diff --git a/notebook/agentchat_lmm_llava.ipynb b/notebook/agentchat_lmm_llava.ipynb
new file mode 100644
index 00000000000..a3a51d3abfb
--- /dev/null
+++ b/notebook/agentchat_lmm_llava.ipynb
@@ -0,0 +1,1363 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2c75da30",
+ "metadata": {},
+ "source": [
+ "# Agent Chat with Multimodal Models\n",
+ "\n",
+ "We use **LLaVA** as an example for the multimodal feature. More information about LLaVA can be found in their [GitHub page](https://github.com/haotian-liu/LLaVA)\n",
+ "\n",
+ "\n",
+ "This notebook contains the following information and examples:\n",
+ "\n",
+ "1. Install [LLaVA package](#install)\n",
+ "2. Setup LLaVA Model\n",
+ " - Option 1: Use [API calls from `Replicate`](#replicate)\n",
+ " - Option 2: Setup [LLaVA locally (requires GPU)](#local)\n",
+ "2. Application 1: [Image Chat](#app-1)\n",
+ "3. Application 2: [Figure Creator](#app-2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "b1ffe2ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We use this variable to control where you want to host LLaVA, locally or remotely?\n",
+ "# More details in the two setup options below.\n",
+ "LLAVA_MODE = \"remote\" # Either \"local\" or \"remote\"\n",
+ "assert LLAVA_MODE in [\"local\", \"remote\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2ec49aeb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# we will override the following variables later.\n",
+ "MODEL_NAME = \"\" \n",
+ "SEP = \"###\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d64154f0",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Install the LLaVA library\n",
+ "\n",
+ "Please follow the LLaVA GitHub [page](https://github.com/haotian-liu/LLaVA/) to install LLaVA.\n",
+ "\n",
+ "\n",
+ "#### Download the package\n",
+ "```bash\n",
+ "git clone https://github.com/haotian-liu/LLaVA.git\n",
+ "cd LLaVA\n",
+ "```\n",
+ "\n",
+ "#### Install the inference package\n",
+ "```bash\n",
+ "conda create -n llava python=3.10 -y\n",
+ "conda activate llava\n",
+ "pip install --upgrade pip # enable PEP 660 support\n",
+ "pip install -e .\n",
+ "```\n",
+ "\n",
+ "### Don't forget AutoGen in the new environment\n",
+ "```bash\n",
+ "pip install pyautogen\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "67d45964",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2023-10-20 12:47:04,159] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "import json\n",
+ "import os\n",
+ "from llava.conversation import default_conversation as conv\n",
+ "from llava.conversation import Conversation\n",
+ "\n",
+ "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n",
+ "\n",
+ "import autogen\n",
+ "from autogen import AssistantAgent, Agent, UserProxyAgent, ConversableAgent\n",
+ "from termcolor import colored\n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "acc4703b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## (Option 1, preferred) Use API Calls from Replicate [Remote]\n",
+ "We can also use [Replicate](https://replicate.com/yorickvp/llava-13b/api) to use LLaVA directly, which will host the model for you.\n",
+ "\n",
+ "1. Run `pip install replicate` to install the package\n",
+ "2. You need to get an API key from Replicate from your [account setting page](https://replicate.com/account/api-tokens)\n",
+ "3. Next, copy your API token and authenticate by setting it as an environment variable:\n",
+ " `export REPLICATE_API_TOKEN=` \n",
+ "4. You need to enter your credit card information for Replicate 🥲\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f650bf3d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pip install replicate\n",
+ "# import os\n",
+ "## alternatively, you can put your API key here for the environment variable.\n",
+ "# os.environ[\"REPLICATE_API_TOKEN\"] = \"r8_xyz your api key goes here~\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "267ffd78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if LLAVA_MODE == \"remote\":\n",
+ " import replicate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1805e4bd",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## [Option 2] Setup LLaVA Locally\n",
+ "\n",
+ "\n",
+ "Some helpful packages and dependencies:\n",
+ "```bash\n",
+ "conda install -c nvidia cuda-toolkit\n",
+ "```\n",
+ "\n",
+ "\n",
+ "### Launch\n",
+ "\n",
+ "In one terminal, start the controller first:\n",
+ "```bash\n",
+ "python -m llava.serve.controller --host 0.0.0.0 --port 10000\n",
+ "```\n",
+ "\n",
+ "\n",
+ "Then, in another terminal, start the worker, which will load the model to the GPU:\n",
+ "```bash\n",
+ "python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b\n",
+ "``"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c29925f",
+ "metadata": {},
+ "source": [
+ "**Note: make sure the environment of this notebook also installed the llava package from `pip install -e .`**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "93bf7915",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'models': ['llava-v1.5-13b']}\n",
+ "Model Name: llava-v1.5-13b\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Run this code block only if you want to run LlaVA locally\n",
+ "if LLAVA_MODE == \"local\":\n",
+ " # Setup some global constants for convenience\n",
+ " # Note: make sure the addresses below are consistent with your setup in LLaVA \n",
+ " CONTROLLER_ADDR = \"http://0.0.0.0:10000\"\n",
+ " SEP = conv.sep\n",
+ " ret = requests.post(CONTROLLER_ADDR + \"/list_models\")\n",
+ " print(ret.json())\n",
+ " MODEL_NAME = ret.json()[\"models\"][0]\n",
+ " print(\"Model Name:\", MODEL_NAME)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "307852dd",
+ "metadata": {},
+ "source": [
+ "# Multimodal Functions\n",
+ "\n",
+ "The Multimodal Functions library provides a set of utilities to manage and process multimodal data, focusing on textual and image components. The library allows you to format prompts, extract image paths, and handle image data in various formats.\n",
+ "\n",
+ "## Functions\n",
+ "\n",
+ "\n",
+ "### `get_image_data`\n",
+ "\n",
+ "This function retrieves the content of an image specified by a file path or URL and optionally converts it to base64 format. It can handle both web-hosted images and locally stored files.\n",
+ "\n",
+ "\n",
+ "### `lmm_formater`\n",
+ "\n",
+ "This function formats a user-provided prompt containing `` tags, replacing these tags with `` or numbered versions like ``, ``, etc., and extracts the image locations. It returns a tuple containing the new formatted prompt and a list of image data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "4bf7f549",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import base64\n",
+ "import re\n",
+ "from io import BytesIO\n",
+ "\n",
+ "from PIL import Image\n",
+ "\n",
+ "import re\n",
+ "\n",
+ "\n",
+ "def get_image_data(image_file, use_b64=True):\n",
+ " if image_file.startswith('http://') or image_file.startswith('https://'):\n",
+ " response = requests.get(image_file)\n",
+ " content = response.content\n",
+ " elif re.match(r\"data:image/(?:png|jpeg);base64,\", image_file):\n",
+ " return re.sub(r\"data:image/(?:png|jpeg);base64,\", \"\", image_file)\n",
+ " else:\n",
+ " image = Image.open(image_file).convert('RGB')\n",
+ " buffered = BytesIO()\n",
+ " image.save(buffered, format=\"PNG\")\n",
+ " content = buffered.getvalue()\n",
+ " \n",
+ " if use_b64:\n",
+ " return base64.b64encode(content).decode('utf-8')\n",
+ " else:\n",
+ " return content\n",
+ "\n",
+ "def lmm_formater(prompt: str, order_image_tokens: bool = False) -> Tuple[str, List[str]]:\n",
+ " \"\"\"\n",
+ " Formats the input prompt by replacing image tags and returns the new prompt along with image locations.\n",
+ " \n",
+ " Parameters:\n",
+ " - prompt (str): The input string that may contain image tags like .\n",
+ " - order_image_tokens (bool, optional): Whether to order the image tokens with numbers. \n",
+ " It will be useful for GPT-4V. Defaults to False.\n",
+ " \n",
+ " Returns:\n",
+ " - Tuple[str, List[str]]: A tuple containing the formatted string and a list of images (loaded in b64 format).\n",
+ " \"\"\"\n",
+ " \n",
+ " # Initialize variables\n",
+ " new_prompt = prompt\n",
+ " image_locations = []\n",
+ " images = []\n",
+ " image_count = 0\n",
+ " \n",
+ " # Regular expression pattern for matching tags\n",
+ " img_tag_pattern = re.compile(r']+)>')\n",
+ " \n",
+ " # Find all image tags\n",
+ " for match in img_tag_pattern.finditer(prompt):\n",
+ " image_location = match.group(1)\n",
+ " \n",
+ " try: \n",
+ " img_data = get_image_data(image_location)\n",
+ " except:\n",
+ " # Remove the token\n",
+ " print(f\"Warning! Unable to load image from {image_location}\")\n",
+ " new_prompt = new_prompt.replace(match.group(0), \"\", 1)\n",
+ " continue\n",
+ " \n",
+ " image_locations.append(image_location)\n",
+ " images.append(img_data)\n",
+ " \n",
+ " # Increment the image count and replace the tag in the prompt\n",
+ " new_token = f'' if order_image_tokens else \"\"\n",
+ "\n",
+ " new_prompt = new_prompt.replace(match.group(0), new_token, 1)\n",
+ " image_count += 1\n",
+ " \n",
+ " return new_prompt, images\n",
+ "\n",
+ "\n",
+ "\n",
+ "def gpt4v_formatter(prompt: str) -> List[Union[str, dict]]:\n",
+ " \"\"\"\n",
+ " Formats the input prompt by replacing image tags and returns a list of text and images.\n",
+ " \n",
+ " Parameters:\n",
+ " - prompt (str): The input string that may contain image tags like .\n",
+ "\n",
+ " Returns:\n",
+ " - List[Union[str, dict]]: A list of alternating text and image dictionary items.\n",
+ " \"\"\"\n",
+ " output = []\n",
+ " last_index = 0\n",
+ " image_count = 0\n",
+ " \n",
+ " # Regular expression pattern for matching tags\n",
+ " img_tag_pattern = re.compile(r']+)>')\n",
+ " \n",
+ " # Find all image tags\n",
+ " for match in img_tag_pattern.finditer(prompt):\n",
+ " image_location = match.group(1)\n",
+ " \n",
+ " try:\n",
+ " img_data = get_image_data(image_location)\n",
+ " except:\n",
+ " # Warning and skip this token\n",
+ " print(f\"Warning! Unable to load image from {image_location}\")\n",
+ " continue\n",
+ "\n",
+ " # Add text before this image tag to output list\n",
+ " output.append(prompt[last_index:match.start()])\n",
+ " \n",
+ " # Add image data to output list\n",
+ " output.append({\"image\": img_data})\n",
+ " \n",
+ " last_index = match.end()\n",
+ " image_count += 1\n",
+ "\n",
+ " # Add remaining text to output list\n",
+ " output.append(prompt[last_index:])\n",
+ " \n",
+ " return output\n",
+ "\n",
+ "\n",
+ "def extract_img_paths(paragraph: str) -> list:\n",
+ " \"\"\"\n",
+ " Extract image paths (URLs or local paths) from a text paragraph.\n",
+ " \n",
+ " Parameters:\n",
+ " paragraph (str): The input text paragraph.\n",
+ " \n",
+ " Returns:\n",
+ " list: A list of extracted image paths.\n",
+ " \"\"\"\n",
+ " # Regular expression to match image URLs and file paths\n",
+ " img_path_pattern = re.compile(r'\\b(?:http[s]?://\\S+\\.(?:jpg|jpeg|png|gif|bmp)|\\S+\\.(?:jpg|jpeg|png|gif|bmp))\\b', \n",
+ " re.IGNORECASE)\n",
+ " \n",
+ " # Find all matches in the paragraph\n",
+ " img_paths = re.findall(img_path_pattern, paragraph)\n",
+ " return img_paths\n",
+ "\n",
+ "\n",
+ "def _to_pil(data):\n",
+ " return Image.open(BytesIO(base64.b64decode(data)))\n",
+ "\n",
+ "\n",
+ "\n",
+ "def llava_call_binary(prompt: str, images: list, \n",
+ " model_name:str = MODEL_NAME, \n",
+ " max_new_tokens:int=1000, temperature: float=0.5, seed: int = 1):\n",
+ " # TODO 1: add caching around the LLaVA call to save compute and cost\n",
+ " # TODO 2: add `seed` to ensure reproducibility. The seed is not working now.\n",
+ " if LLAVA_MODE == \"local\":\n",
+ " headers = {\"User-Agent\": \"LLaVA Client\"}\n",
+ " pload = {\n",
+ " \"model\": model_name,\n",
+ " \"prompt\": prompt,\n",
+ " \"max_new_tokens\": max_new_tokens,\n",
+ " \"temperature\": temperature,\n",
+ " \"stop\": SEP,\n",
+ " \"images\": images,\n",
+ " }\n",
+ "\n",
+ " response = requests.post(CONTROLLER_ADDR + \"/worker_generate_stream\", headers=headers,\n",
+ " json=pload, stream=False)\n",
+ "\n",
+ " for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b\"\\0\"):\n",
+ " if chunk:\n",
+ " data = json.loads(chunk.decode(\"utf-8\"))\n",
+ " output = data[\"text\"].split(SEP)[-1]\n",
+ " elif LLAVA_MODE == \"remote\":\n",
+ " # The Replicate version of the model only support 1 image for now.\n",
+ " img = 'data:image/jpeg;base64,' + images[0]\n",
+ " response = replicate.run(\n",
+ " \"yorickvp/llava-13b:2facb4a474a0462c15041b78b1ad70952ea46b5ec6ad29583c0b29dbd4249591\",\n",
+ " input={\"image\": img, \"prompt\": prompt.replace(\"\", \" \"), \"seed\": seed}\n",
+ " )\n",
+ " # The yorickvp/llava-13b model can stream output as it's running.\n",
+ " # The predict method returns an iterator, and you can iterate over that output.\n",
+ " output = \"\"\n",
+ " for item in response:\n",
+ " # https://replicate.com/yorickvp/llava-13b/versions/2facb4a474a0462c15041b78b1ad70952ea46b5ec6ad29583c0b29dbd4249591/api#output-schema\n",
+ " output += item\n",
+ " \n",
+ " # Remove the prompt and the space.\n",
+ " output = output.replace(prompt, \"\").strip().rstrip()\n",
+ " return output\n",
+ " \n",
+ "\n",
+ "def llava_call(prompt:str, model_name: str=MODEL_NAME, images: list=[], \n",
+ " max_new_tokens:int=1000, temperature: float=0.5, seed: int = 1) -> str:\n",
+ " \"\"\"\n",
+ " Makes a call to the LLaVA service to generate text based on a given prompt and optionally provided images.\n",
+ "\n",
+ " Args:\n",
+ " - prompt (str): The input text for the model. Any image paths or placeholders in the text should be replaced with \"\".\n",
+ " - model_name (str, optional): The name of the model to use for the text generation. Defaults to the global constant MODEL_NAME.\n",
+ " - images (list, optional): A list of image paths or URLs. If not provided, they will be extracted from the prompt.\n",
+ " If provided, they will be appended to the prompt with the \"\" placeholder.\n",
+ " - max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 1000.\n",
+ " - temperature (float, optional): temperature for the model. Defaults to 0.5.\n",
+ "\n",
+ " Returns:\n",
+ " - str: Generated text from the model.\n",
+ "\n",
+ " Raises:\n",
+ " - AssertionError: If the number of \"\" tokens in the prompt and the number of provided images do not match.\n",
+ " - RunTimeError: If any of the provided images is empty.\n",
+ "\n",
+ " Notes:\n",
+ " - The function uses global constants: CONTROLLER_ADDR and SEP.\n",
+ " - Any image paths or URLs in the prompt are automatically replaced with the \"\" token.\n",
+ " - If more images are provided than there are \"\" tokens in the prompt, the extra tokens are appended to the end of the prompt.\n",
+ " \"\"\"\n",
+ "\n",
+ " if len(images) == 0:\n",
+ " prompt, images = lmm_formater(prompt, order_image_tokens=False)\n",
+ " else:\n",
+ " # Append the token if missing\n",
+ " assert prompt.count(\"\") <= len(images), \"the number \"\n",
+ " \"of image token in prompt and in the images list should be the same!\"\n",
+ " num_token_missing = len(images) - prompt.count(\"\")\n",
+ " prompt += \" \" * num_token_missing\n",
+ " images = [get_image_data(x) for x in images]\n",
+ " \n",
+ " for im in images:\n",
+ " if len(im) == 0:\n",
+ " raise RunTimeError(\"An image is empty!\")\n",
+ "\n",
+ " return llava_call_binary(prompt, images, \n",
+ " model_name, \n",
+ " max_new_tokens, temperature, seed)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4123df2c",
+ "metadata": {},
+ "source": [
+ "Here is the image that we are going to use.\n",
+ "\n",
+ "![Image](https://github.com/haotian-liu/LLaVA/raw/main/images/llava_logo.png)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05ed5a35",
+ "metadata": {},
+ "source": [
+ "We can call llava by providing the prompt and images separately.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "ec31ca74",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The image features a small, orange, and black toy animal, possibly a stuffed dog or a toy horse, with flames coming out of its back. The toy is sitting on a table, and it appears to be a unique and creative design. The toy is wearing glasses, adding a touch of whimsy to its appearance. The overall scene is quite eye-catching and playful.\n"
+ ]
+ }
+ ],
+ "source": [
+ "out = llava_call(\"Describe this image: \", \n",
+ " images=[\"https://github.com/haotian-liu/LLaVA/raw/main/images/llava_logo.png\"])\n",
+ "print(out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6619dc30",
+ "metadata": {},
+ "source": [
+ "Or, we can also call LLaVA with only prompt, with images embedded in the prompt with the format\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "12a7db5a",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A red toy with flames and glasses on it.\n"
+ ]
+ }
+ ],
+ "source": [
+ "out = llava_call(\"Describe this image in one sentence: \")\n",
+ "print(out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e4faf59",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Application 1: Image Chat\n",
+ "\n",
+ "In this section, we present a straightforward dual-agent architecture to enable user to chat with a multimodal agent.\n",
+ "\n",
+ "\n",
+ "First, we show this image and ask a question.\n",
+ "![](https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "286938aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "config_list_gpt4 = autogen.config_list_from_json(\n",
+ " \"OAI_CONFIG_LIST\",\n",
+ " filter_dict={\n",
+ " \"model\": [\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n",
+ "\n",
+ "DEFAULT_LMM_SYS_MSG = \"\"\"You are a helpful AI assistant.\n",
+ "You can also view images, where the \"\" represent the i-th image you received.\"\"\"\n",
+ "\n",
+ "class MultimodalConversableAgent(ConversableAgent):\n",
+ " def __init__(\n",
+ " self,\n",
+ " name: str,\n",
+ " system_message: Optional[Tuple[str, List]] = DEFAULT_LMM_SYS_MSG,\n",
+ " is_termination_msg=None,\n",
+ " *args,\n",
+ " **kwargs,\n",
+ " ):\n",
+ " \"\"\"\n",
+ " Args:\n",
+ " name (str): agent name.\n",
+ " system_message (str): system message for the ChatCompletion inference.\n",
+ " Please override this attribute if you want to reprogram the agent.\n",
+ " **kwargs (dict): Please refer to other kwargs in\n",
+ " [ConversableAgent](conversable_agent#__init__).\n",
+ " \"\"\"\n",
+ " super().__init__(\n",
+ " name,\n",
+ " system_message,\n",
+ " is_termination_msg=is_termination_msg,\n",
+ " *args,\n",
+ " **kwargs,\n",
+ " )\n",
+ " \n",
+ " self.update_system_message(system_message)\n",
+ " self._is_termination_msg = (\n",
+ " is_termination_msg if is_termination_msg is not None else (lambda x: x.get(\"content\")[-1] == \"TERMINATE\")\n",
+ " )\n",
+ " \n",
+ " @property\n",
+ " def system_message(self) -> List:\n",
+ " \"\"\"Return the system message.\"\"\"\n",
+ " return self._oai_system_message[0][\"content\"]\n",
+ "\n",
+ " def update_system_message(self, system_message: str):\n",
+ " \"\"\"Update the system message.\n",
+ "\n",
+ " Args:\n",
+ " system_message (str): system message for the ChatCompletion inference.\n",
+ " \"\"\"\n",
+ " self._oai_system_message[0][\"content\"] = self._message_to_dict(system_message)[\"content\"]\n",
+ " self._oai_system_message[0][\"role\"] = \"system\"\n",
+ " \n",
+ " @staticmethod\n",
+ " def _message_to_dict(message: Union[Dict, List, str]):\n",
+ " \"\"\"Convert a message to a dictionary.\n",
+ "\n",
+ " The message can be a string or a dictionary. The string will be put in the \"content\" field of the new dictionary.\n",
+ " \"\"\"\n",
+ " if isinstance(message, str):\n",
+ " return {\"content\": gpt4v_formatter(message)}\n",
+ " if isinstance(message, list):\n",
+ " return {\"content\": message}\n",
+ " else:\n",
+ " return message\n",
+ " \n",
+ " def _content_str(self, content: List) -> str:\n",
+ " rst = \"\"\n",
+ " for item in content:\n",
+ " if isinstance(item, str):\n",
+ " rst += item\n",
+ " else:\n",
+ " assert isinstance(item, dict) and \"image\" in item, (\"Wrong content format.\")\n",
+ " rst += \"\"\n",
+ " return rst\n",
+ " \n",
+ " def _print_received_message(self, message: Union[Dict, str], sender: Agent):\n",
+ " # print the message received\n",
+ " print(colored(sender.name, \"yellow\"), \"(to\", f\"{self.name}):\\n\", flush=True)\n",
+ " if message.get(\"role\") == \"function\":\n",
+ " func_print = f\"***** Response from calling function \\\"{message['name']}\\\" *****\"\n",
+ " print(colored(func_print, \"green\"), flush=True)\n",
+ " print(self._content_str(message[\"content\"]), flush=True)\n",
+ " print(colored(\"*\" * len(func_print), \"green\"), flush=True)\n",
+ " else:\n",
+ " content = message.get(\"content\")\n",
+ " if content is not None:\n",
+ " if \"context\" in message:\n",
+ " content = oai.ChatCompletion.instantiate(\n",
+ " content,\n",
+ " message[\"context\"],\n",
+ " self.llm_config and self.llm_config.get(\"allow_format_str_template\", False),\n",
+ " )\n",
+ " print(self._content_str(content), flush=True)\n",
+ " if \"function_call\" in message:\n",
+ " func_print = f\"***** Suggested function Call: {message['function_call'].get('name', '(No function name found)')} *****\"\n",
+ " print(colored(func_print, \"green\"), flush=True)\n",
+ " print(\n",
+ " \"Arguments: \\n\",\n",
+ " message[\"function_call\"].get(\"arguments\", \"(No arguments found)\"),\n",
+ " flush=True,\n",
+ " sep=\"\",\n",
+ " )\n",
+ " print(colored(\"*\" * len(func_print), \"green\"), flush=True)\n",
+ " print(\"\\n\", \"-\" * 80, flush=True, sep=\"\")\n",
+ " # TODO: we may want to udpate `generate_code_execution_reply` or `extract_code` for the \"content\" type change.\n",
+ " \n",
+ "\n",
+ "DEFAULT_LLAVA_SYS_MSG = \"You are an AI agent and you can view images.\"\n",
+ "class LLaVAAgent(MultimodalConversableAgent):\n",
+ " def __init__(\n",
+ " self,\n",
+ " name: str,\n",
+ " system_message: Optional[Tuple[str, List]] = DEFAULT_LLAVA_SYS_MSG,\n",
+ " *args,\n",
+ " **kwargs,\n",
+ " ):\n",
+ " \"\"\"\n",
+ " Args:\n",
+ " name (str): agent name.\n",
+ " system_message (str): system message for the ChatCompletion inference.\n",
+ " Please override this attribute if you want to reprogram the agent.\n",
+ " **kwargs (dict): Please refer to other kwargs in\n",
+ " [ConversableAgent](conversable_agent#__init__).\n",
+ " \"\"\"\n",
+ " super().__init__(\n",
+ " name,\n",
+ " system_message=system_message,\n",
+ " *args,\n",
+ " **kwargs,\n",
+ " )\n",
+ " self.register_reply([Agent, None], reply_func=LLaVAAgent._image_reply, position=0)\n",
+ "\n",
+ " def _image_reply(\n",
+ " self,\n",
+ " messages=None,\n",
+ " sender=None, config=None\n",
+ " ):\n",
+ " # Note: we did not use \"llm_config\" yet.\n",
+ " # TODO 1: make the LLaVA API design compatible with llm_config\n",
+ " \n",
+ " if all((messages is None, sender is None)):\n",
+ " error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
+ " logger.error(error_msg)\n",
+ " raise AssertionError(error_msg)\n",
+ "\n",
+ " if messages is None:\n",
+ " messages = self._oai_messages[sender]\n",
+ "\n",
+ " # The formats for LLaVA and GPT are different. So, we manually handle them here.\n",
+ " # TODO: format the images from the history accordingly.\n",
+ " images = []\n",
+ " prompt = self._content_str(self.system_message) + \"\\n\"\n",
+ " for msg in messages:\n",
+ " role = \"Human\" if msg[\"role\"] == \"user\" else \"Assistant\"\n",
+ " images += [d[\"image\"] for d in msg[\"content\"] if isinstance(d, dict)]\n",
+ " content_prompt = self._content_str(msg[\"content\"])\n",
+ " prompt += f\"{SEP}{role}: {content_prompt}\\n\"\n",
+ " prompt += \"\\n\" + SEP + \"Assistant: \"\n",
+ " print(colored(prompt, \"blue\"))\n",
+ " \n",
+ " out = \"\"\n",
+ " retry = 10\n",
+ " while len(out) == 0 and retry > 0:\n",
+ " # image names will be inferred automatically from llava_call\n",
+ " out = llava_call_binary(prompt=prompt, images=images, temperature=0, max_new_tokens=2000)\n",
+ " retry -= 1\n",
+ " \n",
+ " assert out != \"\", \"Empty response from LLaVA.\"\n",
+ " \n",
+ " \n",
+ " return True, out"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3d5580e",
+ "metadata": {},
+ "source": [
+ "Within the user proxy agent, we can decide to activate the human input mode or not (for here, we use human_input_mode=\"NEVER\" for conciseness). This allows you to interact with LLaVA in a multi-round dialogue, enabling you to provide feedback as the conversation unfolds."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "67157629",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
+ "\n",
+ "What's the breed of this dog? \n",
+ ".\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[34mYou are an AI agent and you can view images.\n",
+ "###Human: What's the breed of this dog? \n",
+ ".\n",
+ "\n",
+ "###Assistant: \u001b[0m\n",
+ "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
+ "\n",
+ "The dog in the image is a poodle.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "image_agent = LLaVAAgent(\n",
+ " name=\"image-explainer\",\n",
+ " max_consecutive_auto_reply=0\n",
+ ")\n",
+ "\n",
+ "user_proxy = autogen.UserProxyAgent(\n",
+ " name=\"User_proxy\",\n",
+ " system_message=\"A human admin.\",\n",
+ " code_execution_config={\n",
+ " \"last_n_messages\": 3,\n",
+ " \"work_dir\": \"groupchat\"\n",
+ " },\n",
+ " human_input_mode=\"NEVER\", # Try between ALWAYS or NEVER\n",
+ "# llm_config=llm_config,\n",
+ " max_consecutive_auto_reply=0,\n",
+ ")\n",
+ "\n",
+ "# Ask the question with an image\n",
+ "user_proxy.initiate_chat(image_agent, \n",
+ " message=\"\"\"What's the breed of this dog? \n",
+ ".\"\"\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f60521d",
+ "metadata": {},
+ "source": [
+ "Now, input another image, and ask a followup question.\n",
+ "\n",
+ "![](https://th.bing.com/th/id/OIP.29Mi2kJmcHHyQVGe_0NG7QHaEo?pid=ImgDet&rs=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "73a2b234",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
+ "\n",
+ "How about these breeds? \n",
+ "\n",
+ "\n",
+ "Among the breeds, which one barks less?\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[34mYou are an AI agent and you can view images.\n",
+ "###Human: What's the breed of this dog? \n",
+ ".\n",
+ "###Assistant: The dog in the image is a poodle.\n",
+ "###Human: How about these breeds? and \n",
+ "Among all the breeds, which one barks less?\n",
+ "###Assistant: The breeds of the dog in the image are a poodle and a terrier. Among the two, the poodle is known to bark less.\n",
+ "###Human: How about these breeds? \n",
+ "\n",
+ "\n",
+ "Among the breeds, which one barks less?\n",
+ "\n",
+ "###Assistant: \u001b[0m\n",
+ "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
+ "\n",
+ "Among the breeds, the poodle is known to bark less.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Ask the question with an image\n",
+ "user_proxy.send(message=\"\"\"How about these breeds? \n",
+ "\n",
+ "\n",
+ "Among the breeds, which one barks less?\"\"\", \n",
+ " recipient=image_agent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c40d0eb",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Application 2: Figure Creator\n",
+ "\n",
+ "Here, we define a `FigureCreator` agent, which contains three child agents: commander, coder, and critics.\n",
+ "\n",
+ "- Commander: interacts with users, runs code, and coordinates the flow between the coder and critics.\n",
+ "- Coder: writes code for visualization.\n",
+ "- Critics: LLaVA-based agent that provides comments and feedback on the generated image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e8eca993",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class FigureCreator(AssistantAgent):\n",
+ "\n",
+ " def __init__(self, n_iters=2, **kwargs):\n",
+ " \"\"\"\n",
+ " Initializes a FigureCreator instance.\n",
+ " \n",
+ " This agent facilitates the creation of visualizations through a collaborative effort among its child agents: commander, coder, and critics.\n",
+ " \n",
+ " Parameters:\n",
+ " - n_iters (int, optional): The number of \"improvement\" iterations to run. Defaults to 2.\n",
+ " - **kwargs: keyword arguments for the parent AssistantAgent.\n",
+ " \"\"\"\n",
+ " super().__init__(**kwargs)\n",
+ " self.register_reply([Agent, None],\n",
+ " reply_func=FigureCreator._reply_user,\n",
+ " position=0)\n",
+ " self._n_iters = n_iters\n",
+ "\n",
+ " def _reply_user(self, messages=None, sender=None, config=None):\n",
+ " if all((messages is None, sender is None)):\n",
+ " error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
+ " logger.error(error_msg)\n",
+ " raise AssertionError(error_msg)\n",
+ "\n",
+ " if messages is None:\n",
+ " messages = self._oai_messages[sender]\n",
+ "\n",
+ " user_question = messages[-1][\"content\"]\n",
+ "\n",
+ " ### Define the agents\n",
+ " commander = AssistantAgent(\n",
+ " name=\"Commander\",\n",
+ " human_input_mode=\"NEVER\",\n",
+ " max_consecutive_auto_reply=10,\n",
+ " system_message=\n",
+ " \"Help me run the code, and tell other agents it is in the file location.\",\n",
+ " is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\n",
+ " \"TERMINATE\"),\n",
+ " code_execution_config={\n",
+ " \"last_n_messages\": 3,\n",
+ " \"work_dir\": \".\",\n",
+ " \"use_docker\": False\n",
+ " },\n",
+ " llm_config=self.llm_config,\n",
+ " )\n",
+ "\n",
+ " critics = LLaVAAgent(\n",
+ " name=\"Critics\",\n",
+ " system_message=\n",
+ " \"Criticize the input figure. How to replot the figure so it will be better? Find bugs and issues for the figure. If you think the figures is good enough, then simply say NO_ISSUES\",\n",
+ " llm_config=self.llm_config,\n",
+ " human_input_mode=\"NEVER\",\n",
+ " max_consecutive_auto_reply=0,\n",
+ " # use_docker=False,\n",
+ " )\n",
+ "\n",
+ " coder = AssistantAgent(\n",
+ " name=\"Coder\",\n",
+ " llm_config=self.llm_config,\n",
+ " )\n",
+ "\n",
+ " coder.update_system_message(\n",
+ " coder.system_message +\n",
+ " \"ALWAYS save the figure in `result.jpg` file. Tell other agents it is in the file location.\"\n",
+ " )\n",
+ "\n",
+ " # Data flow begins\n",
+ " commander.initiate_chat(coder, message=user_question)\n",
+ " img = Image.open(\"result.jpg\")\n",
+ " plt.imshow(img)\n",
+ " plt.axis('off') # Hide the axes\n",
+ " plt.show()\n",
+ " \n",
+ " for i in range(self._n_iters):\n",
+ " commander.send(message=\"Improve \",\n",
+ " recipient=critics,\n",
+ " request_reply=True)\n",
+ " \n",
+ " feedback = commander._oai_messages[critics][-1][\"content\"]\n",
+ " if feedback.find(\"NO_ISSUES\") >= 0:\n",
+ " break\n",
+ " commander.send(\n",
+ " message=\"Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\\n\"\n",
+ " + feedback,\n",
+ " recipient=coder,\n",
+ " request_reply=True)\n",
+ " img = Image.open(\"result.jpg\")\n",
+ " plt.imshow(img)\n",
+ " plt.axis('off') # Hide the axes\n",
+ " plt.show()\n",
+ " \n",
+ " return True, \"result.jpg\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "977b9017",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mUser\u001b[0m (to Figure Creator~):\n",
+ "\n",
+ "\n",
+ "Plot a figure by using the data from:\n",
+ "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
+ "\n",
+ "I want to show both temperature high and low.\n",
+ "\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[33mCommander\u001b[0m (to Coder):\n",
+ "\n",
+ "\n",
+ "Plot a figure by using the data from:\n",
+ "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
+ "\n",
+ "I want to show both temperature high and low.\n",
+ "\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[33mCoder\u001b[0m (to Commander):\n",
+ "\n",
+ "To plot the figure using the data from the provided URL, we'll first download the data, then use the pandas library to read the CSV data and finally, use the matplotlib library to plot the temperature high and low.\n",
+ "\n",
+ "Step 1: Download the CSV file\n",
+ "Step 2: Read the CSV file using pandas\n",
+ "Step 3: Plot the temperature high and low using matplotlib\n",
+ "\n",
+ "Please execute the following code:\n",
+ "\n",
+ "```python\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import urllib.request\n",
+ "\n",
+ "# Download the CSV file from the URL\n",
+ "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
+ "urllib.request.urlretrieve(url, \"seattle-weather.csv\")\n",
+ "\n",
+ "# Read the CSV file using pandas\n",
+ "data = pd.read_csv(\"seattle-weather.csv\")\n",
+ "\n",
+ "# Plot the temperature high and low using matplotlib\n",
+ "plt.plot(data[\"date\"], data[\"temp_max\"], label=\"Temperature High\")\n",
+ "plt.plot(data[\"date\"], data[\"temp_min\"], label=\"Temperature Low\")\n",
+ "plt.xlabel(\"Date\")\n",
+ "plt.ylabel(\"Temperature\")\n",
+ "plt.title(\"Seattle Weather - Temperature High and Low\")\n",
+ "plt.legend()\n",
+ "plt.savefig(\"result.jpg\")\n",
+ "plt.show()\n",
+ "```\n",
+ "\n",
+ "After executing the code, you should see the desired plot with temperature high and low. The figure will be saved as `result.jpg`.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[31m\n",
+ ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+ "\u001b[33mCommander\u001b[0m (to Coder):\n",
+ "\n",
+ "exitcode: 0 (execution succeeded)\n",
+ "Code output: \n",
+ "Figure(640x480)\n",
+ "\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "\u001b[33mCoder\u001b[0m (to Commander):\n",
+ "\n",
+ "Great! The code execution succeeded, and the figure has been plotted using the data provided. The figure is saved in the `result.jpg` file. Please check the file for the plotted figure showing both temperature high and low.\n",
+ "\n",
+ "TERMINATE\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAGFCAYAAACL7UsMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d7wdRf3//5yZ3dNuzU0PJJAQAqGHKkXEQhMQLPizIKKigljAXgDbx4KoHwRFQL8qCAgIKiAgoKh8AGlBCCKEEEIIpLdbzzm7OzO/P2Zn795LQIMCuTf7yuPknrpldmfe7fV+v4W11lKgQIECBQoU2KwgX+kDKFCgQIECBQq8/CgUgAIFChQoUGAzRKEAFChQoECBApshCgWgQIECBQoU2AxRKAAFChQoUKDAZohCAShQoECBAgU2QxQKQIECBQoUKLAZolAAChQoUKBAgc0QhQJQoECBAgUKbIYoFIACBQoUKFBgM0ShABQoUKBAgQKbIQoFoECBAgUKFNgMUSgABQoUKFCgwGaIQgEoUKBAgQIFNkMUCkCBAgUKFCiwGaJQAAoUKFCgQIHNEIUCUKBAgQIFCmyGKBSAAgUKFChQYDNEoQAUKFCgQIECmyEKBaBAgQIFChTYDFEoAAUKFChQoMBmiEIBKFCgQIECBTZDFApAgQIFChQosBmiUAAKFChQoECBzRCFAlCgQIECBQpshigUgAIFChQoUGAzRKEAFChQoECBApshCgWgQIECBQoU2AxRKAAFChQoUKDAZohCAShQoECBAgU2QxQKQIECBQoUKLAZolAAChQoUKBAgc0QwSt9AAX+O7DWAiCEyJ7n3/ef5T8XQmz0PowxSCmHvGetzd6z1g7ZbpIkSCmRUg753gvtw3/PP89vTwhBHMcEwdBbN0kSwjBEa42UcqPPLb//F/ps+LG8HHi+cdBaZ9fUGJM9f7mO67+B/Hj7e+vlOH5/n/hx9WOslMo+98fn71l/XPmxzv8+/x3/2w3NjZfr+vi554/LH7d/7e+hl2vMC2x6KDwAowjWWrTWxHGM1hpjTPYXIIqibFHyjxezD3CLS34b+b/5hzFmyHH5Y3i+bfvvRlE0ZBE2xmCMyfbrz8mfXxzHNBqNbBtJkmz0ueWPwx+3328cx9mx+X2+mPHb2OPQWj9HSEZRlO3ff5a/ziMNG7pnXmr4cbTWEscxAHEc02w2s+OI4zi7/vljyh9j/v7On0P+3k2ShCRJXrbrE8cxSZJkx+Dfy3/mj8tam82bApsfCgVgFMFPaqVUZsl47T6O4+y9/0R4KaWy/RhjaDab2T7ygtG/zh9XEATZMTwf8hbKcIvJWzFKKaIootFoIKWk2WxSqVQIw5AoirLj3FgkSZItlN669hZcEARIKV8WwZ+HPwav1EkpCcMw+8wLFaXUiLPivKDMW+DDPR0vFYIgIAzDIVZ/EAQEQZDdY/49fw82Gg2AzKPln4O7Fvnn/uHni5+TL1bx3hgopbJ71h+Pv4/DMCQIgsw7IISgVCq9pMdTYNNFoQCMIlhrCcMwE/gwuDAFQYDWmnq9/qKEI5AJG2stlUoFIUS2yHj3rRdEfmHPLzJ+4XshL0DeaslbJ0IIenp6CIKAOI6pVCqUSiWMMVQqlcwC8++9mPBG/njzC7dXdgYGBjJF4OWC9zx4oe/P05+fv64AjUZjRHoB8u73vFLzUu/T37PD96e1JkmSIa59Ywzlcplms5l9J38t8nMjv32vkAOZR+HlODfvAdNa02w2KZVKQ7wUSqkhXowCmycKDsAoQd5S9pN8xYoV3H///Tz55JP09/czadIktttuO/bdd98XtQ8v+LxL8+GHH2bWrFmMGTMms4wfeughJk2axMSJEzMhetVVVzFp0iQOOOCAIRbshmCtJQgC6vU6AL/85S/Zaaed2Hfffeno6KC7u5vf/e53NJtN3v/+92fKxr333stTTz3FkUceSaVSeQ5H4N/F008/TV9fHzvuuGO2eD/44IMsWLCAww8/nHK5nL3/UisCeWvx7rvv5rbbbkNKmSlXHlprDjzwQPbff/+XVTn5b8ALYK01999/P1tttRXjx49/yffrhaSUkquvvpoxY8Zw0EEHZfdsT08PN910E9tvvz277bYby5Yt4/rrr+fd7343UkriOKZaraK1HuJx816NefPmMX/+fA499FDa2tqew795KeEVj+uvv54xY8aw//77DwmZCSFYu3Ytv/vd73jd617HlClTXvR8KTCyMbJWiwLPi7xVKIRg9erVfOc73+Hcc8+lr68PIQRz587lsssue1EWMgxan2EYsmTJEr71rW8xf/78bBHs7+/nrLPO4t57783c8UmS8Itf/IIHH3wwE5p5K3848gLOGMNf/vIXfv/732eW15IlS/jZz37Gd77zHVavXo21loGBAa6++mpuv/32zAvwYvkN1113Heedd15GNBRC8Oijj3L55ZfT29sLQBiGL4ug9Z4IIQTlcpmuri7Gjx9Po9Hghz/8IcuXL6ezs5POzk5qtdqL9uy8kvBCOIoizjjjDO68886X5Tz82CqluPzyy7npppsolUoopTKvypVXXsm8efNQSlEul+ns7CQIgiEx/uHkVn//zp07l1/96lf09/dngv/l8h55JfuKK67gtttuy8Ia3sMlpaS7u5vzzz+fxYsXFyGAzRiF2jfK4AXnAw88wP/93/9x2WWXMXXq1IzI5IW1X5TWrFnD+vXrsdbS3t7O2LFjM/dgb28v69atI45jyuUyY8aMoaWlhSRJWLFiBfPnz+eZZ56hq6uLlpYW1q1bx5IlS1i4cCGPPPII7e3tbLHFFmitM4WgXC5jrWXp0qUMDAwghGDChAm0trZmC5Rn9CdJwv77789NN93EihUrmDp1KvPmzWOLLbag0Wjw97//ncMPPxytNbfffjvve9/7qNVq1Ot1Vq1aRbPZJAxDxo8fT0tLS2b1rV69moGBAaSUVKtVxo0bh1KKvr4+nnjiCR577DEWLVqEEIKpU6dmi3xPT0+miIwdO5bOzs5M8Wo2m6xatYp6vT5knwDr1q2jt7eXtrY2uru7kVKy1VZbDblueYVluKWotWa33XZjhx12QErJsmXLuPLKK3nLW97Cq1/9agDWr1/PwoULAWhpaWH8+PGEYYgxhhUrVlAqlUiShN7eXsrlMpMmTSJJElauXInWmo6ODsaOHQu4EM2yZctobW2lr6+PJEno6Oigq6trCAdk9erV9PX1IaWks7OTrq6uLFa+dOlSxowZQ09PD3Ecs9VWW1Gv11m7di1RFBGGIR0dHXR2dmKMYenSpSxatIinn36axx57jPb2dsaNG8fKlSvp6uqiVqsB0N/fz8qVK5k+fTrWWpYsWUKtVkNrTU9PDxMmTMjGed26dWitaWtrY9y4cZnwzsfovas+z0Pwil+ecNrS0sIee+wxRElYvXo169evRylFV1cX/f39tLS00N7enpHwenp6Ml5KV1cXHR0dQ66vP578fWmMoVqtMnHixMwy7+npoaenh2q1Sk9PD8YYxo0bR3t7O+CE/vr161m7di1JktDZ2ZnxfjynwSv9cRw/5xiG/200GqxcuZIoiiiVSowfP55arUYURaxYsYIJEyZQKpWo1+usWLGCMWPGZNdyyZIljB07ltbW1iH3dIFND4UCMIqQT6PyE27BggVMmjSJjo6OLJ7sra577rmHn/zkJ6xatQqtNa2trZx88snsv//+NJtNrrzySm6//fYsZr/tttvy0Y9+lEqlwu9//3tWrlzJD3/4Q8aMGcOhhx7Kk08+yaJFi7jiiiu466672HHHHfnqV7+aHYuPoV566aX87ne/IwgC+vv72XXXXTnxxBOZNWsWQohMcLW0tLDDDjvw05/+lO7ubiZMmMDChQvZaaed6OrqYt68eRxyyCEsXbqUer3OzJkziaKICy+8kNtuu41SqUSz2WTOnDmcfPLJ2e/PO+88li1bloUi3v72t3PUUUfxyCOPcPvtt7NixQo+97nP0dbWxumnn06z2WT58uVceOGFrF+/niVLlrDjjjty5pln0tXVRXd3N5dccgl//vOfM4/Eq1/9aj7ykY/Q1dXFxRdfzAUXXMCb3/xmFi9ezLhx4zjnnHOyWGyeL+HDC/49bw0LITLehR9Pb80tWrSIs88+m2effTazMN/xjndw1FFH0Wg0+OxnPwtArVZj+fLlrFu3jpNOOok1a9Zw7733snz5cqZMmcK3vvUtJk+ezKOPPspb3/pWXvva19JoNFi+fDnt7e184QtfYPfdd6fRaHDzzTdzySWXkCQJURQxYcIEvvSlL7Hddtvx2GOPcdRRR3HMMcewZs0a4jjmf//3f/njH//IrbfemnmGtthiCz7/+c/T0dHBtddey9q1a7n00kv561//yqte9Sre+ta3csopp/CZz3yGN7zhDQD88Y9/5FOf+lTmeXrXu97FxIkTmTBhAsuXL+e9730vW221FT/+8Y9ZunRpxhF53/vex6GHHjqEt+KFb55jkScjhmFIs9lECMEjjzzCCSecwB/+8Ae22GIL/vnPf/LNb36TVatWMXbsWKZNm8add97JySefzLve9a4sBHfRRRfx7LPPsn79enbYYQfOOOMMurq6smvqPQnPPPMM5513HkuWLMnc9UceeSRve9vbkFJy3XXX8f3vf59Xv/rVrFmzhmeffZZddtmFM888k9bWVlavXs33vvc9HnroITo6Othiiy0yZSBPqAWycy2XyyilCMMwUxaklKxcuZKLLrqIO++8MxPyBx10EB/96EdZt24dJ598Mp/61Kd4zWtewy233MIpp5zC+9//fr761a/yzDPPcPTRR/P973+fgw46aIhXssCmh0IBGEXIk5HmzJnD7Nmz+fznP88222zDtGnTOOiggzjkkEMya/2rX/0qc+bM4Ytf/CJCCC6//HLOOeccZs6cyaRJk3jta1/LoYceilKKVatW8fWvf52bbrqJd77znbzrXe/i5ptv5nOf+xx77bUX5XKZlStX8re//Y3jjjuON73pTdRqtWzi1+t1jDHcddddnHPOOXzjG99g1113paenh6985Sv89re/5ROf+ATlcnlI3YAJEyZQqVSYP38+kydP5vHHH+fYY49l9uzZ/OY3v6Fer3PHHXcwduxYtt9+e2655RZ+9atf8eUvf5k99tiDRYsWceaZZzJt2jTe97730dXVxcknn0xbWxvgBMr555/PgQceyE477cRhhx3G3XffzYUXXohSis7OTu655x5WrlzJrFmzOOaYY3j88cf54he/yIMPPshBBx3EddddxxVXXMFZZ53FjBkzWL58OR/72McYN24cp5xyCi0tLfT19bHVVltx4okn0tbW9hwSmrdO8y5ib20/37VWStFoNPje977H+vXr+Z//+R86Ozu55ZZb+OEPf8huu+3G+PHj0Vrz9NNP841vfIMZM2bw3e9+l69//escd9xxfO1rX2PZsmV88IMf5A9/+APvf//7ieOYpUuXMnbsWN773vdireV73/se3/nOd7jkkkv4xz/+wQ9+8ANOOOEEDjjgAJrNJt/61rf4zne+w49+9COMMdn1/sIXvkBXVxetra3sv//+7L///rS1tWX3369+9StOPfVU3vnOd/LTn/6UE044gXe9612EYciaNWuy+zmfZZLPBhFC8Nhjj3HKKacwe/ZsGo0Gn//85xk3bhxnnXUWtVqNq6++mrPPPpu99tqLSZMm0dfXRxiGlMvlTNG6+eabWbNmDVEUZeGsv//977zpTW/KsjD8fZkkCT/84Q9Zt24d55xzDu3t7Vx22WUsXryYlpYWtNZUq1VWrVrFdtttx6mnnsqCBQs488wzefTRR9lvv/0yJc+TWjs6OjjuuOPo7OxESskTTzzBmWeeyezZs9l1110BWL16Ndtttx1HHHEETzzxBGeccQb33Xcfr3vd6/j1r3/N3/72N7773e+yxRZbcNddd3HLLbdk55NXBPzcyqeO+nkaRRFz587luuuu4/TTT2fOnDnMnTuXb37zm+yyyy4cdNBBTJ8+nTvuuIMDDzyQ+fPnM3PmTP785z/zla98hfvvvx8pJXvuuSdA5nEcXj+kwKaB4oqMEvgJ7T0A48aN49xzz+Vb3/oWs2fPZt68eZx22ml86lOfoqenh0cffZSnnnqKI444gnK5TFtbG3vttRerVq1i5cqVCCGYP38+n//85znqqKM46aSTuOeee7jnnnsol8tUKhUAWltb6erqolqtUq1WAcdMnzhxIh0dHdkCWyqVMktm6tSp7LHHHpmbc6+99uLuu+/O3Kn5POstt9ySmTNnctddd7Fy5Ur6+/vZYost2Gmnneju7mbZsmXccMMNTJ8+nS233JLLLruMHXfckT333JP+/n6mTZvG/vvvzz333MPAwADWWn73u9/xwQ9+kKOPPpqzzz6bv//97yxfvpy2tjaCIKBSqTB+/HjGjh2bLYxbbbUVBx98MBMnTmTOnDlsv/32PP7449Trde655x722WcfZs6cSZIkbL311uy7777ccccdRFFEf38/EydO5IgjjmD69OnZdn0aWhiGrFu3jqVLl7J8+XLWrFnzL3OzfYimr6+PP/7xjxx88MFMmTKFarXKHnvsQblc5u9//3vmSTnggAPYe++9GT9+PAcffDCNRoNjjz2Wrbbair322ovtt9+ehQsXZl6JqVOncthhhzFr1iy22WYbDj30UB544AF6e3u5++67Mcaw995709LSQmdnJ8cccwwPPfQQK1asoFar0dHRwetf/3q23XZbJk+eTLVaZfHixZxxxhm89a1v5bjjjuPuu+/myiuvxFpLW1sblUqFCRMm0NnZmYVP/L2QT2Mb7tY+9NBDOeCAA+jo6CAMQ+bPn88hhxxCR0cHra2t7LvvvvT09DB//nystVSr1SwkorWmVCqx8847c9xxx/H2t7+dE044gbe85S1MmzYtC515pSOOY1auXMkjjzzCu971LnbYYQemTp3KCSecQLVazcirjUaDbbbZhkMOOYStttqKHXfckSlTpvDwww9nSkReIJfLZW6//Xbe/e538453vINPfvKTLF26lGeffZYwDOnr62PGjBm84Q1vYMstt2T33Xdn6tSpPPnkk3R3d/P3v/+dN77xjey9995MmTKFY445hpaWliy84MdqeGEgf4/5z4UQXHrppey7774cdthhTJs2jSOPPJI5c+ZwzTXXZF65v/zlL6xZs4bHH3+cE088kf7+fh5//HHmzp3LDjvskCm5+XBjgU0PhQdglMALFB/fC4KAtrY2jjzySN74xjdireXaa6/l4x//OIcccggA3d3dfPGLX8xcglJKKpUK5XKZxx9/nK997Wu86U1v4hOf+ATjx4/njDPOyFKZvEUURVFmGfk0QB/nN8YMEXIDAwOsXbuWf/zjH5xwwgmZq7VcLjN79uwh5+MXjPb2dmbPns2NN97Ia1/7Wtra2pgyZQphGNLe3s4999yTKTKe0zB37lze/e53Z9uQUvLqV7+aRqPBj3/8Y+bOncsnP/lJJkyYkLn7/SLlFRsPf04+ppkPUfT19TEwMMD69euZN28ejzzySHYNtNa86lWvyvgPY8aMYezYsdkCnC/OVKlUuOqqq7j66qvRWrPvvvty2mmnEYbhCxK0qtUqa9euZf369Vx44YVccsklmUvXWktraytSSvr6+th+++2z2LK/VhMnTsyEUaVSydzd/pjK5TJxHGfxeh9SsdayaNEiPvrRj2budK01O+64I/V6nXXr1lGr1Zg2bVpmZT/++OP8z//8D6973ev41Kc+RVdXF+eeey6PPPJIRrrTWmekOW+5es9Is9kckh3i7xEhREbOC8MwE0Rf//rXM4XOWpvFp/MEVE8C7OnpYdasWbzmNa/JrvmaNWv41a9+RRRFBEFAqVTKhObAwAD1ep2tttqKgYEBWltbKZVKTJgwIZtLQRDQ0dFBpVLBGENbWxulUinjA+TTZ621nHfeeVxxxRX8z//8DzNnzqS3t5dTTjmFNWvWIISgvb2dtra2bG55D8a6desy1r8PsdVqNaSUTJo0KeO+bMgS968918FX0kySJONc+LBha2srTz/9NHEcs9tuu/HjH/+Y+fPns27dOnbaaSd22WUXrrvuOh5++GHe/va3P6daYmH9b5ooFIBRBD+R/cQePul33313tNb09vay1157MXbsWD7/+c8zZ86cbPKDI7j97W9/o1wu87a3vY1Zs2axevVq6vX6kLxzz7j3rz3Rz3MGvJCTUjIwMEC1WmXrrbdm22235Wc/+1lmzdXr9Yw8lSdkeVflzjvvzM9+9jNuvfXWzDJPkoQJEyZw3XXX0dPTw1577YW1ll122YWuri5+8IMfDHGhe2/E/PnzOeCAAzj44INJkoQlS5awbt26IZUNfcEX76L1Y5nPo/bn397ezpZbbkkYhnz729/OlB6tNeVyOWN+e3KX31ZeUICL2b/5zW8mjmM6OzsJw/AF0yV9MZcJEyYwceJEjj/+eN761rcOIbaNGzeOVatWZePoY7xJkmT1Evx3vaAEKJVKrFixgqeffppXvepVRFHEwoULs5DI2LFj2XrrrfnRj35ES0tLJuTDMKS1tZX+/v4hVR89ETGOY9761rey3XbbDSFqJklCa2trVojH30t+7FatWpUpkE899VTmwh8uZKy1tLS0sO222/Lxj3+c17/+9UN4FuPGjRtSzMkLdP/IF7Ty19fDV1/0Ya3Ozk7uvfde9tlnH+I4ZsWKFSxbtgxrbaYU+31EUZRZ33lOh/+eUop58+Zx4IEHcuihh2KMYfHixTz99NPZ/TcwMJBdd69EANn4jxkzJlNAtdZ0d3ezZMkSdt9992ys/HrgxyrvUckTBLfddlsWLVpEd3c348aNY/Xq1SxevJgZM2aglGLXXXdl7NixnH/++YwZM4Zp06ax5557ctlllxEEAbvtttuQMJ6/vwpseiiuyijBcAbzn/70J373u9+x7777su2227JixQp++ctfssUWW/Da176WiRMnctRRR3Huuefy/ve/n3HjxrFu3TpWr17N0UcfzZgxY6jX6/z2t79ljz324M9//jN33313ZmlPnjyZlpYWrr32WlpbWxk3bhxtbW1MnjyZuXPnMmvWLMaPH8+sWbMy60IIwQknnMBf/vIXvv/973PEEUcQBAFPPvkkHR0dHHHEEVkKX76O+Zw5c6hUKtx444189rOfzRatXXbZhV/84hdMnjyZHXbYgSAIeN/73sdJJ53E+eefzxve8Aa01jz22GPMnj2b/fffnx133JGbb745ixf/+Mc/pr+/P1ust9hiC1asWMGNN97I1ltvzfTp0zOhMbwCnHfdHn300Xzuc5/jJz/5Cfvvvz9CCJ544gm23HJL3vjGNz4nbz9fXMgv2O3t7UMUDmBIRbr8dc5/NmbMGE455RQuueQSwjBkzpw5LF++nIULF3LEEUfQ1dWVjanPX/cC0Wc0lEol+vr6GDduXJb+2Nvbyy9/+ctsXH75y19yzDHHUKlUeM1rXsO1117LN7/5TY4//njK5TLPPvssy5YtyzwXxphsv57LIaXkqquu4sADD+S2227j4Ycfpr29PVNSJ06cyG233cZ2223H5MmTGTNmDLvssgu//vWvaWtrY+3atdx0002Z6z7P6gcyS/m4447jl7/8JVprtttuO5YvX84DDzzAZz7zmcyj4n+ntc6yG3z2ibeY8wWt/O+azSZTp07liCOO4Gc/+xmdnZ1MnjyZn//859lxVKvVbPveq9bb25tZ0/7+9spJkiTssssuXHnlldx4441orfnJT35CuVzOQmeeW5AkCdVqNVMoAdra2jjkkEM488wz+fa3v80BBxzAZZddlmXZ5DMfvBIgpaSnp4cHHnggy+QAmDx5MkceeSQnn3wy55xzDq9+9av561//ysKFC/nyl7+chVB22GEHrr76aj72sY/R2trKa17zGr761a9mygEwpF6G32+BTQuFAjCK4C0Yay2zZs1izpw53HLLLZlreK+99uIb3/gGW221FcYYzjjjDK688kquvfZali9fnsV9W1tbGTt2LN/85jf5f//v/3H77bfzlre8hY985COAW2i7uro444wzuPTSS/nEJz7B0Ucfzcknn8wnP/lJfvzjH/PFL36RPffck7PPPpvZs2czZcoUkiRh+vTpXHLJJVx00UWce+65aK2ZOnUq733ve4eULi2Xy9l5bbnllhx++OEsXLiQffbZh4GBAWq1GjvvvDMHHnggc+bMySyNnXfemYsvvpiLLrqI7373u0RRxB577MH+++9PqVTixBNPxBjDBRdcwMSJEznllFP47W9/mxV1ectb3sLixYs5++yzaWtr46yzzsrCEPkYqidWRlHEvvvuywUXXMBll13GWWedRblcZscdd+SAAw4gjmMmTJjAjjvuOOT3w8l++feGL5T5ug1eAOy2225UKhWstbznPe9h+vTpXH755Vx55ZWMHTuWV73qVZknYcstt2TKlCnAoGt65513zuLsANtvvz3jx49HSklvby/bbrsthx9+ONdddx29vb0ce+yxHH/88dn1OO+887j88ss5++yzM87A0UcfDbhsg1133TWzlqWUbL/99nzzm9/k/PPP59577+Wwww7jhBNO4KmnnsoIjaeffjo/+tGP+OxnP5uFCk4++WR+8pOfcMEFF7Dzzjtz/PHHc/3112ex7T322CMLrfiUvw9/+MNMnz6dq6++mksvvZQJEybwute9Lgtb5S3zUqnE1ltvzeTJkzPlKwgCyuUys2bNytJYq9UqO++8c5Zy+JGPfIRyucxvfvObzFO2ZMkS2traiOOYyZMnM3PmzEzBE0Kw/fbbZ4rQ8Ot+2mmnYYzh3HPPZfLkyXzyk5/kiiuuYPz48QghqNVq7LTTTkMs6RkzZmRFkw4//HDCMOT8889n7ty5vPWtb6VWqzFjxowhPIB85sP06dO59tprh3hEXvOa1/DFL36RCy+8kJ/+9KecffbZzJo1ix/84AfssMMO2b6POeYYnn32Wfbbbz+CIGDSpEkceuih7LfffpmyD89toFVg04KwBTtjVCDvls6HATZUtc67A/NpZl6A5l/nrRhfy98vojC0br9/nici+nKk3sU/vJa+t4B9nNnvY0MLht/u8PKlPoTgY/e+L0D+vP3+vMvXE7B8OGNDMUrPsPdx4mazSbVaHeJK9dv0ee3+nL1l6i09j+H8gn/3uuZd3X4s/LXxFiQMlgXOKxD5sc5fTx+SyZPpvFU6f/58jjvuOH7+85+z8847DxmbRqNBtVpFCDGEGe9rLnhBnB+HfCGa4ecQx3EWX/eEvLwFnu9fkb8v8iEZ/5u8ezufEeOvZ/6+8fdHfrv+GLwXIL8dLzS9l2DdunU88cQTjB8/nnq9zrXXXssf//hHLrroIqZNmzYk3JLn5sBgGl5eSOYzQjw7P+/h8O/7fhreU5MX6P78/XUZPidhUMn0Y5dXKLyHyM9Ffy39Nfb3iJ9Tfgx9SCnv7vfHnL93CkVg00PhARhlyKfz+Nd5xm+e7Zt3C3phnK8Y5hfB4Qvp8EXRL9h+cfKlgn3ZXF88yC84wxcy2LC7ezh8TfM8P8DH2j28MMnHc/MxSC+YPFehVqsxMDCQ5dj74/Cks7xwADKBlVcifKzaN0by+wCGEOlerDW0IeUkf23yi71XSIZXnfOCzRM4ff+EvAKWDxN413OeG2CtzSxgHz/e0H3lyZR5oe+PMc8L8e7kPC8iz/jPj9WGBLoXTH4M8kItL6zz28nH+32BKy+cPcHTK1E9PT1ZhUW/7Xyvix/84AesWrUqKwv8mc98hsmTJ2fb8Ofh59bwnPz8/eDvm/zY+HPKj58X8PmeH37b3qORb2Dkz8V/lh/LcrmcXSO/b6+c+HssH/bKK3Ne8fJj6JXgvMejIP5t+ig8AKMEwz0Aw62lPNHnhbZhjMkEiBdw/v38Ip235vxf351vOHktbz0BQ3Le/bF6pcEL4uHwi45f8GBQmPvF0y/U3iKB5wrP4ZadV0Lyedn58csv4n5c/DnkraH8Au2vhScT+vDC83k3Xuh6bOh13n2dF8D5xjNeOfJj4M/BezK8cPBCsr+/P+uh0N/fj9Y6E355S91vb/gY+YcXBPlx88gLk7zHIW+FDgwMZKGJ4VZq/h73x+3hxz7/PH9v5Xkofht5q9r/7oWUxnq9nhEefefIgYGB7Dh8Dn9eGckr38Ovf34O5RVtf25eWclnDAy3/vMeP7+d/PXxCl8+0yc/jsMtda9U5PeZH/v8OuLvC38ez7fGDFdSC2w6KBSAUYL8ZcxP8LzQhkGegBfiXlvPWyT+uV8ch8cP6/V6VhLVLxjD833z28u76721nbfgh1dm29BC4dPC8mGE4WGN4VZP/niGhwK8hZ8//7yVn3ene2smTwzzx5J3z+bHt16vZ9ayX8g9eXFjrmle0ObPJ08sHM6Gzy/QHkK4Er2+VLL3YnhB4t3h/jr5c8qzuf128tsc7kL3x5N3sQ8XGnmXtFdc8gqVx4aIY/nwhdY6s0Dzx+SFvt933v2ft77z3hqvnOYt/HyIIC9o89/NK5/5fPv877xy6e/d4eeUHxuvQHmhmb8HvFKRVw7yRX78fvKdI/12h8f//bz1SiQwxLOQv07DlWi/Xz+X80qvH5fh51cI/00ThY9mFGG4Luddmi/0/bwg8xPdu+WHb9cvKD69zbOah2v3eWtiOAPeu57953mX4gvBW5UbOle/GPr3/Dbz1ufw7+cFWv44vWvTL+DDCxP5bflz8sJguIDMNyTyeeQvBvmxe74QQp7jMdw6958DQ3LIh7t282Phr2meG+DPNe+yHn7OeUEwvH1xfgyHX0fvoh6uwPht54WvTcMG/rdekclv12/Dj3lemRl+/P6+8kItf5399/LKs0feO5C/V/LIn6/fzgsJwrzHbbiilw8D5N8DsnCN/32eH+HDOPnQnt9G3lPjt5OHv0+Ge1T8uXhOS16Z25Dw956CApseCg9ACr8IDHfLeY3cT7r8ZBqtGL4IP5/geb73N3b7/w38p9vc0DlDQVyCoTXz/xvjsaHt/Cfb/m8c1wvd8y+0/ZfiXn458HIf96Y6ThvyluU/83LBc5o2xXP4T1AoACnyFop39y5YsIAHH3yQIAiylCZfLS1fUaxAgQIFCows5L2XeU9lHMfMmjWL7bffHq11VlJ5eNhxNKDIAmBQ+8vHl+M45vbbb+cPf/gDu+yyS8Yu97Gx4a7CAgUKFCgwcpDnHPkQkbWWBQsWMH36dM4444yMx+O/P9pQKADDMDyWevDBB2fFY3xcdDhZqUCBAgUKjA7ceOONLFiw4Dk1RvJZNqMFhQLwPMiT43zs3198T44pUKBAgQIjE8PJqD6jQ2udlVH2WQ757IrRhEKKseE0lTzjdUMM3AIFChQoMHKRX/eHZ57k6yDk029HGwoPQIrhzH4hBNpqtNUYaTBCYK0GKTGkKU8Y4iQGa1FSIoWgtJHcQMEAoBnUxUT6XIEQWCQWgUUAApMjrvhua94jkS+Gk89F3hS9FfnUKX+s/+3sCp/F4bX4fDGjfLU+n+L0fOPkj8vn8ucJo/4znx0yPKWywMsAawE3Rw0CaQXSghVuZiFAGRDWPTcChHAzrcDzw43qIJ7vjrbpAwZXr5GC/Jrj1wBf6CmfejraXP8ehQKQYniail/AjTWQil+LBSFY072W7u7uLOdWWouUTkuMmhs3pIp+hNBucUqFvDugVDNFDplcxlqCsJTlTvtStl6w+Wp1+eI/+Xr0mwJ8fQJfjMSX0PVV1v5b2rYfh+7u7ixXulQqZb0JfG95n0+eb7Oah1IqKxazocpn/nkYhkyaNOm/cuwFNhI2mzLP/15+IqXKQIF/D6N1yJ6v7kTe6vdzfDR6fgsF4AUgLATGEliJEGCRNBpN6ut7Gd/eSalSRgqBQCAEGGMJg42LE/UHCi2kE/0WFE6DltYirUVZb/8bpwoISZToLFvBC/1qtZoV28j3DN/Y8rMvF/L1wr12nS80859CStfVrrW1lYkTJ2YTul6vM2HCBJIkYdKkSZmmb4x53mI9vuCR9xjkK9yBq1LY19dHX1/fqEwVGhEQIhNOIvvPzSWRF/xiZFmorzRyJsmQ9zxs7jujUUEY7SgUgBeAtCCNAJPe5AKUscjE0F6tEZRKZMuOEFhrwPRs1CRo0IrxCoAYnEwCgbACgUVa5wkQDJZH9dayJ6oAWXMP3yDHs1jzpVI3FXirOV/SdHi1v/8E1lra29tpNBrZ+BhjaGtrG8LjyFeDez43n88I8XXY865BGOzyV6/Xn1NMqsBLDyucj0742SjSqACpsLepYJIM+quLy/NvQQz7+3yfF0M6MlEoAC8AC07s+kXDWgIhCIVEqQAQbqHJ7nwBspWNEWEtwmBtEzJVQmaLmd+wFZ4DACiBwmb10L0bPV/i1NfN967tTVEY5TMsvMWcb6v7n8ILea/8eMvc1z9vNBpDGtfkm67k4b0GlUolC1UMb2YEg2WPfYropjjmoxXOPwbKekvUYoRT4LHOA2Cl+44UOY9AgReEsJ4F4I0c/4n3oaQDbLOVyn1JFD6WkYJCAXgBWEALAVK4WKK1GAwJCcZoVKCwqdlucOSiJhtHFqnqtSgbpZMmACSI0D0XIQiFQbntAwJLs15HSsktt9ySxabzXfumTZvGrFmzsg5vmxqiKOKRRx5h+vTptLa2ZgLzxdbL3xCCIOCee+5h+vTptLe3E4YhzWaTxx9/nAkTJjBx4kTOPvts9ttvPw444IDntf611uy99978/Oc/Z88998zIl77u+XAlorD+XxlonAKAAatAp2q4NOBmDUQCAusexSX6d5FTAqxIicmDGBT8/jE6yXKjFYWq9gKwQAIYiQ/Mo4VBC0siLQmWRFgGjCESEAFV26C2EQ8lJEIGCBEghMz9FQiR2v3WIKwlEKBSCzSKIv7v//6PW265hYsuuogTTzyRW2+9lT/+8Y8sWrRoSLvY4R3NPKMdhjLcYZAR7/Ng8008PEEvXwbZf76hyog+vu636yso9vX18dWvfpWHHnooy17Id9Tzxzh82/nt5Y/Jf8/n8Frr2sV++9vf5q677hq8ntZm7wkh+NjHPsb++++fhSLyDVjyDUx8mCXfLc8rDJ5cmG/HWuDlRcZWN4Bx968mVQKMBQPaQhOnBGjNRnnpNldYwCQJ1hissS4byeZT4vIMgWJERyIKD8ALQQi0FCSQ3etGCrSwaXqRoGEMCIlO3WAtduNKBA/IdnQa3/exf0cEFFgDRieUgwCJAWPAOqEThiGnn346WmtuuOEG+vv7+drXvoa1lltvvZUTTzyR/v5+3va2t3HssceSJAmf/vSn2Xvvvbn11lsxxvDhD3+YRqPBT3/6U8aNG8eXv/xlurq6uOGGG7j11luZNGkS999/P7vssguf+9znMqLhz3/+c2699VbiOOazn/0se+21F0mScNJJJ/G6172Om2++mcMPP5wDDjiA8847j/nz57PddtvxwQ9+kG233ZYLLriAu+66iyVLljBlyhS+853vcM011/DGN76R3Xbbjaeffpqzzz6b008/nUajwU9+8hN23HFHbrzxRl772tfyzne+k3POOYf777+fiRMn8pGPfIQddtgh66OeL+uZ74bmlZAkSWg2m5x//vm86lWv4sADD2Tp0qV85Stfobe3l7e85S3MnTs3O15rLffccw/f+c53CIKA008/nZkzZwJDy0fnwxoFXj5kCoD3AOAU94yfY6GuDX95+GGmT5jIDuOKTI1/F0KmVn9q4BtrXRwFn/KXMZYolICRh8ID8C9gpMstdsmAYISbACoISIDIWJ5ZvpxFzy7jsSUruX1lvFGP21bU+fOKfv6yop87lvdx7/Ju5i5fx4PPrGZ9pEGFWGuwcR1MDOjMaq9UKtRqNUqlEkEQIKXkzjvv5Ec/+hEf+tCH+PSnP80111zDJZdcghCCBx54gCuuuIKPfOQjbL/99nziE5/g9ttv55Of/CRRFHHWWWdhjGHVqlX8/ve/p7Ozky9+8Ys8+OCD/OQnP0FrzYUXXshdd93F5z//eU499VS+/OUvc88997BmzRruu+8+7rjjDj796U/z+te/nkajwRve8AZ+9KMfMWnSJL797W+zdu1a3vGOd7D77rvzjW98g3POOYeOjg7mzZvH+vXrM3Lg/PnzqdfrNJtN7rnnHn7/+9/zsY99jIMOOogvfelL9PX1ceaZZ3LggQdyyimnsGzZskwA5/vPL1y4kMcee4z58+czf/78TDkIgoA777yTVatWobXmS1/6EtOnT+czn/kMDz30EH/4wx/o7e2l2WyyfPny7JynTp3KV7/6VdauXVtY+5sQ8goA1ikACYAGm1ieXbuOC6+4kj/dNxddrHr/FhyhUmC1yz6yQtBIDBqBti7sYtJvWgqldySi8AD8C4jcI40CgDFoHROEAb09PXzl698hthIbVni6NJuN4cM2ZRkLBDahZJqUbZ2q7qeVBqd98Hj23WlLBNLRmq1xLk01mGroBZ3P9b/mmmvo6upi3rx5SCmZOHEif/rTn3jPe95DtVrl7W9/O7vtthtdXV1ceeWVvPGNb2TPPffkqaee4pJLLsks56233pr3vve9hGHIxz72MX70ox9xwgkncPnll7P//vtz//33Z9kI//d//8e2224LwIknnsjuu+8OQEtLC08//TS33XYbQgiWLVvGU089xTbbbEOpVKJcLjNjxgyWLVtGpVLJ9p0PMXh3/Mc//nH22Wcf5s2bx3333ccxxxzD3Llz0VrTbDa56667OPbYY4eENpRSXH/99TzyyCMopWg2myxatGhI//dyucyCBQv45z//yf/+7/8yYcIEJk+ezI033pgRBsePH88JJ5yQeTGOO+441q5dS1dX14u8qwr8t2HBzZHUA6ABhePvGA3dAwk9KNZHmoaFEgVr/d+DSJmTktgIVBik3pWc9ShA2g0lDBbY1FEoAP8CwrhBClJjL7AQIqjIgMRY2mutnHzi8TS0ILaKZql1o7Y/IFSabWAJ0ITWULIJgTbMmDo2XdMEVoQEUiIYZM3ni1j4OPTSpUuZMGFC1rRop512YosttshS2LbcckvCMKS9vR0pJV1dXVnLS+8e11ozfvz4rBPWjBkz6O/v55lnnsmIhT5mf8QRR7Dbbru5sQkCJk6cSH9/P2EY8qtf/Yo777yTHXfcEaUUvb291Ot1Go0G5XI5q7oFDHGjx3GM1jqrZdDR0UFLSwvNZpM4jonjGCllxs5/5zvfyT777JO9n3f9n3jiibz5zW8mjmPWr1/P5z//+cyD4iso9vT0oJRi7NixRFFEV1cX5XI5q/0dhiFjx44dUhAkz40o8MrC5p9Yi02tUw2YSGOEorvRxFbbqUtF8xU70pEHIWRKrbBYIdy4agiCtMoiLgAgBRuoxFRgU0ehALwQLCgjHJM4zdFXGoLEYuOEsBxQUSH77D4Hl8hnaYeNUoSNNbnImcj91HEAkjjBlgJEqUySxuHUsOpV3qKVUjJt2jSUUrzzne+kUqmwfv16WlpasqqF1loajUaWNVCr1ZBSUqvV3F7T7T711FOsXr2atrY2brrpJiZOnMjMmTOZMWMGW2+9Nccee2xWPS+fztdsNjOuwK233sphhx3GO97xDh599FFuvvlmwjCkXC5Tr9czAV+tVqlUKqxatYooili5ciWrVq2iXC7TaDSyUrtecQmCgIMOOojtt98epRSNRiPjRfj9a60zBcFv3zd4CoKAnp4eoiii0Wgwbdo0kiRh3rx57LDDDsyfPz8rs+w9BX7MvGKVDzMUeOWReQDSyeTDdrIk3fwNQiIZYlQJW4QA/j0IsK7IgvOqmIQ/3XE/sYb9992V1pbq0NIKRfrfiEOhAPwLCGuzGuI2TSeyQiKFRBhLNRA0EkstEKnbceOaASjbD1aTqhdpOqACoUAqSqWA2PgMHEG+OnfeA+CZ7J/+9Kf56Ec/yne/+13Gjx9Pf38/M2fO5O1vf3v23Wq1yrp164Yw9D15zhW5kfT39/GDc86hUq1w7733ceKJJwJw6qmn8r3vf58VK1ZSS8vpHnjggUzfeiustVndfaUUc+bM4Y477qDRaPDwww/T19dHHMcEQcDs2bO55ZZbeOaZZzjmmGPYf//9+fWvf83ixYtZsWIF69aty7ID8lb3rFmzeMc73sFZZ53Ffvvth7WWnp4ejj/+eKZOnUq5XM727xUH7xkQQmQejvb29ozBv+WWW3LEEUdw1llnsc8++7BgwYIh6ZVaa2q1WhaeqFQqzkNhrSNsWos0GmUNBpcdIiwIDVK4xHMrLBabkqb8zSWygjX5vDSfWmWBxFoC7ZQgIyskCAI00sZYEWKEyz7wC/FocMSaLLdcuBx+4Wv6u84YMknc/BCS2FoiKYgRWBHhomUlSgZK6VzUVhGbgIFmDagh4hhKro7HZgWftu+5kTkC//D7Zkh9f6uRxAzU4SfX3MqAFmw9a2u2bakSARVrwURYEYCQRYrlCEKhALwgLFYYrASRut0TFRJLhZaSQDrGfkvg1m+3mKshdvy/hOz4l18p5VJrTY535gltO++8Mx/60Iew1jJt2jS++93vct9999Hb28uUKVPYc8890VrzwQ9+kG222YY4jmlra+Ozn/0sHR0dBEHAjBkzOOmkk5wioTU7zN6e173uIB577DFOPfVU9t13X1QQsv8Br6atvYMH582j0Wyy3Q47MmObbahVynzqU59i7NixSCmRUvKBD3yAO+64g1WrVvGe97yHgYEBtt56azo6OjjxxBP529/+llnWb3rTmxg3bhzLli1jzz33ZM8992TixIk0m00+8IEPMGnSpKy40Qc+8AHmzJmTkfp22WUXxowZk7H9vTfk+OOPZ8cddxxSJ+G9731vxld473vfy/bbb4+1ltNOO41bb72VdevW8aY3vYl58+bR0dGBMYYvfOELTJw4Ea01nZ2dnHTSSUycOJEBoAJUjCFIEkKboIUmwlBCUvLFGxTYwLhmUn6pFT6K+vz3igYGrKU96cEKTVKawgDQZiIwPVjVTiScFVZKvz8aJrRO884DX1xDQSwgwVAhQeoYbAAyIJKCPgQRYNQAyhgk4+i0BpX0Y4KQqFQmtmWs2ZrQdNJBP5I2NsucdYNTklKlyoUfB2soQP5zV+AkNAnSdqNkGwvVFOKWMay2AVvjUp/DxFCKu9FhJyhFUCgAIwbCFlRmYFCYelhrueiii9Bac9JJJ2WfNZtNli5dytSpU4dUjhvePOKlQt7d748bcs2Lch3pkiSh0Whk1uuGGh550py3zLXW/OryS/n973/PlVdeiVRB5trTaY61VJLEWFe6WLhFQlid7cN328uX18132vJ19/MVC/2xeK6Cf99vz2/Dn7M/P+/Sz59XvvaB5wTk6yD4feR/97e//Q1wfIMLLriAxYsXc9VVV2XFibwS0dfXR7VadamE5TIVIagkGtsYYNnKZbRP3RJbqhJaQVkLRNOgMSRli1KDXBLn6ZHPKawCgypBYqHXGsYka7BxnUZtKnUE7bZBoHswqo1IOgUgxLHeR4NdG+MS9QMjEUZgFTQFxEJTsTGBBhELrJDUywGPrO6FUgu7tfQQNBOS6nikSZBJH7YU0hBlbp7fzxd/eTdH77klXz1iMuWww3kRNiek7EjrBbwvLppWSxTe7Jcu28kA/U1oD2NEczWrdTuvPuPXJNUufnTyAUyqVSnVymxVllTitRjVDkGIGkE34PB1H+Dqq6/mscce43Of+1y29sBLu66/UiiCNiMQXlh617YXaP5m9jdqEAQZuU8IQb1ez4r+eFa8d6/7PgLlcpm2tjb2339/jLEkWeOh1GhNqx52d/fw7LPL0/dtVjI373L3JDshRBbL98fluxb68/GC2Xc1HBgYGFJzP98oyMfyfZnffKGgKIqG5OP39/cPKdkLDCEL1uv1jPx47rnnctppp9HS0sLFF188ZPKDU0BaW1sxxrisBSFy3p5Ba14CWhvnEgoEsqJIZEBCkLquFXkxLYY98u+H/o2wgsZZXMa7WhmdE9g1wTZDCsy5AJlAa5tasQpUQGQsv7jiN/z25nvo7ougVEELxwEgyAl44XMD8s7tAg425U8MDrhIH7YMsXAqpkEgVInECFb3Gb79/R/x6+vvpi8CohhrdDGyIwyjwWO4WcELsrzW6q3+vLXthaWvce8FZt4CLpfLWfqgZ8UbYzjmzW9JLehBQaqNxlpItCEs5a1q0NaghKBWqw0hyOVL5uYVEWNMJlzz3gy/zSRJaGtrY2BgIPue5zjkPS3DKxz6fXmPQrPZzLwf+fi/36Z/niQJBxxwAPvuu292LJ5/4PfrvRc+zKC1RipFviSKzV6l5pQFo11p2n4D1UBQHhbrfz6vkV+KtU2/aexgbruLhDOocOQrs418+O6XwsdPgLzgtkIgjMQgiKxh/pOLmWA6qdenYltb0QKUcGNGyr0AgxA+JrMZI09BSe9YL+wz5JT9fivAKkJVRhuBDEJQAQSwvq/Juv4mTQOUymgUmKF6V4FNG6PRgBjV8AKv2WxmrzMhnYYD8sLEC7BGo5HVrPfKgreq88VznLCUBMGg4G00nUteG5u5/qVSGGvRxmaEubwg98J/uHvN79srAt4LkD+P4V0M8+eVt+Q918Bb/J6k55/795vNZibMvdvfH5vvQuh/k1emvDLhv+/LA5fLZXespMI+UwDcuQosNm645xISAb0J9CeGWFu0J1/6E7FeMLkqNnkrKhCOdGpliR4Nq/stDTNo+wu863b0lGLxCkBW1ccOLlRSSYR0HpQk/YaqtCIrLSRCggxIrGv+I/y4ZoNk0uvly3ptphAwaOnn7738PejGvU/D6n4wYYVYCGJtkTJgzTqNDco0jKIeG5AKoURBABxhKBSAlwDeIv1PHxuCd3d7IeSFuBdY+Zr6efj4fp63EIbhkLa23jXu6vO772pjCUPHcmxETdavX8+69X3EceKWj9zx5OPq3lKGoda0T/3zAtp7IPJWfZIkGUM/764fXvcg/9d7A7wy4rkDURRlrZE9fLaD37/fT55rkFcqvGLhKy7W63V3LM7RnNKpFUY4q1xgsDpxQkcJVvdqvnXu/+Oy3/6BvkZMrHOV6+D5DXcBeM8LimtunMvZP/4di5etBxG4/OucFTe64FP68i571yfT9f91WoEW0NdMWF+PiFFYEdBMQFuBDRTevW3SEt2bfaq6F/6ZEjBcGXLcCoMkQfLV8//Iz6+5hZXdDUAigxKqXCGsljCyRGQkWgisSd1dhUQZUSgu13+Il7v2e97K9n+TJCFJkqwtsBeieTJc/q9XEBqNRmYx+3PxMKm731pLM4pIjCGKEuqNJolO0n0YZCBJEve9PAvf1+Uf7uL3XojhDy9sgyAYQs7z1rkPKXgSoT9ef87eA+L37xUbfwz58fPb93wBH5LIFyXKj50fX1+syDdZUpnr1FvkTskQGFpqZYR13IlS1fLE0jU8u7qHgXqfq07HMGd0FoNNX6YPIRVSSLSRzH9qGYueXUM9aaY8gtEp/NOgyDB3tXHKjnDv20BkTPagVEOFFRAhxljKIciU0SZySqgQgzntBXyIJX//OOGvrSC2zsPyxKoBnli2jpXdfRgsOtEooUgSQTMRJCKkaQyEQeH7H4EoFID/EHlrPe/G/m88NgRvvVrrOvbl3ezeA5DPBPBubS9Ih/ewzwtTv53evn7CUBHFMYnWVKoVQFBvNJyHQCnCcsX1Q4jd7/v7+zPBn7e2vUchjuMhHff837zg9e/nBTOQER431C7Yj5MvSJT3KOTJg8NDFPnwx8DAwJBxyI+Ff9+fQ/53WQV0AQjvDxDpwqpBGKxwcfuGrBCJEhqBEc+xuRhM0h4aHvCsAkNAJCs0ZYUY356SXM92M2robS7bXw3hOXhsaFbYwSsx9BsWMJZACCqlMkK4MJZT1DZ3V4Ad9hd8emqkLYkU9BtoBC2sj6A/0hjcvBCAse4axVakPBUGPWIFRgwKEuCLxIYE9H87o3JD+/BMeWMM/f39DAwMZHXpffqcr/w3GNMXmWDOt/31xD+fDeCVhlq1Sk9vH61trVgE/QMNqtVKWk0voa+/npHiYmMJjM5K+3ph7fPuvTUOg96LRqNBvV4nDENaW1uz8IT/vbewvXAWwjU+8ufvrX3vmo+iKCtAlA8B9Pb2UiqVMqvdZx7kswSSJKFWq2VjPTy1MM8jKJVKQ9MWSZzwtmCtwAqFFQLp7fu0ymOMoCnLrhIdg42lBkX9hmMBg3eTs8ZiUSKSZcfKFgps8pxvjgZYBBpF4BUA4X0CmW9gyBnb9Cu+cJDEvzagXKfOKEoA5fgDm23FOj9q+TvQkmePGAF1I6g3NA8+vpg+E7C2HtMzUGciOL6KtY7cKiVRYohNyq/YyE6oBV55bK4z4b+KfxW3fyn219fXx/ve9z7e//738+EPf5hddtmFk08+mRNPPJFLL700E1qe7Oetby/4fZqeF7heGBpjQAqqLa3EiUYIKJdLNJrOwu7tH6BcLlOrVTEIyiWVCWXvkfD7GF5jwI+PP/ZHHnlkiFcgX2vBEx1rtVpGBvTER611plR4D8jAwABa64znIKXkfe97H7/97W+HlPL1x5NXTvy+8q2DjTHU6/VsTHzJZE8oTJIkJe95ZrpTAgbjqqn3AhenjmWZRIR5atuGrmz2vw8sDHoARLoN50Xw1vFzGNyjAAaJRg7qQ0NOz4/vIKR1D2Fd+qDzj6Tpgsa9DlSItYI4MdjNuRawHSr8HQQIx2FJECRK8MQzy7j6hj/SpwV1AwOR630hrCaQAq1BCEWcheQswiaFB2CEofAA/IcYHsN+qfkAnujW0tLClVdeiVKKq666iiuuuIIrrriCKIpYsGABZ599NgMDA+y9994ceOCBVCoVfvjDH7LLLrtwxx13IITg2GOPpV6v87vf/Y5JkyZxzDHHMG7cOP52z73885+P0dLawsMP/4MddtyJY455E3GikVJx6x//xLyH5mGF4C1vfhP77DGH3t5eLrroIvbYYw/uvPNOXve61zFp0iRuuOEG1q1bx/jx4znyyCOZMmUKt956K48++ijf//732XbbbfnQhz7EX//6V/bee29mzpxJT08PV1xxBe9617vo7u7mhhtuYKuttuKuu+5izpw5vP71r+eGG27ggQceoK2tjTe/+c1Mnz59SDljr1i0tbVlXAB/vZ566imuvfZa1q1bx2677cahhx5KkiT85je/YY899mD69On87Gc/o6WlhaOPPpr169fzpz/9icMPP5xx48YBaQERobM11GXrOYKa41eZzHI1FhIRoAmGUtqGm7Ibut7p/y6ooEhEkHHZh4cNRsvqm6k0ueIIw3WAjNifviHTHHbvARiS1WZMmtqmcMWXXrgC46iGsMPunjRd2Fq0lSS4ZqPr++us7hkgUQE2KNHUPj/FECiZK8alHcHSms0+w3IkYjNWhf8T+JXbTQirY0zcxMQNkmb/xj0auUf2/gAmbmJNamXaQes9H2dvaWlBKUVnZ2fmJl+6dClf+tKXGDt2LLNnz+aCCy7gjjvuQErJ5Zdfzre//W223XZbnnnmGT70oQ9xzTXXsMMOO3DzzTdz5ZVXAjB//gK+9c1vsmDBE8yatR0XX3wx11x9DQB/vf12Lr74YmbOmkVrawvf+vZ3+Mcj/0RrzQ9/+EMuvvhidt55Z1paWqnX67S1tbHrrruybNkyfvCDH9Db28uMGTMYN24c++yzD/vttx9CCG688UaWLl2aWdnXXnstAwMDrFixgssvv5wf/vCH7LbbbkyePJmf/vSn3HLLLeyxxx60tLTwyU9+Movj+2yIfDqhF/5CCJYuXcrXvvY1uru72W677bj88su57rrrsNZy11138Y9//IO+vj5+8pOfcMMNN9Db28s999zDXXfdRWur6/SYcSieQ8EbbrJan1CVMqsHkXcWef5fphxYz1wfrh1kJXKG7WHYbkc6rBc2w1gN1rq5IGwmwryCJH0oBuv6d+DmTJQYnn5mGY8/8ST1egOVtrFz+oIbZ/fHP0/nmv/nPXsvk3fvpcegxjqofwp8178kPd96M8bIkovvC0UUaayxmCTtr6BdWWujjcsA8DUW0nEdMobkbm6bv9+H3L0FXgEUHoAXgABUGtdKcIzjhiijCSAZgGgNcfcTPHrpOYwRPYRJD13xsxu1D6WTzN9rZUgiW+hTnfQGY9jizSdQmbIzqC6MaicSLQSynBHrgCxW7Ul25513HnPmzGGnnXbCWsuuu+7KZZddxqGHHopSimOOOYZjjz2WPfbYgyOPPJJDDjmEvfbaC6UUl1xyCR/5yEcAy/ixXZz20Q/T1tJCV0vIxb+6mr0PeC3n//QSjjz6TYyfsgUTx49nwT/n8adbbuId7z6e1s6xvOO49/L61x1EmKb8Vao1enu6Abj44otZsmQxM7eZwYQJE9h55505+OCDWbFixZB6APlsBh+mOPXUUznwwAN58sknufrqq/nCF75AV1cXW2yxBVdddRW33347Rx99NMCQsIInDvqQwYIFC4iiiJNOOokxY8bQ2trKb37zGw477DBmzpzJ4sWLWbZsGcYYOjs7WbFiBffddx877bRTForwnh5hA6yQxAFgmlTtGqqmhYg2FzqRESU7QJUSVVEhSSC0CR0JxAr6BNQQlOIAAktTGEKbIGODkmACQUxIWViUsJTtABXTTwmT5hsEOEKbUwlGi11btq52QiKahCIEJIKIkhVYW6FXQkX0U4oiyqqdlaWpjLeGXgLqQUjJNJBmJVqXuL9/LN+6cQlPLnqa1rCE0n3osESdhGqyFkwb3UELlURTiZ6FcpMonECCJMAiaSUwqahUI5vlbtP7RMQhVkGkrOtcYqAuIFaWVgZoTwQtpsSy8jS6+noIjWZVvwsFVAKQpkHDWvqDCi3K0GIThImJgg4waS6MTDDodGkLUAzpH4yREKcaQWVz7MmwiaBQAF4AbjFNCS5p3XanOiuEqiCookqdTNzt1YS2Fxv3MRB2bdQ+SmbApYsJx/RORAkry9SCNlTnTthwMka2EskaMQojBBWGlr/1MXEpJc8++yyPPvoozWYzi2vvueeeGXt92rRpgOsIWK1WmTx5MuVyOWu9G8cxAwMDTJ4ymba2VoS17LHH7pz74//H6tWr6Vu9mvvue4AnFzyO7u9h/NhxbLXV1sRRTBgEzJq1LRaBsYbrr/89119/Pa0tNfr7+1i7di19fX0I6cIllUoli/fnyYk+Y8G/bm9vZ8KECQCsW7eOer3OVVddRUtLCwB77703EyZMGJLqmM88gMH+AevXr2fMmDHUajVqtRrjx4+nt7cXgFmzZnHdddehlOLwww8HYNGiRcydO5fDDz884xcMhnnsoKvaHWxmkfpKgBk3XbjYtvHnlUnq3LaEs/CFkFnv9eESXTzvi9GDFyqLkBaozZ6RPkMFJEKR4LgAobbIsEykNf31BnGSIKSr3CiFpGkSqlIQWxiQYKWgIgOcMlVOkzoto8pJmoaoBgNL3p8kUQgS4Yl87sw1PmsGhl4Vi/FXwXtPsk88JD4m8MIclcID8EqiUAD+BYTQCDQClVlYCYJGIqiG7ajWqUze/xiggUVjStuxMStznPY+801cMmqXthCWiUVAglvYEiEyV6S3lH0de09mmzp1Ku3t7Zx++ukZuc0z6EulErVajXq9nhXB8ZaxJwK6fgAV1q5Zy8DAAC21Go89+hgtLTU6x4yhOmYMBx9yMIe+/rUo3aSzpUxrS42+eiM7rjBUxM0mV111Fa95zYG88x3vYOnSZ/nEJz5B1Izceab7D4KAMAxpaWnJ0uzWrl3L2rVrM6Z+nrnf0tJCqVTi05/+NJMnTyYMQxqNBtVqNUsn9Mx///BVELXWtLS00Nvbmyk7a9asyZSOqVOnMjAwwHXXXceZZ57JwoULuf3221m3bh277LLLkCJK1tqNLipjkSmBL723GL44pmGC9EaIEagisPovYS1EGoyU9CQwJihhbQmTJMRa02w2sAgS7Tk6lkCFYBVNJViJpVVJOoIKghBBGWVBDXH7jw5ty6bter3wFzYBWyYQkORCLgZBkjaZNjiOQJ7o7L1sJuVaOuNI5fga7u6WWISfKMMyNUfHiI5sFArAv4SFtAu705MtUlhUKUDLECFKqLATdBNXb7yFjamH2ScgFq6tcIDr6hbgOsclcUIYKpR15XdLKdM5Lxh9Nb84dizdD3/4w5x22mlceOGF7L777ixatIi2tjbe8pa3oLXOmPW+VG+1WiWKIqrVaiYItU5YtnwZP7nwIrabtS3f+973OeHDpzC2q5N3H/cerv711VQCCVE/Immw6y47MXPWbIzWrFy5iokTJ5E0m0yePJmnnnqKu+++mz/84SbWrVtHWCoRhCETJ07kwQcfpLOzk1mzZrHNNttw6aWXYozhL3/5C6tXrx7SAMjn98+YMYM3vOENfOMb3+D4448nCAKeeOIJjjrqKKZMmQK4xSmKIqy1PPbYY9RqNTo6OhgYGGD69OlorfnpT3/KTjvtxK9+9SsOPfRQqtUq06ZNI4oiHn/8cXbeeWdqtRo/+MEP2GmnnbJQQrPZzKowYp9bcfGFkFYHGKIAOPXLp/Ol5YSFT/vbLBvWbjSEEIiwwqPP9nHr/81ln+2m8prtOggChTAuM0QEIYkhtYI1iXUlgnuF5OLbFjOxZPnE/pOoWKeAK4PrRChHj7TytSuzssokCBsjjEKIgCBNUrUIEgQRISZVC3wtLa8AaA1YgbGuPLhFZV4rnbFjJCKvwA7aL+nL0VO+eqSiUABeABYwwlUcE8JpzIE1CKOJYktYlmhClAxBVsFaKibKsZP/NVpRGCvSpd+XP3E878BGCBsirMVa5cg10sVE8xXvtthiCw477DAAtt9+e772ta9x0003cfHFFzNr1ix22WUXtNYcddRRTJw4EXCpcEcccQTlcjnLKjjooIOyWgIzZ86k1tLCDTfcwPs/8H6OPOoo+rXg0MMORYaSv//976xf/gwHv+5AJk+eDMJy+BFHuGwIo2lta+Hkk0/mV7+6nJtv/gP77bcfs2fPZtzYsSilOP7447n88suZP38+3/72tzn++OO59NJLueGGG5gzZw7vfe976ejoIIoiDjroIDo7OzOPwac+9SmuueYarr/++uycK5VKVh8AXGni17/+9Tz00EM88cQTWGup1WqcdtppnHHGGfz2t7/lpptu4pBDDuGtb30rxhiq1SoHH3wwu+++O+3t7cycOZO3v/3t7L333s/pXPgfZXtYL/z9UptAbtH0917hHP33YCw0NNz1wKM8PG8e9b4BDpi1vytMpxOMTpAyINY6zQCwtMoAmxhWDUT86f4H2XZCG40DplBBIY1Aek1NgpV50ufIhQ+aeINcpnY+NkHYIG0q5c41sTItrWzSlWmQdiqEy6wwuJLLjigoMUIR45kpqc80C4ul9QZyUQhZKACvOAoF4AVgBSRSYlDpzWqQxIQioaoExrj1JEpABK5/eyDCf73hHEqmjrQJQ2iy6cojhXATzUqECki0Rsr0vZQgZ61l7733Zp999sm08/3224999903yxjwef+nnnpqJsDGjRvH17/+9ez1zjvvzG677ZYVGRo/fjwnfvDErOWtViXWr15PtVrlzce8GXn0McikQUetRGdHK5E2fOpTn2JgYMB5GSpltt12Jl/5ypeRqWB2RYxcTYB9992XvffeOytO1NnZyac//ense165SZKEj3zkI0O8HC0tLbz73e/m+OOPz0IDPnzgi/TEcczHP/7xzGIHqNfrtLa2Yq1l++23H1Jd0O/vAx/4QFaJsKOjgy9+8YtZASHPAXi+qoT/CsJaVJqmZq1FCQ02TlOoBFKCTKPPo4XQ97JAuPBKdxN6bJWo1EqCoGwBozFJghUKbY1rrIRFNTVIiY0TVBiSSBeeAZE2V3KbNgI0joSpRoE/ZmjGyLA00uwjt9JpqxDCsf19lor/hmP5u+9q62JWGpEpAODrLYrBfQkxGOKC4h7fBFAoAC8EAYlVJARIJNJqSCJU0kTEdcJSBWskUjorRApoplruv4uqz4kRg21d8JXeCDDCdz0TJKEiBMpqsGhO3hL1lf388+Ed7YZ35sv/zqfOBUFAuVxmzJgxLn9aKZCOeCWETOexQklDcyCi1NnqrFk12EEvDAOU8tPbnZW3oH2XQb8vGGTo58sgD89yILeNfIU+j+GVD/3nlUol21atVhtyvvlt5MsvDxfuXoHwx+nHGMAYR95TUpJojVCB4/8J0lCQIDE+gy3BaO0usQEr0xBCoj231IUAjFO6fN37Ai8MxylRrK8nNMJO+iiTSIUVGnRCHDXQtCBLVbT2pN4QoRUysYhIE6RhNtI22G4qGrSQREJThlGhAAxFbr3RBqsEJtGIRBMlMdoapBVYKYn9PZrOGZP2uUh0mpSqJY8/vZLr/3oP7zz6NUztbHMehzRs6d1eQ/gvniRb3OOvGAoF4AUhsSIkQRKmrNZACUJhUDbC6bcBUkhXZcxohNrIu1nU0P436R9vgDgvpKsdn9XvypEA/+1d/BvfzX/n+Pe8myMOPxQVhAgBcbOJKAeOAKkTpA3TVsBp85xU6GrtMhK0SVuxZ9vNWxj5JeBfH+twBedffW/437zSM1wBeqHtvRCy79q0yUxQAq9A+MUuj5zZ5S+1SysN0ShEKSCV+y64agVSKDbex7CZwlqsiZFhlbis6dGKyADCpiQ0nYbygiz/X1gJtkRJQEdQpdSIqFjXvwECkCZNZQtSGtzIl1K+fiSpkeI8myUQAUZI6mhagypWlIkT6zyQ6a+0sT5b2Sm41mKMpRknxNZgUKxY08PcRx7ngP32YnxbGyUB0kowCUibVcUUkJbEIkuBLvDKoFAAXghCknV7s+CcWjKLZQmr3c1tBUIpysoC8UbtoiFbMQReF89lAkBoUzXACxULQsi0otlLhzhOGDt2LHHUoFQKCUJXfhZvWSOQShKoAKlcTQJtbXpsrie4FPne4PnoaX62j+yZb4VTKoRMrRkXHH3u97K1TpBYp9DFUlBH0N+EcUqRWEFTWCoqcNqTdj0J/JJd4PkhBCibUI8MEWWSoAoqQBATYBFGY5RAp21usZYogNBUMFiUVsgoIrAGbIKVgHKOcBerFiM+/g9eAbAIq9JOiq4vgpSKSAoSAmKrCYyL2Os4QhLi5+kQ1dZaVBAgZOAmQlCmESesH2jS0LiCi7gaAwjp3KOeB5A+k0PjCgVeARQKwAvBGoQxKQvfCcDYOHIMYQWrBELHqX/XxZ+tLG/UTe2WlsEiLtJ6EqBOFQzHq82ay8gyRra8NOebQimJlALpwwm+i55xzXeETpAItDEYnbYXloPERGNcFTuf/iPSTnUOPpPbkbFGMoR2OdMIiKMo83iI/BlbizHOYkqsJBZQt5bF6yy2FZLIElZBKMGyvphWo5lWdWmbPuO9wAtDCagoQ78xaFWlIarUtYLAurmLc1ebjF5reRZD0h0xIGtYyo7TowFpSHDySiEQVhIiXGb8iNcBLMLGYJ0Ro1EY4cZvzUBCv7WUWhUlIcBEYBKsKLnVyFf2s24cfJaNsRCnIUwjwQYholxyyr8GEpN6UyRWMtSXkvEQCrxSKBSAF4IxhElM2abiyoIIypiwhXX9CS0tVRc7x4AwrvKVrWzULkLilAWOayaTFpCxWNdcUwzG0t30US/5OiSlpK+vn9ZaCau1Y0ILg1Iyy8mXEpSPoac0Bt9MRynnCreeMUx+7RxFQk0IAiWJtWVgoO7Ii9YOUXdgMGwTo2ggWbymhzPPvZr+thqNdXV2mTyet77zKK664WbGxH18/p3HUi5ZIunqrhd4YVijsVGdQFWJrWT+whUseLLMtJlll2lhdTp7FCYNAXz3lseIn3mcnXfbl34dIlMvDsL58CQSSUgASJ2Gd0Y4BcB5MxPAhazSXpZEccxPL7+NPro56W1vYEbnGOJmk7ISRFZkoUcnr4dVsbDWyXlt0FYighJGkmsMLPCuwA3O+1GzGIxMFArAC8CJXmeN21RrDkqKSlsHq7r76W5ECGERUqOkxRpNOWlulIBW9LntI9FpRy5DkP5VaeW4wYxZZRuEpuelON0MIihhkphGfzelICBJNA0bsK6e0NtIkCqkpCDqW09FWcJQgQxZt3ZNVmAnqjexNl8JzOaek53PSEZgEii3MRBpdL2HrpYqQqmhGk9GsrYYFE0kQlhW9Cf0orFxQK9oY3kfPL26n75oLaW2AKktUhZFgP4duJiyQeuERBvWdfezcuUqmDEljVk717PJeZz/smAR5pnFVLeYji2VadT7HT8jDdF4hrs0wslMxYhXANzN6NhEgxwjaCQJDzz8GH12LX2H74ftGEOgAqxJcuErBjMBLEglibVGWYMSLjTpsweyZlUC516wQ45gFMz80YMRqwAMFqTQGZvbl4AFV7AlDMPsdf77nk3uv/N83fwMYIXL01cpwUsgGNNWpRI4q1wbjbWuQBAS5MYUAQAEVfy0kCnzWzJIvrFDaLICaSVSvrSXTdgAGUY0rCBSVapGYwPNX/+5gvuf0rSW29AlS3ffat6/Txt7TpA0yorbFvXx0BNLOWzv6bxmdhdBNICxLo6tiFHWkIiAWJYIzMZxJV5yCIEWjoWfCEVoNFXdhxFlupMSY8ImZqCHno7xLGoougSMLxlKQHtJoyqtVKs1tCohEUgdYUyMDkqstwIbVCjpOhJNt1TElRoDtpMo6CGWTRq9CXU7jnotJNGgEkNJuZr4ownPNzvcaZohr/5dJFKxtlyFZi9TrEY1YwwhS1U790SClbV2hByg2uyjarZE6BIi7kKoCZSMpMuuoCeQzNVl2m3INGkoJQ2kcpYyUqfR641L8d3UEKNoygo1o5GJpaoCGgr6BURdNRrrQdkQmWhKKsbQoEI7UWhISGjFIlRAvVzCNhI6rSUKmmBiqEf06wAtBYF26r0RYKXNKhBnJEJIw4MjPww40jFiFQCA/v5+arWa615VrxMEAd3d3axfv55arUZrayuVSoUwDGk2m6xZswZwdfB9WVyfJ+4VBV8z3qfUNY0ZYq0KoBxIym0upcwrFsN72o9oaECGxEKgKVOJLaGKWbq+n7sX9lO1AzTKmrrt4f/ba1va21tRYZml/ZaHl/azTyKotbZQ0xKrSoBCmBisBhkSbYIMd+8ctUATKFtDSUsQVYQNabUDiNaAZwbKfPfnt3HkXttw7AGzaJOGIA3LWFUhTlMlAwEEkj6juHfeItdPPRbIJIGSoplY+rWBsgRp0M2Epi4Rt7Sk1dltWp51lGkAz4sXLwg0MCADahKquoESCi1DbvzbM/zibwtZHcVM6iihojqBNVgtUbqMMRXKIqQliFjcE3DudU8xZuBpvvS2fZlcAxNHyEBBMDoElVNuQxBNhBaEEiKcktkMJLGoYYVCSAVCo21C2WrqxDSTCGksxjpFORAKGWt6+npIdAKqxICGWLum11nuT5o2kC+bLbL/N5d7e9PFiFUAfF14X/ylXC6zcOFCfvrTn/LMM88QRRFdXV189rOfZcqUKVx66aX8+c9/pqenhy233JLTTjuNmTNnZgVe8vnnvrBMf3//kPrv+X3/O++NWGQOh1R1933tjSGOY2xsaRpDrGKEksggzKrw+QY8AkCplOLotuUWhU2TTy1wHl5LmsEA6bELggCwCmKJsZrFS5fx5OKA5FVbo2olhE1/OcTt7zgQRmsefPAhtE4IZLsrJ20spgktrW2YkiVJEhpxREJMIi2JBAJNnOWEjGYM8sKHFqTZmLtEoMIyVgY04yYV6fr43XPv/axckVCttdIc6KMcJy4dUClSEgtxYqjXI7r7Yx58+DE6+xZh37YvCOVIazYNBY6S+e0civmQnMtyKJdb6AtjIkc/BhUSlGsw4GoBJIkjR1rhCiVpaxFhCVWqEUvlCK42VR7IGAL4FsECm1ZTzY6CQgF45THiVxcpZebGHz9+PKeccgrnnXce55xzDlEUcdNNN/HEE09w3XXXccIJJ3DBBRfQ1dXFz3/+cxqNBlLKrAgOkDW88HX2NwTfvS7fdnazQKoghWFIEAauMI4PDpKrUeAn9pCh2fTHyYvb4ZPCWogTt2gGgSCoVJDlUvabLE3Un78kDQ1ZAmlpDAwQhiUEIQkKhCIMWmk2NDp2hWcazTqxidHKkkibstFHBwfA5h5iyCMrTvsfbV8ISawVkQatFJFxFFolJZVyiVBKbJJQrlRABGgraTRjhAiQQUCkDcYIIg2qXCMInMATQiHkYNrvSMcQdVIa8qcVJZaIEk0haeJKKzcjN46BVDTihKa1qKCUdkSx9DZjmjakYSUNC1q5AmbPnfY6d63Tt/NGRoFXDCNWAbDWZvF/L7A7OzvZcsstCcOQVatWEccx48eP54knnmDq1Km86lWvYuzYsRxwwAEsXbqURsN1sPOCXGvNunXrWLlyJWvWrKGvr+8FjyFfPW7UIbOCc69TnkSSJFknQv8lzxSWUiCzQjkbsOg24eGS6SKlLMgc2dniLB8CRYKlv9Egtmn73kzweyXINU5RSqFS5bJSdX0KGgk8NL+bh56KUJV2RFBFqJCwXKIZNdA2wWLSLAKNGSUKwFD4sfKlrXKfCJG2RN64m8QC67v7qUeGUrWFBEtsNa3tLcTNCBJDOSxhLQw0NdoKwkoFbS2xtoggxKqQZmyoNxO0FVgZgFQ0Yp2zlUc+BsvvuvF3PD1BqdxGLCs8sCjiwSeeIUZSClswJkJZWLWmh9V1N3ZSa5fpU6lgS22sqEvueuQplqwayIjLFu84TD071rUazgscK4owwCuNERsCADLL3be11VqzcuVKTj31VJYuXcp2223HXnvtxV/+8hfCMCQMQ6rVahb793XyPflv8eLF/OIXv+C+++4jjmOeeeYZPvGJT/xbx+K3MyqQCjVP3PGvhSArBSrS9B5f61vgKgIak1vY83PbU4gtQ+KBmxRM2ivNr1K5EqZKCTAWKyS1jnYSwQavt+dsCqRb9IRgyy2nIpevZE1/g1//8U7WGEP3wFjiajsm0Qw0GsTNJjaJUTbMZOOIJ50/L+wGXg2mim307WEF3b11pImJdRkjBCIIKIUhlSDESAUx1CONDSUmcKWg61oTa4MVisgIgmoLZqDkfi/DQeKasC4FeIQr+97dD6QeAINEIS1EEXRHlitu+CuLJjYYN3tXmpEhxKCsYOnyNSxanZAYSw3o6+9BByV664Kbbn+C8Kl7WFrekkg7ToEhrWyZm+wSx9eA3B0wsod0xGNEKwB5+CYwEydO5MILL2Tp0qWcf/75XHvttXR1dWWfg7P4fdzfGEMQBFhrmTFjBqeffnomzC+88MKNOoaRvkBkEM66zeqfCUAIrLFYY5AyREiBFJIoisims7XuO/nZ7SkEWYnewXHapEbLDsbwM7sk/c94SpNSJEAjirFSDavVL9KTS9+yFmsNVljGju1CiFUMIBiwsN4KaGklViFKhK71cxQjEkNoJUI7RoKUYLCZpSSFIEniVMkaBbA2zXRxHeUsLubuAm//vjJtAWsEcWxoYlDSXYtyuYzRCSbWCKEoVWtQCokkNKMIKRVBGCCkQmuD1TZNv3UCTFoIQqfIjZb0tUEOgEGkCoACsIqg1sK65gp6tGFiqYSyEiUt0gqaCazst1gRUBWSUikgkZJIhzy7tkFbBHVpUWGVCJGSCwUqSVAqYwKlBdVzBzNKbKaRihEbAoBBQe4bu0RRRBzHjBkzhu22247Zs2ezYMECpk6dytKlS1m8eDHGGJYtW8aECROGMP+9NVcqlTJvwfN1fMuTBUd1GGAY8nPVGufuN9a1RNywRj/SZvfweLTF15N3L32MXzoSmfBshw1cfyHSCopOshmtqVuolyvUyyX6JcRSopXrqQ5gtcFEFt1vQQcoUcoIhdo1ZUOqkewXGDq+rpMcRBoiY1nbHxMJm1mJL7SVIU+s87gY68bHSNDWEISKWrmCsIIkBm0D+oyi1+BCVVJgtKtmp8LQ7VeodP+p0kU630fBHB90uFsQGoRGYQgsoAWRlUSqRFwqkyBQFgwaDIiwSo92pYNDo5E2pmk1yAp1HdAMymgEiSgRh4J+bFohMG2T+nyiZuQP64jGiPYAWGuzDm1aax555BHmzp3LNttswzPPPMPvf/973vKWt7DDDjswfvx4rrjiCqZNm8ZNN93E2972Nrq6ujIPwKixqgq8eIhBiWKz1dJXLnQOVBcNEY7Nv5GbjwVoiWP3W4GRLlMg0ZqB/jrGSErVFkRJgAhILGmhKWeJJUlCoBR2hKaaDiGBAVGsEWFAbOGfC5fylzvu4w2HvYHtJ7VudJqoMs5iT4TFSleN0RpNHMVAiJJlmibgHwtX84vf/B/1AUU55f5Ya7DW9/nIH6E76qHPRzacB8Cfk0ZaSwAIK0lQNJUiSr19ZWNAGQJTRosSfcbxM0o6BhtjBRgREtuQZuD6mTSN5M/3PsPj6kmO2G06u23ZCUmQIwjn84BGz7iOVIzMlSSFd+EnietCV6lUWL9+Pddffz1Llizh05/+NMcffzyTJ0/mtNNOIwxDHn/8cd7xjnfwxje+MdtGgQKDiQsmrQSX4zEMZyxb1/53YxnMwhhEYlEI1wjFOIKUVBIjBH3NiAENDeNyrXuSNIRqnAvaaEOS6BE3aQdTwoa+q3G1F2IBd9w3l9vvn8ddD/zjX3oANrR9af0+nHvbkvajAJQMUEEFLcs8vaqXP979AFHUcAlvRjvWph1KShzMCLFDSJ4jGtlpuLicIEFY5wEQ2mKFIpHSlQGxhtAkCGFRMkCLkF7nIiHQCVY3ANdVMbaSOK1wqUWJP987jxv//Dcenj+fKAFkgNV+RHNpwIKc0l3glcCI9wAkSZL1gp89ezazZ89GCEG9XqdWq9HT04OUkhkzZvC5z32OOI5RSr2otroFRi/cumiwqLQQz4a/5GPBUmy8B0Bqi7IGHQiMsWA1Mq2U1tSaOtAIQ7oldAaCuoJ2LDZJSJKAsOQqW440BeC5oRXn/g/CkH7tvCJNIym1j6M7MmlnjI3ZvEUag8QiBSRpJoWxBiUV1kAzSdCVVoJqG1a1I4VA2lTw+xi/8BaqP0JI6a1OExvhS0Vel7VZFoBBWRDGcU1c63EDxlAyCYkAJRSWgIHEZUQEOkFoQGgEAmMEWkJoDVaWiJXFVhIiJJGFmnCNxXwvQDeH/D1RKACvJEa0AuDd/1JKtNY0m00qlQpxHBOGIX19fdRqtSxVsNFoUK1WM7e/1pooiqjVaq/wmRTYFOCWIzuUpJQ9FxmjWRhQaZPYjYFKDCUMFolOLEYalDAkiaURx4Stbayqx1z75weR65eykoj37zuHXbYeizDKCTNr0CNuzRzKobBicPlPrFMATFBiXX8EpdrGKwCAxDivCo546TrXGZrNCBG2YaXEyhKNJKYvNghpEda4RkFWI7K+FUMfPoXNNeUaBRjCZnR0R2mF806JtHxvmssaWNctQIkAIxSN9MaTVpN2VXIKgHXPlDVpuL9MX2QwMkSVBXEEpSCX/pOG1pzXZeQrViMZI1oBMMZkFrxSilKphDGGUqlEkiS0tLRkaYK+ciAMZgwYYyiXyyRJMjpK+G4Az3W8potwlt83GPf0gs8KEEaC0EjbBDRGQyzKNEWJQKTtPox07YqFBpRjcyeu5r9Eg9VYGw7uxbr3XLPwTWzWW3A9zGRqpTgmvmsOD8K6mCnGlUERMscWz/gBOUd3VutcuHRA0sqAKKSRQExo6whjadgS/QQIAWtWd3Pr2uXE3avok5oJJmb7Ld6IMlDXMXWZoIQF7QRdIl0et9A5GbWpDa0F0Pj+Fs5drxFIaoFlwFiahPSoNlpbalRIUlKfABsDATEKgyAgIbSuu4RVAVpAaGNiyiRCZa7rBGiYgPaWMu21Cmu7+zC2QSwsVDrT7AqngBihsEKl3A7v+nfCyVrpqICb2v36IuDUI5yUFyVAYE2CRhLLCCyUTUCYuLFvKkGARZUEuqERcUJgY7SAyIYEQhHYJsZojKgBgioxdSokqsWRK4FEOk9rQOL2mfFnntfXVuBlwohVAIQQmQcABmvw+/fyVfyGV/Tz5X3zZX613tjI40hGan+lxWx06v/0qVgNASUbUtEJwvYDdSI66KGdPmkIxFpnPSUSa2K0aaBlgBUSJaAsDKFuIojRVNBAydG+wAik2vSaqghsagW6rA6DBFsC4Sx9iQETIUwlTRl0IkwLlw6J9f3mHWxaL00KCSYhQNOvSkSyxRVEkQ1KDCC1oV90IsMWgmQZFVlhmS6T1DoxJmJ1ewfoFkrNhKgsqbdCIC00E0xJUFeCmgVp0jKrm2SSgMA55kNiJBWrkTZCiTKhMAQmoalaWVudQtlGtNkmWlRw9LRerCnTryooBGNsHzVrQFWwJUWiBWVTp0eU0aJMi0mQuknTCrpNmc6aonNcyMD6bgKtGdCSAdVOZC1GSZpWomUZo8pgIRSSENJAkEAjXdrryJf/WIFreJWEyKQMUmNkLwNK0R/2Q1PTnpSpNcrEWrG2UmIyTcIWS9zToN0KQtNHI6jRbzsITExJdNO0EivGY+x6OnQ3URwShR1USyXKAnpCl//fqhsIKzGq9jy8kAIvNwr1azOFYwMPvfxCuG6GqxuatQMaq8oIgyuQEziSj0jLevqtGKGQMnDlWOOYICwRWajHBivDoXnyBJu4K1Vkvl8x9F2HlLfnLP80RSxTHGzmNpb+yznYF1jufGlpUlKrNYNeGl9QRSgw2tKs1x1/IDvOkbuIDqHcpcW4hHBeAm2hEdmUd+HGNhDefz04ymTPRFbGG+FrClhXr0KKtC6DfY53xIUK7OAdPUzSjwbBPwS5zqc61ghRopGOgfcBOh4ASAE2iahVygRSpYMhXIEk434zvJSzTb0mCJkGyRw3Q/lQSlpVg9QbNBrago9kbMqrcYGXHGlMTwyKkXpT890LruXsi66gJxHYUg3iBJUklKwmsDHKuiitFgotQqxwnpQ4jjEyQMsAUWnFUMK3/0AokIHLCx6xEBhD2u9cOoUpTafCJjn3sSM3e7f/4DK54cUuUwAg6zHh329GFqtcnFzIkLIqOyElh4lB8VzhNjIgUoFt03x7iRUhkZCEJeEEiRyk4w2lkA2+P1xQW9JW4UIiU+Fl06yAfAdPawzGKwfW1RGQ7kOXLu8TP0aunjUE2miM0SAFslwiFhUgxBjH/AfQVjrlyRrGd7bS1d6CAHp6+txcFwHaGGwm0N1ApYwCjEy9ZkKA1QTWhVSETcAmgyE2IbGbtEEw+lGM/uYMC9a4piDGuEWut7eftXGJtXFAJCUDkYVKBSFA2SRVADQCg4v0l9BWYazAaEOsLXUZ0A/UjauiZ1OrDkK3oI8YQZUqSP6p9VV6JUYGCISz/q3BLX96aK31TGqIdKF7YQVApAQs48lRCJQqoSXUBWAlJQJnQ3nuC6kSknIUR5Scyg7W9VSQUiKlpGlhAFxr5bS6YkDe2+HvJ5EpBXnL3ded01ojlUQpmeX7Ow9DzmJN3/fXajgXaEixx1EAVzQNiBNiK6gLRRPhuvtZp1wl1t3NSliOOHhPApugBMggABUSa1y4MK3v79Uxx3GRjlMhHTNGWOvuUWsdnyNVMtwvBkNmBV4ZFArA5oycwHACyJIkMT2mCh2T6dYWghCQ6NhgY4Midizg1P2fCIWVIVY4l7iVIcv7Szw7AD2xyia4EWCFa9MqNtlmAEMTpZ4jTq1b+LSVDDY2TV2n1nEqJL6R0ODvPfnt+fzJ3nXtUqoGO0wKIdGiypLemLUJSFVCuiYEGY/ShyEYacKfIfI/68chpMBIWNYHTcBaATp5jtM/HaH0/fx1IxsLrY1L90tb1PrQihuuQX9Cvny1lHJozGcUQRtLrA2YBKSgYS1L+w114+5rkRI1E+HmrcKy69ZdiLhBIAR9/fW07W/qyDdeAXBq8aA/QOY0J5Peo54ym59hG9/4qcB/FyPZH1sgh+eta5CPbzLoQB2+tNlU+xcCdKWDhxY+xQ1//QcnvGEnWpqGMBRUAuOs/4zV7khSJl06SoEiSjTX/elB7v3rHcwZZzjzo++mpNx3DO6GcxkBbGIB1g3RkmxK+LPp4QqMdeecWJUudiK1ONMYp5ckftzTvzbbx3PhFQCb+62/Uk8908+3L7yS1lqNtdE4SjZ0i2ZK9gtyF9eKTWxI/wXE8Gdpie0/3fsYl9w6j2MPfhXH7jUZaTUq+5b3y7sTtuC8MHn579MMrUm3+ZwdDiLfA0IMnz8iu5wjaFiHNKnKn48UEAqg2cQGJeYtXMqPr/0z47aZirZuXlosmsAx+IWmgqWmLFJonlj4JKEVWFFyGRpYhEhnthjsjimEBDHolcnufU/h8Mc5ZGUq8Eqg8ACMInghkrcifZ15Y6wjjzFU0PisCfdXYI2hoSWRrLCub8BlswUljDZ4x7TRcdoREJLE8QFCoDnQhwxK9CQBy/oFPXFAT18ThdP9k/Qv9sVker8M2JD0TLUmk1iQinpTYFWAkZIESKxMvZoCEoPRFqu9UDGug6B1IRaeJ9VUa43WGmOM6zGRpqhKIVnfG/NsX52Hn3mW9vZORGzTroR5RzhpWdaXZlheKggJWGc5SqWwxhCogGdWdbNo7QAPPPqkqwqYtpGVmWCTWRkbnZAR/HxH0DAI3f1ureNmSNc2PAgCkrQJWEY4tJZABWljJ6eAWPDaACO9QviQjpW+CZdy571sbQ/zl/dx+98fI9GWUFqiOMGqMrF2mRBlLEo3kNoVybKqRIIg1pZyKUQJxxVw7n+nCAdKYrRBqABrBbEFLaRbADJ/ATw/K6bAy4XCAzBK4LVtTyDzr2XaqEepQTaTJ0NtCMZaZFAiSoxrt+y2BkGQLYouNY4hDjwBSBtjgUhWGcAQiQisq7hmhe+qB5gY1KZ46w26kgVkRDCkSMdP0IxBhWViI0isdU1TRAmhI0QYpIXlLFLazLoUQpAYg0GjnkflHrJQD1LS0TakRyoSFdBhFWVCwJUKljhlAAFWDgYdRuKiatPUSiEEkSxTly00ZQkNhBlfPD3DIfH+VDEYYsW7oTfaDDbrGrwcg/vEppGbdLujy+P/HLhglQVhsBbqVtErao7tLyTCahC+5oLLuqhiKaMRxhCUS2ijiQ2Z0995AwdXAylA2gSscRkzMqRunFfAllohLTXsQjeFB+CVRuEBGCXw1r8nUoHvcmhSj4Aj/FnritgMegEYxna2WJ2gkwiRxvWsDNAoIpMSfNJF1U926eN8JsJaS6wqNGQLkShhbQIkCDu4aLjiLpsinuOUBlylPiE9a1kQG1iw8CnmPvo0sRBYGWCswlgJUiC8lE/TA6WUBGnHyg0hnwUAnk5lEUKBqtEMyzTCEkYLyoSucIsdjBTY1JmqR6IES931WWajFMSiRF21EMtS6tUwQ6P81odqxCCfJPvQaQDWuvvfpwH6yoBDXPx2kHS5oRTB0YD8+fq0UjfhLSYsEwWd9JuSm8/pQpAIiTZOASghqEhDEjfT8JcCVXLV/4wL/vn4vhEQCIM0iSu3LiWRFfztoaU8/FQfTVvGClcXRBjr+jDoTdQbuJmgUABGEfLpTd6l7FoXP9c7YOxQW9GvE1obhIkoKyfg3U8UcaJoxBJDANIxiZVw5UIzSpZ2LN9ElmnKKjEKazQYR+JKzepNNwTwHLjzUorBNAkBxkqWLF3G5Vdfx4CxREhEqUpiJVFi0/EeDMgbk9ZOeAESoK8DkC3Yzg+NFSGNsEQjDGg2NVKn5XPJfc86B+wIFP8OOWtdCqcANGSFWAQZjyIjO0KmNPi0BzlIshhMJbQQJyl50NcBMLlsgbzCa0bsyG0QG2pTPujlMJBE2DgmEiFxqZ0kqLksFZOSe1Ek1qKEpSVUbDlhHJUwINGGxAhQAdrYlARokWkKqgWsjjDRAIFSCKnoSyyXXHMjv7n5Dtb3a4yQg4YAMcJsqsbA5oFCARglyFdG9II/CAKazSZx7CZZvnLiIBN3EBbHFRC6SWgTpNGEEmzirHgVSrSQWKFw1fFsZgEIQOjExXRFgJYlx5Y3rvyvJyA6M2QTVAAGNZQcnFs4EOBrwicahFIgFd0DTRAQpaOAEhghacY69cU7NrRJNFonad70czGYBZB7nV6ZREt0EKCDgCQ2rvyqlGnHQrILmJOBIwZ5zqPJsgAkWigSEaKFGuIkzvRHctHjvEFv7SA/0IJOvAfA/yZVAAZdCYP/RtrgbQQGDYPUlxIorFI0raBuA2ICR2ZNr4G2zkBQAsqBYOupExnT2eFme8pnMcZmdQDcVp1SVgoVJQVYQ2QMVkJP07JmIGF9IyL2/gKjwbqeDAVeORQKwAiDX7BcLN/HMZ0FmeTKGUspaTSbLHxqMctXrsnIejZ1d0qlshU1q+jlyVQS1yRFJ2gLTy1azKKnl9FMXOQOhHN1O1oQeOGVZQe4+uqadPExKWUrY9RvgpN+A4fkvcu+VDLCEf601lgkIqwSWZdeZbDEieaxJ57iiUVLiBvNLP7v2eVSiCECO7/rLASQ8wBYY+jp7aWvHtHfTOjt7yduRiTGX3u/HeGzEBnZkiwl5gmXYpqliYk8O8NjWNw+pw9I73mxJr1sKRs99ar4MXLD5bcxtFajzf2/yapX6UUf/Deo6Pi5PKhkWdb39PHY/IU0+vudYiSES80VjgDsKn2m+fyek6FjOlrKtNRqg9cmC1kNjos/grjZJIkjwiBESYkV0NBQ14Km1qmfymK1a8Jki3bArygKBWCEwaAxJK74nIUYjSVBJAJjJQ00im5Es5/5fWU+cOMzfPvWBdQDBabhcvWNRGgLxpLgpnCgBwh0P+uo0UedcrlEYEL6BXzoirv5+h3PsmL1AJMbCcK2ESlLq1xBaCHRCmEtdSogSwTN9bSpiG5bYUXYRRy2Euoe2mwfEkGjPBG7SearOSFcBteuRDnSUpUIq0r0qRZsXGe8WUVTVng63AYEdNFNWa/nn90NPnL9ct7zqwU8nrjqcwqN0RodtNPU0jVJ8koSLue6YaFuIZaSyCSIUIGyWDFAVT9FqdFJksxkpRlAV5fQmnTTQQMpNDqEplQoQlq0YdMd1ZwTXzhF0pP1tBUgQpejLhSIBuOaS9myfy11yjyjxtCPgKYrMRuxjCBZio1jei2sbYV15QrtcoA92xMmJN3EtskamTBQSjCBJJJjaZa2oG4FMuimNYnRwQB9whAkLYyV/SiWEukKfVZStwnUV6L6m9g4QpvGKziCz4cIaABJygEZbOgFJl0fLL3ASm349IW38b6Ln+IfKyxS9tOe9NIRraLctOioTBBIKiyhTT+OEIaV4RaYsIIMQ7AxJdukImKEjYkNJKqKCVqIKZEklrIMsKpGTAWr64TCZQ5F5VbWmYDIQsUmtGOplQRC1khE6ysycgUcCgVgNCGLb3oLSKRxOzsocIe7usVQ28pYl9+fWEHTuN7sTQ0rVq6h3oxoJjGqFFKpVtHG0EwMiVBoIejpb6CCgFAJTFQnEGQlccEivLa/SQr/FFlagxuXYXkBmei2QCANKg2DkDSxRtO7fh3Nej8itca9ZyUQjizpjVD38JT1lF4gBvc0pESKHZyoJrWOnYoic7nrYtOvWvdcM97BDqarprGT7KtSCJQQqNQ7YIwhFCUCWQYBOnD3Z2AtEzo7OPaogygJRUmUCGVAYF2qkzJpwyQr0kJNwpEJbUpOFa6egwpd5oG2YMsVRBgiUchNMWslN56DjpDU02bTdsc434Y2lrVr15IYV60PIdN7Od2IVERJjDYaEZaxaX+PbDc5Q92m1n8QBARBgJQi3c/gFgfny1AnzeBjk75TNxsUCsBowfDFPyXwGZt3uT/X3Tb0NwKtNYk2WKXoSyr0NKFUbSW2jvxjg4CBZoNmrJFBBROU6LOWhoVGYlISoUbqJhWVFg7yQnIUePu8W1UIUDpKQyAJSEtgLVI3XN50uhQ6MW7ScqpmgwLQ16TPXNN2yHKehVecu1e5Qi25xko5KudLeeovGRy9Iu+WB4sCKQlwnSRDLAiD1QZrJcgKJgxY3QtB2EoQNRnX0sK0CSWINFFfjNQQWkHJQqANSlsCq1DGKayucY2LeVsrwSpiJN0NXxJYYaXAKl9xYVNDXqTmBbXFtd522TmhcAt9EjcBMCLAijBr62WEQgauwJSVJWJC+pt6cJuefJG+1tqQxJFTmJQaFOYp0dBnXmQRL+vCMM8pUlbgFcemeFcXeBHwhr3FZi+MsVnXLptXxT1sLhovACFJjEFraBjLI4tW8pcHV7O+blBBld4oIg4EVknCUpkgrHLH3x/lkt/+jadXr0erMpHWBMJQEQkq6kfpGLISQiMbFi+o3KgFNk5rnDvBLrGUSRApydGKEINyVdVsgrIbbjmd9QJwNn3actBZqxbpuBYpkVILRSQlCSG+r7pvQLRJe1aeD2kxHk9cdc9dSEBIQYBzG6u0jJQMSwhRoSkC/vHUOi6/9m76G4IWDB2lELSkVqpgtUIZ19q3IiAwBqktCoWwrokVRmGMIBIpiRPFmu5+rrj+nzy2aAVaBxgJTSnRdlNcKvNXP6PZQlqZz3sAhHVEVinc+03San6Q8i0ksQHSst6r+zVLV60nadZz23QESykESRJTr9ddmmV6zfzn/rikGOyJaXFtsYdnJhR45bEp3tUFXiQG5buzCjIPQCboh2kAw9yH3gMQBiVsUOLJlWu49a5/0D2QYAhoJoZYgJYCldYGePTppdx0x708sWI1SVBGKklJWQIiiAdccZGM/T6ynX9+MfMGeiBy/eisRFtLkkRgDTHQFAF1oWj47mrP1/rEWqwezHUX1qW2CcAI6V6nIioRIQ0paCDRuIZEaqgW99INwEsEJ/QHvR9WgxUSKxUKqBjtcsatq0BjKRNTYu7ji7j9gcfQokZgGggducp0MiAxJaLYKRVKgEjr1pOGULSUCKuwRhIJiUa6XvVBidvuvp/7HngESjX6paSOGEy73KTgelLkuyK6AUoJerniUAbQrmwiTSGoiyDj/2gU2rix1yJg9YBh6epudKOOTRVP0lCeEGCShChqIoSr+ucjWUCWieHDUib9bYZNcRg3Y2yCga0C/wlsJqactWpNWsxH5L9DajA8l49vrHGkNSER5TKr+huUqu1EsSMMxtIgpCRQAc1Igw3Qskw9scS4/GubRAgdUQ5Vah04d6pv2zISkdlBdvCVkDLtdlhyypMQIBRGSppAH46iNUDZlQzeUAXGzKFgHV/C5vraW8+CN5n3wCkAkgZQQiCt76kmR+zi6tz+qQfApAoACiPd+Smj3bmpkkuLtIpESNY2NaJ1LNoojGkipEEoQX+iaTWaGIH13ZlE4gi0ys2FREik61DlUtOsRCEZ0JqujjH0DjSJkTSUI9dVXtkheh4Mc//7t70CkLsfvOppBdQRNHDnBa5TpVAB0kQgXAhAGEGZBJ/hk1FjsGkBIEugFCpQKCkQid9LygJIf+QzlXzp8KGHP0Jv2FGEQgEYRcgmqgV0QpzEadqfJIqAQOJyb0nbf8m06YffgkUbizSQGBDlMk0CrA0wCSQYEqEJsIRWkBDSFCEirNEvA4wK0UZjhSZUyvUet9a5Fk1KPLJpmdyRNPmNRSinxtQHBlBpd7lEW7SQGFFBCIWm4VzLQcBd8wf47i1XIVrGsLBbQakFG9UzPsbggupIVVZIJAJjDaUgQCeJCzeoAGENAZooiqmVW7jxLwt5ovWfvP+NBzChkv7eaIwKnbX7igzSi4NAYLRGKpWmsiYIROqFEiSxa0hjhSJGECvnrr757gXc+chSYlVDG0kQQlAJaGLpM5qljz1FT6zRCAKhMSIGBVpYjHA56iUZENWb2FAhEoWMJbEV9CWaoNRKYuH//fZOuro6efsBO1ALNrGRTbX6fPEoL6BN1ERWWjDC0jTCFY9KTfX7FnRzxZXXs1q3YBmDUCE6GgAdE7a009vQlIymFHeD0Vgs2iRIJM04TjkFAmsNKr1vtUmQAU5hEIIkdtdR66Etlr0CLRzzs/BBv8Iohn/UYdAi0DrJ0s10asLmLX7vFRjiHTCGwDhrNBKSppBYE6C08ye6+t+aEAgISGRIUwZpgQ+BkG57Jo1TRyKgYRQENRAB1maN7EYO8paUHay4Z4VwqVeiREyQPpcMRIbf/fF+Hl0Z8fDSJqsbAXUtEHLD+rYQTghWSiUCKYkaTYSFUAUkBqxOKAeCclhiIIa/L1jGPf9czLOre9BJAughbtgRBZGSHlNmpbUW3YxITIKWoAKJtQJrBHGgaCrJ+nrMJb++kfnLVpGokMBKEhsTWUPDWno1rK436UsiVLlMpCOUMmjTQARghEULgTICtEGn4XNlBbIUkgQhBpe2NnfBYu74+0JWrq6/0iO1AWwo5OPIurJawyCIraBpIQYIysRac9t9j/LEygHWD8RYXJaQlJJAGJIoItKubPVO0ycRBsqxDISgXCozdcst8Px+JQVBIMlKNecyWbwn8rnNlOzzPC/wSqBQAEYZMkebEESRqwDoi51k3uZ03fDEQQ9X516jEjehEwSRAYxEapGmsSVINBUhKZG6Y1PCoUnJcE4wBjRFmQEtiJUgRpJoXIlQT/UegchKqoKLKeNT8wbLyFSqNZasWINom0hdtZMEVaI4rYE2nHcBLuSSJPz/7P15kG3Xdd4J/tba+5xz783hjXgPeACJiQM4irJJiRQ1WJNt2SW1bdktqWRXldtDdIUd0bJCYYfD7Q6GW45wORxuV7Ssclm2yrak1mDJCo22SJUGSiIljiBIggRIgpjx5iEz73DOHlb/sfe5mQ8AKYICyXziW4jEe5nv5h3OsPda3/rW9x09chSLicb5fY969QW1SQPOKUkarg7CIresUsmmhFRaEHZjLqlFj99qV6rw7b1TVnkoMHVfnOj2RNhFSM64skq4rS1CDsWICcOkMCUGU/qsxNIHw5OZemPSQA4rhOJe5yralSphzmUjZqNfT3DAHGWehHSYrRafQ+4FTFishuKl4CEILJLimpYnryxg8xb6XB7aNi3OMg2JHHtc0+FFeP3dt9F4X5IDVZwT7r7rNGMFryL4xtexwCp5LdQWQWnnxPg8728dh/iYfpnEzQTgj1Fcd5+pMAz9enHNn+lx1L24juzEmHAx4ik9wtISEDSPdqwRR2ImQkeRBU6AJcNyKupiKAOe2GxwzVoeu5SYZwEt2u4Wbzz973XVn22dRXmLa5/68ctZJIUV041NlknppcW0oXGFjLZOwA48aYqFaLW1uclquaJtGpwIVvBTnCqkSI6R7Brotuh1wirWs5oCz+753khhZuSqYpmrvrxZZhmHwoFoOoL3PLXKnBsyvQjWbrAXA9kGNltPo46m2vpmaTA8iqcB2pyw5ZxT2xu0lumkTFC6akiTas/c13G1xL4+wEoEfIe65kt5iD5jHOje7f9EFMThuwl7AZ68mpknwU03Spd+coSVmzKksgGEGLAccanHWcI3HXlYcdTHNdFPVRiGJUeO1DZD7e07UVJO1TekegnUQiBbJoR9a/Ln5AE36PX6xyluJgB/7GIfAej7Ovc7agGMcGv9932gbj9CjEiKqJTEIaSMZMPljCOjVghpE1UaqfAsJesnB0SFrEXLfUXHO9//KP/rv/tZPvTxJ4t+fcqouwHJgCOCckBXwdtQqI1j1WpGYz02LNne2mQ1BEI2Ykw0FiAN41MBB+e2wVehmTvuOEPoe7q2RdUxhIRWO+JsiSHDIgrmNwip9lLjADyX0HmjxHoKoLYALEViHjBfTavUc3U58L//zK/xv//Uu7k8j9DNkK6l8UrYu4LGjMtV5CcqJMWZ0mrD9nTK6c0Zx7uWLkV8PyBWdBskZfI4M59Lvzop+wmAQVKP6CFsXD375q1paKriX4sI7/jdD/L/+dc/zsefucIiQjIj+Cm7A7STKV3XMQxDmSfIZYoip4RYYtvFUulrvV8PkFjHSn/knHRdV6ZVpLw+lHMZhnBA1+JZ7/eG7Fn98YqbCcAfo1jfW7WkjzGuGeUHCtfrH/us3091w1eDTC7mQKaUEeJxaCjTiOAoDf+i92/FHQwwE2JFAZ66eI2HHn2SR588WzXzZQ2d32ixTgDGTdtitVFJha2O4S0wa4R+vkfXdcWeWTKaA5Lj8x53VS0jWpb5M9/4asyMrc1NVATRAqXmGBBAncdcW0bZ1v4B5Y8bzdTODvxpdWZ9LVhhGXFKTOUa3FmueO+HP8YHP/YJri2W7K0GTIUUe7Y3pmXG34qOgprD42hxzNTzpte+nL/2V76ZE7MpLkR8rgY2dTTTrLra1SQqW4G3HVBsL93aUfMwxrPfmfPN2rHz4uVrPHH2PI0mTH2Z+9cWayYMMdEPK9q2QS2jORXjq5w5feIYL7/zDCpSrTyMrpsUMi+s1f9EBeeU6XRSRIFqEje2c1J6fu2Lm3E44mYCcIOF5oCmQpAymeMtkLNj5QxklybuQtwgJU/MAxKUrJEQDRkiOixIObEQIcqAWSRGw0KH9ZknB+GCKhGYmseZsueNvVZJBht4JBv91LPbGUlgIzlYOuazGRdaA9dzLO5w1HbwjWEbx8gcrQt0j8nOl/owfobQ6xmR4wT1AbvTmKuIirQkNynz4RV9T8CAZ9CWRaJUtDmgKiQxTBvyamAigsVEJxklsavb7MqMW/QqbzqdEZfp2ynKguiVxkqZFSQxjT3bYSDkgSCC5QbzM4JqgbC/FIftDwm1AcNjKK0VxfqkHrUMuWeQjt5N8RhD9sQcMMlccSc475WhUXwWprllyAuukZlsbmPzGZInzEksm9N4BjYsIPkSKzdlYdvMxPHaWzf5+q+4hc3ZQPae5I8xTY6rHVzSwDRCg3Ctg+CF6ZAhZwYBjYHpYZ2sEEASRfG/aE9EABtQBnpRLra3cbU9jaC0/VVIyjTu4nNkKROiwXa6QseCeXuEq+4YjsQdW55bj86wfIWLOmHRbnIyneVIHtjTCTtMmDijyStmaYfbZ4moLUtr6SYTYoqoKtd2dkjasmLCCkcvRW0xZw/RDmhY3IwvRdxMAG6wEAzJFBctCWURNV1/70hgLaINCcO7KYYUH+9sqCXMCpUvkrm8E3n3/We5cG5Ocg3XkrAUIZnRmENMGVToKxzbVNWx2AiDFOBZg0EyVggrdSCGj0ukn7MaVsyjkegoauuJrP31++yhCeG6aWU5sKPWP1OGUiu66lhXKFFQORPiSoWlxQxIK3RvUqYAvGvxIogJTgxSIvopQVum0rNRE46AIBLJWnXwVcCBz4kmlQ3SRMAUtMFkXzzosEVBfMp1Mxr8Fge6giglcSTxCJlkQqzV/9k94bc/vsOFvYHGK1M3BRmJpqC5o202iGSCzrA80JAR6cnqMZ3gEDqMDkCGMp0iHc6ElTMWpOITYMrgIAs0lc0epbz35rBOWIwzv3UyJUlxqZAi7YMBvdug1xmC0FJQpCYPaI5k16HO06QFjkhuN9hNCjkydRGnQF6S1DOoww9XOTFr0HZGlBZR0BzxuWfDG6lu7s67KggkrFY9GSFXG6wso2qhFKTxS3bwbgbcTABuwChe9Ad931wu8Keg4B0SjazCbh8LUSw5ggnmHOY9VmU6c2546NEn+d9+8uf42Xf8n8wDhFC0vUvtu1/95pwLuQcqzFpaAq4ituIgR6MRh+JIJoQMTTel6WYE54tvmTSYHE5C1R8WVv+3hq5rL+DAGPba1KdoMBjOOWKMOOcKS1qVvu+LolrKxBhwMh7x+nzXN0rJuVo4q+C0SAOP1stlD8hUSaIvxmF48aMSyrD9iZUYEx9/+JP8h//40zz0iSfIIrh2QkZxQAixws2ZnCMxlCkJrYIzSpEXLjPolbW+hvHt4EuveR3j30WUpmmKVMYBSPvQxQEOwOhWIOt/GLfZ/RT1uXbHB/5e2x45JXwV9/EiiBQ1RjGjDwHnIqFf1kGDcfso0ykZB1IUMY1SMIQYwUDJeDOKjZVVVcab7YEvddxMAG60EA9SNOBrp63cYGZEqtOXVwaBBaVfrEmKkroTzCtJykLZuAm7Q+LskPjo2XNo5+m02rSSypeMfdG8lmsVKb1WySUBcL5hSA6Hx2df6jx1ZFFWQ2KvD8S2YfAQ1dNnuX4FukFi7A+PmP915CbGSQsj5bKwhRgYSVmqWo+fgRmNdwhGo1LkVmsP+tkvOHIOrHoOWCWsraVtLQOpSgXfYAf1Wd0WoyhXplzQjcl0k2Dgu+JmmcThuxkrVdQXkiRmOO/xTjl+dLtMoaRYmOti+LrpqxSuhdUsbexRH/waj7dzStu2lPZ1STIObwZQzv+YDCpUHkVeb/pq+TkJQPntaogkggmEEOjahsYraegL50cUcqJ1Ht90OMk0ZIqbYE1GEaI0JHGIaEXJirzwEIoXiFrE2ZgEVHMwucGu1z+GcTMBuMEiUzbwsvVXeVkzxIRYePokjE8+fpWHnrrC7nLAx0LkiSoVPq2Ve4R5UsLmER7b2WUAZAiQQ9n87SACYGXO3Kqpx2hYk8sC/OT5FWotmoqrWhYHvkVdi5vMeOrawP/5vrM8dWmByOxLfBRfYAh17ImqrFiaBQcTgIPkwJwzW5tb5JTWlXt5ngp9VsnbMPR0XUO/XCBrUlp9PkYnwOK45kSxlOqMNTURsOsSgBszas16YCNumhbnW/YWS5Z9ZBkySQU/2SCa8pFHdkm5XJMxlDZYo8LGpANAxRiGvhD5tFD6C0MdxgRs/3VzUVKsGvblfDnatiWmigDkfDj3/xECWKMAY+R1klOuqfr1GbU3BFVPTonWe+KwKgqAqSSwmsvKYuLI0RW9gGwkXGlBAkFarEp8papsmcwYQqwIVcJhOIvFH4S8TjxuxpcubiYAN1gkEWLtVI8jeGUNECITIi2rIfD233kv7/vYpxlSoomlwkwSMCmjPa4uiOaVPZSVcyAwU6URq/Bc1WCXAuetEYBS/5fxN8tEMz7y0CexABoBK+8xIQzRWEZ4z8cf5od+/Od4/0OPkKw9pBXVZ48RIh5X3IODd8b+pIWKcOrUqVKhYjjn13B0zsVLIecy+3/b6VM0TupCej30j5TNf9J0NE1TXj8bzsk+W36tv27P+v0bJ9ZWVRWKjzGWClMdm0dPFHU+hEDD3pD5jd95D6uYkDo6mXOCHJh2nq6d8MqX3YvFgJILW70eq4OStPUF14nd/o/K45rGk0cEgENKrvicz/f+40oiUP7GOjUoSWvTFB2F2PfcfutJtremiDlaAUmBEOHY1ia3Hd+q5gJaHTETSVoMRan3iJQ24JBKO0AtV75SdQcVQPwNm7b+cYmbCcANFuNyr/UmNrRCw7DEs8ITU+bczpLdIEynXRH3oMjzCAFSRuoTDWb06sjOlSfvBzQHZo2DnCCnfdtPSt9apSQAlhPeOYacuLCzSx4SvsL7SaT0BE3JOBYoS99gzRSVhkO6on7WUApMKqOngeyD9jGWYzwK2sw2NtZjUWNlWUiBHqeKqjLtGu5+6UuYNB4Voyn47YFqVJBkHD92rPAukAJxZxgX7/ENJOPGrKbMqsXy+H1JZpMVwuW1+ZKdZSKiSLdB9h0Xr+wg6nHO4Z3Di6A5MetatqYdr37VK2i9FqqmFNRrjazUREvWSW2tlkXr18grMEKo25PJ9e/x0MRnOOEGORZxKFk/Tqohz8FEscD/Y3PAOUdOEe+E208fYaNTMMVXMqRrO26/bYP77jqBE1BRGgvkfolrJ6UllWMRB7KikDnkygfIaZ2IFWEyJarnMAosfznFzQTgBos6kgsW6/clAcjANRMWFOOP3nXkdoLXTM4BT5lRF+vxkslizHsrvVQ3IediCqQkGjViv6L1ApZxKjjnEVWSlQrW6o3sVAr72HssZRqrngFaIMCUDHENe8lYOc9gckO2/kQOJgCVe3GAyqCqhKQ0Tbve9As5UA5Y3bKutsQyKrC1tUlOoRx7lQM93LJ05xQ5emQLckZFUFViziTLrIIRzIEUkme0G1MMaCSXSt2pUy4KcriGdrpJN+24NAfaGUlbpJlgKCGlUv1bQQCUcq1uzSZItcV1B/ZIWX+V82c1AbBKgBPdb8+kFKuORvnN52raH6bYr+TL25V1p8PqNWMyIi3PRooEU6Ft23r9GdOuYWPSoTmAb0vxkBNIw6xVjk8TXmHoeyY2sOGs6IekiLeAq3yXTCECZ1FQJY8+AeoJ0nJuKTcTgC9x3EwAbsAorcyBksF7QDh/bZf/8F9/lw98+nxxUdMO8y394hqxEWJOqAnECAIfffQSP/Jf3s4HH1kSUoslTwiwMs+083gSnXNYjKgIIQQwQeoEwohEJDLBGcGXH/g62pO1LDhVLoRlyvTiiVnWkwM3YhxsARykVXnnmS+gaRrWinZjf+b5n4nC4ijKimqp2vrW81sXyyIwVCr/8vqV6uUaTAthzcQzSJkOuVHjekIeNF1HHzN7q8AjT/X8mx/9bT71+FmitIVxLrpf31o9jmI1iS0scyGXTX2c1hhfizohILIWqjl4quxZ7+ewRumh6/pa2X+nDnGeIGUDLtJdAqLXJd9rLq6VezTGCGQaJ4U8aRnBVfTQyKK0wISA5si082y6QGc9q/mcqTd8GpA6lhiyEZKRpCGaEKyMCvY4HnzqEj/yX36Tdz28+OIdsJvxnLiZANyIYYD19a8OE+HSzh6/9Nvv40OfeopgRhRFnMPlJatuwmAG1iDSsTLl7e99P7/yBx/g19/zEfrY0TZHCGrMndE4x1vf9AZczrSuzKHnnMkYqCtuf/Ur5UxWYdCyHLuqGFj+y2g2yLCMkShKToKmG7EBUKKY9JRayh20OcWYTKDvV8Wh7wCDfx0HhslHgpZaWtdvY4V64BfWMLaK7BMRUaIJ2RVlt5CEYK4IA30hP/yLHvvzk+tNN5fRsWTGZLaBazs+/LHHeOrcZZL4+qUF+SqNKMqUean2Xd2sqMd1nAIYK/+1W12dHsipzMvLukJmnxtgVkmfhzNjXfP+azJU9Q1Lu0LqOK9oQYYMUC1J0XXPsn/NqIwayAWdKjq/Hi+GimE4vMLEEp7M0c0J3/yW1/OX//zXMWs9HZFWqpQ4ZW1YhkwSxzIWo6WkLQuE3/3wJ/il3/8wv/x7H/qiHrObcX3cTABuxKiQJ9i6AojOsfTbzK0hAn0qEqfTFq6IY0DBPJZaIsrZITDfOMo1m2D+KDlNmSdh2SohDrzxdbcSlstafY6e3lLgfylFg6kRSURnRB0ZCWUzMyke9aQEBjFnTBXLwo3K/NmvDKkg8vgPZQcLofT5jRHW3t84ZN1r3b/l9jf+/a/rXq9uaG5sDZStCtOSACyHQDCIKIuQCIdvj/rc40DF7ZsGMyGkTMZx7uIVXDtllayIL4k7MIO+T2dzKqjU41inY56rsTDyWsuo38EZf1u3auy693NYGyvlXSmgReJ3/Jl6UM9ebwyxOClGINb7bp0A2H7aqeroupacEznFwm/JGXDFJ8AME0cj0FbEauLha77ibr79G1/PtFWs36MlIrU9GVOmj6nYh4mjR7gy7wkCT12bk4+c4XJ4fovsm/HFiZsJwA0W5XbNiEX2lyYlmLCXHIvkyCKobxhCD6lnT1uCeiw4sjUMJoTpJhdyQ5oeI+sml69FfuYX3sUnzy8RUbwWp7Q4FKvVAg/WBADWr10QgAL5w0hOrAtpzlhKSE0AUIelkcR2g4Xt68SPG3FKqQw+5bJhvOtd78NXR7p19f+sNsDzbyX1gc86MHUAY+3HLlb1/sWhviPETIjwwY98jF/69d/i/M7eId2q/vDYF+Mp8HzMiZSMlOHIseNkUXw3Je/X+AciV6SEA5t/Fa1SXR90qQe0EN5KAgAgNbk1G5Ucrm8DHOYYEaE1AiDl8zxz9hy//Cu/yaOPPY76tl5Iz98iMijcHinTKdNpx/bmDE1h3cQrLahCCGzI5NDTeU/L2HbJeIuQhrU9cMqpeDmIMiTjd//gfn7p7b/OACxp2WNGare+OAfqZjxv3EwAbrAoN7xgdGR82Ya12MZOui2gqZBm6Sl3fsoR6YkW2RXPoB2dGqyW0B0lhkQXdmC2xc///id48FNPsNUqoVfa6RaiLV6FhoSIkcxwYvi0wqUeUQ/Z0VVrVbOMiZUFXJSQBdNS0SlGtnh4BUDGjWJ8e2unw/3qMaeIIpBLZ7V0OIQAvPuDH6HPRSDFYagFlFgHyerkAIblTMIxWDET9jkUwpXr6FFWtOW45YyzATQS1SNM0ViY2wOJrmloOvihn/01fuI37ueRTz15KFsruVpGZ1JNiqRuV4miHKcgTTlOWYhJmHrPti9kstBtcG5vFy+ZNkU0j9B/QCgVrlpPowkhFQlbioqdaOELFEaAq6wLYZoDXdMxaENSo2FFmwKC0fvMgkRIDomV3W4DwTILIKcBS3NizkTArFz30cooKGuBpi9slKuyCgGZ4eqXGXzwk0/xc7/zQT711HkaQvnkzoNmMk3RB5ChtPXM04nHe2Gjhbe+8g7uvWWb2EwxHNkJWRNKoMzveJp2RrJi/72lgqZA7zxDcxQzxUnPyjKrJGgM9CHw6+//CO94z/2kDJ2fsuoT5nxtXex/FQQmgYXCdbLAWu6Yz5RE34zPJ24mADdYJIo9KXqUREfRRh/QbHQRpKeQd2wPzYbFGWeuPILLPU9PJlzzHVspcNuwRMIm07ziZPwEVzXz6ParwDo24x6NCPPcktpNLPTMNCAWCMloyUz6a0xWOzR+hsQJ26nFJ8M0kdToLRFdw9I8WTs0KS5nclpyaB1Aaum334sXMF2zpcxgWK7Y6FoaEbwlYobsOvbEE2enWMiMIA7JAZ+WdBZQElk8yYS2cKuIbkrwW6h6JmlBl1ZkbdnBMddt0JapJjbdEtcOLKWl0ZO0wRMtE3yiC7t0Cp+c3Mkj269nWHboIURXTFoGjERFkbIi1TrJaAm5KdeIz1hSoKVJga3lLo14zuuE4egWLu+xFQe65ErFKfMqezXByZxWAyKh9v0F0Qb1EzKeiJK1KRwCc2wPu8y6DfZ0xtIlJuka23GJWGS3y1yWSLQJrj8KYcDSNeaWuQLksAvDxSJ0A3WDSgzU7lb+YiYAEckFpWuBJmcymUeHGeePv4a536CNO3SWGZKSXGLQjUIulV2SGJImTFFUBmayx1967WleddSx0GnhmDgh+AGXF0wMzGb01uG7hkYSM0u0lrnUbXChuY2Mp7UrLCSSZMJWHBARnm5PsDM7TWuwERJTMVTjmAquv8rNFiGvIC8h90BcJwA348WLmwnADRbrqd0DvUsYGfd5Db/t6/kLNBOCNjxyPrHMIOoRbWof9cCQNAU6VUa+WoXy118K4ohWRg2jCcmELA0BT1KPVUGhpnHkOEAOB4hHQnqu4O3hiYMjY3bgR6KkXGbz+yGw7IcyIiZ64NiUBzvvGIZAHxImrrgFUngTYmUCY3ATYkxsuYBLy2K5jNGlPaYYmz7ThF3axTnCfIdGEsQeIdO4YvM69heKUKOsX/8wQgCjVXJZbJ5fkBYSVq15x/n8cURv/cjPAMcbyiAbBGkwmjpuVo5Ko9RJC8MTcQScRKKXovmfHZqFIEJ0iiVjKi2KcnGnJzpXFAVdx4Vd48K1RHalxy5a9ArG+NIc+v2Tfl3KUVtPY1Pj2TyGZ18qZXS1JLrjxIkaSEX2pCY16ihFhyZUKq5ihkuBJieasODEzNEQCUNmsRxY9gM78xXRHFEnPHFxIEkZHWy8WyMXeuAaQRTEQ/UXGN/tzQ3rxY2bx/MGC6UsqBCvI6IVHlqGnMpJFQH1ZByDtPzWux7hh//9r/Jff+tBhlyg+Vw3pnGxVAMRw2nVBCiN0LLBiZbRK1eY2NEcEU/IRQd8gdKbKwqAKTFtPblfImHA5VRUw03IeZ/Odvji+tthPY4nnqy+wL1NS0bwvhzbrBCtkKT61QpQ2smUVcxI02Hiiw97NjQWffXBb9G0LS87tcnUVphvEYFp3GWLhIsLdHGJN911lDtv2ebM0Q1mjRH6PSDQege5uEKWc0ZxCzysnvVGHSXbR1b2U8s6rlcTgMRzK2dV3d+gnicyyko2GGiItKQqwqCW6TxlxFIML6lWy5GhcTht6MwjpqycMHiHiqdNQkrG7z3wAJeHnqbpCEn4Dz/zq/zI/+/XOHt1BdqSOEgG/eJf0fuNqWdH4aDktTJU3e61bPD7La79P6SqTo4GVuvfMpBUdStINA0gCSSimsq5y4aPA5Mc2Uy73HOiZdMlyNAPiW62ifmO3My4sBf5L//tvVzcqSOzuSCLQirKoiMEhwNpQNvyZ/UduLlhvbhx83jeYFE4v2Vetyw+guVaiRiQ41pTvtRVwqAdj5+/yqeeOMeHP/bJQtjzTa1g3fWLsRVSj5OyUowkQxMlW5k/R32RYdXS4Y6ucAuCls1OHGxvTgv3OPY1ASjvNZscWJgOWdSNY786KrhKFKVH6A3MtYSUQYQhFy3zIUZSzrRNYZWrb1mFDH5CkmILbDkxkwHNAys6XnbvS/juP/OV3H60JVAgcBeXtGZYihzb7Pgb3/mt/M3v/tPcd+cJJhpxEnEehmFVkJ71RELdHA+pDECxfc37i42MI2yV0Erp+ZqlYmKrspbtVdXiheA+24dTMl2lAkodr8xFy8KDSizVpeY10XDwHhXPtCIAvSqh8TS+Zbi6QJNxdTVnT404JEKCJy7t8tj5q1zd7YHKaziAlO3HlyC9HSGgikplo46s7h/r69tbJcbvtSYA2TJe3f7oY1bIhZuRMUQhiWEaMa3nLhsuZZpk/Nk3v4Lv/bOv5/hUURvtnR1DVlY07OaGxy4uWMaWbIakAMSaBFSfABsLDkcWX6c+xiIlfxZPg5vxQuNmAnCDhdabYCTFWL3pR/KRVSe6goIWgtU8CEEntFvH2F2FMrefuQ6OL1BygWOdFLh7bDUULfQiUyPqUOeKA5u60u8UR1JPVgeqqArbWzMaNUhDWfylzA5m0+ep8b70sT+JX77bF10uvvA7q8TZ3QztrIyiYWi3xaefWpZjIopTIcRIiJndxUA0JdYP68hMbYkbdlklOH2s5U33HqGLeyxiYUV735R5f98yxMQdp47xxntOsu0CedjDS6aQ2jOVfVjOm8k6eTmMLQBslI6CsfrP660n10U91vq/EumA0VP+s1X/4zP6bEXtEiuOc5ZQIiVvqNMF6kiiJHUErzhxdFlxWRgUolM8ji479hYQptswm5ZZehy938C6I6xSgaevN2A+oOMg66bQFykOXrsHW3b7QyjPRgsOJgJSYX9XhYLU6dodESBlI5tWfw+IaqRKjzGKPkU2Qc3x2ruO8yfv3sRWO8QQCakICJk6VkmIzRYLm3BtFUkp42Sk9eVK/CtUQMNICBEljdiR3WQBvNhxcwjzBosy3lQ0ABKlEWBWMAHMiutW3cxNhKweaxoGHIN5rEr+JxvHh/bV5wpykNDr9pHaRbQ1FkCjxva04cis4ZkrK5w1NfkAS+UmnTSepaV6G4/1dFlADmsCb9SKWqrVrhST3b0+8vO/9vv8/kNPc8m2aKdTFkPgYt/z07/wW5z5K2/lllu2GfoV3juCKLvzRdFDzxkvRiuJE10iO+EZ9bi4YoNA2ruEn24RtGHlIvMk9HQMOiXT0AHbnWPWwMUhsBwiNmnLMU2lDSAlTzmA7R6yyLFOhzTrUbVoRdugzEEU0pdVSesMa4W+kQcwfj1fIiCWOe6NbQXNPa1lPNXJTuuMvAlZW4K0DNbSuJLoNrmI3GSnmAr9oufWbsqHH/wET0432cvHyVre9yI7FrTQboB6loMx6YT9ZPHgm/rCZ2Ljpl58Jg52+a9X/F9L/8g40qfrJ1gnAWOiZeCkVP1SZnoZshDxpKonkBCSU7K68smlCFFFWprkmBl0aog69hZ7a4dFcy19Vh5+6hKb6mi8q/oC7XrjLxLOhTeTZX8yQCiaGFis3ICb8WLETQTgBotshSiFQQRWlDGrIZUO2SjGUxTMBKsiHNEEtIE6djOqh+0vAWUnESumICPRbD0LnROORFrNmTnja95wJ9/1HW9mU1ZMc8/MGTMFX0VCpl1HTtU3vMKSIkKKaRQLOHRRDGhS2bDqpjVkY94PfOLJ8zx6/jKDtvSpNN6t2eCpC1d58pnzGNB4VzgUzrG7WNK0bVELjD1HOuHbvv5N/Plv+CqmEvE54uOSIxMhxsSedfST4wza0ExmLGNhmG8AEwUskMho25ShBFHiUBUHUxmyG0lchy5ysUWOwGDCUP+M9dobJ/uFtaQCKaXigGjGMBQW+ZgUPDsahW9948t45UtOoHGPqUs4ScVzofFkUfosqO9QLa51Lgc6L5ACzjLTuhk1vmEIiWU/sFgFsmsYxNGLEJsJQTzalGt70h5kNFSEo2oNfDFiXaSntA/3h2ITnaqiojq/n7yr4tQx6bpqNpXXdskFwSroXdc2xUVRSmtxSAUtzFa1BmIsI6pNQ0DBC6soBDdF1RWtgKZhyImQE14VRxkZHMwRdMpgvmo0JAYagjnMXEETYtj/jAe+uC6tuRkvRtxMpW6wcOoglG1+AHogNcoqlkxca1av9abPFaIzGwH+g6DlsxHjysge9cVHNjCGI9MqLBc7zHSbk5PMy+46yb/TBTFkyE0ZQVSHdrEuHkIfEpGGrOMyGZ/DAz8skREa58pRygWOVPEMCCtzDLTFIJHiHBfEM0hXzE5g3aPsw4D3jpwyjsTUG8cn8Lp776DPDW14jFYmSI5MCLQEdqXhWp4imnBpSec3SEBTCXQidUxKhCwOsVRUFevrrk/XYWwBFIUeEkqq77GwWIpIjxo4EbxrSFZMpA7GZ6v+AaZdw6tfeoRjm4LEADmQq2mVNhPMtGyQMSBhyax1TMLAZqcYPZ3z2GrFpJlh3jFIxpxnMtnAUmkXLEVx3QYp5LIZ5rRPShvNjNZv74tzIta4Q9MiUvr2Y3IfU938Rch1TRiGofDpGFt++3/q6CvAOGAyogYQcyzk4hzoDDoRJAxsNC2uvo9BHNF3ZC1VpYmyDErKilqksYRaaZ0kbUg5gg0kS1wTmKoDE3wMhctqCcWtDYS+WMf0yy1uIgA3WBgFFzNpeOLqind9+CmuLodCSCs2PHCwIoF1Jc9ohVp//DxgKlDU/p5N1FISjQ1MZaBLe9wy8RwV2E473CJLtoZrHPeJbW9stS2TdkLMwiJkBkoPMEvGLB7S7Z+ySaVIGvqxMVpIfhSTpJ6GecikmiAN2hK1peoBll42hlY+XuiXtJLwcYEPO7i0Iix3meQ5Uy+QM1MGNvJu4VJMjkCOtFcf4xi7OCkZupPC4I4CQZWoSrIyVTHyABQ7vHdzjJg4oimDGb/7wUt8+um9wkExIcZMjglVj+BLc/lApJQ+awJATtiwwlKZxxfnSuWOw7VTwJh5ODkRjuQ9NpcXaRc7tBLI2iMENlJii0LW7B30OaNR0Ow4P1/wro88xaWdHte2qFOcEwQp46EH5Gn2K9Yv/Ga1TjuHgRwjIuCaci1aBu8b8qhgCcWV0p6vU1TueVdVEw9KGKgYnkRcXmHTG12CzWxMV3NOTItpmGLgoM+xiCEhSLfJMjVkcbg84HOPt1LZJ3H13GcuXFnwS++7zG9++AIDgrimvMG4Wk+N7E9a3IwXO24iADdYGBR2+aD8xrs+wn9957u4529/F9q5Mv5UH3SQzoZVfW6rjoDsE/6eV+dcXR0pG2VUSyU7rJb8iVffxV1tz+nNlkmE173kCE/PjY3tW7BhyseeOM98lUE9AUcwJYorCEDOGOFQsniN0gIY/eOhWMJm9QUGpViYxiGXBU6EIKWnnGUkU1ZCGwnvlVbBQo8Mu7z6zjs5c+oY4fyCDQ1oHrDJlNtPbXPP0QVPXdsFt83J2YTveMOtnD5zmi2nVQbYYVrMfnxWsjTkHNc5nlJBm0PaWrG2ISPMDa4uM//ux/4zX/+Nr+YVZ74GkVjc41Kqn2ccGNyPlBIppc+YABSVW0W8w3JL9JFeWqI5+phJccADLzvW8tZ7ThAiJNfRaSDpQBh63vzyOwnNBhee6rFZS1gY1ifaScvZnbP819/8bZ6aTznz0rsIYcBqpStihbx23Rta/+8LGrnujCVhL9dgSoCnklEDKTtUlcGErpuyd+CdjYmAUoytrI4BjloiBsQwcOvxTV537634eY9fJe7cnvE1993Fa24/iU9LyBsIgawRRIgi6GSLZWqKGJNFmjTQpJYkHWvSomWeeOYiP/Qzv8ktussb/8n30jmlyQlzrvCZ8IeSNPzHJW4mADdYjH37FDK7y0DUFm2E1YJ15TQS+ta3ek5FzMMygh6Yx35uT80AxCFaUMDCLypjXLPW8Vf+7Bu4e6Pl1u2GlsTf/L9+G1eTMN04wqXLAz/yn9/F7pULNL6t4ztlhj4r5JxKAnCIkbyMQYqoGyHVQqY0bTDXFJMlaRChmJyMVquMy1rGO0FSYlgu2Owcb/6Tr+cv/ulXcezoJo9cWOEtEsOAAbffeYZv/7pTfPBXH8fiwF3HN/i/f8dX03QbbLqIUJIxqwz2KAfIV/X0lSSBw4uQmhHNCoKBcWXeEzOEnDCk9JspRlE573+QsXc9EgE/sw5AGdHM4lBpWJjSa4fvpjRdh5cBJfDW17yUO2+/ja5Rzq2Mn3/XRwi24uj2Mb7nO76aD3zsHL/91BPERskquCxoFlZmXOuXzGYnSj9bQagtABnra2WkA46sgC/06Vizd4yayBfjrgRrPojzDY02XNspIlXrqLf9mAS4ytURq8e99pR8EzjqHN/9bV/HlsGsSXz9G+/jyOuE00cdrVws7ZY8YLkHKU2+qB17q0iMGc0DjQW8gUoq/BkpkwZZHXt6BBa7BKNA/laFh6RBpdxfsv7Eh/UivzHjhk8APlezjtGg5Q/7+WeFGg9B5JrxS2vgGhZ9pI8j2W5/W4cDm/yI6VkeB5evQwCscsfWSYNe36WvWACSI0c7445jU3xeoWRecvoEx+qDJuJwVuDctmkQ35KtQcQBkVytdA/lPSyFYPnk0+dYXTnLfa+8F3Hd/r+rw9QTi6dRPW5lIbt+eTLCMLDZOZCWFJfcevI4L71lq4xXqpBCj1aExTnh9JEpHQP9Yo+pM+462ZUFOMwx15YzJTW9GC1/K5yLsZ6R3j/7h+sAF8vihPPQB9B2wpPnr/GpJ89y5OQJzKoZDzxnZM3MSuJoGZVnjZDWj5qysQix1IvqSa6MjwlFQ4AcgYHtFu4+OcGZ0G1G4rCkmXj6fsFLT3d8/KGeGAeWBJIIDY6wDHTTBrWi8TD0SxrfIBSlwub5UBcblRn32wLP23B7vqXrBZw6MWNvPufsY48xOXobJ289gWit4EWIKYFm+r7n04/tsLOzB0dmB97R/ouJSBGTGoW/Sg8QtYGJn3CymTBLBqtdNsRzz8kZDQO2XCBus4gs1VZIBIYMO6sVNgSkajIobr/4qOPFMUMvDRvtJlmkICrOYeNIoI0jydcfx8N1hd+4cVi7hp9TmNkaHsw5s7Ozw8MPP8x73/teHnjgAc6fP0+MsTrZwYULF7j//vv5wAc+wNmzZ8k5PwdePCg/ehidwDqgZQneCM2tJHcawTOLMA2OkJTeKYOeQNKMmVzlggPXwebyCk0ULhrMaTmWHqN1z3Bp0jJ45diwYDJskpopqomZXaUhsuQIgx1nIygnzTgqgHYEnWIKRyxy2vbo2szOJOBzx+kYyWnBrgrbYcHtuxfxssGFfJyUD9/tK5Tq44fec4G/8ROf4Mm8jYiyaXNuzStIgYv+VqTZ4Hg4x2a4xmbzNNPlnK1dg8XA7ib0RzY4M4VZ3OVxadjdOMktoeeWXeN4hkYgdTOmkmhyhPY4uMxWuEzX79LjIc8gt+CPki1wHrjMSSydBvVovsgJLhHyVa5swGW/wSx1TFI6lDOWV/2Exk84w8CpuMOia/i5Rzf4mz/0B5zHk9wAaqzyJnsCO61nLhu4uIGKYxEvc8Qt2A49EcfF2Yyr6hDzLGm50gpH+nPs5JbAJsfTgtPhHKeGs5zMV9BmSnCnUFE2CEgDRxGWbouLHGF7mjgpcIvf41Z7jEmec83dwrW8wRYDzeC5omeIJtzCNULODLLFhkVayyAnMD2GMlZUuVJ0y3TGF4q93kXj4nLK/+0nH+RHHjpHUCBcw7Kyt2yYeM+Uq2Qv/Py7HuZKd0/ZYNtdFo0Q84RJXhL8nKsbU5rG06yusNpQLjvFIwz+GBPteKmtOCk9bGyQtmacFDghDpvewW63wWp1lVluMBxOjBPOuPT0OZ4OwrnZKXab07h0hEl0mGT6Bkw7hA2CKFPbZZtIk69hlgj+KAudEIGGJcqCAOxI94cclZvxQuKGRQDMikWtqpYe1zDwgQ98gJ/8yZ/Ee7+u5P/hP/yHnDp1iieffJJ/9s/+GXt7eyyXS06fPs0/+Af/gJMnTxZZ15xR1fXvhhDKGNEhQwNkLJFEEF9U5nIWYrp+gTmIArTdhMVyhW87skg13RjFNSocd4Aa2IhUN7sCaY9a7UhaSxG7SjTIjKptruoLFNextmkRFcaCf9RGOYxJFVBGpwxCLha0uYrrlCqtyhjjatVfvehSQJsJrvHklEnDwJvf8lLksad56JMXmW4eJYWAqtB0B9GZ/RHMtQCRyYFzMB600if11W2tIRdZW332diL7X4fseoXCqxivJa0XRDIl2v7VZWPnPyfisKJ1gsYiNdsPAS9VwAIhxcDRyQS/t0TCignKxDtaV6rWVBSrqlqlLwiUXA8ij/bBznkW82WBzvM4RKvr91WuW8NkrD+fe+TX5NmRPnIdGmOlvfE8v/FHjbEqjtmeOznB+JFrG6WiewVRqdDJuqw2NBsSYxk5TQmnDmeJUcB5/8oc74sRaToo61yfi9FPYNRdLO92X6+C6y5gqVV+eR23RjKR0eVCxm9ro+VmvFhxQyMAo0b4OCb0qle9iu///u/nn/7Tf8oP/MAPcPXqVd75znfSNA3veMc7mM/nvO1tb+Of//N/zoULF/iN3/gNUkpr2VGAGOM6Gei6w5dtjoulUIg7ADHacxKA8bGZgha7tkN8S58di2Bo01RFO71u0ctSEoDGCqtdrRh/mERyFSAq21eR7WzN8AhGQzLoQ8bU0XTtgc3I1u/GLB9a/C4lKGCRkBIHjk3p+BaZkvozUfCKOSleAM4Tl4GNtOL2Tc8k9my1Dq+gHkzLbPb4fOuNfxRlObBlrMOAnNhohDYPzDTRCViqGg8m++0ZEw6rFnBR6KtKb3Wrz+JI2rCqLQysKlBaoNXEZqtFGtkJMUshYVr5fHG1JA0LZJizLT1HtXgsSK58CFWyePokiO+w9ThZSWSV+lgpm9UwhKI9gCtiReLreYJxAubzI66Ov1P9Dmz8shcNqTFK0n1AuK/k9aOGx8EHS7lmYioWyuXaM9QSnp6tCWx1yqxRSMWg54XGOIvkSp9sFGeGOiFz/VVevlcxjGKcZdKCNiiCo25QJsiBa/1mvHhxwyIAwNoxLMZI13WcOHGC06dPA+UGmEwmtG1L3/c8/PDDvP71r+e2226jbVve8IY38Pu///t8z/d8DzlnnHMMw8AzzzxDCIH5fM7HP/5x7rnnni/xp/zMoSprJKRsWGvn2us22WEwYmMM2Xjmcs8v/cZ5nroYK4HNQb3VsihJlEaFFpAEeMHEiJpIOlq1WJ0qGPvfroynmTFfDuTc4T2YHRBLtVx6vdetVIcrYjRiKFzKmItbHBTD2WQ1QbL9KnGIS1LXkVTRxrO9uUmXEsc0I9cukmYnikSqV8QJ+3nmuEyWJa2o4+3PYe8/qlQ8nUXaYQe/akGhmXqy+Xr866kWV74OYTSAs4RYADImSsJj2rC3ghawbHgxXBrQuGTYu8w0RIbGk70nuSlNI7St53jrmYSeLZ9o/Qpd7nLL0desHQeLZoIrr+GnhANbD3UTJhk29MS+5+SJk2TKOY94Mp4sxdWyEGc/39HVcaMfkw951hf8kbLh+jRGIaseeNWiFG3GdYhFybOIyYoGQy2eGoqVcNzdYXHpaTYddBYRSXUbfmHhhKqnsf+ODFsjEgfTEqW8vqGsTMjSlYQLKmlw/4OqCf6QFg83atywCcDBnn3XdSyXy6pgV9oBP//zP09Kibe+9a3s7Oxw8eJFXvWqV+Gco+97zpw5w0c/+tHr+v0XL17kZ37mZ/jIRz4CwKc+9alDlwCsWxL15s85E1Ikm1trd4z1tkkB1dp2wipkzHdcXa74xbe/k9Q4MtVu0yoSIEIScFKkZkdUIGmpwqIWgs9aQdCsvkipkJMZy9WAMaHxVCTbqnyxQZUGft7Rw0MQMWRCLBVLGhVJTUnk+vnkugTL1Igq5LZjrwdJmQ0yX//au2mHyDuGDT726NOghWA4/qJct/hrbSm49XlDcslCKtR6dNby577u9Tz0tPD+jzzOIK56KtT0K1vRBDgw33GYwmMU+ep1+lhmwbVhvjIaX+bvHYLmQCuJ247NeMNtp3nXw59kSJ4hK9OQ2D7SstdfZrW3y20njvC1b3gZ00nPq+95Ka1QFRyL1LBrJtDUa3Pcg62w90WhcdA45Wvf+lU4JzSNK4I61BEYRoA7cdCS9oXHZ7re5TmP+HxewSoCcJAgWWStrSYIo5zu2ALIJfGX6pRIZuaMr3zVnbz66FvYbg1nsZAn3QtDQcer0OnapJrRU+S69yylrVIIgployjwKfeuYUqYRRKhCRfttCP9HOVA34zlxwyYAY49/Op0SY2Qymaz5AD//8z/PO97xDr7/+7+f48ePk1JiNpsxDMMa8t/d3UVE1tW/iHDmzBn+3t/7e0DZWP/tv/23X+JP+dxY30b1pk/1hjbT65YZO/CVk+AnHcEEUUco5NriE1CreKnM5Sxg2u4v0uJJUir8oEXJrQC5rt6Yo72vloU3ZhyC94xNhfUXSPF6/6IdrRcWOSdyKot9gevHan/MdUanwMKP6DrHvO/pMXopvecuR9746pfyijtv5+pHz/PQo0+WNpWULd5Zqq2VgxVb4RWMegKlfzsiJ3B0NuE7v+kreM/HzvLAhz5BSu0aphZGASJugEWxJpWy33YKMWJNOb5FAS7jJfOyl97Gn3vLXTz4xKOczQ3Zb0Doue3USfbOXWUym3Df3Xfwl7/5DWxMMkdzSdrUchHEzrXXfYAvUc5oPVaWIWcaFb7iFbdgSZCckWTVC6MmrXLwTnqhB1hr8b//Dg4eC57908/j/JVr0/a5NesN3/bf8bN67pYpaJOUxEstoXHJV7/mTl72tS9j0yJeQbI9++U+hzeU1xwAAZxV7GX9/sYmpmHsNx+DKXsZFlaIss4MkRG3Ge2JBff5nIab8Rnj0LRUbL2RGfP5nPe973287W1vYz6fc+HCBR555BH6vl+z9g9OAIyVf0qJn/iJn+DHfuzH+IEf+AG+8iu/Eu890+mUu+66iw9+8IP0fU+MkQceeGCNCKjq+gZyzuG9p2ma4ld96GI/q48p07QNq74vcKUKISZGK0+RQtDLMbO3tySKkpwn+ab0O3GoObRCySUBMMQ1OFEyZewNbYhmrIai9BUoFZaJI8VSLDlglZXZZFIsiVNJAByZadcyUr1CGL50h+4PiZQzKe+7LKZUbUjqtdY6RXOmEcMRicMKcY6ry4F5ANWGhtI7PTZRNprS9/aNAwxyYKPRSrQKWAwHFsYq1QqgdXGmlEGOxJaDWezxsbgPDqYMsWxmXmuq8lktc790MWSh4ACO5RBL9ekchqCiWEqo08K/MWPoe45utBzfEGa+uAP22QPKtBFaK8du4h1TgaMMOLXSyFIhh55WDS+JzjGmcKWod/tIS4oBy5kND62Ai5nN1uEsIiniBUIY0MaT8+hVYOtr5LMRLg0hmidZQWsMwZIdKITHunz//H++YUDMjlj1iCyXtUGqrXKu10ZKZb5+TDazlbaRM6OTzGbrmSq0ClpHCf/wKBt9jLHC/OX7SdPgBHJKtI0jx/isz3oAHciBkIUrQ+KxywN7wYonRx0DLHPKNZOzA8fsZvyR41AlAAB93/Ov//W/5u///b/PT/3UT/Hoo4/y+OOP84M/+INcvHhx/fic83qDHoYBM+Ptb387//gf/2Pe+MY38uSTT/KzP/uzfPSjHyWEwLd927dx7tw5/tW/+lf8k3/yTzh//jzf9V3fRQjhOi2Aw8b6f94Y2x9WFrWU83ULy7i0jInC3tWr7O7sMMRIECWqw3xD6TBX/wADJGOS0cYRUiKGQNc15NDToNz9kpfStG0l6xQI0Xx5DTXYlMSw2KFV2N7IEHq8GKvFLikOtG1TFp9DeANXHlqt/MufcKDCygHJRcFv6oyJFBLkfBH4b7/1SR45t8Tajta7am9bUqwCibrKdE5MNHJ8o+XE9ia+8UBeM7j3SVtjuiSgLY5MZ8ZGo7SaGGIkiq+dUnCEdd/0MEYUWOQGpKOZzGiatohTjXA8+0XqiBd5gU6MTmLt6SsiRpsHmhwqRKx4g44e8kiGzBzf2mKzFej3mGghIK4BfBmNiRXnmjUa0ya46/RJjrbghz02GlApfgKrmDA9uFT+4ddvBgaEeTSSlHZbtoNn6MW9Bw5Sa0SpUw2VcFuRTh1heROyuUoCLj9ryHQqeKjiRsILcd1b2wdb4WF4p+SYSCGQU67Xuj3nyyzinWHthB/7hY/yv/zwT/OhTzyJes94pkzWHRmeo7p4M/5IcagSgJwzwzDwm7/5m/zwD/8wL3vZy3DOcc899zCfzwHWM/sjApBzxnuPc45bb72Vv/23/zZbW1t8/OMf5/HHH2cYBpxz3H333bztbW/j6NGj3HHHHbztbW/jzJkzxBhvjE1/Hc+i1mQjWyZZ5iC5eH2LmZGHQAqx9Jq9I2mpALCidOYyuJHxL4mUlqglLC6YSGRikTYs+ZrX3cutmxt0Al7rhIEvLQFHWWA2Gk+nBkOp1s6cOsF9r3gZ6oQQA77xJDt8x9sQcq7jUqIFPpbRh96QFNEc2J46Xv/KO/i6N72qGJjgeejTT3NuZ5dlMtQJ2AAyVNKfIOJQEyRH7jy9zf/wV76Wb/iqVyJxgBxpKkx83dIoVRWtwtEt0JBLEuIbohTWNAZqsWxWh9QOeIGwEkfQlr1VJOWMksv7tmIOVVQXi/hrktKS68RoJNRjozgzmjzQWOSg9FG5AscBscSdZ07y8jtOcnyqTCXjbKjJWGkCJIQcy3mWbLRmtBivuus0/+Nf+jpumTni4hqpX9F0HUk9pi8MXckGS6OYCwGraIhvAKn36LPugc/z1I3J/hqtl/1EJ6W8brfFGFGn+60n0fVEC5T7tyBbNeq1/YIjl1FhL0qjjq5tSeEgDXO8wgsPQMjkNDCI8uFHn+HJaytWORXX09pszPupANczHW7GHzUOHQdghK1PnDjBMAxMp9M1wW/sdY2b/ijkM0L4r3vd63jta1+LiNC27Xqcb/y9e+65h9e85jUA61bASCBs2/ZL/Mk/t7hunbCy+WfLldhzYO523Rc0GnGIKNk5cFoseWvuPy4IRWWu/G7rI4hxZJK4fO0cG+2MYy2cmSS2vdCmgGgZmbOmqTBepMmZsNph1kzKYmKJ226Zcu+xMzzx2IcJMVYd/S/2UfvcImUrWurqiDkh0iAknJUFUmJP0wTuOHmE4yePMP24w5LSzo6zkoaFGdI4kABSIFhEcVKczkgDm23H6+49ylFAYw91Nl5kv7UzVqgCpV8uGUdEwkAY5oj35Fz80m2dABzeymiejWvXjJdsCfiO0pov14ezWB0sjYQS8UTxiGplp6f1FL2zTGsBT2QlLUndusm9TtRy4sTWhDPbypOPX2MqCbEerVubocWG2AspKylGvBn0C7a7lje/4iTNsMN2O8O05XzfEyauEhlfQAjsDcbOzopbj3d0KkRAEnhfxu8OPLTes7ywVsB4vVheEwGheFqUaZW62YoQcsRr4TkoI7+ltAGhcP297CsWmJSWywunPbh1eyWlQDNpGOKyuCfK89SbWrZ42hlzA6RjZbmOhhYEI+8/dJ0K3IwXJw4NAjAS+MyM++67j//4H/8jZsZ73/te/v2///dsb28zm82KtCesvcHHNkAIYd2/b9t2zQ8IIawJg23bEmNktVrhnGM6naKqN8zmfzDKjW/rnmQeDTzqKjDeJmYGMZBjKCQ3jLgmjWWclZ51IU8VOc9+7xzHG+XWLcerbj/KLU1ktrzGHZtT2j7gZUBshdNQNqkUIQcaJ9x562le/fK7ODLrIPUowtmnnwbLTCYdy9WqtAAOYRKQLBOr+FNKVRLaMmYJyYGGiIQlGw6aPDAVx3Les8qOpTlyM0Faj1kACZU4Lii+lvQZnzOTuvSW6Yh83aE42FtOQHRFuEkssbUx4/jxI/QhENWRrK6RVlsAhxQB+NX3XOB//ff/mfc88CCTrUnh+sSAywMuB5Rc5/CVKA0JD1q27EbzfgJArpryGSpSkGsCsO4MW0Jt4JYjE05MlZkGsFVhtVOOdqS4PK5CRKVWQRbQNLABnNpsoN+DOCDOI+3kBaNWGXjfRx/mh37kx/nARx/DVOgzRZzoeWJsg3y+MSpDAyxXsOpz6fvnfWnzQhgdE35di1pBIe25ET3I5d+DHERZPseQ0q8/sr0FZgxDT+Nc1Sy5vvof091skd5gJZ6VNPTrPb6UM1kgycHfuRkvVhyaBGCcZ9/Y2ODv/J2/w/nz53HO8WM/9mNcvHiRv/t3/y5Hjhyh73tEZI0IjG2Ag8YhIyowJgsHBYNUdZ00jL+nemgOwwuKLJUHkEsicJ3KXh2zyXDg5iu31GgRuvYEpxKlrEByzlac3N7gv/8L38Df/Z/+NH/1v3sjf+3/8rW84RV3sSEZwgqGBaoJIWKWsNgzm3j+h+/6Vv7Cn3sd2xstTgxyYrGY0w89iBSy0IskgvJix1hFCcXvvf5wbYbkxJAUmLaezekEcmY22SBGwVwiVZVFk1xG+aTiLFL7JZaASAuQRy2F2p8+0MQdp7dNIAKYomRuu+0UX/EV9yFO9ylkVra/EUE4jPHeDz/Ew48/zdnL19hbZrKVFpJYPKC5VzbnkgQ4TNz+dVkfU1QrinrFKMZ0EC0pT1LO01v+xCv4q9/5jbQulc29ciSKBkFNrpLhfSGr4cr58Gb899/xzdz1kjPEMBRL3Wd7EHwOkQ3OXb7Gw59+nEcfe7qQadnnH+zHwXv2Bb4Itff/rCmA1TKwWpWCyLC6/ukaESzHYbzOanKgUtX76s9Hz4kX/IYy3sOrXnEHt956K5aKh8Nn+nB1i6+cCcV8h3q/viLKY65LHT6fd3UzPkMcmhbAWKXP53N+9Ed/lO/7vu9jsVggItxyyy1sbm6WvmDXrTd/59xarndU7Vtnu+v+bYkRYXj2691osZ/LF7U6kZZVEKJvyW7GYE3pcZqvS6end770jMVwNrApK7q4INCycBusmNIIbKZrTIcrPLGcsTGd8u333U128Oa33EZDqfavxoapTpnk82BXcOEUjg1MWzrf8jX3THDAI5cCOTZMvLBjPak9gvWezlpWZjBcJjfbZByaSw/bpPRIR+tiKL3XLOMk8BcuDJjnxIpluXZCg1oADQzq2dMJizZwJEZ8Vo63gS1dsasLpkemhCBMTcgxYTJFskOZE5nRTXqkfwhzDYRTHAsDwjmQAcu3s0PPnncsUBwRb1dI4rB0jHZvjnZAM3B8Bic2tzk2ZDTuQO54xsOyCWzZRXCJBcY07SJ5gfmj7FjHRDJdXmDiCDrDgGafMvbCqgCrqYeUa3CdrHzWX8rMN86w0x4lDJEJAbUy0pUSLGiIJmy4wDWbsJQpm3mHJp9kwW1Ys83J5Sc4rXN6/WoumZL0PF1ziVbuJA8NvYdJWoEWpOZNpxx2+gjQMOSGJjkkz3FOmTplyBC1I+SB1hJIR9YJrcCb7z3KB86c4NNn56RlxjWCkw6XEs6EnCKZjGkARm0Lj1ghe5IzLcZMWuZbr2DuT7IhEQWCOBTDM8rsRkDpaWktkcQTgdaKQl8WZVU3701bQLyI6YSVP01U4WqC1guby4GuN/JswiUCg63YTMLcbXJNEsnDBivmTUuwKW0UHCtMBwafUbdkqj1iDQtxNHmJz0+CuwXNGWPGIA1tDiiX6K1DdANdXWMqLW3fQzNhMEcrxiu24Z6NJU9eSiyb40hvTNI12pygbVhlUNcgaSC5rtA0Y4IhojEjGTAPUqZcYkUjTN1hzXNvyDg0pe9YsYsIH/jAB1gsFtx7773ceeedNE2zhrgPVvvAeoxv3PCdc+vnGb8f/37w6+Dv3IiJAEiZiBG3VvYyHMnGBVnRKqiRREh1rA8TGgzNPTkNJNdg002Sa3BEtmyJm0zJwIYJmwgzMlNWZYbdOwRHuvw0i4feh+S+XES+BYQpMKNUeCkqkjIxh9JcGAySVEZvwlJRFhR1RZcgjXXWKBu7XwF8MSJYJkuBikmCkECMwYRVEqJmxCkOx6wxGoXslJ3Y88ADjyNDLJ1mdYh1TFXpHOTFOfr3/grDx99PSpFucQn52DvJVx4DVVQLxBltnOsPCAEjkC99jN2H342FKyg9KoJfBrZjgXP3AJMZrcxQ9QQMW+7B6ho5VwvenAsxkbiufvkCHNnrK7X9UIxFLvCuiuHqeJdSZvdjlaUNyzmf+PRjnL10hYkkGvVIe4w+wBGukHceJUSQ7jRKZmuW6bwjZ0+skhSmDbgJM3FsUhQHB5uRs4Nc1J2CKBaFrA3tZFIqVHPkqGCBbTWOTFviEGgtIcMSZyMeocUrYoTOxCratj/PMP5fDBaySfRT2jIHcGAKJh+YiU9Va3D9L/WAFnnP9QBdDtDvQFwxUKYMsq+s/X5AUwY8QSAR8QmUlkEdpqAEokISjzNockRIxURIy/tj9FGwAXv8g1z81LvA+vXUQFYH2iKuYxWLXbWXliNdy7DcoWlbJGc2vbCtA5Z6lhmSNHRNQ+sM78YCJheiYiXdOqCRaldeR//E2FcbEeqI7It62X5Zx6FCAMZ+/b333su/+Bf/gm/6pm9iOp0iFTr+lm/5FqbT6Xr2/8bcuF+EqFBeQe1Lj9ryvt542U0LvCxkNCeyFgEWkMqy9kBTRgjjQL93mZNHPa89eTuvfs3L6EzH3gCRBo+nzRFNCZcWPPPwB3nog7/L1/+Pb8W1Bq4o3Kns35/iHJOJw8ISlwdm7SaIElHMbRDFESsrW/GI7i+eox/5FzO0ZCFFECbb2rxErZLtpECpTj1t2zFYg7SbhIXw8Yc/TTMTvPdlvM0GpjkwC1fQnQXnH3wnc51x6tS9bBD4+O/+Ine+4U+yvfUny4S8ZdSKMBMyAfPYsODce/4Tnzx7nje/9P8B3Wl8m+kmPf0AITtYAf0JlnmDNJRKWqZbkDKrCH0LXhzGOPa5voj2yWcvJGT8337XevxbPvAQedavjJJKMk6b1FcOKZVF3jmcCov5Lj0B0ZKsdpMGjQve+idez8krJ+G203zo8nnme9fwFOMm8w3RqP3nhqVskaQ4ZyqC5vL6KCTXsIsybTNDNPo+lD64b1H1OClJnCfj84rXveIeHnjiwwRK9Z7XG72CeQQp/gH49fVarJ6tSjzvJ0XPvpr3KRv72pCjo6CuOzo1rRBAHLQbmJuxWveAKDbbY0/fqqFVqjqROdUWVmllieWqul/OSkbLWtB0iHPkUHUl+gWPf+jd7O1e4cTtr2HlHW02huxZuS0QaBkwv40hfONbvpLl00vuvHW7ILKTluPHj+I+fRlLCbOE80q/jCSNIB7Lhlbi4T6O9Owr0p51Td0kAb6YcWgSAADvPX3fc+XKFfb29vjlX/7ldb/ezPiar/kaZrPZdfK9X25JwHqUxioDzKrBzjhTbfUGsVRv74wj1Fu+KvzhERKmnmxFg/3oRsc3v/le/uIbb2M2a9nyQCwL2CANjbX4vMDvXoN2D9t7mnZ5pSgANW793ta8YhnzkIikFa1G5mEgmbAEejchWtXBp2wARTPe6ubvObjBfFGOrRVMWnK+zvxlnJ0WPJhD1eFdg/gN+uxomilxWOBJeNeUxMsCXRpohsts5SmT4RyLZKThUegmuP5JlHuAHsVockLMlQQgT1D1WL7GRvwkR8NVSIEVU4a0QmyX0GwSc8c0QMtRlnlWqliohEOHtQ0LE6YCRblx/DzweXZ4efb2Pv5tVHWD554vK3hu3TZz5eHnwo1QRzJjiJGUIlr1KUyb4iypRXPh27/29dwlr+QPnj6C/9DjHJl2dFL8FSQYM2dIXmEOeso15s2YpCUSM8gAw2XwHmPKUC+zhiqPm0qC0IiglklxoHORt7zhNI89fT8XYiBpS5LyKQrlwoHVLbvqCxRRrAKsjuS1LFRZ7oPH7GASNXpq1M3QBKgJvJRbTAxSNJxMMJ2xoiQ4kss/JoopVVZPSJkQI4JfG3oVo6ViuVPWhvE9OgxPsHIuFGgsIyzp+kscu3Y/OlzFd3fi0wAWidbRSWKqS0gecw1/7utfwyuGwMuPNEBm2jUcP7ZF2+6iCbrGc+bW05x9+mlW9XON/KsyEloSlmd7C64TIKMoAx7gOtyMP3ocmgRghOKn0yn/8l/+S2KMNE3D7u4us9kMEeHEiRNAaRf0fX8o3fq+eFHIM6OkKXW8Z1TKEktrmU1vmWiOKEUDPIsjWQOuxZmHHHBpwZltz0uPlYW1lQRa3e9MieZwq4vYpx9ATrZMwmVOugH6PUKrjBNZYvWGVXBti3cdMyfosIeo4+KVa7z9XY/w2MmGr3j5HWxMGz729JKr167xhrtPcnriKzN51HDNfOEZANTiozalcz2uIymvFiaCVB2SIiLTNFOGUH429ROGuELUVeQFpj4zpWdCRleXmLVKY1eQvMEkX8WFa5B6xKToLpTluUCfWUgpMltd5EQ/R3eNdNwza5UcLtNPply8vEu8tkAwlpIwN0dlm4xHpeXpq4n3PLnDm19+hK3Orc1yyoeqKocv8LA+O20Ydd4/e3W2L6rsLBZCnsGQIgnBO4/4lpwTmgOCQ3yHdA6RjAxz7thsuHvW8pELgbS8xkQyaTHHCahm8sVHWJ79GJO7X4bf2sTweJtjT76XPATcyWPkxx/E3XYfk1NfSXbKlAG33C2EzLZ25S3jVZlMNki555YjGRd7nEzJ1yUyQC7mVlITkTEBSLXqTxR3zSz7x+HZR4/qcFgOoNYqfTROKpFiJU1qg+kmj10euP9q4rW3bmLR6IeBx58+y2PP7LG1fYSQMjFVW15LZdwSQS3iLWJWbKXLGavJVk24RQXJCbvyJFuLs2zuPU36xHtwr3oFki4xPPkgduIlbNxyhPzoh8h6G82Z13DEeV675Ziwb/bTNa6MHQqQM6dPnuTyhfNYPxKvBaeKy1adGa0SZ6ltwooCjjXBWPTc3PxftDg0CcDB3v4DDzxACOUGaJoG5xzz+Zxv+qZvwjlH27Zftpv/wSWk7P1WEQDb34CNAmHnOt5nsWbYVpnqDhOjHwLJFaW5mUuc3urYkEzLkmIQ5DBaIsJgwt6HfoflH/wiR247xqRdsRV3oRHMF+ixeApW1na9geMQefMbXsXMOz51RXjiqbP8xGMP8ZKTLf/zX/oGXv/qe/m5X38XH/n4J/h//o1v57Z7T1eUQkvVUqan+aLc9XbAqewgPGtGCqn4JlmxOm2bltgHpm1LXEAMiW6jLfPXVSUxYYV46Rwh9nSTrvAo1DF1hlqEdkJihaud3ihAHqCatXRD4lRUvBxBM9xz5jSvu+8OfvvxgYcfe4innzjNkK7RTwKmiVU6SVKH2YSf+9Xf5O0fvcD8W97AX/+m+6j4wD6K9Hke05o6rFsIB4bwnhUHge2xDTC2AHLFphxOBNMG3zQ0ThiSYuZIksmW6Czhk5XNKwc0L9lulLvOnCnvR3p2H/ltLr337dy78ZdoN++gkyPI6hnO/df/Lz5Hjr3hjVz7wO+Q7/kqJn/mleC2+eqXnaQZbmfmW0yLEJSzREqgkw0GDNcaKSa0BStsheIzaNTEuya71HygHlujTukcaAFcd1TGlsUIxUu95q1eF3XUEZEqZwxoQ58dv/Bbv8OP/96n+Pt/5Zu49fbbcN7z+DMXuP+hR7n75a+nNwipHm8zRtNvMS1oAAVtKgtF4QYlbcpAavFH5uqjHyU9/XFsd4eL7/llJsffzPZkycV3/geal95NfuObefCXfpTbb381J77pBP7I7UzkCliLyYwxUUpxoJ04+t05ToyUAlY6AEV0i9GfoXxeq39bX6mFvPCsq+9mvFhxaBIAV7Wql8slP/7jP85qtWI6nbKzs8MjjzzC1tYWb3nLW9ja2lqP+X25wf9QlRBznfs1cN4TU+nz5ZxxXtejYVYrUV3fVgB1xMcE3zQYissJCStcHmoFNoBOMBlvzKL3K3sX2F49itvdZXJsmyEssdWCtCnVttPQCjE654gp03n401/7al5z96381G8/wgcfehwax4Vh4OrekgRcXGTOLx19TIjkNUgsdmBJkC98EhCtCKSgSsyZnCrBqh5bE8OJoAhOHBJXeAs4N8FZHUvTMsJmSFmIpWWZhGayRYwBSR2wiWRPaVw3ZBY4elJalYEo2SnEMvNFv2K5h8TAJMDr7z7D/3T8OO/+336dnUsL5v2SIJnsI+icic+IeXANT1+8xpWVcXFnXmt/W18PfxQC4HXw7LNGOm3UkBfFctlgrIrDYEYKA5AKJN60JNeyMmGVrCgfWoaUUNGSDKkxUaEzUDVSWDKZOF56/AR/8lUvrzz6hMSztDuPIsuzSFqBPwJxl8mF+yFnLLyEbvdR8uoMEwacj/yFt9zFn/qKl3DnEV+SmlGZLjt6PKlpySqomxTNjX6O5BYvBXqXnIsaliuVvK/VfkoBp+Wuo5ruHOxll+NUplzQMs2TMJYidDkX4mGK4FtyLiRRtcJ36KXhcmyZ26SYeeVIN9sgpcjFeZmnH0zANQzDQLPR0OeMil8nbFROQEG5Cq8hmBR/DwxxhqYFbZ4jPtHOzxa0amvG5MJHODp5CpfuolueZbYD9BeYu9vZygmhR9xmGSk0w6lgOdB6pWmK1bhzVcNCIKWMqsOJEmIZBzQVklLbK7UpIOz7Mn35LftfsDg0CUBKiRgj0+mUH/zBH6TrOnLOrFYrHnzwQX7pl35pLfYzTgR8WYYoJp4hZkI2TBzLPtBtVTlQk0oKKgmCyOh/duCusf3qQEg4B5JKZaZKYepTBMVdheZUDJ/2mAzP4HKHGzo2VWBYHnDJo8CHknG+UI28lsmAe2/bZuphiJnoJ/RW8AJvsDMIfXMEE49ZxPAkGQVaaon1RYgEhFwEYkIaX7emTvWAenUogneOY1stZxc9KSuWA0OMSNMWFbVkRa/fTbk63yP7CWot2CZwBLMJYi1ZXbGmtRWkXZSI5XOQV0zyHVh/EWnmEJ+iW845vj3jrmMtlhLSbrNrG2h3AiFB1to/BhMh4Ag6JWpLEoX1lVDQoD/KDMD+OnwgmVgnAwq4YiwlUtjjlA0hxr48XhRzDTurxGzbY37CagjMNjtm0hQteVXMF7XKpooehBzIRKZeODrxFTY2VBJdY0juyRgDgCpbsqAXg6ZB8orNvCAPK3Sy4NZum9Odx5MJKKIl+Wj8BkOZucA34HzHZrdJt9GitkMcymew0BdhH6le92a4XPQMR3UDkTJfX1pj5SulXHgCWu8aLaK3KzOcKC2KJaOPkdw2dPWh5pS5wa7MCH6TjMMsF4Mlc6R2i90M8wzmJ5hFGleuRRUtgmG+JGOj6JdUYqjIPg1PcqIh4nICLzQKbVxBPorPcyarHXLYwbeOSbwIac4VwEvDrF4ZY3HWeEcMPVOvVXPQ0Kr+59RjNiAjpUkUXMNeKAkCUoqZvO4L7F9qN+PFiUOTAIwCPYvFgltvvfW66r5pGn74h3+YGOPaCXAU+flyi3KjGr5pEd8WIh+K+paMMMQIUqRAqVK0OWkl+5SNVMkF6kRKv9WKK5vzpQ9o5jFpMRlQW+HZBITGBrwtwCJmiiSDXJ7HqD06QiXMlVdSg5ZSdTYSyeIZdAMnAUVpBKJ0rHRWZoEtUjaPCvZZkYcpRKsv3HE1gT4bg3p6y2Xu2JdrrGitj6p9GS/GrPPIsGBYJLq2pVHBujJdnzPgm1rZCNsbm2UxNVDZxNIUyw6tG6MXY4Ndzhx1pGuXOfe7/4Hl/Dy3veV/Ju89jVNjePePcu7UE9zyxm+lO/5y3GAkPcKHnuhZysniEhg3cJRNMRlEPEGFKG6N/6xNo1+EXupzyH5QE1RHnwxxheQZxVVJY2GkrmcRgjT8t3e+F7d9C5fnPdJ0DGHA4WkoQHqkSFb7evSDCFEoY2z18yQguy16a7AMwRw7wAkmOIHQTHDNMVbW0l64SLxyFX/bBJdDMbOQTHRa5J5Dj/NCM1FMM04yu1cu02927PZXOX3Coc2EVUpsOIe58T6jTOTEHueLRPdosrVP+isIjHMlUcsiWBaiCO9/8Gn+2/s+xXd9/Wt4w60dDa6M4woViYpkcbz/wbN86uwuKyl6H+KUKErUKbu55f/4L+/lsdWSa4sB9S2p7KTFudNKUiriKgoQ8SYkCgEvj2+zXiyS4UJzO1vdBm3rse4YudmA4RlYrpjLFLv0KHL+cRZ3vIWFeSZmiJbX1HpoHIYTo2vLtVmMg0ryI+IwUWIC101Y7O3w2+/+GK/caviql59GZL/lZHW9uhkvXhyaBGBU8PPe82/+zb9hY2ODxWKB954HH3yQ48ePr0f/RuWrL8sWAGC5bIlDhpCMaNDHjGlxiHMKyYyUDPNCFs/oTSekSgwqinxN05UerDiya4swDxNCvdFaW+Flo1QNVjwEIg5vk0KIs7ROygusuK/5XaxBiyiKN6OpwiaDTsoCh9FYESsKTAFFch0RWn/iL07Kb8DSjOA8zWZHEEewmkAZJEuVhVwm6Set5yV3nOLB89dwUqRtvWghcGmGYeDUkRnf8pb7eMktT9DFOTLbxk+m4IrHgNhANuMltxzju/70m7nnjpcx7efsfPL38fEq/k0XChPeGfNHfoOdcxeY3XaC5tg9zLIw5Jbf++QFLqRTnGozmo/Q4FAbyqYoWuVy95XsRtLe+Jn/KLMAB/8sz1eOFSKFLS+ly1FEqMb3UIhdCceQlQ8//EkWeo7djdvxkxnWD0gKeClwcGFG1PseT48SpPayDUQSWYyom/Sp7F5mVhQUpcFSpNdNGtkg4Ni7chF2dpieOVMg/Jwxr/RAN3afE4hlGhmYacOf/4Y38psP77HoPNJFLq8SwXsGCmFOXbHibZyi4sECEhIje32f41+yAUkZS5HctJgT+mD89rvfy+99YsGJznjtd76VxguLDMkELxksEhK858MP8alnLpNkg5xKayqYYs0GCzre//6Pci5HzN1Ca1NSCJgvjZ+MI0k1pyLgLBafi1zu23WPwHm0mSCtI7THsLiC/gKW7mCZi60xQNSGsLdHe/HTHMkZL01FQypiZ4blRE6JlALeKSlFyGnc/7FsxQ1TfVFK0IYHPv4wv3c88ide/m0Vwaw0gPGXbsaLFocmAXDOFT/wnHn88cfXlX7TNNxyyy381b/6Vzl27Nh68x95AF+OoU3L3k5iCIbvprSTaZmbVseiDyQrxjYZypy0NEVZTqSSjApM6aBk9uKw5MnqSeLxIvRIEQaxYulbqHhlAYvSIDbBclmIR1OaNRO8tgQQV0lHRkMqG6UogaYSk0paYjiyNAhaxE5ct08r26fgf8FjQFjGTE6Oa/M5JtsYMGSripNlYxBLNA5e9er7eOfHPkS/Slga0Ci4VBZWiQOvvuMYd7zkrdz54E8xSdeIMsW5SNaIkxVqc0jGHcePcuef+iqmbkL32P3kxQU2/RzZeYaYj+CaBdtNj9iTsDiPkWliovcdT/Q9i25FK3tIqmmbRZDR/24E/Md4cXBUed7vymuFXAhxqwBFK6+I+O6D4FKuA3PoZAOnG/RZ8MmY+IYWpamiOJHS5pJKkhtEGVCwXDzrNSIWSDEwlYgsL+PDHl17ClKPtwUmWxhC6ww3XMX1e2S62hcvPfBVRRnAICZcSkxlxTEX+M4/9VoevPQJHnhsyZKOPJ2yq2DmUF9+K2JsAo0pakV4x4zitmn742372XLZ2BKgXohkrsSOnZCJZqwC5KKtheby+84ru4uePinSdogqoiWRQhuu9QbdNpoDITcFAZCSkMeUUWkIkvE4YFiTGWPlA4hQ/QHqFEbb0q4uM9ElOjyJ2UvoOk8/RJp+l8ZWtJ0i6Rynd58hbWwhzWR9PZjlqkNQkLPOF+7DmH6Ojo7ZFMTRD5HpZIa3GfM+Vo/Hcoz2EYCbCcCLGYcmARg3+5QS3/M938Odd96J955hGNjc3OSRRx5BpAitjFyAL8cwgcVixa/82m/z4MNTdpcr9hYTphsJxHHx4jnmfSJlcL4l554svkCw9TYaWwAiwnTSkfBYrwRTogitjAKlRaHbW6lERjwzSIvSYabsW3wYjBWUVaBZyi3uiaiEkgBQqlKzMiLkLRbewgjtjWOMX+TjulwFfv9DjzIfImkwHn70MeJXfwVOjJjKYiZa2hyWEypw9MQxlqEHWiaTFpGeGFbQLxAxtjK0zlg++iCztGB1bYd2cQE/a2l1iWMBFujIbDe5CNuEOd18SWdXSQ8+SMp3MazOMeEcM64RhxVk6HJiRwLLjQnR79K5i4icxnEaLFb0pWy0th6prK6DB5KAFwtfWcP/FB5FTMb9H3uKT3z6MebL5oBSXomMksWzDJm5ZPK0QVVIYY6lSKUNVkNYB1rcAIaaCFTMvXxWC7i0ZGJ7yIfeSbP1DRx5zT24uIumHk09EhZs2BIfFsjqPIO1dFKEfCIwSFVIzAGnxmaXmemKLeZ0zIqKQneUc9fO87/88E/ylXdv8/VvfDWPfPIhvuUb3spLT59iBRTtvQKzjyOx6yFWq3xAVaSOfIZkBMvFbnhjm3k0pPGEEPlPP/0b3HffPXzj6+4qs4BNC86Tteh55JzJZri2ZZUcH/vkY+zuLlm0SjeZkE1LUq1KDEbnG6Ik3ChYZLkWBNTJDOpdXKYyDMdJLqJXB8Kv/SiL0/ezunoejjUwXGUWLqP02NMfIP/u/8HeXX+CY6/5epAZUD6rd46ua+iIbGwI3mmxxhYpJFEoPgVSGAmrIRIRgu3z/cepgEJv+fIs+r5QcWiOZs6ZEAIxRr7v+74P5xxN03D8+HGGYeAf/aN/xMWLF8k5XycNfKNHMlhl6M1I2WAYsNwTScW0JxkxJ1ZW+4ohslwlfuuBR7iwO0cbYfBNIeSkPea7S87PEzEXXfmoHiWUMZ9sTG2Jy5FBpiyZ4l1D5xTSWJ1L7b8XqFisQHYFXPVEVQZXEgoRkLDAW4AUMBNMWiJF018tIbn4ece64ItFJgy0MqCpJ5oxaEujuVR92pLwRRHNxjTEveCNqqgk2LrKqhyy2n8swkmlfVF6yLurnl//g48Rg+IEnjr7TJnxR1nlAmPDhF46one43PP60x0vv/00Q070OWMh4mPEta5IuSWYWuTqk5+GtCLakrxzCWJPNkFiLAsohcApsafRJZtHOiBz5VPvoxFDZYY1R8jS4GRekjrXEfF4G2htgVlfO+ZUjoYRtQNRGtmvuSyXyjpQ2kWfj4Fg+ZV84PiVTTmlhOaeTlbkGHnPhz/JT/zae1mudmjSAizRuxkhe6ZpTquB0M6w6QTfSJGGziVBxJdrQKz0uJMUfEETYA6cq2dXUGlQbdBhTvr4A4RPvZeWBK6B3OJDRHMkhuoxsToHoSA+PUCESS7SxCZTVODe446ve9UZzhw5iu88mxtGzpGrqWXXHefdHzvLD//0r/Nrf/AJHnn6YsU4MlmVpHVzF8UsIkpNwLWO45WuezJoFbxT+gR7fV96+kNibxn51d/5IO9//Bl2a2VONjyujPZJIhKJITDpF+iw4PzODjY9RtdtY3HAwhzJAc3FfcBcwzRntuM1xJbM2xlLv01vU0y6Mn6YlmRRgk5JGWS+JIvB3jO0F+7nWBuIq0gKAxsUThZnP8GV+3+Fq48+wJATyQyJhmTP0MzYPDLjm19/hteenGB+m6HdxmuPzyt6m9Fnj8+JbV/oiUtmDEwBQ+OAt+IWWSyOpYzJ5lDIEXUE2tbj0PW+tmKPnnjxktw/jnFoEADnHPfffz+PPfYYly9f5hd+4ReYzWZ47zl//vzaGOig698fh4gGexSS2XY03GpJmkaWMmVqU9wqEBtjt2k4Zhm/GsBv8Ti3ETe2cOxyres4Rs+x/hnUb3NOOkymuOEc/ewIs/4Sl/QUTdrjeL5Krx3PuFNkHJJhQg9pRaNGAMxltBL4yBE0oSL00mJeGFxmU1Z4H9HVVTpb4pMQm02M7QrdRXxY0ZAJ5okI0U1praeLl/AkOhMWolyxKW0+S2cR88cYpKUFnA2gjqgt+7qAn1scwCMq67le7Hn8XwKR6rjnWOK50twBfWCqK6x1hYWOsmdCrx3JjrCrjjib4m2XV7PJN77yTj782IexyYQ2LpjkWEYHG4c1ZXxr1rXgYeIUnyLmPXGYwQpMBpYIyBEaWYDsEleB1sNUn6SVK6SwSdLT9G6Xzp2j0V12mikpbXFs72nC1jbRjpPiNtEEb4lEJnRHWS7P06UVUKYCdFjgmo5eWxzGlH0+/+d6XBNUOdkeoyFKR7QiZzyxOT4PzPttVpNbeGbrtbxCVhzNlxCJXPInQTa5JV5i15SdZkJUReMe3sDblBWB3DVMTGiTMkhmqIS8SQAXPEEiQY1WPKQthGNlbLQNdItPM7dIbk5Ds8U0K941XNYpbtajOmcyZJYtRAebA9yimaCGNSdxMfLNr9jma+/9ZlS3ecZ6GncOjde41pwiWcfxWcfVxZIjLZBajlI2ooUWIqg4Y946MivEGYN4Osk0NakO0oEUbc4sRvIbRJkz8x0zEwZVLra38MzsGOcEjnqPy9Bk0BTQHFlpxmLmtrSHlynXJttcswmTFJikPRrti4hXmhF0ytKUE/2cW/PTPM0WZ7tXkPwMPyyJ0qFxgUtXSO1pVrlBQ0JaSK5jaB3N6nG2bWCVNtG+wUtL0JZOBia2Q59X9BTPgaYPzLKycFNOHmn5f33nazgeHf9vfxvnmXDH6oNsAmfdveS8x5H+Iq0kFm6bhT/FQrfwlpmmHlPHXNifDIpLivJYW5EKGJ1MkdHpouob1Pv+yxMv/sPj0CQAZsa73/1ufvEXf5G9vT1+4Rd+gb7v8d7TNA3f/d3fzdGjR8k5P8fZ70aOPhvn5pGtTeWoc9B0mEK0VP2vtXh1I/T9kg3foJFSRcSAqnFtZ875lMnaksXz+DOG857W+2IE4zzJhIlvGPZWWDdBBCYEfFjSekFboc190U+3KueLgLRAg1kEEj5nmtijblVd+wRBEV9Y+jkmGl9aAxqWuLga9e1KqKMvNoZIt0GWci5TCowKelrfA1hhzo/ywJ9HyLM6h3LgXw7ys5WRG1eh8zrJ4DA0FhEalxOqleQkpfmhYrSNlqo+LJlNJ6VKGXkQjLPy4xx+IZ4VjsT4PBSRZjGCGcs40DpHclNyvIxXWCx26TaULhRI26UeJ1u0TcvOasmqKRDpYMIEKcJFCOJbcC0XdwNHO+VoMykmWRjOStX0mTzqP/MxrVH1bdcjoFo9B8QREM5dvlq0EUxYrvqq9gl9SPQ6IH66hsbXT1m5Ia1FPJHGAt7igTNlZIusUiZpkZ01VjRxF9cKtoSQPYjuc9okIWcfY6MRZKgVYniKeHZOe/QliDsGkmnyLly6gkXwZ44VeDTCljiOdp6J9biNjrSIWEXMYs4HWhtlzsYZJDLrGmX9+T77NexEiqKnU4ZFUUi8uqNc2svYRmmvJBNiLiNzpoo5V9oCqaJbUkeA2beULi9b8LA+C1fdUZbuKJICm/ki2i/x4SiOgO0+DeE8Xb6M5lT5GkUvJIsjmyeLA6dYHCAuYFiwsbFFWp5j48qncLOTSAxsx8fYvHaeY6fvK9e5EzbcEttN5TrxSopzJPf4Ue/HSlVvOZZ73gk5BkyK2iixx/DkPiNtUS4sPh31QhzPupTn++NRJn7h4tAkAAB/62/9Lf76X//r/MRP/AR/7a/9NXLO656/qjKZTNaVv/eH6q1/3vHuD1/kp97xq/ypt76G73rLG7EYoW1oxJcNqXUF8cTo2qY053Oi84pmoR8SDz70CE86Vyp0HL/xOx9AtcCdmhMxZdykQcKCI5sbPLnTI0ccTX+Nl5+6hd29Pa6FJTOrXPxMvSEVk43avy2Kgo0ZXVzg/LwkAChkt+7/OpVC7kvwipfcwskNz1QyS1MsZ7J41E9IFthZpTJrXY1gpCZ1JWMvwDJ1WqDE554EjKzrcRuWg09hZYOm9smLIgFgWnq3aJGHzVWIKPY0OeBz8a9XKY6IkhOzrkUs0S92eNUdx3jJbaf3pyAY560ziCtLco5gobImC/nJUeSAZHWF3Qc/TNs04DqWSZnlMnXgckCyMv/ER/H3PcWdp7Z4/6NXaTcy7aTFOSFrHZUUhzhl1a/QboP7H7nKkx/7Oe57yQn+9nd+c5F5dlrH0F94YrXeU0Zd/PHnIlhUsjSc3VvxwEOPEPwdoMp0ukECokBWpZvOCIBYPf4HkgBHopGAJ9Baj7e+voBCijSWaboJA4I3RVNA+nNIP4dGyd0WoJhAiBnJPdc+9WE24wqJGQkr8qd+hSc++XFOvek76F7yzQSMZnWeJ3/rpxE9yu1//i+jzSaEnpN+wq2d0oWr7F07X66NBnw7IaXlugJVy/XaLW279QgeY6+bz4pHt60rYlF1U8vA+97/AKf6R3jDd34d3k3J4vDthEEyy5RZmbJKxpAD0pTrOZuRpNyLNr6+ZJxCbmZcsAnL5igbtuJY/wzbrXL3sVP4vYs889/+E6sMp44othqwtiONCQCeKMXZz0LE5QHvEhIzGi6TH/0AH/nP/5pX/tk/z/D0J5k89Bhf0dwD3IO6hpgT7fJpjubTODdjvtplazOR+owTJceIWaxTSbmKnRnqlalUTpIXks6Qbh/hUxGylfWKNaK1nzLepA1+5jg0u+g42++953u/93v56Ec/yhNPPMFqtVpD/9/+7d++lgAekYAbPZ68sseHH73MXfdcLZrhTWHDC0qIZdQxCKXHKAKWyKkw8x0gfkIfFuwMiehnDNFz7so1tjc8KpQRohzJKdMQ+eY/9Sb+8289zLUMt263/JmvuY/fee+H2X0m02oRBzLTtaEPssUgldIsGTHo0oCLy7qROazKiKYsNJKRtOJIO+O7v/3rOLPZMfWZYRDUKfiWVRIa8QwU7YK9vT3EMjGXeeTihraPGghFseyF3snl6qiL78F/kFLb7z+/VY46FDsWrRLLVbo29jQp4HNAJZfx0yql6sTIoefMyaP8xW97E3fffgJkpyQTgFgsvdUqDzwaOCEUwSDKsXZpjsWLPP3JD/FyMwgZP5sgg4O+p8UTozG/dI7tvMv3/nffytM//SHmy3OkEEjascAVAZxkOA9YGdl85Owlnrp6jpwCSYoVdtl0y7WB/9wB0v0jdx29rbK1BddusAqJJ67MmSclOmUYMuIa+mSscrGnznVSRarpZHXVJcv+M1fWCd72YV3NAR8XCFsMBl02GpZ4ruJiT7WxYXSjEt+gwy752tM0zYB5QcIcO/t7yGMfIb38ldhdX0+vLe3yKs25+8mTO8pUDQLDLrONCd/6Va9ENk7wY7/zKHvLVAQLq9phXu/sebx6sJyI1Vna7OBV+JkzAEsJp+XohlgmePaWK95//6OEv/i1hSkfDXENroHe/v/s/XfQZll+1wl+fsfcex/zuvSVVZnlq7uquqqt2kgtCRmMkDRo1DKLdhYzIHZh6GADYmEINojYgNDOBmaZhZ2FBQErxCIkgTRq9QpBy7fat9qpu7zLqvSZr3vcvfeY3/5x7vNmldQCukezm5qoE1H5Zr0m3+e599xzfuf7+xqhtxVS1ZjkMFaIKZG0yOyymuG1lWuZNdCJofdbBDVstHvcYQ5437d+E9/0+Hkq8wR29hzTEHGhwvqMSpEiFOVQJokjioOUqMzaKRSMrqiXlxi1HnPzXqqrn+XM6gb/22/5DvpH313yR2xBHE6ySegMo2bE17z3Hj70oS+UllU5QWCtIRuhBxpMyclQg+aISOZQwZnhfUVobDFjLHDUq4uAV8/Q18eXG7dVAQAQY+RHf/RH+dmf/VmeeeYZ7r33Xi5cuMDdd9/Nt37rt+K9R0SOUgJ/r49rix6dnqE3I0LKjLwnmlLweCfcWCp9D6cKsRZ1BkzAiBL6iHEGVU8UoZeaaB3iHV1YYgfZn5eEpoDTnrc+eIqf/vglwsqzU2fe9uAOH/+CA1dkRRGokGKKQgZpCJRktXVH3aWE5H4gZhXr2wzEPHyPBsZW+Zo3nGaM4vo9nExwg2lRnwTjPTqa8NzVwGq1xEvZiBnS2Aq6IGXj1MTale8/d9yCqV8FAmphxa/tcNYQfVncisVyOb07kibyeq2OJUTFacTkvPZOAxRNEcmJO0+e5B0PnmSjAskWxA5wcJEFls1+CBPQInEt+nZDTaY6fAldPMsJmVFrITg5UZJU2NwhRBxCIwGzvM7ZTWXiDW1v6fsljMfsB8vlQ+XEpCoAB8oqJsz2cZzpubaYEWRQNBxJNb/SGasDOQ/W6Q+GQlLLAqusXFrAhf2OXE8x1jGdbnH94kVWyTJrwYxGhCwYO5hQwRqwLhsXhmQKmrXODygmNYaKwCgfQlf8LioC7L+E338RqRVNlk4NE1VM7lExWJMY6xKjHcko8eZlfHqRO/I+Js2IBloRpjFywh+yYB+1pTiTvEA4xkNnj+O3t/lXv/ibeCpiVtQZjHWs1TVDdFZ5LwopDQ6BKK/pYv0ONYBmxdqC4kQtV3o03SCsLmNMRcyQ1NDFTKot1w4C23VbHB+z0ocW42qsseQ0eDFAQe8oEutAeQaauGIz7LFhD/jWN93B+fom7D3FhswJBqrxCEkRNRUuRxwlmyXICMWgKeKtoDGBKfdv4nrOjaBaXIa9L7KVA3efGzM76TnsofaGDd1lu93iIE+ZTpS3393wodzTGyGYCnE1SSN7y57LS0MzGVEPgWdiy7N5qVNSB9tj2PaDaiElvC3P9nqs8Y/XcYDfedw2BYAxBlWlbVt+6qd+ir/+1/86f+Nv/A1+6Id+iGvXrvHzP//zR0FAKSVCCL+nioB1gfNqIiPAS9d22Y81USucLVB7QvAizOcrfuh/+HFOnznNn/3ur8dWgqkcSfOwsDmscQhFZtUmQXzNYbvHREIBwI0ysYlKEj4sqTWxe7BARydI85vUCeZLoTMNSTxBBKeKJaK5JUuD5lIQ9CajKhh1gBIEqiHXOwtYu2aVJyT1NG6E01IsiDTlZ1LG1hNC6rlykPh3H/l3pLCDzz3ZFsa/1QAaUONBPGj4ih/fQfU29JhL4aJDMyFyaw325eaUdgkZpfAlAoYgRZ6Yc+nNSk44u+74FtdCMYKzgsSWRpScGKD9wmI3qcXlFUmKfAtyYTALQwGgyPwmV37lX7Gz+xm2VheQ/gC8Ia1mLE1FbQLr9D4TVqye+CQ77/xebGpJqWfS1MxXLf/0xz/EiWnmv/8z38j28S1qb3G+orcGX2+xiqFYPcdUFBsmY75Cm+Vyf4s1y/pUu/5bQri4O+ef/vSv8AtfvEJXnSFrJoZylv/FX3+G+X/4ImorupxIprjFFXXpoFQQJRhP50a0WqRoICVAWC0nJ477NuFYXeD2vHeRy7/045x49pOl0DI1QTONBmTvMn2IVDExci3atSTrOLz0PMfiRUbRkmMmAC2WJDU2HZLCoDJyDjsClYBXOO0U081oxqdou0SXtRSVMCgS8vD3Euu8fsxTGiR2+qo5+WWGsZasUuIFHGTjWfURbFW2cAVfN6Q8p+sTH/q1T/KFccXu4YJqZ5OknpgCSi6bopiClFpKEiCWZBy23+PBKZxrPPUctnTJ3of/34xf+FmqxU2W9Wlin7DWF2KldgXtE0uyRYLIajl004pds6hgck+cH5DnNzEHl5gyxedDtpOiXgjAiSZysJqxOTmGsYdsi2KNkmxFkAZMRd91PHvxBv/nf/Sv+XPv+328+e7TeGtJKK9cusZf/Ie/gsSOb33XY/zRb3srVeVw9hbed8thQFjbX79eAHz5cdsUACmlIzdAVeWhhx5iOp0yn89585vfzA//8A+zv79/RAT8vegDsH7daxfDGCMHXSD5DZLUKHFYYQ0mZ6rK8dS1XVZuymLRsjneohWhF0uXFesbNCZEDSIGsY5ApvEVpFDgdGu559RJLlyPbFWKF6GabLEKysQHJhJp6ppVV1wYsxYrW5MX5HwTa6fYUOEkEG2iVceEBsnrAkCwmCG0S5HBMtfaAuU7UfAGCRlrB8g0F0HUS1f2uDlbonkTSZGUlT7BaKj41VQl91xlgPi+kjH8gK5564AYEjC40ZMjWCdIH8rmrrGEqAShl4ZlhiWQbDUQoJTK+yHwBkCp6gpVGDkYCYyKs0pJBEwJDQtsWhDVgK0QjUg3o7KlV+uAfv8q+ZVPY3c/hesWiAGNpbcfXAVdVwxUkqGWyAtPfYbR1/R4KYVRToGshr1g6eeZ/VXkhJQFOUfB+G26WCP1mCwMREYGJ82vRgdY+AsqMkD3GSOKYLh884CnLu7SuUnx19eIZo/zIw7mHXHwd0/ihuhkOercMjDiIw5tHMuB7JZ1CIlBeecj9zByhnvOncKFgGkSq+c/idclJgWCaahspE775N0LWFs8EEwJ+8U7j233IGaM2UCCFgmrQFaLJdH4UPrm1qFeydpDBo+hMtDGWFjo1mCMPbI4VkqgjgA5V2WeG8tqtRw4Jv/xa93FiHG+hORokVaqrehDAdq7HlYdOOcR7QnquT7rcOPNkp46kIUZrqc4T1wtOXXiBPsHh6iFVd9ywmX+yg/8fo7HOU1lmFYdL3/h33Fs+QVmTOg234DxNRKEWBlqTcWHwGSsLWoHUkBEMbZCXYUmJYknGQfXrqFdT0RQiXhNjCnP3p//wT/F0y91zNwJjt8x4WCxi8kdrTQEX5MVJs2Yw3bFkxcPeeqVq7zxnjPDGV547tINroca0wc+8fkneN+3PAq1PTrtD6HoaMEmX9/2/xPjtikAoCxI1lruvPNOrly5wlvf+lb+yT/5J9x1110cHh4ymUyKNeRADvy9bgWccyaKkqgw2SHaU2BQP+jnYW5HdH4DzY4klrlm8qhGmwmr3cjImELCMYKYkliXpfRZRQ3eOO6+6yT+5iHSL/EUTbI1jg3m1GkfH+Z4eqY24DUh0SBujucG2p+kSSMsLaqHmHpC6JSaFkwiLvapKGEq/9Eh3CLkMbDP6wlSjWFZOA3JFNqfiiDGEsSyQDDGfcW9vFsQ/2tx1yTQDhZjzhbTmsY50IzVjj458A2xHrOfYBs4iAY3mpKTEGOPtXJ0Il8vMZZYpIsoWE/MgjPg0hwXl5iqwo/GmLgoRQFxeElCIx1T9nBhH5E8UNctudpEG0+eXUdIBSKWzLbvWeUlnkRMEbECxrIyDdnUtKZBxeCNUplSrGSxJDUotwJX1wjEVzPWDaECeQ8JkNQksXSmoTeeLIIhADXrgKdobCmmjBYCnxYCoKIkMzDYjSHkgqwY0iDmKpf8xMjy9W+6G+MsqVuhccYJc4iGvnRYcqaSJdK/jFldGrThA/xeT0g5MTYtfbI4vwkoNu0ycVuYtD43rmA1I9WnMMYVUywtrZ+jGbw21tK1+VVGxQ7Oh0IwX8ar5D9RbxnfELNiHOzPM9lWJCn6/bWcVQdXPSGTxBKkKSZaWpCYPLQfVClISDPm5LHj5L5n2a6I9MjqgLec9ZyqTrME7PICG/EGPs9JbkonDQVfqQi2ptIOoUdjRk0s4WE5kWNHFHfkMIhCb2QoVi2dnTKWhIk3aMwWva05efwUZ07UzEVIRnnimZ6xRJJ3rMSSUyRryQxZmTEr8QxUCjIQpGZpp1iTCCazplmawS9wPS/Xl1petRK8Pn77uG1YdEUiJIxGI97//vdz6tQpfvAHf5CHHnqIvb093v/+97O1tUWMEe89McbXQOm/F8a6YFm/7rQm7GCwajDaA/2RfzjAXGpmuSarZR6UX3viCk9e3qe3Y5I6nHHYnPEMfWwSa4OUskg5QorM2xaTIzVaNjsik9nLxKc/zGZ/jVNmRv/SZ+D6czib0KtPcvjpD7D8wq9iFwvy858gX3sGcYaw7NHljJwXzK48i3SzI9ezIwrXcNouGeOl120GJq/RIZnAViQMkootaaDEgMbBz+OVg8gvf2Gfl262X1Utvz4J3Pp/ocvwxItX+OjnL9JT2NJqytWuJJJzoFO4dBD55c9e5+NPL/joF55llgSp6mKnbKXEtTIEukgJOXFHTQYhZUAjevUlzOwmBouVBC8/gb74m1S5HVjahoqM6RbFI95a1FhWZovLeZuV3aKLAs4jrsKgVGEOs2tHRYSRAvl2ZsTSbdJR03cBwgpPj5VSIOqwURWrYD0yp/nKL+zaYnjdahn+TXKx6zU1QSpQxWtPiZy1A4HMEo0pp1ukuMLpupCALIX/0VilEsXHBbWuUIZESump0wxSwFUN1iq+P1i/EkQy1f4F8pd+Ab30RURykbGJI7kJXRJ8XBBduVtcfxF98ucIF5/FppKbId0+6cZFVA1IjRVfTIKQkqsg9igAy5pBvpcSCXjm4gEf/vwVFjkTY/otzP//+BzO4ulVaGPmc198jl4tUdyR1j1nRXNGVLGk8p6kLq8ZObLdNqpHCXuiSu0shA7tZox8MW/yOWPbFpvLZu5yV0iTdgjkihHsmD05xsqMS56AHwqLGNHQQt+iQJct0Y4IOaM2oXmFBkMw22SJ6NP/HvfKp6lzTxsEkxOjFJgqNLnDrW6iq0OMJkbOILHDOUuUqmSSaJGsCkoSx8qO6E1FHNpXZnCEXPN9brGVjhaB18fvMG4bBKBowUtv/+Mf/zjf//3fz3Q65a/8lb8C3No8cy5hH7+XFACv7v+/eoQQUA0IDpcZCoDh1JALCS00U1K9iVjPS5f3+Yf/6qd448MPc20O2UyRXHz3fI44CaVwkEw2CtmSxYEfYeopNR6jiRx6KjrecW7Ei7/4LwnX72Zr1nPtY5/j+OEbufOdf5L2uY/zyqf+NcJL3Nfcy/7P/3Ns+yWqaoOcEoQFXubMrz4F8yuY0cZRtw0pvfwogsegUrThhZiWMSSMKDErXdczaSawiAQtMa61CNZ4fuFjX+L/9fOf5s9997u5/72PfkVbVVkEBCfmCA7ICPuzFT/3Cx/juQuXefSRH6SxDlUprmJxjq/GxKrh5f0Z//KDv8xm2qeXinneJjlPiLOS5W4yqCebUmgZXRcAhUdgHJi+p3/pSexqD9M0aGxZPP8FFs1VjsU5EgNtLpuzscWUKJgN0uQE+fSjMH6EaXUDLn4eKPbOTgQbV6wuv4jRCmMNhkTKid5M6dyUFTXLxT5xeYBLDxUJPgABAABJREFUQiWJKDpIEG+1RMrJSr6qReDWulr4E0IAqQseIG6IH0447YaOrCGJEE1heJeNscTkrhEAGQIlTFiwUwV26prH7zmJ+DFuKLJIPcYpKoaVCg43kL9qNKwwkuHiF5nvXqYyRSKmpkJtwzJXYGtMaqFKtKsD6s9/iN2XPsqlc1c48/ZvIudIPrzG8sVn2bjnG1Hb4ClzqBQ9bkhXLDt7jB0x9OA980XgA//+I3zh+Wv8tf/2j5bgG171/K//+B02pD4Xh/5nL9zkVz/6SbpwglQVb/716V/XUlQy65QNQxoQiiFpTyxYT1NVhHaOE6EySiSy7FbYZoelKTkGYwFyj5TqHEOgCTcxfopptskn30R7I9LM9hGTyL7CeYcJSzQtwTo6YLQxpd89pLItogEJQmaEhiU3P/xjuFNvYnPnQarN0zgJRdopDScmG7z3kfM8sdzmc1cX1N5Ce0gzLZHMa5VM8YKwiEIwHmMcShiOGwkZXDAF//p+/xWM22YXfbUK4IMf/CB7e3uv6fOnlFitVjjnsNb+nuQAwK33qarEGEEjRot5iMkBKKYnoiUlq1XLIpaUNVsbri0D1xY90db4ekSOCZsT2q9KCM0RvLvmTxvaZAk4uj6Uh12gNpk//M1v53h/kTPc5C65ydn+AuPZ09DOMfOLHI9X2Fm9jOlfYTR7lrGuyundCKKR2iWq9jr0+7+FZ1sWyIIArJm5BeEx6JGkKKREzJnTJ49jKDnpmUIMy1h2l5H9YFmEOPiAfwXXmVevs3IEt0eFRUjcOFyUBcNwJBkrTOlIGxPJjZlrxVJGLNSTq3FJ1tPCcyg/V9ISdLBFLu+64M1pCIBp57to7hBb/OxdOKDq9/CUPnMSRxzijzGwdNuEu97C5L3fyz3f/Edp7rq/BKxkhox0i9dIP99HGIphKTB/FFvUJGoJfQ+xww2mv1ZSSTI8UnOsddRfHTyqvPoa56EIKOTURJGfGdViE61lLpYiYODJDy6MQmkBrDvXilLTM02JDd/zh7/+LfzX7/t9bI1c0cjHDlJf4m1tQV+iCl0uM98aGOVDqoMXqWRR9ltTgx+ziAY2tpG6ITLDVz3e7rMdnmV7/kXS6gYmDZvTwXVsyhgcPhu8rue0LcjB+vQpWnTofY9YT28qrs57Fllvkf/+c7ckWyHWcTCfs3+4xFQNxlUYOwQpqULKZbPXAnnnQYlRruNwUtZMCgnvK2KIOCNUxlJLZlRbDs0W+xTkZZ0EqNajFhw9k3ADmxeY8RZn3/udTO5+DPWTQgI0FaVSbbH0GCmoo93eoDIJ7Xehu4mJBm9H4DP17AnGs2eRdkkWIZIQPURiz50njvOD3/Ot/MB3vZOdkWUsPVMTi4mYFpzIApK74i9CHv68JfMzDJbAr2/9X/G4bRCA9enYe8/jjz/O3//7f5/v+I7v4I477gCg6zre+MY3klIJsbktEQB91Ycvs67+1uI/DXGkrDeOAbgqrOjy3bYeIa6i70DGBqnHdOroo5JygQWbuiKl9ugkIK9amlVLPzVJUQo4a0ss52pO1WQa5pz0Lbuzq2ynGZM4AV1gV/uMU8DrHJd2megCBELoIEc0J2wFtbRobktfMg+nzKEJkVWHdzQY48gtbb7AAJFaTp88zuwliCkVhr4YssCyz7jxxn+aX/A73o711S7liQIhK5iKqhkXYlVSqqSoMXgrGFPkjFJ5+uBYZkM2niQOjKGuq4EDMKSamWHnHjbBYqBuaTul8WYgdQ73NXvqFDA2oosFqWuRlIixL9fOGhY0VKMzbJx4gDg+D3YEUanFIN6D7SAFvCkbT4oJ0wjGWFIqdr9dEnpRjC08ADPE5wyG6oVol5Q0GEy99prJq/4sGQUZcPKabypXV1+LBKjmAlMfUbbKSfVW37x8Pg8EreFY/ap/s4yKQK0ByXB8uok1DU7BO4vE0unOooQc0ZixVUOKHqjQ2ONNxuUAfQOqRVniamKn2M1t1Bxi+gVeO+hafFKOmV00zAdyW8Z0c+gzyYHNBlUhU/r8WeSIz6d5vSFnUIP4EW227C/KoYXhun+Zt/maTwjFPCimRIy5zE+FmIr8TWVAADQVL4qju2SOkJRi+SNF2aEZ7wqLv/YeJ0pol0TjieMNAlIMwiSCgWQKgc9ooIoHCJtQH8OdeYAwPkEyNcYkjPPklNDQHkkcLYpMp3hRJC4gGUie2Ac0LPH9PlXag7CgD5HGJ9RE6HucbTh3bETYKSd/gzCpXSFOuvqoAEBLqqDRgcUyFJC3nvT1fBpmo9yam68zAH7ncdsUAOsxn8/55V/+Zbqu49d//dePZHOqygc/+EFOnDhxFAd8O6MA63p0vT2gqWz2ImR1JAOHWmP6TZJtuV7PWZmzbKSGygA+kKLFzFuOTw5o/IIWS5uP8+Rzcyb2Eo4D+uo0L8k2tk7kXOP7DTbydRp5haVsEfImp8whm3mFDUtMbjFhjvcTxCnWLFiRuHbiUS7Vz3Js+RT84g+weukQx1348Dy5/xVuTE+yOV9Q+47DKhLYZhojdY7MTY9dXWH06b9Lun4Reff74cy7OZ4jVluyHTETixMhxcj+5ByNWfKJlw9xIbGR9tnWOZMIY+BALCsLvk6M+peZuwfoGSR7/5lDWRvWDDnn8RArmaU9wytbj7EXLnJClc8/f5Wf/PhLfP03voGVjFkwojZK3e9hyXRmhNgKzRnbHrBtrjOVBVmOMTMVturIuSLmCf7GF9CP/z3y9iPI1/4l9oGKiFhP1NMsNu7GLw4Z5wvoRo29+BuM//t3kpsJS12SEHbCAXvcQ1s9irVLoj8BdSLHlrbaQRb7NHqIcx3zumFlPRvLOTscYnSPq8uWv/Wvn4PxNs/rW9hqP8W5fsVT5h4Om9McrpRnlxX/l1+8yP/mXcf5rnsdxVbNDfIPSzTgUmAh8E8+t+SZSzP+m6/Z4eHjFbPs8d7QxFSIoS6RTYXocZ496PiRD3+JS/MDtvsaVFk2DZO4S8hLDiensDJmfPMmJ1yL1h1X6RmPT2KWjpFCWt2EJqOVg7xBrZZplbEyx6lD9RikzIQnsZ/7UfST/wE53Gdki0yTFEEiwW+y6mtq2+LCDFnOuTMJxBNcGb+HEwc/h5XDcpKPBjFnaWOH6Q1NCNT7H6GdP8vh+GGmBoKCZHhwMuIXU6b1W2zc6LHjY+wGJcfEaml4sb6DaxvXmV2OfOlyzzjNqbsDXJbSefGZpalAYRxAoqCNMHaXuTO1iLmfD714wBPuHIemwesuVdrDq7KIHVY62qTs18eoVBnngKXH0ZciQjytGnLVsEyFEzFKM876Q1qNXJK3cMfyac7Im9DasZ9rNlZ3MuE4tlNksmRVTQkyRd3djM0pfN5H6Wllg2b/RRoDh/kUi+ZhTuZrrNikmT6ChE9zrF1BB3niGacLuOU1rkwf4Fh3jckv/+/YuOMb8O/9c7TuTg4aS0eJUH4g9bxJXubD+VGer9/IMS5zqn+5FIKUhXQlhov1WbbDSwQ8HVPWR6UkUxKWSgM1SitFQTTRIQpMqv/pi/r/AsdtVQAYY9jZ2eFnfuZnjtj+a24AwMbGRnHGC+E2tQJ+1Ynzt5Se8qqPt8hyrzoZCLcggi9Ttr6ay762t2AAVm99Ng+wcgnQyTicczx43zncly4Q24YoY4IZs8ojMj1dm4k4lgmSn9J3hmZ2g9glfO0RzeSDXVJoEW+Rvse4jHcOWZVkM5sjlkD7uU8TblzC3X2BdOpdOOuw2hSCEooVqK0yMV3xefcO5z2nT+zwsrO0g7puvOZKDDp8r/9TqviBEyylHHAWrERyVvoMr1y+yqc/8xnOP3g/KUPlDdFSoFahmKoA1prSd80lmcyYTJ0Dtkt4k9mYTODwEvGJz3O12mPzbX8GN67wpi9yvKqmcg67juPNGXKHBNAUSaYDcdjmODt33YP1DmM8KS4hBSAT+p5R4yCWAriLCqNt+uDIMeC9IaTM7t4+/TyyNCc47izWOtpVz9gLGMev/NpH+MSn9znXTvkj93xdQd8EXmMLPHzu5z/0q1w9CHzz9B4e/tpHsNYP7nLru1oUBopwbe+Azz/xLMFv0eXCv2hjHLI7ZLh2gco5Th4/xs3VVeqmJsSIS4aHH32YFy8/weHhFToETM2RykITokISCJWh0m1Wy4g/uMkoLom+wWs7tGYciEUxdM0OzeYUrr+MUhGNxQybShSPtaagL6qkvkMGkt1q7wrm4Arbp95IlRORzMRYvvv3PcyvffBTJFtRGYUcCuoh5ZmMgw9ETEpUQxZDVHP0PoAiSFwf3mNp+s3jLd+AnHOxGzavnfealZz0NWtIQSNuqVEKV6B8/3jUoM5gRfnad76F02d3efFLCUc5SRsBb8oGWtcNYDApUUtGtAM6jPVoNaXPFfbsPeiFfYyusEZwGtBuhdRbqFT04qmtK6jYEAUuXYfTjNm7Tru3y97qFHe9N+OBbYWYM72xRDUFjZNATYcYKZkDlNYaJad0aHMq/7HutXyZv70+vvy4rXD0lBJt22KM4Sd+4if4i3/xL9L3PQcHB3zhC184ktXczif/NZz/ZafesPiL6KCZ1wHGenUH/dU/KfzWSazDppiGh8NoxGoe+mOlBRDEszITgh3hrOHM1JNCoFXPCk8vU6KZoDIiSUVVjdAEvtlEYw3LJd5qcV20Dtm7QU3AGqCPOBkgYuOprMGuFogV2rbFm4BJhxjJLCieBaKppPlJIaxtsmRsepwrvfM7TozBOhZZEM2M84qJRhBPEk+9hgG/mnsxqCxUahCPlZKmZp0lqhBiTwihRLYiOCK5b4808oXKNtwJzVjniudCO6PpLrMjCyTMyblEItvUExfXGeU96sVlfNrDGJDRFujgNQClAIixNPa7Ga6fE5stlsceZHzvG7BGiQrkHkSR4ycw9QY62gFbE/sV3grLZFjZKX2zw9JvUdcNGaEPhQ/ifAW+phmNaLsi2+qSMh5PWLahEOSOrHxeBekPlsXGGKyxR7VBuU7lL4orZM+hJ9vnRKcW/Bh1IwIV2Y1IOkDmA1FtY2PM3efPUVcV1lpSylhjeNPDp7jrzCmyrVmII9miHrGqRyz8aGFhhV7GpGAwoQXpac24FMamItnp4LwoxGPnSGfuK0WKHxOlosmr0s6yI7IdgfFoSsRuSTktKibMsd1VRvE6dvY8jV6iXrzMY2cNx+WAka4Y+YhoT8yAODJKTGW2dCkWhz7KPHsth0UHpz8h13AzK7NqC3ADMaOU+PZoBTBHG3seYovtEdy9VsCvV50ipTaUmOEcisLmve94iN/3rkexcVlsxMUigw+FkPDeIcbgMlShxa728axo+0TIDYfuOOaBd9JWx9AEJiWqvMSmFVVVodUGSzdBqxq1HoQSOL2YU8VIkwNjE0jzAzAOK0Ld7zKeP8eGFDvqThpG2jKNB4hmgtQYTEE3tMeoUmn/Va0Er48vP26rAsBaizGGv/t3/y6/+qu/yhNPPMHe3h4pJX7kR36E+Xx+lBlw23oADAvnby8CXn1SX5f6qRjD8WV6g/Lan7z1dyGJJUkBxJ1GLHEIIilFQC8Nc7tJsA2VyUziEmkXGFsTEEK2iCkPfKURM7+JnV8jznbxxpbAFVFCSiVV7tIF7JAtjgNHoG1bAp7Z3h7LC8+DglQjfD/DX32Sul8Qh74m+ZCGRDKOPgmbtVBrxKaIho7RWOilYjX00k3ap9IeNUU65vnKxWq3GOqRLEI2DZgaC9RGcb5i1mYwhqqq6LuMGoeEJS63OGtLeFGKiHGFgJUDGEuIkRsvP8PlD/84zfIVKhPKaa6uSLFnxBJtL3Dlo/+G8PxnIQbC1ina0GNyRxZbOuECOI91ijeRfba4aM+BG2O0Q6Uh1Sc5lGPE6gxXdJuFOYlWW0xNYFNaUrfEeUc724fYITmUjr8YJAVCiKyC0nZl4YyqSFUzXy5JxhN0kPMdFaLr+Vc2k6qq6PueGGMhQL5qFiOyLjvpVXnuFcWNNpi1EfyIZSynbJXyXBuUHHs09FiBqqrKSXE4LZYe8oouCcsidANdlaKMBjUNyQg9SmQIjTJKtp4gFUJETcPSbgw98czCH6cfnSGpQ/0mptlmlJaDz0RFkqpUNakldwty6KCHKre4/iLxcx9k8eF/ib70URaf+QCHn/k5ttNNaPexukByT58GnogWPgcCq9CVayuWMHBhGJQYlozFECJcX3T8i599is9eDOXosC6UUiqFj4K8ymo7xVIAuMHfIGMLZ+ZISFlapjEEGu+oLYNLZcaj1LnDJMUNygarAJEwcFdsBrtcYg6uYmSJqxyt2Wa3voswuY/r1RnCeBMEmjTHSCTmTJIpN8xpZmZC8sXx0hExh3tUKeJiQvoVJrQlOyS15M/+JPLxf4RbXqFHWZgJIzomcRcFVnZcVEwaYCAE1nlt4/X6+N0Yt00BkFI6Yvp/5CMf4Yd+6IfY3NzEe8+JEydYrVbMZrMjGeDtO4ae05cbRwl0w1KrR+et9Tfc+utrCFZrEttai2xJQ/fGajzysve5x2ogSMXCbpJMxSgvsfGAsUnU9QhUEQ3YsEL6OY2seNPphv/yXQ+y1V+jyXMg4STTRyXbGmb7NNohoS/WtQSqyZTq1N0I0B9eJ4aScW60w176IvbwEh6Kj324iWhLNo5sPGdP7hSuXL/ApY5ohKUZE4posOCiRzDfGuL8Ku+FBjKwEk+WBskZG3tSyiwSJOsw1tL3kWw8VV5x/9kTg29BIbIxeBhICsSYWS2X7F14koMnfplJ2kNJLFPZiHpg7DN0lzh44peIB5dgvIF57D34zWOgedh01nusR3JEY2J0/nGOv+XboC4GNZEKTj7G9N1/FPvO7+Pk134fzaPfQl8f40Sd+K6vu58Hjhu2OeSbHz3Jo8cSpl+UE7dxZUMFAg6xjrqquDlrmYdcXCStI5vC2M/DC3rNdRZYLBYYZ7G+sNGTHt0VXlVmEVPm1z/yKbqQConSlhhvMULSYlBTIGeonaGuiponhISxRcpnFbZqqO367kckHWA10dHQFUEeI6CKc3w/w6iQ7UYpqFJpzwTjEe2w2tOaCX77HDI6xnJyhua+x6mqgmhkLbA6KVClJYRFKeAteI24+SWuf/qDHH7qZ9CnP8rNL/0KV37zFxnnFUZs2T0l02WHSoVKQkOH1USI7UCILL9Dj47wPZoWVCZjDNw8XPDBX/0UFxdCsb0WyLe8Mor5UFGJBBVSKumJXkurIoscFWM63EdjhFIaZEgd40pwIhyfeE5uT0vQEALqCigpiZCFnCskeyQkzHIOtGRrcXc/zrF3fgf2jsdpHvsW+p17AcHnFvGGmMGcuZeNx/8gceccwRhEMl4DZrZfskMisMqMqxFRKnoyu1/4EHz2g8j1Z1hpZM4YmwN1mJHFszQbBPEcMXrEDC2A18fv1rhtCoB1EqC1lvF4jDHmyOt/b2+PGCPj8fjISvf2LQIy6+zZ12zvr+mvrinEr274//YWwNHmLxzJpgqL2pCMHWDCUgAIGa89XnsCjtZMEGAzHwzBOrdQA0/CxQXkFhcXPHbPcf7wt72b43kP294sISBauAFST5EcqE0cNmctiWzHTsLxc6hxbJgiCepTKJndqxsQ9mgALwHiAige5WMXeehUQ5ctNqxoTKIzhtaOUbWF0mMn9FKVx14z8bduTP8Zo6y3JZgoIsyBDluQktwRkpIqQy+GlBXnCtN/bCKPPXhXUTkYwRopkbLG4E25DxGo0yHT9jKNdESxJD8iW080fjAK2mWzv05lAtGMMXe+Ebd9GhVXXNa0XEtVQVPE2IrpQ+/i5KPvBTzkSI/QHr+XjXd/P+bhP8DO276T8aO/n5Xbpmmv821vP8MjJy2b4SZ/5J138fY7lMYEFEPKxZ1QxdBqWTy7riUaS4/FutIKCK/Zyl89DUsRW9U1xhXbXtUi7TTDKbVA+mu0S2m7FUZgMmroV0ucAQ3dwL8wA2M+YYlFksjA0DAG1YRXmDplO95gRzusrkBbVGWIDg7U+YANneHjTWx3iCQIforJbSEA5lDkYzlQ5UJAdSfvpR2f4qbdgTseKpu+hvIacoTcUaUZNi3IKUJVyHOSZoz66+zIDFb7mP6Q46NUDvJ+Si9KnzOdOpJxxaUv9jiN5NQW22dNZM3D9U2gfXmWcoszmaRF+9+ZEVlcQWOGaGEznAIylsRgD6wMzPhBecAaU5QjnGzdGPCutNQaZ9AM01HDmVMnaaMhqCDGDRbbgWQcIddorodCREES2Xgmb3g7p971h3F3vYVTX/+9yH3vAOMhlJZJFzP29D2cefd30Jw6D6agL5X2mHaGTVp4AQnE1nRS01d1kQzmBcyvkDQT3KQsd6knYlmZCa04stSoGQ3r1+267v/eHLcVk25t8fvud7+bv/23/zb7+/v80i/9Ep/61Kc4d+4c0+mUvu8LdHi7DlVi3yO+OIaFBJUDpzLgrIVpLUDWSIiJqm7oQyynsAGSNeu4S2MGnbnQ9pQTuOrwsJeYTpVywjCxpZZQIE3bUOcb3GH2iOZhDvKIkRV6BKsRQk9JcrOlDAxzqrSAsIKgmEpJrmGZhU3v0X4+kKuKnSx+RGs2UbHUi1dg/ymmOh+KG4GqLn1OMagboSGyYRec7l7m/tCA38HEmzgCaksP2WogquHiomFmS0/eS2bJsOl+JbcBhsS9SJuFF1eRO1whFPnUodZzcTeRxZBUMK6gE66dc2LiyesAoRigsoS+Y6QRjCP0PeN4gNUZ4i29seRqAmZBtDWikSrMmOgMS2TlN/D+OLk5UTYk15M6i5iK0CZqZ1hkw3jjPLEaY/sVSCAAwVTEyZ241JLqE0jqSaZB915g3F5jKh02LDhRw6YrBK7gKwSPlXICnfXKvF9xrPJcWwR6P8L4yM3ZIV0sG8RvO1UNfgFV3dD2SyJl00CKm6PEDmxB40IeDFsEvGhBpXKmshabw+BfYEqRKoLRSG1LQaAYrHVo7PACJyaeu5dPcWb3i+iZ+1Hx9HiMZvzsZZj/Jmb7Tkg9vi/lS9/3NL5wK4wGxiYOnhqCd54kE1ajO1hWd0J9HMwIesU3Q4ysz9h+Fzu7jnEOeiFaxVjQ7oBKOsRW+KoizuZkN2GhE9COsXHMOmgVIFGj5GVL1hZiSwwdxpoSEqUZsYqTiNIhjMgJrCs+niEpCU/OJQJXckKtlq8hdDGVc32OWBLOGFJMqBbHQc2lpZVSwhqDM0UGajUycVJ8sI2Q7IgeSDFhTUZlRZcTzo6xscR5JyOIH/wO7BZqhYVMmW6cZ+lP0lDih7EG1IPUpMk26sr8xxRfk3TjUsFtrANX+BJGDT2+tAdDi3a7xRgMQxKHrQ1dAq0adnPNK/uBY17IE0MYuE85F8S4FNLyOt/vqxy3DQKwHqPRiD/9p/80Z8+eZWdnh5/4iZ/gzJkz/OW//JeZTCZ4Xzy2Qwj//36pX36oYr0vDyJl3sdcbFc5sl69RQqy1tB1PTlDiKl4mr/mrgynXy1Q6rq3mTHlNL6OuxXDmeObjOgxAjEkqtV1vuF8RWe3WDUn6artcioJczbGFdgRS9lgqWOop1CN0VQU2ilEkp8ypyantcmLA1MV8pqpscfvResJh09/hNmP/0387lPklEg750h2kyjQpQr8KaLfZJqWfOPONe668AEqazBiCV3HVJTTdUeaH/DC3pz/4//zp/lnH3qaQI0zhj5/5XV/2b4zxMATL+7z3/3wx/h//NivkkOPpJYuCj/zy5/iMEGfhTZkViFz17Exm67Aq5rLCTdlxTtHDi1dSKix1P0eVVqhUk5v5XRSnOL6qDDfx8YFIobOb6PVBmFygkOdlpNqgKQOX4/BVJjpGZicGU7kJfbUIiXWdgjfSVIsV0Uci+c+w8FnP0bXBibHz7C/WNHjkVyKqBATNgdUYXfWkTLcvLnL3/tHP8a//7XfpOsDV/cDH/7M80cNpltnSQqkbAyLVYtUDVI3ZOcY0ioKepEVI5bKWFKCHG/1ai0Jp4FaCgqBmGKgpBGnCe8MVeVJKdOHWBQiBo5PHd8wfhn59f87lz7/EdRu0GZHHXvyJ/81s3/xf2D+qZ8FGZF1DKFjs06kLlMMh5TcHiCxuDUaTTA9yf7oHO30LvBbtG4TdYW7EJUS3nT9EnLpWYx35Wv1lNSHkvQoikgzcDcibbDE6hjqPcZYPvHJZ7ix6FAjpLZje2OTVd8BZWNOCjhKQFReX+dUPqah5SWQ1r384Rkv5jYGNSWkq8eQrSWmeNT6q51gpXy/NbcCxxApCofBHtgZCkeCjK2nZJFSVushpF1Sasl40Aq0uDW2zlBSvkaoHZOtITBB61MkOxrWMU82FSqWaKalYZEiWS3kiAmHlBwLB3WNcyDaowhmtEnulqQrz9D0K8YSCXjm0TCaTEjW8KM//THe/3/6H/jHP/7/oYUhzvsIQOOW9uH18dWM2woBWOv7t7e3+bN/9s/yvve9DxHh5MmTTCaTIxOgEAJVVd2WREA15TSUEYKCHXqgBnDGQn4Va1cHpzwDxvmSjy7V+rkfTrEFFVgbW8gAoeoRl+BWcfGmN97Dpuxw6QsrFkb4pofu5TvfO+FjS0vOxet+qoHt/gotO4jdoq2PsWJCbSe01IxlhDMrEpb6jnuIyzlcvYJ0PdE4vPWYbgF+jD33MObJX2Fy+AQczrCjRNvWRHcCT0UFiDh6s0mLYdzd5GF9hgdnz3HMfz+rZBjbmlNxydnuWTb8A+xl5Us3EjfrG5w+toMa/1WZPh0hAKHn8t6M33xlxur6LhnFGUVdxUvX9nnggROIbzC+wtZj3vbI/dxxbIJzHhNanBHEOtBIZQXEsGpXjNKMSEKlIuIHgUcsqoEEHO7jUlnogtQgFf6eR+lOP4C5/jLiLNmOkWzRaBnd9SZk605gsK9Vg0XZ1A6bFmXBBKDAtmO7YrV/gbq+h90bK4JtmItHcyI7ixpDZTMiwrxXVqnjRF2z7CxmtFV69ZXj+u7+QB8T0FQUDgPVT9fz2Xl6tYVAOhDw1DegkQBEKftEn4WY14Y/GdWyKTEgWGsXSIuULPkh8AojWAMTb3jnW9/I5t5Z3Bf+Rw4vP87Zt347DqFe9MjVz1AfPs0Lz3+e+9/2XyHVMUyIZDlA73gj2gvsH9xSjGRFYovdPsnZ93w7pydnwAqt1FT1GH/XG0kvPoMs98lVT24PMZWwSpZUTTA5450tCFE1JZOpbcKbCpWatl1wwlnmhytCTjgBa2qWbWYRtVh5Y5gFpQXGxpGSx6/vphYesGgqmQXGlcPCYOqD5HJQcJ4A7C1XLLrII48+ylMvP0FcLpiMPTlHVOPQHuWWqVo1EIVdYd2XlkRPTOuApB7RXZA9JB5gcsLksqokKyyNsGNAtcQxqRGS8Wzd+QD9eAs6Q3ITYqrKfBGHGbKRolQYjZCXqBkRxRc2f9ijliVow0E2aO5J3U22WbEZb3JDJiQ/IXULnCSiqbjJBq/srQYzpNtvzf+9PG4bBCDnfOTwd+HCBf7Mn/kz/Mk/+Sf5gR/4Ad7//vfzkY98BCiTu6qq25YHoLkQopQS9/rS5X2u7c5JWog36SgkZ+3IXljQi8WcdtkyCJmPWNjlvKBH/7YMUrJCI1hHYFpiho068dY3nCcuD5HU8o57j7Ell3GzS2zoTTbmz3Ksf5FHeJZTy6eQS08WHxRbWO5hiEUtzfcKTt6FPXUeXA1JSWpLMIkKakZw7DxaTTE5YLoVzHuSqVm6DZxTmhvPUq+uEkVoxVDTsbG4wHFzjePhGofRk/2Ec+1LPNJ9gdmNi3xpqVzrXTnBjLdZ5IpRzriv8FavUwWpHK0aFtU2rZ0ASupbkvH0/hgv3ZTSdrh8jaTCPWeOsT2ypBwLMTWWmFXVokFetB2h6/FpjlVFB6qW14DJS5wWBEZmhzjKqTRmW6RbJ+5jNb4DjQH1llYdfR+J9RbpzsfQ5jj1WiAy1OYlpKVAnRnIpiADZr5gM+2RuwUpBpwRulVLjj1iLM7XjKoSVZsHi9kQBn5ItoSYmbUBrD/qIBc3wzVBtbhUiq1IxoO1LIJy4VqBarOxJHXst4kr81WBlF1Dv7Y21kIYVLHl0CulfbW2gbbDFLfWI2KIsSd2K84d3+Ds5janXctGf5l8+Us0i0MkaLGfriDmjpAcbZdQMUQ/Yn7nW1iefJybcYrxNeoBFRqrUI0Yn38zG3c8AFnRasrcbtPf+QizPC7EORGcDE5/1ZjkGkxVk3IgpB5CosktkzzDhQ6vwkatNNrjxQGG+aqn65VeR+y2mUVQzHiHlZnw0n7m4iwT7ZSeMbmUx6Q4oHoK2RhChqh5aOsVmXA2hov7SsiZoPDIo6cxVhjVntitEC0qoJQiVpQUSzukqmpABolfQQoalxANxSQ8H6IHz8HVz1OFm/jcIbk47mVRWkpBq8gg4S33szp5FzraAONJbkKydWlvkAp1RB29jIuOXyNIIFYNeIdbXKI+fJZpvIkxUsKcupvUlz/DqXilzMRqgiz3mabDMk91TPAbANyOBrC/l8dtczmLWYhhPp/z5//8n+dNb3oT/+yf/TM+8IEP8NBDD/F3/s7fYX9/n67rMMYUH/3bcRgDpnjE781X/OzPfYgf/6mfYdl1DMXx0eZfCFBC13e8+OJLvHLpEn27HFhWt4YOLkFp8Pm2a3crYJ1QZlyFsw1OczF86RdM0i7p536Y2ad+nLfUL/Ff3LXPyU/9Y75z+zm++/yM1ad+Br+6wpQVRpfUcR8X9kEjah19diUaNBSb02Q8XSwba4cn+y06dWTxZF/Eetps0YdAmj3NhX/91zj89I9h44KZgK1r6rrGhMjxxfO0fovdTjl74xP8vulLfP5TH+Xv/fiLtNkwNj2HPSxlxCQWj/OvZKzZ16iSjOcwj1hoVWDonMnGc3mv49c//QIhC5/9/BdJIVELjKySU8KKYETpQsIYw4njxzh58iRnzp6BvhtsnC1GhSp3mDSnTh2VKLKY4Yd7lFPp51q3TWd3ivUqnugnhJRpGbMc3UlyI+qcISfUlg1CTYOaTdRu0gK9VBgRrM2M803u3bacaQL3bDoeP3+SrXFFHxIZoal8KUZtVTLkxZDE0wbl+PETg4a/RO7KMCuP6JbDPO5ipI+ZiPChX/sE//Bf/DQfe/JlOmC/S/zIT/87/tG//AAX9ueYyXGCHdHhCbYiURHVFjXJYPJ0KwtikBSKEkLAW0tlShNFQsT7ivGNz3P4c/9X0pOfRHrA75BFSCQaVjRpBbZm0dxJu/UIuxtvpj32BpbJk21NcGNCtwBbMTfbxQvCwipbZu4kZuMcYeMsaeyQheKMsGxbzGST/VkprFLfYZyB2LJjWnZkxqlaMO2cUVrg2n1MCGSF3b0Zz1+4Sq52+MKL15hnx36w/LuPPclf+zs/w9/6xz/D5UXPQsYEGZFz6XUnBDWCGkvIStJCQoSMinDj4JB/+mMf4CO/cYFVCIynSt+3VA5yv2JSV2xvThiSKQbui1BXNQolvGdQtFS6xOYWRyLvvsj1X/9pLv/Cj1HHGzhdIbkvz78m2nVCIFJaNGUxKoiAlGClbCfFJlsDRkORelLRmY3yeSlIQydCMpnx/DqHP/+PmV/4FCNi4Ru98gTXf+lHeWiyQDSy6pVjVWAr7eItxHoLN94sD3W+Tdf936PjtikAcs5H/4kIf+pP/Snuv/9+dnZ2+BN/4k8AHIUBqSp1Xd+WLQDBIKb0Fp2FxWrFqu2PNn59zXlLiguacxzODgfJ1a2uzG/d8taw9pG3+9Hny+/zEmhsJIUVG+MKO7+BOXyZxya7/Lff8w7+9Lc9yOTFn+fd44u87y1bjPaeYKQLRmEPaa8zyjOMrpCUwFVkLK4qJj3UFqlHJca1qsF6ME3Jf/cTlrKF1hV9MFRGcfESJy79KqNLH8P3BZZts7AyE1Y6Yqu/Tm/HtNkhr3ya+/QKpJ4LN3vG0ym2nw0xo1OauMB8hQ9+OS0LhK6oJmxTTipDbzTETJcNB4sVKoZF22FchTVDfCoc+U2sC86vf/ejfM93fz0P3L1TsNvhjq8VGJI7vPZYzchqeRTLKjmVFEYErccggp9scuzt72R6zz1EPMltETEIpXe8DszppCbbTZKdsAS6gYhJUnSxxx9694P88f/iXbzl3A7f8a5HOH/HiaLLzpmm8sVKm8IJSCmRxeKaMYtV4S8kOOKV3JqhlCJEC3vb+Iqo8MUnnua5l17ms08+TavQSeDDn/48v/GbzzBbdfjJNsk2BLUkqYjGEdQQUyrPNQOmNSgI+r7HGFvaLVI2GQfYUc08wbR9hfGFX8S9/AkQiM1pZvWYRET0ENPtw/EzbH/Dd3HmjV/H8Td9C3d8w7cT3ZjEiGRHCCWeQau6SN9SALEs3SbTU/fRnL2fLNWwu2VG0w2qt7yVuqnJKWAl4QU0R/JylzMblvd9+9dyx05FQ8fprTE70ykYQ5syCc+sg+uzjig1dryF2zjO9YXy1MVd9vpIhyXgiQgxrwOZDGpK6Fc+kvcVzaVYxwsXXub5ly7gqhrnLeNxQ4o9zsDO1ganTx6n8r4Q+2yxEHJ+kAoPjkJCxmmH04DTTDq8xvzSUzC/jE/z4v6nYWiwJ2LqC8HQlGJa8lC02RKvjHjUNoNWP2GIA1/FE01TfDUATCIZJeeIoWP5wicJey+iYVmklLNr9Bc+zz2bJdLYOEeVVkx0Se0MQR2zxQqUYpP++vhdG7fN5RQR+r7HOcfDDz/Mxz/+8SPN/9NPP819993HeDwGOErSu91aAAp0A9Gvjj11MOyP7+Nz9l5uqoGUcAlsV/K4o3OoyQRjOKyOsbSbONPjcqKl4gBB+gVVXBCM4aA3VD4j0uFiQ8x3MHNTuuqQSZyzvdKi+590tDHj7Air+5yZVDz6hruZnjqHMY7NuMuOX+CsoJoKopA8nb+DqFvoqoLsUQKTbomVDp0krG1xORMchSW9CuwEg80RZw1kg0krJC7J/jhXNx8gsMIsP80pXbHQmug28Rq4Ue9Q99d5U/wC45ufgRSpnWWjzoTVikDFKOyxkXe54RvmxhbC1hqpzkOPP/donqNxTqZApCHBWKHOGXUniEbx6TImPM9SPau4zUZQNrpryEjYr6esTM1GXpDEs2kCO/E6MzPmqj+NryyWwL2na77//JJ75FnmdkJrR3j22AhXSCKom5LyiiZeQzUzc9ska/H9TXJYQNynXl0j+ynL8Z2ke98L578WxVLlXYREmzykck2DarneRmgFNhU2U6LPDWk6opse54E7NvmDj55jhDAee2bbD1FJZjPchGqMqUac7i9wd/8cY1qSGrx21P0BIxOw0ZJbQbUHZpBWaFSQioRhS3pOdvtsaOKm2+DC9n2k1jOiY4HwjH+AvdHp0ggxmdBuMM5T6rxAXST5hjDfwriOIIe03SboNll3McypZIQIhLCim3WMNWIIkB122TIyc1x1HZ0GWlthujFbS4VuDzWBdPwh7OPfS3PyDaQ734Ocfxd1nGN1wUETWWxMkT4w6jKz3pH8JhvmMmIOYOyIo9PkMIacaIkc3PlmzH1/kJ3xeZrDFR7H0mS6cJHoI6Ha4J33bPPg2BNG97PceSPLumFhIHHA2fA096aXoVVy3OTY7DISdnnl2Cmem+7QyRCBnBSMcqCZXldsrq7jesGppcsJNYFRntH0kVk+ze7kYWYq+OUL7BCRXLPMm2DHbFfCicaifUdVNTiBShJdFpb1CVamwkuJu3Y64VjeZ6pLcjpkijLKlt5MudGc4MXpeXYn9+PZ4q6969ioaCoyzATFOIwlyXlmpiE7xYZdkDkqPW1V49yM7fAKYnqygpHIOHYYJoiDcTzEaU/rxqi1kHqO+xUnV8/wWP8pHpp/FKNzXvZnOTATxqbFOCEYMKHD50KGXNkh0jqBSeX1hcFttUpQDS7gR7Lr18dvG7dNAQAcEfs++clP8pf+0l/i7W9/O3/oD/0hfvAHf5BPfvKT/PE//sf5vu/7Pn72Z3/2tjz9r5m8qoLXhFVhaaY8uZd4/qDHG4MT8FIK35AVREk5E2xDLzU66KP3e/jwEzcZ1cVzPKmyXIWjtD3JjpwnBPEkG3AaaDI0FmylpGxQ9cV4JPTUBqxxdHFwEqg9rpmgDMYuWch2gnVjsJakCSUhbQddRKsxoTpOa46z8McIfgqYwujWSG2KR3ctEZcXqPGs3BZ2tkf+7H/A3XyZSVMR+x5jO1Z2TJVm3DM6pFpdK6f0EHFhAaoEqctGRcfKVSRZkxNe+zDnWCx4sybWamsrYDOYpGi0qGa8rGiqnoyh1xFOqiJlM5lgLT0Gr5EkjsoqdW5LhLIZo/2cKrdMc+KUzqg/8+/J168iGjl89rMcNwf4vCS88gpOFKcrWM5Y9AlVGHkDVjA5M25GdNRF8+xPkpu7mDMucc45FQRIdYjuBZtzOZuL0ig0OR3116MILmYmESQUKWjCUknx5zvolBUNwdS0MmLhtujdCHIxeiqIkkFMsW5FS+YAtqgZsrFUJJp+hddM62pu2jHQUEkmIOyZLTozKpayqWPsG8YxMs5z6PbxktDekImINzi/Sd8lxGaq8YgUDaEPWG9pRptlbqa+BD9FBhmpoq7QD+ssTNoeDm+AE7Kbgj2Oyrh4VjhPNUqYOrAwDSvZxKjgVMEbsquwcYZZh0SZCkx9xLG40XlidYrUJdyzX0IPdzFW8V7J3hNDYCcesmUTvd3Aj4oHQYdiTcej7kXeOi3FXLKGMS1j7VE19Mlw2CrzReAXfuNFfv2ZKywqg69qjrmGxo3ZO1ix6jOaVmyYSBUDSSbs6ybzoGiYMbUZjYr6DYIaTL/geFPmmdqaPiomRRoiNvWFC5ICkhK+W3Kfvc7yi7+A7L+A62ZUoaUhMnKBqi5WzjYqmwdXgfX8M2RTWjgiMNOKpT9GqrbIrgGpEDeirY/RBqV2K6wfgTokCk5BtJBlZWhhqhS1ApJxecm4u8Ld+hJv3dxj03W0bkoQj0stmjMJcBIxWlomYZC5ogwpgZRYbYpyxlJ4J69LBH/ncVupAKBwAf7CX/gLR/CrqmKMoW1b6roowR966KHb7vT/24YqYgsEO5vN+IVf+DDf8kd/P1btUciHDD39tQq7dIwdiuWZF1/mx/7tvydxouhdc8YQsVp0wTpM9PJBiomIWFzTFD3wACtiLFU6RNIKk4rBqqkryJmQlEoFJBVTIA1IbNE60ElHkkwWQ68buJ070M13EzYSTFfYs49DM6E1PRObyP0KISB5QR1nmH6XrbSHu/Ics70DXtSv4fzJNxPqjCyLbbEhcurYNjpbINUGSi79RlcNmmjHKmTagShljA5Sr7X4R1DXkAfTINXCLNcMwQpWHLYqGefGepJaomay5tKKMB2Q8bnHqJBMhRpz5CMvFO9x38958OwGd22Nye0LXP/UL7G1ukRdrTh48XOcH+1wPO/ywuc+yuMCdBld7rJRG0yEPsEEQcyE7Qffye5zP43BIn4D7nyc6Ru+jtGJ80dEqyIPSVSFqn10L4s8r0gTDYrLPWMJhWiVZ9Q65kS8xksWVnlM2N+nC4lDf5bW7ZBRtuJNnMYhLMoQbUVQyDRYMwUdEYHUJ2jsURwwMDSvMtHY4p9QjGjxtrDMtZtRR/DhgPc+dp4Xb1zjS9duoGkbcSP6nNiSjNXAKsKKmpDADbLW1gi9CiMtFteCkKUGfJG55R5JK3y/T37xi+R+RqpKS0WzYIyiumRFxkXLePtNTM6+vej+reCMIilipSExBWrUAUbRCowKG36Exszs8CqjcIXaRjIV2mViqjH9ARvdFaYa6LolWxhc3CdrpkqZd40vs9o+za+mswQ3p42GSXTk2YJRaLnw9FXM3Q3/tw98kGOn7uAd73gvXSvEvMHKJV64ss+u7TCx5Zve+zY+/PmXOWwTMSnzXqlzcTE0OZJMMXSqmFPnBSm0zJ3B+Aafdnn4eCSehnu2KhDPZPM473pwB9/2rH79n2JPRPzyBiNtMf2KSb/CmSXJb2K0LyY9NpGskgbVURaw9QajOx+jOXYCP91kYyMj5iToFE48zOHxtzDeVOLuAcnByEZUGjQXO2pRMDlhNZSDnFqcZu6cKu/anNKOHC++tE+TlkSzthVOeKDKJchprRjTW0eY/3nW8f+Fj9uqAFBVmqbhe77ne4DCCzg8PGQ0GrFarZhOpxhjjngCt/sQAec9zjleuXixLGh5MB+hTODCujXDdiMgjixw7WDOYRvRyqFDH9pL6Z+qGLIoImn4qWIN3KkpRKIQMcZijEMwNHoI8SaSD0AyfS59toTBuYISSLdPY4bNxGWChEJGNEK2W6TNN7H17h8A3ST7A9L0LOqnHIY5E4YTqxG8RjQdYg5fYkcPqPIBog3sv8Jjb/8WunvOYr9UksRcDuxsjEi7HVKDaCamiLFuyF13GD8qLHb6QZ5WpE1ajMXoMfS5WPXWAp6MMaWTbtUy1oCGUNz8RiO8JnKOrNyEpQlUuk+ViymLGluMl0xh9hstdqo7PvG2e7Z56MwUs3eTanGVRveRuOSkP+Qu2eVMvIw5vAix+Djo8gBrPFksdrRD6fFWcN9jSLUB2aBmjDl3H8fGIMdGBFEkUcxVRKmG93mUKlEIBYhRjCi+n8PqOpgG0h5V3uGE7hZWvXg2FpfYInCBbRbuFCYv8HqZKrcs7QatGdObmuSK976RCTk7nAiVL+E2JZumAIXr3PtoHCrFuMZkcK7YJtcOGllyrO75r/7QY3zqmQtc+rmPE5aH9FQkUUzuaGwEP6Z3G2TpaIwl9pmVQBDHWBw2R7AViRGKp8oRrx1GEmOd0V16Ah8OMdO7Bo+BcolSZdhNwqY/yam3fw/m/DeCHaE2YLAY6TC2ImthsWMzyZaH0mbLhptgRPCVYlyAWYs0Blku0VisemX+BGc5RaOb2Nww7mfUWaHruTe/TG832TEVF+UuOjwey6hbsiGZw5s3ae84zcsr2F05HjEG48b07iTGzVC7w3x5jbedO8X3f+ubWPbKxS8c0CEstWLTNDgnOIl0faAyhtplrLZYa9BqUuZcu+QbHxjzh899Eye2JiX8xzZ809vOw43Azhc/gbSOOhWvAAC6TGX2yX6M2hrVliSQTKIUoENB6jc58zXfBv0cqUbUrcPUZxEzYvuRbyBtOXJ3icVHfglT9dQ+ElaJerhJgqAacTkWIq2CccrpCezcc4IXnnuZOzhOkxcsjAcRzBBc5F9lBRxTOko+fH3//+rGbVMArDkAIsKHP/xh/sE/+AdcvHiR0WhE27YA/Jt/8284ffr0V6UL///VWJ/XyHk4jZblu9gby9phFSjoRiGIOdAB3BaLCnRq2Dxxht19GaSBxWmtAOGlSHCSCuEsW5LUtMZhmhqJEWcALNk4zM0X0Kc/jMZ9rGSMGIwBNRVZhfbqBRosdDOwBRoVI0N4S0tOli7vUNd3o9VxtOlZuppRWtHlFhxItkDGxAizq/DEx6mWV2HVYfI+W2bJZhWY0aExU9kemzs8JXioxLBmjC2eBhlhGSJ9Nrz4Ss9y2bI98SgDs5hibDbPyueevsz22PHY+VMYUSRlLs7g0suX+fq7JzTW4H3DKhjQhE0retlkaad43adOPVknHAXTmIKwlLy7SDi8wTieYOp66PapdIWlQ5eJO060nDx4he1Z5rgtBD4o98aIcNh5bvYVWxHaBqpmA2M8lShgWY2njOrHQQ4xeQgI8h7IuGEyFRXd0AIxuRQBosj+FdILn8bcOUNvXMCcexObtqNTh9PEm3mWs/aAX5EN9tOdjPKMnXAdpz0rO6EzIzoqOlE++dxVvIPQOjaaKW+9uyYjZBXWQTMldTIO1sxK7hWnSon5ga5boV0G9rhz2/LiWDAxIiawCkI93kR2D+i7G6h5jOimhG6BNT2kHlMXn5wYMjaXYJ9emuJWmVe4vISUqOMhi2tP4aWFE48eFQBZQE3Nvj1Fki0mx9+MHZ8BCWR6DAFjOmJOaGxADMYEgk3gaszCs+EmaIBgQLoINiNtgJvXCNETnGX7pV/m0e4Md4++nlbvxCZlS5Xc94QwYyO9zNnUcIE30VFjnEU0kEyR12U1YDaJcpzZXOk1sSuOzX5FqCY0bsKpTcPZkeVYUxwyMWOCHRFlgrWl0BUD2EEIXFVINWbWJ3xqacKM45o4f6ohJy3BSs6ysTMF3WPsF0gniNthlSsmdENDuKgQbuo2Y5lQr+WoRfyJkQxmDNv3Qp9Q22A2KzS6klOwcTfmwZru4qc5zJ9gLA260aCrayXzQsu6xJp7pBa1hR9Fv8fmZMnOwROcqx6nji2z4XAz4JmFrCoDAhBjeV/y2wnTr4//vHHbFAAATdOwWq34m3/zb/K93/u9vPe972WxWFDXNc45Tp48eZQPcLsiAKqDbl/LJmqtJefMdDIh5kwt9mi2ihk29bLzD8zvkvwlvuZg0dMGC9ajKCEGwBbtuckoxffc6gCSecvGVs3EKQdtTwxC8hXh5gUu/eKPYcOccwa8r5CuI2RIalhcexldzXH9IdgIqlRSISpYXaA20GaB8Qi8ozOOTqARyG0HfQbjICuRgM724KnfwMdDcGByz3atEOfUztA58KnH5Y60mpeEmME0xntP33Wlt5mEHsNnfvMZDr5pwh2TU6z9ztcnvk9/6SJ//1/8FO98073c+32/H+8yThwf/fxz/OT/+EHOvO/duNG9OHEl0zxH6niAyA7RVhAVlxMRwUo66nGu6QZOlHvvuoM3PHh/kfMt5xhjUXUgPZOwz53cZPNwhWMfdRV0A3piPdXp+xjd+TZgdCRNlL7Eo4pG5kCyDdMUMVJO/mocSOlhrj0gSgmoIAmVjK4ihy+/xM3uA5w/dzcXX3qenW/+X2PscQ5zwxl2+YOjJ3izexE4xovdSUbash2vE8UXhzwKB2Wlyt/9Zz9BGzsqprzhzFke/99/ZxEGipDEFcMqFCtrhnpGQyhzLxs6BPwIVwdsX4JsEItW24Oq1ZO6TK1L3vWWBzlzx2m6555lY1Rzz2bD3pXrxO4QdASpFGpQDGW8ZsgzyEtIGdElfn6JTEaNIWouhS2CNcc5/ZY/gu0rrD82oCeF0GiIYCLRZWw2oBGRniha+t7ZYEcbSDXBnTxDf/EZGgfaOvTGZerRGRZS0T//Ed4qx3nz5GGeNhPUTJm2kJPwsj/GpkTO9xf4khUOGTGrHdmvyHGBNp6pWMZdxYgtKoWqUrTu2FgcclN2SNUmUzdjlHumLiO5RyQTbUOUpswHTdS2aPp741i5MTeWV5lseu7fGfF2e5YxHWgN4kgB8EImQeogG7JzdAlWzRli3mAr78KyLZ87/VY2HniM7EY4TIn2He6JUtPj0EaIqTy+lQWbMqvkqN0pYBvjanLowW0OUeVd+Xlx5XnXjOJQV5FjoqFntPssd+pVztgFJkSCeIKpUNY9fntUDKeci8fEsO6+Pr7ycVsVADlnUkpsbGzwvve9j2PHjh2Z/qy/rqpHdpfGmKOvrT9vTHEdW3/Pl2sXrP9//bXf3WJicD0xBRYtiWiGuqkHN7T1dw16XV3LzQY5kBQ1r3UVEUFdhTpPMkLAEBKId8Qc8AVYJWWhz4UUVlcwMZFx7rDZEUXw0jNZvVyCWQjkrDgpJjZiHCPtcd0utRvgiQy6dDhpEBMQ22N8BtuS7ISQbVlYNTNSi0ZBa0+2triBCaRuVuRHpZ4gdKsS8pEzJLAa8FIWtwJ/lD5/TkMIiggYi6vHdN2SlMr3pQEOLJEDwjIJu9FzEAXrPEY6SInsKm4kz8JOymk1RPzGJhoDG/Em0zhlrzqOGDuYl0AlRVZV3crqAeAPfPPX8fbH7sDaA3QV6N2IOgecaTFhyXE5ZLNfINISaTCVYOKSlCLVQ+/gzne8D50co9jFpGJTG3qEnoTSimVsxlhNpUgwpqARlGCpI7xLAKvF0r02jLRnvLqM7K8wh5fQOGOZT9NX2zTdJR7Xp3k4XeCN7iV24vUh0EdRXxddd1zgtKfPsB8Nvd3ChIpX9hb0WhZXxRbGdQJXVWgOJM1FTte3OIQ+GpYZFtkRTKJPPRHo8MyZ0uQVlWyQliuONZn/8tvfzuV6RKUdZ7ZqvvaRLT569VM04RUa7iitqxzIKDGDzz2Em0heDR2gRG16ohrUOcQoIisMY6Q+x8l3fD+khNnaQS3ELCR8kaihhEqHAiUTQkc2FbjSLpOccZPjjO57nO7Gx6lXkWjHqEQmJtEK2G7FWXeZx+0lLkjist0kqMFKzW51AmNWbMQZlaupneGmKLEaU5lFad0p0CaqVMiqlayo+1c43l/ihjvLUh0SV0xtwEnG5IixhTQXsuAFcgo4nWOlK/4QdUOQivM7jv/mj7yRR6oH2NmuilzG1ZgEExKqLTMyiRF24FposwNn30V/8aNUqxcwkhnd/3bqr/tfFdKkCqJFvQBSzJfEFBfIYe31JmNkhRGPUOOMZ8Np2fRTD7lFUkApXBuTI94oWTxBPRiHMxa/3GWLBU1eAEIwNVE82USiwjJ7Us6ILQVpzrcejdfHVz5uGyx9vZGPRiP+2B/7Y/zzf/7PeeaZZ/jiF7/Ic889x5NPPnlUIFhradv2FhFE9Uivvf53RORIKvhqj4H171r/zPrf+F17H0KB/9cnN1Oq1WKmcWuaFrVKIawZYwqRb107UFLofFXRh0ybYG8ldKZG/ZgYtfSB6XC5w2fFW08YLId9d8i4n+MlETVgNTKNN9miMOxVBHJABuh2REvV7xc7UinBHTlOiWnEInQs8mroky4wckhleypK2EeTDIRiAbvUiij1ESsXI2Agu2J+ih18/W0hflVSMuDLU7wmNOYSDkPR6pf36iglU5EjFTv1gGgmqCGPdliZGrX2SKccjGFuRlzufSk6YqCL4HLPiXiZO/UiTZ4Vlrl4RIWKUFj8lpIkSPll954/STMQ5UnQmYaFjMhBqCzsmBU7HOINpPoYvd8p/WXnSZt3oTv3k4dFTwi4ehPcmGKntC5gXbkwBpIpTvFCMYQxUgAFQUsBYAz0iSonJnEX277Clp1jc08wNbmaQuyY9Ncxq0glEUXoTcNhfYYbHCNk2NAZo1wsVmW8xW707Kea4JpCrFOhSxBNTcSCcaScSa74AjgyVgvgsd9BZ2uCgK0cUSDaitbtoLWjkcydY0ejc+44McbMD7CHV9ixHffsJLb7VzijL1DrElGBGBGjeC/YPIfZy+Q4L/HFMUMOIDq4+GUwSwwZNRPM9I2YrbuLekBiARQoHh24MQtxWOchJRIVC6Zoa8vv9Bb8lDi+kyWbqHdk15D9CBPmbLU3sCZRac8D/QVOrV5gpobdSrg8NyxNQ2dHYEfkUcNmnbExkXSCo6YSoVcleyHZxN6iow4L7lu9wPn+BXyYobbGOYuRkkgpgMQVmsp9HJf6kJEJbNgEOWEko/2KzXCD99w95pE7pljriG5EsgYnGRf28fPnGZsVptkCFeo0h5zw597Gyp2CVLwozPgY7eQsTg0mC5INZFvmKEXNJALeFZ8BoQW7wEso5OacqfpDJrJCbMZIyWZQZ1BfI8tDGkklKCkbom3osoGckLTES/GsiOIHu/RScMzVkbIObUAZlo7Xmf5f7bhtEACRApdfv36dixcv8iM/8iP85E/+5NEpvWkafvInf5IzZ84ApV3w6lN+COHo9L/e4F+NAKzRgVcXCOuP66//ro310VHkiMXtnBuIfq/9nowhqx2keBS2vzGYIejHNw0Xb+zzkx/8NXbHY3o7QUOmaix5tcB2PakPyHgTMYIjc3YM3cGcDT/G2NIvbvJ82PBLrCupH14HuLwqEjADSRpM25KbM+BPYbfOcNhsM5ocQyUjekCNRagxGrCZsmADQTzGemLXYyUX7oKBaBrysIlnLNY4KieYpMXxbGiDyGBEU15W8UQvegYzyLQskeEQqD0yECKDHbFKRVqhWcnWsuxbZGOb/dFdXLxyFZsibZ8Reu6vbvLuzYoL3YSZNGQ7QZLDsRyCVQAE0dJwGXlKr1sT2q3KYpRMWQ9jpO732fJlI9pLEyyWeuifJ7t5FM1cmwQpcpWTTM2UbSc0ZMIRi7kEMZWMvIJ0qKSy+Q/FIaJEY0A8BqUKB8iqp5IGRUmhp+sC1hjMeIO+W3A5bNCaEatoSePz7AdLE/Y56xf4foUFglgWOJpqSizvnpgUcTXzIHz+mQOevbCHr7d58oVXeOUVT+5H5AjGNhysLOoqum7JyCc0lHmR1RO9hcNdKllxfDMjCtu03MFNvIzYzC2PjPfxL3wQvec+JGR0MgLtSe0h+099ivGLn8XsPkfjpgQijS7J2WJ8gxARiYhmklqsacpjZxZAhwzE0RwEcsNStwgawW1gRmc5tOfgFJjVS4iNqFRkf5qFHCOnXRKJWFVUaUWVD0EhGse57jKPuS/yUfs4HzlMXPz407xDWkaVZ2Y26JrEnatLzPsNYj5J1cOm9bRWWI6V/YOLzL+0y6ht+Zatfc6Pd/kSkRdwyNAK7NVijNDkjqgOkwNWM20XEOmoZMHUTjg+stzhFrzlzIgNlIrSb++lrC0uLUk3niZ94oepr3yRtl1ic6KWiA0rcJtEv4EGcMZQ1RUdMFnPPTXlydNyX51AraCiOBJID5rKRk/GGKXRJSYvyFaAhOaEaimA7MvPYWbXS1aDsQRGJTNjSPhbH5ugpJfaHMlAa0ZH69YaQYXX9/+vdtw2BcD6JO6c49/+23/L3/pbf4v3vOc9RxbBBwcHnDhx4mgDTykdKQKMMdR1/ZqTfoyRpmnouq7A8ENhEGMJzXDOHaEHxTnrd2cIlFCfbNCUj3y5rb2VYrXuX6sqSWzpa6kgOZW+JBW1A3Igq7K/6vjEFy9wuHOCedhgKqAaqUzk/nNnuLKYcWVZTsSOFX/8O/8AT18QHrhrD55swVRY7VEMUaakDKT+qBwpDmCRTENvaiqnbL3xHbiHvhbrT1NNj1E/+A1kNjAsMXlNUOuJpvTMjfZFkjQaIWGOFEYWSE2yTXmYcyyQpHF445FugEP1VSfu4boU/HlABoQSMDIUAOU1txhqNEaS8YTBCDWLJUihJCyjcjkr/+HXnmDDT6GqSSlyf7PHxBp++eqUfXMPvRlT56IAYF0YUiR5XiNVhDEZm1ek1S5VWlCbgGTFOuHsds1WWkLMVKfOI2mJXn1+uNkNHQaPorlF/JiNr/kuKm9gvEnNEqObR0UHxIHw5CCDuLJhmiNFoBCNRf0EE1pq15frnQqManNPZRJBYSUb9OMVc93m3s0Jr1y+yiyMCM2JIv3KB1iTEKDtAzTbqI7p0pyE4qwSszIP8JkvPcON7gA9eYrnLl/m8rUtxhtnQQVjapYrQ06BihbXXuO4D4xSzyh3HMQVj9+xzdfdcw9ve3CHaW14w6ktvutr7qE1p7jv1Bbb91TcMf8Iu098mGOphOWIdkyqGh+uYxYznMkwPUPqE3l1mYTD+6ZMoIEolqVIQD0F0i+uikNJZQTMFqcf+EZWO3Mw22ze9U7ox2Sep33xAiPbY12F3zpHPToD7fNYFnSVI0uD62sykawjjrVzzsXfYNJt8mxMPHsz8fXHD2lWlpY3YM2Cs+1TzFfn2N14Iy5aGgOtCKlS2jznpZnjUTvmMX2SN21c5cfbFc9GIWiJAU/Wk7IyMZGAIqEt5FzjqKuGr334BO88t8Wb33COk92cu8/dyYZmjAZIico7jBbeRP/yl4i/+SGq9lJRfOycBHWQhGb7OGbnBOqhmm7izt/LoRxBJ6xZKOthVKkE0DyokSxIg2hB73A1xpUHV31DNnYg7wkZZXntJSbhGi63VGJomRKpIXcgtvBhAKORKq+ocvHF6PFHz+hRHszrBICvetxWBYCqUlUVZ86c4dFHH+XYsWNHJ/iNjY2jU3oc7HPXJ3pVPYoHXqMA3nu6rqOu66ON3jlHVVWEEFitVv+zJAoW24yhoEiREONr0IWj37ZGKoYwmbLpRUgBYypGrpCWsoKtJ4R+zNKOiWaE0RUhrNhyyiMPnKC5VnP9uYtkwGnmPY/exyP3O04f/gar1BNNjU89ICQcWRVi0cCv++8oZBE6W8NIGL3lXeipN0K9yXRynjx6lM5U1AqSXHmLNtL7XOxic4e3gp2M0bnBHIUdlLjQ8vTHkkgmJaf9CBGRW8+wkfVcSMXPQHNpTWQhDAVAIVn2BZpES4yquPLrrONglUnak6xj3xmW0RPDHDvewuaWzcWLTMc32epGpNF9dDQ0prQOCsENMCWNz2lgnBNjzcjyKjYdMM0HuLTCpFSsWHcm1IczQkz4M+fQdh+urE8yloih0ozmAH6brXd8N+QeRiuszhGZIMOjKCiWgNGiGFFSQYNykT6oKWZT4CBkbJU5uohBkXZGnRaY1DPPNQu3Q5UC33q249nDV/jwYWDhtkgps1JDMo7cKUaKl8SiS5iNCnIkBKULGbWlv+xHmyxCpnETOvE0tsIaT1Kh7xTtF2z5no1+l1G8yUbq2Qj7zL1wfsfyX3/nO5g0RbJ5fCR87zc8xtydoO4id5wWNq5c5Oa1p0hbp7HWIf0czwrpF7iuRadjdPM0qU2kMCNHBT8qyJmdIGLIUmpHi8PmQp7NJLIxJb9et9i5//ez89ActcfZvOtBtk89TP/sT7Pkg9SmxznLaOMMTE5iDgRJCyqjyORutN1Als9Qh4QNkZ3+Eif1AtfmSrd1hpHtmbRLRrnmZJ7xhvwiV4MlVJ7QW8QoQYU6KUYMV+uT9HmPzXyZ04snmeguxjrEF4vdVg0xZUYEwBXzKsC4mnmX+K5veRvvPd1g26u84ZsfJJlNfJEhDY6hhW+C7cj9qqB9JuGqmv7sw7C6QrdXYzZ2YGMH9Ya0eSd64p5SQJlwa40a1i9T6BMIaYDuBJUxWQqiGtRQVRuoH4FxaDUmuroUdViMM5jlPpJmlMjmFnIkY0srdEiPFBJee0ZpQZNKuynpLc3/EQLwegHwVY/bpgBYS/v6vufee+/lr/7Vv8p73vMezpw5Q0oJ7z3f/u3fTl3XWGuP+vt1XR+d4lWVw8PDwWPcsL29fVQQ9H3PzZs3SSnRNM2Rp0DXdUeEwt+V90FhtBdLbF/Cb9aFyvqbhpOcaulh6gBxF3/bAmUSeiwZX3u6ecd+G1lGi3ENpBZjlJw6xlXZpDT2WANGArHbY6c+iW0XOJPpTYVPApLLZqlAGny7X/UAqRiCVLS2x588w6Gp8RxjOjlBr2NWChVT0NK5Fkl0PoMtfADRiFpBcxx8AWxhgsvQp9VY+uCieO8Gotu6JNKjj2ZdlORciEeiRC3Rqok1QlBS7wy5GABZRwRCyvzUz32Yp/Y7EsqPfuAldrJh0nj2u8jP/8In+M6DCxzTiml/J4wsvVYgmWyLbj8PN0lQag1s2Eh//SLLz36I7StPUYWDwqQeV8Ruge5doQn7SG5KvOvwdorHvhbzFM0gQidTutojHWzaXYQ5lkDGDeZOGUNXyCRaDHDMqy6PIiSxDExRUugxFqLL3HzpZSbmGD7MaSTgmgldd8j5+pAz+jkeaJ7ixqxl1p9FJLNkVNL72hbnDTkZrK9RWj72yS/y3MUXCHljQBwaUlyhxtNLzSpb6r5YAaVU7pNNLZu25YETjv2nPsE43sNm2OXG5jbbdcVxZymNpwxxRmPmRDmO056x7CPXVmxvLki5SOVqyYjp8VLY+0mAZouYlSBN0afXhRVPHg+bUSGK5izYVCOScNaQRMnag1YgZ0n+OrgtuuypqJHpncSmIukKNOPchEoaCAETA6PqkFRtcmjvZLy4TBVu4lQ4OfGcD7v89K9dZrvPyCizoyu2Q8+9qePt40M+uzxgXmUWVSbbTK3CdJlZpZZ2c8yh1NSTFX5xwMQuEGsJWlIqs69omgYW+zhXsz2uMLbixMnTzG+smFiL232W+ad/luaeR2jOfy38f9n702DLsuu+E/utPZzhDm/Kl3NlVlZl1gAUUAUCIAaCJAQOIiiZIqVu0VRL8oe2ZZsfrLC+mQqFFA6Hwo5ghD84HOGQLHaEW9YY7JBEU2I3KZLiIFIkQYIACFahgJqHnPNN994z7b2XP+xzX2aBRTazWIBKUq6KV2/I9+49Z59z9l57rf/gT4zPdMPhm69x7aWXufTMBxCXzYfqHpZi6OaXaLTiqFBOmwnBTbDqCFtPImZz5CX1JHEECiKC1dyCM6yxO6DGoTYrdRhgqUrh52g5IdmS5CYM4tGUbYaHEJjTYbowcvsion3G40SDjmJpTiM+tZRpSRnB6T2zhN4FUT+Idx7vmQTg2MPae1566SUAfvmXfxk3muPEGPmu7/ouvPeklN5S1l+3Dl555RV+4id+gl//9V9nuVzyD/7BP+CJJ56gbVv+4T/8h/z0T/80KSUuXbrEj/7oj/LUU08hkvvz9wIE78UH3G9YNcdGMUVheN/ZmjO1ZZVqeo04bUEtrS3QCEEMVgcqHYgYOuuyTapzlClgmzsInmpzm84VdMOKThvqoaDuA9ZlTew0maKasMEwKyYsImi/xCn0ksYeXiJYUHFoCBRpmZHQMsfS5sVGNaOwSyEVQmDOkALRjha7WoAGMlVgNPtIEZKiDECHxkw/CtRY02LDikoWoJFOLckWDK7AhsgstYjCYD0Nc6IWeFmhJuSeoma/+UFLDFAQMNKjOHr1tBJJBErtKRLsH/X8i3/3m9yQitn2w7zWNcxcReoOSaXj8889z2ddmfEMRlGpCdGirqEYWy4dsJJqrCoo3gSOXvki137jp5jwAlXsc3KTLNZ4TGzQuAIpkLqGtMhAyJid/WRMBtAs1atWMBWolpgRoHe3NGRyz1pyCTiox2kAsfQINhnqlAWbkjd0saDGYtLA6qXf5NOf/vP8hq3pn/0yceipTM8nnjhBWrzItr7MpeoUz7EkWZ/FiCRRmI4UDR1T5gb2rr/MP/vZF/jaXsPt6ZNMuhvMtybsNXMSE5yLRF+zioqRjhgibcxoja14xAdnHXtf/lXMo6cZgKo94k994hM4q1gDMSkaQcYyr1qykiCZMmrCKt9jCTQqiQrikDtCIiTn6ZNmu+VqxkoqJmPCZscKXG6XuKyuKGAJuUlgTf5gg5CEpRGOnLLdHlD1hzifqXZWF1gzEMXj3ECMBUE8gYCmCNbSqWK8UHQD+zdvcLbO9FaNEWOVh9ItLtYtm76hlAaRzPpYBkHUMpfItumRdsVREhITiAPDsKQjA2Q/cuEEt566yK/+5k1Wdsp3fPBhLk4Tf/E738fVpeHMrKR/5Vnu/Oa/5HRqcI9+Bz3gcZgwMLz0O+z/1i8TH91hMDMop6D7pJPvw579FsL+L+Y2lC2wdkY0E9LkLDGVFJK1AdZrrBz/j+NNzPjFCHMmb060INqSvtxGAW9WOBqQrCTpwmo09ZkSBJwu8VhIwiAZMFiGDhMGUl2w8nOSJGyAMh6ycnMK3WeWOroEjXH4UanQaG775FaaR8dq7AOMwNvHeyYBWJfxi6Lgn/yTf0Lf98e9+nXff+3Kdu/ivwYPAmxvb/MX/sJf4Pu+7/v4sR/7MYqiwBjDSy+9xM/+7M/y1//6X+fxxx/n7//9v89P/uRPcvny5Zxd3xNyT38phHCfSUAGjrl8sFRW+fTDFb9YJ+6kXY6ITLurJLPBgTtJHCKD81S6YCOsOEqePT/DGGjFUofAybhHIxNWtkCaJc43xJmwdbTNVmuxhXBQJtpK8EawQ0mipHFQ1wbTgtNDSC7LerqQqTp9ZJJuMohjsGcQ7mDkNtOwxAWDST1e8gNV2ISaXJ6XpMAByA6kDepUQR9APH1pqaoOYxJ027QypyxepereZMIboJGVTKj8hCNbUHUtZ4Z9iLCop9zpzjHEKYW/zmASpqtxSTi0Qq9bZHPTBscKzIxGttgz+1iXmHV7zCLcTjUHm5eIbaJcLWHWsVoJ3kxopOXQlSzKiyS3ZCUFVW8ZQk+Y3GA+5L7m2hS300hjPMlDFV/nUV6mGm6MND0POkPEY8MBaEfwsCKL4+iaqTG64EEuS8/Err8BZsAsw/+OZ9cifxhIHlqdMdclyZSsxDEJjq3+CGEv2+36bYIRZv0e70+f47FHSr44f4j/35ezlezcBeanLE1aEWWBlZ4UewyRQgc8C5wuiJ2nK8+wGV5jxx7yRvVBvjA/w2Z/g6f1d6jDQ7T+MTRsYrrbtMnSa48zezhn2UfoTMUjYY8nm+fZPaq46j175UkeCS/zkSe2CapUjKBbO8eaTVYYvFNqqSFaWuPZMgfY2KBqiTqhczNsPMI7D4XDh5zcloVipifYsxtMRDBYqruPYiaoZzQFDrvGWUINtikwZknjHTeNcqq8xYnli2iYsJRNZtxE5YCVnbDhB9p4gsoYTnZfhGEPnU45HBakYocgJ1BbMBmuspkWtOWUm95wKTyL9yvmw5LTi1c50VzDxnPcKTx79Yzt7k12+ueZhzt05iStP0vsDaXexpc9VTriBx7d5Mnt9/Efvvwy190JnvnwZS6UR/y3H9jiwG2yyRITXudCeh3T7TNIxRGGuVSUybLVvMozy89RdH+GPdlmNgRIijz2WXY++GeIz/8cQ/cGxIRGh9dA3wxMfIXp3kTK0zjsWxcK4Z5E6u6wWoDhkMJs0dkJbX0epwN1eoVpuAVFnj/m/SEVoG6D4EpcaKi1xg6BjoZaWnb6BRPgqNrhFfsEN9mjVOhufJmb9imuNC9xvr3KUZvY8yVbkqGeVi1oi5DnpJhTiwfxh8R7hgYIefdvreWFF17gb/2tv8VnPvMZ3nzzTZ599ll+8id/kuVy+ZaKwL2L8zAMTKdTPvShD3H58uVjTADA1772NXZ2dvjwhz/MxYsX+cQnPsGrr77KcrkkjD369Wt1XXf82kVR3Pc5ZOybjh+S+fvWsEzKQRK0qAl+wutHkVhVmKJk1fR0EY56y9VVz9EQaPC0ZsIgFUF87tWKQ/FELel9yWDBpMRJM7Bz+AKTG18m3X4O4mq07sqdQxsT0VREUzHRjjIcQtsRxOO1o0rLUWRDKLTLJWYxxw/2uixvjrcCBhVBkyX6GWpqDtnhZpiRKEb6X8KKkPqIKYGjO7C6w4btqeKCYlgyL9Y6AHnfK0TW1e2u7WnalqAC4ki545irEOO1ika5fXjEEBVxPpfuxx5wUOhNRRTH2u8+YnGiFCYiCNvpgKftS1zhZaRbsnIzalewe+cFvrv4MmeH1xhMSW/dsbQyo8WpakLWlZARxS+jsSuTE2g5Q4sKihmGdbX0/h43gUyTZA2IzDdYJxXYEuMKjLGEIUMjRRO2u8m5IjI0LWpKSmuwB69TNzcoYsuQDK2Z0tpNBnVosrRaIsbibd5VO+cR4yhNYtrf5FJxyObqNSbtDWqatW4T/RBQcYgruHZzn6aPIBZBcRoozEChHbWEPEICaMTKSI+VYxhqRm26LHCT6ahCdDM6t4FNPWVaEK0hFFNcaJixIiZI4rKT7/2G6UAUhzIFkIpgSlQmWemwrhlcRaRgJTOO1JPKLfB13hLHLFxlUlg3po5JP1nvI2FjQzQFM2l5fHieD+rzbKR9luI5MjM6CoItSa5EUqLFU8UFV+Qqj4aXobtJMjDdBB9XbHNElXrEewYpMiBWI3L9BsbYsa2XsgmO6vicZpBxbkYpTgJIyuwgKwzFjDvJZz6fZGa/oyWLdXjue+/sDK0VWmfwRYGXgtRtcmDPM6QNLBWVVSQ2CAcUdoFagdRShgN8s48dIoUILgZoe9RMuD14rveRQ0q2+te5Yq9xNl2lau5QAz7FbPg03k86XpW7vKIH8XbxnqkADMNwvPD++I//OA8//DAxRlarFbPZjJ/6qZ/i05/+NJPJ5LgKsP59Y8xxa2DNDriX57+3t8d8Pmc6nWKMYXt7+xgPsC7/G2Nomobnn3+ew8NDmqbht3/7t3n66afv6zwiiooiJuWetAi9c3zu977Cvznbc+nbd/ml332W//vPvsSTVy6x6num8xlmiPz0L/4W/+gf/3MuXrjAUx/55KjXnujFoxgSicJNaMVwJyUmHuZeeGZ3hq9uc/rGl7j1b3+Jk//V36IqZ2g/YOopttujtVOiDNTDdeJwnXRnhwGP1wOICzBZcrOIDZ3ZoZDyLu5XI5KXT3JtOyvDheSQ3SsEeob6UdoqMhTXcAlIPcYkjFgYoL91Ffvylyn6O8iwYCYH7BYllh6KTPhxBKwoJOhWLV3oCJJ3bstVg7KFXaOMxfPCS4d87gsvs2haQpozEi5GsRFHLzVQ4nQ/4+ulwqSIDgvSpOCCO+Jj02d500z5N+khVsUJrA5Mf/e/5381/Q16O+Fn00O0BkijhKxahCzziw6IlKM3umSAo1ra6gxv+EeYSmTqNzIe3RhI95lvC9g162XEkEQs5clHiK9toLHDVgUMQ/7l0CJHb7A7+RYunN4hJEta7hO+9O+ZsmAS8zXtzIxkJlhdEc2UlcwxRihig40NtigYsLjUclbu8B2nO17bX/BCs81VO4PUjfgGQa2nHQxf+MqLhLZnUCWFgNPEpg08emrKQzJDUt6QrxdLSJByJwWgD4obFBMaJGXA3OHWI4TpaXb2fx9ZREJVoNOHcLeu4+xAmDwExeY7SwBkCaagGsW5kpxisfsx6hOP0YvQiiP5CldNOXBnuWM22J4/BM2NLHtnhEoEm3q8BuyYZK6XSy+JqfR0UThbNPzQiVe40L/MbLjOUhydmRIx9GZCtDUm9bR+zmZzyIfNV/lg+xrcfojh3CME66j7PR4qes7V78v4Eu+xArpY8OYXfodzMUBdjmV2citEbNbEEEeh5MqPNuAtrhiT6M3ztDsNlBOQnNAWHKF0qK3vf1yt4XBkDWwYxSeLVE8yPPQhrr/0G+x2L1BNADfKCKWG5DwmLKgHg1stYICNAnZcwMlA1/U8u+j4v/7D/wDS8RhX+a6dm1Ryi53hJjWXcKlDjL/HYdDw7qm7/Ocb75kEwHt/vAi/+uqr/J2/83f4/Oc/T4yRra2tY+Gf9c78Xg2AtZDPve2ANbd/jf6PMR6zAfq+p67zzb1e/I0xLJdLPv/5z/P8888D8Oqrr953AgDk/rLk3WocqTyDFV65uU/0j/DbX32N564fUp4YsM7z0Nltrr9yyO1loNg6zxtHyuZ+T2snDGYgqkEko98nvsAYz81U05tDPJHv/9CTfNfsKqef+zz6+kvIcECtYPqBweT+fzRlVpBDkdRBt7jr8qYBq1neVVRpzIz5mADknmrEaKLIVHs0mexIWEzZ/fj345qPsrPxFLa/TnH1f0Sf/Q2gy0p+9RYcLhjaBUfXvsZ0OEI1shP3EKkpZICpQ0LKyQB5EY19IOhAMCVJE2n0VBhU8RisWK7euMqrb1xHrUfFHWf8MSlJXN4p4ym0YRBPkAmoklJLsJucZMGn7e/yVdnmZ9NZGjMhpoHypX/L+5sXucSTRPtJrEt4XRFE6akoJYzXY8CMWgSjXAuIY/epT8HuCYrUYC48wRLJoD5zf49bdk17q24Ffsrs/Z8k7f8u/UtfykComMaNWkJcz5Nntvhv/9Kf5dQv/Rr+WkL6PRgOmJgd5vR4lIWUBAo6PEfRkkJPwYqCSFHPWLoKk3p20x2u2Guk1DEbTlHYR3EkNASSOHo1JD/hsE0UuOwdEDtK8Vw8PedHPvsYDxdXqA3EFHH0uY2UehSPsTWM6aUVyUIyIthTj7P9mf8dQ7lD8e//n9BcpXrkw/D+72b1ypcoVagf/Qhx5wrmfnepAKZBTYlDqBTM5hU2Pv2/hQvPEMUQvMv4BTGc+OD3Mp09SrG7STh4Odva9gO+SngdcBqw4857fa2sJCoJ4Ap2OOBy/BKXixu84TqCKjUdtTbckIrOTqh1RUtJNRxwies8tvw9uPMUeua7cKbi//jffA+tNXzooS2M3gZSVjcYljTLA2KKmBSRNFAy1qSMRU1JMD77ksQex2iypAlMwfTJj3Pu4Q+i5Sxb6QpYenrtSWbGWnj3jz+ulg7JOKbYIKHDbj3O1lN/ioWUyLOvImGFmgwe1pQ9PYwuMxW3a0Bhd1rwlz71NHdejrzw8h6prPjijSN2tkrOdS/z4eI1/OQW83SIJWEySZJcs5TxnlrX5h7EHxbvmQQA8oPTdR0nT55ktVodL86vv/46ZVkSxi3eOhFY0wHvVfZLKdH3/TFOwFrL5cuX+Zmf+Rnu3LnDZDLh6tWrzGYz6rp+ixTwyZMn+at/9a8eH8/f+3t/7/7PYSy2MSLAMzLFgnOsBiXgOegt89OPspApQ7vPB973BKvbb7J/Z0U53WLZdCySoReftbvJJT1DYtNZCgyvm0grHRp7Lp/eoLYX0Bf26OQWJq0oUoLYohKOd5KJBAaMDWhYYDUQzARnSkQ7fIqo1CzdDmqq8VHK52E04TSXOdV6hnzBsBefQfslUl1hrjdI+7+VaYYuJwhZ//ZNQInDAiuBFBIn5Q4aCyobYXMLbuWJB+0xGExMqCjBeVLsEeMImXoAozJYlERQwYw8Y5WcAISkqL2rr1BoQzAWmORdkREau4Hvr/Jw9zWacBoN30VKEfEe0+xRHi2YV7eR0mCHQDHcAA30fkoRh1HTYEAkZJqUWFQ8KhX+xBXs7iNIPAS7O9YhJesM3FfoXaZGPmvUFOi5J9H3fZz46gsQO5Lmknkuc0R2a8vHnjzL4lfBxJ5S80xb0XGaPU7EWzRMSMYTjKOJgkmRqRmorBJNwSp5kvbM9IjZ8jU2B4MLDeJKTH9EjJFUFPRJoKxzhSq1ICarPA5LStvzqSe32JV5thWOCtJBaEbHQ4Ndn1f2bYb2kG5V01XnmD75WcRNWf3mP6GKhnTiMdzpDzHYGal3pJPvp52cvN8lKo+sjdltMkIZgelF5MmTpGojH5MzqAmYoaG8+DTVQ59EOeJAa2ocNnRYDTgCjpgXofFxF82sD6cDYiwbcZ9zy312yiXPH+7jh44L4VVOxOt8Fc8gFXM9Qu0pnPacMQdsda8jB6/gUk+l8H0ffoRlTMxlH1JL0R/h/QZCw7x26FFCNSJhwHsdZXsNyWQfBFLExIDTSJIpfcrJAWc+QCWWYGdEciItCGikxTESeO9jYLORl0XxuoC0RO2U4vJHmVx/luJrFjQQksN6Q9KelGt2GBOJERBhNrH8uU89wrN7z9IuvsrCnyZuP8IdljwZbnO6fQE33CHGNouDjaDEe0jY+et160zeU93u90y8ZxIAVeUf/aN/xMc+9jGeeeYZ/u7f/bs899xz/MRP/ARf+cpX+LZv+zZOnjwJ8AcW/2EYcM7RNA1f+MIXePnll7l9+zY/93M/x+HhIQ8//DDee37iJ36Cy5cv89M//dP88A//MJPJ5C2tgjUjYH0897ID/rjhARn/xppxt6pZvjSpyUplfs6N/Z69o5tM0z62XxDbJdZ6jtqBPgrGjztwzbtvIVvu7hbZPezLhwPGDZQOrILpIxo6RHqQSLx9k+6Fz1M2+6g1+NAitEQMNkZYXccWsO9PUzJlFl5EktDaXY78aUjFqLmfRnrVXTMQbC55t0nZsDugnoES7Al0pMIlMTR2g5AspZ1mTK4TULCDcNbeQXEUNsHkNCC41IIMGLW4JHQ+V08k9KQQ6XulLn2m/g0RTOb/D1Hp+0hKZJEjyKIvKtgU8dqgMkVlgoonesfCbtIN16jDAZsypXAFRpdYcyovTM4w8YIbjggv/z5c+31kaOjrDUp6rK4w9BgdSMaSKOjcnOQ3CG6DIAWl2QA8pZFcyk9yXzCAEY6avxrV0XqEym+TNi/SRJgQ8IVDgx2thOeYkKhcR6SByqOdgpQ4Bq7wBleGFzjSOUEKgrEcNQ2EBtMf0fdLboaBN/qePlkqo0x0xcROCLZkKRMmMY4FjZKIo03QhsBkJLWWotDcIV17kfr0x/FhQWc3cd7A4ojmhd/FnjyF371ExRYxRdKoJtnffoOJFDSyjXGnOTLCpp+jlaV1O6Q4w0838w7dbrNMBVv2WK3mPgbX0uJyfpoUyDS4JJCNsjsqpxTDAjqgOIkOhsPBUbiKQjrSWPq3mu5599z/X1NZdejY0gUnyp42Kl9+8U3qMzf4WPw8RbzGv08AFVU8BN1kSFVmGSw70uvPwpUbnDi5i01LCpdV/YYbL9M9+/O4R5+hLFtMWGZb8ekUkTVjX0EsyDjFpxUuRkiG1uxm8KhWiD2TFR9V6GWTZE6z4TeRBP072T6rUAi5JaJLlIEutiRfssim5RgDPRsENlGzwuteTt6tJegG1q0gHGJ/93/gE/u3ifND/u3K8MrBDlESpXdMujukbo8jdRxi8JKln6IIfvR8gNF7Y/zqQfzBeE+Nyq/92q9x8+ZNfvRHf5Qf/MEf5M/+2T9L27b8yI/8CH/tr/01vPfHWIF7RYDW4j5t2/Irv/Ir/MIv/AKf+cxn+PznP8/v/M7vcPr0af7m3/ybGGP4/Oc/z4/8yI/wfd/3fcc4gnWr4E8ca8W2CGjCxERlHUWSTGExnpSg8BOsqTBSUkukpKP2grN5gRRjGNoeT8SmloIeE1q8SczNwK60VKVyZp44t1FhAmBnJC1p1aPSc+e13+HaF34RFj1NKrHdIaUNLKmJbgphiaSAXPwo+uRnkGJCVEez/QTzRz+GRDOq8eWdBCllq13JZTYLlEZQO8NMdkiWLDLkp0RXEU8+xOTpb6e4+C1QnaTpe6wXuqhQ1DxctDy2o2hqSNMziBZY7Sg9TCdTXEp4bxhE8WIJQ0ddCkj2BRNf5BaLCmrsWNnIVrICxNhjNVKEgKUnigIFKWUp3dZv0qYJxngmhWFqhE9e3uTU9ozoZwyVoR0Sp7nDtV/8x/CFX0HSQFBLW23RT7ZJo1iJFWhTgT//fnbOP0nA0YghmglqXB4zMVk0/X7DGDAe6yxrSJbaDdRvIdZTjFTJhCJWkFhSOIsLKypdIcPAYGt6t4FDeWZ+yDP+db7lfE2KA4jh9VdepvImyyBbT0dBkxz4mm4IGOvokqH3m3R2E+MKEpZl2+dWkMnStc7m3b9LLTE23Hn5OQoNiE2ojvbYzT5f+sV/yRtf/Pcw7FHqgG1WDKslpAx2W914ERtWWTYmjmawQ9aMqKzQrxbo6C8xN0pJ8w4e1ZJANngQDSQHfZHvrzkdVrs83kayeZOpEKmYbJ445r+nvsMbyRIekgvRFjmW8UYEz0CRVgxROfQnee7NJS/9yq/yw+du8Z07d3ApYGyJiT0TWqItWfhtkhNWv/97LJ79HC60SFgcA2av/vrPsfzcP2fxuX9Faq5iwhJIENeLniCSZb9RMBKR5jZVOCK5LdKJ9zM9eYWkhkZKgqsgCfXuZYaz34q9/FFIkitu9xsBphEqVYi5+sgEehlo4yFSKERPcLuE7Q9RPPq9UJ1AfdY96GSDWOww7L3O8t/9v3n6zs/zp7evc1JujeDQiiOZECWSDNyME/aBLglJLWu/wrUtNxpZO0s+iD8Y75kKAGQcQAiBoij4oR/6IX7gB37gLbvwe3foy+WS6XR63Caw1rK5ucnf+Bt/A+fccYUA8m7+8ccf52//7b99LAp0Lz5gXQF4N0LGfq9ofhDtWDq3KVPCTBJsLnKDCp6IDi0Wzcp3YjGi2QU7DXjtEc0IXnBMC8F2DVUMfPBMwceubFJIgGjpzRa9zKA/oNCbbA3XEVWirRD20aAMxZzoHLY/wk9n7H7yB2F+hf7lf0NMS8zl7+TSJ34QmW7lXYXJFsXjfLZuCNxTZjNY/NhiMAxmRuu2mH7405inPguvv0h37bdw/S0cLcYaTKtsuz0G22KtR80GiRWOjvm84Mlzp3nzt15gr1sh05PYkJCYJXLvkVPKxyK5SZHeUiofXfQ0UqYBSERjUM2UsEgkqqGTOUv1iBfO2sRf/u7HOLPRsTBznHV4DOe619hpX0VCwFnJCPads1TzCfz+HXBkEaaNM5Qf/zPoxafojR2BZWuONKPIzzvpRsq4k1M8MABRCjB1Rv1rR5KSNIKfUojYuMKwIkqPpkjnPFJMKA9vsc2bnB1e4cqnH+IL/+yF/Bpk8akkliiOQPZdWDeAong6U9GaCcnUDHiO2kC9fQKxOYkunMGEAafZpVJCg4srXAqIRLCGlCL0CzaH28zYR/QA4gE+dSxD5v1724/tiAbVlsrkkrEMQl06DEvoG5IKYVixERfACvzkPsc1Y4ByPptprmlslVmyTW1LjTN1NpkaJaxjjJgRb+HGF0hrpL1kh7pctctgx4KBaBIrrTgoz3KgNdXiNc6nLzMNDRMXwGYzqkk8JJiSAz8jBYdLA2XYy6wel0hiMhtneYPd1VcZjraQ/U1KWlSyKM5aWwHJLZYEeHpMe5NprLFb55l/+LPohacIYrKpk4BRx/zRjzDf2YLZFK/mnYErYx7ZaAR8Bd6w1IhjQWEbxCmpFYLM2PjYn8efOEX7G7dJL+zT95FoKwYMzi7YGV7M81rRM0m7Weo8GfbYQF2BKSquhQoBzojJAlDrg2A9HaxttR7E28V7JgEQEWKM/NiP/RiTyYSu66iqisPDw+Me/T/9p/+URx55hBACdV0fq/eFEI4rAmtg4NvpB9wrEwy8xUL43Qpd96nVgBlhKZIwRDRGrCrOZJKK0Sxe0nU94jwh9SSbXQONRqwO2DSMUGlLFI91jgvb20xu3uTE6mXqawNp+hTGFaBzMFOGV75EMRyC7uXqfRr75mFAyxElC3TFJsWpJ6F8mKU/jRmW9CefwGxfyNWVFBDrQEqi5ONlXHAdmlsPCCoJi9L1hmS2OKzPsnXiCaR4CLYtWmxSSsfi6teYdFMqcUzCATNpGexJ1G2SzD6WntJ3XDxvufkbEdFASAGvdxMAgHU6lAu/ctx3zRcgAxkzOS9RpEA0hkEsxJgrLrbHDUtsMaebbBCHht3Vqzy55Zkdvsp+3+NsxQnTcaV7hY3hzogmCphhQfSX0e2HSe43sbJCQ4tWO3DuQ4T6JKhQkRfsBMRR8fAdFKrHRQXQgJXMxIgYrPjcktEO0YJIZkfceeVZqq1HmZ3JynialEhWSJySKF1D3d/m9DyhoRsdCiMRSy8lSSKeiNGQaWRAFEuUu3KwvSn46stvcEaLPMlqyve1DkycUpmIiQNec88Z4bgQW8aG7Xib6dGrxOd+g2F2SHnmYbwbtd/DCpLDm0iMDSU1vSmJpqCuaghHUExZ6Wbutcejkb52v1GP9swJzaxXnGZaLTiMTtnv56R2yvlyilrQmAjNIlMVU04aFEOSgmhKkrgR15BwBEqj2NDgjLAIQmNnDKai7FeU/ojNtGSqS2ADgEnYI1nHkd0iRkeVGiQsQQ1qpqhUKA2yuoUbbtHceRH3aoVPzVgagmwoNT7vWCBhwgK5+hXot9DpLlx4Bq13SVHxJsvlOI1osYOe2UbiEYKheEd3bMJJVnJs2OFWv0PSGWcIIANiPWo6OjXoqSvo1iMsqivY9GsUDJRGCTGi0lDTIAEqtxipv5YqKE25i+IJUfitr91k83TkkYswKde00nzca77Jg/jD4z2TAAAURcEP/MAP8MlPfvK4J9+27THq/9y5c8dAwLXpz9dT/u51+7v3Z2uk/1r6d10duDcZeDciGHA67voMOTM3CiaS0pAXS6NkoYpc9lr2ESkmBDOg4rK+hqaRfpdI+GzJaieskuHyYw/x0X7C+5a/xu2f+oe0Ox/nwnf+VewqYgZ49Qu/zMXNHsIB0hV426HljCG2mBSwIW/nD4qTbM1OQ5wxe/q7OXrzJJx9jF4KCgPeZJW6gMs7aAQZMddeRw16MWSRXosvLMP0HHruadLOk4TqFM6uSMlQa0N79CYST2Sv9rikcj3J7KJ2jlJiCaTQ4EzAhkA1t/RDh9OEpFG6WHS9p35L/0o108okxUyYlDUGQLPcrbGY1FMOR9j2NtP6Nlan7MWamgM++0hBoR322heZdbcwkrhSHhESVNpk3IMJ+OGIAc+h28W7OZPuEIvQl3OSP8NKSjxQpFUGYtk6gwTfqqf2x4qsm5bDkBCN2NEPwWIQHUZJZCWIpxDDrS/+PLWbMNv+NoKxVNZR2oixkSy/Jrh6wh6GqrT4NABKLwWNmVEaZRYXFKkBBK/Z3tWS8NohqaOn4kvPf4nbTbZrttZBGCgYmLrsDS9Rs5f92B+PMCaePbOwR3HtK1x//RoHJ6/xvu/9oWzvm8B6zU6S1RzEocZhzj9D7BrYvQxqmT7x7SzLDaZn3w+mAL3/aUzUUTHSEkf75YKExETSAmM2qM99jCEk9MSF3HZKPf3RPi6lUflSiWrGqoknjebOhswOKLTDG2Xr3Fn6pqETReNAmsyJrsSkPcp+j04K1BnKeIAxEEwBSY9R/WpLoqsJAkUacpJEpD94A//SgBsWqGSBXhIj+l0wYnO1ou/hld+jkVPY3SuI32GlhhKlFLCxB13SyJSVL9k2BpuOKInI/S4REujR7B1x9ltxHziA3acxailxMOQ61mRWIVXF0m/hz34CXvqfqA5ehO42vUloObqqiKGVKUd2gyAlWyHS+5oUEhojn/u9F7h+/Xf5xPdvcPaDlxAZqcpkU7CoI7XhQbxtvGcSgLXy3nd/93fz8Y9/nBjjcTtgTfkbhuH463sBgPfaAEMWCVp7A9zrGrj2DljHu1n6zy+YBSiTyTu+JJKFgayiJhIZUAmIi2ACCgxqWfSgxZRomhHJmnJJe8xkk1gGCgZTIdOaJ588yV+5fJHPfO03mf3M/4s9KWD4fnzfUyPcWbyJL7OULrKNT4cwvUjoF5TsY8MAZsKyPE2dSiye6Ye/h533fYD9U5foyOJBbtTgDxYwdlx2BUvKi48moACNWbrfGabnn+DSxp9Hdx5jz8KOt5ikOIVNF7BxhcQBUpeBQkbA1KgUjH5+SASJA86MiOqQ4LgCMHLIx5K/WS+T449Fs2RQbg1krEIwBUEcReypUsPctjiOKKlZmAlbbsGnP/Ywklrk1nNUzXVi6Dht3mShW4AhmimGA6rUEF3B0p9gYmsmMWLFo8WMIHNaEaqUkHCYFzxTkEZL1mNA333cT8cJgILogEjBWuo2VwDyVUmjdPDp+AZD+wbYRPR1bhyFJUYSkiJ9FBZ2Sm1shiSEDrUwSEFnaox0CJopjAheM3XMpZ4qLfC6RIsJy0Vk7/AI57ex1uCj4sJAabNADnGdtGh2tWOs1sSecjjAHCwoTcC0dzIzYM12kSwx3fsTJDujMwXzD34Wc/kj6O4TqN+gfOqz+IvfijlxgejmKHrfE5kkcBJBBgaxKAGThjHpMAxasv3Un4JHHqHfuUjSSKE9sV1wbOptTG6bGI8af9yGyQj4gE8dVpSt8+cJV1+hjYfY2NKYc3TFNtbcwQ8LWr+dLamHNldf0KzWl7JbZmcM3VjeLteemGIoZUCaO1ibiGLz7tqV2etD1kkA+cE42MNWDpxncBOizUA9ABn2wXT0fsptEUoD89jhtSeXR+7jnvXCYKChYHLl2zhz5grdxqOYqNhUEXuPUcv89BZSG5rKs3Xlw8SvnMfsfQXp9ignnpXLjpbGTTlyuxzaHQIVZTxiiAlsoPBKGoQ7ewccHiasxFH3MctS9+Tq1YP4w+M9kwCsS/NrXv/aAOheOd41x39t6XsvJuBeUZ91n//tpITvjfXvvluJwBp6kiQX35RsBKOiKHG0wowk0cyDTcqAoU0GnD8W2IkMpJQBgUmFKJloFI3P1rfGcH4LNiYGaRrm9R1Ih0jX4GLPrFDEdhAiSIVhD+oNMOD7W4gORLfFQao5IQajoG4T3a6JUmIko+mdVcSsd5zH097d3poGwOXvBRJCsFP8iYc5Mht0wBCUQvLOxPUBhgb1xTGqPTuJjf1TgcIKlZO8M40NdWWw7YDGAUUJYXQITJq5zDKugeuj07te4rl8bsfyODgNVFaZl0LX7GHSDq7aQJev4auOIfbo0RuQOowMbMZ9Ju2ATLfpTUkVwYR8z3RS4cUx2hdmNoKtR+BUQLUZx2YNpOQdbETGVsf6/NbUUtYvdy8eIhPqNtIeR+0tiG02kxGDN5EQW3CG5Kcc7RXMBGIYsBpI4y5dJLerrAas9rgkFDqM3weK1FOGBYrDFPlcVZU49DjJRk+OLBq1rsZIzO2t3BkTiAMSVmAMpZkj/SIjDoxFCofSZynr4iRRLAsM1daj+I2LLLQADG52DpmeIYmljUJhYX6/Q5vIaF0Tx0UiIHRZflhgSOBnp6GydHaGpEhBRONwt980gjuTZOtha9bCWYozUFolDANYm0G84ZDKJm6pZ2U2qW2dkfkEguRnopCAYxipo5nO12pkSaLQLMGY729PaQ0SW0iRGAMMEUmjNmWKaOoRjYhVCErlFJxjMCabJKcGKwZ8hNgTJHEAnBCYSwRtWWMl/vh3bCQALdDVu9TVLmIctEdIdCQtsGKRjSlJWxqFenaCSEmRJF8HaxhSQo3D2ilLu8nKzojicDEzkyQlutUyj1FR0A8dEGDdliKbnq1lvR/UAN4+3jMJAMCP//iPH+/m14v9etFeK/4Bb5HoXS/eX//79woCAceJwr0/u/e1340Q8oAeSsE8Dbhwi1o20VSytXqdzbjF84cFP/P7h2A3aPsVC69sbWyzmVrqr95gmD3KnWEDGaacbo9YRbhV7dBpxyS8zGm7zWUu4DRBdwDiaGONesPC77PTvMT5xQna2ZP0U8t0OCR1Z0lTx7JYsH2rhQjRReqYmA8NwWyBfz/YgLUx9699tt6xrNXqv+5MZXKXcIsHhalC8hMaroDA+dQh6Sv05oAiGIxEkinYm86Y9olXps8gdslm+Vtcq0/xhvkE3/raF/nLm/+aD0x+ji/ePsH2o9/DzZDoh4/y69ee4v/0/3mWw+XApz6wwwtXn2cxsYTB40xBAlaxZ+Vq6n5ACsNr0ws8vff7bLcv8eVym5sbH+RIn2VRnuGwNEzbfWZJMX6OefMVlm88y8QnVDZh2OJc3GMWb9HZnsKexLSJyeI2Ib2IaE3fJopJpEpHFP2SidS5qlGeIlGP5j0RtT0Bh+d+5KUTnkPQKYQStMzGSAZKGVCXrWEbV6JhiulWoPtsXv1V0j9+gdnyiFZryjjg4wI00VVbrGSDEqEcDlkW56kOr/F/m/89Lr3xBWI55b+rP85/Z3+US23iz81f4EzzHL9h/gw3wzm27StMmgXXJ5e4Yx6jMDdpyqvsUfPQUcmF9hrVyUOYRsKLv4b72R8jffb/TG1PUA8DHBgW0wtMw7NM9QCTzhHdoyy6s5TFjCEEuuSYl4E0HGHLHRTLYCxmLEiXKaP+o5lgLfc1osfhYO27cFwTlAr8+FM7/pI/w0wgAMlsYXe3iV+1uAF0vuKALTrzQd5/9Nv8rzf+FY+tXuWOu0A3GRhI1LbFPP/zmGGg2/gwnXE80n6RC/3v8ZVOubr5AXbDIRvtNSYm8Bfsl7l18FWkFpCIvvE/4P7FTc5+5v8A208iqSYWWyzrgDpHHE6zmWYU7ipBrnLNewRl50s/iT739/DdHnQ9unGBl2LNRTPDjfKLVjXPH/YshY3soMwgkyPdSZL4+6aJrcwJNghsdXtgPId+ygrhhB2w4Yii26OfgDKjTJs8RI/EIw4mn+Sofpa5fwXpSjb6OdQHmHSHM+2bPLx6njd2zvG16S5/cfEzuFnJsjpJ18DtyUVuFYJ2N5joFC1OoUY4Awx4Iu+xhe49FO8ZGuB6N79OAL7+Y/07f9yf/1H/fu97vluLP5AzeE34EZiWRWcswTioNjmIni9/7TqHRw396oDaBL7/yYv87z96ikurl9nlDmX/Btv+GtHss1cULF2J1UAdI9NBKJPLyoIyls5NQWAtFUzOQlLumVsNGALOJrwElIKj3mRTDgxnL14GX47VVwPGZUTwmu53PGb5Ze9+CPcIn+efioz8gOzsZtaZty1opCaoh5hLvI6AQSlSR20SpigoGdiMe9TL1+n33uTy2R3+zKc+xNk6sDsxtOp5+VbLzVu3CcPAb3/+d7l28w79SH2SEfyXLZYtQ7HJ4QB0h1m4qNiknNSExe1cWRGfe7RxgdUBVGkX+xzs3SHFiAw91ghoxprIunrkPM7nQmy0JWJtNq5bb9WPN+X3jtY7u8fyX1tE1lp3gpF1V/auPoVoOqad4SA2Hau928RmgSdg0tj/D6BDxEtiopFZe4NJWFESODN3PHxqykMnp2xOSrwVRDvmlQUbIQ0UEvj4M09QFjWrkDi5VXLJ3uL7ntxlxyudcbSuJppc0emOjuh+/9cxfQapKQIh0zMFgaRMSsGkJUY7GAJOoXDm7n03lrItsr7NjscG7t5+9z+4d+/f9X+8ZZ64Z94gfzbec+ax96OTDSjzz7xEJv1tduItHpoECjtgbYap+thgQguxR3QEDBpPXIvzFDXJZuc8xSAGvBMKazFqoY30+3e4/vJzcONFDA3QoRpxtsAlRYdVBtcNiomJGTCJHXde/Qo3v/osXHsjW+32S6bOHJ/vvWO4HsS33Kn3PNf3N6zr1/IIPidtkn1ENNnM1pCxt+c9QR3RbbD5+FOZRmxNrqgppLgWLhPOnz2DjQ3TcMCG7fG52UphBQ2BkEBxIwZg7c2Rn5f7PYf/kuI9kwD85xACOG2oNQuBqMlGPkkcSzOlLTe4frTAe09pEhPp+K/fd5ofOHHEh1e/y4X0OieGr3Eu/C7E17leTVmUU7z2TENkOji8VgTr6RGi2aA3c1opx3dPIAZNEYkdTjtEOyQuEJcYzBzZuIjqhD4k5hceQcViyMYombds7vumSJJ56IrCaMrhRhSkypSF3SFVJ6HcQJyjiC1GEnU4pB4WYB1uOOJ09zLn4msU3S0eP3+Cb3nmEc7YQ4qwx5FMOYyWsijRfkWzPCJgiGIwJFzqMSnjKpI4lnZOZ2q2ykhR1bTFFqTAJBxmYKWxVNpSxyVm1LZPKYMkTZkXG2sZk5osPZxCHFsdhmEwNFpiqxniBFlXmO7pJOnxt+sJ9/7aTFkfLbeFVO4CHz2M+I6xNcaAoR8n1dwDjkOLTy2FiVijsMre62IdRWrYJfJwep2N5U3KbkE1LCl0hdMBp1nG1ocFhQ5on7A6sF0p/4tvfZSt2RaxKPHxFh8Iz/JXHndcmiQaV7EoZqySASZMy4piuAoaWOsg0S1wqc9l7ART7ZHlq0zKiJnPMeMZq8mI+oxnz+ntWuSVe8b0mxX58bC4C0/Q2s3jNlaZVpyMb3Cme4nT7ggJ2UhHY2RDWnzqs122apZJpiDYKb1WqJ9l8SpTEq3LrUJJGBVscjnHa1rmsoKDF9BwDUxDChEdDC4IPi7BtagTjC2YpUMm/XXq4XW2BOha8ArdIZPQ390UfYPGya35HnaCmlxBLAAxlpXWtHaOYDPLhESTDK2ZwfmLucWpBSoe6zxOQKwBZ3n44UvUseGMucOWHuDDClIYgbGKKTxqJlndcNywmBF+ax5wAf7QeFAZeVcj6+xbk3lFKiUYg5fEwSqw9Bu0pgAFbxKEhvPpOvLzP8kH9n6Tr4ZNzmxHava5Ldv8lnmSxiuz5iZmnDwwPjvAkYi2ZmnntCMQT1IGBwVVTOgg9blPXw/Iap9oL7Jnd6m0oygcTHbosXi3BIVBiyz3i97XDHH38YoIAaPgxGZ5XrfNLXOSLm5xalgxMS2lyUyO6bCHtAmkZKIHPF1f4+K8YZtDOGyxr32RreU+W2aL//7ffY7ujKXvEyWB7RMnuJp6kvGYGLEpIDGDEaMYeimwRUlc3mQySbjdM7z+ZsNGkbK2AlBqTxHbDKYTi6YVNg55txYjgzYYk3LLVy0xJqwVApZifpJbqSa4TWy7WGu/jdv2+0f8v30IAwVOJJskieJUEFGCRhIZN+F0QOlzOp8s6jLC3aceGU1S8JZUVHRDYLsIbDR7fOfOPh+Yd9y43TNJDT4u6anxCj40lPGAygSMMxQYuptvslvCw1szfuvNgO1e5yPhC1x5pWHz6Cyv+Es01YQeTzvYzByI7VoEIeNsVntZ8XEEyXLwBvF3/jVx/3WYbMDtiNqQMTEio7xu7uSuGSiMDI+7idU3PiSfAMHP2aOi9nNsusOUBVfqW9w5PGAj7GcbyjRQWuGkG6jT6NOgOeEJI6OnSROMm0BzAKUnuXqsSARMjIh4EIMTZYM9rn3pZ6icZ/t9T+GNwVHjYk8cFmBDBusNyvIL/xPD/gtUr32OyhhoBtRkZsw09qj8wXrUu7k8ZgeFDKRUkWzwRXaDXJpNVLapwx51zFW34KDHMxUHtqTry1ydswZCQhKsGKhmm7juOqdl4IOnDHXqMBR4icSh43ef3+fw/ZfZntbHgpu5JfvAEeCPigcJwLsaOe8U4ohLNkw0sB32mdpAR24HdCGQ1IMtEb9k9dWf4gnfcKl6nFPbkbPNdX49HJHCFolAoa/T25JD7+ldopZITy47D+uMNzW41CJiCZonfzRTvEhL+v09znzHX+HwcEr11UB/80WiVtkpzOxDMgwU+Jgys+8+n5fjSURzac5KFvQI1Ql2nvo05ekJ5s3fJL3+eYwOIEXmOasHv83M3eBDm3fYjbeopUWjYg7fYDc1bIjjyG3x5q0VhZ1QoCwWC1bGkrzJzoMp5HL9uDDYcERBR+GUv/S934qcvcQr//zLNFcDXrO6IuNkqAqRAgm3sDFkWrk1iEQ2dubEeAjJZCUyZxhcyfZDT2CeafB3Boav3iEZP1Zf1vLJecd77xJ1v1PQmoIpgJVcfbAY0A4luxuqGKwOKC0YYVBLUsGbiIkNxCGbrhgHyeCc5Ts++hTV/vN8S/EqF/2C/dAxIeLcCBgbwIaGE5OISwEdBLxlWigXK8/HLmzyr65dZXcW+NbVGxRffJby6AdZ6EVaCZTOUKYZMTpEFE2ZYy9pIDV7GO2zjI6f4Ns9jp79ebpUMj03QaxFrAVb5lKxkqsdJOxaVRE5toS+W1X5Bk/wQk4SxROLghTy7rZOCx6Zr3jhxiFm6MCByMDcC5v02HuBmuO9GZIhmoLN0rOz/wa1nWXrXQfWpCx8lSyiBhsC0t+kv3bA/lc2qS/sUBUFRI/ERFU5MEOW9R0s+1/6t7jbn2Nj70WMnRzzI4wB0/UkuTt234gwDCQs7Wgc5iVgWCHGs/3kR0nd1zj6vZtUkrOiqJCMZN8PPOX0JBKF0B5RjL3H5DzBVpTGcsod8Gi9xN46wNmzFE4QB7/1pee49d0PM5+7UQNk7cmSE5IHXgBvHw9G5V0MBZKZwOhPFYF52OOh4WVOss/QNqCKt442OVYyYWkSVXmEi3dww4rd/pDHl69xarGPCVNs8vjU0znYrzytbZjEfeapydr1xKz0FQ8o4hIEotpRrlRBDMkZmhbk/DPsfuQH4NzjqMu9x4CA9Kh02a12SGND+08wChrIxsig9Qbnv+UznP5T/zXmiW8l2azYNtiSwijOlESzTQwdp9KbzOKb0C+wKWaDou42pR7QmAmDm5I008yGkKhmG1njQXMDIoohmlxePDW8yVm9ztas4gc/cobve7hhe6Ok8Rt4banTkmBKBuPzhEWFGY6ygtsAWIuYgNua4iRPxqCkNNBExW+eY/dbvwd9/EP0tqJPhq/fS93rIvFOWtVrVkmuLOhYWYiQehKWld0gSE2uSeTrZ3wFxmS0+ijCA0ocIupKUOXUuW2mzes8yhtM+5sQI9pHCD0xRcBTS+TstmOj9iSEHosxkYlTHtnumFUJwj4b4Rrbw00mscEVHsIhvl8hSRjUE8fdepkUl1bosAc6kPAM0VGlnmnzJqVZon5GJ3UW1XEViL3nfhqyDsQ9o6njsKDp64fuXY/8WAhOR8GZ2EBI0B0yc1mKt4lm7M/0TLTDp5aYxt6HZhaHESAGvDWcqRKPhhe40L9ElVYQR8e+GBG1IPl6SXfEyWpB0V0j9QdURYEZIHagRUEy0FOhUlIdvsp2fBNvGmTZoOWE5C14n3UpvuH98AiiDAJB1tWbrLzqTj+C+cCnWNoNgskJnipIjOCEVj1x9wLmxC5ilBTJC3dR0kaHtTWFCcyaNzE0+ElBszjAaWAQz4EqjWR3wRxpvHe+8ffHf6rxIAF4N0OElXgink5BNVIuX+aJ8FXO9K8xdxDajqFvSeWEIzNDJ7swLjAqFoaeia5Ih3dISbGxw9PTJYiuwHCIXP1t/OoVGG5h+iOm2uAOXqEMh5AiyZT0hwuMCMkWIIlgpyQ5hVYnQUqsCNYWGZilcnwnKPfJ++VeMBEcIwbHSXnA0tkttDxLKM+yPxSEYkZDgcHQxQrKswiWMtzC6gLRAFGRECl0YGpXJDGEUYmwHwK+ntOHCGHIhjziGJznYLHChxVPyMucP/oiG5Wy3T/Hqf0vYoeGA3eCGCKbrFj0ylDNCK6g14xDcHkQRlmBIQO4EphksCJAQH1BMjO0PkOoTrHUClyJxJB9koyuERHr6vc7jkxhSvdkECEfgxYcmV06tw0h5V21y0BQTSn3ndP6BQTrS1I3kM1RWtzyTU7oHUg9g5pxp6gZI+An2GHFpl1iQkdyEwZb0fUtRVix036VbTkkNofUJlFqxMQe0sBcGlzqIHZZsth5ZAiYfh9ZvoIMe2jsM28+2IxVMQtMWKLlNgvZZsWUWG2RxI/6AWlMZkY5pXuAXfn/33itd2NBNFeMTDjC1AK9YJ1ipWcxGEJ5hihCYQMzzVWpgGddaBXI90hsKUxgsnyNj7sXeHz1BaZpH5yioc8tqhhA1yylmAGzukQk0XZZ+VB8lfX/jWGwNTHBLB1RhkWuYhW5nH5kJjS6Bhxaol0nVvnuendTgvzc38W/ZG2PnAfN0NlD7JkTrOwcMJQChQTEJHq/zYo5flLTDw1iCtqQwCaKoiZExypG5vEOmVlsqKsCUk80Bde7yO1uyBiYkKWRGYWuHsTbx4MWwLsYCiwQihG8lOKAu/5lHg9f5RF3mcPUUcqM2mSt8cE4DvZ7JtUOsV1ldHDlWC57rq1uwPwmvruNGRqcq7DLwPTl3+fW7f/A5OJTTP0tXCXsHV4n/eq/xqYFaiqSOHTssw7iMTLA5iVMdYZYOuy0pg0dVcrSskidgTmAvIN+mZDp8KgF48eS7chMF+iNodINbH0Ge+oxVte/SKgsREtwu1h7CmyBMEBSklFsBKLgC8+pCqadUiaDTdCro2sa8BZPT+WEgy5xJwhffeUNNk3Dh+QVHr5yin+xeJ3lz/0Sy1vXiHv/G7rqBIkCs7rFztZFms4wwRNjQNIKM67aikElgvajFbNgiESjYC1JJhidktw2rakZhsiWZOEWUlqLsr2FqX+/IEBYC7rmSRRjx5nVgNuG3fcTjjror4MobUqUujaDWfcfss4CxmY/AzLH3wwLXGxpB6UNFjE1OMsQB0LbM0lLyu4qhTbEWNI4Sz2tsAe3KH/zn3Gaz+Jjj8ZEtAmxBqeJSeqxmlBdMagSsVgRDp/9VV77lf8v55svs10UyJAobZkFb1AwkSaVDPPHUE1IuUVYJ6MCMgz30E7v3qO5wJG+4R0AFbI6n1i8VYY7B7i6RkJiGBqqjYvsp8BgKjS0VLFFJdKbCRU+73Ilq1LULiHdEcXwBn/6xCWWMmWnX0Gv1EZIXiG2+TTNeJ5R8Snmu0EFkcAgJV4NGg3BFmgK+Nhm1ocFSIQQacodjC9gf3HM7vlGhmR0yj27S4P1BVhLTDPCicdJ07OAZQIYzUDJYecxdKYQn6MqLXEleOuwaQHtHsaU6GSLOmbMUsBhjOC0J2D5v/w//iXf+y1P8tf/l59hUsn4HL+z5+6/lHiQALzLEVVRTaMaoBL2X+Ps8DpnyofY6/vsyR2XCEucnbHhauJCCCvFGOUothxMCpquYc4rFOEWG4XiKambyMnmALv4EkdDy+R0QRw6ZqUlHVzFpeyQFY3Fx3zj54VsxvTxTyGbJ2hDotYOU1oMgSoZ1MxIa/SuMe+gApDFSbI7XjGW/eKI3LYjS6CmPvck9bf/AHu/cIRtX0PVQHUSzEbu/YqOfdDM+daU6VKbfkW13MemFTEWFPVmBuWFhmlhcEnp/YShNtw8OGDXNlypjrhiW37pdoO//Txy+wZ1d43glcE7NqTnOz70ONULX8alCg1LGJOmXMe1qCRIGRRoVLJKnYzSscYRU4WaDYKbEgJZLpj8O1mJMMc7FSLJR6Kjscw6pQLBU24/zKPf/hcoX58j/+FZdHVIKg0mxgyVE4Mak6mRo4+g0YDXgBnFf1RzmbanIMR87EJk7i2fev9FPjFdUXxpn+Q3iMYQU0d/eMC8fZUL85bVnSXiHZ1VOhJWHVaLrINAi5iRmhgDxfIl5oe/R1kuCQkIKSsRJs23mzPI7DSnv+szSHOInH+MZsRRKHcXrbXIy90xulcw+RsXa76FxoLgpmh1gvrJp5Ev/SakFkEITFCbXS0nRJTIYByoB2nHpSgRQk/W7G15NL1EV52gavNr7M43icnD4TCKiEm+9xJI8qAVUQxIR7Ius3iSUBQOk5YZLBoLMAOkBvD4+SXqrV36xRdxb/Nsv7vLY8Zt3NVWsOP9l5NXt32Ox7/rv6LarBA3oUgJYoNWG1z6U38Jp/vwe2+QtMe7CmHF3BywKwt8ihwOgqUDgcFkBQinAyrC4CbcOjykKnMBTIwg4t71Gsd/TvGgBfAuRyGKpyWElPufqaPqb1PbxJCgcMJMeua6ZFMXTGSg7yJlWTHRBi8DvS8pZcHj9mUe5RVY3aEblBKDb4+YScvcR9Q7uphFj7zmsmDKAqy51DvquPepZphdoS0mmAJirbSxgzhkETCdECgygtfef7c6S9IGItBhCOJQLDLKcHhGSc7Nc8jlj7BnT+BoiCrYyUnETHBjwsQo+JQrCp4+Qjnc5oRZsOECMSUO+yw4VDiDGRa0yyOYnuCNNuFnm9TtLebhgK2Dr/Bt5UtUw01ILVv9dWpWdMlTiHLl0oxaO4iB2XB7ZE0YSH5MnDKi24ztdyEhYwKAGII3JDNlFR1tSG+ZSNctgHT3B+8onGoeX7I2uorJssnFLvXDH8GeegIocoHAF4iJWfZXLEiBmpIoPvdaibiRPx3U0kuRr7d1xJCAiNGBDRP5yMM7fOCUwzaL7BhohdJECgYK1zBZ3MQe3MFaQ2cSprRo8gxpTjQOsREvAwZQTVQ3vsw5eROTjoiuwFpBQs9a1aDvA0exxFz5dsz7vgO2ztIihPXY6b3DmJf9HGN74BscVvOYxjTlpmyzP3uU9Nh3kmRCSUvcv8nUCFbBG6UQGR0WDcmMYDaR/Bw4h6krEGGjvcrmcA0rA0LBdDKjmjqwPclGOmMzaC8ZSBPQSSZESk80AdUIg+IZkNU1NA4MaTZikTI9tK5P4c49jkzKY42DcRjf9Vi3knzK7ENVSyDrHJAEKSbML30Ev/MYaj3EHmJDMpukix/Cn30KyinWCkSDhsB2vMWp4RqTuIJqghBQ43K1BYPVPt8ifsLeUUNaK4OLZBXCB/GHxoPReRdDgIqISUcYHEnAWYOJHSqObuzH23aPLVdhi0DRCcXcw1FA7rzCzumSSbRcomc234dy4BdeS2hVU88ntKuc/aaYiDg6P8Wg2aCENVMcjAZEMxOgH7Yp6nN04piZRG9aok15wYvQ2+wuV7BCbTUu3vcTCQgZ+0AmA1rApCGXg001+iJMsOVJDu0WD+sKdIafbJNwFEYRo8SoJAZsHEimQI2jTivK1Q367jp+9jjLoYbYMvNKYQLBeX7jy2/ywkuHPH2yZ84h20XL6fgm3z3r4c4rDDzEB88UHBztYTbOElcvUvavk9o7ZMGQA4i5xJorAIYkCY19ZiqojKXmXMpVEXoR6mLOfPcMZr6T+7YJsHnPf7xuvcPFX2D0XQgojiBZktmKZxCAGcVQg/GoSfTDQJX6rFkgBZgql0pH0SMZhaES0OExdjKmA+b4iktYsWF7djci24dv4qYDQ7sC3+LFo+0CIbJJl93uYmJFIFkl9SUr2SWaEswhJiwxIRAXRxw8/zl2+gNa9ajdpZIe2ibPQDWotbTqwZ8Hu49qSU/mkKuYsf2h+TKMCPJjKOA3AQQoqYfkMfNzpPPfwt5yn/PlZVaxoE4rvuWhDWbDEW7o6W0YG2op1yckV9WiMQTj0MLSD8CgWDdQSZcBj41gh4AxHSINg7X04ih7gWRJOkeZjBLXkUiPhgHpEvgD2r2X8F1LTHOMNxjNyZsGj1YbDAw4uVfu5xuxM874AonZAjobJVkEg0UZVGjtJkJkSofEFWhkXza4Lo6zbsKWrVCNxJRfrVxeZ3jx19iIHyKmvOtPxjKYkiiMuBYlYCiqAmuzd0YPCHbk0TyIt4sHCcC7HKUG6A8oyzlLNRjjsK4gxIIOT0iJzcrwvg89xhOPP8z5U69woEs2pOeTH3uaajDY6wMXQuBEcxU/m/LsiTM0YSCGBUNlGZIgZY36KdFN6cKSSeGz5KesgWcjdcoKO09/G/rQJVYjiCxqh698nhzi2jHbYBgyHe5tuMJ/ZGg6BmiF9Y8QsjZBxJoiW6WaAmKFVtvY/T6TJf2EAYOXUcPfS16IYwcqiFhMSnzqw+/nuc8pB6pIOSXogKDEbkFVnaIzcw6DJUlC2gPMsGAab1OzD66j8xU//H3fwd7+AeWLLxHbV7EHrzKxHU300O9lxPCxtNxbwYzHK7ncnTZ7ha3tkzz8iU8h9WmkKI5//90oq2bgvx73uNe7GqtkwybjKPLqSQqQSiGX8bMgVBKXvxOLpnU6kts1g1qsVIRkR+pifs/KwxMXz3D2ykO439pHYsSSKB1ov8SmjmihHAYIA5IG7NTQ7bUU1QbqTpPqGtIRNnZISsSmyZa0CkXhWAQZcRICozqeNQVFvQEyAWlIUua0Usd76S2tqXurVPeUB76BIZIgJHx1goc/+f20bYuYAvVTpuaAz3zvt3Hr3/2PGIloVJz3SFB0rNzkI7UEsbQx0OFQl6mlMfaorfEmswySDohNDCIEk+VsRB2qJaplvoISwYwrpChGW/rVLZzWYLZQGcbbRtEBVAqSGV1Tv4HjFDFYDTAaCSmeyHj/CWCFFWBwTExmnuAMjRhuibAhni1fk1C89UgQrETOlS3f/qHH+bXXXgTIap8ytrbGEe5DwjhPNmtUkpWx7fKgCfCHxYMWwLsYCrQCOJtFTkRRWtQcENIeWFixjfqLfPsj5/mL7+vY9beZSkc0iYsf/lacmbKodrhlEw+ZV/nI8pf58OR1vAuEzrDVW0xfcjh9CF8Jm8NVJv1NGkqOJjVSO6Zti+8rCDWtrzn86F+GnYfYCAkfVywnj/Fy/TSx3MGkfSZpj0rvEMQxiL//CUI84ClUmbO2DzEkM0FtlXvPYYEZDgDDSqYQDKvSs9ycYmyJi0Xuc6ohyQT1FulfYcIhpq/5c+87z6QqORCH9FeZpSNadghymg2XpZGvmU1eiTOCmeFNiboJxtTQJLb6JU+dnPHJDzyO1ZYkA8RDpF+OfPtTJC2I3RGYBTrsQ4x0fcytADOiidWiOAzCXID6PObx/wa5+Fnwc/AWZIKlpgZ83sZnSlfmiP2xIwG9WFQcaMSjWE0giSjKoJFUgIYeEUMaasAR6zN0YQMpLG44oOwPscYzuIreBIiRSh11XBFCJKUjnDlA7SbLdJrzO57N+gATBGxBb5TS1YhOaUwJveWADe44j7qOFDdY2YfpS2VzsodtW1L5EAduM+NRquuIzcCtZbuJtTskBrQMNNWcNk2YpI7N7g2CXdIWGxhXcxLYAax4MFOMmBHNMAJPgQwamdzvHXvfEaUgFQK1YXLmabYe+Tao6ixdmxzmoRN0RSCoxYulZh8xME8LfFyO1xBmqaEKDdZ4xBjUWIjgtCP6gSRHSGxAHXWr1EEZZIpYy5xrlHqDTko6pkyHJdb09GVNMBv4RihSQym3UYkkV2FIVFzDVInbeoFrk/fhtaPs9sbcSfEpjcuoGdVL8uiatevmfYTB53vdFVlaHKGCLKttc8Nnk2zeJOJIk21CeYKdFHhclU1VVHZJug1mCWZAsJw5tclTD8/ZQWhkgg0D5w6e4/2L32PeHBLNnAFP8p5OwEhDFfcgKvHBPvcPjQcJwLscATkWMRHybjzpQOEH9vYaXr+urAbPlhmov/IryJd+lWJYEGMkiSN2A9FZOi8UdsnW8kUe4jqbRcMsdtRhQDC0ktX/irigpEFFCL7moE1ICLkKbQuiq+jrcxhbUCcwSWnSJq91myQ3R8hUO0uHqiOpfQcbqoyRd+SSrYO8A2XEAmgarX8hOyDmknqwllSXIw1y/b6GlGzeXa5uYtIKtKA6eB3TL7CFpXQ9pfTEVGBNxU4BlekZnOdGE+kpch/QliRbw6BMtMeHVfZH0D73jfdvQR8QDGo2sGpwBrARMww4MTjrxq14pqFlalbeUVQiiJlAcQH8GbAFWAPiMXg8gpUMRkIc98DY/1ihAkFMHp98lKPe4F3rXJGAdktMMkyKEk2WVJ1klSak0GNocTLk4xYhjNa8ph+yIp9J2clPF6QuoNRMKkVf/xLxzTfAKEVpcKFjJkK7OsSqIEVN6zxJFILBRXB6h5k9yLoMbvM4mVRZkcitEzEzYgBN2QUv2ayaWWiHa26RJBBtiTGOiQi1gDEOTHbYWMsBrxMAEQPyjuyA7vNa5B171riuSEzQke6ZkkElEVyW+zUYXGoQVVzsMXE43ok6MhBT1hd41EmQmK3CNXVIHJAo+F5xYQRxGqFggdUlaVT/cDYiNrd4rJ8wNR4ztEh7hxQD0RWIK3E244PupJMs2MKkiDM6VunIlUA4nrNyocW8BWnxxw2DyXr81oHJDBDPMWwGI3mDUANGLOoq1FbUJHaBCSlXObQkq1tmHQUxBkdPERPJFogJ7LgFVyYdZexI4hFX0CelFVBixgaMvhQP4u3jwci8y2GxI1gn27NGaoI4jCiHB7e4tX+HQE+7us6rX/ol7vz2L2Da7B2vfUuVlpThkMokYgLiwBlzwGW5yslwjUo6lAE3dGjyJONAEj4GtDP4ncvEcg5pCXEJOKyMGbAA4pluneXcI+/HVJtjsnJ3d2rfUX3w7f9IjMFYh6rkHZDxYD1nH3oYxqWsqCoEw71KXZoU7Xu0a0kjtuHW85/DLa9hwwqDElIWTdkwLWfsIbP2GpXpOTpaYKzLuytNWX52FJQjDbmXO5bVwxuv538Tm/n+zSHG2jzheI+SspKeJu5Kz+ofccbvfhwTKsVwb/e2ECg0L6JgEA3Y/oDkSuzZJ5GdC9m7YFQB1CFiiKj1RGuR4YikkaCB2DeUJmBtoHIBFje4/Ws/x/Lai0jfI7QUzXUe2RiY9DewJOYmYNJAJ46SltPty5xpX6SMy6yJ4GzW/NeI3E0LqQtLDC3WWdCIlYgnIqEnds1bivv/aUS27F3H+hpl7wBHFJsXfwNric230kPvYgRk9LWwIhk9bwUxghl9IBhFj/KYkoWI+iybbKxB0pCTQgsi2Wq8k5KeEqZnOPnEx9jaPQ1mbMeN6ZQx96/98W7GGtiZP+V2j64zkbccVkJQjC3QmNkRZ0+fpDRKHAZiTIBkRo4aNJn7rmD8lxYPEoB3MQSwaiCVWTlODUlqEgUSWqx29ATUK+3yKmn1JmbvJQoTUXGkoWcqDfVwgEsdQbNr1m68wZXuOc4Pr1GzRKXHdg0pOqJ4kOx1b9OE6oPfQ1PMwQ1AQKSgMI61EhkUnDj/BE99+/djJztg63EHVWCxWP2TTMB3wVh6/CGjsYvLSZGtuPT+Z4577baeHE9+SrZ2zruifLx5MlXk2pc5Y/Yp4wqrEWMMThKbsuLU8AYn2teYsiKpItYfn66S+6SJzK3W2KApYI3QLA6zk7GRvNDHNqOGB4NWc6yYLFtMumeCXNMrvzmRd9CGvIDevTIOcq+VhBYTKEx2fPQTzKMfpXrkGYzLToLR1FkDXkNekDQx0eylbmxBVQomZV957Rdov0fce4WpbTEiWO3YZp8PnlR24g2MBDZMiyPQS0lJz5X0Mpe7r1D3+5n3YSwlA4aEpiLvYhU09uSa9yiXnDoMPSYNyDBk0aVv0tjef7zdk7F+ru75LGQcTT2Dos4ARns3gRzhlsfXNtd1Rpnj0CMp5nqPKwDJbR9VkiqqAZuGsYIA2Boz24WyzmNrEsYpzipJHE2yLHohTc5w4RPfz8mLl/PzrjkhV3H3PKt3T+mbl+J+/aiOtR15q/XT8XygivEFZrKJRphXDhOasaIBpAwryJsKf4+T5oN4u3jQHHmXIxd5HaI5e41SEMRTmwEzLDlqWmpXUNlALQumbpGFZbSAfqBOHW1aIdHTyzYYz0a4wQf0K9ReqKUFaXH9Ek2btLbG6SFWIaUJafoot0LJjh3wJuPpXBp3rsaA1vjpWdzEgs4za0AKQLG6FgP6k8Rd1nvW2M9gNUZ/BBEoN7P6oWJgku1BRcxomaoYY5E0JgjGICTOhjd4Yv4MX2RFR9bAZ2g4Wa+4bK5zoyx5XZesjM0qYElRO+ryj/asOqwgOkwaMDL2JQWSWCRmbrYGiDKhG0B9ojDrc1qPTLq7GfsTjdP/fNztc5u7F2XsK4mSrU5NyV6YsDk4MEKbhGrrYXSRd3WqhsFUOMm69DEpuneTqrlBJ3Bjf8lyKUhcs0sGjK6Y6wFGl0hSrEZO2CMm3CJdu4GhZ6ZHVBLpTUUVWj5UvE5pJ2ylfQZx1NZSmDBaNFeoZne7ZrlktnOS4c6AcxnLTgq5GhBjPrdv8Lj+ieJuESgv4WlMMkc76hxZ8lfrDRgMhGzGZMxbqzjrV0nrJCApqV2wboOJqwhRKceWT1IgBUzq8v0swiKVNG6brdKBL0B7dOgxbsC4KUJJOySSTPCnn8hgxqQ5KTduhITee0T6dZ+/ebF+R5Fc8n9rHjI2USTSxMTWiRNw/ZBhsY8PRxgNGGM52j9kf++QdL7GGHIicXdKehBfF+/dZPs/ycj2k+t+sRkR2ABzXTGloY+WoxZm3lP0DU4D2g8Mbpohz7HDSMCJ0EvJYAqmaY+PVFd5equldgMiPX5oSFHoJFPsNCR8KrDmJLtPfITkCggZVOcYBWpMAlejskHQae6Pmax6l6U77pf+9/Zj8Jbv8hxJVEuyJSoFWd4mu8FpWeUpcXQPO/67lIjxLr97lm5wadJSpRWVU+oil5hP2gWPF7d4enNF0e0RJZ9DVTgkjZOyye9HXGDTEqsBiT114SFkjXebOuiXJFsij3+ERcp0SE0Bxsk2l9qzNe03a3r03FudzcJOadQGVHGwc5HqfZ9BLn2CldvKO22ZEaUijZvSNJahEUVCh7z8+3Dzq1irfOWVq7x+/SbO2ywcZA0MS2xzGzc0IAkRZaoLJstXGK49h6SBaTyidkqkxMWeD01u8qnZVc76o5zQWZetYVMi4gmZaoI1QvnYkyRXQjUhupKUSzU4jcg3I7N6B5F75G/zU8m70vV9IetKgEC0BdEWufplHdZanF1XAdaVAHOsm2esy/a9oSP5Gqm3MdYjBHQ03Cb12eYbQ8ITty5RPPYpOPsYybhchHCABGIMOCE7Mg6JwY7PnqvAFOPR3kNXPY7/yNr5ImNlZN0CGMdWI3btcWIcQ0zMXeDDjz2EjV2uaBHQMKApj4/q/eMY/kuKBwnAux3SgekxolhVkISVyHTYYxZWo1jKDJsKipCXXE2GYApISt+2iDF4awl4OikpdOCCuc1Ze0ghWXSHviGREa5qDETFqkH8Jrsf+g4GPwMHzoA1Iz1NEjpyxHNlQggyFiU1c3VlPYnd30m/5fPxdyPwRxl3L2LyzsMWqBiSmhEcx7HMrbDO/PNEquSWgAzKCbuilsD50yd47JGLTD2csCvOhKuct2PbBEdVWOaVzVOrJBitVVnewaQFhQQ0dHlxH62bJXUYDdjdc/DMtxMnuxnJPJZ1c2p3d/L+ZqxRoor/up1lQjI9DIhY2LrE7Dt+BPepHyaeeQLj1qprJieAksdeyd97AvbOa9DcAh1Y9kqQXGoOIaLGIxZKF7HGoKWHnRNULrHJgrK7jSFR64pCu5FimDjJbS6bq2zrQT7eojpeCBGLGY+nqGr0oSs0boNw8hFk4yy9muPqw3u5Z3vvkd27qMi4SB0v6pqwJtMwsZ4+xFHcSqm8G7Ud4vELZi3DvOA5yQqawVbohcsogqQMGhRj0TRgQos1luRmzK58K5sf/bPoqSsMZpKBqCIIkTg0mNgwiQuMJAKCWo/mIxjphPlk1nUAedsz/ObEumUoxmdXz3snD9Vc/RClKqvsbOkLtkr45AcvU5mItcKTj17k3O4sa1qqGdWU37v31H/seJAAvMshNGAHRBSXIjoscNqyq3vMh31Wy56BKSYabMiAM7wniiHFcUGyDtUhl+yLOSTYSHtsxAMsMV+02MHIhRfjwEI0CRUhzs+xLzOSy/Qi8CCKSt65xrGinaVGuWtYo2uE+Tt5YNaLv7xlIslYHsHYjHhW8vuK2LtgqMQxCPD4nY3BmLFcqgoDVNrhJXByc8bD507itWOmKzb668zDHlagw1FKZOoVYxJWAxoyz13aO+jqJjIsMbFDUk/yPoOiUktqlujWGTh5ma7cpU93zyatWxaAaPzmtQDSkNsY4zsmlAFGYSVLcNvomY/C2Q/TT8+OoMmYyQijC52mRCrmiC2oLPj+DkLI86srsyukJoIRoq0gZKu5oEJvN9Htcxhgoi2uP8IgeA24cdelSZHQsNnfYDLsYVKP+pKgFrWGpllmlWQDUR2x2GFZX0BPfZBu+hANBXghxpDvx2/wuL7jULiXBTK2+0fVytwGMAAp4Uw29zLGEWPKAD8R6sLlZziFe5LLMd+NEUNuh/R40umLGexKGGExQkoDNnYYY+jVkzYuwIkn0OIkqdpE3ZSUJC+WJHxsqfs9DB1rX7z1My/CH3hWgcys+AOtgW9cSH7Tca3P5f98nOMxjEmwxoAlEcKQsSwh4ocjzs0tdlhhNHHx7Am2p0WuLpr18/qevaP+o8eDBOBdjbyAqigpJXzsSHs38LHj8rTn8oZCTLSDw2HxaM5ubYFNAZoDnC/RJNjQYEODmgIGmMYV265nUhTYJCQdMP1hdg5TOxqHtCAdiTnLyRX6VFOmJpfd10526D3Z/lpHXe9pwL0Vo/zHi3W3+u6kYb7uJ18/laixdy1DjMuYgLcpsYqufb11TH4UE1tmTrEhtwRmYZ8yLoniGKTCp44yNZgY8JKyPTLA8jrhuc8xHO1RjKXtmCBERcMq746KbVS3ObRbLAfGwmsW0V3Tib4Z2vPr88/exOsRHBO48V+FLL28MHPUn0ZtPS4nPYwLPCkzIW6HmgOzSYqBwnSAkoaBEBUxRW7+GJvPs1nB0BLVsnRbqNsCDD42OO2Px8BpP0oLKyKKSQ1V2KfWFVhHLwVJDGWRxVxIEExJZIdrnGdZPcaiejQnMQnSaCP9no4/0DIf7bM15l398b9nSmBCcM6ByV4fXhImdnmhH6tbZiyVqcj4OokgRb6ekul4CUPX96gOFPSEmBDr6GVKb05Auc1ga4KtwHpQpawqSBEbFkhojkW67j2N9cddyN296cB/hOVhZECwrrgduz7mqokhopqyoqImNkyHa29Ry4DRQGU1A5kl30mZ9Poev6f+I8aDBOBdDSFRkrRCo5CGjn6RedEnzSEXJh2OQDKCkYhhyBuKGKlSg1/tEfuO0jkeObXBlu2ytavJNK/tieXszgnMIKgoNh5QaZcNsEXArrIE6+w0W499D2Z+EdE2y/uKgAwIEaOZhGc0YemOlbTyKWRVv/s77bFcd7xArpMLPeaurxOC/JEf8mOVLjO6170lkfgDQwvGIiLY2LFVG1zq8AxY7TAaGaRETUWRWsrhEIYOLEhR4IyFvWscPf8FTFhhTUSikuKYEsWWmCD6OVKd4sxTH2N28uxYHjFZw528PTXrfuw3PPJ7ZGOh3BqRsafpVPHj4tuKIUhBQcjXwARiHEhWMn98Mmf+we+gfPozJOuQEMAKYkpA8KK4NGDWJdflEhk6EpbWzMFtIORxlxEPoWLwxCwxLIqaMnP1U0dBhxpPKxVRLDEekVKby/xmgt+5wpkP/znKx/80pz78A2w+/AwaLBHN3Pr3YNP2j6r4ZLpozDvV9TXThBqX83uTd+Q60lAdAdFwnIAfJ+IiMJpyBVOgMoJdY9YTDFGRocNpJKaE0wHRSBCDLyeoGf0dfJWv+8Z2xnUQUE3jgvj1H+s76+7GIPcExuf5m0AP/HrYYb6/vq43oesEQHEZzkJZl5jhiK0JnNqaYFJgVjqcBoymXGkkX5cH8fbxIAF4lyMxA2YYdRAUFyM2gV2u2Eh7GF1hCyXpErF9tsgcOiZpgR/2EB0opxMuXDrNyTriJIHLamElPbP5FKMONYKLR9Smx2JRWyCuA3uEnWxx4vHP4DYvQZJRDAMYs2Gjme/vaDG0Y3Z99wzuN/4AjWg9oaw5y8fc5XQ84d0F/Y0TzViu+0Mnf4FosrqYSQPbtcFJxI2TpwLJVGBKfGrzbtUoxCyJaqxBwgI3HFDokCc9BesLyrJA0sDQB4KdYWZnOPXMJ5mfvYgZ90Zp3KEJ3FOS/0ZH3pvFEV62Lj0fC6zGRZ7syDudSTpEjAETCSnS2QlYg59uMnv60xQf+WwWRgoRNRPUTTEieI2Y2GOTYGKEdgkxjTv2Gtwk2yTHvPvXUWbYEnICgDK4DQYz+tMzoMbSmTr3wbXB+QgG2mjw84uc/9gPUT70UaaPfoLy8reAn2C9RyW+d/drX39vHvPzU6ZQruVu4fgeT5rld0n5vi+M4g3ZtU/JO/41dkCO/zgD+o77+YqIwTmLNEsKSRRlhUkdRcyCQ9ZZEAhiwVcZazPfIRo3vq6lP0440j3P5vpDudfA+g8+09/gGB/8/H458Uh6dzwZqZCW7I7pjCAxYEPDmYdPc/HMCSyBeeWxGlmbcckoif4g3j4eJADvZqgQcKTRA10QSmMxISvwbZgFznSo7Vk1t0i6AgQnShUXmMVNHIkUemxa4vs9TOjHSRVSdwhdh6ilT4G02kOGFSRLdAWBBtIB0QhDtUujs9w3NDL2VnOJVcgCW0KP0B2XtJWxP/gOHnv9+u/u6VvnBzAd90nXvHpdr/ajDsAfSQATIUjmlwuJjUx9H6l82QgkSd6FGh1wsc34wjSQkqIxwmKPWgJeRpyDCmItMST04FZWTrQ1qhX4GUGFZPSeHbiwhmx9syKJJYjcsyjqmMC1kBZ4BpwmjObye8KAJBJCZ+d59G0JfodUnGGVHDgBO2M5GOIwUGjW9Tdp1BboV3l4VLPOhCkwmrLZkYKaDOa0YxtAjaWxczozGS/5cLwbTWIYhiNC32QH3JGlMPgdDoaS3kwIbotgyqyiqOmb1Hl+9yKvnffc7zJ+MiY3kIyAAV84pnWZXSwlt/80RjSNScAaWACZ4TGyZe6+CeitGzB0mKpCU8LFJoONjY7X3YDzRDVovZFVNxFIjg7ueQbXC+M9z6jeXfzv/fxNCfm6r+WeL9bjMnqOOCuIsRADtYmkG6/jJeGNYVoX46I/zmLH5/sg3i4e6AC8qzHyz9d98DTgjY6TZpZ11dQzb68j175APDpAVLDGQgho2ENTSVg0mGstRZPwCFE8sfBYSUQaEINvbjHsdfghL4CDLWm3LqH1xfzI6zIDBUWxBhhVubOq3Pp4s3XnMc9c1j/7k+aF/3MAoqwMKOpAPMlaogEXFZGEEUVVELVZL9wYsDqK0gQwjkmRmKVDZtLcpWFpBOPp1BETuKT01ARXZ5O+/ZvQHWBdADUELCkkUtxjePOrLKOltiCFhzQjyhxrHSYmhAHFEqNDTPlNSQGU0T52RCGsd4teDEn//+39aaxsyXXfC/7Wioi9dw5nvnNV3RpYI4sUZ1KU2DIp672WZMlQu58FqAEbNtw2+EUf3B/cMGDDggyIcBsGjIYAG/AAfzBs+EkWDNtAA5qMJ3Y33U96mu0niUOJM1nFqrr3niEz994Rqz/E3nnynHtuTby36gz7R566efLkGBkZsWIN/yUYAYdRphliBxy4Ck0G6qlDlY2j1kjtHA0jqLZIsgF8C2jxXilTZCGOJCU+tThriZZjwo6EpAhFmTfmflG23KHQW4OmBaScl4E6Wq1IEsCXaFfW9+3f+hW29mfYuMSVCdICvBA0KxnGyePsXfsEcuURSqOrnjl9ZoBYhFSTpKQBvGXBGoVOvz/SuBE0MZ9Ck1HYAS0RV1xiJMJz8jUW/qsUdkDS3P/Ca0t0eb1oteLAX6K4/K48/7uggsQFWr+CfP2PaZs5s0vvpi1vMl17DE8eR3VF3vTE0SaPC5dprn+U/WafqtrpenQs383xd3fCdQ+eHNIScjASvAh4j6SYv88IjZUkCfhUM2aGqrCobnCwZpSzb/P1X/7nbC++l3dNb7A+9tkLguA7oaxlpdHAXQwjcz8RqDSh5hCxLMdrNaaJ5IRXbQuVgqfbP+Lmd36TcbyTpXiTQyVgtFhoKC0ir86YaE7XXfgJszQiSaSpaqTyXJt/Bas90W/lzWFyjfAD/3cOrv0IIzO2Fl+kWXwLwVN4yHX+5WGzO4TckoNlFlA+gZdv5W0fWzr6RJ573SFXOvimQClog6P2iTKmXDlRGNEcgQmh3KAtBb1zm7LdZZvbaGrY0ZpHZn/CjfStrpI6EtpdTBL7qaAhULbGgV9jHjYImvDNLSrbQ5qEuZK62EBTy5bbRfbuMAqP4PUgJ1MyJullUh1wzBBmWBjBwQKTahnrfZCYCA2OkoizBaQWh0fVs9AJSSeURKr5N4EFL5WPc5UXoNpkVs1ZT3tIivh0J8eB0zo02yRKHLtMRCjaO+z667TVOuX82zgic8YE9yqla5D2gDQeE1OLBJ9zEq2llDlBW8QapK2ZyovMwmXm5Ta1jpiUUyZxjyLVPP6d/0oopqSowG0IdxAFT0MhBzSP/iC28WHKzRbvJoTYhb1OEcnICnztPub61syOlgLvFFm8jMeo168jt79FKZHUGlNeYi/VFOMnGO81/Nm9X0X9t2liRSzX8XVNkRbsh4JxO2NeXOHW9oe59vG/iH3nS6gU4ANOWzbi17Fvf41m40kOnvoLyMMfJV19uFOFaLEwxdKLOKBpC2TtKcqP/Z9oWmF05WF2THLC7esgx/59kCiGJlhowa4IE5RxWaDtPkFaSEqtm0QZU7T7XOJVIGLP/wQvrX2Vm//r/4P3tr/GtXft8OUnPshjV9bYpyAAY/YZDIDXZggB3GciuROgSZbAjVLkchSVvPl2Lqle3z1zwnmyK98ysy7HLifKmXTRrWzq53Iei4TplK2nniOURSdANOKANWJ16e115b0hDLWa0uZImuGt6QSU8ttq0dwkxnkYr6PrlxBxlPUtHuVFbu7/N67e/hOeKV9mrX0VEaHRKldUtrd5WL7DpHkVyFnXWfinzafZTnRm6fFUn5uXGASrc6OgLpnOCahFUjL2oyfi8SEh9V7XzObBc5dhdZimlZ23lsvNINAm5Q5rUEzRdp5d9gkOWmGRFIg4ySWqy5SvZYz1MOp7l3GjnZxqF5NVi5QKjXkaP8lz3U+Z65gkikj25PRCQLKUdT4aPnHiIBlFWXL5+g3W1rbyYn0Km7fERJfKKlkdUcGSUcec7U/b4kLAr20QxlNU+7yWnKBrfaLsSd/GvhzQGW29YH1zE791mTYF9lIgSkW0blwM2gjbly5x9dEnCJM16hbwFYtkNElgNEKcQ13B9Mp1Nq4/goTxqUyuzBMwEawl9K2v05xA3vwtGpU0pK70L2jC4RhdvkpRVRATLjqu3nyUD3/4I1Rl0Q85mGCxf46Bkzh937QzTVa3S/kiRqBxFVF6KVcjpCa7Trskobsz7lcma2cELJeeLtGoNwByOo+CtTlGbQWShCBKW13hYPtZ5qNrb9N7f3OI1ISUDQBh0Qn2AN7TqKdFaVqjFs/Cj0heuBoOeI98kY/F36b+L/9PPrXzKtfkVQSoXV7gtuxVnqpeQZvdziPT4lONswZPu9zEQHOeRjXGigngKOIMb3PMIiaKc4ImQyRgmw8zsxKsZixz6pz7/cDRI5d0eU3Trjhy/Ai0wk8vU196D1AQ2gMKdSAldukpUrmF0FDJAiESEaIc5jUcYTUILALOdSVqll3eTYOzllly3NEd5tUOr8oWCx0hQOjiy85axBLa5X0sE0CXMdnuMdtcJx/7lfsUuv+9gqBZVVIdRpa4nly6wSJlGV8TzR0oxWU9Bs0pqiourwGuqySRu82AbkrmfBUU3AgLW7B9k4WfsrAAPkCh1OYIRYW4wEKEhQmEEvG56gINJAnUbYIGxFU00dG87aP2+mSzsEXibiejbmBznDXUOsFcQNMBFluSBiqX8ji4gnpWo1VJpKXdPyDFLK7k+myiCMMW99oMvpEHiTgaKfHqECLBWkbMsqAK8TDBB7rFtr/YJ8TZ8molEVPbnTz7ZKO+wM6IWuD8JCcdmuE2bnLp+//P+PkXT6H9KyC+yzxu84lR+45pjiiCV0hti4mjcSN8I1xfq/m+tQOKW/87kzsvE8KIQnazgqIb4cTYslvcHNUw78YtLQha4X3oKosFcwXJHE4hVhNiDOjiALUaI6vRJQ2dqp4g1SaXP/p/ZPHF34Y//iK0C1p58EEAWfk5PNZ0pVndH5KBkwDlOlee/SB6833gJ1TtAV4CcesKax/5cdqtG8j+CwRZIJKyooK47qF6rYWjz5/3fwGXW/FaIgvamOFJEMY0kwrZ2ibeOsB2b1GkBSp9bokgliO82fPFUpQlf/KJ4AKUodNYCCRzXYLq6SKX6KdluWMC3PoWk6fezezlz9PXr5sUmDlITWcoSK4GICecLo/hd71BQ5yjrCoW6kAnjJ94P6PyFov/723Si39MtDnqBZ2s4zY3c7dQJ4SiU350Dud8rkgwo1zfQMoK0E534/SRBKK1hLSLYzO/RmsAR7pyE43fIX3nTxHJIl8uLWgbo3QF4guy1QQSAqEoQaCxXnnU52ojk9NoU54KBvPovmJ58+0W0z6JK0lu3lKlAzbSLuvsUtLV+Ft2AhzfTqxvEbo850dITf7ps/Z78RAcjYxJUizrZONoC33kA8gj73/bR+H1MBGS26CWTm9fhKT532hgFnHSErRBvZJC7mtQxjs8VL7K5cWX2fCvMp59i5D2AaMVj6SGst1l3ScsZberpDmBSAg+NwVSTytjIrmagFDS+nE2DSx1NdFKq57kxhiO+fgq+vTH4dqz2UBpjb56+kGzfJbuc+6/si57z7tDTsBkzOjGuyje9TGgwrVzFgvYX38Se/TjxDAFW+CkAc010ta1rJZ7Zd5bPtWivaEpoA6nCmb4oiSGNXji3TBeo7A5RdrP6pOitFrmBM6+CmSZcd69pe79WFKaNmVpHHGnclUSgzYmTApiL1foS9i6irkSUpv9G8UYCVl62zSf/EV9bvnbVQXckwSIoC6AGyNbN3FPfIBFtUWUgrpV6lqJfgJrG10IyGgT+bMywRcFVnX6AWWuFIht7n55+swquvTF7PZXyJ4SHI2rOLj0JHblMYjZOGpxObF6NO0sByB2Iab5AbQtMeZjlRNAPSlJLs8cOJFT+FU72wRaXEpdrXgX60eRlJimXS7xKtfcLlXcA2JfQ7S8/2Fs9lByVC2hKddqU8+6vSCfCBMQ3Sgrtrmqu3cWh2n8FlZcf9vH4HURx0H1KLf9ZZJMQYosfNKdlJQGlRnazrJAjxkWDOa3cC9+iVBEZLdBNJEbL+WH1VhDc0CSgkSZT2spt84Rn1uyRgJ7jGnMIanpNqqufbAGErnsrcXThG0O/A63i4egeIg9f426ukysdmjwb4sB0H+e+eR/mDfSVT92oSBPTB7clFk5Bi0oQkGt68ymzzLzV5mZAC2qWaly9dGF/mTeC9Lkv/ZKbKJ++Sry6uqJlkNSTV0TVWkObjNqbiPxACyR/IgDt0GrRX6G1aYs/SFYuvCDgguCsCIPfcpQoCgrTAImQhAAR5IRLQGbH+ROi2GU9fhTg4nrNBO65jZyaMDdRW9QJPBFBVqxn0oIO8ytQH3AlWNaN8KKdZCA086EMnK+jCqtCZaEpolYWXWS2nm+xHj6RjYCNdrlRUHEg07Y1R2+I1eIbhNbSP67JUptaOf1MndE1OXOmCn3SAkiefMH2iaiGrLnZeBEBgPgPiKAp84bUWpADHE+T1Jgzfa5URzw3usjQnOnu9fhl7JPk8rx1k4hzPUntIQ2C5qvfzlrjzuHiMPMsBvPoFefRaWg6DzFEahFMLf99g/E6yC+wj37P1A99lFcsYPpCIpRdst7l9X90gK02w5Gk1x7niKpzr0NUlnSaoWZQptFhoLVKLCnFfNiRKNTEMkKfz5AXZOKKXW1jYUqGw8GaAARWi1zYxwtaXG47cdYXHkv+vAHsMkN1h57P/MbH0Kvvwdn96Nz4uvRJ+StGIVdGYczsqCMCqjgyjVwE2YIUYy1yzcYPfxepk98Lyqj7m4JvMeSUTqPxRwSUclSvvlpejUGIVnK6oDO5SRUHyAmWoHgC3ycMdKIWsskQBlnEKFtQbYeQ3Yep/HruU1tTJ3ug+TkrD5RBrdUgbRlUsPpQyEb3N1455eqJD9CiwqtF3jvswEpDkktyQzvA4smIkXRJbZ2npTDBhz0rYAoJsQYMac0SYnekXSMcwXWLCAlaisZbV6FpAgRa3OiK06JLqDlBCknWfthvJa9iymXbZancLVPQNu1Jq4j2WM6uYp75P1U199DDDuYd0g7x9W3Ke0gG6YmLBYLzLKip4qBtRz6qgQfOg2EUzqnTgNDDsD9xvoYfXaXthJoBUqMMh2wFl9l2t7OPb0dHLpFjxoCRydtVtVzEiG2iBMWMsarx4nDf/BTXH3qz+UThiWMnMGeDymj1fSC04EvGH/gxxjPXsE2H2bRSu5XoA7nBN82yLyFAEU5oZFJPs13yXkRR1tu0raRkWWFc2eRYAsSJQs3pam2mbe5xK9FUQ0EEWRth/HV5+CF30LqV3AqLKKg1pLMYX6SE91coLr5Xqr/4a9gW48R/YRw42mqH/6/wvgazldvy5gu4//HMgKOCiZnpUIh0JjQisc/9CRbf+YvYFvPshf6znxK6wqCaFZTM5+79B3J/D9aBdAbpL00ayI3a3EYRTujQHHqiOZwYQTNHnt+nY2rz7L2dEv97d9izMv0ymypN3DvkQh32Cb3dJHVJqWT0eoXzjxvcyJuk7NxJLfkVRKII5nmLn5oNqY4qf0unS1UdM+QP88aSKK5qY/VqEHSETFkLw9mOLFccVCNafyIWePY8GNmbsp6uZ5fqTNIDYUv3q7hesPkrJacuAidPNGlx9n5M/8Tm9NHqf/f/x1nylRrJotv0yxeQTe7cF6fXKoGi31INSp5/VsmYi8N2oGTGAyA+0h2kWpW/moSSMFCKnwUJmKEVDOK+1TpAGi6L/fRCWqiXZZ0v0IaSEItouZwEjEteNk2uNyJYrD9NLb2aL6tNSAlZZds1aAUp+wbYCixfARfboBOiXEfiwlcIDYznAudpC1Et07tLlP7bVi8QvCOxgQr18HVUO8uVdiKtMixbZR9t8l+PWPTlFRUWLWWlf5GG8jlR0l/+jtk3cbcuAnpWv76cumutukVZPRxRMfENuHCGnb5/aRUUpkuXY0PlgRLFUJW3PWHBoCRuwNGBI9l4chyG65OIVzJbmUsy/O6MSWKxgYvVdasWLEQO6f8XRwqIQrSGqXUTLWldA6Vil2/TZIdJgFe9I+wVj6Crb9M3SSSl5wRLy7HwkWPlqT1pa59lcBpxFoQT+wyF7pmul3YSCDW2YvShUvAci8AUdD8vsW5HPG716HUj0AOlmW/2a8F3hrKNCO3tfYQJrlJGCnngoiCVsx1jTvtmMusc0s3uFxskdwIsQabz07lai/k8ARSMHKdzES5A5fHOKswzbLG8aUvMQmJcXGAuKorq+5MSjH2Xv0O1eIAN80lvLYywUxOHu6BUzklzjaJgKpDXNbvnly9ia5dxu7cxhAcDcFyh7coAbqeeK+FWNdtzXIi4EwC4fozpJd+j4SRquvM2GCacvw1unLZR36mh32+ThMxVPg6Ap5KhbqtsbZFkkFRIOpIUZj5Hdbf9b3423/M7Aufo6AhLWqSn2Axt8qNXXlcsBqNDWU6YLpzjfH6Zfijb7EwTyFZ4bCVioYyrwopQWpQHKJdhQAOrKE0I+mIpKN82o05sW3hrhDVMSbh+p6qD5wV93jvqV96muCwsRIUQPAO2AB1QEkFiDQgnoWf5nrqWOeEs26z6jfeN/JuHJGy3adITVZTrK6x9twNZpOHGIcFZfkUc3+N9Z1H4PI27atfI4QS82HZAfLwFHz4vNqd3U7fbAUsLltY529UzEajhq4ZYJPzMtQt+8/3OS2iniiKW8aiT3iHBvgKdIHkKENn/xuFNUia56SP4JAwyVr/5KTZiFLIhPWHnsOPL+G2HNO0gfgt9lBGOLwLb8covWkcdPX/2UvqgWST7LmMNUk9FAXV3ld4eAqj4g4QQCUffroKk9uvvog7uI3fbhEJ3a7fl5Ocyhl1KhgMgPvMQhxlcqgDccrWzWeYf/5hbP9LpOgOj1ficoKUNJykVX3UbX/YNSwvAiO2PvD9xF/97zRNi8oGBwITizkuCGjKeQhJp5y2L0CujshLvqRcNVH4LPOrzpHUgTosBvb9Dlce+xDM/5j6a3+Is+9Q+RZzDW0zy6cbRiQUbw0jWzCOt1m/8iS8/5M0X/hlogWyrrrDxBMpEBQi2bBSzU2DtOvoZjWBBXNK9lAmFhnFA8xV7LkKEKq4i7gx8OAW1v403qfn9ddlr0Df5Uy6/+dNNUiN4kh+Ain3o/CdZyiKo/FVDhWlbFQKelhpYMv/3PVKeu+WieFoqeIBBcI8Kk24zNb7Psza0x+nYB8XHmG3GlPtXMZvT5nfckg5Qr3vIrSr2JFnWZYjnq4pS68ctUyEtEjfKdIwiJ22x1LEKG/8iWwkRoSgbpnDcTeWVUH76p8+VYCET3Xn5smNhSSU9D02U+cpsLDGlec/illL0AMm75oTJ1eZA14c3ldkc/t00df+ixnOGirJXriIobGmbhaUMTGxW5S1YItd0tpmHiOxbMSr0MQFabEPqc25Q5I/l8QQAngtTtt8OPM0ODQaAVAnuO2rNNU6fpnq0zsPHYlO/hdYRkCXrqvu1NetOMtGNGbM68Toyk3qZIhXWinZBy51iYMJy18Eq0+rQzV3+urLopoFXgVtE+aUiOKKgMUp81TR+gnu+uM0prSLGd4nrAKZ14ilLL7UxbVLm1GlWc7E3r7GIglhPCZMphAKkjgklDnEkMC8Q5ODxpAynxjMIsQFyRc0GNESFueYCxyQewykOMdp9bZsVHd/hnZYPdKVB/bbircFUFGTe9CHZLgYMY1dRnoA612kS3fCkUc/6oY/PJsvX0zTEkLDqJzQ4KnTiHL6CG6coHmFVF7mVRPWg8fFlGuyyxK8J7aaE1d7RcalB6Jv2nKofXGaOCzfs264emOg88qktkumFJZNdfpTfJ9DodLpNsBqDGT5/e9leldjOxiaCyTzcwuI8/QJw22CVoUUFb92Ob+e+jvIJDAzT1bDV0yVJkJ52lZ8y+FNW2qlZIGtBqhEcutf73D1DDloIQhzyad7s3j4HS4DqV5gqTn8bmAsF9FBCOBETmFe6FlGKBDQiOguyQ6oi8ss5Ca6AN82fLPc5puhBBaMFgc4SmpuQH0VV0+ZL6Z8I1xiv0yYW/BK8TBfr55n7h3O3ULLffbcDm18loN2iwmvMJp/gWs1qJUk1y3fMavA7b7DI3Ivkragt8HNiNWoa7ervBge4QvTj3O7egbYZ8e+AnIA02ukZLjZHOaOuHmdOyFCioRaaXWD5Gc8cXnGutXckQ1m7iZTVcLsK8Qqcavcpp15Nu/ssklkfyTcmgYwD02VS7fKDVK4TJQ1RimxTW7NGkfXEa14mMQVNXyxztujMe6OaQBC7qVQghuDjgCfy9QAweEs533VgOgeyILEOpqmXI4vU9ULKG5Qu0TwXyFVu7w6GUEFTnfxcgfUEHE4rWjddXb1MWI9Qg/2SBLY00DjDij1FUY+u6EbC6CJym4xEpjpmLp4jIYtbpUbfHPtSW4VzyIJqvY2oxghrXcTIqJpAlSnUrI26oQojjVrKGxGkpKF20LchLE2aP0qoTnA+8DM5QoTxy7WFtyur7MfrpBcA+kOZmPm9QSqirYQFtUmM71EGj1CLXu44kXMIjOgcZHdoskdHCVSpG9Qll+ldTWtetZU2AI0TCFcA38Vxu+G4mmmvuC6CJMuBj45hdVwagKM2feXEEpG85cZp29wi8RtSiQmxO4gTQFcIbmSL6w/BXyTzcV/R6stGn+FbUpGf/K/wPw28+jZ7fUybB+106iBeDo4bfbg2aY71ahkBbQst+rYfvxZ3OfXYd9jUTrp73z6EneAS0BskBSZjCfUVIS2QRpj7BagLUWbXVnJKY9+6CNQjEluRJsCLgnOg1g+DQKnO/YlSzkbkAJjkdvNWqQYr/PYh78f+2+7cLslxkj0HpEii/1IduObFrnXfAuo4/LGlCeLS9zwLfbSbSRGkAIef5j4lc8jjSPNRxRrI/AHwC5eHNE2mbU1VahZytUKWF+GCbkHgx2Wx+VT3tsT+7/rWU74XO/520oMIZ+q9fB69WCCiwHfTnA2gihdX4SsOY9GkDnmDojuAPP7iLPu88viVEpaHq5UBdKh3K2iNOYJAotixNazH6SIVxEdI75ccZf3c7arMjiN03blZN97SJYeAOi8F/n9WHfKL9s5ThMjL9xe7FOVCU+LtgsmwaOLPdRmSF0zcm1+ZMvzW63LNTAF8mci3Q+dgNNhyWZ+VRy7fGQYT+OYcjjDbZnfkltvI2T1Qi26nh2WFQ4BbXM+i5ki1uKqCXVRwOwWsMjpPUBeHKTLhRk4icEDcJ+R2Kn0Aaoe8IQn3wNrV0iuYNWZCIIEwYoZjPahmKN6i3HcxdcN0kDR7jNuZ2grUENbK+nSQ9hojbmfMtMpUcqciNSpaOWHzovw21Gt/lZwCGigkUAkx/+EljBdZ/Lc+7HJZl4ALOUvsJREzZuGqe/azvq8sAXlxs6Edz+0zY1xorLcz150RHr+eebjKZQ7+LiWE7nCnCT7OBO0nVKngLk2b/hd8mR2ScdDI2D5yuXI53cquWfcp09aVHA+l/NFT1VX+LaCGNBYICknmAkRdA7ugOT2sLAAnx/CEykku73zRtSNhbgsgEM2OGpKJM6Rasz43R/j0kf+R6y8mse8X/xFaVWzvPIpHdJ7sRQz6uPz0m++hqYW54usB+CE2DaQaiQlvLY5Zk2LtzkVB4g5sBJjhJoSyP4fsxIsQAq5hbZ1/54T+kZJhw2THGJCa0IjBW0rmDPUCeoKinoGVEDISbyTEWlUYgffhvZV2j6kFWss2gl5JwM9w8jcZwrXHbdijmUdJKEpdmB0hVZCVvVbLnLCS/Um32zXaUaTbNi3ULgJzkJnxs4R2SeL1oyQsJOTz8KYg/Iqs+oGUmxgjtyEhCIvrDklmdNo+wrgEUxLFminl9iglohSQLnFggpDUXVEE0xC3vBViM6TtCJKhalAWzNyNZfcnLD7IiPrkqa0oqke5Yt7uT7aieXM7JD7pksErRPV2iWSVl1mfU6w0+5ydx5htXo70RlbZ2izOiw/k9yd0gdEHS61lKkmWLsSk8/kxKycb7BUD+zcyaW1jKwmSOfxWt4zq95lAWCHTi/jNeJMgXUO/A32tMC8IycyRpJALdAIufulndbMlbtZihileGgAmJFEactN5qMbfGNW4qdXWciEGCZYTLRNog1Tcst6g7YBlEhJkjEglJYNgESJSZlDVeayMdDVb5x5VtZCg+yZktBVWghuuk0bRt1aGGmaxKSZAWNwE/KCWWBVoHnlS/DtPyJ2idCSFsRkp7Wu5FRwfszIU4AAWItKibh89jaF5McQNjDuECwStG/oIlRP/gWKjW387PewP/kNGp2i60/T7v8+QReYb5kVc8Q1BA3Ubg0nY8pqwhOf/HF0/kX8xmXm9B6AQ5Uy1N6mWvU3jwOQQI10sevcwMQXI7ARKWwgbsR05wqzzuXeb8J56AJGibmcWa1pQZkO8Is9QulYODAtCFsf49GPKOs7Y+KXfpvW7+BlB9ox3vYRu8XmE+9j75uPEObfziEAO0yyU2PFYOvpTtGnkOVLPbaH5pN15yESBz5grQOZoe5lRHdBa5K2OUHQckjJJUVigW8nSDuCWEPwBCIVC7wYh2MhIKGTcwbDsf7Ic/C7Dh8qzO+wH0oaEVyMFF05rOGJ9HkOfUniaTRd76Z3YGcPQB8pMJqk7PprXHn3B3jkJmw+/V6ar/0Bi89/jtAsSNOHKB55jsUf/3+o0i2ijkGURkqijhFTqghOJBvFFMCiS0Bc8biccXqbM+dF9u56wYngnbH2+Lvhax9GPv+/dpa3skHMnhK3hnWdFpNz2O7X4Cu/R3q6xiRAbEhuBAwb3b0YxuU+Y5a6HUMhxTzCOmahU0i3qGxOobYUDFl7/sfRm9/D7h//zxx88U/Y2nmUuPUM8etfwettmmAcFA1opJSCPQmM3ZiiKKme+16kfhgr13O7lc4Vq8byNZzWD1joztp9++QuW1ddATLCRpeZMWFt53o+g1vCWwMSUSLOQCxgSWHkaeMCn1oCjlZDV03lcWvv45GPPQvz3+ab3KKoKpJdwsV14EWiWxAffpy2uklYvMjhFtoX39mynNjMVlzURwMDpwVZUdM/8ur6apI+BJDrVDGdgbtFdPvgIWoimcMnQU1w0SNtQWhHSLsGcR+KgmANlc1z4yvJc866sE7qmguJevTmcyRfYBKIWjGXnNOeN8262zHLHEYwQ63LeJezYQAc5gDYoTcASBqoi2vI49/H5XAZCyOK0Rov/W//mWJxi7T5KDz/Sfb+9Av42Zy5TqjUWLiS1lcAlMlQNRrxRNXcEEyy/oCcyt5+b4E+f4RuimoWinJGFrS6+gg89n7SF38PSQ0iBWMxSIHo10hasrBAEsWHBrnzja4UkFwG7U/jt/T0cDqPMd8lKaVlqdHqZTNjf38/J5at/NxPhLQs/xHJX9IkFclPCGlB1dwitPNcApcSNlqDyRWa4hq30xpWbWN+gnUxvgRoUJIY87bN8X4taFWhvAzlw0ABqfOSWV6CrNu1Tp/4J3ntT52crRm181goIeYWwLiK23HM/ugGhA005tj8yAGaEMnxeot0LVYjVRBc09C2SlNsIN0Gh17CymswuoatbbMrJVKuQ3JEMXZjII12mLkRrSmt5ZOIkZOODivlbPXln9ryyhPpbBUBymrUTc4sTytdV6Fohhk5vOK6NquApjYL0zRNvk8SopR4a9nw5Ox9DV3b3+wBUPHL+DXVDlasUeuE5HOuSv6bIVaDNQgJR3fmtzl0JsJZwMxwziF1DQh4R+q6KJqOMX+JNLpG8hvY6CozmWAaaN0UK69io22cBqI5UnDcMc8iTMBZbo0kDQdmuZcFDUkaKIUcPnmH3/x9IX+bXH9JctJjAQQRTCew9S6aJGBzVBRtG4zAHmu8HCfY6BKmISdi1rvgWH6Ps0rowL04lwZAn4jTGwD9T9u2jEajw0SdLmaX7uMkMVGi5NOQWLt0dbcJpjLnku4Tmt2uNFVzJZkugH3KECHt42SPEGrACHPB7wvjWLGuIyopEHE0ZtQ6AX8ZLFBqn+mdF1LrEtX9Kd2tkgCWsl6COMyPMVHq+UF2hY6ucmftcbj0BN6XSBgdlr7ToGWJx5HaSGMtqZlxbWuTUG3wnTiF8XaOknrLwkJpm3r8PPPJDSzMQfaRZoJufAgLl5nZLlJNES3I21PuvHd06Fayv08zdvzU038fckvbJILN5ojzaCpxcYKkKWYlbZrS2Bgka8jHuAe6h7l9kDuYCnOriPWCkUsE17cU7oNPWfPeCzgc6JTF9FGacgdLyggYG4Tcy5W+8sJB11/xZGGs04qKoOqgbboQQDaqnEUKO8jeEbJ6ImQvVtHsUkqLuILFwT7UdyilRpuG8voTyOhSHk/L+Sh1KJmZ5nwJa6FrDnZeTraK4bovVZIcCMphQSPpBGSbBg/SEJuEae4H4rceYb75LvyVJ3DaiXtZ1wmgq3TpcqEH7sFp9RB/16SUEBG891l0BGjbFlUlxohzjsViQVEUOfHoPtUeGUJL5/lPDX26TukdV0fGTRKjtE9uGhRAd0FeRPQ2QWbQ3EJtHWtvIU0iWMFoXuGkwTWgsa9pjSwkUMgU2hk+RFpy+ZAAUXLymjuNqiqSGyVLXFA6R61K9FPUeaw+wCzy6Pu+H314Ey69C2eCyIiFhOzsSC3UTX5vKvgAQVuefupJvnZwm/blb+Kv38xuUt0nyhStbnD1gz9J3Byhr/4BOmnw/gbb7/5xdP065m7TLhJCyEJEwUPbidgs6cMCp21Aj3OCiSIgrmspjFAfzPCbBRUlqakoqZA4gjjuEv48aGSRFoxcQywjUWpUFQvrFM2rSGpoU26Sw3KhzbkagYQTB1ox+j/8JIW/hBZjxuSN3lnKzyGHCW3LjgenfXhXEFGcU5jVELI8rVOHTw3jeBtJNdgCLP9b2hyJu4Q0gyJQOFCN+GYPv3Odxz70frj8POaKXMpqkIoN5lpBKHE+9IkG5+T4lr17ajmpNpJzT3J5aZs7hcoGDZrl1RtjARRVwSPv/Rg2vUXYqJj9/34FzGEW8F1vCVxYJqgOnMy5NQBU+5pcI8Z45NTvvaeu66VxkFJa0en+7shCqd1p3Gp8V0pWOaPwDderlnE7Wznl7IFrSUnZ3zc21gSVFpMmH+VdRUgTNN6BtE+SOda2BIksyO0zvQrKHLUs/4rkYkNDcoZxdkPcl/d3PzCyk7dMC0rJ6nytW8NryaQQYrug3H4IP6nA+5yJrhvsuSmtE5wrENPcDdE5ki3QuMfazhrr62vs703QzUtZQS3NwSqSG1M9/r1Qzjl49ffAaqS6Cjffh4WE2Rwpp7hiSpTcrjbgEHfoBTg9I3hv5O7jP9BXAThCUSCWixuTCWvOGNsc386yml0y2j5PQITGBZIfc8dNkFSy6SILqZiUJWn/NslAnD90P3UGQG6bo5g6eOKTOCtAC8oY8dq9Ui1ByywCRZ+m4JEzkgAIXVGvGdQ1WnY5OJpVKQtbLH1GknKo0XXyteIU8yNip2UhDvDrlDffTxMeJTYBJy0iFftpxChWmBuhorkKyNzKmJ9lunyGZJizZRgTa3GuoZUCJ9POR5RwIsxcoLQWnV5Cn/g+pH4x+wxsRGSEt66/iitzv47zMVAPhHNrAADL+L5zDjNjNpvx+7//+xRFwZUrV9jZ2cH7bhO5T2GApQFggDVo16Oaeh8/e4kNJwRb5NIqJyTWgCv48lmKjQ8SLm0Ty5ZUBcIC6hiY+RFTbgMHlJuKBAWrqZnQpE4kLB2gWnX1wbb8X/Y0nK6P2cgxOtK8O0Gt0bgRQoEtZiCRuSi1bLAVApIWJC1g+wlmX/1NCi1wozViHTEikhp8cwfiAdrMiTHm02ScQQo4KWlFcNWYAwRNa9CUMCqpy4p2scf29kMs9l5k7EoahELyaTmdubXj5ADFMoXBKW1KlNdvsDh4kc1ij9JqZPFtMCPoLq1kdUHzJa27jCsepbj2MezOJdq0n8duLngVirJENBAbweXgPmLgpQGyJsAiPIqS8BYJNoeYcnmMH9OK0nTGuYriKTlLR9sYI6lpKOsmNwGzhPZ+Zw2YjjAZZZEkF2l0ChKw0Q7mdrD163C7Ivl1HGu0epk9GROiMCV3+aumNyg3HsYO9qkXuwSdgJbv9Fu/T3QGlGVxqZaEmcvGqGtZYBTlOr6oYJZQM+bqmVhiLzmm40fQeoGaB6Y0NsreV4FEgZMZ+bhxOpshvdOcrp3hPpFSQlURydZ4SokXX3yRn//5n+eLX/wiAJPJhJ/92Z/l8uXLFMX9S5Uz0cP8XItZWEYMJWLtPmOd413KoU5gYVPUNlm/9lHWPyHIWs3eVz/H3BVUTpjpiHj5GhIXNK+8xOThbbhyiTtpRnJb3cKeY4VZvGb55HSugFNJxCDlDnvi1okUePGItUhqc1VDVTGzxNgStfPc/N4fxPn/jflXv8bIlYeu+Gg4FhBnBGsogwPbB9tF2h0QoQ6G0TCXwEQ20bhGckKrLeJG7Hz4U9j1r8LVG+wtc+klNww8dtg6nfn/96L3fOXfLGUXtTz9NP6/vco6B6zZd5B5zpz27gCX8um/TgWt36LceJpHP7KJW7yIa16F73ye9Dt/mGO33pMMfL+SWFby0y4RNQGvIFTmmJhRWUOeryVRlIUIEbrzHSQpOt2Fs0FsI23TQIy5v0TfSholujG1FrQIhY5wBnO/gYUxM5sQqitc+cSPwu9/i70vvMxGsUm0wGLpA1FIBU+/7+OMNhfIn9SkL/4RUGLJYWdpoO6J5ZyGvtKmu1YsYcR8iFnbZrK+BguQKCzyRMUczFLBhApJQrSClpKqe6xGfFdg2oVbB+7i7Jjab4Is3Zrj+r37/3Of+xx/9Ed/xN/5O3+Hf/gP/yFt2/KLv/iLOaZ5n3IADKEWYRrBJ4eFTZw6NAp1eY2FCmP5BmVzm8QOhidJSyuRenMTe8/3w41n8eYoawBP6x3xxsPc2niC6DZh8jhsf5CRbrFN19zDV6BbuejPA07wFNkt5k+nOzUr8u1gfgMh4m0XZYHzwsKV3EGoSIS0z8wVROepbn6EeP37aNx67iFQ5ERJSxMO9DGs2CLKPqqKSxUaC8wX1AGiQiCx3n4Dl77NrfVrfKucMq6/wcSE+MT/Bf2+/xt66VkqUYIIqCc4oeiSu/KpS8gFnKfzq/MdRpgZmnYRM1pKogZmDkhz/OwVbvsxzcMfYlassxXvMJrfyeujAVYTQgRd4LlDTLvUk0vcevzDpGc/BU9/jOaRjxOLTWiVaIFaHdElTHN+SqIEK7u4fmQTYyJk/Qs3Aj8FX+XcGGBE1nXzy/r207erKQ0Jo5FsqqjNcAiNBSwZoglpZ6QwykJI1lKkAyLGTCRH5mzBWA4QZojNedWVNE/9j6Rr72EUX2YfjxfP2GBURNB9al/QXn2e+PxPML/07lyZ0r5KIzX16RumN42YQ6wguZZIgzdduu89gbE1SFlye/IBDthBwi3GMkdSZByN4uCAptzhm9PrzMKMKn4djXOqBCEZjY1ohs3/npzOVey7ZDXer6o0TcMXv/hFnnnmGZ588klu3LjBD/zAD/Crv/qrR4wE6Fx6XdXAmy0RNIEoUBo5qUUrEIcYJDchqeJtH41zklQ5XigJlUT0YNV4uTAW0RAciQiFZyEVmAdGEHYIOqYSwTnABUSqLgMZRAXFo7iurvZ0rRRCzp7GVaBFzmhOM4h1HnvxNCp4SwSZdy09BZjSup3cRVEbTFqIhplnEUckM4RZjrGmAqHCnCd2PkGHp5QGNPGijXnZJhSpxonQ6MMwegLchEKyEImoopJPYyKa49NdVvdp3ajmeLCEpnnX5tdhIjSAWYs4w8opVu4QLTBeNJTmIQnmC/Z0i1fihAMKXCGM3BxINH6MhQ0oNrGwlY2h1BVw+ZAVGbuy11zC6vMISWIsUAk41dzESAtQh0q2Owo6u5WcDnga07ZyZrmRlq6UmOexK+mdcE4FCSPaZEhKKHW+T/cYQkNJDYuaQiNRhcZvYW6dQhuSuvzdN8NrQlz2ytQyoi2uQrFBORoh0pBoiKdvmN48lmskTBMmltNBRfJ3zRzeWoREmtxkkcaQ5qhFNEGRhEKh0YJXtaTVBp/u5PwBywnQCd/lDwycxLkMAfQegD7738zY29vj+vXrOOeIMXLp0iVUlbZtl8mAu7u7/Nf/+l/5xje+gYjwm7/5m3z0ox+9H68IXDfUK0peJrk8yDgsocqNhH1OnlLwkkhEnDX41J6pEqnXInTlOgZ4a2kERB2mASeWT4TWQGpx2vVPcG6Zxb4s71HAIqVGnM0Z+8RCE/gCc1kquHff52rjNcLmu9h48uPoZAv8FDsrojNvgHzWacnd6Cz3XLBEIQ4hYU2iXkQMpSo6tTQJYBvw+PuwrfciLbjZH2Hf/IMuQXCPqV7JWdayhmjeprN0nxJ8OJWb9v1F6DUK6TYpQ5hu7mA7V2HvfyfhcAipFzvoUwG6ewN5HXCCSNelw4RoHmOc57TFfFeL2ZAzo5Dce6GGfMKIlnUU3oFReODI4QUTzcUOrmT9+sMc/PEIq7uyy67Bj4lDvXL52g2KakzfCSjn/smQ//c6nEsDYFX8ByCEgHOO27dvL4U7ZrMZTdMsywK996SUqOuauq6Zz+fs7n63zXS7r31XK5xEDw2Aro+466LNS/028V23Ow+qeE200mDU+URxSmP6bwYxOo352C2CuVe9uYooBd7IqWDSdpnpiaSAOkxyzTnCocxxXeNtDukORXOHWM8xX2K+zGVFQN/eBlnDXXqWqx+7hBYjKDZpkcPGdGecbADkmvN8ok4Ylr/olkity4loLuBQfIpItcF88zFG3/dX2bz6MdYx5Cv/L+K3vswoRVzcZyyRGBWYoNnN1CVDKC4U56gq/V50WgrdxoTl3IVycwvduYJ9JfdGlK5HojmWMfqlr2gpdcsy8Q1RrNhk4TaJ0RCLy0oBUkQ0K1F671iM1qilZMQ5k7eTfqW0I9eBI4mg3qFXH8ZPt+C2y3NNswQ4CZwvuPHks6Tf2iB2ZQS9oWYnF8UMdJxLA6A/1cOhN+Chhx7is5/9LLu7u4zHY/7gD/6AD37wg0cqADY2NvihH/ohQgiICKPRiLZ946pkR5zCvaew+xHn86LZzfHelayWPRRGjhM6Arb0AHQhAlqMFjlDCmmvjREkdfk/Bm1LS2BhU1qZsB7ppH7z9r08PanDnMckb9hOACdo4RilGfbSC/jZywQxYiiou6Y0njzOeUaMsKJENrbAayc8IhgNp1Q38U1xWPG5Mm7dVdYk9tqKXaZctoLYGj42pLrhzto25ZUPsrf1JMmM9VeeZl+28vF1MUNGMesImO8EVhQUzDkkFF3FybGlNtfCvl1v/QHTGwDZkySdaz+po2khYEgooRgTxUHL8ozubLkKgCvABBWX80xEWdiIFxcjNvC5DwPQd23sDwqIUFPxcluy3hZIEvScGK05cXLpW8lXAQmlNcUJ6GgTHW2S7jjmTdevQgAfsu7KdB0rR8RFr32SW3j3Nxs4mXNpAPSJfXCYD/CpT32KX/7lX+af/JN/gnOO3/md3+Ezn/nMUgOgTwSsqqzD3XsD9C18yw4nce6arkvt9ezS6r/TvaZ3Nhw6dW8JmPqsXqeS47jSnTzOUSwrn+ID0QSRQCo3WKw/im4/gfhR904lj0fvolefm3wgeewEmOdg3/xbf0r6jW8z2v8atv4YsWlpgQKjsHyKyhuUsjChdaMsS6uGtwZNs7w4n3Ec5D4TuYk6YKS+PW85pXr6o2xuH+D9GrFYQ3wBAWIxJZbr3JEcm9/YepLiqe/HWw1+DGYklSzgtDSaJH82LnQ6A8erTs5HuOoovZmvJMmbVJMEr0rCoy6AFnnzUe18AiBEECVpABXEub7vJNX0GutPfYS1a4+Bq2gjeA3gRph4goAlR1i/wdpTH0O3H0PkPCW25Umjx66qUz72BDGcllBt0KjifMnS+nEuK05IDh8m2q4vRUc6L4JJD4ZzaQD0p/rVzP7HH3+cn/mZn+HXf/3XOTg44O/9vb/H888/vzQWegOg/1dEKIriTXkA4Li1KcsfdQWx80eZ5oU5txK3ZVw/t5l1mDosJRgVWcfGBRZS0fg1nJ4XI0BJmk/gTqC6/jT+oz+KXnoScRVOunx7rTDxy3gpkl1/QsKpgBekqvDtHvLKi3i5zWj07i6Z0rIoiDV5IbCCpLnveOrcs5aXcJyeE++KgFF1hpOQcguZ3FilWKN63yd5uA7Y2hVimJIsEtsDorSIZNEqREib1xl9/MdzT/XxZaILpG7+igtEdV3OhsO6MT0pPCUnXXkmWT2hdgYAgqnDFSOQLH3kTcgCSEIUn2WBWXZWwDSL3CK5Vl1Rxg89xXhjHa48RtKCaPnk71xJ6lI5kwTWbr6bte0NbPvRc6QDAEdWzdUogGi3JgOuILqc1Kved2GYQz9X7mvhMYtZZ6LTF5C7W3kOrHAuDYD85XFHfgd47rnneO655068T5/xv2o0vBVxoCPTTfocAEV9kbPUpfMMWJaUNTtM5om960oVixFCIM2VJCNSucMdNnFMznxRS07IU1okJ/+JY3TlcVifYONrNFJ0XmzX9VZQfJ/QuawSz/oKWYVOCHFOxT7S7ufxdiWhl2WyZnkiFgqcQtsNes7wj+fmkKAIrQQUv/QtNXRRUS1g+jCmU4iJ1hXZWJYacTXOzVkjh2OSK9ErTwOORrepRfFiqDWI851XRjAU65MCzzPd93RVW94gu/PVdwqcPssfW8qNlcRTiOaN3izPZ+0aLQl4kexNmV6ByTpWbEJXyZNVPdwyN8hwML0O4zXQCVHcuZmzJx2bTLIwlOSvOmgg4kjiiKk7rMlhKgXkMGuyBaTUBVzS0AvgdTiXBsBpoheUkZBLn0h0ySt2JK84n/7JhwMRxAs4R3IlycZUlx/hpRe+wShcPvMGAIDJYVaDFwW/jo08FraIXTtQurBHYhnWzifaZFjT0MSEF49zBaGpcWkGMRHN4cTjLbF0Q4uRk+NyZnwewz6buu9FdvbJVeo5sNSbwIfjp5jfoNUpjj2SSM6BkGwEie0zsRm1+G6zukRNYNbN0tzAp+kSAHNIK3Wx6qMR3FXOx7hm07NX1+Qwx6e3CoAcs+/i9iimWazq8CjisqKl6DLDPyFZG4HsReiPA6spcctwmE5ACkwK+gDPeaE374/nrSxnlHMQAoaiR0anHyvBOZ9rqvpDVdc0aSgFuDeDAXCfOW7LLqdpUeZFtf992WWmWyzkMIdKpPsiqNKYJxbbXH7mE3hZZ/LY8w/8PbwdSMrd0PL23CVHaUnbLW6OhKTYnS5zEt/yxGk5SzpFy6VX4hBrkNTmjnf0degG1mULd6UD+bRvFORFIy+iBUY4F0tEsESNYCI4yychFc2eDhImJY0GlIjXiEjOmLZuQ3e0lBZJCI0U1N0Smuv0u8VUVnJauuc6eZs/L5s/YAkn1mWVdw2QujlJlxOREyP72JKSk0o1t5LuPFApt/8km2ndxqQlSQp6BQRn2aHQb1tq2bNj4knSd1k4bwbAEb8KGKgkpGsUhYB5v3T4d1cdmWE+OBrL1RPZaIqDAfA6DAbAA6SfqiYgRZe4EoFlbbotwwSHEznmTcoihJI6OZJbx115hq3RDRhN35k3c1/JveDVAk4Eh4EpJp62X9z6Lmq5qezhaWD5H+ti3J0APYucS2EQtSBSoMktDavU3U1pUepuOVBy25rQnXDPPpIavGbDSqxBEIIUefO2miS5NBIaAjVCJJpiMu5OmCWCQzt3VBC6Vr39SdR1VRhZH9m6DXA5f+9aZ1cy4M80OS996S2yw3DAss+C5IQz7Soi+oTVPv5Pt4n3yZlKXg7QghZPSIZ2OUjabXX5sVIX9NLcqAqWs/5csPxO926VfmyzBJh2ORMEh5p1WS1p5b7d7b1HUoI2dQeGlFsqn5tKlPvPYAA8MLKVvyTkxWC5FParRjfvDyuorKtJTzAe06LEVGKswSTL5p59DKxGbYTHLcukkuSTj7eEWA3WIBbx1n/PLS+by9zKnB9gpt3mn/uAJw2dnoCACLXkdiC5TS0Emu5kEOj71p0btfBUo1LkmZZyX8ogoRvLGpbZ4w2eOWaRaCWtjEHXSYzABLG81eVQjJFV/gTI7Whzl0Tp5u3xIq7zSG8ArJ5AV961JbQLBywTVjv1ieUXvA+ddAZAv9MZSm2aZ2KXG4T1vexjNoRNsooiDrEcGOvzZM4Lid6rsTw6dV68LPyVZc27NbXf021lPVWX/02p2/Nz2O/8JKLef86TF+mdxwyXDkiSWGievt5alAUEzaU7UbsTUw2xhqZPpjI0ATGCttnGDdeIO+8nbdxkoT7Hwc7FNiXQlZt56GKo+ac/H+WjlEdyESU1YFYj5Tp7lz4Ma08TRFCrsek6MzciupLaTwjTy1ktTA7rgHX5DL0yQH+mPa2ivm8Nk3x2zO7+/K6ERBIh4hEz1FIOefgSSYYRGG89RNIJLQ4TW3pTsrEWkdSSxW8cySlR80amElZiV/2m1i+42kXOz8HoppxIWiO0KFiTv9vW5sxzkdzhUpVkCXEJbwckEnPoDNWId4KpkFznSVlWAfUHgm7jk15aqTsyiCzncZ7LK/c5y4hhpBzbt+xdSQJNFwJR68JUvsjlwcmxkJBzLQyEtjPhC5LukKJHml2MBVEUwSPH9SkGlgwegPuIYFTty8zDNfYlMCFR2QLYI/oWiyW0uQ2txEVeVOuus5dLeHNI05LKlv3RiOrKJ1h7z/9Ec+0xFlrn/gA6eaff5nePKPiNfMIEQMEVy205//0w1OGAl4CrMmP62Pfw6vYPQPFtqv/lP+DqW8Rr38edA6WsW5jeYOfZ76PwIbemJasKdk/M4eZP/8znQP7nkNaPCZ3rP+oIZwvMWhZa4l2gTDO8JaJso+VlLEJbVlx517uJUpAk5xGgiaRFt8g2nXfF52oA72nFUZjHuzF0Yi1LmWpxnbM8nAvnP5A7V1Ky52AsgUnaZUyEOGOeWlwxZuEKxs4zrxc4rXH2Evu07IrgxFE2M4oQSUGxqiKp5pN8nOPVI+4k4z7QG/397M0G83k4CEAO9ieQgDOHWE2jxlyU0hxFmmOqpGoNDTu42rPr1kD7978g4EEmxOI52vhlfPMiLfss3DpFgnCOvCT3m8EDcJ8xLbvoXu/AKjCtIExyeaoYqUnsyZTbbBJ9lvVMdHlDGoix4E5bEqtNdm4+yvr6BO16pZ0Lq/8tkEfUUa1vcvPR60hR0HZuf1FHcNA2DcXmNsVDN7OS4sA9kd7r8hZ2Z1ne/wIhYWnYHCbuBsxPqMdXeMU22XObSLVJNV4je7BGgKLWK1sKXkLugJc8al3an4XlmjFwb6ybs86aFQ9V72VyzJJnHnsvay67tpgu6pL5hhg8APcVARl1bthsANTicDaBcj2fhiwybxLtladp3JTRaJNc/OO6RDXFj66gO0+C38BMD7vhqUCbOEfpP2+Y7I3O7vuUBIuJFqEsx+A9EhfEWJOi4MKUFMENa+oR7loH5a123pPDsM1h3tb5RiuaTn1OsNy7QgpaBXf9fdTPvYzbfibnUzQttA7cWucHAZ9yan/hKyx56NreqgGWi1F7ldCLyPH5I0cu9Sqo2fAKqeYwvyqrrZo4wuUnKJ78EEyukHCIKDoIAb0mgwFwnzEpl+VlOcs3xwwLKYkG4iGM1tj52A9jW0/jtq/mmCGeKGC+oLz2FA9/748RHnovVCNQiH3ZoJybdLU3RYScBJVyi168xyhJVuXTfjOnKgJRC5wfZd2Fgbvp921hKabyVh5DLprCihREsqiP6xJ4I7kV7fpz38/ms0+DbrK4fQuLEQsO0zGgBEBjTiIklHkzs9w58LAL6EUazOOsvvejNVHLa6UzAsRRpDo3S/J0xpPSIKy963vQy1uw/TBIiRfF9YbV2/VWzhiDAXC/6RrQFJY7sNWSxW4KCVkxrIVFEiY3vwe2PoAxypO56xOUvOLWHqJ6aoy5S+w1keSUQgqwOaTzUAXw5nGASUAs0UZBcTTFOgeLfcpqDU01ToxZUnxyRM5XbP+B8N16AC7QcdU6DY8Cw3VJewY0JhTlDhIDrZU4N2MSHJaE2NX2ezOwBmixolz2p+9ULcA6gaELNJ6rHN/yZeWHlb8lySf9IjXQtRLvg4MRML8OkxskmdKieIuw9BwMnMRgANxnBJbJbWo1niL38BMHqliE/doYl5c48BuUVi9PFACNKU43QErQCd57DFjULSMPhIvp1xagSVBoIAQB8eyHbeL8gGt+ROhEP7SaYr46l21o7gt9Vl5WCFr5w5tYJHsPAF0NxQVYX3u9SG+GowHLhngEFpSo3yaiFIBPC6yW3C9ByDoXzIAWqpK58zjtmwQlkAajOB9yCW8Jueu3w2usK8AE5woMh++kvROgkrsBJATzI0iCqc+FlqkFcRfVrnpDDAbA/cYMLyDWImmOak78UXFU65fgYMT02pOksMYCT9A5nkBYEbTAJmDj3PCiW1/HRaCva72IOCE3Qkp5oFwYs/Hc99IefBvPiKoqiPM9WlNKu5hG0uvR16sbQjTDhaJLotS7T5/yGmMo3e1jXDZbOfdrrOTNX2mRVGOSo9KFgJNcjIalXL6XGqyA6DupKUnAPJelrm0yK8aEUBI0a1Jgc6KNzlVN/1vn0HQXM0hd8p8Zm9s7zEKFWFyKeynQ9QPEVDA3IjlAjEp10AB6HQYD4D5zKDqR66edNQQJaFkxefL97N/+BqMPfJLop6SYkJDN/r46HVIXa1wKjq7UxrsuB+DiEQAn1vUIhzDZZO3DfxbSPunWC8SmRpusUy9dEubAvek7Xr7l2PMFO1blU2lCbAGdbFRfYir0m1EWsJIumy9pzhnIQcCY2/uOt7mtU7arta5tcwtpgZOLmdzbpVSemAQoQDeA2UgdTQnjKU03to2CSzkDwBP6HMFO+yMba4ZfaigM3M1wVLrP5Kx9sM7xJxbxGGjFXnGFL8w3qa++B+cnjFRRy/riahCsxVF3j5Mfz9EQLKHWtw66mFPZpc770akpm1Sw/ihsPQWMmc8XOBF8yCr/Olj+R7h7ge3j+G/t8aR/jAs1H2PesMXlNtVAsIi3OocGLBv9UTy4gLjePADEEXWCTW+wV10n+m1ayzFt9GJu/UBfU3rsqiy1nAVCs6s/iYDzmAaSuHwQEICI2oKi94z2AkIkoMXkbuNi4JDBA3CfMRFqcoc7L74TR0kgFW7nGdbf/6O4yRMYFSNpMWKOLxogNbDAqDDLbm9YkGd11gE/2l3s4lBJA1ZjOESE6BRhG1ODchtfFmha5JNtihdqW3pT9KehbuF9S/X8cvjvRXEELGVqRUhSUncnzjK1ua+FdtLfZtQyoiq2KIopTezDK1UO+Y2u8dyf/Un8pSe47UrMWsDlze6CjOXr0R3kOzV1IVmvAZC7ALTiSOo6tcAW5YBAhVLRx0yVBqTNjcMG7slgANxXBJN8hm+BqQSwfKLHVUye+CCPPfQcaf1hFjjGLGi7zJ/83W+BOkuvWhdTZJGFQqxvYHsxVwlHA7HJpX++a4zixjRiFG6UPS91m30kThjKf1+bbAR8F67RFfnmi0DOVM/Ssq14ZuQmVhUG1jW27lz/tY6IfouiXKeO3cmVghpH1IrpEx+m1TUaHMkSRIf6i+qmvodSv9A18MyjkjV/HKKOpLn2P5/5E2JzlAVCuZzTumxdPRgAr8VgANxH+mzVbNNDVg9rAEjicKMdZCTM3QizRGwXWLCVUqzEsnmFrT6iLR//omIWAUM0Z/02lse5BUIxxvuA1OBDIHtiY9c74YLxZibJag7A8Zqre93lyI3v8bzLBkJ33/Os0h8kTRwRR41RGvTfWbOuWZIprQRaN6YMYyxJjkubo+2y1dEpyY3yMOU2djm5VS+eG+D4dLOVC9Z1pMwegDz2yRW0KlmIqWsiZkTE2iOPI9avmXbo8Rq4i8EAuM94YBvIJmxWA4POpeXGAIykuyZsHLrzBWAj//SZfzjg8rK20CNc1Or2F2WdK67Gta8yjtfAgcoBWImJp+GAeTVBps/j4wjxDRcxshogZ/pLVpZ0UgARh+FJYAtailw+VYxJznMgMJ5soJbb/5IcSMrdGcmZ7j0KRFeQKDExJBgmXU22HY53vkvfie3sLzM+AclhLjeZmmKMOQBeAVdyh5KFKJtuna36FmXzbQ5GV1n3u6AFuMA2idaUGK7igCsiCB7KKVXuE/7Ovsl3gH7Dz33+BJW8RhZGp/zpGMkcaJitP8QfPPfX2W3HfKIZseVmqC+BJ7KUigq4XiZtCkwv4Arw5jj738xTxJF46IrZedz4vLdFeuw0tnKjC1JsdU+kG7TVMV5msfuSlsB4vEa49jC4vOldeLryqZPqrLvU6hUN+nvMLeG1vQInxlhf705nD7nrv5CP9m6ZSyH9/JTsv7YTqlHynL17rbioAYAjiBxzHOU5JJ0jdDxd5/kPfIQoa5RlSdvm7/iys3o/qNm18va97jPMYAAMnAmWZ6NukVi6BKMAgTpsYFs7yOWbmAgxyUXVTOq41wK4ErcX9xaz+I6HDS6A1qqkzkWvSFe2CwEsgGXhGSegCubJ/WzF0WsmLN39A0eQY/9Cr4dkWDRMDRWXqy/MMd3YwRjTxjbnTyBdKCD/e+GaVH2XDAbAwJmgF0/tBWqS5HoIU0FkxKy8zl4aU8oUc8prCtmcY3K2+vGAfrqrdiRBrkN/qwvmW5YRPqOIkTU4DMVjJl1SYJkl/l0Xb3ZCDA4WgjjfzcP+5152UldBcCE5ltbcGfg57CmIuTz2WgItlgwUvPNdmVT2uqS04nE6fw6oB8ZgAAycCTxkl6pqrrYCYrew+mKdGx/5Ebz3ML5ELdkdePFaJvWsLqnprgBAn1qK5uYqb/rRj+1iJ7dvOV+Y0FXlZHW/hOQrUw43eQfJ8n4ffUHUIif3dYK/PXfZW8vN6gIZU3eRjZ/er3JkA+/DfJYQzXLJKR26+2OMeO9x7tj4XeThfBMMBsDAmSBv5jkpqD8kxE4/QcKE8Xv+DCBYMSFerLPp3Rx/86tpAF0uhdE1uBlcpm+I3i0NETFHbt8tKB4MNAkqhqiy8GNwE0rpDSxdPojKyt72zryVU0ZvPsqRywYkS6hkA8pSbu+bw02Jtk1471B1xJhwzg3u/7fAYAAMnAmC9eqKh/pzEcEL2ZU9eigvDubO/Wn0tcjZ+HLMDWpIp6xmBpYSJkrC096rYdprCqjkpDdVRS/Iwps3ewXaZeV6gnzkNyBFnFcsjFhU29jigALNOmCaT7EispKw1v3b/3JhJ+1hKOlo8p+gK2W84rMhICKoCKpdUPDEqXevxNSB4wwGwMCZQBNELUjSojkMmGOu1rli3SYgEF1XUnTR6TOiE321f06W0pwkKYKbTKGqoIG32t73Imz+QCfD7RBryB38cum+deEm1GjbhCRHeeUpRlcfoRyvd3kW2sUH7lUBJBd6fzrRXye9V0COerD6299zvE4KSF3gwX0dBgNg4GyQwDSf7tUMkc4XYHmtSIy6dVTx5D3tItIVTt11bfYMZKU6E8kaAaMpbjRB4uxw8x/80ydiXfSfXvxHjChCFMMTQUA1gF/nxvs+hVUJyhF5A7MV4yqLfB0aThd9oFeqUu7avLudv/Nmrf5FTpynq/dfvazHbzjQMRgAA2eDlIuv8sm+F5hxiOVLreS/lWIoscuEv6AyIEfEElhR5rNlUlVEwAfEB0TmuX7t6IO8fa/3DLCsrjDJJYFdIoubnXgAABHASURBVGDSw4BAa4lkBRKuIlJj6LI07bBi8l6lahd0vI8c1vuTfa/wJyyN1xUDVY/f/66huwhpqfeHi1krNXD26DKlc+lVRLpYrBChkwWOAtAiNsu9Ay4gdy97eTFcPQMtqwDIderZhZ2XgqF3ymvQ7yvWGwDQimFWg0TUOVoRbHSJpNu0lk3VfuSPNqYZNqklx42A5VWpG+XDm504YvccxmGMX4/BABg4G3QtEZaubCzHYrsFot/UzCJC37P9AnJSBUB/sduAlidSEdCVyyfcZ+A4hzX7y2HqTq0RWESogZT8Mq3CBJIZMaU89nL3xnRRt6rlrLunEXDCnwfuG0MIYOBsUOYSqgCgJYJRovngGqCk8wa6ALKGv6Du//4LLYDHQDwtjoiQNCA4RilR2pyFrjMPl1joLWy0Q5sCScFJC3IHR4HaBGIBGOaUZHOK9CW+XGxye/Q+NtIWj81voZrAjUAKtAvQ5jLD89GMJdDZSK4APB5lQs45Ec2XSrIgkCK4ImTtAA4z/WUZZjnjg3GfOakIQtAuzHe3MfA6j8bdSkDDeN+LwQAYOBOI67/GktuC0k3ebk1dTmR1gLuwX/m+Zz2W89ZNtBNSyRsyJnhrUKvZp6DWkkQCX2J9joUYIrmZkkBuDgSYZj+Lkwg7T/Dqi8bG2sO5Vvt421WRfPLluAbh2SR395Surt8d1faTrFJxfDE9Mge7fgH34qLOV+TIP0evXLlW7rrwGg/2+jcc6BgMgIGBc0QvrHo8D1BgWT6Z8yYSYglNCW8RSS1umWktGIHUyy91Lm4VQ8yDXeHhZ36A9e3vYev6TfCbQAANWKd9nwRil8C1lHEeGBg4VQwGwMDAOeLupOhcLqn01WjW9UqPYBFSwlkCa/tCt/wIFogSAMlhFuuTCT3IdYotx6UNA+dJ1p1uVXJmvBir/7uw1RgDA6ecwQAYGDhHZANAOpGkXLy2jMkDy04Alg0ASQlJ2SBQIkYOE5gEIh5DcGq4lBPfVJTkR6QkiIM6Zg9BJ8y2jHvbMkFzMAAGBk4rgwEwMHCO6KshDtPNEobmlCpLiKxWThhmilF017U4y9r12QDIrvxghtPcehVggYDmykFx2TnQWtecjaG0aGDgrDAYAAMD54gTVdDNSNb1qrc+K12JCeYpIDIFDdnznywbCUnA5ULL3AkvArm0je5xljpr3cbftxTIDodjLXCHFICBgVPHYAAMDJxrVjRTRUkp4kwhKWaOvTQBtrgmJeIUmkOpWlEwMYwWJIJ5xMBLe0S+/lBkSJHUxwD08KmHCMDAwKlkMAAGBs49OUkvAmaK0wCpppxusfnY+0gHj0CxQY0QJEFMiAscNrJtup8Kw/As7n78rlVzdiO4oRvTwMAZYDAABgYuAKnLBjB1mCriIuVEuPKhP4ukmtZvMDfFa0LqFiSgXVxfaFju6NZ1Y6OPJdjKz0r/hcHlPzBw6hkMgIGBc0uf/Z9340QW54kiiDlUS3TrUUyUuYxp0a5sz3J/e/OdNFBLn9GfEJx1uotmuTGO2MqGn69bdh/owgCDPTAwcPoYDICBgXPGXZut5Hb0kBP6GgMnAYsJL2MSgYMuqG/WqQUl65z6LUJ7+FjWufhZ3fQj1unbm7BS/58NjmGRGRg4nQzfzYGBc8Rh77lDWZ/DFL3DvzuniAT6er6qyxHI5QK+q/HrxYAL6HovSi+zfkTW1nGktBBZ5v8NDAycXgYDYGDgHHH4he516/OGH05yC7gs9euAjeUDhOWD5H4LBbANdII/q3bFymP1iv9Dwv/AwNlh0OwYGBgYGBi4gAwGwMDAwMDAwAVkMAAGBgYGBgYuIIMBMDAwMDAwcAEZDAC60qcTLkOWRH2t2x+//vjfTrpuYGBgYOD+0q+1qz+vx/H13cxOvO68MhgA5A9YVZeXV/8ty/LI7cyMpmle8/FijMQYMTNSSud6Ag0MDAycBpqmWa65wHLtfb31t7+9iJBSQlURkSPrd0qJGOMDfw9vN0MZICef8ts2i5+89NJLHBwcUBQFTdMQQsDMln8/TowRVSWEwGKxIMaIiCwNjIGBgYGB+4/3nsUi96lQVcxsuWmftMavbvL9pt+2LU3T4JzDe09Kafm4IYS39f28HQwGAIeTY9X9IyJUVcU//af/lF/4hV8gpbScML2leK/HWb1cVRUvv/wyBwcHb8M7GRgYGLiYOOdwzrGxkVUtzAzn3BFDYJV+Pe8v9/eZz+f8xb/4F9nb22NtbW15+/N4iBMb/NMnuojMjIODAw4ODphMJoQQltZhCOFEixKyB6C3Gp1zvPDCC3z605/mN37jN4ZQwMDAwMAD5OMf/zj//J//c55++mnatqWua5xzr7l5rx78eu+uqjIajZYeXeccKSWcO19SV4MHoKOfBKsegNFoRFVVQN7YvffLSXGviXA8AWVjYwPv/fIxBwYGBgYeDDFG1tbWUFW89xRF8abX3d5jYGZ475eXz+MB7lwaAPf6oO41EY5XAawaAyklUkoURUHbtsuEwXtVB6wmkvTx/4GBgYGBB09/eheR5al99WB3L06q/nLOUdc1IQSapqEoiru8Bau3f63HOq2cSwOgT+Jo23Z5+gbuuXEfv67//fjtVx/rJFZzBESEuq4ZjUZ84AMfwHt/4nP3E7Q3GFYrEvrX0Bshq/cPIRBjXLq4+t8fJP2Y9l+G/ovRGzz9a+y/eP17OZ4I2d/+JFb/1r8f59zSFdeP8UmVG283/Xte/cz717n6Gldvd9L77m/bj9vqnLjXeDRNQ1mWiAiLxYKUEmVZPvCTyur77MegPzH1p6X+NQKvuQC/lblvZhRFsZz7qrr8/UGyOvdDCMuQYO8NfK25f3w83sjc7x+jn/v9OPVJa/3jnae538+jtzr3m6bhmWeeYX19ffl5tG1LURRv6r31nyOw9CD0j9G/x/4zWF2XziLnMgegL9noP6B+YXrQ2firZSPHk0+cc/ecJKuL2/E61NUN76Qkw+Mb44PkpKzZ1dd2PIyyWlJ50vWv9TzHb7f6GCcl7rwTHB/z3vtzPKn0jX42/f1WN83Vz331+uObBfC2GETHx/y4QfZWjLPXmuOvN/dXN8YHyVud+8df85uZ+yc99jD3j5bq9YZjv2Hfvn2btbW1u17P/Vz3e8OvLMsj6/3rffankXPpAViddH1ZSO/Gfzue13tPXdev6TJ6o5y0CcLR09Nbfey3+lpWN56TTnf3ei2rBsGbeb57GRXvNMcNnOOf1Um3uxdv5P6rl/sFrr/8eo9/v7jX6+p/P+m9vpn3/VrPddrmPpwsJPNmfn+951t9jmHunzz3+/svFgvW19eXhuFbOf2/Hv36Doel4seNi7fjMHa/OJcegN5SX+W1EvfuN73XYfVkdpqtwIGBgYGzzmqpdu8laNt2qd1yP3it0ASc7tP+SZxLD8DxuFyMkS996Uv89m//9uuq+H239G7/1cz/1cSUkzjJml19vNXrj7sFj9/uQfJ6z7n6Gld/X3UNw+u7446PR/+FW33/J52A325O+mxW3dHHP7PXep3Hx+f4Z91fD0cXuuOfR/+8D4qTXND9abwPR/Wvsb/dG3nfw9zPDHP/rc/9vvy6X/Mh7wW9J/Z+sWpkOOe4fv06n/rUp5Zh37NkBJxLA2A1OaPnv/yX/8Lf+lt/i729vQf63P1iuOqm7I2Ce91+dXKu3vek6+91v3fC7fRGX+PrvfZ7PebxRfCNPsbbzfGF6iRX5fHbvd7jwd3u1ZNc/e/UOBx/LavzdvX6N/pZH3+M49eftLne63HeDh703F99zLMy91c/+7f6mr+bub+63vevoTcG7qcHYBXvPY899hi/+7u/eyY1As5tCAAOrcWmaTg4OODFF19ERHjppZf4+3//7/Mrv/Iry6zSgYGBgYGB1+IkQ2Y8HvOv//W/5s//+T+PmR2p0Hg978c7zfnTNuxYtQC990ynU5588klu3LjB+vo6L7/88lI6crXhz8DAwMDAwEmserz6vSWEsFR67T0fZ0UD5lwbAKvWWtu2iAh37tzhM5/5DL/zO7+zPPn3lQIDAwMDAwOvR7+vmGXp4Pl8jtlhp9g+H+C0c24NABE5srGrKnVd8zf/5t/kP/2n/7SMrfWiQQMDAwMDA6/Fai5Cn9s1n89ZW1vDzAghHBG2Ou2cSwOg/3Datl3mAbz00kt8+tOf5pd+6ZfY399fVgnEGM9ln+eBgYGBgQdHbww45/jEJz5xVwLwWegeePpf4Vug39RHo9Hy91/8xV/kF37hF4gxHunmN9ToDwwMDAy8EU461feJf/2pv4//n4WD5bksA1wV4RHJus4/8iM/wmOPPba87iy4ZwYGBgYGTjciwvPPP4/3/ogX4Cx4AM5lGeDAwMDAwMDAa3P6TZSBgYGBgYGB+85gAAwMDAwMDFxABgNgYGBgYGDgAjIYAAMDAwMDAxeQwQAYGLgANE1zpKFOr1qWUlp2Nlu9zfG/97/3t10sFqSUaNv2yH0GBgbODkMVwMDABSDGyD/+x/+Y//yf/zMiwmQy4ZOf/CQ/9mM/xlNPPUXTNMsW1qstTWezGf/23/5bRqMRP/VTP7VsbhJjZDabMR6Pl10PnXODpsbAwBniXOoADAwMHEVV+dKXvsQP/uAP8tf/+l/nT//0T/n3//7f81f/6l/l537u5/j4xz/OF77wBQ4ODhiNRmxubrK5uUlKic997nNMJhPe85738NBDD7G9vc0rr7zCd77zHQC2tra4fPnyO/wOBwYG3iyDATAwcAHoXflra2vs7Oxw+fJlvud7voef+Zmf4d/8m3/DY489xi/90i/xpS99id3dXR599FH+xt/4GwD87u/+LkVRMJ/P+Ymf+Ane+9738s/+2T/jq1/9KjFGptMpP/3TP81zzz33Dr/LgYGBN8NgAAwMXAB6hbLZbLZsfhVC4EMf+hD/6B/9I6bTKT/1Uz/F5uYmL7/8Mv/gH/wDPve5z/GTP/mT/NAP/RBVVfG3//bfpm1b/uW//JfUdc1nPvMZzIyf//mf5z/+x//Is88+O4QABgbOEIMBMDBwAWjblvF4zHg8XnoDnHO0bYv3nsViwb/7d/+OX/u1X2N/f58XXniB97znPYgIGxsb1HVNVVW8+uqrfP7zn+eXf/mX+fVf/3WqquL27dt8+MMfHpIABwbOGIMBMDBwAfDec+vWLa5cubJsgNU0DS+88ALvete7+I3f+A0++9nP8nf/7t/lxo0b/NzP/Rz7+/vMZjPatqUsS1JKhBBwzvGX/tJf4q/9tb+27LlRluWZ0D4fGBg4ZPjGDgxcEHr3/J07d/jyl7/Mv/pX/4rPfe5z/Lk/9+eWnoDLly/z9a9/nd/6rd/Ce49zju3tbb7xjW+wu7uLqvKRj3yEP/zDP+SFF15ARPja177G1772tXf43Q0MDLxZBg/AwMAFwMyoqop/8S/+Bf/hP/wHRqMRzz//PH/5L/9lfviHf5iXXnqJX/u1X+Ov/JW/wvPPP88HP/hBQgiUZcnHPvYxPvvZz/JjP/ZjfPrTn+ZHf/RHuXXrFj/7sz/L7du3eeihh/jpn/7pd/otDgwMvEkGHYCBgQtAL/QTY8R7v6zlL4piKe7Ti/ms1vOvXp9SoigKUkqklIgxLhMKnXODDsDAwBljCAEMDFwA+s27qqql4A/k5MCmaWiaBuccRVEsY/lN06CqxBiXMX4RoW3bI3kBzrkhAXBg4AwyeAAGBi4Aq6d859xSztc5RwgBYJkH0Cv79b+vLhEpJUQEVWWxWBBCQESWnoDBAzAwcHYYDICBgQtAv3EDR1z3APP5nLIslxu/qh7pERBCOGI8nEQvHzwYAAMDZ4fBABgYGBgYGLiADDkAAwMDAwMDF5DBABgYGBgYGLiADAbAwMDAwMDABWQwAAYGBgYGBi4ggwEwMDAwMDBwARkMgIGBgYGBgQvIYAAMDAwMDAxcQAYDYGBgYGBg4AIyGAADAwMDAwMXkMEAGBgYGBgYuIAMBsDAwMDAwMAFZDAABgYGBgYGLiCDATAwMDAwMHABGQyAgYGBgYGBC8hgAAwMDAwMDFxA/v+9suCVzDweYQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "