0dXVJTk4G3k/ptWDBAl69ekWXLl3Yvn278jP/7LkJjUZD/vz52bVrF6NHjyZnzpzpwppCiIyl/ewtXryYgwcPKq9rKx3q6upy5MgRnJ2d6d+/P6ampgAkJSVx69YtPD09GTJkCL179/7oc2xqaqoEdzQazX8+uHPz5k1evHjxyWUPHjzgwoULtG/fHldXV1JSUtiwYQN9+vTByMiII0eOkDNnTlJTU7l8+bKyXdp+/tixYzRu3FiZpkwb3JFpyj6fRqNRpjEzNTWldevWyjRZAOPHj6dnz548ePCAevXq4eXlxdKlSxkyZAg+Pj4YGRlRs2ZNZX0J7gghhBBCCCGEEB/7b98hEkIIITKJSqVK9/87duygefPm+Pj4YGdnh1qtxsXFhVmzZlG/fn2ioqL44YcfCA0N5dSpUwQEBDB+/Hjs7OwICQnByMjoTweFhRDie6MdVNUOGKZVtmxZ6tevz7lz5zh//ny69TODSqXC0NAQgKNHj1KwYEGKFSvG8+fP+emnn9i1axfw5wEe7UCziYkJBgYGAP/5AX0hMtuePXvo1q0b48aN49dff1VeV6lUPH/+nAsXLmBpaZnuc71y5UqMjIxo06YNEydOxNHR8aPvfWn916c8vXDhAgUKFGD06NHExcUpr2vPaVJSEvC+j1er1URHRzN06FB0dXWJjY3FxsYGgOTkZHr27El0dDTw//2nSqXiypUr5M+fH39/f1asWKHsT/rYP5c2WHbz5k10dHQICAhgzJgxxMfH4+npmS7AEx4eTnBwME5OToSHh9OlSxemT5+OpaUlu3btUkKpQgghhBBCCCGE+DSZNksIIYTIRKNGjSJHjhwsW7aMUaNG8cMPPwD/P2Cho6PDjRs38PLy4ueffwagZMmSmJmZYWtry9SpU5VBIanOIITIKtJOMfX06dN006EkJydjaGjI0aNHqVmzJu3bt2fx4sWZ1dR0bfXz82PJkiVYWFiQK1cunj59yvXr17GysmLVqlXUr1//o22EEN+uly9fMn36dMaNG0edOnUIDAykYsWKyvJevXqxYMECxo8fT926dTly5AizZs3C1NSUffv2YWVllYmt/z6cPHmSYcOG8csvvzBo0CCGDh1Kjhw5lOV3797F1dWVZs2a0aRJE4YPH64Ed9JeGwYOHMiyZcvYvHkzVapUSfczkpKSiI+PV9aX4M7fM2DAAPbu3cuuXbtwcHBApVIxc+ZMgoKCMDU1ZcOGDZQrV05Z/8GDB5w4cYJnz56RK1cuKlasSI4cOeTvFSGEEEIIIYQQ4i9IeEcIIYTIJMePH6d8+fJYWVmhVqtZunQpP/zwQ7ob29oB3uvXr+Pl5cWePXuoUqUK+/fvV/YjN8KFEFlVv379CA8PJyAggOrVq1O3bl1l2YMHD2jZsiXnzp1j9+7d6QbUM0NoaCi+vr4MGTKEHj16ULBgQR4+fMjs2bMZN24c1tbWrFy5knr16gES4BHiexEfH8+0adMICgqiQYMG6QI8e/fuZdSoURw9elRZ39XVlZ9//hknJycJiXymU6dOERISwsaNG/H19U0X4NFoNPTq1YuFCxdiYWGBlZXVR8Gd5cuXM2rUKEqXLs2SJUswMzP7w58lfe/f1759ezZt2sThw4cpVaoUwF8GeD4knwUhhBBCCCGEEOKvyV/OQgghRCYpW7Ysc+bMQaVS8erVK2XqFz09vXSVdzQaDQUKFGDmzJnUrVuXgwcP0qlTJ2U/ksMVQmQVH06nYWBgQMWKFRk3bhwNGjSgffv2bNiwgYSEBOzt7Rk6dChv377l0KFDQOb1h/Hx8WzcuJG8efPSvXt3ChYsCKBMbTh58mSeP39O+/btlSpqfzaFlhAiY31qWittf2RhYcHAgQMZNWoUO3fuJDg4mCNHjgBQu3ZtwsLCmDdvHt27d2fKlCns378fJycnVCqVhBX+grYPLF26NMOHD6d58+ZMnjyZiRMn8uLFC+B9X9muXTuKFy/O69ev+d///qdMlQUwf/58goKC0NfXZ8aMGZiZmf1p3yrBnb+vc+fOJCUlMXz4cFJSUoD3f6/079+fUaNG8fbt23RTaH3q/MtnQQghhBBCCCGE+GtSeUcIIYTIBKmpqejr6wOwaNEievbsiUajYfXq1bRu3RpI/2Sw9r/TTqHVvn17VqxYAcjTrEKIrOXGjRu4uLgA8PbtW/bt28eiRYs4cOAAL1++pFixYvj4+CjhmCtXrnD48GElNJPRnj59SokSJShevDg7d+4E3vfbarVaqYw2ePBgpk+fTs6cOVmyZAkNGzbMlLYKIf5YZGQkRYoUoUSJEkD671fx8fFMnTqVMWPG0KhRI/z8/KhWrdon9yNVET9f2u+7J0+eZOzYsUoFHl9fXyWos379esaMGcP58+dxc3OjYMGC3Lt3j2vXruHg4MDOnTtxdnaWc/8VJCUl0ahRI44dO8bWrVupVasWKSkpGBgYpKvAY2lpycqVK6lcuXJmN1kIIYQQQgghhPguySifEEII8ZV9WEkiJSUl3aBCt27dmD9/Pnp6evTq1YsNGzYA6asyaP/bxcWFWbNm0bBhQ1atWqUM/kpwRwjxPUv7PIGPjw9NmzZl9+7dAJiamtKkSROWLl3K8ePH6d69O0lJSfz000/07duXc+fOkZqaqjzx/6kKGhnB0NCQkydPKlXUdHR00NPTU64BTZo0wcLCgpSUFBo3bpxu+kMhRObbtm0bHTp0YOzYsVy6dAl4//0qbQWe3r17061bN3bs2MGsWbM4fPjwJ/cl4ZHPp6Ojo/TbZcqUSVeBZ/LkyTx9+hSA//3vf8ybN4/g4GCSk5M5deoUxsbGDBw4kP3790tw51/68Nqp/b1XqVQYGRkRGBiIRqNR/k7RBne0FXiCgoK4c+cO3t7eSnUeIYQQQgghhBBC/D0y0ieEEEJ8RWmnTFi+fDl9+/alRo0atG3blrVr15KYmAi8D/DMnj2bt2/f0qVLl78M8MycORMPDw+MjIwy58CEEOILUalU6aYx0dPT48qVK4SGhrJv3z7l9WzZsuHi4sK8efM4fPgw06ZNw8HBgeTkZOLi4liwYAEajSZTBm5z5sxJmzZtePHiBdu2bePt27fKMu0AaMmSJbG1taV27drY29tToECBDG+nEOL/fViEuHDhwnh7e7Np0ybGjBnDxYsXgfQBnty5c1OnTh00Gg2bN29m6NChnDx5MsPb/r37MNiett8uU6YMAQEBSoBnypQpSoCnQoUKDB8+nLNnz3L+/HmOHj3KmDFjyJ07twR3/iXtuTt8+DDx8fHK3y/a6XxdXV0pWbIks2fPVsKn2oCqnp4e/fr1Y/78+WzcuBEDA4PMOgwhhBBCCCGEEOK7pp/ZDRBCCCGyqrSDyEOGDGHWrFlky5aNPHnysHv3bqKioujfvz8//vgjpUuXpmfPnujq6tKvXz+6dOmCrq4uzZo1SzeorQ3w5M+fn61bt2Jra6v8rLTrCSHE9yDttFJz587l4MGDvHr1CoB9+/bx7t079PT0qF69Onp6esqUgzY2Nnh7e9O6dWtu3bqFj48PBw8eZN26dbRq1eqr9Il/NTDcrFkztm/fzsSJE3F0dKRRo0Zkz54dfX19VCoVS5cuRU9PjzVr1pCUlISJiYkMNguRSdJOh/Xu3TuMjY3Jnz8/AwcORFdXl+nTpwMQGBhI0aJF0dXVJTk5GUNDQ6pXr07p0qUpUqQIv/zyC46Ojpl4JN+ftP3eyZMn+f3334mPj6dQoUKULl0aY2NjypYti7+/PwCTJ08GwM/PD2trawDMzMyU/Wn7eulL/73x48czfPhwSpQowejRoylWrBj58+dHR0cHe3t7+vXrR0xMDHv37qVGjRrK50h7Le/evTsg08YJIYQQQgghhBD/lFTeEUIIIb4S7WDClClTmDZtGt27d2ffvn2cPXuWbdu2UbNmTWbOnEl0dDTJyckAdO/endmzZ5Oamoqnpyc7d+78w/1KcEcI8b3TDp77+fkxZMgQ4uPjadWqFcOGDaNOnTocPHiQ4cOHK0/56+vro9FolIoZdnZ2VKxYkfnz52NsbKys9zWDOxEREQwaNIiWLVuyY8cO7t+/D0DFihXx8vLC1NSUPn36EBISwqFDh0hMTCQiIoLF/8fefcfXeP//H39k70SIESKJPUptRa3apbbapEaQSMwMBFlCg4QkkiASe5fYo7bYs7VbW+0VI8g44/eH37m+J2irLYLP63679VY518j7uk7O+1znvJ/X6z17Nvny5eP58+dYWFjkWJUgIcT/9T0jRowgICCAhw8fAuDi4sKgQYMYMmQIP/30E6GhoZw4cQJ4OTWeWq0mLi6OZ8+eER4eztmzZ8mbN+9rlWTEm+kHNkePHk3Tpk3p2LEjffv2pXbt2ri5ubF582YAqlevnm0KrYkTJ5KampqTzf+saTQa6tatS/v27fnjjz9o164dbdu2Zfr06dy9exeALl26UKtWLaZPn86VK1eU19Gr0/fKe5sQQgghhBBCCPHvGGhfrRUthBBCiHfm1q1bNG3aFHt7exITEylevDharZbk5GS8vb0xNjbm6NGjODg4ZLsLPCYmhsjISFJSUnBycsrhoxBCiPdn1apVtGvXDjc3N4KCgnBxcQHgxo0bTJ8+nbCwML7++mtCQ0OpX78+kD20qFKpUKvV1KtXj4sXL3LmzBkcHBzeS6jR19eXiIgIzMzMyMjIwNLSkg4dOjB06FAqVKiAVqtlzpw5JCQkcODAAQAsLCx48eIFzs7O7Nq1CxcXFwldCpFD9IN4t2/fpkOHDuzbt4/x48fj7u6uVHa5evUq0dHRREVF0aJFC3r16kWrVq2YO3cukydP5ssvv2TevHmYmJjI6/lfGD16NOPHj6dDhw707dsXc3NzNm3aRGRkJM7OzkydOpXmzZsDL6vzhIWFsW7dOvr27cv48ePJlStXzh7AZ0D3d/umv9/Dhw+zatUqpk2bxtOnT6lYsSLNmzfH19eX2NhYRo8ezdChQ5kwYQKmpqY5dARCCCGEEEIIIcTnR8I7QgghxHt0/PhxqlSpQlRUFN7e3mRmZrJy5UpGjBiBgYEBhw8fxsHBgczMTB48eICjo6Oy7bNnz7CyspLS80KIz9r48eMZPXo0e/bsoVatWtn6PLVajb+/P5GRkdSvX5+AgAAaNmwIZA/wPHv2jJYtW3Ljxg0OHjz4zgZ29X/HggULGDBgAB07dqR37948fPiQRYsWsWzZMlq1akVgYCCVKlUC4PLly2zYsIF9+/ah1WopXbo07u7uODo6Sp8uRA7Rf+0tX76cixcvsnr1ag4ePAjAuHHj8PDwwN7eHoBr164xY8YMwsPD0Wg0FChQgNu3b+Pk5ERKSooSNBT/zPbt2+nYsSNNmjQhODiYEiVKoNVqWbZsGT179qRo0aIcOHAAOzs7ZZtjx47h6+vLb7/9xqlTpyS88x+9+j6bnp6OqakpJiYm2dY7fPgwu3btYurUqdy8eZOyZcvSokUL4uPjcXV1JSUlJdvzJIQQQgghhBBCiP/GOKcbIIQQQnyqXr1TVaVSYWyc/a1VpVIBYG1tDUBycjL+/v4YGhpy6NAhHBwcAHj69ClfffUVCxYsoG7dugBYWVkBUnpeCPF50lUbu3z5MgBpaWlA9j7PyMiIPn36sH79evbu3cv48eMxMTGhbt26Sv+bkZHBjBkz2LlzJ15eXu8luPP06VOuXbtGtWrVGD16NEWLFgWgcePG5M6dm+nTp6PVagkODqZixYoUKVKEgQMH0r9/f2WqLwMDAwnuCJGDdK89X19fZsyYQcWKFfnyyy9xdXVl2bJljB49mqysLAYNGoS9vT3Ozs6MGjWKunXrMnHiRMzNzcmXLx/jxo2jUKFC8nr+l06cOEFaWhoDBgygRIkSZGVlsXLlSvz8/JRglJ2dHVlZWWRmZmJlZUXlypWZOnUqjo6O5MqVS6od/Qf6f7dz585l69atnD59GhsbG9q2bUv9+vWpWLEiANWqVaNatWr06NGDpKQkkpOTmTRpEgBnz55VPucIIYQQQgghhBDi3ZDwjhBCCPEv6E9xlZmZiampqRLcWbZsGR07dgTAzMwMQ0NDVq9eTUZGBhMmTMDQ0JCDBw+SN29e4OUA8fjx43n06FGOHIsQQuQEXR9ap04dEhMTOXDgAE2aNMnWvwKUKVOGChUq8PjxY3bs2IGlpSUFChSgZMmSwMt+1tbWlu+++47o6Gjg9XDlv6HbfsSIEZw/f57Lly/TokULihYtqoQ1LSwsiImJwcDAgPj4eABCQ0P58ssvsx2jbl8y0C9EzkpISCAiIgJPT0/8/PxwdnYGoEOHDsTExBAcHIyBgQFeXl7kzp0bKysrmjZtSt26dbGwsCArKwsTExMJ7vwLur79wIEDWFpaUqVKFbKysvjpp58YMWKEskwXbD937hx79uyhd+/emJmZUb58+Wz7Ef+cVqtV/m6HDx9OdHQ01tbW5M6dmzNnzpCSkkLp0qWZOnUqTZo0ASArK4v8+fPj7++Pn58fU6dO5dixY/z444/kyZNHng8hhBBCCCGEEOIdkk/YQgghxL+g+5K6RYsWREVFoVarARg8eDCdO3cmOTkZgC+//JJevXqxZs0aRo4cCcCvv/5Kvnz5gJdfoi9atIiVK1fSrFkzKleunANHI4QQ75dupl79GXs1Gg0AlSpVomTJkowbN46dO3diaGiIVqvNtu69e/dwd3cnKCiIjRs3smvXrmz769u3L2vWrFH2+64qMqSlpXHhwgWSk5M5ffq0sl9dNR14GciJjo7Gw8ODtWvXEhwczLFjxwBkQFOIj8y+ffuws7PD3d0dZ2dnpXJI+/btCQkJoUqVKgQFBTFz5kwePnyobGdmZgagBLUluPPP6frDihUr8uTJE/bv38/27duV4M6hQ4eUYDu8rJA0efJknjx58sb9iH9O9x42depUoqKiGDRoEPv37+fixYvs27cPLy8vzp07R7du3di2bRsAJiYmSiDWyMiI4cOHk5SUROHChVGr1fJ8CCGEEEIIIYQQ75B8yhZCCCH+pSNHjnD69GlCQkJYunQpnp6exMTE4OvrS7Vq1ZT1+vTpQ926dXn8+DHff/+98rhWq2X69OkEBgZibGzMlClTsLa2zjZgLYQQnzq1Wq0MGL548YLU1FRSU1OVAb/y5cvTt29fVCoVzZo1Y8OGDdm2Wb16NVevXqVw4cJ07twZOzs7oqKiePz48WshHa1W+04HEq2trYmOjsbb25usrCyWL1+uBHMMDAxeC/B4eXmRnJzMtGnTZDoRIT4ymZmZ/PLLL5ibm1OoUCGlv9C9juvWrcvAgQMBGDVqFHFxccp0fq9W0RL/XpkyZdBoNHh7e9OnTx+MjIxeC+7ExcVx8uRJ2rdv/86mQhQv3blzh6VLl1KyZEk8PT0pXbo0AJUrVyY6OpqQkBAePHjAmDFjlGktDQwMsr3n6cJsEmITQgghhBBCCCHeLQOtjBAKIYQQ/9q2bdsICgri4MGDqFQqhgwZgq+vL46OjkoZea1WS3JyMhEREezfv5/ixYtTtWpVLly4wLlz5yhYsCCbNm3C1dVVpmEQQnxW9Pu0mTNnsn79ek6fPo2xsTGtW7fmm2++oVmzZgCMGTOGsLAwTExMaN++PVWqVOHKlSusWrUKY2NjDh48SL58+ahRowZ3797NNv3g+6A/9daNGzcICwtj+vTpdO/enVGjRikDnvrrqVQqAgMD6devHy4uLu+tbUKIf2fgwIHEx8ezevVqWrZsqTyu/zquV68e165d49q1a0yZMoVBgwa9k6n4/he87RRKHh4ezJgxAzMzM9avX0+DBg2UZYsWLSI4OBgrKys2btxI/vz55fz/B68+J5cuXaJKlSq0bt2aOXPmACjV7nTr9e7dm3nz5rF69WpatGgh518IIYQQQgghhPhApPKOEEII8S/osq8NGzYkX758qFQqjI2NyZs3L46OjsDLu7R107e0a9eOxMRERo4cSVZWFhs3bsTIyAhvb2927twpwR0hxGdHq9Uqfdrw4cPx9PTk8OHDODg4kJqayqRJk+jZsyfTp08HIDQ0lNjYWL7++muWLFmCr68vCQkJ5M+fn+3bt5MvXz4uXrzIpUuXKFasGJaWlu+srbopvPTbrv/vQoUKMXr0aHr37s2CBQsIDw/n7NmzQPYKPMbGxoSFheHi4iKVd4TIIX91f9JXX30FQGBgIL/88ovyuG7604yMDK5evUqTJk2oVq0aQ4YMYc+ePRJc+Av79u1j9uzZwP9d+/4Z3bLRo0fTtWtXMjIy8PHxYerUqSxevBg3NzcGDx7MixcvSE5OJn/+/O90KsT/NfrBncuXL6PRaMjMzCQ9PZ3z58/z6NEjJZijP2Xlt99+i0ajYePGjTl8BEIIIYQQQgghxP8W45xugBBCCPEpMjAwQK1W8+jRIx4/fkzHjh357bffCAkJIVeuXLi5uWFpaakMYhgaGlK6dGnCwsLw9/dHq9ViZ2enLJPgjhDic6MbbI2LiyMqKoqhQ4fSr18/SpQowYULF1i7dq0S6jE2NqZv3754eHjw/fffc/bsWa5du0aBAgWoUKECDg4O3Lp1i5kzZ3L//n2aNGmClZXVO2mnfv+7YsUKDhw4wLVr13BxcWHQoEE4OTkBULBgQUJDQwFISkoCwM/PjzJlyigBHv0BZmNj+aglxIem/3pOS0vD0NAQExMTTExMAOjZsyd79+4lISGBkJAQRo0aRdWqVZXXa3JyMpaWlnh5eXHixAl69OjBokWLqF27do4d08fs4cOHfPvtt0rVFjc3t2zXvq/SPVaoUCGmTJlCvnz5mDZtGj4+Pmg0GgoUKEDdunWJiorCyclJro//I9359vDwYPv27SxevJhy5crRqFEjdu/ezcmTJ6lTp45ynjUaDUZGRtSqVQtAeZ+V8JQQQgghhBBCCPFhyLRZQgghxD/wprLx9+/fx9DQkBMnTuDv78+JEyeYMmUKbm5uWFhYKNvoD2RkZWVhYmIiZeiFEJ8trVbLkydP+P777/n999/ZunUrxYsXz7bOkiVL6Nq1K8WKFWPx4sVUrVr1jfs6c+YMs2bNIi4ujnbt2rFo0SLld/yXPlS/X/bz8yMqKgqNRkOuXLl48OABjo6OJCYmUr9+fczNzQG4desWY8aMISkpiT59+jB48GDKlSv3r9sghHg39F/PU6ZMYePGjdy/f5/q1avzww8/UKNGDQCuXbuGr68vy5cvx8nJiVGjRlG8eHEOHTrE7NmzMTY25sCBA5iamlKkSBGKFSvGrl27JJD3J9auXcsPP/yAhYUFoaGh9OrVC3j7KbQOHz7MkydPuH37NtWqVaNQoUJYWVlJcOc/0D/3CxcuZNCgQXz77beMGzcOV1dXYmNj8fb2pmDBgqSkpFCkSBHl/VSlUhEdHY2Pjw+zZs2id+/e8nlFCCGEEEIIIYT4QGTaLCGEEOItqdVq5YvrtLQ0MjMzyczMxMHBgdy5c1O/fn2CgoKoUKECQ4cOZc6cOTx79kzZZuPGjYSEhAAod4DLF+FCiE/drl27WLly5WuPGxgYkJ6ezpkzZyhTpowS3NFNywHQuXNnxo4dy8WLF5UpbF6dsurAgQN06dKFGTNm0L17dyW48y6mUtENbgYFBREREcEPP/zA3r17uXfvHtOmTePWrVv07duXdevWkZGRAYCjoyOhoaG4u7uTmJhIUlKSTJElxEdA93r29fVl+PDhHDlyhAcPHjBz5kwaN27Mhg0bAChcuDDR0dEMHTqU69ev4+npSZMmTRg9ejSGhoasX78eOzs7zp07R1paGqVLl5bgzl9o2bIlCxYs4MmTJwQEBCiVyd52Cq1q1arRsGFDunXrRsmSJbGysso27aL4Z3RVkODlZ5d79+5RrFgxgoODcXV1BWDgwIH079+fmzdvUqtWLZKTk7l69SoAixcvJjExkS+++IJWrVoB8nlFCCGEEEIIIYT4UKTyjhBCCPEW9O9gjYmJYePGjaSnp1OiRAlGjx5N4cKFlXU3bdpEUFAQv/76K5MnT6ZVq1b88ssvjBw5klu3bvH777+TJ0+enDoUIYR4Z+7evUvNmjW5fPkyK1eupE2bNtmW37hxg2rVqmFtbc3OnTtxdHTMNgio1WrZtWsXDRo0oF27dixduhQDA4Ns1RouXbrE2rVryZs3L127dgXevqLD21i7di3e3t40bNiQkSNHUrx4cVQqFeXLl+f+/fvKNCLx8fG0aNFCqcBz/fp1oqKi8Pb2xtnZ+Z20RQjxz+lXaNm2bRtdunShS5cu9O3bl7JlyzJlyhSCgoJ4/vw5q1evpmXLlsq2W7du5erVq1y4cIGyZcvSuHFjChQowI0bNxg3bhwzZ84kPj6efv365dThfdT0++J9+/bRrFkzChQogI+Pj3LO3mV/Ld6el5cXR48eJS0tjUaNGjFlyhQAVCqVEkYbPHgwCQkJpKenY2dnh62tLbdu3cLJyYnt27fj6uoqz58QQgghhBBCCPEBSXhHCCGE+Ad8fX2JiIjAysoKc3NzHjx4QIECBVi5cqUyHQPA5s2bCQ0N5eDBgxQqVIgnT55gaWn5Wml6IYT41M2ZM4cJEyZw7do1Fi5cSLt27bIt79+/P4mJicydO5du3bopA4G6Affnz5/j4OBA9+7dmTlz5ht/h/5g47scSMzIyGDAgAHs2LGD5ORkKlWqRFpaGtWrVyc1NZUff/yRtLQ0hgwZgqOjI1OnTqV58+ZKgOfVYxFC5JwbN25w4cIF3N3dWbNmDaVLl1aWzZkzh2HDhvHo0SPWrFnDd99996f7uXr1KnFxcURGRtKlSxfmzZv3IZr/ydHv97Zv386DBw+YN28e69ev54svvmDIkCH06dMHkABPTmjYsCE7duwgd+7cuLm5ERERobyX6j93q1atYufOnezevZtChQrx5Zdf4uXlhaOjo7y3CSGEEEIIIYQQH5iEd4QQQoi/oP+l9ebNm+nRowddunTB3d2dkiVLMm7cOKKjozEzM+Onn36iTp06yrYHDx5k0aJF7N69my+++IIJEyZQuHBh+SJcCPFZ0B+MXbRoEaNHj+bWrVuvBXh++uknevbsSWZmJlu2bOGbb75RAowqlYqEhAQGDhzIlClTGDx48AcPN86ePZv79+/j6+tLeno63333Hb/88gvh4eHKwHOjRo3Yvn07BQsWZMKECXTq1AlTU9MP1kYhxF8bOXIk4eHh1KpVi4IFC7Js2TK0Wi1qtVoJ/s2dO5ehQ4dmC/Dopt/T9Tnr16/H29ubhw8f0qZNG+bMmQNI+ORV+v20j48PixcvRqPRULt2bVJSUkhNTcXR0ZGxY8fSu3dvQM7hh6L/3HTp0oWlS5fi4ODA3r17KVGihPI8vPp8PH78GDs7O+VzinxeEUIIIYQQQgghPjz55kQIIYR4hS7XqtVqlS+tX7x4gZGREebm5gwYMIBy5cphampKSEgIYWFhaDQa2rdvT0pKirKfr776iqioKHbs2EFiYqIEd4QQnxXd4B9A165dGTduHI6OjnTr1o2VK1cq63Xo0IExY8ag0Who1KgRMTExHD9+HI1Gw5w5c4iNjaVkyZJ06dIF4INXJevVqxe+vr4ALF++nAMHDtC3b1969OihrFOzZk0qVqyIWq0mPDwctVr9QdsohPhr+fPnx9zcnOPHj/Pw4UPlcSMjI6WfcnNzY+rUqdjb29O6dWt++uknDA0Ns/U5RYsWpXz58gQFBUlw5y/ozll0dDSRkZF069aNHTt2sHz5cnbs2MHkyZN58OABgYGBJCUlAdnfM8S7o38/ni64k5mZCcDixYvp0qUL9+/fZ8CAAVy9evWNwR0AGxsbAOVx+bwihBBCCCGEEEJ8eFJ5RwghhPj/rly5Qq5cuciVK1e2x/39/Zk0aRJt2rQhb968zJgxA8g+jUtcXByBgYEYGBiwYsUK6tSp81r1CJkqSwjxOXrbCjyTJ08mIiKCO3fuAJArVy4eP35MsWLF+Pnnn3F1dc3xQfJBgwYRFxfHiRMnKFu2rPJ4kyZNsLKywt3dnUqVKuHo6JhjbRRCvJmuipdKpWLhwoVKIFCr1aLVapW+ZcGCBfTs2ZP8+fNz+fJlzMzMMDAwUALW6enpr02NJ7LTarWkpaXRpk0bTp8+zd69eylWrFi2dZKTk+nRowf58uVj1KhR9O3bF5Bz+i7p3xTw4sULnjx5Qv78+cnIyMDMzExZ7/vvv2fFihW0aNGCadOm4eLiIs+DEEIIIYQQQgjxEZJP6kIIIQRw/PhxSpQoga+vL8+fP8+2zNzcHCsrK9atW8fvv/9Oeno6Go0GY2Nj5Q5iT09PgoOD0Wq1dOrUiW3btr0W1JHgjhDic2RoaKhUovmrCjw+Pj4sW7aM8PBwGjVqRPPmzQkLCyMlJQVXV1fUanWODyRaWloCcO/ePeWxxYsXc+7cOZo0aULz5s1xdHSUyjtC5BD96og6uioj7u7uTJ8+HQMDA/r27cuqVasAlGmxdNds3bt3Z9myZRw+fBhzc3Pl+kwXgtAFd/QDPyI7Xdjpjz/+oFChQkpwR7+yTvPmzfHz8+Pq1atMmzaNxMREADmn74hGo1H+ZiMiImjUqBHFihWjUqVKuLm5cf78eWXd5cuX065dO9avX4+Xl1e2CjxCCCGEEEIIIYT4eEjlHSGEEAK4du0a1apVo169esybN08ZuNH58ccfmTRpEunp6axfv5769esrd7vq37k6ffp0PD09KV++PIcPH8bU1DQnDkcIId6bt7lbf+HChYwZM+aNFXgApSqAriLZxzKl4IoVK3Bzc8PS0pLRo0fz+++/s379eoyNjdm9e7dU3BEiB+n3E+np6aSnp2Nra/tafzRr1iwGDhyIpaUls2fPpk2bNsDrFXhe3af4Z168eEHt2rW5cuUK+/bto1SpUq+9P+zcuZMGDRpgYmKCiYkJiYmJdOrUKQdb/fnx9fUlIiKCYsWK4eLiwq1btzh79iz29vYkJCTw3XffKZ9HOnTowMqVK2ndujUREREULVo0h1svhBBCCCGEEEIIfRLeEUIIIf6/+/fvY2Zmho2NDZs2beKrr77C3t5eWR4eHs6YMWMwNzcnJSWFChUqvDHAM3fuXBo2bIiTk1NOHYoQQrwX+gPdmzdv5ty5c5w/fx5XV1dat25NiRIllHX/KsCj6zM/xukEJ06cyMyZM7l06RIGBgZUqlSJFStW4OLiIgP9QuQQ/dfezJkzWb16NWfOnKFAgQIMGDDgtesu3RRaVlZWrwV4PrY+52Onf850/9ZNHTthwgQCAgIYOXIkYWFhyjpqtRpjY2OysrKoUaMGzZo1Izk5mS1btlCoUKGcPJxPnv5njoMHD9K2bVu6dOnCoEGDcHFxISsri8DAQKZPn45Go2HRokU0b95c2b5z584sW7aMnj17kpiYKO9pQgghhBBCCCHER0TCO0IIIcQrYmJiGDx4ML6+vowaNQo7Oztl2cSJExk1ahSWlpbs3r2bihUrvjHAA3I3txDi86Lfx40cOZL4+HiePHmiLM+VKxcTJkygZcuWFCxYEMge4Fm8eLEygP4h2/qqPxu819/m8uXL/Pbbb9jb21OqVCly5colfboQOUT/tenj40NkZCSFChWiTJky3Llzh9OnT+Pm5sbw4cMpW7assl1CQgJeXl7Y2dkRExMjFV/+hVf7vVf7zwMHDtC9e3cuXbpEREQEQ4cOzVZRbebMmYwbN46DBw9SoEABjI2NpS99R3777Te2bt3KhAkT2L59OyVLllRCVQDTpk3Dx8eHXLlycfTo0Wyhqf79+zNy5EhcXV1zqPVCCCGEEEIIIYR4EwnvCCGEEK+4dOkSHTp04MyZMwwdOhR/f39y5cqlLJ80aRIjR47E0tLytQo8QgjxuQsKCiIkJAQ3Nze+//57HB0dWbBgAYsWLeLx48eMGDECDw8P8ubNC8CiRYsIDg7m/PnzrF+/nm+//fa9tk+/P/799995/Pgxjx8/pkKFCuTOnRsjI6M/7bP/LPTzNlOFCSHer/HjxxMSEkLfvn0ZOHAgZcqU4fLly5QrVw5DQ0OaN29OcHAwpUuXVrZJTEzE3d2d0qVLc/ToUczNzaXyzlvS7/cSExM5cOAAqampfPfdd3Tq1AkLCwsAkpOT+eGHH3j69CkDBw6kefPm1KhRg8WLFxMfH4+9vT3r16/HxsYmJw/nsxIeHk5oaCjNmjVDo9GwcuVK1Gq1UtFO97wNHDiQ+Ph4QkJCGD16tDJlpY5+2EcIIYQQQgghhBA5T8I7QgghxBtcu3aN9u3bc/z4cXx9ff80wGNnZ8fmzZupWrVqzjVWCCHegzcFVn755RdatGhB9erViYqKwtnZWVmWnJzMjz/+yKlTp5gzZw7ff/+9siwpKYlZs2axdOlSChcu/EHaPG7cOObPn8/ly5dRqVSUKlWKdu3aERAQgKWlpQRyhPgIPX/+HEtLy9ce37dvH25ubnz99dcEBARQokQJnj59SvXq1Xn8+DGurq4cOHCA77//nsDAwGwVeBYtWkSdOnXea9/zOfP19SUiIgITExOysrIA6Nq1Kz4+PlSsWBGAtWvXEhQUxPHjxwEwNzcnPT0dV1dXtm/fjqurq0xZ9o6o1WqWL19OYGAg58+fJ0+ePBw5cgQXFxdlHd3728WLF6latSqNGjVi+fLlOdhqIYQQQgghhBBCvA35tloIIYR4A2dnZ1asWEGlSpWYNGkS4eHhPHr0SFnu6+tLeHg4qampdO/enaysLCQPK4T4HJw6dYobN25gaGiIRqPJtuz69evcunWLZs2a4ezsjFarRaVSAdC2bVsGDx5MZmYmPj4+3LlzR9mud+/ebN26lcKFC6NWq99b23VhHH9/f8aOHYuLiwvh4eHMmDEDAwMDJkyYQLNmzXj+/LkEd4T4yGzbto0ePXpw4sSJbI9rtVrOnDnD06dP6dWrFyVKlCAtLY2aNWuSmprKpEmTWLRoEU2aNGH16tWEhoZy5swZZfuuXbu+977nc6J/Pbty5UpmzpyJu7s7hw8fZvv27fTq1Ytly5YxduxYjhw5AkDLli1ZsGABS5cupUuXLnTr1o3g4GD27duHq6srarVagjvviJGREW3atCE8PJyqVavy4MEDZs+enW0aSx0LCwsMDQ2V0JUQQgghhBBCCCE+blIfVwghhPgTugBPu3btmDRpEkC2CjzDhw/H0tKSFi1aYGJikoMtFUKId+PkyZNUqFCBJk2akJSURMGCBbNVqHn69CkApqamwMu7+42NjZWKCl27dmX9+vUsXryYS5cukT9/fmV7XTWN9z3FYHJyMjExMfTt2xc/Pz+KFy8OvKwE4ebmxs2bN2UQX4iP0NKlS0lOTsbY2JixY8fyxRdfAGBgYECZMmWIiYmhXr16ZGRk0LNnT27evEl4eDidOnXC2NiYGjVq8PPPP7NhwwZSU1OJjY2lWLFiyv5letO/p18d58WLFxw/fpyiRYvi6+ur9KXFihWjYMGChIeHAxAYGEiVKlUoU6YMZcqUUaqu6fYlU8u+e+bm5jRr1gytVktAQAAzZsygSJEitGrVCnt7e+U9e/369aSmpiqVqKT6kRBCCCGEEEII8XGT8I4QQoj/Of/ki2tnZ2dWrlyZLcAzYsQI7OzsAPDw8ACQgQkhxGehfPny1KtXj59//hlvb2+io6MpVKiQ0sflyZMHgBkzZtCsWTMcHR2Bl4PrGRkZmJmZUa9ePRYvXsyNGzcAPniFm3379mFkZMTAgQMpXrw4arWaJUuWEBQUhKurKwcPHsTGxob09HTMzMxkIFOIj8S0adMwMjJixowZqFQqQkJClADP119/rVQP2bdvH7t27aJNmzb06NEDY+OXX2t88803rFu3DktLSw4fPqxcq4m3p+sP/f39OXHiBFZWVrRr147ixYujUqkwNjamcOHCeHp6YmBgwI8//ghAUFAQlStXBl6/Jpbr4/fD3Nyc5s2bA+Dn58fw4cM5dOgQ7u7uFCxYkOTkZKKjo3FxccHb2xtA3u+EEEIIIYQQQoiPnIR3hBBC/E95U8jm78I8ugBP+/btmTp1Kk+fPmX8+PHY2toq68jAhBDiU6cbmN2xYwctW7YkOTkZrVZLdHQ0Tk5OADRp0oSWLVuydu1aZs2ahaenJ3ny5CErKwszMzMAfv31V6ytrSlduvR7aee2bdtYvHgxcXFxSgUgnczMTI4ePUqBAgWoUKECmZmZJCcnM2rUKAwNDTl06JASQLpy5Qpnz56ldevWMoWWEB8BU1NToqOjUavVzJo1CyBbgEcX0rlw4QKpqal89913mJubAy+v5ebMmYORkRFbtmzhxYsX5MqVK1vlMPF2UlNTuXXrFtu2bUOlUmFhYfFalTVHR0clwP7jjz9iZGTEqFGjqFatmnJNLEGR98/MzEwJ8IwZM4b4+HgSExPJly8fFhYW5MuXj/Xr1+Po6Cg3GgghhBBCCCGEEJ8A+RZLCCHE/xTdl9be3t5MnDgReLvBBd0UWo6OjqxduxatVvte2ymEEB+asbExGRkZAKxdu5YOHTqwatUqvLy8+OOPP5T1AgICKFeuHJGRkYSHh/PHH38oUweuWLGCdevWUaVKFZydnd95G58/f8706dNJSkrC29ubzMzMbMtNTU2xtbXlxYsXpKens27dOvz8/JTgTt68eYGX03317NmTxYsXv7YPIUTOMTExITY2lr59+5KcnMzYsWM5ffo08H/Xa9bW1gBs376dO3fuALBs2TL2799PlSpVMDY2JleuXGi1Wgnu/Av29vaMHTsWLy8v7OzsOH36NCdPngRePge6a2BdgGfUqFGsXr2auLg4pTqS+HB0AZ7Q0FCqV6+OSqWiZ8+eJCcns2PHDlxdXVGpVBLcEUIIIYQQQgghPgEGWhl9FEII8T9Eq9Vy8eJFSpYsSb169Vi9ejU2NjZvfXfwjRs3MDIyokCBAv9o+i0hhPjY6d+Vf/fuXR4/fkyjRo24d+8eTZo0ISYmhsKFC/PixQu2bdtGYGAgx48fx9XVlRYtWnD16lUOHTqEsbExe/fuxcXF5b1UvTh16hTjxo1j2bJl9OrVi/j4eExNTZXfFR0dzZAhQ2jTpg3Hjh3D0NCQvXv3KlN8AURFRTFhwgR8fHwYNmyYDPAL8ZHJyspi4MCBzJo1i7Zt22arwPPo0SN69uzJpk2bqFWrFhYWFhw4cABbW1v27t2rVAoT/5x+n33hwgWio6OJi4ujTZs2REZGKqFM/WvgGzdusHjxYjp16kThwoVzrO2fg//y2SI9PZ2NGzfi7+9PVlYW4eHhtGrVSqlOJYQQQgghhBBCiI+fhHeEEEL8T/Ly8iIhIYGdO3dSs2bNf/xluUzDIIT4nOj3aaNHj2bJkiUYGRmRmZnJ7du3ycjIoHXr1kRHR1O4cGEyMjK4fv06oaGhrF+/ngcPHlCoUCGqV6/O1KlTKVy48HudouPMmTOMHTuWlStX8sMPPzB9+nRlCq3Tp0/z7bffcv36dRwcHLh+/bqyTKvV8tNPPzFq1Cjy5s3L6tWrlWo8QoiPy18FeI4fP05cXByzZ8+mUKFCfPHFF8yYMeO99z2fk1evZXVTJ+q7dOkSU6ZMITY2lg4dOjBp0iRcXFyA7EET3b7k3P97+ucuKyuL9PR0tFpttml6/+7zSkZGBhs3bsTX15cXL14QERFBy5YtsbS0fO/tF0IIIYQQQgghxH8n4R0hhBD/U3Rfeq9evZq2bdvSunVrFixYgJWVVU43TQghclxISAhBQUEMGTKE7t27U7hwYQ4cOMDkyZNJSUmhTZs2REVFZauucPPmTVJTU3FycsLU1BQLC4v3NoCrP7h8+fJlfHx8SE5Opn///kyZMkWpMLBt2zaaNm2KRqNh5MiRfPvttzg4ODBr1iyWLVuGRqNh3759ODs7SxhTiI9YVlYWnp6eJCYm0rZtW4KCgihfvryy/OzZszg4OGBhYYG1tbWER96S/nlatGgRe/bs4dixY9SvX5+GDRvSuHFjZd3Lly8TGRn5twEe8e/pPx8zZ85ky5YtnDlzBlNTU3r06EHjxo2Vv/u3DfD4+/vz4sULgoKC6N69uxJiFUIIIYQQQgghxMdLwjtCCCE+W7oBWf0vufX/3bBhQ06fPs2+ffsoWrSoDOAKIf6nnT9/nsaNG5MvXz6WLl1KkSJFlGXp6em0a9eOTZs2ZavAA68PJL6vwVz9wc3Zs2dz584d5s6dy82bN3n69Cmenp5MnjxZCfDs3LmTgQMHcvbsWWUfZmZm1KxZkzlz5uDs7CwD/UJ8Al6twBMcHEy5cuWA7NVjJEjydvTPma+vLzExMZibm5M/f36uXbuGsbExU6ZMoW/fvso2ugBPXFwcnTp1IiwsLNt7hPj39P9uhw8fTlRUFE5OTpQoUYK7d+9y6tQpGjRogLu7Ox07dnyrfWZkZLBp0yZ69OhBr169iIqKep+HIIQQQgghhBBCiHfE+O9XEUIIIT4NmZmZyl2l+gOyjx49wt7eHgADAwNlvQEDBtCpUydiY2OJiIiQ4I4Q4n/a8+fPuXnzJp07d6ZIkSLoMv5arRZzc3Pmz59PixYtWL16NVqtlpiYGAoXLvzagPn7GjzX9ek+Pj5Mnz6d6tWr06hRIwwNDUlISCAuLo6MjAxlILp+/fps2LCBc+fOcfToUczNzfnqq68oV64cdnZ2EtwR4hNhYmJCbGwsWq2WxMREjIyMCAgIoEKFCtmu3SS483Z05yw0NJSpU6fSq1cvevfuTY0aNViwYAE9e/akX79+ZGZm4unpCUCRIkUYNmwYRkZGREdHY2trS1xcnFw7vwO6v9tp06YRFRWFp6cnnp6elC5dmtu3bzNy5Ejmzp2Lg4MDbdu2xcTE5G/3aWZmRtOmTdmxYwdVqlR534cghBBCCCGEEEKId0Qq7wghhPgs7Ny5k40bNzJw4ECcnZ2Vxz09PVmxYgWBgYHUqFGDypUrK8uuXLlCgwYNMDQ0ZPPmzRQrViwnmi6EEB+FgwcPUrNmTZo0acKSJUvIlSuXskyr1aLVahk/fjxjx47FwsKC6tWrs3jxYgoUKPDB2jh//nzc3Nzw9PTEz89P6e/3799PUFAQW7Zswd3dnaioKKUCz5tIpTUhcsabXntvWzEnKyuLQYMGMWPGDPr06UN8fLwyjZ74ZzZs2ICXlxf16tVj5MiRlCxZkrS0NGrVqsWdO3fIysri0aNHTJ8+nX79+inbnT9/nnnz5uHu7p7telv8e1qtlkePHtGmTRsePXrE8uXLKVmyJBqNhuXLlzNy5Eg0Gg1Hjx4lT548/yp4Ku95QgghhBBCCCHEp0E+vQshhPjkPX/+nNjYWCZNmkR8fDzXr19Xlj158gQbGxu8vLxo3LgxAwcO5ODBg6SlpeHq6oqvry+XLl1i165dOXgEQgiR86pVq8bXX3/NyZMnOXPmTLZluoG/Bg0a8OWXX+Lk5MSBAwc++MD58ePHMTU1pVu3bjg7O6PVatFoNNSsWZNJkyZRo0YNEhIS8PPzIz09XWm7fhUhQAYxhcghutfesGHDmD59OvCy8sjb3FNkYmJCVFQUPj4+jB49WoI7/5JKpWLHjh3cv38fLy8vJbhTvXp17t27x/Tp04mLiwNgwIAByvMEUKJECYKCgpRpB8V/Z2BgQGpqKkeOHKFBgwaULFmSzMxMli1bhp+fH1qtliNHjpAnTx4A/vjjD549e/aPfoe85wkhhBBCCCGEEJ8G+QQvhBDik2dpaUlAQAAdO3Zk0qRJREdHc+3aNQAWLFjA9u3bmTdvHkWKFCEpKYkGDRrQunVrtm/fjrOzMyVKlGDq1Kn88ccfOXwkQgjxfmk0mj9dptVq6dChA3fu3GHYsGHZ+kQjIyM0Gg3Jycnky5eP3377jRs3buDg4PCX+3yXtFotFy5cwNTUlKJFiwLZqwl8+eWXTJgwAXg5/ciQIUPIyMjA0NBQqeoh0+oIkTP0+4krV64wffp0PD09WbhwIfD2AR5TU1MmTpyIi4sLKpXqvbX3c2ZsbEzjxo2JiYmhSpUqpKen065dO+7evUtoaCgtW7akc+fOuLu7Ay+rWIaHhyvb66q+yLSD747ub9/S0hKAFStW4O/vj6GhIYcOHcLBwQF4eVNCs2bNWLduXY61VQghhBBCCCGEEO+PhHeEEEJ8FipWrEhAQABt27YlMjKSadOmceXKFQCcnZ3p3r0769atY+fOnTRp0oRffvmFxo0b8+OPP3L79m3u3r3LxYsXgb8e3BZCiE+VWq1Wgi6//PILmzdvJjExkVOnTvHw4UOMjIzo0qULXbt25dChQ7Rp04Y1a9Zw7949AJYvX87PP/+Mvb09KpUKe3v7DzoVh4GBAaVKlSItLY0lS5YALwePdYOeKpWKevXq0ahRI7744gtmzpzJqFGjPkjbhBB/Tr/v2bhxI8eOHaNcuXKYmJjQo0cPFi9eDLx9gEdHKu/8ew0aNKBbt24ArF+/npSUFH744Qd69OihnNe8efNSuHBh8uTJw/jx40lLS/tHz494O1qtFktLSwoVKsT8+fOZMmUKI0aMwNDQkIMHD5I3b15l3cmTJ3Pjxg0l5COEEEIIIYQQQojPi4FWvn0RQgjxiVOr1crdv6dOnSIwMJDVq1czbNgwvL29KVy48GvbnDp1ip9++olFixZx69Ytnj17xnfffcfq1aulMoMQ4rOjH7IJCQlh1qxZ3Lx5E41GQ548eahevToTJ07kiy++4ObNmwQFBbF06VLS0tIoVKgQtra2nD17loIFC7J3716cnZ0/eLsB9uzZQ926dSlbtiyxsbHUq1cPgMzMTExNTQEoU6YMNWrUwMzMjJEjR+Li4vJB2iqEeJ1Wq1Wuq3x9fUlMTKRw4cI4OTnx4sULdu7cCcDcuXPp0aPHa9uI92/MmDGEhYVx9erVbNfM7du3x8HBgV69euHs7EzBggXlufkP/i7sGhQUREhICFZWVtjb23P+/HnMzMyUbZcvX86IESMoV64cCxYswM7O7kM1XQghhBBCCCGEEB+IVN4RQgjxSXm1Kk5WVpYS3Hny5AnlypUjODhYqcATExPD9evXlfV1UyyUK1eOoKAgVq1axdKlSylRogRbt25l//79AHJnsRDis6IbMBwxYgTBwcGULl2a6OhoAgIC+OKLL9i4cSPffPMNx44do2DBgkyYMIG5c+fSsWNHLC0tsba2pmfPnuzfvx9nZ2fUavV7aeerffyrA521a9cmKCiIc+fOER4ezubNmwGU4M7ixYvJzMxk4MCBTJ8+XabWESKH6YIeMTExRERE0Lt3b1avXs369evZvn078fHx2NjY4ObmxoIFC5Rt5Drsw9EFRLZu3ao8tmzZMg4ePEihQoWoUaMGBQsWRK1WS3DnX9BqtdmqT+3bt48VK1awa9eubJ9R/P396dy5M8+ePaNMmTLcvXsXrVaLVqtl6tSpBAQEABAfH4+dnZ1UChVCCCGEEEIIIT5DUnlHCCHEJykiIoKWLVtSsmRJAPr160dWVhYxMTFYW1tz8uRJQkJCSE5OZtiwYQwaNAgnJyfg/4I5+gMQO3bsoGHDhowZM4bg4OAPf0BCCPGebd68mXbt2tG9e3dGjRqVrSKNl5cXcXFxODk5sWXLFkqVKqUsu3fvHjY2NhgaGmJqapqt2tm7pL/flStXcvToUX755RdatmxJzZo1qVChAgAXL15kypQpxMfHkydPHnr37k3dunXZv38/ixcvxsTEhN27d2ebakQIkXOysrJo27Ythw4dYufOnZQtWzZbBZelS5fSrVs3NBoNCxcupEuXLoBU4Pm3Xu2j/+487ty5kwYNGmBtbY23tzf3799nw4YNmJqakpKSQsGCBT9Esz8re/fu5enTpzRr1ixbxZ0RI0YQERGhBGDLlStHXFwctWvXRqPRcOnSJcaMGcPSpUsxNTXlyy+/5N69e9y6dYsSJUqwdu1aXF1d39v7sBBCCCGEEEIIIXKWhHeEEEJ8cgIDAwkNDcXd3Z3IyEiCg4OZPHkyXl5eBAUFkTt3boC/DPDo02g03L9/n6+++gobGxv27t2LlZXVX5a2F0KIT83EiRMZMWIEe/fupWbNmko1AGNjYwB69+7NnDlz6N+/P5GRkZiamn6wwUH9wU1/f39iYmIwMDDAysqK+/fvU6FCBXx8fOjWrRsAf/zxB6tWrWLEiBG8ePFC2U/ZsmVZt24drq6ufztFiRDiw0hLS6NSpUrY2Nhw7Ngx4P+qbOleo1OmTGH48OEAzJ49Gzc3N0ACPP+U/vk6ffo0X3zxxVttt2zZMoYOHcqtW7cwNTWlUqVKLFmyBBcXFwmK/EM3btzAxcUFQ0ND1q5dS9OmTQEIDw8nICCA5s2b07BhQ06ePElSUhLGxsasX7+exo0bK89fTEwMO3fu5Ny5c5QoUYI6derQs2dP8ubNK8+HEEIIIYQQQgjxGZPwjhBCiE/OgwcP6N+/PytXrqRMmTKcPXuWMWPG0LdvXwoXLpxt4OJtAzzPnz+nXr16aDQa9uzZg4WFxYc+LCGEeK88PT2ZPn06p06dylb5QjcQmJaWRuXKlbG0tGTfvn1YWlp+8DYGBgYSFhZGt27d6N+/P7Vq1WLmzJl4eHjg4uLC2LFj+eGHH5T1z549y/nz57l69SrFixenWrVqODg4yOCmEB+R9PR0GjRowMGDB9m2bRv169dXlulCdjdu3KBhw4Y8fvyYO3fusHTpUr7//vuca/QnbujQocyZM4fjx4/j6ur6p+vpXzP//vvv3L59GxMTE8qWLYudnZ30pf/S5MmTGTNmDNbW1syZM4cWLVrQqFEj7O3tiYiIwNnZGYC4uDhGjhzJ06dP2bx5M40bN862n/T0dMzNzZWfJZQqhBBCCCGEEEJ83uRTvxBCiE9Onjx5+OmnnyhYsCDnz5+nXLlyNG/eXAnu6Ctfvjxjx46lbdu2REZGEhsby9WrV7Otk56ezowZMzh69CjffPONBHeEEJ8lR0dHALZs2YJarVYGbI2MjMjKysLa2ppixYpx6tQpfvvtt9f60/dt1apVJCYm0qdPHwICAqhVqxZPnjwhNjYWW1tbrly5gp+fH/PmzVO2KVOmDK1atcLb25tvv/0WBwcHNBqNDDYLkQP+rM8wNzenU6dOaLVa5s+fz/Xr15Vlugo8efLkISMjg+bNm2Nubs7w4cM5fvz4B2n350ir1ZKWlsa1a9eA/zvPrzIwMFCet5IlS1K3bl1q1qyJnZ2d9KX/gu48+/j4MHHiRB4+fIibmxvz58/n2bNn9O3bF2dnZ1QqFfAyVDt58mSsra1p2rQpW7duVfajVqsxNTXNtl8J7gghhBBCCCGEEJ83+eQvhBDik7R582YePHiAo6Mjp06dIikpiatXr75xagVdgKdDhw6Eh4ezYMGC1wYx0tPTadWqFZMnTwb+fABKCCE+Zrq+S78Py8rKAqBt27Y4OTkxc+ZMLl68qCxXq9WYmJgAoFKpKFmyJK6urh90qpr09HS2b9+ORqOhf//+lCxZkrS0NGrUqMHdu3eZP38+M2fO5P79+wQEBDB79mxl21f7cxncFOLD0w8EAko4Qadt27Y0a9aMRYsWkZiYyKVLlwAwNjZGq9WyYMECTE1NmTp1KuPHj+f69evs37//gx7D56RDhw4YGxsTHBxMenr6X/aLf9bXS1/6zxkaGip/+97e3kRGRvLo0SP8/Py4dOmScoOAgYGB8t7l7u5OREQE1tbWNGnShG3btmFoaKj8p9uvEEIIIYQQQgghPn/yDYAQQohPgv7grEajoWbNmqxevZrNmzfTrl07EhISCAkJUQI8Wq022zbly5dn5MiR9O3blx49emT7Etzc3Bxvb29WrVql7P9DDloLIcS7oD94/vjxY+7duwegBHNcXV3p0KEDZ8+epXv37hw7doznz59jZGSEVqtl5cqVHDlyhEqVKmWbpuNDMDAwwNXVlcjISCpXrqwEKu/fv09YWBhNmjShb9++NG7cmJs3bxIWFsaMGTMAGdQUIqfpV2iJi4ujW7du1KtXj7i4OE6ePAmAs7MzXl5eVKhQgbCwMIYOHcqKFSu4ceMG06ZNY+rUqVhbW6PRaGjWrBlGRkZs3LgRkED1v1GrVi0aNGjA/v37OXLkCPDn1XfEv/fqOVWr1RgbGys/Dx48mPDwcB49esS9e/c4ffo08LLi3ZsCPPb29jRu3JiUlBT5LCKEEEIIIYQQQvwPMtDKN2FCCCE+cmq1WhkUWr58Odu3b8fT05Py5csDLwep3dzcWLNmDb169WLs2LG4uLgALwd8Tpw4gaOjI/ny5UOlUmFsbJxtn/q0Wq18WS6E+CTcunULa2trbGxssvVpEydOZOnSpdy4cYPKlSszZMgQqlevTq5cubh37x4+Pj7Mnz+fIkWK0LhxY5o2bUpKSgpr1qwhPT2dAwcO4OTk9MH7w7S0NExMTDAzMyMhIQEvLy98fHwICAjA0tISeDnFyJYtW7h48SJly5bl0KFDyjIhRM7y9fVVKohkZWWRmZlJzZo1GTduHPXr1wdg69atzJo1i2XLlmXb1tXVla1bt1K0aFEOHDjAN998w5AhQ5gwYUIOHMmn4dVrWY1Go1R+MTY2Zvfu3TRu3BgPDw+mTp2acw39H7Bt2zZq1KiBlZUV8LLqjomJCZGRkQDExsbi7e0NwNq1a2nRogXw8nOHVqtVQqjR0dHExcXx888/4+zsnANHIoQQQgghhBBCiJwkt6kKIYT4qOnfzR0QEICHhwcrVqzINpWCnZ0d8+bNo1WrVsyePZuQkBBu3boFwLp162jdujXjx49Ho9Eod8O+KbgDfz51gBBCfEwOHz7Ml19+yfz583ny5InSp/n7+zNixAju379Pnjx52Lp1Kz179iQxMZF79+6RN29eIiIiCAwMxMrKipkzZ9K+fXvi4+NxdHRk7969ODk5vTYFzruiVqv/dJmVlRVmZmYAnDx5EiMjI3x9fbOFc86ePUu/fv3Yu3cv69evl+COEDlIv+rIqlWrSEhIoH///uzYsYNDhw4xbNgw9u3bh5eXF9u2bQOgUaNGzJ49m2XLluHj48OAAQOYMmUKe/bsoWjRovzxxx/ExsaiUqkoV65cTh3aJ0HX769du5YbN24oARDdtW7RokUpXbo0iYmJHD58OMfa+bnr168fjRs3Zv369QAMGTKE2NhYjI2NSU1NBWDgwIHExMQA0KtXLzZt2gS8/NyhX4Fn0KBBHD16FGdn5798vxRCCCGEEEIIIcTnyfjvVxFCCCFyjm4gYsyYMYSHh9OrVy+GDBnCF198kW09W1tb5s2bh5ubG7Nnz+by5cuUL1+ejRs38vTpU7y9vWVqFSHEZyM1NRVbW1tCQ0MxNTWlU6dO3LhxgyVLligVawoWLMjGjRsJCgoiJCQElUpF7969yZs3LyNGjGDw4MHs2LGDrKwsXFxcKFOmDLa2tn9amey/0g9jLl26lNOnT+Po6EjZsmWpV68eBgYGZGZmYmBgwJ07d0hPTyclJYWWLVsq25w/fx4zMzNq1qwJoFSYEEJ8WPrVQu7du8fDhw8pUaIEPj4+FCtWDIBJkyaRJ08eRo0axaBBg4iJiaFBgwZYWFjQoUMHOnTooOzLwMCAa9eukZCQwJIlS+jRowfdunXLseP7VERGRuLj40O+fPkYPHgwX3/9NXXr1gXAycmJQYMG4e7uztGjR6lWrZpUmHwP6taty+HDhxkwYACJiYls2bIFPz8/vL29sbe3V95TBw4ciEajYciQIfTo0YP58+fTrFkz5fnQVU7SVe95H+/DQgghhBBCCCGE+LjJtFlCCCE+etu2baNz5840atSIH3/8UZkS600yMjLo168f8+fPx9LSkpIlS5KcnIyLi4sM8gohPhsqlYqdO3fi4+PD9evXmTJlClqtlmHDhrFjxw5lWkG1Ws0vv/zCgAED+O233wgICKBv377kyZPnjfvVDR6+T7qpdXSsrKzw8/NjzJgxymObN2/m22+/pWLFinTt2pU7d+6wbNkyTE1N2bNnD/nz53+vbRRCZHfv3j3s7OwwNTXN9riPjw+bN2/GwsKCChUqkJCQgEqlwsDAQAkfhIeHM3LkSMqUKUNsbKwyhZZ+f7Njxw7c3d25d+8erVq1Yv78+a+tI14/H1evXiU+Pp7du3dz4MABDA0N6dOnD82bN+fbb7/l6dOn1KlTh+fPn7Nnzx6cnJxysPWfrx07dtChQwcePXrEt99+S3R0NEWLFgVenxorJiaGIUOGkDt3bhYsWEDTpk1zsulCCCGEEEIIIYT4iMi3YEIIIT56p06dIjU1lV69ev1lcEer1WJmZsbcuXPZunUrW7ZsYevWrbi4uKBWqyW4I4T4LGi1WoyNjalfvz4TJ06kYMGC+Pj4sHHjRurXr0/58uXRaDRKpZsqVaowY8YMSpUqRVhYGLNmzVKm8ng1x/++B8lXrFhBYmIiffv25eeff2b58uXky5ePwMBAvL29lfW++eYboqOjOX36NH5+fsTExFCgQAG2bt1K/vz5s03XI4R4v44ePUrJkiWZPXs2KpUq2zIjIyNOnz7NiRMnlNelsbExhoaGys/+/v5MmDCBs2fPMnjwYH7++Wfg//objUaDSqXC2dkZf39/Ce78CbVarZyPI0eOsGfPHlxcXPjxxx/ZvHkzS5YsoUmTJixYsIC2bdvyzTffsH//fooVK8bDhw/ZuXOnsh/xbuj+xn/55RcePXqEvb09u3fv5tixY2RkZAAvp8bSfz14e3szdepUnj59yrfffqs8L0IIIYQQQgghhBBSeUcIIcRHr1u3bqxatYozZ84oQRz9UvK6wZ3MzMzX7gjXXy6EEJ8i/WlOtFpttumntFot27Ztw9/fn+PHj+Pg4MCBAweUO/71HTt2jAEDBnDx4kW8vLwYNmwYdnZ2H/QYxo4dy6pVq1i5ciXFixcHXg56ent7s3fvXjw9PZk2bZqy3YkTJzhx4gS5c+emRo0a5M6d+71N6yWEeLOVK1fi5uZG27ZtSUxMxMTEJNvySZMm4e/vj4mJCWvWrFEqibxacUS3Xr169di0aRNmZmbZ9vP48WOlT5Jrt+z0z0dYWBhJSUlcu3aNw4cPU7FiRWW9p0+fcv36dSZNmsSWLVt48OABuXLl4vbt27Ru3Zrk5OQcOoLP29GjRzl48CBmZmZER0dz9epVpk2bRseOHZXPJq/+TYeFhTFjxgz27dsnFZGEEEIIIYQQQggBSHhHCCHEJ2DIkCFER0czY8YM3N3dsy3TDQinpaXRunVrpk2bRpkyZXKopUII8W7pD/ZlZGRkG+xesWIFVapUwdnZmZ9//pkff/yRlJQUwsLCGDBgALly5Xptf8ePH6dDhw6YmJhw+PBhbGxs3lvb9UM2umkLvby8yJ8/P2PGjEGtVisVCU6cOMHAgQPfGODRJwP6Qnx4Wq2WQ4cOUaZMGWxtbfnll18oU6ZMtv5o4sSJjBgxgpo1a/Ljjz9Sp04dZVv9AE9cXBzNmzfH1dX1L3+fLrAosp8PHx8foqOjadu2Le7u7jRq1CjbOrr/q1Qq7t27R1JSEps3b+bw4cNkZGSwYsUK2rZtm5OH80nau3cvAF9//fXfrpucnMzYsWP5448/iI2N5fvvv1cCPFqtljNnzvDFF18AL8NWNjY2EkoVQgghhBBCCCEEINNmCSGE+Ej8VZb0u+++w8DAgEWLFnHq1Cnl8czMTGUwIyEhgcOHD3P27Nn33lYhhPhQdAPetWvXpmfPnsqUNf379+f777/n119/xdDQkIYNGzJq1CgqVqzIpEmT+Omnn3jy5Mlr+6tUqRKrV69m586d2NjY/GXf+1/oVwdKSkrCw8MDf39/Dh48yOnTp8nIyMDIyEgZbP7yyy+JjY3l66+/Ji4ujiFDhvzl+RBCvF8nT54kLS0NeDntz1dffYWtrS1TpkyhcuXK/PTTT8q0QAB+fn6EhISwf/9+AgICSElJUbY1MDBQpmry9PTE1dX1tem39ElwJzvd+Zg9ezbTpk3Dw8ODCRMmKMEd/XV0faqxsTGOjo4EBASwfPlylixZgqmpKXv27MmRY/iUbd26lTp16jBr1iyePn36p+vppsVq06YNISEhFC5cmIEDB7J06VLl73/jxo107NiRoKAgAKytrdFqtRLcEUIIIYQQQgghBADGOd0AIYQQ4tW7TR8/foyFhYVyl2qtWrXo27cvs2bNIioqCnd3d6pXr64sX7FiBdOnT6dixYp88803OXIMQgjxvqSnp3Px4kVlao3nz5+TkJCAt7c3VatWBcDExIQGDRrw448/4uvry4gRI9BqtXTq1AlbW1vg/yozlCtXDni/VWx0+/X19SUiIiLbMo1Gw7Vr1yhRooQy0Kwf4Bk8eDDR0dFYW1szbty499I+IcSfO3nyJBUqVKBu3bqsX78eKysrpXpW/vz5KV26NIMHD8bAwID27dsrFXhGjx4NwNixYwkICCAsLIw6deooFbb0GRvLVxFvS9d3r1mzhnz58uHp6fnGqRF1dEEeXR+fP39+6tevz1dffcXMmTPp27evVKl8SydOnMDNzY1vvvmGXr16/WW1OkNDQ+W5atu2LQYGBgQGBuLt7c2VK1cAWLJkCffv38fNzQ2QoJoQQgghhBBCCCGyk1tXhRBC5Cj94M706dNp06YNZcuWpWbNmvj5+XH37l0sLS0ZOHAgLVu2JCkpCTc3N0aPHs3y5cvp168f3t7ePH/+nAULFmBvb6/c+SqEEJ86jUaDubk5165do2rVqkyZMoUZM2YwePBgxo0bR6FChQCUSgvffPMNkyZNonDhwowcOZJly5YplQJeHSR8H8Ed/f53+/btzJ07l4EDB3L8+HFOnDhBq1atOH78OJ6enjx48CBbu3QBnoiICNq0aUOfPn3eefuEEH+vSJEi1K1bl927d9OpUyfS0tKUsE2XLl0ICwujQIECDBw4kBUrVmSrwDN69GhCQkLYs2cPY8eOZdu2bYCEFP4LAwMD7t27x5YtW6hUqRKlSpX6y8pF+tvp2NnZUbduXTIzM99YlU282Z49e0hNTaVXr17UrVsXgJSUFFJTU9+4vi6QCi8r8ISFhVGxYkUCAwMJCgpCo9Fw8OBBihQpolTjEUIIIYQQQgghhNCR8I4QQogco18mfvjw4QwaNIiTJ09Ss2ZNNBoNkydPpkuXLmzevJkvv/yS0NBQRo4cyZUrVxg/fjydOnVi+fLllC9fnr179+Ls7IxarZZpVYQQnw1DQ0PUajUmJiZUqFBBefzBgwdKBQCVSqUM0uoCPBMnTqRw4cKMGTOGOXPmKNPffIj2Aty9e5czZ85gZWWFl5cXFSpUoFy5csTGxjJgwAC2bdtGly5d3hjgqVy5MsuWLaNIkSJvNUAthHh31Go11tbWrFu3jubNm7NhwwY6d+6cbQqttm3bEhISQqFChf40wDNu3Dh27dpFbGwsmZmZOXU4nw1LS0vs7Oy4ffs2WVlZr1Uu0gUnb9y4wU8//QRkD+88ffqUmzdvYmJiwrNnzz5cwz9xt27dIj09HXNzcwD69etH586d+f333/902kn9AM93333H0qVLWbhwIfPnz2f37t24urq+VnVUCCGEEEIIIYQQAsBA+2ffOAghhBAfyLRp0xg6dCgDBgzAw8ODsmXLcuXKFQIDA5k/fz5du3Zl/vz5yiDE6dOnuXLlCg8fPqRSpUo4Oztja2srX4QLIT5bT548ISwsjOfPn7Nt2zbOnTtH//79mTRpEtbW1sr0KLr/q1Qqdu3ahZubG/b29hw4cAArK6sP0taQkBBiYmJo2LAhefPmJSYmBo1GowQ2b926RWhoKNOnT6dRo0YsXryYPHnyfJC2CSH+XmZmJqampjx58gQ3NzdWr15N06ZNWb58OdbW1sDLoF1ycjJjx47lxo0bxMbGZptCCyAqKoo2bdrg4uKSU4fy2dBoNNStW5d9+/aRlJRE9+7dMTY2VkIiumvk/v37s2bNGg4fPoyTk5Oy7datW2nWrBldunRh4cKFOXYcn5oLFy7QpEkTbGxscHV1Ze3atQwePBg/Pz8cHR3/clvdFFqvep9TVgohhBBCCCGEEOLTJuEdIYQQOUar1fLixQuaNm1KWloaS5YsoVSpUmRlZbFu3ToGDRqEqakpBw8exMHB4S/3JV+ECyE+J28a9Hvy5Am2trZotVoqVarEiRMn6N+/P5MnT8bKyoqsrCxMTEzQarWo1WqMjY3Zs2cPxYoV+9tBxnclKyuLqKgooqKiuHHjBpUqVeLnn39Wwjm649IP8DRr1oy5c+eSN2/eD9JGIcSf0w9Cp6amcubMGXr37s358+dp0aIFS5YsUYKAbwrwdOjQAVNT02z7VKlUr1WKEa/7u2vZDRs20LVrV8qVK8f48eOpVatWtvO6ZMkSAgICqFGjBrNmzcLCwkJZdvToUXbu3Mnw4cPf6neJ/ztHa9asoVu3bjx79ox27doxefJkXF1d/zScI4QQQgghhBBCCPFvSXhHCCHEB/XqYMGlS5coXrw4I0eOJCwsjIyMDJKTk/H398fQ0JDDhw/j4OCAWq3m999/p0yZMjnYeiGEeP/0B8/VajUPHjwgT548GBoaKgOFT58+pU6dOkqAJzw8HFtbW1QqFT///DO//vor3bt3p3Dhwq/t831LS0tj3rx5xMTEcP/+fRISEvjuu++UQWb9AM/48eOJjY2lc+fOLFy4UAZChchB+tdoo0ePZtGiRZiamvL06VOePHnCs2fPaNmyJYsWLcpWyWvlypWMHTuWO3fuEB4eTvfu3V8L8Ii/pt9Hr1mzhqtXr2JoaEjdunUpV64cBgYG3L59m6lTpzJ16lTKlClDly5d6N69O0ZGRixatIj4+Hg0Gg27du2iUKFCUvnlHenSpQtLly7F2toaR0dH4uPjqV27tvyNCyGEEEIIIYQQ4p2T8I4QQogPRn9g4urVq7i4uHDnzh2cnJwYNGgQERERLF++HB8fHwwNDTl06JBSiSErK4tChQoxduxYvLy8cvIwhBDivdHvJ6dNm8aqVas4cOAAhQsXpkKFCkydOpX8+fNjYGBAWloatWvX5sSJE/Tq1YuIiAh27NiBn58fT58+5cyZM+TOnfuDtl83WKwL8ISGhmJtbU1SUhJff/21MmCsW+/69evExcUxYMAAnJ2dP2hbhRBvFhwcTHBwMN7e3vTu3Zs8efLw+++/M3ToUE6ePPlaBR6AVatW4eHhgb29PUeOHMHS0jIHj+DT5efnx+TJk5WfixUrRu/evZVQ+6VLl5g/fz6xsbHcv3+fvHnzolKpePbsGSVKlGDt2rW4urrKVLLvUN++fdFoNJQvX57w8HDy5MnDpEmTaNy4MSYmJjndPCGEEEIIIYQQQnxGJLwjhBDig9C/+9fPz4+DBw8SGRlJ8eLFKV++PLa2tvTo0YPY2FiMjIw4ePAg+fLlU7YdO3YssbGxzJ49m9atW+fkoQghxHuh308OHz6c6OhoSpcuTc2aNblw4QI7d+6kVKlSTJ48mQYNGmBhYcGzZ8+oX78+R48eJU+ePGRkZJAnTx62bdtG0aJFc2RaD93vfPbsGXPnziU4OJhcuXKRkJBA7dq1lQCPrvqD7v8ytY4QOe/KlSvUq1cPR0dHlixZgqurq7IsLS2N1q1bs2PHjtcq8Gi1WjZt2kTFihU/2DR9nwP9PjoqKooRI0bQpk0bmjZtyqNHjwgPD+fOnTv4+/sTFhaGoaEhjx8/5vLly0RHR/PgwQMsLCz4+uuv6dy5M3nz5pXgzjuifx7T0tKwsrJi7ty5+Pv7kzdvXiZOnCgBHiGEEEIIIYQQQrxTEt4RQgjxQSUlJTFgwAD69+/PkCFDKFasGHFxcXh7e2NiYkK+fPn45ZdflGoRWq2WpUuXMnr0aEqXLs3ChQuxs7PL4aMQQoj3Z/r06QwePJh+/frh7e1NyZIlefLkCcOHDycxMZEmTZqwbt06AIyNjXn+/DnDhg3j9u3bWFtbEx4eTqFChXI0DKMf4Jk3bx5BQUFvDPAIIT4uv/76K5UqVSIgIIDQ0FAlwKDrTx4/fkytWrU4e/YszZs3Z8mSJVhbW2fbh4RH3s6r01d5eXlx9epVoqOjKVKkCPByetn69etz/fp1/Pz8GD9+/F/2nzIl1r/36rlLT0/H3Nw82zqPHz9m1apV+Pn5SYBHCCGEEEIIIYQQ75x8qyOEEOK90mg02X7etWsXNWvWZOjQoRQrVgyAb7/9lh49eqBWq6lcuTLXr18nMzOT9PR0pk6dyqhRo9BoNEyfPh07O7vX9imEEJ8LlUrFypUrKVasGB4eHpQsWZKMjAy2b9/Opk2bKFGiBPPnz8fY2FgJ5lhaWhIXF8eqVauYPXs2hQoVQq1Wv5fgztvm/g0MDNBqtVhZWdGzZ0+CgoJ49OgRHh4e7Nix4633I4T4MF59TR47doxnz54pIRxjY2NUKhV2dnb07dsXExMTduzYQZMmTXj+/Hm2bSW483Z0QZHRo0czZMgQ1qxZQ4sWLZTgjkqlomjRouzbtw8nJycmTpxIQECA8lxlZWUp+9I9JsGdf0etVivnbt68efTt25eKFSvSqlUrJk6cSHp6OlqtFjs7O9q2bcvEiRO5d+8efn5+bNmyJdtzIYQQQgghhBBCCPFvSV16IYQQ75Xui/Dhw4cDcOfOHbp06ULRokWVO7OLFCnCwIEDgZdfmG/ZsoWyZcuSmprK7du3KVq0KGvXrsXJyUnu5hZCfNbu3bvHrl278PDwoGzZsmRmZip3+RsbG7N3714cHBwAOHz4MBUrVsTExESZdkV39//76Cf1qxI8ffoUGxubv5yW69UAj6GhIR4eHgQHB1OnTh1MTU3feRuFEG9H//Ws/+/y5ctToUIFjh49yp49e2jSpInyGtdfJ2/evNjY2HD69GmePXuGpaVlzhzIJ+7OnTvExMQAYGtrq0w5lpWVhYmJCWq1GicnJ/bt20etWrUIDw/HyMiIkJCQbNVePvT0iJ8TrVarvGf6+Pgwbdo0bGxsyJUrF5s3b2bdunXs3r2bESNGULNmTWxtbWnXrh0A/v7+jBo1iszMTL777juZ+lEIIYQQQgghhBD/idyWJYQQ4r1Qq9XKv+/fv8/69euZMmUKP//8M7du3QJeDi7r7hSuVq0aEydOZOnSpXz11Veo1WpKly5NSEgI27Ztw8XFRYI7QojPnomJCSYmJjx8+JAXL16wZs0a/Pz8MDQ05NChQ0pwB+CHH34gNDQUeP8Dt/pVCRITE2nVqhXr1q3729+rH+Dp1q0bc+bMYfHixRLcESKH6V7P4eHhzJo1S6kcYmhoyMCBA3n69Cnh4eGcPXtWuabTbbN161YqVqzI8ePHuXTpEnnz5pWqiP9S/vz52bNnD87Ozty4cYM5c+YowR2NRqNMWaYL8BQpUoTx48czceLEnG76Z0P3PjZ58mQiIyNxd3dn+/btnDp1iu3bt9OmTRu2bt3KiBEjOHbsGAA2Nja0b9+eSZMmcfLkSaKjo1GpVDl5GEIIIYQQQgghhPgMGGilZr0QQoh3TP8O7smTJ9OpUycePHjAiBEj2L59O82bN2fmzJnky5cP4LXKDW8K6ejvUwghPmdVqlThyZMn9OnTh/j4eAwNDTl48KDSZwIEBQURFRXFrFmzaN++/Xttj37/O3LkSGbMmIGtrS1jxoyhT58+b7WPt+nnhRAf1rlz5yhfvjwFCxYkLCyMjh07Ympqyh9//MG4ceNISEigZs2aDBw4kO+++w5bW1uWLVtGaGgopUuXZsmSJRgZGck12lvS7wd150z3/9OnT9OxY0fOnj1LSEgIvr6+mJmZKctVKhXGxsZcvXqV77//nuXLl+Pi4pLDR/T5ePDgAY0bN8bExITly5fj7OysPF9XrlwhPj6eyZMn8/3337NkyRJlu6dPn7Jp0yZq1apFoUKFcvAIhBBCCCGEEEII8TmQ8I4QQoj3ZsyYMYSFhdG/f3/i4+PZvn07oaGh7Nq1i4CAAKVixKv0B4H+akoWIYT4FP1ZcEXX982bN4+BAweSnp5OgQIFOHLkCPnz51fWW7x4MWPGjKFYsWIsXbqUXLlyfZB2BwQEMGHCBNzd3Rk6dCilS5d+43rSbwvxaVCpVGzevBlPT08MDAwIDg6mc+fOmJmZcfHiRaKjo1mwYAGpqakUKVIECwsLfv/9d/Lnz8/evXtxdnbO6UP4ZLza779p6sFTp07Rpk0bbt++TUBAAMOGDXtjgOfVn8V/d+7cOcqWLcuAAQOIi4tDpVJhaGiofB65cOECbm5u7N+/n4ULF9KlS5fX9iGhVCGEEEIIIYQQQvxXcnucEEKId0Z/qqzLly+zYsUKBg4cyJAhQwBo0KABwcHB1K5dm7CwMMaNG/fG/ejfvS0DwEKIz4n+4N6qVauYOXMm8+bN4+7du8o69erVo2PHjlhYWFCsWDFu3LjB/fv3efHiBZMnT2bUqFGo1WpmzZpFrly5Psh0NRs2bGDGjBm4ubkxYsSIbMGd27dv88cff/D8+XPg/6bKEkJ83IyNjWnWrBnx8fFkZWURGBjIkiVLyMjIoFixYowaNYolS5bw3XffYW5ujrm5Od26dWP//v04Oztnu+4Tf06/309ISKBdu3aULFmShg0b4u/vT0ZGBgDlypUjOTmZAgUKMG7cOCIjI8nIyFAq9OiCOrprYwnuvDtWVlZYWFhw79494OW51f8MUrx4cYYOHQrAtWvX3rgPCe4IIYQQQgghhBDiv5Jve4QQQrwzui+td+/ezeXLl7ly5Qo9evSgVKlSyl3CdevWJSwsjICAAMaOHQvA6NGjc7LZQgjxwej6SV9fXyIiIpTHa9asiaenJx07dsTFxYWhQ4diYmLCggUL+OqrryhcuDDp6ek8evSIUqVKsWrVKgoXLvzB7vQ/ceIET58+pXPnzhQpUgSNRsPz58+JiopiwYIFXL9+nWbNmtGrVy+aN28uwUshPiL6FQ11/9ZVfDEyMqJp06bMnDmT/v37ExgYCEDnzp3Jnz8/jRs3pnHjxjx69AgTExNMTU0xMTGRKiNvSavVKudp+PDhxMTEULhwYapVq8bly5eZPHkyO3fuZMGCBZQsWZLy5cuzatUq2rRpQ1hYGIaGhgwePBhzc3Nln9K/vnvm5ubky5ePFStWsGLFCtq3b68EUdVqNcbGxri6ugJw//79nG2sEEIIIYQQQgghPltSeUcIIcQ7NXv2bOrXr8/ixYupWrUq1atXV5bpKjHUqVOHsLAwateuzdixYxk/fnxONVcIIT64xMREZsyYQZcuXUhKSmLs2LFcuHCBIUOGEBUVRWZmJuXKlSMwMJDVq1fTpk0bihcvTp06dYiMjGTLli24uLh80MHzK1eukJWVBbwcuFy0aBEtW7ZkzJgxmJqaUrlyZdauXcu0adN49uzZB2mTEOLNXq18pQvuREZGkpycTGZmZrYKWUZGRjRr1ozp06eTnp5OaGgoixcvJjMzU9lfrly5sLKywsTERNlG/D1d0CY6OpqYmBj69evHhg0bWLNmDbt376ZVq1YcOXIEPz8/NBoNWq2WcuXKKQHNkSNHMmvWrBw+is+bVqslb968hIWFARAfH09KSgrw8vnTVTjasWMHpqamVK1aNcfaKoQQQgghhBBCiM+bgVZq2gshhHiHDh48yPjx41m7di0A27dvp379+spy3Z3eACkpKYwdO5Zdu3YxZcoUBg8enBNNFkKID2rQoEGcO3eO6dOnU7RoUdRqNYcPH6ZTp048evSI0aNHM3jwYExNTf90H/qVNN4n3e/Zvn07PXv2RKVSYWFhwY0bNyhYsCCTJk2iZs2a5M6dmz59+rB06VJOnTpF2bJl33vbhBCv2759O3PmzGHGjBlYWFgojx8/fpyqVatSvHhxpkyZQuPGjTExMcl2XfbixQuSkpLw9vamSpUqeHt707lz57/si8TfS0tLo0mTJqSnp7No0SJKly5Neno6W7ZswcPDA2tra1JSUsibN2+27Y4fP463tzeLFy+mcOHCOdT6/x2pqamEhIQQHR1N9erVcXd354cffiAjI4Pk5GQCAwOxsrJi69atODg45HRzhRBCCCGEEEII8RmSyjtCCCHeCV0W9KuvvmLMmDF06dIFgDlz5nD16lVlPf07vevUqcPo0aNp06YNbdq0+eBtFkKI902tVr/22IkTJ6hduzZFixYFXlbFqFGjBsnJyeTKlYtx48YRHR2tVLrR34eu/3wfwZ03tVX3e6pUqcKkSZMoW7YsLi4uDB48mOPHj/P999/j5OSEpaUlT548wdXVVQaZhcghL168YNasWSxYsABPT0/S09OVZcWLF2f27Nk8e/YMHx8ffv75Z7KysrJdl1lYWPDNN99gZmbGqVOn8PLyYs2aNTl1OJ+Us2fPcu3atTcuu3PnDgcOHKBNmzaULl2arKwsVq9ejZeXFyYmJuzZs4e8efOi1Wo5fvw4KpUKgEqVKrFz505likTxftnb2zN48GD8/Pw4ePAgffv2pUqVKlSqVAkPDw9UKhWrVq3CwcEBjUaT080VQgghhBBCCCHEZ8g4pxsghBDi0/Rq1QfdXdsAVatWZciQITx79oyFCxfi4uLCgAEDcHR0VNbV3endsGFD6tSpg6mpKSqVSilNL4QQnzqNRqNMLbNw4ULOnTuHpaUluXPnJleuXABkZWUp09BUrlyZ5ORk2rZtS2hoKIaGhnh7eyvLIXtf+y7pT8E1f/58zpw5Q2pqKu3ataNy5co4ODjQuXNnOnXqpLRB93+NRsPy5cv59ddfadSokVTpECKHWFhYMGbMGIyMjJg7dy4qlYqEhATMzc2xsbGhXbt2GBgY4Ofnh6+vL1qtliZNmmBqaqr0RSVKlKBEiRK0b9+eDRs2ULNmzZw+rI/exYsXKVeuHLVq1SI5Ofm1qiy6MM7z58/JzMwkOTkZPz8/DA0NOXTokLK+SqXCw8MDNzc3PDw8AJTrYpmm7N/Rryz1pp9f5erqSmhoKPXq1WPq1Klcu3aN3Llz8+233+Lr60vBggU/6JSVQgghhBBCCCGE+N8iI6RCCCH+Mf0vrc+fP8/Nmzd5/PgxFSpUIH/+/Jibm1OtWjVGjx6NWq1mwoQJaLVaPDw83hjg0Q30SnBHCPE50QUc/fz8mDx5crZlV65coWfPnuTKlStbn6oL8Hz//ff4+PhgZWVF//7933tbdb/f19eXiIgI5fHFixfTvn17QkNDKVSo0BsHPhMSEoiIiMDU1JSwsDDMzMz+doBUCPF+lClThjFjxqBSqVi4cCEAM2fOxMLCAmtra9q2bQugBHg0Gg0NGzbEysoKtVrNjBkzePr0KT169GDUqFGYmJhIWOFvGBoa0qFDBzQaTbapynScnZ1xdnZmz549zJs3j3HjxinBHf2psgIDAzl79qxSlU38N/o3Gly8eJFcuXKRJ0+ev93O2NiYZs2a0bBhQ7RaLaampsq+5LUghBBCCCGEEEKI98lAq6uRLYQQQrwF/S/Cx40bx6xZs5RpAmxsbOjatSs9e/ZU7tQ+cuQIwcHBbN68mREjRuDp6UmBAgVyrP1CCPG+6feTS5YswcPDg44dO9KqVSsl0Hjw4EG6detGTEzMawEegIMHDzJo0CBWrFiBk5PTe2urfsgmKSmJQYMG0bFjRzp27EhWVhaRkZHs2rWLtm3bEh0drQR4MjMzuXHjBgMHDuT48ePkzZuXdevW4eLiIoObQnwEfv/9dwIDA1m6dCndunVTAjwAaWlpJCcn4+/vj6WlJT179qRr165s3LiRuLg48ufPz4YNG7C0tMzho/h03L17FxsbGywsLFi4cCE1a9bMFsIJDg4mODgYCwsL8ubNy5EjR7JV6Fm4cCFjx46lTJkyLFq0CFtb25w4jM+G/vtQVFQUCxcuJE+ePCxbtgwbG5u32of++6MEUoUQQgghhBBCCPEhSIkDIYQQ/4h+JYnIyEgaNGiAn58fFy5c4MCBA8yYMYNDhw4xadIkGjRoQNWqVQkKCgIgIiKCZ8+e4e/vT758+XLwKIQQ4v3QarVKP6lWq8nIyKBIkSL4+vpSvHhxABo1akSjRo1YuHAhxsbGTJky5bUAz1dffcWePXvea9UL/ZCRSqXizp07VK9endGjRyuDzi1btqRly5YkJyej1WqJiYmhUKFCZGVlsXz5cm7evEn79u0ZM2YMBQoUkOCOEDnk1XBByZIlCQwMBHhjBZ527dphbm7OmDFjCAoKIjQ0FLVajYuLC3PnzsXS0lICC/+A7rp2zZo19OjRg3bt2hEZGYmzszMAHTp0YPfu3ezevZuqVatiZGTE8+fPsbCwIDY2lilTpgAwffp0bG1tX5ueVrw9/SkrfXx8iI+Pp0qVKnh6er51cAeyT1MprwMhhBBCCCGEEEJ8CFJ5RwghxD+2Zs0aOnXqRM+ePRk5ciSurq5otVquXr1KVFQUUVFR1KxZk5iYGCpXrgzA8ePHGTRoENevX+eXX37Bzs4uh49CCCHen6FDh7J8+XKKFClCjRo1mDRpEgBZWVmYmJigUqmoW7cuBw4cwM3N7Y0Bng/Fx8eHS5cucfbsWTp27EhwcDBarRaVSoWJiQkArVq1Yt26dbRp04bo6GicnJxITU3l4cOHFCpUCHNzcwnuCJFD9F97ly5dwsLCgvz582NoaPiXFXg0Gg337t1j8uTJvHjxAltbW7y9vXF0dJTX87/0+++/ExUVRUJCAm3atGHixIm4uroCLyuqjR07li1btpAnTx6cnZ159OgRt27domjRoqxbtw5XV1c59+/I+PHjCQoKwsPDA09PT0qVKvXaOhJQE0IIIYQQQgghxMdEKu8IIYT4x44ePYpGo6F///7ZBhlcXV2JiIjgxYsXzJw5ky1btijhnUqVKhEfH0/+/Pmxs7OTL8uFEJ+1u3fvcvPmTZ4+fUrZsmWB7MEdY2Njdu/eTd26dZk7dy5GRkZMnDiR3Llzf9B2ZmRkcPDgQfbu3UuePHmUqVo0Gk22tq5Zs4ZWrVqxatUqjIyMmDRpEq6urtjb2wMvB0BlsFmID08/6BEZGUliYiKlS5cmMjISFxcXSpYsSXBwMPB6BR6A/PnzK+FC3bWZhEfejn51HF3/XrJkSYYPH46hoSGxsbEASoDnq6++Ytq0aezdu5c5c+bw5MkTSpYsiYeHBz179iRfvnxy7t+RU6dOER8fz7fffsuQIUMoUqSIsuz48eM8fPiQ/PnzU7ZsWfk8IoQQQgghhBBCiI+GVN4RQgjx1tRqNYaGhrRs2ZINGzZw6tQpZVBa3/Hjx2nevDk2NjYcOXJEGQzWkakAhBCfK/1gopeXF3FxcdjZ2bF//35Kly6t9H+6UIxaraZBgwakpKQwePBgIiMjP/hA4uPHj+nfvz/Lli2jdOnSrF+/niJFiijHomsrQNu2bVm9ejW9e/dm5syZ0pcLkYP0+xsfHx9iY2OpU6cO/fr1o0OHDtnWfbUCT0JCAubm5mg0GgwMDDAwMJBg9T+gH7JZsWIFp0+f5quvvqJp06YAXL58mcjISGJjY+nQoUO2Cjzw8lo4IyNDCVHpHpM+9d3YsGED3333HbNnz8bNzQ21Ws3169eJjo5mxowZPH/+HFNTU0JDQ/Hy8sLc3Fz+9oUQQgghhBBCCJHj5JshIYQQf+rVfKeRkREGBgZUq1YNgLNnzwIvBzD0VapUiXLlynH37l3u37//2n5lYEII8bnSVa0AmDZtGt7e3jx+/Jh27dpx/vx5DA0N0Wg0GBsbo1KpMDIyYtu2bbRq1YrBgwe/18FDXZ+u1WqVf6vVauzs7Jg+fTodOnTg3LlzhISEcO3aNWUwX9dWgOTkZNzc3Bg9erT05ULkMF1/ERMTQ3R0NP3792fGjBmvBXcApQJPp06dWLhwIQMGDODFixcYGhoq+5HwwtvRaDRKcGfkyJG4u7sTHR2NSqUiMzMTgCJFijBs2DAGDhzITz/9hJ+fH1evXlX2YWhoqAR3dP2x9Kn/zpvuR9NN+bhv3z6uX79OREQErVu3Ji4ujubNmzN06FCKFi1KUFAQp06dkr99IYQQQgghhBBCfBRk2iwhhBBvpH9H8cWLF9FoNJQoUQKAypUrY2hoyODBg6latSouLi6o1WoMDAyUgYfMzEwKFiyoTKkihBD/K4yMjJQ+NCoqCrVaTVxcHN9//z3Lly+nRIkS2QI8xsbGrFq1CiBblZt3Sb9PNzAw4Pnz51haWip9dq5cuZg5cyYZGRnKNF5jx47F2dk5W4DH2NiY2bNnv9e2CiHejlar5e7duyxZsoRixYoxcODAbNMDvUoX4DEyMmLevHnY2dkRFRX1AVv8edD1m2PGjGHixIl4eHjQp08fKlWqlG09XYDHwMCA2NhYtFotERERODs7Z1tPgiP/nn61ort375IrVy5MTU0pVaoUDRo0ICEhgYSEBABKly7Nzz//zJdffomdnR1OTk4MHz6cY8eOKTcmCCGEEEIIIYQQQuQk+bZdCCHEa/QHeSMiIli0aBFWVlYkJiZSokQJWrZsyQ8//EBSUhIdOnRg6dKlFC1aVNl+xYoVnDx5klatWmFlZZVThyGEEDlGP8Azbdo0tFot8fHx2QI8arX6tfDL+w7uzJ8/n61bt3Ls2DFcXV2pXbs2nTp1wtXVlVy5cjFv3jx69uxJUlISwGsBnvfdViHE2zMwMODBgwf8+uuv9O7dWwlZv4muOknJkiUZOXIkdnZ2DBs27EM19bOza9cupk2bRpcuXfD19cXFxQXgtanHihQpwtChQwGYOXMmjx8/JikpCScnpxxp9+dE/71txowZrF+/ni+++ILg4GCcnZ2Jj49n06ZN3LhxgzJlytC2bdtsU/leu3aNfPnyUbVq1Zw6BCGEEEIIIYQQQohs5Bt3IYQQ2Wi1WuWL8OHDhxMXF0fNmjXx8/NTqkUYGhoya9Ys7t+/z5o1a6hRowaenp6ULVuWY8eOsWzZMqysrBg3bhympqavDWQIIcSn7NU+7c/6OP0AT2xsLADx8fF06dKFBQsWULp06Q/SVv0+PSoqCmtra2xsbNixYwfr169n9uzZLFu2TKlGoB/gMTIyYsSIEX9ZzUMIkXNSU1N58eIFz58/ByArK0uZMgj+rzLJ48ePuXbtGuXLl+eLL74gOjoaIyMjqaD1Fl49pwC//PILjx8/ZsCAAUpwB95cRadIkSIMGTKEp0+fsnXrVszNzd97mz93+lOX+fn5ER8fj6urK3379sXU1BSAEiVK/GmgbdmyZaxatYoKFSr8ZehNCCGEEEIIIYQQ4kOSSdWFEEJkoxt0iImJISYmhgEDBpCQkECzZs2Al1MFqFQqAFatWsXgwYMxMzMjJCSEzp07M23aNBwdHdmzZw9OTk7KdFpCCPE50Gg0Sp+WmpoK/PWUJ7oAD0BsbCxeXl4cO3aMQYMGoVarlWoY74uubZGRkURFRTFo0CAOHTrEpUuXOHLkCJ06deL333/nm2++4fTp0wBKgKdt27YkJCQQGxuLRqN5r+0UQvw7hQsXxsXFha1bt3Lnzh1MTEyU16tWq1WmFOrcuTNTp07lxYsXAErwQYI7b7Z9+3Zq1qxJZmbma2EogGPHjgGQL1++N26v6/fv378PQNGiRQkODubEiRM4ODhIn/of6f6ug4KCmDJlCr169WLp0qW0atUKINt766vnOjIykpEjRwIwa9YsbG1t3/t7sRBCCCGEEEIIIcTbkPCOEEKI1zx58oQlS5ZQunRpPD09KVasWLblxsbGSoBnypQprF+/niVLlhAdHc2qVatYv349Li4u2crZCyHEp06tVisDhgkJCXTs2JGxY8f+7Xb6AZ7o6GhGjhxJQkICRkZGHyTceO/ePZYsWcIXX3zBwIEDKVmyJCYmJpQuXZrFixczZMgQUlNT6dOnD7du3QJeBngSExP54Ycf8Pb2Vo5bCPHh/VnQQ6vV4uzsTL169bh27Rre3t48fPgQQ0NDMjMzMTAwQKvVsnjxYk6fPo2tre1rFWTEmy1btoyDBw8SGBioPKYfhnJ2dgbg0qVLAMp1sW49Xb8fGhrK3r17lW3s7e2z7Uf8e7t37yY6OpqOHTsydOhQypYtqyy7cuUK586d48mTJxgaGqLRaNi1axdff/01QUFBODg4sHPnTgoXLiw3GgghhBBCCCGEEOKjIbfZCSGEeM3t27fZv3//a1Nl6TM2Nlamivnyyy/58ssvsy3XL2cvhBCfulen6Jg+fTqlSpWiQIECb7W9/vQ0YWFhAO90upqjR4+iUqmoXr36a4OQjx494uTJk/Tu3ZtixYopfbfu90dGRnLx4kXWrVvHwYMHadOmDWq1mly5cpGUlPTO2yqEeHv6QegDBw7w6NEjzM3NqVGjhjL9UmxsLKdPn+ann37i6dOnxMXFUahQIQCSkpKYPHkyVlZW+Pv7S3jnb+zZs4fU1FQiIyOpUKECnTt3Bv5v+rHMzExMTU2pWrUqACNHjqROnTpYWVmhUqkwNDRUrpknTpxIYmIiTZs2zfY7JCjybvz22288evSIPn36UKRIEdRqNY8ePSImJoZZs2Zx584dihQpwqxZs6hbty6WlpbY29vj7+9Pv379yJs3r9xoIIQQQgghhBBCiI+K3O4lhBDiNRkZGQD88ccfykCEPl0FiVu3brF79+437kPuKBZCfE50fdr48eOJiIjghx9+YMGCBXh6er71Pl4Nv7yrMMyNGzdo0qQJTZs25cKFC68tT09PJyMjg1OnTvHw4UNl4Fi/ipqbmxtarZaUlBTg9T5cgjtCfHi6Ci4Ao0aNokGDBjRv3pwGDRrQsGFDrl69CoClpSUrVqygRo0abN68mQoVKlC5cmVKlChB//79ycrKYtOmTTg6OirXcOJ1W7dupV69eixbtgwADw8P7O3tGTx4MKVKlSIrKwtTU1MAmjZtSvPmzfn111/p1KkTT548wdjYWOk7V6xYwfz586lSpQo1a9bMsWP6nD158gSAy5cvo9VqSUxMpGXLloSGhlK0aFGaNWvGhQsX+OGHH7hz5w7VqlVj8eLF+Pn5kTdvXrnRQAghhBBCCCGEEB8dGVkVQgjxGhcXFwoVKsSJEydeGwjWH0gaOXIkU6ZM4dGjRznQSiGEeH+0Wu1rj508eZJZs2bRsGFDhg8fTqlSpXKgZa+zsbHBy8uL1q1bv7ESULly5ahXrx7nzp3j5MmT2ZbpBpp1043ofpbKEELkPN3rcMKECYSHh1O7dm0mTZpE06ZN2b9/P/Xr1+f48eMAFC5cmF27djF69Gjq1q3LkydPcHV1xd/fnz179uDq6ipVRv7C2bNn6dWrF/Xq1cPd3R1LS0sAMjMzuXHjBhcvXqR+/fpkZWUBYG5uzuLFi6lSpQobNmygRo0ajB8/np9++gkPDw+GDh3K06dPmTdvHvb29n869Zn4Z/TP41dffYW9vT3u7u7Y29szYMAAbt++zdq1a0lOTmbt2rW4ublx5coVfv31V+Dl+6Wu+pTcaCCEEEIIIYQQQoiPjXxbIYQQIhuNRoOtrS3dunXj9OnTTJ48WanEA/83kLRw4UJ+/vlnXFxcsLKyyqnmCiHEO3Xnzh0yMjLeGF65du0aV65coVOnTri4uPztvj7EYK1Wq8XW1pYRI0aQkJCAjY0NU6dOJSUlJVsAqUOHDqSmpjJ8+HCuXLmiPG5oaIhGo2HdunUYGRkpIZ43hZeEEB/Gq9Vx1qxZQ+fOnZk5cybDhw9n1apVhIaG8vDhQ9q2basEeExMTAgJCWHdunUcOXKELVu2EBISQoECBSS48zeOHTvGnTt36NixI3Xr1gVg8+bNmJqakpSURJ8+fdi/fz916tRRAjw2Njbs2LGDrl278vDhQ0aPHk3Hjh1ZsGABpUqVYu/evbi4uKBWqyUo8i+9+j6q/9qoXbs28+fPp3v37rRo0YJx48Zx9OhRmjdvTp48eYCXledcXFwoXbr0B223EEIIIYQQQgghxL8h9e+FEEJkoxtc6NatG3v27CEpKYkXL14wYMAAqlWrhqmpKbNnz2bSpEnY2dnh7++v3MEqhBCfskOHDtG+fXuGDRuGt7f3a1NFXb9+HQAHBweA1wbDNRoNhoaGpKamYm9v/0EGaw0MDNBqtVhYWACwZ88ehg0bRtmyZUlKSqJq1aoYGhrStWtXjh49ypw5c2jdujVjxozh66+/xtHRkUWLFpGYmEiZMmVo2bKlsl8hxIeh1WoxMDBQ+hBdvzJhwgTq1q2LsbExvXv3xtXVlaysLMzMzPD19cXU1JTQ0FDatm1LcnIylSpVUvalCy/o+iEJ7vy1tLQ0VCqV0pf269ePWbNmsWXLFho2bMjkyZNRq9XMmTOHOnXqkJKSgomJCdbW1syePZsLFy5w8uRJMjMzKVOmDCVLlsTGxkZCU/+B/rlbuHAhu3fv5vDhw1SpUoVq1arRr18/mjdvTu3atbG1tVVePzrLli0jJSWFWrVqKe/bQgghhBBCCCGEEB8zA63cViuEEOJPHDp0iICAAHbu3ImZmRlFihQhIyODP/74AycnJ7Zs2SLTMAghPhtbtmyhadOmuLu7M23aNCWYqBsQXLRoEd27d6dLly4kJCQo06pA9sH36tWr0717d4YMGfLO2/jq4GRmZiampqbKz6mpqcyePZsJEybg6OhIQkICVapUwdjYmPv37zNmzBiWLFnC48ePyZs3L1ZWVty8eZOCBQuyfft2XF1dX/sdQoj3Z8+ePezYsYNhw4Zlq2So64/MzMzInTs3a9asoUqVKsD/9QNZWVlMnTqV0NBQcufOzapVq6hYsWIOHcmnJyUlhV9//RUvLy9u3LhBhw4duHTpElWqVGHTpk0MHjwYHx8fChUqBMDjx48ZOnQoc+bMoXr16kqAR9f/v+rPHhd/T//c+fj4EBMTg729PQUKFOCPP/4gNTWVtm3bsmLFijduHxsby9SpU9FoNOzatQsnJyd5PoQQQgghhBBCCPHRk2/lhRBC/Knq1asTGxvL9OnTqVChAmq1mkKFCuHj40NKSooEd4QQn5UGDRpw/PhxJk2ahImJCUeOHCE9PV0JsrRu3ZoKFSrw888/s3HjRmVKQd00WxqNhlmzZnHjxg20Wu17mTZL15a9e/eSlZWlBHf8/f3ZuHEj9vb29O3bl4CAAK5fv467uztHjhwhKysLBwcHfvzxRxYsWECPHj1wcnKiePHiDBs2jL179yp9ugR3hPgwXrx4gbu7O4GBgWzcuDHbssaNGzNixAhsbW25d+8e58+fB1BeoxqNBhMTE4YMGcKYMWN4+vQptWvX5tSpUzlxKJ+cbdu2Ua9ePQ4cOMCtW7coVKgQoaGhqNVqNm3aRMuWLRk8eLAS3FGpVNjZ2TFlyhR++OEHDh06pEyhZWBgQGZm5mu/Q4Ii/57u3E2aNImpU6fSt29ftm/fzi+//MLx48epWrUqycnJ/PDDD8o2Go2GnTt30qhRIwIDA7G0tGT79u04OTmhVqvl+RBCCCGEEEIIIcRHTyrvCCHE/7B/cgeqSqXixYsXWFtbAy+/VJfgjhDic/Xjjz8SGBhIXFwc3bt3x8zMDLVazYIFCxg2bBiOjo4MHz6cTp06KRV4li5dSlBQEJaWlmzcuJF8+fK9l7a1adOGffv2kZSUxHfffYePjw+RkZEEBAQwatQoLCwsePLkCUlJSYSEhODk5MTMmTOpWrVqtqnAHj9+jI2NDfAyFCR9uhAf3okTJ0hMTCQ4OJhcuXKRlZWVbTrS0aNH8+OPP5I7d25SUlIoVaqU8lrVr8AzYcIEFi1axPbt2ylYsGAOHtHH77fffqNJkyYULVqU4OBg6tatC4CHhwczZswgT548qNVq5s6dS9OmTZWQpO6861fgqVWrFtu3b89WAU28G7dv36Zx48bY2dmRmJhIqVKlSE9PZ+fOnfTu3Rs7Ozt2795N3rx5lW2WLl2Kv78/HTp0wNfXl/z588t7mxBCCCGEEEIIIT4ZEt4RQoj/AW+aZsXQ0DDbIO5fBXmkzLwQ4n+JVqtl3bp1eHp6YmRkxNixY+natSvm5ubcu3ePpKQkpkyZwoMHD6hZsya1a9fm119/Zf/+/VhbW5OSkoKLi8t7mX5KpVKRmJjIqFGjcHFxwdHRkY0bNzJq1Cjc3d1xcXFR+uw3BXiqV68ulXWE+MjoXrOjR48mMzNTCQHqBAYGEhoaSr58+di3bx9FixZ9LcCjC1nb2NhIWOFvrFy5kk6dOjF58mQGDx4MwJo1azh48CDPnz+nRIkSxMTEcO/ePeLj42nTpo0SqNIP8Pj4+JCYmEiLFi1Yu3ZtTh7SZ+no0aNUq1aN2NhYPDw8yMrKYuXKlfj5+WFoaMjhw4dxcHBArVZz/vx5SpcuDcDNmzfJkycPZmZmMg2kEEIIIYQQQgghPinGf7+KEEKIT5n+AM6SJUtISUnh3LlzWFpa0rNnTypWrEiJEiUwMDD405COBHeEEP9LDAwMaN68OUlJSfTt25fAwEC0Wi1du3Ylb968uLu7U758eSZNmkRKSgp79uzB2dmZZs2aMXHiRGWKjvcxeG5sbEy/fv1wcnKiXbt2nDhxglatWjFgwACcnJyUgUqtVoutrS29e/cGICQkhH79+pGQkED16tWlXxfiI2JgYMC9e/eYMWMGjx8/xtbWlmHDhikBnuDgYABCQ0OpVasWe/fupVixYtkCPMbGxtjY2KDVaiW48zfUajVqtVr5uX///iQkJLBhwwYaNGiAqakpefLkYezYsXh4eGBgYEDr1q0xMTHByMgItVqNnZ0dEydOxNbWFm9v7xw8ms/X48ePgZfve2q1mp9++okRI0ZgaGjIoUOHcHBwAOD58+cMHToUDw8PWrVqpVSe0mq1EtwRQgghhBBCCCHEJ0Uq7wghxGdMP4zj4+Pz/9i7z4Aorvbv49+lCwpiAxUQRAUVo7FhLLHErrErJvaCRBFQUUAFEbAiFgS72LCLJfYSjdh7jcaCvWLHStvd54XPzh8sicmtEPX6vElgZtYzA3NmmPOb6xAVFYWenh7m5ubcv38fQ0NDatasSVBQEDVq1Mjm1gohRNb7q7fy1Wo127dvx93dHY1Gw/Dhw/n555/JkSOHss6lS5d4+vQpjo6OGBkZYWJikiVVLyZMmMDAgQMxNDTEwcGBCRMm0KhRo0xBzDcr8IwePRoDAwPWrVtH+fLlP2n7hBD/3Llz52jTpg2XLl1i8ODBDBw48J0VeKysrNi7d2+mCjziwyUmJtK5c2f27duHq6srO3bsoF+/fvj6+lK4cGHgdf+/cuVKgoKCePjwIdOnT1cCPPC6CpqBgYHSz+q+Fh/P9evXKVWqFM2bN8fNzQ1vb29UKhWHDh3KNFVWv379mDt3Lps3b+a7777LxhYLIYQQQgghhBBC/G8kvCOEEF+BiIgI/Pz86NevH127dsXFxYVNmzaxZMkSFi9eTNmyZZkyZQpVq1bN7qYKIUSWyTjoffjwYYyNjdHX16d06dLKOunp6ezYsSNTgKdDhw6YmJi88zOzaprBFStWkJiYiFarZdiwYdjY2BASEkKrVq2UdsD/VU57+vQp0dHRLFq0iN9++42CBQt+8jYKIf65c+fO0bJlS65evfreAM+IESNQqVRcunSJIkWKZGNrP18HDhygVatW3L17l/r16zNlyhQcHR0BMlU0iouLe2+AR/zv3hegVavVpKSk0KNHD5YtW0bu3LmxtLTk8OHD5MmTR1lvwYIFBAcHU6FCBebNm0fOnDmzsvlCCCGEEEIIIYQQH5WEd4QQ4gt3/fp1mjVrhp6eHqtWrcLe3l5Z9urVKwIDA5k4cSKtW7dm6tSpmd5kFUKIL1XGkE1gYCBRUVGo1WoMDQ3x9PQkKCgIY2Nj4O0AT0hICB06dMDY2DhLwjrv+zeSk5MxMTFh0aJFeHp6YmtrS2hoKC1btsy03r179yhQoADPnj1DpVKRM2dOqdYhxH/Y3wV4Bg4cyOLFizl8+LBSKUZ8GF1/6uPjQ1RUFAULFuTJkydMnz6dNm3aKJXVdKESXYBn2LBhJCUlERERgZubm1TZ+QgyXoeOHDmCVqtFrVZTpUoVZZ2dO3fSuXNnbt68Sb9+/ZgwYYLyM5w5cybh4eHo6enx+++/U7hw4SwL0AohhBBCCCGEEEJ8ChLeEUKIL9wff/yBq6sr3bp1Izo6Gsg8EPzw4UO6d+/Otm3b2LVrFxUrVszO5gohRJYaPXo0QUFBuLq6UqlSJdavX8/ly5dxc3Nj9OjRSuAxY4BHT0+PgQMH4u7ujpGR0SdtX8bBzbS0NF69eoVKpSJXrlzKOsnJyaxatYo+ffpga2vL8OHDad26NQBr1qxh9OjRjBo1ih9++AHIuupAQoh/7+8CPElJSVhYWEgQ719auHAhp0+fpmTJkkyZMoWzZ88SGRlJx44dlcpqGQM8K1eupHfv3hQsWJBDhw5lmj5R/HMZr0PBwcFER0fz6tUrkpOT8fb2xsPDg5IlSwKwbt06vLy8uH79Oi4uLtjb23Pjxg0uXrxI4cKF2bJlC/b29nIuCCGEEEIIIYQQ4rMn4R0hhPiMZXzwrXtb9c03gQ8ePEjVqlVp3rw5q1atIj09PdM6Wq2WqVOn4uXlRUhICEFBQVm6D0IIkV1evXpFixYtsLW1ZejQoTg4OHDmzBmmTJnCjBkzaNGiBRERETg4OACvAzw7d+6kefPmlCpVip07d2JmZvbJ2pdxIHLWrFls2rSJ06dPU7hwYbp06UK3bt2UdVNSUli5ciWenp5YWVnRp08fjI2NiY6O5vz581y8eFGm1xHiM5MxwBMYGEi/fv0y9TkSxPsw7ztOKSkpGBsbs379eoKDgzl//jyRkZGZpkbUBXjUajUbN26kYsWKMu3gRzR8+HBCQ0OpXLkyVapUYceOHZw5c4YGDRoQHByMq6srAPv372fdunWsXr2aZ8+eYWdnR506dejbty/W1tYS3BFCCCGEEEIIIcQXQcI7Qgjxmco4EHH79m0KFSqkLJszZw4lS5bku+++49GjRzRq1IgLFy5w4MABnJyclAfcuiDPhQsXcHZ2ZuTIkQwePDi7dkkIIT4p3SCszsOHDylbtixRUVGZppq6evUqkyZNIioq6q0AT1paGvv376dYsWKZ+t1P2daBAwcSGRlJvnz5KF26NFevXuXy5cv4+/vj4+ODtbU18Hogeu3atXh6evLgwQMMDAywt7dn69atUpVAiM/UuXPnaNeuHX/88QcTJkygX79+2d2kz0rGfu/x48c8ePAAU1NT8uXLp0yNCHxQgOddnyn+mYzH7sGDBzRv3hwXFxcCAgJwcHDg4sWLLFy4kLFjx/L9998TGhqaaRqtFy9e8OzZM/Lnz4+enh4qlUp+HkIIIYQQQgghhPhi6P39KkIIIf6LdMEdV1dXmjVrxo0bNwDw9PSkZ8+eXLp0ifT0dPLkyUOjRo1ISkrCzc2N69evo6+vT1paGgYGBqjValauXAmAk5MT8DoYJIQQXxK1Wq0Mvu7YsYPNmzezYcMGihUrRpkyZYDXlXUA7O3t6devH15eXqxZs4aBAwdy9epVAAwNDfn+++8pVKgQarX6k7VX19bRo0cTFRVF79692bRpE7/99hsxMTGYmJgwduxYRo0aRWJiIgDGxsa0bduWvXv3MnToUCZOnMiuXbskuCPEZ8zZ2ZklS5bw/fffK9PhiQ+j0WiUfi88PJzatWvj5OSEo6MjlStXZsuWLSQlJQHQtGlTQkNDcXJywsfHh0WLFpGcnAyQKbgDSF/6P9Adu6VLl3L48GHOnj1L+/btlYBs8eLF6dOnD0FBQezatYthw4Zx8OBBZXszMzOsra3R19dX/haSn4cQQgghhBBCCCG+FFJ5RwghPnMNGzZk69atNGvWDAsLC2JjY+nfvz/9+vXD1tZWWa9Vq1asWbMGR0dHYmNjcXJywtLSksWLFxMWFoaJiQm//fYbefPmzca9EUKIT2vIkCFMmDBBmWYwJSWFkJAQ/P39MTIyylRhQVeBZ/r06VSrVo158+Zl6lc/lrS0NAwNDd/6/q5du/Dw8KB8+fIEBwdTokQJXrx4QaVKlXj8+DG2trYcOXIEb29v/P39KViw4Dunh5HgjhCfP121RDmfP0zGvnDgwIFMnDiR7777jh9//JEbN26wadMmnj59ire3Nx4eHhQoUACADRs2MGzYMC5dusSIESNwd3fPVKFH/O82bNjAjz/+SMmSJdFoNBw8eBBzc/NMv9v37t1j1qxZhIWF8f333zNq1CgqVqyYzS0XQgghhBBCCCGE+LQkvCOEEJ+pjAPM7u7uxMTEANCjRw8iIyMxNTVFq9UqA9QAP/30E8uWLcPQ0BBbW1uMjY25dOkS1tbW7Ny5E3t7+7emBhBCiM9ZxgHcCRMmMGTIEH744Qfq1KnDrl272LdvH5aWlkycOJEGDRpgYGCQqR+8du0aoaGhbNu2jWPHjpEvX76P2r7ff/+dJUuWEBISQsGCBTMtGzVqFMOGDWPv3r24urry4sULXF1defDgAZGRkRQqVAgPDw/OnTuHr68vAwcOxMrK6qO2TwghPmexsbF4eHjQpUsXBgwYQPHixUlLS2PkyJGEhoZSuXJldu7cqUyRBbBx40Y8PT0xMDDgxIkTmJmZZeMefH7e/FsiNTUVIyMj5evr168zadIkFi9ezL1791i8eDFubm6oVKpM12xdgGfMmDGULFmSmTNnUq5cuazeHSGEEEIIIYQQQogsI6OzQgjxmdLT01OmbMmfP7/y/YSEBJ48eQK8HrQ2MDBQpoJZsmQJU6dOpU2bNqSmppI/f348PDzYt2+fMq2KBHeEEF8KjUajDAKmpqZy7tw5mjdvzpQpU/D19WXmzJmMGDGCJ0+eMHjwYH777TfS09PR09NDo9EAUKRIEUJCQjh16hT58uVTvv8xvHz5kvnz5zN79mxCQ0O5e/dupuU9e/Zk6tSpuLq6kpKSQocOHbh9+zahoaG0bt2aGjVq0KFDB+B1MMnX15cHDx58tPYJIf4deT/mv2PLli1YWlrSu3dvihcvTmpqKmvXrmXu3LkUK1aMdevWYWJikqlvb9y4MbNnz2b37t0S3PmHtFqt8rfEvn37AJTgTlhYGJs3b8bOzg5vb286d+6MiYkJs2fP5uLFiwBKgAegQIECuLu74+XlxcOHDylUqFA27JEQQgghhBBCCCFE1pHKO0II8ZlLTk5m/vz5JCYmcuzYMdauXUv9+vWJjo6mWLFiyhusb07LkpiYiJWVlUzDIIT44oWEhPDq1SsWLFhAWFgYPXr0UCoDPHv2jLi4OPz9/bG2tiY8PJy6deu+VYEHeOeUVP+rP/74g4kTJzJ37lx69uxJaGgo1tbWb/3bu3fvplmzZrRt25Zp06Yp/fXSpUsZMmQIxYsX58yZM5w+fRpLS8uP2kYhxId7s3KXjY3N395ffYq+5Wun1Wp58eIFTk5OVKxYkV9//ZXU1FRWr16Nn58fenp6HD58WKmmtnv3bpycnJTps3SkIuW/U79+fY4dO8b8+fNp0qQJAwYMYNKkSYwePZp+/fphbGzM9evXmTx5MpGRkTRq1IhJkyZRtGhRIPM58fDhQwwNDTE3N5efhxBCCCGEEEIIIb5oBtndACGEEP8bExMTunTpopT779ChA0uWLKFv375MmTIFR0dH0tPTMTQ0RKvVkpSURO7cuZWpVXQDShLcEUJ8iW7evMmsWbNISkoiR44cFC5cGPi/AdlcuXLRpk0bAPz9/fHz8yMiIoLatWtnCjwCn2Rw3cXFBV9fX9RqNbNnzwZQAjwZnTx5kqSkJFq2bJmpv/7999+xs7Nj4sSJWFtbY2lpKUEAIbJJxiB0ZGQk06ZNo1atWkRGRmJsbPzW+rp+SHefJkHqj0elUpEzZ07y5s3L3bt3efDgAXv27FGCO4cOHco0DWK3bt0oW7Ysy5cvz/QzkKDIv/Pjjz+yc+dOhg4dyrRp09i4cSMBAQG0b99eORfs7Ozw8fEBYNKkSQBMnDgRR0fHTFNo5c2bF8hc1UcIIYQQQgghhBDiSyRPPoQQ4gtgYmKiTKG1aNEifvrpJ7Zu3YqnpycXLlxQKuusX7+ekJAQjh49qmwrA7xCiC9Z4cKFWbVqFeXLl+fBgwfExMTw6NEjDAwMlKk5dAGesWPH8vDhQ7p168bevXuzrI2lSpXCz8+Pzp07M3v2bIYNG/bWFFoODg4A7Nq1C3g9iLlixQq2bdtGmTJlKFWqFHny5Mk0VZgQIutoNBol9OHn50dgYCBWVlbUqFHjncEdeB0MWbduHfXq1SMxMRF9fX2Zcusj++abbzh+/DjBwcH4+Pigr6/PwYMHM005O3bsWJKSkqhfv76EQz4CrVaLl5cXy5Yt4/Tp02zevJnmzZvj6+tLkSJF0Gg0yu+5ra0tPj4+9OvXj40bN9K/f38uX74MvP03ilzbhBBCCCGEEEII8aWTyjtCCPGF0NfXV6bAWrRoEQBLlizBw8ODSZMmcf78eYYNG8bt27cZPHhwNrdWCCE+Pt1b+rpBQZVKhUqlomLFiowbNw5vb2/WrVtHpUqV8PDwwMLCQtkmV65ctG3bllevXjFjxgycnJw+aVt1FTZ0lTd0AR7gnRV4HBwcqFixImPHjiUhIYHk5GQOHTqEqakp/v7+yufKwLMQ2UN37oWFhTF+/Hj69OmDj48PxYoVe+f6Go2GlJQUhg8fzvHjxxkyZAjTpk3DyMgoK5v9xdL1rX5+fuzbt49p06ZRoEABDh48mGlqrKVLlxITE0OpUqVo06aNBEQ+At0xvHbtmnI9Pnv2LIcOHaJRo0bo6ellCprqAjwAU6ZMoWvXrsTGxlKkSJHs2QEhhBBCCCGEEEKIbKLSyqt9Qgjxn/dPplHQBXgAunbtyoIFCzAwMMDAwICCBQuybds2ihYtqgxqCCHElyBjP5mWloZGo8lU7UKtVnP06FE8PDy4evUqQUFB9OjRI1OAB+DFixcAmJmZZckUNhcuXKBEiRLK12fPniU8PJwFCxbQs2dPQkJCKFiwIADx8fFMnjyZTZs2kSNHDsqVK8fcuXOxs7OT6XaE+A84duwYrVq14ttvv2XChAlKxSyA48eP8/TpU8zNzXFxcVGm5UtISKBdu3Y8ffqUU6dOYWpqml3N/yK9fPmSmTNnKtMyBQYGUqVKFSwtLZk5cyZz5sxBT0+P3bt3Y2dnJ/fH/4OMx06r1bJp0yYuXbpEamoqfn5+ODs7M2LECFq2bKmsD/8XfLt58yZhYWFs2rSJo0ePZqqOJIQQQgghhBBCCPE1kPCOEEJ8RmbMmEGTJk2wsbH5y8GFjAGecePGkZCQgKGhIUOGDKFQoUIyyCuE+Ow9e/aMnDlzolKpMvVp0dHRbNq0icePH+Pq6sqgQYMoUKCAMn2gLsBz5coVhg0bpgR4smPAdvDgwYwdO5aNGzfSsGFD5ftvBniGDx9OoUKFlP1+/Pgxenp65MmTB1NTU+nThfiPWLNmDa1atSI6Opo+ffqg0Wi4desWkydPZurUqbx69Qpra2s6d+5MSEiIEjBcsmQJXbt2JSIiAi8vr2zeiy/Pw4cPWbZsGRMmTODy5cvkyJEDeF0hpmzZsixevJgiRYpIX/o/yHjs9uzZw9OnT2ncuDFpaWkYGhoyd+5cevbsibOzMyNHjqRFixaZtr9//z758+fn7t27mJiYkDt3bglSCSGEEEIIIYQQ4qsj02YJIcRn4tdff6V3796MGjWKgICAv3yYrRuk1tfXZ9CgQcD/vQ0rAxNCiM/dnj176Nu3L1OnTqVKlSpKnzZo0CDGjx9Prly50Gq1HDhwgL179xIWFkbt2rUxMjKiQoUKzJgxAw8PD8LCwtDT06NLly5YWlpm+X6Ym5sD0K1bN+bNm0eDBg0A3jmFli7AkytXLnLlyqV8hlarlT5diP+IlJQUAP7880+OHDlCfHw8ixYt4s8//6R+/fqULl2alStXMnv2bNq1a0f58uUBqF27Nt988w13797NzuZ/sfLmzYu7uzvNmzdn1qxZPHjwAH19fWrWrEmtWrXIkyeP3B//DzQajXLsQkNDiYmJ4caNG+zatYvq1asDr69zKpWKHj16MHToUDQaDa1atQJeh96mT5+On58fderUAV5f2yS4I4QQQgghhBBCiK+NVN4RQojPxJkzZ3B1daVs2bKsXbuWPHnyKNO8vE/GqWCEEOJLoFarmTx5Mr6+vpQvX54pU6bg6urKzp07adeuHW5ubnh7e2Nqasr8+fOJjIwkX758hIeHU69ePYyMjNBoNBw5cgRPT0+OHj3K9OnTcXd3z5b+Mjo6Gh8fH/LmzUtsbKwS4IHMFXg8PDwICgpSKvAIIbLPm/dXuq8fPnyIu7s7a9asQV9fH61Wi5OTE1FRUXz77bdYWloye/ZsevXqxbJly2jbtq3yGcePH6dMmTJK5UTxf86dO4eVldUnC1lKhZd/L+O5MGjQICZNmkTbtm3p06ePEtzJaN68efTs2RN7e3sGDhyIkZEREyZM4MKFC1y+fBkbG5us3gUhhBBCCCGEEEKI/wwJ7wghxGdA92A8KCiIkSNHsmnTpkwDvEII8TVJSkpi3rx5hISEYGNjw6JFi4iPj2fcuHFs2bIFZ2dnZb01a9bg7+9Pnjx5GDduXKYAz4EDBwgJCSEmJuaTDhi+OTCs1WozVRWYPHky/fv3f2+AZ/z48cydO5eBAwcyZswYGWQWIhtlrNCSlpZGenq6Mg0TQEJCAitWrODatWuULVuWTp06kTNnTmW5p6cnS5YsYc+ePZQqVeqtIJBUgMlsy5YtNGrUiEmTJtG3b9+P2v9JyP3jWbRoEe7u7nTv3h1fX18cHBzeu+7ixYvp3LkzGo0GAwMD7O3t2bp1K/b29vL7L4QQQgghhBBCiK+ahHeEEOIzohvAqFevHkuXLs2WaV6EECI76QZbk5KSmDNnDsOHD6do0aKULl0aS0tLoqKi0Gg0AOjp6fH8+XNWrVqFn5/fOwM8arUaQ0PDLBkwTEhIoFixYsp+vBng6devH3nz5mXx4sXUq1dP2e706dPMmTOH/v37Y2dn90nbKIR4v4xBvEmTJrFt2zZu3LhB06ZNady48TsrjehotVri4uIYPHgwzs7OLFmyJNMUeOJtx44do2HDhpQqVYrg4GBq166tLJPgzX9L586d2bJlC9u3b8fFxeVv19+3bx9btmwhV65cdOzYEWtrawnuCCGEEEIIIYQQ4qsn4R0hhPgPed9D6/T0dGUahXbt2rFlyxb27t2Li4uLlPoXQnzRMg7QarVaNBqN0k8+e/aM2bNnEx4eTmJiIpUqVWLLli3kzp0702foAjz+/v4UKFCA4OBgfvzxRwwNDbNsP/r168eCBQtYunQp9evXV/YnY4AnPDycgIAA8ubNy8KFCzNV4NFdB2RwU4jsN2jQIMaPH0/u3LkxMjLi3r172NjYEB0dTbNmzYDMQR/ddH/R0dGkpaWxd+9ebG1tJYDyHrq+0dfXl4ULF7Jo0SKl3zx58iRly5bN5haKjJ4+fUrx4sVxcnJi165dmf5u0fm733W5tgkhhBBCCCGEEEKAjPYKIcR/iO6htZ+fH2FhYezbtw8g0wPwzp078+zZMyZOnAggwR0hxBdLo9FkGuxTqVRKP7lv3z5y5sxJjx49GDRoEEWLFuX69evs27cPtVqd6XNy5sxJq1atGDduHGfOnCEqKuqtdT61/Pnz8+TJE/r168e2bduU/VGpVEqlID8/P1q2bMnDhw/p2bMna9asUbbXXQdkcFOIrKc7RwG2bt3KnDlz8PT0ZOfOnZw7d46xY8dy8+ZNunTpwurVq4HX92fJycmcPXuW7777jpCQEPLly8eePXuwtbVFrVZLcOc9VCoVenp6vHz5kqSkJGUKpr59+9K2bVvOnDmTzS0UGZmZmZE3b15u3brF/fv33wru6K7ld+7cITIy8p2fIdc2IYQQQgghhBBCCAnvCCFEtniz6FlaWpry/+fOnSM6Oprg4GDq1q1Lx44d2b17Nw8ePACgSpUqlC5dmvXr13P+/Pl3fp4QQnwJdOHENm3aMHr0aOX7PXv2pG7duhw6dAhzc3O6d+9Onz59SEtLIyAggKNHj77VL+bMmZPmzZuzdOlSFi5ciImJySdrd8aBfp2hQ4cyceJEzp07R9++fd8K8KSmpgJQpEgRbG1tuXXrFsOGDSMlJeWTtVMI8WF0fdHNmzdJSUmhUKFCeHt7880335A7d24GDRrEjBkzePnyJd26dVMCPCYmJty+fRsnJyf69evHunXrsLOzkyojH6hMmTKkp6fj6elJhw4dmDp1Ki1atCBfvnzZ3TSRgb6+Pi4uLly5coX58+fz8uVLZVnGClTjx49n+PDhyt8vQgghhBBCCCGEECIzmTZLCCGyWMay8RcvXqRo0aLKAE5QUBBeXl6oVCqOHj1KaGgof/zxB69evaJ8+fL4+/tTt25d9u3bR5MmTRg/fjz9+vXLxr0RQohP648//uCbb76hQIECjBs3jsOHDxMdHY2npyeDBw+mUKFCACQlJTFnzhxCQ0Oxs7Nj5syZVK5c+b2VLT7V4HnGz92xYwePHz+mXr16mJubAxAZGUn//v0pVqwY0dHR1K9fP9N1oVWrVpQrVw5nZ2dcXV0pUqTIR2+jEOKf69+/P7Nnz8bFxQUbGxtWrFihVPDSnfMxMTH06dOHHDlyEBMTQ+vWrQF4/PgxOXPmxNDQUKY7/QAZ+8T+/fsr1Vq6detGVFQUpqamMuVYFvu739tDhw7RunVrzMzMCA8P54cffsDMzExZvnz5coYMGUK5cuWYP39+pmVCCCGEEEIIIYQQ4jV5aiiEEFlMN9DQuHFjOnTowNmzZwHw9fVl5MiRzJw5E0tLSxo2bMj69evZtWsXnTp14sqVK7Rp04bKlSszZ84cLC0tmT17NgkJCdm5O0II8dGlp6cr/+/i4sL+/fvR09Ojd+/eREdH4+vrS0hIiBLc0Wq1WFhY0KNHD4YNG8aNGzfo1asXhw4dem9lsk8R3NFoNMrnBgcH06FDB2WKF10FHR8fHyZOnEhCQgJ9+vRh7dq1ynVhxYoVHDlyhPz589OuXTuKFCmS5dN7CSHerWjRorx48YJjx44pFRP19fXR09NTqm316NGDqVOnkpyczC+//MKiRYsAsLS0xNDQEJDpTj+ESqVS+m5jY2Pl+3/++SeJiYmAVJ3MSmq1Wvm9XbduHaNHj6ZXr14MHz5c+Tvkm2++oV+/fiQmJuLt7c2wYcM4deoUly9fZsyYMQwZMgSNRsOkSZMwMzOTn58QQgghhBBCCCHEO0jlHSGEyCK6N4S1Wi1PnjwhNDSUGTNm0LBhQ4yMjFi+fDmDBw+mV69eFClS5K03ik+fPk18fDyRkZHcv3+fp0+fYmFhQWxsLE2bNpUpGIQQX4Rdu3axYsUKhgwZQsGCBZXvN2vWjPXr12NqasrgwYMZOnQo8H+VbnR95tOnT4mJiSEsLAwHBwciIyOpVq1allZo8Pf3Z/z48XTt2pUuXbpQo0YNIHPlgsmTJ9OvXz/09PTo1q0bSUlJxMfHkzNnTvbs2ZNp34UQ/w3z5s2je/fuACxevJj27dsDr+/xtFqtcn7PnTuXHj164OTkxLFjxzAxMZEqMf/CxYsX6dOnD9WqVePChQssXbqUmjVrEh0dTenSpaX6ThbIeN3y8/NjypQppKamYmJiwosXL7C0tGT06NG4ubmhp6dHbGwskydP5sKFC5iYmCjnRqlSpVi1ahX29vbyN4sQQgghhBBCCCHEe0h4Rwghssj9+/fJnz+/8vXDhw+ZN28efn5+AHTo0IHRo0dTuHDhTA/K3yxTf/nyZS5fvkxUVBTr1q2jatWqbN++PdObyUII8Tl6+fIl7u7uLFmyhJkzZ9KjRw9UKhVXr17F29sbY2Njfv/9d1QqFcOHD6d3795K1YuM/3369Clz586lf//+/PDDD2zcuFGpevGpxcXF0blzZzp37kxAQAD29vaZlmfs0xcuXEhERASnTp3C0NCQsmXLsmLFCqXijgxuCpH1MoatdcGQtLQ0pQ9ZsGABXbt2pVChQkydOpVmzZop22UM8CxdupRq1apha2ubPTvyhbhw4QKFCxfGzMwMd3d3YmJiqFWrFtHR0ZQqVUoCPFlkxIgRDBs2jF69etG9e3cqV67M4sWLleo7M2bMwN3dndTUVO7du8e0adO4f/8+KpWKqlWr0qRJE/LlyyfXNiGEEEIIIYQQQoi/IOEdIYTIAgcOHKBq1apMmzYNDw8P5fsBAQGEh4ejp6fH999/rwxEAG8NRrz5dXp6Oq1bt2bz5s3s2LGDatWqyQCGEOKzd+TIEfbs2UOXLl2wtLQkOTkZExMTbty4gaWlJefOnaNx48ZotVqGDx+Op6cn8H+D6xkr8CxbtoxGjRphY2PzSdqacRBS9+96enoSGxvL/v37KV269Du3y9hX37p1iwcPHgDg4OCAubm5DG4KkU0ynntPnjwhKSkJU1NTjIyMsLCwUNaLiYnB3d0dW1tboqKi3hvgefMzxfu9GVZ/33Hr1asXs2fPlgBPFjpz5gxNmzbFxcWFSZMm4ejoCMCyZcvw8vLCyMiIU6dOkSdPnr/8nDd/xkIIIYQQQgghhBAiM3lyIoQQWeDGjRsAbNiwgbS0NNRqNVqtlty5cxMYGIi3tzd79+5l4MCBHDlyBEB561uXsXwzuGNgYIC/vz9paWls3rz5rXWEEOJzouvrKlasiLe3N5aWlvj7+9OzZ08ePnyIra0tOXPmpGLFiqxcuVKpvjNlyhTS09MxNDRErVazdetWVqxYQa5cuXB3d8fGxga1Wv3R2vn7778TEhICgL6+vvLZWq2Wly9f8ttvv1GgQAGcnZ1JT09/a3uNRoNKpeLVq1cAFC5cmLJly1K2bFnMzc3RaDQy0C9ENsgYFpk8eTI//vgjpUuXpmzZsrRq1Yrly5cr6/bo0YNZs2Zx48YNvLy8WLt2LfD6PuzNezE5n/+eWq1WQh3r1q1j+PDhdOvWjQEDBnDw4EEePXqkrDtz5kx69uzJzp076du3L2fPnlXumcWncfPmTa5du0aHDh1wdHQkPT2dJUuW4O/vj5mZGSdOnCBPnjwkJyeTkpICkOn6p/vZSHBHCCGEEEIIIYQQ4q8ZZHcDhBDia9C2bVtsbGwoVaoUhoaGHD9+nG+//RY/Pz9SU1NJTk7G3Nyc0aNHAxAWFkaFChUyDQC9fPkSU1NT1Go1Bgavu29jY2NMTEx4/PhxtuyXEEJ8LCqVSgkm6unp8ejRI06ePMnWrVvJkycPgYGBFChQAIAaNWqwatUqWrVqRUhICGq1mr59+7J582Y8PT3R09OjUaNG5MyZE/g4g+darZaUlBTat2/P/fv30dfXJzAwEH19faWagKmpKfb29pw6dYpnz56RO3fud06DmJSURGxsLL169cLIyCjTvyODm0JkPa1Wq/QTvr6+TJw4kRIlSvDDDz/w+PFjfv/9d37//XcuXbrE4MGDgdcBHgB3d3cGDBhASkoKbdu2lSD1P5QxsOjn50dUVJQSAAGIjY2lRYsWBAYGUqRIEeB1gAdg9uzZ+Pj4MH78eL755pusb/wX6F0Vj+7cuQOgVNxZsWIFAQEB6OnpcejQIfLlywe8Dvl07dqVNWvWKN8DeblACCGEEEIIIYQQ4kPJ6IAQQnwiV65cUR52A3z33XdYWFgwcuRIKlSowMKFC9HT08PExITcuXPj4eHBkCFD2L59O0FBQRw+fFjZdvXq1TRo0IArV64oD9STkpLYvHkzqamp2NvbZ/XuCSHER6XRaJRg4u7duzE1NSUqKoqff/6Z6OhoQkNDuXfvnrJ+9erVWbVqFSqVin79+lGuXDl69OiBRqNh27ZtSnDnY1GpVJiYmLBr1y4KFSrEsGHDlAo8enp6SlU1e3t7EhMTGT58OOnp6ejp6aHRaDJNpRMaGkpAQAAXLlz4qG0UQvw7unBBTEwMUVFRDBgwgK1bt/Lrr7+ya9cuFixYQKFChRg6dCjjx49XtuvRowcxMTFcvnyZiIgIkpOTs2sXPlu6fnHkyJGMHz+e7t27c/DgQa5du8b06dNxdHQkJiaGfv36KZUs4XWAx8PDg+3btzNixIh3VjoT/0zGENvo0aMJCwtDo9GQN29eAFauXMnChQszBXfy58+vbB8eHs6pU6cy/f0jhBBCCCGEEEIIIT6cVN4RQohP4OTJk7i6utK3b18GDhyItbW1sszOzo78+fPTuXNn9PX1+emnnwAoWLAgHh4eAIwaNQq1Wk2fPn1ISkpiwoQJnDp1KtObsKmpqYwfP54ffviBgQMHZu0OCiHER6YbwPXx8SEuLo5x48bx888/4+fnh0ajYerUqQAMGzZMqcBTvXp14uPj6datG69evaJChQpMnz4dW1tbpYrPx5Seno6TkxM7d+6kWrVqSngnODgYQ0NDpX0bNmxg/vz52Nvb07dvX6UdWq2WuLg4Nm7cSO3atSV4KUQ2SEtLU87XjDQaDb/99huWlpZ069YNOzs7tFotKpWKjh07YmlpSceOHRk0aBAuLi40aNAAgG7dupEjRw6qVKmCiYlJVu/OF+HMmTPMmTOHGjVqMGjQIKVv7NmzJ02aNKFHjx78+uuvODo6EhYWhqGhIQYGBkybNo1cuXLRu3fvj97ff410IbaRI0cSFBSEt7c3T58+pWHDhpQvX56pU6diZmaGsbExJ0+exNzcHHh9bVuwYAGbN2+mbdu2FCtWLDt3QwghhBBCCCGEEOKzpdLK5PBCCPHRnTt3jq5du3LmzBl8fHzw9PSkYMGCyvK4uDi8vb25e/cuixYtUgI8AImJicTExDBq1ChevnyJoaEhhQoV4vfff8fe3j5TOfsrV67g4OAAkGlqFiGE+Fxk7LvWrVtHz549admyJf7+/kr/dvr0aUaPHs3SpUvp06ePEuDRDay/evVK+ZwcOXK8c9qPj0X32QkJCVSrVo379+8THBxMcHCwss6vv/6Ku7s7T548oX379vTu3ZucOXOycuVKYmNjUavV7N69G1tbW2UfhBCf3p49e9i5cyfdu3enUKFCmZY9e/aMSpUqkStXLqX6oe5PZd05OmPGDHr37o23tzeTJk16Kwj0KUKDX4Ndu3ZRt25dRowYgZ+fH0CmvvH8+fO0a9eOp0+fsn//fqytreXYf0QZr5mPHj2iSZMmFC9enLCwMGWqsmXLljFkyBCuXLnC1KlT+eWXX5TtY2JiGDt2LPr6+mzfvp1ChQrJtU0IIYQQQgghhBDiX5BRXiGE+Mi0Wi3Ozs7Mnz8fV1dXxo0bx5QpUzKVkG/Tpg2RkZFYW1vToUMHlixZoiyzsrLCy8uLdevW0b17d4YOHcrevXvfCu5otVoJ7gghPmsZp5LSarWkpKSQI0cOfHx8cHBwUAbOy5Qpw+DBg2nfvj1Tp05VptDSDQzmyJEDMzMzcuTIkWnaj09BX18ftVpNsWLF2Lt3L/nz5yckJESpwgPQoEEDFi1ahI2NDQsXLuT777+nXLlyjB49mrx58xIfH4+trS1qtVoGN4XIIs+fP2fEiBEMGzaMffv2ZVqm62uMjIz4448/OHDgAPA6tKNSqdBqtWi1Wpo1a0aBAgXYs2cParX6rbCIhEf+ncTERNLT07l79y7wujpSxr7R3t6e+vXrc+3aNbZu3QrwVvUkOfb/nu6aOWvWLM6cOUNiYiKdO3emSJEiyrlRv359+vTpg7W1NYGBgTRu3JjQ0FAaNmzIwIEDUavVbNq0iUKFCsm1TQghhBBCCCGEEOJfkidcQgjxkfzxxx+4uLgoD6udnJyIjo6mb9++jBs3DiBTBZ62bdsCr6eI6dChA4BSgcfMzIzatWtTs2ZNZeDozUoSGR+KS3BHCPE50vVjvr6+TJo0iSZNmtCoUSNKliypLNe9va8L8ABMnToVfX19hgwZgpWV1Ts/81PS19cnPT1dCfDoptDSarUMHz4cExMT6tWrx5EjR1i6dClXr15Fo9FQuXJl6tatS548eT5pdSAhxNty5sxJYGAg1atXp27dusDrQE/OnDlRqVTkypWL9u3bExgYyLp16yhdujS5cuUCUII6+fLlw8DAACsrKzl/P6Ly5ctjY2PDjh07SE1NxcjISOkj1Wo1xsbGNGjQgPHjx6NWq7O7uV+MjNehrVu34uHhQb58+dDX18fGxkZZT6vVYmlpibu7Oy4uLkRERLB9+3Y2b95MiRIlcHNzY9iwYUpwR84NIYQQQgghhBBCiH9HwjtCCPER/Pnnn3zzzTe4uLhw6tQp5fvOzs7/KsCjp6f3VjUdeRAuhPhSaTQajIyM2LhxIzVq1ODZs2fKgHrGMI4uwGNoaEhUVBQ5c+ZkxIgRnzSw877KZgYGBm8FeEJDQwEYPnw4AHny5KFPnz7v/Ezp04XIOroQYPXq1alatSp6enoMHTqU9PR0BgwYoIQAGzZsyJo1a5g4cSJ2dna0bt1aCeyo1WoWLlzI/fv3KVeu3FtTaol/r2DBgnz//fcsXryYLl26sHjxYvT19ZWpsTQaDb/99hv6+vpK1Unxv8l4HTp48CD169dn0KBBLF68mFu3brFnzx5KlCiR6fpnbm5OgwYNaNCgATdu3OD58+c4OjoCZApcCSGEEEIIIYQQQoh/R0o1CCHER5A7d26qVq1KoUKFeP78ufJ9jUajBHhq1Kjxzim02rZtm2kKrTlz5gBSTUcI8eXTDX5PnDgRX19fzMzMOHHiBMePH1cqjr2pTJky9OvXD09PTzw8PD7pwLlarVb64n379jF//nyioqLYtWsX8DrAo9VqM02hFRoammkKrdTU1Lf2V/p3IbKWSqVCo9EAr8+/W7dusXXrViZMmMDs2bOV6ZrKly+Pl5cXBQsWpF+/fgwZMoT169fz6NEjpkyZwrhx47C1tcXLy+utcKH4d7RaLaampowfP57ixYuzbNkymjVrRmJiotJXrlq1ilWrVlGxYkXKlSuXvQ3+QuiOrZ+fH9999x2bN29myJAhtG/fHlNTUyZNmsTFixff2k53Xba1tcXZ2RkjIyOMjIw++ZSVQgghhBBCCCGEEF8DlVY3iiCEEOJf0VVlePLkCYaGhpiZmTFnzhy6d+8O/N/b3ufOnaNv377s3r2bQYMGZarAA7By5Urat2+PhYUF169fJ0eOHDIoJIT4ouj6w4wyvqkfFBTEyJEjKVCgAPv378fBweG9b/Knp6cr1TA+xYBhxoo7QUFBREVF8fTpU2W5j48PvXv3pkSJEsr3EhISqFatGvfv3yc0NJTAwMCP3i4hxN97s2KWbiomgBcvXmBmZsaRI0cIDg5m+/btDB06lB49elCoUCEAli1bxuzZs9m+fbvyGSqVCmdnZzZs2IC9vb1UGfmIdMfy1q1bNG7cmNOnT1OwYEEcHR3RarWcPHkSCwsL9uzZQ5EiRd5bEU38vYzHbsuWLXTs2BE3Nzd++eUXXFxcePz4MWPGjGH8+PGUL1+elStXYmtrm82tFkIIIYQQQgghhPg6SHhHCCE+gowD0osWLaJTp07Uq1ePLVu2ZFr+dwGe9evX8+2331K4cOFs2Q8hhPhUMg50p6Sk8PDhQ8zNzTE2NsbQ0FBZTxfgsbKyYu/evRQtWjTLB8kz9umDBw8mPDycli1b4uHhQb58+Zg0aRKxsbF069aN/v374+LiomybkJBAzZo1uXPnDuPHj6d///5Z1m4hRGZTp07lxx9/VMIHvXv3xtDQkPDwcExMTDh8+DBBQUHs3LnzrQDPnTt32LFjBwcPHkSlUlGuXDmaNm1K/vz5Jbjzgd4V2Hwf3TG9f/8+EyZMYM+ePRw7dowSJUpQrlw5RowYQeHCheXY/w8y/jzu3r3L3LlzWbBgAevXr8fR0VE5troAT0REBBUqVJAAjxBCCCGEEEIIIUQWkfCOEEJ8ZLdu3cLb25vVq1fToEEDNm3aBLw/wNO3b1+sra0zfYYMTAghviQZ+7To6GhWrlzJnj17KFSoEI6OjoSHh1OyZEnMzMyAdwd4dJV2slJsbCy+vr64ubnh7e1N8eLFSUlJoWzZsly5coW0tDTat2/PkCFDMgV4zp07R/v27Vm7di12dnZZ2mYhxGsDBw5kwoQJ9O/fn3HjxhEQEEBERAQ+Pj4EBweTO3dugLcCPD179swUrH4zgCJVXz5MxuP04MED8uXL97fb6K4V6enpqFQqEhISsLW1RV9fH2NjY7k//kgCAwNZv349hQoVokiRIkybNk05trrfdwnwCCGEEEIIIYQQQmQ9eeoohBAfkVqtpnDhwkyZMoXWrVuzZcsWGjVqBLyebkGr1eLs7Ex0dDTff/89kyZNYsyYMdy7dy/T58jAhBDiS6HVapU+bcCAAfTr14/ExEQaN25Mvnz52LlzJ40aNWLOnDlKXxgWFkZgYCCJiYnUqlWLixcvZnlw5+HDh6xcuRIHBwd69uxJ8eLFefr0KeXKlSMpKYkxY8bQsWNHli5dysSJEzl16pSyrbOzM0ePHsXOzg61Wp2l7RZCvObj48MPP/zAxIkTKV++PBEREQQFBdG/f39y586N7h2WSpUqERYWRq1atRg5ciSzZ88mMTHxvZ8rwZ2/p1arleM0bdo0mjZtSlRUFBqN5i+3010rDAwM0NfXp0SJEpiammJsbJzpWiL+vZcvX2JoaEhCQgKbN2/m/PnzbwV3tFotlpaWBAQEMHDgQE6dOkXt2rW5fft2djdfCCGEEEIIIYQQ4osmTx6FEOJfetcAhG5QwdramsmTJ/9tgMfZ2Zm1a9dibGycpW0XQoisoqtYMWPGDKKioujbty8bNmzg119/5cCBA4wdO5a8efMSGBjIr7/+qoRdQkNDCQ4O5ubNm7i5uaFWq8nKgpHGxsY8fvyYPn36ULZsWV69ekXDhg15+PAhY8eOpX///nh5eZEjRw7mzp3LpEmTOHHihLK9buBaBpuFyHparRZbW1s2b95M/vz5OXv2LN9++y0tW7bEzs4OjUaj3JPB2wGemJgY7ty5A/DB0z6J1zQajdLvBQQEEBAQwPPnz7G0tPzHwaeMx15+Dh+Hqakpffr0UarbnThxgpUrV2Y6JzIGeAYPHkyPHj1ITU2V4JoQQgghhBBCCCHEJybTZgkhxD+k1WozDUzs27ePO3fukJqaSsGCBalVq5ay7u3bt/Hx8WHlypXvnEIrISEBc3NzChQo8Na0DEII8SVp3rw5J0+eZNu2bRQvXlyZBis9PZ1Vq1YxaNAgUlJS2Lt3L46Ojsp2ERERtG3bliJFinyytr1vGpxnz55hZmaGSqUiJCSEcePGERQUhLe3N6ampgA0btyY69evc/bsWTw8PIiKisryKkFCiHdbv349zZs3p0CBAiQmJtK/f3/8/PywsrLKFFLQ3X8dOXKE4OBgNm3axMSJE/Hy8pLAwr80bNgwRo4ciYeHB/369aNEiRLvXE/ufz+dvzq2iYmJLFq0iOHDh+Pi4kJERATffffdWwEelUpFUlISWq2W3Llzy7RxQgghhBBCCCGEEJ+QhHeEEOIDHDlyhLt379K0adNM3x8yZAiTJ0/m5cuXyvd+/PFHwsLCKFGiBCYmJty5cwcfHx/i4uKoX78+mzdvBjI/UJcH4UKIL9mjR48oVqwYLi4u7Nq1Swnu6PqAz8pEAAEAAElEQVTB9PR0AgMDCQ8Px8PDg2nTppGWloahoaHyGbptPjbddCEAGzZs4NSpU/Tr148cOXJkWq9BgwYkJCRw4sQJcuXKpbTJ0dGRli1bYmVlRYcOHbCzs/vobRRC/DuXLl0iISEBa2trfH192bFjB3379iUwMFAJTmu12kz3YAcOHGDy5MmMHTsWW1vbbGz952vnzp389NNP1K5dmxEjRlC0aFFl2YULF1Cr1ZiamiqhTLkP/vgyXtueP3/O8+fPSU9Px8bGRlknMTGR2NhYgoODqVixIqNHj1YCPDoZ/16RoJUQQgghhBBCCCHEpyWvBQshxN+4e/cutWvXxtraGpVKRZMmTYDXU7qMGzeOZs2a0ahRIwwMDJg6dSrr1q3j2rVrhIeHU6tWLQoWLMjkyZMBiIuLo0qVKhw4cCDTw28ZsBBCfCneNdBnYWGBvb09V65c4c6dOxQsWFBZptFoMDAwwMfHh3nz5nH9+nWATMEd4JMEdzJWUQsKCmL27NkkJiZSsmRJWrRooezDq1evuHjxIqampqSlpSnbx8bGolKpaNOmDdWrVwcyD5gKIbLOu4IFjo6O2NjYYGxszKJFi3BzcyM6OhqVSsXQoUMpUKCAss2ZM2coXLgwVapUoUKFChgaGsr5/C8lJCRw7949WrZsSdGiRVGr1Tx+/JjJkycza9YsHj9+jIuLC7169aJXr15yH/yRZfy9nTJlCr/++iunTp1CrVbTokULGjVqRKtWrbCysqJTp04ABAcHM3jwYEaPHk3VqlWVz5Kpy4QQQgghhBBCCCGyjjwlE0KIv2Fpacm4ceN4+PAhQUFBrFu3juTkZPbv30/nzp2ZOHEiPXv2pGvXrvz6668MHTqUy5cvM3ToUO7duweAtbU1kydPpl69evzxxx/cv38/m/dKCCE+Po1GowzuJScno1KplEHEsmXLcuvWLSIiIkhKSlKm5NDR19dHrVZn6eCgbsBYN2DZtGlTjh49qgR3dExNTWnXrh1nz55lxIgR7Nq1i4iICEaPHk2uXLkoWbJkpv0QQmStjH3HnTt3OH/+PH/88QcAxsbGAFhZWbFs2TJq1qxJVFQUI0eOJCkpCYB169bRvHlzIiIi0Gg0SnhQzud/59atW2i1Wm7dusXVq1eZPXs2zZo1Y8yYMTg6OtKmTRvOnz/PpEmTuHbtWnY394ui1WqV39sBAwbQr18/Ll68SKVKlTAzMyM2NpZffvmF0NBQACXAExISwpEjRwgKCmLnzp3ZuAdCCCGEEEIIIYQQXy+ZNksIIT5ASkoKixcvxsvLC2dnZ3r06MHAgQOJjY2lVatWaLVapYLDgwcPGDlyJJGRkXTp0oW5c+cqn3P//n1UKhX58uWTKQKEEF8sLy8vbt26RUxMDJaWlgDcuHGDBg0a8OjRIwIDA+nYsSO5c+dWtpk7dy59+/YlICCAoKCgLJueY/369XTo0IFWrVoRHByMvb39O9c7evQowcHBbNmyBbVaDUDJkiXZuHEjRYoUkT5diGySscpIREQECxcu5M8//8TQ0JCGDRsybNgwSpYsqQRyEhMTad++PfHx8TRt2pRSpUqxZs0a7t69y9GjR3F0dMzO3fmsvNlP6/rBixcv4ubmxpkzZzA3N+fx48c4ODgwadIkKlSogLW1NUOHDmX06NHs3r2batWqZeNefJlmzZrFL7/8Qv/+/enVqxclSpTg0qVL7N69m0GDBvH48WNGjBhBQEAAAA8ePCA2NhZfX19atWrF4sWLMTIyyua9EEIIIYQQQgghhPi6SHhHCCE+UHJyMosXL8bb2xtbW1tSUlJYtWoV5cqVIz09HQMDA2UQ49atW9SoUYPU1FSOHTtGgQIFMg3syiCvEOJL8mafVqxYMS5fvkyXLl2YMGEClpaWpKSksHbtWgYNGsSTJ0/48ccf8fX1JXfu3GzevJkJEyag1WrZvXs31tbWWdb2oKAgJkyYwI4dO3B1df3LdRMSEjh9+jT79u2jRIkSNGvWDCsrK5laR4hskjE8MnDgQCZOnEiZMmVo1KgR9+/fZ/ny5ZQtW5bBgwdTt25dJYyQlJREx44d2bBhA8bGxjg5OfHrr79SpEgR5Z5O/LWM/d6bxywlJYUTJ04wefJkkpOTKVeuHD4+PpibmyvrdOjQge3bt3Ps2DEKFSqU5e3/Umm1WlJSUmjXrh1Hjx5l9+7dFC1aNNM68fHxtGrVCktLSxYvXkzlypWB1y8ZrFq1isaNG2Nra5sdzRdCCCGEEEIIIYT4qkl4Rwgh/oHk5GSWLFmCr68vT548wdPTk6ioKOD/BpBSU1MxMjLCy8uLKVOmcPLkScqUKZPNLRdCiE8j4wBubGwsDx8+ZP/+/cTFxWFoaEj79u2ZOHEilpaWPH36lPj4eIKDgzlx4gQmJibA6/7T3t6eTZs2YW9vnyVhGI1Gg1arpVq1aly9epUzZ84oVYIyBpF0waT3VQKS4I4Q2W/SpEkEBgbSs2dP3N3dKV26NDdu3KBKlSrcuXOHsmXLMmLECOrVq5epmsiGDRvIkSMHZcuWJW/evHI+f6CMgc3o6Gj27t1LWloaTZo0oV69etjY2PzltitXrsTPz49KlSoxb948TE1Ns6rpX4T3XY90P5dHjx5Rrlw5rK2tOXTo0FvbaLVaJkyYwKBBgxgxYgRDhgx567PlXBBCCCGEEEIIIYTIevJKoRBC/H9vVo54V3UcExMT3NzcMDQ0xMfHhyVLllCtWjXat2+PSqUiJSUFY2NjAG7evEnevHmxsrLK0v0QQoispBvc8/X1Zc6cOdjZ2dG8eXMaNWrEn3/+yYIFC1CpVEoFnqZNm1KrVi0iIyO5fv06r169wtXVlXbt2lGgQIGPPmD4vkFOXf/u5OTEsWPHOH/+PFWrVs20ju468PjxY/z9/YmKilL6+Df3XwiRPc6ePcuiRYuoU6cOv/zyC87Ozjx9+pT69euj1Wrp0aMHcXFxBAYGotVqqVevnnIeN2nSRPkc3fSn4u/p+k8/Pz8iIiIwMTFBq9WyatUqGjZsyMSJE3Fycnpnfz516lQiIyPRaDRMmDABU1PTLJsm8UuQ8e+T3bt3Ex8fz+3bt4mKilKOtZmZGZaWlty7d49Hjx6RJ0+eTMdYpVJRq1YtAPbt20dycjJGRkbo6ekp68i5IIQQQgghhBBCCJH1ZM4WIYQg84PwZcuWcffu3UzBnYxFykxNTWndujXjx48nJSWFkSNHMmfOHABlMCguLo7du3dTpkwZzMzMsnBPhBAi6y1YsICJEyfSuXNn1qxZQ2hoKMuWLWPdunVUqFCB+fPnM2DAAB4/foxKpSJXrlwEBgYyc+ZMYmNj6du3rzK94McaMHz8+DHA3w4Ily1blvT0dCIiIrh+/bry/fT0dOU6MGPGDGbPns2+ffs+StuEEB/PtWvXuHDhAt7e3jg7O/PixQu+//57Hj16xPjx4wkNDaVr166cPHmSsWPH8ttvv5GamvrW58h0pv/Mjh07WLRoEX379iU+Pp4LFy7g5ubG5s2badOmDX/++Sf6+vpotVpSU1O5cOECNWrUICQkhFy5crFr1y5sbGxQq9US3PlAGf9ecXd3p127dowePZqzZ8+yY8cOZR0DAwO+//57rl+/zowZM4DXv99qtRq1Wg28vvblypULCwsLTExM5PdfCCGEEEIIIYQQ4j9AntAIIQT/N2Dz3Xff8dNPP+Hq6kpUVBRHjhwB/m/wV/fAO0eOHLRv357Jkydz7do1evXqRcuWLZk+fTqdOnUiMDCQnDlzMm/ePMzMzJAZCoUQn7uXL1++9T1d37Znzx709PTo0aMHDg4OaLVaTE1NKVWqFDt27KBcuXLMnz+f/v37K6Ga9PT0tz7vYw0ebtu2ja5du3Lq1Kn3rqNru5eXFw0aNGDTpk1Mnz6dS5cuAWBg8LpA5cqVK5k7dy516tShfPnyH6V9QoiPp1GjRixfvpy6deuSlpaGp6cnly9fZtiwYbRq1YqCBQvSsmVLVCoVR48epXv37uzevTu7m/3ZefNe9s6dO5iYmNC7d28qVaqEra0tS5YswdvbmzNnztC2bVv+/PNPZQqmY8eOYWBgQOfOndm0aRNFihSRqZn+gYzBndq1a7Ny5Urq16/P8ePH2bp1K/Xq1QNeX0f19fVp164dJiYmDB06lJkzZwKvq+no6+ujVquZN28ez58/p1y5ctm1S0IIIYQQQgghhBDiDSqtjCgLIQQAf/75J6VLl8bIyIhy5cpx6NAhcuXKhbu7O61bt8bV1fWtgeXk5GSWLVuGr68vjx49wsXFBSsrK2rUqEH37t2VN4plYEII8TmLj48nJCSEyZMn4+LikmmZRqOhefPmbN++nevXr5MvXz5leg5d/7d3715atGjBq1evaN26NZMnT8bCwuKTTJWya9cuateuTZ06dZg8eTIlS5b8oG38/Pw4fPgwlStXpk+fPhQoUICtW7eyYsUKAPbu3Yutre07p1QUQnx67zr3UlNTMTIyUr6+desW1atXx8XFhXXr1infv3DhArVq1aJTp05s376ddevWUbBgwSxr++cu471sUlISpqamjBs3jmPHjhEXFwdAWloahoaGAPTr14/JkydTqlQpli9fTqlSpXj58iVPnz4lT548GBkZSV/6L7Vr145t27YREhJCly5dsLCwUI7lm9fU5cuX0759ewD8/f1p0aIFzs7OLF68mKioKNLT09m9e7dM8SuEEEIIIYQQQgjxHyFPy4QQgtdvE5csWZKePXuSmpqKj48PS5YswcbGhgkTJlC3bl2aNm1KfHw8t2/fVrYzMTGhVatWTJw4kTx58vD8+XP8/PwYNmyYBHeEEF+EtLQ0duzYwc6dOxkwYAB//vlnpuV6enoUKlSI5ORktmzZkmnwUDdlSsmSJcmbNy8ajYbY2FhCQ0N5+fLlRw/uPHjwAH9/f8qUKUNQUNAHBXcAqlWrRlRUFK1bt+bgwYN06dKFRo0aMWvWLOzt7dm9eze2trao1WoZbBYiG2Q89x49esSNGzcAlOCO7n2U8+fPc+3atbeqiaxYsQJzc3P69OnDnj17KFiwoFJNUfy1jNMZjh8/nlatWlG3bl1+//137t69y4sXL9BoNBgaGirHdNKkSXh7e3P27Fl+/vlnTp48iampKdbW1srPTPrSf27u3Lls3ryZLl260LVrVyUEqzuWumuq7nxo164dcXFxFCxYkLFjx1K1alVsbGzw8vJCo9GwdetWrKys5FwQQgghhBBCCCGE+I+QyjtCCJHBwoUL6dy5M507d2bevHkkJCRw5swZRo4cyZEjRzAxMaFMmTJ4eXlRu3ZtChcuDLyeTmbOnDlERkaye/durK2ts3lPhBDi47lz5w4zZ85k9OjRVK1alSlTpmQKxuzevZvmzZvj4uLCihUrlLf4MwYY69SpQ6tWrYiNjeXChQusWLGCunXrftTqO5cuXaJixYo0atSIxYsXAzBgwABq167Njz/++EGfsXr1ah4+fMiDBw9wdXXl22+/JXfu3BLGFCKbZKzQMmrUKBYvXkxCQgLVq1enU6dOtGjRAgsLCwDu3btHlSpVyJMnD0uXLsXKyoq1a9cSGhpKsWLFWLVqFcbGxtm5O5+twYMHM3bsWPLnz0/OnDm5cuUK8Lq6S5s2bd6quAbg6+vLxIkTqV27Nlu2bEFfX/+jhza/Jh06dOC3337jyJEj2Nra/uX1M+N5c/z4cfbv38/27duxsLDAxcWFjh07UqBAAbm2CSGEEEIIIYQQQvyHSHhHCCEg08PvBg0acOrUKQ4ePIidnR3weoqAs2fPMnz4cLZt2wZAuXLlqF+/PgMHDsTU1BRTU1NevHiBmZmZTAUghPjiJCYmMnXqVOXt/YwBnvv37+Pn58f8+fNp0qQJkZGR2NjYYGRkhFqtJi4uDh8fH1auXImhoSFVqlTBw8ODadOmfdQ2JiUl8cMPP5CUlMT+/fsZNWoUkyZNYsiQIQQFBf3loP1f9dvSpwuR/XThkeLFi2NpaUlCQgIpKSl4enri5+dHnjx5ePbsGWPGjCEiIoICBQpgamrKzZs3sbKyYufOndjZ2X2S6fq+RBlDHSdOnKB169Y0bdoUDw8PSpUqxdixYwkJCSE5OZnNmzdTv379dwZ4hg8fTrdu3ShSpEh27s5n7+7du7i4uFC2bFm2b9/+Ua5Lcm0TQgghhBBCCCGE+G+RJzVCiK+KRqPJ9LWuTLxKpVJKzLdo0YLExETCwsLQarWkp6djYWFB7ty5OXToEEWKFKFZs2ZcvHiR8PBwihQpwogRI9BoNJiZmQEyFYAQ4stjZWVFnz598Pf3Z9++fXh6enL27Fm0Wi358+cnMDCQJk2asGHDBtzc3Bg1ahQHDx5k5MiRBAcHkz9/fpycnHB2dsbS0pLjx4/z8uXLt/rl/0WOHDnw8PDgzp07ODk5KcGdXr16/W21jb/qt6VPFyLrZZzK59KlSyxdupTevXvz22+/sW/fPjZu3Ejp0qUJDw9n1KhRPHjwgFy5cuHt7c3EiRNxcHDA1NSUVq1asXv3buzs7FCr1RLc+UC68M2xY8d4+vQpz58/p1u3bpQqVQoAf39/Jk+eTK5cuWjYsCFbt25V7qf19fWVn9/w4cMpUqQI6enp2bYvXwKNRkN6ejoGBgYfvM358+fp27ev8rPQ/a2ju+7KtU0IIYQQQgghhBDiv0Uq7wghvkqRkZH07NnznVVyHj9+TMWKFQH49ddfcXFx4Y8//qBmzZoYGxszZswYOnfuzIULF5g6dSqnT59m7ty5SpUeIYT4kr1ZgSc6OhpnZ2f09PS4cuUKkZGRrFy5klu3binblChRgg0bNuDo6MiFCxcoV64cbm5uzJ0796O1K2M1jUqVKnHy5EnMzc2Ji4ujVq1aUm1DiM/Unj17uHr1KoMGDWLr1q2UKVNGWXbz5k3atWvHgQMHGDBgAP7+/uTPn18539PS0lCpVBgYGMj0QB8o431xVFQUPj4+VKtWDTMzMzZv3gxAWloahoaGAMTExDBgwACePXumVOCRii7/3pvHTve7nJSURKVKlXj+/Dl79+7FwcHhvZ+h+13fu3cv9erVY+XKlTRq1Cgrmi+EEEIIIYQQQggh/gfyRE0I8dUZMmQI/fv3Z9CgQbx8+RI9PT3ljVS1Wo2lpSWenp5cuXKFo0eP8uDBAyW4M2rUKDp37gy8HoweO3YsGzduVN7mFkKIL5FWq1Xe2NdV4PHz82Pfvn307duXc+fOodFocHBwYMSIEezZs4cJEyYwbtw45s+fT3x8PI6Ojty+fZvJkyeTnJxM1apVP2obdcGctWvXcvToUSpUqMCLFy/o06cPf/75pwR3hPgMTZkyhe+//564uDi++eYbypQpg1qtVvojGxsbVqxYQZUqVZgwYQJjx47l4cOHSgUYQ0NDpVKJBHf+nlqtVoIjz58/p3r16lSpUoUjR45w6tQpzp07B4ChoaFy39ujRw8mTJiAubk5TZs2Zf369RLc+R/ojt2hQ4dITU1Vrl0WFha4urpy9+5dpk+fztOnT9+5va7yEUBERARWVla4urpmTeOFEEIIIYQQQgghxP9EKu8IIb46z54946effmLHjh1069aNcePGYWpqmmmdQ4cOUbt2bWXgx8TEhLFjx9K1a1fg7bdihRDiS/JmH/euihXvqsBTsmTJ94ZkLl68yPz58xk3bhzt2rUjNjYW4KNXxLl16xanTp3C0dGR1atXK1O2rF69mpIlS360f0cI8ekdOXKEvn37cujQISwsLDh69ChFixZ9a71bt27Rtm1bjh49Srdu3RgzZgy5c+fO+gZ/IXx8fNi8eTOHDx/m2LFjhISEEB8fz9ChQwkLC1PWy3htmDt3Lj169KBQoUJcunQJIyMjCU3+S507d2bt2rUsWbKEunXrKlWODh06hJubG1qtltGjR9OiRQty5MihXEcz/jwWLFjAoEGDaNeuHePHj8fIyCg7d0kIIYQQQgghhBBCfAAZeRZCfFXS09PJlSsXS5cu5YcffmDatGl4e3uj0WgyrVe5cmW8vLxITk6mQIECREVF0aVLF0CCO0KIL1vGygtLlizBy8uLatWqERISwo4dO5T1dBV4/P392b9/P56envz555/Kcl0+PD09nYMHD9KgQQMmTpxI+/btleCORqP5nwZ33+y7AQoXLkyDBg0oUaIEXl5eDB48mGvXrtGyZctM7RNC/PdVqFCB6dOnU69ePZKSkoiKiuL+/ftvrVe4cGHi4uJwcHBgw4YN2dDSz1vG93lmz57NnDlzqFSpEg8ePKBWrVoEBQVRpUoVRo4cSXh4uLKuvr6+UoGnW7duLFq0iAMHDmBsbCzBnX9Jq9XSqVMn8ufPT//+/dm+fTtpaWkAODs707lzZ+7du0dISAhLlizh8ePHyrHWBXeWL1/OyJEjyZ07N0OGDMHIyAh5Z0sIIYQQQgghhBDiv08q7wghvjq6t1KfP39OixYt8PLyonnz5spy3durW7dupV27duTJk4fjx49jYWHxzuoTQgjxpcgYTvTz8yMqKgpjY2Py58/PlStXKFCgAIGBgfTp00fZRleBJzw8nOrVqzN+/Hi++eabTJ+7a9cu1q5di729PX379n3r3/o3MvbH586d49GjRzx58gRHR0ecnJyU9V6+fMn48eMZNWqUVOAR4j/qrypwabVaTp48iaenJydOnCAwMJBevXqRN2/et9a9e/cuKpUKKyurj17V60uVsS9+8eIFgwYN4vLly8ycORM7Oztlvd9//50hQ4Zw8OBBxowZg5+fn7LszftjuV/+32i1Wvbs2YO7uzvPnj1j+vTpNGnSBD09PRITEwkPDycmJgY9PT1q1qzJgAEDsLCwQE9Pj8mTJ7NhwwYMDQ3ZuXMn9vb28vMQQgghhBBCCCGE+ExIeEcI8VXSPcT+u4fZjRo1YsuWLcyZM4cuXbqg1Wql6o4Q4osXEhLCiBEj6NKlC+7u7ri6urJs2TJ+/vlnjI2NGTVqFP369VPWT0xMZMaMGQwfPpzWrVuzZMkSDAwMMn3my5cvlSkK/9fgTsbtR40axbx587h8+TIajQZra2saNGjAtGnTMDExUf5tXYDH3t6e1atX4+zs/K//fSHEx5PxXuzJkyc8efIEQ0NDChcurKyjC/D07t2b06dPM2TIEDw8PN4Z4AGpkvhvBAYG8vLlS7Zu3Uq3bt3w9fVVqpvpjuXvv//O0KFDOXDgAKNHj8bf3z87m/xF02q17Ny5k06dOjF37lzq1aunBNLu37/PsmXLmDdvHseOHcu0Xa5cuahTpw5RUVHY2NhIcEcIIYQQQgghhBDiMyLhHSHEV+19b2XrHnRv27aNtm3bUqNGDdatW5cNLRRCiKy1YcMGPD09qVu3Ln5+fpQoUYKkpCRq1qzJjRs3SE5O5tWrV0RGRuLl5aVsd+fOHRYvXky7du2wtbXNkrYOGjSI8ePHU716dWrXrs2zZ8+Ii4vj5s2bfPfdd8TFxVGwYEEAXr16RUREBOPGjcPY2Jj9+/dTrFixLGmnEOLdMgYLJk6cyNKlSzl+/Dj58+fnhx9+YMKECeTLlw94O8AzdOjQ91bgEf/MrVu3aNWqFYcPH0ZfX5+xY8cyYMAAZXnG++WMAZ6goCBCQkKyq9lfPK1Wy6NHjzL9jut+Funp6bx8+ZLY2Fhu3rzJs2fPsLKyolGjRjg5OZErVy4J7gghhBBCCCGEEEJ8ZiS8I4T4avyb6RPu3LlDuXLlePLkCffv38fc3PwTtU4IIbJfWloagYGBzJgxg+3bt1OhQgWeP39O5cqVefz4MbNmzSI1NZU2bdoAMGHChEwVeHTVLrJiwHDNmjX89NNP9OzZk0GDBinTu9y+fRsvLy9Wr15NjRo12LFjh9KWV69eERISwooVK9izZ48S7BFCZL2M1XF8fX2JjIzE2dmZunXrcu3aNdauXUutWrWIiIigbNmy6OnpZQrwnDt3Dk9PT3x9fbG0tMzmvfn8HTp0iIkTJ7Js2TKqV6/OrFmzMk1BmPE+Oj4+Hg8PD548ecLFixfJlStXdjX7q/Hm3zF/93eNTBsnhBBCCCGEEEII8fmR8I4Q4quQ8QH2li1buHTpEr/88ssHTamwceNGSpcuTZEiReRBuBDii6bRaFi5ciXp6en89NNPJCcn8+OPP3LixAlGjx5Np06dMDY2pkuXLsTGxmJubo6vry9BQUFZ3tZhw4YxYsQI9u/fj6urK/A6fGRoaMj9+/fp2LEj27Ztw8/PjzFjxihBgeTkZFJTUzE3N5eqBEL8B4wfP55hw4bh7u6Ou7s7pUuX5sqVK3z33Xfcu3ePKlWqEB0dTbly5ZQAz6lTp2jXrh1arZYjR45IuPof0N3Lvuue9sCBA4wZM4Z169bh7++Pj48PVlZWb20LsG/fPhwdHbGyspL744/kXcdRpoATQgghhBBCCCGE+HpIeEcI8cV7s9T/gAEDOHnyJNevX8fGxuaDP0cGeYUQX4Pk5GRUKhXGxsYsXbqUrl274u3tTWhoKCYmJgAEBASwePFi7ty5g5WVFefOnSNnzpxZ1katVsvPP//M6tWruXPnDpaWlsoAp+6/V65coXz58pQtW5bt27ejr6+f6Xogg81CZJ3nz59jZmb21jl34sQJunXrhqOjIyNGjMDZ2ZmkpCS+++47Hj9+TI0aNVizZg2urq5MmjSJcuXKKefyn3/+Sd68eSU88g9kvJd99eoVycnJmJqaYmhoqAREDh48SEhICNu3bycwMJBevXq9N8ADEi75WN4MRj18+JAff/wxm1slhBBCCCGEEEIIIbKSPGUTQnzRMj4I37FjB/7+/ly6dIl9+/ZhY2NDSkrKB3+WBHeEEF8DExMTjI2NATh9+jSpqan0799fCe4AnD17Fjc3Nw4ePMiBAwfImTMnWZ0HNzc3JzU1leXLl6PVapXBYz09PVJSUnBwcMDFxYWDBw9y/fp1NBpNpgFnGegXImvs3r2b77//noMHD77VT1y6dImTJ0/i6emJs7MzL1684Pvvv+fRo0dERkYSERFBixYt2Lt3L35+fpw8eVI5l0uVKoWVldVb57Z4t4zBnalTp9KqVSu++eYbKlasSEBAAPv37wfA1dWV4cOHU6dOHUaMGMHMmTNJTExUPufNYy3Bnf9dxr9Xtm/fTr9+/fDz8+PBgwdZfm0VQgghhBBCCCGEENlHnrQJIb5YbwZ3AgIC+OOPP9i2bRtVqlThyZMnREdHExISIg/GhRAiA61Wi0ajUQKOmzdvVpYtX76cY8eOkTt3bsqXL4+NjQ1qtTpLB89VKhXdu3fHwsKCZcuWkZCQoCxLT09XwkdqtRpnZ2dsbGxkgFmIbKBWqzl06BAnTpzA29ubI0eOZLrnat26NevXr6d27dqkpqbi7u7OtWvXCA4OplmzZtjZ2dGzZ08MDQ3Zu3cvbdq04cyZM5n+DTm3/55Wq1WCO76+vvTt25erV69SoUIF8ufPT0REBF26dGHJkiUAVK5cmbCwMOrUqcPIkSOZPXs2d+7cyc5d+GK9+ffKkCFDOH36NPPnzydfvnykp6dnWlcIIYQQQgghhBBCfLnkSacQ4ov0vuDO77//jqurK0lJSSxevJihQ4eydu1aeWNbCPFVUKvVH7SeSqVCT0+PNm3aADB48GCGDBlCnz59GDBgAMbGxnTv3l1ZPzsqkzk6OtK+fXt27txJUFAQJ06cQKPRYGBggEajYfny5Zw+fZpvv/0WjUaT5e0TQrzuG7p378748eO5cOECvXr1UgI8uiBC48aNAbh58yY7d+6kbt269OrVS6n2lTt3bszNzenYsSPGxsbkz58/2/bnc6W7z502bRpRUVH07duX9evXs2bNGnbs2EG3bt1ISEggMjJSCW1WrFiRsLAw6tatS1BQEMuWLZO+9CN7198rp0+fJj4+nsqVK5OUlMSSJUuIjY0FpGKcEEIIIYQQQgghxJfOILsbIIQQH9uHBHdiY2MZPHgwVatWZceOHW9tJ4QQXxqNRqOEbGbNmoW9vT0//PDDe6tWaLVaqlSpwooVK+jWrRtjxozBxMSEcuXKsWTJEgoWLJhpGpasli9fPjw9Pbl//z7Lly/n4sWL1K9fn+bNm7N+/XpWrFiBpaUlYWFhGBsbSx8vRDbQarVYWlrSrVs3NBoNYWFh9OrVi5kzZ1KxYsVM654/f567d+9Sq1atTP3KunXrsLOzIyQkBAsLC3LmzIlGo5GKO//Qixcv+PXXX3FwcMDDwwNHR0dSU1P59ddf2blzJ46OjmzYsAFjY2PS0tIwNDSkYsWKDBkyhNy5c9O6dWs55h/RX/29ogvuLFiwgKFDh9KoUSM6deqUzS0WQgghhBBCCCGEEJ+aSiu1l4UQX5B/EtwpX7488fHxwOtpVgwMJM8ohPjyDRs2jBEjRhAWFoaXlxfm5uZ/u83Fixe5fPkyOXLk4JtvviF37tyfLLjzTz/3zz//ZMGCBcTExPDgwQPg9TQ6FSpUYPny5RQpUiRbQ0ZCfO1092ZPnjwhJiaGsLAwHBwclACP7r7t4sWLVK1aFScnJ7Zu3YqBgQGrV68mKCiI0qVLs3z5cgwNDSWI9y/dvHmTcuXK0bJlS2bNmkV6ejorV67Ez88PPT09Dh06pFQ1OnPmDDly5KBo0aIASphH+tKP40P/XhkyZAjlypVj165db20nhBBCCCGEEEIIIb48Et4RQnyRtm/fTmBgICdPnpTgjhDiq5ZxsPXGjRv88MMP1KxZk4CAABwdHf92+3cNFmZF1Ys1a9ZQv359TE1N/3bd1NRU7t27x/bt20lLS6N48eKULVv2k4aMhBAf7kMCPE+fPqV///7MnTuXYsWKYWZmxqVLl8ibNy/x8fHY2dll9278Z2Xsp7VaLWq1+q1724sXL1K5cmXat2/PtGnTWLx4MYMHD34ruPPy5Utq165N586d6d27t1Tb+cjkRQMhhBBCCCGEEEII8T7y9EcI8cU5f/48vXv35vr16+zatUspPS8PwoUQXyNdcGXVqlUUKlQIrVZL7969Pyi4A7zzLf9PPZg7Z84cevbsybp162jSpMnfrm9gYICNjQ1dunTJ9P2MU4UJIbLOm6E/3f/nzp2bHj16AGSaQqt8+fKYm5szevRoChcuzKZNm3j27BmNGjVi/Pjx2NjYSBDvPTIe67t372Jtba3c286dO5dSpUrh6upKkSJFKFeuHCtXrqRMmTKMGzfureAOwPjx4zl79iyFChWS4M4noPtZbd++naFDh0pwRwghhBBCCCGEEEIo5GmcEOKz9FdFwxwcHGjQoAHbt2+X4I4QQgAzZsygTZs2SrhFNxXKf5WlpSUAS5cu5dWrV2g0mr9c/30DzDLwLETWU6vVSkDh8ePH3Lhxgzt37ijLc+fOTbdu3QgKCuLKlSv06tWLY8eOkZ6eToECBQgODmbPnj0cPnyYhQsXSnDnb+iOtaurK82aNePWrVsA9OnThx49epCQkEBaWhpGRkbUr1+fBw8e4Ofnh0aj4cqVK0pwR6vVsnTpUubOnUu1atWoXbt2tu3Tl+7o0aP4+vpy4sQJdu7cKcEdIYQQQgghhBBCCAFIeEcI8RnKOCiUlpaWaVl6ejpGRkZMnjyZatWq8fjxYxYsWEBgYKA8CBdCfLWaNWtGlSpVuHTpEk+ePOH27dvA677wv6hly5a0bNmSzZs3c//+ffT09P4ytCmE+G/IWO1qzJgx/PDDDzg7O1O6dGnatm3L1q1befbsGXny5FECPFevXlUCPLrtjY2NyZUrF4aGhgAS3PkAuXLl4siRI3h5edG1a1emT59Ov379qFmzpnIcBw8eTNOmTXn58iU5c+bk4cOHPHnyBK1Wy/jx4wkMDESr1TJ79mxy5879t8FJ8e88efKEfPnysXPnTnnRQAghhBBCCCGEEEIoVFoZCRFCfEY0Go1SSWH27NnEx8ejVqspXbo0AQEB6OvrZ5o+YOfOndSpU4dq1aqxe/duQB6ECyG+Lro+7969e7Rr145du3ZRq1Yttm/fjkql+s9VtND14dOnT6dPnz706dOHyMjI/1QbhRB/LSAggPDwcJydnalYsSJHjx4lISGBAgUK8Msvv9CnTx8sLS15/Pgxc+bMYcSIERQrVozIyEiqVq2a3c3/rGS8N+7evTvz5s0DoGfPnkyaNAlTU1Mg8/1v27ZtWblyJSqVCltbW54/f05SUhKlS5dmzZo12Nvb/+euDZ+TDzl2jx8/xtLSkidPnhAbG0tgYCDlypWT4I4QQgghhBBCCCHEV0zCO0KIz9LAgQOZMGECKpVKqcZQpUoVVqxYQeHChZX1kpOTmTJlCr6+voA8CBdCfNkyDuK+y71793BzcyM+Pp727dsTGxuLvr5+tgzSvu/f1H3/5cuXuLq6otFoiI+PJ1++fH+7f0KI7JHxfD527BiNGjWiU6dO9O7dG0dHR27dusXGjRsZN24cd+/eJTg4mN69e2NqakpSUhJz585lwIAB1KtXj/Xr1yuVYsSH0R1/XWgKoHbt2ixatAhra2tlecb74AULFrB//37OnTtH4cKF+f7772nZsiX58+eX4M7/IOOxi4uL4/Lly6SlpVG/fn3Kli2LkZGRElJNT09n7dq1tG/fHldXV3nRQAghhBBCCCGEEOIrJ+EdIcRnIeOA7cqVK3F3d+fnn3+me/fu5M2bl+DgYBYsWMC3335LXFwcDg4OmSrwgDwIF0J82TIOGJ46dYrExESSkpKwsbGhSpUqynqJiYm0bduWPXv20L59exYuXIienl62DdaOHj0aFxcXSpUqhaOjY6Zl0dHReHt7M3HiRHx8fLK8bUKIf+bAgQMkJSXRt29fNm7cSPHixZV7uJSUFHbt2kWvXr0wMjJizZo1lCxZEnhdhWTFihU0btwYGxubbN6Lz9OrV6+YN28eiYmJHD16lA0bNtCwYUOio6MpWrSocl+clpaWKRz1/PlzcubMqXwtIcmPw8/Pj4iICOXrQoUK0blzZ4KCgsiRI4dynFevXs327duJjo4G5O8VIYQQQgghhBBCiK+ZhHeEEP95b4Zw5s+fz8iRI1m/fj0lSpQAICkpibFjxzJu3DjKlCnDqlWrsLe3z6YWCyFE1so42BoaGkpMTAw3btwAQE9Pj6ZNmxIZGUnhwoUxMDAgMTGRdu3asXv37mwN8Pz666+0bNkSAAcHB3755RdatGiBo6Mjenp6HD9+nOrVq1OqVCk2bNhA/vz5M10PhBD/HcOHDyc0NJTKlSuTkpLC8ePH3wqCvHz5ksjISIYOHcqAAQOIiIhQ7vN0/5WqL/9ecnIyJiYmALi5ubFixQoaNWpEdHQ0Dg4OSjBEq9WSlJRE7ty5lW3fvN8W/0zG4zdlyhQGDRpE69atadq0Kc+fP2fs2LEkJCTQp08fwsPDlenMMpLgjhBCCCGEEEIIIcTXTV6pE0L85+kehPv4+GBtbc3WrVtp06aNEtxRq9VYWFgQEBCAn58fp0+fplWrVly5ciU7my2EEFlGNzju7+/P8OHDKVWqFOHh4YwdOxYnJyfWrl1Lq1atOHbsGBqNBisrK1asWEGNGjVYunQpP/74IxqN5pMPmGs0mkxfN2/enEOHDjF69GhevXqFv78/tWvXpkuXLpw/f55vv/2WUaNGcezYMc6ePZtpqkQhRPZ681xs3rw5OXPm5NChQzx//pxHjx4poUAdU1NT2rRpg7GxMX/++Sfp6enKfZ7uvxLc+fdMTEyU471s2TLatWvHpk2b6Nu3LwkJCRgYGKBWq9mwYQNhYWEcP35c2VaCO/+eRqPJdPyuXbtGzZo1CQ0Nxc3NjR49erB//35cXFyYOnUqgwYN4uXLlwCZzg8J7gghhBBCCCGEEEJ83aTyjhDis5Cenk63bt1YtGgR+vr6tGzZkiVLlgCvB3l0b3Y/ffqUsWPHEh4ezrfffsvixYspVqxYNrdeCCE+vY0bN+Lm5sbPP//M0KFDsbOzA15PkzVixAimT59O+fLl2bp1KxYWFgDcu3ePH374gVu3bnHx4kXy5s37ydqXsZrGhg0buHz5Mo0bN1amyjp//jxnzpwhPDycQ4cOkTNnTr777juKFStGTEwMNWvWZNmyZZkqRQghskfGijpJSUlKn3L27Fm+//57Hj16hIeHB9OmTQNen/8qlQo9PT1SU1Oxs7OjfPnyrF+/XqZo+gQyVnBp3749y5cvp3bt2kRGRnL27FmGDRvG7du3SUhIoECBAtnc2i/H4MGDefDgAb/99hve3t70798fQJmq7NmzZ1SrVo0//viD3r17M27cOExNTaXalBBCCCGEEEIIIYQAQF7tEkL852m1WgwMDJgxYwa5cuVi7ty5HD58mBs3bmBvb68MUGg0GszNzfH390dfX58RI0bQt29fNmzYIA/EhRBfvD/++IMXL17Qvn17JbiTnp6OlZUVwcHBPH/+nPnz5+Pr68vs2bPRaDQUKFCA33//HbVaTd68ed+a4uZjyVjVZ+jQocyaNYvnz59TsmRJbGxsMDY2xsnJCScnJxo2bMiePXtYvnw5cXFxbN++HY1Gw+XLl7lx4wa5c+f+ZO0UQnwY3fnn7u5OWloa8+bNQ61WU6pUKXbv3k316tWZMWMG1tbWBAcHK+e/RqNhyZIl3Lt3DxcXF6n28okYGBgo98dLly7F0NCQRYsWUaFCBfT19SlYsCAnTpygQIEC0p9+JE+ePGHRokXcv38fS0tLJRSVnp6OoaEh6enp5MqVi71791KtWjWmTZuGvr4+o0ePxszMLJtbL4QQQgghhBBCCCH+C6TyjhDiP0er1b41mKN7I/Xly5cMHDiQ6dOnU6pUKfbt24e5uXmmAI+enh5PnjxhxowZ/PTTT8ogthBCfMl69erF7NmzuXz5Mvb29kq/qetTb9++TaVKlbCwsGD//v1YWFhkGrTNigHcIUOGMHbsWHr16kW3bt2oXLlypuVvtmHPnj2cPn2ayZMnc/78+UyVPIQQ2Uer1fL48WOsrKxo164dixYtQqvVKkG9M2fOUKNGDZ48eUKHDh3o3bs31tbWrF69mnnz5pGUlMSBAwcoVKhQdu/KZ+ld98rvkrECz5gxY7h06RJGRkYMHTqUQoUKZVou/ndXr16lY8eO7Nu3j1q1arFmzRrMzc2Va5vueD979oyaNWty4sQJBg8ezMiRI7O76UIIIYQQQgghhBDiP0DCO0KI/5SMZeM1Gg2PHj3CwsICQ0NDZZ2XL18yaNAgpk2bRunSpdm7d+87Azzv+kwhhPhSDRkyhDFjxjB8+HCGDRuWaVlKSgrGxsa4ubmxYsUKzpw5g7Ozc5ZWvdiwYQPt27enXbt2DBs2jCJFirx33Tf78StXrtCwYUOeP3/Ozp07KV68eFY0WQjxHmq1Gj09PcqWLYtarebAgQOYmpqir6+v3HdlnEIrf/78aLVabGxssLCwYN68eRQpUkTu0T7Q+47Th4R43txW17/Ksf/fabVatFptpuN59epVfv75Zw4cOMCgQYMYMmRIprCs7u+Vp0+f0rp1a2bNmoW9vX1274oQQgghhBBCCCGE+A+Q+thCiP+MjIMI06dPp2nTpjg4OPDtt9/Srl07EhISSE5OxtTUlHHjxtG7d2/OnDlDtWrVePr0qTJFwJuVI2RgQgjxNejSpQv58uVjxYoV7N69G10+OzU1FWNjYwAePXpEsWLFsLOzy/Lpag4cOEBycjKenp5/GdyB11Py6Nqv0WhwcHDA39+fO3fucOLEiSxorRAiI41Gk+n/9fX1UalUlClThjt37qBSqZT7LV2ARzeFlqWlJU+ePKF+/frs3buXHTt2SHDnH8h4nBYtWsTQoUPp06cPu3bt+qB+XFeBTUd3nyzH/t/JeC6oVCrUajXwf8fT3t6exYsX8+233zJhwgTGjh1LUlISenp6aDQa5e8Vc3Nztm3bpkwBLIQQQgghhBBCCCGEhHeEEP8JWq1Weeg9YMAAvLy8OH/+PJUrVyY5OZm4uDgaNGhAXFwcT548eSvAU7NmTZKSkqT0vxDiq6TVarG3t8fHx4cLFy4QGhrK5s2bATAyMgIgLi6OY8eOUb58+SwdtNVoNKSnp7N7925MTU2xt7fnXYUfdQOgz549A1AGpXX/LViwIACXL1/OimYLIf6/jJWwdFVGdBwdHXny5Alnz55Vluvu6dLT0ylZsiTx8fHkyJGDxYsXExUVleXBwc+drr/28/OjU6dOjBkzhunTp1OrVi0CAwO5du3a336GHPOPQ1dxCmD58uV4e3tTs2ZN3N3diYmJUa5f9vb2rFy5km+++Ybw8PB3Bngykr9fhBBCCCGEEEIIIQRIeEcI8R+hG1SYMmUKkydPxtPTk61bt7J9+3YOHTpEQEAAKSkp+Pn5sW3bNjQaDaampkRERODl5cXJkydp3bp1Nu+FEEJ8PPv27WP9+vUf9Ea+SqXC2NiYn3/+mV9++YU9e/bg7u5Oz549Wbt2Lb6+vgQEBCj9pomJyTsDNJ+Cnp4eBgYGFC1alOTkZO7du5epWgH8XyWP5ORkxo8fz/Xr1zPt29OnTzl8+DAAVlZWWdJuIcRrurCCh4cHxYoVo06dOgwePJiYmBhMTEwAOHfuHPD6fNXd0+lCJy4uLuzevRsLCwsCAgIYM2aMslxmcH6/jMdmwYIFTJkyhc6dO7Nr1y7mzJlD/fr1GTNmDOPGjePKlSvZ2NKvQ8YXDQYOHEjHjh2JiYnhjz/+YM6cObi7u9OsWTMePHgAvA7wrFq1inLlyhEeHs64ceN4/PjxWxVChRBCCCGEEEIIIYTQUWnliakQIptkfJMbID09ncaNG3Pt2jU2bdpE0aJFSU9Px8DAgBcvXrBkyRICAgLInz8/u3fvJl++fAC8fPmSkSNH4u7ujr29fTbtjRBCfDyJiYl899133Lt3j+XLl1O/fv0PfjP/xo0brF+/ntGjR3Pz5k0AjI2NqVChAosWLcry6Wp0fX1ERAR+fn40a9aMZcuWYWxsjFqtRqVSKdeCoKAgJkyYwLZt26hataryGX/88QfffPMNzZo1Y82aNVnSbiHE/7l+/To//fQTKpWKK1eucO/evUwBPFtbW7777jvy589PxYoVKVKkCAULFiRnzpzkzZsXExMTpVLi48eP8ff3Z9SoUdm4R/8tWq02U3WcjPfIaWlp+Pr6cvLkSebOnUvRokWB1/1iREQEsbGxeHh4MHDgQGWZ+HTGjh2rTF32yy+/kC9fPi5evIifnx/79++nVKlSxMfHkzdvXgCuXbuGm5sbhw4dYtSoUfj7+0slJCGEEEIIIYQQQgjxThLeEUJkqX379nHo0CG8vLzeGjhOTEzE3t6eOnXqsGHDBiW4oxvQeP78Of379ycmJobBgwczcuRIZR2dN78WQojPUVpaGgsWLGDs2LE8f/6cmTNn0rBhw3/Uvz169IiDBw+SlJREiRIlcHR0xMLC4pMFd94MZL7p6dOnVKtWjTNnzuDl5UV4eDjGxsbK8ri4OIYNG4aNjQ0rVqzAwsIi0/bbtm2jXr16H/RvCSE+Ht19mO4e6/bt29y/f5/ExETWrFnDggULSE9Px8LCgvv372fatn79+qxevRpjY2P09PQ4c+YMZcqUoWPHjixYsCCb9ui/5/nz5+TMmfOtvs3f359nz55x+fJlGjdujLe3N2lpaRgaGgJw/vx5Ro8eLQGeLHLz5k0aNmyIubk5ixYtwsHBQVmWlpbGzz//zMqVK2nWrBmLFy/G1NQUeD3do6enJzNmzMDOzi67mi+EEEIIIYQQQggh/uMkvCOEyDLPnz/H2dmZ27dvs27dOpo0aZJpeXJyMmXKlMHY2JiDBw9iZmamDBjp/nvu3DkqV65M8+bNiY2NzaY9EUKIT0fX36WlpbF06VKGDRtGSkrKPwrwvC+g86lCLxn/vT179nD58mWSkpIoU6YMtWrVUtY7d+4cjRs35urVq9SqVQsPDw+sra1Zv349cXFxaDQadu/ejZ2dndLWN9sswR0hPq2/O8cyLn/69CmVKlXC2dmZNWvWcP36dY4dO8bdu3c5ePAgwcHBODg4oNVqlenxbt26ReHChYG3K858jeLj4+nevTvz5s2jRo0ayvdv3bpFhw4d2LVrFwBDhw4lLCwMyPwzyBjg6dOnD97e3hQvXjzrd+QLcfr0aYyMjHBycnpr2YkTJyhfvjzDhg1j+PDhys9BF2xLSUmhatWqXLhwgfXr11OzZk1lHd11Misr3wkhhBBCCCGEEEKIz4uMfAghskzOnDlZtGgRPXv2pHr16sDrQRt4PfBrbGyMk5MTZ8+eZeLEiaSkpCjBHR1doCc1NTVb9kEIIT41Xb9naGhI+/btCQ0NxdjYmF69erF582bS09P/9jPeNzD4KUIvugF5gMDAQJo3b07Xrl3x8fGhSZMm/Pzzz0o/7uzszI4dO/juu+/YuXMnP/30E7Vr1yY6OhobGxt27dqFnZ0darVaaeubbZbgjhCfTsZzb/v27cycOZOAgABWrFjBjRs3Mq2r1WrRarWYmppy5swZNBoNRYoUoWXLlvTu3Zt58+bh4OBAeno6KpUKfX19tFqtEtzRaDRffXAHYMeOHVy5coUlS5ag0WiU7xcuXJjw8HA6dOiAvr4+R44c4erVqwBKsBHAycmJwYMH061bN6ZMmcLcuXMzTWkmPtzVq1epVKkSrVq14vbt228tf/78OQBnz57l1atXyrliYGBAenq6cq1+8eIFhw8fBv7vmqW7TkpwRwghhBBCCCGEEEK8j8wtI4TIUjVr1qRGjRro6ekxfPhwDA0NCQgIUB5kjxkzhuPHjzN37lyKFClC27ZtMTExUbbftGkT6enpuLq6ZtcuCCHEJ/dmgAdg2LBh9OrV619NofWpaLVaZWByyJAhjBkzhjZt2tChQwe+/fZbevXqxdKlS7l79y7bt29HpVJhb2/Ptm3biI+P58KFC6SlpVG2bFkqVapE7ty5pSqBENkkYxBv8ODBzJgxgydPnijVQmxtbVm4cKFSHUalUmFhYcG3337L8uXLuXHjBvb29srn6arqZOyrMoZ1JIj3WkhICKVKlaJ+/fro6elx8+ZNbGxsAKhcuTJeXl4kJyezcuVKoqOjGTp0KJaWlpkqkzk5OTFgwABy5crFL7/8In3ov2RsbEyXLl1ISkrC3Nz8reXlypXDycmJEydOcPHiRb755htlme73uVSpUgC8evUqaxothBBCCCGEEEIIIb4Y8sRUCJFldJUX9PT0uHHjBiNGjCA8PJwpU6Yobwg7OjoyfPhwkpKS8Pf3JyAggKtXr3L37l1iYmKIiIjAzs6ODh06ZOeuCCHEJ6dSqdBoNP9TBZ6saCPA/PnzmTNnDh4eHowaNYrmzZtjZ2fHtWvXMDc3Z+fOndSpU0e5DpiamtKoUSN8fHwYOHAg9erVI3fu3JnCA0KIrKULHwQHBzN27FiaNWvGli1buHPnDsHBwTx48ICaNWuyc+fOTNsVKlSI5ORkHj16lOn7UlXn7+nuf93c3LC0tGTQoEGULFmSQ4cOKetUrlwZf39/WrRowYQJExg7dqxyrDNW4ClVqpRynyyVd/6dggULMmbMGObNm0fOnDmJiYnhzJkzyvIcOXLw008/kZCQQFBQEElJScoyPT09tFotO3fuxNDQkJIlSwIgs5QLIYQQQgghhBBCiA8l4R0hxCeRsew/oEyBBfD06VNsbW3Zu3cvuXPnJjg4mMmTJ5Oenk6OHDlo3bo1kyZNwtjYmMmTJ1OhQgVKlSqFt7c3AFu2bMHKykoGJoQQX5Q3+034v8H0/1qAJ+Ng5P3791m0aBEODg54enpSrFgxnj17hrOzM0+ePGH06NHUq1eP+Ph46tevr2ybkpLy1udJJQ4hstfhw4eZPn067dq1IygoiHr16pEvXz7Kli2LoaEh1tbWSrUR3XlbpkwZNBqNMk2Q+HBvhhXVajUvXrygQ4cOmY5nxYoVGTp0KM2bNyc8PJzw8PBMAR7dz0KmZvrfWVpaYmJiwpYtW3B3d8fHx4cLFy4Ar49rp06daNy4MevWrcPNzY0dO3bw8uVLAJYvX05sbCwuLi7UqVMHkBCbEEIIIYQQQgghhPhwKq28CiaE+IRWrFhB27Ztla8DAwPR19fH19cXc3NzDh8+TMuWLXnx4gVBQUF4e3tjYGCARqPhzp07TJgwgWvXrqHRaHB1daVr165KcEcGJoQQX4qMfdqRI0e4ffs2z54949tvv1Wm4ABIS0tj6dKlDBs2jJSUlGyZQuvN/ler1dKhQwdq166Nu7s7L1++5IcffiAhIYHw8HC6devGo0ePcHJy4uHDh9SsWZMdO3bIgKYQ/0FLliyhQ4cObN26lbp165Kens7y5csZMmQIKpWKI0eOkDdvXl69eoVarSZnzpxs3LgRT09Pdu/erUz3JP4Z3RRj8HoarZCQEOzt7Vm2bBmVKlVS1jt69CgjRozg119/JSAgAF9fX/LmzZtdzf6s6aYc00lNTcXIyEj5+saNG0ydOpXIyEiqV6/O5MmTKVGiBHp6epw/f56hQ4eyfv16VCoVRYsWxdDQkISEBPLly8fOnTuxt7d/698QQgghhBBCCCGEEOKvyJMkIcQn4+bmhpubG5GRkQAMGDCAUaNGYWBgoAxQVKpUiVWrVmFmZkZYWJhSgUdPT4/ChQszfvx44uLiWLVqFf7+/hLcEUJ8cTJOFTV8+HB+/PFHWrRoQadOnahfvz4BAQFKVYV3VeDZunVrllXg0Wq1SltDQkIYMWIEKpWKKVOm4O7ujlarZfz48Zw8eZKBAwcqUxyam5tTokQJnJ2diY+Pp02bNlnSXiHEP3Pjxg0AnJycAIiLi2Pw4MGoVCoOHTqkBEUSExPx9vbm3r17VKlShePHj2NjYyNVEf8llUqlHLvg4GCCgoK4evUqbm5umSrwVKhQgcDAQFq3bs2YMWOYMWOGTMv0L2i1WiVUc+rUKQAluBMWFsaJEyewtbXF29ub/v37Ex8fj7e3NxcuXECj0eDk5MTEiROZNWsW1apV49WrV+TIkYPu3buzb98+7O3tUavVEtwRQgghhBBCCCGEEP9I1r2mLYT46nTq1Il9+/bRv39/4uLi2Lt3LwEBAXTu3JlcuXIp61WuXJlVq1bRqlUrwsLCUKlUeHt7o6+vT3p6ulJRQvdWsgR3hBBfiowDiAEBAYSHh9O4cWPatGlDqVKl6NKlC+Hh4dy8eZPY2FhUKpUS4IHXg4wtWrRg48aN1K1b95O3Vxe8HDFiBGFhYXTt2pX79++TP39+4HVVnj179mBtbY2Xl5cyGGpgYMCLFy9o2rQpDRs2xMvL65O3VQjxz+nuz/bu3YuBgQH+/v7o6elx6NAh5TwHGDRoECdOnODVq1cUKVIEyBzuE/+cvr6+ElAPCQkBXvfxbm5umSrwVKhQAV9fXywsLOjYsaNUMfsXdMesYcOG3Lx5k5kzZ1K1alUGDBjApEmTMDIyonTp0hQsWJC+ffsCEBERgbe3N5MnT8bJyQlbW1s6depEx44duXfvHhYWFhgYGGBgYCAvGgghhBBCCCGEEEKIf0WmzRJCfBK6oM3JkyepXr06ycnJVK9enfXr12NmZvbOh9qHDh2iVatWvHjxguHDh+Pp6ZmlU8EIIUR2mTVrFoMHD6Z9+/b4+PhQvHhxAEqWLMn169d59eoVHTt2ZP78+cqgY1paGvPmzWPOnDnExcVRuHDhT9a+jH32gwcPaN26NY6OjgwfPhw7OztlvWfPnlGnTh0eP37M6dOnyZEjB2q1mjlz5jBkyBA2btyoDEBnDGcKIT6te/fukS9fvr+tBHLz5k2qV6+ORqNBq9ViaGjIwYMHleCOVqslJiaGkJAQWrZsybhx4zA2Ns6KXfhqZOxvg4ODCQsLe+cUWmlpaRgaGkpQ5F9KTk5mxIgRREREUKtWLczMzFi9ejUBAQF4eHgooTSAO3fuEB0dTUREBDVr1iQqKkqpTiWEEEIIIYQQQgghxMcidZyFEJ/UqVOnePHiBSYmJsTHxxMTEwO8frtYo9FkWrdy5cqsXr0aCwsL+vfvz9y5c7OjyUIIkaXu3r3LokWLcHFxoU+fPhQvXpxnz57h7OxMUlISY8eOpUSJEixcuJAuXbpkmkKrW7dubN++ncKFC3/S6Wp0A8Pz589n3759nD59Gjc3t0zBHQBTU1PKlCnD5cuX8fDw4MKFC0ycOJEJEyZgY2ODg4ODsq4Ed4TIGhs2bMDJyYk9e/b87bp58+alffv2PHnyhMTERBYsWJCp4s7ixYsJDw/H0tKSwYMHY2xsLNM2fWS6CjzwenpC3RRaHTt2ZN++fcp6hoaGyvrinzMxMSEoKIhJkyaxbds21qxZg5ubG/369aNIkSJKgA1QKvAMHDiQ+Ph4vLy8uHDhQjbvgRBCCCGEEEIIIYT40sioiRDik9BVhmjUqBFLlixBo9EwcOBA+vXrR0pKCoMGDUJPTw+NRoNKpVLWr1SpEgsXLsTHx4dGjRpl5y4IIUSW0Gq15MyZk/bt21Oq1P9j777DmjrfP46/wxJlKAgIKjKc1Lpn3XtrnVh3XXWguFgucKACCqKC4t4TxW3de+/VWtS6d1VUVBRI8vvDX06DaL8dAor367q+17fknBOek3CexNyf3M83vH79mtq1a/P06VOCg4Pp2rUr9evXp2zZsixZsgSNRsOiRYswMDBQluiAtC/g7tixg65du5IvXz7MzMxwc3MDQKPRYGBgoCyZEx4ezsWLF1myZAlLliwBoGDBgqxduxYbGxtlfyFE2jt69CgdOnTAzc1NCXt8jFarJWvWrHh5eXH37l2WLl1K3759qVWrFiVLlmTLli3s27cPU1NTduzYgYODg3R9+Rf+zhz4/hJaBgYGjB49WgmP/K/nUvw9WbJk4dGjR0rH0Fu3bnH9+nXs7OyU1zUd/SW0pkyZQseOHVm+fDn58+fPqOELIYQQQgghhBBCiExGls0SQnwyHytG6D4Q37VrF506deLBgweEhITg5eWVYr+rV69iaWmJnZ2dLAUghMiU9OdJ3dyo0Wi4fv06+fPnR61WK50Axo4dS//+/TExMeHRo0dUrlyZhIQE7t27h4eHB9OmTUvTserGp3P//n1mzJjBwoULuX37NpMnT061vKFuKazXr1+zaNEinjx5Qs6cOWnVqhW2trYypwuRTrRaLa9fv6ZLly6cOnWK+fPnU6NGDQCePXtGjhw5Pnicbo56+vQpUVFRrF69mrNnzwKQK1cuatWqRUhIiNLtS67n/+3998fvz61/9Tjqb5s0aRJt2rRJsZyT+G+Sk5OZM2cOz549Iz4+npCQEKpUqUJAQIByvWi1WrRarfIc3r9/n6CgIDZs2MCxY8ews7PLwDMQQgghhBBCCCGEEJmJhHeEEJ+EfnHh4sWL/PHHHxgbG5MjRw6+/fZbZT/9AE9QUBA+Pj4AbNy4kUmTJtGkSROGDBkiXRmEEJmO/jy5e/du7t27R/ny5SlUqJCyz6tXr6hfvz7Pnz/nzJkzKYIxpUqVonXr1pw9e5aJEyfi7OycZmP9WBjz/v37zJw5kylTpuDs7MycOXMoU6ZMin0+VoiWjjtCpC+1Wk3t2rW5c+cOV69eBcDT05ObN2+yePFiLC0tP3ic7lrVLd106tQp1Go1hQoVwszMDFNTUwnu/E36j9Pq1as5fvw4p06dolKlSpQtW5bvv/8e+DP4+L/u43/tK/6aLjilH6BKTExU/ubDw8MZNmwY1apVIyAggOrVq6c4Pj4+HgsLCx48eEDWrFnJnj27vLYJIYQQQgghhBBCiE9GPvUTQvxnuqVSAEaOHMmMGTN4+vQp8K4dvaenJ/369SNPnjzUrl2bxYsX07lzZ/z8/Pjjjz+wtbVl7ty53Lt3j8WLF8sH4EKITEej0Sjz5KhRo5gxYwbm5uZERkZSoEABZd6Lj4/n3r172NjYKLclJyczc+ZMbt26hbu7O8OHD1duT6sCru53e3p6otFoiIiIAN4tG9K7d2+0Wi0TJ07Ey8uLyMhIvvnmG+XY9wv6uiKpzO1CpK/k5GTy5s3L/v37GTt2LPfv3ycqKgpfX1+Sk5M/epzuWjUwMEClUlG+fPkU2/Xf94mP05/3vb29iYyMJEuWLFhZWXHo0CG0Wi0eHh6EhYX95Vz+/mMtwZ1/Rz8Edf/+fRITE9FqteTJkwdTU1MAevbsiUqlYtiwYYwaNQp/f39q1qwJwNq1a5k1axaTJk2iaNGiACk68gghhBBCCCGEEEII8V9J5x0hxCfj7+/PuHHjqFu3LjVr1iQhIYGZM2fy8OFDGjduzKhRoyhdujQqlYp9+/bRv39/Ll68iKGhIUWKFGHjxo04OzvLt7mFEJmWr68vkyZNolOnTvz0009UqlQpxfakpCQaN27Mzp078fPzw9PTk9WrVzN9+nQsLCzYunUrVlZW6TLWO3fuUK1aNW7cuIGfnx/jx49Xtj18+JDp06cTEhJCxYoVUwV4hBAZSxeae/r0KeXLl+fatWsADBkyhOHDh3902Szx6Y0fP56RI0fSs2dPevfuTcmSJTl69CidO3fm6tWrDB06lHHjxmX0MDM1/e44wcHBLF26lDt37qBSqahduzaNGzemS5cuwLtl5WbPns2wYcOoUKEC/fr1IykpiZCQEH755Rdu3bpF3rx5M/J0hBBCCCGEEEIIIUQmJeEdIcS/ph+yef78OfXr16dUqVIMGzYMR0dHAM6cOcO0adNYtGgR33//PQsXLsTc3ByAS5cucfHiRV69ekXjxo2xtbWV4I4QItOKiYmhc+fOdOrUiaFDh5IvX74U23XFxevXr1O/fn2uXr2KoaEharWa/Pnzs2PHDpydndN1iY7jx4/j6enJ8ePH8fHxISgoSNn2foBn+vTpuLm5pcu4hBB/X5cuXVi8eDEAffv2VTppyfJLae/GjRvUqVMHFxcXIiMjKVSoEG/fvmXfvn107tyZ7Nmzc/DgQWxtbZVj9Jd0Ep+Wr68vEydOpHDhwpQoUYKbN29y7NgxgBQh1efPn7NgwQK8vb1JTk7GxMSEvHnzsnPnTvmigRBCCCGEEEIIIYRIMxLeEUL8Z0uWLOHVq1cMHjyYTZs2UbNmTTQaDSqVCpVKxZUrVxg2bBhr1qxh/Pjx+Pn5ffB+0rMgLYQQ6W3QoEHMmDGDw4cPU7p06Q/uoyvaPn/+nHHjxpGUlISdnR3dunUjV65caVYw1C8W694a6n4+fvw4Hh4enDp16qMBnsmTJ+Pq6kp0dDQFCxb85OMTQvw7u3fv5qeffqJWrVps3ryZ+/fv4+vrS2BgoBIOlBDCf3P9+nVy5Mjxwa5o+/bto2bNmixZsoT27duTnJzM6tWr8fX1xcDAgBMnTmBjY0NSUhJ37tzBxcUlA84g89L/+z516hStW7emTZs29O3bF2dnZxISEti9ezft27cnPj6egIAAAgIClOP37NnDqlWryJMnD927d8fBwUGuGSGEEEIIIYQQQgiRZiS8I4T4Ww4dOkRcXBxNmjRJcfu2bdto2LAh5cuXJz4+nh07dpA7d+5UH2wfPXqUWrVqUahQIQ4ePEi2bNkkqCOEyFT27dvH2bNnGTBgQKptCQkJ1KlTh3v37nH9+nUgdXcFXYDxxYsXWFpaKj/r9kurguGHgpPv/67jx4/Tr18/Tp48mWoJrUePHhESEsLatWs5cuQIdnZ2n3yMQoh/R6vVsn//fqpVq0ZcXBwlS5bkzp07+Pr6Mn78+DSdW74GFy9epHjx4gwcOBB/f39lOTLdvB0TE0Pr1q1Zu3YtTZo0YdWqVQwdOhSVSsXx48eVjjtxcXF0796dIUOGULly5Qw8oy/fhzoXXbhwgcePH9OqVSv27NlDiRIlgD9f/w4ePEitWrVQqVSsXbuWRo0apXoNhtSvjUIIIYQQQgghhBBCfEpSORdC/E+PHj2iffv2NGvWjFOnTqXYVq5cOYYNG8bJkye5dOkS0dHRABgaGirdG9RqNRUrVqRBgwbExsYSHx8vwR0hRKby7NkzvL29GTRoEJs3b061XaVSYWhoyIMHD7hw4UKq7boiYUJCApMnT+bWrVup5sm0Khjqfo9u/LrfpVarlX3Kly9PREQEhQsXJigoKEVnAjs7O/z8/Dh9+jR2dnZoNJo0GacQ4q+9f+3pQgfVq1dHpVJhbW3NoUOHyJs3L8HBwQwbNgytVpvqehd/X0JCArVq1SIiIoKQkBDi4uKAPzuX6TrpHDp0iK1bt+Ln55cquAPvlmzav3+/srSs+OfOnz/Pr7/+miq4M3XqVEqUKMGoUaMoXry4EtzRarVKOKdKlSrMnz+fpKQkDh8+DPz5HOrfnwR3hBBCCCGEEEIIIURakuq5EOJ/srOzw9vbm59++on8+fOn2GZtbc2gQYMYM2YMAFFRUezduxd492F3YmKi8kF3XFwcuXLlwtLSMl3HL4QQaS1HjhwMGzYMb29vKlWqlGKbVqvF1NSUpk2b8vbtW9atWweQ4pv8ugDNsGHDWLJkiVIA1t8vLd25c4eZM2cyZcoUJZjzoQDPjBkzABg7dizDhg1TttnY2JA9e3alGCqESF/688iRI0dYtGgREydOJCYmhocPHyr7OTo6cvjwYQnwfCLlypUjKCiIhg0bEhQUxMSJE1PM387OztSqVYtJkybRtWtXjI2NOXbsWIrgzoIFC9i+fTv169eXZQf/pStXrlCyZEm+//577ty5k2Kbi4sL5cuX5/jx45w4cYJDhw4BKcM5arWa7777DktLS37++Wdev36NNCgWQgghhBBCCCGEEOnNKKMHIIT4vOm6QfTr14+kpCSMjY0ZOXIkJUqUoHXr1gDkzJmT3r17k5iYyJgxY5gwYQJv3ryhQYMGmJiYABAdHc3p06epXr26fGtVCJGp6LpbNG/enKZNm2JoaIiPj48S6NEVCL/77jvy5MlDQEAAefLkoVu3bsCf3+SPiYlh8+bNFClSBFdX1zQd8/tLZeXNm5cdO3bQqVMnxo4di1arZcyYMUpB39DQkOTkZGrUqEGVKlW4f/8+QUFBWFhYMHToUOV+0iNoJIRISaPRKPPIiBEjiIyM5Pnz58r2ihUr0rlzZ3r37g28u94PHz5MpUqVCA4OxtDQULnexd+nm/vLli3L8OHDMTQ0JCgoCJVKxeDBg8mZMydWVlb07NmTU6dO8fjxY4YNG5ZiacH58+czfvx4TE1NCQkJIVu2bB9c9kn8tYIFC9KsWTNev36tLF2m07RpU4yMjAgKCuLAgQOsXbuW8uXLY2xsDEBycjJGRka4urpiaWmJnZ0d2bJly4CzEEIIIYQQQgghhBBfOwnvCCH+koGBgfKhtrGxMWfPnlUKPVmzZqVx48bAuw48AwYMQK1WM27cOM6cOcOPP/5IxYoV2b9/P9u2bSN79uxERkZiamoqhQkhRKah+9a+oaEhhoaGxMbGEh0dzc2bNzE3N8fT0xOAKlWqMGHCBLp3706PHj24ePEi1apVo3Tp0sydO5clS5aQlJTE9OnTsbCwSLN5UjdWeNetwNTUFHt7eypUqMDSpUv54YcfCAwMRKvVMnbsWAwNDXn79i1ZsmQB3i0RVr9+fa5du8YPP/zwyccnhPhndEG8ESNGMGHCBH744Qc6duzI69ev2bVrF4sXL2bIkCE8e/YMPz8/4M8AT/Xq1Rk/fjzm5ubKNvH36Iem3NzcaNy4MQ8fPmTChAlkzZqVXr16YWtrS9u2bXn69Cm+vr4MHjyYjRs34urqym+//cb58+extbXl559/Jk+ePCnmZ/H36B6zdevWkZCQQNasWZk1axYVKlRQlshq2LAhKpWKN2/eEBYWRq5cuejVqxeWlpYYGRmRnJzM0qVLuXPnDt9//z3JyckYGhrKv1WEEEIIIYQQQgghRLpSaaUftBDiL+gXEe7evUuePHlYunQpI0aM4I8//mDFihU0adJE2T8uLo6pU6cyevRoAPLnz4+9vT3FixfHx8cHJycnKUwIIb5o73et+dCctnXrVkaPHs2xY8cIDQ1l0KBByrZVq1Yxfvx4zp8/r9ymUqkoU6YM0dHRaTpP6t/vpEmTmDNnDi4uLkyfPh0nJycMDAw4ceIEP/zwA9evX2fo0KGMGzdOOX7WrFlMmDCBQ4cO4eDggEqlUgKeQoiMc+DAAZo3b07t2rWZOHEiTk5OALx69Yrdu3fTqVMnDAwMmDp1Kh07dlSu25s3b9KmTRtl7hF/j/7rwIgRI9ixYwcXL17E2dmZS5cuKbd7enpiY2MDwIYNG1i/fj3r168nMTERFxcXatWqhY+PDw4ODvL++D/Qfx1avXo17u7uVK1alRkzZvDNN98o+23fvp3hw4dz6tQpunfvTvXq1WnQoAGzZ89mxYoVxMXFcfToUXLnzp1RpyKEEEIIIYQQQgghvmIS3hFC/C39+/fn0qVLhIeH8+2337Jw4UJGjhzJ06dPUwV4njx5QlRUFCNHjqRBgwaMGDGCSpUqAShLbwkhxJduxYoVVKlShbx58wLQpUsXABYuXAjAjh07GD58OCdPnkwV4Ll8+TJXrlzhyJEjZM2alVKlSvHdd99hZWWVZgVc/U4+Xl5eREREULt2bbp3707Lli1T7HPixAnatWvHtWvXaN++Pe7u7pw/f54FCxaQPXt2du7ciZWV1ScfoxDi31m4cCFdu3Zl/fr1NG3aNFXIcPny5fTs2ZP69euzZs0a4M/3ZLp9JYj3z40aNYoxY8bQp08f3N3dKVy4MNHR0cyYMYPffvuNoUOHMnDgQGxtbZVjHj58yKtXr8idOzfGxsYplicU/9yHHrvu3bszf/58qlWrRmRkJEWLFlW2bd++HX9/f06cOIFWq6VkyZLExcXx7bffMm3aNJydneX5EEIIIYQQQgghhBAZQj6dFUJ8kH6Rd+7cucydO5d27dphYWEBvCtSq1QqRowYwQ8//JAiwJMzZ0769OlDQkIC48ePx8jIiOHDh1OhQgWMjY1lySwhxBfPx8eHSZMmERISwpAhQ/Dy8mLx4sX07NmTp0+fYm1tTd26dQEYPnw4Q4YMAVACPIUKFaJQoULK0oM6+suwfGq6eXf69OlMnTqVvn37MnDgQJydnVPso9VqKVeuHKtXr2bgwIFER0ezbNkyAFxdXVmzZg1WVlapwgFCiPSnuw513V5evHgBvHsfp69WrVpUqFCBtWvXcubMGUqVKqWEqXVzgwR3/pnY2FhmzpxJtWrV8PPzw9HREXgXeC9evDgTJkxgwoQJGBkZ0a9fPyXAkytXrlT3JUGRf0er1SqP3U8//QS86xA3d+5cjI2NmTVrFh4eHikCPPXq1UOj0RAcHMz+/fv57rvvCAoKwtDQkGzZskmITQghhBBCCCGEEEJkGPlUSgiRyvsF2atXr1KyZEmGDh2aYjmXzp07A3wwwGNtba0Uq8ePH49KpWL48OGUL19egjtCiC9eo0aNOH/+PKNGjWLt2rUcOXKEoUOH4uHhgbW1tTKP/lWAB0gVZkzLMIxWq+XJkyesXLkSZ2dn+vbtmyK4o6ML8JQsWZLFixfz22+/cezYMRwdHWnYsCF2dnbSlUCIz4RuzqhWrRoTJ07k2LFjdOjQAUNDQ2Ue0mq15MqVi2rVqrFnzx7i4+NT3Ie8L/t34uLiePjwIT/++COOjo5oNBolTFK9enXevn3LmTNnGDt2LEZGRsrrg/h0dH+7oaGhLFiwgBo1anD79m0cHR2JiooC+GCAp0GDBmi1WhISEpgxYwZubm7069cPrVYrwR0hhBBCCCGEEEIIkWHkkykhRCq6QtDAgQO5evUqcXFxtGzZkgIFCgCkKAi9H+BZuXKl0knCysoKLy8vDAwMCAwM5PHjxyxevBhXV9eMOTEhhPhEatSogYODAzVr1uTYsWNUrVoVd3d3cufODfwZgFGpVKkCPIaGhnh6eir7pReVSsXTp085c+YMbdu2pVChQn+5v0ajwdHREUdHR+Uc4MNLlAghMlb+/PkpVKgQERERlC5dmh9//DHVUli///47VlZW2NvbZ/BoMwdTU1NUKhUvX74E3s2xurCUSqWiXr16dOvWjeDgYMaMGcOjR48IDAwke/bsGTzyL5/+61BSUhJHjx6ldevWjBs3DkdHR+Xv/q8CPA0bNsTIyIhhw4bh6emJRqNRXpuFEEIIIYQQQgghhMgIstaBEOKDnj9/zsaNG9m6dSsXLlzg2bNnwLsPyOFdwEej0QDQuXNnAgMDyZUrF02bNmXnzp3K/eTIkYPBgwfTr18/XF1dJbgjhPji6ZajOXr0KC9evMDBwYHjx4+zc+dOHj16BLwr4uoCPAB169Zl3LhxVKxYkYEDBzJnzpwMGfvLly95+/Ytr169AiA5OTnFdt28/vz5c86ePZtq6R2Q5V2E+BwVLlyY0aNHA9CjRw9mzpwJ/LkU1rp169izZw8VKlQgT548GTbOzEKr1WJpaUmuXLmYPXs2Bw4cSBHGTExMBKBixYrkz59fWbJMfBq616HQ0FBCQ0PZunUrjRo1wsXFBXj3d69WqwGIiorip59+Yv/+/Xh4ePDrr78q96N7bS5XrhwDBw5k3Lhx6X8yQgghhBBCCCGEEEL8P5X2Q1UZIcRX5/2lsgBu3LhBu3btOHbsGJUqVVIKE/rfdtU/bubMmcydO5c1a9bg6OiY4r5ev35NtmzZgNTLxAghxJfo0qVLnDlzBktLS6ZNm8b+/fvx9/enR48e2NraAqm71Kxfv55Zs2Yxffp0nJyc0mxs+nOz/hju3btH7dq1efr0KUePHsXFxSXF0jq6ublRo0ZYWloyd+5czMzM0mycQoj/Tv/anTdvHj169ADeLQ1UpEgRHj16xK5duzA0NOTw4cPky5dP3ov9TR96f6wvKCiIYcOGUb9+fcaPH0+pUqVSzLleXl4cPXqU8PBwnJ2dsbGxkcf+E/nll18oVqwYuXPnxsDAgFWrVlGxYsUU3ab0n4vevXsza9YsihUrRnR0dIruczt27KBnz55UrlyZpUuXZsj5CCGEEEIIIYQQQggh4R0hRIoPtq9evUqOHDmwtrbGwMCA27dv4+7uzrFjx2jbti3Lly9PdYx+YUMX0vnYsipSsBBCfIk+Nnfp5r+jR48yYsQIDh06lCrAo9VqOXfuHCVLlgTgzZs3mJqapigwfkr68++WLVu4ceMG5cuXp2zZsgB4enoSERFBw4YNmT17Nrlz5yYxMRETExM0Gg2rVq3C19eXpk2bEhYWhomJyScfoxDi7/m7y9TpvxfbsGEDkZGRnDx5kri4OBwcHChVqhTTp08nX758svTd36T/OF27do379++j1WrJmTMnbm5uyj5du3ZlyZIlVK5cGX9/f2WZwVWrVjFq1CiqVq1KVFQUKpXqf4aBxMe9/zr85s0bfv75Z7y8vLh+/To9evQgKioqVRhV/3ls374969at48aNG9jZ2aXY7+LFi3z77bcf/F1CCCGEEEIIIYQQQqQHCe8I8ZXT/0B70qRJzJ49G3t7e6Kjo7G2tsbIyIi7d+/SunVrjh07xo8//si8efNSHSsfcgshMiv9ue7Zs2c8fvwYS0tLrKysMDY2Bt7NgcePH2f48OEcOnSIkSNH0rt3b6ytrdmyZQs//vgj7dq1Y8qUKWk6X+oXhkeOHMmMGTMwMzMjIiKC+vXrKwGdunXrsmfPHqpUqcL06dNxdXUlW7ZszJ49m9DQULRaLXv27CF37txpMk4hxD8TEBBAjRo1qFmz5kf30b/+Hz9+zPPnz7l8+TIFChTAwcEBc3NzCe78TfqPZWBgIHPnzuXmzZvKdh8fH3744QdKlizJo0ePGD58OHPnzgWgatWqvHnzhosXL5IzZ04OHjxIvnz5MuQ8Mgv95+PRo0fY2tqiUql48+YN27dvp1+/frx48YLJkyfTuXNnDA0NPxrgiYuLw8rK6oNd597/XUIIIYQQQgghhBBCpCcJ7wjxFdP/sNrLy4uIiAjq1atH+/bt+eGHH4A/P+y+ffs2bdq04fjx4ykCPGnVOUIIIT4H+gW/sLAwli5dypkzZ3BwcKBs2bLMnTsXGxsbZf9jx44pHXg6deqElZUVGzdu5PHjxxw8eJCCBQumy7iHDh1KcHAw3bt3p0ePHlSoUAH4syj5+PFjOnTowI4dOzAxMcHR0ZGkpCRu376Nq6srO3bswNnZWQr9QnwG9u7dS61atfDx8SEoKOgvr0vde7sPhQQlaP3P+fj4EBoaSp06dahbty5Pnz5l1apVXLt2jTp16jBixAiqVasGQFRUFNHR0Zw5cwY7OzuKFi1KeHg4jo6OMpf+B/qPXVRUFOvWrcPBwYF58+ahUqlITExky5Yt9O7dGwsLC8aMGYO7u/tfBnjkWhBCCCGEEEIIIYQQnyMJ7wghiIyMZODAgfTr1w9PT09cXFxSbP9QgKd79+7Mnj07g0YshBBpT//b90OGDCE8PJzixYvToEEDbty4wcqVKylSpAjR0dEULVpUOe7UqVMEBwezevVqDA0NKVy4MJs2bcLZ2TldAo+bN2+mXbt2tG3blpEjR6bq+KBftAwJCeHEiROcPXuWggULUr58eXr37o29vb0Um4X4TGg0GipXrszjx485efIk2bNnz+ghfRViYmLo1KkTHTt2ZPjw4cpcevLkSRYtWsSMGTNo0KABkydPpkCBAgAkJSURHx+PhYUFGo2GLFmyyFz6H+i/Dnt7exMVFUXRokXp168fHTt2VPZLSkpi06ZN9O7dG0tLy48GeIQQQgghhBBCCCGE+JxJuwwhvmJarZYnT56wcuVKXF1d6devX6rgDoChoSEajQZHR0eio6Np164dc+fOxdLSktDQ0AwYuRBCpD1dwXDy5MnMnDmTfv360atXL7755hseP37Mvn37+O2332jevDlr1qyhePHiAJQpU4aVK1fy888/Y2BgQLly5ciZMydqtTpdOpUdP36cly9f0rNnzw8u1aJSqZSCqI+PD5B6GREpNgvx+dBqtdSqVYsJEyYwc+ZM5boVaev06dOoVCq6detGvnz5lHmxbNmy2NjY8PLlSxYsWECtWrUYNGgQAEZGRlhbWyv3odVqZS79D3Svw8HBwYSFhdG/f3969+5NkSJFUuxnbGxM06ZNAejduzf+/v4YGBjQqlUr6RAqhBBCCCGEEEIIIb4Yspi7EF8xlUrFkydPOHnyJNWqVSN//vx8rBmXgYEBSUlJODo6smTJEho3boynp2c6j1gIIdLX+fPnWbhwIY0aNaJ379588803PHv2jOrVq6PRaGjdujW///477u7uXLhwAfizs02jRo1o0KABOXPmRKPRpEsBNykpiVOnTpE9e3YlTPT+vK5WqzEwMOD58+fKbZaWlgBKdwIpNguR/j70HkwX/ujfvz/29vZs27ZN2U8aqKYNrVaLVqvl6NGjqFQq7OzsUu3j7OxM586dyZYtG5MnTyYuLg4gVYcX6fjy3129epU5c+ZQo0YNBg0alCq4o2NkZETTpk2Jiori9evX9O7dm82bN6fzaIUQQgghhBBCCCGE+PckvCPEVy4xMRGtVotarQbetafXp/s5Li6OW7duodVqcXZ2Zv369Tg5OZGcnJzuYxZCiPTy8OFDzp8/T7du3XBzc+PVq1dUr16dJ0+eMG3aNGbPnk2HDh24fPkybdu25cKFCx8s1uq6B6Q1IyMjTE1Nef78OadPnwZSFvh1QYCkpCTGjRvHr7/+CvwZ1pFCsxAZQ6PRKNdfUlKScrtKpSIpKQl7e3vatWvHnj17WLlypbJN/Hfvh6BUKhUqlYry5cvz6tUrDh06BKQONdaoUYM6deoQFxdHYmJiuo33a3Pr1i1+//132rZti5OT01+G1oyMjGjYsCGhoaHky5ePcuXKpeNIhRBCCCGEEEIIIYT4byS8I8RX4v1QDrwrVpibm2Ntbc26deu4cOFCisKEVqtVCs5t2rTBy8tLKU7obpdW9EKIzKxu3brs37+fBg0akJSURK9evbh16xb+/v40btyY7Nmz06dPH3LkyMHt27epVKkSly5d+uTj+NAc/v7tuo4/uqVDFi1aBLybr3UhTV2xf+TIkSxbtixF9x0hRMbRva/y9vamVatWnD59midPngDvlgQCaNSoEQBr164lKSnpo/OC+Pv058Xr169z8+ZNZZsu+BEYGMjRo0eV23XhKo1Gw9OnT8mdO7fSvUx8erquRrrr4H26LyA8ePAAjUaDqakprVq14ujRo+TOnVvZLoQQQgghhBBCCCHE507CO0J8BXRLpADs3buXgwcPAu++Wezi4sIPP/zAs2fPCAwM5PfffwfeFSZUKhUajYbly5dz7do18uTJI9/yFkJkSroiuP43+nUFv0qVKgFw9+5ddu/eTc2aNenevTvZsmUDIGvWrKjVapo1a4aDgwPZs2f/pGN7P0i5ePFiJSCku10X3AEoX748xYsXZ9asWYwcORJ4N9/rwpkxMTGsW7eOokWL8s0333zSsQoh/pp+4Eaj0aToYPjgwQO2b9/Opk2bqFKlCq1atWLRokVKeKF27dp06NCB9evXc+nSpXTr6JVZqdVqZV4MCwujefPmeHl5ceXKFQBatGjB4MGDuXz5MmPGjOHAgQPAnyGSmJgYLl26RIUKFSTM/gl8LIxmYWEBwJYtW/jjjz9S/FtE100OoFu3bgwdOhR49xxlzZoVkGUghRBCCCGEEEIIIcSXQz5lFCKT0y9MBAYGMm3aNF6+fMmZM2dwcXHB2NiYgIAAYmNjiY6O5sWLF4wdO5ZSpUoBMHfuXMLCwjA2NmbYsGGYmJhk5OkIIcQnpz9P3rt3j6xZs2JmZkaWLFmAPwMyN27c4MGDB9SpU0fZBu8KuAULFmT69OkYGhpibm6e4j7/K12hsmvXrqxZs4aYmBgKFSpE06ZN6d+/Pzlz5iRbtmxoNBoMDAxwc3Nj8uTJdOjQgXHjxnHlyhXq1KlDuXLlWLZsGatWrSI5OZnZs2eTPXt25TghRNrSD+LduHEDZ2dn5ecJEybg4ODAmTNn2L59O1u2bGHmzJns37+fUqVKUa9ePby9valRowarVq1i6tSpREREYGpqmpGn9MXSD30MGTKE6dOnU7FiRTp37kzBggWVQKS3tzdPnz5lwYIFHDt2jCFDhlCwYEHOnDnDihUrMDU1Zfz48R/tCiP+Hv3XzF9++YUsWbJQoEAB4F1orW7dumzfvp1du3bRsmVLTExMlGO0Wi2zZs3i7Nmz1KpVS17ThBBCCCGEEEIIIcQXS6X9q0XjhRBfNP1ODF5eXkybNg13d3c6d+5M3bp1U+xz5coVvL292bBhA4aGhhQpUoTXr19z8+ZNXFxc2LFjB87Ozp+0IC2EEBlNv8gXGBjIokWL0Gq15MmTh6CgIEqXLq2EFi9dukSlSpXIkycPu3btIleuXCxfvpxRo0ZRqFAh1qxZg4mJSYq591N59eoVbdq0YevWrTRt2pRz585x69YtHB0dKV++PMOGDcPV1TVF1599+/YxZswYDh06pCx5aGhoSIUKFVi6dClOTk4ypwuRAerVq8fOnTu5dOkShQsXxsPDgxkzZhAUFISXl5cyJx09epQTJ04wefJkbty4gaurKzVq1GDJkiUULVqUrVu3YmtrK2GF/2DatGkMGTKEvn37MmDAAFxcXFLt8+zZM6ZMmcLo0aOV28zNzfn2229Zvny5zKX/kf5jN2nSJBYtWoSpqSnr1q0jd+7caLVali5dipeXFyqVitGjR9OgQQPy5csHwPLlyxk7diympqZs3boVOzu7jDwdIYQQQgghhBBCCCH+NQnvCPEViIiIYPDgwfTp0wdPT0/y58//0X3Hjx/P4cOHuXjxIoUKFaJ8+fL069cPe3t7KUwIITItf39/AgMDKVCgAFZWVpw4cYKcOXMyevRo2rZtS86cOQH46aefmDNnDjY2NtjZ2fH777+TK1cu9u/fT758+dIkuKNz6tQpqlatyqBBg/Dy8mLt2rXMnj2bY8eOkS1bNmrXro27uzs//PADKpUKAwMDbt++zf379zl48CAmJiYUL16cEiVKkD17dpnThUhnupDN0KFDCQ0NxcLCggYNGrB8+XIGDRrEoEGDyJs3b6owzuPHj9m0aRPR0dHs3r2bpKQkNBoNo0aNwt/fPwPP6Mv2+PFjvv/+e549e8batWspVKjQX+5/5MgRHj16xO+//06pUqUoWbIkVlZWMpf+B/p/67oOSJUrV8bLy4sGDRoor6lJSUlEREQQHh7Ow4cPcXNzo2rVqly6dIlTp05hbm7OgQMHcHJykjCbEEIIIYQQQgghhPhiSXhHiEwuPj6eJk2aEB8fz5o1a1J8o3jt2rWcPn2abNmyUaFCBWrVqgW868YTHx+PpaWl8gG4FCaEEJnVgwcPaNCgAVWrVsXHxwdHR0diYmKYNGkS58+fZ8yYMXTs2BE7Ozu0Wi2jR49m7969PHv2jOLFizN+/Hjy5s2bpvOkVqslLi4Od3d3du/ezeHDh6lYsSIAs2fPZsuWLWzcuBGNRkOTJk0oXbo0AwcOJEeOHB+8PyluCpF+bty4gbW1NZaWlspt06dPZ+DAgSQnJ9OlSxfmzJnzwfnj/Ws1JiaGX375hbFjx1KiRAm2bt2qhAvFP/Prr79StmxZ+vTpQ2ho6Ef3+6u5XebSTyMkJAR/f3969+6Nh4cHBQsWTLVPUlIS27dvZ/ny5SxfvhytVouLiwvVqlUjMDCQPHnyyL9XhBBCCCGEEEIIIcQXTcI7QmRy169fp0CBAnz//ffExMQAcOzYMaZPn87ixYuV/VxdXQkPD6dJkyZotVq0Wi0GBgZp2kVCCCEywvvF1vPnz1OqVCkOHTqkBGLUajVHjhxh2LBhnD59mjFjxtChQwdy5coFvFvGSqPRkCVLFkxMTNKtYLhw4UK6du3KgAEDCA4OTvG7p02bxoABA5R9CxYsSKdOnahUqZISzhRCpK/Tp09TsWJFunbtSnh4OFmyZMHAwAAvLy/CwsIwNDQkW7ZsnDp1igIFCpCcnIyRkVGq+3l/3ho/fjwjRoxgw4YNNGnSJD1PKdM4ePAg1apVo1OnTixYsAC1Wp3isdc95k+ePOHy5ct89913GTjazOvatWvUr18fZ2dnoqKiUnQIPXjwIA8ePCBr1qw0bNhQuQZu3bpFcnIy9vb2GBkZpevrsBBCCCGEEEIIIYQQaUW+JihEJufk5ESpUqU4evQogYGBdO/enZYtW7Jx40ZGjBjB5s2bmTJlCteuXePo0aMAynIruv8WQojMQq1WK/Pb5cuXuXz5MklJSTRv3lwJ7iQnJ2NoaEjlypWZMGECpUuXxt/fn6VLl/LHH38AYGZmhoWFBSYmJgCfpGCo0Wg+uk2Xtf7hhx+oWrUqK1eu5MmTJ8rv/u233xgzZgz58+cnJCSEzp07o9Fo8Pf358cffyQhIeE/j08I8c/pOu48e/YsxfurFi1aMGzYMAYPHszLly8pW7YsFy5cwMjICLVaDfx53QNKF0SdatWqAbBmzRo0Gs1fzh9fK/3H60MKFSpEwYIFOXnyJK9fv07x2OuHpXr27Mm8efN49epVmo/5a3T//n1+//13GjVqRP78+VGr1cTGxtK/f39q1qyJu7s7TZs2ZcCAATx+/BiAvHnz4urqSrZs2TAxMUGr1UpwRwghhBBCCCGEEEJ88SS8I0Qm8bGijVarZfjw4WTPnh1/f3/WrFlD4cKFOXz4MP7+/jRs2JBGjRphaGjIvXv30nnUQgiRfvSLe6NGjaJ69eqULFmSmjVrsnbtWrZu3QqAkZERGo0GlUpFpUqVlADP2LFjmTlzJnFxcWkyPl2huFmzZqxbty7FNl2Q0tjYmOrVq/PgwQMCAwMBiI2NpVKlShgbGzN06FC8vLxYsGABO3fuJDg4mH379pE1a9Y0GbMQ4uO0Wi3Ozs78+uuvzJkzB1NTU7Zu3crjx4+pXLkygYGBBAcHExgYSHx8PFWrVuXChQsYGhqSmJioXPe3b98G3gX1dIEeNzc37O3tefPmDQYGBrJ0k579+/fz66+//s8wR/bs2alSpQqXLl3ixx9/RKPRYGhoSHJysvJ4rlixgiNHjmBubq6ENcWnZWpqStasWdmyZYuyVGWLFi2YP38+nTt3Zvz48VSrVo3IyEgOHDgAkOrvXb5sIIQQQgghhBBCCCEyA/mUV4hMQL+TxO3btzl58iQnT57k6dOnGBoa0qJFCw4cOMDKlSs5cOAAmzdvpkiRIsrSABs3biRbtmzKt7iFECIz0hX3xowZw5gxY8iXLx+NGzemSJEiAERGRnLhwgXgXWFQP8ATFBREnjx5WLRo0QeXtPlUTpw4wenTp/nxxx+VMJGObjlDLy8v8ufPz4ULF9i9ezeVKlXC1NSU8ePH061bN2VfJycnvL29cXFxITk5Oc3GLIRIKTY2NkX4xs7ODgsLC6ZOnUqjRo0IDQ3lxYsXyv5Dhw5l3LhxSoDn/PnzSlBk8+bN9O/fX1nqVKVS8fLlS2bNmsWDBw/45ptv0v8EP2O7du2iRo0ajBkz5i87jmm1WrJkycKECRMoUqQIa9asoXHjxty6dYvExEQA5s2bx6hRo7C0tMTHxwdjY+P0Oo1MSRc8e3/V7gIFCtCiRQt27dpFyZIlGTt2LCYmJuzZs4fw8HD8/Pzo06cPAL/99lu6j1sIIYQQQgghhBBCiPSi0r7/6ZkQ4oui39Y/MDCQBQsWcO3aNQBsbGwYPnw4zZo1w8XF5YPHL1u2jNGjR5MjRw42b96MjY1Nuo1dCCHSg1qtVjowqNVq6tSpg4uLC6NHj8bR0ZFz584xbdo05s2bR+fOnfHx8VEK4ro5VqvVcvLkSfLly0euXLnQarVp9k3/rVu3MmLECK5du8aiRYto0qRJinMB8PX1JSwsjKxZs2JtbU1gYCBdunRJMWYhRPr75ZdfKFasGO7u7ixatChFt5arV6/SunVrYmNjGTx4ML6+vlhaWirbg4KCGDFiBJaWlixatIhbt24xbdo0/vjjD86dO0eePHmAd8sMDRo0iJcvX7Jp0yaANJ2TvhTnz5+nUaNGuLm5MWzYMGrWrKls+9Djo3ttuHfvHk2bNuXMmTPkyJGDXLlykZyczM2bN3FycmLHjh04OzuneC0R/4z+Y5eQkMCzZ8+wsbHh7du3mJub88cff7Br1y7u3LlD/vz5qVu3Lubm5srxI0aMYMaMGURHR1OrVq2MOg0hhBBCCCGEEEIIIdKUhHeEyCR8fX2ZOHEitWvXpl27djx9+pTNmzdz8uRJmjdvTkBAAAUKFFD2j4+PJzg4mMWLF2NgYMDevXtxcnKSoq8QItOaNm0aGo2GcePGsWzZMurUqaNsu3//PgEBAcyZMydVgOf9gm1azZP6xeVt27YxYMAArl27xp07d7Czs0ux75EjR6hcuTKmpqYEBQXh6emZpmMTQvw9ly5dYsiQIWzdupUff/yRqKgoTExMlOv7xo0btGzZkgsXLuDj45MqwDNp0iRCQkJ4/PgxKpWK/Pnzs23bNlxcXFLMRbGxsRQuXBiQ614nIiICX19f5syZQ7t27QDYuXMnZcqUwcrK6oPH6B7Tx48fM2vWLA4dOkRsbCwuLi5UqFCBfv36YW9vL8Gd/0D/sZsyZQqrV6/m+PHjODg4UKJECQICAihduvRHj1+1ahXDhw8nT548rFu3jhw5cqTTyIUQQgghhBBCCCGESF9pt+6DECLdrF69msjISLp3746Pjw8FCxYkOTmZLFmysG/fPn755RccHByU/X/99VcGDhzIrl27qFOnDnPnziVv3rxSmBBCZFqHDx9mwIABfPvtt9jY2ChF76SkJIyNjXFwcGDs2LGoVCpmz56NVqvFz88PNze3VPNiWhXJVSqVUuCvX78+oaGhZMuWLVVwB+C7776jX79+REZG4uTkBEBycnKaLuklhPjf3NzcCAsLw8zMjAULFgCkCPA4OzuzZs0aWrVqRUhICECKAI+XlxdFixbl119/5c2bN/To0YNcuXKleo+mm8N0y+mJdyHMhIQEpUNZ79692bZtGwsXLvzo0rCGhoao1WpsbGwYOnQoKpWKhw8fYmdnpzy28v7439NqtcpjN2TIEMLDwylRogQ9e/bkwYMHrF+/nn379jFnzhxat26d6vgJEyYwe/ZsNBoNixYtIkeOHBJWE0IIIYQQQgghhBCZlnTeEeILoSvKfugDa09PT5YuXcrOnTspVaoUiYmJrF+/Hi8vL4yNjTl69Cg2NjbKfcTFxbFjxw60Wi3169cnR44cUpgQQmR6U6dOZeDAgcC7LjweHh5Ayq4ADx8+xN/fn3nz5tGkSROCg4MpVKhQuo7zQ8u7fOi2ZcuW0bFjR0qXLs2WLVs+GPIRQqS9c+fOER8fT5UqVZTbLl26hL+/P2vWrPlgB57r16/TqlWrj3bg0Sfv0f6e2NhYqlevjo2NDU5OTvz8888MHjyYIUOGpAix/y+yBNmnN3v2bPr370/Pnj0ZOHAg+fPnB8DDw4MZM2ZQvHhxTpw4gbGxMQAHDhxg8ODBXLp0iZIlS7Js2TLy5csn14IQQgghhBBCCCGEyNQkvCPEF2DHjh3Mnz+fyZMnkytXLiXAo9FoSE5OpkyZMmTNmpXjx4/z9u1b1q5di6+vLwYGBhw/fhxbW1sAjh49irGxMWXKlEGj0aBSqVCpVPINViFEpvX+/DZ79mx69eqFo6Mj06dPp3HjxkDqAM/AgQPZv38/58+fJ2fOnOky1vcLxn+ngNy8eXM2bNjAzz//TP369dN6iEKI99y6dQtnZ2dcXFzYvHkzRYoUUbb92wCPbj6SEMnfp+uitmvXLpo2bcqbN29o1qwZ06ZNw9HRUd7rpoOP/b1qtVqaN2/OhQsX2LBhA99++y1v3rxhy5YtDBo0iKxZs3Lw4EFsbGyU5+nu3buMHDmSYsWK0blzZ3LmzCnBHSGEEEIIIYQQQgiR6cnaCkJ8AYKDg9m9ezeGhoaEhoZiZ2enfLhtYmKCq6sr58+f5/bt25w7d+6DwR2An376CWtra7Zt20aWLFmU26WYIYTILN4v0L5fSOzZsyeJiYn0798ff39/DA0NadCggbJ0iqGhIbly5WLq1KkYGhpibW2dLkVf/aLnvXv3yJkzZ4p5+n26sVatWpVTp05RokSJNB2fEOLD8uXLR//+/bl58yZ58+ZNsc3NzY0xY8YAfHAJLRcXF2UJrcmTJ/Py5UsCAwOxsLAAUs9f4uN0HVuio6N58+YNpqam/PLLL5w9e5Y8efJgYGAgYag0dOTIEW7cuEGzZs0wMzNLse3x48ccPHiQpk2b8u2335KcnMz69evx8fHBwMCAAwcOYGNjA6D82yV//vxERUVhaGiIoaEhGo1GgjtCCCGEEEIIIYQQItOTir0QX4CdO3dSr149li5dyoABA3j06BEGBgao1WoAvvnmG27evMnAgQPp27cvhoaGHDt2LEVwJywsjIcPH9K4cWOlwCGEEJmJWq1WQjarVq3C29ubWrVqMWzYMNauXavs5+HhwZQpUzhz5gzDhw9n69atAEqAB8DW1hZra2u0Wm26Bnd27NjBkCFDWLJkCX/VHFFXxOzYsSNnzpzB3t5eGbsQIn1NmTKFFStWYG5uzsyZM9m9e7eyTRfgadWqFQsWLKB3794kJiaiUqmUAE9MTAy2trasX78ejUaTgWfy5Xv16hUDBgwgNDSUp0+f4uPjw5o1a5R5VprOfnpPnjyhS5cu9OrVi02bNvH69etU+xgYGPDs2TNevHhBTEyMEtw5ceKE8u8VrVaLp6cns2bNIjk5GRMTE+W1Tr5oIIQQQgghhBBCCCG+BrJslhCfueTkZIyM3jXJql27Nnv27KFt27aEh4eTK1cuAOLj46lVqxanTp3C2tqaQ4cOUbhwYeU+Vq5cyciRI7GxsWH9+vUpQj1CCJEZ6HfH8fb2Zvr06RgbG2Nra8vdu3d58+YNw4YNY+zYsUpQJiIiAk9PT0qVKsWECROoV69euo9bP7ize/dufH19OXXqFOfOnaNYsWIf3O9Dt0lHCSEy3q5du6hbty6VK1cmKCiIypUrK9v+1xJat2/fxtjYGHt7e7me/wX9x+zZs2fkyJGDlStX0rdvX+zs7Bg7diytWrVSAjzy+H46b9++ZePGjfj7+xMfH09ISAjff/892bJlU/Zp0qQJZ86cwdPTk6ioKIBUHULHjx9PcHAwUVFRtGvXLt3PQwghhBBCCCGEEEKIjCZfYRPiM6dfXNi1axfVq1dn5cqVeHp68vDhQwCyZs2Kv78/xYsX5+3bt8ycOZN9+/Zx7tw5vLy8GDx4MAkJCaxYsQJbW1v5VrcQItPRBXcmTJhAWFgYnTt3ZteuXVy5coUNGzbg5uamFAZ1+vXrx9SpUzl37hy9evVi79696Trm94M7fn5+XLp0iSNHjlCsWDFevnzJL7/8wps3bz5YaNa/TQrRQmS82rVrExAQwKFDhxg2bBgHDx5Utv2vDjyOjo5KBy25nv+399/Lvn37lrdv3wKQI0cOAJo1a8aMGTN49OgRI0eOlA48aSRLliw0bdqU8ePHY2pqipeXF+vXr+fVq1fKPm3btuXly5eMGDGCpKQkTp06lSK4s2LFCubNm0fZsmVp0KBBRpyGEEIIIYQQQgghhBAZTjrvCPEZU6vVSrv4efPm8eDBA86dO0d0dDTGxsa0aNGCKVOmkCtXLl6+fMmhQ4cICAjg+PHjGBoaotFoMDc3p2zZssyfP598+fKluE8hhMhMbt68Se3atXF1dSUyMpKCBQuSnJzM9u3b6datG2ZmZhw7dgwbG5sUx4WEhBAeHs7p06ext7dPl7F+KLhz8eJF9uzZQ4UKFXj+/Dnz589n+fLljBgxgqZNm6bLuIQQ/5x+5y+AMWPGMGrUKKpWrcq4ceOoUqWKsk2/A0/Lli1ZtmwZJiYmGTHsL5b+e9lFixaxb98+Tp48iY2NDS1btqR58+bkyZMHgDdv3rBhwwb69OkjHXjSiK5LaFJSErt27WLQoEG8efOGcePG0axZM8zNzXn+/DlDhgxhxYoVODk5sXTpUnLkyIG9vT1Tpkxh5syZaDQaDhw4gKOjY6prSgghhBBCCCGEEEKIr4GEd4T4Anh5eTFnzhzy589P8+bNOXXqFLGxscTGxuLu7k54eLhScFar1SxcuJAnT56QkJBAlSpVKFOmDNmzZ5fgjhAiUzt48CDVqlVjyZIltG/fnqSkJFavXo2fnx8GBgacOHECGxsb3r59y/3793F2dlaOffnyJebm5ukyT/6d4M6SJUvw8/OjaNGiHD16NE3HI4T4Z/5OsOCvAjy//fYbnp6enDlzhtjYWKytrdN6yJmG/vw5ZMgQIiIiMDMzw9bWlitXrgDQqFEj+vfvT/369YF3XXnWr19Pnz59yJMnDz4+PnTo0EGCO5+A/mvm4cOHOXHiBPv372ft2rV8++23DB06lCZNmmBhYcHjx48ZNWoUS5Ys4cWLF1hZWZGcnMybN28oVqwYa9aswcnJSf69IoQQQgghhBBCCCG+WkYZPQAhxF9btmwZYWFheHh4MHjwYFxcXHj9+jX37t2jR48erFq1CkDpwGNoaEi3bt1S3Y9Go5EPwoUQmVp8fDwA2bJlAyAmJkYJ7hw/flzpuJOQkEDPnj3x9/enatWqAJibm6PVaj+L4M7ixYsZOnQoZcuWZc+ePQBSzBTiM6F/LW7fvp1bt25x/fp1atWqhYuLC66urgD4+/sDMGrUKIYPH54iwFOkSBEiIyOxsrLC2tpauoz8A7r5MzQ0lMmTJ+Pp6UnXrl0pUaIE27dvZ/HixURHR/PkyRNMTEyoWbMmWbJkoUWLFqhUKtq2bcucOXNo1aoVWbNmzeCz+bLpv2Z6e3uzaNEizMzMKFeuHHnz5uXixYsMHz4cgMaNG2NjY8OECRNo06YNa9eu5e7du1haWlKjRg0aNmyIjY2NvNYJIYQQQgghhBBCiK+adN4R4jM3YMAApk2bxunTpylZsmSKwm9iYiK1atXi8OHDtGnThilTpmBvb6+0rxdCiK/JqVOnKFeuHIMHD6ZcuXL4+PgowR1bW1tlv169erFu3Tp+/vlnSpcunW7j+yfBndKlS7Nv3z4AmdOFyEAHDhzg1atXNGjQIEXIxs/Pj7CwMJKTkwEwNDSkWLFijBgxgpYtWyrH63fgGT9+PJUrV05x/xLc+We0Wi2PHz+mQYMGqFQq1q5di6Ojo7L91q1bzJs3j/Hjx9O6dWsWLFigLEuWmJjItm3bKFWqFHnz5s2oU8h0pk6dysCBAxk4cCB9+/alQIECPHz4kPnz5zNjxgy0Wi3BwcFKBx6d95ctk2tBCCGEEEIIIYQQQnzt5NMxIT5zDx8+xNDQMEXhGd5989vExISwsDDs7OzYtGkTgwYN4v79+1LkFUJkWhqN5qO3FStWjMaNGzNlyhQ8PDwwNDRMFdxZuHAh27dvp27duhQpUiTdxg2kCO74+Pjw66+/SnBHiM/YgwcPaN++PY0aNWLbtm1KsGDSpElMnjyZFi1asGnTJhYuXEjPnj05e/YsrVu3ZuHChcp9+Pv7M2rUKA4cOECfPn04fvx4it8hYYV/RqVS8eTJE86cOUOJEiVwdHRErVYrrwP58uWjY8eO1KxZkxUrVrBlyxblWBMTE5o2bUrevHlRq9UZdQqZhlarRa1Ws3nzZhwcHOjfvz8FChRArVaTK1cu+vfvT3BwMMnJyYwcOZKNGzfy8uVL5fj3ly2Ta0EIIYQQQgghhBBCfO3kEzIhPnO6AsPWrVvRaDTKB926lvKFCxfG2toaIyMjVq5cydixY5GGWkKIzEitVivFvd9++42LFy9y584d5TYTExPat2+Po6MjcXFxDBgwIEVwZ968eQQGBpIlSxaCg4PJli1bus+XT58+Zfbs2fz222/s2rVLgjtCfMbs7e0ZPnw4Tk5OtGnThs2bNwPvlstq1aoVISEhNGrUiE6dOjF9+nRmzpwJQPfu3VOERvz9/fHy8uLBgwc4OztnxKlkKmZmZlhYWPDixQvg3Xti/SBIgQIF6Ny5MwAXL1784H3I0kz/nVarJT4+nnPnzmFjY4OTk1OKZa/MzMxo3Lgx7du359q1awQHB7Np0yZevXqVwSMXQgghhBBCCCGEEOLzJOEdIT5TuoJy27ZtsbW1JSoqirt37yrbdd8Yzp49O3Z2dkyaNInOnTvj7e2d6pusQgjxJbp8+TK///678rOuIDh69Ghq1apF2bJlKVWqFMHBwfz2228AtGvXjgEDBmBvb4+Pjw/Vq1enT58+VKtWjSFDhqDVatm6dSt58uRBrVanyXz5V92BrK2t6dq1K0eOHKFChQo8e/aMJUuWSHBHiM+IfleW3r174+/vj6WlJR06dCA6Oppnz57RunVrnJyc0Gg0yv49e/ZkypQpaDQaZs+ezbNnz5RrPyQkhKtXr2JnZ/fBOUL8PVqtFiMjI+zt7VmzZg3r168H3nVx0Wq1yjJmZcqUAZCgSBoyMDAgR44clChRgocPH/Lo0SMMDQ2V60Gr1WJhYcFPP/2EhYUFV69epWvXruzcuTODRy6EEEIIIYQQQgghxOdJwjtCfKZ0BeVChQrRqlUrTp06RZcuXbh06RJv3rzB0NAQjUbDqlWruHTpEkWKFGHBggW4uLgohQshhPhSxcbG4ubmxqhRo1IEeIYPH87o0aPJlSsX33//PTly5GDYsGEEBgZy+vRpAAYMGMCMGTPo1q0bp0+fZtGiRcTFxdGlSxcOHDiAs7Nziu4An5J+d6Bbt25x/fp1Xr16lWI5kHr16lGsWDFevnzJ0qVL8fHxoUyZMhLcEeIzsHv3brp168Yff/yh3Na1a1cCAwOxsLCgY8eOnDlzhjdv3gDvAgy692QA/fv3p27duhw8eJC4uLgU176lpSVarVaWB/oPVCoVDg4ODB06FIDg4GBl7lSpVMrcuWnTJgwMDChdunSGjTWz02q1aDQaSpYsyaNHj/Dy8iI5OVkJ8Oi+iGBjY4OlpSUeHh589913lC1bNoNHLoQQQgghhBBCCCHE50k+ORYiHb3/Tev/9c1rrVZL9uzZGTFiBK1atWLv3r24u7vj7+/P7t27CQgIYMSIEdja2uLm5qYcJ0VfIcSXzsDAgLZt2xIdHa10rHjy5Anbtm2jT58+rF+/npUrV7J+/Xr69OnDsmXLCA4O5tSpUwA0a9aMGTNmcPXqVS5fvszp06cJDQ3FwcEhzYI7Go1Gud9x48ZRs2ZNihcvTpkyZZg3b16K7mnwbgmtxYsXU758efbu3QtIcEeIjDZq1CgWL17MihUrgD+78Pz444+MGjWKIkWKoFarOXjwoLJsE7ybs3T7urm58eTJE2JjY1Pdv3RH/G90gZAuXbowePBgjh49Sv/+/Vm2bBlJSUkkJSWxdOlS5syZQ9GiRalZs2YGjzjzUqlUGBgY4O3tTf78+Vm2bBnDhg1TAjy6kNq6devImjUrnTt3TtH5TgghhBBCCCGEEEIIkZJKq/sEVAiRbho2bMjgwYOpW7cuGo3mL7+Brdt+//59pk6dyurVq1N0oXBzc2Pz5s04Ozv/z/sSQogvydWrVwkKCmL+/Pn06dOH6tWr07lzZ3bu3EnlypWV/f744w/GjRvH1KlTadOmDX5+fpQqVQp4V+hN72L50KFDCQ4OpnDhwuTNm5fz58/z/PlzevfujaenJ66ursq+x48fp3z58oAEd4T4HGg0GgICAhg0aBDW1tYkJCSQNWtWZfucOXMIDg7m7t27LFiwAHd3dyDlXNO5c2c2b97M3r17KVasWIacx5fqn8zZT548ITQ0lKCgIACKFi1KYmIi9+7dw8bGhj179sj743/onz5WujDsuXPnaNKkCXfv3qVJkyb4+Phga2vLjh07mD59OpaWluzcuRNzc/M0HL0QQgghhBBCCCGEEF82Ce8Ikc4OHDhAo0aNSE5OZtu2bVSrVu1/flCuK2S8efOGFy9esGnTJpKSkrC1taV69erkzJkzzTpJCCFERrpy5QoTJkxg4cKF1KxZk5cvX3L06FEgZdjl8ePHBAYGKgGe4cOHU7x48XQZo/78e/v2bRo0aECtWrXw8fHB0dGR48ePExISQkxMDH369GHIkCEpAjzv34cQImMkJSVhbGys/DxgwACuX7/OggULsLa2Vm6fP38+AQEBxMXFMXXqVBo0aICDgwMA69evp2fPnhQpUoRNmzZhaWmZ7ufxpdIP7ty7d4/cuXP/rePWrl3L0qVLOX/+PLly5aJs2bL4+Pikaae1zG7s2LG0aNGCb7/99n/uq3vefvvtN9q1a8e5c+cwMjLC0NCQt2/f4uLiwq5du3B2ds6QQK0QQgghhBBCCCGEEF8KCe8IkQHWrFnD0KFDuXv3Lhs3bqRWrVr/6f7kG8VCiC/Z+3PY+wX0GzduMG7cOObOnQvAhg0baNKkSapjdQGeyMhIateuTVhYGN988026ncfevXu5d+8eP/30E3v27KFcuXLAu8LmrVu38PX1ZdWqVfTt25fBgwenCvAIIT4vRYsW5dKlS3Tp0oXQ0NBUAZ5Ro0bx6NEjypcvT40aNTh9+jRXr14lISGBffv24eTkJO/R/gUvLy/u3bvHkiVLAP7W46fVaklMTCRLlizKYy7BnX9n5syZ9OnTB3d3d2WpuP9F95g/f/6cNWvWcOrUKV6+fEnhwoXp1q0b9vb28nwIIYQQQgghhBBCCPE/SHhHiHSkX8CJiYnBw8ODhw8f8scff2BlZfWPizvy7VUhxJdOfx6LjY2lcOHCyrapU6dStGhRateuTWxsLJGRkURERNC6dWvGjh2r7Pt+gMfX15etW7dy/vx5cubMmS7nsWDBArp160bjxo15/fo1u3btAlJ21Ll16xY+Pj4S4BHiM/Sh91QajYZatWqxf/9+OnbsSHh4eIoAz7x585g4cSKxsbEUKFCAevXqkSdPHrp06ULu3LklrPAvvH79mpo1a3Lr1i0uXLiAjY3N3zpO//mT98f/nbe3N6GhobRt25aRI0f+rSDsX/29y7UghBBCCCGEEEIIIcT/ZpTRAxDia2JgYKAUFFq2bElycjI5cuT418VlKUwIIb50unmsQoUKPHjwgPXr11OyZEn69+9PZGQk06dPp3r16hQuXJh+/fqRkJDA3LlzsbKywsfHh/z582NgYKAEeGxsbJg4cSKhoaHkyJEj3bpeFC1alGbNmrFhwwYADh8+TKVKlTA0NFTm/Xz58hESEgLA7NmzefHiBWPHjsXJySnNxyeE+Dj9eeLRo0dYWVlhbGyMgYEBu3fvpnr16koXGP0AT7du3VCr1URERHDhwgWWLl2qdNySsMK/ky1bNlq2bMnQoUMJDw9nzJgxf2sO139PLO+P/z3d3+3EiRNRqVRMmjSJhIQElixZgrm5+V8eq/t7/1CQSq4FIYQQQgghhBBCCCH+NwnvCJHOVCqV8sG4u7u7crt8S1gI8TUrVaoUs2bNwsPDg7x58xIdHc2QIUNo1KgRRkbv3q4UKlQIHx8ftFotc+bMAfhggEdXWNdqtWke3NHN3eXKlWPkyJGYm5uzbNkylixZgqOjI46OjqhUqhQBnokTJ/L8+XP27NmDpaVlmo5PCPHX9EM2M2fOZMuWLXz77beMHj0aACMjI/bt2/fRAE/Pnj1JTExk3rx5ODg4KPcrYYV/r1evXkRFRbFnzx50TWLlfXL6MDQ0VK6JkJAQ3r59S5UqVf5ncEefBKmEEEIIIYQQQgghhPh3ZNksIdLYX33zOr06QgghxOdKfx4cN24cI0eOBKBDhw7MnDmTbNmyKcVbXRHwypUrBAcHM2/ePHr27KkEeNJ7vB9y6tQpxo4dy+bNm/Hz86NPnz7kzp0bSFl8vnfvHlmyZCFnzpxSlBYig+hfzz4+PsyYMQNnZ2fGjRtHs2bNAEhOTsbIyAiNRkP16tU5dOjQB5fQevXqFWZmZtJx5296fy7V/ax7/EaMGMH48eOZPXs23bt3z8CRfh30nw/d37wQQgghhBBCCCGEECJ9yadyQqQhjUajFHBmz57N8ePHefbsGfXq1aNx48bkzp1bAjxCiK+afrFWP8By/vx5fv/9d4oVKwak/PZ+wYIF8fX1BWDhwoW8ePGCoKCgNF9+Sr8of/nyZe7fv8+zZ88oUaIE9vb2mJqaUqZMGUaOHIlGoyE4OBhACfCoVCplztcFeuQ1QIiMo7v2Ro0axeTJk+nTpw+9evWiaNGiyj5GRkZKFy/9Djy6pYVsbGwAMDMzA6Tjzt+le+wvXbqEk5MT2bJlA/58/Jo2bcrEiRNZt24dbdu2xczMTEKOaUT/tW3Tpk1cvnyZxo0bU7hw4QwemRBCCCGEEEIIIYQQXxfpvCNEOvDy8iIsLEwpVOi+vT179mwKFCggxVshxFcvKSmJDRs2cPPmTa5evUpUVBRlypQhMjKS8uXLK/vpz5e///47vr6+HDt2jHPnzqXogvGp6f/ewMBA5syZw61btwAwNzenXbt2dOnShUqVKgHvOvCMHj2arVu34uvrS9++fVMsqSOE+Dzs27ePFi1a0KhRI8aOHYuLi4uy7caNG7x9+5bcuXNjYWEBvJsLateuzb59++jfvz/h4eESKvkH9DuNjR07loCAAOrWrcuwYcMoXLgw9vb2yr4//fQTCxcu5MCBAyleB8Sno//aFhAQQEREBNbW1kRERFC/fv0MHp0QQgghhBBCCCGEEF8XSQsIkQY0Go3y3ytWrGDWrFn07t2bPXv2cOHCBVq1asW+ffto3bo1ly9fxsDAIMUxQgiR2emyw7r/NzY2pnHjxgwYMIDp06fj4+PDqVOn8PDw4Pjx48CfRUatVsuzZ8/Inz8/4eHhSnAnLfPI+kvrjBo1ikKFChEZGcmgQYMoXrw4s2fPpl+/fuzatQuAMmXKMGrUKBo0aEBYWBghISE8fPgwzcYnhPh3YmNjefbsGd27d8fFxQW1Ws2TJ08YNWoUVatWpVixYpQrV46DBw8C7+aCXbt20bx5cwYNGiTBnb/w/nvbxMRE5fG6dOkSLi4u1K1bl/3791O7dm1atmxJVFQUjx8/BqBNmzYkJSUxceJEXr58me7jz+x0HaUAfH19GTduHE2bNmXJkiUS3BFCCCGEEEIIIYQQIgNIeEeIT0z/g/C3b9/y6tUrChcuzODBg6latSpubm5ER0fTt29fzp8/T5s2bSTAI4T4qqjV6hQF78TERABMTU2V+TMoKAg/Pz9OnTpF3759OXLkiBLc2bhxIwMGDODAgQPkzZsXa2trNBrNJymi/1UAaMOGDUybNo3u3bsza9Ys+vTpw6RJk1i6dCmDBg3i7NmzjBgxglOnTgFQunRpxowZQ5kyZVi7di2mpqb/eXxCiE/rxYsXwLsuO1qtlnnz5tG0aVOlC0+9evW4fPkyP/74I0+ePAHeBXhiYmJwdnYmOTk5I4f/WdPN5/7+/ly+fBkTExPg3VKC48ePp0mTJmzbto2tW7fi7e1NbGwsffv2pXr16vj4+FCsWDEqVKjAxYsXiY+PB1IHgsS/p3vNnD9/PhEREXh4eDBq1CgqVKiQYj/da7QQQgghhBBCCCGEECJtSXhHiE9M90F4v379qFChAlFRUVSuXJkCBQqg1WpJSkoCICIigr59+3LhwoUUAR61Wp2RwxdCiDSlVqsxNDQEYPHixfz444/Url0bd3d3jh07phRoAcaPH4+fnx+nT5+mZ8+ebN++nWXLluHn50d0dDQFChRQ9v1USw/q5ugPFeRPnz6NWq2mV69eODs7KyEkJycnJk6cSO/evTl27Bjbt29XjilZsiQzZszgxIkTZM+ePU27AwkhPk7/2tP/74oVK5IjRw66d++OtbU1vXr14v79+2zcuJGYmBg2bdpEp06duHbtGmfOnEl1v0ZGRuky/i/VmDFjCAwMxM/Pj1evXuHn58fMmTOxsbFR5tvq1aszYcIEdu/ezaxZs9BqtUyaNImSJUty8+ZNYmNjCQ8PBz7dXC/eSUxMZMeOHZibm9OzZ0+cnZ2VbbNmzaJDhw7UqFGD5cuXZ9wghRBCCCGEEEIIIYT4Sqi0UkUSIk20adOGNWvWYGVlRZcuXQgLC1OK1vrF6379+jF9+nRKlSrFkiVLcHNzy+CRCyFE2tAtewXg7e1NWFgY5ubm5M2bl3v37mFkZISXlxedO3fGwcFBOc7f35/AwEAATExMcHR0ZPv27bi4uKS4z/9q3759dOzYkX379uHq6qrcrpuzmzZtyubNmzl//jzffvstWq02Rbefs2fP0qhRI7Jly8apU6fInj37R89fCJF+9N93Qepr8eeff2bZsmUkJydTrFgx+vTpg5WVlbK9Xbt2HDt2jH379uHo6JiuY88M2rVrx8qVK3F2dubGjRv4+/vTrVs38uXLB5BqLo2Pj2fnzp2sWbOGdevWkZiYiJubGxs3blSOEZ/G69evqVGjBnFxcVy5cgWAHTt2MGPGDNatW4e1tTXPnj1Do9GwYMECOnfunMEjFkIIIYQQQgghhBAi85IKkhCfmC4PFx0dTa9evYiLi2P+/PlcuHABQ0NDtFqtEuCBdx14+vfvz5kzZ/D09EStVktnBiFEpqQrlo8ZM4YpU6bQq1cvDh48yC+//MKaNWt4+vQp4eHhREVF8eDBA+W4MWPGsGDBAry9vRk0aBD79+/HxcUFtVr9ScMw27dv5+7duwQHB6PVapX/6Yr+5cuXB+DSpUtA6uVbSpYsSbFixfjjjz94/PjxR89fCJF+NBqNcg3PnTuXHj160Lp1a+bNm8erV68AaNiwIVFRUSxfvhw/P78UwZ1Vq1Zx4MABypYtS86cOTPkHL4kJ0+eZNOmTbx9+1a5bfny5eTJk4e7d+/i6upKgwYNyJcvn/KeVz+4o1arsbCwoEWLFixZsoQNGzbQr18/Lly4wIEDBzLilDKNDy05ptVqKVKkCL///jtNmjShYcOGtGnThoMHDxIaGsr+/ftZs2YNhoaGhIWF8fz5c/l3ihBCCCGEEEIIIYQQaUQ67wjxH+mKDrpLSaVS8fbtW7JkyQKAh4cHM2bMoGrVqkRFReHm5qYco/9N8BEjRtCzZ0+cnJwy7FyEECKt7dy5kx49elC7dm2GDh1KgQIFSEhIoHTp0sTFxWFhYcG9e/fw8vKid+/eKTrw6Hu/k8anoNVqWb58OQ0bNsTKyornz5+TPXt2kpKSMDY2ZtOmTbRo0QJbW1sOHTqkBIhUKpUSzKlRowYPHz7k8OHDKQIAQoiM5e3tTWhoKMbGxspyTe3atcPLy4tSpUp98JjIyEjCw8PRarXs3buXvHnzpgqbiD89evSIkiVLEhcXx8GDBylTpgxqtZoTJ05QrVo1cufOza1bt2jWrBnh4eE4Ozt/tCOZ/u1Hjx6lXr16lChRgs2bN2NhYSHPwT+k/5p54MABnj9/TqVKlbC2tubatWv07duX48ePY2lpScWKFZk4cWKKLlMuLi4UL16c9evXZ9QpCCGEEEIIIYQQQgiR6clXwIX4D3RFW3hXZIiLiwNIUVCIjIyke/fuHDhwAA8PDy5duqSEfQwNDUlOTgYgMDAQJycn5WchhMhskpOTOXLkCGq1mp49e1KgQAFevnxJ2bJliYuLIyQkhJkzZ+Lk5ERUVBRRUVHcv3//g/f1qYM7uvm8ffv2WFlZ4e3tjZ2dHbdu3cLY2BiAJk2a0LVrVx48eECbNm24evUqhoaGSoF5zZo1nD9/nvLly2NmZvZJxyeE+Gf0v5+wdu1aZs2aRc+ePTlx4gR79+6le/fuREdH4+/vz+nTp5V9k5OT2bdvH3Xr1iUgIIBs2bKxa9cu8ubNm+J9n0jNzMyMESNG0LFjRwoWLAi86zhWunRpDh06xOnTp3F3d2fDhg3079+fmzdvYmBggEajSdUVxsDAQOlSWbFiRWrWrMm1a9d4+/atPAf/kH5wZ+zYsbRu3Zrhw4dz5coV1Go1rq6uLFu2jAMHDrB3714WLVqkBHfUajVz5szhjz/+oEyZMmg0Gum8I4QQQgghhBBCCCFEGjHK6AEI8aXS/yB89uzZbN68maNHj+Ls7EyxYsUYPHgwbm5uynatVsu8efPw8PAgMjJS6cBjZJTyMnz/ZyGEyCyMjIwoXLgwo0ePpmLFirx58wZ3d3cePHhASEgInTp1QqVSUbZsWZYsWcLs2bN5+fIlfn5+2NrapssYdV01Xr9+TVJSEtWqVWP//v3ky5cPgFmzZvH48WPWrVvHd999R9++fXFzc+PMmTNER0djZmbGuHHjMDExkQ4dQmQQ/WsvISGBM2fO4OLigre3NwUKFADedRJxcHAgKCgIrVbLmDFjKF26NEZGRty/f5/Lly/z448/4u3tTa5cudKk21dmY2ZmRvfu3TEwMMDY2JhJkybh4uJCs2bNKFeuHAArVqwgOTmZmJgY4N3ysfpdJ8+ePYuxsTFFixZVHu9nz57x5s0bjIyMePr0abq9HmQG+ks/ent7Ex4eTqtWrejTpw8VKlQA3n0BwdraGmtr61THL1u2jLCwMFxdXenVq5cs/yiEEEIIIYQQQgghRBqSZbOE+Bf0i0JDhgwhPDwcBwcHbG1tiYuL49atW1hYWLBq1Srq16+vHNejRw/mzZtHnTp1CA0NpVixYhl1CkIIkabeD67oL4GiW4YqJiaGrl278uOPPxIcHIypqSkAS5cuJTw8nJcvX/LmzRvOnj1L9uzZP9nYPrZMC7wrHJcsWRKtVou/vz/jxo0jT548HDp0SAnwwLu5Pzo6mjt37gDvitbFixdn2bJlODk5SaFfiM+Ar68vZ8+excLCguLFi+Pv709ycrISlL5//z5RUVFMmDCBevXqMXr0aMqUKQPAnTt3sLW1JUuWLH85Z3zt9uzZw4kTJ/Dx8QH+nF/PnDlD2bJlyZ8/P5MnT6Zu3bqYmJgox7Vu3ZqYmBgaN27M7Nmzsbe3Z+PGjQwePJhatWoxbdo0TExMSExMZPXq1XTs2JEePXowa9asjDrVL1pERAReXl707dsXT09PnJ2dP7ifrgvSvXv3CAkJYd26dZiYmLBnzx55bRNCCCGEEEIIIYQQIo3Jp9BC/Au6gvS0adOYOnUqAwcO5MCBA5w5c4YLFy4wZMgQ4uPjadGiBXv27FGOmzNnDr169WLnzp2MHTtWlsgSQmRKH1paRr/wrVuG6uLFi8THx9OmTRsluAOwevVqLCws2LRpE8ePHyd79uyfdJkO3Vg8PDw4ePCgcnu/fv2oUaMG586dQ6VSMWbMGIYOHcrdu3epXLkyt27dUvYNDQ1l8+bNrFq1ioiICNatW8fmzZuluCnEZyIuLo6HDx+yZ88eYmJiiI2NRa1WY2RkpMwnDg4O9O7dm6FDh7Jjxw7Gjh3LkSNHAMibNy9ZsmRBq9VKcOcDtFotz58/p2PHjvj5+TFp0iTgz/m1YMGCLFiwgISEBLy8vNi+fTuJiYnK8atXr6ZVq1Zs3ryZunXr0rt3bwYMGMDTp0/x9fVVgj4mJiY8e/aMvn37KsEd+e7JP/P06VNiYmJwdnamV69eKYI7K1asYOTIkfTu3Ztff/0VAwMD4uPjcXd3Z/bs2VSuXJkDBw7Ia5sQQgghhBBCCCGEEOlA1ucR4n/42LInCQkJrFu3DhcXF3r37o2LiwsajQYLCwsmTpyIvb093t7etG/fnkOHDuHq6grAjBkzsLCwoE+fPrJElhAi09FoNEpxLyIiguPHj/P8+XM6depEhQoVcHR0VObVLFmyAO+63ZQvXx4TExNWrVrFxYsX6dixI66urqhUqjTperFq1SpmzJjByZMnWbFiBZMnT2b69OkMHjwYOzs74F1QMzAwEIAJEyZQuXLlFB14ihUrlqqDmv75CyEyjpWVFSNHjsTGxoa5c+fy66+/cuHCBUqWLIlKpVLmIV2Ax9DQkFGjRpE9e3bKli2rhAxl6bsPU6lUZM+enYULF9KjRw98fHzQaDRKBx5zc3NatmyJSqXCx8cHb29vAOrVq6cEc6Kjo+nduzeLFy/mzp07FCpUSOnwot8hqW/fvsrvlS5I/9zbt2+5fPkypUqVonDhwgAcOXKEGTNmsGTJEqXD0dKlSzl06BDFixdn6dKlxMbGUrVqVSwsLCS4I4QQQgghhBBCCCFEOpBls4T4C8eOHePatWs0bdoUc3PzFNvu379PkSJFaNCgAStXrlS+BaxfuO3duzezZs0iNDSUQYMG8fbtW6VYDaQoTAghRGbi4+PDpEmTyJo1K0lJSahUKpo3b86IESOUwMu5c+fo3Lkz9+7do2XLlrx8+ZLdu3djamrKwYMHyZMnT5qN79GjRyxbtowxY8ZgYmLCo0eP8PLyYvDgwdjb2wMoxUqtVsuIESOYMGFCiiW0dMt/CSEy1seC1gBXrlwhIiKCyMhIvv/+e8LCwnByckp13N27d1mxYgXu7u44Ojqm29i/ZLrHb+/evXTo0IH79+8TFBSkBHgAXr16xdq1a/Hx8SF79uxMnDgxRYAH4Pjx4xgaGuLi4oK1tXWKoIj+c/RXz7P4uHv37lG7dm1iY2MZNmwYt2/fZvv27bx9+5Y+ffrQoEEDduzYQWBgIJ06dWLmzJkpuuFJYEoIIYQQQgghhBBCiPQh4R0hPuLp06dUrFiRq1evsn//fqpUqZJi+7179yhatCh2dnbs3LkzRaFHV3Q4c+YM3333HS1btmTZsmXpfQpCCJEhduzYQefOnWnVqhVdunTBxMSEsLAwFi9ezHfffUdERASlSpUiKSmJDRs2EBUVxa5du7CwsKBEiRIsWbKEfPnypcs3/atUqcLhw4ext7dn7ty5NGzYEPizSPyhAI+TkxO7d+/GxcUlTccmhPjf9OeJhw8f8vr1axISEvjmm2+Ufa5cucK0adOIjIykZcuWTJo06YMBHl1IQbqM/HP/NcCjI0GRf++vHrvt27fTvn17nj59Svbs2SlfvjxTp07FxcUFExMTXr9+Tc6cOenevTsRERHpPHIhhBBCCCGEEEIIIQTIsllCfJSZmRkBAQEcPXpU6RKh32Uhd+7c1KtXj/Xr17Njxw46deqUaokFOzs7jIyMpDODECJTe78bwr1797CyssLT05NChQoBsHDhQnLkyMG0adPo168fU6dOpUyZMjRv3pymTZty/PhxcuXKhZ2dHdmzZ0/z4rlGo+Hs2bPcvn2batWqcerUKQICArC1taV06dJKAdTQ0FAZS2BgIAYGBowbNw53d3eOHTuGSqWSThBCZBD9boeTJk0iOjqa3377DbVaTe3atWnVqhWdOnWiYMGCeHp6AhAZGans7+TklGIJLf3rXvw9useuRo0aLF26lA4dOuDn5wegBHjMzMxo0aKFcpu3tzcGBgbUqVMnVYBHgjv/jv5rZmxsLM+ePePZs2eUKFECKysr6tWrx65du7h//z7W1tYUL15c6a6jVquZPXs2Wq2W4sWLA9LlSAghhBBCCCGEEEKIjCCdd4T4C8nJyQAYGRkxfvx47O3t+eGHH8iWLRsAW7ZsoWfPnmTNmpVZs2bx3XffkTVrVuBdQWnGjBkMGjSI4OBgBg0aJB+ECyEyHf2CYUJCAsbGxoSFhXHo0CHWr1+PVqtFrVYrSwQOHDiQqVOnUrFiRSIiIihdujTw4e4X6eHMmTNky5aNn3/+mYCAAAoWLMjMmTMpU6ZMirHod+AJCgqiffv2SucOIUT6058zvL29CQ0NpWzZslSrVo03b96wYsUKDAwMcHd3Z9q0aahUKq5fv87kyZOJjIzE3d2dcePG4erqmsFn8mXT/6fk311Ca9iwYbx584Zly5ZRp06djBh2pqL/mhkYGMiiRYu4ceMGycnJFChQQFmy0tLSMtWxWq2WlStXMnbsWMzMzNiyZQs2NjbpfQpCCCGEEEIIIYQQQggkvCPER+kXhX799Ve+++47zMzMCAsLo2nTppiZmfHq1SvCwsIIDg7GxsaG3r178/3335M/f36WLFnCxIkTlUKGnZ1dBp+REEJ8WvoFw9DQULZt28azZ8+wtrYGYOvWrUphV79Dhi7AU6VKFUJDQylXrlyaj/X9Tj7vhymfPHnC3LlzGTduHAUKFGDmzJmUKlVKCezs3LkTtVpNgwYNlGOSk5OVUJIQImMsXryYnj170r17dwYOHEjBggUBmDx5MkOGDKF8+fLs2rULMzMzAK5fv87UqVOZMmUKPXv2ZPr06dJp5x/4WLhSf479XwGeZcuWMWPGDDZv3oyDg0O6jT2z8/X1ZeLEidStW5dGjRphYWFBeHg4Fy9epEKFCuzYsQNzc3Nl/6dPnzJp0iSWLl2KVqvlwIEDODk5ydJlQgghhBBCCCGEEEJkEAnvCEHqQsTbt2/JkiUL8K4YodFo2Lx5M0OHDiU+Pp6JEyfStGlTzM3NefHiBVFRUcydO5crV65gZGSEpaUlL168wMnJiR07duDs7CwfhAshMi0/Pz9CQkKwsbHBwsKC69evA7B8+XLatm0LvAvL6Ad4hgwZwuTJk2natCmrV69O0+UF9YvKMTExnD17lqdPn5I7d27atGmDg4MD5ubmPH78mPnz5xMYGEj+/PmJioqiWLFi7N+/nwEDBqBWqzl37hympqYynwuRwXT/hOncuTO7du1i586dfPPNN6jValauXElAQADJycmcPHmSnDlzpnhvd+XKFRYvXkyPHj3Ily9fRp7GF0V/Lj106BDXr1/nwYMH1KlTh+LFi6eYF/8qwPP69WsAsmXLluZLJH4t1q1bR4cOHejQoQM+Pj4UKFAAgKVLl9KpUydcXV05c+YMFhYWwLvlLatUqcLt27epX78+UVFR5M2bV54PIYQQQgghhBBCCCEykIR3hNCzbt06GjVqhImJCQBeXl7kz5+fPn36kJiYyM8//4y3tzevX79m4sSJNGnSBAsLCxISErh69SqLFy/m119/xdTUlHLlytGlSxfs7e3lg3AhRKaiv4TU6dOnad68OS1btuSnn37C1dWVmTNnMnjwYLJly8aSJUto3rw5kDrAM2rUKLp06YKLi0u6jNvLy4uwsLAUtzk7O/PDDz8waNAgbG1tefLkCfPnz2fChAlkz56dAgUKcOnSJdRqNQcPHpQldoT4jLx48YIyZcqQL18+du3axdu3b1m3bh0+Pj4YGBhw/PhxbG1tAYiNjeX169eUKlUK+HMek/dof49+CN3f35+IiAiePXsGgIGBAYMHD6ZLly4ULVpUOUY/wBMSEoKXl1dGDP2r4OvrS2RkJAcPHqRkyZKo1WpWrFiBv78/Go1GCbG9efMGExMTDAwM2LJlC48fP6ZZs2bkyJFDrgUhhBBCCCGEEEIIITKYhHeE+H9t27YlOjqayMhI+vTpg7e3N6GhoQwbNoyhQ4diZmbG27dv2bp1a4oAj64Dj05SUlKKDhLyQbgQIrM6ffo0ZmZmNGjQgA0bNlCsWDFl2+zZs+nXrx/ZsmVj/vz5Hw3wQNotP6W/NFZkZCS+vr506tSJrl27YmFhwZYtW5gzZw6xsbF0796dCRMmYGNjQ1xcHBs2bCA0NJT79+9TvHhx5s+fT758+WSpLCE+I8nJyZQrVw5LS0v27dtHdHQ0Xl5eqYI7ycnJlChRgoYNGxIcHCzvy/4h/bnUz89PCbD36tULa2trpk2bRkxMDO3bt8fT05MSJUoox+7du5cuXbpw+/ZtIiIi6Nu3b0adRqaVlJREw4YNuX79Or///juJiYmsXbv2gyG23377jYsXL9K8eXOMjIyU51Y6hAohhBBCCCGEEEIIkfGk+iTE//vpp584fPgwAQEBrFixggMHDjB8+HB69OiBmZkZWq2WLFmy0KBBAwC8vb3x9vYG4PvvvydbtmwAqZZ+kQKRECIzCgsLw8vLizJlylC4cGEluKMLt/Ts2RMADw8PunbtCkDz5s1RqVSpCoSfMgzzsUJkbGws5cqVw9fXF2dnZwAKFChAvXr16NatG/Pnz6dAgQIMGjQIKysrunTpQuvWrbl37x65c+fGzMwMtVotwR0hPhNqtRqtVkupUqVYsGAB/fv3Z8OGDRgYGHDkyBElrADv5quHDx9SuHBhCSj8C7rgzsKFC5kzZw4eHh54eHhQuHBhXr58yenTp0lOTmbBggW8efMGX19fihcvDkCNGjWYM2cOfn5+NGnSJCNPI9MyNjYme/bsvH79moSEBLZu3frB4I5Wq6Vz5844OjrSqFEjjIyMlOdWrgshhBBCCCGEEEIIITKefEonBO8+zK5duzY7d+7k5cuXHD58mJo1a9KzZ0+cnJxQq9XKh9u6AM/EiRPJli0bPj4+bNy4kVevXmXwWQghRPopWrQo33zzDefPn+fatWs8fPgQeFcA1Gg0APTs2ZPIyEhev37NTz/9xMqVK4E/C8Gf0osXL1Lct64QOXDgQNzd3Tl9+jQtW7bE2dkZrVaLVqvF2NiYYsWKMXXqVBwdHVm5ciXJycnAu9cFMzMzChYsqAQ4JYwpRPpTq9UfvN3Q0BBjY2Pat28PvOuulZSUxG+//Ya9vT3wbqmn6OhoZs6cSbFixWjZsmWazD9fgydPnrBu3TqcnJzo0aMHhQsX5sWLF5QrV45nz54xefJk2rVrx4oVKwgLC+PMmTPKsXXr1uXw4cPky5fvo8+n+N8+1DBX95pVq1YtHj58SLt27Rg0aBCGhoYcPnw4RYht6tSp3Lp1iwoVKpAlS5Z0G7cQQgghhBBCCCGEEOLvkfCOEPxZ7D127Bhv3rzB2NiY06dPs2XLFmXZK10xGlIGeCwsLOjSpQs7d+7MqOELIUS6q1+/PlOmTKFIkSJcvXqVyMhI4M/QjH6AZ/r06Tx+/JjRo0eTkJDwwQLkf3HgwAFatmzJtm3bUtz+5MkToqOjWbduHWfPnuXOnTvAu2KnfgG/WLFi1KlTh7Nnzypz+fsFfin4C5G+1Gp1iqVHY2JiGDt2LMHBwezYsUPZr06dOkyfPh2AV69esWbNGu7cucMff/zB6NGj8fb25u3btyxatIicOXOmeD8n/j61Ws2rV6+UrjqvX7+mfv36PHnyhODgYHr16oWnpyeWlpasWrWKadOmcerUKeV4ExMTQDpS/lv6XyTQp+sGV7t2bZycnNiwYQOvXr3i0qVLODg4AO9CP9HR0URERODq6krXrl3leRBCCCGEEEIIIYQQ4jMkaz8IoadQoUIsW7YMU1NT+vbty/Dhw3n79i39+/dXukmoVCpUKhVZsmShYcOGvH37lqlTp1K2bNmMHr4QQqQL3dJUtWvXJjw8nH79+hEYGIiZmRm+vr7KfKlbuqp79+5kzZqVqlWrkjVr1k86Fo1Gw+HDh9m9ezdVq1alTp06SlEyZ86c7Nq1i/bt23P27FmOHTsGvFtiRBcKUKvVmJub07BhQ+bOncubN28+6fiEEH/fjh072LVrF0FBQSnCBd7e3oSGhqbYd/To0YwcORKA3r17Y2hoiIeHBx07dsTc3JzExES0Wi2lS5dmxYoVODo6pggDiY/70ONkZ2fHggULyJUrFxqNhqCgIM6ePUtAQACtW7fG2NiYcuXK8e2333L79m0WLFhA1qxZKVGiRIrlmcQ/p9FolOdj/vz5HDp0iKSkJMqXL4+HhwcARYoUYcGCBdSrV48nT54wcuRImjRpQs6cOZk3bx7R0dFoNBpWrFiBra1tqqUlhRBCCCGEEEIIIYQQGU+l/dRffxfiC6ErPr/vzZs3mJqacvjwYVq1akViYiIBAQH069cvxYfcN2/eJG/evGi1WpKTkzE1NZWikBDiq6E/h+7Zs4d+/fpx6dIlJkyYgK+vL/Bn9x39uTMt5snnz5+ze/duatasSY4cObhx4wbOzs7K9itXrtCmTRvOnz9Ply5dmD9/PgBJSUkYGxuj1Wrx8fEhNDSUHTt2ULt27U86PiHE//by5Utq1arFyZMn8fPzY/z48QBERUUxePBg3N3d6dChAw8fPiQoKIhff/2VwYMHExISoswxx44d49ChQ5w/f56cOXNSqVIlatWqhZWVlbxH+xciIyNxcXGhUaNGqbbVr1+fa9eucenSJaX7S1JSEkWKFKFLly4YGRnRsWNH8uXLl97DzrS8vLwICwtLcVubNm2YMGECzs7OGBgYcPDgQfr27cvFixeVfUxNTfnuu++YP3++snSZXAtCCCGEEEIIIYQQQnx+JLwjvkr6H1q/ePGCN2/ekJycTO7cuVPsd+TIEVq2bEliYiL+/v4MGDAAgPXr1zNmzBg8PT3p0qVLuo9fCCE+B/oBnr179+Lh4fHBAE96frt/6NChbNu2jfDwcKpVq6bcfvXqVVq3bs358+dp27YtUVFRZM+eHYA1a9bg7e2NpaUle/bswcrKKt3GK4T409GjRxk8eDBHjx5lyJAhTJw4EQ8PD86ePcvixYtxdXUF3oV0AgIC2L59O4MHDyY4OPgvwwjSZeSfO3DgANWrV6dMmTIEBwdTq1Yt4F1A5+XLlxQtWpQcOXKwc+dO5f3zggUL8Pf3Z8GCBcr+EhT59/RfY1evXk2PHj1o164dvXv3JjExkSlTprBixQrq1avH5MmTKViwIAYGBty6dYvY2FhOnz5NlixZKF++PN9++y2WlpbyfAghhBBCCCGEEEII8RmT8I746uh/aB0REcHatWu5dOkSWbNmpXv37nTt2hUHBwdl/yNHjtCqVStevHhBnz59yJ07N3PmzOH69ev8+uuvKbo7CCHE1+ZjAZ4RI0YwZsyYNP/97xcihw4dSnBwMHXr1mXEiBFUrVpV2abfgcfNzY08efIAcPHiRSwsLNi2bRvOzs5S6BciA504cQIPDw9OnjzJwIEDefDgAbVq1aJHjx4prveTJ08yYsQIJcCj68Cj3/HrY10Wxd8zadIkfHx8KFeuHOPHj0/RlczX15dJkybh6+tLvXr1OHPmDDNmzMDU1JS9e/dibW2dgSP/8r3/txsYGMiKFStYu3YtBQsWBODWrVtERkYyefJk6tSpQ3h4OAULFvzo37y8tgkhhBBCCCGEEEII8XmT8I74quh/aO3l5UV4eDiOjo5UqFCBu3fvcujQITp37oynpyelS5dWjjt9+jQtW7bk1q1bGBoaUrBgQbZs2YKzs7N8g1UIkanoFwx1//2/CuD62/ft20fbtm159eoV9+7dw9zcPM2K5/pz+vLly6lfvz7W1tYEBwczfPhwatSoQUBAQIoAj34HHjs7Ozw8PPjmm2+oWLEiefLkkTldiM+AfoDH2NiYgIAAhg0bhkajQaVSKXPKyZMnGTlyJNu2bcPb25ugoCAJ63xioaGheHt7pwrwHDt2jHHjxrFp0yZl3yJFivDzzz/j5OQkQZFPZMiQIRw7dgxnZ2eKFCnCiBEjUKvVGBgYoFKpuHv3LtOmTSMsLIw6deowZcoUJdzzd1/DhRBCCCGEEEIIIYQQnwcJ74ivUkhICAEBAfTq1Yvu3btTrFgxLly4QLVq1Xj+/DktWrRg+PDhKQI8Dx8+ZO3atZibm1OvXj3s7OykyCuEyFT057SEhARevXqFjY2Nsv2vCoD6244cOYKzszMODg7pUjT08fEhLCyM0aNHM2zYMOLj45k+fTojRoz4YIDn8uXLtG7dml9++YVBgwYxadIkAN6+fUuWLFnSdKxCiL/n2LFjDBgwgOPHj9OmTRtWrlwJkCK4AO8CPKNGjWLLli2MHj2akSNHZuSwvzgfei/7fvBGP8ATGBhI3bp1gXdhyCNHjnDy5EkKFCiAu7s7uXLlkvfH/4H+Y//8+XN++uknYmJiUKvVtGvXjvnz52NiYpLiGP0AT/369Zk4cSJFihTJiOELIYQQQgghhBBCCCH+AwnviK/OiRMn6NatG25ubgQGBlKoUCFevnxJuXLleP78OaVLl+bnn3+mRYsWDB06lDJlynzwfuQbxUKIzER/Tps4cSIxMTFcvnyZxo0b4+7uToMGDTAyMvrLue/9oE5aFXD1x3D8+HFat25NixYt6N+/PwUKFAAgPj6eyMjIjwZ4YmNjcXd358KFC/Tp04fIyEgAkpOTMTIy+uRjFkJ82Pvzhv71ffToUQYMGMCJEycYMmQIEydOBFIHeI4ePUpERATjxo3Dyckp/U8iE5g3bx6urq7UqFED+HiAp3z58gQEBNCwYcMP3o8Ed/6+v3o9vXbtGq6urty8eZMJEyawYsUKXF1dWb16Na6urqn2v3v3LtOnT2fChAm0b9+ehQsXyvMghBBCCCGEEEIIIcQXRqpTItPT/2Bcq9Vy9uxZbt++zfTp0ylUqBCvXr2iUqVKxMXFMXnyZIoXL46BgQExMTFkzZqVQYMGpejAoyPBHSFEZqKb0/z8/AgJCcHR0ZGcOXMSHR3Nrl278PLyol+/fhgbG3+04Ph+h520KhzqfvfNmze5ePEir1+/pkePHhQoUEAJAlhYWODh4QHAiBEjAFIEeAoXLkx0dDRt2rRhxowZGBgYMG3aNAnuCJGO9IMeSUlJxMfHY2BgQI4cOQCoWLEiU6dOpX///oSGhmJgYEBwcDCGhoYpAjwVK1akTJkyGBsbSwDvX9i9ezc9evSgTp06mJiYUKlSJQwMDFLM9Z6enjx8+JBJkyYREhKCgYEB9evXT3VfEhj5+3SPbf/+/alRowatWrUCoFu3bmzevJmjR4/i4uKiLBk3Z84c+vXrx7Jly5RrRCdPnjz07t0bc3NzOnToIM+DEEIIIYQQQgghhBBfIEkfiExFo9Gk+Dk5OVn5YPzu3buoVCpatWpFZGQkVatW5e3bt3Tr1o1bt24xevRo2rRpQ9GiRWnSpAkAK1asYNCgQfzyyy/pfi5CCJEe9OfNc+fOsWjRIvr06cO+ffs4fvw40dHRGBkZMWrUKMLDw0lKSlKKuhkpODgYFxcXNmzYQK1atShWrFiqfXQBnsDAQPbu3cu4cePYtWsX8O68CxUqxOrVqylevDiRkZEsWrQovU9DiK+WfnAnMjKS5s2b8+2331K+fHmGDBnC+fPnUavVVKhQgWnTplG2bFkmTpyIr68v8C4koj8PGRsbA0hw53/Qb7qq++9SpUoxatQo9u7dy+jRozl06BBAirne2NhY6cpz5MgRPD09OXDgQPoOPhPatGkTkZGRhIaGKp2mFi5cSKtWrTA1NQUgX758jBw5kh49erB161Y6duzIs2fPUt2Xo6Mjvr6+5MuXD7Vanc5nIoQQQgghhBBCCCGE+K8kvCMyFV1QJzg4mPj4eKWAM3DgQH744Qdu376NtbU1HTp0AODSpUvs27ePJk2a8NNPPyn7Fy1aFFdXVzp06MCtW7ews7PLmBMSQog0pps3z549y9mzZ8mSJQv9+vXD2dmZHDly0KRJE9auXYuVlRVjx479LAI8Wq0WW1tb3Nzc2LBhAwcOHODq1atA6u4/ugDPhAkT2L59O7NnzyYxMVEZf8GCBVm2bBl+fn507tw5I05HiK+OVqtVgjtDhgxhwIABXLlyherVq2Nra8v06dPx8PBg8eLFSoAnMjJSCfAMGzYMkC4vf8f78/S9e/eIj48nLi5OmS+trKzo378//v7+7Ny5kzFjxqQI8CQmJgLw3XffUaFCBdq1a8fz588pWLBg+p5MJlS9enXmzJnD+fPnadmyJdOmTcPLy4vRo0fj4OAAvLteHB0d8ff3p0ePHmzZsuWjAR7da7pcG0IIIYQQQgghhBBCfHkkvCMynfHjxzN06FA8PT2Bd8GdqVOnUrlyZbJmzZpi34sXL/Lo0SOqV6+eouAbExODmZkZAQEBnD9/Hltb2wzvMiGEEGklODiY7777jpiYGEqVKoWbmxvJycnK9tKlS7N27Vqsra1TBXj0uzikFf35V61Wo1Kp6NSpE8OGDaNcuXLExcWxefNmEhISPni8hYUFvXr1Ytq0aYSEhGBiYgK8K3Kq1Wq++eYbxo8fn+p3CSHShu49V1RUFFOnTqVPnz6sW7eO5cuXs2rVKrp168ahQ4fYunUrSUlJAJQrV47IyEgqVqxIUFCQcs2Kj9Nf9mr27Nm0b9+esmXLUrJkSSpVqsTYsWPZsWMH8C7A07dvX0aPHq0EeHSddUxMTEhOTmbKlCk8ffqUyMhIrl+/jr29vcyZ/5GFhQXdunWjVKlSPHjwAAcHB4oVK4atrS3w7jlUqVRotVry5s2bIsDz448/EhcXl8FnIIQQQgghhBBCCCGE+FQkvCMynSZNmtChQwcWLlyIm5sbU6dOxcfHh379+mFjY5NiXzc3N7Jmzcr+/fuJj49Ho9GwcuVKNm7cSMmSJcmXLx8WFhZotVql+CGEEJmJVqulePHi2Nvbs3HjRi5evMjz588xMjJKEcwpVaoU69atI2fOnAQFBTFu3DiSkpJSdbr51NRqtTL/7tq1i+XLlxMbG4uxsTFt27ZlwIABuLi4EBgYyLZt21KEjvRZWlri4eGRajmR97sTyFwvRNrTarUkJCSwevVqChcuTP/+/fnmm29ITk7myJEjbNq0CWdnZyIiIjA1NVWu2XLlyjFp0iQaNmxI+/btM/gsPm/6wZ1mzZoxaNAgdu7ciYuLCw4ODty4cYOAgADc3d2ZOHEiANbW1ikCPEOHDmXJkiUkJyezYMECVq1aRdGiRTEwMCBr1qzy/vg/0H99PXXqFPHx8dSoUeP/2LvPuCjOr43jv12qgIIIKkqzl8QaezcxdmOLJVhi74qoFEFF7NgVQRS72BA1ahR7BQu22GKPvYEVCwJbnhd+dv6LJe0RUXO+b2KY2fWeXefeYc615+bhw4eEhoYSExMDoIRk3wzw9OzZkw0bNjBw4MCPEqIVQgghhBBCCCGEEEJkPJVe7vaJL9DDhw+pUqUKly9fplixYqxfv54CBQooN78N7ty5w6BBg4iKiqJKlSqoVCrOnj2Lra0t+/btw8XFJROPQgghPg6tVsu+ffsYOHAgp0+fZuzYsQwYMABra+u35s3ffvuNatWqkTdvXo4ePUrWrFkzbFzGxeeRI0cye/ZsbGxsmDVrFt9//z2mpqZotVqioqIYMWIESUlJhIeH06RJE2UZRCHEp8H4fAa4ceMGhQsXpnv37oSEhJCSksK6devw9fVFrVZz5MgRHBwc0Ov1XLp0icKFCyuPTUtLw8zMDI1GI+f6OxjP23Xr1iU+Ph5PT088PT2xt7cH4OjRo2zfvp2AgAAAhg8fTlBQEABPnjxh3rx5+Pj4AGBra8vTp09xdnZm//79uLm5vfXZIP4+43Ph+fPn2NjYcOHCBSwsLNi9ezd9+vShZMmSBAUFUb9+fQDl37rhdb927ZqyvJybm1tmHo4QQgghhBBCCCGEEOIDkfCO+OLo9XpiYmJo3LgxRYsW5fz583Ts2JHZs2e/tWwWwOnTp1myZAlLlizBwsKCUqVKERYWhouLC1qt9q2uDEII8bl6s3huLCUlhbi4OHr27Mnz588ZPXo07du3x9LS8q0i7dmzZ8mePTt58uT5KAVcX19fJk+eTIcOHejRowdVqlQB/leglgCPEJ824+upq1evki9fPh4/foy7uzvt2rUjLCyM1atXM2TIENRqNfHx8cqyQampqbi5ueHv70///v0z8zA+O6NHjyY4OJjAwEB69+6NjY0NqampytKBAOvWraNly5YAhISE0LdvX2Xb3r17CQ0NRa/X4+joyLBhw8iTJ49cH/8/GL928+fPZ8OGDZQqVYpRo0YBkJyczOLFi/Hy8norwKPVatm9ezf29vaULVtW+UyXEJsQQgghhBBCCCGEEF8GCe+IL9a2bduwtLRk3rx5REZG4uHhQUREhBLgebPw8PDhQ0xNTbGwsFCWaJDChBDiS2E8p/32228kJCTw8OFDSpYsSYECBbC0tCQlJYXY2Fi6d+9OSkoKI0eOpEOHDu8M8Lz5nBll7dq1dOzYkQ4dOjB06FBcXV3TbX9XgOfly5dMmTKFH3/8UQqaQnxCvL29+fXXX4mMjKRgwYJUrlyZp0+f0rNnT+bNm4eJiQmHDh0iV65cwOvzOygoiOnTpzNv3jx+/PHHTD6Cz8ezZ8+oV68ez549IzY2Fltb2/eGLZctW0aHDh3Ily8fUVFRfPPNN0ow5NWrV1haWirdjuT6+N8zDtD6+PgQHh5OoUKF6N+/P506dVL2S05OZsmSJQwcOJCSJUsycuRIGjRoQExMDN26dcPe3p6jR49iYWGRSUcihBBCCCGEEEIIIYTICFLREp+9NztJGP6/bt26ADg6OpKWlsby5csBlACPiYkJOp2O+Ph4smXLRvHixZXn0Ov1UpgQQnwxdDqdMqeNHDmSBQsWcOvWLQBy5MhBnTp1mD17NnZ2dlSrVo2IiAi6d+/OyJEjAd4b4PkY82RsbCxpaWl07979reAOgEqlUubs1q1bo1Kp6N27N+PHj6dp06YS3hEiExnPGYsXLyY0NJTOnTuTLVs2bG1t8fb2pmvXrowdO5acOXNy8uRJsmXLBryet6Kjo1m6dCmVK1emTp06mXkon53Tp09z6NAhBgwYgK2trRK+MWZ4f1q1asWePXtYsGABp06d4ptvvlHeN0NAxDCXyvXxv2f4fWXs2LFMnjyZ/v3706tXL4oVK5ZuvyxZstCxY0cABg0aRPfu3SlevDgXLlxApVKxbt06Ce4IIYQQQgghhBBCCPEFevfaGUJ8JrRarXIjfOfOncycOZMRI0YwZ84c7t27h0ajoVixYowYMYK2bduyfPlyunXrRnJyMvC6O0/Pnj0ZOnQoaWlpyvNm9BIwQgjxMRnmyaFDhzJq1Ci++eYbFixYwNatWylTpgyrVq2iWLFiPH36FAsLC2rXrk1ERAQWFhaMGTOGuXPnkpKS8tHnxuTkZOLj43FycqJs2bLA62KzMZ1Oh0ql4sWLF5iYmNCyZUsWLlxITEzMO5dKFEJ8HIZzE14vfXXgwAEqVKiAt7c3hQoVQq/X06xZM/r160daWhqFChXi3LlzpKSkKN2z/Pz80Gq1zJ07Fzs7O3Q6XSYf1edFpVIpIY83gzuG7QDm5uZUqVIFvV7P4sWL0el0ylxr2EeujT+M06dPM3fuXOrXr8/gwYPfCu4YZMmShS5durB06VJSU1M5c+YM7u7uHDhwgIIFC6LRaD7yyIUQQgghhBBCCCGEEBlNvo4uPlvGnST8/PyYNWsWL1++VLowzJ49m969e/Pzzz9TrFgxRo4ciUqlYsWKFTx48AAXFxd2795NUlISv/zyyzuLGkII8aXYsmULoaGhdOnSBX9/f/Lnzw+8XjJw165dpKamKsVatVpNrVq1mD9/Pk2bNmXx4sV07dr1o3/TX61WY2Jiwv379zl9+jQlSpRIt93QaS05OZnJkyfz888/4+7uTrNmzYCPs6yXEOLdDKFBPz8/nj9/zvnz52nWrBnu7u7KuZk9e3b69OkDwKxZs6hcuTJff/01T5484cGDBxQuXJj169fj4uIi5/N7GHc3Mu5GqdVq0ev1HDlyhGfPnmFlZfWnr1+1atWwsbEhJSUFtVr9VlBSfBjXrl3j5s2bjBs3DldX1/cuZQavA1ctWrSgZs2aJCcnY2dnh42NDVqtVrrKCSGEEEIIIYQQQgjxBZLOO+KzZShOBAUFMXHiRFq3bs2hQ4e4desWkZGR3L17l8GDB7No0SL0ej2FCxcmKCiIAQMGEB8fz/Lly3FwcODo0aPky5cPrVabyUckhBAZ5/jx47x48YKePXuSP39+tFoty5Ytw9/fH1dXVy5evIidnR3JyclKkbxatWps3ryZjRs3Ym1tnWFjM+6m8fLlS+XPFhYWNGnShJSUFNatWwf8r/uDcec1f39/li5dytOnT9M9rxT6hchct27dIi4ujrCwMPbv309iYiKAsnQpQNGiRZk2bRpr166lZcuWWFtbU6FCBYKDg9m+fTtubm4S3HkP4+DHy5cvUavVSkeWEiVKULFiRU6ePMnVq1cxMTF557Wu4X1ITU3l1atX5MmTB5BOOxnlzp07ANjZ2QFvd5MzvEeGcwVeL2/p7OyMjY2NLO0rhBBCCCGEEEIIIcQXTMI74rN25MgR5s2bR5MmTRg6dCgVKlTAyckJc3NzNBoNuXLlom3btkoBomDBggQFBXHw4EFiYmLYunWrFIWEEF88vV7P2bNnsbW1pXz58uh0OqKjo/H390ev13P48GFy5MgBwI0bNwgNDSUtLQ0zMzOqVatGnjx5MizgaBzCWbFiBSNHjmTDhg3K9ipVquDs7MzIkSOZN2+e8nPDnL127Vo2b95M0aJFlW5CQohPg7OzM5MmTcLDwwMTExMOHjzI+fPnAdJ1dzExMaFZs2asXr2agwcPEh0dTf/+/XF0dEzXaVGkZ7i+bdSoEdWrVycpKQlTU1O0Wi12dnbUqFGDx48f0759e+7fv/9WgEev1yvvw6JFi9BoNFhbW3PmzBni4uJ49uwZqampmXV4XyQbGxsANm7cyPPnz5XPPyBdMKd9+/ZMnTr1rcdLqEoIIYQQQgghhBBCiC+XhHfEZ+2PP/7g7t27dO7cmUKFCqHRaFi1ahWDBw/G1taWw4cPY2dnh0ajUToy2NraUrRoUWrWrImdnZ0UhYQQX5R3LXWiUqmwtrbm5cuXXLp0iV9++QUfHx/UajXx8fE4Ojoqj+3Vqxe//PILL168SPccGTFPGs+//v7+9OrVi+XLl2NhYaEcR5UqVZgwYQLm5ub06NGDgQMHsm7dOq5du0ZgYCDe3t4kJycze/ZssmbNKku9CJFJ3jz3DP9fqVIl+vbtS9OmTdm7dy8RERE8fvwYeDuIYNyFy/Bn43CDeFtqaip2dnZcuHCB1q1b8+zZM2VeHTduHNWqVePMmTP8+OOP3Lt3T9lm6Nqj0+lYt24d0dHRAERGRvLNN9/QvHlzFixYwPPnzzPt2D5X7/ocMvysSZMmlClTho0bN3Lw4EElTKXRaJSlf0NDQzl16hSWlpbymSaEEEIIIYQQQgghxH+I3A0Xn413dX04ffo0er2e4sWLAxAVFaUUpA8fPoyDgwMAly9fJjAwUCkWGZOikBDiS6HT6ZRi+JMnT5TlUwB++OEHUlNT6d69O97e3koXDENwByAsLIzz589To0YNpTtARjJe9mrixIm0a9eOjRs3Uq9ePVQqlTJ+Dw8PFi1aRMmSJZk5cyYtW7Ykf/78jBkzBgcHB/bv34+LiwtarVa6EgiRCYzPvRcvXvDs2TOePXumbK9cuTKDBw/mhx9+YNq0aUyaNOkvr8nk+uyv6fV6zM3NmT9/Pp06deLAgQM0atSIpKQk4HXocsWKFVSsWJG4uDiqVq3K6tWruXjxIlqtlhcvXhAcHIyfnx/JycmsXLmSsLAwRo8eTVBQEM2bN8fe3j6Tj/LzYnwuPHnyhCtXrvDgwQPlPTE3N+fnn3/myZMnDBw4kF9++YVHjx5hamoKvP5dJjQ0FGdnZ1q1aiWfaUIIIYQQQgghhBBC/Ieo9PJ1PvEZMHw7GGD48OF89dVXtG3blujoaFq3bk1wcDBfffUVvXv3fquTBECHDh3YtGkTR44coUCBApl1GEIIkWGMl/9bsGABe/fupVKlSnTv3h1TU1Nu3LhBx44d2bdvH1mzZuX69evY2dkpj1+1ahXDhg3D1taWzZs3kzNnzo8y7jVr1tChQwc6duyIn58f7u7uyrakpCQ0Go1SPD537hxXr17l4MGDWFpaUrZsWSpVqkT27Nll+UMhMolOp1OCNiEhIcTExHDjxg3Mzc3p2LEjVapUoUKFCgAcOnSI8ePHs3HjRvz8/PD29iZ79uyZOfzPnuH1f/XqFX379mXhwoXs37+fqlWrKtfPhqWzYmJiUKlU2NjYkDdvXhITE3n69CllypRh7dq15M2bN7MP57NmfC5MmjSJ5cuXc/LkSaytrSlYsCCTJk2iTp06PHv2jIkTJxIaGoper+ebb76hVq1aHDlyhAMHDmBpaUlsbCxubm7pnlMIIYQQQgghhBBCCPFlk/CO+KwEBQURFBRE27ZtmTdvHteuXaNixYqo1WpsbGywsLDg2LFj6QpB8+bNIygoiEaNGjF9+nQsLS0z8QiEEOLDMy7u+fr6Mnv2bOzt7QkLC6Nhw4bKfkePHsXDw4PLly/TsWNH6tSpQ8GCBVmyZAkbNmwAIC4u7qMWDL29vQkNDeXQoUOULFkSnU6HTqcjJCSE6OhoHj58SIUKFViyZMl7n0OKm0JkviFDhjB16lQcHBxwdXXl7NmzpKamUrp0aQYPHoyHhweQPsATEBCAl5eXdHf5fzIO8Fy4cIFSpUq9tU2n0xEZGcnhw4fZsWMHJiYmlChRgjp16tCiRQty5MhBamoq5ubmylJN0vXl7zP+osGQIUOYNm0alStXpn79+ty9e5e1a9eSlJREYGAgPj4+JCUlsXHjRhYtWsTOnTsBcHJyonr16kyePBlnZ2cJpQohhBBCCCGEEEII8R8j4R3xSTO+aZ2QkMAPP/xA6dKl8fb2VjroLFiwgG7dugGvgzpdunRRHr948WLGjRuHmZkZ27dvx8nJKd3NdSGE+JIMHz6cCRMm0KtXL3r27MnXX38NpA+3HD16lKCgIPbs2cOLFy8AsLKyokaNGsyZM0dZfupjFAx1Oh1t2rRhw4YNXLx4ETc3N9asWUNERATbtm3Dzc0NrVbLrVu3aNu2LcuXL8/wMQkh/h7jeWLjxo106tSJrl270rVrV4oUKUJ8fDzr169n/PjxuLi4MHnyZFq1agVAfHw8wcHBrFu3jvHjx+Pj4yPXZv9Pb87bxte7b2579eoVJiYmmJmZKT+TEOSHsXjxYnr37k3nzp0ZNGiQ8vuKv78/EyZMoESJEhw8eBArKyvlMb///jupqam4ubmRJUsWLC0tJbgjhBBCCCGEEEIIIcR/kGlmD0CIP2O4ab127VpevHjB8ePHGTduHAUKFFC+FdymTRsSEhLw9/fHy8uL2NhYChYsyKFDh9i7dy/29vZs3boVJycnuREuhPhibdmyhRkzZtCuXTsGDRpEvnz5lG13795Fr9dja2tLuXLlmDdvHg8fPiQ+Ph4TExPKli2Lm5sbNjY2H2WeNBSJ1Wo1FStWZM2aNVSrVg17e3vOnTuHg4MDixcvpnbt2tjZ2VG2bFn27NnDH3/8Qf78+TN0bEKIv8cwTyQmJqJWq8mRIwc9e/ZUwgoVKlSgbNmy5M2bl379+hEeHk758uVxd3enQoUKDB48GFtbW3766ScJ7vxLxgGdN+dt49fUsM2wv5mZmRLUMZ6Pxf/ftm3bsLe3p0ePHhQoUICUlBR+/fVXli1bRqFChdi5cydWVlZoNBpMTV//Kl6sWLF075der5ffV4QQQgghhBBCCCGE+A+Szjvik7dx40aaNm1KuXLlePXqFYcOHcLKyipdgTktLY21a9fi7+/Pw4cPSUpKomDBglSrVo3Ro0eTN29eCe4IIb5okydPxtfXl7i4OCpVqoRGoyE5OZlZs2axaNEikpKScHZ2ZvXq1bi7u7/zOTKqM9lfdXTw9/cnOjoae3t7ypQpw8iRI8mVK5eyvXLlyuh0Ovbs2UOWLFk++PiEEP+Or68vU6ZMoXz58uTJk4c1a9YA6eeSZ8+e4evrS3h4OOvWraNp06bK49PS0jAzM5NrtH/B+DU+f/48dnZ25M6dO5NH9d/y5mfbkydP+Oqrryhfvjy//PILaWlprFmzBl9fX9RqNfHx8Tg6OgIQGxtLrly5KFSoUGYNXwghhBBCCCGEEEII8YmRr1iKT95XX32Fp6cnZ8+e5cyZM0RFRQGvv0Ws0+kAMDMzo02bNuzbt4/jx4+za9cuDh48SFhYmAR3hBD/Cbdu3UKv15OQkADAsmXLaNKkCQEBAVhaWpIvXz6OHTvGDz/8wLNnz975HBkR3NFqtUpxc8uWLUydOpXu3buzZMkSjh07BsC4cePYtm0bu3fvZubMmemCO8uWLePixYtUqFBB6VIghPg0WFtbY2JiwunTp7l79y4vXrxQrs0MsmbNSr169YDXXUl0Oh1arRZAWbZJrtH+GePgzo4dO2jZsiWjRo0iNTU1k0f232Ec3ImPj+fFixfY2dlhbW1NQkIC9+/fZ8OGDe8M7gAMGDCAoKAgNBpNZh2CEEIIIYQQQgghhBDiEyPhHfHJMhR/8ufPT//+/RkwYACmpqYsXryYEydOAKBWqzFuHpU3b17y589PrVq1yJEjB5aWltJ6Xgjxn1C3bl0AWrZsiYuLC507d+bq1ausWbOGrVu3cuDAARo0aMDFixe5dOnSRxmTTqdT5l9fX19at26Nv78/y5cvp1OnTrRu3ZpZs2YB4O7uTpYsWZRiPsD8+fMZNWoUOXPmxN/fP902IUTmMVx7jRgxgvHjx/Py5UsOHTpETEwMarUalUqFXq9Xggk1a9ZEpVIpyzPJddm/Zxzc2bVrF8OHD+fSpUt06NABc3NzpKnqx2EI7nh6etKhQwe2bt0KvO4Ud+bMGSZPnszgwYPfGdyZOHEi165do3bt2hJKFUIIIYQQQgghhBBCKCS8Iz4Zb35T27gNff78+enWrRv9+vVj3759TJ48md9//x34604RGdFJQgghMsOb86Sxhg0bsmLFCipXrkzp0qXx8fHh+PHjNG/eXFlKRa1W4+bmhrOz80cZr2EeDwoKYtKkSbRs2ZI9e/Zw/fp1li9fTmJiIgMGDGDu3LnKY3Q6HcePH6dDhw4MGzYMlUpFTEwMTk5OSrcOIcTH9ebco1KplC4vgwYNYtq0aQDpQgw6nQ5TU1M0Gg2RkZHo9Xry5csHIAGTf+nN4I6fnx8nT54kNjaWypUr8+TJE/bs2cPNmzczeaRfLuNzYc2aNURGRvLtt99SqlQpALp06UKOHDmYMmUKr169Yv/+/emCOytXriQiIoISJUrQvHnzjz5+IYQQQgghhBBCCCHEp0u+6ic+CcbLWm3dupXLly9z7949KlSoQMmSJXFzc6NAgQL07dsXnU5HSEgIer2eYcOGUbx4cSB9QUMIIb40xvNkbGwsFy5c4M6dO+TPn58aNWrg4uJCmzZtqFOnDjly5HhrucBVq1Zx7Ngx6tSpQ7Zs2T7auE+ePMncuXNp3LgxQ4cOpXDhwqSlpeHg4ICJiQmFCxemZcuWyv5qtZrIyEhWrFhBx44dGTt2rBLckW4dQnx8xufe77//TlJSEiVKlEjXMcTT0xO9Xs+gQYNo0KAB8+bNo1KlShQvXpz58+czZ84c3N3d8fDwACRY/W+8K7hz5swZdu/eTYUKFXj69ClLlixh4MCBTJgwAR8fn0we8ZdHr9en+3LB48ePyZEjB56enhQoUACAcuXK0blzZ+bOnYupqSmHDx/m8ePH5MmTh1mzZrFgwQIAli5dir29fbrlt4QQQgghhBBCCCGEEP9tKr189VVkMuNihK+vL1OmTEn3rdZy5crRrVs3evToAcCVK1cICQkhJCSEtm3bMmzYMIoVK5YpYxdCiI/BuLgXEBDArFmzePbsmbK9TJkytGvXDi8vL2W5GuPi+OzZs5k6dSoAe/bsIW/evB808Hj//n1y5cr1zm3R0dG0bt2aX3/9lYYNG6LRaFi9ejV+fn6o1WqOHDmCg4MDqampPHr0SOkSdOLECQoXLoy1tbUEd4TIJMbzhL+/PyEhIbx48QIXFxfatm1Lr169lG46ADNmzMDLywuAXLly4ejoqAQXVq1ahbu7u5zP/8KfBXcqVqzI06dPWbp0KcOGDePrr78mNjY2k0f8Zevfvz9RUVFUrVqV/PnzM3nyZOB/QbenT58SHh7O3LlzuXr1Kubm5sDr0Frp0qVZuXIlbm5uci4IIYQQQgghhBBCCCHSkc47ItMZihFTpkxh5syZtG3bls6dO/PkyRMOHDjAjBkz6N27N0+fPsXb25sCBQrg6ekJQHh4OE+fPmXatGkUKlQoMw9DCCEyjCG4M3z4cCZMmEC7du3o0KEDGo2G7du3s2jRIvz8/Hj69ClBQUGoVCq0Wi2HDx9m9OjRnDhxAkdHRzZu3EjevHk/aMEwPj6eqlWrMmfOHLp06aL83BA4unLlCgAuLi6kpaURHR2tBHfi4+NxcHAA4MGDB0yYMIF+/fpRuHBhypQpA7wuWktxU4jMYbhGmzhxIsHBwdSuXZvChQtz8uRJJk2axOnTp5k8ebLSBdHQgcfb25vU1FTq1KlDYGAgOp2O7NmzS1jhX/i7wZ2hQ4dStmxZ9u7dC4BGo0nXHUn8e2/+u01JSSExMZENGzbQoEEDkpOTsbS0xMTEBL1ej62tLQMHDqRFixasXLmSu3fvYm5uTs2aNalZsyb29vZyLgghhBBCCCGEEEIIId4inXdEpnnzpnWDBg3IkiUL06ZNw83NTfn5hg0baN68ORYWFsydO5f27dsDcOPGDYKCgti+fTsnTpwgR44cH/0YhBDiYzlw4ABNmzalRo0aTJ06VZknk5OTiYuLo3379rx48YJp06bRrVs3ABYuXMigQYPw8PBg+PDh5M6d+4MXDNesWcOAAQN49OgRERERyhxtsH79epo3b868efNwcXGhW7duSnDH0dFR2e+nn35i37597N+/n/z583+w8Qkh/j29Xk9qaioNGzYkd+7cTJw4kbx585Kamkrv3r1ZuHAhNWvWJDQ0VAnwAEyaNIlhw4aRPXt2Vq1aRc2aNdFqtajValky61/avXs3vr6+Etz5yIzDU4GBgTg6OtK7d288PT1ZsGABOXLkICYmhq+//lp53f+qs50slSWEEEIIIYQQQgghhHgXuWsoMo2heDx48GBCQkIAaNeundJGHl7fMP/hhx+IjIwkLS2NdevWkZycDICrqytBQUGcOnWKHDlypFtqSwghvjTXr1/n0aNHyjxpmPOyZMlCnTp1mDNnDiYmJqxdu1bZ1rlzZ06cOMHkyZMzJLgD0LJlS2bNmoWbmxudOnUiMjIy3fbChQuTN29e+vXrR6dOnTAzM+PQoUPpgjsREREcPHiQxo0bkydPng86PiHEP2N8PaVSqUhOTubChQt06dKFvHnzotPpMDc3Z/78+XTv3p29e/fSt29ffv/9d+Vx3t7ejB8/noSEBNq0acPOnTsxMTGR4M6/FBMTw5AhQzh//rwEdz4yw7/ZMWPGMHbsWI4dO0ZaWhoTJ06kc+fO3L59m9atW/P06VNMTU3RaDTKY973HRkJ7gghhBBCCCGEEEIIId5F7hyKj864KPT7778zbdo0PD092blzJ/fv3wf+F+wx3Pxu3Lgx33//PevXr+fixYvK452dnbGzs0Ov18uNcCHEF8lQ/Ltw4QJ6vZ7Hjx8DvBVYrFq1KlWrVmXLli3Ex8crP3d3dydLliwZsvyUYQzNmzdnzJgxFCxYkE6dOrF06VJln2LFitG3b19evXrFvXv3mDRpErly5VK2R0ZGMmnSJLJly8bIkSOxtLR8b8FTCJGxDN1xAA4dOsSvv/7KjRs3cHJywsrKCr1ej16vV0LWc+bMeW+AZ9CgQUyePJkHDx7Qvn17Nm3alCnH9CW4du0aJ06cYPv27RLc+UgM/8bh9bKO27dvp2PHjgQFBWFpaYmVlRWTJ0+mT58+nD9/nqpVq6YL8AASVhNCCCGEEEIIIYQQQvwjknYQH5Vxm/jz589TvHhxVq1aRaFChdBoNJw+fVop2hr/N2vWrJQrVw6dTkdCQsJbzys3x4UQXyrD/Fa9enVMTU05cOAAAKampkp4Rq/X4+DgQK1atQB48eLFe5/nQzIO2fz444/06tWLggUL0qVLF6KiopRtfn5+DB48GL1ez08//UTv3r0ZPXo0TZo0YcCAAaSmprJhwwacnJzQarUypwuRCYwDfn5+flSvXp0ffviB0qVLc+zYMc6ePYtKpcLExAS1Wv3OAI+Hh0e6kPWgQYOYOHEi9+/fJyYmJlOO63Pwvu6RhhBI7969uXHjBhUrVuTJkycsW7ZMgjsZzHAuLF68mAMHDnD69GnatGmDq6sr8Pr1zpIlC5MmTaJPnz78/vvv7wzwCCGEEEIIIYQQQgghxN8ld3jFR2UI7vTr149du3YRHh5Oq1atSE1Nxc/Pjzlz5lC+fHm6dOmCSqVCr9crRdxbt25hbW2Nvb19Zh6CEEJkGOOA45vy5ctH8eLFWbhwIWXKlKFfv36o1ep0Bdtz586RLVs2nJycPspYDcXNUaNGERcXx7Fjx7C2tkar1dKuXTu0Wi0//fQTAJMmTcLV1ZUVK1awYMEC0tLSyJcvH82aNWP06NHkzZs3Q5b1EkL8PYbrrZkzZzJ9+nR++OEHKlasyI0bNwgLC6Nnz57kyZOHhg0bolKplACPiYkJc+bMISkpifXr12NnZwf8bz4bNGgQxYoVo0GDBpl4dJ8u43lv7969PH/+nLS0NJo2baqENNVqNXny5CElJYW5c+cycuRIKlWqxK5duwAJ7mSU7du307lzZ1xdXbG2tqZYsWLA63/bpqamaLVaJcADEBYWRs2aNdmzZ49yHgghhBBCCCGEEEIIIcTfJZ13xEdh/I3iFStWsGLFCipXroyLiwsA7dq1Y/LkyeTKlYtu3boRGhrK06dPlULS+vXriYmJoWTJkhQqVChTjkEIITKS8XI1Z8+e5cKFC+mWoMmfPz9jxowBYMCAAUyZMgVAKdj+8ssv7Ny5k4oVKyqdATKSYaz+/v6MHj0aJycn5s+fz/Lly/Hy8kKlUtGhQweWLVumPKZ///78+uuvnDhxgri4OA4fPkx4eLgEd4TIRG92fYmLi+Pbb79l+vTp+Pj4MGvWLKZNm4Zer6dTp05s2bIFIF2AB15f3926dYucOXMqgRPDNkNw530dZv6rjLsd+fv7U79+fZo0aUKLFi2oVq0av/32m3ItrFarsbCw4NWrVxQsWFCCOx/BV199RUBAAHq9ntu3b/PLL7+g0WiUzz8TE5N0AZ7+/ftz6tQpWrZsKcs/CiGEEEIIIYQQQggh/jGVXu4sigxm3D0HIDQ0lLlz57J69WoKFy6crmAbFRWFl5cXd+/epVatWhQrVoybN29y5swZ9Ho9e/bswc3N7U+7UwghxOfgwoUL5MuXD3Nz83TzYFBQEBERETx//hyAIUOG0L9/f2xtbQFYtWqV0s2mVq1aFClShEePHrFnzx5MTU05ePAgrq6ub829GeHIkSN8++231KlTh5kzZyqBTIAlS5YQGBjIzZs3iYyMpG3btu99no8xViHEnxswYAB2dnacOXOGn376iVatWpGWloaZmRkAISEhDBw4EHt7e5YuXUr9+vWB1+evcScuOZ//ueDgYPz9/alTpw6NGzdm69at7Nq1C2dnZ+bMmUPNmjXfed0rwZ2Md/fuXcLDw5k5cyb58uUjIiKCb775Jt0+hs/wly9fMm7cOLp164a7u3vmDFgIIYQQQgghhBBCCPHZkvCO+Gg8PT3Ztm0b9vb2VKlSRWkxD+mXilm1ahW+vr7cvHkTJycn+vTpg1qtpn379jg7O0t3BiHEZ+/s2bOUKFGC1q1bs2TJEszNzQEYPnw4Y8eOpWTJkpQqVYotW7aQmJhIx44dGTVqlNJRZ/v27cyaNYv4+Hju37+Ps7MzZcqUYdasWbi4uHy0eXLDhg00a9aMsLAwevXqhU6nU5YTAZg7dy69evXCxMSEyMhI2rRpk+FjEkL8c5cuXaJChQo8ffoUa2trpk+fTteuXZVOOYZrNOMAT2RkJPXq1cvMYX+2jOfoV69e0bBhQ/LkycPo0aPJly8fSUlJLFmyhLFjx2JlZcW8efPeCvBISOrD+asvBdy5c4c5c+YwadIkKlWqRGhoqLKElsGbn7sSrBJCCCGEEEIIIYQQQvxTckdRfBR6vZ6LFy9y4cIFbGxsKFq0KIDyjW61Wq3cOG/Tpg1arZbhw4dz9epVSpYsSePGjdPtL4QQnzO1Wk39+vWJiorCysqKsLAwEhMTWb9+Pb169WLIkCHkz5+fY8eOERoayqJFi9BqtYwdOxZXV1e+//57ypYty8uXL7l48SIFChTA0dERa2vrTAk4pqWlAa+X0TE1NVXm8x49erBr1y6ioqLo1KkTSUlJdO/e/aOOTQjx1woVKkRkZCQBAQGcOnWK3377DUC5PjOc0/379wfA29ubBg0asGvXLmrVqpV5A/9MGeboKVOmUKJECR49eoS3tzf58uVDo9GQLVs2unTpgrm5OYGBgXTr1u2tAI8Edz4M48/MgwcPcvnyZRITE8mdOzdNmjQhS5Ys5MmThx49egCvuyT17dv3rQDPm5+7EtwRQgghhBBCCCGEEEL8U9J5R2Q4wzeDtVotHTt2ZMWKFeTJk4eDBw++1SHC+JuvK1euZODAgSQkJLB06VLatWuX7vmEEOJzdv78eYYPH86aNWvo06cPnTt3plGjRqxbt47KlSsr+126dInJkycTERFBu3btGDduXLrlqYx97PnxwIEDVKtWjaJFi/LLL79QuHBhZVtKSgoWFhZMnDiRiIgIXr58iampKefOncPKyuqjjVEI8eeMr8M2b96Ml5cXly5dYsqUKXh5eQG81YEnODiY0NBQDhw4gLOzc+YM/DO3d+9eateujY2NDZaWlvz6669UqFAB+N9cnpyczNKlSwkMDMTa2pp58+ZRo0YNWTr2AzH+vSMgIICwsDCePn2qbK9atSodO3akffv2ZMmSRenAExwcTJUqVd7ZgUcIIYQQQgghhBBCCCH+LQnviA/uXcVjQ2FIp9PRsWNHli9fTtmyZdm0aRO5cuV6b4AnKioKT09P7t+/z/Lly2nbtu1HPx4hhMgo586dY8SIEaxZs4YSJUpgYmLC4cOHMTMzSzcvGgrpc+fOpV27dkyYMIG8efNm8uhf69atGwsWLMDLywsvLy+cnZ3TLRfSuXNnnj59So8ePShVqhROTk6ZPGIh/rveXB7oXcsFxcTEMGDAAK5cucKMGTOUbjtvBnieP3+OjY2NLGf6L6WkpDBlyhQWLVrE5cuXleUHDd4M8IwZM4akpCQ2b95MlSpVMnHkX57hw4czbtw4fvrpJ9q0aYNarWbNmjVs2LABlUrFkCFD8PT0xNLSkrt37xIeHs7UqVMpWLAgUVFRFCpUKLMPQQghhBBCCCGEEEII8QWQ8I74oIwLOElJSTx48ABra2scHBzShXPat2/PypUrqV69OlFRUX8a4Fm9ejWenp7cu3ePAwcOUKlSpcw5OCGEyABnz55l1KhRbN68Ga1Wy7Zt26hWrRqQPgxpCPAsWLCA+vXrM2fOnA8ehHkzfPlnnXwM8/T58+fp0aMHR44coWPHjvTv35+vv/4agDVr1uDn54eHhwdBQUEAUugXIpMYn3urV6/m6NGjHDlyhMKFC1O3bl1atGih7Pt3AzzSDfHfMbwXhgDP1KlTsbKyYt26dXzzzTfKfsYBnrlz57J48WJ+/fVX8uTJk4mj/7Ls27ePli1bUqtWLaZMmYKrqysAjx49Ys+ePQwcOBCNRsP06dNp1aoVKpWK+/fvM3HiRH755RcOHjxIzpw5M/kohBBCCCGEEEIIIYQQXwIJ74gPxjhwM3nyZFasWMGJEyewtramWLFiTJw4kTJlymBra4tWq6VDhw5KgGf16tXkzJnzvQGexYsXc+TIEWbNmpVpxyeEEP9fxvNaSkoK5ubmqFQqTp8+zZgxY1i9ejWtW7dm/vz5WFtbA+mL45cvX2bEiBHs27ePkydPkiNHjg82NuO/59q1a7i7u//tY4qNjWX06NHs3LkTBwcHmjRpwr179zh06BA2NjbExcXJ0jpCZCLjucfb25uwsDBUKhWWlpY8evQIgD59+tCrVy8lfLd161b69+/P5cuXmTlzJv369cu08X/O3tXdyFhKSgrTpk1j7NixODo6sm7dOkqVKqVsN8zNr169QqvVYm1tLSHIf+BdATPj92TJkiV07dqVlStX0rJlSwy/GqtUKtLS0lixYgX9+vWjdu3arF+/XnmOxMREzM3NsbW1/cv3WAghhBBCCCGEEEIIIf4OCe+ID8L4xviQIUOYNm0aFSpUoGHDhly/fp1t27aRlpaGl5cXnTp1ImfOnOk68NSuXZvIyMi3uki862a43CAXQnyO3ux6cfLkSSpWrEijRo1Qq9X8/vvvjBw5kujoaDp37szs2bMxNzcH0s+xV69exc7OjuzZs2fIfNigQQNevnxJaGioUsT/q+4aer2ehIQEpk6dyvLly7l9+zYODg6UKlWK+fPn4+rqKsVmIT4B48ePJyAggJ49e9KpUyfy58/P1q1bmTVrFvHx8bRu3ZpRo0ZRuHBhALZt28bAgQM5f/48ERERdO3aNZOP4PNiPO/t3LmTCxcucPPmTXLkyEGHDh2ws7PDwsLibwd43vyz+HPGn5GpqalcunQJNzc3bGxslG3+/v5MmDCBVatW0apVq3TLPgLcuXOHn376if3793P06FHKli2b7u+Q90MIIYQQQgghhBBCCPGhSAJCfBCGm9aLFy8mNDSUnj17smTJEoYPH86sWbPo2LEj9+/fZ8OGDem+oRoZGUm7du3YvXs3/fr1480smVqtfufPhBDic6LT6ZQCrp+fHz179iQ8PByVSoVGowGgePHiBAUF0bJlSxYuXEivXr1ITU0FXs+xhrkwX758ZM+eHb1e/8Hnw9TUVGrWrEl8fDyBgYGcPXtW+fv/jEqlIleuXAQHB3Ps2DEuXrzI77//zvr16yW4I8Qn4saNGyxatIhatWrh7+9PxYoVcXR0pH379oSFheHh4UFUVBSLFy9WHlO3bl0mTpxI1apV+e677zJx9J8f43l/6NChtGjRgn79+hEcHIyPjw/Vq1dn/vz53LlzBwsLC7y8vAgICCAxMZHmzZtz6tQp5bmM52AJivw9xsGdGTNmULduXb777jsGDRpESkqK8jpWrlwZgC1btgBgamqqLA+n1WrJkycPjRs3BuDVq1dv/T3yfgghhBBCCCGEEEIIIT4U07/eRYi/LyYmBkdHR3r37k2hQoVITU1l06ZNLF26lAIFCrBu3TosLCyUIrRarWbRokXY2toyZMiQd94Al5viQojPnaGAOGLECCZNmkSfPn3o2rUrpUuXTrdfsWLFGDVqFACLFi0CIDw8XFley1hGzI3m5uZ4enpiY2ODr68vL1++JDg4mJIlS/7lYw2FUkdHR3LmzKn8XK/XS3BHiE/AvXv3uHTpEm3atMHFxQWtVguAiYkJZcuWpW/fvhw4cIDx48fToEEDqlWrBkDjxo35/vvvsbCweKsriXg/w7wfGBhIcHAwnTp1olWrVuTKlYv58+ezceNG/Pz8SEhIoFevXuTOnRsvLy9UKhXBwcFUq1aNQ4cOUbx48Uw+ks+PcXCncePGxMXF4eTkxIgRIyhZsiQWFhbKvl999RVubm4sXLiQMmXK0K9fP9RqNampqUr3uzNnzmBra0vu3Lkz5XiEEEIIIYQQQgghhBD/DdLCRHwQOp2OpKQkdu/eTcWKFSlRogQpKSmsW7eOQYMGoVarOXDgAI6OjgAcOHCApKQk4HXRaNasWbi7uysdKIQQ4kuzf/9+QkNDadOmDUOGDFGCO292FzMEeFq2bMmiRYvw8PAgLS3to40zS5YsdO3aldGjR7N161aSk5P/1uMMhdKPETISQvxzhtDNixcvgNfXX8bBusqVK9O7d28Arl+/DvxvfjKEHSS4836GMJSxI0eOEBERwQ8//MDIkSNp0KABZcuWZerUqYSFhVG4cGFmzJjB7t27AZQOPP369cPJyQk7O7uPfBSfP+OudN9//z2xsbEMHjyY2NhY+vTpo4TS9Ho9er2e/PnzEx4eDkBAQACTJ08GUII769atU36/yZUrVyYckRBCCCGEEEIIIYQQ4r9CwjvibzG0jzd4M2SjVquxsrIie/bs3L9/nydPnrB582Z8fHxQq9XEx8crwR2A9u3b07t377eK1lIUEkJ8qU6fPs3jx4/p1asXbm5uys/fFW4pVqwYQUFB1KlTh7179/Ls2bMMHdubc3GWLFno2bMnly9fpmLFihn6dwshPizjazbjc9vKyoosWbKwaNEiDhw4kG4fQ0CwVKlSANy5cweQ8N3f8ccff5CWloaJiclbc+mtW7e4d+8ejRo1wtXVFXgd8rGwsKB+/fp4e3uj1+sJDg5W3gNzc3OGDx/O0aNHyZMnzztDQeL9DP9mBw4cyOHDhxk+fDgDBw7E3t4+3WupUqmUJSnr1avHsmXLePbsGT4+PtSrV49BgwbRrl07evfujU6nY+7cuVhbW7/1HgshhBBCCCGEEEIIIcSHIuEd8ZeMv8F68eJF0tLSlJCNr68v0dHRwOvgTfHixTl69CgjR47E09MTExMTDh8+nC64M378eJ49e0bt2rU//sEIIcRHZiikHz9+HAB7e/t37mcoKiYmJqLVailevDihoaGcO3cOe3v7t0KUH4pWq1WKnX/88Qe3b98GwNramvz58wNvh3uEEJ8mrVarXLNt2rSJ8PBwzp8/D0DRokXx9fXl8ePHhIeH8/vvvwOvQwxmZmYAxMXFYWFhQZkyZTLnAD4zR44coWDBgnTr1i3dXGpw//59AOX1NYR84PV1c7NmzahVqxanTp3i8OHDwOvPDDMzM7JmzSrLDv5Lf/zxB+vWraNy5cp07doVGxub976Whvfsp59+Yu/evVSpUoWTJ08yffp09u7dS7ly5Th48CCurq7vfI+FEEIIIYQQQgghhBDiQ5HwjvhLhpvUzZo1o3bt2kqxZ+DAgUyaNInTp08rSzAMHToUe3t7Zs6cSVpaGnv37iVnzpzKc61atYoFCxZQokQJWrRoITfAhRBfPEMhPV++fABcuXIFSN/BzFBU1Ol0jBw5ktjYWAAKFSqEg4MDOp1OeZ4PSavVKsXMadOm0bRpU4KCgrh79266/WSuFuLTp9PplPM5ICCAzp07M3jwYG7fvk1KSgoAnTt3pkWLFixbtoyAgAB27typPDYqKorIyEhKlChB2bJlM+04Pidubm6o1WoeP36cbnlDw/xeoEABABYvXkxycjJmZmZKEDM1NRULCwuaNm0KoCwnazzXy9z77xw8eJCbN2/i5eWFnZ0dOp3uT19LQ0C1evXqrF69mvj4eDZv3kxsbCwrV67ExcUl3eelEEIIIYQQQgghhBBCZAQJ74i/5cWLF5QsWZJXr17Ro0cP2rRpw8yZM/Hx8aF79+5YW1sDr5d6GTBgAE5OTmTNmpW9e/dy8eJF7t27R1BQEN7e3qSmprJkyZIM7SQhhBCfCkNRsGzZsqhUKnx9fXn48CGmpqZoNJp0RcXJkyezaNEinj59mu45MiK4Y1zo9/b2ZtiwYWTNmpUmTZrg5OT0wf8+IUTGMswT/v7+TJgwgZYtW7Jnzx6+++47LCwsAHBxcWHYsGG0bduW9evXU7duXRo0aECVKlXo3bs3aWlpREVFyTXa36DRaMiZMyePHj0iMjISS0tLNm3alK5DZc2aNalQoQJ79+4lODiY5ORk1Go1KSkpmJubAxAfH4+VlZUS9BH/nuHz9uLFiwDK7yd/9RmqUqk4c+YMT548wcnJCVdXV+rXr4+7u/ufdu0RQgghhBBCCCGEEEKID0nCO+Jvsba2xs/Pj/Hjx/Pbb78RHR1Nu3bt6Nu3L87Ozuj1evR6PTY2Nvz8888MGTKEV69e0bFjR8qXL0/hwoWZNGkSefPmZd++fco3WDOiIC2EEJ8SQzCnbt26tGjRgosXL9K6dWsSEhIwNTVV5sE1a9awaNEiypYtS7Vq1TJ8XIa/d+zYscycOZPu3buzdOlSmjRp8s79Dct6CSE+XZs2bSIkJIROnTrh5+dHhQoVlG2GYEPp0qUJDw9n5syZlChRgkOHDvH8+XOaNWvGwYMHcXd3l2u0v8HU1BStVku2bNnIli0bEydOpEmTJvj6+ipdeMzNzZkzZw5ubm5MmzaNESNGkJycrISp1qxZw5YtWyhfvjx58uTJzMP5Ihg+b62srID/dTP6M1qtFp1Ox+zZswkICEjXFe/N5xVCCCGEEEIIIYQQQoiMpNIb7uQL8Sf0ej0qlUr5Nre5uTkFCxZk5cqVFC9e/K0CT0pKCnfu3CE8PJyHDx9ibm5OrVq1qFOnDvb29tJ6Xgjxn2JY9iotLY06deqwf/9+3N3dad++PYULF2bv3r1s2bIFgNjYWNzc3DJsqSxjJ0+epFmzZhQuXJjQ0FAKFiyobDtw4ADJycmkpqbSoEEDAJm7hfjEDR8+nAkTJnD48OG/tfTVs2fPSEtLw9bWFr1erwRS5Dx/m+FaGF7P6W92Y7lz5w7VqlXj2rVreHl5MWHCBMzMzEhLS2P37t306dOHP/74g6+++orq1atz9+5dDhw4gJmZGXFxcbi5uaX7O8S/t3TpUn7++WdatGjB7NmzcXR0fOd+htc7NTWVAgUK0KBBA+bOnfuRRyuEEEIIIYQQQgghhBCvSXhH/COLFy/m2rVraLVaQkJCcHd3Z86cOZQvXz5dQePPCs4foyAthBCfGo1GoyyV1adPH7Zs2cKtW7cAyJo1K5UrV2bu3Lm4nRqKrwABAABJREFUurp+tOL59u3badiwIdOmTaNfv36kpaVx8+ZNQkNDCQ0NRafTodFo6N+/PzNmzMjw8Qgh/h2dTodOp6NOnTqcOHGCq1evkj179reCIIa55enTp9ja2mbSaD8/f3btumzZMsqWLUuxYsVITEykSpUqXLlyJV2AR6fTcfPmTfr168dvv/3G7du3cXFxoWzZssycOVPpSCmhqQ/jxYsXfPfdd1y5coVZs2bRokULzMzM0u1jCO7o9XpGjRrF5MmTWbx4MS1atJAQlRBCCCGEEEIIIYQQIlOYZvYAxKfrXYWKn3/+Ga1Wi0ajIVu2bIwZM4aePXsyd+5cypUrh0qlUh5z9+5dnJyclMcaboRLcEcI8V9kCO6YmpoSHh7OlStXOHfuHK9evaJIkSIUKFAAGxubDCvgvqsYmZiYiFarZcuWLVStWpWYmBhWrlzJlStXaNCgAeXLl2fmzJmEhIRQt25dGjVq9MHHJYT4/1Or1ajVavLnz8/BgwdJSEh4q9OhTqfDxMSEV69eMW3aNLp06YKrq2smj/zzYLh2rVixIl9//TXz588HoHfv3kRGRjJ//nzy5cuHo6MjcXFxVK1alWnTpgEoAR43NzfWrVvHo0ePuHbtGgUKFCBLlixYWVlJcOcDs7Cw4McffyQgIIDhw4dja2tLzZo1yZIlC3q9XjkXANauXUtkZCTly5fn22+/BWSZLCGEEEIIIYQQQgghROaQ8I54J+Miwrlz55T/L1asGCYmJpiYmNC1a1dUKhWjR4+mR48ezJkzhwoVKgDw66+/MnbsWAICAmjcuDEgN8KFEF+Wv+oi9q6wjKmpqfK4QoUKUahQobcekxEFXOOxJiYmki1bNiwsLPDw8GD16tWsX7+ebdu2odFoKFGiBFu3bqV48eLY29tTrFgxWrZsSVJS0gcflxDiwzDMN8WLFyctLQ0/Pz9WrVqFhYUFWq02XXh67NixTJ06lbp160p45x+4fPkyDx48YOHCheTJk4fU1FTmzJmDp6cn1atXx9LSEo1GQ86cOTlw4IAS4NHr9QQHB2NmZoZarSZnzpw4Ojoqnw8ZNe//l5mamtKjRw8uXbpEREQEffv2ZcCAATRu3JgCBQpgYmKCTqcjNDSUkJAQkpOTWbRoEXZ2dtIhVAghhBBCCCGEEEIIkWlk2SzxFuOb1iNHjmTu3Lk8ePCA7Nmz4+HhoXyTGODJkyfMnz+fMWPG4O7uztChQ3n48CFhYWHcuXOH48eP4+bmllmHIoQQGcI44Lh3717Onz/PgwcPcHd3p2HDhmTPnh34NJYJNB7r7NmzWblyJa1ateLnn38ma9asAIwePZpXr17h5uZGu3btsLa2Bl4XlYcMGcLcuXPZtWsX5cuXz7TjEOK/7u8s5ZOUlETVqlU5e/Ys/fv3Z+LEiVhYWCjbo6OjGTFiBM7OzqxevVqWzvqHzp49S58+fdi/fz8AI0aMoF+/fjg4OCj7GDqsJSYmUrVqVS5fvszAgQOVAM+n8LnwpTO8xk+ePCEwMJDly5fz5MkTXF1dqVevHqmpqfz2229cuHCBAgUKsH79etzc3KQDkhBCCCGEEEIIIYQQIlNJeEe814gRIxgzZgxlypShXLlyxMTEcOvWLZo1a8bChQuVgs+TJ09YvHgxkydP5vbt26jVatzc3NixYwf58uWTG+FCiC+KceF16NChzJo1ixcvXijbK1asSJ8+fWjfvn2mdxwzHqufnx+zZ88md+7cjB07lh9//PFPi8h6vZ41a9YQEBCAu7s7q1evJlu2bB9z+EL85x09epTU1FSqVKkC/HmAx3C9df78eRo2bMi1a9eoVasWPXv2JHfu3Pz6669ER0ej0+nYv38/rq6uEiT5Fxo0aMC2bdvQ6/V069aNuXPnAv8L7Rj/2TjA061bN8LCwpR9RMYy/Nt+/vw5W7duJSoqivXr16PT6dBoNHzzzTc0bNiQvn37kjNnTvl9RQghhBBCCCGEEEIIkekkvCMUhpvWer2ehIQEatasSa1atfD19SVfvnxcvHiR0aNHs2zZMpo0acKSJUuUAM+LFy+4ePEi69atw87ODg8PD3Lnzi03woUQX6zAwEBGjx5Nhw4d6NWrF7ly5WLXrl34+PgAMG3aNH7++edMHuVrgYGBjBs3jp49ezJgwAAKFy78zv2MC/mTJk0iPDyctLQ04uLicHFxkUK/EB+JXq/n+vXr5M+fn3z58hEZGUnlypWVbe8L8Bi2Xbt2jXbt2nHw4EFlm4WFBeXKlSMyMlK6jPwLhte2bdu2vHr1ips3b3LixAkGDhzI1KlTgfSdzgx/TkxMpHDhwpiYmHD58mXs7Owy8Sj+W948Vy5cuIBGoyE1NZVSpUqhUqlQqVTy2SaEEEIIIYQQQgghhPgkSHhHvHVje9WqVVSoUIGGDRuyaNEiKlasqNzUvn79OqNHj2bBggVvBXjefD4pCgkhvlSHDh2iZcuWVKxYkeDgYAoVKgS8XpKmW7duODg4cPTo0U+iSLt3717atGlDjRo1GD9+PAUKFFC2nTt3TilYFilSBIBTp07RunVr7t+/T7FixVi5ciWurq4ypwuRCXx9fZk0aRKlSpUiNDT0b3XgMVyzvXz5kr1793Lx4kXS0tIoVaoU5cuXx87OTs7nv8n4dTb+s1ar5dKlS3Tu3JnDhw+nC/CkpqZibm4OvF7GLFu2bDx48IDU1FTy5Mnzt5Y/E+9meO3e9Rr+nXPifc8nhBBCCCGEEEIIIYQQnwLp2/4f9OYNbONvnC5dupSff/4ZV1dXsmbNStGiRZV99Ho9bm5ujBgxAoAFCxbQsWNHli5dSrZs2ZQb4Iab4FIUEkJ8qS5dusT9+/fp2rUrhQoVQqPRsGbNGnx8fLC3t+fQoUPY2dmRlpbGq1evyJo1a6aN9cqVKzx8+BAPDw8KFCiARqPh8ePHzJo1izlz5vDq1SucnJzw9fWlU6dO2NnZUb58eQoWLEifPn1wdHSUQr8QH5nhnAsODsbS0pLRo0fTp08fQkNDqVq16nsDDABqtRqdToeVlRUNGjSgQYMG6bbrdDo5n/8G43lPp9Px8uVLzMzMsLCwwMTEhKJFizJz5kwGDBjA9OnTAZg6daoS3Nm2bRt79+6lU6dOSsBT5tJ/z/i1u3fvHi9evCAtLQ13d3eyZMnyp18ceF9XHQnuCCGEEEIIIYQQQgghPiXSH/w/yHADu2fPnqxYsSLdz2rWrEmTJk148OABt27d4sqVK8DrooWhUOTq6sqIESPo0qULGzdupEmTJjx79kxugAshvniGZnUnT55ErVZTokQJAKKiovDx8UGtVnP48GEcHBwAuHz5MuPHj+fZs2eZNuYrV66g1Wo5c+YMd+7cISIigh9++IHx48eTP39+mjRpwoULFxgxYgQXL17E1dWV+fPnExAQgKOjoxT6hcgEJiYmpKamAhAUFMTEiRM5deoUAwYMYN++fcD/gtXvYriuM95u+LMsD/TXjOe9kJAQmjZtSsmSJalTpw7jx4/nzp076HQ6ypcvT0hICBUrVmT69OkMGjSIly9fsnr1agYMGMCqVavSdWCTufTfMX4/Jk2aROPGjSldujTly5enWbNmREREAK9fX51Ol5lDFUIIIYQQQgghhBBCiH9Nls36j4qOjqZ169aUKlWKESNG0Lx5c2XbrVu36N+/P+vXr6dOnTps27YNAI1Gg6mpqfJN75s3bzJ48GD27dvH6dOncXR0zKzDEUKIj8rQpSwsLAw3Nzd69eqFWq0mPj4+3VzYtm1bdu7cydGjR3Fzc8uUsd68eZN69epx/vx5HBwcePjwIQUKFGD69OmUKVMGJycnRowYwZgxY9i/fz9Vq1bNlHEKIf7HuIPIyZMnMTc3p127dpw+fZpSpUoxY8YM5VyVpX8yjre3N1OmTCFnzpy4urpy5swZXr16xbfffounpycNGjTA1NSUY8eO4eXlRWxsLI6Ojjx//pwcOXKwZ88e8ufP/95lm8Q/Y3g/KlWqRLVq1QAICwtDp9PRsWNHwsPDM3mEQgghhBBCCCGEEEII8e/Jsln/UXXr1mXy5MkMHTqUwMBAACXA4+zszKxZs1Cr1axbt44mTZqwceNGTE1N0wV4XFxcmD59OpaWltjb20thQgjxRfmzgvhXX32FiYkJw4cPx9zcHAsLC44ePYq9vb2yT0REBHFxcbRu3ZrcuXNn6FjfXCokLS0NtVqNiYkJLi4uREdHExAQgIWFBcWLF2fgwIFky5ZN2f+PP/7AwcEBV1fXDB2nEOKv6fV65Xz29fVl+fLlqFQqnJycyJYtG8ePH6dfv36EhoZSpUqVP11CS/wzxteymzZtIiIigoEDB9K9e3eKFSvG4cOHmT9/PsuXL+fRo0dYWlpSp04dvvnmG8LDw1myZAmnT5/GwcGBcePGkTdvXuXaWfz/REZGEhISQu/evRk0aBAFChQAIGfOnPj4+LBnzx5evnyJlZVVJo9UCCGEEEIIIYQQQggh/h3pvPMf9vz5c+bMmYOfnx/FihUjKCgoXQee27dvM2DAANatW0fDhg359ddfAd5ZhJDgjhDiS2CYy4zDMA8fPkSv15MlSxasra2VfQ1LpMDroqKHh4eybcmSJYwdOxYLCwu2bdtG7ty5M6y4bjzWyMhIYmNjuXz5MjY2NnTr1o3SpUvj7Oys7G88Dr1ez5o1a/Dx8aFs2bIsWbJECp9CfCKCg4MZOnQo3t7edOjQga+//pojR44wf/585s6dS6lSpZg1a5Z04MkA169fZ9euXYSEhLBu3bp0ndPu3LnD4sWLGTVqFPXr12fVqlWYm5sDr4OTZmZmpKSkYGFh8VawUvxzhn/XXbp0YdOmTezYsYMSJUqg0WiIiopixIgR6PV64uPjyZEjB6mpqcr7IYQQQgghhBBCCCGEEJ8TCe/8x/2TAE+jRo3YuHEj8HaXByGE+JzdvHkTFxcXgHSFvwkTJhAdHc2dO3coV64cHh4etG3bFoBHjx4xbdo0xo4di6OjI23atMHd3Z3Y2Fh2796Nra0te/bswd3dPcPmTONi/ZAhQ5gxYwa2trbkzJmThIQEnj17xk8//UT37t3fuRzWzJkzCQkJIS0tjdjYWJydnSUAIMQn4O7duzRo0IDk5GQ2b96sdBkx8Pf3Z8KECZQuXZqZM2cqSwjJ+fv33blzBzs7u7cCi76+vqxcuRInJydy5crF+vXr0Wq1qNVq5bW9fv06np6ebNiwgcWLF9OhQwdAXv//j127dnHy5Em8vLze2vbs2TOqVKmCnZ0d+/fvJy0tjbVr1+Lj4/PWkpW///47jx49Us4JIYQQQgghhBBCCCGE+FxIq5T/OBsbG3r27MmECRM4d+4cgYGBrFu3TtmeN29eZs6cSfPmzdm0aZNyI1yCO0KIL8WBAwdwc3Nj7NixAEpwx8/PD39/fxISEnB2dubXX3/Fw8ODKVOmAGBvb4+vry9z5szB1NSUBQsWMGTIEE6ePEmTJk2Ii4vL0OAOoBSJp0yZwrRp0+jVqxf79u3j999/Jy4ujpo1a7JkyRKWL1+OVqsFXndPu3jxIpUrV2bMmDFky5aNvXv34uzsjFarlcKzEJ8AjUbD7du3KVOmjBLc0el0GDL348aNo3379vz22294eXkRGxsLIOfv3/Tbb7+RP39+pk6dSmpqarptuXPn5ubNmxw9epTk5GTg9XWv8fcd3Nzc6N69OwDnz59Xfi6v/7/z5MkThg4dyuDBg5kxY8Zb201MTDAzMyMtLQ3gvcEdjUZD+/btiY6ORqPRfNRjEEIIIYQQQgghhBBCiP8vCe+IvxXgCQkJoXbt2vz2228kJiZm4miFEOLDevXqFZaWlgQFBTF58mTgdWF36dKl9OvXj+3btxMfH8/atWtxd3fH29ub4OBg4PX82b17d2JjY4mPjycmJoa4uDjmzJlD3rx5M7xLmV6v5+bNmyxbtoyKFSvSv39/ihcvjl6v57fffuPChQu4uroyevRopfis1+u5fPkyarWaDh06sHnzZtzc3KSjmhCfkNTUVDQaDb/99hs3btwASNf5Ra/X89133wFw6tQp2rVrx9GjRzNtvJ+bxMREHB0dOXPmzFvbvLy8mD9/Pjqdjh07djB37lzg9euv0+mUIOTXX38NvO7CJv5/7OzsGDlyJJUrV8bLy4upU6cq23Q6HVZWVtSqVYv4+HgGDhyIn58farWaQ4cOKcEdeL2c5fXr1ylevLh8ngkhhBBCCCGEEEIIIT47ppk9APFpMAR44HW3icDAQABlCa08efKwbNkyTE1NcXBwQKfToVZL9ksI8fn79ttv2bRpEx4eHvj4+JA1a1a++eYbrKys6NmzJ0WKFAGgWbNmZMuWjX79+jF06FBUKhU+Pj4AuLq6YmJiQvHixZXn1ev1GV48VKlUPHjwgDNnzjBy5EgKFy6MRqMhOjoaX19f1Go1R44cwd7eHp1Ox71798iTJw9169alYsWKZM2aFXNzc3Q6nRQ6hchkxksuFShQgJYtW7Js2TL2799Pu3btlP00Gg2mpqbUqVOHsmXLkidPHnbs2IGzs3NmDf2z8/3337Np0yby58+Pubk5Bw8epESJEtjY2ADQuXNnVCoVXbp0YeLEiTg6OtK8efN0174bNmwASDfvi3/O8O++QYMGmJqaEhAQwJAhQ9Dr9QwePFh5zb///ntCQ0OZOXMmuXPn5s6dO+meIzo6mtmzZ/P111/TsmVL6YIkhBBCCCGEEEIIIYT47Ej6Qije1YFn/fr1yvbcuXNLcEcI8UUxLINSu3Ztli1bRq5cuejbty99+/bF1dWVr776Cr1er3Ra+PbbbwkNDaVo0aL4+fkxceJE4PWSHjqdLt1zf6zCYVJSEhqNhly5cgEQFRWlBHfi4+NxcHAAXneaaN68OXFxcZiampIjRw5liTCZ04X4+AzzioFxVx2ABg0akDVrVry8vNi3b5+yn6mpKVqtlkWLFvHq1Ss2bNhAYmIiuXPnfus5xdsMc3XJkiWxsbEhNDSUqlWrsnDhQl6+fKns16lTJ+bMmcMff/xBnz59mDp1Kk+ePOHZs2csWLCA2bNnkz9/flq1apVZh/JFUKlUynJY33//PdOmTaNixYp4e3sTEhKi7NegQQOl6929e/eIjo7m4sWLPHjwgMDAQHx8fEhNTSUyMpIcOXK89ZkshBBCCCGEEEIIIYQQnzrpvCPSMe7AM3z4cPr06YOFhQX169dX9pEirxDic2Xc2QJeF89NTV9/FH777bcsXLiQrl27cvToUUqUKJFuKSnDY2vXrk1oaCh9+/bFz8+Ply9fMnLkyEybG3Pnzk327NlZtGgRJiYmBAYGKsEd4+VEpkyZwpkzZ5QiqRAi8xjPLcuWLePy5cvcvXuXHj16UKhQIbJmzUrLli05e/Yso0aNonnz5gQGBlKrVi1KlixJZGQkq1evxsXFheTkZKysrD5Kt68vgSEcZVCkSBHKlSuHn5+f0m3HysoKgO7du6NSqejRowdDhgxh4cKFJCYmYmdnh5WVFZs3b1ZCU/La/ztarRYzMzMA1q9fz71799BoNKjVajw9PVGr1fTt2xeAgQMHYmZmRkBAAK1bt8bExER57cuXL8+KFStwcXGR90MIIYQQQgghhBBCCPFZUunfvIMtvgjv647zZuH6fZ4/f87UqVNZsGABBw8exMnJKSOGKYQQH43x/PfixQusrKyU/w8PD6dDhw5YW1uzY8cOOnTowP379wkMDFSWEdTpdKhUKuUxe/bsoU2bNiQnJ3P79m1sbGwyrNvOX3U8a9WqFWvWrMHe3h5ra2suXLiApaWlctwrVqxg6NChlC9fnoULF5I1a9YMGacQ4p/x8fFh8uTJmJqaotFosLOzo2/fvvz8888ULFgQgAkTJhAREcHVq1dRq9U4ODiQkJCAs7Mz+/fvx83N7W9f3/3XGc+lM2bMwNHREQ8PD3bt2sXw4cM5duwYkydPThfgAVi8eDGdO3fGycmJJk2aMGzYMOzs7LCxsZGgyP+D8b/bIUOGEBERQdGiRXFzcyMlJYWNGzcCMHXqVAYOHKg87vDhw5w6dYrjx4+TPXt2qlSpQtWqVcmePbu8H0IIIYQQQgghhBBCiM+WhHe+QMY3rS9fvoxWqyV37tzY2tq+tf3PvHjxAp1OR9asWeVGuBDii1G3bl2KFi1KcHAwWbJkwdPTk5CQEObNm0fnzp1RqVTs2bOHtm3bkpCQwKRJkxg8eDDwdoDnwIED5MuXDycnpwwrnhvPv6dPn+bx48c4ODjg7u6uFJdv375NixYtOHLkCJ06dWLBggXK42fNmsWMGTPQ6/Xs3buXvHnzSqFfiExifO7Nnz+ffv360bJlSzp37sylS5eIjo5mz5499O7dm/79+1O4cGEADh06xI4dO9i1axdZs2alYMGCDB48mDx58sg12r8QFBREUFAQvr6+DB06lGzZsrF9+3YCAwM5fvz4OwM8ERER9OzZk5IlSzJ+/HgaNGgA/HW4Uvy18PBw+vTpw4ABAxg0aBCurq4AREZGMm7cOM6fP8+0adPw9PT80+eR90IIIYQQQgghhBBCCPE5k/DOF8a4KDR8+HAiIiJISEigTJkyNG/enGHDhgGg0WiUpWL+yXMKIcTn7Pz583Tq1Iljx44xduxYrly5QkREBN7e3vTv3x9nZ2dl3127dtGuXTvu37//pwEe+PuhyH/KeP4NCAhg1qxZPHv2jGzZslG9enUiIiLInTs3aWlpHD9+nH79+nHs2DFcXFwoVaoU169f5+LFi7i7uxMTE4O7u7sU+oXIJMbBguTkZAICAvj999+ZPXs2+fLlQ6fT8ccff+Dv7090dDR9+vTB09OTQoUKKc/x4sULrK2tlfNYzue/x/A66fV6EhMTqV27NhUqVCAwMBB3d3dlv78K8MyZM4fevXtTokQJxo0bR6NGjTLhaL487dq1IyYmhj179lCyZMl0/643b95M7969uXnzJrNmzaJPnz7A/5Y/U6lU8ruKEEIIIYQQQgghhBDiiyDhnS/UmDFjGDFiBOXLl6dYsWJs2bKFhIQE2rVrx9KlS4F/FuARQogvxfHjxxk3bhxr164FoH///vj5+SndcwClCPhnAZ6P+e1+w5xes2ZNKlWqxIEDB9i/fz/58uVj7969ODs7o9PpeP78Of7+/hw7dowrV67w1VdfUb16dfr27UuuXLmk0C/EJyAgIIDnz58TGxtLhw4dGDhwYLprshs3buDt7c3q1avp06cPXl5eFChQAMi4oOB/xdKlSylfvjzNmjVjwYIFVKlSBUg/p/9VgGfu3LlKgGfYsGH8+OOPmXIsX4qUlBTKly/PixcvOHfuHObm5m+FZA2hKYDp06czYMCAzByyEEIIIYQQQgghhBBCZAhJbnxhDN8o3rx5M126dGHYsGG4u7tz/vx5+vfvz7Jly0hJSSEqKgpTU1MJ8Agh/jMM38wvW7YslpaWys9VKhUODg7Kn433/fbbb1m2bBnt2rVj6NChvHjxghEjRmR4cMe4QJ+amsq6devo0KEDo0ePxtXVFZ1Ox+DBg5kxYwZVq1YlLi4OZ2dnsmXLxqxZs9BoNDx8+JBcuXIpRWkp+guR+e7cucPevXs5cOCA0gkGwNTUVJl3XF1dmTRpEgBhYWGYmJjQr18/ChUqJOfw/8OKFSv4+eefyZUrF1myZEnXcUetViuv//fffw9AYGAgQ4cO5eXLl/Tv358sWbIA0KNHDwB69erF8ePHJbzz/2RhYUHx4sWJiopix44dNGzYUPmMNXx+tW/fnrlz5/LkyRMGDhyIpaWl8j4IIYQQQgghhBBCCCHEl+LjtQ0QGUan0yl/VqlUaLVaEhISaN++Pe7u7mg0GooWLcrcuXNp2LAh0dHRtG7dGkAJ8AghxJfozflRp9ORnJyMRqOhU6dO1K5dm5kzZzJ69GgeP3781r6AEuDR6XTMnTuXZ8+eZfi4DQX6kJAQdu/ejV6vp0ePHri6upKWloZarWby5Mn4+Phw8+ZNqlatyu3bt4H/dVVzdHRUjsX4OYUQmSdPnjzMmDGDdu3aodVqiYmJ4fLly8D/lv8BlABP27ZtCQkJYfHixWi12swc+mevVq1atGzZkhcvXpCQkMDZs2cBlNfV+PX//vvvGTVqFM7OzoSHh7/12vfo0YOdO3cybty4j3sQX6i6desCMGPGDM6dO6f83PA5rFaruXfvHjVq1KBRo0bUq1cvU8YphBBCCCGEEEIIIYQQGUmWzfrMGXdSOHr0KCkpKSQmJuLr68vmzZspUKBAuhvf165do1+/fmzevJkff/yRqKiot55HCCE+d7dv3yZv3rxv/dzQWeHVq1dotVouX75MQEAAmzdvZtiwYQwaNAg7O7t0++t0OkxMTNizZw+FChUib968yvNkpHXr1tGyZUtcXV3R6/Xs2LGDAgUKKAVmtVqNTqfD39+fiRMn4uLiwsGDB8mTJ4/M6UJ8goyXZjp69CgTJkzgl19+wcfHh/79++Pk5ASQbn65evUqEydOZOjQobi6umba2D83xq91SkoKarUaMzMz7t27h6enJ6tXr6ZSpUrs2rULS0vLdJ0ojV//ffv2UaRIEXLlyvXeef9jL6P4OXrzNXrX/3fs2JGVK1fSrVs3evfuTalSpZTtS5YsYezYscTExODq6irdQ4UQQgghhBBCCCGEEF8kCe98xoyLCP7+/oSEhPDixQty5MjB8+fPWbx4sdJhB/53o9w4wFOnTh22bduWWYcghBAf3NatW+nYsSMLFy6kYcOGb21/swB76NAhxowZowR4Bg4ciL29PQC//vormzZtYsqUKVhZWQEfLuz4ZvHyXYXIbt26sXjxYgC2bNnCd999p/z9hv8aB3iyZMnClStXyJ079/97fEKIf+/vBDqOHz/OyJEj2bJlC35+fvTu3fudAR5Z+u6fMX6dVq5cyYEDB2jUqBE1a9bE0tKShIQEPD09WbVqFd9++y07duwAeG+ABySg8/9h/H5ER0dz9OhRjh49SuHChalbty7NmjUD4NSpU/j6+rJ161ZKliyJp6cnJUqUYPfu3cybNw9LS0v27NlD9uzZM/FohBBCCCGEEEIIIYQQIuNIeOcLMGnSJPz9/alduza1atVi/fr1HDlyhBIlSrBmzRoKFiyo7GsoPly/fp127dpx4sQJrl+/joODQyYegRBCfBgHDhygXr16lC9fnvHjx1OxYsX37mtcnD18+DBjxoxh06ZNDB06lA4dOvD7778TGBjIzZs3uXDhArly5fpg4zT+u8+cOUPhwoUxNzcHYMiQIRQuXJgePXoA0K9fP8LCwnB2dmbnzp0UKlTorWK+TqdjwIABrF69mhMnTpAnT54PNlYhxD9jHFbYuHEjv/32G7du3eKrr77Cw8Mj3TXXiRMnCAwM/MsAj/h7jEM2fn5+REREoNPpWLRoEY0aNUKtVqNWq/9WgEf8/xm/H97e3oSFhQFgYWHBkydPAOjbty8DBgygUKFCnD9/npkzZxIeHp7ueQoWLMi2bdtwd3eXIJUQQgghhBBCCCGEEOKLJeGdz5DxTesXL17w008/kTVrVsaOHYu7uzsPHjxg/PjxzJw5k1KlSrFmzRrc3NzeevytW7ewsLDA0dFRboQLIT57ycnJtGrVikuXLjFv3jyqV68OwMOHD8mRI8c7H2NcHI+Pj1eWscmWLRtarRY7Ozv27dtHvnz5MmSebNWqFTt27GDLli1UrFiRgQMHMnPmTPz9/fHx8SFbtmwA9O/fn9DQUEqXLk1UVBQFCxZUxmP832fPnmFraysdOoTIJG+GR0JCQkhOTsbCwoKUlBTy58/PL7/8wtdff608xjjA4+/vT7du3XB2ds6sQ/giBAYGMnbsWHr16kWvXr3Svd6G9+h9AR6ZPz+8cePGMWzYMHr27EmnTp3Ily8fW7duZdasWRw5coTWrVszYcIE3N3dAYiJieHKlSvcunWLYsWK0aBBA3LmzCnvjRBCCCGEEEIIIYQQ4osm4Z3P2IQJE8iVKxeTJk1i/PjxNG3aVPnG8JMnTwgODmbSpEmULl36rQDPu5ZjEEKIz1lSUhLlypXDxsaGuLg4smTJwsCBA7GyssLHxwc7O7t3Ps54Pjx37hy//vormzdvpkiRIgwbNgxnZ+cMKRi+evWKadOmMXPmTBwcHChSpAhr167Fx8eHfv36vfX39unTh/DwcEqWLEl0dPQ7AzxvHo8Q4uMxPvdGjBjBmDFj6NSpE506daJGjRoMHz6csWPH4uLiwqpVq6hUqZLy2BMnTjBq1CjWr1/PxIkTGTRokFyb/UtxcXE0b96cGjVqMHnyZCUQYswwZyYmJuLp6cnKlSspXbo0x48f//gD/sJdv36d77//HhcXFxYtWoSLi4uy7dixY0ydOpUVK1bg7+/PmDFj3vs8EtwRQgghhBBCCCGEEEJ86SS885k6ceIE1atXR6/XY2Zmxi+//EKtWrXQ6/Xo9XrUajVPnz5lwoQJ7w3wCCHEl6Zdu3asX7+eiIgI9uzZQ0REBH5+fvj5+SldbN7lzcBLcnIypqammJmZZWjBMDU1lVWrVtGjRw9SU1Np06YNwcHB6Yqb7wrwlCpViujoaAoUKCABTCE+MWvWrMHLy4t69erh6+urBO2+/vprEhISePLkCblz52bt2rVUqFBBeVx8fDyzZ88mKCgIV1fXTDyCz1tERAQ9e/Zk27Zt1KlT5737GebWhIQEOnfuzL59+7hy5Qo5c+b8iKP9/L0vMGr4+eHDh6lcuTLDhw8nKCgIrVYLoHyuHThwAA8PD27cuEFsbCxVqlT5qOMXQgghhBBCCCGEEEKIT4VU+z5TX3/9NaGhoZQoUYKkpCR27tzJ8+fPUalUShcGW1tb/Pz88Pb25uzZs9SqVYtbt25l9tCFEOKDM+RQAwICKFGiBF27diUiIgJPT08GDhz4p8Ed4K3CY5YsWTAzMwPIsOCOXq/H3NycS5cukZKSgoWFBfHx8dy/fx+dTqfsZ2JiohQ7w8LC6N27NydPnqR27dpcu3ZNgjtCfEJevnxJVFQUFhYW9OvXj4IFC/L8+XO++uorHj9+zJQpU/Dz8+POnTu0adOG+Ph45bEVKlRg7ty5uLq6Kue8+PsMr9mhQ4cAsLGxAUg3nxrvl5SUxMuXL8mZMydLlizh2rVr5MyZ8639xfvpdDrl8/P+/fvExMQor7/h56ampsDrcwNef6YZf65WqVKF3r17A6+79AghhBBCCCGEEEIIIcR/lVT8PiOG4rSh246Hhwe9e/emSJEihIeHs337djQaDcBbAZ5u3bqh0+mUG+hCCPElMRQJixcvTo4cOdBqtVhaWpI7d26yZ8+eyaNLz1AYNoy5XLlyBAUFERAQwLNnz2jfvj1xcXHpCshqtVr5DAgNDaVt27a8fPkSS0vLj38AQoj3srKyomLFivTp04dSpUqRnJxM48aNSUxMZOzYsfz888+MGTOGcuXKcf36dVq1asW+ffuUx2d0aPBL8mbzUMNrVrp0aQAuXboEpA9n6vV6TExMSEtLw8vLixMnTqDT6ciRIwc5cuSQTmb/gPFrFRAQQO3atWnUqBFz5sxRXnsAa2trLC0tWbhwoRLsMUhLSwOgVKlSANy+ffsjjV4IIYQQQgghhBBCCCE+PXJ3+hNn/M1rlUpFamqqUoQwMzPjp59+YujQoWTLlo2+ffsSExPzzgDPuHHjOHXqFLlz55ZvFAshvkharZZDhw6xefNmGjVqRMGCBZk4cSILFizgyZMnmT084PUYDcXO/fv3s3HjRmrVqsXw4cPx8/NjxIgRPHr0iO7du6cL8KhUKlQqFdeuXQNg+fLlXLx4kdy5c0uHDiE+MYMGDcLLywuAJUuWEB8fT9++fWnfvr2yT4kSJShRogR37txh4MCBpKamZtZwP0tvdnx59eqVsq1QoUIA+Pr6cuLECVQqFXq9Hq1WqzwmPDycFStWcPv27XRhHQnu/D3GwZ169eoRHh5O3rx5iYmJwcfHR3kPAIoWLYqPjw+PHj1i9uzZnDt3DvjflxHg9dJZFhYWlClT5uMfjBBCCCGEEEIIIYQQQnwi5A71J0yr1SrfIl6yZAldunTh22+/pWnTpkRFRXHt2jXMzc1p06YNI0eOxNLSkp49e74zwJM1a1ZsbW3R6/VSmBBCfJFMTEyoVKkShw8fZsqUKcyePRs3NzeGDh3KqlWrePr0aaaOT6fTKXP6qFGjaNOmDUOGDOHIkSNoNBpMTEz4+eefCQwMVAI8sbGxyuM3bdpEq1atiIyMBMDe3l7pIiGE+DQdOnQItVpNr169MDc3V35++fJlypUrx7x589iwYUO6beLPGYcgQ0NDad68OSEhITx//hyA+vXr069fP+7du0f//v05cuQIKpVKmSujo6MJCwvjm2++oU6dOpl2HJ8r498lGjRowOHDh/Hz82PlypXUq1ePYsWKpdsXoGvXrjRv3pzIyEiGDRvGrl27UKlU6HQ6oqKiWLZsGSVKlJDwjhBCCCGEEEIIIYQQ4j9N1lD6RBkXZIcMGcL06dNxcHAgd+7cXL16lY0bN9K0aVN8fX2pVKkSP/30E2q1mhEjRtCjRw8iIiKoX78+pqam6cI6xksHCCHE5+x9y5uUL18eAGdnZ4KDg/Hz82Po0KEAtG3bFltb2486TgPDWL29vZk2bRqtW7emV69e1KhRA3g979vY2NCpUyfgdcCnR48e+Pv78/TpU+bOncvdu3epVq2a8pwypwvxaVOr1aSmpvLo0SOcnJzQ6/UsW7aMixcv0qtXL3766ScgfWBbvJ9xCNLX15fZs2fj6upKvnz5sLGxUV7H6dOn8+TJEyIjI/nuu+/o1asXefLk4fjx42zbtg1zc3O2bNmCvb29LJX1Dxk+d8aMGUNsbCy+vr707t0bGxsb9Hp9us8lw59dXFwYNmwY5ubmrFq1ivXr11O3bl0eP37MxYsXsba2JioqSt4PIYQQQgghhBBCCCHEf5pKb/hKpPgkTZs2DW9vb3r37k2/fv0oUqQIiYmJtGvXjh07duDh4cHChQsxMzNDo9GwcuVKRo8ezZUrV4iJieH777/P7EMQQogPzrjQffjwYW7cuEFiYiK5c+fmhx9+wNT0dTZVo9GwZ88efH19uXr1KuPHj8/UAM+KFSvo1q0bnTt3xtvbGzc3t3TbDYXPFy9esGzZMiZOnMgff/yBWq0mX758bNu2jXz58kmhX4hMYhwseDOo8C5hYWH069ePggULMm7cOI4ePUp0dDRmZmbs3r2b3Llzf4xhf3FGjhzJ2LFj6dmzJ56enumWaTLm5+fHkiVLSEhIQKfT4eDgQMWKFQkLC8PFxUXm0n/p6dOn1KtXj5cvX7Jr1y4cHBz+1vmQlJTEokWLmD9/PtevXydv3rxUqlSJUaNGkTdvXnk/hBBCCCGEEEIIIYQQ/2kS3vlEvOtm9Z07d2jWrBl6vZ6lS5dStGhRNBoN69evx8fHB4AjR45gb2+PRqPB1NSUtLQ0Fi9ezIIFC1i9ejV58+bNjMMRQogMY1w8HzFiBHPmzCExMVHZXrlyZcaMGUOFChWwtrZOF+C5du0aEyZMoFWrVtjZ2X30sXfv3p1Vq1axf/9+SpUq9c59DAXQV69eceXKFTZs2EDWrFlp1aoVuXLlkuKmEJnEeO5JSkoiW7Zsf6tLiJeXFxEREbx8+RKAEiVKsGHDBtzc3OR8/hdiY2Np3bo1VapUITg4mAIFCijbzpw5g16vR6vVUrp0aQBOnz7N48ePuXfvHl9//TWurq7puvSIf27Xrl3UqVOHESNGMHLkyL88D94M9iQlJZGWloadnR06nQ4zMzN5P4QQQgghhBBCCCGEEP95smxWJjt79iyFCxd+503rBw8ecPr0aYYOHUrRokXRarWsWbMGHx8f1Go18fHx2NvbA3Dr1i2yZ8+Ora0tnTp1wsPDAysrK7kRLoT44hgKhP7+/kyYMIEff/yRhg0bYmNjw9y5c9m5cyddunRh/PjxNG3alCxZslC7dm2Cg4MJCAigZ8+eZMmShfbt23/UcSclJXHo0CEKFiyoBHfeLGgaCqApKSlYWlry1Vdf8dVXXyn7yZwuROYxzD19+/YlPj6e/fv3Y2lp+d79DefrtGnTaNOmDdevX8fW1pYKFSpgb28v5/O/dPXqVRITE2ndujUFChRAo9Hw4MEDQkNDCQ8PJzk5mZw5czJw4EAGDBhAiRIl3noO4+VpxT/37NkzVCqVEoL9q447KpWKixcvcvHiRRo3bky2bNmUbYb3Qd4PIYQQQgghhBBCCCHEf92ff1VYZKjffvuNEiVK0LRpU9LS0jAxMUGr1SrbHz16REpKCg4ODgCsXLkyXXDH0dERgIcPH9KpUyfi4+MBMDU1xcrKCpAb4UKIL9Pu3bsJDw+nffv2TJ48mU6dOvHjjz+yevVqJk+eTEpKCsOHD+fSpUvA67mwdu3aBAYGUrduXWrVqvXRx6zX69Hr9dy+fZs//vgD4J3BnefPnzNr1iwePXqkbDPsJ3O6EJnvyJEjXL58mStXrgCvz913MTExUbZVqlSJNm3aUL9+fezt7dHpdHI+/0vXr19Hq9Vy8uRJrl+/Tnh4OE2bNmXixIkUL14cDw8Prl+/zrhx4zhz5sw7n+OvwibiNeMGrcb/zlNTU9Hr9Rw+fJiUlJQ/fT01Gg3w+veY8PBwEhISMm7AQgghhBBCCCGEEEII8RmT8E4mcnFxoXz58mzZsgUPDw8lwGO4yZ0/f37y5s3L4sWLWb58OQEBAW8FdwAmTZrEkSNHMDMzy6xDEUKIj+ry5cs8efKEFi1a4OrqCrwuENra2tK9e3d69+7NlStXGDp0qPIYExMT6tWrx/r163F2dk4XlswohmKnXq/H1taWBg0a8ODBA3bv3q38HF536DB09fDx8WHx4sU8ePAgw8cnhPj7DOfrsGHDePr0KUuXLgX40+WC3rftr5baEu/XvXt3ypQpw/jx4ylTpgyenp48ffqUDRs2EBUVxdy5cxk7diwJCQkyj/4/vNkZTq1WK7+j1KhRg3z58nH69Gnu3LkD8M7PVL1ej6np60ava9as4cmTJ+TMmfMjjF4IIYQQQgghhBBCCCE+P1I5yCRarZYcOXIQExND7dq1WbNmjRLgMTU1RafTkSdPHqpVq8aRI0cYMGAAAOfPn1eCO3q9nmXLlrFy5Urq1avHN998k5mHJIQQH82NGzcAlPlQo9FgamqKXq/HxsaGAQMGULJkSfbt28fVq1eVx5mYmGBhYaH8+UN7swOHoUBvKIBWqVIFa2trunfvzvbt29/qqLNmzRq2bdtGoUKFyJs37wcfnxDi3zOcryVKlKBQoUIsXLiQU6dOZfKovkzGQRC9Xk9qaiqpqakA5MqVi9WrV/PTTz/RqFEjRo8eTXx8PPXq1SNXrlwAXLhwAXt7e9zc3DJl/F8Cw7/36tWr8/PPPwOvu3tqNBqyZs1K7dq1+f333xk5ciSQvtMUvP48NDzHzJkzuXHjBq1atfq4ByGEEEIIIYQQQgghhBCfEQnvZBLDEln29vZERUW9FeBRq9WYmpoSGhpKsWLFePToEZUrV1aKzgBhYWGMHDkSU1NTQkJCyJo1a7r29kII8aVydnYGYMGCBco3+w1dAlJSUpQuNy9evCAxMfGjjMm4e866desYPnw4jRs3Jjg4mK1btwLQvHlz/P39AahXrx7BwcHs2bOHhw8fMm7cOPz8/EhLS2PGjBlYW1vLnC5EJnmzi4hxKCFfvnx4eXmRmJjIb7/9BiDn6gek1WqVQOPixYvp1q0b3333HU2aNCEqKopr166RP39+li1bxtKlS/H39ydbtmzA6/chOjqaffv2Ub16deny8v90+/ZtrKysWLp0KQMHDgT+tzzvgAEDyJkzJ0uXLqVXr17A/wKrqampyp/XrFlDSEgIBQoUwMPDI1OOQwghhBBCCCGEEEIIIT4HKr1UGzKVoUDx8OFDWrduze7du2nZsiXLly9XlsG6ePEiLVq04Pfff8fJyYlixYpx+/Ztrl69Sr58+YiJicHd3T1dsUMIIb5kCQkJVKxYkSdPnjBr1izatm2LiYkJKSkpSsixdevW7Nu3j5MnTyrdGDKKTqdLt+xVWFgYarWarFmzcv/+fSwtLRk0aBCjRo0CXnchmDp1qtJByKB06dKsXbtW5nQhPhEHDx6kcuXKyv8bunydO3eOOnXqYG1tzf79+zN8jvmvMF6qaciQIcyYMQN7e3vy5MlDQkIC9+/fp1WrVnTv3p1vv/32rcfPmDGDkJAQ0tLSiIuLw9nZ+a3ln8Q/c+nSJQIDA1m5ciXdu3dnzpw5yrYDBw5Qp04dXr16RfPmzenRowc1a9bE0tKStLQ0JkyYwOLFi3n16hVxcXG4ubml+7wUQgghhBBCCCGEEEII8T9y5zSTmZiYoNFoyJEjxzs78AAULlyYAwcOMGjQIIoUKcKlS5dwdnYmICCAPXv2SJFXCPGfotPpyJkzJ+PHjwdg3LhxzJ8/H0AJ7qxZs4bY2FjKly9P1qxZM3xMhkLk2LFjmTJlCh4eHuzevZubN28SExNDjhw5GDNmDGPHjgVgwIABrF27lvnz59OrVy+GDBnCqlWr2L59u8zpQmQi4w47vr6+VK1alfr16zN//nwSEhIwNTUFoFixYjRu3JjLly8THx8PvN2tR/xzhpDNtGnTmDZtGr169WL37t2cOHGCffv20aBBA1atWsXKlSuV11uj0XDhwgUqVKjAmDFjsLOzY9++fTg7O6PVaiW48/9UqFAhgoKC+OGHH4iIiODUqVPodDp0Oh1VqlRh9+7dFC9enHXr1tGgQQOKFy9OqVKlcHJyYvTo0eTKlYsDBw7g5uaWrkOdEEIIIYQQQgghhBBCiPSk885H9lffNv2zDjyGx967d4/cuXMr/y9FXiHEf9Hjx49ZsmQJo0aN4vHjxzRo0IC6dety+vRptm3bhlar5dChQ7i4uHyUzguXLl2ibt26FC1alJkzZ1KoUCFSU1PZs2cP7du3x87Ojri4OBwdHdM97s2xSVcCIT6ON881Q1cdgOvXr3P37l1mzZrF3r17uX37Ni4uLnh5eVG+fHmqVq3K+fPnqVWrFuXKlePXX3/NrMP4rL15DavX67l37x5NmzZFpVKxdOlSChcujF6vJyoqCl9fXwCOHz+Ovb09er0erVbLnj17GD9+PN988w2DBw8mV65ccn38gV24cIGkpCTKly+v/MxwDl25coWdO3eybt06rly5gk6n45tvvqFhw4Y0adIEe3t7eT+EEEIIIYQQQgghhBDiL0h45yPR6/XodDrlpvXhw4dJTEzk5cuX5M+fn3Llyin7vi/AYygqGW6UyzIAQogvzT8t7j179oxDhw4xZMgQTp8+DYCdnR3ffPMN8+fPx9XV9aMVDLdt20b9+vWJiorixx9/RKPRsHr1avz8/FCr1Rw5cgQHBwdSU1O5d+8erq6uwNvhHSHExzVkyBA8PT1xcXEBwNPTkx07drB7927s7e1JTExk+vTp7NixgxMnTmBtbU3Xrl2pXbs2YWFhHD58mKVLl9KkSZNMPpLPx7Fjx8iZMycuLi5vzdGnT5+mXLly+Pv7ExgYiEajITo6Gl9f33RzqU6n486dO+TNmxedTkdSUhLW1taYm5tLCDKDGX9uvfkZlpSUhEajwd7eXvmZvB9CCCGEEEIIIYQQQgjx1+QuagY6ceIEcXFxyk1tQ2EiICCA+vXr88MPP9C2bVuqV69O8+bNuXPnDjqd7r1LaBkHdwAp9gohvjiGeTIkJISDBw8CrwuD75M1a1a+//574uLiOHHiBJs2beLo0aOsXbs2Q4M7xsvjGJY4fPDgAfA6PKTVaomOjmbo0KGo1Wri4+NxcHAAXgeOvL29OXr0KCBzuRCZKSwsjKlTp9K5c2d0Oh1eXl6EhITQqFEj9Ho9pqamODk5ERwczK5du1iyZAlVqlQhNDSU3r17c/jwYZKSkjhx4kRmH8pn48KFC5QvX5569epx8+ZNTExM0s2pL1++JC0tTZkzDR133pxLHz58yA8//MDu3bsxMTEhe/bsmJubA0hQ5AP4s89e488tw58NS85ly5YNOzu7dM8h74cQQgghhBBCCCGEEEL8NbmTmkFu3rxJ9erV6d+/v1KABhg5ciTBwcFUrVqV6dOnM3XqVAoWLMj69etp0qQJJ0+eBHgrwNOoUSPS0tLk5rcQ4ou3Z88ePD09OXToEPDX4RadToeNjQ2lSpWiQYMG5M+fn6xZs6LX6zMkuGP8vMOHDycyMhKNRoO7uzsABw8eZOvWrfj6+qJSqYiPj0+3VJa3tzd79uwhS5YsH3xsQoh/5ocffsDLy4tdu3bh5ubGjBkz8PX1ZcCAAeTKlQv4XyjB1taW9u3bExUVxZEjR6hRowb58uUDYPz48co1nPhzRYoUoU2bNpw/f54ff/zxrQCPg4MDjo6OLFy4kHnz5qULQRrPpVOmTOHChQt/GjIR/45xN52rV69y7969v3yM8e8o8kUDIYQQQgghhBBCCCGE+OckCZJBrKys8Pf35+rVq/j5+REbG8ujR4/Yt28f3bt3JywsjAEDBjBw4EB27dpFr169OHHiBL169eL58+fA6wDP6tWrKV26NEePHuXp06eZfFRCCJHxcubMSe7cuZk9ezZXrlz5y/3fF2rMqKKh4XmDg4MZO3Yshw4dIikpiSJFilCpUiUCAwPp0qULZmZmHD58OF2xecGCBezatYv69esrRX8hxMcTFxdHWFiY8v/Ozs5MmjSJkiVLcvfuXVxcXGjTpg3Ozs5vdQ0x/L+trS2lS5dmyZIlbNq0iaFDh5KSksLevXuB/4V9xNsMAZ0VK1bQqVMnjhw5wo8//siNGzeUUGSBAgWoX78+x48fx8fHB4Dz588rc6ler2fFihWsWLGCevXqUaFChcw5mC+UcXBn27ZtdOzYkYiICKXLnBBCCCGEEEIIIYQQQoiMIeGdDJIjRw569uzJsGHDOH78OIGBgcTExHD48GEaN26Mq6srABqNBkdHR8aMGUP79u05cuQIgwYNAl4XOOzt7dm5cyfnz5/HwcFBCkJCiC9e8eLF6dy5M5cvX+bUqVNA+mWqMovxGHQ6HRs2bMDDw4MhQ4Zgb29Pjhw56NatG1ZWViQkJODv70/OnDmVxyxatIjx48djZWWl/Fc6Rgjxcej1epKSkqhduzb9+vVj69atwOvz+sCBA5w5c4ZSpUpx8+ZNBg8ezP37998KABr/v06nw9zcnDx58uDn50ehQoVYvHixdEn8CyYmJqSkpACvw4y9evXiyJEjtGzZkhs3bij7TZo0iRo1avDkyROqVauGqampsm3WrFmMGDECU1NTQkJClE5r4v/POLiza9cuAgMDiYuLo379+piZmcnrLIQQQgghhBBCCCGEEBlIpZe7sBnq4cOHLFq0iBEjRuDq6sqrV6/YuXMn+fPnR6vVYmJigk6nQ61Wc+/ePcqVK4e9vT0HDx7E2tpa2QdQ9hNCiC+VYZ47e/Ys3377LYULF2b37t3pCreZbdq0adjY2DBu3DjmzZvHd999l26unjhxIsOGDUOj0dC4cWPy58/PqVOnOHHihBLIdHd3T/cYIcTHsW3bNubPn8+cOXOws7NTfn7w4EGsrKxYunQpU6dOpUaNGkRFRSkBvHddgxkuoVUqFW3atOHXX3/l+PHjFClS5KMdz+fGeN67ceMG9+7do3Pnzpw7d44KFSoQHR2Ns7MzWq2WU6dO4enpSWxsLLly5aJkyZLcunWLK1eukD9/fjZv3ixz6Qf0ZnDHz8+PM2fOsGfPHipUqMDTp0+5cOECTk5OuLi4ZPJohRBCCCGEEEIIIYQQ4ssjSZAMliNHDjp16sTo0aN5/Pgx169fJyIiAr1ej4mJCXq9HrVaTUpKCrlz56ZGjRqcOXOG69evK/sYSHBHCPGlMO4iZrwUh6FwWLhwYSpVqkRcXBw7d+4EyLRv/BuPNTY2lsGDBzNp0iQ0Gg329vZv7efj48PSpUtp3bo1u3btYs6cOdy7d4927dqxf/9+KTYLkUl0Oh1169Zl+fLl2NnZMXz4cCZMmABA5cqVKVWqFN7e3vTr1499+/bRunVr7t+/D/zvGiw2NpYDBw4Ar+crlUrFq1evMDExwdLSUjok/gmdTqfMe/7+/lStWpX27dvz6tUr7O3tiY+Pp1WrVsoSWqVLl2br1q0MHjyYIkWKcObMGZycnPD392f37t0yl35A7wvu7N69WwnuLFiwgMaNG7N9+/ZMHq0QQgghhBBCCCGEEEJ8maTzTgYy/pZ2QkICy5cvZ8yYMWTPnp1Zs2bx/fffo1arSU1NxdzcHICGDRty+vRpTp06Rfbs2TNz+EIIkeGCgoJ48uQJjRs35rvvvku37dixY1SvXp0OHTowZ86cTBmf8TweGxtLtWrVmDhxIn5+fgDMnz+fzp07A6+Ln4ZApsHNmzd5+fIlzs7OWFhYYGpqKsVmITKJ4dzTarVcu3aNQoUKYW5uzrRp0+jdu7eyX0JCAmPHjiUkJIQaNWqwatUqHB0d2bp1Kz4+Pjg4OBATE4OlpSVarZaoqCjatWtHt27dmDt3biYe4edh7NixDB8+HC8vL7p06YKrqyuXLl1i6NChbN++nfLlyxMdHZ2uu4tOp+Px48fkyJFDmZdlLv0w/k7HnaVLlzJs2DCKFCnC4cOHM3nEQgghhBBCCCGEEEII8WWS8M4H9FfLWt2/f59ly5YRGBhIiRIlGDJkCC1atFC2R0dH07VrVypXrsyaNWuwtrb+GMMWQohMcezYMTw8PLh06RIA7du3p169enh4eKBSqXj69ClNmjQhPj6eHTt2UK1atUwbq6enJ6tWrWLatGn89NNPBAcHM3ToULJmzcq6dev49ttvgfTL6AghPh3G12hPnjzBzs6O3bt307BhQ9RqNZMmTaJPnz7K/gkJCYwb93/s3Xdgjef7x/F3ph1BkCBG7L1ae1OzZq1WqSL2lpCEyLB37L33HqVoidiz1GqN1qrae2Wec35/+J2nCdr6tiLF5/WPOM/I/ZzjuXPk+pzrHsrEiRMpWLAguXPn5sCBA4SHh3Pw4EGyZcsGPL/n9+zZw5o1axg3bpzxmOaAV7t27RpVqlQhceLErFmzBg8PjzjbW7RowdKlSylZsiQrVqzA3d39pWVm9fy+OX/VcadkyZJGcMfX15dixYqxc+dOAGJiYv5Ty1mKiIiIiIiIiIiIvA8U3nlDYn/6d8+ePZw9e5Zr167h4eFBpUqVyJgxI/A8wLNo0SKCgoJImjQplSpVokmTJmzcuJHDhw/z+PFjdu/eTebMmVWcEJH3SuzieezC35o1a1i6dCnbtm3j4cOHfPzxx3h6elK/fn2OHTtGzZo1GTlyJF5eXn8bkoyPsW7cuJG2bdvSsGFDvL29yZ49OwDDhw/Hz8+PAgUKMHnyZMqXLw+ocC/yX9ahQwdWrlzJzz//TPr06dm9ezdVq1bFwcHhpQDP7du3mT59OnPnzuXu3bsULFiQRYsWkSVLljjv+2LPF29rjnpXnT9/nnz58tG+fXsmT55sPF+xfyZUqVKFsLAwPvroI1auXEmWLFn0vMYzBXdEREREREREREREEp7CO29A7IJC//79mTRpEo8fPza2FytWjBYtWtCrVy/geYBnyZIlDB8+nNu3b1O4cGGSJUtGxYoV6dixY5xPGYuIvA9iz2lLly7l0KFDFC1alFatWgHw7Nkzrl27xsiRI9mzZw9nzpzBxcXFWDLL2dmZQ4cOkSFDhngfa+zwjdlsZt26dfTu3Ztvv/2WfPnyxbmWwYMHM3DgQPLnz8+UKVMU4BH5j4l9L86dO5c+ffpQuXJlhgwZQp48eQDYvXs3VapUwdHR8aUAT0REBPfu3ePGjRvkyJEDJycnvUf7F86fP0/+/PkpUqQImzZtIm3atMY2ayhk3rx5dOrUCTs7O9KnT8+uXbuMELy8edu2baN///5GcCf2UlkK7oiIiIiIiIiIiIi8PfoI6xtgDe74+/szfPhw6tevz9atW9m4cSM9e/bkwoUL+Pj4EBAQAED69On54osv8PPzI126dERERDB27FgGDx6s4I6IvHfMZrMxp/n4+NC5c2fWrFlDihQpiIqKAiBx4sTkyJGDSZMmcejQIUaNGkXevHmZOHEiz5494+bNm3zzzTfG+eKTtdDv5eVlFJJr1KhBvnz5AIzlWwAGDBhAcHAwp0+fpkuXLuzZsyfOOUQk4ZjNZuNefPr0KefOnaNo0aKMGzeOPHnyYLFYMJvNlC9fntDQUKKiovD29mbKlCnGORwdHcmQIQPFihXDyckpznwm/7ucOXNSpUoVzpw5w44dOzCZTMY26/vpwoULky5dOgoWLMi9e/cUFolHFouFzZs388MPPyi4IyIiIiIiIiIiIpLA1HnnDdm7dy8NGjSgQoUKjB07lixZsgAQHh7Onj17+PLLL3n27Bnjxo2jXbt2wPPlGKZNm8b8+fPZv39/nE8fi4i8bwICAhg0aBBdunShXbt2FC5c+C/3v3PnDqdOnWLy5MmEhoZSqFAhduzY8ZZGC71792batGlERUVRrlw5vvnmG5InTx6nK4+12GztwOPm5saaNWsoWbLkWxuniPw1Pz8/fv/9d/bv30/z5s0JDg5+5VJXsTvwjBkzho4dOybwyN8v1ud51apVdOzYkYwZMzJ9+nSKFSuGo6Ojsc+gQYMIDQ0lLCyMhw8f4uzsrGWz/oW/e+7Cw8P57bffyJUrlxHc8fPzo2jRogruiIiIiIiIiIiIiLxF+i34G3L58mXu3r1LixYtyJIli9GVIUmSJHzyySdMnz4dW1tb1q5da3zKOG3atHTt2pVjx46RNm3aeO8mISKSUA4cOMCUKVNo2rQp3t7eRnDnVflR62Np0qShUqVKTJs2jfr167Nz507WrVsX72O1fv+xY8fSp08fkidPzo8//sjRo0exsbEx5nBbW9s4HXj69evHo0ePyJw5c7yPUURez/3799m/fz8LFy7k2rVrJE2aFIjbHct6L1s78FgsFjp37sz8+fMTatjvjP/lvas1QPLJJ5/g6enJmTNn+Prrr5k5cya//fYb8HxZxdWrV5MiRQqio6NxdnbGYrEouPMPmUwm47k7deoUYWFhrF+/nvPnzxs/y5IkSUKuXLl49uwZ8+fPV3BHREREREREREREJIHoN+H/QOwW/1Znz54F4MGDB8DLxYxy5cpRtmxZNm/ezOHDh43HU6VKRYoUKVSYEJH32unTp7l79y7t2rWLE2551fJSNjY2WCwW4880adLQuXNnAI4fP/7Gx/ZigCh2QGfQoEF069aNR48e0bRpUy5cuICdnd0rAzzDhg3j999/x83N7ZU/J0Tk7UuVKhWzZ8+mZcuWRjjh1KlTL809sQM833zzDZkzZ6ZKlSoJNOp3h/W967Zt215rf4vFQsqUKenTpw++vr48ffqUbt26UbhwYbJmzUrLli25f/8+U6ZMMbrxaBnCfyb2Em9BQUHUrVuXKlWq0LBhQypVqkSHDh2IjIw09k2UKBF79uyhQIECCu6IiIiIiIiIiIiIJAClRf5HFovF+EV4jx49mDBhAgAVKlTA3t6evXv3AmBvb28UdC0WCy4uLlSuXBmAp0+fvnReFSZE5H125swZAFxcXF653Rp2uX37NvDynJgmTRqSJk3KlStX3mgwxmQyGd8rMjKS33//nSdPnsQJYA4aNIgBAwZw+/ZtypQpw6+//vqnAR5rGNP6c0JEEo41mOfh4UFAQACff/45Z8+eZdKkSVy6dOml/a33ctWqVTl37hzu7u4K4r0Gb29vqlevzqJFi/52X2so08XFBS8vL1atWkXbtm3JkycPWbNmpVOnThw4cIDMmTPruf+XrMEqHx8fgoKCKFiwINOmTWPt2rVkzZqVOXPmUKhQIaKiorC1tcXOzo6FCxeyb98+QMEdERERERERERERkbdNv5H9H1mLvCNHjmTixIk0atSIVq1a4eHhQb58+Zg7dy5Fixala9eu2NraxvnF988//4yTkxNubm4JeQkiIm9d2rRpATh69ChFihTBZDIZAZfYYRcfHx8aNGhA3bp1gedz7pMnT1i3bh3Pnj0jX758bywYE3sMEydOZM2aNezZs4cMGTKQPXt2RowYQd68eUmePDnBwcEADB48mLJly7J3716yZ89uzPHWIqmCmCIJw2w2v9TB0Ho/WiwWPDw8GDx4MJGRkcycOZPEiRPTs2dPsmbNGucY6zkcHBwAFMR7DTVr1uTYsWO0adMGGxsbWrRo8Zf7WwM8yZMnp0SJEpQoUYKnT5/i4OCAnZ2dEY7Uc//vbdq0icmTJ9OuXTt8fHzw8PAA4NGjR+zfv587d+4QHh6Oo6MjFouFRIkSAc/vJwV3RERERERERERERN4udd55TbE//WuxWAgNDeWLL75g2LBhODs7ky1bNoYMGQJA9+7dGTNmDIDxi+9169axfft2SpYsGWfJGBGR95m1I02lSpVIlSoVY8eO5d69e9jZ2WGxWDCbzXFCkatXryYqKirOOe7cucPixYtp1KgRffr0eSPjih0Y6t27N7169eLmzZvUqVOHtGnTEhYWRq1atZgzZw63bt0CIDg4GH9/f27dukWlSpU4d+6cipsi/wEmk8kI3WzYsIGAgADat2/PkCFDePTokTHHZMuWjVGjRtGgQQMmTpxISEjIKzvwgIJ4/4uqVasSFBREpUqVaNmyJfPmzfvbY2IHqwCSJUuGo6OjMS8ruPNmHDlyhMjISDp06ICHhwcmk4nFixcTEBBAtmzZOHfuHClTpiQ8PNw4Rkv5ioiIiIiIiIiIiCQMVR1fk7WIEBISQkxMDIcPH2bu3LnkzJnTKD7XqVOHZcuW0bx5c7y9vdm0aRO5c+fm3r17hIWFYW9vz8yZM0mePDkWi0WFIRF5b7yq6wX80cWiQIEC1K1blwULFlCrVi1WrFhBpkyZjLl19erVzJs3j/z58xtLDFplzZqVmTNnUrRo0b/8Xv8L6/w7Y8YMJk6cSNeuXY0uHDExMYwbN445c+bg7+9P4sSJadu2LXZ2dgQFBWFnZ0dgYCDNmjXjyJEj2Nraaj4XSSBms9mYR/r168eECROIjIwkSZIkhIeHs3jxYmbNmsXHH3+Mg4MD2bJlY/To0cDzjlt2dnZ07tyZ7NmzJ+RlvJNid8cpW7Ys/fr149mzZzg7O7/2OTR3xh+TycSJEydInTo1xYsXx2w2s2rVKvz8/LC1teXgwYOkSZMGgEuXLrF7927atWun4I6IiIiIiIiIiIhIArGxWD/yKsBfF4X3799P2bJlyZkzJ9HR0axevZqiRYsSExODnZ2dUYDYtm0bEydO5NChQ9y8eZNMmTJRtGhRJk2ahLu7u5YCEJH3Suw5bdOmTRw9epRbt26RJUsWunbtSuLEiQF48OABX375Jd9++y05cuSgVq1aFCtWjLCwML777jtsbW3Zs2cPWbJkMebiF4OObyK4E1uDBg348ccf+f7778mZM6exDFZMTAxr1qyhb9++REREsGfPHnLkyGEcN2bMGJo0aaJOaiL/EQMHDmTIkCF8/fXXtGvXjlKlSjFixAh8fX3JnTs348ePp0qVKka3rIsXL9KvXz9WrVqFv78/AwcO1Huzf2jXrl1UqFABgLt37xqBEEl4X375JevXr+fkyZMcOXKEPn36YGtry6FDh4zlLC0WC5UqVSJJkiSsXLmSFClSJPCoRURERERERERERD5MCu/EErsovGDBAo4fP86DBw8oXrw4LVu2JEWKFEydOpXu3btjMpkIDAxk4MCBwB9t/61F5vv37/PkyRPOnTtH9uzZSZs2LcmSJVNwR0TeK7HnTR8fH8aPH09kZCQODg5ER0dTuHBhZs6cScGCBUmUKBEPHz4kMDCQLVu2cPbsWQCcnJwoW7Ys06ZNe6MBxxeDP9ZgjnXbgwcPyJ49OwUKFGDXrl3GdutxJpOJAQMGMGLECNq3b8+0adOIjo7GwcHhlecUkYSxZs0aevbsSY0aNfDx8SF79uyYTCYKFCjA7du3CQ8PJ3369EyfPp2KFSvi6OgIwC+//MKIESPw9/dXEO8f8vX1ZcSIEfzwww9GdzR5u2L/rIv988vOzo6VK1fSrFkzatSowblz57BYLOzbtw9XV1fj+EmTJjFo0CC6deuGr6+v/p8iIiIiIiIiIiIikkAU3vl/sQvQjRs3ZsOGDcTExBjbrcu9pEyZkvnz5/P111/j5OTEzJkzadKkCfDqX57HpqWyROR9EntO8/f3N7petG3bljJlyjBo0CACAgIoVKgQ48ePp3Tp0jg6OhIVFcWjR4/44YcfMJlM5MiRg4wZM77RgGPsOX3v3r18//332NnZ0bhxY/LmzWvs8/HHH3Pr1i0OHjxIhgwZjGuyHn/9+nWKFStG0aJF+fbbb//1uETkn9m3bx8PHz7kk08+iROYCw8Pp23bthw9epRly5ZRpEgRnjx5QokSJbh//z6DBw/m1q1b9O/fn7x58xISEkKlSpWMEJ51zlG4+p8JCQmhd+/eLFmyhObNm+u97lsW+9+txWLBYrHE6U53/vx5WrZsyaFDh3BycuLq1askT57c2L58+XL69+9P6tSp2bhxI+nSpXvr1yAiIiIiIiIiIiIiz725tUfeYbGLvOXLl2fr1q18+eWXbNq0iUmTJpE3b16++eYbfH19iY6O5quvvmLOnDk8evSI4OBgNmzYADzvuvNiB57YVMwQkfeJdU5bvXo18+bNo127dvj5+VGmTBnMZjNLliwhVapUnDlzhg4dOrB//36ioqJwdHTExcWFGjVqULt2bXLlykWyZMmwWCxvPLjj6elJ48aNCQ4O5ujRo9y/f9/Yz9bWliJFivD7778zevRoHj58GGceB7C3t8dkMmn+FklAN2/epEWLFjRt2pTvv/8ek8lkbEuSJAlFihShbdu2FClShPDwcOrVq8etW7cYMmQIbdu2xdfXl+LFi/Pzzz/TtWtXtmzZYgS0rXOOgjv/TNWqVUmXLh3BwcHcvHlTc+VbZDabjX+348ePp2HDhjRo0IBx48YREREBQM6cORk2bBju7u48evQIX19fVq5cyY8//kj37t3x8vIiIiKCVatWkS5dOsxmc0JekoiIiIiIiIiIiMgH7YMP78Qu8laoUIFTp04xfPhwQkJCqFWrFp07d2batGm4uLhw4MABo2DUunVrZs6cyenTp+nfv3+cAI9+8S0iH4qnT5+yYsUKnJyc6Ny5M9mzZ+fJkyfkz5+fBw8eMGLECHx9fTl37hy9evVi//79cQrvsb2Jom/sOb1KlSqsXr2a2rVrc/HiRZYsWUKZMmWM/QCCgoLIly8fixcvZsGCBdy/fx8bGxvjHBs3buTp06eULFkSADWrE3n7UqdOTf/+/XF1daVdu3Zs3bo1zjzSt29fvL29AVi4cCEHDx6kY8eOtGjRwtinfPnyFC9enGvXrjFo0KA/nYfkf1OwYEHq16/PmTNnOHToEICe27ck9pKVvXr1YseOHXz33Xf06dOHFi1acPjwYcxmM5UrV2bx4sVUqVKFmTNn0qxZM4oVK8asWbPIly8f+/btI3PmzJhMpjhde0RERERERERERETk7dKyWf+vZMmSHD58mNGjR9O7d2/gj1b0T58+pXjx4ly4cIGLFy+SMWNG47jZs2fj6elJ/vz5GTZsGJ9++mlCXYKISIIYOHAgTk5OeHl5ER4eTu3atTl16hQjR47k66+/xmw2U6RIEU6dOkWBAgUYM2YMVatWjdciYZMmTfjuu+8IDg6mdevWpEyZ0pjTYy/rYjKZWLt2Ld7e3ty7d4+6devSp08fUqVKxebNmxk3bhwWi4Xdu3fj6uoab+MVkVez3q9RUVEsXbqUgIAAoqOjmTlzJjVq1HipY067du1YunQpx48fJ0eOHMbjFSpUwM3NjcaNG1O2bFkyZMjwti/lnfXikmLW1yQmJgZ7e3t++uknypUrR+XKlVm9enUCjvTDc+DAAZo0aUK9evXo1asXkZGRzJs3jylTplC8eHGGDBlC2bJljWUgr1+/zqFDh3BwcKB48eJkz56dFClSaNk4ERERERERERERkf8A+4QewH/ByZMnuXr1KgB37twxlnWxunLlCvfu3aNq1aqkTZs2TuG3bdu2AHTq1In27dszf/58Pvnkk7d/ESIi8Sx2V5vY82RwcLCxz9y5czl06BB9+vThiy++AJ53B6hQoQKJEyfm+PHjBAUFUbFixTjz7Ju0ePFiNm/ejKenJ23atCFFihRxluSK3eHHzs6OOnXqkDhxYgIDA1myZAlr1qwxOuxkzZqVzZs34+rqquKmyFsS+32WjY0NkZGRJEqUiObNmwMQEBCAp6fnKwM8tra2WCwWnj17Zjy2ePFiLl68iKenJ02aNAFeDqTIn7M+T0uXLiV37twUK1YMeL6soMViwc3NjeLFi7N27Vq2bt1KjRo1EnK47zXrvWH989KlS0RFRdGjRw8jrObt7Y2rqysBAQH079/fCPC4ubnh5uZmvH6xz6l7QURERERERERERCThfZC90V9sNpQvXz6WLVtGvnz5mDBhAoMGDeLBgwfY2dlx6dIlQkJCuHPnDp999hmOjo4vLe3Stm1bxo4di62tLQUKFHiblyIi8tZYgzvBwcHMmDGDx48fv7TPwYMHsbOzo02bNiRKlMh4/IcffiB79uzMmTOH5cuXx1twByA0NBQ7Ozv69etnBHf+akmuJEmS8OmnnxIWFkZwcDAtW7akSZMmjB49mp07d5I1a1YV+kXeErPZbNyve/bsITg4mIEDB3LmzBkSJUrEF198QXBwMA4ODnh6er60hFaZMmWIioqiUaNGzJ8/Hy8vLwYOHEjSpEnjhKt1P/9vZs+eTYsWLShVqhTdunVj1apVwPNwVapUqejbty92dnbs2rUrgUf6/jKZTMa9ERERQXh4OOnSpaNmzZrkypWL6OhoANKlS0fr1q0JDg7m6NGj9O/fn3379hn//3nx/0FvYslKEREREREREREREfn3Prhls/6siGs2m9m7dy8dO3bk0qVL+Pr60rBhQxYvXszw4cPx9vZmxIgRf3mOp0+fkixZMhV5ReS9df78eSpVqkRERASjR4+mcePGpEiRwtjesmVL1q9fz5EjR8iVKxcAixYtwtfXl9GjR9OsWTMgfrpeWCwWrl27RokSJciQIQOHDx9+qZPan7l//z6pUqV65bbYHYdEJP7EvtfatWvHxo0buXXrFk2aNKFjx45UrlwZgOjoaJYuXcrAgQONJbQ++eQTHBwciI6OZuDAgcyfP58bN24AULhwYdatW0eWLFn0Hu01vTjvPXv2jJkzZ7Jjxw6+/fZbYmJiqFWrFo0bN6ZWrVqkTp2aatWqcfz4cXbu3EmRIkUSbvDvodivx6hRo1i7di03b97EbDaTKFEiDh06hJOTU5z97t69y/z58xk4cCAlSpTA39+fSpUqKawjIiIiIiIiIiIi8h/1wYV3rMqXL4+Hhwfz5883HrMGeDp06MCFCxcoVqwYBw4ciBPcURFXRD5kMTExbN++HV9fX3777TeGDx9O06ZNjQDP9OnT6dSpE/nz56d///4cO3aMVatW4eDgQFhYGK6urvE6vgcPHlCkSBHc3d3ZvXv3ax1z5coVFi5cyOeff46Hh0e8jk9EXi32+6sqVarw448/0qBBA/z9/XFzcyNx4sTAHwHqVwV4qlatSqJEiYiJieHcuXOcPn2atGnTUqhQIVKnTq3gzmuK/Tzt2bOH27dv07BhQ+D5kok//PAD06dP5/vvv+f69etkyZKFwMBANm/ezLp16xg5ciTdu3fX8x0P/Pz8GD58OO7u7jg4OPDgwQPu3bvHsGHD6NatG0mTJn0pwLNw4UJ69+5NgwYNWLZsWbx2vhMRERERERERERGRf+6DDO/8/vvvtGnThu+//57u3bsTEhJibLMGeLp27crJkycpVaoU+/btA3jtDg4iIu8ja9HcGuDp27cv165dY/jw4TRp0gQnJyeePXtG//79WbRoEXfv3gWgYMGCbNiwgSxZssR7APLu3buULFmSmzdvsnPnTooVK/an+1rH8v3339O4cWPWrVtndPYQkYTRpEkTvv/+e4KCgmjdujUpU6Z8KQTyOgGeFyl8/XpiP0/BwcHMmTOHK1eusGPHDipWrGg895GRkTx+/JiQkBC+/fZbTpw4QerUqblz5w4fffQRe/fuxcHBIYGv5t0X+9/+pUuXqFSpEnXq1KFfv34kS5aMb7/9loCAACIiIhg0aBAtWrQgceLEcV7H27dvs3btWmrVqoW7u3tCXo6IiIiIiIiIiIiI/IUPsoqRMWNGJk2aRPPmzZkwYQIdOnQwttna2lKmTBkmTZpEwYIFOXnyJEFBQdy9exdHR0c+wKyTiHyAzGbzS1/b2NhgsViwt7enatWqjBw5kgwZMuDj48PKlSu5f/8+SZMmZdiwYWzbto0FCxbw3XffERoaaixXE5/Fc4vFQpo0aWjatClPnz5l27Ztcba9uK91LLNnzyZ58uQUKFAg3sYmIn9v4cKFbNmyha+//po2bdqQMmVKLBbLS91brHORg4MDn3/+OcHBwTg4ONC+fXtCQ0OJiYl56dwK7vy92POit7c3gwYNokyZMuzatYuKFSsCGEsuOTo64uLiwuDBgwkLC2PevHmULFkSNzc3jhw5wty5c41zyj9n/be/efNmDhw4AECnTp3InDkzadKkoVmzZkybNo1EiRIREBDAokWLiIiIwNbW1vjZnTZtWjw9PXF3d8dkMiXYtYiIiIiIiIiIiIjIX/sgO+9YnT9/Hm9vbzZs2MCPP/5IwYIFjaKEyWRi3759dOjQgUuXLuHt7U3v3r1JmTKlPr0tIh+MQYMGkTNnTho3boy9vT0QtwPPtm3b6NWrFw8ePGDIkCE0aNCA1KlTv3Setzlvbt68mc8++4yIiAjWrl1L/fr1/3QsCxYswMfHh/r16zN+/Hh1VxNJQF9//TVr1qzhp59+ImPGjMZc82es26Oioli6dCmDBw/mypUrbN68mSpVqrzFkb9fFi9ejKenJ23atKFPnz5ky5btlfu9+Po8fPiQc+fOUbt2bapUqcLy5cvf1pDfa9OmTaNz585UqVKFqKgodu3aRXR0NPb29tjY2GAymdixYweenp5ER0cTFBT0yg48IiIiIiIiIiIiIvLf9kGHdwDOnj3Lo0eP+Pjjj1/aZl1Cq2PHjly6dIm+ffvSvXt3UqVKlQAjFRF5u44fP07FihWxt7dn9uzZ1KlT56UAT1RUFHPmzKFz587kyJEDX19fGjVqRMqUKRN07MOGDaN///4ALFmyhCZNmry07M7y5csJDAzEbDazc+dO3Nzc/jYsICLx47fffqN06dK4ublx+PBhYmJijPnmrzx69AgnJyciIyOZN28ec+fOZfXq1WTMmPEtjPr91KpVK7Zu3cr27dtfqyOZdd60WCxERETw1VdfsWrVKnbt2kW5cuXewojfb7///jtNmzZl//79ODs7c+zYsZeWoYwd4LFYLHh5eeHp6fnKJeRERERERERERERE5L/pg/8oZu7cuY3gzos5JltbW8qWLcu0adPIkSMHQUFBzJ49W0sAiMh7KfZSWQD58+dn6tSpxpIb33zzDdHR0cAfy9Y4OjrSpEkTMmbMyO3bt2nbti3ffvttQgwf+OMafH19GTBgAABffPEFXbp0YdGiRZw7d46jR4/SoUMHvL29iYiI4LvvvsPNzQ2TyaTgjkgCSZYsGY6OjiRLlgzgtYI7V65cYejQoVy4cIFEiRLRpk0bQkNDyZgxo5YH+ocePXrE1q1byZ07NwUKFHjlEmSvWoYQnv9cSJIkCZUqVcLGxoanT5++lTG/7zJmzMjq1aupXr06Dx48oH///ty8eTPO0lh2dnZUrlyZWbNmcffuXebNm/fK105ERERERERERERE/rs+uPDOXwVvXlW0tQZ4xo4dS8WKFWnWrJmKuyLy3rFYLMYn+O/cuQM8L543bdqUgIAAnJ2d6dChQ5wAj3U+TZYsGQ4ODnTu3JmaNWtSvnz5hLkIns/Z1qJ9cHAw48ePJ2/evMyYMYNWrVpRqFAhPvroI5YtW8bHH3/Mnj17yJo1KyaTKU5nHhGJHy+GBOH5XBIZGYnFYuHgwYMcOXLkL89hvcfPnz/PvHnzuHDhAgAODg4kTZoUQPfzP5QsWTLSpEnD77//zu3bt18KUZnNZmxsbLh+/Trjx48HiLMs04MHDzh9+jQODg5ERES81bG/6/7s/ygmkwlXV1fmzZtHxYoVWbJkCcOHD+f27dsvBXgqVarE5s2b2bBhgxGEExEREREREREREZF3wwcV3om9HMrFixe5cePGax1na2tL5cqV2bp1K+7u7vo0t4i8d6xzY//+/cmbNy9nzpwBnhcDmzRpQnBwMM7OznTs2JF169bx+PFjo2C7aNEiHBwcaN26NevXrydTpkwJOk/a2dkZ379bt26sXbuWVatW0bZtW9q0aUNgYCDbt29nwYIFxlhV6Bd5O6zzxokTJ4zHbGxscHNzo3nz5kRGRrJt27Y/7RpisViM+3Xq1Kk4ODhQsGDB+B/4B8LOzo5ChQpx8eJF5s2bx7Nnz4xtsZdpGjNmDIGBgcbPCuv2kydPMnv2bOrWrUv9+vXf+vjfVbE7v924cYNz587x008/AX8E0VxdXVm6dCnly5dn/PjxDBky5JUBnnLlypEhQwb9f0VERERERERERETkHfPBhHdiB3e+++47WrVqxcyZM40OEn/H1tYWR0dHQJ/mFpH3R+zinsVi4f79+4SHh9OiRQvOnTsHxA3wpEqVik6dOjFkyBDCwsIYM2YMI0eOxNnZGTc3NxwcHIxjEpKdnZ1RzMyVKxeNGjVi5syZTJkyhYEDB/Lxxx+TPHnyOEEAEXk72rVrR5EiRdi1axfwR8eRChUq4OLiwtChQ9myZUucYywWi9H1BWDhwoXs27ePevXqkSpVqrd7Ae+4vwt19OnTB3d3d2bPns3333/PkydPgD+CVytWrGDdunVUqVIFd3d34zhbW1vSp0/PpEmTWLlyJfDqTksSV+wA6ejRo6lVqxaFChWiZMmSNG3alJMnTxphNldXV5YvX0758uWZMGFCnADPi5179LNNRERERERERERE5N1iY/mrdaTeE7GDO6GhofTv35+DBw9y8OBBPv744zjbRUQ+FLG7KIwdO5bz58+zfv16Y0mUokWLsmTJEnLnzg08LzBu2LCBkJAQdu/ebZwnW7ZshIaGkiVLlnibT/+qO87rds6Jfb2a90USzvLlyxkyZAjXr19nzZo1cZbaGzBgAEOHDsXJyYkZM2bwySefvBTOWbZsGYGBgZjNZnbu3Imbm5vu6dcUe75ctmwZ586d49mzZ3z66acULVqUZMmS8fDhQ2bPns3gwYNxcnLis88+46uvviJ58uQsX76c2bNnYzab2bVrF5kyZfrT5z72nCuvFvu58/LyYty4cRQoUIBatWpx69YtVq1aRZEiRfD19aVq1arGBwlu3LhBs2bN2LdvH61atWLkyJGkSZMmIS9FRERERERERERERP6l9z6882Jwx8fHh1OnThEWFkaJEiV4+PAhZ8+exc3NLc6nh0VEPhTe3t5MmjSJWrVqUaFCBVKkSMGsWbM4ePAg+fLlY/Xq1UaAx2KxcOPGDZYvX86lS5dIly4dbdq0wdXVNd6Wn4p93uXLl3P8+HEcHBwoXLgwjRo1AiAmJgZ7e/s3/r1FJH5s2LCBgQMH8vPPP7NlyxYqV65sbOvduzchISEkTZqUpk2bUqdOHQoVKkRERASTJk1i48aNODg4EBYWRtasWbX03T/g7e3NmDFjjL+7uLjw+eefExAQQOrUqbl16xarV69m/PjxnDt3jsSJExtddPLly8eaNWv03L9BISEhDBgwgHbt2uHp6Un+/Pm5cuUKpUuX5vr16xQpUoQhQ4ZQrVo1o8PdzZs3qVatGvfu3eP06dM4Ozsn7EWIiIiIiIiIiIiIyL/yXod3/iy4s2PHDkqWLMnDhw+ZM2cOw4YNY/jw4bRp0yaBRywi8nZ9++231KtXj5YtWxIUFETmzJkBePz4MUFBQYSEhJA3b15Wr15Nrly5/vQ8b6OA6+XlxdixY+M81qZNG2bNmgUowCPyLoj93mz16tX4+PgQEhJCnTp14swjo0aNYvny5Rw9ejTO8cmSJaNq1apMmjSJTJkyKTzymmI/79OnT6d37940btyYhg0bEh4ezsiRIzl+/DitWrVizJgxpEmThvDwcO7evcu0adO4ffs2AGXLlqV27dq4uLjouX9DTp8+TevWrXFzc2PkyJHkyZOHR48eGf9XqV27NqtWrSJ79uwEBwfzySefGB14bt++jdlsJn369Oo+JSIiIiIiIiIiIvKOe2/DO6/TcWfhwoUMGDCA3Llzc/DgwQQesYjI2zdx4kR69OjBunXrqFevHgDR0dE4ODjw5MkTvL29mT59OsWKFWPJkiXkypXrrS2FEnsenz17Nj169ODzzz+ncePG2Nra4u3tzYkTJ2jUqBGrVq0CFOAReRfEvrdv3LiBq6ursS12IOTq1avs3r2bAwcOYDKZcHNzo3bt2uTMmZPkyZMrPPKaXpyz/f39OXjwINOmTcPDwwOA8PBwqlWrxv79+2nZsiXjxo0jderUr31O+ec2bdrEF198wZo1a6hatSpPnz6lbNmyXL9+nZCQECpWrMiIESOYOHEi5cqVe2kJLdDrISIiIiIiIiIiIvI+eC/DO6/TcWfhwoX4+vpSrFgxdu7cCajoKyIfnqCgIIKCgti5cyfly5c35kFrIfDRo0cUL16cX3/99aUltOLTi4XIoKAgtmzZwqJFi8iePTsAly9fplu3bmzcuJGGDRuyevVqQHO5yLvgxS4hsf/+Oh1E1GXkf9evXz+uXbvG/v378fT0pF+/flgsFmJiYnBwcMBkMlGhQgX2799Pq1atGDduHKlSpYozp+p5jx9btmyhZs2aREdH4+npyZo1axg2bBjt2rUjUaJE7Nixg6pVq5IoUSJSpUrF4sWL4yw1JyIiIiIiIiIiIiLvvvfyI5oK7oiIvJ4sWbIAMHXqVCIjI7G3t8disWBra0tkZCROTk5UrlyZ/Pnzc/XqVRo0aMDZs2fjfVzW4E6PHj2oVasWu3btokmTJmTPnh2LxYLJZCJLlixMnDiRTz/9lLVr1/LZZ58BYG9vT0xMTLyPUUT+N7Hz4rGDOrH//uLX8DzM9+I5FCD53zx58oSNGzeydOlSnjx5YnTVMZlMODg4EBMTg52dHbt27aJ06dIsWLAALy8v7t27F+f9sZ73fy72v2OrqKgoAGrWrAnAzZs3CQsLo1KlSnTp0oVEiRIBkCFDBtzc3OjWrRuurq7kyZPn7Q1cRERERERERERERN6K9zK8A7Bt2zZ8fX05ffo0YWFhCu6IyAfrrxqsNWrUiLx587JhwwYWLlxIZGQkNjY2REZGGkXDCxcuULhwYby8vLh48SINGzZ8KwGex48fs3XrVr7//nuOHj3K06dPAYwis9lsfinA06xZMwDN6SL/MbE7thw+fJiNGzcSHR39WmGQ2F24FB75Z5InT8727dupVq0at27dYtasWdy6dcsIbFpDj9YAT/ny5Zk7dy6DBg36y58h8npMJpPx7/j+/ftcvXoVwFj6yvocnzlzhitXrlC0aNE4x69cuZIUKVLQpUsX9u7di5ubGyaT6S1egYiIiIiIiIiIiIjEt/cyvGOxWNi8eTM//PADO3bsoESJEgruiMgHyWQyGcXuW7ducfr0aX7++Weio6MBcHJyYvjw4SRLloxhw4Yxbdo0oqKijODOqlWruHDhAnXr1sXLywtfX18uX75Mo0aNOH36dLyN22KxkCJFCrZv307p0qV5+PAhW7duBTCWd7G1tY0T4Klfvz4rV66kffv28TYuEfnfxQ7ubN++nXbt2jF06FAePnyYwCN7P1mDIBaLxfjabDbj6urKvHnzqFatGocPH2bQoEHcvn0bGxublwI827dvp169evTo0UOBqX/JbDZjZ2cHwLBhw6hQoQI5c+akevXqLFy4kEePHhnPccGCBcmSJQubN2/m119/5fHjxyxZsoSFCxeSI0cOXF1dSZIkCYBxThERERERERERERF5P9hY3tGP05rN5jifxH5ReHg4v/32G7ly5TKCO35+fhQtWlTBHRH5IMSeJ4cNG8aiRYv4+eefAciVKxf+/v7UrFmTVKlSMXv2bAIDA7l+/TqVKlWiXr16nD59ms2bN5M4cWLCwsLIlCkTT58+ZcyYMQQGBuLt7c2IESPibdzWOfr69es0bdqUvXv30qxZM5YsWYKNjQ0mk8nowGNra8vFixcJDg4mICCArFmzvvFxicj/LnZwJzQ0FF9fX06cOEFoaCilS5cmOjoaOzs7bG1t4+wr/4x1XoTn73MfPHiAi4sLUVFRRpeXGzdu0KxZM3bv3k337t3p378/adOmNZ7/F98f6/3ym+Hr68uIESPIkSMHzs7O/Prrr0RHR9OtWze8vLxIlSoVjx49YtiwYYwZMwY3NzeSJk3Kb7/9Rrp06QgLCyNz5sy6T0RERERERERERETeU+9keCd2YeLUqVPcuXOHhw8fki9fPjw8POJ8EvXZs2fMmjWLAQMGKLgjIh8kb29vxo4dS9myZWnUqBE3btxgy5YtXL16lc8//5yAgACSJUvGsWPH8Pb25sCBA1gsFhwdHSlUqBArV64kS5YsRvH3yZMnfP/99zRs2PCNjO/FMGZ4eLjRWcDq+vXrNG7cmP379/Pll1+yYMECgJcCPC8Gf0Qk4bwY3PHx8eHUqVOEhYVRokQJHjx4wNatW4mOjqZ58+a6Z/+l2O+PJ0+ezNq1azl48CBZs2alSJEiDB48mCxZsgB/H+CRfy/26/HLL79QrVo16tSpg4+PD25ubhw5coRu3bpx9OhR+vTpQ79+/UiTJg03btxg1apVLFu2jCdPnlCgQAFGjBhBxowZ45xTRERERERERERERN4v71x4J3aRNygoiHnz5nH58mUA3NzcqFWrFpMnTyZRokSYzWYsFguff/45V69eZd++fYCCOyLy4Vi6dClt27alZcuW9OnTh1y5chEZGcnEiRPp27cvJUqUIDQ0lKRJkwIQHR3NmTNnuHbtGpkyZSJTpkykTJnypZCM1d91Qfs7sQuRCxYsYMeOHezcuZNixYqRP39+AgMDjULytWvXaNKkyUsBHs3pIv89fxbc2bFjByVLljS6Ivbs2ZPWrVsza9asBB7xuy32892nTx/Gjx9P3rx5+eijj7hy5Qo7duwgY8aMLFiwgMqVKwNxAzy9evWib9++pE+fPiEv4720d+9eLl68SN++fdm6dSsFCxY0tl25coUmTZpw5MgR+vTpQ9++fXFxcTGWO4uJicHGxgZ7e3sFd0RERERERERERETec+9ceMfKx8eHkSNH8umnn/Lpp5+SPn16Ro4cyf79+8mZMycnT540lgeIjIwkUaJEgIq8IvL+sRZtX1XYa9u2Ld9++y3fffcdBQsWJDo6mnXr1uHl5YWDgwMHDhzAxcUFk8mEjY3NK4M4/zag83fjBvDy8mLChAmkSpWKZMmScefOHZ48eUL58uUJCQmhYMGCxhJaTZo0Yd++fbRq1Yq5c+eqS4TIf8zfddyxBnf69+9P4cKF2bVr10vHyT8zdepUevbsSfv27enRowc5cuTAYrHw9ddfs2DBAkqXLs3OnTuxs7PDxsaGGzdu8MUXXxAWFsbAgQMJCAjQa/AGTZo0ie7du1O/fn3Cw8PZsmULJpMJW1tb43n+7bffaNy4sRHg8fHxIXXq1LofRERERERERERERD4wb74a+xZs2rSJyZMn065dO0JCQmjfvj3169enY8eOANy5c4fw8HDgeSHIGtwxm80K7ojIe2X79u10796d8PBw7OzsMJlMwPP5Ljw8nNDQUPLly0fBggWJiIhgzZo1eHl5YWtry/79+3FxcQHg6NGjXLly5ZXfIz6CO4BRlBw9ejQhISF07NiRnTt3cvLkSfbv38+nn37K7t27adeuHefPnweed1hbuXIlFSpUYMGCBfTo0SNexiYi/8xfddyJHdzx9fWlSJEiRnDH2mFE/jlrODNbtmx07NiRHDlyEBERwfr16wkNDSVXrlysW7cOe3t7o7OLq6srixYtomHDhrRp00avwRtWokQJihcvzvr16zl8+DCXLl16KWTr7u7OqlWr+Oijj5gwYQIDBgzg4cOHei1EREREREREREREPjDvZHjnyJEjREZG0qFDBzw8PDCZTCxevJiAgACyZcvGuXPnSJkypRHggefFpPgqQIuIvG0WiwWTyUTPnj2ZPHkyvr6+RoDH2iknSZIkuLu7c+fOHR48eMB3331H3759sbW15dChQ6RNm9Y4X4sWLejdu7cR/okvZrM5zt/v37/PkiVLKFasGL179yZPnjwkTpyY/PnzM3PmTHr06MGxY8fo2bOncYybmxuLFy+mfv369OnTJ17HKyL/G2vgYPv27fj6+r5yqSxfX1+KFSvGzp07AXVF/F8cP36ckydPvnLb7du32bNnD9WrVyd//vxER0ezfv16evTogZ2dHbt37zbm/SNHjnDnzh0AMmTIwMqVK8mcOXO8/wz40Hz88cfMmDGDKlWqcP/+fSZNmsSdO3ewsbEhdvNTd3d3Vq9ejbu7O5s2bUrAEYuIiIiIiIiIiIhIQnnn0iwmk4kTJ06QOnVqihcvjtlsZtWqVfj5+WGxWDhw4ABp0qQB4NKlS8ycOROz2axPr4rIe8XGxsYoxhYuXJgJEybg7e1NeHg4tra2xMTEAJArVy5OnjxJ9+7d6dq1K3Z2dhw4cMAo4FosFkaNGsWDBw+oXLlyvIQcjx07xrfffgu83MXn9u3b/Pjjj5QpU4asWbMSExODnZ0dFouF9OnT07dvX0qXLs3333/PggULgOfdJTJmzMjq1avJkiWLca0i8t9w4MAB/Pz8OH78OGFhYQruvCFXrlyhbNmyfPrpp6/slGYNRz58+JBnz56xZs2aVwY2TSYTXbt2ZerUqUaAxDo3v9gVRv4dGxsbihQpwsiRIylZsiTTpk1j9uzZ3Lt376UAT6ZMmdi1axeHDh0iZcqUvKMrG4uIiIiIiIiIiIjIP/TOhXfs7OxIkiQJT58+5dKlS39amLBYLHTs2JE1a9bw9OnTBB61iMibFxMTg7OzM2FhYeTNm5cpU6YYAR5rMTwwMBAPDw8WLVpEREQEO3bsIH369MY5Vq5cycyZM8mdOzeff/75Gw863rhxg3LlyvH1119z7Nixl7Y7OjpiY2PD+fPnsVgsxrhtbGwwm824ubnh5+eHra0tly9fBsDBwQH4o9iswr9IwnoxZBAZGUlUVBS7du16aaksBXf+uaRJk+Lp6Um5cuVInTr1S9szZcpE4cKFOXDgALNmzaJfv36v7LQ2ePBgzp49S/78+RVufwtsbGwoVqwYU6ZMIV++fAwZMoQZM2a8MsDj5uZG+vTp9cEDERERERERERERkQ/Qfza8E/sX2davra3869evz9OnT+nUqRP9+vXDzs6O/fv3xylMTJ48mTNnzlCuXDmSJk36dgcvIvIWWMMrKVOm5PTp0+TNm5cZM2bQq1cvY9nAdOnSERQURNasWbGxsWHx4sUcP36c8+fP4+fnR58+fYiIiGDx4sW4uLi8tKzVv+Xs7ExQUBBVq1YlR44cL21Pnz49OXPmZOfOnaxZs8aY72P/DLCOy7rEi4gkrBfniRdDBhUrVmTHjh1GcGfBggX4+fkpuPMvubi4EBwczJw5c0iePDlTp07l6NGjcfb58ssvuXz5Mt7e3pjNZn744Yc474+XLl3KggULKFmyJFWqVHnbl/BBK1q0KNOnTydPnjwMHTqUmTNncv/+/VeGdLTUr4iIiIiIiIiIiMiHx8byH+zJbjKZjLb9FosFi8US55fY58+fp2XLlhw6dAgnJyeuXr1K8uTJje3Lly+nf//+pE6dmo0bN5IuXbq3fg0iIvEp9jy5ePFiHj9+zPbt21m9ejVJkiShdevWjB49miRJknDv3j22bdtG//79+fXXX0mSJAkmkwkHBweKFi3KokWLyJw5c5xzvknR0dFYLBYcHR2ZOHEimTJlomHDhsb2pUuX0q5dO8qUKcPgwYMpUaJEnGLmqFGjCAgIYMaMGXz55ZdvfHwi8vpizxPLli3j4sWL3LhxA09PTzw8PIzAtNlsxmw2s2LFCjp27EiRIkXYtWsXoODOmxAaGkq1atUoVaoU06dPp0CBAtjY2HD9+nV69OjBN998Q7FixVi4cCHJkiUjTZo0jB8/nsmTJ2OxWNi1axfu7u6YzWYFRd6yY8eO0aFDB3755Re6du1K7969cXZ2TuhhiYiIiIiIiIiIiEgC+8+Fd2IXEcaPH8+OHTswm81UrlyZTp06kThxYgB27NhB69at+e233+jSpQsVKlQgZ86czJkzh7Vr12KxWNi3bx+ZM2dWYUJE3isWi8UIt/Tp04e5c+fi5uZG7dq1OXLkCGfPnuXGjRt06tTJCPAAPHjwgFmzZnH79m0sFgvly5enXLlypEqVKt6CO7EdOnSIUqVKUbRoUQYPHkytWrUAuHr1KqNGjWLKlCmUKFGCr776ipYtW2Jra8uaNWsICAggSZIkbN++HRcXl3gdo4i8nr59+zJ69Gjs7OwwmUykTZuWbt260bp1azJlymTst3HjRlasWMGCBQsABXfelBs3bjBjxgxGjx5NoUKFmDJlCvnz58fOzo6LFy8ycOBAVq5cicViwdXVlYiICB4+fEjevHlZu3YtWbNmfSvzvrzajz/+SOPGjbG3t+fw4cOkSJEioYckIiIiIiIiIiIiIgnsPxfesfLx8WHkyJGkSJGCyMhIoqKiaNiwIT4+PhQvXhxbW1v27NlDYGAge/bsISoqCoDEiRNTvnx5Zs6cGa+dJEREEtq8efNo06YNPXv2pGvXrnh4ePDw4UPOnTtHq1atOHv27EsBnld5UwHHF88TFRWFo6Mj8Lxgb2Njw6xZs/D29iZ37twEBgZSp04dAM6dO8fs2bOZNm0ajx8/JleuXFgsFq5fv06aNGnYsWMHWbNmVRhTJIHEDg3OmDGDHj160LBhQ2OuWbVqFQcPHqR79+50796dzJkzv3QOBXf+mdjzXkREBPb29tjb23P79m2mT5/OsGHDKFq0aJwAz+3btzl06BBLly7l9u3bpEqViipVqtCwYUPSpk2r98f/AadOnSJt2rSkT58+zv0lIiIiIiIiIiIiIh+m/2R458CBAzRp0oR69erRq1cvIiMjmTdvHlOmTKF48eIMGTKEsmXLYmtry/Xr17l+/TqHDh3CwcGB4sWLkz17dlKkSKHChIi8tywWC23btmXx4sX88MMPFChQIM72GzduUKFCBX755Rc6duzImDFjSJIkCdHR0Tg4OBjneFPFwtjnOnDgAKVKlTK2DR8+nOzZs/PZZ58RHR3NggUL6NmzJ/ny5YsT4Ll79y4nT55k2LBh3Lx5kxQpUlCyZEl69+5NhgwZNKeLJJAXwyP9+vXjl19+YdKkSWTLlg2TycT58+fp27cv3377LT169KBHjx5GgEfBhH8u9ry3dOlSo4NZo0aNcHBweGWAp0CBAnFCji+GphSC/G/R6yEiIiIiIiIiIiIiAP+Jjz9bizrWPy9dukRUVBQ9evQgR44cAHh7e+Pq6kpAQAD9+/c3Ajxubm64ublRrFixl86pIq+IvK/MZjM3btwgSZIkZMuWzXjM1tYWk8mEq6srEydOpGnTpixevBhbW1tGjhxJ0qRJjXO8yWK69VzlypXjyJEjrF+/nho1atCzZ08mTJjA6NGjiYqKInHixLRs2RKAnj17EhgYCECdOnVIkyYNlSpVokKFCkax2cbGBhsbGwV3RBKQNVjg7+9PZGQkYWFheHp6ki1bNuNezZMnDyEhIdjY2DB+/HgAI8Cj4M4/YzabjXmvX79+zJgxA0dHRypXrmzskzZtWjp06ADAsGHD6Ny5M1OmTKFQoULGPi92O1JQ5L9Fr4eIiIiIiIiIiIiIACT4b4tNJpNR1ImIiCA8PJx06dJRs2ZNcuXKRXR0NADp0qWjdevWBAcHc/ToUfr378++ffuwNg56sYGQCkUi8q56nYZodnZ2pE2blkePHrF582bgjwKgtdhboEABnJycMJlMTJkyhSFDhsTfoP9fw4YNiYqKokuXLtStW5cJEybQt29fPvvsMxInTgxgBHhCQkL46aefCAwM5NtvvzXOYbFYcHR0xNbW1pjLFdwRSVhXr15l8+bNjBkzhrNnzxqBEHt7e2PO8vDwYNy4cdSpU4fx48czadIkLl68mJDDfqdZ5/SAgABGjx5Ny5Yt2bZtG/Xq1TM6qMEfAR5fX1+OHTtG586dOXXqVEINW0RERERERERERERE/oEEXTYrdpv4UaNGsXbtWm7evInZbCZRokQcOnQIJyenOPvdvXuX+fPnM3DgQEqUKIG/vz+VKlVSWEdE3gt/tnxG7M4z1i5l27dvp2HDhpQpU4alS5eSKlUqIO4SKeXLl+fzzz9nw4YNTJs2jaxZs8bLuGMvi7NixQqaN2+OjY0NDRo0YP78+SRPnvyla4uIiGDhwoXGElqDBg2iZs2a8TI+Efn3Dh48yJgxY1i1ahV16tRh4sSJxpwSew64cOEC3t7erF27lsDAQAYMGKDuIn/hr5YVO3DgAPXq1aNixYqMHj2aLFmy/Okx1iW0Ro8eTYYMGVizZg158uSJ9/GLiIiIiIiIiIiIiMi/l6CVFGshx8/Pj379+vH7779jY2PD48ePOXfuHFOnTuXZs2fY2tpiNpsBSJMmDV999RWDBw8mLCyMiRMnGt15RETeZRaLxZgXq1atir+/v9GR5sXgDkC+fPmoU6cO3333HZ07d+bq1atGcMdkMrF48WLOnj1LhQoV2LJlC1mzZiUmJiZexm5jY2PM07dv3zbGeujQIfbv3w88n/Nj50Vjd+A5d+4cXbp0ITQ0NF7GJyL/nPW+LVmyJL169aJOnTps2bKF+fPnc+vWLQBj+VN43oFn+PDhtG7dmtatWyu48zes72NfNT///PPP3Llzh/bt2xvBHXi5w6TFYiFt2rS0b9+eTp06ER0dbQQ6RURERERERERERETkvy9BOu/E7iBx6dIlKlWqRJ06dejXrx/JkiXj22+/JSAggIiICAYNGkSLFi1InDhxnK4Nt2/fZu3atdSqVQt3d/e3fQkiIvHGy8uLsWPHYmNjQ+LEialRowYdOnSgaNGipEuXLs6+P/30E927dyc0NJSyZctSu3ZtatWqxaZNm1i0aBGJEydm27ZtpEmT5q2M3Ww2s3v3bs6fP8+dO3fw8/Mjc+bMTJw4kbp16wLPi8yxg0pRUVFMnTqViRMnsmfPHlxdXd/KWEXkZS92yIqOjsbW1jbO0nUHDhwgICCAnTt34u/vj6enpzE3xQ4YWt/vxX7fJ3GFhYXx5ZdfsnPnTrJnz248bn0devbsyYQJE9izZw9lypR5qeOO9bm9desWTk5OJE6cmHv37mFvb/9S90oREREREREREREREfnvStBlszZv3szDhw/x8fFh48aNFChQAHheyA0LC6NDhw5ER0cTGBjIl19++VKAx1rAUFFIRN4X0dHR9O/fn9GjR1O/fn0ePHjADz/8QHh4OHnz5iUgIICCBQuSK1cu45iffvqJCRMmsHbtWqPrDUCuXLmMjjvxVcC1zsOxC8rR0dFYLBYcHR2ZMWMGHTt2fCnAY3Xz5k1Sp06Nvb09z549I1myZCo2iySQ2O+nli9fzt69ezlz5gzJkyenZcuWFCxYkBw5cgDPAzwDBw5k165dfxngkb82dOhQBgwYQLt27Zg+fbrxuPX5mzx5Mt26dWPOnDm0bt36lUsoms1m2rdvz2effUbNmjWNY/U6iIiIiIiIiIiIiIi8OxIsvDNt2jQ6d+5MlSpViIqKYteuXURHR2Nvb28Ecnbs2IGnpyfR0dEEBQW9sgOPiMj75ubNmxQpUoTy5cszadIkLl++zLhx41i1ahUmk4msWbPy1Vdf0bJlS1xdXUmSJAkPHjzg7t27rFmzBgBXV1dq1aqFi4tLvAUcY583KiqK6Oho7OzsSJw4cZz9Zs6cSYcOHcicOTPjx4+nfv36AGzYsIG5c+fSpk2bOF15VGwWefti33teXl5MnDgRGxsbkiVLxv3790mUKBGVK1dmwIABlClTBoCDBw/i7+/Prl27CAgI4Ouvv1bnrNe0c+dOfvrpJzp16sTy5cupXr06qVKl4sGDBzg7OxMdHY2DgwO7du2iVq1aJE+enH379pE9e3ZMJhM2NjbGe+GQkBD8/PyYO3cuzZo1S+ArExERERERERERERGRfyLBwju///47TZs2Zf/+/Tg7O3Ps2DGyZMkSJ5gTO8BjsVjw8vLC09OTRIkSJcSQRUTinclkwtbWFi8vL8aNG8fChQtp0aIFAKGhoWzfvp05c+Zw8+ZNChcuTK5cuQgICCBDhgw4Ozu/dL74CjvGDu7Mnj2bb7/9ll9++YWUKVPi7e1NyZIl4yzxZQ3wpE+fnqFDh2KxWBg9ejQXL17kl19+IWPGjG98jCLyvxs1ahT9+vWjR48etG7dmrx587J582aWLFnCypUrKV68OJMmTaJkyZIAHDp0iMDAQLZs2UJISAjdunVTAO9vbN++nU8++YTPPvuM6dOnkzp1auB5aGrChAmcP3+eLFmyGPt36tSJ6dOnU7hwYZYtW0bu3LmNbatWrWLgwIE4OzvzzTffvLUlEkVERERERERERERE5M1K0GWzbty4QevWrfnuu+/44osvGDNmDOnTp38pwBMWFkaDBg3InTs3O3fuJFmyZAk1ZBGRN+LvOsyEhYVRpUoVqlSpwuLFi0mfPj0xMTHY29uzYsUKPv/8cxIlSkRERASpU6emSpUq1KhRg7Zt277W+f+N2HO0l5cXY8eOxcXFhZw5c3L9+nWuX79Ot27daNeuXZzlvebMmUOHDh0wmUzY29uTLVs2tmzZQrZs2bT8ochbZp1PYrt06RL16tXD0dGRVatWkTVrVmPb06dP8fHxYfLkyTRv3pzx48eTNm1aAPbt28ekSZMYMWIE7u7ub/My3jnnz5+nevXqZM2alaCgICpUqGBs69WrF+PHj8fd3Z1du3bFCfA0adKE1atXkypVKr788kty5MjB4cOH2bZtG3Z2duzZs+elELyIiIiIiIiIiIiIiLw74j2882cFZGuh9saNG3z++efs3LmTHj164OfnR9q0aV8K8Ozfvx8PDw8yZMgQn8MVEYl3sefFiIiIl5aZsvL09GTRokVs377dWKbm559/pkKFCqRIkQJvb28ePXpEaGgo33//PSlSpOC3337DycnprVzH4MGDGTRoEO3bt6djx47kz5+fI0eOUK5cORwdHWnZsiW9evUiR44cxjHfffcdR48eBaB169a4uroquCPyFh0+fJisWbO+9F4L4MSJE5QqVYoOHTowbtw4IO58devWLVq3bs3u3bvZs2cPhQsXNo61LvOk+/mvbdy4kYYNGzJs2DC8vLwAWLFiBbVq1SJFihQMHDiQwYMHkyFDBvbu3RsnwOPj48P69es5e/YsAKlTp6Z06dJMmTIFd3d3PfciIiIiIiIiIiIiIu+weP1orslkMgo+N27c4Ny5c/z0008ARnHB1dWVpUuXUr58ecaPH8+QIUO4ffs2tra2mM1mY99y5cqRIUMGTCZTfA5ZRCTeWefFmjVrMnToUJ4+ffrK/SpVqkRkZCQDBw4E4JdffqFcuXI4ODgwYMAAOnXqRL9+/di0aRPLli3j5MmTODk58SYzmeHh4a98fPfu3cybN4/mzZvTo0cP8ufPz7Nnz2jZsiWpUqUid+7cTJ8+nXHjxnHu3DnjuOrVq+Pj44OPj4+COyJv2YULFyhZsiQZM2bk0aNHL3VoefLkCREREfz+++/A80BO7AC2i4sL1atX5+nTp3z77bcAxns1BwcHAN3Pf8NkMmEymYiOjgagY8eONG/enG3btgEQHByMn58f165do2zZsly+fNk4dvjw4Xz33Xds376d9evXs2fPHpYtW6bgjoiIiIiIiIiIiIjIeyDewjuxiwijR4+mVq1aFCpUiJIlS9K0aVNOnjxJTEwM8DzAs3z5csqXL8+ECRPiBHheLEKrMCEi74PffvsNgBEjRjB16tRXBnhatGhBhQoV+Pnnn5kzZw4lSpTA0dGRoUOH0qZNGwBjCaqmTZuSOXPmOKHJf2vHjh306NGDn3/+Oc7jZrOZH374gfDwcNq3b0+OHDl48uQJJUqU4P79+4wcOZKpU6dSqlQpFixYwKRJk+IEeGLTnC7y9ri7u/Pll19Ss2bNOI9bgyQ5c+akWLFi7NixgwsXLhiddOD5Mlu2trZUr14d+OPe1RJNfy8sLAx/f39iYmKoWLEi9erVIygoiIoVKzJjxgx69epFiRIljP0HDx78ygCPxWLB3d2dypUrU7duXfLkyUOyZMmwWCyaS0VERERERERERERE3nHxUnGJXUTw8vKiX79+mM1mevbsSZMmTdiyZQtdunTh+++/JyoqCogb4Jk8eTI+Pj7cvXv3jRWhRUT+S9zd3QkJCaFx48b4+/sTEhISJ8BjLZi3bduW69ev07lzZ5ycnBgxYgStW7cGnodoXizYvqkCblRUFPPnz2fWrFlMmDAhTvjG1taWokWLMmTIEMqWLUtkZCQtWrTg2rVrDBo0iBYtWvDRRx9RsmRJnj59yoIFCwgODubSpUtvZGwi8r8zmUw4ODgwd+5cVqxYgZOTE6NGjeLmzZs4ODhgNptJmzYtNWrU4O7duzRr1ozr169jZ2dHdHQ09vb2mEwm1q5dC0Du3LkB3minr/dRaGgoVapU4eeff+batWs4OzsTGBiIi4sLu3fvpkqVKnTs2JGMGTMCGMH2VwV4bGxsjKBVbHqvLCIiIiIiIiIiIiLy7rOxxGPVJSQkhAEDBtCuXTs8PT3Jnz8/V65coXTp0ly/fp0iRYowZMgQqlWrZiy3cPPmTapVq8a9e/c4ffo0zs7O8TU8EZEEYbFYjGLr2bNn8fHxYf369Zw9e5acOXPG2ffChQuULVuWmzdvEhAQQEBAAPA8uBPfHS+uXr1KQEAAc+fOpW3btnh7e5MrVy5je0xMDPb29nz33Xc0b96cJk2aMHHiRBwdHQHYsmULfn5+pEiRgl9++YVTp06RKlWqeB2ziPy52PPGN998Q/369cmWLRsHDx7ExcXF2K9OnTps3ryZPHnysGjRIrJnz07KlClZunQpwcHBJEmShG3btpE6deqEupR3woULF/jkk09wd3dn0KBBlC9fHngebB87diyZMmXixo0bTJgwgZYtW5IsWTLgj7kVYMCAAQwdOpTMmTMTGhqKh4dHgl2PiIiIiIiIiIiIiIjEn3ir/J4+fZrFixcbnyjOnz8/jx49okaNGlgsFtq0acOFCxcYMGAA3333ndGBJ3369ISGhnL06FGcnZ31iW4Ree/E7pKQO3duhg4dysGDB18K7lgsFjw8POjfvz+AEYqxLl8T3zJlysSgQYNo2bIls2fPZtSoUXE68Fi7/Jw+fZoHDx5Qp04dY4wAS5Yswc7OjsWLFxvBHc3pIm+H2WyO8/cX543q1avTvXt3Ll68SJkyZbh9+7axbdOmTTRs2JAzZ85QtmxZSpYsScGCBWnTpg3Pnj1jzZo1pE6d+qXvIXFdvHiRK1euUK1aNSO4s3jxYjJkyICfnx/Dhw+nVKlS9OjRg9mzZ/Ps2TMA7O3t43Tg8ff358qVKzRt2hSz2ax5VERERERERERERETkPWQfXye+dOkS586dY/jw4eTJk4enT59SoUIF7t27R0hICBUrViRZsmRMnDiRESNGYGtrS9WqVXF0dCRt2rTA2+ksISKSUKwdePLmzfvSY/BHyOejjz7C2dmZ0aNH06hRI2O5mrchQ4YMDBs2DBsbG2bPng087xqRO3duY3zWOfvQoUNUqlQJJycnVqxYwYEDB6hbty4ZMmTA1tZWc7rIW2KxWIx77eLFi7i7uxudXPz8/ChdujR169Zl1KhR2NnZMW7cOMqUKcPevXtJly4dAKtXr2by5Mns3r2b3bt3kz17djw9PfHx8SFDhgyYTKY3tkzf+8rBwQGTycTjx48B6Ny5M9OmTWPFihV07drVeM8bFBSEt7c3AO3atSNp0qRGgMfe3p6goCCSJUtGs2bNNIeKiIiIiIiIiIiIiLyn4nXZrC1btlCzZk2io6Px9PRkzZo1DBs2jHbt2pEoUSJ27NhB1apVSZQoEalSpWLx4sVUrlw5voYjIvKfEDug86q/v0rv3r0JCQlhwoQJdOnS5W/3/zdeFbK5du0afn5+LFiwgDZt2uDt7W2EiC5cuICnpyf79u2jVq1axMTEcODAAZImTcqePXvIlClTvI1VRP5c3bp1OXPmDN988w158uShZ8+eTJgwgcDAQLy8vEiaNCnR0dH4+Pgwbtw4smfPHifAY3Xz5k3Sp09PdHS0EUhRcOevWSwWHj9+TLdu3Vi0aBElSpTg4MGD9OnTh549e5IxY0Zj323bthEYGMjhw4cZNWqUEeCBuEtovervIiIiIiIiIiIiIiLyfvjXv/1/VZE3KioKR0dHatasCTwv+oSFhVGpUiW6dOli7JchQwbc3Nxo0aIF27ZtI0+ePP92OCIi/2mxgzrnz58nbdq0ODs7/+n+1jm2Vq1ahIWF0bBhw3gN7sTu2HHt2jUyZMgAPJ+vhw4dCsCcOXOwsbExOvBYl/aaM2cOS5YswcXFhUKFCjF37lwyZcqkQr9IAnj06BF58uRh586ddOrUCTc3N5YtW0bfvn1p06YNSZMmxWQy4eDgwPDhwwEYN24cZcuWNQI81rCOtbuWNTSi+/llL74fNpvNODk54e/vz549ezh06BBlypShTZs2RnDHOjdWq1YNgMDAwFd24IlNwR0RERERERERERERkffTv+q9bzKZjELF/fv3uXr1KgCOjo7A8yIwwJkzZ7hy5QpFixaNc/zKlStJkSIFXbp0Ye/evbi5uWEymf7NkERE/rNiB3e2bdtG165dGTp0KCaTiT9rgmadY8uUKcOOHTvImDFjvM6T1vEFBASQM2dOjh49amyzBnhatWrF7NmzGTVqFD///DMAVapUYdGiRZw6dYrjx4+zbt063N3dFdwRSSBOTk4MGDCA4OBg9uzZw/Lly/nqq6/o2rUrmTJlwmKxYGdnFyfA06tXL3799VfKli3L7du3jS471nkoPoOD77LYwZ0qVapw9OhRY96bP38+Fy9exMPDg3379rFu3ToePnwIPA9Bmc1mAKpVq0ZgYCAff/wxfn5+TJgwgfDw8IS5IBEREREREREREREReev+cXjHbDYbhYlhw4ZRoUIFcubMSfXq1Vm4cCGPHj0yijwFCxYkS5YsbN68mV9//ZXHjx+zZMkSFi5cSI4cOXB1dSVJkiSAPs0tIu+n2MGd0NBQ/Pz82L59O82bN8fOzs7Y9qoQj8ViIVmyZKRMmdIouMcns9mMjY0NNjY2NGnShGPHjhnbYgd45syZw9ixYzlz5oyxPW/evLi5uZE8efK3MlYR+XMpU6bk2rVrRgDn1KlThIeHx5mP/izAkytXLu7evat7+G/EDu5UqlSJsLAwfvjhB2Mur1q1KsHBwYwcOZLatWvTv39/QkJCePToEfA8oBk7wBMcHEzmzJmZPn26Au0iIiIiIiIiIiIiIh8QG8uftXt4Tb6+vowYMYIcOXLg7OzMr7/+SnR0NN26dcPLy4tUqVLx6NEjhg0bxpgxY3BzcyNp0qT89ttvpEuXjrCwMDJnzhynkCQi8j55Mbjj4+PD6dOn2bFjByVKlODRo0ecOHGCPHny4OLiksCjfS46OpoJEyYQFBRE6tSpWbt2bZzuadeuXcPPz48FCxbQvn17unfvTr58+RJwxCLyKlOmTOHatWtERUUxZcoUChcubPxpZX0raGNjQ3R0NJ07d2bdunUcP37cWDpPXhY7uFOuXDlOnjzJqFGjaNGiBcmSJTP2s3Yg27dvH4MGDWLr1q0EBgbSs2dPnJycXjrX7t27yZUrF+nTp9f7YxERERERERERERGRD8T/HN6JvQTKL7/8QrVq1ahTpw4+Pj64ublx5MgRunXrxtGjR+nTpw/9+vUjTZo03Lhxg1WrVrFs2TKePHlCgQIFGDFihLEEjD7ZLSLvo1cFd06dOsWOHTsoWbIkDx8+ZN68eYwfP55evXrRrVu3tzq+2AVj61itj0VHRxMSEsKgQYP+NMDj7+/P3Llz6dOnD8OHD9dcLpKA/izoYTKZePbsGePHj2fYsGEUKVKEqVOnUqhQoTj73bhxA1dXV6Kjo3n69CnOzs56j/YnXhXcGT58OF999RVJkyY19ouMjCRRokTG3/8qwPPicx37e4iIiIiIiIiIiIiIyPvtH3fe2bt3LxcvXqRv375s3bqVggULGtuuXLlCkyZNOHLkCH369KFv3764uLgYn+yOiYnBxsYGe3t7FYVE5L31OsGdRYsW4ePjQ8GCBdm3b1+Cje/+/fukSpXKmJNjB3jGjx9PcHAwadKkYe3atRQpUsQ4x2+//cbYsWPp1asXmTNnfqvjF5E/xH4/9fvvvxMREUFUVBR58+Y17vUbN24wa9YsI8AzefJk437euHEjI0eOxN/fn08++QT48zDQhy52qKZs2bJGcKd169Zxgjs7d+7ku+++o3379mTJksV4/K8CPCIiIiIiIiIiIiIi8mH6R+GdSZMm0b17d+rXr094eDhbtmzBZDJha2trFHl+++03GjdubAR4fHx8SJ06tQpBIvJBeJ3gzsKFC/H19aV48eKEhYUBL3deeBv69u3L2LFjOXfuHB4eHi8FeKKiohg2bBhBQUHkzJmT5cuXxwnwWPdTGFMkYcQOkwwdOpTFixdz6dIlTCYT1atXp0mTJjRo0IAUKVIYAZ7hw4dTqFAhfHx8uHnzJhMmTOD333/nxx9/VBDvNVWrVo29e/cyc+ZMGjRoQPLkyY1toaGheHl5cfXqVQ4cOICHh0ecnwvWAE9oaCg9evTA39+fFClSJNSliIiIiIiIiIiIiIhIAvtHvfhLlChB8eLFWb9+PYcPH+bSpUsvFWzd3d1ZtWoVH330ERMmTGDAgAE8fPhQwR0R+SC8bnCnWLFiRnAnJiYmQcIv0dHRmM1mKlasyIULF7CzszMCmWazGUdHRwICAihbtiznz5+nadOmHDp0yDjeGhpQcEckYVjvQR8fHwYMGICdnR0tW7akUKFC7N69my5duuDv78+DBw9wdXXF09MTf39/fvnlFxo0aEDnzp15+vQpP/zwA5kzZ8ZkMiXwFf33TZkyhdDQUFKmTIm7u/tLwR0fHx/Onj3Lpk2b4gR3rJn5MmXKMHDgQIoVK8bSpUv5h40wRURERERERERERETkPfGPOu9YLBZ+/PFHvL29CQ0NpXfv3vj4+BhLY8UO6Fy9epXKlSsTFRXFiRMnSJky5Ru9ABGR/6qwsDB69erF+fPnCQ0NpUSJEi8Fd3bu3Ak8D+7Y29vH+5hid+iI/XVgYCDBwcGkT5+evXv3xunAExERQeLEiQkICGD9+vWcOHGCjz/+mD179uDg4BDvYxaRV4vd7erkyZPUqFGDFi1a0KlTJzw8PLh58yaHDh2iX79+nDlzhr59++Lv70+yZMl49OgRp0+fZvXq1bi4uNC6dWtcXV3VQet/0KtXL8aPH0+OHDlYsmQJH330Edu2bcPPz4/Tp0+zc+dOPvroI+M5jR3gsb5XPnLkCFmyZCFt2rTqTikiIiIiIiIiIiIi8gH7R+EdeB7gOXbsGF26dOHkyZP4+/vj6en5yqWxrl+/jq2tLenTp1dhQkQ+CE+fPmXw4MHMnDmTjRs3UqpUqQQP7sQuyu/cuZP79+9TsGBBsmfPDsDAgQMZPHhwnABPdHS0EdCpU6cOLi4ulCtXjpo1a+Lu7h7vYxaRl734XiosLIzkyZPTrFkzNm7cSN68eePsc+LECRo3bsyTJ09YtmwZFSpUeOX5FNx5PbHn7J49ezJhwgSyZ89Oz549Wbx4McePH2fHjh2UKFHipeAOPA+2p0+fPk74MXaYUkREREREREREREREPjz/uEpgY2NDsWLFmDJlCvny5WPIkCHMmDGDe/fuxVkWAMDNzY306dNjNpsV3BGR94bZbP7Tx5IlS0aLFi04cOAApUqV4sGDByxatAg/P78E67hjLcoHBATQrFkzunfvztWrV4mMjAQgODgYf39/bt68SdmyZTlx4oRRXF66dCk//fQTNWrUwNPTE3d3dy2tI/IWnTp1igMHDgDEeS81ZswYqlSpQps2bUiSJAm5c+d+6dgCBQrg5+fHjRs3WLly5UvbredTcOf12NvbG/NfSEgI3bt359dff6VPnz6cOHGCEydOUKJECWMpxNjBnTVr1lC7dm3WrFkT55wK7oiIiIiIiIiIiIiIfNj+dcW4aNGiTJ8+nQ4dOjB06FBsbGxo3749qVKlemlfFSZE5H0Ru0PF6dOnefLkCa6urmTIkMGY6woUKADAs2fPWL58Ob1796Z06dKEhYUBby+4A3/Mv3379mXMmDF89dVXtG7d2ujAYe36EBQUhI2NDUOHDqVSpUo0aNCA8PBwtm/fjpOTE5UrVzbOqUK/yNtx+fJlChcuTKFChVixYgU5c+Y0tlWqVIns2bNz5swZ0qZNy6+//krOnDnjBPZsbW2pVKkSKVKk4OjRozx58oTkyZMn1OW8F+zs7IyfAyEhIdjY2DB+/HjSpk1rBHVsbW3j/Kz45ptvGDhwIBcuXKBkyZIJOXwREREREREREREREfmPeSNpGmuAJ0+ePIwYMYJx48bx4MGDN3FqEZH/HIvFEqeLTcWKFSldujSFCxemb9++HD16NM7+4eHh7N27l0qVKiVIcMdq5cqVTJo0iQ4dOhAYGBhn6RxrkRkgMDCQkJAQChYsyLx581i3bh2ZM2dm+/btuLm5qeOOyFvm7OxMp06dyJ49OxkyZIizrXjx4qxevRoPDw+uX79OcHAw8Ee4xHq/Zs2alVSpUpE0aVKSJEny1q/hfWR9jgHGjRtHjx49+P333/nkk084cOAAtra2Ruedb775Bm9vb27fvs3p06fJmjWr5lIRERERERERERERETHYWGKvb/Uv/fjjjzRu3Bh7e3sOHz5MihQp3tSpRUT+cwICAhg0aBClSpUiX758/Pzzz+zfv5+qVasSFBREmTJljH0vXLiAh4cH8HaCO9ZOOoCxZEvXrl2ZM2cOBw4coFChQn973JMnTzhx4gSpUqUiQ4YMpEyZMk4XCRF5e549e4atrS2JEydm7ty55MiRg/LlyxvbT548SZMmTTh37hzdu3cnJCTE2GYymVi6dCmtWrWia9eujB079q2HB99nsefFXr16MX78eDw8PFixYgXFihVj/fr19OvXj/v373PgwAGyZcumuVREREREREREREREROJ4o+EdgFOnTpE2bVrSp09vFIxFRN4HsYutT58+5ZNPPiF//vwMHDgQd3d3bt++TUhICMOGDaNChQoMGTKEsmXLAn8EaGKHY960bdu2cfbsWbp06QLEDeI8ffqUihUr8vDhQ86fP//K4637/9mSOvE5dhH5c7HfT23dupVatWpRo0YNAgMD4yy/FDvA89lnn9G8eXNjqa0VK1Zw//599u/fT8aMGRPqUt5bfxbg6dSpE/Pnz+fmzZsK7oiIiIiIiIiIiIiIyJ964+EdKxV5ReR9tXjxYiIjI+nZsyfffPMNFStWjLM9MDCQ4OBgKlasyODBg18K8MSHe/fuUaJECS5cuMDkyZPp1KlTnO8ZHR1N5cqVOXPmDCdOnCBDhgxx5mnr1+Hh4cycOZOmTZvi6uoaL2MVkdcXO+gRHR2Nvb09AwYMYNSoUXzyySf4+/tTqlQpY//YAZ4kSZKQPn16UqdOjYuLC9OnTydLliwKj8STVwV4ANzc3NizZ4+COyIiIiIiIiIiIiIi8qfiLV2j4I6IvI82b95My5YtjWVrihcvDjxfCssqMDCQgQMHsnPnTgYMGMC+ffsA4rUTWerUqRk3bhy5cuWiS5cuTJ482fie0dHRODg4UKFCBe7du8f8+fOB5/O02WyOE+Lx8vJi/Pjx3L9/P97GKiKvx2KxGEEPX19fvvrqKyIjI/H19cXX15fNmzczaNAgDhw4YBxTsGBBVq5cSa5cuTCbzRQuXJgjR46wZcsWBXf+B9Zs+6sy7n+We7ezs8NkMgEwbtw42rRpg4ODA/v27VNwR0RERERERERERERE/pISNiIi/4OPP/4YLy8v9u7dy48//sjGjRsBsLe3j1PQjR3g6dSpE0eOHIm3MVm/b926dRk3bhweHh5069bNCPA4ODgAUKtWLRInTkz//v2ZPXs28DzAYw3xrF69mm3btlGgQAHc3d3jbbwi8nqsgb+xY8cyevRokiZNyp07d0iePDm9e/fG39//TwM8y5YtI3PmzKxfvx5fX9+EuoR3kslkMp77Gzdu8Msvv/Dzzz8THh4OPH9drCGdF8UO8MyaNYubN28qNCUiIiIiIiIiIiIiIn8r3pbNEhF5n8TuTnPnzh0mTZpEcHAw5cuXZ9SoUZQoUQJ4eWksLy8vFixYwKlTp0iXLl28jS/29928eTPdunXjwoULTJw4kS5duhj7LVmyhC+//BKAfv36Ua1aNXLnzs3cuXNZsGABUVFR7NmzB3d393hd5ktE/lzs+ebp06d8/vnnJEmShFGjRpE5c2Zjv4cPHzJ27FgGDRpErVq1XlpC68SJEzRt2pRz587Rt29fhg8fDsTvEn7vutjP/ahRo1i2bBlnz54FoGzZsjRu3BhPT8+X9n1R7LCOnm8REREREREREREREfk7Cu+IiLzCXxVlAW7dusXYsWMZOXIkDRo0oH///sYSWi8Wah89eoSTk9PfnvPfet0Az6pVq+jVqxe///57nOOLFSvG6tWr1SVC5D9i5MiRpE+fnuDgYIYNG0bTpk2NTlvWe/3FAM/AgQMpWbKkcY4TJ07QvHlzzpw5Q/v27Zk2bVqCXMu7xtvbmzFjxlCqVCnKlSsHwJQpUzCbzbRq1UrPo4iIiIiIiIiIiIiIvFH2CT0AEZH/mtjBlYMHD3L58mUuXbpEqVKl8PDwIFOmTKRLl44+ffpgsVgYNWoUgBHgsbGxiRPUcXJywmKxxGtwx8r6fWvVqsWkSZPo2rUr3bp1AzACPI0bNyZPnjycPHmS/fv34+TkRNGiRalcuTKpU6dWcEfkP+CHH34gICDAuBednZ2NbbHDgSlTpqR3794ADB8+nAcPHjBx4kSKFSuG2WymUKFCLFu2jIoVK/Lw4cO3eg3vqkWLFjFx4kQ6depE7969yZ49OwDp0qWjb9++hIWF8ezZM5ImTZrAIxURERERERERERERkfeFOu+IiMQSO3QzcOBApk6dyt27d43tZcuWpVu3bjRt2hSA27dvM3r0aEaNGkWDBg0YMGAAxYoVe+tj/bPH/6oDz/9yThGJXy927IqIiGDx4sVMnz6dI0eO0L17d4KCgkiZMuUrj3/48CHBwcGsWLGCo0ePkjZtWuCPe/q3337D3d39ld9LnrM+L23atGHTpk1s27aNggULEhMTw4oVKxg4cCAWi4VDhw6RJk0aoqKicHR0TOhhi4iIiIiIiIiIiIjIe0DhHRGRV/Dz82PkyJHUr1+fFi1acPPmTQ4dOsT8+fNJlCgRU6ZM4euvvwbgzp07jBo1inHjxlGuXDnGjx9PwYIF43V8sbvj7Nq1i0uXLnHp0iUaNGhAtmzZSJEihbHvli1b6Nq160sBnpiYGOzs7LCxsVExXyQBxQ7N3blzB1tbW1KnTk1ERARLlixh5MiR3L17l+nTp1O/fv0/7Yz1+PFjzGYzKVOmjHPO2Pe3AnoQGhrK8ePH6dWr10vbHj9+TJkyZXB2dmb37t1ER0ezZs0a+vbti62tLYcOHTKCUT/99BP37t0zltUSERERERERERERERH5pxTeERF5QVhYGPXq1aN+/foMHjyYLFmyGNtmzpxJhw4dcHJyYuHChdStWxeAe/fuMWDAADZs2MCPP/6Ii4tLvI0vdvHd39+fqVOncu/ePQCSJElCu3btaN++Pfnz5zeOid2BZ/LkyXTq1Cnexiciry92EG/atGls3LiRHDlyMGDAAFxcXIiKimLJkiUEBgZiMpmYMWMG1atX/8ul7RTG+3MPHjygRo0aHD58mHHjxtGjR4842589e0a5cuVwdHTkwIEDLF++/JXBnZiYGEqUKEGFChUYPXo09vZaiVZERERERERERERERP65D/uj1yLywbKGXV7lzJkzPHnyhDZt2pAlSxZMJpOxzdPTk/Hjx/Po0SOWL19OeHg4AKlTp2bo0KGcOnUKFxcXzGZzvIzbYrEYwR1fX1+GDBlC2bJlWbt2LceOHaNevXrMnj2boKAgjh8/bhxXq1YtJk2aRO7cuenSpQtz586Nl/GJyOszm81GCMfLywtvb28uX75MlSpVSJMmDRaLBUdHR7744guCgoKws7PD09OTrVu3xpmXXqTgzp9zdnYmMDCQ0qVL06tXL8aOHWtsM5vNJE2alEqVKnHo0CF69uyJj48Ptra2HDhwwAjuAISEhHD58mXy5cv3l0EqERERERERERERERGR16Hwjoh8cA4ePEiWLFlYsWJFnMetgZvz588DGMEca2HW2qisVatWlC5dmk2bNnHjxg3jeGdnZ5ydneMEbN40a1F+wYIFzJo1iy5duhjLe2XPnp0TJ04QFRXFqlWrCAgI4MSJE8axNWvWZMSIEZQpU4bKlSvHy/hE5PVZ54kRI0YQEhJCu3btWLVqFfXq1cPGxsZY0s7R0ZHPP/+coKAg7O3tad++Pd9//z0xMTEJfAXvFuscXqtWLQIDA/noo4/w8vJizJgxwB+vxyeffIKDgwMTJkwgMjKSixcvkj59euMcK1euZOrUqRQoUIDPPvtMYSkREREREREREREREfnXFN4RkQ/OmTNnePr0KRs2bIhT/LYWbosXLw7AoUOHgD9CPdZCesqUKSlYsCAPHz7k999/f+n8b6KQ+2crGlosFu7du8e6devImDEjHTp0IHfu3Dx+/JiPP/6Y+/fvM23aND7//HM2bNhAUFAQx44dM46vV68e27ZtI2vWrCr8i/wHXLx4kdmzZ1O+fHm6d+9O7ty5gT/mgFcFeBInTkyDBg3YtWtXQg79nWNjY0N0dDTwPKAzbtw4SpYsibe3NxMnTjT2q1WrFiNGjADgxo0brFq1inPnznHnzh0CAgLo27cvUVFRLFq0iDRp0sRbpzUREREREREREREREflw2Cf0AERE3ravvvqKLFmyUKxYMezt7Tlz5gx58uTBYrFgY2ND0aJFyZUrF8OHD6dKlSpUqFAhTiEd4O7du2TIkIFMmTK98fGZzWYjSHTz5k2OHj1KqlSpKFWqFDY2NkRFRWFvb0///v0pUKAA4eHhfPLJJ9y9e5dRo0bxxRdfUKZMGXbu3Mn333+PnZ0d/fr1M0JJiRMnBsDeXj8CRBLa5cuX+eWXX+jXrx/ZsmUz5qHYIUAbGxvMZjOOjo40b96c8PBw5s6dawR95PWYTCYcHBwAWL9+PTdu3CAmJgZbW1t69OiBra0tXbp0AaBnz544ODjQv39/mjZtip2dHSaTCTs7Oz7++GOWLl2Ku7u78ZiIiIiIiIiIiIiIiMi/oc47IvJBsXZIqFSpEk5OTgwbNox8+fKxceNGo1ieN29ePD09iYqKombNmmzevJno6Ghj+7p169i9ezcfffQR6dKle+PjswZ3+vfvT+XKlalTpw7Tp0/n3LlzALi6ujJ69Gg+++wzzGYzI0eO5NixY/Tu3ZumTZvi4OCAh4cHGTJkIFmyZKxatYrJkyer047If4g1EHj9+nUAHj9+DLzcuctkMgHPO8BcvnyZRIkS0bZtW3bs2EHGjBmN7fLXLBaLEbLx8vKiVatWzJkzh2zZslGnTh0AunXrRkhIiHFMly5d2Lp1K9OnT6ddu3b4+Piwdu1aNm3aRJYsWRTcERERERERERERERGRN0ZtF0Tkg2INx1i7W6RIkYIMGTLw5ZdfsnjxYqOI26dPHx4+fMjgwYNp2LAhdevWpXjx4vz6669s3boVe3t7Jk6cSNKkSY1zvamxAdSoUYMjR45QrFgxxo0bR+bMmcmVK5exb+bMmY2vDx06RKZMmfDx8TEec3Bw4NmzZ/Tv358HDx7w5ZdfqtOOSAKKfX/DHyGd7NmzA/Djjz8a26xzSuzASe/evcmfPz99+/YlUaJERgcZhUdej/X5njZtGmPHjqV79+707t3bmEsXLVrE0KFD6d27NzY2NvTo0QOAkiVLUrJkyZfOZzab9dyLiIiIiIiIiIiIiMgbo847IvLesnbZAYiIiAD+WCpqx44dAHTt2pWhQ4fi5OREs2bN2LRpk3FMcHAwU6ZMoVy5cqxevRo/Pz/WrFlDnjx52Ldvn7FkypsI7lgsFqOwX6tWLQ4ePIiPjw/Lli2jRo0a5M2b95XXd+fOHX7++WcsFguXL182ts2fP5+7d++SK1cuBgwYQNasWdV5RySBmEwm4/7+6aef+Omnn4xt2bJlo0KFCixYsIAFCxYAGMEd69wye/ZstmzZEie0I//M7t27cXZ2pk2bNmTOnNnoXPTll18yevRo3N3d6dWrF1OmTDGOsVgsRqck65+xg1giIiIiIiIiIiIiIiL/lioPIvLeshZXmzVrxty5c4mOjgagc+fOVKtWjbCwMABatWrFoEGDSJ069UsBno4dO7Jq1Sr279/Phg0b2LFjB6tXrzaKvm+q84K1SD948GD27NmDl5cXnTp1Ik2aNEax+FXX5+LiQpMmTbh06RIhISGEhYUxZswYhgwZQrp06fjoo4+M/dV5R+Ttiz1PjBo1ikaNGuHr68uvv/4KQNq0aenQoQP29va0bt2aqVOncu/ePWNOWLJkCaNHjyZLliy0bt1aoZF/ITIykpMnT5IqVSry5MkD/BGUAqhduzZ+fn7A82DnhAkTjH2sr8ebCGuKiIiIiIiIiIiIiIi8yMbyZ1VhEZH3wMGDB6lZsybJkydn8uTJbN68mRkzZtCjRw969eqFu7u7se/8+fPx9/fn3r17LF++3FhC61Xe1FJZsT18+JAaNWrw7NkzQkNDcXFx+cvvY9125swZ/Pz8WLduHfC8uJwnTx6+/fZbsmTJ8tJyPSLydsS+97y8vJg8eTKlS5fGx8eH6tWrx7m/Z8yYgZeXF0+ePKF48eLkzp2ba9eucfToUVKmTMnOnTvJmjWr7ud/qXnz5qxYsYKNGzdSu3Zt43Hr8/r06VMqVKjAgwcPuHjxItOmTaN9+/YJOGIREREREREREREREfkQKLwjIu+18PBwdu3ahbe3NxcvXuTp06d0796dAQMG4OLiAsQtsL8qwBMfQZ1XCQ0NpVq1agwcOJDAwMC/LdLHHtfjx49ZsmQJ58+fx93dnc8//5x06dK90e5AIvLPjBgxgoCAADp16kTXrl3Jnj37K/dbs2YNmzZtYu3atYSHh5MlSxbKlStHUFAQGTNm1P38BsyZM4d27drxySefEBISYixJGBMTg729PeHh4eTIkYPq1atz584dJk2aRJYsWRJ41CIiIiIiIiIiIiIi8r5TeEdEPggNGjRgw4YNJE+enMGDB9OtWzdjuRQbG5tXBngeP37M7NmzadSo0VsZ4/r162nUqBFjxoyhZ8+erxUaOnPmDJcuXaJmzZovbVOhXyThnT9/nk8//RRXV1dmzZpFzpw5jW3btm3j/v37PH78mDZt2gDPQyS3b9/m0aNHuLq6kjRpUhwcHHQ/v6YXQ4+v+nurVq1YtmwZ7dq1o1OnThQuXNjYvmDBAoYMGcLmzZvJnDkz9vb2RrBHREREREREREREREQkvqgSISLvNYvFwsWLF3n8+DGfffYZx44dY8SIEaRKlYqGDRuSPHlyAGxtbY3i+FdffYWNjQ0dO3bEx8eH2rVrkzhx4jc6JmsoJ3ZhOSoqCovFwsGDB4mMjCRRokR/eg5rMXnFihUcOnSIIkWK4OrqGmcfFfpFEt6DBw+4cOECX331FTlz5iQ6OppffvmFKVOmMHnyZGxtbTGbzWzZsoUVK1Zgb2+Pm5sbbm5ucc6j+/nvxQ44rVq1iiNHjnDkyBFy5cpF9erVadCgAba2tvTt25e7d+8yY8YMDhw4QI8ePShYsCA7duxg1qxZJE6cmFSpUhmBHQV3REREREREREREREQkvqnzjoi8d17VsebChQskTpyYn376iW7duvHgwQNGjhxJo0aNSJYsmRGiiR2mWbZsGRUqVCBDhgzxOjZrEOfmzZuUKVOGJEmS8M0335AtW7ZXdtuIfY7ChQuTIkUK9uzZ88bGKCJvjnU5vAIFCjBhwgS+//57Vq9eze+//07Dhg0pVaoUs2fP5tixY0yfPh1PT8+EHvI7Kfbc7e3tzZQpUwBIlCgRDx48AKBLly50796dnDlzcubMGSZMmMC0adPinCdHjhx89913ZM2a9W+XLhQREREREREREREREXlT9FFiEXmvvBh2iY6OxsHBAQ8PDwBSpUrFyJEj6devH3379gWgfv36ODk5YbFY2LBhA3fv3qVt27Y0b978lef8N6yhm/Lly+Ph4cH8+fONZVlSpEhB5cqVmTNnDoGBgcyfPx87O7s4BeTYX0+YMIErV64QGBj4RsYmIv+cNVT3YkCvSpUqtG3bltmzZ1OlShVsbGwoVqwYoaGh5MqVi5QpU1KgQAEqVapEREREAl7Bu806Lw4dOpQxY8bQoUMHWrduTbZs2di6dSuTJk1i8uTJ3L59m+HDh5MnTx6mTJlC3bp1+fXXX7l69Sp58+alVq1apEuXTsuUiYiIiIiIiIiIiIjIW6XOOyLy3ogdbJk/fz67d+/m6tWrZMyYkUaNGlG8eHFcXV0JDw9n+/bt9O3bl/v37zN06FDq1KnD0aNH6d69OxcvXuTu3bskT548Xrou/P7777Rp04bvv/+e7t27ExISYmw7ceIE1atX59atW7Rv3z5OV4ioqCgcHR0BWL16NT4+PqRMmZLNmzeTNm3aNz5OEXk9sYMeDx48IDw8HDs7O1KmTGksfzd9+nQiIiJwdXXl008/JVmyZMDzeSsgIICxY8eydu1aqlev/soOXfL3Ll++zCeffIK7uzvz5s3D3d3d2PbDDz8wduxYli5dip+fH4MHD/7T8yi4IyIiIiIiIiIiIiIib5vCOyLy3unTpw/jxo3DwcGBxIkT8/jxYxIlSkSNGjUYNWoUOXPmJDw8nNDQUHx9ffn111/x8PDg1q1bJEqUiLCwMDw8POK1gH7+/HkCAgJYtmwZnp6eTJ8+3di2b98+qlWrRkREBA0bNqR9+/ZUrFiRxIkTEx0dzfDhw5k/fz4RERHs3buXLFmyaHkXkQQSO+gREhLCmjVr+Omnn0iUKBHVqlWjQYMGNGzY8JXHWiwWVq9ejb+/P+nTp2fdunU4Ozu/xdG/W/5sTrY+fvDgQUqXLo2/vz9BQUGYTCYA4/XZt28fX3zxBVeuXGHPnj2UKVPmrY5fRERERERERERERETkz6jSKyLvPLPZbHy9cOFC5syZQ/fu3Tl27BiXL19m06ZNlCtXjg0bNtC6dWt+/fVXkiRJQrVq1Zg5cyZVq1YlMjKS0qVLs2fPHjw8PIiJiYnXzhc5c+YkKCiIevXqMXPmTE6cOIHZbMZsNlOmTBl27NhBvnz5WLt2LbVq1SJfvnwULlwYNzc3Bg0aRPr06dm3bx9ZsmTBZDIpuCOSACwWixEM8fLyonfv3jx+/Jh69epRqVIlFi5cSMeOHRk+fPgrjx82bBh9+/blyZMnLFiwAGdn5zjzmfzBbDYbc/LNmzfZvHkzBw4cAP5YjtDe/vlqsM+ePQOeh3Zid9ApU6YMnTp1Ap536REREREREREREREREfmvUOcdEXknWTvNvNiJYfTo0SxYsIB169bh4eFhPB4dHU3z5s1Zu3YtLVu2ZPz48XE6XNy5c4cUKVKQKFGit7pkytmzZ3n06BEff/zxS9f266+/sn37dtauXcuvv/6K2WymePHi1K5dm7p165I6dWot7yLyHzB79mw6d+5M+/bt6dWrlzH3+Pj4MHLkSAoUKMDhw4eNJbSOHz/Op59+yqNHjyhSpAgLFy4kc+bMup//ROzOYv3792ft2rWcOXOGr776Cj8/P3LmzAnAmTNnKFasGEmTJmXjxo2UKlXKOEd0dDQODg5s2bKF2rVrM3LkSLy8vBLkekRERERERERERERERF5kn9ADEBH5X9y6dYt06dIZhVxrcKdr167s2LGDggUL0rBhQ2PZK3jeHcPBwYHZs2dz8+ZNtmzZwo0bN3B2djaK5S4uLsa+b7N4njt3buNraxDJGkrKnj072bNnp3379jx69IiYmBhSp05t7G82m1XoF0lAFouF6OhoNm/ejKurKx07dsTDw4Po6GhWrVrFqlWr8PDwYMeOHSRKlMgIkBQuXJgGDRrg7u5O27ZtSZMmjYI7fyJ2cKdGjRocOXKEYsWKMW7cODJnzmwEdwDy5MlD3759CQ4OZurUqaRMmZK8efMaPwPg+dJZiRIlomjRoglyPSIiIiIiIiIiIiIiIq+idVZE5J2xd+9ePvroIyZOnBjn8WfPnnH58mV+/vlnVqxYwdGjR41lU6xhGAAnJycaNmzI7du3+fbbbwFeKpbH51JZAH/V7Cz297Z+bV1Cx8nJyegUZD2HlsoSeTt27tzJmjVrXnrcxsaGR48ecfjwYYoVK0b+/PmJiYlhzZo1+Pj4YDKZ2L9/P2nSpAHg3LlzHD9+HICJEyfSp08f0qRJoyDen7BYLMY8V6tWLQ4ePIiPjw/Lli2jRo0a5M2bN86+AG3btqVhw4YsWrSIAQMGEBoaio2NDWazmRUrVrB48WIKFiyo8I6IiIiIiIiIiIiIiPynqPIrIu+MBw8ecPXqVU6dOkV0dLTxeNKkSZkzZw5ffPEFjo6OnD17lqtXr2JjY4PJZALAZDJha2tLhQoVjL+/bbGX+Lp48SI3btz422NiB3Re7DYkIvHv1q1btGnThsaNG7Nu3bqXtr84l6xcuZK+fftia2vLoUOHSJs2LQCRkZG0atWKDRs2GMdYAzsK4r2ada4bPHgwe/bswcvLi06dOpEmTZqXgpDWfd3d3RkwYABNmzZl7dq1VK9endq1a1O2bFk6depEdHQ0K1asIHXq1EY4UkREREREREREREREJKGpWiQi74w6depw+PBhRowYgYODA0ePHjW2pU2blpCQED777DN++eUXvv76a8LDw43iuJ2dHWazma1btwLg6uoK/HUnnDcpdnDnu+++o1WrVsycOTNOCElE/nvSpUuHv78/OXPm5PPPP3+pA0+6dOkoXbo0GzduZPDgwfj5+WFra8vBgweN4A7AuHHj+OWXX/Dw8FCXnf/Bw4cP2bhxI9myZaNjx44kT548znz6KkWLFmX69OmEhISQP39+9u3bx6NHj2jQoAH79+8na9asRqBTRERERERERERERETkv8DG8rYq1yIib5C/vz+jRo1i2rRptG7d2nj87t279OjRgyVLllCiRAlCQkLIlSsXqf+PvfuOr/l8/zj+OklOFiGESGgi9t61R6lRVK1WUW3RmrVXBJGIFWKr1dpN7dVSWntF7KqiqFKzBEFCRMY55/eHX04TtNp+SYT38x9xPsN9xyN3+3hcb9eVNStLliwhKCgIg8HA7t27yZYtW6qsNXmhedu2bQwZMoT9+/ezf/9+ypcv/9RCtIikDbPZbA14LF68GH9/f65evcrXX3/Nu+++a71v3rx5dOjQAaPRiLu7O5cuXbJes1gsrFixAj8/P/LkycPKlSvJkiVLqu8lvdq2bRt16tQhICCAYcOGpfg7eZJHz9Po6GgSEhJwdXXFbDZjNBoxmUwKUImIiIiIiIiIiIiIyAvFLq0XICLyTzxabC1evDgGg8Eaxmnbti0Abm5uTJkyBXhYbG/cuDFZsmQhR44cnDp1Cg8PD9auXUu2bNmeWgR+Fh4N7vj5+XH8+HH27dtH+fLliYqK4vTp03h6euLl5fVc1yIi/46NjQ0JCQkYjUY++OADHB0d8fPzo02bNtjZ2dGkSRMAPvnkE44cOcL06dN58OABR44cwcPDgyxZsjBx4kTmzp2LyWRiwYIFZMmSJVXOnpfF3bt3MRgMuLq6Ak8fG2gwGPj111/59ddfadSoEZkyZbJeS96JTURERERERERERERE5EWiypGIvPDMZrO12Dpx4kROnz5Ny5YtWblyJTdu3GDo0KEsXLjQer+bmxuTJ0+mdevW3L9/n0uXLvHxxx/z3XffsXHjxlQbmfJXwZ3t27dToUIFoqKimDdvHo0aNWLz5s3PdS0i8u+ZTCaMRiMAO3bs4N69e+TIkYP4+HjatGnD2rVrrfd+/vnn9O3bl8jISMqVK0exYsXImjUrAQEBZMmShV27duHl5aVxTX8heSNIs9ls/To+Ph6LxcL+/fuJi4v72/BOYmIiAEuXLmXWrFlcv379+S1YRERERERERERERETkGdLYLBFJN/z9/Rk9ejRdunRh0qRJODg4sH79elq2bEnWrFkZMWKEtQMPwI0bN+jVqxdLly6lTp06bNq0CYC4uDgcHBye61r/KrizY8cOa3AnNDQUf39/ChUqxP79+5/rekTk30n+MzxgwADmzp3La6+9hoeHB9HR0Rw4cABHR0cWLVpEs2bNrM+tX7+ew4cP8/PPP+Pu7k716tWpV68ebm5uGtf0F540OjAxMRE7OzsiIiKoUqUKTk5OrFu3jjx58jzx+5j8HaVKlcLFxYWwsLBU24OIiIiIiIiIiIiIiMj/QuEdEXlhJS/Qnj17lrfffpt69erRvXt3ChYsaL3v7wI8N2/epE+fPixatIhq1aqxc+dODAaDtTD8PPxdx52KFStagzuDBg2ibNmy7Ny5E+C5rklE/pupU6fSu3dvevfuTc+ePfHx8QFgzJgxTJ48mTt37rBkyZIUAR54/OdZo7Kernr16uTNm9faSS0xMZH4+Hh69uzJvHnz+Oijj6zXkn8/k389depUAgMDGTZsGL169UqbjYiIiIiIiIiIiIiIiPxLqiKJyAsrKbizZ88ewsLCuHz5Mh9//HGK4A7A22+/zbJly7h169ZjI7SyZcvG5MmT+eCDDwgLC6NWrVpYLJbnGpJRcEfk5WCxWNi4cSOenp507twZHx8f62gmPz8/xowZg729/WMjtABrmCQpI63gzt+7cuUKzs7OhIaG0rt3bwDs7OxwdnamZ8+euLu7ExoaSpcuXYA/v5/x8fHWr1etWsXnn39Ovnz5+OCDD9JkHyIiIiIiIiIiIiIiIv+FKkki8kKbO3cu1atXZ9myZbz++uu8/vrrwMNOC8klBXhu375NUFAQs2bNAh4Wzt3c3JgyZQpt2rRh165djB49+rmve8uWLQwaNIgTJ06wY8cOBXdE0qG7d+/y008/kTNnTgoVKoTZbMbW1tZ6/rRr147u3bvz4MEDWrduzZo1a6zPJgVKHh0HJU+WK1cupk2bRqtWrZg6dSqdO3e2XitZsiSrV6/G0dGRL7/8knfffZeNGzfy4MED7O3tSUhIYMSIEQwcOJDY2FhWrVpF9uzZH/vvhIiIiIiIiIiIiIiIyItKY7NE5IW2Z88eRo0axQ8//ADArl27qFat2l/ev2HDBho1asTrr7/Ojh07cHZ2to5UuXHjBqtWrbJ2bnheLBYL/fv3Z8qUKYSHh1OhQgUFd0TSIYvFQp06dTh48CC7du2idOnS1mtJ58rVq1epUaMG9+7dIyIigu+++46GDRum3aLTuTNnzjBgwADWrl3LTz/9RPHixYGHYaj9+/fz6aef8ssvvwDg4+ODi4sLV65cITo6mvLly7NkyRK8vb1TjF0UERERERERERERERF50Sm8IyIvjKRiODwchWJvbw/Avn37mDRpEitWrODTTz8lICAALy+vv3zP1q1bKViwYIp7Hi3kJv+z/te1PklsbCyXLl2iYMGC1uDO4MGDKVOmjII7IunIsGHDGD58OG3atGHMmDHkypUL+PNMuXnzJoULF6Zhw4acPXuWpUuX/u35JE93+vRpaxgnSdKZe/bsWbZu3cqaNWs4e/YsZrOZcuXK0bBhQ9555x2yZs2q4I6IiIiIiIiIiIiIiKQ7Cu+IyAshebH122+/5fjx41SrVo033ngDgAMHDhAUFMSWLVsYOnQoHTt2JEeOHP/4nc9rrcePH+fmzZtERUVRtGhR8ubNm+LPvH//PnPmzMHf31/BHZF0xGKxYDAYuHfvHk2bNmXv3r3069ePTz/9lNy5c1vvmzNnDpMnT2bTpk24ubnh4OCg8MgzlPT38OjXANHR0SQmJpI1a1brZ/9rMFNERERERERERERERCQtqHIsImnObDZbC93+/v7MnDkTV1dX8ufPby2CV6hQgWHDhmEymRg5ciQWi4VOnTr9bYDneRTPk681KCiIBQsWcOHCBQA8PT1p0KAB06dPx8HBAbPZjIODA2FhYRQvXlzBHZE09m+CHUkhkYwZMzJkyBB8fX0ZM2YM+/bto3///uTOnZvNmzczY8YMMmTIgIuLCw4ODsDzOXteZo+GcpJL/nnS10l/j5kyZcJsNqd4h4I7IiIiIiIiIiIiIiKSHqnzjoi8MIYMGUJwcDAdOnSgS5culC1b9rF7Dh48yNChQ9mxYwdDhgyhc+fOuLu7p/pa/fz8CAkJoVGjRjRq1IgcOXIQEhLC3r17KVCgAMeOHbOO/YqLi7MW9RXcEUl7wcHBvPPOOxQvXvwf3Z+YmMjBgwcZM2YM69atS3EtT548bN26FR8fn78NociTJf+e/f777zg5OeHh4ZHGqxIREREREREREREREUldCu+IyAth48aNtGrViiZNmjB8+HC8vb1TXE9e4D106BD+/v7s2bOHbt260b9/f7Jly5Zqa12/fj2tWrWidevW+Pn5kTdvXgBCQ0Np27YtWbJk4dy5c2TOnDnFujXORSTtLV++nFatWvHBBx8wdOhQChUq9K+enzt3LufOnSMiIoLixYvTqlUrPDw8NCrrP0h+Pm7atIkRI0ZQr149/Pz8MBqNabw6ERERERERERERERGR1KPwjoi8EMaMGcPgwYPZuXMn1atXf+r9Bw8epHv37ty4cYOffvqJTJkypcIqHwoKCmLUqFHs3buXcuXKYTKZWLp0KUOHDsVgMHDgwAHc3NyIjY3F0dERg8GgjhwiL4h79+4xc+ZMhgwZQqtWrfDz86No0aJPfe7vfoYV3Pn3kn8/t23bxpAhQ9i/fz/79++nfPnyOjNFREREREREREREROSVotktIpLmzGYzP/30E05OTpQsWdL6WfIuNUm/j46OJlOmTJQvX54vv/ySnDlzkilTplQr9JpMJn7++WeyZs1KuXLlMJvNrFy5ksGDB2NjY8P+/ftxc3MD4Pz58+zevZsOHTqo447ICyJjxox89tlnGAwGfH19iY2NZf78+WTMmPFvn/u780XBnX/n0eCOn58fx48fZ9++fZQvX56oqChOnz6Np6cnXl5eabxaERERERERERERERGR50/VZBFJVY82+0oq4jo4OBAbG8u2bdsAUoRdLBYLNjY2xMfH07t3bw4ePAhAqVKlyJ49O2azOdU6NNja2uLk5ERMTAznz59n9erV+Pr6YmNjw4EDB8iePbt1zV26dGH16tXExMSkytpE5K+ZTCbr1xkyZKBTp06MHDmSNm3aPDW4I8/OXwV3tm/fToUKFYiKimLevHk0atSIzZs3p/FqRUREREREREREREREUofCOyKSapKHbG7dugU87GZhMBho2bIlDg4OrFixgtjYWOsziYmJ1rFTwcHBbNq0iejo6BTvfR5dbZKHjJK+Tir+N2nShJiYGLp27crAgQOxtbVl79691uAOwPTp0zl16hTVqlXD2dn5ma9PRP6dpO44oaGhJCYmkilTJgYMGEDTpk3TdmGvkL8K7uzYsYOKFSsSFRVFaGgoQUFB5MmTh08++SSNVywiIiIiIiIiIiIiIpI6FN4RkVRhMpmsIZu5c+fSvXt3JkyYYL1euHBh3njjDZYuXYq/vz83btwAwM7u4XS/NWvWsGTJEgoVKkS5cuWe+1qTCswWi8Ua3kkq/pcuXZoKFSqwceNGIiMj+fnnn/Hw8LA+v2zZMiZPnkzu3Lnp1KmTRuqIvCDmzZtH27ZtWbhwIQBGozGNV/Tq+Ccdd0JDQxk0aBClSpVi//79wMMAp4iIiIiIiIiIiIiIyMvOLq0XICIvP7PZbA2w+Pn5MWPGDHLlykXLli2t9+TNm5chQ4YQERHBpEmT+OWXX6hVqxbVqlVj+fLlrFmzBpPJxPz583F1dcVsNj+XjjvJ1zplyhS2b9+O2WymVq1adO3aFUdHRwoUKEBwcDDt2rXj0qVLDBo0iBo1alCgQAHmzZvHmjVrsFgsrFy5End39+e2VhH5dwoXLgw8DI+0b9/e2vlLnr+/Cu4k77gzaNAgypYty86dO4GHwZ2kAKeIiIiIiIiIiIiIiMjLzGBJPhtGROQ5CggIYPTo0XTp0oXOnTtTokQJgBThlt27dzNz5kzWrVtHTEwMAPb29lSqVImvvvoKb29vTCbTc+9m4+fnR0hICC4uLsTFxREfH0+zZs3w8/OjXLly2NjYEBYWxrBhwwgLCyM+Ph4AR0dHqlevzuzZs1NtrSLydEmdX9q1a8fSpUvZvXs35cuXT+tlvVK2bNnCkCFD/rLjjoI7IiIiIiIiIiIiIiLyqlJ4R0RSxaZNm2jRogXNmzcnMDAQHx8f67Vr164RGxtLnjx5ALhz5w7Xrl1j3759WCwWSpYsSf78+cmcOXOqhGH27dtHixYtaNy4MX369CEuLo4FCxYwY8YMypUrx6hRo6hatSo2NjZcvXqVq1evcuDAAYxGI+XKlSNfvny4uLgouCPyAlq4cCHt27fn008/Zdq0adjb26v7TiqwWCz079+fKVOmEB4eruCOiIiIiIiIiIiIiIhIMgrviEiqmDBhAgMGDCAsLIwqVapgNpuJiYlh6tSpLFy4kD/++IPSpUvz5ZdfUrRo0Se+43mNn0rqyJH069KlS+nVqxe7d++mYMGCAFy/fp3Q0FACAwMpW7ZsigDP371TRFLXX4XmkgdCateuzdmzZzl8+DBubm76eX1GnnZGx8bGcunSJQoWLGgN7gwePJgyZcoouCMiIiIiIiIiIiIiIq+0Z18FFxF5glu3bgFw//59AL7++msaNWrE0KFDyZw5M6VLlyY8PJwOHTpYR1A96nkEd0wmk7Vo/+DBA2JjY3F3d6d+/foULFiQhIQEANzd3WnXrh3Dhw/nxx9/ZMiQIYSHh5OUf3w0B6kggEjaSAruBAQEMHfuXM6dOweAnZ0dZrMZs9lM48aNuXjxIpMmTQL08/osmEwm6xl9/PhxduzYwbfffsuZM2cwmUwAODk5UbBgQe7fv8/ChQsV3BEREREREREREREREfl/6rwjIs9N8i4M33zzDe+99x5msxlPT0+uXr3Ka6+9xpQpU6hatSru7u40bNiQH374gfDwcCpVqpSq6xs3bhxr1qwhIiICs9mMg4MDBw4cIFOmTCnui4yMZOHChQQEBFChQgWGDh1KzZo1VfwXSWWPdstJSEjAaDQCcODAAesZkjdvXt555x369OlDlixZcHFx4dKlS1SqVIkcOXKwdetWsmTJou47/4PkZ2RQUBALFizgwoULAHh6etKgQQOmT5+Og4MDZrMZi8VC69atuXz5MuHh4YCCOyIiIiIiIiIiIiIi8mpT5x0ReWbMZnOK3ycvhDdt2pTZs2fTsGFDypQpw+DBgzly5AjNmjXD3d0deNgZo1ChQuTPnz9V1ptUbB48eDADBw7kypUrGAwG7t69y6+//srMmTO5f/8+NjY21r25ubnRtm1bRo4cyY4dO/j888+t3XlEJHUkD9rcunULk8lkDe5MmjSJwoULc+zYMebMmQPAlClTqFixIh9//DFhYWHkzJmTwYMH89NPP/Hdd98B6r7zv0g6S/38/AgKCqJEiRLMmjWLNWvW4OPjw7x58yhZsiTx8fHY2Nhga2tLaGiogjsiIiIiIiIiIiIiIiL/T5USEXkmTCaTdVzNihUrOHDgAIcPH6ZKlSqULl2a9957j/bt29O4cWOyZs2KxWJJMQZr6dKlHDhwgHr16pExY8ZUW+v58+dZvHgxXbt2ZeDAgWTIkIENGzYQGBjIlClTyJYtG23atMHR0dHaXcLNzY02bdrg7OxMgwYNsLe3f67rFZGUkoI2devWxcXFhdmzZ+Pm5kbv3r2ZOnUq9vb2fPbZZxQrVowGDRqwd+9eFixYwLfffsuGDRuoU6cOmTJlIkuWLHz99dfUr1+f7Nmzp/Gu0rf169czffp0OnTogJ+fH3nz5gUgOjqavXv3cvPmTWJjY7G3t8diseDg4AA8DH0quCMiIiIiIiIiIiIiIq86VUtE5H9mNputYZgBAwYwffp0HB0dyZIlC+Hh4ZjNZnbt2sXUqVNxc3MDUna5mDVrFhMnTsTFxYXg4GAcHR2f6wibpLV+//33REVFAdC1a1e8vb0BaNmyJTly5KBz584EBgZisVj48MMPUwR4smfPTseOHTEYDCnCQCLy/CSdCxaLhcuXL+Ps7My3335Lrly5uH//PvPnz8fX15fGjRtb7/P09KR58+Y0b96cjRs3snPnTmbOnEl8fDyxsbEcOXKEK1eukD179hTjn+TfOXToEHFxcXTu3Jm8efNiMplYunQpgYGB5MmThwMHDpA5c2ZiY2NxdHQEeCzEKSIiIiIiIiIiIiIi8qoyWCwWS1ovQkReDsHBwfj7+9OxY0e6du1KqVKlOHDgAB999BFnzpzBz8+P0aNHAw+73+zdu5eQkBD27duHu7s73333HT4+PqkShpk1axafffYZb775JvHx8ezatYuEhATs7OysgZzt27fTsWNHEhISCAoKeqwDj4ikrtu3b5MlSxbr78+ePcvEiROZOXMmAF26dGHIkCHkypUrRQDw0TPlp59+Yv/+/SxevJjdu3fTokULvv76a+voLfl3TCYT77//Pnv27OHatWuYzWZWrFiBr68vNjY2HDhwwNrZ6OTJk+zevZsOHTroHBUREREREREREREREfl/qpqIyDNx/vx55s2bR61atejbty+lSpUiLi6OO3fuEBUVRcGCBenTp4/1fltbWw4ePMi+ffto1aoVW7ZsSbXgDsA777xD5cqV2bZtG8ePH+fChQsYjUaS8oy2trbUqlWL2bNnYzQaGTFiBHPmzCEuLk4FZ5E0sHv3bgoWLMjKlSutn+XLl4/4+Hjr72/fvm0du5e8c9ejZ0rp0qXp3LkzmzdvpmLFioSFhXHp0qXnvIOXl62tLU5OTsTExHD+/HlWr179xOCOxWKhS5curF69mpiYmDRetYiIiIiIiIiIiIiIyItDFWgR+cfOnTvH7du3n3jt4sWLnD17lvbt21OwYEESExNZs2YNHTt2xMnJibCwMLJnz05CQgJnz54FoE+fPoSHhxMSEoKHh0eqjp/KlSsXq1atol69ety5c4chQ4YQERGBjY0NZrMZ+DPAM2fOHCIjI1mwYAGJiYmpsj4RSeno0aNERkayfv16EhMTSUxMxGw24+DgQNeuXWnRogXLli2jX79+/PHHHymeTd5kMOnrxMRE7O3t6dmzJ1evXuW7775L1f2kR0/6PppMJgCaNGlCTEwMXbt2ZeDAgdja2rJ3715rcAdg+vTpnDp1imrVquHs7Jy6ixcREREREREREREREXmB2aX1AkQkfTh+/DglS5akZ8+eBAYGWkfXJI2miYyMBCBDhgyYTCZWrFiBn5+ftfNCtmzZAIiJiaF///706tWLmjVrkj9/fut7nkdwJ/nonORMJhMeHh4sWLCA1q1bs3jxYrJnz87gwYPJnj27dTSWra0tNWvW5Pvvvydv3rxkyJDhma9RRJ6ue/fu5M+fn0qVKmFnZ8cvv/xC0aJFmTp1KvHx8Vy7do0MGTIwb948LBYLI0eOxNPTE/izC8+DBw+so+/s7B7+L5CHhwc2NjZ/GUyUh5KHKy0Wi/VsTfqsdOnSVKhQgY0bN5IpUyYuX75s7YIEsGzZMiZPnkzu3Lnp1KlTqgU1RURERERERERERERE0gN13hGRf+TBgwfUrl2bGTNmEBISYi10JxXF8+TJA0BYWBg//PBDiuBO8s4LAwcOZNeuXbi6uqZ4/5MCNv8rk8lkfe+1a9f49ddf+eWXX4A/x+h4eHiwZMkSqlevzpQpUxg1ahQ3btx4rANPtWrVyJkzp7XLhIiknqSfu/r16+Pq6sqgQYMoXrw469atw8bGBkdHR3x8fBgwYADt2rVj/vz5+Pv7c+XKFes71q1bR9u2bbl8+bJ19F1UVBQ7d+4ESHFOSUpms9l6Zk6ZMoVmzZrRtGlTJk2axIMHDwAoUKAAwcHBeHl5ER0dzaBBg1ixYgU//fQTPXv2pH///jx48ICVK1fi7u5uPV9FREREREREREREREQEDJbkMxBERP7G4cOHGTFiBGvXrsXX15eBAwdaO/Dcvn2b999/n61bt5ItWzYyZcr02MiUBQsWEBQURJUqVZg9e/ZzHZuSvEvE+PHjWbRoESdPnsRoNNKgQQOGDh1KkSJFrN03rl27RsuWLdm9ezc9e/ZkyJAhZM+e/S8794hI6nl0pN7YsWMZNGgQ7u7uzJs3j4YNG1qvnTp1ipCQEBYsWEDbtm357LPPOHv2LKNGjeLSpUv88ssv5MyZE4Dff/+dcuXKUalSJTZs2JDq+0pv/Pz8CAkJwcXFhbi4OOLj42nWrBl+fn6UK1cOGxsbwsLCGDZsGGFhYcTHxwPg6OhI9erVmT17Nt7e3qk6IlFERERERERERERERCQ9UHhHRJ4qeYDl4MGDjB07ltWrVzNw4ED69etnHYm1YsUKOnfuzJ07dxg/fjx9+/a1vmPevHkEBwdjZ2fHli1byJUr13MLxiR/b//+/Zk0aRLFixenQYMGXL9+nZUrV1K6dGkGDRpE7dq1sbe3B/4M8ISHh/Pxxx8TEhKCm5vbM1+fiPxzSSPsAObOncu7776Lq6srM2fOpEePHri6uvLVV1+lCPCcPn2aiRMnMn/+fEwmEw4ODmTPnp0dO3aQJ0+eFO88ePAg5cuXf+zPkpT27dtHixYtaNy4MX369CEuLo4FCxYwY8YMypUrx6hRo6hatSo2NjZcvXqVq1evcuDAAYxGI+XKlSNfvny4uLgouCMiIiIiIiIiIiIiIvIECu+IyFMlL7ZGR0ezcuVK5s2bR3h4OMOGDaNr167WDjtffPEFvr6+3L17l5o1a5InTx5Onz7NsWPHyJ49O1u2bMHHxydVCriTJ0/G39+fDh060LFjR4oVK8bFixepXLkyV69epXTp0owaNYo6depgNBoBiIiIoE6dOty6dYsTJ048Nt5LRNKGv78/o0ePZtiwYQQEBAAwffp0evXq9cQAz6VLl9i5cydr1qwhT5489OnTh1y5cv3l2aPgTkpJIcikX5cuXUqvXr3YvXs3BQsWBOD69euEhoYSGBhI2bJlUwR4/u6dIiIiIiIiIiIiIiIikpLCOyLyt5IXtIcMGcLmzZv55ZdfyJ07NydPngRg8ODB9OrVyxrg+e677/j222/55ptviI+Px8fHh9q1azNgwAA8PT1TJbhz4sQJ2rVrh6enJyEhIRQuXJjo6GgqVqxIVFQUDRs2ZOXKleTLl4/hw4dTt25daweeGzduYDabyZEjh4rNImkk+Tnx22+/UbduXRo0aECPHj0oUqSI9b6/C/AkvQfA1tZWXV/+oeTfp9jYWAD27t3LwoULWbhwIQkJCdbAY2RkJAsXLiQgIICyZcsyevRoqlatmiL4IyIiIiIiIiIiIiIiIn9P4R0R+UeGDRvG8OHD+eyzz2jRogWFCxdmxYoVzJw5k5MnTzJw4ED69u1rDfDAw64MMTExeHp6YjQaU7V4vn79ej744ANWr15N7dq1iYmJoWrVqly9epXJkyfzxhtvMHbsWD7//HOqVav22AgtUCcOkRfB9u3buX79Ov3792fjxo0ULVoUSBkw+asAj36G/73k37Nx48axZs0aIiIiMJvNODg4cODAATJlypTivuQBngoVKjB06FBq1qyp4I6IiIiIiIiIiIiIiMg/pPCOiDzV6dOnqVmzJoUKFSI0NBQvLy/rtV27dhEcHMzGjRvx9/enR48eKQI8aemHH36gfv36JCQk0LFjR1avXk1wcDAdOnTAwcGB7du3U7t2bRwcHMiSJQuLFi2iVq1aab1sEfl/U6ZMoU+fPrzxxhvcu3ePgwcPkpiYiJ2dHZAyaJIU4MmePTszZsygWbNmabn0dG/w4MGMGTMGLy8vjEYjd+7c4datWwQHB9OjRw+cnZ0fC/CEhobSt29fmjZtytKlS1OEIUVEREREREREREREROSv6Z+ji8hT3b59m4iICCpXroyXlxdms9k6iqZGjRr06dMHd3d3Ro4cycyZM4mMjEy1tZnN5sc+i4+PB6B+/foAREREsGPHDmrWrEm3bt1wcHAAIGfOnHh6etKjRw88PDwoXLhwqq1bRJ6uYcOGFCtWjJ07d3LhwgWuXr2KnZ2d9efexsbG+nW3bt34/PPPiYiIICAggAcPHqB88j+XdKYDnD9/nsWLF9O1a1d2797N/v37mTRpEj4+PkyZMoUlS5bw4MGDFN9/Nzc32rRpw6xZs5gyZYqCOyIiIiIiIiIiIiIiIv+Cwjsi8lSOjo4YDAbu3r0LPCyY29raWgvj9erV45NPPgFgxIgRBAYGEhUV9dzXZTKZrF0fbt++zeXLlwGsReOk9Z06dYqLFy9SpkyZFM+vWLECFxcXunXrxp49e/D09ExRwBaRtFWgQAHWrl1LpUqVuHnzJr6+vty5cydFaCT51127dmXevHmsX7/eem7JP5M0guz7779n3759wMPvp7e3N25ubrRs2ZJZs2bh4OBAYGAgX3/99WMBnuzZs9OxY0e8vLx0loqIiIiIiIiIiIiIiPwLCu+IyFNlypSJHDlyMGfOHHbt2pXiWlKXm4oVK5I3b14qVqzIN99889w7XpjNZmuxOTg4mBo1alCgQAHq1atHaGgo0dHR1sJ9iRIlyJ07N99//z1nz57l7t27LF68mNDQUPLnz4+HhwdOTk7AnwVsEUk9f3VemEwm8uTJw9dff03ZsmVZtGgREyZM4O7du38Z4GnXrh3e3t4Kj/wHs2bN4u2332bOnDl4e3tTvHhxEhISsFgs2NvbU7t2bWbPno3RaGTYsGEsWrTosQBP0rmrs1REREREREREREREROSfU3hHRJ4qb9689OnTh/j4eEaNGsWPP/4IYC3oAoSFheHh4cGUKVM4evQorq6uzzXAk9RxZ9CgQQwZMoS4uDhKlCjB4cOH6datG2PHjuX27dsAODk50apVK3766SfefPNNKlSoQKdOnUhISGDGjBk4ODhovI5IGjGZTNbAx82bNzl//jy//fYb8GcAJG/evCxfvpySJUsyduxYxo4d+8QAT3IKj/x777zzDpUrV2bbtm0cP36cCxcuYDQareejra0ttWrVsgZ4RowYwZw5c4iLi3vs+y8iIiIiIiIiIiIiIiL/nCotIq+gpGJ3kr8LriTd26tXL9q1a8fmzZvp0aMHGzdutBZrly1bxvr16ylatChly5bFzc0Ns9n8XEbWJO+m8dtvv7FkyRK6du3K1q1bCQ8PZ/369RQqVIgxY8YQHBxMZGQkmTJlolevXkycOBEvLy8cHBxo2rQpu3fvtnbo0HgdkdRnMpmsIZtJkybx9ttvU7JkSSpVqkTnzp05c+aM9QzKmzcvq1evpmjRooSEhKQI8Ch892zkypWLVatWUa9ePe7cucOQIUOIiIhIEZJKCvDMmTOHyMhIFixYQGJiYhqvXEREREREREREREREJH0zWFTxEnll7d27l8qVKwMPAzxPC7D88ccfjBgxgi+++AKAatWq8eDBA06cOIGbmxthYWF4e3s/93UD7Nmzh99//x1fX182btxIiRIlrNcuXrxIixYtOHToEP369cPX15ds2bJZC/yJiYkYDAbs7OxShAdEJPUkP3P69+/PpEmTKFSoEG+++SYRERGsW7eON998E19fX6pXr279OT137hzNmzfnzJkzdOrUiREjRpAxY8a03Eq681fnfdJ5eO3aNVq3bs3OnTvp1asXgwcPJnv27JjNZmto02QysXfvXvLmzUvOnDlTewsiIiIiIiIiIiIiIiIvFYV3RF5RzZs3Z/fu3cybN4933nkH+GcBHoDZs2ezfPlyjhw5Qvbs2SlWrBiTJk3Cy8srVcIw06ZNo2fPnjRp0oTY2Fh++OEHTCYTNjY21vVfunSJ9957zxrg8fPzI2vWrP94jyKSOiZOnMjQoUPp2LEjHTp0oHjx4pw9e5aqVaty/fp1qlatyogRI1IEeH7//Xdq1KiBra0tR48eJXPmzGm8i/Qj+Rl97do1oqOjSUxMpGjRoinuu3btGi1btmT37t307NmTIUOGPBbgedI7RURERERERERERERE5N9TeEfkFbVp0ybatGmDh4cHwcHBNGrU6F89n5CQwL1798iYMSMWiwV7e/tUK+AeOHCAbt26cfjwYbJkycLhw4fx8fF5LJiTFOA5evQon3zyCcHBwSryi6SBO3fu4OLi8tj58PPPP9OuXTty587NmDFjKFSoEHfv3qVChQpERUVRq1Yt1qxZw+uvv86wYcN44403rO+4ePEi9vb2eHh4KJT3DyU/o8ePH8+iRYs4efIkRqORBg0aMHToUIoUKYKdnR3w1wEefb9FRERERERERERERESeLZun3yIiL6N69eqxcuVKrl+/To8ePVizZs0/ei4p72dnZ0eWLFkwGo3Y29tjsVhSrfNC+fLl+fLLL3nzzTe5ffs206ZN4+bNmxgMBpLnEb28vFi1ahVeXl6sX78+VdYmIimFhYVRt25dtmzZgslkAv48R3799Vd+++03evbsSaFChYiJiaFatWrcunWLCRMmMHr0aFq3bk1YWBjjxo1j9+7d1nd4e3vj4eGByWRSkOQfSH5G9+/fn4EDB2I2m+nduzctWrTghx9+oFu3bmzevJn4+HgAPDw8WLZsGdWrV2f69On4+fkRGRmp77eIiIiIiIiIiIiIiMgzps47Iq+YR7vjbN26lcaNG7N27Vpq166dhiv7dywWC0eOHKFbt24cO3bMOnbnSaOxrl69io2NDTly5FDHCJFUZDKZmD17Np999hkVKlRg9OjRKbrnAHz77bc0adKE+Ph4PvnkE9atW8eYMWP49NNPsbe3Z+3atTRt2hSj0Uju3LlZuHAhlStXTsNdpW+TJ0/G39+fDh060LFjR4oVK8bFixepXLkyV69epXTp0owaNYo6depgNBoBiIiIoE6dOty6dYsTJ07g6uqatpsQERERERERERERERF5ySi8I/IKSR5cOX/+PD4+PgBERkbi5uaWhiv7744cOULnzp05deoUgwcPplOnTk8M8ACYzWZsbNRwTCQ13blzh+XLl+Pn50fevHkJCQl5LMADcPbsWWrWrEmFChVYtWqV9fOff/6ZRo0a0axZM7Zs2cLWrVvx8PBI7W28FE6cOEG7du3w9PQkJCSEwoULEx0dTcWKFYmKiqJhw4asXLmSfPnyMXz4cOrWrYu9vT0AN27cwGw2KwQpIiIiIiIiIiIiIiLyHKiKLfIKSSq2+vv707BhQ+vnWbNmBSA9ZvnKlCnDF198QeHChRk9ejSzZ8/m9u3bTywsK7gj8vw9eo64urry7rvvMnr0aM6dO4evry87d+58bITWyZMnuXLlChUqVEjx/MqVK8mYMSN9+/bl4MGDeHh4YDabU2czL5nz58/z66+/0qtXLwoXLkxMTAw1atSwjikbPnw4bdu25ciRI4wdO5atW7daR2hlz56dHDlyYDabFdwRERERERERERERERF5xlTJFnnFxMbG8ssvv3Dq1ClWrlwJ/BnqSa8F2eQBnrFjxzJp0iTu3LmT1ssSeeUkD3aYTCZOnTpFXFwcbm5utGzZ8okBnqT7ixcvjru7O5s2beKPP/7gwYMHLFmyhGXLllG4cGFy5syJs7MzFotFQbz/6O2332bZsmXUrl2bhIQEunXrxrlz5wgICKB58+bkzJmTpk2bAnDw4EE+/fRT9uzZk+Id+t6LiIiIiIiIiIiIiIg8e6rAiLxinJycGDx4MA4ODmzbti2tl/PMlClThi+//JKsWbOyfPnyx0byiMjzlXws3fTp06lfvz516tTB19eXxMREsmTJQuvWrf+yA0+mTJl4//332b59O2+++SaVKlWic+fOxMfHM3nyZIxGo8Y1/UNP6kyU1EGnfv36AERERLBjxw5q1qxJt27dcHBwACBnzpx4enrSo0cPPDw8KFy4cOotXERERERERERERERE5BWl8I7IK8ZisVCoUCHeeOMNZs2aRXh4eFov6ZkpXbo033zzDTt37sTFxSVdjgETSY+SB3feeecd/P39uXDhAv3796dZs2bY2dkBkDlz5scCPDt27CAxMZGsWbPSr18/xowZg729PXFxcdSvX5/du3fj7e2dokuP/DWTyWT9u7h9+zaXL18GwN7eHvhzTNmpU6e4ePEiZcqUSfH8ihUrcHFxoVu3buzZswdPT09rwEpERERERERERERERESeD4NF1W2Rl5LJZHqs+0zyrhXz58/n008/xd/fn+HDhz/x/vQseZhARJ6f5OdK3bp1OXjwIH379qV79+5kzZr1ifdFRUWxZMkSBg8eTN68eRkzZgxvvPEGRqMReNglxmw2Y2tri9FofOnOp+cl+bkXHBzM4sWL+e2336hevTofffQRTZo0IVOmTMDDzjuVKlUie/bsLFmyBHd3d9atW0dQUBAFChRg1apV1m48IiIiIiIiIiIiIiIi8nwpvCPyktu9ezfly5fHwcEBg8FAYmIidnZ2PHjwgNq1a3Pt2jV+/PFHMmfOnNZLFZF0rEePHnz11VcEBgbSqVMnMmbM+Lehm0cDPGPHjqVmzZoK6TwDgwYNYuzYseTPnx9XV1fOnj1LQkICPXr0oH///mTJkoXo6GiCg4OZMGECnp6eODs7c+nSJdzd3dmxYwfe3t4aUyYiIiIiIiIiIiIiIpJK1JZC5CU2fPhw3njjDd544w38/Py4ceMGZrMZADs7O2rXrs3vv//OjBkzrJ+LiPxbp0+fZs2aNVSrVo1PPvmEjBkzYrFY/jaI8+gIrSFDhrBp0yadRf9B8rFWv/32G0uWLKFr165s3bqV8PBw1q9fT6FChRgzZgzBwcFERkaSKVMmevXqxcSJE/Hy8sLBwYGmTZtqTJmIiIiIiIiIiIiIiEgaUOcdkZfIo10uNmzYwKFDh/jqq684d+4cuXLl4p133qFVq1bUqFGDqKgoKlSoQK5cudi2bRuAOi2IyL+2YMECPvnkE7Zs2cKbb775r8bW3bp1i1WrVtG5c2feeustvvnmG41r+o/27NnD77//jq+vLxs3bqREiRLWaxcvXqRFixYcOnSIfv364evrS7Zs2Uj638DExEQMBgN2dnYaUyYiIiIiIiIiIiIiIpLK1HlH5CWSVGzdsmULAA0bNiQgIICDBw8yffp0ihUrxqxZs6hZsybvv/8+y5cvp2HDhuzYsYPQ0FAABXdE5B9LCn6cPHkSgIwZMwL8o+DOyZMnefDgAVmzZuXdd9/lq6++Yvbs2Qru/EfTpk2jevXqrFq1ipIlS1KiRAlMJpP178jb25uVK1fy+uuvM2HCBEJCQrh165b1zDcajdjZ2QEouCMiIiIiIiIiIiIiIpLKFN4ReQkkb6A1atQo6tWrx9ChQ62fZcmSha5du/LDDz+wcuVKunTpwoYNG+jatSszZszA1taWbdu2ERcXp5E1IvKPJQU/nJycALh//z6Q8kx6lNlsJjExkUmTJuHv74/ZbCZr1qx8+OGHvPbaaylGQMk/V6FCBcqVK8e3337LwYMHOX/+/GMhHC8vL2uAZ+rUqfj7+xMVFaXQpoiIiIiIiIiIiIiISBpTeEcknTOZTNbCq8lkonLlyhiNRubMmUNgYKD1voSEBACaN2/OjBkzOHDgAEOGDKFkyZKYTCZWrVrFmTNn/vGoGxGRJB4eHgDMnTuXO3fu/GUYxGKxYGNjQ0JCAt999x0PHjx47MxR15f/pnz58nz55Ze8+eab3L59m2nTpnHz5k0MBkOKMJWXlxerVq3Cy8uL9evXp+GKRUREREREREREREREJImq9CLpmNlstha6R44cSYUKFRg6dChOTk5cv36dsWPHMnz4cODhSJTExETrc0WLFmXYsGGEhYUxfPhw7t27x+TJk4mPj//brhkiIo9q2bIlJUqUYNu2bezcudN61iRnsVisQZLg4GDu3r1LvXr1rNfkf2MwGChdujQhISFUrFiRWbNmMXfuXOtorOTf49dee41du3Zx4MABMmfOrO+/iIiIiIiIiIiIiIhIGlN4RyQdS+pYMWTIEIKCgihbtiyjRo1izZo1jBs3DicnJ4YNG8aIESMAsLOzw2w2W5+zWCw4ODjg7+9PlSpVCA8Px2AwaISKiPwrzs7OvPfee1y7do3AwED27NlDXFwc8PCcSd4hbPXq1SxevJhKlSpRo0YNAJ05z4jBYKBs2bLMmDGDokWLMmrUKL788ssnBng8PT3JkSMHZrNZ338REREREREREREREZE0ZrDon1uLpGv79++nbt26VKtWjZkzZ5I7d27rtUOHDlG/fn1u375NYGAgAQEBwMPxWkkdexISEjAajfj6+jJ+/Hj27NlD5cqV02QvIpJ+Xb9+nb59+7J48WKKFi1Kr169aNiwIbly5QIehnhmzJjB1KlTiYmJITw8HG9v7xSBQnl2jhw5QufOnTl16hRDhgyhU6dOZMmSJa2XJSIiIiIiIiIiIiIiIk9gl9YLEJH/zfXr17l37x4tWrQgd+7c1mCO2Wzm9ddfZ9WqVTRp0oRx48aRmJjI8OHDrddtbGwwGo3cv3+f27dv4+TkRIYMGdJ6SyKSzpjNZtzd3Rk/fjyOjo6sWLGCHj16MHXqVOrXr09cXBwHDx7kl19+IXfu3GzcuBFvb+8UQUJ5tsqUKcMXX3xB586dGTt2LDExMfTt2xdXV9e0XpqIiIiIiIiIiIiIiIg8Qv/UXSSdSmqaFRUVBcDp06cxm83WQnhSJ4vKlSvTvHlzYmJimD59OqNGjbJet1gsWCwWli5dyty5c3n//fcpWbJkGuxGRNIzGxsbzGYzHh4ehISEMGPGDN58801OnDjBhAkTmDZtGrGxsXTr1o3Nmzfj4+Oj4E4qKFOmDF9++SVZs2Zl+fLl+n6LiIiIiIiIiIiIiIi8oDQ2SySd+/333ylbtiwlS5Zk0aJFvPbaa9ZrFosFg8HAF198waRJk7h69SoWi4UvvviC1q1bW++7evUqQUFBzJo1C0BjbETkP0k6c5IcO3aM+Ph4TCYTZcuWxcbGxhr00RmTeo4fP0727NnJkSPHY39HIiIiIiIiIiIiIiIikvYU3hFJB/6u2Hr//n369u3Ll19+Sc+ePZk4ceJjRfH27dvj4OBA586dqVmzJrVr12b58uXY2dmRmJiInd2fE/RUVBeRJP/1PNA58mLS34uIiIiIiIiIiIiIiMiLSRUckRecyWSyBnfOnz/Pzz//zN69e7l58yYAzs7OfPzxx5QtW5apU6fStWtXjh07Zn3+m2++4eDBgzg4OFC0aFFq1qzJN998w48//giQIrgDqLArIsDDsyfpPLh06RI3btzg+vXr/+hZnSMvJv29iIiIiIiIiIiIiIiIvJjsnn6LiKQVs9mMra0tAOPGjSM0NJSzZ88SGxtL2bJlqVu3LsHBwVSpUoWxY8cSGBjI7Nmz2bBhAyVLlsRsNhMeHk6mTJno168fDg4O5MmTB6PRqCKuiPyl5GfPmDFjCA0NJSYmhowZMzJq1CgaNWpkvS4iIiIiIiIiIiIiIiIi/xuNzRJJB/r378/EiRMpU6YM1apV4/Lly+zatYvIyEjq16/Phg0bADh8+DCbN29m7ty5XLlyhWzZslG8eHFmzJiBj48P58+fp0mTJsTGxrJhwwby58+fxjsTkReZn58fISEh5M6dGy8vL8LCwjAYDIwePZrPPvsMFxeXtF6iiIiIiIiIiIiIiIiISLqn8I7IC27ZsmV8/PHHfPbZZ/Tq1QsfHx/MZjPnzp2jVatW/PjjjzRt2pTVq1dbn7lz5w6XL18mU6ZMuLq6kilTJv744w9mzZrFyJEj8fPzY/To0Wm4KxF5EZlMJmtHnWPHjtGsWTPefvttevbsSb58+fj222+ZPHkyu3btYvjw4fTo0YNMmTKl8apFRERERERERERERERE0jeNzRJ5AcTHx2Nvb//Ea3v27MHGxob27dvj4+MDPBxpkz9/ftatW0etWrX45ptv+Pzzz+nRowcmkwlXV1dcXV2t7zh8+DDTpk1j+fLlvP/++9bgjsViwWAwPO/tiUg6kRTcOXr0KHfv3uX27du0bduWfPnyAdCkSRPc3NwYMWIEAQEBAArwiIiIiIiIiIiIiIiIiPyPbNJ6ASKvuu3bt9OpUycuX7782LWEhAROnTpFxowZKVKkCPAwcGNnZ4fJZMLT05Ovv/4aBwcH9uzZA/xZfE9y5swZBg0axJo1a2jfvj1Lly4FHgaAFNwRkUd9/vnnlClThoCAAF5//XXKli0LQGJiIgDVqlUjICCAOnXqEBAQwOeff87du3fTcskiIiIiIiIiIiIiIiIi6Zo674ikodjYWJYsWcJXX32Fra0tI0aMIGfOnNbrNjY2ZMyYkcjISDZt2sTbb79tDdzY2tqSkJBA/vz5yZUrF7t27eLatWu4u7tjY/NnLs/Hx4dhw4YRHx9PzZo1gYfBneT3iIgkyZMnDxUrVmTv3r1kzpyZU6dOUbhwYezs7KxnR9WqVa2dd0aMGMG9e/cYMmQIGTNmTOPVi4iIiIiIiIiIiIiIiKQ/qt6LpCEnJyf69OlD586dmT9/PoMGDeKPP/6wXre1teWjjz7C3t6er7/+mtjYWOu1xMREjEYjrq6uODo6UqBAATw8PFKEciwWC0ajkSpVqliDOxaLRcEdEflLjRo1IjAwkAoVKnD9+nWWLVtmvWZjY4PZbAagatWqBAYGUrJkSRYtWmT9XERERERERERERERERET+HXXeEUljRYoUoU+fPiQmJjJ37lwAgoODrR14SpYsSa1atVi2bBnZs2cnICCArFmzWkdnLV++nN9++40333yThIQE7OzsrN15njQWS6OyRAT+vgNX/fr1MRgMBAQEEBQURObMmenduzfwZ4DHxsaGKlWqMH36dHLnzk2mTJmwWCw6Y0RERERERERERERERET+JYV3RF4ABQsWZMCAAQDWAM/o0aPJlSsX+fLlY+jQody4cYNp06Zx9uxZ6tevT7169Vi5ciVff/01OXLkwM/PD6PRmJbbEJF0wmQyYWtrC8DFixe5du0aBoOBbNmykSdPHgDeeustAIYOHUrfvn2xWCz06dMHSBngKV++PKBxfCIiIiIiIiIiIiIiIiL/lcFisVjSehEi8tCvv/7KuHHjmDt3Lh999BGjRo3itddeA2D//v1MnjyZdevWcf/+feszpUuXZvXq1fj4+KQoyIuIPEnykM3o0aOZN28e586dA8DBwYHBgwfTrFkzihcvDsDGjRsZOnQohw4dYuLEidYOPCIiIiIiIiIiIiIiIiLybCi8I/ICSF5MP336NOPHj7cGeEaOHImXlxcAN2/e5PLly3z//ffY2tpSqFAhqlevTtasWRXcEZF/xdfXlwkTJlCzZk1q165NREQEq1ev5sqVKzRu3JgBAwZQtWpVADZt2oS/vz+HDh1ixIgRDBkyJI1XLyIiIiIiIiIiIiIiIvLyUHhHJJU9OlomLi4OW1tb7Oz+nGJ36tQpJkyY8MQOPP/knSIif2fFihW0bduWtm3bMmjQILy9vQHYs2cP8+fPZ/78+bz33nuMGTPGOkZr69atdO7cmZiYGH799VdcXFzScgsiIiIiIiIiIiIiIiIiLw27p98iIs9K8u44oaGh7N69m8OHD5MtWzbeffddGjVqRM6cOSlcuDB9+/YFYO7cucDD8Ta5cuUCwGKxYDAYrL8quCMij4qJiSFDhgzWcyK5gwcPYmdnR4cOHfD29raeTVWrViVHjhzExMSwbNkyateuTadOnQCoXbs28+fPp0CBAri4uDzxvSIiIiIiIiIiIiIiIiLy76niL5JKLBaLNbjTv39/OnbsyPLly4mKimLz5s106dKFbt26sXnzZgCKFClCv379+PTTTwkNDSUgIIBLly4BWAvmKpyLyJNs3bqVt956i/3796c4JywWCyaTifDwcOzs7PDw8HgshJM/f34+/vhj7OzsGD9+PHfv3sVkMgFQvXp1PDw8MJvNOn9EREREREREREREREREnhGFd0RSSVKhe/z48UycOJHOnTuzY8cOzpw5w3fffcf777/Phg0bGD58ODt37gSgcOHCDBgwgE6dOjF//nwmTJiA2WxOy22ISDrw3XffER4eztq1a1OcGUkhwgoVKnDnzh0OHDiQontX0iTNBg0aUKtWLSIjI4mPj7cGD5Oo25eIiIiIiIiIiIiIiIjIs6OxWSLPUfKOFhaLhevXr7No0SJef/11+vfvj5eXFwANGzakaNGi5MuXj5CQEObNm0flypWxt7enYMGC9OzZk0yZMtG9e3cVzUXkqSZNmkTJkiVp0qQJNjY2XL16FU9PT+t59PrrrwMQFBSEj48PZcqUAR6O9rOzs8NkMhEdHY2HhwcZM2ZMs32IiIiIiIiIiIiIiIiIvAqUAhB5xnbv3s2qVauAh912kjpZGAwGIiMjOXr0KKVLl8bLywuTyWTtiuHj48PHH39MjRo1CA0NZcuWLdZ3Fi1alODgYLy9va3ja0REniQxMRGA9u3bkzVrVnr06EHNmjU5cuSINbzzwQcf0LlzZ37++WdGjBjBwYMHAbCze5jpXb16Nb/++isVK1Z8rOuOiIiIiIiIiIiIiIiIiDxbCu+IPCMWi4Vr16qzX0kAAGChSURBVK5Rt25dWrRowZo1a4CUAR4nJyecnZ25d+8eALa2ttZiOkChQoX46KOPADh27Jj1vUn3Jv9VRORJHu3OFR0dzZkzZ+jZsyc//fST9fPBgwfz/vvv880339C0aVMmT57M2rVrCQgIwM/PjwwZMjBixAhroEdEREREREREREREREREng+Fd0SeEYPBgIeHB5MnTyZDhgy0a9cuRQeexMRE7O3tyZEjB0uXLmXDhg3WaxaLxdoto3z58gDWgE/ycI+ISHJJnbuSxMbGWsM7R44cAWDhwoX07t2bPXv28Nlnn1k/f+2115g6dSr9+vXj6tWr9O3bl6ZNmzJp0iSyZcvG7t27yZUrl7p9iYiIiIiIiIiIiIiIiDxnCu+IPCNJRfQuXbowefJk4uLi+OSTT1i5ciXwcBxNrly5GDhwIABjxoxhz549wMOATlJ3i/Xr12NjY0OZMmXSYBcikp4kBXXat2/P1atXcXJyAqBXr1506dLF2mln4sSJ9OzZk3379tGtWzfrCC13d3fGjRvHtm3bWLx4MaNGjWLVqlV8//335M6dG5PJpG5fIiIiIiIiIiIiIiIiIs+ZwZI0k0dE/mcJCQkYjUYAVqxYQdu2bXFycuKLL77gvffes97Tr18/pk2bRunSpRk8eDDNmjUDYOnSpQwfPhwnJye2bNlCtmzZ0mwvIpI+zJw5k27dulGkSBFOnDjBoEGDGDt2LAMGDGDAgAEpzpHevXszdepUKlWqxPTp0/82JGg2mx8bwSUiIiIiIiIiIiIiIiIiz57COyLPSPIOFTt27ABgwIABHD16FGdnZ+bPn28N6Vy7do0JEyYwYcIEAEqVKkVcXByXLl0iW7ZsbN++HR8fHxXPReSpYmJiGDZsGBMmTMDV1ZU7d+4QGBhI27Zt8fHxAVKeT8kDPDNmzKB06dJYLBaN6BMRERERERERERERERFJIwrviDwDyQvfAwYMYMGCBbi6uvLaa69x/fp1Tp48ibOzMwsXLuTdd9+1Prds2TKWLFnCzz//jIeHB+XLl2fgwIHkzJlT42pE5KmSB/yKFi3KuXPnyJAhAzt27KBEiRIpuoE9KcBTrVo1JkyYQPny5dNsDyIiIiIiIiIiIiIiIiKvOoV3RJ6hSZMm0a9fP/r370+nTp3Inz8/N27cYN68eQwaNIiMGTOyYMECmjdvbn3GbDYTFxeHk5OTtbiu4I6I/FMJCQmcOXOG4sWLU7x4cY4fP06hQoXYvXs32bJlIzExETs7O4AUX/ft25fJkyfzzjvvsHLlSmvIR0RERERERERERERERERSl8I7Is/IvXv3aNy4MadPn2bbtm0UKlQoxfUvvviCrl274uzsTGhoqHWEVvJiukbXiMh/dfr0aYxGI7NmzWL8+PEUKFCA8PBw3NzcSExMxNbWFoPBkCIcOHToUD755BPy5MmTxqsXEREREREREREREREReXUpvCPyjNy+fZsyZcqQO3dudu7cCTwM41gsFutYm4CAAEaOHEnmzJn58ssvadGiRVouWUTSoeSjsp4kLi6OgQMHMnXq1BQBniR79+7lzp07NGjQwPpZ8hChiIiIiIiIiIiIiIiIiKSuv67+ici/5ujoyJEjRzh06BAABoMBGxsbzGYzFouFt956C1dXV2xtbWnZsiXr169P4xWLSHpiMpmswZ2jR4+ydetW1qxZw9WrV0nK4jo4OBASEkKvXr04c+YMVapU4ebNmwCsX7+ejh078vnnn3P//n3rexXcEREREREREREREREREUk7qtaJ/A+Sd8DIkiULbdq0ITAwkLVr11K4cGEyZsxovc/Ozo5SpUrh7u5OuXLlCA8Pp2TJkmm5fBFJR8xms3Xc1YgRI5g9ezaXL18GoGjRorRv356ePXtiNBqxt7dn7NixGAwGJk+eTLly5ahQoQJ79+7lwYMHrFu3Dmdn57TcjoiIiIiIiIiIiIiIiIj8P3XeEfkXzGZzit8/OrrmrbfeomzZskycOJGlS5cSGRkJPOxqYTKZmD9/PiaTiQULFnDq1Cm8vLwwmUyptn4RSZ+Sj9/z8/MjMDCQggUL8vnnn/PNN98QHR1NcHAwvr6+JCQkAGBvb8+YMWMICgoiQ4YM7Nixg3z58vHjjz+SJ08enT0iIiIiIiIiIiIiIiIiLwiDJWnOhoj8LZPJZO168cMPP3Dq1Cl+/PFHqlevTrFixahSpQoACxcuZNiwYURERNC+fXuaNGlC5cqVWbRoETNmzCBTpkx8//33uLi4pOV2RCQdmjVrFoMHD6ZNmzZ0796dQoUKAVCwYEHOnTuH2WymT58+jBkzBqPRCDw8u65cuUJ0dDReXl5kzpw5xXkmIiIiIiIiIiIiIiIiImlL4R2RfyD5eKxBgwYxffp04uLiHutw0bt3bwCWLFnC7Nmz2bFjBwBGo5GEhARy587N9u3b8fHxwWKxYDAY0mI7IpIOXb58mdatW2Nvb8+0adMoUqQId+/epXz58sTExDBgwAAmTpzIxYsX6dWrFyEhIRiNxsfOmuTnmYiIiIiIiIiIiIiIiIikPbu0XoBIepBU6A4MDCQkJIQPP/yQtm3bYmNjw969ewkICKBv377cvn2boKAgWrduTbVq1di2bRu7d+/GbDZTsGBB2rZti6enp7peiMi/Fhsbi52dHV27dqVIkSLcv3+fN998k1u3bjF27Fjat29PmTJlaNiwIQsWLMBgMDB27FhrB54kCu6IiIiIiIiIiIiIiIiIvFjUeUfkHzp27Bh169alatWqTJo0CW9vb+u17777jj59+nD27Fm++OILOnbsaL32aFBHwR0R+S8sFgs///wzpUqVIjExkYEDBzJz5kxGjx7NZ599hr29Pb/99htVqlTh/v373L9/n2HDhhEQEJDWSxcRERERERERERERERGRv6F/fi/yD124cIHr16/z3nvv4e3tjdlsxmQyAdCoUSPGjh0LQHBwMGfOnCEpF/doUEfBHRH5O2az+YmfGwwGSpUqBcD9+/fZvXs3xYoVo3fv3tjb2wOQJ08ecufOzcSJE6lRowYff/xxqq1bRERERERERERERERERP4bhXdE/qG7d+8CcOfOHeDh6BlbW1trSKd58+Z8+umnREREcPv2bQwGQ1otVUTSKZPJZB1rderUKQ4cOMC5c+dITEwEsP56/fp1zp07h6urq/XZxMREpk2bxtmzZ3n77bfZsWMHPj4+1mdERERERERERERERERE5MWk8I7IP+Tl5QXA+vXruXTpkvVzg8FAQkICAAUKFCA2NpZTp06lyRpFJP0ym83WzlxDhw6lWrVqVKpUiapVq9KuXTtiYmKws7PDYrGQO3duChYsSFhYGJMnTyYyMpJ58+bxxRdfUKxYMTJmzGh9r52dXVptSURERERERERERERERET+AYV3RJJ50riapNFYlStXpkWLFmzevJk1a9Zw//596z1GoxGA8+fP4+bmRvHixVNnwSLy0kjquBMUFMSoUaMoVKgQ3bt3x8PDg8WLF/PGG29w7949DAYDRqORSZMm4enpSd++fcmbNy9dunQhPj6er7/+msyZM//l+C0RERERERERERERERERebHon+OLABaLJUXXi19++YV79+7h6emJh4cHtra22Nra8sEHH/DTTz/h5+dHTEwMTZs2pUiRIgCsWbOGdevWUbJkSfLly5eW2xGRdMRkMlnPnrt377Jy5Urat29PQEAAuXPn5t69e/Ts2ZMFCxZQtWpV9uzZQ8aMGSlXrhzbt29n+PDhmEwmcubMSc+ePfHw8EjxThERERERERERERERERF5sRksFoslrRchkhZOnDhBfHw8ZcqUSfF5YGAgM2bMIDIyEldXV9q3b0+bNm0oW7YsAF999RXjx4/n5MmT5M2bl3r16nH58mX27duHra0t4eHheHt7YzabrZ00RESeZs6cOdZuO19++SUVK1YkISEBo9HIvXv38PX1ZdasWZQoUYKwsDBcXFxSPG+xWDAYDAruiIiIiIiIiIiIiIiIiKQzCu/IK+ns2bMUKFCAt956i+DgYEqXLg1AQEAAI0eOpGLFihQpUoSTJ0+yf/9+6tatS0BAAFWrVgVg27ZtrF27lhkzZpCYmIinpyeVKlVi8uTJeHl5qXguIv/K0qVL+eCDD8ibNy8Wi4Vdu3bh6emJjY2NNQh4//59BgwYwMyZMylevDh79uzBxcXFGvBJCu+IiIiIiIiIiIiIiIiISPqi8I68kiIjIwkICGD27Nk0btyYQYMGUaxYMWrWrEnJkiUZOnQoXl5eXL9+nYkTJxISEkKtWrUICgqiWrVq1vecP3+e2NhYsmbNSubMmXF0dFRwR0T+NbPZTKtWrVi9ejUODg7s2LGD8uXLW8+TJwV4PD09OX36NBkzZkzr5YuIiIiIiIiIiIiIiIjI/8AurRcgkhbc3NwYMWIETk5OTJw4EVtbWxo1asQvv/xCSEgIXl5eALi7uzNmzBjs7OwYPXo0ACNGjKBKlSoA5M6dO0WnC4vFouCOiPwriYmJ2NnZsXTpUtq2bcuiRYto3749W7ZswcPDI0WAx9nZmfHjxxMdHc369eu5e/euwjsiIiIiIiIiIiIiIiIi6Zw678grLTIyklGjRjF58mRq1KhBdHQ0YWFhODs7WwvqSYYOHcqoUaOoVasWo0aNolKlSmm4chFJj5I66Dwq6bwxm83WAE/16tVZvnw5OXLkeKwDz4MHD4iNjSVLlizq9iUiIiIiIiIiIiIiIiKSzim8Iy+9R4vlCQkJGI1G6+9jYmIICAhg0qRJACxdupT3338feNhJJ3lnnaQAT+nSpZk9ezblypVLpV2ISHqXPGTzyy+/EBUVRVRUFG+88QaOjo7Ws8ZsNvPRRx+xZMkSqlWrxooVK54Y4IHHzygRERERERERERERERERSX8e/+f/Ii8Ri8ViLXKfPHkSwBrcmTBhAkuWLCFDhgwMGjQIf39/AGbOnMmhQ4cAMBgMJM+3jRgxgl69enH58mXraC0Rkacxm83W4M7o0aNp3Lgxb7zxBg0bNqROnTqEhoYSFxcHgI2NDaGhobRu3ZqwsDBatGhBRETEY8EdQMEdERERERERERERERERkZeAwjvyUksqbNeuXZsyZcpw+PBhALp3786AAQO4efMmcXFxZMuWjZ49e9K/f3927tzJ2LFjOXLkiPUdyQM8kyZN4syZM7i7u2M2m1N/UyKSriQPEfr6+uLv74+npyejR49m2rRpXLx4keHDhzNhwgQePHgAPB7gqV27Njdu3HjiyC0RERERERERERERERERSd/s0noBIqmhVKlSbN++nZYtW1K2bFlWrlxJ3759adq0KQ4ODgBky5aNgQMHYjabmThxIhaLhSFDhlCmTBkMBkOKjheZM2dOUZAXEUku+XmRFCKcPn06X375JT169KBr164ULlyYmJgYxo8fz7lz55g4cSIAffv2xdHR0RrgiYqKIjw8XGFBERERERERERERERERkZeUwZK8pYjISyZ5AX3SpEn0798fi8XCBx98wPz58zEajdauOkkF9sjISIKDg5k4cSLNmzfH39+f0qVLp9UWRCQduXDhAu7u7jg5OaU4f86fP0+bNm3IkCEDU6dOpXDhwty9e5fy5ctz7949unTpwuzZs4mOjqZ///7069cPR0dH4GHnnlu3buHm5vbY2CwRERERERERERERERERSf9UAZSXmo2NDSaTCYDo6GhrUGfnzp2cOHECeFgYTwruALi5uTFo0CD69u3L2rVrGTBgAMePH0/9xYtIunLgwAGKFCnCyJEjiY2NxcbGxtot5/bt28TFxdG9e3cKFy7M/fv3eeONN7h9+zajR4/G19eXcePGcf/+febPn8/EiROtI7QMBoOCOyIiIiIiIiIiIiIiIiIvMY3Nkpeera0tADVr1sTBwYGIiAimTJlC06ZNWbJkCZUrV7YGeJKK425ubvj7+xMVFcX333+Ph4dHGu9CRF50mTJlIkuWLEybNg1HR0f69++Pk5MTAGXKlGHatGlUqlSJxMREfH19OXXqFGPGjKFVq1bY29tTuHBhbG1tuXr1Kv7+/mTKlInu3btb36/gjoiIiIiIiIiIiIiIiMjLSWOz5KX0Vx0qkj4fNmwYw4cPx9vbm6VLl1oL6nZ2dlgsFiIiIsiRIwexsbHEx8fj6uqqrhci8lSnTp2iadOmXLhwgcGDB6cI8CS5desWb775JhkzZiQsLMz6eUxMDBUrVqRjx46sWbOG0NBQvLy8UnsLIiIiIiIiIiIiIiIiIpLKlESQl47JZLKGbP744w+OHDnCyZMniYiIsH4+bNgwAgMDuXjxIi1btmTPnj3Y2dlhNptZv349vXr14ptvvsHZ2RlXV1csFouCOyLyVIULF2bNmjXkzp2b0aNHM378eGJjYwGsI7QuXbrEL7/8Qo4cOazPmc1mvvzyS27cuEGTJk3YsWMHXl5e1rF/IiIiIiIiIiIiIiIiIvLyUucdeakk744zatQoFixYwNmzZwHInj07I0eOpFGjRnh6egIwfPhwhg0bhru7O3PnzuXatWuMHz+eP/74g5MnT5IzZ84024uIpF8nT56kWbNmT+zAk5CQQJUqVbh8+TITJ06kXr16fPvtt4wbNw4PDw/Wrl2Li4tLGu9ARERERERERERERERERFKLwjvyUvL19WX8+PHUqVOHFi1acOvWLdasWcPRo0dp3749/fv3J2/evACMGTOGoKAg4uLisLW1JW/evPzwww/kyZNHo7JE5F+zWCwYDIa/DfB888039OjRgytXrpA9e3Zu3bqFt7c327ZtI3fu3NZ3iIiIiIiIiIiIiIiIiMjLT+EdeeksX76cTz/9lA8++IABAwaQP39+AMaNG8fAgQMpWrQoBw4cwNnZ2frM6tWrOX36NHFxcXTp0gUPDw9MJhO2trZptQ0RSQeeFvB7NMDTr18/nJ2duXfvHidOnGDcuHHEx8eTJ08e/Pz88PT01NkjIiIiIiIiIiIiIiIi8opReEfSnad1pOjevTtLlixh06ZNlCtXjvj4eL799lv69++Pvb09e/fuJVu2bH9bIFfxXESeJvk5ceLECWJiYrh37x6lSpUiS5Ys1lDPXwV4kksKAensEREREREREREREREREXn1KLwj6UryLhd37tzB1dXVes1isZCQkEDZsmXJnDkze/bsISEhgVWrVjFw4EBsbGw4cOAA2bNnB2DHjh1kzpyZMmXKpMVWRCQdS34WBQUFMXv2bG7evEl8fDylS5embt26BAUF4ejoCDwe4EkaoZUU1tGYLBEREREREREREREREZFXl8I7km4kL25/+OGH5MqVi379+uHu7p7ieu3atTl//jybNm3i+PHj9OzZ87HgDkDp0qXJmDEjW7ZssRbYRUT+DX9/f0aPHk2lSpWoXbs2Fy5cYPv27Vy5coXatWvz7bffWrvsnDx5kubNm3PlyhU+++wzhg0bprNHRERERERERERERERERLBJ6wWI/FNJwZ3GjRuzePFili1bxpw5c7h582aK+ypWrMjvv/9OYGAgPXv2xNbW9rHgzsSJE/njjz949913sbe3T9V9iEj6ZTKZrF///vvvrFmzhl69erF48WJGjBjBwoUL2b9/P5UqVWLr1q20bNmSuLg4AIoUKcKaNWtwcHBgxYoVxMfHp9U2REREREREREREREREROQFovCOpCvnzp3jzJkzODs7ExcXx5gxY/jiiy+4efOmNdzTpUsXypcvz+LFi4mJieH7779PEdxZunQps2bNIn/+/Hz00UfW0TciIk9ja2sLwNq1a7lx4wY3b96kZcuW+Pj4AA/DPTlz5mTDhg2UKlWK9evX88UXXwAPR20VLlyYvXv3EhYWRqZMmVDzOxERERERERERERERERFRakHSlbx589K+fXvu379Pp06deP311xk9erQ1wAPg4eFBt27dKFGiBCaTieXLl7N9+3Z+/fVXBg4cSL9+/YiLi2Pp0qVky5YNs9mcxrsSkfRk6dKlNG3alI8//pjMmTNTrFgx4OHoPjs7O0wmE66uroSGhpIxY0Z27twJYA0K5s+fH09PT0wmkzV0KCIiIiIiIiIiIiIiIiKvLoNF/+xf0gmz2YyNjQ0XL16kXr16eHp60qdPH4KCgjhz5gwDBw6kY8eOuLu7c//+fb777jumTJnC3r17re/ImDEj5cqVY+HChXh7e2MymaydNEREniTp7Ely4sQJRo0axaZNm7h16xaLFi2iVatWKYI4iYmJPHjwgMqVKxMREcHhw4fJlSuXOn2JiIiIiIiIiIiIiIiIyGMU3pEXksVi+cuOFCaTiS5duvDtt9+ybt067t27R69evbh06RK+vr7WAI/JZOL+/fssXbqUGzduEB8fT/Xq1SlXrhyurq4K7ojIUyU/i/bv30+5cuWws7Pj9OnTjBgxgqVLl1K7dm1CQ0Nxd3cHHgZ37OzsAHj99dexWCyEh4fj4OCQZvsQERERERERERERERERkReXwjvywkne5SI2NhYnJyfrZ0mBm/Pnz1OsWDG6du3K+PHj2bp1K3369OHChQspAjz/5M8QEXmawYMHs2nTJg4dOmT97NdffyUoKIglS5bQokULgoOD8fb2to7OWrlyJR9++CEtWrRg3rx5ODo6puEORERERERERERERERERORFpfCOvFCSd7no1KkT9+/fx9/fHx8fH2vh22Qy8eDBAz799FO2bdvGli1bKFq0KNu2bWPAgAGcP38eX19fOnfuTLZs2f62i4+IyNPcvXuXOnXqcOnSJc6ePYudnR1GoxGAM2fOMGzYMJYsWULVqlWpWbMmTZo0YfHixWzdupVbt26xd+9eXnvtNZ1FIiIiIiIiIiIiIiIiIvJEaj0iL5Skwna9evWYM2cOixcvpmLFinTs2JHly5cDYGNjQ4YMGWjTpg2RkZHs2LEDOzs7atSowbhx4/Dx8SEkJIQ5c+Zw/fp1FctF5H/i4uJC5cqViYiI4NatW9bgDkCBAgUYNmwYH3zwAYcPH2bUqFF07NiRsLAwypQpw549e3jttdcwmUw6i0RERERERERERERERETkiRTekRfOsWPHSExMxM7OjnLlylG/fn1++OEHWrVqRb169Zg6dSqxsbG88847NGvWjLFjx3LhwgUcHR2pVasW48aNI3/+/AwePJilS5ei5lIi8k+ZzeYUvzeZTAC4u7tjsVi4fPkyQIpzpUCBAgQEBPDuu+/i5OREtmzZ2L59O/PmzcPb25vExERsbW1TbxMiIiIiIiIiIiIiIiIikq4ovCMvnBIlShAUFETDhg358ccfKVWqFF9++SWzZs3i4sWL9O3bl7JlyzJt2jSyZcuGjY0N27dvx2KxYDQaqVWrFkFBQdSpU4fmzZur24WIPFHyAI7ZbMZisWBj8/A/i7/++it37tyxnh9lypQBYO/evcDDLmEmk8n6XMGCBfH396dZs2Zs3bqVLl26EBsbC4CdnV1qbktERERERERERERERERE0hlVFOWFYrFYMBgMVK9eHRsbG2JjYxk6dCi9e/fGz8+PNm3asHjxYpYsWYKvry+ZMmXi+vXr7Nu3j3bt2gFgNBqpX78+devWxcHBAZPJpK4XIpKC2Wy2BnXu37+Ps7Oz9dqAAQOYNGkSr732Gt7e3pQtW9Y6LuvGjRvcu3ePjBkzPnauFCpUCH9/fywWC4sWLcJisTB79mycnJxSdW8iIiIiIiIiIiIiIiIikr4YLJopJC+YpAAPwJ49exg5ciSbNm2ic+fOBAUFkT17dgC+/vpr9uzZw6FDh+jXrx8tWrRQSEdEnip5cKdu3boULFiQKVOmYGdnx40bNxg3bhxXrlzh5s2bnDhxgujoaAwGA3fv3gXAx8eH1157jXz58lGlShVy5cqFs7MzFStWxMnJid9++42goCAWLVrEW2+9xdq1azEajWm5ZRERERERERERERERERF5gSm8Iy+kRwM8o0aNYuPGjXz22Wf06dOHvHnzAhAXF8eDBw9wdHTEwcEhLZcsIulA8uBOtWrVOHr0KJMmTeLjjz/G3t7+sfuvXr1KbGwsYWFhzJw5kyNHjlC5cmWuXbvG6dOnrfflzp2bgwcPki1bNgB+++03evbsSebMmVmyZEnqbE5ERERERERERERERERE0iWFdyRNJQ/p/N215AGe7t2707t3b/LkyZOaSxWRdO7R4M7x48cJDg6mbdu2KcZmAY+N24uLi6NmzZqYzWY2bdpE5syZOX/+PJcvX+bChQvUqlWLnDlzpvgzIiIiyJEjB/D3Z52IiIiIiIiIiIiIiIiIvNrs0noB8uo5evQo0dHRVK9eHYPB8JdF7eTXqlatypAhQwCYNm0aAH379iV37typunYRSZ8eDe4cO3aMMWPGPBbcuXHjBlmzZk0R3LFYLDg4OFC0aFG+/vprzp07R5kyZfD29sbHx4dq1aoBjwd+FNwRERERERERERERERERkX/CJq0XIK+WixcvUq5cOXx9fQkLCwP+DOk8SfJrSQGet956i2nTpjF58mTOnTuXamsXkfQpeXCnRo0anDhxguDgYD7++OMUwZ3Nmzfj5+fHunXrUjyfFLwpXrw4ZrOZ2NhYAOs7kyQP7jzpeRERERERERERERERERGRJ1F4R1JVlixZ8Pf356effiIoKIjdu3cD/z7A8/bbbzNlyhQWLFiAyWRKtfWLSPqTFLKpWLEiYWFhDB06lM8++4wMGTJY79m2bRuDBw9m/vz5FC5cOMXzSeePl5cXJpOJo0ePpt7iRUREREREREREREREROSlp7FZkqpcXFzo168fDg4ODB06FIvFQmBg4L8eodW3b18yZ85Mhw4d/rLbhYhIkmPHjnH58mUAbt68SWxsLE5OTgBs3bqVQYMGcerUKQ4cOEDhwoVTdOtJOpOqVKkCYH2PiIiIiIiIiIiIiIiIiMizYLD8VbsTkefo7t27TJs2jaFDh1KzZk1rgAf4ywDPo9fi4+Oxt7fHZDIpwCMiKTx6jphMJsLDw+natSvnz5+nf//+9OvXj+PHj9OzZ09++eUXdu7cyeuvv249Ux59x4kTJ2jZsiXr1q0jT548abEtEREREREREREREREREXkJKbwjaebu3btMnz4df3///xzgERF51F+dEWazmT179tClSxfOnz/Pu+++yy+//MLJkyfZvn07FSpUeGJwZ/fu3ZQoUQJXV1fu37+Ps7MziYmJ2NmpeZ2IiIiIiIiIiIiIiIiI/O8U3pE09V8DPCIiT1O9enXy5s3LwoULrZ8lBXg+++wzTpw4gYODAxs2bKBWrVokJCRgNBpTnD3r1q2jS5cuFCtWjI0bN2KxWKzjtEREREREREREREREREREngVVIOW5M5vNf3nNxcWFbt26MXLkSHbs2EFQUBC7d+8GwGAwoGyZiPwXV65cwdnZmdDQUHr37m393MbGhqpVqzJjxgxKly5NYmIihw8fJjIyEqPRSGJiYorgjq+vLxaLhZkzZ2IwGBTcEREREREREREREREREZFnTp135LlKGkEDcPHiRa5evYqLiwtubm7kyJHD2uFCHXhE5Fk7c+YMgYGBLF26lI4dO/LFF19Yr5lMJsLDw+ncuTPnz5+nf//+9O7dm6xZs2I2m/nuu+/w9fXl9u3b7Nu3jzx58mhUloiIiIiIiIiIiIiIiIg8FwrvyHNjNputXSqCg4OZM2cOv//+OzY2NhQsWJAZM2ZQs2ZN6/2PBniGDRtGtWrV0mj1IvIyOHPmDAMGDGDt2rX89NNPlChRwhoGTBqh1aVLF86fP8/AgQPp2bMnBw8epEePHimCO8mDiCIiIiIiIiIiIiIiIiIiz5LCO/JcJO+WM2DAACZMmED16tVp0aIF0dHRTJkyhdu3b7N48WLee+8963NJAZ6goCCKFi3KjBkzqFixYlptQ0ReAqdPnyY6Opry5cs/di0pwNO5c2cuXLhA06ZNOXjwIHfu3GH//v0K7oiIiIiIiIiIiIiIiIjIc6fwjjxX06ZNY8iQIbRt25bu3btTsGBBEhMTKVCgABcuXMDOzo6vvvqKVq1aWZ+5e/cuISEhfPXVVxw8eBB3d/c03IGIvEyeNIYvKcDTs2dPjh49Ss6cOdm9e7eCOyIiIiIiIiIiIiIiIiKSKhTekefm7NmztGrVily5cjFq1CiKFSvGnTt3qFKlCrdv3+bdd99l2bJlREVFsWTJEt59913rs/fu3cNiseDi4pJi/JaIyNM8KaDzNGazme3btzNu3Dhmzpyp4I6IiIiIiIiIiIiIiIiIpBolIuS5SUhI4Oeff+aDDz6gWLFixMTE8OabbxIZGcnEiROZNm0afn5+JCYm8sEHH7Bs2TLrsxkzZsTFxQWLxaLgjoj8Y8mDO7///jvXrl37R8/Z2NhQs2ZN1q5dS548eUhMTFRwR0RERERERERERERERERShVIR8kyYzebHPitcuDDHjh3j/fffJzExkb59+3L27Fn8/f1p0qQJAO+//z65c+fG3d2d1q1b880336R4x7/tniEir67kwZ1Nmzbx8ccfM3v2bBISEv7R87a2ttjb2wNgZ2f33NYpIiIiIiIiIiIiIiIiIpKcwjvyPzOZTNbuOIcOHWLjxo3WawUKFADg7t27bN++nXLlytGhQwecnZ0BMBqN3LlzhwYNGlC1alXKli2b+hsQkXQveXBn27ZtBAYGsmfPHurXr4/RaEQTIkVERERERERERERERETkRaXWAvI/MZvN1tEyo0ePZt68ecTExLBq1SqqVKliLaZHRETw22+/0aBBA5ycnKzPfvXVV7i7uzN27FgyZMiAvb09JpNJ42pE5B97NLjj5+fH8ePH2bdvH+XLlycqKorTp0/j6emJl5dXGq9WRERERERERERERERERCQldd6R/8xisVg77vTv359hw4ZRqlQpli1bRpUqVVLcmyVLFooUKcLcuXPZsmULCQkJfPXVV8yfPx9PT0+MRqN1XI2COyLyT/1VcGf79u1UqFCBqKgo5s2bR6NGjdi8eXMar1ZERERERERERERERERE5HEGi2aJyP9owYIFdO3alY4dO9KnTx/y5MnzxPumTZtGnz59MJlMZM2alVu3bpE7d2527NhB7ty5UxThRUSe5q+COzt27LAGd0JDQ/H396dQoULs378/jVcsIiIiIiIiIiIiIiIiIvI4hXfkX3lSwKZFixaEh4ezadMmihUr9tgzZrPZ2qFnyZIlbNu2jcuXL1O0aFH69etHzpw5NSpLRP6Vv+u4U7FiRWtwZ9CgQZQtW5adO3cCkJiYiJ2dJkaKiIiIiIiIiIiIiIiIyItD4R15qkOHDnHt2jXq1q2Lg4NDimuRkZEUKFCAqlWrsm7duhRBnb8SHx9vDerY2toquCMi/5mCOyIiIiIiIiIiIiIiIiKS3v19ykJeedevX6dx48a0aNGC48ePP3bdzs4Oo9HI77//zvXr1x8L7pjNZgCuXbvGzJkzAbC3t8fW1jZFgEdE5N/asmULgwYN4sSJE+zYsUPBHRERERERERERERERERFJlxTekb+VIUMG/P39+fDDDylQoADwZyAHIHPmzFSoUIHffvuNzZs3k5iYaL2WvAtPcHAwU6dO5ffff0/dDYjIS8lisfD9999z+PBhtm/fToUKFRTcEREREREREREREREREZF0SWOz5Kni4uKwsbHBaDQyYcIEfHx8eOedd7C3twfg22+/5dNPP8Xb25tZs2ZRqlSpFOO1li9fzsCBA6lcuTJz587FyckprbYiIunI08bwxcbGcunSJQoWLGgN7gwePJgyZcoouCMiIiIiIiIiIiIiIiIi6YbCO/K3LBYLBoMBgCNHjlCuXDkKFCjAxIkTqVu3Lvb29kRGRjJmzBimTJlCgQIF+OSTT3jnnXfImjUrX331FTNnzsRsNrNr1y5y5cqV4p0iIk9iMpmsI/WOHz/OzZs3iYqKomjRouTNmzfFuL379+8zZ84c/P39FdwRERERERERERERERERkXRH4R2xerTLRVxcXIoOOvfv32fVqlUMHjyYjBkzMm7cOOrUqYOjoyPXrl1jxowZLFy4kEuXLuHo6IitrS3x8fEUKlSItWvX4uPjk6IgLyLyJMnPoqCgIBYsWMCFCxcA8PT0pEGDBkyfPh0HBwfMZjMWi4XWrVtz+fJlwsPDAQV3RERERERERERERERERCT9UHhHHrN582bq1q1r/f2QIUMoXrw4rVu3JiYmhjVr1uDr60vmzJkJCQmhbt26ODo6Eh0dze+//868efO4du0aDg4OVK9enaZNm5I9e3YFd0TkX/Hz8yMkJIRGjRrRqFEjcuTIQUhICHv37qVAgQIcO3bMOr4vedhQwR0RERERERERERERERERSU8U3pEUGjduzPfff8/ChQv54IMP6N+/PxMnTmTYsGEMGDAAJyenJwZ46tWrl6JLz6Me7eojIvJ31q9fT6tWrWjdujV+fn7kzZsXgNDQUNq2bUuWLFk4d+4cmTNnTjGKT2eNiIiIiIiIiIiIiIiIiKQ3qnBKCm3atCFDhgz4+/tTt25dJk6cyODBg2nbti1OTk4AZMiQgWbNmhESEkJUVBS+vr5s3ryZ+Ph4AJ6UB1MxXUT+jUOHDhEXF0fnzp3JmzcvJpOJRYsWERgYSJ48efj111/JnDkzsbGx1mcsFovOGhERERERERERERERERFJd9R5R6ySuleEh4fzxhtvAFC3bl3mzZuHh4fHY2OvHu3AM2HCBOrUqWMdYyMi8l+YTCbef/999uzZw7Vr1zCbzaxYsQJfX19sbGw4cOAA2bNnB+DkyZPs3r2bDh06KLgjIiIiIiIiIiIiIiIiIumSKp1ilTR25vjx45hMJgwGA0ePHmXHjh0A2NraYjabrfcn78ATExNDu3bt2LVrV1osXUReIra2ttYRfefPn2f16tVPDO5YLBa6dOnC6tWriYmJSeNVi4iIiIiIiIiIiIiIiIj8NwrvyGMKFCjAvHnzmD17Nnfv3mXAgAHMnz8feDj+ymw2W0M8GTJkoHnz5gQEBPDaa69RrFixtFy6iKQjyRu/JX1tMpkAaNKkCTExMXTt2pWBAwdia2vL3r17rcEdgOnTp3Pq1CmqVauGs7Nz6i5eREREREREREREREREROQZ0disV1zSqKxHP4uLi8PR0ZH169fTqlUrXF1dGTFiBO3atUtx7x9//EHOnDmJi4vDZDLh7Oz82HgtEZFHJT8nLBYLFoslxdirM2fO8NFHH3HgwAEyZcrE5cuXyZgxo/X6smXLGDJkCFmzZuW7777D3d091fcgIiIiIiIiIiIiIiIiIvIsKLzzCktePL9//z5xcXGYzWbc3NxS3LdhwwZatmyJq6srw4cPp3379gB8++23jBs3jn79+tGsWbNUX7+IpE9ms9ka1JkyZQrbt2/HbDZTq1YtunbtiqOjIwDbt2+nXbt2XLp0iW7dulGjRg1rZ7A1a9ZgsVgIDw/H29s7xTtFRERERERERERERERERNIThXdeUcmDO7NmzWLdunWcPn2aDBky0L17d5o3b54ixLNhwwZatWqFo6MjvXr1IkuWLEyfPp3ffvuNM2fO4O3tnVZbEZF0ys/Pj5CQEFxcXIiLiyM+Pp5mzZrh5+dHuXLlsLGxISwsjGHDhhEWFkZ8fDwAjo6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"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a bar plot with error bars for the average values of \"s\" and \"f\" for each criterion\n",
"\n",
"plt.figure(figsize=(12, 8))\n",
"bar_width = 0.1\n",
"index = np.arange(len(criteria))\n",
"\n",
"\n",
"plt.bar(\n",
" index,\n",
" list(average_s.values()),\n",
" bar_width,\n",
" label=f\"success ({len(task['s'])} samples)\",\n",
" color=\"darkblue\",\n",
" yerr=[(avg - conf_interval_s[key][0]) for key, avg in average_s.items()],\n",
" capsize=5,\n",
")\n",
"plt.bar(\n",
" index + bar_width,\n",
" list(average_f.values()),\n",
" bar_width,\n",
" label=f\"failed ({len(task['f'])} samples)\",\n",
" color=\"lightblue\",\n",
" yerr=[(avg - conf_interval_f[key][0]) for key, avg in average_f.items()],\n",
" capsize=5,\n",
")\n",
"\n",
"plt.xlabel(\"Criteria\", fontsize=16)\n",
"plt.ylabel(\"Average Value\", fontsize=16)\n",
"plt.title(\n",
" \"Average Values of 3 different baselines cases with 95% Confidence Intervals - math problems \", fontsize=12, pad=10\n",
") # Adjust titlepad to move the title further above\n",
"plt.xticks(index + bar_width / 2, [crit.name for crit in criteria], rotation=45, fontsize=14)\n",
"plt.legend(loc=\"upper center\", fontsize=14, bbox_to_anchor=(0.5, 1), ncol=3) # Adjust legend placement and ncol\n",
"plt.tight_layout() # Adjust subplot parameters to fit the labels\n",
"plt.ylim(0, 5)\n",
"plt.savefig(\"../test/test_files/agenteval-in-out/estimated_performance.png\")\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
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"vscode": {
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}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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