{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Amazon.com 130+ million customer review data using SageMaker and Athena\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "2. [Topic-based unsupervised review grouping](#Topic-based-unsupervised-review-grouping)\n",
    "  1. [Permissions and environment variables](#Permissions-and-environment-variables)\n",
    "  2. [Data ingestion](#Data-ingestion)\n",
    "  3. [Data inspection](#Data-inspection)\n",
    "  4. [Data conversion](#Data-conversion)\n",
    "3. [Training the K-Means model](#Training-the-K-Means-model)\n",
    "4. [Set up hosting for the model](#Set-up-hosting-for-the-model)\n",
    "5. [Validate the model for use](#Validate-the-model-for-use)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This notebook demonstrates the analysis of Amazon review data using [Amazon SageMaker](https://aws.amazon.com/sagemaker/) in two parts. The first part uses [LDA](https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html) and [K-Means](https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html) algorithms to automatically group together reviews based on topics discussed within the text. The second part covers predicting review scores using [Linear Learner](https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html) and [Factorization Machine](https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html) based on contents of the review. The end shows how to predict the rating of a new review based on the models.\n",
    "\n",
    "Note that this lab is tested using a SageMaker Notebook server with `ml.m4.16xlarge` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train\n",
      "Validation set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/val\n",
      "Trained model will be saved at s3://pilho-sagemaker-ai-workshop-kr/amazonreview/output\n"
     ]
    }
   ],
   "source": [
    "# Define IAM role\n",
    "import os\n",
    "import boto3\n",
    "import time\n",
    "import re\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "role = get_execution_role()\n",
    "bucket = 'pilho-sagemaker-ai-workshop-kr'\n",
    "prefix = 'amazonreview'\n",
    "\n",
    "train_prefix = os.path.join(prefix, 'train')\n",
    "val_prefix = os.path.join(prefix, 'val')\n",
    "output_prefix = os.path.join(prefix, 'output')\n",
    "\n",
    "s3_train_data = os.path.join('s3://', bucket, train_prefix)\n",
    "s3_val_data = os.path.join('s3://', bucket, val_prefix)\n",
    "output_path = os.path.join('s3://', bucket, output_prefix)\n",
    "print('Training set location', s3_train_data)\n",
    "print('Validation set location', s3_val_data)\n",
    "print('Trained model will be saved at', output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data ingestion\n",
    "\n",
    "For small datasets, such as this one, reading into memory isn't onerous, though it would be for larger datasets. We will use Amazon.com review open data available at https://s3.amazonaws.com/amazon-reviews-pds/readme.html. FYI, more classified data is available at http://jmcauley.ucsd.edu/data/amazon/.\n",
    "\n",
    "Let's first see what are included in `amazon-reviews-pdf` s3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = boto3.resource('s3')\n",
    "sourceBucket = s3.Bucket('amazon-reviews-pds')\n",
    "\n",
    "#for obj in sourceBucket.objects.all():\n",
    "#    print(obj.key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above results shows how billions of review data are stored as an object in S3. Imagine a situation that we are only intersted in reviews for products in the book category. In a legacy data analytic environment, we would need to perform ETL (Extract-tranfrom-load) operation and may further convert the input to import them into the database. When using AWS, users can simply use [Amazon Athena](https://aws.amazon.com/athena/) and perform SQL queries without any modification. \n",
    "\n",
    "Let's get some hands-on experience. Please open follow the instruction at https://s3.amazonaws.com/amazon-reviews-pds/readme.html and get back to this lab. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After finishing Amazon Athena table creation, you are now ready to perform a SQL query on the data. The below screenshot shows one example. For your information, Athena's query results are automatically saved back to S3. Let's utilize one of the result. Note to set the property of a target CSV as a public readable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AthenaQuery.png](images/AthenaQuery.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Athena SQL query\n",
    "\n",
    "```sql\n",
    "SELECT DISTINCT(product_category)\n",
    "FROM amazon_reviews_parquet\n",
    "ORDER BY product_category;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'arn:aws:iam::082256166551:role/service-role/AmazonSageMaker-ExecutionRole-20180917T155747'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(role)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AmazonSageMaker-ExecutionRole-20180917T155747\n"
     ]
    }
   ],
   "source": [
    "role_array = role.split('/')\n",
    "role_name = role_array[len(role_array) - 1]\n",
    "print(role_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AddPermissionToSageMaker.png](images/AddPermissionToSageMaker.png)\n",
    "\n",
    "We need to add permissions to our SageMaker role (in this case 'AmazonSageMaker-ExecutionRole-20180110T104784') to perform queries using Athena. Go to the IAM console and add AmazonAthenaFullAccess  like the above screenshot. We will also give a full S3 access priviledge to allow this code to access partitioned S3 buckets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AddS3FullAccessPermition.png](images/AddS3FullAccessPermition.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#iam_client = boto3.client('iam')\n",
    "\n",
    "#sagemaker_role = iam_client.get_role(RoleName=role_name)\n",
    "#display(sagemaker_role)\n",
    "\n",
    "#role_attach_response = iam_client.attach_role_policy(\n",
    "#    RoleName=role_name,\n",
    "#    PolicyArn='arn:aws:iam::aws:policy/AmazonAthenaFullAccess'\n",
    "#)\n",
    "\n",
    "#print(role_attach_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't automate this part using boto3 IAM APIs because then we have to give a IAM permision to SageMaker's role which might be less safe than manually assign the role only when necessary. Just in case you want to automate this part, a sample code is given above.\n",
    "\n",
    "**Also note that above IAM role changes may take a few minutes to be effective.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write a few utility codes for Athena query interaction. The below code is originated from https://github.com/quentinf00/API-S3-Lambda/blob/bb5d279396195acfa113f4f074587d714e4ed504/athena.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "The original code comes from https://github.com/quentinf00/API-S3-Lambda/blob/bb5d279396195acfa113f4f074587d714e4ed504/athena.py\n",
    "\n",
    "It is modified and enhanced by Pil Ho Kim for this demonstration, 2nd August 2018\n",
    "'''\n",
    "import os\n",
    "import boto3\n",
    "from time import sleep\n",
    "import tempfile\n",
    "\n",
    "# This is the new bucket that stores the query results\n",
    "# RESULT_BUCKET = 'aws-athena-query-results-082256166551-us-east-1'\n",
    "RESULT_BUCKET = 'pilho-sagemaker-ai-workshop-kr'\n",
    "DB = 'default'\n",
    "\n",
    "athena = boto3.client('athena')\n",
    "\n",
    "# This is the function that start the execution and get the results\n",
    "def get_results(query, database=DB):\n",
    "    query_id = hash(query)\n",
    "\n",
    "    response = athena.start_query_execution(\n",
    "        QueryString=query,\n",
    "        QueryExecutionContext={\n",
    "            'Database': database\n",
    "        },\n",
    "        ResultConfiguration={\n",
    "            'OutputLocation': f's3://{RESULT_BUCKET}/athena/{query_id}'\n",
    "        }\n",
    "    )\n",
    "\n",
    "    results = get_query_results(response['QueryExecutionId'])\n",
    "    return results\n",
    "\n",
    "# This is the function that start the execution and get the results\n",
    "def get_results_for_pd(query, database=DB):\n",
    "    query_id = hash(query)\n",
    "\n",
    "    response = athena.start_query_execution(\n",
    "        QueryString=query,\n",
    "        QueryExecutionContext={\n",
    "            'Database': database\n",
    "        },\n",
    "        ResultConfiguration={\n",
    "            'OutputLocation': f's3://{RESULT_BUCKET}/athena/{query_id}'\n",
    "        }\n",
    "    )\n",
    "\n",
    "    results = get_query_results_for_pd(response['QueryExecutionId'])\n",
    "    return results\n",
    "\n",
    "# This query does not return the result set but the address of newly created S3 bucket with a key name\n",
    "# Mostly used for a query result too big to display and for the use of ML training\n",
    "def get_results_in_s3(query, database=DB):\n",
    "    query_id = hash(query)\n",
    "    s3_output_location = f's3://{RESULT_BUCKET}/athena/{query_id}'\n",
    "    \n",
    "    response = athena.start_query_execution(\n",
    "        QueryString=query,\n",
    "        QueryExecutionContext={\n",
    "            'Database': database\n",
    "        },\n",
    "        ResultConfiguration={\n",
    "            'OutputLocation': s3_output_location\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    run_result = get_query_results_in_s3(response['QueryExecutionId'])\n",
    "    s3_output_location = run_result['response']['QueryExecution']['ResultConfiguration']['OutputLocation']\n",
    "    return s3_output_location\n",
    "\n",
    "#This function polls the state of the query execution each second and fetches the results once it's finished\n",
    "def get_query_results(exec_id):\n",
    "    run_status = False\n",
    "    \n",
    "    while(not run_status):\n",
    "        current_status = is_execution_done(exec_id)\n",
    "        run_status = current_status['status']\n",
    "        print('not done yet')\n",
    "        sleep(1)\n",
    "\n",
    "    results = athena.get_query_results(\n",
    "        QueryExecutionId=exec_id,\n",
    "    )\n",
    "    #print(results)\n",
    "\n",
    "    return format_result(results)\n",
    "\n",
    "#This function polls the state of the query execution each second and fetches the results once it's finished\n",
    "def get_query_results_for_pd(exec_id):\n",
    "    run_status = False\n",
    "    \n",
    "    while(not run_status):\n",
    "        current_status = is_execution_done(exec_id)\n",
    "        run_status = current_status['status']\n",
    "        print('not done yet')\n",
    "        sleep(1)\n",
    "\n",
    "    results = athena.get_query_results(\n",
    "        QueryExecutionId=exec_id,\n",
    "    )\n",
    "    #print(results)\n",
    "\n",
    "    return format_result_for_pd(results)\n",
    "\n",
    "#This function polls the state of the query execution each second and fetches the results once it's finished\n",
    "def get_query_results_in_s3(exec_id):\n",
    "    run_status = False\n",
    "    \n",
    "    while(not run_status):\n",
    "        current_status = is_execution_done(exec_id)\n",
    "        run_status = current_status['status']\n",
    "        print('not done yet')\n",
    "        sleep(1)\n",
    "\n",
    "    athena.get_query_results(\n",
    "        QueryExecutionId=exec_id,\n",
    "    )\n",
    "\n",
    "    return current_status\n",
    "\n",
    "# This function checks the state of the execution, returns true if it is SUCCEEDED\n",
    "def is_execution_done(exec_id):\n",
    "    response = athena.get_query_execution(\n",
    "        QueryExecutionId=exec_id,\n",
    "    )\n",
    "    \n",
    "    if response['QueryExecution']['Status']['State'] == 'FAILED':\n",
    "        display(response)\n",
    "        raise Exception('Failed Athena query')\n",
    "\n",
    "    # display(response)\n",
    "    \n",
    "    return {\n",
    "        'response': response,\n",
    "        'status': response['QueryExecution']['Status']['State'] == 'SUCCEEDED'\n",
    "    }\n",
    "        \n",
    "\n",
    "# This functions just parses the rows and return a list of dictionnaries\n",
    "def format_result(results):\n",
    "    columns = [\n",
    "        col['Label']\n",
    "        for col in results['ResultSet']['ResultSetMetadata']['ColumnInfo']\n",
    "    ]\n",
    "\n",
    "    formatted_results = []\n",
    "\n",
    "    for result in results['ResultSet']['Rows'][1:]:\n",
    "        values = [list(field.values())[0] for field in result['Data']]\n",
    "\n",
    "        formatted_results.append(\n",
    "            dict(zip(columns, values))\n",
    "        )\n",
    "\n",
    "    return formatted_results\n",
    "\n",
    "# This functions just parses the rows and return a list of dictionnaries\n",
    "def format_result_for_pd(results):\n",
    "    columns = [\n",
    "        col['Label']\n",
    "        for col in results['ResultSet']['ResultSetMetadata']['ColumnInfo']\n",
    "    ]\n",
    "\n",
    "    formatted_results = []\n",
    "\n",
    "    for result in results['ResultSet']['Rows'][1:]:\n",
    "        values = [list(field.values())[0] for field in result['Data']]\n",
    "\n",
    "        formatted_results.append(\n",
    "            dict(zip(columns, values))\n",
    "        )\n",
    "\n",
    "    return formatted_results\n",
    "\n",
    "# This function saves numpy data to S3\n",
    "# See https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.save.html\n",
    "# Max data size is limited by the free size under /tmp\n",
    "def put_np_to_s3(bucket, key, sourceNPArray):\n",
    "    # Should add bucket checking routines later\n",
    "    tempFileName = tempfile.mktemp('.npy')\n",
    "    outfile = open(tempFileName, 'wb')\n",
    "    np.save(outfile, sourceNPArray)\n",
    "    \n",
    "    return boto3.resource('s3').meta.client.upload_file(tempFileName, bucket, key)\n",
    "\n",
    "# This function saves numpy data to S3\n",
    "def get_np_from_s3(bucket, key):\n",
    "    # Should add bucket checking routines later\n",
    "    tempFileName = tempfile.mktemp('.npy')\n",
    "    boto3.resource('s3').meta.client.download_file(bucket, key, tempFileName)\n",
    "\n",
    "    infile = open(tempFileName, 'rb')\n",
    "    infile.seek(0) # Only needed here to simulate closing & reopening file\n",
    "    loadedNpData = np.load(infile)\n",
    "    return loadedNpData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create an Athena table based on the instruction at https://s3.amazonaws.com/amazon-reviews-pds/readme.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "query_results = get_results(f\"\"\"\n",
    "CREATE EXTERNAL TABLE IF NOT EXISTS amazon_reviews_parquet_query(\n",
    "  marketplace string, \n",
    "  customer_id string, \n",
    "  review_id string, \n",
    "  product_id string, \n",
    "  product_parent string, \n",
    "  product_title string, \n",
    "  star_rating int, \n",
    "  helpful_votes int, \n",
    "  total_votes int, \n",
    "  vine string, \n",
    "  verified_purchase string, \n",
    "  review_headline string, \n",
    "  review_body string, \n",
    "  review_date bigint, \n",
    "  year int)\n",
    "PARTITIONED BY (product_category string)\n",
    "ROW FORMAT SERDE \n",
    "  'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' \n",
    "STORED AS INPUTFORMAT \n",
    "  'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' \n",
    "OUTPUTFORMAT \n",
    "  'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'\n",
    "LOCATION\n",
    "  's3://amazon-reviews-pds/parquet/'\n",
    "    \"\"\")\n",
    "print(query_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "query_results = get_results(f\"\"\"\n",
    "    MSCK REPAIR TABLE amazon_reviews_parquet_query\n",
    "\"\"\")\n",
    "print(query_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Preview on Amazon Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                               # For munging tabular data\n",
    "import matplotlib.pyplot as plt                   # For charts and visualizations\n",
    "from IPython.display import Image                 # For displaying images in the notebook\n",
    "from IPython.display import display               # For displaying outputs in the notebook\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many customers have contributed to the review  in the current data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'_col0': '34940792'}]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_results = get_results(f\"\"\"\n",
    "    SELECT COUNT(DISTINCT(customer_id)) FROM amazon_reviews_parquet_query\n",
    "\"\"\")\n",
    "display(query_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many product categories and each associcated review data exist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'product_category': 'Shoes', 'data_count': '4379475'},\n",
       " {'product_category': 'Mobile_Electronics', 'data_count': '105159'},\n",
       " {'product_category': 'Home', 'data_count': '6228567'},\n",
       " {'product_category': 'Software', 'data_count': '342135'},\n",
       " {'product_category': 'Personal_Care_Appliances', 'data_count': '86686'},\n",
       " {'product_category': 'Music', 'data_count': '6177781'},\n",
       " {'product_category': 'Musical_Instruments', 'data_count': '920729'},\n",
       " {'product_category': 'Digital_Software', 'data_count': '102084'},\n",
       " {'product_category': 'Automotive', 'data_count': '3516476'},\n",
       " {'product_category': 'Camera', 'data_count': '1838760'},\n",
       " {'product_category': 'Outdoors', 'data_count': '2305596'},\n",
       " {'product_category': 'Digital_Music_Purchase', 'data_count': '1852184'},\n",
       " {'product_category': 'Sports', 'data_count': '4860054'},\n",
       " {'product_category': 'Digital_Video_Download', 'data_count': '5173743'},\n",
       " {'product_category': 'Books', 'data_count': '20726160'},\n",
       " {'product_category': 'Video_Games', 'data_count': '1808486'},\n",
       " {'product_category': 'Tools', 'data_count': '1748610'},\n",
       " {'product_category': 'Video', 'data_count': '437409'},\n",
       " {'product_category': 'Digital_Ebook_Purchase', 'data_count': '19180765'},\n",
       " {'product_category': 'PC', 'data_count': '7004337'},\n",
       " {'product_category': 'Digital_Video_Games', 'data_count': '145431'},\n",
       " {'product_category': 'Electronics', 'data_count': '3120938'},\n",
       " {'product_category': 'Office_Products', 'data_count': '2646491'},\n",
       " {'product_category': 'Major_Appliances', 'data_count': '96901'},\n",
       " {'product_category': 'Home_Improvement', 'data_count': '2640654'},\n",
       " {'product_category': 'Home_Entertainment', 'data_count': '743700'},\n",
       " {'product_category': 'Grocery', 'data_count': '2402478'},\n",
       " {'product_category': 'Toys', 'data_count': '4981601'},\n",
       " {'product_category': 'Jewelry', 'data_count': '1767753'},\n",
       " {'product_category': 'Baby', 'data_count': '1764893'},\n",
       " {'product_category': 'Kitchen', 'data_count': '4882831'},\n",
       " {'product_category': 'Watches', 'data_count': '978042'},\n",
       " {'product_category': 'Wireless', 'data_count': '9038249'},\n",
       " {'product_category': 'Beauty', 'data_count': '5115721'},\n",
       " {'product_category': 'Health_&_Personal_Care', 'data_count': '5332883'},\n",
       " {'product_category': 'Video_DVD', 'data_count': '7135819'},\n",
       " {'product_category': 'Luggage', 'data_count': '349132'},\n",
       " {'product_category': 'Lawn_and_Garden', 'data_count': '2559261'},\n",
       " {'product_category': 'Furniture', 'data_count': '792214'},\n",
       " {'product_category': 'Pet_Products', 'data_count': '2643670'},\n",
       " {'product_category': 'Gift_Card', 'data_count': '149086'},\n",
       " {'product_category': 'Mobile_Apps', 'data_count': '6807166'},\n",
       " {'product_category': 'Apparel', 'data_count': '5906460'}]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_results = get_results(f\"\"\"\n",
    "    SELECT \n",
    "        product_category,\n",
    "        COUNT(*) AS data_count\n",
    "    FROM amazon_reviews_parquet_query\n",
    "    GROUP BY product_category\n",
    "\"\"\")\n",
    "display(query_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n",
      "not done yet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'s3://pilho-sagemaker-ai-workshop-kr/athena/-8511654286911741163/2ae1ad75-c7e2-4597-9033-289c1688fa74.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distinct_category_counter_s3_address = get_results_in_s3(f\"\"\"\n",
    "    SELECT \n",
    "        product_category,\n",
    "        COUNT(*) AS data_count\n",
    "    FROM amazon_reviews_parquet_query\n",
    "    GROUP BY product_category\n",
    "\"\"\")\n",
    "display(distinct_category_counter_s3_address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>data_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tools</td>\n",
       "      <td>1748610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Office_Products</td>\n",
       "      <td>2646491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toys</td>\n",
       "      <td>4981601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home_Entertainment</td>\n",
       "      <td>743700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Software</td>\n",
       "      <td>342135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>6228567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wireless</td>\n",
       "      <td>9038249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Video_DVD</td>\n",
       "      <td>7135819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Camera</td>\n",
       "      <td>1838760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Luggage</td>\n",
       "      <td>349132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lawn_and_Garden</td>\n",
       "      <td>2559261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Musical_Instruments</td>\n",
       "      <td>920729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Music</td>\n",
       "      <td>6177781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Automotive</td>\n",
       "      <td>3516476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Digital_Software</td>\n",
       "      <td>102084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home_Improvement</td>\n",
       "      <td>2640654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>4882831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Major_Appliances</td>\n",
       "      <td>96901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Apparel</td>\n",
       "      <td>5906460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Electronics</td>\n",
       "      <td>3120938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Health_&amp;_Personal_Care</td>\n",
       "      <td>5332883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Grocery</td>\n",
       "      <td>2402478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Video_Games</td>\n",
       "      <td>1808486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mobile_Electronics</td>\n",
       "      <td>105159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>4379475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Beauty</td>\n",
       "      <td>5115721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Outdoors</td>\n",
       "      <td>2305596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jewelry</td>\n",
       "      <td>1767753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Digital_Video_Games</td>\n",
       "      <td>145431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Baby</td>\n",
       "      <td>1764893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Watches</td>\n",
       "      <td>978042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Personal_Care_Appliances</td>\n",
       "      <td>86686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Video</td>\n",
       "      <td>437409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>792214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Pet_Products</td>\n",
       "      <td>2643670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Gift_Card</td>\n",
       "      <td>149086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Mobile_Apps</td>\n",
       "      <td>6807166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Digital_Music_Purchase</td>\n",
       "      <td>1852184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PC</td>\n",
       "      <td>7004337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Digital_Ebook_Purchase</td>\n",
       "      <td>19180765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Digital_Video_Download</td>\n",
       "      <td>5173743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Sports</td>\n",
       "      <td>4860054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Books</td>\n",
       "      <td>20726160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            product_category  data_count\n",
       "0                      Tools     1748610\n",
       "1            Office_Products     2646491\n",
       "2                       Toys     4981601\n",
       "3         Home_Entertainment      743700\n",
       "4                   Software      342135\n",
       "5                       Home     6228567\n",
       "6                   Wireless     9038249\n",
       "7                  Video_DVD     7135819\n",
       "8                     Camera     1838760\n",
       "9                    Luggage      349132\n",
       "10           Lawn_and_Garden     2559261\n",
       "11       Musical_Instruments      920729\n",
       "12                     Music     6177781\n",
       "13                Automotive     3516476\n",
       "14          Digital_Software      102084\n",
       "15          Home_Improvement     2640654\n",
       "16                   Kitchen     4882831\n",
       "17          Major_Appliances       96901\n",
       "18                   Apparel     5906460\n",
       "19               Electronics     3120938\n",
       "20    Health_&_Personal_Care     5332883\n",
       "21                   Grocery     2402478\n",
       "22               Video_Games     1808486\n",
       "23        Mobile_Electronics      105159\n",
       "24                     Shoes     4379475\n",
       "25                    Beauty     5115721\n",
       "26                  Outdoors     2305596\n",
       "27                   Jewelry     1767753\n",
       "28       Digital_Video_Games      145431\n",
       "29                      Baby     1764893\n",
       "30                   Watches      978042\n",
       "31  Personal_Care_Appliances       86686\n",
       "32                     Video      437409\n",
       "33                 Furniture      792214\n",
       "34              Pet_Products     2643670\n",
       "35                 Gift_Card      149086\n",
       "36               Mobile_Apps     6807166\n",
       "37    Digital_Music_Purchase     1852184\n",
       "38                        PC     7004337\n",
       "39    Digital_Ebook_Purchase    19180765\n",
       "40    Digital_Video_Download     5173743\n",
       "41                    Sports     4860054\n",
       "42                     Books    20726160"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv(distinct_category_counter_s3_address)\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's sort data\n",
    "sorted_data = data.sort_values(['data_count'], ascending=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RNCXvh7ndn5GO6WzSjQxm5DIiYhrpgv1fk94Xd/LRUZ+Sdr13IuJZ0r9bPyQlvs+SEqPSZ7KvkJKD14HjSce4Pc4HVpO0Rx7tHQ0cQBqhepF0vhV/m2wyaRTm9sIU4o9oK+b878gM4OGcaEF6DZ6OiJfz89ZeB7NOpzQF1szMzHoypdsh/wM4KCLuqHU8ZmY9mUdyzMzMeqg8PWiFPMXnh6RrPTp71MzMrO44yTEzM+u5tiBNiXuVdG3GXhGxqLYhmZn1fJ6uZmZmZmZmdcUjOWZmZmZmVlfaexcjs48ZPHhwNDQ01DoMMzMzM6tj06dPfzUihlRT10mOLbaGhgaamppqHYaZmZmZ1TFJT1db19PVzMzMzMysrjjJMTMzMzOzuuIkx8zMzMzM6oqTHDMzMzMzqytOcszMzMzMrK44yTEzMzMzs7riJMfMzMzMzOqKkxwzMzMzM6srTnLMzMzMzKyuOMkxMzMzM7O64iTHzMzMzMzqipMcMzMzMzOrK05yzMzMzMysrjjJMTMzMzOzuuIkx8zMzMzM6oqTHDMzMzMzqytOcszMzMzMrK44yTEzMzMzs7riJMfMzMzMzOqKkxwzMzMzM6srTnLMzMzMzKyuOMkxMzMzM7O64iTHzMzMzMzqipMcMzMzMzOrK05yzMzMzMysrjjJMTMzMzOzuuIkx8zMzMzM6oqTHDMzMzMzqytL1zoA6/3mPDefhvHX1zoMMzMzM+ti8yaMqXUIVfFIjpmZmZmZ1RUnOb2QpGZJMyXNkjRD0pYdbGeUpOs6Oz4zMzMzs1rydLXeaVFEjACQtAtwMrBdbUMyMzMzM+sZPJLT+y0PvAGg5FRJcyXNkbR/a+VFkjaV9JCkz0naLo8Uzcxly3Vzn8zMzMzMOswjOb1Tf0kzgX7AUGCHXL4PMALYCBgMPChpKrBlC+UA5OluvwH2jIhnJJ0BfCsi7pE0EHi7m/plZmZmZrbYPJLTOy2KiBERMQzYFbhQkoCtgckR0RwRLwF3Apu2Ug6wLjAR2CMinsll9wC/knQUsEJEvF8egKRxkpokNTUvnN+VfTUzMzMzaxcnOb1cRNxHGp0ZAqiFai2VA7xAGqnZuNDmBOBwoD9wv6RhFfY7MSIaI6Kxz4BBHQ3fzMzMzKzTOcnp5XIC0gd4DZgK7C+pj6QhwLbAtFbKAf4JjAF+LmlUbnPNiJgTEacATcDHkhwzMzMzs57K1+T0TqVrciCN0hwSEc2Srga2AGYBAXw/Il5spXwYQES8JGkP4C+SDgMOlrQ90Aw8AvylW3tnZmZmZrYYFBG1jsF6ub5D146hh5xe6zDMzMzMrIvNmzCmZvuWND0iGqup65EcW2zDVxlEUw1PeDMzMzOzIl+TY2ZmZmZmdcVJjpmZmZmZ1RVPV7PFNue5+TSMv77WYZiZmZn1eLW8pmVJ4pEcMzMzMzOrK05yzMzMzMysrnR6kiOpWdJMSQ9LmiXpWElL5XWNkv63ijbuzX8bJH2livoNkua2sn6UpPk5rtJjp7a2aw9J8yQNrrJu6RjNlXS5pAGdsP9O64uZmZmZWW/WFSM5iyJiRESsD+wMfBE4HiAimiLiqLYaiIgt82ID0GaSU6W7clylx62d1G5HlI7RBsC7wDer3VCSr6MyMzMzM2tFl05Xi4iXgXHAt5WMknQdgKQhkm6RNEPSOZKeLo2ESFqQm5gAbJNHPY7JoxV35W1mSNqy8p7bZWlJv5c0W9IVpVEVSTtKekjSHEnnS+rbWnmJpP6SbpR0RJX7vwtYq3wkRtJxkk7Iy1Mk/VzSncDRkj4t6eo8UjarcBz6SDo3j6LdLKl/3v4ISQ/mulcW+vjlPJo0S9LUXNZH0qm5/mxJ3+jwkTUzMzMzq4EuvyYnIp7M+1mpbNXxwO0RMRK4Glitwubj+XAE5tfAy8DOeZv9gTanvhWUkqXSY81c/nlgYkRsCPwL+E9J/YBJwP4RMZx0F7ojWyov7GMg8Gfgkog4t62A8qjMbsCcKuJfISK2i4hfkvp9Z0RsBIwEHs511gb+L4+i/RP4Ui6/KiI2zfUfBb6ey38C7JLL/yOXfR2YHxGbApsCR0hao0Ls4yQ1SWpqXji/ivDNzMzMzLpHd914QBXKtgYuBYiIG4E3qmhnGeBcSXOAy4H12hFD+XS1J3L5sxFxT16+KMf1eeCpiHg8l/8e2LaV8pI/ARdExIVtxNJf0kygCXgG+F0V8V9WWN4BOAsgIpojopRlPBURM/PydNJ0P4AN8gjYHOAgYP1cfg8wKY869cllo4Gv5fgeAFYkJU8fERETI6IxIhr7DBhURfhmZmZmZt2jy6/vkPQ5oJk0CrNucVUHmjsGeAnYiJSgvb3YAUJUeN5SbG3FfA+wm6RLIqK83aJFETHiIw1L7/PRpLNf2TZvtbFvgHcKy81A/7w8CdgrImZJGguMAoiIb0r6AjAGmClpBKmP34mIm6rYn5mZmZlZj9OlIzmShgBnA2dW+NB/N7Bfrjca+GSFJt4Elis8HwS8EBEfAF/lw9GHxbGapC3y8oE5rseABklr5fKvAne2Ul7yE+A14LcdiOMlYCVJK+brfHZvpe5t5Gly+Rqa5dtoezngBUnLkEZyyNuuGREPRMRPgFeBzwI3kabmLZPrrCNp2Q70x8zMzMysJroiyemfr3l5GLgVuBk4sUK9E4HRkmaQrkt5gZTUFM0G3s8Xxh9DSh4OkXQ/sA7VjW6UlF+Ts28ufzS3ORv4FHBWRLwNHApcnqd4fQCc3VJ52X6+C/ST9It2xEZEvAecRJoidh0poWrJ0cD2OYbpfDj9rCX/ndu9pazdU/MNFOYCU4FZwHnAI8CMXH4O3TDiZ2ZmZmbWWdT6rKou3HEarWiOiPfzSMpZ5VO4rHdobGyMpqamWodhZmZmZnVM0vSIaKymbi2/oV8N+KPSD4W+C1R7y2UzMzMzM7MW1SzJiYi/ARt3ZpuSdgFOKSt+KiL27sz9VBnLiqRrZ8rtGBGvdXc8ZmZmZmZLippNV7P60Xfo2jH0kNNrHYaZ1ZF5E8bUOgQzM+th2jNdrbt+J8fMzMzMzKxbOMmpMUm/lvTdwvObJJ1XeP5LST+UdEUH2l7QWXGamZmZmfUWTnJq715gS4B8E4bBfPSW0FsCt0XEvuUbSvKtnc3MzMzMyjjJqb17yEkOKbmZC7wp6ZP5NtvrAm/k36xB0lhJl0v6M+k3iJD0X5IelDRbUqXfJKpYR9Kykq7Pv0M0V9L+uXyCpEdy3dO6tPdmZmZmZp3MIwE1FhHPS3pf0mqkZOc+YBVgC2A+6QdR3y3bbAtgw4h4XdJoYG1gM0DAtZK2jYippcot1QGGAM9HxJhcb5CkTwF7A8MiIiStUCluSeOAcQB9lh/SGYfCzMzMzKxTeCSnZyiN5pSSnPsKz++tUP+WiHg9L4/Oj4eAGcAwUkJT1FKdOcBOkk6RtE1EzAf+BbwNnCdpH2BhpYAjYmJENEZEY58BgzrWazMzMzOzLuCRnJ6hdF3OcNJ0tWeB75ESjvMr1H+rsCzg5Ig4p5X2W6wjaRPgi8DJkm6OiJMkbQbsCBwAfBvYof1dMjMzMzOrDY/k9Az3ALsDr0dEcx6lWYE0Le2+Nra9CThM0kAASatIWqmaOpJWBhZGxEXAacDIXGdQRNwAfBcY0Ul9NDMzMzPrFh7J6RnmkO6qdklZ2cCIeLWUnFQSETdLWhe4TxLAAuBg4OUq6qwFnCrpA+A94EhgOeBPkvqRRoCO6bRempmZmZl1A0VErWOwXq7v0LVj6CGn1zoMM6sj8yaMqXUIZmbWw0iaHhGN1dT1SI4ttuGrDKLJH0jMzMzMrIfwNTlmZmZmZlZXnOSYmZmZmVld8XQ1W2xznptPw/jrax2GWd3zdSpmZmbV8UiOmZmZmZnVFSc5ZmZmZmZWV5zkdBJJUyTtUlb2XUnnS7qilW2qug1eO2L4q6TZkh6TdKakFdqI77eSGiQtkvSQpEclTZN0SGfFZWZmZmbWnZzkdJ7JwAFlZQcAF0TEvt0Yx0ERsSGwIfAO8Kc24pucl5+IiI0jYt1cfoykQ7sjYDMzMzOzzuQkp/NcAewuqS+ApAZgZeAfkubmsv6SLs0jLZcB/UsbSxot6T5JMyRdLmlgLt8xj7DMyaNCfasJJiLeBb4PrCZpo1biu7vCtk8CxwJHtdS+pHGSmiQ1NS+cX01IZmZmZmbdwklOJ4mI14BpwK656ADgMiAK1Y4EFuaRlv8BNgGQNBj4MbBTRIwEmoBjJfUDJgH7R8Rw0t3wjmxHTM3ALGBYS/FFRLSw+QxgWCttT4yIxoho7DNgULUhmZmZmZl1OSc5nas4Jaw4FaxkW+AigIiYDczO5ZsD6wH3SJoJHAKsDnweeCoiHs/1fp/baA+1I76WtjMzMzMz6zWc5HSua4AdJY0E+kfEjAp1Ko2cCLglIkbkx3oR8XUWM9GQ1AcYDjzajvhKNi5sZ2ZmZmbWazjJ6UQRsQCYApxP5VGSqcBBAJI2IN0cAOB+YCtJa+V1AyStAzwGNJTKga8Cd1YTi6RlgJOBZ/OoUTXxlbZtAE4DflPNvszMzMzMepKlax1AHZoMXMXH72QGcBZwgaTZwEzSNTJExCuSxgKTCzcW+HFEPJ7vcHa5pKWBB4Gz29j/xZLeAfoCtwJ7VhnfmpIeAvoBbwK/iYgL2uytmZmZmVkPo5avOzerTmNjYzQ1NdU6DDMzMzOrY5KmR0RVvzHp6WpmZmZmZlZXPF2tF5J0NbBGWfH/i4ibahGPmZmZmVlP4iSnF4qIvWsdQ9Gc5+bTMP76WodhVnfmTRhT6xDMzMx6JU9XMzMzMzOzuuIkZwklqVnSTElzJV0uaUAu/4ykSyU9IekRSTfk21mbmZmZmfUKTnKWXIvyD49uALwLfFOSgKuBKRGxZkSsB/wQ+HQtAzUzMzMzaw9fk2MAd5F+mHR74L2I+Pdv8UTEzJpFZWZmZmbWAR7JWcLlHxndDZgDbABMr3K7cZKaJDU1L5zflSGamZmZmbWLk5wlV39JM4Em4Bngd+3ZOCImRkRjRDT2GTCoSwI0MzMzM+sIT1dbci2KiBHFAkkPA/vWKB4zMzMzs07hkRwruh3oK+mIUoGkTSVtV8OYzMzMzMzaxUmO/VtEBLA3sHO+hfTDwAnA8zUNzMzMzMysHTxdbQkVEQNbKH8e2K+bwzEzMzMz6zROcmyxDV9lEE0TxtQ6DDMzMzMzwNPVzMzMzMyszngkxxbbnOfm0zD++lqHYdZrzfNIqJmZWafySI6ZmZmZmdUVJzlmZmZmZlZXnOSYmZmZmVldcZLTTpJC0h8Kz5eW9Iqk69rY7gRJx1UoX1nSFXl5VFvttLGPMyQ9J8mvq5mZmZktsfxhuP3eAjaQ1D8/3xl4rqONRcTzEbHv4gaVE5u9gWeBbRe3PTMzMzOz3spJTsf8BSjdDulAYHJphaRPSbpG0mxJ90vasLDdRpJul/Q3SUfk+g2S5pbvQNKyks6X9KCkhyTt2UZM2wNzgbNyTKV2TpD0hwr7HSVpqqSrJT0i6WxJS0nqI2mSpLmS5kg6ptLOJI2T1CSpqXnh/LaPmJmZmZlZN/EtpDvmUuAneWrZhsD5wDZ53YnAQxGxl6QdgAuBEXndhsDmwLLAQ5Jau+/yj4DbI+IwSSsA0yTdGhFvtVC/lGz9Cfi5pGUi4r029rsZsB7wNHAjsA/wFLBKRGwAkPf9MRExEZgI0Hfo2tFKP8zMzMzMupVHcjogImYDDaTE4oay1VsDf8j1bgdWlDQor/tTRCyKiFeBO0hJRktGA+MlzQSmAP2A1SpVlPQJ4IvANRHxL+CBvH1JS/udFhFPRkQzKUHaGngS+Jyk30jaFfhXqwfDzMzMzKyH8UhOx10LnAaMAlYslKtC3Sj7W15eiYAvRcRfq4hlV2AQMEcSwABgIVAasWlpvx8rj4g3JG0E7AJ8C9gPOKyKGMzMzMzMegSP5HTc+cBJETGnrHwqcBCk616AV/PoCsCekvpJWpGUHD3YSvs3Ad9RzlokbdxK3QOBwyOiISIagDWA0ZIGtLHfzSStkW9asD9wt6TBwFIRcSXw38DI1g6CmZmZmVlP45GcDoqIfwBnVFh1AnCBpNkG/y0SAAAgAElEQVSk0ZRDCuumkUZXVgN+GhHPS2poYRc/BU4HZudEZx6we3mlnMjsAnyjENtbku4G9mhlv+sA9wETgOGk5OzqvHxB4TbUP2jxIGTDVxlE04QxbVUzMzMzM+sWivA14/VM0gnAgog4rax8FHBcRHwscWqvxsbGaGpqWtxmzMzMzMxaJGl6RDRWU9fT1czMzMzMrK54JKcXkbQLcEpZ8VMRsXct4inpO3TtGHrI6bUMwawm5nmappmZWbdpz0iOr8npRSLiJtINCczMzMzMrAWermZmZmZmZnXFSU4vJWlB2fOxks6sVTxmZmZmZj2FkxwzMzMzM6srTnLqkKTVJd0maXb+u1ounyTpLEl3SHpS0naSzpf0qKRJhe1HS7pP0gxJl0saWLPOmJmZmZm1k5Oc3qu/pJmlB3BSYd2ZwIURsSFwMfC/hXWfBHYAjgH+DPwaWB8YLmmEpMHAj4GdImIk0AQcW75zSeMkNUlqal44vyv6Z2ZmZmbWIb67Wu+1KCJGlJ5IGguUbqm3BbBPXv4D8IvCdn+OiJA0B3gpIubk7R8GGoBVgfWAeyQBfAK4r3znETERmAjpFtKd1iszMzMzs8XkJGfJUExC3sl/Pygsl54vDTQDt0TEgd0Um5mZmZlZp/J0tfp0L3BAXj4IuLsd294PbCVpLQBJAySt08nxmZmZmZl1GSc59eko4FBJs4GvAkdXu2FEvAKMBSbn7e8HhnVFkGZmZmZmXUERvpzCFk9jY2M0NTXVOgwzMzMzq2OSpkdEY9s1PZJjZmZmZmZ1xkmOmZmZmZnVFd9dzRbbnOfm0zD++lqHYdat5k0YU+sQzMzMrAUeyTEzMzMzs7riJKeXkhSS/lB4vrSkVyRd18H2zpO0XudFaGZmZmZWG56u1nu9BWwgqX9ELAJ2Bp7raGMRcXinRWZmZmZmVkMeyend/gKULgw4EJhcWiHpBEnHFZ7PldQgaVlJ10ualcv2z+unSGrMy7tKmpHr3NaN/TEzMzMzW2xOcnq3S4EDJPUDNgQeqGKbXYHnI2KjiNgAuLG4UtIQ4FzgSxGxEfDlTo7ZzMzMzKxLOcnpxSJiNtBAGsW5ocrN5gA7STpF0jYRMb9s/ebA1Ih4Ku/j9UqNSBonqUlSU/PC8ibMzMzMzGrHSU7vdy1wGoWpatn7fPT17QcQEY8Dm5CSnZMl/aRsOwHR1k4jYmJENEZEY58Bgzoau5mZmZlZp3OS0/udD5wUEXPKyucBIwEkjQTWyMsrAwsj4iJScjSybLv7gO0klep/qutCNzMzMzPrfL67Wi8XEf8Azqiw6krga5JmAg8Cj+fy4cCpkj4A3gOOLGvvFUnjgKskLQW8TLpzm5mZmZlZr+Akp5eKiIEVyqYAU/LyImB0hU3nATdV2HZUYfkvpDu3mZmZmZn1Ok5ybLENX2UQTRPGtF3RzMzMzKwb+JocMzMzMzOrK05yzMzMzMysrni6mi22Oc/Np2H89bUOw6xTzfMUTDMzs17LIzlmZmZmZlZXnOT0QJL2lhSShtUwhlGSrqvV/s3MzMzMOspJTs90IHA3cEBX7kSSpyuamZmZWd1xktPDSBoIbAV8nZzk5FGVqZKulvSIpLPzD3UiaYGkX0qaIek2SUNy+RGSHpQ0S9KVkgbk8kmSfiXpDuAUSctKOj/XfUjSnrXpuZmZmZlZ53CS0/PsBdwYEY8Dr0samcs3A74HDAfWBPbJ5csCMyJiJHAncHwuvyoiNo2IjYBHSUlTyTrAThHxPeBHwO0RsSmwPXCqpGW7rntmZmZmZl3LSU7PcyBwaV6+ND8HmBYRT0ZEMzAZ2DqXfwBclpcvKpRvIOkuSXOAg4D1C/u4PLcDMBoYL2kmMAXoB6zWVpCSxklqktTUvHB+e/toZmZmZtZlfE1GDyJpRWAHUoISQB8ggBvy36Ly5+Xlk4C9ImKWpLHAqEKdt4q7Bb4UEX8ti+XTrcUaEROBiQB9h67dUixmZmZmZt3OIzk9y77AhRGxekQ0RMRngadIozObSVojX4uzP+nGBJBew33z8lcK5csBL0hahjSS05KbgO9IEoCkjTu1R2ZmZmZm3cxJTs9yIHB1WdmVpOTlPmACMJeU+JTqvQWsL2k6aRTopFz+38ADwC3AY63s86fAMsBsSXPzczMzMzOzXksRnmnU00kaBRwXEbtXWLcgIgZ2f1Qf6jt07Rh6yOm1DMGs082bMKbWIZiZmVmBpOkR0VhNXV+TY4tt+CqDaPIHQjMzMzPrIZzk9AIRMYV057NK62o6imNmZmZm1tP4mhwzMzMzM6srHsmxxTbnufk0jL++1mGYAb6WxszMzDySY2ZmZmZmdcZJjpmZmZmZ1ZVOSXIkLSh7PlbSmR1sa5Sk6wrLWxbWTZK0b8tbf6yttSVNkzRb0q1V7nu+pIckPSrp+I70oasUj00rdXaT1JTjf0zSad0Vn5mZmZlZT9DTR3JGAVu2VakV44GzImJD4Igqt7krIjYGGoGDJW1SzUaS+nQwxk4jaQPgTODgiFgX2AB4sh3b+xotMzMzM+v1ujzJkTRE0pWSHsyPrXL5ZpLuzaMm90r6fNl2DcA3gWMkzZS0TV61ba7/ZBWjOu8CqwJExFPtiTsi3gKmA2tK6iPp1Bz/bEnfyDGOknSHpEuAOZKWlXS9pFmS5kraP9fbMfdzjqTzJfXN5fMknShpRl43rJpj04rvA/8TEY/lPrwfEb/Nbe4h6YHc5q2SPp3LT5A0UdLNwIUt9bWcpHF5xKipeeH89hxaMzMzM7Mu1VlJTv+ciMyUNBM4qbDuDODXEbEp8CXgvFz+GLBtHjX5CfDzYoMRMQ84O287IiLuyquGAlsDuwMT2ojrCeBoSbu3t0OSVgQ2Bx4Gvg7Mz33YFDhC0hq56mbAjyJiPWBX4PmI2CgiNgBulNQPmATsHxHDSXe0O7Kwq1cjYiRwFnBcLmv12LRiA1JiVsndwOa5zUtJCVHJJsCeEfGVNvr6bxExMSIaI6Kxz4BBVYZnZmZmZtb1Omt60qKIGFF6ImksaboXwE7AepJKq5eXtBwwCPi9pLWBAJapcl/XRMQHwCOl0YhKJI0EvghsDNws6XXgPlLis2ZERAubbiPpIeADYEJEPCzpRGDDwsjRIGBt0kjRtMIo0RzgNEmnANdFxF2SNgKeiojHc53fA98CTs/Pr8p/pwP7FNrvyLFpzarAZZKGAp8AiiNb10bEorw8uoW+tmskzMzMzMysVrrjGoylgC0KH6IBkPQb4I6I2DtPTZtSZXvvFJtppd5OwNSIeFbS3sC1pJGhG1pJcCBdk1M+8iPgOxFx00cKpVHAW6XnEfF4vobni8DJeQrYtVX2p5kPX4+f0rFj8zBpVGZWhXW/AX4VEdfmuE8orHursFyxr2ZmZmZmvUV33HjgZuDbpSeSSiM+g4Dn8vLYFrZ9E1iug/t9CNhT0qB8jcqpwC+BizrQ1k3AkZKWAZC0jqRlyytJWhlYGBEXAacBI0lTzxokrZWrfRW4s439VXNsKjkV+KGkdXI8S0k6tkKbh7TSRlV9NTMzMzPrqbojyTkKaMwXsT9CupkAwC9Iox33AC3dmezPwN5lNx6oSkTcQkpo7pc0HdgFOBSYJGlIO/twHvAIMEPSXOAcKo+CDQem5euSfgT8LCLezvu9XNIc0jS4s9vYXzXH5mMiYjbwXWCypEeBuaRrmCCN3Fwu6S7g1VaaqbavZmZmZmY9klqfuWXWtsbGxmhqaqp1GGZmZmZWxyRNj4jGtmv2/N/JMTMzMzMza5dePw1J0nDgD2XF70TEF1rZZhfglLLipyJi786OrytIOhQ4uqz4noj4Vi3iMTMzMzPrSTxdzRZb36Frx9BDTm+7olknmzdhTK1DMDMzs27i6WpmZmZmZrbEcpJjZmZmZmZ1pdOSHEnN+VbPD0uaJelYSUvldY2S/reKNu7NfxskfaWK+g35NseV1i0r6TVJg8rKr5G0n6T/kDS+hW0XtLXv9mjt2HQFSVMkVTWU10Y7oyRd1xkxmZmZmZl1l8688cCiiBgBIGkl4BLSD1AeHxFNQJv3GI6ILfNiA/CV3EaHRMRbkm4G9gJ+n+MaBGwNfCUiFgLXdrT9dmrx2HTT/s3MzMzMlhhdMpoQES8D44BvK/n3iICkIZJukTRD0jmSnpY0OK8rjaBMALbJox/H5BGbu/I2MyRtWXnPHzMZOKDwfG/gxohYKGmspDPzfteQdJ+kByX9tNiApP/K5bMlnVgoP1bS3Pz47mIcm36SLpA0R9JDkrbP7d8gacO8/JCkn+Tln0o6PB/TKZKukPSYpIslqXx/kg7Mbc+VdEqh/CxJTXl0qdivXXN7dwP7tNQPSePy9k3NC+dX230zMzMzsy7XZVOmIuLJ3P5KZauOB26PiJHA1cBqFTYfD9wVESMi4tfAy8DOeZv9gTanvmU3AptIWjE/P4CU+JQ7AzgrIjYFXiwVShoNrA1sBozIbW0raRPgUOALwObAEZI2rjKm8mPzrVw2HDgQ+L2kfsBUUqK3PPA+sFXefGvgrry8MfBdYD3gc4U6pfhXJt0qe4cc/6aS9sqrf5TvTrEhsJ2kDfN+zwX2ALYBPtNKHyZGRGNENPYZMKilamZmZmZm3a6rbzzwsZEF0of0SwEi4kbgjSraWQY4V9Ic4HLSh/o2RcS7pClp++bRohHAzRWqbsWHyU/xN3dG58dDwAxgGCnp2Rq4OiLeiogFwFWkpKA9Ssdm69I+I+Ix4GlgHVIis21efz0wUNIAoCEi/pq3nRYR/4iID4CZpGl+RZsCUyLilYh4H7g4twmwn6QZuW/rk47pMNLvBf0t0r3FL2pnn8zMzMzMaq7LfgxU0ueAZtIozLrFVR1o7hjgJWAjUmL2dju2nQz8OO/3TxHxXgv1Kv1gkICTI+KcjxS2Y3paJWXHpqXj8SDQCDwJ3AIMBo4AphfqvFNYbubjr2fFtiWtARwHbBoRb0iaBPTLq/3DSWZmZmbWq3XJSI6kIcDZwJnx8V8bvRvYL9cbDXyyQhNvAssVng8CXsgjFl8F+rQjnDtIoy/fovJUNYB7+PDanYMK5TcBh0kamONdJd84YCqwl6QBkpYlXetzF1WocGymlvYpaR3S9L2/5lGoZ0nH6v7c/nHV7id7gDQVbbCkPqTpcHcCywNvAfMlfRrYLdd/DFhD0pr5+YHt2JeZmZmZWY/QmSM5/SXNJE0te580BetXFeqdCEyWtD/pA/cLpKSmaDbwvqRZwCTgt8CVkr5MSlreqjaoiPhA0pXAl0kJRSVHA5dIOhq4srDtzZLWBe7L1/QvAA6OiBl59GNarnpeRDzUShitHZvfAmfnqXjvA2MjojRCcxewY75Rwl3AqrQjyYmIFyT9gHTMBNwQEX+CdDMD4GHSSNE9uf7bksYB10t6lZSQblDt/szMzMzMegJ9fKCli3co9QWaI+J9SVuQLvgf0a1BWKdqbGyMpqY27xBuZmZmZtZhkqbnG2e1qcuuyWnFasAflX4M813SdSZmZmZmZmadotuTnIj4G+nWx51G0i6kWyUXPRURe3fmftqIYUXgtgqrdoyI17orDjMzMzOzJV23T1ez+tN36Nox9JDTax2GLUHmTRhT6xDMzMysm7VnulpX/06OmZmZmZlZt3KS08NJapY0U9IsSTMkbdkF+9hLUlU/sGpmZmZm1tM5yen5FkXEiIjYCPgBcHIX7GMvwEmOmZmZmdUFJzm9y/LAG6Unkv5L0oOSZks6sVB+jaTpkh7Ov3tTKl9QWN5X0qQ8MvQfwKl5xGhNSTMK9daWNL3Le2ZmZmZm1klqcQtpa5/SD4n2A4YCOwBIGg2sDWxG+qHPayVtGxFTgcMi4nVJ/YEHJV3Z0h3eIuJeSdcC10XEFbnt+ZJGRMRM4FDSD7J+RE6exgH0WX5I5/bYzMzMzGwxeCSn5ytNVxsG7ApcKEnA6Px4CJgBDCMlPQBHSZoF3A98tlBerfOAQyX1AfYHLimvEBETI6IxIhr7DBjUkX6ZmZmZmXUJj+T0IhFxn6TBwBDS6M3JEXFOsY6kUcBOwBYRsVDSFNIoEEDxfuH9aNmVwPHA7cB0/86PmZmZmfUmHsnpRSQNA/oArwE3AYdJGpjXrSJpJWAQ8EZOcIYBmxeaeEnSupKWAoo/lPomsFzpSUS8nds/C7igK/tkZmZmZtbZPJLT85WuyYE0enNIRDQDN0taF7gvzV5jAXAwcCPwTUmzgb+SpqyVjAeuA54F5gIDc/mlwLmSjgL2jYgngIuBfYCbu7JzZmZmZmadzUlODxcRfVpZdwZwRoVVu7VQ/wrgigrl9/DxW0hvDZyfEyozMzMzs17DSY59jKSrgTXJd3Jry/BVBtE0YUzXBmVmZmZmViUnOfYxEbF327XMzMzMzHomJzm22OY8N5+G8dfXOgyrY/M8UmhmZmbt4LurmZmZmZlZXXGSY2ZmZmZmdcXT1eqIpBWB2/LTzwDNwCv5+WYR8W5NAjMzMzMz60ZOcupIRLwGjACQdAKwICJOq2lQZmZmZmbdzNPVlhCSvi9pbn58J5edLOlbhTqnSPpPSatIulvSzFx/y9pFbmZmZmbWPh7JWQJI2gw4CNgM6ANMk3QncB5wKfB/kvoAXwY2AcYBf46IU3J5/wptjsv16LP8kG7ph5mZmZlZNTySs2TYBrgyIhZGxJvANcDWEfEE8Kak4cBuwLSIeAN4EDhc0vHABhGxoLzBiJgYEY0R0dhnwKBu7IqZmZmZWeuc5CwZ1Mq63wFjgUOB8wEi4nZgFPACcLGkg7o4PjMzMzOzTuMkZ8kwFdhbUn9JA4E9gbvyuiuBPUg3LLgVQNLqwIsRMRGYBGzc7RGbmZmZmXWQr8lZAkTENEmTSdPQAM6KiDl53duSppKSmg/y+h2BYyW9BywADu72oM3MzMzMOkgRUesYrIYkLQXMBPaKiCc70kZjY2M0NTV1bmBmZmZmZgWSpkdEYzV1PV1tCZZvOPAEcGNHExwzMzMzs57G09WWYHnK2hq1jsPMzMzMrDM5ybHFNue5+TSMv77WYVidmTdhTK1DMDMzs17K09XMzMzMzKyuOMkxMzMzM7O64iSnB5K0oLD8RUl/k7SapG9K+louHytp5TbaGSvpzK6O18zMzMysJ/E1OT2YpB2B3wCjI+IZ4OzC6rHAXOD5GoRmZmZmZtZjeSSnh5K0DXAuMCYinshlJ0g6TtK+QCNwsaSZkvpL2lTSvZJmSZomabnc1MqSbsyjQb8otD9a0n2SZki6XNLAXD5P0om5fI6kYd3cdTMzMzOzxeIkp2fqC/yJ9AOdj5WvjIgrgCbgoIgYATQDlwFHR8RGwE7Aolx9BLA/MBzYX9JnJQ0GfgzsFBEjc1vHFnbxai4/CziuUoCSxklqktTUvHD+4vfYzMzMzKyTOMnpmd4D7gW+XmX9zwMvRMSDABHxr4h4P6+7LSLmR8TbwCPA6sDmwHrAPZJmAofk8pKr8t/pQEOlHUbExIhojIjGPgMGVd8zMzMzM7Mu5mtyeqYPgP2AWyX9MCJ+3kZ9AdHCuncKy82k11zALRFxYBvblOqbmZmZmfUaHsnpoSJiIbA7cJCkSiM6bwKl624eI117symApOUktZac3A9sJWmtXH+ApHU6L3ozMzMzs9rxt/Q9WES8LmlXYKqkV8tWTwLOlrQI2IJ03c1vJPUnXY+zUyvtviJpLDBZUt9c/GPg8U7ugpmZmZlZt1NES7OczKrT2NgYTU1NtQ7DzMzMzOqYpOkR0VhNXU9XMzMzMzOzuuIkx8zMzMzM6oqvybHFNue5+TSMv77WYVgdmTdhTK1DMDMzs17MIzlmZmZmZlZXnOT0MpJ+JOlhSbMlzZT0hU5oc5SkLTsjPjMzMzOzWvN0tV5E0hak384ZGRHvSBoMfGIx21waGAUsAO5d7CDNzMzMzGrMSU7vMhR4NSLeAYiIVwEkzQMuA7bP9b4SEX+XtDpwPjAEeAU4NCKekTQJeB3YOP/dCmiWdDDwHeAzwPFAMzA/Irbtnu6ZmZmZmS0+T1frXW4GPivp8f/P3r3H2VXV9/9/vQ2QC2AQQu2ISBSDCCYMZAC531EbRVBowCiJUFNs1Z9Y1FT4IWitodiKQBGihQhFxFhAEOUit3AJl0lIMgERKgRptAJiAyFch/f3j7NGDsPM5EzmcmZO3s/HI4+zz9rr8tl7+ON8WGvtLekcSftUnXva9i7A2cAZpexs4ELbk4CLgTOr6m8DHGj7o8C5wLdtN9u+FTgZeJ/tHYBDBviaIiIiIiL6VZKcYcT2KmAyMJPKzMylkmaU05dUfe5WjncDfliOLwL2rOpunu32boa6HZgr6VPAiK4qSJopqVVSa/vqlWtzORERERERAyLL1YaZkpjcDNwsqQ2Y3nGqulp3zauOn+1hjOPKAw2mAIslNdv+Y6c6c4A5ACObJnQ3XkRERETEoMtMzjAi6V2SJlQVNQOPluOpVZ8LyvEdwJHleBpwWzddPwNsXDXO1rbvsn0y8CSwZT+EHxERERExKDKTM7xsBJwlaRPgZeC/qSxd+yAwUtJdVBLXo0r9zwHnS/oi5cED3fR7FfATSR+m8uCB40syJeAGYMkAXU9ERERERL9LkjOM2F4IvO59NpIA/t32qZ3qLwf276KfGZ2+PwhMqiq6te/RRkRERETUR5Kc6LOJW4yldfaUeocREREREQEkyWkItsfXO4aIiIiIiKEiDx6IiIiIiIiGkpmc6LO2FSsZP+vqeocRQ9DyLGOMiIiIOshMTkRERERENJQkOcOQpBMl3SdpqaTFknaVtFzSuHrHFhERERFRb1muNsxI2o3Ke3F2sv1CSWw2qHNYERERERFDRmZyhp8m4EnbLwDYftL278q5z0paJKlN0rYAkjaVdEWZ9blT0qRSvqGk8yXdI+ne8iJQJG0v6e4yQ7S0vBQ0IiIiImLYSJIz/FwHbCnpQUnnSNqn6tyTtncCvgucUMpOBe61PQn4CnBhKT8RuNH2zsB+wOmSNgSOA75juxloAf5n4C8pIiIiIqL/JMkZZmyvAiYDM4EngEslzSinLyufC4Hx5XhP4KLS9kZgM0ljgYOBWZIWAzcDo4C3AQuAr0j6MrCV7ee6ikPSTEmtklrbV6/s12uMiIiIiOiL7MkZhmy3U0lMbpbUBkwvp14on+28+rdVV12U8o/a/nWnc7+SdBcwBbhW0t+U5KhzDHOAOQAjmya4D5cTEREREdGvMpMzzEh6V6d9Ms3Aoz00mQ9MK233pbKk7WngWip7eFTO7Vg+3wE8bPtM4EpgUr9fRERERETEAMpMzvCzEXCWpE2Al4H/prJ07YPd1D8FuEDSUmA1r876fB04A1haEp3lpY+pwMclvQT8L/C1gbmMiIiIiIiBITsrjaJvRjZNcNP0M+odRgxBy2dPqXcIERER0SAkLbTdUkvdzOREn03cYiyt+TEbEREREUNE9uRERERERERDSZITERERERENJcvVos/aVqxk/Kyr6x1G1Cj7ZCIiIqLRZSYnIiIiIiIaSpKcfiLpMEmWtG0NdT8vacwAx9Ms6a+qvh8iadZAjhkRERERMRQkyek/RwG3AUfWUPfzwIAmOVReEvrnJMf2lbZnD/CYERERERF1lySnH0jaCNgDOJaS5EjaV9LPquqcLWmGpM8BbwFuknRTOXeUpDZJyySdVtVmlaTTJC2U9EtJu0i6WdLDkg4pdUZJuqC0v1fSfpI2oPISz6mSFkuaWsY+W9JYScslvaG0HyPpMUnrS9pa0jVlvFtrmZWKiIiIiBhqkuT0j0OBa2w/CDwlaafuKto+E/gdsJ/t/SS9BTgN2J/K7MvOkg4t1TcEbrY9GXgG+CfgIOAwKkkMwN+XfidSmU36AZW/68nApbabbV9aNf5KYAmwTyn6EHCt7ZeAOcBny3gnAOd0dx2SZkpqldTavnplTTcpIiIiImIwJMnpH0cBPyrHPyrfa7UzlUTmCdsvAxcDe5dzLwLXlOM24JaSjLQB40v5nsBFALYfAB4FtlnDmJcCU8vxkcClZTZqd2CepMXAeUBTdx3YnmO7xXbLiDFja73WiIiIiIgBl0dI95GkzajMwrxHkoERgIEreW0SOaq7Lnro/iXbLsevAC8A2H5FUsffrqf23bkS+KakTYHJwI1UZo3+z3bzWvQXERERETFkZCan7w4HLrS9le3xtrcEHinntpM0UtJY4ICqNs8AG5fju4B9JI2TNILKLNAtvRh/PjANQNI2wNuAX3ca4zVsrwLuBr4D/Mx2u+2ngUckHVH6kqQdehFHRERERMSQkCSn744CLu9U9l/Ax4AfA0upLEG7t+r8HOAXkm6y/XvgH4GbqOyVWWT7p70Y/xxghKQ2KsvQZth+ofS3XceDB7podynw8fLZYRpwrKQlwH3Ah3sRR0RERETEkKBXV0NFrJ2RTRPcNP2MeocRNVo+e0q9Q4iIiIjoNUkLbbfUUjd7cqLPJm4xltb8cI6IiIiIISLL1SIiIiIioqEkyYmIiIiIiIaS5WrRZ20rVjJ+1tX1DmOdlT02EREREa+VmZyIiIiIiGgoSXL6maT28tjmjn+zSvnNkmp6GkSn/pol/VU/x/hzSZv0Z58REREREUNFlqv1v+dsN/djf81AC/DzzickrWf75d52aLtfk6aIiIiIiKEkMzl1IOlgSQskLZI0T9JGpXxnSXdIWiLpbkljga8BUzte6inpFElzJF0HXChplKQLJLVJulfSfqWvGZIuk3SNpIck/UvV+MsljSvHR0taWsa8qJQdIWlZKZs/6DcoIiIiIqIPMpPT/0ZLWlz1/Zu2L+34UpKLk4ADbT8r6cvAFyTNBi4Fptq+R9IbgdXAyUCL7c+U9qcAk4E9bT8n6R8AbE+UtC1wnaRtynDNwI7AC8CvJZ1l+7GqWLYHTgT2sP2kpEZ85r0AACAASURBVE3LqZOB99le0d2yNkkzgZkAI964+dreq4iIiIiIfpckp/+tabnae4HtgNslAWwALADeBfze9j0Atp8GKHU6u9L2c+V4T+Cs0uYBSY8CHUnODbZXln7uB7YCHqvqZ3/gJ7afLO2fKuW3A3Ml/Ri4rKsAbM8B5gCMbJrgHq43IiIiImJQJckZfAKut33UawqlSUCtycKznfrrzgtVx+28/u+trsa0fZykXYEpwGJJzbb/WGNsERERERF1lT05g+9OYA9J7wSQNKYsL3sAeIuknUv5xpLWA54BNu6hv/nAtNJmG+BtwK9rjOUG4K8lbVbab1o+t7Z9l+2TgSeBLXt5jRERERERdZMkp/+N7vQI6dnVJ20/AcwALpG0lErSs63tF4GpwFmSlgDXA6OAm4DtOh480MV45wAjJLVR2dMzw/YLXdR7Hdv3Ad8Abilj/ls5dXp5kMEyKknUkl7dgYiIiIiIOpKd7RTRNyObJrhp+hn1DmOdtXz2lHqHEBERETHgJC20XdN7J7MnJ/ps4hZjac0P7YiIiIgYIrJcLSIiIiIiGkqSnIiIiIiIaChZrhZ91rZiJeNnXV3vMIa17KuJiIiI6D+ZyYmIiIiIiIaSJCciIiIiIhrKOpnkSHqrpJ9KekjSbyR9R9IG5dwlkpZKOl7StuX9NPdK2lrSHf0YwymSVpT+l0k6pI/9LZc0bi3ajZf0sb6MHRERERExlKxzSY4kAZcBV9ieAGwDbAR8Q9JfArvbnmT728ChwE9t72j7N7Z37+dwvm27GTgCOF/Sa/4ekgZjz9R4IElORERERDSMdS7JAfYHnrd9AYDtduB44BhgPvAXZXblq8Dngb+RdBOApFUdnUj6kqQ2SUskzS5lW0u6RtJCSbdK2raWgGz/CngZGCdprqR/K2OeJmlTSVeU2aU7JU0qY20m6boyy3QeoFI+XtKyqjhPkHRKOX6npF+WmBdJ2hqYDexVrvl4SdtLurt8XyppQlcxS5opqVVSa/vqlbXe+4iIiIiIAbcuPl1te2BhdYHtpyX9FpgO/LDMrnTM+qyy/a3q+pI+QGWWZ1fbqyVtWk7NAY6z/ZCkXYFzqCRVPSp1XwGeKEXbAAfabpd0FnCv7UMl7Q9cCDQDXwVus/01SVOAmTVc+8XAbNuXSxpFJcmdBZxg+4MllrOA79i+uCzhG9FVR7bnlOtlZNME1zB2RERERMSgWBeTHAFd/SjvrrwrBwIX2F4NYPspSRsBuwPzKrkRACPX0M/xkj4OPANMte3Sdl6ZYQLYE/hoGefGMoMzFtgb+Egpv1rSn3oaSNLGwBa2Ly9tni/lnasuAE6U9FbgMtsPreEaIiIiIiKGlHUxybmPkjR0kPRGYEugvcsWr9dVQvQG4P86ZoFq9O3Os0TFs53G6sydPqu9zGuXIY7qoZ/Xd2z/UNJdwBTgWkl/Y/vGWtpGRERERAwF6+KenBuAMZKOBpA0AvhXYC6wusY+rgOOkTSm9LGp7aeBRyQdUcokaYd+iHc+MK30uS/wZBmruvwDwJtK/T9Q2Ve0maSRwAehsiQP+B9Jh5Y2I0v8zwAbdwwm6R3Aw7bPBK4EJvXDNUREREREDJp1LsmxbeAw4AhJDwEPAs8DX+lFH9dQSQBaJS0GTiinpgHHSlpCZcbow/0Q8ilAi6SlVB4SML2UnwrsLWkRcDDw2xLbS8DXgLuAnwEPVPX1CeBzpa87gL8ElgIvl4cRHA9MBZaV69qWyh6giIiIiIhhQ5Xf/BFrr6Wlxa2trfUOIyIiIiIamKSFtltqqbvOzeRERERERERjWxcfPDCoJJ1I5WWf1ebZ/kY94hkIbStWMn7W1fUOY1hbPntKvUOIiIiIaBhJcgZYSWYaJqGJiIiIiBjqslwtIiIiIiIaSpKciIiIiIhoKElyaiSpXdJiScskzet4R04P9df4SOre9rmGvmZIOrsPbd+ytmNHRERERAwlSXJq95ztZtvvAV4EjltD/Vreu9Njn+WFooPxN5oBJMmJiIiIiIaQJGft3Aq8E0DSxyXdXWZkzpM0QtJsYHQpu7g3fUoaL+lXks4BFgFbSjpKUluZ8Tmto4GkT0p6UNItwB5V5XMlHV71fVXV8ZdKX0skzS71WoCLS7yjS/n9kpZK+lZXwUqaKalVUmv76pU137iIiIiIiIGWp6v1kqT1gA8A10h6NzAV2MP2SyUxmWZ7lqTP2G7ubZ+l6F3AJ23/XVlGdhowGfgTcJ2kQ4G7gFNL+UrgJuDeNYzzAeBQYFfbqyVtavspSZ8BTrDdKmlT4DBgW9uWtElXfdmeA8wBGNk0IW+UjYiIiIghI0lO7UZLWlyObwX+A5hJJcm4RxLAaODxPvb5FuBR23eW8p2Bm20/AVBmhvYu56rLLwW2WcN4BwIX2F4NYPupLuo8DTwPfF/S1cDPenE9ERERERF1lySnds91nplRJbP5ge1/7Mc+AZ6tLuqhfXczKC9TliKWGDeo6qvHWRfbL0vaBTgAOBL4DLB/T20iIiIiIoaS7MnpmxuAwyX9BYCkTSVtVc69JGn9fhjjLmAfSeMkjQCOAm4p5ftK2qyMc0RVm+VUZpgAPgx0xHEdcEzHU9zK0jSAZ4CNS9lGwFjbPwc+D9S05C4iIiIiYqjITE4f2L5f0klU9sm8AXgJ+HvgUSr7VZZKWmR7Wh/G+L2kf6Sy50bAz23/FEDSKcAC4PdUHlIwojT7HvBTSXdTScSeLX1dI6kZaJX0IvBzKk+BmwucK+k5KnuDfippVBnv+DXFOHGLsbTOnrK2lxgRERER0a9kZ8949E1LS4tbW1vrHUZERERENDBJC2231FI3y9UiIiIiIqKhZLnaAJO0GZUlY50dYPuPgx3PQGhbsZLxs66udxjD2vIs94uIiIjoN0lyBlhJZLJ5PyIiIiJikGS5WkRERERENJQhm+RIWtXp+wxJZ9chjpsl1bTBqdFI+nzH46YjIiIiIoaLIZvkNDJVDId7/3kgSU5EREREDCvD4Yf260jaStINkpaWz7eV8rmSvivpJkkPS9pH0vmSfiVpblX7gyUtkLRI0rzyAsxaxl0l6TRJCyX9UtIuZabnYUmHlDozJP1U0jWSfi3pq6V8fInjHCrvtNlS0lGS2iQtk3RaqfdpSf9SNeYMSWeV449LulvSYknnlZeD1hrXCEmnS7qn3Le/LeX7lro/kfSApItLEvY54C3ATZJu6uOfLCIiIiJi0AzlJGd0+TG/WNJi4GtV584GLrQ9CbgYOLPq3JuA/am8xPIq4NvA9sBESc2SxgEnAQfa3gloBb5QY0wbAjfbngw8A/wTcBBwWKf4dgGmUXngwBFVy93eVeLekcqLQ08rsTYDO0s6FPgJ8JGqvqYCl0p6dznew3Yz0F7GqDWuY4GVtncGdgY+Jent5dyOVGZttgPeUcY4E/gdsJ/t/TrfCEkzJbVKam1fvbLG2xcRERERMfCG8tPVnis/5oHKjAbQkSzsxquJwEXAv1S1u8q2JbUBf7DdVtrfB4wH3krlx/ztkgA2ABbUGNOLwDXluA14wfZLZazxVfWu73g8tKTLgD2BK4BHbd9Z6uxMJTF5otS7GNjb9hVlBua9wENUEqPbgb8HJgP3lLhHA4/3Iq6DgUmSDi/fxwITStu7bf9PiWNxaXNbTzfC9hxgDsDIpgl5o2xEREREDBlDOcnpjeof2S+Uz1eqjju+r0dlBuR620etxTgv2e4Y68/9235FUvW97Pyjv+P7s1Vl6mGcS4G/Bh4ALi9Jm4Af2P7HtYxLwGdtX1vdUNK+vPY+tdM4/11ERERExDpoKC9X68kdwJHleBprmHXo5E5gD0nvBJA0RtI2/RzfQZI2lTQaOJTKTExndwH7SBpX9tYcBdxSzl1W2h1FJeGBygtFD5f0FyXuTSVt1YuYrgU+LWn90n4bSRuuoc0zwMa9GCMiIiIiou6Ga5LzOeCTkpYCnwD+v1obluVhM4BLSvs7gW37Ob7bqCyjWwz8l+3WLuL4PfCPwE3AEmCR7Z+Wc38C7ge2sn13Kbufyl6i60rc1wNNvYjp+6XPRZKWAeex5hmbOcAv8uCBiIiIiBhO9Ooqp+gPHXuHbH+m3rEMlpaWFre2vi6Pi4iIiIjoN5IW2q7p/ZXDdSYnIiIiIiKiS9lgXki6HHh7p+Ivd96ovya25wJz+ymsiIiIiIjopSQ5he3D6h3DcNW2YiXjZ11d7zCGpeWzp9Q7hIiIiIiGk+VqERERERHRUJLkREREREREQxnWSY6kVfWOYW1JWi5pXA/n3yzph5IelrRQ0gJJfVpSJ+kUSSf0pY+IiIiIiKFuWCc5jUqSgCuA+bbfYXsylZefvrUXfYwYqPgiIiIiIoayhktyJH1I0l2S7pX0S0lvLuVtkjZRxR8lHV3KL5J0oKQZki6TdI2khyT9yxrG+a6kVkn3STq1qny5pFMlLSpjblvKN5N0XYnrPEA9dL8/8KLtczsKbD9q+6zS13hJt5YxFknavZTvK+kmST8E2krZiZJ+LemXwLuq4ty6XOvC0ldHnHMlnSnpjjKLdHgvbn9ERERERN01XJID3Aa81/aOwI+AL5Xy24E9gO2Bh4G9Svl7gTvLcTMwFZgITJW0ZQ/jnFheRjQJ2EfSpKpzT9reCfgu0LE87KvAbSWuK4G39dD39sCiHs4/DhxUxpgKnFl1bpcS23aSOmaAdgQ+AuxcVW8O8NkyS3QCcE7VuSZgT+CDwOyuApA0syR5re2rV/YQakRERETE4GrER0i/FbhUUhOwAfBIKb8V2Bt4lEryMVPSFsBTtldVVohxg+2VAJLuB7YCHutmnL+WNJPKPWwCtgOWlnOXlc+FVJILytgfAbB9taQ/1XpBkv6dStLxou2dgfWBsyU1A+3ANlXV77bdcc17AZfbXl36ubJ8bgTsDswr1w0wsqqPK2y/AtzfMRPWme05VBIlRjZNcK3XEhEREREx0BpxJucs4GzbE4G/BUaV8vlUfvTvBdwMPAEcTiX56fBC1XE73SSBkt5OZfbjANuTgKurxqnup3MftSYD9wE7/bmR/ffAAcDmpeh44A/ADkALlWSuw7Od+upqzDcA/2e7uerfu7uIH3peVhcRERERMeQ0YpIzFlhRjqd3FNp+DBgHTLD9MJVlbSfw2iSnVm+kkkysLDMdH6ihzXxgGoCkDwBv6qHujcAoSZ+uKhtTdTwW+H2ZbfkE0N1DBuYDh0kaLWlj4EMAtp8GHpF0RIlHknao4RoiIiIiIoa84Z7kjJH0P1X/vgCcQmUZ1q3Ak53q3wU8WI5vBbagkuz0iu0lwL1UZlzOp7LfZ01OBfaWtAg4GPhtD/0bOJTKXp9HJN0N/AD4cqlyDjBd0p1Ulqp1nr3p6GcRcCmwGPgvXpvQTQOOlbSkXMeHa7iGiIiIiIghT5Xf0xFrr6Wlxa2trfUOIyIiIiIamKSF5cFfazTcZ3IiIiIiIiJeoxGfrtavJN3Fa588BvAJ22390PdmwA1dnDrA9h/72n9ERERExLooy9Wiz0Y2TXDT9DPqHcaQt3z2lHqHEBERETFsZblaRERERESss5LkDDGS3izph5IelrRQ0gJJh9U7roiIiIiI4SJJzhAiScAVwHzb77A9GTgSeGunegOyl2qg+o2IiIiIGExJcoaW/YEXbZ/bUWD7UdtnSZohaZ6kq4Drygs8T5e0TFKbpKkdbSR9qZQtkTS7lG0t6ZoyO3SrpG1L+VxJ/ybpJuB0SQ9J2ryce4Ok/5Y0blDvQkREREREH+T/3A8t2wOLeji/GzDJ9lOSPgo0AzsA44B7JM0vZYcCu9peLWnT0nYOcJzthyTtSuWFovuXc9sAB9pul/R/VF4UegZwILDEdueXqiJpJjATYMQbN+/TRUdERERE9KfM5Axhkv69zMbcU4qut/1UOd4TuMR2u+0/ALcAO1NJTC6wvRqgJEQbAbsD8yQtBs4DmqqGmme7vRyfDxxdjo8BLugqNttzbLfYbhkxZmz/XHBERERERD/ITM7Qch/w0Y4vtv++LBVrLUXPVtVVN30I6Pxc8DcA/2e7uZs2f+7X9mOS/iBpf2BXKrM6ERERERHDRmZyhpYbgVGSPl1VNqabuvOBqZJGlD00ewN3A9cBx0gaAyBpU9tPA49IOqKUSdIOPcTxfeA/gR9XzfBERERERAwLSXKGEFfezHoosI+kRyTdDfwA+HIX1S8HlgJLqCRHX7L9v7avAa4EWsvStBNK/WnAsZKWUJkx+nAPoVwJbEQ3S9UiIiIiIoYyVX5XR7xKUgvwbdt71VJ/ZNMEN00/Y4CjGv6Wz55S7xAiIiIihi1JC2231FI3e3LiNSTNAj5NL/biTNxiLK35AR8RERERQ0SWq8Vr2J5teyvbt9U7loiIiIiItZEkJyIiIiIiGkqWq0Wfta1YyfhZV9c7jCEp+3AiIiIiBl9mciIiIiIioqEkyakTSW+V9FNJD0n6jaTvSNpgDW2+0sO5UySd0N35iIiIiIh1RZKcOpAk4DLgCtsTgG2ovJfmG2to2m2S04+xZQljRERERAxr+UFbH/sDz9u+AMB2u6TjgUckPQJsZ/szAJJ+BnwLeD8wurzg8z7b0ySdCBwNPAY8ASwsbZqBc4ExwG+AY2z/qYfym4E7gD2AKyX9Fvgq0A6stL33wN+SiIiIiIj+kZmc+tiekpB0sP008Fu6STxtzwKes91cEpzJwJHAjsBHgJ2rql8IfNn2JKCNSsLSUznAJrb3sf2vwMnA+2zvABzSVTySZkpqldTavnplb649IiIiImJAJcmpDwHuRXlX9gIut726JEhXAkgaSyVhuaXU+wGwd3flVf1dWnV8OzBX0qeAEV0NbnuO7RbbLSPGjK0x5IiIiIiIgZckpz7uA1qqCyS9EdgSWMlr/y6jeuin1oSoFs/+uVP7OOCkEs9iSZv14zgREREREQMqSU593ACMkXQ0gKQRwL8Cc4GHgWZJb5C0JbBLVbuXJK1fjucDh0kaLWlj4EMAtlcCf5K0V6n3CeCW7sq7Ck7S1rbvsn0y8CSVZCciIiIiYljIgwfqwLYlHQacI+n/p5Js/pzK09NeBB6hsmdmGbCoqukcYKmkRWVfzqXAYuBR4NaqetOBcyWNoZI0fXIN5Z2dLmkCleVzNwBL+nrNERERERGDRXZ/rniKddHIpglumn5GvcMYkpbPnlLvECIiIiIagqSFtlvWXDMzOdEPJm4xltb8mI+IiIiIISJ7ciIiIiIioqEkyYmIiIiIiIaS5WrRZ20rVjJ+1tX1DmNAZW9NRERExPCRmZyIiIiIiGgoSXIiIiIiIqKhDFiSI6ld0mJJ90laIukLkt5QzrVIOrOGPu4on+MlfayG+uMlLevh/L6SLOnYqrIdS9kJtV1Z1zH2ss1cSY+U+7NI0m5rM3YX/d4sqabH6kVERERENKqBnMl5znaz7e2Bg4C/Ar4KYLvV9ufW1IHt3cvheGCNSU6N2oCpVd+PpA8vu6yKsbe+aLsZmAWcV2sjSdlHFRERERHRg0FZrmb7cWAm8BlV7CvpZwCSNpd0fZnROE/So5LGlXOrShezgb3KzMfxZcbm1tJmkaTeJBq/BUZJerMkAe8HftFxsno2RNI4ScvL8faS7i4xLJU0oVOMSPqSpLYyczW7xnjmA+9cw9gzJM2TdBVw3RrGOqLE+aCkvUrdLu+XpCZJ88s1Lauqf7CkBaXuPEkbdQ5a0kxJrZJa21evrPFSIyIiIiIG3qDNCth+uCxX+4tOp74K3Gj7m5LeTyUZ6mwWcILtDwJIGgMcZPv5kmxcAvRmmdZPgCOAe4FFwAs1tDkO+I7tiyVtAIyoPinpA8ChwK62V0vatMZYPkRldmlNdgMm2X5qDWOtZ3sXSR0zZwcCj9P1/foYcK3tb0gaAYwpCeZJwIG2n5X0ZeALwNeqg7E9B5gDMLJpgmu81oiIiIiIATfYS5/URdmewGEAtq+R9Kca+lkfOFtSM9AObNPLOH4MXApsS+UHfy0zQQuAEyW9FbjM9kOdzh8IXGB7NYDtp9bQ3+mSTgKeAI5dQ12A66v67Gmsy8rnQirL/KD7+3UPcL6k9YErbC+WtA+wHXB7ZaKLDcq1R0REREQMC4P2dDVJ76DyA/vxzqfWorvjgT8AO1CZkdigN41t/y/wEpW9Qjd0Ov0yr96XUVVtfggcAjwHXCtp/07tBPRmRuOLZc/SQbY7HpbQ5djFszWO1TEr1c6rSWyX98v2fGBvYAVwkaSjS9/Xl9iabW9nu5YkLCIiIiJiSBiUJEfS5sC5wNm2O/84vw3461LvYOBNXXTxDLBx1fexwO9tvwJ8gk5Lx2p0MvBl2+2dypcDk8vx4VXX8A7gYdtnAlcCkzq1uw44piyloxfL1dY4dhd6O1aX90vSVsDjtr8H/AewE3AnsIekjn1CYyT1dqYsIiIiIqJuBjLJGV02tN8H/JLKD/NTu6h3KnCwpEXAB4DfU0lqqi0FXi6b7I8HzgGmS7qTytKrZ+kl23fYvqKLU98CPl0eDT2uqnwqsEzSYirL3C7s1N81VJKf1lJnbR5J3d3YnWPv7Vjd3a99gcWS7gU+SmXP0RPADOASSUupJD3brsW1RERERETUhV4/sTLIAUgjgXbbL5f3xXy3PFo5homWlha3trbWO4yIiIiIaGCSFtqu6WFjQ+GdK28DflyevPYi8Kk6xxMREREREcNY3ZOc8pSyHfuzT0nvA07rVPyI7cP6c5wa4vh3YI9Oxd+xfcFgxhERERERsS6p+3K1GP5GNk1w0/Qz6h3GgFk+e0q9Q4iIiIhY5/VmudqgPUI6IiIiIiJiMCTJGWIk/aWkH0n6jaT7Jf08j3COiIiIiKhdkpwhRJKAy4GbbW9tezvgK8CbB2Hsuu/PioiIiIjoD0lyhpb9gJdsn9tRYHsxcK+kGyQtktQm6cMAksZLekDS9yUtk3SxpAMl3S7pIUm7lHobSjpf0j2S7q1qP0PSPElXAddJ2qircSIiIiIihpP83/uh5T3Awi7KnwcOs/20pHHAnZKuLOfeCRwBzATuAT4G7AkcQmUW6FDgROBG28dI2gS4W9IvS/vdgEm2nyqzOa8bx108nULSzDImI964eb9cfEREREREf0iSMzwI+GdJewOvAFvw6hK2R2y3AUi6D7jBtiW1AeNLnYOBQySdUL6PovJ+IoDrbT+1hnH+t3NAtucAc6DydLX+utCIiIiIiL5KkjO03Acc3kX5NGBzYLLtlyQtp5KoALxQVe+Vqu+v8OrfV8BHbf+6ulNJuwLP1jhORERERMSwkD05Q8uNwEhJn+ookLQzsBXweEk89ivfe+Na4LPlwQZI6u7lq2P7OE5ERERERN0lyRlCyt6Xw4CDyiOk7wNOAX4OtEhqpTLb8kAvu/46sD6wVNKy8r0rF/dxnIiIiIiIulMXe8ojemVk0wQ3TT+j3mEMmOWzp9Q7hIiIiIh1nqSFtltqqZs9OdFnE7cYS2sSgYiIiIgYIrJcLSIiIiIiGkpmcqLP2lasZPysq+sdxoDIUrWIiIiI4SczORERERER0VCS5ERERERERENJkhMREREREQ0lSU43JN0s6X2dyj4v6XxJP+mhTU2PtasxhvUk/bOkhyQtLv9O7K/+IyIiIiIaUZKc7l0CHNmp7EjgAtuHD1IM/wS8BZhouxnYi8pLPSMiIiIiohtJcrr3E+CDkkYCSBpPJeH4H0nLStloST+StFTSpcDojsaSDpa0QNIiSfMkbVTKD5B0r6S2Mis0sqvBJY0BPgV81vbzALafsX1KVZ0rJC2UdJ+kmVXlqySdVs79UtIuZZbpYUmHlDojJJ0u6Z4S/9+W8iZJ88us0TJJe3UT30xJrZJa21evXMtbHBERERHR/5LkdMP2H4G7gfeXoiOBSwFXVfs0sNr2JOAbwGQASeOAk4ADbe8EtAJfkDQKmAtMtT2RyiO8P91NCO8Efmv7mR7CPMb2ZKAF+JykzUr5hsDN5dwzVGaEDgIOA75W6hwLrLS9M7Az8ClJbwc+BlxbZo52ABZ3c3/m2G6x3TJizNgeQoyIiIiIGFxJcnpWvWTtyPK92t7AfwLYXgosLeXvBbYDbpe0GJgObAW8C3jE9oOl3g9KH2sk6ZNlduUxSVuW4s9JWgLcCWwJTCjlLwLXlOM24BbbL5Xj8aX8YODoEt9dwGal/T3AJyWdQmWZXE9JVkRERETEkJMkp2dXAAdI2gkYbXtRF3XcRZmA6203l3/b2T62lNfqv4G3SdoYwPYFZXZlJTBC0r7AgcButncA7gVGlbYv2e6I6xXghdLHK7z6AlhRWQrXEePbbV9nez6VxGsFcJGko3sRc0RERERE3SXJ6YHtVcDNwPm8fhYHYD4wDUDSe4BJpfxOYA9J7yznxkjaBngAGN9RDnwCuKWbsVcD/wGcXZa5IWkEsEGpMhb4k+3VkralMnvUG9cCn5a0ful7G0kbStoKeNz298r4O/Wy34iIiIiIulpvzVXWeZcAl/H6J60BfBe4QNJSKntX7gaw/YSkGcAlVQ8WOMn2g5I+CcyTtB6VpWHn9jD2icDXgWWSngGeo7LE7XfA74Hjyti/ppJY9cb3qSxdWyRJwBPAocC+wBclvQSsAtY4kzNxi7G0zp7Sy+EjIiIiIgaGXl3VFLF2Wlpa3NraWu8wIiIiIqKBSVpou6Z3Uma5WkRERERENJQsVxsCJF0OvL1T8ZdtX1uPeHqrbcVKxs+6ut5h9LvlWYIXERERMSwlyRkCbB9W7xgiIiIiIhpFlqsNYZJWDVC/N0uqaT1jRERERMRwkyQn/qw8ojoiIiIiYlhLkjMMSPqipHskLZV0ain7kqTPleNvS7qxHB8g6T/L8cGSFkhaJGmepI266HuVpK9Jugs4qewP6jh3kKTLBuUiIyIiIiL6SZKcIU7SwcAEYBegGZgstRZ4YwAAIABJREFUaW8qLyLdq1RrATYqL/bcE7hV0jjgJOBA2zsBrcAXuhhiQ2CZ7V2BrwHvlrR5OfdJ4IKBubKIiIiIiIGRJGfoO7j8uxdYBGxLJelZSCXh2Rh4AVhAJdnZC7gVeC+wHXC7pMXAdGCrLvpvB/4LwJWXJl0EfFzSJsBuwC+6CkrSTEmtklrbV6/sp0uNiIiIiOi7PF1t6BPwTdvnve6EtJzKbMsdwFJgP2Br4Ffl83rbR62h/+dtt1d9vwC4CngemGf75a4a2Z4DzAEY2TQhb5SNiIiIiCEjMzlD37XAMR37aSRtIekvyrn5wAnl81bgOGBxmZG5E9hD0jtLuzGStlnTYLZ/B/yOylK3uf18LRERERERAy5JzhAlaT3gBdvXAT8EFkhqA34CbFyq3Qo0AQts/4HK7MutALafAGYAl0haSiXp2bbG4S8GHrN9fz9dTkRERETEoMlytaFre+A3ALa/A3yncwXbNwDrV33fptP5G4Gdu2i3b9Xx6564RuXhBd9by7gjIiIiIuoqSc4QJOk44HPA5+sw9kLgWeAfam0zcYuxtM6eMnBBRURERET0QpKcIcj2ucC5dRp7cj3GjYiIiIjoL9mTExERERERDSUzOdFnbStWMn7W1fUOo98tzxK8iIiIiGEpMzkREREREdFQkuQ0CEntkhZLWiJpkaTd11B/vKRlgxVfRERERMRgyXK1xvGc7WYASe8DvgnsU9+QIiIiIiIGX2ZyGtMbgT8BSNpI0g1ldqdN0oer6q0n6QeSlkr6iaQxkg6QdHlHBUkHSbpssC8gIiIiImJtJclpHKPLcrUHgO8DXy/lzwOH2d4J2A/4V0kq594FzLE9CXga+DvgRuDdkjYvdT4JXDBYFxERERER0VdJchrHc7abbW8LvB+4sCQzAv5Z0lLgl8AWwJtLm8ds316O/xPY07aBi4CPS9oE2A34RefBJM2U1CqptX31yoG9soiIiIiIXsienAZke4GkccDmwF+Vz8m2X5K0HBjVUbVz0/J5AXAVlVmgebZf7mKMOcAcgJFNEzr3ExERERFRN5nJaUCStgVGAH8ExgKPlwRnP2Crqqpvk7RbOT4KuA3A9u+A3wEnAXMHK+6IiIiIiP6QmZzGMVrS4nIsYLrtdkkXA1dJagUWAw9UtfkVMF3SecBDwHerzl0MbG77/kGIPSIiIiKi3yTJaRC2R3RT/iSVfTVd2a6HLvcEvtfXuCIiIiIiBluSnHgdSQuBZ4F/qKX+xC3G0jp7ysAGFRERERFRoyQ58Tq2J9c7hoiIiIiItZUHD0REREREREPJTE70WduKlYyfdXW9w+h3y7MELyIiImJYykxOREREREQ0lMzkDHOSNgNuKF//EmgHnijfd7H9Yo39rAc8aXuT/o8yIiIiImLwJMkZ5mz/EWgGkHQKsMr2t+oaVEREREREHWW5WgOT9CVJy8q/z66pvOr8FpJuk7S41Nl9cCOPiIiIiFh7mclpUJJ2AaYBuwAjgLsl3QKM6qb8/qrmHweusn2apBHA6C76nwnMBBjxxs0H8lIiIiIiInolSU7j2gv4L9urASRdAexJJWHpqrw6ybkHOE/SKOAK20s6d257DjAHYGTTBA/khURERERE9EaWqzUu9bL8z2zfCOwL/B64WNK0fowrIiIiImJAJclpXPOBwySNlrQR8GHg1h7K/0zSVsD/ltmaucCOgxp5REREREQfZLlag7J9t6RLqCw9A/iu7TaArsrLI6Q7HAB8QdJLwCoqe3QiIiIiIoYF2dlOEX0zsmmCm6afUe8w+t3y2VPqHUJEREREFJIW2m6ppW5mcqLPJm4xltYkBBERERExRGRPTkRERERENJQkORERERER0VCyXC36rG3FSsbPunrQx82emYiIiIjoSmZyIiIiIiKioSTJqSNJ35b0+arv10r6ftX3f5X0hW7abiLp72oY42ZJNT2FIiIiIiKiESTJqa87gN0BJL0BGAdsX3V+d+D2btpuAqwxyYmIiIiIWNckyamv2ylJDpXkZhnwjKQ3SRoJvBv4laQbJC2S1Cbpw6X+bGBrSYslnQ4g6UulzhJJs6vGOULS3ZIelLRXqTtC0umS7pG0VNLflvImSfNLv8s66kdEREREDBd58EAd2f6dpJclvY1KsrMA2ALYDVgJLAVWA4fZflrSOOBOSVcCs4D32G4GkPQB4FBgV9urJW1aNdR6tneR9FfAV4EDgWOBlbZ3LgnV7ZKuAz4CXGv7G5JGAGO6il3STGAmwIg3bt6v9yUiIiIioi+S5NRfx2zO7sC/UUlydqeS5NwBCPhnSXsDr5Tzb+6inwOBC2yvBrD9VNW5y8rnQmB8OT4YmCTp8PJ9LDABuAc4X9L6wBW2F3cVtO05wByAkU0T3LtLjoiIiIgYOEly6q9jX85EKsvVHgP+AXgaOB+YBmwOTLb9kqTlwKgu+hHQXbLxQvls59W/uYDP2r72dR1VEqopwEWSTrd94VpcV0REREREXWRPTv3dDnwQeMp2e5mB2YTKkrUFVGZYHi8Jzn7AVqXdM8DGVf1cBxwjaQxAp+VqXbkW+HSZsUHSNpI2lLRVGe97wH8AO/XLVUZEREREDJLM5NRfG5Wnqv2wU9lGtp+UdDFwlaRWYDHwAIDtP0q6XdIy4Be2vyipGWiV9CLwc+ArPYz7fSpL1xZJEvAElT09+wJflPQSsAo4uv8uNSIiIiJi4MnOdorom5FNE9w0/YxBH3f57CmDPmZERERE1IekhbZrev9jZnKizyZuMZbWJBwRERERMURkT05ERERERDSUJDkREREREdFQslwt+qxtxUrGz7p60MbLXpyIiIiI6ElmciIiIiIioqEkyYmIiIiIiIYy5JIcSZZ0UdX39SQ9Ielna9nf9yVttxbt9u1pTEkzJJ29ljH19P6aQVeudfd6xxERERER0R+GXJIDPAu8R9Lo8v0gYMXadmb7b2zf3y+R9Z8ukxxV1ONvsi+QJCciIiIiGsJQTHIAfgF07C4/Crik44SkUySdUPV9maTxkjaUdLWkJaVsajl/s6SWcvx+SYtKnRtK2S6S7pB0b/l8V2+DlTRX0pml/cOSDi/lTZLmS1pcYtpL0mxgdCm7uMT+K0nnAIuALSWtqur7cElzq8b5rqSbyjj7SDq/tJ9b1eZgSQvKtc6TtFEpXy7p1FLeJmlbSeOB44DjS0x7STqixLtE0vxurnmmpFZJre2rV/b2lkVEREREDJihmuT8CDhS0ihgEnBXDW3eD/zO9g623wNcU31S0ubA94CP2t4BOKKcegDY2/aOwMnAP69lzE3AnsAHgdml7GPAtbabgR2AxbZnAc/ZbrY9rdR7F3Ch7R1tP7qGcd4E7A8cD1wFfBvYHpgoqVnSOOAk4EDbOwGtwBeq2j9Zyr8LnGB7OXAu8O0S063lPryv3KdDugrC9hzbLbZbRowZW9sdioiIiIgYBEPyEdK2l5YZhqOAn9fYrA34lqTTgJ+VH+vV3gvMt/1IGeOpUj4W+IGkCYCB9dcy7CtsvwLcL+nNpewe4HxJ65fzi7tp+6jtO2sc5yrbltQG/MF2G4Ck+4DxwFuB7YDbJQFsACyoan9Z+VwIfKSbMW4H5kr6cVX9iIiIiIhhYajO5ABcCXyLqqVqxcu8Nu5RALYfBCZTSXa+KenkTu1EJYnp7OvATWX250Md/a2FFzqNhe35wN5U9hRdJOnobto+2+l7dZyd4+kY55VOY75CJWkVcH2ZlWm2vZ3tY7to3043Sa7t46jMBm0JLJa0WTdxR0REREQMOUM5yTmf/9fencfZVdXp/v88BkwIoSOjN6JQiGEUCFCNMgfFOGADEVpBtEFUWhxQuHCbe/WHDN0SlW6RphUCTYOIoIwGUAkySIAAqQwkYRCVxJahBRwCIUwJz++PvQ4cDjWcqlRSVaee9+tVr7PPmvbaKycn9c1ae204tTZTUWcxsBOApJ2AzcrxW4Bltn9IFRzt1FBvJrC3pFr59Ur6WF7d2OCI/rwASZsCT9g+D/jPuj69VGZ3uvJHSVuXTQgm9/K0dwG7S3pH6cNoSVv0UOcZYJ26fm9u+27bJwFPUQU7ERERERFDwqANcmw/Yvu7nWRdCawnaR5wNPBQSd8OuKekfxX454b2ngSOAq6SdC/w45L1LaqZnzuAEf18GROpZkLmAgcBteuZCsyXdEkX9U4ErgNuBh7vzQnLdR4BXCppPlXQs1UP1a4FJtc2HgC+XTYmWAjcBtzbmz5ERERERAwk2Z2t4IpoXnt7uzs6Oga6GxERERHRwiTNtt3eTNlBO5MTERERERHRF4Nyd7XBRNKngC83JN9h+wsD0Z+IiIiIiOhelqvFShs5brzHHX7mKmt/8ZT9ei4UERERES0ty9UiIiIiImLYSpAzACStKDuZ1X7a+qnd/SWdWI4PlLRNf7QbERERETGU5J6cgfGc7Ql9qShphO0VneXZnkb1EFWAA6m2ob6/F22vYXt5X/oVERERETFYZCZnkJB0hKSz695fJ2liOV4q6VRJdwO7Slos6RRJc8rzbLaqb0PSbsD+VM+7mSdpc0m3Smov5TaQtLiuzuWSrgWml7QTJM2SNF/SKatzHCIiIiIiVlaCnIGxVt1StaubKL82sND2u2zfXtKesr0T8H3g+PrCtu+kmtE5wfYE27/rof1dgcNtv0fSJGA8sAswAdhZ0l6NFSQdJalDUseKZUuauISIiIiIiNUjy9UGRm+Xq60ArmxIu6q8zgY+spL9udH2n8vxpPIzt7wfQxX03FZfwfZUYCpUu6ut5PkjIiIiIvpNgpzBYzmvnVkbVXf8fCf34bxQXlfQ3J9jffujGvKerTsWcLrtc5toMyIiIiJi0MlytcFjMTBB0hskvY1qudjKeAZYp6H9ncvxwd3UuwE4UtIYAEkbS9poJfsSEREREbHaJMgZPO4AFgELgDOAOSvZ3mXACZLmStq8tHm0pDuBDbqqZHs68CNgpqQFwBW8NliKiIiIiBjUZOd2ilg5I8eN97jDz1xl7S+est8qazsiIiIihgZJs223N1M29+TESttu47F0JBCJiIiIiEEiy9UiIiIiIqKlZCYnVtqCR5fQduL1q6TtLFWLiIiIiN7KTE5ERERERLSUBDkREREREdFSEuRERERERERLGRRBjqSlDe+PkHT2APTjVkm/ljSv/FzRQ/k2SR/vw3neJOnzTZa9s7ft95fy5/CWgTp/RERERERfDIogZ5A5zPaE8nNwD2XbgF4FOZJGAG8CmgpybO/Wm/b72RFAgpyIiIiIGFIGfZAjaVNJN0maX143KekXSvq+pFskPSxpb0kXSHpA0oV19SdJmilpjqTLJY3pQx8ulHSWpDvLuWrBzxRgzzLrc6ykEZK+LWlW6e8/lvoTSz9/BCwo9TYv9b4taUy5tjmSFkg6oO7cS+vauFXSFZIelHSJJJW8xZK+Ua6zQ9JOkm6Q9DtJn6tr64S6vp1S0trKmJ0n6T5J0yWtVa6xHbik9HOthjE5qpyrY8WyJb0d0oiIiIiIVWawbCG9lqR5de/XA6aV47OBH9i+SNKRwFnAgSVvXeA9wP7AtcDuwGeAWZImAI8AXwP2tf2spH8CjgNO7aYvl0h6rhzfaPuEcjwO2APYqvTtCuBE4HjbH4bqF39gie2/lTQSuEPS9FJ/F+CdthdJaivHE0q9NYDJtp+WtAFwl6Rptt3Qtx2BbYHHgDvK9d5e8v5ge1dJ3wEuLHmjgPuAcyRNAsaXfgiYJmkv4L9L+qG2PyvpJ8BBtn8o6Yvl+joaB8n2VGAqwMhx4xv7GRERERExYAZLkPNc7Rd+qO4FoZpFANgV+Eg5vhj4Vl29a21b0gLgj7YXlPr3US0leyuwDVWwAfBGYGYPfTmss1/qgWtsvwzcL+nNXdSdBGxfN9MzliqAeBG4x/aiLuoJ+EYJOl4GNgbeDPxPQ7l7bD9SrnFeucZakFMLChcAY2w/Azwj6XlJbyp9mwTMLeXGlL79N7DIdi3InF3ajYiIiIgYkgZLkNMb9bMGL5TXl+uOa+/XAFZQzcYc2g/nrW9fXZQR8CXbN7wmUZoIPNtN24cBGwI7235J0mKqWZju+rCC1/759TQWAk63fW5D39o6afc1S9MiIiIiIoaSQX9PDnAncEg5PoxXZy6acRewu6R3AEgaLWmLfuzbM8A6de9vAI6WtGY53xaS1m6i3ljgiRLg7ANs2o99rO/bkbV7kiRtLGmjHuo09jMiIiIiYtAbCjM5xwAXSDoBeBL4VLMVbT9Zlr5dWu6RgeoenYe6qVZ/T85Ttvftpux8YLmke6nug/ku1VKvOWVTgCd59f6h+n79SdIdkhYCPwe+CVwrqQOYBzzY5CU2zfZ0SVsDM8vSvaXAJ6hmbrpyIdX9PM8Bu9p+rrNC2208lo4p+/VzjyMiIiIi+kavv7c9onfa29vd0dHZbUwREREREf1D0mzb7T2XHBrL1SIiIiIiIpo2FJar9TtJVwObNST/U+OGAdGcBY8uoe3E63tVZ3GWt0VERETEKjIsgxzbkwe6DxERERERsWpkudoQJelWSe9vSPuKpAskXdFNnabWMUZEREREDFUJcoauS3l1a+2aQ4D/sn1wJ+UjIiIiIoaFBDlD1xXAh2tbY5eHer4FeKRsTY2ktSRdJmm+pB9T95BPSZMkzZQ0R9Lldc/Pea+kuZIWlFmhkY0njoiIiIgYzBLkDFG2/wTcA3ygJB0C/Bio3xP8aGCZ7e2BfwF2BpC0AdXzgva1vRPQARwnaRTVs3E+Zns7qnu2jl71VxMRERER0X8S5Axt9UvWDinv6+0F/BDA9nyqh5cCvBvYBrhD0jzgcGBTYEtgke3aw1IvKm28jqSjJHVI6lixbEk/XU5ERERExMpLkDO0XQO8V9JOwFq253RSprOnvQq40faE8rON7U+X9KbYnmq73Xb7iNFj+9b7iIiIiIhVIEHOEGZ7KXArcAGvn8UBuA04DEDSO4HtS/pdwO6S3lHyRkvaAngQaKulA58EfrXKLiAiIiIiYhVIkDP0XQrsAFzWSd73gTGS5gP/h+oeHmw/CRwBXFry7gK2sv088CngckkLgJeBc1b5FURERERE9KNh+TDQVmL7auqWmdleDLyzHD/H67eZrpW7GfjbTtJvAnZcFX2NiIiIiFgdEuTESttu47F0TNlvoLsREREREQFkuVpERERERLSYBDkREREREdFSslwtVtqCR5fQduL1TZVdnGVtEREREbGKZSYnIiIiIiJaSoKcASZp6UD3ISIiIiKilSTIiYiIiIiIlpIgZxCSdKGkg+veLy2vb5D0PUn3SbpO0s9q5SR9SNKDkm6XdJak60r6LpLulDS3vG5Z0kdL+omk+ZJ+LOluSe0lb5KkmZLmSLpc0pjVPwoREREREX2TIGdo+QjQBmwHfAbYFUDSKOBc4IO29wA2rKvzILCX7R2Bk4BvlPTPA3+xvT1wGrBzaWsD4GvAvrZ3AjqA41btZUVERERE9J/srja07AFcbvtl4H8k3VLStwIetr2ovL8UOKocjwUukjQeMLBmXVvfBbC9UNL8kv5uYBvgDkkAbwRmNnZE0lG1c4z4mw0bsyMiIiIiBkyCnMFpOWWWTVWk8caSri7Kd5UO1SzNLbYnS2oDbm2irRttH9pdB21PBaYCjBw33t2VjYiIiIhYnbJcbXBaTFk+BhzAq7MvtwMHlXtz3gxMLOkPAm8vQQzAx+raGgs8Wo6PqEu/HfgogKRtqJbAAdwF7C7pHSVvtKQtVvaCIiIiIiJWlwQ5A2+0pEfqfo4DzgP2lnQP8C7g2VL2SuARYCHVPTh3A0tsP0d1j80vJN0O/BFYUup8Czhd0h3AiLrzfg/YsCxT+ydgfmnrSapg6NKSdxfVcriIiIiIiCFBdlYaDSWSxtheKml94B5gd9v/U5cu4D+A39j+TjftjADWtP28pM2Bm4AtbL/Y2z6NHDfe4w4/s6myi6fs19vmIyIiIiKQNNt2ezNlc0/O0HOdpDdR3adzmu3/KemflXR4SZ9LNdPTndHALZLWpLoP5+i+BDgA2208lo4ELxERERExSCTIGWJsT+wi/TtAlzM3nZR/BmgqEo6IiIiIGEpyT05ERERERLSUzOTESlvw6BLaTry+x3K5HyciIiIiVofM5EREREREREtJkDOAJH1V0n2S5kuaJ+ld3ZTds5SdJ2lrSR9fnX2NiIiIiBgqEuQMEEm7Ah8GdrK9PbAv8IduqhwGnGF7AvBmoF+DHFXyeYiIiIiIIS+/1A6cccBTtl8AsP2U7cckvVfSXEkLJF0gaaSkzwAfBU6SdAkwBdizzOocK+lnkrYHKHVPKsenSfqMpDGSbpI0p7R7QMlvk/SApO8Bc4C3SZokaWYpe7mkMQMwNhERERERfZYgZ+BMpwoqHpL0PUl7SxoFXAh8zPZ2VBtDHG37fGAacILtw4ATgRm2J5Sto2+jCnr+BlgO7F7OsQcwA3gemGx7J2Af4F/LQ0MBtgR+YHtH4Fnga8C+pWwHcFxnnZd0lKQOSR0rli3p14GJiIiIiFgZCXIGiO2lwM7AUcCTwI+BfwQW2X6oFLsI2KuJ5maUcnsA1wNjJI0G2mz/muphn9+QNB/4JbAx1ZI3gN/bvqscvxvYBrhD0jzgcGDTLvo/1Xa77fYRo8f24sojIiIiIlatbCE9gGyvAG4FbpW0gCqo6ItZVA/2fBi4EdgA+Cwwu+QfBmwI7Gz7JUmLgVEl79m6dgTcaPvQPvYjIiIiImLAZSZngEjaUtL4uqQJwB+BNknvKGmfBH7VSfVngHVqb2y/SLVpwUeBu6hmdo4vrwBjgSdKgLMPXczOlLq7184vabSkLfpyfRERERERAyVBzsAZA1wk6f6yjGwbqnttPgVcXmZ2XgbO6aTufGC5pHslHVvSZgB/tL2sHL+VV4OcS4B2SR1UszoPdtYh208CRwCXlj7dBWy10lcaEREREbEayfZA9yGGuJHjxnvc4Wf2WG7xlP1WQ28iIiIiohVJmm27vZmyuScnVtp2G4+lIwFMRERERAwSWa4WEREREREtJUFORERERES0lCxXi5W24NEltJ14/Svvc+9NRERERAykzORERERERERLSZATEREREREtJUHOAJD0Zkk/kvSwpNmSZkqaLKld0lmlzEhJv5Q0T9LHJH1F0uge2h0j6VxJv5N0n6TbJL1rJfo5UdJ1fa0fERERETEQck/OaiZJwDXARbY/XtI2Bfa3fTXQUYruCKxpe0Ipsxj4IbCsm+bPBxYB422/LOntwNa96NsI2yt6eUkREREREYNKZnJWv/cAL9o+p5Zg+/e2/702cyJpI6qAZkKZyfky8BbgFkm3dNaopM2BdwFfs/1yafdh29eX/GvKrNF9ko6qq7dU0qmS7gZ2lfQBSQ9Kuh34SFcXIekoSR2SOlYsW7LSgxIRERER0V8S5Kx+2wJzuitg+wngM8AM2xNsfxd4DNjH9j7dtDuvm5mYI23vDLQDx0hav6SvDSy0/S6qWaTzgL8D9gT+Vzd9nGq73Xb7iNFju7uciIiIiIjVKkHOAJP0H5LulTRrFZ/qGEn3AncBbwPGl/QVwJXleCtgke3f2DbVbFJERERExJCSIGf1uw/YqfbG9heA9wIb9kO7O0h63Z+ppInAvsCutncA5gKjSvbzDbM/Xsl+REREREQMqAQ5q9/NwChJR9eldbtrWvEMsE5XmbZ/R7Xc7JSyuQGSxks6ABgL/MX2MklbAe/uopkHgc3K/T0AhzbRr4iIiIiIQSVBzmpWloEdCOwtaZGke4CLgH/qoepU4OddbTxQfIbqPprfSlpAdX/NY8AvgDUkzQdOo1qy1lnfngeOAq4vGw/8vvkri4iIiIgYHFT9zh3Rd+3t7e7o6Oi5YEREREREH0mabbu9mbKZyYmIiIiIiJaSh4EOQeWZNiMbkj9pe8FA9CciIiIiYjBJkDMElWfaDBoLHl1C24nXv/J+8ZT9BrA3ERERETHcZblaRERERES0lAQ5ERERERHRUgZFkCNphaR5ku6TdK+k42oPtZTULumsJtq4s7y2Sfp4E+XbJC3sIm9tSX+SNLYh/RpJH5W0v6QTu6i7tKdz94akN0v6kaSHJc2WNFPS5P48R0REREREKxkUQQ7wnO0JtrcF3gd8CPg6gO0O28f01IDt3cphG9BjkNNDW88C06meZwNACXj2AK6zPc32lJU5RzPKQz2vAW6z/XbbOwOHAG9d1eeOiIiIiBiqBkuQ8wrbT1A9kPKLqkyUdB2ApA0l3ShpjqRzJf1e0gYlrzaDMgXYs8wMHVtmbGaUOnMk7db5mV/nUqqAomYy8AvbyyQdIensct7NyuzKLEmn1Tcg6YSSPl/SKXXpx0laWH6+0k0f3gO8aPucuvH5ve1/L+10em1lzH4l6SeSHpI0RdJhku6RtEDS5nXjeWXp4yxJu5f0vcv4zZM0V9I6jR2TdJSkDkkdK5YtaXJIIyIiIiJWvUEX5ADYfpiqbxs1ZH0duNn2TsDVwCadVD8RmFFmhr4DPAG8r9T5GNDj0rfiF8DOktYv7w+hCnwafRf4vu2/Bf6nlihpEjAe2AWYUNraS9LOwKeAdwHvBj4raccu+rAtMKebPnZ3bTsAXwa2Az4JbGF7F+B84Et1ff9O6ftBJQ/geOALticAewLPNZ7Y9lTb7bbbR4we25gdERERETFgBvMW0uokbQ+qGRVs/0LSX5poZ03gbEkTgBXAFs2c3PaLkqYBB0u6kipQmd5J0d2pAgSAi4FvluNJ5WdueT+GKugZA1xdlsQh6SqqQKJWrkuS/oNqDF4sgUl31zbL9uOl3u/q+r4A2Kcc7wtsU62KA+BvyqzNHcC/SboEuMr2Iz31LSIiIiJisBiUQY6kt1P90v4EsHV9Vh+aOxb4I9XMxhuA53tR91KrIrHzAAAfhUlEQVTga+W8P7X9Uhfl3EmagNNtn/uaxO6XpzW6j1cDKGx/oSzP6yhJ3V3bC3XHL9e9f5lX/9zfAOxqu3GmZoqk66nujbpL0r62H+xFvyMiIiIiBsygW64maUPgHOBs243Bw+3AR0u5ScC6nTTxDFB/D8lY4HHbL1Mt2xrRi+7cQjX78gU6X6oG1axH7d6dw+rSbwCOlDSm9HdjSRsBtwEHShotaW2qmakZXbR9MzBK0tF1aaPrjlfm2qCa3fli7U2ZEULS5rYX2P4mVUC1VS/bjYiIiIgYMIMlyFmrtoU08EuqX75P6aTcKcAkSXOADwKPUwU19eYDy8tW1McC3wMOl3QX1XKuZ5vtVAkergTWpwpOOvNl4AuSZlEFHbW604EfATMlLQCuANaxPQe4ELgHuBs433anS9VKkHcgsLekRZLuAS4C/qkU6fO1FccA7WVjhPuBz5X0r5RNEe6luh/n571sNyIiIiJiwOj1kyWDl6SRwArbyyXtSnXD/4SB7tdw197e7o6Ojp4LRkRERET0kaTZttubKTso78npxibAT1Q9KPRF4LMD3J+IiIiIiBhkhlSQY/s3QFfbLfeJpPfz6o5oNYtsT+7P8/TQh/WBmzrJeq/tP62ufvTVgkeX0Hbi9QAsnrLfAPcmIiIiIoa7IRXkrAq2b6DaJGAg+/Anqi2qIyIiIiJiJQ2WjQciIiIiIiL6RYKciIiIiIhoKUM2yJFkSRfXvV9D0pOSruuh3smSju8k/S2SrijHE3tqp4u2J0paUrbDrv3sW/KW9ra9Uu9ASdv0pW4X7b1ynRERERERrWgo35PzLPBOSWvZfg54H/BoXxuz/RhwcD/0a4btD/dDOzUHAtcB9zdmSFrD9vLeNNaP1xkRERERMSgN2Zmc4udAbTuvQ4FLaxmS1pN0TXnQ5V2Stq+rt4OkmyX9RtJnS/k2SQsbTyBpbUkXSJolaa6kA1a205JOKO3Nl3RKXfo/lLR7JV0saTdgf+DbZVZoc0m3SvqGpF8BX5a0qaSbSr2bJG1S2rpQ0lmS7pT0sKSDG69T0ghJZ0haUOp/qaRPkXR/STuji2s4SlKHpI4Vy5as7JBERERERPSboTyTA3AZcFJZWrY9cAGwZ8k7BZhr+0BJ7wF+wKs7mG0PvBtYG5gr6fpuzvFV4GbbR0p6E3CPpF/afraL8ntKmlf3/iDbv6u9kTQJGA/sAgiYJmkv4E/lXLvbfkrSerb/LGkacJ3t2lI6gDfZ3ru8vxb4ge2LJB0JnEU1+wMwDtgD2AqYBjQuUzsK2AzYsTxgdT1J6wGTga1su1zz69ieCkwFGDlu/NB5omxEREREtLwhHeTYni+pjWoW52cN2XsAB5VyN0taX9LYkvfTssTtOUm3UAUc8+jcJGD/uvt4RlE9lPSBLsr3tFxtUvmZW96PoQp6dgCusP1U6fOfu2njx3XHuwIfKccXA9+qy7vG9svA/ZLe3Ek7+wLn1Ja8laBqDeB54PwS/PX63qSIiIiIiIE0pIOcYhpwBjARWL8uXZ2UdcNrY3pnRDUb8+u+drCT9k63fe5rEqVjeuhHva5mkWho44WG83bWl9ecs8zo7AK8FzgE+CLwnib7FREREREx4Ib6PTlQLVE71faChvTbgMOg2vUMeMr20yXvAEmjJK1PFRzN6qb9G4AvqawTk7TjSvb3BuBISWNKextL2gi4Cfho6RNl2RjAM8A63bR3J1UwAtX13t6LvkwHPldmb2r3MY0Bxtr+GfAV8pDSiIiIiBhihvxMju1HgO92knUy8F+S5gPLgMPr8u4Brqdadnaa7cfKsrfOnAacCcwvgc5ioLvlaI335Pxz7X6a0t/pkrYGZpa4aSnwCdv3SfoX4FeSVlAtZzuC6r6j88pMT2e7oh0DXCDpBOBJ4FPd9K3R+cAW5dpeAs4DrgR+KmkU1UzPsb1oLyIiIiJiwMnOPeOxctrb293R0THQ3YiIiIiIFiZptu32Zsq2wnK1iIiIiIiIVwz55WoDQdL7gW82JC+yPXkg+jPQFjy6hLYTq124F0/Zr4fSERERERGrVoKcPrB9A9UGAhERERERMchkuVpERERERLSUBDkREREREdFShkWQI2mFpHmS7pN0r6TjJL2h5LVLOquJNu4sr22SPt5E+TZJC7vJHy3pEkkLJC2UdHvt2Tnd1DlG0gOl3kRJu/XUj4iIiIiI4Wa43JPznO0JAOXBmz8CxgJft90B9Lj/se1aQNEGfLy0sTK+DPzR9nalX1sCL/VQ5/PAB20vknQy1TN27lzJfrxC0hq2l/dXexERERERA2FYzOTUs/0EcBTwRVUmSroOQNKGkm6UNEfSuZJ+L2mDkre0NDGF8sBPSceWGZsZpc6cXsyujAMerevXr22/UM51XJndWSjpKyXtHODtwDRJxwKfA44t/dhb0sPlet4k6WVJe5V6MyS9Q9Iuku6UNLe8blnyj5B0uaRrgekl7QRJsyTNl3RKZ52XdJSkDkkdK5YtaXr8IyIiIiJWteEyk/Math8uy9U2asj6OnCz7dMlfYAqGGp0InC87Q9DtewMeJ/t5yWNBy4FmnlI0QXAdEkHAzcBF9n+jaSdgU8B7wIE3C3pV7Y/V/q0j+2nJI0Flto+o/TjIWAbYDNgNlUgdjfwVtu/lfQ3wF62l0vaF/gGcFDpy67A9rb/LGkSMB7YpZx/mqS9bN/WMIZTgakAI8eNzxNlIyIiImLQGJZBTqFO0vYAJgPY/oWkvzTRzprA2ZImACuALZo5ue15kt4OTAL2BWZJ2rX04WrbzwJIugrYE5jbQ5MzgL2ogpzTgc8CvwJmlfyxwEUlEHPpd82Ntv9cjieVn9r5xlAFPa8JciIiIiIiBqthGeSU4GIF8ASwdX1WH5o7FvgjsAPV8r/nm61oeylwFXCVpJeBD5V+9cUMqiVsbwFOAk4AJvJqcHIacIvtyZLagFvr6j5bdyzgdNvn9rEfEREREREDatjdkyNpQ+Ac4Gzbjcusbgc+WspNAtbtpIlngHXq3o8FHrf9MvBJYEST/dhd0rrl+I1US81+TxWUHFh2X1ubamZpRhP9uBvYDXjZ9vPAPOAf6+qO5dV7gI7opms3AEfWdnqTtHHZrCEiIiIiYkgYLjM5a0maR7VEazlwMfBvnZQ7BbhU0seolno9ThVM1JsPLJd0L3Ah8D3gSkl/D9zCa2dFurM58H1Jogo2rweutG1JFwL3lHLn2+5sqdq1wBWSDgC+ZHuGpD8Ad5X8GcChwILy/ltUy9WOA27uqlO2p0vaGphZdY2lwCeoZr06td3GY+mYsl8z1xwRERERscrp9ZMZw5ekkcCKcnP+rsD3a1tPR9fa29vd0dHjLtwREREREX0mabbtZjb4GjYzOc3aBPhJ2XntRaqb9yMiIiIiYghJkFPH9m+AHfuzTUnvB77ZkLzI9uT+PM9AWvDoEtpOvB6AxVm2FhEREREDLEHOKmb7Bqqb+SMiIiIiYjUYdrurRUREREREa2u5IEeSJV1c934NSU9Kuq6Heu2SzurHfnxX0qPl/p6VaWexpA3K8Z3907uIiIiIiNbVckEO1RbO75S0Vnn/Pl59PkyXbHfYPqbZk0jqcqlfCWwmA38A9mq2zZ7Y3q2/2oqIiIiIaFWtGOQA/Byo3QF/KHBpLUPSLpLulDS3vG5Z0ifWZnskrSfpGknzJd0lafuSfrKkqZKmAz/o5vz7AAuB75fz1859sqSLJd0s6TeSPlt37tskXS3pfknndDYDJGlpeR0j6SZJcyQtKM/KQVKbpAcknSfpPknTa8GepHdI+qWke0u9zUv6CZJmlWs9paStLen6UnZheW5QRERERMSQ0KpBzmXAIZJGAdsDd9flPQjsZXtH4CTgG53UPwWYa3t74P/x2oBmZ+AA2x/v5vy1wOpq4MOS1qzL254qANsVOEnSW0r6LsD/BrajelDoR7pp/3lgsu2dqAKqfy0PFQUYD/yH7W2BvwIHlfRLSvoOwG7A45ImlfK7ABOAnSXtBXwAeMz2DrbfCfyisQOSjpLUIaljxbIl3XQ1IiIiImL1askgx/Z8oI0q2PhZQ/ZY4HJJC4HvANt20sQewMWlrZuB9SWNLXnTbD/X1bklvRH4EHCN7aepAqxJdUV+avs5208Bt1AFGAD32H7Y9gqqAGmPbi5RwDckzQd+CWwMvLnkLbI9rxzPBtokrQNsbPvqck3P215W+jUJmAvMAbaiCnoWAPtK+qakPW2/LoqxPdV2u+32EaPHNmZHRERERAyYVt5CehpwBjARWL8u/TTgFtuTJbUBt3ZSV52kubw+28N5P0AVSC0okyujgWXA9Q3tNLbbVXpnDgM2BHa2/ZKkxcCokvdCXbkVwFp0fj2U9NNtn/u6DGlnqmDtdEnTbZ/aTX8iIiIiIgaNlpzJKS4ATrW9oCF9LK9uRHBEF3VvowokkDQReKrMyjTjUOAztttstwGbAZMkjS75B0gaJWl9qgBsVknfRdJm5V6cjwG3d3OOscATJcDZB9i0uw6Vvj8i6cByTSNLf24AjpQ0pqRvLGmjsoRume0fUgWKOzV57RERERERA65lZ3JsPwJ8t5OsbwEXSToOuLmxWnk9GfivshxsGXB4M+csgcP7gX+s68ezkm4H/q4k3UM1q7MJcJrtxyRtAcwEplDdk3Mb1f08XbkEuFZSBzCP6j6jnnwSOFfSqcBLwN/bni5pa2BmmXVaCnwCeAfwbUkvl7JHN3P9ERERERGDgezuVkUNH5IOAva33VRA08dznAwstX1GQ/pE4HjbH15V516V2tvb3dHRMdDdiIiIiIgWJmm27fZmyrbsTE5vSNof+BfgyIHuS0RERERErJwEOYDtaVQbFTRN0vuBbzYkL7I9uZvznNxF+q10vgFCRERERET0UparxUobOW68xx1+Joun7Ndz4YiIiIiIPujNcrVW3l0tIiIiIiKGoQQ5ERERERHRUnoMciStkDRP0kJJl9c972VQkLS0h/wtJP1M0m8lPSDpJ5LevIr7tKMkl/t2VqadkyUdX45PlbRv//QwIiIiIqJ1NTOT85ztCbbfCbwIfK7ZxiWN6HPP+oGkUVTPpPm+7XfY3hr4PrBhk/X72v9DqR7meWgf67+O7ZNs/7K/2ouIiIiIaFW9Xa42g+pBkUj6hKR7yizPubWAQNLSMutwN7CrpCmS7pc0X9IZpcymkm4qaTdJ2qSkXyjpLEl3SnpY0sElfUwpN0fSAkkHNNnfjwMzbV9bS7B9i+2FktokzShtzpG0WznXREm3SPoRsKC7a+2MqqdqHgwcAUwqgRblfA9Kuqhc9xW1WTFJiyV9s5zjHknv6KTdC+vG4yRJs8rs2tRyTiTdWtfOQ5L2LOkjJJ1Rxm6+pC+V9J0l/UrSbEk3SBpX0o+p+zO7rMmxjoiIiIgYFJoOciStAXwQWCBpa+BjwO62JwArgMNK0bWBhbbfBdwPTAa2tb098M+lzNnAD0raJcBZdacaB+wBfBiYUtKeBybb3gnYB/jX2i/2PXgnMLuLvCeA95U2P9bQh12Ar9repodr7czuVFtJ/45qW+gP1eVtCUwt1/008Pm6vKdt70I1Nmf2cF1n2/7bMru2FtVY1axR2vkK8PWSdhSwGbBjbcwlrQn8O3Cw7Z2BC6ieFQRwYl3ZTmfuJB0lqUNSx4plS3robkRERETE6tNMkLOWpHlAB/DfwH8C7wV2BmaVvPcCby/lVwBXluOnqQKU8yV9BFhW0ncFflSOL6YKamqusf2y7fuB2r0zAr4haT7wS2Djury+WhM4T9IC4HJgm7q8e2wvKsfdXWtnDgVqsx+X8dola3+wfUc5/iGvve5L61537aHv+0i6u/T9PcC2dXlXldfZQFs53hc4x/ZyANt/pgq43gncWK7ra8BbS/n5VIHQJ4DlnXXA9lTb7bbbR4we20N3IyIiIiJWn2YeBvpcmcF4RZlFucj2/+2k/PO2VwDYXi5pF6rA4BDgi1S/lDeqf1jPC/WnKq+HUd1Hs7PtlyQtBkY10ff7gL27yDsW+COwA1Ww93xd3rMNfejqWl+jLGM7CNhf0ldL3fUlrVOKND6UyE0cN55jFPA9oN32HySdzGvHojZ+K3j1z1edtCngPtudBVT7AXsB+wP/n6RtawFSRERERMRg19ctpG8CDpa0EYCk9SRt2lhI0hhgrO2fUS2fqgVLd1IFPVAFMLf3cL6xwBMlwNkHeN25uvAjYDdJrzylUtIHJG1X2nzc9svAJ4Gu7rNp6lqLfYF7bb/NdpvtTalmtQ4s+ZtIqgUVtc0Jaj5W9zqzm2uqBTRPlfE9uJuyNdOBz5Ulh0haD/g1sGGtP5LWlLStpDcAb7N9C/B/gDcBY5o4R0RERETEoNDMTM7r2L5f0teA6eWX4peALwC/byi6DvDTMvsgqtkTgGOACySdADwJfKqHU14CXCupA5gHPNhkP5+T9GHgTElnln7OB75MNRtypaS/B27htbM3fblWqAKXqxvSrgSOptq04QHgcEnnAr+h2umtZqSqzRreQDe7stn+q6TzqDZFWAzM6noEXnE+sAUwX9JLwHm2zy4bGZwlaSzVZ+FM4CHghyVNwHds/7WJc0REREREDAqyu1wZFf1IUhtwXdksoDFvMdXys6dWc7f6RXt7uzs6Oga6GxERERHRwiTNtt3eTNm+LleLiIiIiIgYlPq0XG2wKffYXNyQ/ELZxnpVnfNuYGRD8idtL+isvO3FVLuZdZbX1q+di4iIiIgYxloiyCmBxYQeC/bvOVdZABUREREREX2X5WoREREREdFSEuRERERERERLSZATEREREREtJUFORERERES0lAQ5ERERERHRUhLkRERERERES0mQExERERERLSVBTkREREREtJQEORERERER0VIS5EREREREREtJkBMRERERES0lQU5ERERERLSUBDkREREREdFSEuRERERERERLSZATEREREREtJUFORERERES0lAQ5ERERERHRUhLkRERERERES0mQExERERERLSVBTkREREREtJQEORERERER0VIS5EREREREREtJkBMRERERES0lQU5ERERERLSUBDkREREREdFSEuRERERERERLke2B7kMMcZKeAX490P0YYjYAnhroTgwxGbPey5j1XsasdzJevZcx672MWe+16phtanvDZgqusap7EsPCr223D3QnhhJJHRmz3smY9V7GrPcyZr2T8eq9jFnvZcx6L2OW5WoREREREdFiEuRERERERERLSZAT/WHqQHdgCMqY9V7GrPcyZr2XMeudjFfvZcx6L2PWe8N+zLLxQEREREREtJTM5EREREREREtJkBMRERERES0lQU50SdIHJP1a0m8lndhJ/khJPy75d0tqq8v7vyX915Levzr7PZCaGLPjJN0vab6kmyRtWpe3QtK88jNt9fZ84DQxZkdIerJubD5Tl3e4pN+Un8NXb88HThNj9p268XpI0l/r8obr5+wCSU9IWthFviSdVcZ0vqSd6vKG3eesifE6rIzTfEl3StqhLm+xpAXlM9ax+no9sJoYs4mSltT9/TupLq/bv9OtqokxO6FuvBaW76/1St5w/Zy9TdItkh6QdJ+kL3dSJt9nALbzk5/X/QAjgN8BbwfeCNwLbNNQ5vPAOeX4EODH5XibUn4ksFlpZ8RAX9MgGbN9gNHl+OjamJX3Swf6GgbpmB0BnN1J3fWAh8vruuV43YG+psEwZg3lvwRcUPd+2H3OynXvBewELOwi/0PAzwEB7wbuLunD9XPW03jtVhsH4IO18SrvFwMbDPQ1DMIxmwhc10l6r/5Ot9JPT2PWUPbvgJvr3g/Xz9k4YKdyvA7wUCf/bub7zM5MTnRpF+C3th+2/SJwGXBAQ5kDgIvK8RXAeyWppF9m+wXbi4DflvZaXY9jZvsW28vK27uAt67mPg42zXzOuvJ+4Ebbf7b9F+BG4AOrqJ+DSW/H7FDg0tXSs0HM9m3An7spcgDwA1fuAt4kaRzD9HPW03jZvrOMB+S7DGjqM9aVlfkeHNJ6OWb5LgNsP257Tjl+BngA2LihWL7PyHK16NrGwB/q3j/C6/8SvVLG9nJgCbB+k3VbUW+v+9NU/9NSM0pSh6S7JB24Kjo4CDU7ZgeVKfcrJL2tl3VbTdPXXZZDbgbcXJc8HD9nzehqXIfr56w3Gr/LDEyXNFvSUQPUp8FqV0n3Svq5pG1LWj5jPZA0muqX8Svrkof950zVbQI7Anc3ZOX7DFhjoDsQg5Y6SWvcb7yrMs3UbUVNX7ekTwDtwN51yZvYfkzS24GbJS2w/btV0M/BpJkxuxa41PYLkj5HNXv4nibrtqLeXPchwBW2V9SlDcfPWTPyfdYHkvahCnL2qEvevXzGNgJulPRg+R/74W4OsKntpZI+BFwDjCefsWb8HXCH7fpZn2H9OZM0hiro+4rtpxuzO6ky7L7PMpMTXXkEeFvd+7cCj3VVRtIawFiqaedm6raipq5b0r7AV4H9bb9QS7f9WHl9GLiV6n9nWl2PY2b7T3XjdB6wc7N1W1RvrvsQGpZ3DNPPWTO6Gtfh+jnrkaTtgfOBA2z/qZZe9xl7Aria4bFcuUe2n7a9tBz/DFhT0gbkM9aM7r7Lht3nTNKaVAHOJbav6qRIvs9IkBNdmwWMl7SZpDdSfcE07sQ0DajtzHEw1Q2BLumHqNp9bTOq/6m6ZzX1eyD1OGaSdgTOpQpwnqhLX1fSyHK8AbA7cP9q6/nAaWbMxtW93Z9q/THADcCkMnbrApNKWqtr5u8mkrakurF0Zl3acP2cNWMa8A9lV6J3A0tsP87w/Zx1S9ImwFXAJ20/VJe+tqR1asdU49XpzlnDjaT/Ve5bRdIuVL+D/Ykm/04PV5LGUq16+Gld2rD9nJXP0H8CD9j+ty6K5fuMLFeLLtheLumLVB/+EVS7M90n6VSgw/Y0qr9kF0v6LdUMziGl7n2SfkL1y9Ny4AsNy2VaUpNj9m1gDHB5+bfuv23vD2wNnCvpZap/+KbYbvlfPpscs2Mk7U/1Wfoz1W5r2P6zpNOofkEAOLVhKUNLanLMoLpJ97LyHw81w/JzBiDpUqrdrTaQ9AjwdWBNANvnAD+j2pHot8Ay4FMlb1h+zpoYr5Oo7sH8XvkuW267HXgzcHVJWwP4ke1frPYLGABNjNnBwNGSlgPPAYeUv5+d/p0egEtY7ZoYM4DJwHTbz9ZVHbafM6r/nPoksEDSvJL2/4BNIN9n9fTaf/8iIiIiIiKGtixXi4iIiIiIlpIgJyIiIiIiWkqCnIiIiIiIaCkJciIiIiIioqUkyImIiIiIiFVK0gWSnpDU43bfkr4jaV75eUjSX3t7vgQ5ERExLEhaUf7BXCjpWklv6mM7b5F0RX/3b1WSdISktwx0PyJiWLsQ+EAzBW0fa3uC7QnAv1M9l6tXEuRERMRw8Vz5R/OdVM9c+kJfGrH9mO2D+7drq9wRQIKciBgwtm+j+u59haTNJf1C0mxJMyRt1UnVQ4FLe3u+BDkRETEczQQ2rr2RdIKkWZLmSzqlpH1T0ufrypws6X9Laqstt5A0QtK36+r+Y0n/XnmILZKulnRBOf60pH9u7IykD0iaI+leSTeVtPUkXVPavUvS9nX9OL6u7sLSpzZJD0g6T9J9kqZLWkvSwUA7cEmZyVpL0hRJ95e2z+j30Y2IaM5U4Eu2dwaOB75XnylpU2Az4ObeNrxGv3QvIiJiiJA0Angv8J/l/SRgPLALIGCapL2Ay4AzefUf3Y9SLbWo/w/CTwNLbP+tpJHAHZKmA7cBewLTqIKpcaX8HqXd+v5sCJwH7GV7kaT1StYpwFzbB0p6D/ADYEIPlzceONT2ZyX9BDjI9g8lfRE43nZHaX8ysJVt93XZXkTEypA0BtgNuFxSLXlkQ7FDgCtsr+ht+wlyIiJiuFhL0jygDZgN3FjSJ5WfueX9GGC87f+UtFG5l2VD4C+2/1tSW12bk4Dty2wJwFiqQGMG8BVJ2wD3A+tKGgfsChzT0K93A7fZXgRgu7acYw/goJJ2s6T1JY3t4RoX2Z5XjmeXa230NPA8cL6k64HremgzImJVeAPw13LfTVcOoY9LixPkRETEcPGc7QklULiO6h/Os6hmb063fW4nda4ADgb+Fw0zMIWollrc8LoMaV2qmZ/bgPWoZoKW2n6mkzbcRduNDCzntbNJo+qOX6g7XgGs9boG7OWSdqGazToE+CLwnk7OFRGxyth+WtIiSX9v+3JV0znb274XQNKWwLpUy4t7LffkRETEsGJ7CdVsyvGS1gRuAI4sSyeQtLGkjUrxy6gCgYOpAp5GNwBHl3aQtIWktUveTOArVEHODKr15jM6aWMmsLekzUobteVqtwGHlbSJwFO2nwYWAzuV9J2o1qv35BlgnVJnDDDW9s9K/3paAhcRsdIkXUr1fbelpEckfZrqO+7Tku4F7gMOqKtyKHCZ7c7+E6hHmcmJiIhhx/bc8o/qIbYvlrQ1MLOsC18KfAJ4wvZ9ktYBHrX9eCdNnU+1JGxO+V/IJ4EDS94MYJLt30r6PdVszuuCHNtPSjoKuErSG4AngPcBJwP/JWk+sAw4vFS5EviHsvRuFvBQE5d8IXCOpOeADwI/lTSKarbo2CbqR0SsFNuHdpHV6bbStk9emfOpj8FRRERERETEoJTlahERERER0VIS5EREREREREtJkBMRERERES0lQU5ERERERLSUBDkREREREdFSEuRERERERERLSZATEREREREt5f8HRWx16zGFZ44AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "y_pos = np.arange(len(sorted_data['product_category']))\n",
    "\n",
    "ax.barh(y_pos, sorted_data['data_count'])\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(sorted_data['product_category'])\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('Review counts')\n",
    "ax.set_title('Amzon Review Data Product Categories and Associated Reviews')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data for ML Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, let's get a preview on the data set using Athena to get some insights on the preprocessing for ML training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "SELECT *\n",
    "FROM amazon_reviews_parquet\n",
    "WHERE product_category = 'Home' OR product_category = 'Kitchen'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "s3://pilho-sagemaker-ai-workshop-kr/athena/6890954546269221594/57becd26-c63b-4587-962d-bd1943e87e20.csv\n"
     ]
    }
   ],
   "source": [
    "preview_data_s3_address = get_results_in_s3(f\"\"\"\n",
    "    SELECT *\n",
    "    FROM amazon_reviews_parquet_query\n",
    "    WHERE product_category = 'Home' OR product_category = 'Kitchen'\n",
    "\"\"\")\n",
    "print(preview_data_s3_address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>rate_category</th>\n",
       "      <th>year</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00QPR30PO</td>\n",
       "      <td>Premium Wine Aerator By Napa | #1 Best Selling...</td>\n",
       "      <td>11943403</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Previously we had a glass aerator that worked ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00063QPYQ</td>\n",
       "      <td>John Boos Mystery Butcher Block Oil</td>\n",
       "      <td>22753231</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>It is fine but it like the thicker treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000DDVRX</td>\n",
       "      <td>Oggi 7059 Stainless Steel Utensil Holder</td>\n",
       "      <td>10553688</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>perfect for my kitchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008YK3IUM</td>\n",
       "      <td>Kuissential Manual Ceramic Burr Coffee Grinder...</td>\n",
       "      <td>21580126</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>I recently posted a piece to my blog page rega...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KZ64UNC</td>\n",
       "      <td>Innovee Lemon Squeezer - Quality 18/10 Stainle...</td>\n",
       "      <td>17261140</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>I love this juicer! The stainless steel is gre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00DP6BJE2</td>\n",
       "      <td>NuWave Precision Induction Cooktop</td>\n",
       "      <td>12336414</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>This thing is AWESOME! Everything they say on ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00014WEKY</td>\n",
       "      <td>Oster Milkshake Blade</td>\n",
       "      <td>31268106</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>It works only for milkshakes (ice cream and mi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00PIT0YYA</td>\n",
       "      <td>Victorinox Swiss Stainless Steel 6 Piece Round...</td>\n",
       "      <td>43551226</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>They were delivered on time. Great knives very...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0042J2OUE</td>\n",
       "      <td>Happy Sales HSS-TBSP1, Cast Iron Sizzling Stea...</td>\n",
       "      <td>16424002</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Good quality. Check the size before you buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B011J4KLYU</td>\n",
       "      <td>Perlli - Party Serving Bowl Dish with Ice Cont...</td>\n",
       "      <td>4084406</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Finally a serving bowl that allows chilling to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B009YK8GWG</td>\n",
       "      <td>White Porcelain Mortar &amp; Pestle - Choice of Sizes</td>\n",
       "      <td>22889572</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>As described.  Will be using it to grind chalk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00U05NPH0</td>\n",
       "      <td>Pyrex Glass Food Storage Set with Lids - 1 cup</td>\n",
       "      <td>32676424</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>These are great for portion control.  I am slo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00FUF5K8W</td>\n",
       "      <td>All-Clad Stainless Steel Tri-Ply Bonded Dishwa...</td>\n",
       "      <td>51741559</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>I bought this pan for use everyday for both br...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00ENOMFHA</td>\n",
       "      <td>BONUS PACK! Joyoung DJ13U-D08SG Easy-Clean Aut...</td>\n",
       "      <td>2036626</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008AH6DCY</td>\n",
       "      <td>T-Fal/Wearever C9440564 Pure Living Saute Pan,...</td>\n",
       "      <td>43732885</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>expertly made very happy will but again</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000KDW9MO</td>\n",
       "      <td>Finum 100 Tea Filters, Large, Brown</td>\n",
       "      <td>52982482</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>I use these filters for large loose teas in a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00BKQZDPK</td>\n",
       "      <td>PG Ceramic Mug Coffee Classic White Coffee Mug...</td>\n",
       "      <td>18974760</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Love the colors in this mug.  Makes me feel ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0036X4YOG</td>\n",
       "      <td>OXO SteeL Spinning Bar Spoon</td>\n",
       "      <td>27829690</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Arrived on time &amp; in great condition. Wonderfu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00004UE8A</td>\n",
       "      <td>Norpro Taco Press</td>\n",
       "      <td>47963289</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Use it all the time to Fry Taco Shells!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0010SX2RY</td>\n",
       "      <td>Better Chef Top Dual Buffet Burner Table</td>\n",
       "      <td>1842399</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>It's works really well</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002L162HQ</td>\n",
       "      <td>Fred and Friends PASTASAURUS Pasta Server</td>\n",
       "      <td>2340954</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Pretty nice ! Nothing else to say , very happy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00LHXZ6VM</td>\n",
       "      <td>Solid Copper Moscow Mule Mug - 16 and 22 Oz - ...</td>\n",
       "      <td>39073494</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Very happy with these mugs. They stay cold for...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003NCVXGC</td>\n",
       "      <td>Oneida Tuscany Flatware Sets</td>\n",
       "      <td>41986232</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>We bought this set a few years ago and now eve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001RNG422</td>\n",
       "      <td>Cuisinart CVR-1000 Vertical Countertop Rotisse...</td>\n",
       "      <td>33661597</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Use it often...only wish it had more temperatu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0064AGHT8</td>\n",
       "      <td>Bride &amp; Groom Skeletons Kissing Magnetic Salt ...</td>\n",
       "      <td>2639875</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>These are SO cute! They were a lot bigger than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003QP30S0</td>\n",
       "      <td>Prepworks from Progressive International Colla...</td>\n",
       "      <td>52228594</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>This is a great addition to our summer camping...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KCLNIRO</td>\n",
       "      <td>VonShef Frozen Yogurt Fruit Ice Cream Smoothie...</td>\n",
       "      <td>4062546</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Very easy to use and clean and makes yummy des...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KEF0RJK</td>\n",
       "      <td>Fit &amp; Fresh Power Shaker Bottle with patented ...</td>\n",
       "      <td>12148312</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>This bottle is great! it does not leak , It fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00HU7JKC0</td>\n",
       "      <td>Silicone Baking Mat set (2pk) PREMIUM Half She...</td>\n",
       "      <td>6220441</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Good Quality and works great.  Cookies don't s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CJ1HF7O</td>\n",
       "      <td>Mr. Coffee Single Serve Coffee Brewer Powered ...</td>\n",
       "      <td>33503168</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16645</td>\n",
       "      <td>Works well and very similar to the Keriug for ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999970</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B009NGC6UE</td>\n",
       "      <td>Libbey 56238 6.8 oz Just Baking Glass Ramekin</td>\n",
       "      <td>39013440</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Great size for individual servings of just abo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999971</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000JHQ3NC</td>\n",
       "      <td>Pirate Pennant Banner 1-ft. X 18\" Plastic Penn...</td>\n",
       "      <td>12267824</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>I ordered this banner for my son's pirate them...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999972</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000R4OGZE</td>\n",
       "      <td>Plastic Treasure Map Party Accessory</td>\n",
       "      <td>24192811</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>My daughter loves it. We gave it to her with a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999973</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00C9YZ7JY</td>\n",
       "      <td>BUILT NY Gourmet Getaway Designer Neoprene Lun...</td>\n",
       "      <td>22443288</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>DO NOT BUY  BUILT NY.  This item I bought for ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999974</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0073MXRTO</td>\n",
       "      <td>Sun's Tea (TM) 8oz Ultra Clear Glass Tea/Coffe...</td>\n",
       "      <td>12440684</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>These are beautiful, durable, and functional. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999975</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000VLPTO</td>\n",
       "      <td>Fox Run Egg Piercer Carded</td>\n",
       "      <td>3210338</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>It works.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999976</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000UPOJ5W</td>\n",
       "      <td>Vinturi Spirit Aerator</td>\n",
       "      <td>50173082</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Much faster, more convenient and more effectiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999977</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00IK5A4U8</td>\n",
       "      <td>PUR 18 Cup Dispenser with One Pitcher Filter</td>\n",
       "      <td>4353802</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>great</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999978</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0050QP536</td>\n",
       "      <td>28 Oz. Blender Bottle W/wire Shaker Ball- Pack...</td>\n",
       "      <td>41722743</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>I love the conveniece of adding powders at the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999979</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002CM8TZC</td>\n",
       "      <td>EatSmart Precision Pro Digital Kitchen Scale, ...</td>\n",
       "      <td>46523467</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>If you put a plate on it you can not read the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999980</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0015RJHOC</td>\n",
       "      <td>Libbey Glass 149 4-Pack 5-oz. Crystal Juice Gl...</td>\n",
       "      <td>23520837</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>I bought these juice glasses from Amazon sever...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999981</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00DSSLWIA</td>\n",
       "      <td>Foodsaver compatible 100 FoodVacBags 6X10-inch...</td>\n",
       "      <td>52981501</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>Work very well.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999982</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002UPVUPM</td>\n",
       "      <td>ChocoMaker Fondue Dipping Candy Milk Chocolate...</td>\n",
       "      <td>10002529</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>This chocolate works so well in my machine, we...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999983</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00LOCHQ32</td>\n",
       "      <td>Johnson Brothers 16-Piece His Majesty Dinner S...</td>\n",
       "      <td>30417866</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>Wonderful!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003F6GKZU</td>\n",
       "      <td>Chef'n PepperBall (Black and Clear) (2)</td>\n",
       "      <td>17149791</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Love this product.  Easy to use and easier to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999985</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005N0WCE4</td>\n",
       "      <td>Rabbit Wine and Beverage Bottle Stoppers with ...</td>\n",
       "      <td>51901815</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>Open a bottle with a cork. Unable to drink eve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999986</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00086I7XS</td>\n",
       "      <td>Deer Head Plaque Candy Mold</td>\n",
       "      <td>37293424</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Perfect !! Used it for a cake and it was perfe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999987</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00HL3NS2Q</td>\n",
       "      <td>Pink Wash Ball Laundry Ball, Wash Without Dete...</td>\n",
       "      <td>16927369</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>Used it all winter in Costa Rica and saved a b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999988</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004O4TUJK</td>\n",
       "      <td>Lolita Stemless Wine Glass, Quill, 20-Ounce</td>\n",
       "      <td>37992291</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Bought this for my best friend and she loved i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999989</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003NHN8YW</td>\n",
       "      <td>Reflections REF320KN Heavyweight Plastic Dispo...</td>\n",
       "      <td>34195244</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>Not heavyweight. Knives and forks were very fl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999990</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000CFP0F</td>\n",
       "      <td>Norpro 2063 Nonstick Splatter Guard</td>\n",
       "      <td>14589906</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>Very nice one, does the job well. I was surpri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999991</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00PVDA4KC</td>\n",
       "      <td>Skilled Grill Accurate Ultra-Fast Professional...</td>\n",
       "      <td>20174554</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>The thermometer is not &amp;#34;ultra-fast&amp;#34; no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999992</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002KHN92W</td>\n",
       "      <td>Kaiser Bakeware LaForme Plus Springforms</td>\n",
       "      <td>16729950</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>I've had one of these pans for many years and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999993</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00004RDDP</td>\n",
       "      <td>Zak Designs E-Z-Rol Garlic Peeler</td>\n",
       "      <td>25299588</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>I bought this garlic peeler because it was che...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999994</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0007Y9WHQ</td>\n",
       "      <td>Paderno World Cuisine Spiral Vegetable Slicer ...</td>\n",
       "      <td>26097085</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>I like to spiralize beets, carrots, turnips an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00ANCXJR6</td>\n",
       "      <td>Ivation Long Range Wireless Thermometer - Remo...</td>\n",
       "      <td>4170773</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>My brother got one of these thermometers and l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00005IBXJ</td>\n",
       "      <td>National Presto Ind 03430 Pizzazz Pizza Oven</td>\n",
       "      <td>34373067</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>I was very sceptical when I first saw this thi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008GPBGOU</td>\n",
       "      <td>Old German Petwer Coat of Arms Black Lozenge G...</td>\n",
       "      <td>34846333</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>This stein looks really sharp, and I'm complet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00091SDR2</td>\n",
       "      <td>Chicago Cutlery 1063947 14-Inch by 1-1/2-Inch ...</td>\n",
       "      <td>32999413</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15948</td>\n",
       "      <td>My husband had to cut it off so it would fit o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00OVSQ02E</td>\n",
       "      <td>CC Boards Nonslip Bamboo Cutting Board: Wooden...</td>\n",
       "      <td>51263834</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16576</td>\n",
       "      <td>The highest quality cutting board I have ever ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_category  product_id  \\\n",
       "0               Kitchen  B00QPR30PO   \n",
       "1               Kitchen  B00063QPYQ   \n",
       "2               Kitchen  B0000DDVRX   \n",
       "3               Kitchen  B008YK3IUM   \n",
       "4               Kitchen  B00KZ64UNC   \n",
       "5               Kitchen  B00DP6BJE2   \n",
       "6               Kitchen  B00014WEKY   \n",
       "7               Kitchen  B00PIT0YYA   \n",
       "8               Kitchen  B0042J2OUE   \n",
       "9               Kitchen  B011J4KLYU   \n",
       "10              Kitchen  B009YK8GWG   \n",
       "11              Kitchen  B00U05NPH0   \n",
       "12              Kitchen  B00FUF5K8W   \n",
       "13              Kitchen  B00ENOMFHA   \n",
       "14              Kitchen  B008AH6DCY   \n",
       "15              Kitchen  B000KDW9MO   \n",
       "16              Kitchen  B00BKQZDPK   \n",
       "17              Kitchen  B0036X4YOG   \n",
       "18              Kitchen  B00004UE8A   \n",
       "19              Kitchen  B0010SX2RY   \n",
       "20              Kitchen  B002L162HQ   \n",
       "21              Kitchen  B00LHXZ6VM   \n",
       "22              Kitchen  B003NCVXGC   \n",
       "23              Kitchen  B001RNG422   \n",
       "24              Kitchen  B0064AGHT8   \n",
       "25              Kitchen  B003QP30S0   \n",
       "26              Kitchen  B00KCLNIRO   \n",
       "27              Kitchen  B00KEF0RJK   \n",
       "28              Kitchen  B00HU7JKC0   \n",
       "29              Kitchen  B00CJ1HF7O   \n",
       "...                 ...         ...   \n",
       "999970          Kitchen  B009NGC6UE   \n",
       "999971          Kitchen  B000JHQ3NC   \n",
       "999972          Kitchen  B000R4OGZE   \n",
       "999973          Kitchen  B00C9YZ7JY   \n",
       "999974          Kitchen  B0073MXRTO   \n",
       "999975          Kitchen  B0000VLPTO   \n",
       "999976          Kitchen  B000UPOJ5W   \n",
       "999977          Kitchen  B00IK5A4U8   \n",
       "999978          Kitchen  B0050QP536   \n",
       "999979          Kitchen  B002CM8TZC   \n",
       "999980          Kitchen  B0015RJHOC   \n",
       "999981          Kitchen  B00DSSLWIA   \n",
       "999982          Kitchen  B002UPVUPM   \n",
       "999983          Kitchen  B00LOCHQ32   \n",
       "999984          Kitchen  B003F6GKZU   \n",
       "999985          Kitchen  B005N0WCE4   \n",
       "999986          Kitchen  B00086I7XS   \n",
       "999987          Kitchen  B00HL3NS2Q   \n",
       "999988          Kitchen  B004O4TUJK   \n",
       "999989          Kitchen  B003NHN8YW   \n",
       "999990          Kitchen  B0000CFP0F   \n",
       "999991          Kitchen  B00PVDA4KC   \n",
       "999992          Kitchen  B002KHN92W   \n",
       "999993          Kitchen  B00004RDDP   \n",
       "999994          Kitchen  B0007Y9WHQ   \n",
       "999995          Kitchen  B00ANCXJR6   \n",
       "999996          Kitchen  B00005IBXJ   \n",
       "999997          Kitchen  B008GPBGOU   \n",
       "999998          Kitchen  B00091SDR2   \n",
       "999999          Kitchen  B00OVSQ02E   \n",
       "\n",
       "                                            product_title  customer_id  \\\n",
       "0       Premium Wine Aerator By Napa | #1 Best Selling...     11943403   \n",
       "1                     John Boos Mystery Butcher Block Oil     22753231   \n",
       "2                Oggi 7059 Stainless Steel Utensil Holder     10553688   \n",
       "3       Kuissential Manual Ceramic Burr Coffee Grinder...     21580126   \n",
       "4       Innovee Lemon Squeezer - Quality 18/10 Stainle...     17261140   \n",
       "5                      NuWave Precision Induction Cooktop     12336414   \n",
       "6                                   Oster Milkshake Blade     31268106   \n",
       "7       Victorinox Swiss Stainless Steel 6 Piece Round...     43551226   \n",
       "8       Happy Sales HSS-TBSP1, Cast Iron Sizzling Stea...     16424002   \n",
       "9       Perlli - Party Serving Bowl Dish with Ice Cont...      4084406   \n",
       "10      White Porcelain Mortar & Pestle - Choice of Sizes     22889572   \n",
       "11         Pyrex Glass Food Storage Set with Lids - 1 cup     32676424   \n",
       "12      All-Clad Stainless Steel Tri-Ply Bonded Dishwa...     51741559   \n",
       "13      BONUS PACK! Joyoung DJ13U-D08SG Easy-Clean Aut...      2036626   \n",
       "14      T-Fal/Wearever C9440564 Pure Living Saute Pan,...     43732885   \n",
       "15                    Finum 100 Tea Filters, Large, Brown     52982482   \n",
       "16      PG Ceramic Mug Coffee Classic White Coffee Mug...     18974760   \n",
       "17                           OXO SteeL Spinning Bar Spoon     27829690   \n",
       "18                                      Norpro Taco Press     47963289   \n",
       "19               Better Chef Top Dual Buffet Burner Table      1842399   \n",
       "20              Fred and Friends PASTASAURUS Pasta Server      2340954   \n",
       "21      Solid Copper Moscow Mule Mug - 16 and 22 Oz - ...     39073494   \n",
       "22                           Oneida Tuscany Flatware Sets     41986232   \n",
       "23      Cuisinart CVR-1000 Vertical Countertop Rotisse...     33661597   \n",
       "24      Bride & Groom Skeletons Kissing Magnetic Salt ...      2639875   \n",
       "25      Prepworks from Progressive International Colla...     52228594   \n",
       "26      VonShef Frozen Yogurt Fruit Ice Cream Smoothie...      4062546   \n",
       "27      Fit & Fresh Power Shaker Bottle with patented ...     12148312   \n",
       "28      Silicone Baking Mat set (2pk) PREMIUM Half She...      6220441   \n",
       "29      Mr. Coffee Single Serve Coffee Brewer Powered ...     33503168   \n",
       "...                                                   ...          ...   \n",
       "999970      Libbey 56238 6.8 oz Just Baking Glass Ramekin     39013440   \n",
       "999971  Pirate Pennant Banner 1-ft. X 18\" Plastic Penn...     12267824   \n",
       "999972               Plastic Treasure Map Party Accessory     24192811   \n",
       "999973  BUILT NY Gourmet Getaway Designer Neoprene Lun...     22443288   \n",
       "999974  Sun's Tea (TM) 8oz Ultra Clear Glass Tea/Coffe...     12440684   \n",
       "999975                         Fox Run Egg Piercer Carded      3210338   \n",
       "999976                             Vinturi Spirit Aerator     50173082   \n",
       "999977       PUR 18 Cup Dispenser with One Pitcher Filter      4353802   \n",
       "999978  28 Oz. Blender Bottle W/wire Shaker Ball- Pack...     41722743   \n",
       "999979  EatSmart Precision Pro Digital Kitchen Scale, ...     46523467   \n",
       "999980  Libbey Glass 149 4-Pack 5-oz. Crystal Juice Gl...     23520837   \n",
       "999981  Foodsaver compatible 100 FoodVacBags 6X10-inch...     52981501   \n",
       "999982  ChocoMaker Fondue Dipping Candy Milk Chocolate...     10002529   \n",
       "999983  Johnson Brothers 16-Piece His Majesty Dinner S...     30417866   \n",
       "999984            Chef'n PepperBall (Black and Clear) (2)     17149791   \n",
       "999985  Rabbit Wine and Beverage Bottle Stoppers with ...     51901815   \n",
       "999986                        Deer Head Plaque Candy Mold     37293424   \n",
       "999987  Pink Wash Ball Laundry Ball, Wash Without Dete...     16927369   \n",
       "999988        Lolita Stemless Wine Glass, Quill, 20-Ounce     37992291   \n",
       "999989  Reflections REF320KN Heavyweight Plastic Dispo...     34195244   \n",
       "999990                Norpro 2063 Nonstick Splatter Guard     14589906   \n",
       "999991  Skilled Grill Accurate Ultra-Fast Professional...     20174554   \n",
       "999992           Kaiser Bakeware LaForme Plus Springforms     16729950   \n",
       "999993                  Zak Designs E-Z-Rol Garlic Peeler     25299588   \n",
       "999994  Paderno World Cuisine Spiral Vegetable Slicer ...     26097085   \n",
       "999995  Ivation Long Range Wireless Thermometer - Remo...      4170773   \n",
       "999996       National Presto Ind 03430 Pizzazz Pizza Oven     34373067   \n",
       "999997  Old German Petwer Coat of Arms Black Lozenge G...     34846333   \n",
       "999998  Chicago Cutlery 1063947 14-Inch by 1-1/2-Inch ...     32999413   \n",
       "999999  CC Boards Nonslip Bamboo Cutting Board: Wooden...     51263834   \n",
       "\n",
       "        star_rating rate_category  year  review_date  \\\n",
       "0                 5      positive  2015        16645   \n",
       "1                 3       neutral  2015        16645   \n",
       "2                 5      positive  2015        16645   \n",
       "3                 5      positive  2015        16645   \n",
       "4                 5      positive  2015        16645   \n",
       "5                 5      positive  2015        16645   \n",
       "6                 3       neutral  2015        16645   \n",
       "7                 5      positive  2015        16645   \n",
       "8                 3       neutral  2015        16645   \n",
       "9                 5      positive  2015        16645   \n",
       "10                5      positive  2015        16645   \n",
       "11                4      positive  2015        16645   \n",
       "12                4      positive  2015        16645   \n",
       "13                5      positive  2015        16645   \n",
       "14                5      positive  2015        16645   \n",
       "15                5      positive  2015        16645   \n",
       "16                4      positive  2015        16645   \n",
       "17                5      positive  2015        16645   \n",
       "18                5      positive  2015        16645   \n",
       "19                5      positive  2015        16645   \n",
       "20                5      positive  2015        16645   \n",
       "21                5      positive  2015        16645   \n",
       "22                2      negative  2015        16645   \n",
       "23                5      positive  2015        16645   \n",
       "24                5      positive  2015        16645   \n",
       "25                5      positive  2015        16645   \n",
       "26                5      positive  2015        16645   \n",
       "27                5      positive  2015        16645   \n",
       "28                5      positive  2015        16645   \n",
       "29                4      positive  2015        16645   \n",
       "...             ...           ...   ...          ...   \n",
       "999970            5      positive  2013        15948   \n",
       "999971            1      negative  2015        16576   \n",
       "999972            5      positive  2013        15948   \n",
       "999973            1      negative  2015        16576   \n",
       "999974            5      positive  2013        15948   \n",
       "999975            4      positive  2015        16576   \n",
       "999976            5      positive  2013        15948   \n",
       "999977            5      positive  2015        16576   \n",
       "999978            5      positive  2013        15948   \n",
       "999979            3       neutral  2015        16576   \n",
       "999980            5      positive  2013        15948   \n",
       "999981            5      positive  2015        16576   \n",
       "999982            5      positive  2013        15948   \n",
       "999983            5      positive  2015        16576   \n",
       "999984            5      positive  2013        15948   \n",
       "999985            4      positive  2015        16576   \n",
       "999986            5      positive  2013        15948   \n",
       "999987            4      positive  2015        16576   \n",
       "999988            5      positive  2013        15948   \n",
       "999989            2      negative  2015        16576   \n",
       "999990            4      positive  2013        15948   \n",
       "999991            2      negative  2015        16576   \n",
       "999992            5      positive  2013        15948   \n",
       "999993            5      positive  2015        16576   \n",
       "999994            5      positive  2013        15948   \n",
       "999995            5      positive  2015        16576   \n",
       "999996            5      positive  2013        15948   \n",
       "999997            4      positive  2015        16576   \n",
       "999998            5      positive  2013        15948   \n",
       "999999            5      positive  2015        16576   \n",
       "\n",
       "                                              review_body  \n",
       "0       Previously we had a glass aerator that worked ...  \n",
       "1            It is fine but it like the thicker treatment  \n",
       "2                                  perfect for my kitchen  \n",
       "3       I recently posted a piece to my blog page rega...  \n",
       "4       I love this juicer! The stainless steel is gre...  \n",
       "5       This thing is AWESOME! Everything they say on ...  \n",
       "6       It works only for milkshakes (ice cream and mi...  \n",
       "7       They were delivered on time. Great knives very...  \n",
       "8             Good quality. Check the size before you buy  \n",
       "9       Finally a serving bowl that allows chilling to...  \n",
       "10      As described.  Will be using it to grind chalk...  \n",
       "11      These are great for portion control.  I am slo...  \n",
       "12      I bought this pan for use everyday for both br...  \n",
       "13                                                   Good  \n",
       "14                expertly made very happy will but again  \n",
       "15      I use these filters for large loose teas in a ...  \n",
       "16      Love the colors in this mug.  Makes me feel ha...  \n",
       "17      Arrived on time & in great condition. Wonderfu...  \n",
       "18                Use it all the time to Fry Taco Shells!  \n",
       "19                                 It's works really well  \n",
       "20      Pretty nice ! Nothing else to say , very happy...  \n",
       "21      Very happy with these mugs. They stay cold for...  \n",
       "22      We bought this set a few years ago and now eve...  \n",
       "23      Use it often...only wish it had more temperatu...  \n",
       "24      These are SO cute! They were a lot bigger than...  \n",
       "25      This is a great addition to our summer camping...  \n",
       "26      Very easy to use and clean and makes yummy des...  \n",
       "27      This bottle is great! it does not leak , It fi...  \n",
       "28      Good Quality and works great.  Cookies don't s...  \n",
       "29      Works well and very similar to the Keriug for ...  \n",
       "...                                                   ...  \n",
       "999970  Great size for individual servings of just abo...  \n",
       "999971  I ordered this banner for my son's pirate them...  \n",
       "999972  My daughter loves it. We gave it to her with a...  \n",
       "999973  DO NOT BUY  BUILT NY.  This item I bought for ...  \n",
       "999974  These are beautiful, durable, and functional. ...  \n",
       "999975                                          It works.  \n",
       "999976  Much faster, more convenient and more effectiv...  \n",
       "999977                                              great  \n",
       "999978  I love the conveniece of adding powders at the...  \n",
       "999979  If you put a plate on it you can not read the ...  \n",
       "999980  I bought these juice glasses from Amazon sever...  \n",
       "999981                                    Work very well.  \n",
       "999982  This chocolate works so well in my machine, we...  \n",
       "999983                                         Wonderful!  \n",
       "999984  Love this product.  Easy to use and easier to ...  \n",
       "999985  Open a bottle with a cork. Unable to drink eve...  \n",
       "999986  Perfect !! Used it for a cake and it was perfe...  \n",
       "999987  Used it all winter in Costa Rica and saved a b...  \n",
       "999988  Bought this for my best friend and she loved i...  \n",
       "999989  Not heavyweight. Knives and forks were very fl...  \n",
       "999990  Very nice one, does the job well. I was surpri...  \n",
       "999991  The thermometer is not &#34;ultra-fast&#34; no...  \n",
       "999992  I've had one of these pans for many years and ...  \n",
       "999993  I bought this garlic peeler because it was che...  \n",
       "999994  I like to spiralize beets, carrots, turnips an...  \n",
       "999995  My brother got one of these thermometers and l...  \n",
       "999996  I was very sceptical when I first saw this thi...  \n",
       "999997  This stein looks really sharp, and I'm complet...  \n",
       "999998  My husband had to cut it off so it would fit o...  \n",
       "999999  The highest quality cutting board I have ever ...  \n",
       "\n",
       "[1000000 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview_data = pd.read_csv(preview_data_s3_address)\n",
    "display(preview_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above data includes strings which should be converted and/or filtered out. Let's reformulate our query."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "SELECT \n",
    "    product_category,\n",
    "    product_id,\n",
    "    product_title,\n",
    "    customer_id,\n",
    "    star_rating,\n",
    "    CASE WHEN star_rating >= 4.0 THEN 'positive'\n",
    "         WHEN star_rating <= 2.0 THEN 'negative'\n",
    "         ELSE 'neutral'\n",
    "    END as rate_category,\n",
    "    year,\n",
    "    review_date,\n",
    "    review_body\n",
    "FROM amazon_reviews_parquet\n",
    "WHERE product_category = 'Home' OR product_category = 'Kitchen'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "s3://pilho-sagemaker-ai-workshop-kr/athena/994330446047727608/e23aba7e-83cd-4854-b70e-6cb8cbe11f3d.csv\n"
     ]
    }
   ],
   "source": [
    "preview_data_s3_address = get_results_in_s3(f\"\"\"\n",
    "SELECT \n",
    "        product_category,\n",
    "        product_id,\n",
    "        product_title,\n",
    "        customer_id,\n",
    "        star_rating,\n",
    "        CASE WHEN star_rating >= 4.0 THEN 'positive'\n",
    "             WHEN star_rating <= 2.0 THEN 'negative'\n",
    "             ELSE 'neutral'\n",
    "        END as rate_category,\n",
    "        year,\n",
    "        review_date,\n",
    "        review_body\n",
    "    FROM amazon_reviews_parquet_query\n",
    "    WHERE product_category = 'Home' OR product_category = 'Kitchen'\n",
    "\"\"\")\n",
    "print(preview_data_s3_address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>rate_category</th>\n",
       "      <th>year</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000VZ57C</td>\n",
       "      <td>Helen’s Asian Kitchen Spiral Vegetable Slicer ...</td>\n",
       "      <td>20254534</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Joy can come from the oddest of pleasures, and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00HG190GG</td>\n",
       "      <td>Coffeeget 6 Cup 27 Oz French Press Coffee Make...</td>\n",
       "      <td>48432346</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>We very much like the design and utility of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B006LMVOC4</td>\n",
       "      <td>Capresso 261.04  teaC100 Temperature Controlle...</td>\n",
       "      <td>18413064</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>No more hissing and whistling.  This Water Ket...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00JLUP5U0</td>\n",
       "      <td>Epica Homemade Organic “Set and Go” Electric Y...</td>\n",
       "      <td>45792164</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Couldn't be happier. Everything as advertised</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00D6VFUVY</td>\n",
       "      <td>Grazia Silicone Muffin Pan, Red</td>\n",
       "      <td>51703863</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Since the Grazia Silicone Muffin Pan is made o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00649TYSA</td>\n",
       "      <td>Amco Digital Color Alert Kitchen Timer/Clock, ...</td>\n",
       "      <td>29099617</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>works like it should - well done!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004A7XQNM</td>\n",
       "      <td>Kikkerland Ladybug Kitchen Timer</td>\n",
       "      <td>23889456</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Cute, thx.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002LLOE8K</td>\n",
       "      <td>Cuisinart SP-2 Stainless Steel Rechargeable Sa...</td>\n",
       "      <td>50791231</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Don't buy these.  I have had two and they alwa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00DZ6CBHQ</td>\n",
       "      <td>Ifavor123 24pcs Floating Candles for Wedding P...</td>\n",
       "      <td>20738342</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>These work and are LARGE.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00MM6HAD0</td>\n",
       "      <td>1 X Veggie Spiral Slicer, Spiralizer, Vegetabl...</td>\n",
       "      <td>25602122</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Fun and easy to use!! My kids love when I make...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003V5GUZU</td>\n",
       "      <td>Oster CKSTRS71 18-Quart Roaster Oven with Buff...</td>\n",
       "      <td>1639978</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Product arrived in a timely manner and was rea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00D5CZ00A</td>\n",
       "      <td>Silicone Baking Mat - Various colors and sizes...</td>\n",
       "      <td>17027528</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Wonderfull</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0015ZWC4G</td>\n",
       "      <td>Victorinox 2-1/2-Inch Folding Blade Knife, Red...</td>\n",
       "      <td>51903519</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Beautiful tool but need to use it more often i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KIP79CY</td>\n",
       "      <td>Aunchitha Ice Ball Maker - Mold Makes 4 X 4.5 ...</td>\n",
       "      <td>36484568</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>The detail instruction is easy to follow. We t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EACE7RQ</td>\n",
       "      <td>Hello Kitty Cookie Cutter Mold A Set of Two</td>\n",
       "      <td>25131526</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Used these to make sugar cookies for the girls...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00GP40D4S</td>\n",
       "      <td>Rachael Ray Enamel on Steel 12-Quart Covered S...</td>\n",
       "      <td>48274373</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>great</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00IR77FF6</td>\n",
       "      <td>Contigo SnapSeal Double-Wall Travel Mug, 16-Ounce</td>\n",
       "      <td>16843211</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Great size, goes in the dishwasher, holds in h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00FWLJOZ4</td>\n",
       "      <td>Freshware KT-501 Instant Start-Stop Salad Spinner</td>\n",
       "      <td>15789058</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>While it removed some water from rinsed spinac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005BR7JJM</td>\n",
       "      <td>Morning Mug (1)</td>\n",
       "      <td>38861477</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I normally read reviews for items thoroughly b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004495PPS</td>\n",
       "      <td>Snapware Yarn Tainer Storage Container</td>\n",
       "      <td>3351781</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>It works for some large rolls but not all. The...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KWS4QG4</td>\n",
       "      <td>Tundra Extra Large Square Silicone Ice Cube Tr...</td>\n",
       "      <td>48101699</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>For the past 2 years, I've been the &amp;#34;Ice K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001TM6XWW</td>\n",
       "      <td>Hario Box of Paper Filter for 01 Dripper, 7.1 ...</td>\n",
       "      <td>12282095</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>works great and haven't had any problems with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00BTIUYOO</td>\n",
       "      <td>Hamilton Beach Brands 25475 Breakfast Sandwich...</td>\n",
       "      <td>42023596</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>It allows you to make these sandwiches in flash!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000U5NZ4I</td>\n",
       "      <td>Premium Quality Cast Iron Corn Grinder For Whe...</td>\n",
       "      <td>33756850</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This grinder was cheaper then I had expected i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008VGQD1A</td>\n",
       "      <td>Zojirushi Stainless Steel Food Jar (Stainless ...</td>\n",
       "      <td>5006932</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>i love it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KGQ77QI</td>\n",
       "      <td>Farberware Classic Never Needs Sharpening Stea...</td>\n",
       "      <td>17144145</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>great knives, as I would expect from Farberwar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000R4OJKG</td>\n",
       "      <td>Beistle 50509-BKR 1-Pack Black and Red Metalli...</td>\n",
       "      <td>23089429</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I liked it It's  bright and fun but it's huge....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00M3743UG</td>\n",
       "      <td>Sodastream Co2 Tank Paintball Canister Adapter...</td>\n",
       "      <td>27425231</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>The Adapter is a great idea but this one was n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00N2A6PB8</td>\n",
       "      <td>SUPER SHREDDER 4 Piece Anodized Aluminum Herb ...</td>\n",
       "      <td>42888050</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Awesome grinder! Works much better than my las...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001KBY9M8</td>\n",
       "      <td>Sunbeam Products 004722-000-000 Rice Cooker, 6...</td>\n",
       "      <td>1207762</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Great!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111368</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CXRL5RU</td>\n",
       "      <td>Carson Home Accents Original Rednek Wine Bottl...</td>\n",
       "      <td>15279969</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Its's a gift for a friend; I think he will lik...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111369</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000HAVOC6</td>\n",
       "      <td>Pyrex Simply Store Love 18-pc Storage Set</td>\n",
       "      <td>45205353</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>you won't be disappointed if glass tupperware ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111370</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00B4OYEHQ</td>\n",
       "      <td>Go Plus Nicer Dicer Multi Chopper Vegetable Cu...</td>\n",
       "      <td>12483311</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>THIS IS THE SECOND ONE I HAVE BOUGHT. THE FIRS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111371</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000MDB2QQ</td>\n",
       "      <td>Bormioli Rocco Sorgente Cooler Glasses, Set of...</td>\n",
       "      <td>48651798</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>These were a gift for a friend. He loves them ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111372</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00ABH0PYI</td>\n",
       "      <td>OXO Good Grips Cookie Press Disk Set</td>\n",
       "      <td>28847356</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>This press is the best cookie press that we ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111373</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EVQ167A</td>\n",
       "      <td>The New York Baking Company | 24-pack Reusable...</td>\n",
       "      <td>43931066</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I only recently heard about silicone baking cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111374</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000LNV91U</td>\n",
       "      <td>Rubbermaid 10 Quart Personal Ice Chest Cooler</td>\n",
       "      <td>23121607</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I like it. but is not tall enough for long nec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111375</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004PGM3C8</td>\n",
       "      <td>Gracie China Vintage Green Rose Porcelain</td>\n",
       "      <td>20988878</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>This was a fabulous deal. My wife never expect...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111376</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004YN239A</td>\n",
       "      <td>Handy Gourmet Cool Touch Microwave Bowl</td>\n",
       "      <td>16578294</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>cool touch microwave bowl is great, my only pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111377</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0009P34RM</td>\n",
       "      <td>Trudeau Board Room Travel Mug</td>\n",
       "      <td>13719369</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Its in a drawer after using it for a month.  N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111378</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00004ZBSH</td>\n",
       "      <td>Libbey Gibraltar 12-piece Rocks Glass Set</td>\n",
       "      <td>35110951</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>These were purchased to increase my number of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111379</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EZI26GO</td>\n",
       "      <td>Hamilton Beach Set 'n Forget Programmable Slow...</td>\n",
       "      <td>9885509</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>All I can say is I'm very pleased with my Hami...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111380</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B007KK7178</td>\n",
       "      <td>Paper Pinata with Pull Strings, Ladybug Fancy</td>\n",
       "      <td>6073690</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Very pretty, but flimsy and small. Falls apart...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111381</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CFTOXYI</td>\n",
       "      <td>DecoBros Crystal Tempered Glass Nespresso Orig...</td>\n",
       "      <td>19615946</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Beautifully done! Fits my  Nespresso Pods perf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111382</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00DUF4KCG</td>\n",
       "      <td>Sonic Scrubber Power Cleaner and Interchangeab...</td>\n",
       "      <td>21445999</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I had one of these for several years and was d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111383</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00462QPJ8</td>\n",
       "      <td>WMF Manaos / Bistro Espresso Paddle, Set of 4</td>\n",
       "      <td>14105754</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>These flat Paddle spoons are good quality and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111384</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0001V03X2</td>\n",
       "      <td>Norpro 4 Quart Steamer/Cooker Set 3pc, Stainle...</td>\n",
       "      <td>15998604</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Nice quality steamer, especially for the price...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111385</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00GTI75TG</td>\n",
       "      <td>16 Oz BPA Free Double Wall Clear Acrylic Mason...</td>\n",
       "      <td>13678696</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>You can buy this at a local dollar store. They...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111386</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002M9D4HI</td>\n",
       "      <td>Wilton Bake It Better Cake Tester</td>\n",
       "      <td>36899984</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Easy to hold and the cover is a big plus. I on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111387</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004JMZGM2</td>\n",
       "      <td>Cuisinart TOB-40N Custom Classic Toaster Oven ...</td>\n",
       "      <td>3379742</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Very good, had limited space and this was a go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111388</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000PS8E9G</td>\n",
       "      <td>Norpro Self Draining Salad Spinner</td>\n",
       "      <td>15002202</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I would return this item but with shipping etc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111389</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CJ1HF7O</td>\n",
       "      <td>Mr. Coffee Single Serve Coffee Brewer Powered ...</td>\n",
       "      <td>15383848</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Our Mr Coffee arrived as a replacement for our...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111390</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0039BJ3JG</td>\n",
       "      <td>Hamilton Beach 31230 Set &amp; Forget Toaster Oven...</td>\n",
       "      <td>19681569</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>It is easy to use. also the toaster oven has t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111391</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EVQ167A</td>\n",
       "      <td>The New York Baking Company | 24-pack Reusable...</td>\n",
       "      <td>36036515</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Perfect - exactly what I was looking for.  Fun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111392</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008MHT194</td>\n",
       "      <td>T-fal C79807 Ultimate Stainless Steel Copper-B...</td>\n",
       "      <td>32444627</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I love these pans!&lt;br /&gt;Worth every penny.&lt;br ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111393</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000GTR2F6</td>\n",
       "      <td>Keurig B70 Platinum Brewing System</td>\n",
       "      <td>23989344</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Coffee in seconds and so easy.  Anytime I want...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111394</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002PY7AYS</td>\n",
       "      <td>Thermos Stainless King 16 Ounce Travel Tumbler...</td>\n",
       "      <td>38251505</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I usually take two mugs of coffee to work each...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111395</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B007KYPQ44</td>\n",
       "      <td>Aladdin 24-Ounce Lunch Bowl Container</td>\n",
       "      <td>51702729</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>First and foremost, it absolutely did not keep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111396</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00FGD84LI</td>\n",
       "      <td>Eparé Stainless Steel Multi Spice Mill | Chili...</td>\n",
       "      <td>12731640</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>These spice grinders look attractive, they hav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111397</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002HG0TXI</td>\n",
       "      <td>Thermos Novelty Soft Lunch Kits</td>\n",
       "      <td>41585200</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>My son loves it. His lunch box is the talk of ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11111398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         product_category  product_id  \\\n",
       "0                 Kitchen  B0000VZ57C   \n",
       "1                 Kitchen  B00HG190GG   \n",
       "2                 Kitchen  B006LMVOC4   \n",
       "3                 Kitchen  B00JLUP5U0   \n",
       "4                 Kitchen  B00D6VFUVY   \n",
       "5                 Kitchen  B00649TYSA   \n",
       "6                 Kitchen  B004A7XQNM   \n",
       "7                 Kitchen  B002LLOE8K   \n",
       "8                 Kitchen  B00DZ6CBHQ   \n",
       "9                 Kitchen  B00MM6HAD0   \n",
       "10                Kitchen  B003V5GUZU   \n",
       "11                Kitchen  B00D5CZ00A   \n",
       "12                Kitchen  B0015ZWC4G   \n",
       "13                Kitchen  B00KIP79CY   \n",
       "14                Kitchen  B00EACE7RQ   \n",
       "15                Kitchen  B00GP40D4S   \n",
       "16                Kitchen  B00IR77FF6   \n",
       "17                Kitchen  B00FWLJOZ4   \n",
       "18                Kitchen  B005BR7JJM   \n",
       "19                Kitchen  B004495PPS   \n",
       "20                Kitchen  B00KWS4QG4   \n",
       "21                Kitchen  B001TM6XWW   \n",
       "22                Kitchen  B00BTIUYOO   \n",
       "23                Kitchen  B000U5NZ4I   \n",
       "24                Kitchen  B008VGQD1A   \n",
       "25                Kitchen  B00KGQ77QI   \n",
       "26                Kitchen  B000R4OJKG   \n",
       "27                Kitchen  B00M3743UG   \n",
       "28                Kitchen  B00N2A6PB8   \n",
       "29                Kitchen  B001KBY9M8   \n",
       "...                   ...         ...   \n",
       "11111368          Kitchen  B00CXRL5RU   \n",
       "11111369          Kitchen  B000HAVOC6   \n",
       "11111370          Kitchen  B00B4OYEHQ   \n",
       "11111371          Kitchen  B000MDB2QQ   \n",
       "11111372          Kitchen  B00ABH0PYI   \n",
       "11111373          Kitchen  B00EVQ167A   \n",
       "11111374          Kitchen  B000LNV91U   \n",
       "11111375          Kitchen  B004PGM3C8   \n",
       "11111376          Kitchen  B004YN239A   \n",
       "11111377          Kitchen  B0009P34RM   \n",
       "11111378          Kitchen  B00004ZBSH   \n",
       "11111379          Kitchen  B00EZI26GO   \n",
       "11111380          Kitchen  B007KK7178   \n",
       "11111381          Kitchen  B00CFTOXYI   \n",
       "11111382          Kitchen  B00DUF4KCG   \n",
       "11111383          Kitchen  B00462QPJ8   \n",
       "11111384          Kitchen  B0001V03X2   \n",
       "11111385          Kitchen  B00GTI75TG   \n",
       "11111386          Kitchen  B002M9D4HI   \n",
       "11111387          Kitchen  B004JMZGM2   \n",
       "11111388          Kitchen  B000PS8E9G   \n",
       "11111389          Kitchen  B00CJ1HF7O   \n",
       "11111390          Kitchen  B0039BJ3JG   \n",
       "11111391          Kitchen  B00EVQ167A   \n",
       "11111392          Kitchen  B008MHT194   \n",
       "11111393          Kitchen  B000GTR2F6   \n",
       "11111394          Kitchen  B002PY7AYS   \n",
       "11111395          Kitchen  B007KYPQ44   \n",
       "11111396          Kitchen  B00FGD84LI   \n",
       "11111397          Kitchen  B002HG0TXI   \n",
       "\n",
       "                                              product_title  customer_id  \\\n",
       "0         Helen’s Asian Kitchen Spiral Vegetable Slicer ...     20254534   \n",
       "1         Coffeeget 6 Cup 27 Oz French Press Coffee Make...     48432346   \n",
       "2         Capresso 261.04  teaC100 Temperature Controlle...     18413064   \n",
       "3         Epica Homemade Organic “Set and Go” Electric Y...     45792164   \n",
       "4                           Grazia Silicone Muffin Pan, Red     51703863   \n",
       "5         Amco Digital Color Alert Kitchen Timer/Clock, ...     29099617   \n",
       "6                          Kikkerland Ladybug Kitchen Timer     23889456   \n",
       "7         Cuisinart SP-2 Stainless Steel Rechargeable Sa...     50791231   \n",
       "8         Ifavor123 24pcs Floating Candles for Wedding P...     20738342   \n",
       "9         1 X Veggie Spiral Slicer, Spiralizer, Vegetabl...     25602122   \n",
       "10        Oster CKSTRS71 18-Quart Roaster Oven with Buff...      1639978   \n",
       "11        Silicone Baking Mat - Various colors and sizes...     17027528   \n",
       "12        Victorinox 2-1/2-Inch Folding Blade Knife, Red...     51903519   \n",
       "13        Aunchitha Ice Ball Maker - Mold Makes 4 X 4.5 ...     36484568   \n",
       "14              Hello Kitty Cookie Cutter Mold A Set of Two     25131526   \n",
       "15        Rachael Ray Enamel on Steel 12-Quart Covered S...     48274373   \n",
       "16        Contigo SnapSeal Double-Wall Travel Mug, 16-Ounce     16843211   \n",
       "17        Freshware KT-501 Instant Start-Stop Salad Spinner     15789058   \n",
       "18                                          Morning Mug (1)     38861477   \n",
       "19                   Snapware Yarn Tainer Storage Container      3351781   \n",
       "20        Tundra Extra Large Square Silicone Ice Cube Tr...     48101699   \n",
       "21        Hario Box of Paper Filter for 01 Dripper, 7.1 ...     12282095   \n",
       "22        Hamilton Beach Brands 25475 Breakfast Sandwich...     42023596   \n",
       "23        Premium Quality Cast Iron Corn Grinder For Whe...     33756850   \n",
       "24        Zojirushi Stainless Steel Food Jar (Stainless ...      5006932   \n",
       "25        Farberware Classic Never Needs Sharpening Stea...     17144145   \n",
       "26        Beistle 50509-BKR 1-Pack Black and Red Metalli...     23089429   \n",
       "27        Sodastream Co2 Tank Paintball Canister Adapter...     27425231   \n",
       "28        SUPER SHREDDER 4 Piece Anodized Aluminum Herb ...     42888050   \n",
       "29        Sunbeam Products 004722-000-000 Rice Cooker, 6...      1207762   \n",
       "...                                                     ...          ...   \n",
       "11111368  Carson Home Accents Original Rednek Wine Bottl...     15279969   \n",
       "11111369          Pyrex Simply Store Love 18-pc Storage Set     45205353   \n",
       "11111370  Go Plus Nicer Dicer Multi Chopper Vegetable Cu...     12483311   \n",
       "11111371  Bormioli Rocco Sorgente Cooler Glasses, Set of...     48651798   \n",
       "11111372               OXO Good Grips Cookie Press Disk Set     28847356   \n",
       "11111373  The New York Baking Company | 24-pack Reusable...     43931066   \n",
       "11111374      Rubbermaid 10 Quart Personal Ice Chest Cooler     23121607   \n",
       "11111375          Gracie China Vintage Green Rose Porcelain     20988878   \n",
       "11111376            Handy Gourmet Cool Touch Microwave Bowl     16578294   \n",
       "11111377                      Trudeau Board Room Travel Mug     13719369   \n",
       "11111378          Libbey Gibraltar 12-piece Rocks Glass Set     35110951   \n",
       "11111379  Hamilton Beach Set 'n Forget Programmable Slow...      9885509   \n",
       "11111380      Paper Pinata with Pull Strings, Ladybug Fancy      6073690   \n",
       "11111381  DecoBros Crystal Tempered Glass Nespresso Orig...     19615946   \n",
       "11111382  Sonic Scrubber Power Cleaner and Interchangeab...     21445999   \n",
       "11111383      WMF Manaos / Bistro Espresso Paddle, Set of 4     14105754   \n",
       "11111384  Norpro 4 Quart Steamer/Cooker Set 3pc, Stainle...     15998604   \n",
       "11111385  16 Oz BPA Free Double Wall Clear Acrylic Mason...     13678696   \n",
       "11111386                  Wilton Bake It Better Cake Tester     36899984   \n",
       "11111387  Cuisinart TOB-40N Custom Classic Toaster Oven ...      3379742   \n",
       "11111388                 Norpro Self Draining Salad Spinner     15002202   \n",
       "11111389  Mr. Coffee Single Serve Coffee Brewer Powered ...     15383848   \n",
       "11111390  Hamilton Beach 31230 Set & Forget Toaster Oven...     19681569   \n",
       "11111391  The New York Baking Company | 24-pack Reusable...     36036515   \n",
       "11111392  T-fal C79807 Ultimate Stainless Steel Copper-B...     32444627   \n",
       "11111393                 Keurig B70 Platinum Brewing System     23989344   \n",
       "11111394  Thermos Stainless King 16 Ounce Travel Tumbler...     38251505   \n",
       "11111395              Aladdin 24-Ounce Lunch Bowl Container     51702729   \n",
       "11111396  Eparé Stainless Steel Multi Spice Mill | Chili...     12731640   \n",
       "11111397                    Thermos Novelty Soft Lunch Kits     41585200   \n",
       "\n",
       "          star_rating rate_category  year  review_date  \\\n",
       "0                   5      positive  2014        16336   \n",
       "1                   5      positive  2014        16336   \n",
       "2                   5      positive  2014        16336   \n",
       "3                   5      positive  2014        16336   \n",
       "4                   5      positive  2014        16336   \n",
       "5                   5      positive  2014        16336   \n",
       "6                   5      positive  2014        16336   \n",
       "7                   1      negative  2014        16336   \n",
       "8                   4      positive  2014        16336   \n",
       "9                   5      positive  2014        16336   \n",
       "10                  4      positive  2014        16336   \n",
       "11                  5      positive  2014        16336   \n",
       "12                  3       neutral  2014        16336   \n",
       "13                  5      positive  2014        16336   \n",
       "14                  5      positive  2014        16336   \n",
       "15                  4      positive  2014        16336   \n",
       "16                  5      positive  2014        16336   \n",
       "17                  3       neutral  2014        16336   \n",
       "18                  1      negative  2014        16336   \n",
       "19                  4      positive  2014        16336   \n",
       "20                  5      positive  2014        16336   \n",
       "21                  5      positive  2014        16336   \n",
       "22                  5      positive  2014        16336   \n",
       "23                  1      negative  2014        16336   \n",
       "24                  5      positive  2014        16336   \n",
       "25                  5      positive  2014        16336   \n",
       "26                  4      positive  2014        16336   \n",
       "27                  2      negative  2014        16336   \n",
       "28                  5      positive  2014        16336   \n",
       "29                  5      positive  2014        16336   \n",
       "...               ...           ...   ...          ...   \n",
       "11111368            5      positive  2014        16111   \n",
       "11111369            5      positive  2014        16111   \n",
       "11111370            3       neutral  2014        16111   \n",
       "11111371            5      positive  2014        16111   \n",
       "11111372            5      positive  2014        16111   \n",
       "11111373            5      positive  2014        16111   \n",
       "11111374            4      positive  2014        16111   \n",
       "11111375            5      positive  2014        16111   \n",
       "11111376            5      positive  2014        16111   \n",
       "11111377            2      negative  2014        16111   \n",
       "11111378            5      positive  2014        16111   \n",
       "11111379            5      positive  2014        16111   \n",
       "11111380            1      negative  2014        16111   \n",
       "11111381            4      positive  2014        16111   \n",
       "11111382            5      positive  2014        16111   \n",
       "11111383            5      positive  2014        16111   \n",
       "11111384            5      positive  2014        16111   \n",
       "11111385            4      positive  2014        16111   \n",
       "11111386            5      positive  2014        16111   \n",
       "11111387            4      positive  2014        16111   \n",
       "11111388            1      negative  2014        16111   \n",
       "11111389            1      negative  2014        16111   \n",
       "11111390            5      positive  2014        16111   \n",
       "11111391            5      positive  2014        16111   \n",
       "11111392            5      positive  2014        16111   \n",
       "11111393            5      positive  2014        16111   \n",
       "11111394            5      positive  2014        16111   \n",
       "11111395            1      negative  2014        16111   \n",
       "11111396            5      positive  2014        16111   \n",
       "11111397            5      positive  2014        16111   \n",
       "\n",
       "                                                review_body  \n",
       "0         Joy can come from the oddest of pleasures, and...  \n",
       "1         We very much like the design and utility of th...  \n",
       "2         No more hissing and whistling.  This Water Ket...  \n",
       "3             Couldn't be happier. Everything as advertised  \n",
       "4         Since the Grazia Silicone Muffin Pan is made o...  \n",
       "5                         works like it should - well done!  \n",
       "6                                                Cute, thx.  \n",
       "7         Don't buy these.  I have had two and they alwa...  \n",
       "8                                 These work and are LARGE.  \n",
       "9         Fun and easy to use!! My kids love when I make...  \n",
       "10        Product arrived in a timely manner and was rea...  \n",
       "11                                               Wonderfull  \n",
       "12        Beautiful tool but need to use it more often i...  \n",
       "13        The detail instruction is easy to follow. We t...  \n",
       "14        Used these to make sugar cookies for the girls...  \n",
       "15                                                    great  \n",
       "16        Great size, goes in the dishwasher, holds in h...  \n",
       "17        While it removed some water from rinsed spinac...  \n",
       "18        I normally read reviews for items thoroughly b...  \n",
       "19        It works for some large rolls but not all. The...  \n",
       "20        For the past 2 years, I've been the &#34;Ice K...  \n",
       "21        works great and haven't had any problems with ...  \n",
       "22         It allows you to make these sandwiches in flash!  \n",
       "23        This grinder was cheaper then I had expected i...  \n",
       "24                                                i love it  \n",
       "25        great knives, as I would expect from Farberwar...  \n",
       "26        I liked it It's  bright and fun but it's huge....  \n",
       "27        The Adapter is a great idea but this one was n...  \n",
       "28        Awesome grinder! Works much better than my las...  \n",
       "29                                                   Great!  \n",
       "...                                                     ...  \n",
       "11111368  Its's a gift for a friend; I think he will lik...  \n",
       "11111369  you won't be disappointed if glass tupperware ...  \n",
       "11111370  THIS IS THE SECOND ONE I HAVE BOUGHT. THE FIRS...  \n",
       "11111371  These were a gift for a friend. He loves them ...  \n",
       "11111372  This press is the best cookie press that we ha...  \n",
       "11111373  I only recently heard about silicone baking cu...  \n",
       "11111374  I like it. but is not tall enough for long nec...  \n",
       "11111375  This was a fabulous deal. My wife never expect...  \n",
       "11111376  cool touch microwave bowl is great, my only pr...  \n",
       "11111377  Its in a drawer after using it for a month.  N...  \n",
       "11111378  These were purchased to increase my number of ...  \n",
       "11111379  All I can say is I'm very pleased with my Hami...  \n",
       "11111380  Very pretty, but flimsy and small. Falls apart...  \n",
       "11111381  Beautifully done! Fits my  Nespresso Pods perf...  \n",
       "11111382  I had one of these for several years and was d...  \n",
       "11111383  These flat Paddle spoons are good quality and ...  \n",
       "11111384  Nice quality steamer, especially for the price...  \n",
       "11111385  You can buy this at a local dollar store. They...  \n",
       "11111386  Easy to hold and the cover is a big plus. I on...  \n",
       "11111387  Very good, had limited space and this was a go...  \n",
       "11111388  I would return this item but with shipping etc...  \n",
       "11111389  Our Mr Coffee arrived as a replacement for our...  \n",
       "11111390  It is easy to use. also the toaster oven has t...  \n",
       "11111391  Perfect - exactly what I was looking for.  Fun...  \n",
       "11111392  I love these pans!<br />Worth every penny.<br ...  \n",
       "11111393  Coffee in seconds and so easy.  Anytime I want...  \n",
       "11111394  I usually take two mugs of coffee to work each...  \n",
       "11111395  First and foremost, it absolutely did not keep...  \n",
       "11111396  These spice grinders look attractive, they hav...  \n",
       "11111397  My son loves it. His lunch box is the talk of ...  \n",
       "\n",
       "[11111398 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1000000 review samples: 745.4 MB\n",
    "source_data = pd.read_csv(preview_data_s3_address)\n",
    "display(source_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(len(source_data['review_body'][0].split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "8    False\n",
       "9    False\n",
       "Name: review_body, dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_data['review_body'][:10].apply(lambda x: len(x) > 100 and len(x.split())>=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00D6VFUVY</td>\n",
       "      <td>Grazia Silicone Muffin Pan, Red</td>\n",
       "      <td>51703863</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Since the Grazia Silicone Muffin Pan is made o...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_category  product_id                    product_title  customer_id  \\\n",
       "4          Kitchen  B00D6VFUVY  Grazia Silicone Muffin Pan, Red     51703863   \n",
       "\n",
       "   star_rating rate_category  year  review_date  \\\n",
       "4            5      positive  2014        16336   \n",
       "\n",
       "                                         review_body  \n",
       "4  Since the Grazia Silicone Muffin Pan is made o...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_data[4:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00D6VFUVY</td>\n",
       "      <td>Grazia Silicone Muffin Pan, Red</td>\n",
       "      <td>51703863</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Since the Grazia Silicone Muffin Pan is made o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002LLOE8K</td>\n",
       "      <td>Cuisinart SP-2 Stainless Steel Rechargeable Sa...</td>\n",
       "      <td>50791231</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Don't buy these.  I have had two and they alwa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KIP79CY</td>\n",
       "      <td>Aunchitha Ice Ball Maker - Mold Makes 4 X 4.5 ...</td>\n",
       "      <td>36484568</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>The detail instruction is easy to follow. We t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EACE7RQ</td>\n",
       "      <td>Hello Kitty Cookie Cutter Mold A Set of Two</td>\n",
       "      <td>25131526</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Used these to make sugar cookies for the girls...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00FWLJOZ4</td>\n",
       "      <td>Freshware KT-501 Instant Start-Stop Salad Spinner</td>\n",
       "      <td>15789058</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>While it removed some water from rinsed spinac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005BR7JJM</td>\n",
       "      <td>Morning Mug (1)</td>\n",
       "      <td>38861477</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I normally read reviews for items thoroughly b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KWS4QG4</td>\n",
       "      <td>Tundra Extra Large Square Silicone Ice Cube Tr...</td>\n",
       "      <td>48101699</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>For the past 2 years, I've been the &amp;#34;Ice K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000AR7SY</td>\n",
       "      <td>Capresso Infinity Conical Burr Grinders</td>\n",
       "      <td>21959784</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This is now a year and a half old and the plas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002RT91FM</td>\n",
       "      <td>DeLonghi GM6000 Gelato Maker with Self-Refrige...</td>\n",
       "      <td>1175932</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This machine is amazing!!! Five stars are not ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00GURCTAG</td>\n",
       "      <td>Eparé Milk Frother Whisk- Electric Battery Ope...</td>\n",
       "      <td>15492352</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Loved this.  Worked really well.  Stopped work...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00KVLPUB2</td>\n",
       "      <td>EatSmart Precision Elite Thermocouple Food The...</td>\n",
       "      <td>37958169</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I got this tool for my hubby who cooks the mea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00004W4UP</td>\n",
       "      <td>Nordic Ware Pro Pop Popper</td>\n",
       "      <td>12944640</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Ah, now this was a product worth purchasing! F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003QTEBPW</td>\n",
       "      <td>Thermos Funtainer 10-Ounce Food Jars</td>\n",
       "      <td>25031985</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I really like the fun image of Toy Store on th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00JYGSO18</td>\n",
       "      <td>Farberware New Traditions Speckled Aluminum No...</td>\n",
       "      <td>28999386</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I'm delighted to have this practical, good-loo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00LFPS0CY</td>\n",
       "      <td>Hydracentials Stainless Steel Vacuum Insulated...</td>\n",
       "      <td>38473607</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>My wife needed a new water bottle after the st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002K25WUO</td>\n",
       "      <td>Laptop Lunches Bento-Ware Bento Buddies, Made ...</td>\n",
       "      <td>12987916</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>These are really nice containers - the only re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00D6SRLSW</td>\n",
       "      <td>Epica Stainless Steel Drinking Straws, Set of ...</td>\n",
       "      <td>51352034</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I am so happy with this purchase! These are gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000BYC4B</td>\n",
       "      <td>Ball Color 6-Pack Lids and Bands, Blue</td>\n",
       "      <td>11733126</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Amazon is one of the only places where I could...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000RZRYF2</td>\n",
       "      <td>iSi 10-Pack N2O Cream Whipper Chargers</td>\n",
       "      <td>15782661</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Works excellent with my new whipper.  Only one...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00J4H337C</td>\n",
       "      <td>StarPack Premium Silicone Baking Mats and Kitc...</td>\n",
       "      <td>21749478</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This review is for the Utensil only for now, h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0089X2RH4</td>\n",
       "      <td>ChefLand Squeeze Bottles, Clear, 8-Ounce</td>\n",
       "      <td>27986794</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>LOVE these things. I cook alot, so these are a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000VLYT0</td>\n",
       "      <td>Norpro 3971 Nonstick Mini Popover Pan, 12 Count</td>\n",
       "      <td>20097333</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>I haven't baked in these yet! However, I can t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00C811Q0M</td>\n",
       "      <td>Cuisinart Four Slice Compact Stainless Toaster</td>\n",
       "      <td>22912326</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Had to replace an appliance I had a REALLY lon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0067NRC78</td>\n",
       "      <td>Paula Deen Signature Nonstick Cookware Set</td>\n",
       "      <td>18872725</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>We bought this pot and pan set because we rece...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005PFU6U4</td>\n",
       "      <td>Ekco Meat Tenderizer</td>\n",
       "      <td>32408971</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Known in the house as the 'thumpy thing' this ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00004W4UP</td>\n",
       "      <td>Nordic Ware Pro Pop Popper</td>\n",
       "      <td>11072254</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>Amazed! I really don't like to use a microwave...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00FQIUW9A</td>\n",
       "      <td>Capresso EC PRO Advanced Pump Boiler Professio...</td>\n",
       "      <td>52064981</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This machine gives me SOME mixed feelings beca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004K6KIPC</td>\n",
       "      <td>Japanese Plastic Fruit Vegetable Rice Washing ...</td>\n",
       "      <td>29149696</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>It is alright as a fruit and vegetable wash bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0046Y90PW</td>\n",
       "      <td>Silicone Basting Pastry Brush</td>\n",
       "      <td>19180730</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>The top questions I've received on the product...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B004JMZGM2</td>\n",
       "      <td>Cuisinart TOB-40N Custom Classic Toaster Oven ...</td>\n",
       "      <td>52628372</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16336</td>\n",
       "      <td>This toaster oven is everything you would expe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111311</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002QXOF7I</td>\n",
       "      <td>Hamilton Beach 40870 Stainless Steel 10-Cup El...</td>\n",
       "      <td>12152841</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16110</td>\n",
       "      <td>Heating water for french press has never been ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111312</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001GXRQNO</td>\n",
       "      <td>Granite Ware 6169-4 Stock Pot, 5.75-Quart</td>\n",
       "      <td>45311750</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16110</td>\n",
       "      <td>I love this pot and want more of them. I make ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111317</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00E5ICAZQ</td>\n",
       "      <td>TribalSensation® Sugarcraft Clay Sculpting Cak...</td>\n",
       "      <td>20007650</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16110</td>\n",
       "      <td>The first extruder I received was faulty, the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111318</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001C2KY7Y</td>\n",
       "      <td>Cuisinart CBK-100 2-Pound Programmable Breadmaker</td>\n",
       "      <td>22972021</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16110</td>\n",
       "      <td>I've only owned one other breadmaker so not mu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111326</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0006ONQOC</td>\n",
       "      <td>Cuisinart Pure Indulgence Frozen Yogurt Sorbet...</td>\n",
       "      <td>45583421</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>The cuisinart ice cream maker creates the best...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111330</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0000CF66W</td>\n",
       "      <td>Lodge L8SGP3ASHH41B Cast Iron Square Grill Pan...</td>\n",
       "      <td>44941607</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I love cooking on this in the winter. Nice tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111332</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CAX0AYG</td>\n",
       "      <td>The Smart Kitchen Press Measure Vinegar &amp; Oil ...</td>\n",
       "      <td>21154011</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I was so excited about this and it worked fine...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111333</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001THGPDO</td>\n",
       "      <td>OXO Good Grips V-Blade Mandoline Slicer</td>\n",
       "      <td>16102936</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>It's good at its intended purpose. It slices t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111334</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000X9EPT0</td>\n",
       "      <td>Starfrit Rotato Express Electric Peeler</td>\n",
       "      <td>5621353</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I got this product so that I could plug it int...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111337</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000I6147Y</td>\n",
       "      <td>Prodyne 126-B Bamboo Cheese Slicer</td>\n",
       "      <td>49704575</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>This cheese slicer got a ton of use for years ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111341</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005OYE7HE</td>\n",
       "      <td>Hamilton Beach 7 Quart Stay Or Go Slow Cooker</td>\n",
       "      <td>13913534</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I bought this for my daughter for her birthday...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111342</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003O976HY</td>\n",
       "      <td>BUNN HG Phase Brew 8-Cup Home Coffee Brewer</td>\n",
       "      <td>49933819</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>The Bunn HG Phase made good coffee for a week ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111344</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00394NU7Y</td>\n",
       "      <td>KitchenAid Nylon Slotted Spoon</td>\n",
       "      <td>45155961</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>It's not bad for a plastic spoon. It's better ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111347</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00F2RQDUM</td>\n",
       "      <td>Salt Grinder and Pepper Mill Made From Durable...</td>\n",
       "      <td>45916858</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I ordered these as a gift.  Loved the appearan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111348</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B001ULC93O</td>\n",
       "      <td>Norpro Silicone Egg Pancake Ring Round</td>\n",
       "      <td>52827940</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>There is a thick side and a thin side.  Keep t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111353</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0073IE42W</td>\n",
       "      <td>Tribest GSE-5000-220V 220V Green Star Twin-Gea...</td>\n",
       "      <td>13885756</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I am the number one fan of green star, but thi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111355</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000MAFJRM</td>\n",
       "      <td>Zojirushi CV-DSC40 VE Hybrid Water Boiler and ...</td>\n",
       "      <td>47035621</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Pros:&lt;br /&gt;Convenience - great to have hot wat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111357</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000N22JPE</td>\n",
       "      <td>Cuisinart DCC-1150 Classic 10-Cup Thermal Prog...</td>\n",
       "      <td>44835802</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>We've had another Cuisinart (http://www.amazon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111358</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0042IWIWY</td>\n",
       "      <td>JapanBargain Set of 3, Ice Cube Tray Sakura St...</td>\n",
       "      <td>15888385</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I ordered in a rush and didn't realize the siz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111360</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B005JLEH2W</td>\n",
       "      <td>Corningware Etch Bakeware Dish with Lid</td>\n",
       "      <td>18178589</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I've recently begun collecting pieces of Corni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111363</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B0001RT1OS</td>\n",
       "      <td>aerolatte</td>\n",
       "      <td>17155138</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Somewhere deep in storage, I have the satin ve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111364</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B008AFNUTU</td>\n",
       "      <td>T-Disc Storage Drawer Holder for 54 Packs by D...</td>\n",
       "      <td>12965462</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>We had the machine for more than 2 years and w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111367</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B003MCP9SG</td>\n",
       "      <td>Mountain Woods Ceramic Bowl with Wood Tray Sna...</td>\n",
       "      <td>12664561</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>My 89-year-old grandfather used to keep four o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111370</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00B4OYEHQ</td>\n",
       "      <td>Go Plus Nicer Dicer Multi Chopper Vegetable Cu...</td>\n",
       "      <td>12483311</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>THIS IS THE SECOND ONE I HAVE BOUGHT. THE FIRS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111373</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EVQ167A</td>\n",
       "      <td>The New York Baking Company | 24-pack Reusable...</td>\n",
       "      <td>43931066</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I only recently heard about silicone baking cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111381</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CFTOXYI</td>\n",
       "      <td>DecoBros Crystal Tempered Glass Nespresso Orig...</td>\n",
       "      <td>19615946</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Beautifully done! Fits my  Nespresso Pods perf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111382</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00DUF4KCG</td>\n",
       "      <td>Sonic Scrubber Power Cleaner and Interchangeab...</td>\n",
       "      <td>21445999</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I had one of these for several years and was d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111389</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00CJ1HF7O</td>\n",
       "      <td>Mr. Coffee Single Serve Coffee Brewer Powered ...</td>\n",
       "      <td>15383848</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>Our Mr Coffee arrived as a replacement for our...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111394</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B002PY7AYS</td>\n",
       "      <td>Thermos Stainless King 16 Ounce Travel Tumbler...</td>\n",
       "      <td>38251505</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>I usually take two mugs of coffee to work each...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11111395</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B007KYPQ44</td>\n",
       "      <td>Aladdin 24-Ounce Lunch Bowl Container</td>\n",
       "      <td>51702729</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2014</td>\n",
       "      <td>16111</td>\n",
       "      <td>First and foremost, it absolutely did not keep...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3628827 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         product_category  product_id  \\\n",
       "4                 Kitchen  B00D6VFUVY   \n",
       "7                 Kitchen  B002LLOE8K   \n",
       "13                Kitchen  B00KIP79CY   \n",
       "14                Kitchen  B00EACE7RQ   \n",
       "17                Kitchen  B00FWLJOZ4   \n",
       "18                Kitchen  B005BR7JJM   \n",
       "20                Kitchen  B00KWS4QG4   \n",
       "31                Kitchen  B0000AR7SY   \n",
       "32                Kitchen  B002RT91FM   \n",
       "42                Kitchen  B00GURCTAG   \n",
       "50                Kitchen  B00KVLPUB2   \n",
       "53                Kitchen  B00004W4UP   \n",
       "58                Kitchen  B003QTEBPW   \n",
       "60                Kitchen  B00JYGSO18   \n",
       "61                Kitchen  B00LFPS0CY   \n",
       "63                Kitchen  B002K25WUO   \n",
       "66                Kitchen  B00D6SRLSW   \n",
       "67                Kitchen  B0000BYC4B   \n",
       "68                Kitchen  B000RZRYF2   \n",
       "71                Kitchen  B00J4H337C   \n",
       "72                Kitchen  B0089X2RH4   \n",
       "79                Kitchen  B0000VLYT0   \n",
       "80                Kitchen  B00C811Q0M   \n",
       "82                Kitchen  B0067NRC78   \n",
       "83                Kitchen  B005PFU6U4   \n",
       "86                Kitchen  B00004W4UP   \n",
       "98                Kitchen  B00FQIUW9A   \n",
       "118               Kitchen  B004K6KIPC   \n",
       "123               Kitchen  B0046Y90PW   \n",
       "129               Kitchen  B004JMZGM2   \n",
       "...                   ...         ...   \n",
       "11111311          Kitchen  B002QXOF7I   \n",
       "11111312          Kitchen  B001GXRQNO   \n",
       "11111317          Kitchen  B00E5ICAZQ   \n",
       "11111318          Kitchen  B001C2KY7Y   \n",
       "11111326          Kitchen  B0006ONQOC   \n",
       "11111330          Kitchen  B0000CF66W   \n",
       "11111332          Kitchen  B00CAX0AYG   \n",
       "11111333          Kitchen  B001THGPDO   \n",
       "11111334          Kitchen  B000X9EPT0   \n",
       "11111337          Kitchen  B000I6147Y   \n",
       "11111341          Kitchen  B005OYE7HE   \n",
       "11111342          Kitchen  B003O976HY   \n",
       "11111344          Kitchen  B00394NU7Y   \n",
       "11111347          Kitchen  B00F2RQDUM   \n",
       "11111348          Kitchen  B001ULC93O   \n",
       "11111353          Kitchen  B0073IE42W   \n",
       "11111355          Kitchen  B000MAFJRM   \n",
       "11111357          Kitchen  B000N22JPE   \n",
       "11111358          Kitchen  B0042IWIWY   \n",
       "11111360          Kitchen  B005JLEH2W   \n",
       "11111363          Kitchen  B0001RT1OS   \n",
       "11111364          Kitchen  B008AFNUTU   \n",
       "11111367          Kitchen  B003MCP9SG   \n",
       "11111370          Kitchen  B00B4OYEHQ   \n",
       "11111373          Kitchen  B00EVQ167A   \n",
       "11111381          Kitchen  B00CFTOXYI   \n",
       "11111382          Kitchen  B00DUF4KCG   \n",
       "11111389          Kitchen  B00CJ1HF7O   \n",
       "11111394          Kitchen  B002PY7AYS   \n",
       "11111395          Kitchen  B007KYPQ44   \n",
       "\n",
       "                                              product_title  customer_id  \\\n",
       "4                           Grazia Silicone Muffin Pan, Red     51703863   \n",
       "7         Cuisinart SP-2 Stainless Steel Rechargeable Sa...     50791231   \n",
       "13        Aunchitha Ice Ball Maker - Mold Makes 4 X 4.5 ...     36484568   \n",
       "14              Hello Kitty Cookie Cutter Mold A Set of Two     25131526   \n",
       "17        Freshware KT-501 Instant Start-Stop Salad Spinner     15789058   \n",
       "18                                          Morning Mug (1)     38861477   \n",
       "20        Tundra Extra Large Square Silicone Ice Cube Tr...     48101699   \n",
       "31                  Capresso Infinity Conical Burr Grinders     21959784   \n",
       "32        DeLonghi GM6000 Gelato Maker with Self-Refrige...      1175932   \n",
       "42        Eparé Milk Frother Whisk- Electric Battery Ope...     15492352   \n",
       "50        EatSmart Precision Elite Thermocouple Food The...     37958169   \n",
       "53                               Nordic Ware Pro Pop Popper     12944640   \n",
       "58                     Thermos Funtainer 10-Ounce Food Jars     25031985   \n",
       "60        Farberware New Traditions Speckled Aluminum No...     28999386   \n",
       "61        Hydracentials Stainless Steel Vacuum Insulated...     38473607   \n",
       "63        Laptop Lunches Bento-Ware Bento Buddies, Made ...     12987916   \n",
       "66        Epica Stainless Steel Drinking Straws, Set of ...     51352034   \n",
       "67                   Ball Color 6-Pack Lids and Bands, Blue     11733126   \n",
       "68                   iSi 10-Pack N2O Cream Whipper Chargers     15782661   \n",
       "71        StarPack Premium Silicone Baking Mats and Kitc...     21749478   \n",
       "72                 ChefLand Squeeze Bottles, Clear, 8-Ounce     27986794   \n",
       "79          Norpro 3971 Nonstick Mini Popover Pan, 12 Count     20097333   \n",
       "80           Cuisinart Four Slice Compact Stainless Toaster     22912326   \n",
       "82               Paula Deen Signature Nonstick Cookware Set     18872725   \n",
       "83                                     Ekco Meat Tenderizer     32408971   \n",
       "86                               Nordic Ware Pro Pop Popper     11072254   \n",
       "98        Capresso EC PRO Advanced Pump Boiler Professio...     52064981   \n",
       "118       Japanese Plastic Fruit Vegetable Rice Washing ...     29149696   \n",
       "123                           Silicone Basting Pastry Brush     19180730   \n",
       "129       Cuisinart TOB-40N Custom Classic Toaster Oven ...     52628372   \n",
       "...                                                     ...          ...   \n",
       "11111311  Hamilton Beach 40870 Stainless Steel 10-Cup El...     12152841   \n",
       "11111312          Granite Ware 6169-4 Stock Pot, 5.75-Quart     45311750   \n",
       "11111317  TribalSensation® Sugarcraft Clay Sculpting Cak...     20007650   \n",
       "11111318  Cuisinart CBK-100 2-Pound Programmable Breadmaker     22972021   \n",
       "11111326  Cuisinart Pure Indulgence Frozen Yogurt Sorbet...     45583421   \n",
       "11111330  Lodge L8SGP3ASHH41B Cast Iron Square Grill Pan...     44941607   \n",
       "11111332  The Smart Kitchen Press Measure Vinegar & Oil ...     21154011   \n",
       "11111333            OXO Good Grips V-Blade Mandoline Slicer     16102936   \n",
       "11111334            Starfrit Rotato Express Electric Peeler      5621353   \n",
       "11111337                 Prodyne 126-B Bamboo Cheese Slicer     49704575   \n",
       "11111341      Hamilton Beach 7 Quart Stay Or Go Slow Cooker     13913534   \n",
       "11111342        BUNN HG Phase Brew 8-Cup Home Coffee Brewer     49933819   \n",
       "11111344                     KitchenAid Nylon Slotted Spoon     45155961   \n",
       "11111347  Salt Grinder and Pepper Mill Made From Durable...     45916858   \n",
       "11111348             Norpro Silicone Egg Pancake Ring Round     52827940   \n",
       "11111353  Tribest GSE-5000-220V 220V Green Star Twin-Gea...     13885756   \n",
       "11111355  Zojirushi CV-DSC40 VE Hybrid Water Boiler and ...     47035621   \n",
       "11111357  Cuisinart DCC-1150 Classic 10-Cup Thermal Prog...     44835802   \n",
       "11111358  JapanBargain Set of 3, Ice Cube Tray Sakura St...     15888385   \n",
       "11111360            Corningware Etch Bakeware Dish with Lid     18178589   \n",
       "11111363                                          aerolatte     17155138   \n",
       "11111364  T-Disc Storage Drawer Holder for 54 Packs by D...     12965462   \n",
       "11111367  Mountain Woods Ceramic Bowl with Wood Tray Sna...     12664561   \n",
       "11111370  Go Plus Nicer Dicer Multi Chopper Vegetable Cu...     12483311   \n",
       "11111373  The New York Baking Company | 24-pack Reusable...     43931066   \n",
       "11111381  DecoBros Crystal Tempered Glass Nespresso Orig...     19615946   \n",
       "11111382  Sonic Scrubber Power Cleaner and Interchangeab...     21445999   \n",
       "11111389  Mr. Coffee Single Serve Coffee Brewer Powered ...     15383848   \n",
       "11111394  Thermos Stainless King 16 Ounce Travel Tumbler...     38251505   \n",
       "11111395              Aladdin 24-Ounce Lunch Bowl Container     51702729   \n",
       "\n",
       "          star_rating rate_category  year  review_date  \\\n",
       "4                   5      positive  2014        16336   \n",
       "7                   1      negative  2014        16336   \n",
       "13                  5      positive  2014        16336   \n",
       "14                  5      positive  2014        16336   \n",
       "17                  3       neutral  2014        16336   \n",
       "18                  1      negative  2014        16336   \n",
       "20                  5      positive  2014        16336   \n",
       "31                  1      negative  2014        16336   \n",
       "32                  5      positive  2014        16336   \n",
       "42                  5      positive  2014        16336   \n",
       "50                  4      positive  2014        16336   \n",
       "53                  5      positive  2014        16336   \n",
       "58                  3       neutral  2014        16336   \n",
       "60                  5      positive  2014        16336   \n",
       "61                  5      positive  2014        16336   \n",
       "63                  4      positive  2014        16336   \n",
       "66                  5      positive  2014        16336   \n",
       "67                  5      positive  2014        16336   \n",
       "68                  5      positive  2014        16336   \n",
       "71                  5      positive  2014        16336   \n",
       "72                  5      positive  2014        16336   \n",
       "79                  5      positive  2014        16336   \n",
       "80                  3       neutral  2014        16336   \n",
       "82                  5      positive  2014        16336   \n",
       "83                  5      positive  2014        16336   \n",
       "86                  5      positive  2014        16336   \n",
       "98                  5      positive  2014        16336   \n",
       "118                 3       neutral  2014        16336   \n",
       "123                 5      positive  2014        16336   \n",
       "129                 5      positive  2014        16336   \n",
       "...               ...           ...   ...          ...   \n",
       "11111311            5      positive  2014        16110   \n",
       "11111312            5      positive  2014        16110   \n",
       "11111317            4      positive  2014        16110   \n",
       "11111318            4      positive  2014        16110   \n",
       "11111326            5      positive  2014        16111   \n",
       "11111330            5      positive  2014        16111   \n",
       "11111332            1      negative  2014        16111   \n",
       "11111333            3       neutral  2014        16111   \n",
       "11111334            3       neutral  2014        16111   \n",
       "11111337            3       neutral  2014        16111   \n",
       "11111341            5      positive  2014        16111   \n",
       "11111342            1      negative  2014        16111   \n",
       "11111344            3       neutral  2014        16111   \n",
       "11111347            1      negative  2014        16111   \n",
       "11111348            5      positive  2014        16111   \n",
       "11111353            3       neutral  2014        16111   \n",
       "11111355            5      positive  2014        16111   \n",
       "11111357            5      positive  2014        16111   \n",
       "11111358            4      positive  2014        16111   \n",
       "11111360            5      positive  2014        16111   \n",
       "11111363            5      positive  2014        16111   \n",
       "11111364            5      positive  2014        16111   \n",
       "11111367            5      positive  2014        16111   \n",
       "11111370            3       neutral  2014        16111   \n",
       "11111373            5      positive  2014        16111   \n",
       "11111381            4      positive  2014        16111   \n",
       "11111382            5      positive  2014        16111   \n",
       "11111389            1      negative  2014        16111   \n",
       "11111394            5      positive  2014        16111   \n",
       "11111395            1      negative  2014        16111   \n",
       "\n",
       "                                                review_body  \n",
       "4         Since the Grazia Silicone Muffin Pan is made o...  \n",
       "7         Don't buy these.  I have had two and they alwa...  \n",
       "13        The detail instruction is easy to follow. We t...  \n",
       "14        Used these to make sugar cookies for the girls...  \n",
       "17        While it removed some water from rinsed spinac...  \n",
       "18        I normally read reviews for items thoroughly b...  \n",
       "20        For the past 2 years, I've been the &#34;Ice K...  \n",
       "31        This is now a year and a half old and the plas...  \n",
       "32        This machine is amazing!!! Five stars are not ...  \n",
       "42        Loved this.  Worked really well.  Stopped work...  \n",
       "50        I got this tool for my hubby who cooks the mea...  \n",
       "53        Ah, now this was a product worth purchasing! F...  \n",
       "58        I really like the fun image of Toy Store on th...  \n",
       "60        I'm delighted to have this practical, good-loo...  \n",
       "61        My wife needed a new water bottle after the st...  \n",
       "63        These are really nice containers - the only re...  \n",
       "66        I am so happy with this purchase! These are gr...  \n",
       "67        Amazon is one of the only places where I could...  \n",
       "68        Works excellent with my new whipper.  Only one...  \n",
       "71        This review is for the Utensil only for now, h...  \n",
       "72        LOVE these things. I cook alot, so these are a...  \n",
       "79        I haven't baked in these yet! However, I can t...  \n",
       "80        Had to replace an appliance I had a REALLY lon...  \n",
       "82        We bought this pot and pan set because we rece...  \n",
       "83        Known in the house as the 'thumpy thing' this ...  \n",
       "86        Amazed! I really don't like to use a microwave...  \n",
       "98        This machine gives me SOME mixed feelings beca...  \n",
       "118       It is alright as a fruit and vegetable wash bo...  \n",
       "123       The top questions I've received on the product...  \n",
       "129       This toaster oven is everything you would expe...  \n",
       "...                                                     ...  \n",
       "11111311  Heating water for french press has never been ...  \n",
       "11111312  I love this pot and want more of them. I make ...  \n",
       "11111317  The first extruder I received was faulty, the ...  \n",
       "11111318  I've only owned one other breadmaker so not mu...  \n",
       "11111326  The cuisinart ice cream maker creates the best...  \n",
       "11111330  I love cooking on this in the winter. Nice tri...  \n",
       "11111332  I was so excited about this and it worked fine...  \n",
       "11111333  It's good at its intended purpose. It slices t...  \n",
       "11111334  I got this product so that I could plug it int...  \n",
       "11111337  This cheese slicer got a ton of use for years ...  \n",
       "11111341  I bought this for my daughter for her birthday...  \n",
       "11111342  The Bunn HG Phase made good coffee for a week ...  \n",
       "11111344  It's not bad for a plastic spoon. It's better ...  \n",
       "11111347  I ordered these as a gift.  Loved the appearan...  \n",
       "11111348  There is a thick side and a thin side.  Keep t...  \n",
       "11111353  I am the number one fan of green star, but thi...  \n",
       "11111355  Pros:<br />Convenience - great to have hot wat...  \n",
       "11111357  We've had another Cuisinart (http://www.amazon...  \n",
       "11111358  I ordered in a rush and didn't realize the siz...  \n",
       "11111360  I've recently begun collecting pieces of Corni...  \n",
       "11111363  Somewhere deep in storage, I have the satin ve...  \n",
       "11111364  We had the machine for more than 2 years and w...  \n",
       "11111367  My 89-year-old grandfather used to keep four o...  \n",
       "11111370  THIS IS THE SECOND ONE I HAVE BOUGHT. THE FIRS...  \n",
       "11111373  I only recently heard about silicone baking cu...  \n",
       "11111381  Beautifully done! Fits my  Nespresso Pods perf...  \n",
       "11111382  I had one of these for several years and was d...  \n",
       "11111389  Our Mr Coffee arrived as a replacement for our...  \n",
       "11111394  I usually take two mugs of coffee to work each...  \n",
       "11111395  First and foremost, it absolutely did not keep...  \n",
       "\n",
       "[3628827 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_data[source_data['review_body'].apply(lambda x: ~pd.isna(x) and (type(x) is str) and len(x) > 100 and len(x.split())>=45)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'filtered_home_kitchen_data.npy', source_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_source_data = source_data.groupby('rate_category').apply(lambda x: x.sample(n=6000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'filtered_home_kitchen_data_6000_samples_by_each_rate_category.npy', source_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>rate_category</th>\n",
       "      <th>year</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_body</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <th>4547032</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B00EI7DPPI</td>\n",
       "      <td>Hamilton Beach 49980Z Two Way Brewer Single Se...</td>\n",
       "      <td>9552334</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16440</td>\n",
       "      <td>The convenience of the single cup for just one...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>negative</th>\n",
       "      <th>2684373</th>\n",
       "      <td>Home</td>\n",
       "      <td>B0039617PE</td>\n",
       "      <td>Silver Tone Two Sided Sea Turtle Large Hole Be...</td>\n",
       "      <td>30099455</td>\n",
       "      <td>1</td>\n",
       "      <td>negative</td>\n",
       "      <td>2010</td>\n",
       "      <td>14744</td>\n",
       "      <td>The charm is to big for the chain and does not...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive</th>\n",
       "      <th>9755861</th>\n",
       "      <td>Home</td>\n",
       "      <td>B000QRCNPM</td>\n",
       "      <td>InterDesign Sink Saddle</td>\n",
       "      <td>52961487</td>\n",
       "      <td>5</td>\n",
       "      <td>positive</td>\n",
       "      <td>2012</td>\n",
       "      <td>15639</td>\n",
       "      <td>I had one of these I bought a while ago that n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>negative</th>\n",
       "      <th>8338646</th>\n",
       "      <td>Home</td>\n",
       "      <td>B00008OOX2</td>\n",
       "      <td>Honeywell 50150-N Pure HEPA Round Air Purifier...</td>\n",
       "      <td>37426095</td>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "      <td>2015</td>\n",
       "      <td>16598</td>\n",
       "      <td>Too loud</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      product_category  product_id  \\\n",
       "rate_category                                        \n",
       "neutral       4547032          Kitchen  B00EI7DPPI   \n",
       "negative      2684373             Home  B0039617PE   \n",
       "positive      9755861             Home  B000QRCNPM   \n",
       "negative      8338646             Home  B00008OOX2   \n",
       "\n",
       "                                                           product_title  \\\n",
       "rate_category                                                              \n",
       "neutral       4547032  Hamilton Beach 49980Z Two Way Brewer Single Se...   \n",
       "negative      2684373  Silver Tone Two Sided Sea Turtle Large Hole Be...   \n",
       "positive      9755861                            InterDesign Sink Saddle   \n",
       "negative      8338646  Honeywell 50150-N Pure HEPA Round Air Purifier...   \n",
       "\n",
       "                       customer_id  star_rating rate_category  year  \\\n",
       "rate_category                                                         \n",
       "neutral       4547032      9552334            3       neutral  2015   \n",
       "negative      2684373     30099455            1      negative  2010   \n",
       "positive      9755861     52961487            5      positive  2012   \n",
       "negative      8338646     37426095            2      negative  2015   \n",
       "\n",
       "                       review_date  \\\n",
       "rate_category                        \n",
       "neutral       4547032        16440   \n",
       "negative      2684373        14744   \n",
       "positive      9755861        15639   \n",
       "negative      8338646        16598   \n",
       "\n",
       "                                                             review_body  \n",
       "rate_category                                                             \n",
       "neutral       4547032  The convenience of the single cup for just one...  \n",
       "negative      2684373  The charm is to big for the chain and does not...  \n",
       "positive      9755861  I had one of these I bought a while ago that n...  \n",
       "negative      8338646                                           Too loud  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100002582"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11111398"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_data['review_body'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162000"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'We are happy with the quality and function of this paper towel holder.  It adds a unique touch to our kitchen, too.'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data['review_body'][15000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'sampled_source_data.npy', sampled_source_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_source_data = sampled_source_data[~pd.isnull(sampled_source_data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162000"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_source_data = sampled_source_data[~pd.isnull(sampled_source_data['review_body'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161982"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Term Frequencies\n",
    "We calculate both the actual term frequency as well as the tfidf weighted term frequency. For both algorithms, we limit to words occuring in at most 90% of documents and in at least 10 documents. While the term-frequency matrix is just a word count, the IDF calculation adjusts for \"boring\" words that occur in many reviews.\n",
    "\n",
    "We perform two tokenizing operations. First, we tokenize only letters, ignoring special symbols & numbers. We use the NLTK Snowball stemmer to try and get the root of a word as best as possible. Stop words are removed in the vectorization step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation,TruncatedSVD\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.tokenize import RegexpTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\")\n",
    "tokenizer = RegexpTokenizer(\"[a-z']+\")\n",
    "\n",
    "def tokenize(text):\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    return [stemmer.stem(t) for t in tokens] \n",
    "\n",
    "def get_tf(data, use_idf, max_df=1.0, min_df=1, ngram_range=(1,1)):\n",
    "    if use_idf:\n",
    "        m = TfidfVectorizer(max_df=max_df, min_df=min_df, stop_words='english', ngram_range=ngram_range, tokenizer=tokenize)\n",
    "    else:\n",
    "        m = CountVectorizer(max_df=max_df, min_df=min_df, stop_words='english', ngram_range=ngram_range, tokenizer=tokenize)\n",
    "    \n",
    "    d = m.fit_transform(data)\n",
    "    return m, d\n",
    "\n",
    "tf_m, tf_d = get_tf(sampled_source_data['review_body'], use_idf=False, max_df=0.90, min_df=10)\n",
    "tfidf_m, tfidf_d = get_tf(sampled_source_data['review_body'], use_idf=True, max_df=0.90, min_df=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_d.npy', tfidf_d)\n",
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_m.npy', tfidf_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute topics using Kmeans and LDA\n",
    "We are using two approaches to extract topics from our document set. \n",
    "\n",
    "**Approach 1 (Kmeans)**: Using our TFIDF matrix, we cluster documents into N clusters based on their TFIDF similarity. Within each cluster, we count the top occuring terms.\n",
    "\n",
    "**Approach 2 (LDA)**: Using our TF matrix, we attempt to extact N topics from our collection of documents. \n",
    "\n",
    "It's a subtle but important difference. Kmeans forces each review to belong to only one cluster while LDA allows a review to have many topics associated with it. I pulled 10 topics out of my head as a nice starting number. Further testing would have to be done to see if it was the best choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/sklearn/decomposition/online_lda.py:294: DeprecationWarning: n_topics has been renamed to n_components in version 0.19 and will be removed in 0.21\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "n_topics = 10\n",
    "\n",
    "def get_lda(data, topics):\n",
    "    m = LatentDirichletAllocation(n_topics=topics, n_jobs=-1, learning_method='online').fit(data)\n",
    "    d = m.transform(data)\n",
    "    return m, d\n",
    "\n",
    "def get_kmeans(data, k, scale=True):\n",
    "    if scale:\n",
    "        s = MinMaxScaler()\n",
    "        data = s.fit_transform(data)\n",
    "    \n",
    "    m = KMeans(n_clusters=k).fit(data)\n",
    "    d = m.predict(data)\n",
    "    return m, d        \n",
    "\n",
    "lda_m, lda_d = get_lda(tf_d, n_topics)\n",
    "kmean_m, kmean_d = get_kmeans(tfidf_d, n_topics, scale=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show cluster top 15 words per topic\n",
    "Using the approach [outlined in the sklearn documentation](http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html), we first extract the top 15 stemmed words per topic in our LDA model. As a second step, we do something similar for our kmeans clustered documents. Here we just count the top 15 most frequent stemmed words per cluster. Both show similar sets of results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 15 stemmed words per topic in LDA model\n",
      "\n",
      "Topic #0:\n",
      "knife, towel, smell, bag, drink, mug, knive, blade, cloth, sharp, use, white, holder, edg, heater\n",
      "Topic #1:\n",
      "coffe, clean, vacuum, doe, lid, use, floor, veri, great, smaller, open, machin, job, like, make\n",
      "Topic #2:\n",
      "use, water, easi, handl, clean, pan, cook, time, make, just, i'v, pot, don't, work, like\n",
      "Topic #3:\n",
      "use, product, love, just, like, bought, year, great, buy, review, purchas, becaus, onli, tri, can't\n",
      "Topic #4:\n",
      "room, fan, heat, air, set, unit, turn, high, live, instal, nois, temperatur, window, machin, use\n",
      "Topic #5:\n",
      "order, veri, product, look, item, box, gift, arriv, pictur, receiv, love, return, ship, purchas, color\n",
      "Topic #6:\n",
      "br, unit, replac, review, batteri, work, instruct, time, new, servic, custom, problem, read, onli, model\n",
      "Topic #7:\n",
      "veri, glass, sheet, color, set, wash, like, bed, exact, soft, cup, comfort, size, love, isn't\n",
      "Topic #8:\n",
      "work, great, time, light, use, just, money, cup, pillow, bought, product, like, buy, don't, doe\n",
      "Topic #9:\n",
      "look, good, veri, nice, small, price, like, great, size, qualiti, realli, littl, just, perfect, cut\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def show_topics(model, feature_names, n_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d:\" % topic_idx)\n",
    "        print(\", \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-n_words - 1:-1]]))\n",
    "    print()\n",
    "    \n",
    "def show_cluster_topics(cluster_labels, tf_matrix, feature_names, n_words):\n",
    "    d = pd.DataFrame(tf_matrix.toarray())\n",
    "    d['c'] = cluster_labels\n",
    "    d = d.groupby('c').sum().T\n",
    "    \n",
    "    for col in d:\n",
    "        top_n = d[col].nlargest(n_words).index.tolist()\n",
    "        print(\"Cluster #%d:\" % col)\n",
    "        print(\", \".join([feature_names[i]\n",
    "                for i in top_n]))\n",
    "    print()\n",
    "    \n",
    "print(\"Top 15 stemmed words per topic in LDA model\\n\")\n",
    "show_topics(lda_m, tf_m.get_feature_names(), 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for plotting\n",
    "Our TF/TFIDF matricies are thousands of attributes wide which makes it a challenge to graphically represent documents as we are limited to 3 dimensions. We could perform a heirarchical clustering, but the number of documents makes this approach very slow. Instead, we perform a SVD/LSA to reduce the dimensionality of the matrix to something more manageable (eg. 30 dimensions).\n",
    "\n",
    "We then use t-SNE to attempt to visually cluster and represent our data as best as possible in 2 dimensions. More information about t-SNE can be found at [Laurens van der Maaten's site](https://lvdmaaten.github.io/tsne/). In the end we will have a 2 dimensional matrix for our XY plot. Be sure to read the [caveats of t-SNE plots](http://distill.pub/2016/misread-tsne/) as well.\n",
    "\n",
    "Our Kmeans output already has cluster labels in it (eg. one cluster label per document). However LDA allows for a single document to cover multiple topics. One approach could be to perform a clustering on top of the LDA output. But for this example, I chose the dominant topic for each document as its label in the LDA model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_svd(data, components):\n",
    "    svd = TruncatedSVD(n_components=components).fit(data)\n",
    "    o = pd.DataFrame(svd.transform(data), columns=range(0,components))\n",
    "    return svd,o\n",
    "\n",
    "def get_tsne(data, components, perplexity):\n",
    "    tsne = TSNE(n_components=components, perplexity=perplexity, n_iter=1000)\n",
    "    o = pd.DataFrame(tsne.fit_transform(data), columns=range(0,components))\n",
    "    return tsne,o\n",
    "\n",
    "svd_v, svd_m = get_svd(tfidf_d, 50)\n",
    "tnse_v, tsne_m = get_tsne(svd_m, 2, 25)\n",
    "\n",
    "lda_c = lda_d.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Data\n",
    "Here we use our LDA and Kmeans labels with our reduced dimensions to plot our documents. We create a rainbow color scheme which allows for a variable number of topics/clusters. You could choose any other color map which suits your needs. I chose to plot this in 2D, however you could create 3 TSNE dimensions in the step above and create a 3D scatterplot as well. \n",
    "\n",
    "The plot tends to overlap quite a bit. If you sample through some of the reviews you'll see many tend to use similar wording."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nP8NSyvL0W/YcfSYohg8fztatdvkFPTw8PDycEEJYM18ccTzVk4eHh4eHK56g8PDw8PBwxRMUHh4eHh6ueILCw8PDw8MVT1B4eHh4eLjiCQoPDw8PD1c8QeHh4eHh4YonKDw8PDw8XOmzgDsPj77kueiL7DWV8h7MQC4MnNeHLfLw+OTizSg8jjmsQgJgL/t5LppS6NDDwwNPUHgcg1iFRLrlHh7HOp6g8PDw8PBwxRMUHh4eHh6ueILC45hjMAOzWu7hcazjCQqPY44LA+elCAXP68nDwxnPPdbjmMQTCh4emePNKDw8PDw8XPEEhYeHh4eHK56g8PDw8PBwpVcFhRBinhBiqxBia319fW+e2sPDw8Oji/SqoJBSLpRSjpNSjisvL+/NU3t4eHh4dBFP9eTh4eHh4YonKDw8PDw8XPEEhYeHh4eHK17AnUefs0mrY7WsoYEOysjlclHFRH9Ft4/70nqNpcsl9Q1QXgZzZgimTvH3QIs/mef18DhSeILCo0/ZpNXxuNxJhBgADXTwuNwJGt0SFi+t13hgoaQjon7XheGBhRLQjmin3Vfn9fA4kniCwqNLbIz+g/f4AIlEIDiJE5gUOCvr46yWNXEhYRAhxmpZw0S6LiiWLk901gYdEbV86pQuH7bXzmudlVz3jbcJVfydzughcgIlVJROo3/x6J5tvIeHA56g8MiajdF/8C474r8lUv2OkrWwaKAjq+WZUt+Q3fKeoifOa52VDB36FvlFz9IZjQLQGT3EnvAaAE9YePQKnjHbI2ve44OslrtRRm5WyzOlvCy75T1FpufdpNVxa3Qr10Q3cGt0K5u0uvg666zkwi/8nWAwmrS/lJ3UHng+/nvbMrhvOPzUp/6/bVk3L8TDw4QnKDyyRiKzWu7G5aKKoOU1DOLjclHVpbYZzJkhyA0mL8sNquVHkkzOa9hljFmTYZcxhIV19lHa/7DtuYTWzCatjm3L4Mnn69i+dis17RvYvnYrTz5f5wkLjx7DUz159CkT/RWg0eNeT8pw3PveR5mcN51dpuKCMPLCWvyhCFo4yMGGIkoLmlPO1eTPY7WsIX8z1D+wE1mojqkN66D+gZ38cT6MntV97zEPDyFl9qPAnmDcuHFy69atfXJuj+7xu6jzUPWbgVm92JKjk2uiGxzXFeKnLRYj5kt8l6MO7uMLTdvJkQnh0il8PF96Mu8VDcZfnYs2LNWm46/OZeHIcT3beI8+RwjxTyllrz5Yb0bhkTWFFNBCq+3yo5Wejn3YpNXxlNxFCxoARQSYIUYw0V+BDyzziQQtaCkK4ff7D8Lnl/zH4Z0Ua+00+fNYXzKS94oGU4iflkp7w7/msNzDI1s8QeGRNePEWF6VW9D0ThDAj59xYmwftqrr9HTswyatjsfkDtPdgWaiPCY/AM1ZSLjxbvFgdhQPTjomQDsxciN+OvKsa6C4LReCKYs9PLLGM2Z7ZM1I/wj+Q4yPzyAKKeA/xHhG+kf0ccu6xqNLnWMfusJqWZPSoQNoSB77sIacj7P36CpuyYUDqeM6DUlOniAQTf6UA1EfVxV1zyHAw8PAm1F4dImR/hGM5JMlGLqiPnppvcbhJvt1XY25cIsB0So7KJlzIo2P7kQWZDi3kBDZnIv2eXvvp2aiXJtzYrJDQE7PpEHx8ABPUHh8Suiq+sht1lBU2LW2lJHrKCz8tbkULVcdeNN/19A5NAM7goCOqfZCAqCgPcgjtw2gvmEA5WXwxRmCiV66EI8exBMUHkc1xiyiLpy6LpPUGW6zBtHFkIvLRRWPyR2Me+p5rpj/KGW1dTRUVrDqjuvZ8cK3AShaXkHRigruiCUnRXRujP1iv+Zj/9JKoqPClE9XLrWPNwTZvb2Sa04Z1LUL8PCw4AkKD0d2arvZKt+ghVYKKWCcGPuJskNYZxF2pFMflZdhK2QAmlJDFzJior+Csif+xPDr7yHY2g5AqGY/c+fdwxrtRN5CuRCXVCW2RyPFAO6KhJw9ubSvPY5oFEqu3Y0vV6my/KEIG/rt5hTN56mfPHoEz5jtYctObTevyi1xN9gWWnlZbmRj9B993LIEdgn4rLil7HhpvUa7yyC+O+k+Rv3oF3EhYRDU2pjG7QDkFMC0uxPrnAzgjki44dVx1D0fonh6bVxIGIigCuDz8OgJvBmFhy1b5RtJ7q8G77KDgVr5J2JmkW624Jay48FFUZ59oWv7Grgaz2vsO+kSaigZBqXf0rh3o6R+jdo3cF+Ho3rJjuK2XEbPgvKNagZhR3cTK3p4GHiCwsMWu4A6g03ytR7xeOquastNbVQRSnT0c26IJnXmgKuQqAjBmaerGcu9D0VtPajSGs+rqqC6OuXYYlgVY55I3bciHCRQntrhy5ia9kvT3D9IwvV1zgzB4w1BW2HR3cSKHh4GXgqPPuRIVXbrCVZEV7sKi3PEpG7NKgzVljVo7z/EeKo3ViWN1M88HV57nZSRu52NIjcIN81zX5+bi6NLrBDwvRsEv/qNRLNMqIqL4Pq56thzbojaCqmKECx9KADLlsG8edBquocFBbBwIXM2XpWyb/6kcJKdASDW4ePQohEEAjBwTi2teRHb92Tx9n1sGL4bEUzsG8THbDEy6/fJq873ycdL4XEM4VTZbfv2GK88FOrzD3WcGMvLcqPj+pflRrZG3+iygdtOtaWh8WrbG6xeWJk02jaP/uvC8OtHzG6vzh2bUxEhN7tGeRk8siRVSIAybhuzhrR1J2bpOa9uv12poaqq4O67YdYs6tdEU/Zr2xgCoHh6Lf6yCFpDkKaVlfHl+dtDSgDZcM0pgzhF82U86HAaoByp6nzPNDVxX2Mj+6JRBgUC3FJayiXFxV0+nkfv4wmKXsT8gdrl+4kQY/2AWurCIfInhWF6Lb8PRVjdHOTq/GG9OtsY6R/B29vrqT9xh6ObaAutvCq3gEbWwsJpthLNbU1roI5GVTT11CkqW6vZ/fWl9Vpc1dSVyfKcGYL/edB5R8Pl1kntlWQAnzUrITAs29jt27YxFBcMVuwEU1dmpG6lZ5cuH9DjVQGfaWrijnCYdv1h7I1GuSOsLt4QFtbruHbD+4xauQQa9kHZIJhxM0y5uGsN8OgRPK+nXsJag8ApJtc3IBJXQwTKIwgBrXmRpHoFvcWui84i1uCu59bQ2CrfyPrYTgkEW8OZJRa0Ux09uCjK/zyoYirchERxESk1IwAuPp+MRs71Dd2rd2G3bzqswX/palo44Zbi3GmW5GQHyoT7GhvjQsKgXUrua2wEUq/jhA2vMGzRLyG8Vz3E8F6iC3/Eupd/yIroanZqu7veGI8u4wmKXsLuA7VDawjaujtGiLH0o5peLUZzqAYOfudzxFrcO083W4YT48RY/CQf14+f6ufGZH0sUDMJNwO1QW5Q2RlumicoLkos71cMp5ykOvl0gXblZUqg3DRPUBFS21eEEraRdBj7ms9vEAjYn7+tXV2jgVuH74adJ9T4p57n1pGX8OyKIEueGcm51U+lbGM+dzbsi6aq2Yzlm7Q6fid3JF3HFSufJjeSPK0JRDoZt/Ll+AzWExa9j6d66iUycVWUER9NKyvp/+2dtus7B3ewZp76e3QvlH0oqYJDy5VKqWTBG/irWm07sa6kFx/pHwEaca8nf3sB21aO4e2/Dctof2snmy6BnxDJNozF2/dRcHctRWWqOFDTykoeWBgCNNfZiHnWYFV7ZYOxr53x+NGlMmXGFI0mq4C6Wmu8oD1Ia16iIx7/1PPMuf4X5Laq/Qa21nDz1usBWDdsZny7rqqfBgUC7LURFicPOMwiuTdleVn4gO1xCsMqhYmGxpqW1xkaHsqVx3upcXsLT1D0Ek75fwxbRRm5nPThcazZGkIL19q6SvoOBPjwza3cV9lBWfTIe0lNu5u4YAJAKm2AMM1Du5Ne3Egs6BZh3a9YGZHNnbffr2YFZtxiKuKeSDqbtDo2DN+NX/cSCpRHKLl2N4eAXz5sbyMA8PkynzVkip2wufch+1G4+Rqd3ic3l9iX1mvsf7OSwrkJ76or5j8aFxIGeVorc7fNZ92wmeRPCisDeyjCrV14524pLU2yUQAMKmyjX2GL7fYNoQGEbIRFS6hf/O/i/DZu3twG4AmLXqJXVU9CiHlCiK1CiK319fW9eeo+x6k29DfFiSwOTObrK8bRdNEgjntF0L6skliH5dF0CGJFUVXJzJe5Trqr7NR2s/2q1Qw6uIwBSzcSGNaK8CULCWt68Z3ablZEV/O76LKs9MlOEdYVIVixKMD3bkhW8Xz3W6mdtVsUtdVusFrWJLmSAvhyYxRPryXmoB3MDcL/+XbveKDZXcu5/IXHxUUwYwzccAHXbng/61rjS5dLml4JcWjRCKL1QWQMymrt35/y1toUW1lX3rlLiov5aSjE4EAAAQwOBDi5tMUxuHDV9MvoCCZ3/tIvKDyuiOlPv8DxH35EY2s+bRrc+Ua7/UE8epxenVFIKRcCC0HFUfTmufsaa23oIgJIJIvkDlYcqiH/+Sryqisox0/59wfRvs1H/V3VxAYpV0lfUMNXkqwnNtdZ7kmSYhyEvc5cIGihVRmy9WaZ4yKy8YhK52qaiYpnzgxhOyuxM1A7ZnYts3e3MoL3uiIkuhKXYL2Wc/kLN3MXeTG9YwzvZdSiX/MdvsOiyaMy9noy7qfZu6q+/1AqGj9K2TZcOtTRVpbtO3dJcXGSO+w10VrHbbdMHg/AzCf/SNHBJsgPIE6qgOP6U9TaxuR//JtX+yv15Mctx1QX0qd4qiedg03bqGtcS2f0EDmBEipKp9G/eHSPnmOiv4KJVLBJq2NJ506iAfURNhV20PzATkojJFJQ/72Mvd8dQEzvUwb/fovtMY9Emgan9B1mJOojNQRCgIBtXMRW+UbaKO6MXE3TkC6mwoyT2kZrSFVjCIFj/EI6uhKXYAgWs8Cby4PkYRk9R9oZtXIJ95zzfMbtsbvPf7prHt+89e4k9VNHQS5/umveEUsN4paGHUAO6U/wnBEILVUFl6NpzG54mV9yIUMLu5je1yNrPEGBEhJ7wmuQshOAzugh9oTXAPS4sABY0VxDtDB5pCYLYxxaUBMXFPtvqCc0W+mGtXCQWJMff7/UzvtIpGnI1otJ0//L9lhuKcIzdTU1k6lx+XJRxWPRnWj+5CjoppWVKdsKARfNsE/jkQ6ngL+lyyXlNbD2duVZVlKl7EH1Vfa2mnL22Z+gwWG5A3YxIq/Nm4q/XzQ5HfqC63jtqqloDmlFuvvO2d1/M1/590ZybYSEwXEcIN8PPx6b1612eGSOJyiAusa1cSFhIGUndY1rj4igaMq3H01plWp584w6im5MGBwD5RFkpyDWCb6cxPbpdNJdpZCCLrm8Oh3LjJtwMOiOqicT2jaWcfDNGPlXmKKgl1fStiGExWM3brPoSpSyW1zCmnnQqd/iQ9Xq97Yr7W019QxiIKkeQpRlV29i6hQ/Dy6K0maanGjhIFtmXsCWmRckbavVK08wa1qRnnjnjPtfMPNDfMVakmoz1uFjQGuTa4LEfWIA90/I9wzZvYgnKFAziGyWdxd/ba4yStssBzi0oCY1bXSOJHY4gGzyIQbY5/zpKdKl77AjiiAHgTT5xFs9ojKpH2H1UDoSLF0uaQqHaHol2cMp0Am+dogUAJIUV4+OSCIiPBOcVGp5HQkhYdDZCgdbse0gl3CjslGY1U/BPBWxnCUBy621EwbG7MqwY/S7SgnUMtEz75z5/se9qsoiaAeCNK2opD4vxMACh5GEP8iQCVdzJVtg1XJoaYDCMhg7A47voq+yR1o8QQHkBEpshUJOoOSInG/wfVV8vGAn0qR+Ei0++v+4CkRiZmHFVxzla5snHHHPm5H+EeyP1vMuOzLaXkOwE2VQOInDQMQ2G2wm9SOso/CeTJyYbjYTzYUJf1CfxOYr7VUfh5vUcTJ5BnYG9twgDN1sP1wOtkLEpvzqOi4C4Brfg4Rk99JaNFu8Uq05pmIHghz/diU73w/RLqD4/RCzd5QzdVDPvXPmZ2xNW5IbhCWh6dw8ZhF5gcSNkxI6/UUEJ8xVCzYvBE1f3xJWv8ETFkcIT1AAFaXTkmwUAELkUFE67Yic76sTKnjyJmj4cQ1aZQf+2lzK7qz2auzjAAAgAElEQVTi6osqGL0Mbo3aG/sKO4K9liBwUuAs3o06CwpD092Bn2pKCaMi4MIUUYifFjT+KRu4XCuMd+zp6kdAsgHbLS9RV7KippvNBFuT/7brtCHz4DMnA/ub6/3YzVWr3hLs+pwkZvNVbgpexJnzLu7287eb5RiddVzlN80PR+bVd2wDJFSOS5dP5v43Ye5nVlKeH+ZAJMT+odM59cJz1IarbkgICQMtAq8tgTccZhm71juv80iLJyhIGKyPtNeTwehZcDUVrJ1WkWTMNKKtLxdVSR0kKN3w1fmZRS33FG62Cg3YwnDbdS26YdvasbvVj4BUA7ZbmopsXYLTzWZ8Uah8M3HuyjcFOydIW1VQNrmP7Azs5Xcn2yhAVbwLVfuRMY3aMTKh/hJKaN10Y/dtNi+t15LsEwbm1Oy9gdNMy7BLqft1jv4PQvq/OC0OI45Is/oHiVlG3XtQsxk6TKHu3gwkazxBodO/ePQREwx2jJ7lnIbDGnNxRGtVLFtmmwob3G0VmXYp5o7dKdYB7A3YmaSpyDROwW02E2xRgqG8NrFfea2fneOjtoLC180wVeO5W72e1t4OVPspt4QZlAyjy6lCDJxmVP2K4bo5vZvKPq0rs9vof9d65YqWSWpgLQI7HBKAaRF1Dk9QZIQnKD6hGDEXRxRrcZ3qavUbYNYsRvpHsCn6GhE6U3aNZCwqEh17NrEOkD5NRTZxCq7V8Jqgdoxk54QowVaT0HDwvInF0ttO0gkwp4GC3UzDXFs7W15ar9nmjjLIy80sY25PY55pGfdyWbSDaR/u5Kp/PI9f09858+gf1N8yfXLNjGjpRlrcY4xjtsLdro8fp60jkWIiP3cExw+d3Wft6Sm2LUseqV5x0TKq/mI/Y2D4cNtynQcZxpJhHzLtbiiYYV+JbiAn87+00EH696eMXO4JZF+Qy2qjgOTKbWmrzJl4ab3mWmciCQ1O+rdg/2TJwbbU1QO/ECZ/9m7HdjmN3i8+H2681n1sZn1+ZpVktry0XuPXj0gcErjGeW5F34wXN2l1LJe7aSbRwF88/RihVhupVqgrn5w699ziZPVSJggffC01U+4nnb6ocHdMphm3CgmAto7d7Pr48T5qUfZsWwb3DYef+tT/ty1T/9bMU375SKisXsag38xTwkBKqK6mc9432fTEd1Qephr7lNQl1MR9+1uXj+A/xPh4PISR3+mLgc/ycGAShWlmFt3xu5/or2C2GEkZuYx/6nnuHflVHg5OZuLIs2DZsvRV5kxkNWr2Q814yX9eY19zonh6rWuKbyd7yLMvpE/XPXoW3PIh3BFT/+9OluCly9MLie6q0brKE9EPWCR3JAkJgDI7IQFKDeVkmwComgD+LOMqempmcgxwTKqerEIi3fJPGoZAsAZsnfDVJ7nuawsZcLCBmPDhW/MOojVZbZTTGmH0/Cf408zxVFUOJqdmT8rxD6E69s5WNbq9ZdYIxzQcLS6pPnrCtjLRX8HEZS/A9femqMgunRTjfwfMTNnHKfVHRShzQ3SbfttumidSVEjL8txTW7jZQ9w8pnq6XnUmXmZOSRCPJJu0Otax33ZdQ0Gx/YwCPW2xkwZkz+swYV6ybWPI6bDz7xBzkJaFzpmCPZI5JgXF0c7a21MDtoZf8iSXxX5J7kHVifllDFpTbQsAhbUNKg/TgulMvH5hogMGIhSwloRS/JB7HRxXO4KTummntlvVoZCttDcW8O+nxtD+/jDnjvH225PaCECrSoX9t/Nn2nrP2OFkTDen0jZqU7RtDPHAQqkEhUWN9VcH9+Ui/XNys4c4dd4PLoqm1Abvbr3qdF5moIRnb+NWXGnVZycx5x9r7VN4uM0ADJXUFQ8lL6/elPCEsjJ2RpqWehgck6qnox27zvsL/RemVAajICd1Q6ClUg2535k5DhYuhGHDkAgOMow1LOQtEvqOkjRaI6f06U7qJiMzbQutICBvQCufu/Y1ckdV88BCaa+acVCR5dfVOlaZM2pnXzQjypwbovEgOWtlOWsqbaM2Rf6kcDwvk901+20s3W2oqm1uOarsZjtWIWFgd36763JizgyREoltpiv5tNKxSavj1uhWrolu4NboVtuU5G4JAbcMP4lNI07JwPJlw+aFyivKTMS+7gXgeTxlwTEpKPJz7dUoTss/adh13gMO2gxVRw8Ef3JH0FkQZOuC6YCeh2nWLPjwQ976fYyHCz5MEhKZeNyY7QigZhKGUdcOu8y0gVyN0dPfdOyYqbIXOuHCSu59SG3/vRvUyN8QEg8sTNTONkbnRqdqlqd2qbSN2hRgPwOY6K8gz+bT0VCj5alT/Fx8fup+dh1zuhKu5vOnuy4rU6f4+c71gn7FqevcSrfa2b/S8dJ6jbm/28fC9vR1vN2SChbiZ8KeWrdUT85CRIvAhgdhxTXw1Bx44irnrYUvVah4OHJMCorjh85OEQpHk9fTtLtVJ27mQH+boeqw/jBuKBTkIIHmqjI2PHINu2ZOTsnDNHoWXLpQ+ewj1P8vXZiZMXWiv4J7AuNYHJjMPYFxrjYJpwC+gjK13FY1c/fdUJB8we3+AhafusC2w3x0qXPWVquh2SmVtlGbwsne4WSbaYh18FMfRBcEmHFG+pra6Uq4ms/vlI32lw9L2xmGYfM43JQwWleE4Ps3JtRp1tmJ1SEinrDQRVgYAkxe6Fy/wozdLBTgXAbyQGACea0HXe9JU06u+4wj0gzRNEWNZMx+BuJhyzFrozhahIIddgFbO86cR9HLv0xVPw3rT+TEChqvvZF1k/vTQqttHibjuEe6FrdTtHdrgxIEth2z4c6rBwaGCytZfOqCpJrOidmI5hgzYCeEnFJpaw1BV9WMk23GX5sb72Bb7/TzvTTCNp3B2Xx+p21zJyRsLI83BNm9vZIRDeVJ9hhrFtzt70V58WVSYlBGbdcobE0WZoZTg9N1GALMSeiGYx3MuSEat0GlDSgtLHN0g+3wB1g+7lyu+PdGB6N3FnhBdxlzzAqKo53UTv1q1p/YxskrH6csfICYz4cvFuNAaAAN069j1DlXc1VfNRZV8+OjPc9xaqANBESFoLq0nIaifkQ7/GxbOcZdZz5rVlxgzJ4RtXV+qW9wH6EbQshs4G1/vT+F59elpLoWz1XazgDMgXZWRIuPkvlKTXbaxPVMu3I5JVoDbU+WseTd6ax5f3KKN5ObwTnPoqGx29awsRgjeX8owoZ+u1m/GToi9pbqjgg8tzbV46kjAtuOlwSOixLNhUCHUtxouSqNyJj17lHvbkLXapx3DSgdOyM56R+qHc3BPJ763DlsGX4SgLPROxvcXG494hyzAXefVnoy22pPcbBpGx/tfxrhS1bXxIB3cyp5cdU57l5POsa1hWVHkneSQUVIdVpOr/T3b1TSwBhpWztZg3MZyNcDJ9ie3xoAaOCvzqVkfhVFyys4beJ6Lv3mQoK5iY6uPRrk/jevZd2eyUm5ldIlK0y3bcV9r9t2ztH6IHW3nG5/0G4w9jT4+Y+Sx5dG4KPd/Yx1+Di0aETKc0rr+mtJ4/HkmPGsHT4yaZPxH77Hlf/eRGnr4a5fUGEo1VPqE05fBNx5M4pextyR+1CdZU926L2S+iNL6hrXpggJUAayEbWHefTmkak7WTB30mbvJFDZT31RGLhZEBltH02dm0s8RqG4CIJByLcxZANsw15HbpekENTzKzppHFK/xGlXLk8SEgB5gQhzP7OSdXsmx9VkU6ckpzWxm1nYbWtOyeFoY3FYbpBpuiQrb7ylvLTMEeZG5TxrynKtIVWYQ4auv8dPSVIJjdDqCFqE9OvDT2b0iEuY+OR/Zdh6QZJx2x/0XGQzxBMUvYh1RGq88g10sEju4IPoYduR7NGOWwGovAr7ddaZUQdaSidteCdpL4SofFNQWOunvFGjZYKk0ySX/H7QtITa5nCTHmHtUhP61ujWFOHtlqSw0HS+kjJ7dUZ5fkIS1IUTJVbPTDPwN9smpk7xs3R5NC4onNQ9hR3KxuI0UwkGAem83o3n1sKN1ya36ZElUZqaU+tLOGEWgJngatfINH1HbhEEcr1U413AExS9iNOI1GAd+1kXVRGrRQSYIUb0udqoJ3AqDAXQXlcCFtloV4fCiUBZhDOeTbzGZbv9BPI1Gs5NRDi3d5Bi4O6IgGwMIgY4Cwtr7YuC9iCtNlHZBe1BSobpnkLAoYYy+odSpwf1bckdqOGx5eYeC6kGfrPgcCpXenX+MNrmpdbINujoUDaQrsws7KK5r5/rnBnYiUwix804zpYzvYCOJiUoDOre82pUZMgx6R7bV7h1eFaaifKY/MA2YOloo6J0GjKWqmLQIj5yD6dWyEknUM0Y5WMBqi9t4plXavjb09V8fNsevv3bNubMEI5eUAefqrR10zSwunY2rawk1pG8vVE21Oyy/OeXptMeTc471B4NsuTd6Rldkxk7A79ZcLRtDHFo0Qii9UGkTI5jmTrF7xp53a6/jm5BeXaY80MZAXbLJm6m8jevkz8p84ysTq7HWeMWVGelJQxI9f8dLyT/9txlHfEERS+S7c3WkK7pDo4W+heP5riBlyE785FSDQAjB/MRH36Z0V9M1ADZpNVxc3SLs0C1DhwlaKURmmfUUX1pE1t/FqZ1aBR8sDcaZf7+MHdudFZJFL8fSgoWtMPclrrnTZ1yTBmMDy0aQd3zoXgcSssZGn8ITub+N69lf2uImIT9raG4ITsbKkJw3jlKRWOOdZgzIzlZYdvGEIduPZ2vbZ6QEsdi3daKlJCfRzzeIxMu1GX7Jq2Ox+SO+D1qzYvQ/7qdGQmLHo0KL+whiWO4y3qk4Hk99SLXRDd0ab/Fgew6mKMR1el8gOYWStUO5JJaJyIG4f2FfNDZL2UXf6OfofcMtT3c901V426Nbk2bsyqTtOZO22SL4RlkVwnupnnqBtglEDTXBff5lJqoX3Gq6s2MEPCX5ar9Fzm4Hhvk58GqpWrbm6KbbQMPY4f97PtWwiknN6gE3muv02MJD5PYtT7FnRbhh2ABdDS7xmWktB34bOFv+fHYPK48PststL2E5/V0lHKwaVtGZVSdgrTccBvtGjzT1MR9jY3si0YZFAhwS2kplxTb5G3oQXraDXe1rHEXEmAvJAB8UDaohcaGAA2tyRHcWn/nXEjmjsqp/Kw5Z5VTUsGRV4T5r+ZaWnIjMD9IvsnTxynhoOtlBo3a0S4R5g8FMBeBWro8NYjOsCW4CQlIVgGlSyRoqKs2aXWO0em+Yi3uqtzjQsEOw67gZm9YdUNGwuIjBvBRi+Tmzcp17pMqLHobT1B0k4NN29gTXoOUKlNrZ/QQe8JrAFKEhV1n5IYfkbaWwzNNTfy+9R0urqimnz/CYS3I7w8NA04+YsLCzti8SO5geXR3lw3wGQlQF02FEDC89FCKoPAftO+grLr7TMrPTp3iZ/t7yQn88ieF2T1BGZMFyW67QJKh2erSa8anZ9A2d6z3PmQfTFbfYF/dL51R3I5AIFkF5FauFlT7jOfviICS+7cSQKOMXPJFFRxpl22LO20KNkF8VloJcieXA9CmwZ1vtHuCQqdXBYUQYh4wD6DKIdHb0UZd49q4kDCQspO6xrVJgsIYgUeI2cZPbNLqeEruio/SMvV6Wt3+Hp8v3UmOT3VGJYEIny/dyepDPi4p7tnZqVF9bfvaGrRhqcKumWjcUwiyq/ndldmWlYDFCJQTE/R/sSRlO7/fXj+eSQzKa68n/06XVNBuXb+rapMEhTmozozT6L68zLk4UjqKi6BJz7ptVy/biNV4ZImMb2du55wZIiNnA+M9tvMe6xPsZh1DToc9rxNrCfMRA7iTy/kT4+O7fNzSN2r5TyK9KiiklAuBhaBsFL157iOFk9unebld/ISh1pjor9BVR+3si1ZkrToaXbw7LiQMcnwxRhfvBnpOUJiLJWlVzh16hBhPyJ1I/W/IrLO4XFSlt1FkgL/Rj9Zfo1Tz838HD6BgUgGP7kgEqBUXKVfOrqpCrC6drkkFHWZA/rJIimoGlH3DvMzJRjFnhohnzc0GuxKxdhj1rJ0KKS2LZifQDe+xPg8EdZh1fHbVYT6yEQpDC3s2BfvRjKd66iZOMQI5gcRI1m4EZnw8DU353BEO065bEPdGo9wRVsPITIRFsd++oyr2Rzht16644PlXezt/aGoihvK+urK4mDPy8jK2bTzxK433Pi+JFMDgmLIVOtFhM9pM11kYqh9rDWX09mairIs1BTjh1dy4TeAVUc/lk6pYMaXnOijrKN8tvxFgu65M5HKPqcO2UyMZRZPsKuyZA9wypSteRobASGl/F2Z/3Z0tHkl+PDaPmze30WYyueT71XIPhScouklF6bQkGwWAEDlUlCbiA9wieu9rbIwLCYN2KbmvsTEjQeGX+cREas6Kw1oQiRI8/7e+PmmcHgNWNDWxsqkpvtxNQL20XuPt4ZKY8bZ00ak6XWfhpPpxy7FkICW0bR6QZBOwzmR6otSodZRvF/BmxFYAtsFwVruTk9H6kSWSlYsDKZ31S+s12tJk0bZieEpZZy1dmVlla2uDzJwy+grDDnHnG+183CIZWig+0V5PfYEnKLqJYYdw83pyKxe6JWpvsNznsNzKFP/pvBzbAiIxHOqM+Xj1YKIzclJSWJfbCahNWh2/P6magcsTXjtOo+h0dLWzsBqancg7/aBjPYS29WW2o/ZMSo0meXhNzOXSsuN45aEQdWEyym9krONgkNllw+I2qfj1WDylDJqaiVfmM/P4jnoG3Ju5J5VhuP/VbySalrj+X/2ma6VWjefxO7kjI1HhVvGwu/SUx9+Vxwc9weCCF0fRU6x/FpbfDw37oGwQ70+fy6LJo1QeIPy0E0vSvwfxMVuM5Ecft7PXRigMDgR4MQOD/zNNTaxofYczShJeT68erOLd1vIuXYYA3jr+eACeiH7AOvYnrZedgpa/l1NwTtg2oZ4TxvV216DpFO8QrQ/iD0Ucg8aiN49PGwNhh91sxriWto1lWaWtMOIV7I5pl2XVrn2btDoWtu9Mm6HVwDCUP7JEEh2T6qobeDPEysVdGy9mMtM7kqlonmlqSlLbAuQJwU9DoSPuHt6XeHEURyvrn4WFP4GIrg8I72XYol9yArNomDyeFjT8wDkbtnLRylWUhQ/QEaogb8Z3uWXs2bYv+y2lpWlP+0xTE/Pr6+kkxL9a0idiy4RBej6HTVpdipAAEDmS/AkHOLRoBP2urMVfHlFTE5M6KoiPSZSzjYM9nu7cTu3h13xoz1WiXVhrbxMgl7cc8gqlyzfkZl+6Z0oF5lgGI8GfXa0HSMQr2B3T8JSydvbW9q2WNY5eVvF9JaBX1Tv7hjB/HfURRROUcDUEqeGqe2gRwCD3m+CAdaZXRACJpEV3iz3SKe67q7b1yBxPUPQEy+9PCAmd3EiEK1Y+zZbJyt1u3IYtzFi0jKBegS4vXAcLf8Il834CY8/Oevr8TFMTP6ivz0JLnIol6TIAbbEYzzQ18Uq+c+oQX3GUjldCFP2ynPJaP80z6ji0oAatsoMy35HtIGzjHQJVTPxmBYu3w4Z+uxGmjlR2+Dip+jj2O7iaCpHI4mqns3ezL0Gqh9BfXlSj+HbLbmZjstMxjfKrZszBcC+t1whP6LCdNZn3DbbC+L8HOPGvdbw0anc8NbuVhBtvdoLik1LzxEk9m6na1iNzPEHREzTss11cFj4Q//uKlU/HhUScSDssv59Lplyc1QjImHJ3VUiYvZ5+Fg5zyDQqOxiLcUc4zNjjOlwD3Ea8Jsif3MDHuoDw1+YyYM6J3PPUke8wnIzerzwUomlUqr3glfdDcSO0b5x9pLSTzcLNvmROl2HGKiRApbAwjut0TMNTysAcDGd4RpWc4O5l5YtC5ZuCk1qXce7M7/HVPftpqKxg1YLr2DLzgpT90tWtsGIXbNlXcRKDAgFbtW3/qLrPPeG84KHwBEVPUDYIwntTFjeEBiQ2MQmN5I2ShYzZONdPCIQQHIrFkmYadlPuTBHANt0GAWr6fsjysbVLSVTzEwjYp2jwN/rJn9xA4yM7kYWqw9CGddD46E429WFgVX0DSJt6CO1CddK7y/axYfhuRNA+UtquRoKTh8+AvSVZ2Se2+sLcGv0obrPyI5JsVmZPKQPzIzY8o+y8rGS7j6YVlSBBRGFcy5NcyvUEP1a1yUM1+5lz/S8AUoRFYUeQlzZn3qG6qeJ6O07iltJS5u8P0+lL3CgREQT/t4QHB6TWBP+fByX3PRrllus8gZEtXvbYnmDGzRBM9rnuCAZZNf2y+O8DJqGRRFli2m/MFPZGo0jgkJQcjMXibq53hMM809TUran1IEtOaadjfXiwCL/NlEIAF7x3PIfvrokLCQNZEOvTbLdOaauN5e+N+iguJAzMUdSQahOY6K9gEqmOAe+XhfGNyyzRXP6kMOKq3fFZhIpalsjmQFIWWquA07REDXCjXUlpxfV9W9eFKL6qlsHLtlD2yOtcJH9IkNakY+W2dnDF/EeTlvk1H2NrKnlgoZoVGfUxHlgoeWm9/SAhnSquOxjZg6+JbuCa6AZuim52TrO/bBnTTjyNf504khcmn81FTz+Nv9FP6apScv9ZxHNr7YsydXbCrx52vj4Pe7wZRXcxvJ0i7Yl0naHBVE+fyweTR4Gux22Yfh1li36dbMsI5ikho5NupmAY6pym3OnIgRQjudOxciL9+IaoSAqACyII4uevE3c4+txaO4yupibpCm6RzHZtMzDr9+2EjV1pVBG0Nz7bYZfmQwMKAz5qrxnvOisxBIQ50M9cRa5gcph+1yTnkyo/+LHtscpqE52uYdt55KEBjokHswm2c3J9Nqt/iouUMGpuSZ25LN6+jw3H70IEEi9WCxqPyR2paq1ly+j85jzyI0oYDtn3MXd9fz6hcSHWDZsJ2DsTGGix7KrreXiComsYwsGqborF4p3/qCkXc4953TnjwFeS5ELLjJthysXxTTKZKeyLRvnv8nJur69Pil8WQInPF1dTnZ2fz1+bm+P2h/4+Hz8oU73geTU1ccP52fn5PN3cbOt1NdFfHFcnGLrpeNS0g/3CPEU16hWYx25GQSY0KFpewdrb4VANlFTBtLth9Ky0t8ARc/1pOzVKOtuAU/RyNsZn2+0c7ACteZF45LVTxlZDcDkJwaIrU4VQQ2UFoZpUj7WGoQNpfGgkxe+H+OIMwcQpfu5qsH/n6sL2wXmZZNkFJSDMtb0hOYut2Sa0u6yeDSN32kb7axCPg4lnyn3mh5RHkmdMeVorc7fNjwuKdGRbXe9YxxMUTljiIuKdutUV1kqknfDD9zP7oS+k6nunXJwkGKxkMlOQwM/CYbsaPnyhsJAflyfUJOa/IdXvfG80ytPNzVxWVMQrbW2uXleZVp0zb6FSh6eiIVnRXEPFvAo69e+9c+JuNk18g9c6WykUBYwTYxnpH5Gy74OLonH3U59PFdG58drEa+yUdgLs7Q0yomwDRv0Hs+7a8O5xvNYDmQVoOQUolpEbb+/bP36C8l/NJ9RSS31BJUtGL2DTiTM5+wbdtjGxg8rTlfG97vlQ/N36vY0QWrXgOuZc/wtyWxMCrt1fwOKh96jZCIlO2i2tuLE82dDvnGX3D7si8ejmYJvguOIcyptyHO9LR0QFD/o+tztJSIx/6nmumP8oZbV1yhB/13U88MLYuJAsa6m1PV55q/1y2217qrreMYInKOywiYtg4U/U3zausFYGxPYhyS76F5Ra6Lb6+rTbHXJQT61oauKMvDxHDyonv/NX2trSBvdlqoM2qyDc9mnK76BUFxIFM3ZT+sgWfIVKrLTQyqtyC2gkCYsHFyWn+I7FjNTa0SRh4YSta21uFRNv7lrakM909kdakvvd+5BMKfzjVNc6PgpftoxTf3k9tKobMrC1hpu3Xs8JU5t4cdTp8Ta05kUQV+2mAph9YjlTp/hZ3Zxax3vLzAsIdgaYe+diqKkhXFjJ4lMXJI22DfVSurTi1u2nTrH3OvvDrkhSvqSOfMnuMeqg5XuchYW8sDbJbjT+qeeThJwyxN9D45gT4u2vL6hkYGuqAK8vSDgDVIRgyCB4463Uc/p9PVhd7xjBExR22AkD3ZXVyRXWTL3JL91N32vlkuLijASFG27BRt3xO88kEZxVBeG2j7nWdcmCN+JCwkBDY6t8g5GMiKc3/9tZ2Kq8/vIinHJSZp47maQSh8xmUAcGH0qJ6n6icwdicl08k2HL2goOLx1Bv2Iom/2RfezB7bfHhYRBntbKOU/cxV/u/GPScl9ujOiFtTxwawjQGFtWyYZhybEjfs3HSXOug2/+CIDZDlXr6hvs1XVOMww3dc2db7QnJdUDiAWg9jOdroLCqpa7Yv6jSTMhgNy2dr7x/g/jgmLJ6AXcvPV68rTEPWv3F7Bk9ALVziGdfHBmJw2apN8wQej1nHgb8vPgxms9r6ds8QSFHU7CoGEf7WXlKljOgXbyWMKNQHJ1s1ujmQUmDe6iodrArdN3Um1ZPaHssI2IRpCHzzESV6UO35GifvIjGHxfQqD4K1uxo4XWpPTmnGXfNinpch4nJzKZQVm3eSL6Ab4pdQlh5ofC8+vw+2B24fFMDSQGEC+t15izXNkAnq2usTX5lH2camcAZRsxkgZyBhTMFPhMWjCfJpK+bKfOv6hQ/d+srtuk1fHbhmooTc0j5aaucardEMl3ds7IDSr3XPOMyGxwN1N+8GPyJ4Vp25gwWM/dNp/y1oSqbt2wmdQP6aTm9Aid+kt3SEgiZ0a4bYLfy+XUDTxBYYdDXARlg1g1/VK+smgJuabgOeNTaCgtY3Hjd1nHRVxw4gq+/t5Cym45QENoAKumX8bjkycmeXDYBQTdMrY0JaVHNrh1+reUph7bLl2IMYI3G5knzkpfAc6Koeqx83oqmlDBmt/p9S1qCxi18QXGzV9JYW0DLZVlbF0wnXdmns0D5+F5BnMAACAASURBVG8m1qjhr80lf81xtG2y9zLKxnPHjFOUcSYzKKunzyvsT5nxCAEFU+tgy/Fx43BRIbS1gyGz6xxUKQ1DBtqe1zC+R8eEKZm7O8WY3RnU4kFwbRvLHDPNtrUnJx001G1iQGqcSWxryFVdM7RQ2NZ0CLYl9snLhZycZK+n/PxhyQF8Tob4yookL7N1w2byr9Nmcrgp4WxYEYIPzuyMC4n4dXrV6rqNJyisrH8WOlLTdhveTGsn9qeZTmY8vpLi5hYAmosKeWr2dLZMGk/DN8dzbuQvfPvDX5HbqXqvUPgAcxYtA2DR5BirozWc9P5xrFkYsqlBUMBPx4aSUnrYeSbZ1WhIlyPKUEm5pQtJGsEDh6rVb1DCItugKkdVj+7dtPZ2GPy1aia/uZicVnUzimoamHz9YvZSTGym0jtrwzoombeTfrOr8RVHM8qams6zxS3KOF0qbbOazRA2TooqKVSwl4G1joSTKuWJz/8IGfEl6fDNgXl2brcGEWI82VZN7cJU91eDaDRZmDrloOo/s5avnVFuOzszrv2sSzs4rTWHt/49kI+q+6t9Nah6L8fWUSBB8gDk+QU38JXr705SP3UU5LJqwXVJXmb9imHFotTua8AT9gOsj1okpU8cwi9g7gk53DuhwHY7D3s+/YLCyXvJaVs7j6aiEpj7A5hyMWVRlfE2N9IZHzwWN7cwZ9Eyishh0LwJjHn4wbiQMDDnfmqggw3Dd+MbB5g6OmMUvHRKcYqdwa7IELh3+nZcUpx87INN23i/JpEi/e210+hsTa713dmqOvTuuK7aMXqWfszh90Fr8v3KaY1w/vylvDjzS/FlviAQVMNw82g3Z1soyfXSIJ1ni2vCv8C4jBLe2bkAW5FAxX2vOwo2J1XKJmZy6Ydh3qiqpSU3WRWUG0yffqMlN5LWSG0Wpk4zKDEg4igk4sJUQEFhJ2ec+TFIqHu3lOM/yOHuy4Np1X9Jg4mvT+ZJ4IL5DyW8nvT0I1p9wo35ujn2sxunmY2BJmHxjk6g1RMWWfDpFhRu3kt2wsLJoymvIL795aKKUSufTlI9gRIEF6z8I/fcP5HPP5g+95NTwJbTKNjawZuXd5WDTduSii51Rg9x8nfX0BqG2jVKWFReuo3Tvr+WgsGHeL8mtdZGdzjYtI26xrWcWFNtr6N30Fcb+HJjDL2mlstfL3cMtHNLYJcuyjgTw/dTcperkAClfrKmC7GybthM/nnqTPLzEl5UN80QTD1lEDAorqZsbyA+Ql/dkerxZEYLp1e1GMJ0k1bnWEnQKZjOTtAGciRnjKqj7uGhQNcC20ZcfR0/mnleshuzaSYVdLmsLwwJ6ILAnSUfdHLvhOzadSxz9AoKc9CbKSI6acbg5r1kJyhcjNgGEze+hnTI21QWPkADHTSESgnZbNNgSeORLlvokaaucW1SZT6AQEEnZ967Ov77cz9bQ6AgIUj2hNcAdFtYmIVU5+ASgntSy8k2VKZXc7XmRRwD7fInNbgmsHOyQxS5fBZWwdPiICakJCVjq1MqcVCCza2W99QpfvInJc79V3I5i1L+3lGf5PFkPr8/PxY3ANthFqaPy522QsJQsdnZ0xompg9E7Epgm9WNuaA9yP6lldR8WELt1FYi+ZJX3hFc3xzkJxcmp875257MHEG0vinDc9RydAoK60zBiNe3zhgy6PiTKCqBptR0DfF8TPp5nUx6hiBYNf0y5ixaljTrsOZ+gtSArUzrGhsjcaeKepliV+sbwBeQnPXrVSrJnCUbmJSd7A0/121BYRZS+783jaG3r8HXlhBakYI8/rzgurTHMUa71kC7l9Zr/LahOm6YjR+XGMvlblZHnavltRFlk1aXYqi3s2lki1ldZAgSszuv0wzo3ug23uFwfN8GOthIPYNaC9kXbEoRSkIARVH6z9ulrsmmIJJxzluj9q7APrAt0GTY0ypPt5/RmLPgdnXgY57Nzbktyq5gJ7vHROLleDvyJQ/u7+DUXb4kI7WT95UVf5ZhFOZgwmOxVOrRKSjcgt7MMwYX76UU1j8LrTbV6gM5iXxMLueVwL/HngYQr0Fxxcqn1SxD93oylhtMOVDJK5aArXT6XDt1UVdH+TmBEkdhIQSOaTpiso2DTduyO9+yZSpeoKYGqqrIv/lzdF42BoDD+v8H3ruWnL2HEFXDCN59N6fOOJ/39U7TqUrgaPrHq94ZHev292KsP7EWX6m9WqaZaCIViQ1G2gir2inT6PR0mEf5f1meXL3Obga0Ibo/SUgYRIhRX9rklg0ekSPpP6ea9k2hrGtuxFAd9pzlUVuvsqaVleTP3p1Sqc+cBbcnAtvqG6D2852Jmu06mj/VmymdjcJg7gnOsR1WrMGEH7VIbt6sHF6OFWFxdAqKdEFvxvoZN6capy2J+OIsvx80m87DZJ9wO68APvvGWzyp/94yeXyKYLByzSmDuOYh101SsFMXSdlJXeParAVFRek0Pq5flV0DTO3I+HzLlsG8eYmgsupqhvxwD5AQEocvG8Phy8aQEyhhVNUtAEyEpM7aOtoeTX82Up/UsS6SO5AnZj9itGLXeTp1qHZqJieEIJ6ttv/MWq6JRuICzsm4bickDDIRW6IomiSQrKRL9OekPqp7PsSP5vh4sq2altwIsSY/IOj/7Z0UT6+ldGMlU6dkVxTJTsVVXuYcj2GdQfx4bB7XbWhzrBMPcM5AX1aGbLtgwmPN5fboTDNuNyOwWz/lYpj3E2W7EEL9f95PsrNPtJg+0jTndaw5YUOu6zjQGacZgNNyN7qjPsrqfDaRx/72Tgbeuzb5mJ0BKkqnOR5mor+CewLjWByYzD2BcWzjoO0IP9NO2w07A66TUdfufG5t8Ici9L92N2KAGqYbM4euqLJ64gO+XFQRtBzJbJtwupbyMvVMLn/9DFp+OxKRK/H3i8aN94e/uJvF29NnMjAwijNZU56feTrktjt7OZm58vigq5AA+Ec4xh92ZV6wyUmdlama69PA0Sko7GYETuunXAwPPQ/L31T/d3KNdRIC5uU2dSfMWI3V4FwkLtCFW79Jq6PZn2+7LidQkvXxem2/GvvEejl7DnOgsR9SwoHGfvxh1cVZCa9sOlYpQR4IUkj6SG27bKhg36F2FasBOkKsS0c+m4Fp21SIsn3cFN0cr/Vwc3RLvNbDRH8Fs8XIuCAsIzfJNmFO2Z0/KUzFfa8z+Pdb6PeL11m8fR+/fkSSd3lqTIfIjbEhlHmiPqM4k5mOCLz2OlxfGcRvGdXn+9UMwspxhe4jBWM2kClWYZRu+aeRo1P1NOViWPdneGtz6rrTJrhmaHUkEzWVcdwlP4fm5BG1nbEaHMs2OHrLOGHor0eUHM8Fje+QIxMfpRA5riNxNypKpyXZPDJB4Kdj+zTuOxv6fXYbY25bS97AQwRzHAzrVVVQXZ1ynLqCSn52z38l2pK+tEMSmURPx4nB5HAlp5T5sk5FYmD1xkmHk0rKaYQe09uipR0TK06mH18PnMAJWj/HNvmBMwmxWO5IOqo53ftEf4WtK7DVNpE/KUzJvJ34dPV+a16EDSN2knOWS0xH/8xH7k4qrvoGWHphHqfu8mVkUP7x2Lwkm4Id6WYDZuN1aa4gR0CnaReBcsU9Vjg6ZxQAP/otnH+Vco0F9f/zr1LLu0KmaqopF8PiV+HG/07a9k/XzrW1STjdYCc1hhOG/vq9osE8X3oyh/15SKDZn8+Q0KVdViP1Lx7NkNCltjME4TD6jsVi/Gsx9Buzjc/dvYb8QYcQImFYP9i0LXmHu++GgmSdsDmJG2Tu8WUmmxG+8MPWUSqOYRLl8b18wBQqmCmOjwue1bLGsbLaxOUvcM8JX2VRcAq/GPkVxj/1vO12UkLLCxXIZn98tJDu6uSBIJHDmX+S73CYm6JqsGSo5K4VJybNDL4hTuQ1UtPSg0r3/ju5g2uiG7g1ujXlmptGJWYPFfe9Tsk3dsWFhIHIgX5f/9A5ZuNg5jp8I/eUlfIyNVB6repNzvrSNq6duYOFlznbB648Psj9E/JdZxZuswHDeP1Ri0QCBzokMcsNlMDvdnTyvc32eco+bQjZxZxC3WXcuHFy69atfXLuI4FdWuogPiZRnmRwNZbPFiOzqvJ2TXSD47rFgclda3Qa3q+5z9EW0fKxEiyFQ1PXmw3ScSxeT2/Pvot7GmZ2u/C92cCdCUUEiBCzzCgAy0je9hlZjfKo9BJLH7ktpRa1ABZZnovbMzR/hl2xsbhVDnQ7rxnzNW/S6vhtx86k9CFOMyQp4eDDI1NreXf4mFw9gmtOSW/Qfmm9xq8fkVhzVvr9cPn8MFtH7e7SN2T1WAKlsrp/Qr6joBm96nBGnlOgnvOjk52PdSQQQvxTSjmu105IL6uehBDzgHkAVWnqHxxt2NY60FUYZtVAJsn07Mi2BGVPxFq4GawLBjuvs91v1iz1T+dUYGlWrbHHUJlkkkoDsHWLVfskdwxGKo8kdYyNUd6oRW0VFOeQmtDP6Rlm4zXlRDPRpGDCrmC+5tWyJqW+uFsbDXff4um1+MsiaA1Bzm6szEhIgLJPGEKifkgntZ/pJJIvyesQDDu+1jHVSia5x/IDgjY9wq40CL84071jz8ZILTk2vJ96VVBIKRcCC0HNKHrz3P+/vTOPj6q6+//nzp3MJJOEhCQkQMgESFgKRFQwGBAeBLVPrdqqtIoRkcWI3dT+nmqfQrVSaB94nlbQKhEVizQCFdSWaikUXCAgW12CVHZICEv2fTLLnfv74+ZO7tw55y6TdcJ5v168Qm7m3jlzZ+Z8z/kun293QJN8MNoDQQujLSgBcq3FhcvbsP1x4Pgfcwy3HdWqs2i5lABfjB8JSaEiS15L+LIiakjpkrQ+E0pDTZOjMEM13FjgK2437pSgvFpqxAYOc6zZIY+jCQ12RpYWQJ8842DVrBtRUg039gsV5hIFmqRpRO7lzfPAxDk+rHJ58LMj9YYK1OT4ROXg4MK61mgRLt5DdNvpjZG0m6jzAAcqfJpjMVqLIaNlWPpKoV7kxiiuMmiZKaTVI6nWguO9GPmDv+NbH6/CLf98Dq2Zq1CyvSTkXCVSgDz0IyL6efz7hZnYiOvQqopjtILHRvE6k6+unf1CBZ7yHcYCXzF+3HQIhV9WhqRL7t7T/s3f4DuFR9oyedaJJ5GDRLxunYL53AhiyqeRrCc1cn0GTVJEeVxyiYQaCSD0PTRDMuyGxk6aPO/nQlvK0ogFL+1MDMKDw02XMpGaIhm81BTJSKytdQd8/HKBmlZKqlzBXTY6tLCupYVcHKd3H0n1D3JsQWssz1wbjRjVrY7SMOYWDkjaUI+cdxqCrquOdRi5D70VZigiCHUdAc3FQNsF2Pq7EJteD84COAbXQ3QSAs8KEuNzkD7gu7Bw7Sm5vCUGQ9K+g+u/m4PdiZl4BTeiErHwA6hELF7BjXhfzAzr9clxHnmya4n2IPbhs4iZ3N51R1bYBSQj8RGuBNbnfgAf4Qo2+E5RDetsbniIAeFh7IuwddmjcDuCJyfBEYMdy34Y9Bx5fCo2+E5hoacY873FmO8uxrzdJ7F7jxB4D80Yi2TYsdI6EbO54eB1QuKk6+bxqYaMjN/PwStCU1p9OtJUwfJsLBgzEOtfsuKDTVasf8mKrS4PtUCNxtz7Odht5MK6r75Ig88X/Lppu2lAmqC14gwigMf2uYiTOxAcDOcgpdu+NDkGC0aQDZYggmgIfn6YXKj39CFCG4NeztWT3xXhaKmgqqG5jNQuDj7ai+M1f4ffkabp1+Z5G/w+V1CsIzEfGFRqRbFvOIoxPOjxgwx0zCNB64egFtJrHFmFp3wXqK6HT3AFc5BNd/mpYkk5SMQekLvJKZHjEPcseQXJZRWoy0hD/9/8Hx7Iz8cDisdt8J3CR+IVwNKW6cQDmFqB13YB2DMcM6byhl07ygkxj0/FKV8DPhSvUIPKqRzZAM3mhlP7a4gi4PNzOFfbD9nJ9VRTZDQBI5wCNVnY8eBxDi578OMunE+E1Q1cM64iEP9o/WsGXGOSAZUyLcndREIWBaTJcXxvuI3oInrjpFfTpak0iDVu8uut9UjjjCQXFDMUEcB23xc4i68xCj64weM8+uNN0UsNXJJqI2gB01jBhZcoQVA9XSmjHfOUhCP7rVQjjZlchcRHzqJa4+uqF5tQBsDfFUvxkQEjIXNg9m1BgWtSxhmt0130zAr8qbYOMUKmZg2IHFMgLQhKUEeNaXCclDJLEjSUYzjrxJMh94fjAL+ov1MxGiSn+fj1CtRmTOWxOj20BsLiA/gPUlHxWnrQ41/YG9ruluRu0sOoHMfSz1sNxb0uNIu6u4ZIC4AzQ9HLOS2cxQV8BXtbVk40BGSjGqcAvOU6j8Knk0ICvXJ2U0XtLnh89WiyxIAXvRj43r8CwnveQQm48l8zceGeXGoQVE9XykjHPCWyq0hG9v1v9J0JqmNQo1Qj7XdfWUg2jhojbiRSOrNZaO4jvwhi0QTHAUjy4A3xJLEyX53yKRuy13wnA0bDyE6Elg2Ux6fiNd9J4jk23o/hSfVEI6Tl5iFBKnijVVGrkSdPOQAc1cIh4+soDLgY6vYhtbsNV1ZDL3j99hmPqQB3rU4YItLkP5ih6OUcFj+HRZW6yUNEJmpx2B6Hijb3vRzolVdYifE5+LcjLTAZfveNP2OCQsrbdrEe6Yu34fO4IcA88mreiK4UraGSmv1CBXXl3gypxzOp5gQiwMcIiJlchfgTKcQeHmqmEVJT1XRUCZY2ee7eI0CcpJ3JJAAQVM8dCx6zueFBRoKkJGskg0nOXCLtAGLBU1UBeIKFlaXGzaTcqid7s9k+SrfP3B/6Ap9xEupqbrMZSzJaIpKyO6sziTT5DxbM7uU0g1z5aW/7sstVszGTq4ICvUDwZJi3bHNQvwcAsLi8yFu2GSloQi4uYJ2vCJt97+K00NZilKLnFI4+1LsiObVUxgM/PqypxZWXhknplvLL4AAuTkDaD89i0Qs1SKb44GVkWQs9whHgk9HKOHvzZCVdt0WDaFiDrkdTkhUhGqpGf1M8Tawu50yKUfrbxkKr3qbxveE2lNzTDzVzElByTz+ikSgpAlYNBZ6zSD9LikKvIwe5aaj7XZAyloyg1cgoHHeWFhyM7a56E2xH0cuxIho+hGaLuMEHVq3KNpuV+9uDvsrJkNZWNLmsAtmoBt82uzWjBXvFA4BAjnUodaXMBNgNTcyJHrj2pSD++2WwxgX/SXaP0WoRZCoMGgBTOlGq81Zag4tilffBki/JhZhFPRba2JohYCE3ghhrUCI3aFK7oGi7Ea0dkDwWdYfAjlBSBGwrkPqxA0D9eel3ILi+Rw5yF/5RRKOqXQxJ9kW5mzGzs9CS++hsN5GIyOtjwXYUvYC6xhKcKF2Fr848hxOlqwIpq/uFChxHPATVKlAAh/MIDhjL2UHKFZbSh06rAWjMSA4YifbrCzgsfh6iAxVlTQjoSqlTWeVJhLbiNJIOKsciaAJz1XAH0l5pGJ38w1WCrYY7aGWtvg/hGAkg9P5o3S89IyHT1Napz8h1Y8Ebuh+ywe4ouxa3GwkZb4t0XM2MqTz+/LoVP/sRF1Sv8ZOC8GRf1JAE/uQU26QN9bCE4SWK4aUqcBJ66ra9EbajMIjR1fPfGhsNB3cByUg0vf5rDP3fHYEgc8XP9gELfol3Y9yoRix8bTEJO4RA1lMV4kKuxad4ELvis0AzHGVjn3eWPYq5i1bA3tI+kboddhxZ9j3iuGSXV2J8DlH6g+YaoQVS9XYCoghwdikWIVTZYB0QaiySYcd+oQIbxTPEa8iP0UL5PsbBiihwaIZA7KBHQ7myNhrr4PyASJmHeXAh8Q6t+2UmsrJOPBm0A6BV+M/mpBRn5WecZnS1jLHR70k9xdbQjgOh7W7VvH3Gg6cPuXQDyQ5eynSS32kRwFtnvJiUKqWsqlNsjfbX5jkpkUGOyQAIO6jf22CGwgC0wKJ6C/63xsagdNFLPh+erZIicTRj4XpjBQb/4r2gIPPgX7yHKxYbqn/wGACgCnFEw6CGAwI9jOWeypMxACWow4HZtyEOVtyzZC3sZeWozkjFO8sehW/2NxBNCG7GQrsDmNlJJEhiw+8GV80DdhFivF+KQ3AA309AwsKzaPk4BY7/qAoSmJPbnmrpOell56jfxyb4YIMFC7kRASE8pRHRChrLRtHIDoYXLJhnzaI+PhoWajqrGcFDEn4g6LOqpUkGBHcUlFvMqqEZY9L3ZJ1wGj8/7ELJicSgoHaCEzgw2IPi77WiMVlEfDWHKW9HY9JFcy6ZkiJpF3JgsAe7FrjgNXC60kgojxUUu1BQ7KLKv8iGINEGNPsAj7JPh4bQYF+Q8GDqsQbQ+sIo/dW3lJbiklr+ElIB2j8pIoie9ETYLoZmF3kGJ2DJ+X92aJIgjVH9ZU5BU1CMAgD84HAzl4csni79YPSekHiubeddfuowhMzQa/gqbXC9k4G0uWVoiQ5uFUq7HxYA89smfLNjjvFH4Q+23JDjRlRXaStvebJRTsRGFIDlHZOcmWRGp0lvnFqxFdLqn6aITAvk0+5vc3MU/vHXUQDaJ9SyYuC3bhd8CptjdQP/bY/BT+cYm0iVcY7Xf9+AxpSuncs4ADVzJDdsT2o49Xn12EjF6Or5MsFIaB0HgKhL5BTUqEv1uu4aI8g+dXkSULtK5J2K2rW1UMNIAHTXiBsCNTVTJiFTCl4KGZQCuxQPFl0zADPigpVHaTUAgDQpq5+zpAhY7zmF2tlXEFCwILiHWzgv/tbYGLLr0w14i0A1R/77NKSFZF/pKQCTFHCNGAkjAojq5zWyS9bbfeg9h4zD0Z4MEahcjkXIK/PZgTdiW/FTSBOu3mSsjHM0Jnf9gleZ0kqr3O6rMENhAKMS3wOtVuKOYqCGpIV/yCDwZZeIx+Uv6msifYJsPwHU1ATlJEB6HWrXlpHAszw25eoXMCZ3PXO5tBLky+zEHUUKZycGKbUmbvWYS4qA1+tPofGR0CppEqtqa0MMhZah1pMG34dKZAv9gu6BngLwu2Kpyb6HEqPQD/9Gg+Zj1PfHaIzJjPIx7f1Ri/ppZRHJf1PGCYZk1mHs+Cv4u8OLj1pteDAqE3l8KupLga/zJPdVVyPHFvqKGqxZWNaTAbSazyvVTocOKsOkjEu4bvAVJDukpY6epAX/2/+FGBMc3BJjosH/9n8BSF/U6aQCMrH9H1fJo3+Rdu9keRLQMwJmqnDz+FREE9YaepkxOfnAnWuBQauc4JrJ95XEDfudINkJixAaDN61GGicb8xIAORdn1JYUBQBUWjrvW2gfwTpHugpAIfrZvxax0jwQMj9CSdQrQfpe+Lzcfjqi+DPb3osp9uHWq5dGJJZh+tzyxEb6wXHAa1WTyC7rvQOD/453yW5nLogkYjnEBAFXH2jJIzZV9RgzcJ2FAagbcEBBK0QxbYPlt0qySEk8jwKoodoVy7n50ufcUX3N2758qAmP3Os2YBP0hDyS8XXiF2bhuTHJddGlEOaeJs4eu9kQJoEFnIjqKvkcJoqVcONSRt3BITy5CC5upGPmpx8ICc/FftNuDbK56QiKQ+o/f0ZiCnS2ttSbcXg5cMQd2MqVi2WsmYSnJJry6iiuEewUHd98or69vt9gS50g/50wNB1Se+D1gpda8ek5V7Sd7qEzqJmG2EZQf09ifbZ8OmhVFw4nxh4jDLr5/FPXUgeIu0WHA4vWluiMMk1BEC/wM5i7PgrsFrJTaWKvxfd4eiNzQIIfoTs5KI44KXJwcHpnHcaqKq4fX1XwYLZbZgpHpOhBe9kjAR2w0HO9JAnRXUTIr1AczivlcZbG36NexctD0q7FQE0J/VD3AsvBxm8jvKcBdRZMcqhysvngNKWYt2lkOAHymoT8aTDqWnQlVISg948YKheIg5WrLaG9lGnQevSx4PDVKSaEi/UG4vZQHW4aLlqVl++iM8Sz4FXGAJ5DAV/icaFZhF333+Uuns7tG8IyhRGKBwcPNAiBBtiWhe8pA31xI+f3A61u1xSLJjdQ+gF9jb4TkmreUgfKDlQqbdN72jGEg1pNU7/u54vvDM67snctaQwyEgA0hcnrqZB6i8NdJqxCOwUVDQ/UIG6paUQMtzgy+xIWOJE3KZUxK5NQ/NjZPeTJK0N1DUk4UmHzq4PUgXwC2tFuD1A865UxN5aAY6D5m7K1VbwZnTiJcV9lL2wD/mqqDpNejSpxmI2UK3GaOdBraBvecrFkGJPD/x4qfkcLjSPktK9W6IQG+slnn99brnkAtIwFhyktQXPkeshWtpupx/6vbRpOlL97VxQvQRNujySYTsKaK/Ac5BIXMlNRxpKUNcjOwojdOauQQvRYgGn9RnKzATOnTN93ZIi4O+PA6420beYZGDs94Ev1gfvHFofqkDlC6chxrYbRa7Zgv6LshC3KRWN60KznvTuB+3eKSfHtIKzmFz2Jzz0g9AixvWFTweMhd5nQG93qB5XR7LgbOCwxjo5rHOV7N4jBIymjN1mvlKali4sisC7m8YBADIy63BdbnmI+0mmpTkK29tSbwHpbe5nk1qeKlf2tN2AmiQ7B1EUA0V7SXYO/zMxmliIB0jGJZonq8UOieVQck8/A89qjp7YUTBDAe18eZpvWM7bX9t6OqgwTMbvtqAgmryN765J3AgdHUtV5kCklGq4RDgO8NMnNtJECQDvzQP8qoWkxQpc/whw8oP2nQWtFgM+ABYg2WLuNZlyyQwdCpwP3eJUOdPw9Omtgd9JPSuAUL0joD3epGUs5PeLVE2ulyo7vW033JH3XUvRNTWFvrtQY6TuAgCuGVmH7AkXiNdQGhUAsHiB774XgyfuswXdQ62Od3rYLMAf8mICxkLtYnq02EV1Scl1F51JTxgKlvUEegAvGXbql07O2xc3D4Ov0taeFeOXCsbEzcOoRsKMRlJJEbAyRSpSe46TGp0ufAAAIABJREFU/k9S2TTLmpZS/E/NVogX3sBDpe9jYflepDSd0xwLiR3LfhjSHjQISqEh0D5R1p8HIEo/33kQePehUCMBAH4f8NWfgRG3tx+j1WLACsCif3/VaKWNBigqohoJAEguvYJJG3dI/9cIDpvRO5JRtsN9wXoj5nHZgeeIg1V31fwJrpj+DKpRS3srIfU1V2YGKnWyjGZJlZxIpN5HdeqtPwrYeXMrthUEf0/CVZUFpApsuWsdSRVXL4OrL8AMBbTTX2k3SD7+0IgBqH/qOlx6cBIuPTQJl+ZMQv1T1+GhEQOI5xmaiNooKQL+Mr/d/QJI/39vXseMxZqWUjS4D+E/648hQWgFB6Cf0Irbav+NYU3lpkTfhj3wKIoKfw5XbAx5krr9dtJRAOSJEpCMLQ1XNXBkbfvvfJl+lo4ZITvdtNGiIin2QjESgLSSnLtoBSZt3KGZahyO3pEa2XBMRxqa4NM1FH5IMRCjn0ESamlvNUq5ey2jpE4XdrdE4V8H00NiDumxnGGjAkjFd2qDq+6DnWTnEGViHteq/SAZoUjVdKLBDAW089tpTXDk4zOm8vhJgXFVSzP567sWAwLB9+n3aq869fjUchHTGk4hSjUjR4l+TK0/bSoIn8en4hsP/ACtyQnkVPYPPqCea2ZCVCIqfMQJS0JrMZQ88OPfYW30NKyIugGwWoEf/EDz2lq7SwBSGnMLuUeIEnuLG/cseUW7Qp1iQxKc9FW4EuVjjGZEcQA1IG70fdfrEQG07zr0FkbKHdJ3qq5F9YVgIyFPuHl8KkZXZcLVHAVRlNxTJKMCAPHV0idR/flS7gZOf78fXprcbjiGxHJUtVdAe3egNkJy3UVfCWQDLOspAC0TKKiGAcFZTzJ6qpZKzOSvqz/olRkCyq4R4XEAthbgmj3hySzzvIB4gVzNGi+0ms6lz+NTgTLKRFVKtwa0LCY9OL7dWMRtkt6z+mVS1hP8CHyqH/jx7zCj8N12AyYIwJo10v9ffpl4bb2MMa3Xo4bWA0RGrlBXxyjSN+jLa4Qb2LaBg5uy7zD6vss9ItZvElFRBUw/vxEPlyzBgJYyVDoy8MecZTg2YXZg7CRIx/U64722Lw4XmkeFnBeECEx5W1rJ0wyx8vmUk/nbZzz44T4XvKrbY7Po7w76uqQHMxQGmGPNxhzod00zgu5EpEA5kVZmCDh7gwh/2zvG31qFN0eUocjrQTJnLhgpCDwa+Wj0IxiLRj7aVH9kQJq0RmekoX/p5dA/asQoSBOlHnL20+E17cfiNqUGDEbT/RWofeU0RIcf01/9C3mXs3Yt1VDIaaObxLMBnaUo5VWcTk23kxJ3Rjq0phc52KoO5m/I05fXMNvKVV7gaO089N73oAB4nh2LJjvR77l/IGvrIkQL0puY1lKKxw8vwunbAGCO6cK+kAm3qAiYIRWjvp80BEu/9wy2Tg6Wxr9339t45u2lGFJ9AeX9h+Bg7W9x3JEfSIwgBaAPVPjwx1NeCKKUOvtwdhT+70ZJMVkpVa7MerqaYYaimzGTvz5zuRSjEDxA2TXtRiJmchUSFp4NZFuZ7Tx2o38wPumXjW/WHQtyP3k5C6yJU3CDiawneWV73bICzFv4W0R5FFFomw1Yvpx6rjxRKtNgZZS7BhlLFPCt1e3nHS5ESAFewjupyP0BcCivFBaBMpEK+rUIyklY7ukNAchbvlyKUSjdTzYb/KIIi7f9tQuOGET/5n90n4dUE1Pt01+Fm3EPTlfsgEt85JTuOFUrVjW0WqPn1/8iYCRkooUWjH3zl8DSOaYWRiHI8aC2e+2sLsML634CAAFjce++t/HCup/A4ZHqFjJqyzCQK8CluYAzPz8kpfVCs4iC4uD+14IIvH7SC6AF/3ej46o3CiRYjKIHUPplV1onBnohqH3SOfnAd9ZJq2iPoj1E/PfLQlJyPfDjVd9JzPcWY37FIbx+jLC6b+MxhxP97Ddge8JY1PPREAG0WmIxNOW7uCHhppDHa/U2DlrZqqPQBlKvc/KBp6qAe/4kqcqCk16vRe1R44DrF7ZPqt9+GXjWH3xeQqZ0v743Vbq/HE9xy9GOk15TGwG/en6+tCPJzJSCUpmZwLp1sLzxRtAxfu2rYRca6sZJNB5DogR1gf/TEjfu57TVgmn3xF5WTj6htD0GoaVvpQkhHuTwuPDM20sDvz/z9tKAkZCJElvg/EAK4pnpd/3HU+TCPgbbUfQKSKu1V92n8b+v+hF/IgVz3+VQvALwxEqPp7UKDchKJHlQHHsWOAYsGDOQ+NjHHE7A4QSStMem19tYXp3es+QVRHlV30ivV/qyG5gwlSvrVUNDdxgQpfoJrfNCKChoj0m0Xwb7Hvku1vmKqbs5Xb96fn7gNQW5Y+7b0ik1MUZW4WYk6JWvJ9yKbOo9yUgl19EoXI5hKwFQ4kFDai6AgxRgzqgh11fI55rpd220k93VSLcaCo7jCgAUAIBTw3d9tUFarXE2qQd2xRMp+Oy/38Lrh5cgyS0FC99ZuxCHH52heU3O7kdxShkWgGwojBZcaeX65+S3B+epgVsTwV+ZjqaNBl7bC/mYL1Yg79X3YBEE+HkenzzyHWx48UkAdJedUb86zcAfO+enGmgj993IZG6mA5563OFM3LR7Qmqx63FEw6bhcjQMJR5kcTrbC9loMaO2+YUmu0GC7ztlD51OtxoKURTXAlgLSJXZ3fncvQVSJXL1fZQGPskePHbkx7jj9CuwtDnj01pKMe/J38ISI+DgQ7dqP1kieeehp21V11iCitpd8PrqMXlDAo7+70yUbQvumy1P2ndzTvzRe9rQytIotGwovSwWIPS1rXvxSax/8UnEUDrFkXowGPWr0wx8cdZp4m5ug+9UUDBZed9PiQ0hmXXqzoRyJbPScOQhVVOc0oxsvBa0HYwsVaLWuyroDH0vUjzI4QiOe+k85plro0NkN2g8nB2l/6CrFCbh0Y3QJBsqLh5GY2zoF33iK7vx6I+eDRgJJXXxQ/CrsneJ58mINTasS70h5LiWttUvXHZcrNoGUWz31/paonDkF3cGGYuETOCJc9JremtHBbKufQVzlgSryMLhwFf/rxArq2friscpod2n8XPb5Ds0dJH0FH1pqGU21Cv/VNhxHA1BE7lWBpHoseARe1ZQOiutAZUNHDyE91gpt6ElK0JLlY0Fj9nc8E6Th1Hfk1b4iDUZnapxVlQUJMEPlQS/kcfIWU8XmsWASKASC4B5I9qznno7TOupj7NqKHml7P9RBSqeD/6i+90WrMj4PlJrKT7YNg0l2sQoisCUk1ntq1rFl6lKo2fE0xcPwesLbc/aXJ6Av097AkCwHpHyNY1DEWZiMRJQikbeibJf/BrPpd2KmHvKwKd4IFRJvbAXXTPAkLFQ7rxG3B4qCEjSRTLS51qN3sSm3gnI2GGBWyNGoLxuOAbMAuBV6xRD/cl7Qj+su6TKO5O+0KGOyYz3cWg+dstLqXhoVZu/WXRDqLahYXMGUmopGSVAwKVDcgmIIjDycmqwkVBsz1NKr2DuohUAEGQskmEnGgkAcAyqlzKLVCt55Ws6inwchfSHpu9VoPqnZxAXezrQT8A6wIPYh8/izc3AjKlkH76MOki9ami7kahefQrNBZIi7CoBmO5rT//U7XOtwohr5hPKzsENP0SPBZyNbCzCTWeV8UPbwKiD1Fpxh64wJB2VKu8J+nphXFfBDEU3ouV7V37Rd58UsP6EiEpHBtJaCNaF4wI+WOKX1eJEXkZqYFX+8PnFSERwRFqWmJANhTxhRlkTiMbCZkvAs4T5UH5NTfdXBKqjuWoeSPCDs4XuVi12P3zfKgMoQXYaskGqXn0quMeEFdJq3ycVRsqGc1hTOabWn0a80IpGPhp7ErJwPG5Q0DWNTmxaeUVTzg1DcdZpaiOjp3yHcTfn1DRgJHeIjJ6MvRH0YlJm6U3qx4zugRmKboQm2TBTlSASkASZ/JvQQB3HAYsWBflgSatJpZ8/AeStjJyppPyy1/WfGRKj4LgopPafSX1Nb+2owMipL+PuGYWG2qHS0nu1kA1ScwG5EdEnuILJAo9S8XP8Z9NlDKutBN/mVpUFDwEEjEUseMN+dC2p+QVjBgLHgOKhZ4k7C3lSnowB2IfKkDjCdKSh5gTw5Qid/t4igv7OCxbcbTUWpNaqCzGT/bRfqAhqqgRIr+818SRe850MKuwzw/s/kIQeRUFK8Z5QINXJMHoPrOCuG8nJl3zqygIxrd4DxOKuDRsC8hNawnHKtNZ6kCcUzukMKvoDgMT4HAxOuRNRVin9MMqagMEpdyIxPif0AkVFyFk8FL95Mw0LC55DSukVcKKIlNIrmLfof/DgxiJMQBlS0BR0Wqzb/NZ/5nLJqNL6YCehCXvFA2hGCzLqawJGQkYWPJRphmBYVltPGHLBmIF4xJ5FXeF74EcJ6kIKzxZyIzDHmg3x9mzErkmTemhQthYiAKGBl+TsRcDbYsGx48YkPMzoLdGQdyVaHfY+whVs8J0yfE1AMhKH17RX4YuC9Pv72tqNjG6GGYpuJidfyhZ61i/91GppCkAyFufOSc1/zp0LKvTS6imgjB3swnJ4oMroUKcZKkiMz8FI5xMYO/xZjHQ+QTUSstw2B4BTTXBRLR7csOTPiIaAbFQHjAUvWPBATCb5tcp9HiwW6WdRewm4bGRp81QmaiG0/dEmhKbBAggRQiT2YCCMIZvrB7vqq6JePcvV9jSq4SZW5APSe5X8eDacMVPAl5KNjb+RB2cXwXHSmsES70Px0LM4sWEN9Z7JGKn01sOothQtnkNDKRlv5DijZ2CGIkLRk29W1hwcRT62YS3qkAkRbTuTtWs71svagNx2bKlUXs1DRCZqEQcr5lkpGTHKPg+iKP0sKAgxFtOt5NV9tMKCeHiyR7WRlyT6Jm3cgRVZ9+Jl2xSMHj6+/TkIYxAKHsG/33o5KLvJBguyOXKLy3AmZeV7lbDEiRv/uBMrsu7Fa7apWJF1L3I37ITfIYbItty4dTuGLvqp5j0DtPutGMXo7sNsk1a1npfecUbPwAxFhKLnTgi4ato4inysxjkshR8rm86hBB0siDJScc0BwzdK6arRELDaOinUSMgr+AcfDDU8LS2SQVIwx5qN6UgLfHAtkFb3sYodU1lCEgQu2OHv5SzYk5CFSRt3YO6iFQE3Wf/Sy+2TK8H48S0u3LWkEICGgVEQzqSsfK9u3LQTcxetDHLjzf3hCkz58/aQ8+5Z8gpsLSoFYMI965DeUhtGdx9mJxRaEgDtOKNnYHUU3Yyy6jnKmoDU/jPJrh0djOTWlxSRlVkBgLdJAnq6ri8aGq1AlTQ5k/Hn06sRCwfus97d/oeiIuDxx4Fqjb6agG7PbZnTwlnsFQ8E3E/JTQ3IqK+BXfBBbE3AP6KH4ZgzBSuy7iVXkGdmSsaP8H0QATQlJyC6oSlYz8rhIO7MwskKas9QG4pE6PfhBoDXbFPBkb6/Bu+ZGbQKBpWYDWjLMQo1Ex9jAW0arOCuj1PXWELMKKIGizUwWuy0aihQnteeusqX2ZGwxIm4TamB6uqwKCqSdgE6iBzwpmcjbuImIYsf1n6uOpuLRmamFJsxwGnhLA6Ln6MZLYiFAxO5a5HFDwvcg9rC03i1/xT65Gqi14TZ8Rk1HqLFQhyfyHFY6NkTdEzT6Bm8Z2agFR4C5IZeRmFZT+ZgBXd9nIraXUFGAgBE0YuK2l2mDYXRYid5ghRjJYMiZLpRW9iW/bM5jNx3pVwCx+lKibdkDAg2EoDhdqIANPtZqMeT5XQia/lylCAfuxYDfyptT6uNOy+91ppBaUi+SGmwRNIN0sOAC85MHUMdpQFUdUboe/XOskfx8KKVwe4njSSFjjLHmo1soV+n11B8+2VmGHo7zFB0I7SqZ4+XfFwPIyqgjf9TGjASMmKsH/XLSpG+3+QX3MxOAAAcDsT+5nm0bBqGVQo5jsfPl2qWDARITtYOuKvHc/48/PMLcFoE6r3SefXnEahoi9uUio/wf7gTBbApCxDlyVV+rsWLje8sknR02mGujuHtZQUhaqxuhx3vLHsUoggoQy+fzf5PTMdAjPzlCm0tpE4kbMlwRkTDgtndiNiaQDzuupgQ1AyoM/EOJge9hQx3SKGfLrSdgMUS+v+2zKoS5GNbQduELUo/G4xk2zgcwOrVpsdj8bRgujc4mKuuTfAiBmLb4RYuOTjOIKcjZ1JSeMPATB3Dqdl3Yn3h06hypkHkOFQ507C+8GkcmH0bfH7A5+MBsT0gPXLOY8T0aRpatTcMBg22o+hGPt09ERNv/xBRitWlFxYcKp4IT6F2YDlc2YRkjiwdEe+ymw9k09wschUYgV1DQ/tZ/FNcjru4AkSJqj9YLNKEl5lpbGVMGQ+tEn0cikJ2E1bRFfrAoiKgqSn0OImaGt2HmOkbfTfnxBuzvxlS1c4DWGgd0SE3T2dLeTCuHtiOohs5+K1o7Ej6Bhra2o828NLvn38vWrMpj15xnRa0dM374sLoUUDpLVEnOrEyBcRdEel1HUU+/iqqKs7/9Cepl7UoGloZA6C6fUiV6BwPzMTiYJcTIP2uTCeV3Vl62VgyBvptmEmZzeNTMY/LRpxiDRcLHvO48IyEcgexTjypWXvDYNBgO4puRMhw47hlUIg4HRxuJDhDdw05SEQJ6oirUbWPm7bj6FSFT0Kw1wMHdmE5XNXAe/OkY8qdCk0IsSwzHzjXAV96URHQ2Bhy2M9H4SPLckCRMyD3skhYQ5kQlTsTM4F2g4Fjs+9BZ8UB1DsIWsJsOMq24Y6HiQlGJiw9tht5op7coIgvtePWcid25xnrgawmFjxa4YegcMZ3WV+AoiI0zF2MeKEU9XBiF5YHpMUBICYZsMWZ7yNhGlodR3IySlZXhXQRzMkHPClDYasmnKNMJ7VY6JlcUVFAv36Su6kbAscdxWgPjE5tNEQhEntX9FZYHUUfZ79QgT96T8Nnbf+ycC0WfPOLLBzK0+99bJaumgCes4Cui00gZQzgbdbuTGcayoQuchw4WrEZKWtLXTSnV0jYRTUKnUpbyrBYWqqr5Gt0su7obsBIgagR+kLjoY7SE4aCxSi6kTw+FQ9HBUspLIjNwvempnbJ9r+rXAoJTqn/RPmpwyh1F6P81GE03U+Pl1Qdk3YWhoUQDeDpn0g8XpORSo/d5OcDc+cCfJs+BM9Lvyt3BcuXS8aDhhHpEkBT4LBLUYo1yhIgi1Zg0sYdgYfIX3o9KQ9lfOM18WRQjOw18SR+4vvUcNZUZyjYvn3Gg8c/deFCswgRwIVmEY8Wu/Bfn5qoe2GEBYtRdDMk/3NXpSgS9XmM9CDWIX1DBY5eQy7ii9tEnnSOrO3EoqqiIlgIWUk+C4etyx7FKVqfhaIiYP16KWgOSD/XrwemTAlOjwUkAyIQlOkMBK9J9R0oKAi+fldBiLEom1SZ2UGQ+nAraYZgOGsqvtlOdLtyfum51OeTdjBLP4+GS/WWiADWnfRiUqrnqttZdCdsR9EL2CSe7fRrKrNq5JXh2g3Pwl2wIEht1D8/VG2Uhnyd7XkniUV8Db+hr7ZNqYHqrcYXL4bV4w05jfdLrijqKpUUqCaI6CE/XzIg6p2F0apnA8/z+rHLmF9xCPO9xZhfcQivHyNUi4cDZceTXFZhSgzQqKy4oaypoiL8KuPugBqucncjWoA3xFNBiyVSlt+r7tPgUmqJlxcBLP28lfg3RufADEUvoAnk/gkkZAVT9ZeOBwIplcoJ4fVjl/GqW/rS3bPklaCKX0AqUPM8vlj9NCGov7wkfEPofzOsBmpAbpw2GXKQFFWpSqc0txHpOKlplFFpdp3nef3YZRRnngWX5JH6SyR5UJx5tnOMBWXHwzmdQT0w9DDjEtJ8bNv7mdh4geoKEyDidfFkoAhwo3gmxEhxNj/GXkPvdVHe3DOx1qsFZih6OcpJTy2RLX/pZm7cjXncCKy2TgpqirN7j4A9SWWBFp1y61M1UdX6fndDK0wOGEbumIoJBbpPIWFk1a8hm5FcVkGX9Ka5jWjHKU2jdNF4nv1ChdRjW9VbgrP7UZxSFnKK6UpqUowlDP0nM02NNB+r4QpTIk/z1XBTu+jFxHrxzbuOY0hmXcjf0mMNicIwwoQZil5ALKW/p9zX+XXrFExHGnFHYG9x44Ffvk5cKa7fJMKS3N6fmiQsB9BbpSoxusJ86J+SRLS8g+D4dsloQ5OemVU/CVFEXlYu2Z3WSZOoJrSqbocDJ379NN4QT9F3V4nBvcTDKrTsyE5IAalIkIRerw1RwxWmh3r3fOOmHYiN9eL63PIgYxHDA89cG617PUb4MEPRC5jNDQ8xFXzbcZk51myk0L5clC9jZTUgVLUH+N5Z9ijcjuDVnzs6GvuT9SdKIytM+cP07ZeBZ3zAs6L0UzYShiY9I6t+DdkMDqB2ejOU9dQRaFXdyZKe1Euzrw+qdVEj1Niwe0/7alqviyGVcHdCCkjNjhZyI7CQG2GqAVJdBrkjIW3RIpP7Fnn3PGnjDlitIsZfewUcgCGxHFbfGMMC2V0My3rqBRiu3KX1S6BMrgOSgcY/ZyBh4VlY7P5ALv09S15BclkFqoek4p+Zz2LsIv2J5G7OqZsFMw3kSQGgT3obxTPBGUokqW/1qt9I3wjZXaWcJI1kPXUEWlV3XByQn48mXzH1VL/bgsbNGXjhsAhAwIypfKeklGqhpQQgfwZJNQ6krD3aZ1dLDVeLe39J3j3L2Vt2hxc1c8gim4zOp1sL7jiOKwBQAABOp3PCebNNYq52jBSMKdi9R8ALa0VYJlYh/vtl4JM9gAhwFqkafNAqJ2bdmGq4rkE5IdjBwQMpn91I05oF3mLQtMX/c/8IlM9JDRTk3XN7EZwfaKTwGpU7V3d6oxXTdVYRHa2qu20cCyiGQhSB5p2paFgv9eyIjwNiogEs+QzWAZ6Qx3dGIaWR9FcjqbR6FddP+Q4je+O29sWJqgDQDg4+iEFRCRsseNlGbjAlN3Dqjmry3gqrzGboY7IOYvceAes3iaislnYYc+/nMGNq9zYkLikC/pB3GD4neSVsLbVjcFb7596QxIfyPlgs5JoHtQHQmcg7jI4h+onvU2qg1u+2oP61YXDtSwkc6zf3LGJvrYCq/bfpdqMkOkveQ6/i2oh0B2lHct3wCYguvRBy3SpnGn55+t2rWvqDGQpGn2TVUCB10BrcdvE54qoSfsBpnxJ0jqk2rUZ3WpSJvIHPxPP+cx2XF9EZx36hAm+IJymmAhAaeFx5rP37n7qq63YUtN0NDZo7VOs6r1ul9zQs+Y+iIggFj4BvaZeBdzvs2Fq4GMMeePSqNRIAa4XK6CvIq/3z5wGex+OCAJxv9zzJgUkAODD7NvBloYFyouw6bTel7EyntdO6/XagsDBoV+GBAzsFKf5Rfx7Y1pbGa9ZYlBQBuxbnI6MFuJWXRBO5zOBxyLGo18STxGtY4gWkrTkCS7wPQpUNfEqokQA6J0ZB65FBg9a7wkivjbDUcPPzpQQPxXtqX74cD/RiEca+DNtRMDoXE+1Sq5xp+PmX76L/oqwQ6Y+QHYXJ+AwgrWQ3iWfRBB8mbdwR0l9aBIeDWITtCNYWMbWbgWQkthUYV8g16vZRtz6V6a4YBYmocjsGDZsY2H013c9UYbsbJgrI6FJKiiQ30HMW6WeXtF810c8huayCaCSiHAht02pUfqMNyc1zKlD1fs+SV4KMBABwEDEKH4Scq9VEisSuxaFd/Lwt0nESWnUHQePjQkMqenULRiGlv05HWlDDJBLeQe5AS9ttBZK2l/o6zEj0PZjr6SpBvertiJtFE6OFcQDqRWewkeAUMuQoAoYqXEm0DDnF8/2tsRGramtx2efDhMEVsFrbZ1lagRepbWqCyXmYZlhox/P4VGzynTUs3eJotaEl2tOhZj+kOAEJvR2G0k0oG8Mn8jun0RKj98IMxVWC1qq3Uw2FkRoHSJINu7AcTfdXoH5ZKYQMN5ItdtzGOZGzaWeo+ippeS0/HyQj8WxVFVrbHsPzwSHj6oxUpJSGagWpq9KJuxkdaF38tAzO/dwwQ66fFM6OlXGd62aSZcKVVMONj0DXUgIArtmChCXBL8rs7osRmTDX01WC2VVv2Oj1c4BkJE5jJj69/1bUFp6GkOkGLO0B06ZfPBXqZhJFiGp/PcdJAWoAq2prA0YCADxC8EebVJUut3FVEk7nvZnLJQOjRM/gqF0/seDBqwpNOsvNZFQJVouocjs5ltTx4TEiALajuEoIZ9UbFsoMpPPnpdoFZY2CxYLqmx/F3069jPplh0Pkyj3wI7bsEvnaIiByACfbA1EMVFZfzssLemhpXTyykuthaZt75VTce5e8gqTSCmIb14TM8HZX8jmk9qtaqLOBuqqndEezpJJhx5y9E7Htr0GtyMPafTEiE5b1dJVgNjOnU9jzPrD2V4BHEUS2RQMFvwKmfpuag78i616im0jgLeAFwso4MxO3fPIJLvmCff7JjhYM798InpfOiQWP2dxwxG1K7f570UPsFyqo6bhG4MEhGhY0Q0B8sx0JS5ywvJTaeS1tGaZhWU+MLiMnX5oIEzIhBY0zu2Fi3LQ62EgA0u+bVgOgCw0SxQsddlhIRgIASkvxRP/+iFblkja7YjHLMw6vW6fgdesUvGC9EXl8as/cix5CV0BQBz/EQDV5Y6wbFc+fxm3eik5racuIDJjr6SoiJ7+bv9zVlEY8bcfVQoOTNu4IaAI1JfWDO9qO+NrGQCX3g4+thKM5tJNZQ/94fBJzHAvTUrG1Crjs82Gg1Yon+vfHHfHxxCF0+73oITrqdlL7G2T1WpbldHXBDAWj60geCFQR4g3JAwEEq+Zmb9wWpDIaX10Pt8OOV9f/Egdm34ZJG3cghmAkAIDjOFTDjUZbOX6dznL4lWhVYFuAsELcnaVey4gcmOuJ0Xmoe12tIYQ8AAAYAUlEQVQnXyPFJJTYooH7Hw/8msenYuXm41gwbxlVVhqQCuZoPcziahoAGOzVcJVBakBkgwULuRGYz42A1Wd+CjDT/Y7RN2A7CoYh6hpLUFG7C15fPaKsCUjtPxOJ8TntD1BLbJw/D6x4EYi2Aw2NgCMKmDwKeOZXwNRvh5xHDFKjvVBOqyOasglOr1ztmlT87Uz0ep1sfwK49IRUxwIgsHRMQROGohY2CHCDx3n0RxXiOi1llxFZMEPB0KWusQQXq7ZBFKXkSK+vHhertgFAu7EgSWx4PNI/AGjxAvvOAKWqfsc6kh+yEaAVzIkcgprg9LrVLsmAFrSVxHejsaDFFCwvpSL9D9Lf5OLH/hnVyEY1eIsUoYiGgGxUIw5W3MLlMNfeVQhzPTF0qajdFTASMqLoRUXtrvYDRqQ7SNpMGucpO6GRMqFEDtj96N2BGonesNpV9wVv/cXPTWlUdTfKOpq4TalIz56IrLKWgJGQ4SFiJBqYkbhKYYaCoYvXV69/PCnJ2MXUhoHSxlXgLVhf+HTACByYfRvWFz6NamcaRI5DlTMNr65/Bm+9+P8AAHGw9rgYHakvuL2snPxgE5pYXQmpqpzPIO/wmmFM7JHR92CuJ4YuUdYEorGIsrb1LC4qAhoajF1MbRgoPbJfL/yv9sZGbRyYfRsOzL4NC7kRYVcwd2XHP5JUBs1l5krNQEynPGvHIFWV210OeGJDjUIstKVZGH0XZigYuqT2nxkUowAAjotCav+Z0i+LFwNeL+VsBRwn+eiHDtVtOnTqvlEApSEOyeduRP5C7iHubgubVFQBL6wVAQiGjYXW85AC6e8sezQo7RcAWnkH/pizDI8ZesauR11Tclq4FnvFAxAUvfh48JjIXdsDo2P0BpjriaFLYnwOBqfcGdhBRFkTMDjlzvZAtpYbJTNT+qlUf5UDukVtDTHy86Xe1n6/9DM/n5rWSYpBkFw+b4qnsV8IzpRav6ndSMi4PdJxI+g9DymQfmD2bVi3cjGuOJzwg8MVhxOrJxZiW/JsQ8/ZE2Txw3ATNymwg4iFAzdxk5DFD+vhkTF6CrajYBgiMT4nOB1WCU1aPDNTmvhJvarlgC4l80cvrVMJyeVDqiCurCYPn3bc7POoK80BQPRYsNP3CP56x38HnZeabOw5e4osfhiywAwDQ4IZCkbHocQZsLxNWpS249AJ6BrttUyrnVAfH5AsuZvUDDA4aes9D8m4jTo3BNsOpwQ93m6TYiMMRqTADAWj41DiDIHjtB0HJePJLDSZCrUraO79XFCMAjA3aRt5nhDjNgYYVtB1AXQGoztghoLROSgD02r0dhwdhOTyIcUzpMk5/Ek7B4nELnA5SNQ8b8ZUHjOmGnoKBqNXwgwFo+vR23F0EDPxjI5M2iWoM3WcwegrMEPB6B60dhydgNF4RjgoU2JJ9Ep9KQajE2GGgsHQQE6J1eo53ev0pRiMTobVUTAYGpBSYpX0Bn0pBqOr6VZDwXFcAcdxhzmOO1xZWdmdT81ghIWWWykZ9h7Xl2IwuoNuNRSiKK4VRXGiKIoTBwwY0J1PzWCEBc2tFAcrVlonMiPBuCpgricGQ4O7OSd4Qm89F3whEiEMRl+FGQoGQ4M8PhXRhK+JAGCdeDLQd4IZDUZfhhkKBkOHZoWKqhI5xE0TIWQw+grMUDAYOhhJf5XFARmMvggzFAyGDiTJcxKs8I7RV2EFdwyGDmqJEAtArKxghXeMvgozFAyGAZQSIaRqbVZ4x+jLMEPBYJjEjAghg9EXYIaCwQiDrhQhZDB6GyyYzWAwGAxNmKFgMBgMhibMUDAYDAZDE2YoGAwGg6EJMxQMBoPB0IQZCgaDwWBowgwFg8FgMDThRFHsmSfmuEoA53vkydtJAVDVw2MIl0gde6SOG4jcsUfquIHIHXtXjjtTFMVu7fzWY4aiN8Bx3GFRFCf29DjCIVLHHqnjBiJ37JE6biByxx6p46bBXE8MBoPB0IQZCgaDwWBo0mu0no4cOZJqtVpfAzAO3WTAiouL47/44otz3fFcnU2Ejd0P4KjP51sIYG1PD6YDROrYI3XcQOSOPVLHTaTXxCi++OKLvw4cOPAbAwYMaLBYLL1jUIxOwe/3c5WVlQmXL18+Nn78+Lt6ejwMBsMcvcn1NI4Zib6JxWIRBwwYUA9pt8hgMCKM3mQoLMxI9F3a3tve9HljMBgGuWq/uJcvX+ZHjx49ZvTo0WNSUlLGp6amXiP/3traypm51qxZs4Z+8cUXmn0wXS4XN27cuG8AwLlz56Juv/324U6nc1xWVtbY6dOnZx89etR+9OhR++jRo8eE83pWrVqVXFpa2mtiTgwGo+9w1U4sAwcOFL7++utjAPDTn/50cFxcnLB06dIr4Vxry5Yt5/Qes3379vhJkyY1+f1+3Hnnndnz5s2r/OCDD84AwN69ex0XL160Dh482BfO8wPAhg0bUnJzc1ucTqfha3i9XkRFRYX7lAwG4yrhqt1RaLFkyZK0ESNGjB0xYsTY5cuXpwLA0aNH7dnZ2WO/+93vDhs5cuSY22+/fXhTUxMHABMmTBi1b9++GADYtGlTwpgxY74xatSoMVOmTBkhX/ODDz7o9+1vf7v+vffe6xcbGyv89Kc/DVRt3nTTTS233XZbs3IMv//971Pmz5+fIf8+derUEf/4xz/ivF4v5DGMGDFi7LJly1JfffXV/v/+978dDzzwQJa8I/r4448dN9xww6ixY8d+Y9q0aSPKysqs8lh//OMfp0+cOHHUb3/7W9aijcFg6HLV7ihofPjhh4633347+V//+te/fT4fJkyY8I1bbrmlMTY21n/69OnoV1555dzMmTOb77777qG///3vBzzzzDMV8rmlpaXWJ5980vnxxx8fHzlypOfKlSu8/Lf9+/fHr169unzlypWp48ePbwl3fHv27Imtqamxnjhx4hgAVFVV8SkpKUJhYWHqiy++WDp58mSXy+XinnjiCecHH3xwatCgQb41a9YkPfXUU+kbN248DwANDQ2Ww4cPH+/IfWIwGFcPbEeh4qOPPoq/8847a+Pj4/39+/f3f+tb36r78MMP4wAgPT3dM3PmzGYAmDNnTs2+ffviVOfG5eXlNY4cOdIDAGlpaQIAnDx50paSkuJ1OBwdDtaPGTOm9cyZM9Hz5s3L2Lp1a7+kpCRB/ZjPPvss+tSpU9E333zzyNGjR495/vnnB5aXl9vkv+fn59d0dBwMBuPqge0oVGjVlXAcJ6p+DzlXfQwA3nvvvX633nprAwDk5OS4tm/fnqA3DqvVKvr9/sDvbrfbAkixla+++uqrrVu3Jrz44oupW7Zs6S/vFJTjGDlypOvIkSPEXUNcXJyfdJzBYDBIsB2Fiptvvrnx/fff79/U1MTV19dbtm/fnjhjxowmACgvL7d//PHHDgB46623kiZPntykPHfGjBlN+/btiz9x4oQNAGTX044dOxK+853v1APA3Xff3dDU1MSvWrUqWT5v9+7dsdu3bw/anQwfPtxTUlLi8Pv9OH78uO2rr75yAMDFixetfr8f8+fPr126dOnFkpISBwDExsb6GxoaeAC4/vrrW69cuWL78MMPHQDQ2trKHT58OLpr7hiDwejrsB2Fiptvvrnl3nvvrb7uuuvGAMD8+fMrc3NzXW3BbFdhYeGARx55JDY7O7v1ySefrFSem5GR4Xv++edL77rrrmxRFJGWlubduXPnqfLyctu4cePcAGCxWLBt27ZTjz32WMbvfve7QdHR0WJGRob7xRdfLFPuZm6//fbGwsJC76hRo8aOGjXKNXr06BYAOHPmjO2RRx4ZKu9eli9ffgEAHnrooapFixYNjY6O9n/++ef/3rRp0+nHH388o6mpiRcEgfvRj350eeLEia3ddiMZDEafoTdJeJwbP358r9WdP3r0qH3WrFlZckqtUf72t7/F//nPf+7/5ptvlnbV2CKFL774ImX8+PFDe3ocDAbDHGxH0cXccccdjXfccUdjT4+DwWAwwoXFKAwybtw4t9ndBIPBYPQFmKFgMBgMhibMUDAYDAZDE2YoGAwGg6HJVWkoFixYkDFhwoRR8+bNy9B/dM/zq1/9Km3ChAmjenocemzZsqVfbm7uqNzc3FEDBgy4ZsOGDYk9PSYGg9FxrjpDsXfvXkdLS4vlyJEjxz0eDycX0PVWXC4X9+WXX8b09DiMMGvWrIaDBw8eP3jw4PFBgwZ57rrrroaeHhODweg4EZUee6gQSZ8sRXrTZdjiBsIz7RmU37AIpnSL9uzZEztz5swGALj11lsb9u7dG/cf//EfYYv0afGhcClpm1iWXg+vLQFRnju5jPKb+UGmxrtq1aqUhx9+uPq5554b3BVjDHBiZxK+3JIOV50NMYkeXDOrHCNvDUsT6tixY7aUlBRfQkICkwphMPoAEbOjOFSIpB1PIrPpEmwQgaZLsO14EpmHCpFk5jp1dXV8YmKiAACJiYlCbW0tr3dOOHwoXEraLJ7NrIfXBgD18No2i2czPxQuGR6v2+3m9uzZE3/XXXd1bR3GiZ1JOLw+E646STjQVWfD4fWZOLHT1L2V2bRpU//vfOc7tZ06RgaD0WNEjKH4ZCnSfa3B4/W1wvLJUqSbuU5iYqJQV1fHA0B9fX3AaHQ228SydC/EoPF6IVq2iWWGx7tmzZqk2bNnd73S65db0iF4gz8LgteCL7eYurcyf//73xPvu+++uk4ZG4PB6HEixlA0XYbNzHEaU6dObd69e3c/ANi5c2e/KVOmNOmdEw7yTsLocRLHjx+PfuWVVwZMnTp1xMmTJ2PkJkqdjryTMHpcg9LSUmtUVJR/4MCBXWKAGQxG9xMxhiJuIDxmjtO46aabWux2u3/ChAmjLBYLbr755i6JTyQgijgu2nESa9asKd+7d+/JPXv2nBwxYoRr8eLFFfpnhUFMInlMtOMabN68OfGOO+5guwkGow8RMcHsac+gfMeTyFS6n6zR8E97BuVmr/XGG2+Ude7oQrmTyyjfLJ7NVLqfosD57+QyTI8XAGi9JTqFa2aV4/D6zCD3Ex/lxzWzTI/1Zz/7Wa8VdmQwGOERMYZCzm7qaNZTd3EzP6gGghSr6EjWU7cgZzd1UtYTg8HoWzCZcUa3wWTGGYzIJGJiFAwGg8HoGZihYDAYDIYmzFAwGAwGQ5OrzlCcO3cuasyYMd+w2+3Xe73enh6OLsePH7clJyePz83NHTVlypQRPT0eLRobGy3Tp0/Pzs3NHTVz5swsl8vF9fSYGAxGx7nqDEVqaqrv448/PjF+/Pjmnh6LUW666aaGgwcPHi8uLj7Z02PR4p133uk3ceLE5oMHDx6fOHFi89atWxN6ekwMBqPjREx6LAC8v1NIemuLmF5TB1tSIjwPzOLKv30rbyqF0+FwiA6Ho1uqhr8WTiR9Jh5Nd8Fli0GM5zpuXPlofqTplNP9+/fHT5gwYdRdd91V++yzz3ZN0d3OzUnYUpiOumobEpM9mLWoHLfeZ2qsI0eOdB8+fDgWAOrq6qwDBgzwdclYGQxGtxIxO4r3dwpJa9eLmTV1kmRHTR1sa9eLme/vFMISrutqvhZOJB0Qj2S64LIBgAsu2wHxSObXwglT43U6nd5Tp04d/fTTT49/+OGH/Q4cOND5kuM7Nydh/cpM1FXZABGoq7Jh/cpM7Nxsaqzjxo1zHzlyJDY7O3vs559/7rjlllu6RB6FwWB0LxFjKN7aIqZ7vMHj9XhheWuLGJZwXVfzmXg0XYA/aLwC/JbPxKOmxhsTEyP269fPHxUVhW9961v1n332Wecbii2F6fB6gj8LXo8FWwpNjfXll19OnjlzZv2pU6e++uY3v1m/Zs2a5E4dJ4PB6BEixlDIOwmjx3saeSdh9DiN2trawHu0b9++uJEjR7o7OrYQ6qrJY6IdpyCKIpKSkgQASElJ8dXX13eJhDuDweheIsZQJCWSxf9ox2m43W5u8uTJI7/++uuYadOmjdy9e3ds54wwmBjEEMdFO05jx44d8WPHjv3GddddN3rQoEHeGTNmdH4QPjGZPCbacQoLFy6s2bp1a//c3NxRmzZtSlq4cGF1p4yPwWD0KBETzH5gFle+dr2YqXQ/2aLgf2AWZ0q4zm63i/v27TvR+SMM5jpuXPkB8Uim0v3Ew+K/jhtnarz33Xdf/X333Vff+SNUMGtROdavzAxyP0XZ/Ji1yNRYU1JShL179/bqzCwGg2GeiDEUUnaTgI5mPXUXo/mRNRCkWEVHs566HDm7qYNZTwwGo2/CRAEZ3QYTBWQwIpOIiVEwGAwGo2dghoLBYDAYmjBDwWAwGAxNrjpDsXv37tjrrrtu9IQJE0YtWLAgo6fHY4Q//OEPyXl5eSNzc3NHnT17Nqqnx0PD6/XijjvuGD5p0qSRixYtGtLT42EwGJ3DVWcosrOz3cXFxcePHDlyvLKy0nrw4MHOr3TuRM6ePRv1ySefxO3fv//EwYMHjw8bNqzXSt5u2LChf05OTsuBAwdOuFwubv/+/b363jIYDGNElKFYd8KdNHpLQ07ShvoJo7c05Kw74Tat8+R0On0Oh0MEAKvVKvI832VpXzX1h5KOn/9dzldnnptw/PzvcmrqD5ke71/+8pd+giBweXl5I+fOnZvh83WRzl5hYRIGD86BxTIBgwfnoLDQ9FhPnz5tHz9+vAsArr32WteePXviOn+gDAaju4kYQ7HuhDvpF4dbM6+4RJsI4IpLtP3icGtmOMYCAA4cOBBTU1NjnTBhQmsnDxWAZCQu1+zI9AlNNgDwCU22yzU7Ms0aiytXrkR5PB5u//79JxwOh7+oqCix0wdbWJiEJ5/MxKVLNogicOmSDU8+mWnWWIwePbr1o48+igeAjz76KL62tpZJeDAYfYCIMRQrv3Snu4Xg8boFWFZ+6TYtCnjlyhX+hz/8ofPNN98812kDVFFZ90m6KPqCxiuKPktl3SemxpuQkCBMmzatEQBuueWWxmPHjkV35jgBAEuXpqO1Nfiz0NpqwdKlpsY6e/bsOpfLxeXl5Y202+3+tLS0XusmYzAYxokYQ1HhEokCdbTjNLxeL77//e8PW7ly5QWn09ll/RLknYTR4zSmTZvW9OWXXzoA4F//+pdj2LBhpvSXDHH5MnlMtOMUrFYr1q9fX7Z///4TPM/jzjvvbOiU8TEYjB4lYgxFagxHnCBpx2msW7cu6csvv4z9+c9/PiQ3N3fUP//5zy4RBbTyccRx0Y7TmDx5sismJsafm5s76siRI46HH364tnNGqGDgQPKYaMcpnD17Nio3N3fUjTfeODIvL68pKyuL7SgYjD5AxEh4yDEKpfvJzsP/m4nR5+ePtPc6TSI5RqF0P3Gc1T8w6bbzSQk39K7xyjEKpfspOtqP558/j0WLOm2sTMKDwYhMIkYUUDYGK790p1e4RFtqDOd56hp7eW80EgAgG4PKuk/SfUKTzcrHeQYkTivvdUYCQMAYLF2ajsuXbRg40INnninvTCPBYDAil4jZUTAiH7ajYDAik4iJUTAYDAajZ2CGgsFgMBiaMEPBYDAYDE2uOkNx6NChaFkUcNasWUP9fn9PD4nBYDB6NVedobjmmmvcn3322ddHjhw5DgB79uxx9PSYGAwGozcTMemxALCpvj6psK4uvUoQbCk871mUmFh+f0KCqRROu90eSPOy2Wz+Lql0ZjAYjD5ExOwoNtXXJ62oqcmsFASbCKBSEGwramoyN9XXmxYFLCoqShgxYsTYqqqqqLS0NKELhstgMBh9hogxFIV1dekeUQwar0cULYV1daZFAfPz8+tPnjz51aBBgzybN29O6LxRMhgMRt8jYgxFlSAQBepox2m4XC5O/n+/fv38DoeDRbMZDAZDg4gxFCk8T4wl0I7T2Lp1a8INN9ww6oYbbhhVUVFhvfvuu5nCKYPBYGgQMcHsRYmJ5StqajKV7icbx/kXJSaWm7nOgw8+WPfggw/Wdf4IGQwGo28SMYZCzm7qaNYTg8FgMMwRMYYCkIwFMwwMBoPRvfSmGIXf7/dz+g9jRCJt7y1LHGAwIpDeZCiOVlZWJjBj0ffw+/1cZWVlAoCjPT0WBoNhnl7jevL5fAsvX7782uXLl8ehdxkwRsfxAzjq8/kW9vRAGAyGeXpN4yIGg8Fg9E7Yyp3BYDAYmjBDwWAwGAxNmKFgMBgMhibMUDAYDAZDE2YoGAwGg6HJ/wcmb/qum32lTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_2d(x, y, c, sample_size, title):\n",
    "    df = pd.DataFrame({'x': x, 'y': y, 'c': c}).sample(sample_size)\n",
    "    l = len(np.unique(c))\n",
    "    \n",
    "    ax = plt.subplot(111)\n",
    "    colors = cm.rainbow(np.linspace(0, 1, l))\n",
    "                                   \n",
    "    for c in range(0,l):\n",
    "        qq = df[df['c']==c]\n",
    "        ax.scatter(qq['x'], qq['y'],c=colors[c], label=c)\n",
    "    plt.legend(loc='upper left', numpoints=1, ncol=3, fontsize=8, bbox_to_anchor=(0, 0), title='Topic/Cluster')\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "%matplotlib inline\n",
    "plot_scatter_2d(tsne_m[0], tsne_m[1], kmean_d, 1000, 'KMeans Clustering of Amazon Reviews using TFIDF (t-SNE Plot)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for rating prediction\n",
    "The previous steps allowed us to understand how the data is structured, but it doesn't let us understand what drives positive or negative reviews. In the next steps, we look at the words within reviews to build a predictive scoring model. \n",
    "\n",
    "When training these types of models, overfitting can occur where we become very good at predicting our sample data, but fail to predict on new data. To avoid this, we split the data 70%/30% where we reserve the 30% for gauging our final accuracy.\n",
    "\n",
    "We will build 3 models. One that predicts low, one that predicts high, and one that predicts neutral. For each review, we run it against all three models. The model that scores the highest will tell us which kind of review it likely is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(tfidf_d, sampled_source_data['rate_category'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'X_train.npy', X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'y_train.npy', y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'X_test.npy', X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'y_test.npy', y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate model accuracies\n",
    "We try 3 different approaches to building the review predictions: Logistic Regression, Naive Bayes, and Support Vector Machines. We also try a final approach that does a combined \"vote\" of all three models. This means we are actually building (4 approaches) x (3 ratings) = 12 total models. Since we have limited data, we will use cross-validation to split the data 10 ways and measure accuracy in an unbiased way.\n",
    "\n",
    "The accuracy % are printed below for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy predictions\n",
      "\n",
      "LR model (negative rating): 78.3%\n",
      "LR model (neutral rating): 70.3%\n",
      "LR model (positive rating): 82.4%\n",
      "\n",
      "SVM model (negative rating): 75.7%\n",
      "SVM model (neutral rating): 68.0%\n",
      "SVM model (positive rating): 82.1%\n",
      "\n",
      "NB model (negative rating): 75.0%\n",
      "NB model (neutral rating): 68.0%\n",
      "NB model (positive rating): 78.1%\n",
      "\n",
      "COMBINED model (negative rating): 77.9%\n",
      "COMBINED model (neutral rating): 70.0%\n",
      "COMBINED model (positive rating): 82.4%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cat = ['negative','neutral','positive']\n",
    "\n",
    "def calculate_cv(X, y):\n",
    "    results = {\n",
    "        'lr': [],\n",
    "        'svm': [],\n",
    "        'nb': [],\n",
    "        'combined': []\n",
    "    }\n",
    "    lm = LogisticRegression()\n",
    "    svm = LinearSVC()\n",
    "    nb = MultinomialNB()\n",
    "    vc = VotingClassifier([('lm', lm), ('svm', svm), ('nb', nb)])\n",
    "    \n",
    "    for c in cat:\n",
    "        y_adj = np.array(y==c)\n",
    "        results['lr'].append((cross_val_score(lm, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
    "        results['svm'].append((cross_val_score(svm, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
    "        results['nb'].append((cross_val_score(nb, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
    "        results['combined'].append((cross_val_score(vc, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
    "    return results\n",
    "\n",
    "cv_scores = calculate_cv(X_test, y_test)\n",
    "\n",
    "print(\"Model accuracy predictions\\n\")\n",
    "for m,s in cv_scores.items():\n",
    "    for ss in s:\n",
    "        print(\"{M} model ({R} rating): {S:.1%}\".format(M=m.upper(), R=ss[1], S=ss[0]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model of choice\n",
    "All models seem to have done roughly the same on low and neutral rating reviews. SVM seems to have given better performance for our 4-5 star reviews. There is definitely room for improvement. The distribution of the stratified raiting buckets were 33% each. \n",
    "\n",
    "If we guessed each review's status at random, that would be our % likelihood of guessing correct. Our three models show quite an improvement over random guessing, but still opportunity to improve. There are lots of ways to tweak the prior steps to get a better result. \n",
    "- We didn't tweak any parameters in either the TF step or the modeling step\n",
    "- Neg/Pos keywords might vary by topic so we might do this for one cluster at a time\n",
    "- Maybe nouns don't provide much insight and we are better off removing them\n",
    "- \"great\" and \"not great\" have opposite meanings so maybe we should have included 2-grams\n",
    "\n",
    "The list goes on, but I think you get the idea. The next steps just assume we are happy with our logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lr(x, y):\n",
    "    models = []\n",
    "    for c in cat:\n",
    "        y_adj = np.array(y==c)\n",
    "        lm = LogisticRegression()\n",
    "        lm_f = lm.fit(x, y_adj)\n",
    "        models.append(lm_f)\n",
    "    return models\n",
    "\n",
    "lr_m = get_lr(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def plot_coef(title, model, feature_names, n_words):\n",
    "    v = []\n",
    "    for topic_idx, topic in enumerate(model.coef_):\n",
    "        [v.append([feature_names[i], model.coef_.item(i)]) for i in topic.argsort()[:-n_words - 1:-1]]\n",
    "        [v.append([feature_names[i], model.coef_.item(i)]) for i in topic.argsort()[0:n_words]]\n",
    "    df = pd.DataFrame(v, columns=['Term','Coefficient']).sort_values(by='Coefficient',ascending=False)\n",
    "    df['c'] = df['Coefficient']>0\n",
    "    ax = df.plot(x='Term', y='Coefficient', kind='barh', color=df['c'].map({True: 'g', False: 'r'}), grid=True, legend=False,\n",
    "           title=title)\n",
    "    ax.set_xlabel(\"Coefficient\")\n",
    "\n",
    "n_terms = 12\n",
    "for c in range(0,len(cat)):\n",
    "    plot_coef('Top {N} words in ({R}) review model\\nGreen = Associated | Red = Not Associated'.format(N=n_terms*2, R=cat[c]), \n",
    "              lr_m[c], tfidf_m.get_feature_names(), n_terms)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test output\n",
    "To put it altogether, below is a test function which allows you to supply your own review to see how well the model will predict it's rating. For simplicity, I stuck with the logistic regression model and only allow for one review at a time. \n",
    "\n",
    "The program uses the stored TFIDF matrix to tokenize and transform our new review which is then fed to all three of our logistic regression models. Each model has an independent assessment of how likely it is that our review is a positive hit. You could set some sort of threshold or take the model with the higest likelihood to make your determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_review(text):\n",
    "    test_str = [text]\n",
    "    test_new = tfidf_m.transform(test_str)\n",
    "\n",
    "    print('Review text: \"{R}\"\\n'.format(R=test_str[0]))\n",
    "    print('Model Predction')\n",
    "    for m in range(0,3):\n",
    "        print('Model ({M}): {P:.1%}'.format(M=cat[m], P=lr_m[m].predict_proba(test_new)[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review text: \"I bought these knives last week. I immediately returned these when they arrived damaged.\"\n",
      "\n",
      "Model Predction\n",
      "Model (negative): 90.2%\n",
      "Model (neutral): 9.9%\n",
      "Model (positive): 3.1%\n"
     ]
    }
   ],
   "source": [
    "test_review('I bought these knives last week. I immediately returned these when they arrived damaged.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review text: \"This is the best toaster oven I have ever owned! I am glad I bought it.\"\n",
      "\n",
      "Model Predction\n",
      "Model (negative): 5.9%\n",
      "Model (neutral): 19.1%\n",
      "Model (positive): 85.2%\n"
     ]
    }
   ],
   "source": [
    "test_review('This is the best toaster oven I have ever owned! I am glad I bought it.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic-based unsupervised review grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import review data\n",
    "Amazon reviews can span from 1 star to 5 stars. The difference between a 1-2 or a 4-5 might be very noisy when we build predictions. Instead, we bucket reviews by:\n",
    "- Low: 1-2 Stars\n",
    "- Neutral: 3 Stars\n",
    "- High: 4-5 Stars\n",
    "\n",
    "Reviews are filtered to those with at least 45 words to avoid short, uninformative reviews like \"This is great!\". After limiting, we take a stratified sample of 6000 reviews from each bucket to get reasonable performance from sklearn in Python. The stratification helps us overcome the bias in that 80% of the reviews have 4-5 star ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Term Frequencies\n",
    "We calculate both the actual term frequency as well as the [tf-idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (short for term frequency–inverse document frequency) weighted term frequency. For both algorithms, we limit to words occuring in at most 90% of documents and in at least 10 documents. While the term-frequency matrix is just a word count, the IDF calculation adjusts for \"boring\" words that occur in many reviews.\n",
    "\n",
    "We perform two tokenizing operations. First, we tokenize only letters, ignoring special symbols & numbers. We use the NLTK Snowball stemmer to try and get the root of a word as best as possible. Stop words are removed in the vectorization step.\n",
    "\n",
    "For details on Scikit-learn text feature extraction, see http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c2/d7/90f34cb0d83a6c5631cf71dfe64cc1054598c843a92b400e55675cc2ac37/pip-18.1-py2.py3-none-any.whl (1.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.3MB 23.2MB/s ta 0:00:01\n",
      "\u001b[31mmxnet-model-server 0.4 requires mxnet-mkl>=1.2, which is not installed.\u001b[0m\n",
      "\u001b[31mmxnet-model-server 0.4 has requirement onnx==1.1.1, but you'll have onnx 1.2.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mkeras 2.2.2 has requirement keras-preprocessing==1.0.2, but you'll have keras-preprocessing 1.0.1 which is incompatible.\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Found existing installation: pip 10.0.1\n",
      "    Uninstalling pip-10.0.1:\n",
      "      Successfully uninstalled pip-10.0.1\n",
      "Successfully installed pip-18.1\n",
      "Requirement already up-to-date: msgpack in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (0.5.6)\n",
      "Requirement already up-to-date: nltk in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (3.3)\n",
      "Requirement already satisfied, skipping upgrade: six in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (from nltk) (1.11.0)\n"
     ]
    }
   ],
   "source": [
    "# Install natural language toolkit\n",
    "!pip install --upgrade pip\n",
    "!pip install -U msgpack\n",
    "!pip install -U nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161982"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17998"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data['review_body'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>rate_category</th>\n",
       "      <th>year</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_body</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>positive</th>\n",
       "      <th>650027</th>\n",
       "      <td>Home</td>\n",
       "      <td>B004S63FD6</td>\n",
       "      <td>Ridgedale Thermal Backed Blackout Pole 40 x 63...</td>\n",
       "      <td>25893380</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2013</td>\n",
       "      <td>15798</td>\n",
       "      <td>I ordered these curtains to serve in lieu of a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">neutral</th>\n",
       "      <th>6930647</th>\n",
       "      <td>Home</td>\n",
       "      <td>B002RTXE7I</td>\n",
       "      <td>18ML PALE GREEN Classic Snazaroo Classic Face ...</td>\n",
       "      <td>2143749</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014</td>\n",
       "      <td>16121</td>\n",
       "      <td>it is more minty green than the color shown ab...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799079</th>\n",
       "      <td>Home</td>\n",
       "      <td>B003J372NS</td>\n",
       "      <td>Mediflow Original Waterbase Pillow</td>\n",
       "      <td>11852941</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2015</td>\n",
       "      <td>16626</td>\n",
       "      <td>Wasn't exactly what I expected.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive</th>\n",
       "      <th>3892018</th>\n",
       "      <td>Kitchen</td>\n",
       "      <td>B000KUQG26</td>\n",
       "      <td>Godinger EMPIRE STATE/CHRYSLER S/P</td>\n",
       "      <td>22626607</td>\n",
       "      <td>4</td>\n",
       "      <td>positive</td>\n",
       "      <td>2015</td>\n",
       "      <td>16458</td>\n",
       "      <td>These are beautiful, large and heavy shakers. ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      product_category  product_id  \\\n",
       "rate_category                                        \n",
       "positive      650027              Home  B004S63FD6   \n",
       "neutral       6930647             Home  B002RTXE7I   \n",
       "              9799079             Home  B003J372NS   \n",
       "positive      3892018          Kitchen  B000KUQG26   \n",
       "\n",
       "                                                           product_title  \\\n",
       "rate_category                                                              \n",
       "positive      650027   Ridgedale Thermal Backed Blackout Pole 40 x 63...   \n",
       "neutral       6930647  18ML PALE GREEN Classic Snazaroo Classic Face ...   \n",
       "              9799079                 Mediflow Original Waterbase Pillow   \n",
       "positive      3892018                 Godinger EMPIRE STATE/CHRYSLER S/P   \n",
       "\n",
       "                       customer_id  star_rating rate_category  year  \\\n",
       "rate_category                                                         \n",
       "positive      650027      25893380            4      positive  2013   \n",
       "neutral       6930647      2143749            3       neutral  2014   \n",
       "              9799079     11852941            3       neutral  2015   \n",
       "positive      3892018     22626607            4      positive  2015   \n",
       "\n",
       "                       review_date  \\\n",
       "rate_category                        \n",
       "positive      650027         15798   \n",
       "neutral       6930647        16121   \n",
       "              9799079        16626   \n",
       "positive      3892018        16458   \n",
       "\n",
       "                                                             review_body  \n",
       "rate_category                                                             \n",
       "positive      650027   I ordered these curtains to serve in lieu of a...  \n",
       "neutral       6930647  it is more minty green than the color shown ab...  \n",
       "              9799079                    Wasn't exactly what I expected.  \n",
       "positive      3892018  These are beautiful, large and heavy shakers. ...  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_source_data.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure that all data is encoded to UTF-8 based on https://stackoverflow.com/questions/39303912/tfidfvectorizer-in-scikit-learn-valueerror-np-nan-is-an-invalid-document\n",
    "#sampled_source_data['review_body'].dropna()\n",
    "raveld_source_data = sampled_source_data['review_body'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17998"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raveld_source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# See https://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array \n",
    "# raveld_source_data = raveld_source_data[~pd.isnull(raveld_source_data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lasted one weekend. picked up a cheapy from menards and have been using it ever since. i'd hit menards and pick up the little red one for $8\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(17998,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(raveld_source_data[0].lower())\n",
    "display(raveld_source_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_raveled_data = sampled_source_data['review_body'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find removed indexed\n",
    "# print(~pd.isnull(raveld_source_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17998,)\n"
     ]
    }
   ],
   "source": [
    "# filtered_data = np.extract(~pd.isnull(raveld_source_data), source_data['star_rating'])\n",
    "# print(filtered_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# NTM Training\n",
    "\n",
    "We have created the training and validation data sets and uploaded them to S3. Next, we configure a SageMaker training job to use the NTM algorithm on the data we prepared\n",
    "\n",
    "SageMaker uses Amazon Elastic Container Registry (ECR) docker container to host the NTM training image. The following ECR containers are currently available for SageMaker NTM training in different regions. For the latest Docker container registry please refer to [Amazon SageMaker: Common Parameters](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's firstly build up a word counter that produces vectors from input review texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/ec2-user/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
      "[nltk_data] Downloading package wordnet to /home/ec2-user/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import word_tokenize          \n",
    "from nltk.stem import WordNetLemmatizer \n",
    "\n",
    "import re\n",
    "token_pattern = re.compile(r\"(?u)\\b\\w\\w+\\b\")\n",
    "class LemmaTokenizer(object):\n",
    "    def __init__(self):\n",
    "        self.wnl = WordNetLemmatizer()\n",
    "    def __call__(self, doc):\n",
    "        return [self.wnl.lemmatize(t) for t in word_tokenize(doc) if len(t) >= 10 and re.match(\"[a-z].*\",t) \n",
    "                and re.match(token_pattern, t)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raveld_source_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizing and counting, this may take a few minutes...\n",
      "vocab size: 1591\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vocab_size = 2000\n",
    "print('Tokenizing and counting, this may take a few minutes...')\n",
    "start_time = time.time()\n",
    "\n",
    "vectorizer = CountVectorizer(input='content', analyzer='word', stop_words='english',\n",
    "                            tokenizer=LemmaTokenizer(), max_features=vocab_size, max_df=0.95, min_df=2)\n",
    "\n",
    "#vectors = vectorizer.fit_transform(raveld_source_data)\n",
    "vectors = vectorizer.fit_transform(raveld_source_data)\n",
    "vocab_list = vectorizer.get_feature_names()\n",
    "print('vocab size:', len(vocab_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'vectors.npy', vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'vocab_list.npy', vocab_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'vocab_list.npy', raveld_source_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. Time elapsed: 16.59s\n"
     ]
    }
   ],
   "source": [
    "# random shuffle\n",
    "idx = np.arange(vectors.shape[0])\n",
    "np.random.shuffle(idx)\n",
    "vectors = vectors[idx]\n",
    "\n",
    "print('Done. Time elapsed: {:.2f}s'.format(time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17998, 1591) [[1]\n",
      " [0]\n",
      " [0]\n",
      " ..., \n",
      " [1]\n",
      " [0]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "print(vectors.shape, vectors.sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False False ...,  True False  True]\n"
     ]
    }
   ],
   "source": [
    "threshold = 0\n",
    "print(np.array(vectors.sum(axis=1)>threshold).reshape(-1,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "removed short docs (<0 words)\n",
      "(8408, 1591)\n"
     ]
    }
   ],
   "source": [
    "filtered_vectors = vectors[np.array(vectors.sum(axis=1)>threshold).reshape(-1,)]\n",
    "print('removed short docs (<{} words)'.format(threshold))        \n",
    "print(filtered_vectors.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because all the parameters (weights and biases) in the NTM model are `np.float32` type we'd need the input data to also be in `np.float32`. It is better to do this type-casting upfront rather than repeatedly casting during mini-batch training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'scipy.sparse.csr.csr_matrix'> float32\n"
     ]
    }
   ],
   "source": [
    "import scipy.sparse as sparse\n",
    "filtered_vectors = sparse.csr_matrix(filtered_vectors, dtype=np.float32)\n",
    "print(type(filtered_vectors), filtered_vectors.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train = int(0.8 * filtered_vectors.shape[0])\n",
    "\n",
    "# split train and test\n",
    "train_vectors = filtered_vectors[:n_train, :]\n",
    "test_vectors = filtered_vectors[n_train:, :]\n",
    "\n",
    "# further split test set into validation set (val_vectors) and test  set (test_vectors)\n",
    "n_test = test_vectors.shape[0]\n",
    "val_vectors = test_vectors[:n_test//2, :]\n",
    "test_vectors = test_vectors[n_test//2:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6726, 1591) (841, 1591) (841, 1591)\n"
     ]
    }
   ],
   "source": [
    "print(train_vectors.shape, test_vectors.shape, val_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train\n",
      "Validation set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/val\n",
      "Trained model will be saved at s3://pilho-sagemaker-ai-workshop-kr/amazonreview/output\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "import numpy as np\n",
    "import sagemaker.amazon.common as smac\n",
    "import boto3\n",
    "import os\n",
    "\n",
    "#trainVectors = np.array([t.tolist() for t in train_X]).astype('float32')\n",
    "#trainLabels = np.where(np.array([t.tolist() for t in train_y]) == 0, 1, 0).astype('float32')\n",
    "\n",
    "bucket = 'pilho-sagemaker-ai-workshop-kr'\n",
    "prefix = 'amazonreview'\n",
    "\n",
    "train_prefix = os.path.join(prefix, 'train')\n",
    "val_prefix = os.path.join(prefix, 'val')\n",
    "output_prefix = os.path.join(prefix, 'output')\n",
    "\n",
    "s3_train_data = os.path.join('s3://', bucket, train_prefix)\n",
    "s3_val_data = os.path.join('s3://', bucket, val_prefix)\n",
    "output_path = os.path.join('s3://', bucket, output_prefix)\n",
    "print('Training set location', s3_train_data)\n",
    "print('Validation set location', s3_val_data)\n",
    "print('Trained model will be saved at', output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_convert_upload(sparray, bucket, prefix, fname_template='data_part{}.pbr', n_parts=2):\n",
    "    import io\n",
    "    import boto3\n",
    "    import sagemaker.amazon.common as smac\n",
    "    \n",
    "    chunk_size = sparray.shape[0]// n_parts\n",
    "    for i in range(n_parts):\n",
    "\n",
    "        # Calculate start and end indices\n",
    "        start = i*chunk_size\n",
    "        end = (i+1)*chunk_size\n",
    "        if i+1 == n_parts:\n",
    "            end = sparray.shape[0]\n",
    "        \n",
    "        # Convert to record protobuf\n",
    "        buf = io.BytesIO()\n",
    "        smac.write_spmatrix_to_sparse_tensor(array=sparray[start:end], file=buf, labels=None)\n",
    "        buf.seek(0)\n",
    "        \n",
    "        # Upload to s3 location specified by bucket and prefix\n",
    "        fname = os.path.join(prefix, fname_template.format(i))\n",
    "        boto3.resource('s3').Bucket(bucket).Object(fname).upload_fileobj(buf)\n",
    "        print('Uploaded data to s3://{}'.format(os.path.join(bucket, fname)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part0.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part1.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part2.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part3.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part4.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part5.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part6.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/train/train_part7.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/val/val_part0.pbr\n"
     ]
    }
   ],
   "source": [
    "split_convert_upload(train_vectors, bucket=bucket, prefix=train_prefix, fname_template='train_part{}.pbr', n_parts=8)\n",
    "split_convert_upload(val_vectors, bucket=bucket, prefix=val_prefix, fname_template='val_part{}.pbr', n_parts=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "container = get_image_uri(boto3.Session().region_name, 'ntm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code in the cell below automatically chooses an algorithm container based on the current region. In the API call to `sagemaker.estimator.Estimator` we also specify the type and count of instances for the training job. Feel free to change to [other instance types](https://aws.amazon.com/sagemaker/pricing/instance-types/). NTM fully takes advantage of GPU hardware and in general trains roughly an order of magnitude faster on a GPU than on a CPU. Multi-GPU or multi-instance training further improves training speed roughly linearly if communication overhead is low compared to compute time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "sess = sagemaker.Session()\n",
    "ntm = sagemaker.estimator.Estimator(container,\n",
    "                                    role, \n",
    "                                    train_instance_count=1, \n",
    "                                    train_instance_type='ml.p3.16xlarge',\n",
    "                                    output_path=output_path,\n",
    "                                    sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters\n",
    "\n",
    "Here we highlight a few hyperparameters. For information about the full list of available hyperparameters, please refer to [NTM Hyperparameters](https://docs.aws.amazon.com/sagemaker/latest/dg/ntm_hyperparameters.html).\n",
    "\n",
    "- **feature_dim** - the \"feature dimension\", it should be set to the vocabulary size\n",
    "- **num_topics** - the number of topics to extract\n",
    "- **mini_batch_size** - this is the batch size for each worker instance. Note that in multi-GPU instances, this number will be further divided by the number of GPUs. Therefore, for example, if we plan to train on an 8-GPU machine (such as `ml.p2.8xlarge`) and wish each GPU to have 1024 training examples per batch, `mini_batch_size` should be set to 8196.\n",
    "- **epochs** - the maximal number of epochs to train for, training may stop early\n",
    "- **num_patience_epochs** and **tolerance** controls the early stopping behavior. Roughly speaking, the algorithm will stop training if within the last `num_patience_epochs` epochs there have not been improvements on validation loss. Improvements smaller than `tolerance` will be considered non-improvement.\n",
    "- **optimizer** and **learning_rate** - by default we use `adadelta` optimizer and `learning_rate` does not need to be set. For other optimizers, the choice of an appropriate learning rate may require experimentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vocab_size = 45586\n",
    "vocab_size = train_vectors.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#num_topics = 20\n",
    "num_topics = 150\n",
    "ntm.set_hyperparameters(num_topics=num_topics, feature_dim=vocab_size, mini_batch_size=1024, \n",
    "                        epochs=10, num_patience_epochs=3, tolerance=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to specify how the training data and validation data will be distributed to the workers during training. There are two modes for data channels:\n",
    "\n",
    "- `FullyReplicated`: all data files will be copied to all workers\n",
    "- `ShardedByS3Key`: data files will be sharded to different workers, i.e. each worker will receive a different portion of the full data set.\n",
    "\n",
    "At the time of writing, by default, the Python SDK will use `FullyReplicated` mode for all data channels. This is desirable for validation (test) channel but not suitable for training channel. The reason is that when we use multiple workers we would like to go through the full data set by each of them going through a different portion of the data set, so as to provide different gradients within epochs. Using `FullyReplicated` mode on training data not only results in slower training time per epoch (nearly 1.5X in this example), but also defeats the purpose of distributed training. To set the training data channel correctly we specify `distribution` to be `ShardedByS3Key` for the training data channel as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.session import s3_input\n",
    "s3_train = s3_input(s3_train_data, distribution='ShardedByS3Key') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to train. The following cell takes a few minutes to run. The command below will first provision the required hardware. You will see a series of dots indicating the progress of the hardware provisioning process. Once the resources are allocated, training logs will be displayed. With multiple workers, the log color and the ID following `INFO` identifies logs emitted by different workers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: ntm-2018-11-14-14-29-05-433\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-11-14 14:29:05 Starting - Starting the training job...\n",
      "2018-11-14 14:29:06 Starting - Launching requested ML instances......\n",
      "2018-11-14 14:30:32 Starting - Preparing the instances for training......\n",
      "2018-11-14 14:31:30 Downloading - Downloading input data...\n",
      "2018-11-14 14:31:38 Training - Downloading the training image.\n",
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:09 INFO 140632562353984] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'num_patience_epochs': u'3', u'clip_gradient': u'Inf', u'encoder_layers': u'auto', u'optimizer': u'adadelta', u'_kvstore': u'auto_gpu', u'rescale_gradient': u'1.0', u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'learning_rate': u'0.01', u'_data_format': u'record', u'sub_sample': u'1.0', u'epochs': u'50', u'weight_decay': u'0.0', u'_num_kv_servers': u'auto', u'encoder_layers_activation': u'sigmoid', u'mini_batch_size': u'256', u'tolerance': u'0.001', u'batch_norm': u'false'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:09 INFO 140632562353984] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'num_patience_epochs': u'3', u'num_topics': u'150', u'epochs': u'10', u'feature_dim': u'1591', u'mini_batch_size': u'1024', u'tolerance': u'0.001'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:09 INFO 140632562353984] Final configuration: {u'optimizer': u'adadelta', u'rescale_gradient': u'1.0', u'_tuning_objective_metric': u'', u'learning_rate': u'0.01', u'clip_gradient': u'Inf', u'feature_dim': u'1591', u'encoder_layers_activation': u'sigmoid', u'_num_kv_servers': u'auto', u'weight_decay': u'0.0', u'num_patience_epochs': u'3', u'epochs': u'10', u'mini_batch_size': u'1024', u'num_topics': u'150', u'_num_gpus': u'auto', u'_data_format': u'record', u'sub_sample': u'1.0', u'_kvstore': u'auto_gpu', u'encoder_layers': u'auto', u'tolerance': u'0.001', u'batch_norm': u'false'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] nvidia-smi took: 0.125756025314 secs to identify 8 gpus\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] Using default worker.\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:10.045] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] Initializing\u001b[0m\n",
      "\u001b[31m/opt/amazon/lib/python2.7/site-packages/ai_algorithms_sdk/config/config_helper.py:122: DeprecationWarning: deprecated\n",
      "  warnings.warn(\"deprecated\", DeprecationWarning)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] None\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] vocab.txt\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] Vocab file is not provided\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:10 INFO 140632562353984] Number of GPUs being used: 8\u001b[0m\n",
      "\n",
      "2018-11-14 14:32:07 Training - Training image download completed. Training in progress.\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Create Store: device\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1542205961.190559, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\"}, \"StartTime\": 1542205961.190502}\n",
      "\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:41.190] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 31149, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Starting training for epoch 1\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:714: only 32 out of 56 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: .vvvv...\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: v.vv.v..\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: vv.v..v.\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: vvv....v\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: v....vvv\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: .v..v.vv\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: ..v.vv.v\u001b[0m\n",
      "\u001b[31m[14:32:41] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200954.0/RHEL5_64/generic-flavor/src/src/kvstore/././comm.h:723: ...vvvv.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Finished training epoch 1 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) total: 34.8042813029\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) kld: 28.4607408515\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) recons: 6.34353893144\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) logppx: 34.8042813029\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #quality_metric: host=algo-1, epoch=1, train total_loss <loss>=34.8042813029\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Timing: train: 0.56s, val: 0.00s, epoch: 0.56s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Total Records Seen\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1542205961.751131, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 0}, \"StartTime\": 1542205961.190842}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=12001.9930526 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:41.751] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 2, \"duration\": 488, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Starting training for epoch 2\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Finished training epoch 2 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) total: 5.898803745\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) kld: 0.141664317676\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) recons: 5.75713947841\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) logppx: 5.898803745\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #quality_metric: host=algo-1, epoch=2, train total_loss <loss>=5.898803745\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Timing: train: 0.10s, val: 0.01s, epoch: 0.10s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}, \"Total Records Seen\": {\"count\": 1, \"max\": 13452, \"sum\": 13452.0, \"min\": 13452}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1542205961.856536, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 1}, \"StartTime\": 1542205961.751373}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=63880.7492086 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:41.856] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 3, \"duration\": 102, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Starting training for epoch 3\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Finished training epoch 3 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) total: 5.79452776909\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) kld: 0.0579908162888\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) recons: 5.73653711591\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Loss (name: value) logppx: 5.79452776909\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #quality_metric: host=algo-1, epoch=3, train total_loss <loss>=5.79452776909\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] Timing: train: 0.09s, val: 0.01s, epoch: 0.10s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 21, \"sum\": 21.0, \"min\": 21}, \"Total Records Seen\": {\"count\": 1, \"max\": 20178, \"sum\": 20178.0, \"min\": 20178}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1542205961.955929, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 2}, \"StartTime\": 1542205961.856764}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=67749.1671606 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:41.956] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 99, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:41 INFO 140632562353984] # Starting training for epoch 4\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 4 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.75993204117\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.0272018689928\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.7327302524\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.75993204117\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=4, train total_loss <loss>=5.75993204117\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[34.804281302860808, 5.8988037449972968, 5.7945277690887451] min patience loss:5.79452776909 current loss:5.75993204117 absolute loss difference:0.0345957279205\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.09s, val: 0.01s, epoch: 0.10s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 28, \"sum\": 28.0, \"min\": 28}, \"Total Records Seen\": {\"count\": 1, \"max\": 26904, \"sum\": 26904.0, \"min\": 26904}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1542205962.053725, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 3}, \"StartTime\": 1542205961.956155}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=68848.3546323 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.053] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 5, \"duration\": 97, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 5\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 5 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.7315186773\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.0125032035368\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71901549612\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.7315186773\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=5, train total_loss <loss>=5.7315186773\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.8988037449972968, 5.7945277690887451, 5.7599320411682129] min patience loss:5.75993204117 current loss:5.7315186773 absolute loss difference:0.0284133638654\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.09s, val: 0.01s, epoch: 0.10s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 35, \"sum\": 35.0, \"min\": 35}, \"Total Records Seen\": {\"count\": 1, \"max\": 33630, \"sum\": 33630.0, \"min\": 33630}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1542205962.151705, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 4}, \"StartTime\": 1542205962.053938}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=68721.0604877 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.151] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 97, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 6\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 6 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.7217090811\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.00534533310149\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71636377062\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.7217090811\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=6, train total_loss <loss>=5.7217090811\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.7945277690887451, 5.7599320411682129, 5.7315186773027689] min patience loss:5.7315186773 current loss:5.7217090811 absolute loss difference:0.00980959619794\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.08s, val: 0.01s, epoch: 0.09s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 42, \"sum\": 42.0, \"min\": 42}, \"Total Records Seen\": {\"count\": 1, \"max\": 40356, \"sum\": 40356.0, \"min\": 40356}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1542205962.244593, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 5}, \"StartTime\": 1542205962.151951}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=72517.463547 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.244] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 7, \"duration\": 92, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 7\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 7 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.71716587884\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.00225383523087\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71491214207\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.71716587884\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=7, train total_loss <loss>=5.71716587884\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.7599320411682129, 5.7315186773027689, 5.7217090811048239] min patience loss:5.7217090811 current loss:5.71716587884 absolute loss difference:0.00454320226397\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.08s, val: 0.01s, epoch: 0.09s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 49, \"sum\": 49.0, \"min\": 49}, \"Total Records Seen\": {\"count\": 1, \"max\": 47082, \"sum\": 47082.0, \"min\": 47082}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1542205962.337057, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 6}, \"StartTime\": 1542205962.244799}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=72820.5220507 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.337] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 92, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 8\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 8 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.71327761241\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.00165093186245\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71162673405\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.71327761241\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=8, train total_loss <loss>=5.71327761241\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.7315186773027689, 5.7217090811048239, 5.7171658788408548] min patience loss:5.71716587884 current loss:5.71327761241 absolute loss difference:0.00388826642718\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.09s, val: 0.01s, epoch: 0.10s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 56, \"sum\": 56.0, \"min\": 56}, \"Total Records Seen\": {\"count\": 1, \"max\": 53808, \"sum\": 53808.0, \"min\": 53808}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1542205962.436355, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 7}, \"StartTime\": 1542205962.337261}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=67799.6421561 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.436] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 9, \"duration\": 99, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 9\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 9 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.74947067669\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.0333996884791\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71607102667\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.74947067669\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=9, train total_loss <loss>=5.74947067669\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.7217090811048239, 5.7171658788408548, 5.7132776124136786] min patience loss:5.71327761241 current loss:5.74947067669 absolute loss difference:0.0361930642809\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Bad epoch: loss has not improved (enough). Bad count:1\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.09s, val: 0.00s, epoch: 0.09s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 90 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 63, \"sum\": 63.0, \"min\": 63}, \"Total Records Seen\": {\"count\": 1, \"max\": 60534, \"sum\": 60534.0, \"min\": 60534}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 18, \"sum\": 18.0, \"min\": 18}}, \"EndTime\": 1542205962.525791, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 8}, \"StartTime\": 1542205962.436566}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=75303.1348287 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:42.525] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 89, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] \u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Starting training for epoch 10\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] # Finished training epoch 10 on 6726 examples from 7 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Metrics for Training:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) total: 5.72054931096\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) kld: 0.0104845252686\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) recons: 5.71006461552\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Loss (name: value) logppx: 5.72054931096\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #quality_metric: host=algo-1, epoch=10, train total_loss <loss>=5.72054931096\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] patience losses:[5.7171658788408548, 5.7132776124136786, 5.7494706766945978] min patience loss:5.71327761241 current loss:5.72054931096 absolute loss difference:0.007271698543\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Bad epoch: loss has not improved (enough). Bad count:2\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Timing: train: 0.09s, val: 0.00s, epoch: 0.09s\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Total Batches Seen\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}, \"Total Records Seen\": {\"count\": 1, \"max\": 67260, \"sum\": 67260.0, \"min\": 67260}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 6726, \"sum\": 6726.0, \"min\": 6726}, \"Reset Count\": {\"count\": 1, \"max\": 20, \"sum\": 20.0, \"min\": 20}}, \"EndTime\": 1542205962.615038, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\", \"epoch\": 9}, \"StartTime\": 1542205962.525966}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] #throughput_metric: host=algo-1, train throughput=75435.6295882 records/second\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 WARNING 140632562353984] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:42 INFO 140632562353984] Best model based on early stopping at epoch 8. Best loss: 5.71327761241\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Topics from epoch:final (num_topics:150) [, tu 0.28]:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 452 604 1161 312 305 1241 722 89 362 206 1009 1128 287 322 1219 121 745 1521 1255 699\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1460 161 242 303 109 4 1045 292 1264 1165 1289 386 410 291 1104 474 1156 191 906 617\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1090 1282 67 86 1366 1273 985 571 471 1109 89 1413 742 896 533 707 92 657 1510 411\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1408 1352 808 1433 357 355 302 201 381 767 221 371 1474 627 422 681 841 968 818 1046\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1587 216 242 516 1072 1364 280 684 688 444 804 122 1476 1302 151 149 571 542 1529 681\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1279 382 397 83 96 1381 1445 320 1368 1000 548 993 1258 68 361 908 236 308 1211 1500\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1425 1445 478 1261 502 226 253 1352 653 678 102 471 1456 443 429 910 629 291 807 61\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 21 386 808 1151 1156 1076 1484 527 361 945 1429 96 122 692 450 380 1468 1037 333 877\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 652 888 454 242 6 238 458 993 769 83 450 1557 928 67 1572 538 487 591 621 731\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 703 1577 928 745 1305 624 1383 916 757 169 785 1184 1045 1060 909 1102 362 1229 942 1445\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 233 420 478 142 177 709 601 1037 1264 454 1184 1305 475 1379 1174 520 357 932 1500 1250\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 749 1279 709 782 969 563 1413 932 805 846 1468 583 887 4 1578 1303 1459 411 793 664\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 428 530 1548 502 1044 1305 813 1268 1006 1573 1197 818 1011 295 745 568 397 101 404 1072\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 448 1352 302 677 1494 162 1445 212 916 1286 1134 99 1100 236 1549 804 414 708 411 309\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1540 745 1119 162 1586 128 896 1044 132 806 1510 111 131 1218 320 1460 1412 38 2 854\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 563 320 636 160 405 983 1383 777 208 337 1071 743 841 1473 75 729 666 793 280 759\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 212 310 1286 1150 767 896 1425 674 742 932 745 420 22 1305 1109 495 157 739 885 1000\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 776 207 292 1586 280 168 1385 1302 1217 416 8 1546 1249 90 234 1236 362 110 1092 969\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1168 65 1225 1182 151 350 1241 268 1220 154 890 1254 1546 498 101 1211 1429 808 1006 115\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1045 1305 629 553 386 478 100 1475 767 1288 846 431 1020 1480 519 838 1230 677 110 856\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 484 1579 280 908 2 178 324 940 452 151 199 1172 236 83 592 571 420 551 547 386\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 846 101 371 604 263 42 906 747 1572 1539 168 301 810 1236 346 102 393 463 1182 1161\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 324 322 528 643 308 213 985 1044 427 91 460 1087 1392 595 376 32 1184 65 364 26\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 313 703 908 1401 1474 1073 302 246 771 1558 423 22 47 1048 731 757 1132 253 1302 1305\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1236 1401 417 1150 1211 109 907 597 571 1377 298 427 1049 1539 308 974 1429 19 1217 449\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1563 162 1326 1073 359 920 1018 870 360 731 759 1076 407 1218 6 147 652 179 1271 47\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 981 0 360 350 998 1008 885 1360 1127 225 472 169 1290 838 295 1305 898 8 122 167\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1134 1381 291 235 29 777 989 1288 678 707 1373 303 1404 1093 743 1175 1473 745 481 1429\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 370 1127 789 510 416 896 83 1197 1034 758 42 1031 1055 519 657 376 767 324 624 298\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1349 336 1161 293 1476 731 73 1249 787 1264 535 897 1407 823 1327 413 657 414 775 505\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 236 1286 210 1060 1476 131 106 1198 221 1012 1211 42 942 480 1419 303 450 357 564 609\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 426 156 86 1055 722 34 1137 887 1045 789 875 775 1249 1395 411 591 1530 926 1284 1052\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1241 234 427 89 993 934 293 423 1432 1052 84 1211 647 287 1404 1015 1030 1225 915 929\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1061 1085 548 91 763 677 1540 252 958 413 235 461 370 667 532 994 543 360 1128 1264\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1220 1445 1087 1292 1145 759 1500 777 1303 1061 1566 548 1107 1137 1184 755 1385 427 148 709\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1460 985 89 1484 396 322 1237 805 1119 1258 987 163 1510 410 1373 1138 263 667 122 1036\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 664 1099 8 106 1368 553 295 1104 518 109 472 1203 1370 407 1412 862 1288 1366 1035 800\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 883 40 216 884 677 1052 715 1540 458 1527 1276 1251 1105 1426 651 90 1134 1576 1377 1433\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 825 909 767 793 1251 355 0 775 1055 208 1168 916 38 755 985 923 1484 1404 335 611\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 47 1500 1258 224 1009 1519 818 1052 1554 1262 238 1119 856 1008 286 480 463 1051 1572 122\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 828 1366 1053 805 1236 908 653 1163 802 1119 411 117 0 293 422 1211 6 1183 1028 420\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 813 745 663 60 405 883 1413 1468 1182 1008 263 426 410 1041 1086 1515 280 724 896 301\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1236 991 1044 1292 653 427 86 110 1090 1268 813 333 34 1211 213 796 1107 870 156 825\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1165 591 1109 1150 392 131 728 887 964 1211 1518 547 111 907 1321 1049 1383 362 242 993\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1037 169 920 167 943 434 808 617 840 1236 1150 1128 1540 535 751 1058 1008 210 767 1452\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 985 405 810 244 1382 604 947 558 239 642 497 760 1001 162 1407 575 563 680 825 1161\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1386 339 689 1119 1368 313 805 1570 856 1582 823 1413 1540 1410 510 898 265 147 1445 942\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 597 1414 90 99 758 558 1341 291 608 777 1300 1056 948 1328 503 225 1052 964 1364 1410\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 360 30 1294 6 983 8 463 1364 802 371 1265 1241 1023 330 852 625 662 912 71 1385\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1252 1019 1386 1320 1513 35 1119 840 386 380 1044 1285 443 337 1521 1218 399 89 21 821\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 216 884 1218 808 1530 1513 969 383 30 543 987 1475 292 957 536 236 1500 1236 1286 358\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1425 1401 819 1279 701 1151 1058 1132 1255 969 1445 653 838 1195 1517 560 759 1370 165 1426\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 381 1076 960 1008 1172 21 302 793 131 337 1000 1119 957 297 1410 427 8 32 560 1211\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 162 664 450 4 1241 1476 193 1540 767 968 169 207 212 308 1056 520 516 910 982 1279\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 8 789 1039 1225 1166 29 983 770 773 591 532 1533 431 295 139 285 1306 236 117 519\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1305 434 739 416 831 1414 386 169 1276 1386 1132 1468 505 647 604 1127 749 242 513 89\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 797 743 216 775 680 1008 777 1119 993 860 289 1090 206 1356 1208 893 117 1274 1186 1068\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 303 708 597 1433 977 854 234 790 139 1500 993 73 1119 898 1155 887 2 368 1490 43\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1058 1582 1370 427 1051 350 850 538 1383 260 662 582 643 1098 74 1502 527 782 63 1382\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1289 625 410 656 674 521 154 213 224 1540 906 337 775 1203 454 1357 147 1257 89 209\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 96 476 65 728 239 1302 106 357 361 1456 1488 877 870 1473 797 852 1073 90 715 903\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1172 777 289 1320 106 1449 127 84 703 581 1490 740 303 562 429 885 341 428 987 11\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 985 324 1305 102 1413 1182 8 279 777 41 860 1241 701 1329 1357 411 810 1484 1220 6\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 360 162 521 386 1236 210 356 1379 1172 1263 96 427 400 896 757 1177 652 838 1502 798\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 595 1364 1488 350 883 1412 560 1533 1498 1158 1061 701 558 1186 193 1128 358 128 1018 339\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 703 1463 1127 75 280 471 29 1151 117 677 1072 268 1237 206 993 524 1119 278 1036 870\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 208 667 678 532 291 813 1484 38 1468 516 476 1019 908 1255 157 22 110 1249 660 1126\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 773 563 906 1368 29 289 942 236 560 846 386 1468 1587 1286 358 558 1570 757 754 1393\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1051 463 356 360 122 179 731 411 739 1450 242 38 328 110 86 1512 870 303 367 1138\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 444 1137 280 323 776 152 1129 38 920 320 1415 1473 128 1121 109 1217 212 796 790 423\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 162 344 823 731 1401 1392 914 313 295 903 1058 562 971 1576 673 597 555 279 1432 531\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 521 1204 1408 1058 1239 1383 1557 1436 1225 808 1588 169 1237 1071 1166 142 362 454 432 1099\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1352 893 210 674 502 563 298 932 560 689 1034 1341 1064 290 1547 549 312 186 497 656\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1463 456 677 400 887 280 1058 478 802 893 5 1249 323 29 1307 714 1055 1392 1404 1277\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 6 1187 1208 30 29 26 1500 109 528 789 585 652 624 1178 1102 84 212 361 515 1264\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 328 40 1557 1461 116 674 1166 1465 513 1292 346 210 874 431 530 1582 726 533 1229 1032\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 703 1109 109 1262 808 1460 731 132 1292 323 357 961 163 510 878 1006 1208 208 789 104\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1433 324 297 1286 1111 1263 608 312 244 742 350 825 896 643 1072 658 152 478 1530 701\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 518 961 1123 939 1557 1302 427 1408 883 1005 1404 1008 1282 417 862 1178 491 1576 870 898\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1510 805 1184 233 664 1111 1099 1034 533 1241 790 772 538 106 1350 160 743 1218 806 1468\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1208 443 324 510 35 1456 1404 993 1064 1476 1586 971 1145 883 1158 6 1477 408 433 177\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 263 1220 38 360 131 236 818 1556 810 984 1383 1161 865 1419 99 463 1250 783 749 1249\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1264 188 244 478 518 1052 279 953 1404 111 1174 291 1220 169 1073 722 1085 1300 542 405\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 429 359 161 634 1037 252 1364 323 1073 362 689 532 1155 783 767 469 1513 1309 266 72\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1352 1045 910 920 703 100 86 1289 1364 802 370 1007 740 826 759 1054 776 1220 825 405\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 731 934 67 285 464 400 920 1136 647 1250 1151 1545 28 907 742 1387 109 463 1102 115\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 818 597 846 1092 1460 1305 884 953 912 1364 1578 96 888 463 280 1143 518 743 242 201\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 337 252 560 56 1172 1394 333 1019 1302 0 433 1168 1286 34 923 234 1501 326 66 928\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 117 1008 591 1353 948 1404 1171 846 883 148 647 549 1064 390 1394 682 245 337 89 1554\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 290 1061 166 1557 977 513 360 1165 973 983 287 953 1400 381 376 1412 782 350 22 1237\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 819 1058 948 268 519 1490 271 1252 1056 90 1090 985 1229 731 239 1036 782 75 40 1156\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 263 563 589 323 279 595 810 1090 171 1366 216 533 1413 739 1533 1404 75 234 862 532\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1264 714 1433 358 212 854 896 808 790 244 193 308 502 625 923 775 100 786 1045 225\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 652 1241 749 983 239 689 165 1263 4 1166 841 795 1500 469 271 286 856 1494 1540 1109\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1249 1263 79 122 1262 778 1021 357 770 1341 356 421 890 96 147 877 680 1001 632 30\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 564 1473 1305 26 1589 1475 703 169 624 1039 1250 102 86 1460 362 881 1486 226 1197 1282\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 745 571 206 431 497 521 563 122 760 585 1123 1111 75 56 1452 1587 758 1012 316 15\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 360 1364 8 265 65 1178 785 1302 860 358 885 647 234 1490 689 823 336 359 38 573\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 40 6 1109 1048 239 1302 920 1366 1044 538 1137 678 757 1174 1211 350 454 642 786 154\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 604 1289 1285 1252 1091 74 350 1246 920 525 1518 1410 1382 1011 443 1302 810 960 1356 975\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 177 1197 305 1034 285 1158 152 201 1207 884 1090 1383 91 417 1255 43 927 553 96 65\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 381 1000 521 916 287 850 932 1102 1034 1572 4 117 595 1225 1178 289 1250 371 476 1076\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 703 312 1058 1184 1286 1460 298 832 790 1540 514 677 1533 667 247 106 91 1242 343 287\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 303 1460 1182 1510 623 224 287 1279 652 427 885 1433 288 323 560 521 382 51 806 1484\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1565 26 533 1341 83 1381 457 360 1263 1077 521 428 1408 216 494 422 1021 678 1107 991\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 359 272 775 560 4 1211 985 743 1074 819 813 299 737 734 891 360 808 664 1265 267\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 663 212 1236 265 271 166 4 1275 1476 858 505 1266 347 455 1107 34 139 122 386 258\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1582 216 1557 75 444 1550 1386 1579 1533 1370 494 629 1429 775 1415 651 1500 35 1060 728\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1292 1172 647 519 209 622 1385 502 808 309 1486 625 1540 49 141 707 337 366 1302 1210\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1395 581 1229 595 881 47 846 361 1196 1184 211 427 4 1007 179 215 1314 870 1166 1471\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1134 1303 1408 1540 775 4 1586 770 25 580 344 356 755 316 1395 983 969 426 358 437\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 291 1410 1370 749 658 1073 548 1037 285 303 148 1373 1498 573 1353 1111 74 703 1484 636\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 32 521 63 1008 1172 1035 1134 319 416 1069 92 740 159 1533 1127 548 206 179 987 1425\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 236 68 887 152 1045 1051 25 128 1209 410 376 703 1530 710 528 755 8 1150 1306 1021\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 722 1068 361 1134 1209 535 731 1580 1221 246 362 181 1074 244 221 647 405 1519 1007 426\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1134 381 376 775 896 1109 271 25 1473 1460 1174 1557 521 968 1224 1128 1565 478 548 528\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 429 1058 1069 209 1225 71 1426 1061 535 475 755 255 1175 320 1445 221 1006 783 161 1211\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1412 667 653 1251 887 884 1218 595 885 647 1490 714 1255 382 1429 883 456 563 358 29\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 767 1273 234 471 597 1500 856 532 538 1517 292 1333 190 756 1368 115 339 1185 1220 731\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 210 1488 776 1066 188 838 862 427 1412 1211 968 208 434 372 589 115 745 239 456 1325\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 201 1563 89 1407 193 1383 819 560 97 242 677 643 117 993 656 714 775 235 173 734\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 2 923 1473 443 823 928 348 583 279 887 40 239 1037 1413 502 1165 225 1273 166 591\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 793 1163 1500 362 293 560 1368 604 1307 320 210 1452 410 444 1258 1548 1076 1294 429 207\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 71 344 224 1341 390 538 1218 475 244 161 795 1182 285 356 556 1183 1005 1274 722 1137\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1352 1320 1201 1325 155 1445 193 1289 61 242 84 1419 176 832 1182 553 906 935 1302 1101\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1170 298 887 206 1018 47 70 862 1229 678 1129 1039 251 50 714 397 1132 1494 968 739\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 4 1544 757 1058 427 1385 75 431 1253 236 1192 1099 1351 410 254 680 1521 1582 1172 652\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 734 1274 1419 1404 320 1064 423 1236 326 854 431 1430 680 1555 764 850 201 360 397 813\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1072 416 35 287 92 1513 1052 524 1463 932 1005 30 877 313 767 1009 40 534 443 177\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 128 376 210 177 0 75 236 280 1249 677 1265 1045 776 287 652 1225 324 1068 763 16\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 860 179 1408 417 1577 1151 1155 166 819 651 514 910 846 333 427 305 358 1000 969 227\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1563 885 538 1034 422 1375 759 1155 679 102 500 271 386 1437 416 1302 290 1019 247 890\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 4 1540 188 678 783 1225 1302 236 595 405 221 268 885 1210 290 558 790 726 147 391\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1061 782 1590 1218 548 413 1219 181 3 662 1309 806 1521 1074 1239 850 47 920 1533 874\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 678 1352 1237 1129 167 279 1052 3 1366 680 1401 870 916 1500 1220 1093 975 634 362 609\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 589 1412 870 428 75 67 397 1119 1250 1038 649 17 1166 1407 625 253 322 30 1151 1068\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 983 96 1412 627 624 510 1109 1264 63 346 236 193 0 563 313 210 6 518 1379 1557\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1174 560 271 757 1204 92 1189 870 1286 948 169 246 225 1302 448 1170 206 835 1129 0\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1184 1128 1273 410 1172 985 532 463 154 1127 29 287 1052 357 114 337 945 1261 547 739\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 759 1156 166 876 535 1183 167 293 657 289 1385 930 34 745 0 131 525 473 808 1008\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 337 1419 516 1018 83 862 416 1303 1388 209 896 549 656 91 201 234 0 1386 362 293\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1150 162 548 731 1500 221 519 906 1404 1099 1294 1557 146 1444 546 818 1166 1282 476 1382\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1540 68 1018 0 1166 896 129 860 621 102 1197 510 381 233 883 782 1460 942 1490 40\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 246 100 993 43 463 1433 604 1168 1116 1320 770 1498 2 1285 1460 356 1329 591 776 1352\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 703 242 595 285 1285 84 1488 131 516 1137 503 92 129 985 1090 1134 1109 390 186 1341\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1490 291 242 1433 42 226 427 1309 1067 757 449 743 1211 210 528 1012 1036 578 114 405\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1408 1184 813 757 1385 1186 745 190 597 518 1382 427 819 856 885 1020 1364 211 1426 652\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 1128 1201 1237 358 1127 1039 1236 1134 463 747 235 1000 677 1005 1203 1461 818 480 1292 520\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 326 1190 68 1461 311 535 1572 977 947 557 234 235 287 573 288 975 428 1099 785 449\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] 908 454 1429 625 652 38 1320 1044 1366 713 1134 1211 828 80 312 1445 1174 1370 83 428\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Serializing model to /opt/ml/model/model_algo-1\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Saved checkpoint to \"/tmp/tmpI45ZHP/state-0001.params\"\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:43.168] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"epoch\": 1, \"duration\": 33123, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Finished scoring on 841 examples from 1 batches, each of size 1024.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Metrics for Inference:\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Loss (name: value) total: 5.15089035034\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Loss (name: value) kld: 0.00215413980186\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Loss (name: value) recons: 5.14873600006\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] Loss (name: value) logppx: 5.15089035034\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 841, \"sum\": 841.0, \"min\": 841}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Total Records Seen\": {\"count\": 1, \"max\": 841, \"sum\": 841.0, \"min\": 841}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 841, \"sum\": 841.0, \"min\": 841}, \"Reset Count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1542205963.174983, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"test_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\"}, \"StartTime\": 1542205963.168238}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:32:43 INFO 140632562353984] #test_score (algo-1) : ('log_perplexity', 5.1508903503417969)\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:43.175] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 11, \"duration\": 649, \"num_examples\": 7}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:43.175] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"duration\": 33053, \"num_epochs\": 11, \"num_examples\": 71}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:43.175] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"epoch\": 2, \"duration\": 6, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:32:43.175] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"duration\": 33129, \"num_epochs\": 2, \"num_examples\": 2}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 33309.25011634827, \"sum\": 33309.25011634827, \"min\": 33309.25011634827}, \"finalize.time\": {\"count\": 1, \"max\": 534.9950790405273, \"sum\": 534.9950790405273, \"min\": 534.9950790405273}, \"initialize.time\": {\"count\": 1, \"max\": 31143.31603050232, \"sum\": 31143.31603050232, \"min\": 31143.31603050232}, \"model.serialize.time\": {\"count\": 1, \"max\": 17.64702796936035, \"sum\": 17.64702796936035, \"min\": 17.64702796936035}, \"setuptime\": {\"count\": 1, \"max\": 139.91308212280273, \"sum\": 139.91308212280273, \"min\": 139.91308212280273}, \"early_stop.time\": {\"count\": 10, \"max\": 8.892059326171875, \"sum\": 55.69863319396973, \"min\": 0.11801719665527344}, \"update.time\": {\"count\": 10, \"max\": 560.1449012756348, \"sum\": 1420.989990234375, \"min\": 88.96708488464355}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}, \"model.score.time\": {\"count\": 1, \"max\": 6.69407844543457, \"sum\": 6.69407844543457, \"min\": 6.69407844543457}}, \"EndTime\": 1542205963.17957, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/NTM\"}, \"StartTime\": 1542205930.040542}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2018-11-14 14:32:55 Uploading - Uploading generated training model\n",
      "2018-11-14 14:32:55 Completed - Training job completed\n",
      "Billable seconds: 86\n"
     ]
    }
   ],
   "source": [
    "ntm.fit({'train': s3_train, 'test': s3_val_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training job name: ntm-2018-11-14-14-29-05-433\n"
     ]
    }
   ],
   "source": [
    "print('Training job name: {}'.format(ntm.latest_training_job.job_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Hosting and Inference\n",
    "\n",
    "A trained NTM model does nothing on its own. We now want to use the model we computed to perform inference on data. For this example, that means predicting the topic mixture representing a given document.\n",
    "\n",
    "We create an inference endpoint using the SageMaker Python SDK `deploy()` function from the job we defined above. We specify the instance type where inference is computed as well as an initial number of instances to spin up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: ntm-2018-11-14-14-33-17-949\n",
      "INFO:sagemaker:Creating endpoint with name ntm-2018-11-14-14-29-05-433\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "ntm_predictor = ntm.deploy(initial_instance_count=1, instance_type='ml.c4.4xlarge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You now have a functioning SageMaker NTM inference endpoint. You can confirm the endpoint configuration and status by navigating to the \"Endpoints\" tab in the AWS SageMaker console and selecting the endpoint matching the endpoint name, below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Endpoint name: ntm-2018-11-14-14-29-05-433\n"
     ]
    }
   ],
   "source": [
    "print('Endpoint name: {}'.format(ntm_predictor.endpoint))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Serialization/Deserialization\n",
    "\n",
    "We can pass data in a variety of formats to our inference endpoint. First, we will demonstrate passing CSV-formatted data. We make use of the SageMaker Python SDK utilities `csv_serializer` and `json_deserializer` when configuring the inference endpoint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferencing with CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "ntm_predictor.content_type = 'text/csv'\n",
    "ntm_predictor.serializer = csv_serializer\n",
    "ntm_predictor.deserializer = json_deserializer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pass 5 examples from the test set to the inference endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = np.array(test_vectors.todense())\n",
    "np.random.shuffle(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(841, 1591)\n"
     ]
    }
   ],
   "source": [
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0. ...,  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print(test_data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'topic_weights': [0.0066880342, 0.0066496599, 0.0067004492, 0.0066542481, 0.0066710655, 0.0066532376, 0.006659952, 0.0066880821, 0.0066642398, 0.0066551086, 0.0066870502, 0.0066427807, 0.0066690869, 0.006660277, 0.0066480143, 0.0066835359, 0.0066874083, 0.0066624577, 0.0066428408, 0.0066692294, 0.0066704475, 0.0066565815, 0.0066727642, 0.0066403481, 0.0066546979, 0.0066704517, 0.0066564949, 0.0066242432, 0.0066542253, 0.0066715954, 0.0066705574, 0.0066714473, 0.0066559114, 0.0066819508, 0.0066527086, 0.0066928784, 0.0066619609, 0.0066680466, 0.0066910856, 0.0066504367, 0.0066477894, 0.0066871652, 0.0066699646, 0.0066710371, 0.0066711595, 0.0066938992, 0.0066692168, 0.0066508264, 0.0066622128, 0.006691115, 0.0066847764, 0.006642058, 0.006677906, 0.0066432543, 0.0066655665, 0.0066615283, 0.0066464618, 0.0066826786, 0.0066770832, 0.0066739726, 0.0066540777, 0.0066772331, 0.0066554905, 0.0066789202, 0.0066522192, 0.0066531976, 0.0066382685, 0.0066615934, 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0.0066724089, 0.0066799703, 0.0067262398, 0.0066699912, 0.0066943495, 0.006662047, 0.0066816024, 0.0066701598, 0.0066774744, 0.0066842567, 0.0066971038, 0.0066272882, 0.0066583687, 0.0066618677, 0.0066832998, 0.0066636894, 0.0066704983, 0.0067083952, 0.0066649187, 0.0066369469, 0.0066868202, 0.0066879778, 0.0066737742, 0.0066573741, 0.0066647278]}]}\n"
     ]
    }
   ],
   "source": [
    "results = ntm_predictor.predict(test_data[:20])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the output format of SageMaker NTM inference endpoint is a Python dictionary with the following format.\n",
    "\n",
    "```\n",
    "{\n",
    "  'predictions': [\n",
    "    {'topic_weights': [ ... ] },\n",
    "    {'topic_weights': [ ... ] },\n",
    "    {'topic_weights': [ ... ] },\n",
    "    ...\n",
    "  ]\n",
    "}\n",
    "```\n",
    "\n",
    "We extract the topic weights, themselves, corresponding to each of the input documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00668803  0.00664966  0.00670045 ...,  0.00666847  0.00667281\n",
      "   0.00666494]\n",
      " [ 0.00668165  0.00664617  0.00670344 ...,  0.00665623  0.00667012\n",
      "   0.00667072]\n",
      " [ 0.00667916  0.00664859  0.00670422 ...,  0.00667275  0.00666963\n",
      "   0.00665951]\n",
      " ..., \n",
      " [ 0.00669109  0.0066716   0.00666029 ...,  0.00669354  0.00665502\n",
      "   0.00667665]\n",
      " [ 0.00669184  0.00665555  0.00669482 ...,  0.00667214  0.00667284\n",
      "   0.00665996]\n",
      " [ 0.00669609  0.00666555  0.00669974 ...,  0.00667377  0.00665737\n",
      "   0.00666473]]\n"
     ]
    }
   ],
   "source": [
    "predictions = np.array([prediction['topic_weights'] for prediction in results['predictions']])\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note: The following section is meant as a deeper dive into exploring the trained models. The demonstrated functionalities may not be fully supported or guaranteed. For example, the parameter names may change without notice.***\n",
    "\n",
    "\n",
    "The trained model artifact is a compressed package of MXNet models from the two workers. To explore the model, we first need to install mxnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you use conda_mxnet_p36 kernel, mxnet is already installed, otherwise, uncomment the following line to install.\n",
    "# !pip install mxnet \n",
    "import mxnet as mx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we download unpack the artifact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amazonreview/output\n"
     ]
    }
   ],
   "source": [
    "print(output_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'amazonreview/output/ntm-2018-11-14-14-29-05-433/output/model.tar.gz'"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "#model_path = os.path.join(output_prefix, ntm._current_job_name, 'output/model.tar.gz')\n",
    "#model_path = 's3://pilho-sagemaker-ai-workshop/amazonreview/output/ntm-2018-07-27-06-43-35-098/output/model.tar.gz'\n",
    "#model_path = '/amazonreview/output/ntm-2018-07-27-06-43-35-098/output/model.tar.gz'\n",
    "#model_path = os.path.join(output_prefix, 'ntm-2018-08-03-02-14-58-933/output/model.tar.gz')\n",
    "#model_path = os.path.join(output_prefix, 'ntm-2018-08-14-17-14-02-385/output/model.tar.gz')\n",
    "model_path = os.path.join(output_prefix, 'ntm-2018-11-14-14-29-05-433/output/model.tar.gz')\n",
    "\n",
    "model_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#boto3.resource('s3').Bucket(bucket).download_file(model_path, 'downloaded_model.tar.gz')\n",
    "#boto3.resource('s3').meta.client.download_file('pilho-sagemaker-ai-workshop','amazonreview/output/ntm-2018-07-27-06-43-35-098/output/model.tar.gz', 'downloaded_model.tar.gz')\n",
    "#boto3.resource('s3').meta.client.download_file('pilho-sagemaker-ai-workshop','amazonreview/output/ntm-2018-07-27-06-43-35-098/output/model.tar.gz', 'downloaded_model.tar.gz')\n",
    "boto3.resource('s3').meta.client.download_file('pilho-sagemaker-ai-workshop-kr','amazonreview/output/ntm-2018-11-14-14-29-05-433/output/model.tar.gz', 'downloaded_model.tar.gz')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_algo-1\r\n"
     ]
    }
   ],
   "source": [
    "!tar -xzvf 'downloaded_model.tar.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  model_algo-1\r\n",
      " extracting: meta.json               \r\n",
      " extracting: symbol.json             \r\n",
      " extracting: params                  \r\n"
     ]
    }
   ],
   "source": [
    "# use flag -o to overwrite previous unzipped content\n",
    "!unzip -o model_algo-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the model parameters and extract the weight matrix $W$ in the decoder as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = mx.ndarray.load('params')\n",
    "W = model['arg:projection_weight']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrix $W$ corresponds to the $W$ in the NTM digram at the beginning of this notebook. Each column of $W$ corresponds to a learned topic. The elements in the columns of $W$ corresponds to the pseudo-probability of a word within a topic. We can visualize each topic as a word cloud with the size of each word be proportional to the pseudo-probability of the words appearing under each topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (1.5.0)\r\n",
      "Requirement already satisfied: numpy>=1.6.1 in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (from wordcloud) (1.14.5)\r\n",
      "Requirement already satisfied: pillow in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (from wordcloud) (5.0.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud\n",
    "import wordcloud as wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (1.5.0)\r\n",
      "Requirement already satisfied: pillow in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (from wordcloud) (5.0.0)\r\n",
      "Requirement already satisfied: numpy>=1.6.1 in /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages (from wordcloud) (1.14.5)\r\n"
     ]
    },
    {
     "data": {
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OMLqQ5QOr+jg0NsFMaZruaDOGsJAEpMzGWgVTCAQCX/qYmo2tRdCFgald2+RopeLw0Hf2cud9W2hqS/9S+zJNnfXbe6gUHR79wcuXbYvGQ+z+wFqOvXzulzrGlfREu+mOdr3rCd33Uq5UYWhynkk7T75UJVeq0JxOoGuC2VyJfLlCYyrGYqFMT1OGiuuRK1WYmM8zky1QqDikomGqnocmIB2L4PkBhUqVnqYM9ckoulZLhO556lVuvGUlczN55mfyuK7PwmyefK7MijVtJFJhThwawXF82rvr2LCtm7rGBNF4LXkspWR8ZJ5jrwwBsP2mFQghePmF0ziOx9rNnbR11rH/uQFmp3N09jawYVs3J4+e58ypCeoaEmzd1ceBFwfxl+q8N9y8klKhyj/87Qs0taXZuL2HTTf08PTLg1Qdjy2r20knwpiGflU7E6831bLD6YNDZJpTeI6LHbYoZEvEkhGkhFK+TCFbomNlS63+X6hghy0WZnIgJVbYwqm4xFNRmrrqa+0c3yfvVNEQBJest+sFAQXHwQt8IqZF2Ki1iwIpKbsuZc9FCEHUtAgZBoGUFBwHx/cIGQYR00JKScl10YSg7HmYurbcAVRwHB45M4BAkAqFiZgGUdPCCwKqvsevr1pNJlRrMwVSkq9WiVoWhqYtH8vWdSxdp+J5teNogrhlY2gatq6zraWNx84OLpdJLr3O1HVChoGUkrJX6wS/UL7Xqt23Xcp+FYCQbhPSTCRQ8iu4gYepGUT0EAIo+VVGStOUvSq76tbSEEoR1i1KXoXzpRnmnTy/0X4zTeE0MSN8yf4ddCGI6CEMTb+sjqNdeFxolPwqEomUEkPTcXyPqBFCExoV36EaOAgEYd3G1s1fKEvBq2BpBpZm4MuAol8hkAERPYSl1a5Byasd0w08fBlccV+vR0qJL8v4sgyAISLoWhgpA7ygQICLJmwMUetU8GUJ0PBlBYGGocUQ6HiyyFTpKar+HLZej66FMEQUkEv78RDoGFoMTRhL18TFC4pIfDRsDC2KLytkq8cYLz5C2GhHEwamlgAEbpAnbW/G0GJcaJP5QQWJj5QeAR66CKOLpc8hLl5QABkgkRhaGF1EEUJwNDtAe7iZOjt12bWAYHnfv0xi6rWuy8RRc1uaRKrW6DZNnY6+RlzXo1p2CQLJmi1dHN1/lkK2jB0y2XBjD8lMjJeePEFXfxPhiMXk+QWa2jIcfHEQyzYwTJ2xoVn617YSjlhUBHT0NwCCYrbMxOg8dY1xovEwgS9pakuz4cZeDr44SLohTktnHZt29l1yw6pV5h5/6BBHXhnGdTxMy+Duj2xm5y0rCYKAc6en+Onf72dxvohlG9x293p23LwC0zI48soQe545RV1DnMFTE5RLDlt39PLRT9xIKGTy6tHzPPHIUY4eGGJmMsueZ06SzsT49JdvpqEpSSFf4aff38eZgUmcqkcsHuKD921l8/bu5cTTT/9+PyePnce0DL7yR/cu95xUKi7/8O09WJbB9GSW2ekchqnzsc/uYvWGdnRdY2GuwA+/s5czA5P09Dfxxd+7fbnsuWyZb37tKdo76zg3OM3CXJ5EKsJnfvsWOpZuCGOj8/z0+/vILhaZmc5TLTus39LFJ794E3UN8eX3WtdT6Pov9hpJ6QMghA4IpKwCOkIYSBlg6C0IceWK7nPHzhENWWzrv7z31DJ0LCNMKnJ5j7UQgkQkxKULxV8I4m+3l2mhUOZn+0+ysq3hbSWOoHaDeqPF6vta6vi9e3fUevVMnd6+xjdtJAkhSCTCy/++cJzZxQKJaAjLvBgCPD9AE1xxNFIQSAIZYOg6gZRk82WiYeuy1/8qSVtJpqvzrIn3UfIrWHqYiJ6g4hfR0dGF/qaNBSklQ8URJqsz7Mpsx9Ytin6Jl+YO0BfrIefmeX52L3c03szT0y/QE+2kL9ZF1IgwVBzlZO40W9IbOJEbwJcepwvnaLYb2D9/kBXxXg4uHKHkl2gLt/Ly/CG6oh2cyg/iBi6GMOiJdvLczF4+0HQbL87t48bMVppDTbwwu4+kmeSF2X2sTqxgpjpHxa8ggLgRY01iFYcWj3Eyf5oNybV0RtqJ6BF2ZraRMpOYmkFPtItHJ57ADTxsTbJ//iAFr0RfrId9c68Q1SO8vHCoVqZoNy/O7ac93IqzACeOnkdKWLm6hdOnJnBdn66eBkaH52hojDM9mQUhcByPDZs6iETrKLseAzOzaEKQCNm1xBGSF2dP8rXT/whAe6SOP1n3cersOGFh0mWmaAjHMHQN3dTI2GHybpWmsCBmXpIIl5KUGcYPApKJenTtwo32yu9vJXB58Px+Hp88TNKK8NmuW9mS7nnbjYhHJw7ywPDzr7t9S7qHT3ftouiX0EUG6Q8BPkJv4+3OMi/7Dj8Y3cOz08fJWHE+330b4SCGLgSiqVb2iu8DEt/WCQKPeDxE3nNZLOYIpy2qnkbMsmhNJBBCsPqGXkq5MolMHMPSKWZL2BEbIQRPHzrDzGIBgB1rOjk0OM62le34QcALR4dIxkJ0NqbYe2KErqYU9ckYJ0enMXSN/rZ6Xh2eJhWrxemjZydoTMewTYOz43MUKw79bfWMTC/i+wH9bfUcHhyntT5BX1sd7Q1XZxTCe+nMyUmOHhjijns3Lo+kAUgkwwgh0IUgbBoMzswhBIzML7K1o5WM3YJAIN/iYAGn4jJ4YpzsfIF0Q5y+Na3ohsbJQyM0tWeoa6zVIfLZEsOnp+hf24YdMhkfmWP07AymqdO7ppV0fYxCtsz5oVkS6Qjnz85ghwz61rQRTYSYPL/Aq68Ms/+Zk0TjITKNcbr6m2juyHDm1XEaWlLLxxo4OkoyHaOpPc38dI7RszMUCxWS6Sg9q1uIRGufOSnlFftEhBAgLo/WUkrmpnLMTmZZsb4N3dDxPZ+hgSlCEYu27toohLJf5mxhCCdwsDRrOea7gcvZwjmybo60laY31o1O7V4cyIDJyhRTlWkA6uw62sItVP0qE5VJYkaMifIkYT1EV7STkF77nBe9IsOlUSp+hQa7ntZwC1JKRsvniRkxZiozVAOHlnAzTXZjrZ4XOIyWxsi6WUxh0hRqpN6uQxMaZb/MUHGEsl+mzqqjPdKKLnRKXonJyhQZK8P58hi+9OmMdJC2UkhZqwfM5krUJ6L0tdRxdGiSbLGM6/tELIuqoZOKhjE0jYmFPJ0NKTw/wPcDMvEI2VKFUtUhGQ3hB5Le5jpOjE7RkIhimwZL4RUZSI4fHGHLzj4WZgsMn5kmly0Ti4fo7G3kwIun6V/TyvnhOW7/0AaeeuQI3f1NJNMX61WBL/n5gwcxLYNCvtaBsOnGXjRNkFsssfepV7nn/m0MnBijf00rzW1pELXOCdPU2f/8AB099Rzce4bdd64BITh+cJjtN62goTnJ+i1d9KxoAqDiuDy57zQ/e/4EW1a3sWV1Oyu7Ggnb5j+JBJJpGVRLVQqLRaKJMHMTC4wOTBJLRXGrLlbIZGEqS+AFIGqJJN3Qyc0VsEImdS1pfNfHXhqN6gcBPx04yYujw6RDYcYLeba1tBJIyfMjw/z83Bkc36czmeQTa9fTFI0xOD/Hj06eYK5cxjZ07u7p55bOLo5OT/HjkycoeS5JO8Rn128kZtl8/ZX9hE2D6WIRCfzO5m20xxM8dvY0D50+RUg3OLs4z+72Tu7u7WemVORvjxzi6PQk//a2u1iRqcPxPf7v/Xv52Jp1rKlvIFet8Bf7X+K+Vatpiyf4zrEjjOay6EJwd28/t3R2Y2gaSHlZzPGl5LvHj9AUjXHfqjU4vs93jh2hK5nirp6+K9Ym55wcD43tZbq6gBAaN2RWcWvDBoaKUzw8vpeK7xDWbX6tdQcdkQZemjvJ8zNHGS5N84Pzz7I20cUHmrdxYP40z80cZbQ0zQ/PP8fKeDsfbLkBN/B4cHwPk+UFNCG4uWE929IrKftVHp3Yz1BxCiEE29Ir2J5ZyQMjT6ELjelKls5oA8PFae5r201XtImHx/cyWpqh4jusTXbx0dZdy+WQSM6XZ/np2Ivc07yN3lgrL84e56W5k0gk3dFmfq3lRmJmmAfH99RGPnolil6VOxo3sSnd95Y+o2VvjNHCD6l6Mwih0xC+labI7WSdVxnL/xhfVjG0GJ3xj2MbDZzLfgMpfdwgjy8rtMU+Sjq0hZnSs4wXHyaQHlV/jnRoM82Ru6n6MwznvoMnSwSySnvsN8mEtuHLKuPFh1msHAEganbSGf8ki9WjnC/8hLxzisHF/0HYaKIz/imEMJgs/iNTpadpjX2IttivAzBb2cNseS+aMHH9RcJGG93JL6CLEOOFh8k5r+L481T9WXqSX6IhfBMCk+nqHBkrSR0X6z6+LFP15zG0CIF0CemN71qcuu5afVJKThwcZmxohlcPDmNHLEzLwHN9KlaV3GKJPU+cIBKzaWhJEorauK5PqVilsS3DS0+9imFobNrZR31zkleeH2DHnWuJJsKs2tRBbqFIsj6OpmtoukDTNfyozZbd/UyNLZBIR8g0xCnkypiWQSIdQdME9U1JnnvkCDd/cAPxVAQpJY89eIi9z57i/s/spKUtRaFQpa6+lhSZnszxjf/+FJu2d3P/p3cwNZHlxw/sRTc0dt6yklLJ4aXnB/jAhzfx+X92O9MTi3zr60/Tv6qFLTf20reymXgywtCZKT7461vYckMPuqEvTzfTdY2Va9vYtrMPyzZ4+IcH+Mn3XqJ/VTPxRBg7ZHLfp26kpT3NYz89iOcFy9c4CALmZwucOj7GF//gDppbUzzxsyM88M3n+Vf/+j5SmSiZ+jif/tLNPPiDlzk/PEsQXKx4Bn7A0OA0E+fn+fSXb8G2TX78vZf46ff384f/271IKfnxAy8hBHzh9+9gaHCKb339GW79wFrSda9Nplz+Qd7z6hDD04t0NqR47tg5SlWHOzb2c/umPqSULBRKPLzvJKfHZjANnTs39bN9RTuWaXB6bJYnDp3mkf0niYdtXh2ZIha2+fjNG+lrraPiuPzoxWO0ZBIs5EscHZrEMnU+d8dWOhpSVFyPJw6e5tjQJIWKQyYe4d7tq1jd0bj02YSJ+Sw/PzjImYlZpITOxhSfvGUTtmWw59VhHn9lgMNnx/nznzxPLGSxuqOR+3evJxqymFos8NiBU5ydmMP1A/pa6rh/93pS0Yu9Z8PTC/y3Hz/HbK7IqvZG7tu5lli4VjHWdY2IbWJdOmLrkkCQLVZ49OWTvDo6ja4Jbl7Xw641XQSBJFuoMJct4vkBsbDN6dEZetvq8P3atIdIyGQhVyZkGaQTEWYWCkgpiUZsiqUqlmlQKFeJLFWSvCAgFQvXRiBoGs31769hqG8macaZqMzgS5+oHsIQFk3hHhy/jCEs4mYGQ7z51IRABqyM9dEfqyUWRkpjHMudJO8VCagNL50oT4GAzekNxIwovgw4uHiUnlgXq+Mr8GXAocWj2JpFd7SLBTdLd7STsfIEcSNGb6yLvJenI9LK+VJt5FLciHG+PE7eK5B18yTNBOuTq4noEQ4tHmOmOocrPdYmVpF1c4yWag2I6eoMs9V5xssTJMwYtmaRNBM4gUtz6OKNJ2OlMLVaBdCTHiOlMW5rvInuSAfnikMMl0aJ6mE2JNfQFe1YSiwVaUw3smlbN57rE0+ECUcsPM8nFg+TTIaxbIO2jgyaVhtVGU/UGsamrmEbBg2xKHWRi6Mni16F0dIsAIbQ8GUtzk3N5BmbyVKp97BNg/lcEds0KDseuiZwvYCwbWKZtVGXgZRYpkHINphbLNKQjtHWeOUExFR5ke8MP8tUJQtAwoiwNtlOSH97U1XKfpWpyiJe4BNcoWHfHqk1AE1hEqAhjD5kMMs7GTE4Xl7gu0PPsegWEUCdFWdHdCPFqkfUspYbhVHTZLJQQAC5apWJfJ6GWAxT06h4Hl2pi9ckHA0RvmTaWCx1SYNOSuIRG20pbjWkYwRSMrVQIJ0Ic+vGXs7PZImGLHav72HfyRE6G1OYhs5CvsyqjgZa65M0pGK0NiRxnNrIpK7mDD3NGZ45fIaK49HRmGKxUGvU3ryx5x1Pgbne5bMl+te0svvOtejGlcvYXZfmlv5uRuYXuKGrnZBtcHBigtZ4nEw4gqXx0IUAAAAgAElEQVS/cSeDU3X50TefZ/j0FA0tKcaHZ1m7tZsPffIGnnv0KPXNKe77wm50Q2P/M6fY/8xJ/qev/gaH9g7yyPf20diWplSo8OSDB/ntf3Uv87N5/vLf/YT+dW3EEiHGhmZp667nU79/B7nFImPDsxTyZeZmcvi+T0NzinLJ4UffeIHbP7KJXXfVRt4++O29bLihh3vat/PS0yc5d2qCSNTm7MkJNu/q56Of24X5FqdAXmpxrsB3v/Ykf/DHH6Wls46F2QLf+vPH+ejndtHWXY8XePxk7GEmK1O0hJqZdeYo+bVpyhJwApcTuZNk3Ry/1/fbhJeu7+nCGX428Y+0h9vwpIdRMLi/7SPMVOf41tB36Y31ENJDjJbO0xfr4d7meyj5JR4c/xlu4BHRwzxdeZZbGm5ibWINj08+SdEr0RhqwJMeT04/zSc6PkZnpJ2npp7hdOEMzaFmin6RrkgntzTcRNbJ8tDEo5T9MlEjykT5OXbX72BH5gZmnTkeGPkBfbFehKjVO7SKTqwuhm7oJCIh1rY2kElHiYdtoqs60TSBH0gitsnETJZMMkJlaSRgxDCwhUYsFkIXgvZknHgijOsH2KZBPGwRC1sYukapWhthAXCh6uK5PpWyg+f6CGDlujbSdTGOHhjCdT3SdTHau+vRdY1yybksceR5PovzRbbu6ieRipBpiHH05XMgBHWNCUbOTpNIR7jpzrWcHZjk5RcH2XX7ag6+dIbNO/o4dWwM1/EJRSxWrW9nfGSOswOThMIWsXiYTEN8+Xgf3L2GnRu6GRydYe+RYb7x033EIjYb+lvYuqaD9qYUIevKI0d+FWi6RvvadjRdq82qMHXCmTimoeM4LqZl0LyimXDYQkJtFoap47o+lmWgGRpCghS10WYj+SyPnT3NV7bvJBUK8e+fexpfSsbzOb534iifXb+R/nQd//3APh4cOMln1m3km0cO0h5P8FubtiAlhE2D+XKZbx4+yG1d3exoa+eHJ0/wwPGjfGnTVl6dneHDK1bymXUbeWTwNA8cP8Kf3Hw7H+5fxcDcHB2JJL+xei2mpqELQUsszpc2beGrzz6J49VmNNi6QUs8zgujw/SlM5yen2OhUqY1luDB06dYqJT5yvYdDOey/O3hg/Sn6+hIJn/h+hmaxur6Bh4+fYrbu3rIOVWOTE1yZ3fvFe/oju/yyMQ+qoHLb/V8EE0ITGFQ9Cr8/egzbEmvYGu6n4MLg/zD6LP84Yr7ual+HRkrzo/OP88Xu+8mYyWwNIMb61ZTH0pS9Mt8vvsD1NtJbM3kRxP7ybtlPt99F2PlWX58/kW6Ik28snCakdI0n+26kzknx3eHn6IlXMdMJctNDeuYd/JoaKxPdnN08Sz9sVZ21a/lTt1mprLI3w0/wa66tcvfhcH8OHvmTnBz/Xr6Ym2MlqZ5fOoAX+i6m6QV5W/OPsr++VPc3riJifIcgZR8svM2QrpN+C3Wq/ygwkj+79GEyYr0HwKgCxsnWGAo+02aoneRtrcwU36Wody3WZH+Q/LOIEl7LZ2JT7NQOcB44WFS9iaaIndSdIcAjZ7kFxDCQMPC0tN0xD+OqSWYLj/HRPEREvYaFioHmS29SG/qy4T0ZiQ+uhYhE96OL8towmB15l8AGrqozfpoj/8mFX8KNygsl8H1s+SdAdZk/ghDi3Bq4b+RdwYI6Y3MlJ5jdeZf4gY5Bhe/TsbeiiZqdfCkEePnUy/QFKrH1Ex2ZjYTBCPMlvcTMhpI2xve4rf8rbnuEkcAW29eweZd/WhabSj9hS9VteLiPH6c1Zs7aWpLY5g6Hf2N5LJlTh49j27odK1qpqU9Q3axRLnksH5HH5FEiOEz07T3NTI2PAeawIraxOIhdF2jVJ6jrbeBjTt6aw10ozZ9TdM0dty5Bl3XuP0jmwmCAGNphIXjeOx97hR3fGgDN+zuR9e1y0aLnDgyiuv63H7Peuoa4nT1NnBg7xkO7D3Dth217GksEeaOD26gq7eB7t4Gfvy9l2q97UvbELUsfyoTpak1ddkNKRyx2HxDD57rEwQBGzZ38bMfH6BcrC43tuKJMMlU5Mrr2QhYubaVHTevRNMEu29fzUvPD1CpuEAtMZVMR4nFQ1e8ERqmzrad/Wza1g3AtuE+nvhZLdsqA8nk+AI337mGto5MLeERs/G94E3X1pnJFnnopRNs6Wtja38brh9Qt7TOheP5/N0TrzA+n+PW9b3M5Uv8zWP7kBJ2r+0iHQ+zc00X+wdGWdFaz11bVmAZOg1LDRo/kAyOz/HC8SG2r+zg5nU9S/Pva++p6/kUKg6rOxqJhiyeODTI//fYfv7N5+4mFrbJlcr85UN78P2A2zb2IhC4fq3ypGsaXY1pblzVyaEz49y1uZ/mdJxULLyc6CmWa8NNd67pwvF8/uH5o8RCFvfvXg9AxfF4eeA89+1aR3dTmof3ncTUNT5+y0b0N6mMBFLy/WcPc3J0mjs29VGoOPzdk6/g+QE3ruxkZHKBybkcKzoacFyP1voEjutRKFUJpKS7pR3X9Sk7LkMT80zO5VjT3cS5sTmCQNLRlEJKycRcjmi41vMasgzGZ3Ks7Gx4w3N7PxoujtWGwZenCek2GSvCWGmAuJkmqqeIGBcTZZ7vMzqbJWyaNKVjv/B9sS9Z+0JHoy3cwr3NdxEzo2gI5pxFAhngBR6BDJBSYgkLx3cICHACB13oaEIgBEujnWqNlwtTmS6stSSBpJng1oZdtIabQYInfQzNQBcXw/2FUQi+9HEDF4lkrDzBQP4sH2m9B3vBXl7RQBNa7byQIH9xJE5tiK9B1a8SEOAGHoYw0YSOqZnLvfQSiWUZWJaxHCvt5Wm/gnDYWm5MvHakn+P7RCyTpngMy3jz21YqvjQM2/XI5svYlsHkXJ6q42EuJYvi0RCru5soV10W82XypSoLuRLFsoOu66+bOPKkT9W/OE3WCTz8Nxgp+Hp21q0iqocoeBWKfpWCW2b//CDDxZnLnhczo/iBC7IMssI7WWrCkz5OUIvtEvAJ2N7Whq2Zy6M1Lkw7XN/YuNyqu7C20AXaJf++MJzdlf7y8PMLLKM2Ik/TausbDU3MU644rOpoZGaxyEsnRsgkIoRtE00TNKXjHB4cxzA0Nva2MDq9iKYJShWXofE5yo5HZ1Oa6YUCruezsa+F8dkcmXiY9oYk47PZX9mkEUBrZx0vPXuKk0dHaeuqW57urRsa4Uht5G3JcTg6PknCtklHQsyXypwv5Tk2PcWa+kY2NDUReoPvzsiZaQ6/dJYv/Yt7aGhJM3j8PN/5yyfZccdqbrhtNQ9/Zy+3f3gT4ajF8ZeHWL+9B98LeOInB1m7tYvbPrwJp+rxF//2xxx9+RytXXWA5PYPb2TNli4GT4zzN//HI+QXS6xY14ZtmwwcHeXu+7fR0lmH0ASFXPkNr8NtH97ETXevQ0rJvmdO8fIzp7jrvq0kM2+vKiuEoLO/kUx9nEN7ztDUnmHw+BhCE3SvagZgojLJ2eI5Pt/5aVrDLRzPvcp3hr8PgCkM1iXX4EmP52ZeuGzfWSeHlJJddTeSttKYmoGxFHuFENyY2U5/rJczxbN8b+SH7MjcwOnCGRbdHB9vv5+IHmHf/Mu8MLuHVfGVSCSNoXo+3v4bgOT/PfsNhorDdEbamanOkbJS3NpwE3EzhiEMNAQncieZqkzzqc6PEdWjHFo8wguze9ia2gxANaiyMt7P6ugqzp0YZ+TUFNbKMZyqS31LipH9w8Rv6GF+ocz8dI62nkZmRmaJJyNMDU5RyURp7W5g9NQkuqExO75Ie18j9S0p5gdnaN7ew+jZWYJEmExPA/WJKFJKkpHQxdFhmqSpLcXjPzlIpexQ35zE8wMMQ0NoAntpvbUzpyZ45IcvE16qs7/8wmnGh+fY/9wAO29bzaYbe5k8P8/ifIF0XRQrZDI+Ml9LakVtSoUqZ05NUCk7SEltGrSEseFZNF3DMHXsUC0OabqGadamoNU1JXjxyVcp5ius29KFJgTpRIStazpY2dXIscEJHnj0Fb79yAH+cc9JNq9s496b19LXXr+8ZMOvmvMzOVzPJ1+oYJkG8XiIhKlTFYKKF1BxfWSoNqvBNHWmJxcxDZ1I2MJxPapVj0Q8RCIdZbJQIG7ZrKqrx9J1VtXVowvBdLHISDbLvvExjk5PMVWsNawXKmUm8nm+sGEzzbGLsxbOLswzuDBHXSTCWD7H0OIiBaeKHwSEDIONjc00RWNsam5m/8QYAGHTxNJ1bEMnZl2emDA1Hf0103p2tXXw9YMvs1Aus+f8KDe0tpEKhTg2PUXRdXnw9CmKjsN0schitUwHv5g4Aljf0MTPBgc4NTfLWD5HYzRGe+LK66eWfYdzhUk+2raLptDFqbzTlUUWnDxrEh3UWQnWJbt5dvoIea9EUyhNSDfRhUbokiletm4S1i10oRPSreVOroH8eSqBy5NThyj5VeaqOXJeidP5MWarOZ6ePkwlcJh3cmTdAqZm0BauZ8SepiWcwdQMsm4RT/rMV/MMlwbJu2UKbplKUBsNlXWLfH/0adYmutic7kMTgvHyHDOVLPvmTyIQLDh5JivzSCS60OmLNdEWrn9bSVgvKFB0h+lNfpmw0bz8eNEdxg1yJK31hIxG0vZWpkvP4gV5NGGSCW0npDcRMbvxS08DAboWXUrKaBharf0opSSQDnn3DFV/lpI7jBvkkdIj75wibq0kYa1BE5feiwx0EULDWN7PBQIDwWun4AmiZhcJazUBLpaWxg+KCMOojcIP5vGCAsby+dV0R9uJGpFaHVszsTQTTe8g7i8QyAqasN7VhPZ1lzgSQmCaBr9wPYFQpJbICUUsTLPW+EikossjhEIRi8CvVaoDP6j1eqYixOJhXMfH82qLXdc3JaiUHIQmsGyT+qbanGnLvnjQC3VQTatV0GqLN17ssSsVq1SrHg1NieV1gS59Y+Zn89i2sZx4EUJQ3xhn8OQk1WqtAp9MRYhEa19gTdcwDB3fvzgy6PVIKSkWqjz92DHOnZ7C932mp3KUS7Ub41u6zgjqm+LLN7jaGiPwRlOlLmVZOnUN8eUyW5ZBsHTuQhM0tiQ5NzjN5NgC5wanQUqSlwyxfyOlqsuv3biG1e2XJyTOzyxy6Ow4X/noTWzua8X1fE6MTPHssbPsXN1JfSJKfSJKOhamoyHF9hXtv/BlCWStYvLJWzcRfk0vZTxs87GbN9SGmQIhy+RbTxwgW6wQDVkMnJ9laGqeP/vcPfS31hblltT6z4QQ9DRncH2fkG2wrquZnubLF9brbkrT2ZjG0DV8P2BiLsfp8VlcrzYtT9ME21a0c9+udSBria7HXxngozvXEbHfLOFWYN+pEb58zw3sXNOF7wcMTS3w9JEz3LahlzU9TazsaiBsmyy1/4FaskzXNMIhk86WNL4v8YOA1d2NRGyLlqWRRLZp4C29v5om8P2AQELV8cgk39r7+n6StpKM52bIeUW2pdehC6M2pNWv4muXr63leAGD43OETJ2mdOxN99tg1/HK4hFSZoK0laI72kFED/Pc7F7qrDTtkVb64j08M/0Cz8++xFhpnNWJlZwtDL3ufsXy/wVrE6s4tHiUueo8utBZEe/9hecnzBgxI8oLs/so+2UCGWBqJp70GMifYbg4Sm+sC6iNLjq8eIx98wdZk1gBwED+DHPOPK/mBlgR72VVvJ/Di8eZqExT8sv0RDs5kTv1+uf7Jjex126PWbW1DPLVKi3EX+dVFyXjYWIRG2/pPmDqGql4mPHZHPWpKI3pGLquYVsGsai9nGjKl6qUyg6tjVeu+NWuR4wtmV72zJwkYUbYXtdH+C3Owb9UX7yZvnhzrUKCxA18/q+TP2G0OHvZCCQdDV3oID0uvQe9HXV2nE3pHl6ZP0vKirIt00vEsCjnKhQWi2SaU+j22yuDJ31+MLqHPbOn+Gjbdj7QuIlcoYJp6KxsrU31kYHE8wN2rurEtgzqk1G29LaiLa1htam3hWy+TEMyytrO2tTbqGXRlkkQsSyKxQprO5soVxzqUlH6luJuOh6m0FnF9XzikRAf2H7tFh99L1TKDsOD0/z1f33ssrUEV6xt5Yt/eBcAI/NZ8pUqVdcjV67y7NkhPrxuFQ3RKLPlIk7gYkkN7XXWOpgeW2RyZI4f/NWzaJqG63iEIxZO1aN7ZTNCF5w9OUF9c4LpiUXu++JNlEsO58/NMDeV5eShUSSSUqGK7/nIQBJPRmjvbcS0jFpnmJT4fq0DSdNq9SJNF687iqqm9l3wXJ9XXjjNiQNDBH7A9MQiruO/pTrTlVi2ydabV7L3iRPsuHMNB/cMsmJdG6m6WgzPujk0NJpCjRiaQb1dt/xDABfWibpSHFudWMlYZZwfjT1Ixkqzs+5GeqPdAIT1MA12PYZmkLEyaEJQ9EtMlCcYL0/ww/M/QQhBxa8Q0kN4snav6Yp2YS0leSNGmOrSmie3NOzmmZnn+f7oD+mNdbM9vZUGu56JyhSTlSl+dP6ntcXu/SqmZuHKWt0zYkRoj7SRnylTyTkYusHxl8+SSEWXOyOtkMnhF08jA8ncZJZkXYzMigRDAxMk0lGciktuocjcVJbm9gwzE4uEIrUGyvjwLMMDk4SjNvUtKSKxy9cDkgTMOBOsuiuGWUyQCCeJRG18PyAUrq3Vec99Wxk8OUHfqha2715BPBkmHLXp7m/iC//8TgxTJ5oIseuO1SzM5PH8gLqGBA3NKRZm85iWgWnpROM2W3b04fsByXSUaMzm1z+zA6ficeOtq0imovz6p3cQjtp09TXS0p7GtHR23rqK+dk8kaUps0Egmc8VOTY4wfMHzzKXLdLXUc/nP7ydeDTEz18a4FsP7ed//uxt1Keu7qLD75VS2SEatUnEw0QjFsl4mGjUZmIqi2FomL6G5/lUXQ8/CIiEa8+prV8oEQiaG5KE7FrixguCpVHCOiXXJWnbmLpOwrbpSaWIWzYrMnU0x+KYmo6haeSd6mXnpGu1tYs6E0maYzFWZOpIh8JoQuAHAVW/Vr+ueB6mpl2sKwneUntJCEFXMkXMtNgzNsq5xQU+smIVuqYRMy2ipkV/pg4BbGttoyPx+nWHpG2zs7WdJ4fOUvZcbmrvet2RoJoQ2JpJwbs8ma4LDY3a+owA1aV1Es138EMtYd0maoToitZ+eGNDsofmUIawYZO2YsuPr4530hFuXGrraMvxD2rRef/8KZ6fOcZdTVtpCWU4uHhxbSdD6NxUv46j2XOczI2yIdWDpRnEjDAdkUYModMdbaY9Ur/UCSowtbc/ck8IHU2YeEH+8sepLXcSULtOtfWPBJowEaKWvBHiSgtPXOievWgk9z08WaI+vBsNnapfG+2uizBuMIWUHojXplXEW+/qEwJN1GaYXHiRBGy9nri5gqHs3xExO+iIfwxNXBztHdZDZN08ea9AT7QDQ9PxAgdPloib3ejau9tGu+4SR17gM1MpECwFGU0IQrpB1a/9WkNgS/JukQShWoOgxaKES3xVHF9KGu0oSStM94rar160ddYqmo0tF7/Mr12/5tLH3irTrPX6V5YWf37t6y8sUOy6PqFw7VjlkoNlG8uJpgsVpzdypY+zlJKnHzvGM48f51O/dRPtXXUcfnmIxx86/NYLIGrJqndMCHT9yucuxP9P3nsGW5Ke932/zunkdM/NcXKe2ZxmsbvYBRZBAAGQYABNWpQsWhb9QWWrymaVP9pylcqlkmSzTBm0SIsSBZAmIZAASGB3ASw2p9nJ4d47N+eTQ+f2hz5zZ+7OnQCIrgKgp+rWnOnTp/vtt99++3mf5//8/wIf/9Rxfv+f/zVf/VffQzdUvvCrjzIwfH8KBQP5FKW0dVvfLG7WWa+1+ONX3ufPXz8PwNxalYlyDtcPMO7jekRBZKKcx9xlkeT6Ae9eXeTC/BrNjsNKpUGr6xCEEWEUsbRVJ5sw6c/dDJjd76iJoohGx+GNS3NML29huz4XF9cZLma2JxVVlhgqpLcz54P5NG3bpW07u7b3VlutNFmrNfnTV8/ynXevxH2zXiGftPCCkISpUHUXqQYdvLCDIWUICVBlg27YJfKzuFGXSAxx6SBJKp1Iw5ddZEFlK6htI1tkNARZxAscCgOgKKX77IWfHcuoSZ4uPRhnKAUBWdTYk3wQP/Lwo52ExpIokDI1DE0hiDoEYQeikJCQw6khVFHGCVZ6pHwRpwuHuN6exwubpOQ8uqjzifKzLHSXcEOPhJwgq6R5pvQk850V9iYOYwhpypqEHCU4mDxCFOrsNY+SVk0UEvSrU0hRgj5NRRVMDidVnLBFRlYwhS7PFo5giW3EqMJzhSlKSsjzxYPMdxZIKpMouROUtAJhJ2R9Y5MHohMYkc5yaw3LTHCgs49ms8VGtUIkhlRqdY5rR2itt1lcW+X4/kOkZJG6V+eZ0kkSksejuQkM0aHtzfN4/iCmaFO1z2LIZQRBQhGTO7ImdzM3CPCC8L5JaGVJRJZEbmVBG+rLUMolUZWdixhJZBsVaBrqDvTNDWt5c4SRRxA6CMBvjk/xTCmNJansTeVouJcIIhcRGUk08II6QWT3Ml8CsmjS8ZawlBF0eaeKiyAISMQlXfJHnL+236ETdLFkkShcgijkJ0EcFdQk//2BzzHf2SQlG0wky4iCyMLlZSAiXUih/JjcyB3f4eW1c1xtLvNwfg+O63Pxykos8ACsbTTIZS22qm0Gyxk824cgYnOrSbGQZHm1zp6JEleurWEaKoah0Kp3uX59E8uM+dOWVmooikTC0lAEkf17+rfPn03edIaMHzPo9bNmgyN5/tHvfua27cYtSqeqHKMSq50uF9c2kCQxRn4JAqYOH9SucSg9Sk5N7up3mIl4kf+lv/80yV4yQBQFMvkEsiJy8MQo7/3oCoW+NON7y/QNZmi3bFIZk0eeOcjJJ/Zww9lOpk2W57a2ER1w+7vyBvLj1sWbIICsiLi95JrvBTSqcXnYwvQ63/jDH/Hl336G8X1l3v3hFV7963M/eacCh06N8eq3z/L6986zPLfFM589se2faaJKRIQd2GiShhu626i9O1kURSTlBJ8d+BSbzibn6xf4f5e+wW+OfSW+ntCnG9hERLiBSxAFqIKCKVsMm0N8bvDT2+pskiBtf1bF28d3FEWMmMP88siXWLHX+P76D/nmyrf55ZEvYkkm/UaZzw1+Bk3Sto9nSHHgS0RAFiRkS6XTdgjDgGJ/Bt8P6RvO47o+1Y0myYyJ0/Eoj+ZZmdukst6gUE5TWW8Q+CHNWhtNV+i2HWRVptNy2FiuksxayIpEYSBzxzJCAQHNkjBSMpZsYMo7s+LpnIVhaZT60wyM3FTPLfTdXhZfHtrpWxojO9V2+z/ie97g+7xhOS1ORkiSuM3BY1gag7c8X9/60QW+99YVFFnisWPjHNs7yHBfZnu8lLIJ/tkfvUyt0fm5DRwd2NtP/pZSwRv3K3tL8nC39dWtduP7yWwOSRD5ww8/oGhaXN7apJxIMJbJcKTUx1KjwVQuT8t16bMS5E2TBweH+Pfnz7LUbCIKMJhMc7BQ5InhUZabDQqmScfzSKpxmbQfhfzZpfPM12u8ujDHI4PD2+0YTmV4Z3mJlKYxkc0xlc1RsbucX19nq9vl/OYGmiwzlsliqSoPDAzy789/yPG+fkbTMRr5+ckp/vzSRRqOjSbF41wSRKp2l+u1GqutFteqFc5vrDGZzaPLMo8Nj/Cnly5QNC2Olcu3d1DPTFnnkcIBXlp7n47voIgSaSXB4fQYp3J7+KvlNzmQGuViY55D6bGfSCHtyeIRvrv2Hi2/u40YlgSRx/IH+auVN2l4HQxJxQv921BYt5oduMSY9JDr7c0dk7ol6zxaOMSoVeaby2+QUkymEoMMmQXW7Sp9epa2byNQ/E9CxchiioLxGEutb+CEVUQkNKlIWjtMTj/JQuPrpLSDVO33yOsPIQl3D6YY8iDrnVdYaX8HUx4hqU7hRS1EVILIpuXNIPZ4dvPGw1yt/u9cb/wxZk8hvGA+hiIm0eQiXthgqfWX6FKRjHaUEJ+Ot0DXX8YL69Sc8ySU8Tu2RUDACTYomadJqftjwm/8bcTSB7ULAIyaQ5yrXyGrpskoCmHYpeFeQRYtFPHuSe0fx37qAkc1t8vLq1dQRIkgCsmqJgcz/VysrWDJGht2Ez8KOZYbwgl8rjbWccM4ohz2tqdVAy8Itnl5BAEUKY463jou7zVIwyjOlsYM5mKvVCT+jWlqDI8V+ODtWQ4eHSaR0HsEwhG6rjC5r5+Xv3OO69fW2X9kkFbDZvbaGoePj6Ib9+fkCkIMta1V2ni9evK4JA7WV2oMjeTYd3AQRZVYXqxuO1vQg9WF8d/2555a0f3Yjd/d+AvCkDAM7/vB3lirUyyn+fJvPEE6a2GaKtJ9qt0o0u5qFYIoxKTXe4bI9RYNjx8ao5i27ltJRxBAuUN28+3LC/zr77zFM8emePRgP9dXt9iot+PfETvR94vI+qg5ns+f/OAM566v8qmHDpBLGvhhiO3dRK9EsD1mY1jk/RNzi4KAriqcmBqklIkniMcPjZHrlcpFUUjHr9INagD4oYMgiHR6wSDfs3HCNkHoIggiiqjTieJAmyIa1L1lRGQy6iCbziw5bZS6v4wmWkRR+HMl1Fb3mry++QGDRh9+5KNJKgItml4FO2jTCRrk1cHteyUKAhlLp9626XizuMEGXlhFFHR0wcD1K9QCCUvZhxtsIAoqZWUDBBFLagMhSSXBQWXfjnb0G31oQpKrjU1qvoOIxfnKFglFQxK6BJFOJBt8uLVBWtW53K7hhAGW7PJsn4xGlyiYRowURnSV0HsHAYERfYAouEwyrHLIkBCVKQSpH0EQSVQSrJ7ZYqW1QTqfxCsE6KZG56qD2w64FE2TyFj09/XRmG/Fz3Qmzufk5C4pycUSPSr2RXTa2F6dUMpS1D7g198AACAASURBVAz8sEIkKNTdCwhI5PST9x04arsuDdsmqam7BurvxwRBuKccvCgIOEHAYr1BStcomCaCIOAFDXS5RNOdQUDEEgUOJGMYsh+uEiDjBFtYyjAtdwYvbCIK8cIzIkQSdLr+CpqUAwp3bcOtZkg6m+46AilAA+5eynO3ay8bWcrGTvWqMAxZvLzMwGQZ3frxIkfznU02ejxPALIs0t+X3uZNa7S6WKZKPpsgYcVcabIkUi6lUVWZwf4MsiRimSqWFavkaZrCQF+ahKURhhGaJqOpMq7rk/05XYzdj1lJnQElz8zlVTbX6pgJnYl9ZbL5m87gaC7DeD7L2eU1VEnikbFhLmxsUDBNJgspml7nrucY31em0JfinR9c5tCpMTzXJwoj0lkTUZQ59vAE//Zffo/56XU+9+uPo+oKoiRy8vE9fPjWDKXBLJqu0Ki22Xds+J7vBKuHRDjz5jSNWptCOUMqYzIwkuedH1wmW0iyulhhbbEK3AyXuo7H4uwmZ9+Z3UY5u45HrdJma61Bt22zulghDCNSWZNu26G62aJZ74IgsLZYIZWzMEyNdM7i8IPjvPQX79M/nN8mxQYo62U0UeP7G6+yJznFmdpZgl6W3ws96l6DqlujG9psOBtklQyWbHG9PUfFrZJSUmiSThCFhD3utabf4q3K2+z39nGufoGcmiOtpjmU2s+V5lUuNa4wZA7S8TsxKsgYvGP/hYScqZ1FEmQMSceQdDpuhTCK2J/ax/nGRS42LjNiDdMNumiiRlbZWX6bSJs8cPoAEMVqw0HMR1PoJVrH9vUT+AGyKjM8WUKSJQShgO/H/uhBf/ymX9TjMxue6kNRZcb29/dUGG8oZsb+qYBMw6vhhF3CKECILD7YXCGtGuzLFlDEm77cvkODO3xYURS2/701QPHRbXezGz6tINzf/jcsZel89vQRju0dIGnpSB85n2moPP3gHjKpnz8E9g27ETS6rVx9N3/9Hn2b0XX+3olT/GhxHicI+I1jJ8joOglF5b88fpLXFhe4Xq+RN0yyRkzG/ksHD/PG4gLTtSqGrLA3V0CXZX758BFeW4j3TygqeTO+BwlV40ChxGq7xcODQ3x8fGr7/M+NTxBGEdfrNUqmQhDKzNU2Obs+z4m+FCvNNd4NO/RbE4iywCMDWZYaJR4fGkQUfKJI4VT/ILIo8eH6KmFos68Ql90t1uq8vbzIWCZLEIa8vrhAn5VAl2UKpsVAMsneXGFbuS2KIvwopioICXECG0MyeCC3hzDyuNZaRhYkDqVHCfH5WOkob25dYqW7yYHUMA/lD2wHdnJqikcLB9E/EmxOKRaPFw5hSDff80cy48iixOXGAiERk4l+FFFif2oEWZQ5V5+l6bcZNkuYssYD+X1kFIuDqVEyaqKHilKZTMQiDPOdDUbNEmUjR0qxUASZRwsHsWSdI5lxOoFNzW0xoOf54tBp3q1eYbGzQU5NYUo6QRByLDNJVk7SbNu0uzHSWOqp2d1AKiPE6DdRFDB0NX7+BZE+43maTY3VyjT5dBpLGUUSdIaTX2Kj+yq2v0pOf4Ci+QQiMiXjqe1EnirlKZmnt33SovEYUS/Ao4px4Hkk+SXWOz+g4y1QMj9GGNlIgkZCGWcy81tUnffp+ItYyhhiL7ySUMYZSHyGjje7jdFygi02um+gSnlEQWKr+waalIuTuEL8ThcQKRiPYsqDdPxlIkK27LepOmfwgiZDyc+R1x9EEES6gc2YNcSw2c+11nX80IdIIYwCFEEnjPyf2G/ezX7qAkdJRef5gQPb5UJxraaCJauIgogXBiiiiCHFTvmgmcGPQoTevqYcZ2h+/+W3eOXCDGEEo4UM/8NnP4Yuy6iKRNfxSPbUX+5kURTxxrV5/uT1D2k5Di8e38+nj+9H6/HhiJLAZ7/0EP/m917iX/wvf0kun6DbdXng0Smefv4w41MlTn/8EH/yh69SKqdp1LpkcwlOf/zQPXl+bpimKTz85B6++5dntqVDP/n5k6QzJkdOjvJv//UP+L9/7yUEBDptB0W7eTtXlqq88YPLTF9ZY2W5yl/+6Tv09Wd46rmD2xnAu9mFDxe4dG6RD96ZZXOtwTf+5C0GR/M8+czuUvG3WhhGeG7A/MwGX/2X30OSRSxL4+kXDvPAI1P3Hbz6qI0UM1i6ykgxwyP7R3uEjSFBGCHfgjbSFBnb83vbdwcg7mYfXl9hqJDmc48ewjJUljZr2+VZgiAwmE9TbXWY36hxYDgmCg56wbQbCAVJFJFFEdvdmZls2S7n51Z5eP8IL5zai+35fPudnaU8ruczu1bBD2K55dm1CmnLIKHfmxyuP58iYxn0Z5M8fWwSSRR7fROiyBJRJFLSpwijgIjwljrcWG9HRCKIPMRttTCBuIAm7AWMhhAREQWZtDqAgEBK6UMUJKT7XPz/rJgqKmTUFIokk5VSlI0CUdRFETUsOYvsrdNuO5w9u4ihKxw6MoSmyKRMHUspYMhDsSRnjzw76pUYSaKBHvYDApa6h5goT+duClkJRWN/uri9aPLTIXIPKhxFEZokM2il0UQZPwoQEAiJ0BURgX6IjoKYgihAEosgqICEgA9REMNqBYMbT0X/RIl0MYUkS9ucD2EQUh4rIghx8FYQRSRJxO44SJKIKIlIgkpGP0QU+QiCgiYXEZBiWDMiUU8WVEAijBxiqPD9ByosVcUNgv9fiUd9L0AQBardLjOVCiOZDIWeA5pUpxAFhaLxSK+P/d5zIhIvaeOiVVFQsZQ4qxkjl+TtZyit7rmtzv1e5oYeBS1HWklBZMJH0G524OGGPkl5dy66e5miyuQHbmbjoyhis9EmDCPyKWvHvHqrRVHElcYyNa+9vU1VZMaGb2b6+4opTF3Z5uO59be3Wj6X+IkD8v+5mGN7fO0Pfsi1SytomoLvx+pEX/rNJ5nYG2etXT/godEhntk3iev7nF1bw/UDqt0ukEISxLti1VJZi1/5h8/y8n/8gO/9+XuomsKxhye2+a4GxgrsOTzI1nqT/cdHYloBVea5z5/CShq89tfniKKI/uEcB0+OYloa4/vK2/dfMxQmDw6g6Qq1zSbJtMGnf+UR3nzpIjMXlzn9qeNYWYNnPneSb//JW7z0jfcZGMnzsc8cJ1dKUhrJ8dRnjvPGSxdRTZVjj06yuVxHUSQW5zb5D3/0KkHLxYlC/t0ffJ/jJ8Z58csPc/69OV7/7nm6HQfH9vj6//UDnvzEEY4+PIkkiRx9aIJv/vHrnHxiD+YtwdOEYvHZwU/xw43X2Nx6i3FzhJO5E6iiyrqzwUtr36fpt5AFmb9ZfYkRc5jn+j6GF3qcq1/ADT0MSeMT5efIaVk6nS5JJYEpmbxReQtVVPnswIsk5bhs+LMDL/JO9X1m2tcxJJ1T2RNIgsiA0U9Svlme26+XyapxALjuNZhuzRIRkVUynC48jSJoDJkDfH7wM7xZeYfrnTk8X+Ch/EkAdFFj2BxGFuJyEN286V8ovSXBrUm+G4ihW5/jbR9ylyl8e/9b/Myme5Xp6r8iiGwmMn8fRZpAE/XYp0AiqejokowbBNuBI0EQto/huj6vvXGNRx6eZHGxyuhofjtZ6Dg+Z84usH9fP+mUwb3MD0LOnltkeDBHsXjvsucb9sSJie123bAoimh3XXRVxtAUnn9k351+/nNhf5vvX0EQOFAscaB4O2K9nEjyC/tvX2ukNJ3nJ/fctj2jG7y4Z2e58lqrBVHEqf4B9uVvT9bkDJMvH4pJg4NgBd/9PkezJkezIWHUhChAENuIgkQQRORVl9861ADOEgQRkjSBKkk8ODDIgwM7A7wHCiUOFHZeVxRFtFyX1VYT2/d5cmS0B2iAIAqY61zHCz3CKMCLPHQxLlUds7JkVAEv8sirGgudOepejbwWcTx7iD69f0f5cUnP8PHyqduvV03yQv+DO7YposzRzARHM7fTGexPDbM/Nbxj29OlYwDktZuIvTErfv883//AbccAeL58c/tjhUP4fsCVuQ0q9TaD2hADxTRrW02ubm7RbC8zNlBibrVK01xlab1OLmWSTZksb9SYGCpQbXSIovh95/sBk0MFrsxvkE7oiKLA8voQB8ZPMZ7u2z6vKmUYTHz6trYNJF7c/mzI5R37KFKawcROlK+ljDKe/squ15nWDpLWbh+zXhhwtdPiqeJvbW+zPZVpW2PCepqpxNHt58qQ+0lrhwAQBZmy9RxRFDLf/BqqmGEk9YtEkc9882s03Svk9FMIiOxPTvLa1nu8WTnDgF4iq6aJcIiVN2uE0d2Rsj+u/dQEjuy2E0uiej5JSUVVdkpc5rTdHW79DtwSy9UmF5bXewPM59z0Cp22G6tBOB5Pnby7vF/X8/nq99/h9avzveM1ODzUx0Q+SxRFyIrE2FSJf/RPPsX1mXWajS6JpMHk3jK+FyAKAp/8OyeZ2tfP6nKVdMZkcm+sOBAEIQODGV749HGshE67abO5WudLX3mMfDFJ1EMuCaLA6ecOMTJWpFZtk8qYPQJZgRMPTZDOWKyv1UkkdUYnYjlrIxFHXyVJJJE2OP7QOKcemcTzA1RF4tr8JkMDWZ7/zHEiAV564woPHx2lbyDDb/zXz5BKG/h+gCTH5NjPfOIorucjSxK6qbJRaWGZKr/4lcfwJYFGyyZpaew9OICZeByA+dkNXn35Ir/2905T6s8QhRHvvzXDf/z6O+w7NEQqfe+X+242VMjwwql9/PHL73NmZgVDU6i1ujy8f4RHD4xu73dqzxDffPMiURSRTZg8cWiMvuy9HYT9Q0XeuDjHf/jhGURB4OLC+vZ3giBwYLjEiclB/o9vvs6xiTib5vkhX3ziCPlUPD5zSZPRUpY//N67HB4tM1TM8PC+ESxNZbI/z5uX5pElkY1ai6WtBoP5mxOwKIhML2/x+99+izAMeefqIr/6sROoskwYRZy7vsrSZp3raxXMekzePVzMcHisTCFl8emHD/D1Vz/k4sI6SUOj2upyfHKAp49OxmgL6W8Hqqig33unn2EzJJ19yXGWumts+BUs2WDQKCEKEk7QIa8OIoUiihK/rG8gE70gVlKQuHPGURDTeKFPJ3CxA69Xb91BFOIyJX2bxDAODqmShCrd+XmJohgN2Q0cvNDDj4KYFDoUkQUZQzYxUXvB6nsHLVRdRf1IoDKMIrqBQ9t38MJY+UYRZRJ5A128OU8rwv054VGk40cBLd/BDm62OUa3SRiSiiGpO5yhIIxRLOFdAgy3ZpDDKKLt23QDN26zELc5KevbhNC32sL0OkuzG+w7PoKnhGQMHVO5+W6RxfgeqNK91QNlfrL5bddjiRLzrVUMERJyFz4SbLMDj3O1JTRR5kh26I7vwztZti+D3XZ2lC1fWdwgCCNOTqkkjN2De+3A4UJjATf0d5Bi39qvqcTu88RPkp3+z92unFtia73J7/zuZ8kVkzi2x3f+/D1e+daHTOwt4wUB55bXqHa7PDE5ysXVDb51+QpP751gs9PGDXxyWvKuPBiCIDA0XuQrv/NxfD/g/FszWEmdjeUqi9fWsFIG41MlxqdKTJ9dAEGgUE4zd2WVwZEs/UMZiCCZMZk5v0gybfHZX3uUlesbNKptAj/gE194gPmrK0xfXmHgiTHEsQSf/m+fZqXeRE8m+Nb5K4zlszz9XzzEzGaVnGXQdlwiWebdxWW0Izl+7VOHubq+xWQxR73r8N76GocH+nj0Kw9waKCPrXabRtchb5k0HIfcgQK/89Qv7HrNURTRrHco9mc4dGps+zloejVq3iZR1OGF8uMIiCiighuOIuCjCCGfGfg4oiDS8hu4oY0m6qzacxS0FJ8f/CQRIQIiafVmMFURFB7JP0RGjRE9TcfhB/NzpDUNU8lw3DyNpahcq1bATfJhaxO1uwdBzfCt6Sv0J5Kk/H2EkcRstYbYHeaFwjHmekiL65stVqqx+tNMJeCw9SS273O1ukVOjDkfS3qJL4988W9lXNbcJnWvSV7LYEnGHZ/j9fbfsNb5awB0ucze3D9hvnMNBIGEkGXLbpNN57GUj7x7wogzH86zudVibb3O8nKe+YVNRoZzXLi4zOJShaHBHPMLW6yu1shmLVJJg1q9QyGfwA9CNjaa9JVSJJMGV66uMtCfYXW1zuzsBv39GQYHslybXiOZ1CkVU0zPrJNJmxw8OIih35xPd7s2x/P5i1fOcvrUFInUWWQxgyzlaNhvklCPYKq3Bzl+lq3rerhBgK7IuH68zpElEdvzkUSRpVqdkVyGIAxpOS7FhEXLcZFFEUUSAQEn8JnZrLCnmCcIox3H0GSJuUqNrGmQNmKVvhDuqQh5q0VRRKfrUq93yKg6MgKdroveE2LYzQTBQlaOIggJwrCCEG4hSoMgSAhoQEgUuYhiHwgqgni7cIbjeNBDNXe6Lo7jkUoa26WMfhjyjSuXeH91mWfGJpjK3ZwXREGkoBZwQgdJkAijEEmQ8CMfVVTRJB1VVJEFGT/ySSlpZEHGkq070pl4kYMkKDE/4h3M7Z1Puo2X5/7NCWxkUcYNHWRBQRHvnui2PZ/NWotCJsHKZoOZpS0Gimk2Ki2SlkajbZNO6Az3xYhkx/M5e22ZUi7JeqWF3xMx6sslSeWSVBodXM+n1hNB6cun7pggCaOATtDCC10MKYEqarT9BkHkY8pJoijECW1EREJCDMm6iYxExJJT+JFLx28hCAIJKQ0COEEXL3IRkbDk5PY5dDFOylbcDRpeZfsYBa2fIWMSJ7QBiKKQdtDEDz0MyULb4fMLZLUTLHp/xkz9qxCBJJr0mU8j9O5tXsvw2YFnCaIwfp4EmQiPKApxgwaScHegzI9rPzWBo/d/eAkzqdOsdkgXEhx6cBLhI2iRGGIaJ8B264S71dQmDBXfDTA0hfGBe3PtuJ5Ps3uThM32AurNLmenq1gpg3wphWN7BEFIuS+NEIQIUYTveJx54xpRBFOHBvFtl5SlsffAANfOLxH4IZMHB1ie2cB3fMIwZHO1TmW9wb7jI6wvV9lcq+PYHk7XZWu9ycT+fg6fGNlxzbIssffgAHsPDgDEpWyWwrlrqwyVMzGEry/B6EAeURSYX65QzCW4vlRh63KHscEckyNFpl85j+sHBGGImNaYW63i+SG1TpfkYIqp0SIzC1soski5mOLVd6bZP1lm/9Fh3j47h+v5dG2Rxa0GZsEiAtaWa3Q7LicemsDslRwsLWxx5t3rhOHdiSxHSlke2T+yjey61SRR4NMPH6CQsri4sEa93WWwkGKkuHMSf/b4FGEYsbhZhxDWN5qYskKzbXNsfIBkT4UmiiI8PyDwQzpdl4f2jdDsOsyt18hYBr/+7CnOXV+l23IIC7F89288/yCvX5hjZnUrLiVMKXhCSMtzMGSFtKXzm88/yKvnr7PZaNOfSyEIMQrqi08c5aUz11ivtRguZnjs4Bir1WYvqy/x7PEpJgfyXJhfZ6vR5peeOsbjh8YRhHghPL28yfX1KiOlLAJwaXEdx/c5PNaL+J/cS8YyOD+3RrXdpS+bYKwvzkzWbJu6bTOYSm2j+W6Y4/v4YYip3L6g/qjV7C5RBFnjb29x/NNos+1FWn6bKIKKW6egJZhtncGQkyTkDHqUp9NxSaUM/CCk0XFQe5D8O/WhE3icry9wpjrL5eYyy90Kbd8miuIAeFqxGDBz7En2M5UocygzjC7uroYQRRHdwOViY5HLjSWuNFdY6GzS8rp4UYAuqqRVkzGrxLHMKCdyE/Tpu6uE3TjeUrfCe5VpAI5kRhmzSvhRwHuVGX64cZErjSW23FiqvaCnOJoZ4/HCfg6lYzK+e1kURXQCh4v1RS42FrnWXGWxs0nTt/GjAOOWNp/ITnA8O05R75GzyzIpTUO7iyqUiICEiB14vL11lR9uXGS6tUrVbSEJIgUtxbHMGE+WDrIvObCjzWZCQ7c0ZFlCl0U2Wm06rkcpafJuZZpNp7HrOQVBYE+yn/2poXte/09qGTUVo9cEFcI6H8VMlvUUhqxuv//swOWdyjQVp7nL0XpB8NQQU8l+WvU23ZZN0CPoB7B0lVrL3nZ4b1jHd1i366zZNaZbq3xYmwMgiEIu1hf5xuJbd7yGg+lhJhPlHWN5uVPh/eosQRSgSSons+MU9TsTi95qYRQx117nbK8NlqzzUH4PSeXnc15qNroU+9NkcrFqo26ojE/1sTATK/Bttjq8ObfASr3FQrVO1/U4PT7OWCYTI7Rllarbohs4pNR7l9JU1xv4nk//WIHV+S3ajS4zF5YZmiyyNLPB0ESJVN7i2tkFmrUO2WKSrZUahx6a5OqH8wyMl3jru+c4/sQ+Fq6tYndcxvYP8PZLFzj66BSRLNCwHXKaxStXZnl67wRX1zcxFIXBTIoPFldwg4C1RovxQpZKp4ssipTTSZK6FpPk2i4dx6XS7rDV7iD1iG9lUUIQYl60V6/NsaeUv+36oiii3bRZntvilW+eYergIIXyzbHX8Cqs2wt4t3DaCcTH7wYt7KCNISXwIw9V1HBCm5q7gSrqyKKCJaVo+BUKWv9t577VZmpVNjptbN9Hlx1mqhWeGZsgCEPOb6wjCbFia8XustXt9vhTbExFoek6FA0rJhkOQmY7VSxFZTCZ4nqtRs2xWe+06U8kKZjmj8WMFoQ2XX8ZWUygy3fnMNxya9S8JvuSY+jSnVCk4i2fZAREkkoGL/RIKzr7s0UM+fagd6frsLJWZ89kH9Vam3TaoNV2CIKQRqOL6/rohoKqSOzdW+att2ewLB3H8VhYrHDk8BCiKHBtZp3R4TzdrouuK8iyyNRkifmFCktLFRAENjabdLsegiCwd28ZTb33O833QxbXajiuj+BeRBAkDGUiLh/xZ3/uAkcfLK3QdlxylknX82jaDoWERbXTZSyX4bXZefSekMpKoxUnPJfXKCbjcSogoCsy1U4X2w+4tLpB3bYpJSy22h2OD/XTdj3CKGK53kRXZHRFZiyXvXfjehaEEa+9NY3rB/zdQyfpMxOsrtUZGszuSHLcaqKYipHZgCDmgQmEXUiObxbM3u6TVWsdBFGgr5hifrHCu2fmePG5w2QzVo/uY4FPTo7xzFiZjJ7E9a+jSAXCqEMUhaSVEiCC0FO9vbGeFSDD7td/a9Bos9ri3LUVBEHg4FSJujSLJafJa2VEQabj19GlRMyXE3ZRRZ3FzlVUUaOoDSGLCm2/gSYaSIJM228gCRKmnKTtN1BFDUXUccMufuRjSknsoM1M6xwj1l4WO9fIqEWK2hCicFNlXETm3QvzrGzEPlS5mCKfTtBo2xwcLzO3WmGsP0fS1PD8kP5CEj8IMTWVUi4ZK+CaOs2OQz5tsbbVoD+for+YYm2rSV8uie14FLIJurZHs2Pv4D+81Za717nS+gBZkBm3DqGKGhcabyMiklJySILMmr2AG9qoosaodYDLzffIq2Vafp2T2dN4octs+zydoM2exBEScoZ3qi9hSWmSSoZ9yRPMtM5TddcRBYmjmcfp+A3O1d/oHePp294LVW+TS4138CMfXTQ5mT2N3Cs1FASBpDrFVOa3CSMbEJBEE1m4GTR8p3qOg6kpilqOH22+y1RilIwiAREJZXSXsfyfZj81gaMwCJm9sBzLcBoqYRjSqLZxHQ/fi5UzFEXGcTx0I45oKqpMp2X3ZN57tc+SwODYTjUuUYDR/hxJvR0HCsJ71/olDI3jowPMblTwgpCjI2WG8xnOnLlEPghRFIlaJW6fIAjYHRdJErl+dY1u20FRZS6+P7cNt126vsn89DoPPLUPK6ljJHTyfRq6oWImNK6crXNYlZi7uoYAZAtJLr4/h++HaLpCrrQ7oeUN84OA5fU6+YzF7OIWuhpzQ2RSBu+dX4iDRosV/CCkmLOYW6oyOpjfVjM5c2mJ6wtbZNMxp4ckxkonuqawvtVkbbPJ5z9+lHTSYKCYRukpwHW6LrVml47tcmCqjAAMjRVQVInf+9++Q76QpFnvsjS/xaOn96GpEpfeuoZmqFRWawiigKzKFIfydJtdSpaOECksnp1HVmVWZtbRLS0unpJEFFVmyFDJmimOPHUAuRdgCsOQSrOLpkhYusajk8OsmEkMU2VrvUVgBziOx2SP1O7CxSXSaZPZ65vs21tmdbWOpsmMmEn27M1hmjFp5HOHJ1lcqrK62iCMQpIJnc88cpCu7/H2xjyzrQqrboNKo8NYMsfVRkxKOnE0hxcG5AyD765cJq9biIgMHUgzKsRtiOSIQtrklbVrjCVzWGMKZkqloJmMymkeLI6g95wpSRD4/ONHdtzzIAz5wdx1/uLSRUqWheMHhBbsP9SH4/tYqoZhqby9tMhaq03DsXliZJQP19YYTqdpuU5Pzh3m6jX2F4rkTINqp4sbxiUOw+k017YqZI24FGa+VuNwX9/PfeAoq6bIqWmmW/P4kc9Kd5qOX8fovXjj2ur43oiCgKkrFNO7I3qCKORKY5mvzf+I1zevUPfad3TiP6jNIiAwZOb5p8e/wph1u9PuhQGvbV7ia/Ovca25QsPr9goOP2Jt+KA6y7eW32Uq2c9/NfUCJ3MTdyQ5PF+f53++8GcA/Pr403xp5DG+Nv8af7H4FvWP8KMsdSt8WL3Ot5bf5ReGHuGXRp/AlO9ceuaFPi+vneMvFt9iurVKw+vs3gdteL86y7eW32NPaoB/MPUCx7PjdD2fzXaHcurOqCZJENl0Gnxj5m2+s/I+Ld/e8f1iZ4sz1et8e+V9vjD8CF8cfgyjV9q8tdaIlXn2BAhKnBndU8jTDVz+3dwPeacXUNvtnF8ZO82wWUARZcRdHEoBtsUeYg6DKEaU9fYVhHhJutv8LvYU1RQhDi4L8l5uXYR1fJfFTpXxRGH7Wlq+zR/NvrId2Nmtzb+95xNMJfuRZIm+0SKqdjNL2HE83J4qFkDbd/ij2Zd5c+sqda9Dy+vS8p3tMRdEIS+vn+Pl9TsT+OjfhgAAIABJREFUFf/DPZ9kItG3w9HtBA5/MPM9lroVdFHh704+x6+MPXUbMflu5oYef3z9h3xz+R0ATuUmeaTw86us1j+U5ZVvn+WDt6YpljN0Ow6vvXyRoR4nTyFh8uTUGNWOzd5iHlWWuFzZYrpSoe44HKHImNVH+j4JVJMZk27LYe7SCrXNJmEYkutLkS9naDdsqptNwijCTOqszG/hdD1yfWkSaYNCOcP8lRUmDg6yNLOOa3vkyxmypSTlkTzryzVUXWGr0yWSBA6US1xb36KUjEsW56t1+pIJFmsNRnIZ+pIJZFHE0lTWGy02zDZrzRa259OwHRBiDrTVRpO0oVPrdGnYDuWhJA3boT+9+5yxfH2Tb/w/r1MayPDilx/eESjNaX2klNx2WbfUy0BDrCoUhB6SoBBEPoqoEkQ9ZVRBJOx9dkOblHwzSalJKv1G/w4EQNlKMFurkjcMwgimcnlmalWqdpeEqrHRaaOIEpai0HAcRtMZ3CCgYJokVI2cYdD1PNY7bTK6zmAyxUqrSd402ep22JvLU7G7NB0XQ5aJwohOq4vvBaR6QciPWhRF1N1znN/4XcqJF9mT/Z07jhM7dBgxB0gp1l3Jc/us52l704SRz2DyC7ihS93bwpASzDQqbHa7HMmXidSdvvmNBWi74xAGEd2uS7vtUK93GR3N02o7XL26hqYpZDMWmqbQbNoMDmSYnt3g+twmiiLhuj6lUop2x+HylRUUVSabtVhariHLEooiMTKc73F5RiR7aMmNaot/9kcv0eq47DYt+UHIRrXNF587ji4PYfvztN1LyGIa6W8J4f3TZC3HJW3oXFnfRO9RQqiSxEQhx1AmRWlji7Su07Adqp0u602VhKaR1nVmNiv4YUhfMkHDdlipN1hvtbaRRhOFHBlD5+LqBpos4fg+l9c3+fTh+y//C4KQS1dWeP3tGU4dH6EvleTK1TUWl6uU+9JcnV7ig3OLiILAYw9NsrHVZH6xwlalzSeeO8y16TUqtQ5jI3mK+STvnplDlkWOHx7mvQ/n8f0QRRZ59vQB3j0zx+pag0P7BxgazPLa29Ps31Omr5hisD/D1Zk1wjDC8RfpuhfwghVM9RiKGBKFFvXud0npT9J2zyBLBVSpjONfR1f24fhzqFKZMHLwg3UkMUUQdZB7XDtR5CFLOWzvMrqyj8Cb5P/8+o/469cugQCfeGI/T35CRNM32HJWGLb2crX5PiISCSVDx2/2aCkiNoMOm84yk4mjzLbOY4dtjmdOs+4s0PYb7EudYr5zmZq7wbHsU3xQ/T4j5n5suc1i5yo1d4MRay8JOUMYBbT8GjV3Az9y6dNHMYQ0f/rdM7zy9lUAXnh8P//4159FN2VqbovD2T6cwGM0lUYRZdq+TV5J0PS7ZHUdQYBUoJKQC7T8Lv0D/TFzZBSxL1vkamuJY4cHcAMfUbBIKcYdlUOvtc4yZu5n3DqAH/m8V/0+I+Ze+vVRXt/6Nqqo02+MsdydYdCYoOpu4Icep7If43LzXZa7MxhSgiAK6PgNVuw5JqwkAgIP5p5FEVU6QYt20EAUJOY6l9mfPIUuWZzKfowrzQ9Y7s7sCBzFhOIXmOtcIauWaPsNnNDeDhxB/M5RpTRwe2JtpjXP2foltpwqCdlkw6mwNzmOgEQQ2QSB3aOH+Nuzn5rAUb4/Q6flkMyYTB4eIgwj5q+tUa+0CPyAVt1GN1X6hnK0pC6bKzVSWZONlRqlgSwbKzVAYHiytOuCZKve5vLcOkOl9H3Jt0qCwD949mEenhrG9QNOjPaTUjRKA1kWptcYGMmzvlTF6br0j+bZXKtjJXQGxwsszm5gWhp7jw6zML2OW/MZGM2Tzlpk8gkUVSaTs7h+ZZWRyRKrixXWl2u06jaFvjQzF5c59sgkzXqHIAgZnrg/tnnb8VlcrTHYl0bTFCxdxTJUMkmD+eUKCUvH8wJWNhqkEjq1RoeV9TqLK1UGimlc12dsKM/aZhNFjuGSa5tNXNcnkzLQdQVJFFlYraIoEutbTURBYHKkSLPtcG1ug6P7BxkYyvLf/HcvcuXiMp22w/hUiU994RTDYwXcroukSJgpg9lz8wR+SDKXYGNhi06ji26pbC1XMZI6pZEiru32yhhFZEVmYKpMGARIyk65xmqryytnpklbeizxXO/Sbjv09aUxDRXT1HoOR4dW22FkOE+plGJ1rY7n+rTaNvVGdBNW6gd4XkCt3qXTcfH8gOvXN3jowbgWuOp2EATo02OZUDv02bBbFPUEc60qTuBzLDfA3yxdYcBM0fJcgijkeH6QdzcXWWjF5H6DZoaK00EVZfqtNIooUne7SKKAF4V3LQqLiJFEiiQxW6tRMEyWmw2SmsaBYpGXZ2dIaRqLjQajmQzjUoau74MAZ9fXGEgkOdFfomrbRIAqiaw0m6w2m0iiyMFiqZcN9VhrB3E2M5XeoYITRj5h5CAK6n0THf+0WxCF7E9NEEURaSUmADTEEFNKIfUmc8+NA9Bmj4MhimJ+q48+p2EU8vrGZf7Flb9ivhOjAwTAkFQsWcOUNBRRpuM7dAOXbuDihj6jVpGSnt69rAdY6mxxtja3XSpkSho5LUFOTaKJMnWvw6pdo+q2cEKf8/UF/vnlb/I/HvoiexIDuK5P13ZJWPquhNFnqtdpeTbfXH4HEYEhI0+fnkaVFDadBqvdGk2/y6bT5N/MvoIoiHxp5LG7BI9i4sSztTm8KEAVZSxZI6cmyakJVFGm5rVZ7cZttkOPs7U5/vnlb/I/HfklynqO8VwW7S4k+J3A5asz3+NHG5eQBZFhs0BJS6OIUtxmu0bLt1mza3x1+ntIgsgXhh9FkxQMS0NRY1L+MIrwwwBZjMsHxxN9bDktOoGNH4Z4UUDbt3sywrDu1PmgOkNWTeBHASCw6dS3S+I0USGtmKw7dQxJw5RU3NCn7dtIgsSIVaCk7Y4G6wRxUFAVNYjahP4KkjwKxP1gSAopxdiBOJIFiclEmabXpR04BGGIF/m0fPu2Ur/GZpMoiiiN3OSAMDWVVsfZTqr6YcB0a43lbiW+k4KAJWt0Amf7eJqobKvK7Ga7fTdo5nmsuJ+vz7+OHXr8aPMSnxw4SV67d8njUqfCB9VZIJYhPl06iHlHtMPPvo1N9XH6hcP85dfeptWwUVSJow+M88ynYs4JRZI4OlCm2unGZZ1RLP0coTKQTOIGHptOg4KWRpfUexJXa4bK4y8e2/W70b1lVuY2Gd3Xz8z5JfYeHUHVFA4+GKvCTB0dZupITI59AyF+w8rDeYjiEq2Fap09pUKvhOWmxWxhcGy4f7uZ44U4476/XEQABjOpHfuGUcTRwfKOoOOV9U1ODPeT1HcfF3uODPGP/+mXdv1Ol8wbj9jNRsF2vwmScHuw/gZAQIgDxCPWXoRQxG7byKpMn1biV0d+ccdPSpbF39m7/7bzR0DdsXl/dQWBGD11pNRHStN4cnj0tv1H0pntvoqI++hQYZekg+MzfW6RRNokmbFuQ/XfOELVfpuOv4DXE9O4kwVhwLKzzlRihJRy50CJpYxzpPi/bv8/JCCt5AmjkIJhEUYC+i5oUsNQOHJoiFqtw/FjI4RhxPhoAceNyV6zGZP+/kxMhJ4yeOShSTwvIJXUmZgoIUsi1VqHxFSM3LVMjdGRAooikUrqHD0yhKYpLK/U4uBT1txRzuR6AY4b8OITBzGN20twbNvjO69fAkBXJggiGwjR5CEs9dBd++5n0QTihOUj48M0bQcBgYSmkjUNBAQOlou0XDcue82kGM6mWao1UCSJ/nQKN/DJWyZhFJHQNCYLOWRRJKlppIxYKTtnmZhq3Nf9qcSOkvGPVpV81D8SRYG9k30c2tfPk4/swTJVxkfyLC5VCYKQZsthbDiPZaqsrNWoNbqIosDQYBbP83n/7ALjowXm5rfQVBlNk3nuqQNxeVTb4YVnDvHyDy9Rqba5IbZ0dWadg/v6GShn6Nq3c8m4/jyqPIAfVhEFEzdYRFOHMdXD6Moe/KiJKvXTcT8EJPywSjyGJqh1v40kmLj+Mpb+IC37NRRpAEs7Tst5CxAJwiaNls3Zq8vYbkzef+7aCg/bw4xlxqi4a2w6SwAxUlK2SCpZWl4VSVQYMCaoumtsOsuEBNhBmyAK6AZthsw91L1N/MjDDmNklCklGDb3sNi9iiWnCXrEy17kIEYSlpxmuvUhsqDE8+gdlttz7TW2nAbjiTJz7XU2nTpFLU3VbXM4M8rbW1fYmxpiubNF0+8yaObZsOtMJQfYsOtERIxaJZzeu+1sbRZRkHiieJC0Yu2qmm5IFnWvQt2roIgqumTS9GuYXiJeA4k6sqD0SvwUIMQNHareOi2/QVYtMt06z97kMeRb1ju6ZCEJMqIgUnXXaHk1DqcfZcWOk3d+5FH1Nmj5dXJqH07QxQ7i5KkbOhhSgnHzAPv/P+7ePEqy677v+7z9vXq1V/W+zXTPjlkw2AFiJUFSpESJlLVYsizLSixFceJYURJbkZMcJzlHthQnPieOxSPZpMxYkUiFO0WJFAmSIHZggAEGwKw90zO9L7Vvb7/541VXd09XD4aSTg6I3/zRPV1V99267757f/f3+32/3/TdgCCh3D4XZtHIMWGNMGQWSWtJTmaOUNCzBFETRTLR5NQOQYC/CXvXBI42lqscOjVJKmeTH8oghODQyQnCrgRzu+nEmYBsAkmCfYeHkZA4dHISRZU5dGoSIWJll344VlOPCSXrLZeM/c7VEpIkkbMt3n9siwspDCNmjo4wc2wUO2Vipy0QUK+2CIOIo3dN0W44jEzkOXp6ilQ2weBIljCMSGUssoVkT1Ft6uAQxaEMlm0wfWSUsakiybTJwePjTM4MxvxEDx7AaXs9adx36DHppMn+8QJDxVSX7yNeSI8eGGZiNIdlaAhiLK5haGiqzE88eRLL0NA0lcFCEl1XmRrN96oyIyFw3QBVlbEMjftOTSEEJEyNDzx0BFWRSVg6D98zg9wliZYkiaHRLEOjuw9DcsJg6ug4iirzwI/eTWm5gpnQSeWThEFMNB3dJQhFhKoojEwPbmXmFQlFicvv4iqzbeXP3ZLuajNWTjkwM8i+qSK6rsRs/JJELpdgeDgDAjRNQVVkjh4ZRVVkMpmuwyDFV9tcdCRJYmQki+cFjI7mMLtVJjk9wWahqCA+XElIFE07PniKiKxhcXdxnE7oM5ZIExEf9KZTBdKaQSgEbhhQMGyGEylsVadg2AxYSYqmjfEO8B8JmMhkUGWZcqeDGwSMpdPkTIucaXHHwBCrzSZZ0yRnWmiKTMN1kZE4kMsTCsHVSpmxdIYgitAUhZrjYGoaQ3aSlGGQ1HVqjsNwMkXL96g5LvtzW/c1jDrUvDcxlEFS+q15w35Y7I3qRfwoDrA1/BYp1eZ4ZopGUGZYn+Z6+03G1UFSKRPbjrHD1VaHRsfhvkOTvXaEENxobfDvr36rFzSKyQineGzwDk5mp0hpFjISbhSw5tR4q3aD8/UFHh44uudBWJUVHige5i9XXsdWTU5mpzidm2Yske8FK/woZKlT5rPXn+GZ9fOEImKutcZ3196kINIsLtRodVyOHx7tGzh6u77ApcYSKdXipycf4pHBY3EmBwkn9Hm9Osd/uPYdbrTWcSOfry6+zLHMBPcWDvTpMWiywvuKR/n+2nkyWoJTuX2cyu1nzMqjy2q3zwEL7RJ/cv0Znl2/gEBwtbnC99fP85GBe+gEPm1P3XPzW+qUWXGqZLQEPz35EI8N3kFSM5GRaIceZyvX+My17zDfLuFEPl9eeIkT2SlOZKdo1TvkiikMSyMQIftyOXRFJaHq/PL0B/i5qUfwo5B24DDfKfGp2W8x19oMBEq9eyUh0ww6tAIHTw4wZQ1d1oiEwJA18nqSUEQ4YUycu+7WcMK94VmWEpeGO5ELio3UI+KObc2pU/XaFIwkvh5QcqsEIuKXZ96PH0X4UUArcLnRXuf3r3yTpU5lR/uKplBbr+9wsoIgZHIw14O0JlSdX9j3GDV/i1iz4jX5zLXvstQpo0oy7x86weNDx/f8HvuTW+v4ppmyxr35A3xz+Sw1v83l+hLXmqvvGDgSQvBGdY4NL4biFYwUd+am39M8SbIi88DjRzh25ySeG6CoMulMAn2bIMZao8VnXnqN1XoTTZG5d3qC46ODrLVaDKQNDFm7ZVXIdrvVWCZSJrkZm0V/geEjeRR/EMO8Cea8GWDp14wEScPoBY12KTTd9LPfazf/f3Z+HT+MmBrOkbRihb7JXBZFllH6iJG801zZxRvS5+23ek+c8ZYpr1dZurrK2MwwuaHdz/le/ZCAjGH2SHdVWcYLQqw94OTSHr/3e2On6dCud5g63B9GJwioOW/cqpWexYI1AV7kI7pVlX0ve1OfRSTwIhcnbEOY6nLn7a66lCSJyYkCk9tI97f/vm9qJ+nx9v8P9f629fr42E7IT7JbWTRQ7L/m6JrCiYMjPH7PARJ9REpaHY83Z5fj3703SWiHUeQEujKE8jcof/1usYODRRKaRtLQSao6bc9no9bCkBXOzS1zbGKItusThiGT2SylWot9+Swb9RYFy0JVZGpth8lshmrLYdC2CSOB4wUYSYX5jSojySTphMGltQ2Ojw7tTBDX27x+cQlDV7j3+NQuNWVJkrbWHiSEgOvzZRaXKywsVZAlUA0VuatOLSHheSEjQ1ksU2ewmMJO6IyOZNE1lYSl986TlqmhKrEwyEapyey1NeyEgSxL1BsOc/MldE3hwP5BllaqLC5Vub5Q5sjhMfzwCppSJBQ1RBf6qiqDIClochFFTmNqh/GDJXRljFBKocgJTO0gUdRAU0dQ5SxmV3nX8Wcx1P14wTy6OoKmKaTsrTRzLpVgIDFIQk0RiZCICBmFgjGCLzxK7hJ5fRhbTWPICSIt7K5ZEgVjFCdq4kcunaCBocTqlwPGGKqskzdiyHlOH2Ku+RaqpCGI6IQtZGQQAlvJoEgqmqQT7FFf70UBsiTT8DusOBVCEZFQzR6FQ0a3WWqX6IQetmpiKXGisainWe1UYuVWv826W8NUNEIhyOuxklsQRlyaW2V+pcodB0YYG4yTsEdSd3Gx8Rqv155lxj7OTPI45+uv8EL5aWxlgLofYqsSQ+YEKW1zrbjI9dbFOMmXOIQiaSx2rmLIFkVjFFOxGDDGYh/WD3EqJu2mwuvt1yhoU3QakAsnOTN/Bj8IGC2c4mz5TRa8BfwgIBEWySoTLDbW+PbGN8jrwxxL34six7EM1wvQNYVCrv96ktZSPDH4IJZiokjKtv0zFqBxwwqpv2HX6F0TOMoWUizNrZNtOOSHMl21h62DUyL11yPktS2diaEcCVMjYf3VJJ0VRSaT37p52a4UrpU0sAseerKGZAQcz4yTysRlwKnsVtAnkdxa5HRDQx+IgxCqpmBv+36bzmAiaZJImnT8GzheC1MdQVUyfTdnXVc4fWwc29J3qdgYXdja9rHYtOK2ybjXxExuuw/bF6f8toBW7jYlSGVFRu9mGBNpK4ahSdIOpbWa6/BmaY3jhWEyxu1BolRFIWnpFNI26YSJrqvofTja9JuKYjazSPo7yHSHpka2C+MDsFSNh4fiDKskSRzPDffeO5ncck5GEukdQSiAtG4SiQHm2+uEImLQyGKrW+RlT4wc2PH+vUyRZe7ZpuZw85weTnbvp7Q1Y4QQHCnGFWzb+zVkxxHumVx+17WfnN4ZENoug+uEa4TC+YHVot7N5kU+w2axGxAwkZDQZIOav04jKMeQoyjC80LS6biU3tDUnmzw5vi0ApfP3niGi/U422PKGj+/71EeNY/RWnZpr3qIxNbcC1ciHh89zn2JQyQxOfvKHOOTeeykyeJ8mXqtQyabIIoidEPlv9n3E/iVkOFEjuWrFWpWm1bT5cDhYYZyGYbMDCNmjnbg8nL5CqGIeK18lU+MPcBgMcWNBX9PR9+PAmzN5tcOfpgPj5zexWE0YuUoGml+9/yXmG9vsNSp8MWFFziSHkOWpB5sy4181C40YyY1zP904mfIaklSmkUowm7FgIQuK7GcqpllxMpR89ucq14nEBFnSld4oniCAdvGUNU91+5QRGS0BP/w0Ef44PCpHX3OA6NWnoKR4nfe/iIrTpWFdokvL7zETHIYpQsnHt8/gKtGtH0fVZaQkMjqNtltxOJ5I0VK3VrvBs1ML3CxnWcI4iDf5ve/+ZkTQlDz2xh78C4AXQLMBJZigqSBkNh+NMwbSRqB21MiutZaQpdVTmQOYihbC2BWt7HV3XuoYek4LXcHx1EYCZbKdaaGc90+KMyow0RSnACRJInlToUvzL8AxIfH/clBHh08tmut226i+287RO9Ubh+HUqO8XL5CO3T59uo57spP71lqDlD3O7xQuoQTekjAyew+smqSuudgq3rfQMF7wVRVIb/tgNtpe5w7M8fJe+J9aK3RZDSTYrqQYzKf5Vuzs9iWhq4qmLLelYXv78QLIQhFEzcsEUYdJElBlZMYShGJncGKTTW1hdoCqVSKTCK95zoSRC28bpsx7MtAlZPoSg5N2TsIHPcpIoiaeFGZMHKQAFk20eUcqpxC2kGeL7oE+1tzy7pps4+EjxeW8aMaQkTIko6u5NHk9I62bmWR8PGjOkFUJ4q8mABbUlGlBJqSRZETO8bCc3w2FsoUx96ZVzPuXwk/qiNEGLcr22hyFkXS+3IAxeMkiISLF1UIozaR8IgVHjVUOYWu5JAktdevZCZB4If0mwpCCDr+Ii3/2m2Nx6Yc962e134mSVJMpCvpIMssNGpMp/O39Mvj8akQRE0i4SFJMqpsoytFZPpzAd7KIuHhhiWCqNlVA1W7c77Qm/P5dIKffvJOLKP/2Ju6yo8/dpyhQopWJGh6b6LKaWQj8Z4MHE3ls5y9tkQuaXFpaYPDowN4QYgXhFTbMTT87NwSxyeGaTkeFxbXWas32ai3mR7KU260SVkGz124zsxQnqurZWaG86xVm7h+QL3jMJBJoqsqx0eHd13/ubPX+Nf/8bsMF9P83sHRXYEjiM8Cjzx0CMvSkCQYKCZ54pHDZNIJRoczSJLcDfZ0ePm1OTIZiyvX1hgdzvDhD9xBtdYmm05g2wYDhRSKImMYWlzBZBs88sBBEgmdbDYBQpBJJwiIoKCimwbPzd9g2LSZODpAS/ZZbmSJpFPYms56q4UfjoLkkdSLNNw1NCVNtdPA1lOkjZMcKBQwu8Irtn66+60EQtRJ6sd6OsiSpGOpk4Agnxb8nY8eIGkF6JrK3/rgcY4OTCDhkVXHEARMWOMgXBadFSYTh0ipeVZXaiyvN0im8qimRqKaIJdPYqomE/49lFca2EMZDpoDlDaaLK01aDQSnDcXMQyVkdwJ1lZqNNMqWmOK/QcGcaI6gojxxIHuuhruukcA08kRSm6dhGpwd+4giiRjKBpZzSalJUiqVi+p6IY+Kc0kCBukNIPD6XFCEWGrJinVIqPZDJt5VFnBUDRq9Q5/8Pnnef71a/z3/+BDjA6kkSSJjF7gvsKTO/pxb/4D7HPLnKteIG9kESgcT9+JJEl0tAEuNc5yf+GDvfcfSp3iUGpnNW5ai9f39WqdjWWfYeVeJCCfS7A836Kg38nySo20pXNxrULCypHq3EMhlyAsqSy6DhlxjKKmINqw0KywttFAlmVSSRPL1PY8nwMsO2s8s/EKncDBUkw+Mf5hUopM1jyOG5RQpL+a6u5e9q4IHDkdDyOh4zoetXLzlu8VQhBEEa4fE/oiQJbjYISuqd2sxe7PVRsd3ppdZmo0T7XZYbhw+zKc72SGqdEQi2x05pEkjbx9N0j9s8iiq8DkhSFBGO1wtBVZQlMUNEXeUU0D4EUbiMAnKR9F6gMJUmSZTMrqXSMSAjcICcKQaBupuCLJqIqMpigocn9ejb1sR7tB2HNCFVlGU5UujKT/+O9lsrLb4QhFxEq7yUzGJ63fHhu8ZWgUMzZnrywyWkgznH/n+yuEwA9DXD+MYSei69AoMrqqoG6DHt1MFAs7D0dRFLflh2HPiUWKHStVVtBVBUXe+kx8/JNYdapEQrA/OYSy7UB1s0VC4AVx+1EUH8BkScZQ47a3K0r161+/v938ehiJmCg7jA8YkhRnOnVVQVOUPTKdCqqcQpHeO5xH9+aPc7W5wIqzgS8CphIjqJLGwdQ9uGEHU7GRQpVEQqde7zA4nKHjekQ3OeIXG4s8t34x3uYliUcGj/G3px5m43qdqtui0XDIZhM4TszjJoDSeoMgiPDNgEqpSb6QRAhYX61Rq7Rpt1wQAtPSGRzOsLRRYaVVoVJuoWkKiiITBlu1wUNmhocHjnKudgMn9Fh2KnRCF0PqQrP2UBkBuLsww/sGjvYlvpYkiZPZKT48ciefvvoUoYg4W7nGYrvUhVZp1P02G26dnB5zlARRhBv5hCJivr1BzW+iSAppLcF0cqgX2Bgxc7yveIRL9SXcyGexU6bqtqk3IyZztyZPfqB4iPcNHOnbZ1mSOJ2b5omhE3zuxrOEIuJMeZZlJ4beZotJJDlWsltrtfCj6LbXx95z3Qss968Wu/n5y+rvHHANooB20CKrOgjRRoTLoIwjSTJ+FOJFARGie/gPGU+Mo8u3BxtVNRXTNoi2wbdlWSIItziOhBDM3yixsd7goUcOofSFt0AUtQlFmyhqIEsWkpwAERGHjDzCqIEsJeLXJBlZSmCrJk8MneBMeZYIwavlqyx1KoxZ+T3Hfr69wZvVWO00pSUYM4Y5s7aEoajcWRwlZ7531qKgq1yEFBPxbrfyep3nnjrfCxzlEhajmTTXy1Wenb3O0YEBMqbJoG0jyzKhCAlFSCQc5ut/gheWKVgPkzNPs9F5lpXm16m6b+CFZWRJw1RHKFoPMWL/KEn9UC+4Eh/61Z7yD0JQcV9lrf0UpjLMaOrjKJJBzX2Lxcb/S8U5G/svIkKTU1jaGCMY79gpAAAgAElEQVT2jzGW+kkkds/T2Mers9Z+ivXO09Td83hhCQkZXcmR1A8ykHiMwcSTaHKmF4SNtnFXVp03WG1/A0MZYCz5CQLRYrHxBUqd52n514iEj6akSetHGbI/xEDiCTS5v88QP1dtKs6rlJ3nabgXuzCuGoIQRbIw1CJJ7SDD9o+Qt+5HleOKZEWVsTMJwi60SpIkOv4SS62vIETIsP0RbG0f7WCe5ebX2Og8Q9u/QSQcFMnCVEfIGCeYyf4ahjpwU78inHCFcuclKs4ZGt4l3HCVIGohIaHINpY6Rs48zaD9QTL6HSiKQraYol5u9sYqjDp0gmU6wQ1a/hxV9w2ccBUQVN2zXCz/bt9xGbE/Ts3XetxP2/tVcV5hvfO9vp8bsB4nZZzECduYSgIvkvGiED/qf8CMREDHn2ep+RXKzss4wRJ+1OjO0WHy5r0M2x8hbdyxJ1x+tfWXVN3XsdRxxlN/i06wyGLj85Q6L9IJFomEiyKbmOooBfMhRpIfJakdQFHkHcnSm02WJfaPFVBk0KKHCUWLMGoiCPb8zA+7GZrKhcV1mo7LSrXBSrXBZDGLpii0XZ90wmQgY3NmdoGW65FOmGQSJjc2qli6Rq3tYOkaY/kMl5bXqbUcVqpNkpZBvePScjwyid1jHoQRb80u03a8W/ZPliUGtwXY07kENXyEITFbrXJoqEjS0LFMjUMzQ5QrLYYG0gwNpNF1lYFtZ8PNxLKiSL2qtM2f+ye39vj1VovMkM1QN1k7mEpSk3zkboVSqdmiKjk4QczVeLhYZLZUjs9mCDRFwQkCwkgQCtFDyW4laSOi4BKggPBBMpEknShcBWRkZYAHjje499g0sjyALK0R+euAA1IWES6ClAAEo8YpZCXeX0vrDdZW63hegKzIWKZGOpOgXmtTq7Zptzxq1TbJpIHT8amUm1QrbUxTw0roCAH1WkzY3OiS1VtmmjHlOFKg3TLCYCk6Y1ZcPZjb9thmuzx8KXXbPq4BBATRBr77BgX9TmS5iCRJpLU4iZcWid6YbVRbzM6v33KebLeslqZg5GgFHUasrXVWk3WOZe657XZStsHgQApVUfD8gEzKQlMVgiBCH1eo1DoU80kc12e0K2KlayrttkvC0tH1ODHq+SH5rI2mKbhe0Bcmu92uNucZNgYYzBTYcMuEIqTpX8ePGnhRFVW2sNShW7bxg9i7InCkKDJuxyNTSNKqdfZ8XxBGzG1UePbSdV65tsBqrUkQRuRti4PDBR4+vJ/TUyMkjM2s4yaQCExDI5kwWC83OTCxs7zVC0LeWlil2u7sKqrbbCFlGtw5NYK2hyxkSj9IELWBCGkP+cO263FpZYNX55Y4v7jGaq1J041J9yxNZSCdZLKQ5eBwkUPDRSaLWUxNJRIepjqOLueRuDV8yQ9CZtdKvL24xus3llko16i1HYJIYKgKWdtiPJdmeqjAoeEiB4YKZBLmOxKSRkKwUK5xdm6J164vcXWtTMNxkSWJgVSSA8MFHjwwyampERL67nLq2KnbWgir7Q5nry/3soTHxgYZSG9FVCUg7JMSq7Y7vHFjhTCKIYzHxgcZTCdx/QBDU3nyrkNktm30s6sl5ksxHvbo6CBD3UowLwi4uLzBs5eu88aNZTYa8UExbRqMZFPcMT7EfTMTzAwWuqpn/S0IQxbKdS4srXNpZYMrqyU2Gi0c30dTFFKmwWguzfGJIe6cHGH/YB5NUXqAk5RqMWLl+5LqQswRUe84vLmwwouz81xZKVFudQjCiKxtcnJimKOjMZZ/k+9hc9RkSWIin2FmaLeqzI7vEEVcXS1x9sYyZ64tMl+KVXk0VWE4k+To6CAPHJjkyOjAriyuIpuI8L3lJMmSTE5Po8kqK84GjaBF3d+g7K2Q04e43n6LCe1UXJbq+ARhDKtsOR6NjkvKMhDAq+WrVLw4EJ5WE3x09G5SmoUxoVEcSMfqGZvOe/fgrqoyURRzbc1Ew1iWjqLKJFMmUSSIok2IpkBRFfLFJIoqE4UiPvAHIQl7y6GRpJho21J0nNCjE/ps1Jv4y01yWRtd67+eKJLM6ez+W6pU6bLKXblpvqC9QNlrUvVaXGoscTA1ghf5rDgVdFklrVs4gU877PSqa0penU7gMWUPYCk7N0VJkpiwi5iKhhv5dEIPSYZcwqTjB6y1WiR1Hesm5UVFkjmdmybZp7Jm0wxZ5Z78DH++9CpVv0XJbTDbWOF0eh/VcqvbAbA1jfAdFCD//7CICEVW0GUTJANJSiDJOTarjhq+QzvwuofbCFuxEOL2A16mbWClLJRtmVtdVRjIJHtBxaALITbNWwWjBG3vLKIrWwyCMIrnfiTaqHIOQYgQLpHooCnDJI2HkCWVO3P7GLFyLHbKLDsV3qjOMWLlUPZYE18pX6Hux37CRKLAfYUZrtXqmIq2iy/nh92e/fbbTOwfQJElvv+tt3e81qjGzv2mjWczDKWSnBgdYr3Z4lq9yka7TdVxGExZZHSbpGoRig6LjS/R9C8TCgcnWOJK5d/ghCtIyEiSgh+18b0qTe8KZedljhZ+i7R+vDev/Mgnr+V7YO26e5652qextWmKiUdoeBeZrfxbmv4s20kuwrCFE66QM/d2xv2owmz1kyw1v0wQxXBEWYoPKe3gBu3gBmXnRRreBfZn/gGmOoQfhKxXm0yPxntd07vEXO3TmMowtjbNYuMLrHe+RyQ8ZElDiBAnaOEEy1Sc12j7N9if+U9Q5N3rncBnuflnzFb/DV5YRmySZKMiSSpeVMbzSjS8i5Sdl5lK/wKT6V9AkWOlRtM2ULdVNLvhGvP1zxKKNra2H0mSuVD6bcrOSztITEPRwfPKgNS75nYLogYXS79LyXmOIGqyyfgkSzElQRA2ccNVau4brHe+z9HCPyMln6JZa9OstXtJy7X2U8xWP4kfVghEa0cfGt4FGt6FvvcpZ9xDSj1OWk3GFZG98RLU3HPM1T7d93O6UiRtnMJSbareOgkpy6Blo/epQBMipNR5lqvV36fmvonA735HHT/qdOfoZTY6zzGd/RWG7R/pGzwqdZ5nvvFZktpBEuok1+t/SKnzAoKgG7yUCMMyXlim4V6k6pzhaPF/IKXfmnA/CCNePHeV/ZMhKbtJKJp44RqWmMFUJ2752R9Wmx7KU0zbKLKEKsvMDOdJWSb3HZjA0FRytoWlaZzeP8axiSEMVcUPY85AWZJwgxBdVTA0lZRtUPM7pDI6RdtmNJcmsUd1V73ZYXZ+g+jmDN07WLXd4bX5Japth2MjgyxWahweHkBRZI4eurXq4e1a1jR5cHKyx8EoSxLJUb1H31FIJJCgGyiKVWJH0yk2E+2bdBqKLKPJMkIEhOEikpREltNIkoasjIEQCEIkySAK5pAkGyQZpDSK4qGqFpIySOSXu4GiHEgKMIyEhsBF2lZ9sn9mkMl9xR4cT9UUdF1lcChDLmfjurFqoaLIpLMJRjehnlIcUFdVheGRTJyYiCJMU2Oj0uLa9Q0mx/OMDd864PGDFS4Ako6ijhOvdRHbiei2t3X5+jr11u0TQkuAG/lECLRtCmSqrDGTPLH3B28yO2Ew3eWL3F5hDnFi3nF8dD0WljKN7eTXW33fSx3+VpZSY9XM1e55JRQhOf0gTriGEliYysA7tvGD2LsicKTpKpqusji7Tn4ovet1IQQdz+dPXzrH5148x/WN6q7Bff7KDb74ytt88MQB/t4jd5My9R2HaF1VGBnIUKm3yaSsHTeq6br86288wytXF/fs45GRAT71Kz9F2trKum03RbLwoxqanEKVElusjXSl1FdL/MF3Xub5K9cpN/cOjkG86Axnkjx6ZD//8EMPoqk+HX8W3bx/z5LqMIq4sVHlj547y3fPX2W11tyzLH3T0qbBr37gfn7uwVMYWv+pIISg3nH5izcu8sfPv861tUpc6bXNzrPO0xev8bkX3+DhQ/v4uYdOcefUaI8jIwwjzr+1iK6rHDwc42NnV0v84//7q/hhhK4q/M7PfYQPHo+lSyVgJlPA6kOWOLta5jf+6M9oez6qLPO//fxHefL4ASrNDtVmh8sL69x1aJyJgZiH5yuvnufTT79CGAl+6yee4GfuP0m90+EPnz7D185eYLXWv8Lta69d4O8+chf/6EMP9Q0cCSE4c22Rr7x6njPXFlko13aNy3b74itvMZpL86N3HuEXHzlN2jJ7G4navac3l2lHQnBufoU/+M7LvHx1nmafTMvzl2/seU1Nkfm7D9/Fb3z0kb6vCyGotDp87sVzfOHlN1muNnbNmbcWVvn2W7P88fOv88SxaX7x4buYHsz3KpxkSY+dabGbFPCH2Wp+g1WnRCvoYCkGbtRh3Z3HjdrYaiauJFNkbDvGuDt+gOsHPThAO3C41FjC7yrsHEqPcjAVOym6oe7gJrkd2x4M2mG3UVxhqXqP20QIgarLmMmYOH+vgHFSNZm0B27JiSJJEtPJIfJ6krLXRACzzVV+fPw+AIatHKqk9A3kjFp5nNAnpyf7hgcSitGDP8TwCQ/Fl9FVuFBa50hhYFfgKKVaTCQKt4RNSJLEgdQIWd2m6rcIRcTlxjKnEpPYSQNZkVFlelxttwtpDqNo+5Lf+6wfRSAE6raEgxsEqLIc8x6palx9uMc1WkGHpc4iVmqKuGQxAdLW/mWrOnRV2pzIxY3iNeJWfCPbrbRUoVVr06y2sNOJ2CFEYOpqb27IsoSmxVxxt9L01tV93aSJBCIiEm3c4CqGcgBdGUUIt+vEhkgYyN1D+kSiyD35A6wsvdJT33uwcJicsbs0u+63eWHjEoEIkYDHBu/AVAzGk2kOZosktfcWQXbCNtB1ldkLyyzdKHHo+FjvrkZhRKPeIepWAm9OoU1Vo47jk7A0CpbFmlOh7DaYSAywvWBsrfUtVpp/jqpkmE79KlnjZJcXYY3FxpepOK9Qc9/gUvlfcWLgX2CqMXxElVUiItSbYJZeWKbUfo65+qcJhcto8mNkjTvRlAxB1KLhXaTuvk3BfBCpj/vphVVmq59kvv5ZJEkmb97HsP0REloMyWj611hpfp2ae475+p8SiYBDuV9HiJjI+OZgrxeWuVD6F3jhBgPWYwwkHsNUY7WiivMqS80v44brzNX+EFMdZTT5sV2BBwmNhDaFLJkk9YNkjJNkjFOYyiCSpOGG66y3v8t6+7u44SrXap8iY5wkb91L4IV4nVjgY3eFr0PDO89K6y+oumfJGXeRtx7AUscAQTu4QcV5lYx+B4ayM9kZ34MkljaG4lqkzEPkzPtIagfQlSyCiLY/z1LzS9TcczS9S1ytfJI7cr/TI8bePCzqSp6scboblAEnWKHinEEQkND2kTX6k6Sb6jCNSNAJOqhyDIdEinneiolHUeQEflQjCBvUvbcoOy9tG1MJTTKw1QyeF+FG4S6fXghBxXmVt0v/C06whCbnGLKfJG8+gK7kCKMWFfcsK82v0/KvcrH0L1GkBIOJx/dM3naCBd4u/XPccJ2ceQ/D9oextHEQ0PSvsNT8Mg3vIhX3DNeq/47jA/8rsrT34dfzA557fY5CboKE1UBT4sClLP31qDXezWZoKgN9zgt6cqczkrIMUuxej7ev6mokcbm2QigidHOEyVRhT59ktdTg6kLpB+5vNmExmEqSNg38MCSX+JupSG3VO6zcKJEdSJEtpshaO++50ef8st36kcFvWhis4LnfR5JS6MYDKMpQHDjaZrJycwXJGJseiGzcf4srS70ztaTJJBP9YZ6b5/Le9zE0jD2CettNVWUEIv75VwiC7GVBcJUoKiNEC1U9sjekNRK8dmEBz7v9hHbMO+ngRT51v8nYX2OK7IXqUBWJ5KYff4th/KtAyk7n7uhygs6jSAoFPUsk2jS9WSQUnHC9C1m+deHJ7dq7InAEsL5YYf+xUVK53aX7fhjyhZff4pPffpF6J44iKrKErqokDZ0giuh4Pi3X46uvnme93mI0l44dqe68rbccrtxYJ5uydpU6ypKEbehYutaFdYkeJO7med/0PBabdUaTKdLG1kIRRA0i4eJHEUHUQJNjSIUQgrn1Cr/7tad5/sqNXmRZVxVMTcXSNfwwpOMF+EFIEMUwoaVqAzcIu9UpPrpS2HMDi4TglauL/N63X+C1uaVeAGMzgi3LWyTOURQRRFvfb6KQ6YsT3rR6x+VT33uFz734Rm/sVVnG0FQSepyVb3uxfHPT8fjmuctcXtngn/7449w/M4Eix9CZdsul2XD2vM52awc+7cD/gR6ghK4xlEuSS1l7ZizW6y2q7Q6f+t4r/Mnzb+AFQUx2J8lI0vbxidAVhYNDhVsE1ODZy9f5s7MXcPx4gVLkGAZoqkpMxh5GtFwPrwvrWyjX+Mz3z6ApMr/46F2subVeeXbL9Vmq1BnK2GQSMRndtbUyv/2V7/LWwmosfaxrHBwuMFnMoSky5WaHtxdXWW+0evNUkujBHYspm8F0f1ysEILVWpNPPvUiX3vtQryJEAdYLV3D1FRcP6Dt+fhhSKnZ5ktn3ma+VON//MQHmCpmu4fjoHvd94ai2qZFQjBo5lEkBVuxyOkptK7igqkkCZw4eyDJElEk4iCpFAcFkui0Qpd1p9Zrb589gK2aRFFEJCD22bcyEoL43olI9CqKBDHSZ9PB316xt72fQVfla7lToeq1aAYOndDDjwL8KORGe4NOsJV9URWZMIwoVVrks4m+/F6mopPR3pm3zFJjNTe68dflbeTLOb3/3ItEhCopSAScry9Q9Zo0AxdnW5+vtlZ7PEEAbd+nVHaYLubxowi3T5Wbpeq9suVbma0a5HSbuRZECJY7FSrtBm7HR0SCSECp3cYNbtPxEFDudLhaLscZ1TAkY5p0fJ+saRJEEaEQWKpK1XFYbTY5WChQ6XQYSacZS6VQ9ljrUqrNfnsi/l7R1W7gZRuRtYjoBD6KFPNxRV0I6+2a03bZWCiRH84yMF5AVuQY7s1WEEwIQanUZG21tltJqmcSujqy/VMIAZo63q2SlbuBDan3Wk8FTlZ4ZPAY31t7i6rf4nx9gcuNZe7VD+zKxM02VphrrQGQ11PclZ8mryWoOB2cIKDPOeWH2u5+MB6DtZUaT37sTk7es78XIFpfqfOVP3mBhuOyWK2T0DW+eu4Chqqw3mwhazL/6YP3cL1aZdQqMGjmsFWTMNoK8jvhKqYywtHCb5Ez7kKWNgcwImuc5u2Nf07JeZGq+zqrrW8xkf7ZXmCl6lXxIx+ULS/bj+rM1T+NJmc4nP1VCtZDKJLJ5n2PhE8o2iiStWstE0Kw0f4ey80/QxAyav8Y09n/DEsdBeL1NWfeR968l4vl36HUeY6V1l9QMB9Alk/i+rtpWCM82sF1xlM/w4Hcf4kuZ5AkBSEEefMBLHWCy5X/Az+qstD4U/LmvRhKHno1wfGTkNKnOZD9FRLaGIYyjCIlUOQE8bzWKFgPoso2C40/xY+qbHSeIW/di6qrTB4dQze0XUFogc9S86uAxHTmVxlNfgxNyfaqygUBk6m/060w6AcXVphI/QwF8wHSxtGY9wm1uy9D3gzJGCc4t/5PaPlXaXgXqXbeZuVGnnTO7vkMOfNessadvXbX29+j7r5FIFrkzHs4mv/NvnNTlnQct8aF9hIZbVtljgRJ7QC2tp/4WY9Ybn2NsvMym2uXJ1zqQRlNMpAlibbv4YTBjjEKogZztU/jBEsoks1M9tcYS32iO3fiQ2nBepiMfpxLlX9FJ1hgrvZpkvp099q7LRRtnMBjMPEBDhf+WwxlsDfeees+0vpRzq3/Jk64TMV9lZXaBZ59OaLjeNup5XrmegFX5svoymksPYmhDG/u3H2v/162KBI4rs/15TILq1Uq9TauFyBJEpapUczaDBVSjA/lMHUVRZFjDkBZgQjSmrVjiDfhp2EUEYaCN68sx2qfxH6S4wYoSuwjbC8S2EzE6Jq6I6lnGzonx4bJ24lef10/nnNKl56il/AJQhZWq1y+vk651sYPQxKGRiFrs3+swOhgfL5r1TtUSw0OnZzccXYVXXoQPwgpVVssrlVZ3qjTbLuEYYShq2SSFiPFNPvGYpW3zWRV/CViKJoQnV0B9k0oU3RTsmo74kBVYvqQ7Xyk21rAC0LOXFvCUBXunh7b5X9EkcDxfG4sV7i2WKLedPCDkJRtsm80z/T4FhJjex+QJNptjyCIdlAm7LQYeh1FglbH5dpimbnFEvVWzGOXTBiMDmaYmSiSSVm9cVGUESACsZOuQHSruKIwIowEG5Ums/PrvSS474d0XB9ZkvqOl6IoqIrC0fQMK84GRaM/TH7zOo7rs7BSZXZhg3JX+TxhagwV0kxPFBnMJ1H7iD7c3FYYRXh+LAqgqTKqqvTu7fXlMtcWSpRqLcJQYBkqQ4UUB6cGKWTtXe0vdFYYMoqcyBzu3gYJL5QIhUckXAK/gy5nsbS/mQq7d03gyEoavPLU2xw4MUFuYKvqSAjBa3NLfOaZV3uBi2zC5GN3HeWxI/sZTCcJI8FKrcE3z13m229d4bnL10mZBuG2kkZZksgkTYpZm6S107tMmQa/+bHHqbYdam2HWseh3nH4wstv8fbi2lZfgLLTZqXVJG0YOwJHqpzEUscQIujh7gEcP+DzL53jxdn5rgSlzvvvmOHDJw8xkk2hKXEmt+V4zK6VOHt9mYvL65SabR45vI+koVNzXbyw0rdcWQjB5ZUNfvfPnubC8nq88QJDmST3H5jg9NQoI9k0CV2j5XkslOu8Ob/C5ZUNbNPg1OTInhF+Pwz53Itv8MfPv07L9ZAlienBPB+98zD3z0ySMnUiIVgs13n20hzfOHeZUrPN7FqZ//3rz/A//9STHB0dBAkGhtL4Xn8M+82mKyor7QZFK0FWv73MjaYqpBJmvAnIct9KgcVKnc+//Caff+lNdFXh7v2jPHBgkoPDRWwjVoi4ulbmzfkVSs02x8eH9hwbWZZ4/Mg0Xz97kY7nc3ikyImJYU5MDDOaTaOpCmEUMV+q8e23rvDNc5dpez4dP+CLr7zF3dNjyJn4EA2wXm8yX6qSMDQyCYuW6/FHz53tBY0KyQT/xQcf5OHD+3rQQscPuLpW5v/85nO8em2RUAiGMyn+/qN3c2JimLRlkttDQbDj+/z7773MV189j+MHmJrK6alRfuz0EQ6PDmCoKl4Qcnllg2+cu8Rzl67jBiEvX13gD77zEr/x0UfIJ2Meh0h4vWzle8U2vAqHU/vxI59FZ42MnuBq8zVMJYmp2Iwqx/D8EFmOK49MXaXeBrurvOKGPo1gq7Iwp6fQZZW5GxuUK23shI5paLQ6Lp22F3MpJE3WNhocPjBMtd5G0xRKpSa5nI0sSYyP5nr8MpvEymfKs7xUusz5+gJ1v40bBvhRQCDiYGWE6MnGb5osSxiWThBEXZXC3aZK8i4IWT+TkEhvI4puBp09q3SEEFT9Fq+UZ3lx4xIXG0vdPvv40Sb/Sozxv5k3Q1cUcgkdTZHp+DFc4WbTJTWWGn8HkyV5BwSvGTgYyTjrpukqjvDxwpDVZpOR1O1x4bV9n6bvk9DiA2Kp3Wat2eTowACBENQdh4Smsd5qEUYRThDgdq8xkd5dZbtpSdUmbwwhREDk50C4O767IavYqtENxEm0gw6hCG+r2gjAczxUXWV43wBK13HZqLcwNZVC2qaYsVEUhZGRbMyvdUuTdvweU/P0ux+xlHnFa4OAnGFxJD3GscwEz21coOq1+P7629yVn+4Ri0NM2H6mMkvNbyMBh9OjTNtDlB0XJwxw3mOQWdjiADx2aiJ2ntUthzE/kOQDH7uThK4xlk2zUm8wXcxzdHiA6+UqZ5aXeGttDTcISKijvb1s+y4soTBof4C8eR+ytN0dVLDUCSYzv0DDv4QXllhvf4dh+8MY6gAyco/sfKfFhNaH8/8dA4knbmoTFElBof+eHkR1llpfw4+qWOoE+zK/hKWO35TBVUlqB5hI/SwV51WCqMFK689ptqd7AZObzVSGmEj9DLqc28FFpkgmw/aHqDgvs9z6Gi1vlrLzIkk1ru6JhNMl4dYJwjIpfR8Abe8ckWgDAk0ZJmXcjy7nGUl+jJXWXxBEDZreZQAqq1WWZlfZd2ycdB/lLi8sMZb6KSbSP4cqbwbYutXHaGjvsJ4ltMluNdZOi589lbR+hIHEY7Rr8/hRDTdaZmTfoa6C7OZ1ZCTZ7K0ZsmSwmUmTUfvC93r9jzwMWSNC9B5/qXsw7B14JZBvSrErkkJKzZLW8pRFSPomP08IQcU9Q8U9A8gUrYcYTf0EipS46R4aDNiP0/SvcLX6+9S9t1lrPcW+zC/tmV231DH2Z3+5G5DcNmZoZM3T5K37WGp+FS/cYLV6ma8/4zA+lOsL6/aDsJeIbnsXUc0M6h5cWe9la3c8XnzzOn/+/be5OLdKq+3ieEGPUkJRYlXmhKkxPJDmvuNTPHr3AQaGkyQUjZHkAHljK/CyWmpwfbnMjeUKs/PrzK9Uubqw0Xt9frXCP/6Xn++roA1QyNr8+i8+wdRInjCKUGSZXCKBpWm9dXBxrcpv/7tv0mi53HVsgv/8Zx9B1xTmlsp8+Ttv8Oxr1yhVW7ieHycHVRnT0MgkLX7yyVM8escUqq4yNFnA2iZuJIRgvdLkrSvLPPvaVd6cXabedGh34iTy5hlF11RsS2dqNM+PvO8oj9w9Q6ZXtSXQtDtjv7ob6IY4UdhxfD71xed5+c290QYP3zXD3/vx+3pwqEgISm4LNwoYMJMoskzaMmh7u/123w955e0bfO17b/L27Aq1ZqdbzSnQNYW0bZJO9qc3SVg6H3rgCKOFmC+qbyWTKhOFES+9eZ0//carXL6xTq3pdIMoseK1belMDOf4ySfv5OHT+7EtA1AJgqtIkoHKUSCu+Jud32BpvcbV+Q2uLZW5vhTPm037D195iS89tbdK5Iffd5T3PTZCKajghC5hFDJo7h3g9QUAACAASURBVKb3CIKI1y4u8IVvvc6luTWqjTaOFxBFAk2VSZg6hWySx+89wEcePsbYYMxhJISg5sY8UCDIGCZCwNNnZvn0F19A1xQ+/v6T/OijdzA7X+KLT73OC6/PUW10cDw/RgkoShycKqZ59O4DfPz9JylsE2u63loioVhk9S1fUu4mGYOoRd48hK7kdn2nv6q9awJHIhLMnJjAc/0dB49q2+H/ef51lip1ANKWwa//yMP8xN3HdlTKHBopcv/MBHeMD/F733qBUrO9o33L1Dg0NUgQRvGeuO0aiiwzUcgycdNcef36CueX1rY5I4JOEGBrGkVrZ2Y7iFr4URVdzhOINhrxDdxotPj+xTmCKEJVZD5+zzH+qx95mMTN8l7AqakRPn7PHVRaHeZLVWaGCrEzRKyaIfqw03f8gN9/6iXOL8UBLlWWeeLYNL/06GmOjw+iyJufUYkxoeOI+++g3OpQazsUkiYi6hAzQyvQdfQF8PqNZf7js2dpubF6zfsOTfFff+QRDgwXdkAyDg4XefjwFPfNTPDbX/0uq7UmF5bW+P2nXuKfffz92KrG3NWYqOzg4d1KCTebBCiSdEvo1812ZWmDC/Nr5JIWdx8c7/ue5y5f59lLc6QTJv/oQw/x2NFp0tuCiGHk88DBIaLoDjaaLfK2SRB5hMJDkbokkF0yO0VSmRnO8k8+9hBJU+fIaBFL14lEiCJpqHI8jgeHizx4cJLBTJI/fPoMXhCyUmvwzMU5PvHwIZaccpxRiQT5ZIJCMp5XF5c3+N75q0RCoKsKf/uBk/zkvcd3wOYsXePu/WP8xkcf4Z9+9i+YW69QazuEkeDExPAtSzmfPn+NL73yNo4fYKgKf//Ru/n5h+7sBoO27PBIkceO7uf/+svn+dwL5/DCkG+eu8zxiWF+9v4TgOhmqX/w8sp3s9mKxcvlc0QIDiQnESICJBr+BghBJAkCP6DZcimXmtiGTiGdwNTizTIUAq9bTSYjYSqxw9LpeNQbHVotByQJWZZotV1m9g2CEFiWjh+EbJSa7J8qousqtVoHc5vcdSgi3qze4DPXvssr5St4UXxgNmQNS9FJaRaqrKBJCqai40UB11vrBF3YXBQJ2h0fTd2LWYseNv+dTCLmOtq0TaLmm/lpgijktco1/mjuaV6tzPYq7UxZw1IN0poek8hLKqai0Yk85lsbBN2g10DS5uDIKIuNOnP1Cv26Jv01+hwEIY1qG9fxwaC79tzmnJZgKptlKpvd4eC1fJ+UvlUGfnPZdicI9gyy7f5uKop+566/ByJif7KIrRmAIKXZ+FFw21C1VD5Jo9Tc0Qfb0Km1HW6sVckmLRRZot12dwQtflC7UavScGMupkPFeKO9WF1DliRO5cfI6jaPDx3nTHkWN/J5sXSZ5U6F8UShd82y1+K59YtAPNcfG7wDSzXQZJ+ZTIGUbtz2eP6wmdGHX0o3NGa6kuoZK65gnsxnMVU1hmbYBjfqNQ4VdsOcNk2WdAasR3cFeCB+nrLGSVLaIUrh8zS8i3SCJQx1gEhEaLLWF4qQNu6gaD3St829TAhBy5+j5r4BCLLmndjaTN97KUkyKf0wljpCy79G059F06uYepp+58iUcRRb29e3LVVOU7Tex1r7KQLRpOqcZSD3a0goRMJBknQUOUmo1FHkZJfguwAIJMlAlsxu5REYygC6UiCIGj1ltFTOJpEy0faogtaVPLb8EW5sdMgkBIok4YUh5WYbU1O7wi+b4hexXyTLMtnE7ankSJKCrU2jSAaBaBKIJs21Gl7HpziWo+Ev4oRliuYJpB/gfgG4oYcTeYwnhrFV67aD1RDD2UIRdom0O2w47R0VF6HoUOo8TxA1UOU0Q/aH9iQvVySDIftJlppfphMssNF5htHUxzGU/tyOBeshbG2672uypJHUDiBLOpFwCKlx4uA+fuUn37dDXXjTmm2Xf/u5Z+I+R3VWG3+MLFlkrUewtH23PR4/rCaEoNnx+NQXnuer3z1Hs9OtAJIkdE1BU+PEuO+H1FsO9Zbz/5H3nkGSXfeV5+8+m/nSVmaWr65qb9AODRCWcCQBieSIMpBESbMrTWgUu5pRbMwqZmNNbMTsTGyENiZidzSxsbOK3ZE0MqQkSqQ0pEiClGgAkPAe3Wi0764ub9NnPv/ufnhZWZVdWW0gfgCxB9FRqMrMl/e5++4995zzZ2m9wflry1hJk58dP05aT1D120yxeb6+8fx7fPEbr2O7/RckPT/kyhYi6UbUWw6uF8RKfdNgVyHHWrON7fukTKOzjYArs+vUmjamoeEHIZdnV/k//vj7nL+23O3bNtwIfhDhBy5t20PXVAxNQ9NV9I56agOuH/KHf/My337xHO4Wu5QiYsu3IuLiE23Ho+14rFaavHd5gfcuL/Jbv/woGcsE6RAE04CLogxQsQ0urK6R0DT25wuslJtx3tMOVrD9k4M9fbNEcr62QtVt8+jIPlKqiaoo+EHvnNLxfL75g7P8/ldeotqwEQIyVoJsZxzg+SGNtsNqZXvEh6oICrkUtbrNYDa1I6mnKgrfeeUCf/TVVyjX4nm6oaskDI0gjHC9ANcLKNfaXJ5Z5cqTd/NPfuYBkiao6ghbc41WK01+5/f/niszqztq/FbKDVbKjR1ehXuO7KJkDpAxUjSDVt/CIrWGzdeePcNffOsNKvV4QXjjGleEwA9Cak2HWtNhemGdV96d5p99/hE+dnQShOT0yjI1z2HYSnNqeBRVCGpNh0szqyhCMDU2z0gpy//5xee4MreGlLFqzNA1oijC90Nqzfg7Ls+scnV2jf/un3ySQoc8yusZnlt9hZHEIIaic+/AMXThoQiDlD6JG65jaeM7LtrcKT40xJFuaviuj35D9sfVlTJvXZvvWDkEjx7aw1PHD/S1V5m6xmdPHuLi4ip/9eqZ7t8jKZlbqRL5EflMkoXVOlOjty6P2g8p3aDqOttWthRhkNEPYAcLSLl5M7Y9n7VGfHNYhs7JqbFt2Ry92xEU01ZXTglgKANI1efGYGwpJadnFnn1ymz3bycmR/jtTz/MeL5KFLxFKO04DE3Jg/QQSh4ZVciakpypE/hxKj9CQUYNVG0CRRnG8QK+/e5F1htxYGwpk+I3nrivQxpFNP3LGGoBQ43LlmuqyuNH9nJhaY0/ev4NHD/g1SuzvH51jqeO7mdgINUJ8rz14MJQVe4eHGMomb7tScCekQJBEIKIg9D7fa7SskmZBr/+2L18+uTBbgbTBta9aRShIWWEYrqseg4ZbZCyN0taKxJIl2awjpQRBXMSXSQ5ukclkE2WvVnUwEBFI6UVKSU2ByZJQ+dzp47w/bNXuLy8ThhJrq9V8fyICavYqQAlezr68wsr1DoKu0zC5NHDe3YM6d5dGuCeqTGmVyu0PZ/Xrs7yC/cfwzL7r1Y2XY+/fetcd7Xh+OQov/zQyb7qJCEE2WSCzz9wgtevznNhcZW25/P9s5d56th+0kmBpU3sWM3kxxWjyUGKZh5D0cnrWVQh2Ju+m4a/TtEcR3gKhqGR11Uy2SRLiy22XtsCuoHnsqP6kVJyYN8wmXSSTNqMV4M69h1Ni89tFEaoqsJAzkLTVbKZJCurdfI5q0t4X2ks8X9f/BZnazNESBKKzsmBPXyssI+p1CDDiTwZfbOU6buVa/zv577WDer2/IBa1YktupbJ1MT2vjBW/twecRts6e80oW4LepdScqmxyH+4+AyXGotIJEnV4O4tbR5M5MhqSfROm19bu8S/v/B16n7vAkDWMBnPZrvl5z9ImyX0kNKaiFfAapUWdKxehqpSvMMshMVmg4brkjVNgkjiRyFNz6Xl+6QNAz+MSGgqdhCQ0LRuYQAnDBhIJG+L9LoRuqJSdptk/ASGKkipCVLa7bdb0zVypV7F08RgHqVcI5syO0UmYtl9XznHbaLl+VxaXydtGEzl8wTEYalZPYGpap2Kd3sYSea53lplvr3O2fosE1Y8mZBS8n59lgW7DMBwMs+Jgd0ArDtt1pwWh/I/2gDIDxMWZssEfsDk3qHu38prDVaXahw6Fi+U1GyHhuuxpzhA3XE4vbRM3kpwqbzO4VKJfmyrpqS7uUX9oIgkKWMf687LhNKlHVwnz0mEEBSNfnligoxxBFW5U8+gpB1c74RhC9L6PuLrrr9KWRUpNBFbYf2whuOsMDQwtk0VIlBJqKM7Zs4IIbD0KTQlQxi2ccJFhEhiqIVOqzbslDFpISVoygCbxpjNNgoESndIHSGROC2XVt1h5vw8qZxF4oasuqQ+SRDkefPqLHuH4kIl5xdWcXyfYtrC1DVqbSdWjRlG10p+/96JHnvJ5tjhxp8dtY9QQIKMQtq1NmqnAjEI7HC9r5r9VohVg3USqknJvLPVbIHAjxzm7asktHH2ZAc6eW0xwqhF07sMgKakyBiHbrq9pDaOqQ5hB3O0/Gn8sLoDcaSQMvbetAqsqqS6JFgmrfC5x+4iuRPxp6scPzBK2jLJmPfQ9i+S0HajKTurSD9KiCLJi29d4W+fO0PLjl0J+6cGObZvlNHBLKah4fkhq5Ums0sVpufLLK/XGRxIc8+RCSIZ4YXBNgKkmE9x7MAowZZqn4urdZbWYgFB0tQ5MDXUVWBvtaoBFLIWlhnHgCzVGixU66y32uwt9b9Om22Xq3Nr/MnXXuXCtWWsjtBg18gAVkLHDyOW1+tcXyjjegEHpgax0ibllRqtuk1haPN8G7rK+HCcsaoogqFCmr3jJSZHBygNpNE1lVrT5v0rS5y5tEDL9nDcgL978Rz3HJngUw8eQigFpGwhZRvRuVZrjkMun8M0NJ647wCDA2ladkw+1TskRKXe7rt/EI+PJlID5IxY2bh3uEA2uVm1WkrJmYuL/Nk33qDasONn8pEJfvoTx9k3USJp6qxVW7x7YZ6/+d673XNRyFrcd3yKwYE0E8N5xopZPDfA87fnlkEcXP3q6WnKtTZDhTQnD46zZ1cJK6HTbHucvbzIuxfmaTseLdvja8+e4cShcR46kSWKyijK5kKIrqnsmyiR2dKvul6sQtog7SZHByjmd65eOzGSR1d0rjZnaAU2Q4lidwFqowr6159/jz/75uvUmg6qqjA1WuDU4XFGB3NomkKl1ub8tWXOXFqg7ficu7bE//vlFxgpfZaJkTyWrrNmt8gYncJdW45LJCXnri0xvbDOlbl1kgmDew5PsH9ykHwmiesFTC+Uee2966xXW0SR5IW3r3DswCg//+TdGLrKWHIYIeLYlfifioKKG6zihOtkzYOE0ukKWv6h+FAQR4EfMrZ7iJW59ThjYcsD8YWL0z0WtZ88caBHJXIjskmTTx3bzzPvXuiGCUsJnheyVm4SRZLxoZuXdN4JkZQ0PLfrddwKRejoap6IoCeLSBGb5en9MGK90SKScsdMiw1sfdkJl0FG2x7utufz/LmrVFoxA5rQNX79sXuZLKbxvXeJovpmWGlURYgkMqoShrOAhhAZZFRFUYcR6J3XFBRlhLlyjZcuXmejlPhDBya5eyq2tUVS0vaniaSLsSW00dBUfvrUEZ59/wrnF1ZpOC5/d/oSjx3cTSaXvO1qCJGUzDSqZHQTc4cy8L3HSuB6ATOrNYIgZKyYI53oH/h2/75dfPrEwb7V8aSMiAjxozZeZGMoqc4kNwedTkQTJkJRCKWPHznxCqSaQFNMNGEiiVD7kChD2TR3jQ9xeTkO96u2bVbtOjIMGTRzJHSdcwurHBwtISUsVhu4neykoWyqq0Tqh4ShMbXlgbhYaVCz3R2Jo7NzS7w3twzEqwQ/c89dlDI3Lws+Wczz8IFJLi6tIiWcmV1irlzj4FhEIJt81BRHs+0ldqfGGDDivsIOmzSDCuPWQVSh0fZcbNun3rCp19qkEvGgfgO6opLUDHDjAU3Tt+MASF1jYmygb14RAJ1taJ2fqiIYHxtAVeLVWC8K+KuZF7uk0YCR5tf2PMGnR0+R062+27Q0s+fsJBMG2oDJ8mqdlbUGo0PZbTlHgYxwolvbDyWSZrCZXWZp5rY2+FHAX1z/YZc0KpoZfmPvk3xy+Hica9C3zUbfK0oRgprjMmj1qTIkQ7zbsCtJKWnd0OZmzcZ1fKSMJzVFy7ppeGU/XKmUmalVGUqlsX2fomVRTFqstJqkdANDVVlseszUawyn0kgZh2MbqkrOvHVly37wojhDwo0CUnoSv1Ny/XaRsEzEcA5F2yQA6m2HtuNzdHIYRRFEUVz5tN32PnDYpaGqWLrO/mKRlGHE1sQowlQ3rQOjyQEeKh7iemuVCMl3l07zyaHjGKqGFwW8unaRum8jEDxQPMhIIh6cj6WyXfLpo6g2Arj8/gLtlsuuPYPdfVxZrPHct053iaP5ap2VZou9xQFWGy3atkcoIqZy+R2Pi67ktuQabYcidBJqTCxJAtxgtfN3hYyeIaXd+NwQJNSRzrh4Y8V+M8ttp3bEYc4z3d9mG3/Jcuu7NzkiIS1/GoBIuqStWLnp+iFbh4dCqBhq/8yKDRhqqZPDBF5YIYyc7qL2VhWNlAFOsEwruE7DvYATzONFVYKoSSQ9gqhBO5jr2XamkCZbTGMmDdQ+VidTKTKSK/HJu4yuEqLVUebtH94gTemonBWCKNqmBg0jGzuYp+FdouVfxQ2W8aJarJiRDm6wQhjFC4BCAdMysJsb97JAFynuRC20gYRqsjc1QcWrE3TCrW/r/uvsjyI0dAWiSJA3k5hbxmShdHGCBQAMpXhL+5dAI6mNU3Xfwg/L+FG17/tUYcbViW9WQGFLwlvCUNlVKu2YG6drKk89eAhFUSi3n8OPqghhdCpHfrDF6R8ntGyXZ154v0saPXLPXv7Ff/EEI6VMtyDERp5dy3ap1NucvbJEEIRMDOfZoGY3ihpEkY3E5ycePszjH9vfs1bxp19/jS99600ARkpZ/tVv/mTHwrQdiiJIW2Y8fspnKaYtvCAks8OYuNa0+YO/fonTFxe4//gUn//JUxzeM0wunYztRsR2vEq9zcJqjYOTQxBGZAdSaDcIARQh+OT9B5lfrnJ4zzCnDk9Q6ESkbKhwpJTUmg7PvX6J3/vLH1JvOtiuz7deeJ+H795LwlhDiDSafgQhEgRRiO37jGQzmIbGkw8e4lMPHCSK4jymcr3N7/7J93nh7at99y+SkqSqE8oIRSh4QcDlpTUqTZvxQhaEoGV7PPPDsyyuxdmcxw+M8t//+pPsHtu8jseH8xzbH5OC//YPv0PL9tA7Vqu7D40jhGCt3OT63PqOC93nri2BhPuOTvKbv/hx9u0a7Cz2x8el3nT4zsvn+f2/eZlaw6bWtPnm8+9x/7FPxXNYual2KuZS/PavPU4Qhigdu/7cSpV/83vPsLAS78fTnzrJUw/tHKadMDWqQZ01t0LByOGEvZb8d87P8WfffINa08E0NH7hqbv56SeOMz6c747LpZTUGjY/fOsKv/eXL1Cptzl3dZk/f+YNfuuXHyWIIo6WhtibL3Tn0FtxbW4dQUxy/dOnH+Lhk3tIW2Z3254f8ta5Wf7dn3yfueUqnh/y7Rfe5/DRAdqp2JbnRwGKUNAVrZPblWTQepiWP4OpFjF/hJXVPiTEUcDV9+dQFIFrbwai+mHIufmV7urwUDbNsYnhmz6chBDsGyowmstwyYkn6aoiODg1yIl9o2RSH1yqJRDkzAQt39v2mHXDVZreNYKoiZ7YfMhlkyZ7hwq8Nb2A7fn81atnGC/keGDfrr5l6/vB0vcQRe42n3i17fRUgrtrfIi7p8ZQFAMz8Yltrd8YyOmc6PMaRFEdiImad2eWWOvY/QxN5dFDu7eQLbKjMNne9sFsigf27eL8QkwwXFhcYWatiuJHJC2jr8XkRggEXhR27Sy3AythcHCixIXZ1R0nYEld45FDUwykklsqBG3ewIPmge7/94So6XGbhMKOA6ydQ2NjmLrao+hx/YC0mmQ4nUFXNCIpGUhtTKIljr+5CqOr6k0nlbHia7OTDqKIaAebXxBGvHN9sUs2FtMWJyZvbR/UNZX9w8U4K8uNs5ren1/h8PgoLX8aXcmRM++65XZ+XOBEHs+uvIalJjmU2c1YssiifZk1d5acPsSIcgRFFRQLaUZHtk/MLM2kaGS43oonWrPtddqhS1axCImourE0XxVKz6RKSkkgIzRFwekExDuhz2AiQ0LVWbDLvFuZjjMlgJ8YOcnPjN8fk1Q7wA68HiWOlJLRoTyKEGQzib7h2HboUvVatzxObuiz5m7KgIc7k/mtmG2vc6Z6vXuPfHb0Hj47dg+murNKrRW43fyvrYg6waP97od24FLzb91mO/RY9zplvhEMJ/KkFYvKagMh4tyxjcICd0JEHCwUGcukCUIPXTWxdA1VKGQ7g9WUbhDKiNFMhqSmYvsBuio6laA+GCFjKhrz7QpDiSyhjFCFgnYHlpMoktTXGwxPbS4AFDIWe0YKmFsmurl8koOHRz8wMeOHIWnDYCCR6E58F+06WSPZnXAqCJ4YPsY3F96kEdicr81xsbHA0dwulpwKb5WvIpEUjDQPlw5186zswKfmOVjaR0v1CHHexMLMOotzZZy2x8Wz8wgRB/Kffv0aYosdIJsweWNmnrfnFnl/cYWAiCMDBQY7paD7QRHGjlkwEFuKNjJupIwIZHx/RTLiSvMKlmqR13tXMZ2mZH59FVVXMUwdw9CorDXQDQ0jEZd23phA6aaGmYircflRvbsNN1jGZYVbQaCBUFBV0BJGn8mKgnoTYgxAFYmuTSuUzra8PiklXrTOXOMrLDW/Tcu/hsRHEQYKBkJoiI3Mpxuqi/pegKqppAdSPRWKuq0TBqauk95SQfDU1Ni29/WDlCE17yxz9S+zbr+EEy5291cIPc4uQu3EHESdz4CqqSQss2u515WbLxrdDKEMaYc2gjtQHAloBw1AktZytCOVc+UVBpMpkpreeQ56hDIeo2iKddNrNN6mQOvsh0R2lGv9vlrddj3Ygcdcq0ZaNxlJ7kxQRZGk2XZpOR5yyyKoUASlfAohdIKwTDtyGEg+fhsH4scflbrN3HJM0hm6yk89fqxDCG1iY5Esk0qQSSWY3OL2qHk2dd8mrXWII9nG9k6jKElymVM9pHZyixtFVRXymeQt53NSSiptm0o7vpZUERdCuvE5VqnbvH52lgeOT/E//1c/weBAr9tBEIc2py2TXSPxtV4rN1meLZMrpimN5ZGdIG9VVRgdzPI//PqTneIpse1dInG8EF1Tupm7/+ixo7x/ZZFvPH+WSEqmF8osrdfZM2YSRWsQeKjqCIZqsr9UxNiitI4Do+P8qHTSvGmRo1BKMoaJoajd562paRTSVndOs15r8e7FeaSMyZRPPXiIqdHt97WqKjxwfDfHDozx6ulpVspNXj09zdF9Ix1rlSQMJZ4f9B0vSAlTYwX+2//yCfbtKnXfEwQhrhugCMFnHzvK5Zk1vvpsnE10aWaVpbU6wwUdKTefE4oi8HSbOX+BfekpckaWRttB3fJctJIGA9lkp+hMf7RcBSd0udS8ztHsgW6bWrbHX377ra6S65P3H+Q3nn4IK9E73hZCkM9afPbRoyytNfjTr7+GH4T88K0rPP6x/QxOZllptZjMRej0P0/5rMU/+/wjPHbv/h7roxAC09B44Phunn7yJL//lZewXZ+F1TqXplfZdzzFpeY0CdVk0CxwrTnLgfRuDCWJHSwTRE1S+tSO+/5B8KEgjsykwYETk1iZBPXy5sC/1nZ6sorGBjI7qii2opCyyFq9HUqt4bBoe5w4cHsP5X6Iw/h2YC3VYfREHikDFGXzuwspi0cP7eHs3DJuEHJtpczvfPX7PHX8AE8c2cuJXSO33Cc/LOOF5W2M4VqjzUrHSqYIwd7BAnlrM2Cxzx7c9HuUjrw2kpLraxXsjpUpoWvsHtzagcRGnFBur5JmaCp7BzcfDKv1FpW2TREDXVdva1VKIreV1b0VgjBkYa2OZeqYev9QtlTCZHcpDshcXKzSaDo4rr8ZIBfFAY/pVAKQeF6AqioEQUQum6RUTO94CG+1YicQHdtHjEhKplsrKImIgpEmiEJSZhz+C5A2Y0ljGEXUbRcv2JlEC6KQansziDllGhg72CFt3+f6erVLSg3nMmQSt76nALJWokMexjlkK/Vmd0Dar1zwjzNO5g5yKD2FLwM0oWEoJkdzjyJlhKroqKHC2FieatXu+/mUlmDcKvB25Spxmfolym6TrG7hhD5vl68TIUmpBu3QQxUKRTMuTX+xscygmSFvWJiKxrJTZ9Wpc7IwSc1rU+2QI6pQOJ6fInETAkYiWXaquOHmhMb3Q8q1Fo2Ww67xQo8iYAPtwGPZrnarQO6ERafaYyfbndq+qlHxmjT8ziRAKJzI7+7JGNqxzdF29VAYxUGB/ZrUChxWnPot27xgl2n6cd8lhGAqNYgWKeSL8eTN7uR+CcQdZeaULJNkUKfuzZA1pvCj9U7mmYeupPBk/LNoJbGDRdIJnSBqIxWHpj9IrhO+eydoBi5FMyYVFRRUlC6peDsY3z/M0K4iprXZB6xUm7Qcl8mhAeIuS9JsOMzPVcjlLArF/tXyboZsIsFqu40dBOToVDLVzB7CXQjB7vQQh7PjvF6+TMVr8m51mkPZMS7UF1h24gnKVGqQQ9nN0sShlIynsgwkfjRllj9M8N2At1+5wpsvX6bVcJi+vAwdFXPSMvn00/d237trIM++wQKvX59jPJdltJTZMQNjA7KT23dzbH19wxoS/9dP3Wa3XK6fvs7IrgLl1QaBF9CotTE6z9nBsTxJy8RM6ARByOG7p+j4qLrbKFmP3fZgV2CguJPUahCG28cNt7Zh3VgF60bFZIWr1f/IfONvCGUbQymQT9xLxjhAQhtFV3KoIokf1bhY+d2uUgZi6/HgeIFcn2DsfwiklDT9y5xf/9+ou+8jiUjrB8iax0gb+zDVITQlhSqSMLl77AAAIABJREFUlO1XuV7/AqGMQ+VTOQsrlUBRBH7Q3lSl3yFCGSIBTWj4sv8ksX/jwY0c6n4VQ0nghSHtwMMLt1xLYlP1s/GZHwlu3G5n0+tuC+smiy8QBzL/6Tde5/LMKq4foqkKjuuzZyLPb35+kIFcTEwJoaH+/8SqFoRRd+zwQdzMihDYgUcr6Dg5pIemDqMqaZDhj0TIvtZscWllnVI6xVRh+8LWBrLpBL/4k6e2kUY7IZVJsvvwKEEQsVius1Zv0XI8EoaOZepYZqyarrZsFEUhiiIURSGha4wWsiQNDV1Tuf/4br71wjmiIMTzAiq1NnvGkiB9QImPQ+dY3YwcuhmEgBW7SdGMnQt+ELFUazCaz3SPcb3pUO3k91gJg8nRnZWaactkbHCzcvj1hTK242PoGoahMTlewLL630+6pvLoPfvYPV7s2b7t+LxzegZNU7n37inuOz7F1547g5SSRstlvdpkuECPVQ3AVAyqfuOOxj03IqulsbQkA0aOnL45vrl0fYXz12J3RjKh85lH7tpGGm2Fqio8cs9evvbsadaqLaoNm7fPz/HIyAG8TtXInXD8wCj3HZvqIY22QlEE9941yZfSb2G7Pr4f0q7AxwrHKXtV9qZ2MZUap+rVCaIASbiFJP/RKrE/FMSR7wWcf/Ma43uHqK41GByPJ/cNJ2b3NzCSy2zLpekHQ1M7BMomdF3hzJkFLs+usn/X4AcikCIZcbla5lqtwl2loZ7Kv064gu3PowiDpD6GJlKIzo3+9H1HmS/X+Prb53GDgKVaky++8DZff+scR8aGePLYfu7ZM85UMd8tCbkVpjqCQNmWUbBUreN1SkYbmspQLs1bc4uEHcVCyjCoOy66ppI1TVabLZK6xkqzRdo0kDLujAxNRVMUIik5PjqMH4Ssdix1EKtSMonNvY2khyJMNLHdPqUIhcFcioSu4fgBth9Qbtkk4bY6PQl4UUjFtW/rXG/AC0JURTA5XCRh9r+sTU3tWr6uz67T7FQJatseSEkumyRpGSRMjXKlTaPpUCrElhLD0PpaDKWUccWCRpvVRotrq2Vm12vU2nFlPtsP8IIQx/eZXuuVT2e0ZLdMukCwUKlzeGwQQxMcHh8ikzCoth1W6k1Ozy6yq5jr25GvNdq8eS0erCpCsHtwYNv1vwHb81mqbq7GTa+W+e0vfqOvde9G1NpOj/2zbrsoGCS18R575o871twqdujQCFo0/Ba6omEoETV/lbwxxJJ9lWH1EOvrLQyj/3EzFI1TA3t5dvk9moHDgl3m2ZUz/Kr1BAlV51RhCl1RiWRcql0VgoQaK/ImU8XuNkIZkdWTuFGAJhQCGXbVQ+pG5bObPBMavsPblWs9tjMhBMWBFKahoWv977FAxmHWjw8fI6v3n5CHMuLdyjXKnewkSzU5ktseTB/IsBtyrQo1Jrpu0ua6Z/N25Vo39HsrWp5H1XH62l59GfJ25SoPDR4ire1UuSnkrcpVKn7c5owWV/QyfZ3xPYNYmQSed3OididEMqTdsfL4UZOGv4AmTELpE0oPN6yTNSYRkUIrWCaIHIRQECgk1J0HtDdDwUhR82zs0CelpWiHDm54q+pnm9B0bZvUPpIR11cqjBaynT5boKgK7bZLu+1RKHYGscqGSiPCDf2bk2xSUrIsTDVePGj4Lnbox1V3tnwmqyV5bOguztZmaIcer6xd4KmRk7y0dp6gUy3uiaFjZLac31BGnF1d4nhplNHUR6uikZU2+dwvP0BpJEet3OT+Rw91g5ITSQNrS7aDEDFBd//UBHtKBd5aXGC6WqHhuhwdGu57y4U7FN3YgCQkjNqd7Ss9z3yJZN1bZ0Dvtf7nimkmHjmIbqiMTZUQAhRFIQhCdENDN1QURaHddHBsL7ZDSoGmbpw7wZD1JBOZn7vt47RabSIDe9ugW8qoq1zZCaG0uzlFW9VHG59faH6D+cZ/JpRtcubd7B/4bxgwT6GI3oDqtj+zXd0kYe7yIlJKSuPbrUsSKNs2dmd1XhGCuuviBAElK7bXJPTYhmkoKvlkEl1RCGSTq9U/oOaeRqAxnv5Z9ub/axLa6La8QTtYiMkhGS9Y+S2XdC4en6qKiR+0+CDMjEB0COBkty+4zQ9SMIZwQptIhhiqiqneYPdBj0PHw7jwzM2uUQCkJIg2LCzijiqb6YrKutsiZyQZu0kGyHNvxJlLv/KZe3nj/Rl+6tFjvPH+DM22TcbK4ATnsYwjBFENP1zFvEl22EcFA9kk+UySxdU6nh/y9efOsH+yxGgpt2M48laoQiGpGZ2rL8IL5wijCppyDOUm1fxuF0II9pYKrDbbNF0P/4bnzVacODjG0X23r6pt1tpceW+OdCHFmhZRa8e5kY4XMLtaJWFogCCfShCEsYpZUxXWai3yqQTJjgJxYjiPrsVB1UEY0XZ8kCoIQRSuIPFxg4DTi8uMZ7O3Fd+xFXHlxLj/qHp2XJRFxPm7l5bW2TdcBCFo215X5a0qyo65XhCTGAlT69qubNfv5lEtrdRYKzc5uG+4J3toA5mUycdP7e1WHOtWSSRevJ+cKKBpKkOFNFZCp2V7uL5Py3aIojJiyzNIdOzpO1lJbxcbTo+KV+tWVAvCkHcvzncDvPdOlNg70T9wfysGB9JMjg6wVm0RhhGXZ1a5255E1/oLGjbwyD37SOgaSzNrtBsOkwdH0W6wNw9kkxSzFivrDYIootaIn2370pO8vP42P1h7neFEiZyeQUqXuncJUy0QSa/fV35gfCiIo8CPyzDXyk1K45vKFi8Ie1YhUqZxe1VzNuSIbD4Oc+kk/+iRo4Rh1FWY3ClUobArk6Nst7cpTELp4kcNEtowutI7CSimLf7lZx9lz1CBr735PtNrFbwgpNp2ePnyDK9dnWM4l+KxQ3v4qVNHODRaIrnFxhbIOn5UJ3HD6l7dcQmjjfBGBRTBaqvFWrPNRD7LWrONEwYUrCSHh0q0vbjjbHkeTddDV1VMTWXEiMsz+p1j7YcR7S3VDJKG3iMBFygoQusrHxYCElrMpDudjJ5a2+FAKYe5Q2h1D6Sk6Xm4YYATBHCbGZuGppJLJ29qhVMVBbOTW3Lk0CgpyyQIY3mklJDLxmotIWBql+zY1cSO2wzCiLlyjWfeOc8LF69zdWWdtud3Pd1w8+GYpqjEFfPif16wyUifmhrl8NgQr1yewfZ8vvDC2+weLLB/qIChawhi9UWlbfPnL73D+/MxK15MWzx5dP+OpJsfRjSczYllw/E4PbN0k1b2hyRurxAqftS4xZ7+eCGUIQv2CoLYN6wJlVZQZa59nrq/hqEk0TWViYkC7g5VPwDuKezjUHacN8tX8KKAr86+xoHMGPcXDlAy09uUPhv3hqnoBDLslFinh7ixVJOkamCHHn4UsOxUCaOoE7C+CdkJin5u+T3eKF/uec1xfVaaDVptl+LAzjaF19Yv8Vb5Co8N3bXtwSyl5Hprhb9bfKdL8BzN7WLS2q44srS4zW7k43XavBMJG8qI7y2f5t3KdN82pQ2Dluf1SJG34qW183x88AgPlQ72bfPV5jLfWzrdtcGeGJhi3CqyOlehWauRK6QopSye2Lc3Trq4gwGaKkyGEsc7vwkGjH07vFOQ0cfoNcTygRaFfBnGgyc2dRPKrWwdt0Db8TE0bZsGI51OMDIakwSGopHX4wFcKCOmW6u0Q5fUDoRdw/NYb7cZtOLPWKpOVk/g3pCNIoTggdJBvjH/Jhca81xtLvN25WonHwtGEjnuKeztObduGDDXqnNXYfgftN8fVqiqwvF7pvC9gOJQdsdrcnq9wnfOXyafTCCB5y5d4+mTdzGSTu94aflhhUDeLFDVxwni54NAw9TiY6wIhZSWYtAc3Haf6YZKsXBzxYUQgsSWFWmBQlLbIJ0lbT+2tt5u9k7b8VipNCnle9VwEh83XEXKaMdcGzdc65JLhlLo5h1BTFqs2y8SyhaakmF39tcoJh7su61IeoSyl7QNw5BcMbOtWnD39SjinaVFlpsRg1YKU9fQFZWa63C9WqFkpciZJsutFhnT4G5zFF1RaPvXqTpvA5DS97In9xsktV19r40wam8GeAuB03KYv7xEYShLKD3q/nUK5qEP1P9UvDqtwCajp+7ofEki7LBFQk3ihSEZw2S2WaWQSJLUdBQlQVIbp+1P40VlgqgO7Hx/RwTYG5lIah5dubMcUzcMb6lyb7ZdjuwZZv+uEuevLbNvosjYYJb/58sv4jpTJNPTxJWLb+jXPyLYulizQQqlLZPH7t3PxekVwkjy0rvXqP2ew5MPHuKRe/Yy2AmD3qnP8jq5LLEFS8HU9iPxUMQHt0/eiIbr4QVhvJDr+Uhr+30ohODA5FBc0ew2oWoqiZRJLp/i4K5iVz0rEDRsJ76XN/a9kycWyoh6yyGb2iSddU3tLND4SIiVSeogunEvUVRFiBShlOQTiVtGYuwELwpIanp8nKWMVauwzdK14YqIq4TtTLj7QUi96XTFBWnL7C5ClgppBosZkn0qgQKU8imGihleW5jjQKFIIRFHdISRJAiirpVZ11QMXaNle4ShJAhVNG0vYdibI+dHASVjAP0fWKBHExoDRjYeS0lJy/a4PLNZua6QteI8qdrN4xBatkdyiyppvdZiqdZASSrM1GocKGxfQLASerdg1+p8hWiLkm8rVEXpHlcZyW4AeEIxebR0Hxk9RVbLoAoFMBlPf6YjOvmHEWs34kNBHOmGxv7ju7BbLvX1JuN74sohcZnyLdVvdpBw9YOqKD1R+5V6mx+8cZkglOybKPL4vfs/UF6DrirsHyhi3KDQMNUSimmQ7lOCUwhBzkrwKw+f5OEDU3z79AW+f/YK02sV/DDex4VKgy+/doYXL17nqeMH+McPn2Qkl+lMCpTOKlhve7fKRIUQjGbTPLxnkrlKjVI6RVKPB/+6osRlHAfjtPj9pWK8JdEp79q5qDayPeLqRJsXraYqPQMCITTCyEbb4eGsKsoN4Y0R7ZaH69w6uBagmLA4kC+RvIPMCi8Iadou+fTNy8JuNCufiycwuq723OSb77v5tRFGES9cuMYfPP8GZ+eWuwoFRQgGUkkGUklSpkGi8/DQFIWrK+vMrMeBbQJBSkugKGHXA771O4vpFL/04AnOL6xQbTu8P7/Cv/ry3/PY4T3sG46vv/Vmm5cvX+eVy7M4foChqvzUqcN8bO/4ju2XstcGqCkKSePOO1xFCBK6jiToTFQ/OgOl4UQRXdHIaBaBDGkFNlk9SUJNY6oWujAJPMnaWgPH8Zma6m/TKxppfmb8fi7U52kGDitujf9w8RmenniQ+0sHGE8WYnKoc8+1A5dVp8b11iorbo0nR04wYPROhAYTWYYTecpeEwl8f/kMJwd2syfVm/1W921eWbvAn157lnbQO5lJp0w0RyXoVHDbCWWvyR9ffZaEanD3wG5MVUcgiGTEXHudP776LBfqccZaWkvwyZHjZPXtKsSRxACDiSxVv4XshB6fyO/uKbUOUPPbvLh6ni9cex53h2BuRQiKyeS2FeoNrDp1/tPV72EoGicHpuIqbZ0B22xrjT+++n2uNmOSNatbfHL4BBktyZqo0Kzb+G6AlU5su5rj7KkQLwqo+W38zkRMSokdetS8FoaqYwgVRWxkI20Qgxv/xJYJZ/wNG7ZcPwpxIx9ni6Uw/q4WpqphKDqa2J65VDIzlDr5KALB8dyB7j5LKfFliN9pc7AlM64duJttVjQUNvuf43tHkVJ2V0QhDmsfHRvo5gSktAS700O8tHYBieTtylVeXrvA40NHt6kPoo7aSI07OuKQ95C0blAwtq8qD5pZHiwd5GJjHjv0eG75PdbcOgLBkezEtusmknFFyn7Wxo8Kch2CV0q5bUC5cSxsP2Akk477Es9DUxRenptlOJXiZw/ftYPiyMb2Z8kah/t+bygdmv4VIFbjWNpk5+8hraC147P29krFb32PIKXvRRNpAtmk7r1HFLmoyu1lUg7m0yhC6cnliiFxgkUC2UIX21UoUsYk1YZaJamPoSmbfW4oW3hhnJWpKznSxoG+pJGUEi+s4Ie9quJEKsHshUVG9wxt+wzEY6X7xsZRRRpdVQilREGwseykijg7ac9AHKq60e+5wUqX7EobezHUYt9jLmWEHcwRdQgtKSVO2+subunCIqNP9O5Tz2Z2nqgKBGnNIohC9DvIVYs/q2BpaSreKvszUwxbma6qCkATcSW1dftFgqhFw7tA2jiw4/Ycfw43jDOxLH33tsXbm0HK2EItubktOZMyqTUckqZBpW5zdW6dhKnTbLtEkSSffBQnuI6mFjC18R238+OK9fUGlXKLQiFNsZTuOiqeeugwb5yd4a1zc0SR5MylBa7MrvKtF97n8Y/t554jExycGuo7ztaFSkYzOxlHAUG0jB8sYur7YrvajwC5hMnJiRGW602yif7EkK4pFLLJm46HbkSj2qJRaZFMmbGqcss9lNuSZyqlxAuiTjyAi+MFXPPXCcJYYTS/XMMPtpKWkjCYJgxmiMIlNHWSMEpTsJJ9q8neDhKKTsv3KEcBB7KDCKCQtuKCEp33FHIWI6UsV2bXaNku564s8dDJPX2dIuvVFldm14CO3X+s0D2/juuzXmkxPJjtSx4V82k0TaHWcKi5DoWOxVzTFA4dGCGXjX+PF+42j4mUEcgQ5YZ721A02qF9R8R1P2iKRsNvUuqorlw3YGV9053x1rlZ/sW//eubihMgJli3kku244MEQ9UoWXGm1I0EYNoySVkGQhGkc0lW5so72tp6CjZ0fjaDNmfqF8hoaaasMSatMSwtie9o6LraLbbzo8KHgjiKwoj5qyvohobd3MzN0VS1m8wPMct5u3xrEIY9z7wwkuybGKTRdrdZvu4UV6pldufyaMqWjlBGVJy3aHpXKCTuwVC3h4qZmsbB0RL7hgs8fd8xzswu8fdnLvH29QXKzTZhJJkt1/jjH7zJ+YVV/qfPPc7eoQJhZBNJe9uARd8iWdxQreQSCVLDOm7YJJBt7LBJIBM4nkBX4pR2X7TROtYiKeKJYErLYwq9I18WGFs6UK+jCOs5vrKF3ifjCOIg1K1ZAwlDJ2kZVNZbt8wMESIOIH9wZLLv62EU9ZTS3kAmaWLoKtWmzcjAB7cr7FT9ZXM1IR5ovD29wO987VkWOravbNLkY3sm+InjB9g7VCCbTMR5S5qGrsY2wH//7Rf4sxff6fkuXVG7ku9scrOykqYqPHFkL7VPP8If/eANZtdrXFxa4+LS2rY2K0JQTFv8/P3H+PVH7yWb3HmwrQjRo045MTnKP3/yAaw7JI8EgoF0EgjpnRB/NHClMcuBzBR+5LPorJHRJrDDBsvONSQRE8opmk0Hw9B2vKY1ReWxobu43l7lS9dfoBU4XG+t8n9dfIaR2TyjiQGyuoUqFNqhQ913KLsNVtwaY8kBHi4d2kYcFYwMnxg+xnRrBTv0eLtyjX99+ks8NHiYXVaRUEYs2hVOV69zqbGIHwU8NXo3l+uLXG3FhEkUSZoth+XVxg0Dlk0UjQzDiTzn63P8r+/9JYcy4+xKlUioBqtOjQv1eaZbq2xU+3lq5CSfGDrWVxFaMjM8PnSU661VvCjg9fJl/pczf8EDxYOMWwWCKGLRLvNudZrLjSUCGfKZsXs4W51lpr3asy1VUShZqZ68sA0cyowhhOBcbY5/c+ZLHM5OMGEVMFWDZbvKhcY8M601JBJNKHx69G4+Pni422ZVVZm/vkYql0TTVObbZb5w7VlagUsrdPGiAD8KaAcec3Y8mYyQfGfxXU5Xr2MoKrrQMFWdlGYynizyC5P3Y8orBNE6lnEKTS3RChy+MvMy15rLtMPYsuVHAX4UMm+Xu17987U5/vWZL5FUjY5dUsNSTXKGxU+P38eh7Hin7R0SSkrW3AZfnH4+zo0IXdzQ75CfbreUfSgjvj7/Oq+sX+y2OaHqWJrJlDXIz08+tI0AdF2fK5dXGB3Pk0wa6IrKxweP8OzyeyzYZcpek3937m/53tIZJqwiuqJihx4NPw4/vSdziEGl1O1nNCWeJId9BkemovPo4BG+Mf8GZa/Jq+uXcEIPSzN5fOhoNxR7A5GUTGXy3aD5j2JlNbvt8fdffZOLZ+dpNV00XSXwQk7ct4enf/VhACYLOV6dnuX0whItz+Pnjt+FFJLL5c4gtM9xiaTPavs5Bq3Ht9mNpZTUnHdpepcAyJiHSeqxxT+pJjmeO05a6z+584IQ2/PR1DjTA8Dxg/i8R1F3MuIFcVaMIgQpfQ858wTrzsvU3bOs2S8wZH2ir7L5Riyt15lZrpJKjm3LoGi452l5V8iZJ7ddG35UY81+kVC20USGvHkK8QFWriPpsNL+PuEN6i3P8fBdf8chpwAypol+mwRZzwe3/tKXNJLYwRxl59Wu1UsIwcBQFlAQAsLI29xGB6pIoKABslMxzt9mf9vYVigjVt0yRTNPus+iQV/I+LOGkiCrD2CqOtYNExtFJCglP8584z/jRzWWWn9PyXocTWzPnwmly3L7+7jBCoowKSYe6jv+3gleFFAwLUxVu2kGycfumuTK3BqFnMWe8QK/9+UXUBTBvokCmXQLP3QAFT9cQqCh30EbfhwQhRLH8XssaEIIJobz/I//9Em+8p13ePa1S6xVmrQdn/PXlrkwvUw+k+SuvaN8+pEj3H1oguJAqvvMDWSIE/od0kWgKkWEnkDpE4PxQdFwPRZrDaaK+R4nx1aoinLHThTd0Cgv1ykOb19AlzIOUj9/bZkX37nGldlV1qut2HblBV1bWtgRDvRedgJFHYrJT+kjlAGEiB0YH0RxJIQAAXXfwVRju1QgI1quRyltsXHvDw6keeze/cwslvGDiGdeeJ+9u0o8dHIPVjJ2+wRhSKVu86Vvv8mlmXhsNjaY5eG7NwkmTVUoV1qM9SkaA7G6RlUULL33XEgpmZ1bJ5NJkEhs308Z1QnDOqIPKRxHnNy6CvBOUFDYlRxhSayR0jZyoEJqW/iIlu3Rsu/c8hWEEY4f4LkRS60meXP7wqRpaHFVZikJ/A0VuaAdeFxvlklrJhOpncnwg5k9DCdKvFl5j+8uv0hKs7ivcAJlwcI0dIrFNNlbBITfCT4UxFGj2sZpuXiOTzpnbUl9V3uUPQ3HvWXYI3RkZq7fc4uV8inyqSRzy1VGPmBQYSQlK61Wh7joPfWakiahDqEpGYTYeTIJcSc1PpBlLJ/lscNxcPaz567yd+9eZKXeJJKSV6/M8IUX3uZffvYRUAI0kYklZ1uQsxJdqWEYRVRadqcDipi1z5FQUgTSI5Q+raBG0ZzACeOVtbQ2QM1fxY8cVKExlTpGWoulcoam9ZAPDcftDS5ExpU7ULYxvBvH3u1kLwlgIJWkNJghm/2HeZZj5j7semm3wvUDVipNBtJWXLFMbq+csBWhdOMBERqR9DvSTQU3XEfvnMOuZE3GU7lAtkiog7i+5C9efrdLGmUSBr/15IN87tQRsslEV0F0Y/u2Dko2AoA3VtwVIWhtsR5CbL/7zMlDrDZa/Kfn38D1A7JJs/uQsQydkXyGYxPDfPzgFPfuGSdp6N3viSLJhkq2W8VGVcluKWGqqwqHRwcpdLKfoo4iSVWUvtx9JGUnBFF23iMZtj6J+HB0JT8yCCF4t3qekIiRRIlQhszbl0goKVShIhTiEqK38PGbqs4vTX4cSzX56twrLNgVAhky115nrr3Ohru7JyQYukqkG6EIwWfG7mGmtcZ3l9/FCX0uN5e41lrpyFNjYiCSEQnV4KfGP8avTD3KX8+83CWOPD+k1Q7JZZM7DpQHjDS/se9T/Pn0D3inOs0r6xd5rXypoziS3fYaisbHB4/wq3ue6Ks2itus8Lnx+5hurfLDlfdxI5/z9XkuNRb7tvlnJx7glyY/zhenn99GHCU1jWNDw52B5xbpPPFx2Zse4Q+vfJf3ajO8tHY+Vv9AT5sTis6jQ3fxj3c/RkqN74Xdh0aZ3B+rtjZWHcteg28uvEkobx67uO41ulXaNiAQ7EkP8dmxu9EVlzg0Ne5TndDnBytnOVefv+lAsBW6XGos3rBdSKomH+vYIG/Emtvgm/NvIm8xxFx166y69Z6/CQQHM6N8dvzennMpBOQHUoxPDOB5AcmkgUBwNLuLX5x8mD+6+j3qvk3Vb/H8ynvdYy6Jj7smFA4kd7E3bZLoKCY2lGv9jqwQgv2ZUY7kxnlh9Tx2GA/WJq1Bjue3ByZnDJOa5+5YuOKjgMvnFjj9xjT3Pryf11+4xH2PHOS9N6fJFzYtHZZh8IunjvHTx4+QMg3W7BaztRpZ42b2i4hV+4estn9AKflxlK5NS2IHC8zU/xwvqqIIkyHrE10lhyIUBox4Yiz7BGSvN9vMlqtoqoKU8XNmqdZkqpTvBgs3HJdyq81oLkMpnWJ8IMdY+mdoeBfwonWuVv8jQmgUEw90SC2F+MkZIWVIKNs0vUuY2jBWYoBizurkivTCi8rM1P+cg4VRTLWEEHGbIjxW2t9l3X4JgLSxj4HEfT1jGk1JY2pD4L2PH1ape+9j6VMoHYVNbK9ts9L+LkutZ7Z9t5VJMr5/BPOWFpg7szcl1FE0JYUfVWh4l3CCBTT9YPeZIWWEF1W4Xv8CDW/TqhyFEc1am0w+HasdpYNAZWtfaqpDsdojqlD3ztP2Z0jpezpjpA0VZQQoZDSLkjkQH7POLkgZ27XiHijqnLFNNaAkQMqAolEESvHTT8Zj6q2W1Zx5gmLyQZZa32LdfpmFxtcYzzyNSqJzDiUSn7X2D1hofpUIj6x+lKHUJ2+LbNyAKmLi8lbh2Id3D3FgchBNVfiFJ+/m6N5Rgijk0O4SyWSZpnsWVUkRRDV05aNVLAQ2uckb1fEAu0YG+Oe/9CiPnNrHt154nzffn6FcaxOEEZW6zUvvXOXtC3Mc2zfK53/yFPcfn0LXVCIpWXNiPMlHAAAgAElEQVSbcdUvXZJgjkg6CG3Xj6zdlqFTd1zOLa5y965Rcsn+ERt3uuCgaipDEwX0GwinKJKcv7bMV77zDi++fZVm2yGMJIoSZxwpiujmHSVMHUUIqo12D3kkoxZStlG1XYBBQrNZa7Wx/YBM4oNXBh8wrDgrkwhdVfGCkI1QDl1T+cwjRzh9cZ63zs2yvN7gd//0We4/fpXJkYG4bH3d5ty1Zc5cWiAMI6yEwdNPnuTg1Kaism37WJaxY36mqsZuoLrnshEHIKXE90JWVhvs39dfOSyUDJo+hqqO9m5PxItfitiu5LldBDLgYmMaTVGpeDXGEkNxHtyWrMsNRdZOiM/fpg1wA4OFNAlDQ9VUdmWycTtvGHcrikAoAinBc3y0znNMQVDzbFK36JsuNac5X79CUkvwubFPkVBNnl99lXtSp5i7WqbRcLjrrnEM4yNEHKVzSZy2x9ieQSqrm4PZbDJBZsskd7Ha6Fs140a4HT/rVtSaDu9dXIgrIXg+w4U7J4+EgJJl4YTBtowNP6rTDuZRFQtTLaLtsBK3ua04OydlGty3d4Lju0b4+IEp/t0zP+TS0hphJHnu/FV+7r6jTA25uMEKlrGvp5DfSC7dyexxcYOA62tVIhmhCpVJ6y4iGU/yhNhQJCnk9RBVaDHjqw7iRyGGqqMrCYJOcJyuKvx/5L13cGXXfef5OTfflyMy0EDnxA5kk+wmKZKiKJEKli1LtsLKliyvZ3bG460d1+7U7NquDa6prZ1yTe3M7tqzU84jybI9loMkKlIMYk7d7JwbaOTwgJffve+Gs3/ch4cGATRJjaZK0v6qUN14uO/Gc8/5ne/5/r7fvkwSXVXwgpBy02Gl3mK00EkUCdFEsjPYr48glMxVanid5xS3DPLJGPn8W1eKxHrK3Tt436WMEtLNwMNQRn9frNTfkSNb2T1PO1iBDlgU0wYRQqUdrKAKEzcokdBH8cIqbrACCAw1iRl7gMlSjbNT89193Tk6yMfvvoPYbVYs/DCk1V7rEAVgqQamEq0AaKpCJm6vW80JQ8lT56/xt6+epe37PLh3jJ87uAe1HXWPqhBYukY+GSedsqkvN5mtNLFsE91QmZpYolBM4TjtqNRkKIdtaPRl1tr+QrVOw213gaPJaoXJWpm8Ha3ArTIDVCHQFIWZeg1dUTDViEnVF08S096BdtVPWBxI7WDeLSEQ9FkFdEVjV+IuDMXClx62arJrdyR++XbXntRtPj58nCPZUb41c5KzlZssuhWqXgsvDBBEAIytmmSNOMOxAvcV925gG61G3kjy3+x6jOF4ge/Pn2G+VabuR/vSFZWckWAkXuShngN8oO8ISd3iRHEP480FLEWnmEjSM6KxtFzH2FDaEYUvA3Ym+/kX+z/GV6de5tXSFRacCk3f7QiiWgzHCjxQ3McHB+6kx0rfliZcNFP8s90fYnuil6fnzzLvlGn4TtT/dM55NN7De3sP8r6+QyQ0i/uLe5l1VkhoFrEOwCOEQOuyLKHHTHM8vxtdUTmW28loooei+fN8deolXi1dYcmtdc85odkMxwo83HuQD/QdpmCluuesqsoGmnpCs7k7v2tTcG31k9s9+V47g6moSOkghN6l3uuKyoHMyDpwJpRVZNhAKDEEOqFsEE3OBHQmdgIViY+pxkmpLYJgBQhAqB1HKkFcbXBnJoaiJFDEu9P5ABiM5TY43gkhyOUTG9zUdEXlZwfvIaXZfHfuTW40Fii3G3hhJPRrKRGLqWilEWGU2K2K8AuiCZu6ySQEIsD14Z47eGHpclRCTaR9lDc3jtuO7zGcTJM0zJ+6fmg1quUmO/cNcN8j+5gaL3Hfe/fRP5TljZeudbe5ulBiqdHkPTu2dZnDKdPC0rQttSF1JUMYOlxa/j8oxx8jZ92NIizcYJGZ2t+x7LwChGTNo/TEHu0CJm8XcVOnmIwzV6lTd13SHQau0mE4rzRc5io18okYcTMSxxVCUIw9RMO/wXjlT6m2z3F+6X+hYL+HjHkYXU0RSp92sEzTv0nFPUM7KLE798+ZKx1icqHMcG+WW+FrgYah5phvPokf1uiJvw9bGyaUbcrOG0zX/xYvLKOKGEPJX8R6i6CxKmLkrHtZbr2CL+uMV/6YULokjb2AwAvKLDafZr75HUDBVHu6JVMATsNlfmKJ4lCOXN8WK8ZhHYkKQqerkSO9zu+w+v4j2yAMQMPWh0ibh2j5UzS9ca6u/N8MJz+JoeaQ0qfh32S+8R1KrRdI6GO0/GkC2UJRFWJJG1VTt+y8bG2QlLGflj9Jw7vBpeXfYzD5MQwlC4T4soUfVMjZx2kGgobfpN9a07ZbcV6n4V3HD+v4skEQNqm1L7Laay40n8T1F1CVOFrnx1L7ydsn1ulLqSLGaPrzVN0LNP1xrpX/gLp3lbx1HEPN4csmZecUs42v4/iz6EqG0fTnievb3655vuUZC2qew7LbvO2qvqIoBGFAqdzA8wOGOkyTdluSjA2RsVOoSpx2sPCuxLl/UkJKWFyo0bsJw0YIgW3q3H1whP07+piYWebZ16/x4ps3uDm3jOP6NFttXjk7wcTsMp/76L186MH9EdNH1al6LSpeioQ1QBiu/FCsv83POWpz+/t7Oswe+SNjpSqKYHhnL+lb5jZSSq5NLvJ7f/okF2/ME0qJoauMDWbZv7OfXSNFenJJ0gmLmGWgqQqT82X+59//Bo57C2AiVLz2KXz/GrpxnJobko/ZmzKt39F9AAxF7RqrKELQm0pQTK2xvyL2WJZf/9SD/Pu/fo43L02zUm3y7ecvdMeT1UV7RQi29Wf58IMH+dn3HlonIxOPGdy46VKru6STG8kCHQIUru9Tdd1uLuW4EZvN87YSwg8Jg0VARVVvNWEReDJibf+woSsae1JjCARaZ4yLFhHX2sk9d2zj448fxfV8FEWh1W4TseQix+ywQyhRBLSDAFVRyMRsckmb5cChFfgYHcbXlsxGEc37ZGferioKy26TmGYyfBvZr5hqcyC9m6yRRhcaMc3i7txh1LJBEIQdR/Mf+vZsiB8L4MiKmfRtKzB+YYb+0bUBKG1b9KWTnJ2cQwKTpQqlenMdmLRZLFbrlOrrKcOr7i2moTM1X6Ynl2T74NsrpN8aAsFQMs1QcmPHqSo2mhJDoKIrWwtYbrpfIbANnft3b6PmuPzu330/crBquVyZW2Ks10ZT0xvQlZ5UguF8msVaAynh/PQC85U6/ZkkMS3FQrPOqaU5LDUSXIxeWIGuKLSDAEvTCWSIrQbMNBYJpaQ3luBgoZcDgz0kLZPlRgvX93nz5ixHtg1Eqw4d2rumbkRfm+31YsuD2RT9Ha2mW0NTFXQtOo9VKt/bdeh+GHJpdnHD51JKLEPj0PZ+gjC8LYCzGnFthJjW3zmmgiosws5KWHSsFoaapuJeJG3uiZImEbl9zJSrXYFpAdyzY/htj9l020wvV9bOmaiuflu8p8u6uLX0UErJ9cVl/uDJl5gp19jRk+O//9B70Joh1y7M0Gq2SaZs2qHHbKlFydQIQ3CdNj19aVzXZ/LGIr4XRCJqrk+hN03Cttk7UOSJUxEoOLNS5cbiCsP5KGEKZEjD82j5VVq+x2g6y3yjTjsMcAMf1/cZSqax1IBy28FQVGKbvA8/6WFrFqNv0SlIG+uFn+O3OEZsVuLoBh6twCNt2Jiqzv70MHtTg5TbDeadChWvQTuMqNqVdouMEWNvaoA+O7Ml42j1GHkzyWdHH+KD/UeZdcrUvBaBDCPnHSPOoJ0noa0JMN6T38U9+Vs0IuIwPLA1lT6QIb4MGIkX+Y3dH2LeKXcAKgeBIKlbDMUK2JhMLVRom5Wo9FJTI/0rQ8P1I/0uhWhg9RzJo4mj3GnuYry6iGpBKm5hKBpZI85gLE9cXZv831/cx/3FfevOq+14VFcaEXAvYI/Sz28P/jyBF0AZFpaWSMVNPpu6n/dp+1mhgZqKgM2kZjMcy5M11oTJw65Vt2DFbXWAdoGKwFZs/vHYR9AUBUvVCDs19Lqi0PQjWrQqFApWDLsDnkopuVS7yBvl13i4eJy8mcT1B9exMlJ6jN/c+9F11+W23yQIFpGygaoOEJWASkBD4uH7N1GEjaqu6pGoeP5lFCWD510GJELEGNAW+Vf7RzD03VjmiS2f760ROUNGzkrqu5jwCCGwNYOHC3A0maHGo6y0na7gqaXqxDWLgplivtLkjZlZ+pNJEkbEBm0HwW3NLoZjeQyh4siQnJnkRGEPprqxn9UUlUsrixStOGnD+pFMCn7cIpGyabVcVFVFUQRnXrtBq9Wm1VijzquKYKFWZ7ZaR1cUkpZBzo4S960mTEljL3n7Pm5Wv8R45U+YqPwZQmgdFxaJQCdtHmZX7jcx1Y3C91tFyrZI2RajhVUh0PX9oxcELNWbZGM25i1lSrqaYiz9BVRhc7P6Zdxgken63zBd/5tNjiIw1BwKkRPqZo9dVWyGU5+m4r5JqfUCi61nUISBlCGyw4LRlSwjqc/QF39sAzAmhEJ//EM0vBvM1P+eWvsS55f+V3Q1Awj8sEYoPRL6dnZmf4Oyc4qJ6he733eaLr7no+rqFvmNRIYl/PbLIDSEkgVhg2wjlCyhPw5C6/yeRgZLqMadaOoAo+lfoeGNU2tfZKH5JKXWC2hKglD6+GEVIVQK9nsYS/8qV1b+zwgElBK/7aOqagc0FzT9BXKsaVwpwmQ0/Xka3nXq3jWWWs9Qav0ARZhIQkLZRqBzrO8/UDCPktITWMoaw/tm7S+Yb3xry7ZRcU9TcU+v+yxl7Cdt3rFO00oIQco4wP7C73C9/P+y4pxkqvZXTNX+uvMMI8tpgUpM28ZY5lfpjb//HYOba08ABuMZbFXfekIHzCxW+KvvnGR8ZmXddrap8d9+5iHs+JsowqTpXSZl3Ytm/PTlRX6H8b9Vri6EIBEzObCzn/07+vj4o4c5e22WJ549x+sXJmk5HvOlGn/8ty+SSdk8cNd2tieLVNotimYSKRfwwxXUdylufru4PL/EpflFMrbNvWM/OiZT4IeMX5phZFcfdjzqG5tOmz//2iucvx7NgVJxi8999B4euXc3vYXUpuOdv4nTmxBxhEiiKAWEUEmZemSgEwTd+de7CT8MSBsWeXMNVnd9f4Psh6IIdm8r8v4Te7k6sYjvBwz2ZjB0jTAMidsmffkkB3cNcM/BbQz3ZzdoD2uaSi4b31IcGyI+4p58gb6OC6oQgnjcJJOJReybTa9PQUoH37/aBY4ibUOXqldnuV2maG4Unn6n92feKdH0W+TMDEUzh64pJONr/VEYSnwhWWo1abheN3+JmzqZmM22YoZy02G50cQLQ5qOQ18+RT4dZ26pwUAiif42wJ8MJa260wGPovmlL0OCTVi9t0bFq3GqfB5VqBiKxiM99zEWH+LizRlcx2PyZomxsSI/KsjnxwI4AmhWW9gJqyM0FjUaRREcHO7l6QvX8YKAUr3Jmzdn2VbYvHYSOhPuhWXmK/V1nydsk95cglBCJlV8V0Lb7yQECrqS/qGpchC9PDt6csQMnUrTQRKVOKkiSSjMDfTbpG1y5+ggJ8dnkMCNhWVevT7Fz9y5j1Xli5xpkzFt3MCP6luJBsplt0VC79SthiFJw0RXlEioTMKe/iLD+QzLjRaeH/DS1Zt84p6DxE2DULbxwzqmut7lQkrJzaUypyejTlMRgr39PRRSG6FSU9OwDZ2G6xFKyexKFS8IMLTNm6SUUG05nJqY3fTvN2aXuTazRNw2SMUtErcBF6WUmGpuw2eStZIuqUYHzVt3IYSKQOnidp4frDlMCEhsIvr31n1PLJW5trDc/UwgSOkxGr5DRl9D/Vf3K4ELMwtMdcS0RwpZRgoZkNDTl0ZK2a1XXQMton0rHfR7/+GRqJRqFTDUVYQiODzSTz4RZ65So+0HfPfsFY7vHEFXFUZSGQYSqS5LTVdUdmcLSCSLzQYNr81YOgsISq0m2Y4jwv+fI3IYK5HSbXLmWlufaCzzemmCT43d3f1MEQo5M0muw5wIpeSJ6TOM18rsTBrkzOQ7sjaWUrLk1GiHIXekR/7LPINOE1eFwoCdY8DeOCjfmF3mzWszGJpKXz7J/HIdQ1M5uL2PK1NL1B2XYiqBBKpNB0NTWa420VSDo3uGGCy+Oxv6+cklzr54hUwhCQjaTpswkFRX6iQzcYpDORRFMH1tAaEI+kcL7BvdEa2wvyV86XO+eo6snqVoDvDC3AShDEnpFkU7znSjSslpEEiJqWoMxlP4YUjJaTIQT6ErCotOg6F4mgO3OHrNObOcq5zjaOYues00bf8GmlK8rXijqhQi1pDIIwlRld5I30BYSNlE0W2EsFGVAhKfMFhGUYqoak+HbSSRuChKkmhFbmOpRNu70SkTcRHCwg+XkLKNqe2m4T4P+MSth/GDBcKwjKnvp+2PAwGa2o+UbTx/CkPfRhjW8cNlLH0vmtqDpVyhN95L27+B7y9g6nsIwhUCuYimFFhRMxTja++GJhSO92zb0kZXSskbK9e7Iuk7E33sTGxub60IQY+9Ufvkpym27SjSarhYts7he8b46n98AVVT+NDHj3W3SZgGF+cXmS5XMTSN9+4eY29vNKHx2wHzkyViSYvELetloWzTF/8AcX2MucY3OyyeZTTFxNb6yVv305/4CElj123uryBl7mVb6pcBQUJfA6jXvrJeyyLwQvpSmz8zVcTZlvolUsZ+FppPUXFP4/izHf0gga4kMbVeEsYusuYxMtadjDdq2Ka+zvkpOlaApiTYm/ufmK5/laXGizT9CaRooyl5UsY+euOP0RN/BHUL+29DLbAj80+x1B4Wm8/QaE/i+TVU1cLWhshadzKQ+Chp8xCm2oMQKqZaRBCVvaqask5v01SLDCU/gRdUSer7ECKGUAdBNpFhFUVLQ2fiLNQCEdsoQCi5aFslFd1zYy/78r/FdO2rlN2TOP48XlhFUxKkzcPk7HsYSnwCQ80xnPwUSWMvaeMgaibOykIVGYKmxNDect1RmdhB9uV/m9nG11lx3sD15wnxUYWNpfUT04bQ1Wwk2o/GSqWBYWhRKbx6H9LMgoRGyyVmGZ3JIPh+iOcHGLrWzbVURSFpD2wqhC6EQs66B6vQz3Tt71h2XqTlTePLJrqSxtR6yVp30hf/IGnz4AadrtXI2feiCBNFGNj60Lq/KUKw5DTIdmQDEsZuRlKfJpQeSWNfVyLiB29cY3qhwsceuYNEzOy2aFVRyCZjVP0FwrCJpY8SyNs7L/0kRjJl0dOTesdzJyEEPfkkD2fjHN49yNeeOctffusNVqpNSpUm33/5MkfvGKLmRdUhcc2m7YdI6XQ0ud5d+eZWsbMnT08yjh/Kt83V301YMQPTMmjWnC6QNj69zJkr0RxFCHjw2E5+/v1HsIytrdjbbX9DvxWGJcJwkTBcRlUHCElTajSj0t7kuxcN1xWVpu9hqe3OuQkarsfFmUVO7BrplnmHoeTUpWn+/B9eplJvcezACP/sMw9RyERjtyIEmqZg6FpkmrTJNZU79vXGba5ZILiyUkIVCjk7ArNURSGfS3TYMZvNo0MQxgZZDD/0UYVC1rg96/12ETFhA/Jmhpga9QOmodOTS3L+WjSfXViuM5hOsnegSLXlEsoI8EvbkWSMpij0ppPdRZqJUpn4qmC47/P63DQPj4wRU7Zug0IINF3FdTxWFqtk+lNoHf2v28Vka5Zj2TsYjPUCAluN+tJiMcmZM5Ok0+9O+P3t4scGOPLaPsWBLMncepDhxM4R/uKFN5mr1Kg7Lt84dZHjO4fpTW++MrrSaPHNNy/RbK8XynLaHhcnFtgxVECTCsO9727C8nYhhIYiTBreBIHhsoq1up6PpirviGIoZSSOvWpjryiCQjIeafCgdNkwq2HpGg/v287fv36exVqDdhDwZz94nR29OfYN9FCMJSjGEt193xpbGkV3XvR8IsYHD+/h3PQ8fhDyxvgMT1+4zuOH9kTUYq2Pt2ouNdw2f/nSaWZXonLDbNzm8cO7u+UJt0Y2btGTSrBUizqZF6/e5MNH9zKU23ylwQ8CvnPmClfmN4pDA/TmEpi6im3qZBNbgxmrIJXrB2hK1PE12+0IqJPQl0kiRMdusuWST8Qi+qCU1FttdvcXIiHu1YmohOmV6paLAFJKak50X1Yaa/aWQsCAnaMvHnU0hqZ26Ppr32t7flc8dnalysWZRcZ6cljm1qUHa7F1jf/uvgL37R7hq6+eQwLfP3eNEzu38ejBnRgdB7hbY/X3kVSHlRSG+EHIYPL2lss/6SGlZK5VZd6pYCo6Y8loMj7VWKbuuxStJH12mqnGCn898Rrb4nn2pvvZk+rFDQNKbp2dyWJXF8IJPG7Ul2iHAX12iqwR53J1jh/MX+au/Db2ZwYwFI3JxjIlt0FCM9iWKET2zO0Wk41lfBkymsjTDn2+MX0aN/C5uzDG3lQfhqox1Vih6rUYimfJGXGqnkPDd6l5DhLJQCzDittkJJ5DArOtMmk99rY11FtFfz7FI0d3ousR02jPcIiqCCxDJ24ZhDLqp9ZWtqL2HASSuP3uj1noz3Lig0dQOoNg4IcoiqC8VMOKm5FGXhgysL0XRREYtrGlIGDDb/D9he9xT+5eBu1hTvRGAJwiBJpQ6I+nCDtlmquOP04QEMgQXYn03dKmTUJfr6d2d+5edif3UjDzCCFRlQy8zYKCpg2gaQPv4A50oH+1l9VkwtjgiLV53+D6lwjCOm3vGqa+B00t0PZuEGmL6CgigwxbNJwfoKlFgrCK618hZf8MijCptp4ibt2HlAGOdwFT20ndeZq49SBCKARhGad9GlPfQ915CikdTGM/qpIjCAMMVeVmuUw+ZmNpOsYWzngAZa/BswvnIzBfCN7be3CDKPZqrJbAmerWLL2f9Ehn49z70B6EEBw9voOR7cWohPAWvcZiMsEXTtxFw21jGzqF+JoTW63cYPLKLL3DeeK5tYlHKNsIodETe4S8fW9kTR9GRhyaSGBqRUQnm1lZbrCyXEfTVBJJi0TSolaJyp8q5W30ZX8DIQT1RQffrGKYOsvLdZLJKImt1ZxIZFVVePWlqxy5c5R0JkZpqYZlGSRTFosLNQxTo9iTJG/fR86+Gzco4Yd1pPSISgEMNCWOrmRRRPTuBUGFSt3ZZAyWhLJNTB9ie/qfMHv6TtJJj90H+lCEgaHmOwzxrfMzIQSW1sNY5h8xkPgYL/zgDdIpjf2HRlFFLHLUFRHTLWMdJmMd7n43P5DDipvYiTUmnK0PsjP761TLTSbHl+g7MIhmjiGlC9IFcStDe72W2y1nBahkzCMkjT20O/coYh7q6EoaQ812Ra37Eo/Tl3icwA9QczWsmBlpagQ+gXQ37l2oZK1jpM2DuMESS+40S+4SitAZjG0jrRe7elcrlSYnz05RyMdxHJ8gPIquHSMMJbqA5VmHeMwklbRZmCvT15NCOpLFUqTnOTacZ6SngKIoeKHHkluiYBa6CyhCqMT1UXZmf5128Cm8sIaUHkLoaEocQ82jcHtNy774Y/TFHwMiR8Cp5jQ1v05GT5M18ji+R0VEYt9Z6yhZ6+iGfSxXmhzbP8xDd+3c/CDeMVx/BkVYGOrmIPdPcjgtD88PaLba76qfVRSFfCbOL3zgCNW6w5efeC1aZJ5doVF3KcaStII2EhcvmCXEBTaRm/gh+nYhBK22x/nZBVaaDg/tHmNgi7njuw2n1aZSqpO+pYT75twKtUYEhKmKwsN378R+m2qE2aXqBuBIUQpE0/MQoSSJ6Rp7eopsz/9wguurvUjNW3NX1DWFt0p01poOX/zGq0zOlcmnY3zhYyfYNfLOmaYAyaSFbetY5u3hhZgWlSh3J08C6nWHgf7N5+aBPwVk1mkcCSGwVbvzY72l33znIRCYqsmcs8iQ3R8xoGyDnSMFnnntKlJKxqdLlFaa9GSTWzpR3yohtKc/um+hlCw7LRzff1tgS0pJGEpaDZfl+QrJYgIvDLccBSpejXOVKzT9FhdqV2lLD01ojMWHUFWT+fkqO3f1UiymfjqBo3g6xtmXr7Lr0Aj92wrdBjDWk+MDh3bx5edP4YchL1+d5N8/+Qq/9t67Kabi6GqETvphyGK1wRefP8WT565tqCMMgggdnF6obBDHjhDCqDwjCCVhGBJIiXeLM1sgJXXXjUAgIVAUBbUjciaEoNR6iXr7Gqa2+pJFaPl3z17h4swix3eOsKuv0LVovxW1D0OJ43lcnS/xp8++TrkZdTwDmRR7+ovYuoYXVFCV9aCaEII7hvv48NG9/Mfn3iAIJZdml/idv/4un77vMA/uGSNlW50OQnTFvlqeT7nR4tTEDDt6Cxwa6dsAROiayuOHd/P0hWu8fHWShtvm333nBdp+wMP7tpOO7UbpCBR6QUip3uBLz5/iG6cu4ochmqLw4SN7uGtsc1vSfCLGwaFeLs0uEoSS0zdn+ZNnX+fXHr77lrpbQRCGVFsO3zlzhT96+lVarrfpS7S4UqfpelydKXFoez+Dhc0BqLDD/qk0HTJxm3rLpdx0SFgGjuczV66hKQqjPVlKtSYzK1XCDpU0bhrs6i8wWsyST8Yo1ZtI4JkL1/nQ4T1s78mtAwg9P2BqpcqXnj/J985dvb0QrtvGD4I1tp0QjPXkKSRiLNWbXJxd5De/9A229+RImOuTJE1RsE2dwWyKwyP97OjJkY5tXbJh6Rqfue8IJ8dnuLG4Qrnp8HtPPMv40jIfObqPfCKG0Sk5CqUkCEIc36futLm+sMwLlye4Y6SPDx/Z3ML5pyVW2k2+eP1FtiXyaEKlaEX9xpnyNC3fY7x+hs/vvJ9yu8lcq0pCs1hyIrDIC33OrExTcuvcXRgjkCFPzl1kol6i10rSDn1Sus2y26Duu9Q9l4bv4oUBFyqzVL0W12qLPNS7mzuyQ/z5tRewNYOkbpE2bKpYxgcAACAASURBVAQw3SyjCoX5VpUdySKvL07w6tI4vVaSp+cv8Yujd3OlOs83p89wV34US9VJ6hZfGX+FX9n5AHHN4EvXX+az249vCRyFMhJP9Dv140JEGiUgUYSKokEqqXfYNAoG0SAcSJd4fBW8lKhSYiob26SUEid0cIIWgQxQhIKhGNhqDPUWhmUoQxp+g7bugi4wOsnC6v5S+QQ1v4YUPrYaQ/pN3NDFpw3SxpJ2d0zwpEcraDHevMFUa4p9/n6WvSVQQKCS0FPoit49v3bYphU0aXa0e2zNxFYtVKGSNq3udlW/SjuMEjNTie5E5/KRIhr8W0ELJ3BIasnOeUSabaZiENPi66559bqbQRM3iMYEU7WIqbF1TJ1QSqpeFV3RiKlxmkEDp7u92d1eVQq0vQmEErGN/HAJpI+UbTQlF/VPQhDKJoqSxNS24wXTGNpoNKkVEkXEov+jIBQbKT3CsNH5qQMKiogDPggdQxtFU3NoSgk/DPHCcJ0BwGbhhwHPLpznej3SkRuL93Isv3NLsLzWdtE7pXE/rdFqtGk1XXLFJJqm0juQpbLcoFJuki9G/VLDbfPEucvUXRdD1fjY4f30dyZJbqtNKpcg25tGKOutn2FVOyyxzor+1giCkFdfusrSQg3dUEmlYxy5a5RXXrjCnv2DTNxY7O5HCNi1tx+37XPt8hz1WkS9TyQtWq02Yzt6mJ+tUK87TE0uMzO9ggxDMtkE5ZWoZPKBh/dRKCYRGNha/6bndGsIJWL3bOhfIiuHaBs02vUkSqDjlgeIxU30uEmj7tJqutHvukaz6SI7OhN2zETXVarVFjIMSaYKyNYQ7VDDLfcTT1gIRaPRcPHaPmEQks7FCYKQWrmFqinYCRs/CPEbbpdEoaoKQRAytK0DmHghjVqA7wt0w4nK0Nse9apDEIQkEhaxxEYNr+i5xdCUd+ZCpWoq+c7ETEqJJmNoYnOxXSEEqrDxgwxPzH8XgSBv5uixMxjqGm0tHjPYs6MHveOe53kBqiq680HPD7BNA0UVpJM2lqnh+QHpZORemUpYrPaVDb/J04vP88H+R8m8pVxJETqW1ofFfx4oE0pJqb3Ms0svEldj/MLQx2gFXjS23eZ7Y4N5zl2fY75UixhHYu0+mbpGvX0GgY4iWlj86EqiflxCVRVitrGuRP/dRMwy2DVSRIgIK/D8AM8POk7JUcmhpvYgfZ/N2Ear7owQ9UftLbVw3nJcQ0dXVXb3FrD1H92U12m2Maz15Y2O63V1gIQQZFO3fy89P+Cl0+MbDH9kWEFRMkjpIsMKcWOAwwM/fLuXQFI3u7IYQkC95W4gWEzPl7kxFTnGFnNJevPvHmTLZ+LMzFcw3uZeV1yH7coaEBaGkmrNwfc3f65CiSGl11lAuDUkVa+GG7hIPYGmKuvm147rEUq4HWwSyICG30QVKq3A6couHN07TC59mlK5QbnW4vsvX2LXtndfsZSzbPwwuO0cECKyiGHqUbVLfwZVi2QSJhtldqQ2ssidwGXWWSClJyMH6NYChqIzbPeBahJPmJQmagS+pFhMsUVBz7uOHxvgqFlrceLxQ8yOv8VFR9f46NF9vHTlJpfnlvDDkL9//TwzKxWO7xyhP5siDCUL1TrPXR7n5PgMqiLYP9jDhemFjkuURFVVDuzop1JrsWu4pzsAL1TrvHptiqrj0mp7NNw2DbdNq+1xdmq++5jnKzX+7bdfIG1bxE0d29CJmwYxQ2dbIcuugWFC3UcROoFsdru9qeUKX3npNH//+nm2FbPs7S+yrZAhl4hhahpBGFJpOlxfXOa5S+PcXCp3GSiPH9pNXyaJpmhbiu2piuCT9x7i7OQcb4zPEErJlbkl/s0Tz/GNU5c4MNhDMRnH0DQcz2Op1mR8aYUbi8us1Fv8i488xKHhvk0B2kIixq88eIzp5SqTyxVmlqv8m28+x/OXJ7h7+xCZuE0YhkwuV3j12hQnJ2ZwOy/90dEBfvH4oS1t3jVV5X0HdvLsxXHmKjVcP+DvXjvHXLnGA3tGycVtQilZrDY4OTHDy1cnqTkuu/sLLNWaGzSs4rZJwo4SK32TspS1+6Vw59ggoZQoQlBzXKZKFUYKGUxd645XpqayvTfXrTXtlnupCrl4jBM7R7g8G7Gfrs6X+Ndff4bHDu1mtJhFUxTqTpsrc0s8ee4a56cXMDSVA4O9XJhZ6DLKbo22H7DSaHWFv4UQ7Bso8nPHDvCVl96k7rSZWq4wdYtO0q2hCIGhqaRjFvdsH+YLDx9jV29+yzr0Xb0FPveeu/h/vvcii9UG85U6f/T0azxz4QZHtvXTk0pgGzqu51NpOUwtV7i+sMJCtU6r7XXFtH+aQyJpBR4p3eZoboScGaMdBgzHc0w3ysw7Ncpek73pfrYnitxX3MHhXJQwxjSTY/lRnpm/1NkXeKFPKEMOZAcZSxSwVJ37e3byWmmc9/TuYleql0CGjMRz3GwsE0jJeD0qgXNCn8+N3oetRuWlAtifHiCmGTw2cIBAhrxWmuC+nh3cldvGn1x9nvPlGVShULSSPD5wEEvVkMBQLMuV6jxZI0ZKt+i1t2aOVb0ynmwz25rEVuNoio4TNPGlj9UpcYipcZba8yS0VAc4EZGIbeiiKTqmYuFLnz3JQ6isB4OuN67xyvLLXKtfpRk0MRWDAXuQD/V9hKFYdC+90ONs5QzPl55jzplBQWVHYgcPFB5kLL49ArCRfH32HxAI9ib38ULpOaZb02hCY09yL4/2vp8eMyonO11+k2cWn2LWmaXqVfjm7Dd4cv57AKT1NJ8f/UL32DdbN3l1+WWu1C5T9SuoQmPIHuJE/n4OpA6idVbFfenzxOzXOVc9SztsE9fifHbklxmLDxPKFoqIJuRvlk/yUulFHul5lLPV01yuXcYLPfqsPh4uPsLB9B3ddzYIfa40rvLs4lNMNm8ipWQoNsL9+QfYl9rfPbYTtPjLyS8zaA9xR/ow3134NjcbE0gk+1L7+ejAz5HQEhjaNoJwBUVJIhB4wSyRbfQAihKj1T4JjBEz7yUMawgMTH0PESPJxtIP0Gy/jqntQlf7cL3LxMy78YNFIkasi6EN43hniZnH8YMSiojaiKooHOzpIWVZxPTbAzxX63N8bfpV3NDDUDQeKO6jx9xa82JHOsdQIo39o8qKfgzj2qVZTr92g0/+6oNonfHtyvlprl2c45P/9YMATJUrpC2Tj96xl9PTc5yfW+gCR8hIP+F2eWt1uY7bdFE1lfpKg4GdfWgd8fxV3YlU2mbX3n5uji/h+wGtlsfk+BJe26fZ8kgmLXbv7WdgIMvJ18dxXZ9KpUU2F2fP/gHOn5kiHrfoG8gwOJzjxrVF2q5HoZiiUm7guh69fWkM4527YgFYhhY517wNK0EIOP36OFMTJSxL54H37efZ753D9wIsS2fbjh5OvzFOq9nGNDRGd/WSyye4dmkO1/HYvqePMAw5e+omM1PLpNIx7nt4L9/+h5MoisCyDR58/wHOvD7O/GyFVqvNyFgBrx0QBiG+H6JpCqM7e7l0bppatcXHPn2cuZkyX//rV+gfytGoO3z448c4/cYEC7Nlxq8u8PBjB7n7/l23vbYfJiw1S499Jwpbv5NVr0bFq/LZbZ8kb+Q2lFJbps5Q/+ZMiPULuKvPRm7YZjWSepKfGXgcW/3Pc+C9XWhC5Y70AcpelRuNCSTQH0uhCRX9NswzXVd5/fwk4zMlRvqy3TzT1DV+4QNHCNQmljaEpqRRxA8Hrvw4R6Pp0vaCdfl1EIbdxfO3C88PmFmsdCUf4rZBOmaTTcQ67poCVSQQ2hBCbMwvbwVh6k2X2aUq+cxtFIM7oSpRxce1xVKHbfTumUubaVhmCklq5QbpWxicq4LXbuc7SytblyyGoeTc1VleO3dzCwMOHylbeP4lDCWFED88w18VCjlzbcEp7FRCvBUAufX6ZhcrvHxmggfv2kEqbr1jxorjetTqDo2mS3wLN0kJ6IrCfKPOtlSmo+8a/WUrcWxFyaMIh7f2H4pQKZi57qKbbRrr9JUmZldoez7aFoxlAE3R2J3czmxrnoS21qb2jPZwYEcfz74emVB876VLHD80ypG9Q+/ofgRBCCLSsRpNZ9GU249rUkZjbSxhkc4num6PpgxRUIi0L9ei1yrwSM8JSm6Zl5dPcSC9KzITEdHChGXq5PNJlpZqHfHuH0382GRaxYEsU1fn6Rlar6MhhGB3f4H/7vH7+d+/9jRTpQqu7/Pc5QmeuzyBoapIZNfFKxOz+PSJw+zuL/Ivv/JNXD+g5Xq8dOYGjUabTMLmwvgcxewOhBBcmlnkd//uSWpOe7PT6ka15fKNkxc3/dvjh3bzu7/wIJJruMESafPAuvP3goBW22O50eLk+AwQsUQ0VemygG59FWKGzsfvOcinThxeh7JvFkIIhvNpfudj7+MPn3qV7527SqvtUXNcXrs+xWvXp277/Qio2LyWWAjB8Z0j/NbPvpf/67svcmF6gZVGi2+dvsy3T1/u2mneipYbmspdY4P8Dx9+kNFC9rYDyrHtQ/zKQ3fx+997iUrTwfF8nr5wnWcuXu8yyVafq6oI7tkxzD999Dj/9tvPbwCOCuk4r12apNp0GenZugxREK2cKJ2a5EzcJhu3N+TTqxpCuiq6yc8qm0hXFT51/DDnpxd4/cY0QSh54cpNXr0+RcIyUYTA9X0abhspI5H3X3vv3RzZ1s+//Mq3mFqpbjivmKGTjlld6qjr+1xfWKbhtt/RMBdKieP5OJU6Xzt5gYnSCv/bx9/Pzi3AI01V+Ohd+8jELH7/ey9xZb6E4/mcnZpf5xi3Weiq+iPXCPtxjJwR55d2nOCFhav84ZUf8Ms7TnChMsvV6gJ3F8ZIaMYa0Mfti5E0ofD+/gO8unSDf5g8xe5ULx8buXPDdpONZb568w0e7t1D0UwQyJBW0MZWdSxFR+8MPFF99dr3QilxOyCXrqjYmk7Dd0npNnkjQUzTuwzBI7lhXli4RkwzOJIbQe18vlkIBMvtRUICknq6w84MiSs6gfTRhE5ST7PilQhlQCgDAhmgKXrEQFKsjgPg+n5sVUT6ixN/hio0jmSOUjAL1P06S+5iFxQJZMDzSz/gW3NPsCOxiw/3/wx+6PPK8sv82fgf85mRX2JvKhLQbvh1LlYvcLl2icOZIxzL3sNUa5IfLD1LK2jxqZHPEFfjjMZHSek/y7X6VZ6Y+zoPFR/mYPpQ5zlpFMw1analXWbBmedQ5gg9ZpGG3+C5pR/wlckv84+3/xNG42Pd772/9wOcyN/HK8sv82LpeTzpdVZR84QyYgA5octEc4L/NPVXjMbHeKzvcWpejRdKz/NXU18hbxYYsAeQUnKqfJK/n/k7BuwBHu/7EFJKXi+/xpdu/jmfGP4kd2WPIRCESCpehYpXYbw5TsEo8L7eR6n5NTShd1cZNbVI0n7/lm3U0LZ1/l3TADH00e7/Y+axt34FAFOHuHV8089XI5AhZxdKHBsYIGFECVw78Jh1ItacQOCFPlfrs3xp/FkuV6Nx8mB6hA8P3rWujd7an8mODltk/vDTWaYGUFlpbGDU2HGTxfm1hQRb11lutri2tMzEcpmUbfHm1Cw7inliKZuLb9ygZzjPVmuvNy9MMXV5llq5yeCOXhLZeNcJrN328byAUEpuji8Rixm8+cYEhqF1WGqQy8WJJ0wsWwcBTsOhWW1RKCZJZ2JYpk4mEyORMJGh5MLZaXbu7uPS+WnsmMG2sQKXzs9gmDq68e5S01TcYqXWfNs2ICXsPzTMnffu4G++9AIXzkyhCMHPf+YEX/vrV7h+eY5CMUml3GTH7n7Gr81z9uQEMpQYps7MzWU0XWXfHUPdfZRXGjQbLo988BBD2/K0mm2e/OZpRkaL1GsOmWyMVrONDCWNhks2nyCTi3P42CjPff9CxGps+1i2wYc/cYy//dKLLM5XmZ1cZufefqSEwZHNx/Fbo+bV+e78UyT1BJPNaSSSh4sPsCMR9VFPzj+DqRost1eYdRY4kj7IsdxRfCl4afklLtauIBDsT+3hWPYooQx5dfkNTpZPM9Wa4W+nv0av2cOH+z+ArdlMNqd5YellKl6NopXnPYUTFMw8fuhzqnyGc9ULeKFPwczzaM/DJPUEdb/B04vPMedEOcbB1D7uzt2JKlTeLJ/l1eU38GXAp4Z/nkxHXLodepypnOVU+SxSSnYlt3M8dw9CRNckhMKis0grdDicPsid2SNoQuVM5TynK+do+A3SepqHivfTb/dGrDhEZyIWRSglSfP2YI9t6jx230aWtaGpqIqCrm+j3j6HKuLoas+Pz+TqRxS6prK4UGVgINPV9HnmtaucvjzNfYe3M9SbIZuysUx9Qx9dbTg898Z1vvHsWSDKwfdt7yOdjJjA6mqWKwRN900S1n0bjr99uEDM0mk6HqVKg689fYZiNk5PLrnheEEQdvtLXVUYSCdxff+28x0pJaV6kyAMqTttVEVgahoSSd1p0/aDTo4uaHkepqaRHssRCEG15ZK0TUYHc6STNo1WmyAIeeIH57hjVz/ZVGzdOTZaLq+cucmf/cPLzJdqG/NHYaCIJFLR0dQRxFtYgVJGOaDvR7IRtaazzjq+7flUGy6hjHJ9TVMYTeS7OaOhabxn7yh+EK5j8g72ZNg1UmS+VKNSd/h3X3qav/r2G1iGzq2YqqYoJGIm/cUUR/cNc2jXALl0HEURLJQiZ+vN3K9vvdcQASprWwky6Xh3YWRjhARhCSEbaNqe7n6qXi0iWyhGJB+QsBjpy3Lh+jxSSp4/eZ1j+4e5/+j2dSyoVS0iKcEn4ErtBk7grnsOcdvg0x86xsUb8yws15ldqvKv/+RJfuEDR7n/6HbymRi6FuUeYShx2h61hkup3ODs1RlmFqt84WPHMTWNK8slRtNZjE2kW7p3QECmmGJoZy+arhIgqXsuvXaSuG7g8Fa2VaSbeqZykbJXpeLVmGnNoys6fUaRcmXNhfw2j+Ndx49N31avtjhw7w4mr26ctCpC8J69Y8RNgy+/cIpXr09Rc9r4YUA7iLRq4qbB9p4cn73/KI8c2M5CpcFYMUep0WSkmOGuvcNcvblIJmHTvuUF0zWVfCKOtQUz5p1E0jIJZBMvKBNKlyBsRbmZgBO7tjG+uMIb4zNUmi3afkTLDGSI70XlC4oSaTTETJ19Az186Mhe3n9w55ZsnbfGqqj2b/3ce7lnxzBfP3mBq/MlGm4bLwi6pQGKiKwDDU3FNnT2DvSwp79424REUxXu3zPKUD7NX7zwJs9cvMFyvYnr+7T9yEpcVxVMTWMwl+Kjd+7n8cO76d1C+PLWMDWVX7z3ENmYzRdfOMnEYplm28MPQ9p+pFhv6xrpmM3jh3bzyeOHGMqn2dNfYGq5gtKhCAshqDVdnLZPq+3h3uLQFrcMisloAp5PRIr9C40Gl5aWIoAn8EmbFg2vTRBKgjAkY0er4qpQcHyPetujEIuxr1jsrq4M59P89s8+wpdfPMVT569RaTp4QchKoxXRxxWFpGWyp7/IL957iPcd3IEfhBwY7sMNArLxWFccEiJtpb5MtDpcd1y+/OKb/OVLp5mv1MjGbB7Zv4N9gz0kLGNdR79a5liqtzg7Oce56QW8IODM5BxfefFN/vkHHyBhbZ4QGarKowd3MtaT46uvnuW5y+PMl+tdt4WIai5QOywuS9cYzKZ4z94xHto79o7a5k9y1DwHJ/C4pzDGfKvKolOj5DY64IzCcrsRAZFCkDFiXK8t0mulKFoJ3MCn4jVpBm2W3QZxzWTeqbAtkceTAefKM5ses+FHYGPeTLDo1hjR8gzFspTbTU6tTJIzYmSNOFkzTlqPcb2+wGRjmV47za5UD6+XxgGYbVU4lh9lwal1koWozQgh2JHs4R8m38T2dR4bOLDpeUThkxAldpgKQmSBFhCAHkeGc4CGEAbIJnssENQRai9IidAGkP40KHFkMI+i7VynieaEDt+d/zaaovO50V9hLLa5hXLJLfHU4vcZtIf4r0Y+i61Gq447E7v4k/E/4rvz32Ysvr1bWuaGLvcVHuCRnvehoHCMu6l6Fc7XzlPxKiS0BHmjQN4o4AQOqlDptfrYldi96fEPpA9G7J5bgK+skeOPb/wh440bXeBICEHBLJI3ClxvXH/LXiSqku0mJG7ocih+iE8Ofzoqq0OS1jN86eafM9mcoN/qp+7XeGrx++SMHL+07fPEOte9K7mH/3D9D3hu8VkOpu7AUtcSyonmOL+07XPckzv+Q4tE/pcKKaMJlheuOfLMOmX+x1NfxJcBqlBoBi4lt9a1DB60c/zqjkfpt5KU2xew1R7coIyl5ZFIFDRC6RFIB1VYKEIlaucKhvrTZYedSNnMTq1QLTdJJC18P2Ti2mIE0nQiZZnYus6Z6Tk0VaXV9jg1NUtfKklcU8j3ZW67SloYzFOaWQGicUW/RaPC8wJ0Q8W2DaSU3HPfLjRNwWm6BF4QPV9Lx3OjxFYIwfZtefbs7iPbk8JpRgtzR+8a5ebFaQ7s6qF/Ry+e61HM78awdTzHo/DALuy4ief61JwGpm3guR5hGGLFTMJQ0nbaGJaBDCVe28eKm4z15xjrf2eOOqlMDFVTUFUF29ZxHI/F+SqO45HLJ9A0FU1TMa1oAlzoSTEyVmTHnv6IdfLSNVZKdZYWqyAjEVjL1rFsHUVR0DSVgaEcD33gIHbMwI4ZfPNvXwcBuqFF5hyWztJCFafVplF3kKEknrQwDA1NV5GhJN+T5OypCfYcGCRXTG7pZLUagQy4Wr9On9XLIz0Pcrl2la/Pfpt/NPY5bM1mxpml5C7z3p4HOZQ+iK3aKCicLL/JGytv8mjve/FDnycXniGm2tyRPsDhzB2k9RQ1v86H+x8jZ2SxVIu61+Abs99mb3I3J/L38HzpZb4z930+MfSzlL0q3194lkd7H6Zg5GkFLXQluu5L1SvcqI/zkYHHCKWM3EM7fdWB1F5SepL/NPX3eLeUo5yvXuTpxef5QO8jaELlyYVnsRSLQ5mDTLamafpNPtj/fmpenScXnmE4Nki/1UdSS3Ase4SYZvPduad4vvQSHx/86IZ76Ichlqa/bZ954tAoJw6NrvtMrr4bmkLZaWKofUjZJgjrm+7jJz3C8P9j7z2DLEvv877fyefmfG/fvp3T5NnZmdk4i40AFliAIIjALCZRFMqWJbksVUlV8hfbVS6XLYm2q0zStGlbEmWBBEEEEmGBxS6wu7N5d3Ke6Rxv3xxP9odz+3b39MzsLogPAOnnw/R033POPfE97/t/n+Di7GAuFCtNvvbCOb76/bMM5eKM5JMMZqLEI0E01Zcklmttbi4WOXd9hU7Xt5qYHsnwqccP7fF+9TwDWfSL27eHSYzkE5w4NMIr797CdT2+9fJFFlYr3H9giFg4gOt6tDoG9VYXXVX4ledOkIgGaZkW9a7B/lyGXPTuxtJuz9B4rVpHEATy8Qhtw8KwHTKREOu1BueW1pjKpfoql4iukY2G6JgWx8cKjOSTPHh4lK+/eL5ftPhv/vA7PHB4hIF0DMd12aw0OXdthXcuLtDqmpw8NEK13ubG4rZ/qyBo2PZNQETVHvH7WcBGucFXXzhLu23SMSy6po1hWrQ6Jjd3rP/elSX+2z/6DkFdReuxMXXN9x367NNHGczEiAb2SlRDQZVf+9RJljaqzC2X92z3TvjLH5zj6HSBX//0SR46OoYsicwvlUknw4TvwjgSBIGObeNnU/oI6AqFwTjhu0ohVWR5EsdZ3PXXAT3DgL492SeKIk8/NMMb5+eo1Dusbdb5n/6fH3D67Cxjg0kUWaJr2DTaXerNLo8dn+Sh+0aIKmHyenYPK+jI9CB//xce4U/+8nXWyw3mV8v8/n94ka+/dI6JoTTRkI4gQNe0qTU6rJcaLK1XabUNDk7lcV2XdCCI5Tjvy4j1PKhXWnTbJpFECESIqjqOd2+RmyRKKKJCyajStrsogh8+MJCLcWt2g0BARZJ+cn3Cn5rCEcCVd+fQ7hDhJ/SkGcfHCkxkk5xfWuetm4tcW90kFtRJRYIMJ2M8Mj3KSCqGLEkUkhL/y29+BttxMCybQiqG63gUq00Ojg/0Xx73jeT5w9/57D2ro++HoKaiSF1Cyigta2HXZ4eHcvyLn3uSYr3J1dVNlit1io0W7V5RRxZFwrpGPh5hOpdmMpskFQmC50dfGobdr55rvch1y7J7N8J2oysIAhFd4zPHD3BqZpTZYpmLS+usVOo0e8wXXZFJhAIMp2JM5VIMJWPEgz4luGI2UEWZgKRhuNaueFVREBhLJ/jHzz7Kz504wAvXLtOo+VV5URTIREJM5JIcGxkkn4igfkCT0q3ZgGePTnN0JM/llQ0uLa+z2Whj2g4hTWUsE+focJ6ZfJqg6nfkvvTMw/y9Uz5bIx3xB1TJSIAH9g+zuFEluiNC8YsPHuETR2bw8FAkiUwkxGqrgSKJ6LJCwJUxHIdqp0s2FEKRRCKaRte28XryNFkUiWrarm6FIAhM5pL80088xrNHZ3hvboXVaoOOaaLJMqlIkIOFHEeGc2Qivu5WkTz+5c89Sbu3TDIU7G9rJ164eJM/eeltGl2DfDzCP3n2FI9OjxDpufffPruyxVorNtr8/nde4YWLN3BcjzdvLbHZaCMj9uPLXZe+hlhRJEzTphCL8A+eeIDPHj/I2flV5stVKm0/TU+VZaIBjUIixmQuyUhqS2b54aQEP4uwPZf3SgtUrTZj4TTT0RxJLcT3Vy9zobrCR3IzxJQAkiByKjvF86sX+eH6VT49dJQL1RXOVZbwPI/nVy7ydP4A6506ZyqLqKLcL9gIgsC+6AARxb9nR0JJJsJpXlq7ysHYIBk9QkaP8OzgId4pzQPw8cFDJLUQRxIF5lslnl+9xGeH7+epgf28uHaFH65d5VRmislItje3tvv+0kSZwgr0/wAAIABJREFU4VAS13NJqHenenueiWNdwPPaSNIwntdElCcQxSi2fRUBGU/QcazLgIsoj4DjgmcgiBqufQNB0HHdDUSpgLDDo61klljtrnB//ASFwNBd24uisUHFrHAq9RgBaXvWLq2lmQxPcbb6HmWzTE73ZWghOczB6ME+bVlCIqfnOVc7S9fp9M/57bjb94uI2J7NUneRulXHdE02jHU8XDpO547r7DmPeHjuNjNEROBg9DB6z6NJQCCtpVFFlbbTxsWlatVY7iwxEZrkYv1Cf13DMRAFkbJZpmk3dhWOslqO6fBMn45+tbTJSDRGQFG4VSkzGImgy/77tdhqcWlzg0I0yngsgeHYnFlfQ5MkjmQH8DyP5UadkVh8j1n+ndA0Dc5vrJMLhRmLJ/b4EUU1jYahoe+Qk7meS9lsUrN20/kFBArBJL87+VGOxkcBE9ttU3du0rBmEQUVUVCIKKN07SKKFKdr+xJ3PyUqRCZw4gNdm58VjE/neMFx+eN//R1GJjLUq23mb27w+d841V8mqusMxiIsVeukwyGemBojqCq+JLvSorZZJz+W5m6MI9u0sS2HWCbK/gemCMW2n9dgUOPhR6exepIuWfZn899+/hzrc0UQYN/JSRqVFuW1Kg88ex+XX79OfiJHvdTgzEsXCYR0RvYPcun164TjIQRJ4OrbN3Ftl3AixMrNdYLhAGOHhzDaJss31hgYyzB3aYlgJEB+Ikez2sIyLEYOFFi5sUanZTAwmuG+Jw6+7zkUBMjl44QjOrIsMTaZY2JmAMOweeXFS4xP5cgXEtTrHUIRnVgixMh4hsJoivfeuMXSfIljD4xTGElx86rJe2/e4tiDE0TjQUbGM2i6iu24lBttHnxiH++8fpNmx2DsyCCDoykiYZ99aVsO3Y7JlQtL2LbLxbOLjE5kGRn3vT2HRtNoukKt0kYSRa5dXCEU1jl6Yuz9jxGB44mjjIVGiCsxzlTPs2mWGJaHcD2XifA4h2MHdnmpXWvcYDo8yf7INB4eC+1FLtWvcl/8CBElTLTn+RZXYsQUXy6zYRRZ62ywLzLNSncNSRC52Vqg1ZMbR+QwNxq3iCdjjIfG0CS/4BhVInRdg9nmAvujMwzo2X57JYsyQSmwS84McL1xk4Ke51B0f++7N7lUv8Lh2AGfQRs/ynR4krbT4bXSm1TNGoOBPFk9w0pnlY3uJi4eVfPOMn/LdVhvNxCD7ydz3Pu5ZTm88t4tjk4P4qk1gso+TGcDx/vbVzjSdIVkMowkbjMft86IaTncWipxa6nkJ0zt8Mi0bWeXr93EcJrf/fwjTA3v9WxxPRPLWUbz9sb3RIIaX/jYMVY2atxc3MSyXd67ssSFG6vIkoiHLw2yHZexwSS/8IzPIjYsm2qnS1jXsB33jmE94EvaDg5mmMwk+4lhW8wYWRQZS8dxegxXBLAdF1kU+pPdgiCgqzKfe+Y+Lt9a49r8Brbj8sa5Oc5cXUJTZDzPZwN1TRtJEjl5aIT//Jcf569+eJ6bSzsLNB6ysn8P06hUbfGfvv0uhmndk0FSrrUp13aPRQXBL6o8dGSMwcxe6bdlOZy7vszzp69Qrm4rOpTetdy+Rr7KpM8asl3eu7JIo91lIB1DEUWy6cg9A3wkQeDEwCABRUHuXQ9JEskPvF9olecnzvaP6U59OTi2f4hPPnaQv/jeWQzLplRt8Vc/vIim9NhBvWNwHJehXIITRwok1BgNq4Xt7bi/BQFZEvjYo/tRFYn/++tvML9awXZcrs8XuT5f7LO47nQ9tu6jbDBEOuDb09zz6DwP13H9tGDXA1EgrgZIave2BjkQmaLYLXO9OcuR2D7iahRBEFhcLFEqNul0TQ4eKvA+pMoPjJ+awtHhh6ZoNfwX9tZFsxyHd28so0gSAVVms95CkiS6bZN96TTDkRjZeJiZQppba2UUQeT05Xlc12M0l+D68iZD6Rjn51bpdm0ysRAbzRaeAK9emkNTZAzLxrRtDo8OkIl9+JjDLXheAFU6TkSdRha32TaiIBAL6sSCOlMD6dvW8WjW2pRXq6TycS5dW+PW5ipzgkggqLK52WBgIIasSKwsV+h2LRLxEMGwxvh4hmBQpeI0uNlYJijrKILEhlFlLDSAE6/xRC5P045jeTZZLcFqp0RQ1mnZHQyhSluUuFC6xnRkiLrVQhc1grLGcnuTmcgwVxrzaKLi62HtNjk9SV2rML1f51ODj3zgc+N4Li3LQBLFXkqRiO052K7LrWaRqUiWeFTjkegwpw6M9It4ruehihJ2T5bTtA1CskoqHPTTznZAVWSSikwysvvviZ4UbSdGYnFGYvF+w+d6Hl3b7ntv7Hxh3P7/UrdFWNHQJJ/pFA1oPDQ5zEOT72+GKAoCmWgIuPtgvdru8OXXz9Ho+ia7v/TwUT5530y/cd0DQUDCl4+Naio/d/8B3ry5SLXdpdRss1quU1msoSgSCAKJRIj1tRqSLFKttIhEA8RjQRYWfM+HCSXC5z95ZBcb6u8qklqIX514iEq9zfxqhYX5CrVWl1ORaVRFptbsUF7uUpfWiUUCHG4PMzWSYWO9RUFO8MngUQzTxjU91hYahAjwn+17atd3SILIp4e3U3giis6vTDy0Z18eSI/zQHo3yyujR/jtqVO7/vaF0d1yokPx3UldbdvkRmOdstHiucKRXTN+ESXATMRffkCPo0pBZOUpwGZrsCmIYUBDUu5DEGRAQpRH8X1w/LeSH/ceQFKPADoiDoK4u0NQt2oYjkFOz+1i89yOilVBAJLqbqmGLCjElBgdp0vLaQJ+ZyIoBQlKu5+vLU8O957Wp3vhei5XGpd5ceMFqlYFWVBQRRXLs7A9+31sDrfheQaety05lUWZqBLdZXAtCTICYs94HNpOi6bdZLZ1iw1jY882U2pqz/dHleiuQtK3blzlFw8eoaAo6PJ2EqPlOHzr5jVims5kwmdpvLu2yuXNIo8Nj/r7KQjosvyBeUui4PsV3KiUGI7GECWJhVoVw3GYTqbIhcPkwrvfr1ElyLP5Y8y3Nmja3V4BLcqxxDgPpCYZDWV7EjWNuLYfx+0SUoYQBQVBkPE8G8tpEJTzBOU8ktAzJP9bKFmLJUL85j/6KGffusXqUoVsPs5Tz93H+Mx2J3q2VGauXOWB0QJX1oqcWV7lI5Nj/c9zI2lUXeF2n4QttBsdYuko2ZEUkURo1ztAFAUi0b2+M67jEooHUXWV4lIZz3VplBo0qy1yYxk818WxHXKjGbLDaUqrZUYPDZEfz2IZNrPnFylMDVArNkjkYkwdG+fi6asEIwGMjsnitVWiyTCHH9vPm98+gySLfOK3nqRRafHSn71GfjxLvfzBBumCIHDk+Gj/98c/dohSrcXUfUMcfXAc1/NodUwmC3EMy6bdMTl8cgzbdjn+xAzRkO6nq7YNnphMY1oOXcPGxuPIgxPUm126NYe3Ly8yXkjx8V84jiSJfPXFs3zm8SMIAgR1hVbHJKirfPaXd8s7Ryf8WfOHH9/H5kadTtvk8Y8d4sbVVeq13dL8e2GrDVB7sc+m22OBIRKRw3sM+LuugS5pfSmzLulsGPdmGZiuieVZNO0WhmMQlSM8kTlFQNLRRJ1fGv48Z6rn+Nbq94gpUX6h8GkiSpiJ8BifL3yGdytn+fLiVzmeOMqp9CPIwt0nogzXICSH+vsXkHTfwLb3eUyJ9qVngiDgeA4Nq8lXlr6OKiqMhkYQhe229XZEVZ2Hs6N3lLt6Wx6pom/2ffu8f9e0ePvyAmODSfLhw3TsOQRBRZdH7nn+fhbh9Yo/wo524dFjE1QbHU6fmaVca9HuWhimjWU7uK7nqylkiVhAJZMIc/LQCJ96/BBjhdSewoLn2YhCCE2ZQhDkPRNegiBw8uAI/+r3nuU/futtzl1bodHq0jVtbMdBEv2Y+FhYJ5+JIssStuMQUBWykRCe5+66eqoiMzWSptEy0DV/PV1R0O/iwafdxTYkqG175wiCwPRohn/1e5/gL75/hjcvzFOptTEsG8OwkWU/+XkoF+eZR/bxyVMHGUhHWd0c4ercBrIkEQnpeG4dx55FEELYRhZR1P170bDJhAKIsRCu6yLJEp2WgR5QcXacc1EUkRQJs2shCD5TLBDWkSRpl//PFkzL5s+ff4//91vvUKq2KOTifOapIxydGewxanZPVluOy2a5ybnry7zw+jXqrS7X54t860cX+PTjh2m1jL4vniAIDGZizIxmARhIx1BliZnInQqHDqbb8p9mxWKsECUWFVFkGS0g4IlD+B6aFo5r4eIgCjKOayKLGq7nICCg6ja/+fMPks/E+KsfXmC91KDVMTEsu0dYkAgFVEIBlXgkgOXZrHWLiIh39CEK6irPnjrIzFiWb71yibcuLFAs+9vckgjKkoimyoQCKslYiKPTg3z0kX2EAtodrD0E4mG9f07ymeh2UjegBVU/9RI/mGuLPCNJIkMDcRpt//wqEYc3y2f98xrIkNPTSIKM4zmAQm4gRjweYmWl8hO1F/mpKRzJirTLZAyg3vJ1m1ODac7dWmEoHePb71zl0EiOxWKVVCxEPKSzWq4jCgKVpj8DHNRVbqxs4noea5UGiXCA4XQMSRLpmha31svEwwGuLxcRBIGpfIqFYpVkNIjQa662VLA9deAOVez2v7sfJgGQUMT4B/da8OBb/+eLfP2Pvs/n/tGzPPqFh7EsB8d2kWSRRCKEovg+QkPDKVRFQtXkPtPItl0adhtdUlnrlmhYHZJahJJZx/VcRkMDXG0s0LYM5lprjIfyvFG+xERo0J9F72zgeA7L7U3CcgDHc9AllbbT9Y/B8wddt5orBCSNG80lpsIF2r3Eng+KptXlfGUZ23OIKgFEQWC5XWEomKRpdTlTXsBwbcKyTloLc6OxDgiktBBdxyKnRymZTTq2xSOZScLKnVNAPiy2rpMkCIRUdc9nFaNNx7EYDG5X5ze6TRRRQrtHlPTfBIulGis9D6SAqnB8rLCHznsvxEN6f0bF6/ke5eIRYtGellwW8bIRHMdD02Ri0SDgMTSUwLIcqtV2r1D2t2/w9eOiUu+wUW7guh4BTaFcayOIArVGh4DudwKyiTCLaxUs22G1WCMZC1Kpt6m3DP+5VeR7mrZ/GFy9uExuME4goLK+WmVwKNk3sb0TbNvh6nl/drtwOMNCs8wTuRnGwqldyz2UmuF4YoJ2y6C82SSl7WSO7DQ49RCkNNtt4W7TRqH/M3bbetsQe1pe5y6d+S1sDXL8F+FuuJ7ba6+FHcuLP7E7t2JV+OrSV3A9h88NfZGx0DiqqLLYXuB/vfH7H3ArEqo8hrDjVSsivq8sQkBAERVOpT/CM9mP7vlcFCRC8u0pmyId2+GVheu0TYtqt4vjulzYWOd8cZ3nJmeQRYlXlxZ4bWmRQ5ksR7M5rpdLfPfmdSRRpGp0sVyX15YWaFkmz05M+zNXtSpvrCwhCAJPjo5zfmOdR4dGKHfaFNstDmdyjMbiXNr0mT/rzSb/7vwZHNflkaERDmdyLNarPDg4xPVyCdtz2Z/K8KWpZ/Hwep2j3nEL27OCvm+WhygEkMQAMjvuKAGSgQQC4gcu4v0sI5WN8NRzR3FdvyMpioJvvtmDJIokgwFykTCrtQam7TNpw6pKt23QaXZ70mOd0djfw3QqaFIaWfRlUJmhJJZhsT5fJDOcQlblfjTwHmNNQUDTfS8iPaiiqCqV9SqWaaMFNYy2weLVFRzbZWgmTyCkISsSiqqg6grX351l5sQEg5M54pkooXiQVq2DLItYpk1ppYIaUBFFgUAkgCRL6FEd03F44ztnGBzPkp8eIBjSGdnnF7u30nVFQSCmHWYq/k8xHIiqe73ktvCd01cYHoiTT0e5Mut7YoRDOgPJCFfnN8gkwtRbXQzT5shUnptLJQQBOoZFs230B1Uzo1muzW/4KbDNLjeXNllYq/DE8SnCAQ3Xdbk0u0Y+HWN2ucT9+4dI3iNxKZEKc9/JMW5cXSMQ1LhvB9vIMm26HQsECATVXZ4gHh7LnRVGgkN9H6GYEtu6ZHfEgJZlvVukbbdxPIfV7hqDgXsnOPkx9nEORfdTCAz2ZEWgiRouLiE5yGPph5kOT/CnC39O0dgkLIdwPZdCIE8+kONC7Qovb77KQ8mTSKIvTXI9r/9zq33P6VnmW4s07RYCAkvtZfKBAbZa+q3B6U5UrSor3VV+a/RXSWpJFtqLNHrMRq/3HR5u37Mvrt7ZjLvRNvirH13kow/NcO76Cpdure86j6blcGXWL+wbzhox/RSWs4HpbKBIH0w6+bOCbtdC0xXUHX2NQjbG73z2Yb748ftZXq+yUWlSrXcwLLvH7vGT2AZSUcaHUsQjgT4L43a4noHtrAIuhmlSrFeJR4O9gBr/nuh0LXLJCH//s49wbX6DaqNDteGP+3RVIRkPMpiNMZSLk4qFKLXanFlcJRbUiAcju6RChWycf/3PfqH/+87+med5VGpt3jwzx8p6Dc/zGMzFeeyBSSJhHcu1MV1fflsxa3j4SapBScfFY2AwyO/9ykOcWiywsdql0mjhuR4BXaWQTLJ/LEemx94C+Mj9kzx8ZKy/H4JQRRCCeJ7N4o0ytmngOi4byxX+yXMPEIkHMToWgxNZrp+ZJzOUZH1hk1A0SKDnIacHVaqlJrVig1g6wuB4huxQkkazy7//6hsc2Vfg/sPDeJ7H+Wsr/Olfv0251iaXivDPf/sZ7t8/1PfvuR1b3kBPPTiNrip8+bvv4roe711Z5vHjU8SiAbSeT50kCnzpF0/hur5vled6XH7tGvOXl3nud55CC2yPvTpOhVL3BqIgE0xI/Nf/+CFa9iYiMhFdZdO4jum2CMsZauYyuhRFElS6bg1RUDCdJooYxPMchkIP8Lln7uMjxyeZXSqxulmnY5hIokhAV0lGgwykIwzlEmzam3Qcf/IqK6f3yCTBnzyZGErzpS8+xi9/osONhSJrm3XaXQvw0FSZaDhAPh1lKBcnHNTueq8LAkzGI3wql+LUZ06SKST9yX3XI56JsDpXZHh6AFEUWW5XadsmuUCUWFjnv/rNp302ElCyKtzszLHQXkESJFJqnFutRcZDBQKSTq3WIRLWOXJ0GFX7yY1Zf2oKR3dCJODPYC8Wq6SiIZZLdQ6N5MjFIzi9zlO11WUoFeXWWhlZEsnGwiiyRL3dxfVc4qEAkiSwVKoRDWi0DYvBZJRivUU6GiKoqSTCQebrm8w2NwjLOl3X90/SJQVVlGnbJi4eAUnFcCxczyWsBMho0f5NcfXtm34R6tjYPQdxO+EBrXqbTqOD0TbI92JSdydR3J0BA4ANq50SGT1OVkvQcQyyWoKWpPuVV0HBdh1yeoKlzgajwRwpLYblWqx3ywiI6JJKzWqiSxqyKdFxDNa7ZWp2i6QWJacncTyXQSXNhlElpnw4ZpbtuXRdi4iikw/EcDwP07UZCMSIqgEaVgfHcwnJGlk9SsVsoYh+/Plqu4qLR0jWCcs6sijRdSxu1DapWV2mo2niaoCrtSJdx2YqmkIURBZbFdq2RUoLktZDVI0Oo5EkpW4Lw7WJKDo3apt4eOyPZ9ElhdlGibVOg7QWYiAY4a8XLtGwTE7lxjiQyFHsNGlaRn8wXTO73KgXkQSR/bEsVbND2WhTtwyGQ3EKobsnAd0Nra6JYfupa7oio/c8nD4oNhttLMcfZIuCQDwSYKJX1d5CtD9zvF0aFQQBy3IwTfv/ZxvdhnwmSiruF5U9vD77y2fPCciybz54eCqPLIuEA36E81DOf55FUeynj9z+XH8YVMst1lcqnHlrloef2E+r3qXV8Gdeja7F4mwRx/EYGktR2WzSanYJRwPkCwn0oMq1i8scvG+Yp5P7WZgtMrexQb6QoNXsksxEqGw2CYY16httXNMDz2Oz2KC0UUeUREYnMlRKLcqbDURRYHxmwDfHvSfufIwxNY4u6ax0lrBcC+kujLq0mkYQRF8etsPjw3ANNo1NgnKQqPLjpY1sl8TuXHaomhXWuqs8lX2Gg9FDfYZQ0259YGmzP7uVff8Fb0NIDhOUQtSsKmE50jcLfz9cK21yq1LmI8NjnF5eQBQECtEoX792mYZpENU0JuIJhqJRjuUGyIRCOK7HRCJJQFaYSiRRJYl8OMI3rl3mqbEJDMfhmzeucmJgkFQgiCbJnF5a4GS+wHqryZXSJgfSt7UxmkYuFCYgyxzOZFEkkXfWVhiLJzi9tMCxXB7bdbhaXycgqViujSrKdF2LsKyx1qnjeC6qKNOwusRUXxLq9kIK6maHmBogrYVp2SZN25/MMF2bkKRxNDl0p9PzM42tAfLWPEKr2eX823M8/KQv4dFkibO9NLWO6Zu3vjm3xK8/eIxoQMXomNiWgyiEGIp8Yde2bdNmY6FErdSktFJh/4M2hHxT7t//777B6lJ51/KqJvPLv/049z95EK/XF3Mdl27HRJJE9JBGdiTtD5YiAURRQJIl8hNZRFGkMD1AJBEmlo5imRZ6UEPoRRE//UuPYnatvieQJEuoAZX9zxzk69cuc2BokFw2SSUEKVEjnfKffw94Z3WFXDjMeHw/MM6fXbrAM+OjpO4S0iWKAvfPFLAcl4W1CoWsz0ReLtZwXJeNcoOj0wUu3Fxho9zEdhweOTrO1146Tyysc+LAMD967yYLa2XMnp9LOKBybKbAO5eXMHsJqrIskU1EmFspE9AVonfx/tiCJIncd9JnmHqeg2Ev4Hk5BCHAW6dv8O//6EVkWeS/+JefZuZgYfseQeBs9SLr3U3WuuvMRKZIqveWfzyQPM43V77DXyx/A9fzaDsdPp67757rZPQMh2MH+e7aC2T0DLZrMxIc4uHUSVY767xUfAVd0ug6BlElQlSJ4OJyoXaZC/VLhKQgJbPCWHAUSZCwPYdL9SvMtuYpGpu8UX6L0eAwB6L7OBY/ymJ7mb9Y+gaiIFKz6nx28FM9T7M7IyJHiCsxXiy+QkgOUjQ2CfWYqHWrweXGVS7Vr1I0Sryy+Qb7o9MMBQbvWNDfGiSfubrM2maj/14HegXVLaPfCm3zEpZbRpMG92znZx4CFDfqZLIRtjL0BMGXpYUCKrIg8uChEcKhH39i1/W6yFKarhnk0o01VEXCtt3+ZHnHsMilIr5/Syram8SVGC2kcF2XkcEk6R0EhFhAJ6gpFBtt8ASSwUCfOSSKArp2Z3ZR17D5v/7sNV5/d5ZcOoKiSKxu1Hnw2BgAK511KmYNy7MISgEEBK51/Yl4URBReowpO26TTsjEPZGx0BBtu8N0ZBRd2v38S5K4y3rE8xJo+sfAg3C0RbXYxHU8QtEAhYkMZtfGNR3CYZ38sD8BmExFSOXjvgl/o4OIQDTi+3Hmx9J+kV9TWFqt8tVvv4cgCNx/eBjDsnn1zCzlWhtREDh5aIQTB4bvYVK99S7yExVPHBrhK987g+s6NNpd4tEAsXCAgK72l91pSm12TS6cvsbpb7zDR3/1sV2FIxEJWdQQBQXb7RLQVAQ55HsaiiC5CooYxHTbiIJMWMn5LCNBwsNFEQL0zAEQBD9NbyAdZSB97z5ixAxhOhYZLUnLad/NP93vz8kSmUSYTOLHVygBLF5Z4dt//AMOHB9naNzvPzmeS73UxLGc/vt+OprZlba981wWtCyFcJbvrb3CRHiEkeAg31t/Gcv13zuSKHD+/CKJRMgvHn3I0Im74aemcOR63h4Wj6rInDo41vtt9yD38NjArmLKdCGza3sTA8n+ZzuXG80l8TzYv2NbAHLI41ZzHVGAK/VlNFEhoYaRBBHTtQnKKnWzzYZRQxZEhoNpUqpfwXZshz/9779OYSrH6IHCBy4cCQL8/Jc+xolnjjCyv7Dr2G8fVN7ts5Ac4P7kDDktsedYAaYjQ0xHhnbFPm19NhUeuuN3jYXy/jkMb8/kbWGG4Q9NRgnLGieSo8TVYH8fC8FtZtbt+/xQeqL/+3Bwb8reXy1cpNRtMxZJYjgOL6/dYqVdJ6EFOV9eYSaW4a8XLvGxoX28sHydnx89xPeXr/Mb0yf5/so1hoJxZhslWrZF2zZZbFUZDyd5ef0WRxJ5jN5DVzO7WO5upsNfL14mqupMRlL85dw5YmoAw7FZadUpm21K3RYTkRRvbMzzDw88iv4hmUm6KvcZQy3DZLPZel9TzC00Ogbfv3C9L3PLRcMoFn2PrJ3ncMcZBfzUHFn2zU/bPeprs9GlWmkxOORXw23bYXmxTDYXo9Xs4jguufz7aZJ/9hHQFAJ36WDsxJYRYN9o/w7rWK7JaneRrDaI/iEihy3T5o2XrxKLB2k3DTzXo901uHxukdHJLOXNBm+8fI2xqRypbISXnr/AzIFBzr09x7OfPU4orCP1OgKmYbG5Xuf65RUe/9ghrl9a4cQjU7z5yjWe+Phh6tUO87MbjE1lefv0DSRJpF5tI4oCZ9+aIxBUKa7XGJ/J4Xn2bXu6897aSjDZjhHdMnlMqSn2Rw5wtnqW8dAkR2JHUUUFx3PoOl2CcoiAFGBAzzMZmuRM9T2Oxu5jQM/j4XGlcZnLjUscih4iqab4cRCUQ4hILLeX6TgdZEHG9VwU0U+gkwQJURBp2S0s10QSZGpWjdOll7G9vekWP0mk1BQHo4e4XL/Imep77I/uRxYUbM+m43T6vki74EGl2yETDLEvlSam+WyIhB4gpGx34tLBEHFdpxCJElE1XM8jHQwSVlQyQX9wlQ4GUXt6fMtxaJkmB9IZopreN0e1XYe25csEbkdAUUgFggQVmcFIFMd1mYwn+eH8LKbjMB5P9O4Oj4rZQhZ9FlZGj1A12zRtg6rZJqdHcfFo2l0CkkrbNgnJGh3HQnNkWrZJsdsgpKg4rsd6p8FY+O6xuz9r6HbMfge+2919z22sVHnn9I1+4WgwFuW3Hr6ftukvl49GCOsasijSrLSoFBs49p1lapIikR1OEYjopAcTqL2405T7AAAgAElEQVROvygKhMIaoihimjadlkGl3ERVZZqNDqHbWDM7LcmDkbu3b54q8t76GtPJFO/U1jCaDm3LZDAcJaAorDYb5MNhQq7KlZUi2VCYBwYLDFQSJAsJTOB6t87o+CQtz+O7Vy4RlBValsnZjTWGozGeHB0nEwzieB7rrSanFxfQZJmPDI/2E7QSkUBfPnJoMk+na5KJh7k2t47RtQjoKuvFGs1Gl5iuIdge3/nRRfLxMK7jh5vosszmZgPbdtElEc9xef3cHCFFZmWtymqpztW5dQ6MD3Dm2jIn32dQtus84eF5Bi3jTdBOoIuTtBpdFm4VkRVpzz2BIDAmH8K1YFjIMOgN8aObc6iyTNyeIh3McL1YIqypbDSbRDUNSQwwIz1Cxdqk3O7w7OgRsloa23ZodAzmZtt8fODjOIaEgU2rYxLQFUY6BxmKjtKhhW3BiDKI60CYKPeFj1FqV9EUFcEMEvQi1JsGESvNkfARKq0WU9EZssoAnY6Drkvoks5IcIiMOsBL87MUxkU8DxbKXZ7LfYqyvYHrueT1HEk1gYvHJwY+2k9f0ySVT+WfJaUmichhfmn4c6x1/YShj6QfpdtjyguCwFrDYFid4cGkf8cqd5FLR0M6v/acL//OJMI8/cA0xw9s2xK0Ogb/25+9AkBMe4S2dQ1FTBFQpj/Q9f1ZggAkU2E8lz190o3NBv/mj1/gv/wHz3Bg6t5stbtBFFQkMQaIBHWd44eHMc2eDEgW+zHtSo/BjeeRSfpm9kFd8RlOt429VFni4fFhXM+7q7fRnbC0WuH0O7f45JMH+eKnT/hG35ZDKOi3i2ktQVj2275Ab5J+IJDpnSdfLmm5NrIo9SWUISlIu9fPeD/4RQP/3hycCDM4cWdfH8/ziKX2FjDuNra6EyzL6RtgS5LI1HD6A7dPO79jC6oqE7uDrPmDICAn0OUegQJ/rBhT7j7m3JrMvdtnHxQRJUQhOEDVrFMI5N5/hbvA8zxe/PJrpIeSHH1sbwJjf98EgSOnZvjnf/x7jB8e3rU+AgTCOng+g34qmrnrdrYwEhrktdK7vLr5NnE1SqRH7ohEAsiK1Gco/aTwU1M4uri2wUgi5psQ37WAcvdiyu24dxFm77bSWoS4GkISRAoB309DxI863haneRzwhhAQkASxzyAoLpVZnd2gMPXhbjhBEEjlE6Tyifdf+C7IaFs0ZGHXz53fsf3L3u//oPv5N4Eqyqiq/MH2kXtfO4Bb9RIfLcxwIJ7DcGyeX67wYGaEwWCU/3jzXWpml5lYlicGJrlYWUMWJXKBMG8WF6gYHR7NjvHNhYsMh+MktAApLcRss8xEJMVHBib6xeaxiF+0OpryC2iDoRgZPYyAQNu22Oy2eG74ADWzy0srN0CAk+lh9sWz3KhvYjjWhy4cDSaipMJBSk3fIPyb715mfz5DJhLuV/m34LfXvga/1uny5dfP8eLlW31N/oPjQ6zc3MRr2gyNJNlYqxGNBfE8j1q1TSIVJhoLUC41mbuxQXYgRnYgxtJ8idGJDG+evo6uKyiKRCwepNU0qJSaqJqMiMDiQsl/aYc16tX234ki0u3w40D99LWIohOU33/QWjRWWe8uE1OSTIT2IYvvX5RyHBezazM8lmFpvoQgCsSSIRRVxnVc0tkoM4cKLNwqMjiSRJYkhsfTLM2XMLoWju3i2C627bBwa5NKqeWzlTwYmcjy7us3GSgkCUV0EukwSwubuI6LqsmMTWZZmithdG3fFFeEhx7fhyyu49kN8Dp4Xgf/Ve0CCoiJXpHIwXPbICgIggbyGCCgCApPZ59hpbvM15a/ynvVd4jKUbpul5pV41P5n+Ng9BBhOczHcs/y1eWv8KcL/46x4DgODtca10ipKZ7MPIMkSHf1r7gX0lqGifAkb1fepOt2CEhBFFHhyczTJNQESTXFVHia87WziIJIUAqy3FnCxSWmbN/rnudRNIpcaVzCdE2uNK5guiZvld9gub1EUA5xIHKQ+PvM/O+EJmo8k/sYDbvOV5a+zFhonJAUou10KJmbHIvfz3P5T+9eSYCxWJxv35jja1cv07EsLMfh9NICN6tlXlmc56mxCaKqX0zY2bZKggCeh2M7NEyTl2ZnuVkq8erCPCcHBhkOR/nzixdIBAI8NjRKNhDiLy5dpGmaZEMhFqpVTi8usFivMRlPcnKwQC4U4rXlRXRZ4fGRMY5kc/zBO2/ywGCBUC/o4GAs7xeQPM9P/RREkmqQsVCKhVaZkdCOCaBd9HEPUfCLTWPhVD8hU5UkAtKPn5D604bvf+M9Jg8MIgjw7a+87bNBe9dti224heVqnT996yylVhtVkvni8cOcHCngeR6SJDKyL49+j5SbVr3D4tUVXNulMD2AqitEYkH+0b/4NJZp02mbnHlrlj/4H7+1a7DwYViURtfi7NuzqDGNFbnJUDTKfK2KKAg8MjTC2fVVREFgIpFkJpnmWzeuYToOVWOTg5ntDnRU0xAFMB2/cK2IIrPVMiFV5Ug2R7HVYqO17X10cWODlWYDXZIptlv9wtFTD0yj9uQYjx7146llSURu2yzc2sAuG9iuRMoWodxF7Frkgxpiq8fOtVymEjGW54qoukYorGOaNvFUmOXZInLW5Tc+9QCiILCwViGgyRSy20xk07Q5+9Ys8WSIiZmBval3HjheG1UeAd5/MOd5Hu0OHEvvY7PVpljv0uq1AwORBJ2OxLpRJapprDUaZEIhP4pejhCVIuQkm7jiM+nn1yosF2vMr9TptsLMmXMM5+Isrlc5eWCYC9fXOLZvCNEOs1Fpsh4ycBN1ltareCiElAHi4QBv31pkLNmlUu/Q7jroWhK1EWBtxWRdWEcUBU4eGGYsMMaZ9TU2Ww1Ue4Dx4DgLtQbz9RrTyTQReZwzG2usVypAhbFYgsHwAG+vLdM017g/lycspnlzaY2us8JkIsmBxH5uVsq8urpOOhgil3dpGwKCmeLowCBDkShty+LNlSXOuTc5mS/geh5n19cwHJuJeJKZZApJFPn4I/uJ3Pb8qLLMA4dGiIV1FClErCfhFoSfnJ/ITwsUVUbTZBR12wrB7SURzy2WWN+sY1k23V6y4pa/0Vab4PTMiP2iRM9g2fUQRAFZkhBFBV05iO9h4xEOunh6LxFa9FndWxAEAdveMuqX/O26W2bNDtIOiZAkioi9fbUd3wdIAARRRJa2fXg8z/P30fUoVVpYls1QPtmXXAV6KYsAQSlAUArgeh6O7Usew2J4W5rUawYFhO3jFiWigoxjuxieteO4t4s8luXvuygK2LbrM9oEAbn3t53tq+O6/e/e2pYg7G2DXc/D7hXd/O/aS6fZyaC2bPcDT1a7rselm2t9KXM8EiAU+JtN3Ows+HyQffhJJMiKiIyHhnGCjp/0+GOOebttg5e/9iYPPnvsnoUjgGgqwqFHdqe/iqJIMhvDNCzED+FJNBUeZUDPYDgmUSWMJqo4jsvcfJFkMkS51MKynL99jKPlao2wqhK9g+2353rUNhsUl8t0W10/zjSoEU2FSecTKHeY2Te7FusLm9RLDVzHJRgNkhtNE47t1ZVXi3UWrq5QmBwgnolQXa5RWqtimzaBiE5uOEUkuTte3rZsNtdrVIt1zv7oCqW1KsXlMudevoK648GZODJMdMe6nudRXquxcHU7jlsUBXKjaQZG71xZrJebzF9eJjeSJj2YoLxeo7hUwjJstIBKbjRNLB3ZvX+2w61zCxhtg6ljY34FcwfajQ6zF5f8geGhYZTbbijbciitVCitVbBNBz2kkRtJ+8dym5SpVWsze3EJRZOZOT6+56ErrVZZvrlGIhvr+xFswfM8um2D9flNmtU2ju2gqDKheJBMIUkgrO/Z3nA4zrubSzQsg6FQnOFQnAvlVVbadTRJJqxoVM2On/6AgCyIHEwM8JXZsxyI5xgIRjkQzxFWNAqhGMOhOFFV45W1W7xVXCCi6uyLZYkoGher61ypbjAZTbHUrLLWrnO9ViSpBUlqQd4qLtJxLHLBCBWjgy75hrJ+JfzDIx0J8eTBCW5ulHBcjxcu3qTRMfnkfTNMDfjJcpIoYLsuhuVQaXU4v7jGa9fnubC0TrdHjT9YyPGZ4wcoXi/jui4vPn+BQFBD02QGBhMszm0yd3ODYyfHuXFlFcfxCIU1ZFmiXvd9joIhjWwuytJCCdt22FirEwiqeC7Ieo8V1TK4cmmZSDTwd7JwZLg2/+biD3hrc54v7fsInxk5cs/lZUFhKnyQpt3oRbHbGE4DEQUXy68GCiKSoPa8HixEFFBM8uMSr51+E0FW8aQGF85tsLa2xqWLCoXhDKtLFSRZRJElTNPitZeuEE+G0YMq59+dZ2OtxtyNDWRFwrEdBgoJIlEdRVU489Ysxx+epNuxuHphieJanYVbReLJEIGQRjwVQpR8OaNtu1w5t0hhMEAw2OoVjfyXnIdPG0aQ8Nw6YOA5RQRpAE9Qtv2PBIFCYIjfHvtdLtTOcaN5nbJZJiAFOBiZIqsGsZwq4DEWTPJrI5/n9dKPWO5cRxY0Hknez9HYAXJ6DMdt43gGWTWERASB3bPwSTXFdHimH2m/hYgc4QtDX+T05qssdhaoWTXicpaWZaMKJo6j8FjiOS5I73KzPktA1hnQRngk/QAvF18GJ0Cl28F0bOY6y7y0/hqqJFA3u+S1EW7UFykaGwQlnYKeIyyLxOQg46Ecmuhf2y1ooshkeIyEGu+3HTExy+cGf5VXNl5j2ZhjuVUmqoQZ0w8woh2g3OmgyxJt2yYsZlEFhbCq85np/XRtm2MDed8EUlH59cP34eJ3FzVZ5vP7DxFV/XetAJwaGuXSa9e4sHAVG5ecCp8JD5NsC8y+fotMpYWMRTgpcO3mFQ4FFYrtFlnbIx5wKJaWOaAFybccYqaAKAicyBcYjER7bCIIqxqKJDGSDDDbWsLxXNpOF1VUcD0HTVQxXQtFlNEklbbbYKVr+eVIz6XZ8/PbMsdNawnS2vakiyyIHIzl75nm8rOGqQODJLMRLp9ZRAsoHD4+1p88KBeb3Li83Y+otDuMJOJMZVKMpxI0DaP/WavRYXO5wsDIXkPSLXRbXRRNoVQqs3B5mcn7RlE0BVOxcGSXXDxObjCOKIl7mEuGYdNsdolEdLR7sDPnbm7wv//b73Li8WlSTxe4USlTNwzy4Qgxbet+FIhrOgFFIapptC2LiUQSx/MotlusNZuIgkC502Gl0aDcaVMzutieP+jbbLcwHBvLddlot0g2G2iyRFBWmEqmyIa2vcF2Gt6qiswWWXRsMkthOMXirQ0GhpIU16oUxjLgeb3jdxFEgWBIIxwJMDyaQpL9QaBtOUiyxMhYGj2gomoKruuRTUbIp6O7vFSW50v8H//z85x8dIqR8czewhEgCDK2W0UW7+2ZI4syY6Fh7o+OktMi5CORHmMJNppNP+015MdCi4LIVDqFJPhtjbqVSuV66L1zUm91e/5MHs2OSTwSIBrSEQWBZsckHQ8TCWhs1luMDCR8id9GlZXNGuGgxtRQmlgkQDoeQlcVBKFDud5GU2USkQCzK2UOT+Zptg26ps2G2eadtWXuz+U5u7EGCMR1nWulTR4eHEYU4Oz6KrbrElY1//qHw5xZW0OVJdZbLe7LDnB6eYGPj0/xyuI8g+EIrywtkAwEyAZDiIJAWFWoGV1Wmw0KkShvry1TNw3ius73Z2+yL5Xm1aUFPjExxSuLcwxGIsQ0nfwd5C6yLPL48akdA/a/vWmzVi8JzDT9Pqbnwatv3+QHp69y5cYa1XqH/+EPnu/Lv/ZN5Phn//Cj/f77mUtLfO27Z/nNLzzMjbkNXn7zBqVKi3w2xq/8/ElmJnKAQKNl8INXr3Lm0iKr63UkSWBoIM4nnjzEsUPD/ULLd166xMVrKzz7xEF+9MYNrt5aRxIFjhwo8Omnj5DPbRdoO12LN8/M8eLpq6xu1BElgcFcnMcfnOKRExN9RtF/+ubbnL20xFqxTq3R5U/+7FX+/K/fAeDTzxzhU88c7kuFmi2DH5y+yul3blGptsllIjz+4DSPnpzsM5MA3jm/wDe/f47f/sVHuXR9lVffvkml2mYoH+dXfv4BJnvjvnK1zR/+hx/x6IkJ0skw3/jeOZZWqwR0hc994hgPHx9HliU8z+udoyucfucW1VqHXCbKEw9P8+iJCYI7xp/NlsGP3rzBi6ev0u6YTI1lOXqg4Kcs9yBJIgNpv4BhOw5nri7x2WeOEglq9yygWLbDe5cXef61y/3UvOMHhvuFti04jsv6/CbltQqSLJEpJLnTZh3HZXO5THG5jOd6JLJRBsayexQ8rutSXqtRWqnQbRsomkw0GSE3mu6PYz3Xo15usjZXxOiYBCI6hcncLhasYzss31hH0WTSgwnWFzaprNd89u3Q/8fee4fZcdZ335+pp/ftfbWrsuqyrF4suck2trENxtiEFkoIIYSYFEie5CJ5CeF50ggBQjeQYOMCxr3LsixZxepdWkm72t7Onj29THv/mKOzu9pdW7bJ9YDf93tdLnvOnLln7pm5576/v9/v+41QVhsu7WtsJMFIX4xMMoskSQTLfVQ1lSMX74VcJs9A5zAdx7rpPNFLVVM5B7cdB8Djd9O0oA61+FwMdY/Qe87Wn1NUmab5dXiD9jtJlESqGu13tK4Z9J0bZHRgDAuIVAepbCgrVQ5oeY1zR7tKvMHAhWEKOY10KEPNrEpMwNBNEtksLpc6iXh9p/itIY7KvZ5JVr0XMTaS4JWH97D90b10n+knl87bxJFLJVwd5KP/6w7W3TruJGSaJr1nB/nVt57l0LaTJEaTdp2910nr0kbe9/kbmb+yFXnCzX1052n++dPf54N/fgsur5Pn/+tVRvpiFHIFVJfK7CVNfOhL72XeipYSC9hzZoCffvWXXDjZR2woTjaVY+9zhzn8yslJKoR/9ZM/5IprFpb+tiyLY6+d5ntfuh9DN8ik8mBZ3POXt3L3n986bd+cOdDBP37sO9z4sU3Uzq7iuZ9up79jiHy2gKxItCxu4IN/dguLNswbT2tP5fnWvT+j9+wg//zcl2leMNn1q+/8EF//+H9SXhfmb37+x4QmRMGGe0d58gcvsefZw4z0jmIYJqpDoWlBHTd8ZCNrbr5iUuTywqk+vvrhb1FeF+YbW/8WSZo8Kux99hDf/dL9bP7AGv70W78/fq0Mk47j3Tz8jac5sfss6UQWQzcQJRFv0M28K1v4yP+6nbrZ1ZP2t6VuHidigyS0HKIgcFV1C8di/WR1jdubFiEi0OQNFbedS4XLiyJK3Nq4gAZPCEWUuKNpESfGBsnoBRRRZEGoCkWQ6Msk8Cj2uS0vq0eVZFKafc9lDI2rqltQBIlcQeM9tfM5NtqPT3WwOFxNfypJwOFEMkWurmxFRbJdtYpWnYZpl4xZdogd02IKOy8KAvesWUrXyBivnOogW9DYcaaTne2dBFxOfE4HkiiimQbZgkYik0efIFzqVGQW1FXyxZs2MKsiwuj5GPFYmqrqIMlkjmDYQ293FMMwcHucnDrei2VZlJX7iI6kEASB6HCK+FiWYNhDIOQhEc/Sfqoft9tBIp5B123mengoQUNzGUP9cVpmv7305N9GGKZJobgwUiRxkv3spRjIJtg93EFnapS49ub27CYmQ7k+RgpDVDvrEaw4kKdgjJE3bM0xWfQiC04MK4ckulHFAIaVpX5hjsaFYUw0NPM0C9b7WLi+kpzRgyrlufUu26UnmylQ2xDhyrWtBEL2C+nqmxZPOo75S+zxIJPOs+fVMzQ0lxEqs9O+r715KTAeURSAiqoA8ViaU7KIw6HYQrRWKzoqkjweZRMEwX7RxtKIdhILpjkX07RQFIlcNobqVNAKOiCgqG6WSatZ6luFx+vE4VJI5Pcjixrpwgk0cwxJ8OAmzXVlcxCEBYCEU64jq3VS0B0YVgrTzLE2GEKWmhCtQQzTjyjYE5/loStZHprsNge2E1iVs5o76t5fOt+jI4Ocj6VxyXkG0/YCtV5eSY1/Bc2BMOfGomTzDkLmMoy8xOnYCDldp9bbwDWhe6j2+Dg1OkyN109GK7CoLI2Ajm4OkinEmO32MKv+WjSjk3huGFnwY1gZ/GKIu2uvRJXKEAVbE+tkdNh2S1SX4zfn0xgJkizkyek6F0Y1RtQBFFEkq+uUGSsRTIG+ZAaHJFMwDAYzKRKFPB5FJeBwkNV18oZhuzu6xxfPgiAQcDrxyAr5dB5RElm5fA6dJ3rQ4hpDg3FcHgetrfXomkFGyiApEmUBHz1n+vD6FDuaikzII+A3bPtepyzTHLTH4e54nKfOnmZVTR1hlxOP7KQvO4Rm2s5MTlElbxbI6HlmeetsByUsUnqGgqmVyHijSCCZlklaz04ijgRBeEN3pt9FzFtsP6cj1QmuqV3KnIXjJe3RoQSJsXRp23Kfh+ayEOeGR9l65jw3zp9T+k4qRqOniFxPgCfo4fD2k7g8Dtw+F0JRsLg/G2UoF2dDxcIZfxuNpujsGKalpYKqGQIIlmVx8kg3wwNxRBNWVNcS1/NsaZmN3+Eg7HKzrujqF3K6UCWJ9Q2NdMRieFUV07JYXl2DIopohsmSymo8qkq1z0dZKoVTlnHKMrFcFp/qoMztZmFFJV5Fpd5fzGZHmNY151K4i4E2j78BSRIJlXlLE/ZLcTnSBKIoTBHDtiyL08d7Gegbo5DXpy+5EAATNGMI9U10c7yyhw/U3z7td36nPRa+FVJ1dn05fSMJrrqiBVkWMU0Lr9vB3MYKykNegl4XumFQXxFEUSQkUcQoEmRup0omV8DlUJjbWIGmG3jdDhqrQoQDHkbjaTYvb8XjcqAbBl6Xg+7RBAGHk7mRMvb29wIQdDjxTHC5CjtdKJKEKkmkCgX6kkkkUWBepIwylwfdNGn0B5lfVsGhwQF002RTQxN7+3vZ29dDrc+HW1EJOe0FpGGZxHI5Gv0Ban0BDg8O0FZWToM/wPyyCg4ODqAXBeif33WKhuoQ5SEvLlXB5bQdh4+fG2D3kU58HiebrmylKuJ72xkLv80IR7xYFvgDrlIGaFVFgE2r5+BUFUbHTnPT5oXUFi3VLxqyXEQqnedEez8PPbGPeCrHnOYK5jRXMBRNTcqEGB1L89r+c1RXBFg8r5Z0psCrr5/lX77/In//Z7dMIFrS7DrQwdnOYea2VHLNurlc6BnliReOMDic4M8/c32JxHhh+0l+9svdrL5iFu+5uoFsXuPM+UE6ukZKukWiJLJ4Xi0NNWHOdY3wyJP7uWrVbBbMsZ+7xrpwidhNpnL8+MHX2Huok/UrW1m1tIkz54f47n+/Snd/jI+8b1VpLZZM5Th+pp9fPL6PRDLLnFmViKLAyGhqkl6Nphuc7xohkcohCFBfHaKpLsLAsK0xeZEwSyRz/OjBnew70sXGla1ULPNx+vwg//mz7fT2x/jQ7auQZRFdN3joyf089vwR1l45i3ktVfT0x3jgsdfJFcYlBpyqzNolzby46zTZvMa+41188+fbuPmqhdRVBFEVmWJCMrphUtB0ugfG2L6/ne37zzEYTQLQVBPmujVzJxHjhm6w/Vd7efgbT5NN5nC4VernVE95PnKZPC/ev5OXHthBImrLczg9Dq69Zx1bPrKxVBKtF3S2PbKbp370MvGRJKZhrxcr6sv44298lNrWKkzD5MDLx/n1d55nqGsEQzeQFZmWJY186Mvvpaa5EkEUyGcKPPLNZwCLutZqdj9zkNhgHEEQ2PKRjdz1xZuxLIuu03384K8eKHICGpZpojgU3vPJq7n5k1cjyRJ95wb5+dcfo+/cAMM9UV59dB9HXj0FQPOCej75Dx9ELbfHsXNHunj8ey8y1B0lk8jyVz/7IxatmzupP5KxNM/8ZBuvPrqXbNIusXX7XFxzzzquvXsdnoCbZCzNN//4PhZvaGO4N0rfuSGyqRyiJLL+tiu55y/ey8pVLfT1xnA4lTcM6LxV/NYQR6OZLG5VpSpgM5+WZVHIaTz6red48odbkSSRRRvmUdVYjigKDPVEGeqKTiI8LMtiqDvKT/7+Efa/eIy2la1cfdcaFIdC9+k+9r10lO9/6QE+/Y93s2j93Ek3byGvs+2RPRiaQevSRjbevhJN0zm07QSHt59ElEXu/fYnCBVZ7EC5j6s/sJZCXuP0vvM8+cOtLF4/jw23ryxa3tponCBcCPbkdumm+Xz5J58lHc/wwv072Pvs4TftH8Mw2f30QQRRoLa1ipU3LLHV6fee5dArJ/n51x/j3saySbWwbxWWZZFL53nwn5/kxQd2Uje7ivd8YjOegJuh7ih7njnEj/72YSzTYvNda97xyzExmuKBf3qC/S8dY8V1i2leUIfiUBgdGKP7TD+peHpKplQqniGf06jXPFw4kyS4yoFDklleVs9EB6eI014YzQ6MZ3HZ29jwqU5WVTQyEW2hStpC4/3nUVRWT9hmQaiKBaEqcgWN9q4RwCKo2S/H/kKCTF7DUEwG9SQOVWHETNNXdNiSRJGhWAqwKGgGbqeKbpisKTopTESZz82f3riepvIQTx48RV8sgWlZjGVyjGWmd7STJZGWijDXLGjlhiVzaCqzHbGWr2qxib9iREWSREzDLJUvaJqJLNvCfLphoMgSm69fgMOhUFltOxH6A240zUCRxXG9JEGgvqmMxFiGmvowwdDMDjG/a0jlChzq6EMUBVoqI1SHfNNuZ1kWfZk4/ZnEZe/bsHTSRoqUFmcIiYC3GbccQRY8qFKQjNaNQwrZ6beWiEeuQRY9ZPR+nFI5suglq/cjS25k0YtpaUiiC6c0nkngdCqs3dyGw/nmw7vTqbBibSuKKk+pa49FU6QSWfJZDcOwI+yzZlfaAoGKxHD/GKIkkk5mUR0KlTUhghEP+bzGoT3nUBSZxJjtQGeZFhU1QZwulXgsTSGvU1kTJJ3MEY+lEQSBtnlyVtMAACAASURBVCX1VNdHUMSgLUIuBhAFJ4oUQTNjYFkIgoJp5ZBED6pcgSBICJaMLPoRBbUoRG2hGSM45Mlj7+WU1ISLCwoLi4DDWcxUsMldt6xQ67WjztVeP15Fxaeq6KaJQ7IXrXlDp9ztQZUkdFPGtCREIYcoqIiCE0GQcIjVCEV/MFWuRDft+8cyTcSiBpSFnRmkihLVHh8OySZjgg4XBUWnyuPFIcskC3kqPF5CTieKKBFxuUkU8lhYVHq8VHq8uBUFSRDRTXPa4MxFzL2yxT4GUUR2KMxa1EAmmbUFOkNu6ufU2GSiYSKIIoIAjW21djDFKo6+loU4zQK70uvhzvkL8SoqUjHl3q940C2TrnQfgiBQ46pAFiRkwZ6oRtRAScOg5J0njOv4XSo2PxHvtoXb7Pm1UxLy/SEP1966rPR3td9HhdfLoupKErk81cW5lCAIhCoDrLhuEYo6s9lCIppk3ooWhrpGcPmcpUWSW3bilGYmxU3DIp/T8HocBIIzvwe0gk5H+yC5YhZZnd9P8yXR6cbAZNIp4HCytGo8cFTpGdfzmJg5VOubXvx0de34O3+R860HNy6OiTORRu8EWsGw+yNbmHEbAQFRdKNKVaWx4e3grTizXoTHpTK7/qJ7ZtFf2LJIpHMUdIOKogjxxGz6OQ3lk7YXBAHPBJFcezuIFDP/J96LFR4vO3u62NbVSbKQByyODA1yPh7jwEAfbWXlpbIiAQFJFGkJhhnL5choGopXwrDMEjl2cc8D6RQCMJbPUTBM+lMxTo4MM5hOUe8P0OgPcmionxMjw7SGIkCRYLtYcSQAWBw7289zr53CtExa6sq487pliKLAg88dwO9x0tE3ynAsySdvX4Pb+e7RWbsIWZaorBpfb4mCUCJ/orEUr75+lisWNbyhxlEupzEcS3HvJ6+hosyPgE2YXMxSEgSB2qogX/6jG3CoMg5VxjBNGuvC/NN3X+B81wizGspK900imeWadXP56PtX43E7SKZzFDSdIyd7GY4mqSvKgLR3DuFyqdx58xXUV4cwLYt8XkMQxZI9vSyJLC0G2H1eJ48/f5gFc2rYtGbOpHOwLIvDJ3vY8fo5fu+OlVy/cT6qKpNIZrGw2Praaa7dMI+GmvEMwWxOY3Qszb2fuoZIyDt+3s7Ji3ldNzl/YZgvfPIarljYgCyJaLphSwQU33cHj3ez8/VzfPTONVy3oQ1FkUgksxjGdl7aeZpr1s+jtirE4EiS7XvaWTSvhk/fs56Az0VB0/nxg7t49NmDpTYFQeCKtnrWLmvm5b3tFDSDZ3ecZM+RCyWiVJEldMMkkc4xEksxNJoimc6VSNWqMj+/d/MKmmoik57p/o5hHv7G09S2VHLH527AF/Gy64n9PPbdF/AGxsfvYztP88t/f4Yrr1vElo9ehSSLbHt4N49/9wXKa8Osv20FAKf3n+f+rz9G88J67vzCTVQ1V5AYSRKPJgmU+23ZgN5R7v/fj+HyOPijf/0wFQ3ldJ3s5b+/9iiP/PszfOLvP1DK8AE4vO0khm5y5xduorKhjNGBMYITeAVPwMXKG5ZSP6eaqqZyhntHeeQbT/PsfdtYe/MVlNdFqJ9Tzef+9SOcP9bNNz9/Hzd/6hquudt2kZMVeVKl0/JrFjJ/1Wz2PHuI//rqr6Y8I5ZlseupAzz1w61c88G1bP7AGixg+6/28th/voA36Obqu9aWtt/+6F7e84nNfPiv70DXDB799nNs/cUu1t68nLlXzkJWpGLCwuWVH14OfmuIo4ZQkErf+KTAsuDojtM8+cOt+MNePvUPH+SKqxfiKKYAanmN1FgG3wRlc0M3efan29n77GGu/uBaPva37yNYbtdr5zJ5XnlkD9/9y5/z4L8+ScO8GoLl45MN0zDpOdPP7//dndz0ic2ljJrNH1jD//nk9zi97zxnDnSw6kY7Gh+qCLDhdvtmVh0KT9/3MvVzqtn0/lVTyI6JEASBQMTHonVzsUyLMwc6L4s4skyLvvODfPDPb+G2P7weXzGTID6S5Gsf/Q7thzo5uvP0OyOOTIt9Lx7lxQd2Mn/1bP7gH++mYW4NQjH9esV1i/nWF3/GQ//2FHNXtFDb8vbbAruE7fiudupnV/Mn3/wY7mKE4qIrQDaZJVw5eSJ5oX2Q2FCCeCxNMpZGdSgsXddqi/SaCRBEBPHta0ZdDuwBNIvf6yLgc5HO5tFNi4DHiWaY+NwOHKpM10CMXEGnoBlUhF0ks3nyeR23U8KybOG+6SAIAnXhAJ+5ehU3L53Hid4hTvYP0TUSJ5XLUzAMZFHE41Ap93toLg8xu6qM5vIQFX7vpEmia0JG03T1rYaSpz05SEj1oIoSgimgSwb5QgrTspCLjifuojCtIkrIlu1uZAoWTp/CwiX1ON9hXfNvEyRRIK/raLrxhg5zpmVxMNpTElO/HCiCSr1rFppZoMJRg1+tRRIkVMm+z91yTen/YXzyKwt+zrUPks0UWLSsaC89vdM9gijgcl/e9RAlEe8MQobJeJbR4QTpZB6Pz4kv4ELAvqeS8QyKw16EJuJZPF4Lh9P+2+FUWLF+rk3k94/h9btwe+zxVJZFclkNWRERRdGu4zdMJFnE5VYRBHCpLZecloCLxktO2EKVwhO2mtwZ0xEK8UKOo7E+Wv3lVLmmRoUFQaDW6y+RQ9OhasLC9VIByjpfYIoY5cUsgslLGXAqFxe0Ag5qisc/t7SNLIpcUTmeYVDjnUxeXnrsE9utneaziX/PBMclz7DDpdo6N0EPomxfr0sxXZn4dFAlmYhr8vgjCSKyZdHqawBsTcFJuktFMdOsXmAkn0YVZbJGoZhZZI9BmmngkhU006A/GyeselBFmQbvu8sK+1LRV7BLDILh8QmwJIpIIqiyi6B7/JnOZgqMjaZIp/NoBXtMczhkAiEP/qC7RBB5A24Ov3KSXDZP16k+/GEvqtvO9NEtY8bSa0EU8HgcxGLpKeOlrhmkkllSyRw9F6KcPNoDQHQ4wcmjPVPI6mDYTWVNaNqSLbA1NZKJDGOjaduSHtuSPlLuw+1549KK0jHpBrFoikQ8SyGvMZM8migJNLVUTPtusyyLbKZALGq7V5qmhcOh2Fm6QfeM+hS6ZpBK5UglcvR1Rzl26AIAo9EUp472TnlH+4NuKmtcGFbG1oi7VOezeCyZVJ6R4cSkPglFvHh9U0v9pzuXXFZjdCRJOpXHNG1tu1DYW7w/hFK7pmkxNJqyHYxDXkzT4sL5QbSCQU1dGF/AhWVapFI5okP28QgCuNx2ubPX5yyOI8Kk9rO6vd3Ns+fSnRzjjrnzKZgGPekx7l6wmKDDiVuRWV5dg2GZ+FQHo7ksZS43qlxPRtNwyBKiCFc1NpPWC2xsbEQQ7HnWwvIKQk4Rn2pimBK3zG5FEkQ8isjC8jAhp+0+XOPzYVlQ5anHJQvcNqcVn6JhWU50w0RVJW7ZuITXj3fx0t4zrFrYiGFa/P5tq0lnC3z3kZ2MxNI0VL975kO/SVhYXLVqNnXVodJ9eek9r8gSbpfKWDxDXzqPphvkCzqCIJC5hGR1OhTWr2jFV5S0CPhcNDeU8frhC6Qz49vOn1PN7oMd/PSR3bzn6oU01UUIBz1vy0HYsuDAsW4Mw8TtcnDuwnDpO7/XyXA0Sf9gfBJxhGWxafUcaiasZ2bSm1kwp4Yl8+tKhNbEsd80LQ4c68K0LNxOhbOdQ5PaHhxJ0D+UoLYqyOBwgpHRFLdtWUoo4LGfQ0ll3ZWzeOz58TXnxX773Ac3Egl42PZ6O8OxVOmfmSAKAuGAm6Xz6rjz+mUsbK2+xBnOrq5JRFP84T/9HvNWtiAIAtd/eCOnXj9H71m7XMs0TF56YCfBCj/v+/yNVDXZwX5v0MO+F46w66kDrNhiJ0q8+uvXkRSJT33tbioby6aMbaZhcnTHKQYvjPBn3/sUize0IQgCVY1lJEaT/OQrj3DVHatYumn++PmLAnd+4SYa2+xs3lmLGib1TaQ6xC2fvqbUVlVjOSO9MX7ylYeJ9o9RXhdBcSiEKgP4+2JIkojH75qydi1dd6eK6lTx+F2TqpMuIpfOs+2hXTS21fLeP7yeQLGM8LbPXs+J3e28/OAuVt2wtLR9w7wabv/sFtzFefy1d6/jwEvHGOqJEmkq5/TpftwulcVLGt59Gkdj2SwOWSLgdIAgUMgV2P30QfKZAtf/yUZW3rB0Ukqw6lQJV00enGODY+x7/ghOt4MbPrKxRBoBON0O1t26nBfv38nRnafpPN4z6eYBO3q64Y6VuCbYSVbUhZm/qpXOEz0MdUf/B3vgzVHVVM7VH1iLf6LdZJlNQp3Y085A58g72n8mlWP30wexLItr715Hw7yaUv/JisSSq9pYfeMynvzhVo69dpqqpulr8i8XLq+DQMTLUPcIu585xLJN8/FHfEiyiDfgnlaPSpZFOk/3E4h4mbuscTxaZ+WxjG5AQlCnJ44yWoHHO04xyx9iZdV4JFIzDHb0XWBHXycmFu9vXciCyDgpljd0nrvQzqHhfmRR5J65S1i3ZFbp+0sXZBf7rDI8ebFXHnxj+8YjIwMcHOrjjtYF+FQHiizRXBGmuSLMe5a9sdDam0E3DdKahl+dPLkeySU5mxxCFSV8ihPDMikYRnGBBn7FTVj1cD41BAhUOv3EChnihQyKKLEoWEeZe/qMnN9VGKaF3+WkOuSjKjhztlHe1Hk9euEt7dvEYLQwjChIZIwUpmUiCVJJ4M8hTb53TTODYSUQiTA2miYRz4JVFEm/5J1jWXY2m2VZON4gs2Di9lnDdhbUi6snRZBwyQouSaFhVjkNs2Z2dJjJuUMURbx+JzlDJ9QcQLMMMujIgogkywQ93repQ2NH3AqmTkYvUDANzGKEWRElXJJqa4wJwrTnrlsm0VyGMmeWKtf01/VSx5KcoZE1NAqmAZY9WVIlGZek2ETrJW1NIaNmFG689PM37o+ZrqVdMmiS1TUKpl66jgICiijilBQckowkzFxu+WbtTizrtopkTXpS/4sooohLUnBKyltqx8Qio+vkDA2jeOyyIOKQZNyyiohANJ/mcKwHl6SQ0HL4FScuSaEvG8ctqYQdHnrSsVK7bumNrc7fLUiMZXjl2aO8957VU76zLIvhgTivv9bO/l3nuHB+iFg0RTZTsCe2Pic19WGWr27l+luWUl4VIJexx3yHU6VtZStOjxPTMjEsg4yeK12fSyEIRdejgs6l7NKvf7Gb17adIjqUYGQ4WRJp3fbcMbY9d2zKvq67eSl/+Oc34rkk+HbR0GHbs0d5bdtJui9ESY5lQBAIBN3Mbqtmw7ULWL1x7pTfXoRp2kT2k4+8zsE95xnoi5FJ56d1nBEEAX/Qzdf/86PMmj0+F7Asi0JeZ9+us2x95gjnTvUzOpK0F5FeJw3N5Vy5tpXrbl5KpHwqOf3EI6+zc+sJhgcTjAwlSv2x46UT7HjpxJTj2LRlIZ/9i2tBMYDJ/S9JtoDunu2nefaxg5w50UsibltJB8IemloqeM8dV7Jy/Zxpy+ksy0Ir6OzbdY6Xnz1C+0n7XHTdwONxUt9cxhWrW9hy6zLKKsbn0j63o5RpkMsW+Ne/e4z+3lE+/+VbWH3VXHZuPcnWZ47QfrKPRDyLKAgEQm7aFtXxiT+5npq6yaSuBZyNRzkbj7Kxppn+jIBLlVEliYDTid+hMqZlOZuIktLyaIZBoz/ESDZNSvfRPjaCZYFDkTgVG6beG6AnnWBJpIqd/RdI5HOsqKwgpJxB1wJ4pDzNPhXDHEIwRbQCVDhVJLEM02hHQMcvO8jn43hFA0P3okhr8XucdmbGkmbcTpWtr58hr+lIoogsiYR8LtxOlVzhf9Zx83cd5ZGZ54uWZdHTP8aDT+yjo2uEvGbgUCQKmkEqPTXb3uNW8U4gjAXBFpJGGA/aAFy9di5et4MnXjjCP37rWSrK/Gxc1cr1G+cTDrrf0jtL1w3G4hlG4xm+9ZNtk/SCgEkaQxNRUXZ59u2hgBvnDAt8TTeIxTPExjJ8877p2774SSqTxwKCftckfsLjduB0TN1/dXmAz969gWvXzmXf8S7OXBiiZ2iMRDqHoZv2fa6KhP0eaisCzGmsoKWhjIWzapAVu8810yjlGwqGxXBPFJfHQUVduNTHDrdKTUtliTjKpnL0dwwTG4rz07//JUrx2PSCzuhAHE/ATS5lZ1F3n+6jtrVqWtII7NK4nvYBnG7HpPWrIAg0zKvFMEy6z/RPWvtHakLUzKqcdn+WZaHldc4f7eLMgQ6i/TEK2QJ954cwTVv0/DeNRDRFtH+M5dcsxBsaDwx5A25qZlVydMcpUmOZUmVT47zaUkINgCfgRlYktLzO0GCC6qogg4Pxd6c4tipJeCcIY+sFne4zfbj9LloWN1xWHXkqnmWoO0q4OkioMjDlRvAUBbJP7Gmnu71/CnFUXhcmEJka2XW6HVimha5dfmbB/wSC5QEi0+gHOD3F4yu8s+PTcho97QN4Am6qZ1VM6T+HS6WutQpRFOg5M4Be0JHeQaZJpDrETZ/YzP1ff5zvfel+Zi9rYu3NV7Bo/TxqZlVOe83rZlUQHYxT11KJZVnjEUvBAVYaLJ3pXAMofmqa5pTIqSgINPiCNPtD/Pz0IdZWN0wijiRBpCUQYTiT5lfnjrOlcTZN/snaGtPhrS7ULMuacXL+TnF2bJQnO05x7xXrJi1ma9whwg4PF9OzJ5IAQvG/IgItZoW9+MRe7JmWhSQIqKL8G/A0+O2CXJwIZi+ZAFqWRVovMJRL0p+Jcy45wtnEeLTpWKyPx7uOzrjfCpeX2X4Po4Vhwmo5PjmAWHRf2R/tojcdB6Dc6WVZpA6npGBaeVK5PTjE1bjcKro+fn8UDJ3XRy4QzWcAqHUEkGMilglL2mpLx3xsrI+O5CiKKLEkXEuNO0De0Dkc62Vb/xmOxHqJ5tLFY/TRFqji6uo5LI3UoYozvyIsy+JscpiTY4PIokhboIpmXwTNNDgxNsBL/ac4PNrLcC6FZVmEnR7aAlVsrprDFZE6XJfhQDcRBdPg5Fg/u4c7OTTaQ18mTlbXcEgyFU4fC0PVrK9oYUGoekZ3u7ZQJY4J59STHuNIrBfDMmn2RlgYsrN8NNPg0GgP2/rbORkfYDCbQLdMPLKDek+QeYEqVpc3sTRcB8BIPsXuoU4sIORws65i1rTkWHtimNPxQcyii9iiUA2N02THZHWNvSOdxAs5XLLCirIGgupkIj2Wz9CeGOLYWD8nxwbozYwRL9giwU5JpszhpcVfxqJQDavKmyh3eC9rTEoUcuwa7iBv6EQcHtZW2IYHhmVyPjnCq4PneH3kgt3/hoZLUqhw+pgTqGBlWSOryptwvomzmQX0Z+LsHb7AvugF2hPDxAt2OVRIddPoDbOqvIl1FbOodPm5Sp1jj09YRXtjWGTaIr+WZeGSFYKKm4oZCMHfVcRj6RK5cCmG+sfo6hie9juAV186wYM/eZV4LIOsSARDHsoqA+gFnehIihOHuzl/eoDhwTh/cO8WFFWmfl4NXaf6JgVDREHEI7vI6NOXSQMUNINcTpvybs1mCnaJS02IYMRLR7ttc19e6ae6NjzFaGMmgeiRwQQP/nQHLz11mEwmj9frpKouhGVajI6k2L39NCeO9DDQG+OOD62dNuNyeDDOfd95iZ0vnUB1yMyaU0Uo4iWX1eg8O8jQgD3+1jVGmL+kgaqaEKGIZ9I+Cnmd5584yC/ue5XoUBKnS6G8KoAoicRjaY4dusDZU330Xojyic9fR+gSq+xckbirrA4SCnvpOGuXqpVV+KiuC0/J6mucVYEsyxjYJboTmTlJEtm/6yw7tp5geCBBuNxLTV2EdCpHdDjJyGCC3q4oDpfK0hXNU/pVKxi8+NRh7v/RdkaGEjgcCuWVfiRJJD6W4fjhLtpP9tHbFeWTn7+eSLn9bKWzBVRlsvOQrhmMjabZ/vwx7vv2S8SiKTxeJ6Gwh2ymQHQ4ydBgYnrxbyDocJHRNIazadKaxkA6iWYYDGaSzA6Wsa23g2XlNcTyWQIOJ05Jpsbrp9Ll5ejIACYWp2Mj5A2dpJYnoDqp9wU5GRvGo6p4FQeC4AJBBcu0DR2sAqaVL/alXeJsBwLCCEVzCiwLUXQDAmVBD4fP9BL2u9h7vItz3SM8veMEY8kM+YvC0UUi6f/HzHijoFFBM/jF4/vYf7SLu25ZzvJFDbicCl19Mf6ff396yvb29XrzNp0OhXUrWlgwp4YT7f28tu8cjzx1gJHRNB+/aw3eGdwmpz3+oltcVbmfL376WiqmIcLKy6Z+drnrgemCgpe2XV0R4N5PX0t5eCoZVVFsW5ElsGyyaSLMonbldBgqJAhUOVkXaWZhpoqCZpDRCiiChCpKnEoOUOXxoyoS5V4fCT3HqXQfOUOn2hUo6RAKgkCTI2zruQoXT2q8HyZmZF50VRVEAcMwYEJS2dJN86mfU4PikO3sUOs3X4YuK9KM95BlWmx7eDePfvs5qprKqG6uxBP04AlMn6X/fwNvlPVdXuHjlW2n8Ptd7yjJ41L81hBHfYkkEY+bSDHF2jQtUmMZFIedKn850DWdbCZHlau8pGA+EYIo4PG7EESB1ARRyYtwuh0ltnPS70plB/934XApU8oJYPyZnFZccSZY1pQTMgyTTCKLrEi4PFMHUkEQcHmdSIpEKp7BvEy2daYSCYdL5caPXsWshfU8/987OL67nR/89YOEKgMsXj+P2z57HY1tdZPSSQVRYMna2Xh8LnKZgj3QcHGkFUEMTGnHsixGc1l60wkWlVVR45lcinKxVt4pSzzRcWrK72VRZEGkAlUSea6rfcq+o7kMWV2nxuMrCVd3JmJUe/w4RImBTBJFlEgU8lR7fIzls+imSZ03gCJJaKZBR9yOmG+sbZ6kQ6KbJv3pBF7FQVLLkyzk8SoqNV4/SlHkM2/ojGQzxAs5BCDidFPmsh1ECoZBfzrBC13tHB7p58jIIJIgUOn2UuG2RcMlwclINk00l0ESRGo8PtyyOmmAdryLLK7fDFbRKjaRGXcl6k2P8e1T22lPDDNWyBArZEhphUnP3K+7jvDrriMz7ndDZQtfXrICVXSQNTIIgkCZVQmCxP3n9vFkjx2BX1PezD+vuL24+DaxrDygEy7zEQp7Ss97Wi/w7VPb2R/tBuDO+iu4UW0jm9QYHElSEfFhCRa/unCY+8/vwyHK/OWi67ixbj7fO72TJ7uPMZKfnIp8PhVl93Anj3cf5X2NS/lQywqqXNOXbplYvNR/hn87vhVFlPjk7LV8fPZq7mvfza+7DtOfTUzZ976RLp7sPsaW2jY+NWcddR6bCDcsk8PRPmRRZHF4qvPiQDbBj9t381zvCYZyySkj3ZnEEDuGzvFw50HWVsziM3PX0+KbHJWSBIHO5CgN3nHS99BoN39z4CkyRoE7m5bxd4Eqovk0D3Ts46GOg1P6B+D4WD8v9J0iml9RIo76Mgn+7vDTJLU8c/0VLNzwEUKOyUSPZho81HGA/zq3BwubkP70nHV8YcHmKW0MZBN85dDT9GXiNHsj/Gj9h0rEUVYv8OuuIzzWdYSOVJR4ITvDyD/IjqFzqKLEolANn5m7gTUVzUgIZNJ5ZEVGcViktUFMq0BAnYUgCAxkE/z9oacZyadZGKxmeeRjIMBjXUe4r303F9Kjk+x7AU7FB9k+eJZT8UHmBSoxFAO3NH3pUFYvsG2gnR+27+JMfNDO5pqADqIcGO3mqZ5jLAhWc8+sK9lSOx9VuoSknjAmLQ7VTdsDv+v48b8/z1g0jShN04+ZwozZNYIgsGzlLA7v66BxVgVLVzQTqfDjdCoYhslAb4xf3PcqRw9e4JXnjrFucxv1NUHqZlfTMK92gvuMgCLKuGUHfmVm/SLLtHA6ldKi0C4/0rnm/Vdwy10rkQSRgb4YX/urh+nrGmX1xrnc88mrUJRLylRUCccluh+ZdJ77f/QKzz9xCIdT4Y571rDhmvmEIl77/Tuc4plH97Hz5ZM89NMdeH0ubrx9OeqEuZxhmLzwxCFe23oSj8/JJz5/Has2zMXrc6IVdM63D/Lt//0U5073U1YZ4DNfvGFK3xqGySvPH+O/v7+NbDrPNTct5rpbllJRFUAQRZLxLNueO8rzjx9k67NHCITcfOhTm0plugC33b2am99vSxwMDcb5P3/zSzrPDnHl2tl8+A82TxEwlRUJ1Wki6C0oUjUTH7t0Ks9Tv3yd6towf/LXtzBvYR2qQyabLbBr2ykee3AP/T0xfvXz12hurSA8YTFrGCY7Xz7Jz777MulUjquuW8gNt11BRXUASRRJJrK88sIxnn/sIK88dwx/wMWH/+BqnG6VZDaPF8ekeV2hYLDz5ROMDCVpaq3k45+7lqYW2xGpkNc4f2YQRZFKZg2XIuhwsqG2iaDqwqs4UCQRRZRYVdVA2OnivbPaCDpcNPlDdqla0cDEIUncOmt+0YHUQjNNvIqddeGWFTbVziKtFyhzeVClVZPvWcsovlspkkMwtfSZ0ucblrfQ88x+fvzYHqrL/Hzg+mV0D8Twuh18+6EdaJqOz+MkcpnrlXcTRNHWuSu8w+B1OpPn6KlelrTVsuWq+bhdKqZpcaZjiFz+7WVyTdTEi4Q8rF9hi1l/52ev8Oredm7bsuQtEUeSJNLSWM6eQ52YpkXjhGyaNysHf6eQJZHWpnL2H+3Cst647XDQg8Mhc+7CCLph2plYQHdfjFx++utkWhZ9mTEckoyhmjSHIiS0LFnDNqhodIWRBFtjzCkrGFjopoEqSiS1HLIoMppP0+CJ2AYaNSFy6TwjvaNU1Nv6R/lsgYHO8YCH2+ukqqkcj9/Fp792d8nNbOI5CYKAkMxS21LJkR2nGOmLUVYTmjK/kGSJ+rk15Ivl1uGqYEkbquuUrVta23r5EivJsTSPf+9FWpY08vGv3Em4KoBlWrzw8x0c2JOrOgAAIABJREFU2T51rSgI9shh6Mbb1hTyR7yU1YboPTdIKpYmUOazg9aJLP3nBymrCeEJuNGKz8MbtTA8lGTWrArKynzvTlc13TAnTUYFQcDpcWD0x8ikZo50TYSsSDjdDgo5rejaMxmWZZFN5bBMa5It34RGf+Ns5m8ab+X4SltOM5blswX0S5hoURJx+ZxkklnymamCjZZlkcvkMTQDt885JVpY3GjKR9lUbsYBVVZlFqyZw9zlsxjsjnJo2wm2P7qXbY/spvt0H3/67d+nfq69kDR0kzNHuinkNRavbqX9aDe5TJ4Vm+cXTzOPME3GjgV0JmI8cOYwB4f6+cSCK7ln3pJpj+ft4MmOU5wcHeavV27GrzoYy+f4s1ef4a9WbKLRF+Qre7ZS7nLTHouypLyaWC5LTyrOvVesZ011AwXD4IWudnb2XcAC/v2qm6lw25GElJbnXw7sQBUl0nqBtKaRKOT4g0Uruaa+FVkU2TvQwwOnD5M37BISt6zw1ys2MSsQJl7I8UTHKV7sOsdgNsV9J/YjCyI3Ns3h2oZWW+hvuJ/vHt1DwTDQTJPWYIRPLVzxhlov73ZIooDPNT6ZGNOyvD5ygeHcOJHgkCTyhl56vBRRQhZmHpwdokytqxGf7CNvZhnTom9K9ppWHkWuRVXcuFxvXAYkCgIel4psilREfKWX2EXkTZ3jY/2cS47wUOcBsCxCqpuww40qSsQLOUYLGXKGRjSf5qdn9zBayPAXC6/FrzgxzCgFrR1ZrkGVGya1rZkG7Ykhvn96Jz87twfTsgiqLkKqG6ekkNCyxPIZsoZGrJDh4c6DJLQcf7PkBkKqG8uCvkwcVZRZFJ4wfbcsOlJR/u34Vl7qP4NhmciCiFdxEFRduCWVtFFgLJ8hpReI5tM82X2UwWyCryx7D7O842KNBdPArzgJqq5p+zGWzzCQTfCfp3fwRPcRCoaBW1ZRRVuY+qKmTs7QUUSJZZG60n7KHB6qXQGS2hBxLUd/Nj6FOIoXspxNDpWuiWGZnIwPoJvGFKenaD7NcNZ2K6l2ByhzjEcW86bBtoF2Do7aejGyYJekuWSFoOpCFiRSeo7RfKZU0rc/2s3Xjz7P3y65kUXuag7sPk+ozMu8xeWktD5kcfoIWsbQGMwl2T3cwb8c20pCy+KQZByiXf5mYZfB5k0dzTRYEqpFEgSOjXWjijJtgbpJ2UcZvcCDHfv5wZmdRPMZe4EnqQRUFwHViWlZjOYzpPQcOUPn4GgPHakoWUPjjsalJbL8Yv9Zlk0ICoKAadlziMtxzfpdgSAI3PXJjXh9Uwmi4YE4O6cpb7qIxpYK/vKr78PpUhFFYdICo6Y+jOqQ+dqXH2Z0JMWZE324JYgeGKWsNszsZc2oxYWGiEBcS7/hSCWKAv6AqxTksYCBRJLO2BiLqioJel0kk9lSRo3qUPD5XSX75DfCySPdbHvuGIZucONtq/m9T1+F0zUe2KiuC1PXFEGURF588jCPP7SXtsV1zJlgTJKMZzhyoJNCQWfVsjlsvHYBDqc9nkoulbZFdWzasoiujmG6O4bp7YpO+j3YWU9PPLyXsdE0m7Ys4lNf2EIgNF7mUlUTpLYhjGGYPPaLPbzywnFWrp/DkiubS/uQHRKmBC5VwVN0MARb78Trd+KcRlTZtHLk9U40YxifY7ws0TBMfAE3n753C4uXN5X61rIsKquDpJI5fvnfr3Hu9AC93aOTiKPocJInHtpLLJpiw7UL+PSfbiFcNp6RWFkTpLYhgoDAwz/byasvnmDVhrksubKZipAXr2vyQts0TI4evMCaq+bxR39xE6Hi++fiom12Wy0XM3o0w8AwTZzKuCCyT3GUyKCAY/xejzjtMdQl29s6Jbn0m4uoLM6VLi2ZBtudL2iN6zwZpolmmDhkCUGQEYSZ7r+p74eKkJfP3bWBbF7DocooRWv0TE5j/8luUpk8LZVhUiMpnJKE403e1+8mVFcGEEWBl187TdB/cRwQqK95a3qjDlUmFHDT2TPK2c5haioDtHcM8eSLR8nPQHa8GQqawcuvnbalHxoiqKpMdDTNhd5RykLeKfbxbwZBEFi3ooXdhzr46SO7SKZzNNTaz/3AUIJ0Js/1V7XhUN9+sDWn65yJDpDVNRaXVeNTHaW2N6xsZe+hTn7y8C6SqRx1NSEMw6R/ME42p3HdxjYcqkxddZCFc2vYvqed1qZy2lqrGBxJ8tRLR2ckERo8Eerd4dLcUUCg2jXBgGrC/wjCReOKcWimyaASp8Jpl7YuWDMHt8/FUz/aiupS8Ye9vP7cYU7vO1/SERYlkavvWsv3v3w/D3/jaTZ/YA2egItkLENPez8Nc2uYt6IFp8fJmluWs/e5w/z07x7h6g+upbwuQjqeYag7ysK1cwhVBli0dg5VjeX8+jvPo7pUKuojdJ/u48kfvMSyzQtoXTLZFOmNIAoCoiSQSWRJjaUQBNvl/JVf7pn2fegNunG6VY69doYlG+fj9jkxTYtwVRBZkTAN015D62aRizDJJLMkx9JIsoTTreL0ONh05xru//qvefrHL7PpTnvM3/7oXnrPDXLXF2/GG3QTG4y/6fG7XArRaApZlqiumV5z6e3gt4Y4qgn4J5XQyKpETUslZw50cOFkL0s3zX/TVCtvwENlQxlD3VFig/ESw3kRmUSWga4RREmk9g2U/98yBPtfpmn9jzPOlwtJFhGLSur53GQS6KLyfDY12SnF4VSon1NN16k++juHmLO8eVL/FXIavecGMQ2T2pbK0qRPLqYtm7qJVtAnOZDYot5DmPpM+gj2/hWHQl1rFdXNFSzbvICfffVX7Hx8H0dfO0Nd0b5xZGCMI7vPkhhN09sxTHIsw8rNF8sNLQRLwBKmvlwEYHF5FeVuD1/d+/JvtBzMAoxSidlF5xF7ALUsmxYYTCe5bVYbrYEIj50/yZevvIoXu8/x+mAPa6obcMsKH5+/nIjTzdOdZ6a0kdE1enIJvrL6GkIOFw+eOcLD7cdYU92AX3UyOxjhc0vWEHa6iOYyfH3fK7zad4HmQJiQw8WH5y1DN00ODffztyuvRhQEXMWspoyu8eMT+1kQqeT2lvkkCwX+cd82Hj9/ks8sWvn/mYnPROQ1nY6hURrKxic91S4/n523kZSWL+rFwEguxc/P7yNWsEvFrq2ex6ry8ZfSpRGHancAC5P+XBdpPYEsqmimhiJOX1ZlYWGhYZgxDEOjvzdtC+LOELUVBDsa5nappUn7pUTuS/2nKRg6fsXJjbXzub62jRp3ABGBtF5gx9A5fnXhEO2JYfKmztPdx1lT3sRNdQuwrAKmlcY0p3eR2ztygV3DHbgklWtr5nJz/ULq3EEkQSRjaOwbucDDnQc5PtaPYZls7T/N6vIm7mhcimGaxYyayceb1PL88MxrvDzQjmGZhFQX19bM4z11C6lxB5CLGX496TEe6zrC832nyBka+6Nd3Ne+i3sXXF0sxbTHgf5sgqDDRbV7Kik6WsjwyIWDPNF9FEWU2Fw1hw2VrTT7IvgUB1ldozsdY1+0i4FsggXBcbenkMNNtcvPmcQQiUKOgWyS+RO+B5s46kiOAjaJmDd1BrIJovkMlZeUWF1IRdGK49RsfznSBELSJzu4vqaNA9Fu6j0hlobrWFnWSJM3jEdxIAoCeUPnyGgvPz//erG/LTpTUR7tOkzbvEo8PidOl4IoSKiSl7yRmDZ0ldULvNB3ioc6DpAzNFaXN7O+soVFoRpCqhvDMunNjHF8rJ9jsX6WR+pBgKDqwSc7J421FvDq4Fl+1L6LaD6DLIgsDddxW8NilkbqcEn2cxArZNg7coEHz+/nQnqUsUKWH53ZRauvnGWR+lJWS0dymIJp0Baw+3kgm6A/O8bySNO09+fvItZsmsfstpppCRZfwE3n2aFpfmVDksRpM5JKREdtiKraEKMjKcZGU4QqAqTH0mh5bcI8xiJj5MnoObJGfsq+LsIwTPr7xmhqKkdW7AV1ulAgls2SKhQo5+1nYezZcYZMOk+k3MdV1y+YRBpdPJ9gyMPmLYvYu6OdoYEx9u86x+y2cY2LXFZjbNTOMq+pD5fmLBP3UVMfRlEkspkCyfhUF7mzp/roPDuEosps2rKQwCXaKIIg4PY4uGJ1Cy8+dYh4LM3xQ12TiKOBeIrjPQNsmNs8Zf8zQ0RAYboI4PJVLcxdUDepxE0QbHOEtkV1OJwKWkFn5JJFxvkz/ZxvH0BRJTZdv5BQxDvlXFxulStWt/DMr/eTiGc4euACS65sJpPTposP4vU6ueXOlUTKJ4+t4+VE9v5PD41wPjrKrQvbJm1zOXij7S5HMiCey9EzlmB+VQXyW5zbCIKALEv4Js5vgYJusGZxE6oik4ilaT/aw1BvjLZljdNm7r8bMXdWJZtWz2bnvvPsPtiBosgsX9TAFz5x9Vvaj9OhcOv1i7nvoV38w388g8upEPC7WLW0mYGhN18oTwfLsjh9bpB9Ry6UtLlEQSDgd3HXrVcSfgMnyJlQXRngU3ev579+tYefPLQLvVj54HTILG6r47qNbW+6jzfCYCbF2XiUjK7RHAiXiCOAmsogn7rHbvvHD742oW2FpQvquG6DrYfqdqncdcuV/PTh3fzg/h2oiozf52Tp/DriieldMm03wfEAg2aZmKatY2hhkdELJRkAEaFUZlYwdRyijGlZ+GRXSeKgtrWKOz5/A4/+x3N8/WPfwel2UD+3hhVbFnNs5/haZ8lVbbz/T27ipQd2cmDrsaJhikSoMsAdn9titycKLFw7hzv/9Cae/cl2vn3vz+zAkSxSXhumeWE94aogZXVh7vnSe/nVfzzLN//4vlIZXMPcGj5w7814Q5enNQXg9rv+X/LeO0yO+z7z/PwqdVXnnk6TIwYZIECAOZqUKVKkZEXLtmTLvrX1yOu4j/2cHG7XXt/d7voen3e9++ytV/bdynJSsiR6aWWKIiUxiwEEkTEIg8kznUPluj+qp2cGMwBBSrYV3r8AdHehuqvqF97v+31f7nv/HTz8p1/l333g/0HTNbK9aXbfNEm9tFmNnhvo4Y533Mijf/st/u1P/CfUiMqeWyb52d97N4lMnNJClb/5w4eZO7fI8kyJ8mKN//H7n6anmGJwso/3/faPkcomuOWh66ks1XjiM8/w2CefAkDTVR78Fz/C7W8/fM3j5cBgD/l8EiGJTUEU3wm+Z4ijumXRl1ybvCK6xk33H+CJzzzLl//yCSb2DbHr5slunKznejRrbRRVIdqpxmWKKW5883V86k8+z1f/5pv0jRdI9oTHtE2bZ774MmdeOs+OQ+OM7fnuSduNmI4WUZg/v0Sz2tpazfRPjIihkSmmOHPkIi89fpyJ/SNdz6CFC8t883PP025sXAgacZ1bHrqeJx95gUc//iTbrhtlYKIY9p66Hq8+dYpnvvASfeMF9tyyvVstS2bjJHvCh+Lk81Psu20HkizhuR5nj1zkpSeOb9nWVi83sdoWyZ5E1+hLliUyhST9YwV8b6NvUyafYM/hcZr1NkOdtJPCwOrmPkDIfWy1uBJCoAqZuKqh/jP0n8e1COOpHqKqRm80zt5skeOlRWaa9e75RVUNQ1G3lB0qksTNvUOh71IQcFPvEI9On8Xxw990Nf67apn4QUBfLMFiK/SVUSSJVETHUFRUSSKt6xs2oVPVEhdqZe4fmaThhMkmOT3Gi0uzWJ6LrvzwtKitQpVl9gz10rvOzLwnEuP2/DbOVFbYl+slrmmcqS3x8MUjXeLoUG6Inxw/DMClRpXTlWVu7h3uVksB/MBn0BjFx0dCRpNea2Ep8Pw6LetVgmCMen1rvxMIe9lLlRYjxSsnSlXsNgk1wm/uuZcHh/bi2z627ZJIhFXCiWSO67ND/PbzDzPVWKHl2XzszLMczo2Q0yIEQbsTJb9qgbiGmmMSVTR+ZfddvGvkwCafoYlEjgM9g/z2tx/meHUB03P567PPcyg7zEA0TV80gSLkDWqjL84c4x8uvYrje+QiMf7Vnnt4aGjvJg+dkVgPB7ND5PUEfz31HKbn8MWZY+zPDPCe0YPdeUWTZNwOqXv55HuiusCJ6gL90SQf2nEH9/Xv3GT2vL9ngPv6x2i6DnF1jewxZJWxRI5vLU7R8mymm2Vc30dZN95M1VcoWU0SaoT9mQGeXb7AQrvOTKtCwUhsePZPVBe6f96T7tvgCyFLEvf0bWcolmYk3kNeT2wZTT8Wz7Iz3cu/fenzvLAyjRcEvLgyzXS9TDyhky+EGzwvcJDE1ouKZavJR05+Ex/4pV138p7Rg6FCjFXrAsGudC/39u2g6dookkTZbiALiYwW7/pYBUHAbKvKR05+iyWzgYTgwcG9/Nruu+mPbvQjHIyl2ZvuY2+6j9/59t9zqVXhQrPEx84+w1giS0aLYvsuU41lTM9mKNaDAM41ljhenfuBIo5uuuvKoQipTJQH3nnoNY/h+6FSuN22sS0Xz/PxvYBKudm9ZxzHo9UwuXh8hmQ2wY7Dq8mGgoyWYDIxuGHeuByu42OaDrNzFbZtK4bm7LJMRFEoxt84aWSZDqePzwKh91D/utaM9RBCMLmrn2wuztTpJqePz2K2na7XUbhwDs/fbG9NfLRbNr4foKgyqrb5eTj28jSO45HJxvE9n/NntybtzLaNqio0aiYzF1fwPX9NWSRLGJq6qdXztSCEgq6MIUkbicB9h0aJ6FvbK8STBlpECavblynIj718CctySaWjBEHAhSt8l1bTQtMU6tU2MxdXsB2Xesvc0qdmdLLIyHgey3VZbIT3Vj4eo+04NG2nOxY2bZvZWp3zpTJJXSdt6FTabSptE0NVKXQ+07LDz/VEDXRVoWZatGwHTZHJx2IsNZu0bYd01CBt6DQtm6VmE1WS6U3GcX2fpUYTPwgoJuIokkTLduhLhuOl6biUW21sz8PQFHKxGE3LZqXVwvV9stEomejV1/KW7fK5x45w743bGe7N0Ki1SWXjZIvJa1LTfT/CDwIu1UIS52y5hCJJRBWV2968g5H9BdotB5+AwWKaM6UVSmZIUixGTB78iYNUDYfnZmdQJAlNlrE9j/3FXmThI0mCu28eZ9tIlqVSHQEUckn6Ckmu39tHJhUDPIJAcO/tO9m3a4C+TnR6y13iYuMxbrz+LsaGHmCwL9wbRDSFD7znZu6/ew/1phmq3TSV/mKKXM/Wvn/bRvP8m19/kNHB7KbXgk4gx+7JPn73l+9neq5Co2kihCCZMOjLJ9HXtZwe2DPI//6bb2NyrHDF3zQIAtJJnd/44L0kEwbZWJyblWFeXVlAu0xBK8sSe7b387u/8gCnTs5RKjeIxnQ0STAyksOsm5gNE1mW6E/H+M1fuIdTp+dBEhRyCZJGhOsm+zBLDdoNE8dysNoO8XQUs2WhRyM4tovnetR1j+O1BVqeQy4SQxES82YNRch4gY8XBGQjUXRZYWnVJ1OPcyg3hEDCthxuePMBxvYOUy818DyPgW29OJbLbW87jGU6iGoLz/W5/e03sO+OXZx7dRpZlrBMh+Ed/RSHs1SW6xixCO2mxT3vvY1dN07y0jdOEE8Z9I8V6OlNUxzOAWHb5MEf2cP4vmFmzi5gtSyiCYOh7X3E1gUuRaIa7/il+7DbDvIVPJRlReb+n72LfbfvpLxQRUgi5BUycW64b98GAYrvN5EUhXf/2gPc/MBBKss1JEmipy/ZTVqPJQ3ufvdNWO3NHT16TO+qsOKpKO/45Tdzy4MHWJopIZA6Jt6FrjAjntb40B/9GJn8YHd+CYKAgYkiv/mRDzI42YumKZvan78b+J4Z2RzPw3Td7oJeSILr7trFHe+4gSc+8yx/8msf5ab7D1AYyiEElBerTJ+a496fuJXb3hZu1mRF4k0/eRtnj1zksU89TaPaZu+tk2i6xsWTs3zjs8+RyMR524feRKaw2QvnjWJwey+F4RxHnzzFX/2Hh9lz8yQQtmjd/MBBiiO57nutts3KbJlmvY3ZtJg/v4jvB8xNLXL0yVPosQh6LEJhMNslU94IJFnixvsP8OJjx3jkI49imzb9YwVadZNvf+0oCxeWwzjAdRCS4ODde7jznTfyxN89w3/9jY9x0wMHiKWiLF0q8finn6FZbfHe33iIwcne7oCbzCY49KZ9PPzfvsxH/+DvuO2th0gXkixdKvH8V46gRdQNKqRVvPjYq3z+f3yd0d2DDGwrEk0YOLbLuaPTPPk/v03/eIGdN0ysbfoiKntvHKeyXMe2HBzHw2w7xBIyBC5BUAPxT2+OupUN9/pNnCwEspCQECiShCxJXQn3tUASgoxudIRtYm3RFoST+NcvneML508iOsc/trLIHf2j13Tsmm1RMtv8zcmXuwSH5bnszOS5gn/eDwVqbZNoRCWbWNv0tF2HC9UKcTXCvvzV+6TVTuuX628mTBtunXnzElE5RlK9snw0vJ5ZZCmNIiXoH8wSBFevuPpBwNJKnZGBrckjARzOjnDfwC40SebCQoVz55e49ZZtRCIqspDYnxng3aMH+aOjj+ITcLa+zAsr07ypN4MQUSTpyhWbfek+3ja0D2MLTyxJCHakirx37BB/+MpXaHsOF5slnlo8x/smbiCh6kTktXGi5pj8/fQrmF7Yy33fwC7eMrhnS+NlIQQxReO9Y9fz/MoFXi7N0HAsvjJ7ggcGd5NQQ8JUk+QtyVkI26gSSoQP7biD+wd2E5EVXG+ZIDBR5AIQkkievwL+FI43gCy2sZqsti2RDz/j2pxvrGB6DvEOMRialM/hBj79kRS3FyZ4sXSJqt1mulnmQM8AorMx9wKfs/UwITOqaAx3iBHXb7FK1sUUj51JF13x8YIWfiCwvSqSUPEDB0WKokhRJhI53jq0l5dLl/CCgEWzQclpoa541CotDtwyQBC4HTJwMxzfowW8e+Qg7xu/gZiiEQQwN18hCAJ6iykUJVRvxDutJqqQOV2fR5MUhuRQ9esFPt9cPMvJarhBHYim+bnJmzeRRuuv56HsEG8Z3MNHzzyN7Xs8u3yR45V5bimM8UrlEl+de5WaY3KkHLbstTybe3q/s0rv9yrCyHSb6XPLlFcaGFGNobH8FT1jIEz/WZit8PyTZzh1bIbF+SrVSguzZeM4Ho7t0mp2ikerrQfBxrnLDTyarolPQOQKykgARZXQIgqxzsLX9T0ato0sBIb6xtcxzYZJq1PgSqSiG/yCLocR1Uh21AOVchPLtLvEUTJlMDyWZ+rUPMeOTLMwW6Z/KDSjDoKAVtPixWensC2XYn+G3i1abBbnqwRBQHmlwf/1bz57xShv319TeZttB9f119r+JImUoaPJMptdNq+EAEmKI0kb1zaKKpPuiV1VaSOgc003vra0UCXwfSqlBn/0+5+7pu9imQ7Nlo0RUclvUbXP9MSJ6CqPnz3PdLlC23UZ68kwX29QNy1M18FQVSayWc4ul3jy/EWqbZP3HtzPiYVlji8sUmq3+cmD+zm+sMSRuXkGkkkODPShyBKfeuko49keCvEY8WGN56dnWKg1sH2Pnz50gC+fPMNio0FvIsG928d5cWaOl2fmKcRj3DExStrQ+ea5C9RNi58+fJAzyyt8/MUj7OktMl+r81OHruPxM+doOQ5H5xZ413V7uHF4kHrTIm5o2K6HdVlATrNtc2mhiuuGxYhWw6LVMMkWk8jfRU+R7zWUzTYtx2Gl3cZyXQqxGJbncuPuEZZaLRp2+MxerFapWCYRWSaICPIjSdqSx2KrwVAyhd9JBSWo43szELQhsBjMewwWdISIEwTnwLOYHMlAsIzveBDY9BdvYKB3bf3k+iYVe4rx3FsYLK6tz4QQpJNR0slrVxalEgaH9g1v+veAgEXzCGltjIicIh7T2fUa3SvZTJzsa6hcfByq3lF2bd+BIkVZaDcom60OObN1h0RElglKDfpiEXzHwzJtjj5+AlmW8P2w+yOWMPA8j1bdRNNVGi2HlipRLKR48fkp6nuGaFSaVEsNCMKunCAIwvnBcpi4ZxsDsTQlq4mhqCTUCElN7yq9ZSF1C4RJzcAPAuKKFhKzLZsTL5zHdTxcx6M42MPibJmTL4V2IwNjeV555iz5/jSVpTqyIqFHI8gRjR0HRzh/YhbN0Jg6FoY1KKrMxdPzRHSNaDxCNBNn940TDG3hWSSEIJ1Pks5vbbcRBAGSLDG2Z2jL19dDURVGdg0wsmtj6/JqcSU8no/jzSNLKdRImtF9BaCAQMZyToGkEQRZIjHB3tvHEagErHoUKQSEqZkCpWPYHyDLCoVRi8JoHFUZAjwCnI49sUcgL9C3awrZ2U6r0wZeXqyR7IkxeXAU3w9o1UMCcbXOG4lqV5wvXg++Z4ijqLpRbSGEIJGO8TP/2zvJD/Tw1D+8yJc+9kQ3YUSNKGQKqQ1RwUIIesfy/Pz/8V4+8ceP8PLjx3nuyy8DoZfO0GQf7/yV+zn0pr0bXN2/UxQGs/z4r7+FT/zxI3zjM8/yxGeeRVFlonGdyYOjG4ijqVcu8qcf/htKC9VQNVVt4douT3z2Ob796FFkRcaIR/jN//5BJg+OfkfndetD1zM3tcgTn32Wz/yXLyErEhFDY2L/CP/L77+Hj/2fn9nwfiFC8/D3/9bbiaeiPPOFl/jLf/e5MFlClugdzfP+33kHb/qpWzcQQUYswls/eC8rc2Vefvw4f/XvP4uqqUSTBvtu28FNDxzgT371o5vOL9kTx7EcHvvkk9iW25U0a7pK31iRd/3q/Yzv2/hgL82W+eLHnyYaDz2Wbr1vL7FEHwgF/AZQJVDGEOKNp729HgjCDXGoYgj/re26mK5z2bvWCKbX+9iGtt9bf2q53eQvjr3Adfk+3rfjOgIC/vNLT215DGBT4FxC1cgZUX5x/01MptfuU02SN5h0/zBBCEG50YYARvNrBnyqJHUnzNeCF/ibDMYBHN+m4qxg+W0c38INHFSudq+6+EETVd5KV6PkAAAgAElEQVSLKyR048qbMCEEsixQ5CtLUlVJ4Y7iBHE1NDfVdZV4fONmTBKC24sT/MWZZ1gw6zRcixdWprmzkCcILFxvAVXZWrF5e3EbKe3KVVpJCG4pjDEYS3O6tkTbc3ihdJG3De9jqr5CJmKQ0gyCIOB4dYFTHaIhqmj8aP+uLQmp9SjocW7IjfByaYYAOF1bZL5dI6HqNF2bZbN5RbICYE+mn3v7dhCRV+99D9s5geNewIjcAITXVJVzncnf7bSRwLZkDkNWabo2FxqlkDjqkClu4HOiOo8X+OT1ONfnhkioEVquzanqIna/h97ZaFSsNotmqEbsNZJk9Rh+YLPYehJJKEhCx/FryCJCyXoZXc6jygkq5lEMtR9FGLTcOQrGrRhKL5PJAglVp2K3MT0HR3gM9WeoVVudFBQHRWxtsgxhm+a7Rw907xnLdrk0W0aPqORzSS4fJtzAR5PkDQvepmvz2NxpnMBDQnAwO8hkMn/VRYwiydxSGOMzF15i2WrScExeWJnm1sI4k4lebs1PUnfbXN8zgkBgKNoGP4YfJDQbJh//8ye4eDZslfI8H01TeOfP3MbOfZufRcdxeeIrr/K5v306VMYE0JOLk8rESKWjoXIbOH1ilmo5VEy6toumaxuUEn4Q4Ac+rr+aVro1orEIg4NZst3IaUHGMDYo7t4IXMfrRh4rirS1r+IqhOie+/rPAeiGxt1v3hcmyZ2e57/90Re4+837KPSmaLdsvv3UGb71tWOomsyP3L+PXGHzhsPsECiKIpHKRK+JGLg8lc3zfcrNdlctfK1w3PlOIWFto6woUpe0fb0w2zZBEJJP1/5d4kR1Fd8PqDTaDLOx6KEoMm7g8/iZcyR0DUWSqbRDf8s9vQUuVWoYmkLdNJnI9vDu/Xv4s6efZ7pSZa5WR5ElLpWrVNphwWUgmeQ9B/YihODscomYpvGO/btRJInlRpOVZgtVkTgyPY/j+2SiBpV2m56YgSrLpHUdTZFJGTqGqhLTNA4N9vPE2fNAgOP59CUTvGv/Hv7s6edYbjZZabUYz/bQdlyGM2kq9TZ/+qlv8f4HD/OVp0/y1JHzYVpVB57nU6q1eN8Dh8JnLJ/otGp+99OfvlcggPFMD34QsDOXZ6XVCfowokRVlWw0iuuF8e1BEHQTRN3A79qRyCIscgaEz4REg8CvEwQNhFDwvTmE1INQsgTeTFjYCFqdAnEbISVZ3Q03nHkazhxeYHaHKC+wqVrnsP0mcbWfmFrEdMs4fgPTKyOhkI5MIIsIll+lZl8gCDzi6gAxtYgfuNScaWRUmu4CMbWXqJKnbJ3hZOVTDMRuJaEOkta2YXllbL+OLHT8wEaRDJLaMI7fomqfww9cEuoQupKh6czhBy5tr4QidFLaGBCw2H6J07W/ZyT+I8TVAdpuLkxxVbUNFi7r4ToutulQGMwye26RZE+ciyfm2H5ojGalRaNSYufhCZ7+wovEUlGiCR2zbdFbyJPqiZMpptF0lQCoLNYwYjojuwc48dxZqisNcgM9DGV60HQN23ORRLj+vbzgvVURXAiB63hYpkOk01r8wjdOsn3/EKWFGslMDCEJioM9JDMxKssNbMsFIRgYL5BIR3Edn5WFKmbLpjCQobRQQwiBpiv4fkD0CsEQV0PdqWP5NqZnokoqRf3KKrDXgyBo07S+RVQ7jO2ep22/iCR0IuouGu2voKkTGNr1tO0XAR9d3UfLehpFGUBTxjDto4CDru6nbX8bSUphaHupm18CZOL6PZjOUYLAJKJMYrln8P0mzUaTpZMXWZ4tkymkaHeUb5lCAs8NqCzViBgalmmTySfZeXi8q376TvA9szNMGXrXLG89CoNZfvp33sH9H7iLuXOLtOomQoS9h7n+HnrXkTIQVnSGtvfxq//pAxw7eoFTUzPgB2wb7GV85wDp3NqCwHRd6rbFjhvG+dd/9cvkt5AlyorM3e+5mcmDo4zs3nqzJMkSd7/nZnbdtI3580u0GyaKppDoiTO6e4AgCGg4oQy6d6zAT334bTjmlRMCJFmiOLx2LhP7h/nw//sh9HSEObNETNVpe1YowdUz3Pzg9fSNFemfCJlX3/OZmy7RbJgcesv1jB4YZe78EtFohHrDYtv+YRK5BPf89F14ns+FqWWaLQff9Umko9SbFm/5hTcxfmgCpxVWUHoKSfJDObJ9GRp1i9g69t73fFzL5s3vv50d149S73gJFEdyDO3oJwgCHvy5uzDiOqdfPNd1g1c1mff91o8xfXIWTVeprTTQYxHi6RixpEFxJLeJ4KtXWuw+PMZN9+65zPNKIghqCLHZJyUIAkzPpWHbOJ6P6YXXPSLLqLISvu461G0b1/dpOQ4N2yKiKKidDZDpujSd8PWmY9OwLXRFRRaCvliSvzvzKmcqK/TFE3zx/EnK1rUZukNYnW27Lm3XwfF96o5N3LGvqU3M9X1s3yOnR1EkidOVZV5dWeC2vo0GcFk9SsOxmaqVSUd0oopKTNWYSGcZSqR5Zv4SQ/E0hqLQcGwcWSaj//O3XP5zQAhIRfVNCz9VlhlNpdmW3jxOXA6ps5C6fMpXJJVefZCYkqDlNpCvaM5JJynGQgQSvu8xN1NG1RQmJotbqzQII1jlLRKYVqHLCvsya6llbdOmtkW/e6+RZHuqwEKHwDhTW6LhuiTlHLK0tUpKFoLrega2fG09cpE4e9P9nK6FyRrn6iuUrBaO71HpPDdO4HO0PEvVCc9tMJpmOLY5ReNyRGSV0XXx9gtmnSWzwbZEHkNWuS7bT390a3JBAHd2SDUIxw3fr6LIfShyL6uMq++3sN054pFDSGLtGe01UvQaKZatJhebZeqORU4PN9IL7TqXmmUkIRiJ99BvpBiL51ho13m1Mkvbc7rP+6VWhYq99r2zHZPtiJIjqvQhC6NDWEl4gYkkNILAQ4smkUQEIWTi6hgRJQsi9EQyZJUKYfpa07RoNExicT2slPktpKuQjddnhxiOrf2mruuRTOgk4gaRLVJIVUlG65hnr16vmVaVk7Ww/U6TZQ5mh67JxHo41hO2u1lNbN/jVG0Jn4CUZnB37w78jsH7D+ombRUnX7lEZaXBh/7Xt9CTT2C2bR77/BEe+/zLm4ijIAh49aWL/Pc//iLVcovtu/t5x0/dwvj2Ij3ZBEZUQ1FlSst1/sPv/h1Hvn0eALNlMbJrgOJIrpsu2zUVfg1fQN8P8Dyv+35FlrotS98JIrrabbFfbbO7kldD4AeYnZasiK5ueJ8QgkO3TPALv34ff/4nX+b5J8/wwtNnkRWJIABVlSn2pfnRtx7gLe88vGVh0eioqXLFFL/6Ow+RL742SWlENdTO+QdBQMtyaFg2/usgjgQq8chWLYni9VehuucVQQjI5hP82u+8lULfaxunGtGQVOxJxUgYW1euZUliJJNiIpdlLJtBEoJvnbuIIktEFBlZSLjCZ7nZ5OxKCdMNFTwnFpd4+77dTC2XO8cRxCPahmj7eERDkSQkIZip1qhbFneOj/Lq3CICGOvJENNUHj01xVA6RS4e447xER4/ex5DVbl5ZIilRouqabLSaofqCE1DU2QUSQ7bMg2DU0vL7O0tkjF0FCHx7jcdIJ+J02zb3HVoG9dNrs2fLcvhc4+FSaoB4djoOu41Jw5/PyEITHy/ThDYRGWNjsMn8ST4fhVZShIENroUJZA8BKF5MoEPuICELK8pazciAvLaHCP5dRAxhJCRlFU/sM3t8S13maOlvyChDmL5VdpeiQCfS41vUrJOElVyXGw8xs70j1OxzzBV+yJ90Ruo2ufJOrsZS9xHw5mjZJ3AC1wuNL7GddkPIpB4cfm/UjAOoohwHWzIORy/QdtbwfbqOHIDx29wvPJxFClK3blIWpvA9Mrs7/l5LjS+iuM3kITGxcbj7M38NGdr/0DNvkje2E/ZOkNf9AYGYrdg+TVMt4Tt1XHlFiktwlS1Rst1rki+G3Gdif3D2KbDjuvHqCzVuO1t1xNNGLi2y8iuftL5JHe/6ybMloUR15FkiUa5ScTQ2HH9KJ7nE09FGdk1QDwVJdETZ2h7H8q5JQiCjlJJQ5M3CjQux1b/FkvqbNs7SKthkutL0zeSI1tMUhjswff9blp6vdJCliXGdvYRSxqdThtB73APET2cq5p1k8n9Q+T60yQ6LXWW6ZDre32ForJToeU2aXltkmqSIgXMtoPZtpGVcL0Si2+dBns1SFIMVe4FPPzAIqJMglAIAouItouodgO2ex7bOYuiDOB68wT4JPQ3YdpHsJwTaMownr+CLGUIcJBEgoi6C0XKIpAx7VfQlHHazitIIkZUvw0/9SzxPYOMdtRQQoCQJBRVxmxZxFMGkiwRS0aJvwEvryvhe4Y42lnII0tbp5qFREqu28N4LdB0DXk4wSk74MTKMv96117S2Y1VpNOlZT4/dYpfPHgTt7/9hi2PI8nSljK1yyEkAXmDyYFJ4tpGBYHn+3xj+jxVy+LdO/Zw0/0Hrvl7AGQKKW596HoqdoPHF18mrhrUnBYpNUZKjTG4rZfBdXJJ1/NZnKswN11CSIJsIcl4Ks7gaJbZ6RKKIuF5PpFUnGwxrJB4HVl7rRpWDyzTAVWlf3sW1/WJJw0cx2XmwjLpnjjFdT+Ha7ssXFhm9uwC+YEe9I6/ktm0yBRTXDx2ieEdfRgxnZXZMqW5Mpqh0TdWwLUcCoM99I4VWLy4TDKboLJYpbZSR40oDO/auCiOGBrPPPoq8xdX0HSVg7dtZ2As37kGORCRTWoj03P56LFvc6qyzPHyIitmkwu1Mrf1j3D/yHaW203+8sSLnK+VmW5U+OTpV3hhcZaHxndyuNDPbKPGXxx/gZlGjUuNCn9x7AUeTZzlJ7dfx45MjutyvYwmM/z75x8nHdHJ6VGGE+m1RbQkumbFqz4R0rqWsyPL83zu7DFOVVa4WKvwX156kqFEip/ddQhZ6rS5rTeuRIQLKgE5I8od/SN8/vxJnl+cwVAUJjO57uurOFwc5PGZc/zBM4+SiRi8a9te7hocI6qo/Pyew3zk6HP83tNfRZUkZCHxrsk9DMZ/MKv3rwXfD5iv1BnoSW4Yj3w/oGbbr5mEBtB0bDx/6zhOx7cp20sIXrvK6wdWSBIImWq1jXqVyrDnB5imSz5/ZVl0SjNIamsVB0kIPC/Y5LkRUzQK+lprxGyrStNZISFZCBFjq0VcvJNY9lowZJW+deTNQruO5TlMJnPdjYLtuZyrr3Tf03JtHpk+SvQayNSjlbnu2flBQNUOyaiAgGYnZWxrCLYl8+v+7uP5JYLAQVH6WE3gkaQ4qpxHukylE1VUxhNZjlZmaTgWM60KY4ksQRAw16qyZDZQhcxEIkdcjTAa7+HppXPMtKqUrCaZSOg3Mt+uUbNNZCEYjKU7i3TIRPYi1nkRrS3Mw79rci40VA863zboJI+xcWHnuB7NholhaJ3WVwU/cLG9Gpq0WWkxGs9u8OkKgoCFhRoRTcH1/FD9ss5suOXaaJJKXFn7fS42SrTccFPvBQFHy7N87MwzV7gOa2g4Fm13zQ+g6VrYnouhaCRUnVO1eZ5fOddtCR2OZ9mbfm3y8vsNnhcwMJKj0JcOlcO6yui2IksLWxvVP/PEKarlFrF4hPd/8G4O3zq5KVzE84Jwnu9A01VeevQkF45d4kd/5k4UVUEREgLBslW96qiXTkdJJo2uj1AQBHi+j6FeaYl5bX3QsbhOqrPgrZZbNOvmFdvzGvU2tWqonkr3xNCNjesARZYZHMmRzsZxXI8bbp3EiEbQIgrFvhR7D4zQO5hBu4I3TbEvnNPNtk0iaTA4kn3dG4xKq81QT4qIorC1Pe1mCCGhXUHh+UZR6EshSaGPSDyhX/N38Twf03Joti0Gi5vJJllIvGX3Dp69eIknz13k8NAAOwo5UrpOMhIJ0/0Q6IrCV18+zc58lpFkigE1zqvzCxwY7CMfj6FIUveZDoKAmKKyI5frLmnGshlePDfDN189xx0ToyiSxFeePcGS2eLA9kGK8Thnlld4ZW6BQjzGnt4ClXab6UqVmKZxemmF4UyKncVwvN/bV0CRJCqmia6oHJmdR1cVbhkdZsdoqErYNpRjcjjP9pE1lULLtHn6yHkQdHxXTay2g+e+tir5+w2et4jnLeK4x5GkHAROqP4RRthCIxVABAQigeOehcADoREETUBCVXciy2uk25WUK2VngVP15wCISAbbEzcSU1bnpY33aMU+hyrH2J5+JxVriqb7SfzAYbr5OIacww88Gs4MZesMCIir/WxLvY2SeYqz9UcYTbyJqJJFl7OYXommu4DlVdDlHiSh0B+9mZQ20vmfZYrGIeLKFxiM3U5cHcD2GwghMRy/m9PVzzEYu4Op+heoO5eYbT5NVt8FuKyYx2i6i4AgE9nGZOrtXKg/Ss2+wEj8XnqNw8w2n2Iofie6nOVCvYIkJPZle4mp4TjW9hqcrD2L6Ydk/GT8EIWhtQCOdEclWXVaWJ6PSKnUhImfE8hBBFdAgI9IaFiqh9qvo0oKpuNh5GN4QJkm6Yk0vh+gqgrRpNEJ+QlouhWWrGkabhnbN/EDH0WoRJUkKTVHRuslIq0VcSRJItchpIUQxDrev3psLSEufJ9g16FRUpcZ9I9s7+veJ9neUCWe7Nk49gshcHybU/VnqbvlK967xcgoY/F99Om9lOwygV0iqYTr20sXlzlzYo5E0mBsskhsnQLfD3xO1p6l6i5d8dhZrZ/x2DYs5yyeX0OWMkgi2g3NAom28wqqPIAs51DlAooygO3NIoSMLOdR5AKKnENVBglcD8s5hestIYhgOscwtOtR5D5kKYOqDNC2vo1lv4osQ/oy0n/1N0yRIN+xrLiqUvcN4HuGONK+i47fq9iTL5LWDf7gW49t6TUynunhfbuvI/od9OCvwvF9PnH8Fe4ZGWd/YWPPqyQEN/cP4wX+hgrK60VEVokqOhW7ienZpNWtN4iKIjGxq5+x7aEPkRpR8FwPVVVIZWJIsoQkCYbG8ihaGDUdAKmeeEgquX5oAjbYg6xI+H6ALIf/Pju9Qr53I6GgRFQmrx+ndzRPu2ExsK0XNaKSyiUwEgapXALfDQ0iXd/GdVwUVUVVFVzHJfADhCxI5eNoUYXCRBoBaBEdhI8f+B3zVkFxIMNdDx7A8wIihto1Rg+rbwqw+TpXnAXGMg6JqM/dwzsw5Bi6HCUZCag6JVacOXbkfEYyOrcMjaAIhXwkj641ON86TdVpMpkzOdzXx1u3FUiqGRatWZKR8CEtRuP8/s33stgKXfZ7owmqtklOj6LJCr930730xRL0xZKMJNNEFIUHx3Zie+HiYiSR4V3b9nbPNwDKzgqXzAsMRnv5VwdvI6pqmJ7JdGuWhOHzh7fdR0qLoEgyP7f7EG8e2Y7lueSMGKokYXvehva2oUSK37vpXuZbdQSC/ng4aAoh2J/r5V/fdBenqrOk1TQpzaAQfeOGpt/vEEJ0fahWERAm0JXarS2NQS+HLEmcq5fYnskBGzfdFWeFltskGylcUYYMHQWRlCcaOQB+GklaQIsoV1zgS0JgOS4XZkpMjOS3fE9ciaCuIx9isQgD/ZlN45IsyWS0KLIQeEFA3TFxAhlJxJHE1j4jMUXrpmlcDZIkdWLjJdzAp9Ehc2RJ6kYyO77Hgrm2Kb7UqvAfj33tNY+9FZquRUAYW1+x2iTVNgV9symmLMQm4ktVRjspchKri1bLncb2ZtHVUWCtihNVNCaT4abC8hzONVa4vTiBHwRMNZapOSYxJcJksoAhhySTKiRqjsnp2hITyTxu4HOhUcLyXWKKxo5UoXuel/sQBUFIhM22qlxslrjQKDHfrlN12rRcG9NzsDyXmmOyZK4lgKiqjCRJtFt2Z/EeYHt12l4JdQv/qpQWVt67v5Ms4Xo+Z88vYxPQsh32TfR1WzgiskLVadJwTTJa6MGyaNaxvFBd4Pgef3fhpWu9fBtg+y6272EQpqr99dTTjMSy3YroaoLeDxoGhnt49H++yFcfeYm+wQyNmsm3Hj3G0Fieoy9cQEiC8e3FripmZjokXfO9KUYnilsm0i4v1liYq3T/XhjKceuPHebsS+e7igmBYDCaI6cnr9guvdoiu1605gcBLcfZML6F42r4d9f1r4k6UlSZvQdHeOn5c1y6sMz0hWWS6c0Ks8APOHF0htJyg4iuMrmrH+0yNVyt2uKv/uzrXJxa4gP/8h7e8o7Dm95zNew5MMzDn3iGSictbXyyuKV345UghGCwJ0XTtLuFndVx13W9a+XSvivYc90wEV2lWm7xyosXmNjR11V2XRUCNFVmpWrSMjcbvAIMplMMpjeuES3LZcEMVbuZdIweXyO+HNCvJ2lU29w6OMRAXzocj4RA1yWaLZuZ2TLJhMHZ4/Nk0lHapkOl0sLzfW4tDHJmapHr0gU0IXHjwACtts1NO8cplZv0EOHde/egr/MKfe/BfRvOazwbbqxuGxthulzF8TxunRjm1flFrMvIn3tv3L5J7RbRFN7zowfIpmJdq4eIoaL9IxjS/nNDlovIcgFF7Xi7BOHvI4QS+uQJhXCeDJDlwc5raqe44SGE3nk9nLtqbgMZibZnklDj6HI4di2Y5/ni3J8DkFJz9OkT64ijjfB9G1lEkISKKkWRhUZAgOubJI0R4mov6cg4KW2cJfMVFMlAInyvH7g4fosTlU+SVEfIaNtYah/p+M2AIgx0OY20ThUecl2d+bjjISYJFUXoyEJDlaIdJbBNgE9KGyUip8jpu0mo/UhCwVDyyJ3P+Hid46wZWQghMBQV03U4XyvTo0eJyApNt8rjix+n7ITK3cRQhh6tb9NYON1aoulauIHLeLyXU/VZIpKC7busWHW2Jfoo2w0s36Ggp1g0q5TsOjIS2UiS7ckBijesqb+q9hJHqk9wrPpNqs4Ktt/GC1wCAiQkVCmCLsfYnriB+/v+BTKvT50UT11dCXN52uPlcHyTp5Yf5lJ7cyL1Km7oeYCx+D5s3+FU/TRe4KJJGvkgTzoTI5ONo2kKiaSxsVgceDxb+gfONY9c8dh7krcxEd9Dwrg/VMiJeEe8EAASKA5e0ECRsihSjgAXWcqQNMLOBVXuIxl9K0FgIUtpNEVBU8ZR5CIELqrfhyLlSEXfhh+0kKUeFLlAEDhIYnNnxHp8Ny151uN7hjgKgoCjy4s8NXORUrtFLhrjgfHtDCSSvLI0zytLCyhC4ly1zGQmywMT29Flhc9PncL3feabDaqWxd3DYxwo9nUlrZosb9ro+UHAoxfO8tTMNDkjys/sPdhVCXmBz6nSCk9cPE/JbJHRDd42uZO+WIJz1TJfuzDFcrtFXNV4aNsORlMZZhs1PnvqGI+cPcG5apneWJx7Rsa5sW+Qhm3z8JnjnC6tcOvAMPeOjiOJMK62Zlt8aeo056ph+9ADE9sZSqS4VK/x2MUp0hGdk6VlevQob5nYTjEWZ29qlJYXRoLHZL0bYbwekiR1TSIvhx5de//lUaFXM56EUHExmdA39cPLskQqlyDREyPww4lfsHbTrvdMKFvLtN1lDCVNyZ4DFSJSAsdvI1QZWaiIaHi9Kl4N0YScvo2EGrbh2bbLiZcukutLMbFnkMpynUxuVRnhs36TvgrTazCcijHga2S0PJZvoggFJ3CoOMu4QYtiPCChFKg6JaJKqORqujVqjoUkSdxcnCTAp+U1ickyCSdGROksroUgq0fJ6mu/+fo2r/FUOAgbQLITq5k31jY4WSNK1lj7bBAEnGm0eGzpG5ScCe7K34YkJBpug4utaV6uHuUXxj7QbfWIqtprtk9JQpA1oqQiEdpei5iydq2FECiyRzU4z42Ze7oT+A8r/CBAlgSaIq+lbwUBddvG9Nwu4Xc1RCSZnkh0UxKREIKonGCJeUzv6jXnAHD8RSznAsKL4nuhj9ZWiWDhsSEZ1+HKXbBELhsPWy2bs1OLZHqi9PetGcIKwrY2SUh4gddp17Sw3dNIko4sbzaPVSUZ+RpINUFILsgd4sgPfBquxbl6iVwkxliiBy/waThrqY8SAvUaWpu2gtRR5jiBT0ILjWm3giyky65XgO838fwakrS2kFKlTJgohhK2x7QtXNsjYqiMJbIYsorpu0zVlzttsg6vlGcJCBMLhzotdxOJPEnNoGq3OVFd4M0Du2i5NmfrYXXLkDV2JDcbPwYdIu9rc6f40uxxjpbnup5IW2GzNgyisU6krpBDxZFwMeTsJhWcJMJEyi55JQSGrtKTieG4HpGIQiAuW9wh6NHiG37LpmuHJqjd31C+IhFxNSjrSM8Vq8n+zCDvHD50TW1v389oNiwkWeK5b6wtjoUQTJ2c58yJORRF4md/5U1d4mh1Lm82LBr1NoVgzddr1eD54Y8/Ta3S6h7PbFksTa8Qz8RQ1LVWNUXIxKXX17bsBwGqLOOtS1jQIgqJVHic82cXadZN1KuYO6/itnt28dVHXmZ+tszDH3+WYl+abD7ZNXT2/YDp80s88qlnqdfaTO7q55a7dmw67tylMideuUREVxkcySHJ4opj6VbYuXeQ/deP8vxTZ/j7Tz5D32AP1x0eJaKrG35bz/VpNS3qtTaFvnS3VQ2gbTu8dHGOfDKGpikkO7/HhaklatX2hmP9Y2L77n4O3jjOk18/wSOffo6+wR6uv2kC3bjsu3g+rUbnu/SmUDWFvRNrKodm49pa8i3LYercEksrdQb6MpTKDZotm95iilK5yfETc5hmaNquKDKlcpOZ2TKTE0XGRmVm5yqIznFm58qcu7BCIq5TqbY4cXKOUqlJIq5jWS6VaovHv3EC3dDIZ+PcdMMEs1OLRAwVSZaROinB5cUqmUIKWVXwPZ+YEOzWUxw/dYmx0SIHBjYWgFV1s5+ULEld5ZXrhgbFS7MVeod6yPBPH9TyjwnRKRjJV/HDW1URCbH1Jn8VPj5nGxco2VWicjHvzyoAACAASURBVIR9qZ1vaN2ZUAe42Pw6C61vU3dmsL06klDIG/uxvAppbQwvsJE6aa0l8yQL7RdYsY6RVEMlkelVKOgHML0qjr9+TbaVHYCEIhksma/i426h0A0/o8sp0to4tt8kpvThBXZXLbxVsVASCkLILJuvktbGsLwoA/EUI4n0Nams12PAyNL2bDRJQZc1VCGjCIWBWI6RWIEeLUHbs7A8F1WSGYkVGIrmOuEhKlF5rVW/4Vb4wvyfc6r2HH6HUBOIcJ9GeB0tv40b2ESkOA3bRRJep0hnko/GMF2n221hey4RRcFyXVRZZlUwLQuB2TkfVV7zxrI8j7LZDj3PEklM12WmXmNXNk/TcTrrWZl8ZJi218QNrM561cXyWt1zXoUmqWxPTDJnzqFJa0ne7ZZNRFe3VMHlIoPUnRJOYOGvHttv4QVrRvmhn9EYV4Lc8YOTNsyj8c7/IaPKa+s8ef09JTQ0KbxPJdb2jPIWY4sf+IC4psL2d4rvGeIIoGqZJDSN/niSRy+cxfY8/uX1NzFTr/G3x47w9sndTKR7+IezJ8noBncOjfLC/Cxnyis8tG0nru/zkZef43duuYvR1OaNzSoEsC9fZLZe55uXLvATu/ZBx6B2vtHgPz//FGOpDNcV+mg4VnfDUrMsDEVlX77IkzMX+cTxV/jwzXcSVzV2Zws8FjnHod5+tvfkGEqGN4quKtzcP8T5aoXjK0vcPTKGAtiex2dOHuP4yhJ3DI5wsrTEn730PL91852UzTafOP4Ktw+OsDdf5NHzZ/ECn/fu3sMr1XPEZJ2MlmA0dnU3/+82JEkgXWWBLkkSr9V9E5Hj2H4DTYoihERETqBKBgE+iogQ4KNJaw+IJBQi8qoRHsxdWOn2vJpNi1qlxdjOfiAg8MoIeTOBEleTZKVedElHEjI+axJoIQS+vlGlJBBIQuoYGS+T1YqdqkOAH3gIIVHUh1Clf5yqkhCCifgY0+2Zjd9DiXN95jpON6be8LGrTpXnyy9yZ/5WDHltEMtoKe4p3HXV5JwfFqiyTDpqbEqiyRoGo8nMNZmGe0FAVFE39aeHFSpBUkkTUxJXVRwBSCKGH7SJdNpTLk+/WX+Knu9TrjZJRq68yVv/nYIgoN4wSaUNUltUfYLL/gMhIihyAelKyYXBtTTxrR18/XsFgoV2HQnBWKJn7f/vYDie4aHBvW9Isbkz3Yvn+5ytLbOavnYtm7MgcAiwCQnptTHC8ZdZraz6vs/0iVkunZpj9y3b6U+nSGo6C+068+0apudiei6na6HJ91g821VVjcR6SKo6K1aTC80Sbc/B9FwuNEPJdU6PbWgXXMWy1eT/O/0Un7nwElW73fW7T6g6/dEUGS2KoajosoomyTRdm28unKXdSacLgETSIJtPEuB3qrUymvza9yOEG3XH9UjEdRRZZqlSYXwgi7IaOS6tEo7+2iZ03ec1Sebtw9fRF926inw19BmprnF5Vo/z+MJJnls5R0YL54yMFqX4A2iQPTZZ5Bc//OBV3xNbZ3q5e/8QX//SK6ws1njk089x/9sPkc7EsG2XmYsrfOOrr/LC02dJpqNUOp6EqVyCnnwSWZU3JLoGQYDretimS6NudloXoNUhpTRNCc221z1TqiyHyZLeGvkbT+iMThQ59vI0p4/N8um//BY337mDWFzHcTzaLYtMT5zB0dwGhdTQSI4H3nGIT3z0GzzzjZMIAbffu5ve/jRBAPMzZb789y9y5IXzGFGNB991eMtUNCOqEdFV5mfKfPpj3+L8mQUSqShSp6VAUeVu+lqumNw0RiTTUd764zcyc3GF2ekyf/p/f4Hb7tnNrn2DRGMRAj+gUW+zMFflxCuXkCTBL334QdLr2isUWep414XXa2yyyIvPTjF1ap5P/+W3uP3e3cTjOq7r0WrapDJRhkdzr0vZdC1IpKI89J4buDC1yMzFFT7yH7/E7ffsZtf+wY73WUCjbrI0X+X4kWl8P+CXf+tBenJvjAyZW6hSa5i0WjYrpQZDgz3UGyaapjDQl2bq3FK4FvND8/14LMKenf3MzleYGM/TW0wxNprHNB0WFus4jkurZVHIJxgdzjE7XyHeIUsbDYvlUpPJiTiJTnvM3LlFqit1rJZNMhunulzHbIVenUYsQq3cZHBbL2q5yaHRPONDA5uq9a+encf3A67bPsBW04csS2Esuq4SMX741lFBELBsTXOpfYpcZJBBY/sV/IxAQmIo2o8f+KiSgnINSuWtkNCGGIzdzrL5KjGlyFD8ThShM564n+nmE8y2nsZQcqQjoUoqphaZbj5P22uyLfkALdemaNzOXPtlVCnNQPwuVCmOG3ggFag6ddpegCIp3aJKX/QupuqPsWhdYiB6K5rUT8OzUOUBSk6VpLaNqFJgMvUOZppPMtt6moQ6SJadZLRtaHKycy69XW5KkQzG4vexaB7BC2wi0kHO10osthrc2Du4wV5gPRbrDWzXI6FHwtZ8P6Buubh+WByKRiKM/P/kvWeQZNd15/l7/mW+9JmVWVm+qn032nejQQCEoQUISkMrjqihZiVxxJmQRlJoY2epmJkPa2JiIyZitaMYaVfakVaURM1SFClaCCBAEt62QTfaV7vq6vKV3j3/9sPLyqrqqm6AXM4sxT1fgOrMfJnv5s17zz3nb/R+mqZDQojhBwG2FWA7IlElim15tByXRETDcj0EGabqFXIxA0NTuNI8yWTjeK8Ak9OGGI7sJKXmkQQJ27doOGXaXh01GOOp65P4QcDBfJFbzRr7xSInF2YZjidpOBZN2yajRymbbRzPw/F9/CBgeybLpfIyUUVlIBbH9jwissy55cVQTiEImGs12JLKsNRpEWuovDk/Q1/E4L1DI3yg/5/i+hZ2YGF6LSr2HC8ufZUl69a68XJ8p3vGVHsU/hWKpL+JjbSIxKP5X+TB3CdwAhvTa1Jzlnhl+RvMdCZ/rDl7e7i+hxu4GwqnAQGO71K162S1FJJw9z3gRrPMpdoiB7ND5PX4f9EC0k9N4UgQBI70DzCSSNKwLMaSKa5USr0kpWDE+Myue7qUHZcXb93g3oEhvMDn/sERPrZtFx3X5VJlmZPzs3cvHAkC/UacrekMr89Or3vszblbaJLM5/cfIRNZfwDbncuTjUSp2xalTpsXp6cIgoCEprOnL09aj7A7l+dI/6rGgiJIjCXTDMeTVMzVanbZ7PDDm9f43aMPcKBQ5FF7nN9+9rucLy2iSTJxVeOX9uxnJJFCFkV+OHUN03XxfB9BEjYgGd5NLLSanF6c50PjWzc81nEcnrt5nfcMjpDS31l13fY8JislRhJJ4qrGbKNOVFF7r50sLzORziAJoUjmZKXEeCpNVEkTkdPdg04BVnQ6WK3E3378XHuYiRgqju1RWW5AALsOjXWfIyFq72EtpWQlsmo/ApvrZ90tZEFBl6KIP8ZY3x5BEDBrzvF66ThNt0Vez/NQ7j2YvsWrpTcpWSXyep5jmSOk1WT3nt6F5kDgcaZ6jouNSURB4GBqP1tiY3Q8k1eWX2feXCQi6TyQuw9ZkPju3NNcbl5jpjPH7sQODqcPUHVqPDn3DKqo8LHBjxKRdCYbVzlfv4TlW5iexbHsYbbFtnCrM8sb5RPUnQY1p87j/R9ge3zrT2SMflrC625myprEcQVObLrOu6YUmJ6zoYPRchuUrEWSSpqYnLhjYgXh9y8g4ActgsAPOyMdh5X57QbeBoc3SRTvWlyxfBdvzWcSgEhE7R36V2IFKeN1CyaSIKKJAZKYQRRjbIZjsX3vHUV0e9f2nd5zRUHsCipLqFLY1RUFYV2nrT+S4Fe2vQdDXknIhU2T97Xv0btHQcAPAorRBH4QrLnGO4QQFu4QRYQ1SEZJTGK7cwR0C8+eHx66+1NERY+MarDQabDQaVB3OiyaTRY6DQRgR7JAXAnXyBX00fVmiRvNElW7Q8MxmWvXANiW6OsJda9Ey7X508uv8OVrb4b0PkFkd6qfT40d5J5UkYxmdCmDUhcBJnK+Ns+Zymx37oSxMF/DMl0mdmaIyf2oUuJdrTcrIQpCSAERwPH8deMdEJBWjXVo2IgUGgk4hGijDw/u4sHCFlzfp2FZJHUdPwgQoOc4tVYHbrWTvWoZLyJQtds8OfM2uhR2QA9lRnlscD0d5WchZEWiUe9w5s3rLM5VMeI6u/YNs2334KZ0qwfet4vjr4YC0E994ySvv3iZqKHhuh71ahtZkfj0Lz9ItdLi619+tfceK4glCKlsX/qj7zM/W8W2XFzXo9kwMTsOEPC3X36FZ588jSxLKKpMti/Ox/7xMXbvHwmLTb6Ht0YiQNMUHvvYIa5NznPhzC2+/uVXeeY7b6FqCr7nY1suH/nEYT77+YeR1hy8FVXmo58+iqpJfO2vXuGFZ87y2guXiMX1UOy9bmLbLrl8nE/8k/t53+P71qGcVyJfTPHoY3v5yp+/yKk3rnHqjfUNGFESiUQUCsUUT3zqKI8+tnddMU4UQ4Ht3/y9J/iL//0HXL00z1e/9BKapqBqcngoMx0cx0MUBe59YNsG0IIoCDiej+f7qBGND/7cQS6dneHs6Zt8+2/e4Lmn314dD9vlgx89wC9/4VEiP+HCkSgK7D8yzm9+8aN86Y++z9VLYeFK1eRwPgVgde9FEAUOH9vyzhd9h3Bsl3QqythojmvXlwiCgJHhbJeeBn25OK++cRXwyRd1lksWHbeDL9oYCTjx9iTjYzlaVpOIIWFEZcrVKtWLJQ7s3sLFazepVU2SWZHRiSiW1yQeC6nD2w+NY7YtBFFAlkPXM9/zeo2UIAgwEhEcy0XR5J4myOq6JvDSqWskDI392zfXUPP9gNJijXYz1Dn6UdBsPwvhBQ5vlp/ijfJ3OZJ5jIHIVqQ7dJIFQcDyLCRBIq2+86H4TiEKMiOxRxiJPbLu331PJu1/kGJEIb5mTYvJRRTlMI32FFdbc0TlOillgKHYVlpuk5SapeG6tLwqCe0YFxoXiUgGCTmJKqmISEhCHzHtISDA9AXqfoZmZwlVHKfmmIwbD6JJSTQpiaF8mpodyhu0XYgo+/GCgJrdRhZHiCqDLHTC/V5X9rBN209EUmk4NqpU6uaQd77/6UoN3w9YboaFmLbj0LYcoqpCudmmLx6j1OrQsR1aHRfb9Sgm4sxU64iiQELXKbfanJ8zMV0XVZaIa+Haq6sG52ov9ZA1BW2Mjw/9Dn368DpTFz/wsf0O16oNmmqNxVaLgBChP99qoogSBSPG8nKbnZkcN+s1cpEoJxZmsTwPuugix/eQBJfzy4skNB1dknB9n6plsjPbR80yqVkmDdtiulHDDwI0WQIBdBEEKYlAiJhMKjmOl5/aMF5O4LJslRAEAa9LtVRUmbGJPOVSk07bJpGMdtGDDpGoRvQ2mmTGLXKm+vy6f5vrLHG5cYOBSJ6W20ERZTRRoWzXiUgaTa+DJirookbb6+AHPqZno0saGS2J47vktQyTzSmGIgUqdh3Tt9kaG+ZGe5aEEkO6i4EJhFIJN5plXpi/Qj4S5z19Y2xJ9JHVfjJn2LXxU1M4ajs2X3r7FNONGoOxBNeqFTx/lQcfkRWiiopASO05OT/bS0pSemj7qssyMUWl9iM4Wt0ey502ecPYIOpoug5fv3SeM0vzDMWTzDUb2J67xt38R9sgHN/D9jziXeqSoahhh86yyEdlIrJCrktf0mUZ1/eRBZmD6a1YvtOFpYXh+T41p3PXQ5smynRch7cW5mg7DlvTGQbjCV66NYXr+RwpDjJZKVG1OgzGkoynUtQsi5FEisnKMoVojOPzM2iSzINDo8y1GkyWSwzFEpQ6bb568SyaLPOh8a0EAfzxW2/y6MgEDw2PMd2ocb1aYTSZ4matxsn5WXwCDvcPEATw9tI8VdPk4ZFxRpOpUPQ1CGi5FpbvoYoShqwhCgLDWwrsPNTg+oVZBsZybNkziOm6KJK4we3J9X3aro2hqKH+i+cii7fTUe4c4Xf7o2/8QRBg+x4t10JAIKZo2L7F9xeeZ9wY5dH8Q/hBWPX+weILCILAE8UP8+Lyq7xaeoPH+z/wrt9ryVrmzcpJ3pM9Sscz+f7ic+S1T3GmdpZlu8yH+9+PJEgYchRNVHmo7wHcwOMTgz9PTI4iCRI5NcsHCo/wndmn8LuLacttMdm8yi+OfIp5c4E3yicZigxyonKKcWOMop7nW7N/z0h06Mcao5/mEAUBWRJxbnNGsX2P5U57HeXmTmF7HiWzve7gBLBkLWD5JlWnjBd4ZLX+uyZNgiAhCTEc10YUZYZHVxF1phc68a2ELEmkY9GemPJmUbdNbN/tXlsgk4nR7mzUqnADn7LV7olmJ1QdXY4DTYI1EN210XCsno7N3cILfCpWu7dexRWtJ2TtdYsCiiitQ9usFFVkX2Sx0iQZW+V2m7YbisiLIpIY6rVZjotlh58lmzTQFBlD1mg45rvuxAhI+EEH15tbDx8GAtxQ18EPRahty0HVFZRAZjSW5kJtniWzQc02u450FkZXA2lFL0gWJe5JF3lh4QrTrQplq9VFKYXf6fZEnuhtRa6L1Xm+Nf02tu8hAO/pG+ff7n+MkVgmpM9tcm9+EBCsmbOBH9BqmOiaQhD4dLwyXuCib0I/3CyCADRNJhpRCSSBRFRbN6Y1p828WWU02tc7PPXpMTRRxvRcvMBntlsca9gWb8zeIm8YNG0HQ1WQBRFVkmg6Nqbr4vsBA/E4M406qiQhiyL7C0XGYzn+9b6P0nYtYoreRYr+bK1FK7EwU+FP/7enSaYNCsUU1XKLv/6T53j/zx3gfR/Zv+65giCQycX5jX/1BC88e443Xr7MzWtLLC82KA6m2HNglAfft4vD79nKmy9P8toLF4nGNG6vxNqWy/WriyzfJsC9gqDxvYDy8qp2Vr3aptF1aPSDgIiiMJhcj8rdsqOf3/k3P8/T3zzF+TPTlJcaobtVPELf1gRbdxY3tYY3Yho//5ljbN1R5KUfXuDy+VnKyw1EUaC4s8ieAyMce2g7O/YMraOGrUSt0uLpb53i+KtXiMUjZLIxjLi+ju7WapqUlhrcuLrIn//R9xFFgcc/fnjdb0qSRA7cO0FhMM3rL17i7ZNTzN4MHWyDIKBQTNE/mGL77kEOHpvAiGlhHhvQW5s0WWIFODoy3sdv/5uf5+lvnuT8mWmWF+v4XoAR0xnrT7B91wDSbfejajLpbAxZEddZw98eiiKRysRwXQ9N35jqi6LAgaPjDPy7T/Hmy5OcPnGNm9cXaDUdBKAwkKRQTLFjzyCH7psgkdS7a/9KVi4iCAGJpEI6a2DEtTsW87dvLbBtS1jEEQSBPbtCkeT5hTqvvXmNvmycQj7BP3riAB2vTtmeRiBCxS7RVqbIjntkx2UkscEDQ2kEQewWmUU6Xo3+iIqal1CELHawzFimjSFnKXQlAWKpVVchy3SwLBdZUHppu9mxsWwPQRJxfajXwnnsuT6W6ZAvJknG9K4eaLBpzuN7YXNHUeRN5/DPelh+5646M7eHKipUnTo+Pjk106MO/SSi0uhw7to8uaTB3q3FbjNKRRYj5LQ8CSXVzcE16H6fgiKiiipxOUfLa1IXa2TUHKqgoogKThCeuxzfQRREopJBvz5ATI4jCmKXpeCgS6vF5qZj8sLiRZJqFEWQKNtNItKKFmSoUWj5DpIgkteTxGWdHYkiFatNWotgug4t1yapbd7MH8uGe3bHdmg7Ls2qTUxX2ZbPIQkCfXGjSwsTycWitG2HtBFBFMMzliiK5OMGLTt0/VZkiY7tkDGitNwqFXuh9147E/eR0/px/RaBoPZ0nPzAQRRk0pqLkIKheIaILFE04uSjBn4QULcsJlJpUnqEmKpxrVrmoaGxbhMzzPs6jkMmEu3lf47vEdc0VFHCcj2K0Ri6otBvxBlNpIgrGnnDQCDAcmex3ZtE1N0o0p3NMQwpynB0CFVUsf1Q47FWaTE3U6HTtsh1BcYdx2Py8jyO47HnniHUiIQTeBsawSsx1Z4FYN5cIiLpjEUGudi4Rr+e483yWfaldjDVmkUSREajA7y8fJIdiXEqdo2BSB8Vu0YQ+PRpaWJylDlzmYpdo+nm8AJv3Xn/TlHQ4/z6jvtZ7DR4fv4q//HCiyTVCPf2jfChgZ0UIomfWH70U1M4Wmy1eH76Bv/dsQfZnc3z1+dPc3pxvvd407Zp2BYxRWWu2SAXNXq6BkvtFk7XRr1mmWQjP75AZiEa4/TiPG3HWeckUzFNnro+yT/bf4Qj/YM8O3WVr11aTahEug5F79JmVZNkorLSQyHVLBPb88h2UU6CwLov2Sdg2aoiSj4t12TRqjIeC7nmC2adf3vqW8y2qxvfqBvvLWzjE0NH0CSZiVSak/OzZPQIqihxfmmRgXh4SLsnV+C12WnyhsH5LkxwsdXCUFQW2y12ZHLIkkhK16maHdquQyYSIW/EKBpxCkaMIAjt3w8UikQUmbQeYbnTxnRc5ltNymaH7Zks55cXe04WVctEk1Zta+c7df7jxR9yqbbAlngf/3LXowxGU9iWw5ZdAwxP5JmfLtGybE7WZtmb7acvsl7YtWabfO3q23xyy16yepSpRpViNN4r1t0t/CBgulmlLxL7kTnGlu/yjZtv8c2bp9Elhc+MH+Zwtp+212F7fBtptcuJ912W7RL3Zo6Q1TKMG6Ocrr6Nj/+uijEBATWnzs32LVQxLKpqYnhv8+Yi22IT5LXcusRXFkIaiXobPFhC3FD7HIkO0a/nCYKAM/5ZBCCpJLnRmqLm1MioaRTxv44mw3/NkCWRlBHBdtYXQTzfpxh7d4uvF/j0R+MbxmYgMkJCSeEGDm232fue16KEQies1U6nIg+h6Vl27okhCqtIm4ZjUrFXNUp836fddoin7owYrDsmZavNRLcmExDS1W6H6bZca51uzkA0RUSSCAKLzQTow9fYLFvvbL/d8Zxe4QCgEImT1aL4fkBUCn9rmigzHlstki10GiyaTZotm7PX5tg23MfMUg1FljB0lXhUY7naQtcUas0OuZSBgECtZbJYabJ3SxFJELjWWEYWRUZjmQ2fa2PIiEIkFCBkdc2QBIPw8BR2ypO5OKEMVjiG2xN5npq5QMXqUHM63GiW6LgOeT3OeHzVvUgSRLYnCggItF2Hm60Ky2YT23PRJZkRI7NOlBrgrfIt6k7YGEkoOh8f3cdILHPXORkKZa/h43fRZZbpdF3YArzAvOOBaEMI0GiYSLJEOmeQMPSQptyNiKQSkVR0aXXdHDHSRGWVmmPi+D6X6gsEQUBElhlKJFlqt4gqocZAVFPw/QDbCxEruqz0YOV+ELDYauEHAR3P5utTJ3ijdJ1/tu3hriiqx+Hs2Dvfwz+wuHF1kXwhyWe/8CjRmIbreLz23EXOnpraUDhqNkxEUSBfTPKxf3yM9z2+j2efPMOVS3N8/l9+gKihYcQ0RFHk0LEtjP/B50Ia122Cvn39Sf71//IL79ohShRF0tkw95JFkfFMesO8FEWRkfE+PveFR2k1QqRQQGi+oWkKjuNtShkQhBApsvfwGNt2D9BsmDi2B0J4YNcjKtlcfFP3GM/z+fZX3+Rv/+plsrkEv/HfP8H2XQO3iUEHuK7P3HSZP/n9p7l6eZ43XrrMIx/eu0H7sdrq4Eckdr93C/339BOVZEzLwXRcNFUmm4phCz6BLHFxbhkCiGgy4/kMvh9gOi61jkUyGh7ghkaz/JNff4Rmw8S2XIIgQJJFVE3BiGkbCmFH7t/K1p1h7rdy0Nkstu4s8j/8/meBgERqY068sg4VBtI89vFD3PeoQqc1iueJCIKCJDvouo4RiyJJDVx/EdcO0WaSmMXz6wiSyL/4V6MQPI4Ri6FqMlcWSvQlDJIRfd17bSZym0kbHNo/StRQe8+RApmkuoJGd4lISWy/DQQIgthdp8APXFRJRZXyXXdIAVlSwI+iaQa6GF/XaV+oNwkCKE1VmLlZRtcVooZGLKHTalrEExGWFuqYHRtREnDscC6mMwZ9hQRHdg/z5EsXOHl+mmJforfmiYJAJhkN0UyKhOf5eJ73M5cXvVOUrBkaTumdn9iNkOoUHt79O+QUP25ENAXf99HWIA8LkYP4+l5UMd5DrsLmzRZDjmHIsQ3o8hCp4hKT42iSRkSKEuu6c61snWv3UEPWeLR/d3cfF7pOZNKGJv+KBlBAgCiIxBQN168TURQyd5EeyBqrRgF+ENAXC/9ORvTe2juSSSJLUk/bMQgCEvp6jdM+jA0o7bnOLG6w0lQUyGmDVO0r1O1pNCmO6VUQEDHkfmQxSiDUSekhhT+jpRlLDgP0jHbW0tYP5IsszNdwXY9Cf6onOr/OkCYIGCfUg2zVO7QaJplEjNGuDMyqgY+HKKio8gihgcid51LFqXKpcZm0kiYmGyTkBLl8HLNj02pZvf2LgK5LqBS6KbstLtRvEJU0dsQHNlx3OFrkZnuWQb1Aya4yby6TVVMsWWW2xkZIKXHcSB4v8Kg6dXYnt5BWk6iiQtNtY/suRT3GbGepq23qkFBiOL6D7Tm0PZOofHedQTfwuVpf4vjyNNeaJR4oTLArWeBaY5mvXD/J57ffvwHB/uPGT03hKKIopPUIL9+6ycXSMsfnZ9fpg8y1GvzZmRPEFJVXZqb5woGj6HLIq3/p1hSaJFHqtAGBw/0DuL7P1UqJC6UlFttN3pybwfY9dufyEMC55UVOLcyx0G7y8sxNdmfzjCZTHC0O8oOb1/gPx19lZyZH23X44NhW4qpGvxHj9dlpZpp1js/dpj+jqgzFE3xj8gI36zX25/vZnslR7rQ5t7zIpfISTdvmlVs32ZntIxeJ8sTWHfzVubc4t7zItWqZLekMO7N9TJY3X3zdwMd02kQlDWUNVNANfOY6NaZa5TuO706zucZBJ0wEL5SWuNWo4/h+D11hd7tjGT3CYDzB67PTfHhiGzFFw9aBkAAAIABJREFUZVs6y5mleSZS4aGr4zost9vkowYpTcfxQ+ivoShEFAXLcwkCMF2XtmNTMkPIZr8RIx81utanYWX53uIQCS2c1F4Q8OLiJN+8eRqfgAu1OfZlBvnM2BFuXJqnslSnVm5RXqzRcR1m+zpcr1cYjicpRhNcrCySj8Q4VhghqYYUiIrV4c3FaT44vI122+aluRsk1QhDsQSnl+dIqDpRWWG6WSOhakwkMnzj+jn2ZYvszfbz1nJYLd6dKXB88RY5Pcrh/BDpTRb16VaZP7/yKje734cbeOxKfQRJkCjbFdJqEi/wUQSZiBSlZJUxPZOyXSEuxxAJN5A7H+RWF3hN1BiKDPDBwqMklQRe4BGTYxiywXL3uuGmJKGISk/s2PYd1MBDInQPW9GzWa9ForBCCQoIhXTzWh9L1jJFvZ8j6QP/xXSe/r8Mx/WYLdfJxG5z2BIl/MDvCUBLtyXDa5MBq4tGvP0IJCCwYN6i4dbwA5+iPowsyhiy2nMwK1vtEMJLqLNjudfw/Dpx/YGewKIfBEzWl6hY7XXXjxka+eyddSgs3+Wt8i0OZ8NN3XX93gFsbcy161yuLXY/8wptSifwrNBlTMpzewQEnCzd5MH8xF2T5iWzybnqXO/viViOnBZjvt3s6fAoosS+zCAJRafumFTtNq8sXuMXh49wX3SMpKGTTRqocih2KggC2aSBJArYroeuhkWIjGnjuKFjmxv41G2Ljuu+SxqBj+vN4/uNbsEsxCD6gUkQ+CEayYxy8+LMumvtThXDbnjgcaNZ4kazjBv4FCJxRoxVVI8oCIwYaTJalLLV5lJtgbbrYPkefXqM0Vhmo8Bvp97rPsUVnR3Jwl2LRq7vca2xvE5o3HN9Esk4ge9jW92D4F0ET9fGit6N6/lEdIVG2+LGXJmxYqZHd/QCn0WzRlKJ9rSHRow02xJ55jp1vMDnxPJN5jp1ipEEe/rym3bytgu5dcn9tkwW03Vp2BayKHKuOkvbs9kaz2N6DiICZ6rTP5OFo1g8ghGPIEoCkiQS+AGiJJK9TXPG83xefu4ifYUEh+6d6FHIkulQy6Vwm3VvJKoSiW5eRFUUieLgu0Oh3R6CIKDdQQtOEITu+65H0zmOx1e+9BIPvX83I+Obu0KGr9V6lLogCDj+6lWajQ4PfWAP0ib75cJclR8+dQbfC3j8E4d58H277vjbz+UTHL5/K1cvz1NaDqkLawtHQQDlZkj98PwARwqo+Da6ER7wOraLoIncWqiQT8YoNdoEAYzkwnHPxQ3eu2OcmLZ674IgoEdU9Nt0cTzXo1FuImdi6zSOjJi+jkK3Nm5dnuN7f/E8H/+tx0nnkwwMb/7dPvV//ZBITOfBTxxDkkQkCYxYLaT/BR2CoIofNBHFNFDDdisIgkoQrMgtyLjeDAIy2XyUiJ5FEFR8P+D4jRmOjg+uKxzdKXRdWed6BqBLMXQpRkBAXMmtbqK3HcxvlzRIKH0bZA/WxtXFMgEBQ7Eou/cNEYmoocOwIPQKjpmsQRDQK9b5fhBqe0oiF64vcOH6PKcu3grRR93zia7J/PZnH2agL0k8FcW2XHT9/18aR0EQMNO5Qtu7s0nD7WH5NpIgUXXqPdrQTypcz8e0Xbw1qHFFXK/jeLf9X1idbBtCEmRS6hojkbtcRxYlkuLdXcNuD9f3eW3uJqok0a/G33VJTRQEMsbG94qq6+finT7v7f/uBnaPgSAAqqgTkwcBEU1KkGQCSdCQBB0fl6hcIMDH8ipIgnbH64aScgKztyo4jksqZaDEN+4V69zN/IDrF2botLKMbi+uezwIAjy/juMto0W29HLkzUIVFQwpSoBPSgkNI1oti6XFkLrXqHWIJyI4rke51CJqqIyM5ajYNn7gMxjZmPcCDEbyDOjhnjUc7V+90WD1Xgp6Nlyfgjt/B4ORUCR7izHce91EbPiO97M2LtYW+MbUGQ5kBvmVbccYjCYRBZF7+0b5iytvYPs/ud/YT03hqC9q8JuHjvHW4jy6LPMvDt5Lxez0frcTXbHq6XqNz+8/zJHiYO+xh4fH6IsaRBWVX9i1l34jFoqM1qvMtRq8f3QC03O4UikxnkzjE3C5sowgBDw6Ms5Mo05C1SjEDHLRKL979H7enJ+h1GmTj8aIqxopXefzew/z6vRNfMfnkcgg9CuUFuq4jkf/UJr/Zu8hXp+dZqnSoGNkcOMeS40WV8olRhMpAuBKqURS0sipEfaRJj4k8/atOQZEnU8fOYguyfQbMT6xfXcPUTWRzPDElh2Mx/Kh25Mo99BGQRCQUlR+det9zHeaNByThmtRtzscL031utMAKV3nQKFIudPm/qERIrKC7XkMxhNMpNIoksRyu8Wh/gFSegRdkkmoOn0Rg7LZoeM67M31k9YjXK+WyUdjNGwLLwg4UCjy1sIcNcvEUBQeHBrlRq1KIRrj7YvTCB2Pt85OMZpJs20gSzYaxfZ9rlbKKJLE8fkZYqrKPX0FAgI6rsMKUTEgpOWEXWe4OblANK6z69AYtUYHtV/mcN8Qry3c5HJ1GUNWaTpl7smuiocnVR1VlLE8N6RDGkkO5AZ4fvYae7P93GhUuFBZZG+2n7ptYfse25M5juaHuFRdYrpZI65o3KiXcXyPB4pjPZHW28PyXBx/tcPv+B4RMcKh1B5eK73O2dp5kkqSB3LHOJo+yKulN5gxQ+rlg7n76HgmxyunuFC/1KMKHUkfZKYzx5naOebMBb638AP2p+6hqPczFBnk2YXn0ESNgUg/R9IH2Zfcw/fmf8DXZ75DRNI5mjlEUS8Ql2NEpQhPzT/LPcld7Ixv52rzOm9V32bRWuKHiy9wJH0oLDQJYUInIHZFswNabpuyXeFK8xpL1jKH0vswpHd2xvmHFD4h6uF2rSBBgIrZwfI8ogpEZHWd09dip9ErSKS1CAvt5oYDsSIqxJUUEdlAFXSkbuGtP5JAESU8z2XZanK5vhgu/GKcqLofAYl1VraOyfdmLrDW1FoQBdqmzdWpJbLpzVGXju/x4sJVPjl6gJQaCbv1mrwe3Rj4PL9wpYceiis6h7MjqIKFjYUg3DkRemnhGp+dOEpO23xO+EHAy4vXmOmiIw1Z5XAuXIsiskxKXUFcCuxOFdmZLPDG8hQB8N1bZ3mwsIU9mSICAhFtxfK0e/+x9V3EIAhIJ6LdjTp0uguFad9dKhbghuMuyLAmGfH8BoIArl9F0wsURnK93ynAQDRJSo1Ssdtcri327nVXsh/tNhHQPj3GQDRJyWoxWV9EFsNuZFqNMBhdf8gH1rnWBQR3RbgGQcBCp8H3ZtfPEwgolxqAgG2CGk3QcZdJaRPvalw6HQfP8/E9H0WVcTwvTNC75z9dUhk1+tYhjmKKxgcGdvDq0nUc3+NKY5knb53jlyaOEpHvjFpcmxwGhK6AerfTaPseMVlHFkQc36PtWutc136WIp6IcPbkDUqLdbL5OO2WzaWztxgazfHlP34OSRK5/327OH/2Fs88eYZ8IcHl87OMbslz34PbgBBh9uIPLrAwV6U4mObIfVtCbR4v4MLZW0xenEOSRQ7fO0FxKIMoClw4ewvbcnFslxvXlnqvO3NyioCAxfkaW7b3c31ygbGtBXbsHsD3A86dvsm1yQVUTeHwsQkKxVSYmNc7nD5xg7mZKnpEYc++YSa2FViYq/LK85d46YcXqdc6ZLIx7n1gG+Nb83TaNiffuMbCXA1JEth3aJSxLXks0+X4a1d49skzYYd8tkp/McWjH75n3XyqVdq0mhaSLNK3iej12vA9n1olXPciEWUD2kcQYKKQWaM7EvQo7SHqMHzOeCGzrsCxotelKzKD6XcWhQ+CgNJche/8ybN84rc+Qqrv3QnJt+ptrrx1A7tzF2tNoH8ijxZZSy2T0LUH136ClTte8/fKSSi0mdY4sOb5MkGXbvKhe7YS6zYBXc/H8byuU6kYIggVuasjCI7nIYkCuhJqlNleSMG13RCxE1GV3t+25yGLIpoS7leeF2A6DgGgyquICtNxcT0fUQzHWxJFTMdl90AeXZHRZKnXLDOdUDdUlSRUWcILdFzPw/ODHt1S6jYmjuweYXxwo/mKJIrhPkNIA164VVnj9PuzG0HghzQaPNpunVudS3hBOO8838H0Woh3WI8lQUYXVSzPQhVVZOFOR9HV9d8JLErWDAvmFHVnGS9wUUSNpNJHQR8jrRaQhRC5psoShUyciL55YzNEOXnYvknNXmTZmqHulrG7rmqKqBGX0+S0YTJqP6oY6aHX2pbN1GKF/nSCWERFFsUuGja8XkCAJCgoQliwcQOHurPMbOcKVWcR17eRRZW4nKZPGyGnDaGKq9R7URAYTaRo2BbSJgjKHyVc38EN7F5BVURCFbV12poriK8g8Lr/Dcem5dbWIcFs3yQQVKLy8LrXe4AsRJG7uWxEyq773my/07uOLKgoXQMeTZdptcx1Rgi9a/oOTvdzCwh4vkez1sFq291CUZj7+H6ArkroynYkMcXtrrC3hyqquIGLGIi4XckFRZHxXJ+O7aJ2UbeKIhGLa6TSRheR7YfNdlFhM5HTsKHmIwj00FMAGwqPQYDlmwS98VB6cxbuXoQMX+5j+1ZPrFwSZBRBI3TpzfE7ex4hKqtdJ+Ew39clhU+PHSShvLvm4LuJn5rCkSgI7OkrsKdvo/0wgCpJvG90fWIbBGH1ri9q8LHtu3v/3nRNJhtzbMnFGcsaxJUIbdei6Zqcb0whCRL7BtJUbY3xWJ6K1aRPT3KmeoMxow9XdLinP01cGaBfT3XdHnyUssfwLYl9R8eZurLARL5Io9ahVm5RGEjRJ0f4+NZdnD0xRawpcObNa7QbFu/fOkx+IIUkicxMLXPt1DzVozHqS02O7hxgl5qmUmqS6Woa5Y0YH926s3c/o8kUo8kUTafDlcYcOxMj6Gt0THRhgY8WRURpN4GQwfFDe+vfeeMrnKmsIqOSms7DI+PrxnAwnsD2PBqOxZ6+PjquiyQInCstcGJhlnsHhljshDSCo10x8oZjMpxM0h8PLZeXOk0K0RiPb9neu+6R4irPNFaG0baK47jIss/otiSarpBQNWqmScdx6Ddi9HUPBLIgck96kFEjy1ynymA0zaHMMLIgMtSlqI1u7wcEjD6DptTAUFQkUSClRlBEiS3JDK7vM99uMtuqhZS7TpPZVh1FFJlrN7jZrKJJMtPNKm3XQRFFbjXD525TcwTATKtOVFYxZJWJRIasHqVmm+tojLfHQDTFwewolbmLRCSF9/RNkFZFUkKTEW0cW5hAl3R0SWPMGCGjpml5bQwpSkKJ4wc+O+JbGTdGudWZRxYkbnXmqdp1QOJY5hiu79JyTabbcwxGBslqIRdWERRmzUUqdo3x2Baiko7XFVE+XbvA1tgYj/d/mKnmInmtDy+ArJrjaPoIR9NHEBCISgZjRoyiPkDHddAEg48UP4Tt21xrXedY5jARKcLbtfNcrE9yOH3gjmPxDzHCjcpHvc1+N61HOFYc7lEXk4pORjO42lgG4HjpJiWrRVYziKsacVXbSNUQJIYj4TpWcZZ7elu7kv1EZQ3Tc2k4Jt+4eZqDmSFSagRdWS9m33Zt/m7qNK8v31j374EfoGoS6h2TsDBOl2/xjZun+YWxQxSLKfr64ihdTTcv8DldnuHvpt7qJRtb430czAwhCCay1I8o3jkpvlRb4CvXT/C5LfeSVNcjtvwg4EJtnq9eP9mjTo0aGe7rG8PxPWZbdRq2zWgs3Rvfj4/u51x1jpZrc7WxzH84/xy/teth9qSL3bG7rZvVLRgtm02uNZYZiCYZ6qJ8Op5LVjOo2Sam5971NwxhwRRBBd+FwOu9lyhogIAmjyAiIcoSc5NzTOwdQRDCNWgwmgwLR/XFntj17nT/hmJkWosyFE1xtjLL1cZyTzh72EhvCisejKZ6qMG6bXK2Ose2ZH5T3baq3eHPr7zGqdJ6ZxHPC3Adn1xfgkRKZ9G8M1J1w5gIAtGIysR4H+lklFKrQ9t0qDTaRLtd9ordZMmsk1BWv39JEHmosJU9qSJvlW/h+B5/efUNYorGE0N7evd9ewRBgOW7XG+UcHyPnckCardgPxbL8crSFS7U5rhUXyAqq3xy5PC7vpd/SCFKAvvvneh10KMxjUPvCdeFwA/wRR9REkilDRRZoq+QDN3B+lZ/q9evLDI4nCGRivL33zyJHlE5eHSMC2dv8e2vHWfXPUPUym2+/Gcv8su//gj9AykunZvh+KtX2XtolHgigtulkr3xyiS+H9Bp2xx/9SpjW/KcO3OLoZEsly/M8uTfneCeg6OUlup8+U9f4Jd+7SH6B1K88P3znD9zi/2HR2nUTardIo2iyiTTURRVpjiYplBMEYuHc6Ldsrg1VSKRijI/W+X//tIr/PPf+RBGTCOZMlBVhXhCZ3Qst87BbCWMmIamKzTrHc6fnubYg9s3tb1vtyxOvHqFU69fQ5JFJrb3hxpFns/i1BLzN5YASOUTDG0ronQPGXbHZnpyjspiDUmWKIz2URgJ0XLTl2ZJ5hI0Kk2WZ8oYySgTe0dQNIVmpcXM1XmalRZaVGN4e5FELo7n+sxdW+Ct587x1nPnGd09RCxlMLZ7iPxISD13HY+56wssTZcAgdxgmsGtq42y8nyF+alFBEFgdNcgiWxIz6mVGlw7PYVtOeQGMmsKszB7dQlZkei0LMpzVVL5BMPbi6i6Gv4O2zY3zk3TqDR7dMKhbcXe+zqexzdPXeDU1Az/9MHD3DNY4NzMAj+4cJW27ZCLR1mqt/jEkXuYnF9mqlTFdl38IOCTR/Yynkvz3dOXaJoW5VabiKrymXv34ng+3z19kYV6k4Su8djeHYxkk7xw+TonbszgBwGHRwd4ZOcEi40W3zx5nrppkYzofOzQbvqTcV6+MsX3zk7y6M4JPrx3O4IgcG2xxLffukjbtikm4/zcwV0sN9p869R5NEWm3rG4b8swj+ycQJEkhgophgobi/lrwzIdBGF1H/pZaqithBd4lKxbLFnTLFm3KFkzLFnTlO1VFPHFxuvc6ly+I/V5zNjLtvijoaaQErsjQVpARBBEKvY8r5W+zWTjOG2v0SvQiIhdJEyKbfEj3Jt9gpSSp9WxaZs2nr+xyeX5DmV7nivNU1xpnqBkzWF6zZ7dOoR5miyoRKQ4/foYhzOPMRHbGxaEJAnTdnnyzYuMFdLsHesnEdVZtG7y7Zk/wvFNtsWP8HD+M5hei+Plp7hYf526s4ztm/h4iEgoooouxdgSO8i9mY+Q10cRu9pdQ7EkNctkyWxhue6PLJcB4Pg2J8pPc7r6Q7zAQRJktsePcn/u42hrHJU7XpOL9dco23M03Sott0rTrdL26rTdUI4lIODZhb/gxaWvbvpehzOPcTTzWLdQuPpt2oHJN2b+gJIVnkOPZB7j3mzoDmqZbo8Oentcbb7F80t/g+ObqGKEDyW/QCwR6dHs622TM9fm0FWZozuKWM4krr+MKg8Cd24eBUGYh+qS3pPqEAQBy3RoNs1eId3s2Ny4vsS4IFDoT6KLKoORPIooAxuL8qbpcPb0NJqusHf/8B1/95bf4dszf8iSFZpy7U4+wEN9n+42ht856m6Zp+b+EyVrBkEQ2Z24n/f2fTo8t92mhznfrvF2ZY4PDe4kq//48j2bxU9N4ehuEVNUCkZs08dy0WiP4tSLIKQHle0mUUnD8hyWrDquH3YxYnIopBmTw8SkZDcpRjPktHgXZl9HFkTiit7rs9iWy/J8DdtyWF6sY3YcrI6DbTqYHZtmw2TqygKjW/IoikSl1MCxXAbHc8xNh+KJsUSEpbnwGqWFOpbphNcxXay2g+/5GyxA14YsSlxvznGjNU9BS3Mst6v7iAA4ENRQpSyapCGL4rvuvjYdi5NLMxiySkrTqdsWhWiM/f0F5s0GVadDTAknZccNhdymm1VkUSIqK2EnTZZJqpEN9uMAQ2M5Zm6WeloEDj71Tgsv8BlLp5jIhIehgIC5Vh03COjTEvzu7g9ieg47UwUm4mHCFDE0BsZCSGB+MB068bQ1VFtkn1YgrmvMNhvIlkDJbDDixbl1rYych+1qBqfm0B9NYPsOpVKTQT2GGfUwVI0rtWVM12V3Os9YPIEmDtJ2XQaMOAnFJ0ChEDXI6DpB4IUTDY9woVqpzIukVY0v3vMBPjlyAE1S2J4oIAsuPjZZxUBUi6xdXFNqkhSrFtKiIFLQQ0hkx3NQRIm220EVVQYjA6TVJLoUdmnqToMAgT2JHXiBx7y5RMtt03Y7xOUYhhyl5bbpeBZe4OP4LjW7Q9tWmPIaVKxFFDEUovXxGYmluV6bR+oegGUxfN3eTJGqU6PjdbqCgmD7Dpr0k+HM/jSF44WbWURZv1kbioqhrC7OqijzYGELx5dvEhBwvjLHvzvzNJ8aO8iwkWYwHqdst1gw6zQcC12SiakdKvYyTbdO1SnzcN/jRCSZ3al+DmeHeWb2Il4Q8IO5y4iCwKdGw2tJXVTFjWaJb0+f5YWF0PY0qejUuqhCQRCoN0zMTTa2lYhICm7g84cXX+BSbZEnhvcwYmSQOh3ars1Li1f52o23uNEMiwmGrPK5rfeS02P4fgfHnQIkpE2KR7oUOgv96eSrXK4v8vGR/YzHc8iCSMezOb58k7+5cZJLXWv6iKTwS1uOMmyEv+O9mSJReX335UMDu7hQneerN07R8RxeWrjCjWaJDxR3cCg7zEA0iSYpWJ5D2Woz1SozWV/kQnWBJbPBF/d+qFc4SmsRtiRyG1BgdwpBUJHETPf/VwsbftAhCEKKl+d4mE0TI7maoKbUKCOxDGerc7xdmaXtOsQVja3xPsTbUmRFlNiZLPCDucvMtGuIhInani7d7fY4nB2hT48x267RcC3+bPJVdEnhcHYYVQq7/m3X5kpjmb+5fpKXF6/2jAVabqhXIMsikahEJKoS4COh8g6NunWh6wpj3YPxcrMdJltrEqWMGurcqWvQVYIgUIwm+Y2dD/E/nn6S6VaV+U6df//2szw3d5lHi9vZEu8j0aUWNx2L2XaNyfoil+qLXK4t8EB+C1/c98Fe4ahPj/O5ifu50ljE9BwmYjkGNkFp/SzE8Hgfn/31Rzadu2uT1FQmxsvPXWTnngHuf3jHuuf1D6T42GfuJRrVsC2XyYuz7N47yHPPnGN0oo8DR8bodGz+6v98gbNv3aR/IIXr+mi6ws998kiPsmWZDpIksmf/IIEP585M877H9vKXf/IctuXw/PfO8eCju3jkQ3uwbY8//Pd/z4nXrvLEJw5jdTV8Jrb1MzCU7tHVMtkY9+wf4flnzrHv0Chbtq8WQTK5OI9/7BCtpsXifJW/+k8v0Gh0yORi7No7xInXr9GXj3PfQzs27V4Xh9IcOjbB0988xdPfPEl5qcGeAyOkc6FmXLttsThX4+Lbt7h8fpZmo8Pew2M89o8OIckSx585w3f++FmyA2lc10OSRD77xY+RG8zQaZn87f/6XSZPXSc3mME2bbYdnOCjv/5+rLbNt/+PZ5A1Bcd0ECUBWZEZ3j6Aoim8/tQpjj9zhmg8wvJMmWQuwa/9z59Bj2pMX5pl8uR1KvNVJk9eRzc0ktk4+ZEctunwvb94ntefPEVuMIPv+2T60/zS730MgOpSna//wd8TTxtUFmsYSYNf/Z8+Q6Y/hd2xuTU5zzN/+QJHH9vPZ3/v40iSgO95/OCvX+b865NkiylUXWHmygIPffIYH/n8+zBbFn/7+99lebZMqi/J60+epDie57Nf/HhvnBVJ4jP37uX6UomOHe5B9U6o3blvuJ8Lc0vsHOjjwuwilVaHlm3zhUfu5ZlzV3jq7CX++SPHKLfaVNsdfvW9R5AEEUNX+eobb7NQb/LBPVt5/uJ1vnP6Ar/23iOcuDHDeC7NkfEhErqGJIpMlarM1Rp85t59JKN6j7rz/l1baHQsap1wrwyCgG+cPM/ugTz3bRnhK2+c4Qfnr7KtkOPaUoX/9rEHaVsOXztxlsNjg2SMUINvvlTnhZNXmZqrEFFljuwe4cDOwV7RXFFlHNvF836y1KufprD9Ns8t/mcu1F/bQBdciY7XpOM1N30MIK32IwIdz0IVldsQsashCRLz5nVOlJ9mun2hWywKiy5BEOAGNqbfwrRblEqzLJpTfLj4a0T0PAlDR1flDYf4qrPId2b/iJvd662EiITczW0938EK2lh+m6qzwHTnIu8vfI59yYex3bC5eGTbELIkUm2ZJKI6rm+zZE1j+x1kQWVX4j28tPS3XGq8SYCPiIgkqkgouL6N5Xew/A4nK88w05nksf5fY8y4hwBYNtvMNmtULJNBI8mPGrZvcqL8PZ5b/M9YfhtJUDiYfj8H0x9cVzQCaLglXlj6G6rO4l2vWXOW7vhYy61uOheCwKdsz7Fo3ew+b1XbMpONha6NmyQ6pt/qjaUuGjRbDTxvrcsxuJ7HYC6LIIgIgoQgvHNxTRZCp9krzatookZSSWKZDpquoEdVbt5YJhLVkCSJ4kAao7vnle0as50l+vUs6h1yJSOm4Xt3zy01KUpaK3Ku/nI4PrWA/alHSaubA2Zuj5utc1xvnsH0W0SlBAV9jLfLc1TtzobnznZqVO02sHPjhf5fxj+IwtGxgWEOFjYKUgH8yt5DG7qtUVljf3os/CMIxcaKkfQqFLCbkvuElr4ZNYYiSsS7haSCnoSuQ8vKnBZFgaihIRVTRKIqiipjWSEkVlYkNF1hx95hIIS4qqpMpi9OKhtDViT6B9O4rk+93AqvYagoNRnbcvBcD1mVcF0P9S6FI01UeLBvL47vElMiIaTQL+P7NYLARpaK3K3aeqfQJZmhWLg4SYJIIRojH4lhui4xVSMqKz0B16SqE1M0IrKMJslh6cT3sTyv61q2kdvdqHVQFIl0f5IdewaZale52ahSsUInuJZrhwfG7mepOxYRSWEglmATPHl9AAAgAElEQVR7Is5ofJVPPHtjiee+dZK+Yopt+4Zp1DrouThiPKAy00DpE9DaUGnVsW2PwVSMWqPD/8PdewbJld1Xnr/7bHpXacp7VMEUGt40ALIdyXYkRXJIilYSJUqjnZA02tDs7sTM7O5YjUaxsdpdxaxEahSSyJBI0YhOTTabrn2D3ehuAA1bAAqF8j69e3Y/vKxEFaoKQFPcCXWfDzBVme9lPnPfved//udcX1qgtzfFxMQyZalCIKjjCsGsk+XEsW1IkuRFsioarcEwjr1Cm34dtDqCMNtC4Dp5EBIRRWDXTRAhhFBAaLhOESElgDq4BjFhcyQ5iBCeYs11TITchZA7eDMJfP3BzqYY/qbcVLDqPdTqSyEQzXsgpATWNPh5FZtVuacnoZUJyAH8Uo2VehW/ojFVyhFUNZK+IK2BED5ZoWQaTUWGryFBjahhTiSPca10HReXXZHtDIcH33ZVtVjAz9Gh7tvG2oOnknxX2zB/P/kGVwuLmK7Dk9MXeWFhjJQeQpVlT81n1TBsi/d07OBf3fMQqqTTKw+xXF/wkl3wlCcf6zvA2ZVp5mtFTMfmqemLvDg/RkIPossKJbNO3vQIHklIfLh3L4Zt882Js9iu4xkG1038ga0foPtbuoioPn4wc4lvT77Bj2YvE9cCjfvOM9teVQP5ZIX3de3mRGbAu+JEACECOE6RzeS6w5EMveEWvjt1nh/MXOLFhevENT9+WaVo1hvb9hYUipB4T/t2HmobRhESQhJ0hzb6qQQVjV/bdgzLdfjGjTNUbZPJcpYvXnuZr4y/hiJ5gb+O66mlrIa6znbdDdJcn6xuuo/bwcXEtK43xlYPjmtiOQWPPBae/4WyxoRTk2R6QglUSW6SNW3+KGnfRrN0gOFoBr1x/MEbHYaim/fS94VbeLxzhC9e+yk122KsuMS/O/1d+kItJPQglmszVy0yXy1QMuskfSF+ffg4Jxeu8+PZy95Za7TU1OsmEio+JX5HifdWsGyHgE8jHr45GTUcm/larlmYWYujqV5+Z8f9/NdLz3KjtELJqvP03BVOLo6jSjJyo4XEcV0s12tzMRtV4FsnprmGMfyhhqdRxTJYrpdJ+d5+bSK27XDh9ATXR+cw6lbzHLZ3JTj+0M71L95iOI7GAp6HjeO1YWVXyti2y9JCgenJZeZncl7reyJAOHqzZTSRDG3wIxJC4PN5KmKfr9HSJYRnJFqoEG8JIUkSui4IRXwsL5UQQvDOh3bgOi5f/qvnSaUjPPL+vfT0b36tr+La6Bzf/87r6LqKZdkUC1XcTarUW0FVFT786eNUKwavvHCFF5++xMvPjyIaLUiu6zZaL138QY373jPCR37peNNn6erpcTSfyif/1QfRAxqO5eAPe/Ova6fHOf2T8/zaf/oYffd049ououGJs4q56wv81v/9K4TiQVzHxd/wJzr88F72P7gbWZWZGp3hT37vi6zM5ugd6eLQI3vRAzrTV+f44G8/SjwdafoczV1f4EdfeoEP/c6jHHz3Pc0469X0NbNucuTRfdz7vgMsTC7xX3/3rxg/P0miNUayPcGjv3o/189NbDoWGTWDj/ze+8h0J/n7z/+Q00+f5/6P3kt+ucjZZy/y2d//OD07O4lnoty4MEXPrs7118WaP8GbN8cCflLhIEvFMolggMViGVkSdCdiJIIBepNxrsx5ql1JCIYyqSbh47ou15dWmMuXeH70Brbr0h2L4FNVHh4Z4pnLY3zt1Dnu397HvkA7O9vSTC7n+M7pi/Qk4zy6ewht1VR2zdc1bJuVcpXBTAvxoN/7DPNLDKRb6EvGaY9FyFW88dhuXGtTCzn+/BsvYdkO6USYumnzN0++ysxSnvffNwKOSyFbJpII4tjO225etAoJmaTexUDopm+e5ZrM18apNjyOImqSlN61peKozT+AJuvosopf9m9ZzKnZZV5a+iYLtRtokp/e4G5a/X2ElBiOa7NizDJaPEXOmMfF5Xr5HKdWnmSv70PUTWvTcxBQosS0DBOVi2iSj4TWTpuvn5iWJiBHvPAHc4Xx8jmmKpebbVsvLn2TjN6LUWhBkQVLhTJ9rQkysY2ihqw5z08W/obx8hsoQqXNP0B3YCdR1WvjyppzXCudZrE2iYPNQm2cl5a/ScbXi18O0RWK0hGMsFKrENbenF+W4dQ4k/0xzy99nbpTQUJmd/Qd3Jf6RULKxvmPJvnpCY6QtNaHK9XsErO16832wzbfAEFlcxKrRWu/u2CNtduvGiiKdFf3iS+g078zTTDijZ227VI1LHTVM8O2nTJO04dya5iuieEYhJUQlusVMXSfQj5XoZCvMLSjHauRyOuFh3gIKQF8stboINi4D8u0qVaMDd5zjuM20uu8Z41AMBQ6yKnl71FzyuSMBSbKF4ipqXXtf5vBckyulc5Qb7RTpn3dtPsH+U+jz5PQAuuCAACyRoVW/921OL9ZvCWII22NI/xaCCEIqBtvKkmIDe0aym0IldXf3Wwn3fhaTVfZub+nud+ewa0ZwpEDvev+H2/xBhZV4663sRnqjslPly+iSyoRNcjBxBAIFVluwXWDIH62hKuAqrE9dtOMcnUb2+NbG1S2BjZXgG0G07QIR/1EYwEUVaYjGCXhC+C4DpLw2sPaGglUqiR5KiK8f9/6fYr5KukOjwSsV01vorEtgxCCdDqCT/cIPbuh3lJkb3u4Lpqm0tPd0rzvXdddN3ANRNf0r7sVXKeA6xQQooCQU7huDYGO69ZAKF4XqVBw7CmECAMGrr2E69YRwg+utW6y4tozuE4BWT9y18dOkW5/i94a5X7r4LEZAopOQNFpD0ZxXZfuUAxVkvHJXnUm5fcWXrdeSTIyQ+EBhsIDd/3534qQJIFPu3P1QghBX7iFf7HrIT53+QXOZqexXIecUd1QARAIarZF1lgib66QExKSkJsPW1lIHE318j/tfhd/cuk5bjQMlfNmrakoWkVSD/J41wi/tu0YT8+N8r2pC1RsA11TOLa9H/02rWqKkPjN4XcQ1wN8f/oiy/XyOh+0m/sI8ZHefXyi/+BNAsa1EVuojcAj4j8zeJSkHuSJqfPMVQsUN9l2QgvwaOdOPjt0jLh+e+NIIQQZf5j/ceeD9IYS/N2NM4wVlzAcG8sytnxfVPWzO95Oa+Af9uB03SpCqA1ixSNwJaGjK52er4kkkGSBZdz0NBNCsC2SIiCr5BuGhF3BOEnf5mNmb6iFuOZvnoeYFtjU3wggoGj88uAR6rbFNyfOUDBrFMwaZ7LrwxokBNuiaX5z+AQPtA6B6/L03BVs10tsbEmF8Qd0JKEQ1Xp/5uOjKjKGaVMzLAINw9+EFmRfog9VyBvGb01WeLRzF53BOP9t9AVeXZ4kZ1Sp2mbTGH3DPiSZrkCM7dHMOk+xS/lZarbJfZlhhBBMVlY4m53ko72Hf+bv848V41fm+epfPEe6LcbVS7MMbm9j/Oo873r/vnWvE8LzXLE3qXwKSSBJkmfiv1pEkwTxRJDe/jT3PzzieWoIgaY3ZPyN12w6rxC3/N34dzjiJ58t4zgupmlRLtZobYt5aavJCB/46GGO3TfME994lSe+8Rr/7PceaX4+0SCf1uKH3z1LoiXE4x86yMpyicsXZtb9XpYFtu3cVknY3pXgn/3PjzE2OseZU+NM31iiVKzh4ino4i0hegbSbNvRTndfktAaInTv/TsZfXWMP//XX+Lwo/sYOTZMKO6RA+MXpsj0JOm7pxt/cPN2y+FDAyQ71hvdu47L1NU5fvrd1yhlK5TyZVbmcpiGt9hVVBm1oZZQNQVtjdny1NVZfEGdHUcGmyTUWsRSUYYO9hOMBkgYMULxIPmlYvMYK5LSUGZtPKfb9vXR2pPCF9RpH8hw9tmLGDUTRZGRVZnCSolKsUopWyYQ8VLh1sJzQFp7HgSrgkSxWoh1PTJmKpsnX6kxtZInGrj5PZRbiqftsQgRv49PHNnT9EpyXJeOeISPH9nDT8cmefKNUYYySSRJ8NDOQQ5Wa/zFc6foTsQ4vs2bc6+9PFRJJurXGV/KkomEmMrmyURCjWS21dLcejz/+hjhoI/PfvBeIkEdy3Y5e2WGL3//VY7t6aMl3Gi17G4hsIV5+dsBmuTjWPKDTUIBPCXJd2c/x3j5HAD9wXt4KPPp23gcqWSNErKQKVjFpufLrSjbecp2gbAS56HMpxmOHMInBRFCanjPWOyMHOOpub9gunoFB4tLhZMM+99J0B+lVKlv2KZPCrA39iCa5GMofIiU3kVQia7zmnFch4K5xHOLX+V0o9Ura8xxsfASXfYjKLKM43qkwGYFxqpd5FrpND45yNHYh4jUR0iQpCfhjQO2a7Mn9gBPL3y5qdwaL59nunqFvuAeXl+Y8dJnywXu7+i/6zQsyzF4beUHPLf4Ncp2DkVo7I69g/vTHyeoRHBxGusrD7ZrElQivKf1lzGcCqrwsWouPVO9yrem/5iSlQUE9ybfz0Bo36b7VSX9TRef/AG9map5J1TLdWYvTJHpThCKBtA1hb7WRIM4AiFkJALcSTqtSRrD4SEWjSVCite+Va9bxBJBEkkvRCIU8aNUDTRdoVZtzDGFIG+WsVwHZZNHoaLKpDORDQmckzMr5AtVBvvSBBrhBylfF12B7VwpvYrp1hktnmJb+CABZeuCl+drutRQ3XnqtaHwIYJylL2JTj7QvbvpibyKG6UVXlmauO3x+FnxliCONoPlOFSsOhXbwGwYhKpCwt/wo7n1IP488POoIKyNTqzaBiWz3nQ71yTZU/M0mc31WDVgXTYKzVtNEjp1cxQh+VHcjjcjZtn0c93uZ57Rmd2IdzYbseGeYasiyQRk1TPmWkNeOI5Dd1+K7HKJpYUCnb1JfIqCb03iymbJZFuhvSfJ6RevcP3iDMVshUc/fpTQLQ/pW2OF1//Oa6cpW3Wqlonp2FCpIAsJn6wSUnUUISEpnUhKZyNFRCDEZgO3xz7L6q6bP1LXMtJrjp/QEHI7rrPCTaPJNw/vHFgUzTp1x2qkfEmokkxI0fHJb448FEIQ0Xy4rkvNtihbq9t1kYXXNxtSfJu2IL4d4bo2tmsgkHAwkRoGh14YgudzI4QErgNCwnEtjme6GYhEeHZujJeXxpkq5yhZJqqkEVJ0Ur4Q/eEkR1N9CCFYMuaIqylWzBk6/D3IrsB1K8goPNbRx85onJ8ujnFqaYqJ8hIls4Is6aR9IXbGWrm/dYDd8W40yeW+TCexA49gOg694TjxoIRyG0PFkmUQ1/38L7vfzSMdO3l27gpnszMsN4yw074wO2OtPNg2xJ5E57qFuuMUcNwSLhsNQsFTfIRVnX++8wHe07GDH8+OcnZlisVaGReXFj3IjlgrD7Z6276Tx9AqVq/RTw8c4aG27by+PMmp5QlG8wvMV4qoioQuKcS0AF3BOAORJDuirWyPZppm21thX0sX//nA+7EaaXmdwfUVOYGC41RwWUsMaZjWvLdIsh0kSULV1j9Kj6b6+P0D76dmWwg84mgrM/02f4R/u+9xVhoJeSFFpzMY3/I+TvlC/N6uB3lHZoAXFsa4mJtjuV7CchzqeZNOf4z7+od4d892eoIJqlWTe0Qb/9vQIwTDOjv9GerVOuHw5oubtoD3eeqNz747vrnaFyAdD7F3W/s6025FkhGO2FKxp0oyu2Pt/Mvhh5k0sryeneR8bpb5apGyVUcREgFFo9UfZSCcZCiaZlesjc5gDFl4i8bJygpnspNU15CHlwpzhJS3X+sswNx0lqGRTt77kUN85S+e5xP/9H4unJ7g2qXZda/TNIWu3iQvP38FISCViTK8c+vzpygy979nhG999RWEJAhH/JSKNU48sB2t5e6LQ6uQJME737WLp75zmkrZoJCvUK9bHDg6gGVavPTcKKVijUBQp1KqN4tqAKGQTiTq55kfnGd+Nse27W1k2mLEE0HmZnKcOz3B6MUZamuMn2VZ0NWb5IWfXCISDdCSDnPPPo8osG2HUqGKkDwiYGm+SEd30lMSCYHruCiqjGlYjYQsl0jUK26txfDBAX77//kMp39ynue/8QrP/d1P+Y3/8ila2mI4dqPJ5jZrn8Am99ni9DJ/9W+/yoF37eahT5ygnK8ycWlmk3dvhGM761mQW6DqCvpqQlujyn237bmBSABJbuiGGipsXIi3xjj2/oN87Y+eoGOwFc2n8r5/+u51SsvJlTxPnRtlYjnHE2cusVQqkw6HiPh1dEUh4tPxayohn0ap7vkYff6ZlzEsmw8fGkEgCPt0AmuKNkIIHtk9xLdPX+Tzz7yMT1U5MdTL9tYkT54bZWIph4PLnq42VFnm7NQcP7k0huu6tEbD9KcSFGt1vnP6Ii+PTXlkpu3w2O5h3rtnO39/5hIvj00RD/q5f3s/i8UyEZ/uzWtliUTQ3zQozhYqdKSjREOekbEmQVcmhiQE9UaC18TVeYIRPwM7tr7n3upwccga4wgkwmqagBzHdR3kNctJRdIIKBHk2xSxAorNtlAvlmujbdKpsAqfHOQdqY9wT+y+JhE1PbZAMVemZ7id7sBO7k3+Ak/M/ClVu0TZzjNRvkTMOoimyg0zZQfLdtAUBdtxyWjbaU1uR1uzDrE9A5xm61RETXE89SEW6hNMVi5huxaztTEOZnQu3cgRC/oJ+fQtvawkJA4lHuVA5FEuji9hyDfbF2Uhk9Z7OJH6J0xXRilYyxhOlcnKBXqDI2yLJanZJjHdv84eYT3W77NuVzmd+xFPL3yJmlNGFTqHWx7n3uT7ydWvUaiP4WDjNOa4ASWBLDRqdh7TqSELlVb/bnQ5CAh8crBZ2BSALgUIKlGyyyXmZnK0tscIhvSmofSbRbVaxzDsu1IqhWNBMvtTzdeuFCoNg/IwILCdIpK4s49P1a4xV1sgpkWbAUaKIlMp1amU69RrBpFY0PtemtJsfbZdm7Qeb3R2bE5y5vNVlpdK65JOC8Uq5Up93fjrk4NsjxxhonKRulPhRuU887Vx+kK7b/vZJyuXWTHmAE/RNxDahywpfLRvHxIbizudwdgGn9GfF95yxJHruszVCjw3d4XT2UmuFhbJGRVcvErtQDjJ3kQX92WGaPXfPkGjaNY4uThG3vAqvUFF40RmcEujzrWYqxZ4eel6M9o+4w9zLD2woW3u+fmrzFULqJLMsXQ/KV+Yum3x06XrvLBwlXPZGRZrXi9w0hdiJNbO8fQAR1J961JpPHbdZjDcwVxthb5ga0NmrSLLacD12qb+f4DrumSNCmez05zPzXC1sMBUJUvRrOO4Dn5FI6YF6A8l2d/SzYGWbtoCUQQCo24xNb7UqDxuZP9d1+VacZHTK1Ob7NmDEHAiPUjGHyGSCPLYx+/1JMHxILEtJre1hh9KzqgSUnVOpAcJqTols8bJxes8O3+Fq8VFb8HsugRVnd5gCweTPRxL99MTbPEqZOL2N17BrPPC/NVmS8pm6AzGOJJsA9dASHfXr3x2ZYorxQUkBHsTXfSFk1iOzaX8PM/Oj/L6yiRz1QI128QvqyT1ECPxDu5r3cZIrB1dvrvB3MWlblucWrrBqeUbnMvOsFArULMtgorGQDjFSLx907YTgIw/wuHk1glzbzWYTpls/RIBJUPRnCCqDWA6JRwsJBTK1ixRbYC6nUUSGqZTwHIqCNfmwbYIx9JD1OwaqhQjrHYjCwlFktElBV2WcXEomDlKVp6QHEYWCq6Txag/zWpaWZceor0zwsOtfRjOIKZ1A0mKowrQpQp+ZRlhzWMCLUqYBzOeZ5vrLOOYVzFFBFXbXHVhuw6m7SBswT2RDraHM5jY1EwLWRYoQkZFJqBqWKaDI7mNirFAiACylECRkltu28FFlxX2xDsYjmSo2AZWgxhXhOylp71JcnMVkhB0BKK0BSI80DbEfKnIQqlMXzKOhEdUaJKMT1bJV2osFarEU7dXNHUEYluqexyniuPWkOUEa8legUCWogi8dI/cYoFaef3Y1qIHeVf73fWW+xSVY+m7SzRb+54TmQEOJrup2iaWY2NZDj988g3OPD/B0f5ueoKeP9P8bI6nv3meZDLMez62B01TcBNuU+FxK8Kqj3ffxWcXQhDy64T868ka23a4PrfCYMfm1wlApWby41PXeOzwMAeGujwS37Wb1VCpQYb7ZAVNUjYUUopmlZlKjqxRwXK9tpCo6udEettdHL23HlRNwXU8Fa3rOmSXigRDOitL6+OvFVXmvnft9J6ro3NojYV9Z3dLcxIsBHT3JmlJhT2vonu6sC2Hi+emyK6U6exONBVHvQNpEsn1z1hZlti+q4N0Jtr0cvEHNPYe6MXn8/62LZsrl2bRNIVf/KXjtHV4hGwwpDN+bQHHdhgYbuXI8Zvny+fXeO+HDnLq5DWuX12gq8dLK3zwkd0888PzXBudY3C4jbaOOKFGy4IQgv2H+ynkq9y4vrhuLVUq1hi9OEO9ZqFqMtnlEq3tcSqVOqVCDUkWdPUkmRxfQtMVJFni4NGBdcSR67pYhkU0Gea+Dx9lcF8f/+dvfI656wukOhO09qZ5+m9fYnkmS+dQm5f+dxemyMWVEmbdZP9Du+kaauf00+cprqz3hJFV2TMjt9cvVNLdSQorJWauzpNsT2yxz5+tMLXVu1zbITef59DDe7j/o8fwh3SCkcC6faYjQd6/bweP7/HGDp+q4FdVhttSqLLEtkyyoRZy+Nor59jb1ca7dm1DkSQijYS3x+4Z3qBiykRCfPLoXsp1g2K9znKlynKlynBbiqG2ZDOx7rWpGXDhxFAPuWqN4UySTCSEi8ueTCtRW0XTFEI+jZnZHD5d5cG+fqqGiXCglK9hlk3uSWaoVU2SkQCfecdBQg0lZWsywpnL00wv5GlLRqibFq9fmkISgqBfw+dX2XmgD1WVCYTengQ2eKqWlfoNZKHhuBYB/5tr/15F3ixSsWuUrDIJLYZvC7/MNl8fw5HD69RLiipTKtSa132nfzshJU7VLuG4Dou1KUL2fgRect7ESo75fInOeBTbdbFsb85j2jaqInvFbFWhZjW6FYSgqyVGWEvQHdjBZOUS4Hk3LZfy3FjI4VMVokEfkcDmnzuqJhmJnkARKpoiI9/SQSGEIKV3k9DbKFjLACwbs7g4uLi8PD9FzTKbNiK3Ql7j6WM5Bufyz/H84t9Rc8rIQmFP7H6OJT9AQA5Tk6M4rkXZWvKe23IaSVLQpTAuDrocbqSpWRhOdd22b0Uw7KOQr3Du9AT92zIkt8Xx+zVs16E1sHk7/mYwDa/t2LbvnHJrGibTY4uomkIiEyHYMOy3XQdPBe7HcSvcqVWtbJVZqC9gOgbdwZsx96ZpYZoW3X0pYvEAtZqJZdrYjYAcRcjEtTB+WcdyNvoJSZIgnYmiauuLDpqmYOWrWNbN7ygQ9AZHiGsZ5mrXqdpFrpZeoze4a8t2NQeba6XXmsq8Vl8fSd0jp1c5BxeYLGU5n5vFchyGo2kGIlvPwf4heEut9iqWwY9nL/FX104ymp9veh+sYrqS43xuhu9OnePr0df5zOC9PNC6fcvKtiQEVwuLfG70WQzHRpNkfnfnQ3yq/8htFUs5o8LnR5/lq+OvYbsOAUXj39zz2KbM6RevneS5hasoQuKPDn+Ue+Id/Lcrz/OtRpvBWkxVspxemeTbk2d4vHM3vz50gowv0pA2OpzLX+dqaYaEFuZMboxOfwoQOG4FWfJ8mX7eyBkVvnz9FX40e6npSbEVXlka51uTpxkIp/gfhu/nnZlt6D6V/uFW/AG9GXN7K15aHOM/v/HkltuVEHzu3k+S8UcYuzBNtVxn95GB2w5QZcvg/730DJcKc8S1AJ8/9ik0SeZPLz/Lc/NXN/0el/Jz/HD2Ir2hJL+1434ebB2+o3JtqVbkjy78iKlKdsvXPNy+iyMt78K1ZxFyGteFlXoFCUFM9236PZ6cOc8Xrp7EBX5nxwN8ov8wXxp7ha+Mn2K2Wtgg77xaXOTk0nW+NXmGRzp28ZtD7yCmBW57jFzXZbaa53Ojz/LU9EUKZnXDsHu5MM93p89tuY3j6UF2x9vfNsTRqqJIEgqaFMJ26w2SSPVUfcKH7dQwnSKK8OOTkxTsMRQpiCYHUESNiBpFEgoxbbOHqMRAaDtVu4Im+VCFiutKXqujEICCEH50pRtFTHmEkD4EbtVT+7ggyxlsexyB5vnsuMsIKYlLDXCRpK0rL47jcPbqDG2BCIVynZ7WOIoscW16if6OJCvlEnPLBSQh8OtqI15a0JGK0JY0MawrCOFDlreYLDYuICGERxLdRlXkui6nJqa5PL9ISyjIoe4Onr5ynZplsaejjcVSmRsr3n312K5hXp+cYb5YYiidZG9nGxeml1AlmdauCBfmFjh5fZL2aJgjvV18/+IVLi8s8ejOIQZTLfxkdIyaZXG4p5PL80vkqlVkSeK9I9t5fXKGyVyegWQCn6oQ1nUEkK9m2d2uocrdrDXH9ms70N1eZBFE9snsODKIZdr/3T0tpIYicG2axsd/4V4K0xWwby4k+wbS3PfADk69MtY8n0K+/Wf94auj1C2b7lSMSt1keilPdzqGpipcm1miN5PABcbnV2gJByjVvBSb1ngYv67yw9eu8Ojh7USDPl6/Ok0iHKArHePMtVnS8RAHtnWQCPtxXVhcLvPK5UmCfo0TI33Iqmgaa1csA0U46xYNkhDsinag9MnUHYuRWIfXNsjPRxX8jxHdfUnmplbQdIW+ba38yR88gZAER+9fT/AJIUimI3z4k/eu+/nI3m5G9nYDIEkSe25pp99/pJ/9RzaSl5v9TFFl7nv3TZXttu3eBPbxD91MtLv3ncPc+8715tyGbRPfHuddIylvTuM4ZC2DYmEFy3EIazp6V4ATXbubROFYYYVUIshHPnVs0+MihCAaC/ALHzm04XfBkM7Qzg5sy0FRJGzbM/qu101cx0VWZBRFJt0axahbTDLiLEMAACAASURBVE8uN1Xda/HdP/8xY29MkGiNkVsoEEtFSHV6haWd925jYE8Pn/+Xf033jg5sy6atN82jv/rgpp93FamuJMmOBN/44+/R1p9m8vLsOoN9IQTJjgTBaIAv/Puvke5OcviRvQwd6Kd7ewfHf+EQX/7Db3Hyu68hyxK+oI8P/+5jt92n47hcPnWNG+cnuX5uEn94ke//5dNs299Hz46O277XMm1sx+HkE69x+dQYkiwYOtDPw798P+G497zRFYVUeGMhb3VZrd8yRVBlmWRIw3UNhHAQQiGob1RXCCEI6hpBXWNlvspyuULdsijW6mTCIRRJYqFUJletEvX7aAkEKJsmmiKTr9WIB/zYdRu/pBL26eQLVUJJnXKlTjHvLQI1TWElV6FSrRPw6/h1FVmSiPpvjvv3HRhkaj7Hf/iz7yNLnnekT1P56Lv30hILIkkSidTbz1/tViiSTtq3jZwxTUhN/sxjbkpPsFBbQiC2JI0ABkP7CcrryRNFVagUq01S1S8HCSsJFuuTuLhU7CLFSp2gT8OwbLJl73k/vpwlqGuoskSpbmBYNpoiM1cvEvbp3v9lmeVyhVQkREgPENMyrHYXmE4dXRdEAjqqLJMI+7f8/l2B7cS1VizDJVuqbjq2aJJOVE01t1+1CriuQ822iPv8TBTqXlfEJtAk79q0HJPXsj/gmYW/pWzn0SQ/B+IPcyL1oaYnUYvPS+BscUxWzaRXEeH29/6tyGcrROMB3tG3A8OwuLi4QFV1SPkDJHwBtMaa6U7XRaThoyfdYS4CYFsOpXyFVLvnGdsMQXA9IkYSOu5tQmFW0aIn2M4wZbvcVAHZtk0iGcZxXGRZQm5YnPj8WrPw4pd1ytLWc1nTtFleKpJpXX+dBnwaPp+6gQyPqEmGw0eYq41juxbXSqc53PJY41rYiJX6LJPVy4DnW7szehzpFvrmenGJP7v8Eu2BCKok88LCGI927CRS8mOYFq0tEbrTsZ/LHOktsdpbbaP5y6sv8oVrJ5uEi19Wiar+ZhJL3qxRMKrUHYvzuRn+49nvMlct8OmBo+taLlYRkDU+3n+IC/lZfjR7CcOx+cLVkwxHWzmS7N3UL8ZyHL49cYbvTL6B7TroksKHuvfxcPtOssslKhWDZDKMz7++qm65Dq8sjfPDmYs8MfUGQgjiWoCo6keTFYpmzUu7sg0KZo2vjL9KwazyL3a9h4w/giwk7okNULUNElqEhdpNosJ1DQzzKorcgxBvzkjtTiiYNZ6aucilvCeRW20j8MtewpQmFApWjaJZo2R6Mdfnc7P8/hvfI6CoHIz3MD2xTHtngpXFIm2dG1swIqqfNn+0YWrrYLsOhmM3jXTXwnVhZbFAIVtGatzct7aJ3IqqbfLs/BVeXhznlaVxhICY5iek+AgqmnfMDe87WK7D1eICf/DGk4QUnaOp/k3bBlehSgpt/gh1x8RelcS6DlXLWJ8UIXSE3AJCx3Zdzi8vYNgWD3Xd3i/IxeVCfpY/v/ICX7h2kpptEpA1IpqPsOLDxSVnVCmYVQzHZrFW5K/HfooqJH57x4O3JXRmqwX+8NxT/HDmYlMpMhJrZ3e8g5Dio2jWeCM7zeXCXFNRJaDZEhfVAmyPZJCR3jbRs6oUIuXzFkABxVsQhdVewGsVna4XMCxByYyQ9AfAVdCllCdXN20sx0FXFGbKJZyQN1mQhCCgqvgUFQHIQiGkrPHekRJovoc3fBZFTWz5OWVlo7Ji7Qi35bkQgpZIgHQ4TCzspy0ZwXFcLNshHvJ7EyJFRm8sqlbPq1/3PrumDKPIrZtv+03Cdlxen5rlA/fsIBUKcmpiGr+qcqink+evjeMCh3u6uLa0zFyh2CR1wrqOT1EYSrVwad4zVVUkibBPa7Y67O1sI+L3cbS3i+eujXNtaYVUKMjYUpblSoWHhgZ48foEk7k8F+cX+fVjB1EkiclcnnMz80iSxFBKxTBPo8gZZPnmA/35Z8YwLZv5uTyZTJR7jw8SiAS4eGGaN85OIssSh44M0NmZYGY6y+jlWSzLIZcrc/zEEG3tcc6cvsHli7O4uBw5OkhvX5KVlTLPP3uZUqnG8HAb+w70MnFjmbFr8+TzVQzD4tjxIbq6E0xNZfnpS1cx6haDQxkOHepHkiXu5ha0LJufvnSVTGuMwW0ZcrkKJ1+8wgMP7Wy2+S4XKxwf6SMS0PnSj0/TEglwbXYZRZY4tquXsF/na8+eJeDTuLGQQ5Ekjo/08srlSfZv62BnT4bdfa2Mza3g01TGZlfIl2vs6Ekz3HnzWNqOy7nxOYrVOjXTolSt8/zcNcBrkXtu/irboxmOZ9aPk0IIhiKt0DD+94zRvefx22EcuhWZjjjv+5jnjXffI7tp706sa9m6HWpVg7HLc/QNteIPaFimzdSNZTp7WlhaKHD5jSnufXAHiiIzMbZAR3cLiipTzFeJxG6v2HszKJsGp5dmCCgaDi6KkFioltBlhZ5wjLJlcCW3jCQEEU2nYHiJQhFtfTuIZVrYlnOzHWsNHMcBF8/fUJGbi5NVCOGFnKwuGhzbQRIusXiQdNvmlf2D79lDLB2lsFxk2/5+Bvf2kOr02nWDkQCf+Q+/yOir11mcWkbzqfSNdCGrMkLAgx8/TjC2kcgPx4P82n/8GJdeuYbjONz3kXvJzufJ9NysEKe7WvjN/+NTjJ2dwLZsoo0WCN2v8eHffZx9D44wfWUWSZboHGpD1VVae1J88LcfJdJQYvtDOu/7zXfR2uup0m3TwgUe+uSJ5n6sRqveve/znnurJtwD9/Twgd96hEDUzwvfOsXS1Aq/8u8+SiDso1qq8aX/8m0y3UmOf+BQ89je7b1372A3iiRhmpewnQV07SBC3FmNPZxOMpx+81X03cP/8NaxlmiA3/zwca5MLLKQLaGrMtu6U6TiobflmLMV8sYMhlNBkXRK5jIR9WebD+SMAjnTS7s2HBO/vFHVrgqdlN61oeXNaJC/N03oRZNIAZeaWUVXFQzLJqhrHOnv4naYL5RQJIlEKIBlO8zliwQ0taE20ZCQcLA9fxlJEA8FyJYq1A2LkG8j6SULhaTehSp0LOGZMBumvek8WZN8rBpcmK6Bi0tI1dkRTxHX/AQ2Kb7JQkGVVCzX4EzuaX48/zfUnBK65OdY8oMcaXkvmuTDds2G95BoKHm94+iNf6sxOu5q2aX5ObZqH3Mdl1KxSjDoY2mxSE9/ijbDYqKYJxMIsTJboJCt0NWf2uD3cytM06KjK3Fba5FV2JZNKV8hEg/iui7FSm2N0sZtWAmIO5qAmI7J5eJlfLIPCYmElsAf0Okd8AIagg2lYK1mUixUkWKBBv9gkDfLRNUwm/FcsiSxtFikVKwRTwSb59jFm2/dqqpShMZQ+ACvZZ+iaK2wWJ9gqnKZSKRlg+rIcW3Gy+comN5cN6l30h3YseE6mihl2ZNo58O9+5CF4Jm5q1zJL9Cei2M7Lpbj0J3++aTOviWIIweX5+av8NdjL1MwawhgJN7Box27OJrqb5q3LtVLPD03yhNTbzBZzpIzqvz12MuMxDs42NKzgQAQQhDV/PzK4L1cyM0yW80zXyvwxWsn6QkmNkT7uq7LlcI8fzv+KuWGYmV/Szcf7zuET1Epu3VmprKsLJXYOdKxgdB4YuoNypZBUNF5f9ce7mvd1ozaLlsGLy5c48vXX2GivILtOjw9N8pIrINPDxz1FACyzkConenqEkORTlYTQRqXJ1v1Xv5D0OqLcF9mG5PlLIPhFLvjHexNdDIQThNSdSQEZavOhfwsf3v9FK+vTAIwW8nx7Ymz9CoJpieWKeQqpFs3nxg81LadPYlOyladvFH1zF5XpvibsZc3qMpCER/nXh5j6toCqqZw9N0j9Gy7/YPLsC2+fP0U2XoZn6zyzsw23td1D33hJLqkUHcsrhQW+Nvrr/DSopcYNl8t8PUbr7Mz1kZM23ry3OqP8AcHPkTBrDZTr2areb5w9SSTa1VIrgNOGUQNS9gEVRV1K9PRW/Dy4jgnF69jOha7Yu28r+sejiR7Cas+XGC2kucnc5f5+o3XKJg1HNflyenzvLt9J3sSnZtu03Fdnpo5zwsLV3FwCSgav9h7kI/3HSKhB1GEhOU6rNTLfHPiNH9x9UWqtokqyXxy4AgPtW4npgcoFQwmprP0tbU0I2nfyritis2o8/LcFFXTBAEtPk/RVTVN4j4/y7UKmYBXARXApZVFbuSzxH1+jnX04NtCffPfc9IpCUF3a4K2W0yje1pvKogiwZsquLW92bZTbDygPXnwHWXB5hJ1p4gmBQipG4MAJEkQ8emcnpqlLRom4vdxaWGR87PzRP0+DNurBiqShOtCxOdjsVThtckZBlIJriwuM76SZb5YarZFnJ6aZUcmhSrLTOcKjC1nSYaCxAM+UqEgg6kEhVoNVfa8oBQh8KkKJ8cnaYuE6YpHecWcQghBe7QbWShIwo8kbo4Br702TsCvcehIP88+fYlQSGdgMMPTP7nInj3dVKsG3/3O63zi08dZWSnx/e+d5X2/sJ/2Di+Rs1yq8ZMfXeDI0QH8AQ3d5ym7fvjUOVRVZteuTp579hKRqJ9crsIPnzrPB//JQeZmc/zkR+f5xKeOI0mCvr4Uruvy7DOXGBjIkLzLarcsSxiGzcsnr9I/kObypRmmJle8ZKzV10gSuiKjKTKpaBBdlenJJJhezjM6uUhHMkoyGsRxXXozcS5PLnJxYh5VkQn6NPLlGtfnVrgytUSxYVAa8uuMz2WRJYlY0MfMUoGpeI5o0EexUqc7EyMS8GFkLQSCa8UlgorebOW+Fcv1EvO1AtvCaZ6Zv8x0JcfD7SMbnttvB6wdI3SfSjQexKjnuXZ5jq6+JLFEiKWFAq7jUq0YRGJ+bMthebFINB5k9Pw05VKN/uFWcKGQK+P2JAhFfOSzZWzLoVYxyK2UaetMMDu5wktPX+LwO4aQFZlkOszyYpF4SwjfJoTNVsiWqpRqdVpjYSK6zsPdQ8CqgbKgZNZxXJe47scF+iMJQCAL0TAilTYEoxRWSpRzFbo2IQOy83nqVYP2/syG47bZ8cwvFTnz7AXufe8BfJu0nAgh6BhsJZaOsDi5TPeOznXVYyEEgbCfvffv3PBeSVPYdWx4w89X35fuTpLuvkmCdG5r2/CarqF2uobaN/xc86nsODzIjsODuK6Na88hJIdoKsKhh/c0X6v5NPY/dNM7Y+T4dnbd24FrXUWoO8C1cK0rCMVg2/6+dfvJ9CSbRNbY2RvEM1FGjg0jJEGtUscX0CgVqly5PIcsS/T2pTb4Q22FbRlvu4YxBa7nHXg32Op8GobF4lKRVDJMLl8h2Uj1u9P7XNfFshxUVcZxXBaXCrQkwijKxqKxEAJdUxgZbNtkSzdhWjbL+TLhgI/gm7hX3iqQJRUfEWShNYmInwWSkOjwZ5iszG7pwaVJPnxyCMd1vbYySUKWJCRZwjSspuJIrPkTvHO1UihTKNfoa08QDW1tOeG6LtGQoGRlmalOYzo1LM1kqm5j1yxmqlfWKfxXzd4dZ+tiqSJUQkoUIW5aRtdMa9PXboZ8vcpspUjdtkhZQWK3+MAqQkMgcaX4Ki8sfp2aU0IRGvvj7+Fwy+Pokp+l+gQuDrKQkYSC6dRxXRtJUtBEAMMp4+AgkNClIDWngIyKJGTi2uYqJNt2WJwvkM+VaUmGUVWZWsWiKxSlMxSlUC1TLtU8Av8OME2b82cnOXC4/44kk6arpNriTT+laNDPSF8r4YDeUByFUeTMHQ26dUmn099BzakT07z1aLlY4/Qr17Esm7aOOPGWEIoir2vlrzmGRxpucb4dx0XT5JtKqAYCfo3ujhb0W+SWQgjSvh4yvj6KpRVs1+JK8VWGwodRbxF/1OwK4+Vz2K6FhER3YMe6dLypco6qbVB3LKYqOc5mp9Ekmcv5BfpCLfQEW1jMlehIRn9ua423BHE0UVrhz648R9aoIIBj6QH+13sepzMYX3ci2wMxdkbbONTSy/9++ttMVXLMVvP85ZUX2RZJE9+EABAI9sS7+PWhE/zxxZ+QNSq8uHCNL147yT/f+dA6n6EVo8yfjj7L9ZLH/HUF4vzW9vvpCbV4DK2AlmSIllR4XRzrKpbrZUKKzu/ufIgPdu9FleTmwCf5JfrDnqnr77/xJKOFecqWwVfHX+W+1m30BFswHJOrpWlAkDVK9AYBbFSlF8vWuJOjPHgXeK3sPfRrlTp6QMeoGc2HrGXayLJEvWqg6gqO7fC+9AgjWiuD0TRRScfn1z2puWXjuhC0ZLpTO9gZbeNfv/Yt3shN4+K1oP3S4FF6+tMszuWp1zfGYwohCKk6qiNTsQzatCjxVICQovOV8VOY9nriKNka40OfvZ961UD3qSTSd65SObgs1oqEFZ3PDp3gY32HNvhY9YZa2BFt5d+8/i1eWfLUDi8vXedKYYFDyV5qVYNa1UQIT6ovhPCMMV3Q6oIOXxQ08Md1claVJybPrSeOkEDpRogoEh6xYNibVyBuRd705NQPtg7ze7veTU+ohYphIAmB5biEQz527+igRQ/yf134EZbrMFct8Oz8KK3+CFXbbBKUq8jWy/xg5mJTSfRg63Z+bdtxEvrN6qiGl+L0q9uOMVpY4EezFzEcm4nSCt2hBGHVx1Q5z6nJCdpaom8L4mgz1O0q5/IvMhw+zImOnobHjXcO67aFJsletcix0WQFCS8JsmZZbIu3oEoyEU1fk2RhcaN8ia7AEOomppCu62K6dWShNlPzylaB5foMHYHB2xpO3g2E8PbhuJ5Hj+O4aI2JM3jqo7X9+Dev0RiKLK0jUW4H2zUomDNoUpCgmt5QxZKE4NGdQ8wWiuiKQmcsgl9VqBomHbEINdMioGkkAn40RWapUCZcchlKJLh4ahy3VKGl4HDupasISaJUKjMUDXLjjRks2yaZdZm4MENE0UguulQWs8wt27SrCguji3ThozseIxUKslAs41dVyoaB5bhsz7TgU3VgI/Hq0xVG7ulk3/5eZqazjI0tEAzpvHFmwotwdV2KhRr1mqcI6exKsP9Ab3NyZJo2fX0pzpyeYGi4lcFtrdTrFjeuL/KZz95HOhNlamqFyxdnSWUiDG7LcPBwP5MTy3z7m69i2Z43wOjoHNWqwcJ8gWKxdtfEkRCCPXu7eePsBBM3ljh7ZpJ9+3vWLbTeeU8/0ZB33B/YN0i2UCEc1OnJxFjMlwn5dd6xu4/lQgVJCKYW8ySjQfpbW4iF/Ny3px9dVTg03EW+XMOnKcRCfhZyJTRFRlVk7t87QMinEQ8HaGuJoDQk4hl/hNH8PEWzhixJm1ZcAa6XFhkrLXotj8vj9ASTPDt/mY/13X1q5VsRpWKNN14dJ9UapVyscf71G9xzsI/XT17DqFsMbG+jJR2mXCyxMJvjxrUFDMOryL76wlX2HO7j2qVZBne0o2kKaiOZRpIEY5dnGRhuw7EdLNNG11WuX52nVKgyN51l35F+hCSYmlzZkH4myxKZ1ijBNYuAsfkVwn4NFxdZyBuMXkNr/u+6LuWqTb5QoVYzcB1QFAm/XyMS9ePzqdQqdV770TnC8SCZnhQv/f2r5BYLKIrM4cf28dzXf8r8xBLv+OBh2vrTvPy91zENmyOP7mXi0gy5hTyaX2PfAyO8/ORpcgt5LMtmYWKJcy9cxrEdjjy2nxsXppganaFzqI099+3kjecvUyvV6N7eweToLGeevsDSTJb9D40wcWka13Hxh3w88LFjyJskAN8eLq69gutWEFILYOM6OYQUBjwPPCF5CiLXKSCkBOA0/t2wKHCWQU7jOqXG68Pg2rhuGSGCIHRcZ6WxHR3HeBVZ7gQphGO+hixlvFYP4Qe3CsLH2kCQ4YMDPPWFZ/jSH34LzacyfWUORVPYfXyYfNXySOfG8G4YFtNTWSxr/dxNkgSplEooWERIUYQURZKT2M7KuvABANOwmJrOYpmbbCMdIRK5uZDOZsssr5Q5d36Kd54YolCo0ZIIkc2VMU0bn+4pR0qlGtGo30tIylfw+VQqFYPLo3Mc3N+LLEvkchUS8RCVikG+UCEY0HEcl0rVwO9XiUYCG9pOisUaC/N5ANKZCKbrcurCJH3tCXb2t77t1EhBpYX52ihFc4G0PnjXxutr4bgOflknrSexXQd1i/RgT1mjkavVeGV2muGWJL3RGLhsqjhchQv4dZVUPERkk7TD1dS08fI5rpfPslifpGqXMJ06lmM0TaQd18a5pSBv2jbpaJiW8NZWEELIaJJ3jVqOQ6VuEmqsm+4G6UCI5VoFRUibhgcpQuVG+QKnst8ja8439inRorejS35cXMrWCoqkYjkGlmtSsbPYjolfiRJWkpTtHH4pjCrpVOwsRXOpQXRtTRzJssSO3Z0YdYtctowLtAbDTJXymI7TVKra1p2JI8t0cBrzzztCeGpStUHA6KqMZTsosoyLheNWUbizXUvNqZEz8+iyj5yRJ67GCYV9DAy34vdrTW8y23Ya69XG++w6ZauGvUWqGnjqN8O0WFoskkx5NhU3JpcxTJtQSMcUNRwXIrqOJASqpDMSPcH18hls12K8fI6l+iRt/vXq6uX6NBOViwAElAjbI0dQ1rTN/WDmEuOlFQCyRoWvXH8dSQjKlkFvKMFyvkx/W4LZ5SI9ma1DV94M/tETR6Zj84OZi1wpLACegfRvDL2Dri1SZxRJ5nCql8c6d/PnV17Adh3OZKd4dekGD7Vt3+I9Eo937maynOWL105iODbfnDjD/pZuHmzbjiwkqpbJ18Zf47n5q4DXJvdLg0fZk+hsklem8f9x994xsmX3nd/n3Fw5V8fX4b1+OUxOHE6iSIpJEom1wkrCGrbklYNWxgJr2Iv9ZwEbMJwAew1Lu7JkeyXsWpDWlGiJXJISwwTOcMjJM29eDv36de6q6so3nuM/bnW9jm/eUFyYnB/Q6O5bVbduPPd3fr9viOIHTym9p/K4FU9Wj/DZidNYuoFSilsXl2hutDn56BHspM0DAwTTf/vuv8WXETe7NV6v3eJQqkikYirXqFMkb8YTfKUCpGzGiJZ7sDVUUnL1rZscPjcV/z47xa2LixhmvD2dRpfCaJ76coNMMY2UKrY/vNyjXa6z3vOoHirj9T36HY9WvY2TtMlXcxw5N8XnJs9wtb1GPwrohC43OzWymzpThyvkCwdrr3T7Hm+8e4tMyuHRB2YOfJ/nBbz+/EWa9Q6pbILnfuEhnOS9FSyeHT3OL808vMN5Z/tDbyKZ54tT93OxuUIrcOmGHhc2l3mkPMPKYoNrF2PKydhEAd3U6bZdkmmb1aVNUukYbnr87CTsu5sSFS2DFiJFjtVeh25wsKD27hhNZPn7x59iJh1rK7xycyFOtP2AcirJw1MTfGbiNH+58A6XWqtIFOc3l7nW3kApxWQyv2NMXXVb3OrGg40hNJ4amTsQWZXQLZ4eOcoLq5fxZcTbjdss9TY5nhvF1LWBRe5HKzkCUEriSRc36lH3V0FISokEofLRhY4pLCIVEkgv7gIPkvtAekQqIO9YaEInVAGe7CHQBvDhkFFnOhbGVgpfugO4MJiagxt1uNx+i4nEEQpWBU0YsQuHM4VGTB8LlU8oAwzNwhDmcB2gsLTEvjTb7RFGkvcX1mh2+xi6FutHdPqMFzP4YUQmadPsuKQSFj0voO8F6JrGdLVAOXtvhSNHzxFJD13Po6RLqPoI9BharPpoIkHKNjhaSSOVi1LrjGcd4pJln4RpoAlj6LKTLJg09RqqFZBJJ0ittRkpllhf3iSTS1KKTFKBxnqtQSJpcebQKGu3G8i0TqKvqGSTmCGUShkUkDV1EqZJyrYop2MI9LWNOjPFPMer+3PNIU4q+j2fMIxw3QDLjN03pqfLfOnvPEI6E48FuXyS5eVNbMfc0UjQdY2f/dw5VldafPfb79PpnOdnPnUGTdfoba2375MvxJDnxCBBjhGm4LkhX/3Lt3juEyeZmi7zB6vfYQg7v8ccPptLMHu4yosvXKTV7DE7u50+1iefcQnlMoEfYpg+I2UTTTgoFTBedpAqLopPVJK4QZu5qTpjxZBMMokv61SLoJSHbUxRzt0ZEGdH79Av89u6wNuXP1KeYjpVIFSxdfBudNxWCAR+FPHqxnUeLR+mZKd4rXbz3g7AT3GEQYRh6JQqWUzLIAwlrc0+YRBhWjrThyvYCZNXn1+MIf5tFydhUR7Jsra8iWkZQzcxpfRYGNQPsRwT0zQIw4hMPkkmnyBfSjEZlrn4zgKlSpZUxuHWfI1/8l/+Ka1Wn+05Rz6f4h/+o8/yyGN3El8pJVeWNhgv5rgbozwIIt54/SZf/9pb3Ly+TqPRJZIKxzYpldN87gsP8LOfPYuTtJk4MsLqrQ2klNSWGzz0ybO88+JF3K7L3AMzVKdKnHxsjh9+420WBy5XC5eX2VxvUZ4ocvapE6zcWKfX7vPY5x7glb96nYs/uMr6Qg3D1Fm6toJSCithkS6kMCyDqeNjvPPixdgWeaON2/PIV7Pkylna9Ut88tef5oX/5/v02y7pfahpdwslu0TedxAigUKgCBDYYBxBhhcR6GAcA1xkcB6h5UH5KNlEsz8GWET+KxjGBNJ/A6VcUN1B0SgDRGjmA8jgAhCiO58FLUus55eAAUUsXncm1mI0zyL0O8iax7/wIOWJIjfevUUUSR74xBlOf+wY2XKGxvtLsXbaIMGobXT4r//pl1lZbu64PlIph//0tx/jyY8to5vH0LUcUbSKlPV4H7dFrdbhv/mnf87y0uaOdSRTNv/ZP/gUz/1MrLHluj4vvXwF2zbw/JC19TYXLi5xaLLAd56/SKmYZnKiwNVrq4ShxLYNTMNAKsnUoRL1RpcbN9Y5eqSKaRm88eY8I9Uc5y8s4roh/b5P3/UHBSTJM0+fILPLxfftN+f5Z//zN0Apfvs//zQPPnqY0VLmI9tIXSvKbAAAIABJREFU64ebtPwVMmaVXtQgv09z5YMiVBHzvUWkUqy7NSYSo/umzUIIBBppy6LtecO5lhBQnSweiHBzTIOxcm5f6rYvXa60X+fljb9gxb1OpCJAoQsDXRgIdHRhYAhrUHgJ6EWt4efr7R71WkC773Hf7BiFfdBMMQomfubrmsZ4KYu5jfr/QeHoBvdXxgfr2n8fvrfx5/SiJgINhSSQLt9b//KAynSKqdQd5KFUMZ3Li3okjBxisCyUEkvXUQoq9hxu1MKXe7VOt69n+XaDeq1NfaNDqZLBHVDxFIowiGJHQuODi+fJlE3jAN3b3RGFEsM0cZIWQgi6rs/b15fIpRzyaROpurjhNdJ6hbsBKNbdDTaDJmbYI5WM89hux8X3QlYWG8wcqZIvpNA0MfwBKFk52kGXA+fYQhAEEb4Xxk2aAWeuWEix2eyhaYKrjTq9MOCBkTGSg4bJdOoUZWuCVW+edljnaudNqoO5AcTz06udN4bX35gzx3hibsdX//qRR/bVz5ID6ZSr7gbv3lhhonxvxkz3Ej/xhaOG3+Pl9WtDy/qHitOczN29iq8LjadHjvKvr/+ATuix6fd5u3GbZ0ePYYj9L+i0YfN3Zx/hrfoCb9YXaAZ9/uXVVziWHWE6XeL12jz/Zv4N3CjAEBqfHDvJ5ybP7pig+X6cgJnm/gNEyrB4vHJ4B9pFNzQ2bteIBjBhQ2g8VpllLJFnvhuLNb6ydp3PTZzB0kwKVoZm0B0+pIVwECKNYWTgAyaLwKDCK2nXO7Hg2GYvTkRNA7frkSnGRa/yeBE0QaaQwncDEmmHXDmD1bWQUhJ4IaZtkMomKU8UB9siOJUfI23Y9KNY86fh95gbnaS+3qbbce8qHiiE4PD03fnri9fXMUydT3zpYS6+Oc+184s89PT+cPDt4egmPztxioy5U4w6koqNZhc/CKkWYle7jOnQClxCKVnqx52k8akSpWoWJRWmpSMQyEGF/fCxUdSgam47Jk3p7rsNyDYIG1PXOF6o0PD6IGILeEWIwNghWrcVGoIHi1OcyN657pOWxVKzRdf3KSQTSKUo22kOZypcasUdiJV+i7rXJbXLolqh6IT+UCssaVhUnPSBMEwhBJPJ/ACxFNEPfZb7LY7nRmP9Hsf6yHXVYifBNd7efBFdGHTCJl7U40r7TdpBA0tzOJt/kvnuBZb7N0kaae7LP007bHCp9TqSiMOps6SNPO80XxxAl/Ocyj3O9c573Oi8x7Mjv4iOzvNrXyZt5nGjLvfln2Ldu827zZdY9xY5lX2Ukj3GW40XiFTAo6XPEEiPd5sv0w/bpI08x7MP8fLGX5ExCnTCTR4oPEfZHr/rOQkjSavrEklF0o5RPgnLIJ2w8YMIf6DX1PMCXD9E0wRrm21G8vdu0a2QRMonUj6BrNN2f4iupRHoCM2Ki93CIJIdTC2HAjRh4gbzgMI2DpG272frMaVpgrkzMUXXMHXGpksoqTj5wDSaJli4sc7kTIWt7o+uaxw6XEVKxYn74/douoamxwUYFDsSSyEEc5USc5XSXfcrCCLeenOebtfjxvV1Pvv5+xifKJAvpvjei5fIF5Lk8ikeejge03efhXarz8vfuwzED3fbMbFtg7PnDvHX33iX6kiWlZUmTz51nIWF2p4VCE2QStlcu7bGrVu1odZNq9nj/feXWF7a5L13b8fGBIer3Li+zvn3brO4UOeN129w9twU6bTNqTMT/P7vfpuz9x3aUdSPVIe+f4V+OE/KPEY3eB9LH8ePVtC1NLpIEcg6jnEITTtEIK9RLqyh61P0g5BI9YhkFyEMLH0M7uLQsl88v3KFutfD0U0eLk8dWNCeSpX47upFNDTOFSa52l5j1PnxJUc/SRFKDzdqoQsLPRVgZgIuXr5E8ZAAvcuFSxdJFS2shCIy+vRUAyMZELh9CuMWURRw8d1bzB4bYWO1hdvzuX1zA9sxcfs+CzfWSaYd3H68/OipcWzbHGojddouZwaotHTa4Yknj1LbaNPvB6ytNVla3MT3wz0JrG0alLOpu/aBpVS88/Ytfu9//Wtu365jmDrFYgrTNAj8kKXFBmEYYhg6Xs9n6doqa7drNDfa2EkbJ2ljmDpKKuyExfrtOusLNXLlDOl8kupUmanj4zQ32qQLaQzTwE5aeD2PhUtLKKnIVbI0N9pUJotMzI3SrLXpbHZ5+7vvc+TcNMvX11idX2djqQEIQj9iYm4U0zJIZBLYjolh6PfWPd8VSrVBWGjW44T9P0VoWTTnGRAOBD9As38WtAyR+y1QASpaRbMeRwVvomQDzTiJ0PIo5aFkG91+kqj/FRAGmvUo0nsJGV4FXJTchP1EZIWG0Mfj4pHQB8inO2HZJqceP8qpx3dq64VhRL/n47kBUzNldCCRNHns8TkWFxv0+z71Wodb87XB9WGDCoibA1sOlSVADVySYpqqk7B49PEjLN5u4PZ9arUOt27V8P2IaNsx7vUCLEvn1MkJXnvjBpVyhvnknXWfPjWBZel89/mLVCoZdE2j1e7zxGNHYi3SpQbNZp9Dh0r4QUg2m8D3Q7pdjwfvn+Zb372ApgnOnZ3krbcXBsi93devjIuwxPlkz/Xp9n3ymX83Vtj/f4cnO0TKZ829zFji1I+U+5nC4EhqmjWvhq1Z++ob7Y7t+annBvTa7oGNEj8Mqfe6jBZ3Nh0iFfHu5vO8sP5nNIN1ABwtxXjiKOOJI5TtSTJmEUdLYWo2mtB5v/k9vr32r4brGMlnmMoVafe9fYtGuyOKJO2e96Guh7hgdnAEyiOIPHJmldnUGW52z7MZrNIIVnl+7U/43NhvUXYmEQg6vkfd66MJQS+UVBM+vSDAiyKWui1Ol0bohwEt341NJoRN3tr/2zVNMDqeZ/xQgVazTyJhYXgulm7En9UEgR8ShfsLem+PzUZ3KD69OyIVodRO1FJtpYlpm4zPVNA1jUouNajjSKSMHdW2GrAHRdkuseFvUPNqSBUNkEzxnDidSQwBH/2+TxRK+oPcKlKSNa/BoeTovpU8AYyPF8gXkjHidvAezwtpbPboewEIERtAbNOdzZglZtPnWPduE6mQ6523uC//HFkzHn/7UYf53nkiFaChcTTzII6+s8S6n34zwFKvyesbC5S9NK4fs2U+klS15X6dNbfB0cwkCT2ejC52G1xqxhNhRze4v3hoz0R4v6g6GcYSOa6011AorrTWaAXuDirO9hBCMJmKqWf/5M2vsNJv8XZjkT+48hK/PPMwv3fpeZZ6m0Csa/Rbx5/eQ33b0hcID7AXzJoJTud3TuiSmQSVQyU0Y8sqN56kH86Ume/GFo1X2mt0Qo+clUAXGm7ksxhucCY3M1iLTxCuo2vVD6SKm5bB/c/EfPyx2VgQbPxw9UC46ZaO0vjh6o5lcAets31/inZqeCErpdjs9HBDn0jG3NiDQkmFEPHAdLeL23ZMPDegVe/S3owruTcvLTM2VbordHUymedYdmSf4oji+nKNVtcll3bIWgnSZnx9SaVoBzE8UekKK2VgCA0hYkvZSMVdBceKqUpyQP/ZX2pKAD7IWK+jG/jD4xfKOj3vDRzzOLY5ze6TaOkGj1VmsbYNOFnHZqUlmMxlOTs2QsI00YSgZKfQhEAqRTf0SBoWzm6BbEVs3z0oxm7vjhwU+jYai4SBDWY88Q6CaA+l8Kc9FIqbvQuMJWYZTxzmhbUvs+4tcr75fcYTs2x6azSD44QqxNGTjCUOY2o2V9pvMpaY5Uj6HKCo+St4kcsT1c8NE5HD6bMs9C6jVIQUgm7U4unql7jcfoOl/g2OpM+y3J/nwcKz5Ky4kHokfY73W6/G+lvuLQLp8fHKL/Bq7eusuDfphW2ernyJq523WfMWhladB4VjmXz89Cy7Oyh3dNN2LpNSUe/0SN6DkOH2Y1iwBwVxLUfGeQRNJAcuOgZbOklSdQf0Nw1FiKGVEUJH11Js7x4JTZDeRlGwnTvbopTi6OlJ9F3aFPugvP/WkUzaTM+WqVSy3Hf/NJOHiui6xq/86hPDCdLYWB7T1JmZrZDPJ3foByWTFkfmRthsdPF6PtVCilvX15k+VGRzvU3kRxyfG6HT7GMgKGYTvPnqNRSCM6fG2dzo8NxzJ3nrjZsITePJJ+ZIJW0QAssy+PkvPTQQwozHEU0THD06ytGjozHNlviclkoZsrkEDz08uwMha2g5HHMG25jA0IuYegUhdFKcQggdpUIUEkNkEMIkbZ8DFSGEgRA2UnlomAhhoIkPnhDsDi8Kub84SdlJk7cOPoEjiSz/ybFPDPT/TBzd3EHH/ShFJ1yj6S8RyB55a4rjTzhEKkbkSRXSCzfQRETaqOBYGqv9m5x5Jk+oTELpUnaOkzIqw3H+2OmJYWJ++EQ11r4QBjNzW40bxXOfP0MQuFx6d57ySJrySAZQlCsZfucffiZGS/ohX//a2/z+73173+0uZ1OsNNp3faZ32n2++pdvcvt2nZHRHL/yq09w+swkpqkTBBGdjsvERAFd1wgETB4bY3S2imWbPPypc+QrWR765DkyhdSwECw0wdz9MzgpG7fnk8olue+ZkyQGtJXiWJ7Hv3A/vVaPmTPjFEZylCdyeP0uiYyO29MZmS5y4tFZQJIfSfDgJ0+gohjllC2lWbi0hJNyePzzD+JkHB767H1YKYtQylgDRSkCKTG1mJweKok+yB1CJXH02HBAaCUERlwY0h9AqR5B95sxykhNIPvfQujTqKiDUgLIIIMGRAHQBa4hw1sI/RpoOUL3mwh9YtBcNECkQLkoFYDIIqNbqHAeqb2JkFPI8EpcrLKeANVG6Mf5MCYrSir8MBo+N3K5JH/vN5/GD0JMofPqK1f4n/77rw3e7CH08rBir5BI2QIMAuVzq3sVieRo/gx//z/+meE19v2Xr/A//ndf3fPdmayDrmm8d/42pqFz4cIiV6+sxi5nShF6AbpSjI9k6fUD8uMJLFPn+e9eZGqqxNhojsWFGhcvLYMGtxbrFCtpCsU0L//gGtVqBiFiofVkOp6TRDK2PQmiCNvYO4VybJNsysGxzB/bRO0nKTLmCGvuVQxhI1V04NzhbiGEYDNoUfc36UcuFbuIvQ9tfysipTiUzRFJOWwc6bp2IMTW0HWK2dQe5seGt7CjaDTqzPJU5ReZSz+Ape11SFNKkTR2Fp86fY/LK8tstHo8dnyKzAdQ0CxTZ3q0sKPg+eOIo+mHeLr6y4w5h7nQeoVvrPwfdMIGN7vn+ZvVP+azY79J3qrS8PostJts9Lu0fI+nJ2ZZ6bWJpKSUSBIpSTfwudhYRyCoJFIcye3fPBNCUNtoo+s6jVqbdMahGwT0Qj++pRWx2PU9XPdu36fd3r/JHkpvD0XQtA1kKIcyC97A6RFMNC2JlN0PVEpLGklmUtOkjFSc+Sq4dWOD9ZUmCBgZy6EUGIZOvdYZimUrFEndwdAM9iu8K2BttYnrBpw4dSf3zmUTdHteXLD2XApOYsdcyhAmJ7JP8H7rFVrBBkv9a9zuXeZU7gmUUqx58yz3r8frsqrMps/tkap4p75E2YnnfW/Wbg+Xr/RbtH2PpGtz/FCV0j0yBe4lfqIKR92wz2J/g8lkhYQeDyLXOxtDdy1HNyk7Kfr7uG3tjkhJUttsiht+Dy/6YHGyB0vT/Orso/yLyy/SDT2+vnieG+0N3mnEuj0VJ81vHv040+m9jkeuF5DJJg68eJOGxUhiJ+ImCiPWFmrMbaNnGZrOoVQBDYFE0QlcVvotClaSspXF0SxudJcBUModJIAh90JVOyjuNvBtf23rppUDGoHaIsgM7sJQRju2wjA1krZNvpCi3eof+B2WZXD8yOgeGPDuSKQdOs0+rz1/ASkVnWaPpfkNPv2Lj921cFSyU5QOKBpGUjJezpJJOgReF2sLJoiiE3q805jHkwEFK02oIhK6hRcFhCrCi4K4sASkDJuEblGw9kNVSRB5EAYQO8et9DqgQBdpItk8cLA1NZ3DmZ1IrI1OlxMjFcqpJKXUnQHB0vThdRPKmL64H4wxocc23t3Qx40Cmn7/rjDa1X5ruB5L04cTOqVioNuPkDv8xIchDHzp4ksXiUQXBhV7gvvzz8TdAyNP2sjT8Nd4v/kqKT2DPqCNBdJFCA0UJI00uohFB5VShDIgUhGhCjDRkCrEjboE0iOpZwbnQOFJl0hFCAShCpAqJJIBujCIVIQn+0QqRBcmpmbj6CmMAX1uKy5fXiG3bvMfzDyObRuMOFnqKx0SJZNcNsH2ImWv7/POuwucODZGfpebkqYJytmd90/gR8j5kC8VzjFSzZK3kmS3TfS7wRqRCkCpGKkyoPL5XriruJ4Zpgi6HguQChEni+tei4Ruo4vYMSyUkn7kkzbicSIujgjkYBKcUPEYEAaxk4Vh6OiG9mNP4MulDA/usjTP5ZKcPbfzuGWziR16HBAX748dH4u79U2XteUm/a6PbmgcPzmOpgnWV5rU11scPjZK6IfU1ttIqaiMZFlfbeIkTI7OjVBbbzMzWyEMIrLZxJ5tAjgyN8KRuTvi5LEGU5/33l2gUs0ytQvlqQkL29iiqQiMLX2V4RJ2/KVr2/dva0I4aCrcJZWTkcT3Q5SKLXktK3ZEK9opfrgxT8qweKQ8zZHswbTBrSL/1nfd6tQ4mf/bOyj9pIVSCkOzSBklMuYIXpQgUB6WliKULo6exdBspArQhUnOmkQXJqHy0DFI6sU9zYFucB2pPELZQRMWoepiaBkMkRygYCMi5TF6NGBCc5B00YknUVvdS8cxsR3jzqNLQSRdhNDRhEnH9fDD8K6Ty1bL5erl2LX1yY8f42c+dZpkciedXKJYc2sIHTKn0xhCx8PH1iwW/RWy1QxN2cbQdHKnM0gUXdEnfTRNQRg0wiZW0aIuN9EjHVvTKUxvUBQGqA2UaDJy3EUIE4118lMtsodcBC168jLlo2Ab01haGbfnceviEvlqjtkzh8hXsrhRQC0rudm4HaORufOszJjOMGf1o5CkYdGPAkYSaSZTeUzNRHM+yfpanRvXmqRSVRCT5PJZ6hsZhDZGvxehaWfIZm3yxSyry010vYBuOByeK6GsxwhlPy7C6xNx4UckYoSReQIZrceaRUIjjDZR5ulYbFXWEfbPoJknUdEioCGMuzu97g4hBL5/p3GkFCw32iw1Wpw5NLJLTF0gtHysu0TsCKzooZSHriWQSEIZ7LnGEglr36aoaeg8/dTxWKhYE1x+Z4HTMxXWb26Qtk3mL63Qa7vIns9YJYPf7JPPJnAtnaDV59p6m9mRPF4Sav0eE4+M0HcglzCp5oqkE7GD53y/RfFwniW3w+VWnVBKDE3jwcm9Y00QRDHC4CMaGjpFe5p2sLovQv6e1yM0elGffuRyq7dEQnfugjxSWPqWUYai3/VYvV3n8KkDRJylpNXpk9glSnyj896waGRrST5W/iInso8dqBupUARyp6SEaehEMmRuvIx9D2LwUinWGh3Me6BvfZg4m3+GycRRhNA4nn2UZrDBi+t/hid7XO28zisbFT4x8uuMp7OMJDP4UUjL9xhJphlPZ1AK9MHA7UcRz0zMkjBM9A9AO2VzSa5dium8hqHjuiGbXj+ebwgYmchv29ddhbjts0MhMIy9TrBSSbpRe0cea1oG08dK2Im4WahrGuVsCsc22TJrUYQE0Qqmvv81ATGSqem36Id9qqkYDDExVcQdaObGiEKFbmhUR7M4g+ZkzszwSPEUutD3rz8oRb8fIMTOeVS74+IHMWK/6bmkTWvPPGvEmWYycYz3g42YRtl5jePZRwHFze67eLIXb2fiGEVr73iz3G9iaBrLvSYvrFxlMhWbg9S8HjnTwTJ1LsyvcexQmXIu9WPJhX+iCkd5K81YooQm7qBOlnvNIbqhHXj8L+9/mz+4/NIHritUkqVec/h/N/T2OHTtF7Zu8KXp+7nWXuf/XXiHbujzxsApzNYMfnnmYR6rzO7b2bQtgzBpHci7TRs21i4ROCkV6XxqDye06gwmkErhyyie2AO9yKMXeZzKTg/eqdCEg2Yc/lBdog8baoC+ud3bZKFb53p7gw2vQ9Pv0wk9fBniRSGdwGNjmwuOrmsoqbh2eYVCKX1gcSIII65cXyWTdu46wBYqGebOTJBI2UzMVtB0jVwhta8Y+fbImM4OofOtkFKRtC2i6A7Ecfvm9UKPDa+FpRsDS8YeKSN2k7N0g1BFA/cXHUsz44nSvjmyBNVESQ+lb9APA1Jboq9CA6FzEDc3LtTsnJCOZNKsd7q0XY+RzHb60M5j6+hWXITctlNCCEYSGSaSBS63VvFkyA83bvJE5fCOidhW+FE4cHWL75+qk+FQKi6carpgdqyEbf5EDSV/6xAIZlKneWfzRTphk4o9yYgzhRt1eXvzJdJmjjO5j3Grd4l1d4GsWSJpZDmeeYhL7dd4eeNrzGXOkTEK5M0qWxabnuxxofUDvKjHxdZrHE6fJVIRF1o/IFIhxzIPYmkOY4lZ3mu+zInsw6T0LFfbb9EJm1zpvMnh9DkawRo/rH+TojXKiDPFmruAEIKUkUUSn6dms8eL37vCc48cY+pQkXq9i+OYXL6ywuZaj9npMo5tYtsmrhdgmTpr623Gx/IkEhZLyw1s2ySZtDB0jUgqlFQ0W300TVCtZjnpjPBs9RgT4wV0TaCFGpv9HvlcEkfPYSiHUHk7ju0f/4vv8Pr3r+5bbDz30Az/4W9/Etsx2fCavLs5T9HO4EU+UkkmEiVudtdRKBzNjO89TcPWTJpBj6erp+m2XP7vP3yBKxeWePCxI3zp154Y6gTtDqUUUSgRmjhQlw5iSoYghp1n0jaZjIPvh/S7HpncXsHUg9bRafbJ5BLoRtwxfeiJI3EnUqlhgUsIweGjI+iGjmUbpDJOLDYp4k5YGEbomoaUcdnetk3Eh9AYC0PJG6/f5OqVVT716TMk9z02Ys/fd2ktANAP4yJ62rRZ6baxdYOic3CX6/qVVX73f/ga/Z7PybOT/MbvfIpU2uHJ6mEer8zQCTwCtRe+2Qu9gVMMdII719Zir8H3N65/JAtHabNCxhxB1ywEAku/u46OrWdwg5AoCkhZsdba7giiBqFsowBLt2PqqFIoIQlkM3aRwQC9g6Yb6FoSiNGHMVJm7xUhlcem90PS5glso4JSUMqk7qqB1+16tDtx53lmtjJM1rciRnkE1PwGOTPDxdZVsmaMfhpPjA5RC74M8KTHmFPleneBw6kpNoMmxkAjbs1bQBcaeTOHrcWiqkoGg98eUvmYehmJxA1vgpJYxjgKiVLhgMIlmDwxTuJQgUBKkun4PIRS0hqgk+PjEFtcm5pOzevSC31GE1k6MiItBKGMWOm1B0m+QAiLKLQRQscPBBvrLpCg15MkEjZCbBVYU0hp0uspDNOg2+kwc7iKJuwBTcNHKRcpOxjGbNyoUvGyuMMjUESAgRAmQiRBWLEYtj6Crn8SxIegIytFt+vtGP+8MKTddzF0jfRunR9ho+QaShsUIAEhMoThNQzzvvg6FffuPgVx03HLXXju5DgzcyNIqTDMOPdUg2toa+w0TZ3Jw5WYWhjFcgNXWw0s08AhzrfXel1KySQJO0aFbbou+YSDLwU9P0DTxNA9dXfoumCz3aeUS92zps1PV8QCygk9hym2oW0+5G5awqQb9ihaeSYSI9j6wfMXP4r1Xc2B8Hwy7eD1/H2bokDsBppOE99bdzZszbs5/DtrlphN7UVwbI9Q+UMb9K3QNY2ZaoHlRhtN7K+/tz225Bw22wc3zn+UiI1T4n2zNIeHCp+iE9b5Qe1rRCrkzc1vUbLHeaDwKSzdwtJ10lac3+9+Goyl7s1YQ0oVNyDH8zRq8TwvY1pDlknghfheiDbwrNeFgbbt2/xtMh6plI1SaocpR/yePg1/me0TqcAPiTruAFUqYtqd78dyA5gkrbMx0lkkuduFGMiQml/Hlx7NoEnJKlIopSmU0rh9n2I5Pg6ZjMPZ+6aG6OwYwX3wdSI0QTpj7/lmocXjDiKuLexHRrG1JCeyj3Op/UMiFbDQu0gzWMPR0lzrvD04jiYns48NzXK2x6fGTyCIkdq/duQRTuVjl/FbnTovr96gteGRdEw6PW/PZ3/U+Ima7UkV85y3jwXNoD+sUkZK7nKquvcIpTxwkNkdRSvFf3TsKd5tLHK9c2fQeLJ6hF+ZfeRATmEun6RW68TQ6H3sBRMDaPL28Ho+7UZnT7EhaVjD92512RWS270N1r0mTTvHTGoUKet4wVuYxhHQ725L/6OEGugUPb9yma/efpfLrTVqXudDYZvqGx2mZiusLO1/7pRSuG5AfbO3x4ljd9y6ssL5126QK6YRQtDvejw2EEo8KARiSH3c+2IsgNbsuLFt9K5TW7DSfHLsHEpBO+zjaOae4t+dVcU0n7rf2+dVGbuVaFWUVgEW6W8h4JRCoB3Ynd9NwXCDED+KYt2Zzv5W1RDfL5t+j2PZvY5WI06WZ0ePMd+p4cmQr91+j/Fkji9MniM/oETGn+/zreWLfGv5IhKFqel8YfLskKbZdwO6/R/fgPSTEkIIsmaRj1d+YcfyM7mPDV+P/38Cck8MX08ZWZ6wP7/jM3nrDmLC0VM8VPwEDxU/AcRWm0k9wxPlnZ85kX2YE9mHh/8/Vf3ijtfvyz+14//Hy58FYCoV631JGbujBWFENuPErl0LNeqNbpzIJC1++PoNdF3j+NFRFhbrzB0eIZm0EAIuXV5mvdYmDGNUyMx0mb4b4LoBtxZqnDw+xshIllQydoio1zssLW+SStmkkjb5XJJA9fGjDrstUrsdl9p6mzCICMOIIIiGLhzVsRzSew0lBFn9BCezk5gD17pIRji6xVxmDIHAEBq+DDE1nZThEKoIDcGV95f4xlfeoNvxWFqo88SzJ5idGyEMQtqNHggoVOKEz/dC3njlKtl8krmT4zFkGUV1LM9r+OaOAAAgAElEQVT6SgspJZWRHO++fhPD1KmM5ihnExBJ1pc3ufD2AtNHqkwdqVLfaNPvepSq2RjtNKCFuX2fZqNHrpDirR9cY2yyyMzRUVJpewf1bndsoTS23Ng+aAKilKLm9bA0nX4YIJWi4fWHvPoNt0vKsKgm0ow+UObII+NUEimutmvoQmM8mWGh08TWDUaSaW606iQMk7FklhvtOramM5HOcbPdQCCYzuSZbzdQwFQ6z9XmBmnTJm3aLHQ26UchD1Um8GWMmtOA3DbuoNv3uXF1lV7HI19IEYaSa6116l6Pda9Dze0ynswxntypW/T1xfcYT+ZJ6BZ/dP17Q3RyJ/SYTBb4KIap7bxO7t4TjqPW77HQanKmMoJp7c1ZColHGIqqH7AOgdiDHut3XeYvrVAez1Md3328FWHUJDQ6WJTp+wH5VGLP5GD4bqXwvICt+qBlG/te57owmEyMYesWZ3MnsHWLQIbYuoWtWWhCI1Sxc5yG4L78STQ0HN1GFzqObhOoAG+QRwlhkLG3xtftez+YiBkTgMTQSru2RyAVvLe8ShhJcoensAydtGnz1OjhHft15yNia+FwkS8jfBlhbHuuj47nGR3PE0WSeq1DJpsYFry3r08Iwdh4Hs8NaDb7GIaJYTy6z9G9AwMzObvP61vvGaxbfPh7RwgxyHW3FUgUWIaBVDFlaHco2d4GX7RQso3SskgVoQuD1D4Nvt0RqhA/8rC0WMBYErLu3sQ0HArJ8X0LpdtjNzr9THJkuD+7JRgUMFsq7gur3n9MFgRRhOt/NFFHnXCDun+LTX+R49nnABBCx9jWuA6kPzyfB0VfuuhCpxm0sTULfYDI3i9MXccxDPJ2rFFqJ0xOPjxzoDGOH0ZsdlzSu86zJ+8Ub3RhYn9AAb4V1LjZfW/Hsp7n887VBpmEQ88P2Ms92RlCCGxTJ3UXRsSPIxw9zVOVX6QV1LjYehVf9nlh/c8wNYdz+Wf3LTp82FBKsbK0SXOzR6PeYTYYoRV4TKZzOLqBp2usLW9y+Fg8F9WERtK4U5Sq+YtEKorHZMek03b3PHuawToLvYs7lmm6Rqigs9mjUMmQSdo8fmJ6+LousnuKYfuFpZmU7RJpI401cCazLIOTZ3cKvG83JbmXCPyI1ZUm1epOy/straRQSfJOgpy9ty6gCY3p1Gkq9iQr7g0a3grL/evYWpK6twRA1Z5iMnli3+/eAgU8UNq5D2PJHJ+eOMltNlnaaFHOpz+aGkf9yCNQYUx/GlTqQymHF5aGIGs5gxsgLjBth0hv/driWrLtd95KorFXvwP2H/wVak86EVOyDi6ZKBVXY037gMMaY293LDIsAztxR0xrK7YXChSKSKlB8UiRt1L0oniyrmlZLPMkhj4NfDgR0g8KpRTL/Sb//NIL/PXShaEtvCBG8Iw4WUp2irRp4+gGtmbSDX1eXL1CL7oD7yxVMrz83Yuc24dGsRWxtaJGdIA+1PB9QURhUBXuNPtYzr3t80HCz5oQeH6Iru+POIiJMAIhIGd+MEf04BtTB2EjhEMoJZ3Aw9oqQAqQqofaXxwp/v5t/xu6hq3r5PM5nH049tv3zRAaa26bgp1E37YWXdP4uUPneLO+wGsbN6n7XX7v4gu8XrvF2cIEacOmE3q811jk9dotmkEsrvd4eZZPT5waJrzWAF13r0XZn4bYfo/LSNJtxdeZpmm0N7vYCQvTMpBSDTW5NF1DRrFdqC8DNrw6pmZiagaG0AkGop+BDIlUiCFitJouIGlUaAcdIiUJVAyVDWUUX5tRgK3HdtbagJJlaAae9Bl1qgdqumiaoFRMU61kGB/Lc/7CEt2uR6fjkskkmJkqM79Qo9vzCMIIzwsHnO94XG13XKYmS2zUOizcrhGGEZ4bkM8mSBwdZWWtxdF+gFLx8SqV0txearCy2uS5p+MHXEIv0PBukjarg254PP7+/C89yiNPHqXTdum0+lw6v8hL33qfYMt6WUVAhKMbjBt7xY6L++gwbYUQW24Y8XExTH3IKe91POYvLZHJJ4eFI6UUrc0ehVKa2lqL65eWYz2njQ6NjU6MSIokrc0epWoGTRP0Oi7zV1cpj2TpdT3mr61hWjrvvTHP2KEiybTN/LU1srnYrnf5dh0ZKU49MEW/67G2vIkQgiNnx+9osrAFuB5cd0rRCV1s3cSLAvJWCp29WmRq0GwZ7DwXGmuUnRQLnU28KH6WhlKSNC28KCSIIkaTGV5bv42tG3x8dIarrRrnimNc3FznheXrCAQ/N3OK55ev82j1EBnT5vml6zxUmaCSSHO9Ved8fYV///hD/M3tqzwzPotA0A8DemHAbLZI0Umy1G0RSsnr67fRhcZctrSjcLT33EHKtLnUWiNnxpRvY5+CwyfGTmJqOm/Vb/HUyHEeL8cT9tu9Bq9/xF3VtjRf5m9usLK8SafjIaXCcUwKxRSTh4qUBq6ulq5T7/eJdt0rSsUWyCsrTW7d3KDZ7KFU7LQ3M1NmbDw/RL7B3iKV1/PxvQC5z7NascVdju/llGNRzaUwBs9WKRWNeofFxQadtku73efK5ZWh6PAbr92g29nZiCgUE5x4LE1CT9EJW+gC3KiDLgwC6eEM6C1Chgg0QuVjigSB6pPQLdyohVQpJhNl+pEL+ESR5MrlFS5dXObIXJVTpyeJIsni7To3b67T6/o4jsn0jGRqujygVNw5DlIpZkoFUrZFu+3y8kuX0XWNRx8/QibjEIaSW/MbLNyq0em4mKZBoZhiZrZCpZLB1o0dIqlbEQYRCwv1wfHpo2kahUKKI3NVCsX0jvTRdkyqjomUisuXVrjw/hIzs2XOnpsiiiTLSw1uXF+n2/VwHJOp6RJT02VMU99TDNvv+lhfa3Frvka9Ho+D6bTDzGyF8YnCDi1KhcL37tARDV3D0DUCN9qn1qKIndwGaAkVgRCYxhxCs9HQ2AxqTO05MjvDDfvc6l2nZFeo2mP0wzatYJ28NbLjfUrFKNn19Tbz8xvU6x1kpMhkHKZnyowPtLO2H489OZxS9Ps+V6+ssrbaIghCcvkUs4crjIxkY+TdtvpbGEZDkfSPHtooLrikjRJFawp7QGM2hEFCv4NU2/RXcaMupnawHm3WyDDmVEkZSVLG3Qs4htC41miQtxOMG7GRz8byJmPTFax9dBddL6Aduti7jIoS2p1tDKRHL2ximpV9tY0iFXC++SIb3u0dr+VSDocqJvlUgmzigzX8pIz1eHRNsNHs/tjoQrtDCEFSz/JU5d+jFdS43b9EJ2zwvY0vU7LGOZTc31X8w4SmCaZmYwOS+kYHxzGRrqIT+IRb2kNucKf4iqBsTXKNtwBYdxfY8G5TtaeYOzbKzJEqtn3HQSxSIeebL9HchfLy3YBOU5HKfnjNxO1hCIPZ1Mzw/x/XebBsg6mZ8h6pFSEE7a6LPniKuuH+aMqsUWQ6dZpVd56IkFvd90kYGfpRBw2dmdQZ0kZ+38++snaDmhe70+0ebmdTcePj2fuP/FhZIT9hhSMfLwp2lGy21NpBUbCT/FdnP8Ox7Ag3ag1uNTapplOkbYt6t49tGNS6PfLJBI1en6RlYuk6UincIOT6yiY1x0UXGgnTZLXdZiKfY7a0s9NS97r875df4manNlymgFfWr/GnN1/nN499fF/UUa/n02r28b1wfzqW3Mv131xrxtS2XWfc3caj1IWGrRukDYdHisepeS36g8KRlG18/zzS7GOZpxD87W6sHfsTBfxvF7/LXy68MxR2PJat8vnJszxaniFvJUkaFrZmoGsahtC42lrn7cYCvf6dwtHGWpsHHp1lY61NEER7EhchBLlMgiMzFTJp564386EjVc6/doPL7yzguyGf/dUnDnzvvYWi6/pUcimStknf8z/4Iz9SiMFPiKXrVBIpGm5ciJOyj6mP31MXGWL+djGVRCmFdZfCUShjumbeit+7e/Wz6RL/+Oxn+GcXvs0ra9dph+4QXbQ7ClaS50aP8xtHn2Q0cWcy3+n71Fq9Dyz4/TSFG4Vcbq7H6AwtyY33F1lbqJFIO7FrhBeQzCTIFlKsLtTQDR1NF0Sh5GOff4C6v8mqtzEs6mSMFO0wdkJcc2ukzRRu5JExUnTCHhX7GIv9VXwZIJHYmkU0QM8s9mNjAE1o6EJjxKkgEPgyYNQ5WPtlK8qluMug6zG1qVxKYzk6715coJBPcXiuxPlLt1ESWu0uy6tNQhVxeLbChYtLJJMWx89UuXppDSEEmYwzRC11ui6Lyw3WNwzGxvI4tommiVgckbgLGcg+XrTdclVRmUlSmUmS0FNoQuOlb7/Pqy9evlM4AlABiN0TnDtxZ/Hebv6x0xP83C89yrWLSzz61DFGJwvDd7pdl2zhDoXAtAzyxVhEMwyj2N48iJ2CTEtHRgoUFMppdCPentHJIpYd8+GdhIlp6YShxHJM5gYaRd22S6/jUyynmZkb4fyb89TX2xQrGUrVHJvdDpeai6z5TQxNx5chWSMxcKJso2s6ncBlOlUmUgpbNziemSBt7hzf6xsd/q/f/RbZfJK/+xtPo1Dc7m5Sc7vk7QST6Ry32rGpw+FMkVudTfphwGQqx0y2iCKGUCdNk7rXYySRYTZbIG85nC6McL6+ytFchTPFEd5vrJE0TNqBG1v4AnnL4WRhhFBKAiXZ9Pr0w4Ca26Ph9UkY5sDBJeSRygfbNo8msjw7ehRbNwilxJd7E60t2u59hUMoGLqU7ofo/ShFGErOv7vAV/7idS6cX6TddgcaUbHWRCJhkS8kOXp0lN/4reeQSY262x/SpyBOzldXmvzbr77NC89fpF7r4LpxzuXYJqVyhmeePcFnPncfI6O5A++/LYrm3pCkzMMYWtzc6Xk+b1xb5KEjkyRskzCM+MbX3+XP/80P8bwAzwt3PDu++fV3+ebX392xxlNnxvkHD5yjpzdp+CsIROwyIwxSem6YLxqaRdoo0PTX6EYtBKALC1OzcPR0XBRHkjHiItGrr1zlj//lS3zqZ89SLKb5yl+8zivfu0Kt1iHwYxe3QjHFx548xi986SEmJgsIERfvE6aJVBJNCBr1Dn/4+99BCMGhqRJ+Oc2f/cmrvPjCRZqbfXw/QNM1EgmL++6b4r/4x18gvWuCEUWSmzfW+Ysvv8Ybr9+k1ezjeTElynEsxsbzfOZz53jm2ZM7HBAhdvX64avX+T//8Hmeee4klWqWv/rKm7z80mXWN9p39qWQ4rEn5vjS33mYyUPF/RumStFodPnaX73Nd/7mPLV6h37PHzZFS6UMj39sjs///ANMThYRCEZG8zGVd7A+XdOwDJ1mz2XvVGaroxuHphUwkDFlDkjoSRJ6kp1d4T0bSTOoExGRGCBGNKGRNook9fyQFq6UotXs89W/eotv/fV71GsdeoN9sWyDUinNI48e4ee/+CCHpnYjy+6cl6tXV/nXf/Q9zp9fpNtxiSKJk7CoVrP8/BcfJJuLBW+Hun2D9fwoDns/DWFrKbyoQ9qoDOk7luZQdaYG+oshS/2rnG9+jweLn9pJZwOUkkgkm0FrcN6SfJA+q65pyIFsB8R272PTlQOb9KmETVY5pBI7C1fjiTlEI0ZRtoIN3mu+yCPFz2Lrd8YypSSdcJN3Nr/L92t/SaB2zglaPY+F9S71dp9MwiabvLtZ0xaTxjKN4d//rgqKQghGnFk+Ofr3+OrSP2fdiws1f7P6R3xx8ncomHd3JP+g8LyQ999ZIAwi1lebPPncSdKmHWu9SkkyZZPJJbfpIWrMpM/yWuMbRCqgGWzwwvqf8nTlF6nYh3DMOFeUStIJ67zfeoUf1r8ea2Nui3w5w7GJ6T1F3q1Qg7E9UnFjth+1ibZJ0wTSpxe2YuqcMNCFcc/zrTvrjobrDrdtX6gClOly9FRpYFYRxbhXEdO62x2XMJRD06L9tl8IjVPZj/Hu5gv0ohbXu2+TMYpIIjJGiWOZhw9E7zX8Hmv9Np3Q5+36IidyIyQMkwubK/zCobMYTYPVepvp0QLHD1X3XceHjZ+owlHWTNIMOkNOMkB54BIVqa33OBzNVum1IoyUQdq20YVgplom69isd7oIIbge1snYNseqJa7XGnSEj6np+GGEG3joaUGz71HN7KRGuVHAn9x8jW8uvY9CkTZsjudGeKt+GzcK+VfXf8DJ/BhPjczt6fYnkxaZbALbNvYdHNqBR7hLs6E6VaLX7u8pKDX83raESCOlW3RCd4A4StPtu4MJ4TiWdZpItgYP27/tWYhDKcXrtXm+uXRhuM3PjB7lH53+NNOp/ZMOALnPA8AwNOavrxMFEbeurzE7N4pu7Py8VIrbyw1GKlnsAywaAQzT4DO/8jif/7WPYTnmXXVJ7mUfpYrdMYJ/54UPHaGPg0ggBsi5TjB4IA0gckI4d82XtqLj+Vxe20DTBBudHveN709RNDWduWyFaiKz7/nShMbRTJX/j7v3DpLkvq88P+kry5uu9r7HWwADDNzAGxIgSIqeOtoVKVESTydRlHa1u7EXoYuLu9BqQytxuVytKFFLSSRBUiII0IDwZjCYGZjxtqe9767uri5flfb+yOrq7unqmSEWewHiRSBiupCVWel+5v3e972Pde3jTHoSo2LR4AsRlDVMx0aXFRp9IbaGm7i9cRN74+345bXyzbBfq60wvlsgUJ1glXIEQyptfU00tq8888uR7rblICkSlZJBMOInHA8iigJRJYyLiyRIWK5FQPLjl/3Yrk1A9pO3CsTViOfZ4/funSLIlJ0KPlFDFmVc18FybaJqpKp4E6o+Wp76yHDMa+r47rlzOwB7d3ewd3cHi5U0GSuL7QaqqXoGvS0almMR9yt8cNMuclaB2fIcN97TTFAOMFmapq/Jh+VY9IZiXLdnZT34wx/YB8D45CLzC3n2VyPoAVzXQpeinprGtRDxTL2nSqMIgkCnfxOqUG/QJXjEkevUbc9c18ZxZnCdPKLorX55JvBLiGISny/Hp39rB7Ad3CKiWMF1PZWSftmETRQFere2kJ7P0dQW81bOHZeOviSTIwvYlk1LR5xwzM/sZBp/UKNvWwuC6CnM0vNeyV1bZwJ/QEP3a8iySPemJvK5Mi3tnvFi79ZmWjoSJJsiaD6FhBVCC6m0ODFKdgWxOtBwgW689KuybaCKMqZjU7Ir69Q3rusycGGKIy9doKuvCdu02R5tZDC7QKs/QlTz4ZdVYqqXFuOXFWKalxo5kF0gpGh0BKPYrkOmUmZHrAlZFPFJMpIgElJ97Eu2I+ASVDT2Jdto8YcpWCZ94QRBReXmJu9ZcHEJKxq6rGA6NgFFpT0YwXBsmvxBSpZV1z+tHgZzKcKKzmupEW5u7KkRQ5fDL3slMq7rYrkOmqRwQ7yr7rbvBpw/N8l/+o8/Y2Y6QzwRZOeudmLxgKeQW8wzPbVEai6Hz6dUSRNh3WB1bGyB//pXT3PyxBi6X6WzK0FzSxTXhanJNONj83zvO4c5f36KP/jqQ7S0rF/lNCoW5aJRt6NyXAPLMVAkL5VHoEpaV8c3XsJfEw89cl3t1Z6by/LSC+cwDJvbDmyhuye55rVvag7T6OvwyoflBkRBwnZNFowpYkpzdbJqokshbNeiLAVo0LztRSRMt4wsqN6AXpAQhLULdcNDc3zjvzzDmTMTtLbG2LK1BddxGB9fZHxsgccfe4PxsQX+8I8fJtkYQpEkEgEd23HXpImWygbDQ3P88PtHOXp4gEjUz+YtTaiqzOJinnS6SCweXOfh5Dgux94Y5pv//QWGh+YIhXR6+xppao5gGhYTE4uMjszz37/xHBfOT/Obv30PsVh91cLY6Dz/7evPcvrkOC2tUW69bTOu6zIxvsDY6AI/feIY4+MLfPVfP0xT03picHpqib/9mxc49Eo/Pk2htS1WI8xmpjOMjqT40T+/zsWL03zljx6mtTVKsVAhlyvT2b1ism/ZDkrdMUFVW+kux2eXkaQOBMGPA+StLAvGHD5JJ6xsXDqnSTqiKaJUy00Mp8R06SKaFCBQLR6ancnw93/3Ei+9cB5VlWlti9He4ZnEz8wsMTKc4okfv8ml/hm+8scP0dXVcBnB4XL29ATf+PozDFyaJRDQ2LS5ida2GKWSydjoPN/625fYvqNtTdiDJAoEfGqt7O3dpjrySryCzFcGSWg9aFIAQRDZFNzHifQLzFVGMd0KL8x9h9HiWRq1TlRRx3INKnaJop2hTd9CX+hmlswM85U0ITmAfIXpqOnYhFWtpnRvbL9ygVhAV9nX2bHu8+7gbhp9XcyWRzDdCq+k/oXp0iBdgV3oUhDTqTBvTDBevMBUaQBV1NkZuZ2R/GkKtueZq2sK0aBO2TA9D6yr3F/bcUjnikiiyNaO9eqmtxuiINLp38F9TZ/hp1P/jbyVZrx4nudm/4kHmj5HRE1eM2lyORRFoqevkXLZrM51FXRTxi8r+GWFpUoFRZWQV6WEd/q30xvYw0D+OC4O5zKvMlsapjuwi4ia9Ahec4Hp8iAz5WEkQWZL6CYmihcp2l4St7jsFXQZZsujnEg/R8UpYjhlLMfAdA0qdpH5ynhtu4H8MZbGZ1EEDVlUkQUVVdTQpTA3xd9LRF2/CLtQmeRE+nkKdgbDqWBV+7aKU1yjQhsvnucH43+GLKjIoooiaCiiii6FEEpbScS9Z9WvKDWFeL1noNHXRXdgF+eyr7JQmaqZuHf4t9Ks967Z1nDKiEhIgszD7Z5Vy6HZIVr0MB/q2ossihycGWQsn+a6eDvFiknAt96Y+63iHUUcGY6JiLim7rvdH0MSRExsSpZZM7ze1pzEsO0aeSNLIpIgEPZ57HZHLIIkiCiSSETXcVy3Vq5kOw6iKNCdiKFeVoP95vwo/zxyjLJtIgkCD7Ru5xPdN/L/nH6S0+lJFo0C3x44TFcgTs9lSVfLq0SWVZ+EKFoGGaO4Jt2rlC+j6uo6c9XpYqY22PJJClFN5+DcaZTqpHLJLLAn6j1MguDHdWeAty8S3XAsTi6OU7A8ZVNQ1vhY1z46r0AagWdC7kUkrkCURERBINwQor2roWacthqO40nwr2YyO3xhimKhwg0HtrwtL0DFMGmIBNCU+t4Kbx8q4GRA8gzU2oMRWgMhr0zNLmLaM4hmEEmLIHDl2tqo7mN/l7d6P5Pd2OPIwWW2lEUVZdr90XWTcMd1OZeZ5hsXXyRtFOkLJfnqzgfYEm7CwUUSPHNPXVK91fy6JZ1QMax19/xXGbOlfFXJoJHUg4j+tee9+jokW2M4jluLgRcEAV324ZOrvjTV8qwI1XvtOpiOhSSIuIBS9cu6vCO/3Fdkte/CtSRWbQRZlHFcx/MIEiW0qpRckAXCStDzGcBFl3xoooYoSIjVCOllk8B6aGwIceDWzQQCaxN0Sk7GM/i38/ikKBXHU3z6JD+KcIUyUzHO6gfWdgzyVgpVDKKJNkb5aXBBlNvAySKITbhuFkcYQBCC2NYkotyK6xQQxTlk9UZkRUL1qbC6DEcQaGgK09Dkla71bWupfd67tbn2b1WTiSWCtb+Xsfw9QRDQV/na9WxZ+e7lnhmrcS1Rxi5eGoh8mUeB47iMDMyRX460FaDBF6DBF1hzvNiq8rCY5q2s7mtoq21zXaJ13b8B9sRXCOndq/69+rs9YW9QpIgSuxMttW12VbfPGGUsx+GGZNs1t68XM3PIgtdnjOYW6AzUn0DOV3LMlLJsCTfxwswFJotpHmrbTdu71OfolYMXPR+FpjD/x1few+YtzSiKDK6LadmkFwucPjWOrqvE4gHmSgUimq/WBhmGxWP//DqnTo4Rjfr5zOfv4Kabe/HrKi5QyFd4+qnT/PDRI5w4NsqTPzvBv/qNu9aZYDuOQ7lYqev5Igo+XEq41RVZ13XJFlcMUSVJZOuWZpIRnXhzBE1XOXtmgsOH+j3i6PbN3H3vjjWclCAIKIq01o5HEAjKMVTRVyOwBAQsx8Qvh1f8VmqloGvbUctZGSsND6VIpXJ86tO3c+CurTV/yqWlIj/43hGe/sUpjh8f4eWXzvPhj95EtlxhKpOjORxc8/5WyhY/efwY6XSBD35oH/fev5NI1I8oCJQrJqPDKe/vywjgxYU8j373MMNDKZqaInz+C3ey57oudN0rQ8tmS/z08eP85IljvPTCOXp6k3zkY/uR6oyjxkYXWFzI88lP3cqdd20jGPJI10ymyD8/epRfPHmKUyfGeOn583zk4zcjr1rAMwzv9x89PEAw4ONTn72dW27bRDDoQ8Dz63ju2bN8/3tHOHt6gp8+fozPf+FOSiWDUtFYZRfhUqgY+JR6YwYXQQiyPPUQULGdGUQxgiDo1QUX+aoeRVlzienyBJ3+XhBBE4Moos/bf/VcfvaT4xw62I/fr/G/feY2brt9C8HQ8rmYvPj8OR793mHOn5vkiR8f4ze/dM8aX5Ni0eCJx48xNDhHIKjx6c/czh13bSMQ1HBsr/ztu/94iCOHBzBNi0B1UUJVZLpb49cUmPCrCBeHilOkZGcxVqmJ42oL+xMP88zMt6k4RSpOifPZI/QLbyAi4uLiuDYONqIgszt6JzvDW4Crj2VKpkW6UqJ8DcnYV0JUSXJL4gO8OPc9MmaKslPgXPZVLuWPVVNvHUzXwHEtVFFnf/xh9kTv4V+M/0Sh5M09LcuhqzGGLAnYjnPVybgseUEYhvn2zc+uBkEQ6Atez03xhzg0/xiGU+JS7g1Ccpx7mz7ltZtvAaIoYFk2Q/0zOI5LR3cDOaNSNX72SJF8toS16lz9UphbG36NvLXEdHkQF4d5Y5IFY7qmorFdCxcHCZmdkdu5KfEwP574Wo042giLlWleX3wSy71ypUjeSpO3LvfYFdClINvDt9QljpaMFG+mn6Jo57mSIq5o59b5YAH4xADXq60YGU/8kqtUiOkbl+r7xAC9wb0M5I9V05xtRCT6gtejXeZxmKpMUqn6ozZobciigiJK9GfnOLs0jdcGMIUAACAASURBVCbJnFicIKb4GZ5e9EqHTZu2hvXWD28F7yjiqGhVMB3Li3Svvoybwo0ktACTxSVKtsGp9AQf6NiDLqtodcp0lv0slFWEkLSBMeNquK7LZHGJr194kdmy97Dujrbxxc0H6A4m+J2td/GnJ3/KTCnLmwuj/E3/Qf5k93uJrIqf9umK5/K+QSOSt8oM5lL0hlYeUkWVsaqRxMvIGCWG8wu1R7XJF6ZZj9Dmv46A7MNxXRYqK4lxjpNnpRTq7YHpOIwVVl60Zj3MplByQ68g8AYMI/mFdXGFlbJJc3uM6fFFMukiyebwumskCKD71Lqs8mqomsJo/wxTI/PIikQ4HkC/ilT0SnBcT9H1v7yTFyII2h0IggIIVcLSO1dZjCOJUWxnqVrzf+VdLZXKnJ72SpjSxRJd8fq1rwLgk5Ua+Xc50kaRb/Yf5OzSNFHVz5e33c0dTZuveI8vhygIqIpUNyHrVxWiIKCKnjfO1doOT320/vPVA6HVz7okSEh1DEOv9P3L9/FWV4sAQkqQkBJktent5ftt13VY1U9tCW266nG1ajrb5QjIDciChk+OeqaporYiE97oORMUEINVHwwPRXuJvDVPVPUhCAFU7QGPAPJycKpkq+cPheBDkrdWjVeXwDUACcc2CYZ1fAGNrFGmP5OiUQ/SGaxPNFzR9+KX+PxKg8qN/t9oYQzDMekOdHpx3XVkyvlcmYtnJ9eURGy0P9vsx3VzSMoOBEHf8Pe93eR5RPVxoKXn6huuQoMvyGwpy+5YKwvZPIvzOSRJJBz1r/l9w/l5hvPzCMCJxTG6gw0cnO3nkz03v63n8E7B1GQax3FpbYuxd2+XN95YhXg8SG+fJ0VfVq8ldB1ftU+9cG6Kgy9dQJJEPvrx/Tz0vr1rFLvhsM7HPrGf4aE5Dh3s56UXLvDge/bQ3rF2dV9WJIIRf91SHAGBkjmKT/bIRctx8akKuXIFv6bgOi7p2QwzIylCET/haMAjv6pti6JIaBsYZFcPUMPq8pLabxOv7Hm4EXl72+2b+eCH96GuUjuHwzqf+dwBRoZTXDg/xfPPnuXe+3ci+iRifp2GQGDNONO2HcbHFvjUZw/woY/cuGZfQF31lm07HH71EmdOT6BqMp/+3AHuvX/XmvFIJOLnM58/QHoxz4svnOfnPz3BTfv76OldP9lxHIebb93Ehz9607pz+dTnDjA2tsDpU+M8/9w57rl/J42NK15vw0NzPPfsWUzT5hO/vo8P/NoNa8ZjobDOhz5yE9NTS/zi5yc59Eo/d92zncamsJdqu6pqWJUlllYRhisQ17T7tj1V7Yeq3qY4+CQfAfkKKU+CQFxNsmDMIQjePXWwiahNKKKO68LoyDzPPHWGctnkIx/fz699+MZ15/KBD+1jbi7LE9USxXvu2c7uvZ2rrkeK118bxHFc7r5nBx/88Np7Gon6+fwX7mRqKs3Apdna5/lihclUhrbk2zNJe6fBobrwJKiYbqU2V5NFhb3Re/GJAY6lnyVVGaNk57FdEwcBSZBRRR8+KUBYjiMirfPsAy9ApFX3xhxBOYYiagRlj0Q1bbvuuEEQROJaC62W97242rJuGwBRkNgduZOI0sDh+ceZKQ9TsvOY1bQvSVAISGESWhv7Yg+yLXwzsqCyNeSZz0eVJgQkxlJpXBe2ryr9UUQfLb5eTLeCJvq98liqZLW78Tw0ojTSqm/CxSWhtrOYLREPqPhUry2zbIeZxSxLZpGk1oVefTd0aeN3xEt0VNmfeB8uDsfnXsU0baZLg4wXL9Ab2LthG1ssG1RMC0XWaPb1ELK9klSf5KkcC/kKmk9hcT7P+Og8bkLgYjZFsz+EJIkoirxmPiAIAt2BXXyo/Q94bfFnDOZPULJynkLI9cQiuhQkqjaxK3KA66L3oYoavcE9yKKCKuobemVpoh/daMKny5TLJrqu1tRO1wJV8KGIGoVCBVkW14xhVUmnQe3EdMvXPC5a7hNFUUARVMSsSmdHAz6fQi5jXLEyQxAENgVv4E31aabLgwDE1Ga6A7vWEem6GGC6NERBzFCy83QFdrA71sp4Ic0PR45juw5bwo3c17qVBbnI9EKWjsbo2za+e0cRR35JY8KaX5Na1ayHuTHRxVRxqeozNMSF7CzXxdrf1kHuklniWwOHOJ2eBCCuBfjS1jvpCnoKm1uSPXy692a+ceElirbBU1Nn2R5t4VO9+2uqp0rZwqqmXdVDzizzxvwodzVtQa0aI2q6hs+v1Vb1XNfl3NI0Y4UVf6UbG7rwSStpXgLQoK10Si6WF0O4gVHuW4GLW4tfB29FWd4gTW4ZBdPgSGqIgrWW/fUHNC6emSAQ8tUa0cshSyLNjWEUeWNfE4BgRGdqdJ7FuSyyInHL/Tvp3PzW0+R01as7tqx6Ro5vH7y6+42URA6K2IgsJxGEq68E+FWFLckEluOw4NuYNPM6943v22wpy4lFT3IZVnzsiLb+UqQReI2dpijvqtW1kKJxbH6CjkD0iqtJjuNQyFcYH5lndHCO+bkspYKBIEIwrJNsitDRlaClI04w6PMG1xvAsmyyS0Uunp1kZGCOXLaEIAjEG4L0bmlm07YWAkFf3evsui7lkkG5ZKJqMrpfY3YqzRuvDjAztURzW5R9t2yipS0GgueLc+TlC0xPpAmFdfbduomuvmR1AreCcsmgVDIQRYFgUEeUPLJi4MI0l85PkV7IIysSza1Rdl3fRXNbDFVdmfQJSCiCH7/sTTwFQUCqlqvVK2ldOaEKWCmQOqBayiYJMlljGlUM4JfjSNWJKUCpaFIuG+Cufhe8c5HkJMGgBggYFRMEgVAsQMYyyBjlmjqn7s+oXtfUbJaBC9PMTqUpVM2Idb9KLBGks6eB1o4EkZh/3fVbhm07lEsGM5NpRgbnmJ1eIp8te2S5X6O5LUrPpiZaOxL4dG+VPm8VSBtp2vXWWjnGsmmtaViYps3o4ByXLkzVnp/MYtHzZLoMoiTiU1Pe87dBOfOyIiU1k+H86QmmxhYol018ukpTa5RNW1to70qg+a4uzQfv3Vicz9N/bpKh/lkK+TKaT6GrN8n2PR00NHqTzXp7uiXZjYBA2TaJFFTOnxxHUWX23bZpjcpCxDOPP5wa5OaGXqKa/11tjt3W7qkYhodSvPjCOW69bTPBkG8N+bP63qiixFyhgGHbKK7N8eOjZDIl2jvi3HLb5rpl3rqusve6Tt54fYj5VJbhoblaqdIyNJ9Ka1eD5814GSy3iCjoONUVYNd1mc8WaE9EcMOe0bxRNlBUmUji2mPf/1dCViRuuW3zOqIHoLEpzL4bexgcmGV2NsvIUIpd13VSsSyylQqNobXtx6bNTdx3/866+6qHSsXiyOEBbNthy9Zm9t3YU7eNDwQ07rlvJ2++OcLMTIZTJ8fo6m5Yt60oidy6wbkkkyH23dTDhfNTpOayDA3M1ogjx3E5dWKMxYU8sViA2w9sqbuIp2kye/Z28vKLnj9W/4Vp9uztxDRWlCCu6+JTZJLhQB1F5fL4z9u3JDV7C2ZIOK6DImho8tVDSERBoEFtqinLvHJoz1vEdV1Onxpjfj5HKOTjwBXOZffeDp575gxL6QIXLkyza09HVSUKF85PUshXkGWJO+/e5qneLkNLa4zrru9icGCFOPL7FMJ+3xUtF36VISIiCjIVp0hMWDumVUSVnZEDbArdQKoyRtqYpeKUqkSGD58YIKomiSpNG5IB3f6dfKH3z1Ydz/PTu6urew1RO3R6nLnxBXbeuplg1M/9TZ9Zp8ZOTS4iyRKxxjDlQgVZkbAdl5jRx4PhLzOrjpK15ijbBRzHRUEn4WuhQWvH5/rJLlSQFZs7kh/lQPIjgEDFsCnE0hiWRVBfSWtu1Dr5bM//teY6AWiKxK6eFgrl+gu4tzV8kFsbPgB47+F3njvBbTu62dLuEcO243BubI5TQ1P8zge+iq/6XC3v/0rwiQHuTH4Cdf5mFgtFHtqzjctTbi/Hpcl5hqYXeOSW7Xyy69+tOx9RFBm4MI0oiXT2NOCXFcKaD1EQEGWJcNS/rm8RBZGk1sF7m79I2pxlsTJFwcpguQayqBJRkjT5uvBLkdq9e7D5X9Xup1gnM82ybOLuJrbM/gb7ru8mlyvR1uqNQctlE0kSkWWJcsX0FoElAdP05ufL24iiiK6ojM+miYS99M+KYSEIkJR6ubHy2zQkgkTCOo5T9apSZSzLU5opioRhWt6iqCIzNrHglcW2RJEkkQv9swyNzdPYGEIRxTWlzfWgSyESWivT5UEEBLoC24mtMvx3XRcHz2tpS2gfrmsji2qV2JN5qH0HdzT1EVI0REFkYm6JidQS2zob8V9jkNS14B3VslUcE9O11vgABWWN+1q28fLspZoJ1D8OHqFzz0PE1au707uuS9E20CV1w0mx5dg8OXGGX0yew8FFFSXe376Hm5M9NUZckxQ+0LGX44vjPDd9gbJt8b2h17g+3sHO1RNul7ppIwC263JwboBHOvawO+ZNfPKZojeArn5/ySjx88kzZIxS7fz3N3SjrGIcLz9nTdnuHfgqJU6/DCRBJK6udOBLRoklo+T55dQZ7ruuy5HUEIdTQ+uUDOmFPLFEEEEUaGqtz3oGAho9nQ21lK6NkGiK8LEv3Vv7e6M4zmuBICynLwnrkmf+f4UgYdqzSGKIK6SX1uCTZeYLRYYX0vjVjRsD07EZzS/SHYzXnaY7rlubwGeMEgdnL/Ge1h1ENS+B8FomiKZlM5/ZuFzuVxGKKBFV9boG+MuolE1OHRvh6SeOc+7kOMVCBaNiVo1eBWTFW73Q/Rq7b+jiN//gPcQb1k+U3GoKxSvPn+fZn55g6NIMxXwFy7IBr0QqENTYe2MP7//4frbubFs3CHZdl5/9yxs8+dib3HznVh756E389Z8/yaljI5RLJj5d4ejL/fzev3sEURD4+288x6svnKdcNpFliZeeOctnf/se9h/YsmYi8vyTp3jiB68RCut8+V8/jKzIPPqtlzl2dJBcpoRpeiEAmk+huTXGvQ/v4X0fvpFAtTzCxcZ2DbzygSoxjoPhVK4Y04uUBCnK6pfBdR2CSnKV8mbldz71xDGefOzNNfLoZXT1NvJ7//YRYgnP3DqXzqNqMnKjjuU45MxKXXLQdV0GL87w1BPHOH50iPRCnkrZ9LwsXBdRElFVGX9Ao7E5wm995T3s2Ls+CyiXLXH0YD+vvdLPxbOT5LMlbz/V+ytJIj5dIRzxc/OdW/jwp24j2RRGFVWiarTW/ziOy/TEIideG2J8dJ7xkXkmRheYn/XUsYMXp/k/v/LduuUrza0xvvzHbTQ0GniqrLVwXZelxQI//9EbHHzuHHMzGcpFA8dxECURzacQjQW4474dPPzhGzdsw1fv79SbIzz69wcZ7p8lnytjWTaSJKIHNLp6krz/4zfR0h6vG9N+NDVCRNU5MjfMLfFu9IBGqWis63W6gglemr2IJIjsjrUzkJulRa+vvnw34M67tnH01UuMjy/yzb9+nldf6ee2A1vYtbuD5pbIunZBECCoaZ6prGExPDgHgCxLnD41zshwqu5xpqeWwPVIjXS6sM53zzQsJoZTNLbHgLWTP8ctoyvtyKI3TlBlibZ4hOZoyLvXEgiiSKVUubqZ31VgGhb9/TMsLmzc/4iiSGdXgo7OxIbbaJpCc3N9dciy4bWmKRQLFVKpHI7rospeRunl70Hf5uYqUX1tyOfLTE95yu72jgShcP1SBkEQ6OpuIBj0kVkqMjY2j2GY+Hxrxz+qKtNcR9lUO5eOBD5dpVQymJtbKQOxbYfBAe/5UDWZc+cmmZq6vLTDw9joPK7rYpo2i+kCqua1g6uvRb5ibNB/2iCo1XsvIAghXHcGF9MjHLGxHeuaVLWa5EOu9geKqFeNsT1D2sFLK+dy/vwUMzOZuvsYGU7hunjnspD3ys4lr7RzfGwRgGjMTzIZoh7jLssizS3RNYsGtuOSK3oKhs7md1/ZrOPaFMx5fFIIRVxRgdqOw6X5BWbzefoScdrD2+jwb8d1XRYrRXRZqVkeuK5LfqnI+KVpcKGpM8FSKoeiyUQSIaaH5/CH/Wh+lfRsBj2okWxPMHlhhqxPJdIQ4siTJ2jtbcSsmAydGUcURQJhHVVXEBCwLZv5qTSJ5iiFTJEjT54k3hzB0VRmppaIRP3YtoJLG2Y1FdBwXIq4jNijaD6FZFOYaDxIJLqazLS5MDFHqWLS3bhyf5cXxi6HKIpEgj4iQR/FssH0Yo6+1gQTqQy6JjOfKVAxbSqmxbaOJI7jMjyzSKZQZlNrgljIz56eZgYm5xGREJCYX8ozNLNILOSntznOfLbAyMwiPlVhe2cjsiQxNL3A3FKe5ngIAZFUusgbFyZJRoN0N8dJLeUZml4gFtTpbUmQK1YYmJpnajFbXUQXqgnma2FZNtF4gEgsQGdvkgv5eYwqIRKM6Gzd3V7zcXOcAoKgIAhq9fqINKjNNKjNeO//yv5d18Z2UgiC510oibHq5y4l0+TU9Cy267K3pQm/otJ/aYbB4RRzqQLFvMnJU5M0xCOkUlnePD7Cpr5GWpujnDg1hm276LrKwmIev67Q0Z5geCSFpins2dXO8VNj7NnZzvjkIhf7Z9A0md7uJEdfG6ar2ncsLRUBaGuNkcuXcV0IBX1MzaTxaQpdnQ2cPT/peR0H/CTiQRRZJhkP4gLBVR5dGyFnLTBXHgVAETU2h25aozZysJkuDZG3MjUj8D3ROwAomBUeHT7GG/NjfHn7nVRsk7l8HsNwWKx6bCXCVzz8NeMdRRwFZZ2dkS5CyoqUXhAEbm3s5f0de/jO0GvYrsOzU+ep2BYf797HjmgLIcWHXPUMsRybsm2Rs8qMF9K8Pj9Czizz1Z0PrDP2XcbxxXH+9tIrLBlFBOD2xk18pu9mfNLaSXlCC/DbW+5kMJtipLDAaGGRr194gX+/52E6A3GymSJL6SKmtTGrOJpf4M/PPM0f7LiPLeFGOre1IggCtuswWVzinwaP8uTEGVy8VZU7mjazO7axP4TXCCvYrovt2liujeU4FC0Da5WrvOFYpI0ipmMjVxPQZEFCFOoTBJoosyvWhjR6DNt1mC1n+dnEaZK+W4mpK52F47qkjSJHU0N87fwLZIySFxu+iqrYvKOVpcUC+Wxp3XE8x3rPj0dURYqOgWU5ZM3yGrIja5ZJV4rebw8up7hJv7RC5vJjF8sGqaV8tfbzlyePvAhJF8u1sV0Hy3FYqhTXXPuKY7FYKSALYu13L3t4eNfRRVU6kaWmjQ+0CoIgkC1XCPs0lkrlDVUxkigS0/woolR3GNakh9gRaeGVuQEyZom/OPcsj40dJ6zoa66rgGesm/SF2B5pYV+ii1Z/BEWUUGSJvraG2irIuwGLlSIl26yrinFdl2Khwg++/QpPPnaMTNqr8VdUmVDEj6J6xviVkkEhXyGfK1Mpm+gbEJylosEP/sdBfvzoUUpFA1nxkny0qpFdsVAhky7ywi9Oc/70BJ/+zbu556Hd6yaJmXSBidEFQifGyC2VuHBmgnhDiHy2RGapyBuHB3j8+0dxbIfDL14gWjXVnZ/LMnhxmh/+wyts2dFKvGFF/pzLlpgcW0CWJV55/hxnjo9x6s0RQhGdZHMEURQoFQ2WFvMMD8zyD3/9AumFPJ/73Xvx+VQERCp2BheHgNLokequU43Q3rjWGycPUiuwWjYcQBUDiIK0blLhKXpMSsUKlmljGnYt3lvX1VpqUzFXIrOQJxgJoAp+QopWNxLbMCxeO9jP33/jOSZGvFhYSRYJhnyomoIggFmxKBQqLKRyKIqEP1hfKbiQyvHtbzzH3EwGQRDwBzViDcGaJLpcMkgvFsjnFnn80aOkZjJ86Q/fS8VfwXTM2hPo2A5HD/bzra8/s+IlUlUggTeJTs1m6r7n3irrJgRR5fLJj+u6zEyl+eZ/fpojL1/Eth2CYR9NrVFkWcQ0HbJLBaYmFvnhPx7iwplJvvj7D7B5e2vdNse2HU68PsR/+X9/yvSEN/EMRXTCEX/teek/N8l//bMUD37g+po32GrMlXKM5dNsiTQyW8zSLPlp716fetToC/M7W+9BQECXFHyS8j/VH7zTsXVbC3/0J4/w+GNv8ubrQxx+9RJHjwzS2hZj2/ZW7rpnO7t2txOoTuIlQUSoPiu2bbNQJVhGhlP8xZ///JqOWSqZHoFYJQE8wsCiqS2GXEdhJyDjOGVEFM8rTVNIVj0EqxtgGVZdZdwvi0LR4HvfeZUjrw5suI1WLf/69U/dtuE2uq5cUSEUifqRFZF83qZQKGPaNoooocnSOkVNPB6o+0xvhFy2RKVi1Y6jbZASBaD7VUJVUn4pXcQwbHyXNTu678rnEo7oKLJEzrQp5FdIc8dxmZ/PAZ6p9F/9xS+u6fcXCxXmZjLVJMoKclhHFAVsxyWdz7Olda0PqOuWcd0CghsFXGxnDtfJeEohvLjs3FV8TQBMx2Qgf56wEiUohgCXrJUioMRwXWrnsjCf52v/+alrOpdy2Vv4WVZLZDLeRDEU9KFV2/3LIQgCobC+Ro2k+xS6WmJYtvO2l/6+E+CVwTqUrSy2ulJdcGl+gX/z86cZWFjg3r5e/vTB+4j7vX4+a1Y4OjdGdyjOtmgjuC6Dp0a5dGIUQYDsYp7sQp6dt27GNCzmp5dIvT6Eosn4QzpG2WDw5BjZdAFBgN23bUEQINYUIbOQ5/gLZ7npwb2M908jKxKC6KnG0rMZbNOma3srhUyRREuU3k1N9G5pxjBsRgZmSTZHyKSLqKpES3u89k6Iope6us6ly4XOZBRRFNaNEG3LZnpsgfbe+ulVqUyBX7x+gS9/8HYOnxuhLRnh6Tf6uW1HF3NLebLFMpbtkMoUMC2b/okUH7tr75p9LOVK/Msrp4kFdQ6eHuah/dsQBJhezDE6m662SQInh6bY0dVUq6SYWsyytaORx189y0fv3MNPj5wjGtQ5mMrw4L4tHB+YpCUeJpMvo17BMkTzyUiSSKVk4jqed7AAjObS7Iw34V/l9WhYQ9jOEpIYQRLjWPYcbnXxShQ0HKfoEUWChCSGKFdO4FOvw3bSSFIDHoHUypMXL/F/P/cijuvyR3ce4OO7djIzl2X3znZOnhknEvYTDnmqoGBAQ/cpmIZNJlPCshzmUjmiEZ2tm5tYWMhz8tQYt+zvY24+RzZXItkQwnEcLMuhp9szyTdth97uJLt2tnPh4jSBgIamKVy8NINp2bQ0RUkvFWlsCNPSHGEulaOnO0lzY5hE3Fsk9vkUwq6OLEnkjQoxn75hm+C4Nhezr7FozADQ4uujXd9CqWIyv1SgoynKxGyGUCRBWE9gOga2u7IQeHZpBtOx6QklqNheku2Uk6E3kGRqPosqSXQ0vj0La++o2Z7hmOStMqZmo66qVfdLKp/tu4Wx/CKHUoOYjs1LM/0cWxhjW6SZ7mACv6ziuC4Fq8JCpcB4YZHpUoaKbbEn3l4zml4N13WZrxT4Zv8rzJS8zqo9EFsXOb4MQRDYFm3m030387Xzz5M1y7yWGuEfB4/ylR334fOrCAIbDl77QkmyRoljC2P8mzd/xA3xTnpDDfgkhSWjxLGFUU6kJ2olYt2BBL/ecxN+qf6k03VdzmdmeGHmIiXLoGybFG2TkmVQsAyG8yvlbqfSE/zpiZ8QkDV02TM71iWFgKJxfbyTW5JrfShEQWBfopO+UAP92Tkc1+XR4deZKi6xv6GbuBbAdGymS1neXBjh2MI4eavMjYluirbB2aWp2r6WqpG/lysCClaFZ6bOM5pfpGQblKq/vWgZzJazGFUjPAeXbw28ys8mzqDLCv7qJEGXVZK+IA+37SKmbVxyciXomkJrIoz0FkqtjqaGOb44Rt6sULQNyrZF0TLImiVGV1370+kJ/v2xx2srLstm0z5Z4d7mrWwOBbDsFLIYAa5eF++6LpsavBLKmVzuituJgkCjr36qWlwN8PlNtzJTyjCQS1G0DM4uTdfdl4D3TMiCRG8oySd7buTh9l3omkIk4Puf8t15pyHhC3B9Q9u61EQAy7R56onj/Ph7R2qlYXv2dXPjrZvo2dREMKxj2w6LqRwjg3P0n5vknof2oPnWN7WmafHiU6d54oevUyp60e0PfWgf+2/fQjwZwrEdJscXOfLyBZ7/+SlmJtP809+8SEt7jB17O+uWNAwPzJFZKvKlr76XnXs7OXdqnG/+5dMszud45icn0HwKD7z/Oh756E2YhsXffe0Z3jwyyMD5aaYn0muIo2WUigY/fvQotuVw38N7ufOBnbR3JZBlidRshpefPctTjx+nWKjwzE9OsPO6Tm69axsONoIgU7GzVOwsmhjGdCr45dA6s7/LrjKY/aDFWO6iLMfAdg0cd/1q/t0P7mb7rnbyuTKFfJmJ0QV+/OjRdUR1MOqnY3Mz/pBO2bYwnLXecuC9MydeG+JbX3+WybEFJEmkvSvBnQ/uYvO2VqLxAKIokM0UmRpb5PzpCZLNYZpb63fIjc0Rrtvfy8CFaa7f38vWnW20dsSJxgMICKTmMhx64QLP/OQ4S4sF3jw6yKEXzrP1fTFKdgmnSkCLksDuG7r44u8/WNv33HSGn/zwNYyKRUt7nPd88IZ1vjcAoZBOJCqDO4eLs+ZNzWVL/PDbhzhy8CKiJHL9/l4e/MD19G1tQferFAsV+s9N8tzPTnL8tSFOHx/lu3/7En/wHz5I5DLPIYDZqTTf//uDTE+kkWSRnXs7+cAnbmbT1mZkRWJuOsPrh/p5+icn+Nk/v45ZRyW2J95GqpynN9RArlhicWQJ07To6G5Y085Yjs3FzDSjhcUaUd8XbOSGxLszWU2SRHbsaKO7u4FTJ8c5emSAE8dHmZleYnJikSOvXuL2O7bywQ/tY9PmJmzXZaFUpGLbSK5UVblBPBGkpyd5VT9B8Hx5Lr/H6bkckizWLXWTGlpdQgAAIABJREFUxTBl6xK60oVMmELZoGyYxIN+ZElEQEBSpGpp01snj7zJnef/k2wMr9rVShA0uKiagt+v4ThuzQ/nl4UoCrXnznG81KyZbA5fHeLMuybXfgzHcWsm41cbggjV3+J9z7mCsf7G11UUhdrPu9yjajnUJRzW2bS5acPS29Xo6m7AMCyKRW+hJBjy2nVFEjHqLKAKQhhRbkYQotVzkhHFFdWGLgWwXQd3lUq1HnySTkSJrdwX16ZkLeFWqxWWn/Vg0MeWrc3XdC7dPck1fapdvR6CKKwziF8NWRLX/P+KYbGQKaDX8fx7d0BAEhQU2bvXy0TLVDbHVDZHxbIZWFikYBg14kgSRLZEkkQ1vTYulRQJRZXZvr+PUqFCIOKnpTvJ6UMXWUplKWQKhOJBEi1R0nMZUhOLOLZD+6ZmGtrixJujNHclMQ2TRHOMzq0t5JcKFDJFLMsmFAsQjPpxbAfVpxJvjpBojhJtCNbagYZGTwm5/C6JolBTRF0O23GwXZeKaTExnyEa1DFte83CreO4DJ6ZYH56iab2OC1da4lTQfDezmW/JtcFv6Zw644uLk7MMz7nLbbs7GqiMRrkicNnKRtrPWPT+RIjM4skt3bS0xzHp8i80T+OT1UwbZtsscxS3lMr7d/a4RGpmQI7upq4ZXsXJ4emmJrPMDKzyE3VfSiyxEKuwIcP7ObC+BwjM4t17/wysdrcGiM1l8W2HHYlmtkR90jS5bmv47pYjoPtuLhuBcMcRBTnkKQEgishCBqOW8C0xxGFIKqyGVlsRpE7QJBx3CzYLi42othGf2qBXMUjKftT88iyRCSsc6F/mny+wmI6z8RUmljUT1trDEWWmJnNUCh6dguaKmFZDufOT+H3q/T1NnLuojdHbUgEGRmbJ5srEY348ftVbNv13mlR4Mz5SSzLRqyWvjU1hSmVTSJRHb9PxbRsXG/ijySLXLg0QzwWIBLxY1k2+XwZqyGI7TgUzY1NvDNmivPZI175nqCyJXwTmhDixMAUZwan2dHTzMj0Ig/dtp1p8yyWaxKUV+w0KrZFRPWCwEzHJm2UkAWRUjVR7e0snX1HEUeKKBOQfZiOteZlFASBFj3CH+96kMTAIZ6aPEfeqpAxSxydH+bo/PAV96uJ9U+zaBv8YOSN2vc1UebTvTezO9a2IfkjCSIPte2iPzvLv4wep+JYPDF+khviHexv6MKxHZQNbtDOaCu7Y6383aVDTBaXmCwueQ2oIHqG4Ku27fDH+N1td7E3fmUvp9PpSb5x4cWrDsEWKgVemRtc97ksiHx2063riCNBEGgPxPjcptv4y3PPkSrnyFsVfj55hhdmLnou+q5LyTYxHRsRgdsa+/jKzvt4eebSGuIoky4iiIKnuvDIcADyZoUfjrzJ8cVxroazS1OcZWrd523+KDc39Lwl4kgQBGRJpK8twXLk+S+DV+cG+fbgYQznynWrC5UCL832rz8+0KAF6Qv2IQg+BK5toGHYNscnpmmJhDzFEfWHqoLgpaJVHIvLN7Jdh4HcHCcXJyhaZp1vr4WX7ORiuxbnM9P85bnncF2Xj3bvo6d14zKAX0WkK0VGcotIgkhD09pJ6GD/DD/94euUSyaKKvHwh/bx8c8fIJYIriu72X/HFgq58oa+MBMjCzz+/aMUcmUisQCf/s27eeD916OuWnVuao2yfXc7wZDOD//hEDNTaR777mE6e5KEo+u9IMolg7039nDXg7u8zjUW4LVD/bzw5GmyS0W27mrj1z55C60dcc/08727efPIIJWKyeT4AjuvW19u5bouhXyZO+/fyRd//4E1JsUNTWG6+hpxHJef/+gNctkSTz1+jJ17O4nEA4SVFhTRjyx4A0hN8iMJEpZ7hWfOLVb/WzGKd7CRRZ9nfnrZpCKRDJFIrhBew5dmefqJ4+uII8u0mRiYpbW3kWAkQmcwVksZW0YmXeBH3znM1PgigiCw75Y+Pv1bd9O3rQWpWlJsOhZl22LHjZ3sua8X13FxlLWecDVo8PEvHkB2RRLJMIq61sOtoSlMV28jiiLx/f9xkFLB4MzxMW56XzeiKla90Ty5++btrWzevpJ6duHMBE89fgyjYpFIhnnoQzcQidZvBx17Ctf1IVzmSfH6oUscfPYstuVw2/3b+I3//QFaVnnaJJIh2joTbN7eyn/8Dz/i0vkp3jwyyInXhrjz/p0Iq0rjXNfl4HPnuXDG8wncuqONL/3he+jb2lLbX7IpQt/WZuINIf72r56pqcFWI6z4GC+ksR2HoKKx4LhYxnoPuqF8ih+PH2dTqLHm/3dF76x3AQRRIBD0ccttm7juhi7mZjO8/towhw/1c/7cFE//4hSzM0v88b99PwQkckaFmXyeTf5oLS2spyfJ7/7eA0QiVyJvPfjqJL46tnPFibSudCIJgRWfkUyBnqY4miJ7BsiOi+rzQiLeKoyKRW6pyPvedx333L0d0/QmcLpf9fwKHY80yudKxBNBpiYWaWmL1S3lrFSsGtFQD6WigW17Sbw+TUaWJIKaRtG4er95Nfj9KlKVwCuVPMXLRoSeZTteehng92t1t/POZeOE01LJxKmei7bqHggCtRK7puYIv/3l+4nHrz6mkmSRs6fG0VSZRDJUUxv5FJmQXqdkT5CQ5E21PxVlE65bwUtag7JdwnKunJAEnuJIFpRaqRqAKurVcRwEqufSkAzxpd+9j8Q1+GmpmryGDF1WCRvGlZ+PimF517T63XLFIluooFXVx+9G1ZEoSFTsPM4qxUNbJExbJEzJNNnZ1EhIW7n/ObPMdCHLLf4uJFHEMm2K2TKiJDJwaoz2vibCsQAIAr6g59vW1NlApWRw4Y0hWrqT3Prw9Vx4YxBRFpEVmVhLFBQBSZQJN4WxXZd4W4yh8xOeujfqZ3p8Hhdo2dJEoi3OxZMjxJsjBKv95Mo7tPYe1Stdf2lohNMzs9zf24vruqQyeWxnrUG9JIm09SYp5Mp1PS11TcGybQ6dG2FsbomOxpgXNS9JNeJYFATOjswwEdQJ+324LlwcTzGTzjEwNU9jNEhPS4JkNIBPVYiFdGbSea7ra0GRJERRpKMxyqnBKVRZJhbyyDpVlmskeKRanpaMBPBVF8/jIT+vnhthPlPYsA0SBDDKlmehEPC8eWVRhFW+Sa7rMpnJ8tiZc+xtjXKg6wbPf9d1qh6uNiDh4qApWxGQQFARUNCUnYCApISwnDlEIYgoyGxJNhDSPMX07pZmRFFg5/Y2srmS1y77VB5+cA8+TfbKz3Z34PMpKIpEPl9GkkTOnZ9C1z3SKBjQOHlpkumlHHnXpHebt9Ci+lQsATRFoVAxcAIStm0T9gfIlQ2aG2NUTJO5pTxF06Yh5iNdKDGRzyL7RXKORVtXvJbO2N4aI5kIoflkdqvNG5L9plPhWPpZpstDACS1draG9iOLMolIgGQ0SEBXuXV3NwFdw8nblKwcirgyntscSXI4Ncy5pRmGcvP4ZIVPdN3AwOwCQ9OLSJLI5vb1gQpvBe8o4shxXcaLKSLK+g5LEAR6Qg38ya73cmfTZh4bO0F/ZpaM6UU02ssR04KIKkn4ZZVGX5gbEh28p3UnurR+Un5ycYKjqWHa/TEE4IZEJx/svO6K3iYAUU3nC5sPMFPKMl5NHvvF1Flu2N3Jth1tG36vZBk80r6HvlCSfxg4wrnMNBmjhOF4ja8mykRVnV2xNj7fdyvXJTrqqh5WI6hodAcbrrjNRnBsBxGBmOqvDeBXd5ySIPK+tl3EVJ3vDb3O2aVpClaFsm1Rtk0kQcQvq3QFEtzbspVP9txIky9M3qywPdKM47qEZB87r+ugUrFqBNLq/TfrEXqC60vYwFtVK5VNVFVGWdeQuRimTZs/Wrtf5UrVEE0SsS2HuBSgJ9iAKAgkffUTCARBwK+tvHxG2aRJDdPhK6LI0obfA4iqfrqCnkn1ml/mup6xbLXOt1w2URRpTWPsui6lkuGZmIl+REFdY24eVwP0hBpwXJdmPbzumQzrnndFdzy6huSMa356gg1YrkNcC+CXFM/HZdWEynRsnp06z9fOP89YIU1QUbmvZRt7Y+00+kJrzLRdXAzbYtEo0p+Z5bX5YWbLOdJGkX8cOsotyV46g2tTd37VEVV1NEkmKK8d+FbKJi8/c5bpiUUEAfbdsolf/+JdRGP1B9jLSVD14LouLz97lslRT5l2x/07uPu9u9eQRlB9PgMaj3z0Rs6eHOPk68OcfHOEk28Oc+DeHXUHpbv3dSGKWVxHxaf76elr4hXlHKZps2N3x6oIeWhtj6OqMoZpMT+3cYlAOOLnkY/dROSycxUEbyL7yEdv4vSxEYb6Z7l4dpL+c5PceHsPqhjAJ0WQqh2c63irybIgryMzV6B4ptj2GAjbQBBxXRvDLqBLb11qK8kSoiRiVixKtslQboEOxyaqVj2ZXJczJ8Y4c3wU13Vp60zwhd9/kK7e5JrrfC4zRcky8UkKBbuCIMDpmUmCskaD5tWzjxYW0CWFqBqgLR6jpY6CdRn+gMYtd27h6SeOMz+XZX4uQyqTxg0ZV1AV/HKwrUuAiCg21q55IV/m+SdPk8uWiCWCfOjXb11DGi1DFAU6e5Lc/769DF2awahYHHz2LDffsWVNomU2U+T1Vy9RKXtKvIc/ciM9m5vX7c+nq9z93t289MxZzhwfXfdbD6eGmSvnsFwHyRLY3pv0+o7LnpW0UWRntI2PdO67anDDuw2CIKDrKl3dSTq7Grj/wZ088dgxvvtPhzh5YowTx0a46e7NJPx+WoJBVEmms6vh/+PuPcMsu67zzPfkc3OunGNHdKMbDaCRBVJMoJgUqKyRh7Y0ouzRyCP58UiWZ8YaDaksUx5rPJJFy5RJUZQoMYGgIIEEkRvoRgd0rq4cblXdHE/2j3PrVlVXdQI5Non1p5+ue+6+Z5+9z95rr/Wt7+P063OsrZURReGG69atfjfVFWPm4vKuXI6ioBKQM4jCpr/lAQvrJcZ70oj4WVlZkb8liiPLtJmbWcc2HaLxIM26iWXajI51sryYx3IcNFWi0LQp5qrkVst03wAV2GyYrK9XGN6lvMTzPBYX8hiGRTCokkpHEBEIaQqp0K1JnG9lsbiPmFpaLLC0VKBaad5wXFaWi5QrvmhCT2+i7V9sNcOwWF+rMDq2s+zd8zyWFgtt9aF0JtIeA1EUGRxK89KLV8nnqnied1vzw3FcMh0xQmGtrWYkCgIdsQjRoH7LoImv8OgHMB3XQhSkWyrjATieRd2p4eLPQQ8PQZCxPQtBFBgayvDCc1fI533eojud64JAe77kc1Uq5QbeLug7z/XIrVUwDLsdaFIVCakVDHgrmoBIQu1HFUOElXT7mYylknz8Pe9gtVpjPJ0ktkW4RUQgpYdYqBaZiGd81JEkEozoCKLIyMEBIq0xmjwyzOSRYVzX4+zzl0h1xelrCeD0bRHC2fPwOCdXlrEch0aXQH1hnqppID/cS8OyKARUMoNjJAIBLtZK0CkhpDoJ3IBH7GZWMy0+/foZnpue5ZHhQR49OILluGRi23l2Hcdl/uqqv8bJEh292/fTZDjI+47vZ71U4/0P7KczEaErEUZVJEa6U3QlIziuR7nWpGFaPHRgGFkSCQc03nF0AlEQSIQDfP9DB5lazqFIIkFN5YMPHiBbqPD4kTFCEZlEKEhdMDi7Os8erYtYWiee1MgZVY4e7EGPijxwzwDTy3lCYhxVkXj/8QNMLefoTcfIxEK7qsCJokg8GSJoaliWsythvOt5vDAzxx++8DIfufceHh3Zu+szNS2bUrlBIh5qBZ9AEPw11fN0VDGEICh4Hrx7cpw4Eo7psCea4uIrVwnHQwQiOsFwANeyMXJlmo5HsieBaFhIqkwwoBIKaniex4F9vaiaQriVQAnHAsRxcAEpIBPVVKpNn0tRVkREW8SVYKw3g+24aIaCrIjkGxbdmRjlepOSaVAyDDRFRpRFJEEgnY60y4VVRUZtoR0zLRX4spXD81wEQcTDpWoVeKP8Aq/lv4rjWSiCxrHUEyTULkRBoDsdZSFb5NpinqCukEmEyeh9pLyebaqEXYEoPz1+P5dLq5iuzVg0Q08gRmRcpyMRxnHcb1sg+zsqcOR4DhE50C5T281pDsoqj3fv4Vh6iLlanoulLNlGicZGIENSSethBkJJhsIpooqOLu2e8T+SGuD37/2h9vlFFWVCN+BB2moCAr3BOB8/+qE2kbeIQES5OSliwzExbYduIcHPj76N02vzTNXWWa6VkCWR7lCMsVCG/ckewqJGsdrEsG2CqoLtukiiiOO6NEwLXVFIR4I83jVJZyGIokiomkK5VCcSDVAq1AgENWzbwbYd0p0xVpeLdHTFqFWaWJaD63jk1srcnRhh+vIK8WQYBCisV4nE/Dai8SAPdY9xIN7LueIiU5V1imYd1/MIyxp9oTgH4r10BWLokq+odDjZzx8/8BM+T5MFF04tYNt+Tf3o5ObCn9CCfHTwMZbXS2SSYdZyVSJhnVKlgSCAIkucvrDAXcN9SILgywAHNbLrZeKRAOevrnBwqAe5LnF+eZlKza8PHh/qoGGY/ETsPoYG0swt5Rnq9IMbpXqT5y/P0BmLcGiguy2PaNsuM9OrTE2t8j8OH6cWMhkcTLM0m2c1W8K2XSqVJp1dURYXCnR1x7nXG+Cu7h7i8SArK0UikQDlcgPXdclmy3QlYwwMprk2tUpXVwzX9ahWm6TSYVaWSywvFXi8ew+SIBNUD+Erjfjz9EdH7uVDg3dzeWWd16YXubZQpGfCd2Q0WSam61iOw0y+yGBi06H54eFjfGDgcHtOS4KI47ntBWZDte8PL36d2VqeoKTykfGH+cHBIwQVFVnYzofk4TtlrufRdCyeXrrAb5x9kpptMl8rcLmcpT+087D53Wyma1OzTJLXoVHKpToXzszjuh6hiM5Db9tHNB58U4f7Rs3kwpl5LMtB0xWO3je6rTb8eosnwxy+Z5g3Ts1SLTc4d2qO+x+Z3AHBFwRIZ0LYxiuIYgJZO0Y4GkBWJFzPI90ZbW/2giCgKDKqJmOaNkbzxhn0/qE03b03DhCmMhH2Huzn2uUs9ZrJ5fNLDBw1cD2LqNpLROxqBy8Np7Er6eI28+rglvDJnFUEQcT1bEy3Bl7mTYEVZFkiENYQBAhIyg6OI8+D069Ot8un7nlgzC+Pum5uh2UdTVSQBKFd0hxTqiii1OYHExGQRJGIEkC9jaBGLBFql5kZho3qqkiS3E4cXD/H7vR9E8UMntdgY8v3PI/sUpGZq1k8D7p6EwyPd9603bG93ciyhGO7zE2vUyk32oEjz/MPUIuzPidUMhVmcn/vruVM4MthT+zr4cKZ+R2oI11SqFkm1yrr7FM6qdabfmmTt/05pLUwTy+f5xvZS22hjJQWpj/01gpkw/Z+Xz9GgiAQiwV56JFJvvrkaVazZdZWK+BBSFFRJQlVk9l3oJevfsX//NTJWXr7kjcs39r4vd0+i8SDJDoi29Bmm/ciosmbQYt9/Z04rosk+mUMnucRCGmkdjmE34mFIjpH7xvF8zwkSaSQrxGO6OgBhUQ6DC1C75HxLuo1ww+G3KAt23Y5e3qeo/eM7EBXNeomFy4sYRg26XSEvv4kkiiwr6vD55D6Fvc9TVM4dGiA06dmuXZ1lfn53K4loLbtcPLVaSrlJtGozvh4166lyo7jceb0HMfuG93Zl4bFhfOLGIZFV1ecgS2E4ZIksv9AH6omUyzWeO3ENMOtgPnN5oefJDNbhNL+Z4IgENZVwvqdCZd4eFTtMpa7u/rUVpMFhZi8GQhUBJ2B0F3Igi+Cs/9AH5omUy41ePXENUbHOm7Zl4173/h3bKwTSRKp103Ov7HIxOROifdqzeDq1ey2NczD5858CwnNbjPXs8kbc4SVDJKgEFb8xLUkikxm0kxmdiayi2aT5XqZnmAUx/WQFYlDj+zBMmwkWUTbZa4IAuy7d/SmarQRVSMgy9iuiygIlI0myUCQnkgUXZZxPf/vcT2A47p0hXaKJBlNi+xC3l/XdinV9zyPtVqNqVy+rdmWiu4eiBREgVBEZ3WxQHoXknpJEhnpTjHSvfnuJSO+nxkPB4iHW0Gt64AhRyf6tv0/Ew+TiW+i6AY7Ewx2JlhrVrhcWWG6uIqh2+hdAlk5z0oTJqPdXC6vsC5XWK0UqDsm0c4Ad3V1o4oyQU0lGb11MHx1pcT01Cq25dA36JPtbzXLdTm1tDvlxVZrNCwuXl7h6OFB5Ova8N/DlmKi4CtJJ7IGhdUSF8xVOgbSTJ+dI5qKsO/+cZp1g+zsOs1aE6NhMn9pkaEDA4weGkRqKXUnk9tRh2PdKYY7Ns8uoiC0KW22litKouifgVp8Tv0ZP1nvn4uAVjDG54pzdwE5bLenV/6MdXMBWVBxPIuylaPuVHA8CxGJyei97Inc21axqzVMitUG9+ztR5ElPMlkqnyGpNqJgEhK9VHoBaOO6Tgc7xhuCze4rl9a6TgeAe3bJ571HRU4EgSxJdXsZ6UrdgmnxV1guRaOZ6OJOqIgoggqPUGV/tCQrw7V+l5YjhCUbq22Br6Dej0B9u2aKAjE1DuLXDueR75aZ2opj+26JKUoKT2KEBAIaSrd8QgnpxfJujVWvCrpSIhctU5fMka+WkeRRcp1g/Vqnd5ElEw0RL1g4BYdukaTTF/JEo0HOf/cLGN7upk/kyUcDRCO6Jx75hqBkEZtsU40HmRkootSsQa6QyIcIr9QZm2lRKVcJxIN8tzfn2dsspvsUpFkJkJMDfBgxxgPdYxtAwq4nofdWpxtz0Xw/MDaRulY3TYIR3VkWSKZ3u66WabD2lKVvnSc1dUqqVCIM+cW0VQZw7QZH+pgNJMhKuk0mxa1skF13SAYUOlPJLnm5uiOxskXa0wv5Kg3TEYG0rxxZZnxwQxhSWd5tkSp0GDOKNCfTnJyZpGPfekbjHQk+f0ffy/xVg32ynKRcqkBnkdUDVBbb6I7MvmlKplohGbT5tpUlkbdYHW1TDweJLdUIRYPsnApR6NuMHtujUxHlLXVMuGITkTUKS3WSOthZFPk2tUsnutRmKvQN5CiKja2zL/t8zAoqwRllfnsFH/y9Gt83901HpoYan++Vq1huy6aLG8bj43v3ciajs1XF8+1OZiOpQf5waEjxNXdN4yWG4Uk+GpjD3SMMhhKcb60jOnaLNVL2J67TfXvu91EQSChBSib2x3YYr7G4pz/3OKJEMP7O1gzitieg+u5OJ6LLIgE5QBFs0rTMUhrcUzXwvYcBCAo66TUKPlchexy0W8rGaJnYCf571aTJJGhsU6CYZ1ysc7s1CrVcpPEdRB8RZXRddcnHMRX7pJl/4CjyBLhyPYssCAK7RI75yYlDt19yRsSfINPQj046mfsLdNmaT5PWDqAIzSQhVZAzAPbs9ClILoYvGHwx8MDIex/gY3AiUvNzmG4NUJyGl26MRLwRlZcL7M4lWVoby8B2V/7t46xbdlMXfKJCQVB4O57dx4kAUbCmfY1G9apR7f9LaPt7oAaTYtKqUGjbmKaNo7t+JxYuSpmiyTXdT3WjTxRR6NiV1FbaC3breB4NSQhiCLdmgtt22+7NVw3iyiPItBa85aK5HM+R5os+xK7N7OVxUI7ENRomBTzNTq6Np3jwnqVYt4ni48lQjfkfYKWStRIh0/cfV3g6HhmmKQaRJVkxoMZqrk6Q2OdeAIs1cs4rktfOIYoCJiuzfNrV9FEBQG4OznwlgwcNRoW01NZvywzFd4RMLZtl4X5HNVKE0GAVDrsl4Vt4e44cmSIPXt7OHd2nr/6y5fJZMLcdXiQYHBTUtrzPKpVg9x6hUbDZO8uCOpwLMieu4du6759ktUtqjCOS6NmUivWfUf8TWwbzbqBovoBb6uFMu7Yooqmqj5niWM5SKJAIhXesU5uNcdxefbrFzl27wh7t6hWWpbD889d5tRrMwDcdXiArlbAS/k2oUlkWeTBhyd4+u/OsbxU5LOffplUOkJXV7y99ti2w8nXZnjqq2cxTZtj942y/8Dugime5/HNZy9x7L5RDhzoQ1Y2+/LiC1d49cQ1PA8OHuqnp3eTW0gUBfYd6OWuQwO8dmKav/38a3R2xThydGjbnuF5HvW6SW69QrXaZGKym5HRDpzWoepbehaCwlBoHMdzbsmZGFFihOVIW3FIEmUCbK65e/b1cPeRIV55eYov/M1JurpiHD02QuS6vjTqJustRNHknu5tqPDxyS6GhtNMXV3lK18+zYGD/YyMdiBJYkuJzebF5zfnR/tZCgKm7WCYOxUs3womixpdgT1tKfXbMU2SSGpBRqMpZNH3R1RNQb0JD5QgCCg3+Tyq6Rzt9g/NG+vcaCKJJsvtg/P1AfDdknzFXJXCWoXwTZBIs4Uii+XyLeelAIitioeN0vb/lhZXgxyI9bbPZI7n+QgaQUSXFEzXwXRtRAQQQBGkO/bdu/uS6AEVPeDzx11v5WaTcyvZW7ajqjKhkHrbideJe0axDAtBENBCGmOHhxAlkUBEx3VcIslIOxHSM9aJFtRuGnRUJOnO1vHbuvTWF1XsHEuNnYIOqqgzGbmPxzp+mKC8KX8mSSKlapNTlxeJBnWO7OumPzhJQAohC5t797niMgWjzhPB/duAAk3DZrQ3Rb156xLg27XvqMCRKko4eD5pKR7rxhpFM0/dqSEIIoqgkNE6cXGRkKg5VSRBwvOgYpeQBIn+4BABKdjmB3wzL+5u2bamaWG7LiFN+5Yg1kFNZV9vJ/O5IrbrYlgOEU1huCNJIhTgPkkioCmYlu3L50WCSKKIpkgEVAUn4WI7LuEWDFTTZZp1k2KuhiRLFNarxBMhOrrjrK2UiCfDRGMBysU6juPSN5Qmv1ZhdaVEMhUmu1ikVKiRz1URBV/+O79e8dvoiWMYFteqOVbMMmk9RMls0nRslJZCWFIumCe+AAAgAElEQVQLYTgWAVkl2yjjeh4ZPczeeJePdrEdrl3Oku6I0GxaTG5xeCRJRJFFVtYraKrMaq5CKKASjQRoNE1CQZ9fIRkLcTmf9SHH0QCpeIhwSCMRDbK8WmJhuYBtOyiyRKFUR1dlsusVVnMVOtMRH+6X8R3LzliEia40+3o7UbcsGoGgQrXaBA9qVYP1tQoDgyaapjA7m6NeM3C28FetrVWIRAKsLJdIJIKYhk1vX5JIRG9l4ERKxTrJZIjsSpFkKoyu+/0JhTXyudpN1U9uZT2xCFPreXRFviOHrWw1eKO4jIuHIkgcSPQSU24/AHp9sNVovatvJavbFgIC+nWbSn69imH4qJxkJoIdtJmtrVC26qiiTFqLU7HrDIe6WGisUbXqZJsFUlqMolnBA6JKkLgS9hXXWhw8wZB2ndzr7pbKRFq8FJBbr9Bs7NwIZFlCklUf8iuItFPvgk+wLO8CLd6wm23gvnLMjeerKInEEyEUVcYybSrlBsXaKpJuUidHQN44pAgUrdwN2wEQBBncdZCibCzkgiAgiQoxtQdNujVfxW4WCGlEk2EUVaFhW0xX8oxENoMM1UqTysaYhDXSHT5vgrA1C4WH5bQQFPgZHd8HE7BcB1XcrlS5ESyan1nn5W9e4vL5JdZXK1RKdZpNC6vFn2HbLt51ZLUVu4Lder8EwHCylI03iGoH7jhwJIghBE9GaAXiXNdjLVtqq1udPTnLL/2TP73t9hzbobF1/nlQyNfahLvxZOimhwKAZDq8a2DuYmmFA4keqpbJpfwK+ppAs27SP5phtVFhsVaiJxhlOJzhl/e/m/VmFddzSWlhwsru6nbf7VapNPjEH3wNx3EZHEwzMJgmkQwhyyLVapPZmXVePTFNo2EyNt7FXYcGcLdkQgVBIJWO8JM//TD/7g++xuxsjt/62JfZs6+HwcE0obBfelDI11he8sm2DxzsY++vbQ8cWZZNsVjHshxMwydFnp/N47oetu1wbWqVaDRAIKiiqj5MPxYPbqIcRQFF2+B+2b2v2dk1ps/OISsysXSEwmqJaDJMtVgjnAizfC1LPB1l6GA/107PMnp4mMUry+1AdL3coHesi9xygWRXnHKuSq1Uo3Mww9K1LKFokIljo+3fiyeCmKbNb338yxy9Z5ihoTQeMH1tlee+eZl63aB/IMX7PnC0XY717TJBEBgeyfAjP/4An/yTZ3n5pavkchUOHR6kpzeBZTnMTK/x8ktXyedqjIx08GM/8cAN0amxWADHdvid3/wyR44OtcvvZqbXeO6bl6hWmvT1JXn/B4/uUHCLx0P85E89RL1mcPHCEr/3219hfLKb0dEOwhEdx3bJ52tkV4osLOQZGe3gf/tXH9hWrmrbjq8sbDkYhkWjYTJ9bbW1LrjMzqxx4fwigYCKqskoikQsFkRVfbS6Kvgo+fW1yrY2rl1bxXU8PM9lbmadSxeW0QMq2kYb0eC2Mu9YLMiP/9RD1GoGb5xb4Pd/56uMT3QxOt5JpNWXQqFGdqXEwnyegcEUv/JrH2gHjgRBoKMjxvs+cJRP/smzTE+t8rH/6wvcc+8I/f1JDMPm2tQqr564RndPfFvw2zD9DL/Hmzt/fKebKEjE1B6ats1apQaUdlyjKzLJQKBd7uS4HlFVJ6JuT1yZjsNqtYbneWRCIVRZIl9v8OrCIudWsqzVaj7XZCjI4Z5ujvR2E9V1REFgfaXE2kqRoYku9IB/gA4oiq/86LjMFYucWclyZT1Hod7A9TyCqkJ3JMJwMsGejjSZUAhFkXx12FyVRMYXknE9D8O2aVg2dcvkmalpHyklCqxWq8wXd/ZZFkWUhks+W+LwQ+PEUpuBTNt1WavWcDyXuB4grKnYrstSucKLs3NcWlunapjoikxPNMpdXZ3c3dtNQNm+h65Wqxi2HxDPhEI7Ah+KKBFTfRR8qWnQMPzEWDqkoUkK2g2AEhvXT+fznF1ZZa5YpNoio47pOt3RMHsyGcbSKZJBnVA4te27luPStC3qlsULM3Nkq365a8Uwdn1WAEFB3pWPbeNeKsYuyENNIB7Q0VUVIbS514uiSCSxu3/qeR6m4zBXLPLK/CKzhSKlpoEuywzGY9w/2M9wMoEuyzd8X3P1OnXTQpdl0qEgpuMwlcvz4uw8c8UiDcsmrKkMJxLcP9jPQDyGJu9+P0OhgzieTcOp4Xo2uhQirfUxETnGSPgQoqtTajSJ6BqiIBDQFLpTEZqmTSSk+jxwWKxaCziexb7Y/YDPDblYK1KxDPQNJL0L+Uod03awHYex3jdHa3O9fUcFjmJKmImIRlDSEBEZCA7TG+inYpfRxQCqpCEhAV6LBNNrw2N9tRgRWZQREKg0TYrVOv3pO4dEz6wW6IiFCW2BT76xkGWlUOE9R3av17ztPgZ1IlGNdCSIYTutQwb+YigK9CajO76zG1HbhoXCOkcfGEOUfOfQth1kWWrJeu9DlES/3cEUtuWgarKPDPB8gspoPIiiydx93wgbEpRb20imw1yqryIgkFRD1G2LnFEjo4eRBZHOQISCUfdRJ6IfvQ4rWpu/JBIL0j+UxjAsQtdJVsuSyP6JHkzLRlX8chmpdb+u56FIfnmNIotEQhoIAorsy9iLgsA9d/lEvgM9CdyWUoPjej6cD4+JkQ40RWZ8uKPNKD/RlebjH34XmiITUDcX0UQizJF7hv37kiUGBlNomkIsFkSSRVzHn3GyLDEwkEJVZRzHZWJPN4osYZo2oiQiSQKTe3vA82GMmiYzPNqBLPlys67jw3Q3+vpmbalUYTCZIH2HPAuO51Kz/Q1BFHwZ6zt5P8pWk5K1yUkVVXQkvr3O9H9vW6yVcFyPvvD2Z1uvG+31JhTS6A6kGAx1tlRCxHZZoCLK3CMH2wd+SZBwcRERW/w+EpZltyXjZVlCUW+dqdB0pa3+12xYu266PoeYjWNfQRCvR3zcOQH8himKtOshf9s1qoQkCVj4qCONBKJYQxY2IMcCkiCRUNIoonbj7J3ngCCBPMLGFiUJGkl1CFV6c+qJ4C9Jqq4QjOg0HAtdknG2rKVG08JplalpuoKkiMxXShSadVqIZB/dikda98e3apl+AKm1/gQUhb5wlKDi97leM/jS507w9JdeZ2Wx2B7zQFAlEg0QT4bQNN/ZnZtea6OOZEEmpoaJqTFEQcTzXDxcFClOQN4OW7+tvrtlBLEThA2uKY967dYlITdvc/PZeUBzS0ZrN/6V6+2GpPH1EiORNCWzzpmpefZ5HUSivoyt3HrPEKBum/zl7AmulLMI+Dx23z9wlL63IOJIkSU0TeGNcwtMX1tDVaUWBN/fUzbm1f4Dffzwjx6nsyvGYrWMJvmlxxvcBgfv6udnPvp2PvfZlzn9+hyvvDTFyVen28pCtuMHMFVVIp3ZiZqbm83xid9/irW1Crbt+FD4poVl+SXxn/kvL/L5z51AFEUkWaSnJ84//YV3MTC4cdAQ8BwXs2ntIDvfsPXFPEbdYObaHIGQTiQdITuzytjdw6xMryIrEh2DaQJhX8FyZWYVx3FoVJpkZ9eYPDbK3KVFEpkYpfUKhWyRRGeMiyeu0qwZDB8cYCvcMZEI8b4PHOXzf3WCJ798uhXk8jBNHw04POwHdkZGOv5/CQSIosijj+0Fz+Ozn3mZy5dWuDblj7Hr+WMrCAKH7x7gh37kOKNjNy4pjcaCfOgHjvG3n3+Vp756tl02YZo2juMyMJjiR3/sgRu2Mbm3h5/5ubfx6U+9wMnXpjn12gxnTs/55Rqeh+O4uK6Hokjcu0s53Mpyid/7nSfJrhSxLRfHdTGNzb3ub/7qVZ788mmfEFiW6OiI8o9/9nH27d8MUGZXSvzebz/JykoJ23J2tvHXr/LkV7a38ZGf+R72H9i+Lo5PdPGzH307n/qz53jtxDSvn5rl7Jl5HzGEh2P7fZFliSP3DCFeV3opigKPPLaHSqXJZz/zErMz6ywu5FFVGdf1S1fuf2CMd7/nEJ/4g69RbiUdZElqk4S/le3C6hr//ItPYjk7icMfGBrgVx5/lKju+/wJLcBivYTjuttAGfPFEj//N1+iYVn8+jvfTjoU5I9eOsFz07PULBO7FZBTJImIpvHY6BD/6NhRJjNpSoUaM5ezdHTHt5VL1S2Lv33jAp86dYblcoWm5Sf9PUBqoQV1WaY7GuYnjtzNo119CKLQTnQYts0r8wu8PLfAVC7PXLHUDn7Yrsuvfe0fUHfx3YeSCX71yHHCsSDzV1exDJt4C+m4Xqvxz7/0VXK1Oj97/Bjft3cPX782zX888Rrns2s0bKuN2lMlif54jN/9vnezt2N7zdqv//03OLW4RCYc4rff+y5GkrvvdY7r8ZnTZ/nzk68jCiIff887uH+w/wbXulxYXeM/n3ydF2bmKTWbmI7/3oFfqqVKEkFV4Yk9E/zPDz1AdAt/1eX1HM9cvca1fIGpXJ75Uqkt2vOF8xf5+tS1XX/33zz+NkK7lPs6nsdnT5/lP598fdfv/cLDD/ChA/va//c8X3BBEHZXPyw0mnzh/AX+4vQ5lsplDNvvmyAIaLJM1+mzfP/BffzAwf2kQ7v7l3/04gmeunyFQ91d/Pq73s6TFy/zpydOsVTx23M9D6nV3mAizk8ePcz37Z1EVxTqlsV6s0ZnMIwmydyfeh9HE+/AxcV0LYpmE0VUSaoJNFFnJl9gpVTh2HAfoiRRb5gYlv8b+XKDfieKKIsk1A6aTrV9jyFZ5R+WL/NGcYWEGkQQ4Giyn+HuJIZl05XcXV37zdh3VOAoJOuE8Bca23FZLdaxHBfDEulNaSiySq5cA0FgvVwjGtToTkQpVhtkS1VCmkJPMkbVNHnm7FWmVnK8/dA4Q5kE4YDGWqlGvlojHgrSGQsBAvlandViFVWR6UvFWCmU+dQ3TnLfxADDnUlGOpNUGyaW7TLc4XMCeJ5HzTBZzJVRZIneZBTX9chV623pxL5UDF3d7jxbrsO1co60FkKVZKqtWm5FlCjVmliuS1CWsVr1upIgktKD23g4YHsgSRD8mtrdbCvsVgFaFQqoW5JVcsS/5kboF1mWmFQ7kQUJWRTpCcZoOFabbFwQBJLXlTldPzn3HuqnWm4QDGs77l1T5XZQ52ZygaFdIJGB26yh39o3WRJJRXYuDqIoEN4S2No4+FxPVrz1s22/seU67bpb3e3ZXp/tu1NLh4KslCtUmgadkdtHYMii1ObislyHhVoB07XbqkQ3M9OxeS57hfmaL9UZVXSGw6lvGaL+nWY9wSjL9QrydcT0wlY/0POQRQlVUPC2dF8WREzHQUBCbTnbHrThwBuXbuVb8DzvhoeorbbRFvjSzTd67IKgI8kTeNxZYMDDxLKmEcQAsrSdXHWDWO+m33fbytIthJBMzrhGWOkkxoCveIOALgWJyDchuRbjfoWaoLY7absGLg6aGLpxwOkWtimp7ZHSQ/SH4/QEY7tvpp6H43rk61VKRhNBELAcp03iCLBWr5EOBDEcG0X06+jXGzVSepCgomJZNn/xp8/xN59+EcOwicaCPPj4Xu59aIL+oTR6QEVRfdL89WyZj/3K51he9MUWdEmjN9DT5vsDDwG5pUxy52UygtSD6yyAZ4Gwfb8QBLj/kT28/4fvu+32FEViYCSzXf10y7jczpHpRoHIsUiaz8+eRhJEHhwdJVCXCEf9e46qOqMt7qc3yos0bIuf3/N2dFHm69mLPLt6mR8dvv+2+/HdYrF4kP/lf303b5xbYOrqKisrRRp1E9f1CAZVurrjHLyrn7sODZBMhX1eCEXBuk64QZYl7jk2zPh4J+fOLXD2zDyL83mq1aZfTp4KMTLaycRkFyOjOwmWBUFAD6gkbpNsWNeVbeuUIODLcGub5NiBoMrEZBf1ukk0FiCoy6R6fNXHQFjHMmx6x7pJ9SRpVA1CsQDZ2XVEUSS/XCAYDlAvN3Bsh66hDMmuBNVCjfXFPAgQjodIdiZYncuxvpDHqBnb1jLTdDh8ZJBDhwd45eUpzp1doFxqEAprHDjYxwMPTpDoCNN0bYLezmCnqimMTXRRLNaJJ0M71uXVqo/mTgWDrNfreJ6/f2/dN/WAwtvecYD9B/t48cWrvPr6DHbNQlVkOjqjHDk6zKG7B4jHQzcN4JumzaHDAxy+e5BXT1zj9Otzfl9CGvsP9HH8wTH6+lJILeSU67nUnSYiIqZrIQoCPRNRPvrLb+fy+RVOnr7GwkyeSrWJJquE4grDwx2M7+liz3gvpmDiOf4hTBX9sdZ1hXhLtappWMzP59t7pO16dCfDbQRaILCTcwagWKpTrjTo6Y7fUOFp67Pb2obtutRME0kU6RlO8nO/+A4unF/ijbPzzM/myBVrqLKEGlbZN97NyEQnY+OdND0H2zR9nhPPQxJFZFXmve+/m8m93Tz/zcvMza5jmg4dHVH2Hezj/gfGCAU09u7vZWW5SDSi4+HR3xmnN3NnqNDvNpNFkYimUTNNTMehYdmUmk3/gNtC+GyY4dotrtntbViOw0qlSsUw+OKFS1zL5Tm7kqUjHGI4mSCiaRQbDRbLZXL1Op8/d4HVSo3ffOKdCEAkFmBxJkc4FkTTFRqWxSdfPcV/eOkEdcsiGQwynIiTCPoy5YVGk0K9Tq7eYCpXoGFbaKp/X42G7y81LJsvX7jM31/dDHhsFcAxHWdb39p/tx2SnTFy13JYpk0pX2vvj47r8yQtlspM5ws8fXWK//PpZyg1m3SGw4TVGAgCNdMkV6sT0TS6IzsD97l6neVKFRfaQbXdzMOj0jRYrvhrj2Hb2K5FwykTkKJtAnrbcXjq8lX+7fMvMZVr+fWaRlck3EZv1UyLQqNBodGgMxJBvw5J8+rCIv/ptdfbz8h07Pb+77guzRsoEoqSsOs5SMBHrIVUFdNxMB2HqmFSNf3EVM3cibQvrJaZv7TE0P4+4hkffOF5Hrl6g4898yxfvXQFw7bJhEOMpsLEdJ2KYZCtVJktFPnEcy9xPrvGr77tMTKhnRxzpWaT5XIFWRT53Wef50sXLmO7DoPxOMlgAA9YLldYKle4sLrGb379m0iCyAcP7CVbr3BiZYHjPQP0hWNoUgBN8g/jJbPOVGUN1/PYH/dBM57ntRFyQDuRMb2UY2Kgg6AaoObYLDevEVM2EUQJLchPj2/3fTq0CLMzBcb7Mm/ab97NvqMCR1ut1jT4nS88y96+DuqGRTyk80MPHuKvX34Dw7JIR0P0Jn042J8/e4qQrlJpGByfHGAgneDy0jqrpSpXltbJRH2uoC+9eoFYUCdbrPLB+/YjiSJ/+eJZ0pEgYV0lGQqwkCsxnyvRsZpHFAWGMgkqTYNvvHGNdCTEvv5OaobJ5148S9O0sWyHgUyc0a4Un/jK89w71k+uUufgYDfvODy+rU+eB2WjSdU0kUWRqVIOWfQDMqoo0ROKUrNE5qpFNEmmahk82jPKxcU1njp7mfccmmS0I8XllXVemZpnvVonoMjs7engockhQteRX5m2w7XVPCdnFpnPlxAEGEjFeWB8kJ54tE0Mfb01LZvp1Txn5leYzxdpWj5Bd38yxl0D3Yx1budj8YBKw+C1mUXOLWSpNg2iAZ27+ru4q7+LWFAneR3pnOd5VA2TNxaynJpdIlep43jbF0JFknjPoUkOD/ZgOQ5Pn7vKlew6H77vLkzb4cWrc0yvFfA8j8F0nAfHh+hNRneoAayWq3z6xdMU65tImYmuDB84um8b6mirXVhc5XMnzuF6N16cP3TPAQ70bc/ceZ5Hqd7kzPwKZ+aXKTcMIrrG4cFuDg30ENHVHYuS53mUGk1enprnwuIqhm0zlE5w72j/ri+7YduIokhQVcnV6ze8v90squjsiXVxYn0GF49nspd5MDvGgx2jaOJOqKbn+TLjebPOU4tv8MmrL2C6Pl/PwUQve2I7VZO+2y0oq8xXff6hjkC43b9QRG87PpVKE8t2mM/nKTQalIwmYVUlpums1WtUTZOwqjKaSLJWr1G3LCRBYCyZoiPko0z0gEqjbmKZDs2GReQWah/1mtHmIQqGtFuUnTUQxDt0XD0XwzqHKMZ2BI6aTV8q+kY4kg2S1A3ZYk1X0NQAIS/t81C0EIiSIKOK+s3LGz0bvCZbU5OSION4Fk2niiqF39QmKCsSiirTqDZRRQldUtC2QL31gIrcKsdrNkxcy+Xerp3ong1Osev/dTyXkmEQ03Q8z+PqxWX+7kunMAybcETnI7/wvXzPO+9qo8u2vjdG09qWLWs6BnmzQEgOtojEBSRBR5GSb6rvnlfDR+iaCHgIokA4sjnf9IDC4WM+4rJhWjQsP7u/gYj12/AdQQ/f4S/ZJlYNorqGJIrbymfqNfOWCh6N+u6qcRNRH9mhiTLBokit1iS3VuZwMoQALYfKX5cCskpY1trE5CWrecfP5jvZbMuh2TAJhjQGh9IMDvmO4vWPbfMxb45XKhDkewaHW59vT9jEEyEefGiCBx+auGFbu43d8EiG3/j4h++oD9c30zvaSe+WoNToaAcf++0f8YlsW3LRW81zPVzBz8Z3DKTbfxNEge6RzvZL6OH5NAaCRLo/heu521Rp69UGoXgQ+bpEjuu4CEDfQIr+gRQf+oF7t927ByyUSr6fJkk0LAsPPzBnOA7BuMav/fqHmM4XQBBwBagaJo7nElJUruRyrNVqPDE5yVTez8h/YO9eGo6D5boEZJlstcpqtcbBnk7e+b5DHHhshIlUCst1MByHgKJgOy5lo4kuyz6Pyy7js1Ey1defpK8/yfs/eM+Ocdj6vbrT5FzpCoZjoksaVbtOQNKIKREGDsdIHpgkLAepWDWCsk7VrlMwSyQ1layXZbrUJKMlCUo6vYEOenoT/Jvf+MF2+/MLeX7/E19jYTFPudSguy/J//5/fIjOjtiO+9qwVDpC/2gHnirxr/71B+nuurWS5tY28vUGry0tElTUdgA1OhrlkfGDBBWVhVKJ3miUqXyeoUSC6UKB08U10lYN03boiUa4tL5OQJDR6gKZeBghpPDgO/bxiCRSb5qYloOqSFycXyMc0PjQj92P7ThMDHZg2Q6SKFKqNuhM3TkX33eL7e3I8Kc/9EFqpkXVNLm0ts7Hn/kma7XajmtXG1Uc18PxNnnXrh/3v33jApIo8t69k/yjY0cYTSVRJIm6aXFqaZmPPfMsl9bWeWF2jmenZziipTAaFk110y+azhf4i9NnqVkW9/T18PMP3M+R3h70jX3dspgrlri8luPy+jqPjQyjKhLhaKBNAxBSFX762BHev38P4K+1f/zKazw7PYMkCPziww8wlt6J9AkqCpGARigWIJGJEgjvTHZbjsPLcwv83ZUpMqEQP3f8Xh4YGqAj5HM6rtXqPgm3521Tpft2WNOtMlM7y2j4CFKrMufk4jK//Y3nmC+VCSoKj40O8/59e9jX2UFM13E9j/V6jdlCkctrOR4fG0G57tz4PaMjjCaTbZ/uhdl5PvnqSZqWzdvHR/nwoQO73k9/KEp2sbRjHoiCwA/ddZB3TY5TNUxqpsWTly7zH15+ddd2bMthbSGH2TC3BZlNx+FTJ1/ni+cv4gGPjQ7zPx2/lz2ZDLoiYzoOV9dz/PErr/HUpSs8dekK/bEYP//gfTtKBDdsoVTmL06foyca4aMP3MejI0MENQlRkMiWa3z69TP82WuvU2g0+fTpMzw6OkQqEGQwmmgrY2+lwpFFCU2SW+rmoXZCeeu+pcgSe4e7uHuyn4ZhIYkiVaNAtz5CcAvXZ0wJcDTV3/4dy3XwPGjGbGZX8nQmIkRD355S/u/YwJGHT6z4toNjhHSVP/rayxSqDUzbYbw7w9vvGgMBTs8sI0siH37wEFMrOb5x/hoHBrq4Z6yPhfUiH7rfn7R/9eJZ8pUGQxk/qPT6zDIC0BkL8yMPHfJRAbLEkZFenrsww/ceGmek04dX96Vi3DPax/y6f5hcKVZYypf52XfcT6ne5NPPvU46EiIVDvF9x/axmCvx7IVpHjswsq1PiiSxJ9FBRPEPTnsTnYj4vBlSizPI9VyGo0kkQcT2XMKyynNrM3z+1TfoiUc5M7fCn7/wOvlaHdtxsRyXye40dw/1bAscWbbDl1+/yH967iTZUgVNln3Ys+3w5OlL/OPvuZfjYwM7giw1w+SzL5/hc6+cY61Sw22puVmOgyJJvOvQJL/0nofbHEsAM2sFPvnN1/j6hWu+bKEi0bRsPifLPDw5xEfffpzOWHi7w2Ja/MdvvMoXT11AFkXioQCVhsFCvoTrefTEI4x3pTFaB1HH9Tg5s8RTZy+TDAX5xsVpLi2vIYsiddPC9Tz+7txV/uk7HuDuwZ5tfbIdl8VCmflckUKtwVKxzMOTwzxxeJLADY7ChXqDkzOL2zIN0CKurdSoGibHhvs40Lf9gL1UrPD/PP0iz1+exXIcVNl/Fn/96jm+98A4H3nsGB3R7QihUr3Jv3v6Rb5y+hKm7RDW/ajzV05f4q7+Lq43SRBwXId0KHDTwNZupokyj3VN8NTiebLNMtlGmd889xRP9B3kQLyHlBZGbZUINh2LglFnpprj+dWrvFFcpmr7WZmMHuHDQ/cQeQtyipTMJlXLoGptR+xslBU1GxaFXJVKsYEel4gH/PfZcV2imkbN8gPDIUUloqmsVKsEZJmlSoW+qO8wh6M60XiQQq5KvdakXKyR6dxZprrVcqsVms1NjqXr1Sy2miBGW6Vqtx9kEAQFsBCEne2WCjVM077hb7qOSyFXxbFdv/Q2FkBWwDZMFDYRiYIgkFBvo85ajGzzLE23juFUCNwht89WU3WF/vFuJFmkZDZZb1bRRIlg2O9TKKwRbqE3G3WT3GoZ9vXsOKAJN/hXFiRSAb+vjuMydWmFQs53ovcc7OOhx/ehqNKuB75mw8S2NjNzuqShiEo76+ThYDir2G6tXQJ5R+ZVARvXmUYQo4iiTLojgiSJOI7LWniGoTsAACAASURBVLaMadiomsy5hSwXltcIqAq6LGO7DulwiKZlU6w36IyF/fXf86g0TSa7M3TFwsQToXZ75WIN07DbzvhuVixsciJttZfXZ3yFENdhSEgw1uXPF0EQqDsWq40q47EMg+EUz2Qv8idXv4kqSuSMKu/tO3SnT+Y72nKrZeauZjl478i2d+92YvW3CuhvKkjd/v0IWwKJb8Y2fnP+apbuoTRyS/HGw2WhkSWqhLFcm6ZjkFRj5MwiUSXMXG2FmBImrcXJmyV0SSMsB3A8X0ilYFYIywGmqguMhfsRBIGyVaVbT7PSzBFRgsjDGuF0lMF49zbS1I0Dz42eh+U4vL68Qlc4TMUw+ObMLEFVYV9HB2dXsmRCIfZ2ZHhhfh5JEIjpGtOFAjOFIkd6euiNRim1+Dp6I1HWazU84PzqKlfWc4ymkiyUyswViySCASqGwfnVVQbiMZ6fnaNiGEQ1jWy11uY2edvoCNJNBuJ2x9b1XIKSjiaqKKKCKio+klZU8HBJaTEEBBpO0y/FlTUkMUlajbNq5IjIQcDD8Rw8/JLtrb/Z0x3nX/7yE1QqTf79//sPFIp1n2rgJqgpWRa5+/AAQ4MpohH9liXS15vtOsR0nZDqE+8GZKWNgnA8l0w4hCbL9MV8Dr14QPdltIMhco06giDQFQ4TEBUaLX9HEkUaloXYIjwOB1QiIZ1SrYkApBMhyjWfmN52XCzbwbDeWgm1600WRZLBIMnW9u6X6uyezMroYSzXQRElXLeE4+ZQ5IFt19iuy/HBfv7ZQ8fpj0XbczisqTw4NMCPHznEv/7aP+B4Hi/NzXNkIkU8FWJgrLMt3DFTKFBs+MmDJ/ZOcv9g/zaUcFBV2dORYSKTpmmNoMky5XyNSCxIZgvx/VZlONfz+ML5i4D/Xh1o8Q/tZo7tYFsOK3M5eoYyOxOxwOnlFQYTcf7l449wtLenTclRNy06w2GGEvHNfX+Lfy8I3xolhICA6zk4np8UMh2Hz545x0K5jAA8sXeCf/bgcToj4W0IvrCmMhiPc3xwAEnYSXfQE43QE90MYKxV623FyZ5olOOD28d5w0rlOguWL+Cy7T4FAV2R0ZUwHWH/+V9YXbtxvwSBeCaK53noW6pGrqzn+OKFSziex0Q6xS8+8iB7Mpsquboss7+zg194+Dir1SqvzC/ypYuXeHR0iHv7d6cDcD2PiKbxT+47xvv27cHB5kp1hqgcpj/ezU/dczevzC9wZjnLbL7IbKFITzxC07Fo2Bau47EwvUpnbwItoLZ4MWV6gwkiio7ruuiyTF8y1h6DWsNgPlvk0SOj/MOJK2QSIRJqJyVrnaZbJ6z4gfXpyjqrzSr3dwwhCSLnSyuUGk3GWmIutbcqOfb1pikyQV0lrGuIgo+gEQToTkR8HhvPo26YhDQVXZWJBjQM025D+La6pJWmQSykk4wEec+RSQYycf7+zBTJcAD1JqSvu1nTsFEkiaCu+hlYz0eBdMRChHSVgKbgON4Op1gAdFkhIN+5kpvtunzp9Yt4nscTh/dwz3AvsiSyXqlRrDdJbeFjcVyXZy9N87tPfpPOWIR/8d5HmejK4LguJ2eW+JNvnOD//uLX+a0ffjf7ejcDH03L4jMvnebfP/0ynbEw/8MjRzk61Isuy1QNgwtLa4x1ptoBKs/zyNca/M5XnuXlqXnefWiS9x/ZR1hXKdWbfPHUBZ46ewXbdfkX7320rWDmeh4vXJnlU8+f4kBfJ7/wrofojkdomBafeekMn37xdQ4NdPOrH3h8W4AKoFBr8P99/QSHB7r5jR98J+lIiKph8OkXT/N3567wyWdfY/wH09ukYLtiEX7tA4/TtGxeubbAb3zhmVs+78ODPfzbn3zfttSuB5yaWeIPnnqe/lScvb0d275TbRr8wVef4+sXp/neA2N84Oh+IrpGsd7gv7x4mr8+cQ7X8/ilJx5pE6c1LZvPvnKWvzpxjsODPfzo8cP0JaOUGgZPnr7E3568gO36B8r1Yq2N6OjWwyBAMKywlt+sc3VctwW1hmBAIRYO7Mg2H00N8nN7HuUPLzzDulFlvlbgjy49iybKRBQduVViZbgONdvAcjcPtJIgsifWyUfGH+bRrom3HNoI/Pe0KxjZ8Z4mUmH6h9OUTs1RzNc4d2qWd77/yHWlGAIDsdi2/9/XF8T1PEYajXYmI5mO0DeQYu7aKoV8jZmrqwyPdd5QBcK2HK5cWKJebSKKAsNjne0gx04TAQfXnkGSd9+0dzMPG0FQ8RXZto/r3PQa1UqTaGx3Tq1azWircqmawsBIB6ocpFs81DpM3ME88QrgNEF227ehikEUIYAkKG8acqsFVLTW4XupViJvNEhqm/2RFZnxfT28cXoOz4MTL1zh3ocnblkqsZvZlsN6tuzLhcsiA8OZHRxvG+Z5HotzeaqVTbSMgEDFqtChtYImSKhSEsstb16z9Zm23vkbm4TrrCJK/WxwXfX0JUl3RskuFVleyDMztcrEvh6GM0mU1oE+pKkEFbm9Dtuu03Z0bcdlrVIjqPrlO8l0mGQmwtpKiULOVyAcmdgZ+N7o8/SV7LZg2YYVzQb74t2UrCb1RpPcWoVMZ9SH35sGTccCPDr0KD89+hDXqms0HYvhcJreYGLnj30Xm+d5lPI1n+tFcSnlq9SrTQIhjUbNINkRRdNVctkS9WqTVGeMcCxAvWqwvlL0SWe7EwTDGqZhs7qQx3FcEukIgbBGOV8j0RHFsf22E+kwpXwNURQp5qqkOqNE4kFK+RrF9QrBsE4iE6FSqlOvbN5HIhNFD7buo9Ik2RElEgvyX9l77yi5zvPM8/fdVDl0VXXOAUAjZ5AASIA5SSIlUZGyZWkkzzqOvTO7mvHOeObs2OsznvXIHnu9TrJsy5ZlWYmKpCgGMQMEQIAAkYHOubuqunLVjfvHrS50QiKpHVrj5xweNrpvfferG77wvs/7PHPTGRzbppivUFcfIhD2MTE0x9f/7Fnu+8gtxBvCNFVt4TNGgYptMFNO4ZM9TJaTzBs5tkT6SOlZwmqAOX2e8eIsjd4Y83qOrFmgaJZp9MaJqEHmKhn6Q10Yjsl0OUXBdAMep7ODBGQvmkclIxWo58atiTVZpi0SRjddhlBzOERnNMrIfIaWUIhMpYxPUVmbSFDn8xLxekmXSpRMg5H5+VXtyXXTJFksYlQFcl3RWR+9sRhzhQKXkknmy2VKhsE9fb186fUTNAYD3N7VyXODQ1iOc2MmP9dBWA2yObp2xe8XxtiFoFqzr37JvwWCJl9iybGrQVVlGurDhIJe/AEP6fnrM6QVRebh926/uS+yCM2h0KplPiudtSIr/tbjLBs/luYgV7S3mD2wYGUe9HnY3t92w25R/zPAcmwafa4uqmVnKOtvoMhL188Rr4dHN29cEjRagCQEu9taqQ8EmM7nGUimKZVcgxFFlWtsXbOqRQrufvFq90ASAr92ZQwYH5xDSIK27vpVj79R2LaDqVt4fRqV4uqsW8u2+ejWzexpa6052hqWxcWZOUJeD93xuqqmmElev0TJnCDsWY9XWT1YdaNwHAdV8tYkAy7MzvHq8CiOA33xGD+/Zxf1wQD2IlaYjcMCB9S1oHf3opZtY9puklCRpBrx4eb6416LG9JpuAZcl0OH0qL1k25ZPHd5kLH5DD5V4f2bNrA2sdK5WAhBZzTKh7ds4tzMHJPZHE9duMSWpka8V2Ed3dLexr1relFlmUwli+3YBBSfOyYGg+xobeH01AzpconZfIH2ugiykNFkBUM3OHd8GFmWaO2uxyurRFQ/uSpT2sF1pSstKsfzair5UoVv/fgUHk1GJ0/ZKmLYFSp2EVgHwGhhnrR+paqmbJq8kRxHkxVURb5maePN4l0dOErmipwbm8GrqSiyTLRKs1p889viEQ5fGOXyVJLz47O0xML4NZWgVyOVLzGZzhEP+VnTnODYwDgdiSgVwyTi99HVUMeRS6NsnkkjBDREgiiy6142OpchGvAR9ftcdX3doGyYFMo6iXAAB4dzYzOkCyUCXo2I33UL+Eltoy3bYWRunt98/13ctbG35gjm4AZiFr+088UyXz18EoBfvudWDvR318QN1zXXMzGf5WuHT/KDN86zvuWK4OPATIpvHjlN0Kvxb997kFv7Oqq0RJcLfktvB0Isvf6vXR7llUsj7Ohq5dfu30886K9NqN0NMXJlnR+fHeDAum4e2OIGGmzb4fjQBAD3bFrDlvYr5U4P71jPd4+fZTSVQa+Khy+G7Ti01YX59Qf205Woq53Lr2kcGxzn/NQsc/nCksCRJAmCXg9Br4d40FcTGL4W/JqKP3ZlYeE4DuPpLI+/fhpVkfnle2+tnX/h70cHx3nh/BC39rXzr+7bT0M4UOtfLOBnaDbFc2cu88Fdm1jf4mYjpjM5nnjjPCGfh1+651Z2dLYiSe5nehtiXJyeY77gDgYjEylyxQq6YRL0e/B7NdLZIrIsUa4YBHwaqUyRcsVAVRXWdzcSCa4sf1KExMPtW4l7Anx75A2OpUYoGBVMxyZZydcmXneykPBICh5ZoSMQY39DL+9t30JnIL4kk/PThJjXTyVl1sQBFxCJ+tmys5uzp8YoFio8+8RJtt/SQ2PzUgH+1YIkCxoXC1BVmT23r+X4awMUCxVeeu4M2/b0EEsEV3zecRwmRlMcffUStu0QSwTZvqfnquLqDoZbkiRu3C3P/aCJ4wgcu8jyKMT0xDxn3xilubVu1f6NDM5y9uQo4Aq0btzajiRkNPnmxNsBUPpAKdWEnAFMR8dwStf40M3Bq6j4ZFcDZmFxJwTs3NvLk986RrlscOSVS5x/c4z1W9prC7wbhRDUdERwXLehq5VuZTMlDr90YYVYtemYSxa+tmNgOVc2XppHqT0DhXwZY5UgTA2OgUBGVtayUALY1FrHhi3tzE5lSM3lef6pU7R2xF32bMi9bzVW1aJN0mLEAv5a4DTRGKart6EaOMrzxtFB2rsSNSfKWlcch3Qyz5mTo0vciBawMdrMyfQ4ipC5NdaF7RjMzWQJxtznuT3oPoNpvUDBrLAr3oWEYLKUYaKYpj0QX9HmP1k41DZFxXyZx7/4AqZpUS5W8Po99G1qo7kjzmvPncHj1bBMi4ce28vcVIbjL1+klC8TiQV4z8/s5/Azpxk8O0Eg7KN3Yyv1zVF++NXDfPSX7yGbKvC9v3uJD372Dr72p88Sb4wgSRKbbumh0YzxxFdeRVFlcvNFdh7s5/hLFzB0k1Khgi/goXdDK6099Rx+5gxen4qhu/34288/SawhhKLKmKbNBz5zkMunxxkfnOPs68O099bT1BHHsE0yRp68KeGVPSQ8dUyXk2hCZaaSJqaFKZhlQqqfhCdCXItwMnORklUhqPjImQXqnSgRNUDWdN2L5irzeGWNsukK2EfUIF5ZW1ESfz0UdJ3LyRQl02RNLEZA1VAlybW5liVyFZ1MuUxAUzk97RqJTOcLNZeei8kkg6k0I4kMw+l5BlNp2sIRJnN5PFVL6KCq8Xp6gqF0mul8gaH0PL0xd459YXCI9mgEVZZQZHmJG+w1Hx3HTV5WqsLYOCDLAk1TkGX5SgmqDaWSjqJIqKpMpWJimrarQaIpSwLnju1QKhmoqowsS1QqbtuyLPB41Ldl+GFZNsWSXhPdl2QJv09ddex1HNfFTzcs1xlSuPfC41GXsOJM06JcMfF6VMCmUjGxbQdJWuivWDF3u23b6LpZc81UZAlNU9xSykXHO45Tu15CuPO6pik/tWujtwrbcTiVmuL2Zh+a0JBWKaOP+/1sa7m69IFfU6nzeZnO56mYJrptUymbmPqVubUrVkfE66FkGHz95JvEA37u6Okm7PUgWH1tZls2QhZUysZ1y6uvB0mWiDWEGDibI960Ojs65vdxa0fbkr1DslAkWSgS9C42DRHYmNXSpbduCrIAB4eIWo8sqdi2zYXZJHOFArIQ7G5vJREMcHRiHK+qIMDVF7ZMEv4ABV0nW2VN+lWVTMUNdDQHQ0wX8rSHI6yJrQzMXAsBv4cNa5vflrs04LraCkExV6qtUXKVCqcmp7Ach4jXyy3tbVcNbAkh2NnWSiIQIFupcGJiinSpTPNVAke72luJVEXfXRaXjSqpLLjrNgaDyJKEZVnkdZ3pYp50pUiTGaRsSbR0JmpjquXYVGyjds+FcA1WBmfT7tgju9q/ezZ0kM4WEZIg5AlhW0XiWjOlReLYQdXDqfQE6UoRj6xwOTdHfSBIdzRGJOgjlSu+7ed7Ae/qwFHQq3F+YhbdNLlnSwSPkqI9DkGvg2nNAgrNdTrbuwM8efxVIn4f929bgyRl6W6A0yM23zj0Mu/d1cvWrhipvMM/vvIUQa+fR/bsZlt3hExxim8deoVEpJmHdvQT9mj0R6O8+PolLg1O89COdbxydpjjI5PYAr78wyPct20tt3a08cPDZ1GE4K6tazCzOvVeL0bZxCwYxBSNfKZIl1SH1LgGgaA/0rRCcPdm0F1fx761nUts/hYiwYsxPJfm0lSStliEHV2ttRdGCIGmyGzvbOHxo6c5NTqFblm19t4YmWI2l2f/mi729LajLcm0C5RlrhO24/DawBiSEOxb01ELGi2cKx70c8f6Hp49c5nXhye4c0MvXlXBwaFsmK4uhrZUbNKjKKiyXItqr4a9azpoiYaXnCsR8tMYCTKezlKuum+8k8iUyvz1i8e4NJXkX961h31rOpf0u2KavDEySUk32L+mk0Ro6bVoigZZ25Tgh6cuMjSXpr+lHgEMzqaZzuTpbqhjU1tjjZa9cP22tDdzemwagPW9Tdi2jWnZSJKEqkg1Zy2rKqheMUzmsyUa4qEl4uy2XUYIlQVhXYl5bm9oY3usncF8klPpccYKadJ6kbwxR8UcJurppMHXQcJjsiHaS1eolYQnsMLS07BcUT1V/ulwMzJsi7DmpSWwVDhZUWX2HlzHi0+fZnRojjMnR/nyXzzPY585QGNLdMUC13EcCvkK6WSO1o74kr8LIdhxSy/9m9t4/dBlThwZ5PvfOMKjP7NvCTPFcRyy80W+9ZVDjAzMALB1VzfrNrZefQJwHDdQcJP3QwgvkuRftVStXNL5/jePsmZDC+1diSWLnvl0ge/+42vMzrhsmE07Omnrehu2n1ITQl3aB1komLZOxcoTVBvettCfwC3TWD52rtvQyqYdnRw7dJm56Sx/8yfP8thnD7JxW2dN0HUxyiWd2akMicZIjS4P7rPS0BRx3XUsm8EL02TSRSJ1/iXXrlI2eOo7xzn84vkl7To4+GQfUnW+cLBxHANNirEQzvH5NJchMl8kOZvj7MlR9t+1YfXSDuFHCC8sYn95vCp3PriFU8eHmZvO8vT3TxKJBnjwgztXZZYtBHzGR5K0ddXXRJgX4A942L1/DW8eH6ZU1HnqO8fpWdvE5h2dS579cknnuSdPMXBhatV745NV7mnux6eolDM6E+kMhXwZWUg0+IKMF9xy5oHcLMOFJG3+OoQkMVGa50J2ise6f3oCR4oq095Tj6LI6BV3Y7PrYD+Hnj7NroP9XDw1xtD5SRRVprUrwQvfP8HA2Qn6Nraxbks7yZksR547w/0fu9VlDIV83HrPRhLNUWYn0i7jq5phNnULHCgXddZsbmfTHtdp9fUXzzM9nmLXgX7mk3lOvHwB07DYeWAdh585w66D/Vw4Ocro5RmEgJbOBC/84A0unR7Dtm2237aOdVs7+PPfepxSvsyug/2cPHSJhz5+K4GqrpsiKWyJ9CGEwHJ0vJIXv6xgOCY4ZnVDL/DIMhE1jEeSaPdHUISKV/ZgOSCEyZpQMw4OHiRuiW8ioPgoWxUUoaBIMgLXkZWbWCJ4FIUD3V3YjkNAdcWcFVmiKRSiYpr019cT8/mwbIeWUIiQx0Mi4K8xzHXLZENDAxGPh6jXy7pEgrDXQ2skjO04eBUFv6ry4No1BDUPEa+X1nCIsMdLb6yOgmEQ0jzYjo1PVbmvrw/1BgITuVyZl165yNFjg0xOZbAsm/r6EDt3dHHv3RsJBlyzknS6wJ/8xXOsXdNIR1ucp589zehYmng8wN5b+rj7rg34qg6Is3N5/vhPn2HH9k7isSBPP3uaqaks9fUhDty2jttvW4vvBhwVV8P0TJbP//cfkkrncWxoaYny6796H/WJleyhwaE5Xn7lAmfOTTI3l3Pfk7YY9969kW1bOmpj9cVL03zpy6/w6Pt3Mj2T5fkXzzM/X6Qu6ueOg+s5ePta/IuMVxzHYWwsxSuHLnH8xAipdAFVlWlqjHD/fZvYsa2ztunTdZMjRwd57oVzjI2lXCOZtU3cc9cG1q1tvukSu59uOIRUT5UYa+A4ZVjmxpsIBGob8tUgCVHby9iOgzfooaO3AXWRwHJ3rI571/TxDydOcimZ4ref+TFPXbjE+zb0s7mpgZZQaMU6zTAsktPZq7Kpb+pbOg5zUxkyyTztvas7FzYGg9T5llYCqLJM2OclvigR41Q1CXEcVPnaMgY3AssxyJkpYlozum0xkEphOQ4+VWFDYwOmbTFdyNMddV2qpwsFSqZOczBUMwiRhaBkGpRME7/iltILIVYIZt8IiiWdMxcm2b65A/81ZBeuB8cBvawvSUIVdJ2htCstU+fz0Ry+toFQ1OuhJRxiIJViZH6ebKVCMyvHHY8s0xIO1cgHHlkjpkWRkGpil4s1oMxq6VlWr7h7dUmQns0RibmBwAW33rB25blXFZnehnhNgzhbKHNmcJpsoUxJL+P4A3i8NiFvrCayDdAfaeSl6cv8tzefRZYkVCHzie5dXBieo70hysx8npb423+O4F0fOPLw6K2biYc0cqUn0a1R7tmSQJLmqJglpGpGfV+/w63rohjmKA5TlPRWVDnDh/fVYzsSlv06kODgxiK3r6+gyAkUaQKbCgc3THHX5jhB7w4A5pN5rNE8B2INSLLEGy9dZE1HgoYmmVh9yC2PmysQKtvs1CI0tsdoiUU4d2KYrfE445dnmJ/NctfaLs4dGeKeYA/bb1uDLOVxnAqQxDYtXC0RP6BiWyMIKYYQARwnCygIKVZd7LsPqAQ0RkJLbBCvhrFUhqJuMJbK8G+/+sSKzVG6UKJsmOTKOoWyjieo1D5XNkz6W+pXCKBdDcNzabyqQkc8umKQlCWJ5mgIr6owkc5S0nW8quuIs6mtkcePneaVi8Ps6W0n6vdiWBYvXxwiUyyxu6eNiH91xkRnoq5mM7uAhcWcbV+hWb5TKFR0vvzKCZ544zyf2LeND+7atCR4B1A2LEaS89iOw9ePvMnz5waX/N22HS7NJLEd133Ptm0kWWYqk8OwLJoioRXUSCEErXXhKkXURlUKyFIIy84hhIplZ1E1G0WKIYTAsFJ4PDLxSALTnsd2Slh2CElopEpP4VW68KtrcQDdnERT2ohqUTZHdTZG2pHFBoTQSBa+jWVL1PkfQJHrKBuDeJU2ZCmMZc9TMYsoUhQhNAxrlnzlGLIUJeq7/R297v+joAjXeW5eL9HJFeq6EILetc187NO386eff5JcpsTT3zvBxbMTbNzaQe+6ZoJhL5bp6v2MDs4yOjyH5lH5T7/3MTzeZQulhjCf+OxBZqcyjA7N8fW/fYWhSzPsu7OfhqYoju0wNDDDoRfOc/LYEKZl07u2iY986rarOim6/VQAoyqIfPOwndyy9gTRWICLZyf4r7/5DW67eyNr+pvxeFUmx9I8/9SbHH/tMpZp09gS5eGP7F5V6NswLI4fvszsdIZSQadU1CkUyowMzKJXXO2mgfNT/PnvP0ko7Mfr1/D7NQIhL619XkLtASSxOBhqc/bkGMMDM5RLOsWCTrFQYXY6QzbjspOmJzP81R8/Q10sgM+v4Qt48Ps9JNoj1HW6jmiLv2c46uexzxxkPlXg8vlJTh4bYnQoybbd3azd2EpdzA2W5LIlJsfSDF6cZnY6w2/8zoeWlGVJksT6Le20tMcYG05y9s0xvvCHT3Hve7cRS4SwLJvxkSQvPXOGQy+cJxj2oqoKqTn32k+Wp6lU/LT7XZtqgUAIDQebBTZYJBZg+54eRgZmycwX+as/fpqZqQzrNraiKDLlkk5m3mUkbtk+jNdbXhFw2767mw//7H7+7s9/TCZd4O/+4sccO3SZbXu6aWyOommKq/c0l2NkYJaRwVnmUwX+3f/1KLH40sWYEILb7trAm8eHefHpMwxemuEPfvs73Pvebazf0o6qyUxPzPPayxc5+vJF4vUhMuniCqbVyfQ422Pt+BUNj19FSAJ/0MvU+Dx62BU8lhCY1VJaG3fMr1gGRfOdq+N/N6BUdIVfF/JNmlfF69dcMVefhmVZ5OaLNHfEkRWZez+0h/a+Rp786iGCYR/BaADLtFFUmYPv28Frz53he3/7Mpv29NC59krpg2XZtYW36lFobKtD1RTXxCJbQlFkJFli0+4eQnUBTrx8AV/AQyDsw+t3mU65+SL1zRFkReaeR3fTta6ZI8+eob45gtevoagyxqKkzuJZWhYSUc1dqKf0GWYqLnvRtHUKVs61gncM/HKQiJYAAhStJGGljqyRwSv7SVZcNp7t2HhlP23+HmSh4JNXEam9iciRIkk0BlduPJZvllQZGhX3OL+6OCF25fyLWwktsl+1LBsNmXyxgmFaGKZFydZRZAmvpFAsVSiWDfxeDY+qkMqViAQ8KNdgH81nijzz7BlisQD7961BEoI3To3yxb9+kUrF4MMf3I1SLWEYH09z/vwkTU0R1vQ10tfbyPE3hvnCF58nlyvz0Y/sQRYC07QYGUkyPJyksSFMf38z/WtbOPr6IH/y58+SzZb44Pt3vqWgSawuwCc/sY9srsSXv3KIiYl0rTR/OS5cnOLQawOs6Wtk+9YOcvkyhw5f5g/+8Cn+9a/dz84dXQCUygYXLk7xhb96AU1T2L6tE1mWOH58iD/5s2exTIsHH9hSK3e57mj2CQAAIABJREFUPDDD5//7D5mezrJlSzt71zZRrhgMDs2RzZRq91TXTb7/xEm++rXD9K9r5vb9aymVdF47Osix14f4N//rA2xc3/rPwaMqAqprYCAJgeNYmNY0DsaSYyJez0059JZLOvGwf4lbc8jj4Rf37sGrKnzrzTMkC0WeunCJ5wcG6YxGOdDTxXv619Ebr6uVqsmSRGtXgoZV2NRvBZZpo1dMrKs8u35NXZKUF0KgShKj6QwddUtZSpqcQBLvjEi2JrlrMgsTbJtk1SxIkSRawiFCmod7unvxV/chfbE4RcMg4ffTXRerrRxq4/ayfdbNXjtNlZGFoFCs1ALTbw0Oiqq4+pXVfatuWsyXXVZUnc+Heh2WpixJJAJu4LBsmGRKq5tsaIpMYDHZwXFcrTf1KowwB3yKSm8khiYrSLYgk8qjV9z5x7It0nqBgOI+i6L63+XZJO3xCD7JLQGcnMvQ1RxHtzwoQjBeOo3hVAgpMZp87rnDmpdf6L+N0cI8pm3RFogSUrzIDTIz83la4ytLQN8q3rWBI4+qsHddZ9X1SkJT+gD3RRTCg0BGCB+KVIflFLDtDJIawKXh2yhyIwJQRTuWk0WgYjslHLkBSfiRhAfhaEjqGmTpyubQ41Pp3diC4zJUiTdGSDS5Cx8Hh1jV6i+TKuALeNA8Kqoq09qZQNEUCtkSLR0J6luiWJaNz68hSTa2cQ7HTuE4hWqQSMXBA1IdtnHCDSJJUYTwYVvjyNp+xOIyDyFQlRvTCjGq1tmGZTOdya+am+9tiNG5LNhjWK4Ku6bIN5TRX6h3ddXhVw80SZKb4TMtmwXJJyFg39pObunt4PlzgyTzRToTUTKlCq8PjbOupZ4P79l8VaE9jyL/xEoCl8O2XdHtr792ilt62/noLVvwr+LE5jgORnWiyBbLq5ZghL0ewk0ewr4rGjILx13N4W5hwLPsPKnik8T8D5AqPommNJGvHEeRYvjUHoTwkK+8jk9bS1jaS8m4RMm4hEAQ9d9NvnIc05pHk5sAh0z5Fep8d6FIYZKFbyEJPwHPJlSpnpJxAVVuRBJeTCtFtvwyij8CApLF77v9laJ4lFbyleMY9jxBbdvbvdTvGhRMHdO2V130SbLgtrs3kM2UePwfDjEzlWHw4jQjA7MoqlwtM3SDGqZhYdsOa9avLpQgSYL+zW188hfv4ot/9DTTE2leef4cxw5dQpZdC07TsNB1EyFJrOlv5mf/lzvp6m247jggpPqqq9rNvCnCXQysqAOHg/dtIjmb49Ufn2Po8iyq6mrgWJZFpeyWVIUiPh79xF7WbVidDVUp6XzlL1/g8oUp1ynJcZb8H2BmKsOT3z7uunlJ7mLE41F58LF+3v/JrTUGDrgLtCe//TrPP/UmzrK2FvTlspkiz/7gjVqJgZAEsiyx+UAPu35+C92hhbH/ilbFuo2t/Pyv388X/+hHXDo3SWoux49/eIqXnj1TW5jYtuMKYZo2Ho+6qshza0ecB96/k7//wvNuaeMPTnLo+fP4/BqmaVEq6ehlk6bWOh77zAEunpvg8a8cBqDF20jMF0QRC1O0qC607do99XpV7npwCyePDTNwYZLxkRR//f8+g6q6VucLpSq965pYt2ErvkAFlimjqJrCve/bTrls8K2/f5VMusgbRwc5c3IEuSoE6zgOlmVjGu68Egr7ljjALUZdPMhHPnUbUxPzXDwzwcRoir//y+ddOroQNfHQ5rYYj332IN/72hFOvzGy4jl8YvwMdZqPTcFmioUK2XQRzaNQ9DmMFebZWd9GayDGExOneHzkOEHFw9HkEAcaV+q1/FOGz68xPZbGMpdbn1X/JwSda5vcsWRHJ+WCTjgaYHI4yUOf2Ec+U8TQTWzLoVyssH3/WlRV5uLJMfo2tWHoJrOT81x6c4xKedFGbtH729wRZ+DMOH2b2qA6ttXYtCzth2VZ9G/vpFzUicSCVOsIlnRd0dwyqbmpeaRqUBDANEcplp8m6L2HkK+nugZxv7fpGGSNNDGtASEkBIKgEkUSEo5jI6omIyzqj3QNFSBJlti0pZ2PfvxWQmEfoZAPy5qlVH4Wr2c/irK6OOoC9LKBobtiybblIFXZ2JZhUy65ZYSSJIFwj/X43KD11VDSDcbmMkymshQrBrGQH9O08GoKJd0kEfYzNJNGlWVXOFZT2NLTQsjnzjkbNrXy0Y/fSiDgIVwN2re21PG5f/Mg4bCvxpK5bf8a/sv//X2OHB3kA4/srP3ecRzy+QqPvn8X27d1IEkSt+9fy//zp8/wo2dOc+cd62mult7YtkOxqPOxj+xh00a35Gbfrb18/g+f4qln3uTggXWrsoSuB69XZcvmdgzT4odPvcnYeOqqx95+21p27ugiFPSiKBKW7bB+XQu/+3vf59TpMXZsv8IINw0b07T45V+4m3VrmxAC9t7Sy//5249z9PUhDty+jnDYh2FYfOd7J5iayvDpn7udOw/2o1WDp4ZhoVbnd4CRkSTf/u7r7Nvbxyd/Zj8Bv2tosmd3D7/zu9/jh0+9SW93/RI20//MKJsmeaOC7TgoQkNeZW2iyDe271iALEkMnJkkGPKhJq64DNYH/PzKvlvY2drCD85d4OWhEdKlEhfmkgyk0vzo4mXuXdPLz+zYRms4BAIM3aRSvr4T6PWwsI7p6GskeBUG04LG0GJUTIvGUGBJgEMgMKw0lvPOOYX65BCa5KVsupq2C+fxVis9Fp8/qGkEF+lALU7IC7FyXL9ZVHST2WSeRDxErC5wTbH/a0NUBcln6ehvQZIlDOtKxYomS9d9rgQskX8pm6snFhaz3txjHfyy12UcXeUUlm3TGgzT4A+gOIL2vsYaS04IQcU2KVsum9jBNY6qmBZzuSLtsQgBn8amXjfJEw1FqQ/50UyLopnD5so+07RtzmdmGCmksao/r43Us6Gpmbb6yFX3mG8F79rAkU9TuXtLX+3fXm39siOuKLHIRIFWrty5xSoti/+9GKv/zef3sG6LKyi7WPguUZ00FwaVxLL61VB0qXMQQNeiDLRQN0Itwu7BDYIpgEB4H0IgVV9EDUndDCKw4r280dcq4vOiyBLrWxr49w/fuazk7AoUWa4GMVyEfa6t8myuUF2EXf9liwX8nLdmSRdX6o84jkOhrFM2TcI+T+3FFELQGA7y2L6tjCTnyRTLjKWy+DWVx/Zu456NfXQmVjKYlp75Jw/bdjgxMsFfPPca7fEov3j3rcRDq0eWFVki4veiKTL/4uBu9q/tvGovw35vjeoY8rlZlmzRnVSXZ1zy5UrtCbWdEg42pp1FderxKF0EtA0U9XOEvDvRzXEsax7bKVAxRwGHkjFEvVyPT+0j7N2HpjTiOCYepbXKXhAEtC0UjfNYdp6Atgm/tgm/tgZNacB2KmhyEw4WhpWkoJ/Gr67DdipUzFEC2iZMO/OORbLfDWj0Ban3BlYfNYTA69N474d307+5jR999wSn3xhhPpmnWNTRKyZCEni9KtFYkER9iNvu3nBF72YZFEVm3x39JBrC/Oi7J3jj6CDJ2RzFYgUhwO/30Nhax85b+3jgke20d9evovkgaG6PsW13N16/hs/vQ1Z6WHhP4okgW3Z04eBu7BfDH9DYvKOTQqFCS3scVU0g8LL4HbNth3DUzwc/sZfm1iiHX7zA7HSWYqGCJAsidX561jZx33u3se/O9chXGW8kWaKrrwHNc3PTjqLINDc2oFsFAuqVErgFgef1m6+9yVsOIQTx5jARzUPe0KlYJh5ZwXIqSEJFUWW27uric7/1QZ75wUmOvzbA9ESaQr5S21yrmkIo4ideH2LthlZiiZWMBFmWeM+ju/D6VJ79wUlGh+co5ivkc2U0j0Kkzk/f7mY++Im99G9qo74xwtClGRpb6uiOdlIXWN6mBEhLNut9/c386r97D1/725e5dG6S+VTBvS+SQPMoBENeEg1hZMUBp+AKoC+7Fv6Axgceu5W+dc0898OTnDs1RiqZp1wycGwbRZHx+jUizQFa2uvYtbePzp4GVoMkCfrWNfPr/+FhvvG3r3D21CjJGfdZURSZuniQDVvbed+H99C9ppHZ6QyqJtO7rqm2ib21vovZch5ZCJp9EZStdZiGhazKpJwSRdtAAC2+CB/q3M3h2QHmKjnubOpnV7zrpp6FdztMw2ZqNMW6Le0oikxTR5xAyEdTe5xA2EtjW4yte/t46YmTfOsvn6exNcYdj+zglrs38ML3T9DYWkf/9k4c2+HymXHOHR9G8yjsvW8jsfowm/b08Ozjx4jGg6zb2oGqKbT1NNTc8IQQ9G1qY3o8zfe//AqaprD3/s00tcfcflT709geY/v+tbV+NLTWcecjO2jrqa+11dpTj8en4fVp7DzQz7PfPEbPhhbueNhlexvmEJnsH6Aqa/F525dcB9lRSHi8SwLHtZ/FtbLJDpadAUdHkq64HMmyxM5d3ezc1V07slQ5znz296mLBq8aOHIcE8uaJJPUOH1oGNWjYFQMYo0R0jNZKiWDaH0Iy7RITWfxBT3Em6I0d9evEjhysKwkIPB56ljbWk9PU8xNxiFq6wHLtlFlifaGutq7q8hSNanqshu37+hie5VlU7s+kiCeCJFM5shmy1QqBvl8Ba9XI5stYS9LbjU3R9i4oRWPx223qSnCrXt6+MIXX2BoeLYWOALoaI+xbpE+SVtbjF07Ovn6N48yNp56S4GjBdzISsLv05AlwVwyTy5fxtBNMtkSsiKTyy3faDvs2tnNmr7GWn+7OhMkEiFS6QKVKgsulSpw8tQomza1ceD2dQQXlYwvXJMFnHxzlLlkjkQ8yOWBmVqfK7qF16dy9twEpbLxz4GjKvyKiipJVfa8iSzFEGjAFYboza4gTcsiFPXVHMhq7QhBQNO4u6+HWzrauDiX5IlzFzk8MsrFZIrh9Dx/ffQ4l+ZS/Ie7D+IxbAzdxDLfvnhwaiZLNl2gc20TgfBVWOGrfFGPKjObL9KpG8SqzBfXpdCDaedWfuAGsNwR2nQMZisjxD2tCOGr7QsdQF82Fiwki3CoBdMc26Fc1vH6XIapEKLmMgiupuPN7AMc3GBxU2PkpoW1l7dkmRaF+SK5+QJ1DVU9uKotvW7ZS5IKV+uLblWJKYD3qoZZywJ+toEkXHHwa+FsahaPrFCneCnmyzURdlm4TEfX8KN6BuHqABvV/uQKZQYnUti2QzRUJBxoI2uliGlN+JUrpWfnM9N8dfB1+kIJ1Op3r1im62L5FgxeroV3beAIXCtCw0oiSyFkaflLKFb8bDsGhpXEdnQUKVQtp6mK3dlZJKEiS8s3/ld/0G2njO2UUaQIy60Qb+QFuaJlYWM4ZSy7iCz8qLJvSXuCmxSxvQ7WNMWpDweYnM9hOQ4tdSspaouDYgtY39JAyKtxZGCM6Uye5mjomt9TCMGu7laeO3uZ40MTvGdrP55FL1zZMDk2NI4A+lsa8C+afLOlCl89dJKgV+O/fPQBuuvfXfo4juNwYWqW33/yJTRF5l8/eDt9jVfXzvCpKpvam/ju8bOMpzPEAj78nqV1u+4lX3rdu+tjBDwao6l5JudztCy65oZlcXZyFtt2kCU/QpRJFZ8ELGQpgEBCEhqyFMCyc9hOGcspYjtlTCuFLIXRlGZAoMmNZEovIAs/pp2lqJ/FtHLYRpRMcQJblBHOHBoGjuWjUhYojkGu/CaZyhl0XSeo7UdjG3rFIOBpB8dHsvgyhlEmEbnlJ3AX/scgo5f57tAZxouuTemB5h52N7ShSDIl0+DV6WGOTI9iOja9H27nQ5/eT3omx9BMku9cOs14MYsn6OXhzdu4f9N6fEEPh2ZGODw9guU43NLYzt7GLjRZZr5S4omR8wyZKcT9AR64Zz89VoRSscLFbJLD2VG8CS92ZxgaNVeQ1DJ5dWqYIzOjCCG4paGDex/ZzkMf2MWF+VlemhujbtzHieQEdR4/H9u9FXmNn7PzM2zq6QJgppTn65dP8mjPZv7j730MANvOU668hiRFUZY5eBi6STwR4lO/fA/veXQ3E2MpCrkykixRFw/S2VOPP+C55njhD3j4tX//8Irfp/UCR5ID3Nu86aYyjooq8/HPHODjnzkAwHBhjgvZKQ429qNJ7jikWyY/mDjBaDHFx7v2kvC4G5qyaTBZzGE5VRdCbHLGCLZjEvOuRwhBW2eCT/7CnXzwsb1MjKXIzBfQy+4mQ/OqhCM+Gpujq5blLf7O73l0N3sP9jM+kiSfK2NbNl6fRrw+REtHHG91Y711dzdbd3dftS1FCsCygL5AYJQNmuvDfOgT+ygUKpRLOpIs4fWpROsCxOtDhKNaNRGxcspfYHXt2tfH9j3dTE9mmJ6cp5AvY9sOqioTCLoBqPrG8Aqx6+VwbIfO7np+/TcfZmo8zdR4mnLZQNMUGpoitHbEa8GEj37qdj76qaUlrpdzc5zNTCEcONC0hu6QO+7ajoOey+OT1apWjcSmaCuboq3X7M8/ZSSnM7R1J1A0GZ/fw72P7gagvrWOuZksB9+3HVmWeGTZNdx732b23rd5ye/23beZfct+d8fDO2qBG9t2GB6Y4T2f2LfkHquawh3v284d77vidNW7wb3mndXk2ML/H/6525a0/8inD1z5+VNXft7/wBb2P7Dlhq+Dazxy88kJxzEpFL+N45QIB/8l11rvedRNxOv+K5q68arHGOYgmdwfofl+lbXbuzANk7FL07T0NtLa14RRTRxYpkXvZpe5o/nUVZ0ZHUcnV/gbZLmZUOAxZE3inVqSO47D7GyObzx+jNNnxjEMs0omFUxNZ0jEgyvMjHw+jWDwSqBDliXisSCW7ZDJLE0MBgIe/Is03WRZIh4PYZjWKoGbdxa2bfPGyVG++/0TTE1nMatuy6Zpk8+X3cz9MuJsc1N0iQivqsooikyppNfWwslUnkKxQldnojYmrwbHcZiazlIqGXztm0dXyCYANDaGXdHufwbgsrhnS3k6gnUoQgXx1nSwFiPRGKEil1wG7CrrDiEEIY+HHa0tbGtpZiaf55mLA/zd8Te4nEzxwuAQXzoW4ee3bicU8ePxvf0+ebwqrV0Jsqk8iiJT33L98jd3RhfEAr4lz+wC49LGYvm8fz04uALRiyFwtSLnKiM0aOuJ+qqla7bNdC6/5NhKyWD44iSFbBm9YlIuVYjGXOOWVFWjJ1YfJjWXw6iYyIrEuq0dSwgU14MQbgD8nUg5CyGwTJvsXI66hggeRaHO5yVZLJIulTCu4yhm2TZzBbfU2aMohD03FvCVhUzJKrvOwVf5JrptkamU0S0LR3bIzRdrCUjTtqnTAoDAcCxk3OBqb0OM9qo5kyRJ4DiMTKVRZAlZEngJkDZmMB2DiOquj+YqeXbG2/lg59a3GYi7Pt7VgSPLKTI0/0c0hR4l7Nl63eMz5SNM5L6CJDzU+fbSGHgEUHAwmM5/E5/aTdx/5w2fP6efIlM+Slv455ao2ttOBcNKo8kNKwJKq8HBIl06xFzxSTS5ge66/w3lHVDJvxoawkHuWt/Ll156nb958Rifun0nXfV1SMLNYqUKJQZmUrTVhWld5By2ub2JjW2NvHZ5lL964Rgf37uVzkS0mvVymM7mKVZ0uurrUGW3XOzWPleo+qULQ7x4foiD67tRJJcq+MrFYZ5+8xLtsSh7+9qXZAvThRKXZ1IkQgFXgX4V+jvcfN3scizXO1q+WFpBv1zUv798/ijDc/N87j0H2NjSsGp7C5+TJcEtPe10Jep48uQFOuJR7t+8loBXQ+BSUcfTWZK5Als7mmsBtta6MFs6mnjh3CDfPX6Wx/ZuJeT1YNo2r10e5cTwhCtAh0Ii8AiWrWOauHXPsoOwFXxyPZKQ8EkxDEPgWBGing9h2xYOMoZu4ZX24vNWUOQwihSlKfwvKJdtBi4XyObaiER70RL1nL44RiDYj6Jors6KHkSS30fWlriYyaCq6/EFIKf6mZsto3r2YJoWsfW98JN7pP9/hWG7bn7b4y0M5dL8zfmjdIdjNPqCvDw1xN9fPM4D7euIaF6EgObmKE1NUZ46OUwkWMfDLbuwcegJxwiFfbyRnOQfL53ktuYuLMfmyxeOE9F8bI038+LkIEdmRnlPZz85o0KjL8Supk4cx+FHR55ig7eTTbFG0pVS7eE9NjvGP1w6wV2tfRi2xd9dfJ2w5mFrooVkucA3Bk5xd1sfW+MtOI6DJin4FJUXJga5o6WHlkCEU8lJLmWSaPIioX2hVV++VWrzq4+9JAkaW6I0tkQXfe6K49aN6Istd+gSUAv03GgbqyGjF7mcn+a2+rU4wm1DkWR2xrp5c37M1b/xuO17ZIWu0GLrZQfdymEs04USQhCK+FgXWT044TgOQxemmE8V2L63b9Vj9IrBuRMjbNzRSd1bzsQLPHIjmpRgsaiog8P0eJozJ4b58GcPEI1dWwTyepAVmZb2GC3tby2Q7zgOY0NzzE7Os/O2tXR019NxkxbHE8UMBxr7yOglxgppuoKuhpvtOJQtE8uxr5M//OlBXX2I+WR+xTw4O52hkK+Qy5aYGE1hGhaq5pYoFgsVOnsayOdK5HMVInV+FFliamKe1o4YlmkzOZGuOSROjKVoao7i9WkkZ3M0NEeZnsxQ3xhmanIen09jcixFLBGitT2GtIzyfuV9Xc70XvmuL/xu6edWzvPu35YFSRcdc6PntJ0ClcprKGoXYFeHULFKPxyECOH13MbyDdrifurGKUxzhGhMJVyXwLYdGjviqJ7raXQ4VzL31fYsO025cgy/7y6cmtPban270sbVrsdylEo633j8GE8/c5qH37uN3bt6SCSC6BWTP/vCj5mYnF/Zw0XsgoVz167uivuzyrGrJCN/EpieyfKFv3oBXTd55H3b2bypnUBAY2wsze/87vdW/YyiSNetrLnZacfrVfnspw/Qs8r4pqoKkeg7mxD+p4ySaVAwdVfwGQvTmsTh7enRaV4VOWgvEce+GiQhaAqF+MjWTbRFwvznp59jeD7DodFR3t+9BrNiUMxXrtvO9eAP+ZidzCAEqJ7i9T8A4DgUDQNFktyqjGrU03FM7KpD7gIWmC12tSz/arBth8ncMq1KJGShoggNTZbpjEYQuEybi3NzS95nIYGiKq6mXpVh5DjgD3qoV2Wi8WAtqSTLEoGwd8W8cD14VIVwyPf2C0gc8Pg1GjviNHW7+7SAptJZF+VSMsV8qcx0Pk9D8OoblGxFZ6p6vVrDoSX6c9eCAJL6PIZt4uCsGjzyyQoVy8J2HGzLoaUrUZNOkAQ1mZe8USEoe5jNFQhXK1HmcyVOD0yBEGiqjEdTsESZoBIFE8rWlfVqozfMi9MDvDIzSFRzg5AJT5Am/zsjiL0Y7+rAETiYdgbbubEBZq74I6LePdQHHqwKilVLo1CpDzyEJK4uKLsagtp6vErbis8VjQHmik/THv555BtoU6BQH3gQB4tM+TCrl869c/CqCh/fu42xdJan3rzIawNjtMUi+DSVYkVnLlcgW6rwHz9w95LAUSIY4JfuvpV0ocQ3jrzJi+cHaYtF8KoKuXKFuVyRHV2tfO69B9zAkRB019fxK/fu5U+eOcR/fvwZNrzWQDzkZzZb4NzkLKos86v37WNtU/2SiTsW9LGmKc7LF4b5pb9+nKDHA9WXqCEcYE9vOw9sXkvE731bCxHdtDgxMsl4OkO+rHNhao5ixWBoNs2XXnqdWNBPyOthTWOc/pYr5Rdfe+0Uz5y+RGMkxLnJOQZm0yva3tXdyp6eNrc2Wwg6ElH+1X37+W9PvMjv/eBFvnr4JPWhIOCQLpRIFYq0x6J8/hPvqQWOogEfP7t/O5enU3zx+aO8fH6IjkSUVL7IRDrHlvYmfpwfAECWggwNzXDm7AThsI9gwEOpbFT1TNxNvaYpzM5NEQp66eluYGBwFtueRZIEWza1u05rQkXCiynp4JTwaFEqJSiXXIHxSkmA113gKpIP8CMpAo+njM+r0tAQwTQtCvl5FDmIISxk+eberXczYl4/d7etIVUuIgnBM2MXyRkVgqrGj8cvc6C5hw/3blnyXI4XM7yRnOQ3dtzJumj9kr89PXaRkKbRFXbLEDR5mFenhtkab0aVZAqmjkdW2BJvJu69MrkpkkRWL5PwBtjb2IlPcTNiz45fYluihUd7N2M7DvN6mR+NXWRrwtVSUoTER3u3EfFcuSe9kTj1vgCvTo3wcPcGXp0eZndDGxFt8X2TcJwyjriiwbUcluNweO4SR1ODKELiYON61oWbeW76DGczE/hljYdatxJR/fzj8GHqtABDhTnua97E6cw422Od9AUbOZy8TNYosTXawTdGj2DaFnc0uuXIyUqe74y9Tkov0OKL8oGOXQzn53hm6jS247A70cP2ui4OzV3iRHoYWUjc0+SyBC7nZvjCpR8jBHywfTfNvihh1bckMJUzy/xw4iRjxTRNvggPtWwlqKouq/Umh2bLsjny4gUKufJVA0fJmSxPfuMo7T31bzlw5DIuFBDv7inbtmyOv3qJmQk3cPRWIAnBE2NnqFgGquSOrbc19iILQXco5uoM/BSVxl4LiqqQy5Rq2hkLcGwYGXK1xi6dm0TXTYrFCoGAlzX9zRw7dIliUadnTSOqIvPayxfx+jUmxlL4Ax4qZYOWthgDF6fJpAs0t7pi2KNDc/T1NzN4aZrsfJHhwVnAzQ4PDcwSiviI1l0ZoxzHRDdOki9+E8M4BwgUuQ2//334PLcBGradIjn/G/h9DxLwPcLC2FIqP0W++Dh1kc+hKleYdqY1QjrzFLpxGkmKEwp8vBrQUarntDHNAXKFf0A33gQMZLkZn+cAft97EcKP45TIFb5EufIylcoRJD1MpeLqhylyJ7Hob+Gak7jBoGzuT7HsGRzHoC7yf+D1LGbQ2lT0Y+QLX6WiH8a0pplL/QqiuvaLhv93hNiDEIJ05ndxnDzR8OeQJPd/sXqgAAAgAElEQVRdN8xR0pnfwu97iKD//Vj2HPnC31OuvEJFP4ZpDVMsPQmApm6kLvKfatfIcfKUKi9QKH4Hy06iyC0E/R+p9u/qDIlsrsyrhy7R29vAYx+7FbW63pifL1Isrb6eLhTKZLNlolXWgG07zM3lkGVBNLI0CJLNlcgXKu7GD3ccnJ3LoakK4WsYN7wTGBicZXBolk9/8nYevP+KsPX0dJZyxbh+A1dBPB7A7/MwPDxHpWzguQqzUghBU6O7bvZ61X92ULsBNPtDyJKEJNySVFeDdmmp2s1iZjxNciCFL+C54XlVlWW2t7awtaWZ4fkMqUIJ07Ep5ivU1V998l/Y3NuO4yYurqKFpKgS63d2oajyqrqH1+rXVDZPd/xKMssBZBHAozTVQsYL5kh5vUJev7om01gmw8h8ZsnvZKHS7O0lotYjC8G6+nrq/D5SxRKHRsaYyOZoCbsVDx6vRs8ibU5DN5FkCUWRlwSzO/oaVwSM5WpQxHEcLNu6ah9VVSYa8d2UIPpqEJJA1RRkRUapuimGPB62tzbz/MAQ8+USh4ZH2diwmsyDG/Q+NjbObKGAALY0N1Hnu7Ggr1umdu0ysKJp0BoMM6+X8ZbAWcREdACfrNHiixLzBLAdh3jQRzJfwnHcgLciS+QKFXZv6GBwIoluWOSleVTJQ8W6EpwUQpDVyzw5fgaPrCAQ7Gvo/ukPHLnOGQVmC09QNsfwKq3YzkIU2KFizpAsPUfFnMCrtBH334kmJ8hXzpIuv0ymfATdmqFiTVPvf4igtp6KOct04Vvo1iz1/vuJyLsA0K055gpPocn15PWzyFKAuP8ufEoXDgZzxWfIVU7gV3toCDyMLHw4jkW6/ApTuW9QMoew7AKqHKM5+FFUOYxupUiVXqBkDKLJDcT9d+FVmmuLfgmNJdohjs5k7h+p8+3Hp3QBkCw9h+Po1Afurx3r0xQaw0Ei/hubkIUQ/H/kvXeYXed93/l5Tz/n9jblTp8BMOiVIECCXRQpSrJI1dCS5bIuku0kjp3d9eOsk2zi9W7WzvqJ13ayycplk5WsZhVKMlUo9g6C6Bhg0GYwvd9eTt0/zsUMBoWiJGdX5v6eBw/u3Pvec8997zlv+f2+JReP8LuP3s+dG3t58dw4F+YWqdkOpqbSn0uxs6eDbV3rdSokSbCzt5M/ePwRvn1ilDfHpplaKWK7HlFDY3M+x93D/ev4n7Ik8fCOTQzkUnz1yBmOjU0ztrhC3DR4eMdGHtqxib39+XXQOcfzOD05R8N2SbZ0ga7qYFabNm9cLvDMyCUuzS/zW4/chaGq4aBp6nQkojfln0pSaF9fazrrRMAqDZvPv3KcExMzq8/FTZ2G4/DVN06vfodH925dlzhaqtRIRy1s1+XJ42dv2s8RTWVffxdXkcqSENy3dZB8Ksb3z1zk9YsTXFpYIggC0lGL24e6uWe4H0NT8AMfPwhRVrv7O/lXH36QL79+kpHpBY6NzzDYluKX79/PUFuGK0sF4q3fPpeLcZvZH9p8+wGu662KAsdjJp7vk+9IoGoKkYhOb3caRCjmeP1EZpoaO3b2rF4zcGtE1bWvXZ0UurvTN7R5J8TLs+N85eJJemMpIKDmhsJ1tudRcpp0Rm6kcJbtJpIQZAzrhtcWG1UmK0W+NzEKQHckzuZUmFy6Lx9uiL8+dpqa4/D4hl3cnQ9tsD+97Q7+dnyEz4y8Tsaw+IXN++mLpSg2mwzFMy13qYC0bnKysrZIiOsGcW19xUSVZO7LD/GdiXNsSGaYr1X45KZ9N0zakogRBA63yqDUvSavL11iX7qfzYk8lqwxVlngzeUx3pffzbnSDN+aOsZjPbdxpjjFJwfv4sHO7eiSwlRthaPLY3SbaY6vXOFAdoh2M8H7u/bwucsvr37GU7OniKg6H+27HTfwcXyPb04d4/bsILqk8s2po2yItrMx1k5fJMvrSxd5Yf4cB7JDAHyw9zZOFib47swpPj5wx7rzD4KAlxfOs9Ss8L6u3Xxj6k1eX7rEuzo24/lNxDWLgKX5Eq8+M8L0+BKe59PVl+HA/VvIdSRCB56RGV5+6gyvPn0GRZVp1MJF8G13b2L/PcPUKg1eeuoMb7xwjksj03zhPz1LLGGRSEd45GO3k8pEcV2PM2+Oc+rIGMXlKlZEZ9u+PnYdGEJpCZD7fmjxe+Sl80xcmgegvSvFve/dSby1yQu8gHMnJjh3cpJ6tcnApk4OvXsbVlTn/KkpLo/OAIL56QJ3PbSdcycnmJ1c4aEP7qOzN7yPiytV3nzpAhfOTKHpKtv2huehajLNusO3v3yYgeFOZieXGRudxYoaHLh/Mxu2diEEXDob9sdrz44QBPAffv8bAOy5cwO337v5bW+uHujctM7tTpfXhCSvJk///xKSJIjG11dlgyBAViQ8N6DZdIklzNDJx3aRJEEkZuC6fmja0ZPBsDRqtSaartA/1Ea2Lc7xNy5z6fwcG4Y7OHFknIvn5ti5NxQUdh2Prp40R167RFdvmnKxjt106R3IYl5nm+y64yyt/DaK0k/E+iAEHrZzDte9QqCFFKIAG9s5ja7t49pxxfOWcJwzBMEaDcrzi5Srn8XS7yNifZBG8zWWCr9DJvmHGPqdCCHh+yssF/8lQeBjme9BCBXHuYjjjkHLLU0IGU3dhiQlcZwL6NouLOsDoSytiIfoylYoSh+x6C/QtN+kWPp3+P76DRcIZCmLZT5IENQIAoeo9Q+Q5Wzr/f3X9McYvl8kCK5xjwsa2M4ZdC2kGUpCR9d2IUlxHOcchn4Qy3iw9XtnWEsaNSlV/k+qta9ime/DVO7Dto+zVPgd0onfxTQeXDderT9jkGWB43g0Gi5CCGzb5ZXXLnDp8gLp1I3V96npAoePXOaOgxtQFYmp6QLPvzhKR3uCoes0zcbHFzl27Aq37etHliXGx5d49fVLdHWl6O25BaU/uNof3HIzeZVmdtWm+mbTUDiOCJpNB6dlQFEu13nmubNUfgzUSCYTZe+eXp557ixPPX2aB+7biq6H4tjNFgUxHjORJMHe3X082ZnkiW8eo6c7TVdXKlyTuT6VahNVkUmlIz/2pvidEEII+mJp+mLhPOOKjQSBs+4e/FGiXrMpLFdvsGGXJQm9Vcy9PgKgYtssVEOkRtI0UCWZaMJsGZLcPK46bvlBwKWlZfZ13dzwpFZuUlqphlQ1TaGz99byFlfjqqua7XnXabgpOH6Bqn0RS+lDleNsyIR9WHdcXhmfYFdnxw2OYVXb5hsj55guldY97wQNKu4ycTWLEIKt7Tn2deV56vxFzs4v8F/ePManDuwnaYbF+mv7T9WUkCruuquggWvP/9pImga6LFMGJoslKrZ9UwSP7XiUyg06239gF/3AMGMGvVu6VhNDqizzwNAgXz01wsWlZb5+eoR7BvvZlM3cgFydLJb48snTFBtN2qIRHtw4dFMDpOsjCAIKTgkJ8ZZoRlWWma9ViKgqeiROo26jGWo4jwuJ2UaRqtsko4docT+AhXIFL/CJmjobe3NMzBWoNZwwqSdbDFo7gAAvWGMHDCfa+IP9j67+7fo+tv/23UN/mPjJShzhsVR7muX6c2SsB2i4kzTcaQA8v8l0+bP4gUNU20qh8Qq2t0BP4pdR5QxxfQ8rjVeIaJtJ6LehKSF8VJHiJPXbmSz9JTVnjIQRJo5cv8xs5StEtGGSxgEKjdexy19gIPVbCGTi2g7qzhjF5lFy1iOACQgMJY+hdOEFdZLGHShSDFno+IHDbPlLNL054vpeSs2jNEpTDKR+45ZIJ4FM052hUH8VI9aDHzRYqj1FyrhzXbtDm/rZ1tW+Kob4diNuGrx/9xbu2zJIsVGmaC+S0fNYmk5EV1FlmcXmFSw5gaVcI3yYSfLzd+/jI7fvoOm4+EGALEmYmkJE11BlmYZXpuQs0Wb0rwpxJ+JNxooBbfoGYlqKqK5j6eoNk+e56QX+4FvPk45a/N5HHmIgl15t4wc+F+eW+aNvv8AL58b42IGdbGjPoCkyP3NoD4/dto1M9EYebcI0+N1HH8DxfHLXCFgnIwa/84H7aN7CGjP8HSB6nR7Rpx44wM/etfctAQhxQ79BeFwSguHOHP25NI/dNsxkdQpZKKiyhKZKGJrMRP0yESWK6zsYskndq5HNwScf3IDFbahCw1AV4qZOAPzJz35gNVkWixrEosYtKQDXw/97ezP4vk8uG1vH8b+2zVv9favX3kmJouvjexOjbE2389MbdzNZKfL8zGUADFkhpZuMlVZuWPgmdRM/8JmtlckakXWv5a04cdXgV7ffgd6a5BUhExDyqd/VvYG9uW6evHKWr1w+xd35QQIgZ0b5+Ma9PNQzzL899hwvz47RF0vRZkaYr1dxfQ8vCJiplumIrFXcbgUY3ppq4+uXT/H05AXykQRt5npaUxA08YMKstxxiyOApegczA7x8sJ5rtSWeFfHNlbsKhfL8zw9dwYv8OmLZAmCgKhqMBRtI6qG49/mRJ6vTBxmplGg6Tl0WzenQ801StzfvpWYGlZ9FhtlLlXmgZB212bEqXpN3li6TMGuMdsoEJHDe6XHSpPWIvRHcpwtztx04pysLXOhMoc7E8KHDVlFEAo7Nrw1ZOHl0VmOvXKRbEcCSQie+vpRJi4t8Mv//XuRlbDKbUV1VE3Gihq0d4XVwmhL88gPAhRFworoKKpMpj1BKhMlGjdRWmLpjZrNq8+MUCnVSWViTE8s8eozI/zjf/1BNreSuktzRT7zb5+ktFJlw9YugiBgamyRZt2BFmNwYbbIN//6NXqH2qhXbb78F88TEPDQB/cxO7XCNz73Glt293Lx7DSXR2dJpiNMji2iqjIf/aV78LyAr/zli1wcmWZgUwcrS2X+7z/7Po7tcse7tuI4Li9//wwvfOcU+b4MqUyUkWNXOHtigt/8nz5Mpi1GEASYEQ1VVVA0ebU/IjGDmmNj+z4JXcfxfeqOg6EqyEJQsW2ShkEQwEqjTlTTMDWNYrNBQjcICJivVonrOooksVyvE9PCeajYbBDXQm2tim0jCYhpb6219fcp6i2R/Gu/j+8HLC6E2i71mk17ZxLf91E1mcnxJUbPTLNlezeFQhVdVzBNjd23DTAztYKiyCwtlBEiFE5eWQqNMDK5KMtLlVC/ZbrA4MZ2kukIG4Y7cV2P0TPTIbL2uuSf5y/hefMkYv8Qy/wpAIKgBkiriJwfJgQBpn4H8divI4SOqT/A/PIvUms8ia7tRogoflDBcceIR3+eWOQTgEwQNAAPIcIxTQgd07gb15ujXPkrVHUDlvGem0oLyFICWd+PQKJ0E+0VISRUdRBVHcR2RnDcCxjGvahK7w/9/QAkKY5p3IfidFOSPoOmbsEyH7muVYDrTlCtPYFlPkIi9hsIYeAbD+Msf5pq7Wvo+u3IInXTz4hGDfbs7uOpp8/wmb98jp7uDNMzK5y/MEe+M0mjcR0yR4BlaTzxzaOcOj1JMmFx/OQEly8v8MlPHCJ9DQVWCIFl6XzpK4c5fnKCiKVz9Pg48/Mlfv6Td5FouUm5rsfo+VkWFitUKg1m54qUyw2ef/EcuVycWFRn65Y8lqXj+z5nz82ysFimXrOZmytRqTR5/qVR2nJxIpbG9m1dWJZOb0+GrnyS7zx1imbTJRo1ODc6Q6FYI5f70am6qiLz/vfuZnR0jv/y2Vd489gVujqTNG2Xicll9u8b4IOP7kPXFbq6Unz0w/v58798nj/4oyfZujmPaaoUinXGxhZ47AN7eejB7SC/M8ah68PxPCq2TdP1aLouDdflwuLSqsBwqdHk7Pwi2YiFpsgYioKhqFiaGhqr2CeJGD+erqmuq+Ragu1X12NfOH6Sqm2zJ59nKJMmE7FWTXk832eyWOILx09yanYOAezKdxJRVAp+7ZaFDQFsbgtROl4Q8LXTI+zOdzKYSaNI4TrA8Xwarrs6r2fzKVz77W/aA4IWqim4LrHhocpJZCm8p/Z05TEUhYbr8tVTZxjOZbmzr4eoruMHAfOVCt8aGeXLJ06tKxQHQNlZXufCZagqH9mxjaPTMyxWa3zpxCnqjsP7twwzlMkQ1VT8AErNBvOVKucXl9AVhQeGBjDUW+9Fe5NJUpbJYq3Gkclpnr14mYc2bUBrJZxc36dmO+iKzOaNHTcIz9cch7rt0PRcGk54bU0W15L506UyZ+cXV68rXVEwDYVEbL3WVX8qxQe3beGPX3yF84tL/PGLr/CrB29nYy6DoSg4nsfl5RX+4vCbHJmcRgDv2jDEnq71+p5vFapQiKux0NzqFhEE0BmN0RmJYekapUKVWqVBui0erj8lZZ24tnqN8QFAImpw565+FleqtKejGNpVWrRAeQupnNl6iTeXJvhA745btvlR4ycqceT5NZbqz9AeeZSM9SBNb4ZS8zgADXeCYvNNuuO/gC634/llFmrfpj36IQylE11pR6vkiKrDpMy1KrMsdCLaJjQ5e8PnSUKnI/ohEsY+dKWdqdJncf0ympzBULux1EEa7sRqeyEkLHUISx3E9cskjQOrot11Z4Ll+ot0xj6GpQ4APjPlL9FwZ7HU/lt8Y4mM9QBTpf9MxnoXtreI65eJG3u5tsQY0TUi+o+WnVdkiaRlEjVk0p5MRImvy2pfrLxBl7l5XeJICIGuKuuErq+PorPA+fKrtBn9q+9JmjGatUtYkU46rJsvqlzP56Xz41xeWOYX793PwQ29NySWYobOlnwbL42OU2i5tQkhSEVMUpE1CGHg1/DtF5C0Q8hSlKy1QOBNICtrIpyyJNEW/+EXE+mIRKBPIOQhhNTaCLqXCbx5JO22W1b6rp6roSrEIjKuu0zG6MTzXYTwqftNml4D228SUaJU3QrL9iKKpFGnSl+8E0tZXw3sTIZQw1BzYI1HeytBQAgTcCcKJzhbOktSS3J/2/3IksSKvcKLiy9ScSscyh6ix+x5y02W4zu8tPgSQ9EheqyeW7Z7J0V/LM2p5Vm+NX6W88WFVVtPU1F5pHeYvxg5zJ+cdEnqBoqQ+dDQDtrMCPfmh/g/Tr/K7mw+XGyk2jjY3scjfZv5k5Mv8ecjr9NmRik06/xU/1a6owm+P3mBC8VFkprJ2cI8PdEwE1B1bD53/k2UlmNDw3NoN8Pk0Hv7tvAfz7zCn516BS/wuVha4jd23nWrr7MabWaUXdk8X7l0kt/Z+wCGvP7+FkJD13YjiVCs71axM9XDpngHT0we5dm5EXYle9mSyPPx/juJKDpChKLUkhCrzhYAbUaclBrh2bkROs1kSxTwxogpBrONAsNeJ37gI0sSPVaGD3TvpS+SxfE95holThcm+dTGB3hl8TxXqktAKBBYdW2WmmUiioYiZDzWCyNm9Sh7U/18qPc2XN9Hl5XWfSUjX7Nx3Ll/kM07e4jEDIIg4DtffoOnnjjK8mKZ9nyKTdu76exJMzY6S6Ytzod+fv1vEI2Z3P/+3ZiWzqVzszz46B76Nqwvr0ViBj/96fsxTA1VU1icLfLv/sVXOH1kbDVx9PQ3j7E0V+LX//kHGBgOk3p200VV1/rWdX0e+tA+Dj24jWbT5XP/4Wle+u5p3v3YPiAcO376V+/n8HPn+Np/folf/Wfv5+QbY7zw7ZM4tsvFkRmOvnKBX/8XjzK8o5tm3eGLf/4cT3z2FfYe2giEgplt+SS/8JsPk8pGuTgywx/+9hcZvzBHtj3Ohq1ddPZkmLy8iBnRVvuj2GzwnUsXEEKwJZtjvLCC4/vs6chzfnmRquPQFYtTc2wqts22XBsXl5e5VFhmQzqDpWpcLqxwZ3cPS/U6I4sLHMh3M1+rMl+tENU0claEo7Mz3N7VTUx75zgZSZLE5OVFeje0c9VDQ5Yl9uwfZM/+wXVtK6U6hqmxa18/xnXIoOFtXQxvW9PpuupE6Hk+Gzd3riaE+gbX9Fre/b41Xcn8NejSZt3GsV2iCQtF6UPXD1Is/ymOexHTuBdV2Ygk/WgbeCFF0bV9SK05V1E60dVd2PYp/KCMRDRE/xjvolz5LK47gWk+jKZuRRJv5cT69yuCAGz3PJ4/jyzncdzR1ddkOYttn8b3i8hSqtXeI6T9hOOXZcl84vHbSSQ0jh4b49KlCQYGOvjUL93FSqHOs8+dXb9RDkKnsY99+HaefvYMbx4bJ5uJ8r5fe5C7D226rqIesGljO488vJPvP3OG0fOztLXF+eiH93Ng/+DqcR3H49nnz3H6zBSuG6KrU6kI339mBFkWZLMx8vkklqXjeQHPPX+WU2em8Fw/RJBnojzdaptKRujuTmNZOvnOJP/kHz3EF544zJeeO0pnIs59+zfx8/du5hvfOkYmE109X8vUGBpqJ3ETA4PurhTNprsqXC6EoLMnya/9k3cxcmyKY8evcOrMFLqu0tOdYueOntXEgKrKPHDfFjo7krzw0igXL87TaDroEZWOLRm6N2bfMdfizeK1iUn+8NkXsT0P1/OxPY+G67JSD9frJ2fn+K1vPokuKyhyqN2TsSx+85472ZYL8PwSwc20FH+I0HQFK6ojX8MwGF1Y4qunzhDRNNpjURKGQcII3aLLzSYzpQqTxSKu77O1Lccn9uwkpVkoHZDM3HrM2tPVyf6eLl6fmOKNyWk+/ZUn6EslsVSVuutSbdr0pZL8fM9mLp+eQtUV2nvSt0TWXR9BcPMVlywZuL6yut/Yk+/kPcMb+dbIOa4Uivyzb3+PgVSKbMSi4brMlSuMF4p0RKM8vnsnnzsa7p8FEFPTrFRnCPBW9xF3D/Txjw/dwX989XWmSmU+d/QE3z53gfZoBENVCAKoOTbFRpPFao0P79jG3QN9vFVJoCMW5f1bhvnTl19lsVbjXz31DJ8/dpKkaeD6PqVmE8/3+dcPvYvNbTfqg/3R8y/x2pVJHM8Lry/fp9xcozR+/thJ/vbsKIokocoymiyzrb2N33ngHhLG2pmpssTju3cyXijwrZFRvjd6gRMzs3Qn4iRNk3KzyXSxxFSpjCwE928Y5NMH9xPV3t5eWwhBXI0y3ZjHeQtkjywEZdtGEhLlQg274a4K50tCoEoKWouSTxBgqipJM6TwVes2tYbN4koVIeDkxRnaMzE0dW08O7I4QZsZRRYSry+Mrz4/Vy/h/oh6oT8ofqISR+C16F/hZCiLCHKLh+75VWxvkaXaMy39ooCEflvr8Y8WktDQlXAhLmEQiif+aJaMftDA9hdYqb/USnYFxI09yOLWXEkhIKJtRJIMyvZJGs4EMX3nuiSXFziMV0+yYk8TU7MMRHYzXT/HUnOSjN5Np7mJC+XXCQhw/AYbYwfxAoex6jG8wKU/souIkuZi+TBCCDZGD+AGLhO1UxSdBcrOAoE5zFJzgonaaRShMhi9jbpXZsmeoOoWyOl9ZPVeLlbeoOFV6DA2oEgaZWeR4yvfJaqk6I3sJKKkiMgpQOD6NhO1U7QbQ0hCZqZ+ni5rCwJ11WbQb3GFr12V+H7ATKHMxfllIrpGNqLgNZ4FHITcAaggYhDUgTq+c5bALyLk7rAaaR8m8AtI6k6EiODZhxFCQdIO4junw/cJLWzrjSGQw+NKHfjOMYQwkLRDBN4lAncCIQ8Q+EX85iv43iRCbuftCqGYssmu5D4s+a2dBnqsPhzfYcVZQnkLDZOqV2WsOsam2Ca0HwDzrXk1jhWOcSB9gLyZR21tiEfLo7iBy8MdDxORf7CatR/4LDYXyZs3h+a+E+PDQzvosGKU7Abv7d3C/fkN5MwQRbQ/10Natzi2NI3n+wzE06gtR4Wf3ribTcksE5UCqiTTHQmRKhviGf673fdydHGaQqNOfyRN1ohgOx6qLZMzoriBzz35QQ60hwlXQ1bYke7kcnkZCcEvbTnAjnQ4Vm1JtfGbO+/hzYUphIDHBrbR34KB98XSfHjw5m5FkhAMxjP0xVJsS3fcBG2moshvXW2puzbfmz3FQqNM3bPZluhiIJqjw0jwhfFX0SWFQ7lNdEfSROT1Y7MuKQzHO/na5BF+YegeZCFxujDJiwujTNSW+MqVw9yR28i97Zv52sQRLpbnyekxHunaxb3twzw5fRxD1mjXE9yR24Apa6v6SKasheKDgc+Xr7zOTK3I4/0HKDl1vjV5jLHqIt+aOsr97ds4kN3AE5Nv8lcXX0CTFN7btYt2I44kVPxgrRLvuR4Xzkxz5eI81UqDKxfmadRsHPvHW/BeG0EQUCnVeeOFURbnSlRKdZYXyqtCnUEQcPrIONv29tE9sLYZuepK5reSmulslC27+5BkCd1Q6exJc/LwZa6OVbGESTRuEk9aJFqoJysaat14XsD5M1MUV2o8841jvPy90wTAxKV5iitVSis1zIiGJAs27+4hlQ3dVdrySSRJrFL0bhWeHyBJgqxpUWo2sX2f3e0dRFSVhuvSFYuzUK1g+z5bsjmimsZMtUJM14lpOinT5HJhhcVajaxlIQvBTKVMsdmgN5FgslSi6tgMplIMpX6y3Dl/3DBMDSui31S5NwgCfM+nUbORFQkzorN9Zw++5+O5HnYLVSIrMlJLBwZCvQohBLIsceHUBLl8ilRbHKfhgBAh5aZlxaxqCp4X0oF0U8NuOIyfm6awUObgwzuRpTbSyf+RWv1JavUnqda+iqbtJhH9NKq65aYIn7XwuXEulVe1h66GJMVCFFNrbSaERSL+W6jqFur177K08tto6mZikU9g6HcjfuJ0wELXxh82fL+I75colv90Pa0n8Fo0uWsTP1V85yxCMiAQ+P4syWiSj38kzeMfThP4VWQlgwimCPC5fU832nW7vyAI2Le3j927elsb3jBxeT0S4+qleOD2QW7b139VyxdJCgWor26WDUPlV37xXjzPJ/CDVfHca+cdWZYI/BCZ+cu/eO81l3mwtrlqve9q0kYIwfBwJxvf10fnuzu5s6uXrdk2FFniU79037rjb9rYwf/6+x9Fvgny51d+5T6mKqos3vAAACAASURBVCWs2Frf1l2XSMbksUf38dgH9q6ejySJG/pBUWS2b+ti65b8Krqj0Gzw1fNnONlYYAvtSPzd2mD/pETdcW4QX4aQpnQ1XN/H9W1w1v5uuu5qEsTzFlCkJGnTRJWlcMMu1kTkvcBFEFK2IFy/JAyDtGWSaNGpPNdftYYH2Nqe4/BEgsVqlbHlFbzrRNtVSSJh6OzpyvOpg/vZ3tGOAJLpyGqb60MIQVskwj+95xD//pXXOTwxxVSpxEShCCI8L0VIGKpCZ3+Wleki2Y4E1WvcBSVJkGyde1zXuR7cFAQBUUO7QfBaFgaytLaHjOka/+jQQSKaypPnzlNuNjk2MwNBeB66orCrs4NPHbiNpGny4tg4NdtGlcOiWERJrvYnhDSqj+zcRk8yzv/1xjGOTc9QbjZZqddbpjzhuauSTMIw6E0mVvWebhWKJPH47h0sVWs8ee48xUaD1ycmV/tSkSQimraKTrs+lmt15irrXd5UWSJtrfVDw12fqGmLRm7oOyEECUPnv733LoYyaT5/7CSz5QrzlSpBEIS/myzTGYvxU1uHeXz3jlWNp+sjommkLYu4rq1a3QN4gc9ys0CPubZuNhSVtGViex66omD7HkndwJAVogmVaMJcdfBreg51z6bNWGMM5GIRstEIQkDDd6jUmlyeXqIzG8f1bjQGKTsNYqrOeHWZo0uTqy60Jaf5X43a/xM1wwoho0hxbC+sHnt+Gc8PLyBZiqLLHeRjj2O2EDyhHfmPI8QnuDWx463PM8Dn2sWALEx0uYP26GPE9B2to4sfcH4CWcSIa7so1F/F8Qt0xT6BuEb0cLF5hbnGBYait6NKOiv2DBO1M2yMHeBi5TCqZHCh8jr7Uu9ntnGBmfp5BFC05+iL7EKXIihCJaZmuFw5ymD0NirOEldqJxmO3cVc4wKe73Cs/B2icop5Zx5F0lGEzmTtDDuSD2LKca76a/iBy0jpOXYkH8THp90YYrT8MjE1S5uxJnApCYWKuwyNkJO5ZE/Qa21HkiSGO3OYmsrfHD6Fpij0Z5PIkkTDcZlYLvK1I6c5P7vIh/dvpyNp4DdPIxvvQsjt+PYbCClH4Be4Kqwnqdvwmi8iqVtBWAipA99+DYRO4M2Hi04pR+CcRajDSMomPPtV8Ev4gYMIGkhqDCE0PPs1hLoVRJLAPwxBg8AbJwjKSEovgb+mYu/6LjONGZbtZXJ6jnajnZJTouE1aDfakYXCij2Hr/pYssVMY4YVe4WsnqXdaEdCYqo+hS7rzDXmiCkxJE3G8R2u1K5QcSvElBg9Vg9Nv8mbK29ysniSslumy+wib+SZbkyT03OYsslsYxZDMpCFzKnSKRaaC8w2ZjFlE0u2uFK9wtlyqNc0WZtkKDqEhsZic5Hp+jSWYtFr9aIKlYbfYKw6hhd42P6P537x9y0SmsHDPZuYXilRqjcZTKVZKtSY96pk4xH8WsBWrYPebIKlco1zM4vkYhH8wGdvuptsEFY8ayWHS/VlfD/kht+V7ef87CIzK2WabSEMN6dFuLdrkLliheVqDafpcXFliWrDZiiZ4WB77w0TmSQEA/E0A/EbN8n5SJx8ZL0YXhCEOk3z9QqvzV1hf1sPyet3DTeJrt4MB+8ZxnU8egdzCElgShp354apuk10WSWjR1ElmUd79rHSDGkvWT2GJiv83ODd60SphRDsTvfRF82Q1cNz7LbSPJLfyUOdO1AlmaRm0W7E+bnBu6m6TQxFxZRV9meGGIi2UXNt5qo1VKHSp3WzKZ2mNxomq6+UivzapnczX6/wRGGELiuDLsv0GO0c2LQBU1EpNx2ysRj/oO8gRaeGKmQyejT0egmahKN2aKf9xOde5Y3nz7FpRzftXSliCfPvVAQ1CAImLy/yn/7Nt7CiBhu25klmouiGtq5NvdoMK6s3sX2+GoaloahXN1bhNXItbVVWQhdMBOs04IKW21Ot3EDTlJbbRzgz9g21sWPfAEbLdluWJUxTW70ef5i+uGqJe7CrG0USRFSNmK6TsyJMFAtszbXRcF1OL8yzs62Dbbk2xgsrJAwDz/dRpZBKaHveapVxIJni7OICXfEEWcui6brvOE0RzVDo6s+imzcvFBSXKrz5/NkwKby9m5E3LpNui5MfyHHqtQukcnFUXSWVi1Et1XEdj7Gz03QNttHRm+XVb5+gf0ue/i15Ro9dQVFl7LpNs24TAPn+HJVSHd/z6d3UwdjINLVyg7YWAil0FO0gFvlZLOM9NO1jFCt/RqH8J2RS/0uLSiVaUP61jUIQBPhBhYDrK7UufrB+Q+r5KyEFTawhQ2SRJGp9DMt4ENs5Q7H87ymU/ohceiOK0v131PtvFbcoHgnRQlJcQxMJmi363g8XspRAkpKkEv8Dunqdq7CQ1yf5hR7+wyQIlgl3tCZS0EBCQSixEGng1RHIKKrJzZJZQggU5e3dQ2HbtTEpCALqlQZCljAtfVUnZW58EYCO3iyNuk38Gn2lRq2J54bOWIEfoBnqKuV+cb5AtVSjf8t6R0s/CDi1OMexhVkO5nvoiMWYqBTJWREUITFdLhPTNGarFcp2k45IlMFkmkKzwdmlUOx9W7aNkaUFnhq/yL72PPs7u9FlmUuFZXJWBEkS+AFcWF5kqVFjKJkhqRtcLCxTcx0MWWFzJsdivcqlwgqmorAt20YuGmFvZ57zy0vvaOfHO3p7+OuPf+yHeo8sBG3RKIoMMfMRhNDpSWp85qOP4QcBEU3FDpZQgiSaMKm6S0goRFt242nT4n9+z4M0PQ9NlkmrBmXDWDc2fmj7Nvb3dHNmbp6LS8ssVWvUnDBzZWkavckEe/KdbMplSFvW254vhBDs6uzk9x5+kFfGr3B8ZpaVWh0hBDFdpzMWZXdXJ1HLoH+4k4WpFXqG1nTBspbF//ZT78HxfAxFuUHzx9I09nTnr5PMkJClKNo196kQgt5kgn96zyHuHxrkjckpZssVmq5L0jTZ1t7Gof5e8vEYru/z5x95DC8I6IxFkYWL3ioWX8ta0GSZQ/19DOeynJ1f5MTMLJPFElXbRpIk0qZBVyLBzo52hnNZdOWt0wZCCDKWxW/cfQfv2jjEa1cmOD81T7XaIB4x6YjH6IpESfkKK/NFZEXGaTqouorv+Xx61x5+ZtNWvFYBJNJCC7pO+FhWZFzfY9muktFDpI2uKMR1nSAIcAMPVVrTRUybJj+zdzd39PXy8tgVLi4tU7Vt4oZObzLJwb4eNmbSmKpKAMzXi8Q1k4rTIKaaGLLKr96xn5/ZuyvUMY2Y+K3Eky5r9Ee60eW13+09wxu5vacrlJuIWNiBz2K9StW1SZo6hqmtFts0ScGUNUpOY72rXeuyjEUMdE0hFjHIJiNs7m/HMtavBe7tCE1ZGp7D44N7GU6EqPaJ6gqHF8f5rxE/UYkjWVhkrQeZq3wN21vA8ZZw/FDgy1R7SRr7mS5/jpi+Ez9ookkZcpH3cKvkTyhoO0/FPkPDnQIhUWy8gaXe3P3marh+lZo9SsU+E2oQNV4jqm1GV7oQQsJUB5iv/i0zlS+hy52kzbvQlHYy1v3MVkLh7CDwkIRBe/RRPL9B1R6l4lw93qtE1M0YSh4hJFLmIRZr30WVM0T1Les2inW3hCUnyOjdCASTtTNokkFO72OydpqaW8SQouTNYepeBcev0xvZiSRkpuojAPRGdmApSaTWwqvpV1GERs7ow1KSeIFLzS3QZ+2izRgkpXWybE+R0vLk9NAafLx6nJpbIKG2M9+4TBCAKcfI6j2MVS0a3voMsSQkeq0dnC2/CAh6rR3IUpgQO7Sxj1++73a++NoJfv/rT2NooQ6S6/nYrkvSMvn4Hbv5ubv3oisBnhNByN0gLBAqgT9L4C2DlAAErAqEidY/75rHAqFuQpI78cQZhNyLkLMIFAIpiwiaQAPffiN0LAoareO5BEGdIFhhfcVw7bc5WTzJmdIZus1uThZPsiu5i4gc4ZXlV3g0/yiO7/D0/NO8u/3djNfGGSmN0Gl0crJ0kr3JvWyKbeK7c98lpsToNENaTqfZyWh5lKOFowxEBii75VW0j+u7NP3m6qDv4/PcwnPcl7uPHquHIytH6DA62BRdczMSV3eBVx+3/r/6eNFe5Ptz3ydv5rlQucBUfYqD6YM8t/AcFbdCQk1wpX6F27jtLe+Zd1oslmu8PDrO7v48E0tFLi0sk7QMDl8KIbQ96STzpQpXFgsMtKUQwNjCMtu6O/jiqycYak/TsEN+tqmqbGjPML5YwFAVHNdDEoJ60+HM5DydyTiHL02QT8V58tg5HM9jqC3D+bklHrtt64/tIBUAZwvzfP78MboiGu/v2/S24NN3PbCVux7YesPzWSNGlrBCEoqZeliySiSSvqHd9WHIKp1miCgtNRt878ol3MBnOJmlZFd4pTbFcDpHsdlgulpiX1sXF1cmmSgX2JPLo0gKE6USGxM5yk2HY3Pz1O2A7miCM8sLbExuIW8lObOwiACqtsN8pc7ebC+z1RJ/c36EB7qbqLLEQDzNRKmIgkrWNNGkGEYL7bowW+SFb5/gfY8f4KEP7kOSJb731SOcPT5xw3e62g+3DMFNESO+5/P6c+eoVZr8w3/5GB3dKVYWyxx9+fzaW4UgmYmyNF/GbjqY1i0Qtj/GJSKEIJ2LE09bfPi/uYds+40uHOVijZbYzts43io4BAgrkNty7exoa8dSVXriCQjqBNTY15llT0cKgQcYbMmarfHNZGsm0vo4ib7EIAIHkOiOdbaqpjYbUoMIob2t6/nvY8QSFrHELRCrAZSWq/iuz/zMCgjo2dDOtgNDnH7tAp39OXYd2sTRF87RqNlUijWsqEEsGcFpuiRzMQa2drHj4AaunJ+lazCHoio89cVX2birD8PUGD1+Bddxae/JcPnMFPFUlN7hTgoLYXLH98sEgY0kxUNnMzOD7ZymVv9WS3eI0BhEymI7ZwiCKhDF86ZpNl9rCfGvhe9XaDRewtTvQYgIrjeBbR/H0A8hiVirTY0gqCJJCSQpi6EfwnEvUSz/7y1h67XEUahdZuF6cwTYCPQWqlz6ka4ZIUyCoI7vFwiCXq4miK4iq2QppJG53jSSlCTAptF8Ht+v3ORgCgIdz5triWnLgL+KyFCVYRS5A9s+ganfhyTFAQ/fLxOuedcKjELoyNq1SFPBWvLqmsfq8DXP/d2G5/mcOzpOrivF0kyBerVJui3O8ZdG2XHHBq6MzuA6HrOyYGm2SFt3mka1SSRhUSnUmJ9aZsu+AcbPzhBLR2jrSjE/tYKqqxiWxsp8iaEdIX1XlsSaCDLw7Uvnua93gLiu882LZ9mYyvLq9AQP9A3w+bMn+cUd+3jiwlnysThpwwhXdEFA2W6iSDJSa000XipwbnmRj22OM7K0wMtT42xO5/jSuVPc3zvIZ46/waMbN/Ps7CVkSeD6AVdKBS4XV3B9n9s7/99IWv5/G17gsOScpC7PE1HSxNR25hvnMeU4USXLQuMiETVDTGljqn6CuNqBhEzBnsJuRMkFQ8w2RpCFTruxCVsZQRYqstrJ6cKzpPU+eiP7mK6dIqP30/CK+HgoQidQF/GlCtXAoTHbw9KYy+CW/OoYGdU1trTl2HITCtTbieVmhZprk7dSNxqHSIKOWJQPbt/KB7ffuC66Gl0DOboG1j6/6TlU3Ca9yeTqc+G6yQYURAutZKjrqXKhGUQeyN/wfNwwuG9ogPuGBrhZBEGAJrv0p5Os3esaefPme1+pldRri0a5Z7D/lt+t6dlM1KboMjvXyRDcLBKGwabOCA3T5wO5vaxMhkmiIAhwag5escHE0iKaqTE+MkWmM4nn+sTSEag2SaWj1MpN5HotRHm7Ph270sTTUQp2jaBu0x9NUnYbNH2Hiteg6jY5U5ji9uwQipAoOXWiSlgkbUsYfGj3MEEQIoUkIYgoOiWnjoOL5MNys8ori+e5LTNIzW1iyho2Ak92SUY1NEnmtcULDMc7aTPiFJ06aS1DTIms7suSprEOebfcqJMyTExZQZIkNu3qxWgV4CQhEVF0EuqtmUnVus3FyUXSCYt45MaC79V5bGd6fYK900rw7vzmt/yNftT4iUgcuS3NCkVIlL1tBEqFxeYssjSIUHPMNXUcGgjlpzCkU1wqn8YOFLJGL6rWwPF9FCEj5IOY6uA1dDMP252m2jxMTN8OBFTsU6hCRpEztEXfjyI0gsBDlzPkrAeRhYbnTlKxTyIJhYS+m5pzEUUy0ZR2CGQi6ka64z9HxR7FD+pAgEChI/oRLHWImn0BITQsdRCBhOdXqdpnkUWEuLGHunMZRUqgKx0IJFQphaZ0kDT2tywq1yKtdzNdP8fxle8QVTN0GENM189xdOVJbL9BRu/hSu0kICGJcDFUsKdYsadw/Qa2X6XiLnGp/BqLzTHGKkdoMwbwA5djK39LzV1Bk3V6rG0sNcdQJYOU1o6EQEbCC0IBXh8ouwW8IKT4gUTNLXNk5dtU3QoRJcflygnmm2O4gUNESRJV0uhSlIXmZdJaKOrqE6CqMp+4cze3D/VwbnaBxVIVPwjQFYX2RJThzhzd6Xgrs+0gqbtBhI50rjRMYL9BQBwhDTHdgKh7nqS5B0/qxhc1Am8FXzmAIkXxgiPg+aDouPJWVBHD9jyEPEhAgO87KFKA67tI/gy+ehdTVYEpJkkqWQJ/GUnZiPALrXPZAkj4gc/Z8lm2J7azPbGdIytHGCmN8N7O9xIEAQvNBapulZgSI62l+f7897F9m6gbpWAXuFS5xKbYJiQhMRAZYE9qTwvrIFAlFdd3kYXMcGwYTdLQ0RmKDjFRn2BnYiembK5T1L82okqU4dgw58rn2BbfRkYLKzb9Vj+XzEtIQmJ7fDsAJ4onmGnMEFfjNPwGI6URdiV2MVmb5H359xFTYkzWJv+ub/mf+Gi23CP6silGZxaJGzpt8SgjU/MMtKXpyyU5P7vI7Rt6ODE+gyxJOJ5PudHED3zSEQtb95gtljE1la50nOPjM/RmQnpP3DRoOuFn1G0HWUj0ZZMcuTRFZyrG5q4cT5++eLN8ww0Ral85uN48shRu+j2/iCQiCCEjhMn2VJR/vu8uXG+EuBYQBA6uvwRBgCJncbwFJGEihBZuvoSCJPRwfAt8ZDmD5xdbKE8LLygBAbKUom4fRxIxDHXTD0UTsX2PmutwKN/HNy+N0BNLcFdXP98ZH6W7pfV0VcQwaD1O6SZeEOD5YeVub1ueN+en2ZQMBbldf30VPWm0IO2+T7sVYziZZU9bnsvFZc6uLNB0XbakcwjA9su4fp2Y1tvqd4Esyzi2R6lQ5s2XL9C4zspaUWQiMYO56QKVch1FkcN/1+gPRWIGzabLwmyR9q5UCJnXrlqLX6VwBNRrTU4dGWPs/DwbtoaLACEE++7ayDc+9yonD19mx/5BIKBWCVFIVylrP05IkmDzrh7+9ouv88r3z3D/+3ehKDL1WpN6zaaz5+3Tv2RFIho3mRpbpFKqo6gypiyzrzN/TQLUbblpVRBSlMCv4PsrCBFBkrPX0JJcZLmPIGgghILnlxBCx3UvIoQJyCjqBhTl5ovnd0p4vn9TJyrP85mdWqJSqSOpCtF0hLHRGWzHxYqbXDo6jt+iCI2euILTdNi4sxfNVFmaK+I0XSRZMHr8CtnOJJfPTKGoMvn+HJquoukKHT0Zmk2HdHuCbEeCc0fHKReqJFv21/XGC1TrX0eRe5CkGL5foN54FtO4fzXRI6QolvkgpcpfsFz8PRS5E8e9hOcvIl1H5RfCwHEvs1L8fWQ5R9M+ToBPxPrAKoXNdk5TrvwlstyGJKUIgir1xgvo6h5kue2640Uw9INUa39DofgHSFICWUoRjfw0oBIETZr2CVxvAsc5hx80qDeew/dLSHIaQzuAJK0hZHRtN5XqX1Ms/TGaFjrbRMwPoLaKkYZ+L7X6UxRK/wZd24vnF3CcM8jyjRtZSUqhazup1Z8EBEJYKHKeaORjLTRPD7HIz1Kq/hWeN4Oi9BEETVx3AsO4k1jkk3DNetH3AjzPb6EGxZoMQMu99Sr1ZJ06QKudIkuoirxqux34IVXN94OQ6iitFZ0MU0XXb260oWoKxcUy1VKddHuCcqFGz8YOugbbKS5VmL2yiO/5tPdkmLo0T2dflmqpjhUzaO/OUFqpUlwuEwQ+mfYEgR+6A77x9Gl6NoYIK0kIBpNpumJxdrZ1kDFDJ9OAIHSa9X38IGB7ro17egY4MjvNQr3KSqPO41t2EtU0BNAbT9IVS7C3vZOUEV6HA4k0oyshQmqqXKQ/keKe3n7OLi8wX63QHolyT88A05UyS/U6l4sr6LKM4/kUmo0b+uSdGEHgU/NWyOlDzDVGWWxeRhEaDa+E69vocpQucwdVdwk/cFGETt0tkNS6qHslLlVeXk0EzTY8ZKHRH9mPH7ik9B7y5jZMOYkhx3H9JpaSZqp2HEnISEJm2b6CJaeQ1EUS6S6sqP4DtYTqrs3p4jSO79FhxpmohiYYbUYMS9GJKjqLzTIVp4mlaPiBzxtL4wgEeSvJfKNEzbXpttJM1pZxfI+kZlHzbBzfC3WSJJnFRpluK73avjeSZrK2wvnyHA/nt9MbybT6sIznjCApPfjuFUKKrk6Agyz34HvT4XgnZAK/jJASSFIO318gCJphgRsRzo1SHAKnhciUCIIKkpTFtd9E0fYjyV2ryWg/8Dlfvkzda9AX6absVlhsLpM323F8l4XmEp1GG3WvyZK9TH+kh7JTYcUu0hvpwvYdVuwCebODK9UpFppLtBtZym6VilulTc+iSirT9Vk6jDZyeiZ0B0tbKIFM4AfYTQc5I6ObGrF0BN3Q2LCrj2jSolFtouoqTtwhno6SaotTKdRwbLeVbAsXxAvNEkeWx8jqcb47c4KIoiMJibQe4Wxpmt5Ihpl6gWW7iiGrLZ1Qm3YjwVh1kULr+bz5/7B330GSJfeB37+Zz5fvqrbT3dPj3c7uAlgDLECQCIAQDUAeaHA8HUlRlCBS1J0oKY53+oMiKcZRcS5OitAZKRgMiYwjj+bEo3BkgKIEBsAj3AHrzcyOnx7Tvqq77POZ+uPV9M7sznrM7swyPxETMVWv65mq9/Ll+2XmLyfoZxEWgoZbZjsZshn3ifOUb7UvUnMCAtvlme1l+lnEido8L3SvEdgu3XTEqe51bCH58NQRprzabqheCnZnh6y5HlXXRSDItKI6UQLBuCe1xbFaMfN6qhSWEGiK3nm7s1kDZ5Y32NwZUC15PHrfEoH3+vU+V1q47qsHpN6OuyJwpNFc6K8T2G4RPMpOYssHUbmmHffZIyvMBjZnRxGT/vsZsEAvC3GyOnrYIVP5ePzqw5ScBVAbaNUDHVG2ypSDRxHuA6jkSYScQefLoLeY9o6j06fQsomj+0w6LYQeItRV5kofQaXnQEiEqKN1H9IX0XoAokTVWaJsz4KwyNUmCgeth5StSSqlOYTwEMIny68hgVbwftAZIHCdowhho7UiydsMk7MoFVP3HkFTzEZRdJXVBLLJidonCbM+tvTwRYPj1Y8TqT6+rBLIKkeD70VpxXxwnEF6Dq0jalZEzfKoWRFJtkzdCmnVHkTrFVRucbhygGG6zkz1OCWZUS8fYm30LSxho/JVKpbC1ikvbJ9hLcoo2Q7DdIntOKfhPkw3tfHkB3mxu4kt5nhcrVF14FDle5j26wRWkYRbCsmcf4xOHDPMBoCmHQ+ZLzWwq4r312dwZZGcdpDFONJCkCPkjQvHRbjvA2ArHPG1azvY8ghhlqHJ6CWz1F2fpXoDgWZlsJdDEy3Ob7c53qpwYWc/Lb9EM9A8s+FxtBXz4tY1TkxOsxNH9OKIffUJnt9aZzI4jgau9No8tuc+Jms3Jdn2P3HLOZurjExnuNLFEhauKG42gRWwWFrkXP8co3zEocohPOmRqpR5f579lf0crBxkwil6NtjCpubUsG5Ktn2wfBBPepztn+ULq1/g03OfpuEWD9K3JMbWxesbwwjjPH5p2U1ueS3Gg3HGFa1EJTTdJgcrxVTm/jjZu0bjCAdb2LjW25s29V4zHMU4WuAIyZ8++SJ7mw22ukOub3Y5MTvNMIzJU4VONRdWtshzRdVxWR50eKJ3lelKBU9YNGo+Cs21dpenl1e5f3GGauBzemWD55ZXWe8OuLTeZqZWRgL/37PnODA5gefaxElOPfCJkpQoTrGtovLuOtZ4Ws4id0y97GNbkjB5imH8OLacwJITKD1EYOHYCwhsMrVJ2fsQvWwFpfei8hHd8E+oeB8mUx2S7DJaJ1jWJFm+itY5lqyR5Vtocjz7AHF2Dq1zAvcBkuwyYFHxHyNOz+HZB3kreTz6ScyV3g4TftH9d7m3Tcl2WarW6UQjTnc22V+bYDMccrqzwdGJYnjAxmhInOdcH/SwpWSQJrSjEWujPqnK2QiHrA4HTAUl2tGI1VGfpWqDRGVshkMWKnXObG8xU6pQcVwYJ4vU42OYnK3xwKMH+MIffJOnvn5+nAw4oNq4tfeHFzh84COH+Z1/8ef841/4ffyyx8c+9QAf/sR9u3+z98A0R07O86/+2ReZnmswNVfns//5d9JoVXjw0QN8/Yun+Je/9sdFEm6lOXD01lntHvv4CZbPr/P7v/5l/vTffAtBMfTsP/n5TzK/7/Wn+30j9h6a4dN/44N8+QvP8vhfnsWyJXGY8MGPHefTf/NDb3g9rld8H09/4wL/6O/+PkHZ46Pfc5KPfPLkLeWQEA5QBZ0gxwl+hShjWfNk6WmKB+kqljVHll1E6SFFFUoiZbOoZOsUwXsnEfarubDWoR9GtAcjWtUyUZqhlcaSgny6RMWfJs4Va65GL1a5kkXUtEX56CRdoanUPVon51icatBqVhn1QxzXpjFZ5QPfdYI4TKhOlGiNZyhyfYc0zhj1QrySuzucSErByUcPYHsOlhT02n2kcwCV7GV7mvzWXQAAIABJREFUeArLTkhjl2rlR3HFX6PfUbh+hF/2qJT+JgKXKPkPJGoH33uMSunHGEX/LzdmQpNygkrpR6mUf4xR+GckyfNY1jSB9zN023vx/BF+4BIOp+h29iGsF4ijp/D8Ko79CVz/exkNy+TZEMuSZFmO41jk8Y8gVM7WxpNkmaZcfpRySY/z8STEyTeLAJVO8NyHyPJr5NEGljWL55wEXgocue77maj/EsPw3xEnTxeBq5sGJTn2Y7juLxInX2AUPk6a7qFa+Vvk+ZfI1Qxrmz2qZY8kzRHCIc3+M7Ru0us/T5JJfO8jRPGAcskj8B3Kpc/iOIcZhl8gSV9ECA/XPYHvfYSXV9+vnl1lbXmT5kyDUs1nsDPCK3kMtoc0pmu0r2+TplkRbB4HebySi0bwfR86SLkW8NSXXmBipk5nvUtQ9ggHEUc+sJ+J6TpJlFItufy3P/txpCh+f601lmNj2xa97QHXz6+RpTkzS60iN4prk6cZvXafK2dWiaOU1lyD+mSVbnvAzlafPMuZnGvQXt1hZm+LUsVnYroIOg26IdKWqFzTmqlzO1IU+dOeWFuh5DgM0/EwEMse9yQq8o7UPZ+vXLtMMyhxojWNZ1lkec6T6ys8MrdAphTnt9tc6e2w3N1mrlLlGytX+curyyR5XgyFkxJ7PGw2U4qVQY+HZ+dxraLXUjsccW53HTvsr09gvU5OmHuR0hm9dB0hBCVrAk3OhLtIrjMkEku4uFaFsj3JVnwRSzgkeoRGU7JbxHmPprcXR5boxFfopmtU7BYSi0G2hW/VGOXbJGpI3d2DEIJBtsW+yqP0001qziz9nRJXzm8wvWeCcvW1h95fGmzx3PY1KrbHhf4GrrSZDWqc6a6CEDS98m6vj/Wox1K5xdOdK3xq4UHCLOH6aIfFcpMvrb+IRHBfYw9fXD3F3nKLWKWc760jhaTllbk02EIgODkxz7M711goTdB0K7dMBiKw0LqLynJUvoLKNxCyhmUfIU2eAD3Cdh8hz84iZJ08eWY8FDVHjxv2dL6FtGZRyRpCFkOCQWPZB8izc2jS8b31pfNvO9lhPd5kzp/hqZ3nyVTGA43j+JbPc92nua92hKpTYZSHDLMR5weXifMES8gi/6qAjbjN3mzApeEVFkt7eGrnBQAOlpc4N7jEoco+EpVydnCRpttACkl9sspkq0l/EGHbVpGrR2v8ZgWlFKXJIrdQqVUp7m2WJIoz3JJFNXCxxvnTKuMeN9NejYpdzLhacXwebh3gqxtnmfUbHKxGzJeafG3rHJ60KVkuico4VtvDtF9nOxnu9sG6MFinbHlUnIBOMuRkfaFYp+0z6VXRGjbCHsMsphMPKNke+ytT3Fdf4InOJTrJgFm/wWY4ohumdJPiGazuegzSlF4SUXd9Mq1o+gHXBz3215okKqMXx8R5hiMltrRoRyOmSxW2wiHvm3opmF0reXz3o0dIc0WcZFivkyLg5pxeL3/v5e+/VXdF4Egg2F+ZIrCKxFO5ViQqQwP7ypNUHJ+S7bFUmcQRFifrC2S6iNbd+PuNqMfUOG8GsoGOvwxyBukcQ2VnIV8purTbB0CPQNbR+TrCWkCnp4vx4O4HARt0jM43EVYLnW+i8nNI933ofAWdXQX7EFG+htYZltUkzzvjYU3huPLbxLZmyfOVoru2Tsl10Vpvy5dm1VE6YaX/u0TZNaYrn8K35zl9fYOVTg/PsYnSjCxXlD2XAzNNLm3t0O6fo+S51AKPKO0yiDZxHYvW0RzfKRGlCmmVqDsTCKzdRNtT/mFAkuY2Amh6x5F6hC1LoCOUymg4E1iiXNxsUXiWT0tWmAqqCARK7yHXipLtUrI9Gk6N++pqHB2VeNIuEp/ZPqmKOd37CsNsm/vqn+R8r8dWPGBfpYhA97OYi/1NUpWPk60VIZGWV6Zie6jbJClf6fcIs+LCuTHTVZLnRa6L9iatoEQviVkb9JktV1is1jnT2eLE5DTPb62TKcXaYEDF9ejGEWXHZWM44Ox2G6X1eJpnj5nS688IY0mL/eX9PN99vuh9NHiRg5WDWMLiWPUYn1/5PBYWn5z5JEIIjlaPcnV0lVznrzg2wa0X8vXoOoNswGJpkWvhNYb5kAYNfMsnVjHnBueYD+ZpOA2qdpXTvdN00y5XRldYKi+9sYtuvN395f1cHFwkzmNsaaNQeJZHy23x1M5TtNwWq+Eq3H7W3/ekp1+4Spop9k/UEUJw5WqHCcdldrbFVKvKmYvrpIOUk3PTKF0EmpJhxkcP7WP5eofqlEecZDx0YIFuGPPs1VU+enT/bm6ZH37kJM9fXOXwdIs8zMminJrtMTVZVCzKvsvaVo89lSrnr20Rpxn1ss8oTglch94oYnN7wGyrxp7JOnOtClm+jSOncJ0lcrWDZx8hyZaxZJ1B9O/x7P0U02PnRWu6rOBYMwTuSQbR18a9jtZQaohrHyDNV8lVD8eeRakhaX4dgYPnHEIgce29KF3kNLKtWSyryVu5nRTT9Np8ev8xhmnKVjjkE4sH6SYRhxpNFquNIvFxfYKFSp1MKT4wPU+UZxyfmKIThSxVG5wZB5i6UUQvjtlbadAOh9hC8sjMAiXbIbAdPjy3tDuFSapyjjWnxg8BMbHqYY2HfziOzU/87U9w4fQKw15Ec7rKnr0trlzYoDVVRWnNdjwi1TkLD8/yU7/8faxe64CE6v4a62EfjSZTOV7g8MM//12cO3WdLMpZmpukXC16Qh2+b56/8w9+lJUrbYQU7Ds8SxKnpElWJOKOM7Ik4zM/8WGuXd5ia62LEFCtleis7WBJWNg3yQ/+xx9isDMiT3MsS7Kwr8WP/9zHEUJw4n17aU1VsWzJkZML1H6uhOvZHDk5z4//V5/AD1xsx+KRTxxn5sAUa1fbDAYxc/MTzC9NcuHyBr7r8KmfeIyZPQ02tnoIIfBcm5/57z/FwsEp2tFod6aWpffN8eO/9D2cvbiKZ9tUD9S4PuqOZxCxGGUp08FxSrbDS93obx5Wo1BItKYYqi5bKJ1gWWVAIq1plM6gaLdG5dtonWLJACkq77lha9uDEXGW4doWjiVZ2wkpey6NcolOnlOfqdEdRkVyeM8hcIsZUUuNMp1ByFBl2DUXt1EEPm8Oft782pt9qYFg7fIGw+4QL3BoX+8AMNgeUJmoEFR8omFMUPFRuY1Wn2XYDylVAkb9kK0ko1TpEg7WmVpqsud4A1BY/meoBj+MEBKlc2LVxy59joSAPO8SU8Uq/RfE2Lil/5SqVccSLp3NPmdfuEqaZOzZ2yKONMP+D7Cw/6dptztshSl5rqg3fFpTA/q9EZ7v7iae72z2abR+lJ329xLHKY1mhYV5G2SReLte/VsAjKKEwTBmMIopBy6WtBmMBKOwSz5O4IwGIT5Enj9CqeTRqN7aojsYwfXV+2k1H6M/GLGy3sX3Harlz1HyJzh7cY25mTrL1zpMNSskaY1m4+eI4pQozli+NqRebVOr+hw9MIOUAb73GL73GK8nHESgIRxGRKMYaUmynSFZmrF5rcOoF9JZ22Fm7yRZllOq+qRJRp5mHDk2V8zkFqUkUUoWp4Q3mszHl2Z/e8C1s6uoXNHd6rN1YY199y2QRCkqy+m2+zSaZZRS7D00y5UzK5RrJVIJZ751ganFFu/76DFK45wlLw8ETc83i+GWh4vAuRCCww/uZXV5i8XDM7Tm6iQqo5sOURo+vLAHy1ZsJT0e2jPNuU6HjIzvPriPplciyhNWww6PLsxi2Rk/dPQ45zqdcX4SmAgCPn3oGKuDHrlSZEoxX60xXS4S7Z5oTePImI3hJp851KLmbvLpA5Mk2RUem/Mo2YqFSovr/et8z74m81WbOFtjJugwW95LfodmNLobSOEghcXe0kP4Vo2d5HrRsG3dOjV52W7SdPfSSa6w2d+hLPdS8+bY6V9kpFwC2WSnNyAebTJbruDlB+lEXbK4hydmKbsetvSYLz2I1oqKM4WsWGQ6Rlc8qg3xRkZPo7QiVTnTQa24w+giF2OUp6Qq50x3jb++7xHO9dYZZQmxyijZLvOlCa6PthEUCbCVVkz7dWaCOrHKqDk+qXLYUgNSleNbDkdrs2zFA+aDCc731mm4JdbC7i1D35TuovJ1hMyR1gxap2g9QKkVpJwFqkg5Sc4lVL6J5Rwa9yD6EHl6uhgya/lIq0lxgYpx8IgiLUeeISXk+SpSNsnzNWz35I2/xBofS9G7xSoazXTx/yiPOTe4NH7mUxyvHeZU7xxXRteZ86fJVEaYRyAEliiGnlXtMtP+JBeGy5zpX0QKQZInRComzCNGeUjYCen3I5I0J0kytrshU60Kea7wPYf+MCqS0AuB69rkuWJlvYttS6pln0P7p6iUfZRWXBxscC3scHGwQc0OcIVN1fGpOQHdZMjycIv76vOshV3mgga9NMS3XCwhqNj+7newUGqyFfeZCxrYwuK5nauEeUInGXB11MaRFiXbI9M5DadEyXKRQvL09mWWyi166YimV8HGYm042J3RdXXYx7dtwjSl6nrFCAZp4Vs2UZayNhrQiyMC20GhqboeZcclTNNbkoaHcUoYpwzCGDScvriOmykq4zxHtmuj8uJ5UimF7dhkaU40jCjXgmJYXpaTZ6q4f+6fes1cmW+UeM3cDO+c2+7E60XObixTWhHm6W6XNNCo5GmQNQQ+KnsBaR9H51dAziCEj1ZrICchvwayCVgIey+oASp9CiGnENZUkQxZj0APQbYo8ufYYO0B4SKQaH0jH06MtIqADciXJUkcHwsScMddiRWZ6lMM+SgjhcO1dpfuKGIYJXTDiKlambLnMl2vcGmjU1S+rfGsBFqjVXHxH1+YxrEslE7GLWA3pm2X462r3RZ1MR6INkwvEjiL48qQGHf3lOhx12aNRlIMEXs1u12iX/GeIlEhIHBlQKKK76IosDRSFMPgBmmMBsq2ixRFoaEBW1ivGGPciyOe2VjbvTg1mm5cRHhrrlesS8N8tYZv28yWK5zb7nCwMcGL7S3a4YipUplBmhDYDoFtszros1Crc63fZbZcpRtHDNOE+6dmmQxeeza0UTbiVO8U6/E6c/4cx2vHCayARCU83nkcV7p8YOIDSCEJ85BTvVOsRWuU7TInaydpuk2e2H6CpdIS0/5LXeyvjK5wqncKpRWLpUWOV49jS5tEJbzQfYHVaJWDlYMcqhxiK97i6Z2nsYRF3akz68+yWFpklI14rvscJ+snKdsvtXSc7Z9FIDhcPTw+h3IuDC5wYXABIQTHqsdYKi2xGW/y1M5TeJaHL30OlA/cso+v4V5/atMXrxRd1W/MlJIkGb7vkGc501M1Ntt9fM+hNVFmuxsyGBZd1D3Xpj+I8DyHKEo5tH8a1y1aV1zb2i3HcqVYa/dpVAKW1zqUfJc0z8nz4rr1HJtBmOC79m7QSAhBfxTjOhZplpNmOVONCp5r06gEpPkqYfIsrr2AEC5SVMhVB9feyzD+FoFzAiE8BvG/x7H24NkHSfNVAvckWb7FKHkGKQIca248LKiL0jGWKKOJESIgza4jhI9rzxdlHjG2nCHLN0nVOmXvg29qlstBmnBhp83x5jSu9cZvZuvDAafaG+xEEQrNhBcwTBMSlXOo0SRVikvdbRwp+dCeRWbK1VtOylwrTneKYWoPTs1iS4tcxWwnZwFJyz/ximDuy6Uq58+un2aYFq1xJxqzLA87bMcjpBAElsNCuYEe/+0wS+gmIQerkzzQnH9DSTnPP3e1SBK7skO1WSaJUuIwIYnSorWu5NHZ6NIcP4C117pUG6VxPpA+7//OY0zteeNTlF9d6bB8tcNgFJNlOYf3TzMYxaxt9Ma94DLyrJgJq1oNeOiBvVQrAWGe8MVr5wBBzfGYCspcHXTpxCNyrfAth/lyjVQp2lHR2+twfYoJ7/bdqHM1JEzPkozzEt4YzmTLCYRwiNNLOPbc+G8HxfAdtU3FewjfPnjz8d7zZRHA5c1tGiWfYJwLMFfF/VlKgVI39XcpngoQcEtZc2OaX9uybkmM/lq6m70bK2Ll/Bqu7xKHMa7nIG1Jnin8wGXUD7EcmziMqdSLc/TGrG5BxUc5I7zFDkneR6PwrAaWcMhURJi3UTrHt+qUnTnWw6fxrToVe45EDWl4+6na84wGMcsXNnaHYJWrPkmSMTldo73RI88VliXxSy61eonVax0836HeKDMcRAz6UTFEIskQAoKSx77D08iX9QR5/uwKL15cp1ELqFcDuv2Iku9gWZIkzWlvD9Ba43kOs5M1ppoVFudubVFpbw9Z3+pRq/gMRjEqLwJOWa6Ymayy3QuxpKA/jJlqVgijFNuSxEmRJDxJM1qNMhqYm759D5tXkyYZeZaPj+vGMLXixLgx1HHjSpvGVA2vVAzXujFU7caU5uqmIWpCCJRSxW9uSfIs58zjF6k2yvQ6A7zAZeHIHO21bcJeRJZmuL5bDFedKLN2eZNKo0QSpQghqDTKTMzUXzXR+6vJ0gw9nuFvOxnwROfcuIz1GGYRmc6p2sFuUKlsB8wHLS4MVsi1ZsqrUXNKHK7O47/J3tNx+iJxehatE7QOsa1phCihdUiaXcG2FrBkHSE80vwKUpSQsk7gPfrye8i9XhbB+GzKdUY7vkzVmSawXpkP7xUf0ppOcoX1nZT1jqAfxcRpzok90wzihJXtHq5tEWdFQzlALfB49MAiE+XSqwaGOhs9Lp1ZxfEcjj6w+JrDtodZzDOdq2RaMe1XsYWk4vj006Lu1o4HPDCxyBPty/TSiAebi6yOdnhkcj+Zynmivcwoj1koNUlVzp5Sg29uXWSx3CyCjrp4vmrHg93g1N5yi/P9dfZXpvjm1kWO1GbZV5kcfyfFM6PAKoaj6YgsfRbbPoaQ9fFX7QJxMVJF+KCTImWHTtm9vhlf6zcao4WkaMDLKXqAi+K1ThEyQGnFqd45wjxkX3mRbtqnE28zX5ojymO24g4z/iTtZIdc59TsohG9k+ywVF4gzCOWh1c5UFlCaVUMVfOmyLViNphieXgdhWKUjShZAQ23xpn+RfaVF2nmk7Q7IxzbIk6LGfOqFb+YPCbNGI0SHMciTXN836FS8tjY6uN5Np5rMz/bwB7nSIpVRqJSHGkjEdjSIlU5rrQI82T3+THOs6KHIEWQSwpBOu6UcuM+meRZ0aNKSuK8uEc4wiZS6binlSRRRV5S33JI8gyNxpU2sUoBgSMscl083woY/78YmmYLWbyWophhVgiiLOVCt4MjLQ7UJ7ClHA8hLkYa3ejBuNMP2dge8NSL15huVTh1ZoXpYU7Zc/BLHktH57h+YZ04SrEsies7OJ6NF7jF0OUsZ2t1GyEErdkG7/+u4zfSKLyt8uiuDhzdsc3c3Li567W+x5t3786W/zd+D6X17gMnvFQRfLXf6822shbreSkR473ijQYTX+1zt/NqwS/jLbnXvzj98nPsdufOy8+Pt3r+vNa6X22dt7sGiveLisJbOXdfbZ23/xu4+Wf+dneDfT1pnjNIE3KlkFKOWwKL3j1V1yPTijjPi9Ylx31DQwW0LmZVE0gs+foPF5lSnOttMO1XsaSkZDl04hFb8ZA9pVoRmpfWbl63G5WJku3gSfsNfU/hMEbliizN8AKPLM3Ishx0kUtISkmeq2JmI1HMOiKlKFqgxg/Y0nrjSYCTNGMUJmSZKh6wx61acZJhWZI0LR5ubcvCsiTlkrs7ZKMImMndqYkTlaHGFSkpBK60iPKMfNxTuOzcOq3trb9FjtYJiqwYiDuuFEvhoclROh7/X40nMigaRCxZQYqbezHd+2URFC2JN09o8K7syOuUibe7hwJochI1ROscISSWcFC6qHgX71lILISwSNWQIoeazShrU7JbeFb9ttu7nduVj2+mXO4PIuI0QynNdm9Eo1Yi8OzdyrweB1OK/EPFee2/gVwTt3wfb6C8v5O/s8rVTWkA3ppXv/+8+jnx7TqmRGUMswhbWCjUOG9T0ZMxVXnRVCqKpLOJypDjBz9g3OPgzQ0bUzoaJyPPKRKqO4BV5JlBcSPvFqgi3wxOEeyW9fds4OitSrKcYZyQ5sXvFHhFY3GYpNhS7va0sKXEkpKK775mA0scpcWwJlsiLbkb/LwXaa2KvEXCR7zJc/Re8Xr12Ze/f/Oy99rzWFHf1Lvl1WsdX54r4jSj5LusrXfpre0wM9/Esi28ksuwG46HDFuofNxwIMRunrob25OWpFIv3diWCRwZhnHXuNdLeFMWGcZ7gymLDMO4G9zrZRGY8sgw3iveE4EjwzAMwzAMwzAMwzAM4y7z3uwTZxiGYRiGYRiGYRiGYbxtJnBkGIZhGIZhGIZhGIZh3JYJHBmGYRiGYRiGYRiGYRi3ZQJHhmEYhmEYhmEYhmEYxm2ZwJFhGIZhGIZhGIZhGIZxWyZwZBiGYRiGYRiGYRiGYdyWCRwZhmEYhmEYhmEYhmEYt2UCR4ZhGIZhGIZhGIZhGMZtmcCRYRiGYRiGYRiGYRiGcVsmcGQYhmEYhmEYhmEYhmHclgkcGYZhGIZhGIZhGIZhGLdlAkfGu0II8ZtCiL/3bu+HYRh/tZmyyDCMu4EpiwzDuBuYssh4NUJr/W7vg3GXEkIMbnpZAmIgH7/+Wa3177yD+zINnAGe0Fp/9zu1XcMw3n13Q1kkhPg94IeAdPxWprVu3OntGoZx97gbyqLxfnwf8A+Aw0Ab+K+11p9/J7ZtGMa7724oi4QQF4CZm97ygT/SWn/2Tm/beHfY7/YOGHcvrXXlxv+FEJeBz2mtv/gu7c4/BZ57l7ZtGMa76C4qi/6+1vrX3oXtGoZxF7gbyiIhxPuA/xP4CeBLQBOovOaHDMN4T7kbyiKt9cGb9kECV4F/807ug/HOMkPVjLdMCBEIIf6FEGJVCHFNCPFPhBDOeNn3CiHOCyF+VQjREUJcFEJ89qbP/p4Q4n+46fVnhRDPCiF6QohzQohP3LTsY8Be4F+/g4dnGMY94p0qiwzDMF7LO1QW/TLwz7TWX9Ra51rrTa31pXf2SA3DuJu9C/WiT1L0fPp3d/zgjHeNCRwZb8evAg8A9wMPAR8Dbh4Tuw9wgVngZ4DfEkLsf/lKhBAfBX4d+G+ABvAJiqg140LufwX+9h06BsMw7n13vCwa+++EEG0hxONCiB/89h+GYRj3uHeiLPoQYAshXhg/FP6WEKJ+R47GMIx71TtVL7rhp4Df11pH37YjMO46JnBkvB0/DvyK1npLa70O/Brwkzctz4Bf1Von4+6TXwR+9Dbr+Rzwv2utv6S1VlrrK1rrs+NlvwD8udbaDFMzDOPVvBNl0T8BDlFUsv4+8K+FEA/fqQMyDOOedEfLIiGERVEG/QTwA8BRiqFq//MdPCbDMO4970S9CAAhRA34DPCbd+JAjLuHCRwZb4kQQlBUXpZvensZmL/p9ebLIs/LwJ7brG4RuHCbbSxRFFi/8rZ32DCM96R3oiwC0Fo/obXe1lqn4yS0/xdFsmzDMIx3pCzSWudAAvyG1vqi1roH/EPg+9/m7huG8R7xTtWLbvLXgata62+8hd017iEmcGS8JbqYjm8NWLrp7b3A9ZteTwoh/JctX7nN6q4CB2/z/mMUhdhZIcQa8I+Ajwohrr2dfTcM473jHSqLbrtpQLyJXTUM4z3sHSyLnqMofwzDMF7hXagX/RTwW29hV417jAkcGW/H7wK/IoRoCSGmgV8Efvum5Q7wS0IIVwjxcYrEaX94m/X8BvCzQojvFEJIIcSiEOII8H8D+4H3jf/9T8B/oBiraxiGccMdLYuEELYQ4oeEEGUhhCWE+BTwWeCP7/BxGYZxb7nT9SIoZlT7nBBirxCiDPxd4E/u2BEZhnEveifKIoQQByga+v/VHTsS465hAkfG2/HLwCngBeBp4KvAP75p+WWKMbRrwP8B/LTW+uLLV6K1/kvgvwT+JdAF/hxY0FpHWuu1G/+AHpCMx+oahmHccEfLIoqeRb9A0Rq3TZEr4Ke01l+/M4djGMY96k6XRQD/G8UD3lPAJYoy6e98+w/FMIx72DtRFkHR2+hLWuvbJcw23mNE0ZvNML69hBDfC/xzrfWhd3tfDMP4q8uURYZh3A1MWWQYxt3AlEXGW2V6HBmGYRiGYRiGYRiGYRi3ZQJHhmEYhmEYhmEYhmEYxm2ZoWqGYRiGYRiGYRiGYRjGbZkeR4ZhGIZhGIZhGIZhGMZtmcCRYRiGYRiGYRiGYRiGcVv2u70DYzpTOanOcKWN1pCoFFta2MJCUwyn0xqEKD4ghUQg7sjOKK1RWiGFROvdrb+0szDeskCjSVWGb7lI8dL+rC9vAnDt7CpBxQegu9XHDVw838FybMr1gHiUYDkWcZgU284VSZjQnG3QWd/BcWyyLKfWrHDggaU3fSwvfP0MeZrTWd2hXC+xfmUTIQVosByLWrNCHCYsHt1Dd6tPe2WbNE4BWDw2j5SCNE4ZdkPSOOXQ+/ezeHTPm/9S36B+EnOuu8WJiWl827lj23lxe4OS7bC3OnHb5Upr/unTf8HZ7hb/y0d+gIrjveF1a605193iTy6f5ksr55kJqvz6x34EKV6K06Yq599efI6ntlb4k8un+c2P/xgPTy+8xlq/PbJMEUcJQclDyldeP1pr0jTHti2kFAz6EX7gYNvWG93Enbko3yFaa6015HlOEmf4gYtWerfc0UCeKZRSAHi+u7ts2I+4vrwFQLVeYm6xiRACrRVr4TN4Vo1ecoVEjai7i3STq1SdOcJ8G6VTUhXiygoVZ5a6u5et6DSW8BAIhtkGtgzYU3oIR5aI0sukaos4vYwUJaRw0WQoMiwRkKkeuepTcR8kzC5gywZ1/2OI8Tl4enWDi5sdzm92+O7jB/nmpWukeY4QAtey+Mz7T/DE8nWudrrUA5/vv/8oa70+37h4hUwpPv3AMf7tky9wdHaKK+0dTuyZ5ukrK3iOg2tszYJ9AAAgAElEQVRJPMfm+04eveW7HSUJv/vNZzm5Z4apapnpapk/fPIFjsxMcrWzw95WgxfXNknz4ru1peQnP/R+bOu12ze01gz7ERurO/iBixe4PP718wQlF4D5vS3aGz20hu32gPmlFmiI45SdzoC9+6dIk5yDR2fxA/ftnj/82dof8KHJ76bhtF6xfJD1sIWDbwWv+NzZwbMMsx4fmPgoAHEec2l4kYpdpZvt0E26tLwWEskoH1FzaqxFq7jSY19pH+2kTdWusp1sY0kLgaSTtLGE5ETtPsp25TX3XWlNOxrRTUIm/TKeZbM26uNZNjXXoxOFKK2xpKDqeHSTCKU1c6UqqVL80cXn+eDMXo5OTLE26jNMEya8gKrrcbHbwZGSfbUJtsIRgzTBtSwmgzKPr1+ln8Y8Mr1IVbpcvdohChNm5xqEYUocpwS+g+vZ5LnCti3SNKdc8hiFCUoppJSUyy6Vio946T58T5dF3FzpeAMGacyXr1+k7gY4liTLFethn6mgQicaIYUgzDIApktlGm7A/a0ZbGnRiYdsxyO+tHaWv7b3AcIsJcxTjtZniPKUtbCHJSSzQY0oT9mMBviWw5RfYSPsk2nFbFAjUzmfv/osD7X2crQ+w1Y0oJ9GTHhlAsuhHQ9JVU7LK5NrRTseUrJdJtwST7av0k1CHp7cS90NuNDfIrAclipNuklIJx5RcTyqjsdWNCBVORNembob3PJD51qxEbWJVcpSaY5+NqSfjZBIprwGgyxkkI0o2T4ly+erW08z7TU5VF0k14owi8i0YsKtEuYxNadMPx1SsUvspH3CPKbp1hlmId9oP8sHW/cz6TXopyOG2Yi6U8WzXDbjbc71lynbAQ9NHCdVo926pECgyJHYhPk2UkhcWUGM23IH2QZKZzTcJRQZ3eQKNWeefrpGwy3qgVLYCCSpTtBa40qPRMVYwsYSFqlKkMLCEhaZTlFa4UqPVCdcGpxlT7BEpEY4wqXmNMh0Ue9zhEuiYoQQ2MJhJ21jCZuqXaeX7pCoiElvllxnu+vMdEqqUlzpYsvXrrfF2Qa57mPLBrassx3+JZkaMFP5ATLVZ33weRr+Bym7h2/5nNIpYXoJIRwCe4kk3yLXQ6Rw8ay53Xtb8bcxUbaCQOLZc6R5h43hnzJV/h48a4Z+8hyD+EVmqz+CFHeunnmTe70sgtuUR2mSce6ZZSbnJmjO1LGdop64E4dsxyEKzU4cMhVUGKYxge1Qsl124hDXsnbvOTOlKovl+s1l93vaH//21zj1xCV+7lc+Q61RfsXyJE751pdf5MmvnOX8C9f5xX/+k0zvuf2zystlSpFkObYlb3kmzZUi1xrXeqkur7QmVwrftm/57nOtONtboxMPmPJrrIY7lGyPQRoBMBfUuTDY5OHmPq6OOky4pfFn1lFaE9gODbeEI216aciR6gxnemscqExxvr8BwIxf49xgnX3lSR6c2PuWvsf3glwp2oMR9cDHc24fktFac369jdKao3NT347Nvq0L7W4JHNHPRpzuXaJql+lnIwZZSMOpUHPKWEISq5R+OgQE+8pzzAbN3c/mOufq6BrP7DxLmIeUrBLfMfVhJtwJBKJ4sMhHPLPzLNfCawgE+8v7eLj50G7wKVEJ3+o8QcOpU7Gm2Iz7uNKmHfepu2U0ikwp5oIGg6yoMPfSkERllCyX9zf3k+mcxztPUHPq+DslOqs7jLojgmqwG5Dpbw+oTFTYc3AGISVpkhENY5I4IRxEOK5Df3uAkILO6k4RaApc0qSo8GmtyXWOFPKWQMQNf7n5Fa6HK2Q648HGg8wfnEcgaEzVEVIwvbe1G31zXHs3cNWam6A5O8HsvinyTCGE2C1ISlWfzWsdNq5uodWdzYl1trvJb774OL/yyCfvWOAoUzm/c+4p3tfa86qBIwF8auk435FEBG9hPw7XJ/n5B76DfhpzdbDziuW2kHz24IN8YHKBL1+/8KbXnytFrjS2lLcNAAEopVlf22Hl+jZByWM0jEmSjG53xOxcg/n5Ca5f26bZKhOFKUpppmdqLC9vcfjwLMuXt7h0aZMDB6eJwoSFxRbrazv4vku54hEELghoNl/7gfRe0u0MWb3SZnO1S5bmVGo+WaYQogi6BWWPoORSqQW0N3pMztZZ2DeJkIJTTy/zP/78bwPw0U/ez9/7h5/FsorgcmA3kVjU3ZeCv1P+CTyrhp9PoMkRWDjSxxYBjggo2VM4soQtPGLVRwp7N4huWxPYso4jm+R6hCUqKD0CJFIE5GoHhMCWE5SEh21NcONekWQZ37p8nclKiTjNGCUpZc+lUfIZJSmDKObS1jaX29v4tkNnFCLES3ea7z95lDzX5EpzYm6aNMtZ6w6wLYv752fIlOKFlaJykOY5SZZTch2SLKcfxTy0NI8lBTujiEwpTuyZJlOK1W6fsuvSKAcMophBnJBrhf06HWNHw5jf+42/4P/5w8eZmq3zY5/7LvYfmqFaKwL2jmvjeTZKaRaWWlSqPmmas90eMNGq0Jqssra6jRwHqHKdsTw8x/XoMnW7ybHag2zGq1wensURLifqH2A1vEo/2yHMB+wtHWLKm+d070lSlTDK+5zvP8+hykkuDE6xr3yEdrJBzZngXP9ZDlcfYNZaIMpDzg2eYydpM+cvorXmerhMlIc0nEmOVB+g5bUQSGw5yYTTpGJX0CjiPEYDi6UlLIp7wbQ3A4B2NZawKVkBkQrJVbZ73ryWdjTk85dOsVipI4XkUq9DJwoZZjEtv8xyb5tRllJ1Pab8MqujPk0/YF91gkdnFml4RTBMa83FXofzO200mh879ADPddawhWS2XOMPLzzPZFBifTTgB/Yf53J/h0404mB9krLn0O2O6PcittoDut0RzYkKQoJA0O+HzO2ZoN8LqVR9wjAhilKyNOfY8T1Uxo00fxVorUlUgkbjSreo19SaNFwfS0q245CG51N2XPZVJ8h18eBgCVHc0zQIIYjzjC9ce4EJt8xWPERpzaVBm5VRl6P1GV7srvONjUscrc/Q9Mp84doLVB2fuaBGLwn51tYVSrbLXKnGI5NLNNwSAP004gvXXmDKrzBIYx5sLvBn109xojFHMOHQSyNO7ayyPOjwNw48xJVhh81owMHqJGXb5dnOdWqOz2xQ44srZ/Asm51kxLH6DF9eO8fB6hSg+fTi/Tjy5ochxbXROlvxDvPBNN9oP0uuc/rZiMdaD/LMzhkkkkPVRWa8FldGa6QqZaE0w/n+FdbiLfaW5pBC8K3OC3zn1EN8detpTtQO8MzOWWb8FoHlsZP2uTS8zsHKAp50+dLGN/GkiyUkC6VZLgyuEuUxe8uz5DrhyvDr5DqlbE8R5z18q0asBljCZpS1cWQJV1ZpuHuJ8h0kFtvJJUCT5ANwIFEDdpJlcp3Q9A4wymMuDl6k6U5RtqushFewhM20P8dqeBUpLPaVD/F890mmvFla7jSXh+fYjFeZ8mZpxxs03BZZknJ5eA4pLI5UT7ISLrOdtDlee5DTvWeoOxMcqd7PdrI1vp+VOdN7DoFgX/kw18NlBlmPQ5XjTPuv3rCotSbMlhmlF8jViIXaT+NYLXJdPJBaooQtG7t/q3SI0jFSFGVLP34OIVw8a4b14R/hyinifJ256meRIkDrBEuWyNWQQfwCw/Qc0+UfxLdnsWUVACEsHNlEijfeSKC0KoJpyOJa+ysS4Hg9Qgosy+L0ty6AEOw7toeFQzPkWnFtuMPl/jYazX3Mcr63hSUk+6tNojxjbdRjvlynE4/QFA0QjnjpOs4zRTiM8UsuUZiQJhmu5xCUi0ZPrTVZmjMaxGit8QIHP3jpt8lzRTRKdp+dikYlZ3e51pooTIjDccDUsfDLHta4HqCVJhzFJFGKtCSlio9lS4QQpElGHKU4rk00itEagrKH69njxkJNmuREo5g8V1iWpFT1X9EAG4cpO1kfy7YoVTys8XLHtfnwf3SSSj3g2qXNWz6T54pBL6RU9rAdCyEESmlGgwjLllzo7zCIY1yrqC/a0iLOM8quy8ZgiGdbVF2PrdGQXGlapYD7Z2exbzqn4zxlK+5zpDrLuf46i+UWX904y4HKFJaw6KYhEkGsMnzLYSseEOUpi+UmX9s8z1xQxxYWiojj9T003DICQScZ4kgLR1osD9v004iq/ebu2UmaoQHbkqy1e9QrAZXA2/1u4jSn5L/2M1uUZnzt/DLL7W1m61U+fuwgl9vbvLi6ST+MWWjW+cjhJZ66ssJGb0hnMOLo3BQP75vnWqfL2fU2O6OQRsnno0f2sdkf8fXzyyjgo4f3MVkp8YXnziDG2/rYsQM0yyWeurJCL4xZ6/Z5aN88h2dafOPCVS5stPnUg8eYcmxGccJfnLnEWrfP0uQEHz2yj4ubHX7rK0/iOTYfWNrDx48fJMlyvn7hCtvDcHddlnxnBpHdNYEjVzo0nCrDPMSVDlW7OIn72YgZb4L1eButFXWnQsUObulttBVv8XtX/gBLWsz6swyyAZnKd5crFF9vf4MvbXyZg5UDuMJjmI1u2X6iEh7ffoLFYIH3NVqAINM5ZdunbHtsRF0sIanYPp14OK6s2VhCEtguEkGoEh7ffpL5YA+fOvJ9zC5NIaTAdmyWjs+jlC4KWlti28UF35ypIy2JUmo3yCVkEbRRuUJakjzLcVx7dz+f3nmWo9XDNNzGK77HulOnk2zzla2vUncanJg7hiUsGlM1gFsKzZsJISjVAqqtKqDRqvgbaRV/71cDGtN1SrWANM1wboqMdqIR53ttDlSbnOtuMcwSlioTHKg3scbBLa01gyzhVGedXhLR9EucmJjZDcrsxCHnu1t84cqLXO5v8+fXzlF1fKaDMve35vAsGw3EWcqp7Q3a0ZCK43Ffc4aq4yGEYCcOObuzyYFai4u9NjtJxJRf5tjENIHtkCnFlcEOpzprPL11HSgi446UHJ+YZqFSfJ+rwx5Pt1fQGqaC8s1dzIAiGn990ONSv0OUpdRcnxMT09Rc/5aAm+SlHnIvJ0RxBt8u5qO15mx3k14S877JPbuV4iTPeL6zTmA7zLlVLq53ODw3STW4fW+oNM148fQKWabYfHGVubkG169vMz1bnAtnz6yiNUxOVRkOY5TS9PsRUZiyurJDluVYluD0qevU6yVypej3Ih5+5ADnzq4RRSlHj///5L13kCXZdeb3S5/5vC3zyld1tfc9toHBDDAzGBCeA4DgQlyCRiK5G8tYUSuGQtIy1sbuxopaiaJCogkuDUiAC2BB2AEwQ8wMxpv2vrqru6rLu+dtev2Rr15VddVMD0iQAonzT73Kl3lfmpv3nvud73ynd+cL/DtqsiLh2C6xZAhZkYJ3VgDLdDAkEVVXiET1zvZqqREw+AgYkZ4bvFfrjCQAUZA6UeLNC/j1McyQN0DwzdtT2lhnW49xBNd3kIVgkpXFePBX2j4GBLZTBKftUHk+1ZZJbzwCwgaTU2j/sk/ADnJcD1kTwPFp2Q7fvHCNZDjEfLHCcCZJSFV47dYsK9Ua9wz1Uag3tjnUE0urnJ1Z5GceOIomyyRDBs9eu0lXLMKubGpLG/t6u5haKyBsugfvxBq1FuffuEWt0sQybeZvr/HwBw7t6NxvHvfS2WAh4Xs+oYiG0o6W2p7FdH2CiJKgS88hIuP5HpIgM9+cJqlmmGlMEpZjZLUcE9UgYFG2C+yOHmaqfpWyU2Sqfo2F1jSCIFB3qgyGduH4DhW7QI/eH+xnF9gbO4oqqCy1ZgGfkfBeThW/z4C2C3dZxIhoKGioukpttYFtOUiyQqorhqxIFFcqIAoIYtA/m6sORtzANG3irQyWb2K5LnmnhCCKWC2brr5kByhbt4plIiDw2MA4luvy5vIs93UPcKWwwnKjSl8kjunaKKJE3bYYiMQZi6eZqhTwNt3XpmMzXSkgCLBQCxhLo7EUi/UqACFF4cGeIV5YuIXn++yKp2mEo4zHA4bW/v197XfJAwEkScT3wXFcZmbyZDIRdu3qRhQDhxlgZiaPpv2tMAd+ZMz0Wjy9/E0sz+R92Q+Q1DLsT3UHLzTQbUSCF7v9//qC5s7Pputgex4Pdg0z1yiiiBLDkTT5Vh2AoXCKpUiF1VaNpmNRMBt8sP8AEUXnlZVb9IXjDIZTXCot4Pob417VbiGLIg92jfKN2Ys0XItuI8a7ukeRBJELxXkc32O5VUUSRMaiGbJ6hF2xLIIgMBxJU7GbtFybhmvxcM84zy5OULaa5EIJ7ssO8dLyTRzP2wIcKaJMX6ibsl0DQBIk9kSHma4v0HRb7I+NcbM2S8GqMB4ZIqdn2RcbIakG8+JgqJcjiT1U7Bqu7+L5Hi3PouLUiSph7k8HY4su6fTqGfbHx1huFZhvrjAWGUCXNIpWheFwDsuz2/dEICx34fgtFNFAQCSq9ICziIiEqCioYhhZMAgrXYiChA/UnWUEJDwfLK/eeXaO20ISNErWAoYUptcY4GZtgriSJG+tMtO4RVrNUrTWqNglHN+m3xhmsTWLLhlElUSb5aTScpsoooIm6bieQ8nK03QbrJpL7PEPEZXjdGk5FEHBkEOsmcvYnoUkSCiiSt2tUrFLyKKCKr79ItDzWzTtaUDAdJfu2ser1iUa1iQx7SgRbT+GMoLlrrSfa4iE8QCFxvdxvCqmc52mfZu4fi+eb+J4lTbztnbX37mbrZhLPL30TXr0HI90PY4qvHMG+t9nExDQDBVBFKmXG5x/aQLfh4G9PRzN9HEwFfiHqigxHs8gCoGX4eNzMNWNIkq4vo8iiMh3BMLXlkp86fee59i7xjn/6iRLcwV2HejjE7/4MOGYQaNm8vw3znLqhQk816N/JMvHf/4hMj0Bc+n6+Rme+copCqtVHNtleHcPn/zvHiGeCiMIAkuzBb75Z6+wNFvAdVziqQif/kfvIzeUwfd9ZiaX+frnXia/UkFRJE6+/yAnHz+IZqhMXJjl2a+eprs/xc3L89SqTQ7dO8qHf+Yk0XgIs2Xz/DfOcvrF65hNC1EUePTjJ3j3Txzu+CXlQp2v/OcXWJzJIysS7//Evdzz8F5EcWMNsZMPUy01+P1/9w0e/ckTHHvXeHtbnT/+T9/h2MlxUid6UWW5zSby8XFxXA/f95FFAcfziGoqluvQtB1CirLN29IkhaQaZqq+RlaPMlfPsyvaTVYPfKaWa1OxW7Rcm6rdwvO9duChyuHkAJbr0GMEPmpYUmk6Fi3XxvBUuvUYoiBStBqICBStBpbtsrBWxvd9omGdcq1JOh6m1jBpWQ6GpmBaNplEhNVSjVhIp9owOXdjnmO7+1BkiXrTwrQdmi2bRNSgVG0Qjxi0LIeWadPfnegwekRBoD8Zpz8Z5+vnrjKWTTFXKHNxbomPHt3P967epD8VZ2JxlVKjxcldQzx37RaD6QTzxQrPX7vFZx44QlTXAIFvnr/G/lwX4PPtCxM8eeIAz1+7xafvO8xypcazV2/y8eMHeP3mLJlomHeNDxHTNSRRZCST5PVbs1SaLbLRMKIoMJJNMZhO8K0L19jTkyWXiDGQTpBLRLl3pB9NlvnupRu4nsd4d5qnL10nGz1GOhL6YbzWd7UfGeAoLOvsiw8DdAaWjvkQU8Jokoq+A9o/25inZJf4pdH/lpHwcGf7+uKj5ba4UZ1kNDzCzw3/LGI7gr15cWJIBp/qfxJd0kkqSUbCW52r/lAKo52OltIiOwIvhmDwyf4n0SUNTdHQjY32tbdIgZDVH+wRlOwy31t5lpzRuyNwdDhxiNHICBPV69vO7+3+BzBbNmffvIWiyPQNppm5tYLnBouqUqGOpitoukw4rLP7QF/nuMnKGv/m1PcYi6WwPI+mY1O2mvzygQd4tG8cWRRZaFT47YsvM1MtElcNSlaTg6kefmn/A2SNMCvNGi8tTXN+bZFCq8HryzPoksLeZBd7k11okkzZbPK7V17j/NoCkXaqxEAkzq8eejdD0SRTlQL/5vT3GIomsFwX1/dZqJf56PABfm7vPQjApcISLy1Osdyocb20huv56LJMSg91gKOaY3Exv8SLi1Ok9RC/854ntyC5C7UK/+n89ymYTQxJZqVZYzSW5n86/l66jB8O++ZacZU/uvYmv3nyI4zGgpSnhXqFf33qGf6b3cfJZEIUag1sdwMgXW5eoWTNk9HHCEkpFClKKh2hWKgzMJgmHg/RbFoIQKXcpKs7cJRTqTBzs3kcx8NxXPL5GvG4QaXSQhJFurvjuK5HV1ccTVOIJ0JEojr5fJV0OvpDud4fFYvEDPafGAbf35Iaa1sOgigiyWI7Ug/d/Ukc28UybdQdFq2e7zO9WqDSDO5jSFVo2jZxQ2etWsfxfDRZQhCCqMT6O1ltmYQ1FfxggpMlEU2WaNkuh/sTiFuCVlvfY8/zWF4oIQCZnjgmFlfKN4mrURRRJq5EWDNLHB2PU2hUObI7im2USHW5hBSbkAc9hPHlGrleGQSbnBHien2KRJeDLpncaExhWA6jQyqr5SrhdIt0UmFMCIHWxHJaZLpdZuqL9CbiyHIANHj4PHpgjMnVPJIk4Ao+79kzzEK5SiYeYiyTIhHWMRQFz/OwXBfb83BtG02S3jKaomoKucE0U9eXiCfD9A2l3zIivNN2QRJQN4EoqqhxMH4fs82bXCmf5p7Uw1ypnGY4vIeitYLltRAQGAiNYkhhpuvXqTnlzgJLl8J0ab3cqF5iIDTGXOMWY5H96JKBIW1M7FW7TFLJkFF78PFYas2S04fo0QdQBY1ao8bczRKaHtyPSDzE3M1lXMcjk0tQKzdQdYX5myuouoIkiXT1p5i9uUw4ZiDLIpViHVVTmJy+HERrI0HqY7odsPB9n3KxTrXcJJbSSWg6n584xz1d/exPdfHCwhSKKLE/1U3LdbBcBUkUsTyXK8UVCmaDB7qHmKoUmSitUrctIqpK3bbbqcAJlhs1XlqYptBq0BuKYkgBiK8IEpbjookyL69Oo4oy93T1IShiANQpEk3HQZMDsFOQRAbGM0iCgO35xLSN9PB9+/7m0qd/VG2hOcdr+RcRBYH7U+8mRSb44s4+LgjYnsP1yhxhSSOpRokqoQ5jOayodBsRvjt/FUWUaLk2p9ZuM1XLM1FeRhJEClYdx/OQBJF98W6+PnORXCjOnng3LyzdYKFR5p70ILO1IhPlZap2i/f17iYq63xr9hK9RoyMFqaghTvBJNN1aLkOA+EkiiiR0SKcyc+SVEMMRlKcLcxiug7jsS6Gwkm+O3+FsKIyFEkHKReiREINbQu+NJwWl8uTzDdXuN1YJCoHvmNEDqGKKgvNlSDVzQXLc+nW05wtXUOXNEKyji4GoEBIMpAFiZfXzqKJCj16hoXmKt9ZfJljyX2k1BiGpPPK2jmOJvZyJLGHmtMgpcVJKXHeLFzCw2MkNMBioYmmjgUMc3zwoSZLNEwNTZYo1Jp0xSOUmiYtvUFXLIsiS0SUbjzP5+riKnVRZKzrCI5fIarkEAWJqBJnsTnL7fpNUmqGmcZNZEEmZwwx15hCFEQicoyEkkKTdOJKiuXWJWzPwvEd8lbAZujVB4jJCWzPwvUdak6FmBJHFmWiSpz55m2icozV1hJFe4202kVMSSIJEpIgY3otLM/E9Z279lvXayCKGoY8iO0VqbTOYnsFGupePM+kZl7DdmrIoQxR+R5i6j14nk/LylNsnMLxamjiMBIpfE8GN4bnKCSNkySNk/i+T9U8j+s3UKUskhihal6iad9CFDREQ6dsnqZh36RmXSWqHkAQ3j4V/2rlIq8VXmBv9AAPZd9312v8cTHPdakUquw6PEA2l6S4UsHzfCRBJPoDyDvsZI7tcu38DAAPf/gokZgRpNLrCp7r8ezXTvPGs1f51C+/F81QeeoLr/Hl33+en//1D6IbKuGYwcnHD5LqjlNcrfBn/9czvDl+jceePAHAX37lFKV8jU//yvuQFInyWpVIPGC2VYp1Pvdb32Vkb44PfeZBiqtV/ut/fgHf83nvx45jNi3OvHSDdz1xkE//yvso5mt87v/8LrnhDA9/6CiiIJAbzDDw2S5CUZ3zr07y1T9+iX3Hh8n0BIDK3K0VHnjsAO//xL2ce+UGX/q95+gfyZIbzrztfQlFdIbGu3nhqfMcun8URZGZu7XK9PUlPvbZhxjsSuF4RXzfwccF3wF8BMFDwAgYfKJDLuKBIAEWAiawkT4vCSIH4u01ngC7oz3Bx03Eg/Xvd8d6OtvW919fvm/2t96fO7jlOlquTUTWiCgBUHT2+hym7TLWl6ZUbXJjdg3P8zpBqWwiwmK+SkhXAjkZ1ycVC+F6PqeuzqLIEtlEmOVilZViFUEQuDK93GanQTYZ6QBHTcvmwtwSddNiqVyl0jQB2NOTZU9vhgtzS6xUagiCwP5cF3t7s7wyeZtyowUCDKbi7M91IQgC1ZbJWrXOPcN9OJ7HCxPTmI5LImRwoK+b7liEr5y5HNxXUeDYYC+j2Y1gcTJsEFKVzj2stizOzyxStyyWy3Ualk1vIkoipNMVjdATj+K4HpfmljAdh9v5EuVGa8ta8G/afmSAI9gK5AgIeL7HfHOeieoN6k6drJZlX2wPCSWBj89cc55rlQmu165Tdxu8tPYy58sXyKoZ7kvfh+s5XChdZL65wExjlpAc4usL30RE5J7UCfqMwNl8o3CKheYCru+yN7qHpJLcgvj6vo/jm5wpXmKptYQPxJQYh+IHSalBqtObhVPMb2ojoSS2XI/neyw0F5moXqfm1MhoGfZGd5NSU/j43KzdomgV6TF6uF69QcNt0KP3sD+2j4gcpu40uFq5xpXKFeabCzy78hwxJVj4vyfzbtLadj2NO226Ps3N2i3uT99HpK134fs+U/UpbtWnuTd8L4os0d0bJxrTSWejaLqK53nEEyFUVcZ23B31bpabVd7bN8Yv7b8f34ffvvgyfzpxhqPpHCk9xFdvXeJ2tW6htXMAACAASURBVMi/u/8n6AlFma4W+RdvfJdvTl/hs3vvYSye5h8feBBNknljeYb/8egjpPVQsHAWRDzf59n5Sc6uzvOv7nuCwUiCuXqZ//m1p/jKrYv808OBLshas86hVA//7N73YMgKn7t+hm9MX+HDw/voD8f5icE9HE71MFUp8LGRA3xi9BACbFmUjsXS/NqRh/B8n+ul1W3X2h2K8I8PnqQnFEURJc6tLfCvTj3DxfwSj/bvuutzuJsJgsC9XQN8/sZZTq/OMRILUi7P5RfwfJ8T2T48D7oTUZRN+cp5cwqAonkbUZNQpSh79vR2CFOCILB7Tw++D77nISsSYvu6T9w7iuf5iAL09SWRJJGxdjRfksQONdQnjW07WKbDvv19b5km93fZbl6a67AdZFnCMp3OwrxZN2k1TCQ50CUTRIFMT4Kh3T3b2nFcj5VKjaVyldGuFHp70rLdIC3L9XyG0glurRYJawp100ZXZRqmTanRQpZEFFFiIBlr667529aEd1phtcpv/q9fxghp/Nq//kmUhEjVqSOJErbnICJQtmtEIjL7smNMVKexPRNFA1dokot0IQkSt+pzDGbjWJ5Nw23S8AR296QIyTqe7+Hg4IgthrtjWK6B67ugmrQEF1s0SUcN6m6TqJaghsmF/BKe72O5DkVaxBWNy4VlLNdlMJ7gemkNqSqgiBKSINDwbEzPYWG1QkhRGY2lSOrGtuu1XJc13+SRz5xAH46we7iX/mM5apZFzTIxXbfjeOiyzEq9Rl8sTrMNRlUtC9tzCckKuWgMWRSxPZuF5jR1p4rjOwHzEok1cxHLs1BEDVlUkIRAj09Eokvr41L5DU4Vv4+HS0LJ4OOTUXuYbdwkoWaYrl9nvjlN0VojoaQZCI1yuXKasl0gq+UQBBFZDKj0sqiiaQoje3txHJdmzcQyHSRJIt0dp2+0K0jBC6ksTq+R7onje6CoEv1jXUiSSKthEUmE0A2VSDyEqst4rh+8522grNW0+fIfvcQr37vCf/8vf5KPntiP63nIooQoCOxOZBEQkARhk9afwNm1BdJ6mIdzIyhioFXxq4dOIgjBM/zZvccBEBFYrte4LzNIzTIpNVrk9Dh106ZHizGxlidlGBxN9uE5MFepYHsuFdMipmksVCvENJ35agVJEEgbIRRJxHRcHugf2KLh8ONkvu8z2+6j0bYf8HYmCRIJJcyr+SsklAhjkRwjkd72dyLvz+3D9QPWsyyKfHjgUJDmIEgIQH84gUjwbB/q3oXtu53/nxw6ig8oooTv+/yjvQ8htL/7QP/+IN1UCPpTjxHrAEfv79sXjGntY8MRjc/uuh+pnYb/2V33Q/u7/lACp52iLwkiw5EUoiDyaO/uTnvrZkgaD3fd046wSwyHcoiCQFYL5tGIGKNQUcESWaxVORjfxf74KLIgkdYSHa9NEkXe33MSv61JJAsSj3U/gOf7KKKEgMAHet+F7biUSybj0hiiKpCKhhAFiQ/mAp+kUjd54eo0UUMjZmjkqw3qpk3M0GhYNoIgUKwHqRCVponnebxn3wiKLIEfBBJWK3W6YmEEQN/EMk0qGU6k3tV+RyVSagYBEUmQSKvZzud9saMAJJQUx5MnARAQORCNI4sisri+FAje75wxCAjIgowRCtNvDCMLMvtiR/HxkQSJ9dVhxS4TV5L4+Fie+bb9UBR0crF/0PkdAZlc9Kfx8REFJQCMSz9FsWyyUF9G10vEEgaiJFGvNfC891Iu1SlIPq47hKqW0EP7kLIhIhuxTKLaIcLq3oBFKyj4yiBR7QiCICIg0xX+EH7IQxBk7lYjyPc9JmvX3nafH0fzvCC4tvvYMKIocntikWwuSSQZxnM9fOiMz/66F+r7G6xHUQwwhvY2z/ODtYWwCaDwfE48tJsD94x00tMgYN2ce2WSfceHGNnTCwKcePduvvi7z1ItNtANlf7RLrr7U7iOSywRYmRvL8tzhc75G2GNarFBtdxg79Ehhnf3dPzZycsLVIoNHv34Cbr7kwzv6eXSqSlOvTDByScOARCKaDzy4aOM7s/h+z5PfeFVlmaD9hVN5sA9w9iWg+t6HDgxwsvfvUi5UOsAR7mhLCcfP0imJ04iHeGFb19gZnKZ3rcJfEEwxx85Oc7pF6+zPFugbyTLmZevM7a/j65cAs9v0rKuBuARDorUDb6H7S6jyjksdwldHsN0biMIEr5vE9UfQWSrf7XlHLbFInYIwL3N/jvZvnhvJ3U6X24QC+u0TJu55RK26xELa+TLdeIRg2hIoy8b58bsKpbj0DRt+rMJMokwvZkYZyZmOTreR6Nls1yokomHGcmlKVUbrJTqxCN6MJ62bTpfZHq1wJMnDjKT35ASmVotsFyuUag3OD4UPNebqwWGM0matkNYUynWm0iS2LleXZGJ6hqTK3nwIW7oKJJIudFiJl9iuVIjFQ4ChqIgbPFZPM+nYdlYrkvDsrFdjysLy5RbLR7ZM8rttY1zUyWJYqNJrWViqAqjXSnCmsp9IwPYrtv5jb8N+5ECjjab53vcqE3yF3NfIySHMCSDy5UrnCud56cHPkVCTdByWxtpab6P7TtYno3TRlhd36XiVGl5LXw8PN/F8mzENii1bpqo4ngObxROIQgC+2J7O6CP7/sU7RJfnP0Sa2aerBYIU92oTTJoDHSAI7XdxpvF0wDsjW0Iw/p+AAx9Zf6r6KJOSA5xpXKVM8UzfGbwp0moCW7Vp3hu5Xn6jb5A5NC3eDX/OvPNeT6a+zC2Z1G0i9ScOp7vYXvBtQbg7jvTHfJ8n+dWX6BL7+Jg7ACCINDyWry49go1p8bDmfdw+MQwihrk6e7au6FZcTdTBImHekcIywEj7LH+XTw1c5WlRhVJFDm7tsC92QGGowEotyeRZV+qmzdX5/jk2GGiapBbHDiNAqoooUkb3bNhW5xamUMWJeZqJebr5UCoVRC5Wlyh6QR5yoIg8MTgHlJaCEEQOJHp40+unablBIwORZBQJKkt/Chu+Y11C17uYAG747WKEj2hKLerJap2K7hGQaRoNnbc/69iaT3Ee3KjPDs/yYeG9qFKEi8uTHEkk6MnFKVQblJttrYcY8hJlptXCMkpclKU6WuLzN1aobs/Rb3SbOeBqwHLoFAn3R0jno5QKzfpHcqwtliiWTfxPZ9Ww0TRZCRJpGcwQ6Z3w1kVBBjd1YWh//WEhH9UTRAEXMfDdT2aTgAUpbrieK7H2mIJs2nR3Z9CliUc10NRpR0nSkUSOZDrYl9vFl2RkUSxrVHmkw4bHTZRy3GQBZFjAwl0VcH1vE7QRhJF5HbExPW8t10o+77P7K1Vbk8ukxtI47k+YdngvtQhREHA9T0UUSbTXkBpksKRxB5Mz2LNLNFrZFBFJWCkqEG02fFd1sVcg4IEwXjj+9CrZ1BEubMwjMgGkiDitRcWAtCwXUzXIarqqJJEvlknqRmYrovv+8RUDUUU6TLCGLJCTyhKyWxSsy3AD9gtrkPDsUmyHTgqtZqsNRtoPSHu+chBDFlmzqwhNiTmKxXWmoE+U8YIsTuVJt9skAmFuVHIE1ICBljdtuiJRMmEwsiiiCIqDIf3YHkmhhQmLEd5MPM4LbfBgZiCIYXJGcNoos5MYxFN6iWpdvFA+jFc32Ff7DghKcJj3U8SkiKk1C7CcpSW1+ChzAcRBIGIHEcTdcJyjJbbxJDCKKKKTzAv3Zd6LyE5jBTeGJ+slk2qO0YkHsIIB9Fc3/PZd2KESNx42zTkVHd82zaAWqXBpdPTFNeqQQpyW39g3dbHR88P5s1goQUHUt24ntf5XhQCwOHO44Jjg87cF41hOi6W6yK2dXZ8HfqjMYpt4W3LdWm5DhFVRQR6IgGjMRsKoUoSaSMUpFLa9lu+Bz8OZnkmc43b2P47uw+u79LybE5mDiILIpa3lRkiixIym5/71sW0tGlxLQnClv/VzXPoHd+JgoSyqV1xE7NDuYPlIRCkSECgjVZvBexMKRC42tbune1tnIKAulnw+I4h05BVVEGh4bSZjIKIvFM7CCjiVv9AEbb/bzouF28sIMsi+4a7g7Fv0zkkwxKPHwpSSiRRwHHXNSSDIMJ6AZZ1sWBJFAltYrD6PuSSUaLt1IbNC7TAp1E3nc/On6X2Mwj2D9qeK5VZqtTYlU2TMLY+b/GOY6X2/bkznQggpsTZGz2Mh0dIenvGdZCir96xbYOZIvgCkmig6zJWq87qchnTtInGDTwXjFAEqyXi2C6KIiPJEuFIeAvLLrg/G+cMAUjGpj4hoAbzGME86/v+W86tFafMUmvxba/rx8XWgV7P9ymvVnn5W2fR2n7g7YkF3vWhox3tS8/18VyXUNTAthxkVcaxHFzbBUHA97xgvJBFZEWmWW8RS0VIdic6GRpGWCOZjXYAnfW+32pYrC2WWZ4vcv3CXLCtaRGOGlimHQTEry7wyjOXKK5WsW2XyctzPPDogc61PPKRo5QLdb70e8+TykZ538eOc/iBXaiaTClfRZJFYu20NkGAdHeMm1fmMRtBIaNI3CDcZkEJgoCiyLhOMH+bLZtT37/GhddvYlsujWqLSrHeSa0GiKVCqFownmiGQiSmUyk1gmIs0lv7eYIgMLy7h1gyxMU3p4ilwty6ssgjHzlKo9qkVfeJZY7i+8F9FtHwsfB9F0GQ0TmIiIIqD+HjYNo3QBBo1U0Ky2W6B9MdraW3s9X5AkZEJxLfGbAwmxbF1QrZvlRHN2qzBTq9wedk1OC+/UNtANHDcT2qDbMDnu/qz9CVihIP69iuhyQK6JpCVzJC07LpTsXoTkURRZHuVARNldFVhT1D3cjSGgjQaFkY7XE1l4gRC+k8N3GL3T0ZEiGDcrOF5bp8++IEg6kEY9kUp6fnqbUsnr48ybHBXnriEeotk4HUhj+lSBIfOrKHFyam8HyfDxzajabIKJLI6el5TMfliYPjSILAUDoZZBO0rWaaPHv1JpVGi5dv3EaTZcayaa4urPLK5G0O9HUR1oJUwmNDOZ46f42WbfPEwd08fmCcF69P8dUzl9ndk6EnHgHu/tx+GPYjCxw13AbfWvw249FdfLD3A+iiznRjmj+49ce8WTzNEz2Pszs6zu7oOK+tvU7eyvMTPe8nZ2xQ1lVR5fHuR6naVfJmgYyW5tMDn9z2W0cShxkLjzLXnN/2neu7vLT2MkutZT479A8ZCg8iINBwG2iitrWNyCjzzYVtbTTdJk8tfoeR8DAf7v0ghmQw05jlD6b+kNcLb/JEz+MAVOwq9/Xfy/HkcXzf4y/mv8bF0iUeyb6HpJrk8e5H6da6uFWf4omexxkI/WBVuPpDfQyFBjlTPMt4ZBe6pLNqrnGtco0n+z+OLEoI2l2Q5LcwXZYJyRtphDE1cPhKVouMG6ZstegKRTb0fwSBjB7mZjlPy7WJ8va01pbrsNqqs1Av86WbFzrbk5rBvmR3539DlkmoG5V1VCnI9X2n4NrdzPd9rhSX+X8vv4btunSHIri+R8Ox3hHA9k5NFSUezo3yl7PXuVJcJqWFuFUt8Mv7H8CQVTTZYjZfJqJrHBrswcOlP3ScjDaG5dYDjYS2XlZxrYrVsokmQkEKUMvBdYKqYZVCnXK+RjQRIr9cxghpSLJIrdIkmY3huT6Nags2SRmJovj3WoR2/PBA5/NmTRCAvtEsZtMmkYnc9b0QBIGYsfN9MpQNauqJwVzA6LpjUfCDmmO73LiyQK26AShKgkRE2Xli930fQ9LQJLVdhGCrTsgPapqkUm+Y3J7NIwgCvV0xkvEQ78oNb/xmYmcadn9ko6JKXNMZiiW33fudLKEb9Mc8VEnCdl0USaLlBHn7WlJmV0pAEUUs18WQFQ5ku9FlmV2pNLok43gusiiiSlKn2kigSbWVwRmRY0TkDWaHxvpzFXB8Adf3tx0TFwM6clwN/kbEOBF5K4ATU5LElO0C/VElvm2bqitk76isIogC0cTW53vXaOAmm7+d71QCfDtz3DyOV0KT+5HECKEfoGDAYDzOYHzjenYCtob8xJbzfKux9E6dnh9XqzhlbjduveP9F5p5SlaNBTdPTAlxJDF294MI2BYtr0XDqXdAKlmQ0UQNXQqh3KWCVtCGj+1bNN0mlmfi+W4AigtSoJsjhVBFbcvzdD2fW2sFis0W9w31Edf1t3zegUi4ScOtd8TCRUQ0SScshZGErRWDHM8jrKqMpzL0RnZma7m+S8tt0nKbAeuwwzqSUUQVQzKQhYAdGNJVju3tx7QcEtHQtvOURHGLDqHnezTdBg23jiu5AYgvamhyBEXYKsUgCAKSKFButEiFtwPnm8/V9Fo4noOHh4iAJMhoko4hhbaM7etWMy3y9QbDqbfSydv5PtfdOnb7PksEOkchObTt3N/OHM+h6TYC8X7fbYN0QV/o35Sq02xYaLrS0X3ZSaPrznN0fIeGW8N0zfa9CLSc7uxnru9zbW2V1Uadg9lusuHt1a1832e+MUvJLmz77sfRVopVJufzpGMhunWdvpEucmNdAOhhjVatxcrUCtnBNM1qi2qhRvdwllbdxHM8BFFAD2t4rkd+IShIke1PI8o2tWKdwmKJUNToAEeCIHRY8ZtNkkVCMZ0DJ4Z56AOHO9tlRSKbS1Au1Pncbz1N72Caj/zsu5BliS///vNb2sj0JPjsP/sAS7MFXn3mEp/7raf5OUXi2MnxTjVdq2UTCmuBqHvdDMDKthai+DaFac6+dJ0v//7zPPkLDzN+qJ+1pTJ/+JtPbdnHbNq47QqyruthtmxUXXlrYdRNpukK979vP68/e4Vo3MCxHQ7eO8KV1ydRVJkHPnDkjiO2jx0igd+gSAEZYnlpmRe+doqP/dKjhCJ3ByB83++kpLmOy+yNJVI9CWLJjffonRZTkiWJaGjrbyaiITQ1CLhmEmEkUUTdAdCyXY+Doz3EI8E1RkMbY21/VwJVCdiuiejGPchGw/zSw/dtaed2vsih/h4+cmRf57mKgsC9I/08tHu4s9/eXBd7c11bjh3vzjDevTFu1U2LqKHxqXsPbQGKPnhka6XhmKHz6fsOc6f94sMnsD2nHZgNUtBGsyn+yWMnO/uENZVP3nOoHXjwEQUR13MxPRtD0v5G/aMfWeBozcyz0FwkocR5fuUFIBCDdAkqqP1tme3bTFSvsz+2j75QbkMXQN4+ybyVFawi8815wnKI76++CIDpmbi+x2xjtrNfQk2wN7o3iH4JEr16L1cqVzHdt6f/vlNTBIXjyaN8bf4brJpr9Bt9XK1cQ5d0hkJt8d4fEPxY75yBHknAIliv1OL4HiFZQRYkDFmhZpud733fp25b6JK8JcK9KT12i8miSERRuadrgN848di27/R21DOgJv/NvTBN1+Yvpi5RNBv8hwc+SEoLMV8vc6mw/EP9HUEQ2BVL0xuO8crSbQYiCSRB4GgmB76PLElkomEUWcLy6kzXXu1EXyvWAsPRkwzvHWJwd8+6NmpnMPS9YKBZj5R4no8kiWR6Eh1h9rED/YiSgO/REX/+cbTNfWmdsaVoCk47aiaKwo7RlHdinYnXC56JjRO0KQiIkogg/GCL42bd5PrluXdcxLvlOkxXSoDPWDyNdMecvD4WuK4XRMr8gG4uigKiJHT6z2ZzXY+llQqu65KIGSTuwD/uvJ9eu22/3bYggCiJWwQi385USSIX2dDZuvOYjfHMx/NrRNRg37imbwMg1unyntvOq990vZIkbqHQr5ssyETl0Ja05Leyze1v/u13cr3r+28cH1wTCAjihhP7g/QX3/eZuDSH2bLv2ocFQaFl30QQZAxxjLfior+Ta3wnwNaWZ7Klj9C+5nY/vCMS/ffRApaiQ82pkrfWKFhrTNVvsmIG4sJNt8nr+Ze4Vr284/ExOcZQaD9deoKWa21L7bI9i3OlU5TsInElwfHE/UiChOm1uFy5wJXKBWYbU1SdKr7vEZLDZNQuBkMjnMw8QlIJgNE7320fj5JdZLI2wWxjmvnmLEUrT8trAUH5+ISSpM8YZDyyjz3RAxhS4Nx7vkfCCDQd1s+35bY4W3qDmlMlrWY5kjiB73tM1Sc5U3qDmcYUJauI6ztook5ay7IrsoeD8aP0G0Md30AVJQxZpmpt96s83yNvrXKhdIbbjVsstxaoOVUc30UWZEJymISSJGf0MxgaYSQ8TkSMM70QAAvRsIZBAKattJa4UrmA7dvsjR6g3xii4da5UD7DlcoF5hozNN06kiCT1rKMhndxPHn/lnMF0GSZUqOF5Wyw5NdBnJnmNFP1SeYat1lpLVJza9ie3QFhurRuxsK72RM7SK/et6VdSQj0OVqO0/HLXN9hsnadmcYUuqhzNHEvETmK4zvMNKY4XXyN6fpNynYpuM+STlJJMRga4WjiCIOhYURBRhDUbWPi+gJnqTXPhfIZpuqTLLcWabpNREEkriTIGQMciB5mf/wwumgQCm8NKK6fu+3ZXK1cZNlcJCxFOJ68H1VUmW/OcL50msn6BHlzFcuzUEWVpJqi3xjiaOIeRsO7O4zYluPgeT6aLLfP0cPyLCpOmby5yqq5zNXqJVpuE4C8tcZzK0+/JWA6GBphd2Tf39vxyPOhYVr0a3ES2ShHHtrTYadEYiEkRUKSBERJCtL9CebzzSlqCAL4tPtckLLm+z62GYBHRnRngHSzReMhhnf3sLpQItUdwwhpeJ6H2bTb7KUKtUqTfceGGBjtYnEmz+JMnlTXBlBcLtQxwio9/Sne88EjXHpzqpPKNtT2mycvz3H4/l00GyaTV+YZGOva1id3stXFMpF4iH3Hh4gnw1y/MBsEYDfZwu01lucKROMhZiaXaTUsevqTwe3pzKOBH+K5Hp7rdfxzgH3Hh3j6y2/yytOXGNvfRzQRpLLevrZAo9okN9rN2MF+Js5Oszi1Sm60i11HBrn0yg1sy8FqWhx7735c2+XSqzeolRqYDYurb9xkeF8ft68t0DfWTaMaFB0prVZpNUxOvPcAlUKNq6ducejBcUJRncnzM3znT19kcHeOI+/eQ9+ubi68PIEgCGTbVVCvnb5Fq24SihocfHCcwnKZ62enqZUbjB8ZYteRwS0goSyJ5DLbA2jb+kJIw/cVfN9DuGNu26mNDb8ENvsxg+kEpuN2Nvm+x75clkTo7v3xTlMkkQfHBpElEdd3A0kDQejIJkiC2Ckksfnz+j7zzVWWmmvsjQ3TdE269FTA0GsDRJvBoqJVpema5IwMLc8ib5XpM7JBgSuCrByfYG4LUtH/+qykH1ngqO7UcX2XklXaklY2Fh5lNDLyt3Yevu9Ts2vElfiOUZt3Yg23EVyLXd6yfSQ8xPAmMW9FVAhtEk/tVCT7IbFlBEFgLDxKSApxoXSRlJrkauUaI+ER4kqCmXI5SJNpAzuqJAUip23VfVWSaNg2mixTtywG43HU9oRrug5n1+Y5lOoB3+fN1TkyepieUJS4prM/2c2Z1XkqlklM1Vht1blSXOZQqofIJhE9XVaoWibmHVT6sKxyINnNd2YnKJoNBiIJEASajtXWfnnnE7UsSIHWi9XCexuK8k5muy5rzTp94Th94WBAmqoWWWn89St33Gm6rPBY/zhfnLzABWWRB7qHSLVLXhdqDRRJ7Kjoh+QUcSVg2+lSPCjjrkj8QNRFRdr584+5+b6PZTpcOTfDqZeuc+v6Eo1qi3BUZ9f+HA88so+xvT07an+9XZuFtRrXLsxw7vVbzNxcodW0CIU1+oYy7D86yOF7R0h3xd4+0m46VMsNysUG0zeWuNambTfqJlfOzZBIbQe4VU1mZLwHXxG4XSkSUVVG46ltbVeKDSYuz3HmlUlmbq5Qr7UwQirduSQHjg9x5L5Rsj3xLZO9KAoYukI0EiWTDtIWysU6tyeXEUSR0d09hKM6tuVwc2KR15+/xq2JRUqFOrIikc5GGd/fx/2P7KV/KLOt8tf6tU1dXwrAuztMkkX6hzMkUsFvr987z7cx7RvIUheK1Ad3AMye51Ncq3Lp9DTn3rjF4lyBRs0kFFbJDaY5fnKcA8eGSLSp65stSOPb2NaomUxeW8D3fIbHu4klQniez+3JZd588TrXL82RX60iiAKpdISxfTmOPTDG7gP9gfj6Hea5HvnVKrNTq1w9P8PtmysBi9B00HSFdFeM8f059h8dYnRPb4f+fqetgzDVSpNKsUGpUOPUS9exLAdNVbh1bXHHPiyKAulei3A6wVuhkuvlh29eXeDC6SkmLsxRWKsiSSKpbJQ9h/o5dGKE0T09nRLCd7Nysc7V87NcPT/D9I1lKqUGnudhhFTSXXEGRjLsOdRP/3CGVDb2VwZwf9St4Tb4i/kvMFUPtA9bbgNzk5ZM023w7Op33vL4AWOID/X2MFVbRhUVhsLdHQYNBJVaX1j9S27Wr9NnDLI3ehBJEHlq8au8VniRprs1BTtI3VlgrjlDojlKTofeTKyTBgDg+DanCq/x3Op3WDVXML2ti6Z1WzWXuVG7xhuFlzmSOMFHc58iJie4sZqnalqoksRQmxHTcht8b/nbLLRmGQ6NMRAa4lzpFN9ffYaCdSdrrsyqtcz16lVOFV/lo7mf4nD8eKCdSZAS2bC3+hiO53C5co5vL32N+eZMJ9K72Yp2nvnmDJcr59FFg/2xw3y6/+cJGSor+Sq2s3HMQnOWby1+hbpb44nuj6KJOk8tfZULpVNt8GxruzdrE1won+HDvZ/gcPw48npqlQDlZmtLpeDF1jzfXfo6E7Ur1OwKHh472Xxzhgvls3Tlv89Hcp/gcPxE57lHdY2RdHJLiqnju1wsn+HZle8QkxP06H0Mhkb4/uozvLT2PfLW2lZ/1Cmzai4z27xNTKoTE1eIqHvR5O2af5Zncq50iqeXv8lyaxGPrfe3bBeZaUxxvnSKI5UTPNb9oQDs2mGscDyb1wsvcbb0Bik1Ta/RR8kq8o3FL7HSWtp2P/LWKpO1CS6UT/NoLaG3vAAAIABJREFU1wc5mX4YSVAxXQdFkohpgQ+60Jrnv8z+MRW7RNNt0HQbbemLwFbNZb65+OUd7zXAe7NPMB7Z+44CCX8XzXFdbMcjEw/jOh7F5XInMHnj/G32nhghs86K/QF9SEWVCb0D0AgC9u0Tn7qP//I7z/Lbv/FfiSXDNOsWuw4EYtaJTISRPb08/eU3mbgwQ63cRNW2Mg+//icvMT+1RjQZolpqIEoie48GAfTegRSP/uQJvvpHL/HqM5epVZooisRjT96zo09yp+05OsgrT1/kc7/1NKGwhtm00O8AnGKJEF//3CuEwhrz06scvn+M4T0BtX9prsCrz1xm5sYyy3NFvvX5V8n0Jnj4Q0dJtP2qrlyS0X05Xn3mEk/8x/uQlaASY6PSpOv+Xbz8jTP0DKVp1U0c2+HZL75GujfB9774Gu//zLtYnlnj8muTrM0XMaI6ekSjVmowcWaKcr7KxJlp9t0zGmjwXpxl99FhBnf3omgysVSY+ZvLZPtSZPtSxDNR9LDG4J5eUj1xJEXCiOic+stLHLh/F4XlMs99+Q0+/AuP8MbTF0h2xTjz/FWG9vQydXmO/l09gEC+WGd6Pv+W9zUW0RkZyCBLG9W6fW8N15lEEFMIQghBDOP7LQRkEAx8r4AoZvD9CiDieXlARBRTwX6CCsiMZiwENHx3Fk+I4Do3ONLfjyjGscwAkHSsQO/0rv1Tlnn8wDgVu85LK1dQRJmBUBc3qnOIgsBwOMdUfR4BgfHIABfKk6TVOH1GlonqbWpOk4QS4Vr1dlsfUOTV/EUUUWZXpJ+a02SxucZ4dIAb1Vkqdp2Hs8fIWxXyVpmIHOLltfMICPQZGapOk4XmKlktwaPd9931/O9mP7LAUVSJoIoKj3Q9zLHE0f/fEHxBEIkpUQpWEdd3/0rgUVgOo4oq78m+m3uSJzqspXVbd1AE7s5S3CiY/VcDk+JKnAPxfVypXCVn9LJqrvJo93sxbZfXZmc7QIohywwnk8yUy4RVFQGIaRrFZhOnrcHSG412stXDisr35iaZq5VxfY/Tq3P89K6jdBtRVEnik2OH+N/OfZ9//sZ36I/Ema4Uiakanxw7vMVxOZLu5c+un+E/nn2egUiCsViaJwb3EJIVPjK8n4nyGv/izafZncji+j4Vq8Xj/eM8PrD7Hd+DlGawP9XNN29fZbVZI6rqPNq3i/2pbkzX4Y3lGWZqJc7nF1luVvnC5DlSWqA5FFFUTmT7+fPJc/zmue/j4zNdKZI1NhboLdfhlaVpFuoVrhSWKZhN/vT6WRKawSO5UWKqznSlwKnVOW5XizQcm6dmrnGzkmc8nuZ4diMF8b6uQf7s+lmuFJf55f33d9hZMUPjlh2k5miSwUD4BGutSVbNG+hiFF2M0mhauJ6Hrio0TRtFkXAcF1EUUWSRltkuc9mu6CWJApbtIkkCS2tV+rri2I6LaTlEQhqW7aCqMo4TMADCIfUtK139fTHf9ynma/zFn7zMt7/85pY0MIAzr07yzFfP8OhHj3Hw2NCWEuFvZY7jcun0NH/2O89x7cIstrV1AXP2tZt8+8tvsufwAJ/+hfdw7OQu1DuqL3qez8VTU3zlT14mv1ohv1yhvCmHfm56jX//63++4+939yX5V//3PyQzlMR0XQ7Fkls1aTyPa+dn+fzvPsfFM9OYze06Ks987Qy79uf46D94kIfefxCtPZl6no8siUQjOmpbDPzq+Vn+7f/weVRV5n/533+a3Qf7eeqLb/CNP3+N/Ep1W9svPn0ZSZbIDaZ3lC2dm17j3/7a5ynmt4O10bjBr/7Gx3jo/UEFj06RAwQ8v4HlTKFIvawLovq+j+O4vPrsVb7++de4emGmo1Owbudev8UzXz3DwRPDPPmz7+bYg2MdgMX0TKpOo60FtX5+q/zzX/kjLNPh1//9p3jwvft48bsX+cLvPc/SfHHbOb/y7BVWFkuM7ctt0XGBoK984wuv8b1vnGN2ahWztbOmzXPfOk8iFeaJnzzBT/3iw/jA6nKZdDZKJKojiiIrCyW+9IcvcmtikfxKheJaDavd90zT5g/+j+/u2LZuKPzMPznBEz/VgyhsT730fZ/CapWv/MnL/OXXz1Iu1rft8+LTl0hmInzgyXv46Gce3BGAWzfP9Zi8usDn/p/vce71W9vej82majKHTozwT//lx+nqfWcpN3/XzPNdCtYaTbeJgNDWw9KoORUARETCcgRJ2NmdiypxhsM9VG2TuBIOopvrFRPuMNNtstxa4GzpTV5cexZZkMmoWRJqCk3UaXktSlaeulOnW+9FdWJU6y167qiuKSDS9BostubxfZ+wFCGmxMlqPUTlKIIgULQKzDdnKNslGm6d1/MvoYsGH859gqFkkrpldaLvd/aVgpXnO0tf41zpTSzPIqN20Wv0E1cS2J7NsrnIYnMO02ux1Frgq/N/TkJJMRQaQUCgNxILBN83MaCvVC7wpf+PuzcNsuu8zzt/Z1/uvvW+NxpoEAABkCDBnRQpyaJEbbRiS/I2tsfx2I6TyrjywanKJJmpfJhyUo5T9kzsOON4i+zIsmxZO6md4goSJDZibTR6777dd9/O+s6Hc9HoJrpBUlKlKP4/ANXn3nPue855z3ve9/k//+dZ+AtK7joyChktx6A1TFrPIiPTDpqsu0Uqbikq1xIueaOApViIsE4Q3mDGvbG9lxvnWerMc656CluNMWSP0mN0F0tukfnWLK2gyUpnic8t/HeEEBzN3I0syaiyTCERo+XcePZlSWGps0DNq6DLBjElRs4okNXzmLJJO2iz0llipbOEJ1yWOwt8fvGvSGkZxuxJQiFYqtRpOO6uiTNfeJTcda61rvDllc8TioC0liWtZ7CVGF7oUvHKNPw6SS3FVOJOVFkgS+ZNwIkXuny7+HWeXv0yzaCBKqlktR6GrBESWhIv9FjuLLLmLNMOWrxQeoZ1d42fHfkVesybQait0fJbfK/4DS41zlNyN0ioSQasIXJ6HpAoOisstOdoBU1K7gZfXv48hmJyLH0/fbE47S2MKyeI+rcvAhRJJa4mccLOJniqSioxNbErMGS/jUqEH8cwNY2BfBJFltlYqfCtz71Eriv2PHd+mfED0dxVCIEXhKw3mxSbLQaSCQxVoeX5zGyU2FvIM1MqUYjF6InHIndjTaXteeiKQhAKtJTBx371YayeGK7v4wQB18X7ncBnaE8Pv/avPsrls4u0Gp1NtzFNU9ENiZ/9Z+/nyrlF2i2HwbECiiYTuNF72gsCHvzwEVaubVCrt8lm4oxP95MtROOYoio89rE7GNrbS3GxTDxuMnVoiERXz2d8Xz+f+vXHSOUijR1NVfjoLzxALBG9H/ccGOSf/J9PMndlDcPUGN/Xz9K1dfqGowTdHQ/s5dDdEyiKzPyVVR784O1MHRyKSuSEQNMUsoUE2UKCI/ftodP2MC1tG2ilagr9IznGpwcYnx7YXDvuu3OcqaOjvPLtcyzNFDn34hWmjozSrLXxXZ9Y0uLQfVP4vk95tUZptcqD9+5BkiRmzsyTH8hw6dVr9I3kWZpZ4/CD+1id22DqyCj9Y1FZm2HpJLPxLmNMIpWLk84l6B8vkC5ErK58fxpNv87kE/SO5Nh/bIKLJ6/SqreJJUzOv3KVZC7O8FQfIHjhtVn+3R/sngQ5dmiU/+t/f4JkVypDiBq+dxIRNhD+64CGIESWo2dfVvoJvAvIygBhsIRm3EsYLIJkEPiXo/1EGUlKA5Gep6z0I4SDCEtIcopQFDh3co6+wSwrCyUOH598y1UY604FVVJ4sHCE1yqX6DEy2KrJd9Ze4WhmLyktzvn6LH7oczx3kPnWCqZiMGL3s+aU6DdzrHRKeMInpphMJ8c4W50hrcUJRMBap8xYrJ9QCAbtHnRZY8Op4IcBmqRyNLOPl0rnCBGYss6I3f/mjX4L8Y4FjnJ6jiFriBPlV5iIjZPSU9EE1S1jKxa2+j9HQVyTVPYl9vFi6SXmWncyERvf1DiSkbHUN0fIs3qWYXuIV8onmYxNkjUi/Y6yW8ZUTEzlrevFGIqBHwZU3CrD9vCb7/CGkCSJg6mDvFg6wUvlE6S0FMPWMDFV5/179jBXqeCGIXnbZiCZYG8ux40CgRuQlSxJGMpWTRSFT00dYbZWpuy0+OX9x3nv0B60LrDQJ9l8ypzim2szLK6vMZnL8r7+KcS1OpXeSI/HsA3SFfjNvfdyqrHKyvIG+ZpMRa/QUTV0WeI3993DF868xkqtRWOjwd5CL/EVj062Q860ebwrjL157Q2LD4/dRkq/cY1VReGXpu9iMJZkoVGN6OvdhbMfhsw2KszWSowlMowk0sw3qmx0WtzVM0xaN/nw2G2YqsbFSpGEZvCrB+7hUqXIeDLbPUbA1VqJpWaNPanIXelavcxau8E9vSMkgZLT5lJ1HTcI+ODoNEEYcqm6jq1q3FG4cb/yps2eVKSdMpm64bagKgqWrm0rLVzrnGfQPsq6c5maW+TMmcilKxEzuTy3jqmr6JqCoshomsLaRoOR/gxrG3XGh3LomsLZy8sM9WU4d2WFh4/tod7q0OyKAVbqbXRNwfV8Oo7PA3dMbg7g79ZwHY8v/tULfOl/vEir6aCoMiMTBQZHoxfT+mqVqxdX+IfPPM/KQilyYbvFQlcIweuvzfNHv/MVrl5cBiQGR/MMTxQwTY1W0+Ha5VXWVqq8/uo1/vh3v8r/Kn2Aux/at30xIgQri2XOvHx18/k0TI1O241osbKEYWk7Ls7tmLHpUNJjx7ZB0EII5q4U+cPf+TIXzy4iQkH/cJbh8QJ2zKDTdlmYXWdpfoOLZxf5b//p66iawsMfOBQJRKoKpqlRb9zMMAiCkOJylWuX1/jcnz5Dq+EwMJIjV0igagq1SovVxTKSLDE21bsrg8SOGUwfHqa4XKXdcmg1HOq19jYGUtv3WWnWyVsxkobRFfTuIIQHbC35gJPPXub/+92vsboUgTp9Q5nN8202HBauFlldKvPqCzNsrNX5p//HR7ntaESrTmnJLti+c1vXV6o8+41z/MX/+0021mr09Kcp9KXQDZVWw2FlsYzreExO96PtkKFVZJnl+RJXzi9HYuJpm4GRHNl8HN2I+svclTVWFstUSk3+4a9fYHA0z/FHpimXmqwX6+w/OEg8YdGotTn98lWKKxHzVdUVgiDc1FkwLG3Ha25aOrLaIRBNdkpY1CotPvNH3+apv38Fp+MRT5hMTPeTycUJQ8HacoWrF1cprzf4u794Ftf1+fQ/fs/mJPuNsbxQ5k9//2lOPncZISDfm2RsTy/xpIUQgnq1zepyhdXFcuR4aKiY9rtTqB/AVm0+MfSzeMLd3LbQusZnF/4cN3SJqwmeHPo0vebOk0JdijR5bNVgqbPB7emJXX+rHbR5Zv1bnKudIqcXeKjwGHsTt5FUU8iSTChCal6Fq83L6LJBsh7fFHveGoqkcFvyds5UX6Vg9DIVn2YsNoml2N1nRcIPPebbs/z94v9goX2NkJDT1ZMcy96L6ha4VFxn6I21rt2o+1VeKj0HwJ2Ze3i05wNk9XzkdIigE7R4buO7PL36ZZywQ9FZ5bXKCQasQTpBwKXyBv2xOINdjSNfeJwoP0vZjbLd+5MHeV/vEwxYQ5FGEhHV3xMuJXeDS43zLLSucTh9LNo/DFkrN3B3YEECzLauANBvDfF430cZj01hKBEDwQtdzlRf5eurX2TNWaHsbfDd9afZE99HSs8QhAIvCKh1HPwwRFMUsnqOO9LHSaopppMH2BOfJqcXUCV18z41gwbfK36T7xS/jic81p01TlVfYdAaQZM0+lJxsr61qbf3xvCFx+nqSa61ZrAUizsz93Bn5p7udY7mbK2gyXxrlrpfI6EaCFHfEVS52rzMd4pP0QwaaJLO3dn7uD//HgpG77ZjvVZ9madWv0jVqzDTuMT31r/JxwZ/aldQFKATtjlRfp5QBOxPHuSRwvsZsce75WQSbtjhVOUVnlr9EuvuGs2gwYnSc+yLHabhupvado1mB7Od4ZfHf3MTVA1FyCvlF/jG2lcAGLJG+NjgJzF2mbMn1NS7lm0E0fVwvQhoS6RsHnji6CbD6Nq+pU2hZAGcLxa5Vo5coeqOQ38yQanVZq5SoSce49xqkYN9EpfWN6h2OkzksixUquzJ5ZgplXEDH71HoVZdZ65Tp+l69CcSVDodlmt1jg0NMJHLcscDN5LGXhBwbaNMx/PpSyewJtKMJuPU2h0CJLLxFKfmVyJTjI7L4P4C9VIVPRlDjetcXFknE7MQAsrNFkXdR5lMMdibo+y5rBcdMjGLFa/N6OEhFusNLq4UuWt8iEQ2hixJLM1t0Gx0KPSl6BvOoSgyQRBiJSx8P+TyuSWSGRtFlXE6HoeOT7K6WMF1A8qz6/hdF7jhPb30DKRZX60xe2mVYw/uJZ68sd5sNRyunFtk/x2jJLZslxV5sw/6XiTM3ay2N8vEr+tGSUiomsLQnl5e/Prprq6URDIbx3N9BiYKvPjUaXIDkaHR1l596dVZrp5doNN06BnKki4ksRImL379NHc8sp9MT4rXnrnA3MVlzr5wmVQugSxJXcAlcmiVVZlGpUWuN71Z1miZGtmUTavj4bge4k35ESFCREYqQvhIkoEspxAiSrIE3uvdeV8DSU6iqKOIsEooIokGWckReCWQPCQpBQQo6iS+dwZJzgMBrutTXK5Sr7Qp9KfekmPc9bBVk5rf5Gx1Bk1SWeisonsaE/EBik6Zmtckp6dwAx9FkokpFhW3jhNEgP5Ce42V9gYx1URXNGQkGn6Lmt/EkDUEAl3WuFifo9/Ksdgpstgu0mNm0WVtS1lbSEy1fmQutO9Y4MhWbD4y8AR/s/C3/Ocr/4WYGsMNXUIR8pNDH/+RlasVnXVe2HiBDbfEfGuBilfBCVxyepZ7c8eJq3EeyN/HcmeZv7z2GVJaCglwQpePDDzBdHJf9xgvsuFuMN+ep+yVcUOXrJ7lvtw9xNU4Hx54gs/Of44/mvlj4mocN3Txhc8nhp5kLDb6ltvba/YwndzLZxc+x3eK30WSZD4x9HF6zV5WO2u8sPEiZa/McnsZJ3Ro+A3yRo57c/cQ72ZD+sw+puJ7eHbjeZ4c/BgtT1DuVJAkyMRNVFmh7nV4dX2JXjuO33XPUTftMyFEICPRZ0cIvRCC8USWD43u37HdqzNrGGsu92+kyPalcUse9Y1VNtoeldVqVBpn6ixeWmbvkTFuj01ztXmN8kyVa9Vr+K5PPB1jYLKP4+0shtnHcm0F3dUIzAaNgRYjg1l+68jD2353KJ7mX9756LZtjUaHhYslfvbAUQxj+6RJFTIfyE1R2JvA9Xwq1TY9+e3Z1LRh8Y8mtwuaHcnfEGWPawa/vP/WdMA7CoPcURgkCEI21mrUa20m9vbdNAGvew5r7QYP9o9vA8QEgrbnIckSXthmofkyNW8FWidpBxUy2gT5tM7CSoVqo4OmRhotsizhej5LxSrZpI2iSGiawshAhhNn5vD8EM8PKGTixG2DtuNRrrUwdJU9I3nOXFqm1ujQm09g6O/Y4eNHFqdPzPLFLmiUSFk8+XP3854PHSaTT4AEzVqHk89f5q//63d59hvn3pRttDxf4s9+/ylmLiyTysb48E8f5z0fOky2C554bsDaUoWnvvAK//CZ55m/WuTP/+AbDI8X6B/O3mDQyBL3PDLNnv03+t3S3Aa//+++QLXUZHi8wD/+Fx/cpDVvDU1T6BvM4EndtoobWf2NtRp//gdPc/HMAnbc5Cc+fgePf+JucoUEmqHiuwGl9Trf+tJr/M2fPsP6ao2//M/fZGxPD6N7eiPRW0XeZn16PQI/4NtfOcXCbJG+wSwf+fQ9HDg6SixuIisSnbZHZaPB/NUie/YP7MpI6R/O8s//7ZO4jofr+lRLTf77H36Ll753cfM7xVaTy6USclYiaRhISCjy9VKrG2yj+Zk1/vT3n2ZlsYxhanzgJ4/xoZ86Tq4nga6ruI7P+lqNL/31C3z9715hbmaNP/lPX+ef/9snGRrL0w7aRJOWne/7i9+9QKlYR9dVfu23n+Dw3RMkUjaqKuM6UdtnL6+y/8jIzto/ssRjHz7CxlqNQ8fGOXRsjEwugR2PROw9N2BtucJn/+t3+c7XTtNqOHz5b15i6uAQqYxNPGF2s5gwOJbnX/77T20CbL4f8If/95c49+ochqnxK7/1OPsPj9zUBlmWSGQdhLhA+AYXL98L+OJfv8DTXziJ5/nc88g0H/+5+xib6tsUFa3X2rz2wgx/9V++zdxMka/+zQl6+9N8+FP37HjOzzx9hjMvzyKARx4/xJO/8AD9wzkMUwUBnY5Ho9pmZbHMqy9cYd/BIRJJi05XfNlWYqhvQbT5xyGECAhEh4KRQZVvaGl5oRc5RQGqrNJvDjJsj+16nHbgsNYpYygaZadOr3GzIDtAK2jySuVF+swBnhz8OYIwjh+YWEYMVVJotlwMJ8cRu5e4bfD9hasEQUghE6dVbVMtNbFjXYdU2eJx/aeJKwl0T6ez7NL0ml3tvMiZcd/4AT46+NP82ewfUvMr1PwqM81L3JUcJqbruxoGCAShCLgn9yAf6f8pgpYKYVQeAWApFu/t+RAbzjovlp4hJOT1+mkeLDyKLidJaNuPXfdrETsKga3EeLTnA1HJ0U2/Hdtk7bihgypryMjsGc6TSVik4juLeAciIKml+cmhn7lJA8dSbI7nHsRSbD4z/yc0/DozzUucqb3G/flHAHD9gKRpbMoXaJLGIz3v46HCY9jKzuw9S7F5vP+jlNx1TlZeRCC43LhAy2+S1jM4vs9MsYyta8SNm4FXN3R5rXqCpJrmk8O/yG3J27dp+wghiKkJesx+EIK2f42qcxFbjG8rhewEbZ5a/RIVLwLmj+ce4GMDP30TO8dSbR7KvxdFUvncwl/gC58T5ee4I3M3Y/bkLasOfOExGdvLp4Z/kaye77YvYvBbisW9uWhe+NmFv8AXHvOtWUpekbZvUmy1mMxGLqQLs1Ei8dDBYUxTIxABV5uXN3/HVCyG7TEs5X+e9fU7KyQ8P9IAjKdsrLhJcalMZa2GqiqbAL4Qgo1Wm554nGKziSxJVNodcraFIucZTCUZz2YoxGJc2SiRNCKNop54jFzM4tTyChnbouP53Nab59nZOQaSCbK2xeWNDWK6tq1S4Xo0HZcLK+vYus5LVxexdY2FUpXRfJpKs0O94/D60hq2rmEbOheXizQcl6Rp8PevnOP24T6urZcRAoZyKUIhMGSZp89cZqwQJfxPzC5w20APXz11geFsCi8IWVuuMndqaZPIOTCS5eXLa3heQDafYGRPD8WVKo1aGxEKzp+ajyzdZYkwCFEUmZXFEm7HY/rwCGdeuUar0eH8qQUGRnO4jr85t2zWO3zlr55n5vUlhICHP3QEqZsIPPzAPiRZQtUVHv/5B0lm4+T703iuz4F79pDrS/Pkb7wPpIiZFPghhqWxOreBrMjYcRPD0pk4OEwibTOyb4D8QIYHPnGMWDaGF0ZatlbW5oO/9DCSLGHEDTw15L6P3UlpqYyVslANlX13jDNxYBg7aZIpJPnAzz8IwEMfO4bb8Tjz/Ys88uRdLF0tcvK7r/OeTxznrttH+b1//Y/wvKjSoVxt8bXvvc53Xri0c2+UUmj63QjhRCVqYRshaoTBKoo2Fbkndj+7jvgo2l6ksIyEBpKGqh3ufhYAOpIcQ5PT0TbhYloGwxMFVuZKtOqdN9Wi/PK3z3L6/CIPHZ/i+NEx7skdwg99UnqcvBHNQTN6kopbJ0SQ1ZNMxiOmXs5IcU/uEIEIMBUdN/QZtfuJqxYjdh+mrPNY7104gQcS2IqBIevEVAtLNhiyeshqSRKazZDdg62YHExNcqF+jZhqcb52bfO3fph4x678JElixB7mVyZ+idnmNWpeDV0xKBh5hqzBbd+diI/z0YGPkNJ2pqqbismjPY/syOxRJYWMniGhJhizR7ftI3dtVVNaip8Z+TRXm9eoeGUkopKvEXukewy1e4z4DseIJidD1mB0Lq1rVN0qbgDfny2i9qeQkTmQvI28kd+cDAJMxif4iPIEqS0OO0k1yadHPsmlxhVafhNLsUmoic12ZPUMKS25rR2WaiFvgUlN2WDYHiJdSzERm+Lk+hJLrRq6rGKpGtPpHi5WiuiKwnKrhi9C8mY0aTUUlYbnUvM6DMdS9NrbQZXdIj+UQ9VVBqf6UZRIrMttRxlUK27iOR6GbZDtS5MfyqKbOpqh4jmRfaeqR1kpRVPoGc5jxQ1yXZvHTsshnn7rL3I/CLkws0qp2mR8KEcQCsaGspy7tILjepy9uMxj90+zXmrw6rkF3v/QfsJQMLdUYqgvQxCGrJcb5DNxUgmLCzOr9OQS6LrC8lqNbMomlbC4fK1INh2jN5/g3MVl4jEDPwhpthxkWeLY7aMkYiaNeofiSpWJvTco2U3PpRN4PDV/iZLT5v0je9/gOhPSclzCMESRDArmPhTZoOLOocomiqSTjGtMDOfIpGKslxr4QcjqRo3RgSyHpgYo19oUsnEKmTiqojDYmyaXitGTS1BLdWh1XDRVpjefIBW3yKZshnrTLMlVfD+k1Xbf1eCR5wV89W9fplZpIknwvo/ewcd/7v5tTB6joPHIBw9jWDr/8V9/nkatvevxwlDw1N+/wuuvzaOoMh//2fv5+M/fh67fqL3XNJXRPT38zP/2KMtzJZ795jmunF/m2W+e4+M/e9+mEKUkSaQyMVJbHCxkWULtauQYpsbIZA+Fvt3FBX3Pw/H9G0LEoeC7XzvNy89eRpIlPvDkMX7m1x7FsvVt7RuwdT7xiw+ysljmm196jYWrRb7z1dN88ldym4yVVtu9qWTD90Nee3GG246M8Onfeh9y3qRlQiD7ERivBTg5lcmBMcrCxWkKlmt1kqZJ03WxVBVB1Pcm0SWAAAAgAElEQVT9sOtGooaMjeW3sVcEkeZa0jDo2XTKkZAliyAsc50143Q8vvb5l5m5uIKiyjz8gUP8wj95L1bshiOFpqvYcYNf+M334bo+T/3dK5w/Nc/XPn+Cn/v1xwiFYK61zIB1w9lxa5w9eY3hiQK//ttPcOT45Oa9ux7ZQoLxfbcuxZg6MMhv//tPbl7brftrmsroZA+f/tX3MHe1yKWzi6wslJibWSNdSGCaOqqqEIQhddelMJTG7i4SPdfH6uouyLJE/3CWyemdWSteUKLtxbqMre51FoK5mTW+9rcv02m7TB0Y5Nd++wl6+tPb2pgraDz8+O0oqsx//Defp9no8PQXTnL/ew+Q69nuahUEIVcvrETMpaTF+z52500goqarxBMmfUMZjhy/wZ6p+zXK7gaD1si7BjgKhEfVuYQuJ0kauzOF3ixUSaHHTLPULhGL7c4SFQgsxeInej/MeGySS7VVVtoV4qpJVo+xulHj/Mwq+yf7SMRMbFOnVG0xu1RCrbmEXkAxFAR+gKJEGfUwrBIGIYm0jW5qFJcqCAF9wxkgz4g9ynhskteqL+OFLiVnfZNhk7mFMGlWL/Bw4f1YcpwXXp+hp5BgarK3mx2X0GWdw+k7OV19hWbQoOxuUPNqpNU4NdchZdy4Dr4I8Lu6irIk39AX2iUkSdpknXh+wLWlEkvFGuODWRI7XF8Jif2Jg0zGpnbMWEcMrcPsjd/GK5UXCETAq5UTHM8+AEi4foCpqZvlKJIkvSl4IUkSpmxxKH2U1+unaQctSs46bhiZlKQti9GcQHujK8KWCEXIA/lHmVQPUCt1SCalTY2yuudwobTOQDy5ydwKhUO4hRknhGCmeWkTfMnoOR4uvHfXtiuSwuHUnTy38R3mWlepemXO1k4xZI2gSbuzCg3Z4JGe95PV85uGH0tz63TaHqOTPaiawnTyIFk9x5qzQjNoUPE2aHkFmp67WWZYKCRJJq1d3bLeLMJQdDU3I1b+u00kW5YkErax2Wc6LZfvfv4ExaUyZszgQ//LQ/QO55Alidt6CyxV60wX8ji+j6GqZG2bpGmiKQp78zkytsXdw0M4vk8+ZhMKyNkWdwwNIEsSuqKQtS0enhyj7ri4gc+xoT5qjkfW3vk5y8QsUpZJsd6Ixpl0go1Gi7Vak4F0krVag/0DvQxlUyxXajRdj0qrTT4RY6PZQpFlLF2jNxnH8QJ6U3GWKnUcz8cPQ9KWxWq1Qdo2aToeYSiwbB1FVUhlbIQQFPrTVMstluY2yBYSNGpt1ler9A1mGBzLd0v0BaatY5gapWKd3sEM1VKTbD7BWrKC6/j0DSWpVVrbjCBUVWZookDfUJbpo6MYaZNio4kkSTTwiGs6geOiZ2023A5qTwxdisa1euCRHYySBtfZYQCj0wPbruP19ZQVN+kEHlf1MmnXwWn71LwWQpcwB1UkJMrhGtViG11RcfM+nqmRMhKMTg9s6qEJQIvpkelBT4J2w0ExVF5/eQan7XUBL7AtnYnh/GYizvdDLs2u3QI4kpGU6yUakX7dRtVAljIYYRrPD4EYsizh+QFBUCMZM3H9dMSUBbwgcsZMWsamVowkRUmv64Yo9UqLgbEc2cLOLpzXo93x+Op3z3H6/BJ9hRTTk33osoEa6jheiIGJH4Q0Oi6Bp2AZGvW2i6KolNstknGTHjNDGIQ0a200WUUI8Js+QhG0wg6yLGMTmbv4QQCah+0bNDttNENjuNC7bdwZtnu7TpMOE/HBW7T+rcc7etUnSRJJLcnt6UO3/F6P2UOP2bPr55qscSB1246fZfQM9+fv2/Gzre1Ybzq0mnHuH5je4Rhp7s/f+6bHSGgJDqUi7Y31VpPPlb9J0w0iYMkeZMjeflN7zV56zd6bjpPUktyZOXrTb+SMLA8U7r9lOwA6YYe51gKD1iDpMMOd6QyjdhNFlskYFgnNZNhK4XqRVTUS6KpCIETEXEFCINBk5S2z9pK5OMkd2A9bzwu225/3jfXctA0gP5i9KcP/dl/QlqkyMZzn5TNz5NMx+gpJ5pZKTE/2MdSfYbAvTRgKBvvSpJM233ruApqqcObiErqmMNCbZrAvzStn5ujNJxgZzHLy7DyGpjLYl+al167RaDkUN+pYhoYkS1y8uobSdT9SVZlmy8XSI4euNy62vjp3gS/MnqPstPjE5CEmktutvsNQ4HgBlWaHXCJGXCsw23x283MheQz39Wxem3w6Rr3l0JONY0gymq6A7qMEgqDh0lLbxGWFVFIDNyClayBAV1Qy+TS1chNhh+TjFhvFGlIgcJoO622PTCHxrhSmXVsqc/XiCghIpG3ue+y2Hcu/FEXm6PFJhsbynD81v8vRoFpqcPL5K/heQP9wlnsf3b8NNLoekiRhxwyOPzLN8985TxiEvPrCFT70U8ex3oYA95uFBDQ8d/NZqtfbnPj+JTptl1xPkgd/4uA20Ghr+0xL5/gj0zz7jXO0mg6nXrrKhz95D0ZMp7jRILVLGZKmKzz+ibswe2OsNBq0PI8QQVzXKbfbuEFITNMIRaR5MF+pkrYsmt2sIECl3UaWZeK6HgkN2ttf5hKR0+Jas4Hg+vgZdkutbly/jbUarzx7GREK0vkE7/3IHdtAo63na8cNHvvwEU48c5H11RonnrnEBz9xN41EC0/4u5YnKKrMox86zIGjozszit7CuBU5pu3+uSRJpPNxJqf7uXR2Ed8PKK5UUU2N/sEoS4qAKyslTE3l8Hj/2x4vJUlFlqxuYqMrOB6EnDpxleJqFVmWuP+x224ax66HosgcODpK32CWqxdXWJhdZ2F2nWwhsWtbgiCk3XQ2mQNvPOetIYTAkA3K7ga95vZJ8I9zyJJCIFwC4fxQJTACQUKNkdKcTQeX3aLfHGJPfB9hN32e0m0yepSxFQLSSZuBngiQ7ssmojIMQ2NifAsA2h1ThIj+EUTPvhAwdWAIhIic9hQZXRhk9NxmO93QQZag4TisN5pMFXI79pFhe5SC0Yskov7QcbaXCEuSRN4ooMk6BBHrpxU0yaoSLd+jvsXl1ZItYmocHGj5TU5VX6bH7Ns1Gbn9dyCfiZOMmbuWbuuyzpg9eUtAU5M19iYi4Ahg3Vml5leIyWlycfsHUrWUJImcXkCTdNq08IWHL6IF8Fq9wWq9gaVpuwJ0cTXBwdRhqutNXj+7yNFj42Tz8e45qeiKsumWGwFGNzTlIGICXW1ephVEumeD5jB5vWfXZz6aJyfpNfqZa80CgoXWLE7oRPdxl8h1Xf6u61W5jsfKQhlFlgnD6B4n1RS2ciPR0vAbCAoU7BimqlL32hSLNQxdJZfdfa56qyjWGsyuldE1hduGejG0d/QS621H2/W4tLjOxEAOAxUQpHuSpHJxqqXGpv26JEn0xGJR4kbaeeTqT0ZJ57FMenOf67E3n9u2LWEY3T7m0PFX6E1k8MMiQgxt2y9u6hwa6kORJYaykUanoan4YVSSfXphhUPD/WiKzHghw0g2hdt1ADU1Fcfz0dTIvl1TFeJmpOP5/oN7CLvMbF1VaHs+lqZGLlxA3NDoeSwOUlRerukqjVobRZXxvYB8T5IH33cQzVDRdZV7H9uP6IKMuq7itF0MS0OMFdBNjduOjtBpeximhtNxN+cgEOkL3fPYAQBCIbi8vsHZlTX0brtNVUWWJNwgcrnuSyRYqTdouS6yLHF8ZJie+NvT4jLkyB3bl0Js1UCXVepeJEeQ0EySuoWlaHhhQFKzUGWFDbdO3etQ81qYSvTsBmEYtVHRmf7YPvy2TyN0UHpNrjXXCRBMxnvfZknVje8KBBvVgI1qi0wSzs4sk0vFMHSVTMJmbrWMrkaAzdJ6DcvQ6M0m6MslSL4BiNzar2IJk0unFxgYzZO/RTJ2daPOSrGGQFCqNPn+iSsM9KYoVVuoioznBzSaDumUTSpuErN0ZhY2sAwNVVW458gYCpEBzMlnLqKbKo1KC98PGJroYf7KGpqu4rl+5MAWhozs7efahWVkWWL66NhmNcT1UGWFqcTNTPIfJt6Ro1rDdVlu1HH8aCKQMk2ypkWp3WYgkUCRZdZbTYKuRkfVcVhvNfHCgKxpk7ftSLyt3UIIQcf3qbsuedsmb9m0PI/VZoOO75PQDfoT0QBWbDZRZIlyu42qKAwlkmiKwkqjzt9deJ2G62KpGhnLoi8WjwClVotSu4WpqgwkkuhKxIwptdsUW83ILjqRxFRVQiHYaLVYb7cIwvAHdksTQhCEIUjSjnTN6xGEIQ3HJWZEtOym36Tq1bhQv8iF+kUeTz/Ba6eX6MklKJbqyJLMKlWaLYfRoUivxzJ1FlcqNFsOhq5y77EJNEWhVWuhpSOK9HS6h9+57wlGEzfo70II6qUGVsJC22FxvFvstrgSQlDdqFMnQNYUFEnCD6MBX5YlZCTcMIiyPNcXNkKQs20s7ebfD0NwXB9DV3H9gMWVCq7rk4gbhKEgCELi3VKLIIyopKahMTqY5dpSiXw2KuVSZBnXC/C65R+5bIxE3ETTFAxNZXw4x8JymUbLiUSmYwYxK3oZCaLMVBiKm8TW7ukbYSSRJqEZjCez28SLIULwO663TZw6oUZuOWX3GrCd7SFJEsmYSdzUef3Va0iShOf6aJpKtRw5WlU3GsRTNkMTBS6dnkczNDRNpW84w/JcCd3UWLm6TlqOXkznX54lkYmRysbedcCREIKl+RIba5EA7eBonuHxwq792I4bTN8+vAkcCQRO4CEJCVOJFguL1zZYuFqMjjeSY3Bk5wXR9Rjb04umKThByPJ8icpGA8vO7vr9tx1S15a+++f6SpUrry8D0NOfZnzq5tLJrTEy0YNp67SaDqtLZYorVXpHsyRiBn09qR337R/KcvCOMZK5OPm4jSpHbBhTU6l1HJZrdSZyWYKulsdkNirP88MAXVEJRGRFr8oyfhgiS8AOklJBKIjrxhZJHgkZbZvbzsyFFVaXIw2Gyel+xqZ6b7mgGZ/qY3xvP+urNZbmNpi9vErvXXHiqr3rWJ7JxbnjvilCP8QJ3a7TYbeNQYgIRfTsSBJux8WKmZtZrrcTqqqQ7GYJwzBy2/PcAL8r9C0A1/c3mVpvN64DRtIWJobnBZx87goiFBiWxuG7Jm7Z7mw+Qa4nydWLK7SaDguzRQ7eOYai3NhHliUm9vXz3Ldep910+OyffA8kOHzXBLHEzmVAEF0vL/RI/hDup+/MkHCDGhLKtvKftxuBCLncWCSp2TjhzgLr12M8NomtxgiF1BVrl7oL8BBFlgiCcNPVpu1EWiF7hvNvyaZ6p5CFjL4FFAgRKLLEaDZzS+ZHpGOjEngC01BRZAkENOpROUgsaWLI5qb+mEDghS6y1J1Xb3lkY2qc25K3M9+6hicil7m51lXuyz3C3sT+br/aebrsuD6vXlgkDEP2jfaQjN3cTzVZp8+6NaApITFgDaJJGp7waAYNNtx14maaUAhsfWfNujcLXda3LcQE0dgaNwwq7Q7WLcCNPnOAjJ7DtaNS4q3veU2Wcbsus3vTeTYV17fovTlhh7nWVSBiE/VbQ+jKrfuJIilk9CyKpBAInzVnBTd0b7lPvzVITLkB9oShIFdIIklsukxqsr5tbPBCF11ic0xUZJn+vjSx7rzvB7nWQkCt7RA2BVP9wbsOODI0FUNTqTU7JCwDwza467EDNKot1hZKpLbIOvywc/6dtrW9Jdba38NUekjqNxviRGyhqP/pKvCGrnZ0dIBa2yEdszBUFVTYyn2z9DdIV3Tn129k5V3/3tbvb9UfAhjf20c6F8eydeIJa9scX32DnmFkLiIIRYggQNNV9K6MhnELF68gCNEDmbuGBrF0rZsUiN4SXhh2QSToScQ3nbJ3Kku9VZiKxrHc2LZtkiTdmPOILdu2JPnXnQYVp0EzcElqkcbemlPFkFViqsm61iBl27iuoOjVUXwZS/3htAplSWK0L0NfLoGqKGQTNrapRWV+EuS6LCtNUdg32oNMJNXxZkBVo9bBTpi0W86uY0MoBNcWNlhbr4MUSULs39NHXyG5qSPpuD6qKm8SCMJQkMvE6Tgey8XaZjmiaescuGsCVZMJ/BBZiQBwK2aSzMYIA4GqK8iyjBXTGRwr4PtBxLzf4VQ816dVb2NaBsaPQA/yHTeqeUHAn59+lVK7RcN1eW11hd+86x4GEgn+22sn+TcPPUrKNPnCxfM0XY/fuOs435qd4dTaKm4Q0HAdfuPYcfbm8nzl8kXOrK2Rt206vs+xgUF+YmIPp9ZW+PrMZYJQsNyo82t33s14OsPvvfgsoRAkDIPVRoPHxid5fM9eTq+t8tLSIiAwVZVDPb0U7Biz5RJ/fPJlLC2yp39wZIzH90yxWK/zxydPoEgSbd/nSG8/P7n/AMv1Ov/Pyy9gKJG93mI9WpRW2m0uFTfoSyQi0TbXYyCV4FqpQn8qQaXVRgAZ22KhUmVfT4HZUpn+ZALH9yk128SNKGOftSNht2rHYU8+y6uLyxwd6idtWVyoX+Qry1/DFz4PFe7nUPI2il4L01SJ2XokziqiRVc6aUWsFtdnoC8S/DNNDV1TqZcavPiVVzj2vsMk8wnMQGZKT2MIGd/1cR0PWZZ48SsnOfjANNm+KAvtdjxkRUJRVQI/2Mw4el2rQ6TIzcB3fRCCwA9RVBlFU2g3HJ794gnmCwqjk/3kYjZXNkr43cExa1sMp1M0HY/VRoOG46ApCncODWCqCVardVquhyxF+gpGUufy0joDo1lq9TYXl4ok8zbLjQZtfF6+vEA6ZRNocH5hjeHRLK9fXWWIDHvHe8ikIjrz7dODXLy6xnqpwehAlmTCQlVkjh4Y5vLsGpahkRnNs1qsMzKQwbKi0hFZkkglLGRFJpm2qJZb2wakwViKwdjuyHY2bvPooT3kEjdeexljDEtJE1cLWMrOmVIBJDMxJCTshIEIBclMDEWT6RvOousq6XwCRJQhDsIoIzLcpXunc3GsuIGqKtSr7U2R5XdbhKFgfbVKp1tOmS0ktgkQ7hSDozdYYaEQXGmsESK4PTOMCAUri6VNV7a15Qp/8ntfv6XQXrXUJOxOal3Xv2UZ3A8SQoClaqR0Y7NN1x2xKhsN/uz3n7qlzWOj1tm8Pr4XUKu0GJnqQdcUao0O/b03999sIUEmH8fQNGztxkJIdMfd3kQcbYv2yFvpWztdF6ULql9vfgTSWshbgI9rV1ajsQYYmSjsamN/PTRdZWSywEvfu4DvB8xcWKb/7n2kbuGyE09aKGHI+Rcv47QdzJiJ7wXYCRMhQDcigcMwCKmu19l7xzipHejQIhS4rk+t0mJlocT6ao16rUWn5eG5Pp22y+mXZ6+fLK1mh1jc2GxVKEJ60nGCHYSM30oEokUQNlDkONcXiI1am+WFUvSTAr78Ny/xzDfO3vI4y/M3rHYrpeamvsP1kCSJex/dz8vPXuLUS1c5f2qe3/1Xf8uRe/Zw73umOXDHGNlCAq1bLrM1or8ltDcpM/rxCglZUhE7oaNvI3RZZTzWz7pTIaXdOtuc0jIokkrbdyi7TeJatJAWIiqN7MndYIlpqkIuaWPqO19zIQSBCHBDh6pfYcMp0vDrtIPWps6jFzpcrL++bT8/DIkb+i2f/4yeRUImCH1SKRvTUAnCkNnzS7iOz213jiEpN/d1TVYYiCcJxI1nQZZk7s09xKqzzKvlE3jC5VLjPLPNKwzaI+yN38ah1FEGrGEM2dgmhm/oKsO9adZKDUxj5+sgo5BQb13mEDGfbGw1RtWr4AQOLT9yjmx7Ho2Oi0huX7SEIsQLPdpBi3V3japXpuW3cMI2XujjC4+yW+pqsW2PuuNsK1XeKWJqAl028CQXy9JugsclSUKVr5dPa8iSjoS8CXJ6oce6EyVLhBBcbV7m8wufueV1AJhpXkJ0mXFO4OC9CXCUVFPo8g2UQDdUWs0O2hbgJhodtpROBwEZXcfWov2qtTblcoP4D2H4kU/GKCRj6KqC+S4DjSBKRksSFCsNBvMpfNdn4coaB45P0m443eTHWwOQI0fTSFPT77J+oFtyryhomoIiby/3s9QBUvp+/LAR9bU3eZcJIaLErh8QBiECiKkagRfQDgWqqqAqO+uovdlxg+47OQgizSeIngdFllBVBU2VKfTtnEDb8ZhAxV2k7hXps6bR5Rtz+92O0Wq7XLm8xtGDw5uswa0ADkTSHEoQMbs1WYkqRbbMrzwvoNNdc6magqlrNwH2OwJ5158laefv7U30IeLXt0f/TycGQIKW77An0RclVa8LQ3WZxT9McackSViGhmVEwvhxy0CSts8jwzDqE34QRCQBPySUJJotB01T0dSb+0MybbO6UMKyd+/bnutz5tIyfhCiqjKZpM3kSH7Xdm4F2YQQDPSmNgEsVVXI9m5/X4ShwEyYm8+KRCSErmgqqby+7dhvjE7T4fKpOfL9aUanf/hytXfcyLbRbnF2bZV/ce+DCOB3nvseBwo91F03Yph0vxeEIb6IahQfGR3n4dFIkO8/PPd9zhRX2ZvL4/gBVafDP7v73q7QYnRBD/b0sjebR5Yl/vL0a7y8vMh4OkOp3eah0TE+PDXNC4sLPHX1Mo/vmeI9YxOc31jHUBR+8fAdmwyXL166wFQuxyemD3CxtMEfnzzB3QNDfPXyRfJ2jJ8/dIS5WpU/eOl57h8e4TtzV0kaJr9+593M1aq8thZl9y+srZM0TV6cmydj2Uz35lms1DBUlZius1CpsVipMpxJUWl1IlRaCFbrDcqtNhnb4rtXZjky2M/ZlTWG0ilmS2VyMSvKwnVjOjHNgNmPIikktSSGYpAajyaRWzvbbmVgm9uFYHlmlWc+/wIj+4cwLJ0rr11jeHqAwA8pzq0zsn8Qz/G48uosy0kLp+1y7dwCpqVTGM5RLzUZ2NOH7/qszK4RS9nIiszUnROcfeY8rXqbwA+wExY9IwXmzi+yurDO/tuOcmRsBE1W6Esk8IOAtBXVTOuKghsEjGXTkR6SLGOqEUthfqPK3EaFQjJGIRFjeCSLKsu0XQ/JUrhnapz5jSrldodMb4LVVhMzpmNlTOIpE8cLSORtCoUEPakbma10yubuI2M39eNMyuauwze2jw/vPIB4rk+75aGob4+xo6sKQ7lU93aEtIMqVxvPMGgdpezOocg6hnKz/pSiyAxP3Cjr3O1eD47v3N5M/oYY+nWrzTdmT94NEYYhjVoE8siyRDJlbbNC3SkSqe3i5UJA0GV+BWFIpXTDpnxupsjcTPGttyeIWCQ/2ohePtfv+VZ7++WFEp/70++/9faFAsfxCILopbybRrhuaOjGzuV5Emw63Gzd/oNEXDdImzfYBhIyoagTig7XpybVcoug29BUJvamz6CiyKS7JQwiFFRKTWzFpCbVd91H0xRUVUbVVRRNoVltUS3WGNzbHwHIikSz2kEgMGydwL/Zlclzfc6fmue5b5/n1eevUC03cToenucT+pE2x3Ub8Ouh61oE9HfBsI7rU6q3GMzuDkbfKiQUJEmNBCW7Ua+2cTsRe8XpeDz196+8rWM6nZ1dUwZGcvziP30/f/YHT3PqxFUa9Q7PPH2GV5+/TN9Qlvseu41jD+xlbKoXXd+6MJQou+v49vgbE80/tiEIUGQTRfrhzsgNfUpujZyRYt2p0mfuzlzUZR0JCV+ErHSq9HJjAttoOdSbNxwTN6pNFotVRvoy3cn6lrYLQdFZ5XT1FS7Uz7HSWcIJO/jCJxA+oQi7GXZxE2PP1DTaXjQH2i0MORK9R0Cz0UFTY7SbDr4XYMUMdEOjuRMbUYS0fJf+2BtML7QsHxv4JDm9wIulZyi7JTzhMdu8wnxrlhPl59gTn+aO9N3sTezHkCNmkSzLxCydmKUT32VhIUvyLUutrociq5taPoHw8UIPAdi6vg3DFyIq6bvYeJ2z1deYaV6i4dfwQhdfBAQiQBAtaAU7swxVWWa5WmcinyVt7czm0yQNRVKQZYl0Nka4BXgWgK1pZE0LSZLQ5BSanNym0+kLHyeM+ktIyOXGeS43zr/pddgaghBf3Jolp3UdhCB6Z1zXF3I6u+/3xqHHMjUaanSuP+h7p+W4LKxXUBWFsZ4s6i30o34cQ1MUxvqyjPVG1QW+F3Dt/CL7j42zOLNGPG2Tyt1a9zQUgo1yk2uLG5y7tMLM/DprG3WaLRdFiZ6lob40U+M9HN4/xNhQdpNZH+LjhhXi2gSqvDsQK4SgVGlx4eoqp88vcmVunVK1heNGpWhx26CvkGRiJMfEcJ6psR7SSest3XfPC7gyV+T1KyucvbjM8lqNRssBwDQ0smmbkYEMkyMFJkcLjAxkbjILCYKQi7NRwhlgsDfN2FAWWVKp+0V6mKLdcqlWW8iKTG83CXd1fp2FlYglnU3FGOqLtHpcL0AAy2tVZubWScRMpid7cVyfbzx7gW8+d5F2x2V8KMdH33c7B/dGuoFrGw2+9K0znDh9DdcLmBjO8f4H9nPkwPAmq/R6+EHIhSsrlKotAEYGsgz3784KlaUIQJ6ZX2dpNXJyzaVj3DbVT0LbkoSV3vD/Dxm73cN2x+Pq/DpX5ta5MLPKwkqFerMTMXlMnVw6xtRYgUPTg0xP9GJbN8brRq1Ns94hmfG2rYddL6BSa7FRbrK4WuXEqbnos1Awu7jBMyeu7NgWRZEZG8ptlnxLkoSyYyIsmluvbdQ5eXael8/Os7RaodF00DWVfCbGwb0DHLt9hImRPPouYHXgB8RTNoXBH03FwjsOOLI1HVvTObG8iKGopAyThGFQd7dnHAIhQAj8MOS1tRXOrq3R9FyuVErsL0RiWbIsMZ7ObJaiQZTJulIq8fziPPX/n7v3jpLsuu87P/flVzl0DtMTehLCDDAAiEyQAJMIUiRlUaRFyRItayVbu5J9tF7b6z92vceW9/j4nLW9ttLKWiqQChQDmECAAQARiTCDmcHk2DOdu6sr16sX7wm/qbUAACAASURBVP7xqqu7p9NA0jkL4YczmOmq11X1Xr13373f3zd4LqcXFjjUH/tgmKrGbb39ZC2L/lQSLwjjrrCmdjvYyxOZIIo4X1okiCIma1UcP6Dl+ziBz/mlEiWnRaXt4HZkci3fZ6ZeZyybpZhIEErJQGfiUkgkuLJUpphMIqVkYqlC2jKZrFSJZMRkpYKmqNi6TsOrMl9vMt9oYmlx0tl8vcFQNs1gNs1svc7EUjyw1ByXmVqDoWyLnG2T0OI/q+vGi6xZd1icrsQgsBD0jxS6SQmrty0M5Ln3o0d4/quvMLIvpmBfevMqhm3wwMfvJtub4eTzZ7j61nV++jc/zlsvnEFVFfbcsZO5iUX2HtlN/85eXvrGa9zzkTt57i9fQlEVxm4ZpbpYIwwj7v7gYU69dI5Lx69y+H23or2usrenSNqMJ2cj2bU3jsAPqUxV8TssgnQ2QXogSxRJdvTkGC5kYsM9TcULQnRNJQhDFKGQTVgkTAPH9bsTtJRl4gXhimymr0AhtTHrREpJq+FSXqihamqckmXGxmaruybNoMnFxjmCKORA5hYsxSIIQmS00klcmq9RKzc3fB+IB52+4XwnPjOuSAaEkUczWMRSsyTV4qa/v7q2kn5s93sbJXa9W0pGsrsoFkJ0acNb1WpKcSglqoipt7E2X+K0VsawdNZ+W8cvV0hhWH/7w/WFSomMaTGeLeA0Vz5fMm1RuEErvVWl0nbHDwl6e9IYurohpTf261mZkExNl5mZq1LIJzEMjbn5GkLE53i8KBPYlk4YSnJZG88LCIIIx/Xx/ZB8NkFuAz+llu9xpVJmdy6+UUoiwqjSeTZeMrSdFX8nw9peBiLEyncsZWwMaig6iS1iToWiMDw+QBhJNE1BRpJauUmuJ93phNHpeoVdIDaKZGfxEr/HV//kRb75569QXmygqgrZQpK+wRz9QznSuQR2hy166tgEZ47HUslkykQogkatTaGYwjJ0ckmbmuNuuY+blZQ+qpJGEnQbhG7bJ+hQsDVdpXcg+7ZA5Gw+uSGhTVUVDhwe5X/595/m5WfP8PxTb3Hh9BT1msPFM9NcOjvNd778Knc9uI9Pfu5+duzpi88X6CyY3z0MSCkjDCWNqW6cgnazpQkVUzW4UJ/kjvz4ltd1N61NKKR1C7MjtUWAqatcq7aIpEQFNFWh7a4HAL3I42T1KN+dfYJpZxKJRBUqCTVJUkthKhaWamMqJprQmG1PM+/Odn+/1GzR8ny8INz0ulQ6siNdV/GD2MNE64w7yPXy7+UKooglx0FXVMZzK2OUEIK8UeDxwZ/iztw9nKod52j5VRbcOdyozZIXJ7SdqLzOrZnDPNr/EXYkdhOFMLdU5/zEAr35FOktutLblWBlrhBDPhGqUMjZFra+PPZI5t1Znp77FsfKr9KOYjaRpdhxWIpqYikWpmphKCZe5HK5cQH/BvDF0jQKye0MtuO/w0h2jfZXV81tc9V1GE1nOx5HCmEnChvi9L9IroDhtprY0uR6o0prme53vVkpQl3DJoqkxGl6W45HmqLg+AFNz4fePjwvoFxuMTS0Hry/2TI0lVzK7s773m0VRBETs0uM9cXjkW5qJLMJ/uq/Pk0yY28bUtNouXz1qTd59pULTM6UaTobM8mOnrqOEIKhviyf/uidfOSRW0gnLRRUkBE17yxZU8NQ1zdCnLbHS0ev8OXvHOXi1QUcd2vQMWEb/OYvPcYHHzqwRjZ9Y0VRxORslS9/5yjPv3aRxXJjw8bHcimK4K7bdvC//8ZHyWXWHhfXC/jTr7/Ks6/Eps+fefwIv/KzD3fPc4FCy/E4fWqKnp50Fzj69jOn+LNvvg7AfXfs4l/9kw9hmRqCeN7wwuuX+C9feJahviz/7n/+Sb773Gme+P7xrv/b2UtznL44y7/+tY8w2JvhP3/hGZ5/7WJXInXm4iyvnZjgX/7qh7nn0I41czXX9fnDL7/MK29eBeAffvp+fv5T78FQNp+XRpHkG98/yV89eQyAh+/ew//5Lz7ZmXdFwNtneyFryLBGnIIWgfRB2MT+lS6ggAxAWAglhx8qvHZ8gq8+/SZnL81RqbU2/d6eeeU8mZTFA0d28w9/5n6GOpYLvh8wNFakuIpF/1dPHuPFNy5TrrYoVZrU6k63ERlGkiefO82Tz53e8H1sS+cff+5h/t5H1nsVd3dTxoqf771wlq9/7zgXry5051ur6/nXL9H3VIpH79/P3//43RTzG6dsRmHUDdf5m9Y7DjhKGwb7ikVem57itt4+fvHwnWRNi/lmkyCKcMMANwiYbTRI6jqT9Rp/cuJNPnPr7YznCyy0VhbcAtZ5AJWcFn9w7A3ev3MXH9q9F02cYXkhcWOnYfW5pSoKfifNZ5lON5BK059M8tCOMeK4Z8FgKs1AMsVwOsOHdo93Oh+wM5cjY5qUnRhMavoelXZ8w99dzDOSy6ApSryQkBJNVdjTEyPtB/p7ux2Uvb1FdEVld7EQ+3sIQRjFngCqEHxo/3iHNhl/nv39PejK+pNFSsnkUo25Sp3RnhxpyyRh6px85RL/6Z//GUEQYSUM/vXvfJ6Dd+1c87tCCFq1FsefPUWmmGbm0hyqrmImDFK5FCeeP8Po/iGyvRmG9gxw/vVL+G6AlTQJw1ivadg6hqWTKaY5/twp0oUUmqFx+uVz1EoNCoN5NFNHt3SspMmFo5epLtY2nQxCbD78H//ZF7l+cQ5VVXj0p+7mV/63T6Eogr7M5vT85e+8kLQhaa97fHUnf7NBrllz+Mvf+QE/+sYxrKTJx3/hIQ6+fz+VZpsDO/pIWPFkqRHUUYXGYGI47uxKQbvl4bRWUqi+8YXn+fafvLDp4JYtJPkX//c/YF8nOlsIhaTWw2jyHorm7s4Eau3nlB2QdXmBG0kZm6ut+hvY8DGIY04zthVTlYkvgGX5wlS5Sn82RcL4m2tn3yklhOhOOiWSYAMmyI0lV9FsBNAO/eXk93gs6ryeogjuf/QWfvHXP0g79JhszZMz0jihi60aOKHXMRA0cYI2hqLTiFqkCxYX65MM2kWc0EUS+2/1WvluTPPNluwY1hYsu3um6Ksm2UfuH+dX/8XjW15vq0sIQTJl0nA8JqfLG8rUNqpSuUnLiU0bwzCiUnXw/Vg+oaoKO0YLsWa8Q2NeKjdRVYVG0yWTsanWnQ2BI11V6U2sABMCFUPbRRAtsXxtmKbe1eoHXri+BX1DSQmBH3T2N5ZDtEOXRtDa0nB4dqZKqVQnlYppxovzNXK5Goap4TgemqZSqTRJJAxURWV4tEChmCIIQl764Wm++scv0Ki1SaYtHvv4HTz42K3s2NOHZcWm+0KJZcBf/J0fdoGjZe83y44Xm2EU4Xh+5x7z9v07FGEhZYi2CsBQNaU7nhT70vzz3/o0mYEkjaCNtyqhChlzHkxFox366IpKQjNwtYA5v4oWavhRgBsF2J0I2qxuUyim+NAnjnD/+w9y8vWrvPqjc5x84woLM1UW52p874mjXDk/y+d/44Mcfs+eLjjxbipFxMk1UoZ/I3PsSErc0MPWTFpBe0UesEXpikpSs7oebWEYz396C6nu+eP5YdevYbmklJyuneCJqb9g0VtAIOgzB7grfx+7k3vpMftIaek4cbYjZ/rO7NeYX5hd8/67inmsLRhHq6tSaWJZOoEXMn11cc0E/8bSFIWhVIZee/NkrxF7jEFrhLvz93Om9hbn6m9xrn6aVtikHbU5WnmVeXeOnx75HHtSBxgbLGDqGn2bmCrHrJntGaORDAnl8rWjoonYu+mW4b7uHLDu1/j2zFc5VnmNUAZYisXBzO3clr2TQWuYvFHoyLbiM+Zq6zJfuPLb+EF15fN0vD8zlknK2EbyI6Hd9mNPthuZYZpOHnDDEFOx0ZQ0qrC7UjVNaIjOvUkXBvcX38vt2ZXF0s2o3DWhkdPiQJQo2n7sis2xA9y2h7KFn5KmKBzs6aXhxcCCoig4bQ/XC/7aHkdBGDE+0BMnNv01WUvv5MokTPYM9WB05gq6ofHAR++gVWsDcluZWhRJfvzmVc5dnkMQS11tS6cnnySVtIgiycJSnVK5SRBGTM1V+MJXXkFRBJ/80GFiuCFAEQZS+uu+p2bL5Ynvn+DPvvk6S5WYGaMosdF1wjZIWEZsUNxy8TtyJdPQ2DG0tZ+alJLTF2b5g798iaOnrnc9azRNwTJ0kgkDTVVoOh5O2ycIYtb1UF92U8P8Dd6FdlhHEzHjM+pIaXfu6t38NyJJf08G8waZ/VKlyQuvX+IHL5+jmEuRSZlMTC3Ravtcm17imz84yfBAjlffvEpfMU0xl2RqrkKl5jBfavCNH5zg1r2DJDcgDWxUkYwIZQxyrwa/Nz6WEVE0j4yaqNrulUHgZq8X/zz4LYgaoORASUA4D0oPRHOAAbIB2h5QYvXJ5euLvHZ8IpbtqQqGrlLMJcl2rEUqdYf5Uh2n7VNrtPneC2fQNIVf+7n3kknbmJbB2WPXqJZb3NpZD588N82Js1Pd9aGqKkQy7O6Oqiqbgsemrq3xp91wN4OQb/3wLf7wyy9TrTsIIUglTfqKadJJE6ftM1+qU2u0mS81+Mp336TZ9viVzz60jj0X+CGTF+cYGR9YQzj469Y7DjgSQjDXbLAjm2VnLo8TxEyevmSStGHy34+9QcG2uVJZ4va+AXRFQVcULpeXmKrVYmPrLRZRmlBI6DrXalWavsflyhIHihvLclbX3nyRr587zR+fOMZtvf3cOTDI43v38+XTJ1FEPHHKWzY7Mjk+Mr6PL508zovXJ9BVlYSuM5rJ8tCOMX7vjdf4/WOv4/g+7c5iVFUU7A1Ooi7otZru2vn3Ghrh6qdveJ3NolYjKXnj0iR+GFFzXPYOFEmYOQI/pFFzCIOIMAgJw/UL5lQ+yWM/914CNyDTk8ZptAn9EDNhoJs69aUGVtKid6SIaRssTi1x8oUzZIpprp66zgM/eQ/ZngyaoXHovbdQK9VJZhJxh7zSRHlMYFgGdtomU0yhqiqNahNFUcj1bU1PbdXbNGux9467qptxM5ONermJbmiYCQPfDboshE2pj402pm2gqApTVxZ49utvsDAdsxqe+8Yxdtw1xuRSlfGRlfPLVm0W3QWW3BJ3Fe7BFgk0XV0DTAR+QLPeXgNErC5NVwmDtQvVeEI5GwNHG5z/i40Wr165TjYRdy4dzyeftJmrNTA1jabb8fJJ2lQdFyGg3nZRRAw+ztYa3D48wLFr0wzmYqbcxfkl7tk5zFSlFgOP7yLgSFGUboqFjCTNRnvbyWSzsSLhUEWcRhh0uq2KqpDpSNmklLiOR76YwpcB15ZmKIsqNb9JRk9S9ur4MmDU7mOmXWLI7iUMQ1RdpdSoktRspp0F6kELUzUomJm3DRwBLLUdpho1dmdjVk4mv7KQclouuWLqbZueG0GIbemoyuadJCkjgqgGCEZ2RAwGGRAOqkgTSQNF6HH3WgSYukJPn4pAR1FC8gUToUikNNG0EEMtEnprrwUJXK9VSRnGqsZBRCSbRFENOtKNTC4RGxSGUKu2uoyFzSqKIqrleDIqhIgZjXqKQIZomxjnAniej4yg2XSRkcSyDaq1Vjw5jCJs28C2jdjTzVCwbT2+/iotvvfEURq1djeN7nO/+n4se/1CL1rlswDgugGNuksiYXZ8FxRcP6QZbu0Vsum+y3YHjl7p7KfSdpzOSMyMshMGVs5gsl4mQqIJhVboUfGaDNr5+NhGAlcGtKTPktvgFjHMlFOm5NbJ6gk0oXCtVWJveoCCmULVVHKFFA998FbuenAvs5NL/Oipkzz35Elmp5a4cGqSL/7uMwyP9aAV4wns3wRgeeeVoB2WcYIF0sYof10+f0REIEOSqsWks0DByNBrbZ0Y5kchJbeO3QGONFVBU1VKlRZO2yOdtNg72kshGxuQLlcrbPLC4g8peYsAjCV286nhz7I7uTdmhtzIQuwsNrolJdfLVVpewJ6ePLlNEr+6+yYlhqkzNVNmZDDHjvH+LQHvIIpQhcDSNmcZChGDHj1mHw/1vJ+78vdyrXWFFxef5XT9BE7YYtKZ4Htz36FPH2Gp2qI3n9rULyWSEU6wOYt4ubzIox3G9xFNaBhKfP2unsedq5/iZPUYoQwwFZPH+h/nvT2PkdI2TiiMx6a1j7tByMRSJfbIbDmkLXPzhYyARMJgcb7WSWlc8SJxwwBDVUlqOi2/RBDVccM5dDWPEAa6opNQk5SJvc0yWpb96TgRynE8ZqYqJJPxvMHtMMWjMG6I2LaBrqnU6g6zSp0orOH7AQODOcz01teBoggGRvJbLkYFgqJt05uIwb5EwmB0pLCOVfV2arZS58zkPNmETTGTYM3k/F1QSctg30gvQsQSarfl0aw7BH7IxeMT3HLvOL1Dm7Mj00mT9927l/lSnT07ernvjp0c2NNPOmVh6BpSShpNl+devcAT3z/BQqlBpebwje+f5KG791AsaAgU/LBKpK29l0VRxNMvnOVPv/4a1XrclC/kkjx0924eunsPAz0ZDD22rmg2Xc5fWeDY6eukkiY7RzYPKpFSMrtQ43e+9DwnzkzF442ucmBPP489cICD4wOkOvdZzw+YXahx/MwkF67M89iD+9ewdrYugakkMZQkkhg0uhEQurGCMGKx3GTgBl9E1wv46lNvct+du/jM40dIJy2efuEMf/r1V2m2PJ778QVMQ+OOW0b4pc88QDGb5Njp6/yXP3qWSs3h3OU5puer7BjI0ai0SG/DJAuigAuNa6S0BKOJgW3vwVE4TxhOo2o7kdIFWQclHjOiMIobYptdu/oBMFTimZ4JRKCOgzCAg8SwRgTCBKGja5L77tjF9188RyppcujAEHfdtoO+YhpT1xCKoO36XLq2yJ9+/VXOXpoljCTPv3aJ++7YyXvfs5d2y+O+x24h37ts1QGf/PBhHrx7TzzmEwdF/NFXf8zUbAVVETz2wH4evnvPyj5L2bVQUVWFPWO9ndeS3eeXmwNRJHn9xDW++ER8LpuGxkN37+ETHzzEYG8GXdcIwoilcpNv/fAk33vxLE7b53vPn2W4L8fPPH4Ec7WMXwh8N+gCnn/TekcBR1JKLiyVcIOQUEa8tTDHxaUlDg8M8LO3HuKf3ns/50slDFXlsZ27ifApJiT/9L57uFSepWhnuW/kCKam0g7L3DEoMJVdOMFShxFkkjA8fu2eO7iwVMHSFO4bOYKUPpYW8QuHDzOcsfCiJoNplc/fcUfXc+PhHWMUEzYLrRZ5O5Ym3DU4hCVUzizEXiW39/R1zbP/x/fcx8WlJSIZMZoWGPIUB/IJfuPuHq5WqvQnbT686wAD9iRe+zQIAyFshEggoyUUbSdID6SHlG0kLorSg6rt3PDYea7PhRPXcZou47eNxAbH25SuqUwslnE8j1tH+rbdfrkUVUHkLDQpQVdRNIukruMGAZGiEORN6lLSm0zg+D650QJ9d4xyZXKeuz90iEnhYhLheh5SA7UnwbzrMpzMINQ47tLUNFp+gJG2cIOA1EB2jZnuzVYURlw5NUm90qTQn2X+egk7ZVFdjH1JzISBjCQjewdYmquSSFkkMzbTl+dJF1LMXl1At3QSKYtmzSGRtmjVHLI9aa6cmmR07yD7juxEyhuZSXRZFKsHUT/yKRo9eNLDjwIsBXr6Mt0FGMAHP/0e9h3eQbPWpl5pUq+0uPjWJG++cH7LfW2FZU6Uv4IqDHanHyKprQBWU5UabT9Aa/s4bsAtQ31cnC8xks/y4sUJ9vfH2741NccDe8b47qnzHBzoZa7eQFEUGm2P6Wot7qqmU5yensf1A+ZrDdp+0I0lfbeUogp6+rPd6Mul+Tqtpktyi+7R7FS5++9ISpKqQSQ6ngtKnChm2jqu4zMzuUSt0iKTS7AnNdxJkIkICRlN9KF3OrWDdg8pzcYJXVKaze7UEIbQGbJ70YSCrug3gBY3zxDKmRYpw8DW4kVf/3CeRNLspKRVKC/W6blJ5tByBWFEte6Q3uI4RdKl5p4iIkAqIenUfureRTRtmJZ/GUWY2NooTjCFIyNcsYgiNEBFaAqWNogiNILIIWMN0LxBxiwgDi+oltlbKHYfFVioSoZlGtjOvf3ohkbgh1y/vIDn+lsmmPhevB3EbJude/spe1Vm2guk9SSw8T6P7ertSu/WGiIub7Gxz9j8TIXrnRS+Qk+aD3z8zk3NGX0vpFJaWZgahkYiYWAnjQ7gCRnb/Gt3woXQaAcTqEoWVY3DErL5BD39WaYmSjTqbSavLnLnrhwjyQKWEo/VrcCNz38EhqrhhQG6otIOfYYTeYpGCk2oDNk5DCVeOAzaObJG4ob3FySSJrv3DzI23s973nuA3/6tb3LxzDTnTk5y5vg19j9SxFStNdKYv/sl0UQCnzqRDDvXwdsvVcSBHBPNWfamR9C3kBcsl6ao7E71dRlHEE+wnbbXPXc1TSFlrwUdlrwSE83LSCSa0Hm49zH2pPavMZReXUEU4IStlQeE4PbBflod5sd2JYhlQWEQEUaSZr29pQxYVRSW2g7tMOim4275+kKQ0JIcyNzGzuQeXio9xzemvxxLwJrnmfemMbQic0t1LEMjn1kPjgWRz6K3wG7WJ0Etl5SSql/pHouEmiSnr1+En66dwItiyeloYicP9zxKWt+8odYKGuuuCUvXGM5luLS4xMRShWIyQdbeePxa5i8nUua624sTBF1vDklIO5hGV7Isj2mGYjJgDTHlXCOQPrPtaUIZogqVRr3NtasLGKaO43i0mi6qGjMiTUtncCjfAdvjJM1m00VVBL19GbYCZIQQnTj0zZtvG1W53KTeaNO3RXNyu9rRm8M2dDRVwfhbkoW8k2puoc71yRKHbhvFdzzmJ5d449nTFPoyTJydZvzw2JrtpfTxw1mEMBDoSNnmAw/1cPjggwwPpDENGyl9hAiIZAOBSi7r8bM/eQv5bIL/9sfP0Wr7XL6+yJXrJXoKA4DEiyoowlyjCrhyvcSff/P1Lmi0f3c//+gzD3DPobF1HkMAt+4b4uMfuB3X80nY6434l187jCRffeo4b56eBGIfo888foRPffgw2ZyJeoNMcveOIvcf2Umz7WLqereZIZGbjoFrqwMkhBGJpLlmbXBj2aZOEIQ4rr/G1F0Sm/Z/9mN3sWdHD0IIPvrIrbxx8hqvn7xGrdEmm7b4+U+9hwO74zTZ++7cxXM/vsBzr16k0XS5OlkiJQQTZ6fZdXCYdO/Ga0opJWW/TjNwyBmZm2vcCA0pvfiTRovI8CpCO0AQFTj+7CmGxwfp37kJ00qkEIrVfe9IShAbKEokIGNf5B0jBf7XX/swPYUUmVSsnIAVT00hBKODeXoLKf7df/suE1NL1OoOr791nUPjg9QrLaIwwvMCBkYKgMTOmRjC557dI6RMg3qjzdeePs4UIBTBnh09PPrAfiCeF5+fmCeVMNkxsHZMrzddLlydR9NUbts7iKoK5hZr/OkTrzJfqmPoKo8/ehv/w2cfJJ1cO0YP9mbYNVrEtg2+9tSbtF2frz19nCO3jXLr3sHuMaos1PA9f0MPzb9OvbOAI+BcKe5SfXR8f3wjbbuUHYdISobSGYbS8aAeyYDF9hnKnkMxkSBlOYRRjYgAgaDUTmIbM4wmR6j511CEjhfWCKI2ofB4z/BBav4UUs4CCvPtK+wvHqDsnaHRbKMrNof6j3Q7+aamcefA+jjVyJOE9ZCkZRC6URcxHM1kGc3Ei64wuETgTSBIsyujsDubRkoHqBNF80ShhyRAVYdBycfPRQ3CcBJQkbKCohSIogVUdm547EqzVX7///g6tXKTX//3P8OdD+/f/Dh3kM2HDowxUsyiqwpp++3p8ifKMbOm4Xk0fY+saTFZq3JbXz+zjQZeGHDn0BAnZmYxNY1du/po5Q36R/qYuD5JO/C5Wq4wVa/Rm0gyXa+RNk0ulkrkbZuG51J3Y28oRQhsTePBnWPrzHO3q3bLpTRTYc+hUS6duMbAzj5e+tYb9A4XqJbq6IbO/rt2cfXMFIW+LFKCmTBpNdyu0ebE2WlSuQTJjM3CZIl9d+5i+so8dsqif6wHTdcYHCtyz/tv4flvv0kqk+DBnzhEMmOjLqwdRFWxEqvcZaUE4Rrj47F9g+zYOxCb3oYRgR/y3DeObgscDduHmXVOkdAKmMraQX5nMUej7dKXTuGHIVOVGsP5DLPVOrcPD9CTSnQjf+fqDe7ZOUI+YZOxLSIp0TWVjGVRabWZqzXQVIXeTBJT11GEwA3+to2b//8tIQS9A1my+QSLczXmpisszFZJ7DE3XGhEYcSVcytSi0CGeFHYZQYKEXdA+wZyXL+ywOxkmasX5jh0zy56rNwKdtCVVq2VSSa0+IZhqjGNOUtqDQgBrDHvjjXrW0+aFSFi6W3n556+DENjRS6enqY0X+PcW5MUejNbUrhvLFVRMHSt43uyOUNLU9IowgChoClJLG0EIRRMbRAFDSFUksYewshBV+P0pJgCbaCrOZARgdLa8LUB6p7LfLOJH0bYGkgC/HCyc0ziScPYnj5yhdhQ9+rFORZma6Q2MciUUrIwV2Xi0hwQs5V27O6NvedUc0vGEax8R6tfe+Wfm8hf622CDrMwnUt0GXAbVa3S5Nrl+e7PTsvDdX18L+y8g6DaaqOpK1K1ZRPZeP9iZt1m35mUAZFsdczF49JNjQOHRjn+6mWiMOKNly7wwKO3kEqsTG4KxvYS4X7r7S3UVFVh7y1D3Pu+A1w8M00URczPVDmsjpLT89v6ofxdKy+qEkQOkfS7wNHqyfmNTYuNKpQhSMmt2V04oYulbs8ODWWEG/pr5ElRJMmkra5x6JXpEn4QYVs6KTtOX3OjNq0O+GGrNr1m/5YLpnbkMNeeWfNY1WkzValjqCoDma2bYIqqYNsGQRhhGhqKqmxpiuxHEaqiUPNcKm6bnLmxMfRGZSoWEW1BkAAAIABJREFUd+Tu5qXSc0w713HCFnW/RiLMoSkKUwvV2CvpBk8TT3pMOdeQ8oFN3ysiYqJ1uXu8U1qanLHWq3AZXFrept8cxFI3Z2RJKZlpT+NGa73N/DBEUxSGs5k4RGSbBDBFVegfyGJ1YsNB4IYhXhiQMZcXcT4CgaEWUTpm7qZiMZbYzdHyj5FIppxr1PwKOT2W497/8L547RhJ5marNJttDFNnZKSArqvdFMjl83s5lMNbNQ5ttM9tx2NxrkZmG6bE6srlE1iWjmWtSijq/Lf6tbe61lquz+nJeXYUsxRSCd4mYfcdX2EYMb9Yx/MCkmmb4U7abv+OImP7h0jn1473kgg/nCeKqihKEsc7ia4N098vaYeL+G6GKGpi6ntp+2dRRQZN7cPUY5+ZL3/nGFcnS0SR5PpMmbsO9xJKn5S+M54/dCoII14+dqVrwpxKmvzcJ+/h7tt3IJV4LrZcAkFE1FkHKehmLJcNZNBl+6W0FCoqAoWZ+SovvrFicnzPoTF+5vG70BMRZ2tnUYVKWk935WW2atPopCGmozROy0ERCm7ksiu5a8uxMCJCdICoWs1hbqbKrl29JDbzTROQTJgbAmP9PWlGBlZYpcmEwZ4dPbx+MjZwHh3MMzqY745HSdtgoGPW7PkhS+Um7XyaRMoi25PaxGI/LjfyyOhJMtrG/jprq5Nwu8qHVaCDkkWGkiiUNG8yQTiIIs4tlHCDIE7j6wQitX2fUEpMTSNvWzQ8n4Ye4DRrtKslsqZF0/MZzmboSyW7V/ieHT0cuXWUiaklJDC3UEPRNYZ39jC8q4epK4vd69/UNSqOc3N6WySlahPXDxjtz607RrVmm/6eDKJjAfLm6UnOX4nnczuGCnzqQ4c3DV6wLZ2Pvu9WXj56mWvTZUqVBi8fvcwt4wPd98n2pP/W/I3gHQQc+WGIF4bcOTDIpfISf/jmG4CgN5HgJ8b3IYlPEjqobSQDvKhOShukGcxhqjkWvVNkjFE0xSaIXGytiKZYgMAJlgBJ1tzJ9cbzmH6Oun8dXUmQ0oYpuWcx/QytYJ6ssZOGP00YeajbTLD29BcoNZpY+vqo0uVSlAF0MwNCJ160aEDQOeFCQCGKZuOLSRlA4iHQEUoBhAJEiG1yYi6cnGRmYjH24dgGVXS8gLNT8931ysWZRVKWwQ7z5qRGy8bZC80GINjf08Pp+Xl6k0lmGw1yloVEsthsUXVdhOtSTCSoum2anke13Wa20WCqVqPpeYxmsvQlU1iahhAwW6/jRxEF2+bC4iKP7N7FpdJSjBK/TeDIsGIvpaunp0jnU1w7N8Xgrj5yPRl0U6c0U2Hy4iwDY73MTy7FGm0pqS7UyPdnKQ7mkVJidJgIud4M+f4sjWoLw9KZuTJPtpgik0/y+X/5cT72Dx7CMHV6h/NcnS+TSVosL+CllJiqRcEoUPWrCBFTHD03oNlYO7GLPV5inxdVU7rGuVvVfPssPdY4FW+SRrBIRh+Ku4VCkEvY3L1zhCXHQRHQrtYouw4DhTRSxl0UIeJBKO3FiwXH9zk0OoCt6+zrMJJG8vEir+V4eF5AJm2zf2B7qeffxRrZ2cPYnn4W52oszFZ49bmzDO0orklygvh7vXhmmvOnJruPGYpGK/S6vhACQf9QjiMPjDN9rUSt0uJbf/FjBkcL9A3mNiUKCSGIwggE6yjPN958TEvH7Jh416otGrU2vQOb75+t6Xx4bC9JPZY+5XvS3PvIAa5dmqfVcPn2X77Kjt19jG6hsYcOSCVlzETs0Kv1LRYiqmKTsW5f81h6A5PLGztXy4ul5e7dyjbr5Ve9iSTVVHuVxjweR8NoJTmubyjPvY8c4IkvvczCbIXvP3GUX/j1D27IOvLcgB98803mZyoIIbjrwb0MjBSY9KdxwjZVv46tmjfZUby5shJmVyroNN1NF8O+F/DckyeZub7UfUw3tTV0byEgm7SoNFYmZKqmdIMPPNenXGpsChwJoaF0unrLz2qayr2PHODpr71BudTg1efOcfQDFzly/3j3c28Gwq02aly9zfK5dDPeWstyXYEg0dmP2fYUhmKS1N49xv2KMEjoaVRlBZAzFavr5xRIn3pY2/I1BAIn9JhsLZDRkyy4FUYTW7OMLUUnpVvdc0JKGfs4rAKlNVXtsLlXzvvlxXa81dYL7UhGnKm9xWx7es3jactEUCd1E3MS2fk8hXwSy9IwDG1LiW1KNzjcO0AQRTftobRccfqNisJKWqNAodZsc/HaIgM9aRKWsQ44CmXIufppKv4SOb2w4XVR8cq8VX2z+/Oe1D6S6nrgdbUUPWLzBoGUkopf5q3qMXy5dox0g4Dz84tkLJPbBvu3TK9bfq1rV0vdVEmIv1tb07vWEJqSRlOz0ElTiptjKgfSt5I3iix5i0y3JzlafpVHej+Ipq79nnbuXn+f2VQ6vE2falluFvg332H33ICJayX27dVIdUB6gYjT8zr3HCdq4UUuCTYGxHVVxdK1Tvrxu8ekf7kE0HZ9FksNFCk5+/plvLbP1JV5VFVh6IbvUGBg6ftZbtYY2k4UYRNJBz+cRVcHEKgoIomp7e2c2ypC6OSzGgO9Ga5OxjLHat1BoKAKCzcsk4hW7mXNlseLb1zuHvPDB4Z54MhuymGJa/VrpDr3g0hGNMIGRaOIEzo4oYOUElu1UYWKruixibyEncmd2EqCE2enuoCUber81IcPk01bVP0q7aiNLnTqfr3rYWYoBoveIkWjSDNo0ggahDLEVu1t2TimksRUEoCgUEiRzlTXeE/eWHGKbdA1t+4edwG9hRSKprDotEjoOqam0rPq+u0rpsmmV0BnVVVIJ+P4+jCMaHsBqqbQqrdRVZUo2hw6SqgWkYwwb6IhARFSuiiiM0ZKp7Pmjdd/mqHedLOyHQRcWipRdz1UIehNJhnL55ioVGh6PnuKBbKWyUS5jB9G9KWSNFyPhG5QbbdRhKA/tXItm4bGjuECuqbiByFNxyWMIqIo4vKZadod+5PlpttmoSjrSsbzd3+DtXkURbTdgN58CkVR8PyAH712kXbH1P3+I7sY3gBsWi4hBIN9WQ7sGeDadJkgiHjr/AyuF3TWdaIrVXs7DMyt6h0DHB2fm42lGjLi/Tt3M1WrUrATmJpGGEW8MTPVlcMc7h8gZ1nkzXHcoELeHMcJFhlK3oeu2Ahi/WM7rBBIF0NJowkbQ01hKhl2pB7Bj5r0q4fRlAR+1GJn6lFa4SI91i1owkJXEqg3kf5gaBqKUKg0Hfb0b5xkJZQkYpMbzco2eUB0wITlCfrNTX6jKOLM61doNdrYye2N2BzP51qpwsiqaOa34wsRSUnaNNDVDBnTpGDbKP39uEGIqankbZuZeoO0YbC/p4it62iKwmg2SxhJRnNZMqbJvp4ehIDBdJq5RgM3CGIZYEKQ0A0qbYeP7NtH3o4lNZv5NW1Vmq5x+L0H1j0e60gjLhybYOzAEGZirW/IrttG12x/I7ujZyjfSW+hu8hJ5xJrtMApO+4EqN3PLVnySix5SzSCOl7kkdST9A1myfbatMI2CTX+/tpBnF70dia2EmiHdQLpAoLr1Sq1tsveniKWplFz2yy1WlyvVqm2Y88erayiKoJb+/txg4BK26HsxBGVlq6xp7A2vlGIWH43NVOhVG5w6/6hWJa06ji8W8qyDR77+B2cPn4Np+nyjT97hXxvmnvfe4BEyuyCflcvzvHF3/kh5cXGmt8PohBD07pGtKqm8pGfupszx69z/q1JXn7mDGEQ8fd+8SF27O5F7+itozAiCEJaDZcrF+Y4d/I6j37sjm0BHDthMDbex/T1EpXFBk9//Sif/eVHSKat2Ow8koRhHN9uWhqKolCwVs5XVVV47GN3cPb4dY6+fIHjP77Mb//WN/nsL78vjj439dgTKIpZcE7LY+rqIm8dvcp97z/I3luGiaKITMre0ucwCiOaNQdJ7BOmqArJjI3reLiOh500icJYXiUUhUw+GYMgCMIgxPMColASRRFRKKlVW3idm6yMJI16m4SncEemj8gJabltFDVCVfoQIm4kLB+vD3/qLo6/dpkr52Z56mtvkCsm+cAnjpBKW6iaShiENOptvv+NY3z3K6/heyEju3r58KfuJpE06fUKGIqBss34uRbo2mK7VeNM32CO3oEspfkai3M1Xvz+KT7xufu7HkdBEPvRPffdkzzxpZfx/ZXVVNhhMWp6J8Y4kjTbHknLWAUmCXbtG+CVZ8/iewHPfPs44weHGN7Z02EmQRiGMfiptFGVJF44gxENoSkZhBCMHxzksY/fwTf//McsLdb5/f/wHT7xufu595EDJNNWV3oSBCG+GzI/W+H0sQks2+AnfvqedftfWWrw3a+8ztCOIgduHyGVsTFMrQsyRWGE0/J46+hVnn/6JACpjMWeg3G88HbGnH8XSxXmOqmRrdoUjB5aTpNW2ORU9Th7kvs2ZZ9oQmVXcgBT1UlrNiP21mMJgC9Dgijssq6jjuF6fzHdPY9dP1hzRgshSKhJsnqOsl+iFTS53LzAWHJXx7A5LiklgfS52DjP9+a+1U0GWy4vCKm5Hlnb6noSbVpCYFkGyaQJQuC0XJKZzVk4mqJQXGWMLaVkvj3HTHuSHYldJLUkuljvI7bMpnqr+iYLbsw8zBl5eu1etP4cmaSFpqoM9W4s751sTfDMwlN8qP/jJNUViZyUklpQ5YfzTzLdjpsPKS3Dkfy96z6DEIJ+c4AznEAiuda6wpJXot8cXAvAyohGUOPpuW9yuXlx3WdJmSaHhge4tlTtem9sdd2EQUS9tra7bigqihC0Aq9DnxToSo7lVL7lzztkj/KewgM8PfttQhny9Ny30BWDO3J3x75Mq65ZKeMkOTds0whqTLcn2Zs6+LaBYKflkspYFPtvns2oagqaFqd+rh4j80YBS7VxwhZz7RkuNy9yWL9rQyN+XVPZN9TDfLWx7rl3QymqIJO26e1JoUgAwfmjV7n1vnGunplm7MAQibTduY9JhFBQxcp3p5Lp/q2r/Wtfm7XXbCQikquMfH0/BGQnSS0iYoXVPDlbZnYhBnc0TeHBu3ZjmTphkGLQGooBJ0UhiALyRgEFhYQas+x1RSeQAQoKERJFQCtooQqVtutz7vJcd8E/PtbL+Fhv3Iw1ctxT6NzDbmCMD9grHbv1ja7NK5IBS951DCWBDIuxVHOLtX6p0iSdNNdEx0OnWZyJm/Vvzs8wks6yP1/E6jQWFSV+PkLiuB6mpqKratwIEHEwjh+E8dRVEVuC/0IIbNVksjVHj5lHV7Rt9lMg0JDL44RsxX+Iw3bq5Sa5TcbQGytlmnzswH7qbix7y1jxXHcsn6PtB6TNeBwfzHTGgVX70ch7XYXQmn0xdVQ1BnmCIPaO7BnIcvK1K/QMZFdJGCNG8tm1fsObVCglffn1Y5iUkjCS9BdTqJ3jXCo3uT5TRspOwuyega4Z/WalawqjgysSuMVyg6Vqi6EOg0w3NW67fy/J7M0zMLeqdwxwZGoqoNL0fIq2jRcGqEIha5rdiDtNUUgaRvfLTqi9JNTYqM3WYtBm9QlrqfGiN6GtZUToSgeY6WwqO/9LaH0dqhid57efgLZ9n8V6E0vXWKg1tkzv2qrE34Be36y1mTg/s84webPKJy1+4o791Nsul2ZL7B3soedtfG5NURgvrgXJUqa5ZtGTMeNJ4lBmvWFjXwfhXb19zooBk55k5zlguENRF0KQsW42mWB9bYXU7r59FE1b70lwM68hhNjWUqbWXK2zFxSNInk9jxt5JNRYFpNImlTbVc5WLnBX/nZ0RedKpUyl3eZQ/wCJbcCjUPq0wxp91j6csEqftR9DFJiszsdoeec4FxMJ8rbNznyu+1jcQY3NN8NIEsoV6VIEWPoG7As/IAgjErYJkWTy6mLcbdqxMXD6d7mO3D/OXQ/u5aUfnGJxvsYf/l9PcezlS4wfHERRFGYmlzj60kVmp5a49cgYF05P4zRj9pihauRu8GrZsbuXT3/+Yf6f//gk8zMVfvzcWS6dnWb/7SP0DuTQDRWn5VFerHP9yiJLCzWkhHse3twbY7mSKYu7HtzL6y9ewPMCnvra61SWGuy9dRjD0HAdn3rNwTBUHv/MvWTz66/5geE8P/NL72VmconpayWOv3qZyauLjN8yxMBwAdPScNs+S4sNJq8usjRfw/dDDhyKgVbL1OMO3hbJDZ7rc+H4NeyUSbPm4HsBSGIfsoEsmXyKibPTICDfm+HQA/u6jL9LZ2d4/um3qFZanTRCl0a9zdTVWOLcanl85Qsv8KPvnsROGli2gZ0w6B1I8P5P6GRya2/eo7t7+amff5Df+w/fplFr8xd/8CNOHp1g/63DZHIJalWH829NcuroBM1GGztp8om/fx/jB2PZct7Ikje2meTIkCisgVDjdLJON17KCIRYNbEUSOkipY+mFkjnbO573wEunJ7Cbfs88cWXqVUd9t4yhKaplOZrnHzjCideu4Kmqdx53zgnXrsCxECRooiuVM0PQ+Yqdfpz6TWLxDvu3cPTX3uD2akyJ1+/wn/9t9/grgf3ksklYtCs1sZpeTz4gf3suz2eJCtiZYJvmDqP/8y9TF9b4pXnznD96gL/7396mmefPMGufQMkUyZBEFErN5mdqjBzvUSz3ubhD9+2IXDktn1+9NRJFudq9A1k2X1gkP6hPJl8bGTerLe5cn6WU0cnKC3UUTWF+x+9JQZVRYClJkiq2/v7/V0qTbGRrAWOklqaXck9TDnXCGXI6+WXSWopDqZvx1YTRER4kUs7dNAVnbHEeAxed3zRbqb8KMSNAjK63ZW3C6DRWmGv1Jsunh+QSVns6M93FlR5RhNjVKpLRES8VHoWUzE5mLmdhJoklAFLfomL9bO8svQ8Za9EXi9S9ctEqwQRbhBwrVwlY1sMpLfyLBKM7+6LQcooljK9XehwwZ3hi9f+O/3mIOOp/YwkxshoWWw1gSoUfBlQ9ctcaJzlaPkV3KiNQGFPcj95rYcz84tEEewf68U21x9fUzGJpOTFxWdoBg1uz9xJ0exFoFD1y7xRfoU3K68TygBVqBzO3sWoPbbBJ4W96YO8XHqOdtRmrj3NkzNf56GeR+mz+hEoOGGLKecaxyqvcbJ6jLSWJpABrXCtObcXhNTbLo7vk2drA3I63fWlUoNcIb5vKEJgqCpNP+xMmgWG2gusXUgpQuGB4vu43LjAhcY56kGVb05/mbP1t7glc4i8XkBTdEIZ0g4dKn6Ja62rTLYm0BSNkV1jbxs4SiRNdu4d2FR6vFE5jo9paOsYHoPWCEWjl0lnAjdyeWr2CbzIZcgeQRcGoQxwIxcnbKEFaeplk7rTZs+7kImtKgrpVLw4t5MmB+/ezcTZaTRd60rlpYxohWUiGZLWt2Y1RpGk1mizVGnSaLk4bR8/CPH9sGs0vVzL0Isf1jDVPlSxAkTPLcaJWBCDdztH42PvhpKyJxGEeKFLWo9lqX4Udse1VhCiCwsnCgilZDzdR2AEaEKj4jnMzK98hoG+zBr/xu56c5tT7GYb86aaZsi+jZReoKlJhobzWzKOUgmTZsvtevaseS1To+a52JrWSZBcAUkUIbBMjYbncXJ2jtFcll35PMtLGgldpUUi3ZHybskclcy2F9mRHCS53ViCgpQOYTiPpt8KwgAZQGde1G66N+3FE/vbKeRu8GczVHWNrcmKK0D8L6ftUVlqUm+6tDpJikEQ4Qchpy/MrD2eEq5emMMwdUpz1e78SVdVqk77ppRqYRjhuD7pDVIH267PfKnBcEdWWK62KFdjqbeqCM5cnMFpbx1qEkWSq1Ol7s+uG3QZSxDPq0ozZQbGemCb5MObqXcMcHRbb4w+L5/Yw+m1nYK9oriO9bEGKdzgNbcCDNb8TNyhadYcqktNXMcjCiWarpJImWSKqQ2TbCBeWO/uKzBfa9Kb2d5ocbnCIKRWaVEvN/HaPpI43jmRtknnEpjWzRlBSylZmC5z9dzMttsul6IoaMCPTl9hrDfHpbkliukEw4WtF0Cu41EtNWg22gR+iKIomJZOppAklbHXeKzczGe/Eeld81z84E3v03YVhhG1pQb1SqvrJ6TpKsmMTa6YQtPXgkdBV9IiUcXNf6+rq1Rrxpr4VVTyRXeRVtgklCFD9jBmh2FkKDr9Vg+qUGh5HkuO09Xrblde2OR68zW8qMVI4kjnMY+cZSEEJDrgj6ooqKxN2ovCiFqlRancxG37nYm3ip2yyPWkN7yuZCdVRVEEgR9y6ewMxd7MuxI4yhaS/OL/9AFURfDyM2coLzb44bfe5IffWpEUpNIW73/8Dh7/9Hv4z//ma1zueB1py5T9VeeOqqnc/+hBEimTL/3uM1w8O83cdIW5ThrfjaVpCoOjRUxre/ajpqu87ycOceHUFM8+eYJWw+WZbx/nmW8fX7PdzvF+HvvJOzcEjhRV4fa7d/HP/s2n+NLvPcO5k5MszFZZmK2u2xY66TXD+W4Mr2Fo7NvTv+G2y2VYOuMdoAkhCP0wjrkXIpZOSSj0ZRCKwLSNNXKFKxfm+NZf/BintfGNNAojpiYWmZpYXPP4wEia+x57mGLPKKuNVTVN5ZGfOAQSvvanL3H14hyv/egcr/3o3A3HRTC6q5ef/Nn7+NAn78LYJu1kdbX9kwTRRYQwiWS7Y+oZS1ijqI4kRFVSCJFCEQkUJYGq5jEMjY/81N1MXJzjxR+eoVxq8NU/eiE22u5ce0LEpuuf+yePMbKzh9/6zT+jXnVwWh6lhTrFTkhC7GOir6NKj98yxGd++RG+9LvPsDBb5dSxCU4dm1izTSJlMn5wkIOHd224fwMjef7xv/oYfYNZnn3yBNVyk7feuMpbb1xdv7GATDZBvrgxuKNpKsmUxbVL81w+73D5/OyG20HsM/W+jx7i059/L8m0Rc2voAoFQ3n3pDsKoZIxxuIxd9VobCom9xffx5XmRSadazSCOk/OfJ3nF36ArhhIJEEU4EuPXclx/tGu3+j4mQnS2nYT+7iUjjxtxRw7njvUGrEMRwUSlk6j5TK7WGegkME0NCzV5pHeDzLvzjLbnmbBneNrU3/2/5H3nlFynfeZ5+/myqmrOucGGmg0ciJIMIEBpMQgUpIly1awLdkej4999qxnd3Zn9szshzlnd3Z9Zs+Z8dgbnCU5SJZESaZEkZQYQIJgAIic0UDnXDncfPfDra6OAEnZHyTN/1N3dfW9b7313jc8/+f/PLwy/yNUUfP3WnaJsl1CFETuSt3Llugw/zDxdYq2Pw8GlGVtig9aeQVBaJQPO56DY7uEoh8t0eTiUbaLlO0iNypXkAX/c6iiioCI4znUnCqG62ugSILElugwj7U+RUAKENRUFvJlzNsceJq1NrbFdnJs4ce8tfg6J3NvE5Z8PRC/ZKZuHY7I9tgeHm15orE3WBuDkSH2p+7mxOIb2J7Fe7m3uFw6T1SOIQgipmtQsPKYrkGz1sqTbZ/iYvEsJ7LHVl2nKxmnKRwioMgfInEGwbC6rky7ZlmNg5MkBtDNKV+Dbk00qRk+0/UlfjjzHGfzJ6k4ZU7n3+VM/iSaqCIJMi5OQ2tmud/uUGt927YKqJpCc/udXQPXhqpILCyWaW2NE40u617FlQQPZB7hWxN/g+7WGK+N8rdjf05YjiILMo7nYHt+u4+kP8aO+IOko6FfSHFsz/Oo1UyWcqGBkMr+h4a5fnaMof39pFri2J7JTO0ykqAQUTLrQBPXdevOY5O8eXKEyZk8pYqObtiYtu/8ZNeZ0etLsGRi2hCmk23oaAHki1XMelliOKgRr4M7FdugbOkICJTsGlXHJKNFKZg14kqQOb1IzTHrzCOJiOxfcwlct2yHbMEHXCVJpCkRbugJeZ6HXjWRFQnLtFkSZUfwmc9LLq2O7eI6LlpQ9YGZOzxrFTtL0ZohLCeZnMxSLul3dMKSJL93175HAFRZIq5qTLHebduXwhCZLpWwXIfIbRyRPdcjN1f0r3+HKcJ0LZo+KIG28rp4CEvfn5hGkAV8yRaHprZkQ9vsnyuW3PrOXZninbOjXLo+Tb5Yo2ZYmKaNZfvlaI7jNogqK2NgqB2jZjI/vbxPFwQBy3HqEjp3DkWSyBWrmJZNb9sKbSdBIBLSqBkWjuOD71XdpFoviTMth6899+5H/rxL4OtSOJZNIKwhSv88ffozAxzNTmT57l+8juu4pDIxnvq1+witceYRBIFbl6f4yXdOYugmIDC4s4uHPrl/Q1rx2y9faAgK77l/Cwcf2rbufY7jMjO2wLHnz3DxvZuMX5+lsFjGth0CQZWm1jj9Q+0c/tgudt69ifCaDEatvrEZ6sj44sMxfzNgGhavf/80186NoagyH/vc3XT0N/uT5liWH3/7XS68M8LEjTnKhSoeEI4GyLQn6Rxo5rHPHmL7Xf3rdE1M3aJS1n1tnpE5pscWuXp6jFzdJcyomfzga8d579VLt+3rnuEO4sMtzORLJEIBijV9w4dFFH3arlEzufz+KD/5znvcOD/B/FSeWtVAUWSiiRBdm1vYfrCfB5/eS3NHalXJkut6vPA3xxm9Ngt4HHlmH4O7etbVsNYqBq88d5LRqz4AFghpPPmFw6Tb1td2ZueKfP+v3qBarhFvivDkF+4ltvYQLAhIsoTruEyPLnDsB2c49fplpkcXKOV9WnwwrNHckWRoXy93H93Btv19DQCpbJzDdgvIUoKYtheB5Yl1YTrPC3/7FqXCxuK8AgKbd3Ux/MAWRmeyjcXPw8PxbPJmnpgSIyAub+Bdz2W6Nkd7sAVVVlBEiaimIgm3o4ku08sVMURMaWO08jY50z/0xeWQj5pvME94nr+ojV+b5cTL5zn71nUmRuYo150DgmGNptY4g7u6uefxnWzb30cosryRUhUJWRKpVM16yZBHz6YP78r38xSCINDRk+Zf/tun2HGgj5NvXGPs5jzr/VVTAAAgAElEQVR6xSAUCdC7uYXDjwyz//BmVE3mV3/nIeZnCrR2pdBduy5ltgY8kkT23r2JnoFm3j9xgzPvjHDz2izFXKWxwYinwnT2pNk83MG2XV0fun/D0QBf+YOPsXVnF6ffvsGt67OUCjV0yybdFKWlLcHQ7u47usOJosD2fb38j//7Zzj77ggnj1/n1rVZ8otlHNtB1RTiqRAdPWn6B9sY3tvDwFDbba/Xu6mZ3/ofPo7rurR1phBFcZ2I5tqI1Es+1z77W3d28eX//vEP1HFbG+FIgEzzELIUWLeRVVWZ+z62g83bOzjxyiXOvDPC7FQeveZbzDe3Jdixv4+7jwzR1Z/5QLvmTFuC3/xXH8NxXJJNUUTRRZM2I0kpPM9BQMTDxuf0CVAX4XS9GoIQQKiXSAuCQDwV5l/8T08yvLeXk8evMTbijz1ZlWlui7Nleyf3Prqdwe0dOLbLr/3+UaoVg0x7gmJJR5KX14/IBskPRZF49BN7aetM8fqPzjFyZYaZ6RwiPhMy2RShd3MLXf3L429poyQJArptI4si6ZYYn/u9h9l7/yAX37vFpbPjzE3lMXQLWZaIxINkWuL0bWllaGcXQ7u6N+y7ZDrCV/7V45w/eYvrF6eYmcxRKtQa+k6KJtPUGmPLUAe7Dw2wc39fQ5MqKIXpDQ8Skn861u/PYggIKOJ6toUgCHSHevmlzi/wg5nvMF712RAlu9hgtImChCxIKIKK7bncKE8RkBSKVmXdPUJymKjsJ+sUUWk8IZbnkDerdAR9GryqSERCauPvHc3++hwNaY3MuIDA5sgQn+36Ej+Yfo6x6k1sz2qUd4mIyKJMSk1zMHWYB5uPIiDQHuzEq7kExCAlw0SVJZpkmZY620gQRMJypNHOjQTpPSDVEiMSD+HrUIhE5CiWaxKUQrcVsc9oLeyI72GsehPdqWF7DhW7THlFiYkoSATEADElwb7kIe5uup8mNYPnQWdLnGBAuW1Jh+3ZHGq6j+ZAK6/Ov8icPkPByvtiuAjIgkJYDrM7foBHW58gqdw+CROUQny89VnCUpS3s8f8dtolSnZxVTsHooM81vo0A5HBupbUOUJyGLEOnCuSRDy4ei4T8PWzlvo4IPrzcDgSYPvubhIr5m1BEAirKnldr+9DmkgEUiypXK26riDQFujgs11fojc0wLu548wbs5iuieEaeOj1//KBX1XUSKvN7E3eRUTeAGQWBIJSqNFOTfwwWfS141xl7QYpmQhtqCW4P3kPtmfz5sIrzBmz2K5F3vI15UREREFEEwMYhstoPoflODTHI8gfSvPl5ytkeVmDxnFcZscXKeWrCKML9GxtI6CoxJRWdKe06v88D2zH4ZW3rvK333+Pm+OLWLaDIAhoqoQiy6iyhBrSCGgKqiIxM1+kVFnW/3Q9g0X9XWTB37+EZD8BZZh2gyUS0Ja1szpDKTpCS3ILK+vJ/DmyNZhgqZB8I1aQ63oYpg9kiqJAcEUCr5ircObECJIkEo4FCEU0ykXdlwYQBQzdolLSiSfDVMsGTa0xOvsySLc9vHsYTomiNYsmRchk2iiX9DsCKLbtMjlboHUDJ8AlVoxhb+xOKQgQ1TQWq9UNE9Se62EZNsnmGKIkrAPxVrfcIyBpyB+qcsZDFIJ4Yl3DwdPBXYR6krV9oIXYbRJLP024rsuNsQW++p13OH5qpG7c4q9liuKX6AXCip/00BTKFZ3ZxVIDEHddl+xsgZaOFIVcpSE9oSkSsWDgQ+kcCSJ0tiTQbqNVGw6qdUDSwzRXgz6a+sHA/tpY+z/BSBDXcfE+XFHSB8bPDHCkV01ee+4UuYUSrd1NHDq6g57B1dkGz/O4+N5NvvNnr2LXKfg7D23i/qf2rBMPNnWLt350jhe/8TZqQKG9dz1t1DJtTr95jb/9zz/ixvnJhk5G4++GTSlfZfTKNO+/cZW7H9vJp3/7CG09y9daLFW4PDVPJh5hW8eKzbXlcPL1y7z63Em0oMrQ3l7aetJcPTvOX/+fz3P+nRG/RGNNm3PzJa6dHWf34UG239W/rs0nXj7Pc3/2GgszBWplA6NmrrqOZdqceOn8Hfv6/mf2cmS4lYHWJuaKFWRRJKiup1fLqoRtO7zwdyf47p+/xsx4dpW4lmXYVMs6sxNZzr51nQvvjPCV/+UTdA20NMAjQYBr58Z58e/fxnU9kpkYA8OdiGu+r1K+yvNfe5ORC5P+vRWJnYc2kW5bnzUauzbD8199g1K+yvaD/Xzsc/ese48o+uyFS6du8dd/+AMuvz+KuUZc1qiZ5BdK3Dg/wdsvX+BXfv8oD31yP5IsIYpBHGcKlRaENdav+cUyL33zHeZW2K+vCgEeenY/O44M0dOaakwWoiCSVFMsmguU7TKWZ6HVRc9dz8VyraV/p2pZuHd4yk3XYUbPE5GDRJUA7aFdOJ6NXEfxQ3KQmp33M4Ks3h7ZpsOJl8/z93/0EuPXZ1c5uoGv21PIVrh5eZr3Xr3EA0/t5dO/8xDxlM+8WhLddR0XQRQIRwMfyjb55zUEQSCeCPP4J/dz7yPbqVUNHNtFkkXCkQCRaKDBtjv88DDgf5/n85OUrPXuLw2drJY4Dz+1m7uPDFEtG1iWXa9r9jNXgZBKKKStYvJ9mLZG40GOPruPw48MU60Y5PIV/up7b/Ppo3sY6G0mGFLvaDu/dJ1kU4T7ju5g/+FBqhUd03Tw6iVQiiojyiJV3aK9LbFK5NRxXKZnC5TKOn09aZLNMQ49Okw6FWlsOG+3EBqmjSQKG4Iz07MFDDyOfnIfqrJeoNy/8Ienha+ME5fHyMTDfOpL93L0mX0YuoXjuEiSz6qMxoPrWIm3i2RThKd++VDjd9dbEn4UWS9kK6x5bf2BK54M8/in9nPfo9up1seeKApoAYVwNNBgp4qqyJEndgFQrRjMTOUbbiyqLLG3v6NhbLDy+rIssetgP4PDHZRLOt978zy7BzroyMRR6+MwUC89dD2PW4UcEUUlEwrz1tQY6VCY7ekWTmdniPSH+cyeBygWqui6heCB4Ti4okc6FmHeqaFqCqFYgKplYTg2EUVFd2xs1yWmagR7omxr3cJDn9hDrWZSqulEZBXTcbheyKJoMof6egiuAcIUUSGprtZk+0UOURAZiGzhCz2/za3KDUarIxTMHKZtIbkKASFMV6KDLq0P2ZEY0noIKiqyJNWBVz/BJcoyn+36UmP9iSlxwJd9Lls6irAkYu5vcJPxUGN9L1V0SlWd3rbUqg20LMpsjgzxxZ5mrpUvM1F30/LwCEoh2gIdDEQGadHa0SS/zP3zPV/Bci0CUhDR0WgKh30r6/p1I3KUL/b8VoORspFVvSSJdA00I9QP/zElzlf6fg/HcxAQiCnr9xOCINCstfL57q+waC4wURtjwZir6xAauJ6LIirElATtgQ66Qr2kteaGDpJtO0zMFVjIlelp23j8ma6BJEgcSN5DX3gTN8pXGavepGKXkUWZZq2VTZEtdAV7CcrrdSgs12HRKNGkRVFEibiS5LHWp9kW28mNyhXmjVkMx0ARFRJKir7wAN2hPmKKD+ztTOylJ9yPKIgklNs/I4qg8GDmKHuTh5ip5ekJt/ojQXIphWpUTYPuuvSDAGxLNTcO7P73dPv5URAEInKUBzKPsDOxl4nqKKPVmxSsHJZrIosKISlMk5ahK9hDWmsmKsc3LK3URI0n2z7Foy1PAHzoUrbPdH2xMc4jcoy5fBnLcelKJ3zNvxVr1Kr7SRr3ND3IlugwV0uXmK5NUHHKCAgEpCBROU5bsINOrY+JaV875ReRcQT+oXTpWTcNi9nxRe57ei8nX7lIMVshGNGQBLmutbkcjuty/OQIf/L1Y8wt+qBSJhVh11Anw5vb6O1sIhUPoWkykuhr4/3hn77MO2eWGbCSECCmbsVxa6jS8jhe0rcD6ppdK1/3w1uBGy3pcNV3I+uqWVZG4zWPVQLRvlakQjCskm5NYFs2pULNd/0zLIIhDdtyUAMKtZpJMVdFHLjT/kEgJCeRBRXwkGXR/ywr9gdrBapDQRVFFln/3Pm/265L1bZuK9Q+Wy5zZWGB4eb1iUmf8a1QGql8oKjy0jO60X3Wi2p79dfz9Z+9xhlFEARunh+nY3MroTvo1H2UuDWZ5b9+9XVOnh/zvzNVZvtgG3uGu+jrbKIlEyMcVJFEEVEUePGNS/zlP5xoMNgMw2bsxhxzU3nSrfFGV1u2y0y+xHD7Byd1XdcjX6rVNbuWT2Se56EbFjVj+Tta+Y2HgyqfeHQnbR9S82kpAgGFzAoh9GgixPChzSgblFH/NPEzAxzFkmHaetLkFkpUijVmxhbXAUd61eTm5ekGaASwMJNnfipHe+9qscdKscZUvVwhHA3Qtbll1aRgWTY/+vsTfOO/vsx8vUxE1RSaWuPEm8JoAYVSoUZ+oUR+oUxuvsQLf3Oc6dEFfvc/fIr2Xl8grT0V5/AWiZJuIIsbH/Bcx2VhpsClU7f4v//9t7l+YQJVlUm1xAiGfecc23KolHRK+SrxVJi+utjn2sjNlViYymPWFe9D0QC2aVMp+QdUQYBQJIB8BxeuZDLCju5WXrhwjbLuT+7mBnbqkizx6nMnOfb8acqFGuFYkFQmSiwVQZJFirkKC9N5yoUapm7xbp3l9Lv/4dO0dNU1pwSB/m2dSPJ7uKbN2NUZbMteB/RN3pwnO7tcDmNbDiMXJ9l97+A6hH706kwD5Ovb2t5wBloZgiAwN5Hl//lfv8O18+PIskRLZ4p4U4RQJEC1rJOdK5Kt0zBnxhb5i//4jyQzMfbevwXPswkrWwmpm1irPxUMqY372paDbTmYhkUpX12lM1UzTG5OZ+lvb0KrH3QN10AWFHS3xkxtiu5QT6O0IKpEEBFxPJeALDV0ojaKqmNwq7xASyBOVPGzLznzFqoYQRRkoko7UU1Dt+zl2jLA0E1+9Hdv87f/+Ufk62LOoWiAdGucWDKMKPnfa26+RDFbZmG6wHN//hrFXIXf+J+fIpGOgue7qiH4jIWO7qZ17MBfxJBliUQq3NB4uFMICPRG0h8oOiqKIpFYkMg/cZFczFe4PjbP7qFONMXPuMUSIWKJEE0tMb78q/fTkYmvE1H8oBDrwGB4RemH53nkClVmZgtcujrN0cQwtZqJ5/nORrPzJV589SKb+prp7WqiUK5RKuk0JcOUKyalso4ANKUiGKZNsVQjFFQJBlXePnmTWDTAYH8LiiKymKsgSSKpRBjDtDl1ZpRMOoql+JlKRfYFLCVNYFJfJKaEEBFw6qCr7Tkogr8RFRHQXQtZkPz7azEM02Y6W+SFdy+zva8NRZaIhQI0dyRwXY+FYoVASGOmUMayHVLREIlIEMOyyRarVA0TSRLJxCOEAyq6aZEv1wioCtlSFUWSaE5EUBV/I1zRTRaLVWzHIaAqpONhJFFkoVAhpCnkyjUc16U5EWkIWZdrBtlSFdN2CAYUmhNxVFnCdlwWChWiAuTLNXTTIhkJkYqFCIU1mtpjLBarVGZzZOJhwgEV03aYzhYJaQrZUg1JFEjHwgQ1FVuEnG1yemaevoEWYjikNI1QWGuMYdfzmCgVaY1EaI1EyYTCGI6DKAg0hyLk9Bo522CWGm7AIx0McWZmjrZAlLaoytmboyS0AO2RKN+7cZlMMMz+1nauZBc4Mz/DI90DnJ2fwXZdWrv7OLk4x2Ktyt7WdmYrZWZUg86Ihhb8cKXcv2jheR5Fq8aiWSIgKTRrcVJqEym1ib3Jg1imzdnTY+TzVVRVQltUuJYroffM+RnjkEh2MY9lzVEsVFEUia3DHbS1r0+s2Z6vA9IaXBYETSXCpBLL81864Y/fjcQ7RUGkScvQpK3em+mOScU2SKqRxgFUEARS6nIbirbOaDZHUzjUmEMlQWpca6q2yE/mzrM93kNXKNPom6pj4Cq+8PbF3Bh7kgOktQ/e2IuCiCwEcL0Qh1L3I4sbH/pHytOczk3wSOuyGLUsiwz1tuD13P4+ruf7n8miTGugndZAO4d5cMP3Oq5L3qpgODaiIBBXQlQdg6xZJqmGcT2BnFmh5ph0BPtoDfSgOxaO55LWon5Jjm0iCyGKVg2pXjKtiTFEQWBWLxGSVIKSiu05BCWVsm2giTI5s4woiISlJJcLt4jIKSQqxNQQebPKWGWezlATruuwaJQRBIEm9aPpD8miQkZrIaO1sCd58CP971KIgkhCXQ8c3inWjjGA96dvUtZNutK+vuD0TJXYbdZiRVQa393toqybVPQJXM/DdtwNbdJ/rkMQfGZhfeqVZQlFlXn3pfOYhkUgrDUOvgm1fVUSZ3Imz19/+23mFksIAgz2tfB7X3yA4cG2dYkg8N2h177uegZl80a95FtuMI5CAbUhaFytmauMIgAcz2Wyuojl2YTlQN0JsM5xEwQmq4u0BhN+OW+9yXEljCQJDYFux3UpVw08z6Og68x5Bl37Oxp7C8nz6GwJI0siNcvCdBxS7b4AtxoV6Inf3hlrKSRBIal10qT2oFsCiUR4FQOutobc4Di+I5e0Adi51GbDcTb0XRQQ6IzFGS8U7giYuY7LuuzzmlAFmZJdxXBXSwh4QE1fe7Z0cd0cYAEmONMIYl0bTXDRKzpGxeCnibUAoGnafOP5U5y6MI7rejQlwvzqMwf42P3biK6oolgZ0dDqs0wwqLLzYD/p1jjZ+WUWneu5VAyT2WKFWPDO5x/LdvxxsMF8IIoCm3syjblCU2VURca0bERR4PC+AXZv6/xoHbH2HpKI9hH3/neKnxngKBIP0tqd4uJJ3x1sbjK77tBVLekNLZ9gWEOvmRSyFWbGFmnrSa96b6WkM1u3J47EgquAJc/zuPDOCH//Ry+zUK9Z7Oxv5pFPH+Dux3bQ1BoDwcEwTMavZnn9e+/z6ndPUi0bnHnzKn/3X17iy//maRLpKIZl8+6NcVRZxvU8MhuITLuuy43zE7z7k4vcujrNwLYOjjy7n8FdXWTak2iaQrViMHVrnsunRjENi5bOZcvWJS0LURI5+PA2OvozDQTYA66dHedr/+mHgF/i9exvHmGwriHii5zZeHiIgq92n25LoGgyIU0hHFCZyZexN6ijLeUq/OTb72HbDlv39vD0l+5n2/4+YvWDc62sc/r4Nb75Jz9m9MoMnudx7p0R3nzhLM9+5cHGdXq2tCKrfh3w5K15TMMmuKKbPM9j5OIktYrpO1soEkbN4sb5Cd+2eQWbwfM8Jq7P+qKvAnQPtm6oN2JbNu++cgmjZpJujfPwJw9w35N7aO1KIdaBuvEbs/zw68d59bunsC2H3HyJH/7NcTbt6ESNJTGdOVzPXlVL7XkerT0x/vUffQ696lGr6FRKOjNji/zV//E8kzfnl8egbmI7biMj53keYSlMWA432ETukq2xIBOVI4iC6DMTBIGFatUHfDZA8T3Pw/NcUtrq8eZ4Fh4uum0xWSyhiKv77uSrl/nmn/yY/EIZURLZvKOTJ75wL7sPb26UYdYqBtfPT/D9vzzG+8eu4Ngux54/Q0d/M898+QFkVUKSRAzDxjAsLp4ZI54MofwzKfb/rIXneZi208jSK7KIIku4ru88sUQLdVwX0/R/tx2HAAqu51GpGSiyhCJLePiL2dImB88vlVpyZnBdD8OycV0XWRJR6/oTrudhmjZy3SbUdd3GNU3L4cqtWY6fvklfZxPhoObTtevWnqbl0J6Jo614TizLqVtl+/RxSRRR65lEr+6oYdlOY+jJktj4nKWyzsuvXiIUVCmVdG6NLXDp2gy27bB3Zw+VqsHiYpltg76A+NhElompHF0dSd59/ybzCz4Ic2BPLzduzmE7LoZhc9/dm7l2Y5ZoRKM5HWV+ocSNW/MYhs29hzaRjIcaDJrJ6TwLi2XaWmJMzxZo2xpltDpPTAliODZzRh5FkPHwSKoRFEEiLAeYrC2iiQpBSSWt+RnnF969wqWxOUo1g7HZHNv7Wjm6b5BCRef/+tbrbOtpYXwuj27Z3Le9j8cPbOXCrRm+e/wCrudRrBoM97Twa48d4OZMjj/+3pv0tabIlWsUKjUe3LWJJ+4aQjdtvvrSSaazRYR6P//G4wdoioX5k+8fJxLUqBom2WKVgY40X378IOGAyosnr3Ly6gSCAMWqwdN3b+PI7k0Uqzp//P3j9LUmmVwoUtVNDm7t5hP3DDO1WOSrL5+kUNFxXJeOdJwvPLKPYkXnP33rdXpakhTKNfIVnd0D7XzuyB5GprO8fOoqI9OLvHTyKrFQgHuGe3lg5zLzVRQEArIvqul4LpbrYrkOrudiu369v+k65I0ahuMQUzVawxFmK2W2p1toCUVoCYfRJBnXddnX0o4oCCzqVaqWhek6NIcjKIKfbZ2tlklqQQzbxnQceuOJD2+B+wsYZVvn67dep+aY9EdaONq2axXI4eFrrgVDPhDrOC7pTJRkKoLjuJimTSodwTIdmltiWJZDeIUTq+O5lKwaUSXoO7Apyy6EG0WxojOXK5FaYz/vei4lSycoq6ji6vV5tLLA+9kRPtl9CPU2pWMBRWFrS2ZDc4aKrfPO4hUM10YRZM7mb1K2a/SH2ziVu47tOQxE2rhQGKVi6wxE2siZZQpWhU2RNiZrWVzPpTmQoDO0DCRM61kWjRItgSSn8yNUbZ3ucDM9oeZllqgWx/ZGcT2X8eo8tyqzpNQomqhQtKpkAnHaAqnbAk8fJsq2zssz51g0yoQkle2JLgKSyqnsTTqCKfJmhZ/MXqBJi7Ir2cOlwgTzepHOUBOiIPDq7EXCsoaIQMUx0EQF23OQBYnN0VZGyrPM6UUeaNnG+fw4e1N9nMreZH/TAGfzY4xXFrm3eSt5q8K14jQn7RE+2XUXGS3KdC2H53lcKkxxqTiB4djc17yVvogPmnl1wGRJ60lVpEZC1bB8lz5FklCX1kLbaQivL+33VVnCcvy1x6nrcmmK7Dt61tcmQRCwbAdJFBpJuaX7CoJQ120Cy/G1Szz80lqnzjgQBQHdtNje09rQfEwlw8RjQf+wa1q4HiiSiFo3T3FcF9PyD4CaIiGtcF9bCtd1sV2XSs1kZHaRwfbMLxR4JAB6nY0Lvl7hvU/tZX4ySyCkNdwMTbeC7hRJqJ0N8Oj0pQnGp32Wfjio8bmn9rFrqPO21us+6LAaKBGQiaqbqdlTiCtcr1OJEJoqoxsWVd1iLltmoGf5zGc6FtdKU5TsGgk1jOO6hOUAcdUXv79SmkR3TQpWFQGf4XegaTOqItNUL890XY/ZhRK6YXMrl2euUkGRxMbZyXZdLMcf90FFIappTBaKuHhkwiEG1zhZrvzUS6e5sr1I2VokpfYQjQYY3NLaOP94nke+uNp9MqApbN/STuQ2gseiKNIVja86B6z6uwCdsdhtHatd10NWfbffteeQJZDG8zwKdpmKs166w/M88qW1rytIch+eW2CZcb30GX3CwsJklt7tXQgfwUm7Ys1gOEViahdy3cBjdrHEmUsTjfH6yOEtPHFkO5HQxv3leR66aa+SbjF0k/MnbhBviiAALR3JRls9/D78oPCAgKpgbaDdpKkKhVKNlrRfbhgNB4hGNBZzNjXdIleorsJCLNthdCFHJhahWNNJhkMIQt0QRfCNVmqmjSL7gGbFMMnEIh/K/e3Dxs8McKSoMu19zSiqjGXazIxlsQy74aYDkF8sMXVzHkEQ2HPfIGeOX6dad9TZdXg1MyU7V6SY8xkVLV1Nq2zSqyWd7/7F6w3QKNOe4Lf+3TPsPjyIoskYToG52lmkgMqOQ/sYGO4kFA3wvb885pfAvXiOwV3dPPH5w4RUhb7mFGFNJRHeOFPh2C5vvnAWx3bZfc8gv/avn6R3S2vDYhggCXT0Zdhz7xYM3SK0YiIYvTZDrWKQaIrQ0plaVSrnD6jle0myxOCuLg4c2db4u+4sUDJvEFMHCMjL2bnhrlamc0VCqrJh25fYNMMH+/kX//5Z+oc7Gui353koQZn7n95DKBzgj//dPzA/lUevGLx/7CoPPL0Xx3ZJNcdoaomTzMSolecpLPrsrfgKGp3juNy6PI2pW7R2N5FqiXHhnRHGR+aolvVV7zUNm5nxrK/FE9Fo62nasJTH83zdpHhThC/+wce578k9qEFl2R1GEBja20tze5LsXJH3j13F8zwunx5l/PosfXtNHK9aV/tfGS6ufRpFFghmDpLM+LW4yUx0nSBnNBQgHFAR63bGHh55K0/JLpG3cnSHewhIfpmX7ppM6bP0hDuQBZFkIEBIuT1CbHsuumM2rJIBVDHMgnGdgBSjLSD6h7MVk31+ocx3/+J1Fmf8cb9tXy+/9e+eZWC4Y1UfBsMaB44M0d6T5r/8m29w7sR19KrBi994m/0PbqVnS5tfDxxQ8FwPAe5Y//zzHhOzeX5w7AIzCyVcz2NLbzO/9Ogero/P84NjF/mdz95LKKAyNVfgL7/7Nr/z2fs4dvI6i4UKpuUwOVugtz3Fs4/sRJYk/vp779DSFOXGxAKVqsHh3f0cvWcrkiRy6tI4P3rzks8eiYV4+sEd9HemKZRrfPX77zLU38LZq1MUyjUO7+7nvn0D/PjEFV586zIzCyUKZZ1kLMhnH99HR3Oc05cnePH4ZRYLVX7/Vx+gr8NnAv7k3auMjC8gCAKTcwVCAYXPPr6X/s40xYrOcz8+y9R8gblsGcO0OHrPEI8dHiKoKZTKOrIssn9PD68cu8L4ZI75hRLpVATX9RjozTA6tsjO4U40TaarI8XcfKmR9do+1E42V2F+ocTVG3O0t8aRJYlQUKWnK0VXR4pMU5R3Tt0kX6gSCQdW1XwDtDTHGJvIcms8y0BvmtZwjFQg6muCAVO1LK2BBKIgIgli3S1QoDfSTNYsU7Z0XDw60jE+ee8Obkwt8NCezdy/sx9FEtEUGc+D+XyFSrPJbz5xCFkU0erMir62Jr7y8bsIaSqjs1n+/IV3mM+XcT2X2f3Fz60AACAASURBVFyZJ+4aYv9gF2dvTvON185weLiXmmFx+sYkXzq6n6HuFnTTIh0PUzMsKrpJLBzgXz51D6WawX/8+1c4OzLNoaFu7t7Ww93belBlmZdPXeXYuZsc3Nrtb9jKNeYLKl/+2EEUyQcSDcvmH09cJBxQ+Y3HD2LZDn/4zdc4cXGULd3NLBQq3D3Uwxcf2ce1qQX+7Ifv8MjezQx1N5MIB7gxtcivPLSXvrbUugxZzbKYq1aQBIGcrjNdKeG4LjldZ7JcpGpZDKaaKJgGZh2QXF6ePFrCEW7kF+mKJuiMxgjIErbrots2qUCQsKKiiCKn52boSyTZkkpTNAy6YnFqts10ucTWpvWCq/+txKxeYE4v8ruDjxFVgqhryngURWZwazsND6LbauRtHAtGiddmL/Dx9r1ElABD8Y47vr+rJUl7Or5K1xCgapu8OHOa+zLb6joiy9ETTpPRYnfUw1BlidbYxjoXqqjQpMb80iclwKzuMaPnUESZoKQSkjUichBZlIjIQY4vXKLmGDSpUc7kbzKj57gvM0xSjTCj5yiYFTpDaVRRYUbP0htu4UZ5mm2xbs7lbxGRAywYRdqDPjAj1ucY13MxHIv3stdIKGEKVoVAWeWJ9oP/JODIwyMsaYTDGoooU7Z1NkVbuVKcxsMja1aIyBqPtG5HEAQu5D0Goi3sTfUzVllAESXuzgzy0vRZZEFi3ijiei6doaY6aOYxUcuSUEN4eLy7eIOOYJKCVaVqG2TNEmVbJyxr3J0Z5IWpM1Ts5ZJrx3O5WZlj0SgTV0NYrr9H8jyPUs3gO29dYGIhjyAKPL53C7v72pnKFvn2W+coVg1S0SDPHNpOSFP42quncF0fbAppCq4Hn39wDy++f5W5QpmqYWHaDk/sH2JXXxvFqs7XXjlFOhZmdC5HZzrOZ+7dhWHb/OC9y1ydXECRRB7evZkdPa08/94lprMlcuUaPc1JJhby/MoDe+hpTvLjM9c5dvEmu/va+eX7d/vlvqLIq+dvcPL6JJbjMNTZzOP7tqDJMu9dn+C18yOYlsP2nhYe27tlncSDpsgkQkFCqkpLIrpOlPjnPRzHxayX1FuGjV41qJRqyIrEpfdG2H5oM4m2IFU7u47lMjaVRa/LarSkowxvbrstaAQ+q71Qqq151UMQJNLBQ4jCMuu0szVBJKRSKNVwbIfzV6Y4tLu38XdNUjjQtBkAWZCwXBtREFFFGRePtBZHESRcPBzP4VZ5DhGRgKbQ075cEjc2lWUxX2EwnaYvlWzAHl7dmtufF7yGZtBQs6+BJgrCqooUURSQV4AiluXguh4JpY2okkERtUYZ+VLUdIvZhdW6UQFNpr/bPw9ulPwXBdZZzq8MQRBIBINoGwA0rusyN5FlfjKL47iIsrSqPWa9zQBJJcbB1A7fKXrF2lytmcwvlldd1xfmzoCUwfMsEKJQdw0VRYFAWCMcDyEp8kc6V3h4mG4R11veK+aLNeaz/v0DmszubV0NBtlGYTsui/nyKrFxWZHpH2pHr5rEU+FGXzZKqAPLZ/XbjWY/ySuuW4sFQcB1fSH4JXAonYyQSUVYzFWwHZdrt+a4Z19/Q/LEdBymciXOjs1QNS06U3EGWlJcn1kkEtBYKFWIBlRyFR3LcYgENB4YCv1iAkeCINC7pZVgWKszU+aoVoxVwNHIhUmqZZ1QNMC+B4YYvTpDpVhj5MIketUgHF0GP0avTGNb/pffv60DuZ6V8DyPM8evce7EjcZ9n/zCvew/MrTi4RJwPYu42oOAX0ryzJcfYPTqDO/+5CKVos5L33yHux7ZTqgpxNhCnqCm0Ac038bWvlrS2bK7my//26fp3XJ7IVlZkVa5CIHv7HTjwgRTtxbIzZfYuqdnXanX7cPFdPIIgoQkLgMbrudx5tY0Jd2gMxWjXDOIh9bT7RLpKEc+fRA5GlhFmSxaVW5VZxiMdrLj7k3sP7KNH379OABTt+a5dWWaYqHGwQeHiMSDdPY3M3VznlrZYOrm/Ko+KGYrjN+YxfM8Nu/sItUc49LJWxQWykyOzK8CjvILJRbqwEciHaV5BTNro3jo2f088PReUAROzU5StS32NLcTVf2JOd2W4JFPHeTK6TEqxRrVks718xNs3r8ZQVYQBAWrLuAYksIIeIhCFM8r3vaeSyFLYr1u1//d11iIEZJCWMFOQtLKseIRlDR8a264mc+TCAToiq0XvQM/I1K0zFXAkCjIdIfvompncTHpjMeoWXZDj+j0G1e5dPIWnucz/H7pdx5mYHvHOjFIqItC92d46kv3cuvyNMVchdnxLMeeP0PX5lZEUSARC+I4LpnWxAcKBv88x7FTNxifzfOlp+/y3TJcnwVXMyymF4qN7IRh2UzOFrBth4V8hfcvTfDrz9zFA/tVvv78ezz/+gWevH87NycXqeomnzm6h7lcmb/+3jv0djQRiwT42j++y8fvH2ZTV5r3Lozzp99+iz/40kMAXBubo2aYPP3gDjRFJhRU0VSZe/cOUNUtLt2c4defOUQkqBGrlw7u3tpJS1OM/+3PXmyIPALkilXeeH+E33j2EI8c2sLzr1/guZ+c5b/7/BGOn77J6EyO3/zk3VwcmeG7r5zjvr0DBOpzTjLu06dPvDdCKKiwdXMrpmUjSxLNmahffhfREAUBw7A4d3GC0YlFrt+c90vSAiqhkE1Ak9m3u5tcrkoqGSYcUmnJxDlzfpxwSGPHUCfnL08SDCikkmEuXplifDLLxctTHNzbRySsMb9QoqMtiSSJqCvcn7Yotz/wxpQV7AgJwgFf9yWgykSDqzNRAVXm4NZuWpPLh1jX8yjXDN66OMpCoUKpqpMr16jULVObYiH2DXaSjofpa0li2Tam7ZBJRLhnuJd/PHGRS+Nz3LOtl5b6dQVBYPdAO5lEhEwiQlsqxthcjv2DncznK5y6NkFZNxmfz6MbFla9JFYSRQ5u6aYttTxPZEtVzt6cRhQE/vQHbzdem8uX2dSRJh4OcHBrF5lEBMtxEAXQTRtNket9IRIKKI2+8NwqjlsGXAKSx5N9aTxcPG+Rx7vDeDgIQplHu2P422idp/pT4NaQ5BB9cT9DJwoCcS3AllQaAbi/q6/R5k9sGmpoHHieR2/c35Tf39nbYMm3hZf76r/FeGP+Mq/MnONqaYqv3TrG3mQfhzNbWTQqvDZ3kZgS5Fppms3RNh5u2cEb85eQBJF7m4fImmV+NHWaJzr2EZAU3l64yvn8OKIgsD81wL6mAUbKs3xn/G3O5kcZqy7QHUrzS913IwgC3xl/m5Qa5UZ5hoCk8InOA2iiwvH5K1wq+qU5d6U3szPRQ9Gq8g9jJzg2f4lrxRlagwk+2XUXCTXMe4s3eGP+Eh3BFM90HUQU/OTGaGWe12YvUHUMtsY6OJzZiunavDRzBjyY1vMk1TBPtO8lpoSIKkEkQaJoVblamsTFw3Yd4koY23MQgLQaI6PFuVgcxfEcIkqQzlCanFmmNZBCkxQKVgXb8xlzrudiuDZVxyChhGkLprhWnsTx/Gu7novu2FRsnZxZ4nxhFNvzGTN5q0KzlmCytrBKk+SnCVEQiSpBBME/5EqCyLn8GGOVec7nxxmMtXGhYPPdifc40DRAWNaIyP58nwnEkAR4ceoErcFW4kqY0cpCvTQ3ylQ1i4tHezBJQFLoDmkcm7vMkZZtjJTnqDkmTVqUgGijiVKDvSSLEmfyY9wsz3GrMs9wvBPbtZEEk3Sd9ex6Hj85e4PZQonPP7SXgCITVBUEAb711jma4xF++b7dvHZ+hG8dP8ezh7YzPl/gU/ds5+uvvc+TB7Zx4soo88UKU9ki2XKN337sLt67PsH33r7A5vY0tuNy+uY0n7pnO19+9CAI/j7rtfNjXJmY58tHDzKbL/HNN87SmogyvlCgrzlJqWZgWDbNiQgXxmbpaU7ysf1bqRgm+coyKGZYNq+eG+HQlm729PtyEaokMV8s8+3j53nm0DCtySh//tK7tKfiHBzsWvXdVXST6VyRbKnK9p6WOwIjP4+xpB/kiz+bzE/lOPXKRVItcW5dmmLLnl4gCAio4urzhK7bDb2bcEhD2aA8bSk8z+PitWnms2sEtnEpmdeoWRNE1UFCil/C05KOMdjXwuRsAcf1OH5qhKce3kFLOsqSSP6qdZ/V63xwjYh5KhVttGOJ0VOuGEzM5Hnn9C2eObqT0AbaWx82FFkivEJiY27RZzIlAxuz9j3P49qtuYY21IcN23W5tDhPbyxBVNnACt62OTE2TjoUIhlcTR4QRZHuwVaMmukDXYq0Supgdr6IYdoENAVNUtHW9KHneVy+McNCdjVwtDIEZDxBBXcG2Izrgl4xyHQ2+QzDjwAciYJC1V4koS3vc+0V7mKKLBG+jTv6Uixky5y/MrXqNaku35GdLzE/naenLnujyRKt8QjBpXFcl+7wP7w/lyxFtWZSrOh0ZDZwehQEdNNuMKmT8RA7Btu5dmsex3F58+QIR+8borfTT/oGFJnhrhYKVV/yIaypRIP+2VESRTKxMNGAilFnX3qe988KGsHPEHAE0NHfTDCi+YfUsSx61YAmHzTwPI+RS1NYpq/y3r25hfbeDJMj84xenUavmg3gyPM8xq/PNSap7sEWZMXvOL1mcvbEdSp1yl+qOcbBh1e7rXmeg+0ZSIJWFzT1GSWHHt3O2beuYdQsRq9MM3Jxkl0PbCES0MhVanekpAqiwANP76WjL3Pb9/gPiY86LrXH8zwM3WLP4cFG2dJPuxhJwuqJ3HQcilWdUdelv2VjF4++oTa27u1dd0/Ls5mpZekJtRALhejf1tFgHZbyVYyahW05uI5LKBJoiJPrVYOZ8cUGuup5HoXFMrPjiwD0bm0jlgwTCCqUi1Wmbs0ztK+38d5i1mcsASRSEdKtt7dcDUUDHHp0GEWTsT2XsmUii+I6Laq+be2EIhqVYg3bcsjOFjHsaUxnhoDcTsEqcL18hV2JvQREre6I9MGxRK9eQq8FQcB0TRzXIaU1IbISiCsDPs3QdBxMx0bfQHdqZYRlbdUm1XJ1kmo3NTsHOFQtq+GAZBgW7712qSGk3rWphaF9fRuCRkshCAJb9/SSbktQzFVwbIerp0cxdIuabuG6Lk2xEHrNvKNl6M979Hc0cerSOG+dHmHvtm42d2f4MGfXrX0t7NjcjqbK7Brs4Pz1aZx6CdqB4W42dWfoaUvx/GvnGZ3KEgoqiKLAkQObUWSJgKbwxqkbTMzm6WxJIEsSB4Z72Nq3Wq8tEQ0SDWsEVYV0Ikx4BfihKjKxSGDDzOdAZ5oD23uIhjS2b2rjx29fxXVdcoUqmWSE5lSUUtXwAVBh+cAeDCo89tAwjuMgS74zRUtzDNf1CAZVREHggXsGUVWftXP44CbuqmdMBMHfkHS2J1aV4Il12v/WwVZ6u5sIaH5ftNTdQoIBhV3bu9i2tR1FlphdKDI9m2fblvZGCcM/uYRpgz2K7/iyRsi/avCXP3qXaEjjwV0D1Ayb61OLjb/Lkoi65FKyok0hTeFzR/ZwazbLG+dv8v8+f4Jff+wAvS0+sOI4biNz6bouoiAwtVjk//vBCR7Y2c++wU4ujs5y4tLYigbSAPRWhigIbO9rZUevD9A/uGuAjnQc23GRRBGtUQJ0G0HbFX1h2yM4zgyuu4ggBBDEEK6TBUwkqQ1BiAIetj0KgoAoJPC8EpLUheilEVccIFaKla5t76p+v83PwLqs3ZKmxpI86i8qsDQc78R0bKqOydMd+0lpESRBpOYYvDxzhqNtu3mgeRtByX8Gx6oLDWFr3bG4VJjg0badlKwaL8+c44n2vSiiTEzx902tgQQHmjaRMys80b6X1ArtmncXb9AVbuLu9BZkQUQVFURBIKoEOZQeZLKa5XsT7zIQaSWiBLkns4VrpWmOtu2iPZgkXAc2tsU7KVo1TmVvNJIeRavKt8ZP0B9pYVuwk5dnzhFTQvRFmnl97hLD8U7uSW/hhenTvJ+7xX2ZIVJqDEkQicohMoEEsiDSrCUIywEulyawXIeuUIawrLEr0Y/ruRSsKoogszna0WDqtgVStAVSuHjcKs8REFUqtk5PuJmApLIp0k5GjdOsxfHwuF6aJiQFKFo12oNNlKwqncE0rueR0qK0BBLrWGAfNSJygENpnx0h+Eq+2K7DrmQPiiCjSTIfb9/jayFKKs2BOEu8voCocKS5j5nqMdrDe1GlKFtjbYCHJMpsj3fgeB6C4CELFlHxfZ7tHCAiKwzHOxiINiMAtjNGf2QPLiKSIKBJCg82b+W+zGYCkoosyDQHQsxVfoImVoA4rutxfXqBfQOddKeX9Vxsx2VqscjR3YM0JyLs6mvj5MsTVAwTTZHpaU7RlozRnooSVBWcehn25rY0rckou/vaee38CGXdQBQEYiGNbV0tNMVC9Yy9x+h8jv62JtpTMdLREN8SBKZzRfCgpznJQrFKWyqGblp+iTj+nLN2XdQUmX2bOnj7yhgLxQoP79qEIArkyjXGF/JcGJvl5myWfKXG2HxuHXAUC2kc2NzFfKGyvAb8AkUoqNLRlkCWRRQtSOdAC6om09zZRO+2DqKpMAICQSmBuIZRGF3Sc7VdCqUa+hq9nqVY0lB88Y1LFMurzUVEQSGuDaMIEWRxOfGqqTL3HhjgzZM3MC2HkfEFvv/jc/zyU/uIhLTbrgkbMUDW/j400EpfZxPnrkzhuh7PvXSG7Vva2dSTvuP+2auDbCv3To3PIQq0ZmKoii81cH10nqm5Aok1rt1L16nUTF57+zqF4loG1p1DESW2p5sJblDyCxBRVZoj4dv2T7I5zr4j2xpkhrZMDKUul3Dl5hwz80ViG2gFeZ5HqWL4bS6vN4hZFfIA0AtISLJHx6ZWosmPppvm39NGFGTEFbCGuuSUVjUwTJtCqbah7qjn+e7Qb7x3g+uj82svTSrj6xEvzBTq7/fLYCVRbMwhoigSr2MQjusxOVNoGKxEQhr9HekN2U6qItHZmiBQF64WRYH7Dm7ilbevsZD9/8l70yBJ0vO+7/fmnVn32fc99+zM7H3vYoEFcRIHlwRIiYRkkgYlipKlkGx+sKmQ7Q9WKOwPDtuhoGzSDlNCUDJAkARAGMRFYAHsfc3MHnP0HH13V1d33VV5pz9kdU33dM/MLkhHAOATsbHT1dWZVW+++ebz/p//8/+3WVzd5s++dY7P//KjWH0R73zCIp+w2M0sHi9kBt/j5v3633RO9BO1smWLKcpjeTaWttmuNGlU2wz3RZbtrsvCpXUCPyRbSDI+W2bqyDAvf/dtKis1qmsNCkOx8rjvBaxcqxCFEYm0yfBEYTBw7XqPi28sDM45c2KU8tgNKmIURUSEpNVxZHHjZpMkieP3TpMtpNhY3o5FKJ+f58z7jjKaS3HvzCgZ62CxLYBsITmwe79VvDW/RrvjMFRMMTMef+baZouVK5uxEFezx9PPPPAeR1Wgy1migfVz//sIwenJYZa26qRNg3zy4Da7ux6cI19K7V9Q+yBHSIQkS6RzFooi43sBju3huT6JpIHoI9VjMyU0Q8W1PdYWtgj8cDAWawtVGlsdhIDpoyOkshZmwmBro8Hi5VjPaEfHaGu9SavWAQGjM6UDhbF3YnS6OBAxj8KInu8zlkxjKnsXUStpYPYZGmEQ0u0vdAKZiIAg8ve4dkRRG6IOB264dkUqofPQial9i8Xl9kWsnsWR1LGBdXROy5BSEihCQZIgq5u3BSciIgo30f2HjGO0/QoJpYgmZXGD9oAN09hqM39+CYgXprmT4yQzdxZkNpM6YzMlrr4du91trtapbzaxbY/1SoO0Gbt+7XdO+NmJ+05OUi6keO6Na3zhL15mbqLE5z6x6z6MdlwiooGeFfQtMfsOdJoqD4ABIRjoDQkpFnj2ggDXix9CO9UBRZaQZQl3xw5WiAMTix83kgkdre8UJsuxpowsSxyfG+bff+UlvvK986xWGhydHiJpGQRhSLMXA0l+EMZitK6P4kt94VeJrusShHGFo97skDQ03CjAiwJCP4o1m8IA1w9QZImErqGpCkEYstJu4gQ+QRSBFz8SE4qKF4aoPYm265JUNQIvpBnZDB3JYRR1Flt1ImA6/d7EUndiRytqcbPG+nYLQ1NI72Jf3jzatuuxtt3igaMTjOTTvHJpaZ9o5UGXqNV1WN1qkEkYPHV6jvnVLRY2akwP5YiiiBcvLHJsskyjY7O23eKTj56k1XXouR6nZkdJmRor1cZt3RYhZlDdf2ScxUqdkfvSGKpC23ZJWwa1dvfgL9UPQ1OJgIVKjaSpYxkqSWMWWRkligKE6FtYRz4IiTiFCIEQWZlDIIOQiCIPITSEeO+i70EUsuk0B20ATuChSjJu6JNQdJwwbpVQhEQ3cOn4Nnk9Sdd3SKsWw8adRUh/GiOnJSkbaRKKzphVwNjFsEsqBo8UjzBi3vkeSCg6adXkbP06DxYOD+yqLUWnpKdJKgZjVmEAKEG8Abk3N8uZ7NSeotZUokTFbmAqGi3fxot8MpJF2chgKTojZo5R60Z+ZSk6OW3vRmXLaVFz2jw58xR5PUXFbvBG7RozyTKmrPJk+SSHU8MsdatcbKyRkjLkdBMf2Ap6jOnDgCAKwQsE0+YYm70OpqJS7dkcTo6j7WrF2K1r1LFdtuqdGLhsaxySp9DbccFnvdEiq2Z5e2sdWRIYmoqKwX3aMUbT783l5r2E1AdqdsYY2AdCWMrBGh1CCHRZQUSrVLtfJ6kdJoi6uH4VXSkDgox+F9v2yyhSiq53kYJ5N9v2C7jBFpY6hS6X2HZeYSw1Tte7zrb7NqqUJow83LCOJmUwlFHa7iWiaANJ9AWuRfwMcH1/j5auEDEzcgew8YLYFTJuobnRTjMAj6P4e+/oxbhBgCSJQRuyKsv9YoYYHF+V5YELUhBFBDtV9v6mPQaJ+vP2NowwSRJ87L5j3Ds3xvfPX+UPvvkS//QTjyMJQcLQKGeTpE2dzzx+mpmh/e50XcfjemWbzUaHmaHcz5SzWhiFuLrP6FR2MB8lRUIzNDRDZeZEzPKNCJGEjBPsZcccmi5hGhqttk2l2uLZl+b5zMfu2SOAHesINfnCn7/Msy9d2SfvGUQuHfcaSW0OCX3gdyzLEg+dmebekxO8eHYB3w/50jdep9m2+eQHTzExkttjUR6GEe2uw1atzfXlbQ7PlBgbOrgInU2b/MKHzjB/fZOe43F1aYv/6Q++zWc/di/3npwgmzb3SHi4XkC92WWj2qLW6PLEg4f2e54Jwckjo2TSJptbbTa32/zxV1/hd37tSYZK6cG9EEUR240uf/6tc3zj2bdv6Y52q/CCgGqvS6HXJZHav1dy/ABdUfasj7tDksQeB+xTx8ZIffc82/Uua5UGf/zVV/jtX32CcuHGHjGMIrbrHf7kG2/wnecv3r5VWggECjtQhBCCQ/fMEEVxLm073uC+BgavmbqKrEg3FZwEQWQTcuP9hWyCkXKay9c3cb2A7780z8kjIwM9IbgBcv3o1St84SsvYzv7C/Yzx4bxvYDZYyMDEoMfBKQMfXBtVUXi8HSJZ1+ajzub3lnm7DvLnD4+jqrIFHeZSuwW8bYMjaMzQ3vOd+LQCB998gT/8Wuv4noBX/vueQTw6Q+dYaiYQu8zOSFudWt3HWqNLteWtrhwdYNnPnSGUuHgVu+/ifiJAo50Q2Xm+CjnX5jHdTyWrmxw9J4pALbW62wsbSFErAWUKSSZOjyMosr02jZX317m6N2TANQ2Y7csgOJwhsLwDVeQVr0zEM2WJMHIVBEzufchHEQeQeSxwwDZqZGWx3Ok8wk2lreJIrh2YRXXC7hS2abS7HBstMRw9uCLlSulKAxlbpvQblRbsZitHzDTp6Ul0iaHT48TRfH3+nEiiBxsf5OUdqM1IIziKs3Tpw5xdmGNWruHpe9fWCYPD9Nq2TiOR3aXTpQkBONWaVDRjC3sJSBmGQV+wPBkAVmJH/ATh4ZIpE1c22N9cYtOqxe3oEVw4fUFoiginUvEzmf5JNlSiq2NBtcvrtFt9dD0FIEfsnB5PUZUZYlDd43jhj5eGOxJoneiUM6QKSTjJCQMGbISmIpCEHrYQQNF0tHkZJx49EGsKILAD5CE2hfFFkhIdPz2YNEWIkUYtriT1YAqy6iJvQuyJumcSN+FH/kD7SMAXdKouQ3C/kNXEnA7lqYhq/R8d+AeBdDwVphNPUmld5Gmu0XXldAVhSiKWLtepVWPN41CEiQzJuv9++B24fRiwfKd6HZsui0HIQmK+SSphEFFwOriNtn8e68S/KRHFMUJRjZl8qn3n+LQZIk//vorbNdPkLIMPM9npdJgpBTxztX12G2uH5cXN1nbbJI0NeYXNxkppfugS8Sbl9e469Ao9VaPar3DaClDJmngeD4Xrm0wVs5yeWETPwgYLd/YpNxqtpm6SrNj02jbRFH88w7bzfX8WMjbi2m7Sv967rawHRxfCEaKaQxdRZYlHrhripOHhlEVic1Wh5VavKHveT4rtQZEMF3KxW4kxK4yGctgqpDl3NIak4UsVyvbtB2X0WyKpKETAYvVOsOZJA/OxRXbIIqYb2zR8VxGEmlW2k38MOBwtsBqp0XP98jqJq5usNRu4AQBpqqyVdsgr5uktdjS+8rVTb7y9dfpdl1SSYNf+zuPULjDvNRVmQ/dd4Svv/QOl1eqPHJimg/deyR2HMtY+5L/XMri5x8+znffuMJLF5eYKKc5NllGVWSEgEI6MUholL7jmiJJbLe6/IfvvIbteWiKzHgxwyMnpgbHVRWZP/j6SzS7Nu+/e47TMyN4Qcj9Ryb4P7/xEmnLYLyYiYVeJUGEoJCy9jGOdFXh04/dxZeePcfvf/U5JEkimzT5zJOnUWWZQsZCkuLkJ/6OiQFbNmlq8Vi8+A7ffWOeD99/lCfumkGIH0/4Pooiwii6LROo03H44p++BhLFpgAAIABJREFUzPJKLJz6cx85QbPcxu9rFZhyLLCcUk06vs1St4okJJKKwYZdRxYx+LnUqTJhFRgysn/rFJAsOdbD2R0CMTBfsHc9KzKqxW8d+jle2b7CV1de4WJzlM9OPXrTEfc+fFQhYyl7af7rdp0vXHs2BmJEhBt6B7D27rzRiWDAshbErVo7n9WQNXRJAQSykHBCn2utbaIox0q3Sc/3GLXShFFEUouT+CvNLYIwZDyZJa3q/WPt3xjtbBguXN/A0OL1UwhB13ax+o6GSVOj1XFAxJXh4UL6QC2RdxNe6NPyerihP/guUt/VSRISfhigiJjdGUYRfhTSC1zyWpKU+t4AWF0uUrTex3Lri1jKFOXEB1ltfwVTGaPhnEeXCyTVI3T0q2T106y2v0paO0FKP0oUBcjCxA971OzXgQg/7CChUDAfoma/QtN5i7z5IHXn3OCcsiTx4JFJvvby2yiyjKkpjOTTzJTzPHB4nL984xKNrs3Ll5e4d3aM9C0EaiFmKb16ZZnpco5z19eYGcqTSRh72sp2QgjBvXNj/KcfnuW75+ap1NtkLJ2pUg64dvC1CAIq9TaVRpt6x2apWqeUiQXkX5lfRlNksgljoFkzkk9zbKxEu+cwlE3S6jokDsiXVUXGUFVmhvI/c/pGfhjy+vYixzMjWHJ8f7g9lzd+cIGnfuGBQZE4jgg/2uuwdfrYGCcODfPiG9fpOR7//k9fYrXS4O7jYySsuBXsymKVV84tcPl6hXw2wb0nx3nutRvXUBYaqpyi5V4mrR1FlW7st3IZi9/47KO0Og5vz6/T7jh8+S/f4LlXr3J4pkwpnyRhxs6izVaPylaLlfUGm9st/vXvfuqWwJEQgicfPMyVxSp//q1ztLsOb11a419f/0sOT5eZGM2RSZlIkqDTc9mud1irNFjdaHB4pszjD8wdWEk6OjvEY/fN8ZVvnyMMI77/4mXWK03uOTnOcDFNEIZUtlq8eWmNd66so6kKD909zYtvXH/X12xnDq62W0ym9n8/N/Bp2s6eguft4uThER6+Z4ZvfO9t/CDkO89dZHWjwT0nJygXkvhByPpmk7cur3HxygamofHgmSleOrtw2+PajsezL80zv7BJt+fSsz0c16Pn+Cyu3tinXFnY5H/4t9/AMuOCo2momIZGNmXy5GMFJEvd8+wv5ZM8eu8si6s1HNfn+y9eptHq8eSDhxgupfH8kOW1Gq+/tcTZCytEUcSj983y9uW1PULkkiSh6RLaLjv7MIrY7vQG+0JVkXnwzDRf+fZ5NrfbrG82+Tf/7ls8dPc0U6P52FDI9Wm0e9i2z889fozjh/Y6x++Eaah85uP3slXv8M0fvEPP9vji//saz712ldnJIkPFNIauDEDKrVqHtUqT9WqDYi7FR9934l1dzx83fqKAI01XmJiL3Rl8L2D1WhWIH/BbG022NpoISWLiUNyqUR7Pk0gZtJs9Fi9vDCho25UWzVoHgFw5TXYX8tbYag/adRRVJltI7ktoFaHT9lfJaNN7X1cVMoUbm5BOowdewEQ+QxhFt21V0w3tjnZ4Z46NcW2pSmkXeqvpCqvXO7i2S6dlH0izu3OIWEjupnS60bN54fIiy1sNiqn92kySLJFIG0iSGNht75y7F7iEUXjL9hDX9aluNAfXc3iiQCJlUKs02Vyr0Ws7ZPJJgjDk6jsxm6U4kiGZsdAtjbGZElfeXGb12iadpk22mCIMQpavVvqfTTBxeIgNu0nVbjOX2m+FayaNwYPMD0PWOrF9bBC5bNhvYSl5hsy77jh6mqxj7OnX9hDSwdpDd4qO36bu1nBDlzFrAkOOE0JJSNS9Bn7oo0lyvDFW1AG6fVCY8t5E3gnaLHdeo+WtU9ZPgYgGi1p9q4XbR9IDP+Rrf/RDvv2ll+/8gaOI9q4F1HN8XMcjM5Sm1bYJw4jtaptM7s4W9T+NEYYR33nxEm9cWCZhanR6LkdnhsimLdJJk8mRPH/45efJZyxcLxhQVQE6XYc//vordHoujuvz659+qP+biMX1Gv/2P/6A7UaHqZE8R6fLmLrKw6en+b/+7AXSCYNmx+Gjj59gqJC6I913ZrxIp3ee//2LP2KklOEXnj5NNmXyzecuML9UZXWzwVe+d55zl1f5+BO3f6i0uw6dnoPrBVRqLZiHuw6PYKgKqiyjK7Gr3lQxh4RgNJcijCJaPRddVRlKJygkLcZy6VhrJGWRNnVSpj5IZMbzmT16cKok8eDQRFydFoKjuSICgSpLzGULcQVZxG1Vc9nCDZecKELeBYBVt1p8/wcXaTR7FApJnvn0fRzUhBtFId2giSxUNMngzNEMx6cfx/YcTF0mlHqkkyr/4FNnyFlJgsin6zfQ5QSKrPL4mRFOHcoiIdOT1jHEKHlLJwh9fuuTZzB1hV7QopBV+BefeRzTjEgJhX/6i4+w0rmKpaQYTU2Q0HXq7R6SEDx0fJJT0yNEUUTKMlAVGU1V+NwH76Nju8hSXPV2vIBk/1nyjz71GJa+HzTPJEz+7tP30u45hGGEoSl90C7iN37+QRa8LUxXoZhO8F999qk92k4fuf8oDxybwA9DCsl3BxgFYYgX7QfwG67N1VaVuwvjtwRzXC/g5Vev8dY7sbbA/fdOc//RGXqBR0o1UYVMQOxYExIxZsVzQxKCY2EQzxNJZsIq9l267uAd/LckMqrFxeYqa70ar9auDgSOm36PbafFycwEdbfDlfbG4G9USaHjO1TsJlEUkdFuva5v2A2aXo9HSkdY7FQHbdHAoBVsvVfHUnTSioUsSX1AJCCIQvwoRIsicloCXVJ4s77IVKLEW40l7svPHnjOrGbw9NhhFCSmk/kB6CJ26GnE1tBhFDKTzqNKMrqs0Gn1OPejS5TG8szdFeuibLY7dEKPx+6eRZElgr7jzWatTSmX7INZEATRDXaMJHEQGBYEIe+8fLVvJjKHqivk9SIP5B/FCW0ScpKu7/NW/RKykCgbaRY7VTqBgyGpTCZKLHY3SSsWkhB0fJtu4GLIKg/kD5FUDBYvrbN8ZYPTjxwmmbVumwd6YZO6/TqmErep1exX0eQ8Se0w1e6zlKynBjIM0C+PSVrfTGWDrr9Iz19BV0r4YRNTGcPxqwihADKypNJyLuIFtV3HgPsOjWF7HpdWqkiSoJxJgoCnzxxCkgTnF9aZGynw/lNzSJLgnrkxDE3l1PQwmYTJqalh0paBripMl3Jc3dgml7T4wOm5WIdOVbj/8Pi+IufRsRI//8BxXp1fput7PH3vYZq+QyFn0Q5cshmTdNIgkGImw4X1Tb7zxjxrtSaGovBHP3iND99zhHTC4Gp1m3dWKoxl0jx97yEkRRAQ8pnHz/D8hQVenV9hOJs8UDJCVxRmhvLUO72fSfdHATjBDUaGkCRa2x2+88UXY+3Zp05gZrS+29Te/VAhm+CzH7uX68vbbFSbtDo2X/vOeb79owvIkhRbxzs+nh8wVEzxuU8/yHA5w6tvLg30GaMoxAuauEENL9xfSD82O8Rv/+oT/OEXn+fs27H22nq1yXq1GbPW+mtQGITvAtK+EYau8is/fx+6psRMppaN7fqcv7TKm5dX47xGxPniHl2e25zEMlSe+fAZLl+v8PalNcIw4p0r61xeqMR7ySh20PKDEMvU+MTTp7jvrklee3NpwN67U+QMkycnpm9pJqHKMlnj9lbyuyOZ0PnMR+/h6mKVC1c2CMOIty6vcelaJS5I7vrMCUvjmQ+f4ejsEK+9uXRbwN1xfb73wiV+9NrV/WO4K5ptm5fP3WjVjwXtBZmUyYkTjzKdGUfa5dQpyxIffeokl69v8sLr1/D8gFfOL/DWpVVUVekzxHwcx0dVZT7+/rv4hQ/fzb/5/W/uc7DbHUIIJgtZhjOpgaujEILD02U+8r4TfPHrr2M7Hsvrdda+dW7A7N/pSrAMjdPHx24JHAHkMxa//kuPEIYRz740T9d2WV6vs7xeR5ZjtlUYRftkQmTp1mLof1PxEwUcSbLE6HQJM6nT6zgsXdmIByWKuHZhFafnoupKrKdD3IqUzidpbHdYmt+IhbOTBtW1Os1aB0mWmJgro5s3ktlu2x5Y2UuyhHmTjaEQAlnS0aRkLPq564YTgj3v91yfRrPL1co2lq5iair5xMGtJLIi7WFu3Bwb1SZXFqt4fsCVxc0B40gIwer12ElOM5QfKyeWhExCnYjbCAavCZ48PsNitcHxsTKF1P4NgqrKKH2dHvWmFjtdUgc9vAdFMmWiavIA9EjnEwxPFli+UmFzJb4+w5MFttYbbCzGiPLwRIFMPoEkS8ydGOPZr75Ordpi9fomY7MlXNdnaT5OcnPFNOXRHKasxeKWN63QkiTQzRuuC6aiostx1VSWdILIRTqgCrk7wn7FxAs97NAeDHsUNnk3ldSDIqNmaflNZGQs+caYL3XXyKhpFElBFoIHx8Zvm3gIBEFfmHMnRsy7WO+9hS4nMZUsYbhNQJwMd9sOwS7KZ7vRo914b/3SwED93/cDHMdDSGAYKmOTB2tk/bSHJAmefugIpw6P4vkBlqEyXExj6vH8/61fepTVzQZRBKVcknbPIZ+JN1v3HB/ngw8fxXZ8SrkkhWyCWrOLIst8+NFjjBTjnuTRcgbDVFls1/nwU8c5dKRMx3YYzWaw0hq9yMdVAn7tFx9gtJjhWnObsUSahmsji7i9TUsrfP5XH+P1lRXuKg7jKgHrvRYzkwW0jMoD90zS8300RcaXIk6eGuWBM1NcaFYomUnuOT7OzFiBnu3x0vkFRssZerZLt+fyw9eusrxR55PvP8XJsRsA7b4+8Wy05/Xjo/vB3N1tLje/bu3qw9d23ZrqX8Oh6KCIoogN+yrb7gpDxizVoEnL20KWVBy5w6rTxPRTyEIlEhENW2ZSuouF7lm80GY2eT8X2z9iwroLU06x2rrKuHWSFfs8Da+ChIzbG6PqLAERZX2a1c4aCMGUdYbAX8HQp7F0dc8YKpJEMbN/o54wNBLGjY2SscvJJ3eLFuO259D2HdBiFuWiXcO3Q2ZTBebtChcbFebSRa52tuj6LofNEtcaW3R8l+lknstOhZbn8HBymlq3iyErOGFA07Vp+w6zqSJO4LPcqTGbKrLcqXOtvcUTw3Oxa1OnxmQiT0LReKu2xsncCJt2m7Rq0PJshszULZ2nhBC4QcRyu0Xd3cSQFezAJ63pOIHPpt1hNpUnimC91ySp6tScHiNWmvVek0fK04xYPx6w/5MepqwzZubZ7VWnSSrjVgFV7M0vHi4eZtNp8h+uPcu4VeBEZgJFyHR8m+9snGfbaWMpOh8bvWfwN2NWnpOZcf506UXmUsM8M/HQ4HVL3psrzSTKTCVKfGnxecbMPPfkZgbXNK1aPD18iu9snOd8Y5FnJh5CCWW+vvoabzeWaXpd/o/5b/HB4dOcyIzz2alH+dbaOV7cmudQapiHi0fwwoAxMz9o28pqCWQh0eo4vLawymg2FTsftjuxELOq4vqxPmDOMrnc3WIkk2Ikk+KdV67xP/6TP+LuJ47yu//b30czNeo9m+tbNSbz2T2swsmhHJXVGpqmkCu/u3lUqzT5d//qT3B6Lr/3B/85k0dGmLRmmLRusLzdwCejZtDluIg3ahaoOk0sRaeop5hOlAdsoyAKB26QKdXEc3y+/Pvf4ft/9gr//H/5exx+/6FYJkAINEmm5cWMMl1W6HgOqvQkNaeLoZSJcMkpITmtiBNsokgJDGUYgcSw9WEACuajyMKkZ7tsbHsQPknbzyAzTM/dxJMMBEO0nCQieADPh5AWunYUTbqRq+qqwgdOH+IDpw/tGZ+0ZfCph07uG7dP91/76H3HABjdJfI/Wc7xmcdO73l/0tT3vQZxtf+hI5OcmBrirY0KScNgudFEz2houoKV1fHUiK7sIxRY67Z55PQUQRihKwpXtre43q4zpWQ5dXiEuakiY5k09V6Py1tbaLLM6eFhJp84c9t50OjavL20QaNrk09aty0m/7RFEIVYir7HpUszVB7+yGm6LRvD0vtmRhG6ZGHJexkusizxwOkpfu8ff4Q//uorzF/fpN7q0ek6CAS6rpDPWhydGeKXP3EfJw+PUGt0eey+WdY3mwwVU0TE3QCy0AkiZ18hXZYl7j4xzr/6Jx/lO89f4ocvz7O8UafVdnC9GJSSJYGuqyRMjVzG4tjcEDPjRW4XQkA+m+Bzn36Qe06M87W/epOLVzbYbnTp2i6BH4IQKIpEwox1JUfKGT78xPFb5vFCCGYnivzXv/1h/vSbZ3nl3ALVWgfb8bAdD0WWSSUNJkayfPyDx3nsvkN0ux53nx6m0XAYH80QibiFMIh8/DAklzGZm81hKDrlfBJJiIE0RxhG5DMWJw4Noygy5UKKciJB0bIGBb1iLsGJQyNERJQPaHWS+uDIf/M7H+GLX3+ds+8sU621sR0f2/FQFZl0ymByJMcvfvQeHr57hu1Gl4funqbW6DIxenArtSxJjJQzHJnemzPuZIl32vImLB1F9XGC/RIiY8NZ/svPP83/8/XX+OHLV6g1uvQcj97OGCd0Dk+X+dATx/nQE8fRVYX3PXSYKIqYmSgM9JEP+symdpNGmqbwa596kNFyhr/4q7dYXq/R6bnYjofUl6FIWjojpTTJ28isQDw/Rocy/LPfeD9PPXyYr3/vLS5fq9Ds2Ni2N9BYMo14LmfS8bV96qHDB167v8n4iQKOhBCUxrJkiyl6bYetjQadRg/dUrl8NtZnyZfTA92jXDnN0HiepfkN1haq1KttNF1l5VqFwA/Rzbjvdt8GZ+9J932OMPJwg1Z/c7MXqbn5WKosU84kqTY7pIxbU29vcapBpJIGc1NFej1vjxo7QGk0x9pCFVVXQAREkQ9oeypGt44IN2zR8ysYShEZDS8I6DkejhdQSFlcWd8ilzQZy+/t24/1WSCXS9DpuDcdNWSlV2UmebBDXBCGGJY+YPwoqszs8VFe+at3cB2PtYUqR85MsnylQmO7jaopTB0dGbjoTR4ZRtNVPMdn/s0lHvjACbbW6jT61o6jM0VSWYtQSDQ9+0BWzu6KkAAarkPK6QEZVMnidstRGLn9VrW4VTGr5vrXPkCIJBHO7Yf9FtHx2/ihz7azxZg5jiHHaP+R1AwCUEXch53Wbz+XQmI20e7N13rvLRTJIIpCwshnIpvB7VcmwmBHQjaeh8MTBRKZ995+YiV0DEuj63jIsoSiKpRHs7fV7vppjN09yLm0RS69f6yEEKQSBkcTNyo2hV19zEKCIBmgpuOWot0JhKmrex6SXd9lvrHFRDLDmtxCSgo2pDYrG03mMgWqvQ6SIkiGOtdaNVKqzl+tXOH+8jhO4PN6dZXpVA4/EzE6kqFit2m6NrIlYeoay90GiYRGD59lu8GFboXjuTIhEXbgMZpOk0marFebvHDuGv/ws48zM14kCEK8bwRUtlsEQYgi3/qRcZCw5Lt9786YL6/UOP/WModmy8zNlpFv4QYROxx5SEJgyLd/AO87DyG9oEVOGyGpFNhylslqI9TcVbzQIaX0dfWCDgV9jLa/zbazQhSF2EGHMApIqUWGjUM4YZeUWiSp5Kl766SUAkHks+UsAxEZtUwQBeS0UbpB3OaXUoqklCK+GyAI8V0fU1UIXR/P8Qj8EHlg3RpTKcIgRJJ3dBTiBHlHCHGHGaHs0nDYcjostmuxCw4RlV4LXVawFJWkqjOTKuCGAZcaFVRJRiB4Y3uZU7lRdDnWnEqpOn4Y8lZ9nYKeoOZ22ey1yOkWfhhiyAovbS7ghAGWrGLKKhKCb61cYN1ucTTT4oOjR9H6c2a5U8cJPEJg+A7ATkrVGbHS5HUr1jWRJDKaSdtzmEsVSGsGbhjQ9V2KRoIhM0VC0Rjr/83Paswmy8wm9ybXQ0aGzx/64L73FvU0vz77fs6/tUzYjLjr5BiyJJFRLX5j9mk8z8f1ApKWTs92qVRajI/l+OWpx/bVp/6z2fezuLTNs2cv8shDh1BVmbRq8htzHziwlqUIiSdKx3m8dBx2/f6ZiYd5ZuLGdmCnhfFIapTDqdE97zVl+M25pwfHfLR4FIBqu8vR4SKWFt/3w5kkmqIMQKMogpxlUuv2BkYYmUKSibkhJg4NDRx2TVWNj3HTh283e/zBf/snHL57is/+4w/d5mrcCMPSGJst43s+VupgMFeTFYp9pvLOfZrrM7qEEOi3EdUOlJDhqSITh4fJltNcb9Xww5CEqjFkJVlo1VAlmdVOkyErSVrNcL0VokkdnMDn/eNzyJIg9HvkzUcQxGuFrpT6ny3e0LU7LS5f6aBrGXRNYJkhtYaJ43p4vksxF1f4680eUSQzPpxmrBhfhyiKcAIfY2ejGsVFpoPswActrEIcuLG2dA1TU2N2yA6rdNf7gjBmrOk3PY8MRWEklUKRJQxVYTiVJG+ajAVpuq7HbD5HGEaocqzL13XjtuG8acaae6pKEEX0vFhXTbYEG+0OpcS7Y1TLsmCilGU8itB+xnIiPwop6InYlKV/7cIgZOHiGltrdWb64tgZPUlOi2VDdhfevaCFH/U4flTnX868j6XlHstr9YGjVCphMDqUYWI0hypL+I5PNmHw3/2zj8fHEoIIH03O0/PX0KSDdcaEiGUUfvnj9/LJp0+xuFpjfbNBp+vi94XXLUOlmEsyNpwlnTL6c/DOFXlNU7jn5ASnj48PhItrjS6OG7sX65pCJmUyUk4zVEz358Dt86CpsTz/xd9/io1qi+srWzRbPVwvwNBVyoUUI2MW69E1GqJCYHn86m9Nocs6fuiz1LtK22+RUtM0vBrFkxH/4uQZZpNHSCrJm84FTz54iCceODT4WYiYThBFAW7Q4qnHhnn68VlkYSBE3Pnj2u4+F+/hbILPPH6Mp09P0Q5D6q0eju2RTBoUMhZH5oYxdYXICyhnLH73c+8jlU8S+AFO10HIEp7toVsaiqqQsDR+53NP0uu6VNbqZAuxjEmnbceMYl1BlmPyRRiECCnWrZX6OqCJpI4dVlnrvkwQuahYhFHElfUtbN+nnE7yj371ST71wTMsrW1Tb/YIghBDV2NN4YkCqYRBGLn4YZ1f+vghfuUT9yCQb7lvP/fCPLMnxkim9673CStmh73vobi9casPrMmyhKGr5DMWI+UMhezePMXpuXQ7DrniXtAnaek8fv8cD909w/JajbXNBuvrDbodh0IpTTKhIQcRGUvnxKnJ2+IMf1PxEwUcQV+XJp9k7XqVZq1Dq9FFSNagRWl0ukSyP+CSJJg+NsIr33uHrfUG9WqLXCnF2vW4xU3V1UGr1E7ofdcfiBFY194LiAAIIZPS9opjA7HF3q73y4qErMoMpZPMlHK31aS5OWzHQ+uzeSDeSM4vbLK53SafsfYg6eXRLKqusHBpDT9YIQwqqOpxhHh3mjICGV3OI/ouXp4fUG11eP7SIrmEycJmjZnyrUU1G40emnbzQ1AMwIuDIgoiKis1Zo4Moxsasiwx0aflhWHE2uJWLIS3tEWnFTPFpo+ODL5zaTRHppCguhYLZIdBSGW1Rq8TAzZx65tJW+wAOLcf/CCKGE2k0OSYBWXKOSLCW1I4/bCFF2wRRSG9sEvDq9/QExIK0rsc+5tDCEHTa5DXi4NzCyEw5NsDRTdHGEW0vA5e6LNjLepFPfLqDLJQ0RWTycyNY6q6MhDwkxWZT//m+3joQ3f1rYvFYPQk+vT8ftIX9auaO60hiiSTziVYqTTwvIBkymBqrryPkfbTGlEU4YZxK0XP90hpxs4v+olLHDsCxVJ/7BRJGrRn7IQfhmz0mgwl7ly19sKQtuew2etQMCwUIQ1YdOudJkII/AjswGfL7tDyHDKawbCZ4rsrV/D7Llx5wwQRC4pudNukVJ2EqtFyHQ6li2w5Xd7e3sCPQracLm4QEIQRU6m4wp0wNQ5Nlvjyt89SyCbp2i7bjQ7PfPDu/98TYc8L+MFzl/jCf3qBv/OZh5ieKt4SOGr7Dmdr1zFljfvys++JmiuQyGrDrPYuIgmZgj7Ohn2VlFIgqeQHVGc3XGDbWSGrDaPLFniCvDaKJplk1DJCCFShYchJau4aaaWEEBJh5JPXxqh766iS2QeiIhRJQ5VMkmqetcY1xEIL0zJp1jp8dHaC7vVtNtIpVq9soJkadsceLGvdVo9MMY0kicG/wzA2TLCSBsWxPMMzZWQ5HgddVqjYLezAZ8hMMWSmkftjlFYNwijCkBUi4s2eLARJRWc6mcdSNLK6xVo3Bro6nkPD7ZFQNHK6xbCZpuZ2WenWMRUNRUjkdIulTh0vDEhrBinN4FhmiLrbY73XpO7GjKC/XH6HD4we2Xev7I0IU9EYU2KHpV7gYymxdkFaNXCCeKORUHTuL00QRBGaJGPcBtT8WYmD5vkOBf76QpUwDMnnk3Q6Du22zcR4noSlc32hGjPPlrZotR1GR7JcvVZho9Lk4QfmsB2PdjuuZLY7NoV8ktW1OoauslltMTKSJZXU2a51sG2Pre02hXySjUqT4aE0yk3MioFw8c2f9cBXd/TW3l0UkwmKyQR3euZn+q2xQghmjo/ye3/4eYxEXMwKowjb9/DDYN95a5UGV99eYerY6Lv8RLEW5T/4738RIsgUb50b3A5cv90aJssSn/j1J/ngZx4kU0rjiXAgKC0Liaweb15O5IdiodsoYiaTww0CrjVr/ffpZPT9bJ3dkctaPHrfHLIs9gDUvt93hpVugAHLazUMXSWK4sLHQqvO9UaNU8XhuCUxjJ9BJwpD1OwehqKgyzLVXpeSmWC902IkmcL2fbq+N3gtpencfXwUBFxr1FjvtjhZKFOzbQxFRpVkrjS2kRA8MDy+5/OrssxkNoPTB0U1VWa71SOfMikYJhFx/iuEiMElZCxdG+jT7ZYGEEIQhCE50ySh3d7Geye2ml3OLawhSxKFlIV6C9Hhn8aIiPOK3WsCsQJEAAAgAElEQVS3a3tsbzQIw5C1hSqFkbjwf1DMN/5vVtrfBCKGjZ9D7X2IqaRJrpyhXe9ABJLtsXp5PWbJt3ooqkJ+OEOulEbVVdygRddfJqnN4oYNbgXn7Vwry9Q4NjfEsbkb4sNhFOKG3iDn9kKPMBL7NOJuFfHciV3RsnkNXYpd2/wwoOY1yKop1PfgrBibk8TsktGh/WBY1++w3dX7+m8yKTWFKmmDQrAu62hS/J8i4me6Kim3XGtunsZRFNF053ln+3/FCWuMJT7CbOZXiCKFq+cWuHp+kdHZIZyeSyJtghB0Gl3sjk23ZVMYyTFcSLNyeQ1TkvCaPa7UOrg9l27L5tgDcyxfXmNkpszihVUUVcZMmaxfq3D6ieOMHR6Ox88PuXpxna1Kk43VOo7tke/PpaVrmwhJkOoLkZuWRm2rjaLKTEwXSR4eim3nhTVwVQvDkHdWKyiSRLNrUz4yxeRojslbsJ7CyGeh9WWW23+BKqU5lv8dstrxfQSNykqNyso2b758leHJArXNJo2tDqPTRdrNHs1aB1mWKI/lGE0nUFoupYkSyYzFyrVNvJaDPgRO1+XipfX4O8yVeee161x9e5UHP3CcofECq9c3sXsewxN5hCRYvrJBImVy3/Fx3qjZrK43OXVfkaHxPJfPL2PpKlEYsbpQpb7VQrc0po+MsLa4xfZGAzOhM3dybFA4+evET1ymZSZ1xmZLXHz9OvVqm8ZWm17bpl5txejskWFSu9ygjpyZBAGO7bF4eZ2x2RLL12I7vdJIluLIXrpkrpRC6aOnvhdQr7b30R2jKMQPHYTYixb7fkCj2h78bKUMaq7DC1eWKGeSHB/b35pxq1jcqKNrMiOFDJoq0+46LK5uMzaUjcUYd0Vltcb8m8uxOxwyYVR/1+eJt7Yytr9JWouRZkvXmCnnSRk6pUySzWYH8wBL550wTQ1N278QhVHIur1NVtufKLUaXTLF1GD4hCQYnS6SSJt0Wz2Wr1Rwei4LfVAolTGZOX4jURsaz5EfyrC5WmdtcYtmX9S827bRDDWuHGoygROL8qqSgsOt7ev9MEASgpxuAhGqZBC7ph2cfCpSrM8SRl2ScpKyMYwuxWLZUeQShXVk9fgtz3dQRFGEJSc4nDpK02siix//9pOFQBXKnlZBTUqy1juPhIwhZ0nuEtTM5BOoujIA3sIwopFycUOfK60KmhQ/cHY0RVQpbutzA5+K3USWZMatHHOpHLKQmJm8Qe21Eu8N9PpJjuVunZc2F5AQ2IHPWCJDw7U5lC5yrbVFWjOQhEROM1nq1JH7YNoDxUks5Qbz5WNPnMALA1akGsvdGjPJEpKAdNLgt37pUUo3CTZrksw9xTEMWRloW7mBz6iVpuU5bNldDmeKWKqKpWjkDYsHyhPossK9pTG8MCCnm0wmcyhCYjSRJq+bZHSTutNjNh0DAl6fpbFTre76HoasDDYDSUvnN595hIvXK7S6DpoiM1xMk8tYdB2PTs+la7uxwLKINxXtnsNmvcPcWMzUWdyoMVrMsLJZZ2a0GLvm7HKLu1V0ug5vnFui03FwXf+220JFxEDdj1NdEUKQ00bIqsODn/Pa+ODfOyEJqQ8mxd+rrM+CiDdOCSV+rsiSypR1Zt/fAgwZc/0T7q28mnKKrN9j3l5AVQOiIGSokMJPWxRGcni2R2Orhaoq2D03bgHw/LhSJwlc2yMMQuyug6rKqLpCabywB2Qr6UnuKYwTRhE5zeqDv6DKCkQRo1aGlGpwT368P8+zlIwkSTW+l4+kSzGLSFG5tziBHfjkdQsJgSbL+GFI23fp+S5DZgpTVgmjEEvReLQ8S8WOmUkC+NDYMTQp3qzndIvxxO2dv+zA57XNZWpuD4Gg7vRAwFy6QBCFg2LFcrtBSjNwA58zhdEDxT//tkQQhPzwuUs88dgROh2H51+YJ50xqW61mZ0pxUWDCLpdl4WFKvV6J56vUcwGdts95q9uMD6W49XXFpibLTN/dQPPCzAMlYXFLR5/7HC/9SbiwsU1xsdyrKzWKBVT7Jh++Z5PbbNFpxFXdBVVJpEySOUS+zQeoyii13FoVFvY3VhDUTc10vkEycxeDZ9uy6ayvEVhJIuVNKhvtWnVOkRhhJnUKQzHrNc9928Eq1crOL0bjodBEJDqFx57nk/H9Yj649eqdeg0e7z+7AWqa3VqlebAiRRAN1XGZsuDAkwYRmxvxAXLnVA1hVQuwUG6yK7jsXa9OtDYvDmEEIzNljB2iUa3ah021+qEu3QsEhmLRHKvLokiabTrXWqrNcrjeXRTpVuNC69FBD2vgzmkDMxKdkfghzS2WrRqHXwv2LfupnMJSmO5fX93ZPbGZvytSoUgCrnWrNFwbBAwmkjT8z3WOy1eq6yiyTJpTSejGQxZSap2Fz8K2ex10CSZa40aHc/loZEJenhcqW0znsrQdh3WO21eq8QaaAlVI63pt1z7owjemF9lbbtJPp1AV2Q2tltsNTtMDuXYbnXx/Jh1X0gncD2fw+MlCn1W8Z7WJ0kiY8Qs7iC0+xpPu08cEYQ9JKEjhMxwzmKzmej/rU8YSUg3F6B/SsOUFbbdDinVoKjH+YuqKSRSJueeu8TMibHbstidYJuOH7dvd90q/loNTU0hyxKXzy7GUiCWjm7pjM6WSGYTVFdqdFo9UrkEqq5i++vYfgUnqJLRTsWgeRTS9rtYskE3sFGEQjfo4oUBScXEkHW23DoCQU7LUHW2Od+4yP25U6TUJG81LhEBR1LTqEKl7Xfwo4CclsYOXDp+F0s2SalJ2n6Htt8lpcQGGN/ffIl7sico6Xk6QY+62ySlJPD8HhCDUXZgo0kada+JIhSyWvoOhZO9YSkJjqX3tnnuuFrvjlFzL4j6biOMXFY6f8lG74cA+GGb4cSTmGKc9eub6KaGqitce3OJ4ekSEDE6N8zm8jYTR0d5+4XLtOsd7I5Dp9HlnqdP8c6Ll+k2e+SHs5hJg17LprbRYGiyyMZila3VGq7tktyljSrLEpOzJcani7GrWhAOChKjE7G4dLPRo7rRYHymyPThIZaubSJJUt+tWMJSSsgifs5IQmK6mGO11mQsf3tjKoCev8H15pdoeVcASLZnSefmkMWNtda1PV749ptMHh7G7rpUVmqce36ebDHJtQurhEFIr+uiajKL8xu4tsfU0RFe/t47jEwWuXRukZnjo5x9bp7CUJqLZxc5dNf4oCjtOh66qdGsdXj+W29y7xNHYwOwjkOr3uOV713g5//e4zGY7wXoRrxXaGy1qaxsMzJV5LlvnWfq8DDvvLaAlTB4/pvnSWYsfC8Y6Pv9deMnDjgSQjBzbBRJkui2bGrVFp7r02nZGAmNqSPDA8o+xDpHybRFu9Hl6turnH74ENsbsWja+GwZ66YHbCqboDSapV5tEQYhG8vbOD3vJlv3uL0rjLw9rmq1SnMguo2I2U+T5Rx126Zju2iKfMfJeeMMEfPLVfLpBJoqE4YRlqHz9vw6EyN7H9J21yVXSuP7ARARBOtwG5Dk5jP5YRs/7LJToYuiCNcPuLC6SdLUWas1GcokSd6i1a5SaeJ5PrncjWROFhIFPc2Isd+SFOJkI52zBpsZIUTfWS5Np9ljc6VGq94ZaBaNHxoivWsRSaRNRqdLXHpjge2NBtW1BtW1Op7jky0kmThUHtCcU6pxx4V4p13CC0NCAtywgyUf/NnjMQqw/WU0ZYQQjapToawPocgGkpQnit67Y0ZExJa7yaazSctrklJTJG5ZM7l9yEIio2kDi1kAU84CES2vgh/t1S8anihgJQ2a2x2CIGTx8joPKWfoBA4lI4UbxHOr0LdK1iUFVZLxJJWEqhNGESnVQCC48MYiyYxJImlw4ewiM0eHB+2jP+2RVg3O5Mfww7gVSghBQU9QNJJ4YRA7CxFbA49ZGQSCpU5tj7sdwFg5GztWNLs4wY2qo6rIHJos7TuvqajMpPfOxyiKKBoJOr7HRDJLRjPi++imVpwRK67KCCFI9Tf+Ay0hYkHZ3a/lInPPz7tDCEHC1Ol5PvVOj1IuyWKlzupWEz8IUOQ4CbcMlaSpM5RPEYQh7Z7DwnotdnHzg4Fj0Vq1QbvnMFHOkb1FC8dObFZbXF+o3vY9g7Ehohc4fcelHy/uVO0fNg4juLPQ4K1+f7vXzaTJyUeOxC9EMbAO8f+nTowf2Hq7+6UdV7Sd44mbhFplSWImeUN75FYxamUG7zHMG5ViU9EQSJytrjGZynEkXdp3nKx2Yx5FUcRsqjj4ObNrzmU0kyAM2bSrPFqeQb+DZpUsJFKa0QdpBT0riSmr5A0L2/cIo4i25zKbVsjqBl3fI6397IDXN0cYBfSCLqac2OPEeXOYpsb0VJHNaotmq8fQUIaR4UzspugH1BtdLl2OdSO7XZfJiQJRBJal0etpyFLcCpDJmLz62nVOnhjl7LkldE1hfCxH4IexKKsQjAxnmb9SYXamNGAj1ypNvv3FF3n+G2eprNTwXR9NVymO5jjz2BGe+YdPD0Ab3wt444cX+f6fvcL8+SWaW23CMCKRNpk5Mconf/Mp7nrw0GBeXzq7wP/8z7/Apz//fqyUwV99+WWWr1TwPZ98Oc37Pn0/H/m7j5LeBcg3tzv8/r/8EtfeXiHwAhzb49GPnuH/4+494yO7zjPP/82VAwpAIecO6JzITsxJIimKCpSsZFmy7B3LQbZ3154de+y11/ZvdyXv2OPRjK1oK1iSLcmSmCmJsZvsJtlNdjc7oiNyLlSum+9+uIUC0ACapCx7JL6fuoHCubfOPfeE533e5/n9//4xAGKaRlsijiQIzI5l+dpnHubS6VGmhmcxdYsnv/MSzz96rNZed38rf/K1T6IF/GfgWDZPfOMFHvnqAVzXw6iYNHek+Itv/daK2kgzY1n+6ne/xsTQ7JKfex5YhoWsSPz5t36TtVsXnBaPPT/AV//fhyjmypi6hWM7/F9f/3W27Fu7rP1jB8/xuT/+Dh/5vXvRyyYHH36VicEZHNulvjnB7e/bzds+tLcGTHmeh2XYPPP9Izz9Ly8zMTiDUTExDRvbcpBkES2ocuf79/CxP3jnsuudfvECY5en2X/fdmRRrGmhiaKfoGuJxLiQncFxXb88PhRBEkVaIlFEQWC2UsZ2HWzXxXQcoqpGRFFRRJGBuVlczyOmahRMY1EbUUzHpmSZPrv2qpjXL00no8TDARLRILIoUigbxMIBQgEFTZGxHRdZEnFdj7li2a9C8ODia0NcOTPKDe/cuQTodDydnDmAKKiIyJTtCVQxhiiqmE4OPxkZQRAkWptm8HDJWoPEhD7CSuuy+/x5DA8/AWu4C0CsoslsvXEdkUSI1t7GZWU7q4WqyWy5bSOKGAZBoKmrgcxkjsa2FKom+++9B00d9VRKOlpV4y8kt5EVXsPx9BogZ7k2L2dOsDXRz4nsWeq1JCdyZ2kNNmG7NrvqNnO+cIWxyiR7UtvJWnkuFAdpCzajSSrD5XFszyEdqMdwDI7nztIaTBOUAkzqM4xUJpgzc9yR3s+B6ZdJqjE6w62ISFwoDtKg1ZFQY8yaWU7kzlKv1ZExs0zqMzQHGxmrTBKQNKb0GXTHZG/9dpoCy/eAnudhOW7V7VT0y+okEdtxa+L8drXM0nJ8raZ5EWlJFLEdx0/uGSaNcd94aCyTp6Mh6ZeMigKyuBw49nCx3YXzgus5uJ6NIAmsv76XqaFZYqko227dWP1bj9xsgWRTnGQ6Qd+2LkzdItWcRA0qJBqiNHU2MDk4jW3aFDJFEo0x4vVRkukEsiYzeHqEaDJCZnzOJxjg72liV5VvXa2JGUuGae6IIoggCQqxRGeVKQiOZ5K3hoirXUioVVapzf71XWjy6+8TXc/C9RbGtusay8A5x3FxHJd0a5JQWMOomOTmStQ3J2hoiZKZyhOr6vTmZosICDS11XHl3BiO45Buq6OjL83ZVwdp7WmkmK9w4eQInWuaaGiKM5mKkGyIkpstkWyI0rPBx0KOv3CeUkEnP1fG8zwamhMUs2USKR94DceDlEs6juUQjgbpXt/M3HQe07ARJZFQWKN3U9uqDP43Gz9zwBFA59omJFnEMWymR+coF3UqJYO6hiida5sWUe4E4nUR0u11FHNlhi9MMjU6h1G1xG7tblgmfh2JBenb1Mb5E34maej8BHPTeZo7FxgUoqAgIuMsspP0PI9LZ8bIZXzgSBQF1m/vxHIccmWdnsbUqsDLSmGYNqWKWVNEj0eD2K5LXSKEXZ085r+n63pE40EqJRNQEMUGFj+6pRPBUsFqD6eqEbGwoXI9j0tTGQ6cucxsoUxRN7njKjHDxWHbDpVFWTu/DRfHdVcVOAUo5Cq4zsLNJOqjpNJxhs5PUsxXmBqdY2LIPyh297csAe8EQaBnQwvPPSSSnS0yMTTL7EQO8JlerV3+xGu6DleKMyu6ql0dQVmhLhBEQvZtPcXQqqVqoiCjSA0EZF8U2Xbt6iQi4HklXGeWn0Sp3PVcQlIYVdSqDKafLGJKkM54C8FFLJek2k7Ea0BAxLsKyGhoSdDcWc/E0Cye63Hp9CjBikxTXZzucAN29fPqCs/zavr2iRcvIssiHX1pCrkyl89NvGWAo7gaJKZc5TJRfb/qA5Elp3efnuzSHkkQWkFnRxREokoQ0y2+WT37WvsAEeX1RfRW+jdUR+c1yiNWi3BARUhG0BQZRfZIxcLMFcoA1CfChDSFUECluT6GbfsuEfmS7zQCUCgbuPgbmplsif6u9DWu5se5gQmKr+MeNx+SIBJTQtecf37ScB2X/GwB23JIrUJr/teGIAhIqwinCtJPp0h9JV2RWvIAqm5Uq4BbQETRyFsGwVVKwK457q76vySKrI2/MUauIkqsTyzfWAPzVbnzF/EVoFZzaHiLRNkp8ez049xU/3aiysplr4Ig0NKc8NlzyTAb+1t9gVVZYmw8Sz5fIZMpEQwqIEB9KkqqLszFS1OMjs6RyZQolgzGxrP09aaZmSnS2VkPgsDUVB5VlRmfyFIum4yMZmhtTnLy1AiNjTHmtU4e/doBfvDFZ+je2MrbP7gPLaQyO5Hl8ulRZqtOMAv3C0MD41w6NUrPhlZauhuRFIkzL1/iyFOnmZsu8p/+7uOkmnwWmev4sgJPfvtFTN2iq7+Fd3zsRgpzJV768Sm+/dkfoQVV3vGxm2rXidWF+cQfvZu5yRznTwzxtU8/vITtYzoOuYqO6/mspevv2MSWfWs5efgCT37nRXbe0s/ety+IIUeTId85qBqSInPnB/aw8fpe5qby/MP/8yCGbq3KYo7XR3j/b95FubiYUe4xO5njB194mkBYIxReuvZs3beG3/2rj5CbKfLgl57h2MFzS/ZUi8N1XColg0e/cgC9YtK3pYPrbtvIzHiWI0+d4pt//Rip5jj77t5azdJ7nHjBB6bqW5K8/7fuIpoMc+rFi/zwm4doaE3y0d+/j3XbO1e83sCxKxx/7iy7bt/IhrpG8pEY1zW119a6sKKQ1IKEZYWmcBRF9I0cFNEvC9vf2lnbb1iuQ1jxXd1USeZtXWtqc1BrJE6o2sb8wVe3LcLKcgbb+eODNLbV0dW0MG8LgkBDIrLkc4ujxYqhyL7mUSlfYXY8u8zVSRBkRHzQyHbLuJ6NKCi4nklIbqJsj+N6NpoYR5LmEFBxPavKUHprhOf5z2OxFpdRsTj02HE812VoYJzbHtj9hgXlJUmssffC8RCR+HLGVyCsEVh8hhN8BpcqJpDFBZDB9uyqdqGO67kklThb4ut5KXOcKX2GklOhYJfQXZP2UDOtwTQb4n3gQVuoGU1UaAk2crE4RFqrZ2dyE67nMq77EimT+gwlu4zjOexJba+xNZsDDWxN9KOKCmktRUyOAB71WpILxUEKhUv0Rjo4k79IxTEISQFsb2VHNNt1OXjmMrFgAEkSGZqeo6+5ntlCGVHwHfvyFYOOhgRjmRySKFEx/XNZV2OS82MzSJJIoWLQGI+wrqWe0UyeSEDj1cujRAIa1/W1E7iqukQUVFKB7YwVn8D2KqQC2zHzESYqWWzLJZJOIAYUPCxM00FRZUKpOMGIRlm3kMJBgpEgruthmw6SotC7rRPXdZEUieaeNN2bO2rXiyTDOJZDKV8m0Rivjq2lxipXx0Ii1OZK8Rvodoau2HuJqt0LH/LmmVjVsloByobJCwODdNUnWdeyyp6iGkG5gWRgE3ppClmM0BC8HlFYOsdoQZWutU0cP3SBWF2E9r40pmGTnSkQiQVRVF+LSRAFEqkIju1y5NmzpFvraOmqJzOVJxDSqG9OkJstkpnME0uGkVWZeF0E1/U4ffQK3etbaGyrQxRFRFEgkghRzFfo2eCfkyVJ5NTLOueOD9G5tomhgQnKRYOZiRzp1iRqQKWhJYljO3iuRz5b4tSRSzS2Jgm8jrv7G4mfyVkt3VZHoj7K1OgcwxcmqZQNXMcl1ZxYAvAAxOsitFWt2zNTeQZODGPoPoOorbdxmahXMKKx4+b1PPfQq5QKOuODM7xy4Bz3dKQWsWlUGoNbUMVIDVgoZMsceuI1SlWLvnRbHRt2daNIEkFVYWgmi6ZIxIJvDAwIqErVom/hZ/09afJFHd1YTK126ehLMz40QyQerGoBLLWEDYRUBMGf2G3ToVLSa8CTgIQkKAiCVPsukijS15Ti7u3rCSgy8VCAxtjr1eUvXUhdPN8i2V15EhRFAVmSluAq8wKSxw9doFSoMHB8iGKugqLK9PS31ISx52PN5nZkRcaomFw+M8bkqO++1txZTzzl36+AXyp0sTBFDysziObv/EJ2FkkQSARSZM1hPM8lIK9c3qBIKTS5BVEIAkVkUV4EMtk/8YagZJcp2Hkcz8FwzZ+QbwQiIpGrAI6iPY3hFAnKScLy0vckENbY97bNHH/hPK7jcuXcOC89eYrb3r0LWZGQuDYSvXhCT7cmmRzJcPH0GNFkaCkV4i0QKy1erusxmyly8fI0A+fHGRv3D1HgZ/pTdWE62lOs6U2TSkWIRQNVtiI+qLQqrd6jWDK4eGmKM2fHuDI0S6lkICsS6YYYG/pbWLemiYb66JvKFriuR6FYYeD8JCdPjzJWPfQFNJl0Y5wtm9pY05smmQyvaCu8bW1r7f4W+iS1ZNO9uJ8S0eCyxb+tMUG+pLN1Tas/F1z1vT3Po1KxqOgmhaLBS0cuUdH9uS+bKzM4NLOqdlYgLlNxzGsKygr484Ntu0xO5Th5eoQz58bJzJUQEIjHgnR21rNlYxudHSmUaqmLqZtcOTVMMh1fAhx5nke+oJOZKyIKAo2NMYIB/7BTKOqcOTvOqbOjjI7OYRg2gYBCc1OcdWuaWLumicaGa2+oPc8jmytzZXCGEydHGBnNUKlYqKpMc1Oc/nXN9K9vIZlY+ZnNh+v67eTyPtAXDmk01EcBjzlznIw5SlJtIaUt0JY9z2N6pkCp7B9q47EQtuzier4r40oglGHYjIzNceK1YQaHZ8nlyoiiSGNDlJ3bu6hfUetFIJkIkbhGScP8pXL5CmfOjnH67Bhj41lM0yYYVGltSbKxv5U1vY3EYiu7mL6VwvPAdi08liaUFocsi+zfu6b6b4m9exYSQV2L9kzr1i61/7337q21z+xYBBDcf5/vtJbYvPQ5behvxTAsjh0foqM9RbzKMCgVKhw/OEAoFuTX/ux9dPcvzB/lgo6pW4SiC+uVJEvc/r7d3HDvdlLNidrcNjeV5wt/+i8cevw4508M1YCj+Ri5OMUv/t693PPRGwmEVFzXZdsN6/j0b36Fw0+c4Ob7d9Y0VmSl6sC7oZVwLMTXP/PIoj71KBgGZdPEw2ei33jfDt8u2XZ4+l9epntDK3e8f/eqz0UUBRpb62hsraNSMvjO3/4Yvby6aUY4GmTfPduW/KyUr/D1v3wEURL5wG+/naaupet2rC7ChroInufx8lOnOHbw3Krtg8+4Gb08zSf//H3c+M4dqJqCYzts3N3L3/zeNzj8+Am23bCOSDyEqVs89+ArOLbLR/73e9l5Sz+CILDrVh9sOv3yRcKx4LJnsFIEZJmAvPx9rw8uCJFfHQ3B1Xc/i38XqLIFFrtursQwLOXKfO9vf8S9v3wLiWvMtVe/P9qiM8KWG9ax5YZ1y/5GElQSgaVC7ywqF4qoXdWfCETVzlXBw5/nEPATtWFZW0hqOw6u63Ld7Zt49dkzTAzNEIoGlpWlXh2u53E+O4sqGSS0IBm9jCT4DmCu56FKEpIoUrL8PZYkCKQCYV+7BwnLLeB4Bo7jIrgiMTHKi9PH0V0DVVB9AMcVUF2VimWSr5SIShGCooYmqoiCyJHMa+xMbqJOjXMse4awHEIVFaJKGAGhqiNaRhYkGrQUESlCWArz+PgB1ka76Qy1EpUjHJg6wnV1WziVvcBoZZJoIcyO5EbqtSTD5XG6Qm1ISAwUrxCUNJKrgP++LpjL+rYGDp65gipLTOeKVEybimnR15SikityeTJDe32CVy6N0paKEwlonBqaRBQFJnNFutN1WLbDXLFCxbQYzeQoGxbRoLbiuBQFiabQzYSamrE9nZjay9A5nWwmR6moE6yaHOllE8d1STVEsQyfVT45liUU0SgXDSzLRpYlOnoaUAMqHRvasAybQq5MuajXHJlN3STWEKOuOYnreUwOzVIu6TR31i8p010pyvYYg/nvYVXL6aIsAEceDmG5qaZNLCCQioQZms1i2Dae5y75/rZbQRK1mvavKATor/sU7ZF3IothYuoaPFxMx0AQQBI0BEFk+w3rsF2jOgYrbLu1CWjC9Ww8Fo97/9zcu9t3AfSwqO9M4mGwJhVDEjQ61+6tvUuFfIV9d2/xWcARjZ4NrUxVyRIt3Y30bqr2Z14nGNbY87YtSLKIaTrc8+F9tTW0s7rG77xpPdPjWQIhFUVTCEcD19w3vpn4mQSOwrEgTe2pGnBkVW3Eu9Y1L5uQZJjOI9gAACAASURBVFWira8RRZMpF3QGjg1hGRbRRHgZyAT+orH5+l7Wbu3g1YMDuI7HY//4Apt399LW69evi4JMsKpr4Xm+o83hH57k6LNn/WsqEtffvpF0e4qKbVMXCTFTKFGovHGnrXxZpz4eqdl1uq5HsWzQmo4zNpWrfc4ybS6cGsWxHUr5Cmu3duC403heGfA3SKFIgHAsSDFXwdBNLp8ZZ88dm6p9JaBJddUXZlG2TBDIlMoUyj4d76b+HlLRlTfz0WiAiUk/azm/UY8rEXYm1xJcRdTZdT1f2+OqrHTn2iYUVaJSNDh3bAjLtIkmQzR31i9b0FNNceoaY4wPznD5zBjTY762k9+GP3RlUSQia8TV0OrVe55Hxbaq5Uc2AiKaGKkh0yuF7WYBGUWsQxIkQlK4OsEICGIaQaznzbKNREGkOdiMUdJxPBvlp5yNagpurIIVYm0ynA9JEtl+4zo6+tJcOTeOUbH4wZefo7ElyeY9vauyH+bBANvyx184FqR7XTN2FcmOpyL0bXhrULFXC9O0efmVy3z3+0c5f2ESvep85S4CSiRJQK5aka7ta+JD799Dz5oGpvUCqiivSE5zHJfLV6b57g+Ocvili5RKBpbt1vpclkQeflyhp7uBB961i73X99bAjWuFbTscPznCI48d4+irg5TL5iIWoz8WHnrsGBvWtfCed+1k1/YuRHHlkqzXY5K83u+iq2wELl+Z5vBLlxgezTA+kWVsPEtmrlT77o//6CRPP3t21VfsU5+6g/SmyDLwdHEoioTrujxz4Czf+d4RBodnME0bp5qxF0UBRZaor49w/707eNudm4hF/XLASqFCuVCho38BXHEcl6efPcOXvnqAgKbwH//Xe9ixvZORkQzf/PaLPH/4ApWyie041Zp7kCSJUFDhve/axS99eP+K9znvjvbaqRG+870jnDw9SqlsYNtLn5mmKWze2Mr979jBjm2dSw48i8OybB585Bjf/cERAG7cu4bf+927AQEXF8ezUMTl/fYPXzvIgUPnAXjv/bt4232bcT2o2EvZpp7nMTNb5KFHj/HkM2eYmSlg2071ffDfhYcePbakpHw+BOCjH9rHA+++bsV7F0UBURS5PDjD1795iJePXqaiWzi1Pq1aHoc0du3s4hc/sI/2trqf2oboZzE0SSOu1HEkc4DWUDeS4M/VdUo9ddq1s6j/FqGqMlu3dviZ1eo7LysygbBGuaBz5cwYLV0NKJqCKAqEY0FfVPWqiNdFuDrXE6+PsH5nN89+/wiFbHkZUJZur+PG+3agBf3DgSiKdK5vobE1yexElkpRX1Wcd3E4rl8W0p6M10Tj/73DtmwOPvwqz/3gKLc9cD377922TGT8TYcA7X1pdt+5ubZHkmSJNVva/WTsSAajYhKJh3Acl+mxOYIRjeaOhf2Xqsk0d9Zz7OA55qZ82QfP89DLJsefO8PU8CyN7SmM8lJGfqWoc+rwBcYuTxGMBNi8by1NHfUMDYwzdmmSPXdvIztT4MXHjtN/fS8d65o58/IlTN0iEFbRSwahaJDzx64AAmt3dNG7qR2xKtA9MzrHycPnyWeKBIIq3Rvb6N7UDsD5Y4McP3CWgVcuE0mEOXf0MuFYkJvetYtQNEgpX+H8sSsMD0zgOi6N7Sm23LCOUHW+n5vMcfChVzAqJg2tdey5Z2utPApg9NIkI+cn6NnYzqkXL5CbLdDak2bbTeuRFAnLsDl79BLDA+M4tktDa5J1O3uoW0Hs+Oc5ZvQCuWCcxmDUL9mXJWRF4oVHX0Uvm1jHbJKNMZo6rm1v73NfPc5mpmkIhrE9f61LBoJczGUwHV+TtGz5a09YUdjb3EEqKODhIIoqtlMmM1dgfDSLbEVoUvxEv1wJkRaDlCsWDdkmHNujT1pDpWwSjkQJhYLclb4R8MGF9lAL6UA9siAjCgItgUYEBAzDJZxtJREO0OB5nB/OEKMFx3OYLro44QzNdg+aKjE0USBEine39BGQFRRBYUt8PZtia5FFmc5wK01Bf67WxJVBNUkQ6Umn0GSZje1pxucKJCNBpnNF0okI4YBKKhaiJRFjMldkc2cTsWAAVZZ8wW/boSkRxfU8tKBPaAipSrVszbumWLskaiQDm2v/715r4zpJPM9FFGVAqm1hFzuatXfXI4pi1d3VT3IoisTcdJ6JoVlGL06CAOn2FOWCjiAIKKrM1GiGWDKMFlKZHpkjEFJpbF1dOgSqYL95Cd2ZRRKW7ytFQUF35mrnOsdzGZrN4noes8Uys8YFXGxkQcP1bCrOnE8ekBJIgoIkqjieiSdEMD2HnDUCni9t4nkukqAQVdtQhRC6M4tuz5I1L6JJCVzPxsUvdfMBKhdJ0BAFGc9zcLGJKj7rqmiNAAJRpQ1V2ohQhWHGhjOUijp6xaR7TRMnXx3ENGzaOlPkc2VEwS8lDwQVgmGNwQtTBEMqoiSy+8blpcsAqXScO993PZ7rISvSMiLNTxo/k8BRJBakpaueE4cvMHR+okZo6NvUhqotvWVfE6mVQEilmK9w7tgglukLIDZ3rlw+E6sL865P3MzFU6Pk50pcPjPG3/7xd3nXJ25mw66eGrPHcVxmJ3K8/PRp/umzP67pG/VubONtH9iDFlAoFf2a0oimvqnNazwcYHw2j2HZhAIqxbLBa+fGGJvMkV608dECCt3rmonEg5QKOlABRFyvhFT9/on6KO19ac4cvYLnwbMPvkL3+mauv30jgZBKQK5Hk1K4rke5Un15AzJzxQp713bw2uAEJd1YFTjSdd8BLrCIEaSKsn8gXiVc17eUvjo61zWjagrlos6Zo5cxDZuW7ihNHcufVSwRoq2ngfHBGS6cHPFrRgW/jXmBc991x6RBi+KWrWVt1PpRkohrAVRJ8icHvGuKFkpCBA8bD5uwHKE73EtACiAIIqLUhmM7VEoGlmljWzYz4zkso4pceaCXDKZG51BUCVmRUVQZWZWIyXHWRPyXPCit3N+u42JZNrbpYJk2pmGTyxSX/D4znWdmIouiyMiqjKJIyKqfpVkpBEGguSPFfR+7kX/49MMU5spcPjPGZ//zt7nvozew46b1JOqjtXJB23IoF3XycyWmRjIce/48o5em+eSfvZdzrw3T3F5HIKSSbqsjeg32wM972LbDD586xVf/8Xmmpn0RVFWVSaUiqKqE43hUKibFkoGuW+i6RaGg8577dyJW9V1iynJWhOt6HHnlMl/+6kHOX5zEdT0kyWdjBAIKtu2QzVUolQxeOznC2FiWmZkC77h7K5q2+rh1XZfnnh/g819+lsmpXG0hr6sLo6kypmmTy1coFHRePHKJweFZ/sMv38yN+9f+6w8tK8RqQNMrx4f48tcO1Nx6rg7TtDFXEZEF0E0L3bFIqKFVryFLEgcPnec73ztCZq6ELIskE2HUaj9ks76N7uhYlq9843ly+TK/+KF9AETroji2swzwM02bQsF3n5qaznPp8jR/98WneeXYIK7roaoy0WAA1/Wo6Ba27eA4Eu1tq2+KbNvl4KHzfOkrzzEyOgf4QFFdMoSqKliW/8xKJYPDL13i4qVpPvT+3dz79q2oK2wEPA8Mwx+HAGV9fl70arp94RWYlmV98d8YzBllDMeifBVwVCjofOHvn+WZA+cwTZtwWKOrq55UnV/KmZkrMTicqa5XfoiiQDCgEIkE0AKrj19ZFhkZyfDwY8c58dqwzwaJBgiHVJ9FV9ApV0yyuTJPPXOGQkHnt3/9TpqbEziOD+bKkriixffPazieQ8kuMG2MM6GP1cb7ptiONwQcuZ6NX2JtY3sVVPH1hUIXh+WWwHORxUiNfRbQFGy3gumYKGKMQEjl5nftZGhgnC/8yXc58vRpdt+5if5dPdQ3JZZpcAE1gc/MZI5itoxRfV+mq8xiZ4W5oakjRbw+suT+VU1BDSiUqhbLbyRkSSSqaUwViqxtrOd/hvfVmSOX+e7f/piu/lbu/5Vbl+lx/iQhINDS3UA4vnTNUYMqiipjGlatDEsUReKpKMPnJ5ibydNadSB2bIfZyRyyIhGr85k/pm7x8Jee5tCjx+jd3M7F14YZPj9Rczgu5Sv88189ysWTw7T1NlGYK/L8g0f55T95gOnRDI999QC77tjMlVMj/ONnHuJd/+EO2tc08dz3XibZ6DtmPf2dw6Tb60k1JZgYmuGH/3iQ3/mbX6J7Qxu5mQKf/6N/RhAEUk1xcrMFpkYytPSmUVSZ/GyBfKaIqVtYhoVR8S3E588Ol04O88TXDqKFVPDgR998gZHz47z71+9CViRESURRZV57YYBXnjrF9lv6lwBHQ+fG+fZ/fZyeTe0Ioj82i9ky225aj+u4HHjwKE987QCd61vwXI8zL18kFA2+tYAjAboi9QSlReeAgMJtD+xeUtp39RltpRAFga5YkpawWmWY+nIImiRTFwhRskziagClmnwQ8dlIkuAR1zZSsccIye1MlU2f5ScIOKaHXjaQHMlPZEQ9KgU/yZdMRar6QP6bvtjFWEIgKC1696qvjeN6SJ6MZYJh+bo/mixjWB6yBIWKgWHZGIaHYdskwgFCUqB2jcWJYVEQl15jhZAlseZu3V6foC3lj52+ptSCjmDa30d0NPjr9/w73pxc0LlczPzurLY339YbnfclCQT3PI47hCRuR5BaKBomecNAk2TqI6Hq51Y+b8TqIoTjIdrXpBk8O048FaGrvxUBECWRNVs7kGQRURKxttm4jre0JHGFcD2TnHkGxyuvCBz5epQSbrUUUBIE1jbXc2kqQ0hVyFtDaGIERQ5iuAXCciMlewoXG9s1ED0JxzWwPB1Z0LBcHVGQkQUV13PQ3TxhrxEIEZTrCUopouo8S9et3cWiu8HD9fWiBBFJUPE8l5jSUf2/hrBo5ensbQTPq4E8u/b2IQhQKhkk6sIkkr52kiT6QtpNzQnKZZPpiVxN2+3qEEWB4OuwuH6S+JkEjhRNrrGI5jWFQtEAHWvSK2YxO9c1EY2HGJubWdA36mlYMcsF/suz/cZ1fPBTd/HP/+PHzE0XOPb8ec6fGKZ3UxvptjrUgEIxW2bk8jRDAxO12vjWnkY+9h/fQccanw6WDAe5rsd3r1nNmn61EAShdtCPRQJsWd/KdKawJPsvCAIjl6Zo70sTDGt4VBAECRbVySbqI9xwzzaGBiYoFXQmhmb57B9+h/5/OUJjaxJJljDKJqVihXJBZ//dW7njgevobqzjxycuEA1qJMKri9pJsoiuW5RK5ooHlZWilNcJhAPLypgamhPUNcYYHJioMYjaextropmLIxwL0rG2iSPPnmV6zD9QReJBWqo23Z7n4eLREUohiyLmshaW9rNvX+v3t+WWMd3VJ3LXszCdKTS5FVmQCMn+Burp7x/l5adPo5cMDN3CMmwMw6JSNJgcXhC9PP7Cef70E19E1WQUTUELKKiajBpQeMdHb6B/Z/eySTw7W+Cf//uPma5aUVqGjaFbmIZFbnYBOCoVKnzpL35AJBZCCchomlK7RltPI+/+1VtW7E9RErnt3TspFSp893NPk5stMnppms//2Q9obH2OhpYkoaiGgIBp2BSzJTLTBeam8zWBTdtycB2XieEMkXiQZH0U3kJ7o6tjeCTDN/7pMFPTBVRFYu/uPm67pZ+WpgSaJvtuPEWd6ZkCp8+OcezEMC1Ncfp6Gv2smucxYxSo1yKIVaaA53lcvjLN//jC0wwOzSJJIls3t3Hbzf2s7UsTCqmYlsPIaIbHf3SSV48PMZsp8rVvHSIeD3HbLf0rHoxd1+Pk6VE+96VnmJzKI8siO7d3cedtG2hrrSMYUNB1i8HhWR569Dhnzo0xMZnji185QCQSYNeOriVjciJXIFfxD/+6ZRNSFGRJxLAdbMclV6kgSxK24xANBHyBcA+0qt1n2TBJhoMEFIXZYglNlimbFj2NdWzZ1MYnf+XW2vTgOC5P/Pgkl674jpjX7exi5/buVZ3YNq5pwVIssmaZjlUqHqZnCnzr2y+i6xZ7ru/l1pvX09WeIhBQ0A2Ls+cmeOSJ45y/MEWpZPDgI8fYtaOLzRvaSDTGyM8WVmU8ua7H2YEJnnt+gBMnh+nraeTmG9fR1VlPJKzhOH652OmzY0xN5elf17xiO57ncfTVK3z+S88wPplDlkTW9KV5x91b6eluJBhQMEybkdEMzx48x+GXLjE9U+DLXz1IJBLg1pvWI4oC5wanURWJ3rbVM70eEJBiJFVW1XabD0kQSWohmuO+vsh82LbDU8+e4alnz2LbDunGGL/68Zu5bmc3sWopUqGgc/DQeb74lefIZEoEAgrvvX8n27d2kkyEaLyGBoZtu3z/4VcpVK3k73n7FtatbSIRC+G4LlNTeR7/0UkOvXQR07Q5+uoVHn3iOB/98H6Gczkuz8yxs6OVZOiNibT+PERQCnFvy/uv+RnLLeF6pp9t9QRMN4uHS1huYaZyFEkMEpBSZIyTxNU1RJUuKs40jqsTUlqwnDyWVyIit2E4c5hulojSheHMMWecQpOSRJQOTCdHSGnBdkvMGaeRBK2qBSFx0zt3km5L8cNvHeLV585y6LHjtPamufU9u7jpnTtJNS0AVo7tcPaVK/zoW4e4eGoUUzd9FqfrUsrr1YPo8v1UOL46UPxmi4MimgpE/t1BRs/zmBye5auffhhFk/nF3793mfvvTxwChGPL+6h6Nl+yHdMCCjfcu41jB87yrb9+nHt/6UaiiTCnX77EK8+eYdsN6+jZ6DMuRy5M8vS3X+Sjf3A/u9++FaNi8pef/HJtv33iwFmOHzjHb/zlh1m7vQujYvL5P/wnHv7S09z1kRvA85gezTB0bpz+63oZuTBBbrbI3HSejXv6mBnPUpgr8Ruf/jDrdnYzNZLh//v1L3Py0Hm6N7QxO55lZjTDx//4PfRf14coi5i670AkigJ77t5GuqOeMy9d5I4P7mPTnjVLvv/6nd10b2wjHAviOi6Pf+0AT37rEPf96m3IikS8PspdH9mPpEg8/c+HV+za0YuT3Pvxm7n1gd0IooBj+66Bpm4x8Mpluvpb+NDv3Uc8FcXQrRUTpz/PMX/GCUhqLaEiCAJaUMXzHCy3hO2V0D0bLP9grEoxBJTqeFxc5AeqJBFWls/ToavKGl3PxHJLWG4G03WQhDBxdROyGKK9K0BHtw+eL4ztxTOBsOxn8+CK45WrelXVeZOqtIeoIQlBEuEAN2zoWnIvVwMzC6X7Qq3E2sPD9Uxst4zj6njVUggB0RdXFzRkMYTAUva47TicmpiiPhKmNR5ble3t4VX7u4DjVvBw/NYFFVkMIQshrraQf/Pl3D6zSxDCCKIPPs2VKxwZHqW3vq4GHK0WsiIhyiAoNj07Q3hYQBZZDKKIUQQWXLpfrzxtPiw3T0Y/turvg3I9rVKyRgjwgOGZLEPTWeq6mmkN7UKVfFZagi4Aklr3kjYWu9UJ+Np6xVzJZ7hqpi/mjl+6igASq9+7P8YqeJ6J4+o4FBEQkcQgihhe0gfAEmIGQLIqx5KoW14C7HkermegRR1ijQom41iWhCQGkIUwoqD+m5bw/0wCR4Ig0NLlC1vPszjqGmKkmhIrdkYkHqK5q56xKwuOPB1rmlYEmeZDUWXueN/1KKrEt/7bj5iZyFEq6Jw4dIF5l5rFFqiiKNC5rpkP/c7b2LS7Z4lbGPjo5pvJWlm2Q76oYy+6xtBYxrfkm87T37sgAq6oEmdeuUJ9c4K1W4KIYj2StHAQEUWRm+/bzpVz4zz9vSPYlkMxV+blp04jSr5ltet6NVRyzZYOHNdjKldEEkVM28G6RqZO1y2SyTCx2BvPiKUaYyTro0hXaZQEIxotXQ0MDkwAfjlHz4a2FZ+rKIm0djcSCKo1G/lEKrpEfG/W8AEVRZBWBY7Az1iEZAXdtpAEGVkI4HnOtY9PK/xy4PgQzz306orZ0MVRKuhcOj264u923LSe/h1dyy5QKRq88PhrTI5krtm263iMD84Cs8t+17Ohlbs/vG9F4Ah8cbd7PrwfVVP47ueeYqZq9TsxNLvM7WWlv5UkkY07uykVKr77yuv0w897DFyYZLrKNFqzpolf/fjNtDQnlrELXddj3+4+MnMlTNMmGg3g4hGUVQyzsmR8G4bNg48eY2jYf84b+1v4nd+8i/bW5BLWT293A309ab7w98/y3PMDZLNlHnr0GDu3dZJILD8c5HJl/uUHR5maziOJAtft7Oa3fu12mtLxmgil53n09abp6qjns597khMnRxgbz/LwY8fpX9dMZFHWO1vWCWsKAxMzVEyLgKLQmowxWywzV/Lr56nqn3lenqAqo8kykYBGUTdAgJJpokoyQVXm3MQMsijS3ZCkr6eRnq4FtoRlOZw4NVwDjtataead92yrOTZdHaZnc7E4geleg5VkWOiGxe239PMrH7uZxoalOlG93Y20tSb59F89xuRUnkJR56Ujl9m4rplSrkzlGkLdruty4IUBDMNi93U9fPwjN9DWWreklNDzPPZe34tu2DVQZXF4nkcuX+F7D73CRLUkZPu2Tn714zfR2924JIGwpreRLZvaCQUP8KMnT1Io6nzvwVdYt6aJlpYkg+MZwkGVntbVgSMBn30iLbOUXjkqjsVoLkdPLIVWFciuVEwOvHAe23aQJJE7b9vILTeuQ5IW9PpisSC337qBC5emePCRVzEMG8Ow2dDfQuj1tC+q+kwtTQl+65O307ImiSO6NAfjPpW/q4HOznoM0+bwSxexbZejxwZ5573bCSgKmXLlLacuMn84ydtZClaWlJZGEXydjvlEiG9RncHxDECgZI8ioSIGFAw3S0gMAgKWWyBnDCAJQSYrz5NQ12G7cUaKT2C5RZrDt+B4BlPlwzSHb6RoDeO4OiIio8aTGM4sdYEt2F4ZxzVQxYWsgaLKbLy+l67+Fi6eHObo02d44bHjfOO/PMbopSk+8Z/fXdM5Gjw3zuf/5LtMDWe4+V272HBdN6mmBKFIgEOPn+Drf/nI1d0AwE+zItF2XcbzedY0pPgpmc28bnieR6Vk8MhXDzJ6cZKP/cH99C0Sjv1pxCqk42UhiAI7bu7nrg/u5eF/OMCF14aRFRktqLBt/zp+4VNvq7nUjV+e8hO61YRpIKTRs7mdgVcuA3DxtWEa2+tors7pakBh0761fP/vfsy7P3knkiIxfnmKkYsTbNq7hhMHzzExNIOlWzR3NTAznqW1J01rXxpBEAhFAqSqzkEA6c562te18I3PPMzO2zax685NdKxr4Y2cjTzPL6vJTud57flzlPIVhs6OYxr2qpn6lSKaDNN/fW/tXCFX97ayIrHj1g184zMP85W/+D7X3bmZLTesQwu+dcBrAMOxcfGwF+mazgMwE+UDTJUPkjcvYDpZRFEjJLfQGNxDOnQjEaULsbbuvH6fz895ResKE+VnmTNeo2gOYrtFREEhIKdJahtpCO4hFdhx1UF5pUGxsCabTp6M8SrT5cPkzQvozgyOpyMgIYthQnITYaWThNZPnbaVoNy0BIhZzRTCP8xb5MwzTFcOkzPOUbJHsdwC4CIJQTQpRUhuJq6tJ6ltIq711+zjPWCy6OsntsSiVJwJZiov43omdYFtRJUeQEB3ppgqv8Bk5aDfJ17J7xOpnojSTWNoH02hm1atqNDtGSbKz676HDSpnsbgbjzPAiwEMeRrUuIRVlV6UkvZ0wXzEhn9GB4ejcG9BOVmLLfARPlppsqHKVgXsNwCkqARVjppDO4jHbqBkNy6rA+XPn8Pyy2iO5OUrXFy5hlyhq/x5ngGk+XnKFlDK34HRYzSELixWvru76M0aVEJswez+isUrcsr/j1AXWA7itXCyefPsW5XL3VVrbfXE/KeB3UyxnEmywfIGQNUnAlcz0QSgkSUTuqD19EY3ENE6cYHHYUlbc+377g6U5VDGM4smpQiHboJAZG8OcBY6cdkjVOU7FFcz0QWgoSUNuq0LaRDNxFT+5a0/dOMn0ngCHw9o827e8lM+hvqno2tNKySlQmGNXbd0k8+U6qhwOt3dIFH1cKehXek2oeCIBCJBXnbB/fSs7GNR7/+Aueq1u+VsonruiiqTDgaINUUZ89dm7nlnTtorooXWpaDU9UN8TwPy3RQVAlFkWuHyqb2FGu3diBKIp3rmmuHN4CgpqCp0hKW0vqeJi4NzxC+anOtaAqSLKKqMp5n4ThjeN5SmKQuHeMTf3AfbT0NHHj4GDMTWUp5Hdty8AR/UxcIqURiQVLpGIIA2VKF/es7fZ2gKi03mgixfkcXru2iBhRCkQBFw8aynJqmget6uK5b0/HwPJ8R1re5jUrZJBBUiNaF6dvUCghUygaqpiBJIqFIgK3715Cd8Q/jgiiweXdv7Xu41frQmrDY5hY27e3xbeQ9m671zSQawzhV+v2aWCN4AoooYSoyvRtbCUcDCJJwlVOewOaGJmRBRECiLbyzVgurqDLd/S2omkK+qONqMo4toyqNiCydfBvb6uhY3wweBDSlahMrvKmsZbwuvOLapmgyvZtaqXuDrhQrRUtXPcoqh23wx304GuDej+xnw85uDj56jKPPnWN2PEu5ZPhArUC1llYlHAtS1xhjy94+dt2yAUEQ0CsGpmHX6Ozp1n8b56mfhZiezuO6/jjp6qinoT6yYkmqKAq+cO9iC1/XIWtWmDOr2j3Vd+XcwDjPHzqP53nEYkE++uH9dK+gxyaKIm2tSR549y5Onh4lM1fizLlxTpwc4cb9a5dsmj0Pjrx6haOvDuJ50NqW5Jc+vJ+W5qXPZl6Pqa83zXvv38nlKzMUijovv3KZC5em2LZl4RDTmUogSyKJYBC3Os8JgkBTPFrNei1ulyqjz1+oTHthcymJAqrsi8vndcO3lxXFJQcc9yotE1EUkGRx1fI52/HdIjVJWVUwGKCtNclHPrCXpnRsyeLseb6I8JZN7ezd3cf3H3oFgDPnxrFtcW1SIAAAIABJREFUFy2kEbxG6YjnwdxciZ3bu/i1T9xac7S6uq+DQZXgKmCJ58FrJ0d49fgQnueRqovw0Q/to68nvWyMiaJIQ32UD//CHq4MznB2YJzzFyZ58pkz/MID15OMhd7QHFRycuTNKZJqC7yOIL4siExVSiS1EEnNL32ZnMozWmV/hoIq+/b0rfiMNFVmz3U9PPXsGbLZMi8fvcx77t/5usAR+Pbw73vPdWzZ0s6p/Bima1OnBlHFCq5nkG5M8PY7N3PqzCiFgs7QcIZMpkSyJVrt17cWdOTicDp3jFeyL1C08tzd/AA5a46gFGF9zNelEAQJ3ZnFdktoUh1huQXfWdMjIKUQBQVBEInI7QiCjOsZyEKQhNaP4+nIYpio2oMshijol5DFUNVVVkCWwniAJKoklY2E5Gby5gUUMVw9DC6EIApE4iG27l/Hpt193PTOHfzN73+TZ79/lLs+sJe12zoRBIHDP3yNwbPj3POLN/DxP7y/pr3gOi7HDpz7N3+G84wHy3aZLZVpji86VKywif9phWM7PPntF3ny2y/ytg/v58b7dtQAiH/vmN8rZ6cL9G5q4/2/eSfxlF+unmpOEI4uAB+WYSNJIpK8AIwHgguHdUO3kFV5SbI2EFIxdQs1oFDXGGf4/AT52SJd97dy6vAFLp0YQhAFGqplvMFoYAHYF/w5b36RicRD/MqfPsCJg+d48YnjHH78GPvv28k9H7vpdRkLju3y6N8/wwuPHqN7Yxv1zUks0/LLQt5Ef4UigRU1QkRJZOdtm2horePFJ07w0Bee4ulvH+YD/9u99P6UQcH/mRGRNTYnWtEkpTZ2yvYIA9kvMlr8EY5XXviwAyVrkJnKSwwVHqS/7jeQhBAiEu58WY9XXfdZfgi3vRJjxR9yMfd1itaVZXqkJXuYWf0IQ4Uf0Ba5h77ERwlI6dc9JOfNAc7MfZZZ/Ri2W1jlMz44IQoaCW0DW1L/B3FtuWD64vA8j4J1kUu5bzBZPoDuzLASMFO0LjMLDBcfIR3cz7aG/xOpamTjO50KWNX9Zt68wMnZz2C5BdYnf4NwvJ2cOcDpzF8zZ5zE9ZZq6pasIWb1V3C8Co3BvasCRyV7mOMzf77i/QHUBbaRCmxFktLghZd8bv68szhm9Vc5MfN/42GzJfWfSAV3cW7uc0yWn8PxlibfitYgU+VDDBcfYkPdb1Mf2MVKhyEXizOZ/8Z05UUst4Dl5LG9cu1eHK/MxdzXV7x/gIjSRTy9jUhA4x07+mvn24XwGCk+zGDhe6u2saX+D0iLTRhlA72aRLQsh6npPIGAQqousqwvPDyK1iAXcl9hovQMpju3rN2idZnJ8gGuKO30xj9KW+RuZMGfa23bJTNXoi4ZRpZFbK/M+ezfM2ecIKr0ElX7mNOPcy77eUrWMFc/w4J1ianyQYaLD7M++es0h29FYHVpgJ80fmaBo3hLnN/6rx8iHvA377brUjRNJHe5josWULj5/p2s39VDqahjmg6W7XDiyGUi0QCFXAXTtIknwzi2g2naeB7s2NOLLEv0bW3ng733MjWWpTJeoJLTcWwXLaiQaozT2tNAPOUPkly2zPnzEwQCCsGgyvhY1s+KVw8J5bLhT4iuS9eubm77hd20LhLv9DyPvGEwPldgJldGXvRdRiezKIqEadm4roco+hPq9Pgceslk5PIUnevbEIUIXDWRVhwDOSbxvk/ezl3v383o5WlmJ3OYus8KCIZUYskwqaYEDS0JJFmkPhbmtaEJFElib1BDU2S27lvD1n0LNN+SXYGJPNakjeO4SJJIsVDh9GsjRGO+BaNeMRFliff97t2EIhqSLDIzlefIYb+cQJYltmzvIBL17Qrv//hN3P/xm5bcv+u5FO0yWWsayzUwXN3fNHTAe//LdjQxUC0XE5jyRqjkS0iiTIPaRFKt9xejGHzqL38BvWIjVMdFoaAjSr4Irqc7eDLojoVpesiyhKC6iJrMr/3ZAyiKxCM/PEEkHACnFc8VKZsgyRZS9fnd85H97LhrE+GQRiwa4MzAOPV1EZrS8TflerVS1Dcl+KPPf+Jf1cYbDVnxBTP7Nrfxnv/lVkYuTpGdKfjMLkHwtVqSIVLpBI2tCQIhDcdxyUzmGR+cIZIIIVTR/LdyRKKBGqvm0pVppmYKtDYn39D3FhFIB6L0RRuRqhkr27Z58chlZqsluFs2tbF+TdOqbQiCwNq+Jjra62pspmOvDbHn+p4lWkemafH8ofMUS/4Ct293H13XEKgURYEtmztoaIhSKOqUyybHTgwtAY6Cqt++EvzpHGw2taUxbYeA8q9fdlzPw3CsVTW9wK+/37Wjm4721JIF3rYchs5PEAwHSLcl2djfUgOOCoUKhXyF2bEM8fprA7iaKnPfPVtpbvrJajUt2+G5FwZqWk6bN7ayYX3LNcdWS3OCG/ev4cKlSSzb4cUjl7j1lvUkoyG0q1w6rw4XF9vVfY23KpB5rTBcX9NhpJSjMRghqmpkq2sp+NpD0RWYVPPRUB9FrR6Ks7kyhermy/M8DNvBcp3ae7E4OttT7NvTh4NbtVYOEZQV8voxTGecRPAG+noaiUWDFAo6pmkzmylS3xYjFX5jANrPU5TtEqfyr3JH+n5O5o5iV1ljk/poDTgKyk0YVbHPkNyEIEh4eChCCFkMUbbGUcQoMW2tX5AhaIiChiioSEKQVGAblltAk5JE1R5CXjNxtQ9VSmA6c4SVdhzXQHdmCMgNVSHSGcJyCwKCv68yLNSAWlsHJVmic10zbb1pLp8ZrZX7AxSzZQQBUs2JGnDieR7TY3Ov6xz20whBEGhPxmlP/v/kvXd0ZOl53vn7br63ckAhA91Ao3OYnCM5HFJDUhxGBSpQlGRZWmm9x2tLax/trm3asrVnV9axJWu1S5kySYmkREqkmMnhDIeTc4fpmU7oBtBoxAIqh5v3j1soAA1094xInSXpd86cBgpVt278vvd73ud9ntRlr0eONr4fUC7W8D3/isYRbzbCMOTok2f4wp98h73X7+DhX7kP8xq6Hv/QMXVqjhcePclPfvQ+brh3X4ehvvX5yQ9maDdtyktV+kbzBH7A/NRyl5U/sruPs69MUVmpddvBLpycZWhXL5qpMry7nzMvT6GoCqN7BigMZzn+1Gly/Wk0fc0F6crhez6xpMmd776B2x+6jsf/5gU++x+/xq1vP8zgeC9Ad9+9y7Txqis1nvzyyxy5ey8/88/eSeCHfPPTT3LqpSuzDbYNcWV2RBiGkVj3gSEe/PCd/J+//l954osv/lgBR7IkYUnr96vtr3By9T+y0Hg8EqxGxVT7SWm7kYWJ7a9Qdc5Rd6c4Ufx94lrErliLgJDpemR3PxrLo0rRc+YHbc6V/xvnq5/pMIw0DDlPUtuFLmXwwhY1Z5KGN4sTlLlQ/Rxtv8jB3P+MqfRuu+8hIW1vkeMrv89qO5rrNSlFXN2BqfQjSwZ+0KblL9J05/CCGl7YivTblMJVz0sYBpSd1zm+/O8oO6/TgYBQpTianEWXs8hoOEEV2y9GLWyhTdY4giav5xhBEDCQTKIrW/OjtrdI2XmdE8X/QMU5gyrFsJR+dDmHQML2V7D9El7YIGfciLSNBtBaaFKKgnk7blDDCar4QQsvbOIF9Q3vCgiDIhuLS0IINEXuroW2i8XWk8w3H6XYehFNTpFW92Mq/YQENN1Zas4kXtikbJ/kePHfc0PPx0jrB7Y+V2FA3Z3G9qMuCFkyEKGCG1RZ65NUpcQVwTFNSiMQLFcbNNoOQ7kU472bdXST2gR542acoIoX1PFDG8cvd1sLo90IUTU1+kYROe2dO79EPGZEmo4bdzkMaXgzHC/+W4rtl4AQWVhYygAJbQeyMHGCClXnLG1vmbo7xcmV/wvHL7Mz9SFUKU7bdjl6bJqbb9xJJrNZg8H2V5isfJL5xmM4fglD7uncvwWC0KfhzVBzzuOHLeruFK+t/iGqlKTHvPUHzjr6oQWOnpiZ4tTKMv/klkistNhs8KkTR/mV624icxUKqCQEsbjO0lyZeNIkkbJo1G3inWqGEBGql+wsfAFK7RafPnmcVxbn+Zd33svthe0HH+jQXoXoVll1XSGeMEgmTarVFrIk4bo+uq4SixkkkuamZN4PAz5x7GVitkJSqHjBOgDkd35eKdWZXSwz3J9BADv3DDA/s0KuN9VxMhhBiM031VPFo9S9Bu8deivpfOKaziJhGHLPvp1dxtN2gxXAI4vP0ljxmAjHqVbbZDIWsiwRi0duFKoqEfgKQRCiqBCL6di2R08hFdFYO85A10q+Gl6Lz8x8g8OpYQpGChkZXTE652zNCc0gCAP8wMWQLcruCiklYlSs2BU+N/NNDjePsHC+juv6TIz3srBUZXQ4SyGf5NXXL0X7qUhUa21MQ2V4KMuF6SKqIrNrrMDUzAqH9g9SXKnz4ivT7BiJFp2JuIHn+wghuDBdZGKsQBAEvPjKFEMDGVIpMwKcfsRCCEEqG48cbq4RsizRM5BGUiTmp4sUBjIMjV19Uv1Rj11jBeJxnUqlxbnJRT7+ie/xnnddx4H9Q2iqctWFuh+GzLUqZHyXkVgOATQaDhemlqNnQpbYOdqDdY2Kqa4rFHqSXXbf9EwR1wvQN3ysUm0xe6nUFcMeHyugX0OkMmZp9OQTnL8QtYddmC5e9f3fb8iShKn94HpCAoKrst51XWH3RO8mQDcMQ+qVFouXSvQOZQGxqY3M8wNcP0A3dVq11rZueGuRy8UZHsphuz6ttksipkfC1K4XzRFrC2hZdNlZayxNVZFptRymOudckgQH9g1eE3yWZYmJ8V4ScYNSucns7CrNhkNffwrtGoCcQMKQE9S9rVWw7UIVMjsSGXJ6BNxcHhHr9MoXIHKF63y3EN2EMwTmqlVOLiyxK5+jT988l413njlZSBiyitttKQ7QlUFUKUs85qCsaYiE0Gq7EIJ5jWfyRzUEouPsGR1cy2+gSBvFV1Wy+uHovZedACWMY8g9W/6myeugSURrB0SUdK+9V5VSoK6zU+LqSOc9KeLqaHd7xfkVvv6pJ+nf0cPAzh6shIHn+pw7cZGTz08yuLOwSWpgpGMZ/MJ3XmXvDTtIZmNUinUe/cLzTJ2a+771YdZyD7vt4NoepeUqIWA3HYrzZXQj0gXUDHULUNs3kieWNDn25Bme+upRBsZ68L0ASZYil6/O+wM/wLFd7JZLrdTAcz18LzJUkWWpK9q9lvusLFT4/H95BN8PuPOd19GstWnWN7MGUrl4VyQ7DEMc28Vpe9gtp9s6Wy7WKC1V0Yxo+8obcNq8Uqx99sVHTxJPRe53AoGqK/SO5BiZ6EM3NYYn+hgY6+Xrn/weraZNaanCmVemuq1sh+7cwxNfeomvfeJxbn7bIVYXK7zwyKs8/GtvJZYwGd7dx5c//hg3v+0giWyMnsEsz37tKA/90n1vCJibPbfIa8+fo3c4j6xKzJyZJ5G2UDcUTxJpC81Uef6bx5HlyCxk/PAIqq5iJQzmp5Y59eIFinMlnvvGsU1MyUoxEtcuXlql1Whz6dwirX6bbG/qmk5EruPxxBdfRLd0kpkYtXKDdtMm9Qbc/X5UIyRkvvkYS82nI9BI6AzF3sHO1E9jKf2ARIhL1TnPhernWGg8Tru12tG6iaLlOcw1yyRVszuPhGFAsf0S07W/wQvqyMJgJPEww/F3YakDSKiE+Nh+iYv1LzNd/RucoMxi8wlS2m52pX+pqyW5aX/DgKXWs5TbrwJgKgPsy/4meeNGZGEgkDpCxg4tf4ly+yTF9gv0W/ejSle/jm1/mTOlj3dBI0loFMw7GYw/SFrfjyIsIs0gj7ZfpGKfomKfose8bRMbxA0C2p5HTNvKyq265zhX/gQ19wK95h0Mxt9BxjjcYaoI/LBN3Z2mZB8naxzhclfljRFTR7ih52MdtzAPL2wyW/86Z8t/1n3PmvBK4M8D1wGRWLjtelfV8l1uPQdhSErfx570PyKl70UWBhDihU0u1b/JufKf4wRl6u40U7XPc0Ad2QSgAUhCY3/2tyJjhk403BlOrvwBTlBGleLszfw6KX3ftvshCx1dSbOzB6pte0uxsu3XiWk3cn3srZGEdehh+6scK/67LuMsOv6Avp09mHGDMIzIBmEQom7DEvXCJheqf8VK+xUgRJdz7Er9An3WvWhymuiZ8Gi6c5yvfob5xnfwwgYXqp/BUvsZjL0DSQj6+9PohrplTHeCChdrXyYIXXqtuxlP/TxxdaQDEkZtfbP1r3K+8pc4QZmmt8Bc45tkjcOde/AHFz+0wFHTdSm11mluGcPk/XsPEN/moYJISCqdXU9A2/udLpW2bygCFzaLma0nUT1WjF++7kY+9uRj2P6VNTMA0pkYN9y0Lqg11nGiEEIwyFZ68xYqWxgBVYGjcdvYMLENFLpGy+HSQpmBQop00kQQTUoXJ5foH8mxslRhaFwjCFYAG1gH0Jp+i7rX5I2GEKIrYnu1aHgtkj1xbh4Y674Wixscum6kczyX9QD+PRUmAkIqTo0evZ+x2HBU3VnrSybc1GooOg4MTb+O0kGcvdCn7NZotm2CIGTPRB++H7JzJMfB/YNMXljGcTyWV+qkkib79/Rzca4UtfYQkkqapFMmu3YWOHJwmEbTJpuJcfONOzl24iJt28V2PJJxg75CkiAMSacsdo7m2b9n4E2DRkEQcPZSkZ192asu+M7MLjPUk8LSNb57bBIvCLh93yjzq1UK6ThJ69rfG4YhK9UmCUt/Q9f8WnHiuUmSmRj1pRqqprDrwOD3vc0f1hgfK/COBw7xxa+8jG17PP7kaY6dmOHggSHuvmM3B/cPUigkUbap1ApAlWQant0R3BM0mjYzHQ0rtWOjevbcwjX3w/P8rnBfqdyMHL82RLnSZGGpAoCmKjSbDmfOXn27nr954V8qNTpMx22ow0HAwmKVWq11zX2FCLwaHMxe0TL++42AkKbvUHfbLNtVevStYpKaqjA0sJ2bWUgmnyCTTyDJAnmDRW3Y0YILgoCVuRKLM8sURvKb2ozXIp2ymC1WqDk2bduj3rKxDA1DU4jHDIqlOpoqU661kaTIWcUyNBzX444jO1hYrFCpROdTCMGOHVe3MF6LwYEMlqVRKjdp2x5TM0UcEZBJWsSvAkIKwJSTpNW+N7TQFAKmqqtYGZW0Hs01hZ4ElqWxWmpQqbZZWKgwMrTVETMMQy5MF2m1o5bqXDa2ST8rCEMsVWUwlcBvb2bPDg1m0LQIAHIDH0vRCQmQhEoQugihIMvuBrHQKMHT1Uhs8lrC3z9qYcgWI9Y4X53/K0pOkYX2LIZscV/PQ933iGhi3Da8IGC51sDU1E2i4auNJl4QkI/HNrO0Nv54BT2Py78rCEJef+kC3/jLp5FkKWIRhRG7b2i8wPv+8Vs7QG0Ut77tEKdfmea5bx3n3//an2HGDBAwdmCID//Th/jk//HlN3eStonP/edv8ty3T+C7Po1am8APePX5c/zuz/4xqipjJQx+9V+9n12Hhjcd4+jeft7zy/fx7c89wx/9i891LY/3XL+Df/affxGpw4x4/pFX+Zv/51Ga1Ujvb2GmSBiE/P5vfALd0NBNjfveexM/+dF7ATj9yhSvPnsWVVP4iz/42haQWFZkfuG338UdD0ULtcWZFf7kd/+a1aUKnuOzPB8Bvp/4vS8RS5ooqszeG3by0d99uOuG+mYiCAJiCZMdewd47YXzzJyZj3JkIAwCrLjJrQ8e4hd+510kcwk+8rvv5Yt/+h3+7k+/Q//OAg995F4unplHVmR6R3L86sc+yDc//RR/+yffRjc1Hv61t3L7Q9cjKzL9OwsM7+5n703j1Os244eG6RvNs/v6UWRFIt2ToG9HT3eclWWJ3tH8uiuZgFMvXuDRv34eRZXoH+3hl//VB8htYHumC0k++Fvv4NG/fpYv/PG3GNndz+CuPnzg/b/1dv7uz77LZ//wawyP9/KWn7qNUy+c737fE196kRPPnKWyUkeSJT7/R98k15fm/b/5IL0jeeJJi+Hd/VvaCn0/at1x2i5PffllXMdDM1Tu/8Ct3Pu+m9/0NflRCdsrMlP7YrcVaSD2APtz/wRd3jzf5o0sCXUML2iw3Hqm+3oIeGFAn5nCkrUu28j2VzlX/nNsfwWBxGjifezL/iaKtHnhq8lpdqd/BVmYnC79KX7YYqb2Jfpi95FQx7fMb37YotQ+QYCDJDSG4z/BUOztiA7IFIQ+buCgyXFUOUNS20N/7O2ERK6WUfa2Pt8I6IBNPhdrX2ap9TQRw8RkLPWz7Er9YiQCfdl+mEofGf1g5LR1mf6MJsss1SKNo5HsZlmWUvtVBIKR5MPszfxGxKjZ0FIb+AGG1k9euy06ng4TMAzpsiaDICq0CUmgdIoDkiyBCLCUgU3fF+J09m99Ta11ctyrdfAGoUNc3cnh3O+QMQ5tvmakGE99GIAzpf8XL2yw0HicPus+TOdmpqaKTOzuIxbTCUNBpZSn0U4yPphHVWRkYXQZRgKFhDZOzrjuivvi+QGTiysdIyUYyKzniA2/xGJ7irR2F7ocHaMuZ1Avu888z2d1oUK2NzpfnuuTTJpbtDfDMGSp+TSzta8R4qNKSfZn/0eG4u/c0s6ty1kOqv8cXc5xvvIXtP1lJsufIqsfISTLcrFGTz6xTVt/iB869Jp3cDj/LzHkHlptl6bjkUmaaHKK8dQv4AUNJit/QYhHsfUibW8ZQxmKGMhXcUJ/M/FDAxyFYch0pczjM1NR33W7zRpa8HpxmcemziNLEj9/6AiqLEfCorbNY9Pnma/VMFWVB3aOM5RI4gUBR1cXOb60iB8GHCn0cevgMC3X4fHpKWYqZXpiMd4yOkbWjLQbFEnaQptvuS7PXrrIqZUiKV3nruFRhpIpqrbNkxen6LFiHF9aJKaqvHNiDzFV41RxmWcuXUQSgtsGh9iXLxCEIUcX53lhbpaMYVFzbHKywXKpzlAhhblG1RUwPpKnN58kFY8SPN/z8dxITybfl0KWLSQpjhBbUfCq2+BbC09Tc5vsSgxzMDWBH/ocL59hujEHCPanxtib2MnF1gLTjXm8wKPolBmx+rkxsx9ZSEw2ZjlWOo2lGFTcOkl1ezZKEAY8VTzKodQEZbfKq5VJHuy7nVPVKZKqRRjCa9VJGl6LPjPPTZkDmLJO1a3zUul1VpwypqxzS/YQuhyp1J+pT3O8cgZVUrktd5i8lsYOHF4uvc6l1hJpNcEtuYMklTgClaeLJyg6ZRKKRRiEkT6JruJ6PpKQMAyVMISV1Qau52N2rM6PnZwlHtM5dGCQ2bkSvYUksZjeWbCISI/J1CJL9aTJK8dn8IOQXTt7mL64gqGrDPal0XWV107PdSyjddzAp+E4hEBM1VCl9cF2cr7I1EKJ8YEcpq5ycbnMaG+GqcVVXpteJAwhn4pRabTIJWOMFNJMLZboyya4tFLhqdemuP/IOC3b5cLCKpm4RbXZ5pVzc+iqzM6+LCenFzE0hUzc5OJyGV1VGcqn+NbLZ9g9mOf2/aOYmkrDdWj7/rbXFSKxdyOUKM6WSGbjJDoMPSEEdtulUWtTLTeon2ihmyrDP6bMI0NX+ekP3kIspvF3Xz3KaqlBudLiyafP8uzzkwwPZbn1pjFuu3mc3RN9mOZ6pSAgpOHZJDc4hziOR71TOW7bLp/5q+f43Oefv+Z+BMG6a6PnBtgb6PhhGGK3PZrNaJHeaNr8pz955A0xL3x/PQtwHL/DltyGKm17fOJTT/DY905de6NAXyHF7/3r9zMyvBVU+EGEAGKyjilrZLTtbdUkSRCPbwVS2i2XyZOXSKRjZHq2jqNCElgJEzvtUBjOXxFk0TQFy9RIxk1iZkAqYXa065RId4k4iiwRM3XCMCRmasRMnaCjE1Bv2LhudB0VWSKZeGNiqomE0QWbwzCkWKyRKsQJQxjt3w4oWw8vdHCCNwb++WFIxWnjBH5XR6onn+Dg/kFmL5Ww2y5f/cZxdo0VSKdj60yMIGS5WOPbj56k0XCQJMGNN+wgtcHlNBIp9SOnvctsDeIxHVmSCEWIFwQU2zXG4/Gosi1F1cvtwvF84rr2A2mF/GEKRSjclL2LHr2PZXseTdLZGd9DUs7geB6SJOEHAUEQ4ochhqLgBj5+EKIrMlMrJb575gJ3jY9iaSqKJOH6AVMrJc4urfDwkf14YfR5XVEICbstpUEY/bzxdV1RaFVaFOdKICKtsCAMee8/egszZxcoLVWxWw7JTKSNdPe7b0CSBCuLkW1wq95G0RTe+Yt3ceCWMV574TyGpZHIxLj/vTeTzMYRsmBiQ9vs0HiBD/zTd+CmFeaaNZyGjxsEyEKgBHDTT9/IcrnKvNSmsrJIRjfRx1MclPaz3GoQAgdkGTfwiakaw4k0qiqTysW3iJ1qusoH/ocHuPH+fUy9Podju5gxneGJvk1gT99Inrvfef26jmYngjDk23On2Nczws7960WV4d199P3SBNP1Ve4a3M9YYjNQLEmi23YFkW7kbW8/RLt5ZduPnoFMl501dnCIj/yLn+yyuTaGlTD54G+8DVmRiSUjhsfZ4zP81499EUVV+LV/834yhSSSJBEGIbVKk2995hm+/blnuf6evdz+jsMM7url1//DT3eLC2t6l7IS5TjDu/v56P/+PoIgpFJpIssSc/NlGnUbx/X46O/9NKulBo8/9joTu/t47++8h/xQhhPHLzJ4cITMjl7OnV8ik4mxsFDhgV+4h+JyjbNnFqhU29z2U7fjuQHtlsPYrgI7xwqbNJWEkDhw524mbhpD0+SI/el4PP7EaX7i7Yf4qZ4U6ZRJLKajagq3vP0IkiLRaNi89Wfu4I6Hb2Ly/DJ79/R3dfBUTcZ1fXYeGeE3rvswsizTaNhdV99Tp+fZPdHHgx++k7f9zB1dUEGSpW5Hw49bhGFI2X6NmnMeAFVXeTYFAAAgAElEQVRKMpp4GE3aqnUZiRJn2ZF4P8XWCxscxqLukKrbJqGsM+yK7ec7zB2IqcPsSH5wC2i0FrIwGYw/yGz9q9TdKRreLMXWi8TVHYjLlrZhGOCHnSINAllYbGzDavkNphonSapZ4kqGqluk4VWRhIQpJ3ADu6PrGGAqcWSh0KMP4/pF5hqPdPSGBAXrTsZTH0aTE1yt+fJyMGHtXO3IZkgaW3OWEI+kto+J1Ec2gUYA5eUaF167RDxjUVttoHbyj1q5STxloeoKsizRrLfJ9CRpNyNH6MAPGDswRDK3zfkNQ0BDSGvHESKEREzTrppXCmT6Y28hpe+9wnGrDMbfwWLzCVbaL+EGVRab32OHeiPNpr1JrL7teLSdqxM5rhZCQDYRY3JxhYH0ZkaTQJDW+q7KzIJI5L9ZbVIrNcj0RaY4tuOhXpZjeEGd+cYjXU2jXvMu+q37t73OEDF2dyQ/wGLzCeruBSrOaZZbz5EVb6fd9q4oV6DLWcZSP4sh9xCGMLtYZqXc4NbDOxAiYlr1WvcwW/8abX+5+79HlpnmArsTIxjy998e/UOTYTVdl/92/BU0WWZfvoenLs6Q7bSk9cfjjGWy/MWrx/jgvgNYqkYIPDZ9nhfnLnHPyA6Krah6FgIvzc/xqRNHuXtkBzE1Wvz7QcBXz57mxNIitwwMcXqlyKdrx/iV627clhoI8PjMFN86f5Y7h0eZq9X4+NGX+J9uuZ2W6/H510+yN9/D4UJvt8p5ZqXIx4++xM0Dgzi+x8ePvsRv3343bdfjE8de5vbBYbzA58zKCuP9GRRF2nSD6KrCqfOLqBsotIalMzRWwPN8qqUGQvQiSVu1N4Iw5ELjEmPxIXRZ5atzT9Cr50hrCcpujR49y7Jd4suXHmdwV4GF1gpfuvQYd/fcQE5L88jis+T1ND16hi/NPspYfAhVKFxoXGLQvDIoMFm/SFZLcbG5wNPFoxxJ7+ZE5QzXpffihR6apGLoOt9beglLNrghs49nVo4z1bjE4fQeym6NIIzQcSfwOF2d4sbsfk5Xp3hk4VneP/wAz628yvHKaQ6lJjhfn6Xs1nj3wH28VHqNl0qvcWN2P1ONOZaaJZotm3wyzcJihdtvHiebiRY0Rw4OMT7WgxCCEydnyaStqE3I1BjtaKBoqsIdt45jGiqppEG202M6PJglmYhaDo1Oe5tAYMV0rj80QqvtdBeL356a5JMnX8H1fd6/+wAf3HMQtcNoWFitUW220VSZuKFRrDaxXZ/pxRLFagNNUai3bcb6ckwtlhjMpyjXW9iORzZuUUjH2dmXJWHpVJs2jbZDqd4kJGRig5vSubkVYrpKMmbQclyqzTbZhMlIIYOmKLhBwP999AW+fuHMFa9r3rT42G1vwSs3mD41x/6bx8n0Rvdd/0iOU0dn0DSF4V25H2udIyEE6ZTFh953C3t39/Ot75zk6PEZiit1PC/gwlSRixdXefS7r3PnHRO8553XMzyUjYREhURGszaxGO3LnOgiofk3x9IL2fqZtu1ues2/ikvi1bZ7xdajMAKZ3qiLnuv5/+DuVjWvTd1tUXaa5PVtACAhNo2la2HFdSRZ6ogHbw0hBPFMnGQu3gVLtwtJCA6M92EY6gaWy1bb385vnVbD9UWqY3v4nWumKNJVtQM2hixJmyrfnh8wNpS/qt7TWkSCx+EbYuWsFVKqThs3CNBkGV1XefCBg7zwUqTT9czzkyiqzF237yKfi67BaqnBI4+91hX9Hh3J89Z7922ym43rOiOZ9LYaR4oSaTX5YRi19UkyQqiEoQ9X8c70goCLpQqj2fQVW69/VEMRCrsS+9iVWKfmXyyVmVktM5ROM1lcYanWIGUaHBro5ejsPI7nM5JNc7FUodJq03Rdnjk/w77+Aq/MzHFkqJ/ZcpVq2+YbJ8+gyBJ9yQRBGFJqthhMJ6m2bSqtNjeNDnKhWKJYb3Dj6CDudIXZc4uomkJ+IIOQBLqhkulJUBjM0D/aw/BEH8efPkPgB0yfWqB3OIvddjvC9zKu7VEYytI3kqfYYdPoloaVMHjbh27bdPyFoSw3v/96Fpt1Ftp1bM+j2G4yEEvQcB0Kdw8jtRpMezWGHYmG65K6vkD+xn6yrSa6LNMXS+AHAWndZE9mHRBeaFUhhD4rmt+EEOiGxt4bdrL3hp1cKXbsG2DHvoEtr/tBwKvHbe7ZeSN7UutA0PB4L//bb/8cHz/zNDcP7OWevl1XvebJbJyf+Lm7rnFnrMfQeC9D49vLLJgxnbd+8Nbu757r8dRXjjJ1ao5//kcf4YZ7925iVYZhiN10OHfiIguddlohouu2cUSVNvyy8e/VapswDDk/uYTjeAwP53j+uUn2Hxyk2XQ4e2YBXVfwgxDX8UhnYqzaddx6mzOnF4gnDBzbQ1VlFhYqxBM6uVycet0mmTK5eHGV0R09m74fYPL8EucvLDMynKNUbtBsOtTqbebmyxw9HjGFT59bZN+efl45NoOuq6yu1tm/d4BypclzL15ASIL5hQq1WhvTVBkazLK8XMUwVVw3oNm0sSyNvt4UTz51ljCEgwcG0f6B2LU/bBHgUHFOd4GYuDpCQh27Kos1qU1gKn00vdnua5qk0PDa+ESFFC9odUSrI62djH640/a2fQghsJQBLGWQujsFhJTtkwThe7Ys2CPXsWgd44cOS61nGIi/jZgyHN23QkYWCpKQkYRE3StjB2169EFUoROEETgsS2rXmCckoO5OUXengcjJayD2NiQ/QbPt4Lk+CIjFjKitGyAMEZIgDKJuEk1XkBUpMgsRokOYgP7k5uxJQqPXuhNT6d9ynpv1NvVKk/xgBs/2kBWZWrlBLGlixnQ0Q6U4X47aUIWg3XLwHA99i2D0xpMrI4RBGK7nrk3HQVPkq2oIqlKCjH7witpDALqUIWfcyEr7JUJ8as4kzbBEu72ewwZhyGq1SbHSoC+bpC/392v7XDNBuDzF9kOPmrtCwbhy8RygWW0RT8fQOrlLEITIkugyqdei7a9QsqM2SEVY5M1bUK7S4iiEIKYMkdEPdcTffZZaz5Ay7yefi2Oa2rbPU1LbRVLb080tJUnQbDvdjgYhBJY6gCxZ4EMY+jh+hVB4XGwusiM28OMFHC016lysVvhf776fkWSKctvm7Go0YaUNkz25POZlyWBS12m4Lm4QcM/IDgbiCWzP45GpSe4b3ckH9h3s3uRN1+F7M1P88nU3ckPfALO1Kv/miceYq9eYyG5PtX9s6jz3jY7x7ok9rLRa/OsnHuVcaZXBeMRqeueuPRzaoIf0xMVpJCG4vreftufxvZlpzqwUWW21yBomP3PwMJ4f8MLcJXrScXoziU2Js6Yq3H7dThotZ5NT0Oz5SEBO1ZWraG6E7IwN8rbe22kHNqdr05TcKr1GjluyB6m6DQpGllcr56i40cCc1VI81H83qqRwtj7DfKtI02sTAu8cuAdVKEzWL3YXGSWn2vlsiC5p9OhZeo0cM815Km6NHbEBLjQu0fLb5LQUaS1Br5Gn7dtM1i9yqbXEDZn9xBWTht8mDANuyuwnr6epeU3CMOT+ws0cSe8hr6X5+vyTNL02z60c51B6gtHYAIpQ+ObCU9xXuJnj5TPc1XM9t2YPMR2b52x1hiPDwyhtjVhMJ5U1aIUtpNDCURysVHT/7NibIW3FCFQXTxJUtDJpNYEvfDzNYcGp0PLb9Bl5Wn5IxauTTido+w5IATFNoR04IIVYlobVoYnXHJuPH3+RlxfnACjbbe4cHGU0lYYOuFNt2pyaWeLI+ACOF7W3CCGIGzoTg3lmixV6M3FmixUc18PxfCpNm2zSIm5oJC2DMIwW5pVmG78DPPhByORckaVyHdvxiBsahXScWtNGkgSWrnXdwSBkpdVksrx6xeex4TpUay3iUqTrEE+vVyVKyzV27OmjUWuTziUY3GCr/uMYQggMQ+WWm8Y4fGiY6ekiTz83yfMvnmdmdpVGw2apWOOLX36Zo8dn+JkP3sZb7t1LKIXUXXuTAP7GZ1fXFW6/ZZzhbVp9rhappEn8KqKqlqVx/z37usDnG42efLyrG9NsOqiq3O3lliTB4ECGfXu3LpQganuoVFrEO9XcXDbWBVODIBrLftAAoybJ9Bgp8vpWSjiwxiff/JKI9Dt2Hx6+onuh7/nUlsr0DF+7dUxTL0+kLmtXvLx9cWPrj7QO3/h+eFXtgI0RdPRb1sIyNDRFJnYNray1/Wv5NQICpA1LwDWnzI3hBj6qJDPXrFKwEvSakaPg4QND/OpH7uUv/+pZZudKfOex13j8iVNdra5m08bzAjRVZs9EH7/6S/cysWtze1zNtrlUrjDRs/XeX3ufFwaokkJWi0VtAeGaPsb291EY0nXt+3GKpl/naPk5bs3e19U1mmme59XSDLI9QNN1aNgOvYk4lVabpVqD1+aX2JnPUrdtDg30YmkqI5kUz164iO16VNvrMgBBGGBqCneMj/KlY6/Tdl36kgmK9QZD6RTlZosgCOmJxyg2GgRBwMjufgpDWWRF7ralua7HyO5+ZFVGM1TMmN5xUYN0PoGqKXiuh96h4NttF1mW0E0NRZWprtYJrgJMF6wYiiShSjJe4LNXCHRZwQsikNrvJPWaLOMFAU3XxVAi1pQsJBRJIrwMNF1s1fji9DEMWeWu3nF6zQRxRWfZrqNJMqt2E1NWKZgJwhCW2lWanktWt8hoFi3fpek5ZPUYXhiwajfIatFcWbKbTFaXSWsmWT2GEIJ+K0VS29xe7gU+C60atu+S1WOktahgWnZarNgNZCHoNZOYsoob+Cy0qnhhQF6PE1M1ynaLpu+gSjJ+GKAKmYKZwPE9FlpV/DAgp8dJqPr6s+X6lFdqSLKEFdc3PZthGOK0XeY64tfZ3jfv8ppMmhw/NkO77ZLLJ8jl42SWYxSLdZJJA8vSURSZXC6O5/nE4gb5ngStlktItKg2LY1Ls6vEYjo9PUlWV+voukpvXyoS6L9sGPA8j0q1RdBhAqXSFjdcv4OnnznL6EiehcUKtu3RaNg4rk+12mL3RIpyuQECBgez7NndZGysQLnSwvcDfD/gxMlZEnGDtu0hy4LrrxvltVNzJBImu3b1/ncFGgEEgU3Nmez8JrCUYTT5ys66Qgg0OY2lDGwCjiQhWLUbDFle5EwdVKnaZzp/00lq48jS1ec0SaiYSh8Reyig7l4gCF02yngAyMKgYN7GTO1LOEGJlfZLvLj4vzCceBcF83YsZYA9iZu6UhhGPIYb2MSUZNdlcbso2ScJOg7XhtxDVr+OlcUa9VrkaF2rNFFUhVqlSToXp7LaIJ40qVWbJFMWpWKNfdeN0juQjs6B77PabG7SvgVQpBgZ/TBim0JLz0CGnsHMtlpcawDDRoB7hxjYVMAKw63giUABoSOwWSuGGapCXzKBJl9Zk0yRYsTUqwvCS0IjqU0gUAjxIkHysIjrKN3vkiVBXy7BwmqNSqP19wKOwjCkYTvsKGS4uFJmMJukJxnr/C2g5Ve2uPVdHolsHN3U1pVSBJG+6GXnuuHNdJz0QJVTpPS915QDEEIiZxxhtv4VQkJqzjlcoxo5i4fbu3qmtL1dBl5IBBxd7mqrCAtpA7Tjh22k0Cej/f3dui+PH5rRzuuITqudRZahyMhXOfFCCO4Z3kFS03liZppHLkzyi4evYyKbp+m6pHVj07wShFFFUpejQ1alaPtX0zRyA78LVimdhKTteVEbkqZt0Vtqui4zlTJfOHWSEBiIJ8iaFvP1GrqiIHUQQVNRKNdaFEWdQma9Dcw0VOpNe0tvY+9wDlkS27ZVrIUkJBKqhSLJiEAgd3SApptzPLL4LJYcDaQNr9VdoMQVq9vzqAiJIAzwQh9FkpE6FD5N1rrn8VT1Ai+WTuKHAf1GD+8euJcBs4cXV6PjnUiMcqF+CS8IUCSZL889Ts1rElcs5lrL5PUMQsAtuUOktQSvlE7z7MoJfnLwPgpGFk1SiSlWtwIQdv5r+m1OlM8y21okDEMmEqPIQsLtMJqi/ZfRZIVcNk6PkSEIA87UplmyVxmLDTHTXGDY6mWxvYKsSFiywunyFKZiULRLjMYG0JwKq04VXVZZbpdYapcYsnpZaBepeg2W26sEnT7RslPjxsw+eoz11hDX92l56+J/buB3768QqLVsTF1htJCh2XbIJWMslevUWjaqInP0/Bz7R3pJxQz2DOXwA4dsQqPeauL5CQ7sKKDKIbVWk1RMoWW3GOvL0rLbTC8uU8jEaTkOI4UE6ZhJNmGQjulYuoKmCMqNOq6XRFMl3r5zF3nLou441Bybim1zvrK6CUyy2w6iEtH0VV3pDoTtpoNmqLQbNudOzmLGNAa2sZP/cQxDV9k90cfYzgLv+okjvHJsmu9893VOnJyl2XS4MFXkE596gtHhLDvGe6h7bRQhdwFfQ1fRNJlGM9I4uu2Wce6/dx8t16XpRAspS1Vw/ahCZbuRs9Ua6OIHAYokEcgCx/e7k7hpapF9Z2dSe/At+9m/781pTwVBQHGljqYpTE4uohsqo8M5LEunXGny8Luu50Pvu5nVUgMhIJWyKJUaGIaKYag89vgp9u/tp78/zcpKHVWVabddTp1ZIJuJMTiYoVppQofF9f0ASaqQGTCzVNw3rusGa3oAIVNn5sn0JLpCtBtDkiRUTWF1rkS2L73NVrbf7sa4fCF2+WsQtWStAXOe59NqubyRaLUc3E57jBBgWhrzK1XyXozeayRYslDRZWsL9OL7Aa67OYE0OnNlXNGJK+tzkqLI3HfPXpothz/75BO02y6WGQHTsiRRyCcZGspw/ZFRbr9lnJHh7JbcWxYSju9fVVxbFRISomvfLElGpxq9/WekDqNrsVYnG/vBikH+/xVBGND2W8y3ZnECu5vorthLtFilWUtRbdloioymyNRsGy8IGMmkSJs6O3MZ/CBEkSQsTaPatnnl4hyO53O+uMr0SonBdBJL01AkiaSh059MIEmC8Z4crh+BMpfKFbIxC4HgUrnGeN7EinkgVCKHVx+QiVbzAYQ1CB0yPSJ6XWgIceVWzF2Hh2k3Hcxtnse10GWFPms9X9qqH7n5OcsaW1+7/Pdz1WWOlS5hyiqSENzVO44Z0/j4madJqlEOOWClePvgfo6vXuKRuVNYSrSQ+NDOG1hu13l2+QIfnbidlXaDPz/3LP94z13UXZtH5k9jySrtwOPXdt9FztgK5PthwAvFGb63eA5DjoD2nxu/BV1W+PNzzyILgR+GPDR0gJ2JHN+ZP80rK7Nosowpazw8epgvTL1C3XOoOC16jQR24PGb++7l2aULHF29hCIJDFnlw2O3dEErTVcZPzDE0187xt/+6WMU5yvkelMEQeQm99oL53n8iy8xuqefvTfu7OzresvqWn4YAXGwhuJEGVtANhfjjrt2syaKL8mCQm+qU0RYX9Cutf4JIchmYx39lBDfD5Flwb59A0iyQJIkRkbzyHJUgMj3JLZoRNm2z8JCBUWRicV1COG11y9tYt9GLWwhJ05cpNFqdcGnhcUye/f2s1qqMT1TRJKi9wqhkEqbuLZPoTdBpdpCUSUUWaDrCs1mm7PnFtk90dspukRtPWvV/x/HCPBo+VExWyBjKr3bAhobQ5GsjkDweshCkNFjtP1o3vOCFi2vo8sYBhH7qGRfvqktUXXOsjYnuEGTgK3zqBCCrHE9o4mHOV/9LH7YouK8Rm11kmnlC/Rad9Nj3kZa3x+5oUkGurx1vLpcI7fpXeqOyZbSjyrFUVQP3VC7bCLfC0ikTDRNQTdUdEPFsDSSKZNEyiTZKcqGYYjtRlIXTWfzMUhCu6K7m2Zc3W59u/vw2vemgLBFuMlpDV5fXKIQjxHTt2crSUJHk67uMrsGJCqShRtUcYMaodyir3+4K5QfEuXHg/kkI71XBiWv+T2KzNGpOUbzUVv3WphKiriSu2arWuAHUZtaR+NIkiRSHebWxnPYdOe6AJwiTMxrOPGthaUOsTYIuUGDlrvEyorSlTC4PAyl0GVzhWFENunNJS5jkF9+bSNHFkPWULcRjv/7xA8NcJTSI12DV5eXCIl0jexOYhmEIY7v4QcBtud33ceWm00G4kk+uP8gnz5xlKOLCxzoKbA3l+ep2Rn25HswFQU/DEkbBiOpNC8vzJGzLE4sLaLKMgUr3tm+jxcEOF70rywE45kcRxcX2N9TYKZSpmbbDCaS2xWyAdiX72G2WuF9ew8QU1WarstIKtJEemJmmgvlEm4QcKFSJmsZtA1/k9CYrin05hPULnPayOTiHH36LP4Jn4O3jNM/ktvU270W27UfnKpOUXObvHvgPhbaK7xWmdzw/q2R09PU3SYzzXks2WC2uUBOiwaCGzL7OJiK6NWR441ORkuxbJfpM3LssAZ4cvkVRqw+2r7DifJZ3j/8ADktzaXmYpRShiFlp0ZBz/FA7218ee67nKvPUDCy255XVVLYFR8hphjckj1I0CHlxRWLPiPP2do0w1YfFxqXqLmNDZ8UJNU4M815inYZWUhYcpQ0jVj9FO0yrcAm9EKyWoqYbFJ168QUE1lIDFl9lN0qTuBScxuoQmHALDDTnKfqNvBDn5iyeWJJ6ga39g8xUy0ThHBr/zAD8QjllYRg10CeXQPrAMtIIYPjehHtvtJgpJBh/2gvpqaS0BcIvWlGs1mQ2hCe4+CQDOESab3Gg4dVCFdBLHHbRJScC+GzMw9hUEQInTCoIJQdEFTIxTTCoAJiGVyHe4eu557hndE97/u0PZe/fP04f/DiU939MyydvtFkJMy8YZAcGM0xM7mI5/jk+lJdIb7/XiJyE5TpySd421sOcOP1O3j62XN84tNPsrraYH6hwmPfO8UvjfVgKfom4VrDUMnnEpTKTRzXY7XUwAl8Hp28gB8GpAwD2/NoeR77Cz20vKhqvtJokTENFuuNTmun4JaRQbJWBLTGYjrplEVxpU6r7VKtt1GU7a2Vt4swDDl/YYWp6SK7xntpNB2WinWWlqr0FlKsluoEQcjoSI5zk0uMjxVYXKowP1/B83zuumOCgf50BGDJEpculXj2+UkefOAg09NFNE1haanKU8+cJQhCHnzgAJn0m2NEbQwvDFi2q5jymxeFbTcd7FaHSr5NBEGA5wY49mbm55UiDEPqtoPf0S5SZRl1w/jsduYUS9M6o1c0HqxRkiEaF+cXyhzZINR7pVgu1mi3o8RSkWX6e1PMLpYhpAscbdrljl6HEAJNMjHl1JaEqdVyaDQ2zzstz6VgxtmVyhFTtQ2bCzn26kW+8o1juK7HW+7dx0++8zosS+u2B8ZjOsmkeUWXOEUSkV7CNY615raoui0GTQtJGGjbJPNroSkKcV27Yuv5j2Is2fM8v/I4U40zfGPh80giEvqseWVuyN5JT36CMAzRVQXH8xnLZ8nGTHb1ZGl7HinDQJElehIxLE3lwX0TeEHQvT9HMinihs6unhyWpvLQwT3IQlC3HWK6hhcE5GIWGcvE9nwylknaMpEoEnpnECJD6F+CsAFCIrJgi4GURAiN0JsCqXO/6fdc8TgVVSGe2j4dbbkuU6slduayGJexzuerNc6vrGKpKgf7e7tt4WtxNcYfwG2FHZwsz9NrJnj38EEEEVDT8lx2JfK8a/gQAoEmyXx77hQPDOzlcHaQz55/kacWJxlL5HE6xaGAENuPCotCwF2949ycG+FPTj/BsdIsb+nfs+XYbN/ja7Mn2RHPMmil+drsq5wsz3Mg3c9iq8aDg3sZT/TQZyapOC2eXJzkF3fdRs6I8fEzT/N6eYG6Z/PW/r383cxx7unbxTcuvcZUfZWvX3qNm3IjxFSdr158lbt6d3FQjVpdJFninvfcRGm5xlNffYXTr0x15ygBqIbKjffv46Gfv4vMQIJLrYvYgd0pLnoklCSGbGAHNqZs4gUekpCwfRs/9DCVGCkt1S3sAVy7k3b92qxd5o3V9I1jyXbbsiyN++7Zi+8H6J3FtG27CODSXIlKucXQYJbbbtlFs90k3ponlYD9ySzCaNDW59h9i6BgaWSGXBTiqJJOI1xBsjN4oknfaBJFbzB2RCaRgAO3WpiqSz1YwbPb6LJF06sQV3PE5PSPJXgUhgFeEBVshJBQpWvP4wIF+TKL+CCM2H91z4401kIHL4y2G+Cy0PwuC83vvrl9wycIHNhmfSxLOrvSH0GXc1ysf4Wqc5YgtKm5k9QrF7hY/wopbS/DiXeRN26KrO4vKwBVbZu251GIRcfs+hXWQCtNziAJmUyPieG7nVY8B63DBFxzJg3DDfPzhg6SAEiZOrmYSVzX2OANhRASiui4TAeR86vtRwLbhqogC9EtxKyNgY7vo3Ra4FquR0xT31A7+/q5dAjDdT3EiAXD9gvHTkhi63XeLmShdwEQP3BAuKTTZpe5FwQh9abzfTHVwxBcP+CmsSGqLbvLNoJIDD2u5rZ14NsYQgg0Q8Xq6DMKAfV6e5PRB4Ab1OiypYSBJN5YDqJI8a7IeohLvVVC1weQxPbAsyKsTblbrdGOHGWvEiGR/vFie5W8lmZE2aqB92bjhwY4ylsxPrjvAF87d5ZnZmeIazo9Vgzb9/nG5Fmeu3SRquPwX156jsOFPt6zZx/HFuf57vQUsiSwVJV7R3agSjLvntjLp189yh+/+CyKJHNT/yDvmtjDLxy6jk+/eow/fO5pdEXho0duJGeaPHfpIt+YPMtSs8EXTp3kxPICP7X/MB/Yd4DPnjzBf3r+GVRZ4kP7DzGSTLHUrJMxDSQRVYwkBF7oc+vgIGW7ySdPvIJA0BeP87MHDnN9Xz9nVov88YvP0WPF2J/vYdBIEjO0TZPhSrlBs+3QbrtMbGj/WZork0hb5HpT1KsRhfZy4CiurFdYBYKMlkSTVPYlx5hqzPG3s4+S19OMxYdRJQVD1klvoK6l1DimYjBoFrg1d4i/vfQoaTXOgFkgqc1ZkiAAACAASURBVK6pzmuRiPWGSKtxEorFztggWT1JRkswGusnq6U4lJ7ge8svk1Ti6MRIKgmC0Oe16iTHy2eRhYQpG1yX3oOEIKunUDuJhiYpZLQUuqTyjv47+Ob80/ztpUeRhMTB1C76jTz3F27mb2Yf4TPTXyerpbpMpOgcgCUb7EnsJKslWXWqhISMxYeJyyaykNFlFUs2USWFlm8zGhtgxSljyQaWbNKjZ6KWDjFIXLFIKBaykJhqzGHJRoedtD5ICgG/dcPt3D8yhhcEHO7pw1CUDgspRJeVrU4KqsJte0e3PhBhEwiigduvIkSCMCxB2CYMlhAiSxiUEHIBAhekOCAIwzpCJKO7oCNsFwbLIBIIKUXgnuy+LomIRafJMoaibFlwBX6A63hdoHYtVpdrNOs2gR/SN5xj6MdUGPuNhBCCXDbOQ28/zOJSlU9/NnIOee3UHH4QYMgqfWYKqTPbJhMGE+MFzk4u4jg+p88s8A7H48hgXyRIrip4fsTcSJsGTcfFDwL+P/LeO8iy8zzz+30n3xy7b+cwOWcM4iAQBEiIAWACSYmSKJIrSlxZWnntdVlyqZbeku3y2rVi8MpLWqKWQSTBKFEkRYIkSABEmgBMzqFnOseb78nHf5zbt7unAwakXEXCb9VMdd8+9zv5C8/7vM/T1pyoZGNRCODC9PSSuu1cJkZfb47pmWrI8jk/xsH9g+j62tmoxVGrW2SzcXLZGOPjRTo704yNFpmdq5HLxpmcqjA3VyeZjNDWluDipQkymRjT0xWqNYtGw6ZetxlzS5iWi217GIZKLhcnlYxQrZooikxXZ3qZ9pBgqRvWq1VtqZJCXyyP6TqveXIuSaGGyWo6ULIih5nCmn9LWkAAY6UKNctmvFwJS8n8gI5kyI4wVJWaZVO1bRKGHoLIbVky6RgD/XluDM+GWfDTIzz84I41J0pBEHDx0kTL3a69PUlbW5L6VLHl7CdJYknphG27uK6HpinElQxxJbOszblincmpysJnhOL+Rd+k4thL2BJzxTpf/PJzXLk6xcYNBT764fvIZeOvCaQMgHws+qrfcQMfx/fxEU12w+oPhut5KLJMVLv1Z/5XPTJqjs3JnVTcEpsTu5CFjECQUFMUjC7kVcQ3b9Z4mv+9LbH2Im/edS0ZWTQxbn4lpkM2Fv49CHIIZTOIOEJKEU4lm++TFGu+wB5CyxM+TUvfdz8IuDw9Q0ciQWIFMdjFMVGp8ulnXuDPH7qfrtRSur3pupwam+S5a9f5q8d+Y4lr3K2EaE7DBSFru3ly6LJMfzxHtMm0szyXmmvRGUkSUzTajARjjVLoQNZsy/N9nGbWWZdU8nqMmKqT1qJUnJWZE17gM2lW6ImlqbkW93VsZF0iT06P8TsbDvLsxGWeHr/Euwb2klR1vMAnb8SIqzppLULZMdElhbQWIapo5PQYiiRTd21mrRqW76L7Cm/q3kpHZOm1y7Ql+MB/9xYefM9BpkbmMOs2QhIYUY18R5r2niyKKtOgzoQ1QZvejixkpswJKm6FjJpBFgqWZ1F1K9i+jS7pyJLMnDOLIeloUnj9fvaNFxnc0Uvf5pVLnefj9AsXmbw+zQOP37nsb0GzRFdeJSEiSYJc7mYjlwiu63FjeJYNG9rpKKRQVZlEUmOurJOORpgxbjDSOEchWE8k7YBSQhUhq7Xi3mDKusZgZh9FewZLqlLxGqDDtNtASbg41Ji0HCQh49kOqmTg4xGVU7c8fvx6RbCobBjEKn3Q4ggZGjcJVgOmZzeZfQIvsBaVTQkUKYZ8iwvw+dCk1JrsJ01OsS71m3TG3sBY/SlGaz+kYl/D8ctY3jSTjWeZNg+T0XewPvVbtEfvaR2DHwRcLxaZqtVpXxey8HwWmCEhWCCouTbnipNUHZuxWoWduQ5Ga2Uc3yOrR6k4Zqjbh8ALfBKqzt62bkoNE9f36UmnVgZ4mudVsWxGy2UajrPASldDvTDX90gbEWbqdWQhqDlOaHSgyNyzbuA1AUcEPvP9dgBMVCrN5P4v/0yHve48a9GjXre4dGGEfD6B2iz/VxUJ2/V+qb15ns/50SkkIbg+XWRDR5i49wO3yR5bu3Xf96nMVnGscJ7p+wG6ri7TGA3F0ZvndgvvQ2tbpPC+NkE507TRtPBersTGDoEu0fxZEIvoTBdrLY2jlfcBbXqG4fokM3aJvtjrCDiShODevgH2dXQRBKAroSuCJsu8YWAdd/cu1E1qsowqSTwwsI7bu3vC7RSZmBpmPNOGwUf2HKDhhi9WRFFRJYneZIo/vu1OLNdFlSXimoYkJHYXOtmwSOdIERIJTSMldP7V3rAdpakoD1APGrxt23pKfgWzEg7cNdei4pjs7MqTScqozcG8FjTIKVHev30XtU12y70tfG0E2qIJ3u4t3UzOVMj3tS0ZGGfGi0xPlAiCgNvu37oi2+ju/N5WNjsi67y792F0SUUSEr898FZc30WTNSBAk1Ta9Awb4j2t77+l815kSUYWEve338bB3E4kBIqktBa9K0VcifHBwUdRhIIqyXxw8FFUoaBJKm/ruh/Lt7FdjzOT03iNgOFSlYPZXezNbIUgQJVUIrJOQMD7+x5Bb040eqOdvK/vzShCIaeleVfvG7E8O6TcSTqKkGnXs/zuwNtx/LBkLSBoCX8JIUipcVJNR7h5YG3+uqqSQkqNtyjTaTXUSUnctB2EL918dEoqXuBh+Q4JJc5QdZaS3QgnaJKCG/hk4xp+EDBUn+ZKLUAiZGgltQjd0RQRZWn2/ub9AQhlE0JZR9i5Bs2nxQcCAr8I+IjAQYh4EzSCsEsPmt9plgwgIeS25mcSEh5BsLrA7OKoFuuk5OgSPZX5Y+7syzE7Wca2bq285vUekiToX+Qg5rgeEoKkalB1zKZOS1hStmN7D089c45Gw+H02RFGbsyxY9vysjIhxLIMuxACPwhoT8SWaCfF4wbbt3Zz6vQwtuPx0pGrPHj/VgYH2m55Qd/TleHUmRFGx4oUCimi0dBYIB7TuXJtirZ8gt7eLKfPjHDjxiwDfXkuXArL0BRFRmqKBiYSSXRdYf269pBK255keqbChvUF5op1dE1ZBmiFtdoL52NazppMNtf3mDYr2L7LOlYWhF0p5jNIhZ7sipoAEAKm1WINWZGXaaKsFu2JGGVVaWX9IppKOhJhaGaOgHB8Sko6cX1B9FBVZQ7sG2ixsM6cHWFyqkyhPbnqPStXTF4+fh3TchECtm3pwoio2I4bmkM0yz8SiYWF/+xcjXLFJJeNrdhuEISislPTC8CRH/iUmqLYrr+UmTU5WWZ4tAhAMhEhEV9wxbnVZ81vlqa/WnhBQNU1sf0AWUTXFJxEQFRVMdTXD3CkywYD0Y0oQmUgthH5F6Car1ZGOf+5F4STVEmI1j0JCCfNCNGSDJgvcQ9HGBVfFJCFIJAXnMnCdiQkaT5DvaDdJQVB600qNUz+4dQ5Htu5NcyYS1KryMf3w4KnxceDCPfveF7rcyEEA5k0D2wc5MTo+LJz9pv/Fm9/cwjCErgps0rRqhNRtGbyaelbr0ky7ZEkL88Oo0oyF8uT7Eh3Elc05qw6Y/US50oTzFkhoFtzLc6VJkhrEYZrc+zKhLoirh8y5r3Ax/N9VElmfSJPuxHnjrZBGp5DWotQdS0issYjPdv57o1TnJob5U3d24jIGmeK4xQiCcbrJfZku7lWmVl2XjFFoy+WoTeWYUemi5pjEVeWC64qqkzvhg56NyxfTAydG8Fu2Kzf08+u1J4WsNauF2iVY7EAnM33lV7gUnWrGIvYgdGEgXYLSYzpkVmGzo6s+DezZnH+yGU27V9H9BYdKCFkLQ3epMMoCZm01oGHiypFaNcHSantVJ0ZokoKy6vjBS45rQcJmbiSwW8KBVt+nYgUJ6N1UXanMZqMG9OrYigJNCmyalXC6yMkpHlWSUBTU2jtCF3qlo/nlueSVCPIQoSC1kJAEDIrBpPvXdNufaWQReQWSqUkomoXg8nH6Yo9yIz5CuO1nzJjHsH0ZvADixnzGHV3hO2BR2fsfiShNssupZZciSDsB+fDC0IncENWSaoGju/TE0+hywo5I0pUUWm4LrOWT0YP1xmSEGSbPyd0DV1WbmlclIQgoi4INkdVFVlIeIFPJhKh5thkIxGqlt1iN60l/bLCRUJIWUQTNBNAeyJO2QzNNVaLIPCWgGmrhY9LgNc8FxUhQuON+aEqIKBuOsyV66/ZPGbRKZBLRKmYFomITl9+ccJMUHYnKbB+7UYCaOvJLRozIZ2OIjXXjfOfS4tYVn5gv3rms7Wt2wJLJSGTSqRISGmSyVvr34SAjvzq88X5UITcrKT5l0ny/8oAR0HgIwmfpK4to48ldR1YnpUyFGXZwgrCiVFEVVsv1uKIr6BNFFVVoqtMNm/+m+27VFyTpK4zbVWbxx6QUA1USQYCVEWQUnWcwKVo1+mNZlc91sWRiBkkYivU+AtBvpAilY23akBvDmMREyhUbF948G4uqQKQBS19I4CIsrBfVVJISTdnblYO6aZ9LWY+GbKGIWvYkkdcrXFpZgaCOSzXZUvb0oFcsLQdRZJRpMiitvQV1eCjK5xbq801XqYlIrWr/LxSqJLCYBNwa7g2AXVs32OkXiQiq0RkjRmrSkckRdWx8AnQJAVZ+IzVS7QZcSKA6TVoeA0aXp12vYBykwuBECqwykRriWDgrdTSL2pH2YLARyA3J/vBqhkaVVeQV+D8bt03gKLKDF+epGfw9SuMHQQB0zNVkgkDTVPWvM6u63Pu4ljr9/Z8ElWW6dOWiv8KITh4YJDNmzp55XjozvbFrzzPv/nXD1FoTy1jmyzep227lCsmqVRkGaNAkgT337uZ51+8xKUrk1wdmuJLX32RP/xXD6wKGMxHw3So1yzS6SiH7t60ZL/tbWGWuqMj1fr8rjs2tH6+I7u+9fO992xuXbeuznTr840bFoCdgwcGVzwGSRJkMjFkWcLzfC5fmaRSNVulXDeHE3gU7RqGrL4mwALCPmtN1zkBnuOhvMo9b20uBOlmCU9vZumkNROLoEgSEVWBJcusMA7sG2BwIM/lK1NcH57lO997hfe++yDJFRZGruvxs2fOc+TYVQCymTj33bOJdDJCPhMjHgn7BUkSdHdmiEY16nWba9dnOH12hEN3bVqmNRQEAROTJf7xe68suSaeHzBnNehIJmgzlo4Fqiq3GGPnL4zxN59/hl3be0jEIyzuSmRZQtcUMukY6Uy0le2UhECRJGbqjTWmoPPXdn7REeD5RfzAQldWYS0EUDTNUMtgFQ2GX8fQZYN1sc1YvkndXwD9NUlHQSMIQpDH94MW+Oq6PkLQcsqaL23UNAVVDV3rHM/jmStDPHt1CNv16M2keP/eXQD84PxFToyOoysKh9YNcKC3iy8fO4Hr+0xWa6zPZ7kwOc27du+gbtu8dH0YgWCsXGFfTxeP7dyKJATfPnmWE2PjWK7Lrs4O3rV7OzO1Op8/8go/vnCZG8USHYk4j+7YypZCGz86f4mXrg9Tsx0Gcxnes3sHAA3b5evHTzNVq9GRiPP4np0UEmHi5+aFVhAEjJerPHH8JJOVGtlohMf37qQntXyCLYTgrvZ1fOv6cb505TCP9GynP56lL5YhoRpLtnt3/16+c+Mkf3fpBTYk27i7sB5JCDYm2/jK1aO0GXH253tRJJmdmS5qrsXnL73EhmQbu7LdjDfKfHPoOKONMj8aPcecVeetvTt47+B+vjt8is9dfJ5CJMljfbvxAp8fjpxhxq6TUHTual9HSjN4fHAf3x8+je17HGjrZ0uqg2uVWSKySm8sgyGr9ETTtBsJfmv9QX4wcpaXpofojWZ4tG8XN/cqruMxdnWS6lwNI6bTtb6AZqiMXp7gyS88g6zKmHWb7vUFMoUUZs2iOF0OhY0nSuQ6M7R1Z7Fth4mhaWrFOrF0lI6BNlRJxbEchs6OEE/Flhhs+H5AcbLExNA0ruuhKDJ9TU0+x3K4cvI6juXQua5AMhunVqpz/Omz/OzrL9CoWbR1Z1m3sw9JlqiXG4xemcDzfHo2dBBNRjDrFnMTJTzXp1qq0d6TI1NILXGO6zQ2ApBUwjmMFwSk1fAYNOEhSyJMGCttCAR5PRwLVREjpRVQhExSXboQez2Wpt0cYXlaOCYEeNh+6VW/E+C2XNgWR388R0YL5yeyiKBK8aaOXeg6VYgeWrNdzw9LtlRZbsGYAWHZVtWxiKlaCG6zAORZXmj6oMmhsHZP/M0Uooeo2lcZqf0zw9XvYXmzNNxxLhQ/S1ztJ6VvCsvApJCdP99WKAoe7tfypgnw0GSD/mScDSIsXfMDvznuhgyjrV4eXZbxAg9ZhHOMEISSma7V0FWZ9vjqzNB0xCC9iBG6jJkiBB3JxDLw4rU9mwJZ6Vn0e0BnMkEuFl0zMePj4Po19DXE0gFcv4bvh7V4sjCQ0Mlm460KnFC/R6Yzn/yFS9WEELQlYjiej90ki8yHKmlktZ5VGbvzYcR1tt6+ESHNJ1QCKhVzmfGMIeeZf8K8oIHjV1HlVxf0tr0Z5ueEktCIR7JcG66RzcRuydzGdjxujM2xrifHssndorB8m7Jba2nT/bLxKwMcuUGDWfMkihQja+xYM8sbBAGm59JwHCzPw2si2bKQUCUJTVaaCOxrE6gLggDL86g5Nnaz3flynrgaosGapLAvO7CqqKcQgm3BcvaA6boUrQaeH2AoCindaGXZIOwAS5ZJw3WRJUFc1YmpIcq9aVcvxZkqscTKwpF+EFC1LapOODmMq1qTTbVw7qbrUrYWbJUTmo4uy6/p+ri+T82xMV23pfofLopUYsrq9bMNxyFtGNzT398qi3q1CIIA03WpOjaO77WcUTQ5ZJa91mOfb9P2PeqOg+WFVtjzmU1FktFlmaiioki3pg0TUTTWJ9pYF8+zxSkQa2Url4cX+FRdi7gaLu4s36TkzNLwGuT11cGXoKm/1XAdTG9B32teSD6iqkQU9ZayFABCyASBROCN4HtXEXIHQtm44rbRuMHoyUliyciSxfmNSxNsPzDI7jvXthP+dQ/fD/jM3/4U3w/YtaOX7du6SaeiaJrcnICG9vRzxTrPPneBH/7oNBAurO++Y8OqA142E+e3Hr+D4eFZpmeqvHTkCn/5v/8Tb3xgG/v29BOL6UhSSIt1HI+5Yp0LF8c5dXYE23b5b/7gwZb1+eLo783x/sdv55P/+UeUyg2eevospVKNRx7exeZNHUQjGqLZrm17TM9UuHBxnOOnholFNX7rA3dRcm16MkkiqoLpuGiKgiyFLKcgCLBdj4imYjrukv5LksQSynSo9xNeQ0WWMR0HSQh0RcHxPAxVxXJdhBBEtfD53bG1mx/95DTlismZc6N849tHeezt+0jE9BZN2PN8XM9HjYYMPmkNWvqqIUJHJ01fpR8KQpAmqJpLNAhuqemb3sPksjKcpX/v6kjz7sdu4zN/+1PminW++Q9HmSvWeOxt+yi0JZFlKVxkles8+/OLPPHNw9TqNoos8cjDO9m7ux9FlbAdj2rdah3Dpo0FenuynL8wjmk6fO4LzwKwfWs3hq4QEAKR5y6M861/PMqZs6Nomoxtz2e/Ql2XmmOT05eCbD3dWe69ZxP/9P3jVGsWT3zjME984/Cya6EqMtGoRldnmtv2D/LQG7bT3Z1BEOovWO7amcmQAWoQVwyiisysPbMm40hXFFKvUvb06xiu73Km/DKnSkdxA5f5ieb2+AGk653Ytouuq+EcoljHMFTicYNEMkK9btGo28RiOkIS1KoWPb1Z2tuTHB8d52vHT/GB/bvpz2ZwPI+IqvKtk2c4NjzKR+44wFStxpeOvEI6YnBttsju7g5GShXGylW2Ftr5+dUhulNJDl8f5r9/4BCqLPPpZ19gU1uOXV0dbCnk2d/bRdWy+eQzz7O/t4v1uSzv3LWNa7NzfOj2/Qxm00RUFQEM5DIM5rIEBHzy6edZn8+yPpelYln0ZlI8umMrXzz6Ct85fY6P3HFgxetVs20+d/gYHYk4j+3cxo8uXOYLR17mT+65Kyy/dj1kScIPAlzPJxEYPBLZTmc+hQgEUzNV3tK2g3LVZNQp4Xk+mqrQnojzsS2Hlr3jv73h9mXH8HsbF0qt5ueKndEUH9sSLoQXt9Efz/KxLcv1nz665RBB4FN3pxHCx3Sn6Y+rfHTLLvzARREGblDkbX39QMB7BjYS4PL+dXuRRegEtzm1Ohsz8ANOP3eep554nkJ/Gwh48H13ke/OMXF9mqGzI0QSBpePD5FIx8gUUoxcnuDz/+GbbNjTjyxLbNw7QL4rQ3GyzLPfPkwQwNDZYQ694zYOPXYQz/OZvDHDd//mJ7zrjx9h3xtCIHBqeIZvfPL7tPXmOPH0WdJtST78H95LEAScP3IFVVepVxqomsIH//17qFcaXDszzNi1Ka6dHsasWQxs78Eybb7+ye9TrzRChm5U511//GYmrk/zmf/xywxu70U1FIqTZT7wZ++g0LegMblE3Na1uVSaxgl86q7NeL1CXNHwgoBCNIEuyZRsE5+AmmMjiwoH2/tazJH/P4UkNGJqL9Pm4bDMyBnFD9yQMbRKuH4Ny1vq4jtr15holMlqcbJ6DFWKEVN6abjjeEGDqjNEEHiINViWFcfi7OwkUVXD8TwmGlVyejRkETZqNDyHnBEloYb3cqRaRpVl9uQ76YgtjCWqFCNj7CClbyGj7+b07P9Jwx2n6gwx2XiWlB7OkQ1VIaHrrWlBQhtsadTUnVFMd4q4FuN67XKzXY2GVyciR/ECF9u3sf1wnI4rCWpeFV0y6I2uQxdRNEW+ZVfQIPDC6gMhATIEDqG8RRVElMCfRpLy+EENSSRAyv/CwOaCduPajFevKXAeU3tW3SYIfEx3ArcJJBpyjlSkjeEJG9ddmH94ns/kbJWtA82qjNd4zEEQUKybpKMGurr02fQCj4ozRU7vRRarA2FCCCR14ZwVWaJet5EkQW/PgjFSXBtAFjpu4OL4NWruDaJq56seY9m+3KoU0uUcSpDHtoqtedirnCGqIhGLrp0kC4C6axJ5FYfC1xK/MsCRLLTwQQpWXwgEQUDFtjg6McrhsREuzs0wUi1TbdaPRxWVfDRGdzzJtlwbW3PtbMnmSeqrO3XMh+m6nJ6e4Onha5yYHGesVqHuOOiKQkcszt72Lu7q7mNPe2eIOK/yAnq+z9PD15hp1OmMJbizu49Zs87Xzp/m+1cuULEtBlIZ3rN5B2/oG8RQVCzP5dnhIb589gSXi7PEVJXbOnp4fMtONmfzFHqyFJoP6ZzZ4JnhIWzP5UBHN/3JNBdmp/nbk0c5PjUOAewrdPHuzdvZVwgzsyXb4olzJ/nhtUvMNOp0xOIc6hng3Zt30B59dVTTDwLGqhV+duMqRydGuFycY84MX/qsEWFzNs++Qhf39Q7SHl3Obrg4M0PZspip19ne3s629jBLc25mirOzU3i+z+72Ttans0gizBacnJrgR0OXODE1zkStiu17xBSNrniC7fkCBzt7ONjZjYKEYy9Y/K4U80DjyxOjHJsY4/T0JMOVEhXbwg8CDEUlYxj0JFJsybaxKZtjZ75Axoisep9N1+G5kRvMmqu7OmWNKPf1hnXFipBJawuTjISSamoDVFcEIf0gYNasc2JqgpNT41ycm+FGeeGYI6pKLhJlQzrHwc5u9he6aY/Fl3Suq5XBQZgBwi+BvHoHL6tyEzRa+vncdIXnnzxFLBlhw/Yecr+AXe+vQwRBwORUhROnhnn+xcskEgZt+QRtbQmiET0cmEp1rt+YaWkLyZLgwL4B9u8dWLVdSRLs3tXL4+86yFe+9iKzczVOnx3hytUpMuko+XwCTZOxLJdyuUGlGuoHNUyH9YNtq7JgFUXm7js3MnR9hie+eRjTdDj6yhDnL06QTkXJ5cKSMstyKZbq1Jq6RKblsG9PP3Xb4cTYGEOzcyQMHcfz8PyAdfkMpYZFzbKpmBYbCzlOjU4iCVpg8bp8lqGZIumoQcN2MFSVmWoNx/fJRCPENJWorjFbq1O3HZKGznStTlRVObRxAF1R2LG9m82bOjh89Bqm6fDtfzrGqTPD9PbkUBQJqyn6XatZfPTD97NlSze3xrhbfl9r5QZWw4FVEmP5rixtfbn/z+sNhBDcc9dGxidLfPXrL9EwHX7449O8cuIG6wbbSMQNGqbN0PUZxsdLNEwHSRLs3zvAWx/Zja6HmljxqE4utdD3ZtIxHrx/G1euTuE4HkPXp/mrT/+Qwf42OgpJXC9gcqrEtaEZypUG/X051g208cxzF7FtF0WS2JYpYBhLQemQ+eOzYbCdfDbO8OjcqufmuB6lcoNSucGVa1OcuzDGn/7Rw3QUUkRUNXTsWoPSLSHYnu5q7tdBCHnNsoiYpnJ7fy/aKqzcX9cw/TqnSy+zK30bbfrCZFQPopyqTRCJaESjGr4ftNhgiYSB6/k4dmhsoKgLiRbbCgG7sxNTrMtlONjf23JnDIKAC1PT7OgssCGfpTOZ4MlEnKuzcyiSxMZ8jmLDJBuN0JlMMFEJx6+edIqdXR0EQUB3KsmNYoldXR3ENZ2L0zPULBvLdSmbNoosk9B1VFkmoWskjWapI5DQdS5OzVBsNHA8j2JTITYVMbitt5vedIo93Z28NDS8KtOwatmcHZ8koWscvTFCuWFyfnKay8PTjE+WkSRB1NCoNiw8z8doalYMjc1RyCYwdIWZYo2h0Tk68gniUZ2xqTJ37B6gM7/6WBf4Ac68JkezlCEIAmzLxfd9dEPDtkNtDUWR8T2fSDRMEqyVVTe9Em5gUnFGkISKIgwUScf0ishCJyrnqbrjuIGJjEJHdB9RJbdqe63jDQJmxooouso9jx4g25nBiGoIAbvv28bJZ8+R787y5t+9D7EomWbWTA694za61hUQkkBIglxXhkf/4CEUTeEHn3+a3vjerQAAIABJREFUKydvcM+jt6FHNO586z5e+dmZJfsevjiG53o8+gcPkc4nmRiaIt1kt6bbk7zrj9+MWbP5zJ99mfJMhXxXltsf2cPIpXEe/u1DpNtSSLLg6qlhxq9N8nsffxxFlfl//vyrDJ0bxWguph7+nUPku7L8zf/0VUYujy8BjhaHQCAJCc9z0SSZQiRBQEBUVsnpoRbbvJ6ULinostJyaPaDgKpjk9R0LM+lbFvkjOgtJ/N+3UIWGnF1oPV73R3F8qaJrCG6a3slGu7Yks+SSoRdmd7WvFiVkiT1zUybRwjwKNlnsf0SupxdqUkADFklrukkVJ2pRo2UZpAxItiex/p0llmzTkzViTfNHVzfxw0W3LVvDkkotEVuJ2/cxo3qd/ADi7o7HgpaA5brtsTwAZLaZmRh4AYhMDZrHSem9pHWcphenaIzi+nVSatZ3MBBEhKqpKFLBkpTSkSXDCJyBM+HuKYvAzlWiyBoYFs/RogYIEAoSCJBEJj4/ji+X0JV9+J510EYaPq9QHxNIO6XDcevUnWukTP2r75GDhqU7PPMJ0AMpY2ImsXz5pibq5FIRhACdF3B0BfPP5ZiA/NudqtFuWFxfmyK7kwSWZLozy+4+klCxgmsFcsn14oggFRqORs8IheIqb2U7HM4XpmSdY68cWBNvS0/cJizTjCvDZjUNlEsus3Ej34L81qBabnL3HCXbwVJLU5GSyypTPpl4lcGOJqXKVyJaRQAtuvy1PUrfOnscV6ZHKdir2LTODvdbA2640k+fs+DPNi/eh1jEARcKc3y1y+/xNPD15iq15bR5y/OzfDs8BBfOnucN/av52N7bqc3mVpxYHADn08fe4GjE6Pc1tFNbzLFZ44f5onzp7C98AZfLc1xcmqc6f138t7NO/na+VN86tgLTNQXrA9PT09ybGKUvzz0EDvaFrJGw5Uyf/HsjyhaJv/u4CHe2L+eP3/mSY5OjLa2uTA3zeGJEf7TA4/Qk0jxiaPP8fdnTmD7C/s/Mj7C5eIs//7uN5DQVkcia47Ndy+f5wunX+Hc7BTOTULJQ+UiL0+O8c0LZ9jR1s6/3nsH93T3Lyml2ZzPUzRN+tNp0sYCiPfU9St84ujzmJ7Lv9l/F3+45yBVx+azx4/w7UtnGK8ttYIEODs7xY+vX+Ht67ewUUty4/gNdENl56GtKx6/43kcnRjl86df5vmR68xZ5orbAbw4NtzMIuj8xV0P8M5N21ddN5Zti08ee45XJsdX2QL2tndyd3dfi4q6uCPwAg/btzDkyLLSzJlGnb87dYyf3rjK1eIcVWd1TaJnh4d44txJtuTa+JP9d3KoZwABDN0IF4Qb1hWIGCqBH+D7AZIshYzG5gJMyKuj4o7losgShd6l7hLpfIJKsb52uc/rIIQQLRCn3rCpN2wmJsurbp9ORbn/0Gbe/Y7byL5KeZimKjz21r30dGf4ytdf4vyFsdY+RsaKKxxLqGPU051BU1cf+A1d5Tcfv4NCe5LvfP94q+SrUjW5MTK7bHshwuPu7sqgKBL92dAJ5vz4NAcGujlybRhdUSjWG3Snk0xVa1ydnsP1PGK6xnipgtdcMCEE46UKcUNnW2cbz1yqk9A1qlYoCj1dqeH5AevbcpwZm2CsWOG2wd4W+JTPJfjw795Lo+Fw9vwYluVy+uwop8+OLjlmVZWxLOcXHgSFEOjNd2K1KM9UqJVq7Lx325ptFYt1qlWzmYWSaDRCdke10gAhUBSZSESjOFejrz+H1RzoGw0bx/HIZmP09+d577sOkklF+do3DzM+WWJ0rMjoCs9BNhPjvns285533kZHISyL8zyfiZkKDdOhIxdqtQkheMubdlEq1/nBk6eYnqkyV6wzVxxa0p6qyGzZ1MlHPngvpuXw0tGr2HY4MV6iMUPIHrt4eYIvP/EiR16+iuf6rB9sp6cnQzymL9HHcV2PcsVkarrC8MgsjYbD4aNX+fq3jvB7HzzElZlZ2hPxNXUXhBDo8rwTjUAWyaaWwMrfkSSJiPYvQ8X+VYogCNBlg57IACk1u0SjaP+BKMYadsz9/eG285fZbNgtEEmWxIoinLIktRjFASErRxbhuCFLEnKz1HDxXG1xO54fIEsS5yen+czzh7l3/QDpSMgODG6eXS36daRY4q+efo5dnR30ZdIostwCyX1/QSvJ9f01gZb57Hj4PAr6sxl2dXXQ15GhI5tA15SQveiHbGNZbgJqjkfU0JAkQXs2wab+dhRFQpYkNg8USMQW5klnL47zX77wM2zbI52K8KH3300mHuH40WsoqkIspoEQxOI6czM16jWLnv4ckiRwbA+rqQ0oCcH6TR3Em2zyhmnz2S8+w7lLEwC86YFtPHjfADFNkFC7kITc1JGScX0TRQq/F1Pbmu2pqFK4EPcCh4Y7GwrdK7lljBAhCQ48vIvSTIUn/tN36d7QwVs+8gYS6RiyLBCShCSJZfIIbT1ZsoX0kmTd5NA0L3zvZUrTFa6cuk7XukLrXqwUuY40Vt3m5Z+cZuTSOP3NMjUhBJ2D7SQycbRImNm3TAchiebcRSDLoXkBQGmmwpWTN/jCX34LWZaoFms0Kg2MqEZbT5ZcZ4ZYMoIe0zEXuRWbpsOnP/cUV4bC9cJbH9rJw/dtW2RWM78Ym+eW+PTHFIQwWOh/AvyggenK/N8nX2BHroAqyVwuzfBAz3o2Z16fZfwChYyxE03KYPtzVJ1rTJvH6Im9aUVQIgiCpmbQUuAooqgktYUyaEnSKETuZqT6z1jeNLPmCcbrT9MXf/uqC3BDUdiRDZ+1/mTIZl2cMF2XzLZ+BuiNr61/BE1nsBY7Q7TcvxCi6bi9wLROqIOkje1MN17CDWoMlb9FRt9JwViH6zu0G12hy6Acbc3zRdPoYek1FUhSwKb23C1XPAiho+qHEOgEWAhUwmfTI2BeciCCpAwghIFYY+z8lwovaDBZ/zmdsQdXLVerONeYbLwAhOy1nLEPu6FSq1mtfhBCbe7pYhWvOU+TpciCFT0utrd8Prs4dEVmc2eedCyUEVh8TVWhE5MzawI7K4WiSGza2LGsDNBQ2miP3k3ZvoSPzWjtx3TGHiCmruySGwQ+M+Yx5qyT4bmJKJ2xB8hlC/R2BShrzPHnww8CKnUT016buR0QVrxk9RTKvxBo+CsDHPmBiyLF0VbQ1qk7Nn9/5gT/18svUFy08Nea9aZKs2TCa5aaWZ6LHwQkdYP16dXRaj8IuDA3zV88+2NeGhsGwoE8oWrEVI24plJ3HKq2TdWxmWnUeeLcSUYqZf7d7YfYkS+smVUYr1X52vlTfPfKBdK6QVRVmarXqDkO0406Xzj9CoVonM+cOEzJMulNpAgImKrXsDyPU9OTfPnsCT6ee3CJEO78Hl+eHOVGpcS52anWdydqVRzf59LcDF88c5xtuXa+eeEMEUWhO5Kk5thMN+o4vs8Prl7kzYMbebB//Yo6Aabr8pnjh/ncyWOUbQtByOqKaxpJTccLQnvKim1h+x7HJsb486ef5I/23cH7tu5qHbPn+zx//Topw2BrWxvZyHImz2S9xpzZ4K9feYm/P3s8tK9WVDRZRhahLLTjeZhuWCKzLd9OKh5lOqajR/UVs4+O5/Gti2f4xNHnGakuLPhVSSIyX5LWrDt2fB/TdfCCAE2W2ZTNr9nFykKiLRojH4niNbPwfhDa+Ho3dSoly+RGtUh/MkNKCzvGOWeGCWuMdr1zGVg6Zzb4zqVzXCuHC0dFkogoChFZJaZpqJIUXnfHou441F2HYxOjfPznP+GvHnwLO/IFIobG8Ohca3E8PVbk+sVx1m3rJtOeQEgxkAusNZBIssTcZJnAD+joX6C5yrLE1XNjCAG969pft4wjSRJ89EP3ccdt67h4aYKhGzPMzFap121s2w0XqhGVfFuC9YNt7N3Tz87tPaEjV+C3nBIgXOzMAyRycwIiZMGevX1s2tjBkaNXOX12lMtXJ5mdrYUuWKpCMmnQ1pZkw7p2tmzqYMumzlcVztN1lUce3slt+wd58fAVTpwa5sbwLHPFWlh2oSmk01EK7Uk2DLazfVs3G9YXkFRBtJmpLSTjlOomD25Zz0ytQdLQSEcjyJJEZyrBZKVKXNfY1J4nIFyENWyHuKETURUimsrO7gKyJGE5LrbnoSsySSN0shkvRREIZqp1nHnbWAk2bcjyP/zbQzzz8xAwGh6Zo163kRUpZNXk4vT15ujqXHlSMtCf5/c/fD+2FWZuMqnlpQSSJEhkoihaOIj29Wb5k489BEAirhOP6cxIElbdxrXdltaRJAn27elvbduWT+A6HhPjZQxDwTA0NE3Btl1mZ2soikxvbxan6QRkWS7j40VURaba1JSq10JQWJEEb7x/Gzu2dfPS0au8cuIGo2NzmKaDpim0tyfZuL7AXbevZ9uWriWuaQHQ15FZsrCF0J76d95/N/v3DvDz5y5y4dIEs3M1bNslElHp7sxwYN8Ad96+gc6OFKVygw994B5My2Hnjt4lVtgAY+NFPvXXP+L02RGSyQgfeN+dPPSG7aRT0eXaSc3/anWL737/OJ/7wrOhaPvRq7z9bXvJRiOYjkcARCMqj7/rILOzNQC2bl4JzA4FWb0gtL3VdZXfft+dlCsmkiTYsuJ3fr1j0hzjUvUMXuBScYp8b+xr9EQHWnp4PdFB+qLrXlObxqLF/r6ebp67doNvnzxLfzaN43ns7e7kjv5e/un0OX5y8Qoz9ToVy2Jze55jwyuLFgNcm53j+2cvIkuCiUqFTW05Jqs1LM+lN51ioloN+7z541AU4prG80M3qFgWfZk0NdumbIYlaV7g07Ad5vGhqm3z3TMX2NHZzvNXr3PHQC8BMFurM1wsUbVthuaKBASkDJ27B/upWCZdyQSm6xLXNZIxAxELF5GLwbKbxcIBDG1Bk6wl7r1I+qBaMzl1fhTLcsln49TrNul4hEQmSiYTIxIJGWBChMxd3w+IxUITkEh0YcQvFetI8iIAzgu4cn2ak+fCa717ew+yMFqmIYsjBIjC76ostmkOPyvZNyjZwxhyioicXrGUyIhqPPLB+5mdLPH3/+u3uXDkCvvfuBMg7LNMB8d2kWWpZcoiSdKy9/3JLz2DZmg8+ocP8ZOvPsfseOmmaxq0GFhCCNp6c8RSUa6dGWbnPZvZfuemVluLgapgkW2dJEn4vo9tObiOi6zIJDIx1u3o4x0fe5hYKorv+aTbk4xcGg+BpkXzm8WzMs8PuHxtilPnw6TEwb0DIatECIKggeNcJggq+IFFEDSQpEyoC4mNJKXxvEmE0MISILGRjliCGbOO6/t0xJKU7NUTlL/uIYQgpW0iY+xkov40fmBxpfTlUAtI27IkEeoHLhX7ElfKX4FXYYgIBFljD53RB7hW+QZe0ODC3GdRpSR54wCqFF+y0A/dOT28wMTyZqg5w2SN3S39pfljBbC9MjPmMVL6ZnQp03JAWxzz7RWtM0w3jgKhSHdCXYcQAs/3mWuYVG2rBSfqcpbe+FspW+ex/RJz1knOzH6Crdk/Iqb0YkhL1zpB4OMFNq5fxQlqRJXOFhgihGixL9cK23OwfBtZyLhBonksizRum8BU1a2TVA0sL3xfsysQBMJz9pfem6aQefiusuw6vVpMNV7kRuU7DCTfgyyMJf2r5U1xufh56k7Yv0WVbgrR+1C1KDt29pJYpO0Yj+r0FjKtvkYREaJKF3V3FNevM1l/nkL0XhSxcpI2oqns6O1Y8W+GnKA/Ni+8HoRGDvhLAL2QjRQsWVsKIVDk5e0JFHrib2ay/nNK9jmK1mkuFv8rWzJ/iH4TQOUHLiX7PBeKf4PlzSKQaI/cQUbfGVanrND+amHoKqry6mZHDdfkYuU6aTWxzBn9F4lfGeBIEgp+YNFwG6T1hUHE8T2+d+UCnzl+uAUaqZLE7vZO7ujsZXd7B/lI6DA0ZzW4UpzlzPQkp6YneaBvkEJsdZHn8VqFTx19gSPj4UOsyzL39w7y6MatbMrk0WUFx/e4VJzlHy6e4Wc3rlF1bJ4fvc4njj7HXx56aEmd7M0xWQ+Bo4OdPXxk1wFyRoQfXrvEp4+9QNWxuVKc5b8cf4mSZfGhXft5x8Zt2J7H3506xjfOn8YLfI5PjTPdqC3Zz/yj/dLYMBFF5fd27OexjVtxfZ9Pv/wC3718ngD40dBlXpkcI2NE+Le33c2utg5GqxU+/txPOD87TdWxOTw+wt3d/cvEwb3A58mhS3z+9Cst0GhPeydv37CVg53dJDWDgIDJeo0fXL3I966cZ6RaYbxe5bMnjrA9386e9s5W9jsUQ62vKoY6Vivz7Utn+ebF0xiKyoFCF/f1DbIxkyepaZiey/VyieOTYwyViuwvdGE3bErTFZK55a16vs+LY8N86tgLLdBIFoKNmRz39Aywv9BFWzSGISuUbJPhSpkTU+OcmpqgL5lmMJVZE/VP6wb/891vpGyblCyLomlSsS3+9uRRTs9MLrmOY/UyE40qnbEFgCWtZvEMl7iSXKbT0ptIcahngOmLZ9iYybGn0Mme9k42pnPEtdAZpWrbvDwxylfOneDEVJidvFqa41sXzrAulUWWRdOdJgw9ojI7UaJ/UycgCAKLIDDXdowKAjJtyWVlgGbdJt+ZQje01XViXgchhKC9LckbH9jGobs2UW/YOI6L6/lNQE5gBx4XijP05zNMWw0ul2aZM03WZ7KYrovpuuiKzES1Rk8ySdmyiGkq1+aKRDUV03XZXejg4Qd3hPuo29iO2xqsVFVG0xRiUR1lFRvi5ccdCli2tyX5jTft4v5DW2iYIcMlCAIkSaAqCrquEI1qSwCCaNM4IAiA5nvVs0jwub+Jw7c3bb1vXnQtPr6B3OoCiVs627g0OUM6Mm8qEO6zaj1LoP8j73z04/zGw7tpmDae5zczzRKaphAx1GWubPNRaE/w5ocGkUSE1WxR9YjGxh29qLrSusfvePu+1t9t08GI6dimzdxEiXxPtgkcSWxYX2DD+gUGqG27xBNG697MO0kNDoZudooiheVdno8sS6FQuRRut9hF7uTRa1y7OME7f+du+vvyvPmhnZimg99kV2iaSiymYejqsmcgoqts7FvIbs+XxyiqjK4r7N3Vx5ZNndRqFo7j4fvhsRiGSiJuIDcz+clEhKQsk4zKK7r8vXD4CucvhrT92w+s4y1v3t1yVFstkokIh+7exD8/eYqhGzPU6xbT0xWyfSkqTXMJXVe5rymsvlaocg45CJ87TVN48IG12WC/7uH4NmUnTB60GZ14gUvNrbb6bMv75RanG/JZfve2vTx/7TrXZufoz6ZbwBEEHL0xiqEq/PaBPfSmU2zraCcVMRjMZYhrOqmIzsa2HH4Q0JVKMFmtMlWt8c5d2+nPZmhPxLm9r5enLl2lP5Pmrds2k42GQG7S0HnHrm08c/kaZdMkFTHoy6R5ePMGjlwfQdEk7lzfS286RVRTeXTHFjRZ4cWhG2ws5Di0fgDLdXn6yjXOjE9SSMT453MX2VZo423bt/Cu3dv44flLPH3lGposc2hd/xLW74oOayt8VnMcTs2F4MLmVDvpVTRtfAIsJcDKy1gxCeQAVZZDUDyqhxqXgYMg1IsbzGaIqipthSTqGtnlAJeKfR1HqMgiguXNIQkVWdJajlZeYIcacIHX1InpRBY68xbNKy2SIXSPPP70OS69cg1FlZFkiWzHQjlH7+YuXvjuywieYt8bttO7eRVReiDVlmTozDDPfPtwKIadDt/TqeFZzh25zPVzY2hN/bEdd23GtT3KMxX6tnRh1W3Grk7Sv3X1snmAZDaOoir883/9GYPbe7jrbfvp29xF17p2fvq1F0hm40iKxBvff3fzG78YuyIcylyCwEUIhcC3CYIaQkoReFUQsSZDJINAQZFksnqUOauOJARzZp3N6ZVL4l4vIYso/YnHmDWP4/glStYZTk3/R/qT7ySlbUISOq5fo2ifZbjyXarOVTQpi+MX1ywxkoXBYOq9zFknKdnnqbsjnJz+32iL3EF79E4MuS28J4GL41cx3QnmrNMUrTNocor97f/LEuBoPmxvjrOzn0aWDPLGflL6ZiJKJ6qUCB0icXG8MmXnIkPlb1F1QmZuXBskZ4RzA9t1sVyXjnii9WQJIdERvZdZ8xWuV/6BAI+J+rM03HEK0XtJ69tClzch4fsmpjdD1bnGrHmcmNrDtuyfoMmvzoJaHBPWDMP1MXwC6m6DgICEEiOhxpi2iji+Q8HIU/dMGm4DTVZp13Nk1ASmN03dHcH3LbzAxg8sHL/GjPlyq33Tm2G4+j00OR0KVwsNWWgoUpyEOogsrT7mKyKKHzhcLP4dtjdHW+R2dCV8F0x3ihvV7zBWf4oAFwmVnvhvkFAHkHWd2E2JL02RSUT1VsJVleJkjX1Mm0cJ8Biv/5RoqZtC5G5UKSwt9QML168jhERa377AFlsUIZh5Bccv4wc2XmDhBSaOV8JyFxwqZ81XkIWBIsWQhR72u0LHUNqJKp1L2HVCCOLqAOtS7+PkzP+B61cZrv4Trl+lK/ZGYmpP+E4EdUrWOYbK32iW60FM7WUw9T5UKfGaZBdkSbCpv53Bbm9NAosgNJfSJO31xzgCiCgFFLH0oTwzPcV/fvlFphphNjKp6Xx4535+c9tucpHldcT39gzg+T4T9Sq6rBBRVl5guL7P3589wY+GLuMHATFV5QPb9vCxvbeT1BbEWAHWpbPc0dnLV8+d4FPHXqBsW/z0+jW+ePo4f3rgrlVFoS3PI6Ko/On+u9ica0MAv7VtNy+ODfPU9St4QcCxiTHev3UXH919GyndIAgCPrBtN0/fuMZEvcpotcx4rboiQFW2LA4Uuvn93QdaOk6/v/s2XhobZrJeY6ZRp+7Y/Pkd9/PW9VuQhKA/mebRDVv5xNHnsbxQ16nhOsuAowuzM/z1yy8xZzaQhOC+3kE+fvcb6EksLdHrS6bZ3dbBnV29/NkzTzJeqzJULvLZE0f4j/e/mZiqocoyOztC/YObHe3m48j4KC9PjJHUDP5k/510xOPkozG2ZNpaXkQHCt08tnErc6ZJUtOxKyaqrqJqyxdTI9Uynzr2PDcqYebLkBV+c9tu3lrYQMySkB2J2oiFEZeISgZpS2JXJsO03o/je4xdmqEY1XBsl2KxTiSihcLUAciywHV9du7tp5Ol9+XJoUs3AUcBiiSRN2Jk9KVOCJPWBGW3zGBsA5pYuC66ovChnft50+BGNmfz5CLRJVOg+XPdks2zp72T//ap73NudooAeOrGVT6wfQ8RR8K23SaIENLOhSSa6H0zjefXWKBkLw9Jlkjm4niut+T6SpKgPFtDlhuvWl/7egghBIahrlgSUnccJmkw55iYrkMuEiGqqpiey3S9RlTVsFyP9liMpK5zrTiH7YU6Eo7vIxBU7DBjkItFlw2ev2zMW7MnVhHWXy1ea6bpteoMtcVjtK3gHCJQkKQYmqpi6FHSvDbxUdefYbryN+Tiv4O2ivuWEILIGtdZM1S2HLw14XdNU5awf14tVgO8GnWb0ly9BRbeiqPGamHWbZ558jR771hPW0cKIQTRiEZ0DR24+ahVVgcjTpy60Xrf9+3p/3/Je/MgSa77vvPz8s6srLPvu3t67gvA4CZIgARBgDcpkjBBEqYuSwpJtnatlRzW2rGxDtm7lmUpVg45vCtLQVkiJYqHSIEXeAPkgODgmhlg7pmes6fvo+6qPN/+kdXV3TPdMwNKDpP0D4EAqivr5cvMl5nvfX/f3/d7U1HGlTBNvc1ki2JJFEkUBPPVWpvNcbMQQsFeo6vxP0MMOCMMOCNEMqIZ1Ulpa5JHUuLFDUrBMq6Wvqk7zJFjV/jqd44xMljgQ+85QCQSY4hdfZ3s6e/Cj0MMRUMTKgJ4w5Zh7h0bRBMq9cgjFhHv27eTUMZ0pYcQJA6jO7q7ePrkGQqOwy/ce+e6faZNk4/ffUf7c9Fr8NzMJU5UZxnPFFiQVR7YPZwk0eqLLEY13rx9jNFKjmNLM4xmCuQcixcXJ3nDtmEulJfYPbyD08vzHC/O0mHZPLhtlNHeHNXAQ1cS04+vXDrFtmwnHZ02O4c62Znrbms4AZQrDT7z1EvMLVb42AfuZWRwcz0giaToNwjimH7HI2tsrHsoJczWqsTAXKVKIwzJWiYVz6PHTTNdqaAIQdYyqfkBI/lsCxC+2bNDIIloRhVUYVHyz5HS+iBKSjfqwQyaYqEInTBuEihVTDWPiomppKmF8/hxlbwxAqy/XxVVYettI9gpkziOeeC9d9E71oWUksvnZukd72XXAzvxmwETp2ewMg6eH/HGD97L2RNT5DpcNF3Fa4aMHxjHybkYpsY977mbZt3j5CuXcFIG2Y40/+ifvwsAp8WWPfLsCTr6C2i6xuL0Mj/48it87F++j/1v3Mm220cB0HWNJ377nRR600gZku1y+Mi/fAdzlxdxsy4SHzOl8TP/7G1cOTNNs+aR78ljpy36xrr54D97O07GQgIPP/lGuntv7PTUPi+KjWHc1v4cyyYCFdFehK69/pJYwr7OHmZqVSQxtl6lYNfxojqGat84OfcTGkIIuu03sDX7JBOlT+LHJZa8IxTnj2OqHcl4lA38KNHA63UeosO6g9PF/0oQb17uL4QgrY+zv/N3OL38/zLfeIlmNMeV6lNMVr+CpjgItMSlLW4Ss6p5lxU7N21XIgniCpXgHEXvGAIFXckk7QkNKSPCuIYfl0nYNwJXH2Fn/ldJ6cNAMi8fyGSYq1XXeWboSpad+V8jlhHTtW8Ryhol/zQl/3QLdLERKAmoIRuspP51xb2pTs91xyElHXoO0zWohw282Kdg5FBRESh0GZ34UYCrpYiJkMToSqITFBNytfp1zhb/jFj6xDIgZtVsYSXq4SSvLf5ecj1QUYSOInQcbYAD3f+OjLG59EvO3INrjDFZ+TJnS3/Oxcr97ExIAAAgAElEQVRn0ZUsIPHjcssxL5nn9buPMpr54IZAlBCCXNomt4aBpAiTIfddzNUPUvRPEMQVTi//CRfLn0MTDsm96BPKJhljnHt6/h8M9fr5VhBXeG3xP1D2zyTnQPrIDdZBV2tPc7X2dLJvdETrPAy672RX4dfRxXqAUhE6A6l34EdlJkp/STOa52rtaabr322x3PQWO26ptT9BWt/C7sL/Qod1x+ueQwsh0LVVh9sbhURSDCr48eYaka8nfoyAI4EXLeFJcPT+pPQsjvnC2eNcKa9f/P/i/rs2BSAgqcPvd28gYiglV8pFvjpxGq8ldPamwVF+Yd+dbdAojGIuXV4kn3Mo5FOkDYMndu3n1NICnz+TsIG+PHGK92/bxXiusOlF39PRzXAm137IOJrO3b0DfPfyeQBsTeOhwVEyLRqhEILBdJZOx2G2XqXseZQ9bz1drtWWpWnc3TdI2lgt1Rp0M2zPdzJXT4C2FYHuFbBHCMHuzm5MVcWLwkR4eo1IqRCCIIr48sQpzhUT9LXfTfNrt9/DUDq74XHqqsqDQ2O8Y2w7nzj2CgAvTl/l6NwM9/cPcWF5mcNTU3Q4DoaqUrCvL7Wp+B6WqvFb97yJd4/v4NDMFSarJbbnOlEEXG0sJTbfdoFOO1lM1ryQKIpJXVOOEkvJ1y+c5bUWE0cVgsfGtvEbB+6jMltDs1SmJ5epVBrt7Hy5VMdJmYgoJqz7OH0GzUaS8afFGLhycYGUa2GaWpu2fbNIaq4hjKN1j2dd0cm3rNoVFOpNP7FVbtG5U1JnX74HVQjqDR9VUViu1Ek7FkKAaWhoqsrOji7euWU7F0rLeFFIqdnkYmmZPXYnYRi3wc/ACyj0ZNHaFHwdNslErkSj2sSsK9dt0mz4dPZm0Q0NrUXD/1EtM3/Sw9Y07urvT8rSSMoZViYVY7nViepKtvuNwyMJMwWZUIql3DBbkFCIA6T0ScA9taXvorWYLTGxbKAIAykDJCECHSFMhFBav/eTQnGhImWi76AIa00bEgiJpY8irISFRkhSFuS06bXJvppA2OqH1c623KwfK8cSyzpC6K2MexOQCGG2jgmkDIllDdvYh23sRYj1z4hbOR9SNmn6J6l6h8g670KJUwgUFJFadyzJcQaslD+ttHGjaNR9Xn3xPFcvL9I7kOf2e8bRTZUXnj3N1t399PTnqVWbvHTwDLfdM46btpg4Pc3JI5dxszYH7ttKoSuN5wW88tw50jmb86dnyHe43P+WRJ+tUm7wjb97hXqlyf67xxjbnoiNTl5c4OgL5xNNmzdso2+oQBxLTh69zPnTMyBg/11jDG/polpu8u0vHeb73zzOlQvzdPflePM79uFmbJbmKxw5dJ5Kuc7OfUNs2z2AogoW58q8ePAsihDMz5To6t04A9porFKiE5bErd3zpVKd+YUKkDy3MhkLLwyx9fXn3YvqlII5DCVhs0YybNPuExtZgaFYBNLHVl0qwRIpLUsog+TvsdfSgIlx9c1L1H/Soh5W+e7cVxlwRrAVhzF3O1LGfG/+6yz4c9yeu5ddmdtQN8kmxrHk0OELfOt7J7n7jlHe8fa9XGzMEyGJZIwXBYQyJqc75AyHWuhhKhrloElKN5lplABJt5WhGnjEMialmQylOiiYKbRbdEqthwHNMCmtvlItEUvJa4sz7Mp3UQ/DxB1JM1ho1hhJ59ma6WC+UaURBrw8f5XBVJbxTIHZepWRdI4r1SKGqjFRXsRSNd7QO8JzM5eIpWSuUaUa+DzUP3ad0+ncYoWvfPsYTS/g3Y/sv2GfU5rBWLqDxWaNgrU5mKsKwe6+VXHg5NmapLyEEOzt7U4ABLG6/a2EQMWMBtFQkBK6xAgiEi0mooIhR1GkQhREidNnDEIzQYVGVKTH3k0tWFi3wG63LQT5niz5nvX3exzLhFXcmyPTnSXfmcawdDRdpbRcx0pZlJdreM2AgbEuLFtHUaBnrBtVVfC9EM8LMSwd3dS57cH1+pNhEHH55FUKvTl237eN+SuLnHxhgsALqBXrGLZOrVzHSHl0b6vSjGdoNCNMrR+nZ4ktvQZRPEM9KqPJDIGxQO+eGE3JkDJ6EUJF07W2bpLnB3iGxtmZZfamrU0AdEksq8Rxcs2kDIGo9R5KSlikVEhABR0hnNazS9CMAr528QxjmTyaotCbsqiE8wgBncoQ/z3FiP9HhiJMxrIfwVDzTJQ+RT2cIpYBjWiGpGBKxVDz9DoPsi33C4RxnUuVLwASdZ1W1PoQQpA393Fb57/mUuXvuFp9mmY0Tyx9griyslXr3W6gChNXH6Uv9Qj6Jq6bhpqhN/UgM7VnW0yTED8u48erOoJJexq6kqbDuoPRzD+i07qTFVHmlTlc1rKuS+Saagd7O/45eWsPk5WvUvbPEMmgDUys9llFERqGkiVr7kIVm69jFbS2i6iuuAgUqp7P4YvTZB2Luh8RS8FsXKQ77eKFIYoQNPyADlej5vn4YeJIvaOvC7SEwShJnOpUofJ6Rmbccm27UUgk27I/h6P1c7H8WZrRPEE4xYpWmIKOoRbosd/CgPVRAi/FQrVIEEQIRZBPO+1yMU1VKFWaZFyLOJaoqoJtDrGn4zc5vfz/UfROEskGXrSIxwJtnWShIoR2A8BWImUASBSh3dANcIOz0Jojb5zwUhWT0czjpPRBLpQ/w3LzNSLp0Yhm15wDDUPN0mHdyXj2Y+TMfdfp3K6GQFNS7XEtbuAAt/qL1d8oIgG8DEVnZ3oEW/2HSUz/GAFHEMuItQyI2XqVQ9OThC3l822FDp7YuY+UfvOTd6OQwJH5mbaGjKPpvHt8B11rHMFkLDl/cY6tW3rI55wWFc3g/Vt38fSFM9SCgMlKmUPTk4xm82ibTATGcvn1+kRCMJheU7Jk2gxlci16bDLJcA0Dp8WUCmVM7Rpx5JUha2k6Q6kMU+fn6OzPY1g6jm7Q5awCKaPZPBndxG/VqauqQqfltFlSjTCkFvhMVStkTQvXMFho1Dl49XJbCPuBgRF2dXTfcHGlKQqPjI7zqRNH8eOIxWadI3PT3NM3SK/rMl4ooCkK+Q30jVZif3cvj41uxVA1BtNZFht1BODFIVfrC1iqQd6wiSOftJZF1VVCP6SyVF0HrBW9Jt+fvEQ9TCZLPSmXj++5nZxl4/Ql6Hu+kCKKYhRVIQojpEzYGSt2kHbLpSbwo7bryciWrsQeO4pX1UZvEqqioCkKM/XqOj01L/YoBcvk9QQ8evHEZaSE5XKdXWM9NLwAQ9dYWK626lhVml5AqdYkZRncvWcYrSUAelt3L46euPOFMmK52UA4gnR6tb7YsAyW5ys0ah7ZjhRIDymrIKNN13+qrqFqYKXWa0g16j7DW3twszZSU7g4s8RQd+6WkO+fthBCvC76p3ITO9OViGWNpepfU2k+19JYcMjYj5F33osQDkE0w2zpD3HMu6h7h/HDy5j6GB3uk1jGLiCkWPsiXngBVclR815IsrbOO8g570MVyTOi7h9hqfa35J13s1x7Cj+8jK5205f7HXStFyljqt7zLFc/SxDPoSoZcs67ydiPoAiLMJpnpvT7OOZdNPyjeMFFDH2UTvdjWMae1stbMlP8DzjmAaK4RKX5DHHcIJ96HwX3CQC88BKzpT8giBbQlAJDHX/Y7mNyPuos1f6GavMgcVxDETZp+2HyqQ+iihRhvMBi9S+pNJ6lGUwwtfxvUISNoQ3Qk/1NdLUbiaQRHGep+jd44XkUTNL2Q+RTH0AVN9bp+uEzJzn2yiX23zXGsVcuUVys8cj77mDi9DTVSpNH33+AyYsLvHjwLLffO87Zk1N89bMvcMe940xeXGD2apEP/+KDBF7I03/7Etv2DjC2rbcFQielaxfOzLBj7wCKpvCFTz7Pr//Ou2k0fD7/F8+xfXc/YSj54qee5x//2sM4rkWl1CDf6TI3VeSLn/wBv/ibb8cwNQZGO7Fsg/GdffQO5jFMnUbd50ufPoRuaHT1ZfnKZ1/kgz9r0NOf52uff4k4kgyOdnLu5NSmwFEhn0KIBAg/eXqaB+7bdkNhZikl9YbP0986xtJyksjo6c7Q35vDtHUGshn0Ne/GYjDLTOMcab2DSEYEcRNVaFTDZUzFIaXl6DSHWPSu4GoFFrwrZPQuIhnganmaUY2YEF2xf6qAo1hGXKqfBSSRjKiGZYZT49SiCgfy93Omcoxxdye2ujE7r1b3mLg4nwjYywQs8uOItG5hqhoLzQpWOymVLIS7rDRLfo1YxjiqQUxMyU+cVDVFwY8jGpEPpLh3ZJD9fZvbvq+EpWoMpLI0o7D1rorpc9L02GkaUYAqBOdKi3TbKVK6galqVAIPQ1UZcDIseHWu1Er0pdLM1CtkDItzxQVyhkWH5eBoOluzHVyqLDOQyuBoBppy/fN24uI8pUoDQ1c3LZ1fiSCOuFIrMt+ssiXTibNBBnsltE2Y53+f8JoB507M4NV9vGaAYerYKYO56SKd3RmkhHTOYWm+TBQlR7P/rlGMgk5WH2CueQpdda5j8t8ohIDR7b1ousZdb9rRFmpVNZW9d4+tSxZJSfI5ipOSYkUgo4TNZmxQVpu0o/DG99/ND792mKf//BlM2+DNj9+Hm0vxw+dewU5ZRGHM4K4MQbxMFFdRlQyrzopNIllHI0ccN4jiMgINyCKJEdcsh6UE2zZwU+amVuexrBH4R4miMlJ6KEoBgUYcL6KqvYTR1VZbTUzjXjRtDetCgiKgy05hqCqa0kQXGbJ6zxrdqZ++mDo/R0dvjuH0+/BnB4jSZ2iq5/GjEoowcPVhuuz7yBjbW6VEIff0/GHrGZ1BE5trNQqhYGv9bM/9IgPuoxS9k8wsHWZh8SL5XhdNtTDUPI7WT87cRUofwlQ7NgViDCXH7vxvMJL+IIuVY5w/f4TsgATNQ8oARRgYSpaUPkze2o+rj2AouWvG76rO0fX9FRhqjpH0B+h1HqTonaDonaIeXm2xbASa4mCpPbj6MDlzN47ev26Oc20UrP3c3/vHSGIUNGytm2oYo6mJbuR0sUzesTF1jTCO0FWVuXKVnGMRxjGVpkcQxTimTiwlChrD6ffR47xp3X7CuETdP0YsPQy1F1vfvmGZv0DD0W6sJRjGdXQ1w5bMR+iy72Gh8RJl/wxBnDAmXWOMbvt+6pVuLlzyqDYukc8k52BqvoTnhS0pBRXT0KjUmqRsg3ozYPd4L1sGO+mwDnCg+3dZ9o6x1DxKI5wllj6a4qArWVL6IHlzL6qSjK/5Zhkv9hmwE5MfXclwR/fvrgH0bh4XqrPMNItkdJvtuT0thhNcqS0w2VgkpVnsyQ6hCgVNsel13kzO3Mty8yjL3rE2qKopDq4+Sqd1J66xZYMxtj50Jc1tnf+KSCZjzla7b9pXTXE40P1vW8cnsNUuylHIgl8kpdk42uurPthwH3/vFv7BYsUxo0EYN9CEzURxiUulVUT4sdFtSCXmUm0ZR9XbkwI/DgnjGEvViFqCXiuOGZGM2xn9rGFjq8lN9IOrl9s0+V43zYGe/utshyHJ7KyEEILthU7Gcx28Oj9DKGOen7rMu8Z3kDM3vhjdjkut4RFoGo6pI2Cdi5lrGBQsm8VyjasLJbYOdJKyjLbuB4AXheuokSthKCodlsOJ586z595x+ka70FUFW1t9ePakXKJmxCtHLtDVnWHL1m4sTWsfaywltSDg5OI8+7t6Sek6l8pFzi4nbhO2pnNnT/8tgXX9qQw9KZcrlSSbeHJxnmqrDOdKqYQXReRtm4x5vdWgqao8MDDcEpuO2+iYBLwoIKunsDWDZlxhyZsl5e5BURXShRT6NYuX2VqVYwuz7c8PDIywvZDU2a5Qw29FtR7Wl5ZYN9Yk3jCSnJRoMdnWfiMJYp8FP0Gi8xmbnOsgBjroLqSpN32iWFLIJG40K2PVDyJcx8Be069O22lPWlcEujVVaemhJNuomsLojj7SOYckLSlQ1H64Adou45goTEp31oabtjl5+BK6oTF2+xBXajWyrkVH5kcvr/lxCiklYRi3tF82L8OqFOvMXF7ATpkYls6l09PkW5bCTtpiYapIz1ChBda5XDo9TWdfHt3UKM5XEALiOMZxLYa3X/9CFkKnw30CVclT948wX/4vGGo/aftBpAyo+4cJolkK7hOoIsNi9ZPMlH6focJ/RFHShPESS7XP0+F+lK70L9EMzrJQ+QSKMMmnPgBAHNeoNL4NMiRjvxVVyRDJOoriIKWk4b/KTPHfk7Efo2A+gRdOMF/+U2LpkXc+gCSg7h/Fj6bpSD1B1nkXi9VPMV38fYY6/gBd7QQkXjhBIzhBxn4LXelfJpZ1NGVVB8LQBunN/jal+lcpNr7KRpktgUY+9UE0pYOGf4yFyp9iaANJv0WanPMeBCaR/CJdmV/BUPsRwkRVcgmzMLrCTPH3sI29dGd+nSCaZrHyF8SySVf6Vza9znEcc/iHE7zlHfu54/6tDI528Lk/P8h9b9nJ7fds4dmvH+OBt+7mtZcusGVHH5mcw5c/8wKptMX4rv725+XFKrZjIBTBfQ/tZHxn3zoW6ch4N4++/wC+F3L8lUsUl6pMXlqkXvXYuX8YKWOOHJrg8vl5dt8+zP67xqiU6mRyDsdeuYTfDEhnbLbs6CObd9i+d4D+oQSYvnJhnnOnpvjgxx8gV3A589okJw5fwnYMJi8u8LP/9BG6erPMTi1vel/cfWCMZw+eodHw+ea3j5PN2Lz5TTvJ5Rx0TW2NZ0kQRNQbPhMX5vjOM6c4+PwZ4lhimTrvfGw/qVSiW3CtPFre6MVW0+jCJJAe895lOs1BkKApBmEcoAmDTnMIVehYqosqNGIZoSo6pupQ8ufJ3IIV+U9UCMGAPcqjvT9DJSzx3MK36DR70ITBkD3GqfJRIrl5yfDCUpVzF1fLp7O6zX1dqwvfcbe7tZtkLA44ic5Nb+fm2htr75W0aZI2b57JLFgO9/UmZR8baaK1hZTFaq640xprb3et+xvAbR1967bfle9mZy7R+9qyCZPzyLErhGF8Q3fKlVCFQsF0EgBrAxDq7xOxlNTDZH7k6hufP91QGRjpbImEJ2WscRwzMt7d0hdMjjHfkZRNXDo3mzioypBqOAfEVII5OsxxNG4t2yyEaOsapq4xYsi8zhLaFUFsgCiMqJYapHMOo3sGGdu73nHIa/j0jnaxcHWJdMFFVzPk7Teu9Gqlxev24bL7mm3Wx8Wri1RrPqoqGB/e2OlMCAtNG0fTYkBBKCmkrCPlIIqwUdUV5pBMQKU1Y0tXFbblOpmsltAVBdewUNUIRaivu/zkJyme+dwh3vyhexgY7yWtjZPPHMByNh9jAh3XGL2ltn0v4Mrpafq2dJNxt5IxttLJI1SVOp29+YRd9zpCCAVdTZNTd0K5n+e+qXLvr76NTGFzDdwNDgAhYLnRYLnRaOu1rQ1FaNhaL7bWS1/q4dfVx2tDU1KkjcT8IJY+y/Wv4hh7uH9rUka/q9mNa60Hynb1r47vHX1d1z1nTbWAqa5PqvjRLL5/lOXmtwjVQbqcN6MqP5rhTVKCJVEVk5y5m5y5sQ6hLQJ0pUkcx7iOiaIIhnpyxK31exyDHyQgkraiwZiy0FqVHivnuD/1CJBUdJyrTLM13XddsuBMZYq5Zon+wQ4EyTVyW+WHtxqWZnOxdowLtavc37V6XQMZcap8ldeKl/i/bnuy7cqYAJ/d2O7b6Hfftq6tZjNA11UCP6QaNFFUBU1VaDYDVE1herrI4GCBKIyp1306OvupVprohobnxzTiJpqmcOH8POPj3czMlDAMjUKHi6YpRFGMa64/Pi2uoguNZb/CkHPzJM/N4scIOAJDSeO0dI5iKblSKbVZI6aqsrujGy8KOVeZJ4xjdEUlpRkseFWaYYijGTTjgH47mfAEMkoW0YpCFMe8oWcLtqoTxBETxVUrv07bodtZ/wDxg4iOQgrrmhuzYNn0plxenU8+n1teohEEmwJHjqZz8tIcmqpw947kRamtoU4bioouFKYXy1RqXsJKEWIdyJDYEV4PHalKwk5Su9ItpwuBItfToF3dwHVM6jWvDYIpayZaiaBYjIJol+2dW17Ej5KJqKPrdNopvOgWtGwE6wCmxUYNPwqxVI2tHR1cWF7e9EVqazrb8olzVxRLSn6zzTQTgC9DLKkTxB6NKBHZVpTEmjXwwnXtTlXLlLykNEdTFHYWukjpOrEMESitumLRYkMIICaUdRRhoqy5JZLslUIzmk8o42qh/bfkv4IgrrZE0zZnUgVxRDXw1zGOTMXC1dIU/WWaUYMdo1ta9bjJtb92UitZLWOTgN8CE5Fc5+ImadHNvVV6ehRGTF2Yx/cCdtw+nBx1eA5V7Qd1Yy0Y3dRxczp+cz3N3UmbOK5FcbFKvdIE5Tp3yp/oWFqs8srLF9m2vZd8PkUmu/G1DfyQxZkSipqUhi3NlFpOOoIoTOjtURQzfWmBdD5FGEQszhTxmgFu1sFv+izOlOgayHPta0wRKQruR1gpd9DVHiqN7+CF50nzIABSRmTsR8najwIaCIXJpX+BF17ENhJnHE3J0ZH6KJrajW3so+Efo9z4FjnnfaxQsGPpkbYfImM/2t5foq3hUW58F03tojP9cRSRxjHvwA8nKdW/TM5+R6sfIVn7EbLO2wENgcaVpd/CDy+gKR2tbQI0JU8+9Ti62tlmWK4er4mpj6FrfWy0AFCEQ8H98Or50PqpNJ+hGUyQsd+KothYynYa2gkUYWNp2zDXaOJIGVHzXiCWDTrcJ9GUTiQ+zeAs5ca36XA/vmn2T8aSMIzaiynD1ImiOMmKj3YRhREXzsxw9dIib//gXQghaFQ9Lp2d4xtfeJlYSka39rTFsC3bSACktWNKQCbnoChKe/IRRRLfC5idWuabT72CIgS9AwVsx6C0XOcrnzmElBAEIdVK44bsiTCMWF6ocvCbx7FsA0VV6OjOELdYCqqWiOOaN2AQHbh9hPvu2cL3nztDqdzgk59+nme+d4otY91k0haKouD5AZVKk6tTy0xNF6k3fOJYYts6j751L2+4b2tSzrnBM0sXFqpqogiBhYuluOjKmoVI65Foa+k2nX0tHT2WMZbqoot/WJ2w/9FhKCaGYvK9+a/TjBoU/SWeX/wOilCYbrbcYDdZNEspuXx1iaVifdP2r322lYIqk/VZCmaWEWdjV5qViGNJ3HIUlXK1nENVE1OMzX57M3FqKWWr7dV3nhCJs5aqbN7uZm2vtFmpNjl/eWFDEGrD40PSbaXptNzrSt4224eUicukjNf0W4i2G9lK/+qhz4nlWTpMBze78ZjVNJVCp4thaEkpexy3TS8EK3MghUwuAfo7utJJwgPQhEla70UT1oYisZv1P44Tl9iVU5T0PfnX9wIWp4oIAel8ilTWueG1AAjiMl60gF/K8vIz57nn4d1kC9cDUKEfYrsWuqXj1ZO58PVti43HRvLVhue5pzMDVHBsYx1zen2rGqra2zIzgCiKiaWbmB1Aeywr14w9KSWqELyhbwQpJROlRRrRIraSLKB/2iLwAo49f5bKco3Kco04klw8McmF45Pc+da9WI7J/OQSJw6dQ0rJ7Q/tws2lOPnCOWYuztM72sWOO7dw6uXzzF9ZZMu+IXqGuzhx6Cxew0dRFPa9cQenXjrP9/72BbbdMcrdj+wj05nmyLMnyXdn6OjLsThX5NyRSzRqTTp6c2zZN8zEq5fY/8adXD49hW5oWI7JyRcmiOOYfW/cgWHqHHn2JLVKA69xTRWHjNpstZVSpETXSmt9LxFEjOVTDGZtTPX6sZS0sXLdBQJtXbk+hGvWHgnomwDiESu6SivaNyu/TX4XE8bLLNefRlO7MLUhQOBaxhpAfWXfkLwNkn6vNS9JjmtlTaUCaouB00V3+kkiWcEPJ6+75it9SH6//rc3C9liuNJqIXkuqdimjmloxDJurfliDFNts/cjGRO1vtPEqjtiLBPWbdySeNCESoxktlnka9OH+QW7gK0Zba2+UMbclhtZl1xY6Vco4/b+V7aPW+XbSIkq1LaT5vZ0P/XQY6qxtO74xlLdeB0B5yrT7b/FMk60bYXSvj6hjNufL5yfwzR1ZmdKlCsNNE0ln3NoNgP6B/McPXwJ17XwvZD5+TJhGHF+Yo7RLV0sL9WYurrMgTtHqZQbhFFMEIQ0mwGlUh1dT67L+Nb14FBGS3Egv/MWxQVuHj9WwFEQV/HjMmljlDCKmK5W2t/lTJsuJ8VYuoN+J0vQ0ozRFYVQxqy4VUgktpoweyIpCWVEJCVnS3PtwZfoBq3SDXtT6XU045WXf7Xm0X3NBENTVXpSLqoQRFJS8hoUvQZ97sa1tZamkbNsqg1v9SZfc/VURaArKraloygC1zbacEa7P2z8ClIQiFC2qMMbU3B1VUVTFeyUuc6Kd237YSxJmyZq6yaZrJTak4aS1+R3n//udeLZG0UYx1wqrzLEqoFPIGN0KTm3uEip2Wzv49rQFIWOlnZRFEtmatXWA0eS0iwUBM3Ix1QFjpaAfCs1+tfq60xVy+2FRUo3GEi7lP3TVPwJHH0QPyoRyTq21oetduPHRZrhPK4xRhQ3Wg/smIo/QdrYiiRCyohmtEDFP4euZIjxUYVNEJdRhEm3fV+bvnhtKEK0QbCViGREXu9AIOix+jHU9bTulbFS9j2ulEtMVkqcLS4xX69R8ppUAw8vjPCiiLLfZLnZWNf+irbRyrkRQhCGEX4zbE3wA0CFG1BlVVVh6vQcVspkaNuqfsPo9l56BvJMnp8nimPclIl+i5pPPwkxO1ticnIJVVXYMt5NJrsx1SyTT7HnnvG2BlYcJ/eiqipt/EXVFJy0heNajN8+iC99jNjAsgyiKGbLnkGMDQSTpfSoNJ+l0jxILCvEcR0vvNzS+ElCCANDG2xNbgSmNoJAJ4imsWkBR2o3qpo4BCqkMLThpFRM1hIrYfwtQt8AACAASURBVEAVKWx9D6uWoa2XdOzRDM9g6TtamkcCgYmpb6Xa/AFBvJjAr8JE14ba/TC0YYQw8MMp1uonm/p2NCXPCjh6qxo5ABKPSvMg1eb3ieIScdzACyZImffd4u8DvOAsXnCeK4u/Ba1jDaMFVCVDLGuomwhxK6pC70Cesyeu0j9caAvDOq6JZemMbe/l2a+/lpRfDSdA2ej2HoIw4t1P3IuqJFmgbD5FveZteuzt+7/9laSnP8/QWCePvf9O3IxFsxHQ3Z/j7PGrTF5c4MlffZjFuTKnX1ud8K0sbkrLdbIta/B0xmZsew/3v2UXo9t7adZ98p0uvheiGxqTF+aRMubSxBxbd20MJOdyDr/0cw+RTds88/3TFEt1Ji7MM3FhftPzbpoaoyOdvPPR/Tz0ph3YjsHMcqV1dEm2vtxINPxsQ2d6ucxwZ56677fGVoPurItlrL9HNtIvUISCcYPyh5/UsBSbN3Q+zOnya3QYXdzf+Rb8yGPGu8pLS99nR3o/xhqB0SiKqdSaFEsNypUG333uNFGrRKdUbnD4+JVNy4pTjkmuXyetO2hCIZQR+gas1DiWzC1WePXEJMdOTzE5tUy11kTTVHo60+zbNcDenQOMDXe2M8a3ElJKml7IuQtzvPzaJc5fXGBhqUIUS9yUyfhIF3fsHWLn1l5ytwBaJO0FFMsNSuUGp8/NMDVTbJ0nyZnzs+1zs1GMjHQw2VymHgXszPaQ3oAZtAJSxHHMlallXj0xydETV5meKxEEEa5jMtifY/f2fg7sG6KrI91+v883akgpGc9u7MK14nhYqjQ4cuwKR45PcuHyAo1mgG3pjI92cef+YfbuHCCbttFbjGqJRFUMNGG13ver52m5VOfC5QWEEPT3ZOnpyrTP+4kzUxx+7QpnLsxSKjcQQlDIpdgy0snOrT1sG+zk3JELhEHMjjvHSGUcojjm/KV5KrUkWdfTlaGvO9NihUj8aJkwrlOpSFIZmyhMzvfCUpXLV5OFmG3pdGccLp+aotCbIw6vvyZSSkrlBhOX5jl6fJILVxYolup4foiuq6Rdm4GeLGPDnWwZ6WKoP4+bSoDoicsLDPRkGVrjGrf+GrbWBhWPoyeu8OqJSc5fXqBSbWLoGh0Fl13b+rht9wDjo91thmUl8JiqlXltYRaJ5MzyPA8OWQxpqQ2fUT/pcfn0NMd/eJY7H95LrdwAAfnuLN/81HNsvW2ETMHl2A/PMnd5gZ13j6PpKmdePs+pl85z4C17sFImF05McvKFCW57005+8OXD3PXIXr7zN8/zzp9/M68+dxo355DvymCnTEZ3DeDmUy09TY0ThyYY3z/C9IV5Dj19lLd97AGee+oVrJTJoaePsv+NOzl39DK2a3LlzAxSxjRrPuWlKqmsQ7VYp3uwkPR9TZSbz1H1D+PoOyk2vk0YF8nbj9HpfjBZ+McLzFf/mrp/CiEUsvbD5O3HUEUKkERxjcX6U1Sah4hlE03J0OU+gWveiZQxXniF+eqnaIaXUIVNR+r9ZKw3ASrFxreo+a+iKllq3lEUYdDpfoi0eX/SN+8HLNb+lop3iDAuMq8UMLQ++jK/iq52EsVF5mufpu6fJJYNDK2frtRHWyVnyXOmHhxjofo5giipxHDNu+hyn0AVLkIoCIw22HRtRLLMcv1rlJsHkUSkjP10pZ5AvUmZFSRAzGcv/4AgDlnyq3hxyHsH7mZHpp/pxjJfmXqZTjPDRGWGXjvH+wfvxY9Dnrr6IrPNIrGUPNS9m/s7dxBJyeGl83xv/gR+HNJppnl8+A1M1hf58tWXOLx8gXrk0WVm+ODQ/WR1h69PH+a5+VPsyQ7x5NhD7X6drkzx9enD1EIPV7N478DdDKc6+dbMUV4rXqIZBWxxe3jvwN24ur1pEkQIcZ1G6eXaAl+dfoWPjLyRvOEyWV/kS1df5ONjb8bVbdJpm+cOnmbb9l6CxZBs1iGKJUMjHXR1ZejoSGPbOmEQUVyu4zgGlp2sD2emizQbPsVinXK5Qa3qUS43URVByrV47bVJHn74epaXEGLD9/iPGj9WwJGmOKyozMdISmvAHUvTsDUNU03+fb3RZa0yimqB39bvAUhvILQtpaReT+j2hXyqLagnSFg8Sgs4CmNJucVu2TBkIvK9mV2eIKnnbHgB88Uau0Z7bv11I0DXVBTbQFFW0On1oSJAJi/mzSaLYRyRMy0cPUHRi16zDbyEccyF0ublCzcKP4pbD92kNGpLoUBqE1FzRQis1nVVhSBtmDSihOlSDKos+hXGUj1Ag3pUAyS1coPLp67SNdhxncbRyqkwFIWUrlL1zyOERtW/iGuMUPSuJhkCGbaReiljqsFFpIywtG4kMbXwCpbaSSSbNINFhNDwogUy5g7qwRSaSGFrvaibZLklLfAqlVnHIpPEhDJEVTRM1Vr3UJJSstCo893L5/nSxGlOLMyx1Kzfev5KQrXWpFxptAGkRK8pZHC8G1VTkZEOwoYbZOfDIMS0DTr71r8gZq8uszhbRtNVBoY7KYX+TxUlu6cnR29L/Luj0wVWs69rQ9VkK1O88ixZC7ysAnYr9O1Yi7hanSIQATvsrbibaJJIGVJufIOZ8h/R5f48trGHWHrMlP7jxtuzFobYuKSDNd8nv1mzjVAQm1msyrXHtdLC2rIB0f7ftf1Y0TZaG4ow4UcQCpUyotL4LtPF36cj/SSOcQdSBsyW//B1NJK0Y+nb6c78GmKNFoIiHFTlxmU5b3vvAb72+Zf4i//8bSzb4B0fuCsR0yfRFDn8/Fkeed8BnJQOMuLeB8eoV8t8/r89i6qqjO/sp7v/NoQiyRXcNvtoJSzbIJNLxoNAkG85Fo0O5Lnrge185bMvIISguz/Hux6/m77BAvkOly99+hC5gsvgaGebqeSmbQ7cv5VvfOFlegfyvPPxuyl0pXnb+w5w6NnTHPreaRzX5F0fuofOngwPv+s2nvnaqwnI5Jjtfmx0Hvr7cvzaLz/Mw2/exWvHJzk3McfMbIl6wycKY3RdRdUUujrT7Njex/hYF7t29NFRcBMmlh9wfnaJIIpwTAPXMjg/u0R31sUPqwRhyEKlxuX5IoamUvcD3rR7DE1R8PwQxzZ+qp41txopNc0d+ftX/6BBt9XP/uxdaGI16fD9Q+d49vkzTM+VmF+osFSq4Xlh+2fHz0zzW//mc5vuZ9/OAf7F//4WFoMSO9Ij6Mr1c61qrcl3Dp7mqW8c5eyFufY7ZiVeA7598BSdBZe3v2UvH3zXHRRyqZtetyiOOXt+ji987TAHD52jXL1eS+SFwxf5268dZu/OAZ78wD3s3z2IoW88H1xcqvKpL7zAlakl5hYqLCxWqdRW5wZNL+A//el3btinP/rdD7N9ezdLXp3cJo5qK2Uz33j2JH/5uR8yObV8nVvgi0fhqa8fZde2Pj7++P3cdXuSBS9YNmqLWbCh8YimMnFpnv/2med54cjF61xMX3ntMl/6xlHuvn2UX/zIGxkb7kRRBLEMmW0cx49qGGoKR1stTzly7Ar/x+8/ha4pPPmh+/j5D7+Bmbkyf/3FF/jGsyep1a+fz37/0Fk68yl+9397D4ZlYLtKIqotwPNC/vgTz/DKa5cB+Mcfupefffx+TLPFaCUgJiLtdqEbUwglmY/88JXz/N4ffx2A8dEu/t1vv5e+Ld2UFipt97WViKKYV167zF994QWOn56i0byxO1DGtbhj3zD/5KMP0FFw6e/OkE5tru1hGhpnL8zxV194gedfnsD3r2fYf+fgKQq5FO9/x+186F0HcFMmtqbTbbvkrTI7cl24ukGHKYhkhfh1Omb9JERpsUKhJ8v2A6P84MuvIIQg25kmtSa5tue+raiq4NSLE+S7MsxdXaJvrIst+4aQseTFb7xKR2+WbXeMcvz5MxTnK+S7s+y+bxuzlxdoVJsMjPeQ6UgzMN6D2zK/6ezPcXViBkjmyKO7Bth51xaOPHOCZj1h9IdhhFf3UDWFmYtzjO8fYXBbH10DeY4/f5att4/SN9rFkWdPrjuuMC6xXP8qsdWgM/UhIEZVEnMTSch0+b8QxmW60x8jjMvMVj6BlCGdqceRBMxX/5pS41m600+iqz2EcRG9pUcTRPNMFv89pj5Kb+aXEg3G0h+jKjlSxm2E8RKLtb+jy/0IPZlfoOq9wFTpPzFaGMTUhnH0nYjU49S8V+l0P0zK2IsQRnvOIokx1H5S7m0INGYrn2C28ucM5/81qkjhhRe4svx/k7bupZB6N1KGrHcJ3DykjJmvfppK83m60z8LwHz1U0gZ0Jf5p9ws+SelZKI6Q85I8YGh+zlRusLfXD7I/7rjPXhxyKGFs3xg6L42qGOpBqpQuK9jOzkjxdHlizw9fZi92WGmGst89spzfGDofsZS3fhxSEo12Z7u59G+26mEDX55/G04momlGigI3t53B9WwyYK36uRXDup88sKz3N+5gzsKY4RxRMFMIxBsdfvYlRkkiCP+ZOIb7MwMcKCwuYvcRtFhpqmFTU6UJrmvczsvLU2gKxqplrZQV1ea++7fRk9vho4OF9PUMUwNy9IxDJUdO/vw/QjD0Bge6aCnN8v8XAVVWWUSpdMWI6OdaJpCR4eb6GTG0NubJeX+92dc/xgBR4JYBm2Fc3lNCY4qxKa29683VphJq21fszASAsvUSbsmK05Ja0NdQ51LSr02X6hJwLVNDE3bdEEngIxjUWv4rzNHkaCg9UqTXNcNalIFzM9XcNMWPVy/QJJAsdlsu52Fa0A1RQjSuvEjuWZlzCTb4+g6Pa5L1fNohuGm26+cZkUk7nB+nNjApzSbLjODqWotemFETIxlG7i5FO41C53V0r7kWhqqQcbcTiOcIWNsoxEmukKGmiOIK1hqFyF1JAEpfYRYeijCRFMcTLWDqOVIlTG20IwWcbR+LDVhC2nCoRktEEl/Q7E/0TqfzWj9cXuRRyRDovh6hf6paoU/ePEg37l8nuIa8NTVDXpTaQq2jasbWJqGoapUfZ+Dk5fa+5AkbLnB/jxaCywMvICuvhxqGzzUEbcgmDk/tUwYRAyMr1Ift+0dZHy3RFEFDT9k6sL0DVr4yYswjMjmHAYGC9hOTDM43gYaEncy0aoBF4RRoh2iKFniuIaquICCpvaibMDmyuqZZDF2A2wnlh41/xUsbQv51IdQFAsvuEAc19ZtJ6VPGM2z4pwWRAvEsomurta5h9FiIiatGkh8wmgWVcncUJRxJQQGpj6MH14gcUtLbGv9cApF2GhKjihOxETDeK7djzBaII6baOrGehKvN6QMqPlHMLVhCqnHUZU0Xnj5uvOx0usEyFt/goXQMLRhav7LWMbulvbSrUfvQJ4P/+KDNBs+hqWTcq1WNs9jaGiK3/hXe3BcBRFfQEYVXMfksfd0UC0FgMBKKYj4Mpa2zEd+6U5S6fXP6313jrJrf1LKrGoKH/2Vt5ByTVRN5YFHdnPbPVuIoxjT0rEsA8s2+MivvAW/GbTLy+xUMmHQDZUHH9vLgfu3oqgCx03KyPYeGGVsWy9+K0PvZmwUVWH/3WNsaTm46YbWBqA2C8PQ2LdnkJ3b+/C8kCAIV8tnBEzNlDBMjbHhTgx9PaXd0jUOjA8gJe2So9Hu/Do3QkVRGO3O44cRl+eLqIpCre4zM1di61g3qvo/F3DUjBt8b+5pquHq5FcIhT3ZO9iR3rdu29dOTvKdg6cSIewfMbqsPI5hYirXJ3k8L+ALXzvCZ556iWIray8EZNI2rmMSBBFLpRphGDO/WOWzX3qJUrnOxx+/n54bzFGklExcmOeP/uu3OXVupi1ibBoauayDqggqNY9KtYnnhbzy6iUWl6r88pNv4v67xtvaF2ujWGnwze+dpFjavEzvZuHHEZO1IldrpUTcW7uesSKAo8ev8MWvH2V2PrlGmbRFOmURRTGLxRpBEBHFkuNnpviTT30f1zXZvq2HnGG3Szk2ioWlKn/218/x4pGLxC3WVSZtJ6WnpTpBENH0Qp57cYIokvz6z7+Z4YECChq99r7WuY1QN7iWUSxZWq6xVKzz53/zA7518BS+n8whDF1D05LS0xXR7d6eLOmUSUNX6R3pwknfGrtPyoh6MElU7GFmcomRbRtrbNQrDRrVJp39eZbnShT6cq3fS85emOM/f+IZzl+ebwN/rmMmJbItfZBSudEe9+Vqk/nFCpapU6151Oo+GXfz+U6xXOfP/uogL796iSiW7XGnqQrlaoNKNZkDLhVrfOapl0g5Bu9/7HYMQyNv2jzYP4qhanTaNrPNMzRj76eScdQ91MGRZ07yzOdfoLxUJQoiThw6x+XT07x68DROxmbuyhLNuk+j2qS8XGVk1wDP/d3L+I2Azv48vWPdnP3ii3z70z/AbwZ0DRYQrVLIlTAsHVVV+OHXjnD3o/tRNZXXfnCGC8cnOfXSeeIoRqirLBDT1tF1le98+gdcPDHJbQ/uYuddW6gW6zRrHpZjMbZniFe/f4rJs9M0G9cLI0sZUnDehWPuXTd98MMpav4xBrK/Sdq8F4ioNH9IufkcHamfIYorFBvfost9gpz9NoRQ16316sExgniegdRvY+tbSBn7WKp/mar3CqmWpICudlNw3o2lj2BpIyzXv0UjOI2pDaOrXSTC8CamOoClbV33TtWUAgXn3awk+JrhJYr1rxPLOoq0qXgvIYCe9M/d1ATk2gjjZSreIXL2W8laDwGSIFpgsfZZetL/5JbmkZpQ2JsdZsjpQBWCb84cpR4l91PWcDhQGKPPXnUglq1/jpcuM9ssUg2a+HHIRHWGgpHmzsI4xjUJDUc10YSGq9nrNH41oV6X/JhtlvDigIe69+Dqq88EicRSdSaqM1SCBs0ooBysZ6bdSriaxf7sCK8sTbArM8ip8iSP9d2+OlYtnZHRZP45PJL8d+317OvPtf/W0zIqyVwDpAsh6OpOrmVHZ5ooirl0aYEdO/tuOn/7h4gfI+BIEsYNVGG0KOqsEyMM4pggSmobNwNqViyuYbWGMYxjUrrRrr0HMFRtHVjUDDfOXhTyLm7KvM45phkGxK0niyIE5g2saKWULFXqmJtkxaBVt7lJqdnNQggIgqhls75x+H5IvpBC1TYeUKaqrtNoMtY4P3U7Kf7PBx5mNJMnDBMgZy2ZwPPCthuZaWlEYUwUJbWsmZRFl51iplolimPqQdAWy75RRFJS8j16HReBwI8DZptFTEUnpanJ+EAktZ01j+nzcxR6cxgtPSpDTbZJLJ0lfhSTMcZJ64nQXNrYgunlSOtbUMT1Qt2QXLesseO67zJrxOZsrRcpJSl95AbZVEEj/P+5e+8oS7L7vu9zb+V6OfTr3NM9Oc/OxtnFBiw2ICdKoEiC4YBB5qFF0pYtH8u2ZFLWfz7H59gSpWPSNESJBEGDMAEQgJB2Aexic96dnZmdHDqnl9+rXP6jXvd0T4eZxQFpLH7nbOjX1fVu3aq64ff7Bp/5G1zVpJAoQkGTOmudN1qex//60tP87fkzqzzZg+UKH9u1j3sGRymaFramYSgKqlRQpeSthVlen5/B6QarbRvoy3H2wlxyX3QVzdDw3ACprCBhLGJZgG0MOQ3bYNeRUTqN7rqKqLbmWWt2PQppG3Ob5++9Fum0gaapPP/sOU7cVyJXPo2u7iKOA7zgElIYWMYdhNEyQTiPpgwShnP44SyWfhtBOJsklm6YVP0oYMlbIq2mGTC3dkcQQkWVJTrh6zj+OwihU23/vwTR4o1HUut8DU0ZRFHyLLX+DEObwOg95wB+OMNy+4tkzIdw/PO03BcoZ34NIbSb6nwIYZK1Hme6+ocst75EyrgLN7hIw3mCgv0ppMgQ0kja0f46mjKMqpRYav1HdHVsdYFzMzmRhAffIIraBOEycezhB9NEMouiFEFIVFmkHS3i+O8gRYpa56v44eyGc+nqIFHUpuU+T4yPQENThxFoZMz7qXW+xkLzj8nbH0OgEYQLCKGRNu/dpGVr+kIKUhmTVOaGzUccoMgW+YKEuAHRyqIlRtcFhVKAQAPRhchFCJdMzsKJA04tn6IdOhzM7qJsFlcTQEIIsnmbKI5o+B0kAiUr0YVKGEd0Ihcn9LFsHc1KLO2jOKYRdtDjHmpTlcicRJMKzbBLWpioirIpmkhVFfKljSKhYRgxu9DAcTzKxQy6rrC03Mb1AlK2QaWcpuvGVBtdgiCkr5RB11VSKYNyMY2uKTSaDs22g+P4FAsp8lmbdsulVu+Qy1qUCmk812d6oYGhqwxWcnQdn3bHpdV2GS1kMRSF6UZzlZrUdXyWqi28HgKpUs7QarssLLXwe+0ovUsR35/m0ITGvuwR/MhLigJBk7frr266MX3wxF5GhgrEa1BA33nqFG+eSlyhdu4o84nHj92QaIl7/44pFTIseFUmnXkOZiewuf68+0HId546zZ9/+QU6XQ9FkRw5MMxHHznM7okKZk/7a36pybe//zbPvXKRRtPhPz/5Nn4Q8Xu/8QFS9o0mEUmRcGa+zr/9/Pc5+c40AMV8ig9/4DAn7phYRSt1HY83T03yt999k8vXlrh8bYk/+vwPMA2NO4+NbzhvXzHN7/zaQ6vJEICp2Rpf+dbrdB0fXVf5uQ8fZ2Rwc/oSwPhwEUxBWjPI6psnHpaqbf7yqy/Rbrsc3j/Mhz9wiAN7BrFMLaH0LTb46rff4PlXLuK4ARcvL/DXX3+F3//tR6h7Dn6vQLZZfOeHp+g6PhOjJT714eMc2DNAyjIIo4j5xSZf+dbrvPDqJVwv4IXXLlEupvgvP/cwpinpBEu0gwXawTw7Mw+jKOuLhlEUMz1X5y++/AJP/OgMhazFA/fs4fiRUSrlLFIKHMfn/OUFXn7jMnt39tPfn8Ord2g3bjUZFyOFiSYz5Puz7L9tDDtjbgpUsNIJre7ca5d43yfvWv280XL4y6+8xIUricX92HCRz3z8Dg7uHcQ2E7MB3w9ptR3OXZrnjVOTnLs4zwfet49iIYXXQ2k57tZFy288cZJGs0shZ/ORR45wz/FxCvkUUgq6XY/nX73El7/xKkvVNq22y5e/8RpH9g9zYM8gbhjyzMxlPjCyizcXZ1DUDmVrc+mK93oMTlT40OceoNGqcfCej1Doy1NbbPAPfveDqFqiK1Qey6CYMfvv3MngeB+KpmKmNepLTdIFm/Jwnsc/ez/Napu7Hz+KnbV47DfuIYg8bntkH7EMSWVtHv+V+6nONbBSJlIRHL1/Pwfv2U2mkCKds9nRo1Q/9tn7SedtSv9FgfpSk0P37iVXzqBqKvPXFvHcgGJ/lqGdfZQGEwfrOz5weIPwu6YOoCnlZFxd83z64Sx+MMNM498yJz4PgBfOYKo7ekWzKlHsYKgTiB6ieu377PiX8YJprlX/l9UCpB/OEcXdVd0gTelDkcmcpYg0mlLADxdIXMa331+GcZNa9zt0vLeJYgfHv4wgkduICfCCKXR1GHUbVPVWEUZ1vOAai+2/oe78aPUzUIjizpb0/nVthNXk+AoYZEWTz1S0Da7E35x+hTP1KU6U91LQ0/RIMyhCEvZ0iW6MpL+3EnVZH0rvu4MbDCWuthf50wvf487iLipmHk3e3HVzsxBCcEdpFy8sneNHC6eJidmVHtjy2B/3sziOCfwQ2XNL37nz5o5rP6n4KdrtxaT1UaI4eZEUIVcRMJDQy1q+x2SjzsValbbvU0mlaHkuipDYmoZE0PRdLFVjIJWm7fssdtqUbZudheIqxS1vJBDTlVjqdjZAhYUUNJrdVcvRlV9FcUzVdVYTPYaiUtjGbktVJIZQqTW7rMu43BCmrmLq6g20k1vpNWjV2sTbJJ4UKVFVuSVdTgrBYqdNWksGtbKVWuXGxjGULJthLc3pd6bI5ixiYHG+gZ0yaNa7FMtpmkstyrsrBL5gabFJGITsu72MpijkTJOJQoGdhQLmLWglqVJSNC3mOi367TS2YpAIx4Ei1FU6o6JIqnN18pUsvhesJo5Klr3a1W4QsOwkwrGL3Q7znRaGoqLKQRY6LQzFoRP4q4OSpWosdNpoUuKGIbaWbLCjOCatGyx3O+wtlsn1Em03p03EZHWDAXv9QsJSbC63LxLEPkNWgjSI4phnpq7w3cvnVwfYx8Z38T+ceD8jPYTCZt8XbfJISSko9NyOVsJzfJy2S66YIpZlpNwedTF/bYlKoFPaZmHtByFnrswzVM5uSRl4r0Wz6RCGEcdvH2dgMIeqDiBEBojRtf1IYSKFgaKMomvHSJQcQIsiFAlSFlFkjk7o8qVrP2DRraNLlfvLhxlPjSCFXJfkvjEEOvnUJ3GDy0zX/hVSWKSME+TsjyPF9Q2xlDamto+l1p/jhzNo6jADuf8aVfYRkyTDTW0PQbjIdPUPEye01CfJ2x+7/l3CQFUq3GhhnPxOYOvHqWR/l+X2X1JtfxkpbQr2pymkfm71WZTSxtL3s9z+In4wjaYO0p/7r5BKkTD2kQikzCUItzgm6glICpJ3OY59lltfpOk8gx8uEMddpqp/AEJnMPdPMfWDZKzHcfzzTFf/NQiDjHkfeftTIPSeuKQgIsTUDlHK/CrLrS9SbX8JUzvAQO6/QVWK6OoEg/l/zlLrC0xX/4CYCEXkKaV/cd11f2f2Jd6qXQJijuZ38WDfMYytbLiFDfqDW47ZW33uBS38OGB3eoy0mtzTduDw1akfMdNdAgQP9h0liBNx/ZRqkFEtGn4HXWpU/cQloxF0yKgWO1IVZrtVikaamtdGkypu6NMIkrntzuJuuqHL16afoeF3Np2JSnqWz4y9n7RqEccxZ87P8tLrl3tI35hD+4f51pMnuf3oDi5eXuDnPnqcp184j64pvHVqio8+foTRoSLff+Yddk/08eCJvTz78gWuXFtisD9HtdbhkQf3872nTmOZOrV6h48+doTnXrpIGEUsV9s8dN9eFpdanHxnmr07+1EUiWlqnHpnhvnF7ms+twAAIABJREFUBv/oU3dx5doSX/7Gq9xxLGnHpz9ynGdeOo+mJu34yKOHf6YSR4pQ2WHvXveZQDDnTLM3c3jd54f2DXJo33qHxrMX51YTRwN9WT722NE15gsxXjiDG1xGUwYw1Z0suDUsqW9Y0J+/NM9fffUlOl0PKQX337WL3/+tRygXr2sOxnHM2HCRI/uG+dYP3ub//E9P0Wq7PPH0GY4dGuHDDx/eMIc5rsdXvvU6b55O2ljMp/j93/wAD923d1WcFJKF8p6JCscPj/FvPv99XnnjCtOzNf7TXz/PrvE+Cms0j+I4xk5pPPTgLkzFWB1r3zo9xTefOEnX8dFUyf337ObogeEt+96LQqY6dfrMNCl18zHAD0KCVsQ9t0/w3/72Y1TKmdX+ABgdKrBrvMIf//nTfOO7bxLFMW+emmJqpsboYB4h2JKq1mq77Bwr889/7yPs3Xnd/W6ln/fu7OdP/uJpvvHEWwRBxJPPvMODJ/Zy9/FxLCWPpeSx1fKWVPq3zkzx9jvT7Ns9wD/+5Qc4sn94da278j1HDgzzicePEgQRcRASBuFNiwHXI0GASmEkScWpKgOjxU2P9Byf0A8oVHLUFxP6klAEs/N1Tp5Jno10yuS3f/VB7r9797o+hqQPD+0b4pMfPMbcQhPLSuQZPD9EUQSut3XiqFbvUCln+N1ff5gHT+xZp2kSxzE7d/RRKqT4d//hh9SbXRYWmzz78kV27azw4tw1vn7pNJcaVRpel/eN6LT8ZWLrZ08cW1Ek5dEsS83TtJUWtibplqbJVnKEOCyE51HyKkbOI6WlmA3PEwYBSknFy8zha0XOts5QLo9SqKRZ8M4xqOwkKtdpBiZKTrDoTlKWFYr9eYr919eeE4dGNm1TeahAHMcYpkexokPcAWlD3GJ0jwmxA3QBh5FdBkkyRoIMWVs4FT2DkRtDCB1FyVNO/XxPmLrXFzKDFBYCLcHIxN6m77EUJpoyQCXzKz2NxyQ0pT8pKpGgx1f2NTERUez1kkzryf83RozPUvuvqXa+x0D2tzDUUerd79PoJXkSVopOFDvEcYTY5Pq2DaEiRYaC/RiZNXqSUpjb0vvXRhRHPL1wCl1qnGlMsiPVR0az6ISbgwgW3QaWqpPTU5xvzSJ7hfXDuTGeWTjDVydfZCLdjxv6HCuMk9VsMqqFH4U8u/gO/WaeXZkBVCGZdxosOHWqXpsr7XkqZp4hu0TFyPFXV5/hWH4HQRyxM9VPO3BwQo+KmacdOLhhgCBJei04daY7yzSDLpda8xT1FGnNYsltMNlZoul3e+fPUdDT5LUUB3Oj/O3US3x8+E6ymo3n+lw6NY3TdRncUWbq4jz5chbf8/HdgGJ/jstnZigN5Ai8ACEFhb4sV8/Nki2kaFbbvXue7O8qI0WW5+oMjvcxP7lMGISUBwtMXpijPJhn95HRTfv3JxE/NTs9gUpaHVmlkClSMJTOri5w667DbDtJJGQNA6NnKW9rGpaq4YcRbhjghxFlS0eVMnFeUxTq7nrY6Epi6dRSQjOZ77TpBD4pbQ2cN4Zc1qKvvH7Dv4oe6f1csVPr/+7GiME0VUbtrTffQkDX9ZPJ6t10Wu/8gzvKWKvUiY1uNVEU47nhKuR4kxbQCXy8KMnAjmXzq+3oBB4zrSYTMkt1uUVff5ZWy0mcgSxtVdei0p9DIIjC5GdjDeVBFYLlTgc3DNlZKGzRhusRxYm+VcNzVghnvUprl5TacyNgJbEVk8rZ62DTQ+ksSq8K3wl8Jpt1oihiutXgfG2JvGFhqmpS6eO6LtL+YpmUpnPRW6Yb+D2tpBhT1ei307hhSNv3tqUmbta3fpSo7K+dT5zIwY26uFEyoAM4QcArc9O0/WTTn9UNPnvwGCOZ7LYJqrbvbXBWi6KYpeUW/o4yuq5i2jqHT+zu0VnWu/ZtFY3lNnYckCtntvx+IXrIrs2yV+/RKJXTlMoZOm0X35OoWpqZbp1O4BHEEaYS0mcIGn6XqtelYqYxFI1rrWVUqRATsyMV4UY+Ty28wZX2HKbUKRkpEC4DZoUcW0OGE3rlKEP5f0EUN4EEcRPFzmpFC4A4Jm3ej6UfJoq7KCK1Kli48jgoMkMl+0+I4jYgUGVxHUXR1m9jR+nfoMrNF/NCSLLWw9jGbURxF4mOohSQYu2YF5M278PSj61pR45l9zxuWCOnT6Can8QTBp1ggaY/SRyHFM29NP0ZvKhB1vwAsbKHtj9D2TpC07uGH7VBlmn5UzT8aXLpX6fhnSeIHFLWnQRRi5Y/TTesEkRdWv40OX0HpdQvkrM+SEyYWCn3FjgriTAjv6vXr0ni7MYF0Fu1i3xz5gUgQQbekd5HtdXBNFV8Pxnf4jhG6VV6mk0Ho+ctb5oarZaToICiGE1TCIIIKRN05sBAHikFuqLhhB4vV9/i3tLtmIqBG/k8t/g2pxpXEMB4aoCyUSSnpSgbWWxFx49Dcpq9Cr/Orgg3AhUzhyJkD80oGbKL1OvX6Xx1v8X3515j3q2xWYzZFT4x/D7SqkUUxZy7OMfiUov+vgyGruJ7IaPDRR59YD9/MV+n0XRwXZ9M2mTHaJHhgQLlYpo9E5VV2kEYRBw9OMLBvYP89ddf4fLVJS5cXuDAngEsU6NW7/Dm6Un27x5YRawIKRgdKvDogwcQPeHh/XsGqPVQDlEUMTJY4JEH9jO/2KDR7OK6AWnbYGykyPDgzeeY91KEccC0c40gShbaURxxuX2OQWuUKIrWbXJvRf9pLfo6jpNNix8uoyuDCCFoBm1aYZdwTUU2CCN+9OJ5pmfrQOJU9fOfuJNyT7tq9dwrcHxD5fGHDvLyG1f44XNn8fyA7/7wNA/cvYfsDai96dk6z7x4gTCMUFXJ++/by3137dogTbBy7omxEp/56O2cPT9Ls+1y+twsb56e4sF71lM4lr06p5rnOZzdQ9koXr/4G/tjmz6TQjDdrjPbaXCkMEhmC9RRIWfx8x+/g8omc6UQgnzW4uH79vHD587SbDks19pcnl1m1mwzYGcYtDefD0xD47GHDrJ7vG/Tfs5mTD7x+DFeefMqkzNV2h2X5165yN3Hx4mIeuPiHDlt80234/j092X51c+c4Mj+oQ2SBCvfo6oKqqrQbYeJ6YOytS7TjRHGDlIoSCnJlzPrEMtrI47ixI00jpk8O0O2lKY8VKRW7+L5SdLHNjXGR0rbVuWFEAz2Xx/TV8bqbNrcsr2qKnnk/v2cuGPnBqt3IRKXwLuPT/Ddp07z8htX8IOQS1cXIYb9hT4eGd3N0fIgupQoSpNmMPczSFS7HorQ0KXFdPcsqjSYcy+RUvJEJDISk50zqKkjLLjXUKVGTu3DVjOYSoooDqh589iKQzdoUPPmUHrzmRS35ta1MUJi71XicDJJGmFCtIxQJ3rIbwWipChF3EDIMkI7DrdAWTfUkVWKV0o/ghBqoo0aBwihoCp5NKWPlvcqKeM2BHpvTR8hhIql7QMiFJEhbRwHIOqZnKwkctzgGn44j65U8MIZwqiOoQ6zqi8pJFJYhHFzXdvi2KfrnyelHyVrvg8QveJbMnYLVExtJ7XuE3jhFLoy0kMo+z2do+0TSZosYqhDhFEbWz+EFMm1RbHLiph2ShthOP04cRxha0OrybCV0BWNPi3H2eY0ipD83Og92IqRULry4xuKco/2H+P78yd5eek8u9L99Js5TEWjZGT4lYmHeGbhDC8tnaffzK/u3/rNHI8OHOWdxhRLbpOxVB/tyOHJubdoBl00ofCfp1/j0YGj7M4M8ovjD/CDuZO8snyBnJZiV7qfiVQ/7+s7wOvVSwxaBT40eJwBq0A38Hh64TRTnSXKRpZvz7zGkfwOjhcmeHbhHa50Fug38zwx+yYHciM83H8ERUj2ZYd4YvZNjuQTRorvBizN1siV0px9/QpRGNM/WmJptoZmaJx94yrNaptcMcXc5DJH79tDu96lVe9w4eQ1Bnf0MXVxnlTWxDB1TNsgjqGx1KJZbWNYOuffukar3qHY/+7RZe8mfnoSR0KwNqsqhWQiV6BgWiw7XcIeGuOh0XGGM9ltaRYrA89QJosfhnhRiLZmMpBScOfAME9evQjAXKfFueoSxyqDq9/veQHNlotpORTyNvQSFdOtxjqx6GOVwXUW9BsbA82OS6vrMlbZInkUQ9vxaHQ2ikHeSsxPVdEMjdRRC7GJFlEUxWRzFpq2OS0pJsYNAppekhQ5UCqTNyyWnA4tz+ON+VkeHp7g7vv2kMlaPacPNtyDG6s/a37BdLNJzXHYVdx8g3pjJC5kPUhhFGJJnZxmE3M9CaioCjuPjmGY6xN3I+ksg6kMV5t1ojjmxZkpfn5fl9Fsjv5UGl0mFo5SiMQ2t4f+yOgGqpQc6Us4+GHUs4+UErVHm/Sj8JYc5tZGTIwXBeupagh0aWCsESV2w4Ar9eubutFsjl35zRdJ188NF2rLuDdoR0klgW8vLrVI2Qa6oVGsvLt277tjnD25Iuo2SCJF9t6LxTrF7M1hqz/tMT/f4NqVJSYnl6lW2zzw4D527OxjyW0z1a4ihWTAypJRDRacFgtOi4yaVLQX3TZLbiLcXjEzGzYppmJiSiNxN4iT55repj+KY7wwxFQT1GEQRUiRQYhkIxIBirxx4RsjhLJO0+jG6Ho+r12sYuo2miJx/SpRDP35NI7noyqSelti6jUsXWO21qSUsXH9AE1RaHZdFCnwgpCDY4MY+mbPUAysb0cYuXSCeeadxCXEjxUUFKreObywiSotFp1TLDqnknfMvosQm1pQI49FJ/IpGcfQlQIN5xRLzikwD+BFAlMdpuHP4EdtcvpuJArTnefwoiZR7GGmTqAq11EXfhygABKZ2K1iosg0EVGyUGX7xdPCYpPaXJdms0uhkCIIkg1utjcWtloO+XyKhYUGtq3T7frMzzcYHMxz7doShq5i2wYIqFSySKngRwEZ1aagZ3FCZ9P5TJcq+9OjZBQLL4xQY8mIVkGRgqyeRlUUgjCp/kshCInRFUnezqBIiesH7E+NUfc6qEIlr6d5qHIb091Fmn6HTuhS9Vose9ddKFdCSsFAJUcQRBw7PIqmSpotF8vQUFWlZ3ktSKcM5hYa7N1ZIZ0yqNY7LC63EALqjQTtZFv6qtNbPmczPlrm+JExNFWhUs4yPlrm0P4hLENneDDP6yevrbqMAnQdj9n5BtVah4XFxGFrxexBkRKEIJMymF1osGein3Tq714c8u8z/Njj7fortIJeshNBnznIhHaIZ549x9Ejo2Szmws330okGyCJribUj7KRx48ClDVJ6uVqm5dev4wfhAjg3jt3smdn/7YFBdvS+eijR3j2pQv4Qci5S3O8fXaaE7dPrPu7l964zGxPG6hUSPP4QwcxN3GaXAkpJcePjLF/zyAvvX4Zx/V56rmz3HVsByn7+r2PiFBIEqk/bihCMprOU/ecxICEzVGEtx/ZwcG9g5uugVYQ6ztGipQLKZothyiKadS6ZKW9WrDbLAp5m/vu3LmlboUQgvHREnccHWNypkocJyiiWqNLaDRZcs+jK6lVSsxm8f5793LbodENCZOVWKy1cP2QSiFNHMW06x0yxY3U1q0ijiOCqIPih1w9N8fIeHlTJ1GpSEpDhUTzRojVYqBt66tJxHbX4/T5WYYG8res5RGGEbVGF9vcusBbLqZ55P79CFXw5sIsTc9lV77IQOp64TiTMji4Z5CX37gCJCglx/EppWz2FfqYbjeAmKy5iK5E3Ggq8bMSoiez0AyWUVBo+suYSgpdmix70+T1AQbMCcI4WP08p/cx172EHzl0wiaKUGkFVUICgiigGSwRxxEShaa/hBt2MZR345ApEdp+hLaH69taCcJM/osL8SCCANB62fNbQ6Wqskwl81kWWl+g6b6IItKEUZ2c9RAF+0MoIs1A5teZqf8Rjn8JTSkRRi3y1qPkrAex9UMUU59gpvFHVLu7EUjCuE0l/UvY+qHet8TMNv4YXR2m653F1g72klS9vY7IkLUeZK7xeVrua2hKhVLqUyjCJq3fyUL7C0zX/w+i2MELJtdR5rLm+2g6L3Bl+X/G0vYQE6DKPJXMr6KQoe29Scc/Tct9hSBaYqH1/2BqE2TNB1Bkmr70LzPb/BOuLP8LVKVIGNVJ6Ucop34BgD7rHvqse7buwDjmcG6M+/r2r/u4Yub4hR33bzh81C7zCyMPJPIuMTi+j6moSCHZlx1mX3YjQlSVCo8MHOUD/UeI4rg3Xlj8yhontRXmSBzHjNildS5rK/HJkbs3vYTPjN236eefGt143UEU0gldLrXmuL24k0Er2fPGQLPWwXMDBsZKhGFEoS/L1IV5WrU2pqUzdXGOwA8p9udIZSyunJnGc30yhRSlgRxO2yWVs9BNjSiMWJ6r43sBhUoWw9Rp1jo0llv426ArfxLxU5M4ujEEsDNXYHehxIszidXwU9cu84ld+7l7cOSWF0maoqAp6xMmEsGJoVHyhknNdVjqdnh68jL7i32YPb0iKZMqw1p6VxjHPD89yXQrWcClNI0TgyPraG+bRRTF+EGiz7QVXSyMYoIwetdUNQCn4+I6HlEUo9xYMQIMU6VW3UxI9nq0fJ8wiojimOF0juP9g3zvygVi4IfXLvHpPQc5VK5sWvXaLNYdB5RTqVUtqJtVqlYEx/3egqobunRDr6f5o64uBOMoplVrE2XW0/QqqRTH+4e41qwTA6/PT/Pc9FU+tmv/tn27gtgqmesh72uv52a6MJtFtOIstyYMaTJsjeJGLqpcgatev2ZINL7UbQTh4zim5Xs8N32V7g06XeVimocf2P9jbyYgWaBvlzQCUBWFjG1SKfxscPrL5WTTve/AIO22i2loKEJwKD/IwVySjJC9ZE/BuO6CJYD3VXax4i2mCEHVb607tx/5uFEiCtjyPN6cmyVjmChCMNdqEUQRo7kcNcdBEYKybTPfbhPFMZamcahSWac/divheAFztRblrEKj55ZjaCpRHNFxfTRFodFxSJs6mqpg6iq1dpdqq0vGMrANnQuzS+S3caTZLNyoSTuYx5R5pJA4YRVNWmSUMRrhNdTYoWQcIKMNo0obUyky476ELjMoqOgyg6X2EcUhTX8SVdooQsdQ8phKET9qE4uIuncZ1TBJq0MEsUNK38FMd4Z20KKgF5FCUvWqCCHoM/pYchdxwoS6qksdJ+oybu8io20vGmlZWuJeIQWapiSW0iKpvJeKaRRVkk6bWKaGoWtksxa5rIXVQ2b6flKpj8KYWInRpIoqVZzQw1A2T3QEQcxi1eFMYynR8QhC5hstypkUg/nkOb2yWCUmcR2N4pisZSIE6KpKx/VouR5hGDFkFqnYBX5z50dXxSeDKORbMy/ypxe/iRNthI3ffmQMRZGcvzTPyGCBgb4sRg8tcMfRMVRV0mg6FHI2Z87PIoSgXEyTsnXc0OflqQuUx2xIB3SEQ2m3gd0vmTiS5+mzZ8hlLO4ojHH3A2O8duoKGc3GT7vkBwx0H2a7y7QCh6gN1+qLjAwVWFhqMjSQR+vRFo4fGUPXFOrNLsV8irMX55AC7r3r3Tmh/DSHKW0e6/80a8mFUki6nQDfX6DT8chmLYIwpNXx0DUFy9BuceyPEULrUSiS45t+Byf01iUTF5dbXJlMrNNNU+PAnkFM4+bLxx3DRQb7c1ydWqbZcjh7cY67ju1YNW0Iw4hTZ2fwe4iSHSNFRm4BMWaZGof3D/HayasEQcTZi4l9/NrEkYJCv1lepYL+OOFHIZeayzQ9h5SqM5EpbqjS65rCvt39q867K9FoOVybraGpkr3jFVK2jrEmYdLpuqhC4oVbL/JHBgsUctsXZFRV4ciBYb767TcAqNbazC82GB/vpxXMoQt7S6qapincfnRsDXVxYyw1Opy7tsBDx3cjosTNlndhlqJKGz9uIVVJoS+zJc0tCEJmL83jdl0mDo+tJo6G+nOMj5ZW9YX+7y8+g+P43HfnLvI5C0VRtkVQm4bGQF8Wzw+2XHsODxQYHixwuV7j1bkpBlMZvn35HL926PbVYxRFkluji+P5Ab4f4oYhT1w7z+5cCUUK0nGMrea3XOu/VyMII2bqDdKmwYB2lCiOiGNBK3TI6Um/aMoQoR+T1jQ6rseeXBpFCiQKWS3RDxq+0bgCSUy0WhAesvcg11DIgiAkDCJ0Q9303oVhhO8FGGZlzXMg1qzdASy2kgqJ4xhbP0Cf+EcoYvOEaMF6HEMdpeOdIo49NOUOUsax3gkkWnQHI/l/Sdd/izDuYOuHsLS9QELrqqR/mZR2iK5/DgBD3YGh7lg9v6ntoZz6B7jBJHZqP1nzPuSatkhh0Z/+NSx1F36UoEMlGqBQsD+EquTwgmk0pQ8r/cs4/vmeUUuSdBrO/1Nazou44TRuJyKdPYIUFoEf4DpNItEla97X6x9JGLVYcQtOG3cx4JfpRq+B7KJq+0kb19+Lm4WlGgkSv6fJ0227aLqKlTLww5AgjHqU5B47Jow4NTnHoZF+5uotZutNbtsxiK3r+FFIHMVIKZBC4odJIWOx2aE/l8YLQ1qOSzmTwgtCVEWusiGkECw225TSdjIOyGR/74cRuqJsarCwNtwg6LEBtl+DT3erfGXyBdqBwy+NP4ghVbq+DwJGd/czcXAYw9IgTiRxbn9oP3EMZ169zJ6jY0hFcPDOnQgpOPa+fclzLJL2H7xzAtZocMVRvIrKRkCn5ZDKmuuka6I4xgkCzB5L6ycRP7WJI4CcYfLIjl28MT+DG4ZMtRr8yZsvM5jOMJbdmvp1sxBCsKdQ4sTQKN/p6cl87dwZ7hkc5a6B4eTh0FUKeXu1ehnHMZfrVb70zslVqtL+Yh+39w/d9GY4ns9So0PX9UltUvUIoxhDU8m9y80ZJIOmlTIpVHKomrIhsREDnhvgOj6GufnAKxBU7BRj2RyqlGQNgw9N7OGlmUnqnsvlRo3Pn3yV/+ne95M3tob7rn5njyJmq8nite37uEFAzjSpuy6V1K0t5FYEzG3VYFdmkIKWwo+TagUkiKPAC5CKXNcmW9V4ZMdOnpq8RNVxaLguf/rWK+wtlNlbLG97vzaDmW/3881CAMOpHGUztQ71JoSgoJfWHatKuU4va9np0vBcynZqC35zkkx9YebahulwBV79dx1BlAzO2hbC6++1kFKg6wqappJKJT/7QUij7VDI2Ik4PzGuHyJEoh+2gowQJIurIIxI2xsX6oqQNIM2TuQRRT7TzSYHTYuW5+GHST/ONJtEcUx/OpkE256H7L2TURRBb9KS0iJlnkCVpQ3JzaQtEkMdZyDvsrt/F6qiEccwuVQnZ5tkegKrq8f3kmGqIpOkbRChKnIVuZJw+DcuuqSwSBsnUG+AfJtKgbF0UtEJIoeUOkDZPEhMiBvWKJkHsZQSOHkQAXpcJB/ehW5KLFlC04sosUmn6TNg3UcU+0lSyZBIodBLL+NFLQyZw1QLeFETicV8OMWit0BOz2NJGykkbuSwopMWxAFSKKhSQ41D1E0sx9dGX18GtaAyM10lV0yzvNxC0xSazQQp1O0kSE1dV3ENjXTaZHGugSIFURDRV85w9eoS6ZTB2ycn2X9wiFiLKeg5lt3alpsZKcWqW9lwPst8o8VYKQ8IhvJZ5pttbF1nqdUhbRg0ug7ljIIbJBukoUKWk9fm6IF5EUKs063RpZZYxG5B39F1lbtuG1/fFz3q9sF9Q0zP1giCiKH+HK6XmCTsnqiwe6LCstvg2cVT9KfSNP0Ok66POQKTwQJ7dg2wY6JIK+jSFS7jgwN07DZp1UIRglw+RbfTJYhDGn4HxZAcvrOf/dmx1bF7oJJAsQ/uHWR2voHvhwz15/D84O/FVeTvO7phm6nuFbzoulV6JiqjaeqqeYfrBbx+dpJ8xubYnqFbPHNMEFUJomrPFTFxwek3i+sMRGbm67heUpywLZ2Byvb06ZVIpwwq5QxXp5aJY5iaqeG4Aele4qjdcVlcvm4cMTJYQNdvnhwXQjA2VESRkoCIVttlZr7O0MD1deGit8yiW2XUHgTeHdp2JRQhyGoGXhQw221QsdJktPXrNNPQ6C9v7I8oiplbbFApJe+MXDNXQDJXqEJibyN10FdKb4kUXwkhSITptUTPp9V2qdY6lPwmllKgEywRxC4qG+ekbNqkVEhtey91Te1V8QVClaTz9rvQOAIvrBNG3d54FiXz2CZhmBq5coZWtU2wpmKey1h85JEjnDk/R7vjMjVT49//2Q/53tOneeDEHu48uoPhwfyWGosrKIPMNuvroYEcqioJnQhT1cibFqeXFzizvMDOXDHZLAqxbmxJaJ5xsgEUgrxhoSsKOV3BDetEccTPkgHkXKPJcxeuYmoqQRShKgpjxTyz9aSQ7gUhbhAkEiFSIWPqVDIZlJ62rLLtdnPrMfvS2Tkun5/j0Y/ftunvZ6eqPPPkaT72mbuw16BNXcenttymfyi/5vneeEPq1Q5SDlHI7dqGAqmS0o+suqCtjU7b5W++8Dwf//m7KBf2b/r3Ap2MeQ8Zc3NkjkBi64fJWRtRMCvnUJU8RvgIcdslP5Bbg0ayyVuPrDve1MbX/63IkrcfBeALf/JNHvuFnWSHdE6+dJZuO8fdj30OIQRhEDJ3dYnixHVqbOBFPP+NBQ7e9SAjuzZ3RNwqFCH5hR3393RqYfbqIl/9v35A/0iRz/yTxzl5bY6rSzV0RWG4mOPqYo27dg7jhyFt1+Ota7MYmso70wuMlvK8cmkKRQp0VSVnmzS7Lv25NM+fv8rDB3fS9QLqXYe243NlsYqqSLpeIgOzZ6DEpfkqY+U8F+aW0BSFtGlwdanG7eNDTFS2Z8M8cf4iRdvixNj22kEVM8unR+7BUnWKeoYojvnq26e5e2SE3UdG0W5IgK44Xe85NobTcbFsA9kbZxT1JgPIDVPD3mNjdHvnWIla1+FvTp7iHx49RM589zlC+CtNAAAgAElEQVSGzeKnOnEkhODju/bx/PRVfnj1MlEc8+TVizQ9l988eieHyhXyhonWc9EKopBuEND2PS7Xa7w+P8Mndu/fNMmU0Q0+d+R2zleXOV9b4nxtmT985kn+2d33c7x/iIxuMLGjjziOafs+by7M8u9ee4GTi3NAoj/zuSN3ULa3T4IIoJix8cOIlGmwWcY7eREU2o77rtFGQgiGd1Uw7W10lkgEHOvVDmG4mSI99NkpVEVZ3UA+umMXr85N81dn3iKIIr52/gxOEPDZg0fZWyiT7tG6YhKklBMGND2XS/UqL85M4oQB//3dD6IpCpaqokpJy3XZXSyulAC2jDiOaXkeVbfLVKvBaCbHeKpCHMfU/A6a1FePA7EpJ/2h0Qk+snMfXzz9JmEc88b8LP/sB9/iVw8d58TQKAXTwuhdbxAl+lgtz2Oq1eDl2SkeHptgf7FvywXVCuwxiCPCKHHvC3p0o5UIooiq28VSNVQp8cIQRUoUITY9r6VpHO0b4CvnThHGMZPNOl+/cIbPHb6djH7d/S2KY5adDj+8dpn//eVnabjuFrWUrft3hQ4VRhFBnFx/x7+OOgh7OlM110EVAlXKXtvXi6znUiZ37BvZNFHyXgzPC3jxhQtYlk7X8dm9u590weLNCzOkTZ2u62NbOqauYWgK1WYXx/WJ40R/QdMUTF1j/47NHQ7c0MWLXCqmyYM7xilaFqqisG4lnvBAAdhX3mjX2Q4aBLEkm/49urHHcvcCqtQwpI0iFPzIRREaUjtBSr0LVYZYSoKi2z1YWic4uzb8KKDuN1CEQqhGeMQYQqd/UEXEknpUw/UMgihYRf3ltBxDhX+54VxSKNhqQl0LIhdDyaLLLFHs0W/fjiGzNGpdTr2yRKmSxXVmWV5sohsamtbC9wNs26C63MIwdTzXZ88BjYHhAlJVVjdgmrw+/moyqczvzWQYs3dgyoS+k9auo+Gy2rvnfhtGUh1ynASVWat2sGydXM7m2tVFVE3B7foYuoquq+QKNlOTywR+SHW5RTZnUV1q4XQ9Om2XMIjoyi6LbpUwDnG3EInUVYXDA8lCTQjB7v71ieZCymJvf5mlVidZRDkuxdR6ypKla7QdD1NNHGZmLy2w+7ZxrMzNCwDtoMWsM0PZ6COnbZxD+ytZHr5/H7VGl6MHR5gYu96+jGZzd2k/lqLTChxMRWM83d8TuozRFQ0vzKBJFUvRGU/1o0uNIA4xFZ0ddoWMZmMpBvNOjZKR3XJurJTTfOCB/VTrXY7sH2Zi7Oa6Fe+lcKIuT85/HSfssujNUdBK1P0q96Qfo15XEzQbyQbf9cMNOjXbh0SRaQK/voowciOfye48R3LXBbmX1iR3DF0ln701GolpaOts0Jer7cTlrLfB63Q92u3rybBSIXXLib9SIbV6rZ4XsHwDqrpsFHFCb1Vc9ceJMI6xVZ07MyVymrlBdwkSxE8mvXH+C6MIzwsYGchv+q7FJIgmcxvEeiZl3rQ/hBBYpoZl6Xh+F9cL6Do+ttrHtfbzaDKFIjb/DsNIEJKXWnO8Xb+CJlWGrCKH8ztWESBxHJPPJKYogZNQ7gfHt14b3RiK1FFigzhK+krZomKvaArl4SLTF+aoLTQoDyeUNVVVeOjevXQ6Hl/82kvMztVpdVxeO3mNN96epL+S5eDeQR64Zw+3HRqlkLPWrQlrjS61Rndb845cxkKKpIBqqRqz7RZj2Txz7RY7snm2c5/VFMn+Qh+znRaqFGQMFVNNbyFn/N6NYsrmxK6xVX1RQ1MxVZW+dJJIDHuMCkUKFJloYKk9LayIAIlKRLha+L0xlhebxFFMp+2SzdtkcjZO18PzAobGSquMANfxWVpo4vshmqbQabsszjeYnapiWjqlvgxxHPPaixd546VLfPBTt9PXnyWVNqlXOzRqHTRdodyfpd10ePKbbyUaoLeNMTCcJ4piqkstgiCiUEqTShssL7aIogin45EvpUlnTDw3YGGuge8FLM43Nt1b/aQijmM6TYfnv/0m81PL3PuhYwyMlpCqpF1PXE3drkdfTyi8Ot8gCEIKfVlSWYvAC1iYriIVyeJ0lcALaNUTzcDKaNK3nutz8eQk3/7Cs3zyNx8mW0qRL2epzjeSa+4hH+M4pt7T1UllLfJ9WZq1NqEf0m52sdMmhb4snhtQXWgQ+AFmnwpZGN09wInHj3D6lUsANLoOGdPg6mINRQq6nsdCs0PX8xMDgFKOQtpmcqnOVLXBxfllHtg/zuRSnWbX5fBoP4WURX8uTTFts9TscGXRoev5DBWyPHfuKgeGKwRhSMf16Xh+D52UWUUzuX5A1r55QqXhOLhBwMXlZQqWRd40cYKAhXabMIrpS9mkdB1NqOixSb3lEZsd8qbJcqeLH4X4MqbR6VCybTq+z0K7jR9GZA2DUsomjCS1wKW93KIvlSKlayx1ujRcl7xpULAsGq6LIgS2rlPrdrF1nbbr4UcRHc+jaFtohoobBMy1WlQ7DvO9Nv6k4qc6cQQwkMrwO7fdw3ynzanF+Z5mzSTvLC+yu1Bib6FEzjCRQtDyPebaLa426kw262iKwgMj44xtwUK4rTLI7xy/m3/93A9YdrqcWprnv/vht7lvaIw9xTIZXafj+1ysLfOjqSvMtltEcUxK0/mlg8d4/+jEzS8gwZH1Hsytb5wfhHhBeEtUtbW/l1IwP7lMrpTeFBGQUNU00hmTVsvZdHDTpGRvobxuwZkzTP7xsbu40qjx/PQ1/CjkW5fO8uLsJAdLFSZyBVKaRthL8ix221yuV5lqNnDCgLsHrwude2FIWte5bWAAS9NuYToVBHFE3jAZSKXXHZ8gH5JrCPyAVq1Nvi+7oWqf1Q1+48gdnK8u8dLsFFEcc3Jxjj949kl25YscKPWRN0xUKen4PktOh8v1GlcbNTq+z4FSH/uLm2vHnK8u8YNrl6i7Dt3Ap+P3/gl83lq4bhF+pVHjf3z6u6RUHVvTsFUNS9Owewmi+4bG1i1EVSG5d2iUsWyeS/UqYRzz+bdeZabV5M6BYYqmhRuGzLSaPDd9lRdnp2h5LncPjlBzHd5ZvtGufWMsdTt87fwZ6q5Dx/foBEnCqO17XFyj3VV3Hf63l56hbNtYqna9/arGeK7AByd2YygqipRkfwyk3E9rKIqkr5IlDCMGBvMMDOZACHYNl6k1u4mOkxCkTJ0givCDCNvUMHUN1w9WE0qbhRf5pNRsQpNSFfrTa2DRNy7Ct1mUX+u8gxO2KeoDLHuzFPR+ZjtX0KVBRiuy4FzDVFJo0sCNupSNYSwlSZ5stvFZiWbQ4tXqSQbNCu2wk1RqiWkHXdKqTV7LoEudqe4sei95e7xwCPUm04gqjdVqtyIMlN7/a5pKZSCHaenYto6dMpCKwHMDoigmlTbI5m1UVSGKIorlDL4bIIVAyo197Lk+MxfnsFImlTXJg5/EAt5OGRw8PIwQkmIpnSTfpGB4pLA6qq8k5IQUnLhvN1KR7JjoS5xo+nqiuXGMqinosYKe1lCEXBX53yxuTgeOKWeSBd2NSaM4jimnbUppGwG06x2mL84ytLsfK3Pzd7YZNOiGHbaatxQpt0zSaFKlz0iSdBnN3vT6UkpyDX4Y0mhHdIM2u4tFUppOtvc3pqJhKwaGsjX1SkrJ+GiZ8b87E5H/X8OPPNzI4fGBT/Na9Tl2pvex7C3SaNZQlEqi0ScEnh8ShOG27lGbh4Iqr1ewndDDDb11b81aK3Mp5S07aCrK+mNdL1iHOAmCBKG5EoZ+63B6Yw0dL4ziDdcdRAFO5HLrJZVN2i8Edc9h2e1sKY4tpUBTN/aHIiUD5eyW1yO4jvTcTrT5VhI0iiJXqRZRFCc0qqiDKkyi2CeKN6dpSSmQUtBnFbjUnmPAzNMMuqxdiOqqQjFjo0hJKBJx+naje4vi2NcJj2EYJXqUYvVX6yIKY5rVNp7jY9xA+zN0lY8+eoRd43088aPTPPX8OZZrSSF0Zq7O7HyD51+5yOH9wzz24AHed9cuUnZSbMumTPZN9FPMb13k1bVEWzBvWDw+vpsojnl7aZ47KtszCmJiJIJ7+seIiTlXW6DmXaOsbE99fi+GpWuMFq4XXlbufc42143vMRFBHCAQSJH00bxzkbw2SMOfp2Jujuz50p89gxACo6ff8nO/fC+djsdT332bVNrg0G1j+H7IM0+eZm6mxuTlJeyUwUMfPMzViws8/b1TdNouB46McPC2Md546TJXLizwyrPnuet9e4gj+OoXn0c3NAxD5eEPH2VmssrpN69hpw18N+ChDx2mvtzmxR+dpdNOkBsf/Yd38h///ZMUimliEmTxpz97L88+eZqL5+bQdZV6rbNlv/lhgsSqdx10VV2lDK3MfR1/AlVR6foKbuChSomhbhQJry40eOu5c7QbXXRD48FP3I7T9vibP36SsX2DeK7Pw5++i06ry4vfe5tuy8FOm3zk1x7gjR+9w+tPv0O+nGFhpkYcw9JMjR985SX2HR9n4sAwTtvlzWfPcvXcLK/84BR7jo2RK6a5cPIaT3zpBUqDeXKlNEuzdb7xH55CURV8z+eDv3QfL3znJJMXZilUcjSrbT75Ww8TeAEvfu8knaaDaRt87HMPYqfXj587yoUexd4g7lHtbUPD0pP19UA+g61rSGCy2uC2HYMM5JLPojhmudUlZ5uMFPMstzp4YUjK0Bgp5pirt7hjYphi2iKMYvzw/+PuTYMsOa8zvSf3zLvvVXVr767e9w3oBStBghQWUgLJocihGGNrYmbsCYcnwnaEw+EYR9jhJawfY0kzMTOyRhqFZIoiKe4gKYAA0djRaADd6Ebv1dW173X3JXf/yFu3qrqWboj6QeJEIFBdN29W5peZX37nPe95X5eIrhLRVTLRMKosMTy7SFhTWSgHLWye77bvXVlYu+bwgXPjk8xVa7iex9ePHmKp0eSNO6Ms1OpkwyGeO7CPm/OLvDQ8TFTV2J5O8WBfDwJQNk1+fOUa2UiY0/19/OzaTQqNBu9PTvHE0DY+vWOIv3r/IqosE1EVTvb1ElZVvnf5CposY7suXzqwj7dGx4hqGif7e/nhR9c42d/LL2/dpmyaRLWA0PHVwwc5Nz7BxekZNEliobb5/fn3iV974EgUBI525PlfTn+KPzj3GhfmprE9j6LZ5PzMJOdnJjf9bkzdmOGzHJok8/T2XTQcmz9+/21ma1Xm6jV+cOsqIgHLwvG9NS5aCU3nn+w/wu/tO0JE3ZrlA8HifaFUDYS+NglBENA1hbCt3leKs/qMRElEViTqlc2FtQUh0G7RNGVT2vPdOlCCINAfS/CvT3+KP37/LV4eux0gpPUaZ+sjnB0f2fIYVSlwP1ve12ixyHSlwrF8nlxka2FFURCIKmpQbRLXLpwcz6ZkL+HjoeoqkUQYZQOtBUEQGIwn+denP8Ufvvcmr03cabPRPpyf4cNVAM9GsdVS8/LCHP/m/JvU7I2ZAstRtkxeGr294Wdf23OQB7t619SxBEFgeyLF7x88xh+ce73N+Pmba5f40a1raLKM53nUHRvbC2juj/QO8j+ceIjnb1+/L+Borl7lD997k6K5tRC75bq8PT2+4WcPdPXwWO8AmiTTcG0WGjW6wjEU4ePp7/w6hiSJDAxkGRtbxDRtHMcjHNbo70zS17GedSHkEqsWTVu71YWlEAklhi7+auyslNpJSI6iiSFyeh+SIJHTexEQkQSZDr1VFSSg0Suidl/CaaqosrflQOS3QCPXD1ybRIKecs/3yOkZLM9ipjn/K51HKKKxc99aocPlquJGsdVnAPVyg6WZEl2D//DsN0EQUNpz+Ar4thkbQGxpAS1/fvd2i80id+oTROUwCSW2gQ6LcF+A11aJm+lZVJwqSSWOLMroIY0DD+0mnt7cJXF16GLAKmm4TWLy/TkobXZs9/rudLVK07HXuG4GSbWAIa+9np5Xw8fGdWeRpW5c38PyymhSCln8zRfovztEQUQRgrVBVIkzUb8DQMMLknfLCgTSFTkA8dVNXKs2Dh/PqwUuP36gkyCLEnXXXLPuWcti8vHus3q5zMxds58198VajNz17pZp3zy8Na22dx9jECWrjOO7GzRp3V+IgkBaD1N3rDaLYuN2lg2+KwrEooHu2UbhA4vNOvoWZhuu599XW9j6cRZxPBNZNKg7ixSsO2S0nch3aR0tH7YmymS0GKO1OXbH1jqwGZrCjfF5BrpSiKJAJB7CdTYX9F4bAiIKrm9iV5sU5sv0bAsKcne707qOy9J0gUqhuqF4tqJI7N+dZ2gwx5OP7uO1d27y3oejjIwt0jRtanWLd94f4aPrU1z4aIL/8nfPkEmF8XyP+aUqsiySSW689hSEoNBn+g7vzU2hSTLjlSIPdG7sRrccFctiodjg0uIMvg83inM81hsmH9bWaWF9EqJeaVItNch0Jda00ayw4V0+LL7GheIraKLBo7kvkVAyTNavUpLniKsbM7EhkNQ4cLSfk4/u4s/+6BfMTZfYsTfPiTM7uHIxWIuaTZvbN2b5zLOHGL09z+JchWjcIBozePLzhykV6px94TIPPrKLBx/eiRFS+eLvnUYQBOZmShQWazzx1EG6+zMtVpPBviN95HvTnDgz1GoJE9l/pJ/CYpVXfn6ZRs3CMh2OntrOjj1d/Ic/+DlT40tc+XCc575+Csd2+eafvrrpeQ0vLDFbqQbyEYLAdLlC3NDJRcKMFop0x3vx6eXy9Ay+77M9k2ZXLoN01/uzZ3sHRx7ZTb3S5HNfP4MgCIxcmaRZN3nomSPEkmFkVcKoaOx/cDuF+Qpnf3CeernBB69e48nfPUUmn2TkyiSCAP278xw4tZNGNcgFYqkIpz53iPmpIs/98yfaRktHH93DjQ8CFzDfhw/fvEGqM85nvnKKt//uIu+fvYrZsMj1pPjCP/0UP/iTlxm7Ps3+k0Pse3CI4lyZV35wnnqluQ44GsgG7/ttudQayYVtd7WNJcIG/dlAh08QIBcPnuPl7yTDRvu7Ozoz+D70ZRJYXnONyPr2VaztVNjAcT0qTZNk2MDzXS4UXuFi8VUMKcJjuS/Tofe3720BOJzv5Ld27eA/vfs+k+UK+ViUA50dTJTKfDA5Rd2yeHN0jCP5PA8N9OH5gcGS7Xl8//JV9nd28NBAPzXL4k6hwFcPH8T1ffoSgSZauWny7N5B9nfm8IGXbg7TEYnw7N5dfP/yVS5MTWO5HrYX6CGbrtPueNnf2cHJvh7+/N33KTQanB+f5Av796DLMn/+7vub3p9/n/i1B44gqNw80NXDHz7xNC/eGeb529e5XVyibtvBwLUujiSKqKKEIStkQyEe6R2kK7I18q9JMl/ZfZCdqQzfu3GF18bvUDAbNB0Hy3MDSpisENd0DmY7+Ee7D/BQTz+atPHQCQjkIzGWmg0gaInbm83ieX478TFkhcF48MDkI0FFarBr7YPSGY62t4mqajuJ0CSJ/liCpGXSGY6iKTI7DvZhtKxGfd8nbYTa303qBpIoMrSzs71vRZToiyWIqBrZULgF8mxwLoLArlSG//3hz3B2/A7fvXGZG0sLVCyTprMy7rIookoyIUWhKxzleGc3T2/b2RZ21iWJdChEqdlEbB2jIAjENZ2BeALTdUkbofaY+r5PSFboisQQ70qeUmoOXQojCwpqROLEZw9tem0FQWBfJsf/9ehnOTs+wvdufMTNwiIVy2odv4dAsDBXJRFDVkjpBodyXQwl1l6P1ZNaWFHoj8VpOOsru/drUZs2QhumhZIo8uWd+4mrOt+69iFXFuepWRYNx6bh2MhicJzbwlE+t20HX91zkI5QhPlGjZdGbyMKQgsw3TgUUaI3FidpfRzHirXRGYq0q3AV2+TN2Ts82bOLlP7JSNpG78xz/t0RjJCKrqsMDGaCJHbVda07Te7UZrhYHGa8PoflOURlg6FoD/viA3QbmWBOWgUyBKo8HpZnbQoKuL7HklVmuDLFlfIo82YBy3MIyzo9Rpa98QEGw12EpKCaqhCA18qq1EhBpeGaFKwKru+RUEQigozju8yZRS6XbnO9PE7ZriGJEt1Ghn2xAXZGe+kJdW14XBtFRkthew6T9QUul0a4U5tm0SoHehJKiG2RPAfig3TqKTRpY5B9ec6quU0KVqCVkNUSaK22pcnGAh8WhxmtzVK2a8iiTEIJszPay+5YH1kt0XY8lGSJwmwJWZHo6M+sew4dz2W6uciHxWGGq1OU7TqqKJM3MhxJ7qA/3EFY0pEEcQV4u0fYnkPZrjFRX+BWdYKZ5hJlu46Hjy6qZLU42yJ5tkfyZLVEsG9BCFo+W38joa4HcsTWNkF7bpVr5TGulkeZbRbw8EkqEfbE+tkbHyCjxdfo0SxH0S7zfuEyB+K76Q11YZs207fnCMfDJLL3p/ni+e4aXaStwvU9Fs0Sw9UprpZHmWsWsX2HiGzQG8qxJ9bPYLgTQ9LWna/lOnRGokQ1DcdrsGReQ5dS1JwZoko3kVV24pZzA9u+iedX0dUTNH2FhjOFrw4RFns/cS0iuhjiWOoMqqQzFNnD2fmf0XSbHI8+xkJKaGsciaKA43pbMljWh4cohtHl/jb64fkeGTVOwzOJ+YH+TWwVQ81xPOrNrYsmy2E7Lo3minFDOKSuESHVNGWNg1qtbt03KFWtr4BbsiStEcaGYAxCsvErJfC253G7vMBYrUBGC2OE7u+58f2AXXXpxhTpRBhF3vgZCkBPd9N1Q7Npb6oJtOY4bRfLCsAcWRbR1EBHY9G8iSSoKGJoy3HwfJ+5ZhFNUig7jXXnIrd0JEVFQtVVUqs0VrYOAUWMIAkG2e4kqqYQSwRswmZzraGHbTnIqkLXYAdj16cIJ8JEk2sB9eW2vL07u9izo5NC6RiXrk7y6ts3ef/SGIuFKtWayU9fuoTv+/yLbzyCIksslWr4+KQTm+s5dYQjLFp19qZzdIWjvDdz7/QoJCskwyGm6xV2JTJEVQVFKvDxLW5+M2Jhpsi18yM8/OyRtnj56qg4Rd5c+DHTzaBgGlNSfLrj63QaQ9ScwDF4s/HXdIWOfALdUFFUGctcv77WdYV8b4qffe89ookQDzy0A4B0Lko0HsKxXTzXx3O9dZcgk4vx5OePcP7NW5x7/Sa//bWTxJPLa9ZlB0Sfsy9cplJu0JFP4jgunucRCmtkO2IYIQ1JlmjUA9OJaKtlVzc2JxGkQiEimorYKoQPppOBvIogkAqH0GUZx/XIhEMookhUv3cb+eqIJsMks1EkOdC4PfuD96iV6+R60ji2i+t6mA2LRCZKPBUhtBXbWFgZjY2OwPc8yks1svkkmqGQyMSYGV1AlCQ6+9LoIRUjomE2LF790fuUl6p09mVwbOee89i92dUf5/fB3Hq7doMeYyBwsJbWnrciSxzoW8mLi9Y8by7+mNnmKAAJNcsTHV9FWQW2i61ilkAwZz5/9Tq26xFSFUwnKOC4nh8QHlatIm3XJRUyWKrXqVk2IUUlEw7z1xc+JGkYDKSCorShyMSNoCXaa0mKLLPTRIG21pzlutiuS6kRtHlLokguHEYWA8fb5W4BqQVWfrz29XvHrxVwtKy9cvcpLv+uMxzlq3sO8Kn+Ad6YvkWlCQv1Og3HRhQEQopCzojQHY2xO5UhZQQPpeU5NF2TqLyS2Nq+w4JZIqclkUWJYx3d7EllubFrP2dHRxguLJFuPdQdoQj7sx0MJdPEVW1du4fredxaWGJnNo0iivyvDz2B3dK6iagakiC0HcUADmY7+JtnvwIEC56NVNr/51OPtfVyoprWHpMeI8q/Sh2isz9NMhVh9uocWl8GYxWS+/sHj/FM1xCu49LdlVpHte2KRPmPT34hcHkTAwBn+MoksizRO5Rj9EbAxunf2YkoikRUlf60wentKTrScG1xnumaSdOxUUWFJ/J7OJjqZjCeZCiZIqbqGPKKAFjVstri2OOlEjFNQ5VlvrBjD58eGGLRrPDe0m2mrFn63BiKKBNVNSqWGTictY47EHlTiIkfTxg9qen81uAOTuV7GS0XubG0yFS13HIiEzBkmaRu0BeLsyuVJWOECCsqDcdmqlZhezyF2bSplBvEEyEezPXwx2eeCo5HWRYrFrBtl2bdIp4Kt1T/N39YdVlCFoV2292K44OAKon81rYhjnfmuTA3zY3CIiWzge8H90JfNM7hXBfdkVigj+AXOdnVyV8+/UXAJ6xo+N4iCMlgn36dwHpUoD+e4M8+9zttfai7o2jXOTt7Bdt3GQhnOZoaXAfeKZLUFvTURJmUFv5EOYhkslGOHBtgYmIJ9S42m+/7FOwK3xk/y6tzFylYFSwvEI8WBRF9VqFTT/Fc7yMcS+5EXwWYiIJIp94RAAZ3JQq+H7B7zi1d4/sTrzJcnaLumDi+26bDK6JMTAnzUPYAz+ZP0Rfa3A77SmmUf3/rB5TsGl/qfYzf7j7DO4vX+NuJs4xUp2l6Vgs4FVBEiYhs8FD2AF/p+xQdWvKeCxff96k4Dc7OXeCn0+8w0QLPHD+YsyRBRBWDsXg8d5hn8qeIKRsv2n3g9flL/KfbzwPw3+/6CvsTg7wyd5HvT7zGTHMRy3PaxysJIrqkMhDu5Is9j3A6s78NsiSyMdS7DAh838fyHN5cuMy3x3/JRH0e07PXnP9Pp97mdGY/X+57DE0KFnnuFqV+z/eZaizw+vwl3itcZ6Q6Q8M1sX0ncJuBFmtVQhNVekJZvtB9hoezB9FEhbBsYPsOM8150mqCsLQWdF1meN2pzfD/jb7I+4WbrfvBwW+N79/NvMuOaA9f7n2ME6ldwUJl1fjKgkyX3kFSDYonnudTnC9jNaz7ArhVUUUVtcAJcItNfd/H8V3eWbzK9yde43ZtmsaqYxURUEWFmBLikdwhPp8/Td5YAfY83w809lrvVde3KFl3mHPfJ6r0UUVcAxxJYgpHUNCVBxDFKIqvUPFub57f7vQAACAASURBVOoc9Zsay8UKSZDoMQYQENBFg892Pofn+1hVmDAn8D2ftpujJFJrWG0L+HuHiCgoWF6V5eRJFmUs36FglcloCWQkcpkooiDg4dM0bQpbtGasjkbTplhe2TadjKxhRIVDGtFVa5f5xQqO47bd+7aKuYVKkCACqiqRSq5vRVIEed376+OELIr0RZJEFY2E9nGKLT61hrWuFW/tFlB3bKq2ueHnAIVSfdPvt/fj+1RrJo0WmKdrCuGQiuubhOUcISlFTOneVOcIgsc7JGnMm6V2i+lyuJ5HKhZCkSSspk21WMOIaCRbAvXr1ut3TZu6nEOTM4hIZLuCtZtlOZSra1nPekgj1ZmgVqpjRHTMhrkOOFpzzIJAKhHm4Qd3cORAHx9emeAvv/s2127N4Hk+b52/zUMPDHFwbw+Grt7TNSkkK4S1BH3RAIhPD9z7esuiSELVeSDXw5LZ4Hiuh5JTZ8mcJKXmN9Xz+U0N3/PJdic3nVwcz8T2Vq5rw63h41FzimS0fnRpc/ddQaDNclkOzwvYjb4fgEFBAQ+aDZtsp9LSOZLbLZ+rQ9MUGjWTaqUZ6H9ZDrmuOJ959hDf/+bbzM+USKTC6LpKcalGs2GhagpjIwscPz1EPBleY2u++n2pajKKKjM1UcCxXWqrOj6CeXtlfZ2NBvewwMqc3j7h4At3jcMWeUNIZWZssd3+tbKble+M35zmxKf3E2sdvyiKRBNhxm7O0KxblBarrbH1gnH1WmMrCqiagmM5VArBM66ocotp5ON5wfjnB7PcvjxOcWEbYzemyfWkWZotrTvu8ZszHHl4F6mOOHYLBAzE8X38VX/z4zKZV4/vlmATwdpjpHaNjNZF3ujbcr+2Z2KvMp9ourU110sURK7NLdCwbTzfpzMS4V3XpdK08PDbOf7R7i7eHB1jrFiiLxHnaHfgCHe6v5c7hSK/uHmLp/fswvZcGrZNVyxKxbTQW2ZS7XwX2NuR5fuXr/KdDy8zX61xur+P+Vqdn12/QaHeYLpSaXUErLQ9i0LQYr2vo4OfXb9JVFNp2s4/KJT9azWrle0a7y5dpS/cgSwEC+7h2mSQKIgqpmejiDLbwnkGUgY7or18VB5BQsT0bHx8kmqYkl1myjYZtzw83yMihxitT3MitZd5s0DVabAnOsBYbYaEEglQOkEgomoc7cijehI92jwP9PbQEQ2zWG/g+T4KIrPVGrbr0hGJUGg0MJ0ASXxnbJzOaAQPH8txqds2KcPg+tw8c9UaJ/t7MR2XqmmSDoWwbJem45AJhyk0GhQbTTLhMFEtaNFKrHLXsi2HqfEFREkk3RFHqNgIRZtsb4RbSyPMSSsvZEEQCEsKasNH0zUkG6anFvE8j0xXgObLokjaCDE/VaRp2kS7gslifHiO7sEsju0yNjxL7/Ycoiji+h5vzF/nP4+8EhyQCGIUQoAuKXxh13Yeyq13E1gOTZYpNZsUmk26YzE6Gw26olHCioohK3x7/DW+Nfomqijzvx36CqczOxmMp7Bcl/FKEanlPFI0G4iCQGcoypIZ9LNmjDCFZr3Vn64H10mUKJlNLM/F831SusHF+WlUSeZgppO+WALTdegIRVhqBlouMVWn6QZaRZIgMFuvMlUrc60wT7ca5cqHYyzOVzBCKqIooodUHMshngwzPVlA0xTCUQ2r6WAvmgzt7lrj8LA6fHcOrPPg9IM7C1gg6OA7IHWDO4EkpuhUBT7XNcHneneAMw/yLvCK4I2BuARuA7wEuLdRpUEyai/Y7wHboHEe1JMBYOSOgpQHQUN2Z8lITfAKoJ1CENcK7hbLZb45fpaaY/JM9zGe7N7XZnRsFKbrILUSik9KSLJErdpE1xVChrrm5bRolfmT4R9zdu5iGyQJSRph2QACMOV2bZr/cOtHPJs/vYYJYnkWRbuEKiokWQt+NlyTn8+8yzdHf9Fm3miiQlqJIQkiDc+iateZN4v8cOJ1rpXH+BdDn2d/fHDDc7B9h4JVpWhXuVoeRZdU/urOCyxZFXQpcHsQEKi5TWpOkyWrwk8m32a2UeCfDT3LQLhzw/3CMnhW5U+Hf8Kr8x/ScM32OCTlYGFYb+13pDbNxJ05xupz/NPtT5PVNgJ9fUzPYql13hdLw1wu3+FHk29QdRpookJCiSAJEs3WOFSdBpdLI0w1FhEEgdOZfWiGSiwdWccOsz2HH029wd+MvkzBDhZNsiCRUmPIrX3OmUV+OPkGM80lUmoUUQjmvc3HwOPc0lX+fORn7ftARCAk6wGjBgHTsyjbdSyvztXyKJONBSpOg2fyJ3F9j7BkYLoWtre+sioKIiPVaX40+QYfle4gCSJR2UARFSzPpuwEY/BB4SaT9QX+5Y7f5mR6b5sd5Ps+DTdgeCqthFELqYQToftiMAC4uIiCiCFunUDVXZMfT73Jd8ZeodgaX01USKtxJEQarknFaTBnFvnexGvcqEzwz7Y/w55Yf+tcBZqOQ0RTW9fGIKr0oooRBEFsi6wvhySmUeQhArZMHM8p4GHi+/fbPvObEbPmJFfLH274mYBAwuomZETajCPHcREFgcQGTIDl2GiW9n0HnxX2h+d7KIJEt5Fr309duTjpVITZ+TK1usWdiUVOHB64ZyWzUKozNRMwDSRJpL83vYZhpGsyA71pPrg8jut6jIwtUKtb69hDd4fn+dy8PdcGVVKJMF25tYBH1a5hyMavzECzPJe5ZpWm6xCS7y1PsBxhQyWbimzKNvJ9jw4jQljZ/FynZoo0GhbxLa6p6/ncmVhsM7XiMYNsOoosCNScOVzfosPft/XBCgJdRoqBSAfuXc9RrWlRqZs4roukSFimzeTwLF2DuSBpkUSkVfeBZTksk8aCZGZ9mmE7LmOTS2t+pxkKiZ1dTN6aZveJoXXf2SxEUSAW0TlzYjupRIj/849+zp2JRUqVOhc/Gg9kGlR5S7bRcoyVi5iuw85khtcnR3lyYMc9/77tubw4fouaHczlRztF4tonT+MIgutZLdZbYPX6iMgJuoztFO0FFFFlMHwARdSQBZXJ+hXSWh9hOWjvd1pAUIByQ6Yzjm4oOK5LpiOGpEq8+co13n3jBvWqyY/+5hwHjvYzM1ngkSf34boer//iCmce3xOYZkgiqqaQ6Qh0JHsG0mQ643zrz17lsaf2oUdkXv7JJRpVh1w+RmdfAgSfvUe7ef4H55n+qyWe/eIJjp/ezntv3yKdjbFrfw+KKtPVm0Jpgdn53iTJdIRHn9zHqy9cJpmKMLSnq+1qPW9O8H7hJTr1AfbHzyCvAmzvvv8ajk3NstqdIfeKoYN9XP9glG//2xd4+hsPo4dUOvsyBMMYXJNjn9rH+69cJZmLsetwP6qu8NhzJ/jld88RS0cYOhiIAb724w/48I0buK6HJIk89twJktkYO4/0851/+wLHHt/Ltn3dvPitt5kdW+Tl756jVm6w98Q2FqYKfPffvUi6K8HxJ/bxwdmrxFIBQJbqiBNNhDj+qb28f/YayUyUnUcGUFSZd164xPmXP6JWafLjPz/Lp770IJH42rltq2fU813OLf6cmlvicOJx0tpWDHkhcM8V1HZhcat9R5UknfogZXsRVdQZDO9HEleu3ad3bOPMQB+lZpOUYRDTNJ7dvZuZahVNkgirCmFF4VBXJ2nNoNBo0BGJoIgSz+3fS0zX2J3LMl+rMV4sUTUtntmzi/FSmeevXue/Pv0gXz18kFRoZTzykSif37GLsm2SHQyTiwT/hWWFhmXz8EA/MUXjqR07SUVDGIrC7x4+SMoweHLnEBOlEqIg8LldO4jq/3CFtV8r4EgQhIAqa9ewPRdVlJlqLHAksYOLxVsMhDuxPYeiXcX2HaabC9ScBrIgY3oWqqhwozJGSo1R9gKKfFKNUndNOrQUhqRSd5qUrCplp4btO+tekhBUQBdrdT6YmiIdCnFhapozA/2Umk2uzMwT1zXem5jCdl2SRnCRi40mL9+6zaF8J2eH75CLhDnc3cV4sUyh3mCuUuPC1DRJw+CtO+OUTZPeRJxKc7JNRVMlic/v371umWM2bcZuzTIztsiZzx0kEgshiAKSLBKJh9o2p7VKE9/zMcIaYzdn0QyFVDbG+VevkemIMT9dZMeBXlzHJRTRmR5b5M71abbtyZPKxZCmgwVeJB4I0i6HJIg8nNtNWNao2k0qToMPCne4U70/jRPH8wirKoaisD2ZXCsKjM9obR7Lc7A8h9lmsf3JtcI8785OIItBW4ftuRiyQkcowlStzO5klvlGjZFyAV2SqdomnaEoaT3EK5O3UUWZbChMUtOZqJZJagaT1TKvT93BkIO2ugvz05zJ99N0HV6bvENIUdEkiYplEtd0bNdF1WR6+jLke1IBFdH1kBWppc8gEIuHWJgv4zoe2c4YZnPFEnqZ7SAKgRV28KKsB8ibmAb7EghR8OZA3g1OSxNJ3g3mLwAZ/AYIRrCNOwVCLACc1GNgXwUxB3I/uAuAH/xfzIHUA9jgVIPvm78EsSvYjxADr7mVC+p9hef7pPUQ8ieon392usjIyDzpdGRNkt10LX448TqvtkAjTVQ4ldnHpzuOkWlVaWebBc4vXeeXcx/wvYlXkVa1+SiCgoiAJq5NPlzf48XZ9/jLkb+j7NQJSzrHU7t4NHeITj2NIkpU7DoflUd5ceZdxupzXC2P8v8O/4T/ae/XyWkbu/Ysx4XCTS4UbgHwdNdJHkjvoUNPIiCwZJU5O3eR1xcuUXUanC9cJzJq8N/u/FK7He7uaLgW3xp9iRdn3sPDI6XG+EznMQ7Gt7fHoWhXeXvhCi/NvUfZrvPK3AXCssF/Mfg5osrWLY0vTL+L6QVVnSdyR3koe4CcnkQVZcp2nYvFYZ6feotFq8ySVeZboy+zPZInbodxHRd5lZ6c7/tcKN7iW6MvU7SriAjsiPbwua4H2BHtQRUVqnadS6URXpp9j3NLVzEkDdfbGoQQBZGD8e106ilc32N/YpAD8W106kniShhREKk6DS4UbvHT6XeYN4uU7Rp/O36WfbF++sMddBud9IXyhOXQ+rY63+XHU29iuhaHEtv5dOcx+kI5DEmj5jS5Uh7lx5NvMt1cZM4s8BcjPyenJ9gR6Wnvy/Zsyna1rfFSKdSYG1tg94mh+6ry2Z5NTIkTUaJbtlb+fPodvjn6C2pOk4hscCK1m0eyB+k00siCSNmu82HxNi/NvsdEY55Lxdv86fDz/I97vta+X6arFXRZbjHvLEyviCYliKkD64Ajy75OvfkCghAmpD+CKGQxpE5EQf2VQYJfpxCR0EQN13e5Xb2GJhl0G32Ynsnt6nWOhFLMz3v096eJRvXAhCOsk4gaawgBiiK3+ay27d4FHgVk+lYjNhAwnGRRXtOimElF2L87z9xCGdtxee/iKJ9+eA/pLRghvu/zweVxFltuZ5lkmH07u9aATYIg8MDhAV44e4VypcnkTJGLVyb49MO7N71Hfd9nbrHCpWuTbbDk6IE+IncVauJqDLNpr9mPJIqILY901/VxXW/LhML3A9OKrlAM5T4Su9XnpSoSIWNzposiStie29aR2yhmF8rcHJmjIxvbdJtGw+L8xdH2v/MdCToyUepUyGi7cPwGPluDxQWzwrnF6+T0BGktRo+xInqvrloLBkWzteOsKBLGqladmfngHjHYnOE0M1fm5u3Zdb/XDIVMPtVOZj9OCIJAbz7Fzu0d3JlYxHV9KjUTRZYoVxvY99BlKpoNXhy9xVStTD4cQ99A8HyjcDyPim3y7MAeXpkcxnIdVFH/RM1Fy9GsmyxMFzd1ENOkEJ/t+gb742fQJIO+0C5EJBzfRBUNXD9wy5qcL1FvWkzOl9pMid7DeUqeS3V0jn0PbQva2G2Nk799iFQsxFBvhrHheSzLIdsZZ2k+YFt096fZf7RVhBAFdh3sYWR4jv2Hevni108x0RjH9pqU3QYP/G6eqlMlriSY9kdZqCnU4jU++/u7kUWZRXcafX+TJw/tZDC8rf3Mfen3TrfP8UvfOANArjPOgaMDa87f9R2uV87zxsIPORB/iD2xB5G3eA5s1+XS/CwVy2JXKsP2ZKrNvF0dnu9RsuqIKYWn/9Vj7VYpTdR5/J+cpOw2cB2PmtMk92COrzw8SEjS2kXfcNzgH/13T7VbRCVJZN9Duzj02B48L+g8MR0XHJeTzxxhbr7CQF8aTVP40r/8zLrjeeobD6/596O/fbz988PPHm3/fPTRPSyUayyUa9iKyK4zO9jz0M42Qx9R4M7sEnKrzS6sqURDOoq0sSlAyV7g7cXnabg1BsMHtgSOfHxKdgHPdxEEgZzWjed7NNwmru+2xNtFLC8AfGVR5nTmi/SFD6IIBj2h/VTsGhE5hCzKpELBulVoelz5cBJVlRFFEU2VqLoeNVXmo8VJYlEdXVNIGRqzEyVkR6ArF2+/93ricSZLZVRJ4t3xSRzP40BXJ7Io0hVbYeQVi3U+ujwRAPGeTz1cZVqVicUMFuYrhMIqY7dLZLJRqtUmdLj09qboiq7sYygTEAPKZpPFRp2kbmzY4fRx49cKOALQJQ1D0hEEk4ZjYUgq081FekI5Fq0yWS2JJAQ9fFE5TEqNEZYNfD+wguzS09ScBnEljCap6JJGxHNYssoUraAamtJieC3R64ZrEVXW91Hn4zG2pZLcWlgkqmkc7u7ixtwCqZDBUCbFty9e5lC+kz0dWV4dvsNirU4qZJAOh3igr4dL0zNUTJPeeIykoRPRVHRZ5kh3F3/1/gV64nGO93bztx9+hOU49MTjgSvOBmMyP1Wk1LJzbtY3pjU7lsv1C2PYls2xR3aTyyeoVZsIokC+P0P3YIaJ4TmGP5qgUqyzbW83M+OLuI5LuVgjldu8QiIisC/ey+5YHtcP7Nv/480XGa/dW4wZWLF+932GCwXiut4WyBYQOJjo5/zSbeJKiB3RlYlgtl6lYpkMxgLnoqwRXKeR8hJxVedYrpu3ZsboCccIKyqvTN4ma0Qw3cAufDCepCccZ7xaojcaJx8KznGuUWVPKkdM1QkpCsdy3UzXKszUq+xL55AEkbCisi/VwbnZCXwBEtkICG25b1ZTUSVRIJoKUWtaRMIaiiC26fhlu87zUx/QqSd4JLcnWIyLUaALxCRIvYAMrgneNMjbAQcEDeSd4M2DmAK/FrCH5O3gWyB0gZAIwCFBazGXvAAgkrIBe8kdbzGZfEACqQ+kgYCl5DdA/PjW5HeHh9+2Yf2khKLKJJNhojEDadWiebQ2yytzF7B9NwBTswf5/W1Pk9VW9B6GIt0cTg4RV0J8a+yXmN5KJd/2HUzPQnLXAhtTjQV+NPkGZaeOIsg8032K53oeIaWuOPL4vs/uWD/bwl38mxvfYcEsca08xmvzF/lC90MoW1DiK04DWZD4av8TPNfzMNFVQIXv5xmK9pBUI3x3/FVs3+Gdxatcr4xxJLG+2ur7PhcKN/nl3AU8PKJyiG8MPMmnO4+hi+qq/frsjvaRVCP89djLNFyTs3MXOJHaxanM1tXvgl1FFRWezZ/iK32fIiQZQeubFCwu9sT6CEka//nOz2m6FhONeS6XRnhQ3h048oS0djJoejY/mz5HqcWEGYx08V8NfYE9sf41TLrdsX52RXv5Nze+w2yzcNcR+dhe4Epk+TaSIAWt03qKbww8SU5PktXiRJVQcB2EwFVMQGBntJeEGuHPbv+UitNgySpzqTRCp5HkZvUOnXoGTdrAytsPFoH74gP8NzufozeUa7PXfN9nV0vf6U+Gf8S8WWKsPsfLsx8wGO5CEQLqfkyJUHZqbQAgFNHp3ZVHvk/xZEmQmKiPIwoSOa1jw3thvD7HT6beouY0UUWZL3Sf4QvdDwWMtjX3bh/bIl38Pze+S8GqcLk0whsLl3kmfyooGMky1mqwzl+BM9Ydl5TB0B/Bdu4gCHoL+PBx/a0F/3/TIqt1ktU6qDoVFq15Hs58hqSawfM9EkqKUrVIV9dKi6La0ra5W8Q5FtHbr6ylYo1qrYmmrhRvJCGEKuUQWlWEhmuyaJawPJsQQRtZyFA5dWwb594foVIzuXx9ivMX7/Dko3s3bj/1feYXq7x49kr7d7t3dDHQm1637Y5tHewe6uTcB3eo1pq8cPYKxw/2kdzEBctxPd44d6vNWIlGdE4cGVjX3ub5HgvmEoPhACCGQCNleTvTsplfrGwJHAnCspMrfFzdGt+HhaUqjR6b0AYaKI7noUsKXcbm7Tu1msnZt25y/FA/hr5+H57n8+HVyTYII4oCRw/0oaoyDVtsJeXSPUGMmBJiX7wfy3NI36W5Jktiy8lSxG5Y1MuNNa6MohAkRpIk4roet0fnmZktBffdBmHZDq++c5P5VsvMcsxPLNHUFarFOpZpk98WzDmr9SXvFZbtUiwHGk2SKBANa+TSEUrVxj1FxiOKxpnufqqWSW80cV/mNxC07ncYEX44cgVDlogqErZn3pdG3m9aJDJRkrnYlqBeXMkQj68Aj77vIwsaFWe+3aomCLRcID0sxwUfDF3Bsh1CrfZ+z/cJG+qKWynQ1ZPkwLEB3njpKpou89ATe1fpFIEkiziOi22vvEtsz0IURCJypKX7IhORI3i+R9NrookamqThtMADFZWosvkzuVW4vstE/cbH+EZwT+uSzFy9SlzT6Iys/9sN1+btxRuEJI2iXQffx8OnO5QmImtMNQoYkoouqView2R9iX3xHuJqMIcWSw3OvXcbH0gmwlSrTZqmzUBfhrGJRbKZKK7jUa42iUWNoH34H2BN7wOL5Tq3pha5IxVwPY/udJz5UvDsC4JApW6SioUwVIWmZbO7J0cusV7E3sdnwZyk6pTuqwVURCClZrhZ/YiM1okgCFiuxZ3aGGW7it/qk1BFBU3UWLCWCEk6PcY+5s0lLpeu4+JyNHGQhLqSK1mWg2U7OK7XkiTRiIQ1YhEdHwgbGooi4gVEOhpNa91QdsWifO3IQZq2gyyJxPX1c6VlBQSEaNRAUSUsy6FWN/H9wNkvGgnc5EMhlUbDolG3Np0j52o1Ls7N8GjfAJnQ5sWe+41fK+AoKoc4nlzb8jRanyGjJTAti5rdoCeWw3N8erUcnueTtKKEIzqe6yNJIqIkrnvRmA2L3nhAqx0Id7U/2xZZ6+izHLosU2w0uDrncDjfxY35BURBoDcZ58bCAu9NTPHUnp2MLBY4NzbB4e4u4oZGTzzOlZk5GrYTiDb6kA6HuDQzRy4SQZMlXro5zNHuPJ7vo0oS/ckEMU1jvlYjaWwmWuZjNm1iyTCNhsXE7Tk0XSESNxgfnkMUBfIDGY4/Goxdvdrkzo0Z6tUm2/Z2E0uG0Q2VWDLCoVNBMlgu1LBMm1BERw9p3L46xdToAuPDs0yNLjI9usjEyAIDOztX9U6uYOequOKadq9QRBHLdZmv1TjR3U1EW5soPdf3AMfS2whJKj2hdPu67UvnKDTrqJJMXNWIqho+sD2eZrpW5vk71xmKp7m0OIMoCDzRO8QH81PM1at0GGGSmoGhKCQ0nYiicmFhipOdfexLdQQTqKbTYQRCzzkjzKFMJ7bnsT2R4lphng/mp4goKpcnZhhbLGE6DjFdo2bZZCIhFEmkbtmE1aB9zVBlbiwssj2XJq8Gk//l0gR/cfsspzI7OZ3dhYwUtIctt4hpp4MFjXMdQe4PgJ7lcVX2ALuD2Vfqv2u4W/+Q0utFBZbpv8s/y9ta2y4nf8t6Ib/6i0GTZGYaFbbFUvfe+Dck0ukIiiKhqHK7DcT3fd5YuMScGTDieowsX+l7nJy+tvVKEAQissHnux/iUmmEi8Xh9meSIDFvLtBtdK0SIfQ5O3eRO7VAV2xffIDf7fsUsQ3AbE1SOJHexRMdx/jb8bM4vssvZt/ndOYAeWN9QrY6dkZ7+Hz3aaJKaE0SIQgCKTXKc72PcqE4zNXyKFWnwStzFzmUGEK66x4p23V+PvMuS1YZSRD5dMcxnuw8vk78WhAEIorBU/mTXK+M88bCZUp2jRdnz3M0tRNN3Fpk9nhqJ1/uewxd1Hl7dgxdkjnZ0dcaB5XHcod5a/EKF4u3qDlNblQmeCC1K3iprrpmt6tTXCoFCyZFkHiu5xH2xgfWiUlrksKx1E6ezZ/mT1taS8uxYJYYq08HCbsaJSaHmW4u4Ps+KS2M7VvMmUvU3SaLZpGMlmQgErxndEnl0ewh3l26xpsLH2F7Lreqk3xeOk1CjVGwy2S1ja9dTAnx5d7H6L9Ly0oQBFRB5kxmPyO1af5m7GUc3+WtxY94Jn+KnlDA0LE9hyWziBsJqsOe65HtSWNs0kJ7d5iu2VpQB23gdyefju/yyuwFxupzAByMb+fLvY+tY5QJgoAuqZxM7+Xx3BF+MPF66949z5nsfhJyFEOWqZiB6YIiSSS0IZrOErZbgbsYR7LUhyz1oEiDCGIYAQnPtxA/YXoiwTUPkqaGU2XWDBxkPd9lpjlBTMkgyxLisg27DxNzJSzbJbsKdBkazCFJIo7jMTVb4oVXrvD5zx5q25WrcicqnQHby/MYDOfJaUkiqzQhBUHg5NFtvPfgKD//5RUq1SZ//q030TWFYwf7CYcC0Hi5BeX26AJ//YNzXB8OAI1UIswXnz6ypk1tOVKJMM89dZThO/MsFmqcv3iHP/3m6/zjL54kl44iyyuAaanS4JdvXOeb3zuHbbtIosAjJ3dwYHf3ukWzgNBuI12ObDpKJhVhZq6M78PPX/6I3UOd9OZTa5hQvh+ARbbnUrKa1B2LitUkIqv3BWAAOK5Lw7Q3TbI1SSathyhZmwOerufz6ts36O9J8cxnDhKPGohia5wdj0vXJvmLb7/JUktzqq87xcMPBoxCSVCJyDlsr4FwD4F7QRCYaixheTaWZ5M3Uu3zdFyPQqWB6wXC67IqUVmqUZgrk8wFTKjD+3r56cuXqVSbTM+V+avvvcM//71H6OqIt4sfnudT5/1tnAAAIABJREFUqTZ56fWrfO+nH2Baa1t0q8UaUkhraygtx09fvkyhWOf4oX46sjGiYW0NI35536Vyg+d/8SEfXZ8CAkBx3648rhuAmIos0duVZLOQRZG+aBzX90nq969nJQkiO5MZTNehL5qgNxJDwFvTovRJCd8Hx3Y/ti6NLGo0vRqOHyTR+UycfCbO7oEObCcQhzc2mBuW55Tln3VD5czjezjz+J4N/44oBgCnKC6vf2Ewsr397loN5m1kgLH6d38fxljZXmS2OXZf23q+T822GIgnaToOnZEIYWVjsFKXFE5lgiLBeH2BpBomJAVuuSIiKTWKKsoIwvKcVUOTVsYzFtU5fnQwGGddwXECfSH8AEzv6ojjeT6W7SDLEvoWDty+71O2i9i+RUJJI4tbOIYD/bkk2UQEUQDb8YgYKn3ZBK7nM10oEw/r7MxncT2PhXKdkL7xHOt6NpP1W5henZB071ZQD4+KU2Jv7AhROQB+FFGhL9TT1g718dFFDR+ffr8HSQhYvgk1TsNtUnPqhOW165n+ngxdHUG7pQCoLSMC3/fp7U6tuWfzuTiStF6LSRSEoFtpi2kml4uTy8VZbjD3XB/HdVukBKH9e4CurgRbaT6FFIWm49xXO+T9xK/NSsvzfEZvztComaSyUaZGF+nsTdMoWEyK81imQ2mpRux4hMmRebL5BHOTBW5fneb0k/uZvDPPwK4uCnMV6rUm2a4EY8NzdOSTXL0wyuCuLsJRnZnxRfp3dpLKbn7jDWVSbEsHLxhRENieDpLikKLw9J5d7d/vyWXX/BxUp1YqJMsvzH989CCCILAjkw4W4MIKb+Wp3TtB2NqNa2BXF307As0RQRAY2tfT3n/n14KkY/WixwhrPPHc8db2wV8SBOjsW6kCRBMhnvra6ZVt/NYxiCL9Ozo5+cRehFU3WdOymV2sYOjqmkXp/UTTcZBFkZimBS1rqyjAgiAQlnX2xtfbnnaFonxxaH97rCzTxnM89HRQZcYPxmBHIt0ejz3JVpIhBBO/AAzGgmu5N5ULAMBoHPzgb/fHgsRfkSR+a2BnS1RUYGcy09oGzo9MkgwbdCdiyJKI63noihJMoM1mu2IuCEJAsW5dC8/3+ag0QcXeqhLeGnx516p/3/X5vd5fG903W77Y/36A0XzzNrbXJK31oYg6AhKW61I2P1mV/oWFQGsn1xFvJy11t8ml4u12InIitZv8Kjr/3ZFSoxxP7eJqeRSrrWHjk1STa6olFafBWwsftf/9eMeRNSL+d4ckSJxM7+H5qbeoOg1mG0vcrExsCRzJgsTh5BDJLVqOkkqEU+l9XC0HLQ9XSiOUrCpJVSWY1QKnj7H6LJdLQTtlUo3yaO5Qu5q/UcSUMA+k9/DGwmV8AiBnvllsgxsbhSrKPJo9RFqN4fmBOKsnrl3gxZQwQ5E8F4u38PHbIuVLMyXwoWswh+t7XC2PUWxpJ3WHshxKbN9ULFdA4ERqN98dP9vW6oFAuymjJWi6Fnk9i4/f1n/xWsLlqijjtfYRkdeuBgxZY0e0lzcXPsLHp2LXMV2LutPA8z2qTo2ovH5O7Q91sDvWt+l7QRVlTqb38sL0uyxYJaYbi4zUpttjK4syHh4ewT0riCKjl0dJ5OKoxr0TYEEQKFoFUurG91bRrvL2YsAokQSRxzuOrDv31SGLEqcye/np1Ns0PYvpxhLD1SmOJXYRVlWyoTC6LNNwl6hYY7i+jXhX8mU7YzTNcwiChOPOoasP4Et5LK+IdA8tpt/UMKQwx1MP8+7Sq1heIIDcpfewP3EEW5RXHM98H1UW17E/d23vYMdgjqs3Z2g2bf7yb9/hxsgc+3flMXQF23Gp1iwq1QYd2Ri/81tH1oj6L0csqvP1504yPlXgyvVpJmeK/N//7u84vL+X3UOdJGIGlu0yNrHEuQsjTM2W8DyfeMzgd79wnP27Ni7UiaLAicP9fPnZY/zFt9+i0bT5yS8ucX14loN7e+jpSiJLIoVSjUvXprh4ZYJm00YUBY7s7+Nrv3NiQzHtiBxme6QPZVViEw1rnDy6jevDs9i2ywcfjfN//NHPePDoIB2ZWKAN1rQpV5s0mxZffOYog9E0JatBWv946x5ZlujPp9A3Yfh5vt82KNkoFFlkaLCDsclF/uLbb/HBpTH27syTzURwHI+xySXeeHeYuYVy+/o899QRelrgiOmWMd0KTa9ETM2jbJGliILIjmieObO4jr0qimKQYnk+4ZjBkcf3BWujVce9b3eeo/t7efWdm7iux9m3bjC3UOHE4QEyqUiLgVbh+vAsFz8aRxAEjuzv5cqN6TaA1D3USToZpl5qrNn3yOgC3/vpB3zrh++yvT9LT1eCro4E0YiGLEk0zYA5du3WDJevTWFaDqIo8OCxbRza14uhy+RzsaBN8x46JxPVMmXL5FhHHhHhvhIt03V4eXyYgViSd2cnyBi76In86mzuX8dQVIlwzNh6eblBZLQ+dDFM2Zlvr7EBZEm4p2g5gI+H6Zq4voPPskmG3NKvWTHisSyXRt3CCAXz193rnTaA5PvYvoXj23i+22rlFFqmFDKyoKzZ7/1EwB4fpuaU7mt723UZKRZwfY/JSpm0sQ1V3xiskQSReKsgsy/ei4+P7Zk4vo2PQ1QMXLdkQUEWwmTv0thSVZlsepnJtHYtlYgbuK5HqdwgEQ+1HRS3iqpTYbIxyoHEUeRNYISg7dwG2UaXAgMZBQEwCYcD7aFMvJPVVykV3Xzt23TrjNWvb3lca+P/5+7NY2S77vvOz7l77UtX7+tb+XauIkVxk0RSm2XZshB5ZMdObGPGSGJMMoaBCWAEM4kNDDAzmEGMOPZgMON45MSWZDuWZdmSKJmSSEpPFPfHx7fvvXdX1153P2f+uNXV3a+X9+gYCKXfH71U3Tp1l3PvOef7+/6+36QUbdmbR0tpFBnoMc8y62/3fm0HEPNajryRA5ttyyWpJFKTGJqOITbO1e3JPaUUhgWh8gmiTi/5pmEIA1Ozk95223lWShHEMbFSmwymepUtRiJPs76dJCaUvXtCT9pWsYGpWWjoW1mjmk7KMIjuUt/yTvGeAY7cjs/yfJ39R0a5fHaOTtPF90Lcrs99jx5kdbFBrpgmnU1QVq8bkMo4TB4YIptPEUWSRrXN0nyN/UdGCYOITtPlVm+CUShnOPf6DUI/wrTNPYGjJFuzuRPs/PqO22z+5/btRP/Hts/t9SQWQqDrm9/ftA87LIK2b7/9K3bb5vb210NKRbXeYXTw3XeZguPw2NQUNxoNBtJ3b9m++XzHccyNs7MoBUfed4DNgMpu16TfzkaDyTZbwJiN7TWhbX299/eJiRE0QR8cWgcGvSgiFJKRbLZfNzpSyCZWjELQDl0utxa3ZT13Os4fhciZFar+TS40v0NaLzKSuodQ6oxm8v9FzjXvtXAck9dfu86N66u875EDDAxkWQtaVINkgi4QHM5P9Fh3O4cQgn2ZUdK603MsgoyR4Z7sARCiP1TOu6usBslEI6VbHMxuz5zfHgNWnoKZoR25uLHPrR7jY7eweoYCd2r3SH4SjUTovBl2WfLWKIk2qDWwHgN0Lrfn6EZJueygVWAiPbhnuxqCYbuEo1l4MqDqt1gLmnsCRwUzy0xmFE1oibmBZZG7TUB23QluPfw4xHB0po+OE4XJQiSQITe7S/0pwYhTpmLvbiMthKBk5RhJlbcCR0aKe+wCsZL98rbNE9Itk45+onPTc4VE2Ho9YhUjlURDQ99DeH6mZ12/WwghmEwPUbSyrAYNpJJc7yzyxOApgIR6Lwy0XglS4Id0Wy5RsF2Me6eQKqZiD5Iz8jsCjrPdFaq9vpvRHQ5k79zHBqykpM/zAzqxx2x3hYdKR/pGEzOlErZeYsA5gSZMpNpq2S0wsK3jaKJAJJfQtSKBCtAw0TB2ZEb9qIcudA5kjzDqTOBLD01opPUslmYjUpsH9aRPuMHWc1YpZ/n0x+9nde1FVqqthPXxwjlefPlyv6wtiiRxHPOB9x3g0x+/f8f9EEIwMVbin/ziU/zf//EFzpyfp9n2+O7pS5x+9SqGoSdub36PcQ2UCml+7tPv4xNPn9w1gw2JNfKnPnIvnY7PX33zDLVGlwtXlrh0bblvLR9uKkHRNcFD987wKz/3GBOj210ghUiE6meMiW2vP/PkUd58Z5ZX37pBHEveubjApavLWFayf3GsiKIY09R59sljHB4cZjT97sWOLdPoiXzvnAl2DJNjpeFdGUmlYoZf+dxjvPzGdb78tTf44Zs3eP3tW1hWogXm97QvIHFS+9RH7uXDjx/pt1e0pqgHN8mYFSxtb9ArlDGObpLRHSRbwRVDE+TTTt8BaadjKeRS/IOffIj55QaXri4jpeLshXkuXFnE6mnOBUFS3mGaOh/94DE++Ohh/vff+waLK8m4euXNG8wJQXOtzfTRccojG2xeqRJG0WtnbvLamZtYpo5hJIsjKSVBEBH3zoWuCR44Nc3Pf/phCrkUYZSwKHK7lM5tDls3eH3pGlW3QyWV4ZHRyTt+RpGU7GdNi1gp3llbJmNalN6VC9+PRgR+RH2lSRQl2mBL3g2qwcKu248405SsERbci6z6N8ibgzTDJfLmdkfYWEUsutephytYmsN05iimsGmEq1zrvM2V9pus+nOE0sfUbArmIOOpgxzOP8CQPYUmNHRdY6CSxfN2H+P82GXBu8rV9hkWvWs0wxqh9NCEjq2lKVqDDNoTjKb2MersI2sUEbtoeEoV04matKM6zbDKueZpfJmw/xphlfPNlzG0nVlEQ/YU94+MoiFYKrTJWlvH+lZYY869RKxiRp19lKzknLWjBtc7Z7nUeo0VfxY/7qILk7xZZsie5EDuXmYyx7eVc7WjGvPuZSIVYQiLmcwxbD3po0rBOxcWmJ4sMzle7r2m6MQNbnTOAWBrqeSa9Kzt1wGK20MphSe73OpeYLZ7kUXvOo1wtV8y6GiZZF+dKcac/Qw70+StgW3jdqIP69GO6rSiGovuVRa8q72+EnK9cxY3bm/7/vV9nUofwdRsWmEDL3b7oPH6Gm7BvUot2H3uPJraR8kc3rZfXhzx1to8A06aQ/md57GRDFn0rnG5/Qbz7lXq4QqxDLE0h4o9zv7sKaYzxyiZQ9vug2u1GvPNFk/MTCOE5Fb3At24RUrPMpM5jkBQDea50HqV2e5F1oIFIpnoqpXtEabTR9ifvZcBa3QLSSVrWTvqZ/1d4j0DHJl2IuA4d22FXCFFt+0xNF6iVe+SK6SJY0n17By1lRZryy1CP2JwrISmC3wvZG25iW0ndnaz11aIexaJQ+MlUmmLlYU6xXKWtZUmlZGt2YBu5HO1vcQ7jTkutRZoBF0iFZM2bIadIvuzwxwrjDOeLu+6WFRKsRa0udBc4NW1Kyy4dUIZUbKyHC2Mc39phon0wI4OVZGMWfIa+DJk1Cnhy5C/mnuNt2o3yJkpPjp6L/eVk/KKt+u3eG7hLZa8Ovuyw3x4+DiH88kiaz1CGTPXXSNWkiEnT8ZwWHBrvFy9zJn6TepBl6zhcCg/ygcqh5nKDGzJys3VGjS6HvsGy3SjEDcMKTsp1ppdshmHkYF3V/9r6TrldJpyOo1UilW/RT3o7LjtRHqAlGGhlMSLa1haFk2zQCXucu16d0crczcOuNJe4twu1/BAdpije1xDqSRrfptrnRUuNOe52l6mGSZubTnDYTI9wKnSNEfz42SMhOLf9H1em5/ng/v29a0YNS1hkXQjn8vtJc43kvKCVuhypbW47bs1oTHo5MjvIRislKIWdHirfoM3azdY9OpEUlKyMhwvTvBgeT8jTnFP97P1aIce55qz/GD1MrPdKgoYS5V4f+UQRwvjCER/oXl7uFGTZrhMwRrF0lLMdd+mYD6IG4XcLrn6oxyjY6WkDELXEtFZoB60aYRJn00bCQNF22UysR4jTnlLSZZAYN2WyZ/rrtIKk4lGRk9RC1pcac/v2e6a3+zr1oQqph7srdOhC51hZ3eKPmyAJnkzQz1s04k8Vv06ZAoQN1hHRK605/uGAmkjxby72ndD23V/g1bSN2Win9KJvD33N2ukGHQ2ntEaGqHcKiCbZBw3+rtEEgYR9ZUGRs+eN5ARc+5q7xxojDjlLc+5nSJjOJSt7YtETWi7Xm+B6GUwY9zYx49DIhUR93T0IhX3nfKgp44mBMNOhVrYwNZ2FiEfckp3LOlzdJNhp8Tl9hwKtoCImtC2fD6VcShU8th3wTYC+hPPQAY7AjKz3RXaUaInkjFSrAXNO/bdVb+Oud53ZUQjbBPGMY5hUu4ZTQRxi5p/gZRRQSBw9I2yGcMYI4p1lOpg6pNE8TyxjFEIdPH35xryXogVf5HL7WTSvpWYnvS5yfQ+xlPT/dd0TeP4/hF0bWsmU9c1nn7iCKVCmi/85StcvblKp+Pj9yySDV3DsnQKeYdKebu2xObQNMHJo+P85j//BN984TzffOEcK9UWXTeg6wZomsB2DIr5NIf3D/PTH7uPe49P7Oos1j8eIchlHX7xs49y34kpvvz1Nzh3aYFW28fzI0Bh6DrFQorRoQJPPXqYj37wOAOlOztl3R5jwwV+/Vef4ctfe4MXXr5MvdHF86Pe/msYhkY261AupPtahZAsEgE0kYhB758aJAgiSsU0tm0QqxBdJExkX3bIpLIc2b+hDaYJwfBwGt+vAIJyz9gkUh5IM9GCxGVspEi96XJwZoj7T0xy4sgYU2MlvvLcWywuN+l0/b54dC7vMDFa4pPPnOTDjx/ZooNkaDYVZ7tOXTZjc2A6WfCMDOWxTJ1IxtzsrPSBxM1RzKU5Mj2EY+7+7FzvF//y1z7Gl77yKm+dm6Pe6OL7Ea4boOsa6bTF0ECOTzx9go88dRyhCU4cGSeTtpkcK7H/xCSOrtFYbVGobMwxn37iCH4Y8ebZW9SbLr4fEQQRrpcILet6oimZTlmMjxR5/JFDPP34EYZ6bdiWydEDI9v2d3y0hOslIGupmEEIGE5neGQ0ARsL9lagSZA41q2fu4nRIoauYWoahwoVbrYaDKUySKXwoyhhK/yYhWnqWI7ZT0K/Vv9bvr/6lV23/9joL/H+gU8Qq5AR5yCtqLprSXEoA05X/5o36s+TNwf42cnfIFYRzy39EfPuFWK1FQyacy/zTvM0r9W+xWODP8W9xacwdBPPC6muttkpAb7s3eLby1/kSvstunFzx/240e0dq+Ywlb6Hj4/+EsPO9Lbt5t0rPL/0RZrhKp24SSdqEG1KdNzsnuNm99yu5+ZDg/+QdPw+wliy0G7xoZn9pM2N8XrevcKf3fodPNnhmeGf57HKT7HizfLc0h9xvX2WUG3Vu13wrnKh9Qr1cIWJ1GF03dj2/p/P/ju6cZOcUeaX9//WBnCE6t1XW5PcS+4N/uTm/wpAxR7nH838TxStQSzNIlLhNnOpSIZc67zN6epfc6NzFl+6ux4/DXC0DE8OfYbHB396WzvfWflTrrXP9M+tF3f6STpfujy//IVdm65YY/yjff8zsYyYSO/Djbu3baF4ufo1Xq19c9c2fnLsV3mo/CxiEziWgKUtOlHApLHdIThhvy9zevWrnG28RDNa27bNrHuJM40XGbIneXzwpzmafwRTs3vHLRNCQs8RPFRdnlv6I251LzBsT/HzM7+Z9LvlL7Ds3dzGlJp1L/J2/UWGnGmeHv4ch3L395hzMJjJ/r1p0b53gCNT54HHDyOlRNd1Dp+aRNe1RN5F1xgYKlAoZdANnXKvrlrTRH8S/tRP3JvU+iuQUqJpGjKWaIbWL8PStKSUaL0+WirJbHeNP7nxEs8vvUMz7CZlEf0a12TyrQuNE4VJ/vW9n2XY2U5BlUpypn6LP7z6bd6s3aAbB1sG37+Zf52J9AA/N/MYT4+cJKVvnbi3Io//8/xXudCc59eP/ARnG7N86eZpvDhEAK/XrvMbRz9J3kzzv5z9z9zsVIlVzEsrF/jB6iX+zanPMp3dQD6rfovffPOPWfVb/NPDH2U6XeF3L32dC415QpXQdRXwrcUz/PXca/zs9KN8fOz+Xo2sIIolbT8R2lrtdnl7aYknp2cIo/iurZx3i1BGfPHG9/nSzdM7vv87D/0SJ4uTSBWx1D1NyTlG3ppBqR5l+rZJjVSSW90qX7jxvTtew5PFKf71qc8y5GxdGAYy4qXl83zp5mnON+fx43CbFbcmNDKGzdMjJ/iFfU8ylkqynFKpPv1v1Wvy57de5kJrngW3zqJbpxMlZVzfW73Iq2tXuX0wy5oOv3b4o3xs7L4dRSBjJXmzdp0/vv49fli9gi/DbX3rUG6Uz0w9wjMjJ7fUNW+OdWDzP1z9Ds8tvEkjdJFKIUTCCvnq3Gs8MXSUZ0ZP4ugmrci97fMSQ7OZytyPISxC6ZI1BglijaLtYN9hQf6jFDeur7K8lGiFWJbBxGQZv6f9AOBoFqldskibI2M4ezJKlFK0I7dfylYNmvz22c/fcSGkUATxxgTFk0l/NXbRsdCFSOrh7xCmZpDSbeo9Jy43DhIHQNXt728jbPfvrTfql3jnzet3bFcq2RcJVyi8OGBDWn6n/dD7TBtBoqXTcN09wSYATdeYOjLed/2RyD6woaFRMO9camL1zsHdhOqBQstejTONa1xuz7Hk1agHLTqRhy9DQhkRqhgvDrZ89pa7QBAHNKM2GT3NgL19EpIxnDuCkwKxRQ+rvem+VSgyRrrfhpOxsVMWgR9ua2enkD0dAF0kY6gbh+hCYOkJ46EVdQl7gtaL3hr/5u3/7859V6ktgvFeHOJGIS3fJ2OaoBSRcvHiRKC8ZB/e1kYYXSEI3kEIB4SOL2MCUSZtjGHwdxM1fS9GIH2aYR2lJLPudQxhMOyMEciAOfc6OaOwBTgSQlDahepvGjoP37+Po4dGuXRtmbnFGq22h+ppXFiGzvTkANPjG2WJ62VbtmWg6xoLyw0yKYt8LsXwYJ7/5qce4unHj3D5+jILy01cN8AwNMrFDAemBxkfLZFKmVvu8yCIOP3CRW5cW+H+9+3j+KkNRocQAsc2eejeaY4dHuUHP7zCF79wGgk8+uQ95PMpxkeKHJwZopBPoevJ+CtlAtDWuknfr2QyIBKWtK4JIimTkqNeaZiuaQxWcnzm0w9x4v4p2msuy9Um11dqHBweIJ22GKkUmBgtMt5jvSQLhis4epZYheTGmvzWv3qaVrhKysjiWDEr3jUK1ihV/yZr/iwjqUPEKsQQFl7cIWOU+Kl/mGHQPoUlHLqsEqg2Vf8mOXMQTejU1C1+6RdPYfAIvmomC3XL4CeePcUjD+zn8vVl5hbq+H6IbRtMjJU4vH+YcjFz1y5k95+Y5N/+1md545Xr3Lq+SuzHpHSLmewweSNFPeygegtChcQwoJS3EEikkv3SDtGHM9cZ9TEHZyr8i//uQ8wvNrhyfYlawyMIJY5tMjZc4MD0IEOVXH9f/8d/9lFimYjMRm7IK19/k06zy8nHj/Sf9/ccGKGcT/Pso/fQCSLW6h0azS5hT1zZsU3yuRQjQ3n2TVXIpm1MQ6fb8dENjdWVFqNjxX4fu3R+gVdOX+H4WIX//pc/hG5omKaOrmuEkeRyfY2hdKZ/XG7sc72zhFSKRx/ex6MPJpqRmqZh20ZyTgS0Q5+SneLRkSny1o8hagREUZKgWZ+DlswhRpwZvLiDLz1iFRLKgNtd/Bw9hx+3KZojZM3t7JLbw49d3mme5nrnLPPuFQxhMmBPkjeTZEI9XKEWLBGpgGowz/NLXyClZ7kn/TCZrEMUxWzumwl7psk3Fj/PxdYrSdmUsClYFfLGAKZmEcqAVlSjFVYJVUAokwTXbno6rbDG9e7ZPqAshI6mNkrDE0axueuxmprJeI/JaBvGnm5XrXCNVX+Ory38Adc6Z9GFTtYokdZzaEKjEzXx4g6xCplMH8G4Q8JpcyiVgEalYrqv6XmnWHcl23xosYp5q/5dvr3ypR6TJymfMjWbtJ7D0dMoJenETfzYTZ6NmslY6sC2cySR3OpcYMG71n9NFyaR2phHGcLadX5kajaa0BhLTbHsz1MwB/qSMEkIStbIpr7rEqtox767fo7WI6WbDKey21jwSima0RpfX/gPXGi9QqwidGGQMQqUrRFMYePGbarBAl7cZsG7xt8s/AG+9Li3+BSWZqMJQcFx8KIomUttWu524iavrj3H242XWAsWsbUURWuYnFFCqphauEwjXEmYe941vrn0R6SNPJOpw3TDkDCOd6zI+bvEe2a1J4TAMHXooXvrtXwb74PVE0/baYC0tgir9W7AHajRm12Szjfn+b2L3+CVtavESpIxbCazAxTMNLrQ6EQ+q0GLFa/JZGaA/A76DVJJ3mnM8b+985dcbC1gaQaHc6NMpAewdIMVr8nV9hJX2kv87sVvUAu6fHb6UZxNC3ylksXUitfkr+ZfY8Gt80jlIH4c8UbtOnPdKn968wcIEjbRB4ePUQs6vFm7zvnmHM8vn+UfZz+4pT03CqgFHb6z9A5Vv8WtbpXD+VFGU4kN94JX40JzgSvtJX7v0nPkzBQfHj7Rb2Oh1uLY+DBOry4yjiQDhcwWa9a/SySZ9gIHc8N4cUggI6p+i1YPYJG9mzaUHWy9hOo9oJRSFCo57NusYM835/n3F7/Bq71rmDUcJrLlrdfQb7HiN5lMD5Azt9OVBTDv1nm9dh1LMxhPlxlJFSlb2f65utxapBF2+crsq2hC458d/igoRcFx+ihuPexyevUSq37CLnB0k07kAwpLM8ibKbYBR4ad6KNIRbXZwdC1LZP/19eu83+c+yuutJdIGxbHCxOMpUq92t0GF1sLvN24xdyFNTQh+NjofTsu3rqxz3+89iJ/fusHhDJmwMpxtDBG0crQCj2utJf4+sKbzLu1bSg2wIJ7nkh6IARe3EIgOJR/gpWoTSPw+gvIH4ewbYM4lmhC9Ms2yZg5AAAgAElEQVQrQhkT9cBEXexdYrQehtC3iTBvjg0QRW38L4Ndt98tpJI7XrP1EHuwZTaHLrQ+ay1WMaEKEw5zb7COlcTbBFjFSuLGO7s87hUxEvaAjjS0PptIAW4UkjbuDNQFfkjjRoPh6QqZQjqZEPX2Vwj21GJaD4HoM2L2igRE6/DX86f59sobzLvVLedCQK+WXd+xH0ynx2lHHVJBirK1sx6GIXYqRN4em1mM68erlKIRtLA0s3/tpZSkb3MK3Cts3UEjcTANpeTVlVlmciUmskXk32PfzVgmhyuVvotRSq9g60W8uEYgW6SMrVpiulZBCAOpGmiijC7ShLJNrN7997+XY8yZYtSZpBO1eH75q7x/4EMM2ENIFfN67TTdeIO122x7LK82KeYTVq9jm3S6fmLj6wYUcinSaYuVtTaT4yVOHBljfqmBoSeL3++/eo181sEwNC5eWyZlm6RTFi/+8DL3HBhmaqxMtdbBtgz8IGJusZ4wc9I2o8MFxkeLTI6W9yxHg0RY98Vvn+O733oH2zY4dmqSestFoShlUz1hW0E2Y+PVXFavrDIwmOcTTx1jZGyDNbnS6tDyfVqez1y9ia4JOkGSVBnJJ65gK+0Ox0YGubZao5RJEURJFndmoMibs4sYmkYz9hmezPLgQ9OcW1zh6PAgr96aRzo6I8OFLSLMSik6UZ1uVCOl51kM3iRvD1JJDeHFHep+g1AG6EKnG9dZcC+goRMTUbGnWfVvYFmCgewAXtyi665SC2w0obPu9gQK3YxZ8d8mpRcQYgpIkgMqC/uOD3Lo5DCmljhMrpcedOKAtu+TMWxSuoEmBJ0owNFNvDiiGyX3asFMkTEtQi/iL7/0MufOzJJOWXziMw9wuTXPRLrCqt+kaHrEsks3vAnE6FoOgY4mDITQUSpC1zIoFfZ+R3jRArY+jNJixicyFIdr5K0T2MbuWoCbF6otN6A4nCcMoy1270opFudqKKXIWyZD04OsLrdwnISlpWmCYinD0mKdq+cX0XVBNudQW+tw8PAIy4sNBIIwjChXsly6sMAXPv8S+w4M8fFP3b9lH+bbTfw4Yrnb4VarwQPDY9zqrnCzs4QXh0ykKxTsrQkIL5LcaNZ5ZvIgpxdvUQ/cbWylH5fQdY3KaAmzx8S7r/QhjuTfR6wiIhnRiRt8feEP+yVF66GUJJQeeXNor6G/H4F0+eHaN4hkwFjqAO8rf4SZzAlsLZVIlcQdzjd/yOnqV2mEq7SiNV5de45J8zhBEFEobAXQFYrLrde50n4ThSJnlPng0D/gQPYUlpbql8WH0qcerjDbvci1ztucKDxG2tg5GTGePshnJ3+DvngxMa+v/S1nm98HYCpzlEcrP4kpNuYukkRQWSCo2GNEkckP5mcJoohD5d11Kpf9W3x35c+42b3AVPoIJ4uPMZ0+iqWnEUAgPZa9WW52zzGdObpjCdleEUUxQqz/vjtwIaVn0Hvfo5TkZucczy9/kXqYsJ6zRpHDuQc5ln+Esj2GIQwUEMQua8EiVztnEAhGdmBz6Rh8ePhzuFE7KZFFsBYs8q2l/4Qbt7G1NE8NfYZhZ4ZQxmhCoAstIRgI0WPwaCx6N/DiLn7sMeKMJyWvXZe2H3Ik/RSnio/3+m5IJ2rwNwt/wJJ/Y9v+hDKmFnTJGFYv4Rtsm9OFyufl6tc43/whkpiUnuOR8sc4Wng/WSOpUIhlRDWY53T1q1xqvUE7qvOd5T8lo+c5mn8EqRS36g1uNRrcNzq6pf1O1OT71a8SSp99mRN8oPIphpxJTM3uVd20eb32t7yy9hye7LDszfJm7duMODOkTJO0Ye4JTr6beI8AR4pINkluJwOlfISwkcpDCAMNC4SOUiFSeegijSJEqRhJQCyb2MYkkWygYaFpDlK6/TY0YaOJzRbUimrQ5vcufoMfVq8ghOCJoaP81MRDHC9MUDTTPRTXZ9lvcq29zHSmsiObY96t8fuXnuNia4EBK8vnZh7jE+MPMGBlEULgxQHvNOb4f688zw+rl/n8te8yliry9MjJbTeoRPH62nV+9dAz/MzkI7ixz7+78HX+YvaH/KB6iQE7x7868TM8UN5P1W/xL1//T7zduMUr1Sv8wr4nd1ykvrRygQE7y68f/SRPDB7pgRfQjFy+Pv8mv3/pOWpBhz+8+h2OFyYZSRV79pf0M3TlVJLd07XEYnCnWGciJTXnmzbqg9IJ+KOj8anxh3hm6CSdKCDWYj5/7bt8efaVLe0ZWoq0MYyupXrCjJLmWrsHLibXcNVv8e8vfoNXqlfQhODJ3jU8VpigaGZ6kyefZb/BtfYKM5lBnB0Wj6Zm8OTQUVqRy/HCJPuygww7hWThJgSdyOOF5fP87sWvs+Q1+NbiGT45/gBFkaecSmH2bsbxVJn/4dBPolA4lsFst8pvv/3ntCOPh8r7+eUDH9pWqqYLjYqTRyrFhZvLZNM2xWxyjZa8Br9/6TmutBcZcgr8k0Mf4bHBe/rX0I0DTq9e4ncu/A3zbo3/5/LzHMqNciC7tX5cKsWLyxf4ytwrhDLmSH6Mf3r4ozxY3oepGcRKcrW9xBdufJ+/nnutD5Bsjk5UJWOUk0nupqyKEAI/jpnvNij+GNT0t9se+UKaw/eMYtkGAwPJfawJ0dP/2SqAf6fYawhWbNXHKZk5Hh88edeMl/W4Jz+5JzAkldzGoNtxf5RC9rZbZ+mhlYAAENugqUPZCU4V928pGbubmE4Pr+cBd95gayILQ9Nphz6xUhh7TGp0XWPxxgqBH/b0MQTGpvMid8gk7fb9d4q1oMn/deWv+Pby68RKJjpS2THuyU1yIDtGxS5gayamliziXlg5w3+efaH/+UhGzHYTN8h21KGwg/3v3Vyz27fbXK5qaAaR3KD3m5aBbuqou2SNJuwgr0e/V7RCn3rgMa7WwaKNqNgFPjBwYkdR5b3iRGEfhqZzeGBj0ixliOyVJYRye2myaRzE0CeBmDhewZVdctLH1HYXf/9RDNHTQhMIOlGLZlTD0R1iJVkLV8j3qPJBGPHyG9dxvYBYKiZGipy/vMjocIFmy6OQT/H2xXkc28T3I2IpGRrIEYQR+yYrKD9irdEhjGUPFKqxXG1z39EJqvVOX1T40vVlTFNnfqnBwnKibdVodhGaIOVYKEW/jOduI4piLs+tIgSc3D+KY23cr5MzFQ4dGWNsokwm4zB/fYXAC5k6NIIfRcw3Wqx1urS8gIdnJvjGuUs8MDnGbL1BNwgZymWYrTUJpeRWrUHWtnhwapyFRotYKpaaTQ4MlgmimFrXxQ1CbtUbdIOArG31dZog0V/pRGvERNhahqw5QDdu4MsOjXCZSAW0oyoD9iStqEpKz2NpqZ6JhEbeHCSUHoZm0ggWaUdrxCokUj6dqEbc0x1phasM2FO0olWGnYP9779QX2bBTZzgDE3rAUeJA3DGsPDjiHbkM54ukjUTC/O5ToOc6VAPurRCn0hKjpdGyJhlUmmLYycmkVKx7+Awhq5TsnJcay8xnRlCyjZ+vAxIItntZeE1TC2fMPiJEcogli5SRcTKRSEJZZ1YJaCTUuGWZ+lbr13n2pVlPvzRk+Ty2+cK2UKGk08cpVVtk845/Xte1zUGhwvEsSSOYmzbJAwSRmIcS1w3oNP26HYDMlmHfDYFQpDLJ6K/bjcgiiJu3VhldHzvku2C7dDwfRa7q3xgNAHthuwiGd3hYmsWfYcyfiGgGwV889Zl5jstWqHPs5MHmcxtZ5H+yIcAJ2P1tWJSeoaUvgGkBdIjpW8vd3XjJpKYdrTG8LZ3t4dCEUiXYWean574NYbtyS06Q1mjyKOVYQzN4msLf0CsIubdq6wGs8g4z+2c2lB63OxeIFIBujC4t/gkD5WfRdth7lKyhplOHeeR8ieQsSL0JJhyS19WSpESeQ6kT/XBS0nMtfbb/W1yRokDmVP9crBYSl6rXaNsZdmfTbRtLrerpHSDNdcljHcfl292zwNwsvA4z478Ajlju67bsDPNicK64dG7Gwcdx+zpsd1dKBSa0PClj607tKMmL67+RR80KppDfGL0l9mXupdWzUfzBUpLlFAdkWFUVZjInCSIAoKmTqOzhqYJTMugXMniexFrFx2ESHH02BiWZbDgXus7FerCYDx1iBHnHl6v3sLWTcbtEhdWV3l4coK0aRKrmCF7DFtzcGWnv9+LzTaXFqo8MDPKcHYD1PZjF0ffzkpPkoQuZ+vzTGRKlMwMC90GRwpDW7a50n6L12rfRBJja2meHv4cD5Wf3aY1VbAqDNqTfHv5i/xw7Rs0whVeXP0LxtOHyBllporFHQ2Hkuerx0zmBD898WsUza36ojmzxIeGfpZIhbxc/RqSmKvtt2iFa4RxhrdWlhjP57H0/3LY5z0BHMWyQ8s7jaWPEKs2UnnEso0QJkqFmPoQusgQxItEsoqhDSCEgS7ygMSLrmFoBbzwMrpWQIZtYtUmlp2EPohDPvUEgmRSq4BXqld4be0aEsX9xWn++eGPM5YusS5bK5UirdtMpypMpgaSZ0avxGszAPXd5XO8Xb+JLjQ+OfEgn558mJy5MSg6usV9pWn+24Mf5kZnhSWvwZdnX+H9lcNkd2C/VOwcj1YOY2o6ukjxSOUgX5l7lUjGHM6Ncm8p0Tqq2HkO5IZ5pzFLLejQibwddXJ0IXh65CQfGT2FKTaU1vNGik+OP8A7jVm+vvAmV9vLvLp2lY+N3YdtGBTSqf7CIOyV/q1rdtweYRhz9XqiJRJHMZ2OT6mUodsNetaxvXIVP6JcylBvdFEKyqUMk5NlitZ263ENg0Zwmaw5ScYcRTd0Qj9i/soSE4dGsTM2r6xd4fXeNby3OM2/OPKJPhtnPbKmQ8awmckM9dveKcbTJX5p/wf7C73NkTEcnho6xrnGHH984yXaoce19jKnsjnmWy32l0oopTCFgV8HyzCYmRhMwIZeWzkzxYHcyBam2eaI4uQcez3h2lhJvrd6gXcas5iawacnH+bZ0VNbVPxTusWTQ0e51l7mD69+h9lulReWzzGZHtgCcnYij+8sv0MjdMkZKT47/QHeN3CgDzTqQuNgdoR/vP8pztZvcaW9tG3/9mff3xtkBaHy8Hr14WU7zUcmDv+9USD/a8fszSpvvH6TqekBcnkHxzHJZh0sLWGPRHGclB/JOwsMhyraUxhdIykhWxekzplpfmbySYbsAkHc7DtKbXZWgARej1UCnJtaGiFAqQCJjmC7E4hUd8cGCVXcLyPShYatWaDaEF0DYwZDlEhtAgb2ZUf5uelntrymVABEgMY6tKCUD8heQiCDqZkouYxCB6GD2GuCrUAloIUXR2T3KBFUUpHJpfosAU0InB4IJ5W6K3aURBHEe1/bSMY8v/wGL62eIVYSWzP55NijfGr8McpWHksz0Dfdp5GMudic3dKG1ns/EdzeeRj2ZYhE7WiA0D9moBttTDLWSxKFSErYzjYuMZEewdJM4jDG7/jE0d2XG2ubmF+aEBvAYq/vrpetlKwcn5l8kkE7YU8tttoUUk4fYJVS0fT9xIIW+iVwSoEXRmgiGSdSpoGhpRhOPUgrvIl1W5lALGtE0QJRPEccL2FbpxAigxfPokc2hpZCE+8OvHqvh6OnOVa4n5dWv9V/VuSNAg+VHgOS8pHVWhvHMhkZzDNUyfH9V69y9NAInh8xNVbmzXOzLK00yaRthio5wjBiZLDA9HgZ1wsZGsgxOlTg+myVRtOj0/FxbIPhgRzjI0VSjkUpn8wvWh2P0aECrh+ytNLkyMERNE3Q6b47xpcC3CDE0DWyKRv7Ng2dYycn+M3f/kyiYWMbLN/0EiaKgHImzY21OnnHZrJUoJxJcWp8hFgqDg9V0DWtBwDZrLQ7jBVyZG2LlGliGzrFtMNgLoNSiqxtYxsGtmkwmM0QxZJKNt1PCEGyUJnJJqLhAg1N6OSMCpK4n92vWFPomkGsot4sMinlWt8+rffK3lAMsh+lJJrQe21ogKBiT9OJ65SsCdKbNDROlEc5pkb62jJKbRixJH0iKSHbPHcZdLLJXJZ1UFb1y5nTGZuf++UnCIOIdMZGiESLLlmwh6TMKZyesHikWsTSxdaHkuMRWh/M3fwb5Kb3tN7xJfdiHMV89/lznH97jvc/fnhH4EhoAkPTKQ1vZ2AOjxR6gFXyfeVKotdRr3URgqS9nji81rMZT1yCEzZSu+0xs2/ojoy4kpPi2ekDSAVZ02TV7SBFRCfykmfUDskOS9N5ZvIQ3SjgAZWUHf04JNF2CiEEXid4V2MIJK5qftyhZN/Z/GM9NDQeLD29DTTaeF/nQPYUBXOQtWABN27RlmuYWp6VlRb3bBo3k3L5bv9zGSOP2EXLc2Wxgdtbu3TaHvW1DtMHhmisdZJ5vmVgWQatpotpGeSLaYbH7gwSxkrixxGNcENvp5LOcKvVIJYSYxd3xeSzERV7gicGP70jaLQeO7l09d7Zc99cN8QPIorFuzMvkiqmFlQxhUXOyHOz+w6z3UtA4uz28MDHOJx/iMCNOHcmmfv4XohuaGRziStfp+0zNFKgutLCNHVcNyCVsnj48UOJCHjbY2Agu6vrJCSyBr5MyrqCKKbl+4RxDKYJKNy4i6OnEp3G/lxEUso4jBTv3vCgHfk4uknFzhJJiRtFW9bCgfQ433y576i3L3OC44VHt4FG65E1iryv/FEutV6jFi4z717hZucdjuY/gB+FjORy6EIQ3fa4SelZHh74GEVzI0GzOYltajaHcw9ypvESnahOM1qjHdXJGUUc3dhx/f53ifcGcKQ6gEDXiwTBIoZWwo0uUUh9EDe8QCTr+PIGhlYi8cRK2EemOQQqJlZtRE8YM4wXUSra1MZTeOGVpGa71/8iFfPNxTOEKsbUdD49+giNuYCaXKDRSrQ0pFSUixmWV1v4YYShawxX8hTzafZNJgNxM3T53spFunHAiFPkI6OnyBrbwSBNaBwvTPLQwAH+Zu513mnMca45x0Pl/dtu9PF0ud+GJgQDdo60buPGPgdzI33GiiYEZSuLrmkEMsaNgh2Bo4xh8+Hh49uYLonriM0zIyd5aeUCjbDLq2tXeXLoaLLQDBPad9o0k8VyHFPOZ3a0zoylpLbWwbT0PlgkpeLW7BqZtE0QRliWgWnoaJpGGMYEQczAHkKcSsVYesIaSP5XOBmbTCGNYRlEMuabC8k1tDSDz808zkR6Z6pnkrndOzShYe+hEeDoJveVZ/iTG99Doqj6bVIlEyklS50OBceh3nHxgpBixrkroerNIZUiZZv9iVg79Hh+8SyRitmXGeLDwycwb2tTCIEhdJ4cPsaXZ19h0avzg+plfnLioS3A0YrX5K1aQr+cyQzyaOXQNnaaEILxVJmHKwd3BI5W/WuEvVI1P24hlSRvDqMJQcq4+3rq93qMTw6wspLQ4G3b7A9aGcMhrTu4cUA39mhF25kQt0c96OxZwieEoGzncXSbbuzRDNvEMsbUNDrhEq3gJooYqSJ0YRGrgLQxhKGlcaNVTD3bB4+8qErO2kfBOrDte2Ilqfo7C0Guh1KKbuTR7AmAO7pNwUwlwJFWAJFkgEecMrrQem02EoCpBxwppQiDV1Cyjq5PEYZvIbQsQkUIYQI6QsujVIpYxcTxTQzzCJqxl3WxIJSydx73HvR0U8fuPSPW740BO9c/B6t+447XzI+DLTpBO0U9bPPiyhm8OEAAjwwc5ednnt1VQ0myVdcnOSpBycxjasY2++v1qPpNQhmh78HiiZVkxa/3/x/cpJWkC4PpzDjWOtgmRPL8vMPiaT00oWH2bIljJZnJlkibPZtjIajYBRzdwo196kE7AdF6+3ppeY0oTtSw2n5AJGNiqRgr5LENnZu1OsVUiqxl0fA8bCNZtN87NkLNv4wXVwllm4waIWduOGMJ4QASQx9C0zIIkQYlUMR40TIpYxRbL9/V8f2ohKEZnCw8yHT6AG7cRRM6ebOIoyWLU8c2ue/YBFdvrGKZOovLTT78gXtYXksc1F556waTYyVOHhnn8rVl0o7JUKWcuKK1XE4eHce2DN6+MEcmZSOVZHAgSzZjk05bnDk/z+F9QywsN+h6AUcOjHDu8gK2ZXD88CiZtL2xYH8XIUjYM62uvwWk6R+3ofcBhsALqS41cDseI9MV8qUMH9ifMELW7+cnDs7syAQ9ytbM7MHBAQ4ODmwCPJI29leSfjNd3tDD6e+rEJhi69xuJ7YCJFocO8Vu2+8U6VQBU9v4PvuOWeJ3N98QQpBKWaRSyf0ayZhAhrQjj1bkIjD611NTFmhq6+Jd7PJ7l9caDZfLFxYJ7tLR8fa4XZ5i/f/KYK4PGO0WhqFjmDrlHnt4r7haX+MrV8/z7PRBXpq/ga1rnBwpY+liV2ZtpCQvL93iTHWRtGHxK8cfIvfjqnEUxnhd/64Z1+tRDxapOFPbGKFxLBEk+oS3R96scCB7X1LloHo6ZZsiSY4MkDUKrAULKBSdqEFRKPK3AZO6ZpDrAbGRCrjSfosThccpmJVtfaLddGk1XIIgSWhYpoHb8VlZaqDrGuVKDrfj43shq0vJvOpugCOJwtR00sZG31jpdtCE4ImpaTLW7uO8LgyO5N5H2RrbZqner+joJ+pUn60qlSKIItL23skUuycun07dXdIlaVuSMwvEKuJq5226cSLRMeRM9UATHcuCUw/OJPphQYSua4jecyUKJemMxdhkGdPQiXuOi0IIZKzw/IhSObOlXHjnc7MuryDJ2Xa/fLcdNbnSPkveLFGxN8TxLcNAqcR98W4jpZusBR0MoRELxWAqu8V9shXVuN45i0JhaQ6H8w+S0Xef1wohGHImmcwcoVZfJlYR51uvcE/uYRCCII53XLMOO9NMpQ/jRgvY+gCasOhGAc3QY8jJoWsaFXscR0vToU6sYjpRA00EOIbx46VxZGqD2MY0KEhbJ4hknbzzMYI4i6mnAAND8zD1AbrBDQx9CDes44VddG0IGKEbdIhkHqWKWHoWqRqkrGeRqoQQDlLp/aF1xWtyvZ1Q6vZlhri3Mo3h6Ri6jpSy77RgWQaD5WThoWkCx96K2FWDFtd67YynS+zLbLfWWw9D03mgtI+/XXybVuRyoTnPfaWZbXoaRTOzBSAwhd5DVTUqVo5O00XTNVKZRBtn/QbeqbwIYNgpMpLa/aF2IDdMxc7RCLtcbi3SjQP8SCJl4mIie+inbRjMtuqUC9vBKcc2eejBmS2vRZFkZKSw7UEkhGBqcmNiv9vwE6sAgY7du/mEEBQGcgxOlDEtg7nuGtc7KwDsyw5xrJDUr4ZxjKkn9f+xUsRSYul6f5IYK9W/edbptndzKwkhyBo2pqYTK0koE8p/rBQrnQ6HBwbIOBaxVGRTNu/2/pRSkrZNTCPZ9wW33j++e3JjDNi7g2wlM81IqsiiV2e2U6UedKjYG6Uvt7pV6r0sx7Hi+I7g5voxnipO8ce8tO29ejBP2iihofUyqH8/to7vtcjlHO5/cIa1amLzqfUGoQErT9nKUQ2aBDLiemeRB0qHdxWkBph1l+/IcpnJjFCysnRdj3rY4WpnnpnMMFlzAlsvIdDx42pS/qiX0YWDVAFpIyF8R8rH1otkzUlMLbtjYimUEdc6CzxWObHnxPlqZ6Ev1J030ow4RVANEDb02JpH89N8df40sQqYdVdY8KrktgDWEk0fRjPGMIWB0HKgfHrLRIRwUCphyAitjKaXuVM2rGA7ICBzB50jTdewHau/4LE0k+nMCC+snEGhmHertKLuFjHp26MZdVn2a3t+TztymXWTe9PRbR4oHd5TeDuSEfM9d7f1CFXEsl/F1EwGrRJla/sz+kZ3ETf29yz/WvLWWO2BghqC/dmx/ntSSWpBkxFnEBODbrPL8q0qhcH8HQE0ADfuEqoQhSSSkmbob7EM3pcZpWhmcGOfWtDiemeB6UzSL+8dG8WPkmfkeqmnF0WkTZOUZTJZLCCVwtQTC/f5RnIMCsiZY2TN0V6JzNbQRArT2EfCYAtAmOiyg1IRllHG1H58xLHXI9FAdFn1l+n27IeXvDmGnDFGnHE0TXDP/mGmJso4ltlnnRwMIl556wYHZwYZGkh0fw7v26DXbxbCfvaJo/2/7z2WAHWxlNx7cpKsY6Nrgo8+fZysY2EZBvundtet6e+zF9JpeUSxTPSQcttZGAqoNjpYZsJmbTVdOj3R7vUwTZ1iOcPgWImLb9ygsdoiv4ObmlKKKIrptHyCIOqbpJimTiptYTtmf8xfj83scbcb4HYDwigGlehs2rZBOmMnC54d7pf143S7QU80GHRd4KQsMhl7xwVxq+nSbnuUShlsx8T3QtptjyiSGLpGJufcUaRWSkm3k+xv3BP/TmcsUml7VwAvimJq1TbRbWyRYimN5ZhMpQexNJMhp7DlWGtrSblieSCLrmu4Xb9X/pU4u6XSybGKTd+7nnz13ADfj7h4fp4bV5cplbMsLzW2yBkITTAwkO3r5tx+fsMgYbGvi/qblkE262Ba+pbvvD3CIKLT9vH9MCmnTFlksnafC7ZTaEIwky9xvrqCVBJbt8kZaZaDDXfO2yPuLeQfGZnCi8JtC/sfq1CJydBO/XqXDxCriKw5QCtcRRmqz6SLwpjLZ2epjBS3OV0DDDrjpPQ87yyvkLMtZkolOs0ubtsn3+svprAxNjFMQ5UIoterHTaLKZnCZn/2FK/V/hY3bnOl/SZ/duvf8kD5w8xkTpDvyTAIIdh3eGTbvmx+ffO9UV1p3hHYWA9T6Chg1W9xTz7Rr3EMg5YfME+L0UwOe5embC3NdOYIl5dqtDwfNwzp+gGmrlNMp5BK0g0iBJC2Eu2vWifRjss7Dg/tG991v4QQpNMW6bSFrom7mhvEKkYSYwgDN26x5N1gfTU3nT5G3hhAxhKhiQ0QL7NdTBpE37lSaGJjnSYlY6PFOwr+R1JSMB3ShkVas6h2up/43yoAACAASURBVHhRRMayyBoFHig9DiT9IhGbVpi6Rilz94zAdWa4pRk9uQSNguVsqR5ZCxZphomDWkrPJoLfdziHmtCZTh/jTP1FFJJF9zpu3CGIIta63R2FFcZSBzCFwXL3BQacB8hYUyx5LV5dvcnHJo6R0xxsLbUJ5FYE0iMWkjXPJdijHPLdxHsCOBJCI2Ue7v0NFmPUui5vzC0ykEn3EEQThU/TKzGctbhRtxnKZghjn5w9zGyjSd4uJzblqRS6lqXmurT8BpauM57vMl1OHjArfpNOz+VmJFWkaKVxUrcP1AnNeLdQStEOPWpBMpEbcYp7CuECjKVLfc2Nue4aoYy2sUhs3dyyKN8MaxhK5/wbN6mMFpg+NLLl3d3EcQtWek8LalszqTh5rrSXWAs6eHFAy4tpeT5RHNMNN8RWXX9daHb7DSGESNzMlCKKJUEck7KMhDbY0zdSQMcPyNhWv4xrt0mOLiyCuE4bScoYBDTajS7BUkBxMM+K16QbJYvyUadIzkwx32hys95gPJ8niGOCOGK51WEknyVjWXhRRBDFBHGMoWmMF/KUM7eJ6ClFO/JYdOvUwy5uHBDEScnR5dZiHzhUJGWAlUyGwwMDrCvge2HYP0/vJgSClUaHYjYBdRbcWl+IeC1o89zCW7uymDqRTytMWBKRivt/r8eS1+jv9/Ad+mnFzvXLITbHgdwHUEia4SKa0LG1OztU/ajGrZtVZm+t8f+T995Rll3nld/v5vDyq/cqh67qrs4NNBqNHEmCBAmRFMNIGuWRpVnLGkm2x162bMt/yfaM15JH8ow8kqXRcJSoURgqkQRABCIQuQE00DlXd3XlqpfTzdd/3FevqrpeVXdzJC2C8/VCr0a9W/fdc+65556zv/3tnUoZpNtOEyklzpCZ51J9jpCQE+XLPDlwL6kt+sELfM5Vr98UOOrTMkzEBzu28a8tn+Sh3CE0KYXaBk0NOd8uS4ju23YzUzcY1A09zlWnsQJnS/0kL/Q5XrrY+f8Bo4ecloXQgqDI6gJsV3yIXj3DdHORklPn7cJZJmKDnbGpqEeJytQUZCXT5WrXX1+bOnyT7KUiSpscLLqFqilM3DGKosnt35OZiA2iijJO4DHTXGamucy+pLnlJnC+VWChtdlCdX0EbeAYIgHr2BZA7Gqs2BUu1jaWqpmSzrAxQNEpk9XSXa/nan2BRatERu0OhgRhwLnqNGU3yvTFZIMdsbVFryHppJVEZxGRyMQZ3j1ArEuZSLdIyElMycSQTPwwuj7HX2PQDRo97IgNMG8V8UOf15ZPcn/PfjRJJR+PbWBzbLL41vUNbe6NR8+RAHihhePXcYN6BIbeEH5QwrJfJ8TH1B4nDH1kwYyA1i3YWx/lcAKb11ZeYNGaxZTX+kMWFPr1aEMQhjBXrDKaz3QMLFRF4vCBYWaKVXq/C7e5luPy9WNnefTAOL2pOE+/d47794yyq//moNGlCwu88vxpPnz/KrWqRSptcPd9O3n4Y/s2MN40RWbfjr5ORvy5b37I1792DHedLfToeI5f+d+/jG6q9A5n6RvZzCwOw5DCSo1XXzzD+29fYXGhgmW56LpCNhdncu8A9z04yd6Dw5vKlXw/4MzJGV554TSXLyxQKtYJQ4jFNYZHejh4eJR7Hti1SR+nXrM4c3KG949d4cqFBVaWa/i+jxnTGRvP8fDj+7j7vp0Y5kbg91vf+IC/+Yt3+Pl//mlGd+R48ZkTvPfOFaqVFomkzuG7d/CxTx1kfFdf13nBtlzeffsyb792kYvn52k2bWIxjd37Bnnw0T0cPjre2Yytj5WlGr/2q3/Nwlx5w89//p8/yb2P7uJac5l+PY3myhvA6n//b19g6vIS//3/+jkcx+OFp09w4dw8tWqLWFxj1+4BPvXZO9l3cHjDRu/yxQWe/qv3mZspcn26QKNuY1ku/+f/9rUN7BHdVPmV/+PLTOzaqH4ThiFL8xW+/dxJTn14nfm5EoTQk09w5J4JHvvkAQYGN8+dYRhSr1q88OwJ3nv7MnMzJRRVYnxnL5/6gcPRBnWLdWfejCEIYCgKCJEo7oJVwFAUBo2ergYLsiixP9tLxba40Kyj/x1oiHyvhmZq5Icym8yLtgo/cFmyLuMELXq1cWRx7T0ehiHL82VUTaGnb3PZUExKIQsaS40VWq7GWDpNeaXO2Xcuc9dj+8j2pzrSIp0QIJkyUOXNCesxcz/7k/fzfunbhARcbZ5hwb5KnzbK/tT9jMcOkddGtiwd7/Ys9uTXXfdNSFghkVlHbB3jKAhD9ud76TNj0ZjbImRBIaH00BRFZFHEUCK3yrrlkDQ0XD9K0ERJ82gfnTajkrDV/dZ24fsBZ8/Ps3M8z1iX+bVbWyQkTDlG0ytTd0ud68yovdg1l6mTl4lnYjgtB1mVkWSRarFOsk3GMOI6lUINQpBViZE9Q513g2W5eLcAcsiiiCJJWL5LSoL+RBylPbdYfotrzYu4gYMoSGTV3nbfCB0HzluNSG4ljBJgvsdUtcBkMt8B2crOUkf3VRV1kvKtsZ4zam+73B/soEnNK6LLaYKwu45qUskShjamMgBtVpkfBPSba+Ow2x4gpqhEFbzfR6VqcAMtGIirKoOpBJIokI/FuFIsUWlZxDUVNwjItrUSBpJxyi0Lz/dJaCpN16Xluji+32Gc9KaSGxg5Dc/uLPwTsoEkitS9Fn4YIAkiTuCiiSo+AbbvbAByVFFGFERMSaPh2Z3zdisTuzGSitE5V9VtdhU/lbZgwAjtz1zbpV5ubRhU2w0FXVK2BQokQSTe3kz6gU/Tc4hpBmp7AoopCovtDEoQBNRbW9NU37w0jeP5tFyXpKGzWKnTn4qjKwq25yIIAp4f0HRcErrG4dEBDHWLyVIQcIM6ptIPiCAIhEFAqx6xFRq+hdvWj0koBpooU7Esys0WlutGKvKKQtNxuFYqo8uR5bCpKBSbLUxFQZflTl/bvsupynWenfuA89V5Sk6dlu+03bQiN5bV+7Xan5bnoUsSuhw9Rl7gE9PVrdu0TYiiQExXabQisKjqtjr6OO8ULvFO4dItnSfSs9kIXDU8qzNhJGR9WyR8VdD3xtIaWVSZqr9DyZnBD1xy+g6y2ugtt++jFKvllIIYjTuIFocP5g7yTuEsTd/mVGWK15dP8qn+e7oCehfrM7y5cuqmAseKKPOZgft4r3iBpm9xrHiOb869yZMD9xJvuzgK3VhNYUjTt/FDn7hs3nRhcKoyxbvF8zyYO7hpPvDDgHeL5/mgFI0xEZFH8odQRBV8sQ0ctYFHI8vH++7iq1dfwAlcvjn3JhOxAe7r2d/uh24gSsSKrLlNZFFeB7Sswd5bRdAW6BdvYAp0C0EU0NZRskVBYF9ylBGzl8v1Ocpunafn32bY7O06Xzd9i6fn3rqpHpQqKiRkg7rXwg4cFqwiQRh0ZeHV3SZ/Pfsa15vLN3xXi5nWPHWvSVpNEpM2X0/BqfKNuTcZNfswJHVT+2dbKzy3cIxWu2TuUGqCIWNtUx+TDSbia89oKp8klb/1un5TXgNFA9/nWr1EWl27v7qk8umBe/mgfImWb/NW4QzPzr/DJ/uPYt4wz6z/dxiGNH0LPwxJyAaCIKDJa0sRN2jghS28sIUUdmNbBdBWb4nOF4nzsnkb8X0RdmBRcpZ5auCHSatrC9L1bZ0vVzl2eYaeRIxXr8ywUm2QjZtk4gbPfXiRJw/vJqapnJlZJBsz6UmaXJhboS8dx/N95ks1YprG4fEBjk/NIosSn7hjF72peLQO0FT60gnCEE5NL6CrMq4XoCkSE31rG40wDDlzYob/5//6BtenV+jJJcj2xPG8gG99/QOOH5vaxCwp1VroaqRJsWf/IM3GHZSLDa5cXuT86VkSSQPX8ahXW0iSiNSl1HJhrsxv/8a3eP/YFTLZGKmUiRnTsCyXmWsFTn04jSiK7N43SCBGi21ZlgiCkNdeOsvv/dsXqVWa5PqSpNImQRAxkN575zKnPpxmbDy/CTiaurzIb/36M5SKDTLZGPGEjiiKNBo2r710jmNvXuaLP3IfP/JTD20Aqxp1i+XFKt/59lmWFyssLVZIpgwSSZ3iSp3/9CdvcezNS/w3v/xZ9h8a3vCdzYbNH3/lVb719eP4fkjfQIpUysSyXF5+/jSvvXyOL/zQvfyjH39gE2spntB47IkDzM+VKC7X+fD9qxQLdSzLRURkxMyxZJU3sScrpSZzMyWe+ZvjnDh+lWbTIZ2JkUwZFAt1nvvmB5z88Bo//989yT0P7Oo86/WaRbXaIp40GBzOUliuoWkKOyf70dZdm6bJm641CEIuX1jgt37jWS6dXyCbjZNMG4QhLM6X+eOvvMLbr1/g537xCQ7cMbJhfqlVW/ze//sCL37rJIoiMzCYRlYkLpyd59zpWXbuHtjylZNQNXZncrQ8F02SGU+nOVe7ypX6MiW7xgO5A5ve97IoMpbMEIQhvWb8+1bfCCCTT5DJ3zoILQoyuhRn0bqEgEhczqKvE88WRRHP7V7Or4o6kiAji2KHxaUZCnbT3hK4EoDevlRXbRlNMvhk/0+QVvN8WH6Vgj2P5Te41jzL9eZ5EkqWYWM3d2U+xoi5G0NK3LIe061EpGOpI9zwnnpvfpasYXDf4PAGRu+Gdgkimqgz2Ls2//tBiBcEaOsYT+sZlBt/f/t2hCH09yZJJoxbarMuGcSURMTyDF1abYdPWVCJyUkcy6VWatCstVieKdA7mqNnIINVt3HtaO9tJgwWry0jSiLZgTSe63WAI0WWqJSb5HLxbRlQUltTreQ0GdTSGMp64kVIXE4hCVIneSYIoMoSGfP2ntGYrCIQAcmR/rC4IeHZ9GudOUURtbar283DlOId0oEXulh+HUnMkjWMaE1/wzyliSayqEds7NDraEXW3e3LR7OGwWd37cWQ/24gn+8Z4OjGUCSJPfm1RfBdgwNbHtsXj7M7n7vlJWMQBp1OFgWBlmdxrna9o23hBC57E6NMNxdpejaqpLQpbgqyIKKJCgdT4xvz6Lfw5esnixsZHd2OuTHCgPaDdeuoYRiu/rXtha0dTyQWOdEb1Z1eq5aJq2qU9Y/p6MrWQ2Yok8TxAxQpQoFHsykMVSEMoek4Ue1qm5GU0NUOpXKrMJUBFDHWEVr0vQClDcr4Ybghow0Ce3vz7O3d6OpyoL+vc298P3KNUgYkPC9o0+MjEO9r02/zJ1dfp+o20SWVfj3F3uQgGTVOQtHRRIUFq8zTs8c755ZEkYbrdiiFmqLQsj3KjRZ96dvL8Hp+NCbz6Vi7fWtjtFdLkteTbE2wXhPASyj6plK09RPczQAGQRC2BBolQSGnjVOwr+EHt8+q+qjE8HAW01SxbW/Dc30kM8nhzCRvrkTlpn909XkCQh7MHey49dmBy+X6HH8w9S2W7DKyIG0rkA1wZ3onT/bfw9Pzb9HyHf5g6ltcrM3y5MA9jJp9yGIkjxyEkVbOfKvAxdoMpypTHEpP8PmhB1G30NWAqISp6Vn83uVvUvdaHM3uwZR0BMAJPI4Vz/Fn09+m7NYRgH3JUe7L7o9+ObQhXOfMJUg82X8P56rTvF04y7Jd4V9f+BqfHZzn3p699OmZzvjxw4CGZzHbWuF05SoXazP8wOD9PJw/dMv3wg18LN9FFeVbolDfGHktzRN9R5luPo0beLy0eBxNVPj80EP0aElERPzQZ9Eq8fT8W7yxchpFkDqgdLdIKiaTiWHmrSJO+5yHUhPsSgwht6213cBj2a7wVzPf4YWF9xAEEMK1J1UVVWKSyaDeR0zqIhTb1ix4afF94rLOUwP3k1Ej9pAf+sxbBf7o6nOcqkwBkFWTfHod2HhjOIGL5TsREEc0DzqBy3Jb9ynqa4+Z5lKbCSt3nARFQUQWZDRJpu45nQIAAYG7s7v5RN8Rnls4RtO3+crUM1yozfDJ/qOMmr2dTdbq2J1rrXC+ep3T1avcndnNDwzev0njKaGMEIQublAn7JZcEdNo6hFEMYko6ARBIxLNvU2dl49KyIKMLsUoOcvokk6n/EJUUdtlGr2pOJocMXwblsO9k6O8f2WWif4eDoz0cmCkj2feP0fLdpl3qzi+x2A2yT07h3nlzBXuGBtgernMfKkalaUXKlSbm51dADJxgzPXFxEEgXt2jWz4rFG3+JPf/w7Xp1c4fPc4/9XPf5zxXX0EfsD5M7P84e+9wsnj1zrH+0FAsdrsOIkeuGOEA3dE53z5+dP85pUIcJVkCVmWECWx61vwnTcu8cG7U+za089/+8ufZXA4gyxLNBs2C/NlrlxYZNfefhRVwmq5+F5APClRq7R4/ukPKa7U+Mmfe4xPPnUn6WyMIAgol5pcvbJEaaXOzj2by1cmdvXx2S8dpbcvxcRkH/m+JLIkUSzU+cZfvsuf//EbvPDMCT72qYMMjWzOQH/npTPc++Au/ukvPcF4m21z5eIi//63XuTk8Wv86R+8xv/8q1/EbLsdua7Hi8+e5Otfe5d8b5J//NMPcf/Du4nFdeo1ize/c54//6PX+U9/8iZ9Ayk+9qmDG8poYnGdz335KADlUpNf+9W/7pRkB4Ss2FVCYMEqMWBkN6wDmg2bZ79xnAce2cMP/8SDjI7nEUWBqUuL/MHvvsy7b13mW9/4gP2HRognovXHXUfHOXz3OAAnjl/jl3/xD+ntT/FL/+Nn6O3fWJp747S+vFjlK7/9bS5fWORLP3Ifn/j0IQaHswRhyPxsiT/7w9d4+fkz/NHvvcL/8qtfIp2J1k2e5/Py86d56blT9OQS/PjPPMJDj+9DNxQKyzW+/pfv8jd/cQzb6r5+uVIucmJ5gZ3pHjRJpkeLMepHa0rbdyLh8HAjiG/5Hi9MX0ISBa7Vynx+fIh+U8KQBzoGF/+lhiCIZNQhHL9Jwy/hh2v97vsBvUMZzFj3TbYoiAgI6LLSSc62ala0h9gCbLpZmFKSR/Nf5kDyQU5WXuNM9S1KziJOYFFxV6i4K1ysv8947CAP5D7Hjtj+LQWObzeCMCQuR/qnq2uZjG7QY5j0xeLbJvhX37frx50sCV01Z+HmQFG3KBQbBGFIOmV2KkG2YqgIiBhirO2yHJUjrn6vLCike5Pc+1RkJlBeqRJLx1BkibED0dweBJFw/eTdE+3vAdrlrYIAruezslJjYmdvt6/vRBBGwMlYPIvleVwpltjVdmi1/BbXm5dJKhlkQWFAH0UAmrZL07m9/YsT+DhBVKkiItKjxzYYFXmB0+mr2xkvghCdz293QkiI7XmdSp8bQxQkQgJa3jyGHL2TGp7NfLOCv80+XxJFktrfne7a9yxwdLsD/3aO1iSls6C1fBdd0jiUGicIQyKbQQFNVOnV0xvQ4Qj4iNwrFFHGkNROWU/Tu7mjSNNzOpt4U9Juuom/MYIgIDeQvmVxU4g2DP62Gf2gUxIlCmJEUQ5BVxUUSezUz4uigOf52wpgjuez6zCqG7+zXYrQpWyhWwgIeEGjc5YwhMpKDVmN2q6LSgdFtn2XumVzfb5MGIbEDJWm5ZCI6QR+gOP5iIKI5/tUGxZjA1kq9RY7BiJw7OXFM/zptTeouE3GY718ceQeHszvIaEYKILUmSyOFS7xzNwHnWuUBIGW63buY9jWWLrRHeZWQpZEbCcqpYtsTtXOi+Rj/Qf5sR0PtbeS4aa+9UMXP/RQBJ0Al6yaxg/ctraIhC4pnefD8iPR860emEgvq7uAZUzOUnUXCMMA8xapmB+1qFZb1KotisU6y0s1NE0m0dbmSClxPj/4IFP1eeatAkt2iX93+Ru8tHicQaMHAYF5q8C1xiJlt87h9C78MOCD8tZsMUEQ0CWVL408Ss1r8vLSBzR8i28vvc97pQvktBQpJdYGuB3Kbo26Z9H0LKzAYSzWd1NcuFfPkNNSnKlc5Xcu/S1Pm32MxnqRBYkFq8iF2gy1tih2j5biB4ceIqsl2kVmq/pEa3NgTkvzEzs+yYpd4XJ9joJT5U+nX+SZ+bfIa2kSikkQBtQ9i7JTo+nbNLwWkiDxib4jt3U/NEkmq5nfNcFWEAQe772T90sXeK94HitweHr+LT4oX2IyPoQhaVTcBpfqsyzbZbJqkv3JMd5YOb0l8yguGzycO8Tx0kVqXoupxgK/fv7PuTuzh5yewgt8ZlsrXKhdZ6a5TFZN8EDuIC8vHafS7mc/DOjR0lTcGqqobHrfpZUYhzOTHCue429m3+BY8Tw7zH6Sitm53gWrGLmyCRIf77uLOzNb19UfK5zj2YV3aHgWTc/GChzcwKPpWThtEHjJLvMvz341AolEGV3SMGSNrJrkC0MPIyKwYkWC75okt+cpjR8aeZyq2+D1lVPUvRbPL77LseI5clqalBKVBTY9m4pbp+61aHgWduCyOzHc9VpbXoGSfT4Sx1YGMZWNJSyeP0e99Q109T5kKU9MmcCQ+5GE7UsGP6ohCCJOYPHN+T+nR+tFbi9MD6SOcCh1lDAMWak2mStVmS1WkSRhrVxNlqg2HWaLFTJxkyBssqM3g+N66EokgKxIUnS8AFOLpTbLD6otm9liBUOVSZk6s8UKoihw90RUHuf5AUlj42L00vkFLp6bI52J8Y9+7H4m9w5EY1KROHh4jC/80L1cu7JMrRol6WpNm4SpkYp1YcKuT2iFIc26RWGh3HX9UK00I0OTbJzhkWyH1ZRIGiSSBjsnozHUajpMT62QzcWJJ3Vcz6des5AkkdHxPJm2i48kieR7k+TyCYIg7LruMWMan//yPZGg67rPc70JPv7pQ7z0/CmWFitUK00Ghzc7ISWTBl/8kfvYvW+w89me/YN8+Ufv59qVJc6fmeXq5SX2H4o2W+Vik5eeO0UYhjz1hSM8/smDnZK0VNrk4586RL3a4vd/52VeeOYER+6doCe3lsDayAC8oYOJGNOiILDQKrOgFxkyN5YkZnsS/MhPPsSuPf2dc03uHeTJzx7mg3evMj21Qq3aIp5Yu5erX7m+6bcipP7+O5c5d3om6o8fe4BEcu2cY+N5vvDD9/Hhe9c4d3qWqUuLHD46jiAI1Cot3nrtAo7j8YlPH+LRJw50BMD7BtJ8/sv3cPrEdU5/eL3r966u93VJRmtrYwZhiAC0fJurjQX2Jkc3lPIJQMFq0GOYjMaTlK0rJNU4utR3exuT7+OoeQV6tBEMaW08eq6Pa3vI2zh5BWGILksdxnEsaRBPxbqyDm8lIk1Tibw+zCPqFzmUepgrjZOcq77DTOsilt/ACSwu1N6j4Mzz1MDPsjN+x20J228dIVW3RWKdm/Zio85Ss0HJatEX275c7e8zRFEgkzEJgxDX89Ha88pq+dWNEZfjjMV2oopqO9ncdl8NQ7zQw/MDrs4X0VUFx/U4f36GdMLA9fw2uxSSMR3Ldtv7NRfb9dBVmVwm3hHuV9Xt+z0gjHR5fY8+JUWPaXbAtLTa0xHFVoQ1R3Xb9bgd8gVEe72UErnEOqEX7ZmF9Z8rHVEGL3Bv+fxB6BO0qQdRgk5FU1WGU8mujKPouzTiykQHlB40Uwya6dvGE/5z4nsWOPr7jB413p74GyzZFRqeTbaL+LAsbk9nSyg6GS1Owa4x1yrd1Dp5rhVpQQAMGpkt3XS2isAPaNbt26KKFu3GtvbSduCxbEXCpCnFxJRUXMvn2kqJ/lQCU1WpWhZ+ENBogzFbbU7KtRaW62G7HpoiRa9gUaBYbZJPxXC8yFknCEN8P2L89Ge3bktEw2uXgTkeruPi2NFmLqvFMSSFshtp+JStBguFaqe+13I8dgxkKbdd8qoNi5G+KMt1ZbaAqSsEQUjNs3hu/gQlp0FKMfmFPU/yUH7PJvR/VVh7fUmKIAjUbLtDo/WDgKSpRRn523yGBQEqTQtNjZgV/UYKTVKoeRYtzyapGJTsczT8In7oklFHCEIPJ2hiSCn8oEHdr2FIaeZbU8iiRp++B12Kk9dT0TWHwQa9o25RcVs4WziBKYKOIhiYcoaWX7m9Bn5EQpJEKpUWoihimuoGEUhRELgrM8k/m/xBvnLlaaabS9S9Fh+UL20AhzRR4ZH8Hfz0jid5Y+XUtsDRagzoWf7Zrh9k0Ojh2fljFJwKRadK0enuhiYi0qMmyWmpm74wdEnlZ8Y/wzfm3uTtwhnOVK9ypnp1wzECAiNmnp/e8SQP5Q+2x0sI0k6QRoGN/bA3Mcr/tO9H+cuZV3lr5Qxlt86SXWbJ3qifsRqqKNOvZ7cVke4WsiiyN7N91ulmkdfS/Pyuz/NHV5/jjZXT2IHL1cYCVxsLnWMkQWTU7ONnxj+DKWscL1/CcroDR6Ig8mD+IMt2mb+4/gplt850c4np5tLG4xDYERvgn4w/yd7kKOdr0x3gqOVbLNkF/DAgLm9eOI/G+viFyS/w0tIH/Pn0S5uudzWSsskn+u/mR8eeICZtDZxcby7z1srZziKlW0QObZuf64Rs8lj+MGktxnAshXpDqcaQkeOXJr/EiNnLcwvvUnCqnf+6hSREY7dHTXZl2GpSiow2iSiohF0XrtFYdL2pyF0NBUm8NTeYj2KoosrDuU9uAvSTyhprQ1dkPn5oJylTZyibJGnqPLp/nHTM4LED42iKzD27hlks19EVGVNTkSURURC4Y8cAmizRkzDxg5BK00KVJVKmzsP7dqApMrIk8sDuMVRZwvMDLNfj4Ej/hrVA4AdcvrhIpdziyL0T7NozsOFzURTYf8cIvf2pDnCUihn4QZRl3Y5RGPgBuqGSG8x0fbnu2T9IIqlz/N0p/sP/9xIPP76XkR25DoixanIgyxL1aotsLioRiMd1du8b5PyZOb76lVcpF+scPjpOrjfZcfiRpO7XJAgCqibjuj6NskWrFQlk+0FIcaWGosp4rr+lk9jAcJaJG3SMBEFgcu8AYxO9nHj/GlcvL7P3wBCiKHL50gIz7fK/+x6c3KRjpGoy9z40yd9+7V2mLi0xdWlpA3C0IXpBLAAAIABJREFUXQgIqKLMlcYi47G+GwwPoi7fe2CIHTt7N93TvoE08YRGrdb6rl3T1ofn+Rx/d4pmw2FwOMPSQpmlG6a+WrVFKmOyvFTl2pVl7rhrB5IsUCzWuXJxkXjC4PDR8U0lcLneBPsPDncswm+MvlicvliMqmMRCTsHxGSdvJZGFERyWooQl4bXwJBSbTdemad27MWQFbzAwwtaCHQXU/8vNUwpRcmZw5EzxNpJR7kNuIbB1utRQRBIaBppvV22LwrYLRvac8WNIHIQhJQKdfAFfC8SZ1ZVmWbDRlFlXMdD0eToM0FAEOIczjzBHelHmG9NcbL8Gqerb9Dy66zYs7yw+FVSSo5efaTb5d1WSIJISjE7EhiqJJM3Y2R0o6NbdCtRs23cIMD2PBzfp27bqLJM1jBYrNdJahp+GKJKEeCmyTJpffukiiSJHNq/OZFj+82ux0cJ6WgdIAsKuhSj6dfwQoeGV6Xesjl/dQlDU+jNxvH9gFI1cp1OGBpxU6Nca7FQqDKQS1FvWqiqTKHSQFcVEm2w17+JztEqkWM1kSVAR3hfaLOlZ1vX2GFOdj5HECg1bl3jKAxD5loVYoqGJik0PRfLd7H9tXdWXEkjtPdYbmBj+y106eZr3YZX6bCqpXY/1m2fs8vLDCeTdJPbCkKPkKBjBDLXqtLy3X/QMv3vKeAoCEMc18fxPAqVJn2ZOKoi4/k+siSyXG6Qjhu4vo/j+mTiBl4QdNBoz49cKeotG12NqPZhGLE5XN9HEkUUWaLfyJDXksy3SkzVl5htFbsCRzeLHi3BqJmjYNeYb5WYb5UZMjZnlqK2BZyuzGAHHpooszPRv0kY+2ahqDK+53d0fm4lVuwqy3aVQTPT9fO5VpEVOxJXHYvlMCQVI6Fx/65RTFUhExicX1lhNJlmqDfdKe/qFjMrFeotm2uLJTQlAkASpoYkisytVFmu1FFlqeM8smekd1vgyJT7kIVYtKgxVPYcnejUxw4YafJ6knmrzFR9iQoNHj+yq0N5RBAQBToiY3PLFVJxg7ipAWE0qYgChVaNJSvaLOW0BHekR7s7Z4QBM63CBgBYEUWyptlxaQuCkJ5ELCrBC9cmL7i5KJlARDtdFYQbMXP06klW7Brna3MUnQaCIJBSBlFEHUNKYft1VNFEEQ0EJHQpiSbFsIMmqmi0BawFRswedEnBCTwu1hawAxe1i4BjSMjl2uKW1xgSEBB06JLrX9rfL4ukWExj775BBCFyBryxWZIgcn/PfvJamteWT/Jh+TLzVgE38IjLBuOxAY5kJnm0907SSpz7ew4QEokoT8a3d7ZIqjF+dOwJjmb38m7xLKcqUyxZFapenTAMMSWDtBpn0MgxER/gQHIHe5Kj2zq7ATQ9ix4tyS9OfpG7s7t5p3CWK/V5al4LWZDo1zPsTY3y8d4jTCaGETtjFhC6C0kLgsBEbIBf2PUFHsvfyduFs1ypzzLbWsQNQULCkDVyapIBo4c9iREOpXcyYubxQxcRiVV7ZwGB/ckd/NzEDwDQo0U6PA2vhRN4+GGA0nY8WU3zSILIjlg/Xxx6BFWUGTbzW4rHC4LAqNnHL05+iTvSOzlWPM+1xgJ1r4XSBrTuykzycO4Q4/EBGp7FT+74FE3PYld8CLXL21sXVb44/AgjZi9vFs5woTZDyanhhz6mpHfa/PG+uxiL9SMi8CMjH2O2tcKAkaXsVmn50TyuiiohIYak8rmhh3gwd5CJ+ABpJc7nBh9gzOzj1eUPuVCdYtGajejkapK9yb3cnzvIPdm9mJK27TN4R3onPzvx1HcljqiKCsNmjrPFEl64WfdBEAQyaoKfGPsk92T3crx0kdPVq8y3CjS8qI26pJJWorG7KzHIvuQO9iSGu861IT4tbwVT6cPx6xjSRjt1RZ4gYXyZEAdZHv2+mXu2ChGJXn0A27c3ZIDVto6CIAhk4gaZ+MZEl56K1jRj+bV3f7x/M109berR+FMj5ltvKt5ZkKfb7jMhIQlDISRkdqXOaC7DYHajXpbr+RQL0VzV15/q6pKlajI9uQSXLyy0rx1Scf2muoCiJGJbDsXFStfS+0OHx/jH/+QR/upP3+Zvv3aMF589wYE7R7nn/p3ccWSMvrZDjygKlIoNegdShGGIbih87stHqVUjpspv/8ZzDI5kOXLPOIeP7ohKr+J6VzFlx/G4eG6e1185x9VLSxQKNayW2wGL6rVoc7JVnqYnF+/qyKRpCvm+yPmwWKhFZfWqyNJ8Bdv2yOWT9PR2Xzfle5PEYhrVcpOFue3dIddHGAY4gccd6R0sW5VNmxBJkujrTyF30ZaJ+lUk8MOubLDbjWbDprBcIwxDnv/mh7z0rVNdrjfEbZcr1drlSxJQKtRxHI94QifdxXlPFEV68omuY9MLApKaxqPD44RhyKmVRZzA41pzkZCQFbvCvuQoLa/EbOsMuxMPo4gGQRjSZ8YJwpAL5TKa1EAU/GhD+P09Nd00wjDA8msoooEXOrS8tWSCLEssXC9G4GOXUk6Iuu/iSoGYqvLQjjFEWUKSJVzH7zrWrJbD1PwCjYqD7/kkUmbkWlizGBjJkEga1Kot5q8XI4t41+foQ5Mk9TjjsYMMm5MMGjv59tJ/pO6VWbZmuFT/gLw+/J+9MRcQWLarFJ06w2aWjBijbFtMZrJUHTsyEroF7OhqqcRCvY7j+9iex3g2y3ShQMYwWG40EAWBpuuSMSK9ot5YLDKjYP2zG3YtA2fDEQFld3nbYwBU0SCp9FB0FvBCl4IzRzwj8+QDeyOuuhjtw1cZOesfycnRfKdXgzCkUm+RjOn4XkAmY96UmSgLUkew2hMCZioVBlMJdms5QgIsv8mB5BFicnu+bLMHPT/olNzfSuS0OBdrS52KpJRqbFi75NRBJEHGDz3soEXJXSKlbm8iAbBsz3XWZDE5SUrpQTNldmWzuEFAt+IySdTxgyaOX0GX+8hqZtvl7h/OzfF7Cjgq11q8dnKK0b40l2YLpGIa4wM9XJpdoS+T4MLMMntG8nh+wNxKlTt2DnBuehlTU0iYGtWGxd7RXi7OLjOUS3N1oUgQhvQkY0wvlhjoSXLP3hF0SeH+3C5OlK9RdVs8PXucyUT/trbH3SKlGNydHed05TqLVoW3Vi7wg8NHNzGJIsSyxHvFK/hhwHgsz57kwO0veAWB5fkympG/+bHtaHg27xQucig9skm81Qt83li+QM1rIQoChzKjmG3L+VWqe49h8ImJCTRZJhc3t2XgTQ7lCAkZyadBAFNTkESxQ/etNSwWSjV29EclYpoibdkHAjJJdRyxPZOKokBucO3loksq9+UmOVGepuI2eXruOJPJzfdwtcVjA91fTGEEgUTHbqPvU3YavLF8YcPPbN/H9f1O5sP2PEq1JulYhO7LgogmKeBGYtfBNhN1RIcWUNtlbknF4IHcHs5W5phurPDa0jm+NHJ3tFkQopeQIa9lnFd7caN/VfTTvJ5gV6Kf94tTXK4tcKE2z5HM+Ka+t3yXY8XLW16jFzpASEzOklDyhETPrB8EZJPmBqeUj3KsvqxudN+BVaqzwK74EENGD0/030EYKgRE4EZcNogrZke0r89I8EMjjyJ3cWPZdO521nd/cgxdrLEnYTBiHmDZnscLHfr0UVRRwZQ0TFm/qYvjanhhVKyaVuM82X8PD+UOUnNbeKEXjSNJI6GYaDeUTHmhTdMrkVK768sJgoAp69yT3csd6Z0sWjN8WHqWfamnUEWjPf5VdEnBDYqklD5EQaRgT5GQe1GltfLVycQwk+tKlyzf4e3CWXJaChCoupHjY0IxqXlNYpKOE7jcnd1NyamRVRO4gY+xBYYmCAJpNc4PDD7Ao/k7aXgWXuhF5bmiSkqNd8CppGLyg0MPbX+vBAFNUnkwd5DDmUlqbhMncNssSSnqU9mI9PHa8WjvnZ1/z7WWWLGKxBUzApBCMCSNJ3r3E+IiiZGjniLIHMlMsi85xkrzbWbL/zeev4wh97Or95+SVEdviaK8PzXG/tTYTY/bKlqey5zSwtuCjSgIAqoUaf/tTY5S81q0PKtjHiEJIqooY9zS2BWwgwqWVSKuDG74xPdLuN4VEAR8fxEIUZW933W7Pgrhhx4flN/hVPU9Gm4NRVRwAofH8p9mMrGPulfCkOLYfgtF1AgI8AOXmJym4ZVRRC16z4UesqBiB01icgrbbxESoIomda9ARh2k5hRQRI26V0JAIKP20/DKGFKCVlCn4VXIZ4YY6sljB01gDTwKghC3zThRNbnruBQEAU3buD5yXJ9q09o2iSRKIrqpYcS0rowjw1T5zOfuYu+BIb79rZOc+mCaY29c5L23LzM4lOGpL9zNx548iKbKyIrUcW0TBIHh0R7+67aw8xuvnOfc6Rn++i/e4cVnT3DHkR18+Uejkrv1II/r+rzywmn+4++/RmGlxshYDzt399Pbl8I0VRzH45m/Oc7yUnfWHUSAS7ediyCsrQNc1+8wMhzHIwhCZEXa0qZabm+sgyDEtm+d/SO0n88zlWlMSaPo1Egoa0CkKAroxuaS2r+P8LygAwrtmOilbzC97fEjYz2dIeE4HmEQIkniljbpqip33ZBOV8vUXYd3F2fRJZmZWoV7B0bYmxhl2amQ19LokkLVdTCkJCEhdddmtl7lw5V5AC6Wl/j0mETeyN0+7fz7MCK5BBk3sDCkJE6wxmBpNWzMmNZ1nbU+UroesfrDkGa1haoraJtcsKPQDZU9h4YRgmjfIcsSXlv+QVYkRFHAdX0GR7J4rs/1qeV15ZQCqqBzIPUAV5unOFH+Dm5oU3TmuTWUYaPodbckTcOzyWtJkoqBH4Zcq5SZr9fImyY7M9vLP4RhNB+MJdMMxhOdb9RkidFECgHYlckiEu1HItLFmparImqdPWBAgBU0tv2+IIws4m8WhhSjVxvhauMMEHK9cZ6aXyKr9t30d9eHBOTSUbKj6TrR87OuC4V1kgmw1r+W72G011lh569Ih0kRNc5UjzMe201cTuIFIQ3bue29Sst3mW2WOZgexJBUerTYBuZ1Ss2TUftYtK7R8uvMt64wZu7tJEe7hR96XG+e67SjTx9DFXVMQ+mYf9W9LpUdbUkdL2gQhn6bPCPe8n7g7yK+p4AjWRIxdYVKw6I/kyBuqpy5uogkihSqDfKpGAPZJC3HjdyjHI+W7WDqCrWWzc6hnrbIokCx2kCRJRKGxmyhwkq1wf4d0UAWEXii/w5eWDjJlfoST88dJ67oPDV4F/1GuiN064UBLd+h6rZYsascSA1vsLYXBZEnB+/kveIUx0tT/MnV14nLOo/0rmWAvcBnrlXiP1x+mSu1RTRR4amhI/Tr278Mu4XdcsjFEh2a5a2EG/r87cx77EsOcyQ7jtEGVuzA4+2Vizw9dxw38BmL5Xkgt3sTC0oUBHRFxg8CvNDHDwN8f1UXKVJ7rXkWZaeBLERq8/lsDFkUN4m5JWMaA/kkXhDghz5u6NFybVr+WklI1W1RcZpIgogktIGnLo5F0T08xAsLJ5m6rXs4sqGNCdkgryWZbqywYtc4UZ7mvp5d60RdAxZaZb569TU+LF3dcA2r5Wtu26JaFiXihoYfrCLIOgN6miWrwqXaAifK0xzNTnTOHbaFaleReD8IcL1VS0eZJwfu5NWls1yszfOHU69iSCoP5feQUk2UNsvEDXyavk3VbXG+OsfOeB8TiY0TdkaN87G+A5ytzLJi1/jDK6+S2mMyHu/tsPLKToNvzX/IB8WNbVwNL3AoO7MklahmuOTMoAs5Ls+tRKJ/hoahfX8ARxC1t+ZFZVcJOYMXOgiIqKJGw6+hihqzrXOUnSV2Je4iqeTxAoe6V6LiNkjKWRpehTPVNxkwdtKnjaJKOjW3iB96xOQUsqDS9Kt4oYciKMTkNG5gUfcq1L0ipiyS1xMYcoAhxtClGE5gYQVNyk4FXYphSglafp2WX49YAXIa7QaHrvXLF1EQScgmNfcKTW+BrDpG06uwbJdIKQPU3EW80CGl9GMHDeZbZ9iT/AR+aFN2ZtHEBE7QwA1aiIKMLOh4oUVKGWDYPEDVHWXUzGMHNWaaxzGDDF4Q53z120zEHyKjjjDfOosWS7JsX6ZoX0eTTLLaDuaaJ7GDBgdSn0GTkhzOTCIi4Id+R3R7VZ8rCIM1p4z2YkK/BXBOEkTSapy0evsM024hCAIxWV/nFHdr0aOmKWtVml6LlLK2+a63nsUPSmQSP7vhO0xZYzh+lD7j16k2v0bTepWY1H1zfrsRhiHXZ0sk4hqZdHd6tSbJHM2PbLt4rjdsXnnjPGPDPRzcNwTq7VvAQ+TEk1TGqHuzqNJGVksQlAjCCqKQhi56b9+P0fKbXKyf4pHcJzlXPcGO2G6W7DlkUaHl15hpnsMPXZJKjoZXJXKTybLizNDy6wzoE5TdJUREys4iWW2QZXsaJ7AYNCaRQ4+is0BMzlBw5mj5VWRBxZRTLFhXWLFnMaQ4/cYEXuBQcZdpeBUUUWVUPtC5TkkU0dsbulbTIQg2J0rCIMRaJ0y8mnRZKtUZ68t09C+6he/5FBYrW7JaVE1mz75BJvf0UyzUuXBmjpdfOM0H707xO//mOSrlBp//8j2MjeeJxdcYeoIgRBpBTx7i4cf3cX16hePHpnj9lXO88co5rl5Z4hf+h89w19HxznfNXi/w1a98h+XFCj/2M4/w6c/d1dFHAlhcKPOdl85uCxy1mk5XNpLvBzSbNgCGoSK2S+VicT3SQrRdHNtD0zb3lWW5OI6HJIvE4rcuhhqGUdm+LqnktCSjZrfk5D8MEKKqUsd17fFPHuCHf3J7EH99GIaK0NbjdLYQwLVtr1POsj4GYgkWm3XuzPfTH0vw3uIsoiCw4lQ4W7lGXDbIqglkQeuMQVNWGYglWGrV2Z3Ok9U1DHkRSdD/QUtHvndDQBUNhs1onljPmAyCgFhSJ9Wz/XuibLWwPJ+W52HENAbG8uiG1rVUTRQFYnF9W4Hi9WyzTJdSTllU0MV2UiuqTbh5M4nkA1RxbR1g+Q280EXDIGy7Ha+6XK/uAfb15Gi6TmfPsF0szJU5d61CTy5BvW4RBCFBEJBMmSzOl8lko/knkTIoFRoIQmRWcPd9O0GNGC2rbl9uYDPXusKwsbvrXjIMQ5bsaa43z9/0umRRZWf8Tk5X3qDhV1m0p/mw9DKP5L90SwnTbiEIUCk3cb1VB9UI+Fpd8wWhT9OrthN1QiQ3I8axfb9DelgFZEbMCZzAIWy788Y0Fdv0buvpjMkqeS2OiIgpqxzu2Vg9kJSz7IofZtmawQsdzlTfYm/yXjJqd5mFMAyYaV7o9K8iaOxL3nfLwtpe0ECQ+6FdpjeRyKF1q2v7e4rvKeAIIB032jWOGqoS1dM7rk9PykSWJEr1Fn2ZOKVai1RMZ6Q3TTpuoMgSSVPHbZe1qYpMLhVHUySWynV6kiZLpTq7hnIIgsBIrIcf3/EI/+7SiyxYZf706uu8uXyBOzNj5LUkoiBS91rMt8pcqS8RkzX+1ZGf3AAcAQwbPfzUxKPMnS4y2yzwr889zXvFK0wmBtBEmSW7yjsrlzhTnUESRB7p3cunBw5/V5kb1/FIpE0yt2GpPGRkaXgWv3b2b3kwv4eJeB8SAtPNAi8tnmLFrqGKMp8dOsJobDO1zg18Xl06y5nKDE0/AnlavsvF6jx+GBCEIX929XVeWTwdlbnJKrqocnfPOA/kdm94eQZhyLNzH3CptkDLd2h6DlbgcLG6VsD+B1deoVdPoovRuWKSxuHMDh7q3bPhugRBYDSW4yd2PMzvXnqRRaty03sYlzX+1ZGf2ggcKTpHsxOcrlyn7DT43YsvcKW+yFgsTxAGzLfKvLZ8jjOVGXYm+llolai4EQU9Yxg8PDqKqUZgXExXOxpPAGnV5Eh2nLPVWZasKr95/hke7ztAv54mIKTl2TiBx8O9e+mV0uTT8c6kF7Wvh5+eeJTfPP8sS1aFf3P+GV5cOMnu5CApJaJIV90Ws60iV+tLVNwWv3Lwi5uAI0kQebz3AG+vXOL15XMcK1ziX5z6Kx7t3UdGi9H0HE6Ur/Fu4Qp9eoqa1+qUL66GHTRYtqeoukuookmPNorluKiKjKbI6F2o3x/VCMOAi7XjrDhzCAj06iM4voUhxxkx9/J+8QX2Je/jevM8FXcZXYqxO3GU87VjFO0F4kqavcl7KTjzXGuexQ1dNNEgSQ+X6h/S8KpoksFE7BBvFr5Ovz5O1S1yT/ZTXKi9hxvYVNwCA8Y4lt/gRPlVsmo/B1MPcq1xhsuNE6SVHE5gcyTzCU5VXm87g13lwdznGTDGt22fF9oU7KtMJB4kCD1KzjTD5p2crTyPLsUZNg8z2zxJv7EXN7BIyHnOVJ5FEU1KzgxxOYcfegRBE6gxGjvCQuscPfrEag8ShD4SMiXnGmOxe8iqY/TqkyhiRJn2Q4emXyajDVNzlyjZ01h+DU2KIYsqkiCQUeKAS0jQ1jSQABlhFUBiNaMuEuICPmEoQltfIgwDQlwE5PZn/oZzRPc6JJJY9CLbSkFoH7/GhgxDnxAPARXwCEMPBBEB5YbzeB1NHgFl2+tQRIHd8bF2GWP08yCwadqvIwgmQRDNMdH1Km2dFhNN3IUiDcANi9m175AQ1i0+wjAkxN7QpktTSywsVZFEgXwuQSZlcm2mwP7dAx0QaWp6hXxPnKGBDJemlnAcj8mJPmzHZeraCtlMjMH+NBevRJ+NDmcZGkiTTpnU2qXUV6dXmJ4t0pOJMziQ5sKlBRzHZ2y0h0zK5Mz5OQRB4OC+IUxjjSnqBRZL1nEEZFQxgSGtbWJleQxZHiEIasjiAKJ4+wmYj1qEhKiiRp8+xII1iyRI5LV+Fq1ZJGycwMILHCyxQRB6+KGHE7RQRJ24nCIuZyg6c/ht62TbbwACmmiSUvI0vAo1t8iyPU3ZWUARNUwlRRB4SKJMxDJNU3MLVN0V0movV+ofsD+1cUMvKxK5fBJREpmbLWJbLrH4RkDVslwKy2vvFkkUOLp3hDAMUbZgiKxGqidBfrB7yf36EEWRnlyC+x/Zw6G7xnjumx/y+7/zEq+8cIbHnjjA0kKF0fFcV00lVZOZ2NXH2I48Dz22l9/4F1/n5AfTvPnqeQ7fvaNz/Mx0gcJyld7+FI98fH9HMwmiZ67VcDo6TlvF8mIVq+ls0uFptRzmZ0tIkkiuN9lhzgyPZjFMlXrNYm62xO7ERr3JMAyZmynRqFnousrQ8K0bWAhCVB5fsKub1rh/F9Ep2Q9vXs5mmBp9/SlEUeDS+QU8z9+SPXRjZHsSUR9VLQrLNXZMbNRk8v2AlaVqhxm3PrwwIG/GGIgnUEWJ3NhkNC4FGT8MsAIHSRCp+RVKzixD5n4CQuquw2g8jRv47Ezl0MQiTW+OuLKDm1TafN9E2E4ib763YWcDLwgC0rotpxnT8d2AZt0iu0Xp5epv98fj6LJMoMrYlnNL42h92H6LResqeX0Urb0GufHZj84XsmLPMdOMqgsUUSOr9t/afk2AhJJBETTc0GbJvk7RmcdsC4Jrosxd2R0bwOKybZFQNUZTabQu8hHrw3N9DNMk15dEViTmZ0sYpkpvX6ojAI8QETBS6Wh/kO2Jd7TQDClBXhtqu8jZnKm8yb7kvSTk7Ia5C6DmFXm78AwVd+Xm7QbGYvsYje3jbPVt/NDlWPE5Mmofe5P3Rv29LvG/+h0BPrYfzZGmvPn+p9JmO/kQ0b0UUSOhZCm7y7ihzbXGGfYk7mF/qr8tZyDz2PgOeuPx1a5AERTswCImRT+TRIHdA7kNwHFn7N6QiArbfwSgV0/yWH9sS1kISZA5kHqQ87V3WbFnmWle5L3i8zyS/yKqaNwwTwcUnAXeXPkGtTazdyS2h2Fz8pb6OiREFuMoYhJBEPGCgJlGmYl4rqsEyd9HfM/s9hr/P3nvHSXZdd93fu69L1au6uocZ6Yn54DBYBAIEAAJECQByQyiKFGiVrJ0dBTo412v7OO1j71eHXqltbVr6xzpSEdUsCVSEpMIkSApAARAYDAYxAmYPNOTOqfKVS/d/ePVdE9jegCI5nop+PfP9NR79eq+dMPv9w1BHayQTWsLIMCPPCypSGaySKGIdERCxRSQRthgdCSG8m/KFmJYnjTjyr002bahAxAEUYAlDbq6bMbnGiTTknlvnpSRwpIWD/buIG26/PGF73GmPM6ZygRnKhOrtm9nbnWYv5KS2ztG+SebHuEPzj3JWG2Gr199eYl76UdhGwng8GDvDn56zT10/AB6SgCWYxLWQpQh33WnuTU3wOZMP38x9jxfuXwYJVSs3xK16Stmgof6dvHYwG2sZnTrRQHfmzrBdyaPrnp8jebN8jXeLF+76Xu3d6xfMXCGUcS3J97gxdmzt2zvscXLK/4f32/vpsTR9W0P9u4kZbr8yfnvLd2/W93DXfmRmz6TQvJI/x7GGwt8e+INTpavca4yiWtY8QQw9DGl4n3dW/j0yF38p9NP8Mr8BQBsw8A2ll8hJWHLQBemES/QTGHwkYF9XKrN8Oz0Kc5WJjlfmWpPzDS+DsmYCUZSXQwWivhBbKUchBGmoZBCcm/3Vkxp8Odj3+fNxascnjvH4bnVxZb73QIJtXqVsdPJ8EvrH6QZery+cInjpSu8WbqKIRWhjhDAluwAvzD6AP917LmbEkcJlWND+h4aYQkpFCmjiKmNFYmu90r42mO8eZ6dufcBcKJ0CFelsZQLaLyoiWukGUxsJBd0sT13V6yXoVIoaZA2CpjCps9dS6+zlh3Zu8iancx7E7TCOgLBeP0cw4lNODLF3vz9HJp7nHlvgpnWVT7Y8xlOVY7gRx5pI8+Au76NJABPt+hxRtiSOcCzM1+mFpSohxX63HVEhGTNd+ZVSxHrC1X9aSyZJCKk4k9hSgdLJrFVqi2+5+JHDfyogaWSmMKlx92MF1YJtIcfxULeaHz1AAAgAElEQVSAFX8GIRReWKMeLFD2p5j3LuHpBqEOUcIiJKARltBENMIS1WAGgcSWaepiHgAlLTrsNUgUWke0/Dcp1f4czz8PaExjmGzyJ3Cs3UDEYvW/EEYzKNVNrfk9tG6Rch4kk3gUpQr4wSXmyr9Dyn2AWvP7+MEYhtFPLvlpHGsXQii0rlGuf4Va82nCqIIUDknnPtKJxzBUbOvaaL1Euf5Vcqmfolz/Ci3vJFKm6ch8DsfaFtNU/ZPttp5DCIOkcz/pxIcxVCdBeI3Z0m+RdB+g0XoJzz+HFC7pxEdJJR4GNLXGU1Qaf0ut+QxC2Hh+/I4nnDsopH+Fd6r2h9ECs6XP41i7yKU+vfwsh5eZWfy35FI/Q8K+G4ALl2YplRpEUUSp3OD2vWtYLNVZKNXJZly+/+JZ7ti/jkzKoVxpcPLMBHfuH0UqwatHL7NmqMiZ81NUay1Onplgz45hXnnjMl2dGcw2TUZrTRDGyNhnD53l/rs3cfLMJDu3DvDG8Stk0y7jkyU8PyDhWjFCqR2GdMmYI2hCXGMl8kEIheefp+W9AoBt7cMUo++p/uetYUmbHmcAP/LodQZ4ce57BDpgZ+42BhOb6dHrWPRi2l7a7GCmeZlOZ4ikkUPrCEs6DCW2EOkQs2mTMYtk2v2EIUwSRoYN6duwpEPB6kUJE1PaeGEDS7kUrL6YAqdD8lYvgfYo2gMk3oIGk1IwuqGHfD7B5YuzHHv9Mne/f8sSCieKIo69donpqWX4vRDLDnBvF0IQo2gyiZtQdlpr6rUWjmstUbhi4VtIpR02bxsgkbSo11uEYRRXsttUqCAI8VoBbmLZYv06raWnL8fa9d288eoYtVprxW/Gz/hy21ZQfIOIlw6dY362er2Fq57T1OQib7w2xl33bl5qdxRpjr46xrUr83QUU6wZXU58DI10snFLH4efP8vzT59kaLiIm1hOuDYbPs8/c4r5+Sq3HRhlcOSdx4LroYRkQ7qPZqJzSTP0hxmWbaCUolppUqu23lYIXSnJbXeM8uL3z3Ds9cu89vJFdu9be5O+UhRpGnUPN2EuiZ/nCkk2bOrj+WdOceTQOTZtHSCVXk5eTk+VOHH0yqqIo1emrtEKAzqcBJsKnUghaUYeJ8uXqQXNpUJp1uymas0hhaLmezx77SLnSnN0uSkqXpPH1nWSN6z3NOIo0iEL3jStqEGoA0Lt40ce9bBCNVg2yJhojHG6/DKOSmAIs41SNkmoNFaUQqpYHPvWkhUC1zDbf8cmORMXZxjc0IudePfSItVgkcfH/xBHJVmb2k6fu5a0UcCWbry+JKIV1plpXeXw3LeYbI4B0GH1sja1/V39hkDQ546SNvPMe5NU/Hmemvoid3f+ODmzs72ODQm0R8lvkTOL5B2Xq+UyZ+dnydkOlrp1X9jTn6NzXT9SCYqdafoGCiglsB2T4lsSb29NBEFMKduQ3sf56lFCHXCpfpInJv6YfYUHybbb50ct5r1JXp7/Dheqx7BVAi9qEt7Cafl6JFSag8WPMtm4yII/TSWY51sTX+B05WU2Z26nYPVgCBNNhBc1KfsLXGucYaJxkV35+9idv2/F8eIcnl6RVHdUgpHkVq7UzxDqgGOl75O3exhN7cKWLk2tyad9SkGJciAp2v04yiWvOvGj1tJ1EUSUw2lafp1QB7ELXORRD8vUwmWE6HjjPKfLeWzlYggTJQyUMEkaGVJGfgULRghBr7OGAx2P8J3JP8WLmhyae5xaWGJr5iA5qwslDPyoxXTzMofnv8mVdnIyZ3VxZ/FRkir7rvpdgUAJi+vjykyzQjPwfiD9yh80fmQSR6cqpyj7FQpWnkCHKKFwpM10awZXOXTanWhL0wwbLHiLpIwUl+uXsdvwtR6nmxlvlqLVwXRrhoJVYNFfxJIWJXea3pE0tmowVptjNBXbCDrK5N7uLWzK9PHS3DmOL17hYnWait8gQpMyHLrdHOvTPewrrFuieb01DKm4r3sr69O9HJ47y0uz55loLhBEITkrycZML/s7RtlTWIshFFP1KoaK0zR+m/71UO9eRpMD3FZcQzMMaIZBLOwtTT6z5j5aoc++rnVcOT9OZbFOsSfLfT1bGEl14iiTwi2oF0EU8XDfbrbnhnl2+k1OlK6y4NVwlcn6dC/3dW9lV2Fk6dyaoU/Ja6CEXEooPNi7kw47S4+bRSJohD6WVFgqprB5UYApFRnTXULzDCY6bqKXGVLys2vv5SP9e4G4U6uWGjQbHn4bPpwtJGk1PZRSsfOaZdAlM0xemcd2TXR7UWI7FsmME9/Dri1szvRzeO4cb8xf4sj0GFJpbEORMhx63Byj6R5u61j9HnY7WX5904fYXRjhuelTjDcWaIYeKcNhMFHk7q5NDISdTJ6qsrW0hn7RRX4+yxun4mSZ65hcm1zENBR+m0/dkUuSy7j0d+f5Z1se5baOUY7MnWe8MU8zjCGreSvFpkwfI8lY/LVUa6JkDL/1wpD5ep2C63Jv1xa254Z4cfYsry+Mcbk2Q8lrIARkzASDiQ7Wp3vYnhtiONHF1VKJIIpwDINiMtmmo2nWpbv5l9v+Ec9On+Tl+fNcq8/jRxGddpqDnRu5t3sLvW6uLdK7lz53uXMUQlD2p2iG5Ti5IJPYKk/Kja2U30uhMLBlknlvCgHY0iVpZFj0pplWKWptRzlTWjSCCmV/jpSRo8PqxZYuJ8uHyZpF8las6bPYRiVNNi9hSZe81c2CN4mGeFCSViwWTWzHOd26QsVfwFYJWlGDWlimHlZohFXQGlelkEIhhcKUFkooZlvXGE5sXgGVXi0i3SSKqvQ7a6j4k1hmN932AFV/ikGnF4SNokWfM4QtfDKGAUSsSd1BxZ/Cki6m5bYnAA3GG8dBwFByD0oY9LnbUMKix9lMPVzAkgmSRgf97g4iHRDpgB5nI5ZMkjK6cFQKWyWYbJzCVRmmm2dJGZ1kzW7CaBYlO8ml3gdCUK59iZnSb9Jb+F0M1UEQjlOq/RWZ5I+TTX6SMJxmofoFwmiRjsyvEukWDe8IfniNTOLHSLkPUKl/g+nFf01P4T9im+vRBAThLCn3gyjZiRdcZKH6R2giCulfaF+zMrXmk4DGsXaRsO8mCKdQsgBo/OACM4v/BtvcSC79WaKoxGL1C/jBGMXsP2+34xU8/xyZ5I+TdO6j3nyG2dK/x1A9uPZ+LHMjGeHiBRcxVT/59M/Hz6Is8G4oIkqmsYwRyvWvkk58GCVj8d9a42n8YBxTDS5NSgwlKXYkCYKYdhxFbcpxtYnvh0SRxrHNpSp/sSPFQF8+ptHqWCNEIJYcTyxLxX2WF1BpQ+gr1RavH7tCKmlTq7UQAjo7UvR0Z7lybZ6WF+AHAX09OYodK8euSHt4URmBpBku3JQ80tpDCCcW9PVPIEUSw1iphfReCls63N5xLwpFxsxhSgs/8hlIjGBJGxdwZAKIMKSFIx1saSCEBTSAiIQRF9r6pYUlV1I5LOFgWXG/4bK8ALnel7y1T6n6CwwkNrUT6Stj3cYebr9rA09843X+5Pefptnw2Lx9gCjUHH/jMo9/9ZVVz1FrTRhENBpe/EyGEZVyM05ABiHzczVSaYeeNZ3U6x6JpLWULNAavvinz1OtNNm2c5ChkU4SSYsw1FwZm+XJJ45RKTU4+L5NZHNJOoopovZidXJ8kS/83lOsHe1m49Z+Ojvjan6t2uT0m9d44ZnTpNIOW7cPrJjUDw51kO9IMT1Z4olvvM4DD+8gkbAoLdY59NwZnnziKG7ColZtcav31/dD/vyPnmNxvsaOPcNIITlx7Apf+tPnaTQ8Hnh4B8Nrlp/9dMbhsU/ezvmzUzz+tVeo1z3ufXAruXyS0kKNp797gqe/c4yOjhSPfmI/mezy/dE6TrLE73fE4nxtCXVTKTeYn61iGAqpZKxB9cNwH78hcvkkA0MFLl2c5StfPMwjj+0hm0/gtULq9RZrR7tXJMH23r6OBz+0k7/58sv859/6Fne/fzM7946QTju0mgGTE4ucOx0XCH/2F+9bQralMw73P7ydE0ev8N2/PYrW8P4PbieZspmeLPGtr7/G1MTiqhpRBSfBNy+exg9Djs9O0Z1M8fCajaxJ9dDpZCl5NSI0WvuEOkQgyTsuj63dwpfPH+fBofU8Pz5GIyiTNv/7LuT+e0czrPPNiT/kWuM8QeTja49oleTCG4vf443F7wFxktoQJoa02JI5wMHkx3ASNpZj3rIQrtGkbWuFJo3xDppIqx8nohaWmGhe4GLtGBJJ0sjiqCRKGEQ6pBHGGm7XRYZzZif3dn2Con1rU5O3Rpc9yO78fTwz/dcE2udc9XWu1s+QNgsoYbYTbC38qMUDPZ+hQ+7BVorxaoVWGMCqcshxmKZCGZIgahDqOsqJ0IRU/QpCKEwZC+pHuokQBlKYGCK95DiqhMG27EHOVV7jdOWVpeTLmcor7fYZtMIGlSCWUyhYPdxR/DCHZ7/FrHftlu2CWCNtKLGJh3o/y1NTX2S6dYV6WOZ46XlOlF7AlgnMtglIK2zg6ziRc52iteJeaY3vB+3+4AZTDGGzM/c+zlReYap5iXpY4TsTf8KL5uM4KolG40ceXtSg0x7gk0P/C4vePE0VFzB72s54jbDGN8Z/n8nGRYJ2wjNaxb311YUneXXhSUBgtBOehrTYkb2b+7p/AuctkhCGNNmdv496UObFuW9SD8u8PP9d3lh8loxRwGijn8rBHFHbXb1gdfNA90+xNrn9XSfrhZBYKotqj8tdbprxeul/TI2jglUgpVIgwJQmdpuLaQiFrWzyVh5XubjKAQQZM40XtbCVQ9gWf7WkhSFNsmaWpJFsi0SGFO0O8mbsdpaMkks8yevR4+b4UN9u7u/ZRiuMXXwgRqOYUuFIE1PeWsgZ2iKLiQKd9l7u7doGQoPWqPb3bWUihWC6XuX/ePF7HBjoZ12+wGyzStZyafqQNbLMNlocao0xlMwz1aww26xiCJuRdA/ddpZ6T410LnY6WpvqZm3q7QXIQh27zm3PDbI+00Mz9AijuBLsKBNX2SsqePOtGi/OXGwjBSRJw6bDTrK3sI7xeokuN0PGdJhr1Qh1RE8yw+nKFEOJAvs6hjGVumWlRQrJnsIyjSYMI87PXiMUEaEKmZsrIxah1VBkO1KU5qqkMi6VUp2Z4UWiKGLq6gJCwNBoN6PbBlAqro71uDke6dvNjvQa3jzzbT69cycHBgff1T2MBXFdHurdxT1dm/GikDCKq0sp0yZh2Mwv1GimQ+7MbqDZ9EklbRqtmCebTTm0CqmlczKUJJ10liqSBTvFRwf28WDvDvwoINKaMNIYMr6+tjIIggjXMrAtE8s0ODs3x//9wgv8+sGDrO/ooGinebhvF/d2b8G74Rm9LjprKxNDSMYrFf7g5Zc5NTtLwXX5t/ffj09ANfBohfFvb0gNsTbZRxDFToNeGJE0LK5Vq5RaHmvS3WzPD910FwPdQgoD18jGTm4Crs2WKNUabB15l3DefwAhhWJLZj8XascBzcbMPkxpc7J0mKnmZdYkt2MKiy57iNnWOBerx9mcvZ15b5Lp5hW67CGyVhFTWgwntnC1cRZbuvS76zhVeZlqsMDa1HYSKk1fm97V7YyQNYtsyR7gcv0UtnLptodY8KapBYsEkc9M6xpZqxNTWEgh6XfXxTQVHaCEwbnq61jSeVuqWsu/TCu8ShTOk5ImUmuUyJKSVcLwKiDxaKCiErXWKbKqQMosIoVFyuhYcawg8gi0R0olELqEFAm67N4279zFEiFSWPjRDFkzT2xvXKdo9xDqegz71XUSKklCGsx74zhtS1IhJAn7Llz7jjZFLBY7nFr450S6BHSgASVz5FKfwVTDQEDLP0u99Ry5KEbdaO2Tct5PNvlxwMRQvUzM/yqN1mFscz1SZCmkf6m9yJa4ej/15nO0/JMrquJa+yTsg6QTH4Y25QwkWkfUms+gdZNC5ldQsghE+ME41cY3CcI2+lEHuO5BssmfREoX0xii3jpEyz9JwrkDy1yDUh0omUWpblz7tr/nU2uScO6iXP8aLe84rn0Qrevt89yAYfTgh9NI4dDZWUHJFEHooSODqbnXqTXnmJqdZXAgYMe2FEdef5He7k4G+hy6u2oE0WUMw2LduhYXLk3R052lq5ji6ImrXLoyx6YNPVSaTeYrdSKtWazVKXalqddbbN7ai5My6e7NEKiIYl+a/u4claBFLfCQZgy3jtCYQtI20kUToVaxmTFUL1rX2zDy90af804ROzQplDAYTKxd+iwKrqB1GVMNEAVXEPTgCE3kH0NY+4n8NxDGOrRugPawRQJCTSRMiCqABplDhxNIYxgdjoPIgDDR0RzSWIcQKxczKfPWdDHLMnjsE/u5cmmOk8eu8Hu/8x2chAVaE4YRtx1cz7adQzz+lZdv+u65M5N87S9fotnwaDQ8pidLNOoeU5Ml/q9/9zckkhZuwiZfSPKTn717hdX8xLUFXnjmNN/77nEs28BQKnbpbQU06h7rNvTw2Mf3k0zaSCVJJOM+xfdDjr9+mZeeP4vtmJimQkpJEIRLWkwf/PAuDty9EvHc25/nA4/s5Mt/foi/+esjPPvkCRIJm0q5QRhpHvrILq5dmee5p2LB2NVi7/61BEHEn/3hM7E1OYJGw8PzAnbtXcNHP3bbCoqWlJKt2wf5yc/ezRf/5Ps88Tev8eyTb2IYkiCIqNda5AtJPvmZO9mxe3gpsQaxaPSX/+JFLl2YptGIXaYuj8WOSV//qyO88Mxp3ISFm7C4/c71vP+D7w5l8W6jozPN/Q/v4C//7AW+993jHDl0DtNUhFFEKu3wrz//CQaHlxFSyZTNj3/qAM2mz9PfPsbXvvQST/zNa0gl0VHsqOZ7Abv2rRzrpJTs3b+WDz22h69+6TB/+7VXeObvTiw5aPX05fixT97OV//ypZvauKWjC1sZtMKAwXQWo339uuwcZ8pXMaWBKQx8ZNsSO178mUrRnUjzt2OnSBomHU4eJW92pnsvRSwMXqK2mnjvLSLQPoH2IapTDysgNTMTi3H/0I6VV0xgvMW5WMjYaCf0b0hS3TjvvMUc1JIuA+4Gzoav4kctIiIqwQKV4GbnQUNY9Lvr2J39IGsSuwgC8AlQUjBfbeCYBq5l4gUBQRSRtC10m+okhWRv/gHK/jzHS8/TCKs0ozrN1kpbeyUMBHExvZiINWGtVVxhl+fUy+dV8y/SDGfwoxLoCFsVaUVzSExS1jqawRTNYBLb6KToHFhxvITK8P7uTwFwoXYML2rSihq0WsuJIYGg3x3lzuKjrElt42TpJWa9a+/4PEsh2ZjZR8JI89LcE5ytvEozisfpZlS7SYxbIMiYBdLmzZRa3w+Zni5TvEH/SghBpz3A+7t+gien/oKZ1lWi685vb3F/u160XZPaSNlfIGd23DCXi6gGiyvQRW8f+i3PbvWWSWFLOhzoeISUkeWl+W8z1byEH7WY81ayYAxh0u+u567ORxlN7b6lFtRbr3gsD26QtTYjRDxm2Dfo+b71m/9f9UE/MomjNckRYBUl+iU4cHwBlFB0O13t76wcNHpFD1prilYHCMhbN7pOidWP3w5DKgypSP43XBENPHd1jFYY8qG1G1cVLs3ZLr++9yA52yFpmnhRSKQ1W/M9BFFsgavRZC2XgWSOqJ19MKTCMk0G13WjVrFEfacQQsQaRO/gHFd0UryvZwNBFGJJo70IlAgBGzM9WFJhSoNmGDsIpU2HkXRxCYH09wkpBH0jxRhFhKZ/TSda66WKQlwVjAU1DcsgCiMG13ahlMR2rZucMQypcKWFDhVpI0Gn8+61oK5/PyPjLPLxqSlevHKFT+/c2Rb8TtNZSK9aGRFC0NVxM1T0xn1NqTBlXAEMo4ivnzxJ3nW5b228CJASNg51LUGoBzIZPnfwIP2Z5XNQQpIynLd9a7tTKT538CB/c+oU3z57llBr5r06C60GtcCj4CTaImoGUsaTa1sqWmFIxW/FYujKWLW7yZg9XKsfAwRKmDiisELQ+70SQggKdi8Fe6Wb2G0dH7xp332FB5f+Xp/ezfr07hXbh5KbGEouuz4d6PjQiu05K37m16S2AZosnfQ6a9rtiN/zbmdoRa8VRB4g2JTZz6XaSWzpsja5g4u1Y/ja4+3CMgYxVBEIYx0eIUFHOOYQmgiJhRAWmhCt/bY45OoPnCEtepwNLDRfohVOEUZ1LNWJFAZS2CiZAh1hyBSIkEZwjUg3kaKdCDIkjeAqhkySUAplOjjmAJKgTXWapNL4Ji3/NFq3CMNZIt2INYbaoVQRU11HA5g45lbqzWcJo2W4vG1t5bpOkKG6MVU/LT+GCWvdpNF6iVrzKcJoEa09Wv4JEvIAN1qpKJnDtrYhxPUBPr4mYVSh5Z/CC84zMferS78ZRnOEUYVI1xCijeaw9iJEbN4gRQolc0S6yg8jhBDY5mYscwPVxndxrF14wRgt/yTF7P+MwKLln0PKJKNrLUzVScM7jhAWYVSiq2ceQ0mkuMCatQ5r12YRIqIVHGVkjUGkNVGkGBoM2Ty6FSEUM3MVtm7q464D67lcXWC8WSHYFDsDXtDzMCzxPUgaNs9XL+FmDPwI6JG80ZpgeFcRJSQnm1NUKi067CS3dw1jCJdOdydB1MBW+ZsmP0p1oFTH6hfiPRj1sMr3Z7/LwY4HSJs3jmkBUXASIbJE0VHAQusSQuTb70gbkRPNoMMZhDAI/dcRIgE4CNWFNLeCrqGD84R6AcJZhDFKFF4AHcRaIG9xrZufXERIQb4rRrUtTpcwLIN0Ptb5GV7TyT/7V49y5NA5Xj1ykUq5QS6f5MBd67ntjlHOnBzn/NlJOjpXjpn1WouJa8sLuUw2QSabuGG7R73mxQhlb3nMEQI+8/PvY/uuYc6enmB2uozX8jFMRVd3lo1b+tl3YB09fbmlBEssTK3p6c/xS7/xMMdeu8zFC1NUK00sqbATFv0DefbfsZ6tOwYxbIOaF/ethpQ0Cbn/x3aRH8jw4rNnGB9fIJdJsmvfCHtvX8eOPSM8++SblBZqJG+ByE1nXH7ys3fz2ssXefnQeUqLdbI5l70H1rHvwCjFzlWEe03FAw/vYOPmPo4cOseJY1epVZukUjElb+/taxlZ13WTJlAUaaYnS8xML1PQh0aW0UyeF+B5AaXFOgvzKxd4gyNFmi2fzq7V51OOa7Jhcx9RFK0q2A0xWuKj/+g21o528/z3TjF+dT5GmOcSrFnfveI+x/c0puP8wq8+wN33jfL6kUNcvCCp1yJMy6C3L8fGLV1s3tLCNs+i9Wi7b42dtT750ztZv9HglZdKXLu8gGUbrN/Uy70PbiWRsDl7eoJ0xl0xRzekZH2+Y2nOez0mm/EzWQ/jIg1at+dy10V7FfcPrqMRBFhSEkTTVP359zTiyFEJ7sn8HGHGI59O3HK/MIyYnK+wWG3QX8ySS8f3qFYRCG3juBb1SqyJZ0mbe7s+wW0dDwGQMnIIBGvyy4nqVDbBbQ9sI5l2l+bYj/T+PM22W1vWLCJZNp+ZK9UZnyvRnU/xkb5fZM4b51rjHPOtSUr+LK2oQaQDlIzpc0W7n+HEZjrtQd64WOL5qass1Orkky5J28aQkkqzRa3lMVjIUvd8gijCNgyqzRb5ZIJdw718sOdn2J69m+dmnqUWTGKrGKHmqCRps0CPM8y61I4lY4ywLdx8YwwlN/EzI/+aiAhDGEv04qQ5gmP0Es/hYk3DSMdJblOmcVQP2NsRwkTJlahQIQQ9zggf6f9ljsy+Qjm4QD2coRnVkShyVidDic2sS+0gY3YgEHyk/x/TCGuYwiJlvL2moBIGw4kt9DhrmG5e5krjDFdq51n0Zql7DbJOElelkVGa9dnNDCVHyVlvpaMLCoUU+/evw3FWOjlKIdmU2U+3M8y5ykscX/wW5WCBpDmIq7IkVIaC1ctgYhQ/nCdhdJJyV8rMuEaKjw187i1z5YhGMIMhk5jy7aVkkiqD/TbIftdIsbfwIKPp3VysHudK/QwL/hRB5GMrl057gLWp7fQ6a0gZ+bctuDsqyWP9v0writ+RghWbEym5XMzJWg53dq8jbcSf2crl44P/BF97xM6ofz93u3cTPzKJo+sRaU3F82gG8YvgGCZpMxZL1VrTDAMqXotIQ9I0SZkxNz2MIkrNBgnToh74tIIA2zDIWPHL7ochFa9FwrSo+R5eFOIaBmnTXoJBah0L3dX8+IJnbDteRN/AF22FAWWvtYQYSVs2tlI0w4Dpeo2/u3ye/mSG8WoZKUS8UG/zVuebDVphQNK0SJs2tmFw42vthSHlVpMg0tR9n4zloKSMRZC9Fo3AJ7IF1cCjWg/I2g6mvFnk7VbRCgOkEDc5p90YljToaNvl+lEYiwPK6zbVy61NmfbSNbFk7O4TRhG+Dtsw+XduUyPyiRxB2rxZ5DG+Fi3CKMKQEtsySBoGWmsWmk0MYzmZ6IchpWaTrBO/zII4OTNfr+OFIQnLImVZSx2z1pqq51H3/TZdSJG0LCyl8MKQSqvF0xcucKVUYrxSiZ8T2ybVFsH2wpCq5+GFIUoI0raNa8YTprrv44UhtlJUWi00kLQsku3tjSBgolLhqYsX2dPby8ZyGYSgmEjQmUuhtWa2VsMLQ1LWSoguxO9Hrd32SGtswyBrLz/DhpTkXXfpWgBszfcs/b2aE8Vb+dC3unde2/7Rki7NsIxPSNKx3nbi8F4MrTVBWyPDfBtR8Ov0C2VI0PG9Ww0i3wirnKu+gSltTGGRMNLUgwqhDmm1RZJdlcSUFhV/ESUMRpKbSRhp+ty1hDpg0Z9mJLl1CW1kSZOincWUBjkrhdGeCCvporiZYrJq+6MKWlfQ0RzI4vJzEtXQugQYCNlBwhzGUkWEMDBEEk3QFmsOkMLFlBlC3cRSRVrBBK45gsRACIVj9KKESyR9XKMfJZMokQdcncIAACAASURBVCQIx5lc+F+ROGSSH8dQnXjBWVqLp1Zp6Y0VR8nbu20J4gV1hNYhlcbjzJf/H7LJT5F0H0QKl7C8ShVVqDYq6aarBARYxro2vWy5LQID0xgiCKfbTXNu2CxWtvuHEiaZxGPMlv49fvAJ6s1nUKqIY+2Lt6re9mQy025bP1I4RLqIbW5CiUTbsc4DHSGEjaGKCEykcIhFz5e1Xjo70nS2E+Z52yVnueRMl6lmhTWpArYyYtfHwGdDNtZOcZXJotegx02TMCwCHVF0kvhRSM5y4747CDl5+CpHnz+Nk7B5/ycOUOjOrnrGlYUaT37pEBNjMzz4qYOM7lxdi/AfckQ6ohHUAH2zNoyuo3UTITvR0QzQQssGOpxAhxNE4VUkfYCPjhoI2Q3oeJGtfXQ4jg6n0bqG0AWQGXR4BYEDIkLIm921nvvKYSzH4uGfuw+t4dUnj9PRm2fXfbFzkhCC7t4cH/7xfXz4x/fd9P19B0bZd2B0xWdCCPbsX8ue/Wtv2v+dQgjB0JpOhtas5gR2cximIgzi6qxHRLkoGHlwmB3ORmZrdTZ1FmPkeRSPr2fLC8voAsvCMWJEsBSC7s0dPLrtThq+z8Hh4SWUCsAHHtnJBx7Zect2BEFIrpDkoY/s5qGP7L7lfje131CsGe1mzejygiDSEX4UYEnzpvFba43lKP7pv/zou/6NG+MXf/0Db7t9cLjIv/3tn3jH47gJi9vuGOW2O0bfcV9ou0kmbHbvzbFtwynM9K8h1DIlVesmUfNJgtofIeW/QJixuKwAbOMyt++f5OD7Hrsh2b8c/9tvfnzV34zniCuvX8FKszU7zJnKVSSSEMiYReQNdE8lJCnTItIh881zhLrxnkYcKWFw7FiLpGux484Nt9zv5KUpvvv0ZYa686zL9jHgFtEavnL4KOv6SjiuSaYQO5hJoSjYPRToueXxlKHIdqQpVRu4AizToNMZuOX+5VqT77x0mnza5ac/uI/h5GaGk5vf8fy01mzssak0Wwx15EjZFk3fJ5dwWag10EDatQnCkLrnk3Fi5oZtxkVXW7kMJjZxpXyZA133cX/vprf9PWOVOberUvQnbn5XDJnA4NZzbkutHCu11ksMkW43gxCCSFt8+2qJn1r3MDsKt75+AAWrj7G5Ba5WqnjZGmPzi2Qdm3zSXUqkTZSrDOWzLNQbKCnJODZXF2229j6AVdtDNuvw5uQ0HSTYXOzk1NQMParAxFydc94UO/t7lkyGINbLy2RWn6dKIemwe3Hl7WTULOO159jf9WukraGlfbywwpnSf2Uw9SBZa92K7yth0OOOrPgsjFqcL79C2txC0Vn/A7MnIh1S8cdIm8PkrW7yhW72FO7/gY51va1dztDb7mNKRc5avlZSqJvO74cdP1KJIz8KOTJ5jS+eOspsvYYGRnMFPrf3TjrcBFP1Kl88dZRXp8aJtGY4k+Pntu9jXa7AfLPB5w8/w+aOLo7PTjFRq5CzHT6zdTd39g9zpVLiP792iM0dXbw+PclMo0pvMs3Pbt3D7u4+tNZcrpT4o2Mvc25hDg0c6Bvi05t30uHGL+lMo8Zfnj7G4YmrRFFsEf9z2/dyz8AIr01P8Fenj3Fo/AoZy+b1mRia9rm9d7K92E2oNX915hhHJq8xW6/xr+54P3t7lrmzrTDg6+dO8sTFM7TCkILj8hObdnB77wBeGPKHR4+gpGSuUWestEioIz62YSsfXrtphUDz28VMs4qtDDqddyfOveg18MKQvsTqlSatNfOtOqGO6HLT1AKPuVaNoVQe9S4GzVOL05xanOJja3auQCuVWi2+cuIEh65coRXEya6PbNrERzdtou77fP7ZZ7l7eJiPbo4HgEuLi/zOCy/wuYMHSVkWGjh05QrfOHWKmVqNkXyez+7Zw2hHB1prLi0u8oVXX+VKqYQfRRRcl0/v3Mn+gQHOz8/z1Tff5KkLF/DCkJl6HSUEH928mY9s3EgzCHj89Gm+ffYsDT9Obt41PMxndu8maVk8NzbGs2Nj9KRSvD45SdXz2NHdzS/t30/OcXjp6lW+cuIER65eZWxhgRcuX0YIwW/ccw/rCjGC57+88QZHJycpNZv85oMPsrFzeUJ8rVzmT159lVOzs0Rak7FtfnbPHvb196+YtF6/P3O1Gg3PxzHiZF4QhiQti2YQYChJGEVoHWthRTpOhl4XxwzCEMc0qLQ8BrNZ/LCBEJJ57zI5sw/DVjHVzX974bx/iOF5AePXFkilnCU6ZL3u0Wr6dBTTzM5UCIOQnv4c05NlsrkE1UoT2zGxLEVpsU4m43L29CQbt/ThewGtVkCxK83k+GK8fzVGeOV7TNJGDiUMSv4cUkgEgoSRwori59mWLpZ02i5ZekmjxJT2zeKNWrM5leXfbPtpNLHIeof190PfAQT+6/iNx9G6QSL3W1zn34fBabz6l9HRHG7u89hG70pm/vWcjbj+j0DhYspsnCiSy5bvliouu67c0Gc0vfN4/jl6Cr9Fwr4HIQR+eLPofRRVCKMFDBW7JPnBVaRMIUWSsI3m8YOrYMfooUjXCMIZXPt2Il2n0XoJ0xgil/6fEFhEuhy7q73LkMLBUAN4/nlcez9K3pzguJ44eue4vmgJuRHt9PcJ29yGlFlqzaeptw6RtO9GqTxCKGxzJULXkeu5frPeqTYe35u33NgbIm3GieqUaTOUymPKW6NiC3YihonckKi+MWldmqvy+B89ze73baF7sAMncWvdBzdls/veLZz67ceZujL3nkwc2dIhbxV5s/wa/e4Isk21TxsuCVlAGKMIkac0O0t1sYmbTuIm76U8LbGcgzRqEaatCFoeWsfmGFEESmmUqTCtQSoLJSw3iZtUNGsBga9RRkSj1qB7OHhLgvyG5KiAOz68F/kDoKD/fwmtSaZszHZxzFSK/kwGSylqnoelJM0gwDYUTR2QdWxoaiwpCZfQ35LBbBbLUBTc2L2o7q9u/f7fK7zIZ6o5S7/b3XZpXI5m5HGtPslo+r31bgjhIJ37Ea3nbt5mbkIZ6/hvXeIEUUgjbBFqTdJw48R6FOBFdVbvNTWhbi45Mv2PHmMT8/R2ZPjUA7tR6nqRW/PB2zdhGQqhNcp4exmQt4bWmicOn+KuHWvp71y9oADxWLKmr8D+LUOcvTJzy/1u9d3ubIquTNuhSywXVvPJBG83Yv4oSjY8P32erOXS7f7954Faa66VynSnU1xZLDFXq1FuNpmuxsjEcrOJkvFaou753LFmkIV6g5oXF7eVlNQ9PwZKmCZj84sYUlFuNjk1PYOtFIuN5orE0bsJ1+hmKPUQ880TN20zZZL12Z/AkO+uqC2FxUj6I0to+B80vHCBy5VvsSn/cysSyz/s8IKQ2VqNzlRyiZX0dnmAIIxo+D4p2/qhPJ8/UomjVybH+T8PP8tDa9Zzz8796DbSJmVZtMKA33v9JSZrFX5tzx1YyuAb50/y20ee4/P3fJBQR5ycn2GuWefntu8jbzv82Zuv84dHX2Z7sRs/inhjehIvDPnM1t24hsF/evVFvnD8FTYUijQCn98+8hwdboLfuP19NIKA33nleUDzizv240chv//GS5xZmOOXd91OwXFZbDXpTaaRQrC1o4vs9n3MNers6e7jsdEtSCHoTMSLJCUEn9q0kwO9Q/zL73+XZri82A6jiCcvneeLJ4/yK3sO0JfKcGTiKv/x5ef5FwfuZUO+yFStystT1/jc3oN8evNODk9c5Q+Ovsz6fJEdnbfO0F+PeuDx4vQY2/K9TDUqvDZ3DUsq9hYHeGX2KlnToTuR5s2FKWxlcEfXMMfmJyg6KfK2y+OXT5CzXDbmurhYmWeyXmZnRz8vTo/hRyEPD2zmcnWBZhjQ42Z4ZuI0tcCjP5nlYmUe0GzIdhFGEecqs+ws9DGYzHGhMntTF3zo8mWeOHuWXzlwgO5UioVGg4QZa0SFWjNTq1H1lmGGXhgyVavhhzEKpOp5XCuX+Zndu3EMgy+8+iq/99JL/O8PPIAhJV8+cYKK5/Hrd9yBZRhMV6t0ty0ch7JZPrNrF+VWCz8M+ce33YZrGEsIHikEvek0n92zh2IiwYWFBX7nhRfY1t3N3SMj1DyP75w7x8e2buWf3nknU9Uq/+H559nY2cljmzezs6eHrG0zWa3ygdFRPjAaOwJ1JdvPiZT8zO7dHJ2c5N8/9xxeuJIGZhsGe/v7eXTzZlT7XH738GF+50MfoiOxspNshSFnZubww+WkUEcyQRBGtMIAJQSbujoJooipSpXpahUvjJBtl5jhfA4pBOVmi4FshqzVRz0s4agMve7mWLNroYrrmIz2v7tq7z+UOH9mkosXZnBdC8s2KJfqZDIJUmmHyfFFevvzNP2QWrXFhXNT1Ose3T1ZGo3438tjc3T3ZFhcqGGainqtxfRUmbm5Co5j8fwzpyl0xAgzx+1npCuu1heDKkoYWDJG4b118lnknUWAta4hmn9FV+IfIVXXD3wNDOtOhEjSqv4+N06UlLkLO+nQrPwHILq5snqLcUkIiSFuTlqvVpmVIo0QJi3/NIbqIwynKNe+BKxM6vjhFUq1L5F07iUMp6k2vk3CvgNDdRFGVUBQqX8N0xjCkEXK9S+j8Ug69yCEgZI5Wt6JtlOaS63xFK3gDMa7vG5CmKTcD1BrPsV8+XdJJx5BCJsgmCDSDVLug+98kKVzdlCqk5Z3gkbrFaRMI0UK0+iP9WyiEpGuE0YLaN1q6ydJlMwgZXKJipew76Rc/xogSLjvQwqbRrXJzLV5/FZAMpug2Jcj8EMWpst0DRRQhmJhqkQUaTp6c0RhxPx0idJsBaUUhZ4smUIqFtadKbEwXUFKQaE7S7aYjm2ur81TLdVJZRMU+wsoJQmDkNnxBaqLdQzLoLO/gJuy0WHE7MQClYUaylB09udJpFxmJxY49fIFFmcqFPvzdA0VsRyTmWsL2K5JppDC9wJmrs1T7M1jOSZ9a7vIrULrea9ESEjJX+B05RinKrGwK8CO3D52ZPaCcIkiGDtRplau46ZbFLpzTI5NYrsWbtoh8AK8ps/CVImuwTjJ6qYdmrVGrOnjBcxNjNGzpovzRy/hJCxs18awFLnOPDNXZnnij59BR5ryXIWNt8VV3L/7r9/n9adPcP9P3sWe+2OHwcmxGf7uvzxHrVynZ6SLhz57LxeOXubocydp1Vq0mh4P/tQ9DG/pZ+LCNE9/6QXq5QZrtg1yz8cOcPLwWc68fIFauU6r4XHfJw+yfs8aLr15jWf++kVatRbDW/p5/0/eReiHfP9rR7hw9BLpfJL3ffwOetd28cLXX6FWqjF5aQbTMnjkF+4nW8yAENQqLQodMa0uYZrs7Y/7VD+KqHkeOce5aXL9VjSuvkXS8wcNrTXjzWmOLZ7BVhZ781s5VjpN2a/S63bhKoeT5fMIBPsK2yj5Fc5XL9PjFNmZ28zpygXmvBK9biezrQUOzb2Goxz25bdyunKRF2Zf487iHrZnN/Da4knKfoWR5AB+5DNWGwcBd3TsYra1wFjtGlkzRb/bTTWo0+d2MVa7xtbseqy2nobWTcLG4+jgNGCg3McQxnp0eJmw8TW0riLVECrxCUARNr4R0yqR7X03osMxwsY3QNfjRI99L2HzKXR4GVAgTMBEuR9C6wZB/YvoqIq0dqCcR1ZFEgFE/qsEjS8jzd0o91HAIqh/FR3NQjQHwozboIaJvBeIvCPocAIQGKl/jDSWbbFDHVL26wgBzdBrL4Qt/Gh1SrhA4BidNIIJ3jkd/w8vLk8t8PXnjiOAhUqDnev7CMKQ5964wLHzE5iG4r4961nX38F3j5zmm4dO0vICvCDko3duZaArx1efOcaxCxM8ds92do72UW96fPm7R9HAbKlKTyHDY3dvQ0nJU6+e5fSlaRKOxQf2b6Qrn+JvX3iTrz13nJNjUwz3FHjsnm1YpsHTr5zl3NVZMimHB2/byGBXTKu68a30g5CvPnuMLSPdbFvby8XxOZ4/dpGPv38Xtnnzcvjxq0eJtObRoZ1UgxZ/dv4wD/Vv5VJ1jnOVGZphwHyrxpZcLx8d3IEA/m7iFIdnxig6SaabMTU00pojs2M8OXEaPwrZVxzmgd5N2MrgLy4cIWFYXK0tUAlafHhgOxnT4RtXjuEaJldrC3S5aR4b2kW3k+ZMeZpvX3uTea/GaLqLhwe2krcSvDZ/he9NnKHsN+ly0nxiZA+uYfH41WN8eew1cpbLM5Nnubt7lD0dg2jg6ckzPH71OACPDu1gc7Z3dYkVx6GQcLGUYqZaY22xQKXZou75bO7p4uzMHH3ZDOVmk7RjU/d86p7PbLXOdKWKISVaw0y1Rn82w5WFEkoK8q6DFDFCabXQWtMIprhU/VvqwRSuKjKSeRRXxW6TAnnTfLPmT3Cu/JfU/KtsL/wqaWuIVrjAudJfxvsjUNLGC0usy34CJUzOlf6KkneGjbmfpcPZFj/rlW/RCGbwogp+VKHo7GIgdT9B1OBy9duUvQtARHfiDvoSd7PQOsXFyteZbhyhFc7jGJ2sy3wcW+WZabzMeP1ZIh3Q5e6lP3kfAoPpxktM1l8g1C1S5gCd9od442qJjmQCLwioeR5+GNEMYvmGZJuFsqm7k1OTM6SGLE5OzZB1HSZKFeqeTyHpUm62SJgmoY4Iooic6/DmxAz3bVjLYP7WydZ3Gz8yiaNIa566fJ684/LpLbtImSszf2PlRY7NTvHzO/axtztG6qQtm8899TgnZqdYmysgheChkfXc1T+EQHDf4Fq+cPwVFlsxP1BKwaOjm7mtZwAB3DMwwhMXz1D1Pc7Mz3J2YY5PbdpBbyqehO7r6efQ+BU+sXE7Vc/jmStj/Mbt93Cgd3BF24QQZO0YDZAwTPK2y1Ame5OrWNZ26GoLod0Y1f+XvPcOkiO77zw/76XP8lXtDdAGjcbADYDBeHAMZ4ZuhqKnSEmUVmZvT+tupVXc7sbpIu50cXFxf+xenHZPQd1Jq11RWnElGlEaakjOcAzHcBzGAAMPNLqB9qa6fKXP+yOrC8AA4Iii4oLk/SI6oju7Kivr5ctnfr+v8T2emjvPPcPbeGB0AikEA3aal5cu89TceXYWEprI7lIvj05MowiJrWo8fuE0l+vVv1XiyFI0esw0bhjihD79ZoasbjBX30QKgd4RBczqJiN2jnO1dQbtLA3fJYgiql6bj23fRxhHzNbLuGHAcqvGVLYXW9UZTuUJ45iTlWWWWjWCOOLuvjG+Mvs2vWaau/vGeWnlIuPZEoqQnKmu0WfeeLGfMQyiDjJoslhkLJ//gTaV7w5NSh6ZnOTOkQSC+dj0NL//2mss1utsz+fJWxZvLy+z2mxy29AQU6VSV3wvpeukdJ2MruOFISPZLKmrMuG6onDX6CiO7+NHEQXLIm0YrLda3QVk0bb5zN69jBcKjOXz7B8Y4GK5TBTHFCwLLwwxVZWiZbEtfy1nWAhBwbLoSaWuo6kB9No2D05M4AYBYRxz97ZtfG9u7roE01Y7DOWypHUdTUpafoAbBOQyRve7DGbS+FGEpkj6M+kuFUKTkrRhEMYRbS+ppkZxgEDgRg2csI7jJULexZ9CqprRsQEt9WZot1wsS6fRcPD8ANPUWFmu0G55lDcatFoeURTT159j/vIGM+dWiOIYTVNJpQ2q1Ta1apu11Rr9Azkqm01UTaHUk8Hz/KTfxDFReAnFfY44quEpfejmh0FYhP4xAu9VBBqqcTdSvYUoOEMYnIfYJQpXULRdqMZ9xOEqXvtr+O2/Jo42EMoguvlhpDqC7zwFIkUUXiKOqmjG/Uh1J1F4kcB9gTiqo+i3ona0eBKNJY3rJCtvcDz5Dg185xmicA6hDKGZDyNEliicIQzOI5CE/tnutd5s4Q9gaLsoZP4h9ebXabSfQJEFUtYjHUHkZNoSgK5OEEVlViu/TRQ1sc17KKR/pXN9IISKqR+gUv9D/HAZRWbpyf4GhjYNaGRTn8IPl1it/DZCWFj6AXL2p4njKwgCgU6IjR8FaNfZSCfaQr25f0Ol8cesbP6bDiIsTdr6CFt4KykLbIl8b7WhlFkQ5lXUUZVc6uco17/I8ua/RgiLlPkIpew/JYqarNd/H98/ThCuEkUV1qr/G4rMk019hoz1oe5509Yj1Jp/ga5NY2i3EIURf/0Hz7ByaR3DNjBtnY//+sOszZf56n/4Dr/+v3+eTD7FM195Bafl8fnfepSjT5/g2a++SiproWoKhx/ey8EHdnPylQt8+0vPY6YMFFXhwP23cPsj+3jz2ZPd19c3m3zg5+/l1vft4szRizzxn79HrieD03J5+PP3MH1wnIsn5vn6F58iV0rjOT7v+/hh9ty5g9lTCxx74Qybq1XefOYk1X11SgM5Hv/Dp9m2a5iHPnsXldUa/+l3vsYv/g8fZ2THe899P+lhSotHhz7bNVvYCilkVwdNipj+sV6cpoNUFExLZ9uuYaIocSjTDQ3fC+jb1oOdsTrHY0zbRUiBnbXI9+eIwojdd01hWDrEEPgBuqnz9JdfomeowOEP3Mof/85XiDqOeg989m42FstUVjv0zhhOvnyOwA/40C8/iBAC3dColxucPTrDL//Pn+X8W7M89SfP87l/9TGe/NL36B/r5baH9/E3f/A0A2N9VFZrnHr1HL/6v36ec29c5Pmvvcr43lHe/O5x0nmb93/uHqSUqJrCW8+c4OzRGR7++SOcePEs3/qjZ/nl3/ks64tl5k7O89nf+iiKqmB3tFWiKEokD9pXPd9iS6tG3jBpdPVrbvT330cFNybmdG2GHeltjKdHWHc3qflN7iod4KX1N8loKfqNEmktxXJ7DVMxMaROSrVRhGTYGmDF2ehWnzWhkVYtTMVgMrWNxfYah4t7udxa5kT1HMN2P7PNeXSpM5kepeo3WG6vc64xy/t6D1PUc9T9Fheb80RxhC41tGsq6CpSmwZtN5H3GqH7JKq6jchLRM9V62fpjNAQbRB5z6PYX0CIFELph7hN2PoKQp1AqGOErS8jZA9xcAqh7SdynkYxP0DovghRGeIqUtuHkAMErT9Gqnu61LTr7pV2ACWqEflvsVVoiMOLxOEaavq/JXS+Reh8F9X6JKHzFKr9GeJwmch7HaFcu7bXpcaOzDBhHJFSLHSp4oQBraCSCNZf/+kYskAkvc4m9acrnnj5FMM9WW7fvZ0vfv1F4jhBFb10fJYP33ULK5t1vvbc2/zzT9/HQ4d30nR86i2Hzz98CL2jXfrYvXuYX6tQbSQ0/DCKeevcAg8c2sH9Byb4L995g5nFDVwv4Nj5RR67dw+nZlf4+veO8y8+ex+P3rObYxeW+Pwjh9g+UEBXVZ4/NsPpS6t85K5bOHpmnr964R3+8Sfuve76VUWSS5l8/505prf18drpyyhSoqs33l8st2pdI5ogirhQX6MZuCy0Kjy3fI5f33UfqpD88YWX2VcYwo9C/mb+HX5px90EUcgLKxcAmG9u8qczr/LZscPkdJM/PPcSPUaKO3rGOFVdIorh09sPYqs6JTPNmlPn2eWzfHL7AT47fhtfuvAKTy+d4cPDe/ijcy9xa3GUR4Zu4c9nj/LNy8f5wuSd5DSL9w9Oo0uFL828yotrM3x0dB8PD+7iZGWJ/YVh7uvfga0aBHGyD2wFHp/YdoDXN+b46uyb/Pf7Epfuq0MKwZ6hfgRQStmMFBJ37QSEHIMQ7Ozr6SacBDCSzzGYy6BIyc6+K3qEcZzsw3cP9iE7er4xdPdf744gbnGu9mV0mWMy+2kWm88yU/0qe4q/ftM+aqv9TGQ/ybH1f0cQJ9pXYeyz6Z5iLPMYF6pfZTTzQRr+PJvuSYbs+5jMforj5X+PF12RKah5s5TdE+wq/DJB1GCm9jWK5l5MpURWH6fXPEjFO8tc/ZuUjH1k9XFGUu/HCdaZzv8SmkyjKzka/hwXal9hMvtpNJnidOU/Y6kDFPRdXKx/gwH7HvL6ToKoxXrDx1AVSimLMyvr5G2TZ85eZN9QP2dX17lloI8ojthotgiiiCCOsTSNjWaLatsBAW8vLNObTtHyfExNZTCbYbPdZiCbpi+Tulmz/VDxY5M4CqKIDadFj2V3dYuujrrn4oUBA3a6+7/eDoWs7LSZAExFpcdOdRM2W9Ctrck0peoULbvbwU1VIyLZ9Cw3G2w6bf6Poy9iqsmDU3MdsrrZ0Rhy8KOQkXTu7x2K6EchK81GN6EFYGs6RdNisZFkrKUQ9NsZ1I4+USLmnbjRvDsMReOOnh1Mug325EfQpEI79Flq1Wj4LjndIqVqqDIRvR6x85yurtBjplhpNxBAn5VmqV2j6jmMZUroioqhqMw3K5yrraOKRHvJVFQuNyuMpQsstWssNquMpYu0Q5/ztXXyuoWtJvpBdd/hdGUFXSY84KV2jaVWjdV2ndH0FQG8g4OD/Nz+/Txx7hzfOX+eu0ZG+PTevV1UznuFrijkLat7n3psm6BTTVSE4LHpacrtNl966y3+4sQJPr5rF/eNjZE2bk6J2Ao3CHhudpYX5uZwgsSlbKFW6/YxgB7LIq0nfVgIgaWqBJ3kwI8ScRyzVK/z+JkznC+XEypaq9Whm11/bkVKthfy9Hfaze2IWBudSXKrfYw47l7v1uds/T+OY0glv6cp0QjWWXcvUvEX6dP6yKVNbFPj70qt+XGNbWM9FEtpNE1l63E/cXyevv4svX1ZgiBpcykFQRAiFYll6vQPXPmfaen4Xg+KIskXbAaH85iWjtP20TQFTVM6CSaFOFzErf8uUt2GVHdAHBETE/nHcJt/hGrcC5GD0/h/MDP/kjC4iNv4A3TrZxBKL17rz5DKEEIZRKrjINMo2n6kMgwySdD67rPEUQ3NuC/RihAqcVTGbXwRqYwglWG81leI4xa6+YN1La4PD6/9dUL/DKpxB4H7KsR1dPsXicIF3OZ/QjPej1S3g7giLHqzEMIiIvzfDAAAIABJREFUl/o8afMh4thDyBSKzJM2H0ZREpHImASZVMz+C8JoI3GxVHqQIn1VXw6xzfsopH+1I1RtJ7o9naSVru6kL/87RFEFhESRJSAkjr3uNVrG7TT0f8rx+hKHi9tR3zVtCqFgGXeja7uIohqJhoyJIvOAgqYOM1T6IqpyBZWnyCLF3P/Ipt8iImLTK3c2aBPE1j/H81fRpEFTZpHeJk7UpBLfTtY6goFCXs936IoCVV4rFC2FjRA6aeshBDpBEDB7aoE9d+3g9of3oVs66azNalwmCMJuYTwKY6Iwollr89zXXmXfPTs58jO3EROj6SrthsuzX32Vyf3beOhzdycpMU2hvtnk6T9/mSM/cxv7j0zzyrfe5tmvvMq+e6dZvLiKVCWPfP4esqU0dsYiBlYubxAGEQ986g56hopYHberA/fdQr4nw9pCmY/9o4coDuS7mjRxZ66LSbTDfsTh9CcmEtTDJkXjSv+J45h6UKUZ1Okx+tGkzkBH7Pjd4/jV8YP07Ap9N65ECiFYmy9z+JH9DE32M7F/G7Kj1aaqCsrVRR0Bt9yxg5ljczz1pe9x4MG9jEwlJgPbpocYmuxHSMHr3zmG23Y58/oM6wtlZt+5jO8FyTZCwOStY4xMDeI0XU68dDZJUj64hyf/5Hme+fJLHHpoH0OT/cydnGfu5DxP/9mLuG2PfG+22y92HBxjcPxa5KCiSG7ZN3KNwUgcR1T8ReI4IqcPUfNWiOKQvD7UpQS/VyT6m4lLnaX+8BVdgSCj2iw6q8RAQc8SxiEzzXkUoWBKo5u8cXHJaik2PZOT1fOM2SMsO+usumVW3Q1yWoacnmauuch2exhLMfAij8utZWzFpKBnyagpBs1e1twyhmKghm0QYKsWs80FGkGLEWuAvJZltrXI+/vuepe2Vp3Ie5U4aiROfB2KrtQPEbbOEba/hjTuRTAOMo/U7yRsP45Ut6GYj4GIiPxjiGgd/BMQByQDkYFUJ4jkWwh1B/hvdDS8igh1GiFLCJkjjq53w7rSlrKDVro6dKS2H6luJ1K2EwenQOgIkSLy3oTY6SSNrr3fYRwx21xizamy5lZ5oO9WNGmhSp0g9m9gnh4TxT66kuenaT20FaubDe4/MMloX56p0V6kgOVynfML63z36FnCKEZTleROaiq6KtEUBesq0XRVkdcZ2+TSJnvGBhjqyVHM2bQcj8urVWYWN/jWy6fwgpCUlRReDF1FUQSmrmLqGnEcc2FhnZmFDZ54+RSOF1DK3XivIIRg/44h3jq3wIWFDWYW1vnokb0/9J5OIJjK9nJX7zhO4JPXbeq+w0KrSr+V5bbSNsI4YjKTjMnn62vUPIdW4OGEPl4YcLa2yu09Y0gk+4tD3Foc6V7HmlOnaNg8NLiLPivDZKaXDbfButvgYmODg6VRFloVBHCiskQUx/SYaeq+w6pTRxWCdaeBIiQFw8ZWNHK6RV+HqlbxEgmOBwen2ZXrx49D3i7PX7OPubrNrm6da/SYOr+/G6UkBF2Rcm7Qtlde/4Pb3Q/rbDqnGEq9j6a/AEjK7kliwpuatggh0WTqOtqZJjMUjN2ktJcpGLtwgjWCqJUg4aWNIsx3nUdQNPfQax7EDcvMim8SRC2kOoCl9NIMlojigCBqEsRtTFlCV/Io0sRUe9A6kgwV9xx+1MSPGgRRizD2qHkXKBp7KBi7WG+/SRT79Ft3oaWzXN5cZLPVJmXoVNsOtw4P0JdJ0XA9VusNetMpFClo+wGbnYSRH4X0ZdJoiqQvnaLheRRtO9kL2ya6qrDRbLFab/50IY4Ukbg5Vdw2QRShvQthYioqqpQ0/CsQ0WaHV26pW9VlrnFDeHeXTDqzuOH/05pOwbT4b/bfwUjmSsMaikKPlWLTaXfFs28eAm5AL3mvUEQisl3zrgiP+lFIO/DJ6FempncjUG72yBWNNP96z8evOdYOfG4tDiWaKoaNLhWkELQDn6rncKR/gorXpul77Mz1MZzKsdis4kchKVXn4aFEAK/PynDfwESi1m7YmIpKSjMQQjBgZUj36fRbGTKaSTNw2VscxI9CcrrJo9v24IYBQRyRVhNU0X0DkxjvcmNL6Tqf2L2b+8bHOb68zH89fpzFep3ffuCB7ncOrxrgWr7fpalBkoRs+34XPdP0/Q6qKuFS96fT/MY997BUr/Py5cv836+/Trnd5hcOHLiqcW/cui9dvsy/f/ll/sGhQxwaHESRkgvl8rX3828hWP53ST62fJ8/PHqU2UqFf3bXXRQti3dWV/m3L7zwt/os8wZQ3Btdy82qqTVvhXZYYzx9FxmtFxEpeH5As/2Dnbx+EiMWoFoqUXxFjmViuj+xbNZVCEI0VcH1AyxTJYgivDgkUgRqRyAxiCN0S7sOAm1Z1/OovdbrxEQYqX+I2NIAimM89wWkOoFufQ5wiWpzhP4bgEQqQ+ipLwAKofc2UXAZTbsFRduHlCVU/TBSvUr4MA5RtFvRrE8mSIU4JvBeI47q6NkvIGWBmIDAeRbNeOAmYtA3aa+ogu88hW59AkXdCbGD134C3U6sX6VIY9ifR7yHY8VWJIsVA6leWwGW6nCnabb6XIwiMyjyZlSlGIGCqt4YmRIR88rmKWzFpt/sZ8U5TztsUzJKrDinE0vX1DjDqcPMNGYghtP106y4K0ykJmiFLdbddYatYVphiw1vg7SSphku4kUelmJR1IusuWsUtCaNoEE7bFPUiwgEx2vHub2gEsQefuRR1Eusui4RWWxhYwubRlAjiENyxk42/TKa0CgpExjKDbYtcUDDeQopLGzjCACqrvLRX3uQ579xlLNvPs7B+3dx90euF+ONoiRZ2ay1qW402HPXDtL5K2jCjaUK64ubfPAXjpDJX1mYV9drzJ9f5nt/+RpvPHOSRqVJFCZIl8MP76OyVuerv/ckvcNFPvSFI/QMFdh3z05WLm/w+H98jmJflg/+whH6t/egagqariKkQNPVK9o6V+9Z4+Ra//8SrbDBE8tfoccYwFZS7MsdRgrJk8t/SStssiO9m9uLR4gjidYpCgRhhOsFGJqClKJrze4HIaoiu9oTcRzj+WGCDNJujuq10ybl5QrthkNltUaq44J1Ixn6fF+Wz/7Lx1i8sMpf/l/fYngqefbKyxVatTaby1UMS0dVFfpGS7zvk3cwffskgR9iZyxefvxognhia/5JPmFgrI+f/a2Pcub1GZ74o2cYnhogW8qw87YJPvObj0KnULM1ZenmteOXH7Vpx3WsvIkAav4qhkwRxC6nqt9lPH0HTljjVPUpxtN3EEQOraiFJm0UoeGEVVRhEBF10beatHGjOqow2PTmCeOAXiZQpY4T1ju0YxsnrCOExFSyFIppxif76OnLdoszrhNwS2qKVX8dzwtJmTa35fay0apyW24vuqrhOh6aqtJv9HD2/ALpUoZ7S9sJ2hGzxzfYM7UT4UtiCUUtTylTJBOnMRWd7e0xGlWH4b5+7s4dourXKWg5smRQIpWinUNTVMbsYVbcdRSh4EU+buQxaPZiK9duqiL/eDLfpP8JkfcyoX8suV+yDzX1j4jDGYLWnyDUcYTsQzE/jNTvImx/ndD9Nor5EYQyhGJ9DKnthdgHBJH3GlvraMSV/hjHzQR5JHTiuMWWg9qVeK81twSxNV52+pRII5QB4thB0e/oaIVdO1crQtJr5NGlRlZLYUgNL2riRw5OWCN1ncuUICLAj+r8NIZtaKxXm7Qcj3KtRTFrY5s6O0Z6+IUPHMY2NaI4JmX+cFoxqqKgdpLRAkEcQzZlsGt7H//gI3d0kxdCCEScvKbl+PhBiKJICmmLPRMD/Pwjh5IzyCuFz3fnQnpyKSaHS3zrlVNoqsq2/ps7hSXGS4kpQRCHNHy3ezylGsl+UyTXE8XxdXuyq/eCQRyx6tQRAu7tn+RAMVnbKFKSUo3r1uG2qqN3gAJSiG5SJybRlg3iiMlsL8N2nk2vxRfPPE/RsBlP9xDEEdHViDghrmsHXapYSiKmv4X+6V53HFP3XOqeS8G0aPo+tqoRxjF+FGJrGk3Pw9K0rkmQKiVLjQY1z6XPTqEIwXy9xmg2R1Y3OLWxxkS+0JUcyRnmDWlx198DcMMqUujoSpbx7Mfe8z03PA8CIRSkUDvJ5ffYo6GgCrOD6hVdncel5vMstp6jxzxEREB8jXzC1VqQVyKOA5xwHRAM2vdSNPYiEOzIfY6ad4G19hu8U/499hZ/nQenrjWI2OoXo4UcG802Y8V8B8mVFFAne693mL1RYehGhaS/a/zYJI6kEBzoG+KPT7zB0ZVF9vcOIESyWc7oBoPpDEPpLC/Mz7Kvpx9VSl5cmENXVKYKP7o1765SLznDZKXV4K6hUVQpaQc+fpi4evXaaXYUevjmzBkm80VMVcUPQ2IgqycPvSIFWd1gqdmg5QcoUqBLpeuMFkYJ3zCOY4Iowg9DpBSkNJ07B0d5fmGWj07uomBanN5YY6ayya/su+0HXrcXVql6MyjCII5DZLfSEiOEQhC1kUIjJmI8YxHEbXRpEMZtIEaqLpbioylpMlqekpliLF0kiELGM4mYNAJSmo4XBihCMpYudQayiKbvM53rI4gilhtNduUTp4+8YRF2BMTjOCaMYzw/YiCVxVRU5us1sobBkJ0ljK91i1lpNJBCkNZ1bh8ZYbFe5/HTpwmjCFNVMVSVC+UyVcchjmNemZ+n5l5JujlBwOuLi9w5MoKuKLw6P0/ONOlPp4nimMVajaxp0mPbPDQxwXcvXGCuUrmmXXOGwalajZrrogiBIpPKyVqziamqHBwcZDCT4dTaGpvt9g/V11QpSWkaS/V6V2DbUNXu5HB1P/E7/USREi8MWW02mSwW2dXbixeGnN/YuKZKEHZErrdQSEEYEnTeL4TACwPcLX0tsTUcdiYlkfhNbVmDOh0dJF2q2KpGEPu0ggqtoIwT1ujVdpEy9Rs6hf2kx2q1wenFNYIwImXoOH5AMZ24SAwUMlxer5JLmbh+gKYoXFqvUEiZuEGIrio4fuLqONFXvCF3/t3RXQy/K1mToG22ki0ChN1xR0ohZB4hdOI4RAiVmOvpiteE0BLK1DUUWr9Tne0s2kQK8OC9znVdhMRxE999jtA/RhyHqNoBtiZRIQvEqERh1EU4X7PKiq8gQwUgFdkdE+Krx4fO+36A9vIPFVEcUfEqHOo/RN2vM9OcocfoYbY5m9A4UpNcbF5kOjOdvH6L+hDHXGxeJIxDDhYOIhC8vfo2eT3P+cZ5+s1kHNxwN1hz18ioGeZac+hSZ1dmF+ca59iR3sGoNcp4avzKXkYIBsyh7rN5dQgEA2aC3lDEu+22GzScp/GDWeqtvyFjfwRNTSqYcRQzsW+U0Z2DXDqzxF/87rcY2TGAqqmEfojreBiOztpCmVTOxrB0DFtn8eIqg+O9W9AudFMjlbNYnFllfM9w96o0Q6N3uMgdj+xn+y1DxHFC9VRUSTpn8egv3095tco3/+NzvPj4G3z0Vx/ESpt86AtHqJWbfPtLz/PMV1/lU//kA+jmzey8VdoNB98LqJcbVDca3ftAZ3Ow1U/g74c+9OMScRxRD6pst3fgRg5vV15lZ2YPilR5sPQor5a/R8Nrsb7mkbYNHDfRFFnbbDDUm8U2dRotl4xtUGs69OTTXFrepK+QxvECojjRKctnEqHnrWp92En+SSk4/IH9PP3llzj35kUqazW27Rqmul7j+4+/wYmXzpDKWggpuPMjBzn+wmneefEMUpEUBwrYHXec8nKFr/+Hb1FeqnD7hw+Qyqe471N38srfvMmJ75/Fzlg8+LP3oKhKN2EoZdK/Aj/k6FPHmDl2iSiKGJrox0qbHHz/XhbPL/PV//MJNF1l9907OfjgHjRdRb0qERbFEZeab+JHLnl9iJq/kiR/hCSnDdAOqihCox1UaQUVJAqL7VM0/DWkUDCVLK1gkzD2CWKXIPJQpM6ofSub3gJB5JBWe6j4C7hhHUvNs+FcRAiFgj7CinOGfnOafmsnH/qZgzz0oX2oaoI6XVmucuHsCiPbi+jCZnlug7i4lpiPrPv4mQoj20qcPL7M2EQvAwM53FWYHhrBrQW8dWqO1mJMaaSHudPr6HqDnbcM8cZrM/iDkqnpQeS6Tm+2xMpilYszZRRFMrRH48w7C7RaLnfePUUml9ynjJbc/+X2OnEUM5XdnozbnQRkFMVEcR9R0Mat/xlEFWKRQwYBkfcKkfcmQkiieBAlShH4q+B9AyE84qiK1O8AkUYxP0LoPEXkvdr5+2FABxQEBkmZ1yCxHE8Tut+FuI0QRYQ6RhTMELnPE4cXCdvfII7uQeq3EbkvErrPEYcLhK3/imJ9NEGYiiv05RgdCIijCkQVIu8NRLSBNO65LilV81vMNVdJqwmK3VQyDFrTZLUb6eDFSFR0meOnUePo3n3jfOe1M5ycXaHedlEVyc7RXt48O89fPPMWhq4yNdLL+26dQEoFVVG6yWyAcq3F996+wNnLa9SaLlEcc3BqGENTEB0UkqYqKIrk0M4Rzs+v86ffeQNNkeybHOSefeMIAVOjPfz1SyeYHu3jocNT3LlnO19/7jh/+uQbaKrCgR1D7N8xxCsnLvHi8YtsVJv81QsnePj2neRSJgemRnjytbN8/L59pKybMw0GrCzPLp/lrfI8F+prLLWvUJluNMNMZft4avE0L65eSNYIjQ0AprP9DFo5Bq0sfVaGmudQMlI/NCatz8ywOzdIRjPYXxim4bsUjRRO6DPf3OSu3nHyutV10E6uU1DQbU5XlxlN5SmZ6W5C6gd9/ka7xaVaFVNV2XTaXRfnvGFiqRrnNjeYzBc5tZEIj+cNEycMUKWk5joULYvZ6ibr7SaThRJN36PstHl7dRk3CHhobLKrAXyz0JQsJfMgitApGrsJYgdVWAiSva0TlgkjFyfcwIyKqCJFGLs4wQZB3MYNy3hhjXdrY14dYezhhBsEURM33MQNK+g3LURCw7+MQKVgTLPhHLumFTVpE8YeG85xbLWflDpEwdiFpfZhqwOYSg9eVMNQCgSxQ9k5jiYz5I1p1p03CaLmTdcuOdMkYxh/y2Tbe9Otf5T4sUkcCSF4aNsEF6tl/t3RFxiw08SArWr85uEj9NkpvrD7IH9w/DX+9fe+jSoVNtpNPrdrPyOZLKut5o/0+SOZLL+27zBfOvkmz8/PYqkqDd/jg2NTfGJqDyXT4lf2HuL3j73Gbz37BFnDoOl5fHxqNx8Ym0IVAkvVeGB0nC++/SpLjRqmqvFr+w6zs9jDUqPONy6cYqlRY7FR5ytn3uGt1UXuHtrO7QPDPDo5zanyGr/9wpNkdZP1dpO7h7dxZPgHO2G0g1WafogXVjHUQsdGWUEgieKAdrDWOQ6mWsQJyqS1EdrhGgKBHzWRQkWL0vRbBxBCMF+r8s76KqPZLGutFoJEgNqPQobSWWYqZfb29qMIyVKzxrZsntMb68zVKizUa7R8j4l8kYvVTXaXelluNqg4DjXPJWsY7Cr2UnHbOGHAuc0NFuo17h4aZaCjLfXipUt888yZrrbQaqPBY9PTGGqCOntocpL/8vbb/Ktvfxu9cyx3Fc0sZ5qUWy3+l2efxQ9D1lstvnDgAHnTxA9D/uz4cc6srZExDJwgoB0EPDA+fk273r1tG89evMj/9PTT5E2TD05N8f6JCW4dGODxM2f4ty++SNGy8MOQgcwPJ8yaNQyOjI3xF++8w4XNTQxF4R/feSejuRyzm5t869w5ZisVVptN/uzYMV4pFLhn+3Z29/Zy58gIXz91it955plukuhqUewX5uZ4e3mZY8vLXK5W+YOjRxnL5/ng1BSDmQxnqmu8ub6ArWpIIRm0szQDl6rnoArJ7kI/Fc9hrd3AVFXagY+laDwwNIkT1lGlTtmbJ62WCGREEEaY+s21an5Sw/EDdFWh0nRImTpN16OYtnCCgKbjJQk4P0iSfGGErikU0jYNxyWOE95+bzZFPmW+94cBiroD33mS0HsLqY4Rxw5S6U2Oey8QhZcgbhEFF9GMB4iipHpxo0gWxhFRuATSRoh0Bz0krn2PEJ2Ka5vQPwXqNkLvTaSyHTCI4wBwgRBilxi9k8iJrj0ehwiRQtF2o6jTqMaRTgVZQlfXR7C50WR5KREcRwg818eyddptj0bdoVF3iGPI5W36B3O0Wx75Qop6rU2j7iSJTy8gkzXZsbMXXZ3oVCdvnCyQMoVt3ImUN68oAmhSw1Is/MjHUizyWh7d0LnQuMCGt4EhDSpehU1/k1VnlUutS2hSIyZGCtlNDNmqjSlNJtOTuKGLFBJf+qhCxVRMBs1BNrwNLCXZmOhSx4kc6kEdizQtx0dVZFJQEIIwjFCVBDESRTGKIvCDCFWRtJwWadvA80M8P0DXmtRabxJFc8Q8hq5+ukvHa9RafPtLLyAEhEFEOmeRytmkshaprM3Xf+9JcqUMizOr7Dw0RraQ4vBDe3n6z19m7vQiiqIwuW+U/e+b5s4P3soLf3WU5bk1VE1h2/QQBx+4hXs/eogT3z9HdaOO5/ps2znEwFgvR7/7DjPvzGOlDRqVJrvvmCSOYt5+4QynX58hlbXYXKux586pLv3pRrHz0DhPffklnJbH+uIVmsrafJkzb1xk/vwyUko0XWXfPTu7mjY/DSGEpN8Y5rbivdT8Cs+vfZt+cwiBIKclFO8gDFneqGPU2yhKotlRyFo02x6VepswjKg1HZptj3zaotZ0KNdauF7AYE8WRLKps029mziaPbnAxnKFfG+GwR0DHHxkPwDb925nYHsPCzNrWBmbPfftJltI4boB549dxkxb5PrzaLpGrjdDrZyszaYOjXPfp28jki0Gtg8QKzV2va+X3snbadVb2OkMdilgz/2j+F4vflihOAYf+2fvI1ar7Lwnz+BUJklI9fcgrSYl2+bTv/Eo5eUKQgqKA3mEFBz5xB1I5cp4EMUBraDKePp2FKFT9i6xPXWYs7XvMWofoGiM0mfuIIwDivoIRWMbi5UniOMITdqU3UtIoaAKHU2a6NJGoFDzV/CjNq1gE01arDjn6M1N0vDXaYc1UmqJKA7Jav2M2PuTMUoHvZMYC8OIhfkyPX0ZenuzvHl0lp7eDIsLm0RhRKknQ3mjQd9AjlqtTbPhoBs9+H6I6wYsXN4gm7Wo19pcOL/C4nyZTNZicChPs+kyNtGHpipIRdJqebTbHpqm0mq6bJabrK1USaVNdP16tFlR5qgutSlvNjm7vEipL0upP0u90qJWMQj9n8G0QnzfprzWQjcuoxtFNPUguqlRXtex05u0W00md91PoagSRQb1ahbN9PGc/WjqMF6rim7mEFER330ULSrQqHwUO8wTuB+Hdgbf/TV0UxKFDaTSS9DwyfYWQLuTFvtIqQahzBPFEpQpXL0nme0UnaYvMYyPogidKI6R+h1IbT9xcBHiFtK4P7kXzrcRyhBCu2LX7schl1trCCCjWQlyXdr0KRPXtVfnaSWI2zjBGmntp8vFDuC2XaMM9+UJw4iUpWNoKmnL4GcfOsh6pUkYRZSydhc9dGT/OFF0JYFmmzq37hhi12gfTssjl00Elz9+9x5ypkHoh3zkrl3Ypo6la3zqyD426i0MU6OnMy4JIfjYkb2sbDbQVQVTV0mZWX7hg7exXm0ihaCUS6EqClOjPcn4RkKRs4wEYZOxDXpyKW7dMfwDN+L39E2w7jb4m/l3GLbzfHh4DxnNZNDKokuFOE5QaTuyveR0i4lMD4+N7uPZ5bP0mWk+MLSLop5i0M7x85O388zSWVprHv1WlslsL0IIJjM99JhbCZQrsirT2f6uNMmQlSOvWWQ1k1/ccRdPL53hry4dI6ubPDgwTY+Z5iMje3l1fZZeI83dvRPdcwrgg8O7+crsG3xt7i0eHtrFnvwQO3N9pK9yQ53O9nfbYquEt95uUTAtdhZ7eHH+Er12iryRJKZGMzlyhokXBpQsm3bgkzNMWr6fSM+0W6hSoisq660mZaeNoao0fY+imRRh/SDsrkhjwPECDE3trGkUpDTYnvoElxtPcnLtL7F0m9Hs/UT4rLRfZqX1Cqq0udT4FjXvAtszj7HhvM1i8zkUYbDQeIaKe57R9EPkjB0oQierj6PKFCl1GFMpUXFPc7nxJGHss95+g4Y/z47sZ0hpwyhCTyQphEZO34kqUwyl7mO2/gTnq98gb+ygaN6BH4Ef+ZhKP33WEWbqT5JW+5nKfRpDHWAw9SjrzuuEcRtbGSCvJ/psVe8CTf8yUmiMpj9IWtt2074ohLipFtT/1yH+PuFLP0J0L6Lhury6uMB8LVFd77fT7MiX0BUFTVFYatSZqZVpeh7bsnl2FhKHkJSpc2JjFQXBQCqDpaks1OtcrleYzBfZbLdZaNbYVexDCkHeNJmplKl7Lg9un8BSE67s5XqVmeomfhhStCzGc0WCMCTuWJafLa9xsVZB6ZzjtoFheiybipNsbGxN4+jyAhcqZdKawZ1DI2zL5tl02jx1/jyb9WRxJkTykOzu7WNXqZcwiim3Wlyolal5Dr1WigODQ5TSNmEcc7q8hioku0oJZ7Yd+BxbWyZnbNKfMroVjmRyDJBCJYp9YmJUaRLGPgLZ2eQphLGbIJHiEEUkiCld5hFC8NrSAoaioAhBw/dYazWpui6HB4Y5X9lAlwo5w2Qok+GtlSUKpkVK03lp4RJZw6BgWBStZJLdWezh+wuXurb2u0q9VF0Hs5PwmatVsFWN/b0DbMslG7xyu81Mucxmu51oO6UNJgoKumISxx5eFHBho8Ll2goZHaZKo8xWNtlRtDHVLCfXVhjJGizWVWquwWgux0SxiNpBfi3Wanxv4TTrTp0An6lSicl8L82wjRt6FPQsilC4XKlxrpxoDkwU8wxmM+zNTrJUbyT0tDjG9hRqjktfLs1IPsd8tcpKs8m+/n5CP0LVJGfX1zF1jW0h8K2CAAAgAElEQVSZHGnbYGNxE8VKMvZnZpYoFtIcHBykmEuz0mjw5sIiYRQRBhED2xJ011SpxHihwGalwZsXL1N2HVJSZbxQYLHdZGe+SMY2eWthkQvL61hpkzBIKAiWoXHfnil60imWW3UuNZKNl6GobEsXcEOfucYmJSPFcCpH2W3hhD5ZzcSLQmxVI69btMJNFloniIkYtvaik+Old2bJZyxunRzeQn7+eIxsf/eI4Qqsc63WpJCyqLTa9GRS3eREfbOJ5ya6DEIIUjmbzdUq7aZLsT9H4AVIReI0XcyUQRhEnfYRSCnwvQDT1skWEzRRHLv4zpME7nOARMg+jNQvIYSO1/7LDhVAouiH0K1PELgvE3hHsbK/SRyHuI3fS2ho5n3JM9L6MqH/FkIOYNg/h1RHcOq/i6LdgmZecfqK45DA/S6+8wzgI5Xt6PanESKD1/4qoXeMMJxD1fag6LejGe/Hdx4n8F4jDGZQ1F2o+iE065NE4Rxe68sd/QkVzbwf1XiY0H8D33mWpvPLzM0mFbtc3iYIQgaHCpw7s0S10kJRJIEfkiuksCydZtNhYDDP6kqNdNqk3fZYX68zNt7Dzl2JE1LghyxcWsdOGaiqgtu5J7quJWNs28e0dXwvSHRNYsgXUyidKmgUR6y5a/Sb/cRxzKq7SitIBBW3UEF9Zh81v8aau05aTeGEDkEckFJTKChJ4kexiYmp+BUyagZVKEghCeOIHqNExaskrxcKGTVDLaiR1/IstZewVZtWRaHR9qg02pRrTUb7CrQcD01V6C9mmF0qU2s5jPbl6cunOX1plb5CmtmlTfwgZLCURVUkhq5yfn6dyeESeycGu2108cRl1hc3EUqD0ekixSEdRabYWGwwd3oeVVcp9KWJ4irbp6cJ/ICFcxusXNrAtHKM7d6B3avjBi6zZ+dZmd1AqoLh6T5Gtg3h+i5zpxcpz1cxTZPRXQP09pdYPFVm7tQiQRBQ6MuxbXoIO21SXa9x6vUZPMend7jIjlu3IaQgm08ReAGXziwyuW8bkRZTD5r4bsDZ47M4ZZfe4SIihJ6JIo1yk4vvzNPyHRQhSRkW995/iHRCpfqpGIvc0OHFje+y5iwRxmEn4dhGFSrb7SlWnAXe3/MxatUQ09DQVJlsAITAcX2iKMY01MSS1/UpZm3qLTfRGelQcYMwZmm9ymh/gZ4ODfGd75+n1WijGRqpjInT8qhXmkhFki2kaVSaqIZK4IUIKfAcj3TeJgoSnSzd1FE1hd6hAksXlrnw9iyP/ne7EEpMENWJYhcnWEKVGTQlhyJShHGLIKpjqsPoskDDP4smC7T8GRAKptIPSBRh0PAvkDP2ktZ3vndDxhGXW29R99co6CM4UYNWsElaLTFk7eF8/UV25x8hikNOVb/LLbmHWXHOUHYvk9MGkEJh05vHUvOEkZckz4UgiDy8OCmu5bQhYiK8qE1G7WHDu0RKLZJWS7SDGtvTh25wXTEz51c5f3aZyal+fD/k8tw6mayFaWoUS2lWVmqMjBQ4dXIRIeDQ4XG+/c23mZzqp1BMcfb0EkEQMX3LELMzawwM5hjd3sPpkwvcfWQnjbrDc0+fJJO16B9INDprtTY9vRmeefIEwyMF7rlvmnT62iKH0/ZYmF0nX0pz8o05hsZKFHsybKzWadSSZOSO3UNcOr/K5nqdKIpJ5ywGhgvEMVy6sIqmJ5vrqT3DFHszVDfqHH/hDIEfMjI1QKveJlNI06g2CbwQt+3RN1JidX6DdN7GdwNW5tYIwwhFkWiGhp2xaAc+8aEcpq7hhEGnJJLQbcI4JqXqbLjNLpraVFScKOBwzyiDVkIRDL03CNt/jWI9BlEtEfi2fwWp7ei2QRCFXGgscqwyg62aPNB3Kyn15sWgOA5Zd47ih1X67COospvA/kkfi+AGEKoocvCjTba+XkwACKTQO4UnmRSy4gBFWigigxCC2bPLrC5VsFMGvh+yuVanZzCH5wQEfkAUxbiOz8BIkXwpxcBIESEEbden2XK7sh1+EBIRE3eSU1Ec02x7ZGwDPwjJpEzymSt6p2EYMbeyyYvHL6IIwWfef4Bq07nyfeKYsFMIVKSk5fpddoXsJDrUDhra8QK29RW6SbJrGioOk2Kf0DtaPFuviTo/SufH7VDIPARaBzUeIJXtCHF9Mrfq1wmikIgIvVMYiogJ4oAgClGlQhCFGIqe4PWkwWqtjaZIVLmlLSUIowjHD0jpCbVQCMFCpcZgNoOiSNqeR9lxKDtJ4ihrGAhJd02TNyykSAyqZmsVjA6zpuK2SWsG7cBHk5IwjgmjpLi92mxQMBNzoCiOKekWFy6t4/khtWYb29QTerWukrYN1itNdE3pUMzA9UP2Tw3Rk08RRTGNhoPrBZ32jlEUieMGmIbafY+qKpQ3m6RSSdEzldKJFIeYiDgOqfqrAOS0foSQCCRB7Cb3W8gr+2ZiUmoeTSTmCX4UcLp+Hjf0COKAgp7DCT2yWhondCl7FSzFxFYswjhEkxpT6TFcx6dea6MoSjLv+mHCDokSvVRFUVA1ie+FqGpCkQvDkHwpTSzg4uYmmpLsOYeyGRQhu+iytu+jyGTuX282GcxkCKIIVSpkTQNDVa8pG7/Xw/6D4scucbRUrXNmZZ2Vah1DUxnMZVhvtGi4LpO9JeY2NhnvKXJ+dQMhBL2ZFJVWmwenJ8hZJi9dvsTlWpUdxRLnyxuoUpI1DJwOpWSlmXCP9/T2c3ZjndFcjntHt9806+yHIc/OXWSj1aIvlSaIQlq+TzsI6LFtDgwMEkYRby0vIYTglp5e/Cji5NoqK40Gw9ksD49PoikKr526hOMltnoCMPWEL6xrCmMDRWotB88PWd1sIKVgelsv40OlG7prbUUYuQnCaGuQefftvDqdu3Wom1Xeyitf+91Xmg3OlNcZSmfYaLeAROB5MJ1hoV7DCQMsVcNSVeZqFSZyRRYbNQDMjiD5QCoDJDzWN1YWkUJ0JnYYSmdYatTJ6AaKFDQ8j709/WRuIk7tBYs03VeQwiaIyigyjSJzeP5lhLSIYwdDncQLLxFFDgiBxCBt3ouu3ti+fKm9Tj1o4UU+lmIkKBFCBIKUYuJGPinVouxVsRQj+VuxGLZ6u+3Xanm8+Mp5mk2XlK2TShnU6w4jwwXW1hsJCqWQYqPcIIximg2Hqe09XDq5SLE/i9vyqG02KfbnCP2A6mYT30voiwOjJQxT45bbxq5BUpw5epGLJ+cZ3TnI/Lll3LZHtpimVm5wy+2TzLxzmdJgnmwhxezpRQAKfVn23bMT0zaISehsST+4ovkVxhGKSOhs3a5y1diwRRmKOhQmiUIQRrxzcZkgjDg83RX2+0lfIF3zBMXvaoOtePvFs2z+v+zdeZBkx33g92++u+6qrq6+r+meE5jBYHASIAiCBElRK1HkUktZsVyJkna1q3WELK/DXtvhY0O2ww7L9m6EQruWFLK0OtamZEo0KVC8ABAECOIeHDODOXuOnr6v6uo635XpP15Nz4EegCSuAZAfBgPTVdWv8lW/+r3MX14rm4R+SLaYYce+YfyWz4VTC0RhjGWbZPJpHM/G9WwunFoApUhlkgZY32gPlcESA+PlrXU8lIpRqg4q7g6tz5F8nAFKNkAYCJHpTk/zQQUII9edrtMEYSOE2z2Wj5Kt7rofuaTyJhsgLMQ1iwAqFaFUI3lfIw143WNsdkcOXTp/D0QGVP2KNYZACBdh5LfKoVSHJPmVIRlxFILykSpDFMVbcSC5UQrCIN5aWv3Sp20YyRQrwzSS6TKmQbsVMHNhlWzWY2IySaBXV+u88NQZypU8nXaA41p0WgHtVsDIRC9SKizL4ML0Mm7KwTQFd35kz1aP//U0oybL/jIT6cvfv1P10yx0FhnyBlnxV5NFow2bXqfM2eZZbOGAgKJdpBbW8EwPS5gcLB68au297axvtjAMQb3ZodkJ6C1kqDbapBybQtbj/MI6UoHnWPT35FiuJpsYNDsBUSzJpl2kVMk0ST9koJyjt3j1elJKhdQ7jxFE5zGNPHQ7EiDGNHowDI8guogp8kjVwjR7EJi41iSuPcW55jTtuMVSZwGzO6q17PaSNjM0ojqemSKQPqEMaEQN+sUwM083sG2LzY1mN4bAjt0DuCmbk0dmUUoljeSUQ3W1zu0f3k1vf36rzNONWY7Upul3e8jaKSxhsuJvMJLq40JrgSAOEUJQcvLUwga9bpFbCrsu3c/fF7FIKUU7bnKxfQ5bOPR7Q0glWezMcaL+CntyB9iZ3YuxTUNj24NeU+dL1vGQdPyIlGtvLVwb+iEyVsnyMN1pohurdQzToNCTVN5FN/m0NerP6K6loZLnlFSYlsns6QXW5ta56aMVIja7DfoQqQJM4WGZeQQ2QbyKIiZljWCKFK3oAkrFxKoFSmEZWaQKMY0UsWyTskdxzNK1p7itWIVEMuiucSGIVYAlXAxhESkf2/C6a5kk/5YqIpQ+RnddjEj6W40LuByvpAqTtTNIGjmSpNERyQCBwDSSTknLeO26L0pBHMfJhglOMkXn0r8NIRDd0YZCCDqdy5sqtNsBlmVgWSadTncNR9ei00kaD65nEYYS102mB7e7r7FsE0HSQJ45v8r5cyvIWHLbnTvoreSvKVuyCL3RjdGWZWBaJlEUJx0hhsCyDKJQImXSMWJc0TiNuh1XUkocx8IwDWqrdeamlxACvLSLMJJzjMIYvxWQynq4KZvQTxL9nVZA0Am712mybpWUklYY0BixyDhudykIiSVMGqGPRFJ2M9SCDo5hshG0WfObDKYL7C8NkrW6I2dlC+k/gYzPI7AR9n4M5xDX7vZ5pj5HK/ZZ9TfYmx+j3+u5bjxXSuHHawRyg6w9duUCve/1WATbJI5awWnW249jCBchLNL2FABhvI4fL+AYvZhGhlBukHVuIm3vRgjBwsV16hvJPU8phZSKfClDbb2B3w5BgOs5ZHIe6axLTyVJOJ08v8SR0/MM9xWRSlGttWh1QgYreWzLZGZhHSEEu8YrnJtdY7S/xJ4dfeS6I7+DKObxl6ZZ32zxsdt2kk97PPbKNIYQbLaSBJJSsGOgh3zG48TMMp3ursKlXIpCJsX82iZSSorZFPft33HVVLytD0rWCTsPI8x+lNxECAOlJEouJwvBiyymNYqUS0AKYaSR0SwAhjWK5XyIa9fbAvjh6mEaUbI0xkRmiHrUohm16HVLrAe1pOPbzlOLGuTtDDu8CZ6cnqUvm2Gz428th1HrdOjPZSmmPJY2G6S7swbma3XSjs1APodlGMzXNmmFIbZpctf4CH3Zy6O+ts712hyCuNyifL21dsJIMj27SqPlY1smrmPRbAcYArJpl+pmC9extpohpiGYHOntjrSOOPbqPDOzaxhG0ooZGiohgPVqkzCMKRXTCAFSKtY3WmTSDrffPsKqPE0rqpGze7fKFMuQQLYoOAMsdaaJZEDGKuGZWRzDYzNcIW/3MeDt7CaOQs41L2IJm0AGpK1kxLpt2ARxgN+N/RKZbGxgOoymBlmcq3LkhfNJ7mKgwMpijcpAgWa9Q3WtQTbvoRQ06x2KPRlsx6LZ6PCh+/fQNiTfPn0aqRSWYVLwknWCwzgm67ist1uMFJId72zTYKPTYanRoD+b5d6xMTzbfv8mjoIophkEXFoIzTYN/DDe6q1f3KwzVMgRxhLTEFtb+WVcB9MwqPs+nSjqTkNK1ihyLZP40s03ipKblmkRSknatkhZ9nXn/yWLhAUEcYxtJJnHSz0bpjDIOA6xlGz6PrZp4lnJQrmNIJm/65gWRS9ZBKze8rsLkHbX8ehmsA1DkE05tP0I2Z36YhoGnmsnc3/fheFp21Uur338NY8JsZVs2C5QbO3Stc3vvt45ShV0dyuyANkNqAZStZMpOEp2ezUuNWYNIMYw8q9ZWf8159ddS+Q1P19nfvqVSbY4lmzWO0mFqdsoiqUknXJod0JsK1k4L44vnWOy00S70dka8RDHEtdLtkqOo2Rkm2kZ2LaVLDKYu3p74IVzKwgj2QHHbwcszawmw/OFIJNP0dxsb20l73dCjO7aJF7afd2pID+JWErOzq+T9myGyvn3ZeLoetqNDlEYE0Uxjpskh1T38TiS2K7V7QUxUUollV8Blm0S+hG2a+G4No53/dijXU11ewMvN06SHpvGZntrlyTRneIlpSKddVFx8jthGNFpBSzOVdl3cGzrO/LjWOwsYQqTgp2nFbeZb89TsAv0OCX82N/agSlZ/DGmHXdYD9bZld2JZfzouzNd6fV2yLrWG8VUpSSxrKJU2G0cqe7CjgpDeCSJpE53WuMVcR4Hw0jjxz6gqIU1JJKclcMUyc6AikvrUV3e+czBJWjFSKlYWayRzXmkMi6uZyNjie8njdykcSwBQSbrXbXjVTNq48sAz3BR3f9FMsY2LKpBHUmyTbZnOkQqxjUcXNO+FKff61+s1/2jX0rkG7xzdQQpk06vS+uR/Liuva9e22l1ZWfWG92Dr9fxpf1oAj+iVksa78VS5h1bq/DHbXNcNwZuU9/crq4ZK0k99MnaLpZ4441LrrXS2eBMYx5fhuSsFPvy46StN96B99rT+HF/4Qb0mj9cJBv40UJS9xYGrjnUfWEASmEIt/s9VZhGprsGq9j273Sl7doKAI2WTycIr9o8aWF1k4HudLSw21Z0HIsokqRcC8NIRuJuJxmhlLTVlqsN0p6TTMGzLBSqO6JFYHRH2xiGIOjuTiyAfGb7BZ6VilFyg0vLBiQPRijVQckqhjmIEDZKRZc7+4iS61lYCJHf9jo915ilxy1gYOCaNn4cEqtkVEkoQwxhYAqTSEbYho1nuNTana2NEACi7lqAnmUliW8pk2lQhkErCLEMgWMl9dcgjlFKYZsmacd+zaZVb9b218HlDQ5e79potgIuzq7T35eMthZGsjmC74dbG0IkSXeIoiSJns24+LJFpAIs4WAKqztKMUQpiSksIhUmnQUkI34UkkZUxTE88nblNeXZ7j50vftcGEQ0G0kewLbNpLPPTdp/kNxXoyBCGAamZWx1nqYzLrGSbHQ6W6OKLq2VbBsGkqQ9dmkDKMsw6IRJrsM2jO12DH9/JY40TXtPe69XkHQs0rT3Bx2LNE27EbzXYxHoeKRp7xfvi8SRpmmapmmapmmapmmadoN5/+2jrWmapmmapmmapmmapr0ldOJI0zRN0zRN0zRN0zRN25ZOHGmapmmapmmapmmapmnb0okjTdM0TdM0TdM0TdM0bVs6caRpmqZpmqZpmqZpmqZtSyeONE3TNE3TNE3TNE3TtG3pxJGmaZqmaZqmaZqmaZq2LZ040jRN0zRN0zRN0zRN07alE0eapmmapmmapmmapmnatnTiSNM0TdM0TdM0TdM0TduWThxpmqZpmqZpmqZpmqZp29KJI+1dIYT490KIf/lul0PTtA82HYs0TXs36NijadqNQMci7UelE0fadQkhGlf8Xwoh2lf8/MU3c2yl1K8opX7nRyhDRQjxFSHEmhBiRQjxp0KIzJt5b03T3ltukFg0JoR4SAhRFULMCCF+7c28r6ZpN74bJPZ8UQjxVPe9v7XN83cKIV4SQrSEEM8KIfa/mXJpmnbjeY/Eon8vhDjdLd8vvpkyaTcmnTjSrksplb30f2AG+MwVj/2Hd6gY/yvgAePAbmAH8N+8Q++tadoN4AaJRX8JHAX6gL8P/BshxL3v0HtrmvYuuEFizxrwfwD/+tonhBAp4GvAHwIl4P8FviqEsN6hsmma9g640WNR12Hgn5LUlbT3IZ040n5iQoiUEOLfCiEWhBCzQoj/TQhhd5/7tBDijBDit4UQ60KIs0KIL1zxu18WQvy3V/z8BSHEK0KIzW62+sHuUzuAv1FKNZRSVZIK0s3v5HlqmnZje7tjkRCiDHwI+J+VUqFS6gWSWPSr7/S5app243gn6kFKqW8ppb4CLGxThE8CHaXUv1NK+SSNuhxw39t53pqm3VhugFiEUup3lVLfA4K392y1d4tOHGlvxm8DtwAHgNuBB4Ar58hOAA4wQJKB/lMhxI5rDyKE+AhJb9lvAUXgQeBi9+nfAz4nhCh0G29/H/jm23Aumqa9d73dsejSvVJc+XJATwnRtA+2d6Ie9HpuBl6+9INSSpL09usONk37YHm3Y5H2AaATR9qb8UXgXymlVpVSS8D/BPzSFc9HwG8rpQKl1MPAw8A/2OY4/wT4faXU95RSUik1o5Q61X3uOSAPrAMrwAbwR2/T+Wia9t70tsYipdQK8ALw3wkhXCHE3cBngfTbelaapt3o3ol60OvJArVrHquRjDrSNO2D492ORdoHgE4caT8RIYQgyVpfuOLhC8DwFT+vKKU61zw/tM3hRoHp67zVV4GXSCpBBZLk0R//hMXWNO195h2MRb9A0os/RzK//z8Asz9hsTVNe497B2PP62mQdK5dKQ/Uf4JjaZr2HnSDxCLtA0AnjrSfiFJKAYski1ZfMkbSqLqkVwjhXfP8/DaHuwhMXftgNxDeAvyfSqmWUqoO/AHw995k8TVNe594J2JR933OKqV+WinVq5T6MNAPPPumCq9p2nvWOxV73sAx4OClH4QQBskU2mM/wbE0TXsPukFikfYBoBNH2pvx/wD/SghRFkL0kex29hdXPG+TTO1whBAfJ1nE8a+3Oc4fAf9MCHG/EMIQQowKIXZ3A+HzwK93p4dkSIZQvrzNMTRN++B6W2MRgBDiJiFEthuLfpVk8dnffVvPStO0G907EXvMboPPAgwhhCcu75r2XSAlhPgNIYQL/AugCfzg7ThZTdNuWO92LKJ7bI9kDUi7+7zY5j209yidONLejP8eeJWkZ+sl4Engd654/jzJnNpFkullv6qUOnvtQZRSTwC/Afw7krn5jwAj3ad/iWR6yDxJFnyQJHmkaZp2yTsRiz7TPc468CvAp7o7PWqa9sH1TsSeXwfawL8haey1STYOQSnVJllv7TdI1oD8ReBzSqnoLTxHTdNufO9qLOp6vPvYbcCfdf9991txctqNQSSDOjTtrSWE+DTwe0qpne92WTRN++DSsUjTtHeDjj2apt0IdCzS3ip6xJGmaZqmaZqmaZqmaZq2LZ040jRN0zRN0zRN0zRN07alp6ppmqZpmqZpmqZpmqZp29IjjjRN0zRN0zRN0zRN07Rt6cSRpmmapmmapmmapmmati3r3S5Al2q2A54/PoPn2sSRZPd4hePnl4ljSasTkM94TA6XubBYJYxiTMNgaqTM2bk1smmXdiegutmmr5yl0AeB2aLPrRCqkFjGxCpGCEGsJD1OkY2whkCQNtPk7Ry2Yf9YBW5GdWzDwTHc133dZlglaxUwxE+Wo7vQPE01XOFA4W5CGfDSxpPcWrwXz0z/RMcDuDQ9UQjxuq9r1Fp8/6vP8clfvBfHe+PP5/f+5f/Nz//Hn2RwovKa54JOyFf+7Xco9ubYecsYO24aRgE/+NvD7L19B0M7+q573OZmi8f+5jke/IUP4aVdlFI8992jjOzsZ2hy+99TSrGxWucbf/I4pb4cP/MrH932NVKuAyFCeEhZR4gMQjgo1SCOFwGwrAkUgPIRwkWqNobIoFRAGJ3ENAaI5TKOfQtKtRDCBkyi+CKoAMe59Q0/uytJpZheXecvnn2RzxzYxx1jwwBcrNY4ubTCg3um3vBv96O8x9ePHOdjuyYppDzma5v84ZPP8VsP3EspnXozh35zBXv3bTt3VynFyfppUqZHO25TjxpU3F4WOksMeQMs+yuAYDIzwdnmeXqcEmkrxbpfZWd2klfrJ5jM7GCuNY8vfSxhsRHW2F+4idn2PBW3TCtqsxluYhs2rbhF3s6zK7sTo/u3jqKY5dl12g2f+kaLXDHN7Nll+oZLW9eDZZsANGptKkNFWvUOrUaHTCGF3wrw0i6rCxuUBwo0N9vke7LIWJLKukipGLnO9ynwI/7yj77P4986wn/5O7/Azn1DAMRK8oPl47imzVKnRsFOE8qIPfkhLrbWWPfrlJws45kKx2oXEQiSogp63Ry3FMexjKTMM6cWWVuqYZoGG2sNMvkUPZUcs+eWURIczyL0I7y0QyafJldMMzLZhzBee8k1NlpMH5mhudkmDCKyhXTyOgVOyiYKYuI4Jp1NIUQSn2prdaRU7Lp1nIHxCo24ww9XTtPvFWjHAbGSuIZNICNc02bdb5C2XDzTYq5VJWU63F7eQa+bTT6zOOKpuYu0o4iC57HcbKBQjOUKnK1ViaTEMU2GsnkiGbPWbtOTSrPYqPPhkTEGM7kf6XsehhHnTy/RN1ikUMpc9Vyt06HhBziWmfzXNLfiv2dbBLHEEBBEMWEsCWXMzt4ylvHa+1Un9tkM66wHGwyl+slZ2Tcdhy6RUnL2zDLf/NphqusN/uGvfoS1lQYTkxX6B4tXvbYezjK9+XWUirip9CXWOq+St1KoeAbH2olj70Uk99v3dCxSSqlYSgQCBQiR3LOlVJee3/r8pZTEUhFLiefYmIZgrdXmf/ze91io10k7Nv/0jju5d3zsR37/xXqDP3juOS5sVPnE1BQ/f/PNuNYbVxmjKGZ5tY6Sir5Kno3NFt974iT337OLvkqeaq1JqxnQU8rgeTbLq3XCIKLSm2N+cYMjr85xz11TlEsZ1qtNwigml/FwPZsLM6v0lDL0lnM0mz5r1SaOY1IuZVldqxNFkkolh1Lw7UeOsn/fMDsn+zg/s8azh8/x6Y/fjDAEpmlgmgadTkgu69HwfSzTIJYKqRTtIKQdhni2TU8mxQ+nZzjRvfeOlAqs1BvYpklfLku11SaIYwRQTKd49OQ05Uyam4f6iaUkbdu0whDLMKh3fPwoIuM6lNIpFmp1pFL057I0/IBvHD3JJ/ZOMZDPsdnpUGt3KKVTZFyHh09MYwjBg3un2Gx3yHtJuR3LIu0k9bMgjvnqq6/yndOnGckX+KVTHgwAACAASURBVGd33YmHhR9GKKlo+gHPvjrDfbfsoJRL4zkW5+bXKWQ9KqUsKxtN/CDEc2x6ixnWay1afkhfKYsQsFxtYBoGvcUMZ2ZXOTO7yj37J+jJp1mpNgjjmL5ilpYfUm8ln2l/T456q0Ot2aGYSVHMvX79IggiVjeaSCkxhKBcylKrt/GDiN5ShiCMcR2LIIwxhGB2scrp8yvcfesEy6t1Tp9f5vYDY5SLGcIopt70sW2TSilLdbPFylqDybFeTNNgdb1BGMX0lXMIYHm9AUB/b45G06fe7FDIpSi8QZlfXljgf/n+40ilGCnk+a/uv5++bPbKl7ynY1HXW76uiZSKWqONZRoQSS4cn6Ox0cL3QzK5FEoqhCFobLQIg4iegQL5UobNapPWZhulYN+dk/QOFhFC8MzaMZpRmwf6bt+qN/3YZVKS6cYcfV6Jgp19w9cvddZ5dv1Vnlk7xn+970tkLA+A9WCTZ9de5bHlw/wXe79I2S284bHW/BobYYOp7PBVj7djn28tPM2h0m7G0wNv2X33rSalBCGu+uyVSmKqIcRbWm6pFKjL98VL73XswhJrmy3u2z+BuU095vVcOLvC8mKNOI5p1n0AYilJpRzyhTT1zTZ+J6RUztBqBgwMFZnaM0AjDPj2udMstRp8aHCUW/oGOLw4z+HleSrpDJ+e2MVco84P5s5jGQafntiNRPHozDTtMOSeoTFGcgW+cfYkNb/Dh4fH2ddT4dGZs0zX1pkslPjY2CQvLS/wysoiWdvl703uZrXd5OmFi4Sx5Na+QQDaUcitfYM8NH2ST+/YRc7ZNkfxpv4QN0TiSCmFMGC4r4BtmQRhhGObjA0mCRepJK5tk0u7VII0nu1gGALXsegrZcmkXIIwYqhSxHUNamKNWlijFbVYC9YpOQWShkovq/4qraiJAhSKpc4Ku3JTFAwbpRSB9Amkj23YuEaKUPkIDGzDIZA+AkGkQp5bf4zB1BjDqR2kzSS4tOMmUkkcw8E2XJpRnceWv869vZ8ibeZImRlC5RPEPoYwcc0UoAhkgFQxACkzjcAgkB1CFVCL1tkMqygl8WWbgt2DIZKGlh+3UShCGeIYLo7hopC04xaymyhT0iGMwTVNIpVUQpeaDcqpFILkC65QxFJhdhtgBdejUWvzxNdeIOhEXPquP/nQi0RhxPJslYl9Qxz8yB6qy5v88Bsvkcq63UCuWJpZ49mHj4CCW+7bTamS53tfeZbD33uVXbeO0ztUQgHPP3yU2dOL7P9QsjvkmVdmmD2zxPpSjVTW48M/ewjTEDz+9Rfw2yHCEEnS6OGj/O0fP0b/WC8Te4f4+Bfuwu+EPP2tl2nXffbfu4udt4xRquQZnOjFbwcAXDy1wOLMGrd97CbajQ4vfv8Yu+44RrZgYhgVDJHFMMsouUkcLyKMFFI2iaIZTGuIMDyBwEIYBZSsIYwiMl7BcEso1SSO5/GDZwADw8iDkggj/7rXfhRLji0s8cLsPJ5l8eHJccZ7iowWC/Tnslvfj7naJn/6zGHOr1U5vbLGp/btYriQ54np85xfr7KjXOK+yQk6YcgTZy8wt7GJH0Xcv3OCiZ4Sj50+R7XV5tDoIHv7K3z3xBn+8oUjTK+sMVUpc+f4CK0g5OtHTiCV5K7xEfb2VzixtMILM/OkHJv7p8YppFJ8/8w5PNvizMoa902Os7uv94a9kb2VXMOlHjXwpc+A1898ewFH2IQqImNlyFk5fBkAisXOEuOZMSIVEStJLCXNqIkvfWrhJn1ehT6vQtbK4Aib9aBKO2pTckocrR3jUOkgs605VFYCyfddScXaQo3VpQ0qgyXCIGJgrIxlm9SrTdpNnz2HJpIGQr3NyvwGa0s1hiZ6WZ3fIAxjyv0GjmdTXakT+iG2YyGlYmOtTqk3d91zt2yTj3xqP5N7BhgaK289biDYmx+mFfuUnAxZK0UnDig5WRzDZijVQ9bySFsOU7kBUEmyyTEtclbqquum1JfvlkeSyafIFZPk+NBEBcMwcFM2zXqHfClD0AkxLeO6tz/bs+gdLFEo5zBtk1TGJegkMSSVcWnU2kRhklCSscRvB2SLaRzXJt+TRQCuYTGV7cczbRQKP46wDROpJJZhkrc9UqZD1vLoc/OEKiZjOleUQjCUzeFZNo5pUkmlsQ2TguuRdz0MIQilxDMtmmFAbypDxnYYzubo8X70joHVxU3++F9/m//o1x/g1rsnr3ru7HqVxc06Qgjqvs9gLsdGp0MsJYeGBpler6KUou77RFJSyWaYKvcAsDJfxXEs8uUsq/NVWkabU2Ia13TI2Vly1uVKtZSK2dk1enqypFIOpvn6FTalFEn+Kkl+1DZaPPTXzzO1Z4BWy6fdDFiYq+L74WsSRzP1R0iZZTaCaQDW/KPYTFFwbsI0y7wN7Zt3RbXRZmFtk0bHJ5aKtOsQxjEbjTa9+Qxpz2FhbROFIp/2SLs2nSDCMASTg2WCOObo0hLnNzbIOQ7r7RaQfPZRGBP4Iemsd93Y/fD0Gf78xRdRwPTaOrcODnJT3/U7eC4de/r8CodfukAsFbcfHGNirJdsJqm8VjeafPd7r+I4Frmsy+hID6+eWKC/L08267G8Umf63Aq7pvpxbJNvfOcIOyf7mBzvxTAFLx+bZfdUP+WeLN/7wQlSnkNfJUcu4zF9foWZi+vs3TPIoQOj5K9o7BfyKTw3qe7OzlfZ2GhRLKap19scOjjOCzPz9GRSbHZ8gijm6PwSk709zG3U+Pyhm5mvbaKUwjINDs/Ms7hZxw8j7pgY4ZlzMxRSHqOlAp5tMb2yzmbHZ6SU58WLCxwY6ufowjKllMfT5y6yb6DCcr3Jz92yjxOLK5xdXeeO8WFynseJpRVuHuwj4zp87eXjpGwbieLnD91MuVu+OJY8ceY8t48N8+z5We6ZHN1KHJ2vVvn9Z55lplZDAOOlIqWGAwLG+0ukPZuVjQavnFnANAX33TLJS2fmGO8vkU25/M1jrzA5XGZhtcZHb93JE6+cxbVNBsp5itkUh0/OsnOkl1zaZWG1xpm5VXaN9lJrtnnhxCxpz6aYTTG3WsOzbdbrLR44NMXTxy7gOTZRHPO5jxzAda7f9Fheb/DIkydo+yGZlMOO0V7ml2rksh6eYxGEETsnKlycr1LIpajV20zPrDA1XiGKJYurdQ4fu0ghm6Jaa+E4Jhv1Ng/cvZtGs8NzRy4w2JenWmvx5AtnMQzBnh39+GHEWrXJQCVPOmXz7cePJ9eMgp/5+H4c+/plrvk+h+fniZVio9MhiOPX/Z68F0VSAlzuyJJxt9NHsd2NOJIS2zBet25Yb3Z44oXp7sCAHnr6i+SKGYRhkM55NGst4kjSP1pm4cIqI5P9CFPguDayL4/jOkmCCWiELXZmR/BMZ6s0gYyIZIQk6RTxTAfPcBBCEMqIVtxBKoVtWKRNF6kUK36Vby48zQN9hxhN95M2PRzDIpARHekjlcI1bFKmixCCPrfEzfkdvFQ9ddW5lewc+wuTPLN2dOsxpRSxkrTiDrGSWMIkY3kIBK24ww9Xj9CI25ScHK5hkzY9YiXpxAEfqdxKwc5cdayODOjEAZYwSVsepjBox0nCI5QRUilSpotjWG97HV0pxXOnZhnvKzHQk9t6bHmjwczKBrdODmFb5lv2XheXN9hsdbh5YuCqq6+cT5N2bcRPkBspV3IIAdX1JqZpIoC+wQKOa2HZJrVqcj329GaRUpHp3tdipfAsC9s0eWFpjh3FHh6fPc+v33IHWcfFEIJHLkxz/8gEe8sVpFL8f2de5eJmjZRl88LSPH3pLBt+h709vYzniyhgpd1kJJvnQGWAII55YWmez+7cx/G1ZZ5bnKUvnaURBHzp5tuwDYP5Zp3HZs5iG0kHodP971v9t78hEke+9KnJDXK9BoqQtDBZDZewsiYIg0a4iTRTNCOJWTQZTJcxurPsSrn0VSNolFKUVIoRNYBSIEm+nAqFJSwG3L6tGBcrST2skzKTDHE7bvLM+iM0ozqumeKuno9xun6EjJVnX/4QR2vP4hppFJIT9RdZ6syy2JnhrtLHWQ+XebH6Q4QQjKQm2ZM7yJHaM5xtHscQBgPeGLcW7+V0/SgzrTNEKuLm/O14ZpqnVr9Dxsrjyza3Fj9MnzfEk6vfwo99mvEm/d4IgfR5ZeNp5jszjKd34xguT65+B1+2iVWEZ6a5p/wJ1oIljtUO04rrBLJDWX2IRjtN2rYpeSkiGbPebpO2bdZaLQayOTKOzYVajZRlUfJS3D08QirjsOvgGH/7x98niiS2C88/eoxib45DH93HY3/zLAPjvTz1zZfIFtKUKnlq6006TZ/nH32V4cnkc374y0/xC7/1aQ59dB/TR2e548H9TO4fwbJNduwf4fD3T1Bba9A3Umb+7ApPPvQiP/dPPsbhx17l+LPT3Paxm9h1cJyv/9H3iIIYx7WZ2DdM/1gvt35kLzsPjuJ4Do9+5Vlcz2bqllEe+cun6B/poXBNQ9gwDV7+wUl2HRxjdWGDM6/McNM9DoaRA+WjhEUcL2GZQ4TRSQwJjr0PP3geGTZQKkAR4Fl30fEfxiSPMByUbBDHSwiRARVjmmWU8hEi9cbBS0DKsbltZIijC0t898QZ/vG9d7zmZeVMmr0DFWzT5NM37aYvm+HwxTmOLizxkalxHj9znoLnUW23WajVOTg8wNdeOU4xleLRU2cJ4ogDQ/08dPQk5XSa/YP9DBSyPLBrkqFiPkn41RvcNzWOQPDQ0ZMUPI/vnjjDHWPDzNfqPHTsJJ+75Sa+e+IMd02McMfY8JsdnfSeEauYZtwklCF5K0fWylCw84QqRKqYslNi1V8nZXpIJcnbOVpR8vrNaJNQBrSiFqGKyNs5epwSK51VauEmrbiNa9jk7Ryb4SZT2R1krSxltwcUbFYbhEFEFET09OVIZ12yhRRxJLFdiyiIUFFMZbCIjCJkrCiUMrgph55KllxPlmI5i2kZWLZFu9HBtAxyPVkMwyCOYhZn1qgMla57/oYhmNjVz8Su/qseF0LQnyqiuo11gdiKxzk7dVVs3pkduOr3rpUtpMgWXns9VYavLpcQgrDbQL7eDdH1HIamrt/IvTY2XLp3XFk2F5ududdvKF9Scl6b6HFMk109vUBS0elLZ7aOX/S8rffrPkj3hVeV6Y0opViaq7I4W2W7hMneSi87yz1bUcgyDOY36/SkU2Rdd+v7K7q/LYTYGm10/tU5evoL5MtZZqeXMDMGpR1JZ453zUhbKRXf+fZR6vU2+/ePcuCWUfr78697DqsLG9Q3WgzvqNBqBiiluP/jN7EwVwXA9SzW1xrMz1cpl7O4bndUhawzkL6DejiLQiFViFSbBNF50uYQQrw1ldN3m1SKKJZEscJzLFzbxA8jCpmkAR3FycgL2zQoZlPJCA1D0GgnyYVrBWHMwlKNlGfTWK2zulhj/91TWNepzC83mltXVDsKqXU6P1K5F5dqrG+0GOh77d+/ttlmdr7Krql+XMdmda3B8GCRu27fAQjGR8vMzle5ac8gG7UW2YzL7beOk04lCdmhgSSJGMWSarXF/T+zm1w2xdx8ldW1BkEYUau1Xrd8QwMFZueqtNoB+/YMYhjJ1R9LhR8mif5CyuXDU2M8dOQkpmGwu6+XdhgxXMhzZG6RgyMDzFY3Wak3sU2TgyODTJRLKKUYLxfZ01+hP5dDqnmkUnSCkNh1KGfS3Ds1zkNHTjC7UWO10aITRmx2fPYP9TNWKnDH+DArjSZn19bZPzhAxra3RpkBOJbJjnKJl+cWyXkuPZnLsWej06EdhkDyfV5pNuk1XXaNVNg30c+ZuVVG+4rctmeEZ4/P4NgmI33FpFMOKGQ97r5pjG89c4LZ1Q3mV2rsGq3gWBbDlQKrtSZrtSZKKXYMlam3A/aN9/Ps8RkGy3lG+4u8eHoOQwhu2zPMK2cWWNlocH5hnX3j/WQ8Jxkp8LoUlXKWOJK4rs3c4galQpr9u4d46vBZnG5nRyeIKAnBxEiZjXqbfVP9HD21wORomV0Tfbx6ZpEojjm0a4RjpxeoN9oM9RfJZ5L6/vJanZW1OmPDPQhDsLRaZ//uIXaOV1hdrzMzv87eyX487+rP/4NqvrnJht/Gs2wsYbDmt+hxU/hxRNqyCaXENk3COCZWkkYYcKA8gGOYtBo+p4/MEoYRO28epljujlYVYBoG1c0W1kQfg0NXd7SWBy6P0ukfK2N3E47X3q9DGfHU2hGeXD3CweJOPj/yAAAnNs/zxMpLuKbDZtik5OT5ByMfI2uleHL1FY5sTBMjKTsFPjt8P4EM+ObCU7y0cYpG1KLiFrm/cohduRGeXjvKkdo0nTigaGf53Mj99LrJSKft6vnJ4691rjnPd5eeoxP7KKX4+dGPMZTq5Zm1Yzy2fBiFYtXfYH9hkvt6D1IPW3xt/nGO1c7xz3d+fms00oq/wTcWnqQa1LGFxYd7D3CwuItHlp5nvr2CVIpG1GZXbpRPD3wI17z+rJH1eotqo82O/h46QciF5SqTg2UuLFWJpWSl1qS/mGXXcJL0ODO/ymK1TtZzuHl8ANsyOHp+ka888Qr7JwaYGipz6+QQjU7Ad144yanZVVZrTXYPV5gc7GF+bZNYShbW6wAc2DGAIQRn5tdYrTXpyaXZM1rBtS2CKObU7AortQaFjMfN4wPMrdb45vMnqDU7LG802DVcYayvyPmlKtMLa+wcSjo3ozjm5OwKY30lcimXWrPN3Nomu4crbLY6nJpdAQF7R/ooZDwyWZdM1mV4tJyMnuLyKH4hBOXefPffV8/cOVNdpdpps6fUy/G15aRjDIVtmluJ1qTD1MQUAtkdhXVTuY/b+odwzaTt/bOTe3hq/iJPzJ7npyZ28VMTu3hxeZ6/O3uSn53ciwAsYWAKgyCOAKikMtiGgWkY9KUzWIbB4aV5DvYNYCJYnVvHcW1y5Vz3Xvfm3RCJo0hFzHcWcQ2HlJmiE3cIVYglrK3nV4M2UkkKdo5G1KBgXw4oSikaDZ9Go4PvR0ipME2BZZmkUw5OzsK2TYQQr5mSlrUyW8c40ziGIUwe7P88x2rPcXzzMH7cxur+TjtuITDYX7iTi+2z3JS7jfHMLgAWNy/imSkOFu8ha+ZxDY9bi/cy1z7Hg/2fJ2VmkEoylBqn6JSZbhzndOMI+3KHaMYNPtb/OZY7c5ysv0Qj2iRWMQ/0fYYXqo8TqwjX9DhUuo/lxb9CkVzQjahG0SlzZ8/H+M7iX7HUmeN86xQTmd3krAKv1J5h3BnFytu4ZtLTHUpJrCQoCGSMY5ggYCRfYKFeJ1ISqRSWbVEeLGK7ly+RVMbl4Ef2cODeXTz38BHWl2rMn13hH/7nP0N5sMiTD71Ic7PNi98/zvLsOqZl0Kq38dshlZEessUU/aM9lCrJl69Uyb+mobj3jh3s/9BONtcbrMxXMU2D8kARu9toEEJQKGfJFtL0jZQYGOtFxpLnHz1GqZLn/Il5ausNNtbqr2kcVkZ6KJSzXDi5wPzZZfbcNkWxZ5Bkqa8YpXzARAgP170PQ2QQIovn5UEFKBQCB8PoIZX6ue5jIYbIYRhFDKOCZQ4hhAsYSLWJYZRRSlGrtdnYaJJOufRWkgY7JEM7l+tNXl1cZnajhplEpKvKLYQgZdv0ZtKsN1tM9BSJpeTowhKHRga5fXSYWtvn6MISQ4U8QRQjlWIgnyXvuTx59gK2aXJhfYNqq81as8XOSpmc6zJaKtCXyzJf22SokOOeHWNIpXh+Zo7z61WePn+Rhh8QxDEZx8EPIzzb4t4dY4wU33jo7fuFZVjsL9x01WNFO2nESKVQUlG2exEIhp0RQCTDr0n+fpVS0hs6nprANASGYdDvJkmJXje5ySkuDb1NgnvJKRIFETPH57h4eoF8T5Z2o0OuJ8vsyQ4juweZPbVBba2OMASFniwrc+vYjkV5qEQcSaIwYmzPEEszq2QLaerVJqsLVQrlHLc/uB/btVFSMbFvaNtRIr4f8ue/9wjnTy0Rx5KeSpYv/eYn6Ru6PApkcXadv/7TJ/nwJ27i2e+fpLHZ4ac+fxsK+Lu/eg7Xs/n8lz7MyEQvF84s89U/e5JPfPYQ66sNnn70OJ12wK79w3zi5w7R21/YurnVay3+9Hcf5s77dzM22cejD73E9PEFTNPgtnt38uBnD2GSxO5mo8Nzj5/i+R+col5r0zdY5L5P3szeg6N4KQe/E/LQl5/h4rkVfvU//dRVU7qUUnz7b57nucdP8cv/yScZm0ym2q4ubfLDR47x6oszdFoBwxO93PPgPvYeGMWyTeYurPK1v3iKez9xE8987wTtVsBP/fztREHMN/7qWTJZj8//yn0MjfUk06SjmLOnlnjqkVc5e3IByzbZe2CU+3/6wNZ5b9bafPkPH2PHngEGR3r44SOvMnt+lVwhxW337uLeB2/CSyWx8IlvH+XFp6c5eWSWlcUaf/S/f4tcN54ODJf45d/8JFbKot0OsW2TVjvAtkw66z6t0KAlAoSAMIzJpB38IMIyTZr4VHqyZAopTr88QxzFXDy1yE0fnqLoZVloL9OK2/Rw+RowTcEXvnAXZ6aXOPLKRZ5//iyDg0UO3DLKzp395PNJEjGMJQKwLZMwiDjz6hyVoSKpdNIL/PLh8zQ22yzMV5k+tcTIjl6q602KxcuN4x53N+fr32YjmOZ49S8QQN49hEEHIW6IKs1bIpd22TPeh20YKJI4EyuJa1pbje9L1cBLdwypFPV2B9e1aLWjq463UWvxxLnT3H7LOHacTOWWsYTrJI4ODPRTcF2aYcjOnjITpSSB24oCDGHgGua2icG9uwapbrQQQtBTynDqzBIzF9ewLZPbbx3n4P5RWu2A3t4sPaUsTz1zhoe+9Qr33DVFOu0QS8lTz02zf98wPaUMpmkkCdLlTU6dWSKdchgaLLJrqp+HHztOXyXH8GCJVjsgnXIoFdNcnFvn7PkV6o0O5Z4MR4/PMTtX5djJee44NJF8Pq1gK7k1VMzzgzPJKKn9Q31EOYVlmpQzKUwhSDs2pmFgmwZ7Byo8c+4ipmFwz+QYS/U6QRzTDAL8KKaSzfDoyWlAkXUdvnP8DFEsGSjkyLhOdwqcxWbHZ2GzjtE9fhhLXMvir186xkd37eDuiVEafkAll6EZBLw0u4Afxuztr7Cjt4enz83y4N4pHPPy32+0UGC8VKTa6ZBzXW4bGsJpCDLdxBtBjBVK4k6IFSvOnV/m+VfOk0o5FFwHO5LEfoSrYKiQZapSpF1vk64UqVYbLCxWCfyIVqODEUqWFjd49KkT3Lx7iCePnmdxfZP9k4MsVxu4jpVMgStm+dD+CTbqLcqFDK5jJSPT1tf5u5OnuLm/j/snJrC752FbJoVcChkrbNukXMowv1Tjh4fPsncq6bh44cgMYRSzY6RMNuPSaPo8+/J5yqUsxXwa17HIZVxabZ8nXzhLynPo781z5OQ8Fxc3OHpynp0TfSytJtOT+8s5smmXl4/PcnZmlbsOjnPHgfHk+illcF5nhNQHRawU5zarRCqm6KQwDYPFZp12HHKgZ4AL9epWHPJMC8nl+PTVP3mCr/7JE0SR5CM/fYDf/B8+j+vZOJbFrvEK8ys1PNfqzvjo4JlpbMO56v0d9+q225WxxxImD/bfyUbQoBldTnC344AzjTl+Y+pz9DgF/q9zX+dk/QK3FHfy9NpRbi/tZX9hCoUkbbpkLI+fGvgQF1srfH7kAcbTA9iGhUCwNz/Onnwy1ffPzn2To7WzPNB324/9OfZ5JT47/BFcw+Y7i8/y1OpRvjD6ce7tvYWLrWU80+EzQ/dhGSaWMCk5OT43/FHm26sEMtw6zjcXnsIxbP7R+KeZay/z0PyTDKR6WfE3WPE3+KXxn6YRtfjyzMPcXtrDSPr6nWCn51Y5fGaWf/zpu1ndbPLXPzjCP//Ze/iTbz9HX0+WHf09PPziaX75E7cz2lvk7MIaUileOD3Laq3Jxw/twjQM2n6IZRq4VjLCyehOrY5iiWtbWKaBUvDMyRlePDPPXXtGcG2LMEpG6J1dWMMwBD989Tyb7Unu3jPGN587wcmLK9w01kcQxoTDyVI1USRRClzbwjSTa8G2TKbnV7m4ssE/+vhtCCF47OVp9oz28eCtO3niyDkWqnWGevJ8+bGXtjpcDp+e44sfv41CN6lsWgJzmyWgr7zdXXn99aYy1IKLnKmuUUlnSNs2dw+O8ufHXqQ/k+WT4zv56OgOvj97jmcXZ/nk+E7uHRrju+fP8PCFae4cGMY0DL5/8Rz1wGc8P4xE8dT8DAvNOpVUhqLncUtlgL+dPoFjmvzM5B7WOy2K3uVRw65psbNY5qGzJ/mFvQcIOyEzx+fIl3NkimkM462JYzdENMyYGQ4W9yNU0oOsruw53WYU5KU8rlKK6nqTxx47ziuvXOTCzCq1jTZRFOM4FumMQ6WS57bbxvnc5w/iuCYCA1M4r6nwSCSbUZWSXcEzUpScXs42T2ALe+u9pJJX9KxfbVfuFg5Xn+CHq99hV+4Ae3OvXdemHTc5XP0BtuFQC9e3Hu91+8mYObJWnlCGNKMaWatAysxQdvpZ8Re2/dxsw6bPHcYzU7hmikD69DoDnKy/RNYqMpSaYCpfRmB0p6RdUe5reriFEPSmksr59eYGm6aB6yWf3aWMv2EaBH5IFETEUiIMg76RHj7zax+lZyCZIpjJefidcNtjXstLuyC6Gd3r9PIIITAMQRzJrdf0VPJ86ov3MjzVDwrSOe+1n5djse+OSV564gSGYXDHJ/Zjmq8dZaGUwqC8tRaLQeaq7HIsJUdPR2w2AwzD4ODuPLlMX/d3i1sfr0nSAK3VOvzB7z/C88+fY3CwxL/4zz7NxEQyvWu50eTvjp3k1+65nenVdV6YmbvuZ2OIy2swIASuZdEOk+GonTDCNk2ment45NQ0Gdfm47unk74xYAAAIABJREFUyHsuPekUH9s9yf7BfhQqSQBFSaMilnLr3FzLwjIMwu4wa0MY7O2v8KW7byPnOViGgVTJ9fGjrHXxfhaGMUolFdtmK+DchRVM0yCKYlqdkDCM8Vwb17HI5zwaTR+lIAgjSoU0UxMVfD8ijGIs0yCVcpK41v3qSSkJghjLFOR6sgxN9tNp+pQHSwhD0Ddapm+kTPb/Z+/NoyvLrvO+353vffME4AF4mKqAmlGoueeRPZJsDhJJUZYlJRJpSYnjLFnx8nIcrzgr9pK15DgaLMVrqRVJpClGFCXObLJFNtlTVXd1zTMKVYV5Ht483TF/3FcPQAGorhbppMlk/1OF++47957z7j3n7L2/79uxIPVKHVmRESWBeDqGZqgYQY161cR1XCRFIpIIEW/zP2vpSqIZKpIs4bguZt1G05RNnUBJEtl7sIdYIsjpN0e4fGacWoP6eduqFZMzx28wP50llghy6fQ4U2NLJFIhjKDGmTdHUDWZz/wPz1Eu1vxzZ3KIokBnr09Beemv3+HWtTn+0T97vhmUskyHy2fH8TyPH3zzAuVilVgqRCFbYXZyBa+Bqijmq3zpxVd5/eVL7Bzsoj2TYHp8iT/4X77KR/7BA3zo0/chKxLhqMHJV4e5/4nd3PfYzmZ/K6U6r33nEpVynWDI11DLLpX4k999idHrswzs7STRGuby2XHe+uE1fu2ff5DDDw1QKdc5fXyEhdkckViAC++MMj2+TCQewAionHx9GD2g8qv/9FkQ4dzbt/iz33sZVZXp25mmVrV46cvvcPnsOL/6W8/R2ZPEthxGLk9z6fQ4siKSaovS2h7jxpVpTr0+gm3ZPPXRQ83gWktblJXFIrMTy/TtSNPWQGfFkkEUTWJsaoWFpSKiKFAs1WhNhXFcj+n5HLbtUyjbW6OUKgpjU8sYmoIsSbQkQvTv76aUqzB8eoxMf5p0X4pbtXHqrokqbtzER2MBhoa62batleNvjvDSt89z7twEXV0Jnn56Hx09CW5NL9OaCNOTjiMpEm5DwyIaMXjqg/v51lfOMDG2yNJSiQcf2cH2Xe2Mji7S3h7z3xFBoDP4KJoUJ6i0o4oR2oxDiF4O251DkbrZYvn6ibPpUoG5UhFJFFElCdt1kQSRmK4zVyoR0TTy9RqKKKHLsk9jq/tO00Aiicz6gJCiSBi6RK1moUjiu47TQ93d/Ntnnma+VOJYJkNbQ7NlsVbmVmGZTDBKf2Q9TVkQBFpbwnzwmUE8FxRFJBEPsmtHGkH0g+mDh7twXQ9dkak6Fgcf6kMSBIIhn/rx9DP7AMg7JkOHuzE9B9N00CIqjz67i7iqowoSuwfS7Oxv89daUeSpR3fjuh6BgOpn8l84hOf5zsT9R7Zz9FAfkigyPbPC4lKBIwd6EUURy62SSYh88sh2wMPyCqhiEkks89jONhTJZltrAM9zsb0KPUmd3sQ+XODG4jKSJDKVy1Oo1cnVauxsTeHgka3WWK5UONqbQRD8oN5gZxuTuTw70y2U6iadiQjJYABDVbi1vMLhnk4WSiWmcwUOdXeQCASQRAFJFPml+w4B/j709PgU8YBGRywEeHie76a3BQP8y8cf4/TMDJlIhId6ulBFaVXeYDaPNL7CoigTWCqxuFLlUDhMNBXGmivQbkFxPk+qZLN4aZqhziSO41KcWkFLhumXVORogOJ0lonhGXYbGilRJh4yeOGhfc0s+87uVkRRIBUNIQoCPekEjuMj4gTAcl1+cGuUP3r7bT65bx8PdHejSBKeZ9ESl0hEE+BV/WdKDDC4I4Lr1pGlOoIQob+rEwQTRdYQBItPPt+PIIhIkkpvJoEoiCRiAV5/5yY7+lrpbIshyyJHB7vZ0ZGgUqgS1hSePDbQRK9GDZXW+wYwaxb1XJmdbTGi+8KomvJTIVD0o1pPOEZncBVxIQBLtQoVy6I7HKU/tkph93XZPGRBxHU9zh4foVL2KVTD5yfJLhVJZxKUq3Wu3JxFkSUKpRpVJU/WXKTd6CWhtd4z3UgQBCQEZEHCZNXXEIA2PcGOcDeCIBBXI5TsKoog80BqkNMr15isLHB/ci+tWgJJENElFUn0/zVkH1nrei5lu8bN0hRFu0LOKlGyq3+vcbRcm+vFSfJmkenqIrGGjpIuqSii7NPm5PX+iyxISGt0cj3PY7q6yLPp+2nRYmiSgioqzNdWEAWB/lCGrkArWbPYpNndq631uhRF4vHB7Qz2tbOQKzGzXKC7NU4iEmBiIUfNtBidzyIKAnt62miLhziwvZM93b4/FNAUdnW1UqlbPLinF02Rmnnx3rY4zx3dhdgIMFXqFolwgOnlPOWayfh8lt1dbZy9Mc2nHhtiV6YFD9//COoq29uTlGomD+zpafqsnckIOzMtjM77qGVZEjnY38nJ4UkOD2S4NrnAkwf7WciXOD0yxaH+TlwProzPkS9Xm4GjezHHcylbJrOVAulAhJ/fvR9D8qnFiihxtL2TQ20dzfvYlUjRG40hCT46SAA+vXs/ObNKQgsgAB8d2E3FtpAEAUkQeaLblx6QRZGCVaM7GuVwWyeiAIooEdN1+qKriHLTcajaFnuSLURVjVyugChLJNpjSD8mmiC8TwJHgiAgI2+uV3GXeSObrfB//Kfv8+Yb16nX178Y5XKdbLbM9FSWWFLiRmEZTZeRBIXe0MMownqki4hITEmyVJ+j5lZYMRcJy1Fcz6Vil6g4JZbNeQJyCAEBSZCpumUcz0ZEQhZkjiYeZ7Y2zvncW+wI7W8u1jWniipqlOwCy/V5nkl/ksuFU6yYC41rS6ubOAECcpip6i2qTpkVc2F9IO2OwREFcd3kqksBynaJpNpGXEniuB6zhTypYJCVSoWuWHS9g7jm/2uFxJZnc1w9dYvF6RUuHr/OwFDPhquLosj2wS5++DcnSXUkKOerhGIB+vd38/bLF2nrShKMGBx6fPeG79bKda6eusXs2BLXTo0SDBtNaOBaW5nPM3xmrHkfOw72Ek2FSfckOfvqVepVk93HtrH/4Z2c/LtL9EysIKsSR57cy/i1WcauTGOaNsNnx9i+N0Pfnk5e/uIJdh3qJZrYXPhuMwd67TFRENA1hUK5hmU5lKsm4caEs9l3Z6ZXOHnyFrlchWy2zJUr03R3J31UnCghSyKXZucZXljCchxqls356VnGVnIYqkIiYNCTiNEWDvLK9RIvX7vBka5OjnR38q3L16lZFtfml/jo/t3MFYrgwUKxxHevjaArMvf1dvHW2CS5ag1JFHh0ex+aLBMPGHxv+CZ729tIRzaORUc0TDxg8MORW8QDBl3xKH0N/ZP/r9s7p26Rz1d59plBLMuhWrMwdJVcvookiSiyzy1eWCpSrvgaJa3JMHXTQmug+E6+c4vjJ0YIBFT+yT9+Zl37CwsFXv67S3z65+6ne1cHHdvbsGoWkiKB56E2nOhApEE1WiMOuNnf6R4/iJlsj607Z2Ymy1e+doZ/+AsPEo1spInJssQDT+7GMm3qNYvZyZUN54AvLh2NB/nsP3uer3zuOH/7uTd5+OkP8OSHD/BXL77KlXMT2KYfkKxVTUqFKv/4X32E3oE2bMvhlW+c4/N//Apvfu8yH/vFB1ffIw/efnWYF37+Pp76yEGMgIpZtxFEoZmBPPnaMK988zwv/IP7+dCnjqHpCoVsmb/4w+/x9b88wb7DvWzf3c6u/V0YAZUL74xy6IHtqJqvazc9vszEzQWe+PAQsWQI23J47bsXuX5piv/2f3qBXUNdSJLE7MQy//HffJ2vfv44Owczjb5YpNJRfvm/e4ovvfga3/ji2/zXv/kMjz43yBf++BWunp/AcVxqJZO//Ys3SbSE+ZXffJZ0Jo5tO5x+Y4T/9Nvf4offOs/P//oTzfGcGlvkl//J0zz2/H50Q2FydJHf+1df4dWXLvLos4MYQY1Hnt2H63h8529Pcf3SFE++cIChY9sav63fzvaeFno6E811VG5k9V3XxXV9eo6qSHhApj1GsVRnJVfG9TxUTeHoU74TXy3VqVl1inYJ0zWbaODb5nkes7M5zp4d5+qVaRRZ4uc+fT9921oYH1viu9+9yMc+cYS6tar94dgOqbS/HkmSyJ7BLjLdScqlGrIskUiGmJxaoaU1sm4fYLllUvogbYEGpddzqZszDcToT48FFBVZlFiqVsiEI7iChypJVG2bpWqZVCDQgL6L1Gybqm2hiCKm6/oaB3csp47jEQkZyLKIrqtN7bAtr6+qPL9jx4bjIgKtRghNkrE9FxmRYrFGvW6RSISQJHGdHowo0qTDuZ5HyTEp1OrML5UomnWCqkrSCIAkMFsqUrUsnIaYaq5WI6JpSIL/WVBR2eaFcWsO05MrJJIhorEA2ZUysiIhigILc3lkWSIc0TEbAfzDx7ahKBKe52CEpzlyFNpT/nxnuiXK9jyWW6Pm5LC8KhGlA9MtU7VXMOQkASlJwZpEFnRUKUy7cRDH9bBdl4ih0RGJ+LqbiozUcIQMRaEjGvEDZJaF1KATtIVDBFWViWyetnAIQ/HRTCXTRBZEQpqK6TiYjoO+Zhw1GUx7DM+t0JMosDvdgSwsYNkmrldFEqO4Xo2dSZedyQgCMjCN5+mIku/AhGIB2rpS4HmEIwFC8QCiKFKvmciKTPeODmRZbGpfSQgk2+OIrofreEhAe3cKI6RTypUxwgbtva1ouo8OMW2HXMMBEwUBsYEEENcgcAHqts2ZmRls18Vd86B6zjSOdRWw8bwaeDUQIohSC7iTuK4MnoUs9+E5M7iWAiiIXh0ECUEcRJK6/MSapvDAwT50TUFp0E2qxSpnv3cRgKWGoLKqK5h1Czw/IWQENWRVopSrsDy9wo5DfQjaeyue89NooiCsQ7cBpANb6yLeNkGA4JpErh5Q0RsIuGjI4MjebmRZQldlHMnG8WY3rC8/iqnialJsLSX74dR+doV7uFYY52+nXuWXep+jO5hunrfWFupZvjjxMofiO9kR7uZ6cXIDM+BerOxU+dLkKyTVCHsifZTsKhVnFSF1uxDCvfVLpuauCjibro0mKoiIKLf73OzI3VttEh08D8t2MBsIIF2RCQc0xEbQ33FdLo3O8sPzN/nQsd0IwGK+DHgNP7QxznfsP73mdVYphvGwgbxGA+v1S6NcnZzn2cM7yZVruK4fJHc9P8mwdq3yPK/ZN7/gytaBgoGOFK9euMW5G9N4QF86wexKkaCuMrS9g5Cu8fjQdlpi7y6EbtZtRodnsUwbp0Nh0s5jOjZZs0pcM3A8l9HCCkdauricncVpIoP9ojCO6xJUtEYSwqMvnGQkv9D4d7EhEyBQtuvsjbdzJTtHUFHRJYXZSoG+cIKu0BqkN8K6h3Uku8xYIcsHuvv9uIoqN0EWP06do/dF4Aj8B6FgZam5VcJKDEMM3LWj9brFS98+x2uvXsO2XVRVZmCgjR072gmGNMy6TS5XYXomy6MP7qct5OIJDlXH14Kw3Bqe5yIKUiPTL9AbGGC2OsZ3Zr+IIso8mHqOmlPj5Mr3eWVhFsutoIkqjmfSGxjgQv4EC7VpjiWeYKJyg5vlK3ieS39oH6btYtrQqvTy3emv0x3YxvbgIKoX53uz30QRNHQpRLnmohKkbrm4rojgGLTIPYxat/jW5JcBgf5YPwu1GS4VTpEzl3lj6TsMRu8jJEeaVd1CUgRJkBmvXKc/tAddCnAud5wOqc7FcZN0OER3PLbleAJks2VOnRolEFAZ6EkRCOt88JceAVGgVK/z0M8eJdYWoVSv88DHDxNJhejY3c71s2MYhsYnf+t5IukI93/8EONXZjArdQJtISq2jSd4PPap+0g2hE49QJIlnvjEMURRYHY6iymKGKkII9dmCbVGcRWFs++MUi9W2fPwLrLZChfPjjN033YOfWAfr37zHNevzaInQmT2dGJ5IMoiNdtjcaHAhTNjaPEQdqlOqSHcLUo+ImrbYNc6Gh7AzHSWM2fHyGQSHDiwMVB22wRBoKM1yuRc1qeRvUvFuTuIZwgCVJ0qCjLhgMinj+5hMpvjw4P9Pp3Ds3BxeHJXb+PcGqZr0hbT+PiBnSyUithY9KSCfGxoF3P5Mp88mKY9GuYbF6/xi8cOMNCS4q/OXODa3CLP7d1BRzTCUqmMIcnMLRQIGSpP9W3jxPA4k94KVs7kQLyN6ZkcQUNlMNJCOVdjKNLCslUjv1IhaMkEXZm9wRS1sgXvPs/+1Fr/9jZMyxeOT8aDJA9vW7OPWK165AeUNqJ5BEHgoQcH0FSZ7//gyob2I9EADz24A0nyF1ZFlZv8/jvbeS9/33nMshwWFgo+ZeVHMEWV6N7eQjgaoGtbC5quMLAvQyhq0NYZ58zxG9Trq5nAoWPb6N/T4TuUBhx7bCff+tJJLp4a4/lPHEUPrMLUO7sTfOCFAyRb/WxnYM1zZ5k2b//gGqGIwWPPDxJpUJpa2mM8+zOHOXP8BmdO3KBvZ5qOniQ793dx/u2bLM0fo6M7iet6nHv7JoIA9z22C0kSKWTLHP/eFVo7YqRaI6ws+Dx8D+gdaOPkq8NMjy2haDKqKtO97Xa/W1F1mR37MoQiBm2dMS6fHfc3G9fnGb0+xwc/dRRBFFiYyQEQS4YIxwyunp+gVFjNYKY74zzxoaFmfzK9Lezcn+HS6TGqFRMj6KMzBNFr7hvqts21hUWCqorSgIvfRg/6iEQRwRbwPP/ZlESRtkiAmlNDFzUkUSUSMuhMxyjlK3iuR6FRZejmpSlC3QaB3gAxJULdXR+kcRyXb33zHIah8tzzQ/T3+9ogAPF4kJs35pEliUhQp275SR5Z8WnSkixSLtW4dH6SfQe66ezyM9dnT42i6Qq65jvWt5/bm4Wv0xN6irDa1fxdBEFHFP7+lUbfj5YOhmgL+g+7wOomvO44bI8l0KS7ZxBni6V1f4eCGl2xBKIAxVyFWsXc4ptb23KtzEylQMGscbSlC0WUcByXsdFFQiGd+B1V/e40AUjqASKqhouHoSjsSbUgiyKGohBQFOq206CFC1iug9EQl9+RTOG4LhHBRwR0diZwPc+fg1O+qKnrerS2RpqOqed56LqyDl1lBCAYDDapuboUQ0TCdMvYXo240kdQbsVyqwi6gNBAbgXkJBV7Gccz8XCRJYkDnX4lmzsdpY7oxqIYdwb1U2u0ibYK/AOYdYtquY5meNiM4XplYrqLIGSx7Di2s4gstQM2pj0GnosohnHdAoqcQRKTzfHs2ZOhc1c7lmv7IsKbXNfzPDr6V/XoAJJr9O9ufyfRHlv3N/iO0c25ZYrVOkf6M0QCm2fwZ4tFhpeWNhwXxCSSshuEgB80QgRBRRAURKkTBB3/jVdA6gTBDyQhSLjWCA3CNwJ+oCMcWn99SZLo3tVBS2eCUr6CrMrohuojHwV/PVEbQbBYqk6tXG9qnPz/5lulbuJ5PprkXpxRQYCP/VePYFsOtu3ywi88QKQxTyzny8wsFljJl9nZ20YgZWE2ZCG20Nze1GqOyUx1kdnaMjWnznBhgkzAT5Zt1kTJrvLm0gV0UcV2bT9p3qT7qCTUCG8uXWChlmVbqAPbdak7FrIgMV9boWhVfAaA5zBXW+FmaZq8VWa4OE6n0UJKi7FYz3KjNEXeKnO9OEGv205QNqjYNdJ6grxVYq62TEJdo+OkJzi1cpXXFs+RMVroCaYpWlVulqbImSVulaYJSBodRgv3J/dxfOkiVafObHWJhBohE2jldHb4PSPkkuEAC7kSb12bYHRuhVK13vzt7rSq6e/jTNthYiGHrq7KiEQCGudvzSAKAn3pOFoj8LSQK3Lu5jR96QQt0dtr2vrGKzUTSRApV01mlvP0t6cIGzoDHSm+c2qYozu68DyPvb1pAppCPBTg0tg8F0ZnyaSixEIGi/kSsytFVgoVJhdzZFpixMMB+jtSfPudazy2fxuRgI7rQnsizEKuhJqSEOx3HzHP85ibXOZ3/ukXMesWT/2vjzI0uI2J0gqqJJGtV2gzwtiuw0QpS8W2WKiWiKk67YEo51amua+lmzPL0wxEUogIFK0aNddhsVYmpYcoWFVkUSKmxfzPHAtREKjZNh2BCK7nUTBrGLKCIt5OxrgUrRqmazMQT7A3tUpJlFWJZHt8g2zLj2rvm8CR5ZkMly5iuSYROcbuyAFkYWuHvFCocuLEDWzbRZZFHnlkJ5/57ONEIgZio/qW63o+ZURXsIQc4+XjxNUeREEhZ05hulUUQW8ulmV7hV3hXUiiRsGcxnRySILEvsgeLLeGKEgEJJ3x8kk0KcK+yG7ajb2oos720B66A9sBAVXUuTq9xI35ZdpiAwQrbRTLKhcWVghJQ9ieRd30iEfDTGZNdnc+wOh8nlgwSriyj4u5PJK7j4QCkiAzGO1DQOCh5DM8mHwaAQFV1EhpaaQGqulY8klMt8aF/Fscjj+KJEhMVG4QD2jsTcco1k0Cyt0n+osXJnnxxR/S3ZXkt//dp2jN+OiS8WyOQt2kHpfJVosUs0vEYwaL1SIRV0MZSNARjzKZy3OjkMO0bYROg0QgSbZapTS/wEqlQmsshNFA5hhBjf0PrWYzr1yYJGC71Gs2y4tFPA8qpoMe1Em2x6hWTQxdZWW5RCFfRVEk4pkE1bLJYsOxC7dF6elr4eb1OeZm84RbIsQ74siyRP9QNwvTWd76znlU3RfYvnMsXn99mM9//k0+8NTeuwaOACzbRxrFIwau660Xum3Y7XWvoyPOwUO9nD49Sm9Pit17O5iqTRCQAxSsAgWpQGc6guMtElWi1Lwina0iaU8hKAfJmlnGq3mqTpVwOExML7DomMyVHbbHtrOjxV8gbcelMxbh9ZvjnJ2cZalU4f7eLlRJYm+7P5nMLuYZn17BjgaRHHhoWw+W7TA1lyNqqIQNjbppE/JkcCEgKwQVlZxdJWEYKJJECAXLvHfo60+aWZbDhYuTuK5LtWLS0hJmajrL4cN9RMI6p8+MMTm5Qn9/G5lO/x3xPI+5uTwXLk5SN2062mPsH+zC0FVu3Jzn5s0FbNthoL+NgYE0kuSjLBRV2rDIDw/PcnV4hmBAo69vVVy5UKhy4eIUS0tFLNshEQ9y6GAPt0YX0VSZ8cllAobKkcN9hMM6lu1w6eIUUzNZUskQBw70YOgK9brN2XPjLC+XNjhVf18TJbHprMmyhNoIqgj4yETP8zPWtz+PJ0PrNJWCYZ1oIkhupUy1aq4LHKW7EhjBTUuKUinXWVkqkmwNE7zDSWjrjGMEVKbHlvBcD1mROPrIDs4cH2Hk8gwd3UlqFZPTb47Qv6eDroa2UbVisjCTI7tc4l/+2p+zVkmmXrPRAyqViklUk5HW9lsRG/2Wmv3G83Acl+xSkdxyia9+/gQv/fWp5j16rkelXCMaD2JbTnNOiiaChNcgwERBQNdVPNfbVPgYYK5QYnSq1gwMRQ2dmmUzlEkzWyiyUCxjOy6KJNIRjRDWNZJhjZnqJJZnEleSpLQ2ZFFmdmyR+YllRs5PEI4FmB1b4rFPHiaqBslbRb+i2prnRpJEPvHJYwSDWlNP8PYabBgKL3z0EA4ew8NL9GdSjW8J5JZKvmZKxeTtN6+zc08HNH7rqYllisUaO/Z2rOuzgIDj1fE8BxDwcHG8HIKgbr7T/Qm2tb25/WzostwQ3/Qd9bV6R6IoIglrMrBrGjAtm0KxSjiko+oKEzfmGijfe3eMg4rKrlgrBbOGJq1uH03TZnJymc5MfEuxbb+ikE+V9jyP3UkVDw9Vkpt0BX1Nm2uz0wBRTVv3WaAxR7iNdm+PSaTxuSgIvmbgmrETBAlJ0LGcFTzZRw2Kgowux9G9OCG5DVGQQRBQpRB4NFAxHpIAESXWoOmIm1arudv+qvlOeN66382/B6FJX7izjfmpFaZuzNPR10L3wGMNjcvbjrUPFbgtCK8qO/1rIeJ5DoIg4XkCluU0KxqZrsVIcQpJlBgIZdCk9fvCO6+/2bO29p5ZOw4exEMG6djWQqye53FlYYFsdSPVRxBDCGKIVWzIKrBDkFZlABpH1rUpaSl8bUlvHQUfQBJERFEgGA2w4/A2RFEg3hZFaBRY2Kw4gud6vuB8A2lx+xm7fc5tZNl/6WpV7zdbKpaZWMqjKzID7SkihvauYzB0/3Z2DGbAAz2oNn9DvaGDFQ5oBAwFTYSIkkB6j4ijkl3hYv4WYdkgJBucy40Qkg3SRpLDiV3N8/bH+klpMVRBxpA0ZqtLyILERzoepkP316WgpPPB9gc5kx1mrrZMV6CVNj3OhzseYrwyR0KN8LHMI0SVEJbrMFKcZL6+wt5oH8OFCeqORVwJM1qaYaq6yL7odkbLs1iew0Op/Xys81HO50YoWGWebDuCuAYxcySxC8dzmK0uEVdCeB7M15a5XpxgZ6SbrFnkUn6UlBbjaGI3qqgwVp4lpcV4Jn0fYTnAnmhvk0quiyr3p/YRVe6e5e1ujfPEUD/jC1k6khG6WmLoqsJ9u7qJhQwEYF9vmmQ4QGs8zHKhwsj0EocHMoQDWvMd+tB9uzk5PMnN2SW6WqLoqkBfOskDu3oYmVkiGjRojYUY6ExtCBw9vK+PNy6NMja/wpND/STCATRF4oX793D8yhiXJ+Zpi4Wac+T+vnaWC2WuTS4QCWgEdJWLo7MUq3UCusqpkSkSkQDRgM79u7uxHYfDAxlEQSAeNvj04wc5c2Oa4clFBjKp5lpxN5seW2J+aoVgRKcvmKRk1ckEY7gNGrmAQEIPEFJUqrZOUFZZqJWwPYdMMMZspcj9LT1YroPluYQVDUOSMWSFFi1IzDHQRIn5apFWI0zNtkhoQUzXIWdW6TRCzFYKdAQjzcBR3qpyavkWuiizL9blr1u3zYMrb4+wTxJo39b2Y5ur3jeBIxERVdTIm8sYUvBdO7i4WGRuLg9AOGzwwQ8N0dq6MctjNPbeK9U5JEGlbC/jehaGFEcWdCpOrrE5qON4JoqSsbYiAAAgAElEQVRkEFNaAAsPj5qdRRJEVDmGLvl8cscLYkgRZEFBEWREQUQU1HVibroiEw8aOI5AUA5TrNYJ6QIdsQSFSg1TdmiPJpDFEoInU6qX/IitHqIsmEQln7qlynKzXVncOpCmihqyoLAjtJ+3lr+HgEBXYDtteic3qtP0JuJEjK35m67rceXKNNmVMum26DoUpoBfelMUBEQgHjCI6hpBVfE3CgGDsKZRMU00RfErNbkuK5UKyWAA03YIKCoRfevr9/W30ZFJNBZy/5htuwgCKIqMbTsoioTjeARDPnQyFNb9oI3rIUoCnuvTK/YNdaOoUrMShuO4BEMakgBHntxLoi26zjG9fc6FCxPUaiaW9e5BkduOzFKuzEw5T1mqIwk+DzWkaLiey2y1QFILgijw+C/s5egL/bRGw7R2RCk3GBt58siNZ0gSJJJaiopdoepUkAQJ0zVxPAdd1FFkBVEQCUhBJEHyaZJrKgjJksjH9u9hvljCdhwSgQCJ4Hr6UTwSwNBVHzbu+dWyXM+jL5NsUgws26ElGUJXZSzb76ftOL7+iSwRCxsYukK+7jsPmrS5SOpPqlmWw9997xItLWFu3VqktTWCgO8cPfP0IOl0lBNv3SCXr3BgyBdLtG2Xv/y/TtDZGSceCzA7m2P/oI+IGBtbwnYcanWLL3zxBJ/9zON0dyW3vH4iEURVZL7/yhUef2w3oui3/9J3L1Apm2S6Enzjm2f52EcOYVoOX/3aacJhncF9XZw5O8bScomf+dhhXn/9OmfOjLFnTwcn37nFwkKBDz4/xIm3bvDm8REODHVz5co0uXepQnSvthZK7G8ktjhRoLlhX3u+KAh4d2z4wX//t9Jd8zf3HpIkbnBURFFEEAXsNfSoXfu7aEnHOPnqNR56eg83r80yM77ML/w3TzYDT67jB2cO3L+dF37+vg3PtqxI9A60sTCbA2Ftvxt92uRebdtF0xU++g8fYM+B7g2fB8M64WigiTpStTuo2w0U+Hrg93prj4bZvqsTz4OlcplUyK+wEdJU2sKh1QCDIDT0RDxERGRRZrE6h+s5WJ5JV6CP7YNdhGNB9hzbTiQRYuL6LJPuNI5pkrOKxNUo4TWbUUEQNqBNlpdLnHpnlOee3088HmQxWyIRMbj9M2m6QkdPCrlB67Tt9QEx13Wp1yzisSBKQ1BXEAQMOcXV3BeIazuRBA0BaFFj6HIrwiaClj+NtlSpcHJqiuPjE9xcWcF0HGKGzv50mge6uhlK+9o/8hpdjEhIJ9MaJxLWyc4X6Nvd2dQ9qJgmVxYXmwi1O81QFPa0tlI064wUlihZdY62dGPI/p7ECKibiuuD79SvVKucn5vj1NQ0o9ksy5UKtuuLQacCAbpjUfa0trIzlaIjEiGgrGaxNzPHdZktFrm5ssKl+QWGlxZZKleo2Ta6LBMzdAaSSY5mMuxqaSFpGM22RMFAEiNNJBEezBSLTORyvlZHayshVaVsmpyanuEHo7cYXclSNE0imkZ/MsETfdsYak8T1jYPaG91z+O5HO9MT3NudpbxXI6qZWMoMtviCY50dnCks5POyPpqdMGQTr1qNY4pzbnQw+Pa4iK5u1S725FKIdjwzrVJHtjbQzig4QGGrKEIMqZroYoC4GtoDS8tUazXCWsae1pbEfDH5vj4OGdmZxnL5qg7NkFFpS8e40hnJ0c6O2kPhxEEActxWMiVMVSZrpZVhLvjuuTrdfLVGkuVMt+/eYuS6SPeFkplTk5NbYmg64kHCalZRFEmKPewGYbEv7bE9aVlTk1Pc3Z2hplCEct1iWoaO1IpDjfGN9F4Fu7U/NgQBBT9SXcin+eHt0Y5OTXFQslH8aUjYQ62t/NYXx+9sVgz8Ol4m8/NP02mKwrlukmpWqdu2Qz1thPSt34PfCqyQGgTKrwgCFwfX8C2Xe4f6qVk53BcCxf3ntFGACktxkc7H9n0s+7AaiXY+5P7mv9/tGWjDu3te+oJpukJrkfdHUvu4Vhyz4bztxLIfiA1yAOpwQ3H+8MZ+sOZTb8TUYI8nT627tjOSA87I5snsje7p8Px1UBZQNZ5qu3ohu9VGvIJruMSjhgossRj+7dtOO/xoT4sJ4vt2hzb2e0HkT144f49m87N/R0p+jtS644FNIXnju5ad2ywt33Dd1tjIX7m4Y3jFQsZfPDYRrmTcEDjIw/sXXfsmcM7N5wH0NUS4+efONj8W8CnrPWl35v0xuVTY02/MhOMsa2tHYTVEJjnefSEfHTmrijUHJvpSp6eUJzpcp6oqhNTV98DQRDoCW28h0zQnzs7AncUIBIEuGOOmSwvU3dM0noUZY34ted52KZNMGI0ZSJ+XPa+CRzJosKeyEGKxjbfkX6XLFg+V8Fu8DCDQY329rvTsIJyipI1hyoGEZEJyAEgRhT/Ab5TR8iQ797e7dO32tgMpFMMpFPNDNPUSh5BEEhFApzNTtAfSpE2ImQS/iYhk/AfENdzyVt+udOYGn5PkENREBmMHWMwtjrxmLaDiMDI4jKiIKyDR6+1YrHK9ZG5TbPZXbEoXWsqaG0Fq350e9+myJt7MSOgYtwRzHk3C2+yEN1u67a5rkfd9jVRQtEAoejm/Z+fzzO5hX7LneZ5HkFDZf+ODrKFKhO1FQwUaq6N6dj0hpJMlH2Rumy9giEpeAbYiktOqlKwarQHWinU63h1Fx1IG1ESjShnSA7RqreujuXaW26ShW//s/4JCWkqIW3ryVDXfOrH3UxVpGYVls1GOBo2cD2PxUKekdwSA9Ek26LJLZ37n0RTFIn7jm5HUWRaUmESiSDXhmcBj57uFN1dyQ0BF0WWqNdturqS9Pa0NHQVBB5+aAelcg3TtLl6dYa52dxdA0ctLRF27kjz9smbzWP1usXU1AqPP7abHQNpLl+eIhIJoMh+dYlnn9nPgaFu2tqifO/7lymX67z2+jCPPLyD3bs6CIcNXvrOeR5+eAenTo/yzFN7OXp0G8PX5/g//+y1/0KjuLnZlks+W8Zx3KazWa2YFAtVki2RJsXpXswIakTjQZbm81QrJuE173d2qUitYtLaEVulh7RFOPjAdk68cpXJW4ucf/sWekBl3+FeJLlBXQmoxJJBXNdl6Ni2TSmCgB84ugcTBIjGA8iKRCQW4OAD2++qLdP41j213bwAoEoSqZAfvGkJrw/iKMbm62nFLqNLBjvD+5BEqUl9FkWRVEcMQRCRZJHune3E7TB1r05IDhGU/XGu1y1WVsqbtj05scz4+CodRZZEVvIV2pN+gsexHWbGl8j0pgiGdBRF4pXvXuTg0W3kVkpcPj/J4OFeiqUabW2r648hp2g1VjfrPo3LasyNPz1z0GbmuC4X5+f5D2+8yTvT05h3BHpeHR3jc2fP8Ym9e/j43r0E1fVrYalcI2iolHIVJkbm6OhN+eLOhQK/8bWvs7IJCgSgP5Hgc5/8BKlAgM5AlIlSdh2aJ5EIsbxU3LAfshyHV0dH+fy585ycmtpwv2tNFAQ6wmE+uns3v3HfMWwaQVRRQxVXaVWT+Tx/deEir9y6xWg2u2Wb3x25QUA5w8H2dv77Bx/gYEcHnlfDcYuYzgK6nEEigON5fO3qVf63N95EFkX+8MMfpi8e509OneKl69epWOsLe7wxPs6XL13mmYF+fv3YMbbF43dNnHieL5T9jWvX+M/nzzG6kt0Q+n17coovXbzIrpYWPnPkCE9u30ao8dtZpk0yHSUSX59QdT2P33ntdV4fH9/y2n/0wgvsjvoUv7plY3gyk5UFynaViBJEF1aomcvo6l4KdZN//f1XODs7y65Uihd/5uOMLC3z+8ePc2F+fh3aCOD4xAR/dfES+9NpfvOhBzmWyRDQVHZmWqibq2N2aX6ePz9zlql8nsVymblSiZq9mpz7wegoPxgd3bIP//OTD/LcTtDYfE/ueR4L5TJfOH+eL1+6zEKptGF8Xx8f5/PnznEsk+HXjx3lSGdns4rbVm2ajsNXr1zlc2fPMry0tL7N2Vm+PXydv7p4kV85dJh0OORTg833Tv/8STLHdalZNvu709Rth0TQwLFd5hYLtCRD63RS78Vsx6+MlYqH0FQZSYkTVZJ+svw9UNX+3zbbdiiVfLTvu9F179U8zyNbqfoVjTWVkLqxqNPf18YnV5idyxEIqOzfmyEQ2DzwZ7l5ytZNDDmDIkXJlWtcHp1jqL+DkP7ju5+fBKtVTa6cXTPXCu8uDWHICv0RP5DWG773INVdx/WOzyKKTibg+xNVxyQoryIAzbpFoj2OEdZ/rL/V+yZwNFq+Ts3xHbEVc5H7k08g3eX2blc1At/Ju5vT6uHheg7txgEq9vKmYtP3qt6/5gv3dloDpdOV9Be9qmNiuTYVx/IfvDsacj2PqcoSWbPIQ6m9iOKPyK8WaApN+pP0Rng1+PpGkxObB062euDeTT/l/WCW43BzbpmaabM707opL9vzPCYnV8hmN3eA7jTX9bhyaw7TtJlbKnJwMEN7KtrUEZBFkf5ISxNSKcCaTZeA0lhcLddhPJ/DcV3iutEMHN22TcfyfTS8qigxEE0SVrWGWN376OZ+RLutK6QqEqrqi67eTV9QlkU+9cn7OH5ihG9+6zydHTF+5uNHsCyHr3/zLOVyHVkWWVws4mxRLfBupusKOwba+f4rVxgZmQcEMhk/s6FpfuW22/fhuq5fCadY5fU3rnPh4iS27dDeHsNzPUzLblaoUlX5//FSw57ncePKDPlsmUQq7L9/o4sszRU4cN/2JvXrXkzVZHYf6OIrfzHBzauztDYSCI7tcPbEDSzTYef+rqZIq6xIDB7p5QffPM+l02NcPT9B34406c5VDY9gSGPvoV7e/LvLDF+aYs+B7magx/N8+rPyHnUvMr0pkq0Rzrw5woMf2ENLejUQ4rouju1uGaC6uwlomoxtu+s0ku7V5mozVJwSZbtIWu+kJ7i9+dnE9TlC0QCtmQTTNxfwVJdCvMCyuUJQDuDhcX14jhdf/CGhkL5haiqX62Qyq5slu+G81ho0V9f1qNcs8tkymW2tPPOhIb711dOcfvsWiipx+L7tDOxqZ3oqS3dXkiD+5jZtrM/IerhU629g2qMoyjYEfnqFbG8sr/C7r7/Byamp5pqSCgRoDQVxPZgrFsnXavzl+QsU6+Y6Zz9XqJL3quiqLx5/WxAY/KBjZ8QP6JVNk/oWwRhf4NkXcF1NEnmUijWqVXNDJdTzc3P89quvMZ7L+co0kkRbMEi8sdaVTZP5UomyZeF6HvOlEo7ni4Cv1AsUrDydRgZ1DZo7X6vx0vXrjOX8wK0AxAyDVCCAochUTIvZUomyaVKxLI5PTPhBlmefoSMSRhQDSF4QYRM6jOt5nJya4uvXrvL9m7dwXJfOSISYrmO5LjOFAiXTpGSafPPaMHXb5l8/+SSJwNb6WhXL4sVTp/jSxYtkG+ggXZbpjEQwFJmyaTFTKFB3HC4vLPA7r71Gtlrl0/sHm9VLp24uEI4Hia3RqhAEgdZQiNZgkLJlUTVNNiOxBnWVdCKMLPl6niHZoE2PIwkijjeF5UygsR4RkG2M8VeuXOXqwgKiINAeDpEwAriex1ypSLZaw3Zdzs7M8L+/eZx/8/RTdATD3JpbRpNlOpL+HDeWzfKNa9ewNyl+ci/mVwWWm3S8O22pUuH3j5/gG9euNYN8MV2nPRxGFkVWqlVmi0VMx+HNiQlWqhX++aOP8nDP3eUIfnhrlN87fpyFsr8vlESRdChE3DCoWhazxSI3llf4gxMneLq//66BqJ8WK1br3JhbRhIF5nMlPnJkNxPTWVZyZZLxIFuADrc0VZGoVE3mlgr0d6dQdZOctURCbUMTN08M3zbP9citlBkfmSO7VMQyHTRDIZYIkdnWQqIl/J78Ec/zqFUtZieWWZ7LUypUsS0HWZEIhHVaO2KkM5vT5isVk9F70HnzPI9yscbUrUWWFwpUy3Vc10NRJYJhnURrhLbOBKGIjut5TOeL5KpVdrSmmoHkrcy2HOans8xNLlPIVbFMy5cQ0FUiiSBtnXGSrRG/P4aCZTrocQVF2XrfISBSs+cQBQNDyWDbDgvZEpMLOXZ3t276HddzWaxnCUg6eatESDaIqRvZQOvGxfXILheZvLnAykIRy3JQNZl4Kky6K0EqHd0S0brWHNthab7A3OQK+WyZetWvfK3pCvGWMB09KaKJ4JY02i3vz/OYHl1iqcFy+nGb63oszmSZGlsiv1LGthw0XSHREqZnoI1wbGvN57prM1lZJqzoxNX1z54W0DCrZlMm4sdl75vAkeWaJFRfY8J069z2kD3Po1o1qVZNajWbet2iVrMYuTHfhLbX6zYjI/MsLhU3tBsKarRnwsxXL+N4JgE5SZxewKcbLSwUyGbLvshpdxJVlXEcl7m5HKfeGeXy5WmWlot4rkckatDdnWJoqJvdu9sJ3lG6r1isMTOTxXHcde3dDh6AHyhq06M+XM2DxaUii4sFAMJhnc5M3KdsbBFuL5frjI8vcfHiJDduzJPLVRrltGXCEYPOzjg7dqTp6kqSbouCLOC4HuXGZue2ua5HrWZSKNQol2u8deIG2awPw61UTK5dm9n0RRWAVEuYlpaIn5Vxq9ieiSGFEQUJ13VZXCyyvFxCUSS6u1NomozruszN5Tl9apTLV6ZZXCjguB7hsE5Pd4r9Q13s3u0jIzYz13XJ5SrMTGe5cnWG8fEllpeKfmlhRSYeD9K3rYVdu9oZGEgTDK5GXSVRJKAp1EyLQANt4zgulYpJsVgjly1z/M3rVBpioblchStXpje9D0kSSaej7OpLkytUiIYNWiPhJmTfdT1mZrIUtnDiRFGgqyuJHPSr4eiyTFw3aAsG8TyPSsWkbtrEGxNFNlv2KZmeR1d3kmBQo1azuHZtlnPnxrl1c4FCsYqqyLS0hhkc7GLfvgwdHfF7mmhd12NxscCF8xNcuzbLzEyWet0mFNIZHMywa1dHE4mx1oyAStaos1Sv8HB773vONP0kmLDJ/zzPz5LZth+csW0HSRKxbQdBgA88uYcDQ9386Z+9xsMPF8jlKly7NstnP/MYngc3by402vEpVn5JdD8YITVKZLueh2W7OI5f4UKSRGRZIhIxCIU0Dh3qIZUME48HyWZLrK+e4ZumKWzf3kZXV4Ijh/twHb+qQjhs0JKKcOPmPJ2ZBOPjSxRLm1MdPM+jkKuwNF+gXjVZnMtjmTa3hueo1yx0Q6Wt812QmZuYJItMjy/z4r//Dkcf2UGlVOOlL5+itSPGEx8eQnwPu09BEHjig0OMXJ7hz3//ZabHl2jriHHjyiw/+PZ5jj22k8HDvWs0TgR2DnbR2ZvkB988z8zkCr/xLz60TgBVD6h88JNHGb44xR/8669x/xO76OxJUS3Xmbi1QCwZ4uc+89h76nNrR5yP/eKDfOGPX+Hf/4svc/8TuwiEdLLLJcZG5nnoA7t55NmNMO137z90bWtF02S+9p9PUKuaaLpfRejg/f0bKLl3Wqfh0ylrbnVdEiO3VOT868Nohkq6J8nE8Cy7HthGvC2K6Zp+JaRGFaJHHtnJgw8NbNjYTE2ucObMWPNvz/MIGRpL+TIt8VAD9u7D5UVRYMfuDrp6UhTyFVRNIRoLsLhQoFa11j3fZXuO2cpb1J1co+SzSrvWjirG+Wm2Qr3On5w6xTuNoFHc0PnU4CAf2bWLqK43KWGvjfmoo69cuXJHQs2jXrexbYd0dxI9oDXftY5IhD/48Ieo2TZ12y/p+8b4OH/01tvr7iFbrzBWzNJqhNDl1QCdokq0tUXXvbum4/DiqdPNAM99mQy/euQw2xIJDNnfF9Vtm1ytxsjyMsfHJ5gtFnluoB9JFAnLESp2xS9gsqYf2+JxHtvWhzw2zuHODh7q6aE/kSSoKiiShOk4zBWLfO7sOV6+cQPbdXlneppvDA/zK4cPYztZZCGMuMn21/U8/vrSJSzHYXsiwWePHGEw3YahKLiex0yhyN9cvszXr16l7ji8PHKDwbY0v3zo4IaKU7fb+8qVK/zF2bPUbJuAovDcjgE+vnsPXbGoXwXPcRjL5fiby5f53o2bzJVK/P6J4yQCBh/euZNIPEjvrnZ/X7gm8ScAv/XwQ/yjo0cwHYeabTNTKPDvXnud2eLqXnilUOHS6BztyQjBoMJkxV+DtoXa0SQRz9tIzV8ql/nDE29RNk2G2tv5lcOH2JnynVfX81iuVPnrS5f46pUrlC2L87OzfOniRX7j6DGqpoWu+NRSF5eh9jZ+/0MfauZdHNflry9d4o0GUuqh7m5+dt9eVGlzd2RnKkDNPg/YOHIViVXaYd22+dNTp/nqlSvUHYf2cJif3buXp/u3kzAMv8y3ZXF6ZoYvnDvPpfl5ri4s8h/eeJOuaJTuaHTTROJELs9/fPutZtCoP5ng148dY386TVBRsFyXuWKRL5y/wPdu3OBLFy/+vQNjP0kW0jWGetrRFZlcpYYkipiWTTCgNeear/zZ67zx8iVce/Px2LE/w6/9jy8gSiKW7dDdEfffW8shICoUrRxRJdWc88dH5vnT3/02xWyF+z6wm4/+4kOUSzVe/ptTnHj5MvPTK1TKdVzHQ1ZEjIBGMh3lkecG+cDHDtHSHtvS6fY8j2q5zui1Wd55dZiL79xicTZPuVilXrNwHV8GQ9UUv9BHR5wnPnKQR54fJBQx1u0rqjWLcMTYNDnvOC4TN+Z55WtnufD2TRbn8lSKNSzTbhbtUTXF13qMBxkYzPCJzz5GKhbg/ybvvaPkOs8zz9/NlXN1zmig0cigAIKZhBgtUhZlUWkkm7Z3LXmOxsdez/qc8Xic1j7HHtuzsx6vvaNjK45lSVS0JJKSKFJiJkEQIEBkdDc6566cbt4/bnV1F7obDPYfEv3+wUNUV9269d3vfvd7n/d5n6diGE3mEFeff6lQ5fjTF3n6sVNMjixSyJbRqya27WkmyoqEL6ARjvlJt8U4+t4D9O7roqsrTn9fulEEu/T6FJ/5i8cwahaRRJBf/M276Rnyo8ltiIKMi2eoIUkipYonnq1XTf7nn36H8UvzALzno0e45YG9nM5dwnJtVFEhoUY4qHr54l//3jeYuLxAS2eMX/tPD5BsjZDPlHn8kWM89/hpb1xKNRzbQZIlAkGNSDzI7ffv52O/cdemY2CaFkuzOU48f5lXn73E9NgS+WyZWsVo7MtlWSIQ8pFIh9l9qJ/3fOQIvYOt9XbUjVGrGkyNLjI/lWHi8gLjl+aZGl1kZdHL1ctFnb/6na9uWeRUfQq//vvvoXNbFHCRRT/iJprNlmUzcnaGJ799gtdeGCG74rHkG78/5KO1K86Ro8Pc+eB1tHZuZLfG1RBJLURA0khq4aY5KQoCmfkc7f2bg3xvN35qgKPtod1U7DJlq0BvYDtiXaugXNL5ylde5OTJCYrFGqVSjVJJb2qpmp3N8vu///VNj3vo8AB/+qcP0ebfQ8YYQxKUxgZZr5l87ZGX+ed/PkFbW5T/+hcfIZ0O89RT5/jG119hfHxpE8fFSzz15Fn+6I8/wPbtzcDRmden+Ku/eoxcrkJXV4I/+/MP0dERb2rfyhkVLhbmiWleheoHPzjN5z7rtYrceusQv/+HD6KJCpqoNG2WHcdlYmKZf/rSC7zyyhjF4tZ97YIgkEqFeM/9B/jQR45wuKeTyWyOllAIQRDI5Sp87WsvMz6+zMJ8nuXlYtPxxsYW+a3f/MdNjy1JIh/7+E08/LDXT7xYu4Ii+tDEIKIgYRg23/rmq3ztay8TTwT5y7/8KB0dcZ5//hJf/cpLjI4ubBjTF56/zBNPnOEP/uBBdu/Z2PtbKFR59Huv8eKLlxkdXaRWMze8B4AnPWbGDTcM8rGP30RfX5rJpRwV3cR2HIa7vDGfns7w1a+8xOJigYWFAouLhSbHp5dfGuXll0Y3/YpwxMdv/dZ97NzTycjkEoZp05oIE6pTPU3T4nOfe4af/Pj8pp/3+xX+5E8e4uB1fdiugybJJP0BVMkD16Zns2RzZY4c8vqNn3vuEv/v3zyBbTv859/7eYZ3tvPII8f40Y/ONICu9fHED8/Q2RnnYx+/mTvuGL4mM0LXTV56aZSvfPlFRkYW6s4igqd75Lg8//ylLT976HA/v/k795GMBZH/pay4n7IQBAiFfJ4tsV9F02QURSYQVFleKfGTp89z9uwMhmHx+S88x1137SYeC/Cd754kX6giCLB3TxetLVGCAY10Osy3//kE0YifttYofp9CuazzgyfOcPHiHAuLeT7/hWfZv7+HfXu7+dFTZzl/fpZMtsQXvvgcu4Y7uOXmHRSLVRYWCjzz7EU0TeHA/h66OuNEIn5kScJxPSZMOOwnEFB533sP8t1HTzIysoAgwP59Pdx26xD33L2Hr3z1JUbHlgiHfPR0JzZ9gDq2w6vPXear//C0l3DWxZs/+39/H0VTaO+K8+u/+wCSLBJPhRsghepTSKTCSLLoOScFVOLJcIP1oygSRx/YDy488plnMGom24Y7+IWHb6Z/x5qugCiJxJIhVE1G1y3kuujyKkNHlDwNo2giyK/81j08+tWXeeLbJyjmq6RaI7zng4c5+sABFFVCr5keAKdIBEMat927ly9/+if0bW9leH+z5pAgCHQPpPk//q/38/2vH+f0sSs8/fjr+PwK7d0J3nXzdiTZA/PiqTCavy5EWf/dsiyCAP6gSizprbm2bXPrPXtIpML85LFTPP714+g1k1DER/dAmmgyhFMXvvaHNCLxjQwGf1AjGPI12E92HXDp39HKL//mPTz5nZN86e+eQlZkBofbGdjZgea/tiHCirFExSpTsUsktRYiSh0IdD0xcce2qZUNBvf10DqQYN5aJKpGCCteZat/IE13d4LUJtVdn09pWlclUcS0bUoVHcuycV3QqwbUzSwW5vM88+Q5MstF7n//u5ifzZLNVVDUZo2rK01/kgoAACAASURBVMVHAYG8cYUW30FyxiVatVYEQYN3sMbRK9PTPDEy4glMKwqfOHyYXzp4sEljrj0cZldLCy3BEH/81FNNbVayJCHLEuWqgc9xsS2bVcqRKkn0xOqOp/UHdH4T7RxZrGtjrbserguZTJmAX2U9LXM6n+fi0hIAcb+f/3jLzVzX4dnCr58r3a7L3tZWHhweJlurEatrIVbtCoaj47h2E3NKk2U+cfgwDx84SGc00jiXVVFkVZLoikToj8fJ1Wq8ODmJ5Ti8MDHJB3bvRpVcyuYFfHInIht1F0uGwe6WFv7k7rvY19osKNoRDrMznQJcvnn2HGYdBLmtr4+hdLO2h+u6jKys8NlXT1CzLCRB4OGDB/n3R67fYFTSHY1yoL2dlmCQz584Sb6m8+ljr3CgvZ1gDebGl9lxsJkhIwgCraEQraFQ4/u6o9Gm9kSAdCxEPOwluYogsSvahwCEZB+WXUGRu+raYGssM9t1Keo6Bzra+ct776MvvuqG67Gq28Nh+uK3YDkOXz9zBtt1eW5iggeHhtHWMRgWasss2Ivc1N9PWPbWDMtxeGlqsvGermiEuwcH8cubpyO2W2O5GsCws5TNKcLqIAISjuvyyswMXztzBt22Sfj9/Ofbb+PuwcEmm2/XdemLx9nd0sJvPvooY5ksp+bn+da5c3zqyJENTCHdtvn2+fNcXPLabFtDIf7o3e/mhu7uxrgDdEUi7EilCGsqj7x+5t+EvpEkCkwuZ2mNhokFfeBCoVijqpvsHGhFkmB5ocDImWkMw9qUpe0LqI2XvbkiUNVNQgENEZOIkkBZp+dareiMnJkhu1xEViQO3DjII5/+CS8/dc7TN6w/i13JK8AVchUKuQqTIwucfXWcT/3hg7T3bC4N4NgOx35ygb/9429Tyq8VewVRQJYlZEXAsV2qFZ1qWWdpNsfF05OMnJ3mY79xd8Pp1bYdT+NqE6mPcrHGc99/na9++sfMTa40/U0QBARRxLEdqmXvO5bn8xTzFd778RshJFI2zE0lQBzHYfTcHF//h5/w4o/ObTCsWWXVGLqFoVsUsmVmriyzbXcH+2/bwfRMFl036+u2B4ZcOj1NrWqQaIlQKelIQhJZDCEKav18vZbzntZ44xzGL89z4ZR3Px959zAiAm2+FEt61rve0hpDa/zyPBdPTbEwm6WYr1CrGnzx//khL/zwTGPsRMlzuLPq17JYqF6zmDg/leGv/8s3OPfqRNM4yYqEqsq4gGVa5FZK5FZKjF2Y48zxK/yHP3qQnevY5OtjcmSBP/71L5BZ2khGAW/erIJlm4UvoJIvLCJVziMIIgltN0GlWTPLNCx+9K1X+fL/9xRLdZddQRRQFO85bdsOhWyZQrbMyJlpXnthhF/73QcY3NXRNB6qKGE6Nhm7RMYo0SGtgUuO7aD6lPqz/l8vfmqAI8s1uVw8gyCAbte4LnELMp5Q7+WRBS5cmHt7B65PpIq1giaGCSmtTZTX1Xmm6xb5fIVLF+f4/OeeYXm5hCSJRKN+VFVG100KhRq27dDWFiOd3qhS7647XvN9buPaCyD4CMp+XFwWqwXSWqhpYfVYRt4LhmN5B6nvLZYWC3zmM09z7OURbNsTg02nw4QjfiRRoFo1yeUrFPIVXNdlacnTGyjUdM4uLFA2TMqGQW8iRrFY5Yc/eH1LbYq3EpZrUDJyJLROaLQIeL/BNGzyuQpTUyt85jNPMz+XQ5JEIhEfmua5OxWLVSzLobU1Qkvr5nRGXTd55pkLXKrfqKIoEIn4iceDaJqMYdgsLOQpl3VqNZNnn72AbTt86lN3kYoESUWClGp642ZaXCjw+OOnNgEF30TUHYum5rPohoXjuqxn5wuCQCIeJJ0O11ly5gbR19XwSTIBRaGgr27QPaetWs2k0fnlel/qui5nzkzx6vErPPXUOUzTIhoNkEp57lSroILjeG13X/zCsyTrTlqb0TJd1+XYsTH+4e9/wuxsFkkSGRhIs317K8GgRqlU49KleSYmVhqLsSgKhEI+orEAXd0Jpip5yjWTlkCIt+LM89MemqbwoYeuJxBQSbdEkERPeHlgII2mKrz76C5uv82j9puWjSt6DKyHPnCYas1Eqs9PTZPRtBAP/9ItVKsGkuRVa4IBDVmWuPXmHdxwxGsNWsgUqRgmDq7nipYKcuPtQ/S3JwgEVObmc4yMLHD7bUNEowGmplZ4/Pun+Y1P3c0v/9IthMN+ZqvjdPe18rGP3gjgAYgfvYlyRfesicN+JEmkpzvBJ37tKIZu4g9ouI67wbYYvAf49bcPsXN/96bjJMkiiXrrxO/82UME6+1y+w730bf9w6gxl4w5z0137mL/kYG6/tAKjuMSiwe59wPv4j0fPIzjuASCKqFooGmuhqN+fvtP3s/ohTnGLs6hajI+n0qpWKWYqxKO+YnEAlTLBuVile7+NO3dCSzTJtUWRfMpTI8tMjkKfr9Ke0+CZEsEURK54/79HLhhG6qmEE9tXMtXwaNf/A93Ui7WME0bURLQfAqhiDeOXX0pfufPHmqIfh68cRvbdrV7oJkkctu9ezl8yw4CQY0rIwsU8lWuu3mQ4QPdVEoeRT2fLXP8xRF03cK2bGzb4aFfvY2uqza6qipz94PXMbSvi3DMA5VGLszRv70VVZW5/ef2ct1Ng+g1E8uyOf3qOKdfvcJdD2wuALoaiqgSVEREQURZVxGLpSMcfeh6ZEVuAIJZI49e9tjAJbNMRA5voOW7rgd+CYJANBrg+iNrrW+eM5NLRypMPBygaJVpaY8TjQcpl3Qe/ear6IbJ3HSWXKbMyKV5bNdl557OpjY+y6nRG74HF5e+8D1czpcxnSKaqIL71kRVf1bCcV2evjLeAIJ2pFI8MDSE76pkW6iLZ7572wDfPn+OFyenGn/TNJnO9hiaKlNaLrE8l8NxNgq1XwtoDCsaHYEIbYE1VxfwEspMpkS/k268VjHNRstbWFWJrxOo3uz7JEEgta7ly3ZtFFHFsgUu5JbQJAndtrEdh5ZgCBuH0UwGTZYa4sRL5TItoRDpQIBkIMCdAwO8NjdHxTSZLhSomiY+WUWVWrZsrRYFgfuHhhhOpTbVsAhrGg/t2cMz4xMslErMFoscm5lmRyrZ9H7TcXh2fILpgtfeMJhM8tCe3Zu62wqCQFhV+fDevfxk7Apj2SwjmQzPjU9wR7KTWPqN9S6vJSRuWQ5SfW01HYuFWpYOfxw/5TWR8Ksiomn8u3376K2DRmW7hOHoxBXvd4ZUlTu3beOJkZF6O1iJlUqF3missWdQRZUlPUN/sHtTaYY3c/6CKyIKCqaTRxYDa4Vfy+KHl0fI1WqIgsDNvT0cHRjYAAStMv6HUinuGRzk08dewQV+PHaFj+zdR1u4ef3PVCo8PzGB7bpIgsDt/X0c7OjY9JpF6nPh6SvjzBQKW/62d0oIgoAsSZyamCMVCbKvuw2/X/EY/vX5te/IAI7jUMxVqJR09KrBxOWFBltjfdiOSzIaoLM1RsCnkjUXiSoJglJk07kyNbbI1//+aV55+gKSLLF9uJ2hfd0k0hGv8HplibPHx1mYyWJbDq+9OML3HznGw79976YAgSiJpNuiDSfISDxA72Arbd0J0h2eK2ulrDN+aZ4zx8Yo5quYhs0zj52ma6CF9z98C0K98KEoUlOnA3iF5Ce//SqPfPonjd8vCALtPQm6BlqIJoJoPoVqWSezVGR2YpmVhQLbdnXQ2ZdmoVpmuVympOukgs3tSjPjy3z2Lx/j9VfGsOs5RiDko3+ojVRblFDEM1gqZCsszmaZvrIMuBy6bQjDsDFNe0uH1tVwXAPbKeEKFgICVcMiFQ3gu0ZbvQvUHANZlGjREgyENpIByoUqy/N5nv3+67z05DlkVWJweyet3QnCUT+26ZBZKjB9ZYl8pszwwZ4t14doPEgk5nVsqD6Fjp4kXQNp2rrihGNBHNthcTbH2VfHmRxZAGD84jzf/OyzfOqPHiS+iVW9qin072ynrXut1b5UqDJ9ZQmnnn/37mjdknGk+RSC4TDgjbksNu+vLcvmlacv8k9/+2Sj/a2jN8nOAz209yRRNYV8pszY+VnOn5xAr5mcPTHOP/6PJ/jk772Xzr7mIoVYd3S7+p4RFYl0VxJ5nbnIv0b81ABHM9UJClaWmJLEXtepHQxqfPzjN3P//c0b4HNnZ/jud09Sq5m0tEb42MduIrKJWHI8HvQsk90oOX0Sy9XxS4kmNyrwbvDnn7/Mc89exLIc7rl3L7ffvpN0KoyieDTK5eUip09N0tmV2FJMbGO44JZwrAsgtlC1OgjIGooobfkQFQURy7UbmJJl2TzxxBleOTaGbbv09CT5yEduYHi4A3/AcxgzdJNSWWdycoUTJ8aZm81x442DxIN++hJxqqbVoFInk2F++z++p8k97OSJCb7znRMAdPckefjhW5EkT9fFcVwvoauLgfWsS2oU0b+lk41l2bz08gjHXh6lUta5867dHL1jmHQ6jKrKGKZNNlPm1KlJUukwkS3a1JLJMDfdtB3DsNi5s4Prrx+goyNOOOLzHNdMm7n5HN/65nFefnkU07Q5dmyU3Xs62XNDH4WKjoNLd8rbAPUPpPmDP3x/4/imafP00xd4/jmPYXPgQC/ve3CjU4LremyJwcFWcpUar1+eBYRGsuu4DlUM3vvBg/zce/ZjGBaGYTE3m+Nzn3uGpavQaxevcrtK0XZdF0kS6nbWG8fhR0+cxTAsQmEf73vfjRw+vI14PIAkiVQqOsePj/Plf3qBTKbM7Kw3Hv396U17rpeXS3z1Ky8xO5tFlkXe97538b4H30V7exRR9HRyZmayfPGLz/HM0xewLIfrr9/GRz5yA4lkiFBYY8ooUN6K/fUzHJlCBcu2McsO8YiflXwF07JpiYdQFdlrAcW7Xku5Eiv5ilfN9cmUTRPbdRBlkVLVIFesoqkyyWSIVy9OY9sOe7e1I4gCOg6SIpKOhxBUkSePX2ZHfyvpdJi5fJHxuSw3tvcjiSL5QpVCqYZh2JTKOiuZErFYAFWV8PuDrBgLPLf8GB3+Pg7GbuVC8QRlq4giquxMH2S0dJbL5UU63QFc12bGugIS7PEdYaE2zZnMHGlfB3ElzaXiKURBojewnYAvzFTwFI5rsy92EyOl01iOhU8K0Bsc4vXiC9TsCr2xISaMZbJLywiCwHDqXVwonmSuOs6+6I1sa9/TtN65LvgDGv5rrKOSJJJuj1Eq1jymZKbEcj6PP+itecGQj1giRDG/hG05RGIBXEDzyXT2psgsFamUdBRNIhT1E0t6CYIgCASCGoFNtArWhyAI+IPappoGAIoqNzSVgA3vDUX81Gomj37jONlMid5tLVw+P8vYpXn8fpUD1w9w8flZMisltDo75+SxMdq74gTDPn74nZPouoWiSNxx7x6mxpcplXQEYGp8mW996UX2HOzlljt3MT6ywPTkCpFogJuPDoMgcPHMNJZpc/bUJJ09SZYXCmg+hYF1rK6oEme8PELBzBKWmx08XMfllR+9Tn7Za2E+dN9uIpEIZbtCRGlmGLmuy+Jige999zXGxhZ5//vfhc+voigSQ0OeAYUsiXSlo0SCvgarUfUpKKpMIV8hnyvzq//+Th750gveePpVRkcXGd7bhSCsJZZhpRvb0VGEAOdzX6Jmr9AZuB9JlN5ROmvrI1utcmp+rsG8OTowQDq4uZaGIAjEfT5u7unl5OxcQ4hYU2V0wyIZD2KWdA+MeBvjVbIMTqzM8K5UJwHZs9YOR/zYtttUCAtrWoNFslAqcXxmhs5IpKHb80ah2zXyZg7FifH64iKDiSS6ZXlaWbZNyTAomwaSIBJSVfyy7OkPmQZxnw9VktmZThNQFCqmVzirWQaKlMBxdbZipyX8fq7v6kK9xnluTya5rqOdxy9dpmqanJyd5f27djXpkORqNZ4aG8N2XBRR5Pb+PnpiW7fNCIJAfzzOLX29TObzmLbNk2OjHI61kGqPkWjd2Fb1ZsLbV4iMzqyQjAYRBZGFWoaoEsAv2xjWJJq6e8PnhtIpbu7tRRQETMdkojyKi0tMSXgpiiCwI5UkomlkqlVM2yav62SoEPJ766AqygRk/7/othQEiaDSDQgE5E6EulPgTKHAC5Me00GTZe4fGsKvbK1vJokit/X18blXT6DbNrOFAheWlzYARyOZDKMZT+8z6vNx17ZtGwDatXMT2J5Isr+t7d8EcAQQ9nlzvFTVvVaverFstRJ75OgwR44Oe65Opo2hW/z9n3+PH3ztlQ3HchyHkckl5pYK7N3RgeAXyJnLaFKA0CYu0vlMmed/eMZrF//kUW6+by+xVAhZ8hhmpmkxcnaGf/jzRzl3YgLbcnjuB69z9y8comsgveF4giCwfW8X933oevxBjX1HBmjvSRKO+pt0DfWayStPX+CL//2HTF9ZarCI7njgAIl02GOHFKqk0msghOu6vP7yGF/+u6fIrXjP0JbOGPd98HpuuXcvLZ1xVE2us6g9VtPKfJ6p0UXi6TCaT8FnyvQl4hukICqlGl/6mx9x6qVRD7RSJQ7dNsT9H72Bwd2dhKMBREmsu5Xa5DNlT7tpocDgrk6KFZ32thg+n3JNQEEQJBQpjoAHPCiSSM2wtmydW/3dhmPQ7kuTUCOom1xHQ7f41uef5fyJSfp2tPGhT97B8MFefPX9k+u6mIbNzMQy+ZUSqc441aqBZTtIdddur8VdRJAlbnvvAULxAIduHaKjL0VLexRJlvD7lbV85soyn/tvj3Psxx6x4PSxMc6dGOe623diOw4BzXuWGY5FuCvAb/7XD6Cs08F77cUR/uYPvkmlpOMPanzidx+gd3vbht+2Oq+kQJmKkySkdKFJzZIOY+dm+cf/8QTL83lkReLGO3fx0U/dSfdAS0M2YbUN8cfffY1HPv1jVhYKnHz+Mt/47DN84ncfaIBWIgId/jgZo4QmyU3XRZZFTzrH99aMp94ofmqAo7AcZTC0GwGBhGs3WtUURWLPJu1LjuPy+OOnAA9cOnRogFgsgOM4aJrSVLl26xRcTQpTtpYa/14flYrBo987ic+n8Mlffze33jrUcPdZvRDbtrVw6FB/o53nTYULrlsBIYAopQkIAYKSSqs/wmaC3q4Lfkklra1tMIrFGmfPTmNZNoGAyi984DB33b1nUw2bbdtaue22ISoVg0BAw3FdRpczqPIaFd3vV7jhhm1Nnyuta1WLRvzccst2ZFmikKuwMJ2hd7AVdRO3I6UuWrkZeKTrFo8/dgpVlfnVX72dO+/ahd+/SnkUWJzO4GoSd982BLgsTC4j1oU3/UEf5XzFa5dwXA7t7WKwO0FPr+cCU8pXsESo1EwUTWF/3VHKMG2OvTyKrlu89tokd9zlbYh0y24skLFYgFtvXbNtrNVMxuraM4IAra2Rpr/XLyP5Wo3pYgEtohLEIR0PEYusoe+O63KpOIdPUxhKdaBK3mKbSoU3dYwzbJuCrjco9l7VQiadimyqcFUu64TDPn7lV27j3nv31jVxVt8VpqMjjmM7fOELz1KpGJw7N8Pk5ArxeBDXtQED75aXGR1Z4HIdfe/uTvLQB68nvS6REEWRrq4EH/7wDVy6OMf0dJYrVxa9qnVnHNt1sFfyCAjX0oz+mYxHXzhHRypCWzJCoVLjhdNXEASB3QNt7O5vQzdtZElEU2RmlwucGZ2juzXGa5dmuDy9jCKJHNjeyexKnsVMiaHeFnw9LYzPZajpJl0tMSJBl7Nj80zMZfi5m4ZJRAKE6yCKJIoko0Fml9Y2ol2dcT700PVcubJEIV9l51AHe3Z3NdaAhJqm1dfNnugRQnKUkpUnobbQF9yJ5axpxExXRogocdp9vVTtClljCXBRRJWAFEJ3qoiCyEBomInyJXqDO/BJAa6UzlO2ChTNPNtCu2nxdTJZucxUZZS4kmKuOkHNLtMf2sVMZYyKXaLLP4AiqPSHNlrYvpXoHfTsdEMRr00rmgiC6yKIXvISTWxMoAVBoLUueC14L7zp5MV1XcYvzfPSk+cY3N3JwZu2IysS1YrOM4+dppSv8u73Hdy0UnV1TI0vkWqJ0NGTYGWpxKsvjuIPqKwsFRna08Xw/m4isUCDVdPaEfP0GhyHbKbM7ffs4aWnL1ApG3T3pTh5bAwX6OhO0DOQ5pY7dyErEq+8cJnWjjgTY4vsP9zf+H5JlvD5VSZGlyjkK+y9rrndRbdrFM0chmOgO7WmTeTFk+PMjS8jyR7jbn55iUBYo2JVKFllwspawuU4Lt9/7DSy7GnA5XIVfDWTiYmVBnBkWTaxsJ+WuDdutuU02vwUVcZ1YXYmg2lYlEo1ZqYzJFMhpiczdHUmcP3euXUEb0YUFPxykoXqCVr9h9BE0XvOvkNjoeSxOQBkUWR3SwvyNXTlBEFgZzpFQFEawFG1ZhLt8O6hQMRHS+eb08G7Omq2ScGoNVpzVpm7kYiv6aHVEQ6zu7WF8VwO3bb578+/wGQ+z33bt7MtkcAny9cEQhRRQRM1QqrCPQODRH3aBpbwVSajGLZN2TTR6sWYoKo2ki7XdbEcA8sp1J+Hm1fbEwE/HZFr39thTWN7MsUPhBEc12Uil6Oo603A0WKpxHjWa9cIKAoH2jve0HlUEkUOtrfzrbPnKBoGl5ZXmM8VCZVcAiENza++ZWFXVZboaYmRjAa9cXJdLNfGcLxWd6/Fc2Psbmlt/B5PVDuCfZUeUkhVketzyMXT0kQD3bS8Fl3XwXKspgTsrYbr2pTNac85cV1cXlkhW3cBTPr9DCTe2LUoGQgQ8/tZKJUo6jpTudyGxHlkeQW9fs/EfD52pFJbHQ4ARRLZ1ZLmsUtbt/a/k2ImU0CRJTKlCo7r4vcpBDZx2Fo1GFltGdos/D6VlmSEUqWGLIlUnCqmY2K7ZqMt8uoQRYGf/8WbeM9Hb6BoGVxcWma4NY0kCCiKzNC+bj7wv93G6PmvoFdNlubyXLk0R6wziuM6ONAwrNEtC7+icP//fgu264AoULAtJNPEpyiNlkefX+WGd+9ieT7PZ/7iMWzL8dhBi3kS6TCiKBIM+bCtNQOiSknnu196sQEaJdJhfu0/PcD1d+xEUZvXvtVCWGCbRvfAmh6NYdvEA/4m4MhxPCbVK09faHzXrT+3j1/+7ftItTWDy0J9TFKtUZItkcb7p2azVKoG8U1a4teH61ro1iKa3No43uxygeEeowEOb7g+gkCXv42cWQDDJaltroP52gujDO3r5pP/5b3s2NuN4zhcuDhPwK+Sz1eQ6sXXgd2djIwusLRSIlM3MBIEoa6zqRAM+qgJcN3duwlF/IxPZ1jKlWlrjdLdnUAU6/nMQJoPffIo45cWmJtcoVrWuXh6Cl9fnHy5ypGdvQR8CnmzzKKRoy0cJ6at7XGC65zJBBFC0UCjGLlZmA5UdRHDKRKgde2a6ibf/9orTI56+deeQ/08/Nv30dGb3HDtwtEA93zgEKV8hS//3VNYps2LT5zlvg8eZvueLgRBYKy0SM02WdaLpLQwSW3t2aVoCkOHBjzW5b9iUe2nBjiSBAkRkZpTYUmfp93fBWyNagpX/b9t2Zyquwcd3N9LYF2i7roClmOgiH5UMcTGlNzb+JbLOg++/xC33z6Mpm0cGkEQ3tZGSxCCOPYcorwNw7GYreboCSVJqBsTHheXrFHCdu3GWZqm3dAg0jSFtmsozIuigCjKRKPe+euWhe04+GUfap2OeS26+LpXvJuz4omEdfQk0TbQ8lyK5gqK6GezZmbXdSmVdD704QPcdffuBmi0GpMX55gemSfRGkX1qciKRFtviovHx+jc1sLidBbbstl+oBeraiBZNhPnZ7BMG3/IR7VUQ/OraH6V7u1ttLREuPWWHbx6/Aq27ZBZKWHqFqIoYtlm/ZxWf9s1fruw8TXHcVgol5gvF0kHgmiKTLZYxbRszDpNtGJ5gnFxdU3351o3rIvnCJKrVdkWTxCWVYrFGrWauSWh+/DhAW67bahBrV0fsixx402D/OAHpxkbW6JWMxkfX2L//h7AxDBeQxSjCMJ2xseXsEwPQO3rT5NKbaw+C4JAW1uU3t4009NZstkKly8vsKOeCIYUFU2SEN9hvSGKLHHdUBfJaJAXTl9hPlOkuzWGC8wtF3jlwhSdqQiHhnvobolxZcbrWxcEgV19rUiiSKFSY6inBct2yBQqBHwq/e0JRFGkpy3OxYlFKjWDbKlKuWqQiFz7AS7LEsM7Oxje2bH5G1wRSZBZqE0RlCKIiPilIJIgk7WWmKlcwS8FAQFJkNEkP6Zr4uIQVuJU7TKjpbMMBHdRsvIs1mZQRJW56iRVu9RgckiChE8KIAkyfilEUA4TVuK0+rqYqY6hij4kQcZ1HRRRpWjlyBqLpLT2t309VhOlRCrcAIvWhyRtNf/e/rxMt8eIp8KcOzHBviPbGpvf4QM9/NPfPsmh24beFHDkD2hMj694n6+7ZDi2Q0d3gngyRK2uvSRKItWKwexUhlrVYHCoHX9ARfMpSLKEaVrMzWRZmMuxvJCnrS5+f2VkgW1DbSTTYTRNZueeLhRZYmZyxdMIWinSO5DmB/98kvauOLGrQDa/FGAospf56jR+qZnxKUkindtaWJnPgQOmYeK4CiE5tIGx6zguS8tFPvrvbuTUaxOAp3FUrRpNx8vVhfzbEmHKxSrZpSK25RCLB7ju+gG+9qUXmZnKMDW5wo6d7Qzt6mRu3nOeXI3Z8vPU7Cw+KU5AbkGTIgi4qMp2BP51K2s/LZGtVinq3liGVJWY/43tdVtDoaa2nVJFZ2YuR2e7QDTiJ7gFw/daUbNMypZJTq80rokgCKiKTLZUajonRZL42P79vDY3z0yhwGK5zGeOv8rjFy9xx0A/t/f3s7ulhcQWLWyrRSmfLBNSfF4SedXbPDDIYaVaJV+rUTVNapaN5ThYjs2VbLYBAgD1yrmDO4KHjgAAIABJREFUJIa2bNEKKCpRbWPr7voQBYGWUBBNlqmaJkvlClWzGdiYyucboJ0qS3RHr+0stBrd0VjjulVMk8lsjt6qysTFeVRNIZLYOlnZLBzXZXal0NAlMVyLlBpFFRVEMYwstW6aoHdEwg2WuiiISILIsr5CX3CwaRwan6238SuyhL8OFEiCRIe/BVVU37pzcSNcJNGH6zYXLidzeQ+owgPcXp2Z4Uome80jLZZLDdae6TgUdGNDkW62WMSsz+2WUOiaLKbV724Lv/Gz4J0Se3raKOsGE0tZBEGgXDbIF2sM9KTesmBBuapT0018qsL8coFkm5+YKqGKW99/HX0pbr1vH4oqYRsO2Wq1CVAWRZGBnR20dSWYuLyAaVhMTCwhzkYpWgY1yyKsaUiCgCZJ5Go18rUaXdEoIVWlaBhcqeuuxdc5HcuKxM79PURiAbLLJUr5KtWSx7rSNBm/X2nSFB07P8vF02utwrf+3D6OHB2+pnuqIDSvcWFNY65QJKytPdeqZYOXnjpPpeTlGy2dMR58+JYNoNFmx179u8+nML+Y31JGY+0zIpIY8FrAAZ8q0xoPc629VdmqslTLULFrVCWd7Vu8z+dXeM9Hj7B9d1edgSygKF5eq2kymk8hGvGTSATp1hM4jtvQ/lEUCV1flfQQiET9xKIBbHutUN/aGmnKkwRBoKMnSf/OduYmV7Asm8xikXjYT6VmNH6SX/LGejNdqbcSjmthuwai26wVuDSX4+QLl73CmU/h9gf209ad2PLaaT6F6+8Y5vuPHGNpLk9upcTZV8fZXifUDIbbsF0H3TE3sLt0p4Tl6ATlBP+aPfw/NcCR5VrkzBU00dsgWKbN/HSGzoEWjypoeb3tflXZtGojiAKWZdPaGkW9CvQRBYmAHGexeh5Z9DXYTFdHa2uUo0eHqboWmWK1rjkEqiRTsyxkUUSTJJYrZUKqRs32BA99skw6sIUFoyCAqyEIQXBNZCGEIkpNG+HNomhW6xbnNARvwWOdnDo1ye7dXU3g2FYh1tlRM4UCYZ9Ke1gCdAQh1qD8XisEwROHcza1EBeIqa0ogm9DIrEa6XSYO+/cjc+nYNueE9XqsQb2dtMz1N5wlNL8ClpAZd+tO8F1idaZN6m2KNFkCKPeFmUaFoGwH8u0sC0Hte6UJkkinV0JgkGNQqFKtWoyMrNMMOajLe6J1LquSy7n6UDFYm/eltHFS2ATvgBJv5+VWplwwEcooOLUr+X6qsAbVRbBA7FSgQCaJBFUVERBIBTSWFourmkcrYtQSOOmm7YTCGrkalUsxyGi+ZrcXCIRP909ScbGlrBth+XlUl13ZBHXKSJIrbiuQDbrVa8FAVLJ0JbjIEkiiaQ3tz3HvHqfNgKm45CpVekLv7M4R/GwH7W+ARjsTjGfKYIAbfEw7akIve1eZdO2HV67PMPEQpazY/P4VBlBElAlCUkSmMsWKVc9RwzXdUnFgrzw+jiRgIZuWFR0g3Q0iF9TOH9lgYm5DEGfyuFdPZy6PMv4XIbR6WW2daXe0LVOEAT2Rm+gaHob58HQXrQ6EBBVUuyKHsJ1XYJyBFmQkUWFuJJGEEQqdpGE2kpvcIiKVUQT/cTVFlJqG5ZrkjOX6Q0MEVNT7IxcR0iO4rouSaWNPZEbqdolAmKEbcG9+MQgfYFd+CQ/kiAzENiDbbuUawaCAB39Sf7PP3uIju4ktapBuVjD0E1cxyXVFkVRZRzHJbNYoFYxiMQDROqtltm6U2M+WyEY0oilPBBmZSGPXjPxh3wk02FESaSQLZPPlFF9CsnWCIIgkM+UGkKXoYjXuubYng1spaSj1d8ryxKhiJ+WzjgLM2uJiCRLtHYlmpzKqhWdakknlgojCB6VXlHlht7TwPZWZNljTaZaIyiKxNy01x6qajJtHTFiiaBHuRZg2462Rq/+DbcOEQ77OHLrDsIRP9WKwaEbB5EVCRuLw3f2YxRdNE3h6H37WJzP14EmkXRrhHC9dVuvWQiiQHdfeoP1btHKs1ibo2jl0SQ/yXWLztB1fRi6xZWz01SKNZxWm9naAgICKS3RVBEW60zOkyfHyWUrqIrMzEyWjo61amO5apAv14iGvPPq7E8TjgUIhn3IisRtd+5ix3A7K8slNE2hpy9FLldBN6ymTVVH8CYKxjh54woTpR9h2Hl2RW4m6GTwa+8C3mwb+c9OVE0L3fZAiICibCkkvD7CqtrESpJFkbnFPNGIn0j4jYGnTUPwGrzaApF1pSLP+bZUt5ZeH4c6O/mL++7lCydO8sLkJCXDYDyX4/MnTvL1M2fZkUpyz+AgdwwM0BeLNQFdpmN67n3eVzT2va7rYjoOIysrPD8xyfGZGeaKRQ84sqxGO5vtOFiOs0lJy0YSglsCR6okoW1SmLk6wqqGIopUgbJhYNg2JctLEgKSRr5WazhtaZJMWH1z8zLm9zXGwbJtiraBKGrode3DtxqiICJLIpa9NpZhJUBQ9oNbrTP1Ns6FkKo1Mx1wkTdpO1kfNdOiZlikIp7miO7ozFQXaPWlUCXlbYJHAo6j47hrILTruhT0WgPgmcjl+N0fPvGWj1yzTJyrxN5XWeAAUZ+G/Ab3iYCnB/VvIYpVnbNT817HQL6MJAr0dSeZns++vbkpimQLFaq6Sblq4Pgk3EAOTfTjEwMbpqUgCuzc301bj5doS6IHaF4d4Zj3fJ+47LE6jJJBXyQKilfm9CsKhm0jiSJlw0ARRYKqiuU4VE2TXK22oaV2ld3sD2pkl0vYtoNez0ks28HQLSpVA0HwctFzJ8YbbKNwLMDRnz94TdDo6rBsh+VyhappUTXXnoEriwXOHh/3xk8Sue7mHfTvbH9L67mqykTfoFgJ4LoOtlNBlT3QuaKbgAcOb6p8DgQkH0lfjBZB3OotALT1JDl8204k2dsfi6LA4DaPmWPWWf2rulnd3cnG6x45QtjQ+bOa363+/2ahqDLptnpLvuu5pxmGje24WHVtRlEQWTEKJLXIlqy3NxMCIjF1EEVsBvpHz82yMO21wiZawux+V/81CSmCINA1kCbREmFpztNDOnt8nAcfvgWAmmMyU8ngkxSCskZUWSvEuLgs1kao2UVafIMktG7EfwH7czV+aoCjhJompiSQBJk2fxeuAZdPTbIyl6OlJ0lRc6maFsNtacTNbE9XLa1FAVkSG4vY6vwpmHOElFaK5hw2FtImlcnu7iQtLRHGCjkurSxTMgySgQAdoTCvLy4QUlWS/gAOLsuVCulAkGytSjoQJLUVcASATl2kAVWUUUUZZwuatCiIaKLigVb1kw+FfOwcbufEiSsYhsWj33sN07S59969dHbGUdWtKd+241LSDURBJFetYVvLWNYIPv89sImjyIbzkQTSHbEtJrZLwVxGFf0E5CjSJhOyoyNOW/1GzWbLlMs6umFRrRhEIv6GpbnrQlgSmJzNIcuSV6kWIBzy0abKG9rkGjfGVU8rzwHLmx+O42CYFqJhYtnrnO3qgt033DC4JYV2wzgIAqbtsFAu4tKCpspoikSlZtbnm4vp2Gii5xLxZkISBBbKXl9q1TIJK15VLrgFIBgK++jrT2O7Lq8tzZHXa9ze1Y9jeyLyAU31KLNBX/33u40HmyAEQVARxSi2LTQcrgCMuiXoZnPIdb2Ww/pBmkBZSRAw12lxvVPiur1dnC0sMiAkWLDLDAyncF2XWaeIUpOZKeWJaj5EQSTY7uPnB/YyVy2SCgY4n12kPRChKxRlJLfC4bYeFmtlLhWWCUVUOofjBOIag6E0O3rSCAKoikx7KsKu/jYkSUCVJe46vMPTgFGkNwVCAsTVFHHVo9Wr4lqvuCIqdPr7N36gvowG5XAj15YEme3hvXSse39YWUv+VdXTCShWdcYWMvhUFYEkGQmyZYNoQMB1Bcp6Act2CPkilByXJTfTOMbg/i7Cfo2Lp6b45y8+T2dfitxKiT2H+7n53j2Mnp3lmcdPo/kVHNvh/o/eQCId4Ut/8wRt3Qn0mkXPthZuvGs3l05P8dwPXicQ8hFNBLnvQ9dTzVX49heew3FcjJrJ4dt3smNfF//rr3/YsNDVdYsPf/Ioqibz7OOnKeQq5FdK3PX+Q+y6qp3rWrEwneWH3zjOh3/9KIoq853/9TzvunWI3e/q88ZLUxjc2cy2Cu9aq2JKQbGh8+QPaAzvWydEXneXb63rKPUPrtGdK1YZI5bBjTmUBJV4Ik08GWqsh6s6RrbtMHphjr6BlsZx1ocmaoSUCBEl1pgzq1HMVjj9/CVs26PfDzgdiLKAJmqYjtGUzIuSwJ137ebrXzvGxPgyiipz4EAPR46ssRMCPpWQX22Asooqk2xd01WSJJHO7iSd9U3i0mKB8+emCdR1/FZjsvQkeWOMgNRCe+AGwkonPvKAx6B7Z/EfvbAcb3ML1MX637joI4liQxAZ6q0ZhoVubLRff7PhkxTiWoCxwgqrhxYEgbb2GMGQ5jkKXnUO13d1sT2Z5MnRUR69eInT8/OeFpFhcGJ2jrMLi3zr3Hk+uGcPD+4aJubzQC1JkFhrLPHCdV3ytRrfPHeOL506xVyhiGHbiKtJpCjik2UimoYmy9iOw2yxuGaVLkiIgpcwOViblhDfbP4li2JjbTYdB9t1GCvNYzoWu6I91CyrAUAo0ua6hZuFKAhIjY0/CLKET9Vo700R3kSv8I0iUyxj2U6DcW66FkWzQkBS8cs1HLe66ecUsZlLLADyGyQdoiAwly2gyCLt8TA12yCqhD3h+rcZLpYHGq0DCGzXyweazu1N3BObne/ViIdhr7tuovSmEnK5PlbvtL3Q1aFIIh2JKAJCvSAG5YpOuK5b91bDcRxKFY+1s703TShusajnt9zzKKpMV39LIx9xGyPePPKSLDW0YgAcyyHpDzRao9eDDMk643H1367PT0sgiOB6bUWuu5ZnrDrLroZbX5NlSaS7O4lcB0FqFYOpsaXG37sH0rR0bN6ytVW4uEiiwM7WNEF1Lf+ZGV+iVPDuWc0nM3ywF9t2GBldRFEkRFHA51MaQHE2WyYQ0FBkiVBQI5kMYdsOnR1xwm9QQHCxMZ08mus9k2u6Sb5cw7S3dukSBIGlWoaZ6iJD4U32nfXo7EsRjnl7oVrN5MroIrie6YpRf0Y5tlt3xbUIhjTy+Qp+v9ogB/Rva2n6DavX0akTFBzHMxbC9WaKrptN6ZnruAR9CuGAhlKfUwICmqg01uC3G45rkjfGCCldBMW1PeDo+dnGkhMI+QhH/VQr+rWPZTkE1xnYLC/kcWzHkyIQFQpmlRW9yN5Ys9mF7RiIgkREaaVi5wnaCfxXaVm+nfipAY4qdonZ6iQtWgeztQkG/bvo2tbC0ky2sUEt6lsPruO4dHd5DkSu62JY3g2++rAMy61cKT1LSGlFEjZWTQQB4okgfr/KoJqkMxzBch18kuwld5JEOhj0nEQEz1LUL8uYjmep/kZTTJQ6AIWiWSWhBlHFraxHHSzXrqPo9YeXInH06C5eOznB6dNTFApVvvXN47z04gj7D3gW29t3tBEO+zcs3rIo0BoOka1U6U9EgSyC+OYp6nrN9MSaN30oC4TlJAVzGXcLICwWCxAMetoE1apJtWrgumCYFouLnlhrwK9Sq5k4jkuxWCOZCjWYSau/Z/3iZlkOpVKVUrFGTTc9dwDLxrIdJidWMM21Ra0nHcfRBAzTakKj3U0ZVFuHB6xQp7B7SHd/V5KZxXwDFV8Vun6zy42LV6lKBYJEVK2+0Dl15Hvj+wN+lWQyhCB4SUBvJE5I1ZhazHF+YoEjw734Zak5WXBWASEBx1nGdXUkSaCrM1H/XTA1lcEwLDRt431RqehMTXlJvyyJdHau6Qj4ZYWuYHTTis/PclQck/lKEVkUuZxfRnA9x56+SJyTSzOoksxYIYNfVugNx9BUiXLJ4EJuCdt1ifv9+BWFimOSs3VKtsFiwevNLho1+okjSyKytLaZlkSR9cPv15rH1HVdKoa3iQloChXdQJW9Ta2AUK/MCxiWV5GRRIGpTJ6o30cyFKBmWgiAT5GpmV5FPqAq6KaF5bgEVK8Cp7gh2n1xSjXPPSuoKZi2Q9UwCflULNvBtB0MyyZbrhC0PG0D07YxTLvxgC5UvYqdZXuV/2BdnK9Y1UlHgoT9Wt19C+770PVkl4t870svcuDGQX78vdeIxoP07Wjjue+f5vVjY9x+/34qJZ3ugRYO3rQdQRSQZJGVxQKSLHL90Z20diZQVJlXfnIBVZN58OFbmbg8zxPfPE7fjlZK+Rq3vWc/A8MdfPV/PsXc5ApD+7rZc6ifQrbCiecvc+HU5FsCjlo6YsiKxMTlBaKJIJml4pbWv5tFVTeZms/iUxV62uNvKkkBkAUJF4eSVaiL7EvE1RSO45JbKWHoJol0BM2nMLSJRuBqSIKMT/SjSRqW0wwgL85kKGZLJNvjIEDcF0NUhLq+UXNrhiAI9PWl+MQnj1IoVBEFgUQy1NAJBI/mrsgSpWozc8CrNm5iiXtliQtnZth7XV/Tcy2qDmA7OpZbpWot4romsuJDohnMeieFLIpIglB38XSa7Om3Crfxn7Voa4luus6/lTAdm8Foql4k8a7h3GyW5eUiiURoQxu1KAgkAwEe2rOHowMDnF9c4tGLF3l1dpapfB7dtrm4vMx/e+45RjMrfOqGG2gPhzFdE8uxcLAb17Rqmvz98eP842unKBkGsigylEpxU08Pu1tb6YlGCWpqY7zOLy3xh08+yUplDRxxXZOaPYMqby5sajve+L5R4mA6duM6rLJ+M3oJv6SgijKyuAYWmbbNm91yOPX2u9WxE0yHUq3SqMq/1Qj4VMJ1vUsATVSQRalheW7Zm+t+Xn0fiUj4JP81by/TtknHQ7TFvNb3sBxkypn7FwEqkuAjqPYhCVrjpASaWd0DiQS/sGsXQfWtAVR7Wls3rD1qHeRzXbDczRhrG+Nf2NXyMxOaIuM4DpfnVzBMG7vTYSlTIhTQ3lZrj+t68zMW9hEKaFiU8UkBFEHb9FkoyxKJljVZBVmUCGmb6Ctt8j3QnEdc/RnTsFmayzFzZYmZ8eX/n7v3DpL0Pu87P78399u5e3qmJ8/ORuwuFotNSAwgRAgSKVKkqGhaknUWFUq68pVdV67S3dlXdXaVzqU7netcdlGiLPoUzSQqWIJIgCRIZCwW2MXmMLuTY/d07rffeH+8PT3Tk3YX5NVBeKpQAHq63/z+fs/veb6BxdlVKqUGjZpFy3KwWy7NWoultnX6xqhUmkzcXmJ4KEM6HcW2nI5bFoTU99g25k27heP53FguYCgKh/O9nWd7Zb6MY4cNYVVVGB7PhWunmkXTckinTFzXp2nZpJImjuOFiFDPx3Hc9hpCsLhUxjQ11NjO6EqBgixMhFBBQCyi43ZYI7uNBKG+2W6l1DUkOIQFuWKhhiRJxGI6iZQZSo3YHuVyA01XMAMNv01TK5UazM+tMrwh1/J9n1IhFAGfmVhibqpAuVCnWm507p/VtFnedP9mlsu4fpjXQliw8wnQvy96bSgs7voNWl4JU813tjU/td5Enb29wm/90hfuupcAOiglAKflYjUdovFwDVBzLeKK0bUWCwhwfIucsQ/bb2DKKdRdKKD3E++ZwpHlNZi3pmi4NSKKie+GdAXd1CgsrMJwnGKjuWPC5HkexdU6sVjYNV2tN7m9WOT4ngEimkrDK5LUBml5NVzfQpK2whDNSDgAGYqyxUXhSG8f7zaCwCLwywgpT0TRiCoayg7UrrXBdyya76LUDQ1l+Gf/wzP88R+9zJtv3qZSaTIzU2Rmpsi3nr/EgQP9nDy1hw996CD9/alO8iaEoCdqslJrUGg0yUbMHV3QtgshBPPTRUb2b3/+4Uu2ffVZkgQRU2tza2F4uFu8cOtEIxgeyrJ5sFmDfi4tVnj7/BRvnbvD0lKFUqlBo2Fj2y6u6+G6Hp4XdG03CAJqltNZuK59Ju4zAVtboK+2QgHZWqPFtTuLRAwNq+WSjAk0Se08n/difahKEoeyOeKa1q6UhwNXKmluu/4xDA1ZltrQagkv8FEkiYRp4AfBXSZuG1nuA8J9HT4ySL4/xcJ8ievX53nhO1f5wAcPEomsWwXX6y2+8ffvcOf2MgBjYz0cPBgm234QcKO8QkRWGIwm4L7Z7e/d0GUFy3Npui6apJDWIyw2qiw0aggBy806cU0noRn0RmJM18osN2vkIlF6IiZLjRqSECw2aihCIm/GKbYaLDfrrLaa94wg2hhzpSpvTc6yJ5fBUBVuLhZQZIlcPEZUV1mu1hEIFivhMZ4cG+TlG5M8MNCL5/u8cnMKPwj48ME9PHf5Jvt6sxzM53ju8k2GM0l64lEuTC8gS4Iz48PcXCxwe7nIDx3ex9tTcyQiOvv7enj11hS1ls0T+8f48OHxbY91Y/euk+i3i521ZqvtXhF+nsjEiMYNAj/ovMPL8yViiQjVUp2HHt3L3sODABimRv9otkMVC4KA0x8ORexf+vuLROMGn/z5x1kt1Ej3xIlENXryydCi3vGIpyL0DqSJmDqGqePYLlfPT/His+/wwMOjCEGHDnuvEYnqPPz4Ps6/cpNUNsbw3t5dBRM3h+N6FMoNBnP33gUKggDLt+jR8/TofZhyrKMHEwQBd67OU1mtc/JDB9G3MTTYGMutBW5Ur5BQU4xEt3YHG7UWCcdFCCjZFWyjRc2tU3VqJDcUj3zf58KFaQ4fHiSZDBEdy8sV5mZX2dtGSlm2y9xKhb2D62KzF85NMjdb5Mxj+/nTL34vtBVuvx6L82WGRrPYths6drVDkxKYSi91d56SPYEkZOLSYQztIOIHAMN+L4ahqGiKgmPbNB23o52zWzQdp0PlgRB9RgCq8v0V+iUENysrjMRSGKiAQNdVYrHdUQeSEOSiUXJ7ojw+OsJkqcRLk5N8+eIlbqys0HRdvnrpMlFN458/8QSmbLInNk5EXrdgf21mhj+78A4120aXZT5z9AifO3WK4eT2+h4rjQbSpsaGG1Tw/BY7iWPbnkfTdbuErreLaqvV6bxHVRWvXeBSpFD3L2tG2lqHDi3PpWrv3lVei1Kz2SkcKZJEyjAY7O/tdM/vl2KoSBLLpXrnvYopJhIlyk6NdCRBRD+BQCE0z9g5JCHtKPOwFrIkoSsyNcsOO//4VN06VbdOVI68y6KuhCx0pA0i3pIQJI1Qp8YLApKGzo8ffoD+ttaQH7jYXrW9BPTxAxeB1JFnkISKQML1m7S8AgFgyCkkoYQUPSHhBj4Vq3VXWYkAqDv2+x5tBOHcnomZHOgXXJlZIoCOOPq7ubV62yZ8YqbAwT19qBGFgr1AUstiYG5ZuEuy6KKKO55H3ba/Lz0a1/G4+vYU3/yLs1x68w4LU8WtNvWCDp3c20YXKJ2OckDJdwxrXNejvsFwaI2OfT9hqApnRoZoOk4XZbRWaXaOQVFDWn0konHqxNr8vX4thBAMD20VjS+Xw7WTu8E0aLsIcIkoA+htHTRJEuRSa9IWO19zJ3BJqDEMWd9x+0YkBHmsyQHs39dHcbkCAeiKzNhIDxAyiRRVDmUzUiaJVBRFk9l/MN8BJawslHj+6+d47dtXmLq52NF/2hiSJBDSVtCA5/ssrFZ5cMMzpAgJxL2t43aOAEUykTYBVSqleue/rabN7avz971l3w/w3HDusTybphv+MxK1SLe1kz3fYdG6gRfY6FKMWOzEjpIy9xvvmSwrpfZwLHkGP/CJKQmEG0INXccjljTx2xZ8O4WmqdTrLSoVi95cEtv1mFutcHRkvavk+BYtr0qhdYte44Gu36/xJTcPQO0ywF0rj7uLkhmEC2sXRchIQsKQt7/0iiTTZ6TZ3OeQJMHoaA+/8Zsf5ezZ2zz/3CXeeWcay3JoNh3On5/i6tU5XnrxOj/2iYf54AcOEIsbBISVa1WWMBQZ31vAde+gao/eFTodBAGxRATdULcdLAEsr85uU8ZukHohxDrUtN0eXUPHbDwGx/H43veu8pdfP8fExFIHtRSKla8lrTqapuB5PsvL1S6tBSHA3pBo+57fpmftfv5dx0po0ztRCrn+6YTJycMjtByXWBvl1vRs7tSW2R/fvpO5ORzfY7FeQ5PXF422461TwzaFooQTl0DQa0aJqWuOJ3cXcgufQQ0IkXhDQ2mefPIQX/vqWWq1Fl/84ne5fXuZI0cGiZgajYbN+fNTfPtbl2k2beJxg499/Dg9bUFgISBrmMQUrSME/n6JnBHlY6MH0WUl7ET4HheLixxM5YirGo7vt7n1IizcaTr7ktkOdcH1fVRJ5pN7DqNKEookYblJXlucJp7qoeLsnqAHQUBpqcLb375IJGZw7EOHWa7WycVj7OvN8vrtGQYzSWaKZRbLVXoTMZardaK6Rl8yxkq7iNSXiDGaTTG3WmG13qQnHm27C8KDQ3kkSZCMGBRqDUxdxWzDoWdXK9SsFoVag5VaHdf3eXAoT7nZYr5cJRM1cT2vg9y78dYEV1+/CQhOP/MQ/eN9246HkhAkzO6Ox+ztZeamChSXKhimjqarjO3vI5WJcvKDB2lZDomUyXoBaiPOOEyiDh0fYXhvL1/5wgtUVhsMjGQ5+71rLM+XuHFxlnRPPKS6iq3OajO3lzHjBkdOjTFxda6zOOuGO/ud92vjZ74f2sHuPzLEi8++w/StJT79Sx/szCOdexg3OPbBw1i229FPCpnLoQ1vvdHCcdeL782axSt/fZbahgRjLWKpKKc/fpwFMY0buKzayxyMH8PyaqF+FQa+H9A3nCGWjLAyW+TccxewtoFCZ/vTHHvmEPnIIK7voEndXV7dUFFUGacVFo6UQEaVTSIRA0PuprV5XsCL37vO3r19Hfrv3FyJC+enOoUj3/dJxbpRsT19CTRDodm0KZcaPPLEfpQ2euNmdB4fOHJ0iFhs/dhq7iwIiT7zDONKHlmo+O7NHVGv74fDabGMAAAgAElEQVRIGgYxTaNu21RbLSqWddekdqXR6KIUyLKELIcaDt+Pw4oiSWT1UGgfwvGqWKxRqTRZXqrQP5C+KzJGkSTG02lGkklODgzyf7/6Cs/fmsD2PL49cZvPPnQcoTVxfJuEsi4q/dLkVAd5PpZO80snTuxYNIJQXNrfkDf6gYUsouiKjGD7omrddihb1q6FIz8IWK7XabWvb080SsaIIvkOMbWt4ZVIEFEUyoDtesyUyxzp7d1xm2sxXa507pupquzpy3LnnTn2Hxu5yy+3D0mS6EvHOpottu+waBUZi/XhBTVcbwFd2b4JsDHqXo2aU9kV1CdLYWNrbTxbo33MNRfJ67u7k+0UftCiat9EFhEyxsPhVoWgPx4noqrUbJvFWr0jlA1ge1VWWlewvQoBPlGlF0UYNLwVvMBGIGMqOarONHJbo3PQfAyEQj4eQ5EkXN9nqV7vElff/vgCVupbx+r3ayxV6pQbFv3pBLIkQqFi+93pb7Vsl/5cgj1DWayWiwok1MyOBUpBt5aoHwREta2Io3sN1/F49fnLfPF3n+0UjCRJkMrGGBjNkulNYMZ0jLYJj9Ww+c5fv0W13E3vbLUcpmeKJOIRMpkYQRDmCtBep9yDZtrm8P2AlXqDhUqVoVSSVCTMnULqePgdWVnXAVq/BJvQV9tcm2QygqrKmFsMj7pDINNw7+DjkJCOdvIjdRdNHj8IsDwLTVIoOdXdNo7jeFx/Z4Z4KtLRrGo2WkxcW+i4wBWXKsSTJoapUVyucuzMOJneOLGYQRAETN9a5E/+w3O8/u2rHWkOw9TID2fo7U8RT4W/1Q0VSZZ45/UJrp1fFy2PRXQy7joFMQgCFq0Sw2YvCeXuOlA7XwcHP9jaiNy4lo7GDHIDqXumMa/FwGgPUvseJFWTB1PDrNp1Ulq0cx6yUBiKPoSEwA88ZPGDMw55zxSOIGCqcSvUTiDgwdgjpHsTLNxZQdUVmkGAoW5/uEEAtbpFNLrOsXdcD99fh/yqUoSGGzqAJdSBtkDU1gnBDTwqTgOrLcyoCJmq0yCrJ7C80CJSkxRavoOEaAuFJtDkXTq7Qgc8CGxW7TrXKgukdZMU4QJsY3iBx3RjmYrT4NHsIeQNFUIhBOl0lI9+9Ahnzoxz9eo8r75yg3PnJpmfL9FquVy7Ns/0dIELF6b41V99imhcZ65SwfY8HC9AUcYIguo9o46qpUbbmWz7J9sN7HZRbYcnPwgIbVQ3/n3jDCPwgzoCjQAXiTAhXXv4Pc/nW89f4vOf/xblchNJEgwPZzl2bJgjR4YYHExjtBc4sixx69YS/9fvPkulsj6wu55PPLKm+RIK7KWS5n2/rJbrYnseDduhVXO4MblMsVInfmIvpqGhSTIt38EJvJ0zq41nLgST5RA2ORhPIEkCRZFwXHeHn69PEDPVMr1mjOF4Ei8ICxm7n8+aXXX4Dqmqwmc+c5rAD3j22QssLlb40pdeIxLRUFW5A29dc1b76Z9+hKeeOrxOQwigYDWYdSv0RxPvSl/gvRqqLHfESXU5nAhP5AYxZAVJiC3KYLIsoW0oBOvtS7RRtFxSBA9nB/CET0oLFxZN20FXlC3vlt20+Zvf+yZ//r//JdFEhM/99mc5+skTvHJzMkTuZZJcmF4goins6clwfnoO34fBdJJE27I6oikkIgZX55c5PNDL9GqZlBkhEdHpT8XRVYWmHepzlRoWnh8wWVilJxZlPJfhxsIK2ZhJNmYyUyzz8s0pjo/0M5JNQQDpaHgOfhDwxrNv88V/9SUA/vWX/zn94/eOzjRjOi8+e4Fmw+HpT58kFjf4kZ8+w7f+6i2+8oUXSGVjPPXjD2PGdAZGs10IGi/wmbi5wIUXb4KAhx/fR7YvQToXp7BU4Wv/+XtEojrP/NRpdENlYCQbCukLiPdGicYNjp4e5e+/ssRf/8mLRFM6uf44zWaDF549y8U3pmjVff7r55/jR3/2EUqFMi9/4yrVcp2//fPXeOjRvXzgmQcx4zoj+/u4fmGaobaVbqtp8zef/yZ//u/+kmjS5HO//VkefOpBLp2foieX4OCRQYQc6vGpityFpK2u1vjj/+2rTF+b23K9hg4OcOTxg4wO7UcIQcUpIQiYrF8hZwzRJ4+3taFC+tfszQX+4Lf+jOLCVnj90Q8c4sCHx2hhkdPzWzSOhvbnaVkOsaSJpivoCQ1Pccnp3VS8crnB3GyJ5eUKN28sYJo6AXDp4kxXdy8VNzk4ImPo65p8g0MZBocyFAs1PvL0UR770IFOs6FvIMUbr93ixvUFIpHhDpJJFVGSxjhRpQ/Xt1hsniWrH8FoC3i+HyMfj5E1Qxtxx/e5srTMo8PDXWLSm+NGodDl9NVo2izZVaKmTioZ2daZ817C8lzmGmWOZbq1uzwvwDS1e55XhRCosszh3hy/cvo0FxYWWazVmCqXKVlNsrqE36buawQQwO3V1c67cqSvl4H4VjfQtQiCgNvFVZobFv6eX6Xh3MJQdqZvFhoNpsvlXbddbbW4tlLoHMtYKkVSN5D9eMfZpj8eZ282w0KtRsNxeGtunqf37dsVcer5Pm/Nz3WOeV82S1woNFSlsxC931AViUTUCOlXBKzaVaJqJKS+oaHJI6w1NncKP/DxfBc3CMVxdwpJCFYqdVL94fwgEKiSyqjZ7QbapRMT7LrJta2wmYd6uLeXdCRCzbYpNBpcXFxiXzYcmzQ5To9xmCDwEUJCFhpB4BELBhFCxg9cJKEQV4fwAouqM9spPB/IZtGV0BBntdngZqHIQGJnRzzX87i6srLbCbyvolRvkk/Fub1UDO+IEB1Wwf2G2UbRV2pWaDoifOJKKkRG3APtuOW6LNfqIbX0PvcdBAEXz97mD/+Pv2NusgAC+keyfPwfPcrxx/aRzsYwYwaqJiO1kUSzd5Y5+91rWwpHTcuhVmsRbxczJEmgaOtUXtty7ptGrcihYLcsSUQ2rH3VDZq2jh2itO836g2bidvL5HoSu+q8+jjIwsT1q/hBC9v1iJvrTRzX9baMS7KQGDb7WWmtMhjp3bWop6oyxx4Z7whey20jkYOO11nLu66PJESnAWCYWidnrqzW+aN//01eef4SvhdgxnQe++gRnv7MKfJDGaJxo9MAE0LQqLdoWU5X4ajasGhtkDIJCNAlldu1BbJ6Au1dophloRNVB9Ck7rFjo07s3iOD/LN/+5n7dmtXFAkzGuZrl8oz1ByLgl0lZyRItBsXFWeRoj0FCGrOMgcST6LL96+Rt+3+fyBb+QGEj48f+MSVJPPWNFPVCa6fWyQei1Mt1VHSqY66/Ga1e0So/9JsrlmuhxpHmiJ33lPLq9Cj76PszOL4FjvNVJZnc750G4Ca22Qo0kPVbTLZWKblhXDUQTNLoVVFkxRs3+GxngfQduheAQR+jSBogVDIaFFiis5Cs0KvHqfZ2Iw+CIURffwdBxkhBMmkySOP7OXMmXFmZ1d5+eUbvPCdK9y4sUijYfP8c5fI55P85M88gu8HHO3PkzV1PO8WoHQJDe4WjuPRsuxtuwlCCAYi+3F9e0f7TM8v0HIu4rpzSFIc8BFIYTGp/UK63jS6egzPX0GW+tDUg6w9mpOTK3zpS691ikaPP3GAz33uSQZ26GqWSo2uzz3fp1htdHUpgiBA09VdB7TtouW5eO0ub9TU0HWFg2N96Fq4LVUo9OiJti3t3UU2/SCg5a0XiYIghO5GjLtUhoNwcLbbLjstxyObiKLt6rTjtZ/DBhAmV+l0lE/++EluTSzz5tkJkkmTWMzA83wMQyWfT3LkyBAnT+1h377eLvSYEJCPxDEUJdT9eh/FdG2VgBB5ZMihi6OpqJTqTc5PL5CJRmi0iy6yEDRtl6FMkrlS6FyoyDKL5SrjvVmmCyVyiSi+H2oUjfakeOXGFAOpOO9MLzDemyEVjTC5vMr+fA8D6QSO7TJ7YwG7aRP4AQt3lnk6FedTJ4+E0GIgF4sit1GYQ+kHCIIgpDH6PiOZJH4ADw3nQySlEDxz9ACC8Jl7ZHwY3w8L8R/YPwYCZlfLPDw6yEPDeRAw0hNuQxKCjx7ZhxcEeIHPDx1eEzsOxeCl7xOfnx/K8DO/+lQX/Jy0Sv4To5yJp1hoVJmW6xSrDvkfG+W2XEWxdIpW6AqoPhDnpx75KO8U5smbcc6X5rE9D/PxLAceSZHQDKb9BvP1FmOfHOeyW6CnblE/GUH0yWhakad/LY0QEqbSgx+4NJnloR+Jcvpjp7H9OoacoOZcI5GM8JHPxcgaJ4gp+Q7iyrE9GlWLRz5ymGg8nMxd22X2xnznHs7eXODIk0cZ2ZMjnYl1Zp9my2kjCdcHi1gqyk/9i08wf3uJerlBcX6VC9+9TKXt0OLjs2DN4AQ2ZbvIWHQ/OWMQTdIJfJ9Y3EBpUwAG9vbxj37r0yzPFqmt1lmZLXL+O5ew6iFqQxYyju+waM1hKlEi8nqHbe72Emefv8iB46MksnH8usdqfpVZdYF9sTESbapapWLxxhsTTE0W+Ku/egulPe9mslGeeeZIOPcBsgTJmIwXSDRdG11ed0jNZGM88eSh8MlqTzb7DuRxfZ9arUWj3upo+C003yCmhvRFgaDYuoap9L2vC0dZ0+RoXx9XlpYJgG9NTPDJww+Qj22lRQZAw7Z5eXKKxobCkaYqyEJibqFEMhEhl313FuJChCLZGzWA/DbVtFxukkyZ9zW3CiHIRCIdeYAQQRs20WRJ6aKabUSda7Ky4yQbBAEly+KFO7e7imeqnCOpx9GUfJuetTXKlsUrU9M8lM/vaMV+o1DgrbmwsGuqKg8P9KOrMheLS0RknYwWI2UY/ND4Xl6bnsHxfb575w4/d+wYI6ntEVJBEHBntcRLk1Nt1KrEU+PjZJNxilrhXWt3xSI6D46HRT4v8DFkjV49RVqLo6n39gxIQiKmJLaIlW93DgGhuxqs5/UbKRJrAuZrUW21cHwPdsyhA1Q5jql0FypHUynODA2GOlmuy1cvXeIDY6NkIxEkoWDI9y5GHFPXC1v7slnGUinOLyxQtlo8d+sWjwwPbV13EL5rs9Uqb85uLfK/X2Mwk+DOcomosV4kjsfenUtjs+UQ0VV6UjHqlk0sIqi7FUz53ujeCcNgfy57V9fZ7cJq2Hzjq2eZnyoA0DeY5jf+109x4on9O56L5/rb6qOusQHWQlVlkun1ubS8Wsdq2kSi9+e+V7dtrLZL5Jr7XyoTRVFl7FZIwS8uVRjdd39SKhFDDddJYne2DEGA5c6jymkCwuJKsdLAy4fjsG05OJvMFrzAp+42eTC5f4s9/OYQkiB5n4L/nQJPABden+CNF67iewGarvDJn3+Cn/7VJzuGI9udj+d0F9ripo67QT8uZARp9Ecy36d+q8ByCzhSDUNZpwtme9cLSbZlk+1NIKsy9Ubo5ui6fqeQ5nk+iiKH+X3bndnzfSq1Fgk/QJJhxOzB8mzSTrRTNAJQJYOsPoYXuGiSua151buN90zhSBYKWb2XqlOmPzKCEigcfWw/cqCgaApVOeSez5TKHcjeWgjCrv/8QgnbcRkcTIdQ6L4sertSa8gJZuvn8AMHyysTD7Z/0aKKwZnsQWQEy60yWT28yS3PYaa5ghf45I00B+ND2L7LbGOlA6sM6QdrWwq6ii1C6AgRRZdUPtC3P+SLColisRviKhAk1Gi793H3gTikHWX41KdOcubMOH/whRd4+eUb+H7AKy/f4JkfPYamyHzrxi0eHxvmUE9fO5G/+4pPCIEZ07tcBTZGEATYfpOmVyOiJLZ9MAUystRDIDsEQQNJRPGDJkKoCCmG75eRpR5AQggdP6jCBjTUpUuzLLRF5jLZGD/1k6cZHNxZRDbUO1pPLhVZIhM3OyLpQRBQLjVZXqnQ2xsnn7+35CIgtETWZJmYplKv21SqFsvFGgNr+iQCllsVBCFy7W6VaoFgJJEibYQvu+f7LG2i2W0XPgFpI9IWcRREtFDw2GtTqLYcexDg+xVkOY8k1ifkSqXJl7/8GhffmWZkJMtn//ETHDkSdmIlSaBpSiiep251FgkCaLhOG2n0/mL3e0HA9fISJTPBkVS+s0CqtWxajsvbk3OM5TLUrBZLlTpHh/q4MrdIy/EY7x3hlRtTSALenpwjF48ykEpQqDVYqtSoWTa243JnZRVdVcgn45yfCosds6tlBtIJNEPj0Jm9vPrf3iTdm+TQmb1AeB8nl0oUq3U8P6AvFWe2UCaXjOJ4PjFDZ65QxtBUcskoxWqDaFvQOmpoOK7HYqlGy3E5vneATDxE3Tm+h6f4SCbcqhbQJJmK3UKRJOKqjuWFiMEVq07OiFKyLQgCMobJiHl/TiH3EkWrgSJJOL7HVLWE0qb7DcdSvLwwSVY3uVkucCido2K3qNotGq6DISu8WV5Bl1WWmjX6zThXSktkdTN8L4KA8WSW2VoZU1HpM+O0vAqaHEMWGlElR9Mt4gcuMaU/1Mmgjhc4yEJFlxOAhCqtJ4RL8yVefe4SqytVfvgnT3eKq5qhcfDMPl79b+fI5FMMHBygsFyhUW+RycY7HSZdU4hGdMwNSKpIzOCjP/8hXMcl8ANWZor8zj/9T1QKN4BwVjDkCBFM/MAjwMfxW+F8IwvqNYtMLpy3egYyfPxzHw3131yP6WtzTF2ZYX5iqb0tgRd4KJKCF3SLY68uVugfy+G5PtVinZQW58HkIQKCLqra4GCan/jMKVzX40d+9BhRUwcRJs+6uojb/BsIGiBkhJRh2j7BzeoqJ3uGSWvr1rHNps21y7OsLFUxIioHHhigpyeO6/mUy016cgkkSUYSMi1vlSAYxg0sHL/2A+Puv1dDEoInx/bwN1ev0XAcriwv8+z16/zcsWNbF7RBwKvT07wxM9M1MmuajNMIO7nG9yGQfSwzwAOpPiLK+jbWkDeZtvBqEAQs1et4vk//LsgdCOe9CwuLrDbDLn6PaRLXNaKyyqpdwAvcENUswoL5mrrG7WKRhm1v0aMMtxnwd9ev88bM7JbZyfGLuHYNUxtHsHWBEQDPXr/O4yMjnBka3HLsNdvmqxcvUWiExeuBeJzTQ0O4vocuqcSVcD5XJIkPjo0ydj7FzWKRm4UCX7l0kV87c2ZbEeeabfOlixe5vRpqfoym0zw+OkKz0AxzsPs09NguZCHRH7l38f61EAhkIdHyrF2/5/o+yYSB3s63ZCGT1VJU3TppNdHeFvSYUTRZxvY8bhQKLNXqJI0dhFuFwPErlFtleiKPdj6OqCo/dvAgz9+aoGRZnJub40/Pn+cXjh8nFdlZiHiNZuj5Pvl4fAsCLGuaPD4ywjuLi7i+z7cnJvjEoYOcGtz6LFiOw19ducJMpbLrdXm/hOuF6I+UaVCsNUKtqXiEwmrtXaF+VEVitdJkebXGoT19JNQMETmGKt1bY1fAumPifUZ5tc7tq/OdddrDj+/n8InRXffbbNjY9lb6kW17WJbTWXvoEY3cBhfTxZlVquXmfReOABq2w82VIqamkYoY9A6m0QyFRg0c22XyxiIPP77/vrZpWQ79+ST6BrRRVyM+CAg8n4Awv9DkLIoUoy8dkDoWQWuvCarl5hY9ISHCnOKd8g0GI72MRrvRhuv7AMtyO42zNQOibiHzoLP+XDPw8Twfx/GIxw2unJvCsdt04XySj3zyOMYu9DvP9TuOdBuOmFhE76B3/cDH8mwMWfu+CkdCSEhCRRHdY9Hg2Dplt1JqUlisoMQ0JueLLBWquK6P43pE24Lzg30plgrVUAbD81FkCc8PyPfEUZFDE5/SAk3PJqvFOjlcRE5Rc5cptiaxvCo5/e505HuN90zhKAh8Cq0lWr5FRsvRpwxx/vp1mvUW2XyK6IEsvu8zkNy+Q+L7AbIiMdCfQlVCS0KxoZpquWUUScMPZHqMvSiSjs02AlpCQkZi1anS8FoYroUXeAQIcnqyQ1+ruxa27xBXTeatIqNmL7qudvimVsvpQAiFUPG9JSSljiQliSrh4FGvWcxsUEoP9x8WlFre7joom0PTFMbGcnz2Hz/O1atzFIt1VlcbzMyuIuKCk0MDNByHAA/Pm4FgE6Kpa8zYIBKmyhw4NtyNCFj/JsXWHKaSbENstn5DktOoyjDqLtDw9f06BIHbORjfD5ifL2G1eat79uQYGs7sOLD7vs/yUoXGBj0P1wuIGVpnMJIkQW9fgsWlMvX6+ve2QKc3bzsIaLoOdtsdxbJdEnGD+eVylz7JUCRDRo+h3sNCxgt8Si2LdDthEqGAEUvLFVq2u2Ny7/k+DdchpYe/kyWJ3lQU09Bwd7RalglYQ3mFmNmXX7rBN79xEdf1+InPnObDHz507/SFNux9srrKUCz5vtE58gKfimMxFs+SN7sXPMvVOtVmC11VmFutkGwXsCdXSgymEzieRypikE+GLoajPWnuLK8yWSjRaLWd1YSgYrXIJ2OkzAgzq2Vy8Sjz5Sp97bFN1RWe+cUnOfzYQXRTY3BvqJkVBAGVNq0sGTWI6ArJaHgMfekYpVoT2/MI7AA/MEnFIvh+wGqtiaErLKxWiEWMTvdiLRzf43atiOv7ZIhQbDVpeS4jsRQFq07NtYmrOhk9wmy9TMMNKbsp/f5cQjbH2IE82b5EF3QXQre+vkiMXjNORFmmx4gSEHC7UiSpGVxdXcILAgpWg6VmjZFYirwZp8eIktQMDFklIoei5qok4/oeKT2CLAS9kRiuH4rKL9SbjCf3dy0cdDnZpWeXJNQVWVusrsXad8yYwaGHR3nimaNdnSRVV3jmnzzJkccPYpg6+T29zEwV8DwfVdtgE+wHFMshInIgl8QPAgQwb1e5WV3m8dweoikTeYOwpkAiriSZqF/F8iyyWi8gUKUQ9t+otRCiyuCeXOi4psroaugOGk2YXchBj9D5pOHWcPzuhHjsgUFuvD3J1ZkJBvf2MX5iFOHoJCNGFzVVkgTRqM6nPn2KZDLSBbsO/DT4OXz3DrJ6HM9+DUn4WJ7bRhmEUa+1+Oqfv8r0nWVyvUkajRbfff4yv/C5JzlxYqzruPKRM9yo/AXT9e/g+DVMJY+pvHvzin8o8cjwME+Nj/O3169Ts23+46uvsdps8mMHDxJvzyEVy+Ls7CxfOPsmDdcloigd2pOhqwyl0uiaQiy6Rt0OqDsOK/V6W8/Nx/E8LNfl4uJSZ991x+H16RkGEnF0RUGRJFRJQlMUspEI+XwSXVfCHEiE7m8vTk7yB2ff5NTgIKcGB9ifzRLXwwRdEqETY6XV4nt3JvnTCxeotFrIQvDE6Cj5eBwrqIa514YJ+bHhEf722nUs1+WdxUX+85vn+MmjR8iaJpIQWK7LXKXCszdu8l8vXGDN7GSjmHgQeLS8aQx1pEtweS10WeZOqcT/8txz/MrpU5wcHMRUQ+OLhWqVr166xF9dvYoXBKiSxE8/eJQ96TSyJMhH0iSU9WLonnSaXz59in/z7e9Qs22+eO4tlup1Pn34MMPJJKokYXs+U+USX7t0iWev3wh1W1SVz506yVgqhaXZ6BGNeHJ9u7bnsVyvh2gEz8f1w3u2UKvR2KD1c3lpKURzqSEyWJHDInzaMEga94cS0eUIUWV3JIgiS+RTcbKJsLguCxnbd8BtdL4jhODh/n56TJO5apXbq6v8+1de5ldOn6Y3uo6ktT2PlufRY8rIQsfHZSPfRxKCR4aH+dljD/Jf3nqbpuPwe6+/wVtz83zygUMc6sl15C1cz6Nq29wsFHhzdo6LS0v89NGjfPb4Q1sKR4ai8OnDD/D8xATXV1aYq1b5189/i19/5AwP9vVhqiqu71NoNPja5cv85eUrHb3Dd1vE+IcTAfOrVSRJ0GzrGgnCBsi7AcTFTJ3TR0dwXJ9Y20hHl+/d+anhONwplhhJJZF2oe1uF62mQ6kYonhlRWJ4PLdr0cH3fWbvLFMtNbb8zWxLPMhtepUe0dh7ZBBVU3Bsl5mJJW5emqV34P4abQlDJ6apYTGg/dnQnhy5/hSlldA99dxLN/jhz5zCjN37dROSYH6xzOhIT6fQYJhaZ/3qOB7VShNFGiChH+0IPEuSINJelwR+wOydFVZXunWMBAJTiVBp1jumPduNM77vc+vWIoViDdPUw2eqaZNORVlariDLEoahks3EmJoukExEWCnUkKQQLHFgf57lhVJnfsj0JsjlU7uOadVyg8mbi12fWbZLw7bx2lqWtu8yHM0RkbufhTVKZnjud9eVdf0mfuCgSt1j5gMPj6IZCrblUlyqcPmtO3zgR4+xd7iHkXwaRZFZWAkL0fWmzd7hHob6Um2HbFBkGdf30do54XSjQMNroctqu8m7fryhNE8ex7d+oBqQ75nCkR/4eIFL3a0gCYkRbR+aoZHNpzCiOpqucXywn7i+/cshhMD31lE+UUPjxvwKIz0pDFXBVLIoksGqfZu7IXnmmgXOlyaIKDorrTIVp0lMMRiL9lFxGzS98CEr2uELk9HijJq9JJNm52ZaTYeZ6SKjoz2AjKzuYyMUNwgCrlyZY3Gx3LVvgaDfyJA3Mu9qIE4kIkRjBsVivc1hB1NTWW1aDCZjCOEiy4NbIN6RDQNmq+XiOG4oemc53L46h+/5HDg2vGlvYZGr7CyR0vqQd6HrbRa8hq0QSSE0RJeAV9DlbqCpyq5i25VKk9ffmOhC7KiyhNu2J+98psrk8ymG2m4DkiTQjTU+cnjvXNfrKqLIQpAyDO6Uw25gImqwdyi0v4600QKC0JFr84CzU0RVjcF4IlzsBwGyFDr7rVXUdyocOb5P2WpiyAp7goCW43J1aon+TAJtm+sTFlA1XOcmirIHCAeymzcXsSyn04H2vYBA3v7ebIlgDfnw7hKG92qsFQhlIdCE3JVUPjTcz4NDeQq1BuWGxWhPiitzy+zry2Dq60Llp8aHwndPCPb3ZTtFhodG+tt0sXZ3JwiLb0IIjvnrUFkhBJF4hAMnuzsEsiTx8JrrmvMAACAASURBVN6QotNuwJNPJzr/nUvG2DfQs174aP9rTz6DEDDWm+l0dDbe3ois8qH+8c7v1gTrNxsCCGB/Mtf5hmCrQ8X9RDRuEI1vHc9HYuvJ1Y+NhiYGQoSIp7l6haOZfPhMIzjRs94FFsAzIwfDZ1OsX4CN440ABqPJ8DNBR6dup9j4NwG03CIVZ4KI0kdUGSKWMDjw4NA2Y5nA3HQPazWLlaVqFyLSjGjsHe7B9XwWSlUWyzUOD/VSc1vUHCvUS9smam4F13dpenVqbpmau4okQihztjcB7Q6e7XjcmFwik4zSn9uq06FJGsPaHlbsRWJKd1OmZyDFT/zG0zQqYaf06nIBxWlh5tWOk87G881kuiHn5XKT69eXOXliL4FzBc+9BgiCAPYnejZ9t8787Cqf+82nyebiEMCX/vhlrl2ZY2ikGyHRGzmBqfRRc2ZQpRgpfS+KePcilu/VcH0f23NRpVBzLWno/NNTJ5mtVHh7fp5Cs8l/fO11vnbpMr2xGAJYqNVYrtfRZJmfO/Yg89Uqf3/jJhA2s+6sFNg/vi7Q7AcB3719h9958UUs16XpOFiuix8EXbpb89Uq/+Lv/q6djEroikJEVcjH4vyPH/wAZ4aGGBzMdI0rtutxbWWF6ysr/PmFC8R1nb5YLNTtEIKm67BYq1NoNDo0jEeGh/jFh48TURRajsAO7HbjLjQo+eDYKI+PjPDC7ds0HIffP3uW52/dYiydRpYkSlaTmysFCs0m6YjBb5x+hLfm5/jmzVvr54yL61fYTtNHAKeHhvB8nzdmZ/mfv/kcA4kEGTOC5/tMlyuUmk0CQi28Hxof51OHQxOEgBCZPu9ajMkqqhRqkXz84EGWajX+y1tvU2g0+OrFsEA0nExiqip1x2G6XKZhh1II6UiEzz70ED964EA4F0T1LUiFmXKZf/X880yulmi6Lpbr4HjhwsfbcN/+0+uv8/k33gi1+dpuwYai8NmHHuKfnDiBIt/77J1SM6TUrQ5NGyNhGoznw+8IIYjKEY6lHtgywh7p6+WH9+/jj98+j+v7PHv9Bmdn5xhMxNFkhZbrUnds6rbD//Tk4zw2qiEJjc25u64o/PKpU7i+z5cvXqJsWXz3zh1empwkYRjENQ2EoOnYVFo2TjvXCmBHZ0IhBHsyGX719Cn+7XdeoNhscm1lhX/57N93ngXLcZmvVilZFn2xGD/z4FG+fvkKd0pbteTeTyFLEqO5FDFDJ5cIx/vJ2WJYIHgX2xNCYOgqxv0DcYDwHay1bOYrNQZTiftyrBWC9SZHwFYntU1RWqnx0jcudRAuG8P3fVzX76w9JElw9OQYvQMpZu+s4Dgef/tnr/LAwyOk2qjMu4Xr+9RtG0WW6U/ESbQvUiId5dSHDnLz4ixBEHDp7B1ef+EqH/jhB+/ZuU0gsFtu1zmbMZ1YIkKz3qJZb3H76jynnxoB/G1JBbVKk9e+dQV7k5lP3W2w2FyhP5Jj1a4wthPiiFByJZkyO7Qs1/WJxw1aLYdkysTQ1dCZWxKYpkYul8Dz/Q41UlGkTlfP9/xdizme5/Pq85dZmlnt+lyWBJbt4rXvneWFLn2baXZ6RO0UBq2mTbPW2tWgQhIKdXceWdKIqL2dXHJkXx+HHhrhwmsTWE2b579+jsMPj9E/sj5uZtp6jmu5enSXgqYuKTRcm0WrzIF4f9fxNNwSbtAiqfW/P8WxVUljPHqQxdYcOT2PLGQ816NcqKFHNDw/YLFaY7wnA9sIO7daDj3ZWEdQSxKCgXSiQ1UDOiJ5d3NI6zPSnE4ewlBUVDlEaEhIGIpK0o11kio5JggIBwlZSKTTJrlcgsXFCpbl8MorNzh5ag+6biIph9k46a2sVPnGNy52iTivH2f3ETYaNlbTJpWO3tWtZHq6SKFdAY5ENNKZGDXVYzCZoOnYBH4dISJstk9Pp6MoioTr+pTLDaamihw8mCcaN8gPZbq65N3HKiMLlc0T+sZY7xqGL2bdq6FLBoqkhpBIwqXoGuUvRF2FUMhkItI5rsXFMrWaRSKxFengOB7f/e413jp3p+tz1/Oxne7BxHV9CoVqp8ioKBLp9rX1/YC5+VVWi3V6ct2Ik75ojAOZHiQhKFQavHNjjnrTpmk5JGMRFCGzP96PKm2ldu0UmiR1FUNlSSIS0UK6xw4RUzX6onEibYh+s+VgO16o/7BDYS3U2JIJgvVuZDJlIsuCVsvly19+nbn5EkNDmc47BKEji64rZDJR+vvTRCLrvPam62L73j2f6z+EsDyXjG7ScO1tnQ0lBD1xk3Q0gipLPDCQCxGOorvAsHaR5K5r0+6Udn8x/OweEvjtBCg3/r/Y/EH3bjt/2roNgczW49z2GDb93fv/gKa48XnaeP2yuklS60a7bD4Zea2K1r3B7v/d5rN7DQ8Hz2/i+DUqToOy0yCqGGT1u2uFJJImVsPuGsNt22WpWA27eKpgcqXEgf4eEqrBgJnaESptKjEMOULFXUWTdBzfDtFKkqBeayG3nydZChPzat0i37O1cGR5FnPOFG7gUnFKxDY4WAkhMCIaRkRj8uocM/PLyOkIY73pjkB8SIMN2gjf7mu6ulrnwvlpTp16All/gsCdQjKeRrF0rq/Ocjq37hKlaeEY43k+sizhuh6yLEgmt471kpBJaCMktPD3dWcR5FA0+/0U0+USN1eLjKcyjKdDevbh3l7+5Yc+yP/50ku8NTeP4/vMVavMVde7vinD4KeOHuW/O3mCr1263Ckc+X5ALKpvyX/KLYvJe1jweiFnANf3abouJQtarkfNtreMTQLojUUZTCSYq1Tw2ppDJWt7qlNC13lidIRfP/MIB3p6OjSFiGyibaBFZk2T33z0UYI2osnxfa4XClwvFLr2PZhI8MunToZFHVniuQ2FI1noKNL2yJmgfSy/cvoUv/fGWb41McFkqbTl+sQ0jaf37eXXzpwhEwmRQJ7vMd8sokgyI2au811TVfn548fJRaP84bm3uFkoULNtriwvd21TEoJ9mQy/8PBxPnbw4LZ0trVouS5TpXLXfd8uNhYA7TbCC0LHvftZ6t/rHC82fXdzLrsWpqryiw8/zFKtznfaRcDlep3lTe5kISLLpuVWUOUk2ykMpwyDX3/kEYaSSf7s/AVuFUP07Gqz2aE/bgxdUdiTSjGeSe840wngmf37qbRa/Mnb57lVLNLyPG6vrnaohIJQD+mXT53kQ2N7eHlq+n1fOHI8nzcnZklFI9Qtm+wBk/7eBE3L+f8lD2zYNpKAtGncdwPTMHXSPXEKixU8z+f2tQUaVYvopvVFEAS0mg7Pful1zr9yc9ttRSIa4+M59A3N3v6RLGc+8gBf/+L3CAJ4540Jvvz7L/Djv/AEvQM7I2M8z6eyWicS1cNie9zH1Nape5qu8OhTD/D8X7zJ0lyJRs3iy5//DtGYwfHH9qHuIHYdBAFWw6ZlOciKRHxT0y6WiDA8nmN5voTreJz97jU+/PGHGN77IJsrR47t8tI3L3L2u1e37KfhWTiBy1R9npFofufCiiToG0iE2j0EuL5H1W2SVCLEEgOhSU8blRxLpjs5tiza7T4h6B3MdGhuhcUKs3dW2HdkK6U0CAIun7vDs196fYuucNzUKTesztjVH8nQZ6S35F6pbIxIVKdcrOM6Hu+8PsHhk2OdXGvr9faJKLl2wXs9EukoH/74cSauzFOrNLl8bpKvfOEFPvvff5RMbs21eq2JvP473w8oLJYpF+uM7OvrIPUTqoksBKas4/peVzGr5dWQJY2qs4SnOSj8YIpH75nCkeO3qHkV9sYeYKp+k6iSpFZuYFsOuqmR6TNpblA+3xyqKof8P2V90V5phirwuqrQ8IpU7DlUybgrZGu2UGWuVCWqq3hteJjUhqm5no8qyxiqwkK5ih8E9KfiHO43iMUMHn1sH5cvz+J5Pt/5zlVyuQQfeeowiUQESQhatsvsbJGvfuUNzp6dwDA0ms3daWkTE0t84fe/zd59fZw8uYfBwTTRqI6qyp1Kbb3W4vqNBf70T16h0X4xjj00Qq43zsTMLOWmha5IjCZsPG8GlYe69pHPJxkeyXJ7YplCocaf/enL/OzPPUp/f5qxwwOhNeRKtcMtjbVhkarQafiV7flda8dfu0bTaxJXEjS9BpKQKTtFTCWGJmmo7Rer5lbwA5+ByAg5vRchJA49MEAqZbKyUmNycoW//Po5fuIzp0gkQkpEq+VSKtX59rev8BdfO0ur5aAococmqMgShWZji9bD+HjvepFRkti3t490OkqhUOP2xDJf+vJrfPKTJ8L7Jgk8L8B1XAYw8R2fVMJkbDCLoaudarDvB1gVh4rd7FTPXddnfm6VlhVW5T0vYHJyhXi7IJaVdWRPsGxXiccNTFNj8C72jAEhpWrNClhTFUxD3aUgKpDlAQzjaSQpwVri9eEPH+LSxRnOnbvDzZuL3NwE4YRwANM0mWTSZGxPjh/5kWM89tg+ZDWs9FfsVldn+h96eIGP43vUXbvrvIIgFKluVpsYUYP8ntAtYq0wHQQB87eXsGrhwig33ENsk0hss2axeGeZIAjoHe0h2obzB37A4tQKjcpWCLSQJHJDGaJJs5My7zQRL88UqBZrRGLh8dlNmxvnbvPmNy8wc32OZr2FmYgwcnCQkz98jD1HhzHaVLedUIC+5zN/e4lzz13g+psTlFeqxJIme4+PcfqZ4+T35O4ZIu57PoX5VS69dI0rr91gZbaI03JI9SU5/Mh+jj91lNxQtsu6NggCCnOrVApVVF0NHds27W5peoXaarjgSOdTpHoSHcg1hAKO87cX8RyPnsEM8cx6xy8IAuymzeTlGS6/cp2JC5OUVqr4nk8kZtA70sPIoUH2nxynb6QHOapi+xVMMUjJaXC5MsW+2AAZNbbjPZQkiZ6hDJqmEI0bXYUjQ1cZ7c/guB4tvA4ycr5R5mp5gf2J3JbtAahCI6YkqLkhpDmr93c05qxGi8AP8D0fSQr1A2YWSuwb2botXdLJaf3oko65wXHjW19+jbk7y52O7OLUCgMf3MvwULprqJ+eLvLi967xiU+e4I/+nxcplxrr82+1SU9PHIIKvv024OHbb9FwTjAYTRJV1m2UFUWmXG7wH37nb+ntS9Jo2sxNF5mdKfLaSzfo6Y3zqZ89gaYYeIFNwHrXd6b+HXojJ0jr96fz8F4PU9VChER79gr8AFkITg0N8bsf+xivTE/zytQ0E8UitueRjhgcy+d5fGQ0FGuWZT51+AEO9/WiCImoJ7M4Vepy4ZGE4Mk9e/jDz/zEXY/H9ltUnRJu4CKQ0GUdVZLpTdksWrOd508Qmns8NJjk9z/1KS4tLXJ9ZYWpcplio9lBNJmqQq8ZcLhvD8cHBjnS20tcD3UdgsAlrWVIa90IFxFUOZbv4befeYbv3bnFazNz3CmVqFtFDDVGfyLNiYFBHhkeYn82FM39xKFD7M8mUYICwwm9fTUlms4kMe0wmxuR1VaLo319/JunP8qr09O8cPsOt1dXqds2cV1nXzbDU+PjnBgYIK6vU/7cNjpQQqBJ3al1wjD4yaNHOTM0xKvT05ybm+POaon/l7v3jrLkPMt9f1/FnXPnHKYn9Iw0miBpJI00shUsB8myHDgYMDaYcIDDwYTrcw3mAHdxgXUX6XJYcLywwTbGOFuWLEuWZMmSFSfnmZ7QOacda1e+f9Se3d3qnpFkuGvZftfSUs8Otau+qvrqe5/3eZ+n6jiEVZWedIobWlq5saOdrlRqQ+aE7wf3NELQFk/wF/fcQ7kaVMdVTQnYDjXwuH5+r+LW055IrNNDjOs6v3/ngTq4tDmXW/c9o2xy5tWLxJIRXMdFCak8pLaibu7FKFTJzruMnJ3EsR00XcUomSRzMcoFg2giTH6hBPiEIjqtvY38yV13cXBinBfHxhiaX6i3KyZCOg3RGJsbcuxq7SSqFmqtaqKWyFvINUkKgLiq8YGtg+xrbuPU/ByHJie5uLhI0QrmwkQkTEM0yqZslh1NTfRm0mSjIcpuBdVXUISM47vIQq6JeQs84fCe7Vu4rjXL88NjvDw+xly5EqxR4wn2tLVxR083fZksju/we7ffynylTEiViYZkDKcKAsJvovXqRz2EgEwsQlhT6cql0VWFLbVWes/zmJ1cxqxauI6HVbWp1NgrkyMrwO7SfIkXv3uKSFwnEg1cr2RFRtVl0tn4VWQxNg6fQEsspLx5s5tUNsq23d1cPjuF63q88swZ2rpzvOWBXURjOgiBVbWZnVziia8e5NlHjyFJEqGIRvU14EM4rNHb07jmNUWVedcH93Hu2Chnjo5imQ7f+vwLnDo0zG33bmfbri5iyYBt49gu+cUyI+enOX9inGLe4KMffwdSNsTI4jKbmxrqgIAQgr5tbdz/c7fy+b/9LtWKxaWzU/z1J77C7tsGuOktW2npyKLqCr7nUzUspscWOX9inIunJ9m9f4Bdb92KpilMTC4Rj4fQVIVEOsreO7Zw6tAwZtVm6OQEf//H3+ShX7idtu4cqqbg2C7LiyWe+/ZxnvrmYUzDJhzVMVbJfuS0FJqhBuQP37sqK6fgVHh+7iQuHjElRFskx+XSNIokk1JjOBWXimPSEEqybJWp1nQct6e6SasB8L9zXx+P/tuLlAtV5qbz/Ov/+yTv/5U7ae3MIisSju1SWCpz9IULPPKFF5keWySZiVJYKtfXMZWqhaYqVEybeERHEhIb8TOa2zN0DzQzPRbIyzz6by8RS4bZua+/zgh1HS/Q2bVcMh2CijeDKkXX5GaSJNh/3w4mhud5+HM/wKraPPHVVxm9MMOt9+5g83UdxJJhhADH9lheKDE1Os/ZY2NcODlOrjnJb//5B+rAUVTR2ZXpYbg8R+g17u4pvY0Fc5iwnED5SWQcIQQL5iyGW2HJWqBD76d7SytGqUqmJYXpetdMUCVZwjAsSqUquWwMz/MpGFU8L+ixzOp9JNU2TK9Y79e8WiTCIQKd0YBWH9AwAyp1gHQGJ78hHkWVZTI1a2pVlbl9/2a+/+xZhoamMQyLL37xRZ555gytbQGVOp+vMDa2QLFYpakpwYE7t/HvX3zpmoLIluVw4cIMp05N8J3HjpNMhmlsTBJPhFAUmWrVZmY6z+xsHsOwkSRBV1eOBx7YRVjXyETCnJqaZU9nC0LIbORekc3GOHBgK9PTeYyKxYsvDjE0NE13T4Ci27ZDuWRSqVi8//038da7BoNjlkIoQr1m95+PT1JNkdayFOw8MSVOXE2gCq1m71o7h0JCESoRZaXtYMvmFm66qY/HHjuObbs8/PBhzpydpLMzi6rK5JcrjIzMMzGxRDis8tBDezl8eISzZwOXCyEEmXgEw7riuBcwuObnirS1Zbiiq9nb18ievT088fhJXNfjWw8f4fChYVpb0yhKAFCVyyZCCH7xowdoak3huh6XxuaJhluJRwNb6r//X08yOrqAaToYhoVpBoJ5pmnXz+U//uP3UJSAyRMKaei6Sjii8qEP7Wf37h4aG67NXvDx0SSZUE2cVFNkFFm+KvIdjIOMLDfWxsDHtl3y+QqZTMA0s22vrgu25rf8GititsDsXIGh89OYVZs77tyCJsn0JNYj8z/OkVRDFNUqM0Zx3Xzzr//XV3n+ay8zsKePT375Y8RSK4m2aVj81S/9I+cOBpXtj/7Zz/DOX7qrfl/4vs+Zl4f405/+GxRV4Q++9FsM3rIZCCxNP/P7X+TFRw6u259IPMyv/82HufXBG5ktl8ibJt3J1IYOL//+F9/kyc99n4G9/fzep3+VJz77LA///RPk5ws1Gm/wOVmReOzTT3Pvhw/w4G/ch54IM5kvYLsumxpWkgXbcjj6vVP8yx9+icsnRnAsJ6DOSoJnvvQCT3z2WR78jfveED3aqtocfPwo3/i773DmpSFsy64lOsH2nvrX5+nf2c07f/lubn/vzWihlYXg4//8Pb76V4/S2NXAH375YwF4tGofP/2Jf+PFbx3C933e89/ezk99/N2EVjH2xoem+OQDf05pqczvfPq/cssDe2stez6F+SLf/PvH+c6nv8fyXAHXWaWp4gfPFVmRSDUkefA3387dv7gL33dxfRvX94jIOu2RLI7j8unf/zdeeuTQumOPJiJ89C9+hu7dfVQrFo7tQq2oadkOMwtFVEVGici0pOLIkkRTJMFweWHdtq6E6ztU3BKykFElHcPNE5Hjdcq2UTaxLRctJCHgqnavru/g+S5T1XHaw10r+5wMs//+3fVWwgvHR3FiKpqirGn7Taej7NnTg6JIFPIG++/YXLf3nZ7KMz6+iO8tI6Qksr4fx3gYgce5/CxN4ThxVa+B0wr79m+mathrmSs15okSKXOx+HX6ku/gUuFRSvYEVzjqBXuYhvDOq47Vj2u4vkcyFCKhB2NklKtMXZol05KiKRfn/i1buG9gINBmIAAsFDnQHrpy7zTH4zTHg+fJ/EIRM2ERWiXELoSgJR6nJb7yzPF9B883EUJbs1aaNEaZMGZIqUHrYMFZxvUdkCNMV/MYbpBUq5JKWmsgIscYyHXQn80Erow1Z6Ars6rwLYR3CVUbqLEI3YAR6zv4zhlQdxCcYxlwAYHvTiDkLrIRmXf1m9w3sA/X1/DsIYTciixHUGUVWfgELRYWuYjCbZ0tuJVnkaQUKJuJqpuvumZxPC9gO4dC3N3fzx09PfUxFgSJqirL677u+T627xK9isOsJARdqRQdySTv3rZtzTblmmbUlQLl6qiUTZ59+gwtzQnisRBGOSjWZCM6XfE0s1N5EukwvuejqDKF5Qqe65HKxmjvDFqLh85Nc/jVy3iez+COdnq7VwA51/U4fmSUs6cmALhhTzdbBts2PAazYjI9PIeqKehhjUxzil0trTi2QzUcIupKxFIRRs9OEs/EKC6XMasWhYUSyVwc07CwTZvBmzchKzIpVeEtfX3s7+5mYmKJI4cuc8vtm0kmA80qRZIQ2CybI0g1fcbiUoXzhy6Rbkrh2g6SIlMpGEFnguOyNRShO9GBF29HDWlIsqBrSxuyEMg1owUhBOeLl1my8iTVODElwrKdJ62mKDglVEnBcE1UoeCqFW7sj7Gtq4eAcwwxNUpjKM2cOc9IxaDolOlOtxOLWRiuyZgxRtU1iasxtib6NxzLH8dQZRlNkWlKxUi8xqTIqFj8/R99g0tnJwO5B9vFr+Vf9ionq9GLM/w/v/fvdZairMhoIZVcU4IP/8597Nz3xscrrmk1fbc3X7xUNYU737mTk69e4tKZKcqFKl/6x+/x4pOnaGxNI6SgPW1yZIHCcoVYIsS7P3QboxdmeeaRo6+7fSEEze0ZPvSxt/H5v/0upw4N49gu54+PcenMJKqmEIpoKEogCWKZNq7j4tgeDS1JHMelMRJia3PjOkMoWZa456E95BfLfOdLr1BcrrA4W+Spbxzm+98+jhZSCYUDIWnTsLFtF9d2cV2PTdvb6OrM0dGeXdOuJ8sSt9w9yJEXhnj12XP4vs/xly8ydHKCbFOCaCyEUTFZnCtSKVaRVZk737UTx3Z5+uEjddkCIQQxJcJ0dYEG/epOp77vE5Y1GsIpQrJGTk8iC4mibZDVExTsChktjiLJeL5PzA/0K/VVLWSbtrdz693b+d63jmBbLq9+/ywXz0zSPdBMKKJhlE0mhudZnA1YZTe/ZRvX3dTLZ//6CcrFan1/TduhUKnSkIq+pktgJRRV5t737uH04WEKSxUW54r80198m2xTglQNczANm1K+Qiiq87t/+zaUpo2vy1gizEO/cDvlgsEzjxzFrNqcOjTM+RNjqJpCOBpoPplVp3ZdeIGjsucTS6xty6+6NlXPoS2cXXMX2J6B6ZaQkCk5C+RwXkdO5o3HjwxwpAqN3ugWlu1FBuKt+A7MTS7R3JljfnKJ9JZGkuFriPn51NpoVihe8ZCOLAs8HBbNSxjuMhVnnv54Flm++gA2J2I0J1bcQV4vVu9Ta1uaX/zoAT7zmecYOj+FZbmMji4wOrqSBEiSYNOmJt7/gZtpb8/wxOMnWKhZLW8U0ahOc3OK0dF5qlU7AIpmNnZxCEc0du7s4qGH9tLb24jtecyWStzY1U4uGkFR08hKD7zGTURRZO6773qKBYPHHjtOuWwyM1NY9zuKItUZTQBlZwnHvzZjqie6CUUEiWBcDRzIkqydUARiQ22VSFTn/R+4maWlCocOXcY0HU6dHOfUyfE1329qSvC+993EXXcP4rge584FwJHreRims6bK7/s+4bC6BlXWNIWf+ql9LC9VOHp0FNO0GR6eZ3h4fs3vZDIxbNvFdhw8zyMcUuuJzhWAb2xsreD5a8OyHCyL2jgGDAUhgtaOYD+vXTlRhFRvmRNC1BwOwLJdVO31QRzDsPn2t4/yta8dZGG+REtLiq01ByNNk+s/77k+RtViYb7E0NA04+NLLC2V+epXX2XTlmYqSpDk/XDd7T+aIYSg4to1u2lpzetN3Q2YVZvZ0XmWZvJrgKOZkTmmL89i1B5G5w9ewP/oW+vXsud6TF6YJj9fpK2/mWRubUtQc08DXVvbMSsm1YpJYaFEOR+wNxzHxfU9ZkolSrZNazyOXpu6Pd+r03atqk2lWGX8/CSf+eS/88I3XwUEm2/sJ9OcAh+mR+YYOT3O7Og8X/3LR0nlEtz3S3dhux6F6krVyPd9Tjx3hv/9e59j+OQYiirTua2dtv5mJFliYXKJyydH+fTvf5Ete6+90PM8j+e//gqf/sQXmBmdJxwN0bezh4aOLJIkWJxeZuTUGKdfPM/EhWkcy+Henz+AkGt05M5AS2xxepnp4bk1wFF+rsDomUkqtZbfocOXqZbNNcDR9OVZFqaWCUd1Mi0rbL5qxeQbf/cdvvyXj2BVLXJtGTo2t5JsSOB7Pvn5IguTi8yNL7A8l0fVZDRdI812QnITZddAkeT6+Dd3N254Dq+wprxRKQAAIABJREFUB6LREKqqrAHaFFkmGQ8FIJokMGri9o7n0h5Jo0jyBkos4Pg2STVDVEng4zFvTiEJhYwCtuUG4FTtvlQVmY6W9BomQv3c4DNnzpBQk2vu4537t6BqSp2xEEtFGJpZYL5i4KzSRYjHQ8Q3t+C6Hrfcuol9+zbVF6LT03lc10OIJL63hGN8EyHlEEIhqa1tB1A1hYEtLaQzsQ2BSMerUnaaUaQIFWeGrvg9KFIYfJ+R0lMbjNCPf7iez1SxSHMsTmMUiosl5ieXCEV1Ug2JeiL8RiOdirJFWxGwvloYzhgzxS+TDu8nFd5Xfz2rNZJUM8g14wfHt2qJtIQkJDw/AHeEEPXPBH+LAPoRVUBCloJz7zsTeM4RJNGGV30O3ysipDRCSuNWn0T2XXy/iKRsxbOPI5Q+fOtFRKgB353Frz6CFroLoQzg2QeRlLvw3fMgGvHdCUDBd0eAEFLoToTcAgiwz4LQwbfwpShIV3ebuaINtOa8uB54PkKW6hohsiwRljX2ZjZtCP5cifp4vInzVqlYPPHoMe44sJm+viYqpSrhqI5pWGi6glm1WZovousqkixRzFdQVzFhg5ZTFVWVefqJk9i2w87d3av2CSLRQFj/u985TiSqXRU4iqWi3P7uPbX7WqDqKq7jBnOL5yPJElpIRb+hG0kSNHdmkWQJ23JRNRm3xoIMR/X6GElCBIUQ22NpuoiGRERdrQeqkdIH663djh3MiKZh4toepfwyiqogSQJFk9E0hWqxil02EZZHKKxt2PanCJlwrQ2y6pmE5TC27+D4DrIvkdPSuL6LJCQc3yGqhFmy8oTlEC3hLCFZZ8mSichhZCHh4WJ6FjElguO7LFl5EurrtzD/uIXn+7w8NEpDIsbu3rZ6ou25PtPji8xN5a/5fd/zMauvcSYrGDiWs4a58oZCiMDB74dokxNC0D/Yxs/993v5l798nJGhaSzT4eLpSS6enlzz2ZbOLO/5yH4OvHMnLz55imcfPfbGckNJMLi7m1//owf5+mee4+WnT7O8UMKxg2f0RscryxLZpiR6SCWqbXztAsSTEd7/0QN09Dbwrc+/yPDQNLbpYFZtzKpNcYOuyWg8RDoXD+RVNigm5VpSfOi37q231lUrFpVSlUppbYtxIhXhLQ/cwLt/fj+Hnz/Ps48cw6m1lflAyTGouAaOf3Upi7gaZndmAE2uMVWFoD3SUB/XhlCyjge2hldy8dXbi8ZDvP+XDuC6Hj94/CRVw2J+Os/89NprMBzV2f+26/jgb9yFrEg8+oWX6sDRla6BtlzydeflG24d4P2/fCePfuElpscWsEyHqdFFpkbX5nyZxgSalKEl1rGhtpAQgkxDnA997F5aurI8+8hRRi/MYlsutuWuc6qDoIjZ3JFm4Lr2NYYyk8YSp5bHyYXi7EyvFP8EMpZnIAsVRfohRcSuEj8ywJHj25TdIrZvMVIeYjC2h2QmxsWTYwzs7MJyXWzXrSdig4PtfPx/3I/juESjOql0hOVCpV5N8/yVyhEIFBEiobYQkpP1Kpqiyex/6xYGBltIhEO01OwTX9ujDbC8WCYc0dBD10bshBDs2tVNW1uGo0dGOHJkmMmpZSoVC02VaWpOcv11ndy8r5+mpiSW5fDxj7+LcsUkl4ttOP/19TXyB598N0ND05w7N83MdJ7FpRJm1cZ1/UB5PhujszPLddd3sm1bK7GaeJiLT0zTGFvOI4BsNIe4CmUtm43x4Y/cwa7dPbz66iWGh+fILxvBwiKik8vF6ezMsmOVSLYQQX/qkjVNg96Fqsrce+8OBrcHgrFNTQlUaeX33qgQ7erxbG/P8Nu/cx+HDg1z+NAwIyPzGIaFqso0NCbo72/i5pv76e9vQpYl7r9/F4OD7YRCKg25ONXlPPGasJwQEIuFKJfMdQ+bzs4sv/t77+DYsVEOvnqZsbEFymUTRZGIRkM0NMbZPNBCW1uaC5PzLBUMfN9noCtg8qRSUX7jN+6h8jqth2UneP8Ko+3KYW/b3EbVsTE9h7AcuLjs2NXBb/2Pt6NKEqlkFF+m5rqyMqZhTWVXfxvJWBgJwTvfdQO79/QgCUFzy1oXB8dx+d7Tp/jsvzyPYVjs3dvLR3/pTrq6clfVz/I8nwsXZvjbv3mcM2cmmZhY5PixURK7UnUngp+kcD2PqKqtA8T6r+9GUWWWZ/MsTi3RPtBSB5fHzk6wPFcg3ZSkuFji8skxqrXWMAiYMZdPjgHQsbmV+CohYVmR+On/8z285zffQbVcpbRU5tFPPcm3/uG79c84no/jeyR1nVjt3Lu+y8n8eRr0DC3hFZr0/PgiT/3rc7QPtPLzf/wBrj8wSCwVwfdhaXqZJz77DF/4069TKRo89unvcfP9e0CHxYpRTzSWZvL82599g5FTY+gRnft/9R7e9St309TdgBCC4lKZFx8+yBf+9Gu8+K31TKkr4fs+F48O8y//80vMjMzT2JnjZz/5Xm56+y5SDYGwdzlf4cjTJ/nnT36JsbMTfP5PvkL7QAuDt25GkiS6tnUQTUTIzxeYujTDzjsH6+M+P7HI9PAs8UwMy7AYOzdBfq4QbLsWl0+M4rkeufYs2ZZ0fU4fOzvJ4//8DGbFZNddO/i5P3wfm3b1otXmeKtqMz+xyOSFaS6fHOWmd+xCSFA2J5DQkESEmBK0IMuKxAc/8R4e+u8r5/CRTz3JI7Vz6Hs+o5eD1q9MbkVfxXZcqqaDpsj4Powv5NnZ3YIi5DWuY2vGFI8pY4x5a4aIHKU13ElYXqFDJzNRInEdPRy4SVYth4tj87Q1JtdtSxMacTWBJumE5BUwRw9rlAsGB586ycJ0nuauHI07WulrytattleHLEvcfseWNa81NMS5+57tIClI6nX4zmUk9XqwZVzPW9N2uThf5JtfeZUP/OytJFPrha4VKURS6wagP/kgcbUTqQZO2F4FXXpzbjU/DpEOh+hKrsyxpmGhh1XSTevP45VwvSq2twC+hyo34GMhUJGEhkeeaDSE4y1QdWxkEUORYtjuUuC4iYImZ4moPcT0QTw/WFj7voftzuP7JrqcBhxcr4SGjI+LKmfwfRfbW0IIDU3K4nh5PN+h6tqocg5wmSt/G01uIKYPBq/JLSDC4DvgO0j6fnzzBVC3IKnbEdp1+NWn8DHBW0RIN+CLBOAh5LbaZ/YgRAhEmoAimMKvtUWChO/Ng0iAv6KdI6QGPPsYAJKyf8NxLBcMrKqN7/uEaiCN7/lE4iEuHBslFA1arYZPT6CoMl1bWustoqGoTtEsY1sO8VQEo2Jimy7xVATTsLBMm1gygn4NwdONoq27ge17ute93tnXuP7DrF3HdnU3kMvF66yi1SFJEpu3ttLckuLIocvX3AdFldcUTK4WseSbF6vv7W+it3+9O2IAtq0kPpmmFOm7rn4PvNHoiQXr2LopxAaJaX0N4AfP23lzEUnINOiBtkpaW9kPgaA5FJwL23OIK1EU6UcmxfpPi0wswlyhjGmvLWmEIxq//In714EMbzRUTaZv23rAsq07x2/93+/FMgPDnr7BNhaLFRRJIhbS0GRp3RpU0RR++tfeyn0fuKm+DbWWbJu2Q7Fiko6HUVSZGw9soa0rx6Hnz3Pq4GWmxxexTActpNLclqZ/ezt7bh+ge6AZSZK46c6t/P7f/Sy+79O//fXdomU5cGz71T+4n3se2sP5E2OcOzbG9PgSlXIVfAiFNVLZGG09Ofq2ttI/2EZT27WF6AFiyTB3Pbibnfv6OXt0lLPHRhk+P0N+sUTVsFAUmUQ6SmNriu7NzbR15di+pwcAx3ZxHbfO7q6WTVRdoXdrKx/7s/dx6tAwx16+yMj5aUr5YG2YzsXp3tzM3ju2sPWGLvSQyk1v2Vpn3HT1NyFJAt/36Iy0kFDWzhUf+Z37KOaDIl97bwOarKwDltb8ex37mHWfbe3O8at/8AA3v3UbR14YYnRolmKhgiRJpDJRere2suPGXnbs7SESC4p0v/ZHD1JcrpBtTGD7Pok3oJElhEAPqTzws7dyw75+XnnmLGePjbI4V8Sq2gFAnQjT2pWlb2srucY0unxt5+F0Ls77fvEObr/vOk68epkzR0eYHF4IWunwCYU0Mo0JWjoy9A+20bu1lZbO7BrgaGuyjYFEy7r2aMMNcv642kRKa0PdwEH0h40fmVnN9V3GjWE830WVNFzXwShXa/bCVZyooGLZ9Yk81xAn17CaXu2zqa+pfnE1JqNk412osoztG8yb5xFI+Hhk9V5Aw3JdirJLojPBLQNd16zCPfLlV7lx/yYGNpjYVofv+5w/NUH/1lbuvmc7t9+xua62L0TA7NF1pe7YFQqp7FpV/dkoFEWmszNLR0eG227bHDAQXL8uOC1EgB6ralBtWX0cluOQjUaYK1XWMAquFqGQyt69vVx/fSe27eC6Ky4BV35DUlYess2hPjzfQxZKUHmXoKsnR2d38GAVSDUXCy+oTr7JysCV851KRTlwYCv79m3Ctu2aIKuELEtomoKqrmhbtbSk6iCg7/sk4kG/6JVkM5OJkkpFNkTcU6kIt9++mZtu7MN23Pp5u5IcqqqCrEiUapa3juth1zQjQiGV3bVJ+VrxwsxlPHyKtokuyUQVnbxlUInYnMvPMlMtcV26lRmjQDIZJrI1QlaPIgnBxfIC7dEUKS1cT7xURaYxvXIv9Pc30b/BAgwCp6NnnjlLuWySSkd41/276O7OXfO8SJKgt7eB7TvaOXNmkmrVYWG+xC3ZzcRUvV4x+EkI3w9s5ifLhaCK6Pk1h0GF5t5GoskIi1PLzE8s1kEWx3YZOzdFtWJy24M3cvipEyzPFpgZmaNnRyDg61gO4+eDKlZLXxOR+MoDRQiBHtbQwxrJXJx0U5J089pEWBaCmKZj2HZ9Py3PpuiUUYWyBjjyfZ9YKsZP/R8PsOXANiaLZXriITRFJtOS4r5feCuHnjjOsWdPMz08y/jQFKnrVvQ6AM4fvMjpF87h+zB4ywAP/dY7yKyyOk1kYtz5gVsoLpb4zB98Eeu1FcRamBWTp77wPFMXZ9AjGg/82r289YP7UVbdr9FkhFvu34NZsfiH3/4X5sYXeezTT9M92EE8EyPXliHVmGB2fJ6ZkTlcx0NRZXzfZ/LSDKWlMnvv28nUpRnyc0XGzk3Sta29PhZjZyfwPZ9ca3oNoDR5cZpSPkgob3vwRrbc2L9GX0kLqbT2NdHS28j1B7YhqzKWt4jtFXAxEUQo2UbNlnv9Ocw0rZxDWZFo7cgQjmj1Vi4I5o9wKLDbjYQ1trU3osoyEVTcwPN13ZgKJJrDHdi+jYRAl0LocqQO0CuqXAe/hIBISCURC1E1158j06uy5M6jCI2QFCahrOzz0LERHNvlhtu3cPLlCwxbFTZf10k2vj4p9H2fUqnKiRPjFAtGfbdbWlPs2K7g2UcRcjOu+Swl6xYqjrWmUOD7fo0l9fqRULtgVbKXC++oGyv8JEXVcZgoFclFgsV3uFYMuhZQnzdfoWgeRZUyZMJ3kDcPElZ7CCvdTBe/QkwfZLHyFGG1G9evkou8jYnCp4lqA1jOLA2xB4ioK88w3/cx7MssGc/i4yBLMWQRxbAv4/glFBEjFb4F05nCdhdw/BLZ8F3MVx5HlqJ4fpWYNkhY6SZffQlNaQEhkQ7dhu+O4zvD+HInCJWghV5CEMLzFvGdSyBkfOsIvreA783hu8P4TitCHQS/hG+fAbkF372M58SQ1OvwvBmE3IGQO/HtMkIOqrC+exnwEPqttaMTIGIb3mNHv3+WyUuzCEnQs60NH5i6PMfADd2cOXiJRDZGKKpz9uBlwjGdVC7Os984SM+2NhraMpw7fBmEoKW7gUsnx8g0Jdm2t48XHjtKPB1l8MZ+GtpWgCPf9ykVq5w5NcHMdB5ZlmjvyLDtSmIqoFQ0eOkHQ8zPFmlojLP9+k5i8RC+77O8VOH0yXEWF0rIskT/QDM9fY2ob9Bl6Vrh+z6GYXHu9CQTY4tEYyG2bW+jsTmJ78PFoWmqhk04ojF0bhohYGBrC909DUhSsP6bmc5z8tgYpVIVr8ZWam5JceMt/di2yw+ePUshbxCOaNx6+2bitYKLZTkcOThMY1OCpcUSE+NLRGM6N+zuIZWO1J+/ly7O1vfD8wOx/ht2d9PV03B19hfXSFhf+xkBMjKNoYbX2Ehcpa1FyDSFcld9/8c5RuaWcD2P2XwpMAmqva6oMjfc8p/bluf7PiXHYfDm3rpzLcCnHn2ZXDLCnbv7iWjaOrkEWZbYtqt7w22OzS7z6Mtn+Pl795KMBnNqW0+O5o4Mdz24u94SJCSBoshourJmvZJIR7nl7sE3dRxCCDRdZcvOTjZtb+fu9+wJgBvPA0QgEyEFLduapiBeYzZxxQlQqs3/qyXifSDXnOTWe7dz44EtAePYdRk+Pcn89BJdA62kckHb6MjZKcYvTNM72M7cxBK2ZaNoCpZhMze5hKIpdPQ3YZRN2joztLSlsCwHVVNYni8ST0WJxkOU8hUcy0EPqWQaEuy7a2U8XN9FlVTOFYaJKuE12kzX3dR3zXFa/Wy7cvyz1SUulye5KbvxmAshiMZD3HL3ILv3D+BYbq0wJZDkYExVXa0XxiVJcN2NKyzTg+fHWSiU665qrxeKKtOzpYWOvsYaeeOKjlMAwquqgqLJV5UHWHe8kqC5I0NjW5r99+3AsVy8K23MtdxeUWVU9cp1Ufte7SIQvkAVctAqeGW6EgLXtxgtHyWu5ogqWZpCm1H+k2RFfmQyPk3S6YkO1MAdH+EF9NZscwrbdEjoOulI+Kp2i0IIxscXqVZt+voa0XW1TjvTRISM3ovplrC8IldGV5ElVHktmGFUTEqFKtF4iPmZAi0dGRbnihTyFWYml3Fsj1xTglxjnFKxyuTYIr4P7Z1ZQmGVS0MzfPVzL/K2B3eRaYjT1dtAsWAwPb6IIBDYikQ05mcLzE3nicVDgZCXLDE3k2duukAiFaapJcXcTIF0NkY4olFYrmCaDplcjPnpPMWCQXNbmmQ6SqlgUC5UKRUNhBB09TXWL1rP98kbVaLaG+9tlCRBKKSu0UK4Eo5n8cL8t9iVeStROclo+SyypNAV3YZA4Hg254oHGS2foTXSz7ZEgPgfX36Ovtj1pLSNxV6vFiV7iZAcRZV0ZFkiEtEYr4ywbM0ymLzldR/MQog1lrNXJrCrMRKvvB+OaFwLK25pSLJcDESwE7E3J34YkhUM16mDRnbNmWyhWiGiqGT1gOpctE1ao0laIwlKtknJtpCEoGiblB2L5nAcVby5hWGpVGV4OHBzSSYibNrUdNWF1eqQZanuGHHFSSkXir5pIPBHPQzXZqZSZNkyApHsRYOR4XkGd7QTT8do7WtiYXKJsfNTODUNGaNocPHoMHpYY/CWzcyMzHPhyGUmhqbo3t6BEILCYonZ0Xn0iEbHQCvKVZwvrhZCCBQhrXUUA1pDjagbVDV33L6VPfdcT8l2OD08Q2suiVYzDkjm4my+sZ9TL5yjvFxhYWqJ8NZGDHvFGeXlRw9jVW2EJHjLf7mNdFNy3bnWwhr77t/Do596kvHzUxvu98zIPK88dgTf9+nd0cn+h25e5/ohhEBWZPbcez3XPzLI97/8EkeeOsmlE6Ncf8c2QlGdrm3tnD90ialLM1RLVWLpKJ7jcf7gJWRVZtOuHsJRnRcePsSl4yPc+u699Sra1OVZJFmifXMr2uoq/4rRI6WlMq7jrQGOVu+fesUtxQPbK+F4BorIkdXjb0jjSwiBUbFYXiyj9Sr1MVAVien5QsAWVCXmCmU2teQwPYeKY1F2rNdqgQNgeVUkIZHRAg2Tkr1MWI4F4rmszGO+7xON6PS2Z0nGwxSn1vLXQ3KYdq2bgp0nrWXr53h6dJ7p4Xk8z8comziWQywUI29UN9Qa9Dyfh795hMuX52hsiNfb4nRdAT8CIoKkbMJ1p0npIW5u7CIbWqlGxhNhkqkILz1/jk2bWxC16zzXEK8nkVdisvIiTeHdKCIwulisniGudRCSr66n8OMYluvSEFkZI9/zsUwH6Rp0ekVEEShocmMNuDHxfRvwcP0ynldFlXM0xz/AeP5TVJ0xwKMx+iALle9Ssc6tAY7AI28epGSdQVdacLwKmtxAVNtGyTpFTB+kYg+xZPyAiLoJ1ytiubN4fpVc+G3g+yxXXyQVupWYvoOEvou4fl2w0JZySJH3IkQcIQZAxBChu0FKIoffAegIuQ28AoibQUSRwu9BiGjwd+htgBRcW+EHECIMUho5/B4QURA6spQFIYFIIIXegRAq+Cb4FSRtT3C9bwjE+QE4FNaYn1xGUqSAhWTadGxqobWngZauHB0DzeRaU6QaEkiyxK4D25geXWDk7BQdm1twLIeebW2MX5zFrFp0b21j7PwU5mtYyTPTeT77T99nYa5IW0cGy3QYHZlna61lzLZdvv3wURqbEsiyxGOPHGXvTX383C/cDsCrL13ghe+fJ5WOUiwaPPbwET744f3cfOt/XDDeMCz+9TPPc+H8NC2taZYWSzz1+Al+5TfvprklxbEjIzzz3dM0NidIp6NMT+X59sNH+NjH30l3bwMLc0X+4W+/SyoVIZOL8exTZ8hkorz3p28OCnoECdfwpTlOHhtj8LqO+j1vmjaPfuMw1apFJhsjHNa4fGmWIweH+a+/eQ/hiMbBVy7x1S++zOatLVQqFs8/c5Z9tw1w480BiLGUr5CIhdYlcr7vs5SvEIvoVKoW0bC+DmjLFw1Cuopem6+LpSqaKhMOrWeLOY7L1GyedDK6oXvhT0r0N2e5PLtU13z9/zNsx+Xrz5/k7t0DbGpf0V80TAvT0kiFw6TC12Z1vDbackned8f1RFedQyEEiiq/YTv7/0hUfIeCbxINa7wyPUFrNBG0+ZWL7G3pYL5ikAqFMWwbx/eIKiqT5SKzlTKbUlk83+NSfomOeJLGSIwzi7O0x5NokkzVc2iIBzo9sWQYx3bwXJfiUpnLpyeolk0s02bo+Ciu7dLW18SlU+PIioRtOURiIV749lF6trVhlE1Gzk6hhYIWWD2skp8rEooE2maqvvEa1vN9NFllX+56DPeNs89832fcmKXqWvTF2utX1oQxyxPTL18VOLoSkiQRjujwJsmOg11N9LVkibxON9HqEEKgaivrON/3OZG/SIOWoiW83lTgajFv5Zk05tie7EWW5TX7XylVKeUNHNsN5FGsQO8onQvWV6ZhoYc0pscXaW5PU1w2CEc1WjoDg5mIkqEzuhNdiqHJkXr7+H9G/MgAR77vUbSXWbYXsT2bnfF9pBsTaLpKc2cWPazRkb42PTW/XMH3N14HZPQ+SvYMmtSLIoJE3/OgYtmEV4Eqc9MFXv7+Ofq3tvL0Y8f5wIdv46lHj7O8UObIy5fo6M7xvW8v8oGP3MbyUoWh05OUi1WOvnyJB39mH/MzBZaXyszPFlBVmaph852vHQIhiCXCqJqCbTp8/QsvkW2Ms7xQYtfNfbS0Z3js64fINSaYHFtk34HNnDo6Ru+mJvbc2s9z3z1NOKaTycY4fmiYdDbKC987y7vev5ehM1O8+MxZBnd2EgprdPQ0cMXoKK7r3NTVge+D4691k/N8l6pbwfEtVKFj+xYROYYQMhUnj+u7RJVAjd31HcpuHs93WbJmcD0HU1SQJYWYkkIgcD2HeXOCc8VX2Z68jZZQD7JQKDnLxNU0mrTi4GT7JhUnsJKN1archlvE9V1CchRdClNylnlx4RF6o9fREOogrqRxfCsQ29YaAl0k38f1bcpOAUlIRJREQOv3jEC4E5+InEAIH3/V8ft4SMi42AENusaKChhcSu3fgWaDJBR8P3C3kYWGEBJhXWXX1g5+mLgh+/r0VoD2aDAuDaHYOhr1D9se5nmB09ubjWKxymjNGUPXFTKrnKl+ksL2XCKqRlc8Q0TRMNwqC3OBm2AsFaFjSxsnnjvL6OlxbMtGC6kUl8pcOj5CIhune7CD3us6OfHcGUbOTHDTO11UTWFiaJpyvkI0GaFj63q70DcSBcukaJp1QMD1XC6VR8lpGdojLSsfFLDjtq2BOOliMQCMVm1HCEG2NY2syEGffbGKZbtoNdDErJhcPD4CQKohQd/1XVdNVhs7srT1t1wVOBo6fImlWr/5tls2k225ektRIhNj54FBnvvqyxQWSpz6wVmuu30rmq7Se13AGpi4MEOlaBBLR4MF0KGLhGMhurcFDnHPfvklhk+NBW6cYY35iUWWZ/OoukLf9d1r2ze2tZNuSmKUqjzyv59E0RTueN8+Mi1pZGVjdqTrm4SVYO4pOgYTxgI90SbWWb1tEJ7nsbRQondghQ3ouB6OG7RtKbJM2bQCFqPn0hiKU3ZMEhtsWxUaBXsZx7Npj3TTFOogqTXgGC5GxSJaYyI4TiDgXyhVacistyCXhUJXdH0VcKGWLDtVm5Gzk4QiOsnmFI4kb/h89Tyfqallfv7D+2lvX0ux9/0qQsrgmi8iqdfjVRXGyksktDB6ja1oWQ7LS2WOHxnhpeeGkGpg/zse3M2uvb217Xi4vsVU5eWag5qP61tMVl5Cr7hkQiqZcGQduPrjGqokkwmFyEUCZoWsyuTnCwEtProx7VxXWsmE72TReBpJaChSvAYOgeMGOgy2O49hj+D5JrKI4XoVDGcY25snom7B8QrYbh5PMnH9CpqcIRHaRTp0GyAomEeRhIYkNATBf2G1h1z0XmQRRpEy5KuvoEhJXO+KdqOPhIbpTBNSOlDlNIgU4rUthnK29v/WldekVe1Aq/5Gbl71emzD11Ha133GdycQ6nUgX509Horo+H7AOqyUTKoFg1gyjB7WEALOHr5MPBMllopw7vAwkXiYeCqKJEtkmhL0bm9HD2vkWtNMj87jOR7FpTKWYeF5PuXCik287/s8+dgJZqfz/Obvvp2OrmzAKDWdemJiVm0JVekAAAAgAElEQVSu39nFz3xkP3pI4VtfO8SzT59haalMNhdj/4Gt3H7nVvSQSrVq82f/8xsceuUSN93S/x9+Tp8+McHhVy/z337nbWwZbGNpscxf//mjPPPdU7zvpwMNrEK+wq//9r1s3trKzHSev/iThzlxbJTu3gbOnp5kcaHE737iXURjIZKpKIdfucTAllYkSUIPSdx59yCpdIRzZyfX/b5lBWDpr/73e0gkwhw9NMyn/tdTTIwv0j/QzPefPk3fpiY+9NEDNZFhh2wuRltHBtt2mZkrEAlrVC0b03TQ1IChXixVeeHQJXbv6KRUMWlrlrFdF01VsG0HSQhm54s05uLIkiBfqnL01BgtjUn6uxoCoXcfqmZgSBPSVY6cGqevK8fgQOu64/hJianlwDjE830cz0OWArfpp45cwHU9xueW2dbdzPmxWTa1N7BvWxeu5/HcicucG5tDU2Ru29HD5vYGSobJ139wkvtu3EJTOs5CocJjr5zlHTdtoVy1+PbLZ3ny0HkmFwpkExHu2r2J7d3NgGBiPs/nnzzEUtFgsLuJWwd7MG2Hx189Rzoe5tz4HKosc+/ezXQ1BUWF545f4uWzoyQiIf7LW28gHg7mUcdxOXZxklfPj+N6Hjv72rhxSweqImM7LoeHJjh4bgzbdelqTPP2m7eiKQqT83mePnqBxUKFzsYUd97QX2cxbRQePvOVMi9Pj9GXzLBcrdKXzDJeyiNJEqcXZpCE4MziHCXLoj+V4XzVwPJcls0qrucxUcojhMBynfprS1WD0wuzGI7N7W3ddCRSJDMxZscXEZJg9NwU5YKBqinkF8rkWpK4AlK5GIl0lOGzkySzMZq7csyML9aKRW5tvRa4NlpVh4a2oNU/25xE0zcGWiQhEZI0hssTdEVaX0+yFQiMIJasIs/NHUMWEhElRFjSSWtBJ4UPFOwyhmuiSxoJNYIkpFquZ1G0A63YuBpFl2qtd66F7TkIISg7BpqkkFRjVFyz/r2IHMLARA+pdTZXtbY9IQRxJVLfnu/7FJ0KhmsiEMSUMGFZx/Fd5swlvjvzCjekNkPtvZgSxsen7FQx3KDb58p3fIJtvbJwirHKLBktQVjWyWjJOjlmcbbA9NgixXwFgSDTlCC/WGZmYolNg+0UlirMTU1SyleQJMHyQglVlWlsC9b2BXuameoQaa2diJ9C08KI/yRm9o8McCSEIKGmUSWd0coFHNth8tIsTR1ZSvnKun5w13G5cGyE4dMT6GGNXXcOoukqhXylLli4OmShkNTaWJxZ5vArh4kmI2za00suvrYHMxIL7FqXFkpkG+KMXZ5HUWWS6QjX7+nhptsH+Ke/eZL52SJNrSmyjUHrw/FDw2iawvZdXbzy/HnuuHc7uq5iVExM0wlEq/d209CU5Nirw8STYe57cDcXz05x6KWLzM0UmB5foqEpQblU5czxcfo3N3Ph7BRbdrQzO73MW95xPY9/43DgEtcYZ+TSLJcvzOC5Hk2tKQ68bQfqayhyq8XCtdckIIZb4sX5RzA9A03SsT2LgfgeFEnlbOFlQNAY6mRH8lYulU4wVDpKSIpQcvL4+MyZ4xxefIptyZvJ6W0YXokzhVeYq05wUT6KIlTC8mYulo5zvniQjNZCRIlj+xaHFp8kb8+jCJVd6bfi43Eq/xJVt0RESbA7czfD5dOMlM9iugaL1jQ3pO8kb89zZOlpsnoLHZEBXN/mTOEVJo0LgERvbDtJNcdzc98gpeWoumU2xW8gq0ZRJB3LLWJ7ZSShoEoxDGeOTGgrVWcR26vg46FKUVzfBHwcr0pMbaPqLuD7HtnQdpQNekVdx+XiiVEunxxHC6nccOcgqdzGwog/zELumn3AbyJ0XSWZDFMoGOTzFS5cmCGbvTYI5Loer756iePHR4FAELe/f2NdhR/3sD2XS4UFLNclmtTq1FB80MM6rb1NyIrE1OVZLMMmmiBokVookm1J09rfTMdA8LAcPTtBtVxF1WKBcHapSkNHlpaeNz92tuuiSRKJVe1kuqwRVdaXVzRdJduaRpIllFXMw9Uhy6tE0J2A2mvVHMXyC0XKy8GDOJlLXFPTQkgSDR3Zq74/dWmWasVEVmSaOnMrzJ0NtyVo6MgRiupUSybjQ1P4vo+syrT2N6FqCvPjC5Tzwb4tTCzWxYLbNjWjRwJzhOnhOYqLJfS2DItTSxQXS2ghjY7NaxfzzT2N3PG+fXzlrx5ldnSez/3xV3jlsSPc8f5b2HHbFtoHWtYxkBQpiial0OU0sitTdkxs30V7A24VRsXCMAKr4itxBZBTlID9qikyru9TdiymjQKbko24G2hGCCHh1oQnHc/C9i1c3yEc1sg2JuqVU0kWRMMa0/OFjYkVV4kte3sZuKGLoeOjzIwu0N7XhNwcY2K5sKFVrRCCXC7O7EyBlpbU2uq+7wQsDwG+N4+gDcf3MF2nTmNPJCN88CO3r3MXja4CSGyvwlj5eyxUT3J66bPIQsf1LapuiYlylXKkQFIP/cQAR7bnUrEdHC+4XiwzAEM3Ejm/EqYzSdE6jiqniWqB5tRC5bsY9mWSoZuQhI6HQ776Cgl9F7rSBEgUzaN4nkVIaWXReJaKfQ7fd1DlLLrSimEPM1n8HBF1AEVK4PoGmtyIEDKa3IgixZgrfxvfN2mJ/ywxfQeyiCAkiag6gBAyidBuloznkUydTOSOqx+DaVPIG9iWg0CghQJbaUkSOI4XtMxrQXKvKDKVsomuq5imTTQWAKZyTffktWw1ACG38XrF1y17epm+PIuiKbT1NKBoCpWCQa4tTaVYJdOQwCgYZBoT6CEVu2rRs7WFhaklVE2he0sLiqrgWjZ92zvYfEMP4ViglbRpZxfhVSxl1/W4MDRN/0Azre1BgntFT+NKhEIqWwZbCYWDJKaxpnNlmU6NsSlx+eIsc7MFqoZNsVgllX59LaI3EhfPT+M4LmdOTTAyPI9ju1iWy/Cl+fpau60jQ3tHwFiMRHRyDQEjH2oC3Yj6dStLou52/EZCUSR6+5tIJMIIIchkY2i6Wjdp8XzqjrBCBM+RK20nxXKVQydHaczFOXRilGK5iqYqNGRiTM8VmF0oUjEsDp8YJRELMTa1RC4dY3JmmZbGJIdOjrJvVy+Ly2UOnxwjX6hQKFaRJUHZsFBkmaV8meWCwe03baIhG7smI/AnITzfx3ZclstVLv5/5L1nlCTXdef5ey9seleZWd51dbX3DdMwDQIkDAlQlERKpLwo6YhaSkdaSbPSzurMSnvO7Myu5uxopBmNZmQoUqIMSY1EGRA0IDzQJNAwbdDelbeZlVnpw+6HyM7u6q5qGPIDyblfKisyMuLFi4j37rv3/v//+WU293The/Dm5Xlcz8NxXf7uuWPsG+vjyy+fYddIN8cvzfG1o+e4d9cI5VqTv/jqUT7x2CFChsbRs9Pcu2uUfArqLYvXzk3zwN5NxMImWwdzfOPUBAfG+xjKp+hJt6HmAo5dmuODuRTd6Rh///xJejJxuuIRnjt+iUwiwl07hjh+aY6//vrr/PoPHUbXVLYN5VlerfHssUtYtgPtwNG5mWX+7vkTHBzvxwf+7rnjxCMGO4e7OXl5ns9+7VUO7xklHQvjeh6KlDQtm79+6nViYYPtw3leOHGZumXzw/ftQVunchmCKtIzxSVajosP5CNRsuEIDcdGCtEOBPk0HYewqjGcSFFoBgURxUadhmPTchwMRcXxfSpWi0KjTst1sVyXmGYQbnNgprsT7ImNo+kqPUNda6qQDVMPxklVYWhrD/1j+QAOpavc89g+/PacIxWJ31a+dW0XRZUBRYi68TPu+z4Nt4UiFFSpvq2qtIbT5Lml1zmyfAJD0ZlrFBiM5PmBvmCeWLEqfG7ySVbsCgL46MD7GAjnqbtNHp99iSu1OTw8NkX7ebTnLsKqycnyRV4vnSeqhJhtLJHUY/zI4IM8vfAq880CC80iA5E8xdYqCT3KDw88gOf7/PPsC0zVFwHYHO3n/T2HCKsmc80CX5x+lrrbwvVdDqS38t7cQVasCl+Z+yYnS5coW1VOlC9wW3o7hzI7WbXr/OPMcyy1SrQ8i/5wjh/qfwApJK8UTvPM4ms03BYNt0XOTPFDAw+gtzmYuwcydLXhwEIEsDuvTVGjmxrZ3iSj2wKfVlElvud35kaAuJqnpMzieC1s2fy2ihh9xwSOfKDu1qg5FQbCmzB0g96RLIX5MqnreCmumm05PPf3r/A//vOXSXcn+b8+/ytEEkEWeiM4m+d5fOE/PcETf/4skWSYX/rDj9PIh4iHr03isXgoUPApVBgd7+b08SkGR7PUqy3S2SiarqJqklbT5p8/9zK9Qxm6+1KceG0C8K+9Iu17ZIZ0Hv3IQY4dvcLff/YId963lWbTIhTW0XSFcNTAatnUKk3SuRgDI1kGR7Nk8wnMkM6xo1c4e3IGTVfJdSdoNW2Gx/IMjWYZ3ZyndyDN8VcnSGWiQdT0Fk7lTX3ue1hek/HYAc6svsLm2D4WmhOU7AX2Ju8nbfTw9MLfktG7OVd5jdvSD6FKnSfnP4sQgv7QOIuRSWwvmMCjapLbMw9Td1e5M/MYKT1YIG+L38F88wqeH5DpzdTPU7aXeU/uh5FCoggNx7PYHNtH3a3wavFJtjmrbIkfYKp+lgPp95I3g4qDrNHPeGw/i80gi1q2C1yuneS+7EdoejVeKX6VbfHbsb0W+1LvpWwvc271ZaKJXUih4OHi4RJWugEfXYnjehZNt4QmI0ih0PLKuF4DXcbw8ag58wQZUxXPb3GjIh2AY7m8+I+v8rn/+DjJbJzf+dyvkMiY4FsBf4PvYDefQDXuRsg8+A0QKqAGxJ1CB/RgO4Awwa9f24er/CQavjsLItTO4r+AkEmktoPrUc9CrP9qJ5Nh9u4bYmZmhXK5zqf+7Fls22Xr1h7CYQNVVdqVCi61WoulxQovHTnPV79yglIpiGzfdff4hhxK3+0mEORCUXrC8Q4e+Kp/K6Sgf0sv4USY1eVVlqYLpPIJzr16iWatRX4oSzKXoG+8h0g8zOUTk9RKdcKxEDPn52jVW/SNdRNfp/Lj7ZgiZYCuuooZ9wU9Zo7YDQSEmq52+FCucmDd8ppFION91ZFvVJtYbT6cUMxED29M4ioExDa4Ht/3qRSrOJaDGTGIpm4doBRCBKSxpk6jEhBM2y0HI6STG8ySbBOPz11aYGTXIFdOTbO6XKF/Sw9dfUF5bjIXZ+7iAisLZdLdSeavLFEt19m0e4hM71ooUyhq8pFfewwzYvL4nzzJ8nSBN55+k5MvnqWrN83eB3bwvh+7l7F9I0TaZK+eb6NIE1VGgCDzNNMoMB67NffdaqlOwvcZHe/u8A9BkOm8mtWsNFvUmhZTyyXiaYNVu7mhy+X4NgLBql0mZ3RTc1ZRhEo8nGFk67XqM+EH6lzhkP6OQAWKIrl4fJKJM7N0D3Vx6pWLRLfmCOVj6yqPCBFcy5/96TPk23OXALbv6OP9j8Tx/VUU/RAeGr4lCCs6ulTWnC8cMZibWaFaaTI0Eijpyetgxqo0yZp7WG4epyd8CE2GEUgMpYtLAmBjNavvRvN8n0J7sbArl8e1XXpGcpjt99HzPGanV/A8j76BAO4eNw8QNw+sOU5v/Cc6n8vNVwlrm+iN/zgAlltEVRLkoz9IuXmUpjOF5zWI6jvQlTwtZxbbWSakjpAJP0LLmcHxyqgySlTfRtOZQkoThTARbTNC6GhKimzk/Z1zGmpQARTRtxDRt3S212stZqeLZLLxDl8NQKNmceHMHJ7nE42ZlFZqmKaO63nkuxNcPDfPtl39LMwGSodSShYXyui6Sr4niW07eK6HrquMjueZvFIgGjXIdd8Mt4Vg1sxHo+zpDtq5KZMGfC4dnyAUNQjHwnT1p5m7tIAR0pi9sIDVsmm0Kx9X5ksksnE8z2d1uYLaViNcnFhC0RUOPrinE3y/EaYb3Ecf23Ixb+G/qarSCRoBnf18Am6kv/zUc8xOrzCyKUc8EQp4Wr5NC4VGw8K2XK5cWuqQsvYPphkezXVUF8MRo/OuXuX6uDqfbNneSzRm8N/+4Gv09qY49sYkh9+zlfg6Qb31TMqAouBaEvRqMic4/uH3bOVzf3WEz/zJs3iez+J8mQ98cB8A6WSEeJsQF2D/zkFePznFxEyRg7sHOXl2lljUJNUe31OJMKcvzAGCg7uHuDxVCDiallYZH82xVKiyXKzQshzqDYuW5dBo2hRLdVYrjZva/r1o2/py1FsWO/w8mqKgSNmpPNo50ouUglfPTXP/3jFOTyzScly+/vp5Htg/xkMHtuC4LlNfKHHk1BUe2LcxlDIRMdkykCURNdkykGP70HU+p+9zYLyfD961A9fzODe1xJW5FbriEXx8Hj44zqEdw2wf7ub//ZunmC2sMtydpisRYbg7zYsnr6w517PHLhLSNTb1duF6Hq+fn+HlM5OM9Xbx7PFL7B/v5yOHd68ZP85OLXJueomfeeQ2oiGDTb0ZXj49ycMHt9CVWD9oaygKt3X3Y7kOccPEdoMgbEYLxr+EYlKymozFM/j4NJo2m2MZVm2LLckM5UYTQ1VRpMT1PUJSI29GiRo6/eFEkGRwoda08HyfcNRAkXKNz3GjhSJrqTZCG/mLbxMRuGKvUrJWGY70kdDenq8bVk3em7+NxeYKPaEuHsgdCCgY2t3dci32pcYZifTyT7Mv8MLycT4ycD9HCieZbxb4kaGHsD2Hv578Kj1mhnuye2i4Fm+WL/HxkUd5qPt2fHw0qVJzm+hS47bMdp5feoMP99/Pv8y+SMmq8trKWZZaJX586GFsz+HzU1/nleJpDmf3crJ8kYZn8ZPDj+AT8JgBZIw4j/QcYrK+wMM9d7IrMdppe3BdBzEVnaK1yp9ffpy5ZoHRSC/3ZHez1CrRcJt8uP9+dEVDu27d9lbQSUWRtxTrUqROX2hnG5ETcLJ+u5Cl3zGBI8/3MKSJp7is2iVysp/iwiqe57E0u0K2L9WZpDY0IbAsp004drO5jsfsxQWa9QDnWS1U6BnPduT4AHRDRdcVKuUG/XdkOPLMGW67Z5wrFxbXqHD5vs9KscbeO0YpFWsdeUndUJFScvn8ArmeJJGowUqhxtjWHlaWq8xOFdl9cJhTx6a4cHqOc6dmGRjpYnhTnoW5EtGYiecGEqIB/1GaF586zR2HxwmFdcZ39FEq1kgkI7RaAZN7cOnv7onQZQhTiRBWoujSxPIaWJ5FUs9iyjCGEqbilHB9i6iaRFcCItZvxWpumZiawmwfx/d9zldeY6E5QULPYnmNNbCyW1nTraEIlbAaR/HasAe3SVLPElZiWF4TH0gb2zHktZFv1WpRbjVpOHFKCIQYCzDCroPrZShbLUxFRVcUsuEILcfB8lxWGi2a7hwx3aBuW+TCUfKR9QdH1z6G784hRBjFeA9ChMG38d1pHOsVhDBQ9btw7dfwvDKqcT9O6ymkOoqibsG1juJ5yyjaTlz7NEIoKPpB7NZTSGUE1TiEULL4XgnfK+K7c4CPULIBN8Q6FgrpfPCD+7h4YZHTp2e5eHGRf//v/onh4S7SmRimoeK6Hs2mTaFYZW621MnsGYbKgYMjfOxjdxJ6h4ow3y2mSYWGa1Ns1YlpBp4bEIMWChViiRAD4z1EkxGqxSqzF+YZ2TXIpWMT2E2b8QOjSCnoGc2TzMUpzK4wP7FEJBFm7tICvg+b9gy/Y34jAE1RWK7XmatWGE4mieoGCMFMY4GYGiFtXIN8CCk7Y6UqJaO9t8Zv+/g4rovWxre6ttvhylEU+ZYSpfoGWHff87Hb8vJSinX5g240RVUQ7bY717Uj258m3Z2ktFhm8swMdzy6n4k3p6mt1tm0ZxgzrJPMJegeyXH6G+eZOT/HwNZeps7O4ns+g9v7Owp311ssHeWjv/F97H9wFy/+w8u8+MVXWJhcZv7KIl/+1CIvfvEVDj60hx/+Vx9kdPcQUmjU7BkECobMMRrNkzXeWuFHSoFtuShScP16Lho26M7EiIaNgFx/Uz9RUyceMri/Z5yQqlPh5oojU4YwlRAZPYcmNBzfxvGdm/ZzXa9D9vlOrVKqkx/oYnTHAJWVOoXVOmYuirNORa8Qgrvu3szevYNrtqeSK7it5/HdWVxUam6UM+V+0kYcVV6DA7ZaNl//8gleeu4s5ZUaP//LD3Ll4hKbxvNs3ZkDv4UQMWL6AFsSHyOmDyLb7ovn+1RakxvKFn+3miolnu/RdGxsz8UI6xTmSp1FsGU5/Ml/eZJGw+Lf/LuPEIu9tWcfUgdRr4N1KSJMNvx+BCphbROe38RQ+7DdFYRQMLUBBEob9pYCXAx6Apia0NCVbhRhosgIjldhDXHYW9jF8wv8+9/+e378Zw7z8GN7UdqBh1BYZ2g0i+8HMvHprmgAIyCoPtlzYBhFkXTlY5iGhhnWqVWb+D6YpkokalBYqpDuilFaqfN//5v/wcE7NvELv/wgQrnZT5JC8IPbt/MD27d3trm2y/Y7NyOkwLFcwrEQwzsHUFWFrv40RjiAt9uWQ34o2xlvfc9HMzUisRD11TqO/dbvnqYpdOVizLaDprF4QCh7NUFwzdb38S5fXOTYaxP84q8+zI7dA7RaNsffmHy7t+EtbWAoQzId5od//C56+1IIAa4bJEmvBqdu5X0mUxF62hCXRCrCj/30PWzZ1nvLqoU1Jm59gr7BNMlkmFBIp7s3yUOP7mFgMID7zS2uMjNf4sLEEqoqQQHdVAmpGq+fnqZWbzFfXOXSzDKaobJnWx/F1TpDfWlWqnXOTi5Qty2GetKcujBPy7bJZWKcu7wYKDqHDZotm2hEp9awmJgpslys0t+TJBZ5Z9yX3y0WMXQixtqx1sEDITA0Naho0VRURXaIfKt1i2QkhJQBkW80ZLCyTqDN895+uDObiGJoCi3bR1MVrLZIjRCCZDQYC+PtiqL6OsIQ11txtc7UUoknXzsHQDoWZlNvF47rUqo22DpwM8l6tdGiWKnz3InLwbwO3LZ1AP0Wi30pJF2hcKedcyurnJtbZn6lQkjXguB4Mkp5JeibmWKZbDxK1NTxDZ2V1QaGpqAqCl2xCPMrFeJhg5VGg8sLRTRVoRKPkIyEmC6UOLCpv3OvAlqPGq5Xa7fFRJXxb3uyRRMK47FAwOLtHlkKSUjR0aSKITXCqrmGOzJrJtmdHEOXGqORXk6UL+J4LifLl1hsrvC1+Zfx8KjYNWYby53fdRlJdiY2dY5le4GP1BPqIm+kyOgJ+kJZdKni+h7nq9PsiA/TFwq4eMdjg5xavczh7F42xwY4WjzDF2eeZ3dyE7sTAYeaIhRCio4qFUypE1GvzcOu77LYXOFKfY6yXWW5VaLlWgghMBUDXao4vkpYNdFk8O547bWvaCfCXK/Znls9hFDQZArxNviKXM+iaE1heXVqTpGk3ofKt8dH+o4JHLm+zVT9Io7voAgF13NwnKAUf35yma6eJN1DtyadikR0BgYyaNr6l6WqCmN7h3nzG+dJ55Pkh7NUGi1MTQ3wuo5LrWWRGUgRTYRIZKIMjedI52KMbM4TjhlUmi3SfQm0iM49793Ga9+4SLY7wa6DwywVqkTCBrtuG+bJLx9n05Ye7rx3nBNvTDA3vUIyFeH2ezfT3Zti94Fhjr50gUQqzB2HtxCOGCwulnnmqyfRTY37HtqBHtZIDSRJzpcYHs9TbVrsO7SJZ7/+Jo//06uks3Ey3QkyuRhmWMdxXYQnOoz7Xtv5kFJi2y5CBjwajZZN6CpkpPNmBx80aRBSIiw1pwP4mVMhqXWhSoOSvYTm6jTd2jq9+/YtrmaYqJ1u8xIpqEJltnmJXnOU7tAwZ1ZfbrdIogiFhlvF8exAue2GQS6sxvF8l1V7mYZbQwCGEmSirx+2BGuDa3Xb5lJppe2QBqWhA7EES/Uay406zTaMoicao2bb1G2LcqtFRNcJqxpNx+FKeQW6xIaBI7xVhOzCd+evVRIBnnMe310AJY3nLeD7DTznIsJ8X4BB9Vbw/TK+X8dzLiGVAYSMBxVIvoOUeRR1E4EKTTsLJ5M4rWcADVXZmENJCMHwcJZPfOIBPvvZFzn55jSNusXZs/PA/Lq/uarwcs/d4zzy/t3kct/+yeY7xUxFpT+cpGQ1cH0PRZUkUxES7YxkuidFKpdgebrI4tQylTbpNQI27R0GoKsvTSqfpDC7wsSpaXpG8yxOFxBCMLC1920FUNYzx/NImSam2n53fYhr0VtOzi3bYWK+SG9XHFXZ6LwCRcqOYpuqKZ2FkOt6eBsE4q/aetBgCCq09HaW3HN9HOvWzhvQJnMMjqfpaqevYqkoXX0ZLrx+mblLAexv+vwcvuczsmsQzdRRDY3u4Synv3meyycmOfDgbmYvLoCAvrHumzJrcI2Ye8vBTQxvH+D+H7mbI/90lNeePMmZVy5QKVZ59gtHWJ4t8ku//3F6t0fQZAyQOL7LilVjLNpz03FvNLutGBaos1zbrqkKA93XKqGGs9c+94Y3DkgZ0iSqxrhUO8u2+C50aQb8dDcqBUnBUG+a3lziHVWjAoxs7+O5f3yVSyenCEVNug/0s1Jfv+RZSsH2GxRHGw2LhXkXqe0GbTugElFDbE1kiGohwuo1J6a8UufUiSl+4mfv48knjgXK6lIwN11kbGsN11vB1O9AIIlp/W0VNZ+GuxyocPk+xUZjXeLu71YTCLZ15UiaJlFdp+A2WZxcZmRHwJ9Dm8/xnfDd6WoWnWsCFYo0iZv7g++U/NUTYyjdCKEGZNJcWwKE5NDa4ynXfLKEebDNm/R2E0s+gXDg2vbrhtoJNAA3Qa6ubu/KXatE37l3EMtySCSDc8cTIcIRg+JyNQjc3KKPOpUs122Tukrf5rXv9fU8gxv1+fXz4s67t9Kstwi9RUBPCMHdh7fw6T9+hn/6+1fZvXewU/F721uoEEG72kcKLgTtQO8AACAASURBVF9cRNUULp1fYHqyQKrdb64bcKsVC1VqtRbllTrTUwWiUZN4ol1hv1xleblCo2FRLFSZmSoSS4SIRk127Brgq186zj/+3SscunccKQQL82U2b+lm8C18coBmw2J2eoXNW3swTY1SscaVS4sMjwaqb/Vai3KpzsJ8GavlMDezgqYpJJNv7zkqLFUol+qEIzpSkcxOF9E0hZ7eJOlkmA+/fx+GruIJOF9dJjsWp+baiJZKr56ipjgM7c8RDhsU/Qa7buunicPp2hKRrWGUiIaR0ek30qy0GoTDJgdjQyhSoGkqrZaDIgWGoZHviiME1/zr/8lsPa55AaTjYZZK1QASb7usVBpsHcyhyKDaud4KkpOF1TrN6/wEKa4unm/2MaS47n277uX1PJ/FUpVtQ3mKlQDmFQvfWoo8l4ph6Co/94E7OlyPqqLg+R5d8TCTi6WbArmJSIh8KsZH799Lrl1hJKVA32D9ea2Prh1DV1WipkFfWhA2dHRVIWLoFKp1LMdlIJMkYuqBcrOAaEhHV1VatkM8bKCpkoZl47gO+WSUkK6RjUcwNJVas7XGN/P8FnOVv2W28rcAZELvYVP6t27Z1hvNdT0KpRqJqNkRy7nebNslRJhkNBibfd+nsFLDNFQib3EPbmWKUDrVONePv77v022m2RIfRCDYER/tBH0EoEt1XfES2WblF+Kax+RfnVARa8/RVi4fCnfz8ZHHOFo8zZPzrzDbWOb7++67pf/9zcKbvLB0jPty+xgM5zlVvnzL63R8jwuVWVSpMBLJowqFcutVLq/8BxyviqF0sy37/7Uh5rc213fwfQ9V6MS0HMoGKJR3Y98xgSNdmgxFNqMIFR8fUzHZcfumjsxz+G0oV126FGQVkqkw612akIIf+OSD3PbQLsLREPnRLFPFVcr1JlOFMjPFMiFdIzmcJBMLU/cdMvu6mbPqbLtrhPPzy5QXXDbfMRQoaGzvI9WboFJtslysMT23Qjik0785R344Q71hceS1S2hdYe7bN8jkdIFCrUmvIrnzvi3ceV9Qtn1+bpm5+UWcvMmuoa3UWi1OrxQxqxXqisPAPUPMtxrIhQYNy8EZCLF9bzeaovDc+SvsGephbrrFK2en0RRJvWUjgFwqGjjUqwEniOt5VOotTF3j4JZ+jJBKTE2hS4OYlsKQIaJqkrHoPk6Un+d05WVGorvoNkfYmbA5XnqOiJoga/Tjeg7HSs9ypXYKKRQc32Zn4i6kUEhoXZ2HtGIXeX3lGVasBY4Wn2R74k56Q6MUrFmeW/w7VKmzP/VeRiO7OF99nYI1R84YQJUaqtDYFNvD6dWXWWxOsTf5Hi7WjnN29SiW1+CbhS+zK3E32+J3crT4NaRQ2Jm4h4gaJ66lA5iO0IlpGcQNg0fcMNia6cJQVATQcl00KekKRzpyl5OrJbKhCHHDwPE8pBDo7cW35/uMJlPE9I0HQ9+38JwrSCWP71fx3GmEMJHqEJ5XRCq9QQWSVw72wQYRwfOWEF4fvreCVPIIGUX4YXy/AsJAyBSu/SaqjOM5F/C9Goq2KwhSeas3E47eYIoi2bmrn3/9f3yQS5cWOXFimsuXlyiVajQbgZJWyNRIpaMM9KfZtCnH6KYc+XzibUlMfjeb7XmUrGuBQ9tycGwXMxRMkrqpMbJrkFNHzjF/eZHl6QILk0uk8gl6xwKogxHSGdk1yOlvnOfy8Ql23bOVpcllYukIPaP5W3KUbGSe75MwDeq2vYbDZdWu4uNjuesHZXzfp9Jo3Vpq1PcJaRpxMxhjQ7FQp7S5XmnQrFu3+imV4vqBZCEEqVwCzVCxWjblQqUjc7thW4tVWvUWQgpimVgH2iEVyejuQb7x+KvMX1nsVB5FEmEGt/V1yKxHdg3x0j8e5cIbV2jUmsxenCcUMRnY0otyi0ygEAIzYjC6a4jhHYM8+vMP8sZTJ/m7//Q451+9xMkXzvAP//kJfv53fxQznCGkdtPyHZS3GUANRwxCYf1dEdOvZ02vQctrMhbd3uana+J4N1ccqYok8TYqUdazSCLEQz9yCMfxCEcMTswuMqxFMNRrc6vfJogVgpuCyUuLFZ5+epaf+qkxfHcWRd+HAvjNZcpWnbhmoLXhaq4bjK/prgAO7vs+zaZNJKbj+y6uu8zVEorLlS/TZe7A8ipcWP0ipsyhyPdge99bGX4fuLhSZHs2S8I08TwPq2nRrLdugId+e4L4198/RW60aN/4XKq8mVLgW23Hev+vZ9HY2nu/HrfRt2rXt+NWbQoy+z56zMCMmXj4NJ1g3L6aRXY8Dw8PVQSL0x37Bvi5T76Xp598k9Mnp1EUyZbtvdx+5xiqKukbSBOOXAu0hqMGfQNpVE3Sn0vxAx+9jReePcvrr11meKyLRz60B9fxsDybeqXF5/7mJRYXVqk3LKanCnz6j59l244+3v99e6lXW/zVZ15gqVDB9+HEiSmmporcfucY9z+4nXxPgl/45Qd55sk3+YfPv9x5T7fvDEQekskI+Z5kh9tHKpJ8T4JUOorjuJw/GySkFufLrBSrWC0ngJN9aD/ve2QXJ49N8dTXTlJZbRKJGvzLF18jmQzz4Ad2s2ksT09vshMEA9B1lb6BNGZIp9mwOHdmnlBY58K5BaRcpFpt0qhbfPJXH2ZgMIPZXuC2XIfVUouEbtKrxym3mrQ8B4nAjGnY0mfVbtEQNqt2C10qJJIhYqZBzbPwDAhpOolQiHQy0qHEiF63IA69A2Wm/3lM8IHbt/I3T79BoVKnUm/heB537xgmFjYZ7+/i888c49SVBS7OFmha1+axWNggl4zyzy+d4tJsgb1jfR2i643M9+GJl88wW1jlzOQi4/1d9KTj2I7L6clFjl2cZalc45unJxnvzzLcnea9+8f4sy99k7/++mtkk1FK1QYPHdzCYC7JA/s38yePf4M/efybpGNhfHweu3M7Iz1pdg5389mvHWW8P0ejZTOYT3J49+gadMqtLBMLk45eG6uujivDudSa/69dm7/u9hu/832fxGD3Dfv52O4KTSfgKrXc5ZuOsZ41WzZvnpujZTmMDWe5Ml2gvyfF7EKJesOiN5eg0bKJR0OUVgOVQlWVnL4wz9hQlnrTJh41KZRqXLiyxNhwlrOXFtg21k1fPnld4F4QUU2WWyVW7Rq6VDvVOzcm/q/a9vgwJ1cvMxjOE1cjND2LhPbuud02xwY4X51iT2MztudwoTrNgdQWPN9n1a6hSYXD2b2EVZMjyyf4UN9hQKBKBUNqLLZW2m3XMBWdifo8XUaSPcnNXKnNUbbX+soRNcRMY4kVq0JYNTCkju27aNfFLzyvTsOZwvUqgIu/TmX5eiaFStOroAiNllvF9R3UdWhW3o19xwSOpJB06d1rtm1ELryRZTJRmg3rluR00WSErQeDLE7LdmjadhumIemKRdAUiRQtEiFJy5H0ZxKkIiFCukYmGiZqBh1/NSqdTIRJxMNk0lHCpk6jZeN7Pp7ho6oKhq6iKJJY1GR0KEtXOnrTmBI1ddLtY7ueR9TUA/yiqgTcTy2LbDyKFDBfqrK1L0fE0FGkoCsWJhE2yaeiREwdKQW1ZiDbHg0ZOK6HG/VJRk0s20UQVC6YuoaphDiYfhCBpMvoQyDpDg0jUegyfritIqYhEAyGt9EX2tzJPEqhENcybE8EqhoSiSJUVKFzKPMYsl1KF1WTHOp6LIjatn8nUdiTvI+dibsB8B1JWEnRnRjDNHVAoIhAOnw0spuh8HYEoAiN8eh+NkX3tM8pUITGaHQXQ5Ft7X2CRzqVySNRSOt57sx8AHkDMXhE04lo1xyxGzOIQgjSZghVyo6D8E6VzIRMoqpjSHUIUNAjP0UwBCpoof7OZ6ltBiSgopqH25+V9u+Cz77vt9shUPQM4OJ6CtJ4OCCr9RvgWyj6Xnxfdvr76mDrtgNfwXUE81oiEWbv3iF27RrokFV2+HzaC0EpBYoiO5PRzeXz31vm4bPQqKIKidNevF6vRKeoCqO7AjjO8kyR6fNzlBZX2XHXljXj1eb9I/iex9ylRabPzdGoNukd6ybdnXpX/SeFIG6YyLaSoGhXygkEGT2JLtd3WEvVBnab/HojE0JQbjZpOsGElMjGiWeCa1mZL7O6XKF7+OZSbQh4VuavLG547KHt/YRiIcpLq8ycnw8y8BskAVzHZeZCsI8RMhje3r9mnTq6azBQiyhUmD43x+LkMumeJN1D19o2unsQM2KwOLnM1JlZKoUK0VSE3k03OlEbm5SCZDbO4Y/cSfdojt/7+T/m0vEJjj93mtVCjXhED/h2DJUuI4F8Gwt3w9TwfJ/piWVy3fF1uU7eidmeTdVeRSCJqFHiagbHb3WeiW+HXTg2STwdpX8s38n6Xpgv0Z9OdODdCwurvPH6BIfv28LXvnqSUqnemd8KhVqnyslzJhEyAyKMIlTOlpdJG5EgcC8E8WSYTDbOFz77EpOXl7FaDq2mzV2H70aItc9XxZ6ky9zBfP1lRmIfYL7+BqXWAtnQty49/p1kru9Rajax3IC0Hh9sy6VWbpC9rqhUCEFhqcJLz55lYa5MPBHiwB2j9A1kOv3vuh4nj00yM1nkgYd3MjWxzLHXJmjULTLZGIfu3UIqHTjcvu9TWW1y7LUrTFxaAgHDo1l27xsmFr+mGOQ6HlMTy5w7M8fCfBnf88lko+zeN0TfQLrjh11V9zt1YorTJ2dwXY/NW3s6WV8Az/V4/qnTJNMRhIBTJ6bZsWuA0c15XjlygYX5Mrv3DbF1R1+bJNtlaqLAudOzLC6U8X3oysbYvW+I3v70LavrPM/n/Nk5jn7jIpu3dLP/9tEOt1+5VOf46xNMXF5GCBjZlGPP/mEiUeMaB1Pd4sknjjMylmN4NMcbr15u7y/YvKWbPQeGKblNzpeXMFUNRYggkCQVFHG1IlxQc1pU7BaCQEF1z4Ehdu0ZwPWCuVtRJYoiSehhfvlfvX8NtGv7zn62bOtl2Vli2irSdRt89LY9OJ5LRA9Td+uElBCzzTmEJrjnRwdwPZecmSOjpztzu6YpmKbGT3/yfl6eneb23n5OLy/RH4/TE4+jtgPyw6NZfuJnD3eI/aUUHf6N97xvO/c9sK3zfzis8+M/fQ9CCiqrTb7w19/gkcf2cNfhLW0uNI8/+69PceKNCd73yC723z7C7v1rK9kggPBJKfjZ/+WBNfcz153gl3/9/aia5OzpOb750nl+9Tcfpac3SJhVqy1+519/gZnJAgOD14QbdKlwX+9IO3jXrta74ZxX4XeW16JkLaLJKJ7voEoNz1eQQqJLg+XWFLbXQpWBfxxwnmhYXpOQEsPzHRSpdegevtfM930sL6iiVYTAwePQjiHSiRACgWGoGKbKAwfG0HTJtpE8P6Ls5czkIr3RBI/dvY1cOoqHz959fWQmS7i2x6OHtjG3ssq0VSbjBpUzP/6+/bx2foZWW+kO4K4dw4TNwH9XFcnh3aNkYkE/q6rkgX1jVOotbts6wKHtQ0gpaLYcJhdXkFJweM8wxUqd+eIqfdk4g/kkn/jgIY5dnKXWtBnpSZOKBWTsWwdy/OKH7ub4pTlsx2Ugl0RTA26nn3jwAK+dn2a+WCEdDzHSnd6QY3cjW5d3bYNj+Fj4vodEvwmudONvHN9DCZ72b8lntx2XSq1JOhlher6E5/uUVxtMz5eIhHSaLYeX37jCI/ftwDQ0VqtNpBDUGxamoVFrc4FZtkOzZbO4XEERklY7QOi4XidZvys2xucnv86nGv/Cpkgfj/XejeeALgIYn++D8AQqKp7nc1tqB4utFT57+SvoUkOTKh8eeA994SyqVAgpNwdKdKmiCRVFBNAyEBhCQwrJPdk9FKbL/OWVJ/Dx6Ql1cTC9Dc/3OF66wDcKJ9Gkhu053Jvd2/H9QorJnugYLy2d4OzqBIe6dnIwvY39qS08Pvsin7n8JQxFYyCcW1MBtSMxwusr5/jLK08wGOnmsd67233hf8tenKGE6TKGKVrTGEq0syb/dth3TOAI3j1Pz1VrNiyKxRqO48LbULmBwInQFIXeVJxg2nBxW88jZJxYaF9Hdc33YUd/901Bn3Ty6vdtKMsaHqTOpzW/ufE6u5MxupOxm0qgr+53/bZ0NLzm+6vfpaLhm85z3RnX/U4IgdJ5BOSav6pYi4UUCOQN2+Q6JYDAmpI4ISSauBlXGfAmBM7awnyBs29Mku9Ps3X/8E3nuP68ilCva/PGbVM61yOu+7yxrffs6cqtB+Zbm4/QtiPQ2kOA134efBAutKF0rh9Uc0ghcLwaqjBQ5NVrCfpnqVSlYQWlqeVak0TEpGk5nJ9ZZjAXROxjIUmlvhlDT1GuLeN5Pv25JMXVOtGQzvRSmYip052OMVtYpb8rQbneRCAChZCmRSYRIRrS8X0bgexAHgE838JxyygyikAF3+WqNI3gZgjhd6sZUiGsBot8TQSEvT19qU6llVQkfZt70AyN4nyJi8cm8D2f/s09RK/Lig5t70cP6RTnS5x//TKe59PVmyKVf3dZeSkEqpQkzWtBFwEYUgtKbDfoflPXUBXZweBvZHHTRHODiVzTVUZ3D3HyhTNUilUuHrvC5v0j6/KDFGZXAjjYBjaya4j8UJby0iqnv3Ge5ZniTepmV221UOX4c6cDCF4mytbb10pJ927qJhwzqZXqnHv1ElbDItuXIdN7Tf69f3MPsXSUaqnG6W+ep9W06epNkR/OrnfKW5pUJAPjvWzaM8Sl4xPUynVq9WWEXUAKlbDW24ZMvT1LpiLYLeem6sd3Z8E9X7VL9Jj9KFIjpHx7VJQ6Z/B9ps7NoZsB3NCxHDblM+jXVRypqiQSDaTLX3v1Clu29qC3g2L1uoVlOYAOXgm39SJCpmm5+6jarc7CA4LF5gc/fJAXnj6NZTmEIwYPPbqHfE8C16ujqQFnAoAmI8zUXsD2G6T0cRbqx8hHIgzFU98zimoA+LDaatJqB3SD0vqbExilYpVP//dnWC03AJ/CcpVnnzrFL/36+xkdy7Whoh6nTs7w3JNvoukKX/mXN7BtF9t2kVKweWtPJ3A0N7PC33zmRU6fnCaeCOG6Pl/70nH2Hhjmxz5+L7nuAEI5M13kD/7Dl6jXLMIRHc/zWSlWefKJE/zKb3yAodEgoOs4Ls8+eYq/+vPn0XWVWDzE0W9cJJON4bQhnJ7nc+SFczQbAf/D9GSBZ588xaF7x3nzxBQLc2Ve/eZFfuO3v59sLs7MVJE/+N0v0WhYhMPtcxeqPPnEcX7lNx9lcLhrwznp4rl5/vS/PInr+dxx9+ZOgGtmqshnP/U8F87Orbnu2+7cxMd+8u4ONK7ZtPnK48fYvW+Ql49c4PWXLyOloNGwmJ9dYduuflo42L5LQpo0XBtT0VClpOU6lK0mWTOKKiSaVHA8D00GiTJNV9f1XG/kkVOUIKgkXYkhDUKag+u7eJ6DVASu5+BJBw2DptfCU50gC25qGLq6xnfzfJ/JWpkjc1NUXIuqZTGaTaNdV6EphEBVFdR1YNY3brt6HcHnIMg0cXmZgaEMUkrmZlaYvLLMHXdv7vx+veN2rv2GILuUotMfUgpaTZuL5+cDPjfb5eyZWfB9MjeI6gghOoS2wYYNT0nNqVGwJvF8l7w5TMlaJqV3U3dLVB2XllujZC+hSq2doOymJQSW26Tp1qg5JUJqjJSW/54MHNm+x9OzF+gygwr9yVqJTV0ZrjRXyIWiyIzgeHmWZtrhuaVLSBFAsSIjBqaqcqa5yPRiGctzmWys0DecoGK1aERsVF1ytrrItmweXSr0dSXoz66toj+4ZQAIxkJFSu7YOogQgqVSFXwYyqcY68tQsEo41Flq1VGlyq5dcTxiVJ0aaT045qw1i+7omHGDPXuSKELBkHoHTSClYKyvi7G+m2GZsbDBfXvWh5P6vk/VtohqOj7QchxC2ruvSPN9h+nyp7HcAr3xjxHWRjfeF5iolKhYTQZjKdLGt1CB6UO1HsjJ93UnmZipBdyMbVimrins2zFAtd6iXGnQspyAB83zKZRqlCsNLNvFth10XSUc0mk0bSzLxXZcTl6cw7Y9DF0lFApzn3IXhiFZXbY5ay9SrQgeSt/L1HyJZssmq+cYb2rMLVSxbZfHuu/ljYkJNF1BRyMuoviez4CVI2RrLM8UadZb6KGAYH9nc5hkOM7qXIWH47dTn6zxgdCdJJwwlZkq70/eyWR9nlDYIEaYykQFc0DnYHobA1qeuclFQqrBAN1MnZsj3Z2kulJDOw6PjR7CbjoMqz34vs/2+DDdZoam2yKqhvCBsHLNh+8LZfn5TR+i5jYxpY4iFOpui6ZnMxJ55wJEN1akxbUeTCWOIrR11cDfrX1HBY6uN8/1KMyt8Pozpzj76mXKhQqRWIiRHf3sf2AnXX0p5A3keqlUBMtyO53m2C4v/ONRnv78kZuOH46H+IFfeYSGGWSDoO2Uea021rHO2pnFx2razFxY4PgLZ5g6N0d5uYLneYQiJtn+NKO7Bhne1kduIIMR1nF9i7pTQlfCGDfwT7iOS2mpwvnXr3Dm6EUKsyvUKg0UVSGeitK3Oc/YniEGt/aS7Fp/sel5HpVijTNHL3LypfMUZlewLZtEV4zRXYPsuXcr+aFsOxO0dpY89c0LfPG/fhXbdjn0gb08+KP33BJC47kef/+HX+Hki+cIx0J87H97jMF1FoCu47I8s8KJl85y7rXLlJZWEULQ1Zdm222b2H7nZpLZ2JqqMN8Psmu5/mAB+Mazp3ji088SS0X56f/zB4kkwqwslDl55DxvHjnH8uxKUBHUnWB8/wh7Dm8j25e+qS3BsX1q5QaX35zijedOM39liVatRTQVYWhrH7sPb2VwvAdVv3Xww/M8CnMl3mg/j6Xl1YAwc1svB967i9xAZs3zWHOWuFK9hKmm8HwHx2sghYrrO7h+i5gW8CdYbg2EoOWuogidbnMXYbl2gqq3bOaKFWaWg+f06NlpcqkoxUqducJqhxgwn4piO/OAIBk1abZsLs8VsRwHx/UYyCVxPZ/ZtjR3YbWGZbssl2v0dsWZLaxy+7Y0Nescvu/ieOUOOarlLiBQMNRepNCpWmfwfRdwSIbuQRXvrDrwO9UWm1VMVWU01kVI1bi8VOH8mTkG2lwOV5/lTG+KwtwKZ16+gBkx2LR3CPU6bHu6O0XPaJ7lmSJnX7mAlILBrf0YoXc3eEshGErcDEEMq6Gg2miDmHEyFiJiGjfJnF9vQgjGutJruJfu+r6DfPUzz9CstfjKp59h3wM76R7JrXlHWvUWz33hCAu3qDjK9CS5/2N3cenYFSbPzPDEn32dn/ztH8IIX8vg+76PYzk887mXOPH8aYQU3P7IXoZ3Dqw5VjwTpW+sm4nTM7z54lk8z2PTvuE1iiHRVJThHQMc/dpxTr5wBqtp0T/e21FFu95WC4EC0lUFuhvN930qK1UWJpaCvszF0cM6rtfC9VsbXvNGNjiSZXDknQew1rOklmF7fD8CQUyNkxHffpXDRCbG8RfPsTgTZGn7bx+m2mzhuC60n/Wurhj33rsFx3G5/Y5NPPzIrs4icGZmheeeO4OQKRTjHnxvEaGOYToBkeT15nk+Zkjjgx8+2LkXVsvBcRx8ivj+NYLwwej7WGgcpS9yL4o0SBnjnC5rOG6RuGF+zwSPWq5DfzzRebV1U6OrL01uILNmv4W5MrfftZnv+/BBzJDOxXPz/P7vPs4X/uolPvmrjxBPXFs0zEwVef7p03zkRw8xtqUb3/OpVlr09AVjS6tp84W/OsLZUzN84pcfZHxbL57n8/JL5/nUHz1FPBHi45+4H6lIsrk4P/Fz95FMRch0RfE8nxNvTPKH//HLvPDMGXr70+iGyuULi/zNZ15kZFOOj//CA6TSEaYmC3z6vz/Narm+5lomryzza7/1QeyWw//zO1/k9aOX+ZXffJTzZ+b41B89xdzMCl3ZGNl8nJ/8uftIZSKkMzE8z+PYaxP80e99hRefPUNP36E1AQchBJ7vc/ncPH/0+1/FNDQ+8ckHGBsPqtybTZu//YsXmby8xC/8rw8xvrUH1/U48vw5/vy/PU0sHuInfvbwNRJs3+f5p05z573j/NpvfZBEMkyj3uKNF89TKVQZGOpiIJrs7Pt2bL0xyGraFBbK9LTnn1qlyeJ0kaEt3Ugp6Q7l31I9LaiI8TufbzRFSrZms3zA3ULMMNClQjp0bbz0fJ+GY3X6UBGBopMiJZ7ndRJMUohONt3Dx/N9jKjGh37ydp785+P86R8/hSYVQiGD+x/awT33bb1lu9eD5fi+T7nZwlAVTE1jZDTHhz5yGy8+d46nnzyFlIJ4IsSP/cy9xLsjXCoUSYdDJENvf+Hs+z6GEmEgvLVdrSGJqAkEAk0a+Pj4vkfWHEAKFQUFVeqByIRn4eGRbqMnNPm9BZ+9al4bjrk1meOpmQvoUmWhUSWkaqSNMGcbiySMEK8tz7A308uZ0iIpI0zSCFGxWuzv6uOr02fZme4hqhlMVFboDseYqZcZiCQpthos1CuUpErLdYm0Ay6qUPDxsT0X1w9Ssg3HpisUIaYFCmLdmRi6puLisdQq0HCbhBSDmBrldOU8ChJVqpTt1U41TkQJowqVqlPF9V3SeoqMnqLh2BxfWKDp2AwmklxcKdITjVF3LCzXQxWCmm2Ti0RYbbXQpCRhhphaLTEQT/Di9CT3DgyhCMl8tcJoKs3p5SViuk7FCpTPDvT0kjDe+jlpOtPMVb+A61XJhB+4ZeAIghIA2/coWw0SuvmuK1hUVWF8JM9IfwZNU9g8HKhlbwSbc12PN8/PoSoKiViIPdv61+wPdMYMy3ZpNAOOsEhIJxENETEMPM/jUrGApiokQmGiwmB5pQoCEkoIzTFYKddpWg7xiInWDJENRSmUa4Q0Hc/zaC43mX11jnOlCyS64pQWV4mlI/ieT34oS6PaYPc9Ys4oMQAAIABJREFUW5mamEPVVSanZ1A1lWwkgz/lMDGzgBk2iCYj1CsNIokI1F1WXikyV7fQDkrmryzhOh6pXJzqYpXVuQqqpjBV1qmOukRCBqqqE5cG9bKFqiisuA10ze5UWbmuIKREyCQiuHgB37Lv4G0wrjddh6VWibCq4QO25wZqekDNsYhrJj4+qpC4vk/FbtId0ohoPuq3KcH/HRk48lyPk0fO8fnf+xInXzoX8Bz5PkKRqKrC1/76RT76a4/elKVYrTQ6GQ4IFC7mJ5Z4/ZlT+G0ojut6+J5PoivGe3/yXkRvDN8LbpYEEApSdgEe1x5vqKzU+PJnnuXLf/E8yzNFXMdrl48HmV+pSDRdI51P8HP/9qPc8f49uL7NXOM0Cb2HfGg8aJPvY7VsjvzL63zts89z9tXLNBstvKvHQyDaZcDRZIQf+98/xKM/85411+n7Pr7nc/LIOf75T57ijWdO0ag2O4tDIUDVVAa39fJ9P/9e7vm+gzfBQxJdUS6dnGL6/DyteotDj+4nmgxvuIAqzJd4+gvf5OKxCbYcGFlXUtxqWrz4T6/x+Kee5sKxCeyW0xkspBR8+TPPsufwNn7wFx9m2x1jnSoO1/GwmnZHZnh5ZoWjT54kkYny/Z98kP+fu/eOsiSrznx/4eN6m95nlvemq7tMewfddAPCCjQDEkKMDHID70lLWjMIzWjeyDxpBDNAC9OABoSX8NDeVbvy3mZWep95vQn//oibtzIrM6sLifUG2GvVWln3RsQNc+Kcvb+99/dlpvN84b98i2PPnKVaMnAd12+jk0Re+JfDvO+jb+fedx9Y8bxHL03yrf/1Y1754XEK86Xavv49kmSJxo4Ub/zAvdz1jr2rXr/rupx7tZ+v/M33OHXwAlatHVEQRWRF4okvv8g7P/SGJYG3LOjEVF/W13armF4Ry60gCSpxtQtdSmA4eVTFL2sOSslaSfTS5+R6HsWKwdR8gUQkSG9rktGZLKbt1LItPrlgOKDRkowylSkQ0BRsx2V0JstUpkA4oJGOBf1J2jCZL5TRVZ9gz7R8OfD2hjhXJuYQkJHFMI5XxXMtZCGMIKh4nokipX31HCePKGhIYgBJDCMK+pLqpJ9nkwWRoWKGtBYmqQZJpcPLJC/jjTFSLQkuHu6nWjQIRHS6tyxVk4omQ7T2NTF0ZoT+44OIkkjX5uWk5Z7nkZ3OU8qXMSomRsmgmC0xdmkC8NV9+o8PEm+IogU1tKBaX8zCiRAJNYZ8HdI7z/UI6sp123dXsrW7eth5z1Ze+d4RLh0Z4Asf+Rpv/t0H6N7c7re2zRV47hsv851PPnbd48iqzO1v28uL3z7M6RfO86NHn0EP6dz5jv00dqZAEMjPFjj47UN8/W+/SylXpnVNMw++/x5C0aVgTzAWpKWvmfOvXubS0QE8D9bs7FmyjR7S6N7SwcF/OVSvSurZ2uFnpg2LcskkFg8iigLffeRxZkfn2Lx/PX07ukm3JlFraoGO7TB6YZzvf/oJLh4ZQBAFtt62kWgqhKytIyg314OwJc+wbFItVylly4zWnqFtu1w+doVYOrLoGeqEEyEi8dASwN4yLLIzeSrFKtWSgVkxmRmdozBfBKBarHLyuXM0dabRQxpqUKMcMIk3xtAWSVYDGBWTzFQOo2JQLRkYJYOx/kmqJR/0KswXOf7UaSLJMFpQQw9paAGVZHMcRVNo6Wngje+/C8/1ECWRsUKRuVJ5CfnpwvwuSSL33b9lCQdaQ0OEu+7aiOdO45gvI0gtuNUnqTp3ElE0xkpZkloQTZLJzBV59aXL3PvAtnrAf/bUCLIssG6zimn3o3ETABGlnZDcBLUgtTV4C3qT7VcU/IKARgARVSNvVhEFH4CIN0TZc/+2JQA1QCwR5N4HttLQ5IsWbNvVxe6be3n1pcvMTOWWAEcIsO+29ezZd3X9bahhjp7nMT2V48TRQXbu6WXzto76NgvtZ6dPjJDPV4gnQugBhe27unFdt97qvHVHJ/FEiOmpnJ9h1mROHR8mM1/k/b9zD+2dfpvU+o2t7L99PWdPjiy5lqbmGJ1daYyqRSodpqUtQWd3GtOwECWBYsEHEAMBle27l/92LB5kejKPbTlLgCNFlRkcmOHzjzyNIku877fuondNU/26J8cynD4xzIE7NrBpa3tdSXLH7m6ammOcOjFMsVglugiA1oMqb3jzbrp60kyPZRg5P0FmIkO5aNB/ZpSp0QxtPQ20dKY4d2yQQqbMpt3dVCsmQxcnSTZG6d7QyqWTI5hVk1gqQmNbnLOHrwACm27q5vKpUV596iwHXr+N9Tu7GL40WfOHIT9f4tzRQVRdoW9zG4Pnxynlq0QTITbt6VnKyfQaq7OIwFghh5PzW+ITgQDJGthSsg0u52cIyiplx/TJh6sFGvQweatKSNYIyn7LVlBWKdsmsihRsqq0BuOUG21e9+s7sW2X3nCasKahavIN8SUeG5tgZ1vL1WSw6/LM5QHWNqTY1NyEqsnc87ot3HrHBhzXRcAXICjYJo++epSgonCgt4td7TcGHHmex8nxKdY1pIipDcsSMvVkx8IX3tXPPTxYVPX502oZ/lk0WRBZG0ujSzJbky1MVQp0hOOMFLNUHIuWUJSCaXBnax9xNUBCC/r0BwhokkxCC7KvqZuqY9MSjLIx3kjRMmkKRpgsF4irAV6ZHGF9vIFL2Vl0WaFiWzQEQuRNA8Ox6Y4mCMoKGaPCWCnPzoZW4uEAv/+W29AVGVEQWB/pxa21b4uCQEKNY3sWQTm45Pks/nvWmEMU/Ip703HIVCukg0HOzk4jCSIxTWOuUkYRRU7NTHNzaxvPjwxxU3MbOaPKfLVCcyhCRzRGXNNpC0cpWxaXrTkuzc/RGolwcHSY1nCUkKIwUy69JnDkeR4lqx/LmUUUbgSM9ME1y7FpDkaQBIF/hbAqALom09eZXtb+u1qiXRQFNq1pxnE9lEXJ9KXzkW+qInHz5s46UfWC6KznQVMqiiSJuK6HKIDbfBWo6qxRPnieHwc2JMNkcmViER1JEqkUqwyeHsGqWriOR7whSiFTAs9X0g1GdMKxIMFIgEKmhG05BCM6lmEzOTjN1PAstmWTaIyRbktSLVaRFRnHdnx+QaHE8PlxLMNGlEVfVd3zcQBVV4gkQ8xmSkzO5rFtl0Q0yOD4HAFNpVQ16WpJMD1fxDRtHzuQRPbt6EHV/MpT016dV3SinOd0JktIUVFEiZJlUrJNJEFAlxVSWpDZaglZlEhqASq2xUylxK50G7L4C6aqttj6Tw3zqT/6MldOjyKrEr1bO+ha34qsysyMzTNweoTPfuTry1TWgkGN9vYUWq2MVVYk7nv3rWy8uY9ipkwhW+KJLx/kzMuX/B08KBkms4Uy07kiTfEIgmfh2OcQhBAS2wAfQf3eZ57iG3//I6plg7a+JtrXNpNojOG6LtmZPNMjc0wNz5GdLRCMBmplsRq2ZyIJVwNPy7T54aPP8uW/+g75uSKSLNHclSbRHCMUDWKZFoX5ErNj84iiQM+mFWTVPTj94kU+/odfZPTSJFpQZd2uHpp7GlA1hZmxeYbPj3Pp2CCf+dOvMT+R45c+eD/qIhb8po40Ww+sZ/TSJBeOXGH00iQb9qyCYHvQf2KY0YsTiJLIzjs3E7uGf8p1XJ78ykt8/qPfJDdfIN2aoHtTO+nWBLblMN4/xeDZMV76/jHG+6f44N++hy37fTAtHAuwbkfXMt4P07C4fGyQp7/xCsefOUtDR4pUSxw9qFEpVpkbzyBKIj1bl1YmgD/RTlyZ5hMf/t+ceP68336ztZPWviYCIY25iQxD58cZ65/ii//1W8yMzfErf/wm9BWY/6+cHuGRP/4nLh0bRFYkuje107WxDUWVmZ3IMHByhM/+56/T2ttY30eToqS0njqgYrllXGwEJFQxCIJARGlaNVdougYVp4iMSmNKJxROoqkSmmpx2842TNsGL4KqipiWiyILBFSVeDyCKEHV8BAlh3SyjYgWRpUlyqZFUFNq/eEKlu2Do7IkEVBlEuEAkqgRUNbhuB6atBZB8NtQVDntA6uez1MVVBJIgobjeZQMh5D2i8F95OItUbOyHZfm1vgSkDqSCNHU3cDZly5iZUus6emhtW9pxUcgHKBjfSsviyL5uSLRlF8Jc605tsPHf/ez9J8YwrEdHMvBtmwqRT9AMqom3/7Ej/nx559BUvySfkmR2fvQLt77Z++gOXz9ChZNlYkENOSfkNQ8kgzzjg8/zNzYPBePDPD0V1/k1AvnaWhPIclifc5LNsd58wcf4Bt/9726GtpiEwSBhvYU7/2zt/PJD32RgZNDfOWvvs2TX3qeRFMcURLJzeaZHp6lWjJItSZ475+9gzU7e5ZVQOohnfaa0lEhUyLeGKNrY1tdSUsUBFRdoWtjO7IqUZgvIogCPVv8MvaxkXkmxzLcfGAtoigxNTjDjz//DE9+6QViDVFC0SDheBBRlijny2SmcsyN+0mCDbes4cH330M4GqVgDWK6QWTRD0Ycy+FjH/wsAydXeYYVo/4MZUVCqj3DfW/czXv+89uXgPoDp4b55B9+gcxUFttycGwHy7Ap1Soz5iezfPI/fgFFk2tjwT/e+/7iXRx4854l7+DJZ8/yyQ99Adt0cCxfpdQybEpZn6Bx9OIEf/uBR5AV2T+OIqEoMh/6zG+yad86itkyB793jPx8kVRznL0Pbqe3ObXEgczlKhQLVVpa4+RzFRLJUF1WXVVlmpvjuHYWBB1R6sFxxukMx0npCb/9sgb+VCsWQ1dmllTGjY3Mo6gC67ckkBYR/hetMcbKBzGcLOAhEqBq7iVfVdjX3vkLAx5VbIv2SIxEwPcnBEFA1JZfWzweIpkK15+9KAr0rmnimSfOMjmRpW/dVe7ISCRA75rGVYP2zFyJyfEsB589z9lTo/XPXcdlajJHQ2OEStkknghh2y4Xz41z7NAVBgemKRUNbMdlcixDb19jXfFtfHQeTVdoaoktOcfWtgSB4FJHNlxz/CVZRNMVIrX/+2qEAk5Nctu2HS6cHefooSsMXZmhXDSwbYfJiSxrN7Qsq/Ip5Cs8+qmnGB2e548+8ib61i3lPJubLTI9mePpx05z9NBV5RvXcZkcz9LcGqdSMZcAR13daVLpMLbpcOLFSzS1+7xOhUyJky9fpqkjyaGnZth8cy+jl6fZtn8Ngihy4uAluje0cOnkCFpA5dTLl9l9xwZSTVFcx+cZPH/sCumWGNFECE1Xae5MIUkiiiozcGaMDTu7OHNoAEWVyczkOX/U4vyxIW6+ZxOnXu6nZ1PrDQnK1K/T8ygYJre0d6CKYr3CY8Ecz8XFoyUQo2j5pNK257fYJbWgX51pGQQkhbxVpSuUZKZaQBUl4jVwuGBVSSXC2I7Lxdk5SqZJYzhMezxKtlJlKJNFQKA3lUCTZU5PTvHFV49h7NpGQzhEbzrJcCbnB0fBoE/A4HlMl0qM5wqossSadIqiafL8wBDjuQJv2LSOzkSMkmEylMlSNi3a4lGaImE8z2Msl2eqUCSkqvSkEvTPzvOFQ0e5f/0aOuIxNjU3UjRN+mfnUUSRntq5jecKSKLAeL5AUySMLIqokkQyGKBi2UwXi7THYj/xuvvzYrIosi7m+x490STdEZ/MuTW4vD1wwa6tUElogRWVCrvDfqXPcCFLazhKbyxZo3VwkQTRJw8WJDTJl1Cv2BZZs4ouK4iiQEi/Oqdcm1SL3ABxcktgqS+XM6o4nkt3LI7teiQDQQayGYqWSVBR6M/Ms6WhiYJpIIkCTYEoCV1HFSUagiGu5DIICGSrVZpTEQazWToiMRpDPsefLr12GO55JkXjDI5XviHgSECgKRipxaE+T9lrFCaufixBqK/pN7q9LF9LKLL6tsoqoiX1d+cGqHlkSaQhEUYU/XVSD2rsumcLruMiiAKarrJ+d2+dp1UPaT6YKIms390LAmi6SrlYRQsoNHc1IMoiqqbUxDr8pKRlWDR1NWBWTPJzBfSwTigWxDJswEPVFcyqRSAaoPEavti25jjFksHkXJ6+zga62pI1UMznm9M0v0qubFdXpYEBaAvFaA01rPg4/YpQn3NtqJhBEkR6IkkqtrWMeuXfYj9zwJFRMfnGx35UB43e+sHX8/AH7ibZ7HO5mFWLky+c59N/+hVOPH9+yb7FYpVstsyGDS1omoggCqRa4qRarjqdAyeHOffqZf8/gj8BpiPBqxxDBBDlXjw3W99nbiLD8/9ymHKxwtb96/mDj/8qLT2N9bJl1/XITucYPDtGdibPmm2d9Rc1IEVxayzoruty+PFTfOWvv0t+rkiiKcYv/fb97H94F43tKVRdwXVditkyY5cnKWZK9GxZHmxOj8zxhf/6LUYuThBviPLe//QWbvulPYRqgJVpWFw5Pcrn//wbHH/2HN/42A/pWN/C3gd21M9ZUiQOvHE3T331JUq5Mq/++MSqwJFRNXn5h8cwKiaJxih7H9yxdEFwPc4f7ufLf/UdCpkiWw+s59c+8jbW7uyuK+SUcmVe+PZhHv3oNxk8N8aX/vLb/PFnf5NoKoxpWMxNZmlsXaqWUMiU+NJffgejavJrH3kbN79+O40dfuBqmw5TI7NkpnJ0rG2+9pQpZst85W++x/FnzxFLR3jHHzzIPe/eTzTpO9i25TB0fozPf/SbHHrsJN/79FOs393LgTfuXpa1/+bHf8yl4z5o9Obfvo83/od7Sbf6iLdlWJw8eIFP/+lXOf7cuSXnMFko0j87z+6OVgKrLFirTccz1VGy1ixBKYwsK3Uy2qxTQpJl5pxxVClAQm5EkBxESWfcGMB0DTQvgK6GsFyDpkgncdUPcBfIBIPayqjzgozo2ckZBmbniQV8MuZ0OMhYzi/rLZkmAgKtsQjZShXTdogFNPZ0tSP+FCem/1MWU3SCslLnnEimwsTjoSUBjiiJbN63jv7jg1iGzY67Ni9rhRJEgQ03r6FnayflfIX2dS00tPstJgvkdwt/27aN6zl4ooukS4iaiBr2+5398eG3CCyUtpqWjWX5hNe24+K4LoosEU1HaO5tRA/riLX3LhrS2b52OfgcigVp6W3CrFgrtnEJgsCmvev4w0c+wD/+l29y5sULZCazzIzMIUoiwYjOtts28vYPPUS8McapF86Rm/UX0pWOtfX2jfzfj/423/7Ej3n1B8eYG88wcWXazxCrMqF4iFse3MUbPnAv227fWJ+nFpsoCvTt6KZ3WxelXIW1u7pJNMWpOBaXctOk9TDtoTidG9tYu6uX7HTeJ8Ze0wyCgKrKS4hztxxYT//xQaaGZ8lMZZkZma1XJEqSiBpQaehIs/3OTbztDx+ia2O7v6ZI2+rnZNuu/zxMC9ty8ABRllAVCVFV0GN+RR81x9jxPFzHxbJNioUqVcOCWmCsyBKe6+E4DiAgKS7IJrIOgVioTsLqe4AuHiYuMq4Ntl3EdotIKPj5cBfbKWPVVDwExUNT/UqnSGJ5teiCOY5bd+D7T46Qaolz4KGdHH7yNFNDs6zdvpTE9srADGfPjvFLb9nNP//zYd717n2ErxkDgtSMIDbgmK8iqnvQpSABeWHtdDl1bIhnHj/N+bPjPPrJp5BkEc/1mJ7M8ZZ33YwgKKjKJhb4964UfogsBijZEzQH9jBTOYflGITU0A2r3P08mCbJDOWyhFWVpB5YFZiXVWnZd6om11QhnSWfS5K4oozyghmGr6q5dXsn23YuJywOR3wJd9t2eO7JMzz6yDN0dqe5Zf9aGhqjiJLAIx97vL6954FpOsiyuCyIVjVl2XsuSWJ9UfSrx5d+7+G/c888foYv/MMzdPc1smfvGhoaoyDCPyz67cX26ouX0QMKRtVicGCGtRtaUBZVbhmGhSiKbN/dzaYtyytDI9EAkWvUCTXdP38PD9f1FgUZflW5osj0bfErNCVZRA+oCKI/56u6AoIPTAXDGm29jQRCGicOXmRiaJZKycSxXZKNUfSg6h8X/9YYVZNy0ZfaDtYqdxzbJZ4K09rdwMXjw9jmjSnvLJgoCMR1nVdHR4nrOrqskK8YmLaD5TqElCCe7VEwLURBIkUUVZQQJIhIGhP5AjElxMx8maZwjOlMhZuautBlmaQWYtYo0hFKIAkiLwwO8crQKL2pBGXToiUaYTyX59LMHPOlModHxviV3duYKZbIVatkKhWCquKDU4bBkxf7SQQDNEd9H+QfDx2nMxlHlSSaI2Es1yVXrWI7DiXTr5DKVCqcm5qhYlk8duEyv7HvJsZyeb5z+jxrG1KEVZWOeIxMuUKmXCFbqRIPBCiaJl85chJBANNxOT42yZu2buCLh48R0TRaoxEEIF81GM5kedeu7ZyamOTk+CT//qadv7DA0bXmAz8ujlfGcUu4ngXUuDwFCVHQkYQgYo1nxXZcRqezKLJEOh5ClZfOYUFFZUPyakuU61Ww3RKuZ6DjIggyohBAF8NE1DCNwdXXtNVs6fkateuQkIRQrZJeRhZFNjU0sj6ZXhJ439bhz41nZqfpjMaJqFf9w8XXcaC9s/7ZxrQf7G9paFy23WuZ5ebIGUdueHsXj7FSjqlysd4yu5r5fqWD7eZxvUqNgsLnlpXFMKKwdO0xHQfTtQnK6g0TgV+910XcGq+rIMhIQhBJDCEKN8b95PtQpdpxLKiNBUkIIosRFlAmURJJNMaue6wFSzRd3U6LKFTsPIGISLDWnrrYFE1B0RSm8mUuHeln84ENRBKhlTt18HBdA8cr4XpVJM9BD0g0NUSRRRlhBe5f1/UISnq9Om8lU0UJXQnU7mkJ2y3ieb6qsiAotXsRZntqZS7Rn4b9TAFHC2ScZ1+6hOd5rN3RzUO/cRU0Ar/Pf+ddm3nw1+7i83/+DSrFq1wTlulQXNSy9VomiyJtyRjT+eKiTwUEVBCvDqZSrkwpV0YUBHq3dNDa17RkoIiiQLI5TqIphuu4S5whWbxKdlzIlHj6ay/5VUkRnbf+7ut46P13L6lyEUWRaDJMZE/figpWju1y5KnTXD4+hKIp3Puu/dz9zn1ogauDUNUU1u3q5l3/18OMXppkZnSeJ778AlsPrCeyiMC3e2MbHetbuHx8iBPPnaOUfz3hFYLIuYksZ1/xwbZ1u3tp6W305ZNrAVilVOXZb77K7FiGdGuCd/7HN7BhT1/9e0EQCMWC3Pn2vZx+8SJPfe1lLhwe4MLRK+y5dyuu4zE7kfPLyxe1BRllk5mxed79R2/kwffdiaJd5SFSNJn2Nc209S3n9vA8jwuHBzjy5GkEUeCOt97M695z25IAWVYkejd38Eu/cz/nXr1MMVvmmW+8wt4Hd9bVQTzPY6x/itMvXsRzPXq3dfLGD1wFjfzzUNhxxyYe+vW7+NxHvk65cJWLw3IcRnM5trU2E7hmXsyYeSars2yM+ih4ziqSMfN0hlqQBJGAHML2TBRRx/NcInLCr/SRIgs6CaiiTlRJYrkGuhSqLbAOqqghCCKu5yCvoLa1GnfARHUWEYGIptER98f/cCZHVNeIBwKokkTCDSAAqVCQgKJQtixCqvITK0n8rFrZsSjbFobsO97BRe+m63mMFHKcmpkkc0uSu3e8g30tnTQkogRCfrA8kJ1nrJhnT3M7N71uBxtuWYvtOJycn+a0k+UuUnh4TFansF2bgBTg4b+5F9fyMw9hOUzBLta4a8Ik1TiiIJEvVxmeztazF9FokLFcEWs+T6lq0pyIcMt7bqPr9VtBENDbEvXsw2KzHIenR64wukFlx989zO6mVrZ3d6wM1Egivdu7+f1PvJ+zL16g/+QQ+bkigbBGz5ZOtt+5mURzHM/1+LNvfhjXca+RCV90LFGkZ2snv/Hff4U737GPC4cGmB2fAxGi6RDrdvexbncv4UQQV/RJXhfMw0PED9B23L2Ztbv+iLwxw4x7GSlhMlt1maoUiKr+M+jc2MbvffmXGcwfR5QE5lLHUasVXDfB5ESW9s4Uogh3vGM/W2/fxOVjVxg6N0Z2OsfcVB5BgERjlNbeJrq3dNK6tpl4KoJlOYCHJEv1zFahWGF2rsiv/uW/o1io4tSqrjzPY2IyR65Qoacz7Sv1mH57qd82ZyNrCmNTOWbnS3R1pOjpStO3vYuPfvPDuK6H4UxRNM4Bjt8i6uYRkJHEEJIQpGxdRhIjKGIcNZInU30KUQggCjqCILNmv8SffP8uJEFHFqMElbXI4ms72AuKerIqcf7IFcr5CoNnxynmysxNZNl2YB3BWhCtqBLTUzkuXZpifDzDlSszhBa9M+GITkNaBs8ALFjEVQT+WO7oTtO3roXJiRwtbQlkRUIU4JYDa1m3sRXHOY9lDxIKPAhIOJ5Be/BObK9KW+h2itYc/bPjRFStXn32i2BBRaErFkd7jYx0tWzWx92CFQtVJMkn919m15mqA0EVRZHp6Erz4Jt2rbitKAqUSwaPff8EiWSI3/z9+2htT/otAhUTfdEaLgj+MS3TwbScJT6NUbXqKl0/iVXKJo99/wSphgi/+fv309waR5JESkVjWVvxgnX3NfCe99/Bd755mG995RXaO1Ns2d5Z91ECQRVFlejpa7zudV9rAn4bXOfmVp5/6RzhkIbeFGbdgT5GB2eohkQ29ibJXbD5wQ+OsGlvH6l1Dbx06CINjTGcmILWEiFrVgmEVNItcWYmsvRtaiPZGCUUDdDSneby6RE239xX4xsTmZ/KsW5bB6drVUdrt3cQjOgoqkzXel+8YSXzPI/Zog/OSILAro5WUiG/jUgWRWzPIRkIkKlUauIZoCsK56amSYVCFKoGa9JJZoo+N6PrusQCOmPZPKlQkIplMTSfpWCY9KYSdTLglBZeJLThUbUsOhIxtrQ0IUsijZEwE/kCJcPk7NQ0AUXhpo42XhgY4vUb19X9i41NDaxJX+Wz9ICqbaNJEnu7O0iFgkiiyN6uDrLlKvetX0NY0yhUDZoPl/eLAAAgAElEQVSjYcZyeUYyWYqGyaHhUXa1t/C6DTWSblFkR1sL7fEYd6/tpTESpn92ntFcnj+4Yz8l0+RzrxxhvlzBdT12tLWwv6cTAZgtlXllaJRspcLlmTm2NDehXYfw+xfJPM/DcufJVl8iWz1E2byI6czjegaCoCCLYXS5laCyjrC6nqi2E0lIYzsuV8bnsO0UHYtk2Rcf1/UMcsarZCovUzBPYtiTeJ6FJEYIKt1EtZ2kg/eiyx3AjamH+cctk6m+yHzlICXzAqYzjec5/jqpriGu3UQyeCcBuYUNyfSyKtaF31mfTCOJ4qq/e+3ngv/ha56fT3FRwHQmqdrjFM0zFE2/UML1qsxXnqZiD664vyxGSQbuJKxoFBTzOufmX5PhjDNffpb56gtUrCEct4QoaqhiAxFtM6ngvUS1bQj4rfB5s8q5zDQBWaErEiet+8CJ41bJVJ7HdOdQpQaSgTsQkHA9g0z1RTKV5ymYp7GcOTw8FDFOQOkmom6hKfxmVCm14nku3BPHK5CrHmW+8hxF8yymM4vn2chilIDSTSKwj2TgdjSphesJkNhuifnKs9huHoBU4C5UqSYi4TlMG1fwPJfeyE2L9ikwV34Kx6sgChqitolkSwJFW742+8/PpWieZ67yJAXjDFV7DMcrIQlBwup6ItqOZYJOAK4nY7kdeARX4a4TQRBw3DLzlefJVA7698KdQ0BEEeMElTXE9b2kgnchi7Ebeid+UvuZAo5c16P/xBCzExkUTWbP/dtItSyfUCRJZM/9W/nh559h8OxY/XNRFHB+gkZOx3WZzhevURxycZ0JhEWl8cFIgFA0wPSIx9lXL9N/crjeqrTY/MzS4sVCwPHsugrY+OUpTh28CMCW/eu45537V2yNWjjWSg+8mCtx+IlTGBWTlp7GZaDR4v3X7eph9z1b+NEXn+P84QH6Tw6z446N9e+TzXF23rmJwbNjDF8Y5/yhfnbfs2VZNdHxZ84yPTKHHtK46d6thOMhBgZmMKoWa9c1+5xEj5/C8zy2376RDTf1rtgPqwc19ty/jYPfPUq5UOXsy5fYdmA9hWwJPaCu6Kyt393DXW/fuwRQuva415pju7z62AkyUznijTFufdNNK1dViILfBrmhjTMvX2Lg1DDTI3P1ljPP9Rg4NczM2DyyIrHnvq2k25bLqUuSyE33beUHjz7LwKnhJd8FFWVFP32iMsvB2WNsjPpVXjmrwEh5ko6gz92RUJtIqE2ros5RJbXs+pv1riWfrUaambeKeEBcXdpuOFmdRRFktiTW0JmI4Xge3akEYU1FkaTrknz+IrSpgT/pN+hhIoq2DLjtz87xp88/jut5NAZDhFWNOxs2Eg5fBWOfGLrMY4OX+fu7H6ItEkULqBiOzdNXjjIzUeSu7j48PGaNOYp2EVVUqUaqpLUUulQrn3UXqlNs0tEUoiBizeVxS2UaYiFs1yWgqcwXypQNP6NarBpEAjpqIoTjuMjqKoEDkDMqXCpneXK8n0RLnL2h1UufF0DxW99yC7e+5ZaVN5Ig2Xz9rBb4YyQcD7Hz7q3svHsrJTvHWPkSludnZWbEyxhWmvHK5Xp7b6PewZwxgSwqyIJCT2gbqZYEE9mjHJ7+J8KVADFlJ0ktSIPugyKyIqOlPXJcpmjPMzN3hZt5G13C6zGqVv2t0AIqTZ1psrkqWjpOIKjiuX71wOToPNF4iGA6TCFfZXwkQ6lQxfM8Nu7opKGmLGVaDrl8hVg0gBzWEV2vJv1rEU5HSLTESSRCFIpVEiEdVZGYmSsSViSiYd0nYlUl9JoDomgKyRa/8tJxdYpmCUFQEJAwnAlkIYoiJZDFKAXD8jOkYhDbFcETQPBQxCiiGMQNmjS2NSEJIXSlo+ZQ3fh72rmuGdOwEASB7betr7dKCouc6O5au84/f+swly5N8tWvvIK8iNdg27ZO3vymNJ6XR9JuxTGeR1S2AFp9TMQTIQ7csZ54IsiefWuWrKue52I7MRYvDlG1C9czUMUwZzKfp2rn6E1spTfW/lMtx/4/bZos05dIElLV6z63+fkiQwMzxHb5HH2W6XDm5AixRJDWjsSq+11rgiDQ0BiluTXOqRPDZLMlUota0j3Pw3X8OdGyHObnSnR2p0k3Rn05e9ej/+IU2Ux5yTF7+hp58kcnGbg0Re+aJgTBr2wbuDxFufSTk8xblk1mvkRPXyPphgiyLPm+48VJcpnyivt0dKZYt7GV9/zGHfztX3yXz33yKT744QdYU2tZa2qJk26McvLYEPc+sI1k6irAuvi6V7tvUlOAtXevRZdlsjhE16Zo7QhhOQ6nM7OwIU5IECCmUtY92g50U7YszszPElsb96uRBIGONU10rFmaDLvj4Z31v/e9buuS725vjVG0S4TkIOnaHLx9/9pV713JNPnYMy/ynVPnEUWBN2/byJ+87k6/Vc30KwFGC3laI1GaGsJIooAqyXTEY/UsuCBAUyTsP0fXT060x6JoioxdSyh6nkd4UWXz4sTSvu5OAorCwSvDnJmY5q3bN/OPh46zqbmBzmSM89MzN9xVkwjo/OrNu3hpcJh/ePEQ77l5J72ppUIpnufxvTPnyRsmO9ta0BUF13OpWDYxXUe5Vj130d+m7RP36opcB6VNx0ESRRrDoTqg0BAO0RwNc2h4jOliiYe3bPyF8YmuZ57nkDeOM5j9H+SNk7heZcXtiuYZ4HFEIUBc30Nf4i9QFZW2hhhNqcgKoJFL1R5nJPcIM+UfYbu5pQd0JihbF5ktP8lU8V9oj/06DcHXIQnX57LyPJeieZbR/OeYKz+D4xWXfG8445Ss88yWfky4+C264r9LIrAPYRUp82vHzr/FypZfNaLLLlcyf0um8hK2m8d28zheiYVeM8crM5p/dNXjBOQewuouxksGObO6SjJFQBR0stVXGMz+HQXjNB7W1a8dqHCFnPEq06Uf0Bx+Cx2x9yMLEQKyguHYTJYLZM0KO1KtpAMhHK/McO5TFMxTRNStRLWdgMdw7hGmit+qAzULZjBG0TxDvnqUZOC2VYEjn9/pPIOZj5MzXl1+nNozmys/SVjdQHvsfaSD99WBGc/zcGwXQfB5bW03x2D2Y1SsAf9+NX0eVfJjPkmQcT13WauY6cwykPlrTGcaRUwQnP0DzOoa7Gsqev3nU2Ki8DXGC1+iao+ywJUsIOPhULYuMV363orXKhAhEf072kIblypA1kwUdCwnw1D2fzFT+uGy8VtlhIJ5ipnyD5mvPE1X/PcIKn0/9bnoZwo4sqoWQxfG8Wplv+t29qx6weFYiI51LUuAo2QqTKls3HjFkSTR1pCgWF2MynoIYqSWIfU/S7cm2PvgDiauzHDp2CD/7b2f4Kb7tnLg4d30bOkgGA2sKidqOEVcxR9cl08M1Xkvdt29ZdXs/PWsmC0zcMonlOzc0Ep6FTUxAD2osWZHN/rXX6EwX2Lg1DDbb99wlWdAEtn74E6e/vorzI7Nc+SJ02zet7ZePQFQyJY48uRpqiWDro1t7Lp7M5blUCpVCegqoihw5cwIuTl/ADd2JCnmypSL1RXPSa3tAzB2eQrLtImlIhSy5RUzehv29BFviFKxrrLQLyzWrufV/8k11F+VJIrZEoNnx3Bdj0BYJxjRmR6dW/F8bNNBD/tBTDFXZnZ8vg4cWabN0Hl/PMoBmXW7elcdj6FokM71LUuAI1kUcVwXx7tahVB2qlQdg6p71WE2XJOgHGB3chPSwmThQdmpUnEMZEEkJAdRRBnTtSjaZRzPJ4KMyCFsz0ESJHRJpeqYOJ5DSA5guTYlu4LruQQlHV3SKDkVnps9giIo3JTcTFgOoksqRbtMR7CZiOwDbIIgYDhVFNUjbxdQXYWQHEQUBCqOQdmu4OIrrETl0HUJmn+erOrYzFQLRJXlgO7x6QmyRoWP3f0wG1INKwJp93evZWtDM+ng6hK8kiCxMboe27PRRd0nBlykfANgOha2Z9eBw5ZUlJaUzx/wb1HpUSWJt6/fyr7WTs7Mra6G9v+HKaJOTG1AFTUs10QRNVRRx3KrWJ5Rky6Noeg6LjY5awYP/13qCm3nvubfoiW4gbwpMFCcY030Kt9Te3AL7Z1bmDWG+ebwR3xuLlEgFNaWJPwEUSDdHCMSD/oBqOeCBw0tcURRQA8oOLZLIKThOh6q5suJL1hTQ5SmhuWql6spjgCsWcSFdj2TxAAxfTcL61CYzf4xa//X5GauEhcsBYuvrmYs2edGzHFd8paBF1PoOdCDKAgookTZNpEEkargUqwUa2SwcO/btxMTNL7wuef55XftXdqq5o3iGM/iORM4nllfV68dw9F4kD3711AqGji5q8FHKKwhKQKy1FS/qq7w/YiCTFhuZ7p6DF1q5Mp8hNFCnrgeuBFKhJ8LUyWJmKJSLRo44WuTUlfNMh2+9qWXKBarxBMhjh++wsljQ7z57TfTsMLYvJ41NEV58E07+acvHORTf/8Yt9+9iUhEJ5+rMDw4S2dPmtvv3oSmKXT3NnDh3DgvP3+RvnXNDA7M8PgPTmCa1pJj7tzTw9r1LXzzn15GkkVa2xL0X5zkpecu/KtoN3RdpaungYvnJ3j54CV6+hq50j/N498/gXWdFi0BaGlN8L7fupuP/dUPePRTT/MHf/Qg6cYozS0xHnh4J1//0os88vePcdvdmwiHNXK5CsNXZuhd28SB66iANUciyKKEKktYjkNE0wgoCpOFAmvTfkCkSBLBWpXugt8iiSKqJBLXV1Z2fC1zcRmvTjNnZmjWGugItqBLqyt3lk2Lw8NjVG3/Pp0enyZfNYgHdTY1NHJ0YpyEHqAxFCKoKJimjedCMrh6q+RPauP5PPGAzr6uDh6/eBnDsclVqzSEQwzNZ6la/rktVCudm5ymMRImGQwwX6pQMEwy5TL5ahXPg7xhsKu9lfF8galCcRlwBDBXrtAYDuF4LplKBRDY0Jjm1eFRUiHfr+lOJlBlCU2WOTc1g+t5NEXC6LLCoeExSqZJRNNI1db2xXdDFARu6mjjsy8f4fa+bqL6T0/6+mfVPDyK5lkuzf85JfMcIKCIKYJKH5rcjCio2G6xXjXjt0JVUaQ0ipjAcQoUygaKtLzVtmwN0D///5CpHgRcJCFEQOkmqPQgCCqmPU3JuojlzFGyLtA//99w3AKtkXchXMcXLZpnuDD7J5QsP4Evi3ECchcBpQsBEcOZomRexHIzFMyTXJr7CL2JD9MQegDT9Tg/N0PJtuiMxBjMZ0npQSzXoerYNAVDXJyfoyEYqvv8jcEwlzKzxLUAWePquiYgkA4EyRgVUnoQ23W5lJ1jU6qR9YkIZesKpjMLUK8g9gETFxCQxSgCKycHFSmJKEiEFZWSba6y8otU7TH65/+CknURWYwQVLagy+0IyFSdUcrmZSw3i+lMMZr/HIqUoC3yXjJGhQ2JRnTJb+XTpeXnYbtFKtYgU6VvM1n4BoIgo0ktqHIjkhDEcYsYzhS2WyCorkGTV26r8oG+M1yc+whF8zQAkhAmqPSgyx2IgorhTFOxBjCcGQrmKS7NfRTHLdMUeiOiqGEaNpfPjePYLltv6l51bCxYSI5juSvHrwsWSYVxM/qyyk7Pc5gofJWh7P/E8UrIYoS4vp+4vg9FiuG4FfLGUWbLT2DX6HBEQUeTWtDlNlS5i6yjMVaeJ6aEEK8BjzzPZDj3SebKTyIIKgGlh5CyFkkMY7t5SuY5DHsK16syU34MF5t1qT9HlZbyQf9b7Wcq2nMcl3wNgBBlkXjT6k6PrErLlL2KxSqyJN2QWgP4DvJMvkihYrCmeQHttBGECAhXXRpJlnjg1+6klK/y9NdfZnxgmu9/7hkOfucoG/b0svueLey6azNNXemlE6DnEVWa0GvtRfNTORzHRVIkGtqSSPJP3v9cKVYpF/wJKNkUq7dVrWaJhiiKrlCdN8jO5JdVUbStaWLdrm5mx+Y5+cJ5MlM5Ar1XHf+p4VkuHPXJIrfsX0eqJV4nGFNVCVkWmZ/MYdcW++995mme+upLq56PZdp1UKlcqNSPVSkZeNcAfoIoEEtHkBWJo6OjTJdK2K5LR8zP9ldtG8txKBomIVVFl2V2tLRQLRvk5wr++Q/N8NF3f3zVVioPyM74CLZtOlTLV9ns3cXjURRJXG88KhKx1NLxKAoCIe1qH3DWKvDd8WcxHQvLs+qLy1hlmh9NHKQlkOah1jtQBYWMleexyZco2xUUUeGeplto0BK8MneKwdIY08Y8pmPx77of4njmAk16iptTWziRvcB0dZ4HWm7lxdnjDBRH/b7q2BpuSmziTK6fw/NnkQWJrJXnluQ2esJtnMn18/zsUfamtnFr2s9w/svY09iuje05eHg81HI7CTXKE5Mvk7UKjJQnSGsJ3tp+LynttStOfh5MlSSyZpWy7Qc/rufRn51jMJfl5YkRTMfhxfFhzs/P0B6JsqOxFVkQGCvmOTo1juO6xDTdB6+veTUFQWAol+FCxncItqSbaA75PFLgOxMTxQJn5qao2jZtkSgbk/64Br/N7JWJEdYm0riex5m5KSzXpTsaZ13CL5l2PY+LmVkGsvOYztVsSDoQ5JaWjtfMkHmeR8W2uZiZYbxYwPU8WsIRNiUb6468v43FmblpZsolJEGgORShN54komr1bUYKOS5l5qjaFhFVoyeepDUUqSsWqaJGo95Zv3bwndGAFMHFAQ8kUa4RYzok1GbkWiVSREkTUfzFcL46T2sgWpeCXvG68NUvrwX4BUEgvei9rlfrXQNsLCbvvJEA6nrb3HgAdm2Z+0r7XX+bf02ol7eqnJyfIG9WsT2XmKpTdSzmjQpJLUhrMMqF7DQOHk2BCHE1QDLWxP79awmF9CVqVp4bwxW248lrwKv6SRkkPGwq9iSuZxGUW3EsmReeOcdLz13Adtw64vXwW3ezc08nstRYv5oF5UldTtAZvpuCOUHZzoCl/0K1qgEUMyWOPnmaVGuClt5G0m3JZbw/t965gUhE58uPPk8uV0EAbt6/lvvfsB11hVL665koitx1v191/Nj3T/DIxx7HsR0UVSaVjtDW4Qflmi7z0Ft2M/WJHJ/9xFMoquxXjO1dQyweXLKWNzRGedd7b+WLn3mWz33iKWRFoqU1zp33bSaXW7lC6HqmBxQefstuPvfJp/j0x5+o//bN+9YQja8O2C/Y2g0tvO3d+3j0kaf57reO8MvvOUAwpHHvAz532dOPneZTf/9Y/bobGqN09VxfhCCqa0R1bcn8kQwGaItFCamLW/cE4t41HGDXzAej2RyXpudIh4NsbV3O33itCYCISMWpMlGdpie0nBdzwWRRJB4MwFwGgHhQR5UlHNcn/20MhhjOZylbFrInMHBpCj2g0P0aYPdIJsvlmXkaIyE2t1ytmLItxz9BDyzLQRQF5gplnr0wgK4r7I41oXkStzS38oPD59jW18ru5hYc20Vy4UBHB09e7KdTj3BzbwdPXB6gUK5yamyKcqbC7r4OTo1PMl+q0BGP0ZdKcnRozG+XK1c5OjTOrs5W7l7by3P9g9iuy33r1uAYDhtTDVwcmeGrr5ykIRTijds2kIqE2NnYzBOnLjHYMM/969dwR1c3Lw0N49gu96zvI5+r0BoMUyoYGAkbrTbfNUXChFSF9Y0/3SDtZ9U8z2am/GPKpi82FFY30hX7HSLadkRBq62ZDq5nYtgTZI1D5KvHaAzWWo4dt+4LLDbHrTBe+N9kq68ALgGll9bIu0kF7kIWIwiIuJiUzIuM5b/IXOUZbDfLWP4fiWq7CKubVlxjbbfASO6zNdDII6xuoiP6fqL6LiTBrxr3MMkbxxnNPUrOOILhTDCS/xxhbQuu18JkuUgqEOTkzCSO59EZiTGQKxCQFU7MTJIzqiR0nfFigZtb2imaJnnT4MzsNF2xOFdyGcKKSkhRuJCZJaUHGC/mSepBUgGfZF4UVHoSH8JxS/Vzr1iD9Gf+O7abRRYjdMd/j7C6ecXn4iseRylYk7QGo6uIRTjkqkcAh7C6mbboe0joe2vE2yKuVyZnHGEo+z8pW/24nsF08XskAw9ypVCiats4nsstTZ11/3Tpvc4zUfgqc5Wn0OU2WiLvJK7vRZXSCIKI5zkYzjS56mEUKVG//9ea5c4xmn+UonkWAE1uozP6GySDdyIJQfwklEnZHmQ097laC1qWkdyn0eUO4votyLLPAVcqXB8M8k3Aw31NH802bco1Cpt0+1Wg2rAnmCp9G8crIaDQGnk3rZF/jyqlEAQJz3NJB+8jqPQxnPsktpsnIHexJvWfCCq9eKgMlwuMlzM4not8TRBRtUep2uNIYpT26HtJB+/z72nNpypbg4zkP8N8+Rk8bLKVl8kbx0kH772Ba79x+5kCjjzXxaz4gbskSctawRabKIp16eQFsyyHZDK0YuXKSmY7LsVSBUkUFwEqAuCCoLJQYgbQ2J7ifR99G7e+cTePfekFzrx8iZnROQ5+5wgv//A4HWubufud+7jjrbfQ2JHyjyVA0Z7F8UwCUoxKsYrnuiiqjBpQ/lUZHGNRRZUWUJcpD11rqq7UGfGNiulzMC2aSCKJMPse3Mmhx04xfmWaE8+do6WnsR4oHXrsJNmZPIGwzt4Hd6DpKpbtkEpF6pn7SrFaV1QyKmaN3HV1C0Z8x2kBrV3gC7nWRNFXEREEgY2NjXTXSjm1RROVJAg4noflOL6qkizhWA5GbRy5rkcpW75uBCUrEnIsQCCkLwGYPM/DqAFJoiyuyhsAPsh17XgUBYGuRLwecJ/IXkAAfrnz9bw4d5zz+UEAekPt3N6wm6OZc/XgdL7GgXR/835SaoykGqNkVzidu8Trmw+Qt0scmj9Dk5aiaJeJOT5oVXUMSnYFy7O5XByhPdDE1thaYmoYURC5KbmZkfIkSTXGnY031QPNW1JbyVoFKs7VyXXOyLIu0sWtDTv5ztgznC8Msi7SyXB5gnd1PcCxzHmqjkFcWdry9vNskiASVbT6OHA9l/NzM7w6OcqVbIaCaXJkaoygrLCzqZWtDc3IiJQti/NzM7w4PozruXzyvjfVnzv4w28gO89fH3oe03WYr1TQZZk/vOkAuxpbcfE4NjXBx46+WK+gy1ar7G/r5APb9hDXA1Rsm7878iIH2joZLeQpWiYF0+CWlnb64ik81+Xxwct89tRheuNJLNfh2ZErdEXjvGvD9hvK7tuuy48GL/KVcyfRZBkBmCoXeV33Wv7D9psJKSp50+D/PfwCZ2enSegBqraNKAj8+tbd3N7Rg+t5vDQ+zP84cpCQoiKLInnDYMf/R957R8mZ3nW+n+fNlaurq7o6qINaodWSRnk0mhlrgj3j8Tgwxl6DE2C464MX7gK77FnCXth7do/PYWGXiwHDPXsWMPYC9pIMxh6YsT1Zo9GMclZL6pxz5XrTc/94q6uzJHvhXI/5/aGjrvC8zxvqeX7p+/02tfDTB48RMxQ8P48iwqhKQPI3VR3g6uKLzFQHcWWVsNZAk7mVPYl3EdEaap14knPz36AvfwJPuihC46H0x9GVdiYqeXr9O607IoDw6cvVzauLL3Jl8UUeznyC5tAyvENKyUTlBq/PfJn9yafpjt4PQNUvMlg8R1/+BCV3kYiWZEfsYToj+zFrMsxSSvqLp7m6+CLHm36UqcptrudepeguENfT7G94LwBvzf4Vx9I/TMbaSsGd45WpLwDwWPbThNQYs9VhXp36Ivc3fogmaxsjpUsMFs8xXR3Ekw4xrZHtsWN0RQ5iqOHlYxfe4lruFY43/QgTlZtcz71KyV0krmc42PA+sqHt9c8uOpNcW3yJscp1POnQZG5lR/xhGo2t7GloxvMD2ImhaNhecI8VEfCrtYTjaEJBV1QkYGgqe/a2MT2Vp1pdhgNGoxbpxmb88l+BsECmQQ/2ioLdj4+NpTUxP1fhjdf6eM8PHCSTjbO0YAdqYXoAWZPlDZVhRoov48pmFLme1PjtbtGGKNv2dzJwZYRb5wdIZZMcffog4XgIw9T4ic88TjRmEYuHePJ9+8nnyliWTuuWFOGISc7JM1waxfYcssdUnureRi46y3DJwZMeM9U54noMUzEIayEmK9OE1BD7n2qh41CMwYkJTGkRDYVxwmWaMgY3C/2E1RDhHT6/8J+fITcbcL4kG8I0ZRPMzRWpVhxCtf1QVRX2Herkl//Th5iayuF7DmZyksbGFK09W2nKeOS807zv4wl836YqriJCYT76Uyni0RDzlfMkshE+8x9a6ewosmC/Rcceg8/8yhZkfidIjWRDhKZsnLnZQOLYqh070RDmF/7vD7Lo2ZweGcPSNEKGTnJXik/+/DspVG2uTk1jmDohXSN7pJl3dYaZmFxERWCZOqnGKI1tjbzc18/hjjaEofDuT95PPGpxY3oWe8Krr5V+TR66IRyiWA062eeLZUKGRmM0wlyhSMXx0NVA7ltVBNlElGwi2EOllPzF2ct86dRZ3rlzG7/xg++54/NR9irEtChxPYqlmES1O6tGxS2Lz7zjKH/61nlCus7H7w/2hVtzc5wcHSGs6wwuLPDh3jBRRWdhvkioemcJZyklf/bWBb58+iJP79nJZz/wZP29vmtjWJZBpWwzO53HsDTa2hvZ78Q5cGAr59/sxyk57DDitLaZZBIJ+sZGuXphmGKhQsKXvDvTwdjwHONylq6iwc54O5GYxcToPDS7fPzw/vrxKo6LquSZzhV5omc7Q7MLdKdT7G5uYndzkPxyXY/LfeN4uscD2TYsU0dVBIVchcX5Esqix+PNnQgEAyNzhC2dD/fuZnImByWf+WKJNhlmbrJAucnB0FWmCkXeGBymK9VAV+r7o4h2N3P9HIXqZSQuqogEyZ3wuzbklzG1LDFzP36sjBA6rgf58np0iJQ+C5U3mCz8LRIbQ21ie+o/0GA9uK6TSLcaCWkd2NNz5O1zlN0Bxgv/i20Nv1BLKozpZUMAACAASURBVCybL11mSs8zW/42IAlpnexI/Udi5oF1sUdj6F1Y2hauTv88JecmBfsyk4Wvko58hsVqharn0hFL4PqSdCjM7cV55itlIrrBwOI8tu/RFI4SNyxuLsxScV0arBDZcJSy6xDRDSKaQYMVpujYdEYbyNtVFioVuuJJhFCJGj2r5qQKq04gLdAI6ztIWIc2vTee76MAQ4UFtsZT6Mr6gqHEJqzvZGfjfyZq9K65b3Ey6tOA4PrML+HLMiXnNkXnCk3WHkaKi6SsKKENuo0AHH+eqeLfETF62NH4K8SM/eueC1NrJmbcB2wMA5ZSMlX4OrOlbwM+ptbG9tT/RWPosXXQQUPLENLauTX3a0yX/p6yO8hI7g+JGNvRlEAQ5d4aSiRVr4S/ott/I9N0je0HtxJfoWYqpU/evkzJDviAw7WEp6llWPJnhFDQ1QTZ6AfJVc8yU3qOsjtEwb5K0jpaQ5IUCWkbw9MlHorQ2Jr8WVzlSa7myuxMhIjrQUydUBsw1H9P1Z2kYF/Ek0VmSy/QGHrXPypc7XsqcSSEqAfnvuffkThRSom/5n3fl9zun6a5Jbmq6rmZqYogaplrFi8N35tEKOsrV2bI4L539LDr/m2M3Z7k3EtXeePZc9w8P8jg1TG+9NmvcvHV6/z0b/4I2Y6g6uD6VTzFQUofwwqSRZ7r49qriSLv1cywWU+M2RVnXZfOWrOrDr4XfMawjHUkuIoiuO/hHrIdjYzenOTN5y/y2EceJBQxKSyUOPPty3iOR+eBLrbv7wIRfCe3WMYw1Nq4OkIRCEXwnh97hP2P9N7TuSTSMUJRi0rJRlU3IJgTgSqL7/lEdR0LJfjcisSXqFWzJLUuARmoGi0lHVu7m/iRX/7gHZM+S6ZqCt37OlaMLTCsYBzf8/GcOwSmknXPa9lxuTIxTWsijq6qLDoFGo0kUT1Ms5Wmvzi6yWDQEW7mocYDvDF7AV3oPNl8jKQeo8ls5FtTp7AUk97YVrQ1G4JX6w6yFIMnssd4a+4y3xh/hcOp3exP7kRBqfEUiDtKPgKEVJP2cJaoFiauRbB9m4Qew1B0vjH2Cr70OZbef9dx3k4WENEGKmZCCFQU3tvdw9PdPXzpylm+dvMa//7oI7RF40Glt/bMbk+m+LdHHiZ20eSbgzfXjSsJMOyf3H2Qg9kW5stl/t1Lz/JnVy+wK5Wh6Nh8/uxJ9qaz/KsDD2CqGi+P9PPZky+yu7GJ928LYBKe9Dk5NswvPvAIe9JZXN+n6nnoisJ8pcxf9V3mcHMr/+7IcSTw2ZMvMFMu8a7Obej3IFOuKQoPtXZwsKmFtmgCX0r+5Oo5/uTKeT60Yw+RhMFQbpFT4yP8m8MP8Vh7d0D2XSyQsoI1s+q5vDo6SMww+W+PvZdoLdlUch2iuoHnz1OsnsfQ2wkrO5izR/iH8c8hpaQ1vAtNGOScSfryJ+iI7COiBTwtCgotoZ2oQmO4dIm+/OuUvUUyVjc98Swh7U6/cUmlbFMsVOrJ2bjexFTlNn3518la2+vrjycd+gtnmKr0E9Uaa+dU5OTMV7iRf43W0C4yZhcLzgTfmvh99iWf4mj6X6DXOmFK7jwDhTMkjWYGi+dpMFpJ6BmK7jy2X8JSokxXB5ipDpGxtpKzpxgsngcEOXuSUChIHI1X+jDUMGUvx1tzX8WTDmmzEwWVycotnh//PO9o+hHuSz5V/w0W3DkGiqdJLGQZLJ5bPra3gO0HnapSSmarg7ww+T8oeYu0hnahCJWB4ln68q/zZPNP0RE5UL9ya7uw1nZfLXWNvvjCVV588Rq6rtZdrvv2tfDMD8QQaiuq8QgIjcDtcPFxCWtb0EQEKReIxUN0bWtaxauzZEV3nCvzX9wAhiCZq/RTdT5INnznAPftaMXFEkPXRsm2N3L4yfvovzCEYztACEVR6Nq23AmyUVdMySvj+A6zzhyRxjA96VZMTWeiMoXjOzi+i6ZoFNwiRa+E47tAmZyfp6OljcamKBWviu07zNlFSrJEUm3GUAwmvWlaM820tazmpsg2r1ezEULQmInRmInhS5vpcj+egF293YECoF+gaYsH6Ag16Drc0q1gagaOv0gk1EDv3nY8v4Djuwg00k0h0p1tq0hGsy2rg3bD0Ni1u43TQ6OB8pdlUa51Kkeao8QVhZlKmb0NMcKGwUyhSLQhRHMk4E0KGzotiRgesu6jVFyXLd3pALbtOCyWqmxJxWv8Ug6qIkhFQ9yemkNTFVzfZyZfZKFUIRUJ4UufyVyJsKGjKgqWXq0njoq2w6WxCQpVG+cuBTiAico0Za9CzinQHmrBcsPYflDIW+lfKkogPS2l5EBTlvve8ySVsk0qFUXXVVpiMR7c0k42EmW8kA+gaQgiUateFNzMClWbS2OTlBwn6BZcYZ7nc7tvgngyTCIVQQAjgzPMTC4ihCASC7oEo/EQ5ZKN63gU8lUyWQhHTFRFUCnZtU6MILmTSmu4jhtwlihrg1GVPa1Zrk9ML/v1a9xKRVFozSZxXY+GRBgJRCwdX0pm54uEa0lH2wmKkdGwia6ppJIRNFUhGjEpV50AxlejT5gvlREI3r+nZ1XB6PvZpPTwamIHQigoIsSdKrRCiHpCRxU+DbEwI1MLq2oBvqwyVfy7GneLIB1+ggbroQ05hoQQmFob2egHKMxdRuKwUH6Ncmx4XeJlKZHhywqKMGmKfICYuW/D+EsIhbC+g0zkaQYXfgeQzJVfJBX6YbYlU/SmMljaUhIHjrd1IgmoDPZlmtGEypHmNgTwYEsHsiX4nBCCI9mawq2otSjI1bDyfzSRGQEhTV/R+rDxh1pjP0TE6NnkU4KEeYiw3k3BvownC9juCIroxfYCtcXNeFTBR1OSdCQ+vWHSqH6EerPGenP8OaZLz+HJEiDIRn5g02cBwFCztMV/hFz1DFVvgsXKKXLVs8SUxygVq+jGvYHYNcWk6C5xSm08N9d2GR0cRzfbCcdrhTtcSs5tJC4gCOldGCsg9quPESNm3MdM6Tl8WSFXPYeUn0RKqHj2HZNWCfMwmcjTXFssU3LtIP6t+2Ngaq2kQscp2BcBSdnpx5dVVLE5l+l3at9TiSNFU+uqX57nsziT2/SzrutRWNPmbOgaqdSdKy4rzdQ1ulsDicTlBSSQkZQbkLytVHbr7G2jfWcL7/iBw1x96zZ//fl/4MrJm5x54TIv/sUbfOTn3osUPrpiEVaTqIpOIhNDURXsisPcxAK+52/KW7CZhWMW0USY3GyBucmFoBX4DrYwlcOuOHUo1TqWfyFoam+k9/5tjPRN0Hd2gKmhGTp2tTJ0fYzhG+Oousp9D/eQSAckdn5ttVsKrpNNcTRdpVqyae7M8MB7DtwzDE8ucRTp6oa/UbvqMHw74GLJzRUJRS10Q8WpuuiGhhU2yC+WoebYhaIWpqUTrT1Hmq5x4NHdJDYIRu5miqrUpavlXZ5Hb4PnUVFE0E1V+zupx+gvjrLoFBgvT1P1lrkg1i6/tu+wLbqF9nCWr429TF9+kIfSBzBVnU69hf3JHpJGDEUomKrBnL3IolNgsDSOioInPaJamHdmj3JxoY/Tc5fpiXURVi00RSXvFKl4VTSh1ZNPct1CKRAs30cJWKqBrujsim+lK9JKgxH/viKBtD2PpBGqEy0Hzk7N+RYBMagqxIYqG2q9I2Nj60wkuS+TxVQ1spEoD7S0862hW8xXyowVclydm+JwcysnRgOerLlKCQFcmJmoJ44ADmVb2d0YjGOqEKn5qT6SqucS1c36/CK6wVghv2aN29yEEGTCEUbzAfSu7DpUXJe8XaXiBXDUdChMYyjEN/pvYKga+zLNdCYa6uetKwrdiQaeH7jJ3968ygMt7WxLpkiFwgjAdnIIxURXgoBzsnKbvDPNu5p/iq3RQygoOH4V2y8T0ZbJ6IVQaA7tpDm0E00xuVU4FRxQwlQlT3skSUTbOHnge5JiobrKgWkw2mgObWeoeIFqYxFLDe552cszWDxLxuyk0WxHCMFI6TLXFl/iUOoZ7mt4N5owqPpFXpz8Ay4uPM+22AOrupZKXo7B4nkebfpxGs0OBAqOrGAqESpeAV2xyDmTgcpRdYCk3kzenWXRmaLJ2sacPUxCb8JSooS0BO9q/kk0YdbmKFiwx/m70V+nv3Canvjx+twBSu4iQ8XzPNr0E6TNIBHuyCpWTVHNlVUuLj5P3p3lqZafIWN2gRBM1ZJRZ+e/Tlt47zpFxpXPz9r/u67PhQvD/OCHjtDVma4vI4Y6hF99FenPIv0ZhNKEaj0dFFKUOEvVxnAkIKN/9m/OsGtPW70rtmNrmnQmju3lMZQ4W6KPrn9m+RYFaREzzHUqgm9nc51Axn7fI70YlsGt8wPsONyNcQ9FkCVLGyliWoTtdKOg1DvGXOnh49deCxQ4FaHiSa+uYGgqBnE9hisDNcFO2Y4qlDqfXVgNYarfebJOoJEyDyGEhkCDIHW0ArCrIoRGU+QxFHR8XBR0DDW1AtLqA3JTno+11pPNsD3TiKYE5yZqxQFYcrY1FCHY25qlVn8CgrU+SE5IXN/H1FTSsUgAQVcCX0iIgC8TKQOyaCXofD7Q1VL3lxQRHM/UVFw/SK4oIpjLymBxKl9gcG7hnq9lZ7iNWXuBRqMB3THouzlObrGM63jYjovn+UgJzS0JNE0l0RBmejJHsVAl0xQnGrWo4PHcrVuM5XMkLYuS4/CDu3YTtQyiMYtK2b7jHMZzeUYWckGVb41t72mhtT2FtYLb0nN9du5pIxYPcd+hLjRdRVUE8WQYy9JpzMTWQSzLJRsrZLC9twVVUVBUQblkE4muh/2pCuxta+biyAQ7mhppCK8mTFYUQbohsu57UkoSsRCZxhi27UItMZWIWevgoft6WilVHKKRoDt5VzbDruyd4Yzfb6YqVp3QOFCqepGEdQRLa7nrd30pmc+VguTmiter3jj56kUABDqp0CObJgoguG8xcz+qEsL1HRx/nqJ9Y13iqOz0U7SvA4HqWEPo4TuPi0rc2E9AaOxSdSeRjLG3cT/GGk6mpT3nvnSWkusQ1pZVhtftR2v+vkdwzHdsAkHcsOqUCxuZoaaJm4cpuhVs3yZpJJY5VgmuraYkMdTl59r1F2rd3mCp2h2h8BFjFwnr6KZJozuZlJKKO0LJCWCQmhInWYfSbWxCCCLGTiJGL9XyBJ6sMF9+jWT8cVKZGMXc3aFqnnRQhEJYXVP8WLO0KapCtWRTrgmmCCFq6/8SWbUIYJWbJcxQ0ZSlmDT4XpBw0mrviw2TR4owSVhHa/fFpujaVH2XGOB7oyjqFgQ6YX3rinMq48sSKt+niSPD1OjoaQ0qN1WHm+cG2f/IxuoE5XyFkZsTq14LhXVUTblnqJoAwuZax0cAasBzdLfsuaaSbktxvC1F69YMn/3R32Ps9hSnv3mRD/+fTyF0QdJoxah1L23f14kVNrErDudfucrxH7x/HU/T3SyajLBtXwdjt6cYujbO7Nj8pmNUyza3LgxRLVdJZOJs3du+MSRMVTj+g/fz4l8GJNmXTtygZWuGS69dZ3EmT0M2wYPvO7i88Xs+uq4SChlIKem+r4NYQ5TiYplrb97iXR99sC7pfFeTkJ8vUi3bG3ZP2RWH0f4ZovFA2a5SrlLK+yzMFSgVqoQiBsiA8yrTnEQp2zR2punq3cLVU7dYmM5x68IgBx/f8x0nODRDo2NX4Pw5jkff2c3HKRcrjPSNr3ptKl9AFaK+5hxo6GG8Ms2fDz9HXIvQbKWpeDZvzF7g/MINck6Bvxh+nve0PEzeLfLq9FkqfpWYFmZ7tAPHdym4ZW6UhxgsjdNoJHki+wCHGnr59uQb/OXwN7FUg4gapuBWODFzjtHyFKBwuGEXhhJUIHbFunl+8iR/PvxNjmcO0aDHeGHqFH2FIGFR9RzemT1Kyohj1QKDmB5BVzTKXpWCW+Lk7AXOLVxnW2RLLaH1/VHtt30XXVlP1viPYWFNJ7xUqRKCdDhoVa56LlOlIo7v88rIIGenlp+j7Q2NtMdWb2LZcBRtgzUublg8vXUnf3L1PFUvaLV9c2KEj/TcR8K8N7LOomPzlWsX+ebgTdKhMEkrxHyljF/rKgHIRqL86oPv5M+uXeC3z5wgohu8r7uH93b3kDCDxOR7u3tQFYW/6bvCV29eoSeV4ZO7D9CbyqAIHSmduuMW0YL27KHiWZJGMw1GK5YaI6Tdidh3daVqiWx2M1NUhcZMjMmRXN0JsNQYW6P3c2L6T5is3KQjvB8hBOPla8xWh3k0+xOoQkci6cu/jiJ0Gs12Cs5MfdxGs52b+ZNMlG+sShypQmNb9AFaQ8v7l8lStVUlojaw4EzgyiqTlVs0mFsIaQlmq4N0R48wXeknbXaiKyFUoZHUW6j6JYruAp50AImlRil7i7h+FVYkjlShsz12jNbQrhXHXg6Uiu48/YXTNBht6IrJojNRu6IKDUYbU5Xb5J0pGsy2O1z/NXdDQCRikkiEaEyvbN/egfTjSHcAoXUjvQl89ypC3YmqhFFE8Fz6viQSMRkdnmNyfBGlBq9+4ul9pDNxovoWtic+RFRfH5TYXoXrJXHP+/7bxW6cvsXVk311sYrJoRl6jmxb1zV8J9MUDU1Z7+Z9J9S9+ibJmY3GvRdbatdfaRuFcIqIrXpPXTFrx3eZri6SMpz6HnUni67z8za2iGGw4ExS9UqkzfZ1ydMli4XufgXjoe/MUZdS0j87z+ji5gWqtWb7DldyNwmrFh1WKx1daVzXY0mVV8pA5dE0NTwvKPgtQQgNQ8OydDRd5b07dvC1G9c5tqWdi5MTuL6P9CV21aVwB24QX0puTc8ykctv+L4VMuqwwSW7MT9DwbXZg0U0tnyNlrrEN6KoWDvGZq8tzalvcgZPSgpVm/lSmcia+79xp0nwWjx69/sWCZtENlFE/udiqoiSCh1nvvw6nswzXXyWijtKS/QjJKyjmFp2Q8lxoP5chkx9VUxetG/i+HMAGGojIb3zrvPQlQS6kqopkJWouEOs7RbJVy/h1oJ6U2vD0u68twkh0NUUmhLD8edx/UVsb5KkpbJZXGhq2ioajf+/TQKWpm3qy1raFjQ1w3B5grJXIaSGCKnLz36ASjBWJWs8WSWq60yWC0yUCnTFGurdV2stbh5EFZF6on5ZuGMjCNba9yQl+1ZdTc9Qmwjrm4sTLZkiLOLmAebKLwA+Bfsarl8MUDf34B6oQg/42Py7q32u68QUAkVZSlJLXD+PlP6GySOJh+MvFwgUEUYQ+Jplz6bkVpHSZ+3OqIoIMSOIQS1VJ64H9CrSz+FVTyKsdyKUZlRlpSqoiy83TyB+N/a985QTOPdb924hkYmxOJ3jzAuXefcn30EstbpTRvqS66dvM3ZrctX3HcejWLh3VbWNTUcoSZAlQAbVJz94rDfj4gFoyCZJZuKM3Z7CdTykDDoUcs4USaMViWTLjma67+vg3EtXuPDKdU5/8yKPfOjopl1HS9CrlTxGkXiYg4/v4dRzF5gdn+e1r51my86WdZutlJLBq6Oce/kqUkLnrla6925Omti9t52u3VvoOzvAW9+6xANPH+Dcy1cB6D26jbYVErGKElR7YrUNtmlLin3v6GFiYJoLr17n/CvXePgDhwP42ibYVd/zAwdYQHNHGk3X0DZwGKKJMIcfDYJQIYLFx6/J4+bmi8QSgaLdEqeULyWapnLw8T289rXTLM7meeEv3mDbvs5VeNS189noHiuKoGv3Fhqa4sxNLXLmxcu858ceWTeOlJIbZwYYvbn6eYxbZp17CSCuRfnQlidwfa/e5aMLjWON+2hzthANm8RjISzVIGnE+XB7GikD5TJD0blZGKLklvlI+5NI4OtjLzNanmJnrIuPd74PX/pI4ML8CKdnh2nUW2k224NWcDVMfz4IeH0MjqePBd0O5QpT5TKHGvbxZPODwXkLBVMx+EDbo/UKxEPpA0gkZ+ev0WKleSh9gJJX5mujL7EnsZ0mdXN1v7eT6ULhdn6GmG4SVr87HrLNrOp5VD0PS9OQUpKv2piqhq6oxAwTQ1H52UMPcqBpdXC8vrsJNtoFdUXhgZZ2/ubmVRzfJx0K8zOHHuL+5i1o91j1uT43wx9fPsP/cd8RPrRjN5qicmJskDfGh+ufUYRgZ0OaXzz6CKOFHP8wcJM/vHQaCXysdz+CoNPpmW29PNbezcXpCb54+Sy/ceoVfv3Rp0gYbs0ZCc6hJbSLgw3v48L8PzBYPE97eB87YsdoDfdiKpG73gOJxFS1u7Z5l4o25cpy9VwRCu3hveiKxXDxAm2hXgQqA4UzmGqE1lDQ5SWlz4IzTs6d4tmx/2cVNNOTHgKBvUaFQ1MMUkbrJgGKSsrcwmx1iII7R86ZZkt4D1LPMlW5RdnLs+hM0hreja6Y2H6Zm/mT3C68xYIdqJMJFObtMdJW17puRU0xaNjk2BDA7vLODIv2JOPlayuuI3i+TVhL4sjvTCZdCEEkYvIn//MEu3e3YVmBM7llS4oD+xzcyrdRjTzSGwOhIMwEtreAqaaQUhJPhPn4jx+nUgkgIOFwoH5nmjqO51F2NSx1WcnQlzY+wbXPhO4jF11ksbKZ9PDb07KdGZq7muoQ9+HrY3dNGnm+T9lx6J+d5/bMPPOlEmUn4KcK6TrpaJgtyQTtDQkipoGubAARX2FLUJxC1ebm9Cz9s/PMFUu4vk/EMOhIJehpypCOhuvKppuNU3ZcfCnRVQVDVSk7LmeHx7g6OY2mKOxtzbKvtRm9do4l2+H86DhXJ6ZRFYXudIpD7S1EDAMQDBSmyJsVdsZa8aSk6rgB35aq1EUAKo7L4PwCV8anmCmU8KUkFQmxsynN9kyKiLGeS8KTzrrf88rz8KVkoVzh+uQ0g3OLLJYrICBhmXQ3ptje1EgyZG1I/LvReJ4fdDNVXIc3B0fwav6r5/sUqpt3+6iKQBMqES1EyS1j6SYNkdWdNFXXq3c4LXmZUWvZz/JFsJ5bmk5LNMYbI8OEdJ2YaSKUAF6YXNPFH8zZx/UlRdvmzaFRvFrniHuXOWuKYK5SYryYZ3syhblJCCKlxPY8RhdyXJ2YZrpQpGw79Y6v3myG9oYkpra+yBOsY5K5QgnH9diRDWgjHM+j6noIAk42XVVX3M8y1yZnGJidJ1epogpBMhyiM5Vka2NDoLymKPcSe/6zMSEUGsNPkK9eZrL41/iySq56hoJ9jYjeTUPoEVKhR4kYO1BFeFUALYTANDQS0dCKfVtS9cbrpNC+tBnLfwVNubMypOsv4vpLiUsP1y+soQEJulf82p7meguM5r6IsgEdyUpzvBl8addGcIOukE3oRaruVA1id29k/zFjN6nQY99VN869mERSdKqUXWdTFV5NSeJJA0WoRLRIPWmUL9tMzuVoTsWJhow1iR7JXKVEygwzVy3h3WG/NdUMvj8LMoQQGp6/iBB6jSojvGpMX1aQshIoyClxQKXiLlN5aEoM/R5iDIGKpS1zHbr+Io43jxD6PYtmCaHg494R5ud5HpoRQGaXngmBRkTfXksAOZSdAareOKa63hdzvUXy1fO1ORtEjO1B4klKIpqFvwnvkyIMDC2IxVWh4CPxfA9kFUXrYmmVX4kU+aew76nEkRBBoL776DZe//pZLr/ex7NfeImnfvSROkzKdTxuXxziq7//PMU10KCOjjSO49YrKt/lLBBqCvzlB3vg6ig3Tvez41AX2fZGIonwKt6HcrHK2RcuM3R9DIDO3jYUNdhkNGFg+8E8k5k4T3zsIfrO9rMwk+OLn/0qruNx6J17SGbiqFqwkVXLNvOTi0wOz9LYnKR953IgqWoK9797Hy/95SnOv3yVZ//oJVq7sxx77wFC0UDS1bVdRm5O8Kf/5W+Z6J/Cipg8+fF3EG/cvLupIZtg//Fd9F8aZvDqKKe/dYnhG+OYIYNDj++pQwghILtcWChhhXRaSBKOh3nyE+/gzAtXmB2b50uf/WuQsPfhnSQaY3VsfbVsszCVY2JwhomBad7xzGGiyQixZJjYJmooPpLxfAFfLrd7F6s2JdtBImkxJJqioqkKxarNTKHIntYs+4/3cOTJ+/j2V17n1b95i3DU4n0/8TjZzjSGtcyjlV8oMj0yR//lEdp3tLDr/u7lJ0EIOna1sfvYDl772mmuvnGTr//hCzz9qUdJZgKIlud63L40zF9//jnyc4VVcw/rOjdLZbya4yaEwBA6xpoqpqHolBZcRvoXackmaN+SIhwyCK2R1dUVjarv0F8YpexXkUgSejRw/GpV16rngAwgU2HVClrhFYGpasxVi9i+R8aKYSgGeadC0aliqhqGohNSV5ODG0JfdWwpJaais+jkuV0YZs7JkdCj6+b5djbb9ym4Vare5rLO360N5Rfpm59hTzpL0QlItjtiCZKmxbZkirZonFdHBznY1IqlafhSMlcpEzXubT2TwPnpcXRV4V8fPEZE35hg7062UC1jex49DWkiukHFczk1vhzMAOTtKo7nkbRCdCdS/FDPXl4cuk1fTS3O833mKmVihkmDafHIli5mykV+6/QJCo5N0tQRKyqRujA5nPog7eF93C6cor9wmv7iW3RGDvBI048TVhN3Dm65ezFJ+hJVFSQS4VUfTuhZ2kK9DBTPsq/haaSUjJWv0RLqIaFnA6iJlEjp02i082D6oxt2QiXXdMIIRJ3Mcq0JBGmzk8HieebtMap+gbQZVFav5V5m3h7Bky5JvRkhFK4vvsIrU1+kLbSbB9MfJa5nUITK8+Ofx2M9VFkggsrZJraUcNkRf5D7ku9e974qdJL63dWc1loqFaFUSrK4WCKfDy5yIhECEUZRW5HeIFLaCNEI0sNUG4IEkLRB6ty+Ock3vnqGhfkiP/rpx5iezLF1exOhdIgTo0Nsa0jR05Am5wzQn3+WsjuDJkwyofupuDso2PYdHdm3mzW2NFBYCT2nWQAAIABJREFUKPL6104TTUbuCLeWUrJYqfDt67d57tpNrk9Ok6tUqTgunu/XIDwKlq4RNQyaYhGe3r2Tjx3Zj6Vv7gaWbIdTgyN8+fQFrq0YU0qJpirETJPWRIz37unhmX29NEbCG/5W89Uq//Hr32JgdoGndu/gk/fv549OnuHPz1xiplBEEYKmWJR/+dARPnRgD47n8XuvvME3Ll1nulhEIEiETD6wdxf/6vgDhEwtKCIR7K0XRyf4jW++QsG2+eC+3Xz8yH4mcnm+8MYZTtweYrpQrM/b0jUawiEOtbfy48cO09ucWZXkkVKSc2ZoCW1bdx7z5Qov3rjNX567xMDcAoWKTdUNYE2GqhK3LLoak/yLA3t5Ytd2IsbmxYehuQVuzswyMDPPjelZBmbnuTUzV3//9f5hPv6Fr2x6bw63t/HzTx6jPdSCKpR6kWelb/o7L73OK7cGNvy+guD49i5+/l3vQBDAZsquw45UI770Axnr6+PE4qE6b9XA7Dw3p2cZmFugb2qG27Pz9M8ECm0SePlm/x3n/ODWDp7av4PW6OYqmJ7vc3Vimr+5eJVXbg4wUyhRdpw6WX9I10lHwjzU3cGHDuxhd3PTqvunCsGulgxTuQIR0yBmBXvNa7cH+c1vv4apaXzi/v18cN9uqq7Lq7eH+OIbZ7g1PUeuWsWpdWwZmkrMNGlvSPBrzzxFxxrSa9+XLBTKwe8gbC4HkN9HcNm7ma6k6Gr4GQytianC31J2h/Blibx9iYJ9lcnCV4mZe8lGP0jSOooqYvU91XY8YmGrvh9LyQrITqCoNZr7wnc8J19WkXj14FlKWUssBWtF2Q2Up76bcTdUZ4CaZP0fYHvT9zRWc/SHSIXWw67/sUwAuqIS1jZffxRhEHAGLvMV+r7P1Hye0dkc8YhFdINYujkco8FqYLiwQEjdfO9QlRC+P4OPh+uO4Ms8ipJCESE8bxqJi6a2I2UJ318AoaIqzRj6ToSSwJXLnZeqCG3qT6098wACJqgnpESZWCKOdxeuNgiKBmV3EV2siWnWXELpBw0dcxMLZNobiSQiCKEQM/cSMbZTsK9Scm4zlvtTWuOfwFSb60lCx1tgvPAXLFZPA2BprQEkE4EPzNuFmpL1RqagiqCryVBUtoSTWKoOKPjeTC159E9v31OJI4BQ1OLDP/M0QzfGGemb4E9//Wu89c2LbN3Tjm5qzIzNc/10P9L3OfTOPZz+1uX6d01Tw6xho31fMjexwNzEAqV8mXKhSrlQYfD6GL4vsSsOp567wPTYPOGoRShqYUVMWroyRBLbENpytnrs9hS//XN/TKo5QcvWJrIdaRKNUVRdo7BQZGJgmhun+ynkSjR3ZXjyE++oc/ykrW60WpZV1VTe8cwRxm5P8de/9xzj/VP8zr/5Ip29baRbGwjHQ7i2S2GhxPToHIszeT71qx9elTgCSLc28GO/8iF++2f/mMFro/zuv/0S3/ryCVq3ZTEsnemROW5dGGTs1iShaIj3fOoRHn7m8B35lFRN5dh7D/LSX55ifnKRr//hC+RmCzR3ZTj4+J51GG9YhrQrimDPsR188pee4Y//818xdGOc3/ypPwjOqy2FFTFwKi6FxRIzY/NMDc/S3JXh/nfvI3oPAhSO53Freg7X84iYBiFDp1C1KVSqFKsOLYkYYws5VEXg+D7Fqk0mFeUTv/gMC1M5zr98lb/979/i1HMXaNuWrUP7yoUKCzN5JgenWZjK8TOf+9SqxBFAKGLy4X/9Hkb6xhm8OsqX/+vfcfpbl+je245hGcxNLHDtrdu4jsvhJ/by1vOX6t+1PY+8ba8KujeztpYklapDpbo54Xl7uJn3tRxnojJDRAtxYEsPTebqLLyp6hzLdG/4/e7oagz+WpLbu5kQgj2J7YQ1i3k7R3soyyOZw0S1u0sgv10soulkrdh3RFJYdh1eGx2kf3Ge10YHGS3k+NKVs7RFExzOttKdTKEKQdww+e8X3iRpWkyVikwWC/zckYeJGSZh3eBnDj/I506/zlDuWVLWMoztXx96iJ7UvUn8BsoeC/y7l57FUjUsTWdfOsu7t+4kHQrTNz/DG+PDDOdzTBYLvDB0m5Jj0xZL8OiWrexsSNOTSvO7507SO5RhvlKm6NjEV0DdLk5P8qUrZwnrOhFNZ7ocVJ4ebd+KAEquwx9fPsPNhVkarTC+lNyYn+Xxjm7SoQiKkCCXEx5CCHRh0hbupTW8iwMN7+PCwt9zevZvaAvt5r7kk9wpNeRJn6HiPLuS2U0/oyiClrYGNGV15UkVOjviD/Hc+O8yUrqMrpgU3HmORX8YTQmcJkWoRLVGCu4caauznuT537G02UnVKzBbHcL1A9Jrx68gUBgpXUZBIWk0g5RcXXyJsJrgeNOPkarBxwKi6+8uQDGVCJYaQ0pJe/g+lDtwPdyrqarC+95/cNVrrutRKtkIFRTjEKAgZR5kCaEkqdgXcWWRqL6V3LzNs397liPHtnPm1C2qFYeZmTyO4/HQO3dRdp1619xA/lniegfdsfdR8ebozz/L9sRWdqc714kFvN0tN5PHqTjMjs2Rm82z8/D6td33JX3TM3z+5ZO8cKMfew2p8lJQ7XgejueRr1QZz+XZ25Kl7DhBl66qrOs+mikU+f1XTvG1S9eCrhqWulMC+IPjecyVysyVgm6NU4Mj/PQjx9jbml23fnq+ZGB2gSsTUzRGwsRMgy++cRbH89BUharrMbqY4/999RTZeJShuQX+56lzQCBYUHYcZotlvnz6Im3JBM8c7CGqhYhpgRNdtB1uTM+Sr1R5vX+IvS1NfO7F13lraBQBWLpGWNdxfI+y41JezDO2eJ1rkzP823c+zGM7tq5KPrjSWUf8OjA7z2+9cIIXbtym4gbBra4qhI1AgbfiekwXikwXilwcm+TC2AT/8qEjtCY27pj4whtn+crpC/WOoLWWr1a5Prl5519TLMpoeZIbhX4ajDg7ol3rPjNTKHJreg7P99cFIgLYnlniqPEZL+TRFZWBxQUykQhRK0r39ma0FXyV/+PEW/zVucubJmhzlSq5yuZz7mhIYvsuproxhMZ2Pf7hah+fe+EEIwuL9aKAoalYuoHrBf5doWozOLfAK7cG+bnHHuLJXdsxawlQT0ouj07S29q0ikMmV65yfXIGXVHom5qlWLX5n2+e449OnmG+FHCaLnFaeb5PxXGpOC5NsUjQ2bRinq7n8dqlAZ5/8wYHtrfyxJGdvHFliEcPdGMZ/zzIsaFWDFXTdCb+FZnwe5gtf5vp4jcoO8N4Mk/VG6NaGmOu/Aqp0HFaYh8jaR1FEQqO69E/NktzKgaqALx6VxAEXGiqEvuOuyfWSrtLPOSKcRVh1ki6v7NxlbXJhFUmauPdY1fLP3HvmpRQdG1Kd+g4EqiYihUkiX2nhuYIOtqTkRCp2MZ+/UyliBAWvQ1NNTGZzUxBVQOfzPcLKCQw9F4kPprajhAGQoRwvTFAouu7UUQEUYPG+SvgYutFMTY2IaglaILEUZDMhfximdHBGdo60+h3oEFWhUGTtRVRExGqm6z/Uz9OtjNDtjNDaAW01VTb2BL/cW7P/1dsb4qR3BeYr5wkZuxGV1N4skTBvka+eh5fVjDUNO2JTxM1elcdypMb7wlLnU0Aw8UF8m4lUFRTfKQ/FVBA3NOV+t+z77nEEUDP4W4+9Ssf5iu/+XVuXRji0okbXHnjZj3QTbem+MQvfRBNU7n0et+GYzi2w1987lm+/b9eR/oS35dI36daUyIrFyp89feeR9XUAJ6kBipTn/n1j/PYhx9Y5ZPHGiLEU1HmJhaZHVvg8okbNZiVQPo+vi9RNZXdR7fzA595gp7DW+ub4pIi0JKZYYMf/Ol3Y0VNvv3lEwzfmODmuQH6zg4EkLRaa7gQgkRjrC5dv9KEEPQc7uYnf+2jfPX3nufcS1c588Jlzr10FSGWsZdN7Wme+tFHePpTj2DdAx67vaeFHQe7OPH1M9w8N4jv+ex9cCfptuVzkFJSrTj4fuBsLpmiKjz+kWMYls6zf/QS1966xfXT/Vw7fXtVgkIRgWx9567WeufPnUwVgq3pBrY0JOrnvuRvBMSUAVHx1nRD7UdFfV4tnRl+8tc+xld/7zle/8Y5JgamGe+fWkGwKWsJPYVMe+Oq81xpOw528alf/TB/9htfo+/cIFdO9nHtzVv182psaeATv/gMoYjJpRM3lq8VsDXVcE+4ZykhlyuzY1uglFWuOlRsB11TURWFiu2gqSpptZF0tLHumFfsQF3E0AOiOke6zNpzZIxGFp0cMT2Kvkmm3vEdBkvDNFtZIuq9JX80obIj+r8fOH+vWtX3cKW/IbSrN5XB7faJ6qurMJ4vmSmXmCuX2JFspDuRwpeSqVKBvG2jCoWH2jp5Z8c2dFXl9MQoKSvMp/cdYV+mOXiGheCh1k5SVphXRwZZtMtkwhF6GzNsiQWBh6GqvL97F72NTWzgczOcX+DF4X4eau2gM9GAIgSL1Qpfvn6RnG3zk/vvp2DbTJWKSCl5d9d2xvJ5Rgt5XN/HbeskG47yyw88xjeHblGwqxxubuNgUysnxgZJmIECzvaGRh7v6KZ/cR7H89iTzvLp1g56GwMoUUjTeFfnNkKazmK1gqYofKJ3P8e3dBE3TMpOP2XnFpbeHcAepBPg6Qn4A6Jaiu7o/Zyf/3sK7twdOoqW1NEs7k93Er4T14moqfRs4IA0md1E1CQjpYsIFBJ6Ey2hnhVfFXRGDtBfPM2t/Bsk9Zb6OJJg/qrQv6METEiNE9LijJevE9UbMdUImmIQVuOMlq6iCJ2YnqmN76IIDVVoNeiQZKpym3l7lLixebJsMwtrSVpCPUxUbjBZuUXW2rZqTfSkU1eIu5utdErXBoLT03leefkaH/mhoyh6oNLmO5dQjPuRqKiKhamk0ZQIlfICiiI4eKSLG9fGkICuqTiOG8CidIOl0FfBIGXuIqZvwVIbCWtnUIWsqQbeS//Z28diqSiaqTHRP8Weh3rWQdUCXpw5/uu3XuW1W4P1gD4ZstjX1syWZIJEyAIkC6UKg/ML9E0FEtGHO9o4NzqOALY2NtDekKy52+C4Ll86dY4/P3ORquehCsGOpjT3d7bRlWpAUxXmimXOjYxzeniUQtXmlVsDVByX//T+J+hIJTe9C7dm5hjP5eltbuLxHVsp2DZfu3iNwbkFpgtFvvzWBUYXc3Smkjy5azupSIi/v9LH2eExKq7Li339vHv3dlzpkXPKtK3mPuby+BS/89JJzg6P0ZlKcrRzC9szjYQNnYVSmbeGRnljcISK43Jrepbff+UNOlPJehIFWAVHXerm+vzLJ3n+2s06TO9IRxv3tWZpikXxpc/oYp7X+4e4NjFNxXH56/NX0FWVn33sIUL66kSJlJKuVJLj27qW4ZdIrk3OMJUPOpcz0Qh7mpdV89bantYsLVYTrnTx8AipVv0Y5WKFmdE5HuvspC0Zp1i1yVdtFssVLoxN1o+xZKqi0BqNc2ZinHbdIGkFa/3sTB7bdkmlg2Lb1sYUx7d31WMoT0quTQZQMoBsLErvHUii97ZkEQhGCzm6EylYwY/i+T4nB4b43AsnGF4IuE3S0TDHt3WxLZ0iEbIoVG1uz8zx6q1BJnJ5hucX+a0XTmDpGo/v7Mb1fPomZ5jOF+lsTGLU/KeV5vo+04Uiz13r4wsnz5CvVOnJpunNZmiKRdFVhcVylYG5ea5NTNPb3ERjZLV/VKo4XLw1zuMHtzM6vYihq9wYmebBPZ3/rBJHSyaESljfRkjvIBN+ioXKKebKL7JQOYXr5/BlmZnSNynaN9jR+B8Ja0dRFQXTCDoHg9+AUg+KAUJ6J+2Jn0RX7qG6vMIsrQ2BulwURVmVeIgZ+2mNfQxViQRrnefh+T6O7+P5ProaJAmX4okl05XtTBZKxC2r/vwXbZvGSBhDTdMa+xiuXyDvFHCli0SiCQ0fv8Y7WsSTXiBMoO5m1p5HQcVHUvEqRLUInvSx1EDVa2lnbTQ2jkvuaKIGZZJyHSH36o8JVKGiiqDYoCgC15PMLBaQcuO1J6qbXJqbY7ZS4lCmlbCymd+loCpNCKGgKksFbj2YzgpXSVEaQN8JLHfI+9JBUZZjVl9W70mFXEpqfD5LAgQ6qmLR1JIkkYoQjpo4d6jje9Km6C7g4xPVl4vykoBXcsl0U2Pw/DC6qdO6LVufV8Bd+iS+dBjJ/REl5yYF+yIF+woClWCV9xBohPVttMU/RVPk/YhajCaRVD37npSqU2YYn9rzqsQQStOqTv5/SvueTByhCg6+Zx9b97bz5nMXuHLqJsNj02SScXbs76T3XbvZtb+T6VszHHh0d9C+GrGQUlJyHVzfJyRUGrIJ2ne2UHYdTFUjb1eJ3kV5JRIPrXtt15Fu/tOf/xw3zg4weGWE6dF5KsVKINcaC9G0JcWeB3ew+9gOGpuTd+QhEEIQa4jwoZ96Nw9/4DBXTvZx9dStoDOqUEE3NBKZGF29bfQe3b4pL5GqKex/ZDfdezu4eOIGZ164yOjQMKobIpmJ0b2vg0OP72HLzpa6CouUPj7+uuDJ8SuUvQWiiQzv/OEHyc0FWF5FVXj0w0cx1myEvpREIsYq7iUAM2Tw2IcfYP/xXVx54yaXT/YxOThDKV9GNzSSTQm6etvYcbCL9p5WoomNkxXJTJzeo9uwKw6p5iSaqgaqJRuYV5OdDW20WQto39nCpz/7UZ760Uc4//JV+i+NsDibx/d8IvEwzV0Zeo5spat3C81dGzs8mq5y9Kn9dOxq483nLnDtzVvMTS2iGxo7DnTy4PsP0b23nbFbUxx4bA/S8wlHLToaU3Q3BouP7ZfI2aNIfBL6FnRldUv/+OQCg8OzALRtaWDBttFUhXjYYnw2h6mreFISC5mMz+YC6J4iiIUsTF1j79ZmHFHmVnGAmeos96cOMVweZYe6DRePodIwSSNBg55kuDyKoRi0hVqYqc4SUcOoQmG0NI6pmjSZaYZKI4RUi5gedCbYvkNUCzNWnkAVKi2hLBOVqYC7K9S6aXLq7WYCQIK7Qcb/aEs7R1vW/x6jhsFHd+1b9VrZK6CiYqjBevL01p319461tKIIdd3moCkKe9NZ9qY3TgZYmsa/3Hdk07l/c/AWg7kF/ttjT5MNBzxctucxXylzcSYgQD6YbeVg9v/j7s2DLEuu875f5t3fvtS+dvW+z3TPDGYwA2AGIHZwQBHcDDHCkkhKCtFS2LQtiw5HmHLQYTrs8B+2RYaDYoRlU6RJGOYigSRACAsBcAhgMD1Lz0zvS1V17VVv3+6W6T/uq1dVXdU9PZTNAHAiOrrq1n1578ubN/PkOd/5vgnmazWubG5QzXc5UR7CNCRb7Q6vr60yVyhyNFsmZVk4hsGV9Q3mMiVeX1nljlNFayhbaR4/MsHbG+tM5XI0uz6vLC/zxPgEnmVxYWSCCyMT++5RoxHCJtZtNAqN4s36V7jXucyEd4qUkaOn2txovAQIJlOnBkENP+5QC1eIVUjFX0TpmC1/kYxZJm+bmDIFOMQ6pBqsEKgOVX+ZSAc0wg2WO1expEPaLJExS4Oygqw1xGTqNPc6b9GLW5wvfmxfwP9I9mkWO5d5pfJvqAWrjLiH0Wgq/j181eb9I3+HnPXoqjq2kSJvjTHffo2LpRcxhY0tPdJWiVvN73AofRFLuAgBU6kzvFr9At+r/BGH0heoh6vcbH4bKUz+OqwbjkxzofSjfGX1f+NLy/8zR7JPk7OG6cVNNv0Fys4Mzwz99CO11W77rCzXODQ3xN27m3S7OySMy0tVNtauoaM8cfAyCBcd3UJaJ9BaEesAW1gIIUmnHaSUfOelG9SqbebvbHD7xhoff/HxxJk2Lbz+JtMy0lyu/hZ5+zC9aItOtAEolto2JecE4+ln3nWffD/biaeO8N4Xn2Tt7v4yiE4Q8psvfY+Xbi8Qa41lGDxzaJqff/YJTo0Ok3UdDCn7AVpNo9tjvdlmqV7n1NgIV9c2afj+ns21Uopv3prn9155Az+OMaXkbz12ir//7FNMFnID/iDd5/r52vXb/Iu/+DZL9Qbfnb/Hb730Pf6rj7+A+4CEyXK9wdnxUX7lEx/i8FCRWGlGshn++z//CzpByF/emaecTvFrn/4oz8zNYAjBmfFR/rM/+FOW602ur2+y3KhTNVrY7v5rbLU7bLU7vGd2il/+6Ac4OlweJG+01vzkhbN8/tU3+a2XXqHe6/H26jq/f+ky/8WH399XXVNkzOJg3gnimD9+/QpfunKDSCnK6RS/9MHn+PDJIxS8nWBNrBQ/deEs/+d3XuVzly7TCUI+f+lN3jM7xYeO70WKtaIuHz17iBfPnRisA2Ec86t/9jW+eCVJhj42Nc5/9+JHHjguTCkJ6bHaWyfSMQUrR9ZMOOGCbsjGvQrvvzjHJwqn+uU6ikqny6/+2df48tWb+9rLOg5/97ELuH2S37AvN9/eRY792SfP85MXzgx+74Yh/82ffpWvXr+NAJ6cmeRXPvmhB96zZUiWO02kEPv88PlKjd/4xndYrNURAk6ODPOff/j9PDEzMUC5bXMfXV5e43/5+ku8PL/EYq3Or3/j28yUCkwX8nTCkOlyHj+KaPlJKeFucmwNXFpc5tXFZUwp+cUPPM2L504xkkkPOJNipWj0fJbrTZw+H9I+6/ND+WHEwlptQPj8w2rJHNL3iwREKuHvNEi4ViIdJ6qLxiQjmc9Q8D5ML7zKevtP2Ox8iUjV6UZ3ma/9OkeKR+j0EjT+wmqNQ+MlhBSYRn7AESMwKLhPDjhrtsfwNtGy2EaVsCtp0UeGxAp6UcKJZRoSUxZIohUxhkxR8J7BNspEseK79+6xVG8wnsuSsW06Qch6u81wOk03DEnZFq5pcq/eoB3c4ezYKJvtJLCTd1yKKQ/HHGO28ItEKuZ66yYjVp51f5NYx5jCZDQ1Sbe9gNVXrmzoiLKGFX+NVtTBMxxMabLWW8czPHwVUA2qDDtDCYXEu35Y9BVxw4fy/mk0nagDIlHxUlohBGTegfz/qZFpio77wHLT7ba7cYgpzX5SEEANKlXuC8uh0YRxhC0T33i3mlus2okymHgn1XRFGG8NfpPCQ8cOywvJsaHR/IOqDfv3DLGO0Oz1/xPerJ1joR+RK2fRB5S/SZEi51zAMb5IJ7yNa07hmVPEOkAKB8ecIOdcIOc8jmdOI4SB6iPwJYonSsla8U4Ksd04ZK3bZCZdIishkWH6m0Fcf18Gjm7Vtnh9c5XnJmaZ+sxphj59jNVOk6KT4nR5mMVmnZVeCz3h8vz/+KMcK5S526ji15Ps9GqnxeFckfGfOcM/+rlneHntHscLQ9xpVMnaDqdLI/uUiu63OFYsbdYpZDxyaZe58zOMHBvjk2l3sDhorVndauLYJqXcgxEbURyzvNlgpJDB7QdxTNtk4vAImemIJ39qhpRRphtXAY1nFOnFNZRWuLZFI1gh0j4ZawQ/bhKqHhlrhFC1sQqS93zqJOc+Pk4n3mLYOU4zXEMR4xlZIho0elVy1jj1cJlmuMZk6rGEPC3aImuOoNHcbn6D88XPcPFTh3n6xV8iVF1cI7cviy6EwHVtMhl3gGzSWhPHCfLKsgxyQzme/dGLPPviE33SaY2UcvCzaRrEsSKOFYaQg8+afY6n8x84ycUfOUuz3qHTTiLNURQn6mmWwe3rq3RaPqcfm+Gtl2/T6/qcf+YozVqHdrPH8ESBXjugXmkxMlWiVe9gpxw+9QsfIvQjKusNRiaLdNt+//wiKlYs3lpjdLqMVprKWp1sMY3jWWws1ygOZ3HSDuefP837fvwpojDm2mvzHH9shtGpUp8PaYJf+d1/su/5a615q/oHvFH9HLGOOF/8SR4v/SxmH/qqtWZkKEepmDh948N58lGEZUi0BscysEwjYc8HyrmEqNEyJH4YYUiJIQXXWvPEfZllU5jUgjq9uIdCcat9l5PyGL24x5Zf4Xj2aJ8E2wEE8+1FAhWy0lvDEAY3W7c5lJ6lpwIiFdGIGkx7k1xpXuO95few4W/x3colTGGQMjxG3QdnRn+QrBn6tCKfXrxXhSCReu2hiHGNDLGOCFQPS9hY0sVXHWId4sqk1rkRbpIyctiGhx93CVUPU9qYwuZW6xJ5a4QhZxJNQlZsSBNbevhxB0WMJRxs6dJTHWIV4hrJgumrDoYwDySNNoSk4fdYaNQx+lLPVyub3Khu8sL0XkWKe406k9kc7SDAs0zW222KrocANjptUpbFertNpdtlLJNhtdXk/OgYN7e2GMtmaQY+17Y2UFrTCgK6YchzMzN41sMDiAKBKYvknKcxRCIt7zFC2+/y7ca/IdY+rulRsmf5wNA/YNg6QTdIJFKXujf42tpvEag2vbCHZaZ4eeNPeK3yZaS2eH7055nLXCCgwTfX/xUVf4lIBxjCZLFzmeXu25jC4Vzho1wsvdjPACWlaMdzz7HUfRvPzDGXfmKfmlLaLPDC6M9zvfGXXG++xGLnMiAo2GMcSl/EkelBhtOSHllrGOuBmThwZIox92iixuYe63MSaUacaZY7bzOZ2oYuCy6WXkTpmLvtS9xpfY+CPc65wkfpxnWWOm8k84LWQIwtXbLWMOYBmSetuyRZPYNJ7xSfmPhPuVz7Enfbl+hGDRzDo2SPM+Ye7TtJ6oEQca0jIKJW7XDp1XlGx/L81r/8OrmsNyjTbjZ7lEtZEGmEOYM0DhOTwMiVDolVj210UK6Q4tM/8SRf+INXWFuu0W0HvPCRMxw9MU6Mxo9Dar0uU5kcZfcsGasflLzPv3XNMj8sFvoh3/vyGyzdWGHm5CSVlSqHzkwNElNKa15eWOLLVxIUjBSCT545zn/ywWf3lUdtoxpL6RSldIqTY8M0ez6ebTJbKjBV2PGHNlsd/vXLr1Hr9hDAU7NT/NIHn9vHXySEoJjy+LHzp4iV5le/+FWVawTBAAAgAElEQVT8fqnRx04d49nDMw8s+f3QicPMlBLuMtMQPDc3w0gmw91KlVhpzo6Pcnp0hChSGJbB4XKRI0NllutNumHIvXqddLkvWnzANUopj//4g89yZnxvEF70++BvP/U4q80Wv/vy68RK89Vrt/jpC2c5OlzuB2TNQYnM7c0Kv3/pMn4U41kWf++Zi/z4Y6f3IK4hQe3Mlgr84gee5vp6UrrXCgI+d+kyzx2ewd01N0Y6YslfYSo1yqibjNkwjvcEKCwp+2ixpNQijmNyhfSe4EQv1pzOHWOhs4y1513V+J2AOIoH39syDFKWNSAf321Ka+7WqgylUvhxRFF4CUozZe8p2UvZFlr3JaMFWMbeoIplGIN7fpD1mhEz2fweNSalNH/4+tu8uZIIjEzkcvzTD7+fZw/P7BtzjmnyxPQE/+wjz/PLf/xFrq9vcW1tk9+/dJn/8qPPc3F2kq9duc37jh3itcUVukEI9+03F6t1Cp7LP/3w+/n0uVPY99E4GFJSTHkUU/sTyQBp1+aZ07N88TvXWNmqs9Vo89GnTuA5PxwJtINsw69yu71M0crSiX1MYdCJe/hxyKhbJNKKtOmy2tvClgmX5+ncM+TdJ8g557lV+TVi3aYVXKEVvIrnnmOj1iaK4yTohsQzZzBlhlBV8eMVwrgyCBy1goCbGxU6QUAQxfSiCM8yKaVSLNcbFFIeppR0gpCW72MZBk9MT1BKp0hZRzCEQ6w7dMN5ItXANsoIAePZLBPZbF/MRuFHEVOFhIPLj2NsQ5JzHHKOQ6w1OcfBNozBv91vkyEko84ICsWoM4wUkljHGMJgxB1OEEhaYUsrScqiGXaGsKSJIQyGnSEc6RDpiFFnGNdwcA2HQL27gKQQMJHOkbWdd6Bd0BTtAp24S6giDJK9mXoIuqcdBlzauMtoKsNjQxMDpeD7LYhjrtXXiZQmbdrcaVUo2C6mMGhFPlnLoRH0GHIzLLarZC2HtGlzJDdM0fZIWUcS5FhfgcyPVknZ+3nn9n6biHa4ExR3zDEMUaTZWKC21WJ4vEC2vLcMTe+iTTCEiSJO0N2D4CT0oqU9ymSWYyFyHtYBVTO9+B63Kv8t1d63yTtPcKT0z8jYZ1nr3aQdVTGlhRI2tajHqv8KUiS+/zZqvRMle/9Ij1G2HyxolTFtSnYK1zBBaJApHhoV+//Qvi8DR65pUnQ8UqZFLwqZb9QQAoqux/XqFjnboROG3KxtYQjJpfVlRlMZJjI5NjptJHCtusFUJs+N2hauYTKRybHeaTHspblV33po4Ehpzd2VCpeu3eO95w6xVmkihGB+tcLUSIFyLk0mZdPs+DQ7PdaqSUnR8madUjaF51gEUUwUx9SaXcaH8qxVmmgNubSDbZmJ/LBnUg0WWOtdYTb9DFX/LkIYZK0RGsEKQiTZj4X2dyk7R7Cky2r37f49BjTDdTpxhbnMc2hiGsEKGXOE5e7rdKIqI+5xXCPHYucSk6nHiZVPqLrEOmKx/V3q4Qoj7nFm0k9jyYRIecu/gykdQtVlKnXxwP4RAmq1Nl4/Kt3rBly9fA/Xs5mcKXPr2iqWbZBKOWyuN1BKkcl6dNs+Uaw4fHyU9ZU6KtYcPzvBW68u4LgWR06Oc+f6KnGsOHZ6ktWlKiAYmywyf3OdrY0mZx6fwfNsFm5voNHUtpqJ2oUQvPL1KxRH8gyN5VlfrvLqN65y6sk5Vue3cDybTsunUW3TrHVIZV3e/O4tbMeittmkNJLjtW9dZ+bYGLZrsbVW5+xTh7l66S63315iaKKA7ZhIKTFMg8m5YZq1ziNxA2kUK9036MUJ/Hql8wbnij+F2d/1bGw2uXpjFcexiKJkER0v7SZCTff7fS/U/X4zhUEjaqK1pqd86mGDzaDCqDNM2S4y31nkWOYwvgpoRW0cabPhb2IIA89w6akmimRRm/DGmO8scDJ7nGV/hXbUZtqbJGNmGHLKrPc2KNkFRt1hctbDVS9+kMzrIxus+7hSNvwF5ttvkjYLHEqfY7FzhV7cQgjJkcxF3qz/BZ6R43D6PJZ0udN6jdn0WbJWibfr3yTWESPuIQr2KLdbr5O3hvCMDDERi+0rdOMGx7Pv4Y3a18laJaQwOJK5wNXGt7GEzaHMearBKrUgCQqfzr2flLmXLPeFmTne2lrj177zdTKW069djzk3PMbfOnpqz7nT+QI3K1sMp9O0gpC4n03cDvysNJtM5fLMFYo0fJ+JbJayl6KbCwcO0+FckcVGg8lcjiCKsB+ACtxvGj9ewjGn0NqhvjnEnPws4zIhGs2nPSr1kG6YZrHbotrqJhwl3ghH1c+h0TRDn5OjI6zXW3i2RaPjs7jgEOQ3ODZR4sNj/xFKH0xw7hipfbwJk94ZPjP9zxEIUmae7YCG1noAO7dEmuO5F5hMXcAQ0I198lYBUzqAST1skDZTHEpfZNw7gWfsfy+iuILWScDkVPYcc6kxcvYcfng1GUteibnUL2JJiR++jZRZTN3jyeIznMs9RqQaeNYshm4RU2DCaiHieZSQBPE8s6mjDFmfxTMNlKpCvIQwJtHxFjp6G2EeB3MWVJMhI+R95Y8RqOeJ4w2kTGHGizjuJMQLgELLYXQ8n7ShqgisPnLoJqAZn3iGT3/6AoYhOXp0lJ/4yaew++qYCwtb/OW3riOMSQyZAwSm+zHATnhEdI+or6AjpeDQkRH+3i9+CL8bYhiSTC5J0sRK0w7CAeJo2D33iOPsB9sMy2D29BS2YzF+eIQzzx7HsHbesTCO+Ysbd2gHifLPSDbNzz75GGO5B5No7zbTkLimSSfYq4L11uoaV1cTdFPasXnx3MkHkl5Dgrp4/tghPndpmDeWV6l3e3zj5h0uDBTQ9tuRofIelFM5kyLf9ycEcHxkiNduLzOcz3B2ZhTTkAxnkuScUhrfj3kyN0XGPHhjf25ijBMjD+aFS9sWHzl5lH97+SqNns9qo8WV1Q0ODyUI4Ua0idJH0NrkpdsLLNcTotbZUoEPnziyL2i024opj+ePzfHd+XsA3FjfYqFa5/ie+9E0o3bCLfIIJRjdbsD1t5e5+PRhUumdaGlSpqZwpL0HeaEU2J710DKV3SZINr1fu3Mb0zD46OGjpKWFUprZ+5DYWncJonlsa46/TobbjyKuVTeZjSNmsgWEEGx1Onzn7iJhPxn5gaOHOD859sB+EUJwdLjEx04d4/r6FrHWfOfOIqv1JmP5LPVul+/cXmShUmc4ezBK4T2zU3z05BEQ1YTgfJcZMoUpc9yuVtjqJOI2c8UiQ6mkLSkFZ+fGmRsr0QsiXNukkPEeSUnvB9V6cYDSmpyVHgSJhBCkTRdTmrjCQKPxDIdIKxphO0kaY1FOfYjV1h/S8F8h1h1CtUk+7RKEMdOjxUEwNGUdxTLKhKpKpDrU/dfIOgmaOxGcSDiosq5Ju5HMW+V0alBiprQmNOKkfLR/DCBjn8aUOeK4Q6i2aPpvkrLmkFJyqFRMsEsH8H3uPpbdxfO4PcfePzqFEJTsfmmd2P6vnxzpB4p2Hxt2985RB50DEAix62oaeDjRs9ZQD7p0wgdzHG3fXStqsxlsMewMkTNzREpRbXYTdfIDXu/xdJbNXpRwtz6kbVNK8rY34NxLmRYlJ0Uj8GmGPrY0SZs2ZSdFqGJcI/l9O9DlmdPYxih+vEwQb9IOb+FZczxMiS5SbZr+5f5vBhn7FJblUCxnqFc7mKZMuJV2hT4iVWe7xF0giXWIH7d39aWmHV5D6R3kZRRG+N2AoLtfQbLa/RZ1/1VAM5r5NBn7NEJIfNXulxBag6ofW6ZwjAyxDoEkASgwsA0XS7gPDQPZhomvYgIVAxqtqvAu1XD/uvZ9GTjK2Qlh12Kzxkq7iWMmUsvLrQbj6SxLrQZZ22HIS9MJA2ZzRW7VK9xtVKn2uqx2WsxkC9yobTGWzuDHMYvNGnnHZSSVfiAZ4bZt1/mPlDI4lsn8apWljTopx6LS6PDGjWWePX+IK3fXOTY1RKsb8OatFXIZl6+8cp3Hjk6yVU94RIIoZrSUxbFMojjmxuImYRQzN1FCWD69uI4h7KSmU5hIYRKpACEMDGER6whTuIy4J/BVe8B1EemgP8ATGUqNpqcaBKqDa+QRQhKoNt24ngxArfDMIqHqDqafsnOYgj1NqDr4cbMvxVzkXvsVptJPPJCzQwjByGg+mVgQrC7VaDd7HD01wd2ba0gp2FpvcKfWIZV26HVDbl5ZYWKmRLvpU6+2WV+p4Xo2Q2M55m+t8+EXH8fvhawuVXnyuWPYjonr2SwtbBGGo2xtNHnz0jwTM2XcvjyxaRqUhnP0ugHSkJi2ybHz00gpWb67gZtyiIKYbDFFeaxA0AuZPDzM2y/fYf1eFTQ4rk2n2aNRbeOlHaIwxnJM5k6OUxzOEUeKmWNjjM2W2VyuMTJZZO1ehVTWJZPzsOyDSR73jidJ0T7EaucNFIoh9/gehYBMn1wtm3HpdH0arR6F/IMddYB6eI+N7lXy9hQjXhIUOJKZoxhUsaSNjg0OuycxIpfQgJIxyoTtkCLNmKXo+poOMYfco0gMsiID0mbUtAgDk6wskfHK5GSOUQuyaZuyU+Ki+RgAQ06Zxwpn0VrvU4n7QTbXsMiZ4IouyeKcvAOxDslZQ5zMPYOvusy336Rgb6OsNEP2FI1oC4UiZeYo2mPE/cBF2Zliw18g1iFZs8SoO8t06hRZq8Ri5wqKmHV/gdn0WWzpcjL3Xt6sfYMtfwlHpjhfeIFQB7xe/Ur//TfoxW3CWqLs0K51CPwQgeYXRs6wUeqyWWtiGgY5y+HQcBm53qOtO6RyHkIIpnM5pnO5Pk+bptdfAIslm0rQI3AyTOXzxJFCpgVxrHBNm0J5hCiMOeQmKpAzqRxRGOPlHMx+1taPG0S6R8oY3jeGNTqBHasumhjLkIwVkqDCtlLTwkYNS9hoBCnHouMHuLbJcDbLRs3HtU2KBUnGSLMWCEzTI21EaFfj2iZoSdbeQZ4oHdGJNnCNUj/Is99MaZGT+0vNQh1xuf4WjnT6XEMGhjBohk0CFWCIDXwVMOIM9d05xVxq9oFla354lV74NlpH2OY0KVkkCF9D6Q6e/Tg6vodl+MRhnZ4wAIlljBJEd5DYpM1JVHSNQNexzSN40kOpKn6kiVUF05DY1FFxi0i3MdQyOryMMCZBd5OAT7yK1jXQPUxcTOEgzEmEzKHUXaRIoVUNVCV5YuF10CE6upY4oTKFkCW0DjAMiefZaK35zE88RbG4g4SbmSmjnzuGVhWi7v/Tr8MvYzgfAiSuOdwP4GmazR6vfvcOF99zmPJw4pR/56UbTEwWKY7nsA3joXLvP4ymdVJqPTY7jGFK7r61SGG0gGEkfVDr9HhlIZEs3kYGnRnfT0z9sPbvl0/X/Q34NsHxVCHHe2YnH9rvQgiGMmmemp3iyto6Yaz49p1F/sOnuwcGjhzToJTy9rRpCEHO3Xk3p4t5RvIZStlkHZRC9EmoE5aIbhix0q0y4qgBQfa22YbB2YkRMs6DEX9CCI6PDDFbKnB5eY1IKd5cWUtIlqVH0RpFCEk7CPnewhLdMJnLn5iZYDT3YHVaSJ7FuYlRjH7Qc6vd4dZmhWMjQwPfSwoDPw4IVPiwpgYWx4ogiPo+14714oAtv0pCbLvbr9VopTGtRwvsaCBUMRfGJ9Ba86VbN3h2fBrfj1hdqXE0u6OyqLRPJ3gdQxaARxNt2G1+HFHxuxgtyVg6iy0N5ncpyrmmyfuOzD70+UHynJ+Zm+G3v5ug4+YrNa6tbzJRyPHU3DQbzRanJ0YYyuwPHEkh+PiZ46QcWGv9GX68hh+tYRkFYtWh6D3LSPpTSCFYqNcZTqdZbDQoe8l4bHUDvvBXb/Nj7ztDOb/T/r3OMq2ohRCCXuxzInsM94dEdXbMKzPmljGlyZh3H7Jze1iKZPvd64/tbUSgwEIOULAJyY3SCXresYzBXOCYo5S85+mEN4GYtdYfUvZewDWnyTg25yd2EIS9frLKkJKJ/MOD5SnrMAX3Gdbaf0Skmqy2Pk/BfRp7l49y0Bx3/7F2L8C2zAFCLY4VC+s1HMtgYij/wHYG7d0XahIkvlWkVNKmOJg42xBekrQhQdXsLsd6wIVImTamNN4RcWRKk4yZIWtm0BryKZfpkfw+KpJtW2o1WOk0CVTE6dIIKevg99QQkrl0KbkZIThRGEWQED/7cYRnWP0SOZhK7+WxEkLgmbPk3SdZb38Bpbust79Azr2AYxzsW2kdU+l+nXaY8MzaRoly6oMAZPMpDp8Yo1DOoHSIaeSgP/U2/NcYSn8s4cICXJmlqxtJSaTW+PEq1e5LfZ6jxEzTYGOzyfBUed/z7oR30DpZU8O4RqgaWDLPhHcKQzy60vE7Eah3o5DThTHKTgodLyJkGR6RRPzf174vA0cFx+XDM0cRQnCmvEM8tZ2Z2a3dovsy7SeKQwduUgakn9tSmSQQvoeZEIKMZ1POp6m1uixvJjXXSmtaHZ9s2uHtO2t0/ZCVrSbVZodi1mOz1qaQSbGwWqUbhMyNl7h5b5PVSpPlzTpj5RyObbK8WeeZs7MobLLWGFlrDFtmiPQCJWsMx8jSitbJWqPkrDG25ZXTRomcNU6oOuStSRwjy7B7rE8upyhYU1jCZcg52q+nNPHjJoFqk7cmsIwUftxAIBlPnacRrGBKj1D1KDlzhLpLyigihEHBmnpg/0gpOHp0ZwJPZxxazR53b66RK6S4fTVBDRWKadJZl3bbp9sJKJYymKaB4ybKRkMjOUpDGfKlNPliGr8XEseKm1dXOHZqgtWlKqtLVdaXa6yv1MkVUghg8e4mq0tVGvUOU0dGmL++ShREnLwwSyrrYpqSyblhikNZJg+PoGKFm7IJw5igFzJxaIihsTyri5uA5tj5adqNLtWNJmOzZSzLxHYtTNvgzFNzLNxYI5PzyBXSeGmHdM6jOJRlZLJIr+MPxhd6u+IaBqz+KhmJ5/I/TdE4hkYxnXkSY1eNRcqzOXF0jLeuLmNbJvnswVnU3WP+ev2LXK5+nlOFFweBI9dwGXcTB+9OpcpmBULVwZRdat0eKavHcCYiUpI7lQoFt0PWdViqVxBUMKTsZ2/aZByHpXqdrNNhLJtlKDOELQ3sgdKUZGh7c/5DtJcr2h5PFA1QqyROcfIspDD75WICW3pMp04Rap+MWcSSLqH2CZVPM6wgMVjp3h4EkALVRaOpBmscSkPKzHOn/QamdGiEW8QqImMWMYSFZ2SRyH7JUZn13jxv1L7OTPo0U6mTNMJNPCNHWua5eWMxCZLeXsMwDXLlDK1qkimxqm2klISuxb2VDs1qm8efP02qz+G2e64Mo5jrby1BvyS01w3JZF2+/eoK7WaPIyfHCcOYYjlDHCtqWy221hs4rk2ukGJzvc5jTx1muC/ZvNl7i2pwmzOFz3LQ4NBEfYchyRxND+11GsaLuUH0XgiYLOcHrbz3xOyuTB6Mle4ryTngmQaqzbX6H3Es9yI5+8Hz2qA/VIduVCFnT2EIyXRqqs8Pl6wnAvAMF0uYyD4q1BY2PdWjFbUHdf9KxzTDe+SsnXILKTNI4YFINnmxqmHIAobMYcgihtx2PiWGHCKMF/Gj2/3PpjGNCQJ9E0mGIFrANEqYcoRIbRDF6yT8DltImYG4AboDwkOrNiARchgd3UDYF5OAkjmB1hHCGAPh9M+tgNpCxytIYxyNnwSSkAhjGE2MVnXELtJSIQTl8t4NdTrtcPr0JDq+ixBZ0D46TubcSLXw4y0cu4QmKcW59PJtzl/cId7fWK3Tbva4MJom5zhk7L0bsEbYpRP5lJwMa706ppCMuvlHIpb8QbDNe1u0qm3e+MYV0nmPlTvrHD4/O1AFXKjWdtSgpOTJmcmHImHuN1MKxnNZun2ZeiEE3TDizlZtkFw7MlQm674zUboUgpNjQwk3Thyw2e6wUKnvKYHbNteyks3inqNiRxFPJKVmYRSzuFljvJjIKxvbGxkN3ThAioPL1FzLZKZYeEcHvei5jGYzXCYpj7q9WSGIY5QMqQZrjLiHqHUCFiq17duilPJo+QHt4OEBnzCOcU2TdhDSiyK22p1BH2ut6cU+Y94QOSvzSBuJwI8oljN7FM4ANv0KzaiNACy5U16hlWZrtUbQC3kHdxdIfNuVVpNar0fOcSh7KRZqNbyWwkvt3RgKITGNIfhrqjF6poVjmIylMtgyQYksVOuDAGYx7TFVzL9zQk4k43c4m6bW7RHEMTfWt3hyZpJb61s8NTfF9dVNqp0u4/cFFgqey2yxgCFcJrJ/m150j2r3LxnNfIZudIeWnyD7R9MZyqkUDd9nKrczlrXWdHoBYRijLD0AdrXjNkvdZapBjcOZQ3Tj7g9N4EgguVJbZzKdp+ymCKINmsGbfTRPHimcwX7LNWxcI0koKB3S8F+hHVwFwJJ5UtYsAbBZbzM3Ud51DYvRzI+x1fkK3egureAKd6r/E9P5f0jaPoLYRaDsWWafDNknVh064V2EMMnapxH3jU1T5hjL/hTV3l8RxGvUe99lvva/Mp3/+zjmGILdsvW6f98BsW7TCW9iygKGPszb82vMjBRY2WoQK03Gc/j2lXmePjXD5u0OUgiyqYSHdHI4z0athVKaUjaV7BHrHSrNDrmUQzdIRIZc2+LuaoWnT80wXDg4KC1FCteaxI+XiFWHSu9blFIfxBD7KQu2rRn61H2fuWzpwL9v93jeyjHiDGPKRHwjjGOuLW4wXMgwVtwfkMs7LsfyQ1ytrT9SkuIgSoWUef+cclDQzmYs85M0/Ev0ontUut9gufHbTOV+DlPm98QFNBH13ivcq//vxKqJwGIo9THS1jGiIGZpfhO7r7guMMna56n3XgZgq/tVhvyPknMuIjAYdQ/321WEqsZS4/+gFVzZc29BLySVS2G7+4NmaesoUrjEus1i419S630Hx5zYAxZIiMM9XGOCrHMOz5p76LM8yPw4YqXbIGVaZGSObTW6vwn7vgwcid3Eebs78qBO7R87mEjq4M8+yqMZyqcpZFMIAeV8GkMm/DJaaxzLpOuHmIZEiiSr5NjJMdc2B0pXlmkwM1rEcyxGi1mEgDsrFY5PD2NIidQuE95jIKAb1RjzzlKyD9GL64x5ZyjZhxBCUnIO7dyXc3TQRx47jnvaLO85b9u2YYqNVg8tBeOpBDHikCNrjg0i3BlrmFhHbPSuMZl6/L5Bfl+v3gflLA5nePoDJ5BSkM665PMppCGx7EQlSatkQ2rbJrFSGIak1w1Ba2zH4r3PnwTAdkyeef4kcazw0g6nHpvm2OkJMlmX9/3IabTWZPIe+WKa2SPDeCkH05Scf2/SJ1NHdoJZJx6f3Xev2/c7OTdM0AuZOzXJ1OERvLTD0HiB2RPj+z5TGs1THMntOZ7vb5BOXtzp72atw+ZqDS/lJFKYrS4oMEwD05J02j69ziHKIzkW7jYolBXjs0mwUwjByEiOXM6j0exi7crAHGSR9tn0bxLrYB8Mdftz2w6ua5n0wgjXssjYNsOZ9EBFIiENlEzmc4MFfzSbIexDS3UuS9q2mSzk9nEAHNS3PwyWZLctYl3fI9A07MygUX24tOR49il6fb4hW7rMpE4DiVqWRnGh+JE+h1Sace8oI+4stkyy7Iczj9OLW3hGluPZp/BVF4nENTLkrDK2THEq/14s6XG28DyRDkkZWTJmKSmPQ2CZNkcfP4RSiukT45iWge4jh1W8w2shjT63mNa46YMdWCkEhWKadqvH0Fie6mYL0zTIFVKUhrIUyxmEFPi9kMAPMS2DQilNtpBCK83IeGFP+cSI9xhl9xQHzbQCgWvOYhsjSQCF/eNo8KvY89/ev7H/bw8yW2Y4XfgZbOPRSni2elfZ8q9xxv5swk3gJFn1++W5t0k6Bz9rTaijAddIN97iduOLPFb+Bbb5lBzrBLZ5aNCG1hFSJLXpQthkvY/024xISEIv9mvwE2UYKRxMYzgJ9ggjgVwLE0tP41pnUKqJFC6OdRJDOKBDEDaD9JrwENZJEGmEMQ5YSQBP2IBE2k8AJsgy9AlFhXk8yWYxlXxT4aJVAyHz6HgNhAU6Ion0eX24tACC5PN9RRUVL2DYzyftI7BlYQ9kPBE62OnfKIqTkm7LphuGdMIA7e4gVebbG3xt7S2eGzrBlcYSnmHxsfHHyVh/cw7U/59WHM1j2SZnnjvB0GSJO5cX9ghvbLY7A1l4IQSHSu9OfSeMFXe2qnuIgztBQL23A8kfyWYeuQR1LJvF7JfqBFHMWrN54HmmlA8lVQWRcB9qPWjvfrOkidKaQO0vRzWlJPcOPDsAUkrKmRRSCJTWVDrdRLZexgS6S0+16QSw1Q/OaeB3Xn6df3v56ju27cfxAKUE0PYDYqWQhoFCUQuTjH10wP0fZLZjsr5S4+iJMdxdeaW8lWUrqJE2Pbpxb7BmSUNSHMljPSLnjiEEs/kClW6XZhAQKcVocYjimDUoPd1tSnf67/y7t24U0o1Chr1ko6SUYm2XylvGtsk/QrASIOc6e0qIlusNbq1v8Ve35ql3e7T9YKDIu9vSjk3K3g4WGEjhEMQbbHX+HX68gW3sIKlaQYAhBOYuBIbV94f+9ZcvMTtWxJCStGdz4miRLaNCyvNwpUvqEdVqfxBMac18s0batCk5Hr14hRtb/xzHHKfgPkPGPoltjGDKLEKYaB0SxhVawRVWWp8jVFUAMvYZMvYpWgbMjhX3cHYJIUhZR5nI/SwLtd8gVFU2O/+OTnib4fQn+kGqhBtRaZ9Q1eiEt2n6r9MObjCR/SwZ+8Rgvd3dbs55nPHMT7HY+E2UDlht/SGd8Bbl1AdJWycwZBrQxLpHGFfohLdp+JfohneZLfxjyu4R4jgp46q2egOk1PRwgeF8hpevLnDh2BRv3F4miGLubdY5PF7i9soWG/U2RyeH+MYbtxguZFhcr1HIelI7meoAACAASURBVHi2RS+ISLk2ufSDx7wpM+SdJ6n3vocmZqvzFVxzipL3AUyZAxSx8ol1G4FB2j6b+IPoh/pIQgiqQQ2lNVkrk3gescJzLLIPIch+q7qGRDw0cKS1pr7VAgRxFCdItK6PaZnJXjHnEUUK+hy5goT7t93o4rgW7WaX8uRZxrM/w3ztN1C6w1LjtwniLYbTH0t8SExC1aAZvMlK8/f7SLWkNHE8+zNIkcKyNSfPTw/WTiEEJe8DrLY+R6Sa9KJlblf+B8azP03GPoshXBQ9uuEim50vs9X5KqbMonVErJN5yst5hBsGnUaH8sTedbfkPU8t9TIb7T8jUg2qvW89oIcMDOFiGSVK3vNM538Bxxh/5H2VQrPlt4mUQkuFim6DSGHIsXf+8L+nfV8Gjh5mvbjHNze+zUfGXnhXn4tUzPXmLY5mDw1QEwDXm7eIdcyp3PE955umMegcx9rfTakDIo3bxHh7/tZf5F3HIo4VU8MF8v3SpN0DJGUWSfWVfDyzgGceLEH5bjfr2+d3ugHX76wzVExz6ugY8gCHzBAmY96ZfccfZhpY8jfoWD2OpKcwDYNC+eFQbgDX2+mjdHanP3KFnYXW2qW6Zu9ygBJH5uGonPv7KVaKZs/HNk1StoXj2Rw7N/3A8x/W1kHH1+5VuHN1hZHJAlppOm2f2maLyblh0JqVxQq2beK4FutLVSzbpFFpky9nuHZzlSvXVjANydpGgw994BRzsw+etFvhKrVgft9GdrflXZdz4zuKL/fXb88UC/uOPagW+qDv73d84kghpCDohWRL6QPH1A+iaR32kRE7pHn3Ex3bhjdQTAPIWnuh23l7B05ryr2Oqy1d7F1y584u59Lsw5FdI3mH0ubez1pyJ3vkeHudo23VEUPaA9RFrBTtMCBlOfTiCBmpfQTWpmUwezQpuxNCMDU7hOoT1kdRXznFlElZW5LiIwwjLLsvrdovJdFac6f15yy3v0vBOczZ4s8CUPPvsNj+JlJYtMIVstYEh3Mfx5UGzXCZ+dbXaYUrWNJjJv0BhtzTbPpXudf+FkHcougcZSbzAbRW3Gz8Cab0aIaLWCLFkdwnyVpTtKM1bjf/nG60iSEdjuY+Rd6apRkucb3+R3TjChfL/5C0NdqHH9e51fwz2tEaAoND2R+h7Jxgsf1Nrtf/GD+u0wqXKTknOJT9EUAz3/waleA6hnCZzTxP2TnJ3dZX6UZbBKpJEDcZ9s4xk3meze5b3Gp8kfXu6/iqiWeWOZH/cVyjkCB7HmDiHVVDYKPjc6/Z5UihRNH10DpOgkgyi5Z5TGMEIR5S6toP2CH2Bxq0boNqoqNrYMyi1W1AoaOrIHJgjCBkEXQP4l5CuK1qILMQ30MYc+h4NYnwyTxgIez3IK0ziO3PkjjCRfc8hnARCLI5j1TK5k/+6BUuPjVHZavF5dcW+OzfeR9SCHpxjB/v3ahawmDSK3K9uYLSOgk2vAP3ww+S2a6N7YUs3Vjh+vdukynuHRstPxgE+U25t9TrUcyQYsAPsm1BHNMNd9A0acfaQfq8g2WcHX6KbVWqg0wIHrrh6GN1iWNFMfOAdb6/Vh3EDySFwDvAZzvI0rY9CBx1+lxvEvqqfyF+LGnvKuWrdLpU+oGkd2PRrqCoROJKh6puEOl4DzL+QeY4JsNj+X1rbNHOY3dXaYZtZlI7KpZKJYIkj+otSiEouC61Xg8pBBfGxplK5bhxZSVBdk7s+KNaK7Tq9jdR775ULe+4WNIY8AFpDc1dY8U2DdxHfH6OaeDsSmq1/IDDwyVefOwUWdfBNAxK6f1jyOqXvg6uaQwznP4kzeBtMvZpsk6SCIpUQpYcqpiGv3OPhhQcnx6h3uomJZ99sZeKXyVrZijYBcbdUUz5A7fFeqhJIQhV3y/SiiDewo9XaPiXEJiYMoch0ySkxhGRahCpJslOQZK2TzBb+Cc4ZpFD4wePTilMxjM/jSEcFuq/SS+6Rzu8Rrt2DSlcZH/NSBBB25QC/VviwcFMKSym8j8HKFZanyeI16n736Pufw8pPAzh9ks+A5Tusrv+TuuYdi9gs94mVpqhfBrbNND0ueJsk6F8hkLGY6SQZbXa4NjkEBv1Nr0gIuvB9cUNpoYLBFHM1HCelGvjWAZRrFirtqg0OoyXD4YHSuEymvkxKt1v0AreJFIN5mu/zkrz9zBEql91EhDrHmnrGKdHfgPXMFGW8w6lc2AKk7CfWBKAbSaleAcqCfYtiKM+KfaD2+62fNZuLZMtZKhtNqmu1+l1AgxTUh4rkCulCf0E7dqotknnPCzHJApiTMugUWkzPHmGiezPopTPvca/ItYtVlv/NxvtP+2jjkxi1SJUNZJxIMnYZzhS+mXS1tFBYn57j7ltOedxxrOfZbnxu8S6RTN4g9bWFWyjjBQOSgeEqoLSPpYxxGz+H1HrfZvNzpeT5yEFheEc3q7qkMT/blDtvUTQR38LDKRw4f5EiY4HiLY4arPc/B2CeJ2jpf8ax3w0oSFLGkQqJtIq8f9EHmnMwrvX4HvX9n03qyXS3wFKK6w+d0qko0F2N1QRS71VenFCMOpIm1grYh0T6xhLmpjCTNpAYQm7n72KmUlPYgqzLysZE+qQSpAQMCdEsgEasKWN6F831gnKwBTJJGFJk1Al92PKh2fitFZE2kfpGCkkhnQo97lrtNYoImKVLEZSWI9U/5jA8hL4p+qTtiYDVCKE3FEE2dWO6juGMxMlclkXpd95aCXXiYl12JcKTK6RyIhbA/6jSEc0wzaGNPahAZI2EgdM73KQku9qveN3fdh9KR2h+m0C/XYNpDAR7EfsVNtd7mxUmSrlSNnvnIUbPB8dDmQYk2uY/Xvf24OHT00wd3Kc3TCJ2mZrEEQ789RhBCCk4Ni56STL0j91ZCjL2Ege2zLY3GqRyz0486C1oh7cox3ul2bebftRHAdBQXdDPVW/T6M9fSqEgcH+Pr31+jybSxXCIMRNOZx7/yly5UdDdHy/W6IklbxXsVJ0uwnBoGFIPPfRx63SEd24Qdp8GFQ4sSiK8YMIKQWuk1xjq9vhemWTmVyBtXZrsBlqhyGTmRzLrQZ5x8U2DDY6bWZzBW7VKhwvDbHeadMKfA4XSry6tszF0UkWGzUUmrKXIms7tMOAicyOo9L1Q+JIgYCFt5Zo1dr02j5e2qE0ViD0QzL5FLWtJvWNJoZpUJ4oYBgGQxNFcqU00+n304uqNMJ7g3YD1WK+9XVO5D/D4dx5rtQ+R6ozzFT6fVypfQ5D2BzNfQKlFZ45TDeu8Hb1/2Ii9R4KqTluNb9IrH2m08+x1PkrRt0LHM5+nLutr3K7+eecL/1d7rVfohdXOJL7JIFqYsvkvctYYxzJfZxXNn+DUO9s+DZ6b1L1b3Is/2NoHSW8cEhG3ceo+rfpxRVOFz+LJT1M4XC39RXWeq9xNPcpmuESb9d+nyeH/jH14C7r3cucLf4soWpzs/mnDLmnKNpHmEy/Fz+ucbrwH2BIZ3BP72RaazrdYB+fCSTv7Ga7QzvcRcqoG+i4CuYYobK4121QtBWFB5TBaK3xg4goVji2iWnIhJ9JKwxRAhEgrDMgh0HlSAKoEkQKRDpBLbECOAjtJ+cByFEQblLCJtwkuCQ8dLyCCl5CmEeTY9Cfq3fmi0zW5cXPPMmXvvAan/+dv8L1bF74yFnm+iXRZc/DvK/8YMwrsNjZwpIG3ShIJHx/yDZq9Y1EXOKpTzyG5Vh7EEdK76QOpHj3/E+x1lTaHdK7EBvbqMVtk+KdQho7tgeJzIN5Ux+lRcsw0BFUWh0O2pwEOupLR28rCu61h0lQ773nnZ+3EeVKK0LlD1CE221JITg8VKScfvcokslCbs+1CnaWRth6ZH7AMIxp1Dr7vmukYxphi5TpEe5CL/kdP0GhPuL9RUpxt1bjhUNzGEJSTqUg0pimPBDVbJpjfVTHu1fxqfu9+8Qn9L7n9cg8IPeVK26rQd2rNrhXTcRIXnzsJOOFA0qad11C6YBWcIUorlJwn6IT3iHnnMcxTUqex9sbG6StHf/cMg3ec2qanh8NuKUMKamywbrfoBU10fxwKM1uW6wUE6ncQKTANocpp16g3rtErFtoHRGqGqGq9D8h+nsGG1MWKHsfZCz7E2TtdxY3SAIlP4FrzrDc/F3qvUtEqtEP6mwjIkXfL7UxZY6sfY68+9Q+tNFuM0Sa6fw/IOucY7n5ezT814hVF6X9frAIQPbbtbCMEjn7cdLWWXoVn2eOJKqWlmmglErmY60hVIzZDmEnYK6cZ9iyKQ5lOTUzyqs3lzgxPYzcRiWHccIZ6VgI2adPKSbzQ7vj99WnwbHNQfXBNufPkdIvM1/7FzT9N/9f7t4zSLLsuu/83ft8vvRZWb6qu6u975meHtNjMODAEBgAJEEBBClQq4VIStTuitoIrSI2VhuK2AjGigpxIzZC4kqhjV0aCYRIUEuAhDcDMxiDmenu6Zn2vrq8y0qfz9798LKyqrqre3oIfsDgfOrqzLzP33fuOX9DrDr4Xer32rkWQu8ikyV7C2WU2szGkdLuIpRAkw4CyXZ3vDeCQmHokpRl9p4PKZzeb6SwSOkGh0tDpA0zcfTaeDVkuvddy3Yo9GcxTANNl+TLGVQcMze5TLboks6l8DsBCEG6CxjQdY1U1kHFimwx3btmo7nPYemDzDX+K03/IrHqbBCwlgh0DK1AyXmWocyvkjb33bVGu/P+Gst+DkPmman/KX60QKwCvGhu7U5BCpO0eZDR7N+nlHqOKG6x2nkVTWZI57MUBkd64yml8KN5blX/LxYaX0YRkrOPU3SextT6EZtcbpPiZBivUvfOstz6HpFqsNL+ISvtFxhMf2rTvgthoIssQgo0kekVoTQhyJtOt9EiEdoIQqwZTOgbrnMisfG3GT9VWZZCsRKs8sbKGdpRhz6rhCkNJlvTPUvDvZld1IM6Lyy8SDvq8HT5CRY6i1xt3EAg2JvZRd7M8uOV00QqYtgZ5GjuIOdqlzhfu8QvjTyPo9n8eOU0850FVoMaezO7uNG8xeX6NbzYZ19mNwUzzyvLr6MLjayRZdgZYL6zxGOlh/nxyhsM2YNMpNf1GCIVsNS5TCeqUbR2kNL7mGud5fzql6gFs6SNMntzH2E0dQJdWDTCea7UvsXt5qsopSjZO9mXe54+a889k/1IBaz6k6x411jsXGLVn8SL68QqwpQOKb2PPnsPw84xitZETwi2Wu8wu1Cl2fI4nNusg9AI5qn4twDos3bj6IWkOBFMM9V8nZnWaZrhPJEKsbQsWWOYAecA29wncfQ8sYpRKGxpYmxI2pWKaYQLzLbOMtM6TS2YJojbmFqaPms3g87BDWJ562FrOfrt/VtSzIK4xao/yULnAiveNWrBDF6UQAdN6ZI1hylZuxhOPUTe3HaX1sXsap2sYzGUXx+zGtym6k+jCZMBZz+GTBGriCXvCrcbr7DYuUQzXOrtW8HawaBziDH3UQy5Xm3WNnS9OlGVpc4VonRAc4uma9YYIqeP9Ypv+VyKRtPj/KVZNO1urZBYRXhRnVa0Qitc5nrje8SEgKAezHKr8fLdGyFR7++392NqW6MYEseoDhXvFoudi6x416kGUz3BdVOmyBhDlOxdDKcepmCOI9fcAGyDTsujMl9lZNdAUnD4GQlFhOi6QM0uVPnf/+AbVOttDu4e5n/8Bz+H/YDw/1AFzLTOU7TGSOmFLV22ICnsfuelS/z1d9+imHf5rV99ipGBPKfnZ1n12jSCxH62nHJZ6jTpT6X56vVLuIaJLiVpw2RfqYxjGHTCkIbvU/c6XFutULCdruRfQvvwoojFVou3lxbYns0z3L3VlIJ/98ff580LU6Rsk08+tofBQpo4jAj9CNPSaVZbdAyfVr2D1CSmbWBZBs1Gh06zQ7boYsgUppZBBJufvZTex7b0s5hammljjFa0TCdaoR7McLzvt8mbO3rfXepcIFIBo+6TOHqRdrTCZOP7DKVOoAmLbZlnKVq7aYbzzLR+jECSM8dZ6Jxlof0m/c5RbC3ROBHoWFrurrnGNQZRxMy1Xmcw9UhPyNvWC1hallj5ZIz1Dv5c+zS6cPC7LmD1YIZ2mAhU9tkHGEo9QieqcKPxbYK4RcYYwdGK6NIhbQz2npuNEXSpLAmia33+aLV9fvfffZ3JmZW7fpPLOHz4Y4eQJYN2GJBXNiBQ0TUgoq2GuVS7zWiqTD63daFqqdLgD//8Fa5NLvLz7zvIR99/kFDEXKnNY2kGE+ndmGsJoXbn4mctm9xq7HWNiI2hwpvdglJ/F1G1dRF7ZLzE3/0Hz9Bu+ei6hpu2kFL2UDB3Io50qZHWbbw4pGxnOZQfw/oZEuoH0C2D+ZuLRGFMvpzl+IeO9sSxTW1d+DSIo557zbsJL4pQG5AUuiY30ZI7QfjARZjOBgcfKcQDo0a2CgUU0s496RKWTDRUjC10duKuKcmDhNfVdwKwukK7uuaQNfpIaVk0WcfUNMI4RpeSTz98mI8c2LNJyXBtf++XmqdMs4dwEUIQqYh62MQJLfq5f2NBKUUcK/r6sz19qw2fIoWkFjQ2iWPbrpUUwrYoPm8VyT4pfnR7EkfXeXp8O7bSCPyIYt+dz7qeUGxlhih6993tjGkRxHHv3hVC4Gxo5oVxTPCA1y+I4p4TG9BrrnSCoOuu1nmg58KPlrqW20kRoe69SdY6QrXToeH7fHDnTkqp9XwvCCO+9fplXj0/SbPtIaVgsJThVz96kJJZYjVYJVIxP0uzkR9HyTF1i36WNsTu0v+GF05T98/RCW7jR4tEqtWlYFsYWgnH2E7WOkbKmHhgHZc1Qe28/QRpcx+t4CZV7w3awXWCrjuxJl1MrYxr7ETEE0RBiYwxzJ36RkGYFGqsrpGNJlIUnfeTsY7Q8C9R996iHdxIkFFCoss0ljZAythJ2tyPqQ3Qqmqce/0yfYM5luaquBmbTssnlbFJuRbNeofV5TqGqdNp+Tgpi4ee2k3KtTi6cwhT17h4ZY5Wy8f3QzIZmzhOGkRCCOr1Dh0vYP+eITwv4MbkMocPjDA+WtxwTiQ56xH29v0ede8sNe8UXjhHrAI06WDIPLYxTsY8kohpC7lpUpLCYjjzGcqpD3fP35oznGBpoYaKFeWBHCnLpJBxes/n9sLvMJb7HACGVsSQOcqNgPm5KlezPrv3DmEYGrrMsC39L7l08QbNhke48yCjuzY7IwohGBjvw3GtXs7zTm52ALpIM5D+JAXnKaqd16l5Z/CjRRQhhszhGBPk7UdJGTvRxDvrxgkh0GWBkcyvk7dPUvVeo+Vf7dIpFYbWR9Y6StZ6GFsfRaAxlPk0pdT7EWiY+ubjUgTMNf6C+cZfEiuffvcjbM//0+S39yhgKaUI3VU04TLX+CKxalPzzlB2n0cX63Nuzn6EwwP/EYgRwthAoxWAQBcSIRw0Y0/SsAOy9kMcG/x877ob2js3r99N/HQVjpTiXPUi1xo36bOKXG/eZMAqM+oMcaM5yQ53nIXOIrZm80z5JGdW3+Z642aC5hEaPz/4HIqYF5deZTw1yoS7jW/Ov8Du9AQHs3u5Ur9OrGLaUYerjet8euwXeGX5DYI44MWlVwEwhM715k0OaPtoRi1+cfgjWJpNM2xyoXaF260ZFr1lDucObNp3P2rwyuK/Z659loeKv06/s5+XF/6g13lf8i4x3z7PY+XfYtQ9wUsL/5ZbjZdQXTrMfOdtFtoXeP/Q/0zB3HHXjb/sXeOtyp91CzlLqHtA8q/Vv0tKL7Ez83M8VPwstpYj5ZjsGOuj3uxg3pHM3W7+mJcX/4BYRfzc0P/C9vST3Gj8kFNLf0zFv3nXdmY4xWTjJYrmRK9L73ZdTdY6ibGKmGy+wunlP2Gpc4WYEIGGJnRiFTHTOsXZytb3wEjqOM+P/hvuTMMmmy9zfvVLLHYu0Y62/vFM+zQgyBkjHC5+it2ZD2FuoAGVM8m/eyKVKC5Vv86Zlf+MKdN8fOz/JGMMcKX2Ld5c+QKNcP6ubUy1XmO2dZqyvXdT4WhjrHjXeWH2d2lFdy/8AA4X/g6P9v3mJte6W5PLhGFEteaxvNzAGSkghKDqT/Pa0v9NPZinFS7RjlaJepaLilvNH3Gr+aMtt2NreT429vuUtF1bfj7Vep3zq19ioX3unvtK+wzUBFljiAP5X2Bf7mNYWrpHmxia6GfH4W1kH4Ce+J4JBULrB6HjBx1uTa9QqbYo5twtUSD3Ck0YpPQ89WARQ9j3LBw1Wz5ffeFtTr19GykEjxzexlB/jnIqRdXrMJzOstRqMt2o0Q4Dwihme7ZAxWsznsnjxxG3qquAYKHVRArBbLOOQrHqdZhu1hlopJlq1FAKduaLnF2c46mR8U37MbdY48btZVzHZMdvjLNnon9TR39sz9CW+9+qtbFS94dEGzLV000TSOgWnNeQjHfG1iMpdGGjd1+Oa+MIIRh0jpM2hphrneJc5T+zM/tRRlKP3xNpVzB38nDpt1nyznGl+mUaqWl2ZD64ocBzx3VWCk9Vqfm3EcBE5sM4eok169QkOehKZz/gQvvWpTlW5qrsODBM39C6mK8CPD+k3kzooEEYJWiwKKaQSxEFMUXLXteJURHENZAtdCmZSA/Tb99bHPj8lTm+/oNzdLyQVifg6RO7SGUSlFsr9GmEHos3l6istti3e5BcxtlirPsqJ2z+SxYQsoSKpkDmEexgK8yrEALbNrHvoIEbUnK0f4iUsRntN9lcYtGrsTM9QFq3sbX7uzC91yKOYgr9WZ77u88Aitnr85vurYJjY+k6ra7I6ly1ztGRB9c3EKxR1dbHdE2D/AZ9maVGEz+KsR9gBbzYaPZEtU1dUt7CzerB9w1uLlQoZ92uOPbmMKTG4fz2LX8bxjGr7c6Wn20MpRTLzXavMJZz7ETLUkVYmoMUGo5hkEvZtKoBQRQRK0VfOhFjrnsetqHT8n2aQUDGssjZNpoQtIOAII4xNY0ojrm2skzWtjGkZDCTpuo3GLLL5Iz0OyKwlEr0v7Y+T4IwDmmGLTSxjgqOwpjZ6/Ps3EDHv995kELwkV27CaKYmXqNWCmkFPh+yMzUCuPbN1LSFEp1es5B7zaCOCaIo01FxsHMev7Q8gNqnQ7lzDvfP3XPo7Gh8LmmybVvsMxKq02t3dmk4XWvSFAaJn40T917q0cvn23UWfU6nJ6d5fHRMbJW8my0vIArt5d4/on9XJ9Z5vjeUX709g2WvRU0AzqRd88c/b0ahuyiX7p/CyExtSKmVsTS9xPEMUKsI1ckglDFXcZHjB8LgriDRKJQ3fdXgjbTpYYXhbiG2StMxbHi1IXbNJoexfwAnv+h5FkR4HkBaddmYDDP3EKN+aU6lfoKjx/JcXVykeH+XILe6e7rzEKVXeNlchmHWrNDGEas1toM9R/g9uwQxVxivNNs+xzcNYh7R8HacUMOPLwdw9QYHCuiaZLAj7BTZtI07er0aIaGihVRGJHqSpLYpoFSirGRYuKgFkZITRJHca/g7AcRhqGRcW2aLY9SKU0hfzeyUQiJrQ9h60OU3Q8/8LVTSnHh3AxDwwUKxc2uZFEUc/H8DEEQ8Wx/lmI2RTG7vm1bHwI2537tdsCp124wPbXCP/8Xn8DIpRBCx9ZGiVoBP/jGWRYOTPPLnxnFMDa/69O5zcd1PzbExpBCx9aHsdOfYCD9CQD+y5tv8fSO7Qxn3z3bIUFyWWSsdWqqF4Z85+p1jo8MM5DevKYxtAKGtrWOoB8tstj6JrFqY2r9DGV+FccY3/K7G7dvaAVy9gkWW4kekhctdhF169vWZRrdvHsN1wy8BD0edzUw5fr6QhdpdHPPXb/524qfqsIRJFSwXekdPFw4QoziSuM6pjQxpYEpTWISKLEX+wRxgKUn8NGskUGXGpHqcjbjIKGeqYQeFaqImITSJrtJvhf7hCrElCYpzWHC3c52dwxNaLSiNhk9jSENNCFJ6y5lq8SZ1bcomcX7it7Ntt9k2buCImLcfZxaMEPVn6IdrXCp+jXqwTzTrTfIm+NkjAGWvKu0wiWWvavcaPyQbGEU/Y4OeaQ8ZlpnaIQLSDQsLUfWGMbWckhh0A4rVIPbdKIarXCZ86tfJqUVOVL8NO2Oz+TMCs2WTy7jEPghmiY32S1GyqcdrjDdOsWPF/8D9WAeXdqk9X4M6RIpn2a4gB81SRuDZM2kGx+okCAOe1xupRRLnSu8uvjvWfVvI9EYTj3EoHMYW8vjRw0WOxeZbp0i7EJOdWGTNso4WoEh58iW57QdrTLTOkOoPKQwsGWOrDmEpSXCzs1gkYp/i1B1qAZTnFr+EyyZZmfmOYQQpCwT1zax9K1v+SBu0YlWmWmd4szK5+lENWwth6v3oQsbP27RDBcJ4jYlaxe2drfg4lqYMk3Z3kctmMWPmwRxi6BrP36vyOdTzC5UkULgbhAa7kRVppqv9c4VJAvmtaRkjT64VWji/hzkTlRlpnUaP24ihYElM2TNYeyuY0EzXGLVmyRQLWrBDGdWPo+tZdmT/Qgrs6sMTfQzMF6mNFzYhJh4r4cQOnG4BPpPlvgpFdEMK2hCT67/FnockPClDT2heuq6TDpjwMG+AXYXSuhSY0euQBhHnFtaYE+xj2y3a6tJ0V08xBhSUp7YgyblOsVWJo6ThibZnisQK8X1aoUjfQPYunHPfVojwSilNnV/4njdQWbtd6ms0y0wxQSqhR81CFUbL6piyHsn/45WwNX7uV7/JtvS7yNWMZaWIaWXkcJkuvUKeXOC2dZrFKydidjzFrdzrEIq/nViFVK097LiXaXZLfpGyseLakTKw4uqBHEbXVjUgtt4UZWsMU7WvEU9mOlSinUsmWUpvEDFu4opszh6iQHnIebbZ+izE0vVMO5gvAP1zJAuQdxmxbuMKXO4Rn/3mUwim0/htX0sZ/Ncn7INfue/fT8rqy2abY+V0yLIywAAIABJREFU1SZf+uZZLlxLYNRryIUw7lJ1emMGhCqmGbZpRzYFtt4/Q5c95IJjGUgpiFSymLOkTt50WKJOHMf3LZTGseLW9DLVWpvdO/pxU+vzVqR8msFMt/Oso+mPdOc/jTCY7Bb/RE9wXpcuSoXEKgAhieIOUhjEysc1hhlw7z4WiWDVb7LiN/4GhJmf/lieWaFZa3PhlSvYrsXMtXkOPrmvR8QYymVwTYNKKyl+nJ9f4EP7dz0wzUcpRcowGMyud2hTpslwPtPT/blVWaUdBO+on6SU4sZypYfuSJnmlo5q7yZMXcML3r0AsxeGzNbq95zb1qLp+yw1mr17Z7yQw5ASH9XLv7K2xVA2w2y1jgJuLq/S9gM6YcBco56ISXsepqYzlEkTxjFLzRY3KxUMTWMkm2G6VscLQ4azGbK2TX/apRG2CFVIxnhn2psQoGmSyeuLFIouI+PrenpBHCZoPWeznk6n5SUoogdAAjeD5FguLi2iFNxYrVBKpXAdA9e1NmlgAd3ntEEYLwFbW2PfL7Kmxe16laA7rhSC0UIOS9fwwohqu8NCo8lEX/G+1y8p/LV6zoJCCLYV82hS8ubULLrUsHSdB1F6MrU+0uZB/GiZMK7Tl/ogANtyeWbqNVpBSKuLqOtRh2yDQtqh1Qnw/JBKrU2fsYtSOk09bGDcx2DmvRiG1Fj12pTtu9/pU80q12rLuLqJa5i0Qh9bM/CjkE4UkjVt8qbNjfoKkVLUAw+zaxXv6iYZw2LZa3GiPEbeShqySikWV+pIKbk1U6GUSzG9UCVlG2wfKfHGudukUyZT81X6i2nKpUxPa+rtK7OUi2kO7hqiUm31EGzLlQZzy8ncsH24SKXaotpoE0YRKNizvR9ni0KjaekMjCZFg3sxQraKNbTgGuJJ1zdLekRRTBwp6D7jmiaxrMQtLgojgiDqUkPlusRJdzyg9/9rYwkhekLTmq4hZZKzNZsdvvONt3nuw4dIuSZ697O13z38yA6kJnr/l+R9XcOKWCEk3X1PPt+3f5hm02Ppr05vOl7LNjjxxE4mJ5c39d7W9nnj/oZhcmxSSmIVE3ZRiLqUPRq21kVCCui9k9byHl1KLi0ucWRokFLKQZMSbcN3IKE8C5JcSSUHht6VVVn73tp2dCkJ45iUYfRyrCiOu2wf1ds3uuOtNRw0KROX2HA6+VukcfR3Ltj3zvOGAnPSRH2w93egYuY6NQ6wuVl0tb7A9friOkBCwdHiKIPOT/Y+3hg/VYUjIQQHs/t4ZfkNvr/4EiPOMDk9g6WZlK0+0rpLpCLaziCvLr9BpEKO5Q+x4C1hCB0vDPGjiEO5fby6coqp9gy7MzsRSF5eeoNG2OT1yhmOF45yKLePFxZeRArJiDPEDnecs6vnmWxNcSR/gJyRo88q9rpBUkh2ZyZ4o3KWRwrH7usKstA+T9ney/sG/zklaydVf4ofzv8fLHtXmW+fp+LdZJt7kodKnyWlF5lqvsZLC/+WdlRhtnWG/bmPocvNYrtFa4Lt6SepBbOMuScYcA7haAV0aSGQhHFSMHl96Q+ZbZ8hUh6Xa99ge+ZpsulR+gppGs0E1fL6azcZGi+ybedGGoJipnWGS9Wv4cctDuR/gYnM+0jrA0hhoIjoRDUWO5cAsLrVTVsatMJOD8Hjx03Or/4lq/4kAo2d2ffzaN9v4ep9CJHw5TtRlbcrX+TMyp8SEzLoHOFk/3+HqaUx78HHHEk9xKh7Ak3obEs/RdGawNayiS4UAj9uMds6w5uVL7DiXacVLnGp+jVG3UextSxeEFJre13dnq0WyjHX6y9wq/EyUhgcL/09xtNPYGtZBDqxCmhHFebab5E3t/VQD1tFwdrOM4P/jDD2Eo0oQs6ufIErtW/fswuVdi1cx0RIgWmua1QVrO18ZPRfszYLxyrk/OqXuVr/NiCYyLyPw4VPbTmmQCNrjGz5GcCQc4Qx9zFiFbI9/RQlK0GRaTKxVQ3iFvPt85xZ+TxL3mU6UZVL1a8z7p6kMJhn8sIUty5MkStnf6YKR5C4AXKfQt+DRCJYGNKMaqTuIXYP4Dgmn/z5h0g5JgPlLI8cHicmQgiwdNkdKURKxaFyH4YmkFKhESCExBAJTbTTCVheTqibYRTj2AZhGKEU+EHYS1zcWEfEOsvLDXw/ZHT47i7K4nyNjJnY6TbqHcIoplB0EQga9Ta+H2E7Bum0TamcwTA0vLjGper/R82fJFQeb678ITvSz2HKNLkNKMq0MYQmLHTpcCD/GW42vsvl6pcwZJrx9DP0Wfs5WPgMk40fsNQ5T96cYFv6/UBMwZxA61JwLS1P1twGKNrhEtPNhLLp6GXG3CdRhMy0XmO6+RKaMLhW+xrL5kX25H4RL6pyo/4dFBG2VmAi82E0YaJUxKBzhEYwxcXVP2fAOcaY+xQj7iNEqs7N+rcARdHaQ9HaTkrPoXch31IY5M0JdJksBrPmKCPuY1ytfYW0Mczu7CfQtPXFRKflQ7ezv3FOklIyPlxkfDiBF7faPq+dvdUrHGlCMpzOrIucq3YiPK0NYSgNLw6YbVcYsktbznUHdg/xqY8e58btJT767EEyrkUgYlKaia0bSCFxbJNtY31kXPueizfPD/h//uxlpuYq/Iv/4aPsHF8vLgRRnUZwm1Y4gyZsHL1MrCJcY5h2uIAUJim9n2YwQ6Q6SGFiayXa4UJ3/tFohfPdwt3Ali6fQ06BPdlhdCEfaHH4XotMMYMCRvcM0T/WdxeqbziXZbyQZ2o1QYi8euM2q491KKTubxzRC5EgNrSGINtFGUkheHhshK+du0zd87m1vMq1xRUGMvcvkraDkDNTsz2K2J7+PvofADFyv4jimKV6i7YfbBYjeofwwojL80t0wvAuI4C1UEpxu1JjarUGJLP9noE+DF1j1avRiuooYvKOw/6BMm9OzRIpxanb08zVG4zkEsfR1XaHqVqV3aU+XNNAk5LBdJpdpSK6lF0MoiBjWYx1C2lSJK5x9dCjEbYoWfn1PHNDM29jwX5NG+XOQq4QgkbYouJXGU0NAsk2TNtgZNcg5gNAxSxNoy/lUnSajOdy5Gy7q+cD6axDdAdtTEoXxzyGLsv40WZnpQehNdq6TtlxN1H3xgt5xgo5ri6uUO94nJ2e4/jYMOY9Gn1r27o4t8hiPaEPl1yHXeVSIqYfhFg6Pf22BwlN2rjmniSfjBaw9P7u8ybIWhbXVyuUUg79bhrH1Hl0/zj9hTTDfVn+6qXzHN09xHw4Q6Pl0ApbuFkX/adrifUThRCJeH5PHHtDNEMfTUhsPXFOdjQDP04KA5qUDLtZ0rrFit8miKIe2iilm+StRKdFiM33khDQV0ijaxor1SarjTa5tE0+m6KQTTE6mOfG1DKGJsmlHXRdUm96tL2A/lIaFNyYWmKonMPzQmRWMLNYJVbQX0yTyzikXZulSoPBUpYojsm49qZncPPx36cJG4bUPY9iKtW7r5WCdtvjW197i2uXk3f3Yyd3ceKJXZimzs0bi3z3m2+zstzAMDROPrOXE4/tTJBWr9/gpR9eJvBDcrkUn/jlRxgYzOF1Ar79jbe5fHEGpeD4iR088fQeWk2fv/gvr5DPu0zeWkbFMc/83H4eeWwnM9MrfOVLp/nxy1dZXKiRy6f4wIcPc+ShcRr1Dn/9l6e4cG6ak0/v4YMfOdKjQl+9Msd3v/k29WobqUk++vFj7D80mpwLmRSZ7sIhd4uqUoqkINaNhfkqX/zTV/jUrz7OwFCeKIr5T//vixx9eBv7j47y1xcucm25QsYy+cWD+5lcrXJ9ucLz+/fw5fMXOTQ4wI5Cga9evMzNSoW8Y/Opw4doBQFfOn+BKFbsLBb4hYP7Ob+wyAtXrxPEMU9uH2dPXx9/cuo0lm5Q9zyeGB/j5LZxvnopGev6coVjw0P8ncMH+cblK7w2Nc1EqUCJFN++eo2LC8m7RJeSzz50FC8K+erFyyw2Wyw1m/zK0cMc2QDISjR4tzaGuDMUPnXvTaKuBIKlD6GJB3t/u7rBuFu4qxZhSZ1m6DPXrrI3O8jt1jKjXv5nuHBEghz60B2OaVEcI/w8+HA4N0ZeJTfvWC7H7VoVTRXYn8txdWUFKQR7SiX22w/jRSGjbo43ZmawGOOzY4+z3Goxu+qxK7eXAmO0g4BxO08rCNiuH2VbLkcrDFmqtTiaO4rWRXN0og4z7XlGnEGGnIF37OqVOg/Tvpwj3KbTl9/DROZ9LHtXiQmQQuNQ4ZMUzO1Jtyh1nKK1k+nW61T9afy4hepkqDbaRJFCCtB0yZj8FLpjImOTsAGdLixSSoFpZBlyjvF4+R/xrZl/SSOcpxEusNS5RCY9QhwrHNvAMDQsx6DT8u86hpuNH2JIlyf6/zG7sh9AE8YmoeSMMUjZ3kPT9zk1N8tIJkO/65IxUtTDFiioBTNMt04BkNbLHC58irSxXqBKRN7y7M9/nFvNl1n2rrLiXaPpNaispinmFX7QQJOCetOjr5hOBAhjl2Puf0/azrGw2CJ0DLyuBbmha2TcfnZlP4AuHb4/96/w4ybL3nUawVxSYJKC7X2FxAnhHp3IS9WvkzPHeKL/txl2HsH3Y+hOfqYmMUSZQnovUazwgxClEr2QMIoZGcgThjHT86sM9GVYqsDowADIpCLt6EXuVUkOo5jL1+bx/JBSMd3LkaM4Jgh1Sta+9Qq4CrjZWKemOVqJPmt/7+9YKfwowtL13ss47qJGFGtuK8mEb2tFTvb/Eyzpom2hDWJpGVy9jC5tXpj9Xby4TsW/yUprkqBZwLRNFqeWiYLonUzu3lshDEQXEfETDYMEIelENXRxbyqXFIKTxyc4eXwCgCVvmulWjU7c7Im05s0BKv4cIHA0l1ZUJ2f04UUtCuYgRXOQZtvnyo0FTEOj0wlwbIOF5TqObSKlIJtxkFKQckxq9TaeH+KmLEaG7i4cXTg3TafaIZt10A2Ndttncb5G/0CWhfkqnhei6xojY0XKA1niWGGQ5Ujh7xNGMZPzFbRIMjfrYZs6jnie236DarODFMcwdI0LlXmksHG157EjhZACw3a5vlIhZY+i1z7BvpEyqQ2aUsdKv9n794BzhIEuQnHUPcmoe/Ku4yjpJ4jEATRTMJDP9PRb+p0j9N+BblRK0QkuEsc1tqXGkexESpcwmsIPb1PSFH3uLix9G344Rcv7EaPOfmxjL5A8L0dLn+uNpwmTvblfuuf9ITXJ9I1FBsdLuNmt6GBbx1yzjlNLFnfJ4lICMSpuIGSZrJG6S0h6Y+SzKX7zM09u+r+a1+FybZ6JTLknnr24XCebtjGMrceaXahx8focUaTuotIYWoasuZ2MMY4UBrp0iOIOunQxpIvExJAuAj0pYPg3cfSBbkExoTUGcaNLHbx76edHIbZmsD87QqhiLlanH+jcvZdiZW4VFccM7uinPFqib7S4SRzbMQ0+sG8nr968TaQU5+cW+NLZC3z64cMPaABBIv6b2ox6eXTbCHv6+3jj9gyVdpu/PHueoyODG+zLN0fcLaicuj2TmItoGh/Yu5PMu3R527xvyTUfKWVxTGOTtf2DxBu3p7m6uMzh4a2pe34U8cNrN3s28CU3xbGRIQypUbbHSesFdGEipeS5fTv5zuVrzNUa3Fxe5Yun3+Z3nj1JtktN21a4uyngmuuohROjSfNm47kbcspUg8ZdhZaN1vLLrRZ+FGFqSZ7je2HibnlHmNLAlhb6Bh01w9RZmqmw49A7d74NTSOvaZwcS6gVQ+kMQgjCIMLzAsJgc6FAqTZRXEeXfUghyXSppQpYabUIoui+jkzL7RYrnTbj2fXzNpzLcHLHNm4urxLGMV87f5n37drB3oG+eyI8FuoNvvzWhR4i4cjwILvKJYSAfCrRaAm7KIx3Ci+cY6n1HXLWwwj03uLN6OZd11ZW2Fcu9wxxolhx+fYipy5N0V9I88FH9jBYStPQF6kGNQpmDmMLXbv3cgRxci9G6u7m56HCINyRRihiWuGatEKdmBa7cwYCE0HCYjCkQ6zCRJdIaTgbCoVSSk4cWteRvTNvHyqvUXNEL2dWSrF/YmATxV4IwUDXuGXnWB+KmJp/DUEHnZhjD7WBNoZ0iWnQCnPEygcSFHVMYpATxHUsrYgfVdGlQxDXyZg7sLUiURxzYWmJSrvNnlKJ3aUSmpD86PuXuHl9gY9/8jiNeocvfuFVsrkUh46O8f3vnqfd9vmVz56k0/ZJd52/fC/kK186xdPP7mfvviGaDQ83ncwLr7x0hYvnp/n4Lx2n0/b58z9NxhvbVuLi+Rl27Oznl3/lUS6en+GrXz7N/kOj9A/k+ODPH+bm9UU+8clH2LlngFTKBATpjM0nPnmcOI6prDQ2nbNs1uF9799PJuvwyo+u8JUvnWbfwZEHzlM2Rjab5J5vnpnkQ0N5ZqYr3LqxwPO/8BBvz81zcWGJjx/Yx8u3JvnWlWt84sA+fnx7iv/449dxDZO9fX28cO06s/U6//iJx4DExTOMYw7293Ny+zi//4Mf8fi2ccZzOT5xcB+3Kqt868o1thcKXFxc4p8+dZIojvmvb59nNJfl3PwCv/nocf7qwiV2FAvkHZvn9+3h2soKXrdYvtJqU+10+CdPPs4X3nyLMzOzNHwf1zT56L69/NEbp9lVKqHLVWx9lIZ/vkdbG8n8WleY+u5QKiaK68w3/5qF5ldRROgyS956FCmc3npt470fKdVtRiTGFn1WBlPqONJIqMXd6zbmFqn4LUypsS83SKximuHfjFZ8r3hPzGxhHHO9ssLb8/N89ugxXrhxg0MD/QxlMnzvxg32l8uMZrN4YUjN89hVLPL9mzcZz+UYSmdYabfJWQnNYbJW5eXJSZ7Zvp1LS8s4uk4Qx9xardDvpmkGAV+/eoWlZpOT49t4bHQUASx6y0y3Z3mkeKxXTLpXGDJFRoxQX24xuB0kOgVze+9z1yiTM8d6D6AmjF5xJVQeftTg+qRgsdKgmE3hBSHzKw10TdDxQvqLaQqZFM2OT7XRRtcku8fKpJ0ieXOMPns3jcY8YdyhFa5QrbW5NrlIuZjuaXDIu0QWE1zDePoxdmTeh0Qnipv40TyaTGNuEEntRCHXKitJRyCVoha2aIcd/Dik6k8RxC0AcuYorr61XWtKL5E1Rlj2rtKOVllq32RlyuDW9DJLlSbFXIowTpysLt9YoFprs220SC7T4K1LMxi6JAxjDENjfLjIgV2DaJrGgHMAV+/H92/gRbWumK2i3vG5vrDCeN+9kR9CCPbnP86w8xAdL+LsxSlAkE5Z5DI20/NVDEOjkE2xtNJAkcBGLTN5jOJYUa23GSxnqdXb3O7ynkcG771NgMWlOnPz1aSooyXHBbDSbnNqdoYD5X5Gs9ktJ+xOGHKrsooXRr2ErdbpUHJTtIMQx0jQS6amsdxsYes6jmnQ8HwiFVNwHEZz954GhJCU7b1kjCE8r44fNQhoIPUSxaF80nnYIpl9T4cKiKMFtJ8QcRQTEcYeBXMUXT64/kriOKVhCgtDs6iHy2hCI6VlsLWk869LE0um8OMOQewBgnwuxclHJpBS9gq+i0t1igUXTZM9qK9g3fVIk0nSdWejePfeIR47vgOprWM51nQvJnYllvayC62WUjC7XGN5tcnESAkpJfW2RynncnVmCcc06C+k6fghS9UmSkEx45B1bcIooNnxWaw2GCvnybk2N+ZWcEyDlXqLsXJhU+Ho3Uat7XHm5kxSnNu7jb53QEEIdIQw0EihyTxKhYBCyhRSOonDiFZGEaOUj/aATmlbherOD2GwnohHKtH+uJ+Nc85KikZmb3EWJTpHQqMTeTSCDkXz3XH+NSHRpUZKT+7TKI5ZqTTx/JD0PQrt1yYXqdbbpFN3oy81YeIaoxsOFtZQ2AaZHsIiJQdRKsS0s+gihbWBAtxnPwTECUXxjphur1AL2ry5egtbGix5dZ7s3/uujvmnPSrzq7z14kVUrBjfP4KbS3Hk6f0IbS1vEJyc2MaOvgJXF5Nk909+fJqUafDxw/vui7ZpBQGLjSbz9QbVdocnJ9YXaOV0mucP7ePt2Xm8MOJ7V27wxI6rfPzQXvQ7CgJKKSZXVvmjV0/3dIV29Zc4OTG+yc3n3YboLvr/phzExXqTPzv1FtuLhbsKWLFSvD0zz5fPXujNhweG+nvUKFPYmOb6PX1oaIBndu3gz0+9hR9FfOnsBSb6inzs4N5Nos53RicIuLK4jGua7CjdXZy3NJOUbm9AtQtGNrh/TVVqXFlY4sBgP4Wiy7ET2yne4VyqlEITGo5mb8pLc6UMJz/28AMhju4MrVssMQyNHTv7t5B7C1lzCdKkYCS3/szeXK5wfWmFPf1bF3wA+lNpbN3oFWXWtvmRA3v43pXrTFaqXFtc4Y9+fIr/6bmnKW7hYtfwfb7wxlu8OZ0gOdK2xfOH9uIYOrFSDOYyCODKwnLPVfh+IYSGrQ2Tsx9F37DY86KIpu+Tt21ylkWhi8yzDJ0PHN/N1eklzt2Y59TlKdKOxcc/tI1Y1bsaRz9bBNqVThtdSop26q5ru9W19qIWk81XSetlNGFQDxeIlN+jhGeNQUzpsuLfRBc2mtAZdx/lHoCfB9rmnULMW35XSYTQUCqkEUzRDhdIGUO0wjmK1gGUCqn7txBCw9HLPcq1LlN0okWawQyaMBOHsbgDWmIEj1JkTJNKp8Nis0l/yuXN07cAuHJpjiCIaDY9blxf4NDRMYZHCvzguxc4d/Y2Dz2yg3J/1wVLlwwNF3jz1E1SjsnhY+Oku3pJb566RRhEXLsyRxjGtNs+16/OM7athK5rnHx6L9t2lNF1jZd+eIlmwyM9aJPNpRINpaxNobAxBxJYtoFlGwT+5uJ8Pp9iabHO5UuzrK62WFlpbH1hHiCclMmx4zt4+cVLPPvcAS5fmGFwuECh6PLa1VlurFT49pVrBFHEWD6Ha5ocHxnm9773Q/7XD7wfx9CZrdfZWSqSd9bn5rRpsquvRNFxsHWduufx1uwck6vVpB7Q6YBSDGUyjGQztLsNf0PT0ITg21euUe10GLqHRpIUgr3lPnK2TZ/r0gwCBjJpXrh2A3HjJgNpF8fQMWSJvP04Tf8ysWoxXftjorhB0XkGSx9AihSoOHHCi5dpBTeodl5hufUCYZxo9uatR8k7iS6nF4bMNRo0fJ++VIogjllpt7A0nf60y0y9ntC4BdSbAX4csbfU12s8jLtFzldn+Oups7i6yUOl++stvdt4TxSOZut1ah0PQ0tQFIcHBri4tMieUh9HBgY5t7jAnlIfkVIsdbs0hwcGODs/x4FymXIqmegans9MrU7KMAmimH7XJWdbtAIfpSBrrVEgNA4PDDKey/UWTWOpEcZS96b9bAxD2mTsfq7eXmLbwTHcXKJ7owmTSPk4WhFLrt+oQmhdihZA4nQ1MbydiZESWpejemgCEDCzWGWgmMHQE9FFIQTXp5bRZALJNWSKlFbqjhQTxG3ctMlgOUsp76JJQak/oRbdycu1tAy7Mh/ElKke9zKKEyj3RncdXUgOlfsTVIsQmELHMjNdgbsacXfBbWm5TZoeG0MKHUtLd4X0IoTe5pEjYwShApXocIRxTDplk8s4PftKKQX5jENS/1LouoZl6j14qaMVsLqL65iQMO6gFKRMg92DfWj3eisBGWOIHemn0KRJGLapNTpk0w59RRc/iAijmHjN8UNAJmXjOiaaJplbrKFpkrYXsFpr0+oiPlZrbSbGti6erUWpkOb4se2YptYrhimlkk4jiZvFvcILQ+brDRqejyYl44UcxZTDZKXKcquFaxjkUw5522ah0WAom6HR8FlsNDE0DVtPEq37Jfm2lsPSMt1zGhELHzdt4bWCTbznn52IUapBHM0Bpbs+VUrRaHqsVJu02gGgMAydfNahkEv1Em+BQBM6ofKI1YMXoQrGAAVjAESXG658DM0CtTmHFwIK8QCJDasgiiJqzQ61RocgjDB0jb5CmpRjvuM1unMuGB4t4KRMgiBisdKg0fS6HHWNtGtRyru9gunab6/NLDPSnyefNtgzWsbQNZ49uhNd0wiiCF1Kolhxa77CSF+WlG0ShhFh3BXJFgLL0Dm2cxjT0IhjRSZ1d9EgimMq1RartTa+H2LoGtmMTamQ3uQYCdCXcRkt5vDCcEsURiIuGDLXquPqJn32Hrr6m9Arsd0ZAlMf2fR3byw/pFJt0Wh5BGGEAAxDx3VMinkX01h/XuIo7p675NgjFXG1cR1XcxlNDd+11bXwooj5ZoMhN0PeskE4iZtGdxFpayaufm8q7VZhaTr9doaq3070AzQN2zbuqd3g+SEXui4xWxWOkrMiNv5xz+8IYWxJRTO1exe/hp0CptR5om83BTPNudXb9z6492hMHN1GFEZUFmoMTwxgOolFctzVW1jTdPmNkyf4/e+8yGKjydRqjd/71g944fJ1PrBvJztKxe5iGtpBwO1KlXOz87w9M8/B4QEeGR/B0DajcE1d4xOH93Hq9jRfOXeZarvDv/7WDzg/O89ze3cykEkjpaTtB5yemuEvzpzj/OwCAMWUwz988gRD2cxP9F4IooiC62xyeHvQ0KTAMQz+8s0LVFodPn54Hzv7ihhaopn02uQU/+m1N7mxnBhClNwUv/7oQ/dESGVti889cZzL80ucmZ5ludni9775fb53+TrP7p5gd38JS9d653iuVufi3CKnp2aZrKzyj5569K7CUaxiakGDkrledJFC8NDoEHnHZrXdYa5W51998wf8+qPH2F0uYQ+6zLUa+PWIThDimAYjhRS709uoBDWCOOiNFSrFTLtJpxYSRFHitun5LDVb3O7a1CtgsrLKl9+6QNo0cS0Tp6vvYRs6I/lsb45fC4UiVk2ieBW0UXQpeWhsiKxtUet4TFdr/Ktv/YDPnjjGRF+xO+fHXWfEkLRl0hEBU40aAym3Z+0OcGiEQXNCAAAgAElEQVR4gN84+Qi//50XqXY8/uqtiyzUm3zm4SNsK+WxdR0/ipip1vji6XP84OpN2kGAqWl88ugBnt2d0KFVrLi5VMHQJGnLpOH5D9Aw0Gj4F2isnEMIk7x9grL7ERxd59GRUWbqdUrOegGr5fn86XdOI4DxgQIP7xlhqJihbizhRBZ5I8dWpg/v5bA0HUNqNAOfvi10jtZiobPMqcoFnik/xLb0Y+jCAgQFtR0pJH7UxI/bZIx+QJEx+glij1owgyImVjHfnHuZw7ndjKTW1x1xV7fxQXIZhbrru7GK+crMD3hf/wnS+iiKGFvr6+nsCaF1tfYiCpbDmjubJQsIofX0Z3LmLgSSSAWYMkusFE3fZzyfpx0EDGYypE0TYui0A9IZiziO0TTBhz96hD37hxFC8Mz79zM6VuJH37/Iyy9e5oMfOcJT79uHYWj82t97klOv3+CVl67w/e+e57/5zWcZGs7TbvlouuwxB5770CF2701QlVIKcl1B7TUNwwcpmm4VrabHn33+Zeq1DkceGieTsXsNxr/JtC6E4PCxMb77zbc4d/Y2Z89M8uxzB9A0Sd5xODQ4wOdOPNzTGvLCkDemZ3hy+zZen5rm4EA/BcdhtlanHQQolbyn1nSH1ihyLd/nxZu3+IePn2Ch0WTmrcQhWpOy+84EBKSMhFa81GpzcKCfgbR7z1zH1NZF4ZVSlN3EHEEAx0dHMDUNTeoMZ36NTjjNcus7+NE8k9X/wGz9z9BlBiEM6EpXRKpJEK2iSOZrKRzy9uPsKPwzDJnIE4RxzJWVZeYbDZ4YGyNlmDR8n6WwRd62qbTaVL0OpqaRsSw6dyBy82aKT217BD8Kk57d3/Ia7aeicKSUIu4+6Bv/DxJIVt6xGc1lGc/n0DUNXZMcHhhElxJT0zg6MIit61i6xkg224P3Hh4YIGNZ7C6VuLm6iqlp7CwWGMlmmCgUe64XmhAMuGkWmk2GMxkeHRllodUkdY+O3TuFFAamTOF3Eq4pJMgN0dVOsbXcHZOfQLJuTRirkFzaYatFS85NknRF3K3eh+zflUYR04lWupC29QRCEWMaWo/rHYUx9VqbwhYuWCmtSMFa7zxKYRNEyz33o94CCcV8s0HBSay+R1MDOJqJJiSaMHrXMYw7myxiN0asYoK40+vKmJpNxrVZc9sRYv0eMA17E4TSLLgoIsLu8Svl047iRD9IqU2L9HiDptCVuSUGcul7unVkjCFcvdzdps6u8TKWoeFKScoUpEdLPevswbSDpsvefmWGC6CSyTtTTDMykGO50mSgL3tPznQvBFy8Mks241Cvt9k1McDYSCJknLFM/DC858sybZocyg0QhhGtjk/oJ8W2bJ/FfL1Bf9ql2fTI2yn6LZdWx6dcTmOP3pEQKnXHOU3O59o5VRvOqdfu0Lq1xNsvXmRwRz97Tuy8//G950KR9JA2J366Lul4Aa+euclXXniLa7eWqFRbKBSuYzI6VOCpR3bxoaf2MVDOknD4C/TbuzeJ3ftByOe//DpzC9W7tmxZBr/4wSPsGOtL3C4uz+G6JtvGEnvlmWqN25UqQ9kM20sFDGkRRjHnr8zy5W8nAspzCzU6XoBtG+wcLzM6lL/rxZHPpvj0xx6mmLv7WdC05L4+d3mWr33/HKfevs3Ccp0gCLEtg3Ipw6E9Q/zSh46xa0c/uibRZDeZ6Yodprr0hXz6bg5jJjXQK66xBZrItc1Nz3vvqijF8mqTb/7wAt97+TJTcxWaLR/L+v+5e68gybL7vPN3rs+bPiszy9v2dtqM9wYAZ2AIUARAkEECJMSltJIiFNJq92W1G7GxD4rdfVAoKDFW3BUNqCVI0IEgCGBgZoAZADM9pqe7p3vaVneXt1npM6+/+3Czsqq6qnoM9YDBPwKYrsybJ2+ee+6553z/7/99CoO9GR69dw+/+JHjxGIaf/53b7BWbXHq2DB6Nlq4rs+mr52/zY9evQ7Ao/ft4Z7jQ1yqLJDXE+SN+JbnULVu8dVvvEGl1mKwL8OnP3oPqcTWMkbfD1hYrvKTN29y8do8t2dKrKzVaVtutEgxNHqycQ5O9PHRxw5y4vAQuqbiut62Kb7ltfECn0H62a4gEIWpqGSN2Kbnk08Y2ojQAhGyalcJCOiPRYuQK5OL/O33Luy4MNo3XuSTTx8jkELSWoyUGl2vRtOKDBTWy2b9gOVSnen5MjMLa0xOrXLm3C1CoNZo84d/8VPSye3Xuicb57MfP0V2k0vLOzcW+O5Ll7FslyP7B/iFxw9tc/u8M+pNi68/f565pQqphMFnPnaCYiEVJUuEzP09OztHfpgjFjfYc88Yb71wkcnzt0lmE+RGctxsrJFUdYYS0X398cP7CcKQ33vpVeYqNRq2wwvXbvLCtZtRmXSHhWG7Hv6mMTDWkyWmqjuOsoSu8a+ffhTL9fjxzSnWWm2+8to5/vzs2/TETRRJpm5bVFpWd2UwmE7xTx69j6f37/kHJxNUWWZfMU/sA7AN8/E4nz91lD97822+d+UGL1ybpMc0u0zbtVa7ey9kzRhffug0948O7dqeEIKxXIZ/++yT/J/ff5lzswvUbYfvX53k+1cnUWUJXVEIghDL87aVn+20H1nXznECr7vJRQgO9hX4xNED/NVbl7A8j9emZnlrdp5iIoGuyLhBQNNxaNkuzx7ez79+9jSrThnHd7CDjXKExVqdf/VXf89MuYbluVuc8zbH+blFzv/Nd7p/K5KErsgMZzP8X7/0LPuLdyS9QgAZgUpIlPE+NtDLs4f38fULl3E8n5/enOaNqTkKifjGOdsOLcflM/cc5pP3HiAmK9u0OVRZ4tPHD9OwHX7/J69TaVv8eHKKV25OkzVjxHWNtutSbra7wtoxVeGX7znC7zxyX7c8UJIExwZ7iWlRojEde3cQXZZM+pOfQ1f6kESMtnubmn2eMNzLbK3KkWIRQ9ko1dRVhSfumWBmuUq9ZXH+xjzXYypP3DeMIKTkrFHQ8yj8/Gg/JlUNL/C7gsK73eGmEmNvYgRNMlAkc9supps0F+v/icrKUupGWenexDApdWNtEhLyRvkdDiTHSKt3Z/n6YcDZ8jscSk2QVLeya/Ynx9AlFVlaZ8xtHRvrrm2KFHtXxpjaOd7xfaYqFfwwZK5Wo8c0o5I+Avbs68VqOzzy+AEMI0rEmXGNIIg0y0ZGexj50qN88+tv8vqrkzz6xMGuNtD9D+7l+IlR/tO/f57J64v0D2TYu7+P1ZU6Dz26H9PUcV2fmKlRKUcaObtNu3LHEKNWbWG1HRRVRumQB3wvEuheLzmXZYlGw+LyxTl+/cuPsWdvLy9+/1KXGb1ZoDsIQjwvSqhvFukOOu/5nt8VxE4kDA4fG+K737pAEIZM7O0F4J7+Pi4vr/D7Z95AkyWeO7Cf2WqNpK7zpdMn+dqFi1xYWOTRsVG+duEiv/vTV8kaMT595CApQ+/KeCR1nYSusz+f5y8vXCKuaaR1HVmSSOl6R0Mr+vd8LdIFzMUMbq2VmS5XefbAPl6cvMXNUpm/vvgOv7B/L3oHVwCIqSp+GHJ+YZHRbAT4f+vKNSzX5ZGxUQxlmH25/5W4doDV5vPY/gJeUMcNytDdh0oIFCShI0sZEtohCvGPk4s9iSplu31oqipPj09Qtayuy2lvfGMsF+O7A7dNz6btbWAAU81VFBFyPJMDEUf8Nyih/ZkAjpquw0q7he15+GGAISvEVJWm67DcaqIIiUzMwNQ0pmoVErqGKsvMNWpIsmAwkcJUVY4We7ttrrOHhBAYikK+U8d/oq+/+/rmyMVijGezIARpw4j+vcNx7yUEMgIFpaPBE6GVG+3sxsJZj5D1CWD7dzt+k4ozRcm+Sdm5TcsrYft13KCFH7od97PVbZ/z/YDrt5Y5uq+/YyW5XTPAUDLdc9tcI6xId9CjO+8nNA0/DKi4dULiGLJOShuIXMhoUnXnsP0asR2EgS2/SsONaMaqZHYAG7Fl4ruTarourL1qXafiTFFxZmh5pY5zWRs/dPBDl3qn3c0RhCG1tsVgbmdLdIiAs/U+N3SFsaEe1hbKXHvzJrW1Br4X0G5YZPJJqqUGhx7Yi+/6lJerNGstAj8g15vh+OOHUHWVXMbsIuF3C0lEDh2O7VHIJ8l3QD1NljFVFVPdvcyp3rR5fXoKz/cZKKQJwpBrU8skYjq6rnBjdYVyrYWqyFi2S8zQSB8fI9ahsEeMizol+wYVe7o7pu7s04a7tPnCoOoKheEeFE0hfB8W9R+OkBBCQUhbxeQkSfD8S+/wtb8/y3Kp3n0tKlG0qNYXuDm9yuT0Cv/yt54ilVRwA4uZ5lvkjQmyWsRS8f2Al1+7wZXJ7eM0Edd55PQE48MR1d+2XdqWw+hwDwJwPJ9yq81gOhrHfhDw9pU5fvePXuT61AoAxZ4kA71pLNvlnRsLnL88u+17BvsyfPLpozsCR6oqc/XmEl//7nkmp1fx/aALPDXbDs3ZEjPza9yeXeOff/EJjh0YwPV8+nLJbSDpTmP/znKXnWKnz5XKTf6fP/sxL7xyjVbb6RwHrbbLtVvLTM2tsbha46mHDvDtH15itdLEMFT2He+n2rI4NhItTG9Or/LNF98GoLeQ4r4TowQdht+d0Wo7vPDTq8wtVTi6f4BfeOxwBzjaiHrT4j/80Yu8dWkmAos6IUkCAqg1IhbY1OwaF67M8eXPP8THHjuMbmjoMRVFVTbc6wRdxuZuocsybhCxCKIMuwfBMkgpFKEwEi+ibSp1W1iu8s0fvL2jcO3j9+/lFx4/TKAErNlNWp5DXyxFMmEwt1jpXodSucn/8X9/l8mpFSzbjXTnOu21LZeXXrux47mODuX4+FNHtwBHhqby2vnbTM2tcfXmEqePjTBQvLtw4/xila996yxrlSbHDgzyhV+8l6nmKn4YMJ4o8vLKFT7Wv7Mj54c5qis1rIbF/nv3oMVUqq7FXLPGcCLT2dhELL1PHDlANmbwldfO8fbcIg0nuj/8IKBhb9c3SOp6VM7suVt0ddZDCMFAOsn/9NHHGTv7Nt+4cJnlRhPb85mv1rccq8oSx/r7+K2HTvHonrEPxBK6M2RJIpd8d8exnUISgkf3jNETN/mDV95ktlJjudHccowA+lJJvvjASX75xBGMdwEuhRAc7i/yvzz3FH9x9iLPX77e1Udy/QDX36mPNfYV8+wp5La9pwmVYbOXuLwBtgoi4ejffOAUru/zrUvXaNgOrh8wV61tOx8I6dGypNTENv0XLwhYa7VpOu9P28ILAjwnoNyKRIx3iqiE9yph6CKINlRffug0tufz/Ss3aDouju9vO2eps4YLwhAvDLbNR0IIdEXmC6ePY2oqf/nWJS4vLkeVBM0Wq83Wlr4azmb45NED/Np999ATj9ZaQRA5JY0XcgRB5PSX2MEl685w/BJztf9KXNtLSj/NWvtlFMlEk0NqTpxXZmY4WuxlMLX+3A1ZKNWoNto0LQfX80kmNFbsVWzalN0y+xI/Xwk1SUhYnrejOPZ6lJ0aF6s3NjHpIp2gG40ZYrLObGuJ/lieUXOAZWsNN/BYscsEYcCh1AS6rHKhch07cCgY0X1j+w5X6rf4zsKPWbJK9Bt5jqajRMGt5hxlp0ZOSzORGCIIAy5UrvGdxZ+wYlcYiBU4nJogJOTtapQsWgeEml6bhfYKduDQ8NqMxQco6jm80GeyMcuqvUZASFHPsS85sqtEiSpJHCkWkYRgPJPpzqeSJHjsyYN89Ss/4Sv/5SVMUwdCPv3L95HJxfnety8wO72GEVNZWapx+v5I47JabfGXf/YqYdCZ4SXB0HDEfH/48QP86R//mD/5g5eIxw2CIOAX/9G9KLvoEK5HIq4zOp7nO39/nktvz/LI4wfYd6CfhbkKr71ynbfPT+P7Ad/+u3OcODVGoZhk74E+nv/78+QLScprTTQjmiPbbYdXf3ydC+emmZ1Z4zvfPMeho4McPT7M8mKNN167ycULM4QhGDGN0/ePMzoWJeSPnxjlu9+6wKNPHiTZ0XWMaxq/cvwYFctCEpCLKfTG29w3fIiEpvMrx48hRKQb96XTJ6laFqosk43F+OKpk937+4unThDXVEYyaaqWhSErTN1eZXWqwoPxXpRAkI+bfPm+U7w1t0AxEefzx49ycWmZFyZvYqoqH9u/l6f3jiOIcIA9PRsGWY+Pj9H2XH7/zOt8+vAhJnJZ/vjNc1StSAhbCIGmFBhO/WOK8U/QsC/RdG/g+iX8MHJ+lIWBImeJKcOY6h5i6iiKlOloOW7EOsMpG3v/4rE/WrrG9doSRseIZcWq83ghB85N0O4H8f5kDHaKnwngqOY4zNVrVG0LXVFIaTqSbVG22jRdB02OKGlVx6bm2BRicZZaDaqWhaYopHWDrBHbArPsWt+6S2w+pla30DQF4wPUh6+3JUJB4AdIsrQt0yvu4si2W3iBw0LrLd4u/xUl+zotv8x6ulogIwsFIRQkZPxw+2Khbbk0mjZBGLI4V2ZgeHsJjiz0bfTaMHTxg9aW1/wgoGbbWF4EPk03F8nrGbJaiqw2TjF2mNuNl6m5c7xT/Qane76IJiW74I8btpms/4BVO5rIc9o4BePAXa+T7de53XiZq9VvU7Jv4gQb9bYSCpKI/ieQCMLtoJgiSaiyfFd7aWVT9kEIgSwL0vkUe+4Zxfeia7k0tUKmkEKSJTKFFLbl0jtawLFdJElgxPWuw5gkvbfrrCgSxw4PMT1TQtMUFDlC6NesNpXOBLiboHcipnFgTx+aKhPTVTw/oD+fQtfUrpVk0NGKijRVInYMRCDkdPNVLle+QcmexA42NgQSMpJQkYSMQN7Sp5qu4ngBnuNjt+o/n+LYUi9C6MAGEHD91jKXri0gSYLPfOweTh8dIZs2abZtzr0zy/M/eoe1aosXX7nG/vEiv/Kpk8TkFNUOkLmeWVZVhS9/7iFmFss0GjbVRptXz95i/g4GUhiGaLoC4YbIuRv4SJKgr1OTPb9U5T//6ctcvbVMTybOZ587yaP37yUei7Jbl64v8Ed/+SrT82vEDJWnHzrA4X19jAzmyOd2zty1LZc//ItXqDdtTh0Z5umHDzDYG21Ur95c4vmXLnNrepVL1+f5yl+f4X/+58+iKjIrlQauF2n0tD0vcrfxXFQ5KquVhYQfBsid8gUhomydLstIQqLtucTVSPBwXf9CliQUScJxPf6/v32N51+6jOv5DPdn+dhjhziyPwLC5xarfP8nl3nxlWtcvDpPqRJtFL0gIBOPkYnHdhVsdQKfrG6iSh9swxs3dQZ7M1y+vsiekQInjwyxf7yXTCpGGMLUXIkXX73G+ctzzC1V+JO/OcPhDoDvewGteptsIUkoQgpaD+8myt5yXY5nh+hPrC8APMLQQoQebuDhhz5pdYPVemhvP//mdz5Ctd6m3rCZW6rwkzcmIw2Z9RCCgpHEkNWug9PoYA8xIypzVBSJfeNFsuloM++4HhevzrNabmLoKiePDHUYo1ujkEt02WfrMdiX4aFTE0zNrXF7tsTZi9MMPH1s198bhiGvnrtFpdZCCHjiwX34qs8P599h1a6R15MUjbvryH1YQ1IklmdWkRSJdD5Ff88oGd2gN5bY4iRnqApP7Z/gxFA/N1bWeHNmjiuLK6w2o4ScIskkdI3BTIoDvXkO9hYoJOK8enu6S8W/M9ZL4f7lkw/z3OH9vHp7hovzSyzW6vhBQMow2Fvo4fTIIPcM9VFMxHd9hsdkhd/ce4T22H5KCzXWrq5yedlCIDBMlVqlxXOFER6KF0ilTaRli1WzSrNuIwTU6xZHwxT/24OPMb6vl9Ge3a+3HwR4fsDnTx3j3pFBXpua5fWpORZrdYIwpJhMcGKon4fHR9hX7OmW6q3/Ztheurvu+LSv0MP/+JFH+aUThzk3u8DF+SVmK1VajosiSaQMnb5Ukv3FPEcGigxnMvTEtwvfO52ysoRqbkkqChFZ0/+bjzzGxw7t48eTU9xcXWOl0cQNAnpMk95UgpFsmofGR6LnidA6LImN9UZ/Osl/+Ownuy536xEQUrHa2L6PH0a23Yas0HBdhIgY/pokoykyqiYzU6+idUqNvSBgLJ0lDGxcfxlDPcx6sm8sl+XfPvsUv3jsED+5GZ3zejlHQtcoJhPsK/Rw3+gQo7lMd67faczFdY3PnzrGE/vGeX1qjjO3Z7p9HFMV+tMpTg8PcHpkkNFcZsu83nJd5ss1Xrs1Q0xVmSqV+cWThykkEzw8McIf/8Zno/GoKRQTG88/gSCu7SemjFCz30KREqSNe6m0F9CkA+TNGJdXV7B9j4lsriuT8ODRUfKpOImYjhBQ8+o03AY5LYu6g+HIhzvCzv7M2PUJpQoFy7f53tKrjMYHUKQYXujzZ9Pf4Xh6HxktRcuLKg0uVSc5W77MqewhNEnFC310NBJKjO/O/pS0miCtJiJAUdJwApe4bGDKkS6Y5dvMt1eQhcQLS6/R8i2OpPagSRpO4BFXDGKy3k3cJ2STv5r7PqPmAIasU7Ir/MGtr3M6dxgZiXOVK3xh5Fmu1ad5pzrJ3uQw3194hU8PPsXdnskhMFOt0nZdbN9nfyfhK4RgcDjHb/+zZ1haqOB6PplsnGxPVFL/yOMHWFmuEfgh6axJf8fdNpWO8ewnTlCpNJEliWJfmkwuzsuXbjG5WOKjnz2F7glcN3Jcyxci983f/u+fpq8/mhezPXE+82sP0AwjJ0ZVU/jsFx5kbnYN1/Eo9EYAaMzUGN/Ty9hEVBJoeR7XlkuMjuf5wq8/zOxMpGVbKKZoNqO5WJYl+gez5HoSPPnM4e73SUKK2psoMDoWMRWFgPimdUE8oVPsS/PAg6OE7ssExEDoxJVeYtoZQALXQg0tCPsIvQHi4U2ENAROk4SARKJA6F5FhEdIS7MIT8F3qqSFj/BzKGGNeCwPyj0sIrO6VicMInfguKnTY5oc7StydWWF3/3pGXRF5iN795AzYxtM+B0ioWuYmsrDoyN899qNCFSMJ7rmB+shSzFMaRxTHd+1LQA/sDtlkjuvO/3AQXT0TreNuTCMjLZQtuEJE4k89/aMdtnjM801PG8JgkUIbeDnBDjqjycomlHmu0u02aTnIVgvXdr6ubD73s6k/iAIWF6pk0wYxOM716+3Wg6VaouB/o2FSKXaIpkwPjBwBCArMrGEgbcDs+f9hh84XK89zxulP6LlrSKQMJUeeo3DZLRhTCWPJsWRJR1ZqFyufIPp5qvdz7csh2rdotm2o0lElbHa9rb+3N6LYYfWtn0RFXQENkNCdFmNaMchGHKKI5nPsGJdoemtcLnydzTcZfrN4xhyGsdvsmJd5Xbjxx29pyxHs5/FkHdeCK4DTW+t/VfeqXwDN2ghCZWUOkjROERGG8GQ02iSiSwZSELmtZXfp+zc3tJOEIYUU3EalkO1bZHZ0bK4oz3SAUlCQhRdkCpG5SshIamC2amTjTIp8Zi2CWxb7z8fP/S7CinSe7jNZmbXWKu0aFsOMUNlcCBLTFHRZLnr5LFTKIrcdYxYX5xu3sDtVrfrBm3Olb/KO+WvYwd1JBSSSh/F2BGynT5VpTiKpCMJhTdW/5BSB+gDSOWTDO3vZ+bq/Lvtcz9kEXaEhhVAZjNwtFpuksvE+ae/9hgfefQg2qYszwP3jNNfSPO7X/khruvzozPXee7pfbSp0aOP0vLWyGiRbo0sCR46PcGDYTTJhWHEWLoTOAqCELMDAK1fx4SmkY/Hu33+2rnb3JhaQQj4xNNH+ezHTxEzNij1/cU0juPxH7/yI2zXY6g/y8efOop6F1ZAq+3geT6//NxJfvVT96LGZUJCkmqMk4eHOX5wkH/3e88zNbfG+cuz3JotsWc0T39PBFaEwO1qmSAMKbVb0YYnFrncCCJAtGpbXZAoYxjEFIXFZoOBRJKaY1M0E9Rsi4lMjoxucGVyie//5Cqu59ObT/E//Hcf4cThoa6m0akj8NCpcf79f3mBn7w5ie9HWmFtx2W5GoHMLcfZUWhbFlFNvae8ux6AHwbUbKtTvBsidea9jzxziCNHBxgZ6SGXNLvMKz8MOHFkiIdOTfC//+63OH95jum5MpdvLLK/mMGI6yxMl0jnk2gphYbXJKHcnYrvBj5TtQpxVevSmIWUByEhC4mldhlZyKTVaCPfm0/yqWeO0RluTE6t8PqFKbz2RoLB9l3mWxUGzWjxGgJXby7RW0wRM1QyKZMvfe4hJAGKJFOptfh3v/c8q+Um6aTBP/78I+ztZBU3h2BDb2E9NFXm0Xv38K0XL1JrWLx05jofeeQgxi5lSc2Ww5lztwiCkEJPgtNHh+mJJfn4wEn80KdgpFE+QDLmZz3CICRbTPPoLz0AAspLVQxZ4VatzGgiysaHhHgdtrEmJcjFTe4zY5weGcAPwo4mTYjXKV/XJBVF6iTiLDsCBd8lwaEpMkf6ixzqK3TbXA+5U5q62xps83FFdGKpJKttmfpKEwUR6bD1pbl9fYmhdIJBPU48ZVCvtKlX26wsVNF0BavtMphMYsZ19o8MbhtTW/qNiHUiSxJ7Cz1M5HN87uSx7nkLBLIkujpRAM2mTbNhEzMjB0rP82m3XRRZwvN8kskYjuPhBwG6pjBsJhk9lOa5fftQVCkqtRQb61FJbG1/WwhYtcu0fZuJ+BDmJk2ya6urVNoWsiLx8L5RTo4NYKoKa22LuKYiCUFS0ynbbd6cm0dXZOKaxt6ejWSgLsscLOQx9KgU0XH9CKgOA84uz7NmtbG8kLSu4wUBSCGqLDOWTKNLEZv/7dJiV0Q2oeqkNJ3RVAZJxDD1e5GlDfa2EIKkrvHIxAgPjg0ThMEd6/etfZLUtq7HXc+P+lZVsBwP3w+ICYXHx0Z5dHSEpuUgyxLNtkM6bhA3NDw/wLY9WoGDpioYmkJcU5ko5JCEoCdpslSt09MR184n4uQTO5d4KFKKdZ2jaI5XcH8AACAASURBVG3nU7VeJ+QAK60mF5YWmchlWWw0mMjmsF2PK1PLPHRkFHMTo6nuNli1V3FDl/5YLyo/P+CRKsk82j+2BbC+MxKqyb7kKNfqU1te1yWV07nDDMaKbF4wDplFHiucQhZSV5NoT2KY1KZyNFUo7EuOkNXSHE3vo6BHzyg39EgoMZatMg2vxUJ7hZOZg+xPjpDTUhxL7yPXYT4JIdifGiO+uHXdn9WSPF44jYTgq9PfpulZVN06MVlnIj5Er9FDwchtK6vcHGEYORnP1Wrk43HMTY6KtZbNW7MLUUINSBoJapbNmavThIQ8sH8E2/N46+Y8by0ucWCwwEBPinm7xeljQ1yaXkIYMm9OzvKds1c5MtLLYF9UovzqtWmmV5vEcjEycYOq5HHh/DVGC1n2DeS5Ua9y68YtHjwwwqmJQS7OL3N0vJeVapM1y8auBNxYKlENLPYO9DBayPLWzTnCph8lznWZGaeJ6/msLDk8cGCYqeUyV+ZWWK40ePzoBKOFzJY5LteTILeDDEqrZXNrcoWzr99kz75eBoei3ZFQxgnd84RCELoXEdppQu82QhlCiASB/UOE3EcYrELoIOkPQhDJQ4TeLYSUJfSnCf0VhNxD4J5DSAVCVCQRMDSUo1SqEwrQ9Y19WG8iwe88cF9kjtB5Bu6kA+QHVVrORWQpSUw9hCRUnhgf55HRUUJCZEl63yYQba9E2ZlEFipNd4mUNoIqxbH9Cgl1kIY7hyolaLjzqFKMpDpC3Z1Gl9O4QRsISagD1JxpkuowDS8Sa9ekBHV3jiFzHFNJYvsukpAYT+QhCMC9xQd2m7gjfiaAIyEErbrFzakVNFVhYrxAaa2BaUTCw9VaG9PUWC01aLcdBvoz5HuSlEp15ubLZLNxhgZzuI7L5K0VLMtlz0SBWs3iey9cYmw0z+GDA/TkEkzeWqbddhgazJFMGrz6+iS3p0vcd2qM/Xt7qTdsSuUmqY5eQ9tyuHU7KtcYH8vjeQHLyzWaLYdCPsHQ4HYacudXEUvG3pVC+F6iZE9yfu3PaXmrSCgMxk9zT/ZXyRkTaJLZQR3XQY+A6carWz6vqQrJhIGqRDpHkiQorzZwbHeHb9uIMPTRlaFIEX5TyJ06UUkILN+h7raI6vVddFmj37yHUz1f4vzaV6m589xq/IiZ5hkkoRCEPl4YZaKy2jhHMp9hLPHwrguskJD55lkuV76JG7SQhc7B9HMcSH+clDqAKpmsiwNHv9/jXOlPt7UjSxK6qnRs6u+WPfBZs29EzC1cVGHgBC0kIdP2Sggh4wUWhpzGDmpoUoKEOoDj11ClOCEBTXcRP/RQJRNDzpDS3t0S1zBUXDdik2gdQUoBXZbJ3dhY7/e9MAxYaF3gnfLfYgd1ZKGxL/UxDqY/QVobQpPi2/r0YvmvtrTRqre5fvYmmvHuwssftgixESLOnYiYJAmeeGAvTz20f5toqKJIPHzvBN/4/gVuTK1QKjdZWm2Q6cvS8ioUjD3d1rqbrE39tpMMlh9EC2Ntkwh1y/VQO05nYRhy5eYSlu0Rj+mcODy0BTSCaNN+8sgw2bTJzEKZt6/O8alnjpFNm52ygSACP+6IseEePvfxU+QycRbaFa7U5ngwvw9T0dk/3suDJ8eZmlvDtl1uTq8wNtxDtdmO9LA6WzQn8BlMplizIpquLisUzDiSEJE+TwheGJXBeUFIQo0sYoMgEmtfbbe4p9iP6/q8cWGKtUoTWZZ4/P69nDw8hLIJ/BIiYrd89rkTXL6xwErHASSXMBnJZ2jZ7q4W5bIQxFWNtn/3+RCgZLW4bpep2G2CMCRjGCRVnbpso/RrrAYt3p5fwg18+sxIvDofi9NXSPHUQwe4dH0BzwuYWShzZCiPZ3tIcR1Vk1GETNu3aPlteo0i6i7OaiGw0mriZjuAojCJaugFfhjQ8m0s397UN2LbmLgzVEnG8l2aXvQ5WZK2aBaFhFwvlRACDhULkW5Bd9AKZFm6Kxi5OYQQ7BktsG+syJsXp7l6a5nbsyUOdGyUt/zWMGRyeoWpuSjzeXCij+H+aGOY1mJcqc0z1VylYKQ4kNpdUPzDGOXlKnbLZvbaAqquMHV5lvH7xskZkf5hBNKGNNwlyvZ1BuL3Y8iRToIsBOuXue7WOFt9kzAMuSdzgqwSrVkUSWBqURZ1N0bretzZ5vsNWZE4enoUWZbYe3iA2dur9A/n0DQFzVC577EDyIqE37FPD4MQSRbke9PIcmdO6GhlvJtmYLjp/9/reTfqFm+8fjNinsR1Gg2LmKlRr7WJJwxkWaK3N40fRBqRzaaNaUbHHT06xOBw7n09B6Oyewcn8Gga7S3A0Tq4YnsucU1DkQQ9phmBOJLUBb40WUaTZRw/QLpDHHW12uRvX77IFz5yCkNTeOHsdfYPFxgf6OFYvq9rqa5KEl4HURYd1lIQhoyms5HYvtRhiYqNjZUkxTCk7Zpi679fkQV36gO+W1yZWuLmfInnHjzEjZlVJAFLpTrVhoVpqPT2JLk9X8b1fHJpE0WWaLZtFFmmaTkcmehjz3A+6h8Rcm5mvlsK98yhd3e+VKQ0vYlPE4Q2kjCw/UW8oIahHGE0UyahRuLhg8kILFNlmVzKpFRtoeY64rkCgtBHk1T8Xcr8PswhRCTC8UFClRRMOdZda6xfm6SS6Iyt3ceLEALC9VX7xjx1pnSB2fYyj+ZPUnXrXRBg87rt3e7JuGyiSWpXeDskZH9ilL+ofI/vL53hROYAvfpue7woZEmiL5FgtlqlYdsRENuJhmUztVzGcj1yCZNrc6ucnBhAkSUmF0uYukbc0Li1uMYjh8Z448YsTxgTXLg9z4nxfq7OLpM2DYZ60gzlMxwb7SMZ02lYDooUtRHTVPYPFjh7c44njkyQicfQVYX+bBLH9dg/UECSBBemFpjoyzFbqhKGkE+aXLi9wMdPHySTiKHIEvlUnDeuz/ILpw7QdlzOXJvmlx46yhvXZ5noy/HK1Sn2DxS4Nr/yvgSXmw2bN85MIssSz33iHmKmTuBcJ/RnQZiE3jRC2YuQ+yD0AIkwrCNpDxIGiwh5CEILMEBYgISQchBUESKNUDIgxRBSkTBsRO2gIEkBjbpFNhvfUgEihNiVgb45bPc2AgXbvYUq96FIeSRJQvuA7HQAN2hi+2UMOYcmJ6k5UwShT1afYNV6Gy9wGIo/TNNdQJczVJ2btP1Vqs4UppIHJFQpgRs0afsreEGbdrDabTulDlN2mnxz5gL9ZpoDqT4Cv8qYOQ47ONR+kPiZAI78IOCb3zmPqsqU1ppUa21SSYOXL11HAPv39VKfsrl6fZFDB/r55ncu8NGnD/PdH1wikzaZf+UGz33sGFevLRISUsgn8f2wk8lvI0T0UHQcj7n5MtVam7Pnpvj1LzxE23K7ZUYQHffaGzchDDmeHOalH1/D9Xw0VeHW7VUyGZPzb89w+uQo3/zOBb70aw8Ti22n3IZhSK4vs6Pt/fuLkJv1H1Jz5wHIG/t5uPgvyGg72+uF+NtK1RRZwoypVKpNZEmQ6UmSzprohgr2js1023L9FXRl63e1PZeFep2UrhOTdbJaiopbx+4AR7JQGU08zGLrPA13CUNOY6r5yNJSxEioRXpjRxiO309KHdyRirceXmhxvf69TmmaYDh+P/fnfwdN3jlz5IfejhohjudRbdnkEhKp2N1unsie1w6qCASSJBNTsngdpNcL2oT43VI5RTKIyTlqzjSKFKPmzKBKJj5uRLtWe7fVr+4UvcUUyys1dF0hkza7E1sxniCm7F6q9kEiCD1u1L+H3XHMGzRP8WDhn3ad03Y6/s7yP6ftkMolyfVnuqV5Pz8hE4YN7kTnM8kYj923D3OH+10IQS5tMtCb5kZHB2Zy5R3GslDQJ9aPen9nIUkRoybcuglq2JELpOcHVOuR0GvMUMkkt9vkAhHjsuNOtrrWwOoAxl4YcKW6SExWGY1vLV199PQeij1ReakfBjQ9e2OzocgcmIj05MIQVkoN/CCgkEkgyzKykDiSL25pb3MpSLTx3dp3dx5TsSzu6zNQJImqZfPmxWkATEPliQf2bgGNNrezZ7TAwT19rKxFmjuu71NtWSQM/S7gNNQcC8t3sXxvi9PPnZHSDPKJBG6nnE6TZbwg6Nh6R5mriXQEbOjrJabr5zYSbWw8AmoNC8ty6R3OMbK/n1jcwA1cFEnBDdy7jhRFihwnu1oToYeQ8gipHxkJTVY7s9h7D0XI5I2NDZYZUxnoTaN2Eh9Vy6JmW/SY5rsyVN5LpBIGTzy4j4vX5imtNXjl7C0mhvNdkHQ9HNfn9QtTVGptzJjG4w/s7eqzTTdXebs8jRU4ZFqJnzvgSDNU2g0LISDXn6FaqmMHHrKQNrE5BKoU6yRjdp6HvdBHIJHWUiib9BVlSSJ+Fzt5AN/zqVXbpDpuPY1am2Q69p7KsD3XZ3W5hhHTSGdNkumNBNS+I4NI0gagGU++PxdAgMpaEzOuR+W8/8DIZOOcOj2Opsm4btCdi8IwJNZhfWqaErmq9qYplxqYcZ2YqZHp9M2txgK3W4vcmz3I95feYMWu8isjT5FWt69V0mqS45n9+KG/hV0BsD+f3zIXbmYN3/n35gjDkJbtslppUGtarFSieXmt1iJlGqTjBpIQxFWVSqNNpWGhyhLFbAJVlWm0HZaqdYSg69xbqjVpWS496Ti6LrNSaZJPx6PkYz3STtRVheVyHT8IKWTiGJpKtWlF16jeJm5o5DNxmpbDWkdvsZCJI0sS5XqbWtNiqdyg2og+M9qXxfF8ejJx2paL5XgUsnH68qmojB+B5USOrq7ro8gSybi+6TkCdcsmpmnvOletm4IIJCRhIAkDxy9heXPkzWeo2zYN2yZt6KQNg5H0OoMFStUm//FvfkImbiAkQT4V57knxphuzZHV0ndd136Yo9Gy+ZNvvMaN6RXiMY1/9quP05dPEYYhFbfOfHuZmtdkrr3EsNnX0Qba+Xl05yO57dus2GXqbpOF9ioFPUtezyIhMOUY5yvXmEgMMWz20fYdBIK622TJXiOtRutXWUgYksb5yjXG44MMmb3YvsOqU6HutZhrL3dZ/DutCZzQw5QNHuw5TlzZWSR7cnqFv33xbWzH4zPPHEfLaJiaxnA6jX4HIJFNxCKjm5iO7fqcnZzDD0MShkbDckiZOiOFDHv6cpy/Nb+hAxYEtJ1IxzAdj5E2DQrpqMzt3M15XN8nYeg0LYeW7aDJCuPFHEKKQLZ0PEYmYZFPxfGDdZObkJbtEOuwoiJAKt0t0crEY5ucaaMxva8/zzvTSzieT9qMcfbmHAO5FMX07qXJd0ahmOJLv/3Eltdk46kdjhSg3sPWeqPN/wbCGEJ/Dj8AVdta7RFJcwS0bQ9TkWi3HcIAVlfr2PbGHubWXIn/9Kcv7TiXZlIm/+Tzj1DMJZGlFK67SBi2sZyrGFqctit3np1RHwZByH/+2o+5Mb2yrS1ZlvjIQwf42MOHuq9JQsUPbFwRgTwtFLywTdNbwpB7CCQPTU5iqkXaXglNTtL2SyTVoY7TbIgbNCIiiVCJK0Xa/hptbxUnqBESMtNcQ5EkGq5NyW5gOQuMGRLIY3e9Tu81fiaAI8f2uD65xNHDQxR6EqSSBmOjec68fpN6w+ITz93Dm+duMzbSw72nxrh6fZHpmTWmZ9bIZeMMD2bRNIXZ+TK/9KlTFPLRgzhuavQV0xw60E9fb5rJm8usrNZJJWMsr9YxDJWhgSxhEHLkUFSnqOsqxUIq0t9wfZaWazzx6AEMXeH5Fy6hajIH9vVx36lxLl9doN12dwSO7LaDYwX/4E11EAasObcIO2DIgHmShNK76/FeYGH5292aHMcjl4l37NNB1d790ktCJwhtvKAMbIBHmiQzkc3SF08QEpJRE6TUOEnF7JyDzdvlv+BW42VMJcdDxX/BgHkyoqIS2V7KQiWyuLz7xOMFbcr2bQBkoTASfwBV2l000/ZreHdoMkEEnsV1lVz87mI8AoWsvpd1JzmBhECKytS00U1HrmfpIre8PvMUApmE0o8QUlfPZrfF/J1xe7pEo2lTWmvSk0swNJDD9jzm6zUmMtl3b+B9hBc6rHX6VCAxHL8fTdo9K2cHDZxgq7hocSRPvdxCSNK2h/+HOwSSPIIkDxD5Zljdd5IJg7Gh3bNPiiyjd8ptfD9EDVMYUgCsuxK+v5AkwVql2WU3CSDW0a5az7zJ6yVRftDVF7rzYe55QeTgRSR8LUuR3lXVabNi1Rkwt5eJrgNDAH7o0x/LdOnaQtDVrQmJrNnbttvRzNn5d74XzbnNr6c3CfZW621WOmLkPdkExZ7dBe4NXWV0MMfLr0d/rwNsOwlDr0cQhvSbUZu6fPd50ZAVCub2e2Xzhi4Mw6ikJ4h8CW3H65okdL+zw6yYurpIrjdNIh1DIOjTCxiygbIL2wgiRkKk6ddpT0iE/gwg4Yo0mlAoO3UaXpuEsl1f5W79EHTGVaXaxna8blZRliSKiQQJTftvZu1677ERBnrT3Jop8dOzN3nuySP0FbZe20qtxWvnbwMw1Jfh+MENPQFJSEwke5lvreEELhUnGiPruhgf9khk4pipGH3jRWRZYnBfP03hUTDiqJLcvdcloZJQB3Y13dAkDctvU3bWGDFHup8zFIWHx6Pn+m79Va+1ef6v3+CTv/IAmq6yNFfGTOjvCTiyLZczP7pCGIZ84nP3b1lz3K3U7L3GD/7uLe57dD8je4q7HhOEAX4YdEv2NCliHLthJO6uSjISEpou09MX77r7apLSKfHzO+BctFZZ/zvVo6NKCook44U+duBSdurMt1fRemQe7DnMV6d/0NUyCjtt+WGAIiJwPaMmcQOvwyr2UIRMCLiBS0i0AVaFsu3a7Hat2rbL3//0HRZLkWNQs+0QBiHXZ1f4ux9f4jeevZd8JkGj7fAXL55HliJh9Y/dfxBDU/jLF89jux66qvDsgwdZqTT50Vs30FSFmBYd97UXz/GrHzlFPh3nr394gdMHhmnZDuduzCMJQT5t8omHj/DtVy5Tb9toisxgIc1DR8f4+5++Q61pYbse9x0aYe9gnj/93lk0VWaxVGe0L1rnJOORiDAE3fLajTlHRK+zrscZdl9bPx4CErqCptABJ4NdE28hDg37MoYyyELjLxHIeEEVRUqA+QxyJznQ9jz6NiUUdE3hHz1+HMuJnDMlIaEqEs1wlYnEGCv2KlW3Sk7L7iqq/GENz/O5eGOBs+/MkE4Y/OZnokR1QMhsa4mF9ipj5gBXardJq0nyeoYHckcxJA3P9RGSIAgChrRefOFDGJEHHNtlqV3ikjVJv1Zg1apwvTJNNp9GAM/kH+BSfZLp2iL9WoH7M0d5c+0dFtslHsmcJGekcB0P3w14LHmaS5VJrLJDIm9SCipcKF+j6OR48+ZlxLhgLDHAqewhdEklBE7nDpNQTCYbM4SEnCm9TdNvsS8xylPF+zYY+EHId35yma//4AJ+ELlWf+4XTzNXrWJ5HsV4HF3prNmEQJFlgg57T5aCCHSsNWk5Lj2SQJYkVFmCzrGmHmmWfe/cdVZqTaROCawiSyCiuUSSBOVGm7bj0JM0ySVM/CDg669eZKw3x5GRXpKGxsxKhVevTnFyYpC0GeOF8zdYa7Q5PtbfKbUX0bothIblcObqNLeXyrw1OUdfLonS0aNU1lnuhFiOh6YoNCwH410SD3eLSt1iamGNgUKaSofgYRoaa9UW6YRBpdEmbugoikSjZaOpCs22TUxXURUZPwjp60lyc65EMZug2rSQhSCXiXPl5hL3HhkmlYqhqBKJhEk8vrFPb7Rs3rg0TRAEBOGGUxxE5jJftO7vaPsKFClHEFrEtKM0XImrKyv0JZJd4AhCrt5e5uw7M12TkyAMCcPomh0Y27pfN5UCY8mPErEyZZLaCH4YIBFuIRlktD0EqockFDLans5eU+p8Y0hW34+EjBAyRtCLH15Ak5P4oUVWy1F3LSbrK6w5TZ4qFMG/AooF7L5+fq/xMwEcabrCnoki+Z4EibhOXzHNjcllEgmDVCrGO1fmCYOQyVsrXaGt4aEcI8M5Bvoy6LpCPpegvy/Nm+du01tMMTaSJ25q6LrC5auLyLLMWjkCFJJJI7KnDyO1+ZVSnavXFxkfzbOyWmdxqYokCYYGcxQLSS5dnkPXFVIJo5PtFN1s2W6WjXpMwzEDtNg/rJQnCF38YIMWZCq5u2Yymt7KNn0fiFgC88tVRgZyLMysIUkShb67O9kAaHIf6g5AlaGoGIqCH/pU3AY9Wqr7O0v2JNer3yUIPcYTjzOWeOQ9sW52Ci+w8Tv6DOvaTrsyB8KQsnOLplfa9p7rB52JWXrX67HTuXa2Tzu92HmnM4l8wEWCLEs4ro/j+ly7sUzM0FDiCglN37bp3Xx+Ae9fQ8sPbYJNrDRTyd+1TyvODE1vecvrPQM5WjWLWMJA/QCWyT/LEYnNbQeD4zGty9zZ+YObh0RIXMmR1Q1sv4ETtDDD7PueC4IgxHH9TotguR5Nx6XleqQNneGBLIoiUW/a3J4rcWhvH7K86SzCkFuzq1TqUbnYYG+my5jyw4C8kSCjmVtq+FVFJrOJHRAtzXffvISAqauUqk2KuQTpxAY4u1yqM7NQxvfvrh9kaCr7xotdNsnm9svVFrYT9UEuY26pVb8zVEWmJ7uR4dcUiWwiFoG8u3xGEgIvDFDFeyPi73YNW22H+aUqN6ZXuDW9ylq1FTGLbBfH8ag1LJxNJSVCQCKzGQQXlN0aRmCTUO4OfiiShKFs6gcpBULuZH2bFPQ0uvzubkLrIUsSMVnrsq2yWZNypdUFw2QhqFoWubu4fOwmMLxlrGx6rS+f4sET40zNrjE9v8Zbl2Z49onDW46/eG2emfkyQggeODFGYZOg+2i8wEAsS5+RxgpsrtVvk9ez72rX/GGKzQCNYepU2w524G/RVQgJcYPmrm58buDSHxtAQsINNo+/3en6rusxfWOZWrVNq2njuT5rq3XkzvMzDEPKpQYLM2vousrwRIHVpSq27eG7PiN7isSTBkdPjXLh9VtAR3NyocraSo2eQop8X5rVpSori1USqRhDY3mstsvc1CqBHzAyUcSIaczeXqHVtOkbzJLKxlmcXaNRa1OrtAiCu88rM61lXlq5QEzWaXptPj7wILbv8sLSW7QDmz2JAR7NH2OyMc+Z0mVKThVFyHxh5Gnm26u8Vb5BEAY8mD/MwdQIfzf3UzRJZckqczQzzvH0BN9aOEPJruIEHjk9YmkasrZlTi05NV5ceouyW6fXyPF08SQVt8EPls5iBy57EgM8nj/OVGuJHy6fIyDkaGqMRwq7i8bfGWu1JjdmV/mtT9zPYqnGX//oApIkuO/gCJdvL+N3NkaW41KqNnny5F72DuXJJU1evzJNy3b48iceQJIi3aq/+dHbnNw/xL0Hh/jjb7/BjblVerNJLk8tcXCkSLVp0duT5P/9xqscnegjHtP53utXuf/wKI2WTV9PkmcfOAjA9FKF8zfmefLkXpbW6vzo3CQty0FTZX7to6d44c0brHa06KIyvTks9yaylEESKkFnzaLKBWxvGoGErgzjBw0QAtcvIUsJJDSC0OXe8TVazgqyFMcLFnCDewhJYAdtEkqmWxolUIipo/hhG03uIa3fi+Ov0PamOu9H1tearGy5VyQRPexfvzLDWF+W43sGWK40MOUY1xrXcUOXFWuFlJJEfg8lMT8PIQuJY5l9HMvs2/beI4WTLM2VOfPmFWJxnaCTZIrFDVbHqx3pDA8zoXNcP8DUjSX6hnOs3arzjnkbVVOoV1s8tPcE1y/OMZldwHU9Cn6BVDbO9I0lsuMSM9oSa0s1lufKDGT6cNoub7hXKA5mGWj2MxEbod2y2bNnmKRm8kDPxv31YM9xbN/hYvUGzxQfYMjs5dXSBWbbiwSEW8r0WpaD5/uEIVi2R61j1OT6Po7v4wYuVbdMzNQ4sjcSrxYipOq2MZUYUtlmMD5A1kwSUwyKPRqWqPDEiSFk3eHxU0X0IMneMYNUUsYWDe49nAOtyVR7gRN7+9jT14OqSMR1jfnFKr1ajOW1BjcbyzTX2pimTkGNoQYSt6ZXefLIBG/fmOfoUB8yArftk1V13jg/haYprFWaWK7LsWKRudkKfttnXy7H7ekSTx7dQxCGVJsWTx7bw/xajfO3Fnjmnu0lqztFEASslJs0WjaDxTSGrnLxxgKVeou55SqphMHe4TzXp1foScV56ewke0cK3JhZJR7T6M0lefmtm4z152jbLvfsH6Bca2HZbsTu1hTqyxYLqzVOHRyOUrVh5FDneQHttkOz6XSc7WCkL8u/+uJTLK818HyfWsPihTPXqDU2EsWR0VITIVQkYSKERhjCYqOBJiuMZqOEqxCC3/jU/Tx53//P3nvH2Lae532/b/W9du/T65nT6+2VvLyXvYmi1SLKMq3EiGIIdhQjQWIjBQkCJ4agQIEFIZYjUyKliJItkWIRSbFd8vZ+ej9TzvTZs3tbPX+sPXtmzsw5916ZCBhSL3BwsPesvr/1led93ueZodnuUmt2efPyIhdvrGzffxCwaTWxfQ9TVokqOtcb67RdmyEzxVyzxGAkxUAkQdVuYyrhuVquhSJJOL7XS3qEBgYJ1WC5U2c0mqHSbXKlvsqx5AwJVUGTYyQ0mQ8PH2elU6NgxJkwuuAPgHjnc8J7xY8FcCRLEp/4yGmu31zD83xkRSKVNPngM8eQZYlSqcFcyyKbiaGpMh/70EkK+QSf+Mgp5uZLvYFO8MxTR7h2Yw3LCul9mqbwwOkJvvf9S3SbXQr5BJ16l5JU44FT4ywvlrHaNpIbcP7sAoYqc/nKCul4hPXlGvOzGwwXQxDLczwmRrKYUZ10JqTqvu89h4nF9qdYB0HAjTdmyQ1l0P4jRLYloaLsYNi03BJ+4O0BNwICPN/mYz3aGQAAIABJREFUau2v97WjF5Loa1Dohkqz0bmnw9hWtJ1rRIWKJuf63ymSxFqriSQER/QsQ5EcUWVrQRHQctfpeJU+U8f1w8a/JX73bkKRjL7bWYBPy91gO8u0+/5b7jpXql/f5bi285qXK3UUSZAy33kW/v+rGBvJkEyEz1AA0ahOO3BpWBbF6PZCWCAw5C3AL6DhLOP5NrL0zjsERRgoYqvdBvd8ph2vzNXq1+h629a6nudR26iDCAeELffAn/TQdfVdsS28wGWlcxldCvWSUurw2+6zM4IgBBQ3y006HQfdUPADn3zUpGXZpCIGTz44w/dfvM7SWpX/8I03ySSjHD84iKYpuJ7P8lqVP/nyq1RqbVKJCE8+eICoGbYVWUi9evzd/YCqyLsAVlWS6Xj2PVk7nh9Qa+7tU158Y5Z//Uffp925ty30UDHJb/13n2Z8ZK/bY8dyeiwriOjqPZ0vJEnsElm2HI+lco1Gx2aymLlrhsz2vR0U7XcXrudz4eoSf/a1N7h4bZnNSguEQFPlvkuiJETP5XB7P8PUmDk5SjIbgkQiCPs4y7tH/TBhqfB6u4nlbT0rBaEcQog4Wziyqeho92At7T2mzfX6GlPxXM990cfpCeoCaIpCqdkiE4lQjO8FZrzAZcNaIcDHlGO9iZtP128TUxJ0vDayUHrjlENcTaNrOk88eIBvP3+FjXKTH7x6gycfPECsZ2Rh2S4vvH6LRssikzR55MwU+o7fb61bxQ8CDiaGaLptGk6MlBr/sevbf5QRkVUqVuiKtRVB4GJ7DYJgf+BIl3vP07eIyMbbVswGQcC5V2e5eWWFdDZGvdomAGrlFj/81gV+9Tfej6ar/PBbF/Bcj+HxHMXhFF/+4xeZPjJEu9llbaXKo+87suu4a0sVvvmXb5BMm5TWanziP3mU1cUy8zfWWZwv8TO//Chz11eZu77G2HSBwlCKW1dXOPvqLTRN4fXnr/Po00f44bcucODIEJvr9T0GH3dGx7OYba7wn01/jJQaQ5dVPj/3LXJaiqnYQb609DxDRpbXyld4OHsYy3O4UJ+jaKRRhExGS3CpPs9Lm5c4GB9lsVPicHyMXx5/P5IQ3Gwus2nV+bnRp/jhxllqTmvPNQRBwHMb57F8m/fkT/H1lZfJ60nsHoj3iaFHicoGkpC4Ul8gqyV5LHcMU3l3ehS26yEJiEV0cqlYKNi9T+SSUT75xHFeujjHK5fm+cVnzlBrdsnEzT6T1HJcWl2bfCqGoakkojq1VpeTBwZ54fwctuMyOZRFVxVqrU7IsAwCPvH4MWIRHQSMFFL9a+hYNl3bwXJcCukYx6cGKNVaxE2DiK6RT8Uo17efnSwlCF1NA1R5gI5zHc+vEwRdHG8dVc7jeBu4fh0hlJCRH/i4QQWQSJoeiUi0x1ZyCWczPqvdWdpugwFjgrwxgiwUVDmNHMQpRD+OLJkYwTARbxKA2/Uazy0sUIzFOJzbngO3LYevPH+xX/pzZLzI1168xOOPZ5GEhCY0JmMT/Xfv7wJWFjZ7LqJd4mmTiKnRrHV44W8ucvrREICoV1pEojpD4zk81yORNlmaLSHJEodPjdJudvEcj5Xbm5gxg+P3T3Dl7AKSkLBtl5HJATRdpTiSIRIzsLsOruMRTRhYXSdkXCsy+l3kRWQhcyQ+xQ833uybeTyYCY0lHN/rs9+eemiGxbUqvh/woSeOMJJOsdQKdZYMRWG+fYuV7hJ+4BHrSUBoko4jd1BUnVRKUAsWaDkqo+o4a94iLbvJTOwwduARaE3yZo5rjRKz3Qqu7xBXEkiBSdkuMZgYJr2DnXu9vU48YmAoSt8ttdHsko5GSJkG65sNUvEIpqISVVUqtTa6riAjSKZNNspNZFkiIenkYiaaqpCIhuWtEJawOZ5H1NC4OB8CaY8dnnjHv32jbfH7//55rs9v8D/8Fx9mejRPKh6h1uoyMZRhtVRnvdxEUxXWyg0ySZOBbJx214YAyrU2hUycXDpKqdpks9ZivdxgcjjL6madIAi4vVpFVWTK9TZr5Qb1VpdYNCSfeJ7fT0oCJOMRHj45wdJalYMTBTw/4Ny15d3AkaSjykUkYSLLGYRQEITOlqLH/BJC0OraxJMGT44eIBUzkCWJz33ppV3AkRf4vFKaYzKW5Ua3yXQ8z8sbswyZSVY7dRpuFzfwebO8gBf4mIqGJilk9SjL7SqO7+MFHpPxPLcaGzyUm2CuWQZgqV1ls9uE1BCGsi03MhHLMRHLsd5tsNItMay58CMS6/+xAI4AkokID5yZ6H/emfGORXVuL1UYHEjywH3b2xTyCQo7Xp6AgNMnQiHircljxFA4fGCA1ZUqlc0mk2M5CsUk7bbF7YVNhBAUsnGmpvOoqoImSxw7PESnbSMBrYbFUDHJwcOD3Lqxjiwgnwt/nMnx7YHkzlB1BT2qv22m/e1CIJHRJ1hsvYyPx1LrdWbi7yejT7FzBmj5dW7Uv8O1+rfYr1yk3bHZrLaQJEFxKI1thR3q251d9LI9QU88MaRc+0RVDVkSqJLCUGS3m44uJ9CkKJbf4Gbju3iBQ1IbQdmBdgohoYgIppIhq0/3XdXunPQrkkFaG6ds3cQLXOabLzIZe+8O8ARC8GOTN8t/wu32K/veie266IpMqdGmZTnEjLsDLXfWvW7f+/YzuPNad2fZt/ffYqTtPGZopejjBx5bXI6IoRAxEruO1WqFFr/2jkWCQCaljSKh4OOy2b3JUvtNhqP33eHetkMF445nKguVtD7BhnWVgICF5osciD+NIad37d/2ypwr/xnzrRd27W91bF79ztle6xM8/qkHMeP3LgH8SYh3W6KjCI2MPkbbraBJ7/75CAFmREMtJNENhQBodG3Wmy2ODYYswAPjeT75/pN87j+8yI3ZDX7r9/+Gk4eHyaVjtDo2F64ts7BURpYlPvjkUR48Nd5nDJiKxka3QUqLgB7sOu/ONqMKuSc8ea9rFcRMncQdluxCbIm93vtetzK47+zBvM2xdojnGppCJmqGJQd3KRsOCDNC+61Bg2C3i9Sd4fsBF64u89v/93eZXSgRAFNjOU4cHmZ4IEUuHSUa0dF1hfnFct91D6Dbtuk2u8R3sI78wA9ZJve4x6iqUYjFt7WYAge8dQJFQxYRdElFk96dLpoqyRQjCVJaeC35bBxJkjB3MGYlSaJlO/vqAtSdCuv1W+hyhJiSpOu1SWt56k4FVdJQJZ2qvUFOH6TlNmh6DSbMGWYm8sxMFtgoN7l4dZmltWq/THKj3ODitRWCIGByNMvMHa5tru+x2q2RVE06fpeKXUMRChHl3evl/P8hgp7uxUZnd2LED1yiSvHuGke+S1JNoQg51CnbmyPYFZ7nc3t2g+NnxskPppi7voaiSIwfKPLGS6F2mKxITM4UuXF5mXbTIvBDkPvkg5M06x3Ovza7Z36xtlxlaaFEIjVGtpDE7jrMXV9D0xUatQ6dtsXIRI7Sep1mo4vreMxeX6NWaTMwnCadiLCxWiOdi/Po00e5cXn57Z8ZkNUTFPQ0iiTjBz632xvUnTabdp1BI4Mua+T0JK+Wr6IKmdFInq7n8PLmZRpum4rd7JecKUJmKjaIqegEQUDDaZPWYiRUkzGzyMX67L7XsdQpUXdaYbZYMYkpEQaNLEudEn+19AIPZQ5zMjXNfekZvrn6Kn+19DxP5E9wNLm/luN+EdE1/ABWNuuslGp0elp2/lb5hB/+sxyXfCrKxx49yp999y0WN2oMZhOcvbHMRrWJIknomkIxE+P67XWySZO1coPDY0WGcklaXZuLs6u8/4GDRHSV0WKakUKSoxMDtC2HaG9utbPvScUi5FMxjowXySZMtkxaLs2uUqq1uLlU2pWYkEWMmHYKhIRAIaaf7BkvwJYRgBAyQeCxbUcthZ93/PZbIQmdjteCAEwlTsurEXUTxNWeO2HgYHsl5CBK3XqDuBYyUYbjCU4Ui3Qcl7y5/Vs4roftejx5cpJzN1cQIvzOkAyGtSGWO2/fNn/aYuxAEdfxSKSjGKaGosqksnFUXSWdi3P71jpCCBIpEz2ioRsqNy4uMTKZQ9EU1leqRGMGhqmRLSaIxg10Q2Xq0CDzN9fJDyYx4wZmTy9tZ/n4vViwO6PS7WIGBY6YaRKajh+A48jcqm1Sty3SegTH91ByKj/7C2eYTGQYSiWoWl0GYjFWm01sz8Pv9bNxJUFMSfSS/RKu7+AHPjm9yKa9Tttrs9RZIEBgSCbr1hoJJUnFKSM6gqZbx5AjpNQMm/Y6k9EZZCHjs3tdeeLQUFjWuaNcfuseZVlibCiDokjkMjEkSWKomESSeqYDssTkSPaO90X0zS+C3txPU2Q+cPogtuuGGj/6O09WVxsdzl9bptuXNIDDEwUmhjPoisLEUIYt50XXDRNWiiIxkE30S/8lKZRm8PxQwfHIZBFNVRgbCFn3k8PZ3jYSk0MZDF2hUesyNVUIHTHvWItrqkz1HgSKIHBoWa/1ZUsUI42hRkjoOpq805hFUG12WK82OTE5QHIfOZSAgIisMhrNsGm1uFxboe3ZeL0yZXrApNsDJ3N6DMt3Q0aR1Sauysw2S9xsbGB7Lg3HomQ1iKt6T+tyuyZgobmJE/hcqoZ90GqnxrG4YDi5CurbG8C8k/ixAY4CwkHY9h1M2aDhtkkoJh3PQhISh44VaLptup5Fy+2iSQqSkGh7XaJyhJbbIapEqDkN6m6byegQNbuJmdI5cnSYw0eHkGQJWZKQZMHqcg1Nk0kkTVzXw4zqCODM/ZPouoLr+kiS6DOgIhGNQ0eH3vFEvNu00HT1P1rjSAjBROxJbta/S8NdZdO6wbOr/4rpxDOktXF8PGr2beabL1KyrqEIjdHow6y0z+EGnf5x4lGDTDKKooSuJtsXeq+zh/X+treCGRxECAM/CFis1zFVhVxkP3E0QcE4wnT8aa7W/5qOV+Fy7a/2PbokVDTJJK4OciT5Cabi790j0KwIg+n4Uyw0X8QJOiy2X+P7q/8b47HHiClFnCDUQJpvvkDZvkVMyZPXD7HceYudU4dMzOTE6ABBEJav3C02qy2aLYuIETIbWh2bUqVJPhNjfbPB1GiuV48bIkm5TIzYjvKltc4FZps/wPHb2H4L22/j+B1q9mJfp+pm47usd6+gy1FUYaJKETQ5ymDkNGPRh5HF9sSrae9megghyOoz5IyDrHcv0fY2eW7ttxmOPkBWn0YSMo7foePVgICT6V/AVHbr8khCYSr+Puaaz2H7LZbbb/G9lX/JeOwx4uoQbtClas8z13yBsnWTiJwmbxxiuX2WAA8zFmHi2AitaptmtfW3ke/5qQg3sGk6ZdLaEKby7lx3IHyskYhKsZDo6xKZmkrTsncA4yqf/vBpJEnwB3/+AmulBt954WqYPZMkYqbOwckCTz92iI8/faLfVoUQdD2HpBbBkO+dhXB64tj3YhwpsoSyDyX/5OFh/slnn8Jx7w2gR02NTGr/RVLEUPvMl661dwKwM3w/6It/A3Rtl0bXIhMz76qn5vgeEVnZV9/Ist17MjPL1Raf/8uXuTm/gaYpfOS9R/nlTz7IQD6BLMu7ALMtO+qtUDSFdD6BpqtsedFVnToF/e4JCQDLdckYkVB3pVcuHQRVhNNGlmaIyKE73bsJx/cwFY2oEorMuq6PZTm4ro+qynQdl6xpkjD2z6BHlQSHU48hhIQswhJmRSgU9GEQoAiFkcgkkpBpOFUkISOERMTQeOaxw7zy1hzlWovnXrvJwckCQgguXF1haa2KEPDeRw4SuQPs1ySFa/UVbrdKpDSTmUSOptcmz49WE+7HKVbaDbKGiSbL24shgh2L570RELBhrRMEAePRibc9hyxJFAdT3LiyTK3aptuxsW2XtaUqlVKTpflNxqbzGKbOyESes6/cYvrwIJblcOXcbayOQzoXx+raLM1vUlqvs7pUIZ2NMTqRZ+boEKquYJgaq0sVHn3fEa6eXwwZyZLE5MwAl96a5/atDYbHsriOy5HT4+i6giRJXD2/yMU35qlV9moZ7heS2GZPCgRDRpajyQnuSx/E9h0iss5KZ5NrjUUOxkeZiA5QdRpcrM/y2cmPcL52i0u1uT7etrMEzVTCuWrbs9iwanftI4tGmlEzzwcHHsTxXQxZw/E9Pjn0GLOtFb69+joz8RFSWpyfG30vF2tzfGftDY4m3/732opc0uQ9p6f42gsXySSiHB4v0OrafPWFSyysVag2O7S6FgeGc3zj5StUmx2yySjTwzlMQ2V5s84XvvkauqrwM0+e4EMPHeZbr1zlC998nWOTgxyZKKJrCgeGcyyuV5kYzGBoCp94/BjfeuUqL12c5+BonmceOEgxE9+VnBvMJXny1BTfeOkyCHjPqWkOjha4urDOF7/zJglTZ7y4XcothIQQ2wCw4C5Mub3Tz7tGx2tSMMbwApeIHEeXt49ve+tUOs+jymna7jyOV2Uw/vN4QUDDskjoBvO1KicKIahtaAqFVIzvvH6d9UqTVsdmIJMgYUQJJJeYGkN6h/qWPy2RycdJ5w4Cu4Gb0akwIZDMTO7aPggCTj0y3d9+CxSZvmMdFonq5If2Tzy/U30w2DbJqFk2GcPEcsNEjiYLZutV4qpG13NZazexPJeIphIxw7lJ3bKw/dAhNggCJqLT5PUCqqSh9Ji2Aomu10aRVFShktcLuIGL6ztoko4buKhSOBeLKQl0WWfAGOobGgwYQ0SVKKZsot5RcnQ3duFWbFWbbDuf3tE23+E61dRVzL+FNMW12XXKtTbmDtaPosjEtq5rh/v4Tsfid3JZ/Xva8Uh0TcH3A+bmNpjrmVplc3uZ0oosYbvevo6wAhlNGQMkFDmLJExsz6XetUJn4x6LvNG2SEYNoobeB813RrNrsbRe45A5gKlonM6MUrM7HE8PQ0BfwN/1feI5nZZrY8ihO3JEVnkwN4EAjqUG8XrufxFZJa2ZJFSDtheaqWwl/UxFZ761iRt4jEWzuIGHJCzCzvFH4/b4YwMcWZ7N5fotMloSV3G5UL9JQolSc5oMRwpIkmDZK+G0bG41lzBkLdRkkI1+p2L7DiNmka5ncaU+x+32GgUjzYOZY7vOFQQBo2MZFFXe1ZEEQYCiyz0RMPr1hH7gIySI9MS1/J67kON7qJKyLxPBcz0a5SZ2994lGu8kcsYM92V/ldc3/5Cmu8aGdZWNjau7thFIRJU892f/ATljhr9Z/h+pO9vA0cxkgZnJu4tI7hcCCVlKoMl5JGl7wRk6CXn7Wog7fof1zmUcv41AQkJBlrRdi7YAIPDxcOl6NbpejYo1T8NZ5r7sP0CRthcnkpAYiT7EsfSnuFz9KpbfYKH1IgutF++4Vpm8cYj7s7+Kj8/q0vld+j+qLJOLv332zvN8ltdq5DJRltdqpJMmtUaHiKESBHDx2jKxqBE6AFZadLoORw4M9Pdf617iXOXP7nmOjleh41X2fO/6FsPmfX29JMfzMFWFruviBQFKr51FlRxnsr/CKxu/T9Wep+GucqX21T3HS6hDHE1+Ys/3QkgMm2c4nv57XKr+FV2vyu32K3vYWgKZrD7V/03Wu1dwesLjyVwCWZIZPzqKcZdyzZ/2kHri76XuLFJEwVTeXlNsZwhgdbVGvd4hlw0HvYFEnPtGRb+sKggC2h2bucVNJCF45PQkzzx+CEkSqKpMNhVlYiRLMr63PFOR5B5ocm+gQZNk4qpxT8tcRZZIxyN7yr0mRrJM7FN+9m4ik4r2JxOlSnMXMHRnOK5Hubq9oJSEoNLqUGl1ODycxyQckAWiL2QoIajanX3xz3K13WcI7Re3bpe4dD2kJB+aKvD3P/0wg/n9f+dm29rF1vEcj7W5DQxTI6XHsX0Hz3dpe/deEBejMWLqDgBHqIBCEFhYbhtJSEjvEjiKqTo5fXtitbxapWs5OI6HqsqhSGbg35U5pgkN0Y7gOC71loUZ1fFlEdK7haDthALhnmPheaEblSNcFFXm9NFhJkaz3Jjb4IXXb/LpD50mHtN59uVr+H7AUDHJfcdHd9HDAYqRFJ+ZeAJVktm0a6x0N4hIP9mlIbIQJDUDdUe5piRU2u4GaT3UFgmCgCu1v2K9ewFDTnMi/Yscjh/B8i0Syv7CmF2vxo36N2k4K0wnPsCZx6b52g/fYs6q8MynzqCqCp22xdGHxijVq+TsOJ7rYXUdnvjAMfKDKVRNQddV4skIR0+NsXK7THWzxcR0kavnF3nkfUd4z4dPsDhXIpE0SaajPPWRk1RKDZ7++Gk838fv2DRqbQ4eH+HoqTFUXSES1Smt1hgYSZMfSDJ+oECz0eGDn7qvr9M4U8jyP330aRzPQ1cUpnNhn6NKCnHF7L8NQgjeVzzDd9fe4FJ9nqyW4Mn8CeZaq4yZRfzA52srL/HRwYcZjuT5ytILqJJMQo0igJhqoohtR6ap2CBvVq7wxYXvEgQBQ2aOlU6Z50vnWemU+erSizxVOM2j2cN8a/VlvjD3LUxF5wMD9zPfWuF8dQE38DicGEOVFN6oXONyfQEv8Die3LuQrjuLXK19DVnSOJj4CHF1sP93RZZ54uQUT5yc2rXfZz54f3//cOEg89mPPnTHsW0+9NA0H374UFjyGDgIKcKvfuTBPW3lY48d3fV5aijLr3/qsf7nte4a99+fYngHE12RpT3XVnfqPPP4GBktTKpU7Aq3u3MUjCIdt01Wz9Fym1xuXGbIGCKjZam7dQp64Z5j0VZYXoPL1S9RcxbI6jOk9JN0vA5dr81U7DjyLrkHGdsv4fh1MpEnqVtvAqDJEookMVetcP/QEFsdoKYoPH3fAa4ubFButMkno5yeGea2Pc9aO3Tt8u5SOvqTGq7n0+6EJYl+jyGiawqmofUX5u8keeb7Ae1ueBzXDU0kVEXCNDR0ba9YPITi2l3XxQ8gpt/dwMH3fVodm26PgSKJcJ4UMVR0VWE6lWU6tXu+cifD9li2uIe5lDNNbtdqeL6PKstokoam7Z33GDvAynuNVLF+yVH4v+N7eIGHLFRS2v4GLa7n0+nadC0nZOj05FpMIxSTfteJyyDU12x1rDDxFwTIioShqZiGtovZfa/wfZ+zV5fodO1dwNHfJrauqdObm3h+2D5kWcLQlFDKoDcHlSTB9IEinY7db487Q1FkTEO7q/SBEDIBHnLPbRFCV+PxdAqll8j1/VD3qdV10BRl3+Sp6/mcn13l1NQQUkIQVw3i6t3XTLE7/pbU9q9WSPS+T7D77zkjRlTROJocJKJozCQKuO46iCZ7AMO/ZfzYAEeh/WDoWHG7s0bHs1CFQlpLsNrdZCo2TMezKNs1AnxSagw38BmK5NmwKtiB2/t7nbJVI6IYKEIiqezvgLOfq1jLDa3rLN/F8kLQIa4atFwLTQoz0g2nG+r2BD6+73MsNYIuK8hCYzT6EHF1gKiSIy5SSMpyTwQOInKaA4ln8AOHgrF74JWETM44yEzig6hShKiyu8ORhcp04ml0Oc7l2lepWLN0vAp+4CELDVPJUTAOcyDxNMPmg/iBw8Hkh6nZi2T16Xs+96Q2woH4M7iBRVY/0Ge79M5MXH8QscNOUxKCgViMlm2TvsNW2/W7XKp+mUvVL9N0N8hokwyZp0mow7uOGxDgBRYtt8RG9yqrnbO4QYdr9W8ynXiajDa9u1RGinAy/UvElCK3Gt+nbM9he00CfFTJJKrkGIic5Gjqk2T0SdruJkdSH8f228SU3eUNO0MQgnIziQ8CUDAOk4vFiMcMVEVmuJhClsMOQpFlfN/H9cJSEiEgm4qi6+qua01rE8wkPsTfhoZTjBzdLXwdBKy1Wtiex0QqTULfBu9GzYfQCiaX619jvXOJjlfG9S0koaAIg4iSpmAc7utD3RmKZHAi/XPElQGuN75NxZrF8hq9ZxrBVHIMRE5wOPkxcsYMXa/G4eTH6Ho1YuoAm8sV2vUOsiaTyMaQ3oWeyk9TBPh0vDqWt1f74p2ErMh9cWyAtm2HJZO9z47r8TfPXeZvnr/CxHCG//wzT3BgvPCOBnVZCNa7jZB5dI+BzAtC6/d7HTEW0Tk+PYim/uizrImYQSEbY2m1SrnaYr3UYGRgf1aJZbssLJf7nyVJMJROIElSX9xUU2UkSeA4Pp2ugy4rPFKY2FeDbX65TKd7d6BqY7OJ44a/z9RIjsQ9QNT5pfIu9lIQBFQ2GnTbYXJBlzVGzZF7OqpBWKo2mtgBTok4kv4YBD6mLzgoOcTVd1caqUnKLnc9PwhYXK4wOpzBREPtmUlo8pbD3+5UgO8HnH3lFqlslLWlKq7rheWLcQPP86lsNhkcyWDGdHwvoN3sMn1kiFwxQT4T5/SREeZub7K4WuX63DoD+QQ3F0oIITh6YJCBXILNbpuO6zAQjaNKMvOtDfwgYDJW4Ptrlxg0I6jaVnb3J1PnKKVHKHVbu8oSwEcgEwTbiZLVzjluNr5FXB3iUPLjpLQsbatN3a2TVve+Oxvdy5wt/wldr4oX2DyU+8cUT+QpW23GhorEVJ0zj06z1FnjVmuRptriyKltp1W3p4c1MJJGliVaTYurFxaZOjxAt21TLoXt3Pd8Tj04SaPWYX2liqLKROMGUwcHuXVthVwxSX4wRb3SwvN9lm+sMzCSJp2L4fTcCSOmztShQcyYjut42JZDMR7j48cP77mvMbNIYTi9qz1MRAf4+dGn6PoOuqTS9WzKdp1fm/ooju+yML+GImQ+NfIEbbdLpKdTo0kqnxp+AkW4bFrXkVDQ5ThP5YfR5SFsfwNdiqBJPg+k0zyWfT9dr0xShbY7x30pSGlHsPwyIqiQkNd5T34MTYohixZB0GTCVBg0ZogpuT0i737gMN98jguVLyJLOoaU5Fj67xEEDp47B0JHCJXAryOkOL5fRZLyBEELAhtJLuK5c8jKATxvHQK/t906gd8hCJoo6hEgwHVuoun347pzyFIBIb033PPWAAAgAElEQVRzxmzNqbLUWaTjdUJQCAlFKKx1V5GEhI9PWk1TdxtEZAMfn6X2ImvdNbzARZEUZCHjBi7Xm9dY664ybk5Qccq4PV2o1e4KDafBYGSQmlPD9m0SSoLsDramF1gstl9itXMOK1pnJPo4JWu1J5NwB2NDzpHQ70OREqhymrh+MjyGH9Bywox+qNMZRtd2+MbLV8gkTE5NDzKcT6HIEgNSkYQax/Vd1J+COZEQ4Zi5WW3xwpu3eOX8PHPLm3Qtl4ihMjqQ5tShYZ56cIZCNv62x2u2LN64fJsX3prl1mKJar2DJAlyqSgnDg7xngcOMDOWR7mDIdK2HV6ZX0SRJB6bGqdWb/PaxQVkWeKRkxMkYhE6XYeXzs3yg9duML9cpt6yMDSFfDrGsZlBPvDoYcaH9gdl3rh0m/VyY9+/zYwXICohC8GRQoGIendwZKPc5Ny1pV0mGW8X6YRJdlJj3apwKjVNVDH2vItrmw1ePjfHG5duM7u0Sbtjo6oyowNpThwc4sn7phkbzNw18VOutXj9Yugmd+bwKJmUydxSmWdfvc6blxfZqDQJgoBkPMLkcJYn75/mgWNju/QktyIIAlzXZ6PSZKPSZGmtyltXFvH8gK7l8vybt7h1u7RnP0WReejE+C5zle1jQqXe5vr8OueuLXFtboONcoNWx0ZRJBJRg4nhLKcPD/PQiXGyqbDvbDa7SLLE2Him73i8Fb4fsFFpMjaU2cVy2hmSiNCxLyDLGaL6A7Qdj6sbJUaSCQZiMRRZIpOIsFSqUUhF95UH0NVQd2o/VtNWVBsd3ri0gGW7JGIRHjg2ukvPcb8IgoCVjTrnri0RBDCQi3P68EiIb0gyNxobVOxwrnAw6oPYS1b428aPTc8WkXWOJCdxfY+MnmQkUkSXtT67R5dU7k8fJq5GabkdNElFFhK6rJFUY1TsOlPRYbzAI6eliKkRmm6nbxE/e+E2Z5+9xCd//QP7Cvk+/+VXuTp/m2O/fIqG00USEqVug6l4nrZr0/Fssnqs9zeB6/s9i9YwJF/nePQX0bI9VpLn8+SnE6g9B6CUPsZ7B/5rANYWSvxf/+sXWJvf4Nf+l19i5OAgBxLPkGnfx1/8n9/geze+zGf++aeZPjXevz5VijARf5Ih8wxtdxPbb+EHHgKFdksmZRTRpAizizVabYtTM79C1wqtXiv1DmZEo9kKLQ1vr1UoZOJEIxpubYJHcr9Jo9VFVWVcW6JuNYmZOs22xfW5DR44PsadY+DVzRJd1+VAZhvkut16hdc3/xDHbzMWfZTHCr9BQh26K40+CAK6fo3vr/xLFlovhUBS5woZbS/Y1VwPmEp9hKn4U5SqS7Q6DZLZGIqkoUtxomq+b0ccU4s8UfzNt2tyCCExHX8f0/H37fo+0qOLqjtK0KyuQ7PaJlPcdo8bLOxlFozFHmYs9vDbnhug0+zSblmk8/F97Y3Dmlef0USyDxpthSypJMRRlPUIecr4oo2hSzRaNhIqphonYqVYF9BoLRM1NEq1NvleOdAWICY7D3JEOc5seRZZcWlZFgeGBhjODBJVckgiBMaiSo7HCr/RP786UeXm2Tnqm0386b+rVdsvgsBHFQamksTHfVeaM1sRi+pUKi3aHRszolHvWqzUGkxkU8iE2mVnLy9h2y7JeARDU3tMmrehZQcBlufiBj7u22RG/SBgvRu6Bpl3yZMJAYHs0/U9fC+sifd6TmWW76KILRtR+tliRUg95qJE17eJykbY34swg+gToIgwG3j/8XHevLhIu+vwvZeuceLw8L4D8dziZp8BBCGwtlypEzO0vqtQOmmiawqW7TK3uEmzZe0L+NSbHV5641Yo0HiX2AnQ+UGwL14cBAHlaovnX7vZr+8HMOMRRqYLKD2wTRYy49HRu57rbhH2rxEQEJEg8iMY1RVFIpeN9VlZAVDvdmnb4bOQZYlIbyLmuD61ZoeB4TSFoRRDo1lkWQrZSpqMbblhRtDU8D0f1/WplBpEolovWyh436OHePbl62yUm7xxYYGhYor1zQaxqM57HjqAosrcLtVoOQ4pPUIj6PDs2iU2rDo5PUFCNchoyT4b5CcxhBDEVI2262D7O3XvJKLqwB1Jn+0IdQldEkpv7NqnW5CFhizUUNhXCsXa85EY04nsjlJWgR8EmHKEvL57gSXLEu//xBluXF4OWWJjWYyIRmEwxcLNdYpDaWavrRJPRTj32izdts2x+yZo1NrYtoeiyaiaSqvR5er5RWzLCV3XVmscu2+C+ZtrREydTC4cKx3H5cLrK8iyxKETo2h3LArCe24hoRCRA/zAwfVbyELHD1wM2ceQw9JRRcCEmeSPZr+MLCRmYsPk9QiaLKOKEGBXehp1CdWkbN2k3L2G7bcYMh8E6kTkNLZXJa4W2exeI60msf1NTC1K3b6KIhkUjBEyep7VzjJ+YKHJEgQlgqACwqDprNKwb6BIEVLaSL9sZUcDQBE6QsjIQkXtmaYEQQvHeQtVexDbep4gcAgCC0U9hOtcR5aHcN1raNojeN4KkjyIY7+Gpj1CEDRw7EsEQQNZHsex30BRjxHg4Nhncd1beFIC3fjYrktx/S41+za6nCSq5Hcz93v6KxW7wkJ7nryeJybHeGHzeQaMQSQhaGoNVEmj6daRhcxca46oEsUNwPIs6m6dvF5Ak3QSapKYEmPDWmelu0JKS3Oueo6YEqNslynbmxxOHEF7G7Zh1SkRVRIk1NwdbKOwfF+TszheGUWdQuq9S8uNOg3L4mg+T1zbPr6uKjxxcopLc6t85YVLKLLMqQODZEZc2n6LqlMjo2dQf0RitD+uoaoyi2tV/vKPz/L6pYVdSS6A6/MbPPvaDZ597Tq/+atPMzWa25cNFAQB1+c3+MMvv8zL5+Zo3WGmMb9c5o1Lt/nGc5f56JNH+aWP3k/M1HeUNobaNzOFDIosceN2if/5976Bokj87r/4BQrZOH/8lVf5+nOX9hh13Lxd4urcGkenB/cFjoIA/vSvX+e5N27t+wz+0c89xofff4zFep2GbZMyjF0aODvjxsIGv/W571Ctd/b9+35x8uAQ//SfPk7D7ezRW3RdjzcvL/Jv/v0LXJ1d6yextmJ2cZPn3rjJN354ic9+6hGeuG9qX7BnbrnMv/qDb2M7Lv/l338fZkTjc196ifnlym7G1UqF89eW+d7L1/jk0yf4zMceJJ00dx1rab3G7/4/P2B2cZN6s0uj1e3Pe+qtLr//57s1U7ciZur863/x83uAoyAIuHRzlf/jj77H/HKZZnt/85Bz15b5xnOXOHVomH/yK08xNZojYqiUN5tslpo8/sRBtB0A0RZ7aa1UZ3Rw/0SkJExCplEcEBhKWGG0pTsrhAjZ2D1SwX7R7ISlbK7n3XVO7nk+X/jKa1y+tUo2FeV//69+hqPTA/ecw1uOy1e+f57PfellNFXmsz/7CKcPjwBwvbHOSxu3GIuGgL+HDsoBED+a6pAfG+BICEFa3UGjVnfXtQLEeiCQKW+Ln0HIYCka2V3fAUTl7QaYyMaYPjl214qM8koVb6XLg9nJfplaxW6TVM0dJVk7dw5f4a0cxJVXbuLaLqffF5bFSbJE9I4Xaivywxk++esf5N/8t1+gs0PFPVVI8ql//CH+7T//E1r1veUKAoEux3fpANmOy83FBS7VF4mZeliqUWsjSYJKvU2rY2PZLiPFNKVKkxMHh7gyu4bcy57cWiwRj+r88I0bzIwVKFVbbNZaxEwdU1dZLzf2aHz4fkDNsui624vhIAi42fher0RN5kjq4yTU4Xs2fCEEhpSkaBxjofUSAG23vO+2X/6DZ3nvJ+9j5uQY9dkKK3MtDv3c8Xe9EH+7CIKAuSvLRKIGxdHtLFtppcqr373Iz/zae39k57p1eZnzL93gZ//R+9CNvcBRyjA4mMnutt3eEYauYigR4soYhqaE7gKWQyYVw3Jcam2XuOHSaFvUml3aXZuEqVNpdogZGq7v9xbTEsOJwyyXaqiuT94YJaHd29K6vFqltFgGAQdOT+yZuP9dhJPRjD6KEDLqljNgz7HK9wP8HtV1K0MTbgCdrkOrbYVU26kCQoTZmACIahqFRKw/+dJUhcFCAkkIzl5e5F/+3jeZGMluM39EWMIQj+pMj+c5cmCATNJEiHARqEsKbnBv/aGIrHEyPY5xD3v3ru9wvbFETIlQcZpU7SZpLUbdaaNIMhW7SUQOnSKiSgTHd0lrMTatOlHFoOa0OZwYYa1bBaDldvvbHEuO8+h9k3zlO+dYKzX43ovXOHN0lEfOTPadMjzPZ63U4I+//GroatYLXVWYLGRwPK9fSz41liOTNKk3u7x1aZHnXr3B048d6medPD+g3bb4y2+d5fULC/d8NsMDKQxdpdN1uHR9hcWVCjOTxT6g5Ho+66UGX/zqa1y5ubZr31QuRnEwBWK3eOed7aPdsfvtIwhCDaet9rElYilLEpK0lzHl+T6+t30sPwjodG227Ki2ygskIfr7S1LIJ6pU27uYGqos4/Tc7QxdZbCQRIjQveWlN2f5hz//GPGo3h8Pttr2FnV853ieyuwuG54ay3Hf8TG++YNLvPzWHEPFFI7jcWxmkBOHh/sgetowiKkaoPHx4fvxAp+CkaRq15lrL5PTUvwkR1TReP/IDILt5ymQqdo3USWDjLw3q19zatTdoCccboaMozuGzYJxlMeL/4yuW2Eo+gCKiGB5FUqeS0aPoiBBT4fL8R20O0ANIQmGxrMsL5bxXI/CQBLf9YknIiTTUfSISr3aplJqhiYkKYVsPk4QBNQqa9SrbdaWK0RjBkqPEZgrJqlVWqwtVUimo9y+tUEmF2d9pcrAcJp2yyKWiBCN7z8Z3ui8SkQuAgGanKRhz6LKCUDg+A0EEpa3CUicSSk8kB4jwEMCms41VC9K1y1hKAVS+qHte0XCkNP4+Dh+i7a7QVQp4AYdLK+GIulE1QLCLdF2S8iShiGnUISBG1h03BKSkPF8CyFkNBEjquR75haCqFrc45oLIKEwFX8aTY6hSAZDkfu3r0jKIUmFni6QhhAKvrcKuLjuFQh8/KCO763hextIUgYhZXGsH0LQBgRB0EQIFd9bx/eWEcoUECDJu8HsIAjY6F7lOyv/PUeSP8OZ7Gd3CbPLQiGvFzAkg7JdwvJsgqCJJCRiSqzHqBTokk7DbWB5FpqkEldiVOwKmqThBV7IcFCTuIGLKZtokkYQ+Fhel7bXIqWlKOpFGm6DQWNoVxnQfqEKjYq7QgCk1DzKDlDH9jbYaH0TP+igyUVq1msMxX8JVZaRJcFzCwt8cPoAKcPo95FjxRTZhMnEYIZn37rJN165ymeHjxPIPhkt/RMNYm+FZbl87ksvcXVunaF8gjNHRhkdCPvgueUyL52do1xt8calRf7gL17kn/3DZ8jeIfgeBAGLa1V++w+/y/lrywgB02M5Th0aYTCfwHE8bq9WePXCPGulOp//yqs4ns+v/ewjfRDE830830eWdnNNfT/g1tImX/n+Bb790lVScYOHT4yTz8SRJcFGpcnN2+Ea6OD4/tUJQsCT902TSpi0OjbNVpeFlQrr5cbWMBqWTAFKT/dmd2wb1cRMjZmxPPXWPZxTA2i0uixv1ICwzDOpRYm7Zq9cXPTv7ZUL8/zO57/PwkoFXVM4MTPE6SMjpOIR2h2bSzdXuHBjhZu3S/zO579Hs23xiaeO98fkO8P1fL7+w4tsVlqU621OHBzi9KFhkvEIna7NW1eXuHRjhUbb4i++fZZkPMJnPvbgrgTaliOroasYukouFWVxvUqn66DIEiPF1B72D0DU0PYFtSA0ianW27iux0gxxeGpIsOFFMmYgeW43Fwo8cqFeerNLq9eWODf/eVL/Df/6ftx3XAuZVkuzp0sryB8huIe7HzHW8MP2nSdK+jqFH6gIksCpwcc+UFApdkhckflya5rNzR0VcHUtbsmjzNJk0dPT3Btfp3NaouXz89xcLywS/vpzihX27x0dg6AXDrGY6cnEULQcW1qdoeikeBUehRJCGJSHZw5kIdAHrjrMd9p/NgAR7Ab9HEdl6Ubq6zObSDJEhNHR8iPZEOUsGNz8+w8tc0GZjzCgTMTRBMmdtfh1vl5Kms1jKjB9MkxEtk4t87NszK7TjKX2MXsqJeb3Do3T+CH1Hkhwmy41bGZvXCb6nqNgckCowcHcR2P21eXUXWF1bkNogmTA2cmkGWJW+cW+OYfPUs0EaFZbTE8M8j40RE6jQ63zi/QrLZIZGIcODOJHtGQZIlI3EC5gx4nSRKRmIG64+XxPJ8bb85SHM+TyifwXI+FK0tEEyaFsRzlWpuNShPLdhFCkIga5NIxzl9f4dBEgWqjQ8zU0VSZfCbGSDHFzdtRNEUOAaBGh1bHIpOMsrhWxXFDy0W5Vzcqy/KeSaYiSzi+t9sSGI+msxr+XTIw5ew71GcNcHaIeCs73Ke6bYtrZxcggG4rdHbbXK2xsVxhcDzXnzg3a22un7uN7/mMzgxQHMnQrLWZvbxMu9FldKbIwFiOlbkNlmY3SOcTTB0dorRao13vUFqtEU+ZTB8bYWl2nW/+6Usk0lEOnBjl+MPTuI7H/NUVhibCzJrn+cxdXsZ1PGrlJhOHBklmYyzeXGfs4ACBH7BwY5Xxg4M0q21mryyjRzSmjw2jRzQWb66zulCittkkuIfwrucHdD2Xrrs/rVWWBMenBpCk0O57pBAO2KrSc8EifKfGiimWNurUWh3SCZORQgpFCUtPtkBBRQ5ryDeqzT4z414hyxLDMwMs9lyP/i72hhc4rHdvkNKGe5liQb3Z5bsvXKFcDUHdTtem3bG52GPJdC2HL371dX7wynXMiIZpaEQMlcnRHI8/ME0hHiVlGn3gKGKofOCJI7x2boFbt0ucu7LEuStLu65D9MCjqKlxeHqAX/nUQ5w+OoIhqwxEEqg7SlH3i67nsGk1GItmuZudp4ToGRYIDFklpUUZjGTwCZCRUHWFqKJjyKHemRuEDhKGrBFXTTQpZEr5gY8ua5iKjiknenR/wfRYnmceO8Sff/1NKrU2v/v5Z7l4fZkjBwZRZIn1UoMfvHKdq7PrDA+kWF4LJ12u5/cdcGzXxdRVCtk4D5+ZZH65QqPV5d9+8Xnml8ocnAwH6o1yk7OXFnn5rTly6RiW7bJRbu573yMDKY7NDPLcazeZXyrze3/8Qz7+9AlymSiu57O4UuF7L17j4rUVxoczlGutXRpMd05aKrU2z758vdc+LDqWQ6PZ7bOoWm2bP/3K6/zg5RuYkbBtmBGNg5MF7j8xvqdU8PyVJS5cW6HZtuh0bNpdh1K5gdXLDF+7tcbv/LvvEo8a/eNtjSGDxSS6rvT72YSuo8tbIpsSp44M8/XvX6Ba7/DXz17C8wNOHxlB0xQcx6Pe7KCpCu99eAYzstdpaWfETJ1Hzkzyg1euM7e0SanSRBKCh09NkEpEQAgMRcXekbUzFR3X95hvbeD4DgU9Q0z5yXZ3DPW57vgsJDQpgS7vD5pVnQoVp0RKTXJAndl3G0UyGI0+0v9sey5RRWe908T1fXQ5XP6YskFBz+5b8qAbKvc/egDbdjFjOslMDN1QGJsOS2cHhtO0mxZGJJxAK6pMfiCJGdUxIhpnHplGViQkETKKdEMlmTbRdDUc2yfz4XeZKLIsEY0bjE7k7lqWG5ELSELDC7r4gUtEGQAh8PwOupxBlXrsWymCLEI2iePVkcS2WL0smf3ttiKhDhNTiz2nJJm4OoQqmcSUIpJQiamDyELtsXGKKJKOQO6DsOOx9yIJFT9wkYSMLDTE/8vee0fJdd13np+XX+XQ1TkD3Y0cCYAAI0iRlEyRihYtB8kjexzGM7vH9u7Z/WPtc3ZnZ/6YmZ0zZ4JndzXjkUeWJUuUZCWKVKBEMRMgQOTYADrHqq6qrvji3T9edaEb3Q2KM/5DpveHP9D9uuq+d9+774bf/QZJQUKmP/YgsqTf4ZB6+1mbapKhBrX+9vEomn4ESQqhG8dB2DjuZWQ5iaL0IkRAtUIyUZQeJCmMQj+SZKIbxxDCDuQIhAdSoHenqIPIUgShjoB0BwIAj4J9i7pXaKKLVkdUjWLIBpqk0RceIG/ncYRDX7iPFqMFWVLwhUfByVP1qnSGOlFljbxdIKrGWLKXqLgVCk6BkBImqXn4+GStLDWvhu07DEdHqHgVNFn7uTWPFEnF8W2E8Nddty9sVDmO58t4ooZobKakQyHihklPPEG2WqE3kUBXFBzX45Vzt7g8vgAIRnpbefr+nURDEjVfZsnO0/H3YF5Uqta5cnOee3b18dufPMbWBspDAqp1h5d3jvL5r73GfK7EmStTXByd5aF7htaUUbdcvvXiOS5cD1ygju4b5PeeuZ/u9iSmruILqNVt3rk8xZ995WUmZvM897ML7Brq4IGDW5uOqX3p5DqamBCCF165xNh0jiO7+/j1pw4H1CRNRZIC1EaxVCO/XCMZ33ijH+BDD+7k0aPbcD0f1/P4+g/O8JXvv91EWK1Q9vL1BhBAuATufz5C1AAfSYoz0h/lT373aDDvlkKADJIS/N+Ias3mL779FjOLRQxd5d59g2iKQquRwFi1eZctlPnyc28zOZfH1FU+9tg+PvHYPtrSMTRVwfN9CstVXjl1kz//5utkCxX++vlT7BrqYKivdcOx2PcDdI+pq3zisX188vH9ZFLRZnlP5st8/YdnePYHp6nVHV5+e5RfenAnmQY1zPY8pIjMZz59L+2h4FipYvEv/vxHXBydJREL8T/8xsNs6VlvACJL0jr00sq97W5L8MknDiDLEod399OSDBMyNNTGOrZStXjh1cv8+Tdfp1SxGnS2BQbb0/T0pJmcyK1DgwfPDGJhc9PZr6a0YbtjqHILrreAKndjqCqG2tDaEoJ0LExI14iENt5Y1RSFUs2ibm8ueSBJEsf2DfLtn5wnV6jw5tkxPnJ8D5nU5hv4l27MNZOL+7Z109XQ1rxQmOZE9hbLTo2b5UVUSeHx9hRbtDL4ufdf4mh12DWHt394Dt/zyU4v8co33uL3/uVvoBkqX/+332dhPEvPSCeSJNE52IYZNvj2f/wB45em6NsRIF0yXSniLTEkSeLa6VtMXZtl9/3BzpFVtXj2X3+XUr5C15Z2zvz0An3bu3Esh+f+84tMXJ6mc7CNn339TR75lfsY3N3LF//p14m3xOjf0c3omTH2H9/JB371AarlGvn5IqqmUFmu4VgOIJgfX+TMSxcxwwZXTozywMeP8NAn731PTmsS8Mb3TtPak+aXPvcIxWyJZ//193jq9x6nrS9DMhbiwI4eNFVBVWSqdYdE1KRuOSSiIfo70ziuRyxiNLPje4a7sR0Xw1DZM9JFJGSwtTeDpiioalBGLGxQLNfp70w3tUGaz8bzaI9EsT0Xx/fQFTUQwm5QxTxhUfcK72r7K4Sg7C4wXT0NBCKfKeM2Pe+V751h5tYinf0ZZidyTXG22bEsM7cW2XVkK3bd4cfPnkCWZdLtcUr5CsmWKM998VWQoLM/Q6VUZ3Zske9/6TUGd3Rz+dQtKss15iaynH75Kkef2M1bP76A7/lEk0ECUjM0jIYVtSxLlPIVrp+b5MgHduE6Hl/+ty8wsrePeDrK6Zev8PRvPsjzf/U6n/mfn8RzPZ7/0ut8/HeO88r3zpBuj1PMlZm4Nse++4b57l+8zNCeXq6dnSDZsnnHkDRNjvX0bZqYkSSJSGgVfHoTni7AUE8G1/PuKpLX35GiKxO/Kxd3JcLxEKmOJFqj8/7/Y30okkZC78QVFp4I4NH5YoUvPPsG2fzGmkeu53P28hRnL689fvTAAPfuH8A0tOb76Hk+56/O8L0Xz7NcrpOIhYhGjHUC1SuiiYXlGm++c4uFXIn/4w+for0rRlwLNXRr7j7JbTfvTgMyFI3t8bU705Ik0Wmm1x2D9YKTK3/rCKU3tMyVNYVPf+Qwdcvlh69eZm5xmb/+7qnGAjZIbIQMjQ89vJP+nhb+7y+9jC9EkBA1dFTFbbZrQ1f51JMHmZot8Pb5ceazJb70rRMELJ4AiSVLEkMDrfzOpx/glZOjfOfH5zasdzIe5jMfv5fFpTLXbi1w8tw471ycJGTqeH5w3w1d5Z7dfXzuU/fxle+c5MXXr25YFgRaBV949g1yhY3bh+N6jfYxteb4B+7fzp5t3esSR6+cHOVrz53e1Bkum6/w0pvX1xwzDZX/6R8+RmtLrNmWdEVhKNOCsar/OLi7j48/sY+vPneaUrnON55/h2++cCaw+fV8BLBruJMj+waaiaPNQpYlDu3po7+7hSs35liyq7S2RLn3wCC6plK2rYbO1u02MVXNMV1dYskqU3Kr7E114BoJ0nrivZrK/Z0OU0nSFT6MLm9s/tBmtNEdbqHVeC/mGFKgOdmYXAchKLlVLN9moxssSRKhiEEoEoxJK2u41WjaO5GpqqqQSEUan1v9t6AMc4N2Y4Z0bMtlaHvXpuOnJEmkzDtMUe7o4yQkIlpv8+fVn7nz99WhyHrTwAJoUsY20hMMqespECF5Yx0VXwhKjoLj27SZZtMM427hCwvfX0aRk41nFdwPTdqHJOlI6+iLa9uIJG0slr4iyiuxPhHr+hZztbNsNmZ0mLcFu9vNjsZ57l6X1d+5MzIN3aK9yX3NYz30NH/uCnXfteyVsH0rcHV08+uEq3UlgyYnWLZOIaoOrZGAmqfKCqaqkq8Hm5szpRIDySS+COzJnzy6ne7WBIamUndspupTOMKh7Jbx/x7YzQoBnW1x/uDTD7BtsH3Nc46GDR49MsLY9BJ/+Z0TlCoWZ69M8+DBravmAXDxxiw/euMKni8Y6W/jH336Abb23kb/yASu0A8c3IrtuPybL/6UpWKVb/zwDDu2dNCairJUqXErlycdvpPmBGevTvPIkWGePr6HRDTE7EKRRAOR09ORxHF8EuEWnaMAACAASURBVBGTC9dmCJs6pqmxVKjQkoxgOy7RiMlSoYJlu6iKzJbeDNGwvqauhqIS03V6EgmihoFw3gkW6ZIZ1MBfQkgRdLmV1ugCiOXg4pQuJP1A43MSlu3y0xPXeO2dm2iqwi89sJOPf2AveW+ZBatAxkgQUnSEgJffHuXslUDf5sFDQ/yDj95LIna7/rKs0JqO8fQjuylV6nz+668xPrPEt39ynj/67CMoymYC4oLjR4b57EfvJXlHeZ2tCT795D2cOD/G6ESWW1M55haXaUkEDtue8Cl7NsKEjkzQt4SMWnPupcgSLckIHZk4rjcfJKsRSJLaeFvKOE4NX5SRJIMg4RZGljSe+dAOZGm9yYusSCRiIT788C4u35zjB69dpliqc21sgZGeVrq6kvQPZIjdgUyVZWhNRQPB7k36J9udQgiHunsdX1RBCmF7HsoK6ksIlqt1prNF0rEw8d71Y6xAEDa0d3W+G+hu4dCuPn742mXGpnKcujTJE/dt37DvtGyX18/cpFSxiIR07j+wpemYvD/dx85kV6CDiKDuOaSUMrgpUN67FMJG8QubODKjJh/63HF816ewuMyf/8lfk18o4lou10/d4vf+5a/T2tsSvHuqzMJEjguvXeW3/s9foXs4sF1fgeMN7O5lz1yBhYnbglwLUzmmrs3yW//8V+noz1AuVrGqFtmZPO/85AKf+uOn6N/Rw4tfeZU3vneKgZ092HWH488cY//xnbzx3VOcfvECH/rcI+x7eCdv/+AsW/cPcPxTx5o6An3bu4Nr9AWJTJzrp29x30cOvafEkazI3PP4Hn74xZe57+lDjF2cRDM0ekeCgdbQVfo6UnfQ+iRW5gMrVI41As7xUPPYCp90pYOQJIlUPCgn0P2Q1r1TYU2jJRTCbTgZ6EqQfc2Yw8zWzuILl8vF7xHXu4mqHciSumoiFuz2eL5D1rrGxfzfkKuPAtBqjJDWB4PPCcHo+Uke+9QR+kc6ufR2wC9OZmIM7uhi/FqAbpJkCSOksziTZ/vBfnq2trG0sEx2tsAz/+RxWjoSCB9ef+EsHf0ZHv7oQa6cHuPcG9eJpyIM7+vl4Y/eg2t7zIxlefijB+nszzCyt5e9x4aa923rnp7mOQHCUZN7H99N92Ab//FPnqVUuINaKGBxpsCFt26w68gWqqU6s2NZookQqdYYDz19kEQ6GiCqNokVtX/H9XA9H1WRm6Kzm0FNNwtZDhAhdwtJktA1tcn91VR50wnf0lwBz/FYmi/Qt737rg4Rf9cib9WwXJe8VSPnVLjnoQFCaGRaIpQ9m8l8YLvs+D4RVcMXAk8IQqpGZijOJ9IH6IjF6OqI4otlomqGml2icMuhUrF4cEcPri+wLQfd1CksFEm1J6iW6pgRA+H7WDUHM6yDJBGOmsQ0hdkb88gStPe3YoR0Ll6f5V/8Pz9kZr7AgV29fPJDB+jrTq8ZCIUQ1OoOC7kS3/3xOV566zq3JrO8cfomT7ftoejUSOrBYPzBh3aya7gTSZFIJ2/v/HSEknCH4HBfd4rffuY+JElix1BH0GYabVNf1b+tThStziMH6CKxoQvFZm0unQjzO7/6ADuGOnjpzWuMTS9RrliYhkZfV4oHDw/x2APbeeP0LRzHQ1FlXN+nIxnF829fvyRJdLTG+aPffpSfvnGNk+fGmJjJU63ZqIpMZ1uCncOdPPnILgZ7M0TCOq3pKG2ZGJHw2oWYLEvsGO7gf/39J/jui+e5fH2W+VwJz/OJR012j3Ry9MAgx4+O0JqO8ZHH9jLY28K2LR3rEnwQTGR+7aOH1+kwCMBuaEXJkkzNdRAIQoqGLEls6ctsKEx+7/5BomHzPaECVVVm53AnHW239dwkSWpOylc0tEKmxqefPkxbJs6rJ0cZn15iuUG9jkYM2lpiHNjZi2n8fNOMRDzEsYODXLkR9LPbtrQz2BtQ0HUlQGw4vtdsR4asMVHJciA9yOXiJIokI/Pe+sW/qyGEwMfFFy5C+MiSGiBgGvVf/QY5wmG8PErOytEb7iOiRhDCxxX1JrJidSiSjiZrFJ06OauCt5LIRcaQNWZrC8H3VnTLhAAEnnAQeGsSvxJKY/zffCxZXSeBhy9cfLxmXkKSZGRJQUZlRS9RN1TSretpebfL8BpliFVlqI0ybl/H6j7NFy6ubwW6FZIZnEsE9VopK9AXUgME0c+R2Fm5nuDe+CBWrkVDXleGYL62zFg5x6Od21FlHU84eL7N+iSNhCobeF6Omn2aiPlwU+MRQJajjT7WxfXrSJKCKhkELePOOknIqE09w43qEMzcgmdTcmfJ1q8B4Akbx68g3fHeSZKMLyQKziKqpNFirE8M3dmGkUBGCe6NJPPzZH+DOjqrNARvP+eV+7QSMTXZ0C6S0JvUcY+aO4kvLCL6CLrahiyZaHKA3gupKsd6ept3f2XBaGgK9+8ZDDYbGsdeuXCDWFcNTZfQZf3vRV8kyxL37OxlqL9tw7Zj6Co7BtsxDZW65bKwVMLzBWojaeF5Hm+dC+hsqiLz4D1bNxWolmWJQ7v62DPcxc/eHuXKrXku35ij9dAQ8ZBBWNc2RB8mYiF+6cFdqLKM5bgsl+vULYflcp1ISGdmIaDH27ZHSa+zta+1idqfyy4TqVg4jkeuWKG3M0VtA1fXuGEQN03empqiOx4nrfogZ0CKgGSA3AE4AcpIkkFkGj9HgWCd5fuC18/c5KvPn6ZWt7n/wBZ+/anDRMMGlm0yFO1qJI0Elu3yyqlAM9HQVT54/45NjTlURebhw0N856XzzCwUefvCOHO5ZbrbNkaohk2Nx45uJ7FJeZlkhIHuFkYnslRqNoXSbcaILitYnstCrfxuuAEc5wpC1BGihiQnkKUonjePonQgSQq2cxHfLyGhoChtmMaDm+V3AIiEdLZvaecnb13Dcb1AnqNmc+niNJlMjOGR9uYm9wrbQiBQ1c3fU01pxfXmEMIOekARXIDj+4jGBmMiYpIv1YiYG2+O+b5gqVTbgMK4/vqP7R9sJoTeOjfGAwe3rAEHrMR8rsT5azMIIehpT7J3221ZGE1WmK7msTyXnFXmemmBR9ta6dHb+NtK+fzCJo5yM0v87OtvUlhcxqk75BeKeK5PMVdC1RVae1pQV2XwyoUySNA+0LrmONyGpK2OesVCURViyTCaoZFqS5CdzmFVLebGFvne518kFDGoLFfJdKfxPJ9Q1KS1J42qqYRiIVzHbUIUaWhNrAhve67HlZOjnPzBOTzXY/bWAqn2xH8TrWdgRw+arnLj7BgXXr/GtsNbiTZEjtdMgjZ4q97LMV94jUmohi88HL+IoazvxCOazp62O+FuEltjj3Kr9DJld4GJ8puUnQW6wvtJG1vQ5DACH9urUHEXydavk7WuUXVzCHziWjd7Ur9MWG1pJsE81w8QLboS0Po26DQ0XeX4R+/h4smb/PRvTjGyv5+R/X1IcuCcJ8uBi4freMHvioymq3iOjxCQSEVRFBlVU3Bsd6Uq77pXZJga4aiJrMrBOXy/+T3X8XAdF9/zSbbG2HtsGDOsE46aTN6YR1EVFEXGCGl37QhX4rW3b5BORNi7o5ux6SXCIZ2uDYS5IaA6eZ5PJPzzpXJW7NxVVWkiluqWy9Wb8+wa7tyUY9u/s4epqzMM7OrFjLyf0kZQtOpMlYssVitUHJujDw+R0A1qrsv15Rwz5eXABVIEbSikqqTNMKaqsntfD9tTrbRHouTtacbKl4moLcRFD9Ojc7iWy2MHt9DSmWJhMocZNigtlZEUieVciVDEZOv+AYrZZYyQEdBuwzr1qsXVE6P0jHRihk0SHQm++cI7TMws0dWW4Hd/7QF23EVMr787TTRicPbKFNmlCtfGFnBdn4JdpTsUtKUnHtyB43ncWlhas9Mf0OLW9jN9XWl+65nbFsyW4/LTyzfwfXhy/209kJVYrlksVWoMtgY78IVqnaVKjaH29Za1d8bqhHg8avKhh3dy7J4tlBsTOkWRiYYNkvEQihK8i74QgeqGgIlsAcf1SYRMMG4n0jvbEjzz4YN86OGdlKtW0/o3HNJIxEJN6999O3rYt6Nns8tDkWVGBtv4x595iGKpTq0eON+pikwkYpCIhZpJokN7+zm0t3/TsjLpKJ9++tC643XX4cvXz3Jvew+DsTRfvn6GC0tz/I97H2BLfOOJNsCRfQMc2TfwbrcYCHaxFrLLxKImY5M5FFkinYyQL1aJRgzqdScQuTY0sktlujuSFEs1dmztQJNlYrHAjXJieomdI50osoyhq9TqDkuFKsl4iNhdXOckpOaumSxJPHR4+PZOpRRo7Dir6NFdoRTH23eS1COYiowj6kTU9TuS77cQwidv32Ki8jrztQvUvCU0KUyLMcRA7CEyxvYm+hcgrERI6zqWZzUpOmV3gTcW/g0lZ25d+TuSH6Uv8sEgYdQQR2+cGU3W6At3NalBQgjqXoG52jlmqm+z7ExheYH7kC5HiOldZIzttBhDpI2tKJKx4fPxhUfRnmC6epLF+hXKzhyuqCNLGiElTVLvJ2OOkDG2EdM6G4uS25peAoHr1ynYY8zXLrBkjbLsTOP4NWRJJay2kDFG6AwdoDW0Y0Mh8cX6VU4s/hm6EuG+tj/GVJLMVE8xXn6VZWcKTzhE1TY6Qvvoiz5AVG2/a1vzhUvBnmCq8hbz9QvU3Fyw+6xmaDV30Bs5SkofaOoZSUhocuAKuEJFnqqc4OzSl3D8tRtTmhzinsw/pE3vwhdVhNiYBjFbPcObi/+epN7HsbY/RJY05qpnmKi8zrIzhevXMZQ4LcYwvZFjtIV2rUlAAbiizlTlBEvWKEv2TYr2OCUnoBVdX36BqcqJdedN6v3ck/l9HN/Ck9ZT7R2/Ts66xmTlDXLWKJZbRJZUIlo7naF99ETubdzfzRd1nm+Tta4xVn6ZnBWIlZtKklZjBwPRBzHVVPPe+sJnvj6BJkdwhE3Kb0eTdXzhUKy/heXOUXOnMZRWXFEmaR6mTX0q0LyS/TVJyysTCxsK9J64OMVDbUliZpRlt7QO1fR+DF1T2T3cteEmCATjbCxiBogsy8WyXFzXa36+VLUYHV/E8wWpuMmuoY4NN5NWIho22L+9h5+9PUqt7nD++gwPHRpCQmK5Vsd219/zzkycvSNdga6rFCBhFEXCcX1MXWXvtu7bG7EiQLAk4yEkJJKNsdvzA8OPFWbHnZGv1ynUauxobQ00SbW9BFpjG/UPQfJ29e9CCG5OZfmLb73FwlKJvs40n/vYUbraEkiSxFR1kbpnszPRh4nOQq7EzEJAU+psjdPftbnroSRJtGfibOnJMLNQZLFQ4dZUjq7WxIbfaWuJ0deZumt56VW0vmrNbiaJBMGYkNTfXYTZ0A8BHkK4AVVW0kGMsCKHoCr9gA+SioSKLN+dhr4ChNC1gErquB7xeIh0OkLdcu5AXQss2yNsbvaMbocvHFQlQ1g/QM2LU7UnCTV0ZyVJolp3cFwPcxMXtLrjYmhKoEd5l3NJksSB7T30daa5ODrLO5cnmVkoBq59d8Tlm7dpaof39K95HgBLVoXJSp6yW6fNjDFVWaJHzQM1YHNK5s8bv7CJoxMvnOHm+Qk++6efpLhYYvJqMFC1dKVwbY8b58bpHenCcz2MsEGyLYEsSVw7dZOt+/rxPR/d1NFNDeEHSQjhi8A2VpWJJiN4rhdoKKkK0zfmkGWJSCJM/85ujj9zjMHdvXiOh76iz3CH+OjqZqgbGpVClWqphqqp2JbD9z7/Ivc8vpf9x3fy6rdOMj0aTNKCpIjXuC4P378teBb83jju+UiyRDQV4cAju3nxy68hqzJPfOahpjaGEIKqO43nVwhrfdTdBQQuptJK3VvEFw4htQPLy+ELm4jWj+3lcf0yYa0Px1/G8rJE1D6K9hUcf5nW0FFsr0jVnUEzY9TcGRy/QljtxhcWVXcaU2klpK51TGs1t3Gs7Z9wdukrZK1RstY1sta1uzxlCV0O02ru4ED61+kM722WJ0kSvcPtnP7ZZXJzBbKzBYSA/GKJ3HyR5aUyC9NLxJIRJkbnSKQjdA22kpsvkGrdRShicOLFi/QNdxBNhBjY3slPvnGS82+Ocu2dcbbu7qFUqGwAe5RJt8a5eXGaZCZG10CGetVmcaZAuVhlbiJHNBFobqzO+uiGhqapXDp5k6X5ItWKRUtHgmQmSiFbIt2eQDNcOvsznHrpMuffGuX8m6Obos+EEJSrFuWKxUKuhGloTWetFXSY6/ksFSo4rkciGkJTZV47dRPX9Ti4q5eWVDSgKJVqhE09ELizHCzLoWY5JGMhfCF48fWr9HQk2drXSjxmUqlZdLUnUBQJ1/MpLldxXR+5sZB0XI/lukOoOx04W2zgCPd3OZK6iRqTGYynWbbrLFsWA/EUdc8lpKoMxoNB1WskCzVJQWtMJlzfJ6IF/UVS62RP8klU2cCuO8xF6nTubiMUNTBCBkZYJxQxsWo2siIH77skISsSka0dCN8n3NBC81yPnuFOwrEQqq6SK1SaYsvbtrYz0N3yrgNgPGISC5tklypYVjCZT2grbTn4zPhinnfGZ2mJRZjOF3n16nhwjs4MrfEor14dI2bqHNrSw5WZRe4Z7ObtW9PcM9DNcHuGS9MLAMwWSrw5OoEEPLBtgFeujHF+co4n9g6zr6+TU7emMTSVLW1prs9lOT85Rzxksqe3g1evjQHQGo1wcLCbct2iajuEdY2QHuiPVF0HoUtNvRRb8pgpLJOMhNb0y4am0hILIwSEjUBHKRgDAgFeVVVIJyOkk2spHHO1POO5BXojGTrNd7eiliSJkKkT2mTX6b83fGCxXqbqOoQ1nd8YOcj/fvJHWN7Pb+v7bpEvVHnn/CQH9/YxM1cgbOrMWEWmZvPs3tbF5EyexaUSu7d1M9UQ4zx1bhxNVXA9H+GD6/roqkIsYnLu0lRzV6+9Nc7kzBL3HVrvmLkS5arFKycC9GlvV4r9O28n6wRQduzAnU8IkCQKToVTS7eouhaS5NPWGKfbzM0TaX/XwxMOk+XXOZn9PEVnEqCJJJmrneVm6UW2JZ5eMy7XvCrz9RyOcOlwO4goUSSCxbQrLHzh4ok6dW8ZENTcfPCOOTZhVW8Oc8F7V6PolOkOtyGEIGtd5Z3cF5iuvo0nbBRJR5YC1KonbGZqp7nKc8S1Hh7v+uck9YEGCkc0Ez+ub3Gz9BPO5f+Kgj2OhNxMMPnCwxOXmai8ioTCSPxJ7mv7Q8quxUx9ge5QO3Etiu2VObP0Xxld/hE1LzDYuH0tPov1y4yXX8FUkuxJ/So7kh8nX/eYrhTZk+5AUxRcUSVvjyFLMov1KyzWL3Gl+B084aCg4eOyWL/EWPllbpVf4lDmd2gzdyPfQeEVQuCIKpcLf8PV4vdZdqaQkBrJKols/Qrj5Ze5VvweO5IfZyT+ZNPwJKrqJPTwmj7MFy6uqOELF8evYftlNDmM7QV0VukuTjmOXyFv38TyimTrV7lR+hHj5VfxhYvS0HJyhcV09STXlp/nQPqzjCQ+jLZqkVZzlzi79CUq7nzzegTBAt3169TIr9vTs/w0iiRj+/UGyvw24rPsLHAu/2Vuln5C3csjSxoKGgKf+foFbpV+Qsbcxp7Up+mPPrTOAS2oV5XLhW9xsfANKu5Co80E7/905QQ3Sj9kd+pTa5JgumIioZBQMxhyYNBQ9wQtkU/iebPka6/QEftlqs4tSlZATS450xTtm7SF9mMoSSQkvvbTM3S1JNahW0oVi95wD0WRJaGt6PO9v0NV5KYY9max2rTBX5OIhkrVYrqRAAmHdLrb716Wosh0twfUQMtxGZ9ZwnE9LNejIx5bJ6sBkElFiEfXW9ivxGaCzHCbsfFuUbVtDFUlqusNMMG7JU5WrSMbyKb/92uvcW1sgUwqwu9+6v41G4FJLcqct9R8h+aXyk2kTyYZbbJINgtTV+lqS6DIMtWazdRcAd8XG9LVErEQqbvoPQGB9m0jPN9vypJIgCt8So7dHKc3C3kjavUaPbWNryEwCHGp2w6OE5hm+L5A+IL8cvV2+xKwvFxjZjqPZbts2dJKdNXGlaxIlCrWpvchqFsRTe1AV3pQlRZcxyFumLRHo02AQ8jQUBWZcs0ik1hfJ+ELlAba7U5x7LpnY3kOsiQT00KkEmEeOTLMpRuzLOYrvHr65jo9KtfzeenkdXxfEIsYPHhw67q+qCOU4Gx+ip2JTqKaiec667S0/nviF6pnWw2b7traxpmXzvPCF34S0DXiISRJ0N7fwj2P7+Zv/sP3SbbGMUI6j3/mIbq2dHD0qYM8959/TLI1gRHSefiXj9G1tZ3Xvn2Scy9fZuraLN/8d99n17ERRg5tZcexYb7x756ntaeF5VyJ1p4Wku0JHvjYEU68cIbzr14GJPYf38ngnj5kZUVQOLjO20gTiZEjW/nRF19m9uY8h57Yx457h+nf1cOFV68we2ueYrZELBVF+IIrJ0Y58cJZZm7M8+KXX2V6dI77PnKIqWuzvPX8O0xcmeZnz77J/HiWBz52hGgyzM6jw3z38z9i19ER0p23O1dXVMjVTpI0d2N5ixTtQBylJs9Tc6eRJQPbL1JxxlEkE0/UWaqfxvErtIXuo+bOEdYCjrjrlwgoKUGnUHUnSfgjZGsnUeUIrl/G9vLYfgFfcwipa7nlsqQyEL2flN7PjdJPma9dpOTMUveKuMJuTp4MJUpYzZDSB2gP7aQnfIiQ2hLoizQy+wD3P7mPN39wnvmpJY58aA/xdJRbl6e5en2WVDTEuTdHiQykmbo6h5+vURM+jz65n3DU5LFn7uXc69e5ePImbSNtHDgyxMHjOxi9MEVbb5rd9w3xox+fo78x6PUOteN5QXLkwIPbeOMH57ly6hYtHXEmRueYujFPqiPOO69d5eBD29lz3xChiI5AsPeBYVq6kjz8sYOcefM69ZjP0Q/vJdOd4gO/fIQTr11mfHKee+/fSd+2Dg49upNbV2bo3dZBS2tiQ9pZtW7z/E8vIoBbk1l6OpIsFSp88wdnOHZwC4f39jM+neOlN6+TiJrsHO6kNR3ldMMFytBVDu7u40evXsZxPDzP55Fj27hyc46b41kS8RAHd/UiBJy+MEFuqYIsyewa6eTc5WkuXp/ltz51jFrd4Qtff4MtfRkWsiWefGQ3Y1M5ZheKjE/nOLCzl0fv24Ysv390jpJmiKQZDGDtfrQhDqusoYPeqdWzsXaPhExDCFZX2X5oK8YqbnxrT4C2iaY21iYBSDR44neex8qVmvafWgPBdrcQIhDCLzYmG8lEiJCqcTgzsMYetzUeaGTUHZdSzaJi2Rwb6uPSzAKmpmK7LrFQHAmYzBXY29vBROP/1efyfJ+wrnHq1jQHB7oZ6mih7rrs6+vE0FTaE1HGsgXqjsuFyXn29nZyaXqB63NZFoplnj6wg5eu3ESSYLqwTFs8iqEqVG0nECfUFCqWQ65cBSEIGzodieg6fa6a7XB1JkvdcWhPRImZBtPjOQxDpaN384SQQOAJf43tOQSL7TfnJ4lrBlcKi/RGExxs7cb2PN6cnyBXr7A91cbudAcScH5pjktLC9Q9h7QR5nj3VkqOxdsLk1iex650OztTbbjC5635SaKazrVClpFkhn0tneTqVV6dGwMBZXvVLvcdly2EoGDXeGNuAstz2dPSydZ4Gsf3OZebZbSYJa6bHGnrpcUMM1st8fbCFHXPYVuyjd3pdgxdCcTBcyVSyQi262HqKu2NNjg1m0eSJCpVi+xSmZ7OFPMLy4xsDSDgbS1RcoUK4Ya7py8EsaiJ5/v0dqYZHVvYtH36QnDuyjQ3J7MoiszB3X20NfQJV0KRZOqrEmUZI84TnfuwfIfnpk/i+i7aBovM90sIIcjWr3Iq918oOhPocpSeyL20mQFKpOzMM1l9k0uFb2KuEspWJJWuUDeztdnmRD6stnK883/D8srYfpmcdZ0Ti3+G7QfJiJCiMRTPMFcrNdBeABKyJKM2+npX1Dif/2smK2+hySaD0eOkja3oSgxfOFTdHHl7jGz9CjGtnZAaJPQKTpmKW6crFIz5Oes6Z5f+kqIzSUofpCt8iITegyxpOH6Vkj1DzhqlYI/RYg4jkFi0lyi5laYGkSJpyJKG69fImNtp0YdI6L3ocgxPWOSs60xUXqfuFbhU+DotxjCuN0TJttahi22vwoX8V1l2Zmg399AR3o8px7D8MrPV08zWzjJXO8ep7H/hwfb/hbi+dh4k8LlefJ4zDaRQQu+jL3KUqNaFBCw7M8xUT7Fk3eRM7osI4bM79SkklIDIL0Rzet8dPkyruR3bK2P5y4yXX+Vc/svNc0mSjqa0Bzv2d4m6V+Rc/q/I1q+RMgbpDh8horbiCZvF+iUmK29R9/JcyH+NpD5AV/ie5rsXUtPc2/oHuCLof4r2OO8s/SWWV6Qvej9DsScaicrb5BRdDmPIcTwxQ0iJNtuvK2qcXfoS15afQ0IOUE7mbkwliScsCvYYE+XXWaxf4WT285hKis7Q/rUbtkIwUX6Nc/mvUPcKRNQ2+iL3kzT6g8WiM8Vk5Q3OLH2pSVmTJRlTiQAqWWuapN6G5QmuLE/SFWqhwzSx/Syzpa/i+EUi2ggAqmRiectr7uX9uwc5tru/KSWwEt965QItZoouowUZGeXvgauaLN/W2QzWbwJwEaKOLG+Mil8djutTqQXtSlOUJuJ0s1jZoNF1BctxqdYdbMfDUBXSkRDJ8PqETdjUsbzAjCOkaXeCfYIENrfRMpIEnhDUXAdTUVl2LFJG6DbVfYPrUmSZmutStiz898gqqdRsvvrCaU5eGCdsanzy8f0cazhkrYQnPOqejdOYj9QtG7vhEmYaKsYmaJdmNaXAOElRJDw/cG3zfH/DeaOuqWts699LrDiNv9d78K7lCoHr+cxllzlzZYrr44vMZZcplGpUa8G9sB2vYThzGENkcQAAIABJREFUG32paQr9Axlu3VpcU54kBVpL9x0YRFUUliv1O0+JEAJN7cJ3xrDdcTSlHRmYK5eQJYm+ZDLQpatauJ6/KRXN1FXCpr4hki5vVzibH6PDTLI/PYiqyBzc2UsmGWUxX+bE+XE++fj+Jg1RCMH0fIHrjbnUyEDbOrSZJ3wyRpRP9B0M9EuFQBESNBxE/zbiF2qWVXeuIUQdX1QZPOTxidYEVqVKS/swMtsJpyapuhd46NdaGTyqUisvYobDmJnz1Nx5jn96H1sOl3BqaaLRQdp6W1BUheGDg/RtDwZ3SZJItSfQdJV9Hz1EclcPdcuhO7wTCbg1u0THoUEe39bJxStTRCImWm+KmXKVnb9yL1cLy4ydq+NGNLZ+5ACnrk4Rj5jUWqP82p98AlyfTFcKI6zz0d9/gvmJRWRZJp6JNVBQGq29LRx76iDHnjoIgBHW0XSVTHeKI7+0nyMf2g+AZmjojYy3GTFIdyTZd3wnxioaSQDzk1Eks+HSoQWuEcLBVAKXD9+3MJV2FNnA9WuoUoyoMYSptlFz51AkE0lSMZQWLC8flIuL7RVw/AqanCCktgduE4Dn1wirwW7w3GyB+bli83pUVaF/oIODLZ/F8kqUnCJL9QKaIpHQzYZGgI4uh1FFhPnZCvNZj75+WK7WuDmTo6892NmZWi5z4Mm91G2H8zdn8UMqI4cGqSZ1Dm3vpVCu8c2XznP4QB87Btp559o06c4kVcsh57vs+MAOOlvinL42hScE2w9vQe6IYeoq0ViIeF+atr5AhG94X1+zDun2BB/+7APYvss7SxNUe222D21nuponqYeZlJeRDkfIKjVuLozT9WAHV5wFvG6fRz93lEuFGfalejhXnMZJeXQ+3UvFsZASBm/mbxLbH2NwX5SSW2dLuhe1QQebncmzuFCiuydF1XbIFSv89jP38c0XziCAns4UW/tbm4lLUw8y3clEmGQ8RGtLjN0jXYTDOg8fGebG+CLF5Rqf/cS9vPj6Va6PLWA7Ht0dSZ54aAeyFHTw27a0c9/BLQz2BiKU+3f2cGMi23jWPoos8+FHdvODly8xt7jMQq5EJhUNLGm70+9rcWxVlps27rCe5nm33z1fMDGRI5OJEo+F1lH63gul5s7PxqJmoGUEjE3lmM8u07eJNsBK0uhvfniWYqmGosjsHulC19Q1k18hBMVanXylxmxhmbCukwybxEKBsH4yEqI3neTi1BxdyRi25zM6nyNXqmK7HrOFEvPFMtlShXMTs1Qsp4kuCBsaNdthqVIjEwuTK1dZXC5TrlnoqsLicpma42CoURKNcyqyzFB7C7t62nF9n3ylRiYaOELKsoTXsHuXJAlZCp7VnZN5Q1PpSsWpWjZGg8K8MJ1HUWU6ejdHpmiSQsmtYdyxa+wJwZevvcNwIsPOdDuqHGghPD9xlflqieFkhmdHz6EOy8R1k2/cuMATvcO8MHEVkQBNlik7FrIkYygSX7p6mj/e/xBhVePPL5/k4a5BuiMJFEmi6jp8ZfQMiiTTFopytZDd5Gqh4jp89fo5wppGVDP4iytv8we7j5G3qjx74zwf6BnC9lx8ETg1fm30HDHdoCeSoO4FE61w2OCevX2EGwsB23YJhVZg4wr3Hx5CUQIdtFQiTC5fYftwB57ns32onUjYoKsjiWW56LpKOhnB9wMNgZCpEd/ENh1gdr7It390luVyna72BI/dv30NTVYIsQYeDpC1lnll4Qqu7xFWTVqMKLIkr0E3vJ8iQFl8m4I9hiZHOJD+TbYlnkJvLMx94bHF+gBv5z7PZOWN5veE8JmuTVFza8gNrSEJBVNJNhNMsqSsobdVXZslq0rJsVZpHEmo0oo7mETJmWW+dgEQbI09wZHWf9TQBrqdRHf8CmV3AfAx5DgCyFpFpqoLdJhpZAlmqqcpu/OYSpIjrX9Ad/jQGjt6X7jUvQJlZ4GY1gnIxNUoUTVMTG04o0kGI/En6QjtI6VvIaQkUWSNlWWe69dpXd7Oyex/ouJmma6eJKn2NOgDa++zj0vOus62xFMcSP8mpnJbQ3I4/kHO57/KhfyzzNfPc335BQ62fK6J8FpJ7l0ofA3Hr9Jq7uC+tj+ixRhaRZtyKdqTvLn475muvs2FwtdoC+2k3dxD3XOpuXZzbavKOqrcQlhtQQifZWetYyb4eH6JjZeza+s0X7vIUPwJ9qc/Q1TtaGgKqTh+hUvFb3Em918pu/OMV16lI7wfWSgIQJVCdIYPNMsy5BiqpGMROMz1RO5dh7qCYLErEFh+sCgT+EyUX2d0+QVkSWV36lfYlfwkhryipSZwfZveyFFenf+/KDnTXMh/lVZzO9oqJELNW+Ji4ZvUvQIhpYX72v6I7vBhVNlonNdhIPoQr87/qyYqL2gDDkv2LCE1hi88clYgYB3VQmhyhPbIR6k414grBwhrWwBWIfdu39+H928NbN/vaDiPHBgirOvrxqD3c0jQpG753k08dxwhqkhSCN187F2/7/mB6ykESai70dRWQlVuj/Ou6+F6HiFdR1eUDU0gZFnicn4ekMjbVYpWHUGA7tMUhaim0xtNMlkqsFiv0B9LocoyuVqVjnCMn87c4N623uY8MBNav9EX0lRqjsOkbbOrvf1d67ASrufz/Zcv8r2XLuC6Hh9+dC8f/8A+QnegoGRJRlc0VDnovx3Xx2tsHCqKjHIXS/mVWG2MYznupoYZiiRt6lT580RrKIKh/O2lFoQQFEo1vvGjM/zkrWtMzxewHa+RtNQxdQ1NC6Q2PF9QqdlNbTtVVchmS1Sr9jr9EVmS7ipY7YsStjuL5y/jeHOEjQP4QqUjGqMnEWym+b4gX67REg/TsgHaCGg4lWsbPiOBQJdV+iLBOlSSJLb0ZNg93MlLJ68zOrHIlVvzHN7dFyBwfcHZq9PML5UwdZWjeweJR82m7qQEnMlPciJ7i6prE1UNap7Nk13dDIdCwN/OWu0XKnGkKe14ooQmdaOrdbZu3wLIyJKBLyo4noOqBAmeoZ0fRJJUEB6SZCJJGoocoX/kELKkoyq3tTP6N9GnUAyNQ8e2cWV8Hst2qTsu+XId1/OIhQ0yW9txXA8jYlIo10j3tuC4XsCXlCHRmSTWEB6NRk16t3bd1olpWB+39GQC6pnn4csyU+M5BNDS14qmB5DuhbkC2cVAFyDdk0FVA6tZVVXwPZ8rJ29w8fWrxFJRRu7ZEmRzRWC3rskJ4voOLLdITB/E9S2EcNGVTiTcBtw70PzQFQ0JFUNpw/ZKIBLE9F1UnRym4hBSB3B9H8uz8HyI6zsBhYSxG002cfwSdTeLJsepupMYSiuv/OwK3/jrt/A8n3KpTiRq8qf/9BPsO9iPqSSwPIOqoxKXTDLmWj2TxYVl/tU/+w7Vqs2f/rNPYKtw8dYchVKNeMSgWnfY2p3BclzChk48YmDoKnO5UsAp1TTa0zH62lOYukq2UKZuu8xml1mu1OltT6KrCoVSjUrdwXY9JubzTC0UGOltbXTOm3eQnvApOXVajChvZW/QYkQxfQ3Ptdmd7CJrlUnpEUKqTt6pIgFZq4TtuyzZFaqezbJTozuUIhUJU7CrZIwY45UsiqSwN9VDTLu9mHru2+/wja++xT/+ow9y+P4hEDA1W6BYqtErUpQqdUrlOpoqN2lrDx4e4tSFCV59+wbPfPgeDF0lX6yyVKgQDun4vmBytkChVGO4JRq46Zlac+CVhEBTFRZyJVoaDgP5YpVqzaJQrCLLMqapYTbc04QQtLfEOHtlml3DnXS3J9fBL/8uh+8Lzp6bYG6uSGdnko72ODOzBXRdxTQ1stkyhQaq4p6DA1y5MsPCYone3jTtbXHOnptEAPv39gZaUVdnSCYCi+ozZyfI5cpsGWzFF4IbN4Jdg6NHhygWq1y/Pkc0anLwQD/nzk2SL1TZvr2Tgf7MuvubjIc4sn+AW5M5ro8t8h+++LNAHLsr1Vxw+16QMLp4fZaXT1zn1PkJfAEHdnRz7/7BDXebPN9nV3cbuqo2EDo66UiII1t7GgOtwn3DA/SkEzy4bYBcucpju4cwNZWwobGtqxXXF+zv72JqqchQewutscBxY09vB3OFEuNzS3ieTyocomLZ6JLMQrFMVNdxHZ+DA914ns/hwR4iRjC5UxWZsKahqQF/XZVkNFXCdl1MXaVuu6iyvG4iYDsuqUiIHd1tGJqK53iEo8amk6aVWPnrRjtnpqpxf+cAh9qCccX2PH44eY2UEaLs2sxUS4yV8mxLtuIKn7QZpiMcoycSR2+gNZbqVQp2jZlqiYJVI6xqRFSdR7q30h8LdKAWqmVuFZf44/0PkTRMzmZnNr3efL3KSzM3GUlm0GWVm8tL5OpV0kaImGZwo5jjvo4+EoaJLEn0RhNcWJqnIxxjSzyNLEkYukpbZpXL0h2JzrbMbTHiWNQkmQgTWTCIRU1SiXBzsrmZvtrKs6nWbHINMVTfF0zMLPH159/h5LlxNFXmqUf3sH3r2sm3JwSmqhJWtWZ/E1VNHmrbQVjVcYXLtdLYGvTc+y1KzgzT1RMIfDpDBxhJPNlMGkGQ/EkbW9mdfIaF+iUsL9jQcYWHhExHqBND/vn06MKqjq6otIWizFSLDMeDia0tHCpujabAsnAACU0OodwhrixJEroSJb3qGoUQhBqW0itooRWRbhkFTQ6vE1oONIoyhNVMswxHuBTsZVqNdPNcCb2XhL6xa4wqm/RHH+L68gss1C9RtKeR/CJ5y99QdzKh97Er+ctNlNTKOcJqhp3JTzBVOUHevslk5Q12JD9GWG1p1uVW6SeUnDlCSpL96c/Qam5fV5+UMciBlt8kb92i6ma5vvwCLcYwILB9jw0uacMIUB4/n5ZOQu/hQPqzxPUectZNSs48naE9GEqU4dgT3Fj+IXn7FgV7DNsrI9ww18YWaE1Fm1or7yWE8IM20fhX84pcXX4OR9ToDz/AzsTHMZXVqJRA8Ls3cozeyDGuLz/PQv0i2fpVOsL7kBpaMIv1SyxZ15FQGIwdpzdydE2iUZE0OkL7GEl8mHdyX8AVAaosqbeS1nsoe0VUSSOsGkh1MGUdT1TJ117D8QtEIttZts6SCh1DQiGstjfm20H9N0sMnbwywfEDQyie3Ehu3F3T5P0WktyOokUQfgm4uxDwSqiKjKGplLDwfIHtvjv1esUsBgJEiaYoAULasoiagUj26hBASNWYqZTIN8baa8UsBzJdLNVrzFcDBMlUpYipaOxMtTFbLXGzuMRQsoXuSJyBeIqfTd/EE4Le6Ho6Xcmy2JpOU7IsbO/nex89z+etc2N88TsnqFkOR3b38w8+dnRDkeu6ZxNSdNRGO9c1BVVV8GwX1/VxPf9d3ZDtBlUKAnre3ZJD/62tVgJKjk3V3dx6/r1Gte7w+Wdf47s/vYDn+7Smo9x/YAu7hjrpyMSJhnQMXUXTVF45dYP/9OxrTQFz09QYHukM7tW7zPnW1UUy0JTM/0fee0bZkZ53fr/KdXPudDs30MgZGAwmRw6H5DBIJFeiSImmJEqytdYey+vd4/Ue75dd+/ic9dq7lGUq0kutArkiKZLiaDgkJyfMAIOMRuic482hsj/U7dvd6G4AI87aFPl8Ae7tqrpVb1W94Xn+AVXO4rgFBEFDV0R0RSZXq9MZ8/tERRZZLlYJB7Qt2XWCIJAv1WmJb2Xm4FP7lHWMDU2VeezkLk5fHPfp+2eGObCznYCuUqzUee3dEWzbJdsR577DfTiOy9i1WQorFXbsy7I31o7l+onx46lerhZmKDsBXGX3+zY/+olKHMlSHJk4q4/temtU1wshSy0NVwS23AZAkW8Vbd4+etoSvivAri4My2Z+pURnS7yplj7QmcZ/txpVKa8BZ2z8RxQFxMYg4tzy4k5PLDNyY57uvgzjIwu4joeqydSqJpIs4tguwZCGIEClYpBpjeI4HssLRVo74hw61guA67iMXZ7ENm0+/OuPs2DVqcz70FlREGiPRpgu+K5HFd3GsDN4+B2wrijkazU0SUKVFfpSCebLFQr1CBChZpUxbBHDTpCNmQQVheVqGwHFRJMiVC2dOdclEw7TFg4jiUGCyjKWUyQgtyMKEk98YD/79ndSKRv88ZdfaCbAViMoq6zUa4Cwpcq+IAp+e7geI9MrqLKEJPmuYe3pKNGQjmH5fNZy1cR2XJYKFeZWSmTTMcIBlaCuUqlZLBUqzC4VkUSBctWgUK6jSBLLhQqzSwVczxeP1jWFcs1gZqmALIl0Nri/qzE/V2B0eIH9x7uoOxY5s8L9LYMs1ou0BmIIQEBWaRNjXMnPEFF0VFHCdl100dcNqDsWkiDSpsfoCMaZreVp0aPM1PJkgwk0USEob6+HkogFOX6wh7OXJ8m2xWlviTE8vuQL2C6XmZkvoCgSZy5NIAoCBxrJ0d072nj9zDCnL42z60ALh/Znefv8GC2pCD3dSTzJI6KtDUyCIDCwM80b50eRJYmdfS1cuTlHQFe5OjzH/sEO9jQWcV1tCVRV4vKNWQKawtjUMqIocP/xgTsOWv9Qolyp8+Zbw2Q7Ely8OEln9iAjI4uUy3U+9KFDvHtunIP7u7hydZqbw/OcfnuU9vY4Fy5McuxoL/l8hYce3E08HsIwLBzHo1YzqNVMzpwdo7UlyoWLk0SjAeLxIIZhMzW1wvDwPAcPdtPaEmV2rsC75yfIpCNcvjxNtiOxCTosSz4K7MLVaa7cnOONsyNcuT5LPBYgFgngeT7dMV+oNuC7JoIgsHdnG5//5CnSW9DjBEGgL5OkL7O2WEqF/ZGwW2vox0TXFoE7WlMbxK2P9m6kbHQkNlo9Z+NRrk0tMJ8vc6C3jbDiVwkX8mWO7+hkeG6Fcs0goCq8ND5CVzrOyMwyHckoVdNiZqVIQPVRUoos+XpHnkdYV7k5u0xrIsJ9uzcKT0uiyORynkK1zu5sBhm/vwmH7izeuDfWRVqLbpr8y4LY1LFaDVWUeSQ70BSqbgtECCgKmijxnbGr9ETiHG/pou7Y/Pn1c9zb2s397T2MFnPNMUyVJALSLZNeQVgb4W4nxItHWFF5qmuwURE9QFc4ji7J/Ma+e3h7YYq/uHGen+vfz4mWTj7Us5vBeIYfTt3kRn6JL+6757b90Vahawq9XXcWN781hscX+Xd/8iMqVV9foFIzKVXquK7HsUM9fPDhvc0CzPqwXXdD+8zUcrieS1ugk0UjR80xMN33T/PpJy3y1niTNtMRPNy0gV8fgiCQ0ncSlFLNxJEuacSUGIZrYG8jorxVFK0689UirYEodthBFiUkREzXxPM8gnKKkNxCzVlhtPwiCbWXbOgeAtLtKKAupmuT0ePNOVtS7UcSNWpOjsu5r+PGLVr0/U0EyVbheh7zxhKDXu9dU4IkQUGT/D7JdGuIgkdSC7LVMimtDRKQElseJyS3kNQGyJmjVOwFKvZCM3FkumXm65cBj5jaTVrfbBSwGnGlh5Q+SLWyxELtMnWnSFDWSevh20mDbAgPA7uh6XSnyOh7CCv+/FgSVGpOHpp9T5SAnCJnjmI5VVzPRJUjhIPalkLQd3duHmE53qABeQ29oHFAoCWwv3kvbg1RkImrPUiCguEUKVkztHmHQPCFanPGKLZnoAhBOgJHNiSNVkMQRNoCB1HEILZjNHSupjBcm5K9Qjgaw/U8yk4dx3OwnBVsr9xwT3KoWjdJBE412meVguXHuzemiIV0phYLVOpr7pdvD01y/4E+FiplFgtleloStMTCPzPJI0EIYJtncJ05JLn3rvZRZIlwSGOpodVZqhik4+Ftt181c1mlaQV1FVWVqdRqmPaaTfr6cD2PimXi4tERihJRNGqOzVy1RDYUQxEllutVuiMJApLs048so4HI85kSOaNOXAtgug5BeTMtLBkIslCpNHWO7iZGppb56rdPs5yv0JtN8rmP3UM6EdryedEllYQabjojB3U/WWKYNjXDom7at52De55HoVxrJtwiQe09OzPffXjNOc2PfSTP48K1aV48fQPHdUnFQ/zOZx/h3kO9BDSluf5ejUhjTb0a5XKdoavThMM6uVyFZDJ01zQ8057EtMfR5H4kMY6AjOPamLaDJzUQTZJIOhaiZljbXnEkqHHfvh5kabMTpyJKtOnxDYkjgL0DbfR0JLkyPMfZq5Ms5Mr0tCeZWShwbdTXmjuyp9On8yNQK5uYDfOSgKySUINczs9wMT/FVCVHm5TgUnWegZYUYe3H1+H8iUoc3TqAr4ebi8LWk4hbIem3fnZdl+++cJmzlyf42BOHOLJOcLPp2KKKqIpEKPBjwEzXvbSu6yIrEpnWGF29aVKZSJOO5NhuA1bmV00nx5bo7EnT3plo/k1RZdQGVFHRFD74+Ueax740M89KtUrNskgGg5iOQ75W8ykIto1hOwhAUFUYXc5RNU26E3HEhtPQYqVCsV4nomkU6waW41C3bXLVGoZqs1Sp0BIOk6/VWa76SaeIpvkuRaJGUj+y/qpJJMMkkn5H/42vnd6QOFp9SWRRJLJFZ5pMhfln//JjOLZDezZBNBWiUjOIhwMN+KV/L5KRIMd2dRLUfcTBU/fsIh4JoCkyJ/f2ENQUTNvhiRO7iAY1QgGNcFAjoCmossTDRwYIan5H25IIoyoSqixzan8vmiJveGZs2+H1V67x/LMX+V8Pf4Zd0TZ6QikUUWJ3tG3DdQE80LJzwzUJgkB/xK/O7l2nS9MZ9CehPaHUpmNsFYoscWR/J3abje05WLpNXq3wyFM7GSsvUVRq6JLM/Y/0c3pplBviHNcnZ9kTb+foA13IgshIeYmDOzuxWx0sx+ZKdZZ8qMqBRJbnZi4Tkf2E16S+wgOP93M46dP1nnn8wIZzaW84uB3ck6VYqvH62RHuOdzLcr7iOyq8z3zm/z9DEhu0mmiAnp5U89q0RtLXtl1K5Tq27aKpCnpAIZEIsmNHC4oqEY+HSCRCyLJILm9SrhgsLZdJpyPoukIqFaalJcrSUplkMkS5bOA4Pte8XK4TDmnIskRAV8hkInR2Jre0CxUEgd7OFP/it5/m2Rcv8/qZYRZzZabm8oxNLQMCkiSgqwrRsM6OngwPn9zJwyd30prenAz5/yJWSn5VplCpI0sSuUqNXZ0Z+lqT9LQkqFs2saDO6HwOXZGRJRFRFNjRkeKliyMEVQXTcehvS3F1ch7DstmdzTCxlEcUBPQtYMeKLHHPji48z0OTZepVk4WZPKqm0NaZ2OAetz4EQWC8sogmKrRIGyuMt7acLAo82NHLucUZBHykYkzV0SSZim3yUEcfST2I47p4oojjuRStOmcWpymZt3Dr1x08rKj0RxP8zehl2oMRRov+AjFv1BguLrNUrzCUWySsqKT0IIfS7ZxbmmFnPI3jurQFI0xXCgytLKDJMookUXMsarbF63Pj2J5LQgswUy2+52rcjxOe56OOZheLuK5fcMkkIxzd38Uv/9zJpqbS+lBEEct1MZy19lJFmXdWRijbBoookFJjROQf3zHkJzUK5iQeri/wq/Y0tQhvDU2MEJRT5MwRAKZq02hiGtMxMJy7SwLYnktXMI4siHSHEyiiT1syPbuZyAxKKfYlfp4zS39EyZrhtYV/R0ztpDN0ku7Q/cSUTjTpVqSKT3dbn+DLho6zI/IBbhSfZbT8ErO1c6S13fRFHqZF30dE6UAS1rThbM/BcA0icphbRWZNt0zBmiRvjJE3x6k6K1huFdutY7s1cuZY4yz8q1Akia0K76u/uVWIgkRc7UFEou4UqNn55nyu7hQoWbMAJNT+LZN7q6FKYeJqD5OV16k7BYrmFIo4SHcouWkhsXV4WPYMstSy7bOwPlLaziaaSxR87Ys1v0qhIbLu09o8fIq663lMLxQY7N3s7HPns/OoOSWCDeHvnDGG2XCHGyp8m/HyK9vuW3NWcDwLD5e6k8fFRkLB8SwK1hTgo8i2Q5iBr+OlSTFqTg5BEEmorWhSlIpdQJUCCHaZjBZDEiREVAQkXye0/hZKg7Hg4fkJ2HVi8x5QqZu8+O4wRwbXiiWr0yABCGoqpuVssJ7/6Q8XcBHEIJ5Xu6M4MkAkpNPdlmBseoVKzWRqLkdfdvtihO24TM35VCVZEunpSCJLIjXLomyYWM5mpJMkiBxr2cg6aQmGMR2HbGjdWCOsrQR2xTPsivvz+JQepGpbTJXzHE53bDl3Wq5ViWoaJdOkYlmE7pA8Wlgu8eWvvcrFGzNkEmG++Kn7OTSY3XZe5nguI5VZwkqAsBigLRMlEQ1SLNdZXCmTK1SI3EYfqla3mFko4LoeoYYI+X8pdO5yvXqX/dedw7Qczg1NkW9ILDxyYicPH9+xpTzGqqGQZa89A9WqSbVisrRUpqsrxWwwT09P+q5+WxaTVJ2zuF4dRcwgaYcIqSonOrMb1jwCAnXTbiYzbw0BYVvHNQGoOAa262xgkWWSYR48NsD1sQVmFwucvjBOV2uCN86NkitWCQc17j/STyig4jgupmGhBzXCMX+86Y9kcPFYqpc4ke4l6AQ4MzpNZyL205g4ev9DEASefmgvC8tFiuXNAljrt9sqW/1ew/M8Tl8YZ0dPhu4+v+MJNARxt1pgpzKb4Wu3i33t/gC+Hr2TCW/NrdwK4XO4vW3T9xs/3z0/926jbvuLlVsHEkkS6ciuVfVS0SCphpq/cOt26Vjz+76OtYFl1WEsIIn0ta8hJbpa4s3te9r87z3Pa3augiDQ3bq5olirmly6OIXreSiixI5gy6bzWR93sle8m+22i7JtUHctFuolSpbB/ngHpuegSjKdoThX8jPUHIvh0gJHUz1cL8wTU4OAR1wNYXsOBbPqo6aMCtlggu5QkoJZo2IZmI6NIkp0BRPUHJucUW2ep4QAgkDVNnE9D02UcTwXQREY3NfKuakpemMpHjix46cGbQQQDKo8/vg+pqdW0BtWnfeeHEDTZCoVE1WRyecx+nuBAAAgAElEQVSr7N7VjixARybK4nyRaFinYjkk40GmJ5YJBFQuX56iWqgxemMebJe+rhQzkysEdZWgJuOaDq7pIMgeHS0xrl+dYWJ0kZ07Wulsi6OqMgHdp3/8zbfOMDmxzBNP7mf3ng6g8Qx3JPjiZx7g558+zDvnxvna195CD6g89fRBPwEW1sikInS0xJsOIbcKjRaLNS5fmuLatVlKxTqKIpHJRLj3vp1ks5ttWT3PY2RkgRd+eIVDh3s4dKhr7bmRRJyGO5wg+IhJt7Go6oxHGJlboS+dQHQ80qEgruXSk4rh2S7ZZAxdlYmHAkyvFGmLh2mJh9EVmaM7siwXq4R0lXgoQEBVcD2PqmGyO9vCQqFMJuYnsPcPdvBPv/gkoiiwszdDYN2grQUU0m0xpkeXKBdqGxJH9brF333vPJblcOiRLkpOFcvd6IQhCQIf799HWzDS/F5A4GN9+zi7OM10pUBKCxGSVa7l/aTOUH6Rmm1RtS3+ycEH+NU9J3h3aYa0HuK3D9xPSyCMKkl8cuAAEWVt8heQFX5p5xHemBtHlWR+5+ADdEfilCyD8VKeR7MDGI7FbKVEZyjG53cd5+2FKVaMKh2hKLokE1N1VEmmaBo807OHrnCcdxdnkQSR6UqRqKqzL9mG4TjM5pYIygqZYKjhfgOKJGLYDqokoUpSY3HukDfqJDQd03WRBbEpHm86Djfzy3RGYiiNheequLIkCCiSRF9Xiv/u1x5naaWM7bgEdIWebJKebApV2bov8YCaZTWLCY7nElOCZAMJLNchKAXYG+vzn++fQn0jwKeX46NFFCFwmzWZgCquVe1btRZCcjsRJUJAvr37zmo4rkvBqiMIAhFFa8xfXGzXQRFkBMFHdfRHHicstzJU+DbT1XdYMUZYNm5wNf8t0tou+iKP0BN+oIFCEhtObu4GEXNNjHE8/euktB0MFb5DwZpgqvoW09XTBOUM2eBxBiJP+lo3YhBJEEmqMWqO0bzTjmcxVz3Plfw3mK29i+VWkQS1QZ+TmgkT21vV2wHTdbAci61ypooY4HZW8JoYgdU28arN702njOv5CwhNijbFmbcKAQlNDCMg4no2hlui6tS4Xlggo4fvAhEgoMp9GPZ1vLugBvkIH7/FRGR0OXaHd8Xv37Z7J+8UkiBjNhA9Gbow3EKjbTwq9iJVe/m2+69qbnnrED8+Yq0M0HgWtl8EiYKE2nBuEoCqU6Rg5Vkx5wjLcVRRZtksYrk2ASVDXL8XQZCQxSiJwAPN/cC/z6vaaUd3ZjEsm088dIAD/WsMB61R7DBsh3y5xkB76q60Z356QkRWj+M6M9wtfTIcVNm3o503L4xRqtQ5NzTNyYO922rPFEo13rnsG8AEAyqHdvmJu3Q4xHKlti3SxTBtynUDURCo1Ew0VSYiq020TqlmEA/pSKJIvUHpyjSQTz5FVeHe1u4tjw2QCgS5vLjQQGJu/86vOoJ99TuneeP8KKoi8UvPnOCBowO3fd9N16Jk1fzik+fRkgyzozvN+MwKs0sFbowv0tma2JJ+5nkeU/N5bk74AtEtyQgDXZm/15rkTlG1LaqWRWbdvEoQheZ74HoejnP3RSrLdlgp+v2rpkj0ZpMbHN3WR82wGJ5YbGpmAcRiAdra49TrJq7rEtimULhVSGKCePCjDeOBdfqKG3RMXQzLR3tVjfdGz/M8329SE+VNY40siZw63M93XrzEzEKBdy6Nc/+Rft4dmsK0HHb2tHBwNdHoQUtHHHtdwsx0beZrRVaMCgv1Mj1qGsfz3rf+6CcqcVStmdwYXyRXqNKSirCzx6dd3RhbZClXprsjSXd7gsWVMvPLRTzX5zAapsWegTbGp1dwXY+FlRIdLTH6OtMNK8jNC3fTshkanidfqtGbTZJtjTO7WGApV0GSxKZo4u5+X+fo6vA8pYpBX6e/7fxyiXLFYDFXRgD27+xAVSUuXZ/lm8+f58ieLro7Ehze00mw8bDeumCrlA0mx5fI5SrYtusjEtIR2jrihG7Rl/A8j1KpztjIIsViDUkUaG2P09mV3AC9M02bq5enCQZV2jsSXBuaxfM8du1pJxTSmZpcZmpimUQyzI6drSiqjIAvzDxyc4Hde7OEIxqjI4usLJUQJZFsNkF7Noksv/eHzmtMPlYV523b4eb1eeZm881tAkGVg4e7b/tSC4KA47jMz+WZmc5Rq5oIAkSiAVpao7S0bnYnKxVrTE6sUMj77RsMqrS2x2nviG/IWLuuSz5XZW42z9joIteu+Foir7w41GxbURTo7c/QfUu22vM8DMNmbHSRleUynuuRTIXp7kkTCm9dAXAcl9mZPNOTy9iOSzIZpn9HC4Loi46uHRts16FNj5LUQkxUVugOJTFdh6V6GV1SWDHK7Im10xaIIiI0qnwWCcB0bEq2ge26pPQIbYEYM7U87YEYCS1IWgtjujYFs4bhuDw7OURXOO5PqhtJJVEQMBybimViujaW6yDrEv37M5xs6XnfKgs/KSEIAl2dSbo6Nwsnx2I2K7k0A/0tSKLA0MVpwgGVRCxAMhZkemqFeCLE3EweRZEwKibdHf5gXsxVcQybwYFWVEWiUqojuBAOqYiiSK1qkowEESV/20QsyLF716zL3z49wrtnx9izt6OZOFo9XwHIJCNEFIXSfBk1Feah4wMkEgFq9iyKqKNIW79b1YrBf/zKK7z80hDVqomqyQ03CYmObJJsdnNy1XFc3npzmK/91VssLpYIiX4/DJBqj7MwtUIgpOHYLotTK1imTTCiE44HsWby1CybmiQSjASYXJzCc1wqYZ3evVkk3X/fdocy2K6LaJs+MiYSJBEJYnuOz3NPhBoJCT8xkVknTNjZFicRDhAIKii3TEAFQSAQ1IjEg8TTGyHx8/MFvvmNdzBNm7YdMWLdISTBp6A6rtekXOwIpTDqNhXPbNr/ioLA8XQnB+JtSIKAKstcWJ5lMJ7hQ927GSut8JWhM9ieS180SV908/N1X9tGmp0gCAQ8mUNKG+FGX6J4EqLh8XC8tymWKMsi07N5opEAR8JtOAFfOLNSNJA8OBHpQBRFIhGdC0tz3Mwt82hXP47j28ROFQvkazUMx6FmW8T1AFbj//2xJBOlPIdb2snoIURBIGfUuLK8QFsowmgxR0hWOd7agSj44+ZSrUpSDzBdLjJZKvii5YJIJhjieGuWSFjnnkO9Wz6P24UoCIQUlZzhuwKOlRcbKNAsSW17asNPU6xHh9Cg0G8X68WKZVFhuDJM3akRkbemB90amiSjSzK9kSR6gx7oeh6Wa2G4po+uENb0ZBJaPwu1S0xU3mSudp6COcFM7QyLxhBTldMcSPwCrYEDCAhookztVi0kMczO2NO0B48yWz3LROV1FutXqNgL3Cg+y0z1DH2RRzmY+AyaFMNybUx3jR4wUz3L6cXfI2eOoYlRukKnaAscIqp2ookRFDGI61m8ufglFuqXkASBlkCY4YK55WLz/aJa3C6EBg117Zc8REHE9py7R/EKAngud6NKsiGJJdBEGG0XHn7iuCNzZ3es7Y5geSYKWkOfyE8AiSj0hR9uaDrdOVoDB7dBVG2cL2319/V6WQICZTtPQvWLgUWrSlDSEQURx6siCgodkV9CYI1Ssia0vw5hIAhoisz+vrYNc/qHDvWjKTKdqZiPmG0IGf+shOcWsa2zeF4FgQAo++64jyiK3HOgl+++dInJuTyvnxvhiVO72NO/WXLEdV1OXxxnqEHVGexpYbC3pVmUn8kXaY1sXUQv1wzeuTbpz2cth7ZkhErNp8XN50oUynWiId8EJBb2zTnSsTXa2J3uY1TXOJH1k1jqbQS+bcflhdPX+f5rQ4iCwCMnBvngA3uaAuPbRVgOEJEDTZSQLEk8dHwnr54dwTBtnnvtKod3d5KKb75+2/bt2xdzvmPmkb2dtCT/y4yZqiiRDcfQ1rWB2kDQg69XtFKo3LUuqigKaKtsHdejWjfZGhIB10YXOHNlcsN34bDOocPdSJJIvW41zT7uJvzi5+0lDURBIBbSm0nj9xIeULGNLXswQRDobk+wb6CdmYUC18cXuDI8x9i0n2w/sb+bSGgVBOFrs4aia+c6UlpitpZnV6zdR0C6OiFNfd/6o5+YxJFh2nz9796lXDEY6E6TK1Rw3BSvvDPM0PAc/V1pvvn9czx8z04mZnKMTS9Trhqk4iGK5Tq6pvD1771LMhakJ5vklbeH+dTTR9i5BcTWdT1efvsmo1PLdLUl+Pqz7/KhR/bx5rkx6obF9HyevmyKhZUS8U+c5NzQNPNLRVqSEf7qb8/y6Q8d5erwHN9/dYiH79nB5GyOiZkVPvHkYd8m0rAJ6ApBXW0kAzaGadq8+tI1nvvbc4yOLFKvr6FxYvEgH/nYUT79mXubN9myHM6cHuEbXzvN2OhiU9g1GFQ5dKSHX/jcfWQbC91K2eDP/vQVgiGNtvYYr7x4jWrV4LEn93H85AB/+dXXmRxfQtdVPv/Fh3nqQ4cAOHd2nC9/6Qd89vMPMjuT4523RqjVTEzTJpEM84GnD/CJT57YltqxXbieR08kjt7gBVuWwxuvXeelH13FqFvkc1Va22L8L//2Fwhktz/27EyO73zzLG+9cZPlpRKu6+G5Pq0v25ngn/3Lj9HZcElybJfnn7vIs985x9xsHruRgfZcj1g8yKNP7ONTn7m3maiqVU2+/Y13eOuNYfK5CivLZQRR4E++/MIa3U6R+PQv3rshceS6LhNjy/zFV1/jwvkJLNMBPBRFZsdgG5/7woPsHNw4uajXLb79jXd47nsXWFkqoSgyWkDh2PE+xAY1ZzUSWpAn2vcCbECsdYU2LjpXj78nttG2fX/CH8i8+Np33Y19Vylzq5E3a1RtqyneuyrY5lNvfGvyVeSAh4/Gkm5Tkf1pDFWVOXqkF/Cfsf7BtibtVNcVoomgj7hpOF60dyZYmCuQSkcoFWu0dfgoOFmRMOoWqqY0aWiW6dC3w0OSRSzT3uAodbfR2ZXk4KFu2ttjhMM6Hg5F8zJhZQeyFNlUXfY8j8tXpnn2e+dJpcL8xm89TldXCtdzqdct+vpathxoRFFkcLCNw0d6OHy4G11XqFUMHNtHGhWWSgRCGnPjiwSjAVzXRZIlMh1JlucK2KaDJ3s4jotRM3Esh67BdlxF4EdT1wnKCiXLIKOHsT2XolnHadCqKpZJWzBKzbYomDVcPA4k28kG1ygxk5PL/MevvMo/+oWT7NrdsfGaXZdCroJl2psSzYlEiMNHenBcl0BSpo5B1TIZmZ4iFtBZLFXoSydYKFUwHX9x152Mc21uiWKtTiyoY9g2+7OtdCfj3NfWw7dGL/N/XngVSRT5aN9ewu9RR2g5V+HMuTH6etKYloOqSEQiAfKFKqlkmMWlIqIoYlkOuqYwPLqAIIDjeNi2Q093mus35ohEdE4c7SUTCLEnlcFyXUqWieO6pANBMoEQS/Uqu5MZXpgcQZdlknqAumPTEY76YtrLCwRlhYiqUjQNVEmiJxJnvJhnulykZtv0RuNULJOlWpWZSomyZVIw6j7tZZvFuGXaLIwvEk1HiSRC1CsGsyPz2LZDMKzT2puh5FjMVUt0hmNNGuFYZZGYEqQv3EJE1tkZbX9PbfsPLVbRE45nNkwwtp98W+46FIxroogKVbuC7Vl3NWm3PZeSZVAtLjEYa0GTZH+SrEZ8se11uwuCiC7F6A7fT2foXsrWPPO184yWX2KmeobxyquUrFke6/hXRJVubM9hoZ5jMNK17hgCEgoxtZOokmUg+gR5c5zp6jvcLH6fvDnOpdzX8DyXY+lfx/U8XM8vRhlOicu5r5Mzx9ClGKcyv0N3+L5NNLG6U0Bqolh81Np2FsqWW8b1nC31cwAMt9gQTBaRhbXf0aQIUmMfwyniYgNbJ2hcz26gZ1xEQUYVI1ieQMU2WaiX6AnfjX6Yh4fDJsugO4TjmsiifltUFUDdsBmbWfERsLdxINouVFFrKJ54qFIEUZBxPJO2wGF2xz76nhcyAiJK4z2w3SrObTS7PM/B8mrrzkXHw6PqlBAFkYgcYNbz6b+Ws0yh/jZBZWBDm7iejS4nNyGbDMvm9NVJjuzMMjSxwMvnhzmxp5t79/YwvpBjIV+mJRb+qUJj3ykEMYIk70IQQnhe6c47NKK/K80nnjjE//1XrzI+s8J/+E8v8Zv/6AH6simCARXP9SjXTE5fHOOP//p18qUaiWiQT37gcDNR4nkQ0TV0ZevEQCigcnhnFlWWMC2neV9EQSDdcP/0j+M13YrBX78Yhm+sY9t+f2HbLvlSvTnXLpbrzC4WG/qsvjC6LEvoqrxhjuF5Hm+cG+WPv/EGxUqdXb0tPHnfLgqlOoXS9mwYSRKYIw/iRpv7ew/28tDxHfzozWu8dWGML3/tVT79waNkW+NoioztOCzlK7z41nW+8fw5f77SnuDjjx38sVzTbheyKOJ5Hrn62nunqTJ9nSneujBGtWby7CtXyLbE6WqLIzUMMvz2dQjq6oY201WZvmzKR/KZNq+/O8rDx3fS1dAn9ulpJkOjc/xff/kKiytlP5e+ShsVhGbxPxTS8Dx/XlQzLGzHn6fbjkuuWG0ilRzHZX65hNbQ0/Tvp2+8oiobdYo8/GdIEUVs22VhuYTtuE12kwcUK3VmFnw93Q3PhyYTkjVyZpmt+m9NlXni1C5eeucmiytlXjh9nZVClWhY58FjAxu2nR5bIhjWyLTHWXUZFhAIyyqSICJavqmMdZfC7XeKn5jE0Uq+wtjUMl/45Cmyrb7Ys+vBuatTfOCBPRwYbEcUBd69MkU8GuDgrizzyyW62uOMTCyRL9ZQFYkTB3s4ur+LfLHK0Mj8Nokjl5dP3yQWCTAvl5hbLDIysYTneZw81Msb50Y5fqCb186OsLhS5pW3h2lNRwCB6fk8k7O+ZX1fZ4oPPbyPoZF5vv/qVWRZZM9AG5lkmEO7s/RkN1eVXdfltZev8fv//nk8z+O+BwYZ3N2OokjkVipMTiwzsGPtnD3P49zZMX7/PzyP47h87OeO092bxjRt3nlrhFdfvkalYvA7//RpotFAYx+4eH6STEuUz//6w3z/2Qv86PnLjA4vcM+pHTz06B7++q/e4ofPXeKRx/eiNfSU6jWLv/nrt+nqTvPpz9xLPBFiZjrHc397nq/9+Zu0tMZ45PG970lUzXJdCqZBoLFg0nWFT/3ivTz19CFmplf48pd+iGVt/zB7nkehUOOPfv8FTr9xk+7eNJ/6xXtJpyOYls30pD/wR9dlW0VJwHVcIlGde04doyPriwuPDi/w3Pcu8I2vn2bn7nZOntrhZ7R1hcee3M+JewdYXCjx+//+eQJBlf/2dz+Ipq1BFFtaN1beZqbzfPlLP+DG9TmeeGo/u/b4lf2L5yd48YdX+IPf+yG/+88/Qlt7rImYeuv1G/zlV18nFg/yi798P9nOJLlchRd/eIWJ8aW1JBdgu7b/0jcmMnczyVrdZo0SIPkUMwEkNouzuZ6LgEBcCxLXWOtxAcvz7btVUdk2K/6zGpIskkg1YPDb0FDxIN0SRZZ88ff1WkXR2Npiw/M82LpQ9p6iuzvNP/8fn0EQBGRZbED8BVxva10T1/UYG13CNB2On+jnkUf3NJF4t6t4CwIcPdbLgYNdDcG/ddsLAt272hFEgbbeDGKjbQRBQBAFjj26t3F8mgO8IPjJKMtz2R1vwfU8Jit5IqpfqV51HHM8j9ZABF1SqEgmIUVFk+QNzoSu6zEyvMDly1PUakc2nbtlOeCBsoVAYiSi89/84ycBKHs1LM9B8WTGrDLRALTFIqTCQVzPI6SpvlizqtCRiNCbjiOLvtZCNOBbkvdHk/z2gfuaVD21kYh9LxGLBNi7J0tQV6jWTAIBFc/zaG+LEW/QehWl4ToXUEjEQyiyTxFTVZlMOgKeRyDgV5tWXdVkUaI9FGnqIy3WKnSGo6QDQe7v6Mbx/MVeOhBCbJz13mQGD1isVUgFgqQDITKBEAFZJhv2qWklyyCmaSiiSHswTFswTED2+4/ANpP63FyeL/3On/KhX3ucB3/uJIXFIs/9Py8y9NZNQrEg//0f/yaRTJR727rQGjSAQ4keClYVw7EoW3Xkn4EkdkTp8GlN2JSsmabe0a1he3Wq9ppgsi7qqILRSPjfXcV1sVZiplqgJ7zOVQyBzkAb2UBr85m4NURBIqp2EFHa6A7fz4XcX3Jh5T+xYg4zU32XSKwTx3OpODWKVpWUthkBJQgCihAkre0mpe2kM3iS1xf+dxbql5movMZg7MNIQpSQFEBAoGIvkjfHAI+UNkhv+KEmzWlDu7gG9YZgOJ7vhuhs088VrVkcz0LeIunjeS4FcwoXB12Koa/TcdLEKBGlnaqzTN4cw3brzYTfrWG6FYqmr9ejimEiSgdlWyajR8jodylh4LnYzgKWM4Mkbi/EfWuIgoQuRpBvQ/UCv29pSYYbSeutlgu3T1jpUohAQ+MopnYhCwEsqn7beAbKHSr6m89bJtIwwLG8OmV7joTWu+W2hltqCsQDlO0CITlGUm1DETU8LCpOHddzEcUghrPIfOXbyEIYXekirO5GFgPYbh33FupV3bC5MDzDQDbFaxdHObwzy7kb0xwe6CCkqwT127tW/XSGh+vMIsn9eG7hrjSOABRZ5OkH93FjfJEfvnmNc0NT/M9f+lv29LfRmoriOC7TiwUuXJumXDXQNZmfe+IQ9x7qa1KXw5rKAwM92+r26KrS1JlZX1xtnvl6zZp13xdLNf78b99hdHqZeoPWVqtbLOXKzXXL3712lXcuTxDQ/d/QNZlMIsKnP3iE3nV6TYZl8/3Xh5hZ8J/J6YUC//YrP7ojtTqdCPGbv3wf/R0x9HXI8UhI43PPnCBXqHL2yiTfe+UKF67PsH9HO9GwTt2wuTG+wI2JRQzTpi0d5Zc/dpL+rs0uve9nKJKIto4SLQgCDx4d4OW3bzI+m+PlMzeZWSywf0cHuiZj2Q7lqoEkivzqz5+iJRnZsO/x/T30daa4Mb7Ixesz/Js//D7H93YRCelU6iajk0ucvz6Dadp84L7dnL40zlIDXbVVnL82zTd+cJ5a3aJuWtQNi0rNZG7Rvy/5Yo3/7U9+QCTou3gHNN9V+ti+Lj7+2MENbJViuc5XvvkmM4sF//kwbGqG2fx9x3F57rWrnL0yia4p6KqMrilkEmE++8wJpIiwbfFCAPb0tzHQnebq8BxvnB8FYP/Odrrb1wwoXNcjENI2CIUn1BBl2+DNxREkQWQw4OdP3i8g7U9M4shxPQTBtxkUG5Qdx3JwbBdVlhBFEUWRcF0XURRQFD+7q6m+srrn+pNzuSGqLMkSzhZCadCoNrkee3e0kW2Lc2RfJ+lEmOdfHUKR/YygpvoVNl801GPfjnZaMxGO7e+iuz3J6QtjxKO+iPPqRB02Aui2ejnzuSp/89fv4NgOn/2vHuSpDx8i2NBAcl2XasVE0+TmvvW6xXe/dZZCvsoX/+vHefLpg80F6JFjvegBhR8+d4nTb9zksSf3N39HFOGxJ/exa08Hruty/uw40ViQZz5xFDy4fHGSifFlCvlqMyHiOC6u6/G5LzzYSIII2LZDKhXm9/6P7/ODv7vIiZP9Gxa9dwpVksgZNVzPpSMcQRElwmGdcFhHUSUCQRWrUNt2f9f1OPv2CG+9foPB3e381u98gP6BluagbJo2luVssIAWBIFHntjLqQcGiUYDiJLvSHTi3gFESeDP/vRVrl2Z5tjxPlRNRpYlunt9JNH01AqKKqHrCnv2Zbelz7muxwvPX+LihUme+fhRfuVXH0Jr6NEcOdaLJIl891tnefO163z0E8eRZIFazeT5Zy/iAZ/+zCmefPoAsizhuh69vRn+9b/6ZrPy4Xou09UlUlqUiPLeBV+XjCLXShOcTO1hySggCxIt+mba0UxtiYgSIqaEVhuv+behwiTDpWmebDtOWLk7bYyfpbijhpXAGtXxNoXH92sQF0VhA23VdR0UMYbYsES+NTwPKuU6kiQQjQY2DIh30u4SBAFNu/1ifSufAek2FVgVib6Iv1DtCsdRRLFZ/W2SBdZN+raaABqGxfVrc1jm1kKFiiIRiuhoW0CW11enSpUaU9UlekOtnOjrbIhL+9SDsL5xMRkP6JvOafWzJv14Q2wkohOJrC2ubr3mrgaVcPXz4I7Wzdt0rlmWR1SNiOpX3jKBYPOc20Jr7j8xTd+ybVc1ANtDEdpDa5O7VGCtf4prAe7r6NnyXLcLz/OwDAu3MV6nO5N89n/6eX7wZy/z+t+8g+f5lcy4vtYHpbQIH2g/iOdBWHlvi89/qJHQelGlEHWnwEz1XQYiT6BKGykHnuc1Ld5Xw/EcFowFDNdA4O7oMyk9zJ64RFILIotrhYtVzZc7hSCIaFKU/vCjDOW/jeEWqNqLrFJLdVEjsA19du0YAgIyKW0H2eAJlozrWG6NmpPH8XRkUQbBd9pyPN/dSpeiSOLm467auFcdH+rv4mt1pfTgln3jcv06NWcZTdpM6ajYi6wYNwGPkNxKSFkr9GlShNbAQebrl8mb4yzVr9EdPrVlmxXMSZaMawCk9V0EpDjz9TKma9+1cK0sZYgEntrWPOZ2sWKOE1IyqNugqgT8+ZXr+YK6q+GjsBqaUa7ZKFBs3a9brtFs37jaS0ztpFZbZqZ6hoI5QVoffE/nLAkKSa0fSVBxXIOZ6lmywRMbqJng3++F2mVMd20BGZTCjFSGKFhL7I3e69NfJR1JkJDEACF1EM+zcDwDr6FTJSLj4TQ/N9uggf547eIokaDGscFOLo/O4bgeS4UK5ZrZnMv97ISA51WwrXcRhLunNwqCQDwS4Nc/eR+RoMbzb1xjbqnE3NJG1JIAZFtiPP3gPj711JEN2lvrNWrvRPPcqv/brk+sGRZvnh/l+vjitscrlGoUShvXMOlEiCfu27izKSoAACAASURBVLUhceS6HnVzDSFXrhp35Vho2Q5LtSLlYpG90R40LdI854GuDP/4sw/zR//5dd65NMH4zArjMxtdFkVRYGdPhl/52EnuP9L/9zeAuovwgJJpMFsp0buOaTLY28rnPnoPf/ifX2d+ucTQyDxDI/Mb9m1NRfiljxzf8J0gCHS2xvn8x0/ye3/+MrNLRS5cm+bSjRm/IOv4MgKJaJBf/PAxnrp/L7lijaXc6Nbn58HkXJ4fvXV922twXJfp+fym7xVF4qOPbjQOqtctXj07zPRCYdP2q7HV85GKh3jm0f0YSp2b5TkGox2b9hMEgVQ8xPF93dwcX6RWtwjoCvfs793QHzuOi6rLBMNr86C2QIxfHjjln6NtsVypoqe1vxdqdKv4iUkcJeNB0okwz71ylb072pEkkT0DrezZ0cYr7wyTK1a5dH2Wew72sLC8NQyyWrd46/wY1brF6OQSzzx2wEelzBdYKVSZXyoyt1gklQhxdF8XU3N5UomwnxxpckOFDf9oqszB3Vmm5/PEo772g6/bs3UyXRAEwiGNd69MYZg2vZ2pDdbCQ1dmGB9b4uDhbh7/wP4NWkaiKBKObJwET00sc+3qDP0DLdxzascGCksiGeLRx/fyyotDvPHqDU6e2rH2t0SYZMoXWMxkoqiqTHd3ikgkQL1uEk+EGL4xT722Eeo7uLudgZ2tzcSMLEscO9lPV0+KsdFFpiZX2PseEkciAkWjTkL7+yUeDMPijdduIMsST3/kMDt2tm7o5DVNaSKm1kcwqBG8xWVAVX0KWTCkkc9Xt00s3k0UC1XOvD2Kqso8+sS+DRS+SFTn+D39PPudd7lwboLHntxPLB5kZjrH6MgC2WyCk/etOQOIosDOXW3s3tvBqy/5E8np2hIXCsPcn97PteIEQ6VJ33pc9kVR82YZWZAYiPgK/xPVBWJKiIPxfk4vD7FiFlFEmYJV4d3cTXZHu2khwYX8COOVOZJalF2RLr478yYROcAjLUcAj/OFEVRB5p7UbrqDLUxU5nFwGS7P+OLiSpCFeo69sZ67tkH+hxg3bszx118/zTMfPUq1avLSi1cJBFU+8IEDdHYmefHFq7x7doxEMsSHP3KYzs7UhgpjtWpw+dI058+NMzOTR5IEurpS3HvfTnp701tagtq2w/Vrs7z6yjXm5ovousKBA12cONG/LcpvZibHV/7kZSqVtQlINpvgi7/5GJ5oIwtBJDHY1Gooler88AeXmBhfJpercPPGPI7j8sILV7l5c20gHxxs53O/8kDzmjzP48L5Cb72V29t+P2T9+7gI88c3mSLuj5c12NxscjFC5MMDc2wvFRu0kb7+jMcO9ZHZ1eymZCyLIe5mTzXr80ydHWG5eUyCJBORdi3v5NDh7tJJtcSHZblMDQ0w/lzEwzfnOfSpSkqFYM//sOXiMZON88jlY7wmc+cIhzVmy6XtZrJH/3BC8zNrQ38sViQj332KKLua/aEt+CGW5bDxPgSb755k4nxZV8XqS3GwUPdHDzUvaFfd12Ps2fGePZ75/j4J47T1h7jjddvMHR1lmrVIJUKc/BQN8eO9TX7f8/1KOXKlFbKOI6LHtSIpiPoQQ2zbrI8kyPZnkBr9DulXJlqoUYqm8QyLEq5MuFYiNxCAddxSbTGCMX8RFGlUKVWrhMI6+QaE55Ea4xQdK1fty2H/EKBWqmOFlRJtMZQ9bU+zqxb5BcK1KsGkiwRigWJJsOIjefUMiyWZ/OYNRM9rJFsjSM3nnnP86iV6qzM5RFEAesWUUlJlgjHQwQiG8cMz/OolevkFwq0dGcINRJGruuyML5EKB4kHN/azvinIaJKJx2BY4yUX2C6+jY3S8+zM/pUk5LleS4le46rhW82EyTgz0l0SUcRlbvus1VRoi+yPVXK8zyK1hR1p0BU6UCVok2K1urfHc9k2biB5VUBgZCcQUBAEWX6wu3okorr2czXLqFLcUJyGkUMbqAKeZ5H1VkhZ47hejaKGCAgxTFcCU0KIzaoS6oYoebkKFrTVO3lhhj3KvrWZql+nQu5v8Bwin6bNK6x7nlbpsGK1hSXcl/naOoLBKREM8Fad/JcLXyLvDmOiEJn6B6C0hoqSxI1+iOPMVZ+maI1zbmVPyMkZ0ho/c3khus5lKxZzq98laq9hCbG2Bn9IIoYoDOoEFMCd60dKAgKsrQZ2X43YTrlhu7Q1uHhL1qrNXPD97oURxVDVIDF+lXK1ixRpWvDe+d5/njjAUVrhRbXICDF2RV7hqX6dfLmOO8s/QHH079BXO1CErRmG7vYWE6FqrOC6ZZoCxxad70iGX0fCbWPJeMaI6Ufkg0epz14uEkncz2HnDnCteJ3b6FsGrRoXdTdKo5nM1fPMVtbYW+sh7AYoSX0oU1t4GIhCTrCLcm1kK7y0KF+hiYWePrkbl8aoK8dXZWJhQPoqvxTizgKBTV+89MPUCjXUGRpgwumrBzAdWZxPR3TcpAkkb5sin/xxaeo1UxSiTACQsNAA0DANG1kWaQ9E+O3fuFBHj05yKvvDvP2jVGW8mVaAnHSiTAHBzs4dbiPno7kthRA057HdOYY6E7xr//JSQRBIZUQMe35BkKzoZAjSHiehYCEKm/WU6qW65TyVQzD4peeOEKhWEMLKLiO11wbyrKEaVhIso+6TqQDCN4iIKEqLv2tM3i2DSggqGhyiC987AAffzgDggxCDNxlfDc6EYQgiH7CrWLXqdh10lqMgKoQa1VYdHMbENzgrx0Ge1r4F198inPXpnn74jg3JxabAuBdbXEODGa550AP2ZY15sPsxDJ6UMWoWYiSSBiJ3/3MIximRUCWWZ4voqiyT+WyHJLrnMEBnnlkP0cbDuWDPS2b1sIRVWN9slxTZT744F56syleOTPM9fEFVvJ+UjcU0GhNR9jT30Yiuh6J71Kxl5BFjYdP7CTbEufN82OcuzbFcr6CACRjIXb1tXLyYC/7drQjSwK/9un7OHmij/5smpVqjZploUg+uEMUBAYGMvz2Fx4hoMi4q6A4D5KhAIok4Xo2JWse17MQBYWI0oooyGQSYWRJxHYNyvY8rucgBWX+h197HNN8b2tJVZHoaktQEWvM1/PbzllEUeChYwN87+XLLOcrdGRinDrUu6lo7dj+fQIoWXVczyNn+u07Xl4mXzbIysn3zUnvJyZxFNAUfv6pw5y5PMm10Xm6O/zB8LFTg7x9wc+knjrSx6HdWYYnlgjqCrGIL0orSyLpRIhQUCURCzE1l+OhEzvZ1ddKpWZwfmgaTRK5eWMOz3Q4vq+LPZ1pzhRqvPLaNVozUcKyTEs0iF2z6MrEqOZrpEIB7KrFwb423rk4wWtv3iCTDBMSJOJBHc90KearSJ5AVzLK3HSOlrYYH35kP2cuTzI+s0JX+xrKw/M8FuYLVCsGAztbCQRvX3HzPI98vkqhUOPo8T6Ct2wvCAKZ1hjhsMbU5AqGYTeTEaomN5NMouhTV/Sg2kh4CU1+6HrOrCgJJJLhTfoqmirT0hpjcnyZ5aXye763rcEIcU3/e9EJbNtlejJHKKzT23/3TgCr6K3xsSUW5gtUygamaTMxtoRp2D92NahYrPlaSAL8v+S9d5Rc133n+bkv1qtcXdU5BwCNHEkiMYuZlERFKkumLcnWeL279o49R3N21ju7M2dmPbZlz3hky7LlkahgKpCSmMQAMIAECSLn1OhGo9G5u7ryy/vHKxS6iQYJUj5zZPl7Ds8hqupVv3r3vvvu/d7v7/t98YVj7H9zIcM9NjaH78PsTBGrqn6YnixQqdg0NCXQQwvJLk1XqEtHa5ONjJZAETIV1yJnl1CFgulbFJwyRadCQg2MagcLY4xVZqjXE5zKDxOWdcquybpkH0dzg0QVg7gapmCXqm3tE5Z1DmcHuKFuOe3hBroiTdTpMZ4f20fBKVF2TTrCDQsUSmFZ50TuPHE1HBhK8utdGjKXLfHmnnNEIjonT44yOZFnLlfiwvAMN2zu5UeP7sE0bfL5CtnZIl/53TuJx42a6f0Pvr+bp586iGk6RCI6nufz+u6z7NxxnI987Hpuf9/KBYSnbbu8vvsM3/jrHUxN5THCGpqmsOf1s+zbe45yyVy078uSRCikUshXmJrKMzg0xcx0A57nI0kyFXcMZd6ueaVic/ToCBPjc0GMpxXEa5sVm2z28iS7WLyy5l5RZTRNoVg0GRvNMjIyS2NjnLfb4HNdj5MnRvn7v3uRkydGcV2vNkY5joseCiZjl/zJACbG5/hP//FnjFwISoIv1abvLZzj2V8c4cablvHFL91GLB6qEk0OBw+cZ++b56hUbMyKje9DoVhZML5pmoJtOYyfn8HzvGo9OLV7MZ8rc+bMOPG4wfs+vBJP96m4V3pomKbDSy8e5/vf283ERA4jFHhV7dt7jl/84jB33rWaT3xyK7FYqNpmPhMTc+x65RSNjQkmJ3PsffMcoVAQpZrPV3j++WN84AMb+NRntqFpCsMnR/jef3qc7HgWx3bRQhr3f+kOtjywkZEzY/z1H3yb3/yPn6RvfTcArz+xj12P7eF3/+vDDB2/wGN/+TTty5o5d/g8xVyZvnVdPPSHHyTdkuLAjqM8/8jLNHbVM3h0mHKhwootS3noX3+AWF0U27R54bu7eOWxN7DKFrIqc91d67jn4dsIRXRsy+Gpbz7PnqcPYFsOjuXQt76bT331Q8TTMWzT5plv7eTlH7+BazsousIdn7mZ7Q9ej25ozI7P8aM/f4Jjr54kkghT357BLFlXXOfFcP74CH//b7/Pl/7kM3Sv7kAIwez4HH/xr77JB75yN9ffve5aBDH/LHHJQHqicoyCM8b+6X8gaw3REFqJLDRK7hTDhdeYMk+S0NqZs4L0IUMKsSEV7OQqQsH3vcAnyTeDmHq/QtYcqqWBldwpZq0BdCmOIoVQhI4s6Ugo88Ygn8nKcd6Y/O+kQ0tI60uJq82oUkDomm6erDXIQH4Hnm8TV1tpNNbgI7Bcm1krT2e4CXA4PPt95uwLNIRWUKf1YCh1qJKB6zuU3RlGS/sYLgaEdXN4AzG1mVx5mqHSCHVaQDhlQsvI2ReYMc/y5tQ36IhsJSQnsbwCWWuQM/lnKTszJLVOstYgkhC0RuO0ReJX7MALJAy5joH8C5hujtbwpqohd5GLpb2cL76Kh0N9aDl9sTsWEF0CQZ3ey8rkh3lz6htMVo7y8vh/pjt2CzG1CZAo2KMMF3czXj6MJFSWJO6myViDEBJhRSOsaNW5mYvjm7ieieOXsb0yeXs0uPq+R94eYdY8V22jEIqkIwvtqr5M8yEhE5Ljb1sm43k+qiovWMxBkM7WaKxh1hpg0jzOnqm/oTO6HU2K4PoOlpdHkyK0R7bSGV6Oj48iFEDQGdnGWPwgp3NPM1LaQ3FskpbwRuJqK7IUqIjKVaJw1hygyVi7gDgCiCgZlibuZW5ymKIzye7Jv6A7ehspvRMQFOwxBgsvU7InianNtWumSholt4wkZCQhU68lOC9PvO1woYgwYaUe+S2KLkkSrOlpYWVXE4os4QM3rulGCMFENn+FKvXXCYosWLkkgyBo05qRuJevmmNbZGcdpmcv95tM2GC65FCeKXO0fIG6ukht5318bI50OkpvXyMhXWXtslb6ujPEBk2yVpEvdN+DLAu0qgHx260BfFwsdwxJG2TtShlZiiBLcSx3jIoziO/bSMLA9y00pRlFSqHSeMV9MD2R4/DrZ/F9MCI6/a1pNF3l4vlpHMslHNNJZWJcHJxCkyUi0RAb1jeDdRJ8G+TmgBSyJ8EvgNyJrCxlebcK9mxAECkp8HxwzoI3AcoyhL4Bz/fYNXWEYzMD3NX7ABElxEBhFK/kLZjPzEciZnDjhl5uWN2J7bi18nhFDrx5ZOlyO+H7ZKcL+FPg2A5z00XKZYve5S0Mn50gUadx4uB5PNcn05xgfHiG629dvoA46m5L09125caCJAQhWaVgWwsMsiGYq67obaKvoz44R++SIpma7898g2nHN9k/8x0aQstZlriHpV0NdLdl+PCd62rHSpJAVeQF3kOpughyVmXGqzB6boiiZVMXDkQfsiRhOg5tnXWkwgbD2TlMxyGiaazpaCUdCVNx5zgws4OJ8nEcXNY3f5WYetk/sWjPcjT7OFOVk+hSnJv6/4Cw8t7I+6lCjno9jvo2Gzqlil0rZ7tudSf1bzE2lxUJ3/WZHpujb2Uro+U5xss5Xp8aIKNHmajkWRpupmI7OL+EWGI+fmWIIyEEjZk49968kuGZLLPFMp6AWCTEbVsWyllXLllognmJnJGEYPWyZlb0XX4/qYb54PvWcL5qKj0+mmXg5BhCEvQ2JOlvy9DUkuLI/iHqMjEqc2USksLUSJaMriN5Pp7psrG/jVKxguf6mEWLrt4GvKLN5HiO2ekCSVnl7KkxknVR+jrr6eusX/R3mhUbz/OJRkPXtCNh2y6O7aLryqKqA11XkBWZSjmIG7wkGZbmDxRcJotqry1aWRPchG8dmIUQ6CEF1/NqJMi1QhKiKid/b0SN63qYFQtFkTDC1/Ywtm2H1145zZM/28/FC7PouoqqBaWNlwikXxa27WJZDqWiyXPPHFm0LaOxEJGoXruelhUQVqFqSdt8CCGC16vfM2cXmbUKjFdmcX2PsKIju6ImY48ooWrErElCjaDLGqsS3SS1KGcLF5my5oIIa9di2sxRdk1arRxH5gap1xNBXb+QCMs60+YcrUaGmGrg+C7t4QYaQkkmzFmmzDkmK1naw4FR8sXyNLc3bvi13dV/K3bvPsunP7ONnt4G/vqvnmf/vkGmpvL8xm/eTHNzkr/6b89x6OAwIyOzxOMGtu3y9NOH+MmP99Db28iDH95Ed08Dnutx/PhFvvfd1/jW371ENBrippv7a3/n3LlJ/se3XmZmpsDHH9rMlq1L0HSFwXOT/OTHb3Lq5Oii17y+IcaXf+d2bMvl8OFh/vRPnqy9JwkVTQ4e7peOrKuL8OXfvr1alurxo0f38NPH93LTzf189OM31I7VNOWKXaRly5r5/f/jXkzT4blnD/M3X9/xjtdvcHCSr//Vc5w+Pc7GTd3c/r6VtLenQcDkRI6BsxNs3NS94LdFYyFWrWpj85Y+1q/vIpEMg+9z7NhFHvnOLl54/ig3bO5l2/ZlgI+uq9z/wHruvHMVuVyFv/pvzzJwdoLPff5GVqxsq32vLEvEoiGyE4FiNSi3U/nMZ7djWQ5Tk3n++P/6CZZpY8gaEV1Fka4cDw/sH+Tvv/kirufz8MM3s3pNO6omM3x+hp89vo+fPb6PSCTERz56fS1tDoIx48knDtDSmuL3/re76eqqx/N9Xt99hh/+4xs8+cQBrt/cy4oVrbz0o9exKzYP/4dPYsQMshNz1DUF5uqu41HMlXDnxa/apkMpX8bzPFzb5fTeAbpWtvHl//I5Svkyf/Ovv8ML332FB/+Xe7Ath2OvnWLJxh6+8rUvMDM6yzf+6BG6VrRz2ye3c/z1M/zs67/gwd+7lxWblzJ8YoTv/6fHybTWsf1D1zMxNMnOf3yNe3/zdlZuW0alUKFSNNHDAUF6YMdRnv32S3zq336Y1r4mjrxygh/92c9paE+zcusyXvzH1ziy6wS/9R8/RaI+zks/3M3uJ/a+Y18SQtDR34oe0dn77CE6V7QhyRLHXjuFVbboWd2xaBjFrwuEELQYG9iYeZi9U9+k4IxxLPtjTosnEULG8U00Kcqy+P0YSordk3956UC0eeVbWXOQ16f+ioI9juc7eLg4XqVW1jOQ38FI8U0koVT/k+mMbGd9+gsLPJVkoWJ6OYaLrzFcfA1Z6FXzaYHrW7h+UBqX0Zeyuu6TJLXOILHTdwjLoarCRCAJhZw1zJw1hEBCFjqSUPDxcD0TDwdVCtMeuYm1qU8gC42QrCMLuerbFGJl8iMU7DEmKkc5nXuKwcJLKELH9W1sr0hErWdj5jdxvEr1ugQLnLCymBLaZ1niAXL2BQbyOzhf3IUsAnWU41cQSNSHlrMp80US2pUR3QKZpYn7cH2LE3M/Y8o8ybR5OjCjBmyvgo9LWMmwNH4PK5MfQZGuLLccLr7Gvulv4vlutZ0cLDdoI8c3OTT7XY7PPY6EjCQUVCnCqtRH6Ynd9o59qeTOYnr5K7x75kMSQWrj+HSOVfPm3IoIsTzxfqbNk0xVTjBYeJELxdeRhYqHh+fbNIRW0mxswFAWlsdrcpQN6d9Al6KczT/PrDXArDWAICBzPN+tmn2DLLRF/aGEkOiN3UnJmebk3M/IWkMcmPkHFCloS9czCSsZNqQfJmud40j20dqxdVoTlldBQpAJJbi5cU0tNXAxKJJOSu+94vVAHQtSVRkmAEmRcT2PeDho53+q3f1fNXh+hZJ1GEVOEVL6uDS7EFIYWVmFkGKEo3OMjTsUChUQgvr6GLqu4hNsrGUyMaanCwHp5vsISVRtSILnbVjTeaBrM67vEQ9deymyJjcgh7bg+xZCaICPIBhLdCWYCwihVt9XESze9o2tdSTujiCqayZFkXBdn8Zq2fclj8S27npmJnKUSxYeYST9FsAFNMAhKOn0CVRHISAKUgzQQWggoiDVB5+p+n0JBGuTfSyJttU8jTrCDTSGUujSlWuHS5AkEfjoLFKBMR9Ckuha2lQ7xnEC9ZemqzS0JFF1JQgw8X3CUZ3OvkaMyLUToQk9dNVUSiEEuqYsqMK5GhShs67uEyjCqB2rqfKCEsXFkImGuaO/r+oT6yELgSwJHM9DkWQs1yWkyPg+tCXj+ASWKka1jEuXYqyv+xSDhV0czT52hSozotSzMf05zuSe41z+pWqp7nuDLiuU3MWTPSHYXN35xmnyRZNYRGfzmq4r2teq2EiKVCtV64s1kNQM+mL11IdijJSyXMzlyE6bi1ZJvRf8yhBH8/0QfB8KplX7kb7vY7pO7eIKIbA9F02ScappT57vo4ZlLFzKjlWtjxdI1c+G6wKiJpY0cC2X4cEpWjvS6CGVcFRnvdaDYWi1xb0QIMsyqirj+z6W5WLbTq3ERA+ptHWkSaQiRKI63UsaERLoobe/pHrVNC+fKweyx7f5+KWFjaYrlEpWsEP/loGuXLZwbJdINFQrE3iv8H2fctm6InXF831KRRNZlhYshK7xW4Mx8T1uA8uyRMjQyM4WF1VBLIYjB4f5r3/2DKGQykOf2cqy/hZicQNdVzi4f4iv/clT7+lc5kNTFTRdpb4hzu/83p3UZRaPtwyF1GDhC4RCAflXKllX1GH7vo9pOviX0h3wWRZvJ6yEaJIjDE9k0TyPkK4wns9hzkmBya+s0apnODcxhmOUicdTeOMhzoksLbE0w2OzuDkFEdY4Oj1OT6KdqKHSEwliGten+hgqBSVK19Ut51xxFPDRJQ1ZyKxKdiMLCdt3AlNeI01Y+ZfhKQJQXx/jlluWY4Q1btjcx/HjIzQ3J7nxpmUIIVi7roOfPb6PsdEsy5e3MHx+mp//bD+xuMGXv3I7/f0ttXupq7seTVP42p8/w09+/Cbr1nUSTxh4ns/OF44xNDTNLbct56FPbqmNM+3taSLREP/vv38My7yS8JQkKSjJDEM8YSwoGfN9D993kYVRCzCVZYl0OuirrusRrk4IIhGdhoarR3UHkyeZaFTGMDwikXfuA7bt8PSThzhxYpSbb+nnd/7VHaRSlxcCfX2NbNl6ZSRzPG7w5d++vTZpu4TWtjomJ3J859uvcOrUGDds7kMIwenTYxRLJsv7W1A1JSC9JEEyGb7iN7muh2XaFKupKEIIDEPDMDQcx0WRJSwCjzHTta/o6+WSxU8f20c2W+LhL97K+z+4oXbN29vTdHZm+LdffZQnfr4/KFtb077geEWR+cJv3MR1119ejDQ2xhkdzfLzn+7n9Kkx+vtbaOys542n9nPstVNcd/c6lmzsWdTQ+2oIRXVu/PBm2pY2I4Tg+nvWcfiV49zx2ZsASNTHuPHDN9C2pJnm7gbW37aK/S8cZusHNvHGk/voWdvJlvs3EkmEybTWcXrfOV764W42P7CRcDxMJBHm8CsnaOysp3t1O5FEGEmSKOcrvPH0ATKtdbT2NgI+bUub0UIaB3cepWtVO0dfO8nKrctYuS24h7Z94Dpe+ckbb/+DqgjHDbY8sJEXH93NzR/bQjQZYd9zh1l2XS+Ztve26/fPCbKk0Ru7g4TawVDxZSbKRym7WVTJoE7vpTt6K03GGmbMM8TVdiJKfS1J7BI8PGyvhFs1zReAKoVQpbf6LPh4vo3n29h+8Oz1PI9soUI0rNMc3sDNTV9lonyMOes8JXe6SqwINClKVG2iyVhLW+QGYkoTQgSLRHwYN2dZ6rcjC40N6S/QEt7IZOU4efsiplvA8y0koRKSE9TpvbSEN9FkrEaX49UyOBfHc8nbRdJ6kvpQPzc3fZVzhR2MlPZSsKaYyRdpTnTTFOmnO3Yr9Xo/E5XjpPU+wkqmFjrxVvj4hOQkK5IP0hLexPlCkArn+TZhtYGm0Bp6YrcSU1uv6peiCoNVqY/RGrmB84WXGSsfplwtHzTkOjKhZXREtpLRly3qyQRUSa/L3hgCCV2OocsLjbN9AgUZnqh5PV2CIhnE1SBddX7KnC7FAgKrtigShJU0vpfBcuIIEajLjJB6RRmyEIJ0qI+bm77KYOElxkqHKDoT+HioUpio0khzeH2NyAEo2Tam65DQQ0SUDBvTv0l37FYuFF9nsnKCojOJ45soQiMsp6nTe0mHltEYWsVi0OUo6+o+S7OxnnOFncyYZ7C8EiE5QX1oGd3RW8mE+hnIv0BcbcNQMtieRc7LY7plklo9ughjyDrTpRJFOwhbSIfDNcLHcl0migUczyMdDhNVry3OOl820RX5n8qH9lcKvu9juxM43hyq3Mj8HWjXOYdjH0ZRVxOJ1LFmXesVx8/3AezoTC94/dL3VzyLGTMoKX3rHbL59QAAIABJREFU89d0LQpOhbCik7dLOL5HRNaJVRX4QihIxCi4JYpOBUlIJFS1RsAUnDIlu0KdlkKRZDzfY9bKIyGIq1FKThkXP9hkDlVQhExcjaDPIxcdzyVrF7A8G13TaFvSUPuuOVtGkTRc36PkuMjCJ6lFUYVSTQPRMH1Bzi7i+MH3x9Q4ISnoW5ZnM2Xm8H0PXdZqV1cWQRzBrJUnrSdqfdT1PabNOaJKuOYZV3QqFJxg3IipYcKyvqDfSpIgEgvh+NWwAM1FkSJocgg9dHkd4/kOFTeHq1coOAJNMtDleE1h6foWppvH8UyEkFClMLoUpTOWpD0WpeRO43oWCIEuxdCkyLx29jC9PJZXAt9HlQy0aiKl7/uYXg7TzQOiRs7Ox6Vzc7wKcvU5IUtVpSYldM1DQsb0gu/QpVi1DDrof5ZXwPKKyJKLIunB8VXVT+DPF0OXoouuWyURqDU1KbJAgOH7Po5vYrpzhJV0Tfnp+z4lZwpVMlClhaX0aS2KkdAW9fvzfZ8z5yd57eAAruuxvKeJ1UtarhiDVE1B0xUqJQvPDRKMJSSGSjNYvktY0eiN1/P65DCW82uWqmZ5FjPWDAk1gef7jM3lAw8aNbg53pgaxPQckppRrXu3SGkRxspzRNUQSc2g87ok56QpJsZzJDWDiBoYMM6YRebsMlFFpydWTzoVRw2rRCIhSpaFJyBRH8V2PVRdCmIVRSCJlIQIWG/5ys4bq6aYRWOLm4m+FUIIGpoShCM6Z06PUy5bqNqVO9rzUZeOkKqmmxWLJpHoQqPU8dE58vkyS/ubCekqjvPe2U/P85meylMp24SMy8x2pWwzPjpHKBQQJe8Gvg8zZonG8OLEyjtBUWRa2+q4cH6awYFJli5rfseH96ED5ykWTG6/cxV33rMGuSpv9X2/puC6GmoGoD41EmcxxBMGmUyUocEpUnURlixtesfzSmdihEIqE+NzlEvWAh8Uy3KYnsrXJJgtRprWcGDYbTkuk06F+lSU4Yks5SlBKhZieq5EQ12Yi5MFUnodMTVEMhwm7iQC0zhfIVd2yHgZIrJOwTRpUdN0JC7vAia1GEnt8kR0ZaKr9v+90csLiXOFMYQQLI1dVnD8S0Amc7m2O5kKIysyjU0JlKr5fjQaCkwPK0FJ08mTo8xMF1i5uo3e3oV+XEIIVq5spaUlyfnzU4yMzBBPtFIpWwwMTOA4Lps29SzwP5IkQW9PA40NCYaHp3k38HwTH7dWgvI/G7m5MidPXESSBLfetoJk8tq80YQQl7xXFxCskiTI1MdQ1aBczvd9JElQLJl4XjDZe7uExktoaElhmVePcQYwPQfXC3bd5hPpExM5hoamSCTDbNjQtWBRJYSgpTXJiuUt7Nx5nCOHh68gjnp6G+jtbVzwWiik0VYlPXJzZVzXY/P9G8jPFtjzzEFe/OFutn3gOm77xDaSDYubjr6ViDYiIbR5ysZkfYLiXBnHCq5POGbUiCghBMn6BAOHzuM6HjNjc9S3p1G0oN/LqkyqKcm+5w/jez7JhjgP/eEHef6Rl/m7r36PtqXN3PX5W1ixdRm2FXgfndxzhj//8jeA4LlilS0UTcE2Hcr5CqmGeUlUER0jeu1k9Orty3nmWzs5e2CQtqUtDBwe4vN//PF/MSpIScjUh5ZTp/dge+WqQkNCETqqZIAQpENLua/9a4hqSdJ8JLR2bmv+43e1U6oIHYFE0bT49rN7+cC2lXQ11dEdvYX2yBZc38LzL2/uSUhIQkWVDMS8NM/AX4RaKpsQgpTWQ1LrpC92B65v4+Ph4zE1VyJuhImGosHfn0f0NOoZ6rQEapUUE0IiprawKvkxlsXvZ7ZY5M92vMjt926lK92IIgKFUya0lLta/z8koaBLV08u83wbVQrTG3sfHZGtVULGRxIqijCQxdV3/i+3k0Kd1kMk7tMX3YAst9baTxGhmq/PfDjuBI47jq4upz28kUTjw2jKUiTpWsZOgSaFsZ1hyuZrqEovTcZa7mv7GpZzFt89g+00oKl9GEqCJmMVihTMQWShsinzRY6OrOOC5WL0pBBAOKRRn1ps7iZIqB2sTn6c/sT78Xw7iKautrsstAUR9ntHRzg+NclnVq/DUFVkSSOj95PSenD8Sq3vBJu9SlAeKbQFbf5WKJJOS3gD9cZyXM/Ex8N2IW/6pENphBB0RW+s+R8pwmDWmkDWFIxq+bbjeey6MMRTZ07TEovzB5u3EdGC886ZJj88fpTdI8N8etU67ltybUbenueTL5vvaNL8zxWSMJClMIqcXKBgEFI8UPD45ap/0GVc2rha7JrMD70AGC5O8P3zLzBWmWFVopvfWfLB2ntnCiP85MIrdEYaOZE7T84u0WTU8cmO2+mKNOHjczo/wmMjLzNWmUUAG1JL+WDbdiJyiBO58zw6vJPPdt3JingXE2aWb5z9ORtSS7mjcSM7Jw9yNj+CJqkMFEfx8Lipfi13N12PLqs4vsuLkwd5buxNKq6FIevc3Xw9mzMrcX2XR4d3AkHFwFh5Btt3uKNpE3c0bkKXVbJ2gScu7ubI3DlsL5iX3dG0iTuaNiEjmDJzPDL0LBdKkzToSX5v2UeIVlWRx3ND/PjCy/x+/8epq87ZJyqzfO3Uj/hEx22sTvYyXpnhH8/v4HxpHM/3WRbv4AOt22gKXfZ8832fsjvLibknuFjaj49HWE6zpu5jZPQlCBH4+AwVdnE691xVieqR1pdwQ/2XUISO7ZU5lfsF5wuvYnsVwKfJWM3G9OcQQma4+DrHsj+tJRJm9KWsSX2UuNaC73tMmWc4PPsoJWcaz3cIK2nWpz9NWu8FfC6W9nMm9zw5+yLLEw+wMnW5D7i+w2D+Fc7kn8PyCshCoyu6naXxu5CEwkB+J1PmaWShM2uew/ZKNBlrWJ/+JLocp+zOsn/6EabN09UyWo3lyQfojG67YpPl3SJrDbJn6u/YXP/bpPQuBALTy/PKxJ+zLHEvnZGFQQmarKLJam2eOR+5QoXHXjjM+HQeTZW5Y0s/xiLCDSEJSvkKU+NzWGY7hiIHoVRWkZfGTzJnl9kU7kOV1AWlgL8MfiWII9/3mbPnGK2MIolAqux6l8VbPkF9JJ5P0bEIKxopLWCYDVnFkFVajCTlhE3OLuMDIVklZ5VxlMDMWkJQH4ri+h5D01nOTs1gqCqaLGF7Hh2pBMPZOQxFxcNnIldAVxUMVSWia6xpvdJAbT6uddK6fEULXd31HDk4zNNPHOS+96+/bIhaVfa4rk88EQwWrW11rFnXwa6XTvLSjhO8/8GNqNUJ/eREjueeOYxje2zZvoRwRCf3Ngll14JTJ0c5euQCGzZ1ccmo9tVXTjFyYYY16ztpab0ymevtoEgSt7T2oCvKe5rY67rC1huX8Oorp3jyp/vp7MywpL+5VrZn2y6FfIVoTEetSg3tKquqKFKtPC/wl8rx6sunKJev7qehaQqGoVEsVhgdzdLT27DoecfiBpu3LuHM6XF++uO9PPzlW0mmIjXvKMdxmZ0tETa0Wvs2t6bo7Wvk4P4hdr10knvfvz5QtHk+p46PcuLYxcWvoSyxpL0eVZExdI3u5jTyvJKM1b0BwSlLEiFdYcvqLjwvGIjkavKAqsjYjkvUeG+1993RJrqjb38P/DoiXPUFg6AdJCFqhJ8QIEuBAeglwm9kZBbTtOnoSNfSD+cjEg3R3Jxk4OwE09OFwBOpZJLNlpAkQesi91c8YRCLv3uVl4dVTYSx37Pi75dBoWgyPpGjri5CS0vqmu9/3/eZni5w5vQ4x45eYHR0jmKxQqViMzWZxzRtfM/H94P5pmk6pFIRDEOrEXhX/3Io5MpUyhZm2SZ0FZ8513cpuhWGShPUadFaqmA2W2RurkRvX+OCtLNLEELQ2V2P+cxhLgxPXzEZaG5OXuFtdyklFAGO6wUm9KkoH/zK3dz60DaOv3aKR//LzyjOlfjM//mRYIzx/GppcoD8bLGWSgZQKZnYFbtGeuVnChhRHblKgpYKlVrynO/75GcKROIGkiyRyMQCU27bBQM8xyU/nSeejlZLniVWbl3KkvVdjJ6b4Jlv7eQbf/gI/+Y7v0s8EyOajLBqez+f/XcfpVbGICAcDyME6GGNQrZYOzfbtLHeqd3mobEzw5qbVvDqT99kyfpu0s0puldfWTL06wwhRNXXZvFxQRYqEWXxcnlZqISVqxtfvx0MTeUjN68hXfW9EUJCFQYq1xZ+IYD2cCPNRgb5UmKiCNLT5ifE2Y7LU6+e4dZ1faTaF3538NtllLckeQkhAsJC0jBkHduMoIp4QKZV8W5/uyTkKxQ+7wZCCHxvEtn3qj4Ybz8G2s45yuZraOoSZCGjeOOE5Q3I8rWds+/7eFIM15vFNneS1jciiwZ0SWKu8CoVax+a2ocqGdSHLqs9AwP1BL6fwHYrNSPvpkycpszim4W1683be3UCrGlooi+VRpu3ARu0o47Ce/cDEkJCExGolrQdnZ3g+XNn+dKGFLqiBP5P8+6R+tDCjS9Vkrh/ST+u5/Pm6MgClVDaMPj82g3MVSqUnWsfn1zPI1cy37M9w68ygs10HdMZJaQuJNIkKYOib6kSRyXO5+eYrZSJaRoTpSId8SSWG8zNTddBFtLlihBVpd6IEJID4/wv9b2fR4d3kHcWrmdsz+Xo3CBhWec3uu/Bw+cbZ3/OM2N7+Hz33WStAn937kmWxzt5qOM25uwS3xx4grga5p7mG1iV6GbvzEkeHX6RL/XGeXxkF/iwLbMKSUiUXZPXZ47zqc73cV/LZo7nzvOPwzvoCDeyLtnL4ewAj114mY+130pnpJEjc+f47tDzJLUofdFWsnaBI9lzPNxzL12RJg5mz/LYhVfoi7bSH+9g7+wp3pw5yee77yatx5mzixiyXpufNepJfrPnPp4f38cb08cXCBJ6oi0UnQpHs+fYXh+ke+2bPY3ru7SG6ym7Fb49+At0SeXLfR/A832+PfgLHh3eyRd7H6iprjwcjsz+iCnzNNdlHkaVwgwVdrFn6m+5sfH3ialNnC/u5sDMd1mR/ADNxtrAsN4r14iVgfxOTmR/zuq6j9Kg9+P4AXErCRXwiSoNrEl9jIhST9nN8vrkf+d0/lk2pj+H7ZU5nv1pUBLW8GkgCEAw5EtzX0F7ZDNpfQmvjP8ptr+wD0xWTnBg5pHg3MJryJrDHJz9HiE5QWdkK7ZXZrCwi7Wpj7M8cT+z1iB7pr5Jo7GC7thNKEKjLbKB/sQ9SELlbP4FDsx8j/pQf9WH7r3fGwm1HQmF88XdJLUOQGK8fBTTzZPWe69KhFcsh2Nnx6qqObg4OceLb5xm96FBhBBsXd/D9o29i86hJUmQaUpQ35KszWfHyzn2z5ynNZzkukw3hmUg6dIVvlPvFb8SxBGA4ztElAgJNcEEFUKqUnvEqpLMtoZA2r8YY33pYm7SF48Bfuu/88IkpCqUbZuIruH7EAsFuz+KJGG5LvHqv1VZRp3H0lmug4ePLgVEyEQ5z1MXjiJJgk/3XP+Oi6NEMsInPrOVv/zTp3nkH17h4P4hevsa0XSFXLbE6MUsK9e084nPbAUCIuPDD93A6MUs3//2LgYHJunqqce2HPbtHeTk8Yvc8r4VbN665KrJS9cKWZYwDI1vfv0F9m3qJpOJMXJhhl0vnSRkaDzw4MZaaQsEUd6jF7MUiyaVssXsbBHbcjhyeBjHdTEMjUQyTHNzslZGNztTZHIiR6lkMj42Ry5Xply22Ld3kObRLIahUd8Qpy4dQZIkZFniuhv6uP3OVex47ij/4f9+nPUbOklnYriux/j4HHOzJX7nf72T9o5gcrVmXQdPPr6fF549Siik0dicIDtbYu+egUDp8za725GIzsbre3j8R2/y9b94lg3X9RAKKVTKNms3drFiZXXXUBLccc9qLo7MsvP5Y4xcmGFpfzPxuEG5bDE5kWd8fI6HPrWFLduDB6xhaDzwoY2cOjHKI//wCkODU7R31DE7W+LgvkFicYO5eQbFlyAJQbiaaJSKvfMEPRl9588Ecn8fWYgr+qzv+5iOgyoHO8We79cimf+lYbF76u28yS4ZrwfG14sP8pqmBCqM6sLddQOiUVGkRckmuWpw+K7PXYRRpeTi8Y//ExCUhTnEE8YVhvtXg+/7HD1ygW/8zQ7Onp2gri5CZ2eGdDqGYajomsL4+NyCzzc2JmqE7TtCBG2Qmy1SyJevShwpQkaXVNrDGSLz5PKO4+E4Hpp69dQcXQ/ayjQvlz3X3gupVx2nLykdAQYODRFJhAmFdfo2dNPY1cB0NWY3VjVHPLnnLI2d9eRnCux/4fCCdLJCtsjrT+4j1ZSkUqyw97lDLL9hCdGq6mtmNMuepw8QiYfJTsxx8MWj3PLxbRgRnevuXse3/t0POLLrJEs39TJ6doy9zx7itk9tR1Fl5qZyTI/OkqxPkGpIsOamFbz+5H7MsoURCbHxfav54Z89wczYHK1LmvA9n9nxLNFUlHAsRN/6bvY9d5jzJ0aIpaIc3HmU2Xnxt7Zp49gOVtnCdVzKhTKVkoGmq0iyhKqrbL5vA9/6P3/AhVOj3PrQNhLp9764/1WH63mcG53h5cMDlC2H/rYGtq3uQpYkXth/mrCusXlFJ7lihaf3nOCG5Z2kogbP7TtNJhHh5PAEyajBbev7aEzF8DyfofEZXjw4QMm0WNJaz41retBVmcm5Iq8dHaKtPsH+MyMossS9NyynIRnl+PlxXjo4gOW4fPjG1bRkEtiuy0sHB3Bcj+HJLCu7mjg1PElvS5oblgdk3t5TFzg0EBgU37C8g5VdTVimx0/2HaEuZnDywiSyJHHTmh6WtGYYm8nz1BsnePbNU1ycypFJRLhlXQ+ru5txPY+DZ0fZd/oCvu+zYUkba3tbUGSJuWKFZ/eeYmquSGsmgf1LqK/no1B+Cs8r4bgjaGo/4dBNeF6BYuUXuO40hn4dIW0Dpn0M2xnB9cYRIkI8/CCmfYJi5UUiodsAge87VKw3KVtvIosEEeNOZKmOYuU5LPsMPkHSk++bFMpP4niTUF1seH6Fsrkb0zqIJCJEw+/H84uUKi/geUUM/XpC2iZkKYmqdGHZx4EqQSPXoyqXS4dM+ziOM0I4dCueN0fRfJFI6NagHKZc5pHDBxkr5ulNpbmrp4+QolC0bZ4bOMOZ2Rnius5dvUvoiAfKwR2DA4BgzixzZmaGlfUN3NHTh+26/GLgDIcmxlhd38g9fUuRpcBa4tjkBE+cCVJkbdcjoqr81obriKgqF/I5dgwOMF4s0pNMcUtnN+lwmL2jI5zLzuJ5PsP5OdrjSe7tW0pIUXhleIgfHjvC0FyWvGWRMQw+sWoNMU3nyOQEr48MM1su0xCJcldvH03RWJW8EgsIrUsQ1dffuks/mJ3lxaFBHuxfTlwPUbZtfnj8KFvbO+iMByqcRDj0tqER/7whoUgpBBLz5zi+X8K1DwMenq9wJiszUSrSGo2T1EOcyU6TM00c3yMdMjBdF8t1SYUMhnJZlIxEcySGKimktCiGHKLoXBlXr0oydzVfR080KNtZn1rCqfwFTNfiWG6QrFVgY2ophqyjyxpLY23smTnBzQ3rSKgRPtR2E3999mf8xakfYXkOX+p7gKQWxfECUqvFyHBT/VpiapikFuXNmRPsnz3F8ngHb0wfJ60nWBJrQxKCvlgbYUVn/+xp+qKtCGBJrI1t9auRhYQiKTw/vo+xygz98Q6SahTXdzlduEC9voq+aOsC3yJZkkmoEWKLeK+ltBgrE13smTnBhrolgOBg9gz9sQ7SWpwTufOcKYzwua67SagBkboy0cVLkweZsfK0GMH6qOTMMFo+xLL43TQaKwHQ5RjDxTcYLR0gGr+TwfzLZELLWBK/s6ZKvATPdzmb30FXdDu9sVsXUekI0nofVrUcWpUMYmoTRXsyUIkLmbCSYaJyjLw9Sia0hITWXu1Pl0p9QxhKaoFq8RLOF14jpXfTE7sZXY4RU5qYts5yNr+DtnAQAhFXm1kav4uQksBQktTp3WStC0Dgs9Ya3oTtlfB8l0ZjJadyz1SVU78cNDlCZ3QrA/md9MVuR5djjJTeJB1aQlRpvOpxc/kyf/qt5xm4sLCqQJYkrlvVycMf2kLyKms/SZJo71v43Y1GnPV1HZzOjfNS4RTXx3pJ+FEs1+XaM9Gvjl8Z4sj2bKbNadqNdiQh0BYxaYZrU/YsZjw8H7GQTiykL1hoCBHELr/d4sP3fY5kR6m4NlvquwFIagar61p5bOjgO57Xpb+zflM3X/7dO3j26cOcOjHK4YPnA78aTaahMXGFuqCru54vfuV2Hv/Rm+x78xy7Xj6JJAR1mSj33L+OD33s+gWEznuFJAk23dBDJBpi10snmcuW8DyfltYU975/PWvWdSxYLB0+dIGv/+WzlIpBqUixUMF1PR797m40TUGSBStWtfH7f3Rfjax54dkjPPbDNzFNG8fxKFVLTr759RdQFDkgZO5ewyc/u622Mx+O6Hzqc9vJZKK8tus0L+08gWO7CCGIRHV6ehsWlPesXN3Ohz5+AzuePcIPv/86qiYTieqsWdfJHXev5r//xbNXvQYhQ+WeB9YxPjbH4YPDnDoxiqLKJJJhWtpSsPLy5CuZivDpL9xIMhXh1VdO8dwzR7AtB1mWiMZCLF3WvKC0T5IEa9Z18OkvbOepnx9kx3MB4ZhIhrnxln4aGuJ88xoMh98tiqbF+WyWlGFQtGw0WSaqa4zm8vSk6xjN5ZElga4oZMsVWhNxjo2N05VOYTkuluPSEIswmssT10OYbkB4xEM64/kiDdEIqfC17Tj/s8O75FzCYS3wsSqaXBZoX4brelQqNrIsan5hiiKhqjKO4y1aauV5Xk1F927g+mV830IW701d8MtCkSV0XcEyHSzr2s7fNB1+8P3dnDo5xk039/PxhzZTl46iKAGJ/MzThzh0aLj2eSEEM9MFPN+no/2dPW4kSdDT30xHbwOafvXHX0jWqDMitQnYJWiagqbJlCtWUEq9CEqlwJ/PMLQrCK1r7U67HtvDgR1HMKIhXMdFSBJ3fPY+ABL1cW7+6Bae/vsdvPHkPlRdpa4xyZQ7Wzs+kYlz5sAQf/7lb1DKlYgkI9z80S2oVWPFZEOcI6+cYP/zRyhkC6SbU2y+bwOyKrN88xJuuHcDP/qzJ9DDGpWiSefKNjbftxEhCaZHZ/nbP/ounuuh6gqlXJktD2wk01aHJEtsuGMNA4fP8w//7gfoRrAxE44ZfO6PP0YsFeHGD93AucPn+dpv/y3xuijRuggN7Zf76K7H93D01ZOcPTDExTNjPPL//JiW3kbu/NwtNHQE5budK9pINsQZOHye1Tcu/7U2xR6bzvOd5/bR25KmJR3myTeOY4RUru9vJ5OI8oMdB2iqi3Hg7EWGJ7Lcc30/hbLFE7uPsaKzkdU9zbx2bIhsocxv3XcDM7ky33luHx0NKdrqkzy79xS6prBtVRf5klk7rr+jAaeqVgVoSSdY29fC/3jmTbIbltCSSeB5PkfOjVEybTzP49DAKOt6W3ji9eMs72xkZGqOx3YdYdOydkzL4TvP7uWL928mHgnx3N5TJKIG21d1c2Zkiu+/sJ8//MRthEMaPS1p4hGdVd1NdDWlaEwFxOCpC1P88KVDrO9rxfU8vvfCASIhjd7WDE+9cYJDZ0fZvrqbsxenmM4V/0muf8Xci49H1LiLfOkxVKWNinUQfBdD30K+9BNkuQHLOUvZfJ14+OPV0jIFTelGllLYziCGfj2Oe5FC+UkiofdhOUMUSj/H0DdTqrxCNHw/pcoOIDDtNbRNzOT/Cs8rIUspTOsoxfKzxMIfDLxcRBhw0dW1+H6ZfPnnaGo/ski+428SqJTN3ejaehxnCNs5i+AuAA5PjtNXV0dzNMaPTxylNRZnXWMTT545yesjF9jW3slgdpa/2beH39+8nTrD4ND4GIcnxrmxo4sldWmimhbM42WZ9U3NDGRn2T8+yvt6+rgU1N0QibC9vRPP99k5dI7RQhBaULRtvrl/L4oksaqhgdcuDDNWyPNbG67j7OwM/+PQAe7tW0pHPMnTZ0/TEI6wvaOTtlictngc03XZ1t5BTNPQ5WCMnymXMBSVhkyU58+dxXQdvrjhuvfUHxRJ4tUL51nb2MS6pmaGc3PsunCeW7u6kSRBWybJTL60QBX+6wQfB1lcOd8TIo6irgVh4HsFNjenA+9ZKYird+YpZGUh4foegsAKZKpcIqmHrml9F1FCNXN9AFVS8HwPH5g2c0xbOf524AlUKWj7olOm1aivlcOl9ThbMyv585M/5MG2G+mKLFSZxBSjpoZUhUJCjTJnF7E9h2krx6ncMH9y8gdA4P+at8u1cxFI1Gmx2vGBqir4rQAr4118sPVGXpw8wKtTR1mb7OG+5i1k9MQ1/fZNdcv4xtmfM2XO4fk+F0qT3NN8A0IIZu0CObvE984/X1MXlV0TTVIXmC+7nontlRYoL3UpiiQUym4W17cxvTwZrXlR4sb1bSw3CByQFqEQPN/lYmk/A/mdOH4FEExVTtMcXlu9Jjr9iXtxfYuDs99HFQZLE3fREdmCIt55HVt0pqoeQpfKlBUiSoYLxTdqv9OQU0hS9X0EslAD9T0+ZWeWk3NPMWudAwSWW8D1bOCfhultDq/l1NzTTJtnSGqdTFZOsiH92bdtX0WWSMXDhLQ5LMdFVWQyqSjr+9v4+D0b6Fkkwe7tkLcrFGyT9XUdNBpxNFdj77mL9NT/0/hA/soQRz4+lheUEElCULbsWnTcVKXAkxeOMl7O0RpJ8f721fxs+DCm6zBRydOfaOSOluU8MvAGqpAZK+fYlOnkpqY+jsyOsnPsFJ7vc2NjL+vT7TwzcgzbcxkszNAXr+fOluXsmjjLwZkRDEXlntaVTJtF3pgcpOIGZR4f7lrPZCXP359+DcdzeXVigE8mHT11AAAgAElEQVT2XEdLOEFGj9QUGUXH4tFz+3iwcy1xNcTjw4fojWVYnQoIByFAVWW23riEtRsaGB+fpVyU8DwbVROkUmlSdRKeb1bTACyEBP0rWujovpVzgy/gmM2oSppkXYT6+gyK6uH5FQQaofA0v/WV1YFKRD+M662ib1kj//4/P0h9YwTf91E1nw9+eBXvu3MFTU2XPTN8P1BKfPYLN3Ln3avJ5So1X5FLUfHz/T76lzfz+390f61kYqg0wLHcYbojffTHAiY7EtUXRM9vu2kZS5c14/k+lmcyZU7QFGqpSaMh8AKav6gTAhoa43zyc9vZfncvIxOTxEkFCqmwRsmYIVR3+aaPRHQ+/qkt3Pa+FczOBpPHcESnuSWFokp84HeXMGiexlNsQGOkfIH9s29wfd1WGkJNdHRm+N//8D7GxrKUSzaSJAiHNRqaFvqLCBFcm8984Ubuvn8tc9kSluWgKBLhSIhMfYzwWxQNg5Ux1Ot8vrr5g2SnS3iuTyweorklSaVi09XTQFNzglmrwJOjb1ByKnyh567aQ3AxzJh5YmoYVZLJWkV+MbaXe5qvI1Ytr5ksFjk+PklHKslcpYLn+XTXpbiYy9EQi7J/5CLbujvJVUwOj44H9baA6/lYrstoPk99LMLA9CxJw2BwZhZZEtRHg3LR1sS78736dUZ3TwOhkMq5c5OYpoNhLGz/3FyZC8PTJJMRMplgxzMS0amri+J5YwwNTbFy1UI5fTZbIjd3pRLtnSChIEtRFOm9+Yv9sohGQzQ1Jzl+bITz56fo7Mq8Y5Lk6MVZzpwZJ5WK8NAnNtPd01B7z/M8CvnKAnJNiECRcakkVVDdKPCDpNu3QgiBqinvaDQdUXQ6IpkrXs9koqTTMcZGs8zOFKmvX9j3Pc/nzOlxdF2lsytTK5N9N7Bdl9WfuI7U5g6sioUR0qhrrSPVnGJoLovjeWz+/DYy13dgF03i6Rj9Xa3Mjmdryhvd0PjYHzyAEALXcWnqbiDVeHlyGq+L8sl/82BNpdTc00ii+lv0iE7vJzay4f0b8PIm4ZhBU3cD4eqOV/vSFr78J58lP1vAcz1iqSiNXfU1n6JwzOChP/ogN390C/nZIooikWpMkm4NJi1tS5v5V3/xBS6eHUeSBI1dDeSmcsSrJTE9azpJNSbZ/uDllD9VV4jM88hSdYW6phSxuhjN8/rIryNeP3GeXLHCmp5mJCEYuDjNriPn2LCklTU9zZy6MMl/fWwXqiLxuw9uJxk1KJQtVEXmvs0rWNXdRHM6zree3sN0rsT+0yNMzhV5/9aVyJLE0Pgsrxw+x/9P3nsH23ned36ft7fT6+0F9wIXHQQBgl2FTZJlyY60NhWvmxw78XrX3k0mkzY7yUwm/2QmcbyZJBtvLO96bLlKLnJULIkWKUqUQIogAKKX2/s9vb/1yR/v5QVBgiLt1Uxk7W8GMxfnnLec95zzvM/z/X3L6bl43PHDiGcemOPw5N1dzLRjcmCsuMd+faMkSeLEzDCGpvLdK0s8ef9+rq/s4PkBz527SSmTYG48jmB+9cYq37uxyhMnZ0GS+PADc7z/xAxLW3X+1edfpN7uM5RLcmSyTCZhcXiqzNGpOwu7b5y/Rco2OLQLal24vc7L15YpZhJ898oSP/vUKR44OM7iZo0rS9s/kOsvSQamdhhTP0PffQXPv0lv8CKSpOAFt4iiOmFYARFh6icw9fuQ9oxWkyhyjjcWJX6wjCylMI0HUdVxGp3fRfaTqMowln4KhE/fexkkGUUpIr9pIeX6l9C1OSzjDuDhe4sMvFcIoxZ+sIAQ701SpapjyHISP7iJ61/F1E8jy3GIwqFCkZ85egJZllhrt7he2WE6k+Vr87f55ZOnODMyRs/3+S+//hXOb23wxNQ+/ChkJJni2SPHMNU3ja2SxEQ6w75MlnObbzL6liRKToKC7XBha4OW6/Krp87gaBqXd7ZZatb5nz74NKPJFPtzef7nl17kYwcOIgSMJlP842MnsFWNxqDPlco2j45PMJvLsy+To+v7PDQ6hqnemXc+ODrOvkyXju9yu17jVq32thCY91pDiSTHS2WeX1rgaKnMd9dW2JfJMpJMgRD0XZ92392Tr/+olYT8JnrsneaYEF0C/zVkZRwR9Qi6SXwvIFQVlm9uomoKuXLsD6nqCoOeT+AFKKqMGoRQtniHkLO3HF96x8S6hGpRNDL8wvSHKeh35uuGou3JzStui+9UrnAwNcGl1gJL3S32v8m7sxe4e0BPsCtZz2gJFEkmoVocTk/xc1PP7MnLJIk9HyJgDzS6c753ylINniif5HRujpudFb6w9h1+f/Gr/PqBT8QG2u9S084wOT3Jq/UbSEiUzRzTTpx4aCsGKc3m2YkPMmnfGTMVSaZk3AGTZVlDlQzcqLP3mC8GRCJEkx1kSUWRdLywiyBEegtMoEgqiqThhR0E0V1JmwANb4lXKp9hNvUk484ZVMnkO9v/x96VkCSJhFbigcIv0fY32ehd4LXqHyChMJ18/F2vgamk8cI2QsTzQEGIG7Yw5OSbPpN7M9zDyOVq46/Z6F/gTPE/xVIyNNwlvrn1v77rcd9rJdUyI85JFjovUjarGEqKvPH2EJg3VyZl81//J0/T7PQJwghVkUklTMr5JIb+d/ddGrWzfGLyju3Fdiu2xfhBgdk/FMCRJMVxsbqsxx5Hisx4LoOxK8+wVZ2HitOEIuIP57/Hqfw48+0KQ1aKn9l3mt+58RL7kgWuNbZ4auQg7x/az2duvsThzBAjdoqPjR9joVPla+vXOJEbY75dxVZ1fn72QRQkNvpNvr01z4+PH+V2u8IXli8ymypys7XDvzjyQb61dZvv7izwsfFjPFaewVZ1PjRyCPUebu+2ohGKiIv1dY5nR7je3OKR4r63vS4Itwnls+TKA5LWB+kMnkdXpzC1YXru9xj0ahj6YVz/GhIKln6SkAsMja2iqxpCbBCJPiFHcAdbQISpH2cQfJPhiTyGNkNncJZOfwvLup+pA7cJoxZB+Ch97zUyRZWRsTNvS/QIgohee0A2Y5PLOgRBiCxJtBtdqhtNCsMZtN3UosgLGB1Oky0m2XY3uby9xGF1lFOZY+SNIldbr7MZdkkEghQpLrcuEuoBiekUBxNzXGtfZrtxhUJK4XDqGKEIudq6xBqQjI5gYnGtfZm232RfYj85Pc+SepXt/Cbp1FH2Jw/R8BusttYZIV48uJHLtdYlukGXyfw0s8NFrrYvsRZ0sMQByvIw7zv6AN5WBSHHN4eyUSalZXAjl1AELHUXyJtFhqcTLPUWOZQ8ctcEww39OKpWCCIidFklU7LJlmwGu89ZirGbRhbSD1wkwFIMOkGfLbfOqRGFsVwOR431zb4IiYyIsbksjmqiSDIfLJ3gT5df2NPKRyKiF7qEUYStGqiSQjvo87mVF3myfB8lM4MfBaz2dmj5XSIiLMXA0jTCSLDeauP6Abau0fd9ttpdGv0+OdsmZRq8urKGFwQ0BwPWmi0MVSWMIjZabdKmSXMQs5EUWSLv2KRNk4SukzDe3ePgP5Q6dHiEfTMl5ue3efnsbR5+ZP+eTMvzAl785nU2N5ucOj29J600TY0jR0Z57dwi33zhGo88eoB02gIkgiDk9YsrrK83vs9R712qnCIh//8H6qXSFvednOT6tQ3++guvMTVVZHQsuyfViiKB7wdIkrwn7xK7c1EhCSLB3uQ+igQrKzVeeXn+LqaPEJBwTHQjTiyUFRnHMRgMfHa2WoRhtHe89xJg8G5VLKU4fmKcr/7N63z9a5cYG89hWXEiShCEXLq0yuVLKwwNpbn//qm/1zEq/T43whaTx0aQgNVWkzVcrq8vY2saE6kMmixTK6mYqklHUUkVkmTeBGIJIXDSNuNzb03KeuP5WPK2pYfM12os1reIaht8YHqa5UaTxU6LH5s7QNo0qXS7fGNlGS8MeWhiHEWSuG24NDMRs/kc5VyOV9fW2FzscGJomJlclmu1KleDOqXhBI9OTtB2Xb46fws3CHlofBw7oTOfifDDiJQpoY2meWF1lXBrhUOlEkcPHORmtcrlrW1ytsUjExMYqkrgh7g9l6Wrq9w6v8DP/stPYpg/2uNPrd1js97mb16JZT2REByZGkKWJFRF5r6ZEf78mxe5/8AYI/k74KCqyGR2wbxswiIIIwZeQL3TZ7veuWt/hybLewse29AopN47oV2SwNRVFFnG0FU0NWYYCASVZpd6p09w9hoAQ/kkY8V4QafIEkO5GOjUd1lNwTuw+N6oSrPLWqXJl1+O95dN2kyUs/hByMALSO+mPSZt4z1FPr+3ighFG/ARYoAkmahKEcd8ElO/f9fbw8QPlpAkk+/HK5QkE4GPEB5C9JGQkSQLgYcQPpHov2PjW5YMwqiB2As7kGj1PodlPIqj7afWXOa9ds0lDEzjNL3Bt0CSSWgf3zvvgmVjaxp+FGKqGm4YEkQRPd8nvcsKsTUNQ1Go92MwSEai5Dh/Zw+NtXaLP7tymY/MHGBfJvbB6/k+iixj7wI/WdPCDyP6Qfy+M6ZJUjeIhMDUNLq93p13fY9L3/M9fu/ieVZbLYYSCW7Xaqj/HgsoRZZ5bGKK/+fcK9ysVXh9e4tPHDy8OyeEQsohm7B+YEa0P4wViXvIeoSHEANE1EKSJ1hfrLC1WiNbSGJYOrIiU91qUdtuMTpdYPnmFoOeRyJtkSkkSaRtLMcgECFu6BOIkFCEDEIvln29Axjw5jqYmuDLG2dZ71eYTYwgSzLdYBAzf5BxQ4+/Xv82buTx6wc+wRfWvs3nVl7gn+7/SWwlHjtW+ttcaS1yODXF7c468511fnbqaUxF50Rmhj9ffZF+6DJqxY2lht/5vo3dN9dGvxr7ZCoW+5PjHMts8nL16t4cP4hCfBHE6wEhcCMfI/LRdlMO05rDkfQ0FxvzuKHPY8VjpLR4rJ50ypSMLIvdTQ6npjAUjUHo4Ub+XWCWreQomAdY6Z5l2DqOKpusdV9FEFI2DyEhM2Kf5Gbra2z2L5E3ZgGxx1KSUBhx7mep+x3K1lEy+gSCkCBysdX8bipmQM7Yh63kafnrtPx1Ckps2RFGPi1/HVNJYyoZxpwHuNH6G3pBJf4aiTjNM4xcot1wlyBykSUFCYVR5xSvVv4d24OrFM2DdIItVnuvMuk8spdk9k4ViZBeWCOhlklrYwgidtwbRCK86zWRCAiFhyAiEB5B5KFIKpIkE4mASIRxkIOICCOXMPKQd5+XJY1x50G+V/ld2v4mE86DWMrdpAMhBK2ei+cHFNIO3b6HZqgcHhp+R1A0ikTs1yxJuzJscU8LizCK8PwQy7gDODmGzn0Twzg/oLXaDwVwBKDu0g0jERFGEbVuDz8MMTWVzX6LFzZvEoiQtV49vqEpGvtTJUbtDHkjwVa/TUIzmEkVmEzksBSdrX6bitvhRmubru9Sc7t7VLZj2RHyRixDmO9UudLYQJMVQhExbMfJbgfTZYatFGN2hlvtHYDdAUzepV/eW0p3X26MV6vL6LLMiJUhY7x9EiZEnzCsosi52FxXDDC1w4SiTSQ6uP51VHUEWXKIRBfXv44kGahKmSCqoso5hPDpe6+hK5MIgt39FTC0md3XDiNLBp5/i4F3FVl2CKMmQVQlYX4AWXq7L0R9u8Wrz1+lPJGnXe+yvVrDsHUsx8C0DbrtPp4bkExbtBs9klmHbDFJWstQNoYoGmVyRjygypLCIOxzo32VQ6kj3Gxf55HC+7jRvsqwOUpeL5JSM0w5+zBkk17YRZVV1vurmF2LCXuSld4ik84+UloaTdbJ63kEgjFrAlVSSapJBuGATtAmq+VY7N6mE3Q4kDyErdi7SR/xeVxvXyFvFHcNyO5Ou3rjxyoh0w27uH0XCYmO33rbNTpbvUZGd1jvV+kHHvdlZ7jWWiGjO1xuLuFHAWfycxzP7OOb2xeZ72ygySqPF+No2RvtNQYr38KNfD4y/AATTomvb55jsbuFKsl8ZOQMY3vmoXfO83Zng+e3LuCLkNnECI8Vj/KtnUu8WrtBP3SZcoZ4pHCYutfhi+tn8aKAg6lxHs0f44Oz0zQGA6rdHvsLeUxNZSiVJGHoDCeTGKrKo9OTBFGEoarMFPJxpKyAyVwWQ1UYSiVwdJ2xTCqOXFaVdxzk/kOtfD7BT/zkKf73f/U3/N6/fZGtzSb7DwwRhhFXrqzx1194jWzW4WMfux9zd8ErSRKPPHqAl166ycULy/zb332Bhx6axbJ0lhYrPPf1S2ia8jZpVBRFdDou7sDH8wM2N5qEYYTrBawsV7FsHV1XsS0dy35vUcLvVIEf0unGx3Jdn2o17la1WgNWVqqYpoamqSQSxq6/U5yI+MQTh7n0+gqvX1zht37zyzz2+BwjY7nYS6PeZf72NnMHh3nq6fi3USqlKJfTXL++wV/9xfd4+plj6LpKpdLmy1+6QKXSvssv6Q3GUbgr5TNNjdn9Zb75wjW+9KXzKKpMsZQiDEIURWbu4MieRFAIQa/n0e95eF7ATqWN5wcEYcT6Wh1FUdB1BcPQSCRMZFlCVRV+7KP3ce3aBl/76iVUVeHEfRMYhsbSUoW/+cpFej2PZz/1MGPvQTp3r8pbFs9M7UdX4t//wVwRWYK6O0BGImvG/hkzmRy1QZ/6YEAkxN/rt7jUaNDxPOZrdRxdozEYUEokWG026Xk+KcPg+YUFkrrBieEh0obJcrPB+Y1NPnbwIAXbZqFe51a1xumxUV5cXKSUcDi3vk7WspjKZFAkiRcWFrA1jQfGRkkYOt9bW0NCYiiR4Pn5BQ4WC1yvVHhmdpZvLy2Rt22+cPUaI6kkLy1VmEhnmMnnWLy8zOd+84tU12scPLOfg2dmf6RlagCFtMN4KcMvfeTM3mRQlWVURcb1YlbPidkRaq0eV5a2ODodd5v9IKLa6jFWzLDT7O6GK2jkUjajxTSf/sgD2LsTSUWW0bU3WDLSD8wXrZRNMFHO8uwHTuxJ3jRFod6JGZRvjXp/o6Tde/Rbx7xSJkHGsfj5D53eA5s0RabVc7EMjdrufpvdAQPvvRsaf78SwmPgngXhEYo6hnYIgJ77IkG4jSRp2OYT99zO9a/h+pcA6LuvoqmjSMi0e58jDKuY+n2Y+gn67ndp9/4c17+BquSJojoD7zx+uErf/RbwKKZ+P83u79Pq/gmyZGGZjyLLKTz/OkG4RiiagMD1r+B6F/DDZfruWQztMF4wj+tfAWQG7jkM/RiGeohW+KdYxkMob2owSNLbfQ81RSZp6NT6/dhM3/PoBwF5235jIyTevt33q34QewPN5nI8Mj6+t21C1wkjQcf3yFoWO70uuqLgaPF3X96dr0VCvA0nUqTYP+nNJM+tTpcXlhb4rx5+nIOFIr//+nmuV3be83m+tSRgXyZLxjR5bmEeIQSzubgJJBDsNLu4vk82YaHxgzGj/eGqCCG8tzFRJDmNokyh6icQQrDvkM/YviKKouwldIZBxOT+MqqmUBrJIslxgI0sSyhqvP56butV5jsbvN6cpx96/LuFrzBk5nh66NS7ntmYXeQnxx7jq5vf42zlCqqsMIh8Pj7yCLn8Ic7Xb3G+foufm3qGMavIR0ce5v+6+Ze8sH2BZ4ZiJl9Ctfjyxst8ZeNlal6bA8kxjqX3IUsyp3MHudVZ5zPzX8RWzDiVS1b4xakPUzDunXj65jpfv8U3tl/bk5K1/B4fHj6zR0J4qXKJa+1lbrZXWe3v8NnFrzFk5Xm6fIqckdrzdHp++zwRgqPpKeRdUCitOfyj8ffzZyvPc7W5hKFouJEfp8qNPsYbt0lF0jmY/nEu1P6YF7d+E0XScKMOc+mPkNEnkCSJ6eT7aHlrfK/yu5hKJk5eU/M8VPzP0GSbA6lnaHqrnN35bSw1Qyh8Mto4pwufJqNPkDOmOV/9LI5aRCBw1NKeF1Ig+lxu/AVNby1OgRR9DCW1J2VzoxbXm1+m7W9Sd5fwwi5e2KVsHWbceYgh8xhjzgOcr/0RhpLAC7sktDLTycfvUq7cq1TZZMw+zfnaH/Ltrd/aBXpUHO0Ou3yz/zor3ZepufN0/C0u1P6EtD7KdOJxssYUy53vsjW4zM7gBi1/jddqf0haH2U2+SQJLU5SzunTWEqGinuLEeskm7VO3LQ3dAa+j6VrREJwe62Krql869ICpUwCc7fx0uz0cf2ATMLG8wO8ICQMI2rtHgfGi2zXO6RsA01VaHQHJC2DVm9AwjIIo4hu3yObtGh0+qQdi4HnIzyB9AMiQf7wAEeSSlJLIksypqZSTMYyGCEEL27dIqEZPDUyx0q3jiB2198ZtOkFHt3AJaEZDEKf6qBL2Uzh7kYdPrd+nV+Ze5TKoMNf7PoQScQGa2+UrWgcyY7w6f0PkdLijtF3dxbQFTV+tXSnW61JMoPQJxARKvI9b5QzqSLPb97k5Z0lHivPoN3DSV0g8MMtFDkDRGjKMLKcwPPXCKMaqlJClmxkxSKKWihyjoF/Kda7KzlUpYQkqUiSQxhVESLA0o8TCZeBfwVTO4amlAEFWbLQtQ4SKpoyhCYXUeTsPel8kgSJjM3YTAm37+GkLIojWeyEibQ7wLt9DySJ0mgORVN2EzksHDVBSktjKRaL3dtU3G3SWoa6V0MISOsZRqxRFrq3CUWArTrYqk1ayyBJEjc714lEhKnY9MM+STXNkfQJFjq3kJE5lDqKrSZIRANSWjpOPJENLOUOMNf2W+T0HAU9TpRZ7i2y7W6S0bJUvcpdWt97lSzJTNkznK19C4Cj6RNv+4wd1WSjX2Nn0ESTFZZ726iywkxihKKR5mprhbPV6xxIjnG9vcqx9DSHUhMkNYsrzSUsRec/Gn+Uy40lXq3fpB963Gqv8wvTT6PKCrr89gS6UEQ8t/kap/MHOJAc5fcXnmPMLvD+0nGutZb51OQHKJtZdtwmoYh4X+k4pqzz+ZUXeahwCNvQWe+2mSnkydpWnIgix4mCbzCG0tYdb62EodPzPbq+T9FxcIOAQRCgKwqmqtLxPFRZRvsBufT/qJSqKjzy6AGCIOLP/uQsn/2Dl+LvnIj9dWZmy/zUTz+4m1p4Z7vxiTy/+mtP8Tv/5hv87dcv89zXLqHrGraj89TTRzlydIwv/r/n7zpWo9HjX/+fz7G4uIPvh/S6Lp3OgF7P43/47z+PpilomsrDj8zy7Kcewrb//j5oN25u8nu/+02q1Q6+H9Jqx53ms9+9xY3rG2i6gqaq/KOfPrMHAgGMjef5jX/+IT77By9x/vwSn/md5/co/LIskck6TEzc0W8nkib/+Oce4d/89jf4xt9e4VvfuoFhqIhIcOToGL/2z57m//7Xz911blEkMHclgbqu8uRTR7l+bYPXzi3ym//Ll9D1+Pc0PV3kv/2XH98Djno9jz/+w+9w9uxtfC/A9QIqlTYiEvzWb34Fy9LRNIXpfUV+9deeIpuNGw2z+8v88//8w/zRZ1/iK1++wF9/4RyyLCOEIJdL8Olfej8/9tETewDa37V0RblrWyEEgQgZ1VJ4kYcE8YQ4DJlMZZhK3z1W7L9/ml/7336Rwti9gatjjx+kPFkgXUyhrtQZTaXwwyhOGA0jio6Opd05fqXX42i5zGjqzuJyLJViIpNGVxSuV3ZImSYzuRzPzy/ghSFPzMzwvbU1Xlxc5BNHjlDp9nh8aorRVIogimi7HmPpFKOpVAwiSRIZy2Qqm+Xby8u0PZe26zKbm+JQsUQpEV/70niBD/3iB7Acg4nDY5jO3z1t8B9aPXRokksLm/zh377GeDFNszPgocOTzI4WeOHibXaaXf7Jxx/m6vI2f/r8BXLJ+H448Hz+8tuXuL6yw7mbqxzbN0w+aXN6/xjnbqzy2a+fY7KcpdV1OXVgjMNT72zeGUYRS1t1bq9VqTS7XLy9ThQJJsvfP2X1Q6fn+MyXzvK5b14kn7Rp9QY8fXruXSnzCUunnEnypbPXWNlpcGxqiMmhHE/df4DPfPksf/b8BYoZh0ZnwFP372col+SxY9N8/oWLrG432Ki16Q3eOT31XpU3DvDk8P9IREBGn9qTYEiShWmcRlOmsM0PoCpjqMowmjpBGO6gyAVkycQ235BZSMzf2GR0Mo0kqdjmB3f3o6HIedKJT+MFt5F1B12bo9MMeemrJ3ngsSSF0gPIkoOEgSxlEINnwUwgSQqqMkMm8av4wTySZCJLCdLOz+MFN5ClJJbxMLKcIYyamMaDmJyJ49GRkVCxzSd3Ty9OPkKSUZVRTO0Y77YUSBsGH9t/kD+98jq36lVWWk2GEwnuKw9/3+1arsuNaoUrlW1WWy2+s7rM4WKJspPgbxfmObu6wicPHeGby4tYqsbJoRGmM1kOFYr89rlXOJgv8OrGOo+OT1B23l1yvS+b5Yu3rvPHl19nJJnk8YkpLE0lbZh8d22FG9UK5zbWsXfHt47ncb1a4crONuvtNt9ZXeZQochwMkml2+PSzhYrzSZhJHhlfY3ZXJ6cZeHoOu+fnOa3zr7Es0eOkTOt3U9ewjY0FPmd5VT/0CtudN/rtyUIw2XC/hqKOoNhzWG8Rar/bgF6oYiYdIbI6SlO5+b2HrcUA1VS2ZcY5p/u/0nyenKPjfxY4RhH0hOoskCTVT5Quo9DqUk2B7EcMaXZjNpFJCSGrTy/fuCTTDtDsTGymeKfHfgEg9Ddm4+NWSV+fvoZqm4TQ9aYdIZI7rJ6HNXk56aeZqW7zbZbR5NVhsw8ZTOLLMl8Yuzxu9g9GS3BL898lJyeJBQhjxaPMp0YphP0kSWJvJ5i1CruAZ5JJcmJ9Cz3ZWb39qHLGrqs0fFcDEWlpBd4Jvc+JtMZ0kqaIIpQ5dhL6URmhjG7yEa/ihf5JFSLvJ4BJPq+z3KryWQ6Td6Y4dHyr1N3l4iEj60WSOuje4wdRy3wQPFXaHorDMIGkqSSUIuo0i6jUxvi0dJv0GwxQlgAACAASURBVPLXcMMWsqSRUMsoko4vRYwlPkrbX0ORZIbMWUIhCMQAL3IZRCFz6Z+g7q0gRIihOFhKkaQ2StWroEsySW0CTSlQsu7Djzx02cBRh4kikGWTucRPMWY9RtevoUo2BWsSQ07S93xGzEco6Mf3/Jk02eZE9lOosoUsKUwmHialj9IPamiyRVofo+1vkdTKe+992DrBkDmHlPkJQEKWBMZu6mdSH0GWNIat+/Y+I0XW7k7vlHUcrYQm2yS1Mb52fYkwCjF1jbRjUmv3uX//6C7IHccDW4bGjZUdEpbBqzdW2D9W5PWFTdKOydRQju7Ao9HpIwHNbp+e69HsDDB1lZcuLTJRytBzffYN59hudNiqd5AkuLy4tWeZUEg7f6+gnbfWDw1wBOwanMXdglb/jpv+pJPn29u3+cKyRzfwUKTYUO212iprvSa2qnEgVcKPQl7YvMlL2/OMOxmmkjlG7DRfWr0MxF06ibjjpbxpUB9PxK/7k4VXMRWdBwoTyJK0By69wTCSJYmZVJE/X7pAy3f58fGjbPVbfGvrNoudKn+5fIGHitMMWSlG7QxXm5vMJIv3BJfCqIqpH0aIAFmySJgfAMBQZ1GVUfphj17kYSlJNEWNUVFlHEmSEUjIKESEMdWZiFCEyLKNrJgYyjSqkkARw0jICEBVp4EQgY5pvo+qVyWjqei79ExVlbFsg/JYjvvfdxBFlSHrkB/K7PmSRMRfcjP5RnxkrG8ORUR8ZeN/QRQSCWgHsWmttBu7qUkaoRDIxIOkIqlEQnC+fo651BGEgLpXZxD2SWlpBtGArf4mXhgwCFyCKMKSHbb6l7kiXWJ/8iDr/TWWu0u4oUdSSTNkjPF64zUaXpNRawIE9II+kpCRkPHDgIXuLXYGW9xsX+Nw6hgbg3XWeqsEUUhay5BUU8jIeJFLWnv7xLhsZnm9sUBGjxcz6/0qU06ZF7YvEomIpt+jH7rYqsn7isd4uXqN2511nh66P/6+2UVSapzYsNzbpuI2KZgp0rrztmO9UX4U0A0GjFoFkqpNSnOoeW0mnbdP9nN6kpKR2e0cwyDwuby1yWq7Rd62Obe5TslJ0PFcNFkhY1rMN2rkLZumO8BSNSxV5VY9ptUmdANb1bhZqzKaTDGdyTLfqDGVybLZ6aDKMvtz+Xsmk/xDrZGRLD/17INMTuaRZQk/DMmUk/zEJ09z4NAIjW4fS9cYmcrzE588zdhUnnqnh2MadFyXx943R2Y4yepChVqlQ8/32T9VIjGUYG56CD+K6HQ9HEOn63oYmsrhw6P8N//dxzn/2gJbO1vohsrhg9NMzSRYXeliJkJGJy1C4eFHXTRdY+5ImtKwiizpBKKHJifwwjaGkiaIeggEk9NJ2sEtjGiWQAxQJAMJGS/qcOxEmYAjHD6e/76eD+m0xf2npui/S2z60HCaXuDjhyGOphOJiJHxHP/k159i/vY2V26tU290SRsmTsJkarrA/v1DezfQUAiOn5zkl/+LJ1m5VaHT6ONKIQenhjhydAzT1PnETz1AvpBAViSEiAE0y7oDdBQKCX7jX3yIC+eXubG0yWqrST6dYHQsR1v2aTQqJDSD0Asxh00OnxqL2TySxBnDpOUNQEDGMOkFPkPFNK3QJS3suNsty8zNDfEb/+JDXLmyxtpqje1ah0zW4aHT+4h0CSFLtLoDVFWm1/fYN1PiE8+e4dCRMSQJvCDcvY8JZAlm9w/x7M88zJHjY6zsNMnnTKpRFZAoGlnWelvsS4yz1t9CQqJk5rjeWmQuNc1GfxtTiWfkEQIrYSAfUWnIbSq9Or1wQNHIsd7fZsQs0ki2sY7aKIYcM1dkGV2RUZVYYnRufZ3FeoOzqys8OTPDsXKZl5aWuVmtcnJ4OGZxylLM9JIkRlMpLm1t8/nLlxlOJTFUhYubm3Q8l37g40chh8tFvr20xI1qhftHRpjKZvje2hqXNrc4Ui4hhGCxVueL168zlEgwlEhwqFRisV4nb9tIxB3BVD7JySeO3uOb96NZkYhIJHV+5kP38d2ry2y326QdE8tRaXl9al6Xjz5+ENNROTpbptbr0hm4OIZOwjZ47Og02/UOHzgxw0OHJ1EUmXTC4pPvP87F2xts1tukbBPb0qi0uhi6wplD4wRhRN/z6Xs+CTPuZl5b3mZlp8GhqTKtnssrN1YYzqc4NFkil3KwdJXJ4SyOqfHk/ftJWgaZhMWPP3aElc0GG/U248V0zJQKAp46dYC+77PZaCMkeOjIJMldcNvQVD71xH28eGmBpZ06B8aLVLs9RkspfuqJE1xe2KLe6WPbOpapMV+t8/DRSTRNYave5oGD40R6DCS3Bi7qrgfaG83IOBZcImncYWKaSppR5/TbPoOYvT2KbT76pkflmHmkHUIIQa3S5vaNFrlCkky2zeZGg9HJPLXtITbWDJIpi3wxyes3lrAdA8uaJl9Msl6pUyylMc19aPI+3K7OlddXcBIR2dwBvvGVAYePjzO9P8H8zQV0Q2V86iQ3r25gWRXmjo5iG3ECrxCCMBKo8iy6esdTQwjQtMPou39LEvTdG/TcryBLGVQ1XqRGkeDU8Ah9P9hlMMqcHhrBUFUkJJ6cniFpGNysVjmQK/C+ySnSRhwy89Do+D2TV9uey9XKDinD5L6hYebrdRKGiqGFJA2N+0YKrHaq3G66GJrEoUIBXYv49H0n+cLtc6x0N/nQzD6ODxVQZInpXBJDj3AjFy/0OVossd1vxHKSQDCSNviF4ye5VqnQ9T3CKCJv2fzKydNc2N4ECX755Cmag3g+2XIHXN7ewtI0TpSHuFWrkjIMCrbDZqfNtUqFw8USiixztbJDwbbJWRYSMJnOUHQcHhodR9l97wJBZ+DR6bt78qMftZIkGU0pI70tTUtBUWcJw3tLJoUQtIM6DW+HtJan6Vd3m8SdN71akNEs0ppJHOuepu5tk9HTVNxlNFln1EpQ99fQ5Qk2B4sk1Awlw2JrsIjjHI2N180MgiZu2Cej62wP5klrBWSpTVrTafkVtt2VGOhTkuT19F2eRQUjRRD1yOhp6n6NjcE6RaPAtrtDRkujKQJBl5SWpxM0MH2ZdtAhECGGYtMLerSDDu2gQ1I12XG3qLhbjFojyJLHpJOnoMfm4a9vb5HQdVKGQeQaHCmMcr1SAQl0WcGLQvp6xEKjymwuxyvra4QDk7HyEDeqFY4Uy9xoNpAkSBsmq602BTuD7w4QkcbVeg1VbnCoWGSz3cZUVEw1XlOaygwBXZA0dty4sR6JiJyeR5M1jF2iQS/sEWESIZCFoOpVsBWHkBSqkkIIga0W8SKP+c5N2kGLrD5FP+wxiGSWewsEImDb7dIPuyiSiqHE68gAg4q3ia3m2eyvkdXzDESCEIuW38GUs7T6Ed2GQ8fq0OwN2Gy1yTs2CXMfHoJNN6Dd32S73SFjmaSsHCVLImAb179OUhkhjLZxvVbMnqJHyhglCNeQRZ2UIhP410AZRotWGTKHGbjn0bUZVKWEFyxiyAYD9zwJ2cbREwjRQ9cOoMh3mGZvyNxa/hqVwQ2OZ38aVTIxdAVd0dmqd2h1B+iaSqXZZavW5uBEiULGIZ+yWa+22G50UGSFsWKG7XoHPwjp9F2StsFWvU134NPoDGLPIkWm0/dQFZnRQpqbaxVaPZetWpt82mF6OE+93WOn2SXjWHus33/f+qEBjkIR0gpaRCJi4Af4YbhndPpwaZqpZA4JiY+OHyWjW1iKxqn8OLOpEgXDIa1bpDSTDw4fIGc4lK0kjmrwszNnqLidXSZR7MD/ycmTSJKg6XVBikMAnxk9QGXQRZNVHFUjpetMJrL4kc9MqsBEIksvGDBip/ix8YOokooXeSQ0nVOFMR4vz2AoGinNxBcRuqxwLDtCUrs3xG5qR9HUCSRUZCm1N3mJgM3BBjuDNQzFRpFUMnqBzf4ioQhJalk6QQNDsXfNwSRSWo6Ku85M4jg77hoJNU3g1WgHdWQUvMjFVGwsJUHFXWMqcYSGX8FWknvA0YOP7GdmtkwqY6O+SQqiKHcWkteba+wMWozZeRzVwI180prN1qDJsJVFpYAmJTlXW6Ad9AjCArKSoGzkkDCZS57kanODpDrOer/HIGxhK+O0fJ8brS12XIUgSpHUhrDlDIEnoQ0KRA2DvprhUn0TVZVw3BmWdnzCdIVBGJLuHSbsqtzqNAkC0MJJemrEquwyls1T9g+jhBJhoHCuuoFq6Xxo+GMYio4qq+T1Au8vPYkiqZhKzMZx1ARZOY8pv72jndOTVNwm04kh/ChgrVfhYGqcW+11Pr3vGS42FrjUXEQIwaRTYswu8uX1l3mtfotRq4AiKXtSAIEgqyd5vblA2++hSErM5HmLVleTVZKaxXq/gqOatPwuWT0Zy+6Q6AaDvTjRt0rx5F1Pgplsjr7vs9PrMZcv0PZcqv0e8406Ldflys4O05kMC406ElB2ElT6PRqDARPpDKos0w981jttur7PdrfLTi82H8+aFiPJH51I7JHRLM9+6qG9/69WGjQknyOPTzPwA755dYEj42X6Fnz8k6dY3KlT7fRZrbWodXpMFDIojsqZx2bxg4iXb69w7MAkr86vMfB9bm5WGPgBw9kkyzsNTs+MxfLBoTRPfmg/O/0WQkQkNEHHv8r07BzJMZCkRToetP1lMsYcj33YJKnN4YZ1AtEnbxxlu3+OrHGAmnsZISLSxhCV/nkE++j4y+hyFjes4UUtJg5ZlOdsVKl1l9HhW/2ARkdzPPsfP/y26/TW1wkhuFDZIBSCMIqoDHoULYfNbpvR0TQTpWGcQY8HymNc2NkgslXm+3Xywmat26LhDphMZritNhl/oMhjmTyvVzbZnytzqbGD1IPE/Wk6UcRit8GwkiC56+Oyl2wiSWSzDh/44CHGGiWW2w06vsvhXJlbrRpd3yNrWFiqxvSpYQ6dGafje+z0u4wl0jTcAaosM+akqLl9VFmmGg0Yf5MRqBeEXFndRs2azBbHsKstdFVho9dlZy0GzINIYGgKlxY2efz4Ph764BxJx+T2epXtegdVkem7Prapk0vZjB0dYmq2xMJ6DWF41OQGqqyiSgqdoIcfBSiSTCAiZGRAsNrbRJIkrrcXaPkdZEkmq6XI6ikc1WK9v81qbzNOhHHrqJLCSi+Wzub0NGfGx5CROFwqIUngaDqmqvKrDz6AqaoYqsoDY2NMZbLcaG1RDdp4hKTTCufqyyRUAy8KeHB6hKVWjfFUhnP1ZXaiNrIVMZlP0Ap7JJMqc2qOzV6Lxf4OoYg4MzFKSjMpJxJcr1QoKCaHjBTD+QxyIHg0XabS7+HoJvgh9WaTKBLohoZmqLRqHRIZBxHFxlj+bnKfpqs4qR+NlMe2P+B2e4f1QQNtImJUcbBUnc2oQb2vMrU/Q0JXObtzG19E5KcNRvMpGvUYCD00WeLJ++825lyrNan1+jxybIrFnTja/NrGDu2BSy5hE+kSV9a3Y0891+fo+BCLO3XunxujXEyRMHXG8mnOL26g6wrFfJLlSoP3H5qm6blYhs7JA6NEwMJmlZ1Ol8eOT3Frq8bxiSGavQEXVzaZGy/uncvA90mmzT0pniRJjBRSzM4USRg6AxHytzfmmcxlaPgDJiZzaIrMeqtNx/e4sVPh1PgIpw6NUev2mS3mWfc6JB2DjWZ7d2wQ7HR6JAyd5XoDVZZ58sDMu7JmU86zyLspUvfySQvDiMsXVsgXkhRLKTRdZXujie+FLM7vIAH5Yopb1zfQNIViOc31y2uomsL8jS1sx7zL9y0MIm5cWefkmWnshElxKM3CrW2W53eQFYnAj1ie3+Hg0dG7wgYiIXjtxioJ0yCXttFUhSCMqLd7GJpKyjGptXoM5ZO8cqXL3OT7UeQSlYaPbUpsVltMJtK0I5fbqxVGCimyocF4IcPt1QqFtMNj4xOcGikTihBbsQhFhC98To0MoUhK3DQklheqkkLRsfjU0aN7i3JFktl2t7nUep2DxQM4iRzTiUkaXpNBNCBl6lxv32B/YpYzEzkcZZyyWWKhu0giMLDsDg9nh+kHfbYGW+wvjCPa28x35xkyhxhEXSbTGUxFQ5Jgqdmg5/v0fJ8D2QIJPW5eJjSdS9vbZEyT2WweR9foej4jqSSRgOuVCj0/4OTQMMOJJNPZOw1ENwio9nu8vL7GoUKJifTdKXYD36fR7f+AMpp++EqIEDdYRpOzmNqBNzGnA0L/CgKfCAPUubskr4Hw2R6skNLyVL0NDNniduciiqQSipBe2CavD9P0d7CVFLKkkNQyyCgEkUcnaDBhzxES0AmaZLUBvbBLw68wZs0iROxHU3U3MRWHrcEy8u58JhA+qqwzCLus9W9hKg4JJc1y7zpJLYvpV0mob24SSwQipOE3qXl1HMVmobsEwHJvjbyeRZM1brRvkdSSrPXXKJlFmn4Lvx9wMLmfxe4Kjmqx6W9hKAZ5Pcdaf4Mdt0LDb5LXc4QiYrXVZCKdIWOa1Ad9av0+N2rVXR+viLxts9xs0Pd9vN2GXN6ysTWdluuy1e2w2elwtFSm7bqstVtcrewwk82x0WkjAROpDIaioioyAsGlnTgE53ApTzXYQJZi9UzNrVE2hwgiH1Ox8COfdhDbdTR9CS/ysBWHtf4qWT3Hen8tthiRVFJaGhAEwsdSLISI8CKXVtDEUmw6QZv27t+yJJHTCjSDBoZi0vBrdMMOgQho+DX8yEOTDbpBh+n0LJd61zCBnuezVG2gqwqJXauHIBQEkc9yrYEiyTiGjiLH79MPlvCDRaKoQRi1AIEkaWjKGO3eX6HIGaRwnbgRcACBTxBVCL0GAi9+Hp0orONxA4hwvYsI4aGrk3j+TSzjTrOh4S1xsf5ndP1tcsY0Q/ZxFEnmoUOTsQ9mGOLvml9rikw5myRpGxydHkaVZWbLub1Zpq2pnNw3giSB64dYqspkMYsiScwM5QhDgaGr1Js95saKaIrCsakhXC/AGiuRtA2KGYeDEyW09bgJ2R14d3kf/X3rhwY4EkLsmWP7YUS92yfcvUkbispU4u44upxhM2SlmE0V9x4rmAlG7Qyjzp2BPGvYZN/iMZQ3HK61Vlju7ZDSbMpmhpbfR1dUZEIuNJYRQEZzaAexl4cuq6z1dwhESCQibMXgtfomBSNNJCL2JUcxFI2O7/JH89+jH3h8dOQotVaPMBK4XkCrO6C4K3fw/BBTTxBGEYIO3Z5LNmWTcGTaQYOMXmQQ9ZAlmZZfQyAI8dnsL5JQ0wyCGO0WRPiRB0i4YQ9V0mj5VfzII6XludW5wFzyftb7CwzCHp5wafs1/MjFf1MCRyplkXqXyXbHHzAIfZp+j27gxiZuUUjFbeMoBr0gour2aAcDvCgipxdIazZxVIKEoybZ6vcQSCgSeFHAsFVis9/AjyJUVAw1g4yMH8WsqqBrEfVCyqNp1qqtOE2lAorqgpDwXRXVT6MJhUiVafcG7BsaodLqQgi9fgCujb5rCD3wXEaTWYatO0ydpJYiqcUyjCAKuN6+Qjto83D+vnsyMDRZpWhmSGsOuqwy39lk2hlmX2KIv1p7CUcxyRsp+qHHc5vn2XGbyJLEA/k5vCggpcUdK0PRSGsO+5MjLPe2+f3F59AklQ8Pn6YbDnhp5zJbgzqfX3mRx0vHeKJ8kue3z/Ny9TozyWFmEyNossrRzDRfXn+FmeQwD+YPvikOVJDTk6iyTGaXSm1pGkNOAkNR2el2aQwGFGyHru8xmU4znEgSCUEv8Kn2+yR1g0Hgs9XpUHIcUkZ8c9vudohExHa3S8ow7k5S+REsPwjpeT4JU0cmZi3qisJYPk3aNpGIJ4yKLBOEEVEkaHQHSJLEwPPpDuKuQNqJjT5lWSIIIxRJYjQX7+ONim+4XVTJjCnC2hianIj19JKBIht4UZtIeLE5n3BRJANLjQ3ivahFP6ziRR1UyUKTbTTZIRRxDGsYeciyTiR8kG0cdXhXwhkBCmHkUnWvkdBGkJAJhYeEjCqbhMJHAoJoEEtlkfCjLrZaRJUtIJYyrnaaCAFz2QKXqlukdZO2PyBrWNiqhqmoyJLEgUyRl7dW6AU+NxsV5rJFJpMZ1rotxhNpLEXDDQMW23UsVWOn36HpuZwujbLWaTJVyiAQWJZ+T8aUKssULYeDuSI5wyJjmIRCoEoSAvCiEJmY+TOTzhNGERPJzG4ChYyj6+z0uxQtZ2/xA1Br9Rh4AZVmk3zKZjiX5PLCFmcOT9DsDDB0lWqlxcxo4f9j786CJcnOw77/z8k9a6+7b923l+nu2QczA2AAmCBIACRokiJBibJpirRo0RbDUoRtKcIPXiQxQk9kKCRHUA4FgiGbkoNkcJEsLiYIkCJEAMQ2A8zae/f0dve6Vbe2rNyPH7LunV7uYLpn6wXnN9HR05VVWZlVmV+d/PI75zA3UaVacou7TDsDoiRjtlnha69d4iOPLnNxtcUwjInSlM3OgDBJCGNBR3YpmT51q0In6dKK22yFHRQKS5i04g6+6dGN++NS/qKRXLFK9NMh6+EW10brKBS2NElVRphHWNKiYVdxpI1j3TpY4nS5zHT5xm4hC7UqrayHLc1isE1DUDEdwjwlz3NsR+K7JrEqprX1HZO67VG1PabdCivBDo4tebIyx9nuJlLAwkSVhl38NjuGgWoFCDvk7JnzTM43eP21a3glF3uxyWo7orvdJ0tzbNdi+eEFXn9theWH59m81qa7PSCJE6qNMkceXwIejMSRZ9o8VJ1hqdTk8mCbSbdcdGWmqJ4xRdFALhlOMWixae9dIMw0KvtO4CGlIM6yYrBNVdxh9myLiueQ54rJik+z7NHqB8XYR6bBfKNKs+yx2ukRJilRktIZBmz1hqx2ekRJyiCK2ewOCKKYLMuRQhQ3AfMchGC+UaXqu5xa2SSMEwwpxgMcm4ziBNcyb6gEB6g4DkuNGtd2ukX8MySDKKbhu0hZVDKXHJu655JkOXGWsd4fcGSqSc11iZKUIEnohRGPzc1wcn2LuWoFpcC1LHbiop3pGRZhljDKUqxxhbRnWoRZihT+uFp9RJJldOOQWb+Cb1p7lSZ5rjDMYnB+pRRZljMc3xWematTaxTHuWFITLOYAGa71afXHZHEKcNBRL8X0m5tsb3VJwziYnw638YwJHlejP25uDzNwcOTgOLi2XUWlyepj9uUuxVVJc/m0mobGA+UmmaYhsSQgkbFxzQkaVrCdw7Q7gV0B11MU9IdhKxv90nSDEMKRlHC86eu8IP2MXYGIa5j4ZVNLgyu0o67TDg12lEX27CxxsMGxCqhbpUZpCNKpkeap3THgwfPuJPMu1Nsx21GWZFYmXCa1KwaaZ4RxiFJnjBIBwzTgJpVo2pWSPKEXtIjzIrxlXZnX+4kXcpJhU6yQ9NuEGQB7bhNFgqGcQICjjaavLa1WXxvSUiU2igUYZry5MwscZbRiyLqrott5ARxws54LLleFLJUqzFKb6yyvdrr8uvfeQHbMPg7Tz1zw4DgAkGj5BU9Ad+VCHAvyslVxK0jTJmY9gfJsxWEuHVcVykMLGnTiTdxDX+v4siRpXG7QlCx6uQqQwqJZ5QpGTUs6VAya6yOLtJJNpFI+kmbDXGVXtLCNUrsJK3xdU+Ca/hY0mKY9vCMUvF6WSXLE9rxOpawMYXFIO1St6ZQ5NTtKUxp8pGJR3msdggB7CRdTGHgGx4Nu8GpXjGZgCud8aDdIRNOkzRPWfDm6aV9BukQ3/C5MLxEmIUEWYBv+jTtBv10QNksIYRgyplEUFRMV2yHq90utmGwMRwwW66wORxSsR2ans9MqczaoM9mMKQ59Gh6xU2vYRyzMRwyXSoTZylXuztsBQFhkuKaJvOVoi1ftm22w4Ba4LLa71NzXCwp6UYR036VUnqQjAylYMaZxTU8TGFgS4dBOsA1XBzpoICKVSHNU+bceSxpcaR8FKUUpjSxpY0hDBa9g4yyEb5ZopRWcKSDJW1SlWAJiyAb4kgPz/Bo5E1ycnzDp2SUmXKmsWVRiRTnMbPuHI7wWSgfYmo8hlQsUzKRU6u42OOeQHGeIayi/TdR8pn1K4AiUQlZvo1lLpOrANOYBgSmMYdhrFIME7NErkaYcpooPUOetzGNBUw5Q5KtYcomabaOIeukqj3uclzDNObI8hsnrfGMJofKH8eSLiVznjAbEhKADVEWkIiYklsnzWOUtPAtg0xkeE6JIEjpd0f4ns25CxtMNMtkuSJLM4JRTKXssrTQZGWlQzp+zHUsglHMVL3EpSstLNMoxu0MY+JmmalmmaXpBpZpFvG//O60i8SdThX8HlGtqMVWtMXR8lGCMONSq8PxuSncN+mPN0xjLGlgX9cwGiQRnvHGj/mbyZXi4mCdIAsxhUHF8gjSCMewCdKIMCvWvdvvtBjrqBhXyZEWUggMIQnSqHhtFnHAn8I1iq4ZvSTCEpJ2Z8Qr51eLiwbTYKs7pFYqBjZ1LKMoQ8tyfMciy3Mm62WeObHAVnSNXrKNb1YYZQNMYWNJZxxQDYKsj29U8IwSOTlpntBLtplw5uglbQQC1/AZpj1s6dCwZ+jEm8T5iFzl1KwJ2skmU848k87CbX9JcZbeMkaQGA9SaAhJprIiGI6fMx4uESjGlCrGpirGntrNqkoh96a+fENRJm0KgzjJUEphmbIowzYkea72BoMNooRWb8jCRI3d3nOmIcYj0Bc3PJQqqm4UoHKFYQjMN7nLWDRMIhQKR7r7Jo6UUoyyGFMaSARhHlMyXKI8IcxibFlkdF3DIsgikjzDEga+6ZCpnFTluNIiVRmpynClTZKnBFnRPbNsFnfyRllUlIYi9mZaC9KIVGX4poslivGl4jxhlBYzT/imwyiL8caD7+3+f1E2XXwbmVJYUhKmRUm6ZRjEWTbutiLIckXObtm7IM0VhhAYsvg+M6VI84xOGLI5HHCsOUnJtnf79N/v7aV9A+JKpXZAEwAAIABJREFUu0eSZczVK8WgrXmObRrkSmFKSZxme6WjcZrhmCZJlu2Nc5BmOa5tFkmj8d3vNFPYplEkAq47HtM8YCe+QN0+ihQ2FKlWMhWNByA1xskil1zFCGGMu6wWXUWz8dSqveQSdfsohnDIVIQUFvl4Rh4pDDKVIIW194UJirGA0nzEevACUd7DMydwZI1huk6aRxjCIlEBntEkzHZwjTq5SjGky5T7GAKXV7fX2YlCJlyfhXKVy/2d4hxXsFSpcam3w8FKnZ045HC1uff8Ka9EJxpxsFInVTm9OGLaK/Fae4ODlQY7UTGTi2eazJWq7EQjDvh1Xnjhdep1n4dPzN9yvkZZuneBvdvPG26dWW2/38Hd2BYkMZ554+/KZmfA2aubuLaFbRkMgoiS5xBGSVGx6Nl0ByNOHJzh9bU285NV1rZ75Lka93EPcG2L+ckq69t9hmHRrWKmUWFtu0elYWDVYg6WFnDG8WF38gYAQxjjx0xSlY4rkBh/t3Lvsd27/0U1QIolrb04bUvrjgazjfN0/DkVMd4QxUWyQu3FhWLQ2rwYYHec3DCEJM5TBAJDCK4MO3TjgEOVSerjxFGSZWystnGkwdZKBwRkSYbt2piWREhBGmfF8ZmkLByZYdgb4Xg2WyttpCExTAPXt5k5MLlbwfHAxKKiG2d+y1TPN9v9PncHyCx59i3toSzPiZIM2yyS3ApuGI8lHydNd7uOOtfFuSTL92JflKRYhkGa5yhVzIwWpymOZe6957dfX2EUpxydnaBZ9jClJErSvfVGSTa+C15wzDcmHVFKkeY5hizaYVGaMoqL2UAPTzb2tsc1TeIsQ4pi/5Msx7VMojTFHL82VwrHLB6zx793AsF3tlfYCAaULRsEeIbFyqCHa5o8NTnPa+0NgjTGNopK9Jrt0o4CSqbDs9ML2IaJUoqNtR3On15ncrpCqezyyncuc+joNJZlUm+WmJiq0GkPOfXyVap1n3LF5eqlFlIKFg9McPrVFaZna1TrPitXtrFdi8eePMCFs+vkec6BQ1OcPbWKY5vMLzW5cHYd27F48tnlG8ZDW93qUim5dPoBV9d3mGqUcR1zr8G10x9x7MAUF65tM9UoEUbpXhXSaqtHvewVgxULQRinrLa6PHJohtWtHmXf4eB8nY2wRZjFpColzlPKpk/RYoCVYIMpt4klTCxZVJLkSrEVtZnzplj2F0hUSqYybGkVv5nCGLdji/gU5zHWeJkcJ0aTPMEat5kEIClinCnG8W88a1amMrYGIa5lUrEdLMMgyTOiNGNjMGDxunHabMMYV4bme8fJbszfPf12z7frf5+TLKMXR9jSuL7ds3fM5uNqW8u4YQKd+z0WwTgexekmveivqDrPYZtvTPv+xm9oAkjETVXzRTxJyVQ6rjIqZlTdHfai+L6N8bFUrMsQxTizu0NHFBX2klQle+soZtsquvVa0kEKST/pcCU4DQiW/GOUzOr4Jle0956ZysbDZWTjKebfGFs0V/l4PEFRHFtCkuQpinxcxVTMqLxbLWUKg0xlnB+8zow7RcksIZF7zzeEQaKK2dESlWIKA1OYKGCUFDMxG1ISZylXul1W+j0kgg8uLOBbNmmek+Y51jhZvrudYZpgGyZpXvzO77bvpRDYhkGWF+3NOMuwDYMoK9qpL66vsVCtcrjRfON7E7t/3d6hev113s3fM7z57LX7VanvuvkxIQTDJObL68XwM6nKOdvdIkxTylZxk8Q1LLajIUrBQqlK1XZ5vDmHUilB+GUUKYas41iPABKERGCgSEGl4zHfQGCxO+Nl0Q1TgMoQwiRXEVI4KJUUj49neIMcIW69+ZarjIuD79COVyiZdWzpIZEEWY9cZfhmDd+oMUw72NJjwT9Blki6vRGuY7G61qFS8RiFMVmaI2Ux8/DcbI3VtR2EEPQHIeWSQ5bl1KoeG1s9fM8mjjMsy8B1LaYnK5j7d097R/Honkkc9ZIe2/E2i94ilnznpVRv+YZvsd/7XWR8t9fsd5JEcUoQxsU00UIUJ75hkGZFA3i34SUE5KqYnrZy/QC2Yv+T+LZOPNRes3O/k/FBocZjo7zZ7Czae2f3x8x5sBpI+57kaZYjBG+ZlH5XNkDl5GR7s1C8/XWkewME3ulrUzUizSNM6SAxSfIARYYUNrmKi6QVKVKYJNmQJA+o2ovIN9nm62PPm8Wv65ftEjc1ym9+LIoSVlY71Go+zUbpfYtt322bQdww8Pmt+wu7h9mbxeZMZaR5dsfJnftBPk6C7CbzbvZWyb07eM39/sHdE42zdyJKUqI0o+zYN3SrervyPC9ufLxL4+kNk5hWOCTLFQ3Xwxi303ang+/FIfn4fN0d83KQxERZynK18ZaJvLtlv/Ph+riz67u1Dd/0PLx5HbyxjmE2whLmLXFrmBbd8B35zmb3vB27s63dvC+7CdG75H6PRTA+eLI8IM22MY0GhnzrAcvvhlzlBFkfgcAzynszj73XoizClGYxHMXblGQZO2GR/Cxb7/75UkxOEVF1nLt5Pty2NM/pxiPKlkOcZYRZcXPOlkVSuEjcFlGoKFBQlC1nfG04JFdDpKwjxdufHOZOFcUFvfENNmuvim43dkpMFHmR+ETiGP4NM8K92Y3Mm9/j5uV3cI3/YCSO7vYGaJr2rrjfG0g6Fmnag0HHIk3T7gX3eywCHY807UHxQCSONE3TNE3TNE3TNE3TtHvMvV+npmmapmmapmmapmmapt0VOnGkaZqmaZqmaZqmaZqm7UsnjjRN0zRN0zRN0zRN07R96cSRpmmapmmapmmapmmati+dONI0TdM0TdM0TdM0TdP2pRNHmqZpmqZpmqZpmqZp2r504kjTNE3TNE3TNE3TNE3bl04caZqmaZqmaZqmaZqmafvSiSNN0zRN0zRN0zRN0zRtXzpxpGmapmmapmmapmmapu1LJ440TdM0TdM0TdM0TdO0fenEkaZpmqZpmqZpmqZpmrYvnTjS3hdCiP9bCPE/3+3t0DTte5uORZqm3Qt0LNI07V6gY5F2u3TiSNsjhBhc9ycXQoyu+/fPvpN1K6X+tlLqV25jG35WCPG18Xt//qZljwoh/kgIsSWEaAsh/lgIceSdbJemafee+yAWzY+XtYUQO0KIrwohPvxOtkvTtHvPvR6Lbnre3xVCKCHE33on26Vp2r3nXo9FQgh3HH+G123Xr72T7dLuPebd3gDt3qGUKu/+vxDiEvCLSqk/e583Yxv4Z8AHgGduWlYHfhf4OWAI/FPg94Gn3s8N1DTtvXUfxKId4L8GzgMK+JvAHwghZpVS6n3dSk3T3jP3QSwCQAgxBfxPwJn3cbs0TXuf3C+xCDiulLr2/m2S9n7SFUfabRNCeEKIfymEWBNCXBNC/KoQwhov+4wQ4rwQ4pfHd+EvCiF++rrX/rYQ4n+77t8/LYR4WQjRE0KcE0J8EkAp9Xml1O8Baze/v1Lqq0qp31BKdZRSMfAvgCeFEKX3fOc1Tbtn3AOxKFBKnVVK5YAAcmAaqLzHu65p2j3kbsei6/wq8CsUSW1N077H3EOxSHuA6cSRdid+GXgCeJwi0/wJ4Po+scuADcwC/x3wG0KIQzevRAjxfcDngP+Boorok8DVt7E9HwcuK6WGb+O1mqbdv+6JWCSEOANEwO8Av6aU6t35rmiadh+767Fo/NpjwP/1NvdB07T7312PRWPfHCevfkcIsXTnu6Hdy3TiSLsTPwv8Y6VUSym1QdFV7OeuW54Cv6yUisflk38G/I191vOLwL9SSv2FUipXSl1RSp29kw0RQixTVBz9g7exH5qm3d/uiViklDoOVIGfB772dndG07T71l2NReOKgl8D/r7uJqtp39PudrsoAb4POAg8QlH9+B+EEDrX8ADRX6Z2W4QQgiJLffm6hy8DC9f9e0spFd60fH6f1S0BF97BtswCXwB+VSn1797uejRNu//cS7EIQCk1Ukr9W+CfCiFOvJN1aZp2/7hHYtH/CHxVKfXtt/FaTdMeAPdCLFJKZUqpryilEqVUB/j7wGOAnsToAaITR9ptGd/JWqfIJO86AKxc9+9JIYR70/LVfVZ3lbcZSIQQkxRZ8t9USv2zt7MOTdPuX/dKLNqHDdxS9q1p2oPpHolFnwT+SyHEuhBiHXga+DUhhG4fadr3iHskFu27aRTjQGoPCJ040u7EbwH/WAgxIYSYBv5X4P+5brkF/O9CCFsI8YPApylmPbvZrwN/VwjxcSGEFEIsCSGOAQghjHFgMwEpiukdzfGyBvBF4PNKqX/yXu2kpmn3vLsdiz4mhPiIEMISQvhCiH9EMTD28+/ZHmuadi+6q7EI+BmKbiFPjf+8Mt6GX373d1XTtHvY3W4XPTH+YwghqsD/AZyjmH1We0DoxJF2J/4RcBJ4DXgR+CrFLB67LlH0oV0H/jXwC0qpizevRCn1ZeCXgP8T6AJ/DiyOF/+3wAj45xRBbUTRfx+KKa+fAn5JCDG47s/0u7iPmqbd++52LPIoBo/sUNyd+wTwI0qprXdp/zRNuz/c1Vg0nmV2ffcPxTgjXT1Qv6Z9z7nb7aI54PeAHkWyaBr48fHss9oDQuix9LR3gxDiMxSzCh2929uiadr3Lh2LNE27F+hYpGnavUDHIu3doiuONE3TNE3TNE3TNE3TtH3pxJGmaZqmaZqmaZqmaZq2L91VTdM0TdM0TdM0TdM0TduXrjjSNE3TNE3TNE3TNE3T9mW+9VPeFw9c2ZNSim4UIYVgmMQYQpDkOb5pkeY5liHpjEbUXJc4y4izDNc0EQhsw6AbhfiWxShJmfR9PMt617YtSTJOX1inWvE4uNB819Z7u3Z6AecubfHoQ3P4nn3L8lGYcObiBhutHq5j8dixOSYa5b3l19Y7bG0PePTYHLb13h3CWT4gy7uYsoFCIYVPrgKEMACBUglSOOQqQgoXRQYqRwiTXIXj54cIJIqUNOtgm/PkKkIIC4EgyTYwZBND+je8d66y8fqKfwshyVRU/D8GmYqRwkJijJcLpHj3jpF3QNztDXiH9o1FeZ6TJDlKKaR8YxeFgJuLNoUAEJimRIj3/uNQStHe6OJ6NoZp0OsMmVpovCvvvRONMIWkbDv7Lu/FIWGWMu2VUUpxsdfGN23mSpV3/N7DMGY4ipmqlxBCEMYJw1HMRK2095wwTtgZhEzVSxjyze+DpFnOdm/ITKNCluWkeY5zXexI0oxWd8hE1d83poRRws5gxFSj/F3f580k+YgoG6LI8Ywaptz/83w/9UcRUZIyUfERQjAYRewMRyxO1t/ytXmek+UKyzSIkpTtfsBM/e19Nu+hBzIWvWdvlis2rrSYmG+QJSluyb2z1ytFGERYtoVpFb9LSZSQxCle2SWJUpRSOPv85mvaA+5+j0XwAF6nadr3qHcUj+6VxBEASZbx2tomppQ4pkHVdVnt9vBti9YwwJKSIE5Ic8VkySNMM0wpsQyJZ1ts9Yccm5lkqlx66zcD4jjlzPl1skwx2SwRxSnDYcTUZIXhKMZ1LAQwGEYkaUYUJUxPVgnjhErJpd0ZEkYJlmVw5ND0DUkQBawP+oRpSpJlND2fQRyxVKtxsrXJQ80JrnS7lEYj1gd9Gq7HVKlEKxhiyqLRtT7oI4XgQwtLKMB/l5JHSZrxnZNXWV6YeNcTR0ma8WdfOc3yYpMTR2b3vXjtdAO+/p2LHFqcuCVxpJTiT770Gn/6lyc5dnga17FYXpxgovHGc66stDl5bp2HDk2/Z4mjNNshiL9Nmnco2c+SZOu49gmC6Hls8wACg2H0Ao51iDTbxpANFAlKRXj2Y4TxGRzrKEH8IkJYOMYBwuQMQhgMo+cxZB0pLOL0GiX3I7ckjnrxZaK8Rze+hClcbKOCJXwUOaNsm5I5ixQGw2QdhaJszdF0TiCF8bb3ebfb6u0kHK7v4vp+JEfutk4n4OVXrlKtemR5jm0ZSCHpD0Ms08A0JWGYEIxiUGBZBh/76EOY5nf/PjaubHPxtWuUqh6GKWmt7rD88DxXz60zd2iKna0+Sikm5+qsX27hl10GvRGGKZk9OMXFV69y/Olluq0+pZpPrVnm0qkV4jDB8Sw6mz2OPnEAaUgGSczZnS1Kps1CucrFbpHkKds2G0Efz7Q4UGlwbqeFIQQP1SY509niQKWOY5qcam9iSMmR6gQXe9tkSmEIwcnOJg/Xpzlan6Abh1wbdJn2S1zobhNlGUdrE3jmnceuTj/g+VNXOTw/wUNLU1zd6JArRaPic3G1RasbUPUdzlzdZGm6wYmD05S9IiETJSlX1juM4oTDcxN0hyFr210mayVevrDGervHRx87RJplXNvqMlHzefHcCs1KiYeWJpFCsNrqMj9ZI1eKU5c2CMKEH3jm6NtKjoRZn7XRq0gMZr1HKMupW57TS4b89pUvsBl1+NmDn2HZn7vtcyvLc9r9gCjNaJY9ukEESlH2HHpBWBxD1RLdICTNciaqJTqDEVIIlFJsdgdc2dphrdPHtS3CJGWy4tMLIjKVU3JsoiRlFKc0yx6Xtjpsdgd88OgSYZzQH0XM1Mq0BwGDUUy95BLECVGSUi95VD0HIQQKxSgdEecxtrSxpc0oG5GqFIC1cI1Je5K6XWeUjTCFiSEMRtkIz/AYZSMc6aBQpCpFCkmap5ii+B1QKMpmGSnuqQTW26ZQDNOAJE9xDWdv/6M8QiCQwiBXGSXT51qwRtUqUzJLhFmEIQzWww0WvXlyctI8wzFsXh9eYdGbwzVccpVjCpPWlTYXXr5MZ6PLkScOIg1BtVnh1DfP89jHjnH1zBpJnGJaBkmc4no2o2FEuV5CCBgNIg6cmOfSyascOL7ApZPXAPDKLqsXNvjQZ55i9cI61YkKvfaAfnvA1OIEG5e3cMsuT33iEV7qbPC5l77FY5Mz/J3Hn6Vsf/cEUzAIcVyLMIgZDUKEEFSaJXrbA9ySw+XTqxw4Noc0JGEQUamXGHQDLNsky/K9x0aDsEhsxSlCgO1Y42a1wCvdXoJ3JxzRCgJAIAQYQiJEcV66pskwSajYDkJAmucoBQuVCpnK+cMLZ/ijC6f50cPH+fEjJxgkMYnKKJk2/SSibrtc7HUoWRYTrk9rFDBfqtKJAtI858qgy8FKnYbjsRb0mfMrrA57hFnKkWqT1WGfuVKFk+1NlqsNbGngGiZJnuFbd5bEy5UiUzmC4lxWSpHkGTmKk50NDlcnmHBKKBRhmuKZFu1oyH9au8BPLT9BqnJOdtZ5rDmHQDBKExzDRKEwhByvG0ZZgmtYvNpZY8IpMedXibOUz187zSfmjlK13b3nG0ISpDFBGuMaFp5pMUgiojxFILjQa7Hg15j2KgzTuLhmEAaDNKJsOgzSCM+wUCjiPMMUkihPsaWJGO9zw/EemJhyrwizgO14kxlnkcvBGapWkylnDoA0T9iIrjFhz2IIg5O9byOAJ+rPAbAVrWIJm7o9SZgFDNMBE870e7KdO3HA+qjHidrse7J+gEzlXOy3mHTKNBz/rV9wE6UU2fV3MBXEWYohJXmuMGQRj25uT0ghEELcd1lNpVSxX8Yb52SW5QwGIaWSg2kaBEFMtxcwM11F3ls3tO5791TiKM1yNvsDKq5De5iR5opBFNMJRkUCJi0qcxq+R5imrHX7VD2XxXoVU0p2RiOCOGGt22eu9tZ3vJM0Y3t7QLnscuVaG9+32emOSLOcUZjgeTZBEJGmOYYhqdd8ENBqDRgFMUmas9MNWJxvEEbJLUmQquPgmiZl28Y1LSZyH8cwmC2VKdsOy/UGmcppeB4lyxo3BAWmlFRshzBNCdOEJMsYJQm+ZaGUIopTRmGCQmEZBr5n33ACJWnGaBSTZjlSSjzXwrHN614b8yOfeIzSPnf+lFIEYUwUFUGn5BfrFkIQxcV3YBqSUZgA4Hs2jl0cRlGcstXu85++cQ7bOsH0RAXDkFRKLoYhyZWiPwiplF1+9ic+RK3i3fC+YZTQH0a8dPoahw9M8vOf/TCmaVAtF3c+syynNww5dmiGRx6aw3dvvRjNspxgFJOkGUIIXMfCdczbvgBTShGFCVE8AitGYKFUSJKt4ahD5CokydZRKibNtzDzaXI1wqCCUglKJeT5iCTbxDQmMeVEUQ0kfYSwUSoly3uYxgRKJQhhIDBQSjHsh8RxihAC069Rs5tY0ifKdnCMBrYsk+YjHKOOLUvkKkXaNlKYOLLG7YT/JEkZBTGlsosQgtEgJM8VeZ4z7I2wHQvbtYrvexRjWgZCSgxDkCtFFIwfE4JgEDK90Ny7u/wgUUqRqwQhJEopPN/goaMz5KpoLDuOhW2Z9AcjxPhiIcsUm1tdqlWfZqM0PubTolqN/Y/B/s6QUtVj7dImQT+kOVuns9lDmpKDx+cZDS7S7wRcObPG8iMLfPMLr/DQkwcJg4hr59bJs5y117dwfJs0zopqmjjF9W2+8YVXOPzY4m4JFK3RkJdaa/zwgWO81Frj6+tXcAyTY/VJMqXoxiG+aXO53+HV7XX+4Qc+zjCNaUcjSpbDV9cv86MHT5DkGVf6O3y7tcqnFo+ilOL0ziYV26FiOQySiK1gyB9eOoUALHmCE423btQppchRxec5fixXimtbO1RLxfG6utVlea7Jd86ukOWKY0tTZJlia2dA2bPxXZveMGSiVuLVi2tUfBelYG6iytWNHZ4+tlRczNkWUgpePL3CZL2MZRgkaU6cpLx8fpWdQchmp8/idB3XNjENSZJm33X7c5UDjI+HG79rU9hIDFyjimPs/9u0Hm7zp+tfJ8hCHq4eYtmfe8vPbNdWd8hfvnaRQzNNWr0hF9fbuLZJEMX0gohG2WO2XuHUtU0OzTR52rV5faNNrhSubfIXr1zAsUx6o4gvvngOxzaZrpU5eXWDJ5fnWJqss97pc3Zti/lGlf4ootUf8vDiNO1+wMuX15mqlvjyyUuUXIvOYER/FDJdq5ArxY8+e2JcdZtwfnAez/SIsohZd5YXOi9gS3svGRJmIReHFwmzEEtalM0yAsG0O82p3ikOlg7ST/oArI5W8U2fKIv2EiFPN56malVv+7O7l8VZwpneBapWmVEWoVCsh5tIIRllIb7h0bBqlK0SEkE/HXJxcIVu0mfanWB1tIFv+lzoXyLOEx6rHSfKYgbpkKvBKjmKZX+Rtdc3mV6aZNgN8MoOrZUOtckqQkBno0ccJmxebWG7NsuPLPCdL53kie87wcWXr1CbrDK12GR7rQMIdrZ62K5dVGPmitnlKWpTFdprHXa2eowGIXOHp7nw4mUG3YDjzx5GCfjCpfN88fJ5Xmtt8MPLDzHllbANg7Jt7xs7r51bJ0szkijFdExQsL2+Q68z5MCxWc58+3XKdZ/Nq9uAwLAMWittHv/oMcIgpr3RBSAaxfTaAxrTVbI0p1zzuHZhgw98/GG80q0J3usVvxOKJM+53N2hPSqSsb0owrcsDCl4cmaOVjDk7HYLz7L2kjbzlQpRlvE7Z17ha6tXyFTOxxYO8vXNKyyVa3SjkGvDLkeqE1Rsh1GaMEhiznVb1B2XF1trbIcBAuhGIw5U6lwddKnZLgoYJjFhlnJmZ4ua4/JSa42K7dAOA0wpKZk2Dzfv7GI7SGPOdjep2R79JGIt6JLkOQulGud7W6yNehyvThNkMZcHbY7Xpnm4PkOUZyhgkERcGXR4uD7D+V6L090N5rwarmnRdDy2wgHzXo0XWlep2i7DNOZ8r8WsV+GjM4cwpUGqcs73WriGSSsc8lBtilfaq/imTZAmHK5M8MXVM1jjBJBSMExjXuqsMkpjLGnQdHyUgkOVCb62+TqP1Gdpx8Vnea7XomYV712xHJI851MLx5hyy2/18XxPSfOEC8OTDNM+vlFiuXSCK8E5BmmXOfcgFavG2f4rSCE5Xn6SVrzORrhC3Z5g2T/GyugScR4x7SzQS3a4GlykYU/yUPlxukmbjXCFpj2NKVxm3UWuBRcBGKZ9rgUXWfKPkuYJJ3vf5kpwnidqH6Zs1lDklIwKnaTFjLPEKEtxDAtH3v51wK4oS3mts1a0ldOEF7Yvsxn2caTFcrnJStAlzlNmvSpHKlO8sH2ZTCkeqc8iELzcWcE3bT40uczL7WtshH2OVqZYKjX4yuYFlIKPTB9ilCWc7q7z3NRhNkd91kZdjlSmON1dZ7FU56X2CqnK+MjUYRq2f8t+jJKU17fbDKOEOC2u3YprZZcky6n7HhvdfpHsBYI4YbJU4uG5Kdy3UZBwJzeZM5Xz/PbrPL/9OtNuhc/MP0HZdAnDhCzLcV2LLFfEcXrDtWRRFKBIkgzbNknTjCxXOLZJtzdiZaXDww/Pg1LEcUaa5Xzjmxd47NFFpqcqDIYh//EvTvFTP/kMlmUQhgmWZWLbxfXL+f4G/9/KiwRZvO92P15f5NOzj2Mbt58m6cYBv3flm2xF/X2XT9hlfvrgh6nbd54cvFlR4RtjmBLbsdhp9UnilMm5+nt+M/+eShw5lsnHDh9ESkGa5ZhScqBRG9dHjrOpardbjuDRuRkMKTFl0VCfqpRwTZM0z2/r/TzX5kPPHMIwDNI0wxhfHBiGJMvyvb8BpBRIKZECGnW/yOSOTx7TNDCNGzOaUgjmK/s3Xo9PTqGUonLTHTUFTJdKe1/603PzDOOYpudhjjOmm9t9/vDPX+HilRZpmuG6Nr/wN57j0NIkAKMw5gtfPsW3Xr5MFKeYhuSHPv4IP/DcMRRw5uIGv//577Cx1eezP/wkn/n+R/feP88VV9c6/O4fv8BGq49pSn7gI8f5xHMPYVsm33zpEt966TL1qse5S5uMooSPfOAQP/lDT+HYJi+fXuFPvvQar5xZodsf8ed/dYbJZpmf/6kPM9kok8Qpf/DFl3nl7CpKKf7hL36K2aniM0rSjK+/eIm//MY5Xjm9imMb/PN/PWJxrsF/9deepV716Q1CfvMPvsXZixvMTtX4ez896DlDAAAgAElEQVT//XtJJSi64H3r5ct84csnGY5iUIqPPXuUH/vk45jGbd65z3K+8henuHxhg5/7pQ8V37twMY0ppCxTdj6GEEVA8+2nSLINbHMR25ijuOxNkcKnKn8AKd5IjAlhIp0yUvrUSz+OxB7fsQsxZJU0zfnCH73Iyy9corsT8Av//Sd54pllHFkhJ0eKN05Vwa01w7uX2mmSIaS4IZF4vfOn1/jD3/0Wv/D3Pkm16nH25at0Wn3iKCXPcibnant3kx3Xxq+4tNZ2KFVctjd7VOo+vfaQJE4pVT0mZ+vwACaOQLEensQ3mkhhYFk+i4tFdd711VaNxo0/APMLdUzD2M3VMMq65CqlbL5JA13B2uUWM0uTGKZBGERMLzUJBiEAlmMBirnlKdZe3+LQo4s0Z2uEQUxrtYPlmHgVl35nOI6NFHfOpcCyTRpT1b1tkUIw61eYL1VZHfZYKNc4VG0SpgntKEChuDrYYRBHeKZNlKV045BcKeZLVZqOx4FKnYvdbbbDgLJpoyiSO8XfOavDHutBn4OVBjNemcVynRn/9rqtDbOQb26/ypHyIgf84u7eTLOCFII4TekHEVs7gyKxH6d4tkml5DBVL1PybPJc4TtFI8iQgjTPiZKiimAwitjuBQxGEfWyS6s7JM0yDCkJo4Q0y6n6DjMTFdZaPUquzZGFCZbnJljd2iFKshu6KN7yNSrFSzvniPKEpxvHsW/qNpqqCNeo0rAPYMn9uwD5hsuEXUMmghmnccOyIE4YhBFVz8Exb20A90chZc/h2aOLPH/+GvPNKjXf4S9evcDxhSlm61XWd/o8sTzHla0OYZIy16ywuTOgP4qo+S6HZpp86dWLbEdDji1M4dsWjmnwzJEFoiTjhQvXyHJFbxRxYLJOveQx16hiGgal9TZBnGAYgmePLvKbf/kd5ptVnj26yJdevbgXsHKVk6oUW9iM1IjV0Sq9tMdB/yBpku5VwLTTNlJIKmYFIQQzzgxls4xv+myFWxjCwJQmcR4za86ymq5iKYuKWdmrPnoQ5Iw/L2nTTQZ4hsMwDVjwZomymDiPEUIwTAPSPMM2LEZZiGe6NOw6gzQABYlKKJs+mcrpxF1m3Sm24w6+4eEZHjMHJrl8emVcETQkGsWkSYZpmaRJShInNGZqdFt9ttd2mDkwyeqFDZZOzGMYksHOkJkDk7RW2iRll8mFBkopDMNg80qL9lqH/s6QPMtpTNeoTlTwKx5Bf0SvPUBlivlyBd+0mCtXMITkhdVVao7DhxYX9/1s6lNVvvT73+C5H3mKzmaP7vaAasPHdky8kktzpoYQgjgqEunhMKI6UWZmaYLXT16jvdGlUvc5eHyOr/3JizSmKmRpRhwljAbRDXF+P0opVgY9vrl+jY/MLvFYZQLKCtuzWe33mbBcklFM3XCZrnuktRxDSgwEo2EIuUIKwYFKjZdMa1zZWVQDHa1N8o2NK3iGRcmy2RoNcQwD37RphQHndrZphQHDJKZmF0n1YRIXSfvRkJ1oRDsMWCrX2IlGjNKEKb+EZ5qULYfnN6/x2cOPftf920+SZ2yFQ3IF7WhImKXMehW2wyGeaTPrVXh9sM3GqI9A7P1+7Earqu3STyPiPOPkzga9eETZdDCl5KXtVcqWTdl06KcR21HAQqnGgVKD9VGfKEv31uMaJi9ur2BJg0fqM8R5RsMwGSYx53tbbIdDjtem6UQBicqwpcF60MOQkmnHxxQGC+UaE26Juu1xdbiDKSWuYRGOk0+dXkCcmzQcH1sYJFECiHEDbG8MgaLqKkyQpoFhCMS4rb57g+1BlamM1dFljpQf4dLwDIYwaUXrHPAf4nJwliXvCN2kzYy7gCFNtuNN4jzEM3ykMPCNMpvRCoqcnJwpZ44gG7AZFQmjYdonykI8o0TxoRefpSM9chT9tEvTnqZhT9JPd5hxFwmyASvBZer2BJlKQUhO9VZpRwMeqS2wVJrAuIPKMUtKqrbLme4Gj9bnWAl2xlVyKae662yM+nxq/gRf27pI1XK52G/xQ/OPULU8Pr/yGlGW0okD5r06rw+2KZkOZcthkMasBl2WyxPY0sSSBqM0oZeMmHTKvNJZwRCSy4M2gzTifH8LATTtEs9NHdpnSxVpVlSVGlJimwZSChRQsm3SLCcet3dKjo0pJWXHxjHMt1VtNEgD2nGHul2japa/63G+Ffb4V+f+nNd2ruGbDjXL51Nzj3L6zBpXrrU5/tAsm1t9oijhyOEpVte6BEHM8sEJNja69AcRpZJDuz3ANCRT01XCUcLps2tFD4As5+y5dcpll+32gG9+6yJzszWeevIAtVrRQ+DCmU2uXG1Trbg8+8whXNdiddThP1z7Np14uO92B2nMD8w8gn0HaZIgi/nTtVc439/Yd/lyaZJPTz3K9tl2cb2/0GD9yjaOa1GqerTWd4piiWa5uIFsCBYOTbNxrU2e5ywenmZrdYdgMKIxVeWVr5+n0ijx8NPLtNZ28Mouw96Itcvb5FnG/KFp2htddlp9ZpYmmD0wcdv78t3cUy0sKQQlZ5xMuSkJmuQJ/XRIolLCtLjz1rBrDNIRURJTsyrkKDaCLr7pspMK4jyhYVepWvtfuEgp8P2iFNlxio/CvfmN9+E4d5ahHcQxwzgmTFNc0yTJsr2yQgU4hoEUgizP6ccxVcdhtlymbNu3lGv/+V+d4fylLX7usx/GsQ22dwLq1TcuXv/0L0/xxa+c4m/+6DMcmG8wDGIatWK5AB4+Mst/89Mf5XO/9RX6w+iGdXe6Qz73W1/hyIFJfuKHnmRts8tv/sHzVMsuH35qmWAU8+VvnefHfvBxfvG/+BhX1zr8xu9/nYePzvHEiQWOH56h7DtcvNLiJz79BE88vIhpSOrjyiLbNvnJH3qS5aUJfvsPnyfN3kjwmYbB048ucXhpgs/91ldp1n1+5q99ENs0qIzHWqhWikqlP/qPr/DqmVXymxKE33zpEv/m33+DH//UYxw/PEMUp7iOhSIjzlPkeCz4nBzJG+PPiN3/RJEcfOLpZR46MY9lvjFOjKTYB2nc+H1IWUFgjMc9uu5x49YugKZRjB0ixfXrKLpVmqbi0z/2FI88scS//JU/IRwVWXAhDAxuTczsF6azLOeP/93zHHtknocfX9rnGbB4YJLP/sxzVOs+lmVw+OF5ojChtb5DfaJCpe6RJjmgMMyiEqoxVcGQgtkDE3i+Q5IUVVFKKUz7wUsa9ZMNrg6/xTDdZtF/hk58hTnvUbbCMwzSTRTQtJcZpFuAwjea7CTXMDCZ9R5jIziJJX3mvSdZH71GyZzAlj5Xh8+Tk1E2p+nElxFIZGmOp7//BI3pKpvXOpiWge1aSClpb3SZmK1jjr+HA8fnsMdVdqZl4Po2tmMytThxQ1Lj0KOLrF9usXBkmsn5N47haa+EP11chD3WnKFmuziGycqwS8m0eXpqkYbj0nR8TCkxheTxiTkMIfBNi+9fOAzAXKnKB6cXkUIw5ZWZcH0kRVIKBPOlKjNehe+bP0SSZfi32U3tTO8Sn7v47/lbB/9zDvizTNZKe13PTEPSHYTUSh7dQUh9nDAOo4Qnjs5hSImUYu/5nX6AZ1scmmtycK5JEMZ83xOHMaRgcbq4I+NYJk89tECrO8B3bT5wbAHHMplrFsnsVnfAZL3MdKPM1s6Aiu9iGfsf7/004LevfAEhBI9UD2HLG/fZlmUMYRPnQ0rs/+M9503wvzzytwmzmEOl+RtO8v4o5NL2Dg/PTeGYt/5sT9XKnL62xRdfPMfydINT1zbZ7A74wOEFLMPAs00sQ7IzHJHliiBKOLfWYrsXMFOvEKUZp65tsjxdJKwUimbFY6JaQopijLZuL0AosD2Bi+RrZ69Rt23Wd/pcWttmynORqeL//dJLHJudQKHIohRHwaAXFI3Wqsfh0mHiPKZu1+knXZ6tP0bVahLnEQpI85h59wRRnmJKA0d6WNJCiJwD3gKmkOTkJHnKrDOBIQTz3hTmOFHvGHd//Kh3iysdjpYPkamM45XD9NMhH596DkuaHMwiLg2v0LQbTNh1hlmAJSyW/SWiPKJmVfEND1tafKD+OFme4ZkexyuH8QyPqllh0mlgSoO5w9PMHS6S27vxQinFwpGZveMwjVOunFlj6dgcrZU2tmczMffGeFhCCA4+cmuSZ+n4HGmSkSOYmK3jjGNY+lSK49v4VQ/DkPz1hx7l8ckZmq7PYqXK2a3Wd+1i0Jiu8vHPfpD6ZBXXs5laaFCpl+huD5CG5PGPHgPgkQ8eIRiMqE0UiSFE8dqHPRu/4lGqenzir3+ILCkqCm3P5ugTB9/05suuVOV8/vWz/Porz/NPjn+M8tWQJE6wXRuv7NJvlFi9uEGWZMwcnGLr2jau79DfGVKpl6h/+glc2+QfPPuf8dPHH+NAtUHVdnl2ehHftHhu5gDb4ZC649FwPAwh8EyLD04vUrUcJjwfU8jxDSioOy5N16dqu1Qsm1m/gmdaPDu9SMVy+PDMErlSWFJyuNakfIfd1AA80yJTOad2Nlgq1/FNm6ZTXNgHWcKVQYdjtWmajs/GqM9Cqca1YZfNUZ/LgyIZ3AqHrARdjlYneb2/zcFKk0mnxOmdDZ6ZXOTyoEOeKybcEo5hcnnQpm57dOOQ1aDLhV6Lx5vztKOA56YP4ps2TzTnGaUJs16Vbjxi8f9n772j7LquM8/fzfflVK9yFapQBRQAIjAAJBgkUqJISVS0gmU5yePQM/bInu52u90znrVmetbqWT22pt2zrLE9dttqt2VLlq0smaYoiZliRAYBIhVQOb/83o3nzB/31QMKVWBQu1dLnNn/oHDfDeeee+85+3z7299OZMmYNr4I8YUglIL9+X7qvoelaZiqjqXpUQA614et6UjACQMmMt1Yms6ebG+nb4OqxzNPnCDXnSbwQ+rlRsQMNzSyxTQoUVrjwpVlwiDETtjsuWOc+DWs+reiCRkSCD/SflS0dgpxlI2QMrL02cPMtC7RbfUzGBtl3rnCudpJeqwhfOnRCpu0wgYgETJESIGCii88POFSD6ok9BT1oEwjrNIKm6go+MKjHlQIZUBcSwIKoQzJGl3MK1MstKY5kL0zCtDrNr12hlAK3DAgrr/x915IiRMGVH2Hmu9gaToxLQJeqr5DIKO1xTqhIWfF6YunURQFTVHRFJW92X667AS3FIY4XZrjxZUrPDS4l5uyfZwuzzOUyNFlJXBCnzW3wWA8y0A8x8Mzp/nQ8AFW3DoaCsPJPMOJraVF4qbJ/oHNqXTrfjrAzu7Cpm3XAj6+CKn7DoaqkTReT99OMtNa4EpzlvHkCH129w3Bo5LX5Ep9hUAKqn6Ly40VGo5HpdrC90IuXFwiDAXvffc+wlBw8tQsD77rJlzX54knz9LdnWGt1EDTVO64Y4xnnj3Pgf3DoMDwUJ6jx6Yi0kM7a+jwoe28+PIknhelv3tuwJmz8532rJNBBmI53td/gHmnTMVrUQ9cFlplyn7zde79xpbQLR7o3ctYspuq36Lityi5DRadCqIdPZs+v4g35bDzwDAXTkzTarj4XkBppUYsbqKbOvVyE9MysBMml8/OU680MWMmSzNrlJZr3HRoO6qq0Kq7ZAspVE2ltFKjtFLDbXmc/MF5erd1sTizxtJMCd8L0HTtrQkc3chc7yRr7hQ1djPfWqYvVmTVLTPTnKdo5Zlzluixu2gGLepBE0PVaYUOuqIT13duOFez9V08/xUscz8x+77XvG4YlnDcp4nZ96Kqb5z6HooSrnsE2zqMqiZYqNWoui7nV1fpSsSpuh4Vx6E/lSRmGExXqhTiMYYzWU4sLtCXTNGTTG4JDsRtk0bLpVxrsn/XIKNDXZ1Fox+EPP3SRe69Ywd3HxxD19RNWjSmqZPPxjtA2bX26qUlVkp1fvbDt9PblaaYT/LN753kxNlZDu4bBiCXifPA23Yx3J9nqD/Hl759hOXViJaXTtoEXSkMQ6OQTdBXTG8YTBRFIZW0yaZjmxxCVVXIpGLYlkHMNkjGLfq7Mxv20VSVbDpGKmlx/RgVhoLnjk6yrT/PnXcMURIrZFQLXVFZ9pZotvUyWmGThJ5EStHeplGwCuSMPEEQ4jQ9YnGTVCa26fyeG2CYkcZDGEp0Q8MyDRT1qqPtuQFB2wGNKIQ6qqoS+CFeWxtCVaOB2/MiGrXVTqVLpWN0dacxtmDwSCkJ/BDfD9u5vQqmZaC1n7Hvh6wsVXnpBxfI5pMMboso87G4iapFec6tpgsK9A/lMQwNVVVJ5yPgqqs3gxAS1/UxNQ3d1DDbtFEpwXV80kbEzFMNDU1TsWz9LZk7XPamyZgD6GoMQ7WxtCSB9PBEo63FYDLXOoGUIbaWxgmrxLUcMT1H2ZtCyBBNMTBUm4ReIBAOZW8WTTXJ6j1M1p8haRQpWGNUinN0pXIEQUit3KBWahBLWFw4MU33UB5d10jlEsxcWIyiD9t7EFIwc34RKSRj+4coIrkeSuwezFMcyKPpV5+PrRvYbRDH1g3GM9EkYmoauqJSsCOAOZWNFt6KonT2AdrAUBTt3ZHt6uyTNq/uP5K+ypQZTr6+yPK6SSk5U7tMM7wKZtumgW1eBWDWQSHH8xnpyxMEguHePJktRHxjlsGBHQP05lMYukbCNilmk51rbe8vdPbLJG3WtUmutUwbnFIUpXONrZwjKSUrbpmZ1hJD8Z4t709IH0mI4MbpbpqiMZLo3/I3U9dZrTfww60n/kzc5j23TkTvp6oy0hM5mJq6Do7DcDF7lSWragwUMhH4q6mMdK8XAIiilLKtHzBQyKAokLBMDhWL1MsNjEBl7dUFbjKTJALItyQfvGkHC5NL7B4qkFpz2ZbJUC83ufzyJBk34NyRy6iqys1vmyBjXB3XbVVlrnmFVtDACStYWoq4lqPmT0b6IsJE0VJt/ZIMKi2aQRVNMRAyQNfzrLoXiOsFuuOHbviMflxNURTyZvs7UiCpJzrbU7rAUk3SRhIVlZRx9f2GiKllqdEiyXcD5i4tomQU+gd6cYVHl5Wn194IFl1/7WuHFd3UGb1pEEVV6B0tbpmSeaN7ALhybgHP8dk2EWl39W4rUhzIo2gR6JtWdA72RsCTGwQkTfM1y/4apk7vcDQO2fGr30UyG41j8dTVcSFbTG1oS74nS/6aT9WKmW8q/QLAC0NeWpwFIJGJ06ulMGMmbtMlnoqRKaZRNRXP8enZViRdSNGqt0h3RWyrdQmAnkSSnsTVNKi8FcP3Q6Qr6LcjfyBrXfVH8nYcKSVFEpsWgdfu17n39rZ1HZRm4HOg0If6Q3wnlqrzrv4JhBR4YpVWsIKtmwwmUrihigAUmthanrGUQsJQcYJVfnZ8O5YmCKXLz49PEMgKum3TEzNJGgpJPc4ntt+Crmr0xFLcWhiMdFmIALpoEa7w3+y8A1VRmKqX6I+nGUpEgZEuKwFtvLhgxeE63Zb1lMK0EbGzQiE6+i6DieyG/Tb4rO1/K80aiXSMnm3FSPDd9Umk41hxk67+HCgKbtMl3xsFJZLZBNrr6Bu+FUxTNFa9RYbiYwzGt6MqCjV/jbHkHhTAFU367GHiWoJldwEhQ0YTEwhCnLBBWs/QCCoM2CMsuwsktAxFa4AVd46knqUVNvCESytwiKlJ6n4VXdUxFSvS8REOGSNPl9lDyVsmHkuiKTpJI0NMS9AMPbJmnG47g6ZEAeL170UIuUH3R7bdKBGKto8Orgho+i4FM0Ez8JhI96IpKqqiUPGaLDk15poVbi+O0G2nuCnbj9oGjN7eu4NTpTl0JQrCrToNUobNcCKPE/pUfYeRZJ6inaTstciYMVqBjy9EB7Tsj2XojaUJREizrfsloRM0X3+HZ6pV0qaJLyJmkaIo2Lp+9Rtvs8Kj/cFQtU0BqBOlKT4/+Sx7Mv38yo533PCZy/a5diZHSehxAvnaKfwpw6Zop6j4LWKawUA8R+CFLC/XME2dRMLE80Kef/ESQ4N54nGTF1+apK8vy/BwF7qmks8nOH5ympdenqS7mCYeN1lernJpcpn5hTJSgqGreF7A0eNTJBIWSys1ZufKzM6VGRzMsbbWYHi4gG5IfNFkNFnk07se7EgkCCn57Kvf4e+mXiSUV0kJYShwvAAhBK4XUG+65NLxjnxLzDI6AHO96fGxvjuI2ZFOnpCCxxfP8m9Pf4N6EPm2Tssjm4vTPZBj/vIy+Z401VKD1cUKA9u78b2A8kqNkV39qKrC6ZcuEfohxYEcsYRNvdKiZygKFue60+S6UxFjKWVTq7SQUtI9mKd/pIuLp2YIQ8HIRB9DO7b2TX8Y+7EAjnz/DEZ4mZH0gwzG+tBVnYFYL6EU2JpFf6wHrc34WBfLk+0XIXZd9NE0duH5J3DcF14XOBKyTNP5DpZ565sCjqT0CMUikgjxHMpEjvJ44apzM1+rUUxEkf09xe4owq+q9CaT2Lp2Q/rg228fp1xt8qVvHeEfHn+Fd9y5k7sPjmGZOi3Hx3E8errSndS5N+NEL63WWF6t8SdfeAqjLTq9slZnYnsPoYgG21wmTtyOnFFNVdF1tfPbf03z/JBKvcVwfx40ged7pPRUm+JfwVB0qkEVXYmomS3hAJI1b42EngADZq+s8lf/4QnKpQZjO/v45d94V2fyn5te4yt/9QPGd/Vx+sQ0pZU6fYM5PvDxQ4yO9yCEZHZqhW99+SUWZsuEgaB3IMuHP3mYweECx1++zPcfPsEvfvpdFIophJD8w9eOUKs6fOxn78R+nUozjbrLt7/yEq8cn8bzAhIJi/se3Mvht08ghOT5p8/xxHdOcebkDNVyi8cePkEiZfMLv/ZOij0ZGnWHL/z5U1y5uISmqfz3v/0QPdfkwlZrLZ78zimOPH+JIBQMDOf5wMcO0T+Up15t8Rd//Bij491cOr/IwmyJZMrm/R87xP7bRv7LPtj/CqarFlVvAU/U8KVDM1hFQSGUPppioikGlpoklD4ZYwBBiIqGphiAQtYcZNk5T8bopxGs4okGeXMEN6yjoGBpaUwlga5YoAgUNYpcbpvoR4hIHHRwvAfTNhBtgHJoRxRNMtqA7/COPkQoOnpU15v6OtFyuDo29L7BVLLrj7vR/19v+/UmkTjC42JtBl8Er7u/bRrsH9saYLl2n6HurYGrN9qu60Hv17Lp5iIlr3ZD4EhVdAw1hqH+cBFoSZQiJuTWY62iKBjXLFSuf/xChrgiiqTF25pBpnrj/ddJjtcufdLpOH4zSmFKJGMkMjEsy0AR0X6pTJxkKsbIjt4ozdsLSKTjZE2dMAgxbZOl2TV6Bgud99NQY8T1PKCgKQaKogIKGXMAN6xiazlC6SFEE0O1aYUl4nqOQLiYaoKk0YMTVjC116bL/zjbhvu65k9VUcmZmdfcfx1YqK3Vmbu0SO9IkUJ/DluzGHwTGlrr51LaKd9vtq9FKKAtaNo5n6qgm1u7oOsL+K3YdW+knT+MvZnjJLDUbHBubRWAWCrG2Hj/BiAHYPv+bZ2/cz0bn9VrXa/ecDl2cpod27sZHMht+v3NjE3X7qcrCtvTP3xBFEWJqv6CRrk5hyeqiKCFocbxwlpb008QyAZeWEVVilS8y1haBk8YaIoZpfRLQSOcRkifpJFsnzfS4ZSBRItWXQgpUQFFhSAMqVeaJFI2BT3O7bkhYu201NfrA0VRmK9XWWk1MVQtKsCjaQgkuhLpwYg2uCTaot/FeIJiPArgpnIJbr5vT0fPsWe4q63/uPG6vSPFN9Set4KpikqPPcRwfJyYZtMKFinoOl1GD7qqEcoq44lhhHSpe6fIGUWyehdS+jjBND2mTbfZg6oEpIxhAidDteWyFLhAgWyQwVIMyoFENgZI6RpO06Tl++S0PSQMk7gWgac3ZQ4CMN+aohFU2ZW+ufMMzlUXKHstdqR7MNVIGH91tU6j4aKoCvG4iesGGLqG5/ksLlXJ5yKAPgwlQ3qWIbIoawrd+QSZdBxFgZrvUA1c3tY9hkQy21wja5msuBVCKfBFyGAihanprHgVRlJZQimo+k0WHJ+RVMSCcoTLtkSeuGLjBD5z1Tq+CNlmF5mt1dBVhdF4N04QsNpoMV+rEwhBMZ6gPxn5bq8sLbUrdAdtFiLM1WrYuk5XPM5KM0prLcSjNef2XI7hzEb/6Acr53lm+dXXBZSbYYuyH2k7Lbor7M1MbLlfGERB7oKa4OeH7+GpxVfZluricHYMG507bhtB1zXi7QIELccnnY5FxakaLvG4SV9vhmY7bTqfS7Bn9wA93WkMQ+fwneM0/YDhHd00HRdd1/CDEEPVUA0Vh5D9h7YhLZVcKkVTEfT0JGmIGQK3QdHej6lunF8sdTM7fq3a5Ogr06STNpqqslJukIxb6JqCaehU6w61hksibuL5Id35JAcmBtDb7PSEbm7Qn+0f7WLh+VmOPHmG/tFuzh+fwk5YTNyyjVQmTuCH9A0XyBQiJufe20dZXSojZUjPcJpatcZz3zvG3jvGKPQnuXRmCjupMzc9R6PaJJ7WSRZV7ISJbkQkgGq5wfnjUxx8x57XfLZv1H5kgCMhHaSoo6o5wnARVU2BoiNFE5BoSogi5jEI0ejG1BPRJCObKMoyoKBpvShESKAQLUKxhBQSQS+KEkUadH0QXRsmCC5vuL6UIaFYRYoqKCa6tr4AkNF230FVM6hq5ORK2SQUKyBDVDWLquba160jRJWY9TZUJRp8LF1HiAa64iAJkKLOjnwBRdYBBQwTZAvEGnEjAUgQVlvGTI3+r3ajKBr5bIKf/8gdPHDPbp55+SKf/9oLmKbGPQfHMXQVRVUi4ezrIidvxGK2QVc+yU9/6HYKuauV6dIJu7MoUdXNkfnrbSsNnv/SpmnRwqnZ8siZeQrWVZAuZ+bbfXltGyMHrxpUMNqpY31DeT71q+/kkW8cZW56bcMRnhtw4shlqpUm7/nwrRiGzpf+4mm++oXn+Otq3r4AACAASURBVLXfeogwCPnqF54nDAU/9Qv3RJPTSg27Tctv1h0WZksE1wjsltcaVErNDc70a1mhK8UHPn6IeMLi+afO8Vf/4QlGd/TQO5Bj975B7JjBzJVVHvjAzey7ZRuappBtP8dEwuKjP3MnLz93ga994fkOKwogCEK+9sXnOXNymo/89J3EYibPPH6Gz/3f3+PX/9X7EEJy4ew8ly8s8qFP3EHuPfv43reP84XPPcX2nb0kU2+ubPOPuuXNcS5WKwwlBkhoXXiGwFQt8lYCJ5ScKS+yN7eXy/UZAmmTt4oYqgFo2FqeC9UpMuYEcb1IM3R4tXKFbD5LlxUjlD5FezdO6GKqcXpjcZ5fvkzGjDGR7kYQVawxE5FY/jp9226DtesTkGpEX5l+zUTXDBxmW0ucrFxkqrlAM3BQUMiYSQZiRW5Kb2ck0d+p2rhuUkpaocv5+hTHSudZctcIZEiXmWVXeoR9mTEyxuaFec1v8JWZx1jzqnxi+AEyRpKTlYscL5+j5FVRFZWBWDcHsjvYmRrGUK5q80gpqfh1jpZeZaa1xOXGHCcrFwH4+/lneLl05rpnkuGTww/SZW2MDgcyZNWtMNmY41ztCovOGp4MiGkWA7Eiu9Oj7EwNY6tbC+xe23fTrUVOVS4y3VykETioikLWSEV9l9nOSKKvE6AIRMDp6iQXatPMtJZ4pXoJiWSyMcfvnf3LDX1sKDqHC9spmhX6Y/s6adhCCl5aO8PDC89uas/HB9/F7vTIhnRaVYkE6hVFYaG1yhemHiFtJPjY0P14IuBk+TyvVC9T9etYmsloop9D+T302V1IQpbdWWwtTly/ym5wQo/JxixHS+eYaS3ihN6msRIgYyT56eF3c9O28U2/9Y1Emn2M9Wzo4/7R7tfViVEUje7YZmfmRkL/KaOv0771fQYTB1/zGm81u1Re47NHn2MwleYX995Gzfd4auYyx5fmafg+xXicuwe2cbhviEQ7HSkIQgp92Q4jYqlZ5w+OPIeqwKdvuZOUaXF2bZnHpi5xqbKGlNCTSHBbzwBvHxzdlDIv2ykcp1cWeX5+hkvlNZwwoGDHuaWnn7v6hynGE51FSOCHmLbRYQF98ewJHpu6tCUQaqgqv3nobRiqymrzxmkDFdfh5PICz8/PMFUrEwpJTyLJ4b4hbu7uoyu2WUT2WvNFyHS1wsuLc7yyusRKq0EgBCnTYiSdZXehm0O9g1HhkjYgdK60wtGlec6XVji1ssRMvYIC/B8vPLGJ7XNH3xCf3L2/8wzWbbK8xh8df4GS09qw/a7+YX5mz82YmkYibtFqeej65gCAlJKy63ByZYFnZ6eYb9QQUjKYzHCwd4Bbe/rJ2rFNX9Bys8EfHPkBoRR8+pY7MTWNF+aneW5+htVWA0PV2JEvcHf/NnYXutsAUWSecGgEFVJ6vjPnZM0RfNlCJeqfhN5HK1xBSI+E3kegOWiqgall8EWkIxLT8ghCdMWmESzS8CMWSpR6VMJbVZh6ZRm35ZErpsgW01w+M3tVbF2C5/rEkzaGqXOx1mL/3RPY8RsH3oQU1II1WqHDSsvBD0OK8SR1BRYbdRKGyUqrgRuG7MwVWGo2kEBXLNHpw0DxqIoVcMHW4iSt/Ia5zBVNav5a+x6TJI3NYN9bzQzVZHf6FgBC4dAM5tAUkyB0MLUsDf8KuprECZbQVBtDpHHFGkHYQFNjCOkhEcT1KAg0X6nhhyGrjUikPG4ZTK2VMTSNbMxmYa1GwjJpuB5uELK9K7+pgnZfbJi+2HDn/5aqM5bqJpSyM18IIVlcqgLQanl0daWYnFwml0uwsFDGMKKCQ82WR7XqYFk6QSAwLZ1CIdl+DyUJ3eDenjEUVEp+nZOVy3RZaVRFxQt9AiloBA6aotEfy9FUXKpBCyfwMDWdVughpSSp24RSMl0tU3IcAiGoeS6mqjGQTrNQr5GPxam4DnZbx2s9NXXd9vdEc6+Ejh7uSC6HoarYuk4rCDbMxbHrAPlm4HGqPLOBaXMjk1JGAXhVxVB0fC+gUXdIpWMbWHZnX7hIda1GrjtDflHwkD+KYepMzkySK6bx3Eh/8spSBREKhib6ifVEFdAS11SzTCZtfD/k8O1j5POJTqXidCbGxYtzVBoOMVNHEyF+GKJrGpaiMVuqYuoaZc9FVRUsWyedjlEPQ1rBKmzB1N/KUnGLvTv6MQ0NTVUZ7s9jmTqeF6BpKs2W19Y5VghCia4reDh4gdzga61bJttk9wdNFGMXYNI90oWQkrh5NQg8tvdqynfZvUiPMGgEi2i6xti9DqH0acgjWKOC0ZGAUPXY+UA0jmrKZQw1TjG+C4URjj97Hk3XsN9ghdA3Yj8ywFEQTNFsfYtk/JOsVf43ErH3omn9eP5JFEw8/yS1+p8jRAXD2E06+UuAT63xlwTBJJIQyzxIMv5xpAypN7+I759FIjCNfaQSn6DDZb3Ooopez9BofQtFsVCVGKnEpwAIw1Xqjc8DOpKQbOrX0bQ+XO8oLee7SOmAopFN/TM0rYtQLFNvfJFQzJPL/C8dAMrzj9NofRNVzSNElYT9DkzZQkGA1g/hAshK1EZ9ENAhvAK0J0T7fiBGpdbCMnV6ulK8484JTp2bY3ahHO3SLl1/9PQUh/ZvI5Oyo/KpQkZ0OhnR6UIR/SuFJAxFFElUFMa3FdG1SCx2sDeHooDnBW1h8DcGQqmqSjxmUqo0CYKITaG1xcvX6cJCyHb0UWygDL+erR8vo+AlYbsa2Hq0fWy4yPPHJplfqtBdSIGMolS2tTUrQ1EUMsbVhahp6vT0Zcnk4sxNr23ZhvvevY/bDo+hKAoXz81z5LlLbSFpA13XaNQdYnGTvqE8u9sCdf8Ylkha3PvgTSCj4c4wNZ74zimaDRdNU+nqTuM6Pqal092TZtv2jdVgVE2lUEzR1Z3eFCUrrdQ5dfQK9z24l4N3jqMokCsk+Mz/+jXOvTLH+K4oMn3r4THuescuNE3FaXr89Z8/SaXUeMsBR6HUqPt5MuYoM80ysw2ThGEwkkxyqjTPdMNjby5JxUuyLTlAyXWxNI2S22AgnqUe5OmPd9MM4HxFYbaRoC8WkLNStAIfXTGZrFcpe2Xu6R4jqYeUvSbNsEHZK+MKF1/6ICUpI03VrxDKEK2tIQBgqTa2ZtMfG0BKyZpX5Sszj/Hs6glKXnWTA6ApKvuzO/itiZ8jpV7VRJNSsuSW+ObcUzy29BL1oLUh9eE7i89xW243PzX8AMPxjekNXhs8uVSf4ebsTi42Zvne4ou0QqctmB05a48sPMcnhx/kgZ7bNwgXX6hP88eXvkIgQgIZ4otIG2GqucBMa2lD+/vtIu511S8k8Ep1kj+79HXmnBV8sdE5UhSFpB7jfX338BOD921in67f/6pX4csz3+cHqycpebVOdbRr++7m7AT/ctfPEW87Lc3Q5fOXH+Zyc45AhPjtcvI1v8nx8vkN/oitmmxPZOmz44T4nX4BaIUus81lWqFLI2zRaqfq3d99aEMbhIxS0LR2mmsjbPFy6SymarAvM86355/hleokXltvQiJ5UjnKk8tH+OTwe7gtP4EvPNYbJqXEEz7fnHuKb88/TcWLhJc1RaMWNHFF1NemalC0cmgJrQMnCSk3jdmvxTp7rTFeuaY9G/d/jXNe07lSSgIRiQ7/MKk3P45W8RyemJmkKxZnf7GXvzh9hONLCx3WBMA3LpzlYxN7+aV9t1GMJaK53PE7TK9W4PPs3BVagc8ndu3n4clz/MdTLzPfqEXl4on6+cWFWfZ19W4AjqSUlJwWf3XmOF84e5yS02pXm4zE+b964TS39w7xK/sPcXtfpIVmWgZO06O21qBnME/Nc5mpVWj4Pk7o4wYhrcDHCQMsTeNX9h+iFQQ0PA83CDZU/pFSstis89mjz/HI5HlqfpuJ19YZ+cq509zZP8Q/O3gP49n8pvdISkkr8Pn7yXN87uTLXK6W8EOxATBVFZWsZfMnD/4E+4uRDxdKyZfPneZvXz2FJ6IKv76IAjDHluY3vX8Zy96yUIsvBPP1KpOVEnXfo+a5hFKSsaz2uB0VV9jKb5FScrla4o+OvcD3py5S97yOdoaKwhdfPcED28b49VvuYjC1USogeuZTrDpN3jY4wqNXLvLY1EWagd+pFqpOKvztq6f4H269iw+O7+7c03zrIk8tf4n39v0TCtYAAJaewyK34Ru2tDQSgapEgY/1Pg1EC00xNxT5MNQ4Cb0bXY2z4s7w6MJ/5K74T6HpKoapke/NRv/2ZBChoLRcY/veQSordeyESTIdo1pqsK51LKXEFxFYpV1zHU+0+P7Cf6I3Ns7bB9/HtQvGmwqRcLcTBjR8j65YFJCmnc6zbiVvnmdWvsKaN8dAbCfv7vuVDdqTS84UL6x9k1V3lvHUbdzf8/Obnvtb2VTFIm/f3HnmCiopYxRBQMOfwta6MbUMkpD1EVwCSNlmmarcPNTXEVJfH3+iQEk0BwfteQFgeq1C3DRfd+nfCFyOrF2h7DX4yPAhDDWGrqvsmoh82kjAX2WgP4sEMpkY6XSMXDbeCehGgEw0Z68DuZKAineZUHrkrV1kjAQP9N6Muv4ttK/vhB6BFKSMWOe+ZHv+jIYs2dFCOtDT1xm/m76PkIK0ZRF296ApCkLSPk5uAIgAepLJq229DoxXFIWUZd0wiCOlZKFVZrZVekMB/yg9LWCqOUfBzFFaqXHhlTluvnO84x8B+J5PMpugazCP03TxV+uk8kmsmEnPSJHyUhXP8UjlkhiWTiJz40pjhqHR3b0x6ycVtzm8axtck2K/7pus35fS7ueoH8DQFZRQQbaTaqUUQIhEoNxA39i2jI5syrV+yvrf+UwcKSRO00W1VcyYwUzrIlW/xE2ZQ1uc0UWE02j6LkIMLi6uRRpsQ5s1qgBMLYWhJtAUG7VdhdtqBzCjdmuoik4oHKS+XsFZRUGlOJDjbe+PwF1jC3maH9Z+ZIAjVUkgRA0/OI+iWPjBFaT0UNUsUrZQFJt06r9DiDKV6r9HiI/gB5fw/DPkM/8zQtQoV38X27obIcq43svk0r+NRLS3H8Q0tqZpCVGm3vw74vYDxOx7kTJAVdME4TQSh1jsvVjGXsq1P8D1TxDXBrDM/ZjGboRsUK7+LkE4g6oVMPQRkolPUKn9AXDVaZDSJwimyGV+CU0roiBR/DOg9YBaAH0UpADFoMMy0keJHpHCOuj1re+d5OzFBTLpOI2mS63ucGB3hE4qisJPPHgzf/rFp/nMnz5KMZ+i5XjcuneY9953E2EoePy581yeWeXS1Ar1ukMQCnaOdnPbvmGG+/N88F0H+Oojx3j6xYvomorrB3z4gQPsnXjt1JB1i9sGdx8c4+uPnuD0uXmKhSQffNd+MqkYiys1nnj+PFdmVplfrvL1R0/Q05Xi/rt3UcgmXvfcr15a5OjpGY69Ms30fIkvP3yUwb4c992xg3jM5P67J5hdLPP7f/Z9+rrTuG7A9uEuPvreWzakcfywFk9YFLvTHV2feMJGCIEQEjtm8oGPH+LvPv8sf/h7D9Pdl+GB99/MTQeGb1iu/nWC8dfsJ2k1PZ557Ayvnp7FcwPqNYd6zXnDbKXXskbdpVF36B/Kd4CuQjGNpqksL1UZ39WHaen09mc7aL8djxgcQfDGKhj+OJndjga5oc+y045IOg1agU/asEkaFqaqEbYXILqqcWR1mlAKJjI9KERlh5edOpqikrVilLwWvhCUvAaqorDmNjlRmuW2wnDHAfCERz2o0xJNNEWj5JU6laOiBXXYdjSiilLr7kktaPK5yW/yxPIRVEVlZ2qYvZkx8maaUApmW0tM1ue4JTuxATyRSOpBk7+4/C2eXj5O2kjwUN9d7EgOY6galxvzPLd6iqdXjlPx6/zq2EcZTmye3Jqhy99MP0rFb7AvM8Zt+d2k9DhzrWUeW3qZy405vjT9XbbF+9iTGe0ctys9yr/Z+6sAlPwa/8/FrzLbWuKjg+/knq6bNziEhqpTtDdGcRVAVzRcEYlJ70mPsi3ei61ZrLoVnl45xivVSb4++wTbEr3cVdi/aSFW8ev8+eQ3eHL5GLqiMZHaxt7MdnJmmkCGzDYjNtQtuZ1Y1wjjxzWbX9/xk3jCRyD5+/lneWThB+xIDfGLox8goV1bUVEhrknccGYD6KGgcCi/h52pYXwZMNtc5rMXvsSaV93Ux5qqYBv6pu992S3xucvfpBE4vK3rAHszYxiqzmRjnu8tvsi52hRfnHqEkUQvTniV4SCRHC2/yt9MP4oCfGzofu4s7ENXtQ6b6VxtipF4H7+x8xPkzTRpI0EgBBdW1hjrym9gJLyWnVteZSSfJXaDsr9CSuaqVXqSSQxN49Jqia5EnFz89dP6Wn7AN06f4UB/L7t73lxp8R93u1It87svPIml6fzy/oOMZws0fZ8fzE/xD5Pn+MvTR1FR+KcH78KKWRH7Otw4Xpcdh29dPMvXLrzCYDLDQ9sn6E2kaPgep1cWGUxlyNobAwMN3+ePj7/A5185Rkw3eN/2XRzuHyKuG0zXKjwyeZ4nZiaZrlX4vXvfw/5ipNeRL6Y6zJCfnNjHe0Z3ErSDR4EUnFxe4HeefhSI3omkaWKoaofyv26rTov/86Vn+PqFVxhKZfj4xF72FCJx1rOrS3zj4lkeuXweNwz53XvfQyG2cUHiiZC/PnOCPzr2PGW3xfZsntt7BxnPFtBVlYVmjVPLi6QtewP4oikKn9p7Kx8c2w3AkaU5/t1LT6MpKr99x9uZyHdds1gRZO0YMV0jECGhDNsaYipD6TT/+9sfwAkCmn7A/3XkWb4/dWlDG0U7KFYuN+kqJDsFElZaTf7Nc4/z+PQkvfEkH927l5va9356ZZFHr1zgq+dfoe55/E+H72MwtTmVseI6/P7Lz1ByWrx9cJR7BraRME0uldf48rnTTFZKfPboc+wt9jCeLbSfR6RJcy2ofj0rUFEUlOuWFOv7GOrmRaGq6Fhapn1+gRs2sFMG++8ajVgN7apkXX05fDfAbXnE0zZ9267q6xUHrqbdCUKeW/0GO1IH6YuNdbZLIkaQkP4NxyxL18lYNw6AFe1hHuj9BZ5f/SaNoMz1nPr+2Djv6vkUTyx9EV84NzzPW9UURUHjOtaXooM0CBjkcqNGM5xBSomtGeStJF1WagM4d6PCE+u2Lr8RSkEqbVDy6iyW1joaWJamk9JtcmaChG6hKAox3eRgYZS5ZqlzLUVRNumIKqpC1W9BEeaCElOVlSh9UtWJ6yY5M0HGuJbBqBJIl6a/SN6aQFM0NG0zK1NVVJadKgttNq+iKMQ0k6KVIm9FhSfW7dp3036dFF0hBatunWW3hhNGATdbM8iZCYpWagPjeR1oUriBRiOSS/UllpzNfsdWpigKjbDFdHMeXwQMMUij7naKC6zb7tvHMdqFXvI9WXwv6BR3URRlE1D0euSBqwzzOmWviRP6hFK0qyFGzyhvJjYx6oWUNHyPUAgMDEwtRYrB9r171JwXENIlbR9+Q/e+1d+u43H6B+fQdJ0D901gKCbWDarnggIyBCRqW1uz6fg3vGZc70YiiRsbZRA6gP0N4D4FBRSI/SMyjdbtRwc4UrOAguefwTR2IkQFP7hEzH4nQTCJoe9E13oRSpT3J2SLIJgiCC5Qqf0RAKGoIMQaYbiI71+gWv8zQEWEJcJwZVOltnUTsoIUdSzzlk1aRrrai6nvQFXzaGoXQlSAED+4gus9Txiu4PsXkXJd1FVBuYGko66PoWv9qOuTqHXnm+6nd941wdi2Is2WRzxmsm0g3ylpDzA6VOC3/skDXJxaoVZ3SMYtRtsCkigKyYTFtsE8Pzd4e+eYWFtfxzA0Hnzbbia2dzM9X0JK6C6kGBmMnIf9uwbo6UqRbAvFqqrCpz56mP5rcvdNU+cD9+9jfFuRcrVFLhMJXkf3r1LIxsln49yyd6jdJAWtvXBebTTRVZWfePAAlmUwXYpoqr3pVOfcXfkE77pn19W222YH7OgupPiFn7qT7x87D55kMJthdKjQmXD+c01V1Q1sHWXDbwpDI118+rcfYvL8Ei8/d4E/+fff4Zd/4wFuuX07tNNM1r9xKSWNxhtzMMJQ8NgjJ3nk60f5yU/dzcBwgbWVOufPzG3cUYlQ9TcLJemGim5otFpXGR2RcLfsiKgrivKGdHPeCrbmNllo1ZisrRLTTLJmDF+EZIwY52vLhFJQ810WWlVMVeO2wjDNwGVHuptm6DPfquKEPjvT3VysLkcpHFacc9VlnMAnbyVYdRsU7SSBDLlUW6Huu+zN9rMzNdF5sZpBk7gW70S8trJAhDy+9DJPrRwlpll8eOBePtD/dpJ6bAOVvhW60bd2jaMipeSZlRM8tXyMhB7j18Y/xuHC3o4zc3fXzdzXfRufefXznKxc4Kuzj/Or4x/dVDFMIll01vjwwL18YvgB7DY4JaVkX3acz5z9PLOtZY6Wz7IzNYyuah020HgqGgeWnVInv7xo5RhPDr4h3YodySF+Z/cvUrCyJPSNE/XB/G7+4PyXOFp+lWOlc9ya27UBOPNFwPeWXuTp5eMkNJuPDL6D9/XfTVLfyMhqhg6qoqJeI6ypKWoHRAulIG9GY3BMsxhNDJA2NgLhFW+OVWeFhF7sTPiKohDXbeLtdisoGDcoJa8qKrahbwKbAxlS8mp8evzjHG4DPwD3dN3MSKKXf/fqX3O+Ps252mW6LKNdtSaqOPrE0hGaQYt39dzORwff0Xluw/FIAPQzr/4Vy24JJ3TJte9vulLi/Moq/ekUT89eodRsMZBJkzRNzq+ssr2Qo9xyWWs2GcikGc5lOLO0xGA2zcXVNc4uLtOVTNCXTnFsdp60bdGdTPC1k2e4a2SYvX3dnFpY5I7hQdxayAtXpklaFinb4spaiZhhcGCgj9Pzi4RScu/YCH3p1JbMjre6OUEAKHzmvofYkSt0Iq3vH9tFTzzJHx17nr87d4r3jU0wRIzZCwsMTWzUNWoGPl84c4Jf2HsrP7vn5g0gSygEngixtI3v5NOzl/ni2RNoqsJv3/F2PjS+u7OPlJL3b9/Fv3ryEZ6Zu8IfHnue33/H+9Al1KsOVjxa0GUse4OoPkDNc6NvjAh86YrHEESAzbVt+ubFM3z74lnGMnn+7dvfzb5ib+feHxrdyR19Q/zO04/y5Mwk3750lk/uOrBhUXpieYE/PfEiJbfFh8Z38+u33MlIJteJ5kMkfN0M/E46yPr2/kSKgWT0Law5zY425WgmRyoWUg+aiLZuyYIPKxUNSzOJt4HkVujQCl00RWVbvJ9BLbuloDVK5I8trdQY2daFrmt4IuSr50/z+PQkQ6kM//ru+7lnYOTqvW/fyUPbJ/gXjz/Md6cuMpzO8psH79m0IBdSMlev8S8O3cMnJvZ3FqlSSvYVe/iXT/wDlyprvLQwy2g61/HPFCCQHhV/GSkFMS2FqcY60fdAerTCOqH0UdGIaUnMawD0UAY4YR1PuG2NNRtbS25YPENUMVTIkHqwhqroxLU0hqWjmtAIygTSQ1cs4noKTdGRUuCEDVa8WSYbx8mb/VhqVPI9ZaxLFkTvVSMo4wkXQ7WIaynUjjZqgBM2OqBP1LZUp21aux2mGqNBeeOjUhR0xSCupzHUt05Fx60sAiGiymevNT+HUjDVWOUbM0c4unaZ6eYaVb/VTvGy6Itl2Zsd5MNDB5lI920AkG5kvgg5XZnhe/OnOVq6wmKrQj1w8Nsl6GOaSd5M0BvLsjvTzzt69tBrZ/BFSN7anDIEEQAz1yrz8OxxXly9xGyzRMlv4IUBqqJgawZpI0aPnWFnupdDhTHuLI5jq3p7Ho80+a43Lww4W53jW7NHOVaaYsmpUvej9PekbjOUKHC4a5x39+9jW6Jrg192rc23yvynS09T8ZocLIzyoaHbmGuWeHjuBE8tnWWmuUY9cKPUPt2i185we9cY7xu4mfFUT+e8Ncflwuoaw9kMmbjFXLPEfKvMpfoSk/UVTpWnO+zJ05UZ/sejf7NleyzN4D39+zmQG2R/dhcJPY7fkhimBkrkj/7D3AmeWT635fGGqvGJbYe5Kfv6/t26+SJkoVXmuZWLHCtd6YBczcDFF5GmUUK36Y1luCkzwHv6D7A3O9h5pwIRcqmyhqao7C7kccISUq5raaoIPEytB0W5MXB8PdPICQPKjkPKtEgYBoalY8UsVO0qszqhp2+Qcm+g6qOAhpCSputtqhDeCj2+MXOEY2tXUBSF2wvb+cDArZ2x+Fq7/hpuGPDlqRc4WZ5GURTuKIzxoaHb3lBfv1H7kQGOFCWGqqbxgwvE7Qdx3OcQsoymFgiY5NqmrvvOippE10dJxD8IKCT5KLq+DSFq6Po24rH3oSgmCh9B17cuTx5d22wv7LdAXJV1xs9VC8UatfqfE7PvwzIP4weXNh+31XXQNp3rzVpfd4a+7s1RpM41FIV8NkF+CwaPrqkcvmV0i6OumqoqjA51MTrU1dm2Um9y5NIc2ZjN4HCehXqd3nSS6VKFkdEuzi4sM1mrcFNfNzPlCqWmQ6EQ455d/ZxfWuXF6RkmeopolkbXSIam57Onr4eVeoOZcpXFVgPVUPj7069i6TpvGxshaZscm55ne1eehuexVK0z1J+DmEo+HmOmXKXiONzU19MBphRFIWYbDI0W2JEv0BV/bY2Df0wLg5B63SWVttmxu4/+oRwv/eAis1Or3HL7dpIpm3q1RbnUoLsvQ3mtwcWzC4yMvX6UPAwFc1NrDG4rcPDOcUzL4PKFJdzrUGpdi6qd1aqtG5xpa8vmk3T3ZDh9dIqDh8fRDZWLr85HVau2/38rig+QMWP81OhtmJqO1gZbhhM5VFVlJJVHRSWmG3xoeD+6ohLXTR4a3IupamiKynsH9kQV7TSjXao4KmO8LVlAIYoM7UhHAEJCt7inZywS8tc3piYk9Kvf8I001ZNyqgAAIABJREFUX2pBg+dXT+GJgMOFfby37y5SxuZITlzfPCn6IuDZlROEMuTm7E5uyU1scOJVRWEw1s393Ye4WJ/lePk8C84qw/HNrKP+WJH7e27vgA/r1x2MdbMzNcxMa4klp4Qn/E0Rof8cMzVjSxYUQMHMsDs9wtHyq5T8Kr7wNwBHVb/BC6un8WXAPbkD3F+8HQsLzw86qTeOF2DoKpqu0fA8glAQhIJMIhJKfKOpqKYaJ230YmtvToj8qkXi2F4YUf2vtd2pEQ7l92zoV0VR2JnaRq/dxZXmPMtuhfHkDgLpEpVMDpltLWOoOsPx3k71rXUbiveQNhIsOqusuJXO9mwsRsP1cIKAhWqNvb09vLK4RHcyScv3sXSdhdoKu7q7OLO4zLZcFscP8IKQhWqNmGGwp6dIzfXQVIWLK2v0p1MM5zLs7imSse0IrPMDZsoVBjJpVppNjs/Osy2fJRSS2UoVXVOZXi2xXG/8kP3542+aonBX/zDjufyGNClL03jf9l18+dwplpoNji7NM9QzRqE/h2lv1oIZzeT45O4D5O2NAIamqsSuc1SFFHzn8gXqvscdfUM8OLJjg8i6oij0J1P85K69nFhZ4OjSHGfXltlhZUmmY1jtiLOQgmV3CUM1yJn5zewVYKFeRwHGcleZhmXX4fHpSZqBz0PbJ9hd6N5w74qisK/Yy+29g/ztuVP8YG6aD4ztJncNgPH9qUustBoMJCONqMFkmjCM6h36YUjD9UnaJnHNwPED/CBKAfTDkFwijqqyZVqkrZnUgya1YL36pkpcs0loMQpWjppfxxM+aT3BgruCJ31ulJwhhSSfS5C9pgJu1XV49MoFhJA8sG2cQ72DG9qhKSp7Ct18cGwXn3npaR6fnuTn9tzMwBaso135Iu/fvmsDs0FRFHbnu9mVL7LQqDNdq+CJsPMOSCQnyo+x6s7iiiZD8d0cLnyIpJHDlx4nyo9xtvpsVJ4dwfbkzRwufAhLiyNkyPnaS5wqP0mrLaJdsAa5t/uTpIyNYt1SSqabZ3lu5WvsydzD3szbETLkTPVZTpQexxMtTC3GLdl3sSN1CEH026u151lsXeYF8Q1sPUlSz3F/z6fa4JBk2ZniO/N/RjVYxVRj3NP1MQbjEyiKymzzHC+ufZuav4ZEkNLz3F382Abm0v9v0AyrrLgzpI0CWbNnS78kFIJjpSv84bnvcqoy0wEk1q0WONRqC1ysL3GyPMN/u+OdvK17J4Z64+WolJInFs/wh+e+y0yztKmSlxAhvmh1yr2/vDbJQqvCz4zexXRjjZwZZzCe23TOM5U5/vj893lp9RLudYU5hJTUA5d64DLXKnOiPM2Lq5OMpboZimdR0Ynpm4XmfRHw8NxxPj/5DJcbKxtkA4SUlP0m5XKTc9UFXlq9xD/d/R5uygxuOaZU/RZPLp1lvlWmFXrszQ7y2Vcf5eW1SVqhv2nfqt9isrHMK5VZ/vnu97InM4AEFmo1VhoN+lJJrtRX+M0jf03dd3BFgBv6nXRXgCWnyiPzJ7d8DnHN5KbMAEkjJBABVb9Ot9ZD31ABXY+AkLPVuRseb6k69/Xs5iYGt/x9K7vSWOH3Tn+b05UZWqG/KYjqigDXq7Pm1TlXneeltUn++e73crhrvJMGKKSkFbio7bSuij9LF/tACoRwUDQdVdmaWRIKweX5NbKpGPlUNGI7QcCLC7NM5LvYmStEQVldpVl32s+ihCcc+mMjm08oG4AKioIioeH6mNdlxJiKjheGfH/xlQ5wdndxgqL9+v7jqlvj6zNHOF9bwFR17irufN1j3qz9CAFHKrrWj+sdxTDG8bzjBOEMmnbjhattHsRxnsL1Xo4YSzLAMMYxzb3oTi+u9zKaVkRKLwKUpIPvv4oXvEoYLuB4L2Po42hqEcs8SLX+OWzzEBJBzLr3NVorkYSEYg3hVQjDhfZmgR/O4HpHCMNFXPclMG9D07Ze2Kxby/f5u1On+f7FSzy4Y5wP79lNzIg0iVwvoFpzSCYsmi0Py9JZWa0zPVdiYryHs+cXGB7Mk88kCIUgbptU6w62peO4kT7ResW1IBTU6g4DfVmkiM6dTFjUGu7/y957R1mS3fd9n1u5Xn6v3+v0OkxP9/TkuDMbsQE5EgTMABgCQZoSZR9KsiTbR5bP4XGSbJkiJYqSLZOUSIrEIUUQJEAiEMACWGCxAZtm0+TQM53zy7Gy/6jXb7qnu2d3FisbAP3ds2e6X7+6VXWr6ta9v9/39/1iaAqxqL4t0GK7Lt+6ch1JErwyv8SjE2O8Mr/I/XuGeWV+kXdPjmN7HueWllEViRdn5jkzMsQzN2Zp2A5XVteZyPbw1NQ0I+kk55dWeXBsBF2WcTwP1/f4yoXL/PLD9yFLEglDJ6KpaLLMWr2BoSr0RCM8fWOGh8f38PTUDMfy/TxzY4aUaVBrW7x7/3g3q6ZJMqv1BoOxONnI9mmZ6/t85fIV/vLiJU4NDvAL95wiroeLyee+d4XpqVVef3mateUqf/oHz9Dbn+D+Rw5sa+d2NOoWn/3t71AuNYjHTWrVFpqucPBoOECO7M2xZ7yX3/mNb5Af7aFZt7qTaIBioc6Tj5+nuFZjdbnMd79xntnpdY6cGGHiwABHTo7wR//+e/zObzyOrEjUqy3SPVuzKKlMlP2Hh/jqn73ExVfniCcMPv6p+0llYly7tMj5V2a5fnWJwlqNL//ZiwyN9HDi3r3khzN89Gfv5fOffYZ/8b/8BZqhsrxQ4t0fOsbYeO9dB6J+1KFKMmk9su0zAE279XlKu7UYiam3AhLJTZ9v/lnb5CC0OYsf35XS+sZYtypMN5dQhMz9mSMk1TcfmFjrWMjrss6hxBiGvH1RKYTgSHKcuGKyYhVZaK4xbPZtGycOJvZ0WTeboQiZTMf5qeVZeG9g3fpW4fphtr/htrADF6+jO9Rwwxe57bvbhHjXrTIzzWUUIXMqeYhLVwu43ipxUyediLBaqrNWaeA4Hroqk0vHiEd0as0wY35gtJdE9M1dO1NJEVf7iSrZN/7ybQiCgEqzTTpqbtE1gHCxeDQ1saWMbgOGpJHoBBHrbpOqW+zU9IfYmCzuFpRk46+bmWuOQ82yKLVaRDWVnmgEPwgYSSdpuw7nllZo2g6VloUkBI7nUbdtCs0mfgCZiImpqrwwO0+tbeF4IaNFEoK6bRPVNOq2zXqjgSQJalb4HVNV6IlEqFk2l1fWkCURWovbDsVmqLFjue5bcuH6UYUiyRzv7d+WrRZCkI/F2ZvqYaXZ4PzaMh/tnwjFMW/TmpKFxH2Dw+TeQEh6A4VWi6ulNWQhONU3QFI3tt09QghO9A7SH41xrVTg4voKE/kksiohyVJXg6tgF/ADn7S2ffHlBwG90eg2nuV6q8GF9ZUwMK8onF9f2bat63vd52SqXKDlOqQJx+G26/Dq6iJ+EHAs189QJMk3Xr1GwtSpNtsMZBJdCRzH9ehNxlgp10hHTYqNFpoiczDfS8zcyiwRCAbMHANmrqu1JoTYshhOqNGubsug2YsmqexUaR4EAY1mqNukyHL3utyslJmtVohpGvf05zGU7QsdWQjuGxzGVFSmygXm69UdA0dn+vNdxtdm6LJCzgwTFnV7q4tjvVOi9e7+n8f223x75Q95pfwtHsx+DAmJrJbn4dwniCpJCtYCT6z8IXlzPxPxUzTcCi8WvspY7BgHEg/g+jYtr4Z2m8ukAOabl3h67fNMxO7hQCJk5c82LvLC+pe5P/sxcvoIc81LPLX2p0SUJMORg+xP3E9S66XmFHkg+3EGzX1IQsKUY1h+CzdwqFnzvLv/M0SVFBcqT/PdtT/m4/n/hpiaJqIkOZp6jJTai+NbfH/9i7xQ+DI/OfQPtl+gv8ZoelWWWtexvCYJNTRFkIW85R3xYvEGv3bhq9xsrHWSbj2cTO9hTyyLhGCmsc7LHRbStdoyv37xqwQEvKvv0M6lVEHApeoi//rK48w3Q+bIZLyf4+kRBs00qiRTc9vMN4vcqK92mUine8bYG+vFlDVMWd3G5l1tV/nXVx7npcJNAAbMFMdSI0zE+4goGi3PYbFZZKaxzkKzRMGuMxnvp89IdKpKAhruClkOd9sMGTfn+FeXv07FaaFKMgcTQxxO5Rk00zi+x436Ki8Xp1luV3ilNMOvXvgK/+PRj7Ev3n/HMXiqvsqvXvgKr5VmiakGpzJjHErmSagGJbvBq6VZLlYWaHsOrxSn+a2r3+afHP9pYoqBIsn0xWKkIybNdouYohNVbjHDi3aDdasGQEwxyEd2Fnc3ZZWUFiGrZ/ADj6gSpV62Qq1ZKUyynu7Zi+W5lJ0mNadFxWkxU1+n7e9ejnUnZLQotu/S9hzSWoTBSJqJeB8DZoqorFOyG1ysLPBaaZamZ3Ozvsb/deVbDBgp9sbD+EHDtUOB8I5WldEpkQ3wMdUJZKljSrULZlfL1FoWmXj4noyoKi3H6SZNXMfDdbyOLIkgoaYp2Ws7VgoESPjuDWRlkgC9O6/arOUnSxIP9e7jC3MvMtNY51JliUuVBXLGndeiQRDwUvEmi60SAGOxHCfTI3fc5q3gh2qWZegPIcsDKPIgkchPYPgFhIiia6dR1bDDhNCJmp+i0TKJRdIkE38fy3qBIGijKvsABUkkScb/a2qN53DdOoY+gRA6QWDhurMQjHSc1WZQ5AEQCeLRX8B2XsN1Z/HcFOsVl1g8jvA+TqOuQ9Ci0ThDMpWmXNSRxC9htS/QakRJpX6FK+clJva3aLfnqNaKmJEPcO6VCxw6mse1o6j6HiTez/pqi1hcolFvo6oKyXSE5Xqd3zt7ltlyhblKhXfsGWU4mcT3A67dXMV2PFRFptW2sW0XRZHxPB/H8ShXW/RZLk+/cB3H84gYGo2mhWW7mIaGZTkc2j/Izdl1YhGdSq1Fs2VTKNWpNy3y/SnW1uvcc2yEWKcWcsOWVOoIstUtG1mSmMhmGEjEWE7E+dqFq7xr/15uFIrcLJSotW1s10UIQcLQkSVB23FQZYmkqTNd9PH8gKFkgvFsBsv1eH56jqRpUGm10ZUwQBTTNQw1pIGqikzDdpAlwXi2hy++dpF794SBmJplMZhMMJJJbY3UC4HluXi7CAjVbZvfP/sy51dWeHVpiXdPjHOoNxxcFEUmmY7y8LsP4bk+5WKDIIBW0yaeMPjgx06xulRmIJ9GUWUmDw2SSJndMq53fuAoc9Pr2JaLbqiM7+/HjGg4Tmj1+elfeozpG6tUy032TPShqQr1ToS61RG5brcdPvbJ+4jGwwmVJElIksSZB/eRSBkszi1gRiPsPzTC/Mw6vQMmnl9DCB3kGT75i2e4enGI4nqJVCaJqodW1ogmmlnlyPE8h471IISCqnpAC3A4cMzgb/93o0xfyeDYLvmfOs34ZD+yIhGJ6nzsU4dIDkxhuTlUOUN+OMPP/vxD9GR3pgD/dULoqhLaoHqdevvbWTW25yDt9Lnvhvf6ps+dTpBDl3fOgIRi0j6GrFK0K9i+Q0Q2yBrpuxIILtk12r6NJink9NSuAYSIYpBU45SdOutWGR9/izAohOVlu2UMu2KFuxbcvTVsCDyfq0zxQvECN+oLFOxKR5DSww887NuyiJtRsCs4vkNUMckZKWLZBLIkocgSmqogSRK5VAxVDe2bTV3FcT0MTaXRsu+qBLbhFqg6i0hCIqENvkGwZjvipo4mb4ijbxLxRaJX33mSF2qObEzofVzf6U5MFaGwN5rnRn2eK7UZqm4jLK8LwoDSpeo0JbtKUo0yYITBroCwhGdfrlO6PDhAyjS4d2QIgSBpmExke3hlfomGbXPvyBCqLDOZ7cH1fcY6OkeyEBzsyxHRVA6pGr3xKMcHB2jZDi3HYTSdQpVl9mV7mCoUycVipE0DTVFwPI+hZILVRh1DVkiZBv3xGLIk4Xo+b6P+4w8NPN/Hdj1UWUbexHCThKDH2JmzElG1LoNotdkIjSVcn8D3t4ikykIwFEu8qaARhIyfqm0jC4l8LLnrXWwqCn2RGFeK6yw16whNYuHGGtF4jZ7+JKgBqtBQ5Z0vmOf7FFstMubWwEKp3aLWCWj80+e++4bHW3fsLWWMVduiYlkoksSeZJqornFizwC+HzBfqJCJmWEgToQahJosM5xNIUtSOB/pzIVuh+W4zK6ViRla+M7eKE3HRZUk2q7bncfRCXjmMyYhz2k7ohEd23Lx/Vui4yvNOpbnElFVejc5fm2GEIKEZpDSDZYaNZbqtR3bz8cSO74rhNg0XgdbdRhVoXMk+Ri9+igAk/HTTNfP0Uq9h6iSYjh6CNtv4foO/cZYWNblljsuTCqmHGe1PcNI5Ag5Y5heac9t46BguX2T8+XvcTT1GEeSjyALBSewuF4/S1zt6QaEBsxxDDnKzfprjEYPE1WSxJQ0slCJKimS2m3mIEiMRg8zFj0e/p6UuFl/jeX2TSbUND3aIHElgxuEyY0Bc5yp+ivdAOD/jxAptZc90aOYcoyl1hJFu8S++ASmbIai9e0qv3PtO0w31lCExIfzJ/m5sYfYE8t1r3QATDfW+L3rT/L1xddZblf43evfZX9igLyZ3tbflu/yxPIF5ptFJCF4R26S//7wR+gztgdEa26bqdoqN+qrPJDdh+27vFaaxQ083j9wbEuS7uXiNK8UZwgI2J8Y4H84/FEOp/JdgesN2L7HTIfFsyeW65TUB2hyCsOvd78XBAEXKwv83tSTVJwWEVnjk3se4G/seZDUpoRjQMBrpTl+49LXuFCZ51Jlkd+feop/dPjDpLXdtV7nm0Xmm0Um4n380sRjPNJ7cAvbs+q0+NzM8/ze1JPYvsvZ4jSvlmZ5KDdJQNBhK4fBhH93/9/qbuf4Hp+beY7fvvYEAKcyo/xvJ3521+OQhcALXOpug5JdJhlNMX9zDdfx0A2VR3sP8GjvrQDHUqvMf/vyH3OlurStrdvNTHZCSovy6bGHuNlY41RmD/sTA0RuS5S1PYc/n32R37n+Hepum6n6Ck+uXmJPLNcVuq91DD/8wMfxQ7fOAAfXL6FI25Oem5GMGuiqsuUYk7rR7VPXdvFcj76RLBDgBS6GvBuf1EWIW/vTVYWlco16295iAjEcyXB/doKZxjqW7/D40jkeyk3u+P7ZQMO1eH59ioYblkM/kN1H7w7PyQ+KH6pplqLkUZTQsUFT93c/V9W93Z+F0PCDM3z1qfM8dlol39uLEvnItrZkuYd66yEihorRKRcRQiMa+QlqzZDqno7ENrVrYuj3g34/l8/PM3Vlif2HhygXh/H9IkIS2FaKZNpkfuYGqXQUM3o/0ZiOqmZYXbrE3gmZ0tperlw0eODR/TQqN9DUgyzMrJBMxSiuD9BsrKOqJer1NoapcerevWxeC2y+MYMgwLY9VEXCdT2EAMf16UnHSCUipJMR+rIJEnEDx/VIxE0cx0NTFVRVJh4zWF51uDm7juN4NJoWbcuh2bJpWy5RU8c0NDLpCH2bdJKmCkVeWVrk3qEhRlMpHp3Yw/ml1VDQTNfY35tlvdFkTybNSq2Oqans7UmTjYaspxdnFxhOJTmW76ftupxfWuHYYB9RTSOqaeFCWZbojcewPY+TQwMIYCLXw4sz8+RTCXRZptBo0rRtys02+3I93CgU2ZfrwVRV7hkepNK2iKjqlglQEASoktzVSdiGYHNnb822n37wltV0tdLk/Msz5Ed6uPT6HJIs6O1PMntzncX5EpViHd1QiUR0rl1cwHE8Wg0LQ1cZGcuxNFekUW1z6fV57n9kP8vzRYyIRv9AGl1TqZVD1zndUFmaL3Lj6gr3vWOS4dEehsdyZG9zEJAkmX2HZYb3VVFkDUmscSBt4fsXqbVWiOhnqLe+TTTyMIdO1fCDFp6/gsMl3LbO0N4xBvdE0dUJmtazyFIPnr+GHyzQsPrwvDUSaXj43e/Y1mW6oXLmoQmW66+yWPsshjJMLH6Ye9+xD+kOdcF/XeAT8GrpBiORHE7gklKjJG+bAKxaVSKyRkbfyggqWjUUSSar37reM401Kk6DezLbrc8Bltol5pvrPJA9gN3JbsuShCrurgTMC7xwYozo2szvBIFA6bAanMANH6HbHi21W/P//x7cwOPxlef5/Ny3KNk1evU0eyIDpLQ4MSWCKilcrc1wtnR5x+1t3wndSYSEqWrsTffguKFegucHpGJGV2BwYwFhOx6KLJGI6OiqsqtTye3wgzDb9VaEU0VnETpTKBM1NPoTW4O1qrSLeN8myEIhpiRpebXONjIP507wfOE8Z0uX+fzct7gnfRBNUllqr/MXC0/ScNs8mD3WLU2UhGA0nWI0ndrS9kQ2DCSFCQGXmmUznErQ23F66YtvDy4PJhIMJm7d84f7bzGLH43dKqfuie48+RrP3mKpDCbvPOn7UUejbXNxfpXJgSzp2NZAym6BYmlTAMDpiOoXlssM1tpbRUkFb0pfZANe4Id6DALUO0xgpU3BcMfzNjFrREcFUsILPOp2jdHonm3bK7JM3baxPG+rCKkXjj+ykDiW67ujoDFAxohsKcdyfR/X9ztZZwVFklADiUq9RVLW8JoeyAGmoWI7HtVak0TMxGrZaLJMo71dYBxgoVihFrRJmAa264bnGwS0HRdZkhACUlGDxVINz/NJRk0G0juzQzcSUQFg2S4bFCjHD8drCbGNebgZ8ibdSNvfOTCly8pdB0Q0yUCVtO52MSWN5TfxAgc3sLlSe4Eb9Ve7Y13FWe2mCgw5ygPZj/Fa+QmeWPlD0lofR1PvZE/0SEfCAVpejZeLj2P5TQbMcWQRHqPvu9TdEouta3x54d8AIVOg5dbe9DlIQsaU493v63KogdT2wrLCxfZ1Llaepu6WEAiK9hISYYnbDyov8eOEirOGE1g07ApReYCKU+m+A30Cvr92jfPleQJgf2KAXxx/hKHIVmdDAYxFc3xm78OcL88z2yxwvbbCC+tT/OTQPVs0zSB0JpturANgyhqP9R2kz0jueO0TqsmJ9AhHU0NInRKl8VgON/C3abXdqK91xd7v6xnnQGJgR60hXVaYTAwwHg/FiTd0brygjePX2bg/2p7D1xdfZ65RQELwUG6S/3zPA6T1rXNBgeBYephPjz3Er174MiWnyQuFKS5XFnkgt++O/R9TdH5h78M81ndo27idUE0+NnwPT61e6ZR12VyuLnJ/dhxdUVisVDnUl+sEV7dqPW4OQMlC3vL329H2LApWiYJdRhKCtJSh3bTxOw7dt0O5g+Op5/o0am10U0W+LREnyRK+G75rHkhPcF9qHENRuwY9m2EqGh/KH+eV0jTfWbmE43tM1VZpezaqpHQkJcJ5ki7H8TuaxBI6nl8nINQL3PEYPX+budKGe6DaOWY9ouE6HsXlMtmxJG7gbkncbdlWRBGyyoZ7JkDM0EhHt77bFSHzUG6SL82fpeU5XKgssNgqMRzt2dbmBlbaVc6V5wBIqRHuze7tzt3fTvxQBY6CAJYLVV66OEs8aiAJwXq5TjoRYTCb5MrMCsmYyb1HRulJhraZC6sVXro4G4pA5zOcvThHpd7mvffv59WrCxwdH0CWZV44P81qqc67zuzjlcsLjA9nKdVavH5tEUWWODIxwKWbKyytV9mbTqLpKrqhoOkqvh9QLTfw/QDT1NA0hVjCQNMUGrU2kiSRSEZoNtoUC2EE2jA1ojEDy3Jotxwcu4ZuqDjlJtGYwUDSJOhkk/riMT5z6iRPTN3gIwf2k4uEA40sS+wf76VSaxKPmtQbFoahYuhhGZuhqxyc7Cdqajx0pqOTYmjU6m00VUZVZA5MhBMtz/XQdRVVkfF8nyAA23FJxAy8TROhAPj8ufN8/vx5/sl738OedJp9vVn29d4qsRjJpBjJhIuHuKEz0clAtxyHvdkMD+4ZIabrlJotDvf14fpeN3uXMU3WamEZQn80Rm8iRs2yKDfbDCYNPnJsEDdoogiNnzo13JmElADBR45nEVTxCTg1pqCIGEJY1NwVDDnZpT27vo8ipB2zRTFd5+dOnuAvL17mvuEhhlPJrgXo5u9KkoRlOZSKdTLZGI1aGOjbO9mH7/nEkyarSxVkRWL/4TzXLy8RS5gszBZAhCwlSZbI9SaIxnRkRaK4XsN1fHp647z6wk3uuX+cmRtrNGptXMdjbaVCs2HTbjk7HrvrraFIPchSEtudxQ+aKFIv4BEEDorcjyQlsN05FDlL0z5LRDuJ460gUHH9IlrQ7ri7mNh+GVnK4HmrqEoex1sNywg6Gc7N+1flNEOJv4ntrVOzXma18RcEdZe0+RhJ4543jNj/OCIIAhZaBV4qTrHaLpNUI1yuLnCmZ4LXytOsW1UCAo6nxrhUneNIcpSVdpmL1Tk0SeV0ZpzXSjfZG+tHl1S+u3qeuGIQUXReL0+z0CxyKDnMTHMNx3OYTOS5VlukbDdIa+Fi3JR1hJBwApf2bXb1bwRT1ruuZE1v94CGG3i0Om1HFXPnTPVd7fkHR0DAheoNPjf7TWpuk/f13cdP5h+l3+gJg1hC4Pgufz7/xK6Bo4hsdISiXSzPplBpcm12FRDUm1bowqHIGFqYYSxVW8iyRF9PjEbLJpeOMTnS+6ZOXhY6cbWPmNL7lgJskpBIRgxGMqlt48KbaS0goOnVcDtZN0lInEhN8l+MfYQ/mv0GX5j/Lt9Yfh5FyDiBS1yJ8MGBB/jkyPvQNjHJmo5DodUkqqookkTFskhoOo7v0XQcoqrGcDrJs/OzPDIyiq4olFotDEXFC0J9KERYUtz2XExFCV0LW83uz6V2C0lIxHWNYrOFKsukDJ1iq4UkBKosE1XVkFUaQFTbXqb34wQ/CGjbzrZSy1Dsd2f6v+P5HfFsiKlhsibZE9vZ4fMubkdTUdGVMGBas61dv+cGPnUn/Htc01EkCcdyUXtkJFngBi6Ov/t4JRAdTTGPQ7293UOMqjqSFGpD/L1TD3DfwPBrbJRQAAAgAElEQVQbFVpuKV/UZQVNDrU4qlYbPwhYL9YpVhqoikw1CBcKuqZQqbXo7YlTpUWl2sLzg6722e0YSCfYn8wiS1LIrOxEyjbY26amhvqRvaHOHSIMjnneLozI0BGeaOTWvR1XdWRJwvF9Gs7ufWd7Hu3OfbFTOVqnY+4abmDjBbfmJm2vgSI0JCFzo/4az6z9GQ9kP85QZD8SEgVr4dbuhMRgZB99xhgFe4FLlWf49vJ/4KNDf58+Yw8AilA5nn4X69YcT699nnf1fYa01odARpci5M39PNL7CTbMZ0K9wJ0Yz9svkB/42H6re+yObxMEHpqk03QrPLnyR6S1AR7M/mcYUpRzle8yVXv57jvpxxyGHEMgMOUYXrBVILvmtPn64uu4gYciJD46dGrXRa4Qgol4H/dlx5mfDZ3RvrNykfcNHCV2W+l+EIQMxPDnYJtm0k5tK51EmON7JLUIy60Kru9teZdt1klyOq6Hd8IWYxH8UONIvnV+c80C31u9jE9AVo/x8ZHT9Owiyi0LiXuzezmUGuKZtatUnRbfXbnE/dmJOwZDj6SGeecOQaON887pcY6nh7lcXcQLfG7W17rB44Rh3JGt8mZhyDpZPYMsyWiSilvxGBjpCUuh7xKz11dYW65gmBpeJ0nguj6e66NqMvFkhNJ6Davt4NgumVyC4/eP76gtmdai3JMZ45m1a9i+y7pVp+U5KEJBlWRyZhQ/cClZU9h+jaxxFAgQQsHzd9dKdH0/FErvJAo3ro8hhzpEG9GfTF8KM26gSjpROU7FKWB5O8l8CAK/HgYgfehNxhjpSW0zCRBCcDiZ52Aiz8ulcD3wQuEGA2ZqV53QFwtTrLRDTcpDqTwHE4P/SRiTP1yBIwJsx0VTFaYXCkQMjaMTA1ydXWNxrcIDx8a4NrvKWqne/f7U3DoHx/pYXKtwfXadWtMik4zSk4ySipnYjkcsojGQTWLqGr2ZOD3JCFbHNSoZM+hJRrkys0qh3CAZNTh2fIR03ESSJPIjPQQB3Ly+wtBID5quMjreEWQUoW2qLEucfmACISDblwwZALLE6QcnkCSJY/fsAcL7a3x//5abXgiBicTPnTzBp44f2xKdFUIQjWhI2hIEJfpjIwhuo8t1SpqymVh3m3h0q1vJZkX4bl/v8BlAy3Z4fWV52yT1zcBQFN5/YB+SEFTbFpeX1qi2wslZpdkmZui0HYfxXIa6ZXNzvcRkXxbH8yjUmowPSkhyg4J1E0NO4AUufuf/AJ+AAEVoCCGjSRFcv00AuEGbkciZbuCoLxZjN48dWQg+fugQP3HgQChuKwRz1Qot12Fv6pbFtOd6pNIxEskIyXQU23LQdAXfC4jEdGZvrDE22dcJIprsGe9FSIJ0NobvBeT6AgaG0qyvVGnU2yiqQjIVJZ40KRfr3PfwPtI9cVRNodW08LyAdCaK7we7Cu7qyl4s9yoCFVlKIQVRFDkHQiBJcQztEEFgoav78PwSCfPDyFIcRR5ECBlFyuIHbRSpB0XOYYjjCGQkEcHxVjDU/QQBrKxUaLVshoczqGo4RPiBQ9OZoma9jOUuossDRLX9NOyruH6ZvtjH7/p++VFHQMBsY43xWD+qkIkoOjHVwPZdmp7VLUHzA5+kGsX2HRquRZ+RQpMUiladjB7viDKGTDhVUlCFwmiklz4zxTNrl7B9h5yR5JXiDZJalIOJYdY6gsVZPUVUNlizSsw1VziSHH/T4tO9Rpq4GqHSrLPQWt2Vll+0qpScKrqk0aun/5Mzi95MQVsQBFyuTlO0qwyZvXx86J3kzdyW43cDj4pT37WNrJ4iIhsU7QrzrVX2pofJpmK0LAe3I4jbm4mjawq+7zOQS+K6HqauoqvtkDshdlqm7PSJR8MtoEkRdHl3OvpukCXBYCqOpshveSKQUnNbJr+SkNAkDUPSOJme5FT6ABISMcVkLJZnuKPDsrE/z/d5dn6GlusymkwxV6l0rW7rtk1SN8KStHSay4U1JjM9rDQaLNWrtF0Xy/Ow3JCBockyCT2s78+YEZqOjecH9MViXC2sc6JvgHwiwasrSxRaTQ7n+nhpaYHTA4M4HcYIBPSYESZ77l436kcJfqe06fbSSM8PmK2Wt3EigiCgYrdZaYb3/nAihRDQblo/sDNm1oyQNaNMV0pcKxW6Je23o9husVivoUkyw/Ekhq5y+p0HkeRQ50hBwZANWl5zx/3IkuDeoSEsz4OgYyctBBnDpNeMMlursFCvondYQ28WCU0nZ0a5EKxwtVzA9l0O7evfyqIMbv0jbTzfXUeWTSyvTadtaiq5RLTLRtgN8U1r4js9x77vI8sSvn9rTB5NpIhrOqvNOrO1CvftMF4HQcBSo0a53SamagzG3r6EjuU3udk4R0rtww1sphuv02vswZCjNN0KqtDoN8aIykmWWlMd2/oQtt+i6hSJyglSai+T8Xu5XH2OlndrfFYlnUFzH/vj9/H1pX/Hs+tf4F29n0aXo4xGD/N84cu4vk1GHyTUl6mibBL1V4WOJCTKzip93hgg0DouZz4u860rFO0lInKc2cZ5ALL6MI5vYfkteo1RUmofDbfMantmy3vIC1ycwO7MSf0wAC8CJLExP3JxfQe/Y7Pt+HbHifOtj9c/jPADj6K9TESOI4RCWk13F7zT9TVmOsygpBbheHr0jm1JQnAomeeL4ix+4DHbKLBu1YmpWwNHuqwyEg3H+JZn86WFlzmQHGBffABNunP/Or5L3Q319m6ft4zFch3hZI8nVy5zIj3CA9l9RJXtWq+3Iwh83KBNsGmlcb48T8EK7+ehSIbJ+MBumwMhe+pAYpBn1q7iBT5T9VUarrXt/Dfjvp69GJskDDZkC/zAx5RDNmC/mULuyCbUnBZ+hyVasyz8nUTV3gLW7RLLrVWGIwMEbSiu1ciPZrfoeL4ZJDJRZEUO3ao7QfbAD/C9AN/3yQ2E767l+RKZXJyR8Ry7XRohBFkjji4pnWSg05GOELiBT7nd4oDowZCTt66bkFCkZKdyYue+kSWJ9UqDTCJk6gadpEPFttirZCAI8FyfdtNCM8Mko+W38PEQO7F9AguCarhd4FNutJAlQT6T4PaIfkqL8FBuHxcq81i+y5Mrl3hn38EdXQJbns2za9dCl0Eh8VjvAWI7mOK8HfihChx5ns/U3Hon2xtgux6zyyX8ICCdiDC3XMJ2PFzXZ7lYJb0WQddU5lfLtCyHZMxEliTyuSSW47JarOH5PqahcvbyHMf3DVJrWKyW6miqQiYRIWJoqIoUajv4AfneJKoib6LDhRdyz95eZCWMsEubFmYbcxZZ2YGid1sbm7+/7btiO/04CAL8oE3bmUWSIhjB0BY7+M3YQgW9PSO9Yy37zu3MVSosVneui38jhHbfHRqzrnFiZKA78VmvN0LRa0UhoinYrs/x4QFURcZyXOqWTcaIoEiClDbChiJKJ4R2ax/csiYPCAgCn6I93X1AJSFoODb2Lpm8jWOUJalrqzhbDSc4o8kUQSAR+AGJVIST9+3dsY0gCDjQEb3e6MdIJ1g3MJTZEpTbELDO9SW72w4OZ7p/T2WiW76fH92dhijLKUzpDAAqe7qfa+wNu0jKddrZcsbdnzrmc+id0k8lGOx+X1PD9izLY3GxTOAHDAyk2Ci5dbwC642/IqJO0Bf7WXSlDyE0dGWImvXKrsf84w2BIWsst0pUnSZN12atXSEi693sliwJWp7NaruC13m5RzrOXk3PYq1dQZMU8maGvNnDtdoSfUaSiKKjChlT1tAkhV49SVyNMFXbqBMP75m0FmciPsSKVeT7hde5J3OAQTO3y/FuRUyJcDCxh5uNRc5Vpig7ta7t+ga8wOel0kUsz2E02s/gbcGZtwuSkLpZwrqz82JyM/wgoO3ZBAREFIOYYm47rpJd5XJ1Ztc2MlqC8Vie9WKZZ9df52R6PxPDWYIAXK/DktyJoQEhc4aOjlAQdDOZrY5F7HYImm4BQ04QU++edRTVNCZ6e96y+LMbOJTsVeJqsrvv1XaRLyx8Bydw+czoh5mMj9zx2rq+T7HV5h3DI0RUjevFAu8YHuULly9iKAr35od4Zm6GrBlhPJVhbzrN2aVFbN/FVELDg4Smgwi15o7m+ri0vsZMpcx7x8a5uL5Kqd1iTyrNPQODXFpfo2ZbVKw2ddsiH09weiDPWrPBS0sLuL7Pib47T85/XOB4oUbgZri+x9mVRT7tOpi3CSXfKBe5WS4iC8GxXB++69OuW7iO+wM9vxFF5Ux/nrPLC7y6tsRCvbpNI8nzfZ6en6bQatIXjXGopxdZksj03dJaEIHAC7wtbj6bIQBTUmg3HZbsGgOZsKwra0Y50TvAXK3CN6ev897RCfqjb94QQJVlzvTneWp+mgvrK5xdWeSh/OgW7ag33ZYkdwTgfWq21e2Dt2N8FEJQrjQZHLhVFtoXjXEs18+Xpy7z7MIs7xudIHWbE57leXxn7gZu4HMgk2PgLvrmjZBUe1luTfHV5lUsP8ykH009giJ08pFJLlaf5omVzxKRwwVQSuvrbtt0qzyx/Ad4gYsqGbS8Gv3mXrJaftt+YkqaB7Mf54mVz/Ja+QnO9HyY8dgplls3+PbKH2LI0ZA1jcSjvZ/C6ATi42qGvDnJS8Wvca12lqSa5aHcTwOgCgNdknhi5bME+NScAsdT7yKp5fADnyFzP+fKT7LQvIqPjxASunSrpPP18ndZa88w17yE5Td5cvVPSGv9nfPXeLX8bQrWPPOtywgknlz9Y3q0PIeT70CTzW3n+KOKsr2C7beAgJSapmAXGA1C8d2Zxjq1jhlFn5Ekob7xeWf1eBiIDcJSr4JVY09sayLAlFUezE3w5fmXKTtNLpYX+J9e+wIfGDzGA7l97Iv3oUk7l14qksxQJENU1rcFuI+nRxiNZbleW2GpVeLXLv4VD/dO8oHB4+xPDBBXjF2fZSHkkBXprpDy96LJca7VVrosppFIFmMX/bYNyEIiuykAUHVa1Nz2HQNHe+N9W36vuxavlWZQJYV7e/aiCBldUrqzC7tTpkyn3P3tUpmMyCaSkGh5FnogY5jam3aY3Yye3gQ9vYk7BtyTmSj5sRyKIqFqdy6xVYW8VVOzk3SIKCp1x+rIMhgdk5AwW2B7ywixO2tZliR0TaFtb5SzBRiKSo9hdt9fsiKR6k3g2rdK3kw5hirt0K6QCPwy4CDQ0BVlV71MIQQP5PbxxfmzzDeLXK4ucbW2zP36VhmLMIld4FotNIvoN5IcS4/clebp3eCHKnCkyBLHJvMUKg0USeLyzAoRU+PovkESUYPFtQoTwzniUZ3Th0aIGBq5VJSl9SqmrjK7UiabjjK7XCQe1TkyMYgiS0QMjdMHh4kYGkLA5GhIfU7FzTBIJEuUa20cz6NYDcsVzhwOo+We77NUq9HoKKjnk4kuK2U3WK7LfKWKG/jENI2BeHzLBXR9n8VqlZa7PbgRVVX64/FuECkIXGQpjiZnkHa6CTsIa24DKu02ddvG6Sx8FElCVxQSukZE07bdSEEQYHkedcumZls8NT3Nar2OKsss1WpcWV/ftq+IqjKUuLOgpgC8Dp3d8lyEJPAIMFQZXVEw1Fvbxg2dntjGSzpAEyqW57FYreIHAflE4pbLnOtSaLVoOU63/C2q7UMToY2z6/uokozj+SAEnu+zUq9Ts7dTuzVZJmno9JgRFEnqDvSltSpLN1bZcyhPLBWl5TjMV6phVDgRXn8vCCi1WtQtG9cPdVEiqkrGjKDK0q6TyM0sMM/3qbQt6nZosy0JgaEopE0TQ9l9gGy7LgvVKl4QYMgy+WQyrGPd4euu77NQDTP+shAMJhKYnbZ3at9xPGIxHVmWMM1b95sm5xhJ/h0kacOJwaPl3MBQRtDlPoLA3zm6/iOOkl2l5bXpN3LhQifwQzccfGzfZTKR52p1Hlny6TWSHEmNULDKZI0Io5EBvMBDlzRkEepcZPUkru/S9NrElSgxxcAN3JC9iEfLr3IifZKyXeNi9TqP9h2h5YXBiKyeIK6aeL7XdcuKKxHe23cfFyo3OVeZ4renvsjPDL+bQTOHKpSQxek7VJwGJbvKidRkl5GkCJn39d3P2eJlLlRu8Lm5b/Gx/KMklGgYgHXbnC1d5vHl59FllXf1nianp+7UXW8ZUcWgR09CHV4qXeLh3AkyehIZCbdDI4/IRje7KQuJnJ5CFQprVolL1WlOpQ+Ez77vsdIu8Cdz32S6sbjrPhNqlPf038eV2gyvlq/yO1Nf5KeG3sWgmUURCoEIqNo2VadBya5xMr2/y9jZXPMuEAwYoWPMXGuF1yvXOJM5hCoUfAJcP7xv4mp/N0N9t5AkQcy4eyr4ZsTVFMlN1td1t0XRCgOal6o3kYTUocCHoSVFUkgoEWJK6CSiyjJ7UimenpthLJUmG4nynembjKXSCBHawKcMg6Rh0PZcLqytcaS3j2ulAj2miev5eEFYJl21LJ6Zn6E/GufMQJ7nF+aJahojiWSoQSMEludhex4Z0ySqaR2tGEGPGUEgiGs6pvrG+k4/DogaKoq8tTQkAL6/OMvnLr/O+/dMktR1vCBgvlbl3776PDXH5liun3v68uiByv7T4xiRH+weEkLwkxOH+O7cTS4X1vjNs8/yt46dJh9LIAuJluvw/NIcv3/+ZSzP48N79zOe2u6aFnT+U3exQAZo2y7nbi6TiZvdwFFc0/iZ/Ud5dW2Z5xbn+N+fe5JfOHKKkUSyw+4MaLsuJavF62vLnOwdZH9m60L0g3sn+fr0Nc6tLfPPnn+S//L4Ge7py4clfUJgex4Vq810tczJ3gF6zJ11tgaicRKaznS1zLdnpziQyZEywkWf7YVZ39s1GN8sVFXmxCbDkvDcdT554BjPL83xrZnr5OMJPnXwOKmOs13JavONm1f58tRlMobJT+8/suux3y36jD18cOBvE1czrFsLeIFDj5YnoWY7JTLDfGTw71K0FxFIZPQBbL+N1ik7iqs9PNb3N6i7JfzAw5Cj9Gh5TCW8rmmtn/f1/00y2gBCCAbMCT40+F/R9GqAQJcjPNL7SdateRpuBUnIxNUMae2WY7Eq6Z3vzNH2mujEKC21aLUcjpkfYah3iKZfoe6WicgJstow1WIbXVd4uPcTrFlz2F6LuNpDVElQdQpsTKqyep6YkmQsdqy7P00ykYWKEBI5fYSkmmVv7AS+H1BYqxFN9iDdpe7gDzsiSgI3sIgqaWQhE1NiyEImCAJW29VuufxCs8T//PoXtmjn7ISy0+wmWhzfo+5uL5kXQnAqM8YvjD/CH9x4irLd4GZjjd+69m3+bPYFjqdHeLTvICfSo/QZocHFRnLE9T3OFm6Sj2QYi2W3OKsNRTL88uR7+K2r3+Z6bYV1q8YX587y7eWLTMb7eUfvft7RO8mgmUKX1S3JHoHADdr4gQ2EQY/FVqlryvNS8Sb/6OU/ueOzH0C3rAjA8hza3u7OY4qQtolnC0Jm9Vikd1Opk2AzfTIgfDePZdJ3xc7cDY7vokoKfUaWlmeRNDQce3cTkjvhToSHzYhEw2Rs0W6w0q5ws77GSrtC2W7ScC0sz8H2XZZaZRru1hJqQchglUVYRixLOpocZaOPfL+J7S5iqjvrSwVBgOf5HUe1sEVNlolpWjj2do67tFxGNTYYR+2ufuh2mEjaKRAh0cUPfIr13aUihqM9PJSb5E9nnqNkN3hi+SL3ZMa2lCu6vs/Z4k1W21UkBCcyo4xGQ6FuP/AQSG/r+uyHKnAkhCCbipJNRfE8HyGgJxUl2RGEnBy9JaB5ZPxWpjHesUQWksDzfLKpGKMDaaKbLFNz6VuR3WRseyT8wFgfV2dWw3KyoVsTDdvz+PcvnuUvL10ibZr85kc+xNH+/m3bb8a55RX+4Vf/irpt84ljR/mHDz24JVNcbrX5lW9+i3PL261k7xse4p++971kO6KgkmTSdheQpZ0nABsBo6vr6zx1c5qXFhaZLpeoWaGbSERVyUWjTPRkOD2U54OTk8Q2aUK8urTM165e5XqhwEypzGqjgeP7OL7Pbz7zLP/2uee37fN0Ps///bGPouzwoAdBQLnd5lvXp/jezWmuFwuUWqFmRS4a4eTgIO8ZH+d0Po+h3uoT269Tc5aQhExa28tMqcw/+OpXadgO//JDH+Dk4CDX1gv85aVLfH92jpV6Hdf3SRkGw6kUP3X4EB/aP0nTcUgaeniOQUDDtvkXTz/DE1M3th3rWCbNP3v/+2g4DunOhC/wA9oNa0v0+/LaGn/nS18hqqn8qw9/mP54jK9dvco3r00xUy5Rt20iahggfGh0hI8fPkQ+sbNrCYRsifVGg29dn+LJm9NMl0qU221UWSYXjXJPfpAP79/Pod7cjgyDUqvN//rEdzi/sko2EuH/eP/7ODE4sG1/fhBwfmWFf/z1x1mt1znQm+Off+D95BO709eFANf1utcybMdig+HidTKNfmCx3vga2djPU3ZWkYVKn/H22z7+f43VdoEAiCtRzleu4xNwID7GYmsF23c5kBgjrZkI4ZBUI8y3bBpelaSWo2gXKdk1RiL9tLwaduCQVA1WrSIr7QIPZo8TYDJVnyPtRjiVnmC1vUJKi5JQTS7VrpNSI9TdGik1SsEusScygLKpTl8SEifT+/nJ/CMdPZ9LXK/PMRoZIK5GwpeSXWWlXWRPdIDDyb0oHTFSIQTjsSF+ZvjdfG7um3xt6VkuV6cZjfSjSApLrXWu1edwfZd39Z3m3X1nttVhv13QJY17M4e5ULnBldoMv37lj9gTHUCTVFpum5ga4RPD7yWlxbvHfjQ5wWh0gBv1eX735l/yavkqcSVCzW1yoXqDptvm3p7DPFc4v+M+JSFxOn2Qjww+zBfnv8sLxQtcqc2wJzpITDHxA5+CVWHFKjIeG+JYamJH8UyAyfgIY7E8N+oL/O6NL/FC4SIJNYLtu9i+y4M94xxMDG7pv+VWgev1eVpem7Zvs9ou0ujUxH+/cI6CXUGXNHRZo0/PsC8+vKtz3RtBk3QGjNGuCHoQBPQaaY6mJnh2/XV+f/orobh6ZwyROyVrE/Fh3tt3H8dSE2iSyqn+QY7k+lCkkPrv+H53MqpIEu8ZC0slf/rgYWQhkITE/p4skhBInUmX63s8NTfDaCLFWCqczB7I5rYwQQGO9vaxv6eno1lxq99mqxWqtsXJ/r8ebCNJCJKmgbKpbyDUWDia7ePfvPwcj09fZ08yje25nFtf4XqpwGAszi8cPkV/NI4A8pMDu9L87wZ7k2n+7sn7+ecvPMWXpi7x+toyR7N9mKrKSqPOyysLNByHx4bH+LlDJ3d9DypCoeW3dtUfDvX2AsYHb7FwhRCc6R/il0/cx7986Wm+dvMKLy7Psz+TJd7R2lptNliohUmnX3/sg9sCRyPxFH/v5AP82otPcaW4xq88/S32JjOd4JegbLWZrpZouy5/8MGf3jX40heN8ejwGDfPv8yfX73AdKXEWDJ0OKzaFmf6h/ipycNb2GB12+L5pXmqdpum41CzLa4U1wC4XFznP5x/mYSmYyoqKcPgnmgenXB7qXPuv3jkHn77tRf5vXMv8cLSHOOpHiQhmCoXuLC+CsCnD5/ig2OTb1vGOaIkiCjhvCGqbE8eCCGR1HLb3Mw2IKPQa4zSy87lS4YcZTh6sPu7JCR69Dyb+deKpNFv7swC39zOUCR0dGo1bZ5+5QJPPX6B/UeG2PvzB4kbGTY4G7bl8M0vvcLYZD9nHtrHcGSr1fXm8xyOHOROGI3esmRvNW2+/h+/zjvePYKS+/HSX/MCj8XWdZJqL33GQZpeEz8IEwJNz+7yWSodwee7QZhk2VlkQpVkfmbkXgbMJF+af5mXizO0PZs1q8a3ly/y7No1xuO9fGDwOI/2HqDfTCIJCUNWGY1lw7Ly2wYZWUg8lJskp8f5j9Pf59n1a1TtFlWnxUvFm7xenuNL82d5KDfJhwZPMBHvC0veOs+UJFRUKY4iGTi+tyXos9gqdS3R3yy8Tj/uhg2R580BFj/wsbxQ3PpOz3rNsjtt/+DjgR/41J0GNafR0STyaLduXfu3G0EQsG7V+MbSOZ5bn+JSZYGWZ3eSUFs5VEEQbGexilCbsdRuEQahk1vuBUlEUOQMgt2f1UTUIGpoXTkHSQiqtsVas0GqY9Bgxgw0Y2sbOzK8gjqBV0HIo4CCs4uo+AYissaDuX18bfE1qk6Ll4s3WWlXtjgQNlyL769dJyDAlDUe6T2AIsm03XXqzjQxdQ+60vO2yUz8UAWONkOWJfYO3Z12wVBviqHet5YRDwNWY9s+1xWFM0N5PnfuHAvVKt+bnuFIf/+u3R8EAd+5eYPleh1Nlrl3KI96G0NJlSXG0mnajkvbdWnYNku1Gpbn0XCc7s0mhIBARusKsG3fq+15fOXyFX77hReZLZdxOgOv1Lm5S60WC9Uqry4tUW63ec/4+JbtrxcKPD09Q8t1IAh1ipqOEwozahoRbXtGMKZrO55/EARcLxT5redf4JtT12nYDrIkEVVV/MBltdHgwsoqj1+7zqdPHOfnT53sCpv6gUvVmSfSOVc/8KlbNpV2m5VGg+fm5vj1p57mwsoqXhCgSKFjQrHV4kapxIOjw9A55qploQiJ4WQSWQr/3Zft6fb3Yq1Gy3GoWzayELQcm5gWZhsDEbqmOJZLpKMf5fkBNcui1GpxcXWVP3r1Nb58+TJtN7TGFUKwVKuxUK3y2tISry4t8Y8ffYTJ7Pb7NwgCLqys8H9+/3memZ2l5TgoHbZS03FYrtU4v7LCE1M3+MzJE3zi2NEtWXUhBP3/D3fvHSVZdtd5fu59PrxJG+mqMst731XtvZDUiBYyAzIIBrMzsGP2DMvM7AB7WFhzzu4BhoXZswsHtyMkEIKWUMt0t9S+u7qry3uXld6b8O65/eNFRmZWZVZ3C7FH6t8fVVlRkS9ePD2p9UMAACAASURBVHfv/f6+Jhrhp/fu4be/9xKDCwv83ydO8JuPPnIHIDRTLPLHb7/Djfl50uEQP713D5l3YYqZpoauqZTL9cZ3zzOU/T1u1//6vovrF3F9h/naJHHtg+kzYqkWE5UZ0nqcuudgSI3Jyizz9Ry741swFZ2wanGrNE7JKZOzi7QaKSpujRuF0YaPjErRKZPU4yzW81iKQYeZxlJMJitzTNfmMRSdLqttxaQk6JpJIam5NlcqQ4QVCyV8ZwdPEyo/0fUQLUaCl2dOMVSa5GJ+ENtzAhabYpDQImwMd90BfKhS4fH2I7QYCb4+8SqDxXFuFseaDJ82I8nDbQd5ov0e4tr79+Z5ryWE4P6WvZTdKt+ZOs5waZJrhREUITEVne2xjXdMCLpCbfzsxqf48shzjJSm+MbEa0ghCCkmvaEOPtP/JGk9wZX80Lqfa0iNn+x6hFYjySuzpxguTXEhd7N57CzFIK5F2BjOIFmfTtxupvjZDU/x9+MvcaM4xitzp8H30aVGi5HkSGozNbdARFteWJ3JXePPb32DmlvH9QOvgqVn//dm3uHl2dMoDQnfweQ2fnnTp0joa5ttvrdjvDzprHt20yPKVAziWjjoxgZMbBzfpeiUeW32DDcKo/ziwMc5mt6FKuWqruXtY5veYG2uXCzf3uXUpORYVy+GojSB8dAandDgs+6czKUti0c39Dfj5j/o5Xget2YW6W9PETKWj6siBT+76wAnJsd4fvgmp2cmqLsuYU1nb1snP7/7EI/29q/wTPzB7I8iJY/1DpAwLP7k/Akuzs3wD4NXAlsAVSMTifGZHZv51JZdZCLRdccbUzHvKp3wCbyDgn1fIblWVT6+eQdpK8RXrp7n0twMxydGqXsBazei6aStEHtbO+iL3TkfVKTk4d6NtFgh/uLiKc7OTnEzO8+FualGd14lZVpsT7cSXc9cmgC4++e7DlKo13h9fIQTU2O8MT6CKiVR3aA/nrrDSHuqVOR3jr/ITLkU+JN4XpOlcGFumsvzM817rCMc5fcf+Si7W5cblbqi8Pmd+2kLRfjKtQtcXZjl9EwgX47pBgOJFB/fvJNPbNlJRPtggRbvt0xL45EP76FUqLE4d6f9gqqpPP7UPnTzB8ta9H2faqWO46wPAvyoVkJrpT+yH/BRpUpUjQbpZfjY3jLrJKwatJuxdcfMtSqimYS19e83S9V5rGMnB1IbOT53g+9MnOdKfoL5WoGyW+d8dozr+Wm+O3WRL/Q/wLGWTdQ8h5HSPK7v0WpEidzGgNKkwo5EF7+28ykuZsd5fvI8J+YHmarmqHsOgw3fptdmr/HZDffyROduog0pmSL0ZWbTbaBPWo+Q0EPva6HeakZXmXffXmttyVA0NkZaMZS7X8OqFIwu5uhJxDG1738OAeD4DiHVos1MI4VkvligWrFx7bubln8/5fk+t4oz/MHV53h77iY1z0ESeBktHeO4HiKk6JiKznh5gTfnblBfcS3iBxLeuWqZfL1GwkhgKsG44Ps+qhJHEeG7eCdBrlSlZjtkWmLN/bJdr+kFLKQIwr2GZuje2k7dqzZN/O/coIFQN4LQkQi2d7UFYQjryiIFexI99EfaOLM4zHBpnvOLo3RZyeZ7bhSmuJ6fAqA/2sbORCABlkKn7ExgqXcnu7zf+qEFjqAho3LcgGYmJXXHRVPkHckWL1+/Rdw02NeTwfP9plTIcd1GM0swXyoRM00sXaPmOKhSMrIQeNv0phL4Prw5OMymthYy8WgDBQwmr/sznWxKp7g0M8vLt27x2b17SFhrT1rny2VeGxrG8322trSwqz0AmVYaz8ZNk9989BGqjkPZthnP5fn151/g8uzsHdsLqJBrV91x+PrlK/wvL71MvlYjous82NPDvX29dEQiqFKwWKlybW6Oc1NTPNrfT9xcrZ99YtMAB7syzfSPb1+7zn9+400sTeOXjhzmoY0b7vhcq0G/vr1ROFko8DsvvsQbIyNYqspT27bySH8/HZEIru8xtJjlmUuXODc1zR8dfwtFSj6/bx8hXcPHw/Vt4vpqnw3H93lzZJSzk1MsVsp8ctdODnd3EzUMqo7Dtbk5LkxPc6ynJ5BfAbOlEinLAnzCus6/vvcYv3TkMOW6Ta5a5TdeeIG3RseA4KZcqFabUjh8qBSra1Iv667L//XW28yWSuxqb+fpHdvpjsdRhODW4iJfv3yFd8bHeW1omD8Nn+K3Hn90FWPI932Gsll+67svcmZykoih8+ndu7ivr4/WcIi66zK4sMhXL17k8swsv/va62iKwqd371q1QJNC8Eh/P+O5PP/5jTd59dYQf3byFP/u/vuaIFPFtvnL02d48dYgpqbxhf37eWJgYFVXYr42jCZNwkoKpRHpXSzWmJrK0tEZpDf5+BhqJ63hp+64LmeKzzTOm0OLkVn3Ov1RLcdzmKrMMlNdoNNsJaXHUKWCKQ0W7TxX8oPsiA8wWZ1jrrZItZESNFtboCfUQU+oA8d3yVhtzNYWiGsRHM8lpoU5X77OQj3PTG0BQ2qYUmeyOsd8LcdkZQ7wma9lg8+2WjiXu8YT7UfX9SuzFIPH2g5zX8texiuzLNbz1D0biQxkYHqcdjO1iq20VBW3QN6+ydF0K5/v+zA5u4jn+0Q0iw4zTVpPNK+bsfIQZbfI5uhOImqIzzXe3x9eZtO4vkvNrWApYVSp8kjbQTZFemgx4ljK+hPDiBbi6a6HOJrezWR1jqpbQxEKIcWkxYgTv23CowjJgeRWBsJdjFVmyNslpAiYMj2hdmJqYEj+b7b8FIbUCTWMAqezBc7dmuTxfVuCY6caPNF+hPtb9jJRmWXRLqw4dhZpI067kUIgyJWqxEJ3mmdKITmY3MYGs4s/fOl7XJ+d4rMP76c9GiepR+kw083PX6o98U38q82fXjOIYLo6j+3ZOL6LLjWSehQpYKQ82TD0zvITXQ/i+i6qlAyWxhrXkUHOLuD6HnXP5mNdD/JU5gH6Qh3IFfT9786c4C9uPUtXqI1/u+Wn2BDqRJNaYPbtB5PDbL3AX418h1OLV/nu9An2xjM47gim2o0iItjePJpM41HH922qzjC60ommpJDCaHr0eX4VIVSksPB9G3AJqQ5C6BRqF7C0AXy/3vS0c7wCutKKEAa+7yKFhhAKVcfhxeFBpkulQC7seTzYu4G++D+NfPKHpRzXIx0N3QFC+H6QmParRx7g09v2MFbIUXbqzNRHyUQtHurYCPg4XuBrlLfzGNKg6BSp+AX+h6MPUnd9tqfSOJ6DIpSG75CHKtRV7LicnaPm1mg1AsmuLzzu6exmd2s7Q7lFpstFBotD+KLOj3XfS3soBCKQoy1FXitryHbutrByPY9ULISmKKvmT4IA3HmibxP3ZfoYKWSZKhUacmxJ3DBoD0XIRGNrpg9BsGDc355hZ0s7Y4Uck6UCRbuO7/uEVI22UISuaOyuwJEQgq5onP/pvse5lVtkqhQ0/zQZSDb7YgnM2xjD7eEI/+now0Eaz7uUoah0R+N3vB7WdD6+eQeP9vZzI7vAYi1gbSVNk75YkrQVWpN90GKF+I/3PMT17DyH2rvWPPKmqvLZHft4qGcjfbEEs6OLPPfmTcIRk+GbM2R6Ujz84T1EoiaO7XL+1BBnT9zCcz127u/jwNEBNF3l5tVJblyeJBw1uXphjGQqwkc/dRjD1Bi6McObL12mkKsQS4S4/7EddG9ooV5zOPfOLc69M4QQsOtAH/vvGcC2Hb73zXOEwyZDN6dxHY8jD2xh5/5grnj2xC3OnxyiXKrRnkny0JO7SLZEAqaiqqyZJDg3nec7z5xkbqbAox/Zw+5GgI3juDz/tdOMDAZz8WKxyrbd3XzkE4fwPJ/Bq5Mcf+kqtarNwLZO7n10O7qhUi7VePGb55gYXaCtM0G18v4STn9Uqu5VqbgFVKEhhU3BKQZhQEJiyGXwYke8i1/d/pEmyLKyPHeZ+eI11llCioC1plnUKkEicZA65SEaPy9dryk9zEe79vFI+w4Gi9O8OnOVl6evMFyao+rZnFoYYqy0wK/vfpq9yV7woezUuV6YYmei+w5wRiCIaRbHWjdxOL2RodIcpxeGeGHqIpdy45ScGreKs/zelW8zU83zcwMPoUsFVZgYShwQaFIGjN1GPdm5m3+24Si6lCz7tS7bVPjYyNt8dSSQ1EPrhpSsVZ7vM1vL02beRUUA2J5HxXH4QXhj257DmexFTGnSabUSVeLEk6F1/Xf/MTVdzfH7V77DG7PXgMA765O9h7m3dTPtZoKEbq0ap74zcY5TC0OrgSMRgOotVoi4vvJ69KHBWhJCYz02VqFco1K1aUtEVlmNZGsVNCkDqb7tU6/WCTUMtHVpUnRyDbP81SWUDFJdlm+HjXcH+KOaxROduzi7OILrezw3eZ7HOnaiKyqO5/LG7HUW6kVUIbm/dQutRnA9SKGC733fFgnr1Q8tcOT7PlP5Alem5tiQTqBIyeDsQhDfXq2xWK4QNQ0641EuTU5zbGMvs8US16fnSIYtuhNx3hkZIxOPkbAsXrw2yJ6uDja1pTk/PtVMAxldzDFTKHFkQzdSSoq1GqWawbnxKYQQHOzN0B6JsL8zw9XZOQYXFrg2P8/hrq47bm7f97k8M8twNocqJQe7ukhZFmPD8+i6SltnvPk7ipSEdZ2wHtDfbp9gLJUQAk0mkQTayZUX942FBf781CnytRopy+LnDx3kk7t2EjfNZpfXa3oY1TBV9Y4JRcKymiCY63m0hgNWgRSC9kiETem1zZorto3n+YR0rWls9s2r1zg+Oorv+3xy1y5++egRkpbVlB/c09PDgUwn/+vLr/D68AhfPnuOPR3tHO3pQSAJKcHNtLILWXccnr1ylbZImN945BHu7eslrC8znh4fGKBs20QbN58QgQ9RdyzeZFdIIQhpGiFNI6IHfzePL9AeDpMyQ80XnLqLXVtbazySy3Fvbw+/+egj9MXjSARSkRzq7mJ3ezv/+hvPMpzNcnJ8nMlCgQ3JZVTY9jyeuXiJc1NTqFLyuX37+MXDh4gagXGf7/sc7elhb2cHv/5cACT+19NnONiVYWtLy+quq6Lw9I4dnByf4LkbN/japcvc39fLQxsD1tzbY2P83cVL1ByXxzdt4BO7dt4he7O9KrPV65hKjITeTUxrxzBUUukIirIkP4nQHvkEWoMJtjz4OaRCjyJQiKxBXf8glCIUdic2s8MfwJA6nVZLIyZZ0GG14HgulmKwNdpHf7gLSzE5nNyF47sYihZ0JTwbUzFoM1NIRCOpR3JM24shdQ6nduE2wAGB4Ce6HsZsgCsf63oYXWpMVGbottqJrQBO/BVGh2LFn4bU6A9nEOGg47DyXhINLf7tvxdV42yK7uDEwqtsi21Yc/tLEpnAxDq4f3Spsivev2L7jQG1Ps/53Ds80PohJJK+UCcbwquBxWUZ5NJ2G9e2L+gwUrQbqWWTQz/4HsJf/j7+iu3EtQhxLYLPMkDfIM6gSY39ia3NJ+eS7txopH/4zSaEj6kY9IeXn+u379t8ocw3Tlzmcw8fQK6wFPMa2xQCUlaUnzv0EH/2wgn2x7bTkYji+8tsjyVqtRSCjNW6rpH5uew1YlqYxXoeCMC4olNmojKDKXUKdomMlabbakcIQcWtUnIqSAQ1r858LUtfOENPqI3e0GpJV84u8vLMaUpulY903svR9K41F/WdZgtHUjs5tXiVrF2g7BRx7MvUnDFC2maqzjBhfReF2hlcr4BHHU2Oo6sZQtoWwKdUv4zjLuB4BeLWvVTtWygySsW+Sdw8iuPl8P0aFXu4ocEXVOwbhPTtjZSVCiF9G6qIoknJwY4Mz926wd62DiaLhbvGkn9QyvU8opa+yldrqTx8NKkwkEgxkEiRt/O8MX+LlG7i+i7DpWFsz6Y33Mvp7Gk6zQAgHK8O0xprpT/cz0hlhKuFKfoj/VwpXCGkhNgQ2oC+gu01V5tDCknYDXO9eB3f99kU2UREDcyvd6Tb2F2PcaN4g+5IjLHKGNPVaTJWhopboe7V2RbdtgL8EZTdMmUnMMJfatosLbCECPwJ5/Ml+jvv9EhaqpCmsS3VyrbUewsEuL10RaE/kaJ/DR+m91qmqrE93cb2dNu7vjeqGzzet+ld3/duJYQgYVoc6rjTXHq9Cmk6A8kUL00MEjPWTo7SpMKe1g72NFhOF0aH+e43zvLYR/eyY18vL37zXMDk+cheblyZ4Nt/f4qDxzbhex7f/ruTROMhduztYXGuyAv/cJpjj2xn576+oLOvBQDgs397gngjfCSfLTefjZfOjvCtr77DPQ9tw3M9vvXVk0RiFl19ac69M4Rdd3jkw3uYnszytS8dp7e/lVgiRK1q072hBcPU+N6zZzEtjQ89feCui+9YMsQ9D23lr//0VWYml9PfpJTsOtDHhs3tlApVnvmr483n/+ToAs988Tg79vUSihi8/sIlwlGTA8cGOPHadU4dv8mDT+5idjrH5Nj7kyn9qJTj17G9GrpiokuNhJYAAnVDXA8107xszyWhh2i9DdAo5sosTGWples4jouiSBzHpV6pIxWFnKFiRUzqNZt61SYUtZCKoJyvouoKidYYLZlgTh1SdXbGu9kU7eDJzj08N3mer468zUK9xEwtz1eG32JztJ2IZmCqGhvDrbhVF0elmd7l2C7Vcg0hIBQNAjbanQgfSu7iofZtfG/qMl8bPcnVwiQlp8bXxk7xSMcOtkQ7sNRWTCWFQKIKiOtWc97h+C6tRgTJJK6Xx/UKCBR8XIRQcLxFNNmK79dRlRYcdx7w8bwOkBt4r5Iy1/cIKwaO590VcDJVlZZwiJjxj/O4A4hpEfYlduL6LmE1hI8g3R5De5+Jau+lTswNcnZxBB9IaCF+cdPDfLRr37pm6K7v3SlV8xtyb928/eVGU0s22EFro2qWodGeihBd4Q8ohMDxPDKRKJpUcIRDrVInlo4CgpgaXKO2t/b8xHU96raLpsqAcdqcoDb+z3ExDRWBQFEkihQcTG2g1YwyU81zNT/JrdIsW6IdLNbLnFkcwfE9Wo0Yh9P9K6T9Poq0+EFIFFfWDy1w5Ho+N2YXaI2ESYdDvDU0Rls0zM3ZeQrVOkIEJtSZeJSeZIK+dJJLk9OkwiHGFvNIISjXbTakk9Qch1TYojsZo1CtMVcsc3N2nqP9PXTEIkznixRrdTQlmLhen53n5OgEYV1nc1ualkiYJzYP8OzVqyxWqrw+NMyejo47wJ6KbfPK0BCFWo3WUIgnNg3gex6TYwuBp0Tnnd2jdy8Fxy+gieSqVz3f59mr1xhcWEQRgs/s3cvn9+9rSr+avy0EoYYU6gdZpbrN2YlJDvV0ETdNFioVvnHlKo7nsaWlhS8c3E86FFplEq0KwdbWVn7h0CGuzM4yls/ztUtX2N/ZiUsdReh3mAn6BOf5lw4f5rFNA3fIHkxNXeWVJBsTqpVa5Hcr2wt0whB0QDRDJZJYW5bTEgrxi4cPMZBKUS5UuH5ykHQmRc/WDDva27ivr5fhbJaxfJ65Upm+RKK5H2O5HN++fh3X9znQleGz+/auYoAtGdDu6ejgZw/u539+8WVuLizw7WvX6U8m0Vdcb0II0iGLXzl2DzcW5hlcWOQP33yLzS0BuPGHb77FXKnExmSS//boUdrC4TuOR0xrw/XrzFSvk7XHiWsZuvSDRCImnuc3BiIVXWnB9co4XgFDbW+eF0Npp+zV0aSO+ICZQELjmvU0yvkK+VoBz/NRVUk+V8G0NDRNpSTqKIqkUrHJisAbq7Mz0ex0m8ra3YSIGgCV2m2P4JUeNppUg0RJPU672dJMHfN9n7HKEOeyb2N7dTZGtrItupcz2eNMVkexlBAHEscYqdxisjKCKjRc36U7tIG6VyNbn6fkFompCY6mHyGkhgkp4VW08vHKMGeyb2F7dTZFt7Mjtp/JyiivzT1Pf2Qr/Wxl0Z7n9bnnUYVGxS2zLbaXDjPDCzNfZ6w8RNaeZ3f8MP3hrXd8fx+4MDzFi+duYrsu927rY3Omlb9+9Qy6plIoV3l49wAtsTDPHL+IrinU6g5PHdlOJhXnr189SzxkMDSzyBP7NtPXluTrb19iarFARyLKjx/ZwcmbY5RrNg/s2MjX377IkS29REydv3ntLIam8uDOfoZnszx3+hqFco22eITFUoVP3rebkdksb14ZBh8e2tVPd0uCv/zeO5y4McZcvsTRrb0c2dLLC2euc218lpCp89ThbXQmY8RCRpMhaDsuX33zAoc2dbOpM82rl25hOx6P7b374nEg0oPne6T1AJT18NCERihqokoVz/eQIgDkdanh+E4T6PLxGYj0YMhlKv3Kqnp1ZmuL6FKlxUjcVU5QbCzsDamhSgWpZlBkBMfL4fpVXK9E3Z1GU9KoRAnpWynbN6igIYWO4+VQZISKfZOaM0LZvokq43h+lbo7G2zHKyGExHbnsbR+NKUVU+1hrvQslrahEZUbNFvawhFSZojL87NUbZv+RHLdff+glBQCt/E8frcyFIOUniJjZai5NfJOnuHSMBsjGwkrYdqMNnJ2joSeoOyWGSwNcrN4E0uxaDFamKnOcF/LfWhSY7A0iI9PX6gPQxrM1eeIqlEqbgVd6mTtLAv1BSpuhY3hjSiNpCHXd8naWYbKQ0ghmaxOogudLquLmLa8kAyivJXmhHmhWm6YoYfQZSBjzJeq2A3Jz3y1TMkOgj9c3ycTiTFWyAWm7dFEs0E1VykzWS4Ez+JwlBYr3GzMlB2b8WKeol1DEZJMOLaKoeN4HhOlPAvVMiBIGiad4SAQY75SZq5aYmMsha4ERtzjxRw+0B2J4+MznM+SMMzGdgpIIeiLJojpRsPw3WGkkKNQrxHVdXoicUxVo+LYTBTzGKpKrlalJxJnsVal7NTpiyYIaXpz/4cLWWqOQ8Kw6IkGLFjbcxnOZ0mZFrlalcValZCqsSGWwFQ16q7LaDHHc8PXubQww6mZCeKGSUI32ZRI3x1kSYR48un9RKIWds3h1PEb3PPQNt565RqmqdHRlcR1XXRD5eyJQXbs7QmMzzWVDz19gHBkeY7j+z6aplDIVYjELHYd6MNshHCcePUam3ZkePypvXi+TyFf4fhLV/jEF+7DdTzuf2wH9z++g+xCiUtnRpifKRBPhtl9cANz0znKpTrxZJiJ0YV3vU90XaWrN00svtq/SkpBz8ZWHNvlm199h227u3ngiZ0IITjz9iClYpXuDS1Bk6A1yqnjN9ixt4eTb97g/sd38OCTu8guFDl/cuhd9+FHsSwlSlRNEVJjlJxKc3QRQtAbThNRTXJ2mfHyAtl6+Q7gqFqqsTido5gro5s6qY44ubngXq2UilTLNfq2dTE7voBp6eiGRilfwak7FHJlVE1tAkdLn2sqGgPRNn4h/BAdVpzfv/xtCk6VK/kJruQnGoEZHjPD8wy9PUwsFUHVVEr5MtFUJGCMFarMTSximBrpTJJ0Z5K2njSf6jvCnkQPv372KwyV5piu5ji/OMqWaEfj2bU87x2ItvO9qcs4vsvNwgwVt05I1PB9B9+vBV6hQm008CrY7jRCGKgIXL8Mfh1NeX+SIg+fglNlsDhD2og054i3V8DafF+bXreCNLUql/LX6TBb2RnbwsC2H7ziwPd9Ti8OUW6YXe9J9vJw+/Z1ZXm+75OzK2um2q7F6obAQzio9Z9/ipQsFMp4vk9nOtb8rM3JNK2hYGxRFAWpKNg1G4Gg1czQyvrH5MbYHKNTi0TDJrlChUjYwDI0hiYWaEtGUKRkerGA78G+rRn6OlP0hls4mNrAdybOM1vLc2J+kP5IG4PFGa7lA7nyzkQXW2KdK57noqEKqa27L99P/dACR1IKEpbJjdl5NFWSClvcmlskYuroqhJMYqXA0FQSIRNLV2mNRrgxM4+pqURNg7ZohKhpoNQlUgiG5rOoUlKq10lHgiStsUK+EanqMryQJVuusr2jjZ5EnM54lLgVDHrbW9vY29nBy7eGeHHwFp/avYvu+GogaKJQ5NWhYQAOdXexuSWNY3sk0xFc9/3rnYOJoofn1XFFadX/5Ws1Tk8EngZdsRgf2baFkKY1HNTXdCBq/C3wcQl6fks0Sg/B+1v4e75HoVrDblCur87OMZLLogjBoa7MuqlrUgj2ZzrpT6U4MTbOqYkJZkol0hFBwZkkrvfcsfcD6RT39fW+p0QAVUr2tLe/6/uWKoiYLhNSl70UNENDiMqa79+STrOnowMhBIWFIk7dwWhMfKQQ9KeCzmXddSndluR2bmqKqUIRTUru7e2lPbK21lgKwb29vXTHY1yameWt0TF+eu+eO94vhGBHWxu/cvQe/reXX+Hs1BS///obCODM5CSpUIhfPnoPO9vb1jwXC/URfN9jZ/xD+PjM1QbxPJ9CvkootAx4+L5P1RkhWzlOJvaFxnHLM1n4Eonw51ioT9NidL+Ho/2jV7WqzYXzo/h+QFvPZJIUChVqdQfP9YhETey6Sy5Xobs7SThi4roeWoMe7/l16u5C855TSZLNuyiKIBY2UW5LSrq9pAho1Labpebm0GSUqudyYuFVdsb20xfehOd7TFXHmKyO8nDrRxgp3+DE4qu4vkeX1cuV/Hl2xg8wXhlCEQq2V+exth/n5dlvM1S+zo7Yvjs+N6GnOJi8l6JT4MTCK2yL7qE7tIFt0T1k7XkAHM9mrDzERzo/jePbnFx8g/7wFg4nH8CUFh/LfGZ93TaQScV46sh2JubzvHD2BhvaUlwdn+VXPnovpWqd505f48cObOXWzAL/9mMPMDg5z3Onr/HPHtjH1fEZPnpoO0/u34KqSF48fxPX8/iFJ4/wjbcv8+bVYQ4OdPHFl88wPLNIImzR15rA1DU+fHAb//D2ZQAqNZtsscpAZ5qJhTwtsTC3phYZ6EjRGotwYXiSF8/f5L/7iQf48Xt2YLseK/xtSwAAIABJREFU/+7pBwG4MjbDuaFJPnpoG+eHp/jWyat87uEDq76nrqmkoyFOXB8lk4pxYXiah3ff3eAVIKyuLYUOsXZUr3EXY8fby5Q6aSPOVHWeM4vX6At1EFXDjdSRQKZWcWtczQ/z3el30KXK9thGImoaV6oIoeL6JYTQ8PwqmhJM5A21F01pJyIsDDWD73uoMkrZvo6lb0GKEDHjEEIo+HhoMgEE4LQiIghVR1Na8PxA+qYrrehKJ/K25K3+ZJI3x0bZ39FJzDDJV6oslCu0RsJYmor8ASTH/DCVpWuYmor3HnQGilAIq2FMxSRrZyk5JWJaDE1omIrJRHWCuBYnoQXAUUyN0Wa2kdSTxLU4cS1ORA3GmS6rq8nwm63NMlefo0VvIaElUISCIhRajdYmiDlZmWS2Nst8fZ652hxhJUxMi5Gzc5iKSc7OEVLC5Gs1NEUhpqZIaC1UbIeJYp4vXzmPD2xNtpCyLBbmy6iK0mSkvDY+xFeunyeqGwzlF3kgs4HB/CJzlRK/fewJdqbbOTM7yR+dPU7FtfF9H0vV+PmdB7m3s4+65/L1wct87eYlNEUJQjEMk189+ADbU23UXZdnb13hb29cwFBUaq5DRDP4tYMPMJBI88r4Lf7m+nl+78GP0hGOYnsuf3n5NK7n82uHHsTD43dPvUYmEiNXqzJXLZOrVfm1gw9wuL2bkl3n/7lwgrcmR9EUScVxuLezl5/feYipcpFff+M5uiMxri7OsSvdjuN7XM/O88lNu/iZ7fsZL+b54wsnuJqdQxWSmuvwyc27eHpgByW7zu+8/SIJw6Ts2JRtm/lqmac2buNf7D5Cvl7lW0NX+d7oIKOFbPM77ki1sSmxNqt8qXRdIdTotseTIcrFGq7jkl0oMT2R5e1XrwLQlkkwsHWJ3SiIxKxVoBEEc5ZPfeF+XvvuJZ75q+NE4xZPf+Yo3X0tFItVOrqTgUzJ94klQoyPzDd/t7UjmG+rqmya8uZzZb78J6/guh5tHXEWZgtNIOr7Ldf1OHX8JkPXp/mZX3mMSCx4FucWyyzMFTnxWiCb0Q2VLTsDxlelWCMasxACDEPD+kemF/6wVsFeoOhkqbgF4lovju80mf3bYhk6rDg5u8xCvcSJ+UEGGobSS9WSSZLuXM1Q798V+JM6dsA8CsUsth5c9ptdmkPMji8059prlSZVjrVs5pnIO5zPjlH3HMpOnW3xDIZUiVo68QdCuK7b3KZh6dSrNq7j0rOlE6lIzJARyLVtF8PS2RbPsC/Vx1ApSJierubW9PM/mNrIl9Q3ydplhkqzXMxNcH/rlhXeOUt8pNtLYGqbVv37vZYiAuuWlBG+a3BJwIper8QqxrHru3dlL/m+32xw/lOFpQT74ZOtV5oMot5Qet1GLEDFrXMtP7lapkZwxH38O8gEAbPVJGbe+677srEjxcrzoikKneEoytJcQ0CiJYpmrJ+GvbI8z8MyNJIxi1QsRCJqYTsuiYhFLGJi2y49HUkQkGrI30xF49GOnbw5e4OsXeaN2et8qHMPb8xeo+LaqELyWMdOrBXAmo/XkESul/D2/dUPLXAkEGxtbyUTj2HpGooU9KViSJnF86tIEXQ0hZjhYG8MRc6zsUUnFbbQFLD0KomQSsUZx6POsf4WXL+KqZq0xsKEtHYszWCgJY0iBSFd58ntm5FCEDZ04pYBQjQvjIRlcqynlzeGRxhrmE3fDhydHB9nqlBAVxSO9vYSMwzsmsPiQhHP/f7gXtcrIaWOFKvpZtlKhZliACa1hEP0xuP4uBTtITzfRhEWPi6eX0cgUaSJQMHz63i+je87uH61SWOLaH2IdRYla1XFdkiHl/0HpopFCrU6hqKwIZlcvqHWKFNV6UskODE2zkQhT75Woz1qEdE619RipkIh6p7LfLlMzXWBQF6y5LVUsQNZme25RPWAtmlpGrlqEHEohcBrGGrfTtUsNYyx01bAjhJCIGQwaKxVfckEeuM7V4tVXNdrmmhDICFbGh7sFQkRvu8zlstTsutEdYPeRPyuKQhhXacnHufSzCwj2ewdINRSLfkdXZ6d489PnuLZK1ebr39s+zYeG+hf93MMGSap96BKA9/36LC2g6egKIJcvry059TdGRbKL1GyrzFT/DsA6t4cQijo0iSpt33gYmeXKhTWOXS4HykFc3NFUqnARE+IIMFRUWSQAIloeiloKzwVivUbXJ77TereIgKFbanfJlfoZ3BkFikFm/ra6M0k113s+r5LtvoOg9n/guMVaQ9/hKj1YVzfpd3swlSsRqdlkYSWIqrFaTMyXM6fI6RGiKgx4lqCqBpryEAk7WaGiBojrbeSs9em1A+VbjBWvgUCSk4Bf520j6TeQspopeZWGp2N92aQ6Ho+pwcnGJyax3Y9cuUqPj7paIi2eAQn6uG4HqVanUwyRjoawnU9TtwYpWY7hAyNzZkWIpaB7bpMLOTZkmklHjLpa0tyY3KO1L7N7Oxp4y9ePMlv/fSTGNqdzxYhBPGwSWssRM12iJg6xWqNt6+PMpUtsFioUKmvLVudL5S5OTXPd8/dwHU9BjrXNojf2dvO37x2jqGZBaTw6YyX8L0CQkbveow8dxIhIu/6vqUK/IPkKvaf79u49mWk0oqQAeAd08IcTe3iRmGUb06+zkRllm2xDUTVEI7vkrWL3CyOcTU/xKJdYF9iCw+17keRJkojXlsjjan24nqlpoeRpW1CkSaqjDSOrURXMygyiuPlMZSupofS0rE31DulNooWxfXz6EobpnonIH1pbgZLU5mvlPE8n1y+yvB8loG2FHu7O4mZH6xFW6FSY2Q2S0difR+LpVJQ2BTZhCpUImqElJ5qGPTrbI1uxfEdDBkcH8/3UIRC2giAA13q7Essg8im0kga9X22Rreyyd+EoRi0EUiyJLLpmeb7Pv2RfnpCPViKxeHUYTzfw1IsuqzgHKtCZaJY4H8/8WrTwNpSNXK1Ku9Mj3N+doqUafHjA9tJmhZ+xCcWMlYxrbK1Kv/h0EN88epZLi/O8R8PP8QfnnmTK4uzbIgl+bOL7zCQSPG5bfuQQvAHZ97gi1fOsrulg7Cmc09HDwfbukiaFvOVMv/h9e/w+sQw21NtFO0az4/c4Eh7D5/dFrBeFmoVOsLv7f4DKDl1TkyP8e8PPsimRJqK45AyLXzg7akxXhm7xa8depBN8TTn5ib53dOvs6elg55ogqlykV/cdZid6Xa+eOUsv3PvE7w1NcrZuUkqzm6euXmJwfwC/+M9j5IyQ7w4OsiXr51jX2snrVaYQr1GxbH5jXsepdUK862hqzxz8zKf3LSTFivMz2w/gKmofHf0Jv/+4IMkzRDaewBZKxWbXLZMMh1hejJLLBlG01XaOuIYpsqnf+4BdCO4DhR1eXtrTTt83yeRDvPhnzzIsUe28Rf/5wucPn6T7r4WUukIi3NFHMfFc31mp3K0ti9f84EPzuqNTo4uMnR9mn/1n36cVGuUuen8ez5X69XorVlee+EiH/r4QeLJZUZSS3uMjq4kn/zCfU1wSlEknheAXAtzRTzPp1yqUcyt3Xj8Ua+ImiBrT5PQ2zCkiSGXJY8pPczRlk1czU/i+h7fnjzHw+3b6bQSqxfs68xHNV29q9yptevd5aSe71FvME6WTLvnawX6wi2EohaR6J0JiSt9a9Yun9qKxLSwaqy5BN8QbmF3oodXZ6+Stcs8O36G3YkeEvrKz1xvzv/9LeqVRvhKmxm/6xYqdgAmr1VSCCLq8phZsKuU3Tphde1xdEkKH1XDqEJ5z8qO91tSiFXPp4pbx2d9AsaNwjQnF4bueN3xPRzPa67b3u/+qoqkpy25yvZBCkEmuvxsklKQGWhnPbnb7TXQ3cLGLr85Jw3sXmj8fOe1uLTPO+Nd9EfaOLU4xM3CNDcL01zIBmE2GSvFznjXKoDI9z1CagZVhFb5bP1j64cXOBKgqwrpyPJNJy2ffH2EqjOGEAohbSN1N4sUOqXaHKqMkAhtw3YXcbwctp+lUJ7AVDuD7qZXoOr6SCVHxGhBUzTMFRKuZGgZAEiEVnd8VSm5f0Mff3X2LKO5HC8NDvHowEBTAlas13np1i2K9TobU0nu6+1FkRJXkTi2932b5UlhocpYYDa64sRXbYdiA0xIWRaWpuF6VcrOBAIFKRRcr4aHgyGT1L0sUmi4fh1VmNheEderoeGiyjC+76450K9XuqIwlS+yvT1IBclVq3i+jyIlyXWMw5dKCEFLKDivNcelUKthez41N4e/6vYMHqdVx2GuVGaOMhXHZrpYojMapeIEtMDZUhFFSjoiUQxVoVivo0uFiWIBXSp0RCL4+ER1g+htwJHnN3xbVjygQlGLcKy65r6nLKsJxITjYcKx0uo9XnUQl193PI9ctYbvB2h14l0SgVQpSTWOUdm2m+d6rYoZBr9w6CBvj45ybmoagL0dHfzCoYPEzPXBwPnaMIYSJUQCKVQUoeHikc2WG9TOAAJThBkY3jpGc1Ea0vqJ6HuwCYyQP0h1bmKKfLVGZzxKfyqJMARz1TKjWoGy7aBLFUUEumPX9ZEE19BCqUTSCFGu2uhSoTeSwPfrVJ0p6t48IPH8GooUOK5HxDAYGpsnGjZIJ9dmn7l+hfHCV1isngDA8YoM6PsRCApOlogaxcMjokYYLQ9SdSvknSyGYqKKwDdJIFly/fHxWKwvUPOqFJw8HeadC3fP97iQe4f7Wh5HESqTlbF1j5UilKbpclA+ilBxfQfHt1FQ1+xK1WyHVy8O8rOPHSJfqTE+F/hM5MpVcuUgwUIIgampzBfKFCs15vIlTE1rhh+oDR8uRUrS0TDT2QJV22E2XyIRtlgsVrg8NsP+/i5O3hyjuyW+JngkAySweV8vFiucH57kX374GBdHpjlxfRQIEuhcz6NmO2iqQtQy2NnTzucfOYilB/5xuqpQvo0V3BIL05GI8NL5QTZ1xgmpl3HsSRR1ACFieN4cQpgIEcH3FkAoCJnAc+eQqgF+Dc+dBnykTON72YAiL0L4Xi44tzKGU38LKTtR1AF8bzHoNinteN40vl9A1QMavCZVPtR5DFWqfGfqTc5kr/Hm/HmcBltVkyohxSSuRXi84x4+3HGMFmNtSZgiw8TMw+teHwKBKmOocsUC8F3GmUDWHCdi7Fnz/yO6wbnpKXK1Go/19VOSdQq1Ggul8vvI8PnRqWTE4p4tvSTD1rtOeoUQGCIY4xShoK0wrF0Cgm6viIzc9T1CCELq2pH0K99jKRaWEoxrKz9XY/lnz/cZL+S5ujhHzXVwPa+RvKqzPd3GP991kCc3bA7SBBWFfLmKs4Kt3R6KsjGeYmMsiSYl3ZE4HeEo+XqNwdwC13Pz3Jvpa0QvQ08kwdcHLzNdLrI50UJPNM5MucR8pYzjuYQ1jcVqpclO2pps4Y3JYbojMfa3ZRiIp1b4Rbx7+T7sSXdwuL2bmpdHUYroiknVdXhjcpiEaaErChOlPJqiEtF03p4eo6chZxuIBzK4TCTKxliSsWKOwdwCs5USb0+P0RWJU3ddpkqFgF1k21yYn+aR7n40qXBfpo+dqYBhfKCti68NXmahVqE9HCWmG1iq1kh9M5sx0u9W+cUyf/df3yCeCHPunVt85JOHsEIG9z++gy/98ct89S9fJ90WI58t8/CP7aZ7w/oJq9VynWf/9gRSShRVUqnUaWmAQw88sYu/+fNX+fKfvILneYwMzvK5f/HoXfctGrfQTY3Xv3cJRZWM3Jplw+aAcZ5bLDF4dYqbVybJL5Y48dp1tuzM0NIeY3Yqx7WL40yOLaBqCum2GP1bO9A0hb/7f9+kVrWZHl9kdipHS3uM7Xt6OHhsgGsXx/mbP32Nju4k+WyZex7cwsDWTu57bDvP/NVxcoslKuU6hfwHa060VBW3iO3VyNvzbAx3sy22LEPXpMJTXft5beYqN4szXM6N839c/iZf6H+ArbFOzNskRp7vU3SqzDY8W/Yke+kO3QkOTVWynFwYajKaQoq+pr9syanx7clzjDSYQS1GhO3xDCW7xkK9REIPoTSA7ppr8+L0ZbpDKTJWgoQeWnue4tqcXLjFqYVASaJLlW2xtSVICT3MJ/oOc7UwyUw1z0vTl4mqJj/Tfz8dVnyVDQEEIRUFp8pkJctgYYb727aQ0N97cq3vB6BYwani+nePdO9JxElYFsYaPnkSQSaUJKTolN06t4qznJy/xX2tW9YkAKgNuR1AwS69KzvJ9VfLrJeCmO7WOIdgXrYh0tqQGrqczY4wUlpg2yopVnAcB4uz/NG1Fxgv39kIVYVEERLHWx90WvKeXOmPFAQ7NHwupeBu4J4QYs10Rt/3V8nkfAImlabeCbit/Od6xzNtRHm0YwdnsyMUnSrPTpxhvLKIFIIDqQ10h1KrftfHwfGKSLVzze19v/VDCxytVYo0MdUMmkyhCB1daaGuLCLRCGk9BN1WiallcL0KigxjKG0IoaCKMIoMoYgQ4KHIu0+E1qqNqSQHuzKM5HK8PTbGeC7P5pagWzeSzXJqfAIfONLdTXc8GAwVRdLaHqNSfv/AkRACiUbNmUJXVps/er6P17gRlkAPKQzarPtWvGulAa2PbBjXLRm5AsimRE2sqwNdq0xNZWtbC5a23HEMtgLvxVx/5cXt+T4eLjU3j+NVMeTqzmrKsoibJoaiUHNdOqNRIrpB3XFwfZ++RBxVSkxVw3YDICqs63RGo6hSYqjqqv1bWVFdRyosm6yKQIddLa2tCdWU5RveCOssTufo2vTuN+USXXJpH94LSCdvO0Z3q2Ktjr1igm17LsV6nfaVzrxr7NXV/PcwZJie8AGSetDdV1UFTVdYcmxTlTjp0IeIGvuxtD7wvcY2JXWnSNnN43h1UP7p4tr//6zhxSyu51GxbfpTSUaLOa5lZ+kKx1msVRgv5QmrOu2hCLfyC1Rcm43RFHXX5Vp2jrCm02FF6YmsbRoeDZs8cnQLvh944JjG+v5jvu/h+ssgpo9LSAmxPbaHkwuvo0uLdr2HTdHtaOIaL858A8/32Js4wnhlGEWoGIqB0mCHeb7LVHWM56e+huu79FqbuFW8zsXsaabKE7wy/Tw7YvvoMHo5ufAmISWMIU1cPC7lTnMhf4qqWyGsRumy+jCkCrhAAJb51EnqCVQBz09/ld3xQ3RZG1hKsEAE8lhNFWxsj/EPb1/AMjRCjUG37rh84+3LlGt1jmzpIWIZZEsVvvrGBYrVGk/s20zI0Akb+nLMOHBkcw9fef0c/+XZNwD4xL27ee3SEKlIiMf3beavXz3LheEpXM/n9Uu3uD4xx5dePkNPaxxVkc1tKVISDxl0peL8/fGLAIQaxvut8TDJiMWfvfAOx7b1saWrhXNDk3zxpVPomsrDu/qJhUyeP3ON0bkszxy/yBP7NjPQkWZ7TztfevUMnzi2DcEVPOcmnnMdIdPg1/FxkLIdz7mGoh9ECgOnfhxV3IdUunHtc3jOIIp+EM8dC4w2/Sq+X0aIMFLtw7OvIFQXX+nAda7hOjfRzMeRMgm+Q8DUdABJWIGnOg9wLLWFkcoMc7U56h5IHCw1TIseIWO1ktQsXOcGvrcBX0Qa50/i+3WEkLjuFIqSaVyrDq47iZQhpGzF82bxvQqKugHfryKEgbiNUer5Do4XsAQ0GX9PXmn9iSSbk2kiuk7SsAhJjZhlkLAsspUqpqY1QcUPQhmKwoXL07i9aTKZBN2ROP/94QdwPJeNsfdu6uz7fpMl+V66ri+/dJkbN2bYuLGVRx/b0Xz9jdevcfXqFJ2dCX7sw6vBvdnZPJcujvPAg9sak+3VlYlE+Z0HnmAot0ihXqPmuhiKQlsoMNluDYVX+KZAeypK2FyWJxgN7zApBLqiNuYbgX9Rvl6jUK/x1RsX+I62xKry2ZRIoUuFQr3GX187x1vTY8R1A0NRGSvk2NMSjN+movKFHQfpisT41vA1vnTtHI/1DPD5bfuIrJOudvu4rEoZeCZJQc0pMVY6R3/0KJ4XZrFW4Xp2jj8480bgc0IQitIRCoA7TUqkkI1Ou4JcAWbXXIdsrcJEKc9UKYiW9/HpisSapq+aIkkYZvPcKkI0/FTufp7frdo64+w51s+tW9M8/FP76NrdTtGpkeiK8vH/5n6unBtlsVDC6DVxwjBZzpPsjvLEx/av2o7v+2i6yuYdXYwPzyMk/OTn72XTtgye59G3qY3P/8tHuXRmBN/zeeCJXfT2t+LYDk8+vZ/OroYk1tT50NMHaO2IE0+G+cwvPcSta9NEoiY//2+eoF4LpCrlUo3pySwbNrfh+7AwXyCfq5BsjZLNlliYL3LkwS1IIZmezJLpTaNpKgeODZDPlikVgzlgOGqC75Nui/Ezv/wop4/f5P/j7r2D7LruO8/Puefml9/rnNHIiQAJgiBBSqQoiZIcJctp5PI4jKzxlqvs9VZt1c7O1m7N1s7UbJqxx7s7ssdre+RReWyPR5aVrUBSgRSYABAkiJwa6Nz9crjx7B/39etudAOkbO+u5B8K1d33vXfeuencc36/b2g2Okzu7E/ocwIeeHgHhqlz59YK+w6P8cDDUwyNFv5GCIfv51Ddf42wTCVYJG+syyAIIdiR7ufju57idy7+NbPtCs8vXOCt6iyPlKbZmRkkY9golejyLHfq3Gwuc6W+QDP0+FfHfm7bxNGyV+d3Lvw1uqaxPzfKVKqfiVSRvJnC0CSdKGCuXeFs+SbfWbpMO0rmIh8aOUKflWG5U6cRdZiISxjdx0IQR/zJjRe53Vpld2aQvdkRxt0iJSuNo5uEccyyV+dcZYbvLl1hoVNFCsGjfbvYl9t+vq8JwYnSLn5u6iS/d+VZmqHHZ2Ze4fXKDEcKE0yl+nB1i1BF1Pw2850KM81VLtXnyRoORwoT31PiCBK3rfgeaPCNYen6FnOctRBCsCszyO7MEGcrt6gELX77wle4WJtjb24YU0tou7WgTSvyeKQ0zVRqjNmOQX6DZl0nCji1fIWFTo126NOMPNqhT8VvMd+uAolo+F/MvMyrqzdwpImrmzjSwJUWhwvj7Mpslhk52bebL945w2y7wvXGEv/b+S/wk5OPMJ0eQEOw5NU5U77JcwtvcaOxxN7sMHdaq9TD9TmzFIL9xf5kNdO9Vt+q3uF8dZZ25NMKPVqRTycKOL16o3c8z5Vn+FcXvkRat3GliaObONJkzC1yrDi1RWtptlXmdPkmVb9FK/JpRz7t0E/c/rqItRWvwScvf52SmcaWBq5u4UiTnOFwsn83OfP+eQldaDzav4uhmznmWhVeWLpMPeiQ0W2eGty/JdEnhUUnWialJvm7VDn+vkoc1Vod/DCiL7v9zaMJE9eY2rRNl/eAb28z/1x7WK/9/nYRRgldwu7CJ00peWp6B5+7cJGFRoPXZmd7iaNXbt9hpdXqvWcjgqXV9La1d38nkUDzVGJJvGG7IWVvIKh7flIJj9tUghU0oSFJRCcNYVAJyggEGSNHGCcXcKRCYmJcmSaj59A1g+9lhuGHETOVKlPFQkLv6zqdRUrR8O6fJFNK0fCSh7ImBCnDxNYy2DKPF9dJsdmdxNENpgs5FBuPoehaHQoE1oYFh8FQOlncJBaLqqvflGSMvXDzeYiUIoxjoq4tI4DfCZKr5D6XiBCCOIyxXQu5TRb/7tA1jVQXnRbGMc170F/WIt5wjPS3ETevdjr80WunubS8jK3rKODi8gqfeu0M/9UTJ3uueXfHVPoRgtgjcZRav4+CINwy6ZeaSxhUmKt/OxH5Ayw5imM9gSuz/69ynf+/juFMhtlajX2D/YnoYzrPsJvBkjqRijlYGOrBaHdmS0RKYUu9V12RWuKftt3lc+POClZcQQC35ys89eiensPXdiE1m5LzOJXOa8QqoGifwDXG2Gs4jDiT1Dotrs3UubBcZjJ3lEK+iqX3kzb6GbRG0DWDAXsES7MoWv28svptdmcOUgomiTuS1dmAZqiTae7i0dQB4lWo+xGj0WFSfgVdGRwePImlWWSNImPOLjQhGLBGKZlFHs6PEUc3IZzlWCZLGF5AEzbHsn14cUBaNGgHF4jiJkp56FoeP1rE1nfwoYcl9XYWxwRT30kYCQbzGT5wbA9py6KUdbmxsMrO4RIfOraXlG3Sl00qg7/w9DFybrJg8oKQlWqTfcP9NDoepq6zWG7g6jr9/SluL1Q4uXcS3wvxwohjO8cYK+QwdEnKNNkz1MdgLk2j4dGfS2NIjYd2jtHpUtfWIm2bfOzJB6m1PPIpG9cy+YnHDlNuJNXlUtZFAO99YDdPHtqJJqCYdgnjmE4QcmBsgGLGhdBFNw4QBueIo9sY9jNEwZuouIKmTyONxBFIk8MJWkyYCGFi2M8QR3eQ+h6UahP7ryKNIyAMIETTJ5HGIZRqEUeLoNoJOqkXAUFwHqkN4PkvJvx+YXM4lUK5GpqWJ44WUdRR8QwqeoOQccLgPEJIgriMQEeIFFG8iK5P43Wex3bei1IBQfAmqAiQSH0CTSsSx4uo0Cfwz6IbuzHNY5uu72ZwlYsr/xyBzsH+/wlbf3uRzeuVMidHE3fNlu/z6q07hFHc0514fOckA9ntEXw/iKEUNFvrc4h+N8XP7tsejXW/CIKI06dvcuzYFPo7eGbt2TvMpUvzvPnm7U2Jo527Brl5c4Vzr89sSRyFYUyj4XWfpVtHQFPqPRe2twsFBGFMEMX0nmBi049NkbNs8pbDJw49wtH+9cWdJgR5y+blhTt85up5fu2BR3lkaAxNCG7Xq733CSHImRYf2XmQd43u4MzSHL91+jvsL/bz1Ng0mqYRxOs16UgpVjotitbmyb7o/jOERUyEQCYJJdvlSN8Q/+3x92yiYDi6wWw3GXSvfbN1naLtciJf4lcOrSP8hBCkDZN2GHT/vv8xXU9ivPO5niY10nvSTO0wmGmWudUu06ovYGgS09Zxj2WptwUhsCraXJ5Hl4raAAAgAElEQVS/xXiqwKMnd9EIO1T8BjkjhbfsUau0sE3J0YcnaTc9dEOyNFtG0wSdto9pGTx0YhqvHVCvNCkv1yiUMjzyxJ5ef0xL55F3rf+958Aoew5sRs56UUgnpzj4zDReFBLGcQ9ZeqdThVGD41P7aYU+pqYnibBMClPqPPXBw/c8FoYuGSilyO0ZRCkoz1dZna3gd3wyrsXhI+N0mh6aZuD+LbWWvh/DlZkkWYQgpW81+9GE4MnB/URK8ftXnuNGc4mFTpXP3zmNqenoXdOQMI7w46i3HnPvo12DStyyFts1ZtsVdKFhSxNTS4q4sVJ0ugt/RYIKet/QQX5kNEFmN0KPVuhxu7XKjnR/rw+xUpT9Ji+tXOOV1evYmoEpEzS5UqqnkRSj0BAcLUzxizvfRc5w75kMNDXJR8YfphX5/OdbL7Pk1blcn+dKfQFL6uhdTaJQRT1KHUBGf+cyIWuRAAsSwf1k3//mVKSimeYjEw8z01ph1W9yvbnEH1x9Hle3elIfYRzh6CYDVpYd6QF2pMY3tdEIO3zq2ne4WJslUjGhionVZo+zSClOLV/l1PJVNBIpGCk0dCH51d1Pb0kc7ckO8eHxh/nDq9+kHfmcKd/kamOxR6Pzo5Ba2CaKYx7p28kv73w3v3v5G5soa+tOnuvx/MIFPn3jBaI46Wek4ru4LnCrtcKt1gqChBK49v9YcQf7ssNbEkcXarP8X5e+RtlvEnXbvLuw0Ag7fHn2dbirzZKVZjoz8LaJIyEEE26JQ7kx7rTKlP1ErmZnZpC92eFtz38YN7saq3938X2TOAqjmKVqEyk1cimbcqNNGMUIARnHotnxCaOYXMrGkJJqq0MQRpQybk8jI2WZWIYkihWOqdPo+NimQbnRSrQdHIta2yOOE5hcMeNSb3t0/IBixiUII+ptD00TlNIuVxdWuTa/wok9E5QyifPG0eFhdhaLXFxe5qtXrvCRA/tRwNevXkuEHfv6ODw4tGlgkVKjdQ/NnLcL0QXf312pzdoWfSmX2XqdxWaDhUYDzawx07qKJR2cLqLK0ExqQQUhkmSRFJKYmDBOqCCNoIqbSqN/j/nIWClqnke53SZrW4xkM6Qti3YQcKNSuS8UMYxjrpcTSOFgOk3WtvDjBjERttwepdEOLhLGFaK4kQizomPrEygUYbxKGFdARWiai64lybwgmsc2pomVj2vsQWpbk4y6phFEEYG2PpEWAhrl5tvm0eIoptPyesiv+4UmBOP5HK5h0A4CZiqV+1aj2kHAzUqy6BvNZkmb2z9YgyjiT8+9wV+eP4/UNH7xoaTK9wevvsZn33qLsVyWXzr2UM/laWPMtd/iTusssYrYl3sfA/ZuOp2AOFabtAoAvHCOpeYXyNsnexomupZDEzolaxhb+/uBNgLoT7vcrlaZKVcYz+ewtBDJEiiBVAGGTAMRAhNbayeDtZDEqtl1tRDEcQMld25pe6XcZKKoIaWG3j3GkYq7VtRJRc+PIkwpu5V0nYHUj5MyDqAIcY0dxCqhSxbMEjZZbqmQpWqToUI/Jb0J0ato8kEcmUB6za6miaGZpPUsKT2DWnGpVlu0mis4jkncdrCNDGEYE3RgeblNx9OwTAFZk5YekLMKuHoaDY28WcLQTDJ6nlglBgOu1FGqTRg3cKVMvsvcQSt4C6V8pJbrJsJjFB62WSfrjuKFM+ScQ6zUPIYLGYYLWTJO0mfL0BkuZBkpJVp3a1HaUFyIY0UYJQuDvOvQ7Ph0vBBT1zH1xP3o0NQQ5Xqb1XqLtheQtkx2j/fT9gKiKCaXchjIp7EMScsLKGVc5DaolXzKIZ9aT8SmbHMTIgJgrG99Qu0FIV985QK3lir82CMHMHSdMM6BcNC0ImgDhN53EVoGTZ8EFSST4WiJOLyGUm2kahIFbwAKTY4R+q8jhIk0DiO0LKxZyWoQ+i+j6TtAtRBaHgiJgsugPIQcIY6WUKpDGN7AsT+YJHtEA9M8QhheR8oROt6zSDkKcRVNy2AY+xGYCOESx6ugWuj6LnR9klBeRIgMYXgOpTyUamAaDxJGdxDCQcVNwricJLfudslVipr3BlXvDSzZR6zeWXGl6nl86o0z2FLnibFJ0pbF7XKV1WaLqVJhqx3vD3D4fsjcXIWpqX4Gh3K950W5+3xqtz2iSFEspkilknum0eiwuppMKDMZh3zepdHocPHiHF/50uvkcg6uazE0lEPXJdVqi1q1nQhxFlNkupp9w8N5hkcK3Lq5vKlPg4M5RkcLzM1VNm1fWWnQbvscfXCyd+8Efkil0kKTGvV6G8cxKZXS6LokCCKWl+u0W36y4DMlw8P5XlLLkBqmLt/xUmgqW+BQaZBvzFxlOJUhb9nUfY9OFJK3hgniCEWijehFEW+tLnKzXuFAKVmsNAKfc0vz9LkutjQS8VMh6HQdV0dSGZbbTU7Nz/DQwAhvLC9wbnmeJ0e3F7vXhEGftQNDs5BC58nRaf7XV7/J6aVZDpcGiYGFZp2dbyNODZAzbd41OsVXb17hSmWF8UyOII5ZaDV4oO+dOzH1O4k4+VurS+zIhlhSp9+997PbdgwGR/KMpwoIYGemDyEE7TDAlBJHGrSjgFbooyFwdJMhN0vGsBHAm9WbVP0mtjQZWc2xulDDawc0aw7tpoebtonjmMALqFVaTO4ZJopiGtU2jWqLdsun2H/3vG3d6OVe20IVcbOxSicKWPVaIMCVSWJgVyZB7696TVa8JmEcY2iSnZnSPdtXKiKKV2jUoLJUIwwiAj9k7sZS4nSpS9J5FxTUKy1SWYfBifu71f0gRjOqsuzdxtVzDAtr2/2zpcEHRw6zKzPAX95+jZeXr7Lk1WmFPn7oAUnRLa1bZAyHcbfIEwN7tiQN1mLELfAzU4/y7Px5lrx6L0nUDD0Uqqfz02dlmEz18YGRB3jv0AHyZopIxezPjuCrEEtbp1jZ0uCjE8ex7xjMtss0gg5eHNLpijFrJKi/vOlSsjI8NbifD48fY9DO3fecJolcm1+afjfHijv4q9uv8drqDWpBm3bk01HBetuGS8aw2Z0Z4v3Dh+izMkRxsuZNrOKTRNCIU8CRJrY0MDSdMI57NKogDilYqU2CyHnTYVdmED8OGHOLPfHy+4UmBB8YfgBXmvzZzVNcbSzSDD0qfgtBQtO3pUHedLdQDtdCCo0+O00j/N6dTgWC9DbJM1e3+AdTj5E1HP7i1kssdGo0Q49a0EYKDUcajDgFHu/fw8emHqPPzvDU4AFqQZtxt9hLEt4dGcNmxCnwvSTQ16JopbctlNvSZNDObdKLeqeRNVyMd6gTq2uSD44c4dmF8/hxhC403j2wj5K1fbFMoKG6TKO/q/i+SRwFUcTZG3OkbAPXMvjCKxdodDxcy2CslOPq3AqjfQklaedQiW+/dZ3J/iKHJgd56fJMIpLsBewfH+TG4ipHpoY5c32OkWKWi3eW8MOQqYECV+dXafsBKdvk8MQQF2eXkEIwVMhSabbpBCGtjs+Th6a5sbDKuZvzjPXlKHVF1YYyGY6PjXJ1dZVLy8tcK5dRCq6trmJoGifGx+hPrWcNVazI5JwtC/F3HhoF513dk75+4ouOw65SiTcWFpmrNzh1+zbv2z3OA/lHMIS1qfK0UYx0Y4RxgBe3MTSz9/oa1C0Rd753sksIgSV1bpUrTBby7CqVGM6kubS8wrn5eVZbLUru1sy8UoqrK6tcLyeTzr19fZRcFy9epRUuo6FvW3KTWgYQSC2TVLejleR3ElceQw4gkAgh0USaMFpG6GMYcggvvAX3UL8wpeSRkfEufUygYoVpGziZ+1cAlFKUF6sUhwv3dXrYGAcHBuhLudyqVDk9N0e145F3tn6PUorzi0vcriYUjkNDg+S20SqKleLV2Vn++PQZGr7PE1OT/OKxh1AK3lxc5Ns3bvLHp89wdHiYh8dGtyTymuEyfdZ091wnqAnXNTnx6K6eUHivT4SYcoCcfaJH8xQIakGFW62LuJkcrvb3o8rfDkNyjk0plUyo/egWzc63MfVxpMgSxRWC6DaWPo0XXiGOmyAkhjZIFJeJlYemuZj6ji1tHzs0Qcbop9nyGBnIYZo6N+qrVP0OFb/dq173KKVAxW8jRYZIxZiyiqu36bfSjKSyKKWwDZ2BkT7G+nIIVUdo+0GkWKMaboyj+UeT85rqUlPVmjCf6o4ZCY11x47+7mvr/O562CGjZ+mz+snqyWQ+ZR7uVWrCaBlN2GhaGrpC3KCRsR7ufnvSlzWRbtvYTRiVkWYGgU4pq/MPnz6GuQENMd6f52eeeADjPggJxzI4PD1Eo+3jBckizzL0XkInGdcElqkzWMz0+OxSS2gcqosSy6VslqoNhkrZbWk220UUJ4gITYC5DRTc1CUfeHAvoDC7tF7dfATQ0ORo95gk9LEEJttFxGolrPSvdF+XSONA9z06pr6zt33jcU0+GwI60ti/4bOHuq9LpOoQR/NY1gk02YfBQRCCOFpB16eJojks64kk6aM6SDmE0oqJtlJ4ASnH0bQcQmQAHV3fRRxX0bQCQrhAQlc0jAMJnU1Lo8sRougO8i40Uaw61L3zxOp70wN5eGiEV+ZnyVkWI5kM/Y7Lrv4SuhTUOt4mrcIf9Oh0Ak599wrf+uYlPv6JpzhyZAKAL33hLNdvLFEopKjV2gwN5fjpnzmB54X8+Z+dwvNClFJMTvbxwz9ylNk7Zb774hVu3lzm+ecuUCimeOaZw9i2wVe+fI6lpTqNRodSMc0//MUnsLfRa3i7mJlZ4fln32K13OSf/Y8fBWBpqc7vfvIbFEtphBDU621+6IePcuTIOKe+e5XXXr2O1DVOn77Je95zgI/8xLFe4ihlmxzZOYLs3ou6liQqBCJBXHepapbUkySGrvOrh0/w7958mX/x8nPJfS8ET47u4IG+YQ6VBnl8eILfPfcSWdNi0E1zYmi8hwT2opDP37jAxfIyhpYgAx4dnuCRwTGUitib7+NDU3v5w/Ov8meXzzHkpjkxPNHTDRGAIxVmN2kWq5CKf4eiOYGuWzw8OMo/2HuE/3T5DT594QxCCEq2y28++HiSeDcS+q0UGo5u9Chrtq4jheAndh4kimP+z9dPJWYFQrA338f+Yj8CgaMbGBsWSlJouN121uKh/hGODY7y22deIGUYPDo0wX/xwIl7ntMdu4f45V9/P6aud7WukjG1YCb3mBCClGFti1YXQpA3Uix3aqR0jR37RpnaO7JprSYEvTEYEnQT0LVn94jCzdotSimC8BpCWOj6aJeKp/CCc+hyBF0m+kquNHl8cLqrA9NEE4KMYaN39SwV3e/dIB+wtsCO4ip+cA3berC32FKqTa35F+QHPszx9x/u9Wli7zC5YjpxguvuC0ohNNHbl79PkTP6sdL3RtyshSY0dmeG+I29z7A0VedSbY5bzVUW2nUyhoUrTYpWmn4riylMDhSG6IQhzcCnEXg0g4DxTI6K1yGlm/z46MM81X+AJb/G6ZXbeLFPpGJsXSLQcDST/flh+s0cWdMhayTzKCk0ht2txWgpNH5k9ChPDx3gTqvM7dYKq16T1WaT27OrDA3ksJXBgeERhvQ8BT1Fa9ln2aqTyznMzVbIZB3SaYt2O8CxDfwgRO8KMAdBxNHcBMVWip+deJSZ9gqf+v0vcvvmAlLTSOs2I+kMP/yBR3nvkYexpE7N7/Dy8gx5c60w6NDwA35+6klqQQddaMw0qsw0askx1A0Mmegwrc3lgijiqDvFg0d2EMUxfhDhaCblWgvXNgiimDCMSTsmtZZHxrVodfwEvaQJjrhTHDw0xpcvvklgB3RiH9+PGMhm6LMzjDtFRjM5GmEDS7Mp+2VKVpFO1MHQ4L879GPUwyaOtFnoLJA3CliaSSfu4EqXZtQio6cJ4gAv9nF1h0bQJK2n8OI2zbBFqqunp5TifO0iI84QPzH+MI/17eJSfZ7ZVhkvDjE1Sb+dZU9miFG3iKXpLHpL7Mtn+PD4J9AQW1BBa/FTEyf48PjDm7ZtnAPf99pGsOQvUQ/LjDkjvXvhkdI0RwoTm9o7XTnH7fYsD+UfYMwd6c49oy4LRtGb7wkNWxr3EGiPSZzP1x3bxt0iBTPNQqdKv53leGl62wRhYmqjoQn9b4VIuzu+bxJHjmmwY7DAcq2FUopC2iafsrENnWqrQ9qxeGT3BM+eu0InCBkqZHnq0DRNz6fZ8fnQQ/v42tnLGFIjCGNevnKbib48d1arLNeaFNIOnSBiIJcmiCJsQ+fK3DLX5lfZNVwijhOq0rGdo1ydW6HtB+weSaxiH5hcF+PSNY33TE/zhYuXWGg0OTs3j0Kx2GxSdByemt6xiWcYxTG+F97XLeB+IYRAbHOaDCl5386dPH/9OiutNv/h9BnGszkeGh3ZivQRyUXsRxFGF8kAIKXE2iCGKYCsZfZQONdXy/dEDkkhyDs2O0sJJ3kwneapHdNcXl7hjYVFvnb1Kh89eHAL0qXa8fiLN99kvtEgY5k8vXOajGVRD6wulXBrskqh0BhB68nuxFh6iB/KbgJLJszr7kK35QWk7aHuwjBGN7IIsX1yJ8mmb3AC0QRO2iZXyrwt7HvNee2dThAmCwUen5zkVuV1Ts3c5ls3bvDD+/ZuOb4rrTZ/fu4NVtttCo7De3bs2Jaqttho8MlTLzFXr9OfSvGPjx+nP5VCAZ84fpzLyyvM1ut88qWX+Oe59zOc3ewOUzCTQW62dY6imcBOhRCbXMHWQooULf8Styq/haWPItCw9GF04+EuWubtUVc/CBFEEZbUGclm8bu0RlOOobs/3nU2TPbV1Hckzo5aroemEcImWbgLIEYIe0s9I4pjXjt3i9mFKlPjJfbvSqrFpiZphUnlPWdYrHgtbKkniyJN4qsosRNFkNJNIhVT8Tu4wkBKDT+M0DSFCqqouIKwdmw7uVtzQUJuy+btxXavaUJ2K2EbJ/N671FkyEGSxNPd37v5/uhpmCAxZB8JrTR5pN0tXi017b4OjdAVU5aSXGr9eK8lgje19TY3tBAag4XMFtvW+8WbtxeYLdcoplwe2TW+5XUhRI/qvB5rf2t3/b0eihgv6mDJTNdpcuP9f78zp9/1c/Pvur4b9N3E0XWi8Cog0fVDCD1xC9T1ybWeb2l5nWa2/ppuHNj0nuS4ba3YSznB3RHEVWr++fvsy/ZxdnGe/aU+5hsNrpZXWVxtcPb2PP1pl488ePCeWg4/iJHJ2Hzoh45w88Yy8QYNu2azg2Xp/NRPPUKl0uJT//7brK42CcOImzdW+PBHjjG9cwBdJujGXbsHCcKISqXFx37uJJalYxgSpeD9zxzCNCW1WodP/tuvs7RUY3z87VEwd8cDD0wghOCzn3m1ty1WipWVBu9/5hBHjk7wV589zenXbrBnzxCvvHKNEyd2snffCJ1OwO49Qz3bd1i7r9cW7op3jUxytH8YW9f5kR37eu6pv7D/QXRNQxcaU9k8/83D72al3SaIo4TiZTlIIShYDr9+9CSL7QZK0XM7W5us5y2b33zwcSpeIjZraJIBJ0XKiImCc2TMA/zakRP89O5DhHFMwU4WeMGaY0/c4TcPrJDJJDqTqpu4X3s2mlLykV0HeGJ0kobvowlB1rQo2i6RivkXJ59hOJWmz3EZz+Qp2A5PjU1ztL+AF98iYwzxCwce4kNTe2iFIYamUbAcsqZNpCL+6fEnyZrrSdPxjMM/feQhprIJAqARzGLqgv/ywcdYarWIlHpbgWwpNbyWR73l4W4opoku7UtFEXGsCPyQKIqxHRPd1BEC/CggpdvsygzTCDtomkB8D5T2dFbiBddodsrochBTnyYIb1Ft/kc0LYdtHsUxj+KHt6g0PoVlHsIy9uOYR4niGko1iaJl8lLHNg8SRssEwXUiYWIae9C1LHHcxAsuEasGhj6BFFnq7S/S9l8mE1ewzQNIrQ8vuIiujyFlGk2AH17FkCP0jxQIo0Vi1cGQw3jBZeK4jCF3ojHA2y1Cf9CiEzWp+IuEyiOdvj+yJJmbG4y5RcbcImeX51ixWri6gdlNcDqGwa1aBS8b8srSbaYyBTpRSM33yFk2pxZuUbRcmoFPqGL67RRj1gDj6TxhHDGcynKlukIr8MlqGd5cWaJou/QN3x8BnxgwSLKaQzbnsD83QhhEnDs3w1LUz+H+CW7fXmV/aoTr15ZoRB3anYB2y2N65yC3bi4zNd3PndurtFs+jmtye2aVdMYmDCPCIOb4iWnKC00ODYyzPz/C63MvY52t43d8qsurzGplWqMHcN+XrE0ilSCdmoEHQnT1IwWONHsL/kjF6F2kjRQaRdNlTyZBOQEslhtcurVIJmUzXMrSbPnc9qucuzrL5FARQ5dcnlni+IEJzl+f5/DOYWYWKixXm2RTNs22z96JfoaDIg/uSDRPV2stcimb2eUqRIJr2hX82ONw7hDL3jKWNHmrdgEpJAWzQKxiCuYY5WCZPitPOVjGjwNKVp6bretkdYdFb460niJSkuvNK+xMT9OO2miC9cQRilW/Qp9VQtck46kS46n7P5cWvWUu1i9xrHBvuikklGlzw7woUhGLnSVKVglTu3/RJBFib25Jwuia3IRwUkqxJzPFteZVKsEqu+U4nXCJRnCLlDHWpZCtmxDFsYkQkliFSGERE3bXViFh3KJgHWRtPLnWWGTVawBwIDfKjnT/tnNWISSmzKL9nSocfR8ljirNNmeuz9H0PAZyKTJOAnU1dEkQRdxcLPPNN68xWsqRdayew0jathjKZ/jqmUtYhs7kQIG2H3Lq0k0++NBe+rIpPD/EMnTGSjlW6k3CKMbUJQP5NI5lEMWKsb48ermObRqkHQtT10nbFuV6i5cuz3Biz/rE9+DgAA8MDfL89Rs8e+16wueMY46NjrCvf7OItdQSu87QSzjoa9XuKI67/EpF3fN7iu9hFFP3PBzDQHYRQLqmbbsQOjk5wUcPHuSPXjvN2bl5/usvf5mPHDjAyYkJim5yfFp+wO1qlTNzc1i6zi8de2hb9Ard9ncUigxnMsxUq3z2/Fvs6evjyNAQutSS6nocd105TKZLBfrTyQCtCcHPPnCYV+/c4fTcHP/6Oy+w0Gjy3p3TFJzEMnyu3uBPX3+dL1++Akrxwd27eWb3rmTBolk4stij5m2McqPN+dlFbq8mDkLFtEO11aGQcmj7AZqm4YchfhgRK4Wl65zcPYFrmSg0ImUQRTGhCmn56yLSkYqpez4Nz0dq63zb4lCe0vDbwy1N28DvUrveSTi6zs8fPcqZ2TkuLC3xL7/5TRaaDR6fmCRnW0Sx4natxqfPnOFrV6+iCcGP79/PoxPjW859tdPh9195le/emiFjWfyjhx/i+FjC8xfA8bFR/tHDx/idF7/LCzdv8X+/8gq/fvKxTS5rBXOMWrDAgL2bjHF/zQmppRjMfBS1wW5dl3kCFZI1iuji+2Yo+VtFGMfMVCromqTl+xwGNC2FxtaJiFIxQsREqkYY14EGUtjoWgYp3F4yZPOHkh+NVoflciOB/2f7EjvrbAldaBhSX/uC3sfWUEF3hxdEGFKj3vYAHaHvhHjpbfczVgFh3CCKW8QErCGEhDCQwkKKNJpYdy+JVUwnbiOAzj1c9DYuChI3iU7yHarT0ydLHB9NpLCRWgpNfG8PNKUiwrhOGDd7/dYwkZqLoa0lWf5mkWgGJE52YVQnVp1upUcihYOuZdDugueXm22G81n676HLt7nva8ekTqTaG9pOLOw14Ww43hFV/yadqMqQexRLy94zmaVQxLG33q6KQIgN16LTOzc962EEcTTfpbrd35FlrUqWnMsWSvk9mq1AR9NsdJFCE2tjy3YTmK3I01Zwi2Zw/W2P292hCcFco07V8xhMpcnYFodGBllttb8ng4cfhEgqwRraXclTTWrsmB6gfyCL0AS6rhEEESMjed7/gUN861sXee3VGzz2+G4OHRpNDA8MiaZpmKaO2U1mtloe516fYWZmhU4noFxu4v8N9Rg1TSQUtbtOfy7nsGv3EJmMQ6mU5urVBXRdUiymuHp1EcPUCcOYYmHzPRR6p4ijOwmCzXqStG5gR88RtFrkzBMgJWHzeXJCQ2pHgMOE3vNY8Sqjho5hv584miX0X8MPBIb9flIyYkz/ayBC159C0/oJvO/gN+8gjQMUrb1k4s+TJMBdDP0ZQu8Ufusz6Na7ce2nmUxLQu95VNxAN0+gmaME7c+g4grD9iKWvSbMHZIx+jeNSYYmGUllIZWMZV5cI4jLGFqaQTcmVjV0TafPCQnjMq5uUQsusupVcfQ8GiF9Towlc/hRA0U7cWz1r5EyNTLmLjphmZiQSLXQ5ZvE5IniLK1wiUZwh/H0e5jIOkhhEcTNXt9ipVioNWh3bbsztkXJdVmeLTNzeZ7CQJa5m8s4KRsnZdFudug0PXJ9Gdy0g9Cg3fCY3DdCYSBLJw5Y6CSaNMterfc987UGrfu4xNqGzkguS6w8/OAySnk02l+kkP44ioAoXkUIKzEHIEapNlFc6VJlE/pSy3uRVuc5HOsxNJFCsZ8wuk0Q3SYIZ+j4r5NLf4xq80+I4gqGPpGgZfU0UbxKHLe77SXznTiuUW/9FbZ5BE3aNNpfwTIOkLKfot76PFKW8MVVOv5r6HKYRucbFNK/jKFvdS39QY6YCEMzMPje6Ti6phHEEZa0mW81QMGRvuFe4TZWiorXYcjNUPM7rHZarLRb5Eybop1IhURxohujaxplr41o1rE0nYFsCqWg6ncYdN858l0pxdz1Jc69cIkTHzzSG2+jOKZWa1Gvd9B1jYXlOv39Wfr7M+iGJJd3CYM4GXfDiIwusazkPncckw4JBb5SblKvtymV0nz8n/0kjUoLr+3zb//Jn3L5zA0qQZ1vLZ1FKUVKd3DMxPVUAGgeLbWa7LeWjMkFM0PecBlx+rqyCAl1ai2CMKLtBWRci0bLY5hTfWIAACAASURBVGG1wfhAHqUSW/mF1TpeEKJLjbYXUGm0WVitJ+smQ6eUS2HoklzawQ8jPD9ksVxnfDDPa5duc2jXAIEQGJqJHwdUggqlqIhSClOa6ELSiDpd6lmaUEWYmknRLAKKZtSgHiZ6bl7sE/gBjahBqEIaYfIzeS+s+GV2Z6bJGwn1P1IRjbBJrGK82CclXdJ6CoWiHjZoRx1aXVHsMA5phE0yRrrLjGiQ0h2kkDTCJs2whaHp5IwsAsHt9ixfW3ied/U9RskqUjBzRCqmHSWOm53YI6tncHWHalAjZ2TJbRAG9+OAWlAjiENSuktGTxC2fVax13+lYprhLH5cI/KvECkPRw6giGmHixgyg0DQiZbJmXtoBLdQKiJtTGDKfG8O1YkCPn/nDIGKMITkmeHD3STj9iGFixJ/t/Oi75vVXsax+OjJQ6C2Vp3vrFbpBCEfemgflpHYHo+VckgtqQw/sX8KL0wWUJahc2hikD0jfbimwUR/nv5cMqhYhkSpvl6OTxOCg+NDhFGEZehM9OfRpcZALoUmBLqm8TNPHNlS8c7bNk9MTvKdm7d47c4dQqUwpeRdU1Nk77J7F13RvzUURzMI+MKFi8zW67SDgJYfsNpuMVNNRBqvra7yv3/7OxQcB9cwcHSDwXSKD+3ds8XmPm2a/MJDD7LSavG5Cxe5VanyyVMv8e9fO42t64ngYBDiRxFeGPLE1CTR2+jx7CgWeO/Oaf7DmbPcqdX477/6NYazGRzdoBOG1D2P42Oj/PJDx5ipVim6bg9VNJ7P8RuPP8ZvfedFzszN8bunXuJPXz9Hf8oljBVLzSbVTnJjPz45ySceOU7RcRAIXNnHROok2garx7UwdMlQLo0mBHfKNaTQyLsOGdvCMQ1aXkCl2SZtm5Qybm+Q9MKQL126xM1ylVYQ0AqSJNGl5USzYanZ4t+8+CJ9rotjGLiGQdFxeP/uXYxm7yG6viEyhTSVxRrhO5xoCyHYVSry6ycf47dfeIFLyyv862+/wB85p+lzXbwoZLnZotrpoGsaT01P80vHHtqCNoqV4qtXrvLZ828RxjFPT0/z4QMHNqG7DCn5sf37eGNxkb86/xaffesCBwYH+PCBAz2E07XGCwg0DM0hVMlEbr5cp9nxKWZc8ql1hxZN2EiRohG8hVLJxNJSo2j6EGV/kRFnq57PD2LYus7+wQEylslqq72tDpVSijCusdJ5keXWs9S9twjiKgKJpQ+RNQ8wkHqGvP0giIQ+uRZSauwYL+HYBmPDBZwNdCpXN1ltn6LuX7hn/yzZT7/7XmRXtyihSMmuJlCIim5BXEbIrbobawmApn+F5fbz1LzztMNbBHGVWIVowsTQctj6ACljN1nrIFnrELYc7t6jLoZmbkIp3t0+QBBXqHpnWW2fouFfxIsWCONGt78Wpixh68OkjV3k7CNkzcMY9zI56LWraId3WGl/i3LnVZrBVYKoAigMLY9rTFJwTtDvvgdbDm465gm9ISKIYmwjGRfb4U2WWs8jEGStB8hZR1CEVDqvsdj6GnXvLTrRPEoFSOFg6yNkrcP0uU+Ssw73YMM51+alqzPsHupjOJ8g+jrhHIutrwKQtQ6Qt46hiKl6Z1lqPUvNO0cnnE0ojcLB1ofImPvpd99D3n4Q0a0OWTKHq5c2OF9udz4Dat6b3XbfpB3eJlItNEwsvZ+MeYCCfZyS+wS6SG+4lmM0OYjUdyPE9hPttfYb/mXKnZepemdpBbcIojIxAZow0EUGWx/CNSbJmPvI28dx9NEtyAKlYmLl40fLtMM7tMNZltvPE3UXrmFcZ7b+nzHk9gl7KWz63HdjySHePT7FtUqZPjfFaCaLldNpBQGVVhvX/LutrP3/HWv31BqovUfpEWKTc9zatNAwdE6e3M3hw+O8+MJlPv+500xN9ZHNOl3NtJg4jonjBJZ/5vRNvv3tS3z0J4+j6xpXrixs+t4un2dL0rpH9bk7UbfW3w3bpZTr6N0uIM00Jfv2j/ClL76OZek8/d4DTExuribH0W2U8jCcH0IIh6DzVaLwCkJYhN630YyDKNXCsJ4h8J5F06eJwktI4yi6cRCEgd/+AkKYqLhKFAyjyX5UvIpuPYGm9ROFVwm959H0CQLvOUw5RBRcwkp/nKDzdeJ4BanvQ9OnMeynEVqeoPN1ovASQjiE3reQ1mPE8QqG9QR+6zO9/huaTRivJx/ujla4zJ3Wi6T0ASwtS9m/iiYMOlEZpSKE0Mgak9T8W5gyQztcoexdQhMGaWOUxfZZitZuXH2Q5c4bSM3G1fup+beo+jexZZFmMM9s87tkjFFco59WuEQQN1nuvEHR2stq5wITmfcAieHJ//K1b/LdGzMAfOTIQX7t3ScoDuaJlSKVcZC6pLxUI45icqUMfcMF3KwDShH4EZ2W30PGpXWHydQAt5qLjLt9vf3+7ede4Pkr1+6pIXlsfIT/46d/LKGjyQH88CZhtEQYr+CYRzGN3VjGflz73QgElnEAQ5/EtU5im4e79IwIQ99JxvkREpMUDan1I8QsEOMFF4miZfzwGoX0r2DoE9CVPbDNI8SqTdr94LomjvUgsvPXySXcfU/HP4NtHiGM5nDtd1Np/BGJnEIOP7iCH1z6e5c4qgertKIajkyz5tIYq3Ua9v1ib76PqUwBU0r25qMuqkaStx0sTfKukamedXqf46JrGiOpbLdwvr4q2FccwNQku3KlLgo7mT+ttFtMZvLYXRMT7R1oxkRhzGvPnefzf/Ach0/u4cDBUaIwxjAlpVIaw5CUiml2TA903SiT7+rvT571QgjCMMIwJDt3DXQlHiCKFIYhOfnEnl6SvjCQozCQJBEyeRcEGEKnZObQhMCPQ2xpEsQJ9SxvJM/lRMc16lJYLXQhuxId2+hLmToHdwwx3JcUmiaGCpi6ZLgviy41pkdLSZJH13n62G4MXTI+kOiXbdy/Qsbt7ctof44wihnrzzPWV2JU5LvaUpKHCg9haDoFswAkNNsgDjE1g72ZPQgEmlg7f4KH8g8ihaTP6iNWiT7VkDOMIQwG7QShJxBEKuJ87SKvrp7lx0Y+yK7MDmpBnT+b+SuyeipJmmiSHx35II2wyVfmn8UQOvWwgSVNyn6Fryw8y4+OfAApJF+a/xrv7n8MXeh8Y/Gb3XWPzrv6HsORNq9X3uRq4yaOdJlwRzlePMpiZ5kvzX+dYXsQL/Y4kj/EnsxObrRm+M7yKQ7nDvCuvkcBWPKW+c7yS4QqoYj/0PD7KJibKZKaMCjahzfTervnsNCly/hRlYI4iK6lyBhTKBKkf1LSFMQq5szqTc5VkjF6V2aQw/mx+1zhiiCuEMZNLPnOHVjfLr5vEkdS08h29V5aHR/HMgjCGAGMFXMUjjg9wdTuB3q/RrHCtRIutx9EiVuOnQwasotKKrfbzFeTxYulS/woImNaNIMEMqy8JNvt6AZ526Hhe+wqlkg7WzPrhpQ8NjnBUDrN7VpSSZnK5zkxPrYlydRF9FKvJQmTesfj9195hWur5W2Pw3KrxZcvXd60bTyX5eGx0S2JIyEEg+k0/+SpJzkyPMxXLl/m2mqZSrtN3fMSwUkpSZsmk/k8x8fGsO/jzkX3/f/4kUcwpc43rl1lrl7n0nKiLG/qOlnLJG0mCZui42BsOA+aEDw6Ps7//MEMf3z6DN++eZOlRpO3ms2e89q+/j6e3jnNRw8eZDSXW6etCA15DzpZxrYYzGUYyKbZNVjC3vSQSmaijY6PLrVNr9U9nz8+fZYzc3Pbttv0fb525eqmbUXHYXdf6R0ljhqVJqtzZaJDWykq9wqpaTy9c5qxXJZPnznLC7dusdJqsdhoIDWNlGlwaHCQH92/jx/au4eBdHpTGk0pxVuLi/zeSy9TbrfZ3VfiE488TJ+7VY2/5Lp84vjDnF9Y5MrKCr/30ivs6+9nf39/D8GSt0ZwZA6rK259+uqd5Fw5Jo/vn+q15UfzLLW+mCAu0JLkkRBIbTcxEdE7FLb9fg8vinh55jZThQKVdmcLvU8pRSec5Ur537Dcfq5nJb4WnWiOqneaxdbXGc9+jLz9INpd13W10WFxpYGUGoWci7YB3rrY/Coz9U/fs3856ygl5ySyW/FbMwYYyHcX/6oN96ANhnGNO/X/xJ36n9MKZ7Z9X4c71P3zLPEcUqRJm7s52P/PSRk78GOfSEXY2r3oDRHlzqvcqP47at4bBHF123e1wxmq3hkWEKSN3UwV/geUmCZvuj0NjY0RK4/l9je5Xvk9msFlYrW5Wu1FizSCSyy1nme+8XnGsz/HYOr9aGJ97F4qN3jz+jxHdo8yUEhR9y5yafVfAoKR9EdJGVPcrv8pM7U/wYsWtvShFd5ktXOK+cbnmMp/nJH0h5HCpeUFPLF3itny+nXQCm5024bB1IdIl/Yw1/gcN6t/QCea525BxnZ4i3LnZRaaX2Yi+/OMZn4KhUw034RG1tx+fPGiRW7X/yNzjc/SCbe224lmqXpnmW98jpL7Lqbzv0rK2IUQWsKxV2EXZbYdN17hR0vcqn2a+eYX8MIFNrtarsUcjeAStJPk8q7CbzCe/RiCzedxuf0tblX/mE54J0GMqUbPnRGSZOP16u9uu58AplZE18a4XY+JuhOv2XqNIIqQgWD3YB+7Br53etX3eywt1XnzzTvMz1U493qi5bhv/72d527cWOK7L14hnbZZWqqRy7k9Ef5iMQ0KPvuXrzI0nOf48WkMQxKGETeuL1GptHqXUKcTcOHCLFcuL7C4VOPUqavs2jVIOm1z8eIcF96aZX6+wqnvXmVqqo9SX5pLF+d5/fUZlhbrvPjCFUbHCtsKzEMyd1ldaVIopNi9ZwjT1Gm1/N4iK3mTgSb70bQ8SoWgmmj6DnTjQYSWIgpnEMJFaCVQAagYIVw0OYLQcijVBnyk8SCaHEXIfoRw0K0nCb3vkDgAKoRWwLCeBGH32tPkSEI9ViFoGYSwESIFaEk/5BS6+RBCpInjueR1ra9LV04iWdxpxETbyc3RjlawZYGR1GPMNU+RM6cAwXLnDUrWPqRmo2s2eXMaVx8gVC0cfQBX72O5cx4pDIbdRwBFK1zA1ovowqEVLhKqNq1wgZQxxIBzhMXOWVwjQcNbMgsoVjrnyVvTm/pb63isthJEacv3UQrSeZdULpl79o0UCPzEdXXNTXYdDavw2n0YltHdFnO5Pktat5nvlNmXTbQk/TCk2vZ6KPu7o9ZJXPnanRdoeS+Rdt5Lxz8LRBsOorrrmN69iFbosoQQFkJohNEKlcYf4tqPY+jThPEKigAQaJq7qdDQa2t7Y0DoJqtanW/R8U8nyDQ5jFIBtvkAlrEPxzqOId/5vPAHJTQhiVRIJ2oSxTEXbi5i6JK9k2/vkriRyrNRi8vtrplcbf2Z4XS3GduIG68XSDe/NuCmcY1EAH27z20XfsfnjRcvJ07KSiXjT7cbPefJbVbJG8e1NVDARqfKtWWW8zb6pynd4UBuals0eYJYyW3Ztr0OThL93XngWv/WtCGdtb83HLM1J7vt9COluWH/dEkQRhzYMdgtVqy/JuXaOltu2WZus56z5Nb1tN47wOtt6EL/f8h77yBLrvPK83fT5/Om6pV3Xe09Gt4bEgBBM3QSRYqiDGVmZEI7KymkjdnljGYUuzIzo1VIoZW00srMUCOKpOhEDxAgDOGBBrqBbrSr6vL+eZf27h/5urqqqxoATcRS2O+Pjn5Z7+W7eTPfNec73znc3nUTE/WL+JdYf1JS8SrcVbiFLiPPF+a+yppT5Gz9Aik9wf099/DE6jPMtRYICWkF7UhqBUk7iMqPm36Nslfjzu6b6TELpPUkqlC5vftmZlsLvLPv7aT1ZAe8Cqn6Nd6du4+8cVlk/Ej6AGW3jBtedsbO6GmOZA7ghi6PrjzJfGtxC3AkhEDF2H5M6RyztQ0VS1ew50MpOVNd5G8mHqPkNoipBu8YOELBujobveUvowgTXSR+YPpG8EMEHBWrTV45t8BgT4aXz8xyYLyPSqNNrdFm31gPUwslTEOj2XLXEW4pJfl0nLNTy/R1pxjuzXJ+ZpWRvizTiyV0TSWVsJleKKImNVZooonIUlVVFHblclwslzFUlYbrMp7LMVUpU3Ha6Iq6hW0QSslLcwucmFvkg0cP8J/ufTvlVjTJFhtN/tuzL+F4Pgf7evjQNQfXQSQ7ZlKttPC9gIxt8b/ceccbWtZvjJih05tMbvs3IQRZ2+bHjxzmffv3cbFUZqke0Y0lElvTycZshtNpcrHYVZ3ONkYhEec3br+Vjxw5xGSpFLVV0GHkxBhIpfDDgNlKld2FLuzOKOmFAYGUjGWz/OLNN/C2PTsIfSi2mlEJmG0znsvRFY8YXa+Vlik5TW7uGaHitnHDgLwVI5SSfDzG/3b3XTQ8l95kslOCIFEuaR4g8TpuGFJK4paxXgboBj66ouKEHnuGcnzkyCEUESHfoQw7CPj2YWgqu7u6Nh0by2X53fvvIwhD9nZ3o3U0UPrHe7HiJrHUZdDmluEh/us7HwBgYqbIwa4eejOb752mKOwvFPiPb7uHyVKJr508y5deOM07Du3mjj1jjOdzERNrm3slAUvX+eWbIkHLkUyGPd3dvDQZWVJfv0FnRQjBnq4ufvf++5iplJEyYtRcWg9ljEFW25PoisVg7CimmmCkkOX8whr9MWvT9wdhC0MtkDQO4oVlksZhFuv/SE9iAFONYatvDVe1sKNX4QUBw9mtDhpOsMLZ0n9mufFNoruhYKg5LK0fXUnjhxXa/hJusMpk+c/oit21aTsvBKTiFkLA3FKZPTt6Nk3ctj5IQt9FIFuE0kNKH182CGV72/YKIag02oShZKQQA0xg69gSSo+p6t8xVflbQtnq6AvlMdUeDDWPQOCFNZxgmSCsd8qS6mhKDEOJMhWq0Cg6q1iKRdrIbJqIgrDNcvNBzhX/ECdYvNQ6NBFHVeKowkIIlUC2CcIWgWwSSoeYPkrRTbLQWmQ82cVoIn/Fc9dmpvYPXCz/FV5YBEATCUytF1PtBiFw/GWcYAU/rFJxXqKxNoEXVhhMfghFRJuYeMyk2fa2Eb2W1N0zTJT/grnaPxJIB0PNY6oFDDVPKD3a/gJOsEQoHdrBAudLf4wiTPoS76U7FWeuWCEds1inU2yIunuOqerfMlP9e/ywga5kMLUCptqNlAFtf4F2sEgo2zjBEhPlP0MSMpz6SfLWLrywueVeSilxgmXOFn+P5cZDSC6xxXLYWh+aksSXLdr+PK6/gi/rLDW+RtufY0/+fyVlHILOok5uI0wtpaTuneF88Y9Zaz22rjunCANNJFEUC1VYhNLrlN41CGQLVcRImQfXGVMbw/EXaXoTBJ3vU4WNQFl/DQqakti2VBmislg/VJiulBlKR4tpVVHQFZWFWo3TiysUUglu3znyhsmRf0nhOj5hEK7b3tfrbcIg5I679mJ02FXJpMUHPng93d1JpIQ9e/uolFfo68uzc2c/tuUgZUguH/ITP3kNUxeXicdcVNXnyNEssdhR1lZLjI93ce2xJF35EoEfo16d48DBFAfI0aiv4DpJAqtFo77G+HiB8fECzaZD24nKtRtNh0IhxXvfd4xWy6XVchkezvPBH72edAd4OHx4iLGxbkrFBrOzRSxT45UTs5RKDVRN4ed/4e71jZgQMYS4lCxTUY3r8doP4rvPoBnHAAj8s8jWP6HqexBKDCGSG54/C828hcB7DRkuoyp34gVlpHucMCq6QdOGCN1XcJ0nULW9qPoepEhFzkUijkRDCItAJGk2P49l34/UjhI6D1FvPYFhHsPQhvGd55Ctr+DJSPfHEpdElxXK7jwxO0sQhizXog13dzJOTOum5Jxltv44aphlvvYapmaS0nZBaCOERbstUDUd3xckrAHmm09TaS2R0seoh1N4fiR5EAY2s/WX6Y55VFpFdNUiRKXulnHc5+iOj1N2LlD1pql786SNMRaaz9Kn3vCGz6AXNmh4MxhqJkqCaJGZhvBVFKETyDaK0AhCB9WwQUkjpU7db1N0a6w51fXymyAI+eixI9y7dycN16PSalNqtXj47AXOrxQ3fW8QllEUGwjx1+cUpaM5dBZdHUTThhBCQ1WyOO6rKEoKXb0ErG4Y66VHKBsoShrHO9vRJcyjiCSN9rcx9QMowkbXxlCVDGFYxvFeRdeGI4ZbsEQY1vGDRTQlh6qk0bUx6q2vkbDfhaLEsY1r8YNFhBGV0W0EEd8qUTCH0YVJTItKfFqOR7XR3rJXklISBiGNWhun6RL4QVRSq6vYcRMrbm2Zi6WUOC2XVr2N6/jIjruvnbCwE9YWEDoIQopLlUhIuieNDCWy5lFtNaiEEt3UiadsDEvfvI8Lwuh7Gg5zF5Y48+IkoR+yMlfapFeqairZQmqLPq2UknbDodVw8BwfiUTTVOyEiZ2wtpQVv5m42sb/0nHfC2jWWrSbDjKUaIZGIh3bcm2qquB7PkvzpWhveJX2V1ZrtBoOqWycWCraa3iuT3GxjGkbpLuS+F5AvdzEbbsoqkIsYaGn1Nctaw+CkGY1amcYhKiaSixlY8e3d+D7biOlp8gbOWzVwlQNfBnQ8Jtk9DQxzabLzLHsrLLxtx/tFaOk12h8iNu7buRk5TQvy1e4p3AHPVb3+lpW6TCkLkVOz5DVM1hbAK/N1/LM2gusumsM2P0EYYC3AVT6XiKQISdLM8w014hpJl4YMFlf5luLp7hYX0FBcHthDw/0Hd7W4e1SWFo3NW+CQLZ5HST8u44fGuCo3nRwXI/5lQqWqdPXnYpErA2N1XKDlVIdzwtARJfecjyu2TvIxYVo8dHXlSJmGXh+QLESLbbLtRbFcpNG2+Ho8CDX5wehgySqQkEVgh2ZHG4QMFkuMZLOsCuXB8RVARZFCAxNRVMU7hwbXT++2mjy9MUZvn76LMfn5vmRowci3XQJ7ZaL6/j4fogdM3jb+A++rEd0GD0Hegoc6Hlj9P+NQlUUhjMZhjNbHQkA1hpNhjJpjA66HMiQ55ZnsFSdA7ke5ptVUrZJj53gVCmg5Xukkyb10MFpRDawCd3gtfIyvgx5ammKuudy39Bullt1LtZK7OvvZrZepuk7zDcqNHyP+UaVXekuGr7LfKPKWDKLEwakDYuG5yCEYKZeZl+2h5wV40BvNw+M7WauWSFr2BSdFmPJ3Hq730zkYzHevXfPluOGpdM7urmvR7NZRrNRucXvnXmEWsvZAhxdCk1V2ZnP8/GbryUe6gzk09ww9Hq0w+j5G8/lGM9tph3OrkXZ9+uvEOgVQnBNfx/X9PdtOVdMy1BxdRShYqgxqs2oRG4gn8LxNtPrNSWFqRbQ1W7Wmg9TbT+PoRZo1zzcikLQDdpbwMwoZhh0xePsLnQxVSxvWhCF0mO58U3Wmk9waRDOWTcwkv44CWN3tMnAp+XPMlv9R5Yb32C58RAbmT1hKAnDkKP7B0nEzM0ZdmAg+SP0xB8glC5+WMMN1pivf56lxteu0uJIFN7zA5BhxDgSW7M6Te8ii/WvdBysFLpidzGY/DESxq4OI0ogpY8XVqi75yk7z1N1TtFl34mmpDrfFJI2MttanJbazzNR/rN10EhgkLNvpBB7O0lzP0ZHvyyQLZreRarOKarOCXri9xEqeSbrc9vavJbazzJd+dsOaCRIGHsYSv4YOfsWNCUOCPywTsV5iZnqP1BxXsIPq0xV/pqEPk7WuiHK+ocS09C2zdbVvTM0vAuE0qXLvpOh1Ic7/WJGi46gyFztc8zVPksg6/hhhfn6F0gZN2NqMZK2RaPtIsRmFnLU71PMVD6JL5vkrJsZTH2IlHkQVViAxA3KLNb/mbnaZ3DDIoFssFD/Enn7ThpBAyEESf1KNzKXudpnWG58C4mPpqQYSP4Ihdi9nTIxDUmA4y+xUP8iC/Uv44Ulqs6rTFX+hn353440pvRdHRbF5vDCCpPlv2Ct9UQHNBLY2iC98XeSta/H1gajvpEBTrBCzT1N1TmJRJI09my7QMzZN2No3R2WRxQrzUdYqP8zkWNjlh2ZX8ZUu7Z8FkAIg5Sxm3ftTNPwXE6uLNMTizOezTEQT1FzHAxVRfsuxvV/CTE4lCMIQlRNob8/s74pGR+/bF1tmjqHD18e949dU8B3ziDUXqR8Dd+N9FpA0l8I6O8RREvkIqG/yJ5dBdgFQgmQMiAMptHNPDfdFCKUHDJYQEoHIXxkuMT116bRzLsRGzKiUkquu37HetnI+u9MiHUnOID+/mhuPHt2gdnZIj/24ZvIZGKcODHN889NEgTh5Qy+cSts0OZS1FGM2IcippySQLqvoGr70e13IZQEYKJb71gf/4QQaMbNqNoBANrSYqJRo8e4nYrfRvezxNFYCm4mr2sQWoQOrHg34FVmSakHySopZODT5g4ELnkZY6LZoMd8J+dr0+RJkZY6C97N9FkpFtwajfIS1+czgKThr5E3I8H5xWqd3/ri16m02/zn976DPT1djCTehkRy4rVVFtaG6e1KkUkkcfyAWCrG+elVdgx2sVBusWskhe1ez9pSkYa00I39nC0u43gBppmjWt9LTWroxrUdLo1K241Awx37d6OZAd3WYRCC1dZJUvowuvLGCZ9S+1WKzkniWl80tsgAS+um5S9jqlkC2cILG9hqAV+2yBi7sbU+4ppFj5VhqrGynkRtOx5aPWTMTDG6swtFjXQ45yvVTcCREIKYdTOVxiKl5iNoxg1oSgGJoBwcpNr8EhnvQSztvahqhqZ3M832w4Tlb9KV/gCVZhZNkdScKrqmUW2FhMFNVFoPo2sjGFoXLdfADR9gqfgtVHEe07ie4fwIhjaGbVxLo/0ISfsBhIhRaX4bRJpW+0kUYWPqB7DNawnDMpYRgboJ+17q7W/RaD+Erg5i6nvfsG//pYWmGHRb0VjjB5F1vB9sZY7VSg2ee/Akzz54krkLkCjSxAAAIABJREFUy9QrDTRNJdOdYuzAIPf/xG2MH7qs3dluOrzy1DlOfOcs5166yPJskcALSKRj7Dg4yPX3HebG+w5hWJdZLI1Kkz/85b9BNzT+5z/5aV59+hwPf/YZ5s4v4Tk+3UM5jt62h3t//Fa6+i+XQM9PrvCNTz7BuZcuMjexzNpCGQH8wb/+q03u1/m+LL/2Jz/N8J7La+dWvc2J75zlpUdPM/HqLKvzJcIgJJGJsfPwMLe+5xhHbtuDbv5gkhdSShqVFo9/6XlefOQU02cW8FyfbCHF0dv3cveP3kT/ju5NYNXyTJH//eN/gWHp/Nqf/DRDu3o3nTPwA/7hv36FJ79ynI/8+rt5x8duQ6iCxYsr/M5P/zn7rt/Bxz/xAR7/0gs8+eXjLM2sYlgGe68d4/6P3cauIyPrbMON0W46PPvNkzz1tZeYOj0XAVO5BHuv28HbPnQT44eGtv3c617/hn/hUiHb5sjqGebbC5TcMrOtedzQRRUqgQwouiUC6XfAJGgFbXqsAt1mF19e+CbTzVl6rO51UkHVr2Gq5mWB7E653tZ2XXZEA5hsTHE4c4CR2BDPFY+v/0V23iu5Oktsuwil5KnVc/zDxac7JeYSJ/RxOwDY7mQvH9tx2yZ9q+1CFRYJfXRL1cP3Gz80wNFaqUHL8chn4pi6xsxSmUzSZnK2SG93El1T6c4mCEKJlCEzi2XOz6wyUEijayrzK1X8IGS13EDXVAq5BLrWYmmtRq3pEgaSmL4NdQ6Iw5YysO1CEYKjA30cHdi6Ce+Kx7h3zzgXiyWmO1bzEAlG7tzbx9iuXuzYD/bm/X8ZactiRz63TgkVCOYaVTKmRSAlMU1nqlZCADWvzUqrQd13yJsxTFUjbVgU7ARKpy42oZvkzFhkqdisc3JtAU0orDkNuq04p8vLTFSLuEFA04+sOAfiaU4WI1e7sWSO5VadnBVjtlGJhIY7jkyKENRdh/PlVUxVZ1e6Cz8IeO78LE+encLSNe45OE7D8VirNXn74Z0sV+p86+R53nF0D0+fm+bs/CrpmMl7rtuPqgi+8OwpkrbBzFqFY2MDeEHAUD7Do6cm+NAth/n2KxOs1pp8+YXTeH7Irr48Dxzby+xqma+/dBYvCLhh5xA37xkhbhokN1BanzxzkRcn5pFSYhs6qZjF/Ud388RrFzkzt0LKNnnPdfuwTZ1/euoklaZDsd7k6NjVSxi2i8XWaQr2bpp+iTVnEsMdp+35CCFoXcGIM9Qu8rH7UYRBf+pjeGEZWxtmbbrO0swaqdybFyT8YY9yq83z03OEUrKncHkj2/JmmK19mkBGuiwZ8xh7858gbuzACXwenjnPdYVBuu1jJPI70ZQYc7XPs9EpsNFySZg6rbbLmQtLXH9kBMvU15MBmpJAUy73ZRA6lNuXXYquDIGgJ5vo0LdFRG8Vm8sspQypuWdo+bMAxPQRdmV/nZg+umWTb9JNwthJT/xe3GANVYmtbwQ1oRNT4yioG6ZvieOvcbHylzS9iwDoSoah1EcZSn0UXcls+Y64PkaXfRe+rKOgM9Ns4IQ+gdy8AHWDIhcrf4UTLAOQMg+yL/8fSBr7NpVXGWoOWxsiaRzg9Oq/p+wcp+3PM1n+v7G7h7G1foIgpLtjhnBl1iWULoKQnvgD7Mz9BgFxFMVAEzoNzyWUccazvwJIZqr/A4lHzTlFzXmVpcpeTE3DC7bXMZF4+DKgO3Yne/OfwFR7N/WHoeYZy/xrVCXORPlPCaVDw5ug4pxAqINowmajjaqUklL7WeZqn0HioYkEO7O/ykDyRzaV5gGYahdxfQxbG+Rc6f8klG1Wmo9QiN1H3ughDFfQ9MNAer1PpJQsNb7aAaU8QKHLvp0e++dZXingqDquH1DI2jiuj6IMU6530/auJ5MQPHtqja50m2LH/lfXVPwgpOWAqe+l5Xpct2coEuf05xBCRUofVcTI27cS07e6r10Z35g8z0AixUqzwXS1gnQlF9dKlJot3nd0P0nruxdu/WEOy9aZnlrl5IkZhoZyjIx2k0xenc0gZTXSVlNyhN4iiponcJ5Dt96G73wHRRtEKDl85xlER5wTGaKqg8gwKu+KmD4KMlgGGaBqO/C9V5HBLJq2k1AKFmqV9bFBVRTK7RaaohLTddq+hxMExDtrLkVAxrJJ6JHg/kB/hqNHB3jwmy8SBB7xeIz3vu8wpklHQ0+uP/dSRk6VQigIsUEDS4mjaAMoG8HGK4BQIXSEGpUwztcWeLE0w8H0CO1QYTRuMtdcQ1NitKTFYqtEKOsstmtkdMli6FDwJbpQsVSDEBWnVeHF0gQ35vegqgV0JcHZ6jxzrSoBCep+tAGJ9gkCTZhkjCgZdH51jVOLy2iqiheECKFgqFFSyTLqZGJ5Ak9lreRFZeuWQj4VsXzqjRqeF5Kys7RaNSxdELQVHLdBX1ca09RYLbbRVAVNXHbGVaRGMm6iKTqGGq1xA+mSNHYQhHECCdobJKHT5i4SxnC0+eiM0ZHO0kjH3CAyVpCEtP3ViDkoBGpnbTcYy7PSrkZ3VEp0Q4ucQF2fZMLC1MJNzraXQlN7SSd+hjXnNWw1h64P0fI9Xig6WOq7cNUUoiZpeysoootc/KfwPJ+5ksPESoFQSpruBF2JSEA3ZlzD4cF3cn55lUzMZmpmiUrLwvHvJ2WZ7OntRggFRUmQiL2Xpr8KahIvkJyrv4Od2Tz5jvDy7IUlnvzaNDfc+37WnBa9w02SmSzp+I+8fme+hUJKSXc2ESX0N8TUa/P8t9/9Is8/9AqqppDtSZNIxwj8kOWZNeYnlrnxvsObPlNcrPDX//GfWJxaJdeTJpNPohkaldUa3/7cczzx5eP83G9/kAd+6o7LibxQUlyu0Ki2+O+/90WeffAk8aRNOp/AaXtcODHNK0+dY/L0HL/6hx8jkY4qA1zHw2m5dPVnMSydarGOoijsvW7HJufAVC6Bndg8lyxOr/KXn/gM5ZUq2UKKTHcKVVMor9R46B+f4plvnOBnf/uD3POhm75vho2UkuJShb/+j5/jiX9+gXjSpmc4j6ZrVNZqfPZPv8mzD57kp/7d+zh2z4F1BpfvB6wtlDFtncDfxqVaQrXYYGWuRKt+mcnuewGrcyXOKJP8+b/7FKefmyDdlSTXm4mu71NPcfKpc/zMJ97Pze+8ZhNjrFZq8Nk/+QZf+ZtHMWMGhcEciUycWqnBN//+CZ5/6BU+8hvv4u4P3vCmwKOF1iLfWX2WolvisZWnWHFWOZw5QMHqQlM0FKHQZeSwFJOj2YOUlsv88/w3yRlp+u1e0nqKo5mDPL7yFEk9we7kDkzFoOiWeGbtRdphm4LZxc7EGAC2anMkc5CHlh5jPDHCjblrMVWDgtmFsoEFXfcbPLr8HSYb00igHTjcXbiVa3NHeal8kunmHGPxYZJanIpX5cHFx5htzTHfWmS2sca9vbeRNt4YrBeAJlRCGdIMPKSMnJd7rTQHMoN8fPwO9qb63/AZU4RGyvjBE1V+aICjo3sHOLynj8jOWiKlQFUMersie+QdAznkuvuLwrmpZfLpBPlMdBOWSnVmlkvceHiEvny0cfL8kDCcI5uMvaHzlev7vLK4zDMXZ6i7LgnT5JbRIQ719aIogsVqjc+89ArVtsNgJsWPHj30poU4X1xa5PzKGh8+dnjdKnil3uBTL57gXQf2MJbL0vQ8HrtwkVMLy6iK4ObRYa4Z7MfQVPww5OGzF7B0HVNTeW56Dsf3efvunQQy5PzqGj965CDzlRqfffkV7tm1g4N9PTxyPnKsuXlkiGenZzm5sETdccnHY9y+Y4TdhS4UIWh7Hp99+VWuHx5kcq3Iq4vLJAyDdx/cy0A6RRCGTKyVeOTcBOVWm8FMirfvHqc7Eb9MpQxDBuIpTpeXOeS2mamXmWlUSBomeTO6R7amU3Sa1D2Xo/l+LlTXmKmXKbstcmaM06UlBhNpputlkoaJrWnkRYy8FaPuuowlc3hhwIFcL5PVNS7WSvTHU1TdNieLi+TMGEk9Eo+brpcjMe1GlblGhaFEhu8sTvHe0Ugceq5c46GT5/no7Uc7AM9rvPPYXl6dWeKGnUOcmY+cqaZWy5yeXebd1+7jmXPTPHTiHLfvG+PE1AL/5r6buO/IblZrDZ48M0UQRFpLE0tFlio1VEUwVshxy54R/vTrT3FgqJcvv3Cao6N9ZOI2n3nqJHsGusknNusTLZZrZOI2C6UqI91ZLq6UePb8DK/OLPHua/fy/IVZvvnyObpTcdqez0dvP8rfPPICwTaZn9cLTbEoOlN4YZseew/d6ThNx+XiUom+XGoT28YNVmh5E2TsW7C0YQzZTaX9LPXKKEtTq/TtKGAn3hrU7J5knIm1EvuvYO4V20/T8qcA0JQ0Q6mPENOjiUdKiOsG7cDv/D3FUOpjlNvHIw2YTpQqTeZKSyBgpVgnDCXLixVWlmvs2d+/nm1/sxGE4bqYeaT74IHY/BxIQrywzCXmk6FkMbXt7TsvhRAqprbx+iXtoEUgg03Zk0tARrl9nGgDoTKQ/FFG0j+z7iy3/fkFuog2TTHVYyCWwVA3MhhCiq1nqDgngai0aST98S2g0cbzxfUdjGZ+jlOr/x43WO2IUT/MUPKjGLrKSrnOWH8OtskRXPpsQJyT5ZfpsXoZjo1xoVREVRQOdhXoS7yH5eZDtP05QunSCs4w2n0LmZhFvlq/qvaArfUzmv65TaBRGFaR0kFRMijCojf+APP1z9P0JqEjpJ2LjW7JrgWyzmLjyzjBCiDIx+6gN/GeLaDRpVCETW/iPSw2vkrFeZlQOiw2vkLO/FUUJdhQChSFFxaZr32hAxpBQt/J7txvEvr9XGwvUms1iZk6EknL8dAUhabjkU/FSFgxmq06jbZLtRnpC+we6uLMzAqOG9DuaKNcWc733UYoJU3PpR34KEKQjJkMyTQtz3vLuaoBeF5Aq+WRzcapVlscf/Eid9y5lyAImbywTDxhks7EsSw9sjxXCgilB/BR9b3IsIxu349QutDMWxAiAcJEt3PIsIqi9iDDMjKsItQ+hGwhZRMhkiAUhJJEKGmUYA6JhgwWkeo+zhZXEULghyG2puOFAcvNBoaiUvdc+hNJ3CAgRNLyPG4eGCbRAZIM4zzveEcDSCKUBEJYhMEFPHcVCDuioSpCxJFhBU3bhbhCOF3V9oG27033Y1qPcW12nD47R92Pns+ReIEVp0LWSOCGHrqiMWDnsFSDgJCK2yCtR+sXJ4zs5a/NjdNjZcgacXwZciA9TK+docfKstQuo4goYRUGGkm9gCo03CDg1fklGq5H2t46xh/cuTUZuTH6uqI1rRCCPaM9FHKJTaz4etMhs9cmtWEO7uitbglVGFhqD6+Wl1hrr3BdYZCMefW521Qz2/5it6x+JRhX6Hpk9DgVr8mB9DC+HzI3X6bZdMmkYyTibwzwKkLDDWooaCQBTSgU7CTLrRpZM0bKsmi6HoGUmJpKTzqaUzIx+1KTolK+MCRpmaRsk0IyQdq20BSF3b1dNF0PU1PJxe11ZlTROc9S6wQpYwgvkBTbOaqOQ8GO1rynnp9EUQWNSpP5yRXiSTsSPP7/UQRhSLnWwtqwD6qXm3zy97/E0197idF9A3zo3z7AnmvHyHQl8Ryflbkiy7NF9t84vmlt0D2Y4/2/eC+xpMXOwyPk+zIoqqCyUuMzHUDiG5/8Dne873oSV/Tz6nyJx7/4Av/q5+/hnT99B5nuFJ7j8cQ/v8jf/s7neO7Bk7z69DluuO8wQgjG9g/wi7/7YQDOHL/I5Kuz6KbOx//DBxjY0bPp3FdOwAPjPXzwV+6jqy/D6P4Bcj1pEILSUoV/+C9f4Rt//wTf/B9PcvM7jxJLfn8U/HbD4Yt/8S0e+/xzHLp1Nz/2bx9g3w3jqKpKaaXCV/76UT7/5w/xN7/zOfp2dNM/VviBlINNnZ5HNzR++Q9+nEO37kY3dNYWy3z6j77KV//ucf7xj77G7mtG6erPRuO/F/DQp57kn/+fRzhw004+8mvvYueRETRdpVZq8NCnnuKTv/8lPvn7X2Ln4WFG972xYHyv1cMHBt+9/vpS4ux9/Q+sH7u/9571/7+3/4FNCTaAG3PXckPu2BaO0nBsCK54ryZUbuu6kVu7bsAPQ1q+T0bLcW/hHlp+0JGNkPihwp3dd3JnN3hBgK6q6EJjzN7JrvhuDFWl6Xkd0XZBQTnAXUN3kzJMzpXXCKWCH4Y0PDdyTBeCdhCgCIhrBlW/iZQSSzW4Nj/KQCzLifJFus00WSNOfyzLgfQg52pzNII2QRhVT5mqTsltkNRsWoGDrRoIoVB26+TNJIvtMjkjQVz7wezRfmiAo1A6tP1Z/LBGKD1UxcLS+vHDesexSImo0TIklB59PTlixuW6ST8IcTyfdsfhSgiBrikc3NmH5wfr7kVXi5MLS/zxY0+zqztP2jIj6uxqkQN9PSgIbF1nZ3eexy5c5JFzk7z30D5i22g5bHttoeSrp89y244Rxrui7NcLM/M8PjHFew7uxfEDPn38FR69MMmhvh5qjssfPfokv3jbDdy2Y5QwDHlxdp6JtRID6RS5mI3jR9bybhDw8NkJ3n9oP+dW1/j8iVOkLYv9vQUeOz/Jzu48ddfliYkpQilJWSZPXZzmhZk5fuedb6c7EccNQr752vl1wChlWRSbrXUHtnMra/zRo0+SjdkUEnG+fX6Scyur/Oodt4CIrOFjus5wPEtOj2MqGqPxHCPxLFIKDFUhp8eIGwZNL8rAx1SDuGpy78BuGm2PrG4zGs+hSoVr8gOkTYu0aUVMB0WJfrQypOl5pEyLbitO03dJGhZ+GND0PWxNR0Fw94BFUjcRwE/suoa4brDUqjOUyFCwo4xRqd7C0jUGcmkSlsmDfkDM1FEVwWK5xtn5Va7fOchqrcn5xTW+9tIZXM9n/2APUkI2brOjJ4epR5pBjutTabUZzKc5PbtMPhmn1Gixp7+b7lQc29ApN1ucXVjB8XwsQyOfjG0L9uiqSlfSIAhDulJxZtbKLJZrnFtY5evHz+L6PnsHCixX6wzmM3Sl4uzsy78hOHpljMSvo+hMoQqdrDGIH4acmVulP5disVRj90CUyZVS4gUlGu4Z0lbkIuCHFSrtZ4mn9iPDkOnTcxQG3xritOdWiigicjgczUULYSkDSu3nCTuOcrbWT8Y6tj7+CAE17zJLSwiBpfWSto5uAo6G+7OkRkbRNZV6o41paMxXW8zPFhnf1fNdA0eqomAbeidjK5CyhrhCdFR0tIYuafC0gyWa3hRJY/93sdDYOMlql9kABJ2Spo7TntZLX+LdrwsaXRllt4kb+Js2/YFsUWo/u67tFNPHyJhHt98JXWqhEKSMQ6SMA5Fjl2xRbD1Ff+IDSClott11EdcrI2vfgK0N4UlBSIDXuc+6qpAxrc797MdUu2j7c0hCqu0FTs1P05tJ0HA8+rKpbSvI0+Y12OoOXC9AUxVUVSEI5vG8V7Gsu1GUHKZWwFJ7OsBRJHwd07oiNsiGaPvLlNsvAhJVxMnbN6OJ7UthL/WJrmRIGvuoOCcAScO7QNu/iKVc+txl9ljNeY2mP73++Z7EA8T0EUJNcGRnP/OrFTIJm7htrI83kYWxQBWCXCqOAHYPdqOqAl1VObZrcF1/7hI75fuJA10FlpsN+hNJeuJxyvU2y9U6+/sKxI23Dqv3UshQMr6zh1TKJgxCqtVIF0pRBPGEyTNPnSeesNh/YICBwRxCqGj6oejDAmCE9RfKRrHV7su1lUqnnEEIlI7NsKJeUfKu7yfwJ1GUPIqicdPAcIdFst5SgHXxclUIJBHQd760dkV5uETKcudcMZAeQrFAqOtsHUXEkdJHEmz5HURN/e42SV1mii4zEhLNGon1LrkEDKX12Jbz9lu5TceklBTM9BaR2kvnSybs9fcLNU6XOYraYS6emN8quv+9XEtPLokQmz+TiF0uz7t8ztc/T0KPjE7eON6krOo2b8oaCS7UF1loFRnq6SabiSGBZOLNa574sk3YmV9CJCnd5Eiun5hmoCDYuOq4dF9Slrnpnl36G8BYd3RPc4nYlr9dCjdsIIRGxZ1GEzkEuU2M2FQuztzEMq8+ewEhBOYbiCC/FUNTFBzXZ7XcYKQvi6lrTLw6w/FHTxNPxfjob72HG+8/sq5NZMVMktk4Ywe2yjHohsbdPxKxUTbei2xPmns/cgvf+fKLlFerrC2WtwBHSDh0627e9TN3RkAOoMZMbrzvMC8+8ioPf/oZZs4ucuzuA+hG5IZ66VmNvurSGk4gtmggbg7D1Ln3wzej6pvb2dWf5e0fuYXHvvQ8xaUyldX69w0cLU6t8p0vH8dOWHzgl+7l0C271zWYuvqyvOfn7ubs8Yscf/Q0L3zrVXp/puu7LgXbLoSAt3/4Zo7euW9dH6l7IMu7f/ZuXnzkFBMnZ7hwcma9/K+0XOXRzz+Pqqm8/9+8nb3Xj6+zkVL5BG/78M08/fWXeeXpc7z8+GtvCjgSYruitDf4zDbv3/7Y5X83fh9E0+HLK4tMVkuMpSNCx2KzRlw3qLsubhhgqWq05wt84ppBfyLFVLVMXNcZTmY4VVxmIJEibVg8PDNBKCUHcj08vTjDPYM7WGk2eG5pFlvTMVSVxUYNS9O5a3CUJXeVFafCrmQ/XuhyZ89eDBUOZUaQEladKrqistgusTs5wJpbQ8qoGqrk1dmTHGChVSRtJADJ88Vz3Ny1jzWnSlwzifOWA47aNP2L2NoQGhpCaPhBjXawgB/WEELDVHtwg1VC6ZA08qgbtDYsXaPScNjRd/mShBBYph6VgrxBzJYjjZj3H97Pnu4uEHTQvOiBStsW9+/dRdvz+eLJ02/6uoQQHOrvJWWavDAzz0guSyglj16Y5GBfgf5UirMrq3zxldP8+t23ctvYCEEY8vvfepzPnTjFof5e4rpOEEpW6g0+cd/dDGZS0dpKSqbLFUIpma/UOLeyyg0jg1xYK1Jstig2W4xkM+TjMf6nO28hpkcZyZfnFvi1L3yN5XqD7kS0aKq22+wxu/iV228ibVkEYYgiBI7v84WTp7F1nU/cdze2rvHq4jKf+OpDPDc9SyJmUnMcEqZJVyzGfLWGhsJcrYalaUQbHIW661Jzi+uLTEvT6IrFyNo2JxYXKcTj1D2X47UFBHD7yCgxbet9S+idhY7KOg0eVbt8nA3HAUvTCaUkoRnc1b8Ds8NqSFomXhBQabYpN1oIAZm4zf7BHh47NUHL9dnT3407vcih4V4+duexiAauKKzVmyidMjiAlG3iBgGNtsPhkT6+/MJr3H90NxNLa2iq0hkEQRUKw10Z3nv9fsZ787hBQMLcfsEhRKSzdWlAS1omhzvtMDQVRRE89PJ5So0WLdejWGuSjr+5iUpKidcRpU3pvdT9FYrODLLdRxCGLJXrxNdF9yQtf5L52t/R8iZpehPROfBJWzeg2zrxdIz+KzM1/4JjNJfh2elZrhsaWO9/L6zQ8ma4tDlKmQfRNm7CJLQ6JZSXQhUWafMgc7VPrx9rtlyWZpfp70kzNVdk11gBO2YyMJTbRrj5jcMLAhZLNSQSxzPQCeAK1zGEQsLYjal24wTLtP15zqz9HwynPkbWvgFdyb6pRbwQAkPRsdTLk48fVqm6p9Zfp83DxPXxN70pAEgZNhW3hS4uL8b8sEbZeanzDoWksRdTLbzhYkJX02StG1htPUokfH0OJ1hCUfpIJ+xt9RgEKhnzGlRh0QoapPUMoYwEDqerFaqmQ38ihSI0dOVSRl0S0qQ3E5VPD+XT67Xom0MhYx6h3TI4cWqS/t4MO0a6ECKGlG0uO5Qom6zog7BNO6igKZs3dnX3TIdtBLqa6jCwXr9PhBDE9JFOOUmAH9Rwgia2kuqIY0fXFEqfqntq3SlQV7J02bcRCdIK4pbBjv78pnHpythuNLOv4qz1vca54hr3jO7gYqXEbLVKu+nhBiEX10qMdmXR1LfOJu7ChWX8TinI7GwRVVW47rqI5RiGknrd4fDRESxLp9nc8Lt/s7+/7d53NX1HtR9FvVwOHbsKyB05T/qUm23qTuSelRAGzbZHVTgkLRPdOISm7QRhcNnR6jKAeZXG4gUB5VabuuPi+lGS0NI18vE4cUN/3d/CZlBlw/vE5XZ7QUip1aLWdvCCoAMYayQtk4wdCd+K7c6xTShCwVAjcGKuXOXs8urrvv/NxsZ54lKbq+1OnwQBQShRFRE56ppGxK5R1U0jpx+GrLVb+DJgd2Z7XbFLVucQzTOlZotqp18URSFhGOTiNpamXbUvVp0qtmowGi+gdto9cXEFe0/fFkHlq16vUJEyQCAIpWS6UWKhWWVPusBocmuy6rImoaTWdii3WrS8yCZbV1XipkHGtrD0qz8vXeYeQunhh20sdZQdmRjJDSDbkVt2E0/aVNbqjO7tI9t9dfD+rRRSysipqgMKm7pGS/VQhELgh5x69gL1cpNb3n0Nh2/ds62r4tX6XNM1fD+gUWnSrLXxXJ/AD1hbLKPpGu2mg9PaavxhWDrH7tpHtrC5RN+Om/SPRevSSrFO4AdbhKK/l9AMDc/1aVSatOptPDcgCALKK1V0XcNzfFzn+xNHBjj9/ARri2VG9w1w4Madm4S7AbKFFMfu2s8rT53l+Yde4b6P3roVOPoeCLjJXILd14xu6ishBP07CowfGWF+coXXnp+Iyg0FzE0scfHULN0DOaSEiVdmrmiDJJayQUrOHZ+66vd6rs/89Br5Qop48rI5j5SSWqVFo9am0JfZpEO1MYIgpLxap1qJ3EETaZtcd3LTMxiGIdVSk/JanVBKYnGT7t70er95YcBUrcwtfcMkDZNvzZznrsEdfOHCKRQheNvQON+enSSUkrsGx/jO/BRPLUwT6zBu3WCNA/keDuV7qHku+3MFrusZJKEbdNtxAimZrBa5rmeA6VqTUi40AAAgAElEQVSF8+U1jhX6qXsu840KVRk5v1uKwVC8m7hmkdHjOIFPKEPW3Co7ZC/9dh5bM3GaHhWvSb+dwwk8nMCjGbi4TgWlcz1+GFDxmqw5NbJ64gfCSvuhAY40JU3evmuLq4qlR0Js0RQm1jNPV9oIhzISnXW3qel8M3HtUD9PT83wXx5+nOuGBrh1x8iWUpXvNRKmwR3jozx24SLv2LeLtUaLqWKZn73pWnRV4fxqkVrb4ZFzExyfjazjp0plKu02LdcjrusIASPZDIOZ1DoQgRD0JpNoisJkscTFtTI3DA/y6IVJLhZLeGHIcDZDKCWz5Qqnl1Yot1os1xs0PY9ggy6Hpioc7gBcwDooUnNczq+u0fI8/urp5wGoOy5N12OuUuXu3nGaroumKFiaxo5sFlVR6IrFiOs6MV0nJLJ2DTusocliiZiuM5KJKNCj2Sw526bleettuVTS94MIRQh6Ypsn9t5skn0DBf7hiZcJZcgNO4dJxyz2DxX4xstnufvAOJaus3+whxNTi3zysRfRNZW3HdxJ3DSwNwyqihBkEzHarkdvJkkQhvRlkliGvr5YsgyduKVz14EdfO2lMyQtk95Mkrcd2snT56Y5Pjm/XooIEdPB1CMR9qgdBVZrDf7+8ePomsrdB8a5Zqyfv3/8Jf77Y8eZL1bpy22eOK8WkpCKO89y+xy+dGkHVQrWbnpio4wWspQb7UhsuRO2NkJf8septl8kF7sHQWS9ralZyvVG5MLQ/ea++19CVNpthjJpqm2HMIzKY92g2Cn3isLWhlA2iMMGMmQ8lUfbVEalYKgFFGGuW49X621OnZtnYaUSLeYFlIt1ZqbWGBv/7scbRQhMXaNYbTK/JhjKSBTF3bRJEAji+k4K8fuYq32aULqUnePUV8+Ssa6lEL+XrHUdptaLgrHtxCKEQBUqmtBpBS2SWgoEtP1F/OCyFX3K2Mdma+M3jnbgERBu2q86wQpeUOpcox4BH6/jHnH5WjVsfRBFGITSxQmW8YISQkagqLVNebGmJNZL9xQhqHoVBALf9jaB0JHOirbhc9AIQhbLNUqNFjsKua3nFjFMrRfbMGhuWvRKFJFks/DjhlI9AgSRHslGsKzhXUB2BKb9sMFc7bMsNb7xhv1Sc0+vz50hHl6witRzbBJul21a3vR6m2ytH0PdXNK4kS10tWz964WUkpAAhe89MxpKyYOT56m7Loe7e2i6HgOZJFPFynddrvvDHq+dniPflUQA1WqLZKqjUxOEzM+XcBwPzw2oqwoHD282RvCDkOemZ3lyItJjyMVs3n9kP9nYm08wPDExxfPTcwRhxOL4wJH9dCW212iQUtLyfJ6fnuPB185zZnmFuXKVlheVNHYl4uzsznPDyCD37t1JdyJ+FROSrce8IGBitcjXTp3jpbl5ZktVqm0HRHRdB/t66EluvyhO2yYfPHKAXHz7UiIpJW4QcGJukW+fm+SluQWmS2WaTuTCmI3ZDGfT3DQ6zL17dzKYSb0ua84PQxqOy1Ktzly5ysViieem5lhtRNp4bc/jk8+/RPdr2+sCWprKXbt2cLB/+2RMVDIRUmy2eH56jpdmFzi/ssZsuULNcXH9AFNTSZgmg5kUhwd6uW18lCP9veidRJamKCQNA0vVrgrg6KqCRHJhtciXTp7mxZl5LhZLNBwXXVXpT6c4MtDL/ft2ce3wALqibDlPxWtSdOvU/TbjiT50XcM2dWz7zemQhdJHF5Erp0SiCYW8GWepVcMJg0vSgJs/E0qW6nUefO08z03Pcm55jbVGkyAMiRkG/ekk+3oL3Ld3J8eG+rcAXy2/RCBdMsYobthgoTFFpT1K1rz8u7FiBgdvHEdKyfJsCaflvmVK9V8vSk6LhUaNnZk8qlAo5JIM9WYxdBW37bF4cSUCGcYKb5pxc8mB7cLJGZ7++suce2mK1fmoTz3Hw3V8aqVGBM5tU4ps2gZdfVuTX5GLW/Q7Df3wewJRrmxnEIScffEiT37lRSZPzVFcrNBuOviuj9P2qJUaFAbzVy1b/25iaXqVwAvI92awtinrFEJQGM5j2AbLs0XclocVu+J93wNGEEtYxFNb751uaOR60iiqwup8aZ11vjS1iu8FzF1Y4nd+8v/aNvHguz4IQb261SX2UqwtV/m93/gUH/2lt3Hr2w9sAvS/9cUXefbRM/zmH/wY2a7tx82Tz03y6b98lNWlCpqmcs0tO/noL72N2AadqnOvzvHpv3yMmYllVE1h5/4BfuG33kmyo3+lKyrddpxnl2YZTWVJ6CbfmZ8iZ9moQsFQVExVjUyyVJW4btBlx6h7LsPJDKaqcbq4TMv32Jvtxgl8TqwsMJrOcqFSRAA9sSTHlxdQFcFAIoWlRqXMSd3mmsxgZ391eX45mt0RiYILGIx1oQqFQ5lRFAS7UwNIKVGEQp+dRRUK12R3rOMD+9PDqEKlx9qqNfr9xA8NcBSBIVubcyWQdOn1Wr3J6fll/CBkvCeHJhV6skmM16HqeUHAucVVam2X3b1dZDcwNPrTKX7zntt5ZmqGh89N8vhDj/HhY4d4z4G966yj7zVUITg2NMBXTp3l/GqRhWoNQ1U51NcT1YgGwbpd/KU42NdDPh5bZ6QIBPY2GTVTU+lKxJgqlmi4Lju6cnxncorXllcwVIV83Ob56Tn+9Imn2VPoZiyXJR+LrdPI19vYAX6uPL+UkiAMNzkpJUyDf3VoH4f6ehnpWCNfjRa8fiweLTYDKclaNqaqkrFtpJTktxEm35g1Wq7WmVwp0ZdJMpRLf8+lDl4QEEqJqWlYusY7rtnDcqWOpip0p6JFbHcqwS/ceyP92f+XvPeOsus8z3t/u5/ep3cMBr0SBESCvYuUrGpbluRItmTJ13ai2LmJnaw4yyl32fGNE+cmcZFs2XKRKNOSqC5SNDvBCoBoRAem9zOnl933/WOfOTODmQFAir6hdJ+1sNbgzJl9vvPt8r3f+z7v8/h08GhQ4yO37CJX9llJbfEIkiTyyTv3NZNCgiDwnhu2YDsusZDGr737IJloiI/ftpdYI0D6+G17iAY1BtvT9LWkMCybWFBDU2S2dbfR35IEAT8ppchIot8Lq8oSg20pokGNjmSMhXINQYDWeARNkfnFu/dRMyyCqkJw2abYdhzOTM+zUPY1SXb3dKAp/v0lIJJU/TaDiJxBd0q4ni9QqZsWyUiASnOTKyAIEmF1MwG5D0WKr5hTz/OYn8xhmxZrisf8GMK0HYKKwky5zHixRE8i7tvIN5I/AKqURFi2+RUFgXm9iigIdIRjyA1WhiQEVySOMskIt+0fIhhQUBWJoKYQCKrYlrNWTHRNqIpMRypKtlRDU0CgDqxeWCVRoy/2C7ieyWz1B9huGdurkK0/S14/QlDpJBO8g5bQ3UTUoYZj2UrIgsJkfYLWwNKGxnILDatPHwH52jTkKxFTAszrZWq2Saqhh2Y6uWZboICEKl1fG6S/KYoiCgFcz8T1DCy3iCb41dHl1PRFiIKGJAQBAUVU6Qx243g2IioFvU6s0aq2VuAZUGTaE1FylbUDIkFQkcWwL26oSCwdxMG3a140F1gJz7N9w3Bh5WbXsOeaCSDbLTFR/up1zcvKYzvLztnimARcz8J0F5rvU6TkiuvA8RyqdglJkHE9B1GQKNtFYnIC27ObjCvHc5FFGddzm+4msiDjeDaWZzFvTLMh/NYdh/Z3djFTqWA4NhuSKVJaiItzC2xpbyG0DoPzxxV337MdPJidLTK0qb2ZGLNth3rVJBhQEUUb13VXMRZFUaCo6/zNa8eoWxZhVWVrews39fdcVxBZMy2+euQET567hAfcOtjHB3dvW/f906UyXzl8gm+fPMNcubLqdinqBpeyOZ67OMILl0b45VsPsKuz/Zrruek4PHb6PH/58lHOz2Wxr2jFLdZ1hhfy6/59VyLGPZsG100clXWDrx1/gy+/dozpUhnnipbvkm4wmivw6ugET52/xC/etI+7hgbWdfB7Y3qWP3z6EOP5IjXTajKBFmHYDt88sT5rPaqpdMZj6yaOHNfl8TMX+PLh41ycX6BsmKs2qlUTcrU6Y3l/3N89dY5funkfH96zHVWWMRybi4UFMsEwg/G1n62yKHFyapb/55kXOTk1e8W8WxTqOmdn53n+0gifveUA79u5haCyMjGvijIdwSRzehEP39kyGg1Qqeq0XidLRxJVNCnW+FmkP5JCEkTyRm2VkJPtuBybnOaPn3+Fo+OT1C37inmxmK9UOTE1w7MXhvnArq18fP+eJvMeYLx6CNdzkAQFy6tjug5ZvZVUMER7JMqlU+NUivXm+88dG+W29+79/0XiqGKZnC9k6Y0mCEoihbI/D8moH8sbNRNR9Fv3rpdB7boex58/xxd++xFmx7J0DbWz+YZ+Mp0pIvEgpmHxzc8/ue7fS7KIEnh7XMyuBsd2efWHJ/jL//gNFmYK9GzqYMuNG2jpShKKBqmV63z9j5542z7P1H1dHTWwPjNO1WREUcRxHCzzOllOnnfVxJYoCStc2hYhCILf6icKKxhVes3A8yCWibL71s1XbZcb3Nmz7u88D0zdxnVWSy109WXYcaOBqq2dsnAcl8e+9hqu6/K5//BBYokQqqYQCC5dF67r8tR3jjEzkeNXf/t9pFtjiJJAaNl9KwoCB9q7KRg6AVlmUzJD2TQIN55rAUnhof7N/l5NVrmvdyOyKFI2DRRRIqKqdIZ9MkdYUXmof7MvzK+o/MzGHSiSREzV6InG/SSULCEJIq7nd+YoazgWL997L95Si/xRiaW2y8VC9vLbTmrkFMQ3Wcy9Ft4xiaM3i1MTM/zrv3+cYl3nNx+8nXs2DyKJAso6F63neZybzvLrX/ku+Vqdj920m3/xwK1LrSiOQzSg8e6tm7hj4wBfOXKcrx9/gwe2DBH8ETUZBEFgMJOiKx7juYvDTBRL3LlxoFn525BJEVYV7ts8yIHebgRBwHFdHNdFkaSmY896j+GNmTRHx6cIKDKD6RTtsSivjE7Q3UjqPHdphHQ4xK/fcTNhVeXl0fFVQd16x46oKgPpJDXT4rM339hkApmOv9GtGL7I1xJ91fMr5a5fxZJEoRGIediu/7uAJCNLIlXDbGogxNZxwqnoJr//vWd55uxldna38/s/+yAdiSgVw+S10Qkimko0oPnOPZZF1bRIh4PULZuqYdKXSjC8kGcwkyJXqxNUZHK1OhXDJBkMUjVNArLMSKGAaTukQkFOTM1wZ2CAcGMTEgsGiAVXBgWt8ZWb83ho6fcdST8gykRDlPNVSnWTSCJEvVBDC2nEBBFUDaNiMjVdpK03Q8gFRZGxLZupC1MM7upj6swUrb1pFEmkMFMkkgyRECS0kIpdNTDsOqlkmJbY6kRBzbT506df4ekzl9nQkuKLn/4wbYr/PkEQkAWVmNKBKgZRxTAeDooo05GKoZu275KzUGHy8jwbtnUiB2pUzNPEtL3k689RNk+SDt2LrGxEDShvWl/pnYy2aISFWo1MOExEVX1lIM/EW9YCJgoBFu8az/OwXBfdtlGvaAkQBQVx2WPWtBxiAQXdsDg/PMvOzV04tkMwrL2l6pDruliO31bamU7hmsl1mTlBpYtNqd+kJXgH4+WHKRmnsNwijlehYp6nYp5nqvINEtpeuqM/RyKwD1FY0oowXZOoEiMqx5qvOa6Ox1JwvtwR7nrheH7lajn7wPXq0HSjExqJneuDJGgr5tx2awREz9cjW+MyFQQZQVAa77XJGvMklCQBSaYlFKZu2+sWK5PhIMlIsLF/Wd2q5jtBqViWQySsEWhq7Um4bqFhk74ajmdhupVGe9mSkKPtVVlKPokNLanrnJjFTxZC/jXtVljOOPI8B8dd2hCJQgBhmY6f7tQ4UXiVoBxBFmTCUoS6W2PBmKPmVAhJYdJqGwvmLLZnE5VjqGKAmlOhYpcJyxE0MYAsKEjCWw89Ts3PokkyF/ILfjuzDTf0dnJ21tePk3+CkkfBoMrLL11kajJPb18aRZFoaYmhqjKDQ22cPzuNZdlkMtFVzBFRENjX08VAOsnpmTmqpsnT5y9zY29Xw4Xx6hjJ5Tk6PoUHqJLEXUMbSK3BVvI8j5lyhd99/FmevnAZ23UJyDItkTA9yTixgEbdshjPl5gu+Qykpy8MM14o8Tvvvov9fd3rbow84NkLw/zeD58lV6sjiQLb21u5bWM/g5kUAgJj+QJPnr/E+dksViO5EdU0WqNhWqMRNrdl1o0v8rU6f/rCq3z16Al0y0YWRTrjEXoScRKhIKbtMFksMVMqU9INjoxPMZor4D14dyPmXP2sLekG06UKhu0giSLxYADdsqkYRpMhk2i0j62FiKYSUNa/PwRBYK5S5czMHHXLRpP9Ilw6HKQlEiakqhTrOpPFElPFMpbjMFEo8t+f8XUq79vib3biWqCp37YWLmUXePbiMBfnF0iGgnTEo7TH/OtsplRmNFegalpMFEr84VMv4LguP7N3x4prKyRrjBXn0STfzdKyHZKJEPE1GA1rQRRkLLcGCMToxXEdpmpFypZOb3glw9N1XZ6+cJn/+uQLDOd8R994QKMzHqMzEUMWRbKVGhOFItlqlelSmT9/6QgzpQr/5v47mrpIvZHbUMQghlPGcXVqjkVZ99Aa3+vIM2do6003N9fVUh3vJyj+uRoEQcDxXFzPa8RFHvVGwkIQBIJRX+aiVq7jOu6q9qq1UFwo8/B//R6Tl2d56Bdu56f/2QMrGESzY1ke//Ih9GUOYKvG9VaCpzeJuYkFHv6D7zE7vsBHfuNBHvzE7SRbl2Kh0XNTfOfPn37bPi8SDyGKIrWyf32tpb9UK+s4toOqKWvrbK1xWdqWs2bL3yIsw8Y07VWve55HvaLjOu6KZEsoFkQQoL03w6/+/keJp9/etk1BFDhw5xYO3Ll+sUmvGcxO5tl780a27e1dM/Fl6DYz4zn6Nrayc//Amm2U4Js4BZfJpESucGNfdKf0f/afCcuZ6e3hpe+fDi4VK0LRpaJ7cA0Zlh8nvCMSR9ei9S3+9mq95aIoMF+skoyGyMTXplLPFMtMFfy2ijMN16xFPH9phJFcgfaY32p0cmqWvlSyuZmZLJaYr1S5vJCjqOscn5yhPRahL5kkoMiM5PJNanK2UuPY5DTJUJC+ZAJF8ult7+rr5tGTpzFsh0/u39u0IB3KpDk40MeXXjnKeKFISFGYKVXoSyW4c+PANedvMJ3iS68e5cGtm4gFNLriMZ44e5HbBvoalOIoR8YnefrCMKIo8MyF4XUo4qsRUGQe2DLE/3r+Zb7w0msMptMYtk22WmN3ZzuO62E5DplImLplYTsOtUbLWVhVaY1GGMsXKOsGhm0jCv5GSJNlZFEkoqlYjsOdQxvW/PyqYXJ+NothO1yYXaCsG3QQZSJfZKFaw3JcLMfhzMw8m9oyJENBjk1MI4t+f/90sYwmS3QlYtQti2JdZ75cRRAEjmanyERCiILgV7JSCbLVGqlQiNbo2htgw7Ip6waRgEpAufbNn53MUVrw+1YrxRqKJlPOV2nr8XW0ZseyhGNBLp0Yw9QtNu/bQCnnv79e1THrJtVinUK2hCAIVAo1X3/LcjBqJje/dy/R5JvbrPtCtS7DlZfoDe+jaucw3RrdoV0cuTiBLIqoqkxrZ4BKqY7reljOAhXzDTS5k6L+GlFtD/n683REtrPj4CYiiWtbTP644NJCDkkQUGWZVCi47Hmz/j1TMv2gJqyoV9xbwoq/m82WuJyfwLJt5hcqbBvqIBwJEAgo6y5kV4MoirQlImRiIcD1WVDi2poV4CcNMqE7iGm7KOiHmas9RU5/Ccsp4GFjOgvM1Z6iaJyiJ/YxuqI/jSIlfKosAnP6LIogk1ATb1uwZrk2pmvjeSzb/Ppi34tYSxx3PXi4K5zfBATfvS5fJhrWiIXXquQ3koB4hOUwsijh4lG1LEKKsiZFHnzXxal8idZYZAWDdflxBQQ0VcIwbEqlOp1tiYa+kcN615QkaDiuhSeuvzYGpDb6E595U0k18BNlESkMggmevaJov+rTVlSvZPrCQ9iehYhEWI4Sdg0EQSTppVFElaAUZsGcJaW2EFMSyIKCYImEpAiCIBKVY3h4K9i1bxZz1SqW69IfT1C3LUolnWJdZ6pYYmPLT4ZA/3LYtoNp2QwPzzM05ItYC4KA6ziMj2URRRHbcujsSq7620w4xIG+bs7MzOEBr45OsFCt0R67enDveR6vjU6Sq/mJxNZohP293YgCuPZlBCGC0BDPNmyHrxw+3kwaZcIhPr5/D/ds2kBHPIYkCLh4LFRqPHbmAl89eoLpYpnL2Ryff/E1BjIpMuG1xfTLusHDR040x3Ggr5t/c98d9KeTzQSF7bjcu3kjf/j0IZ654Gvw7e5q57fffReZcGjd1nfHdfn2yTN8/dgpdMsmFtB4aPtmPrR7GwMpv+Xew6NYN3hlZJw/e+kwl7M5stUaXzj0Gv3pJEMt6VXj3t7Ryn96z71NcxGAFy+P8beHj1EzLUKqyufuPMhAevX5ApqxyHqQRJE7Nw7wxNmLRDWNA33d7Olupz/lCxSL+Jv72VKFh4+c4GvHTmHYvjbUN0+cZn9fN5GASkILUrfXZym8cHkUx/W4oaeLT9+8j52dbYRUFV9E3+SHZy7w16++zkShRL6u85XDx9nX08nmtpbmMXTHomjXaJeTeK5HpWpQLutIokg6de24xcMj0NB+8/ClDmRRYku8jdqysXuex0iuwB89/zIjuTyCIHBjTyc/e8NOburvIdQoAFmOw7m5LF87doofnL6A5Tg8duYCu7ram0mvgBQnZ1xiXj+NgIBEhoDUjeW6vv7mT91Aa9dSYqNnqI1Y6icn/rkaZEGgIxRt3Bt+0XdxHkRZpGuwDTwYvzBDuVC9riTC7NgCM6PzxNNR7vzQgVVtZ6VcFb2ydoHlR4WwbOG71j506vIcc5M50u0J7vjg/hVJI4DSQgWjbqJew4jpetE91I6iyWSn8pRyFWLpla24rusyeWkWvWbSOdCK0tDyFQQBSfY1p2xrdQKoVtbJzRRWvb6IcqFGMVteVYjQqwbzk3k816O9r6UZ/3X2t6AGVbJTebJTeWKpa+voeJ5Htaxz8rVhZqcKpDIRWruSq8Kh06+Pcu7kBLblkGqJcvDe7QRDS/NbWKhw5NAFsjNFsrMlzp2c4BtfegFJlth54wAbt3VSKdV5/aWLzEzkmRxdIBTWePSvXkAURYZ2dLGzoRnouR7zMwXeODpKYaFCPBVm6+5e2rqX9EcLuQqvPnuO/bdtYm66yIVTEziOy8ZtnWzd4yesFjWZzrw+yvR4DjWgsHlnN/2bfId2PDj8wnlkRaKlPc6pI6PUawadPSl23DhAqGEc4HkerusxOZLl4ulJCrkqmqbQ1p1kx75+Ao1EoWlYXDw9xfC5GWzboXugha27ewmG15adeDvwjkgcGa5F2arjeC6mayOLEqbjoEoSVVtv6huJAoQkjbbA6kVVVSS6WuLNdpy10BaL0B6PUKobbO9qxfZcBM8PlkOawmihwOGJSVRJoj+V5AO7toLgC0w9c3GYE1MzVBt29t8+dZZMOMQn9u+lKxHj8TMXuLiQo2KYyJLII8dO0RmL8skDNzQ3nwcHejk8PkkiqDGYiftuIR6ENInP3LyXR08d4eWRUWzXJRVW2dOdwV8uHXpSUXRrDaq1INCbTLC3q50buloAl23tSba1p9jWnkaVPB7Y0ke+tsCzl84RC4R4YMsG2mNBIk09I4H+TIJIwBf4EvDnRBIEHM9jd1c7n7vjJr5/+jyPnT1PQJG5obsLD38h1hSZrniUyWKJkuNQ1k1iQY2WSEOwEtBkmYimNivz4BFS1aZg4XoIawqDLWkm8yU2d2SarV9tsQiXF3Kkw0FEQWBbeyvTpTJz5SoD6ZQv4CgItMUiuA1Hn2ylRtU0SYSChBSZeFCjblokQyFkUSQd9h0/SrrOdLFM3xrB21NnLvFXh47yqdtu5P4dQ+uOGxoLkSCQny8SDAfo397NucOXSbcnWJjO07O5k0QmSqVYIz9bxHU9SrkypVyFaqlGaaFCNOnbvxbmSgQj/jHOvHKRcr5Cqj2B+pbcPDwWjGFGq6+RN8eRBIWN0dsA2NLdSjTkP7gc3aFe83V+JEHCccvkak8SVrcSDxygWjpDfrZEdipPNBV5W4QH3wnY0pohV62v0PIQBbXJSgEabl9LbZlRVaM9FEEVV86B51m4yxg5/T1pov0DaKpMsVwnGFDJOWWsNSo81wPdtDg9NksyEqQjmfIzAM7cujbV/r0nockZWsMPkA7dStW8zEL9BeZqT1IxL+BhYTgzDBe+gONWGEj+KpKgEZSCdAW7kZd9R0nQVrQY227lTX8HAYGCWac3vBS4SUJ4WSugt4IJcy24nnEFCyqM6/gtiPFw8KrMLkVUCEqhZqLMcOxVFafl6G9JMpUvXdONxQNaW2Jk0v5mSRSiCEJ0VSv20vsdAlIMSVjJlJCEEDSe0KIYpDV0D5rc+qb1hmzrPJZ5GIdLiFIGCCEIEpKwVEl0PR3Ps6AxBlUMEFPaGxtTX6xWFENIoojp2Giiv36k1B5iSgzP89BElZgsoYgydccERAzHwpLe2vUOcO/A4IqgVowJjOcLHOjrWdds4McZmza1Ewj4xhb9A0ubckkWGRhsZXamSHvH2hoGgiBw96YN/P3rp6iaJuOFIkfGp3ho26arXiu6bfN0IwkDsKernf50AnDwzGMgdSNI/nV3ZHySb504je26hFWFf3r7Tc2WqOWIahq/eNMNpMMhfvfxZ6hZFq+OTPD8pRHet3Mr8hrjOTc7z+mZOQCCiswnDuxlqDWzIjmvyhJDrWl+fv8eTkzNsFCtcWp6lpJurJuc8TyP4YU8Xz1ygnJDs+eTB/byTw7sJX4Fuziiabx/11YSoQD/9haWYDUAACAASURBVDtPkKvVOTM7z3dOneXXbr+pYQKyhFQoxIG+lbHaXLnaLBTKksjOjjZ2drVjWw56zd8U27aDrMh+q4YFpXwV23JQNRnX9VA1Gdt2kSSRtKTyr285SDwQIBHyK/5OzUHQBGpVg1RrjI0taf7ZHTeTrdZ44uxFXM/j5PQsk4USm9oyhGQFz1t/zbYcl8FMin/7wJ1sa1+pdRbRND52426iAY3ff+I5CnWdy9kc3zp5hn+57Py4nkfdNpAE37FKkUUikQChkHpd4tgCAo5rNDPbc/Uyry9M0BqMsjm2pAlo2g6PvH6Ss7PzeMDOjlZ+58G7GWxJryqSHujrZjCToqQbPHthmLpl8cjRkxwc6KU/naRu58gbFwlJaWQxyII+RUtoMwnNvy46+jJYps3sxAL1ik5LZ3JNTZifRNRtG032k5OL52/RLEGSRLbu30A8HeHMq5c4/OQp7vzQgVWtS57nOzEubsY9x20WLwRxpfGCZdi8+P3XKReqRK90U3sboAVVJFnE0E2K2QrdG9d/r+s0WrzWGKepWxz67lGMukl07UfOm8amvf10D7Uzfn6ao8+e4fb371sxl7OjCxx56jR4HjfeuwO5EX+rmkIiE2Xi4ixTI/Ns2NHTZH55nsf5YyOMnZte93PrFZ1jz51h6/4NTc0kz/MYOTPJxROjaEGVzfsGmnFU54Y2Nt/Qz/Hnz/HcN4/Q3tey6n7wPA/LsJFkEUmWMHSLL//xkxx76RKtnQkkWUJWJMrFlXGebbsUc1VOvHoZ1/XYe/PGFYmjWtXg0pkpKsU6lmlTKtSYHMkiqzIbNvtFFr1uMnxuhoW5EoZugecnYyRZoq1RbPFcj+HzM/zFf3sM23QIRTRKxTqPf/0Iv/Ab97N1dy8AufkyX/vic0yNZhk5P4vtuJi6Rb1qsHlnD6II89MF/vaPnmRydIFYIoRRt3j864f5yGfu4MCdWxAFgWd/cIKJ4XkybXE818MwLL47nuOOB3fxM790O8GQhut6vPTkaR75s2eRZJFQRMO2XOLJEP1DbQSCKpZp892HX+G5x04Sa9wfT37rdXbuH+Djv3YPoTW0sd4OvCN2egWzyrxRou6YzNQLKKKEh8dAuI3pep6MFkUQBGb1Ah2B1JqJI8NyCGsqqdjalStBENjS2cJ/++h7KOsmXZkYT45fwnRsEloQLSBx784NqJLcXGhsweXFad/GfkNngg/t3rYuxeyXbzlwze/ZnYjzf7/v3ehOkenaYSRTwfZ0VDFKQIpyzzaFe7YlkcUAsqBhu1lm9TySoHJwU5n24M7V3wvoScb5z+/Zhu1MopsLbEoX+d0HVUThNHXjAjE1w2felUAQWnG9Eq47yq39CTTVn8eqq/PAvl4imsjxvG/FvNhrabkOkiCQiAe4e28XrueRUMPsiHdT0g00WSbU0NaJBwPYrstEoYQsinQlYr5uUGMDvkr3qAHLsKmV6wQjq2nTkYDGb73ndj5043b6M0laG21ZyVCQB7dtWnHcC3NZ4sEgLZGlBWb58W7b2L9q/q7ccHUn4+tuwlzX44ULo5ybzlLWr6MC4kEoGqBnUyfRZJhYKsK2d23EsV0yXSnS7QlkRSIcCzK0tx+lQTft2+LrxHRuaCUc9V1ZejYvHaNncwfTl/1gulaqo73JCocgiLQENrI//THiSgeSsPT3s4UKqWgITZGpVqp+QsMDTeogrG7DdOZIh+7G8epEtT2YeYdasc5b6rN6h2K2XEWVRKZLZTZmfDq8JIRWMDsMJ9sQMPbvk6CsULVNKpbBosaQ53nYXq1pKQ9+L/ZMrkilZjIzX+S2/X6kYtvOuqyWqyEcULl5a18jYW6AEAJBwHCLWG69eW4dz0B3iojIqFIYWQj6bjV42ATpiH6UzuiHmK0+xkT5EWrWMI5XYaL892RCdxHVdjJrzOB6DnEl3aw2KVISSQiwWPtdbuV+vTBdm7CsUbXNRq+3QEBuRRQ1cH2BVN1ZP9BZDl/oNt9MNElCGEVMICIx2JUhFQ9dlSnla/TYiMsSOu5VVDXHsgVKdcPX2ltDHHsRluVQKNZQFYlkPNS4XTxYp/dcFjRUKYaAuGK8Abm90b7m4rp1dGcWSciQLVQoVXRaUlGchhuU6/oVK0FYbKODYEAhEtQAEUEIIMmDCI3rWhQUNGkpMWE6C9hupdl+WLV1LlemCUkBylaNeaNIWyCBJEhUHZ2gqJJQI+TMMjO6X7EciLRzvjyJJipM13MEJBVRENmV6F93rtaD47oUDJ2KaaLbNpPlEplgiIikAgInJ2doiYYJqW+vi9v/TpimzfnzM/T1Z7BMh/GxBRKNANHQbYqFOv0DLZRKOp3rbMQ3tWbY093BocujVAyT5y+OcPtgP9F12rc8z+PU9GzTBUyRRN69bVODlm8giLHGdetRMUy+e+oss+Wqz8YYHOC9O7asShotQpNl3r11iO+cPMMroxOYjsP3Tp3jnk2DqxI2boNFUmiwjboTcTa1ZNZkS4uCwK6uNlojYRaqNQp1neMT0+zual9zHJbj8sTZi01tpO3trfzcvl2rxrAISRS5eaCXO4c28OjxN7BdlyfPXeLDe7bTn3rrO0XTsLhwYrxZYdZrBh19GQrZMsVclUjcdyOyTIfeTe3MT+YJx4JUS3VUTaEwm+PidAE8SLfHUQMqhWyZ/fdsQ5IlkqEg79u5hVdHxsnXdXTTZiSXZ2NbutlytB6CiswHd21j6xVJo0UoksT9W4Y4dHmM7546i+N5HLo8yicO7G0y2hzPYW9ykLJd89dDx8WynTfFrrW8Gp7r4nkuHaE4t7UN0hGKNdZaH+fnF3ji7EU8z3fs/cWb9jHUujbzVhAEMpEwP79/D4fHJqkYJsMLeZ67NEJfKkHJmsLxHIqW7w4VlLuZrpQIKUozWXLshXNMXJwlGA5w/NB57vnpAyR/ggxC1oPfiu/rqli2Q7VuIEtSc14Gd/Vyz0du5vtfepYv/s7XGTs3w65bNpFqi2PqFvNTeS6dHOOGO7ex86Afv7f1ZWjtTnHh2Cg//PIhguEAoWiAWlnnhW8f4R8efgn5bbCZXwvJ1hidG1o5/vw5vvPFp5EViXgmgmO7WKZNR39LswWsa7CVdHuCyUuz/PDLh7j3IzcTCGuUFio8960jvPDtIyhrmG94nodpWFi6jeM4uI7rawR5fpImP19CkkRESUQLqsiK7y7b0pXi/Z+9hy/89t/xN7/3LXIzBW68ZweKKjM7tsC3/uxJLh4fZe+d2zhw/67mPRVLR9hx8yaG35jga//zcQRgcGcvlmVz4fURHv3TJxGucv/JisRTj7yCJEvc8t69BMMBpkfm+bv//gMK82UOPrSHjbt7m8+ERCbKB375XmZGsnznz58iN1Pg4Hv20taXwXVccrNFLp0cY3ZsgY/+i4do7Ulz+ugoLz91lk/+8/u49f4dOLbDo399iFefPbtiLLv2D7BjXx/f+MsXeOGJN1aNtbM3zWd/6z2UCzXGLs2x/47NfPRX7lrBaM60xfnE5+6jVjH4vf/zYRLpCP/8P35oxXFqVYNH/vxZUi0xfuHX7yeZiTA7mefz//l7fP0vnue3/stHmoXxSqnOxdNTfOY3H6JnoAXTtLEsB0kW8VyP7z/yGjMTeX7t372PgU3t1GsmX/njJ/nq559hw5YOWjsSeB5Mjizw05+6nZvu3ornejz8p0/z3GMnuft9e+nu15gaXeDhP32anfsH+OAnbqGlI46hW1TLOsl0xE8Cnprgh48e4eO/ejc33bUVQRR4+akzfPEPfsCuAxs4cPvmaxY13wreEYmjFi1OUo1geQ4ROUBSjZBQQiiizIZIWzNw3hjtQF4n0BaAyWwRw7LZ0Jlek3mkSBJ7en072ZlqGa/k0RGOEVVUgrJC0dRxGlo9Aj69WpNkBARSgbevoiAKMgEpju0ZBMUUkiDjATGlk4o9hyRoBKQ4dTwczyQspxEQUcT1xyCIIXBlXLfkt0F4HoIYQxA0ZKkTx83ieRaiEAFRRpJaWdzsS4JIQvXbM0RBwG4kixzPIyApLBrQaqKCJIjElSAeHokrgqxFt45Fps7VWgsXX9NrJsVsaV1hQVEQ6ErG6UrGV/1uMdhaTPQMpH2K65uh511tbFeiUK8zPJ/D9a7eNtNMPIkCbb0Z2vuWjheMBFYkpnqj/vWYXkbPbe3x2y0S6wQi/Vu7cWy3mXR6q3Ab7ky6U8TyDGJKG7IkcvTSJJ3pGK2KRqo1hiiJiIJGKngHppPFcat4OETUbYwVa8xP5pi+PMvGPf1veSzvJCiSiOW4zf5l/7UkipSg3kiM1K1xXM9GbCRmXM+3oL4i1YjpZJvC2OAnjobHFyiUaoRDmr/gSCJ63aJYqDXpp9cLSRSR1KVqEkIIQerEtCvkzEuYThVRkAjKKWrWPA424JFUN1C3c4iCTN3JkfAMWgJb6Y7+HAG5gzPZf4/lFrDcPCXjDSLqNup2DcdzcJZd/5rUhixGoZHYqRhnfevkNyHGJwoCs/USkiDSE04SElUUMUlAake3p/CwqFtjjfm++pLlYlK3xpqMI01uQZESSKJE9ErHkTXgeDYFq0BUjqLbFpbroFxF4y6kKcwWy4S1q7Wt+kG1LIuEw4sVPAsPgyUdp9XjsNzqqu8bVgYRBAnPs7HdClXzMoI9xBuXZtANi7GZPHXDoj0dY3y2gOt5BBuMBVEU2be1m0hQQxTjSFInyxO+ohAgqHQ3/6/b0xhOFk3yTRwUUSIoaWiSjEcQVVJo1eI4nkvI0dBEhYwWxfH8VkHdMdEdE01UiMpB1HArQmN90cQ33+PveB4FXefV6QnCispctUpQlpldqLBQrRIJaL4b5BrB+48risU6p09PMjdfQlPlZqsaQLVmMDtbxLIcunvXT1rGgwH293VxeGwCw3Y4OjHFTKm8buLIchyOjk9RqPsJ775Uki2L7UdeDcQECP7aNF+pcmR8CoCQqnLTwLVZXxHNF+k+PO67tY3mCsyUKquSNo7r28wvpjbC6tW1fwKy3NQlBJouZmuhZpq8OOy7zUmiwI19XaTXEc9efvztHa08dvo8NctiplRhLFd8y4mjxdhF1WTCsSBqQKG4UPGt7uO+uGuyNUa9auA6Lq7jIasyoWgAQRQIx4LoVQMtoCKrEtFE2Gf5BX09oUVsyKQIqgr5uh/fVgwTy3EomQZRRVuX+RMLBNjf131VWYOgInPrhj6+/8Y5XM9jslBmZCHfdLhTRJmqraM0nmOKLIEH1Zp5Xa1q4GvU2a6O65nYrsjlcpaabaI7Ni0Bn1H++sQUc2X/fPck4+xcR1h8OfpTCTpjUc7PL6DbNudms+iWTSawGVlQmKkfw3CqaKJJQdcpGjqtQX+OS/kau2/ZREd/C899+yhG/Ue3X/9xgOt5TFSKbE62EFRlWlPRFfqWWkDl/b98D6Zh8fTfv8Kjf/IEP/ir55AVCdf1sC2/Nbp3U0fzb+LpCA/8/K2MnZ/mH/7uJY4/f5ZIPEwpX8GomdzzkZuYHVvgjZcvvO3fJxQNct/HbuHi8VEOfecoJw6dIxgOYJkWmc4k/+pPPk1Hv//sy3QmeeDjt/LXv/dNvvWFpzj03dcJRwMUsmVsy+H+j9/CuSPDTF6aW/EZpmFx6DtHef2ZM9QqOkbN4OLxMSzT5tlHX+PyyXECYY1AWOPOD+1n9+1bkSQBWZG4+aHdzE/m+MYf/ZC//f1v883PP4ksS9QqOvWKzq7bNvOxf/keEsuE5gMhlbt/5l2cee0SF4+N8j9+42+JJEM4lkO9arDzlk0M7enjya++tOactPdl2HpgkMf/5nke//ILqJpCrVSnUqqzdf8GPvgr9xKJLyvOiwJ7bt/Cz//WT/Hl//Jdnvn6q7z82HGfAeV5OLaDUbfIdCX52c+9G8d2Gb04ixZQ2P2uDciKzzbac/NGvvd3r/5I59NwbM4UZokqATpDcZ/peB37wYW5EhdOTbLthj5eeup0oxXcxbYcZifzlAs1Uq3+mqeoMntv3kjvoB/PBIIqi+mBWlXn9OujCMAbR0Y5e9xPPtcqBtnZErOTBVo7/P1x90CG7Tf0IUkigiwwtL2Lx79+uPksuXx2GlO3uOPBXbR2+oziYEgj2Ihjbcvhwhs+22p2Ms8T3zwK+K2Get1i/NIc+24ZWiGu/XbhHZE4kkQRCRENhW3xlarr8nXa9jquS1U3sWyHTDxMS+Lqi1I6GOLunkHUxqSux4ZZxJtJRjiuS123fOX1wOo+Q1UM0xFaZA8JTT0OAUhqfc1AKUZX43WBsHJ1q25ZakWWWvE8x28vaLQyLH6GJCWbP1+pGpXWIqTU6+/Rvtpc+O541w/LsJgZyzZZNtfClXpXAPWqQS5bJpGKEI7+4zhbeJ7H2EKRsYX1e4MX4eJSMAu4uGTU1VWv5fP3VnpQA2GN7TddvU3uejCjnyUsp6nY8+hOiZjShipLKJL/z9AtAiHfHcN2i0yV/grDmWluZoPyBjIdH2Q8NEm1dP2tRO90RDSVlnCYRHBJ30gR44TkXkrGScCjZL6B5eaRRX8B9TyPqm0hLbMkdjy98f4laKrCjk2d1HQTTZUJBBTmdAtZlgj8yM4goq9ZY48RVragSGE/iYOAJKi42kZfRwgbVYxgKbVGEsJFFgMIiIhigJbgnYwpAxSM1wHQnWkEAdoCHZiuseL5qEhxoto2KtZ5AIrmKSrWRSLK1VthlsP2XNqDMRJqsMl0lMUIycB+CsZRwKNsnkW3pwjKV3eEspw8OX0x+BCIKENoUiuSKDPU4weAV3UUESSSSgqxkThvCYbpiPiM17WIR5lomM5kjMpVGYi+acNCvkYqESaTiiAIQWR5I7D+OY8qXQSklezaqLqZgNRBzR7B9irk9BfJJO/mhi3d/n3aEErXTZu2dJRYeJHF6Qtsh4Oq39biVXC9IpLQt2yUMlF1G7IYw3ZLWG6BhdpzxNStgERAUtkcXfmcXi4IvnheBiPtK/7fEUytIRouXJFc9FuyrwaxUZjYmm4hrKj0xRK0hcMIccFvR/a8Fc6SPwnIZCLc/8AuXNdFkSVS6aW4JpkM092dIpstU60Y694XoiBwx8YBvnHsDcbyRWZKZQ4Nj7GxNbPmWp2t1njmwjCu5yGLIjf1d9MR911GPeIIUi8IYUDg3FyW+caGPaqpq1qa1oIgCPQk40iCiINDxTQZzxfY3LZyrfSfW0vHsl0X5yr3ruN5mPbSNbQe6wlgNF9gLO+v5bIosquz/brG3RmPElBkapaF5Thczua4fQ0m8/XAcTxqhk3rQCuyLFKrmQTSEURFQtZkEh0JqjWTmgeZlhiKItHel16XrbPWvQgQUpSm25mHP0dio8XoaiKtbdEInfGra9SIosjGlhTJUJCFao2KYTCWL7K/r9tnjkoKcSVESo36WpKSuKYD4NUQklux3TqSGEDDoyMUY7icY3eqC0EQqBoGb0zNNp3rBjMpUqFrtzWFVJW2WITz876T5HihSMU0aVHD1J0CYbkNSShQs2s4nktQ9t2tnnjkZS4cH+P0q5cIRgLYlsONd63vNviTAs/zKBo6rudrPUmiSH/H6oR1a3eKT/3Oh7n1ffs4/A8nmbgwS6VYQ9UU0h0Jtr1rkL13LrXSy4rMXT/9LpJtcV763utMDc/7Vum7e3nXu3ez4+YhXnn8BKZuEQgvxfeyLDK4o4dKsUY4vrqAmp3K49guW27c4IuZS6uvOVmRuPW9e4mlwrz0/WNMXpxFr5tUCzV23bJ5RXucFlR58JO30dqb4tB3Xmd+Moeiymw9sJED9+1k6/4N/PArL3Li0LkVQtWu4zI/mWfi4kzzta7Bpf1cuVClXPCfofn58grmeSCk8cFfuZdtBwY5/OQpLp0YxzJt0u1x9t65jX13byfZElu1pxja28+/+pNP8dy3DnPh2CjVUp1Ua4wdN2/i4Hv3cuHYKLNjWVLtaxTkRZEP/B/3ctv79vHi915nemQeLaSx46aN3P6B/bR2p1Y9K7Wgyp0fPsDG3X28/sxpzhy+TG62hCBAui3Oxj19bH/XRlq6UziOS7lYR9VkIssK39F4CEX90ZIcgiBQtgzOl+aZ1yvsSHagSddOc5QKNQzDYmJ4nsqyvYysSOzY17+CoSUrEqmW6JrrRb1mUqsYGLrJ0RdXJjpvOLiRYHjpuojGQwRCS/mBRUaT1yjMlgo1ZFUi3pAruRKO41JYqKDrJqcOj6xgFm3Z3UOmPf6P1gjyjkgcXQnP89Atm8vzOV65PM5MoYwHdCdj3NDfxVCb72iwfC41xW8xa0tGiTUcrkayeR559QTF+sqgXgBu3tjLe3avrdK+1klyXY9iXefs9DynJmaYr9QwLAtVlkmEAvSnk2ztbKUlFkYWRC5NZgkHVQa7loKhRQeCuXKVNyZnGc0WWKj4i1JIVehKxtnV005/JklE8y8o1/OwPRsQkAWR0YU8f/vSMRRJ4ucP7qEtFmGqUOLFi2OMzOcxbJtUOMS2rlb29naSCAUa87T8OwlUDZO/eO4whbrOgzs3cUN/F9lylWNj05ycmKGiG4Q1ja2dLdzQ10lrLHJN21zX88hX65yfyXJifJpspYbreWQiIbZ3tbG9q5VkOLRSnyCgkJsp0tHnb+pqhsnfvXqCi3O5VcfvSsT42XftxBZcElqwqS3gOC5nXh9j864ewtEAl+cW+NILR4mHAvyTg3tJhYNMFcq8cnmc4fkcVcMirKn0pOLcONBNfyaBvGzDv4iqYTKSzXNpboGRbIHjY9PkqnUEQeDRI29wdHRq9RiTMT59xz5yZo68lSelppCWJT89z6OsG1yay3F8fJqpgu96kgwF2NbZxrauVtpikWbf+HpwXJdspcbRkUlOT81RNXxdqR1d7ezqaSegyFetFAJE5AxnSk+A5zIQuRmAmXwZ23FxPZfNsQSWYSNKIqYzj4fLYOrfIS1LluRqBVp7MvRv777aR/1YYaLgC+F3xGJsbVTaBUEkE7qd+dqTOF6dujXOfPVpemIfRRAkZFHi5vZekpq/EHqeR80aIae/suLYnucxu1Bidr6MpskkYiF03fL1H94OjShBACeHiEywkXRYr/VSFtdOsvri0s6y9/l28mW7RN2pEZRCpPFZcQIyraF7ma0+huvp6PY0E6WH2ZD8p2hSmutZuRRBYqKWR5VkHM9FQUIUNDKhO5isfB3TyVKzRpivPUlP7OMIrM1o8DyHbO0ZyuYbjXFHaQndg3QVpuaVMFyDsl0iqTQYgIK/YV0r2VQzTU6PjZIMBwlqCkPt64uSNwbYbA3xvAqeV4d1NY5cCuZlInIHCa2/+XpA7iQTuoPx0jgeDtnac7SEDtEave+6WF6LLAdRCOGJrbDs2SQIAnFtJxFliIJxBICpyrdIBQ8S13bhO8Rdm6F5vSxOWYw1v7/jGRj2HGFlYN2xO57LWKnIa1MT9MTiFA2dvW2dbEqmsRyH4Wy+4VT6j9PW8L8DgiAwO1NgZCSLZdrs2NnDnr1+ss9xXIrFGoIgUCrWr6oZ05dMcNNAL2P5kxi2w7MXhvnwnu1EtdWso9cnprnY2EwnQgHu2byxqc8DLp4ziSB1AGEuzS9guf6zomwYfPGlI0TUa7MmR3L5pr275TgU10i8SqJAVyJOQJbRbZvpUpmZUpnWyOpA2vN85tJCtdactw3r6BsBviNYw1Ladly+fuwUz14Yvua4ZysVqg0XKdfzyNffesHEth0uD89jWQ7tbTGGR7LYtkM8HiIaCaDrFrbtoGkKFy/PEQ5rbIt0rGrbWdSM0W0/meW4Ho7rNpKpMF+pLT2/PJqFyoV6jbptMRBbe57aYxHUa7QICUAiGGwmjjz8BIzluEiiSNYoUbMNWgJxtsd6G0Lvzpsqknh4KJKfMDVdm+FyjrKlM69X6AknqBgmo/mlgt65uSz/12NPXzP+MR2Hi/NLcWZZN5qJx4Taj4fLvH4a3dYQBRGnsQ7c+p693HT/Toy6heu4CKJAPO0XXv15tvCfQQ6C8JOlueZ6HttSbc2C+1oQBN/ifM9tW9hz2/pOWMuhBhQO3LeTA/etluMAuOvDB7jrwyvlQCKJML/5+V9a95gvf/8YkizyB9/7TWRl/fEqmsK+u7az767tAORmCnzpP32DW967d5XpixZUOfjQXg4+tHfNY73/s3fz/s/eveK1YDjAR379QT7y6w+uO4b1IAgCqqaw8+Amdh7c5LPdqjrhoLrClbGumzx//DLbBtrpbk0gigI9mzr4+L/6qTWPm353gpvevXvN37muSyCocuD+XRy4f9d1j1WSJfq2dNK3pZMPXOV9tuUQjQcxTZtqWSfRKIZUy3Vs6+rFo2vBw6MnnGBH0me0Xa8JVDQeQgsoPPizB7j3fXuv6Qa43jobCKmEIhpbd/fwmd96CHUNJrrruI1jcFXZhGg8iGU6FAtVOnpXJ+skSSSRCpNpi/O5f/+BJiPq/wu8IxNHZd3gq6+c4FtHTzNTLGM0HuaaItMaDfMz+3eysW2l6J1lu9R0i3BARW08JBYqNb5/4jxzpZWCrYIAYU1dN3F0JTzP4/j4NH/z4uscGZlsLjCLFvSyJBJQZNLhEPds38inb9uHaTlIor3iZJ+fyfKt18/wwvkR5ssV6paN5bjg+W0EmiyRCoe4c8sAn7lzP5lIGMdzuFSZISRp9IQyLFRrfPfYWTw8bh3qY2yhwB89+TJnp+fRLQvX9cWmIwGV2zcP8Eu372egJdnUvEDwbybDtvmH0xcZWyjSFosQCWj8r394kWNj05TqBo7rL/xhTWVHVxu/ePs+bhrsXfdG9DyPY2NTfOmFo5wYm6ZYN7AaFSBFEokGNPb0dvKp229kZ3fbiiRUvWI06a6G4/DC+VFeurRaK2VbZyvv2buF85V5uqNxtiT86qYWUHFsp7nxnitXefToG0QDGndtHeTE+Ax/+cIR1cuVmgAAIABJREFULsxkqVsWjushif45607G+NTtN3L/9qFV7Y2HLozyP554kWy52jhXTvO7Hh2dWjNxtL2rlU/ffiOiICJeoU/ieR5juSJfev4Iz58fJl/VMWwbz/OQG+dsqC3Np267kVuG+lZUW6+c6yMjU3zphSMcG5uiops4noss+vO8q6eDT99+41Vp/QA9oT2k1F4EBEKyHzy2JiLMFSps6W7FLRrMTxXYtMtBCoQBj4p5Gk1qA0FAQKNesRsubT85GMqkuZBdQL6iKhrXdhNSBiibp3G8GlOVR0kE9hLTtiOLIt2RpeqN7ZaYKD9C3Vp5jfhaN3W62hMMj2exHYf2BnX1asHN9UNCkPtWvOJhUTROoEktBOXuqyYYXM8mp79MzZoAQEAirAwgCTJxJc6cMUtADKywiI8HdhPX9pDXXwFcZqo/QBYj9MZ/AVVc7ToE/jXseia6M43rhcgEorQHY2gN4W1fcHwr6eBBpivfwcNhovz3hJUhUsGbVrVw+dXQU4yXHsZ2/epdXNtNKviuNzV7iqAQkkJoUoCQ7LfmTlfK9MVW6+oFFJlbt/QTVJWmu8x6bCYPyKQjy56fKq6bh3Xd4jwMp4Im1lbMtShotIUfIFt7hpo9iuUWGC58AVVMEQ/sRRTW3pB5nofl5qmY5wkrg8jUcN1so11tCbIYoz3yXorGCb9F0J7gUv5/siH5K8S1Peu2Cnqei+HMIQoKirg6yFkLQbkLUVBwvTq2WyKnv0IicMO630EVJQ529TBZLtEXTzBTqTCczTG3UKFqmsyWKmxsTRO8CovrxxG6btHRkSCfr2IYSy0xkiTS3ZPmzOnJpu4R+Akl23GRJbHJTgkqCgcHevn2iTPots2F+SwjC3l2dLStOFeW4/DS8BhVw7dr3trWwtAKpzoXz5kGQUGQ2slWa821u2paPH7mzbeTuK6HYa8WSxcEgc1tGboSMS5lc+SqNR4/c4HBTHpVO1zNtHjszAXmK/693xmLrnD3uhL5Wh3T8T/T8TyevTjypsf9/5L33mGWXOd55+9UvDl3385hOkweADMABhhkgAABEgSjCDBKFIMoWdbKsmRqvV5LXluPH8myLZna9ZKWREmmQGqZARIESQBExgCYGWBynp7O+eZYcf+o27e7p7tnBpT9LMF9H/yB6b63+lTVqVPfeb/ve18XqK3jWnS10HWFXTu6cF0XWZZob/M2fELynvamYLAQdHbEvOTCJe/0ct3g3MIi5+YzXFjMMFcska/WKdfr1G2bumVRMSxmi8VV31MkiYimYzobt92HdA35KtwPfaqCf8W4SnWj2c7fG2jBdl2sRlu8pingsq7d91p47q+SUJpabwJBeyCCVF2unDcajnFLGFnMNrWr3goMy2q2YVtuFV2K0uHfg8QodX+gSZ76gzojJxZ448XTVMt1VE3hnY/cTDQZAhxc6wy4ZYSIgnp1e4y3A4QQpAMhcvXq20LS8p5H9nnPlvLWNO+iqTCf+oMPEoz+jxPjzhYrjM5kSUWDhPw6o7NZ4iEvoZUpVIhH/ESCfi5OLxIL+YmFA0zN59FUmWQ0yOhMlqBfIxr08fSBM2zrS7OpM8X4bBZVkelrT+ACpWqduWwRXVVwXS/BnNzAZfxyWC+KacY2VyA8rgRZkegdTFOvGry5/zy33rcDx3Y4/OqFNeLYbxV5o0amXuFEbobb2gaaFexXQiodYfPOLl548iibd3TRvakF23bILpSwbYf27o1bwVciENDZsbuPV589ybEDF9l14yaEJChky5QKNdq7E1ddbdm/uQ1NU3j+h0dJtkRItUUx6ya5TJlYMoSmKwzv7OL7X3+V5548yjveex2hsJ9Kpc7ibIFUOkJgAwmYfyx+7oijmmnxf//0Nb7+6mGv3zgcZGdLnGQwQK5SYyyT478+s59bh/tWlSUrskRPOoa+Ims/3JbiTz58P/PFMvlKjbOzi3zn0PEmAXA1cF2XM7ML/JvvPc3ZmYXG5j5FOhJCV2Xy1TrzhRILxQoT2TzZchVdUShW6s3y+aXAbCJb4DsHj1MxDFrCQXqSMdKRMKoisVCscH5ukYlsnkf3H8awbP7Fu+9AlqFkei/FlQFeqWbw2JsnOTI+TbFmsLW9hVQ46FVqzWWYyhV47I2TTOUK/OuH7qE3FePwxWkUSWJn77JOgmFZvHphnOdOjXB+bpGuRJTr+zzHtPFMnpH5DC+dG2Usk+PfffA+9vR1riGPLNvh5XOj/PvvP8voYo6ApjLQmqAz7pVQTmYLjC3meOrEOc7PLfJb9+7jnu3LWUxZkbBtx9ss6hq/c/+tTOWK5CpV5gtlvvfGCcYz+eU5YltUTGPlXaJ7UyvhqH/VNcpXazx26ATPnR6hVDfoS8XoiEUQwFgmz+hijtMzC/zpD1/AdeH+7UMsNCpBEskQXYkoD167xQtqXTg/l+GpE+eQhOC24T62dKwNTNOREEK4yELGLy9XOriuy4mpOf74B89xcHQSn6rSk4zRnYiiyhLT+SIj81leuzDB6EKO333gNu7bMbTGcc5xXY6MT/OH332K0QXvpTGQTtAd9za2k9k8B0YmmM0XN3QY9DaRVWwsZKFQtOap2nnS/s20xkJMLOS5OJel1x/Eth0cx0VGwrBnmCk+itwQy/Urm2jv/wRtfa1vqfT85x1juTypYIDRhn7FUjWaX+mgO/IRzmT+BMspUjROcmLhf6c78nEi+nZk4cNxDarWJJPFb7JYfRFZ8uO4Mo7rvRA1Taa3K8HpC3P0dibx+zTCQR/xlI+qNUGtXsVx69huBcspYTo5CsaJ5tgMe4Hp0uNochJFCiJLIWThQwgFn5xCRuBaF7x2kgZsp8Zo/iuUjQuEtM3EfLuJaFtR5VhDo0nguhaGk2G+8iyz5R9hOhlAENF3EtWv9apNkFCFukosWgiBJiXpj30OY3GBsnkOyykwmv87srWDtATuJqJtQ5XjgGhUJU1SNM5SqB/DxaIz8ocktMAaolSWfPRFP03ZuEDBOE7FHOHkwh/QFnqQlsBdqI2KKssukK29zmTp21RMzwnKr/TQH/s1dPnyLb6XwtNwspEbwuE+RVllq70S+UqNkuFtUDKlKu+9fuN2BcGyrofXzivAdcBdXxtDIGG7Bpa7OpASwrsnfbHPNuZhgaJxkmPzXyDhv5mk/zb8ajcSMg42pp2hak1SqB+laJymbi+ws+U/ENOHkeVOvIqf1de9LfgAudohZstP4mKSqb1Cee48qcAdJHw34Vc6kITWPH7ZPE+hfoKScZr+2OdJB+9fc8z1EFB6iWjbydRewXHrTBS+jix0UoE7m0L0jmviuga2WyekDaJIIfZ1dnM+l6ElGGS4K4nruPhVlUy52jRq+EXClq0dZDJlspkyqdRy65DjOEyMZxgaaiOxwqo5V67y5tkphrpSdLd62giSJNjd1cGmVJwTM/PMlyq8MjLOlnRL8x3jui6TuQKvj05gN9rU7h4eaFRxLUEgqVuhcX8qhtlcDyQhmpXSbwV+VdmwsqUrFuWD127nz599mbpl87UDR5grlnnn1iG6YhEEgtlSiR8cO80zZy5g2DZhXeeRPTvpXUcXcQlV08J2liUCgrp2xYrqS6FI4orJmctBCIF/RUvLUvHXSvJ56Vpql1SjluoGr4yM8bWDRzgzt8BiubKu0LUQnoaldckaZjQE9Psi8Q3vlybLXM2tVCVpRUUaVA2zOZapaoYOf5KxyjydviTFUg3DtKhUjI0O14QL2K5JzcqiySFcXHJGhYCisTvZTVDRGpppDlVzeR3VFRmf+tbXgZBPRxKCqpXhYvE5FElvmEgEydT8RPXlTdjpN0eRFZn+rZ0szuaoVQ282SbAKYF1ErTr3/IYft5RtgyOLs7QFY6hSjKO7XDx5CRPfe1lcvMFEm1R7v3orXQPtyFJEod+epwLx8Zp72vh4NPHqFcN3vv5exm6thejavD89w5w+sAIxWyJYqZMW18LH/29BwnFg+x/4k2OvuSRc5FEiHf/6p10D7czN77I4//tGfq2dXLitfOYdZPr7tzGvgd3o/s15iczPP7lpxk7M83OfcO8/zfuQzTa1GbHFvjaf/h+08W2lCszsKuHT/zL9+E4Lm88c5xnv/UqhUyJX/3DD9G3zaukzy8W+dYXf0Tftk5OHxihXKwwuKuX+z5+K4Gwn3rV4MXHDnDq9QsUMiWKmRItXUk+9vsP0dKZ5NzEAmOzWcJ+nVOjs5SqBscanTBdrTEuTmfY1t9Grljl7Pg8Hakoi/ky12/t9tyWy1VOXZxl95Yur2JPVzkzNse5iQUvCa6p+DSvlbJQrjG9WERVZNoS4Z+JOFqC4VgUzZrnhOrYlO0aQdlHQNGaOsC26yALCUVImK7dSAp6ZLFf1prmVksQQrBtdy833bWVr3/5WZ5+7A1UTSESCzSdwQDy2TJPf+8N5mdynHpznJmJDH/zZz8mngxx091b2byrC+mSNTuoaMT1AKokXxXpvYRAUOfhz93JV/7Tj/jP/+pb+IOeq5nrutx67w4e+vjNV3UcIQne9ciNFPIVvvJnPyLYIG5s22Hbtb18+HN3rFrzL4eu/hYe+fydfOMvn+fkm6MNpzWHUMTP577wbtKdcYZ3dPG+T97Cj791gJd/chxFlbFth1DYx6d+5/7/fxBHjuPy2oVxvnvoOHXTYrgtxe89cDvX9bajyTKW43JhPsN/e/Y1njpxrvniB+9ltdS7vYSQrnFDv/fguy4cHp/miSOn3xJxZNkOz568wJmZBUK6xv9y7y08eO1W/JqCABzXy/qcmVng+NQsu3s7kIQgHNDXbKav62nnozddQyzo5/q+TroTUXTVO47ZIF/+9IcvMLqY47nTI7x39zY2dyaJqgECir4qoHBclycOn2YoneT3330nN/R3ocgSrgujC1n+9qVDfO+NExy6OMWj+9/k9x64HV1VyJZWb0Rc4NXz47RFQ/zL99zJrcP9hBsuXblylUf3H+arL7/BeCbPXz9/gP5UnFR49UJ0Zmaev3jqFUYXc3Qnonzqtj3ct2OoKb5Zqhm8dPYiX3r2Nc7PZfiLp1+hKxFle2e6aW+pqHKz8mZbRyvbOlpxgWK1zsGLk03iyHJsesIx/IrSXIyEEEyNLRKM+FZpHLkufPvgcdqiYX73fo+ICTXOLVuu8u2Dx/mr5w8wXyzz3UMn2NvbycjZWXx+jUQyxNb2FjY32k9c1+XJo2d56sQ5ZElw97YBPrBn+7pzxtOCcDGc5eBooVThvz6zn0OjU6QjIX7l1j08sGuz10qIR5g+f/oiX37uNc7MLPCln75GdyLGru7VrjCFao2/fuEgFxey+FSFj998LR9rtOOBt5l95uR5/s+n97NQXF8c1MUhb04zWzuN45pU7QKtPs/hIl+uEQ36SMdC6JJK16YWJEmgya30x3+fsnEK08njU7oIqIPI/xOE1/6/hibLZCpVinWDC4tZhlqSCEAIhdbAfZSMs0wUvo6DQdE4ycmFP0SVoyhSCNupYNo5HEx8Sjt90U8zXXqMfP0w4M3JQrGG6zhkCxXsRmWAYS9wfP4LlIxzOA2NMg9e1nUJVWuCM5k/ZoX0PJ6Nus7mxO/SqrYipLWtB45rUbEuUrEuMld5CkkoKFIYRQRByDhOFcPJ4rgGXhWMpw/UH/ucV2GG18aVM7NE1eiqjJMQgrjveoYTX+BM5k8om+cbVU5vkq8f8bSThCfY6p2b02iXcAgovdQdk7ojryuYHFQHGUr8Lueyf0a+foSaPcXF/F8yUXi0QRxJmE4W26k02usEPqWDwcRvE9OvQ7yF4AFAEhILxjyO65DUUrQHwxs6D/k1le54EttxNhQa9uB937IcajXTaxWTW9F9dyLExu2C/gbZ5vW8L7eJCWTagu/GckqM5f+Wmj1DzZ5hqvRdpkuPI4SMQMHFwnVtlueQi9T4e65TwLHnEGJ1ebNncBBhIP5PkYTGbPkJbLdK3Z5lsvgNporfbpCIavP4S8cGd5UQ/JWgyQk6w79EyTyLYS9gOlnOZ7/IWOGrjTY2F9upYjslNDnJrvSfE9Y20x2J0hXxtmlvjE1xenah2ap+39bBNWT72x0jF+bJ5SqEwj7UVVWJnj7i+PgikiyRagikBn065ZqxJv5oDQfZ29fNqdkFHNfl2bMX+MA120g1XE9d4PDkDGNZ713bHg2zt6/7EkJX9fSNpOjSEJpIh0P8zt23IKsuBbNMxaqhSjIlq4pA4JM1WvUEObNIzTbwyRplq0rFqRGI1TlRGEGTVNp9ScpWDQeHVj3Oh67dwUyhxGNHT5Kr1vj+sVM8eeIMPlVFCKiaJpbtgIBkMMAnbriWh3fvWtXKcSlWnpJPVfhnd91CKGhTsoqAiypUhJCQhYzpGJ4+mBImZ2aQkJCFgukabEpEmaqOIxCokkbNrqJKKjW7RkyNE9U2bperVepYpo1jL1t8AziWQ6Vcw3UgGPF5VWPCa2XWfSq5ao2/evkA/3DoCPlaHYFXUdYRDdOXjNMeCRP1+whoKrqiUDVNvvLKQRYry7Hfkl7Y5XghZ/1lb53PuatiU4+AW9LPjHCxPOut7d4SwUB/KwH/1RE7qvAT14cQjZbW6UqBNxYnGIq20B9KEtF8ayogbh/s50PXrh+bXQ4BTSMZDOCTZbqDN+NgoUlhNCnBWK7h6tmYOMl0FCEJRk5OUa8aK/Yd3nsNqdUjkHDhslf57QVVkj0tMdsCVWNmbIG/+6PvsHl3P3vu2c7pAyN89d9/l8/82w+T7kmRmy/w02/s56b7r+W2995AtVInkfbWjuP7z/HTf3iFj/zee6hXDP7+Tx5j162bibV6DmxCEuy5Zwe+oM5Ljx/i23/xI37rz3+ZetXgtR8fITdf4NaH9pCZzfPYl54m3ZNi295B4i0R3v3pu/jan36fmdGFVXMz1hLhoV+7B9dxyczk+NZf/KhpQiNJgq17B1B9Kn/5r/6BcnG5is0yLI69fIZzh0d516fuxDItHv/yM6R7U+y9/xpOHbjAU4++zMO/8y5sy+Hv//gxdtw8TCIdA1yGu1tYzFc4Mz5PsVLzdN7SceZzJeLhAJlClZMXZzAtm2LFc2ltT0XpSEU5dHqC2UyJYqWOrirEwwGiQT/zuTLVukl/R5JQQMcwLeqGRU9bnLHZHIVyjeu3rNYMfqsomTVeXzzfkE1xqFgGqiQzGG4jZ5QpmFVkIdHmjxHXglwszTNfLzYrDjeFWknqYW+v7Dgs5CuE/BqLxQp3PHw9Q9f28PobF7jh2n56t7QxeF03XYOtzOdLFPNVFipV/LEAu24bZLNpEwv5KdcMDMcmV6ziAD5N4dzMIu/6yF6S3VHGy14y79Lqb1WXeeCXblilP9X8nICegVZ++99+gNNHxpmfyaOoMqm2KEPbl3Udky0RHvncnQxuW12pvRLJ1gif/uf3c/b4JBMj87guxFMhhnd04Q/oCAF3PXgNZt1CWaHp1L2phY//5j2kGs+HJAluvXcH/UNtnD81TSFXwRfQ6OxJEm8JU7ctnp8ZYdcDw/TubOO/PPkM09k8v3LtjVy7ve+qq6R+FvxcEUcV0+TpE+fIVWqEfBq/fMtubhrobmaCFBm2trfwuTtv5OT0/CqhYp+qoCoysiRWEQpLEOLq+x1XwnIcJhqkRVs0zK3DfUT8qzcJmuJn70A3u/s80siyHHRtbRYtFvTz6duvR1OVVRka79xk7ti8iRNTc/z18wfIVqqcn8uwuTNJ3qzgU9aylEFd5ZO37PbamlYcbzCd5LN33MCxyVnOzCzw0tlRHt6b8xw8GlaPK+G4Lu/bvY0Hdm1eFXS3REJ8fN91jCxk+cmxsxyZmOHw+DT3bBtcdX2ePHqGM7ML+FSFX75lN+/fvX1VtUs86OedO4dxXPg/vvc0I/NZHnvjJFvavVazYNjf7PsEVmyQGvdsxXBLpkHBrdHiXy6fd/EY40vZZ/Da5D528zW8b8829BVimS3hEI/s3cUr58Z4fWSCs7MLZKs1dF3Fv0KwbCloXmpLXIIkxIYZStu1UYSC6ZpNPYH958d49cIEQsDDe6/hQzfsILBCCyIky9y7fZBS3WhUbmX5yfGz7OhMr9oAHJ+c5c1Gi9yN/V18Yt91tESWBVNT4SDv272d0YUcf/PSwXVrTgUSCa0HF5eQkqJmF5rkRLZUwXU9wlTIXlGEd04VZkvfwbQXkaUAhdrrxP23kQjcte41eDtjd1d7U2jTs59dhiKF6Yt+GheX2fITGPYiLiaGvYBhLzQ+JQipg/TFPktL4B5ytUPk60cAz8I2ky+TSoS5OLnI7EKBjtYormtj2Dlst3LF8V0qIuy6Jo5bw3bKjd6G1SXWklAIa5sp1I81KolsHNfGsOsYLHApZBEg5ttDX/RXifl2L2e9JY2ElsQvry3hloRKwn8T21J/yGj+K2Rrhxp/y8HFwXbXa0VR8as9BJUAEUVgufY6ejkScd8etiT/N0ZyXyZT24/lFLHcEpa1ugVZEj7ivhvoijxCyn/r6t3hVcMlKIdQJAUEpIOhlb9aBZ+qkilVuDiXxX9ZVzWBC/j9avNZFkJFbNCS5X1DRpF0LNfAsAvoK0SyhRDIwkdX+JfwK52MF75Grv4GjlttkDkWcCmBI9CkBBF9B7qcwnHyuG4FmqTPaiLQr3QxmPht/Eon0+XHG051duP4AGsrBnS5tVlZdjUQQiYVuIO6Pcdo/m+o27O42Jc8Sx4UIiu+t7xN7EnGmMgVaIuEuLiYXVNZ8YuAUqlGIhkklQoTWdE+IQlBMKAzNrrAwkKRgcE0QkDdMDEt2yNTVkAIwe0D/XzzjeMU63UuZnIcn5njjkFPV8qyHZ4/N9IkSne0p+lNxC55Jk1cZxEhdBA6Qc1zXbXxWuT3dHcSDamMlWfwyRp5s0RYCVC16/hlnRZfnGP584SUBIqQqTeSK5ZjYzkWasNh1nItylaNpBYl4tP5/K034ldVvrL/IIZt4wJlw0AI8CkqXbEgg6kk796xmTsG+5pi0BvBr6rI0lIljmBHexo9PEfBktEkDV32YzkGCa2FgpWjZBaJayqzNYd2fwcCQc2uUrHzTFULRNQYESXKWOUCilDxKwFUSSXKxsTR2LlZSvkqc5NZ/EEdy7Txh3Q0XcE2Hep1k0qxRiwVQpYlOje10t6b4senzvLowcOU6h5hsbWthYd372RvbzcRn45PVVFlqWnUMFMo8o1Dx1YRR0vtr5fGoStRs8wNSfOVMB3Hk1xoIKApTXfegKyT1CPkjTKO4zI168Xsm/paCFzB5dKzfReE1OXk2VC0hbJloEjS8r5ASKta5VpCAW4b6PMMDqyGjpZhYVo2Pp+GaVgoqowkezGcZdlUKwbBsK+hnSpQJB8z1TfBhYRviFs7h1a9TnbuGwIXkm1RJFki3tQXkRDKAK49hetkfoEoIw92gzxYrFVI+AKcfPUctmnzrk/dSSQZYmBnD//xN/6aE6+eI93jJV1lRebOD+2le7i9uZl3HZeF6Syx1ihbrt+EY7vEW6Ne8liVkRWJ6+7cxuT5WUrZMuF4kFOvn2+OwxfQuPV913PjvbsoZEocfOYYUxdm2bZ3EEVTSPekiCbDVEu1VePX/RqbdnRjGhYHnjrKph3d3P6BG5prXDASoL2vBW2dqhBFU9hzzw72PXgd9arJydfOM35mmhvu3UlmJkckGWLLDQMIIUi2x3Acz/nYcV0W8mUs26Y9GWG4p4UzY/NoiozrwvELM/S0xfHrKhNzObpaY6RiQSRZIl+voeoyQhakU2H8PpVwWOf0+By97Qly5Sq265IpVRjP5LAch752rx2qJRZEuYJWz5UQUf3cnBpClqSmJIWEQJMVDNvCakhkaMJ75qOqH9OxGassEtcCpH2x5jNQrhkcPjdJazyEZTvoqsI1+waox1Su39XP2fF5pPYQdRWKiwUCPo2ePd2EAzrz2RKKLBEK6OTLNaq64PD5aRzHYWtfmulskbvftYuZeoHjuRkEAtt1sF2DpXhEUgQ33N3fmMem5/jmGrg4XjW9FCCaCLL3rq3rXguAaCLIve/fc9lrJoTAH9TZdeMmdt24ad3PXH/r8JqfpTvjPPBLyzpeQgiELOgeaKV7YG3lfMms8/rcGC3+IDu2dvEvut7DF4+8yHW7NjMQTa75/P9I/FwRR4vFMgdGJgHY0t7CvsHeNZtzIQRDbSlu7O9iIpNvvtgc18UwbayrTZNcJSRJEA95wdpsocShi5O0hIP4VGXNJqdZ8i1746xf0v8uCUHgMla1iiyxq6uNoK6RKVfJV6qokkzRqpIvVGjRV2eHN7UmuXmwZ91r1JOMcftwP+fnFhnP5Dk1Nc/OznRzAViJqN/H3VsH1g0iEkE/924f5JVzY+QqVQ6MTHL75v7muS4Uy7x0dhTDstna0cL9u4bXbZFSZZk7NvezvTPN6yMTvHR2lKlcgXQwiKzKq4ijyyGs6liuQ8Go0RH0rocAysXaum4j/S0JHti1eRVp5F0jCPt0dvd28PrIBJlShVLdoFYzMAyrqS/ws0IRCnEtjkBg2jY/PnaWSt2gOxnj3u2Dq0ijJaiKzM0D3bRFQ4wu5jh4cZJspUqyMf8s2+HgxSkWy1WCusY7tg+uqf4C0BSZ+3cN8+2Dx8lXa2t+7+KyaIxQt8vU7AIAyYYAb7VuUal7AXk8lSS7UMSxXQxrBtsp0RP7TSThp2aNMVP8B6K+O3BdVhG2b2e4rotflfCrMssbaqexWfb+rckJBuP/hHTgbharL5Grv4ll5z0iROkh6ttFKnAnAaUXISQ2xX6d9tB7AQgqW8lE6izmygT9GpMzOZKxID5fiu2pf4ft/uw93iG1G6wzwCyw7LonCT+bYp+nNXAPReMMJfMsNWsS085hu3XARRY+dKWVkDpMVN9JRN+BIkVX3dOIGqHDGYAauPpaIV5JKMR8uwnUdZ7XAAAgAElEQVRpw5SMc+TqBynUT1K3ZrHdMiAhS358cpqA2kdE305Y20LG8FGxc8Td9UuqhVCI6DvY1vJHFOpHyVZfp2Acx7AzgIsmJwmqm0j49hL1XYd6ybjXQ8y3m+vSX2qM24df6Wz8v0y7rwMbe02yWBI+NsV+g67wh/HmQYqjF+uEG8HM0roa1reuOLZGUB1AQhAJ+fA3RGEdx6FQqhEM6J5FNTJ90V+lPfQgAKocJ6LtuOx5yFKAlsA9xHx7KNSPkqsfpmScom7N42IiCR+qHMMvdxDWtxLSNhNQelHlCLY1gevmQKy/eRNCoMsp+mOfIx26n0L9BLnaIarWKKadx8VGEhqqFMevdhPWNhPWthBUB97SuqlIAbojHyXu20Om9jr52mHq9gyOayJcH9WKTktsE4nANfiVtVm+ZDDAzs40IwtZhlpTVyQM3o6IRPxcOD/H5HiGHTu7m3pGQhJ09ybRdYVa3WJpwsqy5LnnrbNt3ZxOcU1nGy9eGCVbqfLi+VH29nbjUxXGsjkOT3rOP7IkeGDb0LoxgWueACSEFKE9EkGWJGzbpmaaTBUKdMa62Brt88bIaqcv27XZFuknpi233F36GYCg7Fv+juNwZHKGF857pNYtm3p4ePcugpqKC+iKpy+ZDgeb99+wPTfJJa1G23HQFLlpOtEa8mK4umVhOw7juTx3tw1guRYyXoWP6RgoQiGkhsHnvTeTWit+JYDruliNxNBSBZIqqfhkv1el5JoE5Mu3iHT2twIuLR1xpkbmSQ8niCVDCEnCMi1s21muMHM9AeFcpcq33jhOqaFBta2tlT96z70Mt6Y2TIzajtPU7lmCJASLtTKyEHQEI+uuM5lydQ35uB5KtTqF+jJRnQwGUKRGNVxlAVlI5MwyqiozPJDm/Mh8ozp+YzH3jeCXNTqDMcbLWcZKWdr9EXyqQksoyIWGrtFssUzZMNFcwdiFeRZm84QifizTplSskUiF8Qc1covlZsuSpisoqsym4TZCET8lc4aw2okifCzWzhAJd6JJ4eYTNTu2SEtHnP6tnUycn6VeNVBUP+CCMw9OBiFvrLH1doXrusT1AO0B7/ldnM7hC+qEE55gfSQRwhfQmJ9c1phKpKOEE14Cpnm/Jejb2smz33yVH3/1RU+qQhL0N0xWxs9M8/X/+AP8IR+dm1opZcs49lIcBv6Qj9auJEISyIqE7teaNuZXgmXa7H/iTUZPTfGpP/ggsdTViQprukp7fyuSJCFJAn9I9/6mEHRvbucnj77Ej7/6IkLyrNwHdnlyAZIQDHSmVhklDXSmMC0bhKC/PUE64V3P3ZuXTWbemJ3iyPwMiibTPugZ+JwszqOkVE/nND9N36YYs+USx8vz7Limk93pDkanM1SqBtcOrXWqdl2XiWyeumXTEQ0T0DVc16VUNyj44LNf/DidySipJd1NSSaqra/1pElr93nhhhFJQl/ral4o1ymUa6QTYRLhAPlyDb+uEvCplGsG8/kyjuu5h+bLNUJ+nb62OJbtUq2bJMIBjEZFVsiv49NUVEVGVxWCPg3TcohoPoYjLZiOQ82eo2AsUHfyuIAmhQCBJGRK5jSqFESTguhylJI5Q1BNE9c2sRT05Y0aC9UyLi6t/hBhVWeuWiKq+fApKplaBUWSkISgYppULAMEdAQiKJLMfLWE47qULYO4HiDhCyCATK3CYq2CT1FoD4RRJBnDtpiuFDFsi7Dmo9UfRCDIGTUWamX8sko6EEKRZOqWyVTjs1ZjXVckme5gdJVLZtk0KJkGLX7vPZSpVdBlhbB2ecL+avBzRRyNZfIUGu4aw+nUmsqeJSiSxGA6ia7IVBvkjK4qKLKgUrty7/RbgSrL7Bvs4TsHj7NYqvCffvQix6fmuG24j13d7YR8GrJYvWl2XRe/rlLfQABwyQXDbtjLLjnduHgL61IAYDkOuBBW/euKrvan4htqOggh2NEQoTYsm3Nzi3TFIuvaFbfHwsSDgXVf4kIIhhr3olirM57JU64bxALemBaKlWYb2XA6RSywsYNRxK8z3Jbi0Ogks4US45k8rf4A9apJvXZ1i77p2NRsE7+sNAMP13WRFIn56RzpztUZvuG2FNHA+u0gQojmeF08Rrw1FcY0bf4xJcYSEoqkUKgVcGlco0Vv8UqFAkT8OqUN5qlPU5vk4kKxQq5SaxJHFcNgdNGrHAv7NDZfxvo4GQrQEQuvSxyBi+FUGK8cIqK2YzpVZKESVBL0tyWoGSZdqRhWoU4w7PPKKYWO41Ypm2fQ5BZKxglkKczUQoHpbIGt3WnCV8ggvh3gOBlsewbbmWlUhXgvdNcpIKQIjpNFoCCkIEE5iB7YTUdwHwI/rlsGbCThR2+QRgBBbYCgNuAdx3XZNuSuyuQqspfljPtv+EeN3XWrOPZ5XNdaNXO9CpUAEX0XEX0HTrOF6ZKKBGSEULzz22BePfnT42iqzPvfdR3yOva2AIoUIqpfQ0TfjutajWo2L7PvbROlVX+nWJ6lYNZodax1NxPVqsHxE5Ps3NFNwnczcd/1OK61YvwSQshIqGta0xzH4cUXzzI7l2ffzUN0dnqaHrrSQrUUZGRknh07ulCkZZ2XgpVHk9bOZUkoRPTVOkZ7+k1KdYNKQ7RYCIEmx4ko+zhyZJy+vhSqP4zrugwNpJEaYre5QpVvf/8Q73rHTtrTHtEV1rcQ5q2JqS79vaT/NhL+m1Zc76X1S0IgN0Stl9vdJLkDSW7FCwE2XueEkAkofQSUXloD78BliaBYcXwhI7H6+G8FklAJa9sJaVtww480K+rmF0t89+nD7HjPjSSDyw5sq78r6EvG6Y5Hmxbjv2gIBnWi0QD5XIVCYbki0XVdMoslZEWiPblckabKHkGirSO2H/f7uXlTD6+PTVC3bF4fm2ChVKYjFuHw5DRzDXHpTckE29vTawfjWtBoIwQYakmiKTKGbVMyDE7PLnB9d+caTYslSEjE1PAaUutyznzThSJ/8fx+Ts7MM5BK8Hv33M7mdArDshlbzFGs1SlX64zWTQrVOqosUTE859TpXIHeVJxK3cR2HHZ0pYn4ffQl4oR1jXy1hu04HJua5cHtm1f97ZVisKvG1mgR9D60erxRNd78zpUEZDVNwTQt4i1hoolg0xxhKWklWXbTlccwPHfT8VyWyXyheYwHtg0zkEpcdt5nK9U18gyu65FrFWvjuGu6UKRsmCQuw3+5rstCuUK27CU8ZCHojkVRGpW6ETVAxigS04KN87CZmsmRiAeJ/Qziwy4uBaNKyawRC3jXOqTr9CXjvDrqGTqMLGbJVqp0hEKkWiMEgjqyIlHIVfAHdWLxoCca3BrGNGz8AY1azaBSMprtgpocZqZyGEnIOK7FfO0EnQHPaKFSqnH45bMMX9NDojXCmy+cYc9dWwlG/OAsgGuA8OE6lSvOgbcbgqpOV2iZaAzHg5iGRa1cxx/yUS3XMOsW4fjypLnUARu856VjUyuxljBTI3O097fysS88RPew54Z18OljVApVPvWvP0isNcIL33md0dPLRiNCEkgbxB+Xg+u6nD50gee/8xrv/437SLVvXBF4KYRg3eS0ANp7W0iko0yPzNE5kOajX3iIvq3LxM16+zdZltizuQtJWibOHVyWGkjjPj8BxSPHl8w3gqqKJiuYjk1rIEjcHyCm+6haJmHN0+jqbYvT3RprJKRWw3FdDk/M8MOjp/mVfXuaUi6L5QpPnD6HokncecN1+H4GrUDP0dFBV9aPH9uSYR7ctx1Z9uaDbXsk0c07+pAlibt3DzavS3c63kxauMDmnuW9juM4ze4SgTcXbt7RhyJL5E2bTL3CfK1Ee6AVTQ4jSSqK8CELrVH9bhBU2/DLcWSh4bi2p6Hm2iwt9jXL5KtnDlKxTASCOzo2cV2qk7869Rof6N/Jlngrj108QVsgREjV+db5o/RF4sxWS9zZMcC+tl7+7MiLJHS/pwPlOPz6jn1Yjs1XTr0OCKqWyZ2dA9zS1sfLM6O8MD1CUNVIB8J8aNNO8kaNR8++geM6lE2TOzo2sTfdww/GTvHmwiRRzc+Z3NqOgSWMl3J868JRfn37zaiSzFdOvc7dXYPsafnHO2D/XBFHi6VKU/A6FQ6iKRsPLxUOej3sDeJIlgTJSPAKLQNvHZIQXNfTwWfuuIFHX3mTiWyer+0/zJNHzzCUTvKObYPs7utkoDXRFNEVQmDZa0vFgWawc3J6jpH5LFO5AvlKjZppUrdscpUauYq32XeBouVlsiNqYA3Dmwj5LyvouGTrDp6mjyLLq0qKlxD26ZcVeUyGAvga9yJXqTZd7sAToF7Kfi0JT28EIQTtsTCKJFGpG2RKVYq5CpF4EE2/uqmoSjKlmrEqE9p0ml2n2KwtGr5sULWyDcy2naaw2D8GLi4lq4TjOh5rXKlSqHn39NT0PJ/5629veJ1cF8YyXjl31TRXuc3ULbsZoOmKsm610RJ8qrIhiScJma7AteSMKWJaF1U732xVG5/P0ZeOe6Sm7ZDPlLAtG7+vjahvH/Olx3HcGprSRmvwITK2YHIhz2DHFazI3zYwEZIfHBvLmkNV+hFSGFeouE6epZ2CEAEcexHhGshSCrBwkXDcGpK0sQ29EAJZFv+TDMN1JG1jFzHvxSsjI//MsgvvvGubt0G/ghi6106kwmXasZaQ8gUpGFWCyvrEo2nZzM4W2L7NaRBOWkPU+8oQQtDXn+LV187T25OicwWxXDdM5uYLTVcoAL/sp8vfgyzWtvSuhyVRSsKrf26aNq+/foFw2Ecq5YlDKrLnbDk9m+foiQlePTRCezrK+FSIdCpCT1cCx3EZn8wQjQYolmosLJYIBXX6e1KoqkypXGNqJk+xXMOnqXR1xImEfdi2w/hkgXDYx2KmRLlikEqE6OqINYOsYqnGxHSWStXA79NoT0eJRzVK5RozcwWiET9TM3kURaK/J4XfpyEE1Oom07MFcvkKiizR3hYl1RD5rhsW45MZ2lqjTM/mKZZqJBNBujviSJKEadqMTWbI5SuEgjp93Uk0TcEwLMYmsyTiQaZnc9i2S3s6Qksy7F2juTxvHJnjzSPzDPXNEInk6OlMkG5ZWx0hCYH0C6ZrtBLt7TFCYR9zs4VVuo7lcp3pqRxt7VGymfLy3BY0zTkuhSQJ9vX38A8HjzCW9QwiTs3NE/X7ODA2Rd2ykCXBDb2dpMNrs8bgIEQY0dA4GmhJ0BWLcGp2gaph8srIGA/t3ELEt3Gy5q3iyOQMJ2fncYHrujqaRIntOMwVSghA8eloioztOOQr1WbCzHFcaoZXVbSyvbwjFmawJclEroDtuhwcn2S+VKZ1xTlfbqxNQmCdDfF6UOQVDquui+U6XDg1RSFTolSooukqPr9GJBEkM1dAVWUsy2ZhOo8vqJNMR2jvSVIyjFVC0P3J+GXbzVzX5cTMHGVjLUHU6g8RWqfyeQmZSpWzcwt0X0Zk3Ha845cMLwaMB/x0NUhcgLl6jqxRIqVFsGyHmbl8sz3sZ4HtOkxXC+TqNQbD3gbVryrs7Ejz+NFTVEyTiWyeNyen6d25lURLmHjKu6dtlyQVV7pguq6LaSw782pSEBeHqNaPLFRSvuX2lbmJDOeOjrM4nSMY8ZNIR4g02TV7RRXn1SVD305o8QdJ+vxN4eFtNw3x8vcP8dLjhxje3ceZgxcxDYutNw5c8VilfJX8Qolr79hGsj1GrVynXKgSjgfR/BqWaZGdL7A4k+PAU0dxrCtXv7mui1EzqZXrVMs1apU6+YUigYgf3a+Rmcnz3f/rJ/Rt6yLZHmNxOouqq82KqGqxSn6xiGVYFDMlitky/uCVE6LlQpXcQpHtNw/R2p2kXjEo5StEEsumBXmzgO061Jw6SsPZtm4byJKM0nArLVllZCGR9rU0nVzXc2td+bN0ILjq55Iss1GAKUsS920b4sjETNP0QzSSL/duG+Kpk+eueK7rwXVdzs0tMpktcOfmTSjrkHqyJCFr0qp/A2iNvedKUu6yRgXyyr2fi+u4qIrcaDGVaA9ESfqChNUEsmjxIvalhOSqZXs5ARB0W4HlAhDDsZkuF7km1cHuVCcdwYjnRm5b2I3rbjo2luNgNciyT27ew7n8It8dOc7uVCcVy+TBvq3sSLTxxaMvcSwzQ75eJaBofGrLDZzKzfGtC0e5vqWLmWqRgKJyX9cwHcEImiSzf3aMiVKOd3QNczo3z/dHT7I51sIrM6P86pYbCGs6FwqZDS9TZzCKLATn8gtEdR+ZeoWe0NUTpZfDzxVxVDOXe6p9DbHLjeC/5Pc102JyIU9vW/xnKoG9HHRV4WM3XcuN/d388Ohpnj5xjpl8if3nx3ntwgTpSIg7t27i4Rt3saklgQCqdbOpcg/eBB9ZyPL1/Yf56akLTOeKyJIgoGnoquw9VEJQt+xVAV9A0RtSsmvPSZU3rgxYGvfSNapbNrlylXLNYMclgsuKLF12I6jKcrNXtm7Zq1yGKsZy5UzgKki7QOOauI3v+kM64GJbzlXdN0kI4rqfoLqsQySEwKiZSPLadqmArl511kdRJBRxdU4il4NAEJADSELyRLItu0m21S2rqZm1ESQhGsKWq1d/23GoN4JGRZbW/H7VuUgS+hXs3bsD1zJXO4csVFp070Xf2xonV64hSxIBWUKSJFzHE3wMaVuI+W9qCu5aThFVllFV5R/dS/3zAlluQwYUuQfXrSBJ4Y0/vGa6C5aFcASmZXPm3CwvHzhPqVRH1xXuu3MbA30tlCsGz750mtHxRQzTpr83xR37holHAxw9OcnJM9PEon5OnZ3B79N4513b6e6Mk81V+MFPjrKpL8WxU1OYhsWNu/vZvasXy7b50TPnaWuNcuNuLxA6eHiUkbEF3vWOnfh9KovZMi+9eo6L44u4Lgz0pXjn3dtRFZla3WT/gRFOnp2mXrdIJUO8867ttKbCTM3k+OEzx5iZzXPDdf284/atCOEZGjz38hnqhkmlajA2kSGVDHHnvs10dcQ9ImQqw09fPE02V6YlGeae27fQ1rrcTpbQgviCKpWywUKpiKLIWJZDIuG5+hXyVa7f07fKWahcrpPPe9UXsVgQv1/FMCyyWU9wPBz2EQ77PLK6LUY0sppErdVMjLrNnt19qGojs+W6FIs1CgWPrFfVGolEkHK57m1AayahkE6kcaxSqU6hUEXXFeLxYIMosVhcLFE3vFaTS+E4DmOTGY6enGR+sciJ09OEQz62b+mgpytBrW7yze8fwqer1GomCNBUmc987DZUVeal187z2qER/D6NhUyJZCLI5z5xO5Ik+Pp3X6dU9trfhBDMzBX45Ydv5rqd3RRKNb7y6EvkChXCIT+lco3b9g5x921bGB3P8MW/eobBvlZcXGbmCmweSPORD9xIOOTjyIlJnnruBJqmUih6xPVvfvou0i0RcvkKX/rb5xkaSFMoVjEtm96uJB9+aA91w+CJp45x+Ng44bCPbK7C8ECah993A9lcmT//8tO0toTRNYVSuY5hWvzWZ+729L9GFzh+apKFTJFjpybx+zR8mkq65eraCn6RMDtXYH6ugCxL9G9a3f4SCGhomkLLCkLNMC1aYqF1EykAfYk4e/u6GcvmqVkWz5+7SE88xsExTyYgEQhwz/B6IuMuuDVcewKED6QW0uEQ79g8yLn5DJbj8MrIGE+dOs9Du7ZeltBoHvGSFrX1kK3WcBoxx1ypzEK5TEsoSEBTuWWot/l913UZbF0i7TfeaAEENY13bdvMKyNj1C2bU7MLfPfIST52wzUEL0OmvJVxr0RE15vvSMtxmMoVuKWri1gyhGM7+AJa01k2lgojCU+cun9LJ0ISaD4FRZFRFiurxMorprlhbbTrusyVyjxx/Azl+jqaZIriiUtvgEK1xpMnz7C3v5uAqq45V9d1mSkWefLE2ebPhltTDLYs62v4JM2roBAN5+OuBJblXFHfaCPYjosArm/pptXnvZuFEOzr72WwJcmRqRkM2+bR1w9zXVc73bHLty0343O8drWlf5fMWfxyjIq1iCr5UKXl90fflg4e+a37iLdE1pIKUhqcAjjjCLmfXzRxbK+qc3ld6Bpq4+F//iA/+rvneeUHhwiE/Tzyuw/SPeTtMQJhP8n22JpKHcu0uXhiAs2ncvrABSRFJjOdpbU7yce+8BB733kN0yNzfOPPniCSDNE11IYe0D2SWFNo6Uggy56hjiRJJNJRglGvC+KNn57g5e8fYmZ0nmq5zpf+169z/b07uf39NzB+ZpqpC3M4tsNXzniC5z2bO/jgP30nlmnzzS8+yeS5WRzb4cdffYE3nj3BPQ/fTGt3kmRHvHm/hRBEk2Ek2ZPZGD05iaopnH1jlAvHJsjO5Ii3Rvnkv3p/s/pqtrZAxa5StsoEGu2uFbtKWAniV/xU7Rq261C3ayS0OH55+dncqBsEvCqi45OzvHhulJppcl13B7cM9TKTL/Fk49nf0ZHmjiWJkaucjnXL4oUzFzk+PUfEp3PftiE6YmEWyxV+dOwss4USLeEg77lmi7e3fe0Ic8USRyZmuG2oj929HZyamef5MxdxXIfre7u4vq+TxXKFp06cJ6irjC3m2TfQw56+tW11pp1jLP8latb4uuPT5Ba6I59ncdSlmCnRv6ObWavI8ewMmizTFYzh2nD29DTlUo2t27vwB2Gu/DiZ6nPrHlMWQToiHyGsXcOvbLmel2Yu8renD3Bf9zB70z2sfLfUbKv5lknoAUKqTsoXpGZbOLj4ZIWkHiCkaEQ1nYJRY6FWodUfIqhqtAXC1BqFAQ/0bOHZyfN87+JxWvxBPjm8h/lqCdNxKBp1OgIRdiXbMB3Hq5b0BfArarMNbT2EVI2b2/p4aeYiMd3PcLSFpO+tV3muh58r4kiRloVovczaxouuR66sIFh0ja6W2GU1hFbCdV3qVcMLALz/PPLE9ezhHdelVq57/ayK5AVtsQifv+NGHrxmC69eGOfFMxd5Y3SKmXyRb7x2lJNTc/zmPTdz46ZuDNOmbljNAGOxVOE//+hFXjhzEcdxubannRs3dTOUTtISDhLUNXRV4djELH/8xHPNyhIBhBSf9/K9JBA0bXvdEsglWLbTJKFUWaIlEmK9uNtqWK5vBMdxmpnOpZ7OJazUDjKuIotkrviMrijIssTM6AKaruJYDpJ2+WBzqUdUX9GqBi7pzjiRWGBN+epS0HK1kCSB7yrtEjfCEoutN9pdJEk0g72hdIoPXL/ds7dsWH/rkuYRTI6FJinUbAPLsUn5w0SDGlmjhC6pnhNKI5h3HPeywpWue2VXlIjaTlj1mPalYKC/LdF87HLzRUIRP0KSqFvTLFafoT30UWTJT92aZq78GEH5E3Qkwr9wTkZCKFzqOHWV3wQamd7T0/zd//MKO7Z0sHNbJ7l8pRlA1WomxVKNvp4ULi7PPH8K13V53wPXMr9Q5PEfH+HWvYMMbUpz5MQEX/3mq/zWZ++mWjd5Yf9ZLk4ssntXD1PTOf77N/aTTIRoa41w5sLsqqqE6bk8p8/NcN+d2yhXHL727ddYyJS4ac+mxnkuZ3d++tJpnnruJDft2UQ8GqBQqjU3O/FogFtvHOS/f2O/Rzo1JonruoxOLPLTF09z695Btgy18cL+s+TyFX7tk3eQyZX526+/QiIeZMtQO6fPzfDVb7zKZz9xW7NVwXVh//7zHDs6Qd0wScSDFIs1PvzhG/H7dZ56+jhTUzn+yW/cQzQawDAsfvCDN8nlPOLo5psH2batk/HxDM8+exKrUQL9yCM3EdwgW5jNlvnBD96kblh85jN3EAzoFIs1vvNdT1D+xMlJtm7t4IH7d/Ho1/YTDGrYlqfD8JFHbsJxXH7wxGEqFQPLsrn1lmG2bu3gxZfOcPjNMYIhH6Nji2v+rixL7N7ZQzioc/rcLB948Do625YC6+WFamIqyz/7tXcQDvlwXBd/w4Xopj2buOHaPhRVZmx8kf/yl8+wmC3RkvTa4RzH5dc+eTs+n8pf/f2LvP7mRbZtbuf/Je+9o+Q6zzPP3823cu6qzgEdEBs5kSBIgiYlUpIVqEBZso48zmP7zNg7Prsen+PxjnfOmbG9s3s8s97RSPLalqzgkSzJtCJFMScAJAESqRuhG51TdVcON+4ft1DoZleTlK0/ZM7zBw664ndv3ft97/e+z/s8hWKNseuL/MIjd7BzpMNz85Nvz7X1usXRQwPs393NxNQKf/7/Pcnh/X3s39PDrpF2tvWlUBWZXKHCn/75D5ieW6OtYQ1fLNewbYdf/Lk7UVW5waoTGb++yItnb/Dpj99Bd0eMyeksn/visxze30cs4qdeN+nIRPnI+w5SLNX4L5/7EZfH57nvZJSjBwfw+VRuzq7y0fcfIhELIMkS76RN2NuB67pYls2u3V3YtkOxcFsDzedTCYV99A+0bdhYWLaDX1O2LBzossyJbb38/euXG0mTZZ6fmGKl7N1PI+kk29NbMEiFAAgBXHsKQR5AlUK8a8cQj41dY2xxhbJh8pnnzhDx6Rzv7/G0IHkDuwMvpsiWq7w+v8iOdIrO6NZzbU8sgibL1CyLl25O8+++8zijHRnCurbhuGVRJKipdEXDDCTihH36hlhlQ8ucIHC8v5t9Xe2cnpzBtG2+eOYcuiLzgdGdBDW19bhdl1KtzmtzCySDAXZmNouWtkJHNExAVcmWK56D6vVJ7hkaINJgqrRiFbRCouoj7NPJNyQdTk/OcO9QPwFV3XB8rutSqNX54ulzvDw9u8kjQxY9++yVapmeUJRWsF2XJ69OcvzyNd67e6TJqL81zmK9zldffp0ri8uAZyRxangA/zqdsd3RXrY7Xc3fIRL2s3tHR+Ne9s6n424MbW+dZ9h4t99iFKiizHOLExxK9hDTvDUkHQrwgdEdjC0tU7dsLswv8l+fepFfvOMQg+ta+daP33FdTNthcnWN2VyBu7b1NisQvEEAACAASURBVLscQkoHBXMay6kR1w5s/G0EiCVDXHt9muJamd6Rdtr7kg1mp41rN5JGziKwWQT3HQUBEjvTfPr/+Aiu5eCInsC04To4hs3Ou0boPdhLXRGQGkVPVZKoFKv84IvP8tAv3MPu40MgCNy8NMPn//BrFFbLtPen+MT/+n7qlTqu63LhuTEO/cweHMch2RHjF//9R1iZzVIpVvEFdD762+9BViQEBPacGGZofy+CKLI8vYKsyGT6U8iKxPZDA/zR1367UQx1GxpJEpIsISsSd773AC6QaG8QEIBAxI8ki/zCv3sYX8BL5iiazLs/dRIEqNdMfvA3z/LuT51k9MQICALT4/N89ve/Qj5bJNjQpBsI9uI02qHW54GERnOa02y9F5oMpLeDtXKVr555jRNDffQmouiKjO24fOPVi3RGwxzp6+Jb5y6TCgUY7cq89Qc2cHl+mbM3Z3lw9zCvzy7yzXOX+MUTh3j0/BXqpsWDe0ZwXY9tM5ROcqivk7lcgYcP7ibq97FarvKNVy5xdKAbXZH4h9eu0BnzmIePXbrKx4/s5f5dg0R9rZPXjlsnV3uJknGRVi4/utxDUn6ExSnT078CYqqfkKI192CCAJVyHRdvX+9iUjLGWKk81vIzZTFCwn8fimQhCAL3dQ6hijIvL89yINWFT5K5UVglpGhcWVuiL+QxeK7lV7hZXGMst0xc8yELIgWjxuW1JTRJZq5c4M5MP0FF4+m5G8yUcpzPztPmCyKLItlqhQMpj9n05avnyBk1+sNxJgqrHG7rRmoQO3yygi4rXMktEdf83CyuNc6V6yWVGmzWW9p+o4l2fjA9ztV8ll/defQn1sr/U5U4CvtuV2Vyjb7srdrVCtX6hg1SzTCZy+Zps0MkI28uSghQLdV59tFXvE297DksZHpTTF6cIZaOUK8ZiKJIsj3KynyOeqWOKIvsOT7McCbJUDrB+/Zu58LsIo+eu8xjF69xfmqezzz5EgOpOLoqNzWOHNfl2auTPDU2ge04vGv3ML/74EnS4cAmJ7CZ1fzGxRKBilUjomw+ply5uoH980ZkS+XmAhzx61SNW7ahG2+YUq2+oSVq0/dUa83nQz5tg1tcxKejN4K6pUJr+/f1WCqWsRpClWGf52IRjgdp729DeguGDHgBX0TTqdsbx+u67tuilL4ZXGBudo141SDT0TqY+nFwi+kU1FQvoVmqEA/4+PDh3dysLHGzvETFriPgktRCBGUfqqhgOJ5GzZpZ4uXiGFpFYTTajy7pBHUvqWXYXlvjVu1qpm23rDJuGJ8gILxhCljvYheOBxjZ14PuV6nZpufa1YCLi2Wv4eAymy3Q2xb7ibeJ/nOGbTs8/cI47ekIn/jwUVRF3lCljscCvOveXaw1LFWjUT8TN2/3K4eCOu/5mT10tkfZPpjhP/+3x5iYWiEeC6CqMieODnLPHcPkC1VmF3K8fP4m77l/T8ux3Lrbp2bXuHBljt/59Z9hZFtmw3iqNYMfPX2FB0/t5tRd25GkjSL6Pp/KyGCGtmS4JSOvsz3Kw+87QDzqtRD/8OnLlMp1XnltilK5xm/90r1EI34O7evlP/7Z93j98ix3Hbst4F0u1RgcbGMtVyGVCrGwkGd+Ps+hQ/289z37+Pznn2oyKOp1i+mZNfbt62FkOEMyGUIUBdrawhw9ug3DsPn2d86xsJBnWws3CvDafx54YDff/NYrzeOcm8uRz1f55V+6m29+82U6O2Ooqszaaom7Tx6kry/Jl770AnNzOebm1lhYyHPXiWGuXlvkh49fpKMjyunTN3j4Q4fw+zU+85nNiSNBEFAUCUWRPCaBIqO1uG92jrTTlgpt2gzOLeR4/sx1CsUqxXKd1bVyQ5PN++w9OzpJNGj33Z1xxq8veuytqJ99u7r52qMvs29iiSMH+untvs0MiEZ8bOtNEgxo9HbFiUX83Li5wr7d3WRXyzx7+hrLK0WvbW0hj7FOv8+vq+zZ0UksunEuGru+yMz8Gn/ztZcaLDIbw7AoFKrEIn4CAY3RHZ1Ewj5kWaQtFWY1V7l9juRbWj2tz9H/DFheLvDcM+O0pcOIokB//+3rWQBWsyWeeeoKvX1J+ge85yRRpGZs7YgligJ7O9rpT8S5srjMbL7AN1+7RNU0kQSBU0MDRFoG8wKCoCKIYUAGwdNF3JaM82t3HuGPvvcEq5Uqk6tr/MG3f8jBnk7uHuyjJxZFkSQsx2atUmM6l+fC3CLjSyusVar88Qfe/aaJo72dGd63Zzv/cGGMqmny9LVJnr42ufm4BAFFkojoGl2xCB8/OMr92wdbCqYLgkAqGODXTxxluVThxsoqi8US//cTz/PMtUlODQ/Ql4jhUxRsx6FUN7i5luPK4jJjiyssFIr82okjbztx1BYMcKing6m1HLbr8oPL14j6fDywfbB5ri3HoWZa1CyTbckEqeDmtb0rFmFXpo2ZNU8z8fuXx4kHfLxrxxCxRmt6xTC5urTCty+O8fS1SfyqiipJLJdur99iIwnzRtHs9UgE/OSrNf7k8WcYX1rmxEAfyVAAAVgulfnma5d4fOwGNcuLV472dfHA9qENc5Yqyk2JhaphcnMtR8UwqVkWFcOgVDPI1WpcX749Vy4Uinzp7Hn8qkJQVQloKroso8ky8ZCPvFElqGjU7duaeJIo8t7d2zk/u8B3Lo1j2jb/cHGMC/OLnBoe8Nz+fDoCeLFqscy1lSyvzy0ytZrzNti9Xc39hoNFd+A4giBTtbKYrsFibRZV1GjTO3j5qSsUc2ViqTDPfec8px4+3BAUlhDENlx7BkG+vb69U2E6DucXFgmoCpbjUDIMOowwK+UydcumapoENZVASWWxWKIzEuZQV6fXGlg3yS0XKOUrWKbNpdPXCYb96A1XY92vkp1d5dUnLpCd85wHv/9XT6H5VboG23niq88zcngb2/b1cvnFq0iyxLa9vbz+zBWSXQmG9vdz89IMfbu6qVfqPPets4iiwPDBAc4/dZl4JsrAaA+Tl2bIzq2x48ggrzz+Oooqc/jd+7h+bpJaxWD44ADjr9xAUWX23LWDVGfca5EMevdtKV/BqFveseQq2JbN5dPX8AV0dL8G1HGcCrIYAeGWjpnR0GMUuRWhSYi4rgHYCCg4joMgaHjzrojrOp5TreuiSHGExmfN5vKe+dBwf1PDNlepMZcr8LHDo6RCAc9he3GFPZ1vL3Hkui6X5pa4OLuIadueZpyqUqkbXF/K8snj+xlJJzd01ER8OsVanXQ4iCJJTGVzGLbF0f4uZEnk+WtTzOYKpENBUsEAh/o6iQe2ZsBIYoB08AOEzN1Ydg7TyWHYK1TMCWjoINq2QzgRAlx0v4ZgS0RVH35ZRRYkXMfFqFtEYn58PhXHhbjvDsDBdNawnAKmvUrFvIbj3hb4N2yLJ2avMVf2Wsge6t2OLsk81LuDb01c5MraEnsSGRK6H8O2CSgq35i4SN02+dDAHjRJJqCoXC9keX11nt3xDLviaY8FWi3xF1fOEJA1Pjo4ioDA1dwyzy/eRELk3s5tJHQ/x9p6KBg1vjD+MrIocm/nIIdTXTw8sIfv3LxMTPOzP9lJUFG5uLrI96fHKJp1vnT1VY6ne3l3zwhhRWMwkuDi6iL94fjb+u3fDn6qEke9ySgBTSVXqXF9aZVizSAR3DxE13WZXFnbqP9iek5YqvL23BpEWaR7KMPSdBbbsomn46Q6YyiqjOZTqVXqBBsVcc2vgeui+dXmZCEIAtGAjxPDfRzo60AURL716iVem15kdq1AxKc1xclM2+HczXnPglCW+NiRUdqjm1tgXNdlIV/axNyp2iZ+aTObZ2J5laphEtQ3J0xu3fi3qL29Cc9Wt5Xj2Xy+yFq5Sjoc3HTeXNfl+lKWYqPC1R2PEGywugRBIBUK0JOIMr64wtjCMuW60XI8AJW6wfjCCpbt0BkL0xUPgwDJzhiqvpkK3QqW41CxTNr9oWb1yKhbjQn6nwbTsBBtx2sRaYH1w3ur6qDjOlgNC/J0JEhnNMRUNsdcrsDcWoFYKIAmdnrBbiO4chsiamqDTRW1AsghCdOxiSh+ZCR64l5Cq1QzuLaUZVtbvOV5y1drzOcKmx7/cSDLEqFGtUQliesazBX+ElVKUjav4ldGKJZr7yhBWtMpUjQmEAUVXUpgOkVst4aAhCT6cFwDx/WqYLIYpG6voEtJTKfY1N0JqQPUDYe1XIWRwUyzh3v9Ijt+fZGvfOM04ZBONOJnJVtCX7c5Dof0ZstRLOZHVWVWc+VG4khqasz4dIVMW5jF5S1+63WX6dJKgXBIJxF7g8MJUCjWMC2bzvZYkxX147T7dnXE8DeSmoGAhu04WLbN4nKBYEAnEvY2mfFoAF1TWFjaOF5JEgmHfRiGTTwWZG2tvKWjTzCo8YH37+fMmQkuXJjh3nt2sHNnB48/fpFCsUZnRwzTtDHNrZPhrdDREcUybb705RcAGGzYm4cjfjKZCH6/hs+nYpo2KyslyqU6c/M5AgGNw4cGME1PUyUeD6JpMonkm7Q5vgVzJtBiPltaKfLZLz7DwdFe7ju5g3Klzs3pjcmpYOD2ht8T3QRwCfh1Pv3IccZvLPHsS1f5L5/7Ee+5f5QH7tko9u29z2PYOo5LoVjj8196jkTMz8++ey+SKDK/mN8gKCdJIv4WLE3bdmhPR/i5h480nxcFgWQiSKHhgrn+feIbWRfvnGnlH41kMszd9+4gFvMTDOobWnxESaC9PcrMzCqyLDXjHl2VMSyHYqVOW4s4Azz9w2N93VxbzpItVciWKrhAdyzC0b7uLRikLi42rrOCIHU3Ny6SKHL/9kHqlsVnnz/LRHaNlXKF71++yvcvX21qC1m2vanGq8vym/7Mpm2zUCgR0rSGGYq3Nr9xzbnFUqlbFksli6VSmSuLyywVy/zcodGWLqaCIHCkt4s/ePe9/F9PPMeFuUXKhsHT1yd5+vok4LXp246zKQmnvIEh+FYIaiqPHBjllek5JldzVEyTv3zpFf72ldebiaO6ZVE2vI32f/zZB0gN9m/6HE2W+fkj+xlbWmEyu0a+Vuczz57ma+cukAp6SZ1ctcZisYTtuGRCQX7r7uNNgfFbMGybycIafkVpGS+HNJV/fc8dnJ2a5TsXx/iLF1/hC2fONdgBAvlarRmnCgLsac/wGyeP0fYmuos3sqv8L9/4LtNrec/4ZQtMreX5o+89seExURCI6Dq//+67Ge5sY6KURVlXdBUEgYhP53dO3UlI1/jW65cp1upcX1nl+soqn+UskugxO1p9d3tk431SNpdQpQCK4KdgzhJVt1Gzq8iCJ31g1E06+9to64qzMJ0lu5BDViTCMR3BzSOoBxtaR+/sSUwRRfZ3trNWqbJYKuFXVAZiMRI+X3P98asKdctmMB7HryrosowS8fPBf/kAP/ibZznz2GueCVAswEd/+yGibV4S2XVdZq8tMHRgAF9onvFXJgCv/U3zq2zb18e+e3cxfvYGy9NZwskQS9NZZFXm2EP7ESWRtu4klmExPTZPqjNOMBrg0otXMQ2TY+/ZT6VYBcfrLilki/Tv7iHR4bGNrp2/Sce2NIs3l/GHfNimvYGlewu+gM4Hf/1+vv+Fp3n58QsABCN+PvLbDxHPRLDtWUzzNUQx4ckfCCEQRAQhgGOv4FLHSw5pCIIfrwXGwsVGEBRkeQhBiGDYy1zN/u8ADCX+EE32kta64p3fYq3edM2WRC8xvFapEtY1CrU621Jxbw/TuPy32sHcejygKezraedfnDiEsI4lpcoya+UqZqOjRZHExhwvYFietq8oCGiyhGW7FGsGqixRt+ymppoiS2/ZyiyLQbrCn7o9LtdlrfYsl5d/B8vx5D4EQcCx7GY4UjRrXC0seyLhwTiSK5DKRLAtG8uykWWVhP8UCf+p5udWzAkuLv46FetG87GI5uOXdmzWCx1NtDOaaN/w2JOz1xkIJ/i1XcdQGiYrFctAk2Q+2L+bbZHEhtd/ZNso4MVHuVyFYq7KLjnF3t5Ms2iWy5Ypl+rcGx/grnAfkiRgWQ65tTKDaoyPR/cQjweoVAwCtif4vyexMSnouC51x6Jo1Lmva5Cg8pMzL/qpShylQgH6U3Fm1wpcmV/iZnat6Si1HvlqnddnFjYIPbuud2HZ9lvTfQF0n8rgaC89w+24jousenac4fhmQcj19NZWmym/qrK9I8W3Xm0oy7sOPelo0yUN3GYvoySKhLZwi6saJi9PzlBZJ2To4mI6NjmzjOVuTB5NreYZW1ghFd485lLd4OzkrNcPGfTTn4w3jmPz9+YrNc5PzbO9fbN9aN2yOTs5S7FWx68qbM+kNgSVyZCfPd0Zri9nubmyxrnpee4Y7N0c2Lku44tZxuY9WvNgW4KuWARFkenb0YWivr1WJ8Ox8cvKBktB27JZWcgTjv7T+jcVRcYfEVu2qgncbstzXZruf60g4NkOl6wSAD5F4VB/F2cnZ5ldK/D81Sk+dnSUlLYx8HnjNRbXQpsSVCOZJH5VoVSvc3Zihnu2D7QUNr8yt8xy8a0ZYOthWBbnpxfIlsp0RCPs7Eg1W+NkMUIm+Aj5+ktYTp6IfoSwdoQ1WyATC7V0cPjnCNMuUjFnvHY8O4vllLCcKpoUI6Jtp2jcoGYtElaHMZ08llOm5JQxnAKOa6BJcQJKD5LkaVIUS9VNgbllOTzx7BUiYR+f/MgxQgHdS9yYt+9vw7CwGk44lungOi5aQ+PHsd1mctN2XOp1i4C/sTEXxaYeCEC5Um9GAj5dpVIxNnzPLaiqjOO4VBrtuz+uRpwsiS1jZJ9PwbK8JI6mKRgNm+lbtvSt8MavdputDN6/hmHh92vcd98uXnrpOufOTzE0lGZ8fIF3vWsPqbYwTz51ZeNncLuFdP3nNg3CAE1TkBWRe+7eQTIZJBTyUShUEYTN835HR5SFhTx33jHUnOMlSUKWRaZnVgkFdebnc1seo9QQym6lg7QV8oUqhmFx9GA/fd0JXnltimKplWviZjiOgySJ7N7ewba+FF/5xhlOvzLRTBwVijWWVoq0pyPkCxVy+Qod6QiVqsFqrsy77t3JQG+KmzNZ1nKVt/g2D71dcZ54bgyfrtLf41UnLctGFEUKxca43+QykyQRy3belFX7TofYsHYOBHSCwY0sINtyWFoq0N2TYGmpQFe3V1G8da8E9K1brkVB4K5tfXzj/CXytdvX0I50it74m7Bt3SqwmY2kSBIP7RqhPRLmi6df5fmJqaYgs+O6OG9w9RLwGC0Hujs2bdpvwbBsnrh6g//3mZeYyK4higKHe7voinjusOtjDMt2qJkmy+UylxeWWa1UqRgmX33lNfZ2ZTjc09pJRhJFDvd28e8ePMVfvfQqz9242WzZAza5kQGENI39Xe0MtyU2PbcVBEFgV3ua37z7OP/92dNcW1nFcV3PxtncWKgKvgW7brQzw7+6+w4+98IZLi0s47guK6UKK6Xb4xYFgcFknF+64zAP7RrmhYkp/KrSlCTQJJkd8TYMu3VyvS0U5MS2Xu7o70GRRL57aZyyYbJc2njvK5LIga4Ofuvu4+xpz7zpumE7LhXDfNOk0VbwzpVBzbYYLyyzWivT7Y9t+r50KMhvnjxGTyzC185d4MbKWvP7Ws0jiiTSG4ty17a+DXFtRO1hrnIWF4ekNoIgiJTtksf8wCUQ9nH68Ysomky1IeocS4W57+GDKKILxosIUh8oP55L5k87LMfEdi00yUfVLqOLftLBIHGfj764l3DRZXnL9qNbkGSJvSe3M7CnG6NmIogCvoCOP6zfdswSBAIRHzcvzZBbLpDsiFEuVOkcTBNLR7EMi4kL0wRjAfwRP+neFPFMlEK2hCiJVEs1Zq8vICsyXcPtzIzPk1spkGiPYtZNJFlibSHPzNX5ppuerEhMXpxmcF8f0VSYSCJE13A7qws5rp+bZGFymVg6+oZjEdlz5zB9Ozsx6qZX0Ato+EO+xrFEkaUeHGcVUfRam0QxgutaIAgIroogBnCdMgheu50riIjIOM4qtzI9FfMG+foZZDHSYCZ56I5F6IiG+KvnXyEZCtAbj3LHYC9H+rv55qsXifn9rFWqjHZlWKtUeeH6FNeWsqiSp627r7udi3OLPHN1gmtLWX5w8Sp3DfVxsLeTi7NL/O2Z11EkkZFMiqMD3ZwY6uV7F8a5PL+EX1V4YJfHdsxEQvzg0jW+/soFjvR10xELs60tzlfPvoYsioR9Gl3xSFOG5cdFqw4JVVeoVw0qhWrDREpnONKGIooooldMya9VyOfKdHTGCLdwchSQQXjzJJbruhRzFarlOumuFsydt9Fm/EZYlsPlS7NYlkMk6mdxMY8gCHR1xSkWqpRKNWRZJJ+vEosFqFTqWLbbeKzC9u0dLC8VECWRu+4aQRQ37sFmy3m+Pz3Oar3CoVTXT7TA/1OVOArrOg/uGebMjWmWCmU+++QZ/rf3+OmKR5qZ1Hy1xtfPXODc1PyG96ZjQYK6SsCnvu1NjygKb8lUqRgGj1+6zmBbgnQkSMSnb1B8t2ybhXyJ567eBLzkVyzga7IMAERBpKNBxTYsm5cnZhhKJ5oLleu6FKp1vnb2Aj+6fP0N1S0B07HwSWrTpvEWsqUKn3v6DKlQgMF0onmO1ipV/u7sRV69OYcoCBzu72J7e4ors0usFsts79xIr3aBL75wjoG2OKPdGfRGFapuWfzgwjW+fW4M23EZSic4PtizkYosy3zwwE7OTc1xfWmVP3/8RRRJYl9PO6rkKd0bls3VxRX+n8dfYKlYJurXefjQ7iatUve/fU2hkKoxEr2d4BIEgXAswIE7h9DeJmtpKwiiZ+fstNjMCYJAIugn6tfJVWo8Oz7Je/duJxUKrGOSeEwjURDI6BliagyxYVH90OgIT49N8tr0PJ9/+iwucP+uQZLBAFKjMmM5DuW6wUqxwtXFFUbaUwykNk5SR7d1M5xJcm5qnu++Ns6ergwP7B7ytCQEAcOyGF/I8lfPvdLSQW8rmLbNo+eu8J+//yzFWp1EwM+//9D9nBjq9SZsQURXOtGVD214nySWWc6XPW2kdwBcHBQxjE9Oo0pRT5QeAVFQkUUfMW03rrYTRQw0qtwGAmLTlU5EQhI0JFVg9/Z2fvTsGK9dmiXTFqZetwgFNQIBzeu5liRM0+b6zWWuXFugv/u2rsj8Yp5zr0+ze0cnr16YwgX6u5MeHbxS5/SrniPX0kqR8euL/PxHj6EoEulUiMvj88wv5qnVTV4+P0Ww0cLZ2xVH02Qef+YKp06MIAiegHdHJko4qLOtL8Xjz1wmGvERCupUayapRBB/g2VTNywM00IUBUqlOj5daWlPux77dnVz+uVJXnplgqGBNJfH5zFNi10jHRteF4n6CQY0bNshENCIx4MEAxovvzzBCy9co5Cv8uUvv8jJk9vp7o7z9NNjLC8X0DSFY8cHkSSRw4cHeOyxi7S1henp8dy75hdyfO+7rzE1lSW7UmJxscDJkyNcuDDDmdM3yK6W+JsvvcCD7x7F71cpFWt86+9fQZZEdu3u4vChftozURRFQhQFInE/gioyur+HfLHKl776AooscfBwP6N7ujl13y5+9OQlohE/3f0JREXEtO3mHCEJnoB/NOynqz3KF7/2El0dMbYPZTh6YDPDYD3SqTCJeJCvPfoKHZkIk9PZlsykVphbyPPXf+tpTUmiyLWJJY4dGmg+b9sOf//985x5dZLpuTX6uhPs2t6Bqsps603xvR9d5MbNFWbn11DeRksxwM6RDo7s7+MvvvQsA70prIYBwic+vLXz33ok40HCIZ2//MrzZNoiHN7Xy843XDf/MyCXq1CtzpFMhojFA0QifhzHwbIcYvEAiwt5+gZS3MrCSaKnhVeuGYCL63p6NbieE4zrehvmXe1t/NePvm9DojgdCmI0kiUtWUeChiAGoYX+mybLHO3tYlemjbGlZc7PLnBpfonFYgmzwbaO+X10RsPsyqQZTMXpjIYJaq0Z04+NXeOPf/gMC4UivbEov3HyGCcH+4joekszD8dxKRkGr87M8Sc/fIary1nm80WeHJ/gUHfnlrGBLIrsak/zhw+dYnI1xyvTc7w+t8BisUTVsFBliZCm0hWLMNKWYjiVoDseJbIFs3oryJLIQzuHGe3I8PLULGenZpkvFKmaFj5FJqxrdEUj7GpvY3d7esvPUSWJd+0cYk9nmuduTPHqzBzz+SI1y0KSasSDNqcGDrPNHyMha6xMraLNVvnje055DG/dR36xgFiuE1IklstrnpaW7fDLo3v58MAwku2SCXktwP/2gXt47+7tvHRzhkvzSxRqNVRZZiDhiawf7O7w2E5vEXt1x3X+6P170MU2ZGHrc+fiUDAXUEQdv3Q7rhAFgWTYz5m1SXpDMaLaRsMDy7FxXAddFfnA/hFODvVyYWGBV6bmGFteJl+rIQsSiizQGY4ykIyxuz1DezRAVySKhYHjioiCiO0adAWOIQoyZWupGXk7jeLt6PEhMt0JIskgAgKK5hWKFE0EuwfEODg5Lyh8hzCyHddhvjZNzszSqfdyozzGvuhxanYR13XwyxHP5QoX2zWRBRXb9cTbJUFqxMYStmMiChJVChT8syRj3eTMReL6AOA23YgFQWDowADRhpFGJBkiny0iiiL+sI8jD+7DqJnE2iLEM1GvXSoRItXpXTNecsorjMTbo03dongmyra9XgI0M9DGHT97CEEUCMdDiJLA6kKORHuMuz9yjFq5TiQZQpIlEu0xEh2tXakkWSK6hXGDKIYQ1d2Nv94oFTKM67qUawaW7RDya839pevauG4NQfDhuCYl4yKWU/Ba3tbBpyp8+s6DTK/mqFs26XAQVZZ49+5hdrSnqJoW7ZEQqVCAct2gJx7lN+49hgBE/T5EQSAe8HP3cD8nh/ubxjvxgI9fvfsIs7kCggCd0TCy708H6gAAIABJREFUKHJyuJ+BZJxcpUZAUwhqHjN+JJ3iV08eoVw3SAT9BDWVjx8Z5WY2h+O6dEYjRHw6mizzyycPo6veNQGtinobi5eGY6KJm/eJoigQCPuplr1Cfsmqo0ky3YGY57yJi2laGIb1pjq+rbC+kO+6LrOTy0xfW+KBjxzZ8Lr9yU6GoynkdcknTZL5lZ1HafO1cib1IMsiO3d1IssSkiTS15dEFAW0hjGKIHjuvKIgUCzVWF4u0t+fwnEcXBc0TaarK46myi2ZcGFF50hbN+/t3UHqTcbxj8FPVeJIEgXu3THAE5dv8MSV6zw9Pkm2XOFwfxfJoJ9Crc7luWVevTnHaHeGS7NLTSt4WRJJrNM2qpsWy8UyFcOkYhhUDJMr88teFd/1LM+fGZtAVxUCqopPUwjrGrGAb0NmrmpYfPbJM+QqVQbTCfqSMdLhILoiY9qeO8brMwtcmVtGV2Tu3zVI9xsqdookcnywh/9x5nWypQqfffosi4USg+kEoiAynytwbmqel2/Osre7nWtL2aZekCyIHE0O4+Ju0kMaySQZm1/md7/6HY4N9tAWCmJYNq/NLHB2YoaaabGtLc4nj+8jqKvs6EpTrG5mynTHI1QMg9/72vc53N/VTFZMLK/x9NgN1io1on6dT915gEwL6vuergy/du9R/vg7T3N+ap7f+x/f447B3mZ73PRqntM3pplazRH2aXzi2L6mG8p6OK5LtlShUK1RNbwe+GypwmrJy1AXa3VevDZFZyyCT1W8HnhdJR7w/5PZRuCt76Gwb8tWtb5UjNHudp4Zn+DsxCz/9mvfZ39vByFNo2ZZFGt1kkE/n7rzALIoI4u3b6/uRJTfuv8O/tO3n+La0gr/5/ee4dvnrzCSSRLSNZxG8nCpWOb6UpaaafEfHn5gU+KoLRzkU3ceYHLlcdYqVf7Td57ihetTDKQ8AcipbI6zk7OYts2OjjYuzy29rWMv1w2eGZ9sVgMWCyWeHZ/k+GAP8psEPpIkEg5ovKl95j8j+OUONCmOJOgbAj4BAde10aQILnYjIARZCHBLTdzF8ZJIbh1BkLnnzn6qtRJ/8/VnkEQQRYWH37OPvbt7ue+uIf76b1/iM3/9NKGgzp4dnRtcw5LxIOcvzvDUC+MYps2H33eAZCLIwnIBn65QrZl87ovPUq7UOX5ogNGdXSiyxD13jDA5leXPPvsjAn6VbX1JqjWvChaN+/nYw4d57PFL/NlnZ5Elke7OOJ9+5DiqKvPIBw/zlW+c4b9/4RkUWSIY0PjEh4/i01WeO32NM+ducn1yGUkU+G9/9RSH9/dx5+FtzQSSi0vZrKMoEqGgVznc1pfi4fcd4LuPX+A7j19AkSU+8v5D9Kyr2ggC3HF8CKiDUwE3x8hwD2CBC3v3jHrnWFAQRRGBKh/+UK+nEyB1cHlskemZVdKZCPc/sBtFkVhaLrC8UqRaM9m7r4fRvT2EQjqrq2WmZ1Y5emQbx48NNsfgui6PPnqOAwf7OH5skKWlIl//+hlO3DnEI48c9dgyjkPHwTSWInC1sErb3iSd+9qomBYBXePFuRn0pMyDHz/IcqWMIooIQZnTszM4LvgVhT3pNKokEYv6+ZVPneTi2ByGYRFv6AOpisx9d23fwC5xGiKd4ZDOr37qJBevzOG4LiePD5FdLdOWCjcd++KxQPP1ozu6aGsLI8kS8ViAe09sJ7taQpJEjh0aYGTb7c1pMh7kwVO7Wc2VGexvY/+e7qYT3c9/9BgXLs9SrZkcPdBP3bBINpi5oaDOBx7aR3eLgNrvU3nkg0e4NDbH7HwORZHo7Urg86mIosCH3rOf9nSk4WAnce+JkQ2JMEt0OXjnAGFBbRy/bwMD8yfpnPrTjLa2MOPjCywvF9m1u7MhEG9z6dIs+VyFwaE0fp/anK5EQWSwM0HI711DNcvi8uIypZpBKuTFUTXTIqCpiCIUagaqJBPUVMaWlrm+IrKnI9NCY0dAQAP1jubfb4QgCIR0jYPdnRzo7mxsJDe+en3bw1a/Yb5W5+vnLrBQKKKIIh8/tJcHdw2jvokJgygKhHWNI71d3DPUz9XlLKbjMLWWw7SdDdqMreBXVXakU2xPp3Bdr51g/djfzrjfCpIo0hOL0BOL8P7RHc3vaHVu3gyiINAZCfPR/bv58L5dzc+ZLD3H62t/x89kPsD8lTLj127SNZhGNF3ElTKdA21kZ3K8dnUZVVNItUfJZUsU8xVkWULVZKKSSCgaaI4joKkc6+vmaF938/cU1o3j7Z6PqjvFivgF7s38G+LaZhelWzCdKi8s/x0JbZCd0Y26fZZjs4t2iqa3QVyPslVlprqE7doUzAppPU5vh0ayrZ3jZhRdUgnKPqp2nR5/muV6Dk2SqDslTq/OIosSh+I7CMo+SuYCuhRBFn3kjSmiaj+SICE2GCGXz05w5olLnHzffvLZEtt2d5HqiOHay7j1J0DqQBBjIHW/9Y/5zwSO67BmZFmsTWM5JmElSsFcYrF+A78UZs2YR5MCBKQoc9UxUnofq8YcIiK6FKRml9ClIGUrR1COUbPLuNjIgkbdrlC3KxStLK5rk9J6kQQFzafSNXS7NSi4Tkcv0X57zWnv36w1puoKXcO33+sP3U40ao2uAlVT6Bzc2OJz63W+dWtwKHZ7410waliuTUTx/Rhxr7DF/731fXopR65UZXd/OyH/Lec2CUHwjteycxTqr7JVg1lQU9nRvvEcqLLE0BtMDgKayp4WAtm9iSi9ic1M00TQv6nrRxZF+pKb13tZEjftV/zq5nHpikw0JHKtfB1VVCiaJVRRRREV6k6duBrzzKnsKoZjUrNrSILEzshm9p7juJTyZQKN30wTZdbqFVzXZSTShiQJBIK6p4H7YxAUwHNGn5pbJR4NkIgEUBQZVZU3JbUimk5E28iwkwSR3lDrJGPzNZJIvBFHua6L339bAkbTbs1t3noQiwfo6dnMcH0zU4WIpjOqtbd87p+Kn6rE0a0+5V8/dZSaZXH6+jQXZha5PLfkiSs27EpPjvTxyeP7+ePvPr3lxvj89Dx/9K0fUTUt7IaFnWHZ1EzPQu/Fa1NcmFlsigRKosDBvi5+/333NgWIwVvoYwEf15eyrJQqvHR9uina6bouhu1V8MI+jffu3c6n7jywqXVIEAT2dKX52NFRvvzieVaKZb7w/KvoioIgNPSZgFM7tvEv7jrIZ548zeOXrjffG1NbZwtP7dyG47p8+cXzfOmF8ygNe8pbFcOhdIJ/eeoYe7o9CnFQVzcc2y0MpZPcMdTD558+yz+cu4Isedwmw/I0CdrCAT5+bB/3bB/YYAV7C4os8TM7B6kaFn/xzFmmsjm+8fLFRqAmYNie/lQmHOTjx/by4cN70FsIVpbrBn/4jR8yvriC7Xh6P5bjNJNdc7kCf/q9Z1AkCUkQEEWRtnCA33/fvezq3LpC93YhCAK9DbvjVoFQWNf41B37WS6UuLKwzPPXpjgzMYssCtiu13ZyeKCLn7/zwKaQ2mN+dfJ7772bzzxxmvPT87w2vcBr0wvetcTGDPdgW4JQi4qmKAjcPdLPp08c5CsvvcZCvsijr15GkT1HQtN2aAsH+M37jjOXK3J9cbNAbyu4LhtanIBNug6t4FcVupNR/O8Q8VpBEJGF1klI28li28vY9gKOW0YUQwgoCIKK61oIgoYoRrDsKWSpA0me4YFTDscOZ6gba/j1YcKhHLYj0ttt8K9+5QCmGcGne5ba1jpts1BQ52MfPIQoiCiKRDzqbyaOVVXm1IntdGQiuK4nbKxr3saxsz3Kb/7ivZQrBqoi4fer1BqtbC9np1mNVxvPe331wYDadMJKp8L80ifvoliqYTsOuqYQDfsQBNi3u4fBNwRnwYCGosi8+9RuL2nkGDw6dYFHRg7Q2xX3RI8lkaMH+hkeSFM3TDRNIRbxb2AqeYw2cMzruMY5EBQQY+CsghhBlHrArYNTArvWoG8LiFIHgthOMKB5ekiCZ09uGBbJhsuYonqLfbFY8xIqAvh0xTNFWDcGx3GIxfxcu7bIi1xvtv7IsrQhYa9IErIoYdsWpu0gKSIOLqLoSeHbrktQU5kvF0nofmzHm48lQcQny7fdggSB9nSE9vTG6qGiSIzu6uJmLte45hxu5nL0xbwKWkcmSkfmdoDXt46ldnBvL47rMpXL0R4K0d+bxA2JVG2TuN/P8XUMo1bo7YpzeH/fpseT8SD33DnS8j1+n8qdRwZbPicIApoqs39PD/v39Gx4TvZrzfcVKjXWSlX27OzcEIjbjks6HeHEztttJGulCqWaQWci8g5XD7kNQfDWBte57YCoqjLpdJjlpQK5XAVFkYg2bJ9FUSCTCHN1KUuPHEEUBUzbZqVcJhn0U7dsJFEkqKq4eGuGT/EKZ1XTbOhWbJFkEQSqhsFKqUJnNIwkeg6015ezLBfLbM+kiAf8jbYC7/Wr5Qpjiyv0JqK0h0PULZuL84v4VZXhtkTLzVe2XObGiucYE9I1drW3vW3nTlkUiei3A3nLcRoi0N77a7bBZHkBRZSIKEFW6nlEBKJqiFWjgIBAlz/JQs1j4kTVoMdUEWWKZoWyXcNybFJ6hJV6Adu16fa1UbQq5MwSGT1O2apStmp0+VME5I3MmPXsw38KWn2Ox2j05qOeoTSdAylUVaF7MI1t2Wi6Qu9IO5ZpI8kioihgN5iAYoOxjsAmRtf63/MfDxcXZ0ttlVuQBY2DiU8iC5tjVVmUGIm0jvUCsg/bdQjKfuwG8wUgrASIKAF8ktZgE3k6kpIgoIoKMTWM4zr4ZR214WYVVruYK3utagl9uDEuuel2lV3I0dGXxDJt1pYLmLfMAsQQgnIQpDQgvWPYRgCyKDMY3IHesIn3SQHKdh7btRvMEZugHCNbn2W+do2wkmKtPktEzVC21rBdi7pTwSeFMJ0aqqSjijoOFhU7j4tNxcpTs0uk9W0tx+C4Joa9RM2ax3IKuK6JIEhIgh9ZjKDJ7ShiFOEtWo9uwXbq1O1Z6tY8llNGQECWwuhyJ5rU3tRxW4+ZyhoTxSwnM4OExNYteZ6QdY6qeRPTWcV1bSRBR5VT6HIXsni7AG9aDoZpEw7o+Fu28LvU7WVKxtjbOqZ/LFzXxXTWqJk3MZpj1lCkJLrcjixubg1tBcc1qFneOb1lqCOLYTS5HV3uQBA8PVfX9ZyhVVFFl3RMx8CwDXySj4JZxHJtJEHCJ+kIgohIi9/UdTFrJqZgNeJIgZFIGtPx3OuMukW1UicQ0DYUZt/OuVjLV1hYKaLrKlG/F5tHU2+mW3nr+E3q1jx1aw7LKQIukhj0rim5vamFuh5vPK9v9bftVKmYE5jOCrZTQxTkxjnuRJPaWl63P0n8VCWOXNelXDHoDIX4Dx96gBdvTPHM2CSzuQK249CT8Ho37xruQ5FEDvd3edTDoG/TBC2JIn5N+bG0VzRF2jTPh3SVP3j/fbx6c47L80vMrRUajm9eBSsVCjDYluDYYA+7O9PNhIjjuKzmygT8Kj5dRVcUfvGuQxzq6+RHl8cZm1+mXDfRFYX+VJTD/Z2cGO4ioAU5PtjFzOoaiaAGrg1bWDPKosQnju/lSH83j1+6xvWlVcqGQTIYYLQ7w307t3naRuLGpMQbYdo2D42OsLe7nWfHJ3nl5hz5So2grrK7M83d2wfY1dm2pcOdd+5kPnhwJ6M9Sc5OzHN2Yo7FQgnXdT0byO4MB/qT7GrvQpU3T46u62A4ZVRF2JQwSbfQcLqF9VoHju1QLdXQRYmRTArHdYkH/W+6wYgFfE1tp6CmomrylpOjIAgc3dbNnz7yEN9+8QKXsllWq54+RFjXyERCHNvWjeXayEibekptbPb3tfMnH3uQ12cWODMxw9jCCquVMpqoEA346IyFGe3KsLsrTXc80nIMPlXh0ycOcHSgmyev3OD1mQUK1Tohn8b+ng7uHulnR0cbz16dZE93hphf3yAk2QoBTeX4YC+nJ2Yo1w3PTnlbz1sGt6ulKmulCrGg7x2jcwRQs+ucy10io6eIq1GCsh/XrWE7yw22sY3trCEKQUQEXLeKIvciihFsZwXbWcN1K+haCr8exCWKJAYwzHkEolj2OMnEA4jC1loAoYDetKx/I1RVItMWYalaJO/WmCnmSGpBoqoP2e9VjiuWQcUx6YxEKVp1bNelNxQnGvETjfgxbIuFapGFQomY6iOlB3EUl7LPE/aL+nzIjb7pWNRPLOqnblsUzRpBRWOlViaIg6naaJLCfLnAYDiFT/fmPNOxuVlapWZbxPw+OuJRTMdmurKGjUvGFyYg315EBbEdQQs3nEcUbrGMEPzglqEhNi/cUncUFECir/d28qR19eVWVXzreVAURe64Y4jOzhilUp2e3gS9PYkNGyhFkjjUsXWr1M5ko4VWEOiKRJp978OJRMvvbI4ZmFhd4+XZWTKhELos852xMd6zfQQRgUevXOHB4WF8isL4ygqZUIiucJizs7MEVZUdbSnOzs4S1nTSwSDfuHSJI12dHOzs5PTMDEe7upnK5wlpGhXDoGpZzBWKSILAXX19zTG8GVzXS9qslioEdc2jt4f8rBTKxII+VFlitVhBU2V8qsJyoUyxUkdTZNpjIURBIFusYLsO5ZpBxK+TDAcoVOo8e3mC8bkV7t87RCYWajxeo2qY7OpJIzU2AavFCk9euM78WpFTewZJR4Noiky5ZjSdVFeLFTRF3lJH8J8jZmbW2LGjg7phMTu3RmdXHFEUaG+PsbxUJLtSalYrb8FxHB597TKPHBqlKxbhUHcnuzNp/KpyO1Zqca/0xzdWSU3bZnxxhe5YhHBDt0QUBK8NvXFf5Ss1vnX+MqdGBhBbbNgkUWR8cYV8tUZmZwhB8Fx/XrgxTX8i1jJxZFh2U+tRkSR8ypuLaK9HzbSYWmuIp+KtzeuZSpZrkzNLzFZW8MmekL8uKbyen0ARJQKyj6ulGXySik/SmCjPo0kqESWAhIDtOkyU55ksaxTMCu2+OAvVVQpWhT2RAYpmhZeylwkrflbqee5M7cHb/JWpO0VCcgbbNSlaC/ikKLoUptrYWAfkBI5rUbazmE4VRfDhl+PIonc9O65DyVpElyJYTp2qnUMUJIJyCkW8naASBAHN57UKFc05NC2E5vNRs7MIqoytVNHlJDU7jyNbBJU2JEHBdk2qVp6KU6RiuGhiEL+cQBQkHNeiZC6jS2FqThHTqaAIPgJKEklQGpeUQ9XOU7PzuLgoog+fFEUR1yfyauSMaWzXRJfC+KQ4ouAlrar2GhXLKwz45Y3XYs0uYDk1ZNFHxcoiCCIBKYEqefe+LEqMRge3rMA3JQUas12bto6xot+u5ru4SIKCLOoYTgnbMbjFOavZVVxc+nd18tQ3X2bq6gK9w+1EErdiVBncHJAA+yZICd5JAtlVu8JKfZFUg8nQpvXil8LoUhBRkNBEHwmtk4AcQRRkgkoCRVBJat2YroEm+hEbm1pPAsD7f7s+hCxoSIJCWh/YsFZ6CYYqudppFkvfomyOY9jL2E4ZFwsBCVHQkMUQqpQioI7QGf55guqOlsfg4uI4dXK151ko/R1lY8z7PLcGCMhiAFVKE9WP0B76GH5lEHHdHswnKdQdEwEByykxnf8shfp5fEovA7F/g+uaLJT+jpXKY1TNm16CCwdRUFHEGD6ln/bQR0n47kUSfYgCTfFjT27EoWpNUTUnqZg3qJhXKRlXqFtzABj2EpdXfhdxi3bPhO8eOsIf3/L55fJjzBe/BILEQOx3CSjD2G6RhdI3WS5/m6o5heV4iTxPoiGKJqXpi/1r4r4TW59T1yBffZGF8jcp1S82zqnXwSCLARQpRVTzzmlEHSaqbmQQ1W0DRZSRBImIHKFo1FEkCb+sgEBzvdnwva5LW08Ss2415g+T8fwShmMRVX04jkv0/+fuzYMkOc/zzl/eR2Xd1VXV9zE99z2DGdwgQAAkJZIgKZGiKHNXliyuLFkbtjfWsSFtKGJ37YiVYze84sorS1yZtmTJlESKFg+JNw4SF4EBMMAcmKunj+n7qK47K+/9I6u7pzHTwMCWN0i+/8x0d1ZW5pdffsfzPu/z5BKb+qDvNtJJg0ImgaLJlAdz+H644zoujFyazpvMN79Aw3kd11/Gj2LgTBIMVLmEpR6gP/kLJLWjiMLbJ9vb7nWma/8aL6hSSLyPXuuTRLjUOq8wV/8Tmu5FvLBKGDkIyEiihSYVKVlP0Jf8NJJovO35/0viRwo4Ajjfpe4/cHqcDx7Zy/sO7ibs0mMlQUDuKriHYcSvPniK33j0HjRFvmWDe2yol3/3K594V6JVohjXdt4ckigy1pOlbCX46cN7ECWBjVJJgXgy2lCw36CVAbiez1e//Qanjw1zaF9MyzVUhWNDIqPFgDDsIYp8ZCmNoQwgCjZ+eAnHl/jAYXj0wDiqHNByz2JpJ26LoPthgCzBsZEMe/r3QKQgCgph5CKKEYoU4UUNAt/b1GLRpewtHTYIY12eA31F9pYL/GIYEsaJJ2RJRBbFnV+WIOT7X3+NqUsLnHxkD8reJR44lOV9R0+y1pkjo5ZxwiaCEFL3J5nrVEjIOdywjSoaSIKCEzTJagPUwyl+8yOnsP0GQeSRkLOsOTdQRJ2M2ksQeRhSEkXQsZsdqit1ssU0fsthaToW3b766nUKgwU+9/eeiEWsl2o4TQdREqmu1NBNDd3SqS7XUHWVR3YNs0cySOeTpDMJ5q4tYmVMEimT5RurmEkTK5ugMr+ObsWCd2rVYXwt4FMffC9Gd2MvCvGC2g4dLtamGTALLNjraKLCiFVCFkSuNxdZdqqMJcqMDlqkigN8WNzFpfos+1ODhMSsiaSis+pWqXgC660mohD3+bbvMJQoklUtIi+kcW6JvtkOH//gw2SLKSCiFbjokowTepzaNcDR4V7swEXVJNq+i+17ZDQDqZt5a7gdBEFAlxTu2z+IqT/MZGWNA+USd+3qZ9VpkVb0zdphgLlWlX4zgymrBEFIq1sK9ZMUqqiwNznGlcYk52tX6NHyHE6PY2i90AWKgnAdWdqgggrE1qoCunpX93cbpH5h8/+yNEAYNdGEw3HZx21ClkX0HfS6RCF2UtvYbH3+auyUY8oqYRTxi+Oneb0yx1MLVxlN5tElmY8OH6HitPnm3EXGkgUOZMr4YcBTi1d5bW2WrGYynixwqjDMF6deww19Wr7LfcUx7ukZ3rYZXLTrfGfuEnf1DPGXk6/xa3sf4Ks3zvHTAwd5eXWGC+sLPFiKs4Wvrt3gqYWr5DSTESvPe8rjPLs0wfn1BURBIKMa/PzYSSQhZjhGQhZBzN4+SSuoN7Xjtj9s+ykCJhurZDWTnBZvKGaaFRRRotdMv20/rQY22UGL3XpM5V6y6yy3miQVnSAM6dEtKk6L89V5xpNF+hNvIyIMd5xtjqIILwhQJYmJyhoPjYywr6eHo+UytU6HfT09HOkt88rcXGzr3d/HUrOJKktMrq/jBgF9yRQn+vuwPY+9hQJ39feTM00sNbatNhWFc4uL3TI2gbl6naSmsdxqUuxJ8lOPHiKR2BlsqbVsPv/dl2M2qiCw1mjxjz/8IH/y1Ct84v4jDPVk+OpLF9nT38OJsX6efOMatVaHlXqL9x/bw96BHv7vrz9HLmkgiyItx+VXHj8dGzNMLXBjpUra1Dm5q59CKsFao83XXr6Ipsj8yuOnMVSF+fU6b0wtUmnE4NXJ8X5UWeYrP7zArzx+ClmS+NOnX+Xhw7s4NvqToYXk+wH9A1kmJ1cQRYF9+7fuKwxDGg2b8d0lUulbF4leEPDC9Rso0hz3jQ2R1DWeuTrJWrPN3nIP4z05zkzPMVetM1rIcaivyEtTszy8Z4yJlTW8IGS9bfPXZy+yt1TgwfER+rMpfnBtGl2RyVsmjbbH3164zMRyhYO9RUbyWZ6+fJ2H944xsVIhiEJ29+QZzKbxuoxWTZYZyWe51rVgn6lUqdkdDvWVuDC/jKWpaHJcOlfrdGg4DtOVKgfKxbctDdnIYL9yY57nJ2PNSUNR2F8qbkvizLaXWbIrtIMOmqQwYBYQgDW3zrBRQhMVzq5fo5gaJCHr+FGIIkgs2GvsSQ5wrjZJJ4g3jiU9S59R4Fz1OoIg0mvkqDh16n6Lop6lbGyVbix33uRi7W94tPxbVNxJvrfwOxzMfJjDmY9xsfY3BJHHsezP8Wbtb5lpvbSprzdgnuRg9gkUwcCPOryw8jlK+n5q7iwNb5EAn3sLv0rR2M4KDAm4Uv82k83nOJb7JLKg89Lq50nIeSruFEOJ09S9BRreIsdzv8Bw4m6W7Iu8sf5lOkE9dnVC4mT+7zFgnsQOavxg+ffIqAM0vKUYdCLgYPoJ9qQeA2DFucKra1/Ai2zCyEcWNA5nf4ahxKn4mqKAC7WvYfvrOGETVTQ5kfs0vcYhIGKlc4Ur9e+y4lxhf/qnOZ77+c37mW7+kInGU1hKkbo3jxvalPT9nMx/Gk1MEhHhhJ14PI08vNDBj3wKahE3dLusKSFmx3STCE7Q6Zacy0iCTCewyag5Ks4EYeR1gbC4n5pSAl2K37PhPb184tcfo2O7JNNm7LwMxDtck8g7jyDtXI734xoREW7oEBJ2gUGdnNq3bV41pTSmlCYkgO4xCSmeK3eaf3NaH0Hkk9N6MaSthGn8jBaZr/8Zi80v4oVVNtYAAlJXLDkiiNoEQQsnWKTjz1O2fnbHO/CDKguNv2C2/u/xwnUgQkDpMkEigrBFO7xK25ug7rzKYOq/o5B437Z9U0LWutfn0XTfpNp5no4/Q8N8P6vtb7PY/PLmhl4Q5FjIIPJwggWcYIGWewkvs0bZ+jhBKMdrICm+rzByuLr229Sd12OdIwI2rdCAMOpQd17Z8Rnp8gBRt4/fLhx/nvXOcwB0kp9ClfLcqH2O+cZfEkZtQEJAREAijFzcYAk/rO8IRMU8qkFMAAAgAElEQVRt1mSh8ZfcqP1bvHCt26byTW3axg+vY3tT1JxXGUz/A4qJD3YZ+/GF6tJGm8Ys7XOrS6Q0jQP5IsoODJogCGmstzbLClOKTlrV4+SGKBGqImsrDQI/pLc/i/YuKiNEUWB2cZ1dg4VYHLvWxnN8MvlbiQxBaLPc+jo3an+E7U8D4U33Hz9T27uO7U3ScM4ykvknFMzH3xbc8aMGtc4ZnGARTS5TSjzBUusr3Kh9jo4/Txd6RUAmIsAP1/G7Jj3vBEr9l8aPHHB0eF/fpp6cIIhoOywU6o02T79whccfOoCi39qpJFHEVP/udFeeefEq+8fLjA4V3vngbvh+cIsgl0CELieI8AEFWTRQJRU3WCPCIwgbIIChppDFRKy8v8MIEEXQCWostV9AJGZLGVIPECFGCoHv0PZXEJCwlDJe2MaQcxS07RbMG8ZCghCLasrdkjd459r1MIx4/huv84OvnyWZNTiwV8ZSMqy5k7hCkxWviiEl6dFGaQUryILKZPMMJX28CwpppJUyFWc2zmoJLQKxjojMun8Nhzqm3M+6fxVJVMko+xE9OPfd8yxcX6I00oPX8ViZXaN/vExtpY7TdmlWGvSOlXjje+coDOTJ9+WYPDfNsUcOsTpX4dJL1zj2yCGiMOLKc1eQZJFDD+zn5W++xolHD9Oq2fzwb17l2COHWJpe4exT59FMlb137WLh+jLVxSqaLMcZ3Lc886bXYblTo+V3qIQNNEkhiAKc0MOSDc7VpvBCH01SKWoZEpqKoSq8sHqRkAhdUjFEjZnWMqKwBdoNGgWuNxc5mRtnfbXBn/1f32B+apW+wTx9P3WUdbfNm7UFZFFElxTavktKMZhorFA2UihibIFpKb0sOw1EQeBCNRaZ7zczzLerPHZonIvVBP1mhqn2GlEU0VYNXl27QUG3kASB5U6DtGpgyirVls38Wp1ixmJX7527zPyohx8FTDRnCIk4ljlAxa1ScRsMmBv14RaylNiBEv12w6qESApR2hnEOHFkmD27ypui1jdHIWfxj375EbJdTa8gCjmS7eO9vXv47MVnuFpfwQ19LEXjZ4aPokkyqigxmsxzd88Iy50GAFXX5qWVaX525CjjqWJsP1tf4dz6PO/t3cNcu8bTC1c5ku3bZuNpyioIsGTHfWCyGeuIDFs5VFFmor6yeWzFaSMJIg+UxhhK5HACn+/MX+ZItg9TVvnO/CUe7duL6IokNJW1Zpty2kJX327SE2JNgHaFRbtKRjHZlSwiizEwOtVc443KLEdyA6x0mqiixExrnbpns88vk1GNrtsGLHcam1BUj26x3GmQ1xJ4YcBMs8JEYwVFlOk1UiQUjU7g88PVKSpOmz4zQ8VpYUgqNc+mbKSoezYXq/O4oY8iSuxOlSho70xtdn2fC8vLOL5PEMauNG3PY7nZwtJUbM9jqdEkisBS1ZjBsbpGzY7L73RZZrnVYrpapWRZ+GHIXL2BIkksN5sYsszh3jLrdgdFFDlUKrFu24xms5Qti5Su89hDt8/ObsSV+RWiKOKXHj3F5bllvvT8uXiB5webc4XXdUBTFYljI32sNdq8MjHL+ZlFdvcVsF2XBw8cYLy3wB995yXmK3UOj/TywP4RLlrL/MJDx9C6Zd6jpRzvPTzOM+cnNq/hwECJe/bWWKo2+YWHjiFLMQClSCITixXSpkat3WGw8A6A3o9RvH52hmRS5+jRIRKWjmFsvRsbLnXPPXuZ/fv7OXp8u25gGMZlk4IAT16eoJxK8tLULPmEyVfOXuRXHjgVGyhE8DfnLtGXTvL8xAwP7xljai3WBdpXLpAxDY70l2PRVUkmaxqcmZ7l9PAAhqqwp1jg+kqFw/1lJFHkuYlpHt47xvRaDGru7nn7eSGKIl6amqUvneK5iWned2CcQsJkVyHHXK1O2/X481feoJi0OD7Qe9tEVhCGrLbavDR1gz987mXmavE4N5LPcv/YdkMPvVuyVNKyZNQkaSUGmPv0PGnFQhElDmVGWXebtPwOx7O7mWrFoGtSNlEECVNLkZQNkkoCU9IZSpRwQ48XVi+wy+pnf2oEJ3BvEnQVMKQcXmjTCWrUvQVU0aDq3sCLbOreIj3abhY7F7jaeIoTuU/Ro+9mzZnk1bX/SErtY8x6EKKItl9htv0KR7M/R0btxws7WMrNZcQCYeQz0XiGieb3OZ77eXqNQ6x0rlL1bnA4+1FMO8dk81nu7/l1JhrfZ8F+ncHEXSSVEoezH8OSiwSRy+vrX+Ri7esMmCcBaPkrCAiczH8aXUpxvvpVLte/xbB1GlVMMNH4PqqU4HT2l5FFFduvkpC31sydoIYsqNxX/DWI4IWVP+Ra40l69HFkQaffPEFOG+WZpX9FGG13fIsIWO5cZjBximO5T1Jz53lx9f+l1DrDWPJhOkGba81LNP0GRb2XMAqRBJkgWuRG+zpZJY8XuV232whJkMkoOWRRZsGeRUDAkExSShpDzqFLaZbt8whIQIQbupvCzVfOTtM/VqSnN8vFVybpH+0hU0h2+3MHopCYNfuTFaaUQBFVGl51kwGyU1mNhExWvXONFUmQseQtoDWKIvywxtT6Z1lpf4MwsgEBRSpgKfsxlWEkMUVEXBpk+7PY3hRJ7RCGMnrb7wgjl/nGnzFb/2P8sIYoaFjqATL6PWhyH0QBLe8atc7LtLxrNN03uV79P5ClFDnjwfgcROjSdldHAC9YZ7r6/9B0LyAgkdHvJa2dRJPLMRvFvUjFfgY3WMYLK8zW/x2mMo4hnUIQBUJ/AxwS0OXht5y7Rtu7RoTfveb9OwI5pjJ2x6V6HX+GhnOOhcYXEQWFpHY3CXkMWcoRRg4df5a2dw1ZTKHLt3emDCOPxcaXuFH7HF64joASt6lxD7rUD0S0vQlqziu03Eu0vStMrv8rFDFNznjktuesdGzCKCKt6ttEp98asizhdlxkJTZiargdZltV+hPpLvAe98dc3kJ+FxURgiDgByH5rtZbFEF1rUl1pcHQ7u0aUVEUsWY/yVT1s7jBMgIKSe0Iae0uDGWEiAjbm2Tdfo6Wd42OP8tU9XcRBZ2C+dgdPSsvWGfNfprp6u/jBRUsdT8JZS+aXAREHH8R25/CC9ZJqocQdqhS+ruKHyng6Pkz1/nha5Pce3KMu4+PIgjwjSfP43o+K2tNkgmdxx7aRxhG/OXXXuGVN2aYmVvn0N4+Hji9i2bb5annLlOptRjuz/Pg3eNoqsxXv/062XSCiekVRofyPHB6nGrN5snnLlNv2owNFXjw7t3IksizL13jyvVlBFHgkfv2MNCb5clnL/HX3zzLq/05Rgbz/PQjh0haOi+8cp1LE4skDI2H79tDbzHN9ZlVnn7+MooisbRav+UeNWUATRlge/ZcQJU36rZvlmN8q6zkrSELGmXjOKIgISJv1jaKSIT4ZLVxwshHElScoN4VX7x9R+24HtWGjSAIrKw3ySYNFEUmDMJY7V2VsR0PIjY1UNLmFgVZQCApF2j4axhSEidokdcGaXgr1LwlNClBUulBdaZJKQW8qEMUhbT8Cpacp+4tk1KKhJGPF3WQRY2WU6Wkj9MJGyiIaGIC27XptDr0jhXJljLUVurkyrEQt6zKZIopWrU2MxfnkBSZTtshDEJGDg3RO1bi6qvXGT4wQP94mXM/uIgoiTi2SypnkS2luXF5nqMPH6TQl2Pm0hyJlEkqn6RvV4l2vUO6J4Xd2rIwjqKIV55+k47tMvbgAKtuHVmUKOppal4bVZTj9g995tqrWLIeP9kI+owcTd+mHXTIqUlC4mxG23cYs3qZt9dIK7FjSY+WZtWN+5Rp6Ry+Z5xcKU3fSFwiIwniJjuvR0/yemWWfjPeRJmyiibJlPQkkiDE+aIoouF1SMgaqigxbOXRRKkLfNWRBYmm75JVY5AiIWu4oY8kiHGGRBAopi0mFyus1lo/McBRFMVtM2YNkZRNlp0Kw4l+DCkW2fO8ALvloBkqrtNBlqSu5XSIYarY7Xgii6KIMIjQdJl2y8Uw1VgLoavp43kBmiozO7NG/1AeQRBwHQ8rZdwWNIJYA+dmXRxFlMhqJqaskpBVmn6sB9ZrprAUbUcLTicM4lJONYHatS1t+y5Bd2E8ZuUpF0dRRIk/m3iZmeY6Bd3iw4OHkASRuVaNA+kyr63N0mumbvs9G8yjr86cZyxZ4JHybhpeB0WUMCSFT46eQBcUXpq6ge16pAydntRbBXlvjRutNf7ns3/F5foiBc3ifz36Me7Kj3CxusB8u0bLd7lQXcD23Rh4JWZqvVGZw48CxpKxO913Fy5RNlJIgsjBTC8rnSZ24NLyXV6rzCAhktUk5uwqmiOT1xL4YUhS0RCAF1cmGUhksX2XspFiqrnKb772RSpui7Ri8L8c/RjvLW8BMmEUYXteN8MdayMFYYQfhhwsFak7DoYsoysyx/t6abgOSV3jeF8fTuDTn06hyzK253G4XKJkJdBlhZKVYK7eIIwiNEni/uEhvCDWLTne14smy6RUjaNmlkI+jdj2uHdgEEThjnVjWo5H0tRI6Cr5ZGJT02xzlopirT6AN2eX+frLb3JyVz+qLOF2tbtSpk4pk8TQFAxVxvWDzWcDG1pXO891cTIp/tKNYxOayuk9g7x0ZYaUqTPeWyBr/dejaP//HZVKk2eefpNyOY1pahw8NMDuPfHCVZQEdu/pxbJ0Oo53i2inLEmMFrIIwFOr6/hhFAM9pQJHBsqsNlucn1/i6ECZMzNz8dzRZb+1XQ9JFMiYBmlDpz+T2ixVK1hml2EdC7CWUrHb7EA2je16Xd2kgLZ3Z0zU3nSKtKHz/WuTWLpKOZVEV2Q+fvxQ7IxWb3BmZo7/8T99gxODfewpFsiaBpIg4PgBlXabmUqVyco6EysVOn7cD3tTSf7hA6fJWAbLrSZ+FMbviJDgQGIfhiyjShKOHzvXjBkJbN/H0nSGMyW8KDZSWXIqLHQqnMzuJqeleLR0F2KXvQIRncDncDpOBARhnOnvNwpbpVHd55JUikiCStW7wVpngmHrHtac6zTcBdywSVrtZ6r5AkV9H4OJu7plXlmW7Itcb3yfMSsuE4kI6TOO0G8e2yz1eWtcbXyP6eaLnCr8In3msc1Nvi6lyWu78EOHijNFThtl1Zmg5s0TRj6WXEQVEzFLHImMOsBk83k21qKiIDNq3U9R34cgCPSbx1nuvEknaKCKFmmlnwX7HPPtswwk7qKgjyPetM2QRZ3dqcfIKIOxzpt5mEX7In7ooMgGsqCikdwsfXtrJJQeRqz7SColTClHr3GY2fZZRqz70SSDAXMk1qkR5PhfUcENXcp6PwWtRBD5rDrLmFKChGxtgt4pObPJrJUEGU1MsWifJYjczRLAlJLG74JZN64tkcwkSGZMblxdJFtIdoGjCMJ1iDoQLgF73rH//ziFEzoxmwXoBG1iN9l3tjK/Oe6cnR6w2Pwiy62vEeEhoNKT+AAl6xMktUPdMv8NbcEAL1jH9icRBB1JSHXXMhv6XPF3rtsvMlv/E/ywhiRYDKR/mV7r57ob8I3rDen4s8zU/oCl5l/j+PNMVX8PUx5Dk/tIdtfBQRSi3HQrQdSi5ryMImYZyf5TesyfQhZTm8zBiIB1+1km1n8H27tOx59nufVVdmVPEoQha/VY1FkUNMZzv901W6F73c9xee238MMqqlRkT/5/R5dvz6qNQQOJq2trm1q8u3K527b7UvMrdPx5TGUXw5nfIK2fRBKsm8Yun46/QBC1UMUCYRh15+Gtc9U6Z7hR/zxeuI4kJOhL/gL9qf8WTd7SIouiECdYYqb2b1hsfAk3WGKq+q8xlFEMeQSAettBlmKJmaSqYvseCeUdBK0FgSiMqK01iKIISRBRRAnbj+chURTQdQXfD27rxPl2IUsi6/U2QdeduqecQTe2X08URdjedWaq/wY3WEYUNAZSf5++5KdRpeK2OcC2Jpmp/SFLra92+9fvY6n70eWBd3wnWt4lJtevIAgSo9l/SjHxIRSxuMW/jyI6/jpeOI+pjOIHAUEYIYlba6oNIe2/CyOjHyng6PihQVbWGswvbdARBS5cWcDQFd730AG+9cxFXjt/g4fv3cNDd+9mebXBRz5wlHwmVjx/5oXzBGHIg6fH+fYzF0laGqePjXD2wiz7d5d5+L496KqCgMCTz11GU2UePD3O337vPIWcxfBAnh+8dI0PPnoYVZXIpGLdlnvvGuOVczM8ev8+9o2XSSZ1rlxf4rULN3j8wX1cub7MN548z89/5BTfevoC4yM99OSTnD0/e5u73OhIXpdNFD/6eAD2CSMHsSvOG7szGRB5RITI0q2W54qYwFJSN0FMAm+1NtwIQ8rt+DeAlu3y+pV5LFPD0BRml2u0HY9619VMVxXaHTfevOZTZFPGNuAIoNfYQ0iAKMgU9V1IgkJBG+5eW2x1eijzOJIgk1LKbAgmioJMQR/pCqDFwobz9iV6jd3YQR1VNChoI3FZlaWz7/RuFqeWyJYzpPIWTttF0WQalRZ202Zo/wCu7eD7PqXhIulCcvMFypbSeG4Ql4XlkyRXG6QKZXwvwEwapPJJfC9AtzQS6QR9u8oEfoCe0Cnuy3P5zAQ9A3mUrpCd3XL42z99DlEUOfbQXt5XPrEJrsT18vF959QkI4kiEiIIMWVaEWWyqoUoiIwl4uyMKAj4UYAYiQxpRTRVRuyer0ePQYNk2uSXfvMJgiDETGjdhanOPcXR7oJJpKhbyKJE2UjHWVrYLHvrNzM0vA67kj2Mp3pQBGmzHPTe4hgiAqIg4IUhqijRoyeRu+KZfhSiS3JXUFOgJ20x3veTARoBVL06r65fQJPiSWLeXua9xXtIK7Hg8o3JVRZmK5R6M1y/uoggCCiKhGaopNIGC3PrjI6XmJ1eo9N26RvKMTe9xr7DA0xcXiAIwq6DjYJpqUxdXcYwVVYW67SaHe66bzeScWeDu+17vFldYjCRZc1t0WukuNFa3wY1bzwzf8MkIAxIyCqGrHCxtojSBb7yeoK8ZrE71UNGM7qCsBLv7d1DJ/BQRJm8nkCXFOZaVe4pjvDs8gTH8wMEYYgXBgRRhBP4KKJI03c4nh+gR7f42sx5Huvby6iVp2gkOZjppRN4pDWdgwMlri2tYajKO05qURQx065wsTZPEIXM21XOV2c5mh2i7bsMJDJERCzaMcA6bOUJooCynqIdeFxvrOKGAR3fQxUlcloCXZRjzR4tAQi0fIeMapJUdNzAJ6PotP1Y96nXjIGmISvHTGudifoK7ynvvqNn1XRdnrsxTVLVYhFtVUGTZJZaTXRZRhElZho1ltpNVEli2W7RCfx4AxtErHbadDyPCIG7+wc4XN7KfKVuEgMeyW5pdxw34k2P2/FYPzuPba4SRRGnP3AM9TZGCTtFTzrB9883mV5e5/L8CvW2gygIaIrM1FIlZqwtrHJ0tJe5tTqaLHNoqMzVhbVN1u1OjlGaIlNt2pt6SYaq4Ichru/H/3oBWlfjRlcUKvU2a/UWSVPH1BQODpZ58o0JJhbX+OXHTu8Ilv44xkPv2Ue5nKFUTmOa6jaBzygCx/EIgnCbC18c8Rzw/MQMfhgwXszTl0nx9OXrLDWa9FgJ0oaO7Xos11sokoQmy2RMgy+9ep4blRpHB3tRJYmkrvKdN6/x4O5RUrrGC9dnuLZS4eXpWe4ZHewyleP3VpElshvnWK9xpL/MleU1zs4u4Ichw7kMuYQRn2N5LWYujQxypL/MH7/4Kk8c2Y+uxDqDD+4apvrAaX7/2R+y1Giy1GjyzTev8L3LE0iisNkGQRThB8HmykaVJMYKOT5z313cOzbE+dUlKnYbLwqpOw4ZTadgmiy2mpTMBMvtFrIo0XQdRFHknt4BUprGueoclqzhhB69eol112HRjh0Nc1qCZhcEX+rUSco6iih1SyRknMDDCwP8KGQ8WSSp6KiihSYmqbvzNP0VRq0HWHMmWXdvEIQeKaUXO6iSU4e7LJcYqDHkLIudi5tPViT+3U6gUdNf4lr9SQRBjtk+N9mgSYLSfQ+l7v9FhM01V8ic/TrX6k/ihm1kQaXuLRBFW4LWkqCgS6mbhLklNuBjAZFdyYcI8bnROsO1xlOMWvezO/XYpl6RIhqoonnT5xWibtnTnYQqJjb1ZgRBxJDSrPoTRETIgkRW3VqHbIAFmqhhyVvrv6Sciu9Z2DouItrM0wqCQNWdIiH3UDKOoIgmAiKdoLM5r/aN9vDid86haQphGJLYLBUVENS7iPxJkHq3Gv4nJCJCQgJWnSUy6vY1XxCFuMHGniYe64MopO07yKKEFwYYkrq5Bo2iCENSd9g0R3T8OZaaXyEi1jrLGQ+SSnyGWphgubmCG7qb2lgREWnFIooGcEKXFXcWSRApaBnSaswECyOXxeaX8MPYeCJvPkJ/8r9BFlPbvlkQRHR5kKH0P6TpXqbpnqPpnGe1/V36Up8iAop6ElW8dfssIFO0nqBsfQwBlYbfpOE3KWlFZFEmo99DKfEEU9XPAiEt9yp+WEXqrs+DMOqaE6ksVRr4YUBfLo0gKGz1JQFJ0N62zCkIQ5ZbTdwgYCB1q1bqRjTdS5jKGGPZf0ZKP4kfhohCdBObTMZQBnEcj+nrFcIIBofzm/NQ3KZfxg2WN5/RQPqXUMTtfUMQRDSpzFD6V2m5V6g7r9JyL7HS+iaD6V8mihTOz8Rr0VO7B1AlGT8M6QTbWYe3C93S8Jx4/jFllcPZXladVvd7BUrlNKL07sESRZa6mlNdAMpUN934NiLCZaX9LVreNUAgo99Nf+rvo4jbgTpBEDCUUQbTn6HuvIrtT9N0L1PtPE/Z+gTvNE50/HkkIcFY7n+ilHgCSTSYWV5nvWlj6ipr9RaGquAFOTx/jYTeQJVldFWm3nZQZQlNkRFFgeHi27u93Un8SAFHhq5gGiqNm9gcuq5w9MAAu8eKXLy6QLVuI8sSqZSOpsnkMgmSXbu9V8/PIIoi80s11qstWm03Pq+hcmT/ALuGY2aG5we8dn4G01CZml2jWrdp2y5WQmP/7l6+/8OrjA0VGCjHehspS4/ZNSmDbMYkiiImpla4en2JMIxwHA9dU7A7LuvVNkcPDpKydEZvY5+3EY53Fdt9A1UeRBAUBEHH8a4RdcW0ZKmIKCQJojqSkEUSk0jSTg98u2zY7UTE4j/s+BcAkqbO6YNDqN0OFjMv4lKK1fUWaUtH6zow7SSELAryZrZI6k7w0ltocxsLnrcufN56XJ+xDydsowgaESGauOEaIzJ8cIDhAwOb2eeb4+YMx+6Tu7q3vnVMoX/ruYwcHGTk4JZl6qnyic3NzV3vP76Jrt/9wROb57nnQye3nbOyVOP6xXmG95a77kk7aNeIEjK3KauUbt3ASaHEzPI61YbNkfHeTWenDaFYQRQw3sJKEbtaRXEbhKhi/KwM+faDZkLW2JsuoYjbr2njHBvXDGDctKHfuNooimi0HQSBvxMU+0clYu2KAj1aDNTqooYibrWJ7wf0d9/twA+xUgadtksyLVOttEilTXpKKS5fmCOZMrCSBpIsMn+jgtPxSGUSeK7P8FgPs9Or5HuSmAmNXE+SpfkqHdtF75akBFHIgl2l4rToN7Pkte311bIoUnFa/Pnkq5wuDDOazNP0nG26RF5Xz+jl1Wm8MODLU2f50OAhPjJ0mG/PXeLM6gzH8wM8VBrndH6Yv5p8nTCMeLx/H6NmHkNW0UUVWRJp2i49SpI5r0ZGMigoFhnZ5Jm5a3xv/irLToM/vvZD3te/j4V2neeWrwPwWP9eUorOx4aP8o3Zi7y0Ms2+dIm+wQNU2x1MVaGcSSLfQVZIFSVkQSKIQkQEErKGJAiMWHl+uDKJH4WcKgwz1awgCyI51SSlGiiBzLCV4HLtCoqo02eapBVQRbD9dWZadYLI467CCKudJVY7En1mmqv1JVq+w0BCxZAiNElHEWIwteU720r53r5fgamoJFR1073KC0PyhompKMw16siCSMFM0HQdWq5LaJq0PQ9ZlCiZCd5YXsJS4/IjPwy40a7Q9BxGrAJJZWehdUkWGdzdS3W1TjqfRJLj9tuw1667dnesUFG6zEWErblkoJhm/1CRL794HktXSegqggh37R3gqXMTqBM3GChncAnpL6Y5Oz3Pv3/6FUppC9NQccOAfFdcvdbpkLbid2LNbtOTs0CGzz95hg+d3M/+gR6eOnedVyZmadgOn/vmi3zw+D72jhQ5OFTi4o0l/uK5N3jv4V0cGi5jGSqjpRyuHzDU85NTpgagaQoHDvbHrn1vmedEUaDcm6HQk8I01c2MYhDEG/1PnTqC6wd4QUDBMJFEkQ/s34MT+KRNnaSq8qGDexEEgVPD/aR1jU+cOES94/DA+AgZQ8dQZD58eB8rzTZZ00CTJe4dG+Ku4QEsLS6bLCUTfPzEISBOPHzq1BEqLXvzHF4Q8MSR/URE5BImuixz39gQp0b6SWoakhi76W5YN2/cp6mqfOzoQcZ78nz59YtcWFxipdGi6bi4fgxSS4KAKkukdZO0oTOYTfPI7jHuHR1iIBMDHIcKRfwwBEGg5nQQBYG0qrE7W0CTJPwwROm6wwVRhKWohETYvovazV4LCDTdBu3ApWykafsODa+DKsrkNYsbrQqGpFI2Urihz6rTxAsDdEmJgV/i9UJK7WPdnSGIPHLaMAk7z2LnAqqYQJMsDCmzqS8EEEY+naC2TfclZlDsPN9qosWpwi9xvfksr619gfuKv4YmxZvn+H3eREy2RSeocbby56SVAe4u/DyKaHCt8RRX60/edNQG7e+mn2+6Lk1KciD9Ycash1jqvMmra39KSLCpVbTt+/8zwg2amyVsURTSCepoorW10X3LuaMwwnF8gi7z1/di5oEoxX8TBGHzcjq2h6JKaFosjL3SuUjbXyOp9JHX92EHLUw5nn8PnBylUM5gt/kxzVYAACAASURBVBzy5TSprqMhwQyRPwVRhyhqIci3L5n6cQ1DMtFFE1uwyak92/rhcqfKtcYihqSiiLHO5qK9jhP69GgpMmoCWZCYs9fQRZWkYnA8e/v2iaKIaudFbH8KAFnMMpj+B9TCFA2vgR04RETU3CZp1SKrpPCjgE5XYLnq1tEkjX5J2+wTLfdy19IeJCFB2fo4spi6ZVyFuC/r8gClxIdoe9cII5uV9jfpSbyPpi/T8Bz8KEB9y0dVqUgp8VFEQSOKIpp+i+nWLAU11zXM0Uhpx5DFJH5Yxw1WcYN1GrZJy3FxPR9JVHA9n+nldSRJpJje2RxopxAFgf2FHpZaLfLm7Q1WINaJKls/Q0o/wYqzyqqzzLi1B03S33KcwPzsOrqhMDC4RWBoe9epdc4Ql36alKyfvQU0ublNNamXkvURWu4VgqjJavs7XfZMP5au0nZikDCIQgxZQRZ21teFeA600ga+F4+XNbfD5doyda/DaDKPIkn0DdxKuLiT8PyYlS+KMaupUbNRVIkkW+3p+iustr8LhPH9Jz6CKt1+3y8IAqYyRlo/hd2cBgLW2k9R6oKMbx8RKf04xcSHNgHDxfUG1VaHPf2xBpMqS9RtJyYIiDFpodbqsFpvIUsiuqKgazLD/IQBR7cLSRQ30U1BEDazAqIgEoYRnucTdEupcpkExw8NcvLIEGEYbWplSKKAqmxtjgUgn7V48O5xDu3tIwhCDCNeeH3g4QOsrbf41tMXeer5y3z48SMIYoyeO66P7wdIkohpqOzf3cunf+b05uQTBBGSJNJodlAViWbb2fm+pBymdhJRMNgQ1RXVOOMtCirCpqhYHVEwEXaoZ42I0WW6duqiKG4u+KMoIgxDBEHcpPlvoKe3C1WRUJVbB5kEKtmkiWO7NGs2nusjySKJlAHaOy8EPNenVbdxbJcoAkWTsdIG2k0Zb98PqCzWEESBfDmNKIqxE4cvs75cJwxDUjkF86bMam29SaftkslbiJLI2mINw9JI5yw816dZs3E7HoIAmqFiZWIb8LciwRvhOl58f45HGEaIkhizQpL6LW5rgR9itxwc2+XCS9dZma9QHsqxOLO6DZXWDZV0IbnN+htiUfF2s4PdcvD9AAEBRZMxLR2965Dj+QEr1SZhFPcQ3wuoLNc2B0mIB4hsT/IWJNwPWzS8CQy5FznMs7ZYQzMUsj0pwjCi3ezQbtiEQYSsSPH3JrRb+kYURbgdj2bd3nQukGQRTVcxLI3+Qpq+QnozA/yTEBk1RUI2qbhVSnqsz6CJyiYg2TeQQ5SEbp+IPzN1bZlk2mD3/l58PySZNjh+egzfD7CSBrv29pLJJWg1O0iSiKxIJFMGZkLD9wM8N8BMqOw52Idxk0NS23f47Jvf5qXVSf6HA+/nI4Mntl2rKsq8pzzO6Z6tmvi7Cn3EQ3sAyKiSzPv79/P+/u06NnvTpVusjXdrRQqZJAlNpW17vFFZRIC4hCSd5OrSKmnD4Jg+ROjDI+m9dBoBUUfival9ZEydk8OxKOiIlefe4vaF4a5Ugd848NC23yU0hWuLq7hBSCltIXfLp8Iowg19BASCKNgsxRy1evjwwFHOVKbYkyxzX89u/CikoFs8MXS0aw0c39+G8PYGiLrYXmM4YaBLaVTRwg6qgEcY+fSaHaIoQBAmeE+5QBB5CHToN9sIiCjiCuOpMXQpw7LdYKq5ytHcwI7CjQBNr7NZ3ulGPnuLWWRRQhIEFFHultPFnahg6ZiyhibJdDyfG/XYCe1EeYuSrsoybhAgCyKrTpP/7Y2vMN1c43dOfILThbEdr0OSJZyOG7MEpbgdr9WXaXgdkrLOitNAFkTSqkHLdynpKWbbFdadNqmuntn7T+0mrZpMLa/zH556hRv1Ovl8gp++fz+OHzDXqLMctnFbIQ+dHAMEiokEV9ZWmW3VOX5wgEWnxaprc2B3CRC4uLKC4/sc3N/LnnyBvYX4ffupk3v5qZOx2G+z1eH8xTmenL7E2EgPn3n89KZeQRhFOJ5Pw3Z4z8ExEtqds6h+XOKtc8dGCIJwC9PI9QLOX47dd5KWzsx8hYShMm3HTLOEqeH7IW7S5XK1jR8E9OSSrDTrTATL7BktsauU34YNZEyDlB4LxMuSyGg+S7PtYGgxQ1ASRYpJa/OacgmTXGL7OuKtP4/1bC3mryyt8t03r/GeLqPp5lBliZND/Rwf7GOt1WZ2vcZqM3bnC6Io1vOTZdKGTsEyKSWtW8ovU9pWG+X0Oy9jvL84fmcHCrAvXd6m278v3Xtbnci8Nsora39KXhsjpfSSVvq5XP82Q4nTyILOsHU3Z1b/hPn2GxT0cdadaWbbr3Io81HuFHBRRJOivo+CNs4Plj/L2coXOZb7xDt+Lox8/NAhIRdQRJO2X2HRvnBnbUDs+LbauYomWciiTk4djoEwv3ZHn4+iED9ycIMWQfdanKCJJChIXZHZpr/MVPMFRq0HqHvzLNjnOJL9mW2uVzfH0kKVy+fnYt1QU0U3VFzHAwR8LyCTT+C7ASvLdcIgpFhOc+DYEJKgEoQutfAGkiCTZx/toIXUtRKfm1whX07TO1xg5uoisipjpQwQiwiKDpEDvDNb4sctoggSchJV1FnozLDL2sdGqVpWtRhJFLul6Botv0OfkY2NdgRQhJg1mlUtKm4DLwwIiZBu06+DsEmt8yphFCf/0/oJEup+UoLJoFEGIV6btoMOCfkmN8G3VFxsgEZRFNB0L+IGqwCY6jgJdfwdSqNFMsZ9KPU/wQnmsL3rtNyrqOLhHRlySe0wurwlii4KIkEUbB4vCAKKmEUSEvjUY90mwaU3V6I3m8TU1bhc2A9IJXQ0WUJXFVrvsitFwEKzyfMzM+zr6eGBoaHbHqdKRbL6/YiCgh20cUOHIApuOS6WaPAZ21NC07eS0y33Ck6wBIChjGCpe99Ws0cQRDL6PahSD7bfxPanabmXyBpxm20wV4MwomCam8y0HZ2uRYHBvVvro5xmcl9pDC8MulqWW599tyWTuqbQV0xv7olW59dptxzypS190oZ7kY4/F7elXCapHXnbcwqCSFI9wiJfAqDtT+EGa+jyO2mBiRSMR5HFLSmH/UMlDFVGlqRbWEQbSaT/WvEjAxzZHY8zr0/z6vkbOI5HLpPg7uM7o/UJU8XQFL7yrTc4dnCAw/v6eeDUON//4VVm56uoqsS9J8foK91K0xMlkftP7eL5M9eZmFpBVWUeuifWOPres5dip6i2w95dJTYSEv3lDE+/cIWl1Tr337WLw/v6uXBlgS9/4yyKLLF7tMixQ4Ps213ma995g558klrd3vH6FakEUmnHv2/Qf2WpyNstGBoNm1bToVZto6oyqipjGCr1uo1pqjiOj2VprFfbKIqELIkMvwuBb+iKfy3V+OZ/fIGzz16mslTHypgcODnK45+8G0m5/UARRRH1SotnvvoKrzxziYWpVYIgpFBOc/T+PTz68dMU+7IIokBtrcnv/rMvEIYRv/UHv7SZwZm+vMDv/eZf0KzafOqfvJ/HPn4aiIGXL/zut7jw8nV+/V98Ak1X+Oef+SPufd9hfu4fPc6TX36ZV75/iaWZNURJpHekwEMfPsF9HziyDXzauM7VxSpP/tUZzj57hdWFdVzHx0ho9PRl2Ht8hPs+cITR/X3dFxLePDPJN7/wAvNTKyxMrRIGEW+emeS3P/0HCDeBKMce2MNnfvujpHJbWYPl2QpvvHCNN168ytSb8zSqbURRJF9Os/vIII99/DRD++KB5GZx9dXFKv/nP/4PLN2obP7OSGj89//ykxy+e/si1w1reGETPfJZmF7lX3zm33Lonl38+j//BBfPXOep/3SGy69N07FdUtkER+/fw8/+6nu3MmfEGbnpKwv84G/Ocu6Fa6wt1gj8gETKoDSYY8+xYR756EmKA7fPMPy4RhiFzLTnOFe7wr7kGFOtG5zMlJEVFUkaIXmTg9HwriKeF6AbKj2lFNJNTLzega3BPJuP2zVX2NpgAZgJbdsAXyhuz4BVnBaX60vYgYsfbTlrbMSRXB89+lbfiojoeNdR5UFcfwZd2fuunk1PMkHLcUnpGq4fkEsYiIJAwUpgqgpZ0yCfMLG0WOdHEkUSiowqyyQ19V3XkW+0hR/E9fzhTW0RRiGX6wsoghS7+okyVa+NKWt8ZPAE9xV3k1YMvDDgtco0CVnDkjVm2+ukFINO4OKFcebqRG4YgTgjjhAhCToIAqacp+3HLiBJpRcBEUlQUMUkXtgmiDxkUUcWdDQxidaltWc1k/eU95CQd6LaxzHZXGWuvY4X+XQCjyiCspHGkjUkQaTqtXHDANt3Sasmp/PxnKfLMruyuVvKrvqTsWaCiMBivcZkcwU3iPUW3ilEUcBzPPyuFpEuKjSImUa7kjEbt+U7mwxEVZTpN7PokoIhK7dYrVuqih34pDUdRw7QZBk38Kl1HJKqhhMEuEFA3jRjS90ILE1Dl2WarkMYRgyl0yw2m7Q9b8fF+MatJUyN9WqLdMqg0HU2ma/U+e7ZK1SbNsdGe/+z+t+PewT+LL53FgQDPzxOpdammLeQZZGEqZFJGZtAW6xxINBoObQ7LkO9WTJpE0EUkASBTMq4bTlhrWEzt1hl33iZKIp48vkrnDoyfNv11buNoVyaT951hKS+M3NPFAR6rARKKCC3Yf+uMvIOTNrbhe16nJ9fYrwnTzZxZ+CRIAhUWm3cIKCcur3Ifd3u4PgBBcu8ZRy43biQlMvYQQ1L7kEUJJJKLy1/jYw6gCQolI1DDCXu5uz6XyAKMkHodX936o7vdSMScoFj2U/y/MofYClFcurI2x6vSxkGEieZbD7LcudNJEFFk1I4QeuOvi8i4HrzB8zbr6MIOmHko0pJRpO3t/B+a3SCOueqf03dm2fNuU7LX8UO1ikbh9mVfA8Ahpxl3n6d6eYLOGGTnDrKgHlyRwZWp+Oh6jK5QhLH9khnTOo1m3azQ73Wpn84R/3/I+/NoyTL7vrOz3177EtGRO5LZWVl7V1L763eWy2pJSQhCRAMjEFgsMGAjY0Hxsw5w4yBM5oxZgzGNjYYxqwWssBo75Z6k3qrqq6u6trXrMp9jYx9edudP15kVEZlZlW1pHNG6vmdk+dkvHjxlvvuu/f+fr/v7/stBEmxTHecZFfAe5TQBzHjcWruMnU3j4JCzuxFb5GdXzhxnQMP7SASD3H1zDSG2QocCQvpHAPpIdSAiuHdVK6mCpWYnsSXHiW3EIhWt27PVHQGwjfQFnEj1E5gQ+f7kLPiAZpji7bxZK2lTgUCnYi+A1WEkQSBpGCqEERUE9kKLgVKYi2FVunhSwdNiSGEgi+b1JwJ1tTJTLUbTbk9OlVXkphaD01vBk82qDqX0fQDKEK00f/rzdL6UNc595pQSejxjv4pxA0uWomH73tYhk5kXSLd0DVURdxhAedGE0BvLMbOTIahxNZjtK7G24GuiBal5lU3zPMQ+CHpTKwjOO5Lm7ozAS10pKFm0DehU7nZAqLtPuruBFLaVJ1LpEJP4bgetUbwLJueS9P1NqilbzAZKJhLGQSRgnI/H9+X5Cs1wpaOZert6y7VgnLTsGUEtDW3OLwAytVmW5hdMzTUFiJqzerO1ZYSHVhq57PfytYjkny/juutwm0CRwKFsBH4eGvvUyxkdnxeb5txUX0n7bsmcKSpCv09ST783v0AmKaGpip8/JmDRFpSl4/ct501PzoZD/MTn3yQpZUKqUQYRRXcfdcQA31JiqUGlqWRayE9fvRj95FO3nigihDcf3gbwwNpypUmoZBOOhlGIHjg8DZqdZuQZdDXk2yXCH3f03cxt1BEUxVMUyMaMfnxH3iA+aWAS6M7G8PQVZ55fC/T86uoisJ7H9lFfEuiTkGtafNHX3iDidkVVEXhlz75GH2ZRMc+AGcm5vnCq2f4yQ/dz86eLP/pUx/D9SU9iSheM3C46g0H09Sp1pq4ns/k9Ar9fSk0TaVYrLOSrwROiBAMD2WIWyb/5w89Q91xiVkGEdPgyLlJnn/zEn//+x4gs669Fqby/MGvf45jL5wl0RWlezDISB55/gzn37q2AcUDQWeeubrEn3z68xx74SzxdJRsbxJVU8gvlvjM7z/HW9+4wI//yvex995RDFMjlgxz9tgEi9P5dgDjyulpZq8uUas2uXxqisc+fBjd1LCbLhdOXMexXbp6EqwulVieLXD++DX+7b/4DOeOXyOdi5PtT1NerXLqtcstZNAqn/iZJzGsG6VHsxNL/MGvf46Tr14i158imY2hqSqVUp3rF+a5eGKS3uEM29pSyJJG3UZRFQa251AUQTFfIZ6Osv+BsY7gwfa9/Wh652t27MVz/Mmnv4CmKaRzCXIDaXzPZ2FqhbNHr3LqjSv80m//D8RzMfqzifbgFkuEed8PPcD89ArLswWOPn+WlYUiTnOzdIREFSEMNY3r5FmeLzA7scTXP3uEz/y7r+G1UDGhsMnKfJHJi/PImxQAr5yZ5nd++S+YubpEz2CaTF8yGEwLNS6cuM7549fYe98ouW8RCvrdawJTMTAUHVd6jMe2EVbyOM5lVHUA1sFKhRAYhkZP/0b451aDtut5vPLWBIVyndGBLvbv2Fo6/ExxllW7suX3N6OIPK9AqfEyuprD2II88VaWDIc4OBSMWYPp5IZFX3qd0yWEoD+VuGMFxq2s6XgIIVgqVTsCR570Kdl14kaoXQ7iS5+m5xDXLBJaiIhmstgoUXNttsdyrDareNInpltIJJ5stqRzg2tLmiPBtbYzkZK43oeP3yZBXf9da0PHdghIyW8u8dzMtsey9IQSFOwamlBIGmF0RUUVCo702uVinpSYqkZIC/pW2WkS1TcGpYJAUrDtxOokVddGv0MFlVDU4uqpSUIRC2SACBuJBouY9edZu+/haNemi5LeVIxPPXUvvalYR2BLCIHjeVRsm6Rlbcj0bdU/eqIxlqpV4ubGwIHjeFTrTQb6UtQbDt2tsqw1i4cM7hvvpyseoesOiNXfbeb7Zaql36TZeBYhNKzIr3DvXZ8gHIqgaxrbBjItpHHnc3Rcn3rDJhoOUKYDPVvLZduOy4tvXOTEmWnuPzjCA4dHyReqfPmlMxi6yuP37yAasfjG0cuUKw327+pndCjDy29cotZwyKajDPSmOHbyGo7n4/s+D98zFlAGnJnC0FUeODTK26emmZrNY5o6uqbyzON7OHLyOjPzBfp7kty9b4iX3rjMm6evc/+BER69bwcrhSrHT0/ieD4PHtyGaWq88uZVXNdjbDjH4X1BGbqmKkwsrxK3TCKmwXyxjKoqQbC7dc8V224TuWdjEfKVOhcXlwIHUYLr+2SiEfLVGhJIhixWa3UURaHhuKxUa3itfcqNJuVGk1QkRCZ6o18mjQGe6fuXLc4fQV/4Lj7Y/5vE9R6EUDBEmIPpTzLmPkHTC+Tqo3ouQF8TkEs/kvtFQurmTm9v+C4SxgCmGnD6dIf2cG/XPwmC6noXD2X/MZaaoD98kC5zFFONMhZ/As+3MZQIB1I/yGj0UVy/jqUmMNUYdW8VgcBS4zzW/UtobryN8k8pYzyS/cdEtRwKGgdSP8BY7HE8aVOvuCRDvYSVJOVClUxsjCd6/jkJ/YaTtD32OIOR+zDVGFJ6bIu+B086LYRVYJYab9MfhNU093b9eAsNqhDTc+0yvM2sbyDNwHAXmqZ2vAOO49Go2URiFgPDgu07ezBMvR14LtqrrDQu4MoGhhK05UB4pP37aCLEqdcukxtIk18sobdlviX4FZAVEO8+9GPTb3CtegmQJPUu8vYiaSOL1irl93yJIgLxlSBGs8bhGtBeqGvOvQeu56ObwCaIEl82sd0AyaIIC0sbwvVLVOxzuH4ZHxtVRAGPpreIrqQIaQNUnItI6WCoWXQ1RczYg8DAl06bhwdUDDV7R5LlijAxWwl+KW2a3jxoPvlmFdf36QSDKmhKsgP9ZvsOIdW66f7W/d+i1ppaLKBrCntHehAi4Bc1NJV0bOsys9tZqdnA9X3KdnPLuVcVcZTW2IKUFOxV3PBGf8JxXDRNQddvoHgkbhttBAqGmmnLz9/KFGFgagE/oy8DRTwpg7nH9nwqDRtNUbA9D9v3brmurDUdLl2bZrVSw/cD4uft/RnqtoPjeIQsnZVilVQszHKxQlc8EBoqVhqM9qfp7YpveWzX86k3nIAX1HYprFTQ11UuSSlpegv4MqgsKtunOLXwM+2g4NbHvSGa5UsHV94+MK8IC00JxrnF+SLVShPdUDEMjUbDwXM8bMdD11QiMatNBr7WdqapU681SWeihMJ3Rq1wK/uuCRxV5CKhXJUhcwhNudH5urM3HmxqffBHEXSlonSlOus/+7qT9N0E5Onr2TjJaqrCQO9GZ294YPP6xGjYZMe2XMe2RDxEIt4ZGLIsnbGRzv22MkvX+cRjd/Hamet89oUTNO3N8YjZZJSH7xolYhmETYN9AzdIUdcmw6HBzuseH+tshJ30UK40qNVsAiSTys7ebMc+xUqdq7Mr2O6N62g2HL70Z69w7IWzjB8c4lP/80fYdWgYIQQzE0v86b/6Eq986QQ3+xfVUp2/+r1neeO509z/9D5++Bfex8iuPhRFsDi7yhf+n2/w+T9+mT/+rc/zK7//4yQzUUZ29XHk+bMsTOfZvm8Az/WZODdLPB0llooweXGeSqlGKhtnea5AcaXC2L4BwlGL1VYA78KJSXqGu/jZ//0THHp0F5GYRWm1ylf+/FX+/P/+Cs/+1eu855kDHZKKrz93mlOvX+bAQzv4+d/6ITK9SYQiaFSbzE2uMHN1kX3334C1CiG454nd3PPEbpDw7Gde5+yxCUb39vML/8cPYd3mxbzn8d34vmR0dx/DO3sJRS2k73P59DR/+n99kePfuMBrXz3FfR87TKVV3gcQjlk8/cn7AVieKzB9ZYHrF+c3HD9QcVBpeIv48gYSaeLcLKtLL/DYRw7z2EcPk8zFkb7k6vk5IhEzKD1cZy/99+NMXVrgkQ8f4qd+7aOkMjEQwbOdubrI8lyR7XtvrwjwvWaKEOSsDP12gRV7laIQdKmCkJLlO5E9lBJqDZtUPEwktPVE6/oepwvTVJzmHQUpAFQlScS8G9dbRHBrNMyd2J1k0b/dc4QMjXK9SVcs3MGVZSgaD+duqNKMRrOb/bxtEknGjDIe78FuOMxNO4x0JYlHoutoJTfej4qJusV3366FVIOQapC5iZtKCMHa27beofGlxPY8qrZN9BaKIrbvcnJ1Csd30TfhSNvMSvkKhmWwMldA+j5bSbZuVcq7ZqauMZDZPIupKUpH0GirY3QcT1UZiG++gFtYKjE9u4qmKiwslsikO2V1LdMhnb6CpiaQ7N+a3+9dar6/hOucBhpICU7zGNXyY9TUoOw6lYnRqNt0D6RYmiugaSq1ShPHdhFCMFm3SWWi2E0Xv6UeoxkadsNheLwH0wqCOAd3DyAlPP3w7nbS+aHD2yhVGly4uoBpaly+tki2K8bb52cY7k8zt1jiA4/voTeXYGJqmVDIwK00yKSiTM+vMrtQ5MFWEOro29dQFYW+7iTFcp16w2ZiaoVvHr3M9pEsZy/NsX9nHwd29+O4Hk8/shuB4OUjl2jaQSnIxYkFdo72UCrX+fgHDnUoU+qqStwKVCYbjsOFhSVmC2W6omGysWBdefz6LJauErVMoobBar3B2uR7baWAoakMphKcmJrjgdFBTE3lylIeCTTTSb527jK9iThhQ2cyX6DStDk42Mtj4zeQ85pikrG2tz8bSpis1UmuL1Ep2RFc30LoJjVXMldbCCSmJaw2o3iGhus3mKwWiGg6XVaEmWqRrBUlpPVxrVwgpOmkjDCTFZWDXd0UbYe3Vmx2JSr0hOMs1ps03Bo961AiirCQhSS6SFEo1HDsAoWVCgPbV5mdWCKZiXH94gTJTIzh8R4mzs0zfmCIy+fmAEhmY0xebDK6p5/qdB4l6SGTDS6evM7Izl6sSI7ZlUIr4aYQ0bqI0Dq/0Da0xWZmqjEiWhDU9nyJ70vWpLchEB1ZI03WdBVJUPqjqQG9hVAEqqYQjlkoisDzJYalt4+HBOHHyVr7UBUdS9041h16ZBdnjl5lcSbPPU/sId29Nn6pCPNR8OdB6efdhDYCcHwb22+Q1LuYrl9DEQpxPYWGzrWVAleX8sQtE11VKNQb6KpKSNcxWmO27bqUGzZRy6BuO2RjEUaz6Q3lpb508GRQsSGEiqZEUYSFqfViyAxCaKgihCdrRPSx1lymEBd3oQijLVW/hvSReLh+tb1NFXeKOlRRlXD7GJ5fQUHg+n5HkmvtuMF5O595w2vQAdW5+RxApd5st1FwXsGl2WWSkRD3jW9eZnYr86Vktd4gX68zeAvEkbLO15ZAj9XbIrzvNFVTadSdtsIYtEpL/UrrHkRb1Ol2FrTT2r7BMYSAiGUQC5kB15HrENb1NmJtqzWEqWv0ZgIUdl8mgaYqQfDEC3gYbdcl3CqpDls6lqGxttLTtigBb1+nEOS6okFJthB0dcc71moSF8+vtT+7fpGKfWdluTfMb4lk3doUobMmmNBsOJw/Pd1eB7muh2nqVMoN0pkoruuRzcVBCBbni9RrNqGwjuv43PPg9ndX4KjkLGPcgiX+3WiKIujLJBjIJm/ZiXOpKLnU5gRp78S5icdCxGPvrI0Ly2WOv3QeIQTP/Oh72H14pI2oGRzr5iOfepSTr1ykXKh1/G7q8gJvvniWRDrKx3/6CbbvuxFg6B5I85FPPcbpN65w8eR13n7tEk994l56RzJ4rsfybAHpS6rlOjMTi/QMdRFLRbh44nqwGM7GWZxZpVZpMDDWjWGtV5qRvPcH7uOB9+1Hb3FjxVMRHv3wYZ7/3DEWpldYmFrpCBxVCjU81yedi9PVk2wz8IeiFqN7+hnZ1dcBaezIzm+YEG4tKQ2QbP8KoQAAIABJREFU6UvxgR95EFVbh9RSVMb2D/KeDx7krW9cYPbaEnaLk2Sz897uHLZfwPMbHdtK+SqPft8hPvbTjxNNhHnl6BXiMYvTM8s8eM92NL1zwiitBgN6tjdJsivaLsGLJsKMHxxmx4GbeDLfRWb7NovNFXbHxgICVnUZ172AzsEN+3rSZ7lRYba+StGuB7w8QmApGjE9RM6Kk7NibaLxNXL5M1fmiIa3dvoLdo1LpQX8Wyw6brYA9TGPpY/dERR7/T3M1YtMVVeouE2klIQ1g7QRYSCSJqbdnDXbaLLFSTRXL7DcrFByGri+F5SzqSYZM0pvONlCAN2wUr1JKhoiFQl1LLneydgmpWSxuUTTs+kL9SAEVFarTF+aZ+/9YyRzW2eWfCSLjRJztQJFp44rfUKqzlCki/5walNI+p3aWq15walxrbLMql1rkeZqJPUwA5E0aSPSvlfX85go5qnaDmHdIGZsDP5JKVmsl5isrrwjKHsql+DKiet0D2duqTIipWSlWWGmvspKsxoEpxSVtBFlMJImZUQ2lNC5foOGt4SpptCVW5N5NjyHmdoqs/VCm+Mpqln0hBL0h5Po69Rqsl0xEvGgX6RTEUI3BVoVoaGrcfQ7gIi/G02IEEKJrFUL4DpJFmcqxFImV8/OMLA9R2m1ihU2mL6yiBkyqNdsqqV6gLhJhpm6sohjuxRXqozt6+f8iUmssEHPUBempbc4HEVbvQgElqkTDVs0mi6u52NIGB3K8MChbeha8PzW6ATW+oplBrLImqq2MsxrfwJfSgxVIRzSadgOtuPheT7pZIRH7h0LAj9Ri3K1GQRzJEgRZJj37MgyNpQlZOkUyw2S8TC6pm6prjdXLFOoNajZNmFHD7gy/SCwMJbrImwYvHl9hh25DGFT542rU7i+z45cFyFdIx4yOTTUhyIEfYk4S5UqAhhMJ9jZneX0TCCaMpZNs6f3zpKI663sNDm2PMlsrcSuRI7+SILXF68zEksT0UxmqgXuyw4zUVrhSnmZmG5xqKufo8uTRDSDPckejq9M81BuGwKYrRXZneym7tksNSqMJ3LUXJtTq7MU7Tqf2nF/xxijqgonX72EbqgUVyoBf6KESqnOzkPDzE4skWlJUzu2y+piidJqlfEDQ5RWq1SKdaavLhJuBe40TWmRtcKJb1wg3Z1gYPSdt8vNVq3bTC0V6E7FWK3UW5yektVKnWwiQq3pELEM4hGTlWKVvkyC6wurREMmhXIdiSSbjJIv1UjHwhRbZSyJiMX5yUUe2jeCpbX4XPwarnsFoURQ1WE0XaV3OEO6Na+4todhtdZzIgbK1iio72WTSKJanJzVR8OvszN2F1oLuaOrComQ1RZciBhGoJDpeXi+HyBpFAVTV4maBq7nB2S+m76n62c2ASggNAw1KKvyW0p80o/iSR8VFU3R8ImjKWvKgQLWzd3felLhJrRmi7No4/giNpwjpFoYyq0TO7brBWgZVWmPixLJYCaJqX9rLroQgu5olKFEgqhxZ4klS7UoOPlNY1xmi89zPXVGgH3+1tp0Y8sFw/pCscyOvgwCKDYb9EVv/R6pqmC4J0VfJoGurS8HXOejJddpjrc2Z5NRVPXWvpqUknyhRtNxiYYCZO7KQnndHj5yHY+ZrnRhaQO35Hi62TQl3kYS3an19qdIt3h9fU+2+b4ANF3F9/xgfSdhZDRLYbVGLG6hGxqWdXuU3R1d93fkKN8BU1BYtWdJG31orVKQYrXBn331GOODWS5OLTGzVGS4J8VHH9lHTzqI/lUbNl87epETl2dwXJ8DY308dfcOuhIRfF/y2plrTC6sMtyT4ptvB+Uh9+4e5Pse2htkMfNlvvjaWa7NrwacC91JPvrwPnq6AhLhibkVvnrkAtNLBaIhkycOjXHPrkE0VeGbpya4PLVENhnl2IUpbNfj8PgAH3pwDyEzkK7/6pELnLw8Q73pkIqFee894xza0b8l4eV6K1Yb/JevHGV6sUDI1Pm5jz3cEUB6/s1LvHTiMo4XkGM3bJdcKsIv/8iTaKpCrWHz8smrHDl7nabjMj6U45n7d5FLBbLiK6Uaf/PyKSZmV+hOx1o1tp2jxuJ0ntlrS2T6kuw8ONxRhiWEYHCsm8Gxbs4em+j43ZkjVymv1jj0yM6OoNHa77J9Se56cAdXz8xw9OtngsDRUBexRJiZiUUadZviSoWZq0vc++Qe+kazHH/pHLPXlhjYnmP++jLNus3wzl4M48bLEE2EuPvx3R1BECEEqWyMZCbK3PVliiudpT/jB4eJpyO89uwp0j0JHnx6P/2jOUJRszXZfWejIwGEUMFuOJQLNZp1G7e1UK5XGyiaQr3SpFiu42rKOw7OCAQRfQhVmGhKGAjuNxyzePhDB4kmAj6GlXyFhaUS24YyFIq1DcfZc+8oL3/+Lb7+2SNEEiHue2ovuf40oUjQLu/WoBEEbVhzG1ypXiekGkQjIXQRY/2U5/geZ4szfG7yTc4WZ1lqlKm6DRzfR4gAMRNWDZJGmMFwmnsy23hf7z7SepDFeOjANhzXa5Uq+czXi0zV8lwpL3K9usyl0gLnSkEm1/Y9/suVV/jyzNtbXvPfG30PD+fGUYRFtXkCSx9FV28s0KeqeT595os0PIdHczv55Mh96IrKlfIifzN1nNeWLrPYKFH3HGSrdCqmhxgIp/jpHY/xYHZsw0JBSknDc5ioLHNk5SqvL11hupan5NSpuQEvkyoCtb+4HqI/nOIDfft5omd3O2BiaCqrlTqGpm7I4s3VC/zOua+Sb24O59WEys/ufIK7koPYvsNkbZqclUWRCtFkmEQmRjS5eSbM832uVBb526njvL50hRW7QtVt4kkfU9HIWXEyZmxTB9RUNH5u+1OknCCYkUx0BuQFAl2oLDfKfH76BM/Pn2spoDVwpY+uqEQ1k95Qkvf17eWZvrvIWYETogiBqihUHZuIruNJj+lqntl6gcvlRa5Wltp9BKDuO/zrc18lcfnlLfvGL+56Gm8hIMgXirLp4tCTPrO1Vf5u+gQvL1xgsVGi7DRwpYcqVOK6RW8owUPZHXx08DD94VS7beruAk0vj9HigLJ9lz+89BLH89dJGRH+0c6n6A8nOboywWevH+N8aY58s0LTC4KsIVUnbUbYnxzkh0fuZ3eiD11RMU2NufkCs/MFlpYrxOKSOsuYahpLy+BLD9evYSjJ/5+gjTodKkXJEYr8A5r1zyKUNFb4J9CMHlRVpSsXxwwZ1CoNwlGT7XsH0A0Vu+Ewe32F3qEuVE0JSp1bTReJh+jqDspPTTNQoRJCkIiYuI7H1165wEOHtxGOubhKEcVwsCIe20eiXHt1ii99c4m7d48zNJAgGpM4VKi5TVTNJxYxQYBhakTCBnvTfbxw5BJCwAOHtjE3X8QwNXRTJS4sensSjAx28bVXz9Pfm+I9h0ZJJUJIKXnulfM8dt8O7j+0jaNvX2d+qcS9dw2jayqxiIWgpYrj+aiaynK5ytXFFapNm954jEKlTljT6YlGuby4ghAQayke4sNoJs18sYwvJXt7c5QaASdX1DBIWhbSl1Qch4sLS+RrdUxNJRUOYbW4SRDQcF0uL660EU13ap70iWgGWSuK4/tcKC5hqTq25xHWJDsTOfrCcRbqJUpOg32pXibKeQxFw/EDh3o01sVwNEXTC7jVCnadlBkmZYQJqToT5RWQ4G1SirpGExDM9RaVUp1MXxIrYhKOWmT7kizNrqKogvxiiWQmhud4TF1eoFquYzcdpC9ZmM4TiYewGw4r8wW27e6lWmowdtcQQthIrwCyDAgQYcAEWQMcEFHADz6LMOAxaPWT0T+C1SqtmV0pUanbJCIOM0tFJJLVcoBSWSpU0DUVy9AY6U6xWq4jgbcuzjDck2JqsUAuGeXa/CrlWoPt/Rkc10MRgljYREKnMql9hHLhn6Ob9xJNfJqjz1/l/JvXUNQA2fSRn3yMLuvb5/v6bre4niSkRphvTDMcHkMVN0Rj+pNx+ltKhpuVON9cuuz6Po7nb8pjI4S+ruzJw5NVLpUv4UoXX/rE9WCeWUvclp0yDb9BWA1TdIrEtBhD4SGSRqsEFxW1ldCQ+Hiyxp3wT0np4fn1jmM4vtcKTN2+vVzfxfadW55HtK5kdrnESE+NTCKCAKpNG1NXuRVa6VZWajaYK5fZke5i4BaoozWz/SaLzQUGQsMbvvN9Sb3uUFitkutOoGpBUO5Gm8o7b1N8vHZ5loKmRPElVBpNGnaQiAiUXps03FujcXwpWaxVaXouhhokC2qOg66qNFyXqGHgeMEc5voeju+jCoVcOIJ+m1JFz5fkSzVmF4uMD+cwLIPeoa4blSeoKOIGX27CuodtqX+G1moTx/fa6Me61ySstvwmBG6LgFwVKp60ggTrmmL2bTqWbmhtUMSdWDT+nQfkfNcEjjTF6JAchQCC9eaFaU5dnePJwzsY7knx7NELFCp1/uknH0dVFD730tu8cXaSJ+/ega4qfPmNc8znS/zDjz6EqiosFyt8/pUzHBrvZ/9oL74vScVDqIpCrenwF187znKhylN378B2PVaK1UClDJhdKfJv/vpl+rMJnjy8g7mVEn/4hddRVYW7xwdYLlT57Etvc9+uQR7at42ZpQJ/9bW36EnHeM/+bTQdl2K1zl3b+4iFTV49NcG//5tX+I2f+eAtayvXLGzqfOjB3bx2+jpffO0sTafzJdo1nCMRtfD9QHbvsy+exNATbU6DL7x6luffvMSTd+8gbOl8/c1LzK+U+Cc/+BiqKvjzZ9/k0vQS779vF7br8aXXzm4ol1sjRI4lwsQ2ccBCEbODUHnN5idXQEBXb7KDT2jNhBD0DHWh6SqL06u4jkduIE28K8rM1SWaNZu568tUijVGdvXSP5pDKIIrp6c5+J5xZq8tEY5adA+kO8apeDraDox0nE9V2ln29XBLCAisP/nzT/O5//gC/+0/PM+zf/k6+x8c494n9rD/wTHS3Yl2vehWCKA7NSkl1VKDk69e5OjzZ4Pyu2KNZsPBdTyatSauHQQTwpaJETG+NYdIShy/ir2unjaWjJDMxNrXvW93P6Vynb6eJJ63cXJ66AN3MTuxxPOfO8af/faX+fKfvcq++7dz/9P72HvvKPFU5JbIhe9l0xWdoXAvc40lbN/GJ4lu7GNtyGx4Dl+ZPcUfXHyBuXqxDZXXhIquBJkju7Voz9tVrlaWeH35Cl1GlKd79hKydErVBplkMMkU7Bq/dfoLHF2ZwJMBDHo9mk0iuVZd5lorWLCZPdMXKDpIabfkYN0OmG/ds3lz5Ro1zyaqmXz/0GHOFmf59JkvcrG0gCf9dhbJR1L3HOqeg+t7KFuQj0rg2Mo1fuPU37HcLLcJvAWgCqUlcw0Vt0nFbTJbL3C6MM2J1Ul+cdfT5Kx4sFgPmYRNY0PJa9NzOV2YYbZW2JQ8WVdUCvYDAO1rFAh8X9KoBUSLmqHh2MFErRtqC+Hg88rSJX7/wte5WJ7HlxJT0QipBgqiFUQpcL26suGcqlCIaiYlu8HC5RKZrhjJRH/HPooQVL0mv33uKzw3ewZHem1CTQ0Fx3fJ2y55u8rl8gKnVqf55b3PkNajQZ2/3aQ714MAZmpF/qfjn+F6daXVNzoxaL6UXCxtLFldb0W7TkwIPM/H97wNLen5Pt9cusgfXf4GpwvTbbJtTSjoShDQy9tV8naV86V53sxf4+fGn+Jw1zCqUFCERsW5jqVl0dWA+PRyeZGjKxNENZOnevdwujDNvzn3LEvNcqsdVdSWDPpa/5iq5jlXnOUfjj/J0717UIRCJGKiaSo7d/Sg6xqubFB2rqEqFgKFppfHlw66EsVU311E/Teb9Bt4/gJCRBEtx8qwHkc3HwUcBDrxVBUhQgRvZ4NQREEIi0gs1J6/rLDJ5bcn0U0NRRHUK02cpotQBIalY1g6TtNhcTqPbmiomsr3v+9AkOUUDcYOrlIXeUxLoycraKgF7n64iYJOb1in6l9n92GHgnybYg0yXTtZkRbUPIquS1MDVXHZd88AqqLQUHziAxEaroejqUSNECdWFqBXZ3SoJwjoKBCLWHzs/QeRBKUG24cyDPenQQYJmanpPLlkhKmZPE6rhE36klDI4FAsR8QyKCzWGRUx+nJJlpfK3JPtZmQ4y7nL8+gNweJKia5khJ3DXeSLNSxDo6FYNG0XzRUMGFFOnpsGIXh0+wiRsNkOoAoRlD41rk1hqCq2FzgPVbceKDy20FWO7+BJH10JVLpUlDUMBbbfYCyewfF9DFWj4jRZbdYZiiaxVB1DUXGlT9FuENUtZmslhqIpPOmTtaLkrBgJI3AW6p5DXziB63tENIP+SIKy06A7FKPq2ozENvITCkXw8IcOBo6xpKUwK9r+69j+wfa2XH86kKveN9B2GdeIaqWUbQd7ZFcf+YUivcNdZHoTQBW8SdoH9c4FSB2hAj6INHjXQEmCkgB3krB0CCsxwAYi9HXFcT0fy9AZyCZwfZ/h7oB+olBpsFquMZhNYpk6kZBBdyrG4fEB0vEQ3akgUepJn1K1STYZaRPKRkMm/V2Jljpn4LjbzVfx/Vl8Lw9IKsU6I7v6SGZiLM7ksRudpLnvVmt4NUpOgZAaouwWO+bktXHX932qxTqKGiCMPDdIiq6tY4UiUFUFu+kE31k6a4t43dAwwwaKMNDVLprePL5s0nRnSVoPAgEa3FIsfPyOtXFSJDEVk7SRRhMa2jrkqiIMzHYSzcP2VvCle1ueI18229xIQugYapaC6wT8hL6E2zAI2NKh5q0FVDY3TVVIRUO4rkfIvHE99aZD4VucywTQF4ujKQqmdmduftO3UYWKz0ZVNd+XuI7H8lKZke0uIc1AQcdU1yo3fBxvBV86t+U5CniNWm2KhqEFPLGpaOCXO56PbN32cr3KQCyBtkU7+FJyubjCUq1KwrQoNoMqi5hhEtUNynaDS4WVgFPS9+mLxpirlHlkYCQQ7LiNSdlClkmoVxqsLBQZHOtu9XUVQ80g0IKyNVlFU6IYagZP+lwoX2e5WSKpR3CkR0JXKLsBD56qBOtAW7qEVQvECsPhHFHte6Pq6rsmcGSpUZr+RtSDL30Ojw/z8cf2owiFsGXwp185xtxKiYhl8PLJq/zg4wd4773jCASe7/N33zzD4mqF3kwQnKk3HT780F52DgUDx5okve/7VGpNktEQB8b6SMcj7YcKcOTsJI7r8aNP381ALknDdsiXanzljfPsGQk4hAxN5Yffe5jxwSzFSoPzk4tcnFriwX0jJKMhfuSpwxh64KwMZJP86h98gVK1cUeBI11TGe3LsJCvbCp33teSQnddj8+9/DbRkMkPPnEQRQhK1QYvnbjME4fH+Phjd6Eogohl8MdfOsLMcpGQqfPWxWl+7P338OThMaSExXyZ185c6ziH3QyUgFRN7UAbrZmiKhvInyGowxRCbJCxX2+GGcDgXdfDdTwS6ShdPQnmry9TrzW5cnoaTVcZGu8lmYmR6Ipy7fwctUqDuesrAeF2X6rj+Lqhom6itnKr4dcKG7z/Rx5kbP8gz3/uGGeOXuWVL53k2AtnGdndx8d/+gnufnw3hqlTr9msrlTo7k1uKO26E2vUbP7633+N5z7zBr7rM35wiJ2Hhknn4oSjFhPnZ/nyn78KBIPWarnWWuy/swnEkza2V8BU0+3fqprS8QxDps7UzCrZLo/wJlw70USYT/78+zj86C6+/rmjnD0aqLEd+foZdh4a5vt/6jHuemj8jtBz32tm+w6LzRVs3wZ0bG8VR65imhlA4WR+kv906UVm6wUUBH2hJHuS/QyG04RUnYbvslAvMV8vMFnLs9wok7Pi7E70oggFy9DpycSptxacihB0mRHGYrl11+AxWV0OCAKBnlCSlLF1HXnKXPtOxfcr+MrWY8xys8K1yjL/9sLXOF+cJ6ZbbItm6bbihFSdmmezUC8yUy+QNWOMxXKbBjAFkDYjeNLHlT5RzWI40kVvKEFfOEVEM6l7NlfKi5wuTLNq16h7Ds/NnWEkkuEnxx5tv7+lWmPD8eO6xQf7DzBbW6XsNILsuVPjanmpnbVZM4kkaSSCjI4bOMGhiImUMDu5QjFfYce+AUJhg5naKv/h4gtcaCG6did6+UDfXRxKDxFWDVbtGi8vXuCL0yfJ20F2rMuMsiveS384xUA4xUiii1PuTAdh4po1fZfPT5/gjaUrIGBXrJfdiT6yVgxf+lyrLPN2YZrFRomm7/LSwgVGYzl+euwxwrpOC7QQcDAJhe5QooPnqu45TLYCSQqCwUh6QwngeovqJmYoCGg6NyUHpJS8XZjid849y7XKEhLImjF2JXoZjWYJaQZlp8HF0jxni7NU3SZv5Sf53QvP8b/s/wg744F6UEjr2bSP1Fybr8yc4kplkaXm2nvQx0g0g6Xo5O0qp1anuFxexJEeE5Ul/vPll9mT6GMgHIzvuUyMmbkC3b3JltpTGl2J4skGmgjh+BVsv4ipJrntav572Fz3Co5zHNBQ1AxIBSEMVG0I1zmLlDUUtQdFJHDdi4BEUTIY5oOAecO583yaDZtYKoKqKjRqNqGoGSBgGzb923I4TRfP9UG6FFfKjCtDaJqKLy22xx9CoCJbTkbwv4+CiiJ0wjKBNCUSH4GCJgx0NU/MMlHsIFgY0nUsQ8eXEl/KIOgsIBMJEj91x8HUNBKWhaneKPPVblqH6OsU4wBq1Sae56MqAsPQWS6UUTUVBRAykGE3DQ0Fga4HCCVNUVAUQbnaQG0F/mNhk4WlEkJCpdoknQjj+xLbdvF8GRCLC2UDP0t/Ks7jxihSSmKWScWtciT/NjEtSLD1hnLk7QK+9FmxC6hCxfWDdzJtJIlpEbZFBzEUveO+1u4fArRGygzj+B65UIwd8Sy7ErmOfSCYE97bv7P9+WD6RoC7P5zYsP/aZ1W9se1mej1FVdrblDuc+hUVEpmAq9K0DKQUoK0TdtDGCdyRVrRKGKANAi3nTl3jedGhxV8TCZnsHg7uOdWiYFi7l2rdxvE8EpGgxLo7FZSDJCIBQqAn3UKsSAlZOn4LsHPoBp+elDau81bH/YzuCdrxrZfPgxCEot8+b8j3gjm+jQBKTgFXumwWEKmVG7zx5ROEY1ZLWbmCGQrUY5u1JolMnHRPgtkrC3SPZJGez9JMHilhZHc/2/YNooowIW2Yin0GX9pU7UsMxJMoSqud13Qr1lBMt0HlKMIkrG9vj1lNbx7XL2Cot+ZNdPwCDW+udYwQEX0My4hT9xz0O+j8AkHZrW4qcb/eElGLWNhsK6tJCY7nUa4HYkfvdP0vgZlSiRcmrrIrk+WJbdtum1AJqSEyRm5TjiMhwArpZLsTmK3glhA6YWO0HTixvUUcL98mvt7KXL/YlrBXhElE3wFSEjYNLF0jbAZzwuOD21CE2LLkGIIk3v6ubpykj6YoAWG5CBKfazxnniepOw6jyTQhXWd/Vw+WevvQx1rgPBax8LzAR43Ew+u+F4T0bahKGNcvUXcmcbwihhqolksklqoT08MYikbBqQRJNkUQ08PU3AYhxQxoFdwGtn97rqOtzPcltWqzo5QwFDY2XZt+J+y7JnAkpcRUIhvqA01doz+TQGtNzkO5FJ7vky/VqNSaLBcqfP7VM3zj7asArJZrVOpNqi1ZPwg4ggZzyRsQszVYtmXyA08c4D9/8Qj/2588y46BDB+4fxfb+zKA4OrsCv3ZBKkWs72ha+we7ubPnj2G7QQDQV8mTjYZDXhLdJVkLETDdkFC03U5em6KMxPzVBtNVst1qnUb37+9dPI7abc3zk3y8smr/NSH7mcgm0AIwXKxykK+wvPHL3HqajDwlWuBBG+51qBaD5jgB7KJtnLccE+K4xenO46/Vpbk2O4GhwMC9I7jbNweSwbZzVq5sSW5WbVcx/d9zJCOYWooqsL2Pf1cfnuK5dkCV8/OkO1Pke1LYoVNhsZ6mJ9cZnE6z9z1JbJ9SVLZm+tD3zk+Jwhw6ey+exvjB4dZnitw7tgEL/7tMU69foXf+9X/yj/49Y/zyIcPUas0OPXmNeJP7iaWeGeKB1JKzh67yuf/5GWskMHP/sYP8MDT+zBaPBJSSr7x+bf4yl8EgSPX9ZhdLZEv18gmb80bsuFcuPjSxlRTQGHTfY6fmqRQquNLSTxqdigPrrVLOGZx4D3j7L1vO3PXlzn35gRf/+wRTn7zItfPz/HLv/s/svee0Tb/0bvFFAQ5M0PFrVJwikhp43nT+P4SUnTzd9MnmK0F7fpAdjs/N/4UuxO9bR4jaKHL3CZTtQBF4fo+Q5EupC9ZLlQp1xooQjA60EXCCPPP9jyD499YYMzUVvkXb32WmfoqhqLxIyP388H+A1tec0w3WVtGOf4KBoNbLhama3l+78LXOF2Y4X2943x0YB+7kwMkdAtJIG9atOvM1CtU3BoJXeB6qyiK1UIyuW0iyO2xFN8/uBPbV3g4t5fRaJaUEekguq67NkdWrvK755/jcnmRhufw7NxpPjxwkLCpE7UMxnoymLqG7dfxfQdDjZA2o/z8zqeAoISj6bmcLc7yq8c/00autJ+ZUCg6JSSBclO93CASC+HYLtVyHcPU0fUgW//V2dOcLc4iCUi3f/2ujzEe7+lYpBxKDzMcyfB755+j6NSJaRa/sOu97E4EanX1uk0yEd40cFp1m3xz8SIpI8xPbH+E9/ftI2fFKTWbrSygyturQbDmdGEaR3p8dfYUT3XvZSJfIqTp0AXSl3Rbcf7lgY91lJVcKM3zayc+S8GuYao6/2jnUxxOj2zZNyxP5di1Ewzt7qd7KNNxzYuNEr9/4etMVJYQwH1d2/i5nU+xK9FLaB3xdsmp8+L8ef7jpReZquU5vTrNH156iV8/8P3oSgJNySOEumEE9pG8sHAORQge7d7F3x97lJ3xHkz1hmM8Vy/yX6+/wV9OvE7M4ESHAAAgAElEQVTTd7lQmuPFhXP82LaHKFcanLswSzhs4rkCwwy1CDY1wCRmjNL08oS0btgCGfduMVUbRCixFqLIB+mD0FGUGFLWQbqoah8Sia4EMtBCRLh5qRdPR7j/6f3tzyMtxVDXCcq71lDC2/b006wHnEii9T4rQt2UMLjTNiYi9ncHybZbIXfXK/Bt9v+tTAjB8FAXw0M3CYXs6L7hYLYPIzf8NiAA77yue/YPbzj32HD2lkqBmqJ0lKdpvuBwah8gkVKiK3o7iDQSGQBuEDrrQgNkh/O21TkOdvVv2H47+/8SjWda69VIdbidqpW4dTAmuJXN72cr0Yk7EXu4ebvnTuB5kx3fj+0P1PoGW33rnZSOfC9bVEuQMXsouQWGw2MomwTprbDJ4Sf3IqXEaboUlkoszeTRdJXxw9uIpyOAoKsvhWHqeK5HdqALVVeJxEJBcFhEiVuHWK49i8Sl2HyTmnOZmLkvOMlNj+12q34hFKLmXgy1m6Y3S82+TNW+jG5ltuwDUvoU6q/jeHkAQtoAEWMcS4uRCwV+h+PdWhHLUk3Go6O3LItqOh7Ti4W2n7lmtuMRCwe/U4TWvkeJi+TWPqQAMuEwO7q67pjjqGCvMlmboNvqIbTusUoJvidRFAXLuqE8KIRCRN+FqfXRcCepO9ep2hcw1NyWPD9SSgqNN7C9JQAsrZeIsQvPl9iOS8Nx2yifhGlteoyO+xSwWqizXKxSrgfcnIFyZzA+b+tJU1yuEw0ZVDWba4XgWTYdl0f2jaKpW/ebrkSEpx/aSThkYugqe+8d3bBP3DyAqXbj+iVsb5HVxiuE9W1BQCuxbd11CoZYA650zicSSc1rElLNb3l8dmyXN169xMSVJUIhHcfxePy9exj5DnDJbWbfNaNd2c23Sg1ualQZZFfWgg9BGVlQNiQUgalrHBjrYzB3gwhW11R60rGOz5sVpCqKYNdQN7/2997Lmxemef3MNf7VX77IP/3kY+wa7kZTFTxf3njQMiAvXU9kranqxoctA+j/N05e5c+fO8777t3J/u29FKsN3ro482231Y22kUwvFfhvL57kow/vY99ob/taFCEwdJU9Iz0d2RNNVRnIJZlfKSEJIpU3CNk2WrYvhW5qlPIVCstlUtnYukVYEBgqLJc3/G54PLiWhek81VKdSDzU0U6+5zN1eQHHdukfzbVLnkZ29eE6HlfOzrA0W2BoRw+RWAgzZDA41s2ltye5dmGe4kqVw4/u2hQF9e2Yqip0D6TJ9iY58J4d/PW/+zp/+4cv8MbXznD3E3vQDY14srMUrv2/lLdc4HpeoBJXrzQ58NAODj+6MyCeXNcmq0tlPLfFkWNoZJNRktF3Dl+U+Dh+Gf8WjP2WpVOayeN5Hg/cvXFQXG+arjKwPUfftiwHHtrBH/3m3/HNL77Fmy+eZ8ddQ5jfIdK17xYzFINtkQFqXp3FxjJRTYCXQlFSFJ0m07U8PgEU/329+9iT7NtAoiyEIKpb7Ir3Mh7rwUeiKSpSSA7t6melWKWrFRBUW1Lt663m2R08C1HdImvdnkjPlzUsfQfqFpLNAKt2jeMr13iiZzc/Pz5KxjSR8go1u4HrzQMKptLF9rCJJ2vYro3nr6KrA9juBL5sYqgDLS4Cix/sd7D0MeLm5pmtkGbwUHYHC/USnz7zRdwWp9NMbZU9sX5MXePI5Sl6kjEcsUjdK5G1xlDWqZ+oQiGsGcQ0c9MsVEQN0Wt1o4g1ktIAFr/mYAhFoOkqFbfJG8tXA4JLBA/nxjcEjSAog3uiexfPzZ3mjeWrzNULvLZ0mV2JXtbINw1d3VLYQCD4xNA9/PDI/ZiKhiclJxbnMFSVh/qGOJQe5se2Pcj/evJvaPou8/Ui16vLdEdulI+Uy3XyS2V6+lNY68aKhUYJpbWQVIC4Hrpl36hXG8RSEZymw9T5GbqHAoJs1/f4+vw53l6dAqA/nOIXdj3N/uRGtcS4HuL9ffupuE3+9bmv4PgeR5avcqYww75EBF82b6kQsj2a4xd3vZft0dyGwEBvKMGPbXuI4yvXebswhQSOLk/wo9seJJuJsrgUJZuJYZoC2yujtciwfeli+0Gw8N1OkB2sgUKo60ow2v6S9FDVwcAhxyBY23e1d5LSx5NOy9Fb+1GjFXgyAA0pS/jea3jeBI1aDN14AFUbxQwZGJaG783QqL2B7y+hKFk04zCqOrLBSQieaxOkR0BOu+YA2HjudVznLL4/D9JHqCk0bTeatgPEFvPqTf9L6YFsEiBTdGAt8eIjZRHXOYXnTiJlEVBRlCSqOoqq70CIROt6b7VA95F+Ede9jOteRsoCIBEijaaNoerjiJv47jbevwuyVSorFFJ6/AaZSet+NkNJBCpCHsjGDaSxMNhKATE4n4+UFVznHL47gS9XQUqEEkfVtqPpe1qljZuvlYLz2CBdEBpB0K+BYx/FdS4ghIamH0bTdyFEgByRsoDTfBXPm0KICLpxGFXb0S6fvJVJ6eL7K3jOeTzvGtKvgFBRlCyqtgNN276hL9z6mhXW0HRSuvjeYtAHvGmgCVioag+qtgNVG4bbqI0Gx/YBD6SD65zEbzm64CFlDekHbdn2yWVL/XZdf3w3WsOvEdaijMX2otKJ9FvDxQtNkOpOtLdlB7vYdtdQgKJVFYQiUBA3Shtbx17/P6gkzfswtV4a7hS2t8hs+S/Zrv8qqojewfPbGBQM69tJWIdZrM7iySoLlf9OzNyPSmTDvmtS60u1L+PLOqDQFX4SXcm8o/aqew1W7FW6rdyWznbI1Llv91AHl+paO6SjITRVQfPi7ffX9xs03XksffCWAbOKbQOCVCh0R/3x/yXvzcM0Oeo7z09E5PmedVd1dVX1WX2pL90SupCEEALMaYwN2ANj7GE843t3xs+uvWt7bD++xtjj9YWNsbG5DMaADYhTCJAQupDUklrd6ruruu7jvfOM2D/y7aqurqpuBMwsYr/Po0ddb2ZGRkZmRkZ84/v7/sp2Fuq3GoYgiBGiPU+8QAjg25vo8K5lsj5GappMNT5F0d2PJTvWbNMonWGm8Zm2x5Gky78NV/UTJ5pKM/iOzMB9x6bou1nWxLZ372BPCcdSdBb9tr9wlmCjp5Qn0bqdae3SbaKUpKN0aXGAqwboyt1Oo3IMbQKm6p+gy78Z31pf4bU89kkBiRTyuw5Rc1yLm2/bRaMecsNNo5w9M0er+T8vfPb7hjiypYtArGKwwzjh+PgsYZxiW5Lj47NYStDbkSfnOXSWcnQV89xx1Q4cW5FqTRAlS3I3WH+IkGpNEMaUCz53Xj3K1TuH+OX/51McPj3Nrk397N7cz8fue5KphRqFnEsQxTx1fIJdI/24l1lhiFPN0ycm2NTfyetv3YfnWNz/xHHi9NKSxReCuUqDf/jcY+wc6eO2g9uWJNuQucb3dRQo5lxuv3IU17Eyg7MoxnezTCJSCk5OzLNzpC+LFR2bzdRSF6BnQwcjowMcffI0Tz90jJHRgRUhWqefO8fY8elVddt19Wb6hro4dWSCZx45wbV37FlBOJ09PsWhh46hLMW1ty9v27i1D8tWHHn8FPXFBiM7BjLVkxQMbe8nCmOOPHGKJEnZsueFr7hdDGMMURBju/aKjlsqSbm7yK6rNiGVJGiG6CQlTjSWpVYQRH47e0h1oUnQjMgV1mHKTaYgyLDa0X96fIFHvvxMu17QaIZoySWlmutfV5yt/q8zWAS49uBmujvzeK7N1pGVkl2tDXEYL6mhYFnC3jPYyfZ9QzzwmSdoNQLM91BB9/2CUIc8VztBmIY00gYb3R467O2AgzatJQ8YuLx1YRZuJJZ6NiFEZuAqBOdmKgyUzw+EDIXSt/eRvxSkyKNNHa3rl9yv1yvx9m030+c1MCbCYCGwUbIHYwK0aSKRSCykKIAUGNPKQmOEAgHaBCAMObsH17q0v4wlJAc6h+lxi0wGFepJQCVuMVdvEiUpV24ZxLUVOnWpxhN0u1vWLWsthDpiPlqkwy7jGJf5dvrxno1d5PIuSZKZJFbjFlNBljZVCcnejo3rvmN5y2VHaYBvzp4g0pna6TzRblmSsJ3WfC2M5Lt49dBBXJmF60ZJgqsyRYEQAoVgb8cQG3OdnKjPEOqEE7VZtOPQ4+dIU83MRIXZ6SodXYUVxNELhZ/3sF2bYKFOfaFBmmosYD5q8OXJZwl1gi0Udw7sYU95cN1rcqTilr5R/un0NzlZn2UxbvLN2ROMFndgyxJK+GtOiF1p8cqNB9ha6F0qezGeJm+VsduTvm43z3U9W3lqMSOxJoIK9ThkfrbB7Fydai2gVO5H+qqtNsqQ6AaOKrV9xn4wJ2wZDK3G3xI0/3ndPXKFd+H6r+XioV1qImbDw7iyTIezGWMq1Cu/Rpocx8v9GI53N83a/yBsfbJNuNhY9i7ypV/Ddq4jiQ/RqP4WSfxUuw/wUdZoe/u1K74zxixSr/wGSfwMljVKsfO/o3WNoPE+wtan0XoaYxpkxI+HlN3YzvX4+Xdi2ftWPHtBErMYBpQcj5ydLU6kyXHq1d9Ep1N4/mvwC/8h+46HDxA0/pY4PoTRNTIvHAHCRYoytnMthfJvItT6q7DG1ImCLxI0P0CanESbxTZJBeBlJJS9Az//dhznZlgnBXUUfIFm7Y8z/yJrM8Xy7yHVSj/G9Z7VOHqUZu330bqKVN3ki/8HtrN/zX21bhCHXyVofogkfhptqsukGg5SlrHsPbj+G3G9V4BY4/tiWjTrf0EY3Ivr3YVfeCdB8yO06u9B6zlAotQIfuEn8XJvQaeTNGrvJgruxZgaYKOsEfz8u/Byb1iXPMoIpwWC5scJW/9Cmo5hdBXIzIOzDIGd2M41+Pl3YNkH2kToWmU1aNbeTRR+FaWGKHT8DkL4BM0PEbY+SZqcxZg62eRMIUQeKbtx/Ffg596OVGv3cVrXSKInSNPTpMkRkvgIaXKCjICCJH6SytxbWU/Z6PlvwC/8ND+o4bKpSRlrniQ1CRiLnMoUO460mGgtsjnfw8n6DBtzXYQ6ZiFsUHZyLERNOp0cM40aUgi2FHqZaC0y4JcZby4sLZxNB1VG8t3kLZecs52+/Cs5U/kbIGW68W8omWOg8AZy9nYEyzYYxpi2H9EUreQ0RWcftupcUXcpbDYUf4zF4GGidJrZ5ufJ2ZsZKP4wjurm/MtpjCZMJzlT+StqYZaMJG+P0pu/57L+PRfDkQ4b/P4VSvSLIUQWzXIxbEst+e3aqhtXbSBKZ0h0jenmpym4e1BiNekF2ds/32pycmGBDu/yyh2A1CR0u71YcvU7l8s5mV3JRYtkUlhsKPwIi8HDBMlZ5ppfxrOGGCy+Fdfq5cI2jdM5zlbeQyV4DDDk7K30FV6NFB6JjoiStD22Wp+QNyv8lwzGpPSW8/SWlxeNwjhtm4oDCHpKBc6P0i82aF8u80I/0RRj9LqqqZXX79Gffw1zzftoxs9Tj57lxPzvMVz+KQrOHpRc+X0wJiXW87TiM9SjZ+nO3YFnfffzWCEErmfT3Vvkq/cdxhjDzbft+q7LXQ/fN8RRrAMayQKdzoalrGqQGYc9fWKSP/3YV7FtxVPHJrj14DYGujLly5vvOMiHv/QtjpydzlJqNlr0dRZ528uvvmw2rFoj5O8++zBxklLKeyzUWlhKMDqUsco37NnEE0fH+YtPPMD2jb3MVhpMzFX5+Tfdiu9cWmFhK8muTf186Ivf4v33PoIQglMT8xT9ZUZ3fGaRp45P8OypKeZrLb742PNsOTfHns39DPaUOXlujiNnp3n6xCTztRZfeOQImwa62LtlgL7OAh//6iG++expXnnDbj7+1UNAFsN928FtFHIub7rjIB/4/GOcm63QUy5Qb4UUfJd3vOo6uks57r5uFx+7/0mOjs1gjOH0xPyqjqGzt8jtr7+GM89P8M9/9WW0Nlx5606Ukpx6boJP/M1X1lTZbNzSyw/9u1v44J/cy/t+51PMTVa44rqtKKUYOz7Fp973VSZOzXLTKw9w4KYdS8eVuzOfo8OPnQIBm3ZuWFIjbdo5gOM5HHrwGFIKRkYvHUv77SCOEj76F18iaITsvWE7g5t6sNvZZE4ePse//f3XkVIyun8Ev+DRmqtTLPsr2mnD5h7K3QVOHh7n3g8+yM2vOojtWERhjGUp+ke6sazMe2nLrkG8nMPhx07y9U8/wZW37AQhmDg9yyffez8nnh3HsrPsACMburD8jNAyJjP7TeLMd2JxtpaZf2pDdaHBwkwVpbJzuL6NNilKZOax6+HE6Rl2bR+gWgs4O7HA1pHl1ZQojHn/7/8bhXKOHQdG6B/OjMzjKOHwY6e47xOPYbsWOw9uwnZ/sNRGAALJfLRIj9tJwfIxJiCOz6DUCAXbo98r8TSZzPQTZx9jONfF/s6hpfCbSyGLu9a4jkVfV5HnD41RrwZ09RbZvX/4O/LOOg9tYoSwyTtXESdT64aJAlzfs5Xtxf42mXHhnEagdRNtAqyLBl+r8e15DED2cSs7OUqOz2RQIW1nZKsHESXfZWyuwnBXGWXb5K3ub+vDfSE0hkZSZzGu0Cf6sGxrKfTmwtjvRhIuhwQKKNrrr/acTxWfXSM021nXZHuiHITJKrP9rFjB/s5hhnLLZJqSAs+yiNJ06b50OD5dboET9WxFu5WGFF0HKTKDb8uSdHTlKa2TGe6FoH9TL/lSDrV92QfuRH1myVi75Pjc2Lv9MoNcwQa/g6FcFyfrmVH7U4tnaSbD5FSyLlHd5ea5qW+0rajIrr2R1JgNzzGS24WrfASCzYXlMKMwTagnIV1defJ5F6Uk0gpoxOMUnJH2M2eI0xqpiej4AbQZiZKERhBTyrlIAVovkKYnM6UFqz1Gsol+9lucplTjkLLj0UxmidI6/vkJkklIkxMk8SGicBidThG2PkY24c0UJ0n8JM3aH5Iv/SqN6u8SR48gRB6EhzENkvgJmrX/TrHzT1Hqgm9xu+w0eRZjakTh1wia/0wUfBYAIXIIUQZijGmi0zHC1hhJfIhC+XewneuWnqPxWo2Hzp3lusEhRjuzZ8OYgDQ5ik7HSeLtGF0hbH2SZv3P0XqKbFLvgPDAxGBamWpAOJcMf0qTMZr1PydofZQsuxeA165rdl6tJ9DhBEn0BF7+beTyP4Vs+6RcOA5S1ha0ns/2T88SR4/geC+/7KKAMSlR8G/E0aNAglJ3otTgGvsZjKnQrP0JQfMjbbIPEB5CnPfvCdB6miicJooeJomfJVf82aXrWSoLjU7PkSbPEoUOUg3RrP8lmAghfIxpkKbHadT+GGVtI2z9K2HrEwjhIEQeY+qkyfM063+EZe/Cdg6uWd80OUqz9m7C4F6WyaIckAd0puRpPwtx9Di5wrvwcj+KWPOeadL0bPaM6Rni8JvE0TcImh/JyhYeQhTI1HYtjKmQphVa9b8ijY+QL/86lrVaZZ3Gh6ks/PsL7v/F19EgTZ5b7/ah9UvW3fZiR6xjHOnS5fQS6QhjUs42Joi1ptcr8tDscYobfRaiJh1OjicWzlCNAwa8MpaUtNIIjMFVNolJOVWfRQnJWHMeKSTP16aYCaoULJe85SKwGCz+OI34OPPN+9GmxXj1H5hrfomisw/f3oQSeTQxcTpLkJzLfGb0HPv7/440zNGsBfgFDy+XqcxK7kGGS+/kTOWviPUcpyt/xnzra3T6L8G1BsFomvFxFoNvUo+exZBiy242dfwMOfvSqvyLIYSg1+2+/I5rIElTBruKS+FrtuyiJ3dXu04xU/VPkKSLdPg3YMkSxsTtcKl5Su4BOv1bGCqVydkOPblLjx20NhihKVplcqqAI1yCOEHJZX8hZSv2HBjOxiXGLMX8SiEouPsYKb+LU4t/TJTOcLbyNywGD9Pp34SnBkFAMzpJJXyYWngIQ4IlOxgp/0fy9k6EEHi2TU8pz0I983QNkjHmWl8mTudIdZPE1El1kyidItVZeGCUznJs/r9hy06U9FGygBJ5bNVJr7wH17pwgUCQ6Coz9XsJ00lSXSfRDVLdJNYLhElm55LqBmOVv2Gm8RmUyKFkDiUL2LJE2buekruSwBdCkLNH2dzx85xY+F2CZJy51n3UoqcpOHvJ29uxZBGDJtEVwnSKIB6jlZzGmJSSe+AFEUepXkSg2grS1d+SPXs34jgWQyNddK6RtOp7he8b4qhodeGpAuoiSa5jKQ7u6afgeZybq/Cyl2zhrit3sZhUyCmfa/cO4fuKp45PUGu16Op02b61hJDZ4H3zQDe3HNiKvUY4ge/aXL1zmGdOThJECd3lPK+4fhe72oZ7pbzHT/7QDTz49EnOTC4w3NfBD7/0ANs2diMEbNnQRZJuWWI3LSW5ascQvmNjKcUtB7aSpJqz04sUfYe33HUVz52Zws+ltJJFZqoLHBkbxwjDTfuHqTQrPHN2kb5ul74uj6nFBZ4fn8RSktsObqPWDDl5bo6R/g56Owv0dxa5ef8WgighmM/CxeIkXVJaXbd7hHLe47EjY1SbAT3lPPu3DWIrhRDwqhv3UPBdTk3O0ddR5GXX7ODw6aklgzbITLFvfvVBzhyd4Asf/Sbv/4NP8/H3fBkhJWErYv+No2zc1sd9H390RdtatsUdb7yWerXJv77va7z3tz9JvughpaRZD4ijhBtfsZ83/+e7KFyQyrpQ9ukd7OTk4XMMbuph45bll79vYxfl7gLHD51leHRgzWxu3wnOHJ3km198ms9/5KFMlu9mWZhajZA4irnh5fu47bVXY9mKNNHUa8GSV4IQgo2be7n1NVfxhY88xEf//Ivc+8FvYDmKqBVz/cv38o5f+SEKbT+kHQdHuOHl+3jgM0/y3t/+FL2DHUglmZ+u0ruhgzf+hzv417//WtYWvkO5KwtlajVCPvW393P66AStRkRtscG5kzOEQcxH/+yLfPmfH8HLuZS7C9zxhmsY2mfIWRsQa6x8GWM4N1nhKw8e5ey5BVqtiB3bBi7eiWOHznL0ybM4no3n29iOTRTGtOpBZl73umu46tZdlyVoX4xwlcO+8k6qcR2DxlMpJmlgTA1b5rmlbycPzZ6gngQcWhzjvx36JK/aeIA7BvawqdCNLZbNXC9GmmpOnZunv7vI3GIDz3dYmKuTL3jfdZY6bVrE6SSQ4qih9a9PWmwr9GVqmPZk/sI1FyF8lPBYgxNewopQkgtopwul62tRSvKCfbUx2LYkSTVWW8aemphWUvm2fE0uRKpTpFBZuFqkGN45kJEvtsKyJWErI2xcaS2HFRqox6tNuS+s34XbXWUvHWuA7q48xTUUho5UbMr3YF3g85Rqzanqwor0w5ZQOBcQNaFOiNOUKMnIqMWF5lJWGnkJQudySOLllbqp0zMMtUn3Y7UpgjRTmeaUw6DfuUJNtxaEEJTsjOgxGGaCGokROKqMNtGax2zwO8gpwUx4Dk/5lOwuhJCkJl1hHHqhp5I2mlqzRdRI6OstMj/fwGgLy/KXBq5ZGuAQhfsDqTaaqTR4/tws1+8YwXMsXO8upOzC6Ara1DB6kSj8GkbPrDp2IQy4f+wEd45sw7M8XFUk0S1Wkk2GJHqMNDmG492N495GmpwgaH4IradI4mdo1v6EJDmM678ex72ZNB0naLwfrSdJkudIoidQ/ivWrL/W8zRrf0SSHEOqIRzvTixrN1KW0HqeOHqEKPgCxtTa5MOfUiz/LsoaBgSudT48Y9muYGX5s0ThV2jW/xKt57Hsg9jOVUjZD8JB61nS5BRJ/DS2c7BNJqxE1s+0aDX+epk0Eh62fQ2OeyvSGgIMaXKWOPw6cfQoxlQIGn+PEC65wn8GHOYWG9TqISODnUg1guUcJAomMaZGHD2I494MYv0xizEGraeJo8fISEGF474UIctrhN9EmSqo+Y9tksPDcW/Gdq5bqq9OzxEF9xFHD4NpEjQ/gLKG8HI/tg4Zk6m5gub7sewr8PzXYExEq/Fe0uQYRk/Tqv81SXwI27kG1389EBM0PkCSPI1Op4jDr65NHOmFtkrpc0CMkD047i3YzrVI2Y0xIUnyHFFwH2lyBJ2eoVn7U5S1Fdu5+ZKEm9ZVWo33ZMogYWM7N+E4L2m3gyBNThIFnyOJnwESovABVPPD5Iv/ZVUIoJAduN7dbWK23b8kJ0mTZ9rbezKF3TpTJ8vey6XDIF+8mAzOkpiEot3BBi/zeJJMo4QkNZptxWw8YUtFpFNGi/1U4hZdTh5POUsjgVqcjR89ZaOEYGshG+OnRtNh5+hwsndECIGjetnS8YtYssBM4160CQiSMYJkHIHdXlwybUuG7DtyPrw9TTTPPnKcOEzYe8N2ujd0IKVNf+H1GFLOVt5DrBeohI9Qi55CYLfLijBkYT6uNcRQ6e10+S/9X9bOkIVfjQ72omQWdi9x6M2/kvnW16iED6NNi5nm55hv3c/5zH+ZUiZlpPzTlL2XsBAERGmyYgxyMcI44dT8AnEq21kFBa4VMlNv0p33aYQRBdelGobkHRtLSmphiK0UXbkcA6UCUlj05u8hNS3OLP4FsZ6jGj5GPXq63aa02zQbG7hqA4OlH6cn9zLOvytCwGy1sZQ5vBmfYKzyXqJ0nmxJUMNFvk7atFgMvtH+S7YXySWO6sq8h6yVytI4rTBe/UeayQkwa5dpSKhFh6hFh6AdAwUSJXNsQq0ijrK6C7pzt2FMxJnKe2jER4jSGeZb97HQ+np7ESRTR12obrJkiRfqyRjGz6HTBZTqxbF3IEVxRd/4yEPHmRhfJAhi+vpK7NyzetHhe4HvG+IoSBv4VnFpJfdCCD/kyqv7uJoOUpMyoc/QrLUyU6mkRam/yJX9PmW7n/loAUeGWSclErYMV9gybKNUzMWX6zoWtxzYyi0H1maShRB0l3Lcc8Mw2gzgqO4VN+ng6EYOji6zha5tcfd1y/Kwgu9yz42baUTHMvd4p5ehIU0tmWG8dYKBwV7u6ZVZXRJtF7UAACAASURBVI3EV1kca5ie5nj9OJ1D8KoRn83561d4nZzHa2/Ze8k2tS3F3q0b2Lt1w5rbizmXV79kz4rf9m9b/aB1dBf4d7/yanZfs4VvffUIs5OLlDrz7LtxlJfcvY8jT5xm9twi3QMrPVWKHTne9B9fxt7rtvHwl57l7LFJ0kTTt7GTg7fs5Kpbd1G8yC/Iz3vsv3GUeqXJ5l2D9GxYLtPPuVx35xXYjmL3VVsoduYuOM5l97Vb6NvYhe2ufqyFFGzauQGdpniddRLdxJI5bNvirb/4Cq64bivHDp1lfqpCFCW4nsPASDd7r9vGlbfspNSVSUI936Fczq0YG/gFLyvjmi08/KVnmZ1cRClJV3+JgzftWDIaFUJQ6irwzl97HftvHOXxrz1Hdb5BruBx0ysPcNM9B+gd7ODs8Wk6e4orssfoVDM1Ns/U2PzSb5t3L9/XRq1Fo9aiWWvRqLYoOsv31cs5WXt15JbqUii4bNvcy+bhbvK+y+BAmTTN0nwbYxCW4h3/52t5/P7nOPP8JAszNcIwxvUdNu8YYM/129h/w3bK3S/MtPvFgiANeLb6PPWkiStt+i0fnyjz5RCC2wd2caI+zcfOPEIjiTjdmOOvnv8Knxr7Fns6NvLyDXvZ1zFEr1tcYRINMFdp8vDTZ+jtzDPU30GaarbvGmTj5p7vmoSzZImccwW+vfeSg21HWfR4y6sW2lRJkuNkq8CFpRABY5JsZRmBNi2k8LMQNuFhWZtWlJnolJmwxlhjgTPNOcYa8yxETepJlg0t1AnNJOR0Y3bFcUpKhrrL9BTz5BybVurS423HeoGycIOhbJdQQtGsBYwdm6J/uButDQsztSVCvcstsMEvc7Y5T2o0Ty2c5c4Ne1Z5VAFU44DDlXMA2EKxvdi/nHobODeRvet9F5n021LR5xVXkBmustjd1Us9jpY99y66R5aUDJfKLAYhsh0aOnF2kalzCwxtemH+ChdCAAtTiwghSJNlomasMb+UnW46qPK/P/7hFdnb1sN4c2FpIhDphFaashA+Q6d7xZr797hFbKkI0urSM2cJC1s4K8IELyZ/FipN8qGL61iZgWbaxHIK+FZ/5jOFwrU6UZcx0n0xo9oKSHSKEDa2cw22cw1wXnVSpTr/TuJoNXFUj0PyttPONGPTSubwVMcqKlfrSRznGgqlX0WqPozO0kc36/+jrRj6Eq73Sgrl30DKUhbWqhdpNf4m8wKKn8Hx7l67vzENkvhplLWbYsfvYNkHL+hbDJ7/RgL7Khr1d2P0HHH4TYLmR8kVfw4hbCSCDtdbty9LkiM0an8IGPKl/4LrvxYpB5YUS1kbNdF6Eq1LxLFhtVesIQruy9QqpokQBXLFX8Dz34iQy6GVxhh07s2ErY9lYWimTqvxPiz7II77UhqNiFNnZxnsK+G6ORzvLuLw6+02vB8v91YsuePik6+8nvhpkjhTtEg1iO3cRLMaEwUNOvrOK5+ysLxW473t+pbwCz+Dn/9xMg+n5fq6/hto1v6YoPlPGFOlVX8Plr0H27luzfMbU8OYkGLp17Ds3W2yLkdt8ZeAlCi8D8veS6H8m1j2ziykQxapLfwiEBNHj68K8zBGE7T+qU0aRSi1hXzp13DcmxEX+JIZk+D5b6Re/S3i8D60nqBZ+1NKXVcgRNequi4jJImfRIgS+eKvkCZ3Mz9t0dlbJlf0AIPrvYpG7XfbqreAKPgCfu6tbc+j5cUOqbYCv44xUOjwEUITNP6BRi0jjixrB4Xy72Sh22tBrJ9F+MUOWzocWniEVtqg2+mnYJW4svMlKJFZN4wWM6Pw8157lzPAv6lvdEX5xhi2FlZaJmSKju1s7/pVOr2bmKp/klZyijhdIDVBZsmARAkPJQu4qo+ydw2uNUDQSOkf7qHYmSOOMqW+dCS2KrOx9Dby9ijn6h+hER0mSmfbvjsCJXI4apCSeyWDpbdQcPasG6ImhEXO3kaiqwgsnEuEwZ6HFB4FZy+O6kOJPGoNMlkIscJ6RAiBbw8z2v3rjFXfRyV4lCidJjUBYJBtr0lLlbBVN1JIun2fM5WIVpJwoW7csfoouVdnx7GV2UaLZqhJtMZRiuHOMs0owpKCWhhRDSIM2bbFZkCYJNTCCCUkA6XsPbBkgcHij5K3tzFR/yi18NCqNrVVPyX3IIPFt1B097aTqywj59rkvUwZZskieWcnrl7tn3s5WLKIWiOEWAqHnLN9VfjYtwMpHGy1/vhLCpfe/D0UnN1M1j/OQvAAYTJFoqtoEwICKRwsUcZWXeSdHXT7LyVnr2/HoESeorsPNx3EkkWk8HDUTiKOEsVHCONDuPYefPf6pWOSROO4FnMzNfr7L5+5/TvF9w1xlLc6qCWz5K3OVaqjbqeLfrcPjUYJRazjzAC1veIpELTSAF955FQOR2arwsZEtJKznKt9nNGuX6bgjK5z9gypbqFJsC6SgdWio0TpDP35l/NCm0ybiGr0LHOtB7ii57fwVQe+KhProK1kKCKFhURl5pXCItat7DehsMRqIz9jDKkxazLJWhvCJMlMxtoqgosnrmuhGcX49tofPSEEhVKOO95wLXe84dpV22+8ez833r02E+v6Dgdv3snBm3eu2r4WpBS84adv5w0/ffuqbbZr8bZfuoe3/dI9q7Zt2rGBP/jYz69bru1YvOs33og2Eacq/0grmaDobFsilDbtXJtcuxjNRkCjEaxqp3JXgdteezW3vfbqy5bR3V/mnre+hHveuras+Rf+4MdW/ZYv+fzc7/3ot1XHizG4uZff/uDPrKxv0eeW60eJoiyDVpSknJ5ZZLbSoJT3aAYReIJdr9rLQH0bvmvTVcyxWG9RaQQkUtK6jDLhxQ1Bt9OJrzwqcQ1EESWHlsISyk6Od+24ne2lfj508iFO1mdopTFjzQXGmgt8eeJZRksD3NizjVdtPMBIoRunbWrb05Hnhv2b2TrUjTGGMTHL3EyNDcOd8D1InxkmZ4iSSRxrIzlnz5r7KCHxLoxlN5pUL7aNP1tI2YmUZeLkOFL4bY+IArGeBQOWtWmpJ0y15vnaFPeeO8QDM89zojazREZcCNn29bpY0VJvRZyeXaCvVGDfyAApMa10gZLd/21esWlfk8KRNlJI/ILH5t0bsV0LYwwLc/WlzH++snnZhr08Pn+axGi+MPEMN/Zu58quTfgq8/TSxlCNW/zzmUd5pk0c9ftlbulbnvgliaZU8tf0R5JCkrNWDowMEKYpRcdlySjpIiRaM91sgBEoS9LTX2Zmqkr5uwxVE1Iwsjtb5PAvULZVoha6fT+ybGaTL7hsYwxB2iAnE/Q6aYdzloOnPOajShYphaGRVNHoSwY5bugv00UBS0nK5Rye22QxGceWeVyrG0t6lJzt2PLypvEvVmTp3tf+LmMs1lu1LDkuvX6+PRaK0Sah7GxGCnXROqvE9e5GtI1fhcxhuzciGn+3FALl5l7fDoESCOFi2fsRooAx1czoum1CvBaEKJEr/kyb8LrI8Frk8XI/QpqeotX4WyAgDD6Dl/sRlDWMa1nYSuFZa4+7jJ4DkSdf+r/xcm9a5a8jhGj3Xduo1gMOHzvL7u0bKF2gEsxIsL9v+/UIXP81+Pl3IIS3qiyl+vDz7yCNTxC0PoLRC0vkkevY5Hyn/V4LHOclBNYoSfw4aTpOFN7fNpBej1iIiFr3ct5Px3auRaqtjB87y+J0hWtfkSl5jKkQND+ITscBheu/jlz+HQi5kszI6jtArvCfSJNjxNE3SNMxgubHsOwrWds/SLRN0bcvlWE5VyLVBnQ6Bmgc7y6UtY3sWVBY1g6k7MnC8vQsxtSXwuUy5dNZguaHyDynHHLFn8Xx7loViiyEhbJ2kiv8HNX4EEbPEsePEwVfw/VfvW4YbAaFm3sjfv7HqS1EHPnWs+w4uIlc0UMIibK24ef/PXH0TYyeJ01OkaZnkGoEIQSRDpgOz5KXHUwcb5DECTsPlHB9ycrsbwohPMQ6k88szCYl1il521nz2/BixYA3xG29r6ISz1G0OlBCtRMara2svpQ58Av93RIl+guvpSd395LiKNFVDCkSCyULOKobzxrBkiXSOCWJG3T2lViYrjB6YOUilxQuXblb6fCvpxWfoZWcIdV1QGKrDjxrGM8aWuGltxYsWWRb13+llWZ+kr7K3sHFaAaDodNZTSQJ2UVP6X+jz10/6+151JNFHOnhyKwvyjvbGe3+DcJknFZ8ilY6y+nGYTbnD+BbPbhWP64aRGDRiOtM1mrkbZsNheU5bV/+Hvryy3Ooi5XdQgi29nQt/dtkOy39DTBZreFe1CdL4dDp30TZu45WfJpWcrrdpgJbduBaG/HtTWu2qZKSK0aWox7K3lXs8/76km3zQuFa/ezp/SMg84bTpomlei9zFKS6BiZGqUuR1wAS197Cps5fYjB9B634FFE6k/mAtlVLtuzEs4awVS+pMSuEMo994Um6Bjrxix7PPHiE3deNcsX2P1txhiB8DGMCcv7tSOETxUdXbN9/cIQnHj9NR0eO4c3f+ULj5fB9QxwFaZ0gbWIuGHg6tsX1ezYxOrCBbje7aZk6p73DRe/ceSLp/L8RHj3+rcw2v3bZ8xsM1egZonSBvtydKwovu/vaErMXPqlzVCe9uduptE3WXJVv/3/5I385fxCBYL7ZZKrawLMtXEtRDyOGO8ucmlugp5CnFcXEaUpvIc9T5yY5sHEDU7U6ecfBsy3GFisUXJcoSSi4mQN9NQjpK+SZrNY4NjPPK6+49GrYDwIENsPFH0bJ78zF3lKS6ckKo98DY+7/r/HIE6d49ugEWhteetMO+gcySbxjWYRRQjHnEsYJpZxHwXfo6ygQJym2pUhSjfcDnILWUw4DXg8z4QJdTgcFZwhPbQeWB5G+crhncD97y0N8dfo5vjRxmMPVc8Q6JTGaw5VzHK9N8eDMMd68+Tru2rCXou2hjeHE+CxBFNNVyhEGMXGU8L3yGPfsnYTxKS6W4a7CBf2nlGU85/r2jwaERKBw7N3LOwuZeYawbLqujeHR+ZO8+/DnOV6bJtKZ1DhvuQzluuhxC+QtB185eMomNpr7Jg+zEC2nsR3sKjJVqSFEe6BiNLV4ih53OxbfjpLk/IUY5qJ5tphNJInm3MlpOvvK9Ax2sWG4m8pCdk4lJLf0jfLZ8SGeWDjDVFDh95/5DC8d2MWe8iCusqnHAQ/PneDLE4dppRGesnnN0EG2F5fJLEOmWi0VV0+YBawISTt/xHSzjm/Z7Oxc+6Me65T5VpOC7WGMIY4SHMda33D/20Sj2uKZB44gpGBmfI6BLX0oS5GYZeLGkza9XnFN5dWl0OeVyFllctYaGUbbUGI5QNFrT7pKdhe2cLEvoSzT2nD0xBTNZpZqd8duH8/rWwoxMSb9gVYbubbFQGfxshlg1oIlJbZSOCrLKuuqzjXHGkLkkWpgxb2Tsgch85i0gpAdSDlEkurMZ0oIpOzMPIMMGFPPVv7F2s+osrZg2ytJo5UVyOG4dxC0/gWj59r+SMdR1jBBkjDfahJfonNU9t7M+JnL+MsZw8xcneENYTs5QfZzEh8iSY6026KA679u3WvJ9vFxc68jDD6TeefEz5DETxPG+4jidOkqperBdm8hib8FpkUcPdgOE1tbraLTSeL48fZfFq73MkChbEXQirIOR0CanGp7IIGUXbj+PZcMgZOqD9u9lTj6BpCSxIfQehal1losc1BqaEUIV0a89WTEkfBRagsXTh2EyGeklQZjQoxpAed9ljRx9ChpkhneK2s7tnMT6z0LQggsexeWtYs4+jqYFkn8KI5357rtBiBkGc/7IYRwcP1skfHCrkgIgbK2I2UfqZ4HInQ6TRZCArVkgWo8h608pBTkiz6OZ3HZb+gaOFtb5NjiPHcMb101uX6xIzUJJxtHEQjyVpF95WvWtENYC0GcMLZQ4dxilUYQkRqDZ1sUXIf+UoHBjiwT1sXfkChJOTQ2ydhC5YJfe9v/Qd51uH7rMEV3+TvQrAeMHZ9CSkm90mT0wNp1yiJBRsk7oxhjmK41ePz0JK5tODiSUvSy+xemLcZbxxAINvhbWIxmaKRVPJmj2x3kcPVhEh2xvXiQgtXBXDRB2c6+8fWkwnRwBks6DHpbmY8mSE2CQbMYzbIYTWNJhwFvCwvRFNV4jrLTQ16VeXLxq+RViW2FA9jSZaJ1Eke69HubqKeKWpIjloZO/3Z864I5ZTst/VSjTk/+0otO59s71ZpTswscm56jt5DniqF+XMvK3tSL7slAaf2FGils8s528s72S55Xm5gomcCx+lepj7JrSAmSM7jWxjUVX9oEhNEhjAlxnQOk6QxJOoZljSBFAWMCpCyS6gUwCUk6iRAerr2TZvgAUXKcvHcntrWFKH4WbVp49j5SvUiq5zAmwrF30AzuJ0mnyXu3Y6lBwvgpQODYu0mSMVI9j1LdNNJhFqOQIEkI0wQpBvHUCFKIJa9BgEYjIjETCAT7ugeWbC2OPHqcXMGjf3NGNp49eo7B7QPttjAY08K2NoOw0HqROJ1FyB6SdB4lOxFCECcpL7t7L7VaQLMRolyJJdT3nMD+vurVlLBWrEIUcy5vf+VKSa3WLU5X34+n+qnHz9PhXk1v7nYa8XGmGveSmpCSs4e+/J1rPozaJCwGjzHX+jrGJJS9q+jL3cFc6xuM1T5MoutUwifozd1Bp3c1860HmWx8li7/Bvrzr0DrlLHaB+n2b6HgbKcSPk0l+BYbi29iMXiM+eBhALr9m+jwrlrFrhpjWAgfJUwm6M/fgzYRY9UP0Ze/i5y9aVV9z+Po1CyT1TrNKOb6zUOcq9Qoug7PTExzzchG5pstjs3MccPmYVJt0MbQCCMaUeZWf2J2Hs+26Mz5HDo3RaL1kmJpU1cHjShaap/p5leYCx5BoBjI30mHe4D54BGCZJrBwisRWIzVP46juujzb2MxfJKp5ldITYtu7wZ6czejhMN4/V8xJqGVTJDoBiOlN2OJHGfr/4IUFq14nLyzhaHC61HCoxGfYrz+bySmTsHexsbCq5lpfh1bdeCqTs5U/4ltHT/NXPAIOWsjOXuYsdonaCUT+NYAw8U34qhOGvEpJhtfwpIF6vFxevwb6fNvJdIVzlQ/TDMZY2v5Jyk62wAYr3+aMJkm0otoE9Gfu4Mu7xpS02Kq8QUq0XMEySS26qDMm0jjlHq1Rb7gkpqU2KS4bfVGqGNcaWNgSRlnCUWQhthtJVyoYxxpZ527ybIZCSEJ0xBHOsQ65sG5J7mmaw+OdHho9kkOdu6iaOWy1WKjs8H7JSZ4C2ELRypy1vqpYeMk5cCeIZqtCGFgY0+ZQZMN+LYNnjciXTnR6Cx8d2kjXywI0oij9VPEOqbL6SDQMb610lRUCIEtFFuLvWwp9PCqjQc4tDjO16aO8M25E0y2KkQ65bnqBH/47L0crU7ys7vuwpc2IwOdzCzU8V2bXMFte9h8bzr3VvRMlho9ifHtK74t6bwQErGmZPqiD7ZYvv/GGI7VppauDTIS4fXDV3Fj73YG/DJF28OVNpbIYvUnmoscWji7gjiaWKiRdx2mqw1qrRDsAE+VXrDkP6d8+t2+9rUYlKWQUqC1xvMdLHt5ULrB7+CX99zDXz7/ZR6ZPcmpxix/d/zr2EKhpCTWKanRCAR9bpE3b76eN2++jpy1MnFDvR6w4FpsuChMt91YK/6KUk1iNK1k/TSptlQ4yloinYw2eL6zljjphbVN0WPfLbuQSjJzdm7JgN2RamnBZXOhh1/YdTdGC1zLohZFeJaFBCypCNKsP1sImnS4PonWS6qWnKrTSsbJMXCxX/MKpCYl0plnVKwjmrqGLZ01M7lA9o51deYo5F06O3KUi4pmWkOSeXNFus5c61s4qotu/8APnM+RMZki7wXafQEw2ajz3PwMw4UyjpJLY6uLyaOMGFiZXl4IH9pJSqTsYq4CYzPj7N2+Ac+xQbgIVDu0OQLWzxZr2fsRcq334/y5BJazrz2pnwNikvgQjvdSHKW4amCQDfniOv2BjePeglSXN6FNtUEIcJzMcwIExqRZpji9AICydi2pbS4FZY1i2buJo4fQeo4kepQw2kmzFV3Quh6OeztB8wOZeiZ6nDQ5hrAPrKEi10ThA6RpRrBY1k4s+2qMMSilcC5IQJHE31qqr1TDWJcJSwYHy9oBuEBImpxBp5NrEkdCOEjVw4pnAQfRDv0QIodUvRedz2aZtEvaiwvnr6vZJrlCQGLZe5FqwyXrK0QRZW1bIrri+CmyLHzrE0eWtRtlbc7OCWu+L1J2rFBlZRndsufAEjYlu5uC6uRsZYyu/hJSSswLVFUbIDUG37a+rYQRLzaEuoUtHbbmd2JJ55LJV84jSTWPnBrjHx54nGPT8yw0WwRxjDFZEiHPtin7Lu+6/XpefWA3llr5bLSimA8//BSffnJtU/LhrjJ/8ROvo+gtz/eKHXl2Xrk5y4jcCNf0R7sYrTjh3Z/7Ovc+fRTXUvzUbdfxzluvzUJU29+t8dYxhJCcrB9iOLeT442nyFtlBGAJG0d6SBS1eIFEx3Q5AzxbeZBuZ5CcKpL5+iVMtE4x4G3hZP0ZXOVRDysoYWOMppU2OLP4HDf1vBZt0qxMoXi+9jjNtEYzrZGYhLPN59jojxLrcM3r6cnl2NHdw8ZiaU1188WYqNT4tX/5As+MT9FXLPBfX3UbL9uT9YXGGKJ0CtAoWUSbEG1a2LKH1NTRuoWtutEmINUNbNVNapqkuoGjeklNC2NCpMghhEWSZqHuleBBCs5eXGsYISRJuoilOgBDnM6x0LqfvsIbVhFHxhiC8BFSPYdj78KYBo3wPjx7H83gPmw1gjYtHGsrYXyIVC9gW1uI46eRsgBYYARSlIjiZ2gGX0MIH6ODzDvQhHjO1W2PJokQFlIWCKLHSfUMIEn1ImF8GN+5hmbwFWaTO5gOHGpxdj/mgibDhQ56vBzVKKSZxAigGmU2BEV7JT8xsLmP6TOznHr6DKXuIqWuC/s7TaXxQWRbeZam81lCCtmHURFKdgCCQ0+coX+gzPRkBW0MRb/J8fo5thU2MOj3LEU9fLf4viGOQt2gldaYDyfo8zavOyk2aBaDxxkq/ggjpZ9ACodE1xivfYySuw9X9TJe+xg5ezNFZ/eq4wUSzxqgP/8KgmSK6ebn6fSuosu/jiCdINVNhorLkucu/wYSU6cZj5HFkjoI4TDfeoi8vYX51oM4qoconWOy8Rn68y9Hm4Tx2kfJ2cN41uqPs6f6map/hk7veqJ0llZyDkuWV+13ISyl6CnkmazVqAYhk9UaW3s6sZXi6PQs880WWhvmmy3OVaoMlovMNZskqca1LOYaLUb7uhksl6iHEWGchbMVPZfFVtCWw0MtOsJk4wtsKf8EiW5xpvYxXNWHq3o4V/8MfblbAZgPvsXm0lsI01lOVz/MhvzduKqHU9UP4Fm9dLj7aMRnaMZn2Fp+B1K4uKqXKJ1ntvUgQ4XX0V28nlPVD5C3tlB2r+BU9QN0uAcou7sZq32C6eb9gKAeHydKO6nFx2klE9Si5yjYWxivfwpjYjaX3sJE417O1T/LSOlNJLrJVPPLbC2/nR7/RpTwAIkjO9lcehuH5/+ARC9PXBvxSWrhEUY7/xPV6Cjj9X+l7F5BLTrCXPAYo50/w7n6ZzCkuHIDcXyEKEyIdMyhyvMEacQGv4dEp4y3phnK9bezRUwxWhwhMZpT9XFGi5sIdMi51gwddoHEpFTiOgUrx5b8Rg5VnseXLtd0XYESkkSnlCwHS1rEOmGsNY0tLBbjGtsKQ3hq/ZX26WadE9V58rbD/u4Bys5qn4gDe4Yw2nDoyDn6ezPCSAhBM5mmEZ/Dt/rIWwOrJOX/f8D5lpoN5zOLvMu0QRbbX+L2/iK39u3gRH2Gz597ms9NPM3ZxhyNJOQTZ7/Fgc4R7ujbw9jUIpVaCyUlRS2IguR7VnfPHiVKxv+n37dQJ3x2/CmOVacAKNoev7T7bl4+uHcpLO1iaMyS19B5KCmZqtSpBRFHzs2wdSNI1AsmAWKTUk8bYAxpnBKHMZZjkSSaoBUh1XJGLyEEe8qDvHXLSzhZm2WitUinm8OWFqlJ8aViQ66T/R0j3NC7lf0dQ9jSbk8ksmxeBsPQxs5VmSjXg60kfX6Berz2QA8yBVczzhROBpBKUiz7fLeGq1EQc/rZMYyB2fF5BrZmiqNut4ASgsQYQp1gtKQaxBhiurwc1SDEVYpuz+GZ+VkGCyXQFudqDaQQbC/1MpArESQxQWpd8pkzmMwP44K/K/Ec/W2j1bUgpcBIge8rigUPQwNN0ja2zOTxntWL9R34FrwYoI2hHoRLaZlfCHpzeQyGSKcYNKkO136nhMvqMDO5tK8QPklqMTVXY+emvow4QrL0TC69E2sjU7BcWjEnRB6lNpImhwHT9lvLfNPmgxYdnkfOXoNcFBaWtZsk1UtqWGud9zH7XazIsGhMs32ujPhS1ibkZcZiAFIUUdYW4ughICVNTpD3UoY2dCypw4QQ2PZeLPsK4vD+jDwKv45l7+Ni9boxi0ThV8C0ABvbvQWpBkgTg+1a9Awuh0kk8dNL9c08qerrhoguI0UIt60IqrZVYmtNpq3VCwhCcD4cMjMkXmv7surzwmcha9/zE34Lqboxev6ylEpm3q2AFJ3OwDqm++ehrBGEyFQQWUZKzeo+01pBdJgLshJa0qaRVOiQ/dQWmxQ7cy84OcN5pCYjo36QwtTOw5U+kQ451XgeT+UolcqXVBylWvPwybP8X//yRc4tVoFlskiKrH8L4iwhRDGniE1AlGRJLlzpZ++Qkuwb6meh0aTaCqmHEZVmwEKztX5FBRx5/BSVuRqu73Dty/aSak0Up2ht8FxrlYVHlCQcmZwhSlLiJOX5qbmlbVPhGebCCcK0SaIjfFWk3xthPsoWzIpWF4mJLst5YAAAIABJREFUcWUOISSOdElMRGoSYh1TsDspWB0IBI70MKQkbYLVV0UiHdJMqkyFZ/BknmZSxZEuRauTvFXGEjZB2iDULbqcARzpIRCU7C4suVqNY4CJeo1KGHB0fo6+QiFTnl4C1VbI8ek54vT/Je/NYyy77ju/zznnrm9fau+q6p29sZs7RWq0U5Il2Rp7LO/GBHAmiB3DRoAkmCAJgowHGCPAZAaB4QSTie0YEzuT0YwHGVuRLEu0SEkUF0kUN7HZZO9L7fXq7ffd7Zz8cV+9ququqm7SngGt/AiSVfXeu+++++4995zv77toVrs9rq83d2xxpfuvKXmPYss67fBFhHCx1RhhfB1L1Sg499MavJDJV2UZR03Si87jqmmC5BquNYVvH6MXnUdi4VhTJLpBP36bbvQGSniZH5CQSGxAZDKxXctkXpuygqWmMSZAILHUJGE8zDs2PVK9MQKsHOs+tG6BibHUFKleQckKcXIZrVs49iSWNU0U97GtIzj2UQwGS00BKVKUhiymKgJBqjcyfy01BrHiULHI0comQwiaYUDNyyGAU9XNb2WnYGr7/fjgqVmSOMFow9ThCY49uN3/SFLwPzPcF9C6xSC+QIrGVlNsjnXVap6vfulVkkTzxIePg0gZc0v0kgEbUYdJ724pyfdW7xvgyJE+tgxGF9d+ZcsyJfcMnpUdxEGySCd6k0T3t0mQdt+GNiHt8A168ZUsii9tZhHW2EgszNBkbGsCLHcMjEIIKu5DLHT/hF58lUGyyETuk0R6g050PgOWULuynTZf71kzOKpOO3ydMF2h6J7CkvubDJ+ZnhjSDydRUjBXrZB3bD509CCOUsRpOqSjK2YrJXKOzXghT5Rq3lhY4uTkGEXPZb5aZqpUxBhDojNQKYjjEfuoG18hZ89Sck4CgsXel+nFl6l7H0AKl070ztDkyyNnzdJPbjBIl4ZsnZjE9OlGl6i4ZxEoSs4Jis6J0QQlSsFRFareg+SsOcrOKbrxRXL2HFG6Tt1/DE9NUfMeoTF4mcn8J+gGF9F6kIE58dsYk+KqGs3wdYr2cTrxpaHU8PzQLC4D56rew7i3dSIz4O+2JA0syt5Zis4JpHRZC54jNQNSEyKExJYFLJknMX0yvyefSi1PajS9JKDuVrgVrNCKOmgM3aQHBsbcCuNujdWwwSAN6SZ9VsMGk26NhWAVjWbMrdIIW6xHLfrJgPW0yaOc2TZp37oaXGlzvn0FJSSnSnubqkEmU4h1Sqo1bzZWeHxyDmsXr6zJ8RKT4yXsbd46rfAioW7ST5bJFSbuqav0o1aecjlXPkndKVO0ivjq3qRCmR5fcbw0yVyuxqP1w/z2G3/Gtd46QRrxzPJbfGjsPirFYSqVNqSJodsOdixo/iqlTY/UtLDl5D3hDcaYTLK0mRRIBvAYY5BCEOoUJSSWkGgMUZqQsxw6cZYotyl2eqA6z4cmju8rdRqkMQO9k3ETpylLzQ4Fz+HoVA3X7hJEe7Ny9v4cmQcegLQkGOhu9Jg9PkWn1cfP7ZxgnW8v8PvvPMvyoMXD9YP8ytEPc8DPEcTvYEkXRyaUnQqeEiTpRbTOY0gQKFLdxlLjTE0nwxv3vVXd93H3mcBtjpODJEGQpaEtL2xw9OQ0f1XwyM25WLai1+qPtjSXr2HJTHrajQcoZfjwgUMkWmNLSTpcXNpSMpMv7fDVEwJ8y872U/dRwkMKd8/OrjaaqjM+8msI04BYD3akqu1Wjm3xxvlbLC55HD/hImyJEi4Gk9Hd0ya2LPzIsY0A2v0BJd97T2zEdhjiSIWrFBJB2ZnHkrkhw2yrBAru8I/Z/n4KUFkqzHsYooQscneZv0TIrXu10c3hXgjqfo4wSfcAOgRSVgjCmDevLDNZK3Boem8vimIhk+mPtjM0+h7thazAvchjhYeQW5NwrRtEcZdmy9oBjAvp47ifIA6/CRii8Bm8oYn19kqT6yRDmZqUVRz3o8N5SkLQCQgHm+OhyYCU4TcYh8/RWv8l7jY2GDPAmO7m3g4N0Hf7XIJ9JX9i53z4rmVidLoZhhAz6P8rouCrd32Z1hswTLbCREP52z67JUqZdBIIg4g4TIjjZMf5sheOk6VWZR6jqU7wcs57Bo2yfdesBz0SbXD+6paF75tKTYqvcpwtPwYYFoLrdwUAW0HIF196nYVmGwFMV4p89uwJTkyN49qKfhSz2u7RHoRUyjGvNL+NLW3G3RkO+EewhI3n2Hzhkfv57NkTJGlm4nxhaZW//8WvMIj3brjNHp+kPlXGL2ZN0yROefmtG6Sp4dHTc+S8nfMB17Z4cH6G6+tN8q7Do4e27ChyqoAlberuNCW7hkZjSYeqM4ktXWrOFO90f8B6uEDeKtGMVodN2DaH8qe50bvAmrXA0fxZlgfXGaR91sMFwHCz/w5Fu8qEN0cvaaHRzOaOA4IJb56b/bcp2TWOFh/gau9NAMp2nZo7zdXeD6nYE3f4AgsgZzsjYG6l22W2vD8gXs373H9gkpevLXCwXuXUzE5/JilcSu4HiNIlBslN8vZJlMhRdB+iG71JmCwQJjfw7aOAZBBfx5iEKF1BoCg6D2PJEm39EuXcp5DCJYgvUfaeYLX3p0QmwFETSPJoE1LyPkCiW7vvLALPeZD+4Bl6wdfIeR/Ftg7TD7+NbR3FtU/QC75OnFzO5F0YpMxjqVmkLCOExyAKCePXcO0zpHoVrQOkKGCp6SGDJzuOmTztDcL4LVznfnqDbwDgO48RJZcJBs9hWQew1dgWbiBgzM9v29ut/+5Vb3//EsceOozt2UzMjZEr7lR3qGH6JGRz/BFrlnQ0zj382BGuX1vDshT18QKJSilYPpZQeOrdhc3sV+8b4CgzvrZxVG7PiedmCaEQ225uAhtPzTBT+EkKznG0ibE3pQ6Zs9fouWG6xkr/aY5Ufo3URFxt/t7ocSEs0jRAmwiJzU4zvq1t5Ox5lMiz2n8aV43jWdMEyS3y9hFmS7+IpybRJNh7dK6ksKj5H2S59xXitMWR6q+PJG3GGJrBgOV2FykF06UiRc/Fv63bVhjObXznzpt83s1OkJyTbe/huRm6UcRYPo9jWdxuTZNztlJOdk4qtxyjlPSouGdZH3wPKWyq3kNY2xIxUt0HKZjOf5ry0JBXCIGShTu/z9F3svnvZndTsIXKGhDgWzPEuk1CjzH/gyz3v4FvTWeGvQi0iUh0F9+aoe49NgLssgnyvd21hVBYIjcEadTwUxuKzn0s9b7GW41/iqvqTBc+RxQp1JC5ANBPBvTTJQ7lZ6g5ZdbCDaa8MXpJQE55WEKNGCvdpMeYW+Vqf4GqXcKWFmW7QKwTWlGH1KRU7CKNqM1K2OBmkDE5VsIGecvnRPEw3aTPyWLGyIuShJvNNlGSUvRcpstFpMj2LUhiPjF7lFY4oOx6u/itwPm3l3Adi3IphzZm9Jyic4h48CaeVfn/JWgUpANu9pdQQlK0Clzt36TilKg5d+9Eb5ZA4FsOHxg7wucOnOOfv/MsqdFc7qwSm5ReELHS6HB4to5vW3RUcAe/XgDb14v3OplN0gaOmiNKrhEny9hqct8xdZAmnN9YoRUNGCQJedsmZzkoIeklEZaUtMIBrWhA3ctRdFxOVycIdcxamHWElBAcLoxRsPYG2IwxLPQ3WA+7O/6eas1MrcR4MU8555OKCN+qvqtzzwwB2zCNMGQLTL/g4ngZsFGpF8gXtlh3QRLxh5ee4+XGNapunl+/7ykeqh0k1Rt0wxhLFYmSNdADUjGBNgFKGuJ0BUtWiNJlonQpS3Kx7m6wCNko1w7DfSfbiU7pJTE5y0VrQxBE+L57x6JHsAUy7ezx715e3iVf9rn25i1q0xXEEAA6U5mlbPsM0phWHPDyxlUeHTu0K/jnqr2nC1JYdKIreGoMR+1+nWg0vaSD62YTIl/lwZncNUl1e+XzDo5jkaZZWpMU7pBFmlWse3Tiayjh4VtTd50//E2qasEnjJN37TsFEOmUnO1gTMbUbsc3cVQJ63Z2kdjZ+byzBLatODRTw7Le7X5kQMS9sB/FDhlsxgi2pGS112O+vLvULZuvOKxudAmjGN/dG/TIZF8Sy1IjEMqQjN4LFAj/3qW9bDIBDVoHdHt9gtC/A1xz3I8QqBl0eoskuUAcfR/H/fjW9WsM0eBr6DRjOFjOw0NWEkiZxWRffu06B45NUR7LbdtfMKZDmr775KEMlBmaJu38ZHeRtdzt8Z1lTAjDZh6YzJiaxr6v2WUrwP6M3EwhMJRiGsPs0QnyxXuX1ScmJtYRSawpVnIUSu9Nkm+MIe84HCxVf+QYR81onV7a4Z3OG+StEo1oheOFM7uAzlt1bW2Dl69l4RLVvM9/9ZmP8Kkzx3YN7OnGLRYGmkTHmQxObBpvDxO33K2Fb3eYOrpZBkMnbqBJKVpZqlgSpVy7sMjEbI3aRBkhIE40c5MVXDsLzdBplpKLAKXh1z78GJ85cxzPtjg2UR8lkI45s9TUgYzFLC3quVk6/ZBKcpxOUyNEnqPqIxAaGs2IsfgD5D2HdOBgAgu/m6dayrHcDzlaeRy/7GBICdIuB/OnqLtZmvWZ8odYjzIgeyNq48o6RwpPIIVFpGOmvPsRCFpJyLR3itRoEpPQTgbYaUzJ3lpv5WybguPgKosDpdLos6RJipAy++xkPoLKkozlfP67H/soS90uY6UCs6UicZRgDxeMzhAYsdUYBecchhRLVonSBSBFyQIF58FMIibH6KUXMpKBqjBK5xUOnjXPRvAMjppBUEYbG0tOD+dVK7jWLHHaZa33AlIIUi1IdYKSctS4EkJgqUlK+Z8fnQOW+tjo504cEFqfxwArUZ+c5WLHFpE+gQnBUzaR/EkiYxGFMXnr05TtHFIIbGs7A1pgWweoFH5l9JdyPgsv0rqPYx0l534YpWroVLO2vEGaaISEOMyS/PJlnyRK8XIOcZRg2Yo00USDmFItT6GcG34vmkuvXiNX8ilWC7cBR5p2/1+OlFBpuoGQFWz71HCdm43lLz5/kY31LkpJFlkjP+7QTQLmchMcKdxbANS91PsHODIpnszf02JBiZ2yG1uVmch/mpX+12gMnseSJaYLnyfWLdb7zxEk11nsfoma/wRF5wSeNc1S98sgsti/zRtOzj5CI3iR6+0/ou5/iJw9z2r/mzSC54h1k1udf8tk/sewZJGye44b7X/Focp/DMLBtaYpuw9wq/NvsWQeTx1gsvBp2uFF1oPvEMQ3uNX5N9T8D1JyT1Owj7BkYpRwyW07UbUxfP2ti/yPX32WvOPwD37iKT5x4uhdj0kyZAWIIXCwOSUQQlDJ+RQ8565yG4Cicx8rwbdpDF4mNQHaDCjYmc614p5jPXiBxPSZzn8ahrK/vH0YW1UpOseI0xbOPrGFAJFusBY8T9FZoxW9yWzxp7FlGd+aYbX/bYrOCdYHL1L3HsvAKQNC2hTso1xN/oiycwYlPGreY4Rpg5JzH9pEOKqK3OeU1iYi1i1SMyDWLRLd24pt3I3BDxgTM1f6WXxrBkvkSVRCsZRDKYkQUHPKHC3MUXVLgCHWc9jSYvtUfMKrUbFLmUmZlMznplBkfiupNky5YwgBZ8wxJAKM4G/PfAwlJFJIfnz6I0ghWQkbVJ0S0362UF1sd/gv/s3/y42NFp86eYzf+vwncS2Lbhzx8uoCS/0ujlR8eObQrpNhg+Hr3zpPseDx0P3zzE5XiXWfMG2Qs8YZpI1sET58fqrbaN1ByhJS5Mi0vz9akyOAdtzlYvca40ND/k7c3eF1sAng3OtnL9n+6BhuAnvdfsjUWIlaKcdgrc/YZAl1W6KaJSSeyhZCiUnpxAP0kAW0X2kTEoQvYas6hv0p/pCBPmXHwxnGsJccjyCJkUKQt20cZVGyXeZFBV/ZWFJknkXDf2DTV2J/+KKXhDyz/BaDdCebaKJcoBOEtINMktPVqwRp8115RAgEk944trSxhEWiQ8r1TLogpKRSz+9AVxpRjyvdFTQGT9kULBdjDJasUPE/DgiM88C2TlEGcGOfAAS5oVQqu1/dG0BtgP7wuO73OSwhMRjSRNPvZv4Mtx8KR6rR9xXphF6yv4+DEILFyyu01tq01zvMnzyAZSsO+FUeqR/iy7deI9IJf7l4ns/MnOVQfuyezu/RtYAiZ23FoO9WlrDpJi0SE1OyqqMkmrsxhdbWu4zXC2y0+kShwFJ9QOBZdaSwsGSeWHdIhtHEf1Vm1vup4iRlpdXlyNTdEl3urIrrcahYoei4aCIGaYNuvID7HhLoekFEnNyZ8Hr3MsBebKHba7scLzuPYq2ZLhQZ8/duKArAd23GKnny3j5G68aw1ugSx7cz3My2/2/K7u7G4Nl87tb+xrFmvFa44/pW1hy28wHC4P/B6A3i8Ns47pNAtjAwukEUfYcsdczGdT85ZGll71OsFjhydm7oebElrwJQ6jDKOg5CsnOQMFkjYsecb+tzSTnBHYPKv5dKt72PwrJPIdXsu9qClJWRDG3v2pJGW7bF6kKTfMm/x/OOkQTZdjJQMQzePeMVNu/vcKGxxlSuwGzx3ptN7/eqOmPkdIGiVaZglVge3Lor6H9tvckgzo7lwXqVhw7O7JnyrNEYY6g4daa8+dFa0BhDP+3hSAdH7sEGNIZWvEYzWuZk6QmkcEbA0OY9SmtDkmb+oEpJgu6At166RK7oYbk2cZiFlNAOyE9X6Jg2l2+uo2yFn/fQOvM8HPRC6vN1bjS7NLsBYZxQLvgoKbAtxeJamzhJcWyL43PjvHZxAa01Jw9P0mj26fRCzhydQqA4Uji7Y807SEPeaGUm3Dkrk6P1kwA5vNePOWVWww0inXCqdJhG1GIjaiMQzPjjlOzCjmMihcCxMpbxm89foFjLriPLVixeXqZYK5Ir+bi+Q9gPaSw1cVybjbhBUwgOnp5j8tA4IKjnf2J4TBVF+yksK2sY+fYhMAYhbIxzBkwKQpF3z5Bd+9m+CzIv17L3BMYkXG226YYRg8YAIR7Etx18+wQr3QQQhMk8nTAm72zgWV3mKpW7Gn1v1mLQ5FJ3CWMYJfwWLI+C5THQMTWnwKXuMgpBYjRTfoWHq4dx9mmO3V5C+BT8z7IJo6Sp5tIbN2mstHBcm2IlTxTGBL0Qow2HTs2wdH2NUjXP+lKLiQM1XN8hX8rmd498+hyDXki/1Ucnt9+jBDn3o5ksjxitm0TJAqnu71DQ9LoDPvSxk/i+TWwnJDIlMSlF66/Xm/Z9AxwpYRHpYORdsPfzfA5XfnWHJ5AUNmO5j1B0TpKaACVzWLKEEDY1/0mq/mOAwJZlbFnhUPnvEaUNLFlAoLBVhVCHbCQeE8W/Sydu0NMOpCFdXaac+/xwcqtoJwGDtEvNfYRi3kfIw1zrXUEKRdX/DL3gPEW7TDc1dJOI0OTxnMc46D1JThVGoIoQDkrmKbvn7phsJ6mmN5RqpFozSCNWBm0qTp5+klHVLCHppxFVJ08nDnirvcDR4hQFy8UWitWww6RXztgNScitfoO85TKXq1N2dr/4hBDk7cPM5D/HSvAsAsV88WfxrSxJyLMmKbmnhwyfAwghcGSV+eLPsRp8m3b4Q3xrhqJzHICcNberBM9RdaK0yVLvaareI5Sd+1HC5WDpF1jofZml3l9Qck5Q9Z4kSiUF5xTauEhRpuQ8gBRzCGFTcZ9iffA0V1p/ihAOs8XPAjFC5HHUERItgczk1lUWt7qvcrPzFygZcb3zdYJkiaLzSRI9TsEuDc8vj5JzAonNIF0hNSHX218cemlMMlf8BU6cncXPOQgJxWiMyzfaHJ1ysKSkE4RUCz7rnT5518axLZQUrLUDVpod7jswTrsfomTG7Hr54k3OzE8xVS0SpZooTri6vMGjx2dHEx53SDGs2iUeqpwkPxwEjIF+FNOLYgbJVkfOUxYfmJyj6LgjU+Ld6tEHDrK63sVSknp1kz1mEEiUdBG30US17tEbPIOSRSw1jWufvtPv4Eeg6k6VT0w8ySANWYsajLs1ittuyO14wMqgzWyuim/tvVAxxtBNQr6/fnUUQT+Xr+Eoi2opR6sT0O1HxP0Qo+07gBffcqi52fumxvBG8yatqE/VvdsxT3GsA7jWPK41v+eztDaEUYIUgvl8ZbRU2jQX1akmjBIsS4HK4TrWqFsP4EqLCa/E5e4q2hgudVdpxwPKzp03qTCN+fOF1/nLpfM7/p6kmrV2jzjVNLqZJ1vFnaPMAax90rZ2VrY4yFt58lZ2bGxPE4Ux/jA9afw282pXWnhDQ+bloMX/8vbTfGjiPia90g52hxQCR1pU7BxTfpm85e6Qj77bEkLQi/cG8wyZhFggsCzFzHwNKQTiNqlS0fYp2T6LQYvEaH7QuMYHx4+Rt/aW2cwcneLy69eZOzEzMscu2h6fm3mAF1cvsx51udhZ5vfe+Sa/efKTTHilPUEuYwytOGB10GYuX0cISck5hqOqewJBAoj0gLqbsYx8laeXtvcFCAdhTLWYZ229S6no4+ckIfFosiSERd6eIUyb5K0ZfpRAI4BEa4IwvsMX7F7KlpLr3RaT+QJ5W1K0D1CwtvwQ3k1ZSnJrtcX0eGlfVs9uZcyADGTZb5FpdjBpxJDNLIXgnY11XMtiprAHeCAyGWQQxvv6jTm2RbWcZ3O8yF5qsZXWpTGmf49gQ4oxfTZBESkLlEslri/07viuhPBw3I8RDZ7GmBZR+Bx++h+hrEOZTDi5QBpnPkBSHcByHhvNCTfZEPWZ6nAMsGAb2852niBX+i8z/6J0ATYNn4WLSVcR6gCYDogioEFYYDRCeJAugLW3v9hfRwnhMYqzFy6u95N4+V94l1uR7JeodnsN+iFJlNwVkN5ejvSouweQWKOxUQxBoHdbsU6xpaTg/PVJQ94PJYXEVzl8la0fZvyDdz3GncGAdCjBHy/mcK29x4AoHRCbCCXsOwCp9XCVUA84Xjy163sKIUYy9c3xLVfwyJV8pJLZnEUI5iYrFPNb90itNRurbfIlH8u2MEP2je3apHFKZ6OHZSuC9gA372A7Fr1OQLEfcvLw5Mh7zrYUaapBwLG5rOkipcSxFOPVPCBwHEWt2MexrdH4om5bgvuWxxP1c6PfjTEZoAZYQqGEYj43jRQCVzrUnDIGw0KwiqfcYaN3CKAqRcXzqXoeRhss2yKJEry8S2u1TTSIicMYnboM+iFREKGUxHay45DGyaiZmc15HMIo4cZCg0EYc+a+meHfrdEtJZM9739/EMJCCItB3KYbaQZxQsX3GCQGbSRRmknjpXBxVAY25Z13Jx89mB9j3CsNm//ZDMMeBoFoDJ60OZgfIzEpComSCku+O11p9h1uC0uxFec+eDxjHImt58RRgrIUtqOYnh9DSMHh0ym2beF42Xm2vtBg/VaDsB+ycn0d27UZnxvb9l4Sxz7KIHqNMHoDQ4BlHSdMruM7J1AM/d2k4Ot//hr5vMehR+qs5BqA4GhhmqJ1dzXXvdb7BjjKqHiDu5r8CaF2NZyWwsa3D9z2tyK2c+dkw1E1HLWzg7cWLvFm+zzHCscJUp+SU2JxsIyjJkHYNONWZpSlmxRUgX6acCPoo9lACYUSFrcGK9wIehTtw8SmxyAN0eS4Eixyf+ksBaeadTPSNTaCl0h1QMV9+K7HZnXQ4eXGFR6pH2E5aHG9t4YSgjGvxHfXLzHlVVgINrjeW8dTFqdKs3TTwdCgucG4V6SbBERDacnpOPPkWL7ZwMs5BL0Q21bYjoVUEq0PIZN5vJxD31Ysrl8j6IWMTVdw7B+j6Nuc/+41xmeq1KcrFKyTFIonR/trjCFNNVO5z6K1zgZUthgaUjgcKHyenL2z85Sz5zhW+dXR7+8011judzlUeoo315dYCTbwrJ8mDCHSTW52u+TtD3OpdZqi43Kjq+nGb3GwWKEbf5il/hpKNujHEXOFMgu9KoP0C1Rdj2Y0wLeqnN9YpWA/yLFKtsD2rAmOVv4e2iQstr9KzX+MSf9jhLrB5eYfEOWWKRaPb51j2qLd6/LCW9eIksx47+BEhXY/ZL3TZ368Qt5zWG526QYhx2fG2Oj2SbWhnM+Yc41uP/N52ehwaLJGGCe79jxz+8iAtlesUzpRyM1ui0Ga8OOHTpKalH7SI2flkUO2yLWbDa7eWMcYQ8538KccLJnDVRWa4TvYMp9JAYYDtqUmyblPjGItEVnSUqQTcsp51wPv+7UsqYiSiO9tvE7VKdGIWlTsEs5Q0nmps8xvvfbvOFio81DtIOcqc4x7RVxpoYZskTBNuNJd5SsLr/OtlbcxZPTYp6ZOs7GRmWLnfIf+IGSiXsg6Y7d5HJVsn3OVWb63foVYpzy3epH//eKzfGH+UUp21o3KqMqaME2ouwXKjo9AEaeL2HJ/CVUUp7z+1i2arX42yXEUvpexChxboY3hxq0GRw+Nc3OxSbHg8di5gyO/lZLj80j9EN9dv0JqNC+vX+OPr3yHn5p7hMIQYIl0wtqgw5/deoU/u/kKgzQmpxz6aQaeKCWZqZSYq1do9Qf4joWjsij6QRrTiHrZZ9SaII3oJSEXOysj1pIeAmqOtMhbLjnlYMssGW3i/snseOxys6y5eX589kEWgxbrUZdnly/w7PKFO54nELjKoubkOVIY59Mz9/PJ6TPk1HthX0DBdpD7TLZdZXH/+ARxapBKMD27O9NkzC1wsjzNO+1lNIYv33qVku3zmZmzI/AoNZpYp0Q6Ycors3JjjYOnZ+m1+qRJmqXOCcHjY0f4uUOP8/sXv0mkE76y8Bo3+ut8fPIUj9YPU3Z85LAz144DbvY3eKN5k/OtBQSCf/TQFyhbKb1kESlcLLn7OGWAmjOJI102k9zutvBYXetw8eoKk+MlCnmH1PSJTQ9LFjLAyWRMMEvmUHt1o/8GlyVlZhD+HhawBuhEIXGaIh2LQbpB3prEfQ9zR60NgzB+T/uh9SoZm2ZOESVYAAAgAElEQVS/rqdGp4uj36TMmlWxTmmFA6I02RfQUVKytN7h+Nz4nsDWRrs/4g5ulhA+clu6mE5XMKZ/V6DCmD5aL2/tr5oknytj2NjFq05gu0+irPtI4u+SppeJohfwrUNATBx+E2PaZIvLx4fpYFufM41T1hYaTMyPI6VEqQPEmxI500CKEkLm0ck1TLoEwspS7IQN6S0wHYR1HKMbCOFgdAvkOEYaxD6g7V9HCeGh1DQ6vQ4mQZsWUlRBZNHR7SBj0WqdyQiVkPSjCFtlY3g09LayrUHmU5NqaoUc+ykmc0UPN+fgF/b2W7u9EhPTiTeoulPYtjW85t7bsXGkwrfsv7YEo/dr3U3BYIzJvj8254/7B14U7Ao1E+3qeZeYmCDd2+fKYJBC4amtplq31efWpRVa6x2OnJlFCri+tEHey0AIv+Dx0CfO0G8H+Hkv80XcVhpD9VDWuNku0+61A1zfGUm4dqteGLHRC0i1pui7lH0PJSUF3yXVmtVOj16Y2QBU8j55xx6CX3LUGN6ttDFEkaYdxURJB60zVlFBVfGERZxoHCu7qtd6PS6srXGgVGRubp6Hnjq753Y3lSq3/3x7WUoipaTZ3t9zbLMSrekEId0wJNEZ28u1LAquy8mJ8dHZsN97bi9jDN0wojMIiYasHFspcq5NyXOzJpsQuMrGVfsDWJvsos0E8pVelzBJ0Hro66gUnm2Rdx1cS911LBFCkCt4o/0cxAmdQchApKRJgkiy+5SnLPK+i+fYmRQvTrE9h9Zah/pMlVI/wt7jHhbFbw/lagZj+liyhDFbsuNHP3CUxnoWfDA7XqMkfBaCtR9dxlHFnqBo1bCHkz9jDEOQ+K5f2PYT7vYLYL/Sw3jWzPzTZsY/QN2p00k6xDpizBljI96gYlfpJF1c6VCyS+SsPFEaMuFNUnVqWMKin/aZ8qYRCIp2iVCHpCYhZ+XwZY6ctTWgRckaYbrCTPGncO4hRnZTrnKps8R62CXWKTnlMJerc723RqgTJBI1tHQKdUw7DnBkm8QkhGmCLSwO5yd4q73A2mKT9kaPsB9RKPvcurKCUpLJuTF67T6HTh0g6HbZWGnheDarC02EAKMzA13Xt1m6vo7rO7SHpo2brOgk1hQKLkEQUy77NBo9tM6ixufm6kh3iFDfQzco0ZqpXAb8LfY65CybSCcUbZd+EhEkWSe2n8RM+AWu95sUHZd2FLLY7zDh57GlRc3LZf4taYxAsBx0GSQJXS8iShOkfeeCQyDJ2XO0Bq8TJeskpotvz+CpyZ3PExlrIkl1ltbgWHSCkJVWF60Na+0eK80u45UCSZoyiBI6QUgnCJmoFJgoF6jmfd5ZXKMThBR9l6WNDt0gZKMbUCvmKPrvbkHkKIVv24z5ORphNsCH6YAftl/hVOkcJTtj691aavLEw4dZa3RZWG4xM1XBmJRm+A6eVacXL9Jca9Fa6zM2U6VQziFlkTR5hzTZwFYHWB0Y1sIOR4sTlH6Eko00hiANmZB2Jl3rXeOkPELeymGA5UGLa701Xli9hKdsynaOmpvHUzap0TTCHqthh14SkhqNLRVPTZ3myfFj6ADEGAyihDhOsYzAWJn3xvaSQvCxyVM8s3yBC+1FgjTii9de4lsrb4+YMUEa040HBGnEf3P/T/CRyRMoWcEiGX6K/RfnOd9BKclGs08x75Hztzo7BpgYK+E6FuP1ArVyfoe9hS0UH588xdcXf8iF9hKhjvkXl5/jO6sXmc/XUULSiLpc6a6xMmijkPzU/MP0kpAv33ptGHcP1XyWPFHKeTv29LvrV/idt75GP4kY6JhUZ723RKcEQ+ApNZp/cfk7/MurL2YxBkLiSAvfcvjo5An+k2MfpWjvBDKMMcRaczBf52ChTqPR3XP5ZMgArIWgyWLQ5PXmTdbDHr946AP7ss12KyUENc+nYO/PUguTlPguKVpKSj4zfY7vrl1hIWiyEfX5g4vf5M8XXmfCKyKAfhrTjgJinfJb9/1tNi6v4HgWSZyyffx1pOJnDz7GyqDNV269Rj+NeHXjBm+1FslZDnnLxRqCxAMdE+uUMI1JjOZ4cXIIYIZoE6FNuCeDSAlJbEK02UxEk4y5B/aWHwDjYyW8vp1RzhONQBGma3jb5NDaRKT63iayf9NKKUkp570nvxQ5BJYNoE0yBH3eW8fRdSxKeY+C/+5ZFDq5mqXeiH0WRHodrVeGvwmUlcnjc5bNVL4wkmXuVXGiCaL9gS3fs6mU/Z3HUnjD93KACJ1ezaLu7xJWYnSbNLk8/M3Fso5A4pLz7F0tgKSs43gfJ4m/B2ZAHH4Lz/9pjG4TRc8P98XF8T7J7eEdWmuCTjD65iz7DBl7KyFNrqB1AyXzCPt+hH2KTWltNu5vpospxDCJTWzKbv9D+BeKHMo6SRy9CESkyTsYBgh8Gt2AtxdXafaC0fjfDSK6g5DxcoFyzuPy8jpCCIpettBq9gI+fPow5busg9qNHmEQ3bNULTERQdoBAf3egNpEafuHeFcf2ZYS37LejRXU3/hKtWa53WW106PZH9AKBrT6A164fINk2Di+strg/37pVbzb/FqVFDw4N83shEU/aTPQATP+wdHj2qRYwqZoldi7BAKJrwojQKtULzB9aIzyKNY8AxXCoaH2pZV1vnPx+q5sTikEJw6Mo3xB1fU5XKqOzqNNT5rNWmx2+PY7V7P52skjGGP4P779fb759hWCKOb0zCQ/89j9/K1jB0m15jsXr/N/Pv8Drqw2yLsOHzx6kF964gHm65U916/GGBq9gGcvXOGlyze4ur7BeqdPlKZYSlLxPabKRY5O1Hlgbopzc9NMF4uUPW/E8tq+vVRrXr+5zKs3FtmtPNvig8cOMlstDeV9Q9aRYOQTt71uNlp85+I14lTzyKEDnJgaY6Xd5ZkLV3junWtcWlmnF2aA8Hgpz32TYzx1+hiPH5ndkfYWpekOL6Ptn789CPnelZs8c+EKby2ssN4LAEPZ9zg4VuXJo/N89MRhJkp3yoV3K2MMzf6Ab76dHdPLqxusdXtESXZMy77HZKnAsYk65+amODs7xWRpF8/e27bZj2K+f+0Wz1+8zpsLK6y0s7AXIQU5x2ayVOTIeI0TU2M8cugAh+pValMVTj5+jOpkhd7x/lBpbEiSdHSss4buSSBlEL2MkmVS08QM7+1CwPdfvMzlSyuA4ZOfPYs9J3CkjS2texoH77XeN8CRhU84iLBcEMrQ6gRcuLTE6ftmKOQylFYKMZROSCyliOKENDVYlmRppUWx4FEp5bix0CCfd6lX8gzCGK0zRkUYZQOG61iEYcLrFxaYna4wPVFmwp1kws1MZGvOEMwRjH6u2EOX9eHB96VP2a6Mfq+ZrDNctasgoDxcnK+HaxwrHsce0veEEBTdkxTdLYbO3apoezw5dh+esgl1Bn7YUpFTLp+deZBIJ5ypzGILNUKtgzTCkzYGQ6xTDubH8JVDyfaxipIwiLAshVSSY+fmh4i9ACHwcg4TszWM1kRhwpEzs0OqY8ZCUEpy6OQMXs7lncsrxHFKmmoGg5heN2Rqqkya6owG2uxhW4rGRo/JyTLl3BjHK7+Gdw+GsoeGBoNKSD5/+BSeym7GQZJQsB3G/QI5y+ZsfRJHWdxfn8wGHSGJdIoSmSn1ZppYJw6xhMJWkihNydsOzbBCzrLvMI8WQjKd+zRV9wES3UMKF8+awL7NH2J+vEI55+HaWYdsECe4liKIEmyl0CYDlUo5j34Y4zsWJw6MkxpDOedRznk4lsJ3HZTMmB5T1SKubY0ee7cVa03N9Ym1xhreRCMdUbAyH6bNGqsVeP77lzHG8PhDhwFIdEBs+vTDFSyRwxjobPQo17Ob72a0pSHAEOOrHAvBBnP5uwOgf5PKkTbzuWliHTObmxotwgDylsuBXI1r3Qy0DXVCKw643l+/YzsCwbRf4bMzZ/n5Qx+g4uRIlebG8gYLKy2mxkrIyFCp5e+YaAoE95Um+c2Tn+R3zv8Fl7orxDrlem+d672d72VLRayzhYEhxbUOovVOE+rby3MtTh+cRmtNHKc4+3TQRvu0bSeFEBwtjvOfn/w0//Nw/4I05vXmTV5v3tzxukmvxE/PP8LPHXyc765f4euLb47An81t3X5ba8Z93uksc7capPEdvkkARwvjo+9ss4wxLA/a/PGV7/Dnt15nI+oz5VeYy1Up2T72tgVqagxBGtGOAxb6TVbDDq044P+68jxHCuN8dPLEu7oZCyGyOPt9ylGK02MTeHfR2gsEj9QP8Z/d9wl+7+KzXO+tE+qM5Xalu7rjuZ6yka6iWMtz+dVrVIZGodv3q+4W+M2Tn+RkeZp/ffW7XO6uZOd2lLAR3ZnAJMlec6I8jadsLFmgYM/jqN1NjCHr7GVBAQoEJDrmZniRI4UzOzrF26vV6uMoi4mJEqWih7HW8M3kKExCCIWnxkh+hEDr7VUr5Lj/4BTePVybt1fRcXlq7hglx0WIiII9haPevb8RMJqIvxcORhK/gU6XkXJ39pwxmjj6ATrNgCMhctjO/QA0BgGDNNkfSDUQhBkHJ0n3Zqzblhp6E4oRoCCExHYeQaoJdHqTJL5EEr+BuouEK4lfI0kuASDVGJbzCCIUeJ4zYmRqrYfvkUkaHPdDBL0/xOgVkuhV0vQGOrlBmlwFwLJOYjkP3vnxtMHLeyP8wnYeRcoxtF5CpwtZUpv6JYSw2TcRbd/H/v2UEDls9wkGwRfBBCTRKyTxeSz7IWoFn7PzU6TaoKTAUpI4yZqMUmSMxMMTNaQcQmBDz5bMx2pvDyIhBGNTFdJkf/B9e1nCYcKdwxI2/W6I620aesvhcd2sGNhfFYHI9v9HMeVxr2r2B/zTr36b71+9xSBOCJPs3+2YzPnFVc4vrt7xWsdS/MZTT/LL06cZ92ZIbktdlUKOGvF7VQaVGlrxKpPeEaRQmNTg5RwWrq5y7IF5Uq0p+Nn1KYTgh7eW+cdf+SbJLmOLkoLf+OQHOXl0nKK9P3Pt2nqT3336+Wz7rsMrNxb54+dfGW13sdnh7eU1/snPf46FZpt//JVvcXNjywbi6loGWPy3P/FxajmfjZUWylKU6hlIoY3h5au3+OfPfpfvXb25a5LcYrPD+cVVvvHWZUqeyxcevZ/feOpJJgu7A+BJqvn2O1f5X//yhV0fr+V9JksFDlRKXFxYIwhjTs1P4liSYBDd4RN3cWWd3/n6d2j2B/ynH30MIeB/+vNvjc6H7XWr2eaV64v85flL/PKTD/JLTzxI0XPpRhE/WFzk7OQkVX97UILhyuoGv/v08zx/6TqtYLBje0utLheW1nj2rSt86dW3+NWPPc6TR+f39NKCbCx5/eYS/9szL/Hi5Ru7pvguNju8tbjKsxeuUPQcPnv2BH//sx8h5+7ePDHGsNbt8btPv8DTb16k0du9mXVldYMXLl3HUYqHD83w21/4MQpa8sKXvs/9HzrJ8rVV6jM1lOfw0gsXOXtujtpo/TUgit9E6z7GaFLdIdHtkWBOCDj7wBztVkCcJPTjCEuq9xSusV+9b4CjTm/AS69cxVKKw/NjXLi0xPpGD8+1qZRyrG90KZdzvHVxCcdWPHRmnue+d4mjB8eYHC/xwstXOHF0imLe4+ZSk7npKr7r8M0X3ubATJXZqQovvXIVrQ3nTh3gwqVlFlda1KvZhPP2BdHWL7vv7+0DyV4DS93d3yj67iXwlIO3R6dvu5mXGb0ik7ns+XyHEaVur0ptTRAl+L491Jnu/vnOnN6SB0ZRwvp6l1otj7uNahfHKevrHXzfQQr7DonaXuVbW9sY3xZtWBgyhLYez34v3iVGN7dLp3/7e9xeSnrk5cE9H4cs1W57st3msrC8yzpo07izsI1BtHmYpmtbE/pK3h8+9t4uz2YYcKnVIDWaXhxxbmwaJRRBGuyI7Xz8wUNstPporckN98lRRXw1htYRoLEdi/WlJuMHahhjkLJKnDxD1r3JDb1l3B+5KZIjbc5VdsovN+u+0iT/5JFf4M3WLc63FrneW2c97NJPQmKdYklF0faZy9U4UZrisbHDHC9OjqR82hgarT5KSXpBhCstzr92k1I1j692elJZUvGh8ePM5Wp8e+VtXtm4zo1eg34SYUtFyfYY90ocK05wopxJUG01CaQoNbXD53XKL/Pfn/1JFtttcpbD8eIEYRpiSxvbUUOKuEEbjRQKQRbBiwB7M81BaxpDCvZYMY8lJR8cP8aBXJXnVy/y4vplbvUaDHSCKy0m/RInStN8dPIEp8sHGEQJR9wp/oezP4nG8GBtnihJubzSYKyYY7yYZ63bZxDHTFDh1w9+iqLnUvLddzURHyQJdmITR3q7DJ2NqM/vvPU1vrrwOlIIfmzmfv7ukb/FseIESso73sMYQ6gTfti8xe+89TVe2bjOatjhG8vnOVedoxF2cZQiSGMaUZdPTZ/OYk8FuFLxTmcJbTRFy2chaDLuFmnFfTxlY0vFtF/lV45+mJ84kC0YjxTHM9ZanBINJ96WLbdSoIYymHQo7fjU2BkO2nWeXX6LN9sLLA5adMMBedclL11mCzUO58eYL9a5mesxc2xq6Jdy50SiZLv8nbkHeLw+z4trl/hha4HLnVVa0YDEpDhSUXcLTPlFzlbmOVGe4WhhjJylgBICOZKP2VLxy4ef5KmpLGFzPl/DUzaxjpBDpoMmW1iHOsAdAj9nKgf47Qd/BoCc5TBtVXj17Rssr7ap1wocPe4zSNdxh1JzKSyUqNAIHNAheee9SQjfr2UpSTl3bxLlO14rJWP+JqDmM+adGj32bgEgg8H3nH0n43tVmi4w6H+RfOm/RuByO0Ku9RJh8CdDuRZY9sMolQWClF2PU/VxSs4+93YBlaJPKb8/M0sKQRgmI2Bnsyz7BI77MQb9PwYGBL0/wHIeRMo7E/qMMWi9RND7QzCZSbvjfhjLPk2axplJLII0SXnjuQvUpirM3jeNEALLPoNtnyMKv06a3iCJXiVJzmN0A7Bx3I+PJHpbxya7RrZLF5Q6guN/hkHvjzCmS9D7wyHo9DD7mdMbo4cyPCuTmv8HKCEkjvO3sO1zxNGLaL1Mv/vPKJT/AZaaGs11dt/fdLi//i4srP3eU5ArehQr9w4ma1JWwhvkRRXbsUagkxAik/1hAzFpuohOVxGisuc4E6YJg3T/FLgftTKYrKksJXnXGSWgdcOMXW8A37ao5O6UjjuWouA6BEk38/gz8W1MMTFMeA32nQN4MkfBqo2eoXWWXFUo59Bpxt4oF3xyQ6nafL3C586dGDGk2sM06/4QRJACGmGf4tBf5273lc4g5E++/wY3N9p86v5jjOXzPHfxGpdXG1xfb/L73/oeK+0uiU75mUezdLRvvn2F5XaXb719le9dvclTJ45w6ZWraK15/LMPYYzh+nqTf/SlZ7iwtIoSgvl6hVPT4xyolrGkpBdGXG80uba2QaMfYIBjk3W8fdYPSgpOz0zwmbP30Q5COoMBG70By+0Ocbp1cfUGEevtPr5ro4bjppKSeA87DW0ML125yUtXbvKD6wtUcz6PHDrAsYk6jlIsNNv84PoCS60ua90+v/+t71FwXX7xiQcyVlUQ0IsiKp43Avivrm3wD//0aV66chMpBFPlIudmpzhQzTwYbzXb/PDWMgsbbb5/9Ra/9e+e5h/+nU/xgcOzyD3uVwsbbX77S9/gjZvLCCGYrZY4NTPBXK2MJRW9KOLGeovr6xus9wK0MRybrO+aYj46VlHMP/vGS/zJ995AG0M153NmdpJD9Qp512EQJ6x0ulxeabDc7tIdRMxWy9TzOZpLTTaWm1z94Q1sx6Y+U8VoQxwlXL60QqnkYzsWcXIZ13kIS40hRA7LOoittoJMHnn8CHGS8vb5RaamqvScPktBtha8V/blvdT7BjhqNPvEccpao0vOd5iZLJMkGt9zePOdxYwlFCV0ugNqlTzBIEJJwX1HJomTlPF6kbmZKpYl8V2bQRhnP/sOzVY/MyhudKlV8iwut/B9h8nx0h06XW0MK50uV9Y2WO/10cZQ9FyO1KscqJSx9jBf/O7Vmyy2Ozw4O52htKvrXFxZx7Mt7p+ZYqKYRwhBMxjw6s1F2sGAyVKBcwem7qBubpUYaa1bwYC3V9ZY6WQGkrW8z8nJcSq5Lep1qjVBElOw75w8x2nKzWabW802G/1s4ecoRb2QY75aYfI2el+UpPzw5hLlnM99U3WEELy9vMYPF1eo5jw+dOwQlpSkWnO90eTqepPWIPNVqvQ8Dv1/3L1nnFznfd/7fU6fM322V+wudhe9ESBIUOxFFElVx7bklmL5OkpcrlzluCTWdWwnnzjXjn1j39ixLcux49gqlk1SlEiQYq8ACKJ3LLC9zE6f0899cWZnd7ELkFRkX4n/V8DszJkzpz3P8/v/SkuW7nQSWZJQVZnOa8xpwzAkX6tzdbHIbKlCzY3ShhK6Tm8mRX8ufYPjsrrCMGSmXOHi/CKFWh3b81DkaDDqSiXoy2YwGxriyChtWYqzEokNw5BC3eLywiIzpQqW6yGEIKGrtCbi9GXTZNcZ+NYrPwgYXyxyaWGRsmUjhCBrxhhszdKZSr5j6UHFtjk7s8BMuRIZB5sGw20tdKYanYR1ttMWS9BimHhBwFy9GnlOhR5OYGH7FqaUYKFQwbaj+2l2oUwqabB7W9Rl1eQkQvRRdaM0CFmW0GPRufD9WTR1hCCoEgRlwjBJm556T+n5l7T5sMyEqdoOkiTQVQXL8WhTU9zdluJ9LaPUfbcZXrOUQicLCV1WMGRtjUG5JAl6OzJcuDqPaahkEib1anSNLM6WSGbjLLXqhCRhVW06lAQfzO7g5qCP1GiC4mL0HDBjOsmkSUzV0BvnwPGuoCuDOP44hjra/N6UGuPm9EZ+7quP054x2d2WJe9epV3voOpH14ksJPwwQBEySTVFzavihR5xJUFWy1Gt+/zWV59joVrnN7/7Qdoaz7UN8RZ6zCwP9ezECbxmd1gVMjEl2jfH9/mLl9/kjUvjfOaRuxnpiFhqdcclX6k1F8i/8/UXefn8WHO/f+DAHr7nth2o74J998alcX75K08yd7PDJ+/c13z9rcWrPDdzBi8M2Jzs5IeH72Qo0Xbde1oIgSGr7Mn1c3fnZk4VJ7EDj4naItP1AjXfQfEihue8XWZTuost6W4cP2KiXa7M0WGk6TNNLlRmmawvMmeXSakxtqV70CWF/a1Da753YnKBuZkiruOjGyq1qo2mK8QTBr4XMDtTJJ0x0Q2V/FyZTUE7Q6IFPaFSEw7ZbJzFuQoZL06LSJCWTfLJGKdfO4+8wuB8ZRWcOcreIppkcFNLgu3ZEcJwMyESFa+AIZkk1RQFZ4p2ox1NlpizL5IKctT9apNN1K73oskGe1sGVm0/DEM2pfZEiZIIDMnEkSxqXpmkkkUSMp2xNI/07lr1GWOfwvjEIh3tKWTZRiPNyrSoyXKZn//a1/j+nTv54KZNa9ij780KG0BAnZVR5WFoN8yoI1PRb+1XhnxznCMHq/5lFGUEPfbhpn9QGEbx7PXKH+PaLwIgRAI99hBCygJQcmyuloqMZFtI3gA80hSZga4c2g0WS2ZMY9toF8oavwoDw/xeHOtpgmAS13mDWvm/EIv/MLIy3DyOYRjgexepV/8Y13kdCJGkTozY9zbADQ/X8wnCkDAIqVcsCnMleke7Gr9NRzPuxrG/AXg49rME/jgQIEntqPrtawCSMAgpzpcpLpSXmwAihhH7blz7VXzvFL53lkrp1zATP4aq7UdIK0GNkDC08P1JfPcUrnMYzXigker2j1NCymGYP4DnniAMKzjWQapCxzC/H1XbAywzOiKLijK+dwnfO43nniOW+CSy/M6jpFVdYXR3P4r69r4kS6VJOmm1DSewiMV1dGNp7imQ5T4kKUsQzBL449jWY8TiP8r1gkEMWWlcq/8YqXXfHpU1Y3zm4buaMrCl+uIbx/n8S4exPZ9bhvr4sfsOkDSi+3ilRCwXjzHvXqTqlUiq2Wu2HiILBVkoN5Te+6FH1VtOY/W8AKvuEgYBc5N5jKxJsVIn04g539rdzqcfuhUFtRE04/CfHn+Op09fbGwvJKFq7/gsun7AG5cn+N79O/mXd+1HUyJGya986UkqtsMzpy6SMQ1+/L4DvH/7CH4Q0pNL8btPvoTlerx1dZo7B/qJp82GwXLENnr5/BXOzcwDsKOvk194+C76WzLoDTmkH4TYnsdCpcZbV6e5MJfn1qG+G177siTxvpEN7N3Qgx8G+EHI5GKJX/nyk5yfXWazCyFozyRQZAlFliI7Dl0lnYxdF8J76+o0QRiyqbONH7lzH/sH+zB1FYHA8X2Oj8/w+0+/wpErk1Qsh68dP8tDO0aRGx5TK6+Lqu3wv157i8Njk8hCsKu/ix+79wBbutvRlKi5aXsel+cX+d2nXuK1i+NMLJb43AuHGGrN0ZFen3H16sWrnJycIwS2dbfz8w/dyXBHy6pj6ng+i9U6R69OcXZmnjtGB254TC/MLPDc2UsEYUhbMs5P3n8bd28ewmgEJAVhtA6vWDYnJmY5dHmC+7ZtRJElWnty3Pbhm+kZ6cK1vehYSIKYqRMzteY6TwgTy3kVSaQx9L3oyuq5Y3tnmjAM0TSFgIAFu8SUlacn1kpGfecBA29X3zarvUrVxnY8WrIJOtqSHDs1gSRJtLUkOHd5lsG+VgxDpVCu05qNk0wYtLcm0TUloq+aGucuzbJttIup2SK6rtKSTaCqMoVija0jXbS3JknEDQb7W3n96BiW7aKqyyyAuXKFvzp0jCdPnWeuXKHqRMi3riq0J+LsH+jjB/bvZrS9Zc0F9DeHj/Pk6fP8n/ccoCOV4LcPvsRksYQqS+zo6eQX3n8npqbx20+/yAvnx7A8l5Rh8KEdm/n0vbdhrpPAsPQVjx0/w1++fpSL83kqdiTvMGIkr4oAACAASURBVDWVwdYcH921hY/s3IKpaQRhyJHpKQYzWXqTKYQQkS723CW+euIMlxcWWaxb1ByXIAhQ5MitviVu8sCWYX5w/25aGnGHc6Uqddej21gGoZ47f5nffeYlNnW0sru3G9f3+fyrR3ji5FkWqrUG0AIxVWVDLsMvPHgXN29YzS5yfZ8TU7M8euw0r4+Ns1CtRUZnfoBExLLJxAyG21v44QP7uHlDT7PLGQRh88G/BAJdXSzypTdPcPDMBWZKFequG2lyJQldkUkbkU713k1D/MDNuzE1lYl6nvPlaXrMHKPJbsIwpGw7/O2bJ/nKWyeZKVUoWZGpqBACXZFJ6DotcZMDQ/380C276UwmqdWjcxE3l4+RHwScnZ3nC4eP8/yFMRYqNSwvkheamkp7MsFD20b52O6tdKWS130Qub7PG1cm+O8vvMHpmTnKVuSVY6oq3ekU37N3B/v6e9ZogSHqeL05N0nJtXF8n/5kBklIdMf6UKUovatWdzh6chxDVyiU6ugrpBASKkHoktO3IHy5kb4QTQhkOYfjXUIIFVlKYwc2dd/5llMh//+ssmVzenIOSQjqrovrReBZuW7jeB4d6QQtiThXFhaRRTSg3jzUe10K67UVhjA5V2Q2X6azJcn8TIl6zUEAJ145j2O7KKqM6/hk2pJohkoibVIrWxQXytgzdSRZQtMVJmfzdG1oJd3oagPEtO04XgQaXXt9BUHIXLmKrkkNBpnEvDNHxS2T1jJ4oSCtZliw55omlU7gUPNrJJUkQQiL1Trz5WozUQQaPnFCJnOdxEaIBuOx+UXGFgoUa8s0XkE06VIb1/In79zHB3dt4ujVaf77s69Tsex3PQW3XZ/pYpmyZa96/fWFi01j7ptyA/SY2Xe0uJCERLsRAaR24FH3HZJqjM5Y1KHygoBNqS5CwJS1pkQ4CEN0WSUmq+zI9CGLyGRaERIx+foU+FQ6RqlYJ51VqVZsJFmKxrS2JFbdpVKOOouBH6BpCnrjvu5sy0SRwkKgJxXicZ3e7hY0TcGq2bT2RmBduE7LXhKRtFZXYrihQ0LRSao5ZqwxJCGTUAxatBYUYQM+BWcOXYqRUDKU3DxhA8yw1CqavJrR4AcB05UKuVisubhvM3pp48bsUyEElarNxFSBhcUqN+3uoy25H8GyFMQLAmYrUfPhvVphUKVe/VM87xxhWCEMKlGqV2jj+5eb77Oqn8OxHgN0hBRHiDiSSKDo+zGMjyC+SUmf5wUsFJcTkt5xiRiKsgnPO0ul9Fms+hdR1O0IKUcYLOA6h/HcE0Tm2TJa7BF044NNsCarG0yJMvbbMDjCMGQmX6Y9u/7keAkoVdcxOY3YQLuIJ3+aavm3CIJprNpf4tjPoWr7GmbV4HuXcZ3DkdEzPkLKEUv8axRtLxBJrUxDi2RVfoAW04iv8kMRqNrtSHIfgX8Jx366AfKBou1FUbdwbSmawujeQRzbXU4uEgJF3Uk89QtUir9C4F/Bcw9RLnw6AjmUDUhSpsHYKRD4MwTBIkEwi0BZVw73D1lCCPTYw/j+FerVPyEM8tj1L+PYzyErg8hyD0KYEejZ2M/AnycI8khyD0b8B9/192nvMvkvJicZTkash+SohbSC+auoW1G1/djWo4BPrfIHuM4bqOpOhEgQYhEGRYJgHk1/gK74R+gwE+vOzd5rFYYh9bqLZbm0ZSK5fRPfFIK0aTSPo6lr9GbTpM0o2v5ifpELC3lGWltIxQw0f4CCs4Ahx66hskT+Rd4NpGoQAUdWUMUNbRSieHlJEliWRzIbR9IUcqnl+1GWBcUwj0AwkhzAcrRVjBI/CElrBmntnbPz0jGDj+zZQrbBpNs/2MemzjYOjU1gex57NnRz75aNJA2dMIz+njVjLFRrjC0sgiQi24+a3WA3hhybmG6CKd+9bwc7etcyIeO6Ri5uMtLRiuv7b3vtResaBV1Znve7nr/GGkOWBKWaRXtmqVHdWJcKsS7jCBretOkEv/DwXewd6MFvGHgrskQMlQMb+wnCgM/89RMU6hZnp+e5ki8w2J5jpCW3ai38ysWrPHr0NF4QsKO3g1/84D1s6Vrd6ItpKrv7Y/zsg3fwmS88wYXZPIcuT3Dw1Hk+sX/XGoYpwPGJmeb89WM3beOmDT3N90VJdiGaKpOIaXTk4jwYjqDKMn4Q4BOFtWhSFC4S+QsJzs0uUGjI027d2M+D20dJGNeuCVTSMYOebJr7tm6MZjGN33LlzCS9m3uYuRLJObtGuqhULIZHOpo4hap041oXCIWLWEGltyyXl5470wxmmJkusH1XH22jGQpOhfh1FEjfbH3bAEc7NnezbVNXk4mxoScHjW7/I/dub76vo21r86G0f/cAAIoicfOugaZPz8Mr3n/rnkFEw9jr/tu3NN/z4F0RhV6I6EI5MzPH7zz9Es+fv4wiSWTNGBtaokXFXLnKZLHM3xw5xrHJaX71kfvY0dO5hjXi+B4vX7rKYq2OpkgMtmS5tLDIG2MT/NGLbxBTVV67PM5AS4bFWp3pUoUvvXmCWwb7uGd0aM32/CDk2bOXePL0efwgoD0ZZ7AlS9GymSlVODo+xZmZOQo1ix953z6CMKTs2JSd5cXSpYU8v/eNl5guVYg1HOL7smkMRaFYt5gulbk4n+ePXnyd+UqNn7n/djKx6GGfiunE1LWmWjXH5dzcPP/r0DGePHUOQ40c7VvjJjXXpe54WK5HR3LtJM5yPb5w+BhffPNE5IbfAEIyZgzP95kuVZgpV5gslrk8v8ivffgB9m/oJQhDZhbKVGoOmWSMMAyZLlX47GNP88rlK8hCIhc3o9+mKlRsh3y1TtGymC1XyMVj/OD+PdFOhNEDL9dAYMu2zX97/nX+x2tH8IOQlKEz1JojoWtYnsd8pUrFdjg3O48Q8P0374oGvqvzxE2NITOSI4ZhyGuXx/n3X32Giwt5DEWhNRGnJd6C1zAPvLywyB889ypvjk/xSx+4m4GWa7sr0SLr66fO89sHX2C8UEJXFNqScdoScequy1y5ym8ffIEPbB3F9tYOpoai0GEm6BIp6g3vF0WoTNbHCUKfRDxFX3eWuKmTThrULDdabDYqprRRdC5Q82bIBa0omtJcuPtBEUXuRFX6EcJAlTymrQIjyc41+/GdWrqi0JlOEtNUHC+6lhVZRm6w/3RVwQ8ChuSWJpX43SyoBDR8rdTI+E54CGB6cpENW7obkbAyVs1GUWXiqRhhCMmMSUdfjlrZQjMiBl0QhMiqvIp4psk9aHLP9b4eAFko9JkbSFwH7Ooyupv/9kOful/HkGPU+eZNiA1F4V/csZcHd4yyrWdZkhEChVq96WMy2JplsDWLIst8/sXD3/T3rVd5p9o0Z15KC3snFYQh81YZJ4juk7iiE1NUstpy13k9WcvK19MrBu+3A6sy2TiZ7PodbYANg+9eAt3ak2NufAEzGUOS17K3MmobGbUVEOS05ft5KL46jSWupNd8dmNi55rXVla+XufXn32WH7vlFra1t7+r/U6nTHRdQVVldE1Dld5bMdfvpMLQwrYexXOP3fB9QTBNEEyveV0L6+jGBxB8c8BRazbBrTs6MG5A1V+/BIb5AwT+GPXqn+M6r+A66/hqiBiafh/xxM8gVnghRU2j8Iam17BkJLpkBB5Sa4QSLHmcCaDq25iyjiJJTT84Pwxo1ZNIQkE3vwshpaiWfwffO0vgj2HXx9b5NhlZGcGMfwrd/AhCKIRhSCKuN5sMUkNWOj+RZ3D7sl+SrPSh6Xdg1cYaEjUANTo36yS5CSHQTR3dvJZtJaHp95LMqNTKv4/rHiYMS3jeCfBOXOcoKQi59bpMmX/YMjDj/xJZ7qNW+a/43iXCYB7Pmcfj9et8RkOSsgjxj3e/CyGIp2LXvKYTS/x4JC90T0JYxbW/gWt/45pPK8jKKIYk8d7Il11bbuDj+D66rKBIEmEIU1MFpqYK3HrrRiRZ4lJ+ES/wGcqt72kGRMBDGEZrrYafTdkrMmuPExCwTbkZRaiNcTIkCD0M2bihVC0kxAvciDUmJ8m0pZgdz9M92IaZMHA9n3yphrnC9sMNPJxg/YaDKkkEROE7q74nDKm7LiEQv6bh39eSbjbfIQKSOlewXnb2djYZV0JA1jRIGhoL1RoVy6FuO9TLVgQUN8pbIR1zvOtIxIKQWtXG83zCkEaadYiqyshyBG5IssD3ooAjRZHw/YCYqaNp12fmKbJEqWo1ATXR2J+1yZHLJQnBXZuG2DvQgyQE08Uy04UyOzZ0oSkykiTY1ddFTzZFoW5RaqxnO7JJ8vU6nYnItsN2PZ44dpZCzUKWBB/Zs5WRjrWkjaUa7mjljpFBLs7lqTkuL52/wiM7N68r9XZXeOE5vkdA2JwLBmHI+fIccUVjoraI5XtkdTOSeiGIKRqTtQK6rDSahDCQaGmEtyydM3+Nv+a1da3sW5Iknvvrl7BqNnvu3RGpXRI6vh80za8d7zKJ2IfwgwKudxlViRpvYRA1gTeORN7BZlxD0iTSapyNyW78wP+WSvi/bYCjyNT5Oj5DK2o9GvqS2/t6+Kcsr9zm8ntWbqZYt/ijF17nxQtjxHWN79mznQ9sG6U9GQ2whZrFV946yZeOnOD09Bx/9OIb/MrD96wBRvwg5JVLV/jgjs188rYIyPnNJ57lpYtjPHfuEhkzxqfu2M/9m4c5N7vArz/xDFcXixwdn+K2oX5i10izLNfl74+dZnNHKz90yx62d3egKwp11+WFC2P84QuvMVuu8teHjnHLQC/9rVkSmrbKk2ikrYU7hgcoWza3DvaxvbuTbEPeZnkeL14Y4/OvHuHywiLfOHuR+zZv5O6RQWKa0pTrXFsly+ZPXzrE0Ykp7ts0zAd3bGKgJYsqyxTrFqemZynVbTpTa40447rGnSODTJUq7O7tYk9fN/3ZNLoSTb7GiyU+9/Ihnj13iauLRR4/foZtXZFZbLFi4XlLBsDwyqUrvHr5KoTwsT1b+fjenc3f5gUBhVqdN8eneHN8ijuGB4g1Ot1eGEVUJxoLudPTczx2/DSO53P36BA/uH83gy1ZZEmKwDjL5vTMHK+PjTPQkqUtEV0XmiqTSixL16ZLFf7rc69wcT5PayLOP7t1D3ePDpHQtYhlUizxhcPHefT4GV6+eIU/f/UIP//+O1eh/gCTxTKfe/kQE4USSV3jE/t28fD2UXJmDC8IuTif549feoMnTp7FWgc4koVE0bGYrlXIGWbjeEUyg6SSbqLc1ZpNMqGTjOsQX56Y1rwZZKGTVPsRrqB7sK3psSWEjuMewvMniOm3IiFR99zmgvq9ULqq0NeydnG8MvFi6f9+ENCdTd1QT35tSZJg52gP2zZ2oWsKoRfguh6KqmDErr8wW0nnX/p/S1emafZ47fveSb2T9ypCISl9c6a6K0uSBKOdbYx2rjXG70wnm8z+f0iPmsiPKzJena4X8cKAd7IsydsVXpu/iN24zvvMlmhbN9jXd+qD904++62orsF2Mu0pVE1t0sLXfuc/zLG/mM8ztrhI8DZpcetVJh3jtv3DkVRU/7aZsvyjlosgVG9Bkwe+qTMUsWJWPFuEjqbfgSz3I8kda8AEIWJoxr0E/iyyMoimmnS2Lhu7RylhDxAGBRRt16ru56oKncbC+18jyb3Y9b/D9y4QBAtAiBBJZGUIzbgXI/Y9SHL3qmu/5rpU3KW44aXvTqPp90fAizAiGVEYslCqRvIU3+FMaYKCW8MLfMqeRVaLk1Jj5J0KrXqSWatISjVJqSY5LdFIDdLRjPcjyX1Ytb/Ccw/je1cIwzKRp18SWe5F0XZhmB+PmFNiaREoiBkasUaH2fVcktk4/tJ8JQgpF6oNNsH7cKwZXKeKHtMIwxSKuoVKsYwkS7iORzxpEAQhjuVG44zno+kqtYpFtj2F6zTkDOFeEqn/iON8Hcc6iO+PEQTzEFpExs4xhJRDkjtQ1G1o+n2o2s3NY+l7AVbdR1J2EoSL6GoWz20hDKPwFCEJwkAmDPchSSkUpRWrHiMmBdE+yBJBoBIEB5CUDeTnE8RTGq7rIMsSsiJhWy6apuK6CkJ6mGRmBLv+FVz3EL43RhgUiUynVYQwkaQWJKUvOr7q/Th+Fk0EkQWBJFNxHHzfIabuxXd9dEUhkEapOi6GomC5HoYaSaNlISFLEXNYV2Rm6zvI6BHr0mcDIYK646ApSnMxee08HASKupVE+jex63+DY79E4E9FjD9ACAMhpZDkbmTlxp6Y3+lVdmyO5acZSGbZkMwiRDSul8v16HoNXMaLRXRFwb8R2huGuH6A4/lNIEUVGpZfIySk4MzRonc1gCKBIZsY8o1B7yD08UKnKfMvzpeZujxHtVSnb6QTENRtF7dxT3qBj4REUrkOS5EQQ1aaEe9L5Ychx6dniWsq2zpXe5JlTXPVfF6SBHFdQxKRYXpbMt70CoKIAak0mjheEIAk0T3ciV2Lmv9CRI20pfq7N0+xraeDzV1tqCuaP7WazZnTkyzMV/D9gLaOFI7tkV+o0N2TpVCo0d6eYm6uRLVq096eJp+vsHvPBjo61851l0qIyEpgOXRA4DgesnR9CE+VZW4d6kOWIla7F0Tm1pt62puMpqShrwJ0ynUbQ1GYr9aoZhxypsl8pcq5mXmCMKQlbrK1u/2GTCpVlhhozWIoCnU3kq8V69a6wNHKY/rY0TNs7+lkZ29n0w4h1vCg9MOQmKLiB1FTwgt95EBq/C2g6tnEFA0/DOjJpjA1lbrj8sblCZ46cZ6Hdm5al3yxXm3eP8ypV89hpkxSLdF8u6U1SXqFV5sid2A5h4EAQ7up+bphqBy4YwRNU4GQjq6IaTRZnyfvlOkz312z7u3qO2IW5gYWIFBvENu7XoVhgBPUUaVYpK8M6shCRZFWDwzPnbvEk6cvEIQh//zWm/jkbXtX+et0ppL05w4gCcGfvXKEly+OcfD0eb5v3641F4SuKPyTPdsZbLCVPrxzM29cGafquNw2tIGP7tpKOmaQNHT29fdwdbHIxflFbNdbM2CFRIj0Zx68i53XMJx6G8Zo//fBF5gqlfnKW6f4iXtuQxaRweuSEVbC0Pnp+25v0BLXIss96RS6IvNrjz9DoW7xxtg494wOoStKQ5rjN9HOpZqvVHltbJwfumUPn9i5Hd/ykTyBb/l0GSatXX2RV9RUEU2LkkyWfk8YhmzPtbH/ux4iHTPW7E9nOklr3GSuXOXoxDSHr0yyWLNoT8QjhLyhPQ/CkBNTsxEtMpXgh27Zw3Db6mSvnkyKbd0dfPdN21lKNwnCkLii02u2NI/nmZl5SpZNQtf4xL6d3DbUv2q/utJJRjtaeXjbJvww8oby/YDJmSLFcp3WbALb8/jboyc5cnWSdMzg0/fexkd2bV31oOtMJRhqzbFQrfH8hcs8cfIsD2/fxE19qyfLB0+f58RUlDDzvXt38K/uvAVzRae3J5OiK5XkV/7+SY6Mr43TLDkWF4p5+pMZik5EhRcIdMlAluTm2vD0+Wl0TSGTNlFkqQmyWv4CTlDCdook7VbsuovUOIeSMBBCJwxtIMQJfHrN3Ko0qvdCLZ0PLwg4Oz3PwZPnOT05F3WaVrjQtyZMfuy+Aww0BqL/+cpRzkzP8al7bmF8scgzJy9ycS5PTFO5aUM3H9u7LTKPjGkUanVevzzOKxeucmk+j+v5tCRM9m/s4/aRAdqSy4u5r751hmdOXeRT997ChdkFnjtzmdlSxKS7ZaiPe7duJBVbHiCDMGS6UObRo6d56+o0khDsG+xh70DPuoPYUtzpKxeucOjSRCP1Q5CLx7hpoJv7tw6v2r4kBNOFMl8/fo7DY5PYrkdvLs1DO0bZ1d+1qptyZmqO/+epl5sy25aEyY/fv3zMACqWHU1G3iVg4vkBx8anefLEOS7NLZKJx7htuH9NJ3Cptmd6+fvxN7F8l5fmzvPszGnu7txMTF77/sgY22W8tsjnLrzAq/OR90GLnuCO9hEMaS3I99KVK3zh+HF+4sABBrPR73N9n0fPnOHVq1f5xbvuImUY/P6rr6IrCts7Onj09Gkmy2VysRiPbNrErX19GI3JZxAEnJqb47EzZzifz6MpCrf29vL+4WHaEwnCMOTk7Cx/+MYb3D04yIc2b0aRJLwg4G+OH+fQ5CQ/um8fo62tfPHUSS7m87x/ZITHz5zh0uIiPakUHxgdZV93d3MiGoQhlxcXefL8eY5MTYEQ7Ojo4MObN9ObSq0CMKcqFZ48f543p6YoWBZpXWdLezsf37GDlK5zfGaGL508yeHJScYKBT77zDMk9Wgcv3doiI/v2IEmR55L87UaXz17llfHx7E9j81tbXx0yxYGs1lMU2sey1fHx3nszBnmqlU2tbWxu/O9w3Zcr9zA5WzlKnbwMBsTIySVFFW/QlJJYfl1JCHjBi6WX0cIQUbN4oUuZbdMQEBGzaJJGjW/StmZIi4nSChJQvMn8AIHL3CRSeK5ZWDJ181ATfwSJa+IH/oooY4aBlS9CnZgI8iSSf86buBScotYnkVGja3xjIwSqNwI7DE/jivvIib5iNAiAo5MJLkLSWplPWNn2/eiZmHzmguwQwMt+Rl0eXnBk00G3H/zKKahIcmCoWQHbuAjIVh0q6hCIaka9AQ5DEmjz4xSZkNYtc9CqKjaThR1C0EwR+DPrgAITCS5HUlqY3XS1nKFYYDl5/GDEG0Fs8GxXS6fniKVi5OfHWZx9lO0dWdZnHUIgpAx28Kun8EwdTzXI51LUFqsUq9GcpuugTYkIaiU6ghJMD+1iNJoVozs6keP/Qia/j2E4Sy+X8T3LTw3wDASCCmFEFkQGSRJjhgJfmSWeu7kJLIio+kPMTd9gJipUyrWCcMLtHWkseoOZkJncuwhdOPD5NqSlAo1hjbVOHt8gp4NrYSh4Mzxj7Hn1o1cPHeOrr400xML6DEVq+6Qny2TSMdYnK+g6Qo3HdhBPLWdMMjjBzO8+twRXn3hFN/3w3fT1tGOkNKNYxxnulRhtlwgrmtcWlikLRHH8XyEgEzsn3Bq9k6GW1vIF+q0xIuYmsrF+TzDbS0cn5ohoWmkYwZBGLKxNcfxhY9z98gQhXqdQi1A1ApMlUokdJ3FWh0h4PaNAyvm2iFeEKBIMkLZgZHYjBqbjsA5rEYTQkOSzMikVrp+quR3eoVhyFSthBcExJXo2g6CECGgsyONEALH88mZJjFFaY5h65YQtMRNbN+P2CsSuKGDJhm06J20GasZ0xWvTN2v0hnrvi7rSJdNkkoOs8GIjadiJLNx0rklYCjEdn2qdScy8hYyduBgBfa621uSQ0e+W6uNhR3fw655a7wCE7q6xgdXkSWEiFjmcX21/+wSoATRuOsFAeV8uXlvS0Jw1+YhvnT4BBOLJY5emeKn/+oxDmzs50O7tzDc3kLaNDAMlaGNHQxubEcgUDSZumUjRCeapjBxNU9HZ5pcewJJFsiSzCBtmAkdLwxQb2CqnzD0psTX831KZQvLvr4sPK6rDLRlm7+p7ji0JExWKsaWpHLN4+n5SELQk0pFa4kwZLZUZXIxCkwwFIVizVo3lW9llW27aYi9UKlSv458/c5Ng3zhjeOMLRQ4MTHDz/314xzYuIEP7d7MSEcrvWYWSRK0G8nm/l7bNL723G/v6eDmgV6+dvwsc+Uq//HxZ/nGmYs8uH2UmzZ0055MXNcjGeDNZ45TmC0SBCFdGzvoHOpkZrpIOhUjHtcJQx9D24svzxOEFVjhhyckga6rvPCN0xw7eoUwhA98aBf9GzoYTvasMy7/79W3DXBU84rM22Ok1DaqXhFFUjHkJCV3FsevYwc1WvV+FKFR8fLktB4WnUlMJYMbWNT9Mmmtg7I7R0btpOZHE56iO01cydKmD5C3x4k3jNeq3iIdxkYkdB4/cRbX9+nPZnho2+gaBghEXYh7Rjfy98dOM1uu8uqlcT6yc2szPWCpOpIJ2hPx5sNhsDWHIknY+Gzv7miCQ7oi09EwOC7WrXUjIYWA3b1dbOtqXyNjUySJu0YGeez4aV4fm+C1y+MsVKprtiEJQTp2fY2uLAl29HSSi8eYLJaZq9QiM+UgwPWXNK+ryZEhsKO7g4/v3YFbdDlxfBxNU3AdD1VXKBZqTfpjNpdAlgSqqlCr2VQqFt3dWbra0tddJLYn4+zs7eLoxDQLtRq266FIEo7nYzuRYbUIw+axrDRSBQZymSgZaR0wb7lCyq7VpKpDdG4lIbA9n8sLi+zr72maaa865rKEwvINqOtKg/YaslCt8dTp8/hByO7eLu4aGVyDjgshyMQM7tu8seHvVOfwlUl2dHc2kXjX93n+/FgTZf/A1tEmU2plDbRkuW/zMG9NTq+RSflhSFzVeGt+iv5ktrEQEBhyLEo0apzOMAx56vlTJOIGN+3op687ujdichuy0Kl6Uximjl13mlI2319EkpIEQQnXu4qg44aGhd/pdWJihs/+7UFcz+fAcD+SJHjt4jjnZxbYN9jLzYN9q+6vy/OLvHz+Cr25NI8fPRP5Yxk6l+by+EHAd+3dtmrbv/HoN1AkiQ2tGUxd48TELAdPXuDhnZv42YfvbErJpgplnj1zCVkSHJ+YoSeTQlcUDo9NcvDUBSYKJT51zy1NwGZsfpHfeuJ5joxNMtrZSi5u8vjRM7x8/gqla+JMwzBkoVLj9556ma8fP0fC0OjLZYCQN68UuLJQ4PaRAVYy+Et1i987+DKL1Tp9uTSO5/P4W2d48dwYv/Hd72dX37LnUto02DfYy2y5wotnx7iyUGiCSBBNLvwwWDdm9kYVhCGvXxrn//rKQequy9budiqWzZ8+f4juTGrdK/LmlkG2pXs4nL/MjFXiP598gkMLl9md66dVT6LLCm7gU/McpusFzpSmObJ4hbFq1PlSJZlHenZxc+vQus+vuWqVw5OTVJ3Vv2+8WOTN6emmHO/swgInNs6qowAAIABJREFUZ2d56sIFtre3s6Ojg0OTk3z26af55bvv5r6NG6PI2JkZ/u3TT5PRdXZ0dlJ1HD7/5pscn53l39x5JyldZyCbJRuL8ceHDrExl2N7RwdHJif53JEjfNfWrfSmo4n0lWKRR8+c4ezCAhtzOXZ0dvLa1av86sGD/Op993Ggry9KMVlc5JefegrL87ilN5IJf/3cOY5NT/Nv77mHrmQ0mbpcKPAfnnuO8wsL7OnqYnNrKwv1OhfyeRzPA11HlWWGczkW63UmSyX29fTQ3fj8aMsyeD9TqfCfXniBU3NzvK8/Yt++Oj7OGxMT/OJdd7Gjo4MQeH1ign938CAbMhl2dXWRr9X4syNHWKx/8xLKb/dyA5fJ+gRVr9oEgV6cf5572u7jdPkUcSXBeO0KAQFB4DOUGMEJbM6UT5FRs6TVNJtTW3kt/wpBGBKEPgdab+fI4iEsv06r3kZr0MZY9RI1v0ZciSMJmaH4Ri5VL5B3FuiJ9TGa3MyL888RVxK06G0klDhvFg5T82tYfo292f10GOuBeNH4ZHl5jub/jF2tP0lKf2fMjP5Uhp5Eqjmu+4HN2cJfktE3MZB6uPk+WZZIJ5YfUBl1GXTP6cnlZ4Fg1Vi1nkk8RACSLHcjy93r/v16FeBxqfR3yGGc+XNDZNqie09RZVo600iyRGtnhkxrkkxrgnrFJvCj+ZYsR/K2iYtzJDIm8XSUpKOoMomUie8HpKs2iiKTzMSxqjbxVNQUPXtqMpKgqCkUJYNteyzMlZrgTyYLtepMM1Wub0MLyWQM2/bQhcB1PBzbRwgXI6YiyzLxpMH5U5Ns2dVHsVCjtT0CjTt7cxgxjVrNZm6miFVzmJ8poaoKMTOSVtSqNqVCDc1QcRwPu+6QTMca7KkAWVcQchtCaqVYtJi44uD7N6FqyxLcMAypuS5TpTLtyQSdqQRBCAlDo2q71F0viuYW0RyuJ5OibEX2Cx2pBIvVOqamYnteFNTQCEzxg4CqE1kZZM0YtucjCRdT05AlwaXFReqey2y1SlLX0WSJqhOFt6QNA8dXMdV+pitl4qrWBOr3dV8/ZOG9UCFRymtS01elEYdB1FkWDUsQzw/w5RtrSwVR8mm+VqNg1RtNEJ+CO9+QIm9c9X5FklHCG8tkQ0J02UQ05ui+7zO6ewOZ1shLVJYlhntbkKTI5NkXHpqkYgfOutsTQlD1HNpjqxlJSyEi7jpSJFWW16zVlv4nCfG23kOyLOFYLlfPTNEz3ImZijHa0cK/uudW/uT5N7g8v8jEYokvHTrBUyfPs6e/mwPD/dw5OkhnOonaACbswOOiU6Lo1lF8CaVNxmERM6ZjBS6W7+IFPmZVpzOWos24PqO8ZjsUqxHg7PtBBGS3X5+llIoZzbWZEIKWZJxS3b7hvbEkMc7X63Q3lCqFWp1qI+FuslDi0//zMd7u9vKDsMkctD3/uqqZwdYcP37fAf7w2de4MJtnuljhK0dO8vSpC+zs6+R9wxu4c9MgXelkc232dgzyVMzgR+/ez0K1xtGrU1Rsh6dPXuDFc2OMdLRy5+gAtw1vYFNnK7F11pe6qdMz2sXM5Tk8x0NRZVpbk8STBhDieOdwvasEQR4/KKIpw2gN/72lWsxX2bSlG88LqFk2FyoTaJJKn9lOXPnWJWl+2wBHFW8RN3RwA5s5+zKSkNGkGC16NJmVhMycfZmU2k7BncYLHWy/SlbvYap+FgRUapFBp+VX0WWTvD2BEBIpoTBnXUaVdGy/xrx9BQgx5TSOleHywiJAM/FqvQtcCEFfNk1C15ktV7laKFKyrDXAUcJYbbBmKEojhtulfQVNUVqBuDq+t66GX5VkRtpbV1ESV1Y6ZrCtu4PXxyYo1C2uFkqkkwbmO0wjW/pdCU1Da+zLUirIkgznejferYP95EyTUA+47X0jESMlDHFdn3y+Slt7lBwmyZGWVkQzNoIgjIyYb/AAkCWJdIO+6gchbhB1x0xDpVKNugOSEOzb0MNfvv4mFdvhPz/1ApPFMvs39Eb+KOsASBBNGmUhMVnPM5qKJoXbuzvoSiU5N7fAn758iJlShQ9sG2WkvQVDuQ7NUAgcx8f3oxN3aT7PVDHq2O7o6SRlrH+TCiEYas2hyjJ11+P83AK25zUfTvOVGtOlaDt92TTd6dS636/IEiPtLSR0PZo8raisHsMPQ1pjcbwVMYxRzPrywHXz7gHyhSqGrpLLLE+0AzwK9nk0OUkYhFRLtaYcSkgxQs8CoeIHBRSRe0/J1K6tR988zXSxzC9/+F7u3RyBBYcuT/BvvvA12pNxHtm1ac0zYKFS47GjZ/hn79vDvoFeDFVpmravfDZs6W7n5x66g4HWLGkzipIezxf57N8e5OlTF/i+W3exqWtZ1mW5EQX2Zz5wB3sHImP0U1Oz/IfHnuVrx87x0Zu20ZNN4fkBXzlyilcvXOWHbtvDx2/ZiaEqzJaq/MHTr7BYW73Idjyfrxw5yeNvneGBbcN84pZddKUThEDNdqk2OkYra7pYYUt3Oz/14O30ZFI4nsfXjp/jd598iadOXGC0s63JkutMJ/mn79tDxXYo121ePLfaN0QSgi3d7eTi785/pWI5/PlLh6m7Lj/1/tu5fXQAIeDibJ5//3fPrPtM7Yyl+T9G7uK/nHY4XZxkzi7zhStv8NXJYw1aeoNeHfrU/WiCtVRZzeT9Xdv5p0Pvw1yHofRua6ZS4ZN79/KRzZtRZZljMzN8+vHHeWFsjHuHhqg4Dn/51ltoksS/u/de+tJpvCDgCydO8P++9hoPjYxwx8AAcU3jn+/Zw5VCgT964w1+dN8+/tvrrzPa0sL3bt9ObAVwnq/XeXB4mA9t3owmy5waGuKXnnySL544wf6eyJPgiydPMl+r8dl772VvTw9hGPLSlSv824MHefriRb5v5078MOTvT5/mxOwsv3L33dza10dMjRZpru+TNqLreUtbG1va2pAliaPT0zwyOsqOdRhCXzt/ntfGx/nFu+7inqEhFEni2PQ0P/vEE3zp5Em2trdjeR5fPHGCjGHwy3ffzYZMhrrr8vk33+Tw5OT/9vn4di1TMemN9eGHPhsboJAf+oREhrBhGBCEAZtSW7B9i8n6ODm9hV6zn02Jzbww/xwdRicXKxfoMzcQEhA2Fj395gZGk5upeBXGxGWKbhFZLDHPAqpelTCEWWuGkcQmhJDYnNpKq9aGFVicK5+hRW9t7Mv6c4XlCglCh3eTNhXNIZbnPyHR+BRy4+9aJd19h+/7llQIQeghQg8jrmOmonmArMh0D66W6AohCNvWHotcR5pYwlhl6rqywy2EoCVIUy3WiSX0yH9ttoTe8L3r6ctRq9qk0yalYh1ZFtRrDlbdRUiCuZki7R1pMlmJ0W09uI6HEVNp7UhHUtAGCKAoMu+7bytmXCfXlkSWo4RcIUlIkmDPLRtRVZkgCNm0oxczobPr5kGMmMbQaCdBEKDqKlY1hx7Tmou+JaPXpd9y1wPb2XfrMC3taxev3akk2ZiBrirIQmo2qWwvkqP1ZlLEdY0gCNEUGV2WeWDzMElDpzuVbHoT1l0PVZa4ZaAPTZEZ0nL0Zvwm4GSoShNQvFoq4vg+MTVi6ZuKSs110WSZIAxZrNeACLDzw4AwCDFV7T3aOlsuASRVnZKzer6pqgpa45zGVJWiZeEF/g3v8hCouy4ly24yUE0lRZexgZSaW9WM9EKXiltGugErZknNIK9Y0jq2x/mjV+gaaGNwaxSyM9y3fA8Gvo+pxIiF11tQh1Rdl6K8ttHWloivSv9aqmtlbStrBXHy+iXArtnE0yZaQ12hyDKP7NrEUFuOv3j5CC+eH6NQsyjULJ45fZFXLlzhy4dO8OD2UR7YNsJAawY/CLB8Fz8MyNtVspqJEBo5M854LU/RqZNQdRKKHrGcXOu6O5SOx5oNSU2VyaRNpudKDG5oXfea11aAZwKwHJeZQpmebIqedSwglqriOHQkEsQbSXYrm4shrOvneqMKQ9Y9RxCtnx7YNkJ/S4a/ePlNXjw/Rr5So1i3eP7sZV6/NM6XD5/gvi3DPLRzlKG23NuOFZIQjHa28tmP3s/fHj7JkyfOMbZQwHI9jo1Pc3pqli8dOsEdowN8bO82tnS1rzIj33rrCLKqEE+ZtPVG/qlyI80OBKrcRxhaSOooYbCWJAIwMNRGEAScPDbB0GgbCT1B1bf4Vic8ftsAR6qkkVRaSCg5FKGRULLIkkrBmUYVGmmtA8eqk3cmIAxRhIamxtAlE8svI0sqabWTqreIIcdZdKaQhYIQEhU3T0JtoejOYshJDDmOH3qYcporpTKlRvLOqelZfvoLj113H90gYKaxqC/Vbex1QBVdUVYBPULQvImWtK4r/wbRBR6uc2JlSVqOXV+ndEWmJx35DjhelCKkGmvtXoMgJF+rMVEocX4uz3ihSKluU3McbM+jbNnMlirRvix9JgwxNLXZRVhZkhBsyGVQZQmhyOjXJFjcyNQ1+r0hlusxVSpzeWGRi/OLzFeqVB0Hy/Woux4X5xZWfiACshqa6CUX+5s39PIvDuzlbw4f5+J8nt984ht0pZPs6evmvk0b2dHTSUcysUYCYyo6I8muJj1zY1uOn7jnAL998EXG8gX+5OVDPHrsNNu7O3jfxg3cNrSB7gbyvFKmIQTMLpSxHY+xfKHpN/TEybMcm1hrULpUZdum1kDSF2v1VelUi7V6k63QEjdJxa4vz2xNmJiqugY4AtjZ0knBrlP1om35oY8V1JugIMD5S7McPz3Jrm191OoOo0Mdjd/m44c2gjRKY0Jl1ZwGZddCllpQlX5kKctEeY5Zq9T0fnkvVRCGXJhdIBc32dHb2TSGHe1spTOdYLJQoua4a4Aj2/O4d8sQH9q9ZY0B3srKxU3u2bK6s5aOGewf6uN/vHyEYt1aRZGWhMRtwxu4f+twk/K6u7+bW4f6ePytM8wUo8E5X63x0vkxOtIJPrZ3Gx2pBEII0rEYH71pK69dGl/1nXOVKo8dPUNPJsVPPnBb5Df0NhXXNT5xyy62dbc39+/+bcP81atvcXF2Act1V8krRaPbtlLf3/wbMJ4vYWoqCUN7x4u5s9NznJiY5fbRAe7bOtxMsMgOmNy3bSN//Owbaz4jC4lbWjfyG7vTfPnqIV6eO8+8XabmOdR9Gz8MEY33aZJCq54grZrsbRngga5t7Mj0ElO+NWatfek0t/X3N2XRfek07fE4i5aFH4bMV6u8PjHBbf39OL7PpcWowdERjxMCx2dmONDfjybL9Gcy/NRtt/GLTz7JzzzxBEld5zN33knGWC0Hbo/H2d3V1ewIDuVy7O3p4fXxcQqWRUxReOnKFYayWQxV5XLjO+OaRkxVOTYzw0dcl7rn8frEBFva2rh3aKg55mnXaXLcqBzf5+UrV8gYBtlYjCuFAhBdM23xOCdnZylaFrbncWxmhgeGh+lNp5EliYSuc2tfH39x9Og3fR6+E0qTNGbtGUpeEVOOAyEz9hQz1gzJRAov9JizZnFDh6SaJAgDFux5JpUJTMUkJpt0GJ1sS2/HkAxM2YyucTm632KygQBSSgoncNAlnUUnjyIUOo0u8k7kSaQIBUVEzRRVqLTqbQwnRsloGdLq9RcFSxUSMFV9iQvFL6NIMTYkP0BKGyAMQ4rOOa5WDuIGFVLaEBuSD6JIcfygzlj5axSc85hKO7ZfaG6v7s1xtXKQijuBJFQ6zVvpNPdTtC8wWXsRTUpQdC6SUHsZSD6CJiexvAXGKl8nqfYzbx0lCB02pr6LpNaH41e4WnmKonMBRYrRn3iAtDYCwFTtRebqR/DCemN7D6PLGbzAYrL6HPPWUQy5hZo3TVLaiGZoq8Dr6zUkr61Een0AfZXERZJINuZZYRBy0/4hzPiy31JXz5IMWLA0qxNCEPgBi4tVzIbZdszUorhnWDeJLNXw10its0/pdeZ5S+/XVniRGcaNG5mJpEEiuXbxLoQgpqmrmi3NbTZY2NeqA2RJQl/nb0vPu5Xs4KW9v9afcLRl2fJgCbDb1BIxoSzfY3NrK9o60vzVPjgBEu9eev3tXo7vMVev4GciACYIwqb/y1K1xM1onL+Bx5Hn+3h+QNowmmNH3a8yb09hBTU6jb4m4ish06p33FBuE0lEy5HHETR8tRS27t/YBGCuLUWSqfsWju9eZ10t0Btg4cryw5CyvQx4fUsrhFxXllqpjrwCYNUVhd39XWzqbOX4xAzPn73MN85cZCJfpO56nJqa48z0PP8fee8dZtl9l3l+Tr7n3Jwqp+7qqs5Jre5WS62cLONsPxiMzQMLa9JiDA/D7LIDw/DwzAJjZpcBlmXAeMBpbGNsbMuyJVmh1Wq1QuccqivncHM6cf84t25VdVWpJVvsyNr3eTrce849+fzC+/1+3/e7Z6/wK/cf5L4tGzmY3ojtutQcC0Ne1CgVSGrBxv+XZ1yWKaw6HLdefZKMGAj1WypJIs2p8LpE6ZLm8OIp+bPbXLn6usTRYpWLVN/RcgHr5kiIjx7YtaL80auXTqx3HJIk0BaNrLPULx3c2dHCH3zgIS5NzHDk6iDPXr7B2EKOsmlxZWqOq1NzPHnhGp+89wAPbutdsy1aDlEQ6EnF+fWHDvH+vVt57sogz1++wdXpOXLlKpO5Al999RxHrgzynj1b+fnD+4gZAaqlGmePXGLH3VsQJZHsTI5AWGd0dJ5gSMMIaohiCElMIwo6NXcMVdm8av+dXUlGR+bZvruTSqxKzayR1mIo4ltL9bxtiKOo0sxsuUQNhU69brcriLiSjecJSMiEBJ2k5kcVJMF3erLcKmElhYBIWuui09iGgESrvrnxSHl4SIKMG9jUEFvzcJAFlYqVaTygs4USR4pDtzxWsZ69sR7jfHOq4iLerGW5ILBCa2mtfS0ud+tK/9PlEp0R/+X01f9tjlwb5BtnLnBhcoayafkEwOJGPHDxGoJxja89j9ZYBE1RVtX3SqKwgkR5M3Bdj4G5Bb5x+gLPXr3BbHHJ1tur/+XBqtI9y/Gze5xlDgO6IvNzh/axva2Zr586z8tDY0zkCkzkrvDs1RtsSid5784tPLx1E6lQ0Hc6Acp2jdlank4jhYyELIo80N9LKmjwtVPnOX5jlJlikWev3uDYjRG6k+d4cHMvj23vpzeVXCL8lh1foWY2Ssauz8wzMLvArbAo4r18O37E3j9HTZZeN7U1IMsN/agV19jzyJtVOsMxRgv1SRgCSTW1wkVqZq5APBYkky2tStm33TKCIDM3kcWDRucrCkGqzjkcN4uu7ScdiBBVjDfsTvXjBAGIGzpDcxkypQodcb8TylVqFKomqXRw3Ynynq62dduBRbieR6lmMjAzz9hCnmLNxLRtrkzN1UtKVt4TRRLZ3JJaUSetSCJRI1BP0fWfm0K1xnSuSF9zkkRwSbxdEKAzGVvlpDaTLzKTL3JHb1ejfPZWSAR12uMrs+FCmoYmS9TqWYtvFB6QKZUZmZeJBw1k6Y09SxPZAqWaSV9zEk1ZSdZvakqum2ogCgIbQil+bfODfLT7ANcL04yVMxTtGqZr+84Zku+Y1mbE6AomSWhBNFG55T1dD2tdj2ggsOL5EQXfIGJRU6FQq1G1bR6/coXnbtxYsa2iaVK1lzQWREGgP5VicyrF41ev8tO7dtETi60KcaqStGKfiigSVlVMx2lsr2JZHBsZ4dz0dOMSevgRQctxcOqDumKt5msS/BBk0XLUbJuiaTKwsMBvPfFEoxjYwxdH3pRIYDkOFdumZtvENG3FfQip6o98DG93tOkdZMwM09UpNgR72RbZwWRlkna9g7iaYKwySs7KEFFi9IW2MFIeouZWyZpZdkR3EVPj7IjuZKg0SEJNEFGitOsdhBW/TZMEmXa9A0EXqblVApJOTImRt3J4eGwM9SKLCt3BHgJSPYtGkLgtfjuDpRvkrRxbI9tRbzHEsb0qRXucNuMw05WXuZb7CnuSn8Z0C1zJfplkYAcpZTfDhe8jCgo94XczUznBeOkIPeF3Y7kF8uYgLcYdeJ7HeOkFsrVrdIffheUWUUSfuKi5OUaLT7Ix8n5ajEMMFr6NLAbpCb8b26sxWTpKWdtEi34QFwdFCuN4FsOF75G3BmkP3kOuNsDlzOe5Lf07KKLfLqYCuxAFhYH8N9GlFJ3hh5mvnmeo8Dhd4XfheTZjpecIBzZSyJSYn1igfVMLmu5nxSzMFbh8bozZmTwCEEuG6NvSSnuXH2F2HJeRwVkGr8+QXSiiKDKtHQm27+5EN3ySL5sp8eIzl7j9zk3MzxYYuOzrkG3Z0U7PJt+6WRAEhgZmuH55kv139XH14jhjQ/OEozpbd3WQSIQabffxI1eoViwO3t3fIJFqVYtjz10mYKgcPNzX0A2xTJvhG7PcuDZFIVdBCyi0tMXZtruzQVx5nke1YnH14jgjg7OYpk0opNOzqYmN/c0oiozjuFw6O8qlc2N4nkcwFODwg9tWCMF6nseRpy7Q3BYjngxx7uQw+WyZSMxg9+09jdI58LXg5qbznDs1Qi5TIhjS2Lani9b2uO9cugxu3W1PxE8BWczE9sfFS5NRDw+xPrJx6ussOm35n/2Mo0UtHMfzNbU8PK4XpugOptGlNx4I+XHAXLVEazCCJvlTR0nyM/sX5ot+XyQIZCuVdTUGFyGKIiFNpWxZjT4sIsdpNzYSkldO9iVBoinQ0rjOa25PkIipzciihiiIlEplTh25jB7SaOlK0bN1bZdZTVR9+YZ1YMgq2k19ix8AEymaa5e4/UgQfCK4Wl5bd0lXFW7vaWdXRwsfvG0bR68N8/yVQU4NT1CxLK5Oz/Fn3z+KKkvcu8XP3JVv0gZ+M0YqAhAxNEK62rhPlmUzOZ2jpzO5plnVzXBcX1aku2m1g/RyRLQAC5UKVcchJAgYy56hiK7xgdu2kQwZWKaNVbMpF2tEEr4BgSAImFULUfSrXERJ9N2JBZFquYbnQcBY+10MKD4pt629iQ/s3cZLAyM8e/kGJ4bGKZsW12fm+T+fPIogwGO7Nr/uTGdRD3iRQPp4Isp7d2/h5PAET124xiuDY8wVSkzli3z+2ClUWeKT9+wnO5Pj+ulBbNMGAZq707iehyxLjbYXqAtje7huHhCQpZVZrMeOXkVVZfL5Ci1SBKu5RtUxUcW2unD2W4O3DXEkChJD2TyzpSIg0Bz0WdFMtcJCpUI0EKBQq6HJMoaioMsyCd1gSypFp+HbBQek0DJWevVFkoTlp7toL7jUFG1KJzm4oXPV79ZCJKARCaxmnG+l9fJmO5FbTcAWraUFwY/cNgeDjSyHqm3z+ZdP8Y8vn2ShVCEdCnKwp5Mdbc20REJE67WouUqVP33qCFP1rCMAy3HJl6u0x1cztq/XgN8KZyem+JMnj3B2bBJFltiUTrKrvYXeVIK4oTeysh4/f4Vvnrm4Yp9BQ0WRfXte6tGckKZyX98G9na0cmFyhiPXhzg+OMLQfIbTY5Ncnp7l+WuD/C/3HWJnewt+zNPDWNahC4KALAns7WyjvznN8EKWZ64McHRgmMG5Ba5MzzEwu8AzV2/w6fvv5PAm39nGq5fdKbLku7LU78OhDV1sTMXBs1k5e735Xor0JKOokofj5hGFYF2PaO21V2Ptu1Awa5yYHWe4kKUt6N+/gKTTY/Q2tI0EQaC1OcrxE4OEghrbNy9pOdhumbbgPZTtCYxogE27ugjHjPqxSYCNKBj+tfRcZFH8oSfUb2cIgsCjO/s5MTTOXz9znA/u244kCjx5/joV0+KBrb0NR5DlEAWB4C0aadfzeOXGKJ9/8RTD81kiuoYqS0iiyNhCDtdbnYMoCALhNTLQFoe5i0+M7fgONDdnP4JPtt6cBVWqWbieR8xYLVa/HjRFbkR2l47P/9dbZH/fIDRFrpeZrU+6r4WaZeO47qpMTmBVFpjruZiuSUAK4HkuRbtEQArQrIfpCPq2wbZrIwrijyQkKNTJ6eXttuN55GqrB4ICr19GE6w7ZL67v58PbN266t6kDKNxfx3X5fjYGOemp9nV0sLx0VHOTU+zt7V1BXlUrZMvi7Bdl4JpokoSAVlGq4uaPtDbyyd2714VuIhoGiFVpWbbGIpCtlKpi8e+/jV7vfNUJQlDUdjW1MRvHz68KpIbkGWShoFTKqFKEgXTXEF0V217TY3AdxKCcpADyTsan3tDffSG/EwY27WRBInN4a206e2Na9NtbGBvfF/jNz3BjfQENzY+dwc3NP4vCMKKZYu4I3XXis+bQv0rftMcaFlH12htSIJGd+gRUvpudDnN2fm/pOLMkjeHKNtTdMkPIyJjSGlmyq/SEbyP6fKrNBv76Qg9gO2WmKmcbGxPEQ0st4DrmSQDOwlISxbgqhinO/wYihjCcgtMlV+mI3gvAK5n0R68lyZ9yZmmYs8xWX6RZmM/AhKG0sJo6RkK1gjJwHZSgd2U7Alst4ws6pTsSfBgtnKSqNpHV+hhHK/GQvViPQjm+cK/nofruFy+MM7n/uoHZBeKGCE/w8uyHB5+zx7au/wsl+GBGf7qPz2B67qoiky1apFdKHHfu3byiU/eh6JIZOaKfOnvjnDlwjjzs3kcx6NUqPL4P7/GRz5+J/c/ugNJlrh+eZIv/O3znD81zMxUDsd2ySwUCUd1fuW3H6O3vwVBEHjuyfNk5ovs2te9gjh6/OuvkUyH2X9oE6Lof/cvX3mZp75z2ncADSjYtoMR1Pi11nfTVS/Fm57M8tV/eJHTrw4SCgd83RbTpn9bGz//qw+ixHwH3XKpxtjIPNcuTlAu19ixt+sm4gi+9y8nUTU/eFmrWtiWw/xcgSNPp/m133k3za0xXNfl3Klhvvi3z1Mpm+iGhlmzeOKbJ3n/Rw9y90PbGuSR53mczQ4zWpqjK5im4pjMVLPoskbRqlJ2asiCSEDynZJSWoSQHGC0PEdUMTBdm7xVJqxmbyYSAAAgAElEQVTotOtJBopTDafO+VoBQ9boNFI8PXWG/ck+DiQ3EXgLyprfDhAEgdZghKFCBtt10epDC8dxKJVqVCsWiq7QHArdkshfdHW+PjdPdzxGczjEvDmJiEjRzpNUl9oUQRCQeCOBAZ2wbOB6oEd1Nu3rxrYd5LCK5fpOyooo+U57wqLZi0hMXVsOAvyxdMAIrlruuh6m89ZanEN9LqJK6KH19WgEQUBTZDakE3Qn4zy2s5/nrtzgvx09weBshrFMjq++eo7dXa1vuvx/9b5gfM4nKVoT/lzCtl3yxRqFQpXYOtmRNyNTKt9yTFhz7LpouT+iTQR1VFnCtB3KNYtCtUZLNMy1iyMMX51C1RRCEZ2FuTzp1hjhWBBJEsktFOuC/zLTYxm0gEI8HWbL3vV19RbFurtTcTqTMR7Z3seRq4P844unuDI1y2SuwFdeOcttXW0Ijj/GjoZ0qqZFzfQDboIokCtWCRkaju0QCxvEIwapcJCHt2/i0KYurkzO8ndHXuXotWFqts23Tl3kgS0b2dKT5r6fvBPHdhBFkWAsSLFQxTBUJGkpc1EUg9jONAHtdjxvqdLEcVwmJ7LksmV27fG1WONimL5UH1XXJCTr6536D4W3DXEEsDmZoisSperYJAL+iVqug+n4iusly2pkNixGTkVBxJDXT0e7FcIBzZ8AVWtsbk7xvz167+uWl/x/Cdfz1ixDWoRvOe8vl0SRVNAgV61RC9kg+CK6/1AnjXa0NfPbDx3mts72FXWVAMPzmVUNvSpLjC3k0DWlLnR7085/iPaybFp87qUTnBmbRJNlfuWeA3xwz3ZSQWNFA1yxLF4ZWllOI0simVyZ8dkcnS1xQsbS5EIQBGKGXi8r62IqX+TloVEeP3+F44OjvHB9CMtx+cyHHyNh6AREhTnHXtWQLRJR21ub2NaS5uMH9nB+YppvnrnI89cGuTw1y2eePkoqFGRzU4pUwnc1EkWBuKEjSyI1Gx7c3MtP3taK445j2SNIYgpB0PBw8NwirreoMeMAJfAKVGomhnYvhqI07k+pXkq4XtZZ2TJXpHMuIqoFuL+9FwGw68Si6VaYrA4iItJh9CMh09/bzKaeJgQBX3eqDk2KU7TG0aQ4iaYYwrKJtOPlcd0SpnsdVd5AREmzN95DVPnROqi3K+7u7+HD+3fw1VfOMTSXQZVlupJRfve993F3/4YVOhQrsEaJ53JM5Qp85okXyFdqfOrhQ9y+oYOoHkCTZf7L08f43Aury6zqm70lFFlCVxQqloXpOCsInkqdbFmOkKYiiSILJb+U8Y2SN2/VkEkUBGLBN9+x6arvYFKo1vwBx7Jl5WXRQNM1GauMkTEXiCpRdElntDzKhuBGLNdCFX2Be9M1MSSDjJVpkOOqpLG8RCesROrrr42IpmE6DgMLC2xJpRBFkeFMhhM/hAZPUzDIvrY2rs3PE9Y0WkJ+loDpOFQsi0g982bRWe0vXnqJg52d/OK+ffyXl17iz48d4/949FFaQ0vZBXOlEqenpuiKxVAliaFMhpMTE/SnUsTqukR3dHby0sgIAL3xOJIoYtUFZQ1FQRZFYoEAe9va+PblyxwbGWFfWxuqJGG5LlXbJnxTFpBWt7teqKwszV3U+jtYLzcrWxa7WlpQ6oKzZctCFkUUSSKiaWxNp3l5dJSPbN9ORyRCzXE4MTFB9h0sjr0I23YoVU0CqsLAuJ+V2BQPMTA+R2tzL7JtUKn5Ir4RrwlPcLk+NkdHU5Sa6WDaNhEjgCyLWJaD7br+OywI2LZDQFMwLRtZkhDwxUUNTaFcNdFUBU354bKMl0NAaGTvqGIIDxfHq2E6BUynwFz1rL+eINMavAtRULHcEqoYRkBEFJTG7wVBoD14H6oYZqz0PIOF77Ah/F5ag3cC+A66Qr10Sopiu2XcujaSJGorSCbwySTTyZM3hzAdv2yjPXg3mhSnas9zIfN3yIKBIbdgOQW8uqaT5ZbQpCgCEiIyihRG8ASsmo3negiSyOxMnr//y6cB+I3ffS9dG30h5YW5IpGo3jiflvYE//NvPEK6KUIsGSSXKfGV/3aUI0+d55H37Kaj2y+ZqtUsRgZn+c3fex/tXUmyCyX++jNP8E9fOMa2XZ20dfrnNjedI6Cr/NbvvZ9YMsjVC+P81X96gm986Tif+t33rJIZWA+u63Li+ADf+PJxHnnvXh5+z24SqTCVskkhX6GlbqxhWQ7//MXjvHL0Kp/4pfvYf1c/gYBCLuubrgTrJWmyLHHgcD8HDvfz9S++xHf/ee3+DuDCmRE+9j/dw6Pv34skibz47GX+9s+f5MTxAR77wG3MzeT5/N88h26ofPrfvY+mlij5bJkvffYIn/+vz9Hd28TGPr8M33RthkuzHEptRhMVjs1d5q70Vr46cozmQBRdUqi5NiW7yuH0Vl6YvYQkCDzQvItnp89jew6HUps5nRnk5fmr6JJK1TFRRInd8R6GSjPoksqWSDu3xTe+Y0ijRTh1R7WVGmJCfZmH4Llkq1VSb4CwUCWJzliMZH1dUZDwMAnL6xvnrIexcpZJL0PRqhKQVAxZwQ16BCSFkp3j+sw8AUkhb1XZEEqxKeyTnLO1BQKiRnMguWqbruciilCxV2YWCUDC0An9K5WquY5HKVd+Q6uLokAqHORD+3aQDAX5/X9+ivlSmbNjU8wVyj8ycQQQUGWKFX+MJSAgigKxqE408sbGbKIg0JGMIaw3Vq5jMYAUUv3nqykSoi0WYWguw3ypzI3ZBXqbkiSbo4RjBrIiI4oCG4Q2tICCGlColGqNIDcCJJujzIz75fY3V8683vEmQgbv27uN1liE3/nqE8wWSlycmGFoNgNVj2KlxpbuJsZmsizkK8iSQEdznHyxwth0FkkS2dMfaMybBUHwndQ3dNASDfPpL3+HixMzTOWKXJyYYWtbE8MXxzAiOrIi09SdxhMEgsEA0rL5uiL34nkWpnUFTdnZ+N5xXMZH5wmFAlyoZ3Fu3d6OIMBIaZpt0Z43dK/eKN5WxFFU04i+yZdxPVeMyYksqXQY23bQ9bUbb0EQ/MybQICZQonJfIFcpUrc0N9yJvmHge24jGdz6z7wVctmNJMD/JeuOxGniNmIvp4ZnyRf8SPdH9i1lX1d7asIIr8kwlwlgu3VS2V8fZS35nxmCkWuTvsD3g3JOB/es2NFKc0iHNdbNRHw6t8nIgb669TMC4JAazTMe3Zu4fbudv7ou8/y/LVBzk9MMTiXIdGlY7k2lme/7sxXEHwy6K7ebra2pGkKh/jiK6cZzWR5bXiMzekUluU0bKJ7knECskyxZjI4n6FqtRJQmvEkr068iIADYgiZCP7OXQRkEGQ8z0QQZJIhox5xLzBfLJOtVGleQ6Db8zzmiuWGVtLNODM3CfilKBsi/iCy6hQIywkWLUZfPT3E/t09zGeK5AtVdm3r8K+/Z5IIbEWVotycMSWLKVS5D/AQxQiiKJHQ3lh5048jFkoVTgyO89C2TfzCPbf7ApyyTCiwOsvlzWAmX2J0Icvd/Rt4YNumhh6QadtM5wo/UgZFOKDREg0xmfWfoVDdAtbzPEbns5RqKwdCTZEQTZEQ16bnmMzmfUey+rndbEH6dkJbPExQ07g6NUfVshttm+d5XJuebxDDNadGwcqTt3JU7DKdRhcxJYYkiEybC0iCQFpromiXKDklZqrTiIJIa6CN4dIgbXo7JiZlu9wo7VkP/ckkLeEwnzt5kvF8noAs89r4+OvqPayHkKbx07t28R+eeYbf/8EP2NvaiiKKzJbLzBSL/Madd9KfTJKr1fjsyZN4nsfP7tlDRzTKz+/bx7/9/vf50pkz/PKBA4Tqad+6ovCNixeZyOeJ6zovDA+TrVZ535YtSKJfrvGhbds4OTHBHz3/PHd1dRFUFDLVKqO5HB/btYu7e3pQJYmf6O/nxMQE/7G+XioYJFOpkKlU+NShQ3RGl/QM+pJJQqrK506e5EYmgwD0pVIc7OhAFgQe7evjzNQUnzl6lHt6eogFAuSqVYazWe7u6eGnd+3CUBTet3Urf/DMM/zJCy+wv72duXKZS7Oz7/hSNYDr43NcHpqmORlhZqFAMhrEdT3GpnPEQq28cmWCgCYTDek4jksiYvDapWGMwCZyxSrlqokRUNE1hfGZHIVyFdtxUWQJWRJRFYlcsYoe8HVlapbNps40r10cYf+2LtrTt9YwWoJw0x8fHg5le5qIupGyM4uAhCKG0OUkupxmU/RDKKL/jgmCiIhCQEpQsWdxsbDdClXH1z/03Y00Wow7SAR2MFx4gpHiUw3iyPbK1JwMmpSgZE0QkBJIgopNhbU6f0kMoMtNNOsHaAvevfS9oDJbPU3RmuBg079HEYNkzauN5QE5QcWew/VMHK9GxZ5FU2JsOdCLWbVQVJlrlyYZG57nk7/5CNt2dzbKD8I3Tb6MoMqWHUtlNfFkmO17ujj+wlUK+aUgoiSL7NjbTdcGn4BKpsPcdf9W/p8/+x7jI/MN4sgIBdh/Vx/JJt/0pXdzK/vu6OX4kSvkM2XSLW/sntaqFqdfHSQaD/Kej+wnVd+eEdRIppc08TLzRS6cGWHH3m7ueWhHI4NpsYzth0F7Z5LDD2wlWM/C2Lqrg1A4QGauiOd6DA/MMjY0xyd/81Fa230X2UQqzEM/sZuXnr/ChdMjDeJIFiWCssaF3AhtegJJEDmTGSIsB1BFuV7C5lB0apzLjhCUNHRZ5Wx2GEWUCAgKsiAhCyLNgRiWa9OmJyjYFWRBQsDX8is7JteLk2yPdr7l+iL/IyEKIiVrZcanokh0diUJhwM4+POGtfRfl8OrB6hUaUnkICInEBURyzXf8CR/EbIgElEUoopOxTFRRV+bSBUlbElGAJJaCFWUiauLZIqALMhY7toaR5IgsifVtkp/1vVgqlBEKVeIv45j9Q8DDw/XdQkYb+59EQWBHe3NRA2N+VKZqmmvGVR+sxAEP9OoXLOwbD8IaegqluXc5LW9PjwgamjItyCOBEHg2sIC6WCQlOS7jvc1JRmez1C1bF64OsSh3i6a2uMrfrP8WVwsR1tuJJBoirzp5wnqpi2tTcSDOrOFElXLRtMUttXLyFRFpikRbgTCFNk3CvBLNmkYTt2MlmiYDek4FydmsF2XSt1NuFqqkmpPYER0FM2vDLBtB1WRGsdfrZ3A9QqAgOPOs+g+qCgS+/Zv5OjzlxHEuiCP6F+HlBZ7UwHhN4K3VYuWz1V49fgAnT1JlLroci5XZmYmT09PiunpPKoqoxsqC3NFdEOltS3G7Ey+HtGAZCpENlNmdibPlm1tmDUbRZWYmc4TjRlUyialUo0DhzYRDgdIBA32drZybXaeS1OznBqb5IH+1Snb/yNguy5nxqfIVarEjNXs7kQuz8lRP5LdlYjSGY8ykF9ArUcMF8qVRgPdmYitWU5guy7nxqdYKK9kuCVRpCMRfUsH5BXLahAdiaC+JmkEMF0orhKXFoCA5ket38gLIIsiHbEo9/Zv4MUbwxRrJsXFchFBoGhXcdaw07wZoiCQCgV5YPNGHj93mZliiflSxW9EtSU3iQ3JOFtb0rwwMMzRgWHet2sru9rbUeS17HzXK0YTiBsyvU1JrszMMTSf4drMPM3h1cSM6TicHZtcOqeb0BWOUbZNwopWL2RysdwaYSUJnsi1oRmOHL/GtYEZZFnkvjuXhNZsr8J05VV0qameyr9EIpj2EB42YON6RUTemZlGi5gtlLg6PUdzNMR4Jl/X9vK1x5rCQeLrPMO3QkCRCCgK0/kCo/NZWmPhRud4fGD0RzrmRFDncF8Pnzt6gi8fP8PH7tiNoalM5wp88+TFhhnAIlJhg/fu2cJf/eAl/vP3j/LRA7tpi4fB87MEZwsldnQ0rxAWfTNYJKFN2xfE9PAwLRvLdhqOEcsvoet52MuE8K166Z0kCitcS/qbU+zqaOHFa8N85/Rl7tuyEVGAgZkFnrk40HjNDNlgY3AjPcEeRCQCUgDHsxEQ6Qn6mggiAoZsICDQFmhDFERkQSGtNQFQcoqooooiKFiuw1ghR75WozsaJ6pqjWNqDYf5Dw88wFfOneOl0VEimsa7+voIqirfv3at0Qb3xGLElwmDgt9m9aVSJHT/mZopF+mOx/jTd72L7169ytmpKUzHwcGlKx5DlkRcz+P05CRl0+TX77iDjYkEoiCwLZ3mVw8e5F8uXeL89DR3dPol2E2hEB/fvZuXx8Y4MTFBeyTCz+3d21guCAKbkkn+9NFH+cGNGxwfHaVsWcQDAQ52dLA5nW6stzmd5o8feYTvXLnCmclJLs3OEtd19rS2Er3JVXJzKsXv3X8/X79wgSevX8dQ/JKGRbSHw/z+fffx1MAAT9+4zlB+lk3RZna3tHKgwye0JVHknp4e/sMDD/CdK1d4amCAbek0n7rjDr5y/jwp453dFs1kiiiyTFjXUJpEWpNRZElsTBAs2yEcDFAo1ehpSxANBkjHQ4R0jWLFJFOokIqFOHLqOv1dTZSrJtGwjmk5bGxP8vzJ60RDOkktSCJqcGV4BtdxCRkac9nSGyaOBDFGOPaneJ7v/CJJXUvLkBgufo+seY1M7TKpwE4CUgolECKqbuTCwmcJq11YTom0vpcmfR/tofs4v/A3OAs+4V215wBwsRgtPE3RGkORQuRq14koS+UINSfHlewXUcQwc9Uz9MU+iizo1MitedyaGGFD5CcYzD9OyR6vl2V79EV/ioCURERiqPBdPBxK1iRBuRWAVuMuzi38NecX/hZJ1Chao8S1zTR3L+lPzEz5UeiOruQKzYrlWCzfOnl8gPNnRlmYLWBZNguzRRzbXTFBkiSRZHrJAVgQBJrb/Kj+/OySyG0goBCJLWV0y4pEc2uMSsUkl12fOFoqfPZhmQ5TExmaW2PEE6tLdxZRLFQpFqp0dCcbpNGPiubWGPqyibSqysiyhOP4/cPMVA7X9WjvSq44rkQqTCgSYGJsSW9SEkQOpTaTMYsYUoAWPU7eKrM3scEPqOFRsU1eXbhOp5GkRQ+jigo5q0JIDtT171TuSm9GEWWyZglZ9I0UNFEmrm5Ek2TuTG1CFtUfqez57QhREJgs59nhLZWSOa6HbTsNw5hspeqP0V9nXOSbP5TJ12qNmMpYZYCinSMkR+kL7XpTx9WiR9gQi68iEoA1h9uCICB4HkFZJ2vmqThVP4i7DJIgEtVWj3lcz0USBXTlrZ8+i6JI1+Y2qI9zbMelavkGLK83zvQ8r2HWAr4m0M2i7z8MJFFkS1fTiv2kk2GiYf0NZ5zLoshUtsjG5tVZXcuxuD2nfv90VeHduzdzYnichVKFZy8NsKUlzU8e2IkiySuyeZbD8zzKpoUiiaiyjKKuvA6O61Ix39g1nc4XKNcDreGARlBTCCzL0lzUG3U9j3LNIqDIKzRI10KhWmO24PeNmizVx9UCbZtauH5qkGDUoKkrjRELMjebJ90UaZTwylILCG3UzHMrr50gICsS2UwZSRLQdBVP8LhSGCdj5qm5Fh166i0L/r6tiCNBFFA1mUKuimU5qKpEsVglEtFp60gwPDTH9p0dnD01TGt7nJePXUeWRUaG5hAlkf7NrRw/do19+zdSKFTQdZX5uSKSLNKzIc3FC+PYlkM0ZjQsQUVB4EN7d/DctUFmCiX+4tmXMBSFne3NGKraSOV2XZea7QuCDi9k6UnESYdf3z3srcD5cb9U6kN7dxBeljmwUK7wjy+fYjSTRRZFHt7SRzpk0B5biog3hfwO3nFdrk7PcbCns9GYeJ5fo/vSjRG++OqZhrDuIpaXgLxVD1tQVQlpKtMFf0I+kSvQFl0a/Liex3S+yN8efZUbc5lVv9c1pSGObTsOr42M05WIEQ34Wk3Ly4ZczyNfrXFufBrH9YjqAWL1ia9TFzYXEHA9jzNjk4Q0lZZIGENVVkxOPc+jZFqcG5+maJoEFJmWuktVrlBpdHpxQ+cjt+3kzPgUIwtZ/vMPjvJvH7mnkYm0eN8c16Nqm+QrNcayeXa0Na0QgRMFgce29fHslQFKpsU/HD9JZ9wnBRczAizH5aUbIzx+/gruOokMYUVloVYmU6uwM9mC53nElGZEfCvd/o3NfPzDd9CSDqPrK7NngnILMbWPmrP6HgA47gKymEIU3rmZRuDf+6iu0duU5KkL13n20pJAcUCR6WtJ8WsP3sHerrY3/Y50J+O8a2c/3zl9md/56hM0RUJULRsBeM+eLXzt1XO33MZ6kESRj+zfwVS+yLdOX+LYtWGiRoCyabG7s5X+5tSK9TVZ5sP7dpAtV/jGiQu8PDBKMmTgeVCqmSRDBp/5qXf/UMRRqWbyjy+eZGguQ9m0uDQxS6ZU5c+fOkYqbBDSNB7evonD/b6z0muD43zz5AWqls1MvkjJNPnBxeuMzGcxVIWNTQk+emAXUSNAUFP5pfsP8If/8gx/8fQxvv7aeVRZomrZHNrUxVzRJ8MlQVqVKaTUNe406fUje4vLA1Kg4SZ1ZnqSr105z4ZYgrCqEVVXls3uamlh1xqW8w9v2gT4mjw/v+82IssIJ4CwpvGHDz7Y+Pz1qxdoDYb5UP92ttYJG8/zOD83zZcuneHI+CDd0T3ct2ED923YsGJfgiDw7v5+3t3fv+J7z/PY2dLCe7ZsWfecRUGgJx7nF/bt4xf27Xvd9bpjMX7t4MF111mEIkkc7u7mcPfSxN52HUpOhZCgIwkiqWCQn961iwf6O3l66iQf6jyMftP9USWJB3t7ebB3pRvh7tbWWx7Djxssx8FynUa7v21jCxOzOZqSYd/1VJORRZHWdIRYUMd0HcKGBgLki1XCIY3utgT5ShVJEkjFg8TDOnfs6KEtHSUZ9fUQVUUkGtR5cP9mylWToK4iigJ9nWmCukpnU4xY2A9cVcsmxXwFIxxYN4tEEBTkNRxfVCnKjsQnUcQQOXOAuNZPKrAbUZAQBINtiV9gvnqWqr1ASOkkqvYCAgltK7uTv07OvIEhN9EevAdFDCMikwzsQBQUXCy6w4+RDOxo7C8ot9EavIcbhVeQlP0UnTCXCmdw3Cq6epCsVSFnD/jC7/UJK0IbfbGfpWL75H1E7UEUZMJKFzuSv8xs9SIeGpvjv4TjmRTtPKKYZFP051moXUKV4vTH9yALGiXbtxDXJQNZkXBd14/UrzOmsm2HL3/2CMeeu8zdD2/nnoe3Yxgql86N8f1vnVqxrud51Gors43Nmq+1oSxzNHMcF9uyV/zONG1EUWiMgf3vVyZF2paDuWz7giCgKDLlUtXXbVoHkuSXsiwey1sxdpRk8XVTG9SAf7616srr4dgOtukH+CzL8UVrRQFd1NADmh/QcD1CWmAZxyEQlnX2JXoJyiITlWtElDguNkVbJG/NIwkqguBrpkbkOFlrvt43iJTtgp/loDaTVBNv2hDn7Y6GgLi39LlWs1AVv2908ehJxInconJEEUU2p1Pka7XGuL3b2IzpVpDFpX5xcZJvu67v4FYPQs0WSg0dQdt1mS2UCGoqkuAHl0TRz/zSVWXpHixqMHq+q3PNtmkSW0hqTRTLDmWzupTB70G+WmUqV/C3WdfxFEUBRZJwPb/i462GIIC2jCSdL5X5y6dfoisZZW9XG13JmH9O9eNxPT/ANjCzwH99/hVm8yUEYG93G6l15qeu5/nX1PFF4l3Xv6YzhVKj8sRxPRZKZaZzRV9wWhCQRAFJFAkoMuHX0WC6GTXLxnbcW7YFsijSEgqtENy+a1M379uzlS8eP0OmXOUvf/AS0/ki792zlVTYaATi/HmVzXgmz9nRSa5Pz/OJO/eyta1p1X5ylRp//tSLtEbD3NbdRk8qXid8pPo19c2iBucW+NzRE0zmCgjArs6Wdc1japbNF146xXyxzF193fQ1JwlqvkalKAr1AKhDtlzlq6+e4/SIXxHSFouwvd2XC6kUq7RsaKJaqlEpVAglQjS3xqhWTRzHRZYlZLkNWWxGFtMIwmpi3vU8dE1Fq2csBSQFSZDQxLdOGBveZsQRnh8hCej+SZbLJi2tMXTDH8i0tsVRFJm2jgTTkzl27+0ms1AiFNZJJINEojpbtrWTy5WJxgxKpRqO4xKLG4QjOolEkJnpPNWqSa1mNWxCt7ak+dj+Pfz9sde4OjPH73/nae7q7WZnWzMhTcP1PLLlCiOZLOcnpilUTf79Tzzwr04cqZJEIqjzd8deYzSb50B3O5FAgHy1xvPXBnniwhUsx2VbaxMPbeldlR20s72FqB5gvlTmn06dx1AVdrQ1o0gS+WqNM2OTfOPMRUzbJm7oZMpL5WG24zYai7UGAI7rYtYjncvTAhdl5RezDGRRbPw2HQ7S35xicD7D4HyGv3nhFR7ZuolkyKBmO0xk83z3wlVeGRqlIx5lcH4pUiSKAuFgoCFyaDou//eRl6lZNjvbW9jakiYdCqLKMo7rMlss8crQGE9euoaHx872ZrqTfoqj7Tmoop++6nke/3TqPKdGJ9jb2cbOtmbS4RBBVcHzIF+tcXJ0gu9duErZtNjcnGJfVzsBTWbfjq4GySQJAod7u3n39s187dR5Xh0e43/95pPc29dDbyqBrihYrstCqcz12XkuTs4QUBT+7MOPrSCOAG7raufQhi6euzbIy0Nj/MfvPcej2/pojUYwbZvrs/N848xFrLow8M2lR4v3QhElFoVfTLfasMVchCyJHHv1Bps3NWPoKumkn3JesEYJSEmK1ihBpQ2h7johCCKylKZmXcGyh1GVvnd0xtFcocRfP/syAL94z36aoiFEwHJdrk3N8/3zV/nCsdPsaG9upKVub2/m3bu23LK2PKDI/OK9+9nSmubs6CS269ESDfPgtl4MVaFq2aRCS9vobUrwrp19tMdXRohFQaC/JcUjO/pJhpbao3QkxG88fCc7O5q5MD6DKAjc1t3Gwd5OnrpwHdN2UJZFRkIBlV+8Zz/7eqKe8HwAACAASURBVDo4MzLBVK6IJIokQwa3dbc1OkxVljmwsZNCtbZKe0sRRe7ZvIGgpjZ0lbx62rrn+WVSt3WvzMCz6ra8sOim6GA5LpIo0hqL0LqMCPdT4O3GUywIAlta0/zhBx/iyNUhhucyxAydgxs72dgUR6wvfyugLOt4r2Xm2ZJI87Ftu5EF8U1NjjzP41pmjsFchsc29qMIby6jUxAEtqeaOdjWSab6463rs2AWeHHuPI+23I4hLw1Ek2qE97YfQnsdLan/P2C4mGWylEdAIKJqxDQdI6lhyn7W7VStSCpgMOOUEB2RnFZlxiwRVjXksMhINUc0HMDExZL8zKFIMEAk6F/r3nY/Arz4/IYMbUW0vjXpv3vJaN323YOxoVm+86Xj7Ni/gYfefxtvBopokNJ3AxDVVhJ/vvaRQYtxxxq/FChOxGhqvg/9JnOAsNpFWO1a4zf+eSUDO7GJU7R9gVc8D0OOEVb2I4sBbM8iIOp4kovrOYBAWOumSd+2antxrZ+sDZOVUTQ5wHwtz0T1DACyKON6rZRsl6AnY3s1is4IqhigO9hLZ48f7b14ZpTN29tXOX0BFPNVThwf4LZDvXzk43cSCgdwHZfx0YVVZI1tu4wNzfniv7JfynDj6hSiKNDSGmusVy7VmBzPsHl7O6IoUqva3Lg2TTQWJJb023QtoFDMVxpEked5ZOaLzM0UaOvwS95UTaZ7Y5pnvneW0aE5NvY3r9nuReNBEqkQ1y5NkMuUiCX+9YNLnd0pNF3h8vkxdt7W3QjSDV6foVis0taV5PLlCSJRA8918TyoVE3CYR2zZhEMapRKpk/GBhTa2+J0GEkqTglN0hEQkQSZqlPC8kxsz3feNOQIXl3/LiRHsT0HQRZw647NpltFFt9ZwTXTdRDw5wXgv2OO47Kw4Luq1RyboUyG9nCE1jUy5RexqPmy3Fwkby1wqXACXQpxe/w+BEFgvljmsy+8xlTOd1At1UzKpk2+UqVWJ25m8iV+95++T6ieEWKoCoaq0hoL87N33rbK4MdyHJ6+eJ1nLw3Ut2c1/p2pZ4I4nsd/f/ksT124jq4qBFWFoKaiqwqP7dqMLbok1qgCeathOQ4nhsd5/OxlwgGN9niEnlScuKGjyTIVy2Iim+fC+AzT+SKO59Eej/C+PVvXNG4BKNdMvvDSaa5MzdavqUXF9MWnZwq+QVKhWuP/evJFPnf0pH9NFQVDU0mFDD60bzu7Ot94oEZXFTqS0cYzs+56ssLO5ma/YqbetgQ1lZ85tIfRTI5nLt0gX63xhZdO8dSF63QmoySDhu8yW6sxkS2QKVUoVmtE9AAf2b9jzf04rsvpkQm+NZ8jrGu0xyJ0J2MkggYBRaZqWUzmClwYn2YqV8RxPVqiId6/dxuRdYKnrudxY3aB7527yhPnrpAKB+lOxmiNhtFVBcd1mc6XuD49x+BcBtN2fOOT3ZvpSvrtdSlbpu+2jUwOTlMpVlEUic1bWv15eH2sXjPPI2lRECQsexRFXtn3bextoljwyX3X9YgqQYJygID0DiaOwpEA+++4aUCxrHPq3+I/rN09abp7lkfNl0odUo1665XlD0BdLMq/CYq8Urjz4wf2kAoZfPbF1xhZyPLfXzvLV6AR7VtMnxPwrZ6VW6SjvRXob07xy3cf4O+PneArr53layfOoSkypm1Tsx0kUWBzc4rfefhuOuOrBeW2tzbxswf38g/HTzK8kOWPn3yemK4jiQIVy6ZUM+lvTvFvHrqbpy5fX+FiJtbZ5fWEwq8tzBOa0pjIF0joOrlaFUXyBzBBRcFQVGRJpFgzydeqtIUj7Glp5efuuI3JXIGz41N87eQ5vnfxKkHVdz3IV2sEZJmfun0XO1qb+f3vPN0gOgRBoDW11AEIgu9scGZ8ijPjUwj4DZQiijh1Rt3xPAKyzOHeHj51/yHihu4z9I6N6dpL20ZgeCHLjbkMXz91AU2W0WQJD59JNh0HWRTZ3Jzitx48TF+TnxIduElYMqipfOr+Q4QDGo+fv8zV6VmuTM8i4D9Hyy3WJUFgX3f7mtc3Yeh8+sG7QBA4en2II9eHOHZjxG+AHF98dlM6wb95+G7+6eR5jt0YWfF7y/Uj1bqk0KSHyJrVupecy/J83XOXxymWagwMzxEJaQ3iKKr2UrBGMOTmBmm0CNerIIkxLGcEz1tbX+mdANtxefbyDV4eGOUPPvAQ927ZsCIrK1OqMFcocWNmgUypSnPUHyS9b+9W3rd36y23LwgC6XCQD+7bzgf3bV+1/H9/7/0rPt+7ZSP3blldQiuJIg9t38RD2zet+F4UfNHEnzywlPJdKtWwbYeP3bFnVdsoCAKhgMbhvm4O93WvS4aEAxq/eO/+NZcFVIXffPTwiu9CAY1PP3LXmuvfDFEQOLSpm0Ob1ne/uBmSKLK5Nc3mNQii337snhWfXc/j1ckxnhsdpObY3N7SzkPdmxjJZ/mX65co2ybbU8081NXLkbEhrmfn8TwwFN/V56e37ub7Q9f49sBlBEEgU6vwU1t2EdUCfHvgMlcX5ohqAT7Qt43OcJRnR28wWyoxVS4gCgIf27qbwVyGf7xwiulSkdOzkzzY1cvB1k5emxrn+dFBbM/h9mb/uBzP5fTMJFcycwQVhY/076AttFpjqebYfPHiae5s62ZLMs252Slemx7nZ7buQV2j1Nj1PM5kB3h1/gqWa7M91sOdye0U7QrPz5xlvDJHcyDGPeldWJ7DU1MnkASRnFXiUHIbu+O9TFczPDN9ipxVoiWQ4JGWfRhygG+OHSWiBBksTdGhp7i/eS9ztRw/mDpJyanSYaR5pGUfWbPEV0ae5UJuiKHSNHtjvdyZ3sFCLc/XRp8nIKn8TPeDGHKAi7lhTmcHqDo1Ko7JvU272BrpZqQ0zfMzZ8lYRXJmkQ913s32aM87JsovCX6k0sGlLRihZJvMVop+di0CRctEEgSytapPqjq+7kRM0xGBimOTq1UISApjpRzb4n70NTNXQFYkwlGD7LyfkV2rWLiuRzFfIZb0SYW5eglQpVQj3RojGA6waVs7Ow9soFpeavsrpRozk34pVro1hqJIzM/kKeQqBAyV5rYYoiSSmSuSmS+gqjLN7QkQYG4yR7ViEo0HiafDLMzm/ePIVYgmgsSTISZHF/jGPxxl3+F+Ojak6diQWrfcaxGKaBBSOpAEiVZ95eB6sSTph0FroJOU6pMmZadEXEkRlEN+9jJuw9rbw0USlsYHfVvauPuh7Xzrq6+Qz5XZtssvDZ2ayBJLBLn/0Z2omkw4qjN0fYbL58eIRA0unBnhxWcvNyYOi/A8jwtnRvnGl46zdVcHk+MZnvjmSXbd1kPnhqWxseO4PPGNk9iWQ1tngpPHb3Dy+IBPTC1qBu3s4OUjV/jnL77EPQ9vp1Ss8cz3zq3IOFI1mcMPbuPlo1f5yz95nPvftZPWjjilQpXJ8Qx3P7Sdjq4k0ajBw+/Zw5c+e4S/+OPH/aypoMb8bIFyqcbD79lDOKJTq1pkFoqYNZuFuQKW6TAxuoAoiuiGSiIV5o2ie2MT9z+6kye/fZqArtK7uYXpiSzf+PJxtmxvZ8uODoZG5/GgoUu5sFDCdX2762y2zMRkFkkWaUpHcBwXUZQIiAa9wR0rSp1sz8LDQwDkOrG9qBlZ//CmHEV/nOB5HqbrYLkuzjIiUxRFTNMPDqmSRFTTblmusxZCSpSNwW2Y7pKWV75S46kL15nKFdb9neO6TOYKcNM6LdEw79uzdRVxZDsuZ8emeOLcVV4PuUp1TXOijekEh7f2ENf/9bVwFUkiGTIYXcgyWygxWyg1slVuhipLbG5p4lcfuIO7+rrXlfSo2jYvXBvi1PD6hh1uXUN1MWt7ERFdY//GjjdFHCXDBgFVvqXEiCD4Y62V3wm0xSL8u/c8QHsswtMXrzOVKzKykGVkIbvmdjRZoi0WWRWQX4RcD4jemM0wVygxVyhxZnTta6pIIlta0/zyfQdXzQGWQxQE4kEdRfJNZhZKFa5Oza27bmciynv3bOUTh25rZE31397LxMAUWkCludvXrrvZvECSkhQq3wZcDG1Jh891PapVkw29TZimzeRElkKhgqnXqDg10lqMsPzWBfnfVsTRG30J/dXWXvf1thEKB9i3f8OajkdBVeGDu7exo7WZZ6/e4Oz4FMMLGYo1E1EQiOoB2qMRtrSkuWNDJ73plfWaQU0lYeiEA9qKI5NEkbjhp8IudzMT8CPwCUMnEtBWPJCaLJMwdPZ2ttb3leCJC1d5eXCUsWwez1NpjoQ4tKGLR7f10de0du2iJst84uAeepJxvnvhCtdn5slXa4iCwMZknF3tLXxg9zY2phLMFkscuTZIsP6ghjSVe7ZsWHEuuiKTCOp4HrRHImyKJ2gNhilaJq7n0hwKI4siYVWlYtv+y6TpWG6IlBFEAHa2tfBH732Yb527xPHBUWYLJWzXJW7o7Otq59Gtfdzfv5FspUpvOsF8qbImuaJJMp+6/05euD7E9dl5JnJ58pUalvv/cvdeQZLc+Z3fJ31mZXnb3s50jzfAGLgBsAuz2MVyPc2RVHBJxulExkk6hRihCz0o9HIPilAoLiSFLu6BOvGo0B0veDTicpfrF8ACCzcDjMGY7umZaV/d5U1WVXo9ZE3PDDCAlqc9cbm/h46y2Vlp/v/f//f7Gp+YKDGVSTOdS/P43BTP7p+lcB8aQxFlVFEGIv2n3zhznJlchsubZdabraib4UVd3WLcZDqb5uTUGJ/aP8dcIfuRK+9+xFXK0PlHT53mUwtz/GBphcubZXbaXepWP4JiphOMJRIslvI8szi3R5+7PwRBYH8hx3//8qf5xuXr/HhllY1GC9vzyWVinJwc4ysnDrGvkOPa9i7XyxXi2r3rzvV9lptVMloMu++x2W1xupRHFpQ9BwyAhKmxulFjMHA5dzYqPAShi+03ERDouhuktcUPXc9pIlFv6aFQyV+U8IKAcquLLIoUk+aDEuFD5EvPcdAUCW0I+Q9Cn+rgOhltFkW8N0jv9C/hBTZF4wg9r0JMzj/wPh/athtYyKKBeB8axQ36bFpvk9cXiSs/vf31/bF0c4dm0+LZpz++sNW1bFZWdjl+bPLvVAz7LiVTFiOawt9G2C+iod4b4+8+3+l1+bc3LvG1hSPMpjJ7sOQ/uX6ZR0pjHMoV+VcfnCepalyvV5hJZnhze50nxqZ4f3eb+qDPF+YP0rQH6JLE1xaOokkSP1i7xe1mg98+8gjv7mzyb69f5A9OnaPSs7iwu8XvnziLKkkkVI0ThVG2pzvcaTf4rcOPEJMjauyIGeezcwvsWF3+5vYSj46M4wUBIfAbB4/zlzev8a3bS/zu0VMf+b2qKGEqGq9u3GY+neW1jTtk9BjKh8bNZ2dnmctkMDWZ72xdYypW4FBqBlWM4Pw/rlyhYjd5rnSSVyqX+OHu+5zKLnKpeYuvz76IE3h8f+cCU2YRU9Y5mdmHKsr8xcYbLHU2OZGZY6W7zaie5YvjTyAJkfZHQjY4nYs0Qf5s/TUWExPsT0zw4sgpnMDjP5l5npRiIgsSRT3D58ce50/WfrSnQdd2e1xp3uYfzr/MRr/CK7uXmImN8FrlCvsS40wYef7N2g+YM0cRPyYv+PsY04kMU/E0ghAtTrPEmIin9sbwaAErMJfKDtGzRLnNh0Q9HD8gpigUY1Fx+5VvXiJfSvLUZ47y2rcvk8qaXLuwFglqFxM06xZf++1z/NE//w6j0zkCP0RRZb72O+c+ohcx6Dn88Bvv02pYOLbH+HSeJ54/xOV3b9OsWexsNvjU508wNp3jL/7166SyJooi8dRnjhKGIRfeWKbXHdCodvnq75zjz/+PH2PbHvlSMnrtt8+xcbvCxu0qmUIC3wsYncqB8KHVeXjvPg/CkIS8j4Op3/vIWHA3hJ/yOgmHNA5RjJDFmqSjSTqEcCBxLNrO/ZsKYDBwCP0QIx7RBp2BC0HAr/7Wk6QzMd554yavfe9q5IhUSvHSlyLklhHT+PXffZo//eM3+Jf/07eRZYlDxyf54q+e4bvfuIgs37ufVVXm8WcWuXNrd4/GdvzUDF/4lTMPCG5nsiaPPjbP6z+8zuqtMvGEwme//AjPv3wcUYp2/IlnD1CvdHjjR9d5+/VlsvkEz37mCJomoygyd3/i7L4Cv/8Hn+Vv/vIC3/rz81hdGyOmMrcwEhW2whBBCHn2M0cx4zLf/+u3+aN/8QN8LyCRivHY0wt752H1VoX/6w9fobLTptsZ4Dge//v/+j00XWVqrsB//k9fRtMUzCEl8v7zJ4oCiZSBbqgggG4ofPU3HieZivHdb7zPn/6fPeIJnUfOzvPiL52gUEoyPpn90LrhXtsQYP/+aE4VBGHYlAxotPrYnreHxKu3emSSMUxDpTdwEQSbge0iyxKO60WoWk3B9SJaXDZl/gKNRlEYkszx3Cj6EGEtCFAqJUkkdCRJRBbg7NQ9vbyzc5NosowXBMwOKUEfF7oYY9yYi8a14fnOJ2L8/qcf20PVl60Ou5bFseLDc6DrtQp3Wg3OTc5QjMeJ6QorjTrzmXsOioos8fyh+QdkPe4PL/C5XNllNp0hPdQ4uri7Ta3f58mJKR6bnaRgmns5yXQuzT9+7vGIkTCS/0jR7PnD+5nKZZAlkdn8g06OCV3jd86dotHrU0yYH0Fx5+Mx/tvPP8ubN9dY2qmxXm9S6/YYDN1xNUUmbejMFrOcnBrjyX3TTGRTH3vddRybstXlNx8/wYuH9z/wnuP7rLWaTCST6HK0H5Wexa7V5UC+gCSIaLLEodGP0r/2FXP8F88/Qd/1yJrGnqyBIAhoivyAs2+lZ5HSdL566ghn5yYJgUdnPl7uIXJYM/mvXnyKzx5b5JUbt7m0vs12s0PHjponMVWhlIozm89waKzEqdkJJjIPP7+pmM5/87lneOPmLZZ36qzXOkNB8YgSqcoy6ZjObCHDsYlRzi3MMJVL7x3TIPSGjYF76x9dkfn6k49weKzI++vbrNWiQl934OAGEfAgriuUUgYnJ6d5enGWhVIO5T501e5aldMvnWT71g71cpPEQ9CasjSGKu8jxHugqe95PuWtJlcuriOKAtVKh5OnZhifylDu1ynq6Y9s6/9L/FwVjn6aGLgesiQiD5FAdauPKkskdA3X91mpRPSm+UIW2/Xou94epUwQBIThZOl4PnWrRz5uIkviHt1osZQnZegcGi0ymkxEyCJBQJFEDEUhoakPVUv/vafP8JtnThBTFYz7bv6JdJL/7R98MYK73cePlESRLxw7yFPzM+iKtCd+LQoCzx2Y5/jEKClDw1AUFMPn648/wtdOHtnj4OqyTDpmoMkfb5ErCAIxVeWFA/s4MzNB17ZxhxQ0TZYp19t4TkCj2+f05Dh//PVfpjdwsQYOyZhOuzfA8XxGM1H35+Ujizw+F3XvSok4MVUhZ0RT8L5Mdq+7/WFV+/sfC0J0bn7v6bP8g1PHsT0vUqiXJExNJalrke6CLPE/fOklvCBgNPXR7pMoCpyeHufY+AiW49zjQQ/FwBVJwlQVErqGKN5bTggIzMVLTMSyqKIS0V1KBfYXcnSGcFXP9/cQZu3OgJSpM5FL7dkU3x9hGNLqDvCDgFzKpDdwqLd6HJ8Y4dBogfbAZuB6vPXBKtWmxeeeOMTt9Sr1Zo+DpcLHIroEQaCYiPObZ07yS0cP0nNdgruDmqHvuXB9/fFH+dLxw8Q1FXl4/J3AJ6ebxBWVjGaAAF5oExLx8sMwxOrZ7J8roigyuiYzNnJ3YBHwQxtFjOOJ/Yf8XgfXW9v77C9qKJLIZDaFZTt872oEV04bOm4QUOv2+NalG1zZ3OGLjxwiqesM/CZtZ4ud/mU0KYEb9NCkFJKgstb9MRPmY/ihQ8+roUsZLHeXnl9FFgxMuUDb3UQWdVTRZLX7KjltkbQ6TcfdRhRkkso4Xmgz8FuIgjKctELCMECX700KjuOxulZjfq6IKAqsr9cxTRXfD2k0LMZG03too3anz82buyiKyNxskSAIeeW161x4b5Vud8D8XBFVlWm2ejQaFrqhsrCvRBAE3LpdpdPpUyqlGCml2C43abX6ZDImzabF7GwBXVNYvrlDr2czN1skmzXp9x1uLJdxbI+JiSzjYxk6nQHLKzvIksjcXJG4qdHsD7i4WWaxmAchQl/cRe1Ft2ZEg73rPhfCXufG9jx0JXKF0hWZatdiKptmx+piyioni2N7na2WPaA2sDhZGqUUi7MvneNOq4EkiMymM9xs1tiXybHcrOH4Hroso4oSqhhZx/pBwO1WncVsnslkmhB4deMObhDReA/ni0wl03tJZhiGaNLw+7KCIkn0PZfL1R1uNmr0PJf6oI/j+yiixMFcgalkmoO5Aj/ZerhouiAInB4Z519dOc9ys8Zap8nz0/s+Mi88OjbGo2NjBGHA6ewiP6ldpeZ0eCx3kJAMy50NKnaLQeBgeQMyapyQkBE9y2SsSECIXz5P37fZHTR4tx51bGtOC8uLaHOaqHAgOUVJv+d8stbb5UJ9GUEQqDsdep49LCpFDkW6pO45DwmAIn50nJ2LjzJlRloA79aXQIC0GudWd4u2a1HQ0qiS8nda7PxZx4cFZoX7/vLAY4GPvHXfC5okMJ3I3Et8/WAP+RoGIWEQSSEvHJ3g3EtH+aN//h12NhsIosCppxaYnCvyh//jt6iWW1HR5r6oVzq8/+YKJ5/YR69jc+H1ZU6dW2B2cZTaTpvqTou1lR0Koym6nT5HTs0wf3CMVNbEc332HRqjttPm5tUtWvWIJrL/8DjPvnycf/0/f5edzQbHzs5x/sdLPPu54xQmMtzcrhEbLtA1RaY3cJBEkb7joqkylWaXsVyKTt9GlSWmSxmMn9J2/sPRblhUtpvMHhxDkoR7BacHCnT3wnFdLv/kJjcvr/Pr/+QlAG5f2+L1b13ki7/7DF/9jcd5/uXj2AMv0jPRlb1CjygKHHtkhqm5In3LRhAEkmkDTVc4cGScVPpBaYTSaJpf++1zdNoDBCCZjmHGHyyyIAg8+tg8L37hJL1OBUVqkMofQr+vYZVIGnzlNx7nuc8dx3U9VFUmnYvTbUfmKpIkEtIHv8qhY5NMzxXodAYRTU4SMUyNZNKITDP8TXR9hieeznNkoUcv+AohOrIik0gaxMxosTU1m+cf/pcv7mlW7l1PdYv3Lq6haZH47X/6Tz6DJIl77rUMf+d//d99cU/CIjpOMb7wq2coTmbJZU1yuTipjImiSly9usWxIcLrvgPzic+bzR7f+Ob7TC4UKWRMtqttrL7D1EiGdMJgaa3CRDFFb+DguH5kuNKzGTgukiiyMFUgm4x9okD037cQBIGiEmdEExACITKskCLtq3xew3W9YY4v0OvZJBI6c9kMB0YKkdaWH6BIEoOBiyyLe9o6oijQaFjksnEkSaTa70duaIFPEIYcnxuh7dgkVA3b93ljc5WjoyPMpjJoksxyo4YiisxnstT6Pb59+yafn18kreksNWqEXjTWWY7DcrOGIcksjhUYz6comiblbpeEqrHWaeIFAVOJFNpthWLMZCaVYS6d5Vx7htc2Vvny/kP0PRddjhA05W6HkVScz53cjxsEpJSPopDOzk1ydu7D118UpqZ+IkJdEkUOjhaZLWTY7DYw0IfaROHQVVJAkaO1k6mphAQf22TzgoCNdptACHnpyAKNwYDdXpeB57E/m6PR77PRaXOsOIIsitxs1KKiW5jnmckZ1tpNDFlhIp1irdVkt2dRiJmMJ5L08ZiZyLIvk0MUBJabdRKqymg8wWqrSd/zKJompqLyJ1evcDhf4PT8BJ86MPdTzdl3C1DHJ0c5MFqg3bcZuC7uUDtJFqM17V064Sc1GkVBYHGkQDFt4fsz+F4M2/fvXY+CsLceNXX1I6ZSLec2TtChZNyjaguCwGg6yS+dOMhzh/bRc9xI2+m+NakT7NIPb3Mwd/oBEMndaNc6LL27Qm2rztTBiYfuu+1cIaY9QRBauN52JJbNPWdD3wsojaapVjtIisB6r8J6b5eMmmDM+GRx8r9N/NwVjgauS7Xb24PotfuD4cURVWd/tHSbUiLOgZE8kihS7nQYSSYIgoCl3SrfvXaT09MTzOQy7HYtwjDS1nE8n6oVJSbFeJxmv0/N6pE1Yzi+v6dyXkxEleSVSp25fJbJzCc7ibxeuYITeIwaOWzZYblXp+CnaDpdBoHLdKyILbg0/S6DQRbJFrlj7TCiZ9ElhRV3m3ltDLtnsdTZJK2Y+GFA3emQDE3yfpIf7LzH4/nDFLQUlwe3GI/lOZqYfajNZ6Pb543rd9AVhZlihg/WdzAUmZRpsFppoMoShydLXFrZptbusX8sx2a1xXQxQz5p8v3zy9xIxHjmyByXV8vkEjEShsZPrq/i+QGj2QS3yvXIqnCyyHajgzVwODxV4oO1HUxNYaaYZXbkXmXd8wJ8z0dRZbrtPq2GxdRcETkmsn6rgqJKpHIG1c0Wmf0jBH6AY3uMDDnSd3nUlXKL5atbnHvhML4f4DoeqqYgaxoZXb8rr4Q9cFE1Gatr8+qPrvDU84cQxIii6PsBsiSi3KepsbJR5cqtMqoscWhuhKW1CoQhc+M5Ln6wgRlTUY7McHO9Qt/2GMklWN1uIIoC8xM5rqyUcVyP504vsFVpYbse48UUl5e32aq0OLpvjHOHZnlvaZPRZAJjWuKd7jpta8Cd7ToLU0UuLW/x5PHZ4R7d64apsvSJWlo5M0bOjHE/PlpAYK3TZDyeYtNq89LUAk23giTIKKKG7we8deE2N+9UUFUZf+ics7hvBFGIkFhNexn5oagYEVFMIAgKwt9Sn+XvU0iiyLMH5nh/bZu/eu86P7i6gq4oBGEk1ugHIS8fP8DXn3yEEJdy731EQcYNLLZ7FxAQ8UOPMfNRQMCQI1eeur2MKRep2TcIQg83R+uV+AAAIABJREFU6CELOk5gERJQ0A9h+5HAZs1eom6vRBoLpoIiGgiCyMBrYHm7iIKCqRTQebCb8O3vXuarXz5FPpfgr775Pp/9zFGSCYPV1Sq1WpcDi6N0LZtvffsyMUOl13O4fqPMC88dxnGjCVRRZURJ5NqNbf7m25c499QCN1d26fcd9s0V2dlt4bo+P3lrhec/fZhvf/cK2UyMnd02xWKSSrWDLEtsbTXJZU0uvPcuX/vqKd5+5zaNhsXoaIpGwyKdivGt71xC1xQGA5er17f46pdO7SVFju+z0+7g+AGe79N1HCRBxHIc4prKVDZN3erhByFu4HN4pES1axGEUaKoyzKNXp+xVJK4otL3XGp9C1Ew8YcabKoks2N1iSsa1b7FdDJDz3P3FoZ7RZ+HXCeCIJDWDXZ6XSzXYdvqEFdUZDG6NzTpo2O0JIoMPA/b9xEEgWrP4tu3l/nHJx/DDXz+xfuRrpYfBuwOt1vpWSQ+DL2+b4fG40kmEin+YvkqOT3GeOLh3ba7sZCcYMYs8Xb9Bt/YepPfnfssOS3JWCzPS6OnCcMQWZTYHTRpuRYdr48fRsUwRZD5wc77LCYnOZs7QHXlnkuVIAhDNOe9+F75PGdzhziSmmG7X9ujCYmCgBf62L6LLqpIQ72ohx1nbcjRv3tOJEFkwshT7tcZNXI8nj/0kf/7ixB3dTxs19ujzUuSiB9ECwRZkvCGSa86pFff1SS0XQ9ZkvY603eT1UiLcIjqc33CIBJULo6l0XQFVZNxHQ9FkYknDWJxDVmRGPQ/qqXn2C6e6xMzNTL5BAeOT1Kvdvjun53n2OlZVE3Gc30y+QQv/+pZzr++zPtv3ORLv/UkN69ucfPqFovHJhElkcAPUFSZ4mgKTVciUWPnQQFa2/HYrrZQFImdeodETKc3cCik41j9yMa707eRJZGB7YEAE0M3uPJajfOvXCMMQk596hCKKvPWdy/jeQEnzy0iSgI/+ZvLSLLIxHyJw2fmeO2v3+P6+Ts8+uxBzr5whDvXt9i8VcFzPY4/tYimK7z1nWgbJ55aYGr/CAcfnWHp4urePs8eGuP9H9/Yy4EG7T5X3l4hCODMc4doVTtcfGMZe+Awe3Ccw6fnIPdgt7k48rCOsUAyFSOZ+gQKQhgiSiLZXJxM/AMIeyBvEvYuAgNAAwQ0AoqFBQga4NdAOEAmvgWhA+4YBNuEzkUE40uYhocpXwNpAoI6hF3wRgCJ0H4FjC8gyoukc0XSugHuCsjz4L0L4WEQ0myXW1y8tIYgCDzz9AF8P+DV127gOB6CKHD7dpVGw+LYsUl+/PoSJ09Ms7HRYPlmmbip8eSTC6yuVvnJ27dwXZ8TJ6YQBHjt9SUmp3I8dnaOXDHBxUvrrNzc2SscnT9/h5WVXfKFBE8+sZ/d3TYXL60RBCFnTs+hqhKvvnoDx/EZ9BxOLo4TAlbfYayQYmokg+cHTI9mKGUTiIIQOf0SCSffdQQ19U92bPr7GM1mj5Vbu0xMZNnZbTEYipGXd1qUiqk9Q6P98yVW12scPjjO0nKZiYksly6v43kBuaxJqx01mPp9F9/32T9f4r2Lazx9bhFNFLi0WyZnGLRsG8f3uVwpc2p0nDe31jlZGsNyXSo9izutBnFVY6PdwnId/DBgMplGl+6hoRqDPuvtJgdyBV7duMNuz6JlD3hqfJqLlTKfnp7jh6u3eHl+kWrP4ka9Rjtn0/dcghBeWbuNJsnEVS3SDAV2rC7Vfg9DlpEEkYJpUrctJEHEkBRabo8gDEmrJk7g0vMckorBwHdxQ5+ErGN5Nl4YEJOieU+XVLpeHwGBvu8QV3QGnoMb+iSVyPjAEz2KcZO+72J5A+KygR24OIGHIAfsDlpca21yJj8fOQA+5Ppr2zbLjRpHCyWW61Wu1So8OjKOQJRrXdwpM5/OUu33eGtrg4xmYLkOP1q7TddxaA76fHZ+gdfWV0lpGglVY73d4rX1VY6XRvCCgFfXbmP7PrV+j7Pjk/xo9RZnxya5VCnzufkFmoM+ETj2P+z+0GSZQuJvN9eHYYjlldmyXscJOkzHn6dmX6RonMSRqtj2DSRZI6sfpNq/TNevoYmPsWltEAJZ7QCVwUU0KU0Y+jSdm/TcMnnjGJX++6hSkqQyTcu5hRfa5PXDuOI6vl+joB+nYS9B0MBEeWjRCOCR54+xem2D4lSe8f0PpwJKYppu/68J8YlpT+y9LggCiiqzfyjnk0jqkaat5VOVWhjSz5Yd8nOVaYVhSKsfiRGvN1p89eRhvnvtJgldYyyVYDaX5crWDpWExWgqga7IvHV7ncdnp0jqGqu1Jp2BgyZLBGHI5c1yRMvKZzi/tslao0kpESdt6Kw3WlzaLDOVTeOHIe+tb7HWaPLiwf1MpJNkYj+davzOoMGp7CJJJcZfbf6EtBrnRmcDXVR4JLufS83IiWkxMcmokdvTi7htbTMVKxKEUQJ4pbWKF/o0nA6KKHMkNcOtbplxI8dsfIQDyUmaThc/DNBE5WNtPjeqTUxN5bHFad5ZXmcyl6LnuFy+s83sSBZNlnnzxhoT+RTpmI4qy2TiEn3HJRnTmC5mmB/JkU3EyCVi9GyX9Urk3DZdSPOjK7coJE0Wxwv85Poq1sBhNJvk5naNcqPDr547jqE+2N1757UlNE3mwLEJdsstrM6A0cksl969w9qtCvOLo/h+QHWnzfR8ieuXN+i0+szsL9Ju9jATBr4XkCsk6Lb7eJ7PyvUy9UqbkYkMruOTyph0OwMEAcobDRaPjJPJx+n3Il2XrbU68aRBq2Exu1CKINjD6PZtYppCPm3y/beXWJwpkkkYrJUbTI1kGC+k6NsuN1Z3KWQS3FjdxdAU9k8VWB9+RldlStkEru+ztLqL6911fwu4tVnlyPxHB4KYrlJv9Ybbuysm7OH7ZQQxTmQPKgD+fd8SiUgKAYQ+4CMIGkHYRxA0JDFNStOZSWZo2H0SSiSQZ0gmbbdKEPqoksq5s/vp9hweOTpFvdnFum9R4PhtBEFClZJ8uBMniVkSsS8OB/2freDaz1ukYzp/8NI5Xjyyj1uVOp2Bs8ePXhwtMFfIYigyfmjjhw5JdYKms8rAb6JKCRLqGKZcJCbnSMijCIKEIsYI8JAElZQ6RcO+TdctIwgiSWUCUy4Qk3PE5AK1wQ3coEdanY6+F3oEoUtCGaPtbmAHbUZiJx7YZ0WRmJstcGOpjD3toSgS42MZVFVmdrZArRaJHzbqXS68t8qhg2M4jke53OKF5w6zf75EZbfNmVOzCILA8s0d5uaKfOqZg5imxsZGncX9IxiGSqvVYrfSptPtE49rPPrIDG+8eZNjRya4s1plaXmHTCaGKAqsrtfY2WmhaTLtTp9DB8eYmy1Qb1icv7DKoQOjuJ7P5maTrjUgHtfZV8ghiZH2UkLT6Ng2VrVBQteYyaXRFYWcadDqDUjGIsfGnGkwcF28IKSUiAORo6IoCkwlUxzOl/jDK+dJqRqL2QKfnprnhel9/PnNq8SG8OzTI+PsWJGbiibJSMMO1N0CkiKKe4UhQRB4YmyKP7p0nv/l3TfoOg4vTO8j9AOkMPq87/mEQ+SlKIpMmkm+fWuJP3z/HZ6ZnGE8kaKoxfjL5auICJiyAmGIKkhsWm3+5cW3qfQsfnnxCJbr8MO1W/x4Y5W+5xJTVF6a3U9S1Tg7Osk/e/OH/GfHzqAKEv6w4TJkfO1RACx3wCuVi1ScJrbnMq7lUQSZJ3KH+Xb5Xf7d6o/QRJVHMvuJKRo9f8C3t9/B8gYsJifJqHGmzRJX26tU7Cau7yEGIr4XIAUioR/iD91ZBFFgUi9yvrbEzdZmtE+BgO/5JMUYGgr/fu01TqTmOZ6Z41prjXebS2z2q3xz6y2eLBxBEkQU4S4iKSpMhSF0vD51p81SZ4Oa3eJs7iBx+T++7sT/n7GyXYvQ0Z0euiqjyBLFVJzV3QZhGJJLmmzX2wDMFLOIohBRZUSBWjvScRm4HocmS4wNxa7NpM7OZoPNO1XuLJXJFhIID9GE7Hb63Ly6hT1w8RyPTD6BM3BxbQ/X8bD7DvGUQXEsTbaQoDSeJQxDajvD/VkY4eLbUf7Tt2z0mMqTzx/mW//ubSrbLXa3mmQLCQojKfqWDUSzjfAhEVVJklA1hZ3NBsmMybljkdZbvd0jFdf33I4i4NR9Rh1Ei8e7Bbd3fniVyfkSiyenESWBd75/ldJUjmwxybs/vMrBR2exOn1e+JWzvP6tiyycmGLh+DSBH3Lm+cPoMY36ThvHdvn0l08hKRLvfP8qxcks+ZE07/7wKlP7P5lG7Lk+r//NJQI/wO47LF9aZ3yuQGWrwUu//gTx1H9EwV2pBO4VCNrDF0QIyiBmQD1LOPgWgjQJ2mMw+AEhAYL2VFQg8lQExQV5FrwbIMjgfQBooJ4F9zwoJxHkRZAPRNsGQAV88JYgHICg7dm2x02dlZUdVler9HoO+VycTMbk/IU7mKbKu+dvk8mYtJo9giDkBz+4yvhEhuXlHfbvH6HRsLAHLi+8cARVjZD3M7N5Hju7j6mpHLIssrgwwoXzd/YOwfZ2k2RS58DiCIIAr7++hON4OI7HtetbxAyVdMakVEzy1tsrZJJRUe7UoagwdXcOSMeNh+oE3i8u/4s0DgE4rofj+pHO5sDFtqOirqGr2LaLZdmYpkbXsqlWu/R6Ns12H73WxfV8EnGDTtem0eiRTBpDJJmCKInoukIYhMNjDH3Po+3YyIJI1jA4mi9xu9nA9n3Sms5UMs358iZ9z6Pj2IzFk8QVbc+J0vV9REEkpxtsdFo4w0KG5TqMx5MRUiae5K9vXudwvsRur8vtVoOe69B1bCRRZDqVptq3GHjuEN0cbXs6lWa5UaMBPDMZucEud8pookzfd3ht9zoZ1aSoJ9kdtMlpcYp6kmutLQxJxQt9dgYtClokrq6IMgeTY1xurkWF/yEFuW53yWpxDEllIpbl/cYdxowMb1SWGPhR0a7j9kkqBqIgktFMrrc3mUsUickaH8btioJA1jAQG8IeUnsmleFwoRghFjUdQ1ZwAp+e65DSNCaTKa7VKtR6PbwwYCKRJKYonJuc5kJ5i+u1CvsyOWKKwoFsnhCo9HtIgshkMoUuyYyYCQ4Xiqy2m5iKStGMM5NKE1MihLDrePQ6/chCPgSn7xBPx+i2ekiSSLfVJ5GJoagK3VaPRMak1+njewGGqeG5PnpMo9fpo+jRNq1Wj1Q+8QC12gnaOEEHTUqiSRkU0cAPbRy/TUwuEeLTslew/TqKaGJ5ZdruOqqYpO3ewQ26TMWfo2EvEVcmMOUStcEH2H6Tyfin6bjrNOwlDLlAy76FJGq4gcVu/zySoJPVDtJy7nzs/ZUby5AdSUeovY8RElfkOVzvNqKYQBRGCYb3zN0Iw5AwuDuHCozrBUb13F6++rOKn6vCURCGbLXaDDyPcjtS0RcFgZOTY0xn04RhyHw+y5GxEqOpBGEYMpFO4YchiiRxeLTIYOiypckyc/ksa40WQRiy1mhyYmKUhVIkojqdTXNztwZApdOlPbBp9gY0ewMmPob7+rCQBIm4rKNLKgklRkzWmIwVuN5eZ7tfRxUjlEJcNtBFhYQScbsmzSJpxaTpWqx0t9BFBR+JWXOEmt0iJkUJkSLKuIFP3W4Tlw2yaoKV7hbTZom4/NEkQ1Nk2r0BW/UWqixRaUe2lXFDI25EOjiyJNKyBgiCQFYU6fQGIMD+sTyaIrPb6lJKJ6h3erR7NglDw7IddlsWieF2dFVBliTihkYhZTJbzLJdbxN/SKelstPiyecOocc0zLjO5mqNTqtPtz2gOJpGFAXiCYPlYZK6s9XkwLFJVE1hd7tFxvFxXZ98KYEgCPR7DjtbDYojKVZXKkB089QqXbKFOK1hQiGKIoIYWWm6rs+1S+vETA3pQ4KxQRBSb/cQRYFCJk7HsgmCkGTcwHY9Ks0uI7kkpqFSyiZQZYm2NUBXZfwgRNcUqo0u3b5NvdWj2rTY2G2yvFZBVSQc16fSsKg2ujTaPWpti0qzS7s7YGY0y/nr6zz7aKQx5PtlbOddJLFIEPYBH8/fQBBiCIgIgo4sT+C6t5GkHGHo4vs7IMjo6umhBlG0wNqy2swmswhCZNtZsdfJqCVUyYgESvMJ3nhnhSAMeeLU3F7So0opnKBD8BDxa0EQEfhkq9VfhPDDgJ5nY2gqZ+cnOTM/iR246EN6I2GEm+j7DiEhpjLBVu8qATqjsWNstlcIHR8zdPGcFM3egJ5X5059HSehQhjDMFV8N0FczFIfrFEbDFB9qHcEfO8y44kFnKCLIEi4QY+eV8UPbZLKBIIgYYjZBwRYIUpWF/aP8Poby3S7NvNzxYc6+PhBSNzUOHZ0gpihoesK8bj2UGR9KhUlebIs4fkBb769Qrnc4vSpWZaWyxFiQY7e17QoEbw7oR0+OE6plOL0qVlGR1LMzhZIJnXeemeFtfUaJ45NYcZUjh2dJBbT0DWZuKmjyBIZTafd6jFpJgCBtKohOiGGEtEGwxBwQkY0k2zcIAhC7L7LiBnHdSLx+0TiXufN9wOeL85RTVoEQkgxnmDQczhg5ChMGfRdl1I8QSyQ+cz4PnRJ4aXRfSR8lS9MLmIGKs12j3PFaRJDZyoBiPVCnhdHqA16SB5oN9pc31xhTlFxHZf31j6INO50lXQhCX2bzysTbNyqUt3dRC32eKKTQMrHkN2QRH4Ce7PD8+lJ0gdStGybmKxQMiPq2NF8ifn00O1IkjDkyAo5remMmgnSlsJ7b90ikYqhaTL1apeQCKkST+jUqh0KqSw5PY3VtjF9nZrUJSYaPCOeoO1bYAvoogZ5mDAKPFM8hiYqe5Sw50uPULFbiIJAY9BGqAjcLm9xuD2JtdVjKRVp5hTHM+yvjTKuZqncapIPZuntWlyL3yaVT/CUfYi+69Db6LFsrrHbqHF8/xzPLpxAFATSapyMmmBfYgwRgREjw69MPoMTuNxor/Fk4QimrHO+voQuaTyZ/6jQ/N/36PRt8kmTMAxJxnRimoIsicR1jW7fppiO43kRIs9zwz3L7JFMgo1qC2eouXI3Tjw2zw/+6n1e+eYlJucKFEfTOLZLfEiZGpvOkUjH0DSFjTsVVq5t8cQLRzBMjTd/eI2bV7cIgpAfffMS5z5zhGc+d5zzP16i37/O4okpphdKiJrEn/7x62QLCZS4xm61zXf+/DwDy2Z0PEtqJMlUGPDqNy+xs9ticqGEpMnomRi+ANVmFzNn0g98JFnkzLMHuPDGMpXtFi98+REkWaKUTTywUH9Yanz33g/DELvvYCYN9JiK5/p7Cw4zYWD3HURJIFNMksrG9xBZiiojK1HhShQFRFkkP5rGiOuR3bvnkYrFMYeCz/9vEQYhgRcwf2SC8bkCiYxJs9ohW0oSTxqoPwWlzoipHHtkhuLIJ+epuXyCIyenI5pXGEBQhaACQgwEFUKBqHjUAX8DQcxEzShvHYQ40AMhiSCohIIRFZz8clR8QoTQAzENYlSgRzAiRFNQi86GX4FgF+Q5sH8E8gEEdBzX5/z5O8zNF9ndbeO6UX5nGArxhI4siWQyJoah8v7FVcaGjQ9JFjlxfArtjEyhkKRcbpHPJx6gsSmKjKJIKEPdQVmWHliEPfHEPm7cKPPKqzd48YUjeH7A/HyJqaks8bjOB1c3MYZz4f154od1az6uJvSLViy6P3LZOIoioesKyYSO5wUoikQQBFg9h3K5xcxMHl1XiMVUDEPl2JEJFFlidCQVyTII4Dg+miYPNbEEFEXixPGpqPiHwHQyzSvrdwA4WiihSCKKJDEWT5LVDdZFkfPlTY4XR0hpOq+u3aFj22iSxMWdMuutFm9vbfDExBTv7Wyz0Wlzq1HnVGmcd8qbdG2HmKJwKF9gpVnjQK6A5ToICOSMGGOJJKokc768iTGcd9/cWqfSt7hWq3B2bAJFkpAEgawRQxQExowMTSdirYwaafYlSpyv30YSRJ4sLLJqVYkrOodSE/zfG+8ybmQ5mZ3h3doKbuBxvn6bUSPNu/VbTMRyyIJAVotzJjfPa7vXeTQ7S0zS6fsOK90dCloSTZSJKzqn8/O8vnuDUX2cTqLEjFkg8AN8ojFu0HPQDBUbn/e2NtlsNrm6XUYXJFRFGjp4yXxQ3aHSt/igssvRYomleo2r1V1m0mmKsTjv7WwhCAJxVeXy7g6271EyTSaTSW7Uq/zZjas8PjbJk2NTXNwtQwApVWMsnkAMBYq6iRDCTCLNq6t3SGoa+ZjJzlqNtRtbxNMx4qkYS+/d4eDpeeo7Lcbnitw4f5tUPs7YbJH3XrnGwskZ1pfLSLLIwslZdtaqJLNx1pa2MJMxREmkvFrh3BdPPVA4itwRFZLKNEHo0vfryF4MEFDFOG7Yx8cGRJLqDCIysmiQVCbQ5TwDr44ixoborDaSoKCIJo7QQRFjKKKJIpqY8giyaFAbXBuulyS8sE/fqwHBR+6rvf0T7knpfFwM3PdQlYMEocWN6z/C0M4Qi0WsAU1XsAcO7VafdMbE9wIsyyaXj9PtDkimYoyMpX8mY9TPVeEoBLq2g+16jCQT6MMFgnEfOmQkleAnt9cwNRU/CLhWrlC1IgSSLElR4QKBVn/AhfUt6laPw6NFDpQKvLu6yUqlzlP7pnlvfZvbtQaXNsvoioxlO2RjBqamcL1c5WalHtlRm7E9PZmHxencInHZQJUUniocoWK3UAQJVZSRBYkz2cUIbijHUCWFs7mDbPdrxGWD+m4HujCbHEUVZDa6FZy2y2xmlKAeMuHnSYUm016J1Zu7lLIZREdkSiqh+NJDz95MKUtIpOE0mkzg9F10TWE2m0YYwmmPjBZp9SPLvnzMAC/AcTyazR5HZ0bZaXbww4B8Okag2QixAbmiBIHHqcNFBp6DbsLpQyM4vofVd7HFPgv7Hm6/mc0nSKYMwjBke73O7nYT1/EI/IBqvUsma7K1XqdSbtHt9NFjCtcvr3Pg6ASeF7ByY5vJ2QIbqzUqOy06rT5mXGf9TpWxySyd9oClD7bIFhJomoIgClR324iSSHWnTXmzwch4hgtvrvDc548jfqiaK4rR5DVZyjA1kmGr0mKn0qaUiuOHAevlJn3LYXGiSLs7IJlNYGYTSAgRcith0G712Cw3ScV1Ds2OkDR1Th2aJBgm+47rc3TfGAhg6irH90daI6IoEDfUPctjUcygqicRBGWYvPYQhTgIAoJgIIppBEFHFPOIYoIw6INyiBAXSbzHYW07AybiKXqeM6SdqJhyMuoiDjsRZ07OcnhxDEkS2Q1alAeRKLYdFIkpk0iCQN2xhm5wAW7oIwsikiBhyhqGrP4Hw01/3sMJXC40bpKQY1TsFgnFwA089sXHWO9VI0qk7yIKArbvRY46wjTjRpa8Ps4HNw2qnR71TA3LnqGpRg4LpvRZNjcdJnIpXMWg2Yg6/X5QwAkC1roDHO8gfhgjmRshqd7jOh9WfxmAhn0bx7comUcfuu9joxm6lk29YfHLX4kc0OoNi3rdotnqsVtpk0wajIykaDR6aKqC63qIokgiYQypa9uMjUU6NR8+x1bPRlElej2Haq3L0G3ggc8IgsDhQ+Ns77QoFpP0eg6iKLK2XiMe05mbLbK2XiOdjjE2lqHesNB0Bcf19xyEatUOF969zcxcEc/1UVQJ09Ro1DqQCahVO3Q6g6gQUkzuueSYpsrubhvDUDl1Zg7DiBYW1YbFxlodWRIjZEbgcL1WZ2C7aKrC9k6TZqyLGVMJiTp0fhDSM1TKuy0URSYR19g/VyImRg6HkiQysGySooqpSbihh+u7aIaCU7cwVJmWZaGoMvnxLN2mRbdpYcoS84k0uqmhKDIThRxm0mD79i65gs7KxTXOvHSCWEzfEzW+G9OpzAPPvSDger3Cd+/c5PToBIYn0vIDwjCgXu1i2y6SJJJKR0mSaeoogkS3NkCzQnwhoFbpIMkigQeleJbt3QbxRYMefSRBpKRnSSr3KDGGrDElRyKZfbuPIAr0egNiroaZNKLxfeiONajbJJMxZE2k3bBQ9ajQVRhN09jtoCARL8XYvLWL4knsS4yR+RDVziQq1KmCQkFPU7NbdIdwfUmIusG6+IuHgDwwWaDjtfECD0EQERERBZ/FuQSyKCNgIiDghR6KqOAGDgk5iTyk7eWTJrV2D1O/t7gujmX4tX/0qQf+z+FHZ/Yev/iVSIBdM1Se/+IjjEzco50//dljPP3ZYw989+CJKQ6emKLa6LJWbrDd6nL0+QP4fkgpl6DW6nF9s8qplw5j9WzMmMbl22XCMOTYZw6RTka5wUCE2EyG7cEAthrMn5qm27Px/IDFk5NMnyzghz5WGFnHR7SOAbIo0fP6pNVIFNbyI22ehGySVqPrSBAETjy5wKU3lrl5aY2TTx9g4cQUb33vA65fuMPxJxZQNYVEKkJIJtIxJEkknU9gtfu8/s2LnH3hCEZMQxqKVIuSyP5j07z1vSvceG+V40/sp9cd8Pb3PmB3s8GFV69z5Mw87/94ie21Gudfuc5TnzvBiacWuHbhDvXdNo88vYiiyJjJ2Md2mT8cI+MZ/uk/+yoAVbtNZdAmqUT3gSLKeGFEnZo+UeDrxz+NE3jUnA592ycp7Schj4BgAiH4u+C+Hz3XX46KSEEd9GcRgg6ICcLQxQvBR0YO2gTiKIR9RGU6QhAFAwR5ISK8SaOI/i6iVADtLFE5TwY8kPeBEEkGpDMmN2/uEIYhhqEyOprm1VdvsLnZQDdUVFVhfq7Ij19f4rlPHyYe1zh2dJIL762STseGhSWF4EO81qnJLG/85CaO4zE+nuGtt1aoVju89dYKJ05Mce3aFtvbLWIxFd1QeOTkNFeubNJoWDzyyDRzswVeeeU629tNDEOhOrDY6rYpxeJ7XXvbj7RLJEHAD0M0Sabvu8iCSExWyGi/WKjHuyFJIpng1DnVAAAgAElEQVS0ycB36OHQCnpk5ShHJh6SnTHpKQ5e6IMJDd/CUwOcwGPKzGG5DpbroqgS7cBBFgQcL0Dwoe3ZaJZNz3NJqBqfmpojqWlk9XvH8sXZqLl6/EPC2C/PLvDunU06lk08UHhxbB7XD7i6scuhRIEnS1NsNNpkTINpNUU6ZiAhIgsSX95/mPbAJmMYfHHfQZwg0hZMqzr70zkMRabvejxaGuf0yAQpXed6rUJz0OeFmX2IgjAs5pRpOT0UUSKpxFBFmaKWZBC4fK98mWmzwMB3OV+7xaFUlNMpokRCMRgzsrxZXebp0kF6noMbehT1FB13gCJGufZKd4eNfo2dfoGj6Snabo8RPU3HG6CIMglFJ6EYNJ0eH1TXGVzpQBASSxjUyk1ESURRZA6nYjw1NkKr3KVd72K1+1xQtznw6CxPT07y9EQWQhuw+LUDBSJWgQehw0J6FHAgrPCZ6Vi0YJeyCKLOVxYPYVk277x7O9KqFE0kUaBqNRkzDD64sE4cWLK2STsiBSVHVo/yCddx6Vs2sYTB7kadQc9BECMkku8HxBJRk3Bno067YREEAd2mRbaUZtCzqW03UXWF0ZkCru3RqLSjAr0fsL60zch0AVmV6Hm7aFKGhn0TRYyT0w4iiRqamEYWDYLQozK4hC5n6TobZPRFsuqjtBwLNzCAg1T7Fl5QxA8cNjoDSrE5ZKHATq+D48fouQfJ6jqqmEWWIgdCVS7QcRuEfoAsFrnUWGfMiAo4oiBgufbe9ZDXE5/oCisg4we7hKFNz5Ior+0wM19kfbWK7wdksnEGAwfH9qiUW0iyhCyLbG3UGZ/MMTL2sxHJFu7v2PwdRiR3Goa4vs9gKLIWUxVc34+qu0OBKsfz6bsuMVXh/2HvvqPtuu4Dv3/36be31wvwHhpJgARYwCKKRbIaNfK4SCPZsa2RPW6T2M5kZSazVpwpyyv2JHGSNYnjxDPjcR15SZ6xRx7ZsnqjxE5QBNH76/2929spe+ePc/EIAo8kwAaQ3B8sLOC+e+85+5x37z77/PZv760UtIL45i3p2AggiOTmULVm10cSL/FnGIKWH8Q9xI5NJwgJogjHsrBNI35OiHjoiZSbk3AnHeeaVvSB+IbyfHORidQgCfPlszOmzy4ze2GVarmF41oYhmBgJE8i6XLq6ByWaXDrXRPMXlhlfaXXsJeKZMpl/8EJiv2v3OP0xNPnqFbb5HIJqtUWlmUie1daz+3NbZP2qFRbDPRnyeUSbB8vvTjLe2edE7Vz5JwMy501EmaCfrdII2zS5+bxZcB6t4prOgQyIGOl2Ju9clLWi3MOXfx/GEoSCRvfj4jCCNezkVIR+CGJpEMYxvMXeQmHMIyj5nFPnMLvxj+XUuH7AZ7nbM53ZDvxhIrdbojn2SAE3baP7VgszKwzN73GvQ/dvFmWixZWq3T8kB2jLwZezk+vsrBUob+U4fipRRzHZHgwTxhGpNMey6u1zUn+TNMgnXRJpVz27hm+6kZDu+vzwpkFhvtyjA9eGgl+6XdSqYtD1QRis1IRV7zuxZ9DI+hS7rQJlWQiU6Ar20w1jzCS2E3GLlwRDDi0cY61Th0hoORkMITBht/AEgYVv4VlGPS7WZY6FVKWy13FneSc5FaBo7d7i0kBBDJktrVGM+wQyJC0ncCPQsaSJWZaa0QqwurN8eQaNqGShCqi5GQYThQ5vbBKOwhBKUzDIOHEkzWXMknW663NCQ03Gi28Xt3l2hZBGOE58VCcsdLW86tF0kcSYQn3ks/DJQegFE8/c55Ktc37H74ZIQQ/eH6aE6cWkVIyPlbi/vt2sb7R4NBzU/hByN6bR9i3dxTfD3nm0AXm5yvce88OpFTU6x1uu3WMmdl1KpVWnMb/zPlehpHFrp0DLC/XmJjoY3p6jZGRAmvrDXZM9PPk0+fYKDcZHSlwz8FJTp1e5MTJRVzX4s47Jtg2XmRhocIzz13A74bcfNMwt906hhCCSqXJ/FyZdNqj2eyQTMYZUaZlUiykWFysxL38Il5m2TJN0hmXdjvAtuML5uhYcTPjqlZvM7dYxrYsun5IqZCi3uhgWQYCQaPZiec9Mg1sy+zNJaPIZTxW1xt4ro3nWgwP5alWW6SScaZW1KujhCF66fYCwxTISCEMgexN4BhnYklUJBFGPOT04mSiqjdBmwwjAj9kdW6DiX1jV1WXdMOQr0ydoeF3+cjkHvKWi5RqswzAS1amu5gNFkm12et7ad0ThpKlhQqj40XqosXR6hT3lW7Be5lx8mEQxvsRbA7Hu5ixIQzRW966dz4Em6nYlmVuzmFjmAarC2XKKzWGJ/oovMq1LZQRx2vTnKjOoFCMJ/u5q7jn8jK+7euiUIacbpxiw9+gG3VIW2mKTonlzhIpK40hDFJWmlpQRaqIQIUcyN1Osrf0rupNovrS3/HVefTLL3D7e3aRzV/dMr4b1SaLqzWSiXjS4mbLJ5Vw6PgBfhAxUEzT7gY0Wz6eaxNGEsc2sUyDRquLY5t0uiGWZZDPJAmjiFYnYKiUoel3mJHTSBQFJ8vZ+hR5J0c76pA0EygURSdPpEJ8GbLhV9ib3cVIYnCzfFEk6fSyiRMpF8MUtBvdOHiRclEqHlbquHHPrePZgKDd7ICCRDoeFiEQ2L02hJTyJdsQQtBuxsPjHdfGSzh0Wl18P8S0TBIpF5Si3ezGq3Cl4jrt4uuv9Xc01VjhdH0BBbTCLuPJPpphm0rQImsnsXvzp6x1a2TsBJPpQUYSLwYClWxAeAHsm+POqi2E0QqN9lcxhIMQKUBiGDmkrKFUG6V8DCOHIkDJJp57EMfagxAmSgUQHANMsG/t1XX0hocFGKaBY1uYpkGr1QUEpilI9Np+nU5AqndegyCi3fYxDEGy106EeIW5i4IgpN0OcF0b2zZpt33CMIoXAfFsOh1/M1PG67U72725sRIJB8O4WI44UFJVXU5srDDTqLArVyJpOSgUfhTRiQJMYbDUqtOfSLHebjGeyXNwYGyre4a3e10EvbbRqdoiP9iYxjVsck4crMjYCSZSfTTDLq2oSzcKKfcycPrcDO/p38WhxQUWGnVQ0IlCXNOkE4ZkXZdOGNLw46kAbi71caa8zmS+yL0jW57Ll2j7AbMbVWzToB2EmIYgkpKWHzCYTWOZJouVOq4VT8mRS3q4jsU3p87jmiYJ295cUKPS6bB/IB4WF09qHC/ElLAtPMvi4MgIRq+jImk7myuztqNuvMiPYcUd9EIQqXhV1FBGOGZ8XxkpiWtayN4cgoGMO2O7MiRhOgQy7K3CZW4uFOHL+BrpyxDXiFdG9mWIZcTtlovbcQyTduQTdSKmXpgHBblSujf6QtBpdikMxvPHrc6XMS0DJRVhEDG+Z5hMPkJ2Hwc6ceahsEAFvUASYOQR5jAqWomDy8LFcO9HmPE0HPV6m1OnlshkExgints2CCK6frwQQDrtYRoCKRXpjNdbqEWwOLWKjCT9vaHOMpI4no3fjadaUNBbwCFua/idgLOHZ3CTDrsObAcUlh3f+4VBxOkfTMULL+zfjpe0sR0bBNSCKar+BSzh0Z84gG1cOXdsPZij0j2DIWz6vNuYb0RM18usd1pYwiDnemRsl8VWnUo3XpRgPJOjFQRU/Q451+MDYzuZa20w1yqz3KkykerbnGOoHQWYQtCV4eZwQ1+GWCL+Pf/Q0C2b8zluRco6fngeIRyEmkBGJrZtEfXmOTRNo7eSqNicqsAw49+z2btX7V1jXld9dEMFjt5t/G5AtxPEXxAnXu3Ksk0sK56I0jAEiaRLu+0T9MYTCwGWY5FIOFj2K49bbDa7RFFceUWR3FzJAOJx/2EUYRhG3GixLSzbwLuk8RLIkK70N1PfBQJTmEQq6gWLQkIVxjcLwsAQgpSZvOF6W5RU1KotLNu8YmnXl9P1wzgTwbZotLrxRKS2idH7zgVBPBTGMg3C3oTbhmGQSt6YkyL6ssts8wQD3nbSdv6KgE+rN1lfHI6KPyORirOTKn488V/eiSduN4VByvJe7oJ+4x38tVEvfdCbM4PLVgpEbb7yleY2uLx+vXyVwZd7jXallztvr3ROLz+Xr/Sey19z0bVs/40gI9lL4rr2m/03ZP8XA0rGW7t/peK5kcxXWCn0Gr3dv0hKKkkraqFQrHfXKDolPNMjkAGXLqKgen9MTDzTe9k5EN9Kr1SvXcv3EyBQIafrFyg6efJ2hq70sQ2bUIYYQiCI5x0LZYRCESlJyvJwjDd2UtAbjR8FtKJ4wYCLN66RirM84p7ruG12cZi1ZVh4r3BjshWlfCJZQQgbpSKE6M29qIJeu9AAYcc3mcLAECmEeGec91BKmoG/GejYnFj+kmaCLyM8M15yPmHZeKa1Vf31dq+LoFfhNIIO7V62tSWMuN4RJp5p04mCOAOJF7/LhhBk7QStICCQkobfpeH7FBIJLMOIb6SjCKXi+Y1c0yKQkqRtbw7DvuoCbnE/+9K6JM7w7oQhlU4nPgYjXmwgPkBF1nVp+vHxXVxtO+gtRpBxnJddCflG8Wrn4OXbpxHICgiTzY+rrMWBI7PU+5kN9IbkqgiM7OZ3XW22h1/cdhjGq4o5vcDOVu3ki/Mhmi8zafQVxycVrUYb0zLxki9NzFBK4bcDwjAimdl6gvBr0QlDWmH8mbYNA0MYSCU3V0uzDRPHjBNVLq6km3cT+JcEhlwj/gwHvWvTxSCRIh7JYQmr9x0ySFtXd3/6BtCBI03Tbhhv9waSros07Z1B10Wapt0I3u51Eej6SNPeKd4RgSNN0zRN0zRN0zRN0zTtBnNj59xpmqZpmqZpmqZpmqZp140OHGmapmmapmmapmmapmlb0oEjTdM0TdM0TdM0TdM0bUs6cKRpmqZpmqZpmqZpmqZtSQeONE3TNE3TNE3TNE3TtC3pwJGmaZqmaZqmaZqmaZq2JR040jRN0zRN0zRN0zRN07akA0eapmmapmmapmmapmnalnTgSNM0TdM0TdM0TdM0TduSDhxpmqZpmqZpmqZpmqZpW9KBI03TNE3TNE3TNE3TNG1LOnCkvSWEEH8shPin17scmqa9u+m6SNO0G4GuizRNuxHouki7WjpwpG0SQjQu+SuFEO1LHv/069m2UupnlVK/fRVl+GkhxBO9fX9li+ctIcT/KoRYEkLUhRCHhBDJ11M2TdNuLDd6XSSE+OBlZWwKIZQQ4mOvp2yapt1YbvS6qPf8R4QQzwshakKIs0KIn3095dI07cbzNqmLPiGEON4r0/eEEHteT7m0G49QSl3vMmg3ICHEFPALSqlvvMX7fQRIA3cAdymlHrns+f8DuA34eWCh9//jSqngrSynpmlvjRu1LtritZ8DhpRS3beoiJqmvYVuxLpICJEAVoFfAf4UeA/wDeBOpdTJt7Kcmqa9NW7Qumgf8DjwYeAQ8M+BTwK3KqXkW1lO7c2jM460qyaESAgh/l8hxKIQYk4I8b8LIezec4/0erp+QwixIYQ4L4T45CXv/bwQ4p9d8viTQogXej1kZ4QQHwBQSn1FKfUXwOIW+x8A/hvg55VSc0opqZQ6rINGmvbucr3roi18Bvi8Dhpp2rvLDVAXDQBJ4LMq9jhwDrjlzTxuTdNuLDdAXfRR4BtKqaeUUiHwW8Ae4mC29g6hA0fatfgNYD9xls9dwPuAS8fETgAOMAT8EvAnQojJyzcihHgQ+HfAPwLywAeA2avY/+1ADfg5IcSyEOKkEOIXX+vBaJr2tnW966JLt5EDfhT4k2s8Bk3T3v6ua12klJoGvgD8rBDCFEI8BAwS9/xrmvbucb3bRaL393K3Xu0BaDc+HTjSrsVPA/9SKbWmlFoGfhP49CXPh8BvKKX8XvrkN4C/t8V2fgH4N0qpb/eyhmaUUqevYv9jxA2iYWA78FPAb/caSpqmvXtc77roUp8CZpRST76G49A07e3tRqiL/gz434Au8E3gn/TKomnau8f1rou+CnxYCPFeIYQD/AviOIOeh/YdRAeOtKsihBDEUerpS348DYxe8nhVKdW57PmRLTY3TpxKfa3avX9/QynVUUo9B/wFcXqkpmnvAjdIXXSpz6CzjTTtXedGqIuEEPuJ5zb6JHE2wQHgN4QQH7zWbWma9vZ0I9RFSqkXgF8Efp94Dlq3t525a92WduPSgSPtqqh4FvUl4kyfi7YB85c87hNCeJc9v7DF5maBna+hGC9cLM5reK+mae8AN0hdBIAQYidwH/AfXus2NE17e7pB6qL9wJFLsgOOE/f8v+xk/pqmvbPcIHURSqnPKaX2KqX6iLMgR4knytbeIXTgSLsWnwP+pRCi1Juo+n8CPnvJ8zbwz4UQjhDih4APAX+5xXb+PfDLQoiHhBCGEGJc9JZs7I3R9wALMIQQnhDCAlBKHQOeBv5Zbx+3AR8HvvQmHa+maTem61oXXeIzwLeUUrpHTdPena53XfQccGtvXhJ673kEOPwmHKumaTeu610XIYQ42HvPYG87n1dKnX9Tjla7LnTgSLsW/wI4DhwDngceA377kueniMfQLgF/CPzcVhWGUup7wD8E/j+gSjwmf6z39C8SD0n718SVWhv43Uve/ilgL1AmnhDynyilvv+GHJ2maW8X170u6qWGfxo9TE3T3s2ua13UyzD6h8C/FULUe+/7LC+9YdQ07Z3vureLgN8jXsToGHG206+8IUem3TBEnN2maa+PEOIR4HeVUruud1k0TXv30nWRpmk3Al0XaZp2I9B1kfZG0RlHmqZpmqZpmqZpmqZp2pZ04EjTNE3TNE3TNE3TNE3bkh6qpmmapmmapmmapmmapm1JZxxpmqZpmqZpmqZpmqZpW7p8aeHr5U1Pe5qqlVluN7h3cPxN2b5SihPlVWzDYHe+703Zx/UShBFHzy+yY7SPXMq73sW5JvVWhzNza+zdPojn2tetHCuz68ydWmD7vjFKw4Wrfl/gh6zNr1MaLuJ4V1/+btvn7A8ukB/IMbpr6LUU+bUSb+XO3gQKQKkQMIkXztLerZSsQHgB7L0I4V7lm0JUcAzMYYQ58IaXqRN1qQUNfBlgCIFnugQyJGHGdbNC0QiaZOw0hjBIW0nKfpWMlaYRNglkiK98PMOj5ObfkDJdnrmsiCuCq/3+SKmo1dpYlkGt1iaRcKjV2uTySbqdANM0UAqEwWZrwbZNqtU22axHpxvi2CaNRpeBgSxeXFe+3b+8r6ldpJREEf+NZAshTMzeZ1epCKl8TCOFQCBV2DtLAgP7Da/vyp02c/Uat5T6sYy4n3KxUacR+OzMFzFew/6q3Q4LjTo3l/pf0y+42u0wXa1wS6kf2zRfwxaujR+FHN5YJJQRd/ePYxlv/j7fbiLZpR2tk7QGMcQ78vy83esieAPu02rNDucX1tk7MYRjm0ipWFirUsqlSFzSPp9dLlNptNk91r9lu3210sAyDQqZJABrlQaObZG9hvuTaqNNxw/JpFyWyw1SnoOUkoRrI5XCDyKEEHSDENe2iKTEEIIgkqQ8h64fIBUYhsA2DaRSSKlwbYt8OkE7DKj4LRzDIlIS2zDxZYglTFKWQyvySVsutaBD0nKo+m0MIehEISnLIVQSA0HCssnY8XFJFVH2lyj7S7SjBqCwDY+MVaTkjuCZ6Zc93m7UYrZ1EkMYbEvuxTKcqz5X2munlKIZVZltHidjlxhO7MCPahjCxpd1DCwUEkM4KBVhGfG1OlI+AgNfNvDMPFKFmMIlkM3e6y08s/ha68vXVR/dKIGjN92zK3M8unjhdQWOlFJU/fhL7mzR4Di8tkjKtl9X4KgbhZyrrrM71/emNWpOlVcZSmbIuVdXyfphxFPHZyhmk2+7wFG10eHJY9NMDhffsMCRUopGpUltrY4wBP3jfTTKTfyOj1KKvtES5eUK3ZZPaSSPjBTf/tz3yRTTTNw6Tm2jQXW1RjqfJFvKsL5Y7r22gGkarM1vYFgm/WNF6hsNamt1ioN5wiBkbb5MFIaURoq0Gx2alSaWbdE/XqK8XKVVb5Pry5LKJthYLLO+UH6rA0fvCGutL5Fx78Kzxl71tY2gy5+fPsIHxncykS0QKcmfnXyej03cTCmRfAtKq71pZBnlP4WwdsLVBo4IITgE3AlvQuBow68w314mkCGmMDGFiSTCFjYZO0Un6tIMW4yJIaRSNMMW9bBJ2a/SkT7dqEtXBhScLCU3T7PeZnF6DSUVfcMFCv2ZzX2tLVZYX65i2yajOwZQlsFqowEIbNNEoYikJIgkLd8n6diUW2360yk6YcRgJk3adTCEoNv2WV0oMzhewnYub3ooFhbKKKXw/agXFGqxslJjfaNJJuOxfVuJU6eWsGwDx7YYHMqxslxlbc1ieaWG61rYtkky6VwMHL0rtcJZ6v5pFBLPHKAVLmAKj1DWSTu7Uconbe+gGc4QyDqRbCJVSMG7k4Q1+IaWZa5e5cvnz7AjX9i8UTmxscpUtcxkrvCaAkflTpuja8vcXOp/TWVabNb54rmTbM/lSQuB6t0PCwSgkND7n6ITBQgh8AwbU8SBr2sNrhlCYAmDry6cYn9x5C0LHCmlOFVdJe8kGEpmXvZ1nbBMV9ZwjSyGiL83pnAIZBPL8GiFa5jCJmH104k2kCqMw4xGim5UQaFIWgMoItrhGpaRwBQOAoNQtTGFi8DCl1WkikhZgwSqRSg7KOLHnaiMH9VJWgP4UZ1OVAYgZQ0RqjbdqIpCkrZGMK/hhtcPQh574QLvuW2CmaUyO0ZKWNarn/9IStYqTQaL8Xk7NbPCxFAR94p6S3slSkGr61OutcgkXWzb5OiFRXaMlIikZKXc4Onj0zx4YAcJ10YpRasT8LVnTnHztkE6QchqtYljm+RSCdaqDWzT5Oz8GkEYcdP4AMVskvV6i75siiCMWKs0EQYkPYd6swtAXy5FpdlGRopSLslGvcX5+XVCKdk52sezp2cpZpIEUcRQIYMfRoSRxLUtgjDCtkxmVsoIIdg2UKDajINOkZT059K0uj5jfTmOz6wwUspy955xFtoVVtp1fBkiEAghmEyXWO7WyDkJFts1JtMlTlWXGUrkOFNfYSiRpRX41MIOphB4ps1IMk/G9uhGLR5f+wJHq4/SCqv4sgOAKSxydh8fHv4FdmcOvuzv4nT9ab6+9EcIDH58/B8zkboVKRVnzy7T9UNuu/XV27oX+TLkZG0RKSWBinAMC1sYJC0XUxhUgxYJ06Hit8g5SZphl3rQxjVsRpIFWmEXiaIVdtmbG8U1X/l6HckGneA0hkgSRCtYZh+RrGGKVK+yVkh8lPIxjSxStYlkDUMkcKxxpGxiCBcFONYIxlW35UAqSSA7mMLGMuJyxh1lvQ4aFSKESXwFMVEEKCV7damJQvLcxlf43up/ougM86lt/yPdaAFD2Aig5s+RcUZph+s4RroXFLKJlA8qvt5YwqPsn8cxUoSyTSeqkrKHsIwkjkhtljWU0Us6FN8suha8Bt0o4rOnfsCPTO5lW+bKntqf2L3/de9jrlFltlFhLJ3j8aVpqn6H8XSehGVzvrbOWCpHpBRJy6bit/GjiKrfYTSVpeAmOVFeoeQlUcByq07RSzKWynF0Y4mRVJaJTIEz1TUKXoJjG8sMJFKstJuYwmCqvkHCtLlncJyE9eIXOena/NKP3Pe2zL4Y7c/xyz/6ntfUQH05Siqe//YxVmfXkVJy4OF9nHrmLKZlMrp7iDCIePrLPyBbTOMmXHbePsHa/MZmptF3/vxxDFPQrnd44OP38s0/+x4D2/q48wO3sTy9yuHvHmfn/u0UBnKsL5Q59I0jDGzvZ2OxwguPHieR9igM5Jg7vUgi61FZrvL+/+oBlqdWmDkxj5KKH/mVj5AfyFFbr8dlVopuy8cwBSCQkcQwDaIwwrRM1hc2yBTTuEmX2nqdTCFNFMU9LqZpYFgmMoxo1tq4CQfDMJBSYrsWfifAtEzcxDunB0MqnyBaxTKygMBQCRAGQogrvgehVMzUK7TDAIgvH3uLgyQsXb2+Jkqh6IBq9VJXPBAJQIFqgEghhIVSUe9xGjBANUFYoPz4r5FC4KFUi7h1EQAhiDQCB4RAqS6oMH5etUFYCJEBYaBkb1+JT8IlF+e4fH68P6VAOL0yGSjlg2yA98NgZC95SwCq0ytnB4Tde4/Z214XZBOQ8RuEQ4SLVCGSCKUk3ahKwipRcFxSZhHbSBGqDoFskbVHezdrF2+ExWaXkgIG3BIK1Xu+t4vev/VKi2e/dYLHv3KYD//EffzwZx7cLPf8hRW+9zfPc/LQFP/D73ya3HieIwvLmIaBZ1l4tkUnCFlvtkg6Nq5lsVirA4LVRpNGt8vto8MALM2s8wf/6ov86r/6FAOjV2Zd5nJJQJFIuICiUEhhWQbDw3kcxyKXS2IYAsuKe6otyyDh2RiGoL8/i2kKOp2AVOrNazBdD1IpgijCMV+aAamUohtFuL2fK6XohCGmSJO0xlEoLCOFbWSRhCgVkbK3Ecr4+2AbeSwjjVIhhrCxjOTm/l4tW0wqxZHVJdbbbeYbVbKOx3tGt/H04iw/tG0nfhTy6Nw0Q6k0S806nz/xAp5l85GJeCXo6WqFPzryHHnX45Edu3FNi8fmpzlTXmc8k+Ph8UnK3TYvrCzhRxGVboeP7diDLyO+cuEMpUQSQXyD//jCDId7rxtIpvipvQeYrVX5zuwFTCG4f3Qb27J5nlma44XVJUxhEMiI5zbOk3FcJIpIRnRlQJ+bJeekKPuN3km++I9iMj2IZzj8YH2e5XadetDlQ6N7aIcBz6zOIIEHhyaZb1ZZaNVoBF0OlEbYmx9kLJUjYcbXg1BKfrA+z/HyEv1emoeHd9IMfR5fvkDF7/DA4CT9iTSPLV2g4re5tTBMqCLO1tZxDJNQRjwwtIONbpPn1xfI2h7vHZrkqZVpfBlR8zs8NLyDThTy708+yUgyx70D27h/cOKK32kg20w3vlcnxMEAACAASURBVIVr5uhGVfLODlrhCjlngmowjSUSNMNFAtlie/r9zDQeJW0PkbZHqPnTrHWP45hZMtYIvqwjVbjZi+5ZBVbbRyi4uzGETSNYAAQDif00ggUC2SbnbCdhlmiEi6x3TpJxxllqH6IezAEwmLiTcvcMUoXUg1n2F38Bk1dvZ0ileOHsAqvlBvOrVbYPFVlYqzExUuKFswtUGx0GCmkMQzCzXGZ8IE/Sczg9s0p/IYWUiu8+f46P3HszxUyS6aX4NXMrFU7NrNCXSyEVrJTr5NIJ7tg9qoNKWwjCkGdPztINQvwg5OHbd5HyXCIpOX52mdVKg3KjTSgltWYH2zJpdX0qjTaObdLxQ45fWGKj3mLf5BDPnJhl/85hOn7A4nqdtUqTO/aM8sLZBW7ZPshqpcmp2RUs06DTDWh1A9IJh2zKY7USB52K2RRr1fj7PVDIkEt53LlrbDOA7NkW3TAi6drYpkkkJVIpipkklmmQ9hxWaw5CxJlGSdeh2fFxbJMDk8Obnet9bhoDgWtaBDKKr8pCkHeSWIZBn5tGIBhKZPFMi93ZAQpOnKmkANswAYUhBJEKOVb9Pk+u/RUSyZA3yVBiJ5aw6EQtQhVQdIZf8XfRlW1C6SOESSR9IL6GrG80aLX9uClzlbdJ3ShgrVOnHrTpczPYwiRAst5t0Ay7WIZJpBSuacev7dbZ6DYZSeRZ7lRpBB0kCsswkVcxz7JSPoZIgTAIZRlFQCSbKCOPQiIQRKqJbQ4QREtI5RNGGzjWKEG4QKSaWEYBqdq4V9EZ/OJ+FRv+Ao+t/gV7c+9ld+ZuACLVpNJ5FscsoQixRJZAbmAbBSLVQiofgUXC3oZlZOlGLaSKCGQXqSKSZh+GsHGMFK6ZwzVyeGYBAxNDWHGHnOpiCgcQmMIhZQ1gG0ki5ZO2R7F7AfpLVYI6J2tTAOzP7wIEKcvDfIOzOG+Ymk4pxWKrzpnKGs0wjrSlHZeD/aM0Ap/VTpOCm+BUZQ1TCG7vGyZjuyy3G5ytrNMMffq8FHuLAySsOHK90KxxurJGwrLjbRKnDR/dWGY0lWMwGaf1HV5bpM9LMprOsdxqcK66Tj3oUvKS3FIYIGU7zDYqPL+6yLfmz5GyHQaTGXbnSuzO91HzOzy7Mo8fhezIldhzScZROww4WV5ltd1kIJHipkI/Cctmpl6hFQZIpZhvVim6SfaVBknbLivtJq0wYKpe5tbiIIdW51BAynJYaTU40DfCFy4c4z2D25hvVtmd6+Pw+iKRUjiGyVS9jG0Y3FIYYLZRJZSStXaTyWyRlO1QD7o0A592GPD82iKNoItUivVOC0MItmcKTGTjhv3Mcpmzc2tYpsGB3aPkUh6RlJyeXcU2TdZrTSaHi8ysVMgmPSaHi5ybXyOT8lhYq9LxQ/aM9TNQSCNEfJGWUmEYgqnFDVIJh32TQ3iOTbPjc3J6mUbbZ6iYYedoCcs0UUoxt1plZrlMFEkGCmkmh0u4joUfhFxY3GBpo45tGYz15xnrz6EUnJ1fY7ZXrv07hzczjpSK02OnluJerT3j/ZRyKfwg5PTsKv35NFOLGyhgx0iJwULmigpVKcWO/dvotgOqazVM22L3HRPsumOSE0+eIVtMc+sDt/DsV58nP5Bl2y2j7HvgZhzXZvbkPKO7hkjlknhJl9seuJmZk/M0yk3Gdg+zNr/B+mKZwA8YmugnV8qAgvJyheEdAwxuH+DEE6cxLZMDD+3lyPdPsrFYYf7MEkIIVubXrvh+1TeaHH3sJKXRIq1aG7/tEwURURSRyCRYurDC+E0jjO4e4vSz5xndNcT0iXkQkMmnGN01xNrCBs1qm4HxEgvnlgmDkHx/jsULK5RG8tz1wdcfOL1RWEaW1cYXcKwhkC7hyv0QZRjfM4xpmSig3GlxZG2ZSEm6UQjE3/enl+ZQm80QaPhdpusVOmFI1e+wp9DHaCrL2eo6kYzrqbybYG9xAM+yWGs3ObGxihBwa2mQnONR6XZYbjfohiEbnRY3Ffspd9pMZotM1cpkHIeU7bDeadHnpTi2sUw7DJjMFtmZK6KAExsrJG2HC9UNhpJpbir0EyrJ0bVlKn6bHdkiE9nCVQWIfT+kXG7S15fBNF9+qrwwjNjYaFIqpV/xdZdSqg6tz6KiOSAC66Y4eKM6qMb/hUj9IlgTIFdRzX+DSP0yGIPQ+lwcYJLLqGgR4X0U3PdB+z+j5CJChSi5irDvRCU+gRBJ8A+B/zQYWVR4Fow+RPpXABf8p1CdLwMgsr8OotgrXzPeZngyLp8xgkh9BkQeomVU63MQnkKk/gG4740PKppCNf8UYfajonnARCR/EuzbUKrR294UyGWQG4jED7PKrbTCNQLVxjNz+FED18ySsUeo+NOAwjWyNMNVbOGRsYfjwOZlWchi81/xkscXDYwW+dGff5iZM0tXDDm79Z6dFPqzzJxeQgFpz+WBnROYhkApMI14a3Hjms2bedsy6U+ncKwXgx0KkFKy1YgHwzAYvSyYdGlZLm5jePjVh9a9HTs4XkkQRUxtVNhRKrwkCzlSiumNCpOlAo5pEkrJt86e58HJ7WS9Yu9Vl4YP48emEQdBLfPFLJSLnw2pFDPlCsPZDO4rBL6VUpzcWOPY2jI/uusWErZNK/B5emGOB0a30wwCnlqY5ZEdu1luNfi7+Zs5vbHO16fP0ZdIstxq8Mjkbp5YmOWphTlynscLq8s8MLqdb86cI+O4pB2HL5w5zs/svZ2JXJ6EbZMWDpO5Ak8uzMJNcTbP3tIAg8k035g+1wucdPniuRPcXOynHQb8xeljfGLPPr58/jTvG5/k2PoKnTDEMx2Slstscw3HtMhYCZRSBDKkHrRJmA6uYaNQ1MMOoYyIhOTp1Rkm00XuH9xOyrL5s7OHsIVJPegSyohG0CXvJJjMFPnm/Bl2ZEovPXeozaErjy6d56Z8P0+tzJCxXR4a2kHBTfD9pfO8sL5I2nb54swxRpM5XNPkVGWFiUyRZ1dnObQ2x0gyy9HyEinb4bGlKd43shNTCJ5ameFDo3sYTmY52D/OLfmtM8lC2UIhGU7ew1T9qzhmhmowxWrnCCVvH6udFwhkE8fIolCYhsNA4gBJa4DF1tOk7dG4PuqeI1RdxlMPstx+jkgFNIKl3nCMGoZwaUfrZOwxBAZgUHB3MZCI2wwZe4yaPwOAgUmfu5dI+QSygcBAIRlNvRfbSG15HJdrtn1mlsrsHC2xvFEnlXBYrzaJIsnZuTV2jpYoZpN869AZHNvk0Kk50gmHHSMlxgcLVOptBgsZRvty2LZJpd6i0fY5Nb3CcF+WE1PLtDoBe7b1s7ReozpSZMB5+ayud6sgjGi2uxy8eZzvHT5PN4jbSZFU1Ntdbtk+SBBG1JodjpxbZLQ/x77JIcYHCuwZ72d+tUqz41NrdUknXLYN5lmrNnEdi32TQzTbXcJIMlTMYBoG5XqLiaEiUikOnZrllu2DpDyHp45PE4QRO0ZKdPyAvlyaQiaxud10wt283lx+/bj8miiEIJ9OvOS1Wy0ylbU9svaVIzQu3/5AIvPS92/R79GOGlxoPk+guuxI38GHhn6OvD2AEAZSRUQqJPEKw9QAdqbvoFpcwTZcRhK7499DJF9TR0vSdLm7NLn5uBX6rHbr7M4NEiqJdUl2plKK8VQJpeIgmClMIhVhCAODOKvq1ZhGDsNIAwLHHAIEigiBxWZnGyCw44wfJCjVO9cWiogwWsMW/Vxr2GOpfZ6TtSc3zxmAVO24MxCFKdIoIkAgVTeuJ0WKQJaRqovAZF/uQbqyzZA3Sd4ZxDZePOcehZf8e/FarC5rJ3lW4bLs2Cu5hkM9bHG6PkPZr2MKg/f2HSDnvPJn41rdMIGjhWaN3/7Bo0xkClT8Dt+ZO8d/fdt9MDDG0Y1l/uD4MxzoG978IG7P5MnYLl+dOc2FWpmkZXN0Y5mPbb+Zj+/cx2y9yv/y3HfIOx4FL8HR9SXyboJG4PMHx5/h4ztvZTAZ93798clDPDQyyY+nc3xj9gynq2ukLIfjGys8PLqDn95zO5Vuhwv1DWp+l5V2g0gphnqBJ0U8XOXzZw5z9+A4e/IPAPGws3937GlOVVYZT+c3gzy/sPduHl24wH8+d5S9xUFSts3xjRU+NnEz7x3aTjsMqPkdMrbLcCrLc6vzCBFHrm8pDKB6GUddGRJIiS8jDAQIgWOY7C8NsdpuMpTMsNiqM5BI0Y1Cjqwvxf8PQyrdNiOpLIdW58m7HkPJDJVuhz35PvouGV4TRZLlcp2/fuwYv/7pD5LbMUwQRvzpV54h5bmsV5vYlslAIc3CWpVf+8SD/MdvH6bR7rJtIE+50eavHzvGr378AbYNFvje4fM8d3qO7UMFTMPAtkwmh0u0OwH/4avPUmm0yaUTTC+X+dDBPXz4npuYWtzg9/7qcbYN5OPEAKX49EcO4joWX3riBE8cnWJypEij1WVsIM8n3387EF+0Ts2scOjUHL/1S3+H4V4q7LELS/zR3z7NYDGDUvClJ47zMx8+SDGb5P/5y+/Rn08zWMiwUWvhhxH/+CfeR1/+sgaLUjz/nWNkCmne+2N347d9kpn4YjKya4jpE3M89aVD7Lx9Ai/pkitlsB2LZDbBXR8+wOrsOvn+LJZtsjy9it8JCIKQ6lqd6moNKSUykpx46iyzZxY5/sRpJm/bxqGvH2bpwiq33Leb1dl1nIRDtpTB8SzajQ5e2mN4xyCVlSrHnzxNp9llx/7tBEGI49lkCik2FsuksglOH7rAvR+7g7M/mKI0XKB/rIjj2nRaXcorVfpGi6zMrrF4YQXTMmlUmziew8ZSBWEIBrb1MXtqgbX5dXYe2P4m1xBvrax7DwlrBxIfQ6WZqXVYmp5jdOcQphUHg/74+HNYhkHSsjldjoN1hojHpP/+sac3A7VLrQa/9fS3eWB0Atsw+M78ef7Rgffy56dfoNxpc+/wNr4+c4b3je3gvqFt/PHx5+hLJAml5OmlOX5+30Eu1Mr8388/xoe27SJju9S6Hb48dZoPbtvJfzpzlLF0lj2FPuYbNR4YmWC6XsExTH7/6DP82oH3MJhK83tHnmJ3rsR4JodnWoRK8tXpM5ytrrMtneeP5g7x92+5k4l0nnK5RRRJcrkEnW6A3w3J55O4veBrs9nlqafPc2D/OAMDWbrdkCCI8DwLKaHV6pLLJQnDiEqlRT6fpFpt0+0GpNMupmlQr3c2Gy3tdoBtm2SzHiKcQvmHEOlfBaMPiOKsI9WMAysq7gRAhRCtACGgUNEMqBCR+jRCZMFIAUb8nnAG0r+CUO042GTtBPd+UE2U/wQi/asI7xFQEfR6tIX7MIgEqvXZ3s97okVU9zuI1C+BNRaXR8R1pjBHIfVzqPpvxRlTFykfgiNgfxqR+AS0/grV/muwdkNwEuU/h8j8dyArqMbvgvMAefJk7CEUYAgrTndHIISJY8QZTqZwKKhJHCMFCKSUVNcb1MpNlFRk8iny/XFwr93ssr5cJQwi8qU02WIKwzAwDIHjWlsG9kzLxHFtRC9AZBkGWS9uZLebXdaWqshIkutLkyukkVKxvlzFTDmIjQ6dSNJUJslMrwGtoFFt02kuYZgGpaEcXtJBKUW93KK60UBGknQuSXEgzthaX67iJhzqlRaBH5IvpcmV4o6IKJKUV2o0qi3chENpMHdNc8G9HVzYKHN0aYXtxTxPnp+i0u6wrZAj47o8v7DIWD7L8aUVzqytM1+rUUomma/WGMpkyHkuR5dWuHmgj0IywaHZefozaQZSKY4uLZNxXWzTYLFWJ+24bC/m+OKxk9y7bYwHd0yQsF/+XEoluX1gmDsGRzCEYLpW2XxO8WKoak+hj4NDowwm0/zlmWMkbZvb+oc4ODRKpBTPryxiGQaHVxap+11WWg0Wm3V22UWGUxnuHRnDNV9sqg6l0pvzJQkhKHoJLlTLBDLiJ3fdRqXT5rG5GVZbTQA6Ych0tYxnWtw7Mk5fMsVfnj7Grflx0rbDZGoA2zA3h6IBTKbjIabxPFCSduTjmQ5+FJGyHPbk+pjMlOhEAeudFu8ZnKDkJulPpPjW/FlGUjm2pws8vjyFL8OXnLdKt81jSxe4f3CCIxuL+FHEWqfJ/uIIE5kioYxYaTcYSKS5tTiMZ1qcrq7R76Wo+G2GExkW23FG0y2FQW4rDTOWyvHE8hT7i8Mstes8vz5PwrLJOh79XoqiF9dPDb/L7zz7JMdXV0g5Dr9+/wMkzBJT9a/hmnmyzjba4Tpl/yxFdxemsFlpv4BtJPDMIp5Z3JwvCwUb3VN0wnUGvTup1dY5Xf86jp2k376dC9VvMJS5nfXqGbbl7qHZadJqdilm0kS+TeRY1NoNTFuxymFq/gzrneNYRhLbTGNIH0lApHwi5VPzpym6N+Gacb3w1MIsf3T4ORq+z8PbJ/n0rbfj9YKdQsRB0EjGt1pBGNHuBtRbHUzTYKCYIZ10MQyBa1vsHO1jYa1KGEkiKTefC6WEELp+SK0ZDw0Kwvha4LkWA4U0a9UmUfTuXJ1aKUU1qKNQ5O3sZlCkFbZphC2KTp6xgTyPH5lioJCh0mizuF7jzNwq/fkUR84v4IcRA4UMeydenFIhn05gGAZBJAnCiGImQccPaXUCDENQ9BzSnoMAWt2AqaUy69UWeyeGODG9hGWZ7JscIu05uI7F3skhao0Ojm0xOVzkyPlFyo0W2wfjm/WyX0UgyNlXBv+26oi4/GdX85qX0wibtMMORTf/spkhofKp+CsATKRuo88dwxTXdvtecIb4wNBnrvh5sZiiWEhddbYRgGkYZIzE5uOk6VJyM5v18htNCBNB79yIV7u+XxmsU0phWgmEsLiW6X0kEVPNI70BzC9yzD76Ux98SWjn0mCOUpJItTCEhxCCkeRuRpK7eSVXdvht8Zl6lbJn7CQfHryXmzLbcU2HlOmRta8u2H4tbpjA0ZlqPN70Z266g24UMl0rszNXImHGaVvNwOej22/ipnw8rv3iB/QTO28liCIk8JWZUzyzMssj2/bw3YXzpG2Hf3rnwyQtm9898gTnauuvWo4f3bGXIJJIFN+cO8v3F6f4scm97CsOkrRsDq0s8PEdtzKZfXFix6zt8rGJmzm0Ov+SX+vZ6jpPLc/yzw7+ELtyJc5W1/jNZ7/F+0Z3AHHWzT+45SCj6Sx/M3WSb86dZX9piKFkhm4UcktxgIztct/QdlK2w0y9gt077r+38zbmG1UWmjWW2w3uGdxG3vE4VVkladncVOin6CXZVxzEFAJTxBlIUin6Eim6UUTe8ThQGmYwmabkJREIbMPEuKQBNTFcpJhN8ujhcy85T5ZhcPDmcVzb5G+fPMHff+Qg/+fnv8NKpYFSiomhIj/3d+4hkpLf/JOv89TxacYH8vENRzfgxx/az1Bv/LhlGnzr0BnOL67za594kHw6weNHp/jbJ0/w0IGd1FpdOt2Ah27fyY6RErZlbqYFL23USCccfvj+fRQzCSzL3OwF3zs5hGUaHD67sFnuIIz4m8ePs3/nCJ98/wGEEPzb//I4X33qJJ/6odtRCvZNDvHJ9x1gvdbif/7jr7GwVr0icOSlEtz94dvZfdcObMeib7TUGwYG2VKaD/z0gygpsWwLwzS4/8fuxjBNDENw90cOUG20afgBXjbBw5+6HyHim7TyRoN7fuRuXM/GcCxuunc3o7eMkcp4uJ7N+3/qAbrtAMezGdoxiJd0ueejd2AYgu37xuMou2FgWgY/9qsfjc+vYxF0A2rrDaIgYnB7P+1Ghzs/eBv5/hw7btuG7dq0Gm1s1yZbypAppMgP5MgUUrTqbVr1NkPb+2nW2jiezcB4H4mMx8rsOv3jfay+w4JHoaxS6TyKVG08cxdhGKe3Xry4bnTiIRr//Z0PYhkGL6wvAeCaFncNjPAfz7z04lX0kvzIjluwhMHvHH6cmt/BFAYHB8f4xM59SCmZqpUZS+d4anmW+4bGiZTiyNoSlW7cYO3zknx0+03kPQ8pFYPJNGerG2Qch6rfYb5RYzyTJ2nZJC2barfDarvBUqvOYCqNY5jcOzzO3YNj8cAtKfnazBlKXpJlo85co8rpyhq5wOFrXzuymU0UBBFewiaZcLjjju1EvcZ1udzk5MlF5uY2WFmtUyykmJzsp90JOHtmiUIxxchwgcOHZyiV0nznuydJJhykVCSTDq2Wj+taCAOCQLJr50AvqDQARh7V/iLCfRCcg1zdpUoinLvAuhkhLm3AKIR9G1h7gABhTkB4Kg4coeLglHMf4vIGibB6Q+QuawwZRTDHUJ0vIdwHeuXrvVcYcZBrq/IaBXAfirOO7H3Q+VtQ3V5DKAS5AbISB7yEi4GBIRIIDCQRoWxj94YTOUYyzmpTEQYGluHFWSCHpvirP/wuAjBMg+03DfN3f/ZBolDyX/7wu8ydW4nHzNsGP/bz72PXba9tzr/yap0v/P63WZpZxzQNbNfi47/0fvqGC/zZv/4yXsKh2/aprDcYneznJ//bjwDQ+P/Je/Mgua7zyvN33/5e7pmVmbVvqCrsIAgQIMGdFClSEiVZsiTbsizv3e12e+wetz0dEzEzMfPHdEyMZ/FMu8c9MbLs8DayvEoUZVEUN5EgSJAEiI3Yl9q3rNy3t84fL5GoAgoktcRYtr8IRCQq875333bfvec753zlBl//w+/i2C6VYo1dB7fwiZ9/ELvt8rdfepHl2TVcx8NuO3zxt54iP5jmq//pOVzHw7FdWg0bw9L4/G88Se9QhrdfOssLf/MmAHbbYfc9Ezz5+XvR/wmBR/lolBP+Ir4fsFCtsbs3z2RPGtsLJ7P1tsO1Yok9fb2s1hu8OTNH0jS4VFhjazaUKKqyzIWVAhM9GSZ6Mjx77gLlVotio4muyOzozTFbqiBLElPZHvYPDnQX4e8VhqJ25zwSEIhQilW1W10WZstzcX2flueidLLMTdfBDXxaroMqyxiyzP2DI3x6agdBAIaicLawgibL3ez1ZhEEAUuNOi/PXOWjW6ZIGWFycCie4Gd37SNjmggE05USXhDg+j625xEE4Lk+TuCGkhA/9IcQIlSfSpLoSksADFQCN/TzUpHCz66H7AvuyQ4zWy9RbDdIGxaSELy+fI0zxSUGI0k8P+D5+Ytcra7x8uJldqVCMPhKda0759rXM8jLC5c4XpjjQHaIvZl+Xl68xNnSEkPRJJosIUSAIiSEgB4jwu50L2eKi1iKyoAVD+eGIlwWKVJo1ttnxXlu7jxVp83B3DCO73NqeZnDc9PEdZ2m4zOVeRA/8JBEKJPote4ib+1DEipJbZy4OhxeX6EwEn2ku4CTJZ28eQd95kGaVYerL69gRKZoBdBQKgixn0uFJgHDuKkS+bH7KS6VWAjqrM7JKFoJM9Immoowsf9+Bqz7wJeQFaXjMQWOX6PUvoQuJ0JT2HUA/mqjwZG5WUrtFv2x+AbJS8TQ2Daco1RvsmM0T6PtkEtFqTbajA7qtMRVGkGUrZMB15YWcSSHsVGNS3OXaQmLyf5+jMQSF5Y8FM1Fi5ZZrLlk+wxmF5fJ90PSzJOKWWwdzhKz/mnJYz9oeIHHSyuv0/JafHrwI6gdMONC7Qpvrp3gi6M/zs6xXrYN55Ck8N79xY/djSQJJEmwYyQPCBRl4zP+6P4JZEli13gv20ZyoWWCEGwZyCAJEa69OjdJAOwYzSMAWZYY7UsjRPgMX48t/T34QRACCLLEUD4FQYDcWU+9tHwEXdb4cO+DyHxwWc90fQ5Zkhkwv38f0XdK73K8dJovjnyGWGeRHwQBXuDg4xMEPk2v0vU0gtDoev0cRxEaithY3CBkT7bwWJf0IgQfNMlAEjKqKhPpgKTrw/Vt3MBGFiqqFCaK3MDBC1zARyAhCwW5s0/5NoBREAT4eHh+51hu468uIaFKxoa1Z2cLN+274yQkVGSh3BacC4IAJ2jjBS6aZCALhSBQcH0XL3A68jYJScjhMXDDgiIEfTz8wKXulZlvXgjHoqDdMSS/+Vyam/QbvEDCCVob/iYhoUnGTfPTW/vtBx6qpCELFT/wcAMHvzP2SUJG7UjYNjt+n4DZ5nLnWH0Opnf+02Uc6bJMy3NYazVoeS5CQFTRuicma0YYiSU3mFK3PZcXZi9zbHUePwiYqZUwZAU/8JmulRmOJknpBookMxxLMlMrbbrv67ey7Xm8PH+Vt5Zn8ToSMj8IacVSB3wRAmQhbUBWhRDIiJD1sy5Wm3VkIRju9DtvxUjqJnP1MgBD0SR5K4ouK6R1E8fzmIhlGLfSaJpMo2GjBhI7kjls22W0L4XjuEiSwLY9JDmOE/cYtVL0JeL4fkAirWMYNwYQ1RGoqsx4NI3r+ghJ8FDPGIYe/mZvqg9dCx/AB/vHuDmEEJ1BZeOxKbJMImKgyBLJqIllaCiyjNeZzI72ptA1BUmIblbG98MzPZRPkk1GUNeZFF5bKjKzVOL/+foRhBA0bQdDU7Bdl6nBHu7eOcJXnj9GKmrx2IEp9mzpQ5Jlnji4ja+9corf++tX2DqU5eF9E4z2pjuSjc7LY13XPT9gbrXM/XvGMDvnYLQvzRtnpmnZDrqqsHUoi6YqRAwNTZWx3ZsGXkmw676tSIrUzW6vN7wUQtxi/qqoN/4vKzKaoXJhfpGKYzOV70FTZFpNm3dPzJJMR6jX2rSaNrIiEU9YXL4YLvh6+5OsLFfw3PA879k/QjIdDgraOsZAEARoBp3r5qGbMjvvGSb0eFG5sRj2icZNAiSESCGEQnYg3UVIMn2pTSUj12Ns1xBL11ZI937wSnH/GKLSOoKlbsNQhliufINmS8VzjO65CAh1OaoUjgWG/N5DaUI3MGW16zPjBwGqJJM2zJB5J8t4friwiWs6+3MDRFWNDw9PkrMiFFoNErqBoSjhOCQF9EZivL44zWg8xUqzzsVSgUN9I/zZuXfIWhEO9g5xsrDUnVCrfj0ZlAAAIABJREFUskxKN7uZ9SAIAaA7evoYjiW4u3eI0XgKv+aRzcbYtWuQv//WScbHsuzaPcirr17g/PlF5uaLjI9nSaUs9u4d5siREFTetWuQWMzg8GsXcD2f1ZUqu3cNEo2GE49IRGfvHUO8+OJZlpbKpFIWkahGu+0yNZlnfLxjJB3kIPbvwD5K0HoGnLdCORqEK9PuiO3AhkyQDMLa/KUs5E62RiJAJmQpQbjSMm8Fjd4jhJSC6H8BzlGC9vPQfgUR+7ddKdvtG6o3vJKEfKPvyhjIvQSNr4A8gDB/gkBKU2yeou4u4QVtEtoYTXcF268jCRk/cJFQsJQcuhzHUFK0mw5/+6WXmLxjiIc/sR9ZlRECDEvnlaePMX9lhZ/69SeIJky+9gcv862vHGF85wDS95ElPPz377AyX+QLv/lRrKjOs195nW/88at84b/8CK2GjWnpfPG3Pka92uL3/9u/4vQbl+gd6cGxXbbsGuDeJ/Zw+d15/uI/fpv7P7aXbH+KD//EPWiGimO7/OX/9R1OHL7AY587SLtpY7ddfva3n0KSBb//3/01p9+4hBnR+fZfHOGeD+/mzge2cu3cAn/yv36TPYcmGdve/z0f049iBEFAodFguVZnuVZDFoKoroWyjFqd5Vqd1XodSQjmyhVc3ydtmhiqwngmRTYSYaXe4OzyCinLZKFaxdJUorpOy3UZTiUpNZtEtHCbiiTh+j7zlQpxQ/+eCnREVA1TVvjmlfOsNhshW4PQIPtbVy5wtVzkjlwvmqxwsVjg6YvnOFNY5snxKZK6wVfPnuTF6Sv4QcC9A8Ob7uNqucjx5QVmqmWOLswykcrw9YvvUnPazFbK1G2byVSGrekevnPtEn2RGCnDZCqdIQC+dvFdFus1As/n8ulZGit1Ep33Z3Glgm5omFEdTVMIAkhmY/heyP51bBfX87lDylB5d4131SrNhs3BXQO0ewZwfY8eI4IA9vcMMmwlSSkmOjIHUoPsMHqImSYZxeQLW/ZRa7a4OzZIrxalT42Sx0QyFZBcHK/GeNLC9QMcquQsCyFctqQsYirIQkVTVQotm4gisdRe5b6+fipOBVMV7EinKdglHumbYGeql6h6e1+gkM0o02rYyHIQvvqFwMftTAMkHNsFbDRDw/c8EJBUJxFSgCzpyHJAIpPAipnYLZvyahXd1IilYmimhqxIiEAh25+nUW2SdHx0U8NzPAxLpzBbIZUPPRlzQ6HVgwA0KcZw9NGu34cuf7AqkEIIto1ulOdNDYVJ5+XmKnW3wlzzAlo8ytaEjCpC1szYRJWMliWixdi1NYaEQ81dYefuAFUEaHLAHekofuCS1OLEVJNkzNysC/8sIiAEj9xgI6tuMjrOoNmH0lncrwcWZG3d59u8e5TOuCPf3PY2cndFXr/Nzfu6fiS7+TcP50IfVOnmJNF7RBAEHC68xbA18AMBR17g4fgha/p61N0SR9e+wZq9QM0tUnOKlJyQcfRG4eucLL24YRv700+yP/0k8rolvRvYPLv4ZWYaZzb8NqIkeKLvl8kbo/h+wNx8EVVVOh6DYZwqv8yba88wFTvIvT0/znzzAu9WDrPYvETbb2HKUQasKaZiB+g3Jzet7BUEPmVnlfPVN7haO0HFLeD69qbgUVLL8bH+XyWubpT1Nt0a043TXKi+xUp7Gte3sZQEQ9Z2JmL7yBvjm7K0nKDFqyt/xfnqGzze+wuMRe5guXWV0+XvMt+8QMOrYsgRUlofu5MPMWLtBEI100LrMherb7LcukbBnme1PYMfeBxZ/TveKT6/YT+WHOfjA79GWt/oL1VzSzw9/3uU7KUNf8/oA3yk718SUzefK7qBzasrf8nl2nHuy36GscgezlePcqn2NoX2HJKQSWv97Ejcy2hkD+omJt9e4FN3m9yRnOLdyhVavk1i0719//EjAxxtT4VeHr974lWyZoRPjO1gLH5jMSpL0gYqMYSeHX9y/hi/deeDjMZSfGvmAocXrgEQUzVqThvH95GERN2x8TqaR0WSaXWyYY7vUWyFkoIL5VX+6Oxb/Pqe+5hM9vDC3CWem7nY3Z/orFn826CmN4elaniBT8O1iaoabc+l4TrEVIO1VgNNkm+YNosww3Xh4hKlUoPRkR4qlSapVARJEpw8NcuObf3MzhXJ5+O02w6FtTp37R3h5OlZ4pM60zMFNE1hdKSHZtPBNDVm54qhxERTmJlbI5eN8+7ZBQYHUzSbNoVCnX17hzcMGh8oBB2j4BtgwnpModJod1DzgHrTJh2zbsgd5HXH3YmYpTPcm+IXn7oHqwPGyJJE3DKQJMHnH9/HarnOm2dn+PI3XudXfuw+to/mGetL86ufvp/ppSLPvPYuf/D06/zW5x+9bVlOSQiipkal3iIIQgCgXG9haAqqIiMJ0G7KtN480AkhMGI6jt+k6bZQJfN7Lm2pqwrDmSTqunOhagpbdw6gqDK+F7BWqKKoMrG4SSYbo9UMF2TxVHitrl5cvr2xXFDGbb8WLnKFFnq+yP2E2QI19JERRkf2owAe4CGrO0EkNsCE70W5HZzsY3DyvU35/lGGkPCDFn7QQpIFiZ44ga0hOpOYhGaQ1E2enb5ITAvZgAAVu8VSo0bZbnGtWsTqSD02O4MhHLvxm/5onF4rxlKjhhpN4Pj+TS3CkISgLxLlSqXIh4a2EARwpVwkrZtU7BaDsQSL9SqldvOm1je2oUgSd/cOMV0tkbeiXQNHRZaIxy1UVWFkOIPnB7x59AqTE3l27Bhg165BKpUmJ0/O8sbRy4yO9bC6WkVRJFzPp1prISSBYahcubrK3HyR9JVlTEtFkiXSmSgjowblcpNcNk6l0txQBSsImoAE+kMIuY+g9n92mDjR8J93iUDOhdIvv7zRMuc2t2rgnAVvFoI2gTeN0A5u/sMbLTryNJvw2bA7/5cIglb4N+1ehDRIUPufwVsJGUVBcKNN4IT/3u81G7TBLyGMD4MyAUJHADF1iJg6QM2ZJ6r2E1FySEIlCMJ7IpSq6V0adWGpTLVU58AjO8je5Bd05s0rzF9Z4e++9CJCEqzMFTEsHaftfl+m9uePT7Nl5yDDk3mEEOw5NMFf/N63qZXDd+mOA2Ok8wnS+QT5oQxzl5fJD6VJpKPccd8U6XyCdjs0k7dbDk7b5dq5BS6fnqNRazF9fpF46gbL8457p8gNpkOZQi5OtdRgZb7ElXfnkRWZU29cwmm51CtNyoXapn3+xxgBoCsKB4cHkSWJA8MDxAy9C8rs7sthaAp7+kO20WOTW8hELBarVRJGmNgZTiboiVokDINrayUkITgwNMBcuYKmyIylU0R1jaRpEtM1dCUsPQ1QrbWoN+1OssrF0FWWC1UyqQgjUoKEZrJarLO6ViOdtHiyd4IT8ws8ND7GR8c1ekyL//qeh6i020ymMuzsyVO12wzFEqw26mxN97C9J4tre3xsYIpLxTUa1TazzhqJhMUDqeGObNZDUUJPRb0lcZfRS9sN53Bb0z2h/2O9znK9TlTVua9/mEulNcrtFmnDxPMCPja2lYulAo8MjxOVVJoXSwRegN12aNbbeK5PtVSndzhDcbkSWhCUQ7atJEnEMxFKhRqKIpPNJrh6NjR71hWFXDQEn7zAJ6mb9FlxenyDs69fRUgCvZOkKvsNWla1yxp22g5vrsxCEJDsibHz4BYut+Zpem2EcMmZMRpei0B4BARIwmfIylBzm7S8FmOxDGkt1pEGBlTdBjHVouyU6TMTRFSNsdssUjbcZ37A3KUlFjuyeQJQdQWn7RJLR0hlw2culLWqFBZLTNwxTH44BHnMqMHeR3YC4XyhUqiimdqmY4vr+xRq4bZkKXxnAKy0W4ioymqtzkqtTtqycHwPSzVImMnbggzfa8S1AQw5QcaYQCAo27MktIEb5shSAlUyyOpToRQyGAUEklCQUDoyOL9bNvufW5SdKq8V3mamPk9GT7FmlzA7FZwabpPnll5htrlAUk3wE8NPdRk8fuAz11zktdW3KbtVVKGwN7mDPcntSEJizS7xeuEY881lBDBo9fFAz0FMxaDsVHl19SgLzRX8wGMiNsqhzH4MWefNtRMU7CISElfrs6S0OA9m7yFnhL46y+1VXll5k4JdJKHGuLfnLgbMPJKQWGyu8MLKayy3VrkzuZP7swe68tTDhbdwfIeG22K+tURaS3JvZj99Zo7p+hxH197h1dU3Oadf5lT5HGktycf7H0OXNapOjTeLJ7lcu4YiFHYkJtmb3IkqKdi+w5HC25yrXO4wjMQta4y232C6cYaqs9Y5dzeS137gd9k31+NmKRWAQCKuZoipGZpelZpTpOYWaXpVHL/d2VaA7wdUq80NbRtuheXWNLpkoUkmrxe+RtOroUthAYtCe5Yr9Xc4UXqBJ/v+BVtjBzck7IIgYK55nucW/4i55jksOUFMTaNKOhVnlZpbIsAnoiTJ6SPkjbEOi+ZG+5KzxPNLf8yF6pv4gYchR5CQKTpLXK4d51jxWR7M/SS7Ew93q52tb19xCyy1rlK0l/CCt3hu8Q8p2guokoFAUGjPcbV+ih59iGFrZ3fqOFN/lxMdYM4PXIIgvDqbnXcPd1MgTBYKSTWH7bdoeXWqToGGV0bqeFLdLgICKk6BheZFrtVPcbV2khPlF5CRkSUVx28x2zjHucoR7un5JAcyT93ibaVKCkNWnldXj9Nn9JBQf7hsI/gRAo7ankvL8/jcxO7Q90fR0N4ni3/9crVcl4vlAkeXZrrfHcgN8nsnj/DN6XMkNIPXF6eROsyA4WiSF2cvk9Etpmsl5uqVDdtteS6XK2u8sTSzYWEeU3WEgNcWp6nYLXqtGH1WjIYb6txLdguvw3xK6xZb4ml6zCh/fv4dDuaHeHtlDl2WmUxmuFZd2/SYqtUWjaZNo2FTKjcJAmi1bKZnCkxO5KnVW4jl0CS0WGrgej6FQo1itkG93kZRZGzbY2m5TG8+QancwPN90qkIS8sVEnGTZrNNu+WwtlanVgsBFAC/w3iAGwh+qdpkuVil1XZYXKuSTUaJfICFxmunrjKYTdBsO8ytlPnwwa3vqc68c2qQI6ev8ea5GbYN56g3bXRNoScR4erCGpfmV+lJRDFUJaQNeh6+H3DkzDV8PyAe0YmYGu0VDz8IcD2PYiXUU7faDvOr4SQ5ETW5f884Lx6/RMQMqxAdvzDHh/ZPoqkfPLvqBw4LzdO0vAqj0YMo6yp9+H5YBed2IYSgYducnF1CANlYBEXWwupAvTew4XQ2NIO7jtu4bsgQEVKIjO9KmMiK3DUcvF7lIfADQmaRjJBz4cJU6kGSwm0HgY0gCvggoqEkB4/AL288Rq+zLz8EXK9/9jw/zCD+EzOhXR8J417WGs9Rt08TUfdQa8dpNcpcf1gSusFPTu7hlYWreL7CT2/bS9qwmK9VeW3hGluTWU4XlpCEYFsqy/39o13px/19I8Q1g7vyA/RGQrmmtuwynkyQMSx+bsc+ji7NcmZtmR3pHJIQ5K0oh/qGUSUZx3a5cGqWKzPLPLplLDTon62iHK9ib2vzuak9HJ6/hoTgJ6f20BeJIyF4oH+MtH4jOyoLwY9t2cHLc1c4VVgiZ0VQJZl4UmPfvhEUReKBB7aGALAXoOth/4MgIBo1+MiTe0Jmm6YwUy6z5rdYKTYYubOXwA+QZAlJCO58bIKm75LQTa60y6S2JUiaJnusIRQ5ZMVsyCS65wgafxb6BgVthHZ3CMoIE6E/EHoDtV8HoXVkXdeBawU2pZlL4K8RNP4Q/ELoQ6TdceO7TdhGQdCE5tcInHfAm4H6HxNo+0B/ELwrBI0/7ezLBWUHyLlQRuG8Be3vgnuZgGfBXwHjo4SIlroOXV+3X38ZCAha3wbxIgRNhPFJLP1hhBCYSg+CTuWs90haXKeb+35I717/fEqyxOBEnns/ckf3XEcTJsr3MOatD1mR8Vy/8zgEeK7fGSPCbYffhX31PA+ps09JlroysvWg6RvfOcXzf/MWT/7UPfT0JrHbGzOwRkTrnrr1ciIrarD/4e1dP6RHf/yufzJsIwgBYg8fWZUotBqYqkK50kaRJFqug6LJBAJqrk0gB1TcNmvFBiOJJAPxMAHQG7vh27GjN9f9nDA3Jldierj4G0vfeJednl5gqVAhYuqYukqhWGO1WGNqPI+GoNZsULAr1Optrs4KHNdjyIyxM5Pryskz5saklKEoZK0IW9M3iogsrJbRW4JEUUauSVwrrqKp4SJ9RZhcnQ495HLZOKmyQr9pcXfvIEIWaEIhpZnEO/2fr1ZY6ABn2zvj54mlRaqOjS4U+iIx+qMx3GS6+44LdWnh2KYoMv1j2e4zdP0+liSJvpEeBAJJDlnFlWJ943OG4KND21EkmUa5SSxpEYmbuI6Hpqt4noduaBQWS0RiBlLCwjB1fN/HioaSkbFIHyNWnvlmgayeZM2u0Guku0+DIsnkgiQjkXzX9wzoFmRwO56Xye/BrFlIglQu3q2yWl2r0W7a5AYzmDGDVDaOEAWa9TaZ/hRGRCeW2rggWX8eYh0W12ZzhFrb5ths6G0zmEpQbLRoOQ4N20GWBIok0bAdIrpG03G4Y6Dvlnv1BwldjqJfX2wFAYac2AACXa9MGVEztxjV3s649p9LeIHHi8tHOF0+x309Byg7Fc5WLnJnKgQNdVljX2o3tu9wtnopfHY6t0DDbfK3c8+S0hLsTmyj4lTxgxuAx0vLR7hSn+XuzF5s38EPfPwOINJwm3iBz7b4FtpemxeWXyOpxtmb2slye5Xnll7lvsx+pmLjvF08ydPzz/Ezoz+O7Tv89ezfo0oq2+MTzDYW+OrM03x+5JP0GjmSWpxDmX385cwzzLdusEMCAuabSxwpHOOezJ1sj01wpPA2VafGT498ipgaZXt8kreLp5iMjrE7sRVD1jvVxHyeXz7MdGOevckdVJwqzyy8gCkb7IhPcrp8nueXDnN/9kD3uJPajQqsAEktz6cH/x1+R2ZWc4p8be53WWnPcCDzUe5IProBqNElC+mmuY8sFA71/BgHg6cIAp9z1dd5duFLG38jSQwOpDc194aA+eZFVttzJNQs92c/S1YfJiBgrnGOo2vfoOys8Prq3zFobiWq3khWOX6LI6tfY7Zxlj5zgsd6f46sPggIllpXeWXlq1ypv0O/OcETfb9MTEmjrAOOWn6dV1b+knfLh4mrPexNPcaQtR1V0inZS5wovcCV+gleXv4KCTXHWGT3beVfV+snWGsvoEkmD+U+T1rrQxIyZWeFldY0o5FdG+Yid6QeZVviHgBK9hJ/O/u/03DLHMh8jK2xB7rzDwBJSBhygrobSrM1WekojzTuy/4UqhQCRUcLT/Pyylc27d9m4eNzuvxdFKGxP/UEw5GdmHKMkrPMu+VXOVd9gzcKTxNTe9ib/NCGcdYPfMpODVM2aHgtbN/BkH+4Fa9/ZICjumvTch3++NwxIkqY9XpiaIpHB7eQ0k2mkj1dxpHjN3H9JttTPXx8dDtfv/ouKd3kQ4MTLDaqKJLMgdwgn5vYzZHFaTJGhIcHxqk6Noas8NmJ3fz5heP87eXTjMbT/NjYTrJGhIlEhk+N7+Rb0+eIqjIP9Y8zVy93yiKGLIOf2bqPb147xZm1JT4xtp2cafHq4imen52j7jg0XYffP/U6T41u4578ML+2+xB/d+UMf33pFDkrym/suZ+cGSVvxai7TpdtktINtqWyWKpGf1+SXDZOtieGJIU6xqHBNPG4SSxqoCgyuq5gWRqqIrP/zlEsSyOVtHA9H9NUmdiSx/cDxsayrFbqBLJg755holGdnkzo4j82msW2XWIxA9/3efnkFf7m8ElkSeJnHtvPtsEc33jtDFcX1ohHDF5+5xInLs7zuUf3MtqbJh4xkCXBSD40uh7ryxAxwxfw1FCWN8/OUK63+NRDu9k7MYAQgnw6hqrIt0woxvrS/PLHD/Hto+c4P72Maag8sGccBLiez1vnZmm1XUxd4VMP7mbrcA4ElGtNjl2Yw/N80okIP/34fuKWQaFS56svvsPSWpVUzOQbh09jxBU+/vAOHtg7juf5vHLiCgAfvWc79+0eo9xqMjmU7QJjiiwxOZglbhmde7RN23NJaRZu4CAJGV2OId30GF2YW+F3//YVirWNKP71GMkl+e3PPcJAKh5m9jq03KbtsFqpEzMNkhHjFgnJ8cvz/N7XD3eBoptj+1CO/+ozDzN3eZnhySyK/hBCfJABQ4QshqAFHTM3gKvnFsgNprl4coaBsSzZ/hSVUoPn/+ZNHnxqL5n8D5sA+aMTAplc9DMIJFyvgpDLeI7XnS5KQjCZ6mEy1bOhXd6Ksi2dvWV7T45OdT/v1/MULxV5aPd4928PbpvEsLRwu8keJpMbtzsQjTMQDScXPj6pnhizz5zgZx55nIhhcGhijNkXpqmt1tmxa5Ad6Rw3x8fGtm48RiFI6iafGN9xy2+1Dp1c0259GduOh+f5WOsAZCELpitliq0mvZEoCGg7Nm3XRQiBJsvUPQfX96nZNpIis8XUbmEeAqBuR0R/peP/Y4DUixBmyMo0PoZQ94VMOSkDQROkHCBB5JduSME2RIDQDoHxeNhO7kWIzqJKO4BQt27SRgXtLjx5Gy3tk8hCwhdRfLeBTC+O9llUXHyhIMt9+J6MJrt45FC0x/DUhwmnvQqBH6CJfrTYb9MtmaLegVBGQEQImk8jtLs6AFMArWcJ7FcR+sNAKCXpnuf3gN/T+QSpbJxXvnEcRZVRNQXP88kPptlzaJJv/flhNF2ldzhDs94OfdTkUIbSrLWwOx5C9WoTw9SQZIl206FebeI5Ho1qM2xnauy+ZwuvfvME545dJZqwePOFMwyO54jGQ2Dy2HfPMblnmFqlwdJ0gQefujMElW7T/eW5IpGYwdj2ASrFOkuzBaKJ9y6bmxtI0T+WpbRaZcddYxBAs9FGt364k6R/6FioVVltNDAUhfFUGkWSuVouElU18pEoxVYTuVMYo2A3EAhMVX2PO+X2EQRBtyyzEIKt4zkmRrP4HeZ22wmz+FpXeh10fUakDrDp+2yQoX+Q6M3FCYI4+VyiOyuX5dC43dBV+vuSeJ4fyu5He5BlCVmREcDObA7H95E7rO0gCNib70OIsKS1HwQMJxKdKoBSlwV6s5x8fdy+9ze+6R/Nkh/KIK/zZxFCYCjh9uOpCPH9Y93zuj7yQ5uzgERHLhMEQRcYshT9Vg8NwW29WBQhd9t+0BBCdBmCEBZFCbwARbuxj2jyBgB4ndF4u+TReyWVYobOfVtGcT2vWzXX831kSQqBL8/retoIITBV5YfGNoKbxlAhUMUmla9uOncfxLj2n0PUnDpnKhe4J3MnD2YP0vRazDYXut/LQmbQ6mWgkedC9cqGtgEBtu9gSjrjkSHSWmqDisQNPCQh6DWyDJl9qOtYJHmjh3sz+yk6ZWzPQRUqi62Vbm4hq6d5JH8vKTVBRk/y1ZlvsNpeo2AXWWqt8m8mf46MlqTm1vn9S3/K8eIZnuzLYcg6o5FB0lqCzV5OfUaOJ3ofIqHGCAh4dfVNGl6TlJYgocaIqVGGrD52J7d126y2ixwvnWFPYjspLYElmxiSzvHSGbZERzhWPMWOxBSP5u4FBIV2kbnm4ob9ykK5Sc4kuvMAQ44SV7Ob+uqsDyEEqtBRO3MOvVNQ45bfSWAZm78z236DnDHCR/r+FXljpNt+0NqKJGReXP4zVtozLLYuM6Hu77YrOkvMNs4CcDDzFMORHd1nZjSyi7bfYK55noXmZZpelbR2Q7UQBD6Xqm/xbvkwlhLnQ/kvsi1xqGsGPmBO0W9O8s2F/8yl2tu8vfb39BpjWMpG8O16nK8eZdjazod7f4kefaB7DNd9pIS4sR4VQmDIEQw5nEe6vo3U+b5su8w2mhSdeve+Cwho+7MMWT2da6MyU18lAHr0OHuSIyFLU/re5awNt8L9uc9yf89nkKXQh2kgmGLE2knTq3K1fpITxeeZit1FRLkh4fUCn6rT4EB6B6fLl2l6rR+6QfaPBHAUBAF/eu449/eN8uDAGJIQHF64yt9cPsU9+SH2ZQfYlx3ADzxabomSfZW6u8xQ9D4+vWWYz05sQZOiuIGN7VeRRQtF6Hx8bAufHJvCC2x83LC9V2QgEuXf3nEANwjNRgUStl/HC0p8dst2Pj7az3zjLYYifWjyFLZfo+HWUKUID/YPMBy/QNbYSVTtxfNb3Jk1ebj/UVTJxPZreIHT6U+TgajgV3fvRJcTSELpTiAeG5rcMEzt7elnb08/rVZYXUiWpQ3yjVinKs31qkYA2vWMXmZzKpokCRIJi0vLa1xYLDA1kCWZtFCNG5OB61WN1qoN/uT5t3jrwhwAhqby73/iEX7mibs23fbPfuRA9/PW4XCR+otP3U2tGVIgp4ayPHbX1C0TiMcPbLZICyd020ZybBvJUXfa1N02uhzSu/t74/zcpw9gKRqWolG2m1S8JgnZ5IG7xnninm003LDSUttzWbPraJbMF5+6C02ScYNwUnmtvsZLi+f5VOROPnbfDu4/MIYb+CRUEy/wOVy+xE9/fB9J1Qr7ENj87McPYMoqhXad8+UlKk6TD/VtQxEqMSVHSBLdCOS0HY/Z1TKLxSqe598iJ/N8n2KjSTYWwfX8TtZFDuVGS0VKtSY7hvOM5dMbtNuNtsP0cpFay8btGHyuD0tXmb+6wsylZeLpCGeOXqHVtFHUsDJSq2kzMNqDpMjMXVlhYLQHRZW5cnaBse39yLLExVOnGZnqJZNPcPnMPOlcnNXFMtfOLzK4Jcfuu7cQTZh4ns/qQomTb1wmEjPYc2gC4/uQvPwohuc3WW08TdJ4AF3uY7XxDLa7BUU1NzyzpUKNU29e4d7HdzJ9YZlquUEmH+fE65ex2w533juJFdV558gl6rUW8WSEnftH+cafvUZhqcLeeyc+VRSfAAAgAElEQVS474ldzF8r8MYLZ3nwo3sYGO2hsFzh7e+ep7RWY8/BcXqHM7z23GnqlSaTuwbZc/cW0rk4htlhbghBPBUhmjARAtoth6MvnWXXXeGi5d1j17jz3hCYAii3WsyUygTAUCJB8nvM5jZbNifPzzPYm2KoL4UiS4wmU4wkOxmnTbJX68eBlXr9PRcWQpigbLnNdzooo5s3lN+DaSI0xCbthBQHbp1wCKGCMsGSfY2AgIbbwPFtbP8CUSWG7QtMOVxkyV6DlreKKVu0/RYZbYA1p0DZKQIecbVAvzmEroys228UiHaoM1EC9zLCPgZBhcA+itAP3f5YbhOarvBTv/4EX/vyS/zx7zyDokhs2TXER75wL/se3Eqt0uDZ//c1PNdHN1Xu/+heMr0Jjjx7imPfPcvKXJF6pcHKXJEP/+Q99I9l+dofvMy18wtUSg2+9uWX6R3O8Imff4hDT+yhUWvzd196KTRqH0rzY7/wSFeOrJsaX/mPz1IrN7n78d3sunsLqwslMrk4cgdUkJWwqpqiKtzz4d0sza7xx7/zDJneBBO7h0h2KqclMjHMdSWD4+kokbhBLBXhs//6Mb79F6/zR//T08iKRP9Yjk/+/IP/ZMYigJ3ZPKaibPBVHE+mQGxcvr6XF9318Hyfqm3TcGxcP2SJ6bJMVNMxFIWlxTJLS2V27hxAVRXMmxYUkUCj4ThUHRvb80IenSQRUTUi2m2A4NtEEAS0PJeabdP2PHzfR8gh40SXFSxNQ5VCZqv2HiDPdaaUH4RFVGq2g9MBHzRZJqJqJA3zA/ctCALankfDcWi5IdiNEChCwlQVIqqGJssISWzwNnyv2JgRDsJzaLc39NNS1O45DD0ar4uLb+13EATYvheeO9fFDXxAoIhQOhdRNfQP4k/V2bTje1Tb4X3hBwGSJGEqCjH//Zn/N/er6Trdaxpw/f5QiWo6khDdyozvF34QULPbrDRD1rzU2U5M038o1ZvOr64yGI9jaZuPFZV2m2cvXOT+keENrL0gCCi1WkQ0bYPn6j+HaPk2DbdJVs+EwISkktMztH37fdtGFIvPDH6Uv198kS9d/gqDVh+P5e+n1wgTbY/m7uP55Vf5yvTXyegpDmX2sTMxBQGcKp/j24uvkDMyRBSLulvHW8dWSqhxdElHCEFGC8uW190GRbuMLGTSWgIhBBHFIq5GQ9DpA0SfmcXosNEs2ezIlW6vJoCwSlrRLnO1PsNqO1SWpPUkI1bog1awS2yJjnLdkDlv9LDQWv5A/flhRxAEzM+XMAyV8bHsLe8NCZk7U4+TM4Y3gE6yUJiI7eeNwtNU3ULXf+l6NL0qDa+MIqlk9aENY5gQEkk1jyFHaXsNqk4B1uEqLa/O2coRWn6NPfFHmIjt31BBTghBUsuzI3E/l2vHudY4TdFevC1wpEsmh3o+RdbYWAhECLGB5fR+EdcsBqwMOT+BJl33Ee4YrgupI0PzMWUNWchEFP0HUmXE1Aw74vdvsEERQhBT0+xNPcbV+inmWxcptBew5MQNX2NJZtDKcaZyhaQW3bRS4A8aPxLAEcBys9Ytb990HebrVSKqvuHF3HBXuFp7ET/w0KQIy81TVJ2wYtZw9D4WGydwgwZRpQ9FMvD8NoaSZK19kZZXou1VUSWTnLGLqjOPHzioUgRNjrHaOoskFPqsfdhelfnGGxhKgj7zTgqtc5SdaSJKnpQ2zlz9KJ5vMxJ7iIa7ynzjKFa8B9drcq32MgKJqNqH49epOUsEuIxEHyahDeEHAUW7SUTRuhkVAmj7YeURU1WxAw/hhui7HwTo8u3d498vfN8nHbPY0p/pmvJuFrbjUWveGPxrzXZXe/79xA9C5J2uFzlXWaTqtPix4b08N/8uXhCwNZEnoZm8vHiBiKqzPz3MW4VrPNq3je8snEUWEvPNMkEQ0KNHiCg625K9zNVD6eBQJE1SC7Nmbc/l+NoMM/Uie1IDRFWDw8uXSKgmB3pGeWnpfEg9lGR2pwZ4eekCbc+lzwwXiwKJulvA9hvo8sYHsy8T42cf389yqUal0abesrk4v8q5mZXu+a802yxWqqQiFsOZEC32g4ChngR7RvtwXO+WLOWWvjS/9JG7KdaaVBstak2bc7PLXJy/US0wnopQrzRpVFvUay3sphNm4U2VXQe38M7hC1gxg50Hxjn9xmXS+TitZlipqFyo0Wq0MS2dWNKi3bKx7dCMfXxHPwvXCpTXwhLHgR9w/vQMa0tlVhdKjE71Ygy+v5fCP4ZoOO9Sax/H9daQpSie5yAL65brYVga05eWmdgxwJlj1xgaz/LC14/Tbjr4vs9r3znD/vunOPvODB//wiGiMZNo0mR0ax+ZfIIDD21D11UGRnvwPa/rD/Pmy+eQZYmHn9qLYYVVyFRVxvcDXnvuDLsPjm/W7W6omky92uLy2QU0TaG0Vt+QObY9jyvFIgGQtkySpsHchUVOHT7XNbBfH5GExYEP78GMhgBTxNTpyybCBc6633U/C8Hi1RXeeenMptuzYib7H9uNZP3/lLVVd4P4/l6epmzR9BpE5AiOpJEUaTRJ604eTTkcT8qAKqm4gYsbuCiSQlrrQZVULDmKJd/GQ04IMD5N0HoZnBMgFITxKGgP3AIKFJfKnDp8HsPS2HnvViJxkyAIqNfaCBEmFZK5OJ/51cchCGi3HHRDw7B0Wi2H/Y/s4MAjO6hWmnieT09vgrmZNRK9ST7zK4+F3enIdsyoTrPp8Njn7qZYqLK8WGZyWz/LS2UWFsqMT+Z56JP7OfTh3bRth2QqihnVqZbCe/iO+6bYeWCMwA+IJCw0XSE/lOGLv/0Ujutj2y7pXJx/+d9/mmjcQpIFP//vP06r0UDWmhhGmiCQMCydz/7rx9D0G1OVT/3Sw1253ei2fr7wm6EhtxAhYGXext/uH2vEdb0z9lx/ljavpnKjKkzAcr3G89euIAvBIyPj9FgWTcfhhWtX+PaVi5xfK1But9Bkmd5IlD35Xp7ashWt6rO0WGbrVB/qTQrOtutybGmBZy9f5OTKEsv1OkJAyjDZ3pPlQ6Pj3N0/SFR7f1DA9jyOLy3w3ZmrHF9aZLFeo+k4Iaig6wzFEuzM5tiZzXNnvo+Ypr3nHKjh2Lw+N8vz1y5zcmWJQrOJIknkrAjbMj08PjbBgb4B9PeoFBcAddvmneUFjs7PcW5tlelymYrdRkIQ13VGEkn25nv58NgEQ/HEe/apard54doVWq7Dgb5BxpIpbM/j6MIc3758kWNLC6w1m0iSIGdFmExn+MiWSR4YGn3Pc+d4HqdWlnltbpq3FxdYqFWp2mHCLqbp9MdibMtk2Z3Nc9/QMJHbGGNLHZnbcr3Gc1cv8d3pa1wsrtF0HSxVZTSRYn9fP09NbKU/GnvfOajr+xxfd3/M16pAQEI32J7J8vDIGIcGhkka7/98tlyXI3MzPHPpPCeXl6jabSxVZWumhweHRnlifBKlU6VrQzvH4UIhPAZdVshYJtdKJXpjIct+qVanxzLJRaMs1mr0RqNcLRZZqFZJGiZRXSOmaVTabeKGgR8ENB2XarvNqaUlIppGj2XxzPnzbM9m2ZnLcblYRBaCHbnc92Qm/48xZBF6zTa8sFpUyLhwPlBbSUgMW/18YeRTXKvP8cLyazw9/x1+buyzqEIhrSX4RP/jrLTX+O7qG/zN3LfIG1kiisnLK28wEhngY32PIguZK7WZDdtue3bXO8b2HQQCRVLQJY0An7ZvY8oGbuDh+A4pdXOQ4ZbjRb7lHnu/UIVCXInySO5etsZvzNVkIeP4Drqk0fAa19WxtH3nH1T6WCzVb0kQXA9TidGjDyE2MQ3XJQulMxdybwIOJSSEkPEDHze4FVT0Agc/cDvgzcb3Rcuvs9wOvYr7zYlN/WOFEMSVDKqk4/htivYCA9bULb8DSKp5cvoPXvE5Iuv0GjeYPetlzJvF9fFyvRzze4mklrvFv+h6ZPVhdMmk7TcoOYsMcYPx5gcBfhBwKLMLRfxw2ZrX40cCOBJC8Ms7DvC1K+/yf596HSEEg9E4/2LnwQ0lYWvuMqacIakNs9Q8ScWeZSh6iJXmGcr2NBVnhj2pn0aWVBYa7+AGLVy/he3XUCULTYoiC52Sc43V5ruk9PEwyyVkeoytyEKn5ZXp0adI6eMMRu7B9qrU3WU8v03ZnmY4ch8pfYzh6AMYcgJZqJhymiDwqLkraFKEvHkH07VXkSWVXmsvdWeJplckwRDTtRJvrswwmehhpVWn2G6QNaNdSnVCM9AlhQvlFQxFJabq3J0LK4z4fsAzb7zL5cUCHzmwjcmBWyUxN0fLcZldLeN6PtuGcxsYLOsjETHYOZLn2nIRRZbYPzlI5DaDyc2x/uGRJYnBbIJkZ5F5/bsPCny5vsdCs4QfBJyvLFGyG6y2a/zCxH1IQvDaymWGIykeyE9Sd23qbpghK9kN4qrJcCRF2wvLEJadJrbnUXVbWMrGYynbTQrtGlWnxUKzwpPpIcZiWT7Ut42i3eCt1WnGYz20hcTVWoEBK0XWiDJbLwKhfKTH2ELLq6BJNxaFQada1ifu2YmqhDRsPwj4q++e5H+Zf6lboW0wFSdhGVi6itq5Jm3HRRKC83MrHJgauiVDOphN8vlH7uzux/V8vvzsUS7Ov9bZObQabWqVJpViHd1QO+XUXZy2S7XUQNUVZFmmVmogyRIDYznstsuFEzNs3zfK2lKFcyemsWIGjVqL0moV1/WoV1thlrPlUF6rU1qtIisyhqUxuCVPPP3DpUL+Q4albiVlPoqpTqBKKSQRZT5SBxY23Ou6rrJlez+vv3CGRq3Nocd28Np3zjC1e5D+kR6icQPX8cj2JegdTHelEZGYgeu4JDrnzLhuItq53tVSg4mdA2TyCXzP5+jLZ2m3XYYn8izObO6Ntj4kSWLPwXEOf/s0ruNy6PGdKOukI4okYaoqGcsiGwn7cPrIBX7333wZz701mzY42cfWu8a7wFHLdlguVEOmSU9s00onF96+wv/xa1/GdW7dXt9YjrHdQ0QSJkvFGp7v05uK4QUBjuuhq6HEyvE8LEPD83yuLhVxPZ+hbILp5RLbh3OUai0qjRb9mbAU84XZVXaN9eL5Pi3bxdQUPD/A5pFwwej5NG0HU1NptGxeeOcij++fwvMCnj92gUf3TWKoCrIshT5eskRa60gGr/vrbOJ5ERCQ0JK3MAPeb+yrFeuUVyu0GjaufYCtBzZnWUH4XH/ld57mmS89j27pfPG/+TSf+FePU1ipcurETHgtehPMXisgyYLB4QzXrqwgyxLjjV5mrq3i+wE7dg1y/Ng0vf1JUtk4Fy8soWkK+cEUC3NFLEuj1XKYPXYNWZHYtmMAM2bSni2SySe4dHmFSrlBvj/JtSsrxGIGnufTO5DecJyyIpHKbpycK6pMPBXh0rlFzp2aZfe+UVI90W67SNzAUb6J484gSzsw1G0IMYQZ0wERMmQQGB32re16eL6PZmkYEZ0gCCdptudiSB+8St6PctTdStfItOXVkISEIun4gYci1E6FPQ8v8JCEhC5ZmHKUK6Ui//6FZxHAn3ziM7h+mv987Chfffc0NWfjRP5yqciZ1RV29eTZF80xOJRBkjfes0v1Gr//9lGevniWlUZjw3dXyyWOLS3wjYvneHh4jN84eC9jydtX2azabf7TW2/wV2dPs9Kob7pkOrG8xDOXzpPQDf7DI4/z5PjkptsKgoC5aoX/7Y3DfOfqZUrtjeWPr5SKvD4/y9cvnONTW7fzC3fsZyC2+aLxcnGN//HwS7y1MH/Ldq7HyZUlvnnpPH93/iy/efd9PDA0ctuJ+WqjwX84/DILtSr/w4OPkrMifPnE2/zhiWOsNjeew2vlEm8v/n/kvXeQpOd93/l53ty5e7onh52ZnZ3NAWEXGQTAABCiRFGUZKVTLJetK59KV7J1Z+tcp9OdZJ3ls8/S2Sdd2TKtSEkURIpiAEgiEAQWaTM27+xOjj3TObzxuT/ent6dnZkFSIJ3JPSr2qqZ7en3ffvt533C9/mGefriyW2BIykly/Uav3/yDb44cZXlWnXLe3c+v8xXbkxwuKuHg13d2wJHhqoyVSrym6+e4fW5GezbfBmvrK3ytckJvnLjGr/+yBPsy3Vty9pardf5o7dP8xcXzrFYu92cvsTZ5SW+MHGFB/oH+dX7H2Es07FlvyilpOLY/O6br/HMlQusNTbK/a+srfKVGxO8MjvNgwODmxhfFcfh7eUlCo0GUV3naP8AFdvh6uokUsJwJs3J+QUeHRnmxlqB0UyGs4uLRHWdqWKRQMLdfb1cW11jf3cXpqaCAL8FIF1ayXNsMPRqSVsWl/N5LqysYLQkkLtyGyXm77dKaHH6It2cWDvLYLSXilfjSuU644mQ3exLn6Zv0/BtPOlR9epYqompGDiBy3R9joQeJ2d2MBof4kzxYujNJSVT9Vk0oWGpJnsSOzlTvIAdOMQI59cCQcNvMtdYYq6xwJ7kzfFyqj7LpcoEQ9E+ThTOEtdidJlZ4loMXdF5bfUU+5JjzNQXyNsFnuh6sBV97uEELk7gogVO+3pvD2LaqgSCqBphvrHEcnMVTaikjSQ5s4O+SDdvrZ2h0+xAVzQqXo1Os4OoGmV3YpSTxbfZlxxHFSrnS5f/fwOOfD9AU1UMY2tygqVEiapbg8Y3/dXkputP6Z1kjT6WmpNcKL1Cp7mjFUMvaPhVrlVPUPNKdFsjZM2NTHHbr1P1wrCZ11c/z7niS2wlI7SDOm5gowmdul/Z9jMm9ew3HV60Xd1+H76TPq8RNbHtdRuKRURNYAd1qm6B9bRmCIGjhWYeSzUwFZ0eK4epvrfzoe8K4AjgYLaHg9k7RxrGtE5WGudp+kUsNYWlppmtvQ5IuvSDNLw1rlWeJaH1EtGyLDfPoaBhqHF0EUERKorQMUQMtSXzSOh9rbQKFQWt9XMYmz1VeZWMOULNW0UTMXRhEhBGhE5VX2Yg9gglZ4pV+zqKsOi09rJmX2Om9hoJo59AemjCQlMibapdxoxwKNtLVyRBVDPojSZJG1b7sUsaFk3fZb/aQ8FuYPsegZSoQlCo1vmLr5/h0swy+3Z0vyvgaLVcx3X9bdlDvueztlJBNzR+6tG72N+Tw9Q17t41QK1Qp6mpOLYbmp8qAt1QUVWVSMxEN8II8WszK2RTMYqVBtl0jI8/fBBdUyiU69iuj6Yqbc8DRRHEI2bbyHXT9ciApUYFXVHpNBPENJO0EeWZ6VOMJ7vpiaR4eekqq3aN+zpH0BWVL8+dp+LadJoJTEVHQRAQAjhv5G9Q8xz6ImneLs4xUVkha8bojiRZs+skdIukbiGAmGbwlfmL3N85wj25oXBBG0nRG03xwsJl5utF+qNpQOBJm7x9nbK7xGD0LhJKK+q1UGVmucjBnaFmd12rf/viuma7nJyeIwjg6UO7iVsGfhDgeD66puL4If3cUvUtadlCCJSWkeR6yZZJ7fDuXqyIwe4jO5BSUi01uHRqkkbN5oEPH0AIwfxknrseHsf3AqIJi1x/Bt8PiCQshvf04vsBA2PdOJ7P2IEBSms1xo/sIBIzwyQlRbDnrh3MJCx0Q2ub374fSlVidESfxA+qQEAgHXRDIZpo+ey0kiiQMLqnl5MvX2HPkSHS2TjHPrCH8ycnWZkvcvj+nURiJtGYSdAyFRdC0N2f4fyJG3zx06/x6NOHuXR6musX52nUHaIxk4NHR3njhYtcOj3NoWOjROMWV87OUis3iSUj1CpNjn/1PAvTq7z8pbPc88g409eWmb62jOf6xBIRst0ponGT+akqfUMbJ7NeEGBpGtlotE21H97Xz9O/8Djl1Qq1coPCYompi7NbAj+KooQeJy0flOm1Ipqi0BGLYrb8y/p39fD0LzwRHq9Up7BcYuriPK59c3fS8wPevDyDEPD4kTGOn59ECME94wPM5Utcml7m7vEB5vMlZlZKDORSxC2D+XyJ3QOdXJxeYnalyIfuGScIJLMrRfYMdXJ+comVUpXOVJxirUGj6dLdkaAnk+DE1Vl6MgnuGR8IEzIDSTwSshl8P+Dqcp6opZMv1Tgw0oPR6reEFEgCglsG6JuD9a007K1/3qqarZCCIJDo1p0Hds/xmbo4i9N08VyfuWuhJ0KxWCeVitJsOhQLNaLxcPeuXGoQiZqomsLSQpFoLExqK5cbqJrCwSNDmJZOZ2cC09LRdZVKuUGjHoIKqXSUVCZKvW7T1Z2iVg1TMrO5OKapkc5EmZkUXL44z5F7htsSNUURJNLRDZLqW8t1fQxTY8/BgQ3yM1p3NAiqKEoS11/G0EaZWS1RqDWo2jZNJ9yhTEUtyo0mvekE5Ybdlu6uv5awTPb2b/b4+l6svD1L3S+jCQNVaAgEJXeFmlckY/RiqhG8wMWVNr506Yvs2rBLKQkX25+/dpnPXblELhrlsR0jDCSSaIrCcr3GxfwyuqpxqKuH/GSJyck8w8M5TDNcyFddh3/z2jd45nIY67wnm+OhgSEGk2kEMF0u8fLMJBOFNT539RIVx+G3n/gInZHopmfA8X3++tIF/vDMCRzfpz+R5LEdI4ykMkR0narjsFAt8/bKMhOFNVKWxa5MGNEcBAGVcpNEMoLSCohYqlX5zVdf4ssTV9EUlYOd3TwyuIOeeBxfSq6urfLq7DTT5RL/9dxpVhsNfv2Rx0mZ1qZrixsGju9j+x7DqTR7sp3szubIRaLhRtbaKi9MXWehWuHcyhK/89o3GEqmGM28M9N2ulTiT86f4fdPvUlU03l8xwhjmSyWplFsNriQX2GlXuP+/q19vaSUzFTK/ParX+fZ61fxpSSq6ezqyLI310k2Em0DS5dWV1ioVjnWN0BndPsNnabn8e/fPM7l1TzD6TQPDgwxmu5AANcKq3x18jqL1QpvLczzb19/hd967CP0xDfugEspqXsuv/vWcf78wjn8IGAs08ED/YOMpjtQFcFCtcqLUze4spbnKzcmqLsu/+qxD2/J2PKCgD95+wz/9dxp3MAnbVo8OjTMwc5uDFXlRqnA8dmQiXQ+v0zN3cxmiOsGyBDsOb0wD0JQbtp0xmIMZzLka3WCQKIqgobrIoGYYVBoNpF+QKHRpNRs4gUBjZZp92KlwnSpSMW2MRSVtGURMwzWGg3qjstgLkcu9v7ZPNuuDEXnqd7H+ML81/jUjc+QNTOMxYeJqqHWaLo2z5cXX2LNKVByK/zJ1DP0RXp4sudRPOnz/PKrrDlFNKGhC42nex9HJdw0fz1/lsuVCQxFR1VUHut8gJzegYrGY50P8IWF55mpz5PUExxO72ufEyBnZDhZeJvnl15FU1S+v++DRFSLiGrxif4n+fLiS7y+egqtdc5diREkkjdWT3O2dInZ+jyKUPnjqWe4K72PezKHiKgWgRJKQCFkFCf0OEqLfSOE4InuB/nSwot8avKvGIz08kMDH0US8LG+D/LC8nH+dPqzCAS6ovFDAx8locV5IHc3i/YKfzz1DEk9wUC0l7Jb2fQshGle8h29jL6dclt+lYHcHKgBoAh1g0zs3VZSz3Fvx9O8uPynnCw8S9FdpsvcgSIUFhrXmaydJaaluT/3cZL6bfNT6bSTY+teGduvb3UKACJqHE0xUMQdXOm2CD/5XijR4oRu+ZpQ2p/ZkxsZf6pQ6DTTTFRniagmWTOFyfsUOHo3yF1M62Is+RSSAF2JoKCRMXeiCA1TSTAUfwQnqKIKA0OJsUt9GqBFhbuV3h2aAbuyjiasWxpWiJ6qwmBn4vuYqq5R8ySG+iCO7yIxmakVMdR7cIIGM7U6pprDUp+g6ul4DYW+6KPoChhKHEmAIlTiek+b6pcyLFKGBS1jWsFm48SUYUGENnNGaS1WFwtV5lY3Jl+9U3WmYqyWavhBsCWtzg8k+cUS1XKDWNxCm6uT7kkydWGearnZZkXoRhjNurJYoqMzwZEHxgCVIAhoNF3mnRKXJ5cxjdDHaWywk6W1Crl0jOVCFdf1ScRMLEPn8HgfhrJ10zMUjacHDuAEHkv1Gsu1Or1GFl1V8Dwo+w6DVieeDJgsldgR6aRgNxhMh2knKdPClwFeEBBIGO3qIm1YxHUT2/fYmejEUnSimkGXlUAgiLXYSD80dBdu4BPTTD7Yu4ea62CoKnHN4geGDhPIgJhmtkw4dbLmCGm9H+sWqVq51mRupcSeoS5MffvHK2bqZKIR/EC2WWAJyyRfrhM1dS4Wl8jbNQ519NFlhZO1QMq2/8FWJYRgaFcPQ7s2ArB2w8FzPUb29hFLhINtZ1+4I7xWa5COCKqui+s5pHfnUA2dsuOSPdBDICW+EMQzJqlElI5YlGNdN82UU8fe+6jH74YqN99grfEsUnoILIL6J3HtsIdwnTA6PJmJEfgBd903yp57hhFCMH5ogGqhguf6xJMWheUye48MMXlhDlVXiSYsolGdRz96iLOvXKG4XKa7L819HxhncFcv5dUKPUNZxvb24NgekZhBNmZy/xN7qJebmJZOrVQnaun84E8/xNpSiYmz04zfNczg6IcRikIqE0WIcDfpwNGRDeatEKYamZpGxW4CKbwgIL4ry0/+zz+EGgh8z2f26gK//TP/kfx8YdO9URXB8EAHnhegKCHgcml+hYGOFLt7cqhCsGNvPz/3v/wInufjuz7z15f4N//wD5ifuKmF11SFbDJKKmYRMXTKDZtjuwcxdY1itcFapc7Caplq02GoK40iBKmYRblhoyiCdDxcQKZiVhgp2wjBheVilYHOFJOLBTzfZ6AzzVqljqoI6k2X+dUy99zyeYQQbezH1FXOXV8ImU6iwWJzBUWoRJQYPj62X0NXLGy/3tpBU/Bl6J+X0nNE1AQykFw9PUl5tcq++8aIpbaWqXX0pNF0jZnL82S67mw0b1g6++7fxaU3JkhkYuy7P6Rl9/ZnWFkqISVkcwnOn52hqydFpiiQAsEAACAASURBVCPGudPTdHYn2XNwkImri9i2x67uFPWajaqG3jHZriQ3ri7R259B11QqlQaDO3IoikI0aiCB/EoFKSWlUp2OXJyFuQLVSpP+oQ6uXl6guzfd7pOicYuf/O+f2gIUCktKSaPu0Gw4dN3yvtY3gaENU2m+iKkNo6udrHsIKEIQMXRWKjV0VUVXFToTsbb0N5ASTVHIxCLf0V3A/6+rN7KTQPobJpA9kREq7hpJPduaV4T3IG/PYWwREf63Vy8xWSrw0MAQv3j3UUbTHVhauMPs+B7FZpN8o053LEaQcjZIM3wZ8Pzkdb58/SoADw3s4FcfeISRdBpLDVPPGp7H9+/azW984wVOLS7wyuwUX7x2hZ/Yf2iTB0zVsXn2+lVs36cnFufXHvoADw/uIKrpYYKclNieR9mxubyap9hs0p9M4vsBC7MF7Kbb8nsUOIHP565c4qWpSTRF4aM7d/FP7r2PwWQKUw3nIXXX5Xx+mV9/+XkureZ57vo1Ht8xwvfv2r1pUp6NRPmJ/Yd4eHAHHxgapisaI6YbbflRzXV4eHCI3z7+MlOlIteLa7w2P9sGjhzfp9BokI1GN232vDo3TaHZYE+2k//27mMc6Owm3vIz8gKfsm0zX60w3rE1Y8X2ff78/FlemLqOLyW98Tg/deAIHxvbTcaKYKihsbTjh8e6urZKbzxxRy+gimNzcXWFhwaG+OWjD7A720lED8HJuutwtHeA33n9ZWbKZU4tLXJyaZ6n4xvlIAGSV2en+dzVS7i+z7G+Af75g4+yM9NBVAt9QGzP42Nju/mtV1/i+NwMb87P8cyVC/zju462jcTXa6K4xjOXL+AGPlFN5x/ffYx/sPcACdNEEVB3PSbG1/jNV17krcX5TdYLpqbRl0zQ8ELfSdf3KTVtOnIRUqZF3DAYSKWoOA5uEDBTKtNwXS6t5BlKR4lZDSZWZ1FVjYK9zFpzhYurZXoSCVxW6Emm0LQKqYjHmeUrjGczZONN6v4qdhCl6tZQhIovPUwlhrGdRPl7tIQQDER6+Kkdn6DhNzFVE11o+NJHEyq9kS4+0f/khvfoSsgiEgh+fOjjNP0mfiAp1RxEQ2fGLVGzHXayn97oKJqmkDAiSE9judjA8TxSRhcfTjyF7btEVJNd3bkNnjs5s6N9XkMxSOg3mazjiVF6rS6agYOh6CS0OJoSqgEOpfcwFh/ecL1RLYKuaDze9QBShtIzgF2JYXJmGiEkgQxo+k1GYgP87MgPU/VqRBQLVSicL18kqSf55MBHWbJXwnWJnsRSDSpeBUu1eLr3cWpejZgWQxcadb+BqRg0/AZe4BHVoiw3l/GkR6/Vy3eqdF3FsvRtlSjctjH2bksIhX2ph3GlzUvLf8bl8utMiJOhn5sSodsa5q7Mh9mbvH8TMKUJo/3d3pf9foZi++98LpQN5trvlwrlfFv7aYUs4xAw0rcY893AIyBUu3wnyGzfNcDRuylFqES0jRToqJJt/6yLCPot7uVRLcudymD7HQJdSZK3VxE0MFUdXbEouw4Rzed6pUxcM8mYKnVPstxUCaRH2mjSG0kT1TY6qN/K+r6TN8HtZd5iSugHAVdmVyhWGltKQ7YrPwhoOB6FSoNCtUEuGdvA9tF1laGx7lCiIgS53hSRqInX8thRWjpyRVEIgoCxA/1outo22tU1ld3DXTiuT1cmjqopaEqoCy7XmmRTMbraSWph8sqdrl8IQcoI799qvdk2z4zrBqtOA1URSBmaOXpek6iu4wdQaDZJGRZShhTxhudhaRpDiTRZM9Zyy9dJ3eLC1nmbN9H6eQFiiklMu/lAZs2NbcXxG5SceQQCVejtONeIqTPcm9lE9b+96o6L4/l0JePtifpSscriWgVfBgyPpJmrl9pdtgQu5VdCr6dc7mbbeBd9uhkxOHBsaxnMUqnC3FqZmBUu3Ku2TcKyWCpX8ANJLh5lpVIjboVGbx2x99dEaLtqepPEjYNE9DHqzlWKvoNmREEInIZDaaVC90AHJ164QK3cwD0wwOWTN0hkYuTnC+R6M0xenGPmyiI7DwzieT6KIlie9dGN0O+lZ7CDRCbG5MU55q4u0j3Qgd1wMCydTGcCRQgqa1VW5wtYMRPf9eje1c2VU1MsTa8QT1gIGbLMEukoRovl0Ww4PP+3p3BtjwP3Dm/eyQLSEYtISwYskRRdm5PlNe7p6qMnmqRaqqNoWz+n0wsFrk2tkIiZpJI70FSFoWya/kwSXzbINy4Q03qIJQfai7NGrdk2RV4vIQS5VIzLM8sMdWXIJWMkY6GvRL5cJ2oZZJNRbNdjdqXEzr4sMytFVks1lgpVOhJRrs6ukC/VqNsua5U6q+V66//z9OWSBIEkE4/geD6FSgOQpOMWC2sVlotVppYKZBJRlgvhz6O9WV46e53Hh3uwgzoFZ5GYmqIpqghCA8SivYKKBlpoAtn062iKTkSNE1HjlFcr/Od/8Wlmrizwv332n7Lz8Pb6+sJSkWunJ+nb2U1/C/B1WpIRTVHaCVeqofHxf/Ikhz94ADNiMLS7j0BKrIjOfQ+PI4CVpTIH7xpi1+5eVlcq7Ds4QEd3EqnAzj29IcjvuuR6U+QLNXRNQdEVxg/0o2gKO/f24noBmqZgmTrJxE1Wxp79/WFbkZJHn9iH43pMXF3irqMjG4yLFXWzRO3WUhSFWqVJLL7ZOFIIhbj1CFHzboKghkBnMBthMJtqnRsarkvU0Ak3gqAj/v7uj24HgtY3fyLWZuZHX2Rsy2OcXlrgI6Nj/E8PPcZgciNAaWkaSdNiKJVuj/d9fRnU1vi1WKvxR+dOU3Ecdmdz/A8PPML+zo1srphucLi7l1+69wF+5WtfZrVR5zOXzvP4jhF2pNIb/rbQbDJXKQOwr7OLY30DJG7xRNKEQDNCo+3e+M3x2fcDqpUGK4slRsa6AZgsFvn0hXPUPZf7+gb4p/c/zNCtn08IEqbJff2D/OLdx/hnzz9L3XP5i4tvc0+2l2bZIR6zWC1UW38uOBDv5N50uAhpFB0a2AQSTEOjK5fgg8OjXC8W+J3XvoHd8mn6if2HgDBmfqFcoSOyOUHnQn6F8Y4sv/HIE4xnM23z60D6KMKnJx6jJx6F1v9JAqQM2vOKK2t5PnPpPA3Po8OK8M/ue5inR8fRhAJShoxIKdGFRjyq0xuJ4zQd6uUGqq5gmMamuYIEBhJJfvX+hznQGd7TZlAHJDE9ykdGx7hWWOX33nqNim3z5vwsHx3dteG5LTVt/ujc6RDgSyT51Qce4Uj3xkVcRNfZ19nFLx97kKmvfJHZSpnPXb7EU6O72JO9yZp3fJ/nrk9wo1hAEYIPjezkpw4cJn6LeXXcMDjc3cMvH3uQX3ruC23Jn0SSt0vEDZPBbIyq10AQxsNrQmnN01ScwGasK4ErPX700D5MRefN2TmG0mm64gZz9TMMZRIE+DT9VX76rkOU3SV86TKQ6aDpl6nLS4x0xUgbA5ScefYPVImoKvXgBo5jElUzLDUvk7NG6Va3DoP5Xi5FKCT0OAl986ahpZr0RLZXQiT1OEk9ju153Jido+40MDWNhuPQnU6QVE2CIMBpwOxakeFcyKyvOy62rVCzBbGUQVSLbpJNxrXYprXX+vWmjCS3b80IIUjqCZLbmAfHtY1zfi9webt8ltHYKLZv83b5bZAwGB1k2V5GIEjqR0jpqdDLSDWouiXmm/Pc13Efp4qnyBk5FKEw35in5tUYjg2zbC/j+A7DsWHmm/MoKOyM72SyNokqVDrNd1aWfDulqAq1us23AhDdqQLpUnSWUIXOY12fYCC6py2nTugdGwydby1TjZLQOmj6VSw1zo7YgW+J9fS9XlWv0Jap315Nv0bdC8fRpJ7bsAniSZ+8XeRIZpxzxQnmGisMK33vqVzte+bbCCM7JY7n4/l+23RVCIGqiFCn2QIn3u2OYyAljuvjtrwSIPToMXQVXVN5uGvnxmephdz1mSn8QGIZGqoi2JPqbl1LaDi4fr2O54fR1S3anSoUdE3B0LRtpVrblev5nJqY+6bBw4ipY+oqiggNrzsSEZRbYlyFEG15A0AiFdlg+vVuDMCilkHUglR8o+nhfQfDBZPtenheSIcUQtCwXQxdQ2/tem9X462o84PZ7ltsQW9ei+svEcg6ujqCEDfjh/d1dLWN595Nksp6HGzYtoKwba2DXIqCoalot11rQEDDL2EokQ1UyLVKnauzeUZ6s+3k7S3vG7BareN4Pnv7wom4HwRUGk0SUQsExHWT9QZYsW2WazVSltVuY+9F9aWT7MimN7GjRpoZ1mp1+jJJVBGCh++fffx3rog+AkhKzeMEgUMscRemmgyjNS2d4X19xJIR9h4dpVqsk8rF6eiO43tw6IFxosnQIyqeitHVnwmlfAJc22NtsUSyI45h6UQTFpmuJIce2k3/SBeL03kCP6B7IEulWCPbm2ZhMk+uJ42qqyQ74owf2UFHd4qeoSxLM6uoWigfXS/T0nni43eFk+UtwB/X91GFoCMSLrqlBFNT2d/RRVw337GP2dHXQTYVwzBUDF3F8wNKjSY7smkgoOnlUdCI6XeOUwcY68sx3J0hkDDUlabWdOjJJHj62B4kIXgy1pfDD2R7MTs+0InaArQ/emxPe7fs5586hqooDHamODjSg9bahRdCMNiVbnnghBIFgeBnnzyK0vr5Zz5yL4oQ3FhcY6Sng+6OJBFdJa13cnvLDyVqsM5QXa91Vml+vsD1czOss2W2KyEE3Ts62Xf/rrahs+P7nJieD/sByyJpmdRdl4Rl4vk+zd4IsViMK2trqEKwVmtwsL+bjliUXFcSJ+FztbLEcDaHETF46dXLJBIWpqHjeT6Npkuz6eL7AdGIQdPx6MrF0TQVzwuo1W2CIGDXaDfJxBYx1a2OwDC0UKL2TbJ71jckYrcdOwjq1J3TQDhWNt2LxK0PYOk3GQ5CQMzcqPl/P7GLbi/fC2jUQq8d3w+l4tKXNOt2yxidtrxABpJ6tUGuN7PpmU9ZFj++7xADiWQos8UFGfr0edIjkAGmGn4fmqbQ05tGb40Hr8/NcLWwigCe2DHKzm1kWQLYl+tkTzbHK7PTzFZKXMgvbwKOdEVps2CWqlXWGg06rHfHEvPclmyk9adfn55krlpGAE/vHKcvvjX7VQBHunsZSqa4VljjRmGNV69M0k2UWNQkGjFQVcHkzCpBIBkZyuL7krmFAoGE3q4k8ZhJVy6Brqgc6uohpuvUXJfVRr39HTRcl5VabcsAEkvT+Pj4XnZnsxScaapeHokkpnVQ94p40m5ZJYQ+XoYaw1SidFrhhs8LUzfI18NgikeGdnBYSXHltauku1LIIKC0UiGZS1BcKaMbGkEQYFgGmqZSXqvSM9xJ9/DGBagiBA/2D7I314UQgkAGLDYnkTJgILoLUzU52NVNRNepuy4L1SpeEGwwgD6zvMCFfJhQ9dDAEHuzWy9yBbArk+VAZzezlTJL9Spnl5c2AEfFZoPX52fwpSRjWTw1uovY7Q7trdqX6+Lunj6eu3ENCOdOpwsTDCfC4/kywA081pwKTuAR1yxSeoyFxhopPUbFq3N3ZhdRK8OxgYEWkCfJmqOApOmXiWlZoloGIVS8oIkqDJyghkSiKSYRNYUuTNygiWjJRkwlitGKPY+9w8b135eSrXXWul2F5wcYmsp9Y4N3fN/hod4tjalv5g2GpSs6ujBCO44tVrRSyjC89Jtcc0HYrtZZ/gEBESXCaGyUa9VreIFH1sjiBA4xLYbt2zT8Bm7g4kmPslum7tdp+A0qXgUFhcHoINP1aVJ6ikAGrNgrNLwGGSNDQEBCS2AoBm7gkjbSmIqJoRjY33pO0R3LdcMQpsHvQLDN9eppzhSfpz8yzn25H0AX7y5lLKom6Y/uZsWe5nr1NPtTjxDXMnd4783V4XtdilARhGtgT7pbyvm+U1VwFim5y6SMjX2qlJLZ+iVcaRNRk5vYVqpQiKgmZ4vXaPoOU/UF0kaCbvW9+46/J4Aj1/eZWipw6to8F2eWmFkpUq41kUDE0MmlYvRnU+wd6mK8v5OhrnS7k9qqpJRU6jYnJ+Y4PTHP5dkV1ip1kJJsMsaewS7u2TXAkZ19RE39ZkMRoSHnualFSvUm9+0axPcll2aWcX2fsd4s/bkUtutxfmqR1y9N8/bkIqvlOkJAOhZhtDfLsd2DbfPp7RqhlBLb9ShUG+RLNWbzJU5PzLdfOz+1hLZNgoOhqRwa6SUeMbFdjyAIrcv6cyk0NaRozuZLTCysbvl+CBdxw10drY7bb/3T2gPkreUHAVNLBaZXiu1zxyyDYrXByWtzvHphiqmlNapNB0vX6EzF2T/czUP7Rxjt7dg2iSIIAi7PrrBSCidMO7pSDOWqSOmgasNAFWQFVRlq64CllJy+Pk+pNeFe/062u8flus21+TxXZle4NLvCbL5IrZUuF48YdGcS7Bvq5q6xfsb6cm3PE1OJ0WXtwvarGyiziYjZkgVue2sBsHSd8Z5cG/iEsC1rqkI2HkUVChW32e4KhYDeRIKItrXn0TdTgZTMLBe5sRQaLSejFodHezeYfKaiFqno1uknxWqDtycX8YJwArB3sItM4v2185807wMEpjoAKGixATxHIhSBYerkekPmY/dglu7BcIIo/Sl0s5eR/f2bwNf1klLS1449DXe6BltMEyEE8XS0/XO6M3y9o3vjzkxHT4qOnrBNJ7PxTecQQrQXfluVIgQTa+F3P9rRQSADSrZN2WkyknrnwaXedHjl5AS24/F9jx0gYuihxEEIFKET1wfuqM/ecC2KwFA0mo7LaqVOZ6oljd3Qfwtu7SJuJSwat3zOm81XYNz+jGzRzyq3GKsqLep6T0eC3mySSMss8pudIkgpmbwwS6VYJZG+s+eFlLBwY5nCYonkvnirzYSvWbqOogianheyhOJRCrUGMVNvm4g7hH39ev8pBCw0SyT1EHiORg0euX9XW5YWeiaEUstKzSadjKCqCrbt4rh+CygSTM2uYrZSzKQM+/cgCE001yffQog2kPfNlKIIGjWbarm5YRLmywpN9wKGNgxAIJt8RzjW30O1ulhk9voypUIN3wuwGw5Du7qpV5o0ag4dXUnyi8XQP831iSYsOrpSm4Cj7licA53drTYQUHFnaXprZMzdLNtLSCkZioXm00IIpqZWyGbjCEVwbmWJmuNgaRq7O7J3jHi3NL1tPN30PG4UN8tc05bFeEeO68UCV9dW+T/ffJVfOHwPe3OdRLTtd0RlyyNuHXj0g4Azy4u4vk/MMNjVkb2joW3cMOiOxblWWKPiOChJjbt3DIU+gZqKAAZ6M0jA0FWQMNzq19cB6PX7E9d1IloIHLl+gC8lmhA4vk/Ncbcc++OGwf19Awgh8aRNmMqax1TjIVCktIBA/Jb1QqztYeEHAWeWFtubYU/sGEULBHbdIRK3WFsssji1Em5MNF00XaVWrKN36eGCx/VCCfTIRqaYoaoc6e5tzyckARV3lYzR05KLCBKGialq1F0X2/dwbwGOAim5mF+hZDcxVZXxjmybxbpVGarKYDJsH67vc72wMeihZNtMlsI2kzIt9uU2x4OvV9I02ZnpQJkU7Q3JsUQfw/FOwvzBUKIRELQ8QlVAMpboR20B/BE1BKFvznsECb0TiSSud7bHr2hL5RD+ntsQjGC0Unpv/T+ADvXbT3F6P9ULJ65y/8FhVEVhabXMzoHce5b2dH/H3YwoJVbyDZL9m8fcQqVBo+nQ35Xe4t13rmszeXb0ZrAMHV3o5MyQMdRr9VJyS1iqha7ozNXmyBgZdEWn6BZDP0AjiydDcCmtp1m2l7lSuUJci5PSU2hCI6JGKLmh/UhaT2MoYZS7pVoYisFUfeo7KlWLRg327+sL5zrvMR6y5izgBWHane3X0TQdwfbjx3oZSoQ9ifu4WnmTmfpFTqx9mWPZjxHVNjKZA+lTdvME0idtdN/R5+hbLUuJYSgWnnRZbk7hSQddvHNq6HtRTb/GmeLzdFnDmEq0PYcruIu8Xfo6IOiLjJHWuzb0k5pQeTB3iOn6Ej1WFk2oGO9xWMh3NXC0DnB85uWzPH/6GnOrpTsuyFVF8PCBEf7lT3yIbHLrSbvnB7w9uch/ee5N3rw8TcPxNv7BXJ7jF6f4y6+f4dGDo/z0h+5hV//NTk4I8HyfrlSMmGnw+qVp/uUfPUux2uDnPnKUH3vsCJ9+8RSfO34hBKNuq9cuTfPZV9/miSNj/PyTRxnu3jpd4k+fP8k3zk+yVq6TL9co10NdMIS+RJ967q1t70MmEeH3f+mTjPd3Ymgahq4SVfUN7JsXz1zj3z7z8rbH+Pknj/KPnr4fQ9Hw/EVs5yyWcQRN6+V2ZNfxfD7/2gU+9ZW3SMUsfuvnPkoiYvJ//91xTl6bw3a9Tcd/4cw1PvvK23zykUP8yKOHiLZ2kqV0kLKGEFEaNvyX597ia6dCf4VfeOpe/tFTKXzvGqoW7lY4/jwWNz13JPB7n/0Gp1og26/88Af48ceOoN52j23X4/nT1/ibV85xeXaFcn1rSiDAl968RFc6wfcd28vPfPieEBySHivNa1S8ZUbU+9GVEGRRVYUd3Rm0d1hUNVyXpuvh+j6uF2Bq4W5v3XYp1OpEshppI9KesCpCUGo2iSffm3SAiYU8//wPv4Tj+fTnUvzuL36ckZ6t2+KtJSU8e+Iy/+6Zl7Fdj/H+HL/zDz/2vgOOis1vEDP2E9F3stb4KgkjSTSx2XD31ghsGeSR/hyKcQ+w2Xx1/e+llLiOR7VQo1Fr4jnhYkhVFYyIQTQRIRI3N0m7bj/n+nmbNZvKWpVm3UEGAZqhEUtGiWdiaPrmY3hBQLlph8agMjSRH0llWG1sb0J4a1mGxoHxPpby5RDccD0arncLu+ab18VrqkKp1kDXVAbvwMyWLWleebVCo9rEcz0UVcW0dBIdcazYu9vVur18z6deblAr13Ftj7wfoKgKhqVjxUxiqShqy/j7TiUDyekXL7wrzEMAhqm3TbIBLF3jgZGtd2JHsh2sH/jWdnf7MSereQajHUQ1jfQW/kqO7YLtUl0u4bs+iqpgxUx0Jfy8+3ffTDmRUrKwWubK3AqHRvvIJTcbHkPIiKmsVamV6niuj1AEVsQgno4RuUXyts7YW2cMr5emdJCJ/QiKSCClTcQ4gCI2SwhmbqwwO7XK2N5eLp6dJRo1OHJsdMt2/r1eyWycIU3BdwNUTWFxZpXeoSyu64ME3dToG86hqCLcHArklvehKxprR6AH0sX1K6iKgSJ03MCl4dfaC9/QfyrsF2quw1SpGAKVQcAfnj3J565e2vZ6/UAyUQzBAM8PKDQ3J5PFDZP/5uAR3l5ZYrZS5gvXrvDWwjz39PTy1Og49/b2k7vFtH+9FFUhmY7guaFBetm2ma2UQl8fz+f/eP0Vkub2E3ovCLi0GjJjHN+nKX0StzGkb5VcSikJFKg6DrZj4wUBfiMEIWYr5S1ZRaqyvlmw+bWMadEdi6MIjZ5IGJ1s+zUMJYIQyh1TlYp2k/lqKEtIWxYj6QxDnd0M7Q4XfNm+DjoHslgxs518CTf7iEa1GTIrb3tuNUVh+Jb0O196RNQEqrjZz6mKwjpRI5Ab05MansuNYgEvCJBC8JlL53lldnrbzxFIyWSp2DqXpNDcmJi2VKtSscNNu55YnMQdvk9FCPriCUxVpeF5qEJhONZD9A7g43qFrDuJKhQCKam6TWKa2Z4f375d8E6/v9u/+ftWQSB54/wUi6tlphYLHBnv59psHsf1GB3IMbdc5Pi5SdLxCA8cGubq9ArX51fJpWIMdmc4eWkGRVE4ONbLSqHK4moFU1d58NAI5yYWyBdrDHSl2D/ay/XrZeIRk8GuDMfP3SBfrKGpCg8eGuGFt64yu1zkiaPjVOpNZpeKYdpof5aIqXN1Jk88YrBnuJvXzoUBHQNdaXpzSf7s2RPsG+nhI/ftJpOMsje5F4CEnuDezL0ALDYX2ZPcw2BkECEERzuOMlcts1JrkmCUpu9xcbVIXB/ADXyqrkARgqSRJl+vYal9VF2HRS9AU6JYqoaqaQRSMmjuJaJGcL13NzdbLykldb+MEzQIpE8gfYruUtsvb82Zx1TWA6NUDMUiKreWjn2r1RvZSVRNMl2/wJ9P/a/EtCQCtaUEUUnoWYZjBxiKHSB2i2xNCMFI/DD3ZX+Ab6z8Fa/mn2Gqdo6h2H5iWhqJpOYWWHMWWbGn2ZW4l0e7fgzjOwAcGWqUkfhhlpo3uFQ+jq6Y7IgdQKBgBzUC6bMv9fCGQIpA+tS8Ep508KWHLz0qXjguuoFN3p7BkU0UVFShElETrXHgdullmquVt6h5JUbjh4lrGdacRS6XX2ehMUFa7+Tejqex1I1M29CWxWSqtsBApIuotjUB4Nup71rgSErJzEqRf/1XL/Lm5Zl2jLmuqcRMnVQ8gq4qVBsOxVqjLTHa2ZvbNkZeSsnrl6b5v/72G1yezSOlxNBU+rJJutJxpISlYoXFtQq1psNzJy6zuFbml3/oUQ4O99xc9LUYJbezmi5ML/HHXz3BX3/jHI7nkY5HGOpMEzV16rbL5FKBSsOmbrs8e+Iynh/wP/6DJ0jFNn+xZ64vcOb6fBsoUxWFIPDbw7auKijbIPamrt3yEEImEaV6GzCSTcbY2Zul2gyvx23J6raaEClKDF/eNOWWUuLaHqqmbvLysV2Pb5yf5OrsCicn5lrSkTTd6TiqqrBcrDK/WsZ2PWbyJf7w2TfIpWI8fXQPQoAMigTuORRtBOjfcOxwMA7avyEDpLRpb8V9EyWlZGJ+lRNX58LFs6oQswx6OpIko+GEZaVUY2GtTNPxWCpU+PMXT5GJR/jRRw+hawYZY5CU0Yel3FzgqIqgN5vEMu48gYkaepsGuy61MXWN3kyCdDyCELDYqLArGa6iDVWlZNtkvc0gSAIcaQAAIABJREFU3DdbAti/o4edfVkuTi+zVKhw4uosQ13pbVls69V0XF67OI3TAgPv37uDztT7zyDb9uaI6jsBieMt4Os3vQoCP8B1PISiYDdd4sn151dBSg+kv2V7lFLiNF2un53ijWfPcPnN6yzP5KmVGviejxkxyHSn6B/rYXj/AA//4FH6dnZvOZivA0anXzzPm8+d5erJG+TnCniuRywZZXB3Hwce2s0Hfvg+ugazG1LvTFWlJxEn0pIBeDJgplKi7DQZS78zvT4IJPGoSf/+ITRNYaVSo1hvIJEE0sENKuhKbENE6DuXwG4lfGx7Xj9g7toix//uJKdeOM/C9WXqlQaGFd638XtGePgHj7L32BhmdHs25633MPAD5ieWePXzJ7h6apLZKwtU1qo4toth6qQ6k3QOZNl5eIijHznM+D0jGwA93/OpFussTa0wf32ZpamVEDgC7KbLlz/1EtneM5vObUQMjj15BLMVJR/4QZuBI4SgvFblxb88Tq20ecKoaip3f/DAlt5JpqqTNkKvlNs/q+/6TF+e5+t//ToXXr/G0uQK9UoD3dTpGsiy/6Fx7n/6LvYc3Yl+Sypa1NIp10L2o+d4vPbFU8xeWWBwdx9HnzxMrVTn+N+d5I0vn2bqwhz1SgPN0Mj2phk5MMSjn7yPw4/uRTNUPDdA11UMUwOC8HnBQAgdhQR1+w2a7hUUJU4y8qFNn6+wVmNqYplm08VpuriOh+v670vgyIoYmLek7WW6khvkFluBh1tJy+O6scWyVkEROsotoIWUUK026ciGY3XNc6g64ULeCwLOLi+962sPkDj+5rFKEYJjff38i4c+wB+ePsG5lSWWalW+NHGNl6Yn2Zvt5NGhYT66c5yRdAZVrAMeErsRyiwB6p5LozUGOYHPicX5d39tUuIGm01HpZT4UjJXKXN8bobLq3mmSgVWGw1qrkPT80LQyfOoOFtvNBmauiW7MaobIAQ112nPsVShU/FdXN/HUFWanoepaW3YXQhBVNOpOQ52y/csYZjE9Jvz27DPgEz3TVb1upS2zV7cxqheQZC45VgCgS+3ZkxtVa7vU27dB19KLq3mubSaf1fvlVK2P9N6VRy7fW+SpvWOkegxfTP72g8CPBm03itbbDAFvyUbVoWg5jnM1ArsSfXgy4C5eonReLYl12sFkBB64wQylEf6MmhtPoaL/kCGkkKvFV6jiTvbLvx9rIbtcmN+lQ/cM0a+WEPXVAY605y5OgcylElJKUnEzDboGjMNTl+ZIxG1UFWFXUOdXLyxSL3pcnhXH5emlnn7+gJLqxUevWsnL52aYNdgJz3ZJPMrJQIZsJAvc9fuAc5dm6fasBkb7CQVj7BvpJvnXr9MNGIgA5heLJIvVklELRbzZRJRk1rD4dG7d3Li4gz7R3vY0ZPh4SOjJOPbyLaBbqs7fOZu+f7rnstSvULJaYa9bauNSKDuOdzd2UfNc5iulBhJpnF8n+V6lbhhogCdkTgz1RL7Orq+pXblS49X83/DtcpbuIGNEzRxgyauDL2Mvjj/++jCQFcsDMViOHaID/f+XOjd+G3WehpcVE2RNfup1NZYtqcQt3SZ613MueJL7Enexwe7f5aYdhM80oTBvR0fRUHhdPFrzDauMNe4ws15TThq6YpJVEu1k+7e61JQuCvzYRYa15itX+ZU4TnOFJ9vX0Va72Zn/K4NwFHVK/Lcwn9i2Z7GCRq4gd32Kio4i3xm5l+jKya6YmIoFvdnP87B9OOb5so7YgfpMHo4WXiOydpZAAIZtIJYuvhA148xGj+8qX2sA+O7kzva4Pi7sWz5Zuq7FjhardT5/S8c5/iFKYLWwv7wzj4+dNc4h0Z6iEXCHQLX81kqVDg/tcSlmWUeOTC8ZZqVlJL51TL/zxdf4/LsClLCnsEuPvnwQY7uHmx7J1SbNm9dmeUzL5/l8uwKZ64v8B8//yq/8dNPtmQUgrHu7JbnOD0xx5nr86iKwicfPsSTR3fTmY5hahoykFxfWOXPXjjN8YuTuF7Ai2cneOre3Xzg0Gbj4p/7yFE+/sBNN/lSvcnvfe4VlgoVVEXw0x++hyOjN4GVoEXNVYRA11T6OlqUccdjdqXYkhzIdijfvWMD9GWSxKMmgZQUaw3+4AuvcfLa3OYvQ4JAbz/svh9w4qVL9AxlGd6zkUZpOx6fe/VtHM9nR1eGH37kEA/u20HMChNEqg2HN69M86nn3mJutUy5bvOZl8/y0P5h0lETGawACojNBqsSGQJFtBbngKZ2fUvSVlPXeOTgCMcvTtGTSXDv+ACHR/tIxyMhVR2oNR1OXZvjP3/5DWbzJRq2y+eOn+eh/cMMdsUpODPYQQ0jGmmbWKqKysT8Kp3pOFF1e3ZQzXap2Q7dqUR7nmk7HivlGpWGQ088RtW1W14u4YSlbDfbxrnfTgkh6EzFuH/PDq7Nr+J6Pi+du84TR8a2ZepB+AxNLxe4OL2EBJJRkwf37cAyvmu7kW+5LG0Ha/Wvoqsd+LKBdgs42Gw4vP7SZZoNh1xXkrF9/aSzMYQwQbiwxYRXSkl5tcpf/+6XePEvj7Myu0awBUiyPLPK5beuY0VNdh7eQd/O7i2PVVgq8Zl//yW+8icvU16tbNjkLq9WWbixzMnn3+aNL5/mp37tExx8eE97cV2xHeqOi9fydZMS6q7TTl58p4mKH0guXl+kJ5dkZDBHRywSvg9BIF3q3gIRbfN136kkMgTHtzm/7/m8/qXT/Pn//rdcPzeNdxtTdGV2lasnr/P6F0/x+I8+wI/8ysdIZGJ3/CzNms3X//p1/uY/PMvMpXk8d/OzlZ8vMHFmire+chYrarLr7uENr196c4L/9GufJj+7RrVUp1mz29+r03D4/B98dctzJzvidA/lGNjVg0BQL2/cfS+vVvirf/cFlqc3y4kNSyeejrHz8A68oIonm5hqBoFKxohi++6mLtFpujz/6Vf57H94lulL85va3up8gctvTfD1z7zOJ/67p3j65x9vLzg9P0yoDIKQKffCXxznlc+9xf4Hx8n2ZXjmd7/Mm8+eoVFtbjrmlRM3OPm1c/zYr/4AH/6pR1A1hWxXkljcRPpLBO5JVPMxEHFA0nAvkIg8Qd0+gecvoyobx4GOXOgNtpavoAhBLGG95xT776a6tf1uJw18p+d1I9ikYWlZvKCGlJK4lmwbcAsBqXSUG9dXQvlbELQX94aisjfXeUdWz4ZzCsFYZmsQWldUnhwZ42BnN1+5cY2/vXqZq2t5aq7LW4vznF5e5O+uXeYn9x/m+8bGyUXDeZeiKvhe2G5d32+DP5aqsb+zi+g2fji3lyoU+uOb5xcVx+bz1y7zZ2+f4XqxQMPzUBDETYOopmNqGklNw9I0aq6DfxvCoikqCcPc0oNQEYLlepXFepkrxVUUIGVGSBmhp9xyo0pnJE7atJgoriKBXCTKfT2DOMFND05dUUEEFNwVnKAZere0kh2jahxf+jhBE0uN4gZOuBgRgrTesVliIbgpU2t9Fk0Y2MG7Yzf8v+y9d5Bk2Xnd+bvPpzeVWb7LdHe1n3bT4wczgwEGGHhDkaBIkAQpLkXsLhVSrLSMXZEi10ixklYKUpQJUuQC0AYIUgAIASAAAsRgYMZPj+me9ra6qrp8Vvp8+ezdP15WVlVXtZkhFAsg9EV0dFVWPnffe9ec73znBKHE8deE/Hfle+iJbcE83iKxpyDY07PRQc4Ngi6IqXc07G4VhqptsCqXUvLc4hWW2g12pIsstutUnBbFWIq5VpVQSvpjaXwZ8mppOtIcVVSOL1+j10ryg4XLNDyHtGERU3Xuyg3yammahGYyb9fIGjE0RWF3po+zlXkSmsml+hJ7M30cyg+j/1dgPPw4h6pGoO/sUrWrH7pYrrNYblCqNUknLfryKU5fnqeYTXLy0hzFXDJiT0pJpW6zUKpjGTpN22WuVMd2PNIJi9mlGtOLlS5AurjSYHGlQbXRxjI0UnETQ4+cFWOWTrnRYqUWOatahkEYSpptj2wqFrGc+wfIpROkEhbxjoSIqgh0XeX6UpWedBzV3BqcWP8MhjKk4s7TF09RjEXMXScImGvWGU1lu893UjfwwpD+eBJL1bt9maqIiMEnI/CpGIvm45rQGY7vJq6myeiFmy55pJQsO9PEtDSGMElqOeygjiIU+qwxIGK9VLwF8sZgV3TaUKO+oewuUHbncMM2o4m7SOsFtJuUOamKxnB8DyktT0Zfo4lLQi7Wj/Pdxc/iBC0OZt9OrzWK1tGClTLEDdvMt69wufEap6rfZzi+hyO5J7qlbBFrJsG9Pe9nZ+oY15pvMGtfpBXUAUlCzZA3B9kW30uvObpBZzbaXqHXHGE8cYhea/Qtl7EJIegxhvjA0G9wpfE6062ztPwaqtBIaFn6rO2U3DaW6mJ11nsC0RX4vpO48dzXx7Ge97EtsY8rjddZcWZRhEqPOcTu1H30x7ZvKRruy4CztasMx3p5vXKR3akRssbWAvBvNX4kV3xBEPKNl87x3ROXCaXE1FU+/OBd/Np77yOX3EzpGu/Pc//eUVzfR7kJ8u/5AV945iRvTM4hJewc7OF3f+EJJgaLGyZWhUyCkWKO/aN9/PZnvsnluRLHL0zzX547xSeeOIZQBHPleoehtHHi4HgBmqrwd568l48/fpQ2HidWZhjUM+zM9tKbTTJczPKbf/w1zk4v0nZ9vnviMo/ctX1D9tCXAdsGU2xXct2HcbHaIN7JAgsh2LOtl4cPjHePbbseFxeWySZibMutIbepmMlIbw5di4S5IVoAVKotWnWH8f48mVSMtuuTv0m5kcRF04ZRRAd1l5JkJrblQksCLcdjtDfLb//cOzl4g3ZOTzrBtmIGU9f5p5/7No4XcHm2xNmpRe7fM4IQaUJ5HRnMAhsdKQQCRelByhoILVqoE96acdRZ8ECI568J9Hl+yM7BAv/7L76LwZ4MihJlBSKx3GiXmbjFwL0Re+iffu7bNGyXuZUab1ydY6R3F3QWysvOVQwljq7EsB2Xpu1uydxaH6oisD2fpXqzW7aRTca4a2wATVVIJHS8MOwCAoKorl9TfjiZLUVReMfhnXzrlfNcL9U4fW2BE1fmePuhnTedsAWh5PjFGRYrkQvNruEi+0f7fyIzbdnYQxhqAS8sk7EeRFPWKP2GqdM7mKVSaqCoCpquAC5hMI8QsWgmc0OTlBeqfOp3Ps93PvcsvhdgWDpDe4fYfnCE3m09aLpGbaXB5JkZrl+Yo3+8l7F9w1u2bWWpxqd/9ws89afPEAQhhYEcRx4/wMSRMQxLZ3m2zKvfOcWVk1OcevY8f/D3Ps3f/Rc/z73vPoRQBGnLZCyX67hTAUgURRD4d5ZqDsOQQraTYZGRCcDqvhShoStJVPHmSip9PySTMIkZ+ibwKghCXn3qFP/273+G0myZZDbOwScPseeeHWQKaVp1mysnp3j1qVMszazwxT/4K8Iw5Bf/yd/CjG19Hm7b5fO/93W+/O++SaPSQtNV+kYL7Dw8xvBEP2bMpFlrMX1+jpmLc6iayl1v24O2RcIglrAoDOUpDOUJw5DJ0zO0mw6qpjCyd3hLa/pEJka6J0nfaJEwlJtArnQ+yQd//QkWp0rUyw1qpTqTZ65Tml3TjZEENLwZ2kGJHusgupLCCwNMVd/Uft/6f3/Ap3/38zTKTRKZOHvv3cmBh3aT7U3TqtmcP36Z1797hsWZEp/5375A4AW8/9fegZUw8fyAHYM9myx7J8/M8Cf/+M8488IlegZzPPJT97Hj0ChmTGf+2jIvfeN1rp2ZYWlmhc/9869QHO7hrkf2Ui41sFsuxaJNpJ3nEoR16vZTuME0dfsp/LBE3Dy2qd1S6RiZbBzX9YknTA4dG9/AyvlJCsfzsdseQRjSartoqoKuaVH2WlOo1u2IvaVrkQ6PlGSSm12FNu03qBBKL9K08Wsd/Zco1M5+pIyA3NWSsaRp8I/uf5iHt22t3bKa5VwdOyHqAleNQYIOCySUIYEMMRWd4VSaXz54lA9M7OHEwjzfnrzMD6avsdBscGGlxD977nu8vjDHP37oMfKdUrtVtp+uqhGIAuRiMf6PR9/B3kIRNpRTCSQhofRRhYEkjBzIOomYsHNugkif6I9eO86nT75Gw3MpxOK8c2wHj46MMZzOkDQMLE3DUFTOlZb5zae/uakUz9RUrFto/GRNi2I8QW88yWyjRl88haWpBFIykEiR0s0oU6/pqEJ02AcCQ1G7cyg3DHADl8X2Ai2/TjtsYSgWqlDps7ax2L5OUs/gS58VdwFNaDhhm2Tq6Ja2zesjspCPYai3f4YgmsOYnfuR0HU+efRePjCxh5VKpMklFIHr+tSbbVIJqzvP1vVIcNYPQkrlBpqqkk5ZWKrWzbp7YXBb5pMvww1z8HboMdOq8L5tB2j7HtcaJZ4Y2st/uvQifbEUj/Tv5HvzFzlWGCWQIXuz/bR8F0vV8cMQPwx5uG8HT82dx1I12oHP9VYFTag8ObyPb14/iwDGkj2sOC2yRhxT0fhvhWlbh2XoPHJ0B0srDd5xzy4SlkEybnJoYigS+9dUknGThw9vZ6g3y4MHx3Bcn10jRZq2i6GrFLJJhnszPHPiCr4XcHDHABMjRXLJGEuVJg8eHMPQVfLpGEnLIPACju4eJpO0uv+rqhI9h9UWO3tzpHIREC1lZHA0vVBB0xQGelIkLJ1M0uK+A6MYusbb755gudy47Xx+NULpc77+EhOpYxTMyBxESkmPFd/EjjNU1rEHN45hQRhyuDjQldhIaBneN/jf3/b4kpAXSl/mYPbtPNr3twG41jzFijvHkdwTACy1p3h2+Yu8s+8TJPWNLuWBdLnWPEXTr/LT237zln1BTE3xgaH/cdPnK84s3138LFVvmXf2fYJDuce3BDi80OFrs/+Bk5WnudJ4nUPZxzcBPJpi0GuN0GuN3Pba14ehWDxQ+AgPFD7yprbbKoQQKCQZSzzA3fknN/yt6dt8dfZ5EsUU/bEIKErpeT4w9Bt/4+OGMkATOjuSR9iRPHJH2/hhQMWt0wrafGHmO+xLjxNTf/iaTD+SwFGp3uLpE5e6+kOHtg/xC+88Si4Zv2UWwrjFoD1frvPMqatIGQ14H3xg/ybQaDUURbB7uMj779/Hv/vKs/hByHdev8R7ju2hL59ioVq/KctiYrDAe+/ZG5WLhVDz2vSGEdonhGAgn+L+vaOcnV4EYHKhjOsHXQZTSMhUa4kz1Sm2J/vZlx65o5KLUqPFfK2xyXWm3nKYK9XwgoC+bArVUHC9CP2PxwxSN6Exr49oMewjcbvXsapjcbN4zz17OTDW3ymxC7uuBqulGEd3DjFczHJ5tkSj7TC7HGWEoiHYAbF1hyVlGykjy9ioNMi75bm3PZ9Xr0Qlc9WmTT4V77KyVKFgGhpTyxUqTTsCjtbRlS1dIxkzObx9kB0DBU5cmaXtesyt1BFCwVQTSAIK5jhaJ5tXbtgUsolNi6wbQxGCB3aMkE/Gu/e+0rC5tlim7foc2NtHO/AIWGMG1N2odOCWOFmksLkWYv3fOr92PtvWm+XoxDDXS2eot9o8e2aShw+MdwXAbwzbcXn+zDUkUenkA3tHb1oW+uMaXUFrDBLGXQA03VMoIoGudsQVRfQOpDNxRnb0kkzHAIlQeztlahvbz3N8nvrTZ3nmSy/jewHJbIInP/Eo7/nlx8j1ZdAMDSHoCuDOXJzDsT1y/ZvFHKWUPPOll/nBl17C9wLGD2zj47/1EQ4/uj/S91EEgefzjp97iK//ydN8+T/8NbNXFvji73+dnYdHyfdn8YKAmVqV4Y51dSijZ6M3dmuGzmqUazZTcysIBMP9OdIxi8V6o+M+opPSx980cCRE9EwltwB6FqeW+eK/+StKs2WyxTQf+4fv5x0/9zDxlIWqqYRhiNNyeflbJ/mj3/wspbkKT33uOe5+50GOvH3/pn4qDEPe+MF5vv7H36FRaaEbGo/+rft4z995jKE9vVhWJC7teDZO26W55DJ9cYaBvVmcwO5mGAWCXUe385uf+mT3uXFtl3/yU/+aq6emiSUt/u4//znGD2zWLBKKgFCyNFOiNLtCdblGcXiNoZHKJfnQJ58g8EPCIKTVaPPZf/YlvvH/fHftOqSLJERXU+hKBORV3OYN5UeS6fOzfPnff4tGuUk8ZfEz/9P7eOLjbyOVT0btF4TYjbfx7JeP82f/4ivMTy7x1T/6NiN7B7n3ycNIIvDpxqlzs9Li9PMXmTgyxi/81kfZc/8YVjyGomj4rs+D77+bT//u53nl22+wMl/h2a8c5+Cje+kfynW6bx2h9BGZLhhYxj4MfQdRObJEUwrcGHMzZZLpGKPjRcyYTjafiESTw6Dbb/2kANnLlWbEQi03mC/VGCpmKNVapOMWfhAlQZbKDUxDI2bqxC2Du/fe2qkIws67GQkGC6DqltgWj1jPmqZgdhJUpqaS6liht32fitPeBOquhhN6zLSWqfvR+5HWYjT9NlkjScO3CWVUQmQqOk7oUTDT9JpZNEWlGE/w+Nh27hsa5szSEl+5eJavXDxH3XX5+uULPLxtlA9P7AXW9INiWiTID9D0XOquCxKq3iJ+uFo+5SGEQt1b7ozVkriaJpBBx8VMJaUXiasZLldW+IvzZ2h4LhnT4h/e9xDv2j5BxjS7rkqrsdhqcrMReKnVjJ7FLURg45pOyjBJ6gYDiRTaemaqEN3aslWWweox47rRFSWvOw5uoLAvtx+JpO6VSWgZVKGiCIUesx8VFQn0WcP4oUvJXbijjLuumDhhqyssfTs0RFdV0kY073GCgJJtR9pYTYd602F5pUGjY1Iy0JthqVSnZbv0FTPoukpppYGuKQz250inLFKm2Z2PVx2nCzreLJqu22XNRuevYqoax5ev0R9LE0jJi0uTFK2I1RG1t8BSdebtGpONEm4QcK2xwmRyBa3j+KcrkfbIiZUZmr7LtniOV0vTxFUDQ1U5sXKdpXaDiXQvhqpxtb7MnkwfSeW/MY5ujMFChoGeNH5YQsoV9o1HFQpesEIoq9y9J+qv/KDC+BBoSh8t7zQLyyF37xlmz1g/jldhqLeKaA7RnG9woeaSTFkULQu/7mJLQdwXxBUNu9rGsnSuX43caVUvpG8wx6FdQ8xcnOfKySns3jSD2/vID2QRQpBLryXMUx3ZkOGOkPZwb7b78+1CSokiVI7k3tnVPO38BVWwAaheX1IsO/2aWKcNqXQm6rJTYrl+mxuF2Nd/fqNWmpQhA7Gd9Fvbbzhb0ZEWCFh1g43YNcOMJw9xtvrshuta7RDWznvt9xtjoT1JxV0ipefYnjy8JWgEEbsxoxdRUHBDe8OcZfVKVh30Nl4jG1id3e3WfX7j9zfvZ23LG/e1/vurnx0vX0AVCm8rHtzwfVMxeN/A/RvAmTWQce0+rT/u+nuk3KQN32rYQZuT1YsdJ8k4db+FE3qYt6h+eSvxIwkcXZ1f4dxMJGSYjBl84P69DOTTb5mSLqXktcuzXF+OBAb7cyke2jd2S3tGRVF45MA4X/zBSWaWq8wsVXnl0gyPH55AwmZRbaKB9L49I/TnUxFKKRRUITbUaquKwu516q/Ntkuz7XbBAwWFjB7nruwYKe3OMj/RfgVD2TSF5MZSo5ipYxkaSdXoUt1VReD5IU3bvaOHVgiDUDY7TjfR/KZeaWFY2pbb55Ix3nZgvAueNFsuFy4v0N+bZmggslXsSccZyKW4PFtCSlip2wRBiCp8pPRQ1H7YEhNSQEZtrygWmpLfUlNgNfwgwHY8FEWh7fksVZvkkrEuOOT6Pk3Hw3Y9EqZBre10NI9UEpaBH4YU4yYjvVlOXJklCCXNtosfenhhm1BGmbFVZ7VCJsHZyYVN4q83Rigl5+eXGcymmejrQdFUkjGTsb4chqYREmIHHgprA0xP7PbPg9v2eP0H57sT/Gwxhef6qKrC8mwZw4p0MxKZGDsPjvDowe08feISDdvlpXNTzJaqjPZubX15bnqJ89ej97KYTfDQ/vGfmEXaarjBAqqIYfuTOP71SG+m/Qr9qV/oAkeBF1BdaZLJJzBjOl03rLCJlCsgd8C6soC5Kwv81We+R6tuYyVMfvofvJcP/w/v3lJ7Ipa0yBY3l1GsxvJsmW/9p+/TqtkkMnF+/n/9MA+87+gG3R1VNRgY7+Vv/88f5PqleZ776iucevY8L3ztNZ78xKPEdJ2hdDqypRfRBMIJgg3aGbeK/kKKUqWJrilomkK5ZVNu2lE5heIRyDaWmn9TeVhVUWi2XZZrLUZ617JgYRDyzJde5szzF9B0lSd/+THe96uPY8bX2k5RFbSMxsMfvofJ09N88fe/QXmhytN//hy77h4neYO7Watm85d//BTlhSpCETz8kXv49X/5cdxEhanWK6TDIm5ok9Cy1M0SsbEUPUMeVWWOthtjxZ1FIklqebbF95EprCtjbDldVyshBKlc4qb303N9JGDGjE3gllAExjpQVtVVzBtAWoGKIrSI3i0ESNCUiBWx6hzk2i5P/9lzzFyIWFOP/NR90bO3rv1UVUHPJ3nnzz9M4AX80f/ypyxOlfjaHz/N/gd2ETd1FisNtg9spl6nsgl+4bc/yqHHd7HsvoEmdhJT86gxg51HRvngr7+Tcy9dolmzufrGFCtzFZZXWgR+wMBAA+m9AdoOFCWNqe2k3Pw8tvsqCJ1i6tdQlY33Tjc0Ji8t0Ky3KfSmyfUk8cOQVy/O0HZ99o32Ucwk35L18o9aFLMRkJtPx9kxXMDQVXw/QFEUgjBE62iAQJSUuROXokgjIWLgBDJAFRpJfW1hJFZZt0KQ0HXGMllUEVnNX1op4Y2Hm4Sro/1Klp0aXuhT8Zqk9ThO6JHWE8zaK/RZWeqejW6o2IHLnL1C0VzT5VFE5OB17+AQd/X2MZRK869efBYnCPjmlUt8aGIPhqlH/awBUQnDAAAgAElEQVSUZEyT4VSGEwvz1B2Hq5Uyd/cPYAfVDnMmTtVbwFKTqELHCZrEtDS+dGn6lag81K9iqgksJc0biwvMNeoA3DMwxEd278PcIhEppYwcxrbQGpQyKle71Zjod0pSBJKgw77ypY+pGKxqwgUyQAiB0SlhyFkW/YkU50rLVJ02k5UydxX7UISgx+zfsP8bF2mGYhLXbl+mIIRAhhJdsW5amnJjxDSd8WwOTVFo+z6XViIgpq83Tc4NyKZjTM+WGdvWQyppMT5SQCgCVVUIgpCJ8V6mZ1fQdTVKXCQSpAyTmuMw32hQc5ytS9+Inrf5ZgNnnY6WJlTeMbCbimuT1i36Y2nqnkPOiBFISVI3edfgHjJGjHcN7iGhmaDDh0cPkdJNdqYKJDWT9w7tRwhoeC7HCqMkNZOK2yKlW2iKQtlpcW9hlJhmkDNjJDWTuPaTlUC7WYSyjetfJ5QuhjpAEFZBKGhKFi9YRggVVaTwwzIg0NU+/HAZ2z3bqVgQhNLGD5Zo+1eR5j3oaj9+WEJ0nvcwbNGTa5C2DuD613GDa4wPz3L9/DBN28M0NJYWahimRiJpYfg6k5cWyOaT5AtJKisuK8t1dEPFiptUy01yPUmKw3ma1SZT565z5eQUA+NF7nn3YcQduoOGYUiz6WBZBpq2mfnfCqq8XPoaK+4cbyv+DMUOS+ZE5WnqXol22MAPPfak72cscZCQgMnmG1ysv0wgA/qsMY7kniCUIc8vf4mUlmfRuUZSy3FfzwdRULhQf5krjdcB2Jt+kLHEXYQy4HT1Wa413yCt99DwK91zOlN7jov1l+m3tnN/4UPdz22/zssrf0nLrzMc383+zNvQRWQscuO8reTOcqr6fe7Lvx9LTTLVOsOcfZn7ej6wZTt50iEkAqTUWwDWnmxTdmcJCUhpPQgiqYIXS2d4rXwRXwbsTY/yaO8hNFROVq7wQukM7cDlSG6CBwr7WHHrfH32RUxFY9GpMJEc4p39xzAVncnmPE8tvErDtxlL9PPugXuIqSbHV84zYy/R9G2WnRrvH3yA7clBJpvzfHvhOLbv0GNm+NDQQ8Q1i+8svMpXrz+HoWicKF/inf3H2JceZcWt8fnp77Hi1vil8ScZikWJrq/PPk/Va1HzmwQy5PHeI+xNj1L3bZ5efJXp5iKz7RK9Zo6Pjz1Bwdza9futRFKL87biEc7WJvFlpCEW3gaAfyvxIwkcnb423xXfLWaSHN5RxA8jerUblNHVNAKNQLaRhKgihhdUMNQsgXTQlCSGku2+2J4fcPH6MrYbIRE7BwsUMre2SoaorGpiqMjMcpWm43JuepEnju6ikEqgq5s7jpipsX+0H6WDLta99pbCVKnY2qTdD0PcdSVfEQosWWxXwIL8HdQmror6NdrOpglzGIaM9uVYqjS61F8J1Bo21XqbOxIIkh6q0oOq9HS/n84lkHKzqw/AcCFDby7ZbZ9azSYMwg2uJYambRCQtl2PsCNkqKj9cDOxMyFBrFpFuwSyxq0sjJIxk4f3jXct77u7WXdPbrRNv/GaXD8gtu5cI1FrnZgavfCGuja5URXRvYe3ipihM17MoalrE81VkGup2iTfH9nzrmbdwo6YZNK4Qej0hsNousr2/UO0Gm2suImmqwR+iBU3KA5GgJDn+qidge/YxDZ2DhZ4/fIsM8tVXjo3zWjvRvoqRO/Qc2cnWam3EALu2z3CtuIPr8P7UYlIEwIazuuoShxNZBFC29DmmqExPFaIbLLXacUItR/BEIiNQoqvfucUc1cjhuHe+3byrl98BKEpeF4E6AH4Heck1/EwLQPf8yOWnhrZRUO0MLlw/ApXTk0DMHF0nGNPHNzSfU0IQSIT5z2//BivfPsNnJbLdz//Ao/81H0ElkK17RDTNYIwRBC5/gwlbg5Y3bjv1T7NcX2yMYtc3OqUgao0vev4oY2p3Tl4JIG+XKqTeF9jNVSWa7z4jddwbJe+0QKPfPTeDaDR+tB0lXufPMw3P/N9VuYrnHruAuWF2ibg6NLr1zj30iUACoM5Pvr33kMqn6QdCMaShwllgBe2MZU4ppJAEpLUct2fVWGgCBVLvf0YcqvQNJXAC7h+aYFkLk5xuOdNAbFCKATSQREGopNBtAOX2VaT4XgeA43SfIXnv/ZaVMZUSPHuX3p0A2i0PnRD420fvZdvfPq7XHptkpM/OMu1s7MM3jWM4/nUbRdz3diFgD337eTQI3tRlJC2X8HTGljkOmXFCuMHRsgP5GjWbKqlBnajTW9/Btf1EWoWlMtsELyUbRLWQ4Rhk6nFJTzPZPtgpCkYhiGD2/I88sQBgiCkstKMtpKSVMwiFafLyImZP/7la6tl1dnUTdi3N2Se7yQUoeCGkU5ESl/NQq8NIp4X4HaSYpGQ9TBfvnCOlbbN96Ym+fDufYxlNmfg45rJfT1RaXnNb5HW4mvM1kRhwzjlhj5u6KNtsagQQhDXdd4xtoNPnXyNpVaTpVYTKSPXs3jCgA478YGhbTw1eRnb9/nrq5d459gO+uO7uvsqdDQ9Nh2jU6ZW95cxlTghMjpG5287cvktQSMALww5s7xIy9+c1XIDn/YWn69GxWtQbpRo+m280MeXAb1mDjuI5okpPc6KWyOummSNNNvikYunqijcNzjM96auIoGvX7rA20fHSZs/XLccIRScsKO1dgePlCIER/oGKcTizDcbPD87zbVahV35AoaukUyYDK5jzca3YJNm0ms6nVkzxu58gev1GuW2zSvzs4xlslv2iTWnzdnlpU06UxkjRsZYe1/yZtRHR326jSevEsgx8qbEUg280CatpwnwO5ftk9QlqtBJanSYWpFLkaYY2H6FgXiiw8qKygrbQRkpDbyO7qaCih1UaAUVeq1d/CSF412lYn+LULZIW4/QdF8nZT6IQKftXcTxp9CUDH5YQVWSKCIRgUuEKEoCx75GzNhLKNv4wRJN51UMbZggjEBbIzaEpmRwAxs/LFO1v40iEkh87jq6dZmsEIJUej+mpXXnSlvF0nSJ8kKV/Q/spmcwx7mXLm25frlZhKFkcb7G3GyZPfuG6CkkNzybCS3DA4WP8K35P8EN18pYV9w5Ss4Mb+/7OPP2Fd6ofI+h+C5KziyvrnyTe3s+QM7oI5QBilAJpM906yy7U/dxf88HAYGqaCy2r3G6+gPeVvwZnLDFy6WvUTCHccIW52sv8GDxo3ihw9WOkDLAvvRDgGTevrrhWlpBjW2xvaT0As8tf5G8MchIYh9bRUJL0/KrLDpTDMZ2cKXx2hYMprXIGQPElAQ1b5nT1Wc5mH0MU4135yhB6NEOm5yoPM3F+ivowmRn6m5UodIOXL63+DoPFw+yIxlpRKlCZdmp8Zezz/PugXvIG2n+fOo7FM0MSS3O65WLfGzb4zxQ2M+fTz3NtngvY8kBvjTzAw7nJtiZHOSLM9/nRPkS9xf2U/ObHF85z69sfy8J1SKjJ6I5sJHk3f33ogqFP732bS7Upzma28UjxUNcbc7Rb+V5ov8YuogIE3kzzcdG3s4fXvoqbrDW719rLWAHLj878jgnK1d4auFVticHOV29ymRznk+MPcmXZp4hayR/qKARdLCAMOR6axFNqAQyxAlvXZXzVuJHEji6XlqzO+3PpdDNEnW3RcufIaYN4kuHyJElwAvrtINFgtAhZx2g7l7BUov0xh9mdfSzXZ/FSr27/0Im0bV/v1XETJ3edYuOxUqDtuuRS0SObjd2Opqq0ptbU1ePqTpFK7kJ1tgAJMmN1DUJ1H0bO7g9VXd9GJpG1W5vEpetttoslBtUGjY7hiJEVNMUCl1g5/acZIlHKCtI2QQiEdNE2iLTs7WbViZhbShhCqVEyo0TByE2EoVWyY9S1pDBAugBt3s8pfQ6rjy3Pn8hbl2+sEojrDbbLFYbNDvOd6uCfm3X49piecM2QehR9xcwlY1toKrKlsLpN0bLcXljZgFFCArJOJpq4PohK/UWmqpSdW18GbBKXBJCdGyVb1hE3HBZqqZSHMrf8YCYipk8vH+c1y9HrjTfP3WFDz6wf1Mp5kqjxWuXZiM9lpjJvbu3bQDTflLC6Ig65+LvQFcKqIqFoQ2gKWsT4MAPsVsO5VKDQdsj0XHckOEKoiPyuxpSSi68cpWwI+p65PEDBAjOvTGDqiok0xZt26Nte+QLSeaulxkdL9Juu4Cg3XIp9mfI9SQIvKArDK2oChNHxm7qmLMaQxMD9A73MH1hjoVrSyxOL5PaUaDhOrRcl1KiRcoyKTs2TuhzzBq+o3YSQKVmc3lqiVLQJp+K05NMoCghmhJDvEkxQkUIaq32phLP6lKd6fNzQOQc1D9W3GrzbuT7s11L6kalxfLsCtt2rwn4SymZPDNDvRwJwO46up3B7dECzVTjnQnO2lWuLc7X7mpKX/3r35zV0qrbNGst4um3sghU8MMWkkb3PPusNIZQu3TqmQtzVJYipu3wRD+9I5vLv9ZHKpdgx10jXHptErveZvL0NIMHhiJB9kaLnvUsKE1l5+FRdFMnkA5b2YrHkhZGR4PIczzaLZdKo9RxIkwilCx0xSEFlnEAKVu0/GkWKz65ddjcwmwVp+0yeTliPVZKDQ4cGUFRFLb1ZrAdn1wytsnt9Mc1vI5IsaqsiW7S+ScAN2x2S5CklNF7d9tnUkEVOoFsd0su148VlqUTBCFhpwToSN8g+4q9PDN9jfOlZf7L+bN88ug9WwIrq2NsWotHCbP1NPzOf6EMMRQNU7352CGlpOE6tDusnowZZcLbbQ/PDbo7u39oG2OZHGdLS7wyN8tfXbnIz+w9sCUjav2+g44TVkaP3ns/DNdpE0lqjnPTkrxr1QrfvHJpy32rQtmkY7I+Vu+NKhRMLcZCewWQXXHrqJTOImek0JSN7fvQthEKJ+MstVq8NDcTsbB27b3ltb75iMCQm5WWbBX7ikUO9fYzf/USU9UKXzh3mn9wz4Ndx84tjyLlmnvZujbOmCbHBgb53tQkDdflr69e4onxHWS2AMjOlZY5uTh/x+dZ8+YoO5M0/GVUdHzpkNAKzNonGI7fTc2dpRmUOkkBpeOaFmn2KUInlD4ZfZCqd520PkTZnUQiMZQEVfc6Q/EjlJzLhATE1Rx2UEFB/YkDjiJWkELSvA9NyWOoQ8SNw7S98/hhiVC2CGWMmL4LRZg0nFeIGXtQhIXtXURVkiTNY7Tc01j6TgxtCNefx9AG8YONjnxBWEdVMlj6Lmz39JbvYyh9yu1ztMQcsaCXnLYP5SZiw77ns/PwGMlsnGtnZth3/0TXbdYPbZbtV3DCypbbAujk0Yxxto0W0DRliz5CIITSLf1aDVVoDMYm6DGGAMmF+st4ocuyM91hLe/dILANYCgxhuO7SevF7jFKzgztoMGcfZlIn65M1VvqsKMz9JojSOQGoepIn2dzH5ExivTHdpDQsmSNPlbc2ZsCR6aSYDi+m6nmaTJ6kZq3zKHsO27aTn3WGNuThzlReZpnlv4zs/ZF+q1xdMXED12q3jLz7SvMt68QSJ/9mYcZS9wFCAxVZyK1jRdKZ6h6TR4o7EdBsOLWWHIqzLaWWXFq1D2bqdYi+9JjFIwME6khCmaG4ViRaXuJgplhurXIcLyIF/rYgcPV5jz3FyLDqdFEH9sTAxvIAqaiU3brlNwqTb+NHUTSLKaqowsNQ9E3lKRFbnnKJmKIrmiMJQYYihVoBQ5vVK/gdhIFAgVN0dAU5baukW81VKGyPTlEw29x3V7aMkHzN40fSeCoXG912TE96ThJYwhT10gZ21GEQSh9JAEgCaVPWu4CIdBEjJg2hCo22hH7QUDDXtXngaRl3laDBiLL++S6DGut5eD5IdVWu2vZvj5URXQBEyEEQWeAfDPLCwVBzojAiOIdopFSRiyYUqO1yRlpIJ+mmElSbW5clHl+cEfU9mj/PoIYa4+LYH6qRK3cpDi4mZ1iGvoGnZxU0sIvhF12xa1CKD0oWthhbWwGPwQmyDXAKwjryJvYn9/+uiRL1SYvnZvixfNTXJ4tUW21abs+rufjByFBGBKEmxlEbtjCCyPGlpRrjDFL18kkY7dtW0vX6UnECELZnWymYibbijk0VeAYPgvt+oaJ6NVKGVPTKMbjWw6i9abD2asL5DPxSHxQRK6DrbZLGEoK2SSzS1V680kaLYd4zKAvn+LhA+P85++fYLHS4OzUAmeuzXNk59CGY5y9tsjFTpnaeH+eY7u2/cSVqa0PS1sDUHQl16VRAwgF0tk4qqZixdZNUqRHGE6jams6I+2mw9zkYlT+aGhsv2sE09IJAonveyxdqqFrKlbcoNV0SKdjTF5eJJmOoWkKlUqTMAzJF5L4XsDUucj1UFEVRvYM3vYeJDNxekcLTF+Yo1m1mb20wIGJXlqe1xVaDzsLtbAjintbTTWiBWaxkCKXjrPYaBAz9M5CQKFTuX3njd3Zq+14nb5MonXo4wtTy9TLEbNk5sIcv/WR/xvlFu+W53gsz64AkRNbbaWx4e++6zN3ZQHP8VBUheE9A5yfLhFOLZNNx8ikYqxUWpiGhu14GLqK4/poqkKr7VLIJQnCkIWlOol45L5iGRor1RYT471Yd9DHdUNAz2AOI2agG1uX/d5mcxRhsr6fbPpuxHjtfDZzcR6nGWm+DO7o64JqN92nIhjZu8YCuHpqmoc+dj/ZZIzhQmbDOaqqwtDOjji+hK36a0UVXRasDCMFAVVTEYqClA1kp6Rh9YpMbQxVyWLquzG1OtNLVUZ6c5g6pNIWruNR6E2RSFroHZfA5WqD6aUKpVqLRw/u6AjW/vhHy1+i5F4iCD30TsbWUBLoSgJDSbLUPoemmJhKmhAPQ0nSY+667bsXSh9NRIicE9g0g7V3JJ2OcfTuMawO2FeMx/nVQ3dzvrTMUqvJn5w4jhP4fHBiD32JBKaqdUtd667LQqPBc9evMZTK8KGJPZsApu9NTfLC9RkeGh5hNJMla1rEO7bqoZQ0PJeZWo0/fO04dddBEYIHh0e7TETdULtXN5rJ8olDR/i/nvs+5Xab33vpOcptm/fu2EU+FsNQVcJw9dwcpmpVnp+Z4nDfAE+M7+gKz6pCsCtf6Aoivzg7zYnFefYXetFVFSkltu9zdnmR33v5eU4vL3a1ENdH2/dx/eCmrZ/RE/QlOm6TAnYGw1iK0X38V+/bjeArwK58gZ/ec4BPnXyVcrvNv3zhGaZqVT4wsZtCLL7hPtQch8lqhcVmg/fsmLgjZpKUEido0Q5bJEX2jjSOAFKGya8cvps3lhaYbdT57KkT+GHIhyb2MpxOY6kakoiN1XBdllpNnp2ZIm1afGzvgQ0Ak66qvGv7Tr584RznV5b57rVJ/vDVl/mFuw7TE4ujCkHDc7m4UuJfvfgsK217A6B/q2j5K1hahnZQRREatr9AWh9AQaPiTmMHFZygDgh6zHHm7FMUzQka/iKK0MjoQzT9UofVuYIuLDQlji/bJLUCIHHDJpaaxg4qJLUidlC9gzP78YogrCFQcIM5VJFEU3q65e6hbKOILLrag6pkkCjEjD14wUIkRK3twA9LrDS/gq72oio5FBFHCA3bO4frL6Org9jeOWruNXrivUjp0XJPbkjcrYaUklL7dV5Z/B1sfxFTzXG093foiz3QlY5YjepSjRe/8TqqqhBPx4glLXbdvcaaccMq58p/yIpz6qbXnlXvIVf9DZyGxcjonbODFRQMxeqUgSmspskFCrKj53djqIqGpmw0uRAd0D8q/9K4r+f9ZI0+ltpTXW2eVWOD20UoVx0MJVKGW4JL3fMXCuOJgzzd+CwX6y+T1PNkjOJNr99U4jzW93EsNcnF+itcrL/M6eoPkIQIFHTFwFDi9Flj3JV9jL3pB4hrEdtdRfCBoQeZaS3y3PJpPjv51/zK9vcgEGhCRVNUdEXjyYF72ZEcJOyYMqwSDwJCNKJ5bQTQaOiKyv09+xlNrDn93igY7YQen5/+HoaisT05gKnqm/rhrfrlrUIVCjHV6IB2ont2e1IjvLB8hv94+S/pt/Lc17P3jvb3ZkMRAjf0KLt1dia3of9XmA/9SM6wPH9tIW7qGqqI4XghtqdH+TahARq256OrBqamRfasQYipJWl7PrraIhOz0FUlsg3tlL4JETmS3FkINFXp6haGYdhxedNIWjcpl1i3cFhlutyISN4qQiSz9gqnKpMczI6zN307sUsASdNxkULg3WCzXG7YXJxZZqXW5PEjE6hGdH7JuNmlpN8uFBFDSofIAScKw9SJ32QRoiobr9n3Q8rVFgN9dwCEyYDQv4KijQFbtLHQER2WjxAaqpJ5S5l/zw94+cI0n/rWy5y4PIsXRFlIy9DQNbUrNmrp0e9zKzVKtTWbWkON02ft3rTfTNLi8M6h2+px+WEE7OQS1lpGod7k+MVpgjDk0MEB+mIpYh2HJAGYHRvgm83pXD/g3OQC2/qyNGyXtuMhpSSbitFqe0wvVChVmrhegVpHtDKXijPam+PYxDB/dfw85brNM6cn2Tva12UUBWHId9+4TNv1EcBjB7eTuwP3nh/H8IMqbjDP+hYu29+lJ/4kqtJxdpCwMBtZwSLWdF8kLlI2QDod4FPQbjo4rQi0tpImiUyMVCbG4XvGomNEAlmw7ohy3WfNRptVQXkZSuqd8hwhBOn87ctYdVMj0RF/9FyPRrUVlcf6AePFHIV4HCcI6E+kuwup20Wj6XB9ocKomqc3n6LedpguVzk2NowqPELp0g5s3KCKoWTucIIlsF0fPwi6+i0AteV6dyLUqLQ48/zFOzpHiNrRczbSdD3Xp1mN3mPd0MgU0tiuh+9HgHur7eJ32GHppEWz5dC0XXqycVIJC7vtUqm3cT2fZsllfFsPfhBSqdv4fgBvAjiSUrI4tcyZFy7SN1ok13enbdXZHtCUOKvCmhJJ3kgwb1e6GcxGuRmdF5DMJtD022ef0j1rz1V1OWLqJmNmZD6xvjRTCOLpWOdcJIH0aPnLpPShzhi9OQI/xAklmdxqia4C+B0WQpNK68tk4x+i5byMomTJJrd1+9JkOkaz4VCv2eSLKRbnKpx+fYpd+4fozaaot9pYd8D2/HGJuFaI2A74+KGDplj4oY0QKjEtR84cAykx1CRe2OywJG73/EgMNd0VyA4JiXVKrYUQGIa2oaRcURQe3jbKJ4/ew789/iIrbZs/eu1lvnzhLNuzeTKmSdABnmcbdeYbDdq+zyeP3stmOXVYbDb53OmTfObkawyn04yksxTjcSxNwwtDFppNzpWWmK3XUIXg2MAQ7905gUCQSJhR8mkV8FEUPrBzN/ONBv/x9eMs2y1+/6Xn+eK504xmsiSNSKOw7jhcb9SZb9Rxg4DffvixTee1v9jLfUPD/GD6GlcqZf7RU9/k3sFhBpJJ2r7PdL3Ki9dnqLsO79k+wdnSEhdWShv2oasqCcO4Lbt5NW7mdrPVPTRVlV86eISK0+Yvzp1hsdXk37/yIl86f4bxbI6UYRJKSc11mK3XmG3U2Vfo5bHRcdJ3aKpjqnFyRt+bsq4WQnCsf5DfOHY//+b4C8w36nz65Gt89eJ5duV7yJgWEknDdZlrNJhr1LF9j5/dd9emMjOA8UyOXzl0lP/z2e9Rdx3++PVXeP76NDtzeQxVZaHZ4NTSIg3P5b07dvH89SmWWq0tzmxj5M0xKu40PeYOFKER71hlZ4whTDWNrsRBl1TdGUIZULR24QZN+mL7UYWGE9RJ6f3U/Xl0JY6pJ1GEhpQhdlDBUBL0WntQhIYmTFp+iR7z5uU8P64RyBa62oskQNcGEEovLb+Jru0gJgaYtacZNyfwpMdM6xrbkw8RB3zpo6AREwIvdNEUHVVouKGLaQxgIllqXiajDJCOjdIUM6hqLyl9B6pQI3H90MOTXgeIMZH4VJxztPyILd8Olii3T1OM3YPKxoqSVE+S+95zGKEIUrnkpgSKgoqpFTH9PFIGyE5Fy6ocCkRugOM7emlWtmY0eaFD3VvBCVs0/DK2X79lOftAbAcX6i9xuvoMOaMfkAzGJm76/f7YDi42jmOqCWJqEjdsYykJ8kY/7aDBpforSEKqXpTgDWWIHdRpBlWcsEndWyGmRmunuleKQCAtR9Vb4kD2EUIZ0g4aNPwy7bBFzV8hIwroiklSy9FjDnGq+n3eVvyZWwJNQghSWp539P8Sx/LvYcmZouFH2nOq0IipCVJaD0VrG6aykeTR8tucq01hqQZDsQLz7RVCKemzcozEe1GFyoDVQ923SetxKm6TJafCC8tn6LWyXG8t81DhABk9yd70KEjZ+X6LrH7ze+HLgIX2CsfyuzEVnbq3sU/JGAmmmgtcqE/TZ+XJ6AnswGHZqdIKHEpulYKXIXELXeJARhDX+wfvJ2+kiWlRn912PSrNNj2pOIEEo7WPBzKD5M1BZKjiylVdPGi5HjFDQ0pw/IBK02Ywl47KtzvrBC8MmLOXCWTIorNCwcyQvsW1v5X4kZxlaevot64fCTwt1hucvL6AHwT4HZcuTVUYzWcpt2zank/aMllp2gznMzTaDmOFHOM9uci+Vl2jZXt+sH69dotY+y5EIFYQhsyVa7i+z7aeWwMhmlBJ69ab0yBA0GdlWbKy5M3bLwxXI2EaXZeR9RE3dcYH8vTnU+vaYPVVvTNF9wjNtoGo3hsBrusT+HdWSheGIdMzK4yPFLqZzJuHArdyZJJetDBfp4uxvuMJgvCOrDNfvXSdf/0X3+fKbAkJ5FNx7t87wrGJbYz0Zsmn4uiaiiIEXhDwh3/5PN84fr67vUBQ9xbxQpv+2J61z4W4g+cKyk2bSssmG19Xj5+Kc/fOYWqtqORwqlFmLJknTQQu9SWSBDLEDYItbX+zqRiP3zOBqUdZPt8PQayVRgZhSBhKdE0hDCWqqmCZERj78IFxnjl9lVrL4fmz1/jwgwcY6bhJLJQbnJ5cIJSSno15Ff0AACAASURBVHScuyeG74ix9+MYNec41fZzGzJctn+ZPE+s/d50WFms09O3UbA/ApIsQv8qin4XQmj4nk/QWbirqorayYKvlptBh53oB7iOj2npeI6HpmsomoJlrWWdJBLPXQXAuSMQQFFEV6xZdo5rqCrZWJTph0hzZKXdosfaWoj0xrDbLkIR1JsOEknb86nZDqs9hRtWial96MqbG6z6skkcz9/AVnTabvfnwmCO7QdHuu12uzDjJsWhjYLOYSjxnE4bKgLT0jm6bxt+GFJu2t13K2EauGFAyoihxTWSloEbRKWrfQmDMJSYukooIK4o7DB7SSZMuMM+EaL7nuvLsv/BXZt0mO5oeyCQNi1/gZwZgdhlt0ndb3fLnD3PR3bqXVVNuaUT5mpoxlr7e45HEEquL1dJxy2GM+tKcwUb9LUMJYWl5jZR9deHogiEhHrVRkodoQ4TTUN82t5pWu4rhNJGoJBJTLBc3ZiPnZ8tc+q1KWqVFrWqTaPeJjuQjtgu5Tr37N5G/IfsIPL/VxhqEkNNriuXFAQdF1FV6OTNHd3vyk4Geb1bz63CDWqgQ1LLoAn9pnMUAWiKwsf23kVM1/nc6ZOcXlpitlFntlHfcpveRILhdHpLKn7OipGzLK7Vqlwqr3CpvLLlPuK6zmMjY/x3h48xmEwjgEJfGt8Lu32uIBJo/uWDR4lpGl84d5pL5RUmqxUmq5tLThQhGEim6E+kNvbbQpC3Ynzy6L00PZfX5ue4WC5xqVxCV1VCKfHDkIxp8rP7DvIrh47yB8df2HTuvckED42N/JDLx9bOsTee4O/f8yBpw+Srl84xW68zU68xU69tea1p07xl6dyN+1ekSp81gmCzfudNtyMC8D60ay8pw+RTJ1/lxOJ8V5tqqyjE44yms2hbORoLwft27ma+Wedzp99gvtngtYU5XluY634nZZj83L6DfGzfXUzVqncEHJlqir7Yvu68F6K1QELrlPXo0ehlqWlSWh/tsI7nBNhzAjNuooVpfF2FxgBq3MB2/S5LVA168TUFLexDNzQ816c319cdd3+SImncTaDVQKhoSo7r9gzXWlfI6nkkknP1U5hqnHZg80b1NWJagpbfoOyW6LMGSWoprjYvYioWPWaR6dY18kYBS7U4WX0dUBiIDXOxfp5YNknJXiKtZVhxSyhCpeQuogmdA5nDJNQ4upJCoCIJEKiYan7L8SfwQwbGe/FcH7ftcfWNKfY9sFZGqCsZdmR+lR0ZF4UAN6zjBCtcrn6OmhuVpvp+wLXJJZy6xeDQ5kqLhfYkF+ovoQmDyeZJ7KDOvszD9JjDGEqE3hpKjMHYBKrQyGo69+TfzaXad1iwTfpj+xiMTaCgMBSbwFBiHSMgBXDJ6j0cyT3B5fprBNKjaA4jcEnpBQ5m385k8xQJNU+PepDW/8fde0fZcd13np9buV4OnXMDjQwCRGQWRVG0EinJlsaS7bHlGduydr1ex/Wss3fX650zwbMeWw47TvTIki1ZwbIkUzQlikEECYoAkXNooHN4/XK9yvNHvX7oRncDDVp7LPF3Ds5Bv1fvVtWtW/f+ft/7+35/DYVRd4aTpWeZt8cxVMHzU19iS/JeklqSbvUgBavKOesSw8n91K0EM16R4wvPM2NfxQ9dXl94hh3pB+g2RwBBtznC5errqHRSaETFUEQzPjIUlSAM0BWFIAjxw5CKY2MoKfLKXWTkEF2WsX0fRZKouQ6Op+IQVUb0wxDH91FkmGoUGLfm0CSFd3XfQ0wxkJqZSK8WzvLi3AydRpatzaSKdi1N3W9wsnSFh9p3MRjvRBUK7+m5l2/OneCFueMklRjDsS5KcxVito65oFKKV7BqNvGUScN22Cdt4rWr50hgsDnsRRRCylKVwA/YrW3ka3Ov8VT5Fe6Nb2Mo1sUFb5yT/igxWefw/FkqrsX9bTsZinfRrkfxQ0Ix2JLsRxEK884MTuDxxfGXkIREp5Hl7W0HuDJZYqpYIWnqtCXjUB9ga+4AlYbNqetzLfZKEARUGg5tyRiz5RoDbRlK9Qae7xOEIb25NKamIguJlBrnbGWUHqmNmPzt1cKD71DgKJswW1k+c+VaFLDGYxwY6CVoUnAUVcbxfDRJIqcbGJrazCQSSAImCmViShR0aapMppkhEYYR5WyxnO2tzPF8ynW79XcypkfUhSbN63aLqyQEU1YZRch0x9IYt+D1L1pAyIJTRZcXs6tuv4ALITBVhc2dbSsABcvxOHV1ivb0Da2lKHMKEnF9XdSU6DcNwrBOU5QJIWB2YgHbckFZ+/dhGFK3HPK5+Poq3QgZIQzWEseOKIoKIAhCmyBc7jTYrod7m+CtXG/wia8d4fJkBBpt7e/gp564n4NbB1YVPbdsd4VukSxUOozN+KGDId+5wJmpKWzual+WmVWzHM6Pz5KOG+jCQJMU/GbqqSJJ7O/pvUWLUaZXemkm0Dp3GgH2bepja38Hh89d59rMAofOjNLfniYM4cTVSa7NLCCAnUNdbOpbO031u91MdZiEvgtNvsETL1ovIC/RslJUmYGNHcuFbwAhZQm9KrCkRLCmoDTHjud6eK6HYzmceuUSnu9jmBqBH5LKxSnNV6mVLfLdGQxTY/r6PEEQkO/KsHnPUPSeL+ophSEN68bctJb5XoDTaAabiowe03B9n454gqxpIoRAlWSyhomhrI8ulU3HIIwApDAISZuLadjN34YhsqStqTWwloVAMrZ8kTOXaDht3reB/+3PfhLN0Kg0bCzXxQ8CvCDSy1DliO7iBwFqM3gTkmC6XCWha8Q0DVmW0Jpaa4Ef4FgOMVOj2rCZKJep2g4JQ6crneTKbAEhosBo+noVTZExNZW4FvWhqshMlSrENY3BfCaqFnQHwBEhlObKnP/WZQZ39JHrWl/Z36X9JaE2AYVoR9eQVbale9CblZHMuIEkS/iej91wCfzbX59duwHWmYlIaHpLfzu5VUSal44WJ6iiy8lb7sgIITBjajOrZTFNXyCESkw7gJyKdDMArEaDVMxbtm5ksnE0TeH6lTkUVSKRMtFVBaEspqe/uYK1kt3AagpvXq8UW9XHDDlaGzRZxg18Gp5HUtUJgZSmsyXfzpNPfACI6GaL64wQMgmll5DI2ax6ZezAot3ovuUGV1zT+NC2u3hL/xAvj1/n1clxRktFyraNIkmkDYOhdIZt+TZ2tLczks2hSOAGEQgoNQsMPNDfx3/9nndzZHqC03OTTNcalG0XN/DRJJk2M8aGbI77evvZ29VNUtNbc5KqKqhqNPct2BZzVuQbxlSVH7lrD48Nj/DKxBiHJ8YYr5SpOk4EkhsGw5ksuzq62JJrY0MmEm/3goDJepmUapDWDQ729PF7j72HZy5f4sXro0zXqnhhQNYw2ZTN8bbBDezt6sFUVT66az/vHB4ho5uIkNYarWgaYRgyN1fBqzh8tGcXThDQ355h7nqJqUaBZNLA9wN0XaVYrOH7YVTRTJWjfTkhSCQM5uYq7Nrdj94UehdC0BGP8wv3PMB7N23lyPQkr02OM1WrUnGcaP3XDPoSKe7u7GJrto2keiOzXJYlTEnh5/bdx4/dtRdVkemOJ2k0XHQ9mvs1YVD3XOYaFcarJSqeza88+DBlp8FAMkPRtpisRxqkuqwgEY3HtG7ynpHN7O/u4ZWJMV6bmuDSQoFio4EsBGnDYCCdYWu+jV0dXYxkcujy6jpZCU3jY3sO8pb+Yf7pykWOTk9ScWziqsb2tg4eHhjinp4+VFnmtx9+lHnLoiueQL8FYLfa2L55rRMIMloUjMalPIX5EkeeP0EqFxWCybanqFcteobamRkvoGoKmXySc6+PksjEEAJyHWkWZkrcde8mYslvf8D2L22SZCJJ0Trghx4VrxxJPtjTbE3tZMAfZii+kQWnQMktMhgb5ujCq/TFBukzB7lWv0IQBkw3JrF8i8HYML2xASyvTn9siA2JTWiSTkJNEYYhCSXJ1dqlJiguUXKKxNUkTmATVxJ0xu5nOPWvKDnnyOjb6Im/FbFKNsz4hUkWpktcPT2GJAl8L1gGHLmhy6wzQ1zJMBjfG62pgcNk7bkWcLRIyb5ZciPSTavTZfTTa25eMa62p++PKGTOayTCBvekRyC4DmGNPqOfPmkDyIMgt4PzKrIwOZgaAVEhdC6B3AZBDeQ2+lSPvuxmQAEpBf4YhA026hob9Z2UbIVXFmK8XrToSVo0ancxHL+XjGEgCYm6FWI1BHlxH21GDK86j+zEuFwpk9A0dG83PdzFXd1d5OOxJiU3wAts5u0xBsy9XC85tJkRMDxTq9GbTGF5UXGjwXSGuXodLwxaGZ4JTac3mSRnxDg9N0N7LE7FsdmQyTFTrxKEIQXLIq5qpHSdd/fcu+rY25DoYUNTMHvRik4NQ9Z5rGv/CqHpTiPL9/W9pfW37/kcP3WepKazJehl7OI0tXIdz/Wx6w7JTIwPDb2VmWvzTF+fo6ZWON95Fd8P6Bvp5J7KFjzXozpa54o5Rjod46P3PL5iM+7Rzn2t/3eZeb637yHcwOO5mWPc37aDPdlNTFjzfGHsReYSZbwgknEJw5B0zECRJWRJolCzSBjRJmXJirKpM3EDv7kR6Ho+1YaDoSoU6w2G2m9o296d3cJIop+yV/v/JVb7jgSO+tozSE2NoMn5MsWqRVsixpXLBRqWSyweVZGxLIe2jiSe4xPEoFi3sS0X23ajIKQt2m02NZXefLoV500vVKg2nNvSbeoNh8lCtJsjhKAnnyYVMzCaAcTtdvYUIbE13UVc0e9Id2FRPPAOBP8JEZSsBt2Z5VlK86Ualu0uy8KRhMBxvWXV3G5nspSB1oQcNjO2ouya211mzNRIJgzkdZW9DFu8XyGWByaRXlQMSe4mEvmMo8pdywKVYq1BbUmWwmo2PlfmzLVpwjDSsfrgQ7u4Z+vAmkCi6/vU7ZXK9LocB95YCmCx3mC2WkNTZLozSTRFRkgi0mRwPLJ6jE4zcUc0RwA/dPEDm5AAN6ijySn8oEEkVhxpgqlSHC+wkISMJDQUYZBNmLx110aOXBzHsl1eOn2Vx++JOLiHTo/ieD6GqvDQzg1vKjrIzWYoQ0SgpIvjTeOHVeLajmUZSEKSsOo2vudj5RNNcKNBiATCRFJGWs6LETdaAtZW1Y40d4Qg3Z7EjOsEXoBVs9FNjUQmRiJtkson8T2fXGcaRZVJNUXoJVki1xUtjmEQUphaW8hx0WzLaen8qLpKKpckrmkYio3rR++/H0YlpmPK+oGeMAipN9wWt3ykI4+uyHihQkztRhYRRWH92Zbhqvz8dHsaSRIEPizMlAiDEEWVGZ0qUajVqdoOluvh+j69mRSyJKg0HHQl0vyo2TYpw+DgcBQQKJpCIrNI3fMoTJeiio+KTHsqQabZJ7oiM5jPMDpfJG0atCfiqIqM3pwj/CDE9X3y8RiGqpCLm6iy3KrOs847RlZkGnX7zgCnJeaHNm5Qww8aSFKCObuCF/ikVBOQyHSkol1w26U0W8a1vTWrqi3a/NSNQgDZzjRThQp+EJIwdcJV6M1hGEYUKgRuYHGrVTUMQ5JpkyCIFAlC/yqoNzI2He8aNftlBIKJ+R3oSseyMZFvT9LZk8GxXWRFZsuOXmIxjUtXprA9b9WKkN/Ndr40S8GuY8gKSTWihb0+N4EfBqiSHPlBvktc0XADn7iqcU/nAHkjzsMDQyvaEwi0Jl3BDVziSpKUWB9gKQlBbzLF+zdv4z0jW6LU+/DGdyEOZfcyqlSh6E4ieQqOX8GQM0giElAPQo+OpODRuM9jG4ZIqH2oIt7KG5aEQJYk1FtszF0qF/jDE4c4Pj+FLCQ2pfP81sG3M5TOMJBK8/7N21rXtrj5Jkui2V83AIOZeoV/99JTvGtwCz+8ZQ+SEPQkkjyc7mO3lMeyoszKeEynWrZIlGTG6vOoqozvB2QtGVn2KGp1crnlfkCtZlOcqbK/sxfH8XAaHjWnQblsEYYhju3R05vF9wIq1QYNyyEMQddVurrT2LYLq2zqhc2b2trWzpZ8Gx/cur25uRR9XylbzEyXUSSJwqUyVypRlqWsSFTKFooiE1dlulNZLMth4moBWZYYHm5vaYZN1EpcKS/QZsTJGSYbMznGqiUkIRitFpGI9J0m62UEgnu7BkjrZhPYSvDujZt554ZN+GHQKu4hiRvi4TeLYq9muqJwd2cXO9o7cIMbDAFFklClKBPcDwK2t3Wsy0dquB7Pnr/Ert5uejMrq4d+a3SMY2NTJHSNx3dtJa5pmHGdzXsiugsI0vkEjbpNprke64ZKMhvH93zMpIFrR9VqjebGxM3Vet9s5gU+884sAZGgui7pVNwSk9YYSTVN1Sszbl2PNnWERhD6FJw5vNBDlTQyWpbr1ihO4NBl9GAHDa7Vr5LX2phpTKEJjQ2JzVT9CjmtnQ69CyewyWsdxOQEAoEht3FX/mdbGUeSiKiiQRDiBT6SkFBkif4tPSSzCYbvGkAAc5PLi92oQiOn9bZoaatZEIR4nh+tXUuIDiEexcZRQCJnHkCwhh8VlCAsQ1gj2n3Xo4wiuTtqzLsAQQHkAfAnQdlBq0JvUABsCB0IZkHeAP48hHUQJoQWhAtIop/eVJShGdc0kq5L1jRJaBoBYWv+02QZSUj0p9NkTRPLc5ElCcfz0BWFbMxsvVcld4bjxWepe2UO5N5H0VJRJZmOWIKsYdIei1N1bPwwRJVkkpoe6QOrGookEVNVUlrkcwyk0uTNGHXXRZNlUpqOKsmk9Qi8v9M3RRKimWSxHuYMpHJxVE0hkY7eW6uWaG0Aa4ZKOp/A93wS2RiarhJPx2jUbGRFJp42mRsvoJsavSOdqLq6bm1dWcgMx7s4VboaVV3zbEaSvQym2xnJKK0gWpZuFBhpT8db9xUleMDSbElJEmzszDNXqdGeSiBLgpJb5dD8ydbG4bg1w6OdB0go62MTrNe+IyPAbf0daIqC5bjMl+scuzzJA5sHKBZq0eIWgmXZSLJEMmVSKtax6g6O7VGvOyhKtDgtIsOKLLG5r524qVG1HC5NzjNXqt4WOJot17g0EfHY47rKSE8+EidOJ25bUSoMQ2qew/nyDEOJPCl1fbsPEoKUGqPk1iJnrOk8SDeN0GAFWrM62JRPxcmn6nj+DRE2SYrara0jYwEiAVZZziGJxf6K0sE0U0VRFZxgbQBKCIGQBHUrqhJ1WwtdQn8SiLK6lmb61BouPm2oShqQUeROFLmdRUArDEMm5kssVK21mw9DKvUGxVp0TCpmsKm37ZbZZ7WGw/j8t1fosC0RI5+MkTT0FhBjqAp7R/pIx3XGGiWqroNyO+X9m553xRmn7F4jCD2coEpMaUMWGpqUwPbL0NQiUaUYdW8WU8nTZe5FkWUObh2gM5NgfL7M6dFprs8W0VWFY5cjDnlvW5p9m3rftI4Q0BJVLFqHKDdeQRI6Qmh0Jj6MKkcBlm6oJNOR3kossRiEqwgRRygbo4W82Ud6TKN7QwenXjqP7/lcen2U/Y/tYtOuSC9pPbSSFlVNVRjeGQEggR8wemrsthmDlYUa06MR5z2RjtEz0ompqmzM36BwaZJEuxmn6DTWamaZNWyXmKmhawqSENRshxNjUxzc0I8sS0jizrKNIv24EFlazIq4cT/tfTnS7SnmJxYoTBWZujrLxt2DbO7M4/rZiEbiBxTqFr2ZJLKQWtQSiAAeVZaIa2qL3tezoRPNVHEsl9Ez41SLdVL5BBvbc8vOH4YhHakEpqq00oWXfrfU3sg7UZmvosc07n/vfpLZxBtqIwg9gtBlcSLoj+UpOvXWRsHg1l7MhEG9YjF+YYp62SKZXRvsXhxXizZ81wBCEthN/am1ZklJyKTUPhRx63VOliVmp0qomgJh0KQeL4LyIY53lZT5GCCRMAJkWVlWaGDiegEhBMObuojFdbr7shSrEVV9pljBD94YAPedatuync0ASKBJMiGwJdPeiluCMMQLAxQRAS2yEMQULfKFVwlc/TBgor5Am5HElDV6zaF1XUfRqXG+PEVOjzOS7EISgjm7zqXqDL1mjv54DidwcO0yssjgBzaS1Mw0EjINv0C02RMF1aqkoQgFU1HQ74BaGIQhT42e4/j8FL+y7xH6E5HGYVo3Wve/3qIfpqyyu62bgcSNneowBNf2CGwf4YbE4xq4EUgnyxK6HlExalUbBMzNlsjmVm4gdXakyGVvaIp5XpSh6HoBqirhuQGqJpNtZrMsOm6SLLX8Vs/1V+hxVl2HL4+e5V2DW0hrxop79VUPx3KpOj5+EOK4HooiY1kOpUqDtnwC3VCxbY+5+SqyLJFKmSx1JPoTGdqNRCsjSJYEbUa8qWMWtgC+7UEHkpCIK2prvI3OF1mo19nd1/3PFqlfDHBXo/6FYcjhq2P0ZFIM5m4PfDY8j6dOXaA9EV8VOOpKJbmkF/insxd5dOvGCDhKGGy6q791vqUWZRhF79XQtp4V7VmWy/RMGUkIOjpWnu/NYJqksTu9Hy90USUNU46xL3svmqRjyCb7svehSipZNYcqachCYWtyJ42ggSIUDNmg5lUj0Ek22Js5GAHbks69+YdQhUpcSXBP7kE0SUeVVBJKEoFAlW5k4cli5UbIXLXG8etTdKUT7OjtRFZkPM/H96LKtDdn9/p4uEEDP3RvCUJks3ECN35TKCOQJRPXX0kZXd5hB6BZ1IlFkWxhgJSN/MWw0fxcBTYSZRXlm7/NR3/TrCop1CaNzY+OD6vgXSCpGOyIdzCUzUQxtOuS0LQV88St/M6b/ZC4kmFH+iF0KdbUOlp5fEcsvuy3q/lHYRiSN2PLPutNpv5ZAGuHkeWHBh8jvQ4NH0WR2XjXQOvvtXy4oW0rmR2L19/WnSEIQtJtyXUVe1o0SQge6dzL7uwmbN9Bl1WyahJTWW0TL7qO9RCeZQk60okWGC8JiT6zg04j2kDLaAmMNbT0/jn2HQkcbezJs6W/ndcvTVCxbL546BS7NnTzlrfv4OZIWQhBvv3WWkBCCPZs7GGoM8fJq1PMlWo8c/QCG7ryaz58zw945sgFZksRT3uwM8vekT4kSbB/4/pKVvthQMmpc67k0qYnSKi3f4ARVa3C5eoUCcVsVVbTVLml/RGGIcXKcnAkCEKuF0ps7MizdFYLwoBaw6HheAx2ZsgmohdXliUSMX1dL6sQOoa2p/W3rEjse3gbnucjyYJbgPRAtLNfKlnrC5LlLhTjnSDiaIpMLnmDtnh5skC57tOeXroQ33h+lu3y0qlRyvVbB8BL/DRuJ14ehCGvnR/jytTqWgxv1FKm0aJPLlrFsjk3PsNge5b+tgydZjJyym71jG76Kq52IEtR+ViBhCzpSChAiCHnEELgBTaqFCOh9qBJcRDRUjnUmeXAln7GXzrFXLnG65cmUGSJ2VIEYj6wY4ju3JvTCbrZbG+Mtvh7MdUhpit/gx9UWsCRVbPx/RDD1PBcnyjFQiYMSgi5g6VTvhCC3W/ZxnOfeRmn4XLoS0d46/ffR+9IJ4ui1+s1WZHYemCETHuK4myZUy9fYHasQHtfbtV2wjDkxItnmZ+IMpNG7h6kvS+34jgg2v1R1xfAOa5PveFSs+xIA66ZLVSzHRKmf8faRhFgEFW+TMaW02fberJsPbCRb37xWxQmi7z8laMMbO0hpi+/1vbk+s4phGDzvmGyHRmmR2c58/IFzr12if2P7VpJXRCC5BpFENZ6brIit6q++Z6P7wVrgntWzeb1b5wikYkzsLWHga23pqKuuAYEptIOBGhykjCEhu/iLany2Lupiw27+pmfXGDs4hRnX71Ix8DqFWHCMGT8whQXjl4FoopvI7sHW8C2F4RrOjNeYDHXOENXzCRG+xpHgev6aLpGW2cKKICIRU5w6xpsytY/IYkY1cY2yjWTvvZMawNBkgQzU0UkSZBrS9DdlyVuaNy9sYdyrbFMH+vNYIlV3klzrR3tm6zo1nm9MMpbO7e1nrcTeLw8d5EHO7ZgmncG2JwujVNwavz8tne1Pjs8d4msNsUPb3gQTYozkHhg2e9urkRzc+WwOy1sYfseo5Ui/Yk0b+kZ/mdRE7OGyb/b+/CyzyRJMLK568b1iuWaUUvfmyAIWShUV/SjEALD1DBW2Zc0bvrPIg1tVbtJDzIMQy6V5/n7K6d5uGeYtLYSpE2lTPbvG16jwWaGePN+NmzoaF7v8qM0SabqOcgE1GwHy3XJxU2SejQX1h2XQs2CMCTXpLP4QcBstcbTZy7QcD2Shk5c0+hOJxFCUKjV0WSZuutSd1wyMZO0EYnDFusNqo6DALIxk4Sutfo9OlcdLwyJaypt8RhuEDC2UOYLr5/m3g0D+H5AJmaQjZkEYUTrqNhRexnTaM3hQoDlelwvFKNgNxlHlSOR/r5smt193bxwcXRFnzuez2y1hh+GZEyDlLHcb151brccTp4cI5dLvKmAo/liDcfziZsaMwtVuvJJsuaN+8touSX/X5n9GVPixJaArJp2Y31NqTfAHH2JLsvSz7Pa6v7LzRY91+XPZurqDNNXZwFBZaG6rKpaVCZdR0FbM1NaCEGt5hA6NzMamhqC0tqbJkIIEGvEqYunErdKZFglY2TpJYYx0DKAAkKQaL6ra+mt3YnfqUkGbfqtY97VfKf1nvOfsxGtSQodxp1R/N/o9QghyLS/8XfZkDV6zPyKz6P52EPcobTDoi0FBVNqnLsyN7QPc1qaa1NFpt1pRm6TIHEn9h0JHOWTMR69e4RzY7NYtsvRS+P85dOv8tF330s+FVs11TUMm5SihkOyyRNc1mYqxjv3b+Hs9Rk8P+DLr5zhwOZ+dm/saZWljtqJNDKOXpzgK4fPtCr8vH3PJtrS6w+IhBAYskpaM5vVb9bn4EgI2vQ0GxPd9Jg3JklVlunIJDg3NksQhBy9NM5779se6TsIQdzQODDcR0Jb7sSYukouaWI5pPMblQAAIABJREFUHvPlOqlm32zsbyMMFwGURcLJkv5c/BcualAsv7c74W9rmkJ7W2JdLqIQKqKpLyNJIcNdeWK6Rq3hcPb6DN86P8Y79m1ZtihEzyzk0NlRnj5y/jbtCxKmRiqmU6w1KFsNRqcX2NbfsQxEXKQLXpks8DfPHcNahaq2HrvhdK7SvzcFlJmEyUh3G5oiY8gK5i2oQ4vt3ozFKcIkoZirJnctVl5a7XOIJqBHdo/wlVfP4rg+r5y7RjpmULcdsokYB7cMvOkCs7VMCIXZ6t+hyDks9wJB2EBXesjF3oHjeIyPzhGL63T2NBetRf2voARSjqXg0Y77N9O/pYdLx0YZPTPOF//oaX7oV76XVH71LJMwDAn8AElaLmYshGBwey/b793EoS8fYez8JM9/9hWe+OijaObyaj5Rxa55nvnEi/iej2aoPPD+A+jGymBRlWSGUuun+HTmk9SsSFND1xQyMZMggErDJh2LIwkVRdxZaqypq/TkUysWtngqxn2P7+XY82eoLtT4+qdeYtvBjdz9yI5VgbdFupvnROCJtooYf/+WHnY+sJnp0VmqxTqf+/2nGNjSS3t/rgX6rGgzuJERcCtTVJl4kwpn1Wxmrs2xee/wqu9je3+eg++8O6I/vCGHRJBUB0ioN5y6ebuKsiTdOZGJ88D7DvD6N85gVRt89cnnuevBrSsquIVhSL1i8cwnX2Tm+jySLLH7LdvoHu6gHviMzRYZ6srCGhmQmpxEk9dR5U+VkXSVetVGSFmijlkEuiSS5ttx/SkUKYck1ChTdilVrSNFMmVSmKu0sjnG5kpcnSqgyBL5VOzb5hz9S5vlOxxfuMb+/AYkBKdK42S1OAlF5/D8ZbzQ5+7sIB16inOVSDz4UmWanliWoXgbfz92hKOFUWbtCntzgwzF23l1/jJBGLb0FoMw4GptjhMLY8hCcF97VNXn+MI1yq7F9nQvG5Id5PQEOzN9vDx3CYjmok4zzdZUN3N2RIWdsopMN0rclRnACTxOFsfYkuriQnGBkmOxr72X12bGGa+VSagaBzr66G1m+wRhyFi1xGuz41Qcm45YgoMdfWSbFKia6/Dy9DWuVYqcLc5i+x5/de4IEoLNmTbu7x4EYM6q8c3JUd7SO8xYtcTJ+WlCQrbnOtid70YIQcWx+frYJQp2HYFgf0cvO/MrwaIF2+Lw9HWmrSp+ELT8uO25Tg509CEExDMGr89Ncnm8gAC25TrYlu1Ak+TW+/Xs2CXazDjdsSSHZ64z36iT02M82DNEWjOiudqqcrIwzWStggB64in2d/SRbAbWM1aVb82M8c3JUS6XC/ztxeOkNANDVniwe4iBZCZaNwi5VCpwbG4Sy3PpT6TZ39FHQtVaPtxkvcJrs+M83LOBS6V5zizMIAnBXfkuduQ6CcKQTx4+hiJJlBsN5msW79y+iYc3D1NpOHzi8FGuL5QIgpDhtiw/dPBuZCF47vwVvnH+MrKQKFoWg7kMH7l3LwB/89qJiN7sOMxV6zy8eZh3bN9EoWbxl4deY8FqUHccNrTl+Df37SOhaxStBp945XUuzxXQFYXOVIL/6S0HWag3+PKJsxwbn6TqOJwYn+KhkSHeunmYktXgyZePMlutYTkuPekUP/nQgei5IvjHk+eRJcGCZfH2rSM8vnMryi0kFOqOy98fP8OxsUnCMCQbM/nxBw7QlojdMuANwxAhCYaH29b1rn+32MWxOa5NLWA0hcHrDYddI8szrqJx6GK5k1TcUWx/AT+MNrploaPJGUylk7jSiyLFEcjrAg/C0Cfg1vIaEgpCSE2aurtsU7hzoJ3iTJmtB0eYn1hOVXODBhV3FvWW4A8oioRVX34NAglD6cZv6rkt9kH0Ni5WxBII1qcheeN+Q0K8JSC7tHZfCQHoTf/HxfJmqDiXsfwZ/NBBQsVQ8iTUQeJqX6SNuM5ria4jqjS39D4WYxAvqFJ2LlHzxnGDCiBQpSQxpZuEOoAuZxBCbh4fEjTp/LfqkzAMmue8ce/SKpVao03Lpf0sc7O4f+u8oYvlTVP3JrC8Wbymzy4LA13OElN7iCv9yGLRl17PmLy5b+Ro/C32TVin4lyh6l7DDaKMtKhvuoirAxhyjhAf2x1FU3qRhLlmTAAhblCl6l6l6l5v9jWoUoKY0ktK24AqJaOebbYhQsFcqUbiDjaJ1mPfkcCRLEu888BWTo1O8/Rr53E8ny8eOsXlyQKP7hlh+0AncVOLtHo8n2LV4spUgdcujNGZTfKx99xHOr58ApAliXfu38Lhc9d58dQVxufL/PanvsZ7793O/duHSMV0QFCuN3j57DW+8NJJxufLCAEHNvfznnu2o76BSlLdZpqEaqxLGHvRAkIW3Colt05Gi/jUqiJzYEs/3zx1lSAMefHUFT7xtSM8dNcG0k1BWV3ITBUqBGGZoc4suqoQ0zXuHrmxkx0EAZOFCtVGlC3gelFp7lLNYnrhRpWUq1MLvHjqCinTQFNlFFlCVWQShk5HJnFHVbWEEHj+apDF7X+3b1Mv/e0Zzl6foWLZ/OGXXsJyXHYNdxM3NPwgop4dOj3KZ144zkI1EhSr3kLnqDuXYmNPG69dGMNxfT757FFiusrO4S5MTcULAopVi6MXJ/jsi8e5MD5HKm5Qqdu3rdhWrFrMlWtR3/oBrutjuS5nr820flu1bF48eYX2dBxdU1BlGVWW0DWFzkwS86ZdyCAMWajUKVQsXN/H83wcz6dmu1yamGsdt1C1eO74ZUxdRVdlVFlGUWRMTaE7FwXlt9vh3T7Yybb+To5dnuDYpQnScYMwhM29bewc6npT09SWWlq/F03uJAgbJPS7EcjIwkQgkcrE2Li1G9fxWsFrGJYIvMtIyhDippHeOdDGEx99lD//9U9TLlR56snnWZgp8+5/+whdw+2YCQMhCVzbpVqsM311ltOvXODRH3iQga3LnbJY0uR9P/U9XD01xsTlaT77e/+IbTk8/MF7yHdnkRUZq9rg8olr/MMfP8P5I5eRZIkD79jNvkd3rgp81CsNGtUGruPhOZGA9/jF6Vb1Mcd2uHpqjHrZisS+NYWcqmAmDBRJYqQjDx032lPtXqxqg4ozF7Xn+ExenWmJdDu2y7Uz4/iu32xPRlEVOhImseTyhVNIgnvedTenD13gqSefY/ziJB//+f/O9/30O9lx/2ZSuQSKKuN7AVatQWm2wrWz4xz9+ine/q8fZP9ju1bcrxk3ePwnHuXUS+eZujrLsW+c5nc/9t94x0ceZvO+YeLpGLIs4bk+tVKdhZkyp1++wPDOfu551923HTtbD2zk6NdPEQYhX/yTZ+je0EHPxk40XcX3I7Fy13aJp2NUizXGzk+Sakuy475IWNN1PKrFGm7DxXN9XMejXrZYmI0cjzAImbk2x5WT11HUqP8Wn4upqHTG0mhNJ0tRZe5/Yh+HvnSEw/94lGPPneZPf/VveOIn307vSBdGXMdzPObGC3z1yef56pPP4Xs+Q9v7ePwnHsWI6zSqFnFDi0CcVSoFCSHwA5eY0h6Vtb6FBWGI73pYVgAiiZC7uOGGhFjOcVxvDF3dhuPFSceXU2PnZsrUa5GWYbkYFUbIJky89gwJQ1tRxOC72RQh883ZC3QZGbrMDE9NHOeJvj28MHOOmKKhSQqfGX2Fj2x4iOemz+KHAQfzG4jLOqas02GkyGgxdmb6yGlROnunkeaZyVMcbNtIVoszYRX59Ogr3JPfGIkdC4Hje6iSTEoz+dz1V/mxjW+lzbg9KOgEPl+bOk1/LE/JtXh+5ixbUl08P3GZw9NjvJC9yoXSHEEQbZi0mXF6E2n8MODw9HV+//hLgMCQFRZsi3+In+Hn7n6QkXSehu9xYn6K8WqZstPADXzOFCLAI7Uka2GyXuEPThziYnmeU4VpHN+n4buU7BF25bsQiGZWt8WpwgzPT1zmozvuWQYcAUzVK/yHo88za9UYTmW5UJznZGGKx/o3sSkTgQEV1+HPTr/KcxOXyWgmfhjwyQvHeHxwKx/Zuq9VqORzl0+hyzKykJiolfHCgLiisSPXSVozsH2P/+/UYU7MTxFTVPwwZKpe4aGeYf73vW+NytDXq7w6PcalUoG663KxOI+pqMRVjbvbugHwwoCnr13gT0+/SlzVMGSFgl1nc7qd/2XXffQ1Qbor5QX+8MTLnF+Y42RhCjcIcHwP2/fYkesEYKZSRRKCH71vLwk9otNLQvDs+UsUahY/9fC9OJ7Px597mUOXr/E92zfx7p1bGCuWyZgGH9y7s7nJFL27c9VIa/PHH9xPyjTQ5UinKGXqfGj/LkxNZbJU4fe+/hJT5QrDbTmeOXORq/ML/PyjDxLTVNzAR1cUOpJxvm/PDi7MzvOBPTs5MNjbLIYQZVp8cM8ODFVltlrjPz/zIuPFMt2ZFEEY0p1O8qH9d3FxtsCTh46wt7+HgVtQ3V4dHeO10XH+54fvQZVl/vyl13jm7EU+tG/Xikytm01Tb+9vfbdZJmlydXKemWKVjuxKDU4/sJlrHOFa5css2Ccj0ChoEBCt/RIKsqSjSAl0OUfe2E137GHyxt3Iko7reMzOVkinTSRZWpbNN159hvOlJ9e8NoHErrZfbLYl4fo+lntDk09WJHbct5krJ6+T7VwupCwJBTe0EcGt45pYTCeuLZ8LQwJsfx7Hnyem9rNY5W208g9cLX+WkABVSrC77ZdJaWtlA660mjfGibn/jOXPIJDoij3EluyPIdYI24PQp+xc4mr5c8xar9Lw5/CCelP/SUKRYmhSmjZzP8OpD5DRt6yQFHAaLpWSRSJtojc33dygzJnCHzNvH0OXstzV9vOktI34YYNZ69Vm5blLuEEZP4wkSWShoUoJDKWdbdmP0R2PRKrLzkWOzv42AR5xpZ897b+KJq/cNJuxXuHcwp81wR1Bp3kf2/M/tfJ9CkMulj7BWPVpBILh1AcZTL5vSZ94VN3rFBqvM1l/gbJzEdev4oX1FsVfEgqyMFGlBBl9K/2Jd9MRu++2vgyAH9Y5u/BnzFgvo4gYd+V/jqyxgwCXOesIF0t/Tdm+gBOU8MNIGkYWWmv8b83+OD3xh3GDeVynSFLfy80ktTAMcYMyk7VvMFr5IlV3rNnXN9pTpSRxtY/B5BN0xx9BkzLNLLdmcZeFKht68qjrIsDd3r5jvax8MsZPv+9BHM/npdNXaTgeRy+Nc/TSOIamkDB0JEnQcFyqltMKyt9298iqwb0Qgnwqzs+8/0EUWeKFk1cYnV7g9//+m/z11482K8YICpU6hUrkkCqyxL5Nffz0+x6k/Q6yjaC5g+s7jFYLbEl3rv93zd92GzniS/iPsiR42+4R/unIeU5enaJqOfzRlw7x6eePt6rd2K5PrWGTSZj8l598L71tK6t91W2X3/v8C7x2cQzb9bEdF3eVSjvPHrvIs8eiagKaIqOpMoaqsG9TH7/0/Y+QS64/oyCZNNi8sYPYG0A9e/Jpfuhte/hPf/ccpVqDsdkSv/Opr9GeTpCKG3ieT6FiUaxZGJrCO/dvwdRV/va519cUF88mTD740F1cny0yU6xy+to0v/5XX2W4K0fS1HE9n6mFCpOFCkLAo3ePsGdjL3/85Zdb2khr2VPfOsdfPP0qtuvRcDwcz1txHZOFCr/xV18Foh1MTZXRVYWOTIJf/vDb2L1hOVjgej5/9+IJ/u6F4ziuh90UNr/59q5MFfi5P/li1K4c6UPpqsxgR47/40e+h/7226d05pIx3rprA+fGZlioWixULSQhePveTS3U2nE9LNvF1FUkSbojEPG7xbygSMU+Shh6GGo/bbHHkZsULMe2uXRmktGL07zjA/tp70ojpG4kdRMRdXJ5f8iKzCMfup9KscZnfvfLlOerPP/ZVzj81Ot0DLSRzEYVBxt1h+J0ifmpIkEQNEGPlfoJO+/bzI/85gf4i9/4NNOjc3zi//48X/+bl+ge7kDVFCoLVa6eGqOyUENWJPa+bQc//GvfRzKXWNEWwBc+/lVe+cpRbMvBthzchotVs6mXo7E+O1bgP/zbP0I3dTRTRTNUNEPjwPfs4kO/+ERL/HvRvvzfvs4LXziMXXdwLAen4dKo29Sa7RWmivzux/4UI6ahmRqarqKZGnse2cGHf+kJ4qnlc0syl+AHf/n9lOYqvPr0McbOT/IHP/skbb1Z8l1ZVF1pgS3zE0XqFQvNVLn/vftYzYQk2HpgIz/6W/+KT/zO5xm/MMXr3zjNiW+eo6M/T7YzjaLIOA2X4myZhekStuXw47/z4XUBRw9+70Ge/fTLTF2Z4cQLZ/g/P/x79G/pwYjpeK5Ho2ajaAof+Y0PkGpLIqsy8SUVyy6fuMZf/ManKc1WcGwXp/lc6k16sut4fO4PnuKpJ59rPouo/1K5BG//v95Bx470ss2ydFuSj/zmB6iV6px55QJf+9Q3OfK1k/Rv6SGRjmE3HCYuTTN1ZZYwDOkaaudHf+uDbDkQpTwnTJ09m/pIxw2c+uq6eKoUI6tvRJMStwyWDENjeGd/BB5ICrJ+35JvQ4KghiQl8IJpdHWYQtlZBhzJssTQSCdjV+bwvGiXUZElphYqZGIG+XT82+Qa/cubKslsT/dyvHiNum+TUHSyWpxvzp5nMJ5HkxQmrCILTp2QkP25YQ623UhT74vluKhNsy3V0+rDXjO7TG/xdGmcbiPDI13bW5+N1xeYt6vM2VWu1QpY/q2LTSxap5EiryW4VJ1hplFmS6qbRPNcpwvT3N89yH/d9V7Smk7JtlvAyky9yh+cOMT2XCc/u/sB4orG+eIcv/jNL/OJc0f5tf1vI2/E+NndD1J3HX7tlaepODb/8YF3r3odC3adk/NT/PK+R9iYztHwPLwgaI3LtGbwo1v3MV4tcb44u2obL06Ocnxukv/3oSfYmetkvFbmJ579LAc6+rivc4AwDPn62EWevn6eXz/wKPd09OOHAX917gh/eeYI93YNsLsJ6AAcmrrGz+x+gN8cfBRNlik5NqmmdIGhqPzrLXswZZWOWAI38Pnk+df51IVjjNVKbEjlWtlAX7hyio+fqPKr+x+hO7484LpcKvAHJ17isf5NfGzHPZiKyumFGX7l0Ff5q3NH+KU9D7cy36frFSbrZX7jwKMMJjPUXHdZNnII7BvsZSifXfb+vXZtgrGFEn99+FhUDaluMVGKtHxMVUWTJXRFIa5py/2CEHb3d7OhbTmt2vZ8zk3PcmVugULdYrpcoeF6WI7LiYlpHtg4SH9uuR8rCxGVnZYEhqoQX0Jbtj2Pi7PzXJidp1hvMFkq0/C8G/c00Et7Ik5c01BliesLpVsCR8fHp7i2UOTTr50kJGSiVCapa82+WnueU5RIOyqqHvnmsf7ODIJoHpZlic7cDRDFDWpcLv0t54tPtrIrIosyZSKIxSUIXNygiuVNUbRPU3Gusr9jI7Kkc/bMBBfOTtI3EMmI7Dt4g05mB0WK9pk1r00g4QZR9qPrB/RmU3Sm4q3xNnFpmtmxeTzHpzhTYuTuoRtXKCQSSp4VoqFLzHV9RidnySZl+sP8kscfzSyR1pJotqeQUPuoe1PYfqSTO1N/ad3AURiGzNRfZsY6jB9aqFKKuNq/atYNgBc0GKs+xfniX1J1l9ItpRaQ5QYV3KBCrTLGrHWYkfQPMJz6YFPHM7ruqbEC5UKNwS1dLeAoAl+uUbTPoEkZau4EptLJheJ/53Lp0zjBzUVaQvywge83cIMaSrMKXyQLotHw56l7E9j+AnVvYgVwFIQ+hcYJ5hpHWdRBEUhsDv7NChkEL6gx3zhG0T6NIiWQl9xLGIaUnYu8Ov1rVN0rK4TPozEpEYQuQejiBmXq3gSz1mGGUx9ka/YnUG4DHoVhQM0do2ifQRYmVXeMpDbM1fLnOVf8i9azX2p+aOP7No5fQm7SE/2gCs0CRsvbj+7hXPHPmKg9SxDeWIsXC/Asttfw51honGa6fohtuY+RVDcggJih3na+ulP7jp3VhBB055L8/Pe9heHOHE8fOc/12WiANpwoKL/ZFqun3SozaKgzx8+8/yHa0nG++q1zlGqNZWDRoiVNnUf3bOIHH9nDhu718WpvtqRi8EDnRrQ7EAkUQFqNo0rKMkHsqD9SfOw99/Gn//gKx69M4gchs6Uqs6XqsjYMTV1z+gvCkPlKjflyfY0jVprTzHCpWg7zlXqzIs76TVMVtMwbG2qyJHjb3SPMlep85oVjTM6X8YOQqYUKU0sypJKmzuP3bOMHH9nDqWvTfPr5Y2tqKgkheGjnBko1myf/6VtMFsrUGg4nr04tOy5haDy8ayMfffe9SJLgb58/dlvgqGLZzBSrtzxmqXlBgGcH1G0XIcBetWoRlGsN5pp6W+tq1w/wfIdaA2J6tSmOfnuThODebYN8/qWTXJuJ3rfObIJdw92t3aXjFye4ODbHzg3dJOM6g11v7P34TraKc4y0cR+GMshs7e9x/UILONJ0lZ37BmnrSi0Rx4YwKECwEFXFuGknR49pPP4Tj2LGDb765PNcPjFKo2Zz7cz4qudPtyXXrH6laAr3P7EvGpP/6UtcOXmdsfOTjJ2fXHacEde5/4l9fP8vPM7gKoJ/izZ2YYpzr11e02cKg5BqsU61uHzO6B5qx19lXE1cnub8ty6vCdyGQUitVKdWWt5evjuLv0qlRyEEbb1Zfvz/+TCdg208/7nDzI0XmLk2z8y1lQuzrMgM7+inrXftcSnJEg+8bx+JTIwvfPxpjj1/Btd2mbw8w+TlmRXHx5JGq7rdrUwIwdC2Xn7gl57gM//lK4ydn2R6dI7p0bllx+W7MzRqNu19ebbs20Aic8PBrRZrnD9yhVpx7TnarjvY9eUBfSxlcrBS40J5ir25wWXU2+Ed/Xz03/8An/v9pzj0D0dYmC6xML1c8F9WZDbuHuD7f+Fx9r9jdyubbpECBrAWhCALDXk9IsciWoMW2775S13dSKXxLJoyjCp3kE4szxzu7MlQKVl4vk+uPXoefhBiOy7E33zlrzclu/jq5HEs32FTqgtFSKTVGO/o2UVOi6MIuQXOmMpNWjvrcRRv8ifDMOSrE8dJqSZv7dzK5erKd2Et0ySFnZk+ji1cIyDk0a4drWvIGTEe6xsho0Ui1tklAkCjlSLnFmbZkmnnK1fPAeAGPgFwsTRHzXVI6+t/trqs8JaeYTalIy2v+Dq125ZaoVEnrmotqlzOiGHIKkXbIghD3DDgyOwEtu9zqTTPWCV6lxYaFiWnwaXS/DLgaCSd5+GeDcSa15I3lgck3bEUl8vzHJ+fouY5jNeiMs11d32gHcDphRkqrsNj/Zta59maaWdXWxfH5iYp2hZtZrSGxRSNt/WNMJSMgKGktnytkUQkTL1CuwTY2J7jwZGIGvjw5mH6l4lNrz7mJCHQlZXtPX36AocuX+OJXVvRFJnTk8vH262zelZ++ez5y3zt7CXev3sbMU3j1MT0su8XwbHFpWk9oVR3KsmDGwdBwEMjQ3Slkret5Ob5AZNTJQYH2ojFvr00kX9Juzw+z7WpBdJJkx0bulrZ8WEYMFV/gYulT7ZAI13Ok9V3ElM6UKR4VKnWL1H3Jqm4V7H9AgKZNnMvmhyBgw0ryky6dnWOnt7l9Pm0NsJg8n14gYUX1vCCOg1/npp7nZudF12RkSUJa4k/3d6XZ3Zsnrsf2c7s+HLN0jAMsf0aMWVtyrgsS6TSMdryyZsGToDlja2sCKttIakOtcCD6fpLDKc+iCytQ+s2dJi1DuOHkV5rXOklb6zMnl689snaNziz8CdYXhTDKFKCrL6dhDqIIsXwghoV5zJF+xxeWKPuTXBu4c8xlU564o+yeENCwMToHJ39OZLplaBJgIftzzNWfZpLpU/hBhU0KUNSG0aXc1HmVlCj7k5Qda+TUPuJKTd8T01OYSpd1L0JvKCG5c2Q0bcuO4cX1Km4V1kqnmv7C1jeNKq2YdmxblDFcqN7VqU4cbV/2fealEISSgs0MuQ2EuoAMaUHrUmha3hzlOyzVNyrTYCtypXy58kbe+iKPYi4XYGixedAgO3PMVH7BueLT2L782hSmoQ6iKG0IQkNL6hT9yaoutcw5Q4Saj8CBV3pwwtKLB1YYRjS8Oc4Xfg40/WXCHARSMTVAbL6NnS5DQixvGkW7FPUvUkCHCZrzwESe9p/GUWkMTSVUq3BrUDRO7XvWOAImqrrbWl+8j338sS92zl2eYIz12a4PlekatmR1o6u0ZVLMNiR466hLoa7csRW0fFYNEkS9Len+bnvfYj3HNzGkQvjnBqdYq5ciwKUVIxt/Z3sGellc187lUKNueky16/OY8Y0EFAq1tE1BU1XUJ2A//Vt91Cr2ZiGysJ4iYVrRRzHR9MUJBHRBcyYTmH+f3D33kGSpOd55y99ZmX56uqu9n56/OzM7KzHGix24QiAJIwI4kgKoJOl7qSQTowQeTrpdOIdj5QLKYgjFYG4E4/kUSBBAgRIOC4W693s7rgd2z09bauru7xJ+90fWd09Pd0zu4BwEQu8f0x0TWVlZn2V+eX3Pu/zPk8Dw1BxKi1+46ffT2EgEv3MJbdR1ECELLRLLLXX6dGTpDUbzwvw/RBZlrhn3zCj+QyvXl7gtSuLzC2v4wuBKsn0pG167Bgn9w+RTVi4ro+iRP2WYShQVQVdVvjHn3iURtul3XaxbQMkWK+30FWFUAiuFTfY19/DlZV1pgo5hADX94l3xQxDBGu1JkgQ0zUqrQ4fvu8AyZTFZG+WbCLG/HqFnkQkcN3xfMJQ4AYBWdvaEvP6ux95kM+89wRhKMjFY6iyvJ2ICkEooslak2Q+8eBhjo30cW6hyNm5FVbLdQSgBHBgvI9Hjk2ybzBPPGYQ0zV+7x98AsfxKWSiikiz42457gRhpGP12F2THBzt5TtvXuPiQpGl9RqyHLXk7Rvs4b4DoxwY6d1im/2zn34cWZLIp+IrsD9dAAAgAElEQVS06h0WrxWxkxYDoz0oXV2Nj953kHtmdk5et0an5XLj6uoOC27D1Bif6WdiMKLBh90WvI7nE7cMnjixjwcOjrK0XsMyNHqSNsVKg75MAsfz8fwA29Sptx0SlkGzE7Hwrq+WGS9kycQtzs4t09tthXNcn0zC2tOBZqgnxcmpIeaLFWRJ4vjUIKN925XCYrmBLMvMrWww9n2Cqu/2UOUEHW+WUHQIwipt/wqBaBLTpvHcgLXVGv3D2S3rXQBZ7gV13w6Rw0AENP0WsiSjxBTe97mHOPr+/Vw5fZ03vnuepblVmpU2QRiQSMTJj2Q5eHKa8aPD5GdyNP0moRDdhWrURe0LH1VTec9P3sPM3ZOcf+ESrz91geXZIr7rRw5hx0Y58tB+xo+NkkxHjCaAIIxAyripR4mJ4/GZX/0YH/mlx4FIJ06W5C2Hs1vD9QNURcYJPVqxgJrcod3xafgOeTPBWqfGh37lfRz7xDFUWUFG4kzlBiezY2y4TZJaJGBadpuk9BgVt4mpaBiyRhiXCWMy5yoLSMC+5MBWhVySJAYm+vjsv/gkT/7sw7zxnfNcPj3H2sIGnuOhGRqpviSTh4eZPDbG2MFBsv131m1ywpATTxxh390TXH5tlosvX2X23AK19Tqe66OZOsR1jp+a5uhDM4wdjLSEthaI0t6puaqrvO8z7+HgfdO89Jdv8NbLV6ms1UAI7FSM3ECGmZMTDO8foFKsUpxfZ/TgIIWxqN9v5u5J/vWX/8mO+eGdhKzILPW2qfsdAnFLdU2WGD8ywi/8r5/m8U8/yCvfPMPVN67jOT66oZEfznHfh+5i5u5JErnIMcRzfRRVIezOyU7HQ9c1fubXPs6Hf/FxDEtn9MAg7WbkcKrpKk7bRTNU1O58aMVN/uHnfzFiWWkKg5MFFq6vM3e1yORMgYGh7JYNOEiY2gEMdZJA1Gi0JertnUD90vwG5fUGM4cGiXd19ixdJZuI4fl31r/4YYzBWJpO4HGptsLfnNyHrRocSg9ypnyDiXhkRX4kEz1vbr0WM3qMpu/wQukKU4k+4qrJ1UaRdafB1foqSc3iQGqA1zbm+PbKOXRZY3+yH4GgE3hcrq+y7kTFmZV2hevNEiWnHukoWRk23AYLrTIVr8VsY43hWJaDqUG+snia6USBPnObKZLSTRL63mYcpU4TNww4u77C9fq29kghFmc61fO2LUG3hq6o9MW+P5fCzdifyfP/XnmTF1bmOdU3xMXyGi3fZSrd05VI8NnotKi5HZ5avLbjs/f2DZMxdgrd9lpxLHUPjQ4hWGs3+c3TTzNXLzMYT5I1Yqy06gQi/J6W+qVOk5iq7RBUV2SZnBmj6bm0/W2dRlNVyZt31unZK06MDPLi7DyFZJxMzKLpeuTjm45KkDQNlmt1Ss0mlqaRsu4M+M2tlxnKpDg6WODccpGNZgSWW7rG4YE+nr16nZMjg8R0DTcI6E3EUWUZTVGwNJXFcpWN3hymphLTdebWy/Ql4xwb6ufSaolScxt8l4AXZ28w2ZPlytoGXhAyko20ofwwpOP7BGFIx/PxggBVljk2GO0nY1sUkvEtYe+3CwkpWsP+iLk8yrKE50cujzdfOU5QZr72lS5IIpE1DnM49w9IGweiFv+uJs4m86Xjr1F1L7PeeYP+2CNbLVMHDg1Gx/ACJqZ6dxy7xzpJzjwRsZaESyhciq0XeW3tX2wBLJsRhgLH86l1tv/ftA18L6CyVsOyd4M3gYic1fzQRZV255BCCNbX6jj1En2F9E3zkoQu53adgybHGbDfS6nzGiCouBepuBfJGkfeVh+r7s2x0TnDJrLfbz+Mqew2nRBCUHUv3gQaSeTMY8ykP0fWPIomJ7tjH+KGVUrt05zb+A80vDmcsMyF8v9JXBsjZUyxqY2TH0iD2K3BChGgtdx8iqp7BQmZseTHGUv8OLY2gCrFkCSFQERsmro7SyA6mMq2ILQmJ4lrw6x3TuOFTZr+IkKEO8AZL6xScy8DYCm9dIJ1nGCDprdAQhvfcU5uWKXlR8VXQ85g36T3KEkSllpgJPFjrLV76bcfIWscw1Rz0bh0tZBC4dHylpmr/ylXqn9AKBy8sMr1+p/RYx5H62o3lt0as81FJuPDJFV719gI4bPaei4CxUSLkcRHGE9+vKvlFUeWlC3wtO7N4QZVYmoBQdDNMxpdraRNJlGHy5X/i5XWMwgCNDnBdPpnGbSfwNYGtvShgtCh4V3nau2PmK99mRCP5eZfkzZmmEx+Bl1TyKfsPfU7v9941wJHQgjqbYewm+SnbJMHD43xwMExFFlivR4JPeuqQhCGKLKM6wd0XI9kzHzbUoInhUwO5Tg81u1t7z6hQ0LqnoOtGlFvedOlVKzRqHeIJ0yKq1WCIKRRa5NIWvQPZsimYqyv1em0XUI3ZHUlqj7JskQiZZHrSWBaGtVKC12PNGcmh7P0DaVpeu4OSq8iyYzGehmN9eIFPrPX1giCkGbToV5vMzIaiVr3Sgb/6Cce5vLlFYaGsrz11hKFQprFhQ3sQGVtucZGuUEibmLFdOZmS+ybKTA7u8bYWB6v4rK21ODEI4OousLzV25Qa3c4OT6ErMlM9/cwt1FhoCfFuYVVWq5HLKazVm8Sq+gslKtIwFA2xeWVdQYyCRRNZmIghyxJXFxew/UDau0O5WaHRsfF8X0e3DfKUDYSZh3tyzAi0ly5sMTihRXMGYml+XXSPXGctke77dLXn2bxeonewQyVa2UO92T44MemWb6xweT+AZ779nn2HRrEtHTOvnSN/qEspWKNTC6O2oHqYpX+3hRvLRVx/GDLFrfc1e2YKuQ4PjPEkcl+3CBAU2TWu+wzn5DZYrSQNTWVlu9zdKRAwjJ4+dvn+eef+z3ufuwA/+Tf/XfEu+h8XyZBX+bOehBXzt7g8//HN6htNPD9AM/xGZ4u8K/+y98iYUUPNMfzeeXSApIsUWt26M3E8fwQU9foTceZHuih7XgM9aS4tFiiP5tko96iXG9j6RqmrrG8USNlWyiyxEKpih+EVBttTl9ZpN5yuPfAyJ7tax3Pp9hlsdmWznuPTe1g8Y32ZzlzZQlZksgkLLzumP4ohaEM0QjO0vauoil52t4cfljF0iZQtaiNaWG2xOSBfqwuM0gIB8K1ruhvNB4bboUbrUVUSUWRZOJqnFqmxsDjvagPSjwon8AXAYas44YeCS2OpZh4wmPeW0D1FQIRgTkbbhlZUrAUk5hisT85Td9oD32jPTz2qQe2qqlNx2O93iSfjLNabWAGJp4T0HY9bEPn7I0VTowPcn6xGOmFjfSj5uOkYyYr1Qa5eAxZlqi3nS29MEuPLJcvLBYZG8hTpcnF2jKXmquEIkSWJLzQ52J9mcMDQxT6+glEyEAsQ70kcbhvhnOVBdJ6jGKnTjw0CICY0NFkmarXpum10RtLtAMPS9HYi7bb8gOknM0Tn3uU97geiiwjBKzXWiyuVbhreghFkVirNDAdF8cLqDU75JIxVFVhuVQjn4mjKTLnZ1c5Nj1AqCsceeQgE/dOoqsqa+UGSdsgZur82TNneezENLqmoBgalXoLXwmpBw4xVY/sv7tinLIk44UBXhhgqzqpiSwP/OKDPPLLD8MW+AdxzUBXNjWIVAIvRDO0rYVaPBVj5u5JvtcQQqA0ijQ9B3mPKtmNa2tcOXODyYODHHnvUfY/dJDCSI711Rq+61MY62G91ODyhWXyA2lCPySVs6mVW6RyNhdevU5PfwrfC6i3PNSYSWWjyezFZUan+hDApTdvkO6Jc/I9M1E7g6owcZMFbhCEUYtDf4pW06FabtKz5ToUUmt/HVm2cbxL1NvHCMOhLaDuxuwaly8s4To+dtzcAqdajkej4+IHAZ4f/EgJ+KvhVd7fm6Qe2BTMJIqk8JGh47y2PkfJqTMa70GTFB7p3U/B2jmX91sZPjZ8grlmiaEgiyarFDs1TuUmaAUuFbfJmJ3n02P3c7a6gBcGKLLMR4aO83p5nphi8MvT7yWj28w1S6iSytH0CPPNdXJGgtVOjawRJ2PYLLY26LfSqLLMQCzLVKKvew9H0ZVb2DMyRsTm+duH7+ORwYld73+v8M8mxP7fEnf3DvHB0Rn+/ZvPMZqIxvWnpu/iof4xJElClxXShsVkKse/fegju1hNtx79dnliKATfWbrGM8tz/OYDH+K+wgiyJPFX85e4WN7dRhcBEnvXjntMm7bv0biJpRSEIRudNjFV22W2cafkNWkaW26KmyGE4NHJMYq1Op9/+iUUWcbSVP7m/ScZyaaQgPsnRvidp1/kt77+DAcKeT774EkQYKkqmiQThmLLbECWJR6dHucPX3mT3/7GMyQtk5l8D6oko0gS7z84zVq9yW9/8xlMTSOftPm5++8iZ9nYusZjMxN89cxFXl9Y5okDUzw8Pc6j0xP8Py+/wb/51rMkTYND/X1osowMDGVSeEHAf3jqeWodh48ePUB/KkHTdfnia+c4v1KkWG/wu8+8zKGBPj5yZD/3jA+xXK/xu8++hKVGuqo/c99x9vdti16HQtAOXCxF25p3JVliYCBzG2blD2+M9EUulo7n70rgq25kTKNKFqOJj5EzT+xyn5NQMZQMupwmqU8zHP8gm3fLWrGG5/r0D6Rp1Dtcny3Re4sWkSRJKOgoXWBHV9LsNUMoioyuKsR0feu5euPSEpqusnBpGVVTmLpru21MECJLCk7QpuatkdV3SwTIsoRmagix834WIsQXTUylF2mHTIFE3jqFpfbS9ldxgg3W2i+S1me6bW17hyCk1H6VdhCx5QwlQ2/sfuQ9wCxftJit/Vca3hwAcW2UYz3/Iyl93w4wRpJkdDnNgP0YIHi1+D/hi4iFdKPxNeLaL6HIJmbMIPDDO8xXLsutp9HkOAcyf4e8/kEIdFqeIAgD/NDD0FSEyGDJORRVot4K0dSocG8ZGgl9AkUyCESHhjtPIDo7zFRa3jJtfwUJlYH449yofxU3jICovtj9SDe5ija9RbwwytcS+gSqtBOoliSZieQnmUh+cmv8bp33ZEnD1obZl/4cVfcyq63nAEHVuUQ7KG4BR17oU3brtH2HpLpbukYQsNp+HlWOMZ36WabSn+mCadKOY6lyDEst3PQ5H03pwVBHt0AjIQTrnde50fhaV6NKZTL10+xL/9wuQXFFNkjq0xzK/j1a3grF9vOEeMzXv0LefIj5VQNDUxj6HjuF7hTvWuDIC0LevLZMwjJYKFUxNCVqUfN88ikb14tAouHeNNeWN9A1Bcf1mRzIkU/FudNyI0Twh1dOkzEsfnzsMKqsbG2+3m7z22e+w09PneBItp/BkSz5vgi1VTWZ4bHc1sSx2ee7XmqQSse2KqAzhwa2EHZZkVFkGVmRSKajhCzwQwxT5XK1xH+5/Cq/evxxEt1+dy/0OVudQ0IihkGj7JFKRxdfKm1TqbQordUprdWZmu6jUm7S15eiUm6S6rYTNJsOpVKNTsfHcwOc5Qqzs2sMD2cxDA1DV6lWW1uTQ6nRYqUS6fms11us1ZocGMiTjVtdO9LIXtXUNHqTcfJJG8cPcH2fC0trBGEksN2TiCFJ0WKlVG8x05/n2UvXOTCQp9rqdPex+5Jbml+nWmkxMpFnZamMYWnUqi18N6BuG5gxPWJGIahVWnRaHmsrVcam+rBiOrIiM3+tyNW3ljFMHdPSo+q4GWmfaIrMgcE+QCC6LCY/iBbJmiLTdDxy8VhXWHXbrUzqCoupStQ0GDeNLfFV0UXkb9uPs0cI4QM+heEMv/TrP06lVOf6xWX+8g9f2EVzFUSC6AM9Sb7zxjUK2QSeH5BLxNBVlSAUNNoOpWqTIAgjZpfrY2oqYShYq9ZxXB9VUZAlmVqzQ7Pj0olF+gFxS8fS9xZsPzO7zIX5iDI+2Z/j4Gjfjonq8vwaSdtkpJDh6devcWCsj7umvzcr8Xd7mOowXrhBKFrIkk3KvBdFiuYVIQIGRnKEYbjzoaDNgAjgpgebLmvEFAtVVnEDF13WMGUTX/jYagw3dDFVHSEgbSTRZA1N0nADj6QWx5ANQkL80EdCQpc1fBEQVzervN3jS9vJ0tXVdS4tl3jv4UmWKzUycYtrqxvoqsJ0Iargh0LQ6DgRuFSps7BR5Z6pYdZqDUxN5fJKiWK1wUA2SbPjEjOiBdh6vcVkXw6U6D7PWjaapFDxWqR0i2xXzF+SJDq+hy6rZHQbJ4gcTlqBS6+ZYLVTJWck0GWVUIRkdJua12HEzjHbWCOp73aXcFyf75y+SrFc5+jUAJoic321wvF9g9SaHWotBz8IePmtReaWNxjqTeEHIZqisGjqpGyTSzfWePDIOOmExWKpysHxAnMr5chpp9ZC11TOXlsmFbf48fccJhWPzmNlvY7j+ZRrLeR+WGxXonkdCU2OhF7TegyJ6PmS0i2K7TqarNBjxllolrs/k8R9vWMYqoYIBaWlDZZnizRrLbKF789W9uZwA59ABIS3MI58N6C23qDSZdD6XkCukKJcauB2PPIDaVYXywR+yNB4ntJKBd3UKC6VSefi1CsyzUYHfz4gljDpKaTYWKvjdDz6h3P0DWd54ZvnqFdb6KZK4Ac7WuU2Q5Ig2xMnkYr0P0xr5xwUdPUSdHUSSfJ2LGA1XaW3P40IBZat43SdLm1TJxO3ovaaHyHQCECSUkyaLwMaUhAHdYK4avJw3056/6H0brtkTVY4nB7mcHqbAftE/+Fd243GexiN73R/erL/yI7Xh9NDHL7lGCeyY5zIjm29Xm1XeWn9Kn7ocyA1uOv+vV2MJzNMprL8+dwFDmR76bUinZ+VVh1DUSnE3l6Y+wcdncDnUqXET00f5UOj+9EVhZwZQ5ej68tQVE72DvLsyhxPLV3j8aEpTEWl5nYodVoM2UnMO7iiboYAmp6HJisU7ASqLLPaavDcyvUdDCGIlqiGouKEAcVWg/6bxkWSJA53xba/eeMKk6kclqJyqVLijdIyJ3sH33G7nyxJfPaBk7vWau2mw+LlIo/3j3A8mY/c+VQVb63NtbWIGRj6AT81sR9fEsQ0Dd8PmL+0yuO9Qzh1h8tnbhBPWnRaLrqhMmLH+fTkDKgyaigR9ob0WdF6N2kafPaBE5RbHcIwpOTX+U7pAh8evAtT0Xh0epyjgwW8ICTdZTYdGujlHz3xUMTUNnQQESteVWR+/oG70RSZWsdBkqDHtpElCVPTePLgNI/u2wYSTE1DV6N5fWjI4uHUAKeykxiqQtbe2cLjBB5fuvEqHx68i7Qeved0vKhYcBvm7g9jVBsdas02pWqT4kYDQ1NJdtuDhfC3GDeSpKLKd2azbb+3vc1Lz11B1aKcqtl0MIzvz54cIl3QesfpiqZHkR/MMXtmnma1zdFHDuzYXpYU4mqGUARdZ7Xd5+77IUoQUhjcA8ySTALR2cEYidpkB8kYB2n7RUBQbL3IaOJjWHLvrv1vRiDarLaf33qd0CZI6lN7jmfdnaPYejE6HiqD9vtI6tN7tldtfr7HOkHGOMha52UEYbeF7uPY8iBOx8X3AqQ76pYKeq37KVgf5OJ8nXKjTcf1SNkmthk5u61s1DC7ZiBBKCJNMkXiyFg/CW1sa7xa/mIEHLF9T1XdywTCRVeSZIzDFFsv4IZVat5VgrCDfFNBou7NstnSltQnkfbQgHonrYGSJKEpcXqt+1lrvUSIhy/atP01knpUxJMlif2JMbJG8g7XtqDHPMl48id36THdHE7oUPWqSEjk9AQt9xK6WkCVoyJaIDost57GCaKWyoQ2ynD8A7vEzG8+f11OMxx/P6XOK4TCpeUvUeq8TNJ+HEWSfqBz0bsSOPru8jWGYml0VSERM7h73xCuHzC3UuZ4f5a4ZRCGIY4fkIqZDPWkI5q6BDFD3zVAz63M0m+nGE9022oElJ12VK2+5di+CFltN3CCqDdW19UdAnd7TWa9hehCeruFknrLotYNfIrt+g4xb1mSSetxFEkmqyVgRsLpePT1pVCUqJVroD9NGAps22D//gFS6RiPPnqQMAwZHMrguQGptEW91sEwo4f35FQf2Ww8Yj+ZGvsPDOA4HoahkdUU7psaRlOUSNjQ1LBNg5PjkVvFw/vHcf2QpGUQEi0W4qZOKATjvRn8ICRpmfhhCAJUXebu8UFSMZP3H91H0jKY6MvhesGe1OUT90/TbjnEkxb3PbIfWZXpD0JcxyeZjhEGAs1QOHjXCLIsYydMZo4MoxsqB46NoCgy1r4C/UNZ4kkLRZVBRFWfdtOJQLhbf7ebFkWTfdmIevt2v5+yjQZ/XxEWCd03sONHue+JQ4DEpTfmeerPX9u1qaEpHJ3ox9BUPnr/QTqej23oaKpCve1gaCpHJ/pJxEwK2QRBKDg1M0St6WCbOo2Os9WGFjH2LGrNDqahMjXQg+v7u5wHAWqtDl9+4TzlRhtNVXjf8ekuELsdXhBQb3fw/IBsIsZG9Z3rZf2wxEb727S9q6hKmiBsktCPonb738NAMH+1iOv6TB0YwLQ27Tt3Mx1TWpKkFi3wJbYtTJHADwM6QQdd1ghEiKWaW9vdHFuipe9QmMHxIwtXzw+pdxw2Gi3ipk5fOoHj+6w32jQ67hYI3HI8xvJZVEWm0XEp1hqYmoala9i6Ti5u43g+TcdFliUUOdJZubfrzHRz9BjJXfoPp3ITrK/WyDUszJiO5wbkahaZvLXlXqa1wXRlpFbIPq2XZNzeofEGUTEhFIKJwRz9uSRrlQalSrSAzSZj1JodZFnG9QLGChkG8imWSzUG8ylWN+rkUjZWscL11TLphEXb8Wh2XDIJi2ffnGX/aB/NtsNAT4rh3nQ0fi2HRsuhJ2XzjVcuMdqX5mCuwL6gF11WCURIIEJUOWoD9ru23ZaiUrCSGF19u7wZJxSCy7U1bi78aLpKGIaUV6uszK3RP773gvLcjVWycYveVJyvvXaR4+MDDOaiBWzTcfmr0xdpdDw+ct9+hBTucvH0g4CNUh3D1IgnLUxLI5WNIwRYMZ1UNo4kSwRewNylZUam++jtz/C1P3qRu+6f4tKZhah4kbWJJUxS2Ti+HxJPWlw9v4hAMDLVh+cG9A/n0G4jCttpe1y9tEql3GRyuo/hsZsBC5lU7EMIESLLNqa2SvMm9n9hMEO91kbTFPqHsrz0TFThrrcdml1W3Peqv/duDyEqqPrdSJKNCOv8IHUKftBhKBojdg/39UyR0N65JlFfLM5/f+wh/t0bz/J3v/NnxLTod5QliU9OHeGj4wfffiffQ7ywMs+zy9dZadVZbNT4y+sXWW01GLCT/MTEQdKGhRv41FyHL82e57mV+UjGwIzx5PA0jw5OYKka7xuaYqFR5fNnX+QPLr2OIsn4IqTXsvnVk49ReAfAkSJJnOwd5E+uneWfvfBXDNhJmp5HUjfIWTvnVkmSOJjpZdBO8usvfYNBO0lCN/js/rs5kO1lLJnhV44+wOfPvcTrpSVMVaPUbjKZyvKzMyfQ5HcGqkqSRM7erW/SbDjUyi3CQKB4AaHj46kBy81u648QXXkEhXxvAlmWCbyQ9WKVVDZOpdzGb7gEfgCShOt4qLpCp9gknrSorDfoKaRRhLR1HjFdR5Kh6NRwAoeG18EPA5acBk7ok7cSdEIPXVVYapXJ6DaKJlAVQSOMROPjSgZPBDRp0/FcEoZJSotR9Zu0Oi6qrNCXSFJyGjT9Dmk9Rka3afodSk6DdbdO0tbpS8VZc2osdcr0mSk0SWGlU6Xpd9hwGltgvRAgKxKttkcYvHvv1+81VEWiUo/yppip75A5UGQTXcnghXX8sMFq+1ny1j2Yau4Oe9wZD7/3AIapUSk3cR3/ts+QdxKWrjGayxDe1G4lyRL3fugEmq5SLdV2bC9ExDjK6P3YarfF/ZafTpIkDFOj3d4J6ALIko4mpwmFs4MRosox+mOPUmy/3LWtv8JG5w0G40/c9twrzltUnbeiY6IwYD+2JwgRtbm+SDuIiry6kmLAfhRZuvN9rstpsuYRSp3TCHxa/hJ1bxZbGySZsekbyiLLt8+HFMlgLPkxLC3BRL++1SJu6SqBEASBYLQ3GkM3CLbWg6oiEzM0VMbQlSRuWOna1DcxlGz3O4WUnXOEwsNQsiT1SRL6JHVvlqpzCS9sbjGAhBDUnMtbYF1Sn9oC7b6fkJBI6hMR+CQ8hPDxw2292lAI5lvLaLJKr7m3RIeMzmjiIxhKlnKnzdVyBPyMpzPkrO051Q1d5ppzJLQEPUYWWz9MILY1bDv+GmutF4kuQpl87NSONrw9z1+SyJiHMZUcLX+ZQDiUO+cIW3fT6siMFrI/sM6QdxVw5IUBdc/hz6+f46Ojhzg42ofVpdgKIJeO4YkwYn+oBmkp6lE0dBUn8HHDAEWSCIVAkSS8MKDhuXx5/jyPDUyT0S10RdkSqw6FoOV7gIcuK5jK9nAEQlBzO0iApWqostIVq/JRJJlAhLhhgCbLWIoWAU0SGHI0aTiBTyBCYqreFV7zccIACdBlBaN7LAHdRUonEhBUFOKqhSrJdEKXfCJFostk2ryRb35tDe6kL26DGtIOK8ubPw+QzW4zFhRFZiSX3nqd6zqmWbdUnG7+/CbzZhMI2mRYbf6dsaM+8E3Hi1iX3bJLbFGSiCdN4sloPzF7s+1H7Nq+t3/7HDe32/Fwye3ev/UOhAn30vn5/yUkG0SdW1239gpFlknHozE09Z1tO/EuUJGI7R6rZCwax0xi28lg8/3UVnVo99gKIfCDkG+8eokXLswjhGCyP8cjRyd26d3sG8mzsFoFKdK8udlZ40clBD4xfRpd6aPtXSMU225SuqHSP5KjWY9sS98ubgaCbh5zVVK2mEO3brfn599hwcDSNcbzWTRV5vHDU1t6BJvHfvLodKR90WXTpLv3qgS8Z//Y1nab2koSEqLL1hNC7Git3Us8ddf5SxLzV1ajtmM/wHV8fD9g9hxUTucAACAASURBVK2lLnAitsD5TtsjlbW564GpXfuxLZ1jkwMUK3VMXSWTiPH+e/cjhCCfibNarhOGIcemBri+uoFlqYwNZohbBroetfxlkhZ9WZtau00+a9PodChkE0wOZZkayuH6Phfni+iGTNtzyaYsvCDAMjUMTWGkkCWtW8AmGzMgCEVUtNA0vCDE8XwUIZM34nh+gKGphN37qycfx/MCGh0HU9MojPeSHsgQhCFyV49u04xAkSVMParaDWaTEfgtYGmjxlhvhpRtYmoaMV3jxMQQf/HKBUxZw9TU7nk4qIqMqamYls7DHzoWtbOpmxVRGJ7s3fpbiEhDIfADQOLahSWmDw+RzMY58dA+vKCDJIeosoEkqSQGElFiNpTAJSCuG5wYSqEoEsV2E11WkCS2Fo62qmPFdA4dG8bpeHswkgSufwPHv4apHSJpa+jaTuaZYWice2OeuavF7cW5ItOXSbBYqu7pqPrDGmGwjhAtED6hKKIZD/NOJgEhBOvOGWQUMsYBNpzzQEjWOIR0S1Lhh22q7lVy5m4mEkAgXJreEnFt6LaOPpuR1mOk9ZGuK845kvoEqmzxYGGMkXh6lwDzZiiSzKneQf73Bz7Iy8UF1jstDEVFc2WOZ6KWkVAIrhfLmLrKh0f344Y79ayEELRdj5wR4xcOnmJfOgIkXT/glcsLDOaSDGSTvHZ1kXmnQlI3iGs6E6ns1txoqSoS0drtq9cvkjYM7i+MYGtRkWyuVuY3Tz9NTNV4ZHCCuKbziwfv4b6+Yc6srbKwXmEonWIynkP1ZOqtiNnySH4cQ1UIPUE7dJFlGaM730mSxMFsL79x/wd4vbSME/hMp3uYSed5YeU6hVicULjdZFRmJJHmX977BC8XF2l5LnnLJt8FmBQp5L1DE4wkMry6tkjb9xiKp7i3d5i0sfnsDxlNpPn5g6cYsGMIEey6Jm4XPb1JMj1d7ShxU0GDm54DovtP97UsS5x8z36QYPLQ4PbV230mIUnkBzJbyxtJ3lkZF0Lw1OoFVjpVQhHihQFv1ZZ5szJPXDWQkGgHLodSQ3xj5SyP9h7gWqPIpfoKxzJRW+X7CodYc+ostsostDYYj+c5kR3jSzdeZTLRS06Pk9ZjnK3cYKldpum7fHrsPv5i8XVUSWG+tc6h1CDPly5zvVHCCT3G7DyDsQzfLV4kZ8RZd282RBG4TvD9FxjfpWFbBjOjfSjKts7RZhuYoWTpMY/T9G4gCFlofJNQ+IwnP05K348m23uyYG6ORNLiwtlFrs+tIUkShf40fYXdztDvJLwgoNx1Wt6M4nwJOxUjmYsz/9YSk8fGtt6TJJmGv0EnaDAeP7H3WkwIwiDcs/3QD1u4QZlAtEno+5FuSq1z1gni2ggV5zxeWGel9SwF+5GtdrubI+iKYjtBxFC21F7y1qk9v2Mg2lTdS1tOW7Y2hKn27LntzSFJEjFtAEUy8IVPKFzq7hyF2EO0Gw6NaovEHda1MXWAuBa11KZvKj7fmgPe7voXYQJbG6bhzeMGVTp+kXhX1NoNqzS6YuemksfWhkjq4yw1iYTQ/QViWqH7/Vs0/cgQxlCyWOo7dy+/XahybOu3FwhCsS2uHlNMLMWkE7oIxJ7XiKXmSegTQIRDLDVqCAEDiZ05kkAQEjJoDQIhbrCMH9Yx1WgcGt6NLkstav1M6tPvCBTTZBtTzdPqjksrWGIgLSFXjdu2H34/8a4Cjs6XV/ny9fO8ULzOutMka9i8pzDOh0YO0PRcfv/Ka1yuriEhcap3mB8bOUhCM7hQXuVP585Q7DSxVY0nh2Z4uDDBlWqJL82d5dmVORabNb6+cJH7e0f50EhEU7xYKfJvzzzNeqfJgJ3iM1MnUGUZPwz4zvJV/mT2TVq+x/sGp/nwyAEE8PtXXsVWDVbbdebqGxzMFPjM1An+ZPZNVFnhExNH0SSFr85fYK5R5n848jA3mhX++NobLDVrhAj2p3v57L5oMmj60fdaaFYIQsFPjB+mzzaoeA0yeoK8kdqVnAV+yIVXZ9FNjYmDgyiKTHGpzNpiGafjYdkGPf1pevrTe7YLOB2Plfl1Noo1Aj8gFjcZGM+Tyu6ucEH08A78kOLiBhvFWiSG2mX+FEZypHLxHdvfGrVyk5X5deqVFmEQ6XmksjZ9w1kse2/BTEmSqFdaLF8vUa+0kGSJdC7OwFjPDrepzQmqXmnx1uk5CiM9DE/2Uik1WJkv0ax3UDWFnkKa/tHcVtJ0cwgh8N2A1YUNNorVraQmkY7OMZG+Pe1WkiWEgLWlMis3NnDaLvFUjP7RHKld9ucy0S33vd3B0aG3f4t202Hx2hq1ShMEpHNxCiM57OTu9p7NMbo1Gm2X1UodXVWRJKg225y+usTvf/s0bTeq4H3s/kP0Z3fTMiUk2o6LH4QcnRpgeni3aN8PeySNuxGElFvfQpYMFHnbbcN1PIpLZRbm1kln42Tzu4GzldUqmYy9lSBsRrncZKVYpb+QJhE3KVeaJBN72/ZWqi1UVSZumwRBiB+Eu/YH0Gg6LC2VGRzMYMcM9g/0RmCHru3p/rJF396sxN30nnoTtXvnnbJzP+2mg+f4JDK3vzfCMNwS5d9/10i3tbMruijLXYAiSi6EAEWNgBNZljFM7Zb5J0DVVMYHsowPRNWe3szOBOOeAyPdryWRSKmcrryFpRs4UoyW0cGQdQbHbCTJoyHaDE7reFKDOadGclDCUx3KQZXCmE47aLAU1slPwGgyzcJyjbH+HH2ZbdHdYrXJl185j6YopGIGjx+d5rmL11lcr2JoKvfPjPL8xet84v4jXFleZ7lSYyiX4tWri7RdjwNDvUz05fjqqxdQFYWEZfDkXdP89ZmrLG7UGMwlefzIFIsbNf70hbO8//g+Dg8X8MOQp8/P8syFOY6O9nPfzAiaIm+dV8fz+e75WRbWq+iqwpN37aM3FX/HFdwwCKnX2sSSJgNjUWujqin4ssdK62VUySJrHGK23uJ6o4IiydiaThCG2JpO2WljqRp5y2ahXsUJfSxF456+IWKSxtKNMpVyk0J/+hZgX9DxLiMBrn8NRRlA1zI7ruF0zmZqfz8iFFt2wbapoykKuqrstAD/oY8QSbIQ+IC3K/Hq+OsIAgLhokg6XtgiFC62NoSMStNfJG3MIEsadXeWjHFgx+Kz4S3S9lepOJew1B6coExMHSAQbZygjKUWIjHPrktSy1+l7ReRJR1VMvHCJqaaQ5F0Wv4qptKDJCm0vCXWOqcjwVQsTvUNcapvU1geGh2H+bUKqZhJLhnjxlolAv/SCQ6bfUiWRCEd59JSibiq03Y95lbLnL+xyr6BHkb0NIoms1ZtUKq1UGSJbCLGcxfmGMyl+MjwAVbKdS411hjry+CHIWu1JkM9KUIhGDHS7B/KI4TA1DTqbYfe9Pazeq5W5stzF/jU1FH+xtTR6PqTJK5V13lzfZnr9aidUpIkTFXlVN8wPUGM08UFjLpKQtdYKdUobtQZLmTo68Rodzxery1i6iqFXIK+XHLLkUqRZA5m+ziY3U58hPB5YiiGrmrUOs8T0w+gK5EY+r50nn3p3c9cx5tFUwvsz+TZn9lLSDfEDRYZiPXw6eljdLw5/DBA20N0d6+QZCmSdbhNbNRbXJxfw/MDelIxZoZ7o8Kkeuf1juuHXF4o4XoBEwO5raIXgCcCrtRX+ejwCZqew1PFC1ysLXE0PcxMsp8/mnuBmGrw6sYsBTPNxfoyGd1GlxU+0H+UZ9Yusdqp0fA7mIrGiJ1jMt6LJinYqsH7+49iKhplt0GxU0OXVc42Fthwmyy3K/zK/vfzreVzeCLgxdIVFEkmpho0A4e3asscSg9xND3MfHOnu6dhqDSbzo8UeBQEIee6bdz1ZodsKsZQb1TMVSWbkcSPsd55g4Y3RygcFhrfYL39OlnzCIPxJ8gaRzHV/G0ZMaW1GnOza2SyNqoqUy6/cxfhW0MI0WUCy1vgVv94L698/c2oU+Lw8K7tZRTutDZXVIVUxt4yi9gOqTv/bkTW9rcUhk0lR591HxXnAoKQjc4bdPwitja463hOsEGx9TKCqHiTM+/C1nbrLQGRe5m/7RooREip/RrqHVqkNqPuXt9yGhMixAurm1+F4am+OxZETbUHTU68ba5xe8aSSVKbYJVnCYVLw5unxzoJQNsv0vGLSMjEtRFUKYatDSNLBkHYouZeIW/d3d22hBtUuueUx1Cytz3mZkTXRfR888MmgXC6QusBAp+ae5WQvU02fBGgSArWHVrfdCWNLkc5u0TkVpmPbYP7N4cmRbqWQgR4wXoXiIrmi4Y3T4i/OZC0/GVWWt+943cD8MIaQbhN0/aCBqV6BVX0/Tfr/t0c7yrgaDrVw9+YPMbl6hqfmTrJkWz/lqDfH197g/lGmZ/ffy9CwOffep5QCD4wvJ//dP45DmcLfHrqOJerJb5w8WX6Y0nGElk+NXmMy7USPzl+hHvyI1iKukXZnW+U+fuH30OPafMfzz3LX8yf52Njh/HCEC8I+IX993KtvsEXLr7EvnSeqWQPy6068415PjtzDx8dPYQiSRiKyrrTQpeVLcmbstum2I4cSZ5evsZsfYO/f+ghALww3HoAl502WSPGj40c5JXSDb5w6RX+6fFHsBSDgrm3K1C75fCffv2LJDM2//C3fpoXvn6Wr/3B813gyMWMGRy9f4pf+defIt2zndSKUDB/ZZX/+jvf4tzL16iUGoRhiG7qjO0r8NHPPszdjx3Y4RQFcPnNG3z5C9/l4uvX2ViLHIckCUxLZ3iqj5/4xUc59djBXYmJ5/q89t2L/OnvPsX85RXaTYcwiCrryYzNxKFBfurvPcHMXaM7Puc6Hm8+f4U//b2nmL2wRKsRVe4SaZuDd4/xib/1OBMHBpBvShKuX1rmn3/u93jyU/dy3xOH+eLnv82Nq0U6TQdJlsj1pXjfJ+7hwz/zIIn0Thr26Wcu8fU/fIErZxcor9Xwu+JwdsJiZLrAp/7O4xx9YHpPEA7g6a+c5i//4HlWF8o4LRfLNpg4OMCnf+VJDp2auOk8BUgKuzyQ32H4XsDZl67y51/4LhdPX6fV6CAExFMWEwcH+eTffpxDp8aRZZl62+FGqcJANkkyZu4CEM5eX+Ff/v43MbrVk3rLodyIrIYNTeUDd8/w4XsO7MnGur6yQawrkBgK8SMnjA1g6xG4HNP2AdKOaruqKsTiJtOHBnZptDSaDs2mw3eeuchjD+/H0FU6jkcyaYGAbz11AU1T6MklaDYd1koN7JiB43jU6h2CICSViqEqMmulOpm0TcwKmZ0rcf3GOifuGiWZMKnW2nheQCYdQwjBm2cXUFWF4aEsrhMJ2TcbDlZM3zOZDkNB2/cwVfV7ZtwJIahuNAhDgWnrESAcCpLpGJ22i+f62AmLhWtFmrU2M8dH8RyfIAjRdBVJjijftbLbPZcQTVORZYlWo4OdiNoWPMcnFjeoV1rMXlji2APTxBLmOwNHJbpi1T6+CHBClx4jTdNv4wmfvJHBDXy80KcTOAxYeWzF4qp3g4RqE1ctrjUXSWlxJGBqaHclzw8Cam2Hn3v0BGnb4urKOl999S0ODPWyuFHj4HAfqiLz5vVlZlc3mBnM8603r1ButImbOudurNKbilNtOfzsoyfIxC0cL9LxyyVizAzk0VSF6f4eRvIZXK8LtEkSJyYG6UnE+KvXL3F0rH/HeS1t1Pjqq28x0z2PhfUqvalbQezbR6vpcOaVOQD2HVa3q48iJK4OoMomblhlIJ6lYCc3hxtfhCiSzEDgk9AjrYO0vmkVr2FrOlIYsUCFEJi72KASqpyl6TyPpg6jSAdQdW0Hi8hpe5x/4wayLDE02sPoZC/1lsOF+SLlRotHjk7+yMxHstKDwEWRc4TByi6Xm41uBbvuzmGp+S7lP0PLXyWhjWyBRFG1P/rbCSqRToSks9p+AUvpwQ1rLDW/gyrFqLmzhMJDImIRBKHDRucsKX2CYvslOv46iqQTEhBXh1h3zqBIOpHbo4MqWehKkjB09vxOIOi4HovrVc5cX2Ywm6LpuEwP9HB5qcTSRo19gxGQcXFxjWTMoNl2mS9VaDseby2u4Xpd/ayuhoypafRl4pQbbQqZBH4Qsl5vcnV5HV1TsU0diei+sU2djuvT6ricub5CfyaJH4Y7gKNoPpR4q7zGtdoGhqLS9j2+ev0ifhjuCcrkM3HuOTzaZQdH90MuGcM0NOKW0U0QBLqm3nR9ClruJZrum+jqQMRu12Zoum8goVHpfJuM9QEcf56OfxVDGUZXCrS9i2hKH6qSpe1dRFf6MNVJGu5rpORHcUWHWvu7hKJN0nqYhvMqEhqWNkmp+UXi+l0kzHtpOK9iG8cQCOqd55AkDVXuoeNfQ0YjZT2Kprw9g2Ez3rpe5Nf+819SabR57PgU//Pn3o+t3JnxLYTgqdNX+Td//DQtx+WTjx7jlz9y/1aBRJFkkprFtXoRr8syyxkJFltlNFlBADPJfv7kxis82X+E7xbf4mh6mIu1ZSxVR5VkQiHIGwmeXbvEkfQIY/GergGOjirLyJLEaqfGhtvk4d79nKsuossquqxxsbrMaqdKj5FgOJYja8SZThSwVYOr9VUWWhskNZN2sN2+JHX1RMJbVZR/yCMIQ1Y36iyXatiWznDfdn4StXIe566ef8r58u9QdS4QCId2sMpic5Xl5tMk9HFy5l0MxZ8kZczsEg5eXamhqjLLS2XCUDAx9f0zSBw/oNRosdFo059KkLYtjJiOoiuMTfdj32I1L0kyqqwjId82wRZC0Gx0iJu3FgqjdqKEfpC4NrEL4Fckk77YQ8zVv4QTbNDw5lnvvEFMHdx1eZQ7Z7ccxTQ5QcF+GFXe3TYKkQ7OJnACUHbO8HLxzNsPzq6zD7cEpj3H59qFJWaOjpDZoyC6+X1up7XzTkKRDBL6JCATCJeaey1iLwpB01ugE5SQUEgZ+6KOFG0UXU7SCUpdhlVkFtP2V3DDiJllqwMYyt75shAhTrDBRucMq+1naXg36Pjr+KJJEEbAUeRs5ncBu73B3qrXoB10CLm9U6IimVuaSgIRAc2atkNrC6Li+6bpjSSpaEqu26rWLf4FawgRzXd+2OBi+fe+hxHejkB0SNoaI7m+H+i66F0FHMVUnZxhoysqGcOi14oe5i3f5eW1eT4+cYzDmYimdrJniDc3ljmY6WOlXeMfDz/KWCJL3ozzjcVLXKissi+VJ2fYGLJCWt/e36Yt+6neEU72DGEqKpPJHMVORDc1FJWHCuNMp/IUYkm+OPsm840Kk4moX/d4bpCTPUNb7Wa3i83Lb18qz3dXrvH1hUvc0zvCkWxha2rKmzbvHZxmMJYkFIIvzr7JbGOVml9Dk1X2J27f17i+WuWvv/Qq3/zjlxjZV+DEe2YIg5Cl6yWy+SSxxDaNUAjB8vw6//lf/TnnX53l0Klx3vuTp9A0heX5dV74xll+93/5Eq7j8ejHTu6gC9crLS6cniPXl+LEI/vJ5BN4bsDlN+Z5/bnL/N+/9TVyvSlmju8EgK6eW+QLv/EVNtZqnHrsIEOTUQWqUqozd3GZSqmxyxYzDAUv//UFvvC/fYXqRoOT79nPyL4CgR9w6Y0bvPztC2wU6/zSr32Myf+PuzcPkuS+rzs/eWdl3Ucf1XdPz/TcA2AwwAAQbgrgAZIiRYuWZIm6V7vy7kZI1lr2WhHrtcPhQ1ZYK8uxFqmVLFnWRYqUxEMECQLEfWOAue+emb67q+s+svL67R9ZXd093T0zgLBrkC9iIqarqrMzszJ/+fu97/u+d2BoY7tVIDj52iXOvHWFRCrKh//+PVhxg+kLi7z29Gm+/IVnSOViPP7ZoxtIp+JihXNvXyM/luOexw+QSEexWw4nXrnEqdcv819/0+FXhzMMjG2eMF49v8Ds1DI79g5y7+MHCYKA4y9d5NTrU/z+v/kav/wbP8rwzjVzaUl+bw9DIcJj+/z/+VeUClWOPLyXwfEegkBw/p1rnHj1EiuLFf7Rb/4443sHkCSYL9e4ViiTjVvsyudIWmuLbtfzw9aeDf5aEgnL4GN37eUnPnSYWGTrSd/kSC9CCM5cWSQWubnx3PcS6nWbat0mCAS5bAxzC08zWZEZ2ZkjIEDptLBKkoTn+bzw0gUEguVCjUqlxckzs+GkKhtjbDTHtdkiO3f0ousqS4Uabx67Qk8uzkqxzosvXyCTidHXm2Dv7jzH3rnGgX2DJBMmU1eXOXVmjt6eOLlsjJdevQTAzh297N832G09bdkOL71ykXuP7uTlVy/x4P2TqJ3vMRABnggnuJ4vmCpWKNs2B/v7SBhbK/8gvPYCAgLhhxMrIXPlbCiFFX7AC397nFQ2Rt9Qmitn58nlU+w8OMzF49O0Gm3yozle/Nt3GBjrIZ6yKC5W2XtkjK/94QskM1Hatoth6qR74rTbLrIsUy3WiSYi6IZKJGZy7u2rDE30Eolt3s8w5lfgi9BAXJFUYqrF0eyBdVUcCbnTxgzhtd5jrE00ZCmcMN6R3hNOHSUJn6AziK8pnzzhdmXKAkHSCtvFVk30BzIJPnZ4D7IskYlZaIrMs6enkCWJ8d4Mxy7PsbM/y23jA51Y79Cnz9RDAk9XVR7YN87VpRJffeMMn3voMKlYBL9TzPBFAIRtOS3XC70ICKX5XhDg+gGKLJHPxPnY4d0oskwmFrnufAV4wtvQarLh+lYlZEVGkiWi654himwQ1fJ4QZOolmc1znb9eQmxNm4kDBVVUruf9QloNdvMz5ZIpq3ryBCJmPkgpr4XRU5Rb8jUms5Gz0IJPM/H9wJkpdMCZ+oc2pHH8/wNrQnf6xBBHdd+ClkZAlRkZePz1VCS1N1pZEnFCap4QYuoNkhE7UHgh/9EgBB+519Aw53rEksKGjFtmFL7LLa/QkyLYqn9mEqWZfsYDXeOuD6KKq9eAxK6kiCl76HqXiZl7KZen8YRZUwlh6X04gQVYtoQTXd+y2PyA8GlhRUqTZtKwyZmGAzlkgznUrx9eZaeZJTRnhSyLJG0TIJA0Gg79KfjaKrCdKGMCGC8P00QCNKxtUVVbzJGPpOgUG0wXajQ6hBEfhDaGwQiTOj1g4BULIKhqUwtFXn4wASrwRkA/VaMfzB5iL+6fIp/9OLXutYEuUiU//HAXRzM9iFEwOq4IHUIqai5dt3fSttG+JqPJKk0nZOocgpDHcHx5okZh4lok1j6XtreNJZ2iLrzFo4/Q8L4AerO27jBMqqcJKofRpY0JBQC0cZQBtCUXJgUhYYs6bTda0S0XZjaBFHjdlQ5gyJHCUQTx11AU3oBQb39OqY2hiInsb2pd0UcbYf1x77V2H15foVCJVSXXJwp0HLcLnEkI/Gh/n28VbqKIWvcm9vJeKyXN1emmKov81DfXnqMOI/272NPIk87cBmN5rg7GxrajkSztAOPM5U50nqU+VaJZxdtdlh50mqSmVoVRZJpOxCVLb41fYZ98WEqLYfbU+O8snQZLxAkrTjDkV5OVab5YuFN7uvZSVyNoss15ppl7u+d3OArF6poBf4mdcr3LjRV4Z6DYzRaDoosEbN0NipMZHoiRzii/nNmGt9mpv4tas4UAo8Ah4pzjqpzkbnGMwxEH2Y49lHS5gEkFCRJYvfekDw9fXKGVtPBsTd7Cd0qgiCg0XbY0ZPpJhVfOzOLoioszxYpzBUZ3be2tvKCNk7QIqNvH/IihKDZaKPiXPeOhC+a+F6LqDa+iXaSJImksZu4toO2X0QQsNB4nsHYYyjrnpWB8FlovogvQqP5mDZM1riN7YrMQvgbLBRW9+XdIlRIhfdoEATUSk3atrOpULF2PAogb7ivtyuF+127g+4vAxJRbQBNjuEGVZreLKIzH6k4FxD4KHKEhB4mbEbVQXQlie0v03Bn8IIampyg5S3iBg1kNGL62KaW23Be6LHQfIEr1a+wYr+NFzS7aq7wuENlEJKEJClIQibY9P2GiKkWVaWBLm9PmkmS3FWchZY0CrX25iJK2AYXEHSsd2TJwAvWfLdCo/n1z4z3RkALoNKwWZCqWPks8i363N0MH8hZ1vWnSIhQpWPIa271hqJ2o4+BropIkSQMWe2YW99YJprWI6hyOKFVOpWJ1b+/yhDKSKhS2L62ipQRehBdv8/hxDnchhus9Tgfzg2RNaM8O3+JP77wJmPxDP9w331ASFLFtXAAXvUTyelJDDV0W78RFq8VeepLr/PZf/iD3Pv4QaJxEyEE9Wor9A1Zt+ht2y7f/NOXOP7KRT798w/xyZ9+oKtGatsuEweG+MK//Cv+5g+e4+A9O+nJryX87L97B7/2258j258kkY52zafLKzX+r1/7c449f563nj+3iTg68epFZqeW+ciP3cPnfvVjRBPhAibwA6qlJs26Td/QRpOxlYUyX/q/v8PyXImf+t+e4PHPHg0JMAGlQo0v/e7TfO0PX+Ar/8+z/NK/+Ex3m6uYvbzMPY8d4Bf/j0/TM5hGliUaNZv8WI4/+a0neeVbJ7n3wwc3tJHd8/hBRif76RlIE09Huw/92allfud//yIXjk9z+o2pLYmjpZkSP/wLD/Oj/+vjnfMPj36qwO//66/yyrdP8sxfvcmP/i+Pd9oqZERQQBJ53q3qqF5p8pe/+wyFhTI/9Y+f4NFPH+lW7EvLNb78hWf4699/jq//8Yv87D/9BHpEYyiT5OLCChfmV7g4v8KHb58k0/GvGulJ8fceOESh0qDluER0jcFcgnv3jXFoPI9laNsSCb4v2DGYYaQv3a2wfr+g1mhz+uwcmqZiGtqWxFE7sHm28DUuNU4zZk3ySO8PEVEs2o5Hrd7iQw/vo1RqUFipcfHyEuOjPbiuT28uzs4dvRy5Y4xkIoKqyKQ6iqEgEPT1Jjm4f5DT5+awIjqDHRN8TVOZ2NGL4/oc2DfIsXeucfXaCkODadqOh1hnCByPmUSjUqwGxAAAIABJREFUJidPzZBOW0TMtf2fbV3hr2f/AEc45PUJdusfJmtZOL4fmtHKW8uLPeHyavFpTlReo98Y4pHeH6J/OMPSXCms5g6k2HlgiAsnptmxf5DZqWWCIGB4Zx9CiPAeF3DbfbtotxwWrq3gtj2a9TYju0JSuLBQYersHD2dCONY0uKOB3bz2lOnmNg/hGO7jOzq37RvAL7wOF55jdeKT5PRe3mo5+P0mYPoW1TFFGnr/69i/e/siA4iEMidiUDFLfKV2d+n5BbIaD3cn/gsA+lEl9joT8bZk8vwtddPk0lYfOSO3aRVnaihMZJLEY8YPHJggq+/fIqLcysc3T3MaG+GgUyiq/pqex6vnr/GfKnGRH8Wy9B57cI0MysVSvUWCdNAb8Ol2QLn5wocyPdSrrX47snLFCp1vnvyEg/v38G9k6M8eew88YjBRw7vZl13L0VnmS/NfIGGX9t0/BIS95ufJpbIkR/KkFhXma25M9h+CT9wietrk+6Ku8JfzvweFa+05ffz0f4fZU/89vC78gIajTaL82VGxnNksuuVUAH19vOkrE8CYDuL4b2xzmBWVRV27R0g2xOnXg0l2Q3bYWa5wnBv6n2VY/93h2SgqDsRwkVW8l0Cwg9CotSQ+5G5EpJAso7jV2m5bXRJpuFdotKewZCnaHqz1N0FYto0aWMvCXU3bddB+JdYqJ1ADfJE9DSuXwZZp+bN4XkOQlao2ldoeUVK7TNhZVYISu3TWGoeRTKJ6SPocoKGN4ehpDCUFCX7dMcrQu2Qimumwa7vU/dCMqcnFWOoL8n52QJzlSo7+jK8cWmGUqPF5EAPi+UaAsHkQA/vXJ7DDwS3jea5Ml8k8AWpaIS4qa8adBH4Ae9cmmMklwoDQkyDRrPN1eVQwZAyDS7OFBAy7OjPkIlZYeiEqSGEQ+AvAx6ScPjEsMo92TRVL0uAjCo5ZMw0SXUFVVzDd5sg6SjKMAKr0064sXW2SzALiUA4+EELTUniB00U2UIIh4ZzImwZEeHcseVexA+qyJKFCNp4fqnTKp3sKMgUHH8BCI1YFTmJLJkEooEXFHH8eYRo03LPkzB/gKZ7FtcvIAi9jCQkXL+AIidw/SIgo8hxPL8IUvh/RUoiSxGEeO8L9/UQQKkZmionTGOD+lmSJEb7MmQS4fNzcrin28K3+v5QNMtQdKPJ8ofy+zf+3B/+/HgkTAN8qJM6OBHvo+G1eXn5ApOJPI7vstCqUmzZ7Irlma1VWWo2GIonOBgfo6zb5CIW54tFhuIJ9sfHQrNrIVFtuewwB9hlDTFbr1KRajw8uI+0ubmtR5IkUklrkz/k9zIkSWKhUOXc1SXiUYM7JofQN/ighovvmD7KpPZTDEYfY6H5PAvNF6i0z+IEFQQ+tr/E5eqXWGi+yGTqpxmJP4EqRVBVmWq1RV9/kkq5SbvtbbsvN4PcSTxVZbn7bE32Jjj/1hSNSpOD929MpZQllbiaw19XGNq8zbClvla1r3sndKwJyeStiUJVipCPPkjBfgOAYvskdecqSWNX9zNNb56ifbzbQtZn/UDXNPpWEFNHSJn7Oi13q+tVGUQbkDrdDquFtFAlBSFxlDbCAAIjom8oFm2H5UYDx7dpex4RTaPlupiqFibpKmFISMIwKNt2RxkoSEcipCKRjp9cHkNJ4wZVbK+A41dRZLNrCh5VBzA7LbS6kiCmDlN1LlJ3p2n7ZWTJpO5eBQSypJPUd236zgQ+07Vvcqr429h+ITwbkkFMHSVp7CSuTWCqOXQ5hSpbqJJJxbnAiZXfxN9EyIEnPMpujbQXJ3WTtTmdM5y1rC0FJjIyqrzmcywQKPLWafCKZJIz79xWUXUjqHKKWt2kUG4w1n/r19JNt/u+bel9gqaEhppLrToVp4Uuq2iyzIFMP68vT7Mn1QtInCjOsyORZTiaJm1YvL58DUud4Fq9zGyjwmfGDyLRiUqWZZY729PkNXPs7RbG7cDjWGGWPaleZuplSu0WA9Zmr6H1yJpRThbnO6oliTOlReROf23FaRHXDD4zfpC9qV7+85mXWbLDCsv1LiO+CKh7LfanRkloN460FAjuuH+SRz5154Y2sc3eOrA0U+SFbxynZyDFYz9yN+meNc8WM6Jz9EP7+Ns/fpGLJ2e4dn6eXP/a8ZoRnV2HNvYEI0EqG+eOB3bz6lOnWJhe2cRQh94AUCk2sJsOVtxElmUUVSHdE99SCnn6jSkunJhm7+ExHv3hI2vEkATpnjgf+/H7eO2pU7z2ndN8/HP3s/fw2IbfNyM6n/yZB+kbXut3jSUiHHl4L1/9wxeYuxr6Hq0/R7FEhF2HRjYeniTRN5Th4D07Of7KRZbny1sy8KlcjA995gixxKoZNeRHczz22aO89fw53vjuGT7yY/d2Fs9tJDkDNzEZvR5CCC4cn+bU65c5dM9O7v/YbRuUWpneBA88cTvPfOVN3nruHJ/86QdJDiZZKFc5NNpPPp3gzExoJusFAcVWi1jC5Oc/cRQvCKjYNjFDJ6JqeEFA3XNoBd5G023DCO9NYHa5jKrKJKJhC9xW5Mr3KnRdRVUVctkYiW0eoMvtOV5a+RYNr8pCa5o98dvZEduLaWik0hbPvnQWzw/oyyfYv28A1/PJ55PIqoQZ0ZAVCdt3OHthnqlry1iWzkB/Ciuuo2gyuqkyv1Tm1IU5IpZKvj+JaamslOq8+c5V+gfiTEz0oCoK+f4kc/NlLk0t02w65HIxxkdzfOfZM3zio7dtuF7bQYs5+ypO0EZ4JgmnSj6qkbUsVop1lparjI/2YF2nNKu4RV4ofJNCe56Z5mX6lFHaZ1PMXCug6qEiCEXCMDTsZpiaU6u10KI6516fIpdPEU9bKKqCFTfxXJ8Tr1zCjGhYcbPbvio0mXqtxeh4D7VyE01XsBImsaTF0nyZM8evsfvAEPJ1rXUNr8qLhW9ytXkeTdLJGX30mdtXDm8V8nXFAV94LNozFJwF3KBNKqbz0cMj6z9AoioY78sztrMP2YeFK0U+99Bh6jWbcydn0Q2V/UaSRMpiJJEkE7f46OHdXRn8wkyJu/J5zPERjIiGXbO5YzhPHzrxhEUmF2P6zVnuuHOCTE+c08euEVNVPn3HXt55fYqhRJrA9jgyOsDB/l6sqNH1AlpFQEDNK1N1S/jCJxD+OsWQhIg7mKZOtdKkf3BtspLUxtDlJIqkb5hcBwTUvQplp7DF9qDpr3lVaJpCfiAd9v9v4ckWBA0a7VeRpSiOF2Gm0Gbn4JrqoVpuMnVhkWq52W0dNnWNluMyW6iwazD3vlXV/ntDknREUEOSE932B9cPeG06TLjTFJmVxh6SEZOVRhNNSZOOmNQVhUJjnFx0Pyt1jYWaRhCMEjhRjJTDpcvLnbF9L/VGm2w6SsXzqTfaLASCWDRJsZSGfGjavmP4c6iGQ8srgASKpNFnHUWWVAbVhwHo4XB3v3ORO7r/rzltzpcL1B2Htu+FbV8Rj96xOGkjQgCM7khzsbzCYtBgbCLDSquJEVX53KN3dhd9ct2jXGzQmm0wmUhSLTWR7DbZVBKn7VGvtXAW6gzEIySEyoFEeN1KLdihxWjUbZpLTY4M5Bnf2ctCKVTj3rFjIEzXDWr43kVEUAFJQpZS9EV0eoNZFG03YOB7lwm8BQLaQIAImshKL4GAldZr+KKFJidwgyqm2oeEguOXkCUdRTJoB6FvlBA+MW0CVY4SN+7CCyooRhxZMnD9AsnIw+hqnph5FxAQM+5AlTPEzYeo21BsXMMP9qLIFgnTouS1aLsVHH+ShXKbuNmm4QxRt+v0JnZi6XFkKYKuDBA3juKLJkJ4RLRdSJKGqY7SlmeQUIgZd3bua4XtFsHvBkIIXN/n9PwSMUNnX74XQ12b/0iSxKOHJ0gmfVquzf6xQXypTsMLkCU1HEsCBxBhO5GkEwgPv6NQSGh9m8bp62EpOo/nDzLdLBLXItyRCVOCAWKazmgiRcIwEAJaXkiWpQwTXVHxAj/0bxMBLc9Dk0M/N9f3aXrutmb8sZjO6FjmlsJZvpfgd3wWM4noppTo9ZAljbg+SkwbYSzxaUr2Keab32Wx+TINd4YwzWuWU8XfDsnD+CeRJY0dO3tBwLkz8/T2J7bd/s2gyBKD6QSGqnbn7bFUFN3UcB19UwuhLMnYfh31Bi1YQSDQFRlFVjbUfSUUotoOHL/AjYJveq17Mcu92P4SLW+R5dZrxPVxZElFCJ+ifZy6ew0IzZ77rPuQtzDQXttnFUVaIy3T5gFuz/0TVEkC9y1AgDIKwTL4K6AOQVADOQ7+QrivogHqOJI6CYDdcFA1BacdFiSlraprwFKjQduVWajX6I/HkSWJc4UCju9jaRq1dpt0JELcMFis15EliQdGR7sr3Yjai6nkqLtXsf0VnKCMKqzu8Sf0CXQl2T2/aXM/c81nsL1lWt4ihpKm5l4BCMdRbXzD/gkhKLdPc7b0+S5pFFH72Z36Wfqs+7DU/JZm7T7tbb9DXdbYGRsmrllbEovXw/E95mpVctbWnlO6rHeKQV7H21EHXXSPafUC0+QYu9I/Re82Juk3gh8EvL40TdNxbqajeVf4wBFHUVXnYCbPFy+/w/MLl3k4v5NHBib41NhB/tOpF/k3bz+NIBzwnxjeS58V41OjB/jG9BleWLhCw3M40jPMnlTYEhRRNQ5l8vzNtVO8Xpjmgf5xHsnvpMNpdBH+HF4Olqoz0yjz7955hqVWnUPZPLuSua6Uueu6vu6hcVfPMN+ZvcBvvPMMUVWn5btk1Si+EDw1e4EXFi4TVQ2ansP+VD85w6Jor/Y0iu62AhEw3SqgqzAe7ac/sj3LGIka7L1zbEuX/+sxd6VAuVAjlYvx+tOn0a5b7LcabZy2h+8FFObLrG/PXt23arHB/LUVKis1Wo3Qy+TCiWkg9N+5nljZe2ScvqEMrz99msAPuP+J2zl0784w+lnamri7eGIGgJFd/SRSm826ewZS9I9kWbi2wpVz85uIo1w+Rd/QZpM0M6JjWjpt2+ma8q5i9fjKyzXmOmbcrUabdssJW3JEeHxbiYR6B0OV0ob9lKXQHDsbY3muTK3UWKesEiDdut8IhFX6axcWaTXaNBttnvvqsU0m3+WVOr7n02oElJaqDO3s5b49Y6iyzMWFFSYHcmiqQsW2+e7UFDnLwheCQrOBFwRISPTFotTaDoaqoisKjh/6rfREo9w9tKYwiJo671yYRZVl7to30jVI/H5A23aJx0zyfcktDathdZwILwRZkrtmj4EUkD8QRfFDBUldqbPrcJZLlXmceIsLtRly+wxiKZ0LtRkmdvWh9QoSZhTL0GmlGsSSOoN7YzSkBvc+NsqsXUCKCFbsMocfGUBVFM57V7nzvhHikkUkoiOAH/nUkW7rzoVLi4yP5IjHtq8cmZrK/p5e4locz/NZXKpSrdsb1EvrIa9ryzB0nbF7dlDOqqTGszi+jzB1jjyyl1KtSe9kL+gKU0tFDj22j0wuyR0f2kfDdbAMnaMfOYgIBIcf3YuiK9iOS9IyOT9foHBtkZ0Hh2l7Pi4BRx87gFAk+u8Zoapv8+STpO7iQerIomvuPCCFVUAJZBRkSUORQnN6X7j4ot1ZnHghGSLCxYrfqdBJSKhyODEzlFtLDmw2HaTlGo7jsffgMPPTRQ7dNcalM/PMTa8QsQx8L6BcrNOo29x+dAeFxSqpTIyrFxcpFRvUqy1yvQn6BlLMz5bYMdnPhVNzNBttfvgn7yOejCBJITFfXKnR10ih6SrVchN5Rw/VcpPico1qucldD+7eNC4ntTQ/NPDT1L0KLb9B06/zdvlFFuxw7PX9ANf1EGLjGFP35llovoGl9TGk3t99PaYm+fjAT1JzyzT9OnbQ4ljpRebtq5vOj+f5TF1aQlXlLTzjJBQlTdu7ErbdBHuwjI3XsNNR2M1cKZDrCxcWsgS6quB6/vs5N/pgQJIQwu6qPwIELdelPx4nYRpYmobj++QTcTRZJhWJUGq1iBsGSdPECwIMNSy+JUwDRZaJmBqaplCutEinLExTJ2i2MXS1O+ZFLYNUMsLsfOihoUpRBqIPIoTXMYC9NXJOlsLinamqRDWdiKqSjVjUnDa5iIUsyUzXK/RaMaKahirLYaKaLGP7HlE5XDgpSni9RGMmyVQUTVMwIzqJlEWjZqPpYbuLqoWtw6vec5quUnI80pkY2Z44UkcBko1bPHxgB5FVZa2cRjPu7ih/5HWTHx8kAwhQlHG6PoWCzmcMJOFgqLmOV4ZAAzQ5hS9a6EoW259HkS2i2ghe0EDtJExJkoyp7dhwvgx1rUAX1Tcm3Um+wVxpmYVqX9gaK0u03Cq5mEelaSMY4kqhxHDGwNR2c7pS4+E9KfqTa9tU5LUFlrZOzaC+h2r2raJmt9EVhZQVphpfD12X6Rkq0vYb1KQWtm3Q8iuhl5bwiKlZbL9G06+Q00epuIv4wiWtD5LQem/69yVJYjiaZXidaklc9yxZVfOljI0pUdvh9t4BWp5L0tiYVLsKh2XURAFJmeADuMx6z+jPJXC80AfmVoy/JUlCk6L0RO4ibe5nJP5xLlf+gpn6k/jCxg1qXKl+hd7IUSx1kHrNJpuLc+C2IZYWqjfd/nZQZRlDU9HXrY2mz84xuCtPfrx3k0WGEAI7qBNTt1dlqKrC+EQPbtPasA4QCPygiR+02G51LkkSUXWAjHmAucYzCDyWW68zHP9o6CXXSVMTHUPkhDZBQp+44TWoSGZIrnSEgU5QxhdO6Ikk50CEqkjkLAgH5HT4GirICZAiIFLh+4Tjpef5FBaqaIZG32AaYxu7it25XMewes2GY09PTzgnaTYp2zajqRSqLOOLMOXcXEcYh+1lIxTst3D8Mq5fo00JN6iFBLY2iiqtqp0lEvpOQELgU3MuE9d3dJPDLC2PrmwkGQUei82XaHpznS2o7Ez+BGOJTxEmVG59XoNge5WlE7iUnCox9eZJygAx3WBXJkfT3bzNdtBGlVQUSenMWTUCYXcKcnSDJoTwwoAKf2s1980gIZFNWgTl93dm9IEb0WRJ4mcm7+axwTJO4NMfiSMhkWrr/M/j97LcbuC0PUZTacymSrld5+7IIGMjSVqKj9/02NXby5VTCwyMZNB1lUf0Me6ZHOH06WkmR3LYdYdHzXFyuThSAPVGi8fTu2i7LlHF4Bd23YsqybSFC0ik9Qg122GlVebu9Ci5SJSFep2m59L2PIYSCXYlc/yruz7GhSuznHvpEgeUJGpM5/WV42SFy9FmEjMVoTpXJS3g5XNvYvXG+HQwxtU3r7H79nGGokn+7dGPkzUjJDXrhgkWEE6KMr2bU6+2QmG+jO8HLE4X+S//7uvbfs60dFxn1SAsfJiuLFR46kuv8cI33qFaanRUQ2GLX6N2vWxzDZOHRvif/sVn+PPf+TZvPneWN589y+B4D4cf2sMDT9zO2O78JtKrsFBGUeTwuLaQ+eqmRqLj51KY3XwzxZKRLUxXOwtKSQoTEdbdQ0II5qaWeepLr/Hqd05T61SyFTWcPNYrzbXPsllIaMUjG1oCVxGNh/tRKzepljvbkDREsIwkGsCtx4wGfkjmAZx5c4oLx69t+9l4yuLs9CJTrzldU+TLi0V+8cP3hJN4ReFAXy9D8QSyLOP4Pp7voylK97SEyYIBQRAgAFNVNwz6gnB+XW22aTvvXU78QUQ8blIsNWi1HEhvXSnoMQZ4IPcxphpnmIjtZyAStmg2PJu61GQ8medKYyHsFxdNGlqDKgaVRoOYGqERtCi7dcai/cgWtKQWDa+Joku4eFxszxKIgLsyu2k0mlSDBmdrVxmyeghCUTRmRCNprLURrXpCOI7H5M4+MulwcbUd/EAwXakQBE3uGRwmkYhgGBrWFvdOUsvwUM8nOFF5jaHIOGkmeHN2gRXXQdJkKq0WbREwmE3y/NvXGOvNkNEiXF0pU27ZOAocm5ql5XrcPpqn5bhoikJE1zhzaYlqy+bBveMM9qaZrlSRNJnvnr5Isd7kjrEBlqoNivUmA1qCraS8USXBA7mPYcoWveYAk7G9LNtncIIGSW2YurdIXO1HIDrGznXcoEXbr6BJFiljFJcmDXcJRTaou4tE1BRpfZyCfY6Imr5l4iiZsugfSrOyXMPzfBp1m1bDQVFl6lWbyf1DFBYr9PQnWZwr43sBvhfea47rY0Y0GrWw1Xhxrkx5pc61y0tdUrBtu1Q7Y0oyHcVte1RKDYbHekgkLVRVIZWNcvyNKxCTeKl8iawR5UBqiIbrENV0dNlkPLqPlVaTpGGgyhIL9nSXOEJAPGlhmtoG0slQUphKBuu6FKZLUyVWyga37TlMvBPPu2TPbkkcybKMZemUig1c9/r0EomIto+2exFFTrNvdALHlTa0royM92C3XGrVFpP7wrSZluPSsJ3Q58nz0b9PzLEBJCw873Q48df2oisK94+PbhiPr8dIKtlt3woRLphXv8edO8LF9ug6EfFGH56OkiwIiMcjmIbaXQC+W0Q1nYPZNV+/9UmJqxiIxje8v9WCdGyiF9HlWDZ65pgdRV3vNtHdYxObt6upygaz0NAfw9o8vGyoLupbvq5IJinjYFdhd72K3PXHkFbVAV2VwrtvYdIVhcn+HDt6M0gSuF7oexYzdHwhcDyfg0P9XdX+oeF+EubN207+v0bMMFhpNBlKb/39yCiMRg8jCAtYgQg6Hl3he6qs43UisDXJIGeOdQxyNa5PsLpV3Gr67HawNA1LWxuX6o02bx6/Si4bZ3w4CxpU21PEtCE03v1980FE03YQApKxCIvFGrVGm0T01q6v1fEjbezjUO5X0eQ4l6t/0YmCn6LcPotdSfLcM2eIxUx0Q6On99aeuVvB8wM0RcZdVyTWTY2TL56jslyldyRHqnftegz3z+i2pW8FRZHp6U1sah+TJZWEsbcTKrD9uKzIEfqtB1luvYYb1Cm2T1J1puiJZGh5C6zY74TbQ6M/+iCafOMCsybHiWkjrNjHgDApzQkqmGoWtH1sWOioHcJYGWDjoLZxzHedUMk3MJrdljSCcI2gygpsQQRbySRDyRuvbyRJIqXvASQ80aTlL+H4FdyggSbHSOg7u4ogSQJLHcBUsth+gbJzjqx/J+0OmZLQJlCvO1duUKPUSbEDiOmjDEQfuqmpt+0Xur9zPWRJZqldpNfMENvG/2nDtjyX88UCMW3zedRlnZgaw5DDFD5L30/bu9r9ZhL6BIpkEIg2btCg6lxEiB/cUiV1M7TdcE70Loa3m+IDRxxBONnYnVqrJBSWqpw9OcPAUAax0MZQJF5+8TSJtEV5pUF+KI2iKrRth1bTQfS6LM2XSaYt/IhOYbrMnoPDXF66RgKdRs3GW3bwcDh7bYbSSp1Go43n+lhHdUxNY6ZaZXcuS7HZ4tzSChOZDJdKRdq+D76MCCSulEvoikJfLIYsyQxEE2TGDXaaGWRZot1yMCwDx3bZGQyQG0xT70TSy4ockjRtjyAIf9ZUleFYiheWT2EpBhOxfvoj2zPgYXrDrV1IIWEiGN2d5yd++SMbDUevw+jufPemqKzU+b1/9Te89M3j7Do0zN/77FGGd/WTSFtohsaL33iHP/r339hyO6qmcPiB3ew8MMTxVy7y2lOnePvFC3zlC9/l6S+/wWd+8ZHQw2idMiJUPEg3vClXW+CCLdQRiip3F1k3wyop9h//2Rc59dplDtw9wQ/97IP0D2eJpyxkRebbX3yNL3/+mW23se1urq9IrJuQS8ogN5KzbrmfrJks3vPYAR799JFt/66qqTgpDS1ukIuHExbX91n9umO6zr6eG1fp1hsIb4WDE3kmh3u4NFvAdr+/iCPX9TdMNraCoZh8qO9TwKc2vB7XIgyYWUAwERug7NRJ6TGGrV5UWeZ8bZohq4eoGqHPTId930YcP/BJ6TGW7DKqpLA7PowmqyT1GP2BS4+R4q7MbuJaFE/4lJwaUXXrCZuuqwwO3Lx6rCsKO7NZXC98oDSb7W3vOU3WOZp9lKPZRwE4N7dM0gr91GRJwlQVqs2wWjLak2ZmpULU0JnoyyJJEovlGm3PYzCdoDcZ441LM/hBwN7BXlqOy0A6gaGp2K6L7XjUbIdaq01fMo4sh95zQ5nktlHrqqxyKHWUQ6mjALT9Gi3fBCQMOUFU7UGTI7hBC1nSMJR4J068hSrp6EqcQHiokkmAR0TNoksWppKkzDUs9dYMYnVdZdf+ASKWQSJl4bo+O/fmadRt6tUWiZTF0lyZsV19xJMR4skIsUSEZDqKEILJfQOcOzGDEDA+2cfSXJld+wY6JI6OtdvAdX0GR7Komorr+uSHM+i6hm6qjE/24TgeqqoQjZnkdiQpSmGvvhcEXKgUKNot7uodQpElXpy7yr0DI/RENlbQXNensFSlL79RSegFTbLmHnzhbmDR6602DdvZtm1jPSQZBoYzmBF9C3VbQLX1JIY2SdN5g7iZJBG9TpFhahw8vNFLL2oaRCNhstf3S6LaKgJRRVaGwkp0935TKddbtN3QDDwViyCECFPHri1xcmqBuZUqvh8QtwzG+zMcnMgz3JNCUeQt73MhBI7rM7Nc4diFGWaWK9Rth6ip05+Jc9vEAGP5NBF9e++76+EHAaVaiwszy1ycXWG5XKdluyiKTCoeYTCXYGIgx1BPkri1Znq/uX1RUGm0uDi7wonL8yyV6nh+QDoeYedQjoM78vSmYiiyvOm5GASCatOm7XgkoiaaqnDy8hzPH5+i1XY5sKOf+w6MkYyauJ7P6SuLPH8ifG88n+Gh2yboSUW7+9Rqu9SaNqqikIpFKNaaPPXGea4slMgmLB45vJPx/gyyDAvFGs8fv8yluRWyCYu79oxwcEd/2OqyxSkUQuD5AQvFGqemFrgwW6DSsDFUhd50nIMT/UzksySinZCLLXIpWm2XatNGliRSsQiyLFGqNTl7bYkTl+YpVJsCYaqFAAAgAElEQVRIQCZhsXukh/1j/WQT0c58cHsz1iAQlOpNjp2f5czVJapNG1NXGctnOLxrkKGeVOjRed1xhe3xPqam4vrhc7Vcb2FvWXCSsAyN+DpDttXzrkgaC61LpPQ+omoKzw8o1ZqUgzqJqEFE12i7HhdmCpy4PM/McgXX84maOuP5DHfsGqQ/k9igQNkKQgjqrTZT80VOXF5gfqWK6/lkk1GO7B4in91cqNUUmUa9TbVmU6nZ9PUksDQIcL+vPNcc12dqboVas02tYZPPvjdiR5NjDMc/ymzj27S8RQJ8Wt4S+UyM+x/cQySqE4ubXUL4vSBhmWQcl5i5ZuA9fmCY7EC4nrq+uCxEQEzLktR63/N3djNSQkKiJ3IXMW2UUvsUrl9lsfkCOfMOCvZb2N4SABG1jz7r3pv+PUU2yJi3MVt/Ck80sP0Cy63XiWvjnWO+lePY+BkrZmJ0VJjbmWOvx2pa5PpiAx2/XiEEQeeZRaejR9Axy5YkEvouFEnDF20a7jS2X8QXLSJqT0dhtLaPppolqg1h+wVqzhR19yp+0EZCJq6Po0ob5zCBcHGCSvfniNKLJt/4ehXCp9g+sa23mxDQ9Nu0/a3Ns6+HIsvEdIO0sXmuLiExZ8+R0BIktSi2dxWx6kVFaIye0HewYr/dUae9wXhihYi62Wf3hseEwPX8Dc/X9wMfSOLoeoRtUIAUSuk0SQmjqDMxVpZr3cplq+mg6Sq53jhu58HkuT6llTqBF5BKxxCBwHN9iss1Rsd7eOPli+w7NEyl3CSejJCLxxiOG+zKZtEVhaFEkr09PRiqylgq1THSllBkmb25HIEQRNZVHkzLYHCib8uqmSRJxJLWDSWeuqxyMDkaMrLau2tpuhHiqSiSLGNEdG67b9cmU+ntcPn0HG989wzp3jg/908/weTto12VkAjEBtJnOyTSUe778EFuv28XV88v8OxXj/H0l9/gy59/hv6hDPc8frD72WQ2RhAEayqd6+D7AY1aGEO/qjz6u+DssSucfPUSA2M9/Pyvf5LxPQPdxDWn7d60R91uOmEb2xavu+0w9SgaW0ulEf4iyD1I78IcW5Zl4qlQXZLMxDj80J5NviXr4fo+EhKKHA7QuUR0UxzkjdByXGZWKiQtk95kbBOBdH56mcViDcf16M++9z70DyJ8P6BWsxnMp27pwbkeiqQwbK2Rclk90Z3g+yJAlzWyegIkmIyH5f6EFl7DAkFc7XzHndckSWLUCqv1o9E1Y+heI/V3fgi4gc+VUgnf1xiMJwgCgeN6t7TdgXSC6ZVyN0ms3LSxXY9aqx22EZs68YjeJXqixmqqmyBuGuzK5yjWmoz1ZijWW7RcF0vXuFood5cuuwd6qDRt+lNxbMdlqVpnZ3/ulm4ZQ4mjK+HYKSERISRArh/x1hQC4SRHl2MbFAGB8Ogx96LLtzbOqJpCX4e0S3Tu11xvAsfxKK7UkWWZXH+C/FAaWZG7n4Hwu06moxw8Ms701DK53sQGf6G+gVT3c7fdvUamHDqy1noyNBYSXAszJdK5GPmhDNfKU6Q7yrSEbpLSI93j80RAy3XhOuJIVWXaLYd6tbXh9Zo7i+PXsNSeVWU7EC5CR/IZojeoUK7C8wLmZ8uUVurddqL1WE2rEni03JPIcqyT+LQ9GnabQqXBYPbWVZzfK1D12xFBo9NiEEIIwW998TmOXZjj7r3D/LOf/EFWqk3+7DvH+NtXz3ZJJYRAUWRMXeOe/SP82o89SiaxdbRzsdrkG6+c4W9eOs1isYbtePhBECb9aQq5pMUP3jnJpx44yFDPzc9z03Z45fRVvvzcCc5dW6bZdnA9Hz8QXX8mQ1OJRXSeuHcfP/Oxu7YMWhBCcPzyHH/y1DFOXJqn0rBxvLAtXlNkrA4x8A8eO8x9B8Y3peo1bIfPf/UVXjp5hf/hE/cwkEvwr//b01xZKBIEgm+9fo7Lcyv8zEfv5tUz1/jPf/0SVxdDM23T0Dg1tcCvfPahrrLilVNX+fzXXmGoJ8mvfPYh/ujJN/jaS6dDFaWq8PrZaX75Rx6kJx3jt770HK+cukrDdtAUhSdfO8c//rFHuWvv8JaLU9vx+M5bF/jzp99mZrlC03bw/ABZktA1hWTM5MjkMD/x+J3sGsptOVa/duYav/OVF7EMjV/90YexDI3f+/qrHDs/S6Vhh0URKfQSjUZ09o/38wtP3MO+sT4kyScQoUenIq+pO4NAcPzyPH/y7bd44/w0jZaD7wfIsoSpawz1Jvnc40dIxSNdT6rrEQiB2nnv9772Cs8dn1qniFvDE/fu4+c/fjS0TCjWiUUNEvGQGK24SxiKhaUmWSzW+Od/8CSLpRo/98RR7j84zl+/eIq/fuEkK9VmGBohBLIsEzFURvsz/PADB/jo0b1dde5W19rscoU/fPINXj51tXMfeZ1rTeEvnz2OrimbvrmJwRy/9Mn70HWV/t4EybiJLVaw1Dy3Osf7XkAqHuHQrgFatovr+besNroZwrmqgSxL+EGAZYUG6lMXl9iz/715FaqyzFBm4zhlRk3MbfZZkTQkJFp+jcgtqovfCwwlQ2/kKKX2KQJcSu1T2H6Bon0SrzPGZ83biahbB4Fcj5x5B1FtsBNT7zBTf5KB6COYas97IsAkCdK98VueX7Zsl5VSA8f18PwwIczQVWzHwzJ17LZLPGpQrrXQVAVZlhgdzCJJ4bkwlBxNb5aaew3XD1sTI2o/5nWqLk1OYKkDrPAOTlCh3D5LIBxUOUpUHdxCiSMhr6M3hHARbF8QFkJQd6dZaR3bVnEkASkttFm5lVPbdF2arkNfdOv5Y5gArCCED3gY6lB3w7qSpjdylKJ9EoFHuX2WpebLjCQ+8a6/V9vxKNaajPZneL/caD9wxFGr0aZUqNE3mO76uPT0hQOAYWqkMjGctsvE7jy6oZLrTbC8WGF0Ry/xZKRb2e3pSxL4AZquctuRHUTjBrcdGUOWZQxT49CdY6QyUR75yCGSKYv8UAYhBNGYgaoo3V5sVZYxVZXADygXmmiGSqyTRqYrCqXlGkW3SU9+bTFXKtRwHY9cX3JD7DuEF2hxuUZxsQoSDO/o3cB+S0DVa3GxNseexDDj0b73hSkcmewnnoywNFPk2oUF9hweu6Xtzlxeom077Do4xM6Dwxtay4Ig4NqFhVv6+7IsE0ta7L9rBxP7B5GAr/7RCxx/5RJHHzvQ3ZfJ20f4+h+/yNXz89Srra7pNITnrjBfZv7aCrqhsmPf380AVwjB9MUlfC9gfO8AI7v6N3xfTttj+tLSDbexOFOkWmqQ6V0jUIQQLFxbobxSp2cg1U2vAwHCu6GcdSuoqsL43kE0Q+XCiWmKixXyo9urIDRFYbFcY7Fc5+BoP8em5rh3cgRVXU1RWPve1xtgr0ICCrUGZ+eX2dmXZTKf20A8qYrMwkqVuGWSit0aAfm9gtXJy1Zqtu0gBJ1ktCBM8+ik8glRIwh8ZDmBIinkjO0XXGEk6Bavv5/60nXQZIXhVIpWO5ws+H7AwlJ1g7fZdohHDD50YCe+cPCFQz490jF4FNy9cxhpi/0ezCS6VOnewTUi4N7Jke7r2XiUw+PhPZ2LW93XexNrRNqtYvUab7SdUKWhbe5rX38fbLVtWVJvuUVtO/hBwNmlZcw+i3tuG7np5yOWzuR7nCyvHkN+OEN+OEPdbaPLKr4I8ERAodUgEIKdqSzLrQaD0QSmunkqISsyuqVvaiXLmvuwvSKqvNHU2vMFF64uEY0MEbNuTFDLskQ2FyObi+G0PWzbXVdZlojoh/CDMroygiTpt5TsVKq1cL0gJC5vQfX0vQIh2vjuCWRlnEA0NpQaCpUGs4UKVxZiXFko8vvfeJ1vv34OXVNJxyPoqooXBDRabarNNvlsAmuLYoMQguVynX//Z8/y/PHLuL5PKhahLx1D11Qc16NUbzGzVOGPnnyDk1ML/NqPP8pYf3pb5VKlbvO7X32Zb756lmqzjQREIzqZhIWmKAQdVUetGSrVkjFzS8LB831eOHGF//AXzzKzXEGRJbLJKMlOKEPDdihUGrx9cY4rCyU++8ht/OTjd2KZ+ob9KVabTC+VefHEFKV6i6btMNafYbFUo1Rv8ZfPnaA3HeNvXjhFud5iPJ+hWG1SrDX51uvnObJnmI8e3YMiyzTbDvOFKrWmzX/79ls8+85lBnJJGrbDYqnGsQuz/Ol3jpFLRXnl1FVyySi5ZJTZ5QpXFkr80ZNvsG+sb4OqBsLI5P/65Bv8xTPv0LAdYhGdoZ4khq4RBAHleovlUoOvv3KGi7MFfuXvP8ThXYObFOettstcoYIsSzz15gVOTS3w9sVZEpbJUE8STVNxPY/lUoNSrcULx6dYqTT59c/9ILuH07SdU0hShIixZnB+4vI8/+K/fItrS2FrSDpmkU6EJFHTdri6UOI3/uy7PHBoozpwFbqioilK6PXR8aMTQuC6Po7nYzse7Y5yuVxvgYBKrcXlq8sM9KdIxNfIbl90CsIdZdbcSpXnj09xcmqBr798OiTnk1EsQyMQgnLdZqXS4NTUAlcXiiiKzBP37MN1wrEnGjO6Js9zK1X+7Z8+w8unrqDKMjsHcxycyBM1dZZKdd6+OMf8SjUkwRSZhGUSjeiYuoplauQyMa7NFBkbyiJHBMoNjI2/VzFfqHLqcuj7eXjPMP3ZkGSoOpcRwsPS8qhS7IbPai9oMVd/GscP7RcUKdI1N66UmzhtD0WVKa7U/385JgBV1ukzt75+308okkGf9QNMVb+CE5SoOpdYsY9R7LSpaXKCvuj96/x9boyoNshY4tOcWPkPBMKhaB/nVPE/sjv1c8S0Ya6PqF+FEAFuUKPhzqApYcsbgG5opLIxVO3WuiKaLYfFQpVaw0bTVHoyMeaXqxTLDYb6U+iaSrnWYn6pEj6TfJ/RwbB1WleSxLRhmt4s1faFjjE1pI39yPLG8VGWVJL6Luakp3GDGkX7bXzRJqL0EdNHN+2XKltY2gDYbwJQc6/QcGe3TKkTQtD05jhb+gI1d3N7/Soc4dLybaabi6S0ONZNvI40WWauVkNXFAZja0pFIQRlt4zTab+VJJlAODj+AhEtVFopks5Q7KPMN5+n3D6NL1qcKX0eTYnREzmKtk0xM1R5Odh+gZa3RFLfR9TUOkXc929u9IEjjqYvL/ONP32Fn/8nT3RJA0WVuxXXVXQTv0yNnv4k8UQoy42tJiFF1z4XT4bbMTsV0dXq7vrXdOPGSWt2y+VLv/csQ+M9fPJzP9B9/aWnTlJcqvFjv/ShLqny7NfeZn66yE/98kewYtebsMH0pSW++9VjvPXCBX79P32OyYNDGz5jyBqu8LdtFXov6B/JcOThvXzri6/xjT9+iXRPgtxAqmtQ6rk+bdvFsV2S2Vj3dSvWmaTVbOqVJmkjvAE81+fssSu8+dzZLf9e4AdUSw1UTcWM6l2JfBhTy//L3XtG2ZWd55nPPvncHKpu5SqgCjk2QufAbja7mdTMomSSEiVZ0bYsecae8dIae+aP7RlbtrxmHGRriaQsKpAUKebYgezIRqMbjUZsxAIqoNKtm9NJe36cW7cygJZEmdT3B1i37jn3xL2//X7v975oRog+62uqP3uOjjK8vZeLb0xw/Jlz3Peu/Z3vNOsOL3zzDaavzrPv7rFN7blvP0SHMVUp1mlUW2jpUKzMdTxOPPcmp1++fNM9LM6V+eF3T9E/0oXefoZK+SovfOsk9WozFANfspwWBsI4CEqGt1SJErDjwBDb9g5y+cwk3/38y3zoVx8J700bpPDdgEa9FVaZIwbPnRvnzal5Tk/M4rgeh7f1UG5ViGhxrPbCLwgkV6fySGBsMNt59v1AMpxNcdfYEHVn/cJter7Eu+/dzUy+wsRcgWT7Gv6oQI6/zfD9gG1bc6RTy46GbuDQCppsNPAKFGw1QrHa4sLkPCM9aXozccDHcS8SyDKWcRQh4vjSo+nXkYCpWGhCx5MueWeWq7U3KTl5POliqhEyRjcjke2k9S6UtoBew3Wpey5xw0BX1OWJqC1UWfGKTNavMN28RtOvYygmPdYgWyI7iOvpVf37ru8zUSyhYLA1kESjFtmMB0i8wKcZ1Dc9X0u1UYVGzV2g5s3TZe3AEBpLwtTAinOVKKhYamRD95uV24Tb+e3tgva1jW7gbubT9GsdxtDaI7QUG03RKdQa5Kt1epK3Zm5KKWn6dSYal5msX6HuV1GFRpfZx3BkjKzRu+r5lgT43lV8tY6ijiCESiADWkEDX3oIBJYaQaIwV6khgO09XatAuUAGNPxa51xV32Rhvoquq+RyqxMNVzrUvSo3mtdZaN2g6pXxpIeu6MS1JD3WEL3WIBE13rlegQwYjoaJkqGobImnqXmhpbqCwJfBhnbRnudjqgpda/Tz6u4cU/UXsJQUo4n3Lv9NtAFX/9YuTL4XcOHcNFPXFxkYyhAEAcNbuzvXTqq7UTXReT9uNU5KGZDrMkmnwwKLUP/6TlA/NiFdpPSRQR5V7d/wK4VKnT/4+su8eGqce/dt4V137WJLX5pExKLhuMzkK5y7Nsuh7QOY+vp0r+l4/OmTJ3jmxCUURfD2w9v5yNsOMNqXJRYxqDYcLk0t8GdPnuClM+McPz/Bp795jH/69x4mEVlfvXf9gM89/TpfevYUnh+QiJg8emQ79+wZYUtfhohl4Ho+s4sVLk/nuTazyD17Rta1oUopOTM+y3/98otMzpdIRC3effcu3nPPboZzKTRVYa5Q5eVz1/nC909y9cYif/rka/Rm4jxx394NW/GfP3WVnUM5fvcfPEFPJs43XjrHf/7L56nUm3z6W6+Qitr8i08+xh3b+jl2boJ/9+ffZ75Y5fj5Cd52cGwV2DNfrPHMiUv8wruO8s47dzK1UOZf/fGTnL8+x/OnrqKpCn/v0UN84MH9BIHk3/7Z0zx/apzL03mu3MhzcGz5fvpBwDdeOsvnnzlJo+Vy564hPvDgPu4Y6ycVs3E8n6s3FvnSc6d4+tWLvDkxz+9/5UX+xc8/zkjvxm3JjZbLl37wBl4Q8PAd23jivj3s3dJL1DaoNR2On5/gj75znIsT87w5McdXXzjDb37oThQlgVjh+lqtt/j0t17h2mwBIQT37Rvh4+84zI6hHIauMl+s8tKZcT773df49rFzeP5GY7Kk6XqhLpeUfPLdR3ni/j3Umy7VRovnTl3lK8+dbosuh7NOs+kSi1pk0+HYHeCTNHIYyvpn7sXT44CkvyvJJx4/wqHtA3Qno3i+z/hMgT998gRPvXqRasPh80+f5O7dI/gNj4nreca25ch2xXFcn2/+8ByvnJ9AVRQeu3Mn//CD99OVjCLasgjnrs3y/37xeU5cnCIRtfiN99/H0Z2DZBMRfC/gBjA0kAkZTUqMcjDO36iV0Y9B1BoOmUSUluvhrJApuFH7PlfLXyRl7iJjHSRpbCeqD6IrMQRKKLfQbkmaqj7JRPWbHROKbvsocSMEjsa293Di+Di+H7Cv7eQsZYAX1PBkEz9o4MkGXlDHl422ff3ScyPbOkECTdhoSgRV2GiKjSpMNCXWMTJZCiklAS5eUMWXDl5QD/cdNHCCSseRC6Dl55mtv4Sldrf3ufQbJqpibbqYXxlCCFLmLpLmduYbx2j5BaZrz1DzpgCI6SN0WYduO5cWaAzG3kW++TpT1aeQeExUvk3ZuUJ/9O1krYNYaheqMPBlCycoU3enKTuXKTkXKbbOszvz6x3gSEpJpa2feDuRTUdJpyKhZ0BYMey0oIen0CYEbM1RrTuUyo1Oa5uuxIjqQ9B4mYp7tS0MrZAyd61iCy1F0tyJKmwcv0wxOI/Ew1KzRDZgZ2kiQtY6xFT1e/iySdPPc77wB+zJ/AYRbQBFqG1R8yYLzRNcLf8F+cYJNCWClB6+XK/d6wc+TuDRb+ew1dth2wly0SiaCOUW1BX3NG2kKbmlFbmtwA9qHXFsgJg+yM7UL/L6wr+h5S9S96Y4Mf+v6YncR699f9iip0SRBHhBnaY3T9W9Tsm5QKF1FkNJcnfPv8c2DYRw/0bxhB874Ghke45P/OPHiKxRvd/sRTJMfZU48c1euL/q38IIdWaCYHVi+uC7D+J7fiim3I7Al/hewIYLLwH7jm4lm0tw+dyNdRVSRSgM2FnSRoyI+jfXlxiJWbzrY/dy+cwUz33zdRbny9zz2D6yvUk812d+qsCbJ68TS9j8yr94P3ab0rl1dz/pXIKJS7N89TPPcfihnSiqwtVz0zz5xVfQjY0V6p2Wxxf/2zPMTS2y/55t5AbSGJZOq+Fw8Y0JfvDVE2R7kuy/Z7VzQK4/xU/93P189ve+zWf/w7eYny6wdXc/vh9w9pUrPPnFV8j0JHjvJ+4jmf3rtaoJAdv2DRJLRnjz9Wt847MvcOCebQRScuHkdZ78wjGsqHnTtUsyE+V7XziG5wXsODiMlJJjT53huW+8Ts9ghgffe0eHUSaEScAIpXyVZmMBt+UxeSVkPDlNl2sXZqiVGximjhUNdVKUNuCWziV44hce5FP/+qt87Y+eZ+FGkX13jxFPRWnWWsxNLXLu1XGOPLyLx3/2HvYN95CJ2Yz1ZInZBooimapeYzCyFavtFBVISaXeolpvMTa47DhSdxwWaw1itkkqYq27v+l4hJMXp/H9gO2jCRadOeJ6CkNsILrwExZCCC5emQ3ZgAPhovtM+TjPzH0VN2it+37a6ObDg7+MZaSYL9bob7fuBbKBxEERUUS7ejTbnOJLk39IK2jwSO797Igf4PjiD3hl8fsU3Txu0CJAogoVQzHJGj3c1/U4B5P3YKo2i80Gx2emONLbz0BsNcPtcvUsT899mRvNazT8Or702oCNTa81zP1dj5PUsx0xUQmh9oSqIGUoqm23GQlTjat8ZfozNP317aIxLckT/Z9gKLINVWi0/PKGLQcFZ4EvTv4BJXeRlNHFhwd/haxx85YjiQx/e+ozNPwavdYwHxz4JeL6aqbWfGuaL07+ATWvsm4flhrl8Z6PsCtxBy3PI1+t0XsL4EjK8HefX/g2FypvUPereG3wx1QsUkYX93c9zvbYftSlxFMG+N4VPGURQ+0HVOp+la9P/zHX65eIanE+MPCL9JojpGyL+gaaHhWvyBcm/huLzhwpo4snun+RxbxHNrt8vIEMuF6/xOnSMS5Vz1B087T8Jr50CQhQUNAUHVuN0msN8VDXe9geP4AiQvHKyXqBpG7T8j3eLM6TMu3OtY5uINoIdED+Vms1aBzVe+iP3LsqqXK9UD8tapu3pS+kGxqj23voziVotbxOguoETZ6a/TJny8cxFIu3df8Uh9L332JvUPXKfHX6j5lqXCGixnh3388yFtt7y+1+IkIYaPoBpKwRBAXUJYHTFTE5X2K+WOPth7fxa++7l97M6jaD0b4sd+4KF2Brx3EpJW9cnuY7r7yJRHLnrmH+8YcfYKAruVyYM3SyiQjZRIS5YpULE/O8eGac1y9O8+CBrav2KaXk/LU5vvbSWTw/wDI0fv5dR/nI2w4Ss41V3x3pSXNk5yCNlou1gW5Sy/H48nOnuXojj6YqfOCBffz8O4+Qji9X4rf0ZejvTtKdivKv/vgpitUGf/GDN7hr9zB9G7RQ+4Hk8Tt3sGMo1Il4+I4xvvz8aa5M55krVPnowwe5e/cwhq5xaPsAu4a7mS9WmZgr0nTcVcCR5weMDXTx6OHtJGM2UdvgoYOjnL8+R6nWZNtAFx98aD+5VPgu37dvC69emKTWaDGTr3BwbPmaTc2X+PJzp6k1HcYGsvz2Tz/EjsGuDpvIMnUOjPXRm4nTcjy+d/wCZ8fnePq1i/zCu+/chPkF9ZbL4R0D/JOffoi+rkRn4WCbOo8d3UEgJb/759+nVGvy+sUp8uWdZBMOSlsvRErJiUtTnLg42b5nKf7hB+5nx1B35zcjPWn6u5IgBf/lyy/geusLTYqiYOlapwiVTUTJJpZzt4VSja+rCqzQFlxi8XbunXQJpIci1i9ZWq5HdyrKP/jA/Tx0cBRNEUiaECyyf2uaX3ninhCwm84zOV/kzetzjHWlMQy1U2gu1RocPz+B6/n0ZuJ89JGD9KSXmTOqAnu39PK++/dy+uoMtYZDoVJnKJcGJIVSnXQyQqUatlKahoon6wTy5nqJP2kx0pvmxIUpbEOnJ7s81kgCmt480940s/UX0JQYmhLFUGKowibAx/XLuEGFll/ouIfF9a2MJj+KoYRzfK3aotV0qdVbzM+VGRjK4AQlTuf/I/nmG0jpIfEJpI/Ex5ctArmkOSO5XPpzxst/iUANC26oCKES0fq5o/ufdwCSlZFvnOCN/O8SSKezXyl9AunhBss5Rtm5whsLv4siNERn3xqqMBiIPsruzK/f1jXUFJse+z7mG8eBgBu1ZzvnkIvcg6HevkuxEAJTTbIr/St4QYPZ+ovt1qazlJ3LGEoCVZihuGD7nELgrYbED7sf5PKatqs3GRoMrRmvb/b76trvbagxK0jELBKxla6FKjF9GFWYnZzCVNNEtLUC3mFE9UFMNYUTFPDazMOYMYKyAbNPCIUe+x4y1n7mG68Aktn6i1TdcWL6Vkw1jRfUqXs3qLmTOEEJQ0mwPfVJZusvstA8vm6fmqKhCAVd2dyVbWX4MsALAgqtBvlGnVw01rlmETXC9th2dEVHyhZSeqzVvhVCoTfyINtT01wqfpamP0/Lz3O98g1mas+23TlDh0+Jhy9bbUA1vJYZ80CoXeiFuq1vpYviVvFjAxw16w7f+YtXuHRmEjti8kv/7D2dBffVN2/w4vdO0zuY4c2TE0gJ9z22l/13jaLpKk7L49yJcY49c45apYVp6bzzo3exdWcvxYUq3/rcyxy4Z4yTL11i/kaJvUe38MgTh1BUwfS1PM989QT52TLJTJRHnjjEyI6wejkzsciTf3mc/GyZ/pHsKoetwkKFr/6N3TAAACAASURBVH/2JSbH59m+d4AP/dJDiBUvTKVU5xt/8hJT1/J09SZ5x4eO0Nu2idd0FStirKv2To3P8/U/eYkP/dJDdLc1Vs6fvM4PnzzLz/z6I+ssJN9KCCHYfmCY3/y/P8rn//OTnHst1Pbx3LDn3bSMUIvoXQdQVrQlDW/v4WO/9U7+4vef5suf+gHf/JMX0XQVVVO54/4dPPCeA/zH/+1z635PUQSqpnDm+FVefuosUkpUTSHwJbqu0juc5SO/8SgH7t2+ajtVU3n0w3cSTdh84fef5s//0/dCYW9C4eehbTl+9jcf49CDO29bGPzm12SIj/6DR/nWn77E5/7T9/jyp36AqirohsZDTxxi+/4h/r/f+cKG2+umxjt/9l6icYtv/PELfOUPf4Dr+ggFeoeyfPy338XuNS2B+dkS//VffpHp8Xlcx6NRa1EtN6hXmvyH/+VPMCwd3dDZurufX/s/P0C6O9G5ng+85yCRmMVXPv0Djj11lu9/5bWO1oBpG6S64rztfYfRVJUtuQxBIMlX69Qdl2ErSsrIYijLYGQQSDRVkIyvRs8jpoEb+CxUaiQj1rohPJOIMNKXpuV4NJUyBaeCrUYxlJ984CgaMejOxvG8oKNxFMgAJ2hS92r40sOXHm57og9kgC99FEWsadGSCAxC2+XwffKky4Jzg5pX5Xz5BFdq53h18VkkkpiWIKGH7/ySRfpk4wpfm/5jal6Fh7rfS1Q3qLsu2goGTiB9zpdP8uXpT7PohG2VETVGRI0hhELLb3CtfoGZyQnuyjzcAT4c3+dGpULKDFvrTEOjUXZCViABTtCi7lfxpY8XuHjSRSJp+g2coH3uBDT8AgHhpNSoNtu6YCYpuwtLjXKxepqF1izjtTfb7KnN31kvcDlXPsG1+iVUoTJm7qcx6+LpJdI9y+CRlEvHF94PT3od152IGqMVhNo8uqowW6oylssSNQ1qroMnAxL68jsgpWSycZUvTPw+N5rXkUgsJUJCT6MKDTdokW/N8PXpz3I08/BKPycUtR9N38GS4E8gfQpunrnWNHE/iRuELjQtz8fQlqjCy29TIH3yzhzzrWl86SMUSancQNNV+lfoG/0w/xTHCz8AJLowOu+aIhQ86VH3qpTcRUruIrPNSX566NfYHtuPE3hENZMeO4EqBIaq0mgv7IK2cOWm6Y9knWNmIH0ESvhv+/pV6y2uTCyQy8Zvi0QppUTXNQIp6e1PkWzrPBmKRb89zIv57+AELV4vvsjuxCEs9eaU/anGOG9WXqfuVxmL7aXb3JiZ8xMZ0sf3riCEjpS1Db/i+QG7hjP8/ffe3QFLLlyaIb9YY2xrNydPT5BJRTl8x3o6f73l8uSrF5kvVknFbD76yMFVoNFSCCEY68/y6OHtjN9YpFBp8OLpq9y5a2iV413T8Xjy1QvMLJZRFcHbD2/jZx65Y1Xr2MpQFYWYvX7OkFJydabA86eu4geSbQMZfubtd6wCjZbC0FQe2L+Vh+8Y42svnuHCxDwvnRnnfffvW8disk2dfaPLxh+5dIyedIwr03litsG+0b4O+GkZGgNtLad8ub6hYcLOoW6S7VZtVVHYPbLsHrd9sItcahl4GOxOYukaxVozbMdqhx9IXjg9ztUbi6iKwvsf2MfOFcDMynuQS8f4qXv38MLpcepNhxdOjfOhh/Z3jmFt6JrKxx870jmPlaGqCvfuHWEol6J0dYaphRKlqkcm3iJoLzzqTZcXTl2l2nAwdZV33rWT7YOrj00IgaGpPHp0O8++cYVj59Y7vtYdB1UR2BtoWG0UAohGDS5eqTEyFJpNKKi4gYMfrBecVoTgHUd2cN++kLkmpQR/DuldROg72NI7yNGdg4zPLFJptJiYKzIQi+E6fqdNrdZwuDYbtuINdqcYyq3XEVTVsH0tFbOYL9Z448oNHM9DV1XKlSaLpRrlSpPB/jRLWdNf1fXtxzXmi1UK5XAdVCg36M1qbfAig6llaXpz+LKF77do+Xk2HrVCR7C0uY/d6V8lYx3oaNRMTS4SjZlIoNqefwLpUXWvU3XHb3l8XlDFY32Lmx808YONHaCdoEzZuXTLfUs8nKC4wV8U0uaeW26/HIKeyH1cLv8ZDW8WX4bjgaGk6bXvR3nLSjSCuD7Koe7fYbz8Za5VvhaKjssWTX9+061UYRHVh1bpKamaSjT+tyM/IRAkjDE0JYrvh/fG1vqIaL0bAjO6EidhbKPiXu18ljJ2b/qO2Vov+7K/zamFf89i6wyBbFF1J6i6E2uOQyNp7GB76ufoj74dT9ZZaL624fEqLMlQ3Fr/NHSeU/Dlem68IhRMNZz7AjRMbQApnTVjW6j9NZb4WRLGNi4W/weF5ik8WccJSqvEv1cfp4qhpkiaO9FUnZEei5brYWzAOP6rxo8NcGSYGve8fTeGofHUV17FXzFRV0p1vv+11zl4zzYO3jPG1PgCn/9vz9Ddn2JgSxevv3iRv/z0c9z1yG4O3J2lmK91WtBaTZdXn7vA/I0ie49uYXhbD4l0FEVVyM+W+LP/8hSDW7u559HdvPnGJH/6n5/kN/7l+4klbL706WepV1rc+449XL80y6UzU+w+FCZgsYTNw++7gy996lmuX55bxxy6fHaK0V39HH5gOy9+9zR/+aln+eV//lPrWrNWRjRmMTtV4I2Xr/D29x/CdTyOPXMOiVylLWSYOu/9xP00Gw49g7d2UFqKIAi1fH7r//kZrpydZvzNG1RLof18Opdgy84+BkdznbY9CAVfH/nAEUb3DHD2+BVq5SZWxGDLzj62HxhCAh/7rcfJrGlr0E2Nj/z62zny8G4mLs1SKdTCFgjLoLs/xY6Dw+QGMhs6oBmWzgPvuYNt+4c4/9o4CzNhz37PUIZdh0bo6k0h1oBuuYEMH/utd5IbSHfu/cpIZGO87xcfRAZylai2bmq87xceZP/dY5w/cY1GrYUdMxnbM8D2A0M06g4f+63HGd0zsKrNZGC0m4//9ru485HdDI7m2HvXKJdPT1IrN8jkEmw9MEh6OEWLAOEFYYuRblJXPHbes5XsthBEtFSdktMkaVg0fRcpwVQ1jLQJusLZwhxD0WRHkO3Qwzvp2tXF9VPTjF+ZoVFrkUsmyOYSjO4ZoH9rN6qmcCNf4qU3rzGSS5OwLQLskNmgZ9t0SIGqCjRVZXK2yL4V1HkZyBAUUr21a10ArkznuXvvCKVqk0KziabP0W313d5D+GMejuvTaLqkVggX74gfIGPkqPtVWn6DvDPHCwvfouItD9xNx6MrFe24tog2+yrUaFl9ESUBZ8uvIoG4nuSuzCPsiB8krqUIpM908xqvLj7L2fKrNPwax/JPsydxBFt0EdH1VZTTfGuWZ+a/wqIzhypUtkR2cn/XOxmwt6IIlYIzz9nyq7xaeI4X89/FC0LgQFMUDKF1qkW1egvHCcfcXmuYjw7+OjW/QitoUHTy/HDxKRZaN1ZcqTCZV4UettMguXF1jvHz0+y9e4y+LTn2JY9ytnwcX/qcLb3K/uRdN2WlVbwSb1ZOApKIGmeLsofpS7OkcslVwFHW7OUjg79K3a/S9OuU3QKvFp5jsnFl1f5UoTCQTqIoAjcIOF+cI6IZxJJdnfNuBnV+MP81ptvW8f3WFh7sfg9DkTEsxaLsFrlaP88LC9/hWP7pVcKJUlaQchHIARuzbYSAmGlQbd2GE4cImV8hW3Vpe8He5BGu1s7Rb29hLLaHXmuIlJ5BV0wafp2J+iWeX/g2041xim6elxa+x6C9FQWduWYZW9XpNhNENQMnCO9xRNOZrVfpiWzMxlI1hegaQNkNKrSC4ioibTJms3WwC11TWShUGexN35QSHfgBpWKdRn319VCEwmhsD11GH9PNccZrF5hv3WDQHt00QQtkwPnKCZp+A4FgT+IwkR+hsOnfeggLRYkTBGWEsrm76v37t9KTXr6P8/kq1ycWaTRd/EAyn69umOgWKnVOXppGSujNxNk9srmeoqIojA1kiVg6TtXn8lTorrQSOFpirkgJ8ajFY0d3rPr7W4nz12ZZbC9QD471k0ttzi42dI17923he8cvtNuwJnnH0R3rWulitrEKqNLa4toQ6ralYsvsWkVRiLRF/R3Xw19TrdVUhXTcXlX8i1qhIYAfBOsAONs0QofIQHbEvYUQOK7HsbPXCaQkGTE5tG1g03sghGCgO0E6blNvOtxYLDNfrG0KHA3nUuwY3FwL0dQ1erMJTl+dwfUDqk2H8OUOx59qs8XZ8dn2uZkc3Tm0qRtvOmazb2vvhsCRIgSZiE137DYZ4qLNZFDD6yVlyDiSSBDLpgZLEY+YHN4xuKIVMwDZRCgJEF2oikJfNoGhqaFrZ6NFrdai2XA7Y5kXBLTarFDb1DYdw3RN7QCSLScUBDY0lVxXnFTCZqFQJWIbaEKnyzqA8XdpPCIE2BJRk0bL4+p0nohlkIxZDMXeRVTrJ998nZJzgYY3R8sv4AUNJB4CFU2JYKldJIwxuu076bKPYqlZVgobDw5nkYHk3OmpjnmEpkQYib+fbvuudccT+AGO46MbakdeY6PQlRimusyq94KAmUYZTSjMNRPEjZ8BQtOQAIlKaIAU00wavksr8DAVjYJTp8eKEyBxfB9P+jh+gKbc8Zauo632M2r/Mq4yhxA+frCIrQ0S0/sIZAXfr6IIu81aDx1il3NIQSDrqCKGooTXKFxL5NiR/gVykXuZq79EoXWaqjuB45dDhpfUkZ5Jwh4gZY2SNLbTZd8JIsNkfQFNKLjSxwt8DCV8l0zVYK65iKHfSzTowVR04sYO5pplEHWcwEVrt+mrQqEZuERVi1779talSWMHO1K/0GF2RfXBTRlXuhJlS+IDnbZGgJ7IPazKraVczk8EJPWdHOr+l0xVnma+eYyKM44X1ACBocWJaH10WYcYiL2DuLEVgUZ/5BFUYSBQSRhjnX1bqkmXmepcGwBFGJ1tAaLaQMddL5CSwXiS3miMqLG53plAI2rsbY9r6/NIVTHose8haWxntv4C+eZJSs4Fmt48vmyGMgeKjalmiGoDJI1tZKwDpM29qMImZosNCzR/nfixAY4UVaFnMMPgaPeGA4AVMXj0g4fZdXCY+RslTh27wo3reXoHM/zwqbNs2dnLE5+4D01X1wn+SiR7jmzh7e8/3PlMCMGZV8eZuZ7nrrftQjd0+keyvPL9c1y7OEtuIMX1S7N87B+9g/13jrLrjmHOnVieFHVDY2g0R1dPkvnp9Sj00GiOxz58hHRXnGjM4vP//fsszJToG86u++5SJDJR9hzewskfXuKBd+2jUmzw5skJfvpXH0bV1c7y07R0furnH3jL1/jaRJ6IbdDXm+LAvds4cO+2W24jRKhDtG3fINv2DW74nSc++eCG28WSEfbfPcb+u8c22GrzcNoT8sDWbga23tp+UAhBz2CGn/tf373pd1LZGB/8+w9vuK1pG+w6vIVdh7es+7sVMfngLz+8bpvB0Rw/8w/f0flsz5Gt7DmyPKBdLuV5LT9Nsm3FaCgqpqpxujbL8BPbSDstVCEYS2Y5NjfB4e4BxssFtiXD5+OHs9dpGZLzN+bosaOUnBYNz0UVghlRwzqUoW9vBBC8fWCsI+a+FFJKbEOnP50gYuoIJDWvGopLth8kKSWGrhFfw2RruB4zpQqFWoM9AznWIkc9mRhPv3oRAezbnaRAgLYBhfwnMVzXx3X9VWKtMS1BTFtufci3ZjlReG4VcGQZGoVKg1zKgZRAoAIBQVDe8HeaQYOUnuU9fR9jb+JOap6DLwNSRpSMkaPPGqbgLjDVuMqCM8Nk4wrbIhkkEq9NLZZScrL0QybrIWAyHNnO+wY+Sb810hnn0kYXA/YWEnqab974M4L2okARgpRloci281nUJNHWlLNUm+Ho8thQcYucr5xYBRxJwkpgTOsNqduEDLZUV5yuvjBpGInsoMvoY7Y1yUTjMvOtaQbs9e02S3GtdoH51nR72230GoOMl2ZI96x2uDMUk6HI8phS96pcr19aBxwpiiBuGe1JW1J2mpSdJtvb75iUkivVc1yovAFAUs/yRP8nGIvt7TCjUkYXA5GtJLQ0X576NDV/ibouEMLG966jattgkyqh6/ss1huk7PUtn+tC0lksdeYpBDti+/nFLf+MhJ7GVqOr9pPUM+TMfhJ6mi9M/HeK7gKTjSssOvMktP5QHynwQwZhs05cX6pyQcPzKDlN+li9oGs1XfKX5xhaM/b6shWCheryQlVRBE3H5erkAsm4TS4Tx7oJWKCoCj19SUZGu1HXFA0SWppdiTuYbo5T88ucr7zOoL25YGnZLXC19maof6JnGYvuXW4l/DsQQgh87xoQIDbRz1AVwfY2cLcUPd0Jpm8UWchX0DS17Xa3/tmbK1SZLYTVeU1VOXXlxk2BnutzRZbwk8VKfZXGCYQtRzcWw/ejKxldx0653ZDAmxPLlfKdw7lb7me07epXazpcml4IF7hrgKMQ2FnNltFVBSFCJ6CVrm5C0AGFAsk6mv8S6LQyn1SU0MnUD1jnOBU6nIb/Xyl3UK41mZgP80fL0Lg8tUCpttrNcGUUKo3OsVQbobj4ZtGXTdzU+UqI0DQAABkWTTRtoN2qJihWGswXQ85IKma1tfs2Dk1V6G+DM84adpahaixU68Qti8HU7buw9uQSmO0ipiZMal6BhL4eCIvaBsM9axlCbamI9memvgwGOa6Pooqwe6e9ytQ1lZhtUq63KFQa684B2vov9SaNdgtvzDYxtFBrMF+ocfHKLKVKg2TcRuhN8s3TGJEkhvo35WX0Pz+6UlFmFysYuooiwrE/iYWmROi276bLvhM3qOAHjbCNDK/dCqWgCA1VsdrtU/aG77Tv+aSzMd726O5OsXwJMNgoLl6Y4XOfeY6PfPQu9m/AqtwsJJKL5XlsVUdT+tC0D6MrKn2GTc0NDTUyZoSFZo1Sq8RYIku3FeNiaZ6BaIrZZgVdhYJTpyti479FIfQL5wp8/s/h5z75Ica2Jag7JwEPz79CrXkZVUmhKiGAoipJWu5FABQlSiBb6GoPhjaCwO6wVIQQqJhkrQOkzT24QRm3rd2EDLh+rcAffepFPvyhB9l3dB+qCJnXFysh2F/x6rQCF8f3iOkWLd8jZyUpez4x/QG22Y9S91q0Apez5Sl2J4Zp+g4Nv8V0YxFNqMR1m7gWoddO4/g+5WaLTMTeFIi1tCzbU59Yf3+kxAuCVaY8itDpidxHT+S+Dffl+j6aolDOVyjMldBNHSHAsOOYhUfoE0eJ1/KYsVBrTTQ1RGAxMDJG1Fhuu0xbe0hb6xlkZbeKG3j4MuhMp6piMhh7fMPjUYTgSnERXwbsym6+jg1/V7spYVsIBVvrZiT+fgZjj+P4JTzZaDuyheLhSzpe+lIL248wfmJWe4lUhGyb1aIbKrqh4jRdmg2HcrHGwV3bOgPN2gHJjhj0DWfXtTbNTi5Sq7Y49cqVjoPb4ft3kMxEqZYaBF5ANhdWjqyISbbn9ie9dFccs51YdPen8H2fWmXzhADCKtddj+zmxAsXuX5pjqmrC1i2QWo0ybVqnohmhOgu4UulCoGmKDiBjwQc30MVCqpQCGRo4+oGAb12HN8JiEbCxaEfBFSrLVotd1kE2zao1R08zycaNYlGTOr1Fs2Wh+t6ZDOxUA2+WEdRFVLJCI2Gg20bKIqgWmsRsQ3q9Rb1hkM0amLoGtVaqAsjA0ksZqKqCsV2y186FaXleLRaLo7jEY/b6LrKy69codlyOXRwhK7szR0a/qpxcTFPTzRKwrT44eQEi406DwxvIWEugyhXiwUShkk2cnsOByvDCXxqnkvKtInoOv2RBIutetv2W6XqtogbJrP1KsVWEyklPZEYfZE4x+enqLoOmqKQMi1avk/Dc5mpV+iPJHADH0vT6LKiqCIUuV2LVCejNomIxbX5AsmoTSaVxlAMZptTJPQUKhq+LylVG+3kapkVIwT0JuPELHMdswugL5ukN5tgYrZIxNJZ8PxNhIp/8kJVlbZt9FsT2ZUSsqusroO20OjmtN8j6YfYmzgKCK7UZvACn/2pLdiqQZfZy57EEaYb40gk041xRu3DQFgtA6j5Fc6XX8eVDrowuDf7GH3W8Lr3xVRtjqQf5GTxJa7VwwTE8X0mS2UGYlkQUCzVaTRchvo3dkvaKAJ8yu40GXNrKIKtKriOh95efKb1LnbGDzDfukHJzfNm+SS91vCGi3s3cDhTPk7Tr6MJnX3JO7HdKKqurmKfvpXwgoAz03OYukZfOk5Ct2i1x0oIdXXOlU+0hbBV7kjdw2hs97p2OlWo7Ekc5nzldV4tPNv5XAgLRYmytjd9ZUgJpXoTW9duSW92PZ943MK29VXfNVWbXnto0+0UoTBkjzEa28VrhedpBS3yzhw95gjbEj0ohElMwjAxlPYcCahCsLJAtxSRqEnf7v5Vun0Ajl+l7s0jhEra2Lasg2Pq6LpKX3dyU6vrpQgCyaXzN9B0ldHtPURXah4g2Je8k2OLT1P1ypwpHeeezDvWaVxB2K442bjMbHMSEGyJbKfXGvyRzBX/U0NoIJ1NNXZNQyNmr9ZCTCVt4jErFKeOWdyxf4iNLsvMYgXPD9+tU1du8M/+69dv+7DqLbczDi3FfLHaceLr70pg6tqGgulLsw3Ako3jEvActFvaZwvL2iK9mVvnXbapk4lHmCtUqdZbFKuNdTpHuqZuuoBRFWVda9tyJi9ZewOUNui0PkJmgHETva+V5PSFcp16MwQiZgtV/q/PfHfT7daG4/o43nrttKVIRK1VYNhGsfpyCDxvCk3tA1W23QrD+5lNRm/a6iCEIBmzMHVtFegipcTxPaoth9ZNjnVtNJsujUYo6BrqqKib6gUZmrbK3VUgkCKK9K8hpFyHmUopURSFzIrcMhWz2T/ax3S+zMRckZfOjPPuu3ehrVi4Vuotnj91lVKtiWVo3LGtvwPYJuMWXdkYw4MZbEvHDRYASXAbrpA/SdFyPepNFyFg32gf2WSUesPpmLQEgQzBDd9G18K2QVUJxYGFBN+VeIrACcJ9KELBMJaNPgqLNUrFOr19KSJRk3ji5m1TzabLxPU8tdp6/cmbhQDG4l1YqoaparR8j0BKLE3H8T0MJfzcUDT6IglUoZA0bHYmc+iqiqFqaELBl2ki2lt3zwuCAM9dYh5aWHoo2SHx0JRuhDBC/SGhARJFRNq6SiaBrKMIG0XEQMLly3MUFqscOrKls54LHWEzq1zEYto8JtcwlGwHNALoszLU/CYZMxaCNYqKIpTQPEMopI04CgJbM3F8l6rXoN/OYigacT2LG3j0WVl0RaUVuETaLViVZov5Wo2EaVJxWrh+QKTtbhvVdSotB1VVqDsOMcNEIqk6DlFdp+a4vDEzw52Dg/gyaD9XAlvTqTpOKLSPpOF6mJoKCI5NTHKov4/qXInxU9dJ96SoFqsoqkI0GSU/XSKaTBIRCeanFmnWWpiWQzbjEW1j4k9/7kUSmRhHHzuw7p71290M2Dk05fZAmUBKIrqBHyzLXtRKdb71qWd4/OcfIpG9NRtxZnyeF77yCu/+pUeIxEOwVRMRNCXCq0++wfHvvoEds/j473ywg2H8bcRPDHC0ZHO9NjRdRdc1yoVa5+ass+RtV4PWRjRuk+tL8dFfe4TEEhVahGyiictzIAT1arioD/yAVvP2J4FG3cH3w4GhUXdQFAX9Nnq8c31Jtuzo5bXnL5KfKzG2p58pynjVAB9JIAOKTiMEHnSLXjtBxW2CgLQRYb5ZoeyGFrjdVoyUESFjRigu1vnKN05w99Gt7NzRx3eePE253MAPAixDZ2wsx4mT1+jrTeH7Ae9+bD9PPnOWVsvFsgweuG87p05PMDdfIQgk+/YOMjG5yLaxHNlMjKeeOcs9d4/xyqtXCYIAKWF4MMP5CzPU6iGotH1bD5qqMDlVoNly2bdngBszJWbnShiGRjRicu/dY7x28hq+F2AYGg/cuwNN23wx0PQ8zi/MU3MdTFUjF40xXizQG4uRMC3OLczhB5KeWIzFRp2kaTGSSjFZLpG1beZqNZ66eplDvX1MVcpIGafqOCDgeqnIjmwXk+UyV4uL6IrK9kyWC4sLFJpN7uofpGsTUCmi6Rzq7qcvEkcRoj3xWPRFErR8L9xXKmyZ2ZftwVSXKmsKR3OD3NHVh63p3Ns7gioUuqwIWxMZdEVhOJ5CVxSWMiJ9A60nTVEwdY2Fcg1L11CFSsboxlajne00NZzQnTXCvXOlKoV6k8Vag72DOYSirFqYvHF5GkWIEHTSo+T6+tH/jljP+n6Apip43lt3Z+pOxYjZIbtFSp8gqLSBhfVhKTZjsb1oQqfqNdGFSkQ3MZXlMSJn9oVJMJK6VyWQAb3ReGfxX3Ly5J2wlSChpxm0RzfVEIqocQYjY0zUL4eiyiIUNZSEBUFdV2m2bn98E4i23bBsC/QJUl1xUt3Lk6EiVHbED/Ja8QWqXolL1TMczbyNhL6axiylZNGZ53r9EhJJ2uhiS3QnQV5iRQzi6ehfCRBwvQBVUag0WwyIBGnLpthqdip0db/GVCPsl7eUCKPRPaibMOcMxWI4so1TpZdxghYQEPhzoA3c8jiipoF7G45jighbR9caQ9xOaIpOQguvq5QBjt/ACXyavoMqlHChq6g4QahPJBD0RGMMxpMoa1ZWvh/gOB5asPpZShpb8II6EX21i0mr5ZFJRsllbg3yq6pC/1CGYqG+bk4WQpAz+xmObOds+VXmWze4Vr/I3sSRdfv1pMeFyht40kUTOnsSR9CUvzuV/aVQlCyqvgfB5jpBa7USx6/nyXXH6etNYVt6h0m4NupNp5MrKZvkSJuFpiihKOcKgLPedAna+zM1lbnpIotC4LSLT4ahYUfM0MG15WIYWuc3K+VQ4yKdjTIwnKW+gkljmbdOU1VFYLVBSz+Q1DcYPlN4rwAAIABJREFUy8RSv/cm8ZZGmDagcZM/31Y0Wm6nSCHgpu02a0PTlnOAjUJXN86ZNw+B5892ih1Nd8X91Ddv31oKQ1M3lB4oNVrELZP07bAul45dVymW6h12lUSiCI2KmydtrG6LVxSBvgrkFu0Ft77h5RFCYJgaxYVqBw+MR0zecXQ7x85fp1hp8JlvvUKj5bJ/tA/L0ClWGzx38gpfe/EsUsKu4RwPHlhmQzquj6lrjE/k6UrHMM0Yltr9I6/8/21HudZqt6q5od5ZsYYqBC3Hw3F9avUWybhNo+mSTUUplOrEYiau66NpCs2mR6XWRBAWqwf70/R0LwO88YTN+bNTTE8VGdnaxZ5Nuhz+uqEpKsOx5Txk1ZpRXyYfDEZXFy0idlvI31ze5q+Sm+zZM8A//z/eRyRiIoSCtqKNbrkGvAJiV9cyVsLfdF2PYy9fotlwOXhoBPUmj9vwSJb//XeewF4j5RHVLKLaStHqdqfOmnW0EAJL0Unot1dIv14scW5unkzE5mtnzxM3TRQhsHWdQ/19vHTtOjHTpNJySFomgZQUGg0OD/SzUKvz6uQ0UcPgSn6RQqNJRNcZSiUpNcMczgn80EVaUdmSTnF8coq0bbN3NEf/aA+KqnSKjooi8L0AVVMRAkb2DCIlKKpAXQGIXzs7SffAxm3huvLW4JKIppOxbdLm8rV1Wy7njl3i4Y/ec1v7qJXqnH/lMu/4+IOwBmfaeXSMZs3hqT99jiCQm4gl/GjixwY48r2ARq1FrdzAc33KxTpCER2B7M3CtHR2Hx7hxe+d4dSxK+T6UzTqDqlsjFT25m46+45u5blvvcGJFy9x8O4xPM+nVm4yuruPXH+KRCrCS0+ewY6YTI3PM35hhh37B0ManevTrDs0Gw5Oy6VSrGNHTYy2M9HV89OceWWc4e05Xn7qLKlsjO6+JDKQNOotKsU6nhdQLTeolRuYEQNNUzFtgzsf3sX/+L3vYFo67/inR+hNh3bFYfd52How16jQF0miK2F/qa6oCARukEMCigCtPWlZqs7gQJqxrd34wZKwF2wby7FYCKnI+cUqPbkk73vvHXz2z15ifqFCs+lyx8Fhdu3opVZ3uHRlnp/+4FHmFiqcOTvF2GiOc+en6e1JkkhYzM+Xee3ENbaN5Wg2XRIxi8GBNJNTBfbu7ufaRJ7LV+aIx20EMDdfoVxusGdXP1u3dPO1b75OLGqxb88Auqbxtgd33vK5qbkOb+YXWKjXieo6R/o16p7Dy1OTdEUiTJbLSCRz9SrdkSjn8/OkbZupSoWtqTTdkShJy2IklabpubwxN0PL89jV1c18vU7WrrPYaDBfr2GoKoVmg2ulEl7gc6hnc12fgaUJJwgrLS3HxW25aIZG3DDYEc8iXIlQwFRUxNK6UgVL1fCFggzC/3dadNpf0W8D8V4o15heLOP5PjcKFbwgR92r0mMNdsCFjkj2mgliS3eGWssNNQECh1KrTM7KdgZOQ1OpNR32jfXhSoeItt4V5yc1FEXgeT6W9dYWoaqqMD6zyI7BcIIXQkPKJlKabJS9xvQUaSOk3S+xBE1FX/XN0Hq4nSBIh6bvdQT3AMpekaoXtsLFtGRnfxuFEIJecxCl3Y8etKnAgjBB0FSV7tuogKw6Z6GTNreiivC4C3MlmjWHbG/YNiCEYDAyyqA9yvnKCSYbV5huXFsHHIHkau08BSdsTxmN7iFj5FjwCyzOlOge3Ly992YhkXTHIwxlU0ig7LQotFl/AA2/SsEN7XYtNUKPtXmSKoSg2+zHVOw2cCQQSgpVG+NW0+hQOnFbfDxFVajV6ggFBgc3Tl586dHwazT9RigWKz2Ctph5xSt2zjsgQAjoj6TIGFECCSnT6rzrvgxwg2BDZp1uaGRzidA4YUW0/BJVbwaEIGGE7jS+H5BORphZKON6PreS1QyCgLnZMqVCncQG7i2mYrMveSdXqudoBnXOlo8zFtvdBryXo+gscKl6FoCcNRACjUHQee7+roQMqritH6CqI2jG7elomKbGmXPTLBZq9PUkSSU3SfRXXKaHDo7y8ccOYxoahZkStXI9zGVk2Lq4xAzSdDU0xxCCoFinpGmk2gu/lZfd8wKKxRrCCyltlXIDIQSjO3ooFWt4XoBp6qFTrR/geT4ykGSyMRBiVauwDG799kiW28mEYL3Tz99w/Cj2PjbQxW9++AHScQs/qLe1MpR28cAnkC4SH01EQ36wkAz3pAlkqCGz7rkXb+04pWyhql0s6fOtcsy7je2XAOm1n2WjES4v5PHegqtPEEhKlUZnvBJCQVdMLDW6Tgw3hAPX0oqaIBtsdgXWfqoIwX37tvCL776Tz3z7ONdmCvzHLzxLOh5BUxUajkuxEj7Dh3cM8I8++ACDuWVQwXE9Ll2bJ9Vm9Lf8ImXnKnFjvYvXT3J0JaMslmpYhobr+1TrrY4WGFKSiNlEIyaWqeNLiecHuK5Pve6QiFukUxFq9RbRiEk0YpBdU2ywbYOHH92LogoW5te7ptbrLWZmSjQbDslUdENGo5SSVtNlerpIo+FgWTp9/Wlse3WeGgQB+YUqi4vV9nikkU5HSaejHaOjJSJCrdZidqZEsxl2acQTNl1dMcw2u7pRd5ieLtA/kMZ1fGZminieTyIZoa8v1QGEF/NVbtwoEgQBqqoysqULTVudI87NlanXW/T2ppi5UaRWa5FM2vT0JDsauUEQMD9X4cZ0kePHrtDVHefMqQlUTcE0dUbHcsvC77UW168t4LVBlOGRrlXFhJVmOcVCjfn5Cr4fkEjY9PQm0Vfo605NhgLy2a4Y01MFmk2XSMSkrz+JtcYEYSAZ5+piIdRvsywe3DrC9y5cxtY1Xrx2nbRtc2pmlkzExtY1xrIZCo0GTddjOJVkV66bHV1Z8rU6Qgg8P+D0zCwJyyKih21odw0PcWLqBinbYkd3Fwf6ejA1rXM+cxMLJDIx4ukYrYbDjatz9I/mKM5XEALKi1VkIOkbzRFNRhBAs97i0slxkISfJyI4LZf5yTyRmMXc9TyaoTG0sx/d1KiV6kxfng1z7K3dxNMxAj+gMlNCvVGmLCrYI5J0bjUIGQQBUxdniKYipLoTFGZLLEwXCDyf7qEsmd6lQqBk9to805dnyPan6erPoKgKsVSU7sHMOqaR53jMjM9TKVRJdSfJDWf/xtlIPxbAkZSSyWvzfOkPn6WYr1KrtfjM732b7oE0H/zkA2i6SiITJSB0ERFCkMrGMNsDwZJ20V9++rmQ1qopvOfn72VP2gpfpJRBDadDvVuKobFuPvlP3sm3Pvcyx545h6IIhrf1MLKjh0jM4sO//Da+8kfP84f/9hsMbOli/52jRGIWUsIbL1/h2W+eZPraAq7j8Yf/7pvsOzrK237qINGExeEHd/DGsct894uvYEdNPvz3HyLy/5P3nsGSned95+89sU/nvn375jxzJ2dgkBNBgAmkSIFBpEhbgZJsWSuVgne1W5Yt26sqyVatJNdWyVqVRFGUSVEkzQBmEjkDxOTB5Hhz6ts5nfTuh9O3b5wACloB3AdfMH1Pnz75vO//+YdoiEqpxlc/8xwTl+dxXY9vf+ElfvT0ad75oVsY3RVQ7bfu6ccI6bR3J+gZaidqrNepd1lxXM+nZtvYtqTh2U3ASAQGfrqKZegtqm+5UqdcaQSof81BU1U0TSVk6gG9XAbg0dR08MCzLCPoEIZ0FEXB0DVMU2NqJk8uVyESMRnd1MmRY1eZni3wgffupVK12bqli3c+sANFgenpAuVKHU1TME0NRVHo7k6yc1svPd1JwpbB40+fIh6zUJvdMV9KDEMjl6uSy1dIJsI3nAhEdB03FORYHJ2dRgBlu0FHJIKuKuzp6GK2UmYwkQySlbwgQrri2PRrCZKhEB2RCJpQeGbsCpqikAkHMrCq46Aogr54PIhWrNfxfJ+9nV2krNXTJNfzyJaqwTUKWKrG/ESO7FyJcMREUQXVcoN6zaa9MzDttaImi3MlPNdDMzS27urFsAyOXpoiX65xYHMvqWhAUazZDrlybT2jrlmmrtHejLk1dJXhzjamc0WqDRtPepTcYssceem+01V1lVcDBN3bgfYkc8Uglvz14gXCWoikHkdKyb37NiFlIHMrOT5z9XFiWvInIlXN83yK5TrOBvHp1yspgy73sm+LwGslcKx3GA8pISw1uLY1RaHq2US01R3Ztde9riiU7UDCKJGU3ELLrDmmxdGV64PscT3VGmBrikJY13H9IJXP83zyxSqbhztuep996VCwx5DSp9PcRb1qU8qvzlEJqxH2JG7jXPk4da/KycKrbIntRlnRibV9mxOFV5DN1LC9yTsQBAyIRCaG7/ncSOa1UZmaxnyxQi3jEDUNMlYEcwUQW15xP4S1KKZyfdgjqsXRxEpA0WO1aeX60lWVmWLA4BtoSxIxr32OpJRkOmKtweoysOtTcBa5VDnN+dJxsvYcZbcQgEfSxvPdFoC0sjzpc6E4T3fYpsOMU3Md/CZLpO65qEKsY6tAcA/ks+VVgQwQmDRK/FWyVF8GRvqmfnMRtUIIQiGdqq6uCmFY+ffR6G66rX4uV85yrnSC2fokQ5EtK46Tz7nyCXL2Agoq22L7iSkpTrxyiUx3ku7B9E8MeKRoPfh+LjD6lXI1OrNB+b7PyFCGdFsU23apVgPAeQkgXnn9x8MrzaAFo30ZYmGTExN5RM1DcSTSl7j5Kpqh0tHfjqqrFBdKFLIlKihEIqHWtRq1zBbDpe65bN3Z25IQ1WsOiiowDI2hTcEz5lqdbSlXewQVqxunIa0st2XuHDBpo+F/rnfRG5NsRy2jJZHzpWSoq43e9hgV5xxV5ywg0JU4UroIoWN7C2hKDE2JIfFw5QTZaoFE6FYM9ccD2JdKCBVViTc9jgKm5NL5rNXtG8q3601vyrWlqQqu55Ov1eiI3Rx7VNdUImGjBQxI6eNJh5B6Y1ZjcAZUWPLX2WBx3dAw1shqTV3jg/fsYnwuz9effx3L1DF0Dd/3ySSi3Lq1n72be3jHvs1kkmu85mIWybhFOhUlZOpoWgemmlwHcr3dy3FdxmZyGLrGffs30bWm2XQ9tspSDfWn130GkFss8/QTp0gkw+i6SjhsMDC43AxbzJb57N88y4lj4wgBbW1RenpTqx6JUkpmZwr8w9+/zMkTE7iuh6oKtu/o46M/czv9A22t9+sLz5/jq1/+EYVCDd/30TSFoaEM/+rfvJNMRwCG+77P+XOz/MMXXuLy5XlcJ0jQ7exK8K9/7SFGms+y8fEs/+1Pvsd7H9nH6yfHOXd2hkbDYevWbn7ndx8h3HweXb2ywLe+eYSxqwssZsv8xz/4MLv3rAYXf/j9E7z4/Dn2Hxji+LExisUahqHxwIM7ePQjBzFNjUbd5VvfPMLrJye4cG6GifFFJicWA/CiK8Fv/PZ7iDXT0bILJR77+mGuXpnn0sU5/t1/+BD33Le6Ke95Pi+9eJ7HvnaI7EIQphCyDO6+dws//eFbsZoNni9+4SUW5ov09rVx6vVJqtVAInjPfVv5mU/c2QKkPN/n3HyWq7kc3fEoiZCJrqiEDZ2dnZ08duoMtw30oQhBsV5nIJnAdl0UIcjXa/Qn4+Rrdc7ML2BqGjGChlh/MkGuVqMtEgpkamqwzqhh0HBdjk5Ns7+nB0NT8RyX//ln3+HO9x/g4Lv3MTee5bO//yV+7c9+jm/8+feZvjxH/9ZucrMF0t0pPvo778fzfI48+TqlXIX58SzdIx185LceobRY4TO/9w90DrQTiphohkZbdxLX8fjKn34b6QdyOt3Q+MT//iGMkM7TX3qZcr5CcaEECvzyH34yuO4Bx/Y49MPjvPjNQ3zo195NIh3jxcdeY248S73SoLRY5hf+88cAWJjI8vjnn0PVNRanc3zkNx9h9MDwhven67g885WXOfzESVKdCbLTOR7+5L0ceGj3hsv/uPWWAI7KDZt6SHDLh/cCtLpNNddhzq7RSOq889N3MdmoMDtdBwk/99vvacXTR2Ih3v3R27j7Xbtw3eABUDQa5O0q7ZkYD/ybgxx1pths95IylzuXqqaybf8AfZs6aNSCKGorYmI22QZb9/Tzq//hQ0HEdPO3lKbJ4bZ9A/SNrKYPhiwDw9S49717uEfSYheZIZ3okvFs2OR9H7+jhf4urTO2IsVJUQRW2GgBVWtLSsl8scIzJy9x/Mo0l2cXKdYaeF4gO8vEw/S3J3n3ga0cGAnSwC5dnqdatWk0XOY6S/T3pYjHAy8Ez/NZzFWwGy6Hj1xl354B2toiDA6kiTZ/3zBU7rlzlOMnJ9B0hTtv24xl6ezd3c/sfJH2dIz29gAtf/Hl87S3xxjsTxOJmui6RioZYXhQ0p6OcuTYGJevzHP7wREG+9MkEmFMQ2PzSCe6prJlcxcvvnKBI8fGuP+eretMVFeWqWr0xROkXafJuPJYrNVIW2Hqroupahyfm2EokSJumvTHE9i+j+dLpstlhpNtjKTaCKkaESPQow4nA4Cp4bnMVyv0xuLETRPP98lWq0QNg/FigbQVZkdmeaI9my/z7z//faYWiyhC8B8/+hADyRhSQiIZxpcSKYtEYiE0XSXSPPZCEWi6RjxhQfOlZjsuM4tFHHdZFnL44iR//LVnqNsbS4p2D3bzx7/wCACZeJRkxCJXDuSIUtikjeaAfcVIKhI2iK0BfIq1BuPZPJWGzYCMNmO/PTxfMjmfZyZbolxrkC1UGOlLMTI8Sli9Prvv7VJLHh1BR/eNTT570nHCptEcmPitxIuNShEqSvPxq6CQt4OX9WDk2sCNIgQzlRINL3juOP6yrl+/CdBOV5oAYdP2IR4yMZXAdD8aMVupMjdbilDRRAhNCYEIAgyy07nV4BcKA5FRknqaRXuOseoFCs4iKWP52TlTH2emHkSkdoX66QwFfj5WLIQVDbU8k/4x5UnJRLmAbP4HYPuN1v8bwrzh+daFvkoKKGUNzz2LZhyEa0iJIJgQpiPWshHtNUoRglKp3pSgLP2G5FLlFE/OfoPx2iXqXiWQbaAS0WJE1Ti6bqAKjaKTo+jmllcooeTUSbgWmqUyUS4S1oNjGVI1pspFtqTWs9Q81yO/WKazJ7UOsDOVJKaSaD1DdE1F1xQGe1KEN0izXFuqqjA0kqG3v41QaGOmYlRLsC22nyuVc5TdAudKxxgMj7aWrfs1LlfO4MgGMS3JaHQXCiqu41EuVIF/3AT6rVSecw5Q8PwqqrrxYHFlTU0XqNVtxidzOI6LbbtUUh51zyWiGexu70JvgradqVgraWo6W6ThuMQw2XrrppY8f6mWGnK+L/FcH+n7qE320VK1J8Itb5/ZbIlqwyEVC56B1nWY42uvAYmkL7OcrDOd3ThgYGUt+RpB09/vOqbQ/7T1xt4ZbfEwkWanPluoUK41gCgCDV1pQxE6vrRxZQlLHUERFl7T40TiIHEw1HZU8Wbsr4bjXUUVNQx9mGTMaiWVZYtVGs71veZK1caGnksC6E3GSVo3H/MthCBsma25gCI04noG/TqpnKtXoIGShg2k20KA3XDWYf6+lDx/4jJPHblIImLyqx+6mzu2DyIJfHpChoZl6quM6JdqMV9lYbFCvlgjnYoQMRp4ssEbvR7e6rVYrAVNAkW0jNk3eoZfX8a58d8i0RCjW7sIWQbxhEV7+2pQ6vnnzvKjVy7xsY/fwe13bmJ6Ks8Xv/ASpdIysFyvO3zzsSOcPjXJp3/lAQaH2rlyeZ6/+atn+MbXXuNXfvWdmKaGbXt8+7EjpNtj/NpvPEwkEigmsgtloivSRBcWyvzdZ58jn6/yC5++j4Ghduo1h7nZIpnM6u0rleo88fhJ7r5nCx/9mTtwHI9KpbGKibNzdx8jmzs4/NoV/vz//uE1j9GVy/Ps2NnLb/zWuzENncceO8y3HjvM7j197NrdjxnS+chHb+OdD+/kj//wW2zb3sPHPnEHmqagqgqRFXL3nt4Uv/q/PMSF8zP84R88tu63pCQ4lp9/kf6Bdn7pX72DSMTktR9d5utffY3uniT3378dtWkZcuLEBB2dCX7ndx8hZGp851tHeeIHr7P/wBC3Hgzkm4oQ7O3pYltHOyFNb8rwBZu7E/QnE3zqwF6yTok7BvupN32KJNCXTBDRdQxN40M7t6MqounjKrlaztIfbcP3JBO1HG1mmE4rRipsYWoaH9i+NWjE3oTc1647DO7o5dFffy+zYwt84Q+/RjlXwfd8hnf18ZHffIS58QU+95++QnYqhxEyKCwUefhT97L3gR0gwbQMnvziC9TLdX7xDz6OXbf5y9/9PFdPTbDzri289xcfQNM1yvkKf/7bn2NxOkcsFQEBJ54/w5lXL/D+X36I/q3BHP0dP3MXiqrg2i5//jufY/LCDIn2OLqp866fu5+ekU7+/o++waEnTlwTOMrPl3jpW4d55JceZNttm3nqH17k+W/8iP0P7trQr/bHrbcEcFRp2OTqdWpmMGFLhg2ipolfrTFfCYyUG75H3Av0kJahk0hHW5rrimfz/dkTuL7HHe2biBsWL01eZF/bAJ1Wgr0Dg8xNV/CRnC5MUXVtesJJLpbmaDdjHM+Poykq7+jcxhNzpyEPSSOM7btENJOSrGNUNTKhGK8vTqItKryrexcv5S6jCEEmFGMk1sEPZk/hFDzu7dhCXyQwmY0mVr8sVVW5psm2Y7vkFsqcPnIFKSUH7tmyTqPu+5JT47P86WPPcfTy1IYdngvT8PLZMbb3d3BgpAcQ7NnVz55d1zZYPXTkCps3dfDIe/a2Pju4IiVMCMGmkQ42jaye2O7fu5xkUK89xm23WBjG3YgNJs39fSl8f4b+vt2IppfIQP/yIP/eu4OuclcowaM/dcs1t3VlRQ2DfV3rJWOO5/HK5AQNr0jaCHOgu4e4adIVDR7229uXJ6539QWI//G5GSSSrliUVMji0W070JTVhpq253Fyfi6QCykSz/dbgxvP95krVJjOlQLPCEOhq6+Nzt5U6xj2DKTXdWMGNmVaf4dgsrhjsJOedJxYeHlC6/mSSt2mWKtjO946iVnPChPRhVKF2XyJPYPdPHvqMlvDVtO/ZcX1JGCxUKVhuwx1t7V+JxkOUY1F6IxH8fAYCHdjKoE+ORULM7tYZqi7jWTMwlbzZBtlQoqFprz9wSNVVUjELAz9DVI7ZWA2a2gq2wY7EMJHEdHAwHCDEiyn7Egk7WYcT16fWSNEwMhbe95vtpQV3U9FCHrjcTojbaiKoK8nRU/3xjGo1ypf+njSJqZ3NgNsxDq2zJLEaziyjZw9z2xjkqvVCyT0dABI+g7nyscpOnk0obMtvp+YFgQS2DWHSqFKKHxjUGdtSSmZWCwQMQ0sXUcRkDItSk3/N9hA3nCDEqsmIRLpl/F9A7h2J96XkqQVCsIM1niRBc+B1eeyXneQUuJ5PqoquFI9x1cm/oqFxjQKKv3hzexO3EZ/eBMRNYauGKhCxZMeT89/k5ezj7fWpQgF23epuTau3zSwX5J+NLdtrcExgKqqyKah6apt8/KU3Sl0dfWzvVZ3uDK1yP5t/esSGjcq3dBalPuNSlM0tscP8FL2cfLOAqdLR7g9/U4SeiDfW2jMcKl8GoDB8Ch91nDgmRELYd5A2v52K1XfiudeRNH6bso4JxEPotq7OxOomsLiYhlVKMxVK/THtFXXfGdbjIHOFCcvzzCzWOL0lVnu2TPcktu/0WpPRBnoTJEtVpkvVDh2YYqedPwN37sC2D3ShaoE0fZHL0zxsXfs23DCDsElfeLSdMsXaftg53WZff+09caezYlIiC39GS5OZSnXbA6fm2Brf4aIsZkIQXKk79t4soqmJFjv0/TmhlJoSjuiyapMRi162hPMLJbIl2tcmc7Sl1lvVA+BVGtsLoe9AbikKgpbO2+cjruyJMF7bmn8qwiF/vD6pKNrr6ABIspGwI0EbNujWKitOnzz+Qr/4weHyJWqfOSBvbzvju3XNTlfWcm4RToZQdNVopEQDW8WT9Z5s8/PP3eN9rfT2RbjyvTimjCQf3wZhkZPXxvHD19hdFs3C/OlFojjuh7PPn2GvfsGeOjhncTiFl1dCbLZMn/5359orWN2psDzz57h/T91gNvv2IQQgs7OBCeOjfPSi+eZnyvS19+GEIE03LZdfF/Slo7Q1Z1Y96w6duQq587N8Dv/2/u4/Y7lpNlt23vWbb/jeGzf0ctPfeiWdWy2lftoGNoqcGqjisUt3vWePWza3IkQgg89eivHj47xyksX2bW7P2BjJ8MIEfj8WpZBW1t0laxsqTRNDUgCcWtDzzPP8zj02mXKpQYf/9k7GRwKmkntmTjnzk7zw++d4NZbh4k3Jc/xuMUHf/oWRjYFaZcPvXs3zz17lqtXFlrAkRCCqGkwbxd5anqMbiuOqep8f/J1XBE8I747cZJ39+4kqpm8PjdNRDOoeQ5lN0icjukhyk6DpGHRH2njscljHEwPsT/dz8nCBHdkRjien+D1/DSWqrMr1cPh7BgLjTIfHthPh7Z67L1k3g6g6ioD23qJp2NUS3WCYAAPVVNJ96SIpSK4totmqNRKNYyQQSQRpndzF9aKpsT05TkuHLvCZ37vi/i+T26uQKNmU8pVePEbrzF1aRa7ZpOdzuE0AlA9N1fk8f/xHAPbe+nd3IkQ0KjZvPLdI4ydnsJuOExfmqNRCwgCsVSUTG8b4ZhFx0CayQsz15wn1Mo1Js5P852/fpKnv/QSpXyFSNxqKVrerHpLAEed8SgdsRUnWaxJ3oB1ao+Vh0wApqIRUnUszSCph+kJJ6m666NKU0aEk/kJFu0yMS3E4cUr1D2Hhu8yUy+Qt6u8o2s7veEkP5g+yYXSLCWnTn8kTcK3SBphfpS9xD0dW8jZVd7Xu5tMKM5zc2cZq2RJGhZXKwv0hlMgg+729YZNfnMvFRTmp/J89k++h2O7vP+EpzqdAAAgAElEQVSTd9HWsd5zZKFY4b9/9yVeuzABQEjXGO5so689ga4qVBoO84UKDcdle3/nTQ/a+vvaSLcF58D38zj2q0i/hlBiqGo3rnsOVR1AyhK+t4iidiCEiaoN4zrnUJQITuNZQuFP4PvzuI3jKGonijqAax9BKHGEkqBR+zqGeR9CJFHUDL63gBAKnnsFoSRQ1SFc5wSIEJo2iuscRygxNH0fihJrbp+k5jiBHMwIutYVt8GZwgwKgvZQlKuVRfrbUoQjgSls1inxemGC4Vg7k9U8Yc2g4bk0PIf+SBtXK4t0WXH29nZwpbKApWmcKkyzI9HNeDVHw3NJGWHylLl9sIe5eomTpXH64wliyvVfAmvPwY3OSbXhcGFygWyxiqoohDPBIHjXQCf/8RMPU2rG8JbrDV48c5WXz46t+/5zpy5zdnKeE1dncX2PvZtHWXRnWowLgcD3ZaBFXzN5zFVqjGcL2J7LZiuC7TuoIhjEn7gwTbZYYSFfxvMkI8NR6v7COpnM27U6O+J0ZOI3M0dbV57nU6o18H2JpmpI2cCVFQy2X/d7rh+8SFPG9YE3X0p0ZdnKeKW0quHXbyjnWloGAgbOdKkM0iARCjU7O2+MVq8pJkljEKVpyyelbDEVVmqqVaGyJ3E7JwqvYvt1Xi+8xvbYPkzVouQWOFM8isQnrrezI36gxerRDI3B7X0YIf2G+7ZRjXa1M5xJETYCv4V8o7aGPRNqTaSDY3P9a9j2Gyuuc4EQYeD6LC1FBMyuuuut2wdH2vhrftP3feo1B9f1sWnw4sIPmqCRws7ErXyw5+daksOV62p4dXSxesLvSZ+0GWl6j8DmRJpQ04Q/pGnc2TNAylzPAgiFDXbftxUhWHO84hhKFOTq8+D5wTuu1rCJho03fJ7WlkDQGepjOLKNI/nnma6NMV69SDweAPBnSkepeEVUobIrcRumauG7gaxKvwGr6+1UUrpIv4hu3IrnTd/UPXBlPMvkVMA6EwLa01F8JJuTabY0wxiWqi1mcdeuIc6MzVEo1/n2y6fZNdLVYglde7s2NoVNRkPcsWOQ4xenKNcafOvFU9y6tX+drOdmautAB6N97ZwZm+PkpRlOXZll96budQbNUkoKlTpPH71Iw3GxTJ27dg1ivQksxR+v3th+GprK/fs28eThCzQcl++/epYH9m2mO70cD60oJgqrAdk3Ysy7slF1/eUlEg9VBM28qGVyy5ZejpyboFK3eebYJW7bPrBhutpcvsyhcxM33JabLelLqtXGPwJ2keAvgnRgAzaWpql43mrZ7cxiiYVC4KcSakrUpFztB3mtKlXqdHXE6e5MYBoqBn2oQsdQ3phv4Fu9yjWbesOhXG00J+E+jnsVz8uhKClUJYnnz6MoCTwvS9BRUkC6CGEQvDfNIA5dhPD8BRQlguctYhp7mBzPoukqE2PZQNbaVHVUKg3y+Sr7Dgy2/CeFEGQyMawVjJ7ZmQKFQo2jR64yO1NofX7hwiy5XKUlrdJ1lY99/A4+/7kX+OM/+jbbtndz2+2b2LtvgFjcap3vsbEF4rHQNT0HV1bI1BkcbN8QvHmjFYuFmsBQsB3RiElXd4KpqdwNvvnGy3V9JsazJJLhlkQPAvP9kU0dnDg+FnhUNYGjTCYW+EA1t800NEIhfV3Iji8lDc8lpOpcLme5r3OUHcke9rcNkLer7Ej2sDPZw3cnT1J1G+TsKppQMNXAiH++XubW9CDj1UUUIdie6Oa2zBAJ3SJlRnB8j4prszmWoeG7XC4tUHLqdIXixA0LpMAwdUqLFRrVBgsTWcq5MgBCESgbMJM8z2NhIkulWKWwUMS1PUJN1Y2yJiQIoHOgnZHdA/z0r78HzdDwXI9Mb5pXvnuEI0+e5F/8+w/jez4T56db34kmwzz66+/lxcde48kvvsjD/+I+Lh2/yjNffplP/btHibVFmb0y31q+lCuTnc5jWiYLkzkSTSae9GXzGSVbdg6hiEnv5k7u/+idDO3sw/d8jJCB+iZckyvrLTHKCjrVG3x+zX+sLlUopM0oF0pznC/OYiQ1rpQXiGohNsc6GassMlFd5Gp5ge2JHkKqzqn8FD+36W6qns1srUh/JE2XlUBTVMKagSIU2owoV8oLRLTgpX2+ONvyDAIwFBVLM1CEIG1GSegWXaEEI9FMoGP0G5S9Iq6/ROlU8KSL4zew1Ci+9BAIKl6JsBqlvbuTX/i370XTVVKZ2IYvq2NXpjh+JbgIIyGDn71vHx+8fSfRUMAI8XyfhuNSd1y6U+vXIaVsYXAr/9axItlA+mV8bxYpa+AvoCjtIH0c+zUAdH0vjnMUISwUJYPnnkc170dRe1C1EaQf3JyOfQxFnUFRUmjadiQNFKUbVR3BdU+BUPG9y81kpwqmcRuOcwLPu0JwaTr43iymcSsrY81n8yX+9qlDxCyTn3/wViIhg6prU7Br6IrKiZlJEoaF43uUnQZ3dYwwXy9TchucL86RMsKczs+gCEFPOMGZwgwCwdniLNsTXQiC1IWa5zST7CS273KqMI0nfSaqOTbHMhiKtmrgcb26+cFbAAZu6mmnI9WgbcUgPhW1uGtbP0IoSEnzXHvrgCNTV9kz1E0mHmGkK000ZGCqgbGutyYeVlUE6cRq49m4FSIaMrDMCEnDolQtY/sOlmYy3NPG5EKBLQMZ8qUa5ZqNGq6zlkX1dq1rPYtupuq2i7fCAFTi4PtFfNlAuY6UYCnes+HdONXMWTIARhDV4igo+PhU3CKudK6bbld2i63rVcoAfJ0ulojoBv3JxBsm1ZtKjPbQFgSBhEVRFUJhk2qpHlByV1SPNURXqI+x6gXGqxdYtOfptgaYqY8x35gCYDiylbSxLM0sLBSZvDDDwLbe1svyZmtp8E9zkuN6Hg3fQxPLSURhLWDs1P1qYDjt14hxbdZV1Svj+UuDo4AtJNDw/QUUZXUHcolLpCgKhqZhbcCAqHplHH91c8O23VaiX9HNMVa9AIClRbkr/S4SetuGzw8fn7q/2gtGIvGkT7ZRJtuokK1X6Y0EDBBD1RhqAjGuv3rAJ2DDzqTjV1CEjrFGlqoqgnrDZXahRFsigrZWWuxLGvUgWVSs6AqpmtJMK7VRVKWZPKoFwKOusjtxkOOFl3Glw+niYbbH9+P4NhfLr+NJj4zZzWB4cxP8k5SLNTzXo3vwJ0OqJmUFzz2H7+dR1Run9wF0dyaYmy8SMnUiYYNcoUqnlqJoN9YBT7qmcv/eTTxx6DwXp7K8cOIyX346zccf3LeK6bpUvi/JlauMz+UZ6U6vk4Ppmso9e4b59kunGJvLc/j8JH/3g9f4l+++lXQisjHoU65Td1w6ktHWNSeEIJOI8NAto1ydybFQqPD3TxwhFbPo71jtaVJrOHzn5dMcvTCFlLBjsJO9m3reYJrYP18JIdi3uYfdI10cOjfBmbE5Pvf91/jkwwfoy6xnQEgpKVUbXJlZpCMZpSu9MXt9bdW8OmW3SsqIo18zfVDgeYtI2cDQhzB1lYPbB/jKM8fJlWo8f/wyDx4Y5fYdA6vOZd12+d4rZ7k4kf1xD8O6SibC3HP7KNaPwX4TgJQeeFMBeLQBeKPrasuGYakSkRCWqTGf9/n+j86iqgqbetLL0hcRsKdilklPe5zOVKxleqxrKpfGFpidL7J3Zz/RSIiYMbj2Z9/2Nb1Q5PSVWWJhk0KlTiqu4rjj+LKMIotI6rjuOIoSw3Vn8GUJTe3G8xdQlSSgNMfyPgIdIUw8r4bnB3LUTEecsatZXMfjwMFlxYMMzPmC624Fm1lRxCoJji+D5VzHo1JZfh92dyfo62tr+f4IIdi9p59/+7uPcPzYGC88f47PfuZZ9uwd4Bd/+f7Wcr4nEcrNBS4IJQAk3wx/vYCMvGJMLYKGyj/pMFus/6dobsbK8b2qKRuCLmunQo7vcbY4S91z8KTEUDSKTo3Jao6EblFy6kxV86SMMJ6UDFlx5uslfBk0olzf42JpHk/6xLQQDc/hSjnLYCTNZDWPJoKgmKhm4kqv9fNd4WAsq6oKO+4c5ZmvvMLUpVmy0zmMG8jpdUPn6qkJvvrfvsv8RJbh3f2097RRylU2XH7fO3Zy+fUJnvj7F7AiIRRV8NCn7iOZieO5Hq985wiNamPV8dJ0jZ5NnXzgXz/MF//rY3QNZUh1JtAMjcNPnAAhcB23lVCpagrf/9tnCIUNpi/P8ehvvA+77nD82dO8/uJZpi/P8cQXnmd49wCj+4a48/23cPjxE4ydmcRzPEZvGaZr6I0xPm9UbwngaG05vsdsPY+UkLPLtJtx9KYXhwCqXoO65xBWTUKqTsmpU7CrDEfT7EkFcqxb0kNoIkj/SBlhDraNUHd88o0G+1NDCFenZvtsjfaiSh0FQbZaYzTSQ6HeQJUaA5F2PAmWqjNTLdEdTVHxGnSEktRdj9FYN3O1MjXXJaaF2RrvASQxvWkI6ddYbMxSdvOE1AiqUKl5lUCHKXQW7XliWoK8s4AiFHRLo3vg+oPeQxcnqTSCycaBkV4+fu8+2uMRzi8uENKC+FFP9ck7dSKuQc6pt258TRFUXRfHC2QL3ZEYUcPcMGZViAgCHV8WcZ3DgI6UVRQlg6J2g3MMkHjeBFKWECIcfEeEcZwX8f1CADwhQbpInOYyOuAG6/am8Lx5FLUDRW1HUTMI1wBUNG0LQokj1TqKuloed/jSJI+98jr9mSSfuG8fEQyklMzVSyQMi5FoO4t2lR4rQV6ttSiPS7THmXqRiGYQ1y0yZozxSg4pJQnDYqqWZ6FeoaNeYr5eYqKS40JpjsFIG2kzwmKjypZ4ivl6CV1Rr8vSqLg1ZuvzCII47IZvYyoBKOn4DqoIvm/7Dgk9TkgNAEqJ5MLkArlyjT3D3YTNIEHA8aao2yeJhu5BUQLEfyNzW1VR6E8naI+FSceCCXzNq2L7jVVmjYoIkgpyxSqjA8sPlkrDJmaF6EnFKHlFim4ZT3oIRdCTSdAWDzO9UKTWcBhMhbCl/aa8LN/OpaqCkZ422hMRNFUFJKa+F1/WbmiQGXhzucQ067rHURMKo6k0ET1gdST0FDE9ScFZpOjkyTZm6bI2lqNKKZmuX8WXAbtJVYJrsmyvZ2XebAmxzH4SyMDUti1KNLmesRDXU2yL7WeydoW8k+V8+QQdoV5OF49Q92oYisnO+EEsdRkgTnen0AwNw/zHp/YJESQt1TynBZ5F1Chps5OSm6fmVZiqXSVjbpyUGDxfJqn7tRWfFfGli7bkVSUUjCZwJ6VPw6uiCMFAej0YFaxvirpfXbGRgeGn63iYpkapnMdugkEJPUW7eW0Gad2rMFefXPWZqWi40qfbSpIyIpzLLVJxbFRFodO6scns2lKEgevXsP01aTei2W3tSW3oL+D7PpePj7EwmQskAs2BZzhmUckH6V2priS5mTwSKOcr3PrwHgYjW+m1hhmrnudi5TTZxhwVr8h0bQyBwqboTtJmJwClfJVGzaazN/UT8yxSlASqthXfn0Nyc8/Y9nSU3u4UFy7NUirXiUaCxJ6S3cBdw24TQjDa384vvf92/ssXniJfDmLID5+b4MCWXoa62jA0lVrDYSZX4uJklisziziuzx98+j3rgCMhBKN97fz8ew/yJ196lnKtwZeeOsaxi9McGO1ltC9DxDKwHZfZxTKXprJcnM5y964hfv49BzFXUOlVVeGn7t7J2Fyeb790micPX2Bqoch7bt/KtoEODF1lYq7As8cv8cKJy9QaDt1tMT79yG3rGiFv5RJC0J6I8isfuJP/8oUnuTiV5WvPneDYxSnu3DnIlr4MIVOnYbvM58uMzeU4N75AtlDhNz96300BRz6S+cYiC41FTNUgcQ3gSOIGLHKlrbVte0a6edfBLXz5qePM5cv80eef4JE7d7B/tJeQoTGzWOL545d5+uhFUnELSgGYt1HZjsvRC1NMZYtU6jbVuk2lZnNmbK4lcTt0boI//dKzRMMmYVMnEjJIRi32bOomk7w5KbyEpsdRCtSNPQPthhP4S664p/oyCT54907+8psvM7NY4jPfeXXd9xRFEDYNutqi3L5jkJ996ACdqWC7kvEgUezE6Ql2besltoE/6du9hnvaiFomsYiJZeoIYWDqO5A4BCMZE9XIAC66OoTjXkLT+nDcEIa+pbmM2lxebXp1+eiyAagYhsa9D2wjEjWJrHi+RKIhYgmLqak8jYaDrqsBkJ2rUK8tj2E6OuLE4hb33r+NRz6w77rPTFVV6OxK8HDXbu65bytPPXGKv/vb57n3/m3sPzCEEIE/ULFYY3a2QE/v2jTYtSXeNEurcqlOPl8l0xE0eqqVBrOzBfbfMrTuNxUhNkyXu9nSNIW+/jSvvXqJ7EKJcHMO6no+V64s0NWVbJl7B794c2UoGndmRig6NSzVIBOKcn/nFnRFJaKZ3N+1BVPRONg+xEytiKGobIl3BGoIKXkte5WIZrAl3kl3OMG9naPNgCvBnZkRTFUjrocIqUHIy6HsVTqtONPVAuOhKNsT3dzxyC30bOqiWqyR7knhOh7xdIz3/9I7CTdtZFKdCT71e4+S7k7xnp9/AJDk54pIKenf2oMVDUK2PvV7j9Leu5p5lulL88n/40OMnZnCaTgkMnES7TGS92wj2ZEgO7VIqVAj2d0WJLDbHre+ex/lQg0p4c6fupVKqU57fzvv+/SDXD0zSbwtxoOfvJdEJkFuvsh7f+khSrkypqmz94FdaIbG2cOXuXp2mvb+dj79Bx8HBOGYhWZoPPCxO9lyyyYWZ/KYlk7Pps4f+9q4Vr0lgaOK2+Dw4hUUBBW3TlxfpC+SZq5eIK6HmanlsX0XXVEZiXZyoRQwRraHu4lqQadsV3I5WjmqhyjVPS7lsxSq0+zKdGI7giNzUwzEkmRLNu1WmJMLc3i+z5RS5o7uflx8ZiplOq0YMd1ivFygzQyjKiqHsxM4vtc0tq1ztRTQ6QaiKSw1mOjEtCSRaKwJeC3dbkv/EnSE+hAIehkJJpc3uCN9XzKbL7VYDVt7M0SbNM283SBfzDNWzKMrKlHDYLpSwvV9LE1nplJmNJUmV68RN0zKjs1ivcbt3esnmkKJo+k7AB8pHcBB+kU0bTNCiYKII7VbkVJF+PNo+h6ECKMbBwENVdsC3hiqthlVG8RzzuJ742jaTjRtG76fRdU247pn0bRhFLU/eNEDmr6HIK1IR1E6EGL1INCXkmOXp6k7q7vkmqKyI9nNaKwDQ9UC7yGhIAkmjH3hVJNRpuBJv3kGBIqAvkgq0NMveQwhUYVCj5VAEYKtic5mKK4IHlxN74UAALw2KHClMk4+e5mQahJWLdJGCkUoLNiLlJwyKSOJoegUnRJ7kjtawFG14ZAtBbHhSz4jEvD8HI4/iy/rKFx/cJwtVbk8t8jd24aaXQOJ67tNY+L2wIsGaEuE14FPuqownS/ieB49GQuvGd++VPfsHWY+V0bXNcJRn4l6ZZV/zv8fS1UU9m0O/MSgaSarprlZgqjtuxSc6nXlKBKYq1boidaJ6DpJo51OszcAjtwcFyunaDe70NZMDKSUlNw8VyvnW1IrKSVhQycUCtOXiL8p4516pX7NzowiFLbG9vKjxafIOQtcKp9hS3QP49WL+Hi0G30MRkZX+QjVKnWmL87S1p2ireuN+S+tLSmhzQyvmjxbWpTB8ChXKueoe1XOlo6yPb4PYwPpad2vcqlyugXkBIC4gxAWQkkihIIqNKJa4AFi+w2m62NsjW08eK16ZS6UT2KvMDhHBhOTJWNLZQU7yl9zD67eN5+L5dPMNVYDR570iWgmoSb1WwgoOzZ1190g5+/GFdba6Y3cheOvPsdd7XHa4pFVkoG1FUmECUVDqKqC7/utTq5uaCQyweBY6UkhfUnXYIZQ2ETRLLbF9jFVu0LBznKpcpq6V6XilbDUCDvjt6IINbiWYyEGt3TR3rWxB8vbtXx/EVXbjqLc/PXf15NkajpHqdxgaLCdimsT0rQNG0SKENy/bxOlaoMvPH6Y8bk8PzozzpHzk0E3n+De8aVsSZoHO9uuyehRhOBdB7dSbTj8w5NHmVwo8PrlGc5cnUVVFIQInmO+L4NxjJQcGO3dkLfbFg/zy++/g7rt8szRi5y6MsPZ8bnlFDI/iPtGwEhPmk+/7zYObOnbYE1v7RIC9m7u5rc/dj9/8dhLnL46y7nxeS5OLiwfs+Y58P1gTJCMbuxXslF50kXiE9YsYtp1xg3SJpDeLp8NXVP55EO3MD5X4LWz40zMF/jrb7+yKgnO9yVb+jP8wntv46++9TJnx+c3XH2lbvOZ77zaZIctM1+D0JBgmUuTWS5PL7Y8AIUQJKMWv/cvH7pp4AgI5GkiumpflvezaXdQs1vbYbseZ67OMp0tYRo6UgZpcGvtpKSU1G2HC5NZLk/nyJdq/NbH7qdas5mcyTM7X2T39j6yucpPHHDkeT6WqdOTibOQr6CrSvDeU5cCFpbHz0v/UtQ0Ah1V7UJZZWy+5sA2P1uYLxGLW8TjFq7rtbyCNE3htts38Z1vHeXVly9yy8Fh5udKPPv0mZZvDUBXV4KDt23iB987Tld3gqGhDJ7nM91Mi963fxBNUykWa5w5NUV3T5JEMozreti2G0j2VzBm9+wdoKsryZe++DKaptLTk6Rhu8zOFBgYbCedvvlrUkqJbXv4vk+jEXgZNuoutZqNqiro+jJbKZ+v8vSTp4lGQ4RCOo//4CT5XIVbVvjOQsBGSaejXDg/y/RUjlRbEAW/5Gckm+wr1/Np1B2khIbtUq8HadG6rqGqCvv2D/K97xzjsa8f4kMfPkgkbHD06FVOHB/n0Y8cJHwT3oVrSxGCTitOp7UMbg/HlsM4hqLLJImYvvpe8aVkd6qXhB5qBVr1RZaBux3Geon9zmQPF0pz6IpKfzgAeIyQzuZ9Q+uW7duy3CA0LYNNewJ24BIzp2toNeBshJaXWVlCCKLJCDvuGF33t+Fd/XT0pzlz6DKhaIhyoUo4ZtHWnSI7kyfRHiMcDyOlJDudR7dMhncNUinWGNjajaIKZsYW6BrMkGiPk0hHyc0VKeWrFLIldt45iqIqbF1jlK3pGgPbehjYtt6H682qtyRwFNNDPNi5E4nElT4KgpCqMxBuRxUKo7Gu5qRfYCo6g5F2fCkJqdemtY4kUvTF4vhSEtZ1HuwfCaLrVZXeWLyp/Rct0Hgpqvod3aOYaiBJangehqKgCIWG5yIJ6Hc+kk2xNIoQGIrWYqEIIVCve4jf2GTb8bxVyRbx8HLqxK72DhzfZ1s6Q8N16QhH8JqyNFUIXN8nrOs4no8QUGg0VoElq7ZKiaMo1+5klR2bl2dj+FJya9edtJkBw0A3DgCg6aNo+vKNpKrLN6lu3rb8ubYetFKUOIZ5/zV/u1Stc356YZUkCCBhWEQ0E0NRm5P21cdWFQK1ebzXsoSuNf5auw5geR034dw/HOlnKBm46IcUk2hz0NZtdSIQaEJtvWRD6vKDM2To9Kbj5Cv1JttIBElKb8B8WlMVLkxnsV2PVMRix2CaSNMsbuk3VUXQ0x5f17U0NJVk2EIRweQ1olroyrKxasjQ6e8MHuK+9Nmkbse4QRT8T3otGUP/uBVSjcBw/TpVshvYnteSaoSUMFvj+7lSPYftN3h18Sm6Q4MMR7asAmAafp3DueeYri9LGgO5kkoiFFolv/1xSwhBPB2jXt2YGREA5b30hzeTKywwUbvEpcpp5hpTCASbojuJa6snx7VSnUqpRrIz8WN5HK0sRUDJaZC362xNBoMDXRhsie3hUO45ym6BU8VDbIvtZ1fi4Kr0NE+6nCoe4lzp+Mo1IpRoE9heki6bdFn9iJyCI21OFl5jd+J20sYyU0gicX2Ho/kXOVs6tmobfV8Gcq4mNT6utxFSw5TcPAUnx3T9Km1GZtVx8KXPRPUSzy98h5pXXbU+T0rmakU0oRKJ69zZNYjn+1ia9mMBhVV3gZIzjkSSMpeNQhPR66clKZpK/9ae5WNwDQBs7d+FEGyP7+dQ7lmy9iyXK6cpOXkAeq0heqzB1nKqqjAzvojreAxv6/6JYR2pag++N46UBRR1iRUq2DXcjaaqWKZGPGIG0hwCD4YLl+colOrU6zbTM3lCMQvX3xh4FEJg6hofuGsnW/szfPPFUxw5P8lCM+HLlxJNVYmHQ6TjYfo7kzy4fzPd12C6CCGwTJ1H79vNrqEuvv7CSU5emmGhUKFSt3FcH1URLSZJf0eSg9v6N2SqCRG8n37rY/exdSDDc8cucWUmR6kabFfY1Olpj7N7pJuPPLCHrf0d6JqK35RzOa5HvlKjpz3O9sEOtvRnqDsOtuvRsF2klLQno2zpzzDQmaJUaxBqJrN5vk8kZLBruIuIZWDoQTNKCQk2jaSREkIRjflaubW9ruozMJRCFwpaWGGiXCBumHjSJ2wZbB3poLtSR4SD9FdDXR4baqrKwe39dLbFeOyF1zl8boKphSLFah3P9YOkpBXH7I6dg+weWc+OzCQj3LVrCNvxGO0LrhcpV8Iny/eFqghG+zLcs3sYXVNJJzrQNROxxk+ppz3O//rxB3jshdd5+uhFZhZL1G0XXVNIxyPsGenmU+86wEhPOxcmF8gko2wf7FgXBqCpKtsHO1pJbTdbwX6vnlhapsat2/pZLFbpSsdWXT9NJSxQa3mj9qTj3LlziIbjMtSVoqsnSVtbBKXZBHz8tXP89bdfYSpbZM9ID/fsGWagI7nKz8nzfSq1BucmFvjha+eYnC/w7LFL3L17mNu39dPblWTzcAdXx7Ps2LIxc/XtXAuFChcnFpiaL5Ar1bj/wGbaEsvvv6VaacC/BBYJrgeirZDPGiqvvXIRw9TYNNrFnn1BcI0Qggcf2smF87P83d8+z1e+9CrRqEn/QJr2Felm4YjJhz92G1/8/Iv81f/zVIuJYxgaB28bYW9zfY26w9JoD9UAACAASURBVHe/fZSpqRyKorTeOXffu6VpSB2sr6cnxc/94r38/edf4s/+r+8GjFlFIZWK8OlfeeANAUflcp2vfvlHjI1lmZ3JU640+NxnnyPTEWfT5k4+8MEDxJqm2R0dMRYXy/zXP/wWtZqN53k8/O7d7Ni5WrIcCum846Gd/M1fPcN//v2vYpo6Q8MZfu03HiYcNqnXHR7/wUmOHR0ju1CiVKzxP7/0Ki8+f46OjgQf/fjtpNNR+vrb+PjP3sk3vnaI//P3v9p6f95+xybuf8f2VUmv/1+UIsQqYOlmqjucoDv81mocWdEQW28ZDqSWBMEg7b2p1jNqKUyGNf9vhnR8X5JIxzBCOp7royiCTG9g7O5LiWHoN22V8mbXWxI4UoVCdA0CKaVE+oJyrUHNdlppYopSx9Q0QoZ23cm8qWmYK3Y3GbLwPJ9yvUG1bmO7ASBjaCrRkAlaEPVurni5h1ccLUtbD1JJKak2HMZzBXwpiTVf9EsvUN+XFKp1itU6QgTmg8lws3MkPaRzBNQuhLq+ayabHb+VKWqKWO6IWJqOBcSN6yPDVnMfYs3lpJTYrkexWqdmO3h+ACaFDI2YZbbMp1dWVDfoisS4XMzdtOZWSkml4VCs1rFdt9XRsQydmGXe1GBCSsl4tsDVufUGcapQbgrMudZ6645LuW5TX3FtaaqCqWvEQiYhQ1t1HDy/Rt29iqqECWn9rHt5CkFECzMQ6UVKSblukyvUsV0PARi6RiJstIChlbV0nZwZn2OwI0kiEgo68iKCpmS4GcAxEQ6xb7iHhuMSCRlB9Lj0SRrpFjtoCexYu7a5YoXZQsBs60xblNwqvvRbptorSxEKpvqT1VlbWRPVS0zXx2h4Nep+jYZfb8aeBxPYslvghzNfIa63YaohQoqFoZikzS42RXbcnDZeCGKadUNzak/6dIQjLakawN7EHVwqn+JU8RBTtSt8eeIvuDX1AIORUTShsWjPc6p4iLPFoyT0NkpuAduvNyeECh3R1ca1s/UJJqqXqfkVGl6wv2W3wFw98CGqeRWenf82p4qHmvsawlQt4moKtZZomfRttN+mEmJP4nZeL75G0Vnk1cWnaPg1LDXCnsTtiDX7H4qYSH/1urKNOcaq56h6ZRpenYZfp+IWGW96Adl+nVeyTzJWvYDZPBch1cJUotS8NgaiqRZYLoRgOLKNfck7eWHhB5TdIt+Y+iwTtUsMR7ZhqRGKTo5LldMczb+IoZgtT7pgBSFWduhVobEluod243Hm7WnGquf50vhfcEvqXjJmDwLBojPH+dJJXi++1jQ3F1Sb63Mcj3LZIRYLIaUkriXZGtvDQmOamlfh+zNfwvYb9FpDKEKl7Ba4UHqdI/nnKTiLdJg9LDRWmDDqJre1jzQZRwpR3aDgLHKicLJ1LTe8GnWvynj1YvNbkpcXH+dq9VxwbpvnN6rG2RzdQU+4F3+FIXjJyXOudJyGXw/W6dWw/TpXq+day/xo8Skmq5cw1RCmYmGqISJqnM3RnYS19QPvVd57Zg/bYvt4Ifv9/5e7946y9LzrPD9vfu99b06Vc+ekTpJaoaWWlZwzxtjYHjjG4DUzwHDOws4OcHZmgN2zO7DAcDCHIewAZkyyjHOU5FZWq1tS51BdOd6qm9Ob94/39q2qrmpJtuFg+9d/9O2q2+99432e5/v7Bq7VLtB068iCwp7YEaLrgUYBmnWTVuN7l17+oJXv1fHbLBDPXer8XBDgE+841jbRcqhY51htXiKuH0QRoqiKzNhIlivXFnEcD02UKTmtzn1vexZ1p4IPOL6NJMiIiAwO6Hz8/ftYKY4xVyhgmwKWa6EpKtGQxkiml2w8est0s/UlEHjG/ML7jlNvWeRLNSptMEdugyCpWJhsIoLeZhXcYDT5Ph22ie/75BIRPvroUd51z15mlktU2sa8hq6QTUToScc27FPLtLk6lyekKhRrTQ7v7Gf3SBddiSjzq1VUWWY2X6JYa7J9IMPtuwewXZfxhVVs16Vcb6HIEruGusilooQ1lWQ0RM2xcJPw4+87RC5kYMgqzy9NY3kuXaEoZanFB95/kISqk9bDXCuvEG2zu/enuvnAuw8iCgIVq0XNtkitm1veAPUGuxJ84l3HqNZN5lcqVBstbCeQicfCOsloiO5UFFmWsF2XhhWwyW6M5Ud2DnBk58aGnCzKyIJEzamzPmVGVWQ+/PBhPvzw4XXv3gx4CILAYFeST777bt597z4WChWapoMiS2TiBgO5OLoazIc/8Y5jt7wnomGNX3j/fa953/i+S6H2N2jKDiL6Hbd8Xy4Z5Tf+zSO33pDXAN/r+OEcv22U47eNbnxPPJj7TS0W+ZMvPs/McpHbdw3yn376UTLxWxu6P3zUZ6grwe989jtUmybnJhYYzSZZWqliWQ6ZVIRGwyQZ/+dNHfvXrkzCQJZEDmzrpWFanWsOgQdRvdZCkkRkRcJ1PDzP4waMJ8sSCODYLggCihKYk3tuAIq6roceUtm7f4C9+7eW23d1xfmFf/9mpqdWuLy8iJpQeGDnLiYn8vT2rksu7k3wqX/3CFOTecqlJoIokEiE6elJBPsBJFJhdr5ngLf5h7BNF0kSSaUMBgbTaJpCw7E4vTrNvV3bOHh4mO7BJIuzRcymgyf59OaShDMaX5o5z6N9uzCyOmPv72PXvt7AE9V1UCQJEQHTcxAR8CXI7IzTN5oiJCuYrtNJbQ5FVHzZ7wAHobDGhz9yD9Vqi2q1SSoVYXAo2Lf1JUkix+7aRl9fkuWlwMMynY7QajlIsoQsS8STYQ4cHCRzE8ilaQqhkILjuDz3wjjZ7hi/8h/ewdxcAcf2SKYMsrkYkrRmCv3u9x6h1bIxjLVGcSod4VP/7hEy63wobdel0GySDIVQpVuPF67nbdm4bNp2QAyRt14Xer5PodEgpCiEla2tDC6fneWxzzxLs25usYUgje7eh/Zy252jbfYrHbbZjTHoxvrW84LwDR8Ih1WMiMbXP3ea5568dMtGmBHV+eDH72dg5Hv3F9Jex5PpX6t+IIGj9XUDjDk7tcizlya5Mr/CfKFCrRkkLmiKTDoapjsZZUdvhoMjvdw20vuaQITn+SyWqjx+dpxXJuYZX1ylVG+B75OMhBjryXB0Wz8n9o+Rjoa3ZOX4vs+V+RWevTxF0gjx4IFtrNYa/N1Tr/LkuetYrstYd5oP33+IO3cMIooCp67N8ndPv8LZqUUkUWR7b4YP3HuAYzuHEHHBGQdUkPqoNi0WChXKjRbFepNKvcVKtcHs6lpSwIvXZrBddxNlWRZFDo70sm+om1uV7/vYjhuc18tTvDKxwFyhQsuyUWSJnmSUnX05Htg/xsGRHjRlI3Di45PWw2jrvhQsx+U7568zu1omaYR45NAOdEWmZTmcujbLE+fGOT+9xGq1get5REMaPckYewZzvOfYPgYyiU37aNoOhVqT2dUy86sVXhqfpdjuCq5WG3z25Cu3TFAZzCQ4sW9sS0p3AMT5LJeqvDQ+x6XZZa4urLJYrNBoa/TDmkI2HmFHX5Y7dwxwdKwfQ1c7GJHtFQONtt+Zn3RKEgPjvoZp8eLVWb7xylUuz+Yp1hogQCZqsG+om0cO7eDAcA/aOlM9VZbYP9xDJmYQN9Z3831kKYMgvBEBlECh1qRQbaApMp4vEVEShCQD34dz0wucHp/DB7JRg0cO7ehMvi3Hpdo0MTQVVZTRRRXXX2O6LRQqPH52HMt1CasKx/eO0JN8YyadP2x1uvQUz69+G89323l07RSDtuSr5TU5U3omGPxuCBoFge2R/YwauxDegFhNRGA0EjyrrwU0JbUQF/KrlMwWMU1HIPAOeiD3Lkr2KvPNSfLmAt9Y+vu2AaqA67u4vk1a7eahrvdwcuWrzDSuIQoChUYTx1nhrsGBzuderJzh8eXPB0DjjT++1zle27e4UDndPt72MQsCffoIb0v9FDnl1ukjAgID4TEyajdL5ixzzQkABsJjZPXNtFrf82nVTbx1YPlk4xJfnP9rWm5jy/1zfIertbNcq51vf28LiIJIVM6wQ38/TcdnNJbiBhtdEVTuybyZxdYsE/WLlO0CT+a/yDMrX0MQRDzfw/FtonKcB7vey7XaOV4pPUugW/AQhBjrQeMuvY+7Mg/z9cW/b8vbLjHbvI7UluK6vovj2cSUBA92vZtrtfPt7YEeUujtNTqTW1XUuT31AOO1Cyy2ZlhoTfMPs3+CLoURETG9FpZnIgoitycfYDSyi8/N/XnHcFsSxE0duIXmNJ+b+zPczv3cTsJj7fm+Wj3Ltep5bpiwC4JIUknzseFfJiRHkFibzCybc3xu7s/bcpittzdeO8/12sW17SESV5J8ZPiXtgSO1pcq6m3W0UnKdmC+m1a72BbZt4EVJggCqiZjtn50gCMEFUE0EIUcvlfa8KvFmVWKKzVGduVwxTqCICG2p3ODA2kuXl5gZChLIh5m2WvQFY50ZNWWZzJRv4IsKGhSiKKVRxN1IkqMgrWCEY4wFNHQpBALzRk0yUcSHLpiUaQ3NPbA/EqF/+0Pv8jP/9hx7j4wTE86RrHeZCJfCPbBcWl4DpcW83iej+U4RPXAN0WVJBqWje0G7KTdvbk2EBUiPhLqgEo3l9+WPEmiSMIIBYvBaJhirYHr6W1PlmCxEtZVImENqe1Zs1yqsWRV8XyI6BqRsIauyBSqjbbkIwDufXxCksJAJIHpOvQZcSRRJKmFKLQaOL6HIkqEZYVMyMB0XUQEWq6DoajokkxYVtBvWhAtV2vMlSrEdJ1G23fOUTySGYOmbRPRVFRJwvI8Fio1bNfF8Txs18XQVPoTccLq1vMggSDZcv0Y/r2UJIr05xL0574/2fBrl0fTvvSajPc3VGIKQR7i9ZY4vg9XZvLMLpeQRJH7Doy+JmgEgZx4uDtFSFc6CbeaLhMOqTiOi+f533MT8we58sUar1ydD4BMBI7uGeiY6Huex9Vzc6iaTCQWYnm+SDQR7jCQI/EQsixRqzTxfR8jqlNYrhIyVHwfVpfK7L9jlHgqMMlfalbafjYiC80y9XZC9q54N60eFyEuo8sq49YKrW4HISLy0uoUAP3hJGWryUqqwehglrxZZdVpkFajTJTmqdkmo9EMbgKyuWB8bLk2S2aNqBdmtbTCUqvMdK3IPbkxJqqrTDkFdu7MUbNN5hpl/IhA2ohQzQdNuO5knG37u0lmDGYbRS6XlugKxcjqUc6XFhg0kkiiyHKuwbHcCDFZ53JliYgaZsBI8uLKFGqryD69Ddz6PnpIItYtIItJwnIKAYHZ2QJzC0XCYY3B/hRXri2hqjJDA2mK5QaKIqGFVL7x7fP09yXZv7cfI6Iztq2LXDbG+MQy9bpJJh1FlkVapkOx1CAeD9Ns2qS2RVgp1HCxSCQNXjh1Hdf1uOPoKCuFGtVqi7HR7Ib00lBI5eChITzfx3bdwJPJtnnswkXevmsnvdEoTtva44b65UZdXV2lLxYjqmmB/QfBuilfrxNSFDKSFChnfL9jERKcHp+TU1P0RmPc0b91cERxpcqpp65QLTe3/L2iygxv6yKaidBq2liWQySiEwqpFIt1ZFnENB3CYZVCoY5l2kiyxJ69fRiRLJPjSzz7xMUt1bAA8aTB2z9wa/D79Wq5XOP60ipHxvpR1q2zfd+nadkdUsd0vkRXIvJdMzm/n/qBBo583+fK3Ap/9eRpnr44SaHa2PIazRcqnJ1a5FuvXOPePSOMdadveRItx+X5y9P8yTee5+LMcodpdKNWqg2uLqzy+NlxvvXqNT7+8B0cHOndRKX2gVcnF/jdz58kGzMYzCb4m++8zLdevdZhrCwWqywUK/z6jz+Epsj8X//4OOMLq51jmG2zZ37zI29md18ExCy0J2ZPnhvn0199noZpYdoOpu1g32SA9uS56zx57vqmY9QUmU++5a5bAke+HzBa/ufJV/iHZ86yUqlvorEvFqucuT7Pt165ypuP7OTD9x2iJ7U2mJuuS6HV3PD/LNvhsefOc/LCBIPZBIfH+kgYIf78my/y2PPnN12/1WqDyeUil+aWuXfPyCbgaL5Q4bf+/tvMrZSpNE1qreBc3Kh8uc6nv/rclscIcGLfKPftHUVk8ySgZTt84YUL/N3TrzKzUqZpbW3oOJUv8dK1Wb586hIPHtjGp956F6loGM9vYTpz4NtocvcmerciSVSbJn/0lWf54qlLFKobZST5cp2Ls8ucPD/Be+7ax0dOHA5AKaBhWlybD/wNguO9wQ4zMe1xfN8iot9zy+MGWKnUaJoW9+4e5snz19k7sI+kkW5vx6dUb/FHX3mWpuXQnYgy1pNmZ18gg4noKqoi0RWPUHeb2L6DKqrcSHX4+stX+G9fegbLcdk72MU9u4dfc19+mCqIZA9iZkEgqSQYCPUFUhBBBgTw2wORoLf/bYKgAcHE3fVdfD9MzWlLPQQpuJauT1YdRhZKROVuKnYLz5cCrzRBwHSdToKj63s4nkRSHUQTJbr0fiKqxgNDoyji2iAiCiJD4W38WP/P8mT+C0w3rlGxizTdJnLbc2fE2MXdmUfo1gdYNZdRBY2kPIDmKbRsh7ptY7S7NjElQU4dotisB5HE7YSLpm3j+UGMO0IATtdMi4imYjkuqpdGjoWJZmIsVWpoiowmy4TWAc6CIJBUMxxK3sO12vlgMiBIHErcgyFtBhAUTWFgRy/6epNMKUZfaGRTGtnrVUiKkdQihKTNhr4ZtZv39X+c51e/zYXKKUr2KqbXass0Ywzq27gv+zZGjd0ogkrNrpBQE2jKIJI8wvphVEDkWPpBdDHM6dJTLLVmabbT0yRBIixF6Q7v4Hj2re3tadTsCnEliSZpdN0UjtCrD/K+/p/hyfwXmGlcp+aUKdtFREEkJBp064McSt7D7ckTgM/u6CFqToXYLQA8XQrTHx77rlMQVcHAc0VMx8FxPRRJwnQc8BR6tGE8gkmf3zZJNx2HsKp2mi7rWUoAYclAwMPxrA4A5PoBe0sWAkPnGyyKwfB2ekKDTNQvATBq7CajbRzb/Bsyvx8RiRqAICjI8p5gzNzEQhaYm1xhYCyJp7bQxBySGDQZJibzLOUrJGIhGk0LeUTjanmVsXgaXQ7Aou2RfeCJWKZDLtyPLMn4rkBa7QtSc0QRfIiFU7TcJiWnEHw3ChKWZ5FvLeO3wVpBEMlquQ1yZc/1qNRb2O7a/Kpp2ZQaLcJqIJkvVBsslqskjRCyFEjMSvUWiiRiuS4N06YrHkEAXM/n1IVpYhGd3cO3Nvs8e20ex/U4uLOv08nuScc2yAEA+jLxDT/LxA26U9HA+De0NparbRNeVZaQEDmc7SMsqyS1EL7v0xOOdbbZb2wEaW++F3uNzftxo5ardZYqNVq2Q1hVKNSbKJJIqdlCkSTCikLRbGK5Lo7no8lSACQ5LjWzRlc0wo3x5+by8ZEFmZyWCXwavSqOG8SgW84MgqChK9sRBQ3Pt7CcaRx3FVlKo8pDiIKC7S7heU18LFyvjCL1oki9gIfpTCCLKWQphe87mM51ZDGLLCWxnIVgD3wb211CllJo8giCIOH7NqYzheOuIggKmjyC1Jbje36dhnUuSHiTh5DFFAgCrlvGdmdxvTqSGEdTRhAFre2VVMe0x/H8FpIYQ5O3IQpK299xBcuZAYT25wSJdZ7vsVpptMdpkfTrgEY3qlQLmGAARhvwdBwXTZUZG86STUdvea1/WCsRCRHSFDRFxrRsag2TXNsYXJIlcr0JBAHyCyVc1yPXk6BSaiAIAs26iW06xJIBmFQpNbBMm8GxHM2Gidmy8Tyf1aUy2Z4Ec40SLddGl2ReWJkkpRmktQjfWriIJskYkspMvchMvUhM1am3E5Pf2r8fH5/ThWnuzW2jareYrK3SH07yTzOvkFQNhiIpnstfZ7lV4bn8dQ6mBnhmeZyUbjBZW0WTZIaMNIIAVjvZazSaISSpvFqYpy8c53p1hb5wvDO+SUJgYeL4HmdWZ1loBGBXyWqS0sL0GwnKVouuUIzeUJzTqzNktAgz9SKKKGG6DiM3SbMc36JsXkYRwgxGAgBicnoVx3GpVJrIsojjuExOreDYLtcn89x3zw4URUJVJdLpCJom02haFAq1QMZ8bZmjR4bJr1SpVlukUxFWCzVSSQO7Ld91PY+5uWLbA0kiHgvRMm1On54kmTKo103uP75zw776vs94ocDpuXmimsb9I8NkDSNg9JVKPDczQ0hR2JvL8dLcPImQTm8sxmMXLnCwp4fd2SzPTE/TF4uxN5fj2ekZ9nXlUESRJyYmUCSJ27p7uLC8zEqjwaPbtzEQj9/S9xEgmY1y2+2jFNpMQMu0adRMCvnqBl+s3Xv78FwfxwmYZ4oqY1sOgiDgOC6iJNJqWlTKTbK5GOFwMM4NjeU4eMcoZtPGNG0s06FablK+hc/nUqlKy3YwtEAxUKw3ycaMIKCo3iQdCePjU6g1yUTDqLLEtYVV9g50U3FaRHSNUr2J7Xo8d2Wa43tGiGgqxXqDXNyg3rJYLFWJ6CqiKFKuN9FVhZ5kdJNs+PutH1jgyPN8Xp1a4Lf+9ttcmc8HrGxBINHW2qejBqosUW60yJdrHZnR4dFeYuGtpTOu5/HlU5f4o688w2KphigIpCIhtvdm6UvH8H2YWy1zbXGVUq3Js5emmFst80vvPH5L5goEAMjnnjvHS+NzHNs5SDysc+raLMulGhNLBT578hVMx2W5VOPu3cOko2FOXZtloVDh+lKBr7x0meHsHkJSL4gJQMD1fDzPQ5ZEZEnF0ANkvlRvdsCuNSnZxv3RFBlDV/F8j5ZrAz5hWd+wv5/+yrM89sIFbMdFkSUGUzF29GaJhTXqLYvxxVVmV8vkK3U+8+QZ5lbL/Mp7H6ArsZbGY7oO7i0e3JblMJ0v8dhz5/nLJwKGQk8qRlcigqGrQVJLqUal0aIvFWOse7Oe1XRcFgtVGpaNLIkkDJ2W5VBuBAa1siSSNG5tEpl4De8NSRQoN1pcXyzgE9Dqs1GDwVySZJvlM1+scG1+hZVqnVK9yWPPnyMa0vi5Nx9DUyOoYhrLXcHzmojSRuBIEOAz33mZl67Nokgiewe7GM6lCKkyS6UaV+bz5Mt1FktV/uybL6JKEh978EjbDPMGlVzC0NdkSZKYIqTuQ5Zen/qoqwqVhsnp63Po6kZjVEEQ2DfYzZ6BLl4an2O5XOOZS5OMdqVQFRlJFKg0TJalGsO9UUYjA4Tax1dtmjx9cTKQ3AnwwP4xMrEfnhSb1yvfW8F3xvH9FoIQ5u5YF3dFH0AQuwAbfLtNL1PwvSL4VUBDUo8gtK+L7Tk8u3KFmfoqiijRcAOqbN1xOZH9KDWnRUhSWWpWaSou47VFUmqUutNiwMhQthrYnkvJljiR+Sliapi98YC+XbOafPrlFziY6+HBoTEgWLj1h0f4wMDPsWotMV2bpWo1EAWZpJKh3xjE80RKDZc9xsPsMR5GQGCxWqVh2+TrdRqqSsO2OJw8Tsbfz+OXr3OgvxvTcemKRpgqFAOjfUXBcl1641FmixX6EjEmV4s0mzYzqy3KzRKe76PJMv2JGDu6Mriexx+eeZ6BaJy3b9vJQ13v5aGu997yGtQti89cfAW14nAiM0iqay2WelfsILtiB7/r62p7LtfLq1ieu0luKQgCWa2Ht/b8BMfSD7JiLmJ6TURBIq6kyGq9hKRAdnA0dR9HU7eWWwiCgCro3J46wf7EHeRb81SdMo5vIwsKMSVJVutBf8PbExk2dtAb+nlWzAWK1gq2HyQyRuQYGbWbiLx2fn588JOveR6GjR383NivBYxTz0VsR9reqr44eZG6Y3IkPsD0coOCtkrdsgCBmmnieiKD9gcwVBVDU9FkmYZl0fBt7h0eQlcULK9BwZwABEy3Cvh4voNIi6I1heXVCUtJGm4gQVYEnaQ2jCIEY5YuhUmpWSbql1BEjf2JO1GEzRRuz/NwbDeQN0o/Gos1hBt36zpwUhA6Ub2CIBCS+zcYz6qqTL3eIr9SZdf2bhKqjh2OdsYAWZCxKiIXzs0Si4dZzVcJGxqJRJhGwyQUVqlXTSzLoasnTrlkkUx1IcXWWHMrVp6p+gS9oT4KVoGjqTtIqa/tSdGbjNHbZqb6bf/FWsskqmudY9nK70EQBJotky+cPM/xg6PsGu7a0qPL9Ty++uxFhnpS3La9F0HeyEjbfGrXfqYp8obG2I1KrxvbJKBvHTh08zbfqCx5q9rf183+3i0AsRvvv3me1b4vHNejbgUA/q3K931qTr3jh9Gyr7Bc+SN0ZTu+7yIIMmr0ZxBEmWL9H6m3nkWW0ljuAjH9TSSN91JufoNy/QuE1P24fg3XLdCd+BVUqZd85Y+JhR4mHn4Ez2+yVP4DksZ7iIXeRLnxJRrWK6hyH55vo8p9aJFhPN+kWP9HKs1vo8r9+L5D0ng3IXU/AKXGF2jZl/G8Gj4+PfH/FVnOUTOfod56DkGQMe0J4uG3kYq8Hx+LfOWPsb1lJCEOOKQjH0MTh7CcKZYqf9Bmh1sIgkJX7N+iyN2IgtC2Agjunyszed50eNuWnls3zuVyqcZXnr9Iue1Bub0/Q6tlE9JVVgo1dE1hvl4lX6+zLZUipv1oSPl1TWH3SBdnry2gawphXeX8xCKqHCRea5pMpd7C6E9A02TVNGlJASPYyIYp11ok4jq2JtHdG6dUa1LDJZQMEVEFLNGnKxM8X4ookm+1qNogCxLdoThxJcRUbbUd+KAEzTXfI61G6DeSLDTLDBhJimbQpNUlhaZrIQkimiRjey6SIKBJQdqoACBAy3NoeQ6aqNAdjTPbKKJLCiLB2JjQwlwoLbAv2UtCDTFeXaEvnGCxWWWhWWG6Hrx/oVFmrl5iKBI0bXbEckiCyNniHKooMRRJYboO0/UiXaEol8vLhGWFmKKTC0WJKMH3YDSqk83FkCWZiJxDXTaqgAAAIABJREFUFDYu02VZxPNErl/PY5qBubbtuCSTBl1dcSzLIRrRUdoqBkEQaDQtopaDKApomoyqyjQaJvjBfd9oWNQbFvMLJa5eXcJ1PWzbJR4PB+Mpgal2xNAZ6N/clLI9j1cWFqiYJqbr0LCDRrwPnF1aoths0nIcLubz9Mdj3D04SNO22ZPLcdfAIBWzRVhReWA08B7ujkZwPI+FWg1JFHnrjh2sNAKAt2qaLFSrm/YBwPFcTM8lJMns3NfP//5fP4htObSaNs2GxfileX7/P32eUiEAdwIPLAFVk9oNk6A5LisSghCwkiRJRFVl4okwkiTi+2BZLg++4xD3vXk/lulgtmwc2+Wpb5znz3/v61vu29dfucpgNkF/Os6Z63MdexVREGjZNgeGeri6sIqhq1yYWeLE3lF0VcF0HF64OsOBoR6euTTJcC7JUrlKy7LRFZmXrs/RnYhyZWEVs92caVo20ZCGaTs8sG+MXPy7CBZ4A/UDCxwtlar896+/wJWFfEeSdmLfKI8c2sHu/lxAO0bA9TxWqw0uzS5zaXaZYzuHtvzS932fqeUif/XESyyWagjA0W39fOi+gxxoS7HwAybKq5ML/PWTZzh1bZaZfIk//caLbO/N0J+Obznwe77Pt18d50P3H+L9d+9HV2S+duYKv/+Fp6g0TU5eCCQZP3nicPB7VeFbr1zl/3ks0Ei/OjlPwzpM2EiBGOhEj+8ZYXtvZsN8wXQcfuex73B2ahGAd925l0cP7diEJooCZOMRLM9hrpnHx2dHNFh4up7H185c4atnrmA7Loau8rYju/jAvbeRS0SQxCD1plRv8fjZa/zFt1+iUG1w8vwE23sy/PRDd6CrMj1GlJplbSnjg6C7+MUXL/LC1Rmy8QjvunMP9+0ZIZeIIAoinu9TrDU4N72E43rEQpu9mXqTUX7zI2/uGGH7vs/zV6b5/S8+DUB/Os6vvv8BYqGtB+ZoSNsyrh4CRtDxPSO8eHWWka4kR8b62TvYRURXOzRj03Y4P73E73/xaa7OB4bcj58b521Hd7GtN4YspZClJNIWtOq6afHClRkysTAfuu8Qjx7aQSQUgECW43J5Ls8ff/U5zlyfx7QdPvf8OY7vHWFbTxpdkRnMJYJO2I1jx8f1yvi4SEJ00+fdXOlomNu39zO7Wmb/0OCmZyJu6Ny5Y5BzU4uYjsvTF6d4+9E95BIRQqrKYCZBMhwipkRIqNHO+Z/OF7m2EEhGEkaII2P9qG/A8+KHpQREfFwEKRO89goIYgZB0Dsdd0FMAk7APBKjgNdmHAWliDLDRg5VlDFkjRWzStmuk9ViyKKEIev4+ERkDQ+flBolq8UIyxoiQjDRURQUUWonSqzdwy3H5uTsFFFV6wBHa5+r0q0PML0q4LSaVEyTpqqwXF5itRlEw/dGo4RlhZLZothqIiJScyxEBOK6xlA8gSpJ7OvrQpMDxlFM18hFI2iy3O7eeuiyTH8yhoBAOhIOzK+jBqoUGL4bqorcptf6wDPz0+zLdPHWsR2vew0ajs0TMxP0+SHu0LJUS3X07yHVY335PrRch6Zr39JQUBREMlr3JjbL91KCIBCSDAaNzWkb30upokZvaJje0PA/y/Zc3+PxuXFGYil2JG4NRL+Un6Vit7gzPURECxhEyXCIummTiRgIBH4GsiTRagP8siSSVNa8/YIgCxlFDKEIGqZXx8dDFcN4voMi6O1/G/h42DeZfJftAvPNQIaQ03rp1Yc2RcKJokCmO0GraWFbzg+sN8D3Ur7v0XQb2J7V9s6L4Toe2Z5E0A11V9DltXt2dDiLokg0mxapVIRZp4oqBRNhtx02IstS4F8hiviZCKoiETY0BBFi8TCO46GHFDRNQZJaG5ozuqizM7qLgrVKUk1Rd+u3ZLB5ns+rV+eZX6kQCavsH+slHgk8+yzb4dT5GUZ70wz2tA1DfZ+rM3mWCjXuvW0U1/O4PLXMpcklzo0vENYDlqQAjPSl2Tvaje/DtZk8E/MFXro0S6VudhogfbkEB7b1dEyObcfl8tQyM0slJFFgbCDDcE9qgw/lqYvT5FJRktEwZ8fnqdRaJKIhDu7oI9xmBfu+T6na5Mr0MiulOrIkkk1G2TWc67znwsQiLdNh13CO89cXyZdqgeH2WA/puNHZTr5Y4/zEIo2WRSYeYd9YN+F1DSPTdhifWWFmuRQsEKNhdo90kYyGkCWR1UKdqUYBWRKZXylzYFsvTcvm8uQyA91JdgwFz3fDWZNsOO4y0ejPEFL3Aj6iYGC7i1Sbj5OJ/jQhdS+V5rcoN75ENPQm8F0EQSEd/SiiEGKp/PvUWk+TNG7dAADwsHC9Einjl9c1vCRsZ4Zy48tkY58grB4CPARB64D6mjxCLvZJPK/FfOk/03KuEZW7iOr3EdHuRhAUivW/o2GdIsn78H2bpn2BpPFeovpxgmTcAJyvNL+BKvWQif4Unt9kvvRb1MwXSMrvDHzuelKkYwYr5Tpff/Ey2/sz3LF7gEQk1LkGnudTrDYYn1/lC89c4MmXr+N5Ptv6Mhze0UcuHiEWCTG/VCJiaNTN+i0N6X+YKxM3uOfACAhQa5gsF2tYtsNoX4Zyvcn0YontA5mA/VCo4roeyVgY23FxXBfX85laKhA3QrQsm2rDJKwp+ATN5lwyCgj0hhPM1EuIAuyMd9EVihGSVA6nB1kxa7Q8m9szw9RsE8tzCEkKY9Hg/ooqOqPRLGcK02yP5YirIeYaqzzcnWKmUWOmXuRoeoi+cILuUJyS1eD29DAFq05aM/DxWWyWGYykEAWR25J9bI9mCcsqfeEEDcciLKsIwEfG7iAsK4gI/OSN14LAoJFCl2RUUSKrR9AkGV1SeLBnJ5IYhCj1hOKokowmynSF1ub0Dz28j+P37UKPuiyZFcLyGlDj+z7zCyUGB1IMDWYYn8iTTkfIZaPEY0HDO0h/S7K4VCYa1TFNG9OEkWGVbCbK+QvzDA2mkWUJy3ZIpyOUSg0aTYuW5RCKaCiySDwRJhRSuHx5ka7uOLt391KvmwhioIqQJLGTBCsJAj3RKKbrMpxIYrkuk8UiyZBOKhSi2GwylEgQ03Qu5vOE5AXG0ils1+ViPk/GCKPJgd9TzbSYKBaJqhp7u3KUWi1Ozc3heB4z5TLg07BspsslbM9jRyZDQg/WgLP1ElcqeY5kBkip4baEXUHVFGKJMPVqC2ndemVlpcqlywuoikS9biLJIsmEgdUG2URRIBzWqFSaSJJIOKxi20ECXyikMjtXRNdkensTdPclyeRuLbFVJIk9/V2ossRiqUZfKkYmZnSApJnVMo7rsm+gi2+8erWz7r0x8jquh+N5ZGIGvckYg5kEgiCQioRxPZ96y2TvYBcvXpvFx2dPf46rC6sbVDr/XPUDCRzZrsvnX7jA81em8X0IqQoffdMRPtqW89wM3mRiBjv7srzt6K4OwnpzWY7LZ596pbPo3TvYza/9+IMMtE/+jYqENB7YP8ZoV4r/8Fdf5fz0Ehdmlvj7p8/yybcc22AId/M+vPfYvg6y96YDY3z5pUucHp+j1rLY2ZvlPcf2kYlFEAQ4tnOI4VySs1OLLBSrlOpV0tp1kLchSCFS0TCp6EZzvaZld+RMAN2JKLsHgvQK1w1c19eDSK7n0nIt6m4rWCz5MLVc5LMnX6baNJElkY+cOMxPnji8gZ4NEAvrfPj+w0RDOv/1sSeptSw+99x5Dgz3cnRHP/lGnZbrYHtb6+ZrLZNvvHyFvnSc//iBBzk02rcJvEhHw4x1pzvu8jeXrirs6l+LRfR8n7lCZd3vZXb2ZXFljyvlPKbroIoSDccmpYWRVYFzxUXm6mWyoQi6JBNVNKZrAb1VNWT+zTuPIKsiCT0Ess/Z6gK6pCCLImOxNMf3jOD78Gt//bUOu+3s1CLbehPY7jKa3HcLv4Vg/z7xyJ286869mwxFj+0YJBbS+dX/78tMr5SYWSnx5Zcu8YlH70QSBK4vFFgsVomFdSKdRZCL7zXxef0vgmKtyTOXphDb7KG3Hd21QScrCgIP7B/jS6cuMpUvcWU+z6nxWd5yeBcNM6A8FmtNdvZmO91tx/V4/so0q23Z3e7+HLv7cz8yVGwAQcogSbeSAQZGj53X8sgttzNorMWORmQd2Lw475i/apu72N8PzX1XJoPVNqnWZClgL+IjEiSpOX5gsu/6PrIoduRoN0DW7niU7vjaRMb3fVJGaNO+3XwsAAPJLeQa3+XkOaWH+M3jD7M6sUJxpkQsFXnDqWqVVotKywQEZFEgbQhIYvt5jidwPAeBFo7nILUT0XzaiVQQ/P06JuU/CtV0HP52/FU+tvPIawJHP7v3GK7v0R2OIiWD87L++q9nhgTyMjrvuXG9FDFEVt8IGG58ktr3VduvyvXttlwtkK9dqpxh2ZxHFmT2xA4TV5IbWGO+7+MKdUb35RDZ2ijzh7nOV84wXruIKqqoosad6QeQFalNp/cRBAnXC0CBick89YYJgkCzaTF/YY6+2zLobSPmxxcvsyvWTV8sQTS2dfKTIAh0dcc7r/sGUpuuuSpqDIVHmGvO0K33EpG3bmZ87dmLtKwA6Fkp1+nLxvnUjx1nsDtJo2Xzx//4DB98+BADXQmQAqb1yTPXeebsJHcfGMG0HE5dnObyVJ5SrcnEQqEDHMmyyJ7hLmzH48yVOS5PLbNaqaMvy4ginSbN3tEAVKvUW/zN10/z3NlJjJCK43i0LJt33reft9+7N5Cl+R7/8O1XScZC2I7LfL6C7bjEDJ3hnhRhXcX3fV6+Mseffv45yrUWuhYA7JGQxi9+8H5G+gLm1eOnrnF1epmhnhRXZ/I4bsAgjxk66biB63mcujDD//jyC4H8U5aoNkxGelP8zLvvoi+bwHU9vnN6nM9+80wHlC1UGvRk4vz7D52gLxvnhfNTfOWZi/R3JRifXSGXjJKKhZleKiII8OsffxQv4rFiFtYSVaUUIXVvRxoG4Lh5mvZFVmp/gYiG59fxfAvfDxjeitSLKvUDPiF1Dy3rEvDavkkCApoygioPbXguTWcKUdAJKRv3wffb/pLqQWQxiyc0kcQonlfG912a1gVq5lO4XhXTvoYoGgTAl07SeC+l+heotZ4mHn4rhnYHAtCwXsV257CcWcDDdVdxvUrnXt7Wn+U9x/fxN986w9xKmd/6q2/SnYqSS0YxdBWnLbssVhusVhqU64HR/Pb+DP/Le+6hNx1HFIWAldBevIfdG03VH63vIkEQ0Npm9i3JpisVpTcdI6yreL7PUFeKkKbQnY51jvzGvN/1gkCOA2O9KLLU8atxPT9gx0AHoE5rEd7av2/T5w+si2S/uW7EtcuiyB2Z4c7PhyMZPK9By3ycroSGrt2BIAgMdaRhG5mS29gYxX5zSNL616lbvdbWXie1tXVcTNW3/Lm8LqImbGiEDQ3bayEJKra3BvimkgZDg2kGB4J9HhvduK8QnMM966LYH3xgT+d1LrsGbNzMHKo2TZ6+PEl6OE7TcqgINqbjQ0bhlYVFNE3BlkSqvsP09Tkalo0gCBwY6iEXMzjS10d/PI4qSRiqyjt270ISBAxVJaZrxDWdrGGQMYLjDikKD46NYTouiZBOdyT4HlAkkftHRhAFgaxhkNB1LNclpuv0RGPIokBE0+iORgJ2+7p1jSbJbZuHjbYut6pmy6bVtDADi2NkWaK7O069blKtNqnXLWzbxXZcNEFhcamMIkuYpkOrZeO6HpqmkE5HNyl/bq5MLNwO31I5PNrHUltWtlwKJMp9qTiGpvLE+euMdqWYL5RZLFW5vlQgYYQ4NT4TMODCIRzX48zEPEkjxHyhwtWFFfpScZ6/Mk3CCNEVjxBSFZKG/i/S2P+BBI6WilW++fKVjhzmxP5RPnz/IQxde82LI7+Ge/v1xVVOXpjAJzAf/tD9BzeBRjdKEASGckk+ePwg/+Vvv4VpO3zr1au85cjODUDG+upPx8klDDw/8HoIqSpj3WlOj88hCDDWkyYdC3Mj1UJXA2r02alFbMejUK0ylqyDV8EXcxv25fWqadqcnVggGtbYM7gW/YwgoIgyRjsK0/V8Tl6YYDofGG2Odad53137iehbd2dlSeSh27bx3OUpvnbmCvlyja+evsSh0V66jQhjidSW6XIQPISKLPGTJw5zdNvmlLj1x/f9Dq0128LxPJabVeJqCF2SEQSYqZVoOBau79N0LEpmk5pi4ngeddui5DXZls7QchxKVpOi2aBgNjAUlW2xLBFZa8u6utgz0MWzl6doWg6LxWqbmQKOtzVlEoJr/tDB7beMGt7Vn+Whg9v5yydOYzsuJy9M8J5je+nPJOjPxsnEDGJhbd3/UbDcKTR/bNP2bq66adGViHLH9gG+cvoS5XqL5LqEP4D+TIL79o7yV0+cpto0efzVa5zYN0YmanB0tJ+QKm9gbFUaLZ48H3hqqbLEQ7dt7yTi/GjVa92R6xesUGg1O2anjh8kSYRkhVjb7K/QahLTNFRJxvM8qraF6QT66aiqtu/VtW0WW01UKfCvqJgmtuehiCJxTb+lpMj2XIqtJrqsEFVUXN8nrMi0XBfb84hrOg3bouW6hESFsBQsrt32c9B0AqNkwQ9AJblteO14HqVWk6imBZ4jto0gBKmKIXltge4TsCFrttWmg4tokoShqJsS+1qOQ9lsBQ0BWSaiah3Wout7lFstTNdFk2SGR3uI7RlGbrMFfN+naln4+OiSTNUycX0fQ1EIKwEbJl9r8OzkNK7nsTOXQZHqQMD4lMUY4NKwVCxnGVmKIwlGGzgScNwiEe0wqrz5O973fVbNBs8uTnGusIjtuSS1MI8O7GBbPIMArLYaPLUwwfniEooocTDTy709w4QkBdtz+eurZ9gez1C1TU7n5xAFgTtygxzvGUERRS4Ul3lmcZI7uwZ5amGSfLNGdzjK24Z202cEXiqu73G9XOCJ+XHm6xVSepgTvWPsSa7FX1uuy7XyCk/OX2e5WUMRJXYnczwysANREPj23DVeXJ7lzMpc598A7x3dz/5UN0L7Z88tTtFwbPalu3nn8B7CcjBOCEIQJ36puMx35idYNet0haI81L+dkVgKURC4XMrz5Px17u4e4unFSZYaNXLhCG8b2s2AEV8nwVr/ZAkIgoTY9vnzfZ/55hTPFb6F6zt06wMcSBzbAtjzaTl5avYEUWUMQxl6Q+PmD0uV7FXuSN1Pt742jlbcAoXlCp7bjyCJbUmTz+TMKiFdQVVlLDtY1Duey0yzRkLTMWQV0wsW55bpcPXVaSrFBjsPDpLueuMyLNd3WG4tYns2tmfRcBto0mZWoO/D//GJtxCP6JwbX+C3/+Kb/NN3zvGpH7v3DR27EVL52NvuYGGlwqXJJd73wG08emzXhnmgJIl88OFDNFo2V2fyvOWuPbz/wds2jLu+7/P4qaucPD3OL33oBAd39uE4Hn/5lVN85msvsW+smx2Da8/98+em+Ol33skvf/gBFFmiUm8Ra3utlWst/uSxZwlpCr/28UcY7cvguC6FSoNMfKNs+9z1RW7b0cdvf+rtRMIa5Vogb4LAbPjPv/g8u4a6+Pi7jxHWVC5PL/Of//RrfP7Jc/zse+9GFAUO7+pnrD/DQFcCSRJ58cI0/+dffJOXL8/Sl42DH8hIPv6uY4zPrvA7n3mCX/yJ+/nZsbv4lf/2Ra7Pr7B9V5S0trboFpE3BTcIgoIidZGN/BRKh8Emo7SZQj4mPjYCIp5XRxRDgAiChI/T9hky8fyNZrTCFmCuKOj4uHi+uWVTQGj7nHWuH2C7iyyX/1+SxvuIRI5TbnyNuvnCjbuAeOitGNrt1FvPs1z+Q7oTGoZ2O5IYQVceIBF+R/u7Q9jAEldliY88epTuVIzPnTzL1FKR6aUS1+cLQVNFCBptsiyhyRLb+jLcd9soj96xk7HereWZggBL9RpDiX9JI/F/3YqGdXYOBqz+Gw2EG/NBVVm7t25uON0AX7eqf6nv7WBciSCI/7ySnX/pUkQNx1tLBduze3OIyD9X6YrMvoHgubccF1WWiIa0tu1J0FzUFBlVlghrCpIgcG2p0AEAdVnecL/HNI3xUoFTS3NokkRvLEZIURiIr40zWWOzzYUmy4wk176rwsraGvMGs+jm1zfKBwxZxfbcN3QvpZIGt9022PEt6nxmWCWbfX1lx4bPfp0G6YP7twHBPX7XjsENvzsytmbwfef2tVTBPQNb+/m9/679ndeffHQtyXLf4Mb396fjWK6L6Trg02Yef//P2A/kqm9yucjMSpAeFgvpvOnANmKh1waNXqt83+f8zBIrlYAp0ZOKsW+w+zVPoCAIHBjuoTcZZWK5yEqlwauTCx0D4Zsrl4hQtktU7RJZvRtJVIm3vZYkUSSXiDBeKpALG6RCYSRR7KRgeL6PaQPy7rb0xcd2l9vAz+unaKmyRL1lbZnk0HRNXN8DP2ByvXh1ts3wgdtGel43QSIa0rh9+wDfOT9B0wrS7VarDSqCSb7ZYFsidcsF7UAmwZ07Bv/FJ/H9RpyMFoZ0H6IgthOfwPG99sAfUCldz0MSRRwvSGvw2gtQ34fucPAlsdfvRhRElHXHFNJUuhLBgOP7Pk3bxvWCCZT/Gkklewa6iOjaLY9fEkWObuvnH545S9lxWSnXmcqX6EvHsZ0gHliRxXUsOwFZ6gZha7BufemKzORygflCmVrL4olz4zx82/Z2Sls78lOVuXPnIP/0wgXKjRavTC4wXygz1p1mOLu5s3NxNs9MPnguuxNRDo72/kgt0L7bsj2P337uSU4MjvDW0R2Mlwr88uNf4aGhMX7xyN0sNer8+lPf5BeO3M2udIbn5mf4nxfPMl0pBVLJ/iHet2Mvg7G1wfb3XnqG0USKpBbiby+fZblRZyia4D/efWLD+zr74Lo8MTPB/zj/Mh/de5ATAyP87qlnyIUNzq0s0XIcfmL3Ab4zM8n51WV+YvcB3rN9D5Ig8OLiHJ+9dJarxVU83yMbjvBjO/by8PA2NFlmpVnnV5/8Ou/atptTi3OcX13G933u7hvkY/sO0W0Ez0zNMvnspbM8MTMRAF+ixHA8yc8fPsZofO0+Kraa/PdXT/Hs3AxNx2Z3OsvPHryDsUQANtQtiz87d5oXF2YpmS3ePraLf3t4bVB0PY+/vvgyc9UKO5IZvj55lbJpcrCrh5/cc5AdyTTdsQj7e7sxHYeErqLJFq4XRRBUfFwcdxVdHkGT+3D9BoKggC/g4yGLMURxa9lrwWzyu6+c5EJhiV3JHJokcam4zMFML9vaYNAfnH2K88Ul9iS7qFgmnz7/LEuNKj+x/RCO7/ONmat8aeoS3eEI2VCE2WqZx+fGCcsKd3YNMlcv82eXXuT5pWmyocAU+J8mL3C9UuA3bn+IsKxypZTn/375SRRBpMeIca6wyFMLE/z60YfYkwwmDScXJvj0+WdJaCEGInHKVoszK/M81B/I5izXRRaDDMCYqpHWg8mbts50PasbDEaSfG7iHDXH4s0DOwmvmy28uDzD7736FCktTI8R5aX8LM8vT/NrRx5iOJpkvl7hzy69yAvLM2RDBpIg8uWpi4yXV/mNow8TVTeCDL7vY3pNQEASJBzPZrE1zbeXP89iaxpZUDiUuIeYnGHVrLCxAmNbQxlCEgPZ2xtJM/xhKUVQeKl4kh59EEVU2RHdh6yI7RhrBwEJ33fwgbvv2BbIdCSRYtuks6rYaJKM2k6oEdtwbmmlSmG5wsiuXsKR786HxfN9bN+m5TVZMfP06lsn29y5b6gjTds+kGX7YJbLU8u0zDdGn1/fBAM6viRbN/yC36/9vY7hYjm8fGUOD5/5lQqr5eDcOI5LqdpgerG0ATga68twz22jHdlZeh0gNLNcYrlY4+PvOsa2gWwAKkgivZmNbEuA/5+8946y6zzPe3/frqf3KWd6AwYdIBoBEgS7SInqsi1Llrsix3GuE9fYTrzusq+cOHauFa84145lxZFjKSqULdkkRckyewNYAAIg+mAwvc+cM2dO2+27f+wzBzOYGQCU5bVk+v2HxNlnvr3Pt/f+yvM+7/MkY0Hu2ddHLOz3QSp2jWUwMpVjcGyeXX0tvPSmD277yVLBwOgs5YpNJGSSjIXq11+sWEzPF1AUQb5YWXWehmSE/FKFhkSE9sYEyWgITRFUqg6OdDFqTpsbha5mMdQWitYJYuoDSFmtfd4ECCr2FYqVYyhKlGL1OInQ+xHCwFBbKVVfI6D1UrYvYDujG55jOUy9G4HOYvlJIoGjSGmhKUlU9RoQ47NVr/2N/4y7aGoG18tRtk7Vr9GTJSr2BTQlgan3oSphHM9fq0TMw+TL38Jxb0dTMzjewhp5gXDA4JHDWzmwpZ0zg5MMTswxky9StRy/RMXUaUxGaGtI0N/eQEMiQsDQNlz/jBcKxM0AoQ0Sq++EUGpzyHLcbC248vj13/WBQT+Bpdcs6r+3oaCoDbju1Pe43X/MkATUOI64Bhz9Y663dU2lLe2PYSsZ79dkRMSqPbgnJZlYeE01xcoIab4mZkQ3VjG1vhdRtm2Wqhbp8DXn86CqE9Ju/VzRaIBQaG0V03cTNwOObvT8/2OF63mcnPITnTHTZFumEe2dChy9NTJVd+NoiIfZ2d7MhTOjvgMykmQqQqlYxTB18vNFjICOqgoc26VasTEDOoGQQblokWmKEU4EuTx+rdavtzlN+roysPUiFQnS15JhcHqBsmVzcWwWy3HXdWxLhH3dkrHyMHEjSRCjThFThCAZCXJubgbbdUkGfEvZ5YyYlBLH9bMsUoTxZJWyfRFFCaIZ8ZsugpfL89Y+uBJFKFRc34FobrHI8Gyufk17ulpuOkALIdjS6otmly2byYUC5yamiWUCxG8i+re1rZFQSHK1eBoVDU3RMZQgS04OQwlgKEE86dQ+W8DDI6anKTo539FFz1B2l2pW1pK4vg7bS0qb/w0nAAAgAElEQVRUIYnoBhLPF12tickZ+HoryyUoUhVI6RJU14I5xg36WFXEqnvueb64q6pEMdSNHV76sumb9m9nQ5JEJEi+VKFs21yZnOOOLZ2UqzamrhIOrGAcoaKK6C1tikxdI5uMUrEdsskYd27pJBI0kV4J1xlA4qIbe7itp4UtbQ0cuzjCVG6JF85eXV+o3HZ46fxVcsUyihAc3tJJa3rtQvmfUyhCkAgEOTUzxX0dvZybm6FoWZydnaZgVbmSm6dgVYnoBs+PDvH7x5/nvo4efnT7HpYsi8+/9QaXFub47TvvJxPyNyYzpRLHJ8bYnmnkR7btIahp5CqVNRttgcByXf760lt8+fxpPrplF3e3d6OrKqOFPAO5eX50224+/9YJfv/48/z4jtsIGwaPXjjDXW2dNIcjFG2Lzck0H9m8HV1V+NbgJf7Lay+QjUTZ19yK43lcyS/w2VOv8uHN23n/pq2cn5vhc6dew1BVfmH/nUgpeWlsmL84c4J/uecgm1MZ8tUKlxfmVoGvAC+ODfHe3n5++eAR5itl/uTkMf74xDF++8j9RAyTsG7wiW27OdrWxe+88gwLldWZawnMlcs8NnCBj24x+Fe33U7JcfgfJ4/z+8ef5z/f/RC27TK1WKAlHmNTQwNCNAI+Q8+TFq5XQFMSNTBeXNf6xvGt4Qu8Oj3Cb+5/gDuaOlGEoOzaqMJfOr88OcQLk1f57QMPcbi5E1d6fHP4PH969hj7GtroiqX8LBjwb3fdRW88w1y5yP/1wtd5fmKQPRk/k1iwquxvbOOnthxEFYLHhs7xR2deZKiwwOZ4A5+/8DpxI8Bv7L2PTCDMfLXML7/0GF+5fIpfve0eSo7F584dpzOa5N/vu5+EEcCTkqJjE9H9BdKHenawM9/Mc+ODfKBrO0dbetb83p3pLDvTWS7lZ1m0K6uOFW2LPz//Gl3RJL+29z4SRoD5aolff+WbfOHiG/zCbl/su2BXuS3Twqe23Y6qKDw5fIHPvPk8Vxbn2J1ZnTm1pcVLs9/mzOKrKKi40iFnz1FwcggEO+MHOJi6j9nqIk9NnaDoVii5VaJakKpb5ZFsK1HVIhM8VGcsvVOiK7wZUw1iexa6YiBQUFSFWDKMokk8YRDQ/LIz09Q4c24Mx/EYGJwmnYoguzR0RaUxFCZnlWg0owghmBlfYHG+yNULE9i2S1tPA4Z5axtdXdFpDbZxbvEtQmqIkLb+eioZvWZQYRoa8UiA0akcFdtmPRBDSv5RdGGqlsNCoczCYom/O3Z+1bFdfa2Er9PEakhtbG+8VKrieZLmVPSm83s8EiQSXD95NJsrUrUd3jg/yvmr1za06ViI7hqTpVy1eeyFt/i7YxdIRIK1EjqPas0BaTk0VUVXVVRFoGtqXRgX/LlCExq257sWKiKEoXXCddekqQka4z9HrvgNphf/O4owCZuHCOi+g5KuZFiqHsN2xgiZe4gG70agkwi9n5nCZ5la/O+Yeg/hwGEUEUJKB1VJAQqy5pjo8+1VVCVFU/zfMF/8KqXF/4YQAVLhjxJS0xhqFk2plUqiYKhtqEoMXWsmGf4QudLfoigxgsa2WsmZQMoqhfLTNZc4naBxkKC+3zd0MO/D9orMLD2K7VmYWpZMJIuihPzxu9YPiiJobYj7LK5/YLTH4nX28DspPM9DehJVU6lWbEavzuJ5knDEpLktiaIoFAsVrlyc5OSxAa5enqJctDADOu3dGbbf1smWXe3EEqHV74SUXFmcYyA/zx3ZTpLmWmMbz/OYmy5w5eIkp18bZGJknlKpiq5rZJpibNrawo59XTS1JjHWYcILEUBVV5fneq7H9GSeQr6Mogha2lMEw75Ln225DA1Mc/bEEJfOjrEwVwQksUSItu4G+rZk2byjjUTq5k58nieZm17k8rlxBs5PMDY0S2GxjOdIzKBOPBmmqTVB35YWWjpSpBtiaAEVyyvhyvUdn1eGbTlMji1w4fQo50+PMDu1SLViEwybNLUk2FHr90Q6si7B4PqYGs+xtFhGCEFzW5JwJIDrekxP5LhwepS3TgwxPZHDtlxCYZOm1iT9O1rZtqeDVEPUd+UEyo5NWzTOpmSKiLG2ssWxXXLzRcaGZhk4P8Ho1VnfBa3qoBsasUSQbHuKzdtb6enPkkiF6217nuTilL8nP9DZSiRgMlUuULB9ge5bkTewa5iBZTlomkq1aqMIgaqpmAEdx3axbQfD0KhWHRzbrWsfKTXNo2VwWdfXf9evL/PeKIpLFSZH5/G8mmZjY4x46u0ZD02NLVBYLPsVP4ZKc3sKkEwtLfnrv7fV2sbxfQkcjc3l64uHllSMkKkzOO2XBMWTIeKpMMNXZtANlVgizPDANG3dDcxM5lEU4deul21s28EM6Khhnan8tZKihliY4C0skEKmsUqNfCpfoGzZ6y4oArqOJlRc6SJQaikv/5gAQoZOMhDc0IVMSgfpDgEqHgJFmOhKmutt3jeKWCiArq0eEDwpiethTMW/3pnFIqWqDyIpQtzyxr8xESVYY0e5nsf43CKBlI7teTeUL8nEwgjFolCdI6hGiertFJ0ci/YMqtBpDnRTchcpuQUcaVF2ClheuWbRrCGEQtldRBUGqlCJ62tBmiVnHFn1EELgSRspXTzp1O2hdSXkC+85cyAEhhIhE9iO4Fr5i+N6lC2bmcUio7M58qUKhbJF1XawHJeKZfPm1fFV55U4WM4EitCQshUh1j4TDbHwTVlypq7SEAszNL2A7XjM5Iv1AS9kGkznCkSCBqau+RogePVrv1EUKlWmckt0NCSIhwKEA6YvhFq3oazRhk2DB/ds5tVLo3hS8vTpAT58x841YuXT+SVevTSCJyWpSJA7t3ag1Rwz/jlowqwXihBsSzfw+JULVF2H0zOT3N/Zy9m5GWbLJQZy8yTMIGFd55tXLtIQDPMzuw8SrU2gIV3nV599khPTEzzY1VdvVxWCT+0+QF/Cr0H3F9usOg6Sr186y6MX3uLHd+zl3d2b64tUiWRPYzP3dHRzbn6G4xOjPNDZx9X8Ak8NDVB2HBShcE97N4dbOnA8t0ZFFjwzfJXB/AL7mmsMAinZ39zKJ7btwVRVtqYynJge59TMZH2MXmb1RQ2TvkSaiGFwT3t33Q57eZDoSaT46Z37aQ5HcKTHWCHPVy6coew4RAz/+WwOR9EUtV4WtV6kgyE+umUn3TU2U9m2+fTLz3AlN18vg1q+NsuzmK3OoSs6Vq2sxpF5NKERUkO1sjqTvL1IQAngSKcGtldRhYonXVqCWY5ND7M91cSuVLZeErZ8ja7ncXJ2nK5okm3JRhQhUITK/oZ2/oc8xrmFabpi/r3c29BKR8QXAo4aJh2RBLlq2WeFAjHD5Gi2p34v2yMJdEWlYFWZr5Y5Oz9FSDf48/Ov1ft+oVrG9lws12GqtMRYMc+P9e8jYQTqbMuY8Q8TF18Zk6UCE6VFHm7vr58jaYa4M9vFowOnKTr+HBPRDI629GDUsn9tYV//oGBX17QppaTg5BkpXWHZ5l1BJaRG6Ivs4D3ZjxHRYoS1KJ/oepBnpk/SGszQHc7y9PQbqCJFSBM3Zef+UwyB8N30PAtDMXzbZ9v1F/bofpnQSt07CecuTLCtP8tCvkRTKErJtpBQc3m0kFLS0tXA6JUZHMdl+NIknuvRt2PjsvKV4UiH8co4aSNTL6FcL1ayYizbZalkETB1DF3DqUkR+EyDWruuy2Kxsqad5fFvWavxRivg9TK/uq4SCRn0tTfw6Z99ZE2J9fWbKSHW4Cr1CJp+KfxCoXzTzYlYpj+tE/FIgKCp89MfOMS+LW1r/k5TFc5fneaLT77OB+7eyQ89sAddUxmZynFucHKdk63474prcqTDvLVAzvaZegG9n+bEv0Nw/RpYYGqbaIr/2/o76K81/L7RtWaaa8f85JWK7U5ie/PEQu9FFSFsbxYpHVwcSvZ5FCWOoXVRst5CUSK4Xh5Qcbx5IsYeWhK/seJcPiOqIfYpKhWXfL6Epik0xX++dg0KyfAPEgt+EKvqEAgEEEIiPYlVCZOJ/msURSIlnJ2ZZ6RYpDkimC4WmS3txVD3ETE0dE9naKJKPDCHoarkKhVczyMbjdKVSH7XbBfPk1QqFqapU3Vd3pgcJxMKkVA3dvf9pxT5uQJXz4+TaozRvinL6NVZ/t0n/yfVis22PR385mc+xuJCiS9/7jleeeY8hcUSnnvtXTz2rOBvvnSMfXf08dGfOkrv1pZV792y/o96PRtJSqyqw4t/f5bHvnSMKxcnsar2Kkt1IQTfUgXZthT3vHsXD39kP+mG6Ip300N6JTy5tKrtSsXmC3/8FM88eZpgyOBX/9MPsu+OPvILJZ746nG++bXXmJ8t4LnX9jpCgFAEuq7xM7/ybh7+8P4NXTyllBQLFV555jyPf/U4A+cncBzf+XPlMCWEQCgCTVNoaE6w+0A3H/mpQ+gZgeNV2UjQQ0rJ/GyBbz76Gs88eYqp0QUc11uxzvdBiCe++iqbd7Ty4Ptv48iDOwjehGXzl3/8FM9/+wyKIvilT3+Eg3dt5sW/P8vXPv8Cw1dmsGvupSvPoekqW3a288EfOcz+uzaj6ypRw2SquETJsUlw7T2QUjJwfoLnvn2GM69dZfDSJFbVwfPkdUALCEXBNDW27m7noQ/t4/C9W9ENDct1aYyGCZtmfU0mkYwsLdATvbHD53Lk5oucf2sMz5NYVV+zCAmRaIDWjjSXzo1j2x7ReJD5mQK27QKScCRAOBpg89Yso0PzKIqgd/PGpiplt8x0dZq0mSa6gR7g+PAcv/NLX2JupoCmKXzoR+/g45/y9QxvJSoli//vdx/jxCsDSAnb9nTwK7/7AyhCsDmdZnMq8z1j8n1fAkeLpWr9pUqEg2iKQqY5RrYtim5o6AZs3Z1FUUDTNRpbfHeXbHukNuGreJ6LInQUVWXJtilVfdRWCEHQ1Nc4ka0XqiLq9egAxYpve7jRd0EQ0+NrX/EaiKQJgX6jGkPnKigNqGoWy51GVxtuuTyvbNl+uduKs3tSUnVt9BpwVKraOK4/SWuqesv6NEFDq7OnPAnFSpW2SIyibeN4LqxZgPhh6hqqohLWEkS1FFE9RVCNkjKyNW0jlZAWRyDqoo0+Q2g5U6bgyUYKzjxBNbouQOFKG1faOF4FTQTQRADLKyJQURQdyyuiCA1F0WvsJKPeR56UDE8v8MyZKxy/NMLQ9ALFqlVzgPB8UWEp8eRagMwXYTUo24MEtG50dW1p10ZC6itDVZT6M+ZJj5Jl10UE2xviLFUsX1Q0E0dKC8edQtYygDcK6fklKd51ExSoIAIgi7U+hr29rWRTUcbmFhmcmuetoUkO9a8uMTw7MlUvH+1pTtPZojJSep3mwBYCtXv4zy0E0BlLMFcuk6tUGCks8t6efgZy80wUlxgp5GkKh5HA2NIivYkUUePahN0eixM3A1xamFsFHPUlUzSGrmWx1uvZ4xOjnJ+f5ZHefh7o7F2T2fQ1kVRCmk7cDNR1kxzpi2a7nselhTmeuHKRS7k5X3/JsihY1TXCglvTjfXaaEPViBkBZkpFPClRhWB/cyt3tXXx2VOv8tjAeR7s6uP2bDtt0dXlAL2JFHGzBmagkAyEsF33loUMlyMZCJIJXstYbkqlkUjGiwV2NTSzM9tEoeKDE3lrkaJTxJUetmczU50BBIaiE9N93aCIFmamMktQC2J7NqZiotW0drzapmbRqtIRSaCv59iJz66J6OaqeSWo6QjwgZTaSxjRzVVMrOWS2ZX/XskuWy67lUDFtX0zAgmlGjgDsCeTpT2SwFA1LM/F9lwStX7+x4iq6y/wwvpq7ZKoHqBgV+uaB6qirAKs/N/iM2Mtz8LxXHRFr4/2rcE+bkveRdlZwpYOGaOR3sh2usKbiWrxWhsKilAIqAbnFoeYsxaZrMzTFSxRVcOEZcc7TY+WgaVztAa7SBuNvJk/RsHJYxgmrV0ZDENjyRlDEWadddTX20hTY4xw2GR+oQiGwmy5SNGu1su0wbccDoQM5qbypJviOG/DfUVKD8ut0BHpR1O0WhnU2jj+1jD3H9hMNGRydWKeSyMz7O1vI2jqlKUkFg5weWSWYrlKNBxgaGKe05fHUa8bzzRVwdBVpuaXcD0PRVHXGAioioKha77Tk+Oiade+EzA0dvW18tXvnODNi2Mc3NGJrqoslavklyo0pSK3tCYEaG3wy8Cefv0ymzoaaMnEcT2P3FKZSNC8paQkQGc2RTYT4+nXLtHf0VgX5J7LFzF0jXQ8TLFcpVy16WhOEjR1bMfl9OVxZmuldrcSqlBJGgmWnNqcLxQE6wPzfl/qG75CQqw+5mHheAtoShqQCKFju3PoKKhq1C8Jq2kgeV4RKW0kVRQR8NsS651L543XB3nxxYvousYv/OLDK38NczNLPPnkKT76w7cTCBjMzxX40v95hQ//wAGy2QQekqora1p/OiXbJqib6IpK3AwyXSwiEWiKQtm2Kdo2pqqi1lj7jutRrTqE36aTZz5f4otfeJkPfHAvlZCk6jg43loQ859q6IZv475Sc9CqOtiWw8xknotnxvjmo6/y0tPncB0PIcAM6HiexLb8OaNSsnj5qXMszC7x07/wENv2dNQrJlzPo+hYa7i/paUqT/7Vazz6v15gYe4a8KOqCrrhA9CO7eI6ktGrs3z1z59nfHiOT/7SwyTTkVo1hge4KMraTbvjuDWjAZifWfTBrz97lif/6jXKJWvN96UE6UrQoLkthdjAuRl8Hbmvf+FlHvvysboFPICiKgRMrd6HnieRrsRyPcaGZgH44I8dJKbGCKrrJ/illIwPz/PFP32a5799BmtF+a+qKb6BQq3tasXm9GtXuXppitnpAh/6xB0EQxsn5xzbxaraSAlXL01RLlb5/B99h9mpmqi8IggEdTxXYtXurVV1OPX6INMTOYQi2H90M7lKmarrUnVX75s9T/LYl4/znb89gWOvPqZpKpqh4joutuUiXY9yyeLEKwMMXZ5GURUO3rOFq7MLFC2LhoikpWbmYqoaDYFIfY67WaQyEfr6s3WzCU1T/XVtjYDS0BynucXf28n+ZhRF4HmyzjgyDY2u3oa6FtpGsWAvMG/NE9EiRLUorutRWCgSigbQayWv2bYUm7a1Mvl3Z7AteOPlyzz8kf00Nt9cJ01KyWiNtbX8HOza3000GsTMlSjX5vbv1Zrw+xI4slc8ZIamomoqXZsDeN45PK9ApWqjmWH8jMcEutmN5y2gaW3Y9hVQTFTVIGAeRgidRcvCqgE+QnDLCwRfRE9hOWl+Mwa1wHcbcaSDed398SRkI1FMbaMud0CEABUpLVQlUs/E3Cxcz/O1cMzVA4EqFJacEq706Im0YDluHaVfFrS7lRBCrLLjdTxftb7OKNjw7yCqp4joMVThbzLUt1nnKqXEUIMoQl23/+N6F/EVTgVCQIJr92plRtD1fGaSInzr6L87eYn/9dRrDEzO+Yr6qkI8FCAZCREydMJBg6Cho6sKlyfmGJ3Lr/hxKrqaRMoIqrI+nfBW0F0hrn1PSv8a/WdNUqxYNMQjKwA+D8db4GZOJoBfkx/3XQeslWCn0AALatoAQgg6MgkObe7k68fOkC9VeObMFXZ1ZesOfrbr8tSpASq2T+N8YHcf0aDOZDV3SzTad2oIIWgMRYgYBscnR3E8l+2ZRt6aneL4xAiTxQKHW9rrmhvrVkOt85mpajd8dqquw2y5xNH2Lp4dGWRvY5b7O3tXjWtqDXBAXAMf4JpmxNXFBX79uW+TjUT5+NbdJAMBJpcK/M4rz645X3DFmLWCSFnvg4ZQmP9w+B4GcvO8MDbEF8+d4svnT/Nbd97P1vQ1xy5TXS22/t3OYVLK1d22nAkE5oolBmbnyVcqbGpIEzfiKEJBUzTcGnvIlS7T1Rlagy140kMRClEtgiZ0bM/GUA20GoPQkf6E2xSMMFVeouw4a9hQQkA2HGNgYpCK67AMly1USrhSkjJDqzVabhIbgbBR3SSimxxsaufnd96JpqzNRAU1nYCqM15cvCEbYhmsl/XykVuPmGFiqCozZR88VISoAXd5GoORunujuMFvWbAWuVC4QoOZpupWsaWN7QXZEr0bXdFQEJTdClXP42JhFBhhX3JH/b7sT/bzZm6AglPmaMNuUnoOy51FrvuS/dMORahYXoWqV/HvKQq24zF0eZpko46eTNXLl+cXijiOy9xCkdHxBRYWisS3xijYVcKaSVsoSUvINwXRDQ1NUwlFAmSyCbr6b114VREqQS3EZHWcZaWTxkAz6grGV9D0rak//blvYxoaYzN5UrEQ7z+6A0UITEPnPXdu4y8ef5X/8CdPkIgEsWyHVDxMobialRYJGhy9rZfHX3yL81enMHSVO3d1c//BfrRatl/TFO7Z28c3njvNyFSOUMDgts2tvPuOrQRMnXfd3s/MwhKf/frLfO3pU6iKwLJdGlNRfvYjd5JJ3NraJJMI8+OPHODPHzvO//2n3yQRCdY2Gwqf+tDhVVpJN4rmVJSffv8hPv/4cX7jjx8jEjR9toCUfODoTu47sInOlhR7+9v4i8eP89KpQaqWg+d5dDanbn6CFSHwywuXQ0oPy7NwpYWhBJF4qELD8qp40kUVKkIotedNEDTvRNW243j+RtqVLppiENA6CGgdrBxBXG8JRQQRQiWgd6xzJXCz0uDDd/QRDBo88cSba47F4kHuOtqPXgMwHNdjZmaxvgFVhGBf1n+WhRAc6ehcxWLoS6WpL+pXXlltrHzr7Cijo/M89PCuG17j9eHWrsO2XQxVJ6RvDMD9U4ylfJmpkTlC0eAaVt/8TIE/+b0nGB+aI5WJcuCuzew+2EMiGcbzfEDn6Sfe5NLZcWzL4dybI3z+j77Dr/7HHyTdGF0FHq1s27YcvvHFl/naX7xYs1JX6Ohp5Oi7dtC7JUsgaGBVbYYGpnnpqbNcOD1GtWLzzJOnicSCfOJn7yOW8E2JJC6q2Ngu3fMk4yPzPPr5F3ji0VdRVYXdB7rZsqudlo40gaDOUqHC0KUpzr45QjgaoL07s+EcK6Xk5LEB/up/v0hpqYqiKvRtbeHQ3f30bsnWS+LKRYuJ0Tkun5vg0ltjzM8usedgN5mWEFPVM0T1LOtxVOamF/mzP3iS489dwHU9DFOjb2sLdz24nbbuBgxTo1ysMnB+ghe+8xZDA9MU8mW+/Lln0TSF9/3w7QRDJrblUClWCIYDaOuQCZ56/E0qJYuFuSVaOtIcvGszO/d1EYkH8VzJyOAMz33rNBdOj2LbLpNjC3z5z56lb2sLiViQTChMRL9ub6oqHDiyiWe+eYpAUCfblmLLrnZ6+rM0NsfRTQ3bchi6PM3LT5/j3Jsj/rw2U+DLf/Yc/TvbaUnEGM8vslS1WCxXiAUDhFSDnaksKTN0S/tbTVOJRAM+8O9UUKSH5TqonoKpapRiMOdWMDUNDw/LcYmbARzPxXFsPLtCxDCIBQPIDRKgy2SIqBYlpsfqz8bCbIHTxwbYvLuDxtYk4WiAw/dt5cQrl1kqVBi8OMXZE8M0vvvmwJHjeLz56hXmapVZyUyEfXf0oekqiiLqRjLfq/i+BI6MFRuVsuXXRmuqiUPMz5QIo1Ya5KEoCVQ1g+clEMJA07JI6eF507BMeRcCs8aYkZ7Esm+t/lHib7qX+ztgaDcEnQR+dqdg5wkZ1w9Qkny1SrO+gaOAV8CfTB0EGqoI+yVvtxC241K1HEKmwUoet59ND1KpTfamptY3brbr+bS8WwjX9bCdGp1YQEPEz86lAqENXdVW/LDaYlLWUP/l67v+v+t95sFyL8j1tzdTM4vMiSUqFRtPSoIBg3LZolK1SaciuK5HLl9C01QCAZ10IkxTQ5Rnz1zhjx5/kcncEooQdDUleei2zezpbqElFSMRDtbBtart8Jm/eX4VcCSlhSICKEocRayP3NvuzQEez7sG7ChCENA1LMfhwugMADtVlY7G5YFD3lKZmudJEuEg79m7BYDzY9OsXKjJmrXtcmiqwj07e3j69GXml8ocuzjM+PxONrVkfCR7Ns/ZkSlfRDwZYW9vG0FdJ+wm6yWB/1wjFQySDoQ4NjFKRDfpiCXYnMrwt5fP40iPzclM/fPB/AJ5q0rcMJHAUD5HrlphU/LWaLXLoQmFBzr7+Pi2Xfy/r77AH588TlM4wq6GGwv+r4yB3DzDhTy/fOAId7R2IIF8tULJfntAoJR+ZldXFLalG9icTHO4pZ1fe/bbvDA2xJb0xlbv323MlktMFZeI1frx/PwMihC0RGKkwyESwSAVx/8dhqKTMmpsQEGd8ZLQ4+iqXn92lwGHVTbvKz470tLNH5x8jqfGLvNwez+aorBoVRBCkAmEOdjYzt8MvsUzYwO8p3MLtufy7dGLBDWNnemNKcxvJ2JGgENNHbw0eZW7st3sSDUjkUyVljBUlWwoRjYUpS+e5rGr59iTaSEbiuFKyWylSHMoWrdlD2k6qlC4urjAvoYqqlDQFRVN8TeLy8w0T3q4UmLXmEyqUGgMRtmWbOK5Cd81rSUcZ3gpx3MTg9zd0kNEu3mmPqQGaTBTZMwkRadE1bNQUDBVf340FJ0lp4gQCoaiowoVZUUWMWcvcaU4zr7kZmzpUvEUtFswDfinGJujOziz+Drj5WE6Q31EtTgzTp5qxaZScoilNPRaJr1csZmYzDG/UERVFSoVm7Si1BhosGAVCao6KSOEbmr07+lg9MoMwZCBGbz1/nOly0xlhrZQG6rQOLv4FiDIBv0NeyIa4uMP7eXAtk4uDk8zPLnA7k0t3L6jq64hoyqChw5tIRULMTA6i6oq7NnciqoILo/M1sFzz/MwDY2PP7SPzmyKkYl5xi9O0pyMogh/DLp08ipDZ8fYt7WFpg/dwWuvXkKX0NmSqpfDxCMBfvJ9B7mtv5VzVya59OYQt/nGT9YAACAASURBVB/ezO7+NhI1LSZFCO7d38fSTIFnvvwyOw9vpm1Tc11DchkoPbyrm9bGBKcujTGTK2JoKh3NSVpW6OPs29pGS0OMkGlwdnCSZDRENhPj6vg8kZBJJhHm9h2dtGRivH5+lFyhhGnqNKejREI+iJSOh/n5jx7l5dNXWVgqk46F2L+tg0vDM/Vr3tbTTDhkoCoKDckI9x/YzNkrkwxPLvDw4a30tmaI6yGCaqA+vlW9CkPFcxTdRTJGS40FqlJwctieRViL1cc/T7qImlZmszKPJjTGK4P0RnaiirU6l+oq5yr/fIVCmRMnhpibXSIcNtm7r4tUKsKlSxNcvjyFbbn09TWxZWsWTVNRFJ8xcf10dvHCBGfOjBIMGnR0XJs3JXDhwgRvvHGVTCbK3n1dBIMGI8NzzC8UsW2H0dEFenoa2bGjjdxCkZMnhsjnSyQSIfYf6CEUMjh1apgnHn+zZsddobMrw57dHQhFcO7sOIODM4QjJnv3dpFIhLBtl7fOjDI8PEckGqiXZpmqhqaobyNJ/f0fZlDHDBrrQn6VssXwlWkamxP8q19/hN0HezADBorivze79ndx26EevvTZZ3nq8TfxPMnZk8O89NRZHvmhg6iqwFDUuisn+O/1+VMjPPaV4xQLFRRVcOSB7fzwJ++mrTuDVtPxklKy5/Ze7rhvG//zv36rznh65pun2LyjlfvfuwcQKCKKJysbKoS6rsdLT51jMVcingzxkR8/wtGHdhCOBn092mVGmu1SyJXILRRJZqIrmI+wCkB1PV5++hylJR8E797UxC/9Px+mpT1VLz9avn7X9XBsHxi5cHqUjp4GTD1Ek7KV9fY9nif5zt+c5NUXLtZBowfefxs/8BNHaMz67ovLbe+7YxOH793K5z7zLU4eu0K1bPM3/+cV2roaOHzvFiavzrA4V6BzWxuRdYCj8eE5APp3tvEzv/Ieevqb0Uxfd1YVCrv2d7H3cB+f/6Pv8Py3zwBw5eIk50+N0H6wlYligf5UhlRwtQ7etts6+di/uJumliQ793cRiQXrQMfyte8+2MOhe7bwZ3/wJC89dQ6A4SvTnHtzmP5DXYwtLPrVGak4MaDkWsxWikT1t1ce6kqP01NTLFkWi9UqjeEwuqKSDAY5NTVJxXHoSaYoWFWWqhapUJB8pYrlutze1sbGcOS1JN2cNUdH2AfSPVfiuZLe7a2EIgGklCiKws59XbR2ZbhwehSr6vDy0+e484FtdZB8oygtVXj9xcv1Z7G3P0tHbyNCCExVI24G1pSA/kPi+xI4SkWC9YTA7OISridRlCgaW5DKNfHE5QHGsl0QaTRVQVFaARspi5QqklBAYuoaiYj/IElgsVzBdt1VANV6Ydku+eI1gdZo0KyXbK0bQrBo54mus/6qui5DizlszyMbXscSUgmBYoHwRZ5duYgib+3hDxo6mVgYc81LL7GlW18wJMJBjNoD6LoeC8Xy2sbWiVypguVco7olI0G02qYjoGlrhHuvhUvZPo8QElVEamwZ8MUMLUDUBAqjgIsrl1BFFFcWUUQA252tlR2WCBs7UZS15WDlig2upFS2iIRNqlWbUsXyJyxPsrhUxpN+3ewyNTRXrPDoS6eZyvm01z09Lfyb9x1hR0fzukwsn6V0/Wcqppq9IW0iX6rcNJtvex75kq/poCoK0YBJJGDS39bITH6JZGR1XfCtxGSuQKFc5fWBUQKGztXpeXZ2Zll+dIWSRBErWVqCbe1N7Oho5rmzg0wsLPLS+SH6smk8KTkzNMn4/CJCwI7OZroak3gsEVTjGMo7o37/u42QptMcjvCtwcu8r68fQ/UdxcaWFmkMRchGogQ1jff29PO7x5/jT04e5662Toq2zV+ceYMdDU3sacze/EQrQwh0VSEZCPIv99zOfzn+PJ957SV+564HaYncmoVoJhAmqGk8PzZEQNOYLZf460tn33YNtCcl37p6iflyme54El1ReGtumpJt0RB6e8J+i9WKDwqVlijaFnPlEufnZghoGtlwtL4InyuX+MLZN7mvs4eq4/CXZ99kV0Mz3fEkQ/M5ipZfQruvvbUm97H2fTbU61hD67ylKz87mu3mUm6WL148wTcG30JXFMqOw09s2c8DbZvYlc7yQ327+erAKR4fOodTA1s+ufV22iLxNVTt7yYUIfjYpttYqJb5vRPPENFNn/0pXT659SDNoShxI8Cntt3On549xq+89DhxI4DluTQGI/z63vsIBP3xPxUI8a72zXzx0gmeGruMqWp8atsh9je2MV8t86VLJ5ksF3hlahjLc/m9k8/QEorzga5t9MTT/OSWA/y30y/y6698k7BusmRX6Yol+UjPTsxbEISN6mG26L5oeWaFTfjKsmVoWOczP87kB7E8h7xdYqY6T7OZozUQekeC2Ak9zR3p+3Gly4I1iyNtNF2loTlOQzZFwT2DpkQJ0EprNoFhqETCPqiqKoL2RBrLdf3EkVDqfelYDudPDpGbXeLwgzve1jUJIKAGmK5MoysaMT1B2b22nkjFQ3z43t0AtDetnzFdlg04elsvR2/rXXVse8+1MXHg1DCxVISmjgzvPbIdz/VYnF8iloqgKArVssXLf/sG+x7YQXtXI9tTEY5s6/R1AqOr57lQwOCOXd0c2t7J4p3b/TZWlJ8qisLDh7dSKpT59l++wOjlSdo2+cBvuWpzYWia/s5GQgGDntY0Pa0bg/6HdnTV///0wAS9rWmymRipeKjuRCTwJRDu3d9Xd25zHJdCqVrTShM0Z2J86N7V7JeVAs67NrWwa5MP2DWnY/zg/Xs4NzjJM28M8MkPHFq3dE5XTNpCm+rvi1LTc0uZzUjpl4QuA0ZSevV1jK6YVNwiUS2BLm6tlMu2Xb76leMUChX6t2T9RGSNHTQ6Mg81QfQvf/kVfvKnjtLTszFjK52OEAmbPPXUOe69bxtarR+XChXOnx+np6eJZ545R7li8cADO7g6NMvjj53kwIEeotEAbi1Jl8+VyOWKhMImL790mUrF5oEHdxCLBTFNHVVVaG9PkUpFEIrgzTeHefKJU+za3cGVgWkGr8zwsY8f5uKFCb7xjTfYu7eLq4MzTEz45jNC+Gs72/uHj/vfL1EqVIjEQyQz0TVzKviuXI/84AEOHOlHXaG3KoQvNtzameGj/+JuLp0dZ2hgGsd2efbJ09zz7l3EEr4zVioQIqD6z6ttOXzrr19nfsZnUXT1NfHj//oBsu2pVecXwtfXyban+Ogn72b4ygxDA9Ms5ko8881THDiymVjC8LXgvNUaRytDepLhgWmS6Qif+Nn7uec9/WiaRAiJz/L3yQaGCemmEOmmZSDEQcoyvmSHCfh7CM+TDF+Zqbe/+2APHT1rXbmFEH55lqbS2pGmpd1nEzqySt4ex/JKdGoHV/3NxOg8Tz1+ss6y232ghx/5mXtJNUTX9I1uaHRvbuYnfv5B/vC3vs7lcxPMTRf45tdeZee+Ll9Ye2iWbE8jxNc3OYinwvzYz93P1t3tCCE4tzBN1XXYkmggoOm0dqZ5/8cOcfr1q+TmlrCqDudPj9C1v41ctcJCpVIzhbp2bYlUmI/8xJE6yHV9LPdLtj3FBz9xB6deu8rSYhmr6jB4cZLefe2ETYPWRIxU6Np1T5UL9ETTt0QOWQ5NUTnU1o5VW6fpqorjeQQ0ja5EAiHAUDVcz8P2PIKahsQft8I1ZuGNdmchNYShGPUvuY7LzPgCkUSI5o50XfA7kQpz6J4tXDgzWgdOx67O0dnXeMPfMnB+gquXfYMF3dA4cNdmgiEDV0omlwo0hMLfU6fC70vgqLsphSIUXOkxPr/IVG6JnuYUwxMLDI7OkUlGoFbWo9TojZbj+qLYNYErVVVoSFbYtilL0NDpbPBF7zwpGZ1bZLFUJRO78c9fqlTrui6qIuhsTNZYPeuH67k40qbBbFpzk8O6TkQ3/BKSdW+gAtIGbxGphHxrUnFrm0CgBo6s16pPJJdSkk1FSUaCTC4U8KTk8sQsd27tumnbIzM5lio+ayls6mTTMZZsi3y1QtV1iN5AwFtKD1fmcWQOx8sRNnbgeAu4cgkQBNQ+HG8Bx51BCB1H5nG9RYTQ0ZQUZfsccL0D0rXo6kiTDPvgxXK/Xq99cD3gcmZ4ivNj00h8HaaPHtnNrs7m+st7fdiOS6G8mjqviCCqeuON8chszj/3jcClYoWZml6Bqau0NyRwPY/5Qonh6Rw9zSmiIbP2ezSEMGrMrY0jVQObeprSdDQkMLSV9qYCz/XZeKrWxXK/JiNBHtiziVcuDlOxHJ4/O8gHbvdt2589cwXH9QiZOg/u2YSpayzZNmW3gOkWCKg3wtvfOWG7LpriT3LL2leaorI908QLY0McaPYFTrtiCTpiCTpjCWKG76hze0s7v3n4Hr5w9hT/8ZVnCagaR9o6+fCm7YR1A8t1MVTf+SisG6tsbleGqii0RKJ1R8PWSJRfOXgX/+nYs3zlwmk+uWs/TeEosdrxhBmgMRTxdWE0lfZYHF1R2Zpp4JcOHOHrl87y+uQYDaEw7+/dQmMoXAeCNcX/fkQ3qFZsNFVBqIJ0MMSSbdVBmaCm89TQGWbLJSSQNoN8sLWfuxo7QMLiQomkMEkFgqs29lHdoC0arwNCxyZGefTCGXLVChXH9h3nXn6adCDEL+y/g46YvwHdlEzTFo3x2VOvUahWua2phR/ZtptUIOjr42HREIl8TyGEmBHg53bcwSOdWxhZyuFKScII0J9sRBUCTdH4sf59HG3pYaiwgCYUumIpOiIJFCEwFPjF3UdpCF4bMzRF4WOb9gAQUDV2p7P89sGHSAeuLYK6okl+fe999MUzALSEY/z7ffdzKT/LVKmAKhQagmH64hmU2oJ1X0Mb//lwhgu5afLVCoaq0hZO4FRdBgvzdDemMBSVn9l2iDubu8hZZQKqRk9NwNsXQW+k20lxuKmzfi2GohKpPRt98TS/ffBdXMjNkLcqJM0gW5ONmELj0sQs3eEUv3XgXRSKFoPWAmXbJl+uciTew8x8mRPeOJlwCFdKilXbB+y5ltjpSCUoVKpEAiYLpTJV2yFo6HSnk5iahlkD/sbLs5ScEl3BZe2wd06p2pKziC50cvY8BScHCC4VznAgdTeKGqwJnCqoIsBKc4ax8RwXB6ZIxIJkm+MENZ2gpmN5DgFVr5c4WlUHw9BpaktRXlEaNjeV5wt/+G0KubU6Orqp87Gfe4CW3gz90a1UvDKWZ1FxKySNtYmdG4XruJx56SIXXr9CJBHm6IcOMHppkuHz4xQWlthxZz9GQOfRP3yCdDbF7qNbuO2ebbz+92cYG5jikZ++F1VVePavjnHu1QHMsElje4alXJHn/vpVdh3pZ9vtm6gUq7zyxAmmR+Zo6W1i/4M7OfnMWUYvTfLIT9+LGTQ48/JFzr86QDgW4uiHDxJNhkk0xuprCNtxeeq1Szz7+mW2djfx8OGtVCyHs1cmKZQq9LRm2NrdxLNvXGZhscymjgx7+9s59tYQY9M5Rqfz9LSkuTI2y9OvXeau23rpbUtzemCcL3/7BO3NSQ7t6GJrdxPPvH6ZYrnK++7ywbzXz48wMDpHS0OMA9s6+PvjFxGKoFCscN+BzShC8OKbV6jaLvu2tLGlq4lMInLDJKcqVMLatXm7ajlUyr5bnON65BYLNGdiWLaLovolzhXLJhQQWA4YSjMLdolENIiiKFw8Nczf//XraJrKez9xB80d6XrfTU7mGBiY5hd+8WEymWtrWin9krRCoYJVdTh7ZpSJ8dyNgaNMlE2bm3nxxUurn0td5aGHd9HX10QiEeSF5y9y//3bAQiFDD7wwb11kElKaO9IEYsHqVRsqlWbwcEZPM+jt7eJtvYUju1y+6FruoPPPXuB3r5Gdu/poGUmwRf+94vMzhZ4/fWr7N7dwXvfdxtTU3nOnfNNVBSh4L1N7b7v99BNncnhORINUWLptcnvppYkd9y/bRVotDKEELR1Zrjz/m0MX5lGShgZnOHC6RH2H9nMYGGBfLVCSziKoaoMXpri5PEr9b9/8P17aem4MTu7o7eRg0f76+2fPTHM+Mg8sUQjyCqeLNzw7wGOPrSDu961Fc97nWrVF9RWlDhIB0+WUJQEigjhySKKCOMDRUE8bw5N60ZV21mWBVgp4F0pWXieRN1ASHtlPwEIKQipKUJqapX2q+O4nHxlgLEhnwkUCOq896MHSW0A6C232b2pmYc+tI/BS0/gOh6nX7vK5XPjpBJBEg3RmmD3WrBFKILbj/azY29X/dhC1S/D91a039vfTFtnmlxNh2pkcBbpeCQDQQLrkDSWgaGbhRCCzt5GOnsbeevEEABz0wUqFRtDU4kGzLpDuet55K0yVffW9frAT8pFzfX3sSH9H85ktj17FWPaMDX693RiVexV/a3pKgeObObxrxxndmqR6ck8b7xymdau9IasI8d2ee2FS3UNrebWJHsP9/nln1IS0HQmlgp0xhN1GYF/aHxfAkdb2hoJGBrFisVcocSJK2N0NSZxPUlkBeunXPUwTQ1NVahaDpbtEqtl2qSEWDSAIvwHr7+1gWjQJF+qMDg5x3RuiUzsxhv/qdwSV6bmAQgHTDZlM6u0fq6PqlchqsVr9vGrw5OwZFskAoENStXyICKgNKAoYRwvhyJujc0hBLieZG6xSF/LyoFVkLeLdXHssGmwrb2JcyPTeFJycnCcj7vuDR8m1/U4OzJVB046GpI0xyOUhEM2EiVfrZIObFRPqhLU+1GEP7xIJIowEK6GqbWjKlEEBqoSwVRbawDLysFLRaDgyFoWh9W6QctaQOsh+Bv929cOqrJU9oGwaNCkLR3fEDQCKJSrjMzmNjy+UZwfnfFBzA3allJyfnSaYs3pLmjqdDUmkVISCRromrKqLlUIDVWEbghEgS/K3ZzQaIxH0FSFVCS4StRXCAPpLbIS01KEwr7eNrKpGEPTC1wcn2FkNo+hqZwbnQagqzHJ9g4/+6oqJmmzE10JrprU3imxrKMja6UJACcmJtjV3IypquQrFYbyOXY1NfNgVy+Hsm3ETL9mPRkI8nt3P4SmqIR1o1beAPuaWtmSauD1iXFaojE6YnFMTWMolyNmmqSCQX5+32EEgoCmrboGX0we0oEQnz7yIOYK4deWSJRPH3kQiSSk6fzawbswVB8sfF/vFh7u9ojoBn2JNJ+59z0kAgEUBI90b+butu6aSLFK1DC4o6UDQ/XbTgeC/Nd7HyFiGiyMLxKKmMSTYX5i+2040tcLQ0rubOngtsas744kQXiSV//+ApMDs8R3Bnjz2AAfjW1i79beeimUkHBXexf7mltJ1ECuO1s72dOYXTM+CiHq3wEwVJUf6N/BRzZvrwugBjVfQy0VDjJdWGK+VKIzubFrpOd5XDwzxsjgDC0dabbuar+hyCWArihsimfYVANxCpUqniNB9++RJhT6YmnaQz5YtPzOSemLiB9quqb3IaVEQbAjda2MrTEYoSm0OlmQMIMcyXav+sxQVMK2QbQcIFcs09IXp1J1OH7Vdz3c19NKUNNwFyReSdKeTZDSQnztldMslqrcvrmDA71tjM7kmZ0skYmG2N2Z5cL4DMGsju14tIk4mXSYkdk8M4tLxEMBdnQ0c3p4klfzo/Q2p+lsSCAKgoClUSrYEIWTY+M88cZ5trU1sae7hZlSESUkmF0qsVAq0xNKUak4LJTK2DU3S01VkBKKloWqCIK6jut5XJ1fIBEMUrYd8uUKbYkYVcfB1DR2xruZreSYqMyzK95NNmhSdUffURpHZbeEVIKcXTyB7VkE1CB5e6E23kK5ZOE6kIreiS94vFyi7ttMu663auNyfSiqglAEnufx/3P3nlFypeed3+/mUDl1juiARk4zg8EMMIHDLHKGIiVK4tqUjlartb22jnR0vMdrOayto7V9pJV8rF3La0teSZZWWYyiNAwTSM4QGRjk0OgGOufqynWzP9zq6m50N4DR6gPJ5wtQt26/devWe9/3Cf/n/5fVdR+gVrE5/9ZNluYKhPvx+t/opsrHPnec1iBFVsviBz4ztWk6Ip0klfeXOAovFoyozvV377Dn6QHm7i+yOLPCh/+TU0TiBpIi0bO7k30nhhg60o+sKgwe6uXGmVFc20NLaBw6tYexK5Oceu0pUq0JCALSrQnyCyGR651L4+QXi3zgJ59DkkVUPRzj+vfuhlLLRpiENKI6N07fZeSpXcRSm31DWRIZ6Wtlci7PB5/ZTS4V5b2704zPLvPjrxzB1JXGeRKqIvHm+VG6WpJcHZ3h1RcO8Ndvhu05nbkkuiZTKNcQBYH+jgx9HWlOHtpFf2cGTZEZ7snx9TO3cD2fhZUS1+7N8omT+3jzwii3HyxwbWyWT798kOmFAjfG5tjT14qmyiwVKpy+dp/dvU/GsbTRZhcLzCwUWC3X8DyfStVmZJfL/FIJTZUpV+tkU1ESUYOxqSUCQlTZkZEuNFXha3/8Pb71hVDpMZWL8drPnEJpIOBrtZAbMRbb3NZWKtX48pcuUq3aqKrE4mKpSaz/fk1WpKYEu26om0RsksnIpgDV8zxOf+8eV65MYBgqM9N54olH+9qlUo2F+QJzcwV8P6CjI4WihPLdhqEiCCHHlt6YB7os05tMor1PTs/vZwt8n0zbzr5y31ArycfIhwuCwP6n+pB/P+SwKZdqTIwvcuz5IaKK2vz9fT9g9OZMMxg2Ixp7jzzMl7XVFEWiZ6AFTVeo1xzqNZsHd+cZOdCKKLYgSZ2P/HvdVHnq5DC6ruK48TDRI2gIQgTXu4ckdyCJWUAJgzpAEHQQFNb4adeKsaIo0NGd5taVSQAunh7l6oX77DvS02yze5R5gcOqPYEhb+Yzs2oON96baFKNtHel6Rtqfaz/Iskiew71kMpEWZovUq/Z3Lj8gI++dpRyvoy0g3qXaWrsP9qLqq3PZV1SQjXYDc+rEQn9wzUrFavMlkrYqv/35rNsfp6hEtvwjFarFp7nI4siZcuCBgtUzXOIq/oT8/nUvQplN4+AhB+4jZbcMppo4gY2tm9hSFHcwEFAwA88NMnE8S1UMVzPvMDF9muk5J07B1RRxZTW42TPC1iaXaVUqJLrTK4nCwWBrr4sQ3s7WV4oEvgB575zh5c/fojUNslagPxKmeuXH+A35sP+Y31kW9eLAgJh6+w/pFjK9+Wq1t+SYn9PG2fuTFC1HL545jqH+zvY1ZVlV9f74wNZu1mH+tsZbM9w4d40+UqNr124xVBHtgkZftgc1+NrF26SL1cBGGhLc3jXo8kjTcnEDZxtSa0FQvnlpLbDBiW1Iah9gEyAA0/IbwQh2qhUs5pqYGvfyfU9IrK+rqIiS7y0f4C/vXCLquXw3vgsl8dmODbYtSOMbWJplTev3gvHlSSe39NHLh7h8tIcU6UCKd2gIxrbketIEJQtG40qdbIG5wQgUJqAoofla3VlgAAvrKgGIenzGnKsZjmUajaZ2PtriQmJ+IBG4sl/xCLj+wHv3LrPxOL7TxzdmJzn9swS+3u2ItAghKi/efUe1UbiaE9XK93ZJIosMdyVoyubxNCUDYuKjiSl2Sqju9VEUWiiVjaru4Wgc99fIgiKCEKiMTZ0ZOI8t7uXBwt5CpU6F+9NIUsSy6Uqkihwck8/rYlw8So585SdRXL6IJq0/YL2g2yW5/Ht++NMFoocbmsjruv8f+9d5sRKD6/s2sW7ExO8MzHBx4drHGlv57sPJihZFiO5LJOFInPlErqsMJzJMLaygiSJZAyTg22tvDk2Ts6M8Kk9e5BFkTfGx/hA/y5kUeQvr11HlSXSusGxzg6+MXqPxWqFY20d7E+3hPxsQF0UqEo2ddttJloMTWG6UGg+y9lkBFNV8f2A+3fnuXV1kmQ6QldvlhvvTZDMROnqzXDt4gPiSZNdw21cOX8fURTYfaCLC+/cpb07JC28dPoe+470YtVdLp0eRVVlYkmT6QdLKKrMwaf6m/Dw51/ZS29PmFyRZYn2rjRW3aE4V2ZiZY5MS5zF2QIHn+7ftG6YivLI6s5G9TVVCpNyD1vZslmsVKk5Doc723dMZ85MrPDbv/olHozO09Wf45f+1WewoxKKJNGVijO+tIImKyRNnZnVIpIoMtiS4e78Erqi0J9LcW9hhWzMJKZr3JxZQJYkutMJ3r49TsLQOdrbweRKAct16UjGmM6XcH2P4dYsC6UKq9U63ek4ruejyjJ+4JOLPf5Z8nyfN6+PkTR19nTmUGSRL5y5juf71G2XlXKVkY4cF8emOdLfgabI6IqCoYZok+5MAlEQqNRtLMflG+/dJRkx+NbVe7Sn4uTLNc6NTnJkVydfOX+DTz61l7ip4TcI++uOwxfOXOPzLx7lC6ev8aPP7ue9+7OYqkLC1FEkiZ5ckmw8Qi4ZKp22x2Nhop91nhhFFHF8P0xUNvgEZUlCJAzAj3R1oMpyU9RAkcSmuIQkiHSaWeJKBC9wqbg1ZOGHK4Gd08Kk4p74YVJKFk3SuVe+iSGZ1Dy/uU9K4uZKaSyqE48byLK0Se5aQGC+XkQRpZDvSwiD/GxbgkQ62txn0i0xfu5XXg0d29Uq5WKNd//uKiuLYSKm7tWZr89yq3QTQzRYsBY4mX3hiRzTetViaa5AJKYjAOe/cZXnPnmM+9enQlUbRaKjv4Vc53qwZEQ0InGDSDz0nYyojtTwcURRwIwbaKZKPB1tJiuMqN4MViqFGvFUhHRropmEN6J6M1CqFquc+/oVnvvkMR7cnMbZRl0ubHNT0FSZRFRHkUPOrfZsnLZMyHNy/uYkhXKN4Z4ctx8sULdcREGgNR0lZmoIAmiqjK4qTbSmpsromkLU1DAbYhQRQ20Wm9YSq7lUlIiuUqnZjTa5LJbtMbmwyjtXxskkTDpzCWYWC1uu/WFzHY+F6TyyIpFpTSDJIm3ZOMm4ie/7iKKI63qoPZFAGQAAIABJREFUikxPexpZErFdL6RCCAJaMrHwuyihylYQBBTzlWbbTKVU25SwzGVjiKLApUsPGBnpwHM9DFNlYmKZWzdn+NmfexFRFLh7dz5EHQZBiHxyPXzPx3FcJCnkO/L9AMf1wpYRx2smi6pVm7t35ojFdEbvzpPb0LLz8LSs1WzefOMGz58aZt++Tr75jevk8+stTLomM7tSobBaRVFlDENheLidWs3mlQ/uQ5JCxaVsNkZ7e5Lx8UWWlkpMTCyzvBSOE1VVjnd2P/a3+EGySNwgEjPChPM2z3pbVwrjCZToWtoSpDJRFmZX8b1QGaxed0iqOpooIwkiVt3m/t355pzKtSdJph+/NwqCQCodaSaOgiBgZnKZIHDx/Bkk8dG0ANmWOB3dGQRRQlUPNI8HgYss70IUYxvQnZvba325F4H1AF2SRJ5/ZS/vvHEDq+YwN5XnX//3f8XxF0Z45ROH6R1saaBGt183RUEmqXYTVVo37Wu1qsXY7bnm67auNJnc41H/giDQ0ZNpJo4Abl2d4uRLI5TyFayqjZDdei1GRKVvaDN/ZkxRqXvOprhREAQ0I4xX1tTiTFkmGzUeQWfyZCZJ4mZ0kh+gSCItkSjGBoqWnB6j6NSfqF0eoOIVGC9fpdMcZsWaIaN1smovIAoSphSn7OZZtCaw/BoCAlm1kwXrAY5vkVRa8QIHP/BxAouk3Lrj5wQEWP46qleSBNIt8QY6b/M91w2Vkx/ax+Uz96hVbcZuz3Lj8gTPv7J327HHbs1y/27YphZLGDx9cgjdWPeN/SCgaFlPTHXyJPZ9mThKxUxeO76XOzOL5Ms1rj2Y4ze++Db/5Y88T39rGkPdnD3z/QDLcSnW6lTqNl3Z5BaYbjJi8NmTh7g5tUDVcvjy2RsMd+Z4+cAAUX394V1Ts/rWlVG+cvYmnh9gqAqfPnGAdHT7/s81C4CqV8b2bQzx4fcC0jtA9kITG/xGAYHvEwQOwo40bg+bQKVuoSnypiTImvyxLmlNxMLRXR2c3NPPN6/cZblU5d+9fppfUE6yt7uloSDXyIj6PguFMv/362e4PR326e7uyvHhI8PIksRQMkOrGS7k+g5VlQAoWTaO74VU10HY8lezHQx1nZhWEUWWqlWiqoooCERUFU2RG86VSJMkXAjbsFJRg+VSlfnVEufuTNKWjG1QHnvMnRJCrqd0zGSxUKFQqXPl/izDnTn0DVDAIAjn1Pl70/zhGxeovw+p4jWbXy3x7795jl/45PP0ZFNNtFoQBNRsh6+cu8np2xMEARiqzEeODJMw9UaQJJGIPCRJLCaQtWeaY/gNB2v9mtfbNx/txHts1/4nCgIfPjLMV87doFy3+c71cVJRg5rt0JaKcXJvXzMpaUhJ6l4RcRt03Q+DWa67VlDi1tISP3XwICPZHK+OjBBVVQ63t1N2bF7u7+fi7CyX52bpS6a4s7yMJAi0RKKoksRUsUjVdfmJkf188eZNnuvpYW+uhWMdHXQnEvhBQHs0Rs1xcDUNx/f59PBevnz7FivVGgXLoj0ao8WMcGV0prnWKbLEro4Ml+9MNwPr4Z4ci/kyiixRt12eP9iPriq4jsvk+CJHnh3g6oX7XDk/zuCeDnbv7+K9c2N09WXZd6SXuek8iioxN5Wne1cOTVcaqksamVwM1/WYGFugd6AFx/G4duF+Ux3k/ug8kiRSKdVZnNs+eElloozenKG4WmXX7nYCvwreFIgZEJMIwn88jDZjmoy0ZqnaziNTCMXVKvMzq7iuz9J8kaujMxjdMZ7q6+L23BJV2yFfyWNqKpbjUndckqbB5clZdrfl6CeF6/ssliq0xmNcnpylP5umJx0Gp+mowUqlxt9dvY0oiuzrbGGpXG0io8YXVzjcEyZGzo3fRxJEBlszT5Q4AjAUmX3drezpasFyXB4s5hlsy9CRitOdTdKdSXCgp417cyu4fkB3NklLIorr+fTmUhSrdc7cnWCwPYvluFiOiwCNtm+3maTrTIfjiKLAvbllbk0vMNiWYbVSw/V9UlGDpwa6WClVKddtBtoypKMm/S3pZgvx39dMdeck4unlm5ScKh1GJmRACqqYSuemlq0fFksqGeSGIlaX0Y8sKtQo4bk+NcfFrYbcQmucDPGsyfHcQJMMc6EUytObqsLxzK4mEs51PB7cmSO/WGLoQDepXFi1NaM6L/zI4U3XMDk630wcGZJJp9FFTI4TU+IsW0tNtZjH2aXv3uH3f/1rvPDJI/zI506gRzRunhlFkkU0Q0UzNaSH9vLOoTbe+cpFCstlRp4e4LtfvsDs2DzvfvUCJ37kKJIsEk2YCI1ky82zo1w/fYuAgEx7nIFDnbz1F2f5y//jb2jrz3D4hf2889VzTI/N8M6XT3PkpQMY0fA6RFFAN1RunL7LrbOjiJJIpi3JroM9xE0dWZb40tvX+MizI6iKRERfD4hipsZKscqdiUXScZPWTIxU3OTPv3mZSt1GVxXO35zgzoMF5pZLpOJmSKidTfD1M7c5vq+X7tYkb10YZXJ+le9cvsexkW6SMYP/8PpFFEnk+P5e7k4uIooCqiJh6gqmpvBgNh8mAmMm+WKVty7cZXI+z3cu3ePFowNoG56l6fFFfvO//hO6B1v5+f/uNeKpCLqmoG/DhbTRXM+HICBiqITFJx/XDWkhXvjkEZYXixiGyrMf3I8ki/h+KFEdjet8/BOHeOvNW3zn27eJRnU+8tEDdHQkaWtP8uUvXSSZMunoSGKaGpWKxeuvX+HO7TkWlor83u+9zcFD3Rw50ssb37rBjRvTrOTL/MEffJs9ezo4cLCbgV0tjN1b4PLlCQB+/MdDP0nXFOKxzeuQpik89XQ/58+NcffOHKoq0dqaaPpMh4/0Mj5+nt/93bc4dqyf554f4sMf2c+Xv3iRv/yLswgCDAy0ksvFOPXCCH/+p6f597/3bZIpk77+HPIOrVo/6GbXnR0RnaIkEk+aT9R6pBtaM3EEkF8q4VguoiYwWy3Sakaxaw5z0/nm36wul/m93/o71MfM0bXxatWwGBsAxUKtEUco+P4ykrQzACAS04nG15BxG5MiCpL0aCXDhztEBFHg6IlBPvaZp/jGly6FvtFsga/+2Rm+/fpV9h/t48iJAY6dGCTTEkfVHkaFBBSdOWy/Sk4fah4t5CuUCiGgQZJEWtoTTbLtx5lhqqRzcSBsqVyaKyDKEu39Leimtm0Hh26opLObkdA+UPdcvIdoMzbxKzVe275HwapvUVaDBrLfD6iULYqFKvPTeeZnVqmUalTKFnbdwXE8rLrD3RvTG+4MrFRqVKoevZnkhusOEbJOwyd+nEXlJH2RfQRAUm3BkKJkta7195UUrm/jBx6iIKGKBoYXQ0BAFlS8BkqJIAj/3cG8wKPqVvEb9ysIwiJK7SH10DU7+FQf/cNt3Lg8QXG1xuk3b3LsucFNCSEIkfPvfusGthWKwfQOtLDvSO+m765KIeH4PyQW+/vSyxKFEN1weXyGv3r3Kp4fcObOBPOrZU7t7WNvdyuJiIEoCNQdh8VihQcLed4bn6W/NcUvvXoKVTa2jPncSC8fODDI3128TaFa57e/+g43J+c5ube/2ba2VKzw7q37fP3SXQrVengte/t4cV//Jinp7SzAp+rWKDkF4tpWZJQXBJTs7SfKRvODKpY3hfyYhWqjpWMRMjFzU8JMQMD2XWpWgYFouFhGdJWfPHWIsfllRmeXuXhvmn/1F2/wqWf3saerpRkojS+s8M3Ld3n31gP8ICCqq/zUqUP0ZEOESkLTmzwrO5nn+9xaWOR+fpVGVwu9qXCDzldrVGyHlmikce9guVpFkyRe3NW/I2tSezrOQFuG5VIVy/X4o7cvYrkuh3d1YKoKru9TsxzKdZtkxGBfz9YscHsqxlB7lqVCBdf3+Yt3rqApMs8MdRMzNRzXYzZf4uydCb567haLxQptySiLxQreI6D/G01XwhbK794Yp2rZvPrMPnpySTRFJl+ucfbuJF88c42aHT7wB/vaeW5P77atkL4fMLNSZHo5RC9YjkvddkPU2P3Z5nkLhTJ//t0rGI2qpqbIaLJE3NQZbM80kEchMbkophCErYnQ/tY0e7tbOXt3ktvTi0SN8JfY3ZFjoG19TnuBg+1Xcfwa8PdoUfg+t8VKhVtLi0RVrcm/ggDnp6d5prMTWRRZrla5vrhAxjBojURJ6jo9iQQPVleRRBFZDDeUQr3OuelpBCGEsGuyxHtzc2QMA8vzuLuyjO15mKpKRFGapKgBIa+SqarETZ3e/WEL1NomqcgS6YRJuWrhBwFtmTjDPTls12VyrtCcS6IkEonp3L0+je/5ZFsTTN0PVYxUTWHq/iKKKrOyWCK/XA7JUUWRSNxg8n7YyjU9sUy9ZpNuiTP1YBlRFEhmoqiagigKLMyETmAQBNSrNjOTK/i+T0d3hpnJZepVh96BFtK5GFP3l0LYreBAkCdwHiCoR0F4NJpUEOBArpWUpiM3pKKbmoyNjXK5WuXe0gp96UfPyWxrnF2727l9dZLBPe109WSYdWoUajUUSaRmO3h+gCpJpEyD5XKVIAgYyGW4NbvISHuOlUoVx/MYbMky0BIe39/ZSkzXWClXSRg6LfEoHak42ajZQNlIDRoegXylRks8SkRVebCyyonBEIrvuh5XL9xHFARGDnaj6VudZXENNUlISrq/p62JXIxoCqW6Rc1xw1ailTCRl4oYnLk7wbnRSfpb0k2UahCEfEvpmMmb18awNijrbdzzLDdcc2zXC4sMbBUOUCUJVZb47s1xntvdR1vq8Tx9nudz68oklXKd/Uf6MKOPr05qokJHrJu9iT4EAor2HVy/ui3a9wfdrhbOMRTdT1JNc714kf7IMKpmkMpFuT6/QNlz6EwmKNRqYct6pUomYpKLRbBcD8t1kUURx/UYac+RaHCN6aZGtj3J6lIJ131/JL5+EFBwCqw6eRasBfbFDzz+bzyf21cmmb6/SLVUx4zpvPrzr2DXHSRFIhI3ae3NbqGpOvzSXgYO9qAZKkZE4/hHD3H0A/tQVBkzZiBKAh/9mRebRNjdu1t45ed78fwK0cQKekzguc+1YVcDAmWFQF+k/4UqQ88fQ1J9xMQkH/3HzxDYevM67LrNj3V/DIBIwkQUBSKGyqdePEDdckjGDJJxg/4GNYAgCAx2Z8kmo4iigCQKxEyNT57aR7XuoMgShq5g2y7drakQgR7RkSWRk4d3cWioA9MIC2YffGaYl48NoSoS8YjOR0/soVyzMDSFiK7x2Q8eRpUlhnpy7OrMIAoCpaqFLIsokoSmynzo+G5efirkI9yIqg+CgOnxRcZvz5LIxB7ZyviwLayUWFgpk4oZuI1WyOVChYihEu1L8/n/4bUQGZUwuDe1jKpI5FJRlgsVUq1xnv/gHmKGSldbqoGIE/n8T5+kXrNRNQVZCpXUPNGh/2mT3iP9SMJQiG4yYMp+QP+RKJ37BlDFvVTdCpouUTcKfO7zJ5BFmVrNRlNlYo3g/8DBbnaPbEaZKIrEB17Zy1NP94foMyNUclQawXdPT4af/dkXqVsOpqmhyBKpVITP/sRxKhWLAIhENHRdRddVPv8zp6hV7cY6HWCa/3Hoiu9Xs2rr/LEPoxckSdh2n9rOZFncdG69MS4IuL5P3XMJXI9Kqd48p5Cv8O3Xr73/iw4aCa/AwfMXEcVHx1SyLDXnwT+E6abKZ3/2BTK5OH/zF2eZmwqTYcXVKt978waXztzj9b++wImXRzj+4gj9w22bUKKOX0UUpE10EJVSvdmmJogCZvTRcdhGEwSByIaWUcfxmJ9axq3Z2/JWAU2y+I2WUHVMWXlsK6YmyVxbXuRY29ZkXRAE5JfLvHd2jDNv3+bujWkqpTq27eK5Pr7nh9y9De6ljVNOECBp6HQmDNIbClS6pNBhxjGkJ5uLmmgSUzaqMwZoktncg7bjzTXk8D49rMgb7LCWBgTUvBpRJbqOHBPYIMoQerFBEOATUhgk01GOPDvA7atTeJ7PtUsPWJwr0N2/WaV4eaHEnevTzXtz9LkhIg/Nh1WrTrXBSfoPZd+XiSOAuKnxn330BJ7n8/qlO5TrNvfmlrk3t4wiSeiqjCgI2K5H3XaavkYmZrLd7ycIAnFT5599/ASqIvG1C7dYKJT5D9++zJfP3iDWCJBLNatJBK3JEi8eGOCfffw5Ek9QPVVFnU6zh6zWsk1Pp9B04B5lAgKymCCqHkESn6wFSxAgGdFRlc19s4Ig0Bdpw/adTccO9Xfwzz/9Er/1pe9wa3qBm1ML3PnrRZIRA02RcVyPQrXelInPxiP8py8d5YOHQrTRk5osioy0ZFFlibZYDK1B0iwKAlU7hDlKoogfhCgZ2/PQZZlIA8q9nSVMnc+ePMTE0ipz+RL3F/L81pe/QypqoDXaGmzXw3Y9Pnx4eNvEUczQ+KkXDjO5VGByaZX7C3l+/Qtv05qMYqhKSA5ZqZEv15AlkU89u4+Dfe385he/w0qjdfFxtre7lU88s4ff/fpZTt+e4MK9aRKRsI2jYtkhiW8jATDQlua/+PhzOyLaHM/ji2eu8WffeQ/H83E9D8fbGiBNLRf4X//qTSAM+GRJQpFEenMp/pef/hi9uTCYDon8CrBNkJWM6Hzg4ADXJuYo1iyKNQtJFPjQkSHMDdWeIPBwfItwxv7wtIesWXciwSd2j0AQkNADAiw+vWeYkmUjij6tUZNP7h5CEX06Ygaf2jNI0XLImga9yUSjwgWr9Tp5q0Y2qnOovRVZFDjV28t0qYAveIhiwMv9feiyQlLX+NDgLkxF5iODg9xeXKIlGqFsWdxemuNUTwxRsAkCH1E0CAKbRFQnEfHwgwpQQJYkZKnIvl0diKKAH/isuBUSu6OwKJAwYxhRDW1JxVY8kh0RilqdsmSRGo4htkiIskgiG0HVJAb2tBNNGhw+MYAsS6SzMZLZKJIYJqMEIXRecu1JVgtVoqZGLG6QzERDWXVNZvf+Ljwv3F7rNZvegZawxSAog9iCoByEDejKYt0iX6miNColvh80UXRP5zo5lGpjpVKjZjtIoojn+7TEIpiaiiqFEsilukVAQMkp4wYusiA3yMxDJKOZVfgn/+OHWFpcJZ2Lk8hFMIsSguwxmE6TNEMJ3rgecuaF7WQSAtCXTWIoMns7WsOAQxLpTiXoSiWIaCqHuttZrdbIxSKcGg45pNIRk85kIuTCkCTak7EwaRyE61FnMkGsgV6Ym8rzb3/tKySSJv/i139yi0MuSxKvHBwk2mhtEQWBjx3ZzdRyActxSUUMJEmkp4E8EiWRO/NLOPgk4yaFukXdczmxpxffD/josd3ETI2D/e3Mr5bQlTjJiI6hKRwb7KLi2FTsMFjev6uNfK3Gx58ZoR54fODQILPFEl0tSbzAZ6lW5ZmRbvB4YhRofqnM//Mbf0t+qcSv/s5P0xN9PEeLJIh8ZeZd3lq4hCyKvNLSQkwKn40fpuVosjrG3dI1Sm4BQ4qwYs3TH9lNIh0hluojXw3bgnRFxnLdBu9YyMmjyhK1hm8kAAulCqay3vpcWq1QXCnTtasF8wlaTDaaF3iMV+4hCWHLVs2rEpEf7a+UCjVGr07huY2gRxCIpTYHK8o2c0bTVbSO9YAv1bKVuyyWXP9sPaYQk338QESXdaxgicCcJxZvJQii1IJJjHQdQw6DVS8okowMoUrr4yqqTCS+dT+OR3TikXXHfCMAQpYkssnN9yBiaEQMbcP5MrHIZsfe0JRNymfZ5OZ7Ykpqs40NINEQv9AUmbWu9YfRQg+PsWZ23eHWpQc41uMR1Lbjki9UyaSiSA1Z9fmlIhFdYTFfCflEA6jUbCzbpbMlQToXp1CqMbNYIJuMkIga1C2HyblVSpU68XiWzIYANR43iMc3+9YrdpGquYwuGhhyFD/wqfkVZF9GjshoUQFNDKjaVWwErECiNaqgSdoWHiVdV5q8Q2smCAKappDLbT7uBQ62W0EQRPSYiB6TCHCwAgsxkJF0n7jeKMYIPk5QRkBEMmySjZYjLwhROXXPRxJklB8i1VlREojEDIxtEhWCIDyS/3XTuaKwiUDbcVx8P0TQV12He4Vl+vwYtr0+R0VR2HZteOxnCSH/lSCoyFJPgwbk0eeH4ic+C/VbzeOSoFJzV0lqXVheCV1KUPeK+IGLG9RRxQh1r4gXOMSVdvzARRVNCs4M2dQgn/788xx7fog3vnqZ89+9y/TEMo7tUqtYjN6c4d7tWV7/wkVe+tgBPvLpp2jvSiMgIIkqfrA5qe84XjNJEaqmvb9El7rh/JDfTsbQdr63kiJuiWdv5hdQRJGUZjwyeVRzHWRR3FJw9zyf986O8cf/15uM3pzBqoe/iyAKRCIakaSJosooioQoiYiiwNx0vplMFAiBGKvVGlFNbbawF+wa98sr7E/p70tVbc02JnY2Hd+EpNr85sMJpO3GjMpRbN9eT0T5AfWa3UDJhsfm66vcKU3zdHoIQ9U4/uJuvvnlS8zPrDI/nefi90Y3JY58P+DmlUmmJ0KS9FQ2yrHnBreQ01uui+16P/yIIwh/qHTU4Jdee4HDuzr56rmb3JxawLIdHM9vkjWLjcq7IkmkYwaH+tvRHpExbk/H+cVXTzHUkeWv3r3K7EqJmu02k0WyKBLRFFqTMV47vo9PPL2HTGyrAyEQokrSjfcMVUETNYajYR+i4/mYqkI6ZqLJEhFNYTidaXJ4CAhEdJV0NEzWyJLYbEERkJCEffhegC+B47koUhi0xE29+Zm6KjcJowc61lUsbNdDaizkaXVrxVeWRJ4e6uZ//kcf5o/eusTp2w9YrdTJN6TrBSGUI4ybOof72vnxkwc5PtwTOioN84MAp6EEtZHYK2ZopKMGdTdUwYmqGkc6tvYVxx9msF8bo/HS9X1qjkOk0b62fprAC/v6EQT4/W+d597cMrbrsVSoEDTuhSSJ6IqMusM8EASBZ3f38t985iV+7xvnuDW9gO14TR4jSRRQ5ZCn40eeGuEnTh7G8Tx2tV2H+ZBkfDsTRZFURMdyohwb7OLVp/fSnorxx29f4vLYDPlyDdfzEQghphFN48RIL587dZj9vW3bjgmNFjQ3JNkOocAKNDLwOxLibXh/8zkiotyLIEZDFT/hIVlyQeD5PX188cwNbm0gxT7c37Fp8XQDmyDwNikF/DCZJssMptP4foW69T0cJ0dKtUmqAb5bQxCTdEY8wMP3E6TVBdIqIMhoyn5EMXxGI6rKU12tlKUlVn0PwXZZdPKUxDKj1RWisomj2FhBwGplgahsUKkV6TXbGMnl8Bp8MHtzUVz3PTx/kcCvIEopfL+Irh7Fdu8hiWk8fwVJSBLg4nmL6NpT+ES4X1lk2SqTzJo4uJQ8i1RrhLlagQWrTLY9hojAVG2VWsJBFSV6IwISOiuVGlNzi9QcF8GF5SUHURRYLdVIBeF3XK2E64apKuzqbA3XjuT6mhltBAb55TKRqM7A7vbwQQ98Am8KAQlBXie+zFdqXJycIaKq6IpMvlojF4tiKDK26zG9WqQjGUeVwgr7XKFExbbZ39FKTFM50N7KQrmC67vcKY9ScsqoooobOBiSiRe4ROUoXsLFNSvMBAVmyqArOrqaohaodKfXkqzrcz4IAsx0onm8u/H/IAjoTMWbx+OGRrwRLHYk19ffjWMZjbW07riosszxXV1NboD7d+dYnF1tBPNbt3tREGhJRJktlBhfyrMrl8ZQFYbas5vO29fdSs12ePvOOO2JGHFDpyuXYLlcZWxphbipIQkiC6Uy12cXKFkWpqKwatdxBZ98rY6hykzmCyxVqxTrFpbv4ooBs9Uyy1aNkZYc783MUXPCQkB9yaU3leSZnp158zZaEATMT+eZGFtA19VtlUG3s+OZPRxLDwNQdMqoQhlNUn7oWmcTSpqs1kZKyRKRYwxERkgoobqgKAikzXVF0Wig4rk+tuXg1j0sz0EUwn1JUWUGs+lNleO5iRWqZYuluVVSuViTQ8jzPeq+gyGpO67vsijTqrdhSAaKqGJIjw+QVxYKjN+aeaLvvRHRsJPz73s+Vt3BsV18z0cQQ5UezVBoMU9tcPDXxtrcBrL52Obn3G20R7iOB0GAKIX3UNVDhOWTBCSu62HVHNwGD50giiiqhGaoWyr4O94HP8BxXGzLxXO8ZjAkyiJq43reT3BUrVjcuDj+ROf6fsDU3CqFUp2hvhwduQRt2Xjob3bntpy/dhkRQ6U9l2i+TsYNBrtTgIMgPD5BGZGiHE0+jS49GknRF+kPP3eHTLHv+9h1F9tywvkhCEiKFLZgK1vJiYv2DAVnGturYMgpys4iPg6GlEYUJFzfQhJkDDmFIpqs2hOszRtDSiIJCpZfDpMJvkVS7SarD2xzZT+YFhD6k+VClWhyc0wUBEETBfM4C/mr1s9VFBlRFEhpOruTOXRZRigFm56R3sFWXvvcs1uSgI81QaC1I0mAj+ctIAhPis4JGorQDm5goYgGkhj6yiVnAdevI4kqFXeZAB/br1JzV1HFKBV3Gdevo4gGNa+AH7hIskj/UCs/8wsf4hOffYbz79xtomyKq1V8P2BhdpW/+oN3uHbxAT/3Sx9h8GCOqJzDDzYneWVFbvr0QRDg2O8PLbo5ISfieyH9wU6k59s9XwPxDBFFQRV33m9tL2x7H05lSGyI93w/4OZ7k/ybX/syMxOh+JRuqowc6OLw8QH6hlpJZ2NEYzq6qaKqIQXLb/y3f8nZ79xpjqNKEreWl9FkmagWJm5lUUISxCeWnveDAMt1m7y5awXKtTZboKncLgghIk5piMes0a+Eok9CszV8+8/xWbaX6TLCNjjP8ynmK2ERJQhLOxWvTtWzsAMPA+gdaGX/0T7mZy7j+wHvvnGTD712tFnkqVUszn/3DrblIgiwe38XPbtym31WGmhyw+AfUnH2+9rLWktEfPzYCM8rGOLcAAAgAElEQVTu7uHG5DyXH8ywtFqhYoXZO1NVSMUMhtty9LWn6M4kqQcu1bpDUjWwPBdTVrD98OESEJAVkU+f2M/BXe3cnFzgndH7+HaALskIKjzT38O+3lba03HimrbjxvzSgQEONAL+VHSzJLwiiXzymb2c2hvCYdMxs4lqgjDR9NMvH+PVZ/by1rUxBtuzPFhcpbfRzvStK/eIGRpPD3Yxs1KiPRXD0BR++bUXqDfam1IxE0kMFWmmlwu0JmNoiszsSpGYqe2IYPECn7JTp7Mlzi+8+hwfnhni/OgkM8slbNtDUyQ6MwmGOjPs62mjM5nYogxWd12mVgvsyqSRG9/ZUBV+8dWTrFaf4ut3Rnn50ABTxQJ9qfffylSo1/nCtRt85sA+UsZDPeqKzEv7BxjuyHHh3hT3ZpdZLlXD4FVTyMQi9Lem2Nu9M1mZKkucGOmlrzXN+dEp7k4vslQqUrFvYGp1Dva+wtODI3QkY1ilOpGYwa986iUEVcKUZRzLRVBlquU6iiYjyRLZiMGv/aOPUq1a5NIx8nMFjvS00/OpBBfHprkxtcDsUgFNU8hETfZ1tvDcgX6w/VCyfAeoqF21OZbO8eF/8ikMM9y8rEbvbzS2/UZYLtWplOq0diSRJYmWRgVSEAQkqR2knUkCM7EI+3tauTW1gCQKHOzroD21WepTFU06zP2Y76Od8gfTgoZiRmieN08Q1IAAUYg02v08AlwEQcHzZgiUkeb5uiyzr6WNa4UqsiAhIZC3S9Q9mx6tFU1Umn3Piijj+h5i4OMT0BqN0hoNf7cg8AmC40CA680iiRkQJETBRJZ7G4TpHiATBFV8v4gg6EiI7Et0EcQDJEHEb6gWqpJMdySDACiihB9ATyRDEIAkimiizHS5iOt54WYpiywWK8QNjZihNfm0IEwyC0L4/D9qw05lopvmeBAI4OdBzEETFwFtiSivRAbC621wea31aRPAvo6WEOfW6GlPGDpGA0mxUq1zfyVPxbbZ15Zjf3xPCHfesGkGBMiCjICAj98IxgCE0Olge/LPR0ndPsl5251jqAr7O9fXKdtyGbs916zAPcrmCqXHOkiaInNqqA9ZChFwu/x0Y76Fjk5AQH82Fd59Idwf1xLwfsNZksQQUeX5AZIoNHjUQudMl6UG8XXQrDivFTmAZiJop8v0/YDxu3NUyxa6vn1CfqO5jX3cC3xsP3SArxXGSMtT9Ji9aNL7E8/4fre4kuTZzAcwpQiSIOPjr3P+QTPZWKtY3L89y61LDxi/NcPM/SXKxRqiKBBNmHT25xg50suxU7vJtIXt4u29GTRDQTdV3A1JiVWnwsX8HU5k9hJVtvchgiDA9V2mrCk6jA58eWvQGAQBlWKd5YUCq0slLn7nDoWVkDx4enyRd1+/2iS53mit3SkG9nZRWK3iOh7pbHRTEBmOW+PSO3e5/O5dHtyZpbRaQzcUch0pDhwf4NCJQXoGWze0A2z/HW5cuE9+qUSmJc6eo31hInMqz7uvX+HG+XHmJldwXY9owqBnsI0jp4Z5+qU9m7gm6lWbGxfGqZYtdu1pp60ny+pSidPfvMa1s2NMjS2ERLFRnZ7BVo6cHObQc0Mkd9jv165tZaHI3atTjF6bZOzmDEuzBey6g6LLpLIxeobaOHpymD1H+jB2aO9cax3OL5VYmS9w9+oUU2MhZ2V+scjZN65jRrcm/eIpk6HDPdTqzqa2DUkQcGyXW5ceUGgoXj1swwe7yXVsUAoCfMr4zlUEuQ/EXuYmVxi/NYPn+rR0Jtm1t7PJj6NJO3+X8ZszzDYq7L3DbXT257ast2vPw3vv3uXK6XuMXp+imK+i6jK59iT7nurn8HPD9I20b5pXkqihiCaSoCKLOqacDltTpBS2X8HySqiiSUxuxQlqqGIoxS4iIgoyPh5RuYWqu4wdlN832uH73TRN4caZUQYPblU381y/SUb9WLUw18NqFOohlJOXJBFdVtiVCP3JlVppU8tyJKZz4uU9m1S73o/5QY0AC+EJQ14BkZw+RNW1KLlVFFHGC3xKjoAuDeA1OkhUKYGIgBt4pDUxDNQFBUdwcIKAhNqJ5amI2JTdOiAgZCQ+9OmjPPeBvYzdmeONv7nMxXdHWV2p4Hk+N69M8ie/+za/9KuvQYQtxZBYXEdqdH4EfkC1XN/6BXawIAiolNbpUkKC/DiV1Qqe5z3xnC3YNWarRfamWlF36ELxAp8HxQKtakDOjDTHLhdrfOVPTzM72UgaGSqf+fzzfORHj5HOxrbla6rX7C1OhON5JIzN8U/VtYnIKpr4ZCpiq7UaN+YXERFYrlSIGzoRVWW+WCaiKcQ0jYSuM7G6iqEoLJYr7G9vZblSRQBKtk3dcUmbBiMt2R0/Zw31vnFNbBYrGv9EZZ12PYXcKNYoqsyzL43w7a9fxbE9HozOM3Z7jn1HehAEgcX5AjcanG6qpnD0xFYOJIFQlGuhWv4H7Qz5vk4crZksibQkosy7ZXbHWjiu6cRVPWQLt+ss1ascyLQzVlwm5ZvMlIoEBOxLt3F5aYY9qRZury7i+T4BUHYsRlItFGWLXE+EV9oHMWWVQ9l2bucX2Ztq5UZ+ntG5ZV7uHCCiqGHg1nDa1ngUIrpERF8jhQwAPwyGGq+TEYMggHdv3adctzA1lbZkjJmVIp2ZBIosMdZAzFTqNu+Nz9CWjDKzUuTsnQk+dmyEUs3i8vg0qeggFcvm3OgkCVNnV1uGb18fQ5UlenMpvnT2BkcHOjnS38GFsSmO7urCdj3O3plEV2WSps6DxTyqLNPRE2XJL2JKKo7vEU1pnDzeQ96qhgFtJMODyjK+4FCXbTx8JlcKzJfLzBRLtMViuL5HvlYnG4nw1tg4tuvSmwoJY+fLZTw54OriAhenZvjI7iHqjsNILsfZqSlsz6NmO6RNEy/wKdTq5KJRWqMRbi8u0RqN8nR3J3Fd31HxTJZEenJJenLJrW8GQfM3CgKX9WpiwHqA6iHi0JmS6XpmD7CXunufu4v/O7Y3R09qLwnlOHcuTzIzvsDB54cp3F9h+HAP4zdmyCsSRkTjzuUHdA+1IasSgR9WSSqLJTpTcU6/e5enX94LFYcDyTR7InEWonlyHUkqxTql5QqJiMGDmXlGb87Q0Z1B0WRKq1UicZ1cW4Ll+SLzM6uUCjX6urNMXJuje1cOv2BhaDJyzWfs1iyJdIRaxULTw0Bg/sEK6VyUvtb3n9gp1SzuL4S92DFD54OHBrdtUVyxJ1BEHVn84QrWNpooRjH1F5qvfb9MEFiIYnpzIk3ZQxDYeH4e8SHuqKhscDwTIhEFBCKygeU7JJTojgH1wyYIIoIQolcUIYZtuZQKVerVYqN6ZyErIqrmE41FMcx1BGJE3t4RV0UZ3w+olGqUi/VGsiJAUWViCYPudIKeTPh8BQFUbYfIY8gp1wLZes2hkK+EG34QICsykahOLGmsE2gGQdiuFmwOQDRZbkKPQ+LEOsWVMvW600QWqKqMbqhEYjrdqXViU0GATMQgFzVDHpfHVK03muv5IV+U75KIiEhSmLjyAn/HKlYQBNSqNqVCtckBIYgCiiKh6SrRuI7WQAXUXAfLd4jJ+pZE/JqVijWuX3rwRNcriSL3l/L0ZpIYOxBJC4AmShRWKlTLFq7nhU66oRJLmBjGkyEW9IdcBdtyKa5WWKlYIRpSFjFMjUTKbCQSwzFd12NhqUQuE20qIG00x3a5ev7JEBAAc/UVZEHiamGM0fI0mqgyWZ3j1fbuUDb5faiR/qDYeOUO3WY/SSXN9cIFeiNDJJT1Ysz0+BJ/+K+/xtWzY6wulbYd4+qZe3zjL8+y50gfn//lj7HvqX5W5otcPXuPdCNpAuF8Lrs1JEHECXauZHuBR8FZxfZt5uqzTRTUmtmWy1tfusDf/dkZ8kslivlKGOA0tvMz37rOmW9d33bsj/zEcf7z/+nTTIwv4jaKI2sFE9/zGb0+xZ//zhucf/smVm1zgvXOlUm+9/WrtPVk+LF/+gFe+dQx1B1QCoEf8Kf/5hucf/sWR04O8y9/9x9z8+J9/uA3vsbtyxNb+H+unxtnZbHIgWcGNjnoq0sl/t2vfomJu3N85udf5vmPHuCPfut13vveKN5D3FE3L97nra9c4tip3fzcr7xKW3d6y/PnuT7n3rzBn/zbb/Lg9uyOSeRzb97k9T87zcmPHeJz/9WHyHVsLtAFQcCFt2/xp//nNymuVFhdKlMu1pportFrU/zWP/+zbcfec6yPX/3Df4rv+1uQJPWqxR/+5t9y7ezYtn/7y7/xU3zgR59a39sCn8CfDduEhAaX6Nwqv/Mv/5rFmVVGjvTyL37787R0PrrAWC7U+P1f/xrn3rpJNGHwi//bT9D5EOeH7/tMjS3yJ7/9dc586zq1ir3p/dGrU5z+xnVaOlN8+ude5IOfebrJERNTWokpjSR+AAQ1wAJUvMDDFCVUMY4pqYBGQk4QhlABCCIgQuAQk6OUHe2HTm22VrHItCeJJiNb5qzvBxRWQgTF44iaa1Wb/PK6il0qG9tCeq3pCi3t62tKfrFEtWz9vRNHIaGCSMCTidwIgoAsaExUF9FFmSm7QM0L55If+CiizGxtFU2UEQWRDiNJzbORBBFZlCjYVSKyRpuRZKE+T0TWqboWc/UCHUYSOSqSycVI52IcONbHzfcm+PP/99tcOj1G4Ae8d2aMa+cmOPmhfVuQQPFUhGTaZGm+gOf5LMwWcBwXRXl8OF+r2k2RA4BMLkQQerbH++nxtn2Pol3f0Y8BMGSZQy1tzFOn5rrNpOL43TmuXrjf4NIUePrUMD/20ycfqcjnuj7OBqSU4/kslitEEwYp01gHbIgS09UCnZEkMWVn0EdzXN8nFzEp1S0QBDRJYrlSYS1OzFdr1ByHsmU3kU2257FYCdu+LcfFaAg5PQotHRbaNhR8RBHdVClseA4WrSK3S9P0RFqIEN6bPYd76B9u4861aVZXKpx/5w5DeztQNZmL74421fHaulIceXZg2+8bEDCYyjyWJuf92A9E4mjNqq5NybHQZJmVUr5ZFa06NpIoULDrLNerxFWdgl3H9lwWamXazCg112kmIdrNGPl6ldHCEoasEpVVaq5DxbFZrFVYMaus1KtY/vqmX3OncfxVREHDC+r4fh1NbsHxCqhSBsfP4wcOkqCDIKKJGTQ511So8X1YKFSoWg7Pj/TxxtVRFEnk2EAXVcsmFTWaELnubJJdbRkO9rajqzKyJGE5LpNLq3Sk4uzvbWOlXEWRJaaWCgy2ZxlsS3O4r5101CCqa1iOy9Ryge5skpplc/XBHP1taTRZplJ06GlNo0sKZccipYbIpaisIQoCGS2KIavYnktM0bE9D9vzuJ9fZa5UZrVW54NDA8yVJ7E9l6pj81xvD9+4c4+0aXC4o52iVac3kaDuOAykU7w9dp9aymWqUEQUBJ7v6+Hd+5P4+Dzf28uZiSluL4ZkxPdWVtjb+niei40WBAGev4ofOCiiTmBfAjEOgQX4ENgIciiPGvgVECQIfAgqBFIGQerGDyxcP48f1PH8YqNaZ4XytI7HynyBSrGO53kszebp3NVKfqlE70g7M+OLiKJIR3+OuYklhg71YEY1PM+nVrFYmS9gmBrZtgSe65NfKDb6qQEBWjqSTE8sIwjQv7uN2YkVJkYX6OrPkWtL0NaVanLGtHenmbAXKOSr/P/svWd0ZOd55/m7uW7lQiFnNNDonNjdbOYsBgVLojRKVhrb4/FY4zm2PPZO2tnj8ZFnZu1dW/LIa1key5YVLFk5kxRJMbMzO+duoJGBQuVw890Pt1CN2N2UNGtJZ/8feAh01cWN733f5/mH4kgGz/MZuzxH//o2pseyeL5PV18a8yZ8DFY7j/vPX+V8PUlva28bG7tXuxY+IlJD0/+L6HO0gIlKgbCsktLCiGIMWN3wN9DRr8Fy82mwHsNyCNvymakVadPjN02r9X2fatnk2IHLHH31IpfPTTM3U8CoWoGMKqKRTEfp7m9m0/Yebrt/E81tK/1AFrZVyFXY/9w5Djx/lrErGfLzZXzfJxrX6R9q49Z7NrD37g2kW4I45WhIXSEhuXpplpefOY0kidz+wGbaOpMcP3iFl58+zeljV8nOlfA8j0g0RGdfmu171/HAm3bQ0pYATBBWT2PyfZ9SIUiTOPzKRcavzJGtp69Iskg0Hqa5LU7Puha27Oxj254B0q0x5soVrubyuJ7PYHO6cVeePznO4Zcv4qySjhhLhnngTTtRwwpHz0+AALdt7kMR4FR2mprrsDnVSlwNLdm/SsngwAvnOPrqJa6cnyY7V8IyA9+lSFynqTlG72ALG7f3sH3vOubCwaJtfbwVXRQbkphspsTMZJ6pq/NcPDvJxTOBnGd+psBX/+7FVf1nonGdjls6SUfDa06OXMfjyvlpXnn2DMcPXmZqPItRs9FCCumWGEObO7n74W1s2tHTKG4tRzFf5elvH6VcrLHr9iE2bu9h6mqWp751hFNHRpmeyGEaNlpIpqU9ydbd/dz54GaGt3YHJqr4nDo3yebhDro6kriuRyFbYWYyx8ToPBOjGY4fGgGCmOFvf/FVEqmVLJdQWOXuh7fRUTcjbjNTbE0MkFSjHJw/gyRM/EIaYwPk7QztXhc+Pnl7nk5vacdf1WTmZ4qU81WSzTF6h1rpHmwlnori1xfSJ/ZfpJircurQZT7/8Sf4nf/zPfQNt9PckURWJCJxvVH0jcghEkp0TZla0JZxSahJLpcvEpbDhOWV0pVivkq5WG10tUNhlWx9ohtLhkmkVy+cN7XGsUwHVZMJRzRCutLY5tWLM/zlH3yDc6+NIisSw9t7WL+9h3gqglmzGTk3xenDV5gcyfB3f/w9VE3mnjftvKE/SrVk8NrLF/jcnz3B6Plpeoba6BlsJZYMY9RsZsazjF+aZdMt/Q3p7Wo4+uJ5zr02ypkjI7T3pBne0UtrVwrX8Ri7NMOJ/Zeolgxe/eFJWjqT/PPffxPasi6xKAmIksjUaAaA9p406zZ30t7ThKar1Mom549f5dKpCcqFGk9//RCJpigf+OijKyRwlbJBMVcBAZItUfSoxvx0sOAMhdVFkdBL0dbdhCyJtKXjFCtLGQ2KKrPnvk0k01EqJYNq2WBqdD74O6tCQBCiiGIbQl3C3T/cwfC2HuYm84xemObSqfEbFo4mRzOcOxZ02Lv6W9h0S/8KKfHkSIa//ti3OPzCOSRJpH9DB5t395NIR7FNh9Hz05w6fIWZ8Syf+7Mn8D2fR997ezD+LZYr4uE5p8GdBzGFKMaIeqcQPA3XrSf9immCBaaCICYAF98vIXp5YuiIvgS8vnnszzJESWT8wgzRePDsLsf0RI5qxVwiU18N87PFBltNFAU6uptW+PiFdJW+wVYkSQyKI9MFZqbydPT8eAx3Hx9RbEaSum/84UVo1mIYrkVrKI7tuYTr6yLbd0mpYVRRRhFlIrJG1bFwfBfHc0kqYcKyiixIJOr/H5IUkmqEpKITkq4981pIYfvedYR0lfGRf2B2qoBlOVw6O8W+ezeihZY+n7quMjDczsUzQTDOzGSOXKZMS3viuoUS3/eZHs+SX8QUHNzUQSlbZvLKLB3rbv5etVwXw3UabPm1IIsSuH4jpRVgYiSDUU+9UzWFnbeuIxRem20czHuNFQxHw3YQLXvJtgWgORRBvMn1SEskQmcijh/3GWpJNyRrAgIIXJOkQSOYRhQE2mPROjs7KIbVl3FrIq2mSSkppHpysCgKNLXEUDW58c0WLYG+jG3Z1Bxj122DXDk3jW27HH75Io8+vodYQuf4oSuBT5IAu24boqV99bm+6bicLs3Rm0iivA6P4uvh56pwtC3djirJRGUVx/VIaCHCiortOWiizC3N3SS1EJIgEFM0ZEHktrZeZER2pDuQRBFFFBsSiN5YCrmeDmN7HnFV466OfsKyQqsexfE9dHlhQPOx3ByCIOH7LqabQRIj2F4BSQxju0U8LBxKyGIUdxHrQJEkNCWQXCiSyNh8npAqo0gSU/kShu1QqpnMlypM50sM6VqQMCGJVE2b+WKFmXwJWRLJFCtMzBe4msmTL9dwfR9ZFPF8mMmX0RSZTLGCKkvoqsJcoYzn+8R0jWhIQwAiksrmRMdSIg5AfT4kCAJN6tLKflNYZ7ZcoSsep2SZFE2TuXKFTKVKRFWJqCpCoCRhqljCcT10VaFoWBTNIPXpfCZD0TDpiEeJqioQyAOjqookCiRCITRZYqCpCcfzmC2XmSqWSGjaDU25fWwmC3+O7WXoTf5e/QF18d1REJsQBC0oGOHhu7MIUjOBIa8H7iyIXahyJ63RD2G5kyT0+1EllU27BzBrFpG4TuTejYSjoaAgtNFj8socvevbCUdDbLttCFEUUUNK4BcRC7H99vUomoIe1WjtTAWSNkmkWjIoFwIZgW066GGNiZH5xkCSmS7WoxhHSTVHG5V5VZNJt8bBp8EgaOtKkZku0DPQTFNLkJJiGTbZuTKdfa+fCZSv1PjBkXOUaiaaInP/9sGGDHMxREFGFkPIN+FZ8PMMy3WYM8p0RZJkzSqztUAe1BVOMFULFkEJVSdrVhEFgZ5IkulqEdv36IkkmTcqlByTtBrmWG4STZTZme5izigjCcHCerScw3QdWkJRMmYFx3PpDCdIqNfOu+/7jI9k+MpnXuCVZ89QLFRXSJZLhRozk3nOnRjn3MlxNu/qX7Vw5Ps+V85N86W/fo6DL52nWjZXbGdqLMtrBy7zyrNn+PC/eQMDw+0IgkDFsslWq3TEYyiSxNXLc3z5b54HQI9oKKrE3//FM8zPFJcUmUqFGtMTOU4cGuH8iXF+5Xcepqs3DlJrowu9GBOj8/zDp3/Ey0+fprpKZGkhV2ViNMOxA5d56alTfOAjD/LYO/cykE5Rs53ABHjR5y+cnuTL//O5RkzvYnT1pdl71zDN4SQePkOdzUFIgO+SNWurmj/Oz5X4h796lme/d3xJ+ktj//JVJq/Oc/LICC88eZJ3fvhuBt7aT8YsMxBLA0pQpH3uLH/7509RLZtUKyZmzWp0rubnSnz9719esW2A9q4Uv7H5bcj66q9wx3F58alTfOFTzzI5mlniKVEu1pifLXLh9ASHXrzAw2+7hbe+/w5iqyyIi/kq3/z8K0xP5CjkAunQ5/7iac4cG1vCRCgXryV87H/uLP/y997InruGAxamKARR3sDJwyN8+k++TyFXoVI2MWpWw+izVrX4zpf2r3o8yaYIw1u6aa8vLjfF+5AEEQGB3U3DlC0L083g++4SaekvAlrUdk4WDhGRY1ieRUhaujBrao3z2PtuY8cdQ+x7YDPNHUkicR1VkwPz4mKN/c+c4nN/+gSzEznOHx/j+KsXecM7b8WoWUxcmWPjrr7G9nJWifOlcXrDrQ3z5cWoOhUOZveTs3LIgsy8mcF0TfRF+6WoMve/YzeD9/Ti+h6toTgHnz7DZ/7bd3Bdjzse3sbj/+K+VdkJ4WgQey+w1BDXrFl87a9/xLnXRlE0mbf+83t49F37aGqNo2gKnutSzFV55huH+epfPUt+vsxXPvUsvUNtrN/Wc91znJkp8Lk/e4L56QLv/s0HuffNu4inIiiqjOu61MomU1fn1yy0LGDk7CSCIHDLPRv4wO88Smd/M3pYw6vL657+2iE+//EnqZYN9j99igfevpvhZdIfQRDYsmeAR969j57BNjbs6CXVEoQayJKE47hkpgt85+9f5Ft/9yK26bD/6VM8+p7baF+2sN5z76Ylx37hxBh/+QdfJ58pM7y9l1/5d28mtspCP0jLDDyZWrSlRQJNV3nbh+/GMh0c2wnGhI8/wdNfPbS6z40g4OPgY7IQgqBHNG59cDOvPn0Ks2px4Jkz3Prglut6Px198TylfBBOsvvejcSXsU8s0+Hbn32R114+jyyLPPa+23nje2+ntbsJVVPwPI9SvsrLT5zgH/7HU2SmC3z9M8+zbksX225d6UUkoOCLUQK/GwNRbAeEoOkoqAhiMyyEjPgWPhYCMr4QRrxO5PvPK6IJnd4NHUhrMIquXAiaJ9crHPm+z/FDVxrskWhcp2+wdYWxtiSJbNjaTaIpQnauhG05vPrsGbbt7r9pf7DFEJAAEc+bRRRXUSmsgbbQ0sbW8pStxb9f/rvGNvTEklSu1SCKAr2DrbR1ppidClJQyyVj1dTDUFhl2+4Bnv3ecRzbZfLqPFcuTAfNuOtUL1zX48yxMXKZgOWiqjJbd/URCqtB8fpmDQaBlKazLt50w/Sysm1SdW20RSzkWtVqjBOSLJJsWslgW47JsSxTE7nGz4ok0pmIMVIpUbWuMTJlMRivrseEWoxGKqwgNOJZbqa0stpnPG/1IlpQWBKWNGI812NyJBMcd93jKCQppNQosnBt66IosO/ejTzznWPMTReYHs9y7sQ4/UOtXDgdNBijcZ1bbhtcM9VQkyUiyk93TvRzVTgKyypbUm1YrsP+iXGKqtUwpHI8D8/3yKsGVcvC8oLkqYptoUkyt3X3kFwWHx9RVlY5tTUm4rrSg67UGSu+i+cbS1LPYur6VV3VExGdW9Z14fo++D6KLDFbKHP/1kEkUWQ6X6IjFSesqewd6q5Hpvvcu2UdsiRi2A67BruI6yHaklEimorr+WzqbiUdi6ApEm3JKLcO92A7Lp7vs7mnDU2R6UzFmcwVUWWJRDhUj1YW6kyXRQ/qKs/s8gc5pes8tmE98VAI03FwfY+9Pd3ENI19Pd0kQiEe3bAeSRDJGwb3x9fREYuxp7sTRZS4taebbK3GGzcOE1VV0uEwDwwNIgqQjoS5f3AAXVaYLJWIqioCsK+nh7Ci3JSll+3OUTRfRhKjIIQRtTuDayUPgxBdZWBaeAHYASVZCCK+u5K/veRTkbhOJK7jeh5RNRIUAesdQk1XqRRqJNLRJRPgBcplbPHEZh1IyzAAACAASURBVNHcS9EUNuzsAyHYRkdPE+3dS7tts1MFOnvTtHQkllBQm1piuI7Hll19wQRWEFi/patxRG1dKfD9IKXC9TANC1ESG/KgtQZo3/epWTZfevE4By+M4ROwje6r36fLESRK2AiIv9BsIx84W5hFEkXyZo2rlSwhSWHWKHEqN83WVAdTtSIjpXkSqs6Z/DTzZhVdkrlazjFjFNmW6sTxPeaMMmktguf7zNRKVF0LRZQ4mBmlWYtyIDNK2TYZjDdztZLjka5Njf0YuTDDJz/2bU6/Norn+YSjGj0DLQxv6SLRFHTuMzNFrl6eZWosy85bB2nrXDlB8n2fyavzfPK/fpvTR68iiAL9Q23s2LeOvsFWBEFgZjLHwRfOM3pxhgPPn6NSMvit//xW+gZbyVQq7B8d55GN60nq1+55o2rxxNcOMz9XxKhZbN87wK7bBmlqiWHWbE4dHeXwyxcoFWrsf+4ssaTOr//uXeiag09tyT1UKRt84S+f4bkfnMAHBjd2sGPfOnr6W1BUiWrFZHJ0nkvnppi8miUaD7Fhe08gB7MdWqMR2uNLFzvDW7t4x4fuIpspUS4aZDMlLpyaWCIDcT0v8P2p+/XIvkiLHmGyUgzG7zpqVYuv//1L/OBrh/Fcj+7+ZnbfuT5Ii9MUalWTqbEsVy7MMHZ5FlmW2LyrFySFuBIKFHqLzOsTyQiJehqTYViMXpzFsV30sErPupZVKejp1lhg9LqKr5TjuLz67Bn+8r9/l/x8mVhCZ9dtAVsoltCplAzOnRzn2IHLzE7l+fLfPI/juLzvNx5YVU62gIMvnufU0VHGrsyxbkM7O/cN0tkbFKevXp5l/4/OMjmWZezyHJ/9Hz+kd10rejxEUypCpN5RFASBaFxvSH1cx+XKhRlMw0ZWJPqH2ladAMUS+pIY4bHqLM/OHqVs11AliTe0tpJUfrGkIQtYF92EJulUnTKDkU24hsycXUYSBURRxLYdNtw+yM77N2GYDq4sUqyaUDMRgGhY49437WL6apYvfOJJTMPizJFRHnp8L67rkUxHcR0XSQ4m9y1aktZQYk3GUUSOsDU1yJHcQTbENjFSubyi8yyKArFkmKxlIgkim5tTgadP/VaNxHU6etNrMoFsy6FWs9DraVW+53PywGVefvIkCHD7w9t4z28+tETeIIoyTa1x3vT+OyjMl/nqp3/E+KVZnv76IdZt6lzVT2kB89OBf9AHP/oYj7x734r9isR0mjtuvOD0PJ++4TZ+9d+9hb7h9sBOwS4TlXXiTREeedc+zhwZ5cXvHyM7W+TssVFaNwaL08BkNTjnfsjnnR+9p3G+XN+i5Jv4tk9EjtDe08Tjv3ofx1+5yMVTE8yMZ5kem19WOPJBN2jrTSIKQSEuO1ts+D5pukpbd9OaXkum5WCaDvlilb6ua9sVBAFNV5cwpSKx0A2ULiKeM44kbwJBQ5JFtuxdR0dfM+OXZjl56DIzY1k6+tKrzlHKhSoHnjmN7/skmiLsuXdjwARwPcR6PPyFE2P86FtHsW2X2x7awgd++9GG4TuAKEqkmmO84Z17KeUqfPZPf8DcRI6n/jGQcC7M4aaqRQpWjZDUTdas4dcbnLbXgu25qKJMqx6j4pj4fpSsWSVal8UsyHEMx0GXFTbdfI3iZx6VkoEgCiSbY6teo8x0kReeOkl3f/OqBeGFucdLT59u1Ch6BprZsG11FtDQpk627x3gR987DsBzT5zgljuGuOX2oWty9+vA8zw812+kqqnqlps+VtOwMWsWtu0iSiKWaTf8/kRJxLachsG65wZyzmrFpL0rSSgcjEmN4oh0YzP9BQZzZZFfUSIZRpJWfk+SRHbW52uXzk5h1Gy+++WDDG3qoql5tbVOna15aY4nvnEYpy6f3bC9m/VbuqhkS4QiWuM5uhnpuut7TFaKxNUQ+nWKNLbr4SxLOg1HQ43iu+t45OpM97X2u1ys8f2vHKS2rIHo+T6FmoHpXGORS4JI1qrSpP14ksb/rxA8R9GgkVk/7qpjMm+W8JateHvXtXLL7UM88fXDVEoGr+2/RK1qMl+XHPYPtbFpZ+8avpygywoVyb5pW4ybwc9s4cj3fXzPX+IeL4piELcsCKxvakYAypYVxLnXb96EpjHjeaTVMDXbpkUI43mBuWowWa9Ty1Yx1IPrGKAiNKq/oigjCitftqstoEOKTHtqqbSlNXHtu02LEtvaFkWo9jQHb5xEOMQt67oav18sG2pblNizELUOsHPg2ssyEbl5j4/rQRJFBtNLu1nLTa97k8E+dyWuVekXS84W/z74/rW3ajgRMCPSkWvnoyV6cw+/7/tY7hSmM0q48XKoe56Iq0thFiAIyk11qGuOw6VclqFUUyPSOKSrK8zIbgaiKBBvWnpsy++7dGuMpuboikWj7/lcOTsVFP/qKTJGzUKWg9jKWtXEqNm4tosgCqRbY5iGQ3tPU6APr9Mv50tVLk7NE65ry+eKFQ5dGOO7h85iux4RTeUdd2xb02A9JMWxPZPXo4v+eYQqSjRpwTlQRInBWHNd/Sywo6mT0UqOlKrTHIrSrEXYnxnF9lzaEm2EJBlVlNiUaMfHp0OP06bHiCkh0qEIRtWhZBvElBD90SZO5qbojabYEG/lUGassQ+1qsnXPvsSZ44FvhudvWne+su3c8cDm4kl9MZCIDCddBi5ME08GV5VM26ZDt//ykHO1E31duwd4Fc/+ijdfc0o9UhWx3G5/407+Ownn2b/c2c5e3yMb33hFX7ltx/B9Xy6EnGWh/l5ns/FM5PoEZV3/co9PPRLu0ikIoFcyfe597HtPPXNI3zhU89SKRkcevECb37XNobW64GkdJE59txUniOvXMR1PdZt6OB3/svj9Aw0B2kiQj2VxQ4Si0YvzZLPlumqFzAE4Mp8lppts6ntmv/Fug0d9A224ro+nucxNZblv/3+lxgfyTQ+I0kirakY4Xpqout55OsLh7JtklRDCIJALlPiwHNncWyX7v5mPvIff4lNO3pQ1GD/fM8PEpVMh7HLc8xO5hkYbmfaL5Ozqo0XeLFQo70nzX/4v96LUp9oX700y8f+7RfJZcp09jbz0f/yDppaVkojRVHgQjaLZTsrGoXjVzJ89e9eIj9fprUjyXt//T7uengruq4iSsF77KHqLg6+cJ6/+pPvkcuUefIbR9h953q2LpN/LMbsZA5Zlth370Y+9FsP0daZaiwQbMthz53DfOIPv8nMRI6RCzOcOHyFXXcNk8mWSTdFsDyD9Tva+I//93sbbLlivsIf/JvPMXYlQzSu85v/4S109680mRREoeFzA3CuNEZMDtMXbiNnFfF8CeUGY/3PK0YrF5gwRnE8izlzmtbqNjJzNk2JMMl4mOlMIP/Ol6pBEEJTlJn5gPXruh67t/TSnIqwYWcvmq5g1mzy8yWqZYPXXjpPNB6MIQuGxp7vISKiiGstzgQ0USMmxzlXOosiKmhSEH/s1qn9C8VMz/dZH2+5YWd6xV8QBYr5KooSzNsMw+LQc2epFGuEYyHuffPONeUNIV1l5x3r+cGXXqVcqHHhxDiluozveti8u5+7Htv+Y8V+L0CWJfbet4nudcHY4/ouR3Jn2ZJYR6vWRDgWYmhrN/ufPoVlOsxN5blUuoQkSRiuQVgKo0s6WSuLJmlYnoUqqhTtIo7vICKyJbEFTdVItcbpHW7n0plJjJpFtWw0wgQ830MQPC6UjtIf2UJCXZmCdiOIgkA6FaG95afwXPle3Tbg2mDV1t3Ehh29TFyZY2Ysy4WTY4Hnk7Rybn75zCQTVwL5/ODmLlo7k1x8bRQ1pNC/pRvHcTny/DmKuQqyInHvm3cRXiM4RFFltt02RDwZppCtcOn0JLlMiZZ6YbDiWEzViogITNdKdIRjZIxy3bumhirKVBwrkCZ5HhmzQnMogoCAKkqUHZOIrGJ7v1hsbLNmYdcT6laD47g8+Y0jDG7sYM+d6+vvQ6HRJJmfLfL1z73M+JXgnSvJInc+tGVJQ2AxVE3m0bfv5tj+y+Tmy+QyZf7uEz+kUjLYd+9GQiFlSWLwQkPGdVxmpwqcOjpKU0uMvXcNv+5jnZvKc+H0ZN0LUGdqPEulaKBHVHrWtXL14izxVBhREinmKjiOR0hXSLfGWDiayavznDg0woZt3XT1BUXyBWbVYuaS7wdzvOefOMHEaGD8rqgy/cPta/pFNbclePDNO7l6eQ7bcjh28DJf/dsXeceH7iTVHGuQAxbW0TOTeb7wqWe5fG4aCFgqD755J8lUhFoxYPGtmdC8CkKSwrxRXSITWw2SKFCqmdTsa8bp3f3NhHSVatnEMm2OvnqJ+x7bTiQWWsro8iGfq/Dtf9jPwUVpagtQZZn2eLQeEBVsu02P0aSFUer1gIXt+MEPwRp+0YTJ94MGezD1FJYUV26m2FffLFBv1i+bjC008APlm9BYLvmeT36+HLDn6owjVZQJScoK5lckorHnzvW88ORJqhWTq1fmyOcqjaF0953riazhD+X5MF+r0hqONEy3fxr4mS0cmYbNgefPsefO9ZimQyFXoX8o8A+RRJHexLUo5OXoiMVXpd0ZNYuJq/P0DbY1JuoLsEyH6fEsfUNrJ3GdOXYVVZUbDI/l8LwaljuL4+XwfQsEEUkII4tJZKkZUbh+kcHzLSxnCsfL42MhoiFLzahSK8IqMcOeb2HYl/B8A10ZRhTDOG4Oy53C82uIqChSK4rUgiBcv0LvehUsd6bu7WMhCBKiEEGV2pHERH0QgmK5hmE6xKMhwrqK51vU7HP4voOubEQSQ3i+helM4Ho5fHwkQUeWmlHE9KrH4fsutjeP42Zx/TL4PoKgIIspVLkdcRU5lO/7eH4Z28vhunly1e/j+TVcr0TZPIy0aBEhIKLK3Why54pt2N4chr3S5FFXhpDF9JKBbLSQJxXSCSsKrlelZp9DEGR0ZRgBBcfLYrnTeF4NQZCRxSZUueMG193H800sZzK47nXPoAXU6g0IUVDRlQ3g61TKBqomY5kOtulQq5rEkmFkWaKUr6JqCpZp4/vBc1TM19AjGolFxaqz47P8p88/0dC81iybsmHi+xDWFB6/fSv3bxtcQSFegO0ZwM11J36ekbWqjFfylGyD7kiSuKLj+UEhYcYoI9flMheKM9TCKd7YvYXT+SnwfXoiSfJWle+On+SWdA/pUISDmVE0SeJUbpI5o0xbKIrpOhyav8ru5m58HzRJoTkUXCvf9zl7fIxXf3QG1/WIp8L82kcf5dZ7N6zsuqkyelhj5761I4AvnZ3i+SdP4nk+bV0pPvivH2Jo09LnQpJEegdb+dBvPUR2rsiZY2O8+MNT7Lt3Iy1bW8nXarirUKh93+eOBzbztvffgaiBJFyj58aTYR76pV28tv8SB184TzFX4dKZKwwORRH8pc9HIVcNXozAhm3dDKxvXyIPEcVggRbSVbY3DSz5bkRVaI5GkJfR2SVJXEJxj0S1Ffd2qWoyOpMjqgdeTrIo0hQK49Q8Ysq1MahUqDE/F5gQD27sYHhr19ICsgiSHJhjb9nVx5a6DKiSzTYmFr7nk89VcBwXLaQ00hEjUa2x6A68nEJrUv9DFQXbdZccq+t6/Oj7x7h4ZhJFkXjrL9/Og2/ZVdfSXzt/0bjO3Q9v5fypCb79xVfJzpV4/gcnGNrYuaZJpe9D/3A7H/jIg/Qu80OQdJWd+wa5++GtfPNzL2NZDudPTrD9tkF8z+fClWlqmo2gGgzG9yDV3wOed63pIwoCkdjax7vk2CUVRZCpuAaGa+P4Aq5v1E1qf7HQofeQUJuwPYtzpeM0pTV6W9pRFQlJEknGdERRwLQc1HoXvKM1kEdcGss0JsPRuE4orGHWbGzLxbYdbMthbjJPR++iYp0Aluc0EuxWQxB9LGN5Fp16Fwuz2KliiYl8kS3trciyQFhWcX3vdb8nREFA05VGd7xaMjh9eASAdGuc3vXtazf5BIGWzhSRmE65UGN2PEshV7lh4eiWuzesiBl/vQjHQ2zeM9BgN/m+T1pN4NYZWYIQdJoVVcK2HKyazZC+HlmVcH0XURBRRZX2UDuiIAa/Q6Tm1TA9k6SSRJd0EIJz1NQSRxQF3Lp5bM2xODQ/hgDcku5EqUeI/zisYMf1mMkUaUlf/7zdDARBRhCjsMi8XpYl7nh4K688eYJq2eTA06fZe9+mhln1AizD5uiL5ynmK2i6wp77NhFPRbh6epxKEfq3dGMZDicPBvO4eCrCwMaO694fTa1x4qkIhWyF7GyB7GyxUTjqjiRo1sK4vs/WVAeaFCRqub5fT5QUkEURp/7/sLjtEWAhCfQXCR19zYTCGolVniNZkYjGQszPFvmLP/pO3R9xmFQ6iu/7TIzO89S3jnLm2NUgQEIQ2Lyzl3se3rrmHFMQBLbs6uPxD97JV/72BQq5KpfOTfHJP/oOz37vONt299PWmSQUVuthDYE8fPTiDBNXs+Tmy7ztl2//sQpHLR1JIrEQqhZIynvWtZCZLhBPRog3hekZaAmaRL4fhENIYsAEX9Soz8wU+fynnkGWJdq7m9iys5f+4TZS6VjjfWyZDuMjGQ6+cI7jB68EDGgBNm7vZuuuvhXG2AsQRYEH3rKTi2eneP4HJzBrNt/58n4unJngvse207uuFVWVqVUtLp2b4rnvH+fS2Slc10NRJB55+y3c+eAWBFHAKBvEUxHUkHrT47Th2kHS7Q0+r4oSqZDO4qejd10Lm7b38NLTp/E8n4MvXuAzn3iK+x/b3mClGlWLq5fn+OG3jnL6tVEEUaR7oJmpq9kGk0sWBTRFXjL/EYWgeDt2eY7cfJla1aJaMahVLKplk6nx+Yb1geu6HDtwGUmWCEc09LBKJBpCjwSEgL6htlVTqy3TZuTCTODvVjGpVoOifbVscu7k+LVzVLN44uuHOXlkhHBEIxwJth2OaGghBd8PGFfXWKUeNddawTgSRIFte/rpGWjh3Mlxxq7M4dWtB5LpCLfcPrRm0U8SBHa0Bcnv0usoDN4IP3OFI8/zOfLKBWanCsxO5dm8q4/zJ8YJhVX6h9q4enmWU0ev0tQcZee+Qc4cu8rUeI7uvjTRuM65E+OIksDOfYMUshWuXJgm0RRl+54BLp6ZopCt0DPQwsUzM5w/NYHv++y+Yz0jF2Z48YenuOeRrWzbPcDJwyPk5ksMbuwkGg9x7MBlpsZzbN/Tv2Kffd+jYr3GbOmzVKwT9cKNgYCEJMZRpXbC6iba4/+KkLxuFbaTi2FfYrb89xSNl4Pig28gCRE0uZtk+BGaI/8MVWpHWDQxdrw8V3N/SM0+x2DzJ/B8i5nS31C1TuJ6ZUQhREgZpCn8Zpqj70ISVlJMbTdLrvoDCsbT1OwL2O48nl+rF26S6MoGmiOPk9QfBj/E2HSe8ekct2zpJayrOO48I/P/HsudYkPr5xAEmdnS5ykYz2A504CHJMbRlU30N/0hIWXdkuOu2RfIVL5C2TyM5U7guHl8XEQhhCp1EAvtpSX6PsLKliXH7vk1xvIfo2QcwvZmcb2Atlezz3Ex8+tLjlFApSv5UTriv7H8ylGoPcfV3H/G95164kLw0K5Lf5ym8FtYeKpN1yEsKw1nessZ51LmIwiCxlDzX1A2DzFf/TY1+3z93CuoUhcJ/X5aY+8nJPcCy6+7R8U6zlz5i5TMV7HdTL1wZDf2I6iCq2hSD4PNnySsbmDHbWsXBhrHvEhzXSkaqKGV8ZRV08Z2Ax21JAqEFIW2ZJR/dud23nLr5gbzYjWEpBhVN4ft1QhJP/nE8mcRvu+TVHQ+vP7W4OfGf2CheeADJ3NT6JLKvtY+PN+nO5xoTPpnzOBlfL48SlQO0RlVmTEzbE41IwjN5K0iLWGZmuuiyR7D8S6isk6bHpxT07DZ/9w5SnVPrNvu28Tuu9cHL3zXRq2zApZPXOuNlsaiUSCgVB968TyZmUKwrXs3rigaLUAQBDp70zzw5p2cOzFOqVDj+SdO8Nj6e5gplVftNGkhhYffths1JDNjTFBzqwxEhhv3XTiqsXlnHwdfOI/n+cxMlhDELpA6luy9qskoioRtuYEp9nyJ5pb4TXXEcjWDXLWG5boMLTLHvhnEwhqyJDSOzScwWnR9H9u7tvhVVAlVk6mWTSbHssxNFdAHVxailqMznECT5Lq3FXWfB2HVycnNwLQdxrIFulMJwvVUtexciYMvnMe2HLr60tzx4OYlRaPFkBWJffdu4KlvHqFcrHHi8AjFQnXNwpEki9z9hq309Dc3umxwrTMnySJbdvXxxNcOYRdqzE7nkUWRVDKCJAnoWo2KX+GnwVLck9qA63ucLo7QrMYZiA2vMJb8RYEoSChC0F33fA9Nk4gvkt0r0WAMiIa1huE6totte6xrTuLVbGYrOfLz5UUNNR8tpKKHNQizpGDi+wG78nqXyfEc8lYOSRCZM2dIKkkkX2OmVMZ2XRRJxMMjJCnE5Nd/f/tAOKxRrZq4rkdmKk+uTs0v5Cr8zz/6Npq+NoupVjUbZs3VsoFVWz2ZbAGKKtPR29wwdF9eaLkRI30BIV2lveeat6AgCFRdI/DjWvScNBpSro8u6qjy0mNRxWvvXs/zkW0V1bCxyi6GU8KrMwmM2iLPNj9YOFmuQ280hSxK+L6H7Zk/VoCF53vMZEpkchXafsLikY8fNFOXpVoN7+hj3aYuTh68zKlDlxm7NMuGHUs9n+Znixz80Rnwoa2rid33bkSURERJajTZcnNFMtOBN4xRtfjbP/7edVOazJpNtl78N6oWRvWaDCYkKUHn///HUggClUKVRDq6UsoZ1Xj7++/g8CsXOXl4hO995SDf/+ohtJDcaGA2NiMKDG/p4oO/+eCa4R0LUFSZt7xnH7GEzhf/6kfMTuUpF2sceP4cB54/d93vSrJ4w3fyWtBCygrJ9OJEN+0G6bIACEFhYH42CL84Vk8hlGSx8X3LtJf4D0qSyNDmTj70kYdWZRovRjIV4cO/9RAhXeHp77yGWbM5cWiEE4dGkGURRZMxDWcJQyykqzzy9t28+9fuJZYIlCmiJHLp+FXSnSnCi1g/10PGqGJ7biBDuw7ypsGsVWE41dzYbiwR5vEP3snIxRkmRuexTJvvfvkAz373GIm631GlVKOYD8JEorEQ7/jQXXT2pfnkx75Nse5zZrseTWF9hQ+U7/t85hNP8eqPzlzXtslzfU4eGeXkkdEV/6ZHVP6PP/vlVZuw83Ml/uvvf5mp8ex1j900bJ78xpFV/61vsIV3f/hukkuUJwJxRV/icbSAeDLCnQ9t5typcYq5OkNMgM07++pFzOsUyfWfrBmyGn7mCkfVssHopVn23jXMC0+WUFWZRFOE6boxViFXxTRsYokwoigEiwnf58ThkSCmrt69PXPsKqVCjS27+jhzfIy56QLp1hijF2dwHY/ZqTzR+kMyM5Gju7+Z9u4Um3f0Mjdd4Oj+S7R2JDhxeIRUOkK6NR5QgFfptJvOCFdzf0jVOoEspYmou5BEHc83sJzJoCDjzdMa+/Cqx1yxTjJR+GNKxv7697cjCjqOl6Vmn2e6+ClMe5Tu5P+GKq9kRLl+mfnKtyhbR/B9i7C6FQEJwxmlYh3HcIK449bYBxFYWgyw3AlmSn+D4VxBldoIq5uRhDieX8WwL1E0XsCwLyIIGgntETzPpzkVpTm5VGrleiUMZ5RM+R8pma+iSm1E1G142FjOOK5fDGRhi+DjUjReYrb8uYDmK3ejKxsRkLG9DDXrLGZ5FMuZpD/9x6jStS63IAgoYitRbVdw31hnqNonkcVm4qE78JAwXRNJlFAEHVHqp+KUUUW1PokKJoiasplY+FfQRA/XnSZXewrPL7MabM9FFpcX3uaYq3yJbOVbCIJKRN2CgIrlTlCzL2GVx/G8Mj2p/7TEEyuQ140zlv8YFfMoIWU9LZF3IYphDOcKxdqLuH4RTe4hHXkcXR5ElTuu+QrdgEG2cI4gMDUM9r9E2TqPLnexrj3Nv3xkH/OlKoblEFJlOlJx9qzvZl1bE+oqGnLXMzHcSSLKAK5v4/o2nv/6k9t+XlCyTWarZXJmrZGoZXkOpht0wV3fRxaCBRK+wIGZMURBIKWFKZg1WsMR4kqYklMjJuvElCAiXhYkVFHGw0eTFEKSStwL1ym3S/fBNOxGypaqyWy4tYdjhUlEQaBo1eiPpQMZlRamYgd+IpIgoMsKectopED0RlO4FZfRizN4rk8kGmJoc+d1o3MlSWRguJ1YIkwhV2F8JEPUk+hOJlbtXrR1Jmmt+yp5eMxbs/RH1jcWLKIoNiYrQWJhFd+vIvhLDavTrXG6B1q4cm6acyfH+duPP8nDb9vNph29KKp03YlNSg8hiyLpSPgnLk84nsd4ucC8USGtXXv5JtNRBobbOfrKJUbOT/OZjz/BW957O9t296NqKwu0C8iaFWZqRTr0OJokY9sumdkiw5s6fyz9eUssQs22G+fX931ymRJTY8GEJt0aXzNpYwHpljhaSKFcrFEq1MjOlmjrXD3dKBIN0b++re6F4FGpWTiuRzJ2LQ43FtcbzC6zZuM4LoVSjZ7OFGFVpGzMssLV/cdATAmux23pzYxWZrA95xe2cHSlfI7R6kUEBFpDnUTlldIh3/cpzJc5c3SU0fPTjF+aJZcJ5GimYWGbDtWK2Zh0QzDXKuaraCGFfKbUYKQKAnTqzairsIMXIAkScSXB5cpF2sR2dElHRCSlB16KmiyTMUvMm2XaQvE1jWPXgu8HRRHLdHDdwNTYsYNxt5it8PKTJ256W47trm7avAiKKoMCBbuCJimICJiejSaqSIJIxTUQEVBEuW50GjRmFHHpOZIkkXB06X1YsMsY7kpT/hvBdVzGL89x8dQ4V85MMj2WpVyoBoUwM2CL5efLQdd64TueT8210SQFAQ9JUPixBheC+UMipr/ua7fqtlAQxBjLbWWTLKH4WwAAIABJREFU6SjbbxvizNERcpkyJ/ZfYv22niUL/rNHR5mpL9K27RukuZ4epcdC5GfrRsLFGk7dcLlaNnj1h6duet9cx8OyHKZKJVRJwvV8worMbKWCriiokoQsilQsi7AShMDYnkdIkiiYJqokocsylfpYrCsyjuc1JDSJ0E/HLuKfGvm5IsVcmXg+Sqp16Rjk2B7N7Ql+7aOP8oW/fIaj+y9jGjbGsoKtLIvsuHUd7/31+9m4/eYSzrSQwgNv3kmqOca3v/gqZ46NLfECWg2JVJjNO/vYvLP3up/7X4nm1jibd/Zx9NWLS0I5XMej6qwM/IgldPbds4E3vXsfw2uoWpajpT3BB/7VAzQ1x3j6O68xPZ4LmgeOh+Nc+5uCKNA/2Mp9b9zOI2/fvSQIQ5REkm3XT2Rbjv5YirxZQ1tTzhwgFQqxTheXmDOLosCGbd186F8/xBc//VwwJ/X8gL2zzMeoZ6CZR9+xl0fedgvFfJVUOtp4h8X1EBnHoCm0rMFdZ/L8JMOW566+1m9s/wbvkxvBcTx8b+nfEAWBVi2JKq587woC7Nw3SDIVaaTiaboaJNJdp4Hyvwo/c4UjURTxPZ9KMWBCOI5LuVijkK1QLtbo7GmiUjI4dvAy8WSYU0dGaW5LYNtucPOVDURRQAspSJJJuVTDsV0kSaBSMigVauTny8iyRDwRxnGCSUU4oiEQfEZRJEJhlXRLnLauFNm5ErWKiW2sXCR7vkWu9iQV6xgxbS9dyd9Dl4cQBAXfd3D8AqZzFdudRVeGljycPj6Om2Gi8CcUjVdI6Y/QkfgIqtSJKCi4XoWydZCx3B+RrX4XVe6gK/HRFUUD3zfJVr8dfD/+GyhyBwICljvNdPGvma98nZnSZwirm4iH7mBxKzEkD9AW+xCSGCWsbkcWk4hCIDcznMtczf0BVesEmfKXiCp3oyoSNcNakWzgYzFZ+ASCINKX+gOi2l4kMYLvO9heBt+3UKSl8gZRUEjo9+D5BjFtD6rcGcjiEHG9Mtnqd5go/BlF4yVKxn6awm9qsI5EQacr+dH68ftMFf+cauEkIaWfdOx3OVseozncwqw5gyzICH4L2eoIqqgSliLMmTNE5ThFu0ZCezvteje+N03ZOoLprCwchWQFXZYxnKX3gOeXyZS/TCr8GG2xD6FJvQiChOPlmS5+mtny58lWv0c68jhRbc+i6+8xX/kmZfMQYWUzA+k/QVc2IAgirldltvxZJgt/Dkik9EcJqxtxvDKzlacJK32ElT5sLw+AIiaw3Tw+PooYx/YKgI8mtWJ7eTzfQZVS5IxDGM4UIbmTprjBe+/rQJNasL08Ph6yGMP1KnjM4nhRPCxcr4oqNeN6FQx3hoJ5gogyQERuJiK//tS2nyfUHAcfyFsGNdfB9gLzed/3G3TSBcZPzXEo2SZxRUMVJebNKm3hGMOxrsbjJrA0eWN5lPBqKOaqjU5qJBairb+Jgm3g41NxLM4XZrE8l62KxqXSHEk1jCyKaLZM1qyiiBJ5q0qbHscqm0yNBwX4UDgwZb/eZEEQBNIt8cALIlchO1diZiaPp/tBzOoypFvjRKIaru/geDZhaaUP3GLJmecJ4M0DKkhtLJyodGuct73vdj73/zzD3HSBZ77zGodeusCmHb3c9eBmNu3spa0ztaRzv4CyaWG5LnPltaKh10axYqDKMmLdA08WRDYkW4CWhmm2IAgkmyK89b23Mz2eY2osy/7nznH6tTE2buvm1ns3sHPfIO1dqYZ55gLCksqedC8hWQEP4gmdZOrHN3HMVY0gknbRImshEhng8rlpfu/Dn77uNgKKf3CuHMelVKyt+Vk9ojUifz3PZ2KmQKFc49Zt1xK5xLphPwQyNNN00BQ5KGAiIYs/WXEnYxY4Xxpn8dvnZGGEh9p3E1d+tg0xf1x06n0MxbagiSEEVvozVssGLz9xgu9+/mVGz09Tq5iBB19YRQspyKocdN8FocGShMCktLsuwUimr5mqWq5D1TVvWHCIK3He0PYoqqihiApVy+ZCZp6aZbOzs4OkGqZJi1zHK+n6qFYt0s1RVFXGqFmN1JpoXKejr/m66WaLoagyekS7LmtIEOFiZQK/RENeKwsSllcvzAoChmuSVKJkzAKqKNOuN7Eu0rlsO8KKYnxSieH53k2zfnzfJzNd4BufeZ6Xvn+c+ZlCMIeVJfSwihpSAk/D+jVtfA+w/CCRMyTKeLiEpAiauDIt8WYgCgKVmrWmp83rgwVemeVFY1mR2HPfRp766gHmJvMcfv4sDz2+pyErNGsWB545TaVoEI3r7L1/E3pEC4I/qhYNRnjNbhTQ9IhG10DLdZsiy/dB0GXGC0UG002cy8wSVlSKpoEqSY1CYTocZrxQxPGCaxlWVKq2xVylSnc8TsEwUGWJlnCEqhPEhIckme0d7T+F8/dPD8d2yUzmaF6lseA4LqZhs35zJx/9w3dw4dQER169xMiFGWpVEy2k0DfYyqadvaxb30a6JUatbCDLEq7rBeOTJGKbTuM9q+kqtmmjaAqu7bBjdz/9gy2MnJ/hzIkxpifz5DJlbNNB0WRicZ3O3ib6htroHWxhUqvR1NxE3qwhiyKyKGG7LoIAVdsOjK5FePuv3cU9j+9EFARsxceSPIrVEiF5aTLrQvMQYN6oMGOU6NQTRBUR26sgCeqitZlPW6/OR/7zGxgf3cnY5SwjF6eYmypSKpWxTBcEn0gkQltHmr71KTZs7aZ7oIloJLlGII0F+JSdGpIgIgoisiAhJyTe+OE9bHugj7OvjXHp2DQzUzl8G+SwRHtnkg07u9m9ez2J1siKtZumq2y4ZYDEItPzD/zmA/zS+24DP5grCopEpWriekEjMkGI5nAYwRMoV008L5gXv/UDd3D/W3aiyBKIAq2xOO2igIRItlBtyKplWeKuh7awfksXp46McvzQFWanClimTUhXaGlPsmFrNztuHaCtK9WwJvj3f/xuqmWTeFKnuSlKu7yyMSaIAr/2u4/ynn9x7+u+xxcgigLdA6v7wqVb4/zvf/pebGttKffNbL9WqlEtGcyM52irhyNVHCNIiV/GeBQEgZ6BZoa3dHPghYBp19HdFMjU/gnsQn7mCkd6RGX3HeuZny2y5871yLKErEi0daWoVU0s0wEBbrl9iJb2BFt39+M4Lus2tFPIVTBNh85kmA3buqmWTUYuzrB5Zy8tHUkunp6kZ6AFo2YHGlBNDm54fGIJnR23rmN+rsTwli7ufmgLs9MFwhGNjp4mzp8cJxwNrfB28H0H280AHiF5Hbo8hCxdM32WSaBJa0TB+lA0XqJovIIm99KV/B1C8rUbQRQipPQ3UrVOM138K3LVJ2mJvgdNXrk9Te6lI/GbS74fEqJ0Jj5CzT5LxTpBrvoUUXU3onhtABSFCC3R9zd+btCpiSCLt9AceSdj1llMdwLHm0dTI6iKHAz2ywYgx5tnMP0JotreJbIyxV/LN0ogJA/RER9a8rcBJDFKOvI2CsZzFI0XAh8lHlnBmFoNqqgxFB1GQCQiR5EEGVmUA5q/qDXozYIgoEkamqghCRLX584Ek77VFswhuZ/uxL9FkdoXXbsorbEPUjCew3RGqdlniWq3sNBx83Eoma8CEvHQnY2iUXDsYZrCbyJT/kcsd5qafbZROCrblxDFEKKgMlN5kiZ9H4IsU7YvUbGvoEpNmM4MipQiJLVTtE4TVQaJa1ux3Cwg4HglsrUDiIKGLneQN4/TFNpLWOlhvPQ1mkJ7UKUUWeMgoqAiChquV0ESI3h1dshaiTu/SGjVI7TqEYYS6cUKtVWxIBVbLhkLviPg+w6uX0YSk/i+i+VcRJUHEIRraVOroVyqYdc77bFEmO5kit0d1yZui//eQHRlIc/yXKZqBUKSTNWuNjp1gSfBjRcUwUIleE0YNYtazaIkutjuypempqvIyv/L3nsH2XWeZ56/7+Rzc+icu5EzQJAgCVKgSAVSpCQq2LIsB1lO8s7u2qUab9k7Httjj8dpXDO7I3s8K1vlsBpLVjKpQImiRJFiFgCCCCQyGt3o3H375nDy/nFuX3SjuwGQ1FRJmn2rULh978nhC8/7vM8jIwuFhJrG9q+fZQ+CKgQJApwV10yWpVaG8Qt/9wznX52kuFjlxe+e5vvfO0tPf4YDh7Zw19t3sGFr9wp9IUWSUGW5pd31eiIVM9kx3EW0mcEqOg2emryIJivsa+uhJxJmWSVJ4ra3bEE3Nb7wd9/j7IkJysUah589x5HnztHVl+HWuzZx533b2banH8MMyxUX7RqTtSK7Mz3oQqFSblAq1unpS/NGyreWMuHLJ4/F/FVGSaVU5/TxK2utumYEftCySV4rFEVqCRJ7foAsCyLXZvuu3SYBrueFmi5+BYI3preyFOPVWUYrU3SZV591279+GdKPerxaOsrW+B5MY3VZqeu4fOXvn+GLf/MU1VKdWNLkwL3b2HPXZroHwvJ9M6qj6gqXz07zV7/7pZaludWwW+KhlVLoDhr2iSo1t7HKKW15+PjMNmZJqmn8IHQD0xWZ/lSKy4t5EILAD3UblpdpvZ6Ix40WGKDqKqI5mdq0u59//Rc/TSRhYtkuQcu0RABhOZ/n+biOh9oU1Hc9n4VcBctyyWZj6Nrq9qHdSNFjZkN3s6Y4eMWtIQuZuBqlaFdQJYWUZjJTL+A1waDrhR/42L6D+TrK9eYm83zqjx7lxW+/ShAEtHenuO2t29i8Z4BMe5xowkQ3NVRN5kt/8xRPfPHwVXAnCJisFUhrEbJGAiew8IM3NsEJgrCcVl+n1PX1hUo49ll9vYa2dLN17yDzUwXOnbjC5XMz7G2LhwnB8RyvHhkNdWY2dLB9/3DrWTJjV/V2VE1Baopq9w6382//+hdoX8NVdL1wfZ+ybRPTNLpicUxVIaHraIqM7XoYqgIBdMSiYdkyYCgKjm/QE0/g+j4dsShBs1zQVFWmK+WbNnj5UQhJEiTSMXzXX5XoWtL6EUIQT5qh+9nBjStYH0LA/GSexdki1aaIue8HzF7Jke1KoRkqc1dyqIZK4PkMbu1h6tI8g1u7mRqdRzNU7IaDqcvc844dDG5ZWeK+tA8AN/C5PHWJgl2naNdb7PC8VSeiak1wKtSi6uiIUdLKpHSDilWn5FrM1MrUXYeAkHnebkS5vTNkLzm+x9+ce57PXzrGxzbfzk8Nd1P3ZtCkeMjwI3RBDAAjmqRzi82mXdvIN3QkoeD4DRRhEOChy/EQ8PXLWN4EValM4FRI6yNcG1P1ORasAiWniiHrNDyLmGJSceuYso6Vchh8W5aD796KHwTUPQvbd3CaVQHRmM60NY8fBGyJX0322A2HwnyJSNxoOSX2DrbR21yk3rA5c2mWWMQgX6qRL9bIpiLULYdE1GBmoUwQBOiaQiphIid0DEPj4vgCU6+M0p6JoSgylarFHXuHEKLJrJcEXb1p2rtT3PPg7tV6YU0dtyAA2/WQJBjccHX+7QU+Dc9Bl1ayvIUQDIy0N1ntNrqp3XTJ4pJWnG6q6/ZZmq6wafv6jDDP83Gs629jrUSxF/iU3BreGm3k0rm0EnsCbrt7E5G4QaFQw/d9FCU0khCSQCCo122UZnK11Waa2g+kPf+hA46EEAxv7mJ481WUfv/BTSuW6R28KuS4+9bh1ufR8zNk2uKtdaMxYwVV/0a0xZ23XH2ZbnQMSyEJDUMZQqBQbHwPs7aFtPkuVLmjdbPWHzQFlBrPAy5RbQe6MrDqBQBBVNuDECqON4PlTq4JHEW0nahy56r1w3Ku/VTt49SdM7hBCQ3jmn2sHeEgsgeERBBY+FgUKzITM3k2DbUTv8axLartIarvWwEa3cw+1gtJRFDlpjuJXwZuLvOlSipZdZkD3DWzfoEgvkT3F1cnMu51xld+EDRF2FYLQsf021aARrAkHppCl3ux3DEcf5EAH7FE1Q4CXK+IEDKKlFp1zSRhIkkRAtfBC2rN80oRUQdIaFsJAg9dbiel76PmjmF583hBA8+vElWH0eUOLG+OlL6XmhvW8EbVoVaZXsObIa5tQZaiyMIkbexHCAlZGKSN/VjePHV3irgaAnCSMEnqu1isv3hT9+DHIYQQOO4krjeNIvcSBHW8II8q9+F5C/hBFUXuxvdL+EEFVRkgCHxcbxpVGQQEjjuGLKXxgzIN6zBR8wGE0PC8HIHcj+NO4PkLyHIHkojguKOAjK7tQhI6ju21OhlFlRHSyknYjbpDXVYYagJKOT90HAnPjZvK2Euy1BJo9Jr02vUEESXpagfl+g5ucKMJvQRBpXkWK7epagr779rEwEg7z33nNb7/vTOcPTlJo24zcXmBmYk8L3z3NPc+uIf3/PQdpJq18aoss6Etc0O3j7VCCMF8voLZmQ5r6xWN2zr6MRWVpLay9l9WJPYcGKZ3MMvR587zzLdOcebEFWpVi+krizz2hcO88N3TvPXBPbz3p++gvSuJ6/tM14ts9TvRZaXJqH3j2fyqZRM39JbuGkGombAUnT0pNmztuWlMKhLVyXSs76C0ZEcMIZsoV6jh3eA6m4bGppFOUkkDW3JQpDdHqx6KdjES6yGlLXMgjbST/DFlGwFElTij1XPUvRDE6DR6UaVw8jNxaZ7Hv/D9ltvYz/z6O3nLQ3tJZWOr7OfLhdo1fZdgca5MW1dyRRLI9T2Kztrl2lfXFPiBx8v5I6iSwr7UrZhyFFNVyEQiSwuxYFVIqhGyxtqW79fbvqoqLdA6njRRmu1VKV/Fcz1GxxYoFGuUyw0MQ0XXFJIJk8XmpNRzfeYXypimSntbnGhEZ36hzEKuzI5tvavKbRMKGFIORRj4eEhCIa16YUm2nycqu8giHPNsTXShSbEbgqBCCNqNFFHZvGm20TOPHefYM2fxPZ9Nu/r4uU+8ix23DWNG9VVjj0jMaG1VAFFVJ6Garb+rbgH0N1auIwSt6/qmQ8gIpR/WMDrRjFDw+rnHT9KoWhx79ix77gyTiedPXiE3GzJu9929mWji6pizkq/i+xUGtvYQTRgozeOsFGuhjfp1bMJXHYMkkVXC9Te1ZVex066d6K33/dJvnu+TMg1i2ut33f1hDUWVKeUrdPSvxzRfzei7dqigNZ3QdFMj25lkbjJPIh1FViSsmo3VcIjEDSKZKFbDQUiiafQSYDYNLYyo3mLzrjd/UIXMhkQGTZIxZJWi3WC+XiFjRJAQRFWtpVtqKipxTSejRzAUhYDwvrabMRzfY7FRWzXmcX0fH7+pB2fgBfHm/w6alAC/CvgIJGJqF5oURZJCZpIim8SUDpyg1mxHXVQpgiQkokrnuu1ERDEZUiLElLB9nazP0a6nmbfy9JodTSMiCbnJenJ8F0mIJrAvUCWVqLK69NR1vBZgssT2Wh6SJGFoajNRpCKJCD0dSSp1m1rdpr87jef5GLqCLElUmnphXe0JVEUik4rieX6rHXF9n9HZRWRZQlcUGo6L7botN+dERKdYbYTlqJqC2xRTLzcsupJx0rHwHL4/P87F8jwfGbkVeY3nwPd8Lr42xeZdfWjGzY07apUGk5cX2LizF0WRsS2HRs0msQ4z3PcDKqUahqmhNXWrapUGk5fm2birb7WBTTPWZL0CEdlY1/3s8vlZRs+HrnjxhMn2fYOMX8kxNV3AtlwSCZNqzWKgP0u1ZuF7AW1tcebmSnh+CPbu3TP44wkcvZkY3vSDoYTWHIdnx8e4q3+A6A0afiEUUpF3UrJeoFD/Nlfyf8JC5YskjLtImvdgqttQpOQqYAAgCBzqTmgzWLaOcG7uY2vuw/OL+IEFWKHr2RqUZ13pX9N9TAgVQw1BL9ubwverK8rMQztIi4Y7Rs0+ScMdxfHm8PwKfmDjeDNNUcMACK0F3XWoy6G+z+ubGARBgOvnqDvnqDtnabhjeF4eL2jgBzXq9pIA3s1PsLzAh+YxVh2bqKq1kGsAqZlpgDATGdJY1x9kBEFAybKQhFhj0iyIaNvXaQhkRPOeXL2GSz9KqHIbNec0tjdDELgsd5xzvEU8v4gkDFQp0zxuFUXEWKi/RFLfgSInCfDxAxfXr6KIOLKUQJbiBELBByxvAS+w8QMHqTnoNZQeEto2QKDJaXSlrfV86kobIKHJWZL6LvzAJqpuoOpcoNA4hix+8EJrP6zhBxa1xncwtH34QRnLPoaiDFCuPUqAi65uo1L/OuCjqdup1B8DJCQRxXZOI4SBovQihEHg5whwmwwjgeWcQlNHqFnPoMjdzeVjeP48Qqjo6jYQofvCUrbEaoRWuKGjYFguZ3te6G6BWME+Wj40CLW5JGRFarFzfC9YIVi5XjiO29IWUTUlLEXixhlsH++6jIUwJMLGaO1uSJYluvoyfODn7+Lt793H+demeOmp0xx78SLTE4tMX1nknz/9NBNjC/zyJx6goydF2bIZzxdouC69ycTr4rZ4vs/l2TyJqEHEUAHBxVKOs4V5HhrYSm90pQ6AJEl0dKd44IO3cvc7dnDxzDRHn7/A958+w+R4joXZEl/+x+e4fH6Wj/8fDxLNaESUq/2JY4dlYtWK9YYEsgNgMl9iQ0dzIC9YYVm/dU8/v/kffhIhwGtmHYMgaLbkAbIInxufAC9wUSSVQPg0vDqapF+XVag2s1s3yuSVyw2uzBXo602R6PQxpNcHIFwbGT0EtuqexfnyBEWnSr/Z8WNbpgaQUtsYr11goj6KKlQyWkcIHPkBZ4+PM3sltHDedWCEd33k4CpRV7iasVwOLMaSJm95aE+z/O3qYNaQtTCD7Tms152rksqdbXev+M7zfVRZZiSbDt+7pj12RNFel6xVEATU6zbzc8XWe9HekyaVjZObLTE/VWBhpkj/5m46OxLhpERX8X0fgaCzM4kQIWNm65ZuhABVVfA8j2hUx25KF1x7TGVnkrl6A73ZrybUfnQ5QdWdxfFrCCQ0KYomJ1AlsaqcYK0QCBbtEhHZJKbeGDyqVy1eee489ZodCgP//N3ccmjLKnb30nWqFGsrLKA93yetRegww/dMk5ZApJtPNrSWEwJNlVc5VL6hCBx85zSS3I2QVzLQZVli523D9Ay1MXFxjmPPnef9v1ghloyErCs/IJGOcuC+5eOsEEDIz5VC57rOJJmOBDPjORbnSsxNLq5banIzce147nris2uFLEk/NtpGS2HVbdp7MiTS0TdcHpNqi5PMhs+mEKIlyi+EwLFdMl0JdENraSgNNOd0XQNt64J368VI4irAlTUijCQyq5ZZ2saSi+3SdwOxkK3m+B5pzVzhkKdKMr+y5SAP9W9nKJYlpZkg+ldoDYYbWvov/NAd2U/DLaDKUaTmWL+1zhLodp23s0NPryhVHon1ISGIq1HkNfrqa/XXgDWX002Ntp4UZmzt51XXFLZuWPnOriWzsNr06er3yz9bjst8uUbDcTDUEGyShKBQa9AWj2CoChfncmHyXZYwVZW4obNQruL5PpmYScW1eWTsBNP1Ih8e2c+18Izv+RRyVdLtMWQl1BorF2vUqzaNqkVHb5gcrFUsak1XuVjCpFKqk+0MnSqthsPx588zM7HIrYe20tGbplquU1iooBkq7d0pcrNFnvnGCTbt7GNoSzfRhEGlWCfbmUSSBHbDIZ+r4FgOuqGR6UzQqNkszpVwHY9UW2xZmbholpOvfgZcx+Ppx0/SaGpljWzpZtf+ITw/oK8vw+JiFdNUUVWFeNwI2UZq6HqXbQudDUcvzfOD0JeEHyLgKAgCJsslTs7NYigKt/f2c3x2mvlaja3ZNgqNBjPVMookM5BIslivU7Yt2iNRdnZ0cnR6ktlqhZ54gu5YnLFCAcf32N/dy3ipwESpSH8iSZsZpWRbtEciTJfLuIHPZLmMJsnc2dfPmdwCV0oFxotFbu+9OfE2VWpnIP37xLS95OuPU7NPUy+fZb76OeL67WQi7yZp3oMsEiteLj+o4wdh6YjrLVLz18/ytSbs62X7hQGs1cELZBE21L5fbzqHXb3mtjfBfOWz5KpfwfUXESJElGUpjiT05vE1H7YgbEQSMYNoZDVIJYvX57zhBw4V6zAzpb+lYh0hwEcSWnPfESShrjjem41Co8HFwgRRVWOuVqUrGqNohc4iuqxgKgoz1UqYbVBVBhNJ2iLXn3jENB0QeE2Nm6shkKX1s/TrhUAmZd5HqfEcxcZTFBv3EtdvBSQ8v8R85TPY3hwRdSsRbVdzLYmUcYCZxgUWnQqaNMBU/SxuYCEpe9AkA4FMI7AJvAAnSGL5NQy5g5JbBzQSageyMOmM3g9NBpSpXH3Ou6IPhnsSKh2Rt7WWiWkbCcG7H/8StVYEbqj9pPTj+0WE0FDkHurBi6hyP5qyFcs+jiL3oqvh5wAPSTZRlSEcbxxF7kaWs4CNLGWapWo2QugEgY0kxdHVLdS8WRACIQxM/TZEE+SLJc2Wg0m5EAqj1l2Hw3MT+AQUrAYDsRRV16bTjFFzHbzAR5VkbN+jaNWRhMS2dFiam0xHmbi8EAqrLt5YB6haarSEHSMxnVjMwEyqLRevdS4cslAwZfMGkxQFhNYETNcq8gtDCEEyHWX/wY3svnWY6Ss5nnj0Zb7ztVfIL1R46akzDG/q4kO/dIj2WATbc4mo6k1PkJYdNplrxGC7IwkKVv26trMhPT/CngMj7LxliPvffwtPfv043/nqK8xO5nnlpYs89sXD3PaxXahCDrM/UoDnB2HpSeyN6/40HHcF62fJzpYA8vNhf6IoMgv1GWzfJmfPYcgRDNmk4pYwJJO0liVnzaFICgKJgrPISHQzSXVtkeyl0DWFUqWxZpZyKSIRrTUA9AK3OUB+8zX5x/LnGavOktWTPDl3jId67qDTuP7x/qhGnznEYGQDQghc30VtsrZ8P2BxthSWx0iC/o2d6zroea7HlQuzK1y4hABFWb28E3jYvkvRqdJu3Hy5D8Bcucpsucy7tm8BQJMUHD8EmpczHD1nz6d1AAAgAElEQVTXu654qRnR6OhKtnQkYgmTrfsGufjaJLVKg8NPnWbr3kFiTc2s5ZOT60UibhIENIGllcu3GdsYibcjSxqy0BHNvi6uLi9LaLLIb5I9VHDKuL6HfpNMu0bNplwMGcbRuEHPUPua4GwQBFRKdSZHF1qlehCWJhfsGiU7im6GfYjjXxWcleVlGmSet2LdtfaRSkboanv945trQ0gpZHUr6yUAO3ozbN8/zNTlBaZG57l0eor2nhSjTWOILXsG6BvpWNbOBKFmab5Coxpq6OzYP8xrR0ZxbI/vP/kae+7YdNM6WADlUp3pqQKyLLV0UiUp1HOTJIFtucTiBo2Gg6LI1KoNFFXGargkUxFkWVCrhlpcjuORycbelIbdD1vEUlEqhdq6c5CbjdVVFWEoqkzXQNuq79da9s3s8/X8rgiJvthqHZ1OM95yvn09+9LlcFvXth83055cm8hZAoHkN9mfdjcB1jdaGXIzoOryz5oic8emfvKVOhFdRVvWBy0lMLpSsWWFImFio1hroMoh06xg13m1ME1GXzuR7fsB0+MLfPfRY3z0Xz9APBnh2cdOUMxVSLXHMWM6Y+dmOP7CRfo3tDO8tQfdUDl1+BKzVxb5iY/fi91wuPjaJPNTBdq6QmB6/Pws505OsDhX4p0/eRulxSrnT15BliWSmSi6qfLq4VGmx3P85MfvZeZKji9/+ntsv2WIqbEFHvqZOzn23Dmshsv5k1e45917ufWerQC06wmyehzpmvsZBAEXTk9x9PkLQNjX33P/LiLLXCMzmVjztQzXjUR0lubtiWbfmM3E3rDL4LXxwwMcAU+PXWZjJsNgMsXS+RUbDZ6fGMdQFKq2gyJJFBp1crUa9w6N8NzEOFnT5PjsDHFNp2LbzFerTFVKHBoYwvE9js/OcKCnj8NTE/TFk9QcG1kIzuQWKFkWm7NZLubznMkt8MrMFHf0DTBWLN70sQsh0OQOOuK/QDryEGXrJQq1J6jaxynUv03FOkLWfj89yd9AEcs6YSER3miZVOQddMQ+csN9merWta9fcNVKfvW1bYIvQmb5oN0PqkwV/5Jc7VFkEaU99hESxkF0pR+BjhAypcYzjC3+PgC24yKEIJ2IrN3Qvc6GvWaf5Er+j6g5Z4lqe8hEHiSm72+Wbin4gcVk4T+Srz/+urZreS62F5A1FfriCTRZxvJcSpZFvl4nqqoYsoIqS8Q17YYYrGgyjcaLBYSAzujKrLl4Q2CKRCryACXrJYr173B58d8QUbciSzEsd5y6cx5VytKV+FU0uat1HAHgBR6KpOEGLo4fAo+GHMcJLCQC/MBHlQ0c30KXYshCww1s3MAm2iwfkpaxm5bfteXfr7fM/ywhhImmbKDaeBJd2YwkEjSsw0T0gyFAJHRUZRhZakMIA03dhiTFcd0pJCmJLu2gYR1BUzeiKsMEBFjOGQQSvl/GcS+jSO0IEUGWO/D8fFhGaB9DkXuRRYp4wqSzO8X0lUXqNZtL52bo2tiGJsv4QYAhKxSsOg3PpT+WZLpWxlQUfC+g5jrosoLrB6iSRDSu0TuY5dVjY9RqFpNjC+w/uL64XhAEzM8UW+LJmfY46WycKat63RIlPwiwfQv5BpMlIUyQ2kDcnHirEAJNVxjc2MlHfu0+UpkYn/nrJ2nUbV47Nkat0iCejDCUeWMAghf4TWr31Xe+04yR0gwi6o1LDoQIhXH7htr50C8eorsvw1//6deoVSzOnBinf6Gfsu4wUSsQT3Tief5qQOp1vGiaIhO7xha4oztFIhmhVKixMFdifqZAT38WXTYoOHm8wCMIfAhCHZeAANd3CAhwfIeoEiemxGkaaF9//6qMLEvUGg6xNRIJ4TWBStXCcXx0oOzm6GLjzZ/kOmF5DvvSmxiOdvHt2Zdx/B9fd8dXS0fZENtOUk1zqnyUgchG0loWBKhNoCiApljw2jFzZZHnHj9xUwlHXVLoNNJk9dcHGAghyERN8vWrOgyqJBNVwmcjloi02pr56UJYNrsG0BWCQGEZyBIQppsat79tB0999RjVUp1nHzvBne/Yyaad/aGzLmtPYEJGNSsGzEuLXft8CyQMZXXb8cb69zAMSWMg2k1aS9zc5FCWWmCH63o41trPte8HnHjhAmPnp1d8HxCgyaFrp0CQ0boxlpkUROJGq3xicb68Aki8NoQQeK7P9FyRxDpshJsNISWQtX3r/q6oMgfu28Zz3zxBtVTn9LHLzE+nyM2VMCIa++7eTGQVKzNAVmRUXUVWJG67bxuPf/4lSvkqLz7xKnc/sJvt+4eRrsOYCpqJQEmSmJ8rkVsoMz1VIJEwSKWj+H6ohdPWnqBet0lno5w7PR1qtjkeiYSJ1RR0rlUtHMcjv1ilozOB43g/XsBRMsLGPYM3XvANhB8EzNRKTNaK1D0bXVboMhP0RlKrqgGCIOBYbgIv8NmVCXXfxip5FhoV/CAgqRkMxbIrxKy9wOdyeZGxyiLD8SyDscyayaC663AiP0nDddiT7QvZRM04U5hlul5cAXgPx7MMxdY3GfGDgFfz05Qdi73ZXgxZpWDXGC0vUnUtNEmh04zTG0miyavbQstzuVLNM1sv4wU+CTU8t2tL5yEEjY/lrmBIKjvSXdi+x6VyjoJdgwCyRpSBaJqoerWvdn2P08VZPN9nW6qL8WqeuXqZ3kiS/lioX3e+OM+iVaUrkmAknl2Tiez4HpPVItP1Io7vEVE0BqJp2ozYquvc8FyOLU4QV3V2aN3UXJvRco6CXW+W9sYYjKUxrmF0Rk2NhUaFC7l5XpwbZbZeRgBPTZ9fwaTK6lF2Z3rYvLufo98712JZ+57Pln2D7G2OeS+fmaa9O8k7PnhbWPYoCbbdMsTibAkCSKSjbN49QFt3ioPv3IXn+cxMLKIoEvn5EqV8jeFtPQxv6eau+3fR0Rv2HdtuGWwa2gR4nk+6Lc7bPrCfR/7uGfILFcqFOh09afo3dNA71N66j0KINUHAuekCj/7TiyzMlcJnbnMXtxxcqQu89uO3EriT5R/cLO6HCDi6StYDwXSlwsvT00Q1lbrjkNB0otEwe5mvh2JnS+/vUi1zezTKzvYOJkolemMJeuIJSlYjpEwTUpdVWaZQaTBVLlN3w8x0XzzBQq2G7bmtfOhaNZM3Ckmo6EoPuvJ+MpEHaTgXma9+noXKF5mrfIaotptM5N0sKe/LIoIipQEfCZWYfgBJvDENCNfPEQQOiJUTnAAPx5sFQJHSrXK2IAioOxfI1x4jCCy6k79FZ/znVpRLhed0tfFVFZl0MoKuKW826UAQ+OSqj1BzzmIogwxn/xxDGVlR0uf6Jd4Iw6UnlmAwubIOOwgCvCDAcl3MN8JGIAh1U34wTD9CPZZ2uuK/jO1OYbmXsb0Z8ECVM3TFf5mkeR9RbSfLXfR0Ocpw7JbmES1z6EK0ShhXD4jDgbjl10NWkhBU3TqXKlfYkhhGu84E3/Vdym6NtLZ6EuH6HieKZ9mWGMGUf7xo2QBCSESMt7T+1tm5apl45OHW55j5zqUFW2Foe1ufk9GfurotbeW2IsY91BrP4PsFhFBZ0sIyTI1dtw1z/PAlbMvhe988yZ33buOOzmt0K5ov5FAiw/VS+Tv3D/HME6+GJREvXuS+h/YQT66dtbHqDsdevECtYqEoEtv3DBBLGNSnCrjXyVRLQiALBUOOcF0kRIog6W9Z//frRCSqs//uTXztn19iZjIUDbTXmWTdbEjNwUOkWepTshs8OXkBXVa4tb2PmHLzehWGqbH39hHaOhKMV+apVSwKlRqGaTAUy4AfsnGutXWVFaU1EXZsd31LWKBuO9Qdl9GFPLv6Qq2jju4UG7f18PILF5ifKXD4mXM8+JMHSGkZUtpqqv5SU9Fl9q5qT64bImQcZZMRoub1r0tbk4ott/qWq/uRFbl1vq7r3bSDkyopfGXyOQxZp+hUGK1Mk9UTvLf3LqKvQ4j4hz0m62OcK5+i6pYx5SizjUkGIhuAkD0ysLETVVNwbJeT37/E6JlpBjZ1toQybctl/MIMn/nPj3PuxBWEJNZlmQRBaA8sGoKNUi9e2SfnFvE8v/VuBX5AYaHMwkzIzJAVGUmWUFUZVVewXY+UeVV3p+E5reeqozdNpiPB3GSe0y9f5vB3X+PWt4YuWQTgeh6u7SJJEpqhMrKpc5m4Z2jFfuihPXzrC99n4tIc//X3vsxPfPxetu4dJJIwkSXROtZG1eLKpTleOzLKwft3sWnXOgYly8IPAhzXuzrRueYVCJvVJY238MfrZXCFEBiyRs4qkFSiaLJ6w/cqljDpHshy5tgY1VKDF799iv6NHWQ6Qra674flaS8/c5Z/+uQTrdKFpTBkhbrrYPsuDc9mwZogusx5KJWN0T2QZXZikanReZ7+6jHe/bMHicSNFlDkOm54rrKEqsjYztV21fd9rIaD54RuxJ4b/qtVrNZrXcpXWZguhEYJioQsSyiqjGZcX6R2655BNu7o5fgLFzj85GmyXUnshsPg5i7237N11US5vS9L91BHy11z8+4B3vaBW/n6Z55ndmKRv/53/8L7fvEQew9uIpowkWUJ3w9C3ZKqzeTleU59/yL7D21lx20jdHaniER1RjZ0oKihKU+o+xIaA3R0JtB1laGRdnILZdo7EmSyMWzLRUiCVDrs7/oHs82M//8fN4oAKFg1Hhk7wSNjJ5iqFbF8D0VIdJpx3tm7lY9uOkBauwo6+0HAfzz5HWbqZf7k1vfwjYnXeGbmIgW7huP7xFSdPZke/vft97At1RW+rwEcy13h9499gwd6t/G7+x5YAQpB2P6dKc7yGy98iYRm8N/u+vCKZb5y5SRfGTuF7btUXRvH9/j17ffwa1vvRllnQuT6Hn995lmO5Sb49Fs+wmg5x+cuHeVMYZaqa6NKMttSXfzhLQ+yOXlV+NkPAs4WZ/nMxSO8ODdKzqriBxBRVLYmO/ng0F7e2bsVTbrq3Fp1LP7g5W9gKhr/55538PnRl3lh7jIVx8L1fdJ6hP3ZPn59x1tbYFfdc/h/zjzHfL3CB4f38g/nX+JKNc9IvI1P7LiXBavCX772PRYaVXqjSX5nz/0c7BxuATUBcKWS5x8vfJ9nZy8yV6/gBj6aJDMSz/LegV28b3A3sWVg1UKjwu8c+Sq9kRSf2PlW/t8LhzmyME7FsfCCgDYjyt2dI/zqlrvoj10F8o/lJvjU2ecYq+TJNSrUPIeqa/Fbh7+y4pof7BzmP936fhZmilSKoSuxYWrISuiWvoQXCElgRvUWsGw1HHIzRcqFGotzJTr7MximRjFXYXZikVjS5MKpCd7+gVuZHl9EiLD9l2WJqbEFIjEDTVdYmA23kZsr43sBZlRD1RRkOXSMT2VjXD43zaZd/aSysRZj22o4zE7mm7qiAtf1GL80z2NfOMyJI6MEfkAyHeW9H7mDaMxgYbaEEOA4HrquUshX0XQFw9RQmxp/AQGu46EbGqnMDw7E/qEBjiQEhwYGOTU3S81x2N3RyUg6fGg6olF0WQnBHCEomg0WJ2uczc2zt7OLhuugyjILtSovTU6wq6MTKRJBAAndYG9XFxcXc2xrb8dQFZyCz6ncLLvbu6g3102ZBpoqk42aPDM5RmcsxoVijmHSWJ6LKslUHRvH9zEVha5IfE3LxNb5CB1T3UZ3/NewnHGKjSep2qdIm/cvAwIkYvpeytbz1JwzON40mtz/hiiZdecsXlBFZuXDEQQNavZJAAxlYEU5meMt4AVlJBEhru+HaypFg8Cn7pwnaGqaCEkwPV/E9wMyycgbOs7WtnGwvSnAQ1eGVoFGEGo7We7NugI13coCl7U0WIQQKEKgvEGxQklIxDSNtkgEWQh+ED4+nl9hvvJZLHecnuQnQlARCYSEJHQE2vUpossGoUEQcK5ymbpr0aaHGhMZPclUfR4BFJwyg5Eexu15bN9BETIXKuOU3SpD0V5c32PeWqQv0oXl28w2Fug1O8lZBU6XLnFX2z4kITFVn6fLzCIQTNXnuFydYlNs8NpH50c+bOcCjjeNrm7Gsk+ja9tQ5PXcAcH15ghBThffb6CpQ83vc4CLLGWp28eQpQyaMoIflPD9WnObLiCIGofC51fIiCb6pGoy+w9u4rEvHCY3V+LUy5d54isv88AHbm25dV0bfhBmV9ayq99zYITBDR2cOXGFk0dGOfLsOe55YHfoxLC8jNb3ee2VcZ77zmtAyDa6495t+EGALG4oCYvlN5isj5FKZW4q07486jULRZFDUEGswyTwAxZmS62MeTxhrlumc7OhyjIxUw+tZIGEpnOwawiAjHG1vWvUbCRZXFegMwgCFhcq1KphmUgkppONx6iJcBIWZoCkFXRjAN1QiUQNoEQxX2V+pkh7V3LNfViuG9a4p5OtREc8aXLnfds5e3KCaqXBN790hMENHey+dXjVPYYQfPb9AMd2wgn7TV5D0UzwLJZq110uCMDQVaIRDRE4Lc2VpVBVmVgi/K5WtZidzDO0qfOGfcuGWA81z8LybCQh2JEcJq6Yq7KUP+oRkaNElBiKpKFJOrtTB0goV8vHRrb3smlXH6dfvsz4+Rn+6ve+xP63bGlNuqfHcxx9+gxzk3n23LGRycvzTI/l1t3f5Og8X/7bp7DqdqgHUbNo1G3Gz4fJJ9ty+Ye/eIxkNo5hqhgRHSOiMbi5m/d89C5qjo295DIRQLeZIiKH7VR7d4p9d2/m2186TH6hzKf/9Gsce+48Xf2ZMJFVtakUa+w8MMK9D+9fpfulmyof+KW3Mj2e4+RLFzl3Ypz/8m++wNCWbnpH2tF1FavhUFgoMzuxyNxUAU1X2L7MQOV6UanbnB6bpW456IpCxNCoWWH7YuoqihxqcdiuR8N2iEcMhrvXAGNXhCBnFekxb05vR1Yk7nz7To49c478QpknvnSYxbkSOw+MoJsa1VKds8fHOfHCBfwg4OD9u3np26euAnsBJFSTqKqjyxJJtQ11WTIxEjO48507efXIKFbD4ct/+xTjF2boH+lEkgWNmk2lVKezL8PDHzuEHwRYy4CjwkKFf/n00+TmSjSqFo2aTaNuMzW20ALBH/37Z3j2GycwIxpGVEc3NTp70zz8sUNhKe06kcxG2Xf3Zk4dvsTomSmmx8PndM/BTbR3ryyZFEJQWijj2C49Tf0VTVd470fvZn4qz4vffpVLZ6b4b3/4CMNbu+nf0IluqNiWSyFXZm4iz+zEIggY2hKyViIRjUjkxuPDzu4k6Uw01BCRxJqlxm9mbPw/U9Rcm78//xKfvXSUvmiKnxjeS1qPkmtUeHHuMv9w/iVs3+V/2fqWFQwigJxV4ZOvPc1MvcTBzmH6oinKdoNnZy/x9MxFFEnmD/Y9SNaIIgnBvmw/XWacY4sTTFQLq4AjN/A5ujBOwa5zb89muq4pRfvw8H7e0rmBktPgcxeP8tzc6E2fZ9lp8OzsRR4ZO0FGi/Lw4G5kIZitl3F8b4XGUhAETFTz/PHxb3FicYrdmR7eO7ALQ1a4VM7xvZkL/PmJbyMLwf1921axVMari/zFySfJ2zXu791GuxFj0aryxORZvjl5hoiq8/v73oW2LCF9pZbn61dO8dbuTUxWCzwxdZZPnX0OgAf6tpG36nxj4jUeHT/J7kwPSS0s6c/bNf7zq9/l6ekL7Eh38a6+7ZiyylStyJPT5/gvrz2NHwT87MZbVzGVLpYX+LMT36bq2jzUv4OMHmW+UeZbk2d4ZOwkhqLyW7ve3nKy7o0mef/gHmzf4/jiJI+OnaA7kuBnNuzACypIQsHzbXqjbRSqY5w8dQ6tPc+Zs8eJZHfQtyWOkalRcUKWZqSrgpZUabh5LL9AOe9y4fJxzIzC5YvTOCmX2JBJ9XyDI0fPsufQBjYc7OXpo68gb5DwMwE5SvTf1snhY6exow5SUmLs8gyk4JXT5+nekCWyxSBnldi8u59o3CC/UCaeinDp9BSKInHbW7eBgIXZIn/625+nWrZQNRmr4VAth1IRQRCgqDL3PbSHg/du58KZKeamCmzZ1UelFMoFyLKEbTlcOD1Fd1+GcrHO4kKZrt40bR0Jkuk3N2dfHj8UwJHl2fj49MbjdMWjrRq/2wc6SaoxgiAsz7F8G01Scb0EuUaFu/uHiKoqlwqLRFQF13fpjmfpTcRRm6BOzauzKZtmd0cXc/Uq4+U8e7o6qbkOpqKCE+AEHqoqMVEtIiuC3Z0dbEq1cTI3Q8OLcy6/gKko1FyHqmPTGYmTMSKYkoQfODjeLIqUQRLm6rrOZS+LJPRrOGWClPkO5iufp+6cZ67yT3THfw1ZWjlRCAIf1y8QBDaasrYAeM1+jVL9abLR97dYQ0HgUWq8RNk6giQMYvqBFXo8IftIIrQKL6/YXhD41JzXyNefAK5OdhRZpmJZvNlYLhztB1WCwApLV5rhBxaLtceouxduanuqHIKMjjeH4803S4B+cJ13RFV5x8iGsBH7AW3XdicpNp5GEhpRbSeKlADkN3TcC3aBqfoc3UYHr5bCa9ZvdjJnLbJol0ipcUrOeRbtIntTWzDkULMpqcY5sngKgJgSZaFwhi3xIWpug7OlUbrNdmJKBF3WOLL4KoqQmajPYMoG3UbbdbVfflTD88uU64+hKcMIdFx/FtlrR5bSOO4EIJDlNEFgARJCaLjeHLKUwfWmsN3zBLioSl9T7DqCjCAILBx3HE0ZxPXmEUgE2NStlwgCB13dgRA6kojh+rMtp74NW7u57917eOQzz1Ov2XzuU08xNZbj0P276OxJNx0jAhp1h0qpzsTlBUbPzfC+n7mTdNvKwU9bZ5L3/cydfPLSPJVyg3/8q+/guj679g+1RPqq5TqvHhvns5/6LjMTeTRN4Z3v28+Grd0sWA0qln3DV8CQjNcNGC3Ftx55mdGzM+y9fYTBjZ0kM1HMiI6iSniuT7XS4NLZGb74d89QzNdQNYWte/qJrlNOUas0WmUEju3hOi4zk3ms5mTLtlwun58NHV1UmdpiDbtsIesy52rzSJJEVNXQm1Typx8/yckjo+y7YwPDm7tIpqNEYqHTi+8F1CoNxi7O8ZXPvsjifBlZkdi8o4/ObJKqbyMQYUbJ8amUywwvs5iNRDU2bOtm/NIcxXyVR//7CyRSETq6k0iyhOf4WJYTWh83M/yqfBUgFEJw19u2c/bkFZ782itcPj/Lf/2Tr/H+nz3I9n2DxBMmsiLhuh71qk0hV2H03Awnj47y3p++k537h27qHgVBQLVuUWs42I63rvNSEAQs5MrM50ps2mngyisZErIis3lnL6eOXsZ1PL7y2Rfp6E3T1ZtG1RQ818OxXWzbJZGMtPS+zpTGqbh1uow0kpBo05PIuBTtKZJa9zJ20492pLU27ml/kIgca2kbLYUQgmxHgg//b+/gH/7iMS6dnuK1I6OcfvkyqqaERhaORySmc9/79/MTv3ov//TJbzEzvrju/uanCzz+zy+t+/uSk9vEpfkV32/bP8QDH7kDU1GpNMJ7rEoyG+Id6E2RViOi8b6PHSI/V+LoM2eZm8zzzc+tdOmUZIlI3ODeh/ev2rcQgp7hdv7VH3yAf/n093jumyco5aucfOkiJ1+6uGp5WZHoGWwjepPC85btUq5ZVOs2juuxobetlQ0u10JzDFkSuL5PreGQit3YKCIgQJMUCnaZTiN7Qya7EIJbDm3h/b90D1/6m+9SXKzyzGPHeeFbp5Dk8L0NfJ+u/izv+9ghdt4+wqXTk0w270dAwFStgC7L7Eh102EMoiwDjhRV5p737OP8yQmefew45UKN7z7y8qrjOPC27bz7o3dTrjZWJEjLxRpPPnKUxWbZxFoxN5lnbjK/4rvuwSxv/+Bt1wWOFFVh312b+fpnnme+6RQUjRvsP7QF41pAR4AR1VcwMoUQdPZl+JXfeZhMZ5LvffUYxcUKrx4e5dXDqyf5kizRO9xOPGW21l+KIAiYr1Sp2w6O56PJMq7vh6w2P0CVJVRZxvE8FEmm7jiko2bItvsxHBP9j4rD82P88+jLDMez/PtbHmIknkWRZBzf40xhlt8+8hUeHTvJnkwvD/RtX7Fuw3O5VM7x+/se4J6uTRiyghcE3Nezhd86/ChHF65wIj/Fvd2bEEIwGMuwO93Ld6bP8fzsJXakVjohVxyLp2cuEld17u4cxrwmCTEQSzPQZMC8NHf5dQFHtu/x2YtH+dDILTw8sIsOI44QUHMdaq5NSrvalji+x2cvHeVYboK392zhN3e9jZ5IEkkIaq7NV8dP8X+9+hSfOvs8O9LdDMZWgtdFu0HOqvL7+x7g9vYhFCHhBj57Mr380SuP8/zsKJfLuRUMp4VGlZ/feICPbbqDiWqB04VZjuUm+IVNt/OJnfeSt2qcWJzktfw0VdcmqZk4gc9jV17jyalzPNS/g3+17S30RJKI5vnub+vnj48/wT9dOsrdnSOMJNpWHGeuWf727/a9i73ZPhQh4fgeW5Nd/PHxb/HMzCV+bkORgeb59UfT9EfD669LMt+48irtRoz7e7vRZY2SPY7jB2iSR9F7le33pNh+z2YMJU0QFOhIBNhekaKdJ6p00TaoIAudojMWzoFjJW55d5w2fQdWEGemkcNTfbruzTAU7WbGXUTfozGwqxNFKORFGckTDGzvxOp30DWNumdx58M7mWksIoCyW6crm2G6vsDOAyPkF8Ly4P23bWX8/MwKV13fDygX68zPrJbJicR03vqu3Xzolw4RSxhs2NrNhi3dRGI6VsMh8ENgyfcDuvsyLVdR1/XQNKU1bvpBxQ/F6GrRLnK5NslwpA8ncBFATInwSv40+zM7mKrPEVVMpupz+EFAj9lBW9pmrD6OX/OwcXlg8yATtRkSKuTsfFNoWeJydZK0lmRrfJiUbmB7cTRZwfE8VFmm7jpEVJVBKcVQIo0myVieh6EobM90YCoqg4kUpqzi+D6qLKFJMlaaa0MAACAASURBVGoTBfX8IhOFP8MP6kS1vRjKMIqcAXxsb45C/duUrRdQpCwJ406Wu44JIYhoO+hK/DIzpU8xW/476vYZ4voBNKWPsDwqT925QMM5T1S/hf7Ub61xBQUChcni/03DvUxM348QGlXrBAvVz+N4cySMu0lHHmixeoQQ6OogprqJunOWqeJf0pWw0eVevKBC2TrMYu2roZDvUu1NEBAx1OuWT9xsCKEQ1w9QrD9N1T7FVOkvSZlvQxYxLPcKxcZTFOpPokrZJjPp+hHRdqE0l50o/AXZ6AdQ5XaCwML1S0S0bZjqSm0NP2jg+WWCpgiy7U01GUshG8v2JgAFgYwkGZhKfBUr6s2ELMXQ5C6q9ikuL/4OhjLYZJuEuleKlCKm7yWuH0SV26+77yDwkZBQJQVdUtEkjRPF89ya3sFsI4cua/SaHVTcKl1GO07gktLidBltnC5dAsJSnR69vfV3QEBciaJKCrKQcAIXRZIZivaSswpoktqy/vyxisDFD+pIUgIhGUgiDggs5wx16yUQKpqyEdsJAaJE5H3YzjkUuQuEjOcVsOzj+H4hBIiEgSr3IUsJPL8AgOtN4Pt1FLkDx5tCoBEENerW82jqdlxvEtUMQWJVU/jgz99NtWLx1NePU8zX+OrnXuLJrx+nsyfVckqrVS0Ki1WKi1WyHQnu/8B+rlXskGWJO+/bzuR4jkf/+wtMX1nkk//+UXoGsrR1huUQpXyNsYtzNOqhs8/b3ruP93z4DgxTIyMLbhvsw1Svz+zwAg8vcKl5FWLK69NKyc2V+OaXj/CtR46SaY/T1pkkFjdQNQXX8SgWakxcDsu/ZFniwFs28/b37FtXy+KxLxzm6cdPYjUcbMvBtlzshkO1yQZamC3xn37vy+iGiqYpaHrY0W69ZYB9H91Fw3NXOBcVchWe+sYJvvPVV0i3xch2JEJQQw8n66Xm8VVKDSRJsOvWYR780AFcXUJyRWvyqGoytfrKtjQaN7jvoT0ce/EihVyFZ544xei5Gbr60ihKWDpRr9tk2uIc+rnb6ErGuLamJt0W4+f+17dRKdU58tx5xi/O8ck/+grtXUlSzUy5Y3tUy3Vy82UaNRvdVHnHw7e8rvtkNe17rydKrGkKu7f34XoeqAWcYGW5jqJIHHrnLp594lXmpgoce/ECf/gbn6FnIIuuq7iOR6NhY5gav/qbD9I/EjI3VElhqr5Aw7OQhczmeB+acBirvswm6W5i6nqW0T96kbcXCFSfi9XTeL7HntQBdLk52ZUE+w9toXsgy3PfPMGJFy+wOFfCc30S6Sh9Gzq4/b7tjOzoQwjB3e/aS0dvhp6hNmYn8uiGiqzIBH6ArMqk22L86r99GCOqE3kdou3JdBTT1NgQyTDc1BgTQhBXr4I2QggGN3fxG3/2Uzz/+ElOvHCB6fEcVt1GM1QS6Sj9GzrZf2htHUdoioBv6OTjv/c+7nnPXo6/cIELpyaYnyrgOi6arpLpSNA9mGXLnkG27B2gax378EDAvb9wB3e/bx8xU2fHrSOksjHspuC8sY4BwLUuQcm2GL/yO++hWm5gRnViyQiO55Fr1MkYJtuSI+ETHwQgYNeBDXzizz+M63r0DrUjqyv70EjM4H2/eIgNO3t5/psnGT07TTlfRVZk0u1xtu0f4s6372Roaze+6/Pr/+EnyS+U2XbLEIascktbf6uUxJCjTVfY0N46pmokMzE+/rsPs/+eLRx75hxj52aoVRoomhKC1H0Z9r9lC5oqs3VkJfsv1RHnA7/9Tio1i6xhEtdv7hkxIzqZjhv3AyPbe/iNP/0QlVK9td7O20ZWgTGSJLFx7xAA1WItLDGOh4nbjt40v/zb7+H/4+69g+w6zzPP33fyzaH7ds4JORIAwQQSzKQokhIlirIpeWTJLtuyd8alKdfM7M7WzlZtzc7u1Lp2157y1mgd5JU9tCyLlESaIimSAgERIAkiZ6DROYeb0wnf/nFuN7oRSEKibEoPiwD69rnnfifc73zv877v89z16BZOHrrEmSPDzE6ksas2uqGTSIVp7qhnzdYO+je107rCfc3zJOmZNOVilRnhUJYuhYpNybbJlysETQNdUUiG/EDfdl0czyNXrrCto4V44FenTfbDoBsaLe1JqhUb3dQ+MkG7BFd6fH/kBGXX5rOdW1YRGbqisj7exJ6mXv78/CF+MnWJ+1rWLMdd4Hep3NHYzb3NA8v6QJoQbEw0sznZwmsT57mcm2dvs+8qrSkKD7Wt40fjZ9g/PcjTPduXq46klBxfmOBCdoaeaD076js/dgKwO1LPb/TtWuWsGtFNIvrq79BYMc3rExdImiG+1LeTttCVarugZvBI23renx/lByMn2Td10bejX0HuakLh0bb17E51LVfr6EJlV6qTgVgDxxcmGC9mrjnfmxItGIpKUzBKYyDCeDHN7oYudEWlzgqRNEOMFBYoOH5iIF0p8oPRk0QNi6e7t60ap6lq3N7Qw+ZkC/umLnJ0YZyuSHJV1ZGuqDzVuYVtde3LOlaGqnFbQzed4QRjxQzTpfwycXQjhLQGTNXCUCJ40sHDISo7EULxu2bcOcJaC660CWvNgERTghgygpT4BjiKRdGepuBM4+ES1YM1IwqNJiuJqehE3CBaLT6rejaqUAhpARSh0BduJaQFqHo2hqLTZCXRhYYrXSzVd1IGv1X4zoc3Mz+doa2ngf5Nbcut+qGIxa13r+XyhSny2RLSk0RiAfrWtbBtdx9bdvUsu+ZGV8hLaOGrYrDQL75N9hNBHAFk7TxVWaXsVsg7ReJGFF3xq4gKTpGQGkBXdCzFIOcUmC7P02jVLQe3s5UFhgrjtAebiekRsk6eoGrheM7yMtVUtVUK+TeyD1zC0hc6bgZWLRRWQ+B6RTLl18mW99fai5YqfmxcWURX62gIf4mQsf2a4F8RBqnQMwh0pnN/Qaa8j1zlIAL/BpE4eLKKInwNpBuhLvQZKs4I07m/Yib/bQQCT5bxZIWgvo7m6O8viywvwVRbSIW/yGTmv5CrvENp/owfwOIhZRlLH6A19g0ms3+CJ/2WhKrtUvwAQcWbQTzwAPnK+ywWX2I69/8ym3/OFw7G338q9DSammAi8ycfuB8hBJbWS33488zmvk2mvI985TBLbk0Cnbb4H11DHOUrh5nKfhPHy+DJIq6XpVrTg5rM/imz+b9BUYKoIkTI3Epz5HfQ1JtzmbkRpJSoIkTQ2EyxepaSfYaSfWblUSFQmS8+T8zaQ2vsG1i19qfroc6ME9XDzJTnWR/tw5N+f2trsIGKVyVj59GESkewBU1RERJaAg2YisFApAtLNZitLBJQLVJmkopXIaKFiRtRAqpFxs6zKdbPeGmGoGoRDrUwXZ6j0aq7ru3nLzNUNYGutmFofVdIU8BxZ3G9DIbej642U7XPARJVSdZII1+QVNNa0NU2XC+DpjbhySW3RMmSe5imNmHLMYQw0dU2FBFG13qo2Beo2KcImrtXjSmWDPHs795LQ1OcV54/zOTYAoWcX3lzNRRFUN8YRdevf11MS+fxL+4mHA3wg789yNjQHEMXphmqtaMsoa4hyp6HNvHUb9xBNOE/qILGVY5qS61kK1rKBIIGs5mkkUK7nn7WDdrPltDUmiAaD5LLFJmbzjI3ff3sdihisWvPGr74W/dc08qwElPji1w6M7GK/Fk5FomkkC9TyJdX/SqSDHKnZuA6Hgpiufog1RwjWR9hdirNwmyOhdnctfvFP8877xrgC1+9m/aues7mZig4VZzaOAxTI3CVPpCiKGzZ1cOTv34b//CtA2TTRUYvzzJ6eXWFR/dAE5pQ0DQN9SrdECEEDc1xfutfP0J9Y4y33zjD/EyW6fFFpq+qBACf3Olb10IydZ3ATlz5f+k6g38Xe1KSiAZXVRsJagWZtX8IfMcPVzrMludRvWurZnrXNfP0b+7hu3+1n8mxBabGFpkaWz3OhpY41eqVlpmqZ9MeSNEUqEMVCqaiY3sFLPXnd4D6pGG0eImqVSFnZ3CkTd7JLRNH4N8zrd0pPvPVu3n4md2+tkGtdN0wdayQwcxEmtFLM0yMzpNsjNHQluToTy+i6Srx+jD5TIlAyCSzUCDREGXzrb0kUhGEENiew4nMRXrDbcR0v2Kk4JQYLIwzEO7AVK/cw1JKULiu054rXS7kRklEojz8hd3c/eltODUdLyEEquaP96O0S5qWzubdfazd1kmlZOPY7vJnarqKbmi+K9YHCCOXHIfX1Wk+s3sDtzS1LL9u3GDeXImVxxYImuy4e92q349mM/zv777Fv9t9N02h1cRVY1uShtYEU4U8dQFfe+dq6IbG1tsHWLu1k2rZxnW95XNkBU0MU/PHoKls3r16XbOUlV+C43m8fPkClqbxWO9aPzEbC3LXo1vZec96qmUbz/OW22c1Q605iimEQ6vJAM9UmO5SOTA+xxN9a3lkYMMHSjbcLDRd+0Di8Hp4+wfvEYgEuOOJncuv6abGhh3dDGxup1ysLt8fg8eG6dvejWnpmAFfc2lVi7bjcvSNUxx4/l2++O+fYv1aX/stX6lSsR1iAWu58sxdiglqzwZD+xVMon0AWjvq+F/+7DeWXQqDNymgnq6WGMov4EnJYG6O/zb4Piv176SEsUIGiWS2nKNckwxZgqH65NLVotKKEMSNIK70KDmrRSXWxRtpDyU4l5lhJL9ILOFXiDnS4/D8CJlqidtSXde0sX0c2FbXSuAj6CRezs2zWC3SHa6jK3wt6R3UDDYnW/ne8HHOZWYoufayuyRASDNYG29cJo2WoAmFmBHAkS4Vd7UepKXqBDW/pVgXCrqiogqFpOm30AmoGbKw7JI5XswwWczgScm7cyOczcys2KOk4jpkqr4e8VQpi+15mCvmuqhu0R9NXSN+bigKUcPCzi9Q/QimF4YaQVNUVOVawkRKF1ONYSghv/vnilUbEFmVxDKUEEGtAV0Joio6jdYVwkoIQZzINRzB0twRVM2rtg1flzMQQtCzroWedS1cjXgyxFf+5QPYVcdv+5V+otcM6Mvz8UeFlJKimyGgRlB+Acn9T0TEV2fGubN+O9REfG3pYCkmG2P9BNUAA5Hu5e0UoSAQtAWalgNWVSh4UtJg1tVacKDei2OpFvVGgqAWWL5BbsZWsOSMoytRNBG54fs0JUZ74t8RK99NvvI++eppdCWMIgw0JUHI3EzEvJ2gsWaV0PSqfahxGiJfJmrdQbb8FoXqSWx3Bp8ZTRDQBwib2wkaG24wcomuNdEY/RqZ0htky/ux3TlUJUrE3EE8cB+m1nUNaSWERir8DEF9AwvFlyjb55F46GoDEetWYtadqEqCkn2Gsn0ZVQ3S3V63vIjx92EQNNYihIGuNFxvcDeEoTbTkfgfiVq3kynvw3HnEcIkoPcSte4gYu6m7FwmXzmMqbXzQULZqhKkOfp1IuYu0qVXqdjDSFxUJYal9xI0rhU29mR1mRBTRBBFDaKvINckkpxdJKQpeF4BWbOS9ScEF0vvRlPCCERt7Eatfcl3YpGyiqm1ENTXoIogrjsLShwhgpSdS4yl/zcK1RNEA3cRMW5BVSLLn+zKEhVnmHTpVRaLL6OrDbTH//sbVh2pQmVbYvXitSngl4ZuiF1ZWLYFm/CkZL6SRxcBLuVniehxTEUjEoqhoBA3EpTdKqpQydtV1kT6KLlVNKHQE+qi0YqjCEFv+MMFR38ZUcyVKOUCaJ6k6IwwO3cEw5zAULcitRSVXImqMYXjpHAdDzv3DrZ3DEUR6Fo3njLFQm6SRPwuXP0knsyjqU1U7PM1MqmVSvUMjjeD4fSiqQ2UKofQ1DoMvYtS5T1UZXWWRQhBsj7C575yJ3fcv54Lp8Y5c3yUmYlFioUKQihE4wEamuOs2dRG/4ZWkvU3bguIxII89vQutt/Wx5G3L3L2xBhzUxkk/gNszaY2tu3upa1Dx7DcGzadbdnZzX/8r1/Bc/3syFJAowoNFQ0pbaQ7hVAaURSV2/auo7u/CWSeRHwRaZ8DrQchdP+7482x91Ob6F/fyuC5SQbPTTEzlaZUazUzDI14MkzP2ibWb+2kq7/xhi1qS/jcV+7i/sdv7OhzI+hBjaFqnsHsAn2xeqKG/2y5/d71dA80cenMJJfOTDA7nSGfLWHbLrquEkuE6B5oYu3mdvrWtRCqCc/2ROooOTZh3UBB0NKaWDWXLsEwdT775TvZvLOHY+9c4sLpCXLpIqqmEI4GaWyNs3ZTO8GohadwXYc7IQStnfV87RsP8+jnd3H83UHOnxpnYTZbE0vUSaYidPY10reuhfbuFInr3C8NTTH+/R//OnbVwTA1pO2QzxSxwhaaquBcJWbd1dfI//ynX8Z1PIJhc1l4XaCgKwYl71qSTdc1Hv7sDjZu7+K9A+c5d2KMzGLBr5CIWNQ1RlmzsY3G1ivkYFswxcn0ZUYK08T0EK70UIVOVG9AU342HbtPKkzV4nzuBOuj25gojVzX7c53HNQxzOtXydQ1RgkEDLoGGhGKgmFotHWnfMHOmii2pql4nudrb0WutNyU3SovThzgi50PrSCOypzOXKYz2LyKOPKQHF44w5b4AOZVrR6u9LiQH6U71EJjLHlDUf6PCiEEpmVgWj/b9ZZI0pXyciD0ccKVHovlEu4NqrMrrsu3Th3hSxu20hK+PtmpKIJg2LrpgPxqqIrC3o4eX/x/5euqQihi3VSlSNy0+PKGrRRtm6JtfyxeIdKTZOZzZGaz6KZfVRqOh4gkQkwPz1LIlojWRWjsqKdasX1to4pNKV+mtb+ZcqGCqqk4tsv08CyJxhi6oTF+aQq77NDc00AkFmD49Biv/fWbmAGdZGOM+rY6FqbSVIp+63VzT6Pv3vep7QweG0Z4Phnk2A6l8QyFbBGRDNPY6csgFOeyzIzMoeoqrX3NSKEwMTxDIVOkoaOeWF1kuZrgVwGu9MjbZXRFW3ax1OIaEunHZYqg6FR86Q/ht5sZioaHxJVXnhOaUAioBtlqmZxdoew6/O3g4Ru2tgdqovIr9wF+3Jeyrr/GWZJQuHqubAnG2N3Qxd9dPsJPZwbZkGhCRTBTynFg+jIR3eKOxl4M5eMPtpsC0Y/UvL9QKVJ2bVKBMIZ67ThUIag3Q2hCYaaUp+I6q8SnTVW7vk298J1ipby2SEIXynKVoqhdCU1R0FbEG8tntPbemXKuRg6V+ZPT+254PAFVx/W8a66FpWrEbzDOJTL2w5xdV73tOmdXCA1zZTLpAy6AIvRVrprXi/lvxAPczLY3/HxF+dC17EdFyc1xeOFH7Kr7dM2k5uPFJ4I4MhT9us5OCcOvDlq5MFmCpV7LLoa0a1niwHW2ux6kdLG9LLoSZUm8ejT7beoDe0gGdt/wfUJoBPReAnov8cDDjOe/R0f0WTTl5hTMFaETNNYSNG4u23LlAFx0JUEq/AVS4S98+PbLn2sRsXYSsXbecJu2+B9d+eGqO0ZX6+iu+883O1pgyVms7gPHHDI20p/65kfan6ZEiAfuJR649yNtHw/sJR7Ye8Pf5+wi3x3bx6Op3TRYKysaJMgS7dE/QFLFcScoV4/jeTkUJYDtjAISy9hOQ+hJUsFPoygByvYJNCWFrg8wlf0m6dJrNEV+m+bY19GUyDWfL6XEUFsYz/wxhcoxXC+Hpsau2e5m4UmP89lJSm6VglPBVHVsz6EtWIepaL5TQmGOqB70yzF1k8nSIrpQMVWDRuvjqbr6pGLo5AiByA4WR4osTKZxnXuplKqE41DMduN5knDcpKlrC8NnxrArVeKp3UgpMYMmwUg/C1Np7OYA/dtXOKnp/Sv+vTpLbOprcL00pfJhLPMWhHL9jJemqbR3p2jvTnHvY1uvu81HhbpiX49fbwMpkdW3oFoB8z6u99SNxIIfHAB6GSh9H4JfQihh4skwsYQOxdeQ3gzYvaC2gdDBm4PSDwkEv0T/hlb6N7T+XMe3hMZmQWNLI0LcXIAppSRQzJK0giTMK/p1VsCgu7+J7v6mmyKkLFVfJd5sfYAbmWFqrN/awfqtHTfcJleusFgoETJvvB8rYNCzpomeNU0UsiUURRD4CIuTfKaIpqlYIZPetc2AL5j+yv+3n7b+JgZ29BANW9dUZwRCJv3rr71uilCoM9uoM9uu+3martLV30hX/40F6FcipFpUPN89KqRZaEJDoJO1ZwlqCSz12vn0lxUbozuYt2ZIGimCapiofu38K6Wk6tnknRISianohDVfDNPxXPKyhBt2CagmQdUnhYpO2XddkW6NMNIJq4HlVqycXaDi2dcEbAWnhBDwYNOtq9ZcJbfCRGmWV6YP0mAlCakWcSOCJlQc6ZKzi9xWt4mwtnq+cKVL3ilhew5WbXyKEJTcCq708KRH1bOxVJNQbexSSspuhaJb8VvitFobQe24BODiUXarmIpBuDbOgm3z4uA5RrMZ2iJRKs6SqLTkyMwk706OkalWaA1HebCrj1QwxNsTI0zkczw1sAHX83h7YpSy67C3vZuS4/Da8EUuZxZJl8uoiuCe9h46ojEcz+PtiRFGcxlCusF9nb30xBKcX5zjpcHzvDJ0kWy1QtQwebJ/PQOJOmzP48D4MMdmplCF4J6ObtbVNaApCgcnRslUyrjS4/TcLF2xOE/0rUO/TnC5hHS5xN+fP8VYLsN9nb3c2dpZe73MS4PnqA8GOT03i64o3NvZy9pk/YcmV3VVRb+qSkpKyVA2zZsjg8yWikR0g0d71xAzTF66fJ5Huge4sDjP0ZlJvrB2E6PZDO3RGDHTwnVc3nzup0xenmZxKkNjV4pA2OT+Z/dwYv9ZSvkKY+cn+NwffopyocLf/q/Ps2nPOgSCRJP/XXBsl8OvHefCe5d4+Ct7OfvuJQaPD2OFLQ6+WOGJ33uIsQuTzI0vMnZuAq+mnfbNf/s3bN27kbmxedbe2sfdn7/tmmMuF6ucPHCWUr7M6LkJPvsvH0U3NJ7/k5dJNMawQhbxVIzRc+MceeMU0WSYzGyWx7/+EPUtHyae/suD6XKG+UoeU9EYKsxiey4RPUCp5g6WNMMsVgskjBB6LYaKGyGmyxnKrt9F4EpJSDPYmujCq7U5BzWDP9xwz6pWp6uRMIKrWrzAJwr0myR4dEVlb3M/r46f5c3JCzzTfQtxM8CJxQkuZWfZWtfGuviHmzP8LFBvkPS9Gl7NMfyDRiBqJLDHtYSMXzV0c+dFiJtXpHQ9/5NbgjG+seleLPXGdEJnOHnNtVKEWEVM/SIgpcdI8TSjxbPYXpkGq5P+8A4MNcDF3Pu40qHgpsnZ83SFNtEZ2shw4RQlJ0vZK5KuTtMRWk93aAsL1Uku5N6l6OYIazE2xPagKyZnswdRhcp8ZQJFqKyL3k7SaKbiFTibPUi6OkPRzRLSYmyK30NES3Ih9x4z5WEsNcSa6K0E1SinMm8BAg8HSwmTtefYFL+HgBphtHiGkeIpNGHQE95Kk9XNcOEUWWeOslug6GRpCfTRG9nOXGWcE+k3uJg7TNnNE9YSbI7vJaxfLVzxs+MTQRx9EmB7WSbz36cl/CT6zxic62qS9sgXUMXHX+b44bj53M8V1lng4XExN4amaMyW03SHm5gozRNQTfojbahCYaq0wGDBb/noC7fSYPkVYIvVHMOFadJ2nrAWoC/cStzwS/UKTpmzOV/INKRa9EVaiekhLuUnsFSD1kAKRQgG8xM4nstAtJ3J0jx5p0RQtRgsTGAoOhtj3YQ0i7Jb5Wx2hLSdJ2XGGYi0IRCcy434FTJOifZgA5fzkzQH6ugINiCRjBZnGC5MY6o6A5F2EkaEdDXPeGmOiBZguDhNQDVZF+0gqFmMFWc5uniRd+fPEtECJIwIveFWOkN+YKOqSVx3HiRoSiO61uU7awmBrkoUJYQQKlK6KIqFwMDUfOLA9bIUqydRhEnE2okqrp818Rdp9T77jrvsbvdRUHJszi3MkbACdEavPJCHMouUHYcddb21dsYrDx5D8atFPOnRF2nkineSoD/SzEIlT6EWbHzwY+2XG7H6KPHGGBOXpjEDhq9d40k0QyMUC6BqKoGwRWNnys98Fiq+LWfNAltRBOF4kHD8ZsnjECHzHhQlct3syT8L9B3w81xvJQHBL4JYESzKAtIdQQSeRmh9LD+GlEYIfgFuUJn5s0FC6Ydg7gHtxiTM9VD1XMYLWc5n5qi3QgRC+idK+DRimUSsj5oYkRz+8QlidRG27Fn3odsefOkILT2NrL/1CsEphOCuJ3eg6Rq6ptLdWoeiXOvc90+B09lh1kTa6Qg1sm/2GPPVDHHdN6tIVydIGNeWgv+yougWGC8NM1S4gK7oRPU4xlVl+XmnyMtTB7mUH0MVCk1WPc90PICKysH5E7w9fwIpJXVmjMda7qLRSvLmzGGGi36ra9bOU2fG+Hz7/US1EBOlOf5u9DUc6ZAwouScK+55p7OXeWv2KCW3wu/2PUXS8DO653LDvDF9mIu5Mb47+jqaovL59vtotupJV3P8cHI/F3KjPN66h911fgWwlJL3F8+xf/YotnSI6WEeatpNV6iFg/MnOZUZxFBU0naekBbgmfYHSRpRsk6BF8b3MVNeQCLZkVzPnfVb0IXGW7NHWKhmcaXHVHme7lALT7TeDQh+PHKJ14cv8UBXH2O5DGO5K22w86UiEcOkKRTh5cvnkUieXb+VS+kFzszN8tTABjwkFxbnyNlV9rR18e7UGPvHhnmsdy2HJkcZyWbojiVwpUe2WuHQ5Ch3tHZyaHKMvz19jD+6dQ9xM0B/oo6YabGzqZVUMER9wJ8f35+e4PkLp7m9pYNMtcKfHXuXb+y4g554kguL87wydIF72rsZSNZjqeqHmlMEdYPdze389eI85xfmuKO1E4EvyvvDwXOkAkHuae/h9PwMf3Xyff7DHfdhaj9bWJApl1GFwtpkioMTozx39gS/uXE7R6cn2dXUxjuTWZK2wgAAIABJREFUY+wbG2JvRw/HZqeIGCYx05/rJZKNd6zl7DsX2XL3ek7uP4uqqXSub2NmeI4zh86zMJXGClkYls6ez95KOB5CqbWHXTx6GVVVeeLrDxGtj/L2D96jfU0LkUSI42+eIvfMHWy7dyPv//gED3xpD7qpc/nkCPFUlL1fuJ3ZsXl+8p232fXItYkAK2jQua6V6ZE5zr5zkYXJRRam0kSSYZ78/UcQwk/C/P0f/xBVUwjFghzfd4ahk6O/UsRRQDFQhcBDkjBCTJbSBFSdvF2mwfCraYKqgS5ULFWvtRlJBFBvRlCEQsGp+C6LQEDTsVQNRQgGYg3c1vDR3A9/XqyLN9ETredSdo6zmWl2pTo5MH0ZD8mtqU5CH6Gd7BeJqGHVBPVLy+2QKyGBnF3BkR4xPXDT5NnHhZhhoQkFQ1XZXtdOc/CT2CYuqHpl4nojEo8T6TeJaEk6QhuYLF1kvHSO9bE7abJ6MGvFHrOVEc5lD7EhdhfNgV4s1Y/PbK9MQI0Q01Ocyf6UgBqhL3wLF3LvogqdNZFdTJQucnTxVfY0PMPl/HGmypdZG7mVI4uvEtNThNU4g/mjnMseYiCyk/nqBO/M/5CdyUe5kHuPrtAmLuQP0x5cS85eYLR4hrAW5/3FV+gP30LRzXJw/gUeaPwK0+XLnM4eYGv8fqJ6PUcXX6PObCOsxWi0upksXaQnvI2gFl3V2v5x4BNDHEnpAR5XNSGyZFUNAk8WUUQA3/tbIISC4+WZyD1P1V1AVYJ4soqlNdIcfpzF8jvMlw4iENQF7iBu7WAy/zxBvZuEdQtzxf3Y3iJJazfD2b9gvnSAgn2ZmLmJxtAjSOkwV/wJc6W30JQILeHP4HhZZoqvgZTYXo6m8CNEjY1U3BmGM3+BEBo98d9BE75Q11zxDTKVYwhh0BB8gIixBttLM5n/PmVnEk2J0hp5CkOtY6bwGtnqCVQRoDH0MCG976YX5flihXSuhAQc13eBUGuuO5auUShXaq8ruJ6Hrqm01MdwcXlh/AASP3NY9Wzagw2MFWf57d7HcKXHt4deo86MIQS8Ov0eX2jfy/pYF8fSlziRHiSiB7lcmKTRTPDVnkfxkPz10CuU3Ar1ZozFag5LNQhHA7w+/T4pK05zSx2KUHlr9jh5p8xAtJ0Tmcu8NnWY/kgruqIhgJ5QMwqC50bfYLq8QMpM8ObMUbbEe7m7YSt/eflHdIeaGCpMETfCBFWLglvm631PciozxGvT79EaSJFzirwxfZTf6n2MydIcf3rxeQYi7cT1MEOFKY6nB3mmYy8Fp8RsJU3JrZB3SmiKStXz+6WFUNC1TnStc0U4LTD07uvcv1zzWsUZByGQ0sHx0rX7fvXkL6XE9dJkyvuQeJhaK6oSoew4VFwHU9UoOTa251JxXeoDQQrVKiXXIWkFODs/y8HJUe7v7CNdLhHUDfLVCiXH5ujMFB3ROJlKmUpte1dKFksFklYQ23PJVSvEzQAekmylRMy0aAsmP1GB8y8Krf1+hcWS+Ob1sHQe1uz0A+ur+5k/SDD4xvvUUdWPlhWo2g7P7TvGY7vWkYhcW/HjuC6ZQplk5MMtOKU7hyy/CO4EQklC4AmE2oSs/BhZ+THC3Is07/cJTHccSi8ivVlwJxHWA6CtQVZeRyCRXgZh3gXGbeDNIQt/6X9I+PcQIop0x5HFb0P1PaQsgrEbrEfBnfW3FQEI/Q5ChEB6yOohv+pJ2gjjVjDvBucClF9GyjJCXwvWp/39OSfBy4EsIwKfBrUHyi8gS38H9nHQeiD4tH+MHwGaohAzLLbWtRC7ygq4kCnyg2++TqIxxsjZSTbfuYadD25i6PQ4+79/GLtiM7Ctm92PbuXA9w8zP5kmPZfFDBg88Gt3kGpL8sZ3DuE6LpODMwSjAR7+8h4812P/Dw4zfnGKVGsddz5+C3XNcY7+5AzH3jqDY7tsuWst2+/dSHYhz1vfe5fp0XmaOuu564kdFHIlfvLddxBCkFvMs23vBrbvXc/+7x/mh998g1A0wNF9Z9n7+Vtp6krx1vfeZfDkKIqqcM9Tt9K9oY3Xv3OQH37zDZJNMbo3tPPgs3fS2FHPj/76LU69fYEHn72TTXesobBY4JVv7+dTX7mHUDTIO68cx3M9tt+7gbdfPMKFo0NYQZO7P7uLMoJyqUooYlHMV3xRZlWhWKwQjQVILxRQVL+FStdVqlUHTVMxDI35uRyWpS+39W3Y3I6h6H6iohrA9pzlahNNMakzb44g/KTjTPYIET1Oa6ATVajoV7XiudJl3+xRLuXH+PXOhwmoJo500YTKSHGKV6ff4en2+0mZCV6dPsSLE/v5YudDpO08w8VJ/kXXY1iqwd8M/4jTmUG2xtfw0uQBGqwEDzTeyvncMMfTV9xNtyfWEtFCPDf6Kt6KaqR1kW5UFBbtLM92PUJICxBQDRBQb8b5bOtevnn5BcruFVfW6fICL00e4OGm2+gJtfLW3FGeH/8Jv9XzJDm7yMX8KF/reYK4HuE7o69xJH2OPalt/Hj6XUDybOcjLFQzPDf6Gi1WPWuinSzaOY5nLvLlrk+RMuO+WqBQyNtVXh8e5Mn+9TzU3c9UIceRGd+aWQjBHa2dzJYKFOwqJ+aiXFyc/8B53JOS8VyWpnCEbY3NONJjrlQkblnMl4qEdIPPr9nEruY2msMR/vLEEbLVCo2hMJvqG0laAbY3ttIR9ROVtuvy0uA5OqJx1tSlKDsOB8aHeXdqnJ54EsfzCBsmz6zbTEg3Vo1jupBntlQA/BaQ7lgCXVUxVJX+ZB2t12mHEwge71vHfZ29bFho4D+/u5+FcommUJjpYp6Zor8/s7a/67XNLO9LCNbXN5AKhshWK+SqFd4cvYyl6SSsAGO5DPOlIr3xJEOZRUayaezmK9WHmqbWqnUDWEHTFyved4bLJ4bZdu8mgmELz/Xjg1A8RCQZRq2RRnbVYWZkjnhDDM3Q/Oq7UpXGzhSxVJRn/s2TJJri2BV//bbyilohCzNgEIoFcWwH13GX9wv+c/zUT8/z3ivH2H7fJoIRC9f1KGZLRBLhmqOpX41Zypfp3thOQ0c9T/2rR2lf86tDXgMkzNAq96/FaoGoHiDnlInr1686FkLQGrzyvF25TkpZYdpDCYbyCxyaHWZXqvMjV+X8PEiYQe5rHuD9uVEOzQ7REoxybGGMhBHk7qb+f/Y1bl+knqQZZKyQZqyQvkZvqehUObEwjsAn3K6uxPqnQlc4SSoQZqKQ4cTiBI2ByD+Zy7IixLI21QfN0UJAe3AteSdN1StjKEEWq9N0hDYgkSSMFjbG7l4l2o2EmF7P5vg9K/SBJHVmG4YSoOIVqTNbmauM0xu+BU96bInfRW94O1G9noPzL+BIm7Q9Q0JvpCXQz0T5IrowURWDc7lD1JttJIwmVEXn7bnnyTmLGGqAvsgtpO1pOoMbmK2MUXQyTJUHMZUAdWYrIS/O2exBFqqTSCBldrA5cS+2V2akeIqCk6bObKHB7CCgRmkNDhBQbyxZ8bPiE0Mc2c4lPC+DqtbhuvM1G2sDRURQlBhSVnC9BVSlHinLGHo/QkTxpEOueoaG4P1M5J+nJfwk86UDhI21TOVfpjXyFBKPidz3sLTWmm6RX4FRdeeouDMYah0t4c9ScWbpjv82hlKHIgxcWcZUU7SEP8NY7u9YLL9DQGsjUz5Gb+IPqLizTOSeJ5jowFQbaIt+kcvp/wev5sxVsC8xV3qL5vDjlJ0JxnPP0Zf4Q6YLL+N4Bdqjz9bKLWPkqmdYLB+iJfwZ8vYFxnLPMZD8o2WR7I+KdL7EsQvj1MfDuJ7H7GIBISAcMGisi3J5fJ76RIiZhTxCCPrb61l6lKpCYV20kwYrwT+M7eOptrv588GXGC/NcSY7TFMgyeOtt6Og8BeX/5G3Zk+wJtrBbXXr2Z7ox5UeZ7MjfH/8ABm7QFCzmK2k2RTv4a7UZsJawO95/rCgulaG/lDTTlqDvuOFguBEZpDTmSG+1vsp6owoZ7MjPDf6BpvjvRiKxr2N2zmbHWGkOM3T7Xv5LxefZ7w0x2vTh9ma6OfO+k0U3TL/1/nvcio7REL39Ykeab6V/kgrZ7OjfHPwhzzm7GZNpAOBwoXcOA807aTJSqyqArlxRcj1Xl/9mqbEiJi3UKyeZjr3F0hZJWzuQFNitZbJBUrVs6TLr5Mu/RhdTZEMPo5AYzy/yIXFeXrjCU7NzTJTzNMRibG2LsW+sSG/v9kK4HiebxtsV3lrbI7tTS3sHxtmT1sXpqqRrZb53oXTNIXC2J6HEOB5ENJ1qp5LxXG4vbWD47PTZCtlbm/tIGKYn5Q6mH8SrCSBbMelWLERQhA0dXRNxa3ZMjuei6lrBAwdx/W3cz1J0NQxNJVy1aFsOxiaSsDQqDgutuMua8SYuobrediOh6H5Fr9B069uKZSreJ4kZBloqoLrSQrlCrbjMTy9SMVx/f1XbV8To9a2dHFinlePXODX9m4jZOqYukbFdilXbTRVIWgZVx7yzgVwziOCzwIqKLWKS/MehJdFutOIpeV25QAIAxH6GjL/f4C2HmQJqocg/AcIaSPLLyK09aA0IIK/jsz/3/gJAEBpQgR+zSeYgs/6ZA4GKC2IwNPI4p8vbyu9SWTpu4jgF0Ft8ceGCkoSzPsQOMji3yL0neDNQ/UYIvINsE8iyy8jwt8A60moHkGEvgxaL/DRHSek9DN76UqJltDqwMtxXE4cOM9jX9vLk79zP7qpkVss8KNv7WP97n6iyTA/+uu3aB9oYnJolrmJRT733z3MW997l7eef4/PfP1BpoZmyS7k+czvPYCq+m1hrz/3NtNDs9zzud0c+P5h3vzuIT711b0c+tExBrZ30b+1CytkoqoK+194j1y6wD2fu5U3//4Q+39wmLU7ejn21lme/TdPgJT8+Lm36dvSyW2PbmPwxChd61u5/bHty/asneta6VrfxrnDgxz4wWFa+xq564kdnD98mS171nLLfRuXdXPue+Z2pkfmycz7Yu+haIBipsTgiVHW7OjhyJun2Pv0bQyeGOX4gXM89OxdDJ4Y4aW/fJPbn76DUtkmky5SqThYlk5TS5yFuTye6zE7kyMStahPRcimS+RzFUrFClJCMGSQyfguS0vCydsSfRycO82RxQv0h9tImXFK7gJ5Zx5FfGKWNR8LAloIXejEdN+V5mony5Jb5UJ+hFsSa2kLNKwKfEaL04S1AGujPum0K7mev7z8Q/K1CqKBSAd94XZc6dIcqGe+mqXkVRgqTvBs56M0WAk0RSUx/c7yPpfEyK9+BpqqTkA10YRKSLOW28MAENTcOVePfbqygILChlgPYS3IzuR6Ds2fJG3nAUlnsIk1Eb+9qiPYyEI1S8W1OZW5hKEYvDp9qNbqVmSqPM+a6JVt10Q6VgUErueRr1ZIWAG/OkI3lgmYXLXCn584zFypSGMwzHAmTSp4pWJUUgt6JcvrF1UIdja38Z8O7eP/PPw2tuvyqZ4BApr/fTFUleaQv2i31JrWy3X0yFZ+xkyxwFguS7bqk2u98eQy6aMIqLeCBLXVsg6elByaHOONEd8NtTkc4Xe27iL+AUQPgKaIZX0lU/Nb1B3PQwKHpyZ4ZcgnCxuCIX53260kPyBrXXYcXrh4hiPTE7SEo0wVclQcB0vT6IzGOTE7jaYobEw0cnx2mny1eg0RJZb/8LEwuehrHSXDy3POVZv4x2Go3P74DgJhi1e/9RMe+eq99GzpwnFcmrpSVIpVrKCJqGm8jJ4Zp77VJzPGL04yeHyYycsz1DcnMSyDzGyWSqlKfrFApVRlYXKRUCxItC5CZj6PADrWtfKT7xzk8okRVE2lriVB37ZuyvkyjZ0pKsUKwcg/R+fBLxZLra9LAtQAMT2II90a6bNkkOFXkUkpaxXz/t8r5yZNKDzcto6Ds0O8OHqKOxq62ZRswaxp13pSUvEc8naFiG4tf69+XuhCYVeqk3orxDuzw7QEY4wW0tzXsoa28D+/DEN7OMHtDd38w9Ax/mHoGG3BODHDqhkVuLw3N8K+6Uu0hGLc3tD9C2/3uhHqrBAPtqzlT8+8xXOD79MVTtIdqVsejyM9qq5DwamSNIPXiHX/PIgbQXRFZaFcYLKUpSOUWG5hhitr94KT4cDsP6ApGgE1SsFZXNaqVYRCUI2sJo1q7w2o0VWi0lW3zPuLr5C154joSeYr48R1v/tEEzqmGqzNLypIUFDoCm1k38xz5J00jqyyPfEQCgq2V2GyfImK5z+Du0KbMJUAAgVFqKhCRxHa8vHYXoXF6iRnsj8FoCXQR1CLoQhBQA2jChXHT48sH9svGp+YFZYvhlVAyjCSCooIgjBQlDjgAi661oHn5ZCysuoEqUqIkNGLriYIGb0slN+h7EyiKkHCxgAgUYRJ1Z1f9ZmyVuEkhIIqLITQUEVgWZ1dFUFi5hYCehuW1oxTE/YM6h0E9S4MtY6pwj/iygq6UFDQV+xbUnYmyVcvMF14GSk9XwAWl6I9TCp4LwHNt8iVSIr2CPnqRSYLLyKljSLMZfvWm0EqEeaubb0YtcDWcSVDk/PUxULUxUK0N8ZRFQXZI/GkxND9TC3S/yKFtQBB1SSiBbFUHVVRqHoOY8VZ5ipZpsoLCCBdLVBvxrA9h6PpixxdvIgjXTLVPDmniCs9AqrJ46138OrUe5zODLMx1s29jduuqzslV/wJkLLi1JvxVRmI6fIik+V5/tvw6yhCUPUcwpqfcdYVjbAWwFINYnqoVqkkKDglxkuzFJwSJ9KXkBJsz7eR9pBE9RBNVhK1ttiVtYXh0iJD4JNWV08uH4SVLYDXI+AVEaI+9AVK9kVy5UOMLv5HNDWGQK89bKu4Xg5XFrC0bhojXyFq3bnch+x6HhXHBSTbGpq5mF5gPJ9lNJthXV0DEcOkLhBEImmLxBjKpKm6Ls4KEVApIRUIcVtLB39/7iRl16E1HCOg6ayJ1vP+9ATpcpn1dSkOT0+wWC7RGYvzq9yidiN4UnJyeIqfnh6m6jjs6G/nzg3dnBqe5o1jF0HC+s5G7tnSy4FTQxy9NIEQcOeGbvpb63nh7VPMZQuETIMHtvfz9plhphfzGLpKxXbpaU4ym86TKZSJBi1y5Qr3bOrF1DVeO3oBz5Ns7Gzk7s29HLk0zr4Tg0QCJov5Eo7r8e75UY4NTmC7Lndt7KEzFef7b5/i5PAUILlzQzdr2xt48Z0zTMxnMTSVx3ato7U+VnPm6UOoHcjS8whjG6itvosWKlJoq1O0ShJZvYiovgMi5pNMbskngLQBkH7ABw5CKEihs/JLIISKFBaggbAQYmkuEP62K+FOIpQo6JsRtd9J6YBzHlk9CnjgLQA+qYC+DqF1I2UJ7CP43z8TiQpYiJtsIfakJFutcDm3wLpEI/Grqo6idWH6t3VR3+xXiU0Nz3Lh2DCVso1p6TWrel+/p3t9G81dKXo2d/DuK8f9U6kq9G/roqkzVTs2ycVjw0wPz1GtOBRzJSLJEKZlcMt9G3n/jVPMjS+y+9FtSODC0SEKmRL5dJFCpkhLTwMgaetvontDG5Wir//i2A66qftuU6aOFfTPeW6xwMm3L5BPF1iYSqPpGp7r+cSUrmJYV7YFvx1jpQOUZuis3dnL2cODBMIWruPRubaF/S8cZuTsBG985yB2xcYKmfT0Nay6jYQQKIqgtS1JNlvCdSXxRJBUQ5RkMozjuEjAcyW6riKReK7fBgqQc0rsrFtLQDWZKaepeFUEKhEt9Ss3Q2lonM4e4WL+NIZicWf9g0T0K+30Uvris3qt1XgllgK2pXlbEYqvilG7GKZi1MxFas6BtWBPyisCs8qyXOrHD98J7UoiRlmiqJecB1UdTai40kMIBa/W/uJJSVOgjq6QXx06EOmkJ+xra4nacV39zFYVhajpVwNJIF+tkK/62itjuQzvTI7xP9y2l85onD89cpB0xXdZNFUNx/Nt1x3pMVXI+zb0wj9HMdPisd41NIXCpAKhZZcgUTvf14MQiv8sX1GxJQQ0hcK0hmN8fu1GNOFftysEix94X32NVSF4sKuPPe1dyz9HjI9CkAvU64m6Avd29HBba8fy/sLGBycwF8slXho8x29uuoWdTW28MTLId86dRBGC1kiUw9MTtEdi9CWSvDM5StIKLpN2QhE0dTcQT8VoH2ghWhehY10bPZs7OPiDw7z/4xNs2bOOWH0UI2DQvalj1Tlo6mrADBikepuYnUwzenGarQ9t5eQbJ3jlWz+hc10bnevbsIImux7dzlvfe4eB7d009zYSSYQ5+dNzKIrgrqd2U8qV2f+9d6hWbN575RiKqrBudz9TQzMcfvUYm+5aSywVpbmnkfRMlje/8zbBSID7fu1O7nhiJz994V1e/dZPaOxK0dR1c2YxvywYKU4gAV1o2NJfS6ftHCkzga5o5J0ilmKiKSoVr1rTnxO0BFKrk69CcFdTLw+1ruPF0VP8hyMv82DrWvqjvnxFulrifGaGkcIi/2rDPWxINH8s4xdC0BupZyDWwLH5cV6bOAfAralOwtr1nLkkZddmsVqi6jpUPIeFih/0z5bzXMjOYKk6pqoR0y1C+s9nh24qGk93b+O9uRG+P3ICT3rsaerDVDUu5eZ4bvB95ssFvrbmNgZiDdfMB/9UMBWNT3ds4u3ZIQ7NDvHv33+RR9rW0xKM4UmPqVKOc5lpyo7N/7T9UWIfo1NdZyRJWyjOqcVJ/uzsfu5rWUNQNah4NhHdYke9P3dl7XlmKyPc3fBFNGEwVDjxM31e2StyuXCc7YkHqTPbyNhzV4VC15tHVYJajHWx24lqdYQ0f73dGlhDwVlkU/xuBCqutAlqN27zawn0UfFKbIjdha6YVL0SUS153c9cgqoYuNImU53B0x0sNYz6MSbUPjHEka73ouu9tZ98vno1btz6I/CZbv8//0GtKUE8WcJ2F5eDcU0Jo6Bje1kcr0jJGUMstwgpSOnWLOxtBBpCKCjLgqpXxlR153G8LFV3AQV1WUz7amgiRNgYoDP6m6hKEPDQlDCaEqHsTOLKUo2w0dGVCBFjLd2x32bZRv5DxFwFGqbWjuOtQVP8G8nUNcyrBEu39F8RKw3cwHVl6ZQKsaRqs/S3v5iL6iG6Q8080LQDVShIwFJ0Fqo5/m7kTT7VspvtiX7OZUf5zuibtTMq2BLvZSDSxuXCFC+M78eVLo+27EZTNKqeg6w5LmTs/KqHiiKUa8oew1qAJivJr3feT0RfcusRy+1sK0e99E5d0YjrYe5t3M72RP/yoUa0AJfyE6hCuSETvpQpWSk+57oe6XQRoQhs27cSVhWFqu1gmTqO44GAUqlKIGD4i2IhCAVNgkFjeeFn6Wtoi/0nisG3yFfep+wM4Xp5FKEiiKDSQn1kG7HAbiyta/keqwsEWSyXlkU105WyX6Gi6mxKNSHwM4SaohDSDBRPUHEc3rw0iK4rnJieYjizSFMwREQz0BSFpkCYRCDAYqVEwrCYLRRYzBYpRKoUq1WqjkvZcXBsD6EJXNfzNX0+wOr4VwmKEHSkEgS3GQxNL7L/1GU2dTfzo8PnuG9rH+s7GnE9yXy2yP5Tl3n6ri20p/zM1TvnRnBcj68+tIv9py7z+tFLLBaK7OxvZ9/JQe7Z3MeB05dRhOC2dV3sO3GJ29d3cWZ0hsn5LA/eMkBHQ4L/+o8HaUpGefP4JZ7YvQFD17g0+VNURdDfUkdDPMTRSxO8fXqIWx6/g0d2rkVVFX7/03cAcPTSBBcn5nlwWz9HByd4/dhFvnjPNnRNBSUMwWcQ7iSy+FcIEQbzztrRXzUPa11+1ZESRYT+BSj14E4BFnAl47jqfT+r9Y6IIL0CwltEKlGQHuAiSz9ABJ8BtRmZG6rtX9yAGBJ+RCaLIKv4hNVHu29VRbA20UBfrI6GQOjagE1T0Va0NWi6RmN7HQ8+eycdA83YVYdwPMShHx1jfmqRUr7M4lR6ORMtBMutDktINMaob03yyG/sQXoSVVdBwPpb++jb3MGhHx3n5b/6Cc/+2yepa4ozsK2bu5/ahed56IbOxOBMzbpVXCHsanbJiqpQLlSwqw6qqnD59BgjZ8f5tT96nPPvX+bIm6drZ0ygKCu21ZSaFWztQtaCekURDGzv4r1XT/DT/PusuaUHM2AQjgfp3dzBM//6U/77JMvOcteDFdBJ/P/svXeQZdd95/c55+aX3+t+naZ7csAMciICQRAkmDNFkyLFlXftXctUqWzLqrLLZa+1qyr/4dpduWyVtd7ySpZka7W73JVkaWVKlCgGkQQJEgQBIg8wqXumc/fL4aZz/Md93ehJyAJ6iPOpmql+74Z3bvrdc37n9/v+anls20JKkaWt8NKzyz/efJ7ry/sp5ws82TrNifJ+qg7k7Ar2G6qR9dZzU+UubqxkxSsa0cZl+kaB5XEgP8NjjZPcUD6EKx0SnVJxCuwJJuilj3C2t8iYW+ax5kn25qa2o4EujQIAMu3BXJ2nWqeZ8sY41T1PK84mzbacVLFKRqLVCalOkWTp8J7lkmrFRthC66xtWX9BE+sEpVNinW6nF074VVKteaF7nv25aR5vPU/dq1By8he3b4cNcaTD0dJeojTmeOkAjrSJ0piKu0MQ/Qq3Ws52+MD+w/zhyadYH/TYHA5ohpnTueB45GyH7144x6Mrizy1vrod6XP92AR/ceYkv/XEj5DAE+srvHPPXrSGXhyxNujxB888jmdl0TWfOvLSGmIANd+n5Pl8+dknOFId4+6ZOabzRT5x+Dj/8vEfkqiUih/QiUI+cfg4U/mrpxsIIcg5Djnn4mdGa83GsM+Ta6ucaTXwLIuHlxY4XLm8xPel+wsch8C5/Blc6/d4emOVM80GOcfh4cUFjo2N49k2E7kCjyxfYKXX44fL57cv2VyxzHy7yV3TsxypjLPa77G/XKUwchytrLZJKwUi36XVTyN/AAAgAElEQVR2fJb5zR5H7jvOwsIGd3/+PjY2u1n1v7zPueUms3ce4eEfnsZ1LMbrRfqBz97rZlhZafKOT96J5zmcOr3K3Z97J/mcy8ZGFznqq9z7iTu49xN3AHDmyXnG99T41C99iPyOAg+f+eWPXnbcX/jvPn3Zd/d9+h3c9+l3XPTdR3/hfS95bn86EGxGTcI0ApEVW0lUQqQipoM6y4N1ElLqbpVW3MWRNkorZoL6ZXuqeXl+5Yb3MJev8ifzP+F3nv8+wzRG60zIuuB4XF+ZesOijbbwbYf3zRzje6tn+c7KaQ4Wx7h7Yv9VU62+vvQ8/8cz3yFWKdEOx9Gfzj/Bt5ZfwJWZrtPfOXwHnztw2+tqmxCC45Upfu22j/DbJ7/HX5x/hj8+95PtiePpXIlfPH4fXzx0599K9bdX0869hSr/+NYP8zsnv89Dq2f4X578OtFogtqTNiXH597Jg294VNSkX+SXjr+Lf/bE1/nKwtP82fxTODLTfPvAnuu2HUd5OU5NHuSxxtewkiIz/mHydtY3z1llrtQ5DawiiRNf9t3x0r2c7PyQ/OAkBbtK3iojhaTsjOMIN5OAEQ4ltw4IhmmXTrzBo5tfxRIO08FBTpTeyY2VB3h046/4wcZXEAL2BEc5WLiFkjOOJRwKdhVHergij5Swx7uZXhTyaOMvGUQRNXeaeyanCaziSOInm4goOWM4Muv7VJw6+/M38u2VP6FgjXH/9CfJ26+/sNIWu8ZxtPWmz0rwqYvCG9kxeFdajcKddziPhAMINDYagRQuvj1HYB/kXPv3EQjy7lF8e5qSdxPLvb9gmCwSpevk7KykuC0L+PY0C+1/RcW/nZp/L1J4244lOYoWAojSJufbXyZMG1T9u7BEkebwMdb636Yfz3Oh84eM5x6g4F6HN3yEhc4fIIVHzjnKZO4BxnPvZbHz7+jFZ7Bkgen8Ryh6N9MKn+B063exhUvZu4Hx3P2grYukabOQ6SGDZBkhLMr5zzJW+Hkcq0ovWRg5zjRSBETpOr49jdJhlnqnhtuOsFi1sYSL2nKovYTuhyNt7hm/nj+78D2mgzHG3BKtuMfe3AR5J0COKpmc7S3zo8bJbS2gbjLg+xtPU3bymYNH2CRaIYVgX36Sr688yp5gnDCNeb5zgaPFK1fc2eJocY6aW+Khjac4XtpHqGIsJEdLVy8LX3by3FE7xo82n6Pk5HGlRTvuc3Pl0MvOoRadHArNDzefY39+kim/Ri4JePyJBQAqlRxKaeI4wfMc1tY7xHFKLufiOhZaQZykpKnihutnyeWyc6+1ZnmzQ7NrkfPfx0b7TqRQ2FYW5TQ1VubJs2vsKc9xZqmHFBvEaUqSKMoFn2PeGIHnoFLNIIqZKU9z+twmtxyaptUP6TSG1MsFrC48srbA7WNTPH9hicOHJun3I245fBOd1oBCKNG9lNlejolckWmZw+oLpotlNpsNbrx+goULm7ynvo94mPLsM4uUKzk2NroUiz6HD01uRwD8NBPGKd94/AV6YUQUp/SGEYMwJkkVe8bK+G7WqVnebOM7DpPVIoGXlSBda/eYrhUpBB4ztTJPnV3Bc21KeZ9qIUc555GmimIhoDL6rhB49IcR/TBirl6hkvcpBB4rzQ5hnDBRyfZfCDx6w4iHnj6LEIL1do8oubKA+ma3z0qjw2OnF9Fac3Cq9mIgUPwsOvwOQmgQRbAm0aoH4TfQ0UOgQxjk0f77Mw0htQnxSUjXMo0iIWFkV5QCrRyEAh3/GD38NiI+h+58Gek/gLaPIpRGKQeRAkJlMQbJj1GDb0F8Djr/Fiv3AYS9H+Echd6/RMsi2MdR9t0ouR85+BpCFlA6K22vtUQrC6FU5l9SDkJlmjhKHoX+H4F9HBl8CC2Kl0mR6VGU4Va0AGRpG6fbG1TdgHE/jy2ti0KhXf9isezyWJF7Pnobf/NHPyRfCihW87z3Z+8Brbnwwgpf/t++wuZyg/f/3H1oUqQD0gKlUwRZhZR7PnIzf/WvH+IPf/PPcT2bW+6/nn0nZvj6lx+isdYh7IfMHZ3G8Wze+Ylb+fq//S5/9JtfwQ88bnvwBizbwnFFdl5FiuNJhMwcbgdvmOEHX32cfqfL3R+5ldpkCZXGfPX3v0m/1adYHaXmiJRDN87y6DeeornW4r5P3om0BN//ymM8/9hZ1hfXEUJz14dupTpZpjZd5vnHzvDgz96DQHPirkOcfPQsf/ybf4nt2By7fR93vP/G0bv0UqH1TDzVdV9dV6TgBJztLSOFpBX3cISdORBUH7WVFnmNE6ZDpLAYpD2GaR8EPNN+jBtKd+BZLzrHLCG5b/wW/jj8Br99+k/xLZcxt8xn5x5kX36KO2sn+PcLX8eRFgLJJ/a8C0+4SC0RIpsMSZVCaglK4AmX94zfwb8//9ec6SxRtHPkrRyoLC3ur5d/yPOdeRb76/y/F77JseI+3jl+E77lUfeq7MtN8W/m/5KyU+TTsw8w5pR5unWa7288yen2Iu2wz+agxbvqt1D3qtxTuZGvnP8uFhZCwvsm30EgfIQSyFSQJmnmnFIi+6cl91dv40+WvsXvnPozHGlRdHJ8Zu69WRU3aaMTON1ZBzI/p0ZjCcnBWpn79s5xqrXOdbU6n7nuBJ4jwVJ8+PBhHlqaZy5f5uNHj9IMByz0Gkhb87njN/CD5fPMFsr8/A03k7ddwjThP7zwLB85cJQT4xPEqeIPnnmcsSDHe/ce5FNHTmxH/kzmC3zs4LHtKJu84/ILN9/Bw4vnaUdZSiZCcHN9il+89S5+srpMNw4p+JAwZL7XYU/ZR9g5lgbrdJMennzRKZfqlKpbJlIRwzTCljb9ZMCFdo9vLp5if7mMJSye2Vij6gXsKZb45OHj1IIcWmvKns/HDx2j5F05WiKMEy402rTSIU+vr3KgnKWGPLW+Si3IMZsv8fdvvJ3vLy4QpQmfO3YDy90uYZxQdnw+eeg4t9SnKbounz58goOV2nYkVas9IJ/zOH1mjThOqJRzrK61QQhmZipsNrpEUcLZs2scOFDne997gWPHpul0hsRLLcIwYWmpiZSCVI1i17XGtiXff/gU+/ePX3Y8iVL08pq5+w4wHzXJ94YMkpic7ZKoFCEyXaxhGlOwPQZpjCdtWvGASb+EQhGrNNOYVCl52yVSKRXHp5/E7HmJCmHXOvvyM+zNTY/GZxlbr1NLSOaCqZGVH2VTJANaSfeq80d1v8B/duweHpw5ytPNZVaHHZTWFB2f/YUaxyuTVHeUbRdC8DP7b2Yj7HGoeLkzSo4imSpuwJ31q+vdPTB1hMbxAZFKOFQaZyp4MepDac2Z5U2U1uQ9h5Lwuc6dpFrI0R4MyY27bHb65D13WyagH8a4oc1Gu0+j2yfwHLrDiPvHD3NdeYLrKq+sYujWMdw2NsfB28d4bOMCpzvrxEox7ue5ZWyWvfkqrrQu6n/4lsPPHbqDQbLGuLNGHEdkli9BiDyWGvDgVJH9uRn2ButE8XOQhjw4kXKiVKfqLBPFbTQ5PjituW3sRsb9Fx34H997I/cOe9T9i53Yh4rj/Pc3f5Dn26s83VymGQ0QCMb8PAcKNY6WJwhsl0QpBJC3Xb546E4Wek1sKUmUGk1GpASWgystPjZ3PbePzzGTK5OOdIw0EKXpdvW2u+r7+Sd3fpLHNy+wEfaQQlBzc5yoTmeFmYYRLyx0KMX3cni2ypnFBnfM7SOMEhZWGxwp3odtSy6stSjmPFzHYrPdZ2/+VmIrZXG9RSHwEAI6/ZB9/l0crt/FRqtPOZcj8Bw2myEnvI/hJh6PnJ5ndqLMAxNfINUJT7W/w83VBxlzZ+gmDX60+VX2BEeRyTiqeTM31kuMFXI0uiEkOe6u/QyNbszR4L0ILWmtwWytzKlGhzg+wq2TdzA/bDDsKAYlwdHiPWx2eyw3OxQDj9vLn0RrSZQkNHoRc/a7yLu3cn6zTbsk8IuZtvEbwS5yHGVOoVbcZJgOKTpFmlGDvF2kEW1gSwdXuiwOznOseIJW3MQdqfzbznvQokSb24gJqOQ+zkacEFu3E+llpvxpQh3QSUIc+zi2C2W3QtGZ2I7ksGWBfeW/R5SuY8sSUtjMFb+ANVJar+ceBDT9+Cw55zDauhtLSjz3IGd6G+StEh11Azn/Ziy7wPJQszfvk1ofoObEtOIuz3UjtOzQS0qUgi/SSzYItSSlxHyvzXjwWR5r/Ihpv4pl7efCoMG4V2ZpsEnO8ghVTJgmTPoJveiZkdMnT4REphuE6QaW8PHtaYpOjXa6hmvV6EQv4FnjRGoTV1ZIVC8zJ6pDqkOK7hECOca+/CRVt0je9jlQmMIWkn35KSpukQP5aQZxzGONF4h1zLhXYS6YpGIX+cjU3fy48QLzvVWuK+2jYOWQWCRKcb67wSPDkwghmfSq3Dd2E0JLbi0fZWXQ4Iebz1L3Krxv8nacUW7zmFfOxLAv8VLX3CJf3Hc/f730Nb61cgZPWtxWvRGbHgdyAa7oULVDpCexaHEw55OXXT44eZyiNeThtW8DsCc3jdR1cnLAoXxpOx0usFyOleZwLRuNpuYW+eSed/KDjWeZ76/wnolbua5Q5o7bslKizijaQGmNFIID+8YzIXIpslLmmSQC586tY1uXh81vdgYsbSZEScr59SaHZ8axLUHgCaT0WWn0eXZhFa01/TCmEHi0zw4pBT6pUviuQ72cZ73ZQwAWkufn17AsybmVBnvrFdrdIc31Lq60sZB0mgPCcp5hP6ZWztNqDVg4u45v24RhzNR0hULgUfA8HMui3wlxpEWrNSCKErq9EM+z6XaHo8ijt27G481iEEU8cXaZf/Chd3ButcHCehPftbGlZGmzQzkfkCpFzncJ44S1VhfPsVBaM1EucHppg94gZGmzTbUYEMaZo0Bs/wfb0ZOjj55jUwg8zq81kULQ6YdMlAt4ts1aq4vv2vSGEY3OgFNLG/ziR+/loWfOcmoxGyw5tkWcpAyiBM+xqBZyHJiq8el7b8C2JJYlX3yJ2EcRsgw6QcgKyDFAgXPDqOoZI8eQhY4eQvgPgJyB5AkY/jnkfg6R+yKIgPmlLsPBJ+nP90jiBFteR+DfyDBMkXaM568QeJL1jffjeuA4K7iOhWMVWVu7Ece+mcEw5eBBl/q4hw6+AGoZoRP6aYlnF1sMo/spuy3Gi+NcaA2ZFhU6w2N0wzms1grDyCFnf4TG2jyB4xDG78AVB8h5eYL+kLVea3vQMkySLBVHgO843DL7Yji8IyWTQYFuHKF0lh6TakU3DpE5yad/+YM4xWygobQmlil3fvgmZm6YJhpE1ColcqUA27G54d6jHLwnj/DGmNojaUTPcvzjUMxp1odPEFjjtOIz1I8c5QO/eIzWeo9EtqjtUYT2IvseSNjTLZPINof2HcD1HPYeD/nYl3zamz08z2JsehHLKvHh/3QS250nsLp85r+8jXxlkShe4Mb7Y6YP1dHaIle6gOdX+cwvz9HvbFKs1PCC6/BzLkQPce+HChy77T50ukS+0ESrAcdvSzly0wOQPE9QGiKtLDLpo3/3AP1PeUzMJejkFMWSy8/80vvZWG6BHlAdW0IkP0bLCSACkQfVB2GBHgIW2IdJVDZRlGpFlKTYUmbp1LZFnGadTikz5/r1xQN8d+MJvr7yKEeLs9T9CmHaoBOvMR1c97dlCt5UGvE6gZXnscb3aCcNXOmxOlziRPHiyk9CCOpehS+OhKKVVhTsHK7MHJvvm3wHt1SOEKmUspOnaOU5vbDO/sF+coHLc6dX6PSGzKh9pKnmx5vniRLF/c69xGmMG7vc5J9gsAYb+R7OeoGZ3j72O4c4kB9jqlTZfnd70uFzc+9jI2ohEFSdIoNOSLygmG1PskdPknZT3LbD6TNLrPktpt1x7g5vYuH8KieO7ae0VuD8xhrjG1WKaYEXhhdoNXoUkwKOZ3Ous0RjvcvHrnsXZ5cWkY5FQfkEZM60ByfuZGnQ4pnmMu14yHRQYj3sUfVyWEjes/cgR3pZpFMrHlDyPU531qnlfe6cnWbcyxOqhJlSnZVBhzPdDQ4Wx3jn3lnm8hWW+m1urk2TKsWFbpsH9h5gtlimFQ5RWmMJQdUP+MzR67ev0VS+yMcPX3fRNTtWq3OsdvHA15KSm+pT3FSfItUpjzdPshlvYgubIAjZ69k82zmDhaSd9MhbPnk7oBl3mPbrtOIuSqeMedVMhNwZ8sHDe7ipdBRXZtWsGGlnfPzgMcIoIYwSio7LB/ceIU0VYZRgSTFKJ82ci43egOcW19hTK/HAxAEcK+vf9cKIQNk8cuo809Uit5dnyPsuq60us06JZy6skfMc7p3Yy+pml247ZI8sMuMXtwe9SikWzm8yNZkN3JNYMTFRYjiItwXzu90e9XqJhYVN9u0dY2ysgG1bbGx0tqOfG41MzHvQj+h0w0wTMFWMjRW5FI1mIwgZu3cPfZEwHPboJiHtOLuGY16eCb9AI+wz390kVAkTfpFeEhFYDsuDrBpfMxpQcnzOq4RIpVTdgH4SMxkU31BNl92EJeRLKhbs7LcrrbClpmy7hGkfR7pEaogtHGIVIYWVlZTXKbMFn5n8XmzhkOoEWzqkKkEKTSfexJU+ihQQfGB2HyW7un0PJSoiVD0sYSOQ3DFe447xGgLJMO1k1Y2FQ6ojpLCJ1YCaV+DvH7119PuKSDXROsC1Mmfq6eUNklRRLQRMFkt8fOwm9tYrnFtr4Dk2a6pLJR/Q6A4IPAdLCnLa4ezqJmh4YSkT13/f0WOMl65eYVfplGHaHp0rF4EgVH0c4eFbirsmxrlnYhKEwMJGkSJFyiDNdL80isAqEdgOf+fwnYTRT4jj50nVLEK4aB0jxBCpzvPu8Tbvn7qRVLdQqgHJSR6czFx8QrxAmk4ihMdHZ6fwnNuwRsLKQgg+te+mqx5Dzna4ubaHm2svZrfsdGoN04Rnm6v4ls1yv8076vuo+0VaUUg7WqHs+vSSiChNaUcD6n6RPbkqzXCIpMliv03BcdkY9hn380QqRWnFVK7E5w7celnUbBglPPLsAs3ugELgYeOztDbAus7mufkVzq82uev6fSyuN2h2BhyZG2dxvU2nP8SxMl3SJFUjKZdsf45tUS3mePL0EuWCz/H9U5xZ3ODo3ASxE3NmaZPxSgFbeCidBb9shOcJ0/5IG6lKYBVZ6wyZX++wt1bj1HKH5VYH2GC8kGej2yPVmmPTdc6ttakXSgyimKVml7V2n/FCnrX2gG91znLvkb189SenuX3/Hhq9AYlStAZD5sYqfOe5MxyfmcCzXdZaId8bLvCuY/upF19dpeersascR4lOWBycp+aOszxYop/22Yw2uTA4z4Q3Sc0bwxEO/bTPWriKROJaHr4cw5M5pKghcViPIs72TjOX20esK/RVgUTFSNFnPVxjZdih4u5D6yaSAnG6loVE6whPFgBFojawhIVSTaJR5SvHyqokhEqzHjrsL+zlbK/BII2QCBJdx9IWni7zbOcCdT9iZTgkb0+xHGoibXG6u8r5wTrXlWaJ0gqzuXGWh12+v/Ec99evx7P2U/QmSLBZ6C0RpSnLwwaQVTvLNHwqlL0bRx59hS1yZOXaNRIbKVxsWaTsHkcKn7yzH0cWcHUVgcS1qlgiIEw36MfnYKSP8DOz929fi62y85+dewCAjV6fpJnnvtxd24OoZlPzxJnnybsF3p2/l/YwRLThgKzwvecXKfke13vXc70NnTBkIpdncWPAc8MmqVa8Z+odTJeKlz30t9eOcnvt6GX3hxCCWd/i56ZtpJzKZu0tC63P8IWZSYRYoVLSQAGVPsTnpy2ktQZ6gQdrCQ9WxxHWJOg+0uqSs5/l52encUaaS5N+jS8d+hhxeoE4aSFweUd1ijsrY2zNlCvWyOVttI62UyAtmUfpaJR2orFlHbHj5XnTjRdHRCmtaXQHrLa6lPM+4+U8je6A9XafYuAxjGKWNzuUAg/XtijnfYZRgufa+K5NnKRMVktYUlIfbZskit4wIuc7+K5D4DqsNnsEnsORfVNsbHQZHyvgOBb5vEe5ksMbzfL7vkOlkiMME+I4IQhcjh6ZAmDPTBXLkkzUS7TafUrFIAv71vptk6oWuC4n9k7yR999gnI+YKJcoJz3ed+tR/jmT17gb548zYm9k7z7xoPcfXwv/+H7T2NZkvuu38+th2aYX23wW1/9AYHr8P7bjvLIyQU8x6aSD/Aci7FSnlLO3/7Od22qhYA7j87xVz8+Sao0Nx6Y5rq5CfphzJ9+/ymqhYDpWpHxcp59E1W+/DePEXgOY6MOykytRL1S4Lf/4mHec8thTuyd4NTSOr/3tR/h2JJ333AQH4uxap4wSuh2C/i+Q6nok6oQrTRxnEUh+oFDqeAjiNCqCfYREH426JfVLEVM5kfaKBadfoXeIEKKMkoV6cdW5kQdaq7fU2RtvUNvWKVczqGBTi9E6xyl6i1cWGoQxWmmbQQImQN5EIA0GTJMVlAUWQ/zeH6ZYdqlPYTVriDnVnFtC0sWSVWBfpzNXHZCyVj+AJOVMZZaHTrDkNlKmUEc0wsjXNvGEgLPvnhOVGnN5rDPZthnKldkI+zSiYe0ogGuZVOq+Dy0cQZXWoQqoej4bAx77Bmr0IpSvJxmYvSM+DmXuWNT9ONVQjYJkxblegFbaFIdo9HYIkCLBKcy5PDEYbrxEpoeoU6Ymc1sSD+18EcC6Ig+1YkSlbrAknUQNug21SkPIRJce5r6Hp8o/glp2sNxK+w5dBCEhVKbaGLqs3lUGmI7B7I0WSlB9/G8lJn9FsTzILsgbeYOSRBN0AWQAehz6ERTLs9TLvVAPw7RGljT5Iu3ks9vAhLSPqguqCbbKd/pPIhSFuEmHAZqmofPrNILs9nbou/h23bWaUtStNbUi3nWu32iJEFKyQOzt1PJvZiemCiHMW8vtnh9GhO7hSk/i8A9UDhG3ZsmsHI8234cV9pEydpI90tlul9oPOEw5WUC8gJFmC4hsdGkVOwBtihgWz5aiSzFOnIYRppmu4lWmiBwScOEvoqwpGAsX86E+QsuliVZ2+yi4wF1q4YrAvZN1ig4HpM7ZuqFEBScHIUdVZZ68ZB0U7E3P0VQ8NFKEYUJm502sUyYnhqjlOSxNiX51Kex0mHu0ATjYQXHsxn0QsJ+RM7xmZ4YI0kUfuAi+oJCL48fZDpqzsgZXHGLWMIhb+WZ720yFZSQQnKgOEZgORQdH1dmkdzWKLLEKYzhWjaHVX00gLPwZCZoPZMrk3dcWtEQR1p4VpaCkWrNp4+c4M9Pn+Qrp0+itea2yRnePXdg+9jDJGG+0aTk+8iRbuJW/2krelHDdpXbOM0cpsMkoeC5zAQT2ELiWx7VuJhF4IsXJ6tc4RCqGHtoU/dr1NwSlrDI2QGRirOnTcHJF9ZY2+wRjIoslIs+7e6QYt6n2xtuazuW8j6ua9HphRzZX6eY9wFBOeezv15FAJOVIqdXNmgPQnKeQ5yklIIscrZeyrPR7dMeDNmXrxKnKYMopjMYUs75LDbaDKKYgzvu83I5xx23l6jXX3TwKKVZOd+gsdJmpl5mbrpKmihm6iUQkCYKt5Kj6NrYtkRIyXg5oFzNE+Q9jh2b5sJig/37xxmrXT5YEsCBwvi2No1GM0yzSEUpxKikvE3dL9KJh2g0BdsnVAmutLKJuNE1y9kugzQm1ekobTJ5zdnZP21EKmRxcJZh2iew8pTdMdaGi3iWj2/lGaQ9YhURq3AkDGwRqYhIDXClT82dILDyrIaLJCom0RG+DNBojpfu2Bal2AjPsBGdpWCPk6iQnF0jVoPMHtkTbIRniNSAqjtLYJXZCM9S8/bSSzbROmWQtrGlhy+LTOdOIIXg5gMz22L2jm1RKWTZFQenxsi5Dgcma2itSVSWRaFHKeGWlOQ8B9uSNHsDlHrpuyFUPVaGL6B0gi09LOGwEZ7DEhYSG0s6BFaZRIUUnDrDtIMrc0SqRzdeo+hMUHVnKThZZJ1lTSJlGSmKZBWbJZoYKSvbsi6W2IMQHpY1zba4h3DYKsakiRDSJ1Eprbi/nWqmRvpysUq30+T6aUgvCZkOqmyEnZHotEvZyW3r7imtcKWk6gWc7qwzY5U4UKwR2A5lx2eYxqwNuvSTCEtIIp1SsAWTuSIC6MRDyq5PznYYJDHDNCawXSaDy8ePkFWojJOUiUqBOM2qh/cGIcMoYapW5MJai8W1Nv0wYrJWpOBnUf7j5QIrmx1Ac3CmxsJaE7TgwMwYF9aarLd61Eo5ZusV+oOIWilHvZL1fcfLWT9eCHClz/31n2VleJZYh1TdKSb9/QRWkSgYUC/mKAUeC5stJkoFlpsd1jpdJktFllsdXMuiXspTzvm0h0OOTddZanZIVMpEqUCzP2AYJ5RzPjfOTfHM4iobvT6b3T77xqscnBjj9FqDo1NjHJ4YY5hkhXHeKMRrKRv9t4CGzPO6MlwmUjFFp8hauErZLtNO2hTtEr7lE6uIklPhwmABR7jk7Ty+FZC385zunqLqVmlGDbpJlzFvnGbUZMKfoJtkN7QvAzaidfb4RdL4BzjWFEr30TokSi4gRQBCIIWPa+8nSs5hyRqWLJNzb2aYrNAMn6GjjiKEQ8H2WRxsMu5lnRN0VsZ5ob/O0eIeTnWXmfDKRCpmLWyzJxhjJWwyF4yT6JQxr8jKsMm53hrXl/fSjHujl5bLud4q+/ITLA8a5GwPKQSedKi4eXI7RNy2jOdWOt+VPl+qNp+d74REdUaC4C+tC9ENQ06vN7BGGj1Ka6q5gPlGk24YcaQ+RpRmaTKdYTbb49k2geOw1u3h2zY516EbRoRJgmfbzFZKVHLBqyrhqFQLlTz/4rGLAloPECIPIgBS0Alad0A4SFlFpYugBQgLIWto1Rht10XKMaQ9t2P/A9rDr2HLMVLdRqCJksVtgytFgGPvIVENBBaWrIO9pt8AACAASURBVCBljmH8HJbII4RL0X/fRY6jS9mq0hWnCktmIrHrrT7LjQ5HZsbwXIcoTnAdmzTNNJPEjmuXjl5UcjQjqLQmHe1LjSKdLCmIE4VlSTzHuuza72zLi1yuLXZpeflXIMR3reeuXWYQtdaEccIgjHFH+mE5z0GNwmGTVOG7W1XVFP0wIh1VQtuuqhbFOI5F4DqEcYJjZdXTHNsijJOsszqKEtqazXVti97wxX05VtZR7Q6ibJZVSnzHJkyyWeMtbbMtHbNBFDOMEvK+O6qqltAPIwQClSgef3IBraDdHRInmUZXPudRLgWEYcz6ZhbJdvMNc8ztyQYNOnkGokcyp5E1A949CDm2fZ7SVGX3LDAYxnT7IeO10awVAsfJKskplTket4R4Ibtn1za6rG92mZksM1a7OCRaab2diqfR2DKrMidHHRMpstnxrf0prWkMBiy22kwWC0yXiiRKobTejmbJRIAh1Vkqi7tDsyhKE763Ms9m2Kfu55nM5xmmMbawRoMNaEeDkdiwpuwG9JOIkuOzOuxQdHyOl6d4/G+exc+5HL1jH0pvtV+N9NhGQrrCQuk0m4HVCVLYaJ2+mFG34zm0hJ2JjOsocxqIF7X9RsnMZHpT9uibiCQ5h23tHennQdah3Pm8W6PPFiQvjGxpBOlCpmO1JWiOhvR8JqBuzWTbJKdG+7FBN0FWwDoIJNl9Ej8DMg84oHtZRJtaHTmNMm2sSE9yai2rxATg2hYCgWtbbHT71PIBtiVp9AYorSl4HrPVEgX/xfdglA5oxctU3BmcTAfop8IWZamMmYZQomKUbtMe/hBNiiWKKN0n1X2K7i2E6SJKD3HkGP34FJbMI5DYskiq+xTcG3Gtie1nUKPpdIYsrba2O9hT9RLlYrDt5NhKR1aj1M+dz6sQL691l6aKJEqQlmSrUozW2b0sR+8/TeYMEEKglcJ2LJTS2+tv/XY2aQEqVQhLkMaKjZUW+WJAeSx/2ftqmMa4lk2YJgSW84reZ1frEwshGCRxFp1oOSO9J003Ckm0whKSnONclEKy0Gjx2IUlarkg0wkcvbs7wxDXzpxXwzjBd2z2lEo0BgNWu72sYtveWQ6MVXfcDJe0a8fHUEV41hU0MTXEieK508tcWGkxMVbEsiS+Z9PrhZSLAc3OgI1Gj/pYgbFKnjhJGYYJe2eqFEbVyLYGyNl1l8RJuu282rpPhBBYo3SxVCnsS+y7lIL1To+lZoeZSompSnH7/hCCkY5axqAX8vDXn6E2UaTbznSomps9auMFEILmehchBcN+RKEcUKsXWZrf4MZ3HGT2QD17VpI008pxXj7y59JrfpnI/I7lV7uHXqaPdK3bIngNaoWxilgbOX0QkLeKhGpIlqJsIYBEx9giKwzjSJcwHZDqFFd6KBRlp0Y7bqBGEiZKZ0WN9gQHtvvZnXiVftoYRXuk+FaRSPWxhYdnFWhE82SC3h4FZ4JevI4jcwzTFlLYpDrGtzLHbNmdufLB77i+L2UjtojihF4YUwy8yzIOLj5HQzbD+ZEMSYojfSLVH6VLOkjhYAmHVGVVfWM1wJE+qU5IdULeruLKHL615Xh9pZfp6rrBW5878ZAfbZ4i0SmOsHFHfdNm3Kfi5BBA2c2zFrbJW5nTxBYW3WTI0eI0k37lRfuhFZYQRGmapf2PUh23xpNhmnCqvY4lJPuLNVxpjaLXNLFSWX9PZ9torZFC4kh5xectVYrn5ldZWm8zM14mH7g89vwF7rhuL3GScnZpg/3TNWzL4uTCGgdnxhDA6aUNpseySflizqPZzWxPpRBsRy+dXFhleqxEvVrkJy9cYKJa5PDsOE+cWiTnuZw4MPWSZz1MEh45fZ6xYo5qLuCpC6tU8wEzlSJPL65SCnxumJ3kyYVlbMuikvNxLIvmYIgAlpodfMfm5r3TnFrd4Ka5aRq9AY+eu4DWcMPsJOfWm0RJwsF6jTjNJiTGi3mq+e2Jttdlj3aV4yj7Q190319toHolI33VYxE7nCnb6ySkqjnysoLSIWnaGOkYCRyrviPMzyfTDcpvG6qXetG80rZd6YXzUtu+Ver5l7LzWNLRQMx5mdKvW1ztGF9DK7b2eJXv2bH8ykbx4nVGS7QiVc3RwEyR3Sc9lG6j0bjWDEI4aJ2QiZePNLhGldDidIm8dyfi0gpRL0OSqlFpdmvXXOfXyDXdeF5D5+haJE0Vg0Gm2bRV2XHruQw8lzPz6ziOxUS9SCHnXSQC/UrZrs70KnSwtNYkaTboeCOeg60Bj32VDsbLbduKMu0LS0oqbrDdUbnIZr+4wfa2kcoKpLrW7gjq1ToFXss5vZrtvNp+rrT86vb2sq0vGaBd6T37dnZiQ3YtE9VEo5DCI0yWsWUZx6qSqj7ZoEyiSEhVlzBZJHAOkqgmnr0HWxYu2V/2zG1FULxRz96bgdajanvW5SLfu4EwSeiG0SgtWJOOnGFKq+0+05Z+hzPS+9Bk/aqC6+E7r99+bD1CYRRvRxlvIYRgGMakSuO79mW2+o0+p6/UHqepotvqY1lZyncaZw53y862S1OFZUvSJNM/sWwLrTS5goe9O/tQu65Br4HX1De6bEJ7p00XXLRse5srrHPpq2XnNi81ab7z806utu5bwZXacLXztj1ufBParbSiHQ9IR9q0cvRbUmQTb5CJmCc6zdYh01zbCDtM+GWKziuvpLbliBdkUX+v9xm+Wj9hy95uOa1ebvz9Uvt9tdu+mn28VPsv/f6VODIvXfSyjXwJdp3jyGAwXNNc6x0kY4sMhp8OjC0yGAy7gWvdFoGxRwbDTws/FY4jg8FgMBgMBoPBYDAYDAbDLuPtoW5rMBgMBoPBYDAYDAaDwWB41RjHkcFgMBgMBoPBYDAYDAaD4YoYx5HBYDAYDAaDwWAwGAwGg+GKGMeRwWAwGAwGg8FgMBgMBoPhihjHkcFgMBgMBoPBYDAYDAaD4YoYx5HBYDAYDAaDwWAwGAwGg+GKGMeRwWAwGAwGg8FgMBgMBoPhihjHkcFgMBgMBoPBYDAYDAaD4YoYx5HBYDAYDAaDwWAwGAwGg+GKGMeRwWAwGAwGg8FgMBgMBoPhihjHkcFgMBgMBoPBYDAYDAaD4YoYx5HhTUEI8btCiP/2rW6HwWB4e2NskcFg2A0YW2QwGHYDxhYZXinGcWTYRgjR3fFPCSEGOz5/8fXsW2v997TW/+QVtOE3hBCnhBAdIcTTQogvXLL8TiHEY0KIvhDiB0KIG15PuwwGw+7jGrFFvyuEeH7Uvs+/njYZDIbdyW63RUKI64UQfyaEWBNCbAoh/j8hxKHX0y6DwbD7uAZs0YwQ4nsjO9QUQnxXCHHX62mXYfdhHEeGbbTWha1/wDzw8R3f/as3qRlt4MNAGfgF4F8IIW4HEEIEwJ8A/ydQBf4d8MdCCPtNapvBYHgT2O22aMSjo++ffJPaYzAY3mSuAVtUIesLHQWmgKeAP3yT2mUwGN4krgFb1AT+LjBONkb7DeBPhRDiTWqb4U3AOI4MrxghRCCE+E0hxJIQ4rwQ4p8KIZzRsg8JIV4QQvzayNt8Wgjx2R3b/hshxD/c8fmzQoifCCHao1n7BwG01v9Qa31Sa6201t8BHgbuHm32fmCotf7nWusQ+HWgCNz3Jp0Cg8GwC9gFtgit9W9orb8BRG/agRsMhl3FW22LtNbf1Vr/nta6obWOgP8VuFkIkX8zz4PBYHhr2QW2qL+1DBCAAibIxmmGnxKM48jwavg14CbgRuB24AFgZ07sfsAlm/X6BeD3hBAHLt2JEOJdZFFD/xXZbNmDwMIV1isAt5HNoAFcDzy+tXxknJ4cfW8wGN4+vNW2yGAwGGD32aL7gXNa695rOhqDwXCtsitskRDiOSAEvgz871rr9us7LMNuwjiODK+GLwL/SGu9rrVeAf4n4Od3LE+AX9NaR1rrrwFfA/6jK+znHwD/Qmv9jZHXel5rfXLnCqPQxt8CvqO1/ubo6wLQumRfLYw322B4u/FW2yKDwWCAXWSLhBD7ySKOfuX1H5bBYLjG2BW2SGt9DCgB/zHwvTfo2Ay7BKMNY3hFjIzEFHBux9fngD07Pq9prYeXLJ+5wu7mgG+/zE/+BrCPLD1tiy6ZMdpJCei8zL4MBsNPCbvEFhkMhrc5u8kWCSGmgL8E/qnW+o9evvUGg+Gnhd1kiwC01gPg/xmlxD2qtX72ZfZnuEYwEUeGV4TWWgPLZIZii73AhR2fx4UQ/iXLF6+wuwXgqlU/hBD/M5lu0Ye11t0di54Cbt6xngRuwKSPGAxvG3aJLTIYDG9zdostEkKMk0UP/IHW+tdf1UEYDIZrnt1ii66AC1yWDme4djGOI8Or4V8D/0gIMSaEmAD+B+D3dyx3gP9RCOEKId5L5om+UnWP3wL+cyHE/UIIKYSYE0IcBRBC/BrwSeADWuvmJdv9FRAIIb4khPCA/xroAd95Iw/SYDDset5qW8Ro3z6ZCKQjhPBHs34Gg+Htw1tqi4QQVbK+0V9orf/xG31wBoPhmuGttkXvFELcI4RwhBA5IcSvkkmJPPKGH6nhLcM4jgyvhl8FniaL8HkM+C7wT3YsP0uWQ7sM/F/Af6K1Pn3pTrTW3wa+BPxzMo2ivwZmR86gXwUOAmeEEN3Rv18ZbTcgM1hfIiv7+HngU1rr5I0/VIPBsIt5S23RiL8BBmTikP/36O+73sBjNBgMu5+32hZ9DrgF+NKOZd3RwNFgMLx9eKttUUAmqt0gi1p6gCwqae2NPUzDW4nIotsMhteHEOJDZOr5h9/qthgMhrcvxhYZDIbdgLFFBoNhN2BskeGNwkQcGQwGg8FgMBgMBoPBYDAYrohxHBkMBoPBYDAYDAaDwWAwGK6ISVUzGAwGg8FgMBgMBoPBYDBcERNxZDAYDAaDwWAwGAwGg8FguCL2W92AEW952JPWmkSt0gsfRcoiefdWLJm/bB1NghQ2WQXmN7gNo9MgXsW+tY5pDb5JqpoI4VHy78e2Kq+/LVoBKULYZFFp2d8vd9zZujFgI8TL+yWz9RPAekXrv1601mwOBix2OhysVck77hXXi9I0q7FtWdvf9eOYdjhkIp9HCsn5dosoTTlYrb2GdqR0ht8mTq9cbCBwjxM4xxHCuuLyXcy1Xo78LbdFrwStNb1kA0cGeFZmp5ROiVQfzyqgdILSCY4MrrqPQdpGIPCtIkqnJDrElbk36xAuIkwTUp0SWC6RyookepYDZMc6VDG2sHBk9jzEKmWYxhRsj1inpEoR2Fd+ll8PWxG5Qrz8bf1K1n01+0tSxWavT5gkREmKY1m4tjXaDwzimFLgEScpYZLiWhYTpQK2de3PB2mtabUGeJ6NZUmEEMRxQhSn5HMuaZqdR8uS2+9Nx7a55LQaW/R6flxr2p0hSZxSq+UZDGM8N7seaarY3OxSreax7YvfUb1eSBQn+L7Djx45S68XXrbvXN7jjtv3EwSv7ZnVWnP27Drz8xvMzFQ4fHjyqs9UqhTnGk3216rIl3nutNZEaYpnZ13jhVaLqULhon7ASxFFCc88u0i7NeD66/dQreYvatfySotaNY/rXr3rHcUJjm29IhsBEMcpliWQUtJo9rAsSal4dbu/W3g11/CNINUJAoF8FX2qKO3TT5tU3JnLliVxytnnl6nUCrieTamav8IetrnWbRFcI32j3YZKFf1eSJBzsexrrj//qtFao3SMRqNJ0TrFkt5oqSC7jQRaJ6Q6RgoHyei8CIElnNf8u9kuXt7GbyVaCfHK+mJb2ykUGoXEQukU0AghGSRdfCuHRhOqAa4MkEJm/WoVEViF7fFtP2mT6gRL2Gg0Oau03YZB2iVK+5TdrCCm0ikasF6hzVoPz5PqhLq3F/nS4+nXZY92i+PoimitiXWCI+yXvbiDdIhEIoXAEvZ2ByEZDUIskb2INZpYJUQqpmC/OEhSustS6zfY6H4ZS5aYqfw3jBc+v7081RHL/YdQOmYm9y5AoEmRwiW7BgqBRaqzTpIUNv1kBVvm8GRl9CClWMIn1UNAYgmHfrKCFA6+NcYgXSNVIQVn9qL1lY5JdYQlPSxxcUdL6ZCl1q8ziJ/GljUOT/zBa3Icaa3QeoAQLhpFt/8VhMiR8++lP/wWIMn77yVzILmAhSYCnYKwSZJFpCwjZZ7u4C/xnRM49kE0IZkrzEXrYXaeRG7H3wH98LtIUcR3b+bF+1mPHFWXs9brYUlBP44BWO/3cS2LQ9UapxqbCGBvucKZZgPXssg5Dhv9PqnWzJZKfGd+nl4UMVsqMd9cBQSHalVObmyggb3lMg8tzBPYDnfNznKh3aYdhlR8n0cWL3CwWuNEvU6YJDy+ssz+SpX5VpPGYMCRsTHyjvvyxouE5fZv0g0fvuLyqdJ/gV8+guCn/0Wzm0l1zCBp4siAVMcIIREIEhXiSJ/N6Bw1dx8CSawHODJHrPq4Mk8/aRCqNlV3P6vD5xj3DqF0AkJgC5cw7eFZ+Zd07mRtSJjvPYIrc1jCpRUvMuEfpZ80CawyvWSdtfA0ZWeapcFTHCjcQ5h2UKT4VonNcB5P5ghVDylsAqtMO17BlQHdZI2yM0POrtJPGvhWiXa8TMGuM+Ef+VvpyC/0NjnVWSVve/SSEEdaWEKyNGjyzokjnOmsUfMKLPQ3GaYx+/JjNMIeR0pTzPc2GKYxY16BxxrzvHvyGGvDDq60KbsBz7WWua48zUzuldlArTVL/Q6pUniWTScOyTsOUkhSpVgedCg6HgJBznaIVErVCyg4HmutLkpppBQopTm/0aKc9yn4XvYesiRxknUuUqXxbJtYpURxyoHJGt/96hN8809+xM//yofZe3jysralSnG+0abVH5AoxUSxQJSmFH2P1XaXQZwwVyvT6g9pD4fMVstMlgsvebxKpwySlazzI9zM7pMiEAhhIxBonZLqCI0CNFK4SOEgENgyjy2CV31f/Pg7J/nqlx/m87/0PvYdnXrZ7VdX2zzz3BK1ap7/n733jrLkuM48f+kzn3flfbW36G54NBreESRh6Cl6jcyIkkhpV9Ks9khHs2eP7Gp2V+KMKG84ogwpUAAJgiIBwnug0Y1Ge1vV5d3zLm3sH/nqVRXQ3QAlSsOh9p7Tp1/lixcZGREZce93v3vDtj2EEHR3J5mbK9NoOmTSUebmy9i2h6rKbNvaR29P+ntq078XyedrHHh9nKHBLIViHcvUiEQMZudKdHclmZ8vo2oKpXIDEQg0TUFRJDo6EpiGRr5QQ9UUjhydYvvWPg4eOk8yYTE9U0TXNXq6kywslolFTVRNYXq6yEB/ms6OBH/yp08yMZF/S5sG+jOsW/dB8n6DzkgMVZLxRUDdc4lqOsrbOJHy+Rpf+K+PcuTIFKOjnfzqr9xDb28aASzValRsh45oFMf3yDcaPHHmLPebW0lbFvlGA11RKDabpE2TQAhqjouuKji+zysTk+wbGcZSVfL1Bp3RKHXXZbFWI6rpWLrGYq1GRNOI6DqLtRqmqpKLRNi/f4zf/8J3KBTq3HvvHj71yb1EIqHRZNsexVKdZMKiVKpTqdmYhko0YlAo1tF1FVWVeWX/ObZs6iGTiVEqN1BkCUNX0XUV3w+QZIlazcYPBJapcfDQBP29afr70xQKdRIJC88LWMpXAYhGDSqVBkJALhtDVuW2+eZ4HrIso8oygRDIkoQQItTCWp9VWf5X2Qfy+Rpf+MKjHDk6xbp1nfzKr9xLb8/F120vcKh6iyiS2tqvigTCJ6KmafplAuG3DLZwHQvwcYMGQggMJcpU/QiWmiBnjND0KwgCVMnAEzZCBOhyhEB4eMIlqqZp+hWK7jQlZ+aCwNH5M/OcPDRJIh0BJK6/c/v3vY/+f/mfXwqLFb72Z09z32f20XGJ+Q0r4Ae8c0DjnUjTdTm3WKDhugxkUjieT91xUWUZVZbwhUCTZRRZRlMUsrELOxCL1Qb/7YFn6etI8sl3XXlRIL7uzbNoH8OQEwh86t4iMa0XRdKoeXMEwiWuDQABimRRdScRCCw1R4e546Lg7nyhwhf/8Xlmlsp4fsDujX385L3X4XsBZ8cXSCUjaKpMveGg6yqu5yMEWKaGEKAoEp4bUK3ZSLJExNJJpyLo2jsD6sdrR/ACl4CAmlcia/RS80rYfh1NNlAkDVXW2uCQLCkk1AzDsW0gAipunvnmGLKkkNQ60WUToQRU3AKB8Kn7Feaa5xhiOxElwUzjDBISvdZ67KCOJ7zwPih4wkWRNDzh4AuXqJrCCxxmm2fJ6n1vBxz9i+QHFjgSQlB0K0zV50nrCebspfZ3EhJpPUHBKZPVkzQDh/HaNIPRHpqezfr4IBP1WQBc4VF162yIDzLdWCChxYgoJufrMwxGuokoFkW3Qpeh0HRPILDxgyK2e25Nexy/RMUZI21sxg1qzNSfJa4NoykxZBRsv0hE62ah8RoxbYCo2stM/VmS2igZczuTte8SUXtJG1vINw/T8ObojlzHbP05Imo3ndZV1N0ZJEnB9qPMN14GJLLmTgr2cRy/SEfkCuLa4L9Kf7veORrOqyhyFkPbiOONETGuJQjKON5ZTH0Pnj+N7R5HkgxMfTvVxuMY2npUpZdq8zuY2mVYxtWocoZA2PhBnrr9LAhBxNxL3X4WVenH1LZSbT6KqvRg6btRlW48fx4/KOD50wAochZN7b9gW88U8liaxmSphCLLeEFA1bEJhODU0iJX9Q1wKr/EUqNOxbZpeCF42HBd/EAQN3R6YjEW6jXGS0XcIMBQFZ4eH+Nd6zeiywqeH5CMmUhITJRLvDg5wU3Do/hCMFUpY2kaWSuCKsuUbZuHT56g4bl4IuCKnr63tFkIgRN4CECXVSQUUtYdaEoPflDBF2VsdxwvWOAHwbFTzlcJ/IBkLo4kSZw7PMHRF06y994rSHUm/0c3799Mis55Ku48ESVD3S/gCxvHr2EocWRJo+RMElWylNzJlmdAwxcuQgSk9H6coB4CTaJJ3VtipnkYCYmo2oEXNOmxdrwtcCREgBs06bW2M1Z9CSQZ268gRKgYu6KBHzi4QZOY2kFMzRIIj5I7Q90rktb7OFt9gW5rC45fxwuagCAQHjljHRV3jpiaww6qCHyiapaiO0WnuZ5/DUfpWG2RwWiWp+dPMBrrxA18FEWmP5IhpUUwFZ1Fu0rNC9/pqmejygpVr4kuq5TdJhFVp8dKkTNi1DybRbtK1bMZimXpMC8NnqyWQAgO52db76dP03PZlO6kaDeYrJYwVBVTVql5DuuSWWZrFTanO/H8gFdPT2LqKplYhFK9SdwyODoxTzoajmfE0Cg3bGpNG01VUGUFU1dIWCZCCArzZU4emqBxAUYGgK4q7BrsWXbQrfLVwcbu3NrC4p15z4TwKDqn8QMbRTYJhIMXNDGUJBIyimziBTUUycAN6jhBEU2KochmC3TsIKGPAnDu+AwnD53n9g9ciSxfWkkpF2vMnF+iWXfewaiExu7wYA5NUxAIPC/AMnUymShCRDFNjXjcAgSqotCRi7+ZbfRDL3XPJkAQUUJHxcWYyvlCFSHg6PFpujoTDAxkOXVqlsGBLPl8lUwmytFj00gSqKrC4lKFXTsHmZzMMzrSgef6qIqM5/k0bRddV2naHpalM9Cf4fkXTpPNxrBtD01T6O1JIkQ4hvfdeznTMwVqNZtCvsbRY9NUKk0AAmC6WmaxUWdrthM3CDian6fhuVzR2U9cNy74PADlcoPZ2RKu6zM/X6ZcbtDbmwYhmCpXODo/T8IwqLsupqpSbDR58uw5LuvpZv/UNLqitJlFQSBQZJnNnTlqtsPppTw7urvAtHh+fJyhdIrTi0u8MTvL5X19lJpNzhdLyBIMpJIcm1/g8r4+MpbF9HSBUqmB43hMTCzhun67zY7rsf/AOOlUlEOHJ7BtD88P6O1JcebcPNu39BGPm5wbX6SjI0G1ZnPw0AQ7tvVRLDXIpKOUSnVsx6NUajA0mCWdijA2voiuK3R2xjl+coaBvgzlSoNjx2fQdZVGw6FWt0nELfyETLw7gqVpuL5P2rKoOg6GouD4AZIECcNgrlrFUFXihsHWjo6VZ3BCEFfTVGT5X/bCrRnDuTLlUqMNHHl+gB8E6KuYV55oMtc8Rc3L02VuYMkeI2MM4QR1phtHyeqDeMJGltSWgbpE0ZkmZ4ySM4dZcsZJii5kFBbsM6iSScE5jypbCOGjy1FCQ1bHVOLUvAKabOAL/4Ltl2UJu+ly7uQsQ+veCvz/exXfD2jUbFzHQ5IkNF3BihrtPcL3AxrVJq7ro6oKVsxosxabdYcgCAgCgQgEVsygWbMJAkE0YSHLEs2GgwgEiirTqDlIkkQkZqCuMv6FEDhNt13WjBgYlhaSB4TAbjjhe68qNOs2QoBp6e0yy3W4jkez7uD7AYapYUZ0ZFlu1eESBMFKHQGYkZU6lvvhxOvnOfrqOfbdvRNFkVFUBTWm4BGs9JkIEAiKTp2kHqHpuUTVcP2TJQlVVtrr/Go5cm6Wsdk877p6yyXfRyGgUG9QajTRFIXxfJGEaVBpOsiSRNP1SFoGSBIJ07gocOR6PqcmFwiEWFFGLiCqbJHW12EoSdygTlzrR2tF8GhyFKXtjJJDx5U0gIyKriQuObfiEZM7rtrE5HyRrz7xOmMzeRCCUqXBmfEFhvpD50ip0sDQVPwgoL83TaFYp1iqo2kKnhcQi4Z9OzVTYMvGHrKXZgu2JamFa6EvPHJGH6YSxVJiITEFGUVScYVNusUYCoF3HQmZvDPNueohBAFpvZu55lkiShJDsXij+CQ5YwBTiVFxlzhfO4qlxCm7i2iyjqXGmaqfwFSizDTO0GOtY645RlLrwA4amEqEiJKkyxz5ZzO2vhf5gQWOADRJpeY3aDZt6l6TycYcCS2GIsmM1aaIa1HmmkvkjDRR1UJBphk4NPwmTd9h0S6Q1hMktBhLdonZ5hIVr8b68ULclQAAIABJREFU2GDLmPM5UDxORk/QY3YT1fdQdw6jyjkixq61bZHjRLVeotoAAS6CgJSxkaJ9AkmSqXuzWGoXIOH4JRL6KKaSI6YPAeAFddLGFgLh4AZVKu55urgWQ80S14eRJR1NiVN3ZzCVDJKkokg6tl+k6S+hSAaq9P2hHwsRYHtjON4Elr4dTcnSdI/g+0uEIWZRdHUQTR1EkVNoygC6OozjnkJVenD9CTx/AQgwtG0IXFS5G10bbd0hXE1cbwzXG0eRMwgRMo+CYNn7KOMHBYTw2+UVOUmt+TgSGhGz+6Lt1xWF88Uii406A8kkXbEY0+UyvggICEPKZEnCC4K2F60jEqHUtJFliZRpEtN0dEXBFwI/CJAliaRpMpxKIUlhmYptk2/Uma5UiOtG23Bz/VCRmKlWmKlWqLsO2UiEtGkymLiwR0EAM40Ci3aZzcl+oopBLvZxMsJG4CGEx3zlT1mo/GXI5GrJ1OlZ5sYXSHYkGNrSz9z4AoX5EoqqMLpjkHq5zvSZOTzXZ3TnIFbMZPzYFKWFMn0beugcyDI3tkClWMOuO6y7bAhJkjj7xnkARrYP0KzbTJ+exWm6DG3rx4qavPjwfurlBpfduJWBTb1ke1IUF8pUS3Vi6SizYwvkejNhyMJMgf6NPf+qNPP/UeIFdptSKhEykASClD5I0Z0goqZbFFSJtD5AxZ1FkVTqfiH85xVo+iXq3hKqZIYeUiWDjEpS78NSL+6BGpvOY7seI30pElo3qmyQMYapePMYSpy6dz4kAwsbTbaIqhkEPhVvnqZfxgsckno3RXeaDnM9UTUbglRCIPlldCVCRE0TCB87qOMHLrbwqXl5UvoAy+9lreFwamKBYqWBpsqM9uXozibahnrDdjkxPsdAV5pscmUDXizWOD4+x55N/UTMFabkYDTLWHWRgUiW+WaZLjPBQDTDWHWRRbtK2W0ggC4zidJSmhabFSxXR22FqlmKjiGrzDXKlJwGtu/RGUkwWS8QVQ26rXcGbsqSxPXdwyCBHwgkCRRJZq5eYUu6E1PRQi88Ak1W2JLqRJUVhBBsGeiiKxmGhrl+GEq2ua8TSZIIWl77QAiECA3TQIT1v124zLKEYABrlDPpTf+/5Yu3fV6NTnMPSDIgWiHJ4XXBagMpZNGG/4crn+0X0ORYW/l+46XTvPrUcW573xVvmy3xqpu3smX3MOkWEP12EouZRKNvBQ5yuXjYulYV30vo3w+bnKhMcao8TV8ky2ismy4zeUEvo+cFKIrE0EAO23aZni6SSkWYmi7QkYszv1ChpyeFroXv1vjEEucnlujtSVGpNGnaLsVSHcfxqFabGIaKbXvksjESCYstW3ppNl2ymSiO41OuNBgeyhGJ6LznPbvwPB/fD1hcrPC7v/stjh6bbret6XvUPRchwAt8oppObzQRGiaXkHQ6wvBQjkKhxuhoR3teND2P8WKRIAiYKJbIRSPs6ulhqVYnpuu8MjlFVyzG0fl5MlaEmK5TbjbZ3t3J+myWuWqV0UyaddksANlIaEANp9NMlkrMVirMVas4vk9nLMZAKkW+3mChVsMLAoaHO+jIxVnKV9m2rR/DWFHi4zGTTMtA0XWV9aOdnDwzRyoZoSMbJ1+o0debZqAvw/rRTmbnSvT3ptm6uZf9B8bx/QDb8ShXmuRyMXZu78d2PPp6U2xY10UibtHVmUBRZIrFOj3dSSIRnRdeOsOG9V1kMzEOjk3TPZii7rpUbJu0ZdFwXZQW4yuuGXhBgKYolG0bPwja9qEQgkcfO8LSYoV77tlDKnVh4zIQoq0jLYf8XWwMh4ZzFIo1Rtd1ksutAP5nZpeYK1XYM9pHzAzXgbK7gO1XcfwaFXeBmJqjL7Kd+cYpIkqKvugOputH2ywjXY7QZW2k4i6gSBtIab3kjBEafomomiOu5pioH6A/shOQKDkzuKJBzhjBFx6GEiNnDDPfPH3B9me7kmy6bADHdsl1/ftwpi3NFDAsg1qlgW6opDoSa9Ze1/F45cnjPP3Ng+TnK0gS9I928vGfu4N0Lo7vB+x/6gSPPvAK5UKNaNzkpnt2c/Wt2zBMjacePsDEmXnKhTrlfI07P3QVzzzyOqVCjc/84t0Mb+rhyYcOMHVugUjC5Nj+cTzXZ++7dnDrfZdjRY3QKbNQ4eG/fp7jB88T+AHrt/dxxwevYmBdJ74X8NTDBxk/OUc8HeHo/jEaVZste4a499P7yHUnEUJQrzT5p6+8zMHnTtFsOPSPdHDnh65i065BfD/g6W8e5NzxGZLZGEdePUejarN59yD3fWYfue4UhYUK3/zy8+x/+gTTY4v86W8+jGFqdPVn2P6pzTSjHjHVxAl8Sm4NVVKoejYb4iEI6QY+ZbeBKitsTHQzGMmuGQshBC+8McbpqUXuunozl1ICTE1l10APQoSA51Am1dZDwkicUC9Z1lv+JSJJEpaaxVKzCAQma1N5mOpbU3ssa41vl6bFMjSu3T5ModLg6YNn2tczqQg3XLsRQ1fbYL0QAiRQlVBfC4JQF5MkCUmCIBAUSnViEeMd6Q+SJJExutvtXA6Rj6nhu//mVDPLfy9fa3gVkloHmmwQ4JPUOvGFhyoZ9FmbyDszqLJO1ugnp/ex6EyS0XuIqAlkZCwlQbc1wnjtCBW3gCJplNxFfOESURLob+OA/n7KDyxwJICSW6XglEnpcTJ6kprfIKnF23Taut9kINJNxa3S8G2cwCXvlLAaBvN2Hl3WyOhJ5u08nUYGVVLI6EmKboUlp8RgtIeSW2VjfAhZitKV+CzJyO3IUgxTW7emPbKkYCjpNngTUTtRZQtDSbPQPEAgHCBAk6M0/MUQPZXjlOyT5Kw9WGonmhyh7pax/TymmkWRNEw5Q8E+gS6nqbrnaXgLRLRuLKUDSZKRJR0vqBFIThjm8n2QQNSYq/wxxfq3GMn+Ppp1I4a2CSEa6OoQshxDUXLIshUCWEoOSbLQ1AEazkFkyUCRs6hKD7IcIRA2shzDdo9i6rtwvTFkOY6p70ZVelHlHLIUQZbieP4MgWigSDFcf4pA1HDcU/hBkUDfhSp34gdLKPLF8wZtzOaIajpbFZmkYeK4Hpak0hGLEtcNFElmIJkkpusoskxU05El8IIAU9UQCBRJxmopNQLoiye4ZWS0fY8dXV0s1OqkTJOr+/rxhaArGqMzGsUPBH2JBIVGg32Dw6RMixuHhinbNnHjwl7Spu+Qd6oIwJBVEEWEN4mi9CIpWUBCkROhRbRKZ64UaixO5Xnj2eNE4hZP/cOLdA5kqVea1Ip1nKbD+NFJetd18/xDr3LzR/ZSWqwwdWqWc4cnuOszN/PiIwfQDY0Ne0YQQvDyPx1kabpAo9YkP1NA1VVOvHKGkR2DvPDQq9z68X24jk8QCHRTA0nCipmYqwy5I8+fZGT7AOWlCtVinf6NPf+SKfkDKxljhJq3iCHHsdQkEhKKpKPJESwlSSA8JEkmZ2xAky3MVuhYWh8iED6WkkKVTbrNbZhKkrgWejxV2US5SCjmsjzwxOvky3V+6RO30B3bDECHuY6cCOdpWg8ZeauN57Q+0PpuoFWLoIP14ffLG5oQdJob279JaKGi0m1tIm+fx1TiWEoIoLqez5899CIvHhkjm4xiaCr37NtOd3bFMzSzWOK/fPlJPn7XFdx57eb29ZPn5/mtv3qMP/ilDzLYvRo4yjAYyVB06pTdBv3RDIok02uFINqlwsyEEGxNhiBlpxkai4OxFYVqXbzjYj8FwPN8SktVquUGgR+g6SrxVIR4KoKsyu179OpxlubLLNbDMJ50RwI9riMp4ZgFQYDVDHAVGzVmUpmv0qzbaJpKpitBLGaubXcgKC5VKRdqSBKkO1r9dwF9xXM9lubL1CtNFEUmmY2TSEfayk3gB8xN5rFiJlZEJ79Qpll30HSVTGeCyJvvLURLEa+2vLwykbhFMrM214rvB1RLdcqFGq4TlktkYiQz4b01OUrgBxQWyizNlXn9hdOU8jXOHptGbuVV6uxNE0uGoWxBICguVigsVsJHlSTiqQia8dZ5v+whzs+XaTYcVE0l0xknEjPbz+37AXMTS0QTJoapU1gIy+qGRqYzgXUBoOmHWXamhukwEuzPn+GlpZN0GAmuzW1mJNa1JtRr08Zu1m/o5Hx9kSQGXWYKU9FIdTVouC67BwbRlRVvfSYTJZmwSCYjaxTqvt4wFNDzfArVBrIk4QeC3sF0mDtGlkLl2olh6BozS+U2+JlORIjHrTX5kCRAk2XKjt1KIRCAgLhmkNANPN9HkiSUC7DZkskIn/vcHcwvlOnsiJPNhqCDQNBwwxCM/mQCTVF4eWICU1PZ3t3FN4+f4MbREaK6zmS5TC4aRZFlLDUEeJZZSgemZ0gYBpPlMm/MzpKLRLB9H0vTuKynhxOLi6QsC9v1aHpeC1yGnTsH+LVfu49Gw2V4OIfRmutCCGbnSszMFjl5WsfQVQxTIx4zadoutu0iSSGgJMsSh96YoLMzQTwezv+OXJz9B8YIhGDDui4mpws8/dxJdu0cJBo1OHR4km1behmfWEJVFLZt7eXkqTlkWWLHtn50TcE0Nbb1dbGjrw8/CHCCINSBlg3IN/VxqdlcY0gWS3Ueeug1SqU6t9yy9aLAUdV2eHlikpRlcnlf70WNsmQywufbY5hojyGAqsjUmg6ev8LKCISHLxwsNUla72PBPsvp8rPkjBGcoMap8jMktR4WnDMgQVrrw/arBMJvhRonmW4cocfaQtGZouEX6Y/sxFQS4Vw0LbzARpIksvowc80TzDVPYsgXZiPMTeaJxAzWbe7hladP0D9y6b3nh0EOPHGUIBD4nk9Hf4bdN21t5+0JgoDXnjnJf/9/vs3N9+zm3k+vw3M86lUb09IRQnDstTH++ve+zW3vv5KNOweYODPHg3/xDK7jc8t9eygt1Tj43Gk+/NlbePIbB/jKHz3BfZ/Zx8tPHOPpR15naGM3lWKdJ79xgHs+dT0/8rnbOX9qjof+8hlMS+e2912B3XT52//6GPmFMu//sRsxIzqP/sMr/MXvPMLP/vr7SaQiVEsNHn9wP7e9/wo+/FO3kF+o8JUvPk4kZvLRn7kN3w94+MvPc3T/GO/++LXEkxGe/PoB/uy3HuZ//d2PkutOUi03eOKh17jl/sv58E/dQmGhwlf+MKzjR372duKpCLe97wqsiMETXz/AR3/6NnI9SXRdRcqqKGqY9sAXQbheS+D4HrqsgkQrZFTibHV+DaDi+wGFSp35QpXXTk7iBwEnJxbaQFBvLknMWmEnCSGoNmyWSnVcz8cyNDpSsQuGZwWBoFRrML9UwfMDVFUhHjFIRc235LJr2A75SoOm42LqGrlkFGsVUD6XrxAEgmwywlK5Tq3hIMsS2WSUeMRY40ATQtCwXZbKdZqOi6GqZJNRou8wB56qKiRiYfuMlk4TCEGp2mChVAsZa5ZOR3JtTr54LGR/l2tN8pXQObLaZRE1dXqyCYrVBsVqg76OJKYePmMY1h/212KpRk8uQcRYae+bQbCU3sV85TwgSGldLDgTeMIhpqVptFJLKJJKREmgygamEiOqJJlqnGIoug1P2IxVD7Mpdi3zzXG69HW4gY0gwPVcVCXCTP0ci41pFrQJOs3h0FH5Nmzwf478wAJHEtBj5eg0M638CxKbE8Msb2+SRJtNsuydkiWJDfEhJCQ2J0aQkZAlmf5IqEj1R7padcFotJ9Fu0if1UlaD1FzVUkRU65styFMMBvmqFAkg5y5B6nlWu20rgYgpg0QUbuhlXTPULIh4INGh3U5QoR5kHoi+wCw1E6G4++BVpmMub1VRqMncj0CgYyCaL1/TX+xxWQK8ETj+9K3XlCgbh9ACLuVxwJ0dQOaMthKxqwRMfZCK79OxLgWkFHkNKra3+oDFVXpaI2VTtS8AUGAhEY8+v7WS6MSj7y3VaZVpyQhoWGZ12ERfo5Zd4SJ1ESTgCamvotLTc2oprGx5REMhOBEYYHTs0tEhzVGMq08F5LESCrd+nhxNHkomWqX6YnF29eThknSCA2wda3k15IkkWwBQ5IkEdE0+ggNQEtV27+/0P1UWSFrxKl5TVRJASGDKIB04XA8ANd2Of7SKeKZGI1qk2atiaoprL9smHK+ytTpWVIdCQY29bL12o1868+foLhQ5szBMeLpGItTeTzHQzc11u0cYv3uYVzbZfLkDKqm0jXYQcdAltJChXWXDbH12o08+tfPYFg6nQNZgiCgd103kizh2isKnKIqbLpyHUeeP0GzZnPNu/f80Hr8NdkiqYVjpIu13jVVMdqbzDK7RJYkklrfGhavJEmYLQqu+T0wJG69ciNNx13D1rnQb1f//dZ63+rDudS9QzBKapdZKtXYf2KC9+zdxn037Wjn7lldRS4V4+PvuoLt694ZeCi14q5SeoSkHlnTT2/720s+68XrEEJQrzZ57IFXeeE7bzA/VcDzfKyowY6r1/Gj/+k9RFpKxPx0gW///Uu8+uQxSvkahqmxadcQ93zqetZt60OWZRzb409+/et0D2aJJSxefvwohYUKmqFy+Q2b+PBP39Zm1/h+wLH9Y3zli48xcWYeVVfZumcYM2q8hSFSylf57tde5dlvHWJproSqKYxs6eXeT+9j655hFFXBbrr80f/5EP2jHZgRg1efPEZhoYJuqlx+w2Y+/NO3kcqGzKDADzhx6DwP/PGTnD81S7PuoJsqHb1p7vvMDVx967Z2/7z46GEef3A/E2fmadZsVF1haEM3H/jJm9myexhZkamWG/zN7z/Kkf3nmBlbJAgCfv2zf9We7D/+v9/DdXfuAMD3fF558hjf+NJzlJYquI7Hr/7Rj7LtipG3jM3s+SUe+dsXOfjcScqFGqals2XPMPf/hxsZWN+FLEvUq02++J+/xsiWXhRV5rWnT1BcrGJENK64cQsf+qlbSWaiP7Rr0ZtlorbAwcI5JEni+o4tJLQILy2eoMNIktQj+MIn7yxiKRGMljdyqpFv6UQZCo0Gh6ZnuXnDKEZkZb8d6M9eMuzR9QNOjM9TbzqU6zZBEKCpCrqm0pNNEDE1yjWbifkCmhqGfV+7ffgt9UhAXA8NCBkJVZI5Vy5g+x6bU50cm5knbhps6spdcM3r7k7S3b3C9BBCUA9qXDYUZTDShyarLZZo6GVWZZnPXL4HVVHojsXYHfSghA/aNmJMVeVDO3cgt+7x2WuuRpHC9XAolUaWwgMzNnXkkJevp1OtcJIwF9DoaOcF+62zw+Qj74sjyRU0bQuyEqEzF+4Lmzd0I8vh2nrLTVugxQxYGZMMPa1nVRSZrZt7Ea0wzasuH0WIAEWRec9dlwGgqjKD/VlAtA0HSQpDVkJdV0F9m4TfSXMFgBZCMDmRZ2GhfMnE3gC6IpOLRoholw6ZuNAYLouqyERNnSBYMeM6zQ3kjJGWDq+QM0baiWMTehdCCGRJIWuELP8wOW0QgpqSimkl6DI3oUgqSb27Fd67zKhsPWeLhSlLCmmjr8VQuLDhpRsqRw+Mc/7U3A9AcoF/G+kcyDI/sYRQZFRNYbUi4DoeT37jANuvHOE9n7iuDeSLVhi13XB56btH6R7IcteHr0LTVdZt7eX8qXmeeeR1rrtzOwLIdMa59vZtzE3mOfj8afbeuYNSvsrx18bb90pl49z5oatIpKOMbOrh+MFxXn78KDffs5upswscOzDOj/7S3ezeuxEkiMRMfvvnvszpNybZvS90miXSUd77ib109qVxHY8jL5/l9JEpAEpLNV587Cjv+sjVbN0zjCRL3HTPbn77544ydnyGXHf43sZTEd77ib10D2RwHY+jr45x+vAkAIap0TfSQbYriWFq5LoTZDtbDiMfGmWbeIuBKAKBrMjYdhh6JysSruMR+AGX50bW6Fv5Sp0/evB5Dp+bZWKuiCTBf/qDb7S//+VP3MY128J3IAgEpyYW+NvHXuPwuVmatksqbrHvslHuv2EHHalYe21tOh7PHTrLIy8c49xMyHY3NJWNAx189n17GexaySFYrdv8yTdeZP/xSQqVOlFL5/qdo3zyritJREMGz5e/s5/pxTJXbO7nu/tPMZ+vggTbR3v42B2Xs2Wos81eHp8t8PffPcDrZ6ap1Gyils5l63r52J2XM9CZ+p73dNfzeeXYeb721BucnV7C8306UjHuuGoTd12zhWR0ZW07N53nr771CqcnF3A8n2K1ge16dKbj3LR7HZ+5+yr2n5jkvz3wLL/4sZu5fucKwcDxfL76xOs88dppfus/vptI18WBrpia5rLULQBIktxie4drVqrlvF3RByXSehjBlDP6kSWVtN7N1Eyees3DzxuQjRMzNGq1JmoQUFVVypUMUTlDsWIwb08RieisH+38vutEP7jAkSS1FvxVi/abFnBFWvv/2s8rm+JyHavrUiRI6wlSehxdvvAGV/eWWGgexVIydEcuAyG3qW5yiy0gSTKKtOLpVKXVHjUVlsuxurx5kTLyquuhmEqOrsjVIMTbxn++U7Hdszj+zJprkiQjrQmFUy/4WWK1N3tZIZEAbVUYhbGq/KrPa557dT16KxhCwtKvQpEvvVCs+U4IAiHIxCJ0Jd8UBvE9GqL/GteXRZXkVlL2ZdaYSuDPIqnrkbgww8JzfUqLFXrXdYfKnwCn4XL8ldPYDYf+jb00qk3OvnEeu+nS0Z+lWW3SqDTpHulcATUkKUyIKUmomkr/xh4alSY9Ix1ke9OUl6ooy7kEWsBGNBnh5KtnmTg5Q++6LqbPzDF/fpHzx6bI9qTpGenk2X98mUjcItP9Lz/F7wdZJEmiajtM5ksriqgcGhtOK0+EFwTUHQdDVdux1XXbZSCbItLyUBTKdRYKVSxTYz5fZbQ/i+P6nJ8tMNiVpiMdQ5YlxmfyTC2UCIQgl7qwt9MPAmYXy0wvhkkCo6bOQFeKVDxke5ybXiIIBL0dCc5MLlGt2yTjFqO9GQxdC09laziMzeSp1m1iEYPRvmwbpCrXmpydWmR8pkClZlOsNnjtxCSmrrF1pAtNVWjaLkfPzVG3HVIxqx3qslpkSaJhu7xxepp606UjHWWwO4PaOilLApqOy9h0nkKlgWVojPRm28oHwPhMHtcL6OtMcm5qiXKtSSJmMtqbxTTeWTy35/o89g+v8Pdf/C6XXbueOz54FZG4yfxUEQCj9dzNusNX/uC7HHjuFHf/yLVs2jVIYaHCN/77s/zF73yTz/4f76N/tDN8F22Xp75xgNEtvdz/YzeSSEXZ/8wJvvOVlxhY38VdH74GVVOYm8zzpf/7Wzi2x6d+8d1kOuLsf/oETz98YE0OFM/1efivn+fxr73KTffuYcfV66gU63zjS8/yF7/9TT73Gx9keFMIzjm2yxMPvcb67f2878duIp6yeOXJ4zz2wCsMbezm9g9chaop1CpNvvLFxykXanzqF+4mmY2xNFvi9JFJ4m9iDAgh6B7IcNM9e0jn4kyNLfCVL36XB/74ST73mx8ilY0RiZnc/x9u5KZ794TP1HT53G98sM04yq4K2VA1hb137WTHVet4+psHePDPnyYIAt4sjarN33zhUY4fGOPuH7mODTsGWJwt8vUvhX3+H3/tProHsmGfN10e/8f9bLxsgA/85C1E4yYvPHqYxx54hZHNPdxy3+X/Lk6uAVBkhZ3pYXqsDKYc5tXQJKV9GiGAEzicr4/RZw0iIZPSo3SZIXPSVBW8IAjBk1XydrlrDF3lsg19+H7AfKGCJEvELQPT0DD18CATzw/o70ziegFjs/kL7o8BMFurUPNcAgS+CNBb80hTZAr1xtuGrK0WAZTdCrP2PMOxPlQZkGTUVcCAvhw6dREmkyRJ6KsAldVlpqtlivUGGzpzWPpKH6vvwKMrSeGpZ6qyAJKKokhIsrSqr1fq0C4wf2VZWgPYrMZ8wjrC3+u6vOr62zbrHUsQhCeglcvNNSFlFxIvCJONx4y3PyTkYmLqGhFDX3NCpNxi4LdltT6P3FaaVyfWXT2VJZT2d6vLv0VW2xCXaH5Hb4rEuQVKhTojmy6eWuGHSXzfR5KgcyDD5ivWrVkrPNdn4sw87/3EXsxVjJflKeDYLlPnFhnd0ouqheuEpqv0Dmc5uv8clUIdSQIrZqKoCpqhEU1YKKqMqip43spemcrFiLZ0HcPS6OxNc/DcIs26w/x0AUWR6ehNI7Xal8rFyHTEmTi7wO7rQ+Ao3REn3Qq1UzUVK2bijIe5dJdmixQXK3z9S8/x+IOvtZ9PBKEDanlZSnfEyXSursPAsS8UGSIYOzrN6QPjyIqMrMrkelJ4jsf8ZB4hBMlsjBOvjSECQSRhkcrFicYtUh0Jym4NSZLxhY+neXziritYKtf5f//+KSKmzi989Ob2WHSlVxzgUwtFvvDAMziuz4+/9xqyySivn5riwWcO07BdPnv/XnQtXFeef+Mcv/fVp+nNJfnkXVeQTUbJl+vkyzUSkbUs5gOnplAVmc+85yosXePbL5/gwaffYMtQF7dcvh5JknA8n4OnJsmXa9yzbzsDnSmOnJvly995DVWW+eVP3oqpa1TqNn/44HOMzxW4/4adbOjPcX6uyNeeOsQfPvg8/8uHbySXeud5KwEOn53h97/6DL0dCX7uQzcQMXVeOXaev3vsAE3b42N37GnPqb/+zqucmVrkx++9lsGuNC8eGePPH36J9+7dxruv24pl6Gwf7SabjPD4q6e4dvtwe18oVBo8d+gcGwc66MzEL9kmSZJQV+cfWrNOXVxnWVnzZBQ0LFMhYno4doDwPXxPQlU1XMenXvNJJS1cJwjzcl2A3f39kB9Y4OjfQkzlndHgYlq4KTQdj5dPTrBzpIdU1LzkpiiEhxeUCIIKAg+QkSULRU4ifw+n0kjIaJKGJ4o4XjG8IlmochpZNlnJQfHORIiAiv0SQVBD+jdIovW9iCTpqErIJBIiwA9K+KIhnpD5AAAgAElEQVSCEA5h/xkocgJZWkHJhQiNU9f320dVX0jC+sr4otyuT5J0VDmBLL2zvBsrdQkCUcULSq3cTWHbFSnWCjeTLxir64uAmrecCHclEvZiYsVMbv7IXir5Crd/8gay3SmsmEk0GWF05xBDW/s5+MQR1KSOm5PYvXs7juaz4c6NyB6M3r8R34Lu6/qwslFKTp2a12Tnndt45pWDnKvOI3kG6a1ZTEkjr9XZ897LmG+WyG7J0e/bTNfzdIkcsiJzzbv3YLaQelVTyHSnyPVlMCM/XCEiQSAo15s4vk8qaoUJOoFivUG+Fp5ys1itEzXCPFlqh8y5xQKZqEXFruB6PsO5NJOFEj2pOBC+Z8fH5/ijrz3PcE+GE+PzbBjsRFUkTk8u0ZtL8EufuJVMMsLkfJFnXz/Hq0fPs3Gog1/6xK0kYyugbsN2eeS5o3z7xeNA6J1tOi4fu+sKbrp8PYok8eCTbzCzVGZdX45jY3PYjksiavGLn7gFQ9cYmynwVw+/zMR8AVPXsB2XDYOd/Ni915BJRJjPV3jkuWMsFquUqg32H59gYq5IJhFhsCtFxNSxXZ9Dp6c5cnaGY2NzfP7DN3D71ZvX9KXtevzNt/dTqjapNx2atsuH79jDHVdvQlMVFopV/uaf9vPGmRk0VcF2PLqzcX7q/Xvpb3maHnnuKKcnF9k83MXRltcsFjH4hY/f8o6Bo7nJPN/62xfZs28TP/Gr95K4ACAnhGDsxAwvPHqYd3/sOu7/0RvaIES2M8Hv/PyXefLrB/jY5+9o/0bVFD72+TvZtGsQSZLYtGuQ158/xbH9Y9z03t1EExavPnWcidNzfP63PszVt2xFkiVGt/axOFPkwHMn2/eenVjiyYde4/IbN/ORn74NrWUoJtJRfvNnv8Tz33mjDRwBaIbKxz5/Jxt29CNJEht2DHLoxdMc3T/GDe/ehapZYVjRfJn+0U62Xj5CMhtFlmVueM/aHH6SJHHdnTvZe9fO9lq47coRps4t8NQ3DlCrNEhlY6iaQu9wjmjCxGqBe4Pruy4I1kiSRCxhEUtYoYJ+AUtWCMGpw5O89vQJ3vfjN3HPp/ehtIzFVC7O//XzX+aZR17nAz9xc/s3hqnxyZ9/F0ObwtPZRjb3cviVsxx7bYy9d+0kEvvhAo6EaOC7RxH+HKp5KyKoIMlRBiIrCdLHavOktSijsW4Ey+GrAlVS6TX7SKhJ6l4FL/BR5dBJoKsqMiEj583i+wEzM0UOvn6eM2fmKRRq6JpKR0ecHTsG2Ly5h0TCInWRY99Xv5bL4PdSw31LOVPV8ETQPr3LVFRUWW4zgJZBreUk2KuNx2XRNIXOzgSyImEqBt1mDvCYb7xAytiBrmTw3ID5hTIiEHR2JqhWbZ566jiHj0ySTFjs27eJHTv6kWWZ8fFFvvPoYebmSnR1Jbn55i2sa7GIKk2nbQgLIajXHRZboZhvlkQr3G/FuBYIbBRlCEleAW0bDYf5+TKyLNHVlURRZObny7z22hjHT8xQqTTRNIXuriRbtvaxfVtfO4TtQiKEoFazWVyscOLELGfOhuPnOj6mqZHLxdi0uZfNm3vIZsL1wAscphvHyOj9xLRsew7Ytku97rC0VOWVV88hRJiofnq6gO+/FQQ2TI1E2qLSDPWcdZlMu53hGJbwvLf+TtNUOjvj7TCS2UKFasPGfdM9fD9gbi5MqJ3ORInHTEqlOkePTfPGoUnm5ksIAalUhM2be9i+vZ/uVp9eShoNh3Nji7xxaILx80s0Gg6JuMmuXUMMDeVQlLV9bZoapYUKtapNZ0/qLWE8P6xSLzcZ2tpPMhdfk7oAoLXwvI3T9+3vsczkW/68pv7lj29as5ZzNS9nmbm4SbTyO01XL9oeIcK5fPdHr2F0y9oT9boGVub0hep4c9total3tJN4MgTCPNdHNzViyQiqFoJkpqUTTURaDP8wUkBrOeMOlc7R8B3qXpMeK8P23AixFlgfMXVGejNvAcKFEDz26inOzeT59Z+4m10bwkN7dq7rZXqpzBOvnebe67cz0pul1rB54KlD5JJR/reP38pwz8VThQAkoyY//f7r6cmGoNlAV4o3zkzz+plp9u0axWi1pel4fOjW3dx51SYkSWLHaA/Hx+c5MTFPoVynO5vg4KkpDp6a5mc/sI+7rtmMIstctqGPTCLCb3zpUZ45dJb79u24+Hr3pr99P+ChZw6DBD/z/n2M9obr2Y51PSyV63zjucPcvGc9A10p8uU6R87Nsu+yUa7bPoyuqWQTEb714nHmC1Wipo4sS3SkYtx6xUYeePIQYzP5dp2vnZhgJl/mM++56pL257J4no9te0SjBq7rUSw2SCTMNbnwLiXL4eKD/RcbH6nFLP3XOZ1vWX4ggSMhBFX7JerO68hSjFTkNjRl7akFjjdLqfEdAtFAliwS1q0Y6trTrFx/iVLj2/hBhaixh6h+RbsTPT9Pof4Igai95f6SpBE39iKkOF7QbJ965Hg+tuvSsF1SLQPaD+qUm4/jeFNY+jZixlV4foFi45tUms/RdE/hB7Uw+bXaS0TfSdp6F1HjckLw4uKbv8Char9KofYgdecNPH8RJAVd6SVmXkMmej+60n9RKm1YTxDmEfKmcfwJbG+CUuMxBC4IKNa/RdM9+dY+QCVmXklE3/mm+nzKzSdpuqcBiZR1O7o6jN9i/kgSaPLKCxQIh2L9W7j+LLJkIjs34NQTZDoSGGbIfAjrO4mmdJOybicQLuXmU5Qa/0TDOYEXlJAkBU3JYWlbSUXuJm5cgySZ+EGAHwhiptFmg5UbT9Nwj6EpHSStO4GAcvNpivVv0XBP4PsFkBRUOYOlbyVtvYuYeS2yFLnkS7YMGFWaL1BqfDcck2AREChyGlMdJWHdQNK6HVXOrhkXAdiBt3Y/kyzeLqvs4OaVTcu1XVRdYWTHIP0bekJqtiKh9pvEd2UIohKvLJ4lORwhrUc5X5mlIprMJ+rUhE+sWsNQNMpAYluGhGYhKxqaqjPTLHGuMs+u3mFOVmaIaxbZyzo4XZnFkwMGNvUysKm33Y4jz5+kkq9x3T1XtL06PyxSrDd45VSYF2PncC96zCKia+wZ6msnyV6o1ElFTFQlPMJ0KJdGkcOcHyDQFIXRjgzamxTWYqXBHfdv5tqdI/ze3z3Fj917De++fhu/97dPMbNUIpOMcPW2IfZsHuD3/+4pKvW3nrj1xulpvvLYQd69dwt3792GrikUKw2ilt5WtARw+MwMV2wZ4Fd+9A40VaZUbZKKW7iezz989yALhSq/+PFb6O1IMr1Q4r98+Qn+6YVjfPDWXQz3Zvn8R27k/FyB3/jzR/nALbu4Yfc6JIl2jHciavDRO/ZwZmqRX//zRy/Yl03bIxWz+NkP3QAIHnj8EF/97kF2ru+hvzPFd148zpGzs/zMB/cx2pdlqVTjN/7iUR5+9iiffs9VWIaGAI6em+PKrYP88qdvR1NkKnWbTCJyESXtrTJ5dp7iUpUrbtxMLHHh3BwAE6fnEIFgy+XDa8CQ7sEsnX1pTh+exF9l+PQMZukdXgmlsaIGiXSERt3G9wOEgKmzC1gxk97hXFuhjcZMhjZ28/oLK4lXF2aKLMwUWZot8dBfPtO+Xlyq4Hs+U2cX1rS1dzBH79Cqe8cM4qkIjVqY1BZCiv6+uy/job96hi/+56+x54ZN7Nq7kVz3WmNKCEHg+yxMFxk7MUsxX8V1PCbPzOO5/ppn/n5KEAjOn55FUiQ27x5a06a+4RzpjnjY56sMyN7h3BrlPRI3iScj1Gs2InjnDJX/WcSzX8S3nyUI5lCNvXj20+wv+ZRYycN4pjLLnT27yTs1Gr7DcLQDXZZxApuaVyWjdzHfLK3JfSSEIEAQiLVj22g4PP/Cab761Zc5fz48GSxkikkoisQ3H3md3buH+MAHrmTL5t5/9ulaEmAqKjXX4f8j773D5DqvM8/fzaFydXd1QncDnZCITDCBYBKjAimZlGSJVvLacljZ8o5zmNnHI83s2ruyx5LH+ziMLMm2LFlUJiUNKUoMIkUSIAmSAIgMNBqdU+Wb790/bnU1Gt0AQY78DOU5fAig+1Z9dVN993zvec/7goAdxPoSOc3AC0Iyht4URp6ZKfMfP/ENpqbKK8bp62vlj/7oblpaElS8GpP2DN1GjjBy8MMqqpRnbq7C//Wfv025bPGbv3kXTz11nO9+72XqdQdRFNn//Bl++7feSjqt85m/fISDjXtOkkRefnmUX/3Vt2AWDCbLFfzGPRZF8MKLI3zqU99d9fjuuWcn7/vZa9B1BYgg8hHF1kZesNTMfPToJJ/45DdRVZn/8O/vYX6+yr985TmOH59qupgBSJKEaSjsuX6Y+++/js6OzIp8JYoijh+f4utf388rB88xP19rXL+o+ZmSJKDrCuvXd/Ke91zNrp1rCSOfOWcEU86QJE+97vHoDw5x7Ngk50bnGRtfoFiMpRKKxTqf/E/fWlU744or1vC7f/D2GCLzlzMvpqfL/PF//CbT0yuv4dq1rfzRH97dFDl3fZ+xhTKbe5bn/eWyxZ/86UOMjs7z/vdfy9VXD/CFz/+I5/adwrLc5jwhigKPPHKQvr5WPvzhvVy5a+2quV0URZRKFl954Dl+8IPDLCzUm+dKEAQe/cHhVUGhHdt7ufPmTQRegGaoqP9Klf03U0RRRPdgB8WZMo7l0ta9fPEqKxKdfa2cPjKO42xFX9SnadzqaqN1a7TxTFFUicAPmDg7RzJtrGDAXiqKs7FOYSafwLE9ZicWyOQTGKZK+5ocYRAyO1FkTX8sp1GarzE/U6F7bdtl1dlbOjJohoqiyQxtWYMkxwLLi2uM1/M8FCWBMAwx0wbtvUt6jIv3Y2vXkqSG3hD3Pn87QIuaYtyaa+j7riZAsDKCMOLo2WmCIOSpl0/zysmlLpPJ+QpzpRqlWuxwOVuqMTFb4oZtA3S3vbbQe19Hnvb8UsE9beqkTJ267S7LyXIpk+GetiWgTZbIp02Onp3GC0K8IOTU+ByaKrOxr70JfgkIrO3MkzQ0jp2diR3wpMt7zpTrNiNTC/R3tlDILTGVVFnmiv4OfvD8cSYXKvS0Z2PplMY5hcV1N5z3BxAXZ6/e1MtXH3uJ/UdG6W3PEYQhzx4+S1s2yXDP6u3JQRA7BAqigCQKTRB+3bo2LMvjhz88zI4dfQwOthOGEaIYa0PGGpFx/hjnRA1xbxaBVZqvEwQIgnhfZVkkCKLznhlicx8kSfiJ6R29aWe7uvsy46X/F0lIoModZIzlD5Cq8yzjxT8liMpIQgpRMNCS7172Gss7xHjxUwRRhTXZPyShXtnc5gUzTJY/jRdMrvhsUUjQk0shq1eTVfsQG8ycIAzRVWUZshhGNWar/0TFfoqMcSeSkGSy/JeU7SeIInvZuG4wRs3ZT8n6Pt2Z3yFj3onA6khjGFnMVv+BmcrncIOxZdu8YIKae4CK/QQd6V9b1g52YTj+COcW/jgGsKIKQVgF4gd6hMdc7Uurvk8UdLqE31kJHOEzX/sGC/VvAiKa3Ics9XGiNMup8jyb8+30pZZ6YaPIYab6eWrOfmQxj+l0U18YINeSao64UP8287UH0ORBVLmHueqXKVrfIQhLK4677r5C2X6M1uQHKKR+oWEpHVFzlpzIitZ3ma1+EU3uQ5V7KNa/w1ztawThworxLO8QZesxWpLvoSP9MSRh9dagKIrwgkkmSn9O0XqYoOkOtzjWFLZ3hLL9Q4r1R+jIfIyEumMZeGRKKlkl1uBopJ5wwT1yqZAVmRvvuwbzvIXvpmuGabELqAmNtGKQV5Nk1QRe6COLElboEhJiyCpZNUFOS1L3HcatIpoox1WMwCGrJsiqCdKKSdmr44V+s4Xzwoq0KEt09rfTPdhBpvXS9MyfxlhMPnNJE0Veasc8/3vfnVveNrrYrnB+t9ZqRU7TUFnfV2ByrkI+bbJxbQdJU8PQFSoNW3ZZlpr/rxZPv3ya1myCO6/b1KzmpxP6ite1ZhLcsGOAlobI7SJraXKuzKunJ7ll9zDDfQVEQSBlauzZ1s/jL5zkzms30pJJIBsqhqYgigK6Kq8QKhQEAU2V0VXlopU7Q1O4adcgrdkEURRx484BHnvhBEdHZsinTZ49OEI2ZeB6PsfOTgPQkklw4Og5am/Z1hRbzKdN9u4YQBdFRs/Mkc6YzEyWCIMIWZGQlTgJjaI4UbMtD1kWkSSRQkeGWsUmjELSucQlq561io0oiSQuYFIoqoyR0LDqSzbDAMlGBXHZeWnY9UIsZO1YLqomo2ryEn1fFNBNbdl76xUb3/U5e3KK0kJ12Zhrhztp71meqCczBuIFyVScGETNnEfVZN56/3V09Lbw2Lde4Ot/9zgP/eNT3PC27dz+nqvJ5OPEyrU9vv0PT/HkQwdi8CufwDA1FmYrlw3OvaGIImrlWAQ8cYGot6LKGKaGVXPwz2vpS2bMZmvc0nELlyJv/lRH6B1BNt6Kbz1EXGiIGK2dYW1ue7NIM2uXERCYtErUA4cesyVWSxRk0kq2ocEWLltwuEEQs3vO/53r841vvMADX93HwkKNXC7BQH+WXM7E9QJmpsuMnpvniSeOcO7cPP/u/7iT9es73xB4FEYRE/UKYqNKqggSlu8x51h0JdLUXQ+/AVjJcswqCoIQ1/GxHZ9isYbvh1SrNlEYC8mqooIdOERRgCToiA0ZgTCMqFRtxsaLPPzIQV555Rzr13di2y7Hj09x7tw8DzzwHMmUzujoHDu292HZHkePTnDs2ATf+97L3PeBa8gaepMFJQjxfN7RkcF1fVzHp1ZzKFcsoghs21v23YlwCMNZBHH5s8P3A8rlGJT5zndf4sUXR5iertDVlaW9PY2mKSws1BgZmaNcsXn44YMA/NJHbyadXsn4mp2t8MPHjuB5Prlsgra+FOm0ga4rlMs24+MLzM5WeP75Myws1PijP7yb9jUaCbkFoSHxUK87PP74Uc6cnmleq8V5UxDiBclqrX6SGLsN+WGIGi1f/CqKRHshTRiGOI6P43gUi/XmNTxfz8jUFPIJY8V9FUUR1YpNsVhn//7T7N9/muefP0MuZ7JuXRvptIFluYyNLTA1VeLVV8f567/+IYU/upvexqI9iKKmbpVte3zlged44IF9hGHE8HAHV13VTz6XoFKxef750xx+dRzH8WNmW1uadFqne02e9q4sC5MlRk9O09aVoXewHTuo40cuCSl9yYLuT2NYNYe5yQXqZQvfC9h8zdCy7Yoqc+M7tvNPf/Ewhe4cm3evI/BDyvM1Nu1aSzJjcO1tm/mbT36Lh7/yHMPbejh3cppXnj3Jne+9Bt28vA4QgLnpMt/70rPsuH6Is8enOHLgLO/632KGcPfaVjZf2c9DX3wGBAHdUHj4K/sodGVjdu5ljJ9tTXLdHVt4+CvPoekqXX0t1GsOsxMldu0dJrHK9+5i0daVpV5zOPDUMexaH7Iq0dXX2mQUX0qzcul3In4U0qJluCKzFkNSsbi0WZIfBNRtF8v12Hfk7ApNs41r25v5le36uF5AOqkvaw+9WKQT2grwatG06vwwteXr5VjmZalbJAxDqpaDLImYunLe60BTZDRVborkvxZrcDFcL8BxfUxdQZHO/2xINVruqo2CbD5tsnWgix8fPMNwbxtr2rLse3WUYtXiyvU9TfkFQYiBrK0DXTz24glu3jlE1XJ45eQEe7etoy27urbiU08fZ3q6zNq1rWzd0sOBl87iOn5zruroyBIEEeWyxYEDZ9l15Tr27TvFQH+Bg4fOYdse27b2UqvZjJydw9BVkkkdw1A4cWKKnp4WUimds6NzFIt1rt8zzPj4AlPTZUxDZdeudbzwwhmKpTqdnVl2X7nuss/jpeJNCRwJgoChDCMJKYKwjO2dJq0vUSCjKKLuvkwY1QGBIKpTdw+Sj+5tTtYREbZ3ijCqIglpdGVo2YWVpRZakx/ACybwwyJ+MI/tHccPl6q6fmg3GAbxmLoq43g+ZcuhJb0SYKg5z3Ou+Elqzgtocg9J7WoMdQMCMo4/Stl6FNs/jeuPMF76MxSpg6S+e8U4URSyUP8mk6XPEERlRMHAVHeQ0q9DFnP44RxV+zmq7n7OLfxxoxVuZURRBFGIIMgoUisKrXElyDtFEJWJgZ91yOJK7SRB0JDF1VHUla+NEwtdkmnRL101kGSRmYki/cMre8Ld4BwTxf+HqvMskpgjZ74TU70CUTTx/Akq9lPU3YN4wRTT5b9Gk9eRMd7WOM6VE67rTzBR/BRV93kkIUXOvLsxXhLPn6LqPEPNeRE/nGG68ll0eZB84mdWnQD8cIax4n9iof4gAiK6sp60fgOq3IuAgOOfpeo8g+Uepmz/AC8Yp6/lv2AoG5rjzThlRAR6zJYGrzaAyG5WMS6MMPJww3rcZhc5SIKOlo8IqWL5ESE+YkKiO5kkiBygyo2FTQCcqEyiJGU69CzrU12N69SoDsgGHfpyHakoihhMxtekoGeaj4ROI7fCPlySRNouSpX86Q9dVajZLi+eHqcjlyLRcEpYbAGJiBcii8j/4jbXj/v/VUlaZnV6fkiiiKrE2xVZQmtSnYXL1vOYmq/QmkmQfA3HiUzSIKGv1Jmo1h0cLxYLPP/adrammZ6v4Hor20HeaEiSSK4BdAqCQNLQUGSRYtWiZrmUqjajU0U+9+Bzy97X17mcep1O6qRMjbmJEudG5hCleXw/xDRV0lkTWRKxLBdVlZEViZnpMqIgkEjqFDoyJNMGoihSbAAhF2MWZluTBH5IaW45cOM6HtWSRbY1iarJeG7QPKbXOn4jqeHYHk5jISkIAlEYYVvOMiZNIm1gpnTu/uD13PW+a1aMtWIhIrz25wuCgJHQ2HPnVnbfvImx09M89s0X+Ppnn8DzAt73sdsAOHl4nK/+7WPsuWML93/8drINYe9//swjPPiPT60+duPv/xG8RhCExjkPKC0sZ/86tketYtHWlUVRZLyGdsS/AvP6TR2i3EfgPEsYnMOzv0foHeW27neR0Qeauo09ZiuqKHOuvkBBT5NSDJzAoeKVMOUkoiDSrmeZtktNBoDrB/S35JuaOlEU8fzzZ/jSl5+hUrHZtq2XD3/oeoaG2jFNjTCMmJ2t8OBDB/ja1/Zz8uQ0n/v8j/jt37qryRR5PSEIAklFbexOvKDIaSaaGLcGm6qC5fmEUUhra4rf/723U6+72LbH7GyFv/j0w4w0NEkWQ5c0knICWTQIIw8vLGKwVHSMoogf//gEH/nwXu64YwuVis1n/vIRnnjiKD9+5iSZjMFHP3ozN9+0Ecvy+MQnv8n+/ac5enSSUqm+wl5++/Ze/vRP3otle9iWywsvnOG/ffYJ6nWX5SEgCElAIdZ6XKWVPQj53vdeIZnUeO97r+auu7bS0Z6JheHrDs88c5LPf+FHjI7O8+STx7j22kGu3zO84pxu2tTN3XfvIJ9PsHVLD2vW5EkmdURRwHV9Tp2a5m/+9jFeeuksp07N8MSTx7jn3RvIqh3oUhIQSKV0fvmjNzefBbWazRf+4SkOHRojnTb4+K/fTlvbyrwxkYifN0EYciGW2Nqa4vd/f+kazsxW+PQq1xDA8QJcP1gGJl0Y+/efRhQE9u4d5r57dzMw0I6myfh+wOnTM/zd3z3O8y+c4fTpaR599DDvf/+1VAKXs6USmwsFdFnm8KvjPPzwQXw/4OqrBvi1X7+NzvM0G++6ayv/+I9P881vvUDC1PjAB/awZ88QpqkycnwK23KpVW1kNf4OBZHLWP0YbmjTafSTVzsvqV/y0xS6odLR20oyl6BarK/YLkoiV928Ed8LePI7L/P0w68gSiK9g+3LWrnf/+u3870vP8tj33oRM6nxtvuvY88dWwijCDOlk84n8PwAzVRJ5ROEYYSe0Micp63VN9SOY7t84c++h+cG3Hbfbq66ZRO+HyKrMu/+5Zv51hd+xJf+6/cJgpC+4Q4+9Ft3kWtLEfghibTR1CaC+NuYzprkC/E8Jkki7/i56zBMlUe/vh+r6iCrMuu39nDlTetBgERq5RiprLkkgN2Iwc3dvOsje3n82wd44sGX6F3fwfs+diuZXKLZVRcRM4REgaZ5iEBD61cQKHt1esw2WrUMCXmVNtVVviaKJJEwNLpbM3ziF99KxyoaPIsggqEqqIpMsWLh+eGqepXnx2W3P73GyyRRJJcy8fyAUs1uuvVGUSzJYDkeKVNHli8f7DA0hYSuUqrZuJ7fdFmLolhnFGiKY8uSyPtv28knPvcwf/W1p2jJJEibGh+9+xqu3bJuWQ4qiSJ3XrOBT/z9Ixw4PkapFjvK3bRjcFUQHSCZ1KhWVDRVQVVlBvoLvPLK6IrXpdMGtZrDKy+Pxgy62QonTkzT0ZFhYqIIAhTa0lx99QDHjk1y9OgEk1MlVFVG1xUkUaBSsZmcLFGrO3R1Ztm5cy2TkyVePDBCf3+B8bEF3K09GJfpVHepeFMCRwCqvBZJTOKHs7jBGBEeAvEBh5GF7Z0kwkeT1+H4p3H8EcKojiQ0JpcowPVHCSMbRepClXuXjS+LedpTv0BEQBT5BFGZidKfM1/7avM1upRlyn6ZjNqDTJw4taYT6BdxlfDDWXxnjoS6k67s72KqGxEFg3hR6JIxbmGs+J+puy/h+mcp2Y+S0HYiXPBg8YJp5qpfaoA7EvnEfbSnfwlFakdAIsKnJfFupsp/w1ztS4QXYa04js/MdJJ8+o9J5mM0N4o8RuZ+m4rzI0RBozPzcVL6dau8W0C8CPvmwogi8MIAJ/BJyJe+KaMwWlGJWxrHpuI8gyavpTPz66T1mxFFMz7myCVn3sN46U8pWQ8TRBWK9YfIGHegSBIV22m6NzTHw6HiPIsqddGZ+Q0yxq2IYqIxnocX3MtY8U8oWg8RRRZF60FyiXc077PFCCOX+dq3KFk/AARSxo10ZX4TTZlbPOMAACAASURBVO5vCpeFkYsbjDFe/L8pWY9geUeZrf4ja3L/ockIU0X5vOOOiCKLKCwhELBay5rlz2EFJWreBLqcRxET1P2ZRvXYxwlKGHIrGXUds/ZBREGhN3kzoiCxLlmIxUZFecUkv3pFY+l352/9X2yNBkDdcenIpVkjiViuRxhFjJcqlGwb1w/IGBpeEGIqCk4QoMkSjh80qfmqJNGdTa/qKnPhqX8ji2BdVbAcb5lV8aohrP4BiiIhSQKWu1xzpG676A2G0U8qoijCcZeAbS+IFwOaEjOqVEXm+u39fOTtVy3bV1kUSZrnW5vGsaavlfbOpcRelEREMQZiFl3tEGBwuIPZmQqeF8QU+/4C2ZYk+x47wpU3bSSdW31u6xloR9VlDu07xc6962PnGODcqRlmxhfYfdPGBtvlMsE1Adb0F7BqL3Du5DQ9A+0IAtSrNiPHJgnPu4aFrizZ1hSH9p/mLT9zJeZ5DJylVpPXF4stRqIooGoya9d3ctf7r+PQ86c5eWiJyVqar+K7Pht29JFrLAgd2+PUq2OrztWCKCDJEp7r4zn+G9b4EESBvqEOBFHk8POn2bx7XXOs0RNTLMxWufXeNSuYVf8rhaztxUciCmeIgjFk/Tay6gac0OdoeYw1ZiuyICEi0qIlm2CSIAjIotL8WRVl1pgtzbm+6rgxgN1gLdm2x0PfeYlKxSafT/ChD17Pli09zflAFAUKhTTveucuRs/O8fgTR3n11TFeeHGE227d/Ia0FCLAbohjy6KIIor4YUjY2L+K7eD6AYaqYJoaZkNPL5nU0fWVuYYf+TSgfTSpFUlYWcjK5xNcd90QqiqTzyfYuaOPZ545iev69Pa2sGtnfA+mUhJXbF7D/v2nKRZrVKoO416VK7rbMdRYjFySBNJpo8n8mZwqXbKiG0ULRNHKRfdihGHILTdv4r57d5NOLy0QTVNjz54hJiaLfPazT2LbLgcPnlsBHAGkMwYf+tD1qI059vzroqoy69d3cu+9uzl2bJJ63eXIkXHuDXciKTSt5z3LJamKtPS2oGgypVKdVCqejxRFYmCgwJqLFI9qrouuKOiKvAykF0VhlWu4OuNekUTSpn7RBVl8riLW9rfyoQ9eT2/v0n0tyxKDg+3ce+9uTp6aZn6+xsFD56jXHWwp4GypyFBLC5okceTIOKVSHcNQuf76YTral7fpZLMmN9ywnh8+9irVqs2Jk1Pc2rjXHcuj0JVjbrrcBLUXiz+6lKDozmBKaUz5J2Nq8z87REmkWqzT1d/edANbjCAMsQKXUIy46vZNbLluALsxv3hyCEmBkltHlxSu2NNP36ZOTo/MIssiibTBsdFZkqZKfn07ucE2Tp6doX9nL4Xhdo6dmSY/2Mo7d61tfp6Z1LjvF2/CsT1GRufQdIWp2Qpq2aJadUgmNba/ZRPbb9pIreaQSOlU/YBTp2doa0tzw9u2c+1tm5cYvwLc8d6rm8xWQRAwUzp3ve8abnz7djwvQJJFdENtMqNueNs2rrl1UzNHQIDb330Vvre8kK8ZKrfdt5trb78C1w04PTXPXN1mpm6hSBLFqkU+ZeB4AUlTo1p3CKMIWRLp68ihKjJdRgtzTokxa46CFuc/i06OrufHc+QqLNzN69p5/ugoJ8dm6Sks5U3RorRI4+eWbIKu1jQvnRjn3EyxqeHzrx2yJDLY3YofhBw6PclgdyuSFDO2T5ybpWa5rO9tW1G8Pj8u3JI0NAbXtLL/yCgTc2VSDZDIcT1ePjlBS9pcJmQ9MrVAGEX8zvtvZqingCKLjQLnypxmaE0b3W0ZfnzwDMWqxYa+An0duRWvg/gcq4oMAhw+PMbwcDvHT0wxMjLHxEQRWZY4dXqaSsVizZoc/f0FHnzoAPfcvRNVlWhpSZJOG/T1tTI2No9hxHNle3uaHz72KsPDHYyNLVAopBmfKMVsLjEGGvXGaxMJjc7OLIau0NmZ/YmJZb9pgSNZyqPJfTj+GVx/hDCsIkrxg8oLpnD8M4iCQc58B5Plz+D4Z/CDWSQxBo6CsIrjnwEidGUdsrj84l7oIiaEMue7fkEDBY68RiKy1K6iSMt71M9/hyy10ZX9XZLaVcse2JIgk9SuojX5c5xbOE4Y1am7LxOEJWRp6QEcRQEV52nq7iEATHULHen/HUXqPA/ZllHlbtrTv4IbjFOy/vuK8xdFEQsLNc6OlOjtayGXXbSvdxGaKu0CkphBFpd6UFfrrz3/9xeLNiN5mcsaAVjqKb0wRMGkM/NxcubbEQS5yfAQRB1dGaKQ+gWqzj6CcAHbO4blTSCJJh3ZFKtdE1EwaM/8KvnEOzlfDFwQJFShj/b0L1J1nsEP57C9E3j+GJqybtkYrn+Wueo/E0ZVDOUKurN/gK4MLqNqSoKMIQ7RmfkN6u4hvGCckvUo+cS9JLWdQPxwXTqtEYKYQJC6EYTVv4aG3IIht5NS1iA19j2pdBGDTg13PxQkQUExr0IStCYrQRYlZJZPfK4fMFYuk1BVCsmLt+TN1eqkDX2Zu8z5YXs+siRelpvMT2NkEkaso2M7DJutREDRspgqV1Ekic50klm3zqzrUbEdFt3BdEUmAmacGnnTeE074jcaW4e6+NLDL3B0ZJpdG2JhZM8PCMKo6Wp0qShkk3S2Znjp2Bi3XDlEytSoWi7PHRph21DXipa0/5GwHY8Xj51jYE0rAnBsJLY8XdfVQiahM9TbyqmxWWq2x5pCrNlRt91Ga8TqoJfyGtUwaOgxNFq7BEGgvTvH239uD1/8zMP8l9/9Mrtu3ICZ1JibKlMt1bn3ozeTypj0DrVzy7uu5Ilvv4gkS2zc2cf8dIWHvvg0fcOd3Hj3jte1QBYEgStv3MBT33uZL376YcoLNVraM7z0zAlOHhxrJp2CINC+Js/b7r+Wb/z9k3z6D77Crhs3oGoysxNFRo5Ncs+H9zKwec1lfzbAuZPTfOPvn2Tt+g46e1sIw4hXnjvJ9LkFdt+8qdHfH9HWmUU3VZ546AD5QpowjHj+iSOMnZlddVzD1Fi7voOD+07xjc8+ztCWHhzHY2DTGjoa592uO8xOlnBtj+lz802dpkRKR9ViJxxVV+jf1MXet27j+1/dR+CHrN/ey+xEkYf+6Wk27VzL9XdtfV3n/N9aRGEZWbsaRb+diJAomISoxgvzY7xSGmF3FFLxLXrNNupBrDHhhwEQUfOrBFFAqEWUPIuiW22wSGNwu2w7yA1g6MiRCQ4fHkMUBa6+aoDNm1fXL8pmTfbsGWb/82eoVh1eeOEMe68fft2VTAFQRBFBVhAR8IG8bjR1mAba8jHbQHl9aWq8eBeQxSSa1MqFOUFXV64JggiCQEdHFtNU8TyfdWvbyGSW8sL29njRX6+7OLaHpIpYnn8RH9TX3DNEsavhXLpa/ggtLSluv/2KZaDRYqiqzJYrekildMpli7FzC01djPNDFAQSlzCsEASBwYEC3d05jh+fYmamguPZlMMzFPQBZFEliuDM4THsmsPaTa9vzhEb7SiW52P7Prr82s+kC0ORJbwguCQIpygSN9+8KW5BE2LGbsx0ih3zBgYK9Pa2MD9fY2KiiGV5kBSbLTW+HzAzU8H3QzIZle41+RX7KQixYHkyqVMs1jl7dg7H8TAMlc6+PG1dGSZG5zHMWJvGDS26zSG80MGQUqjiyhbyn+aYnyzy+Neeo3NtG+uvXLIlP1GdoujW8cOQjGow41XIGwlm7Qpl2yYTmJiyypRVQhQEBo12FhyHFiMJgoDtelQtBz8KUVQJsdHuXQ983GrIXLFO9/mCzVEMyCTSBuMzZRDjtsNyxcZxPDzPp1pzMUwVXwBZkymX4nZQw7AbIsPnOz4LJFJL16pac5qsm3R+9TYkI6FhJFYfY1FXRlEaRgSaQr5NwXZ9wtkilutRrtp0tqap1ByiKCJpaFRqNmMzJQQgn0ng+SGR6DHvlKkHDvNuhR3ZWNsuYaj0d7fwyHNH+ZdHX2Sopw3b9dnc30F7LmYM33rlMIdOT/KXX/0Rx0dn6G3P4fkBJ8fmUBWJj7ztKnQ1Zui8++Zt/NmXH+eTn3uE23YPk0+bFKsW47Ml7tm75V8FTBIEga2DXVy/tZ9/fuQFilWL9T0FRqYW+NaTB9m9qZc9W9cRa/5EzFfqFCsWC5U6VcvFC0KOjs6gKTLZlEFLOjYjeNcNWzh6doY///Lj3L33CpKGxrOHRjhw/BzvuWV7U9Qb4PR47NS7ULGYnI9bvlVFoj2fInOeCZYgCOTSJnu39fPVx16m7rj8yjuvI5OMTUg8L0BuaGH5fuxo1tKaxDRVDEPF8wL617XR3ZXFMFRkWeTGGzYgyyKKItHf38bPvvdqCoU0kiRy262bsSyXXD5BNrtkspBOG9z9jh1kMgaVik06bdDZGXeQtLQk8f2gmVcmkxp33rGFctkimby0odfriTctcCQKKoa6ibL9OI53hiCsIEv5htbMOK5/Dk3pI6FtRxZbcf1zuMEEqtzXoMqWcLwzABjKZt6Ig5goiKSVbmRhMcmAE+Nzq7apNd5BSruOhLZ9VSaBIEgk1K3IYh43qOMF0wRRDZmlCTGM6lTsJ4lwEVDJGLcuA43OD0UqkDXvomz9kIjl1Ogoiu0jszmTfMvFrQwnRufxc5VYU0WRmJ+pICsSqbQRV+sbwl6eFz/Esy2rJIcC1DyXqrdSyPfCCMMQWRYvymow1A1kjFubLCwrsDlePcNgci2mrKMr/WhyL3V3gSAqE4RzCFy8PU5Xhsgad6wKzsRAYB+asg7fmSMIq3jBzArgqGw/ieOPAJAz70KT+y4qTqfJAyTU7RStcbxgmrrzIgk1djDyoxA/9PGiAEUQid22Lt6nLApKw0lOgyiuwDqBj6mo6IpM3XVx/ICkJiELWequh0VcrUhqKo7v4wUhgiCQ1jX8MGDf2XN4Qcj9u7bFug9u7FySVFU0WaLueZyeX2BjexuKKFJzPbwgrsKkdI0gDHn46Ak2tLfSk81g/CuBI/8zo1i1EAUBx/WpWA4ZU+eKznau6GwniHzqQZWEqSMJElGUBAHCKEAUJIIwZLZeRlE9qn6ZmAAjYkqXx96rWS77Do+wULE4dnaGuu3y4I8OUcin2D7UTVsuyY07Bjh8apK/euBHbBnsQldl5st1rrmij5t2Dr4m+0PXFN572w7+9utP86f/8ChdrRnGZkoUKxY//45rSKxSyV8tTozOcOzsDGenFlioWPz4lRHqjkdfR55N6+K2x6Sp8ewrI0zOlgnDiBePjXHTzkEG17QiigL33bKdv3rgR3zqn37AUE+BMAyZXqiyd3s/t+weRl6FaRIEIeWy1Vw0CWK8UFlcikXAzEyFXM7EslySCY102uS2d19FMmPw5Hde4pt//wSBH5LOJ7jyxg0ojcWposq8+5dupqu3hR9883ke+9YLmEmdHdcPc9f7rqGzoZMhCJDJJ9EMZcXcnMklkFWpOce1deX46L+/h6/+zQ/5+n97HEmW2H3zRt7zq2/h0a/tbz7kJVnizp+9msIanR987SBf/q8P4rp1WgpdbL5yoMmSij870bAjXvpcgTjJ1Q21+dlmSkc3Vb7/1X2UF+qxc9OaPPf+4k3c8q4rG+MJ9A138MHfvIvvf20/n/nDB8jkE2y7boif/9238cVPP7JMiymKIiRF4tZ3X0Wt6vDDb7/Id7/8LKm8yUf/4B7auuMk5vSxSf7hz77L/FQZ23JRdJkv/X/fR9MVWtozfPTf30PvYDuKJvOzH7uV9r48Tz50gEe+9hxGSuOqGzfxtvuvo7UrRxhFRAKk8gkSGYOIeJEYNgSeU/kEyYxB9G8QX/KsbyNpu5HVXRBFBO6PEaRu3DBJXk0x71YoezFwVPVsnNDDjwJi2acQP/QQgFYt1WAkxSfJ8X1eODdOa9IkicDRYxOUShamqbJpU/dF5xFBEOjpyZNIaNRqDmfPzlGp2G+IAi8QtzVFRFRdh9PlBToTaQRgvma9bkamJmoookQEVNzjRJFPSl3OymnJJ5d9Zw1DQZJiy+98PrEsN9EajBjfD5pi965/aW2Ri0UUOQiCjCBeHHbq7MzQd57g/fkhCALJlI5hqJTLFrbj4XnBG6oiK4pMqsFo9LwA1/dwhBpuWMckG2uPFGvYbenXrXEWNNq4bd9ntFhioCXf1IW63LBdn6SmXrI4pesKOxotUFEU3z8HpidpM0w2tLShKBL5hoab74dYlkuky3SmUiiStITfEf8dhaszeGOR2vMKqo1DWZipIkoim3b0xccd+YxbJwijAEXUGEzu+DfTprYYhd5WyvNVkrlEU1wfIKckMSUNLwxIKTqqKGNIKiXXIq8mGE53EEXQZWQRBYGkpNO5LbvMLXa1GF5biJ305iqYxuq55uZN3av+/o2E5wUcOTbB6TOzDA+1U6s7bBzu5NCr47S1xa5/MzNl0imD1tYUc3PV5vewsyPDyVPTDPS3M79QRVVlTENlbHyBdNqgXLYwDZXdm3qWsVn6u5cDMuv7lkuE+FGAKWvktRRpZSmPVGSJ99yynSiCB59+Fdd/hXzK5LfefzPtuZhR055P8e9+9ia+/dQhfvTSKR58+jCyJNLbnuOWXUPLdI/2bF2Hqat895lX+caTB7Fdj5ShsW2oq6mFJIkCuZQZawWd95UWBYF82sBQZHwvBBVSpkZLOrEC/F38/WJup6syv/Ku6/j2U4d48sApvvnkQXIpg5t2DnLfTVvJp+L1neP5PPT0YR5+7gi24+M0mF3/5999D02VuG33eu6/fReKLDG4po0/+OBb+OpjL/O57zyH4wX0tef42H172bu1v6nj5PkB+bSJ5Xj8xVeeaD4bZVlkY187v3j3tcvEvUVBYO+2fr711EFSksbujb2IgsCrJyZxHR9JEnEcn3LFImGqaJqC7cTMfsfxAIFUUqOzK0fC1DATGqOVErXIJyWrZAoJBEngXK2MoENXaw7H99FlBccPsH0//v60GMiSHLctiiItnfH1lkWJcuSQUWSqbuwAmkhrSKaEJr1+AP9i8aYFjgQUdHkQEPDCGfxwAY0+IMTyjhHhokpdqHIfqtxN3Z3B9k6Q1K4GBPyw2BC+FhrskNd/qBEhdX+OnOqyiE7nUgbaRSpgoqDFGjqCysWafCQx3WT8hJFNFC1PQuI2vNhpRxA0Euq2i15sQRDR5bVIYhY/nL5gG80H6qVofmMjc4wcOo5uqiQbAoqVkkXfQIFjh8eplOokkjpBENI/3EE2v/rk7QQ+0/UaNd8lqVy82rXIFriYK4GpXIEomCyev4gIL/SIaLhlCEZje9x2J+CiKhIt8urgkaluQhLTXOx6iILWFMSO8Amj5eBXFEXUnQONVkkFQ914UUHzxf1TpELj83yc4BxR5BChoksKY1aZolujTVNikczLADQFBGZqNb7z6jEEAbZ1drAmm+G/HzmO4/v05XO0pxI8eWoEzw+IgFuG+9l39hxEcSJ358Yh1mQzDLe1cnQ61vGyfZ9nzowyWizRn8+xd2AtZ+YXePT4SXpzWVRZ5oGXDuIFIUEY8pbhAVRJ4rETp5gsV9g70MfG9svTwfppClGMKw6FbBLlAqq/F7qM1I9jB0vtBq1aBwvuLIqoYgd1UnKWiu8zace9zEk5TX9yE62ZJNdcsRZZkkgndK7e3EfCiJPjK9Z38Ko7xsToHMeOzYMj0tmSwgl9/vnl55A1gd/J305bLkkubfJLP3MdT710miMj09iOR0vGZG1nvumasL6vQD5tNl3dFpwak3aR4XQnkiCydbCTX3vvDTzx4knmS3V62rN88K276V/Tsux4E7rKNVf0UcivBJ8nZsscPj2J7frsXB9XpV89HbuSre8t0JpJ8O63bGfrUBc/fuUM86U6b9uziduv2dDQdhJY25njN953I0+8eJKRiXlEQWC4t41N/R1IjQXcUE8buio3k60wDDl2Yoq5uSqeF1tMm6aKJIrIiohpqBw7PsXAQIGZmTKiKLJ3zxCarrD3rdvYsWcY24qZGbIsYaZ0tEZSKggCZlLnlnddyZU3bcS1PURZJJUx0c0lvShFU/j533t7LBx+gajnz//e2xtUd6N5P/UNdfDRP3on9aqNIEAqm0CUBLZcPUAmv5QMSkqF4d3P03/FBmqVJK43SjbzVhLp7rh6GrpImsBHfv8uRFFE0oSYFRsBQsRHfu8uBFHASMZzcL6Q4f6P3x4Lb/shAjRtgBV1KWlUVJmb7t7Jrhs24NgesiKRTBvIikTvUAe5tiVqtxeEHJ+bI5Iidr1vB4N3rcf1Yve8ZFuG0VKJku3gZyXu/8O3k9N1im6Fqm9R8euk5QSRGFHP2xyrjBJEAbqs0ndXB+mrrwM3duhJJVqZUT3CWpU5q04URtz8sb2oisSsa7FQsVBECUNWuO3XbiAEJtwag9FPrrL25ogQms+liCiyEQjZkF7DuamXOFw6x3CqkzYtzZHyJH4YNkBUkYSURBYV/DDgZHWStGJQ0DIggCbLyI3KvuN4jI8Xm85Bo+fm+OEPX73oHs3OVnAbLajVqo33BnTRoihuT1uTyiIJIros05/Ok9F0oojmvr2eaykKIh16AUkQifBxgnmSF7SVGsZyMf9FZxpJEtH15UDw4j/DMF40dGZSFFIXL8RdKgRBIyIALl5ga2lJXhIIEhvtCPE+RZcEdRZZNXNzVUolK2ZNOR5BEFIqWRTP06mRBYV2fQhDihlWsiwRBiF27fLNO5pjiSKKJFFzXRLqSo29ywlBEJivWg1x9NUjnTaWscM0ScYNArzGe+JWwobmaRQRBOEyQQBFkSi0p5FlkVrdYeTsHFu39i4DDqMoYny8SLlsxbbcrammZorvB7y67zRT5+bJtiQZ2NRJr7mRRfMTWfzJMXffLDFyJGYk2nUHscMkiEIs38MLAxKKStnxmK5Z5HQDN4rIyilMWcVyA5KqRqcRtwIKgtAk/JRdm7rn0ZFYXSdNliW622Owda5WQ+9OcIXcTyTA6fICc1aNjfn4Oz9n1+gwU5dscVwtap6LISvML9RwHJ9UMm5Vsi0P1wuoVG0SCY1y2aJnTZ6Z2QqKInHqzEwTgLVtj1rNIQgCFFmiUraYnCrR3pZm3/OnGRpsZ3q2QndXDjV9+evRmmdzuDSCIWuICGxO9zXPYU8hy6+86zruv30XURQiSxK51NJaSBAEWjMJ7r9tF2+/bhOeH8StTKpC0lCXCWHLksSu9T1s6C1QtV3CMEQSRRKGSqLhbplJGPzeB96CIknLVlSmrvA799/C7FSFWtkimdB4/207cbyA1szytdm7b97OO/ZspiWzWAgTyCYNbrlukBuvHMD1AlKaTiahNwEriMWy775+M7fsWi7KvhiqKuETohAX7fq7W/nAO67kA95uoijC0GTSCQNZEhmrF+nQ0zx3+Cxff+IV3nfbTjb0FlAViSCIOD0xx+e+s49H9x9jcE3rMuBblmNjgE1rC3S0xMwuWRIRdJkgiJAVkbbWFPW6g+v5MXNNEEgldTw/wDRUDD1+BvlByLMTo3QkUmRUjaMLs6xNZ0mpGpbvs2BbHF2YpT+T43RpgSta27EDn5Jjk9dNZuqxFqcbhkiCwGC2hRPFOdZl8hxbmEVEIKfrnC4tsKm1nW1tK7WF30i8eYEjQURT+pDENGFkY3snSWjbiQioOS8QCzuvRZN70OQ+6u4B6u4B4P0AOP5JwshGFnNoci9vxOFgUZS45s+gSikWtSIupi0iCBqavJZLK8NINPVsopALdSv8YAE/iNsDJDGBKl8aTZelQkML6kLgKH5o1msOlzr04Su60RggDELqNZdCZ4YoAk2X2az1IjU0RHwvwDC1i1YAvSik7rvL7H5XC1ESyeRMxIuInelKP+efP0EQkQWZoGkZLDbFyqPGf7brY3kesLLXVJPXNV9/kT2CxZauKNZGOD/CqIrjn218WsB48VNMiX97yWOM2UnxdQ3CIiEekqCzId1NX6KNlGwAEqKyCc4DyS4WURTx6tQM7akkt68fBGD/6BiaLHPPlo18ft+LSKJAezLBvGWT1XXmaxYV2+Xtm9dzdqHIKxNTrMku9u8vVZyrrhtbt09Ns7tvDcNtraR1PV58RBEVx+GO9UNMVqqcnJvnzg1DbGovcMeGIXpyr23b+dMYCU2ls+GaJkvLFy+KoNJrDsWwYOQx40ygiyb9iY3IoowXeqhi/JBNyDEdVhU0ZEFmqLeNod7YHra7kOGX793T/MyP3nMth0vjfP7UE3z4jhvYno+TAy8MeLU0xhdO/Qg/FVcuBEGgNZvknhu3cM9FjuFtezYt+/lMbYaHJw7y8Q2FuLouSWxa19FkBl0s2nJJfuW+61fdtnfHAHt3DKy6DWC4r8Bwo3q2bWj1eUwURdYUsrz/jl0XHee2q9eveM/gQIGBdW1MTZdpa0uhNCjCixXh1tYUuqbQ1ejrXmRPSLJEpiVJhhiAqizUmnTyKIoozVbwXZ9cR5Z8IY1ddwj8YBklPd4HgZb2lfe/IMS/L9YsRudKKLKIoSoQgRP5SCkVPwiZsy3CMMaNrXKVxWdLRzZEFEFNHEFPJhDFfpAsFvzTTNZjxx4vtFFMnZTcwmRlEkNOIQoyfugSmgFZtQMahgeiKJBIGStc4tzQ559HnmRnfoAN6fjaKKpMvrBSj6PQtTSvhlHItFXh5YlJgoZGQtbUaU3E7MNp2yK0Qqquiy7LDPZ1ktF1wnqIHqq4lockiVihQzGq4tpeDBpIGlZkI2YFREEkJZtUah7YcUFivBqLnadVjYwuUXRsRsslulNpqq6DYwpUPZdE+MbYIG/mkNQr8axvErgvAXbcuqbfRo4k7+7ZQxCFTZvmnBon47qk4IUukiiTVfKoksL6VBf1wGk+wwUB0rqGKIi4btAEEup1l3/5l+cusjcrw3X8ZSLvlx2CQFLRaNENRCF2uyp7DnXfY10qTqwNSljqDwAAIABJREFUVXld4JEd2JyqnSWIHDJSioTSu4JtfKEL4nm7s9Ih8bx/u75PqVy5JJhxqYgit5GLrF4sEoRYlPpSx3o5pyEMQyYmSuzbd4pnnj0ZC6bWnEYLT0AYRPhBuAx0EpCQRQ2l0VrluT71soWsvP4qtR+GqJLElo52ujNvTN9HkUTqjtvQaFs9TFNtzutCoz3OCfxL7m9G1xgpFfHDmIm9+8p+Hnn4ICNn53jw2wfo7Miyfn0HmibjeSEjI7N85YHnqFRs2tpSXL93uFmcKXRlWWjo6PleQEhIPShT9Rcoe3NszuxFly7fYv6nIXrXd3Hu+CRt3XmmrSpl18byfbKaTs33SCoqM1aNkmuzNpWj5nn4YcRYrcz6bCsFI8mcXafs2qQVjYxmMF1fMkWoug7zthU7iOkmVc+NWUyqRlrVmHHrbLlxiL50jrFqmW+cPMTGXIEoiph36hQdm3YzRRCGTNWrOIFPRyKF5XuUHJukqpFUVObsuK0ur8catF8/cZA9XWvJ6zqW5eJ6AZbtMjdfZWGhhq4pFIs1TFPj7Og8qZTGsRNThGGIYShMTJYotKUIo4i5+Sq1mku15tCST3BuPNahaWtNNRnSryeyapIes4Aqycw55WXbFg1HksbFi/VRBK88f4bx0Xm6e/KsWdvKU48dQhBgeGM3J45MUKvaaJrM2sF2Tp+YQpJEeta10VpI8cTjx5AkkZ1X9zN6Zpbx0Xly+SSJlM7pE9NARL4lxebtPZw6OIa0sYv2zizHDpxjYmyB3nWtDG/sYt/TJ6hWLDZv62XdYHvzPNR9l+fnznK2Os+WXDdl0aKQTPLY9DEG03G+fLIyQ15NUPUdErKKHXiIgkheM5m2KvQm80w5JZI1jarnUHQt2vQkB+bPcW3bOuqBy1SlQk+YY8qqMGGVuLtzK48fOIGuyNx307ZlINW6zjwPPnWYYsPtcRFfC6OIfYfPslCpc9vu4SZAOTTY3rwe57MT439Gq26DWMC8YCboTCQ5VVpAFkUEQaDmeVQ9l7Jro0kykiCS0w3GqmXCKGIgm2esWqbNTPL81BgtuommapRcGycImLVqtBgmdc9j2qpRdh1a9Mt3AnyteNMCRyCgSB0oYgHbP43tHwdouKwdRxSM2CkNreGYpmF5xxoC2Sks9wgRYTyG1P4an3WRPRAkEnI7SaUTaOi/lOucVRfIJY0VbRQCYlNj6Y1GEFUbVSkQhSRLekSrhySYF23Di4gYG1tg/cauVYUkIXYSSOvxAmhRXHrxpm4tKCsqWhe2xEGcKCiCSMFIokuvfUslUvpFoRJJXF51EAA38nACd9V8K4oiFmoW9QuEfpeP98arz0FUJ4ysxk8hlnfodb0/irzY2U4UMGUNUz5vghcu35lMl2VmajVKlo0myxiKguP7zNctZEFEEkRUWUaTpQZTI8ILAop1m6rjktQ0/DCk7nk4vo/ToJEv1C12dnfy9JlRiMBqCO3VXY/QjNBlGUNVUESx6XAiSyIl26bdT6DKb+Ip5A3Gyck5jpybJpPQ2drXScZc6n2XRYWMmIt1zyLIKq2Nu0tYfptFoAgGdujhhRGSEKI2HjJO6OMEHrIoYUgqoiCgSjLbc708qC6fP2RRZEu2h4KeXjZ+FEV4YYAVuIiCiCmrSIKIFwb4YUAQhYREmJLaBHOjKKLuOziBhyGpTVFcJ/RxGwtuQ1KQBYmQCCfwgfhzNElBawith1FIPXAJwhBdUlYVYL9UeF7QrPDDUpvfIhAsQGOBFVPhwyhARGzS/iVJJGmo+K7PwNpWVF3FtVyCIEQzVAQBHNFFEAXSaR3fC6hXLBRVAaKmAGbghzzz0Ius//+5e9Mgya7zPPM55+4398zKrLW7unqp7ga60dh3gABJcTUhSpRsaeSQZijJY3tmgv4xPxQznpiYiPEPT4THlsdhz9iSHLJkUdKMFpMiJRHcN4gEQIDYGr1Vd3VV156Ve+bdz/y4Wdld6OpGAwQVID4Eoqsys27e9ZzvvN/7ve+9B5mYrWI6Jsvn1zjz7AU+8ivvw8nZNDbaREGEm3MIvJAojJGawHYsAi8YtfDarrUrIVxY3+bixjaljIOp62Rsk0a3T86xWNlu41omgyAkiGIMTTJRypG1TKZKeQx9jiRpo4hQKsCUFTI6WCqDRKMTbWFKh5I5haPlkUIjUTFh4oEQFIwakpu3SCQqYbG3yZGh4+KtxrrX4qX2Aj99+x30ggCloOw6Nxxhd9iuU06aAM5mJrlaLLmmQABDLcFrtjTE5RQpo9WPIvKWPXKbOVapXve97y2mURqaeTdCq5AEr4Iw0YzbQY7x0vYC+zNVxqw8LzYWKBgZ8oaDJYc2z0i8eMBieIHjuVNs+m064YBppwIine/9KCJJEpJYEQwFfjVNMjaWxbyBCcgbo1zOXhWIfQuhlKLh9TGkpGS5ZI2dsTCdw/pBOHK0vNUwpIEhdLJ6Bo0Mmrx+4X6jNvP0vZtsW9cJZHJTp6+bRZrLadysPf2NltlvNZIk4YUXLvNbv/0NFhY2SBKF65qMjeUYHx8nl7WxbIMkUTz77AKbmx0gdQNr+CvotomlZdBNnfxYjjiK33KrmhSC5mBAczBgrlx628/kWD5DMAS49tqGpsnrXg/jeKjvtXd4UUTb90bHdODAGD/3c/fx27/zTRYubvLP/4+/YH5+knwuBRDOnltna6tDoeDw8z93HydPXtV7am33WFms0+0M6E+XOXpqBlvLpoLYWgHtBtqVP6kRxwlez+fEw/NsXdnmRHnf7pFcpM/WfCHVhUzbx9NP3FGZHM0Fn1s4jSk1To6NkzEsXq6voRQcKpT5zupl1vodLrUbfOzAUb6ydJ75YpVuGPDJQ8d5YWOFvGmxP1dkY9Blc9DnzqqOJiWLnSantzc4WCiz3G3x7ZVFJt0cecvmSrfFuWad1V6HJ2cO8oVLZ9ifK+LqBifHJnipvs5EJs/45H4eeiAthgkhODY/iZSCmZnyyEV3pxXz6JEdCRHFbcdSPbiZ6XJq5KDS8VXKlBm44442M/3mz0MQ19nsfYlIdag47yNjzFM0s3yvfpopZ+wtX7cwiDjz6hV+9pcewjA0nvvueWb2VzBNndMvLw3zFxPbMVm6tEUYxNz35BGe/c45DENDCsnjH7ydKIxZXqzz6Ptv45lvvE693iWXt1PHWD9E0zTGann6vZRRqZTCsg2yOYelxS1Ov7zE5HSZhfPr7DswhjGcXza9LqbUKFkutqazNkjBQl1KOqGXrluikI3BGndV9vF8/TL73BKmJnl2a5HHxg9zobPFlFOg4ffpRwHTbpGVfpMxK0PRcnllfQVbM3i1ucptxUkaQQ9NSopZh2dPL/Hc60sc258WVevtHt944QLbnT63HRhP1zvdAd1BwPJmkz/75sscn53gxNwECTEohRT6aF4J4g2udD57XfdKek9ZzOR+EUu/iknMl8ZoeANOjo2z0u0wkcmy7Q0oSpuDhRKbgx7jmSxSChzdwJQaDX/AwUKZtX6Xk2MTLLYb5EyTmptlEIUULZuS7eBFES9vrTPuZlntdZjJvTPF/nf1yGbIKoZWw4vO44eXUCrBjxaJkjqazOAYRxFC4BhHkcIlirfwo2Uc4yhetAAkGNo4uvb2RL3ixKcdLlOxdlPjbjyNirfVErdr2+ratiydNwM9hDAQN1gkOLbBxETxpgOVUopekIIJOcsc2kImJAqsa/pwd1L6vcxtEqXQpUbVuYGOyzUnzPfCdBC9Aer+xvPXjzxMaWLKvcGxaKiPsKPDs/f23v5iIr0eQxtoTFzz5HXg1s3CMW7bU1/prYQQguMTVVbabT7/6uucmprg8FiFK80WXz13gXv3T1NybGxDJ2Oa2LqOa6YWjc8vXyFv2zw8N8tWr8/r65t0fZ/T65vMFPI4hsHZzTqHx8roUvLs0jJBHPPilVVKrs1suYij65RcB3N4P5ycHOf55RU0KTk+Xv2Rju3dGFPlPP0gxNAkOcfaW29iCBTdaBHSjTw+d+UHXOqmdNFHavM8XD1CM+jz+eUfcGXQwNVMPj59J/P5ydH2bvQ9b4x+HPAXV17gfHsdXUqeGL+N+ysHealxmW9tnEEKSSPo8Wh1nicnjgOw6jX53YVv0Y185vMTPDVzN4bQ+craq7zWuoIfh5wq7edDkyfZ9Dt89tIzOJpBKxxQs/P8/P4HyBsOrzSXeXr1Fbw4ZDYzxif33UPWuHUR0MXlOt2+T7HgUhkz2YouUzAqJMSEScqAUyrBkjYxMV7cRxMaeaNCRi+goXHuhYtceu0Kpm3wwEfv5Cuf/S5OxuLuD5ygs93jha+/yuRcjTufuI0ffOWVVJeh4CKk4MqF9bTtYKZCY7PNc0+/jOWYvO9TDzA1V+Py6SspWNEPeOFrr1Ks5qntq/DlP/gOURiRxAlP/PyDPPOFF+i1+mi65GOffj+aLofJouDIeIW5aikF7YaLdKoldF1jppBHl5IoTjBNHcPQMI2h5gY+Sg1I1AAhLBzrMXRtAuuae7CUTCGFNgQM92A9XXPDLPY2cTWLjG7xevsK8/kpwiRiw2uhgEbQ5bntC0gE87lJMrqNl4QsdNdph31czeJIbhJXt1jsbfL19Vc43V4mZzgUzQxHc9NIkQJKS/2UKTthl5hxy4Bg029zsbuOKXUOZscpmKk2Rjvsc66zigIOZscpmVlW+mmr4rSbztcbfotu5DGbqaJEwmVvHa8XMpepUbML6FKy2NvAkgZ1v0Mv9jkwfO+9FCppkoSvkSRrgCKJF9niQZ7bXmGxt0nJzLLc3+J94ycASdF0EYghCJsyuMQQnI6DZLSYK7kOjx48QMY06ATeqHUxl7P5B7/+JEePThArnyTxUvdZEpRKRvqDAoFSMYZuky95BHETSIaGFlGq54MgUeEQCN5d8ZRCUDDt0YLSi1IKfqAbaELS7A+IE8XBsVsvsAigaBYomXnqgx8gQh1DvjOuVpoUHK5VbuiqewtbQKleqot3o/gRcc+VlSa/8x+/yZkzqxiGxmOPzfOxj55iakeQ1dDQpKTV6rO8tD0CjhSK7WCZgjmJpWVI4oRSNY+TfXvizlnLfNvMLEhbONp9n9XtNpWsu6fW3RtfSZRiKptHE9cDSjvR9n0c3Ri1nui65AMfuB3Pj/i93/sOjUafZ59dGLUxFwsuJ0/u4yMfPsnjjx/FvoaV0G0PKNdyaLok9EOEkCQqxtYz6EmPt+OC+W6OfnvAwitLNDbb1EZAyl5Jy7WviV3/ANxTm+LV+gb1wQB7TOdgoczldnP0p2Ec89jUAWpOhqqT5bGpWb5w6QyakBwqlNn2BmhScqRY4Uixwr21aRzdYC5fYrHdAGCj32NftsAH9h0iTBIuDl9f6qTz3kw2z0MT+/ibtSUm3CwHckUenZolY+wNVF/b+bbz814Y714dcrtfe/Pugq3+V7nQ/BfEiU8/XORI+X9i029xqniIsvXWxzKpSSxL59xrK5QqKVNo5XIdTddGMiRCCgxDw+sHtFt9Li9sYlo6mibJ5CxMS0cOnVQvnlsjDGMy2dTkYme/e12P1SsNTFPnyPEpKtUcva7PS89f4oHH5imUMhTLGQ4eGUe7Zm1ZsTJc7G6hCYmfRAzikE7oYUmdQRQSJBGOZhDEERfam0y7RTa9LlU7yx2laS51t5l08jSDAUESUbJcSpZLONxW3e+xL1OmF/nMZiusD9qUzAyWqfOh+49xfnmLf/Mn3wIEckicMA2Nn370BE/cfRghBN98cYE//uoLtPs+E+Ucv/Shu8llTLb8SyQqYcK5ihEEcZ2l9u8Rq+5110ITGWruh3cBR5OZHJOZtOVtctiueS3As2/48857QghmVdq6OZ3N049Cqm6G6WyaV05nr94jiVIMopC6179hK+jbiXc1cCRlyirq+H9DGK8N2UbnieImjnkM00jbOWxjHl2WCOM1/GgBQ6sRxqsItCGodGMa381CobC13e1PfhhhaBrvoGP1rpDCGrVWJSoYtrPdZB9VNAKa3hhxooZOXjfe2TBOWGzUuVhvcLQ2Rr03wNAkrYGHEIIwTjA0SdYycQyD2yauF3bcQeL3mibT/bsK6pimjpazsaxbE1U2pEYzaBM6e7PGDE1ScJ0bT2I/YkhhjjSNpHSZLv4Gze2DqTibH4IATcqRDbiUgiCIqY3lcByTTtfn/GYXXevT7fm4jplWLKSgVHCpjt3aw5yzLD55cnf70UeO7xb9nLsGH/WjiO8tLvHR4/PXtKjBpx+4hyRRxFGMYer80j2nAEiGLhCPH5rj8UNzo8//1Pxh4jjBURKnatHvetwxPs49+945QcJ3WyilWN5qEUYxtUKWjP3WdRq+u3mOhc4mf3/uYbKGjSE0JIKnV1+hG3n82uEn+P7WBf586Qf890d/Cle/9cq6Uorvb13gfGedXz38BFtDkGcuW2UQB5zvrPMbJz5BOxzwnxa+zYlSWiXthj4/NXmSrG7xf5/7KneXD3A4O85d5QPcXznI2qDF7y58i/srhwiTmFeay3z60Ps4nKvxH85/ndOtFebzE/zJ5Wf5wOTtHMzW+P2L3+G57Ys8MX78lvc/n3c4e2mD1Y02xbqGMbVJN2qRM4poQsOPPfpxh4IxlgJKKqEd1vHiPnOZPFEU0a53MW2Dsz+4yKNP3Ut1pkyvPUip20WX/FCTqdfq8+I3XmP22DT11QZ21mbiQJUoiBh0U/2OBz92Jy9963W2VrYpVq9Ouk7WZvbYFPW1JipR+IOAhz9xN89+6SVaWx1aWx2KYzlm5ifxg4iXnrlEoegSRQmmqSE1SWO7l1akhhTkXN5m8eIW+YJDsZRhfbXJ0dunmB7aSUdxkzjpkLGfRAgDTVava7PWrwHRb8aeAPhe/SyWNDiUneD/fP1zfObo36ERdFkZbBOrmK+uv8Tx/D5WBtv8sHGRXzjwGHW/w7c3XiOj25ztrHBHcZanZu6nG3mse016kcd20EUTkoSEtUGb37rwZfZlxkApNu02U06Jhe4af3z5O4zbRbw44Gsbr/Dpgx8gTCJ+9+LXcbX0ufrq+kv86sEPcrazwsvNRf6Hox9HIvjzpe8x7hSpWQV+9+LXCJOIrOHw9NoP+fsHHmcuO87nlp+lEfaYy9SQQpLRrfcccBR5X0QlTTTjDnYWHi5lJp0BJTNL0chwKDvBhF3iG+unmXSKlM0sAoEhDcI4ZeMqBZZmju4ZXUryQ+0Kw9BGejFJotB1ydRUiSDephNcQqkYKU2UigGBF60ghYWjT6Po0goXsVQNUJiyQhBvEyTb6bZlHoGO4NSu41JAmCR0Bz3254r4cUQ/CsgaKWtwulQgiOIRE/pWQ470ERMS5fFOLeC7foDs9pitFLHeFtNWACaCH4/TVhTFfOvbZzl7dhUpBY89Os9nPvPhkYPctdHr+cTXADuxijClg0QiEFiOSbaYQTO0tzz3SSmYKRTImNcbB9xqJEohJByolXbpsNwshBBUXZeSfeP2sLliibxl7XLqC4KIixc3iOOEhx48zMc+fip1jTU0CnmHWi2/Zwvh9OwYpmUgpSCbT58dP+nT6K/hJX2KxntL/zFbdLn7ydsZ358WXN5OKKVY7XXw4yhtKfIGPL9+hbrX51i5OtRLSljutqk6GapOBl1qlGyHhj/g+Y0rDKKQ2yo1ypZDyUpz/yCOeHZ9mYXWNi9vrbE/V+SrSxf47NmXeGRqlm4QIIVgNl/E1nQqtoupaRSsFLiuuhm+cPF1Pjw7T8G69edTqYQwaaBUhKFVhsX+vY97J27+TCj8eJMo6Q5/3kCpAEczudRbYxD7jFtvzdNR1yVPfuQONlabSCmYPz6F65okCiaminiDdH6QUrC+2qTf8ylVshw9MY1lGVTH86l+pKPx0PuOsrnW4r6HjxDHcaoBFUT0ux6d9oDq0IXy3GtXSFT63TOzZaQUHD8xQ2O7mzKS1FV2cdaweN/4kdG5OVmaRinFbLayK7u50NmkZGbQpWTD6zCXrdxQGkUIwYRzvbB/kij2ifT8bW92KBgmv/ZT97HR6tHzAjRdIhLFeDnPVDUPQUIYRtx/234mKjkUMDteolrMgkgQSLJ6aVceZukTHCx9hiCuEyZNorjFtvcdomTvZ2anha3d9QijmKW1BhNjeUp5l2anj2ObqTRGnODaJn0vwLYMoihO13JJQtQKiayYXuiP1sJBmOopTZo5Zt0ijv3OGRm9q4EjSF22hNCJkgZhvIofXUTh4xjHR6LGuixgG4fwowX88CKWfoAobiKEiW0e+5G+P76GbmYZOg8fPzBcSP54rMg1mR8BFXHSJeHm4oSJ6qPU9e1jSilUoshkrBs6mEHadlTNZjA1jbxtE8YJlqGTsyw0mfZomrpGEMU3nMB3RCy3Br3r3ksFwK8eQ5wkXDi9SnW8QNZ4855LXRhMO+PYcm/wTyAwdY2eH7xlIc1bCSlc5JDuniR9YtVlMEhY2+xi6BrZjM3qZmfIGFBUx3KsrLXIZFzCKKTRHNDrpewK2zbIZCw8P6TZ7KeJzpsAR0mSgEiHpZ0xUAwZDAo1cpNC7byengNNSE5NTeBvDziz0GB8f4WN5W3cnI2mSXrtAROzY1x87Qq5UmZINRYYpkZjs0N1qkR7u4th6uQrWS68tMTJh4+w8MoShUoOJ2th2iYoNVqkv1fC1DWOTVe5tNG4qchirGLOdi5yobtI2SxyV/F2HD1NOi501zlRnGE2s9sh52x7lSCJeHr1Fep+h5VBg37kvzXgCLjcqzOXqTLhFKhYWXQhWRukVbv9mQrTTolJp4guNDa9tKo8m6lwKFtDlxquZtIJBygUvdDjfGedVa9JK+wTDcHqCbvAscIkY2aOMStHK+zRCQdc6m3xSnOZxe4Wdb/L5V796r4pxdJghdPtCxhC587SbZSMwmhyVEoRBBH3npwd3b+5/By6JkZjano/K4RIlzE5o0zRrBIlAVJoDDyPy2dWKVZzuDknTQ4UdLa7bCzVKU8UEFJw+cwK83fPMXdiH6ZtMDY1SXfIENKkJI5ihBS89O0z9DsDskWXcy9e4tLrK+x7ZYl9Ryc58/xFOo0uB0/sw8la6IaGaRnEUTJ8NlM3Hl2TTM2UEVIQRzGWbeBmrLStZfisuhmLMIiZnC7iDKnhhlmhWLrK1NxpTe77zyBFhoz9BELc+PlSStGN+rzQfIVO2ONo7iCHsrOje27KKfN6exlbMzicm+DKoE4v8phyyiz2Nnlw7CgfmbybC901/uDSN/HigAm7yC8eeBwBvNi4yNc3XuVDccDx/DSXe5toUuPvTN87bCkU+ElIL/LY51S4szxHVrfRhOQ7m68zZuX55bkn6EU+//bcX3KmfYVW2KcXefzXB59EKcX/dfaLnO2scLI4yzc2XmXda+JoFkv9LT4ydReXehtc7m/xmfmPk9Et/v2Fp3lu+wIHMjWCJKJmFfi5fQ+hy5SF9V4LpXw08z408152ku0iGh+aqKXW48NjTl1g7ZEeYKIS/MTHjz22g222/Da2tvc4Y9sG+/ZVUuFbL+TipS0efXQekOgyiy4zSGGDSkgIMGQOKWwMrQgqRhMOuswTqz6GLCCFianKSGGSKB8p7OskoQXg6AaS9D7SpMSS+sjO/fhEFfUWMZ9YJaz7W1StPHEyQMm3z3p5YxQdm6MTtR/BSVTDsB6E4RG/0zEYhFxc2CSOFbmczcOPHCGb3TtvCoKIdnsw+t0fLlQT4lTNMYpT/bG3oV2VJIqtXo9EudTeZmoQxTGdwW5w62ax46r2en2LQ6UyRXvvxf+FxjYrnQ4Vx0U3NZJE8Z3vnuPLX36NWi3Pr/zKoxw5Mn5LeWS34/HKcxepThZTB2KlqFr7acp1MkmE/iYyEz9p0e94vPC1VxmfHUPTNarTezMBwyAatoOnD69K0vkvCmIQcMqucpQCtmmgehE/s/84hqUTK8UL8Qoly6YbBriGyYdnj2BIjY/MzqMLyS8fv5t4uLYBwfum54YttzG3l8a5uzqNretYms4H9h0GpXB1k4/MzhMnCZaukzFMak4GKQT3j88QxjEPju/H0Q0Egm6Qasu4ukHBurnmWJg0udD4F4RJi/ny/4yuanjDVq3ACzEdI2V8SmjXu7h5J7Vs5+oaTdM17MwOs11QsO4kaxwlUl0qzuNoMkui6kghr9F63R1BFI9cRtNnJtVMTLcIVs5kfz4tvkcoqvtL9IMQN2uRzdkjdqpuaGia4MhtkyMwxHaNkTRDuZKlXMnSbQ94/nsLqDg1Yuh1fba3uuyfq7J0aYtBL3X0KpQyeF7IS89fGjpqawgproPy33iOhbgKxSilCFXEhJNjZyE04WRphd3UQXOYXxlSpx97uJqNrZkpCP6G7UZRxIWzaziuyepyg+p42loXNgdMVLKMTxY5/eoyTb/N9nITyza4/dQ+xss5xsu712qJgna4SZD0yRpldixdDVliOveLpEYWCYnyeXH903SCV258H0UJz/zwIpPVPEtrTerNHtVyjr7nU85nuLLRHM2FnZ5HIecQRTFZ1yKftTl7aZNEwcZ2hyiK6XshtqWjFPQGPhnH4uE753YxJn+UeNcDR7YxjxQWUdLAj5bwwgVA4Ji3I8XQtUa4OMZttAZP44XnsY1DxEkz1UHSD7/t7zakQ6JCkmGrkq5JZsZ+vNVMTRYwtBpBvESi+gTD1rsbRRhv7EmJA1hba2HbBvoNxCBh6BSSzzGZT6ly08WrKO21Yl47oQh2tZMpQlBgynRAfoNKBVHSIEqao991PZ2sb1UnICFhK2gwbt+g3VCkNvFjuVuzO3+rIYWLpR+g5z+PImAQvM7M9ENMT5WI43Qg3D9TYkelQ0rBzFQJTaYOLaWiSxBEWGY6+GpSkijFeDX/pmyBgR9yaXmbQi7VEAnCdOLNuhbdvk+z3WdirEAUxwRhTJIkhFHMVLVAMe9yR3WOQjwdAAAgAElEQVScl757lkI5y2vfv4DXD7j/p07Q6wxobLbRTZ3LZ1eZPTZFtuDQ3urS73rkii6nn1sgX86ytdrg5EPz6X0gBHKodbR0bg3fCzl4+wzvTCPAuycUkHctnjhxEPsmVdOl/iq/s/BHXB6sYEuLXz3493i8+gCQ2mL6cUiC4lr/CUc3yQuHA5kxDmTGeF/tOLm30OYFw7q1puMNmQSxSohVgjHUNvGTCAVESdpeYkqNMImwNOMNEzIs9bf5/Yvf4ZHaPPO5CX5QvzT6HlPT0ZAj+Sal0sVpVreZdspUrAxz2Rr7MlcTyHbU5T8v/jkvN19HCslHvSf51MxHsTWLRCkuXq6zvtVOhde7Ph998vYbH+QwNMDRMqClz7iTsfngf/UIURhx34fuwHRMjtx1gAOn9lGo5en0PSZOzXDPB05QrOZ54ucepNPoYrkW0tDY6PcYc1yEggv1OrEX8sB4jWI1RycKOZQTVOeqLA46zD95lJJlU6wVePgT95DJOTz08bvYWmlguymQtHj6CkfumuPQ0Ynheb06flZGbmRiBPgOz/7uwxVp248UNlnno0BMEJ7dxdbcKxISvrH5N/zR0ucJk5DD2Tn+yfyvMmalTNkZp8Kz9fMsdNe5r3yExf4GQRxzZ3EOQ2pMOWVszcDVzKGelOJCd41vbLxKkERs+W1ilbYfSSHTJBh2Vfpm3Ap/d/YRvrv5Os9un+eR6nEeHjtKM+wx41awNAMFlMws9aBLI+hyobvGvz//JZSCdthHAWUzy7Rb4eXmIq5mUbLS35e2zrLU2+R3Fr6CFIK63+VgZpxkmCxOOiUc/e2xin8SQsgyQe930ILnQaRirob9IZqhy3P183SjAVJIPjhxCkPq2NpVh0AATei4mktG93A0c0/2jqZJjs5PUMg7NJp9Xnxhkac+cRfFYhFTu1rhVm+8b4ctcaY+tku6yuL6Fmaf3YWlnTEqGtp6F02bU9VJTKkNix9vHVwZs0rcVz6FKTUCeWIIYr0zaW7Jdajl336RJL0e71zV940Rxwn9Qbpo3bGiv9Hcdflyna2tzuh3S2aQegltqJcZxwlu3h4xMHecg3beu1n+JgTkLZuy+/bFWHVdY6ZS4K0AbAJByXawb8IGy5op4zseDsRhGPHDH14mCCKmporUavlbLj52Wn10Q2PjSoNs3iEhZt27RC9q0g7rlMwa2rt/iXXLYdoGcyf3oRJF+SYSGItnVrnw6jLjM+UURFxtEkcx1akShqmxdG4d0zGZOVjj8vk1ZucnmTs+hetYfHDfYbphgCk1xpyUXQKpYx5ARpqsdjosNLZpDDwUioxh4hoGTd/D1nS6YYClpeDMZDbH65tbBHEKiN4/PYOjJ7y+vUXb91jrdak4Lg1vgKXp1NwMgygabffBqRl0cWPdMT/eoD74JrrIkqgAfxDww2+8BijcvMvWUh3TMahMl1l8dRkhBeXxIpWpEqsXNwgGAcVanpOPHccayiIU7fs4Wfs3KCIsbRwpjBQQUjHdaLDnfry2usF6u0PRcVJh8E6XKEkouQ45y+JKs81Y1kUAW71+aqTg2uTdabbDrdQgIQkwLZPSIZOVwQqWZo1ymX7cp2gUSUjI63ncjM2d986lZgIqXc9pmsQwdaZnK0P9sdTIJApTJ7e0yKYwTP2mZIY3hgIudJbpRD28JKAfeTiaRdUqsdRfJ2e4SCEZxD4Vs4CfBBzMTFM2r1+rm4bO0dum8AYBk1NFDFPf1YGh6ZJc4QialhYWpSZx3JsBwIowScfcOI6J4gTbMkbkDwQIddXQafRXwyLqjvaVUgpT1zg0M0a76zFdK3B5tYGua2w1e+iaRq2c5ZkfXqJccMm5FpomWdloUavkKBVc4mEBszdI3dyOzY3zrR9cIJ+xyb0JgeStxrt+VDO0cUxthkF4hiC6jB9eQBN5HOPISDtGCCtlJmHhRefwoqPEqkvGuAtDe+tiYjshkAi06y76jzOkcHGtU/SC51HKo+N9m7z9xEhX4NpQKqIfvEocN657TwjB+HiBK1e2iaNkZH0pkKNtpZoF0ejz1/7tXj+n3ynR5M4DqfCjJaQQ1NwMWdPcNc0rFTMIXiOKN0evxVFCJmff+k2sFN2oR3gDtxyBYLKYY7L4zvVv7tq+kOTtx2j2v0iierQGT1PKPIWl7xt95mYUQNsy9kR5rVvQSeh0PdbrHQZ+SBBGOLbJwAuZmy5zZaNJvdmn3uxTLrr0BgH9QYCmScYrabKn6ano3dZak0I5i5uPcbI2K5c2qa81yRRc6mst9h2eoLHRZnu9TaGSpTpT5srCJquXNjFtg8ZGi82VBltXtllf3qamFJZrsXGlwdjkW6PN/iREFCf8YGGFUsbh1NwkxczeCfDKYI01f3MoFj3gXOfSCDh6oHKIP7j0DBndIm84jNk5juYneaQ6zzfWTxMm6WSaAj4anXDAptehEfZZ6tepOXnG7QJeHLLhtWkEPa70G1zpbzNuF7m7dIDPXnqGp1dfYcvvkDdSMKrud1jobPDXqy/RCvpkdJtpt8zZ9uqex+DHIV4cYmsG616bfnxjq2iAMSvLnaX9bAcdanaOdjjAvqZ1qh12udhdIlQRKDjfvcQg9rA1K03sCy7VSrr42qrvDXjfKEYgtoCxqd0txOOzY7yysc6zS5cYz2S4Irt4iUGlrVhobDPmZmgNGpihRi8IGRgKQ2o8393kjto4PRfanTZWxUUmWTKVDMm2jxizGRgGuozx9ZiCqWPYJmMzFU48PE8URFSmStjXuK69cfxM91sNW2523rt+/EuSJmG8wsD/HprIEMSXMI15RirRe0SiEs52FvCG1211sM6mXx8BR0UzgxCCtUGDn56+n5ebiwRJxLhdRAz/23WOgT9f/h6zmSrvH7+D57cv8MzWmdH7xhAQ7UUemtAwZep4eSg7wXxuime2zvBXqz/gjuIsk06JK/06raA/anN7aOwoGd3iUHaCX5h9FEuahEnEmJ1qkzxYOcJ/Wf4+htR5X+12JIIxK8eBbI1P7XuIguESJBEFMzNsqrkqwP1eDc24DSEclPIRMjt0sbB5uXmJfuxRMrN0I49B5KftjsOqtFKKRCV4yYBu1CFIQtwbMI4Ajh2b4s47Z/na10/zyqtX+MIXX+RTP3sflnVV/H7nflHDxUKv5+E4Boahv2USjS7EqEXNlBpSCIrW2wcbhBAYwqAwHI8c7Qag9Hs0dF2SH7ZMBUHE+nrrOha2Uop6vcuf/5fn8byrhiK6MOklfWI1LEZEMa2tLk7GHm27NGRG+n7E4uIW+/aV9wQPFLDV79EPA0rTb+96Hhqv0OwPyDm3ztrRpLwh02gnCpaNsYs5LzCHbWvnzq3z13/9MnffcwDbGmpjCpAiBbKyWRvHMUe569T+CpfOrNFu9jn14CEkGkWzRtmcoBs10G9gWvOTGoap01hv0W31adW7TB2s7Xn9i9Uct993EHOYF88cqrG2WGfmUI0wiCiO5VOWjWtSquXJlTLDopSgbLuUb9JqCFCwbQ4US0zn06LKDsOoGwRpUUNK/DhK2YxCULDT3ONsfWukxzpXKNHyPQ6XKiOwSQpBxjDx4ghDSmxNv25+fGN0/FcI4wa6nh2do4Mn96f5gBBMHRxHN3Usx6Q4lsd209cR0GsPCL2Qgydn0a4BO6UwcIx9u75HlxqNoEvW3fvcTBVzlFybnG0xCCL2lYustNrsLxfImCZz1dKIiTQ3lC+RAmJCVr1VikaRTX8TXeqUzTJhEuInPp2og6u5tMIWW/4WlmbhZB0c3aFY3rtYb72DLVGQTisTToVSkgcUXhzgxQFjVpGymcfRbYKhy1qsIraDNsaNDFsEGKZGEuupSdMen7lVGZVYReT0MYTQEAgGfsirZ1a5bX6SRFOstbuM57IUnL2Bx+ZGm7XFTWaPTWPnbE4cmcKydI7NjePYJrZlMPBSF1rXTteRH37kOH0vIJ+xCcKIYs6hVs6lbOOsjW0aTFYLWEbaDfOhh48x8EIyrnnLLb+3Eu964EgTOSx9lkH4GoPwDGG8ga6NYWpX3Q1S4cd96FqJIFrBC88CCkuf+5FcziytwP7so+hvUyPp7YQUNlnrAerdz5KoAW3vm1SiBWz98HUJQBiv0xo8nbJ+9gjdkOlEt+uG0dBkHhAo5RPEV0hdPm7tYRFILOPAzl7Q9b9PNfvLZHSXrHH1PO3sX6P/FyNxaYB+z6fT6hNFN6+kX/OFSATbQYvSHgjy30ZkrHtxjOP0gucYhKepd/+Q8fw/QorM3onTaKHoI8TNqa43i7FShofuLIwWRkKkvf+GrlHKu6kOACmqv1MJVgrMocONbmgcu2eOKIgwrNQhT0rBgaNTTB+sceHlZebvmiXwQ048eJgkVmi6RDd0Hn/qbuIkQUqJ1CT75ycxbYPSeAGVKBZeXebIHftH1rTvpcg6FrVCBi+I0IZtmHtdw5yexZIWXuwjEFTtq8yb44VpfmbfvbzWusKm36ZsZRHA3aVZJILXWlfQpeTO0iwgWO43+N7WeWbcMpd72yjgg+O3s+Y1+fbmWWp2ni2vw99sXuAj03dwKFfjqX1388L2Io5m8AsHHhoxDQ5kx+iEHv044Of334+rmVTMHHeVZlPhUAT3VuaoWjmqdp4nxo9zrr3GPrfCp/bfT0a3sJTO/ZVD2JqBAE4UZxizcjiaySf33cszW+d4tbVM0ciM3NkAbGmSMzI0whYAFbM0ErYXIv1/s94lSRKa7QEH1VVg/2bPiR8HPNd4iX404N7yHXuOBY5hMJ7JUBxa7G70egRxTDcIWO60OTU+wfl6HS+O2fYGvP/AHAdLZcquy2qnSxBHVDOZ0fOWWqNCy/N5bXOTOydSRtHalQaNzQ6ZnM2h22aw7Buz0sIk4uXWada9OneVbmfcGrvhZ6UsYggDgY6uT2GE55Di5kxKgaBsXgVvXd0hb1yd8yypYwqdrGGTM5zUfS+RGJo+Os4dQG4HiDmQqfF6+wrd0KMdDcgZ6fkEmMvU+Mb6q/zWhS9zJDvJk+MnWext8FerL6BLjV7kczQ3jaOZPFo9xh8vfpf/cOFLREnCpFPiSG6SWCWc76zyZ0vfG7r1GXxi+j4ymsVspkYv9onDAYdyE6PvPFHYz+evPIurW2hC8sGJOyga7pBBt5sZ+15zVhOyikq+TxJvYWX/IUl0EQBXt3F1i5X+Nr3IByE4Wdg3fM7EcL5IMKVJ0SzRDhN6kX9DxR/L0nnqqbt45dVlNjc7/MmfPIc3CHngwUPsmyljDXUVWq0B6+stLl7a4tLFTX7mZ+/h4NxuPZcoSuh2PTwvxPNSHYzNzTbdXtq27vkhr7++ysREEcvWWTMjLMsgl7Mx3qCrE8cJ7fYA34/whg4+29s9+v0ULPUGAadfX2F7u4dl69iWgWnq5HL2LTvDvZ3wvJBezx/tk+9HXFzYJIpS4G5zs81rp1fIDzUdLUvHcUyyWevHMm86jsnBuRq6Lun3A772tdOcPDHDxEQ6Vsax4uLFTT73+Rd46aXUTWlnXwEcrYA5bMtP5/2rz5Vp6hw8WENKwWAQ8MUv/pDZ2TGmpq4yT3w/JIoSdFtnEEYMwnA0d974GqYyC4NBej9cew1dU+cG8p17hq3rREkyYqfsFX4c0w8DktFxaTzwwCG++a0zNBo9fvc/fZs//uPv7TJv0XWNYtFlfn6Chx86zN13H8AwNHptj8CPyGRt6httZg6X6UdtFKkmX9F8e27O7+YYmyphOebImXSvqIxfOzenuXCplkUfCSLvNFDdWuzk0zuURkeXOLnsddso2mmufZXUe5X5myhFNZPBkCljdsxxGXOugozjuNft15vNI0op2v6LuzVcbZPJg+PX7PfVyBR2gz7FsTxJojCsN3el7UYDJp0yRWPvfKCazVzd50z63ROF7JCxpcjtWt9dPU6F4lTxFErBhDmNpe8uEiQk6b8qNVUQQqALnVhFSN66/hlce16uXtNr92uvtrWikRt9JDVfuKZgpNI8S9M1ekkLXXgIklH73a7vThSbV5okSpHZQ/vtxvt2/f7FKmAQtymak6Ntr2+1OXygirQk2/0BE/m9CQ1RGLOxvE17u5eytaSkOpT8GCul/2b2AM1zGfu6+0oIweywkFoaFg529jHjmLsY8O9UvPuBI5nFMg7DQND2vkWiepj6KUx9ctfnLGM/hlZlEJym430XkNjG4T0T70QFxEkbpQJ2LI+jpE0cb+98giBewQ/PI4RBIlLqmRQmmizsyf55p0IISc56kIx1Lx3v23jhBVaa/5yJ/D/CMo4ghY1SHn60zEb7/6Hrf4+0meP6gdzzQhYvbVEoOEwObywhBK55csigGVDv/r/Y+kEy1l0IYZO6ovhDXYK97GwlGfNOdFkhSup0vWfY6PwWJfcpTG0ChCRJBvjRAhud36brP0N6m6XgUSZrEfjRrbeqKUWsElzdHtLi//bD1Cap5T7NcnOJMF5no/MfiZI6lczfxdAmU+0HVHpfqTZBtETXfxalQqaKv3GVtjgMpWLipEWiAhRBKiCuAsJ4YzTZhfEWfnwOTWZguJgUwsSUOaQw0Kw3TzyFEJiWMXI+GB2PbWDaBkfvmqXb6uPmnLQSck1ob2j92KkiGKZOHCfMHp3Ezb396vC7ObY7fbwwBY0a3QFZ29wTPDqSm+Nnpj/ED5unmXEneXTsvtF7htR4YOwQD4wdGr22sd7i8uU6edfhWHOKEydnRhXi44UpjheuWqN7/YCLL69gOSYfzZ8gMRLiKKHf8WmvdFGJYtYpUZNumpBtC9TwchQMl5/ff3/KPtiZoFYa3LVvhrULm2i6xsHtAmHDo+7EnJKTnNKmCDsR2YJDTlpousYnZu4kjuugdB6t5BAytZKt2jk+MX1X+mWCXZNz2SryqZmP8d2t53B1h49Pvh9Huyq6u7XdJYxStlUQRMQqphE2EAgqVuWGFb41b4PfX/wzelGf2cz0dcCREGmbwsVGE0sPmcxlUSjq/T6JUoy5LrVMhubAwxjaofTD1I2i4rgsbDcYRCGuYbLZ77Ha7bDS6eAaOhPZHOe2QyayaRLguBYLWyt02wMGPZ/b7j6Aae09lbbCNn94+fNc7C3xP5r/gHHrxgxYITSE0kFoaLKAlFn8JEInJFIxmtCQpCCxHI6FCYrHqvfTDrt0oi6Pjt1HzboWjFM8Pn4IpWAQd3m8dghN6ARJn6dm7sbVDFa8VRxd52f33YWXdHmkeoi7y4dQSlGxckQqJjMcD2YzVf7xkY/QCHvkdBtT6sxma/z0zP00gx4lM0vNLmBpBjW9wN+rPsJ20sU2DcbtIq4wUQJ+fuphljtbxFJRMF2KmksYxsiB4NOT78e0DSpmer51KfnkzP2sDrbxkhBXsyibDrGK+OTMvVjSIFYRoQowpY1UN3ZV+kmMyPsyIFHJBhATR2eQqsM9pbtRKMpmDhSM20VMeU3lGo2aPUE/6qGhESVJ2hao1J7rNiEEt98+za//2hP8/n/+Lpcv1/nDP/ob/uqvXyKfd9F1SRIrPD+k2/XodDwqlSwf+9ip67a1sLDBv/13X2F7u0cUxcRxQhjGdDopcFSvd/nNf/0lDENH1yWaJtF1jU//N4/xyCPzu9rpLl+u87//s88RhjFxlLYDXLut9Y02/+pf/fVoW7ou0TWNT3/68aFO048n/vIvX+Jzn/8BUZSkxxglDLwQz0vBkGeeOc8Pf7iEoUs0XUPXJZOTRT7zmQ8xuYfJyI8amiZ56KHDfP3rp3nt9ArPPXeRf/q//AmHD49jGhqbWx0WF+s0Gj0eeOAQmYzF00+nuhuh8mkGV3D14shVbXN5G93QmZxLbarvvXeOw4fGOXtujWefu8jyP/3/qNXyGIbGYBAwGIScODHNP/zHH+BAqUjHv6q9uXi5zj8bXsO97of19Rb/8l/91e5rqA+v4SNvfg13mJ2dwCeIY2ru3gtsTQoMqVHv9yjaNkEQ47omMzNlTp9eGQGdb4yNjTZnz67xzW+e4Zd/+RGe+sRdNOtdKrUcY5NFXn3uItOHS4S5AZrUCRP/ugXeeyGiMKa+2qA6U7nJGJvgxxu0/Zdp+c8xCJcJkxag0GUOR58hax4nax4lYxxBk3uzxJRShMk2Le95mv7z9ILzRKqLJlwy5hHK9sPkrTswtR2piDRGPwlBEG8zCBcRQsM1DiGFzSBapOF9j4b3ffxoDYHA1KrkrZOUnEfIGIf3bG/dcYuMkhZh3MKP12n5LwCKWHl0glcJ4vp1fyeFRdY4gnyDTqs+BLX74UWC0dpzd5haFUffh1KKK/06YRJzBwev2Z/0eBPl0wvPkygfx9iPKStAh83+MzS879MPF0hUgCZzZI15Ku5j5MzbU/06DC7Xm2x3+5yanUSTkkF4GT/eAhSuMYc5zP16UZPFwRlMYTObuSO92iqgF1wgVn2ksMgYh29yTSP64SIt/3ma3rN40SqKCFMbI2eepOw8SNY8hhTm6EqmbV07RC1BP7pIOOyyyZrztNYjLvxwkeP3H0bmNbaDdcrWGJAjTgb0wgskykcTLq5+mO2NFu1mn+nZsV3zYHoc52l6z9IJXsOPV4lVgCYsTG0MR99P3jpFxjiMpdfQhYWlZejHbYpMIoQg66YtYR3fZ7nZouQ6ZMzrxTxUosgVXXRDI1O4fh3lRSt4UeqiautTWNr4SAN0T5fn4fjXDy8MnzVBxjiILgs/llzoXQ8cCaFjG4eQIkMYr5ACF3cg3kAD1WUBSz9EP3iZMF5FE3ls49CeIE/ff5HV9m8Sxdsk6qr9cZykrROJGrDe/ndsdX4PIW2ksJDCwdBqzJT+t11tSj+O0LUSE/n/jiBawo8WaQ2+RD94Eds4iiZyJKrHIDxHFG+Sd95Hkgzo+N+5bjs7bImNjfYIOALI2++nYX6Bnv8cg/BlLtX/Ca55AilzoKKh4HZELf/rFJz379qmEALHOEbJfYqt3h+SqD5rrd+k0f8clj6HQCNK2njhGaKkSd5+BIC29y0AipUsj3zgKNk90N69IhXQlbiauyMj9LceQmgU3A8RJQ1WW/+SKNliq/sHNPpfxNRmhgyuhET1CeMNwngTiMnbT+65w2G8xlLjfyWMV0kSjwSfJBkQqw6KNNna7v0prcGXkMJBChshLHStwnjuvyXvPPaOHJflmFhvgQq+E5om33OC2NfG8laLzVYPU9c4OFHmcqOFpWuYmgYibWVzTIMoTrjTvY8H8g+lFZlQsBX0yDs2hpR4iY9SKTMAwPMjVlearK21OHFyhlazPwKO3hitRo/L59bRDI1cwaXbHjA2UaDd6DHo+YRBxIGjk6wtbWPZqbNLbbqEJY0hQySNQc9n+fw63iCgudWhudmhPJ6n0+yjaZLJA9W0CrPSII5ijtw5S2moa6FURBS+SBxvIGQWy3wMSO3UN/0uYRKNWE47AoUAx3PHOJo7RqxiLKmzk95IKTh8oMbC5U2WV5uYZvrepreBlwyoWHvrmCUqYam/SiNoYsob368V1+UDBw+mdSEhOFgqE4QxQqT6dEIIZvK7AafZYrqA++iRq64e90ylAN58ZYwoSXhlY537pqcxhoLaURhx6sFDhEFMLu/cVENu06+zMli/4fvXhlIRg+D7DPzvEpqnCKJlOuIRmpHGIBogpcakXSVWMYPYx5A6raBDwczzkckn6Ed9YpXQDFqMWWkbiZ8E9KIGlmax6g1SWnfi0wpbtMItoICe6Egh6ccNLvWalM0yR7KH0eX16YEUknGnyLhzdeHrYLI/U2XDS5l1O5XArdUWi2fWUMB2GNNwW9hu2urR2u5RGS9gmTrtXgfPTqv3YqiDEBDgzeexczrr/jKDqEeoQiLlEaos24HBxf42jszQRbEZJJjSQkNjwtmXamK9R0KpDppxO0l0HqV6kLRBm+Fyf5Mz7SskKiFWiim3jGleHZd1qTNhTw23oZjPT5Go5IZONJCyK5588jizsxX+9E+f47nnL9Hr+TQafZJkWHHWU3vniYkCDz5wiLHq9VXVOEnwBiGeP3TsEQLT1KlUskRxsos2n2oexkNQ4XqKSZwkDAbBqNgk/n/y3jzIkus67/zdm/vLt9a+9VLVC7qBBhobQQBcQIAgIYIixUUSJY40tGRFWIulGc+MODMej8LyjCYmJsIOWRZtUStJeUxStERSogTKMgEuIAkQawPobjR676rq2qvenvu980e+et3VXQWCsiiTnNN/VMdb8mXezLx5z3e+831XbevaUEoTxxmJyIjTlDCLkUgSMso1j0rgYRcMIpWSZFkOwBqa6mAByzWwXZNur+VPAFiC6lCBgm1j2VvXknGSEgRXQAZpSHzfwfevZ6hnmSLLFGGYs3KCJMmNLTRoCZWBAkppbM+iGyc9JnG+D5nSGDJnTcRKUaq4BLFPqewSJAnCFGRK45gGExNVfvEX38of/OFXOXNmidnZdS5cWO2NmUGlUuCH33krP/mTd3Pu3ApnzixRKXuY0mLMuwHfyB2CvKLLxMwoWZr1iyaTkzV+4Rce4OOfeJyzZ5dZXGwwP7+R61EZ+TmZmR7GNgz2Dw32rzuldS523Qn7LOlEZZiWpDbob7oh5IUOpRAKVKxJEnUdM11KyeBgkU43ZmCgiHFN+4UlDepRmLceSdF3RavVCliWwahf5J0Hc83QRqPLpz/9JI988QWEEDxw/2EOHhyjULjiopplORh44cIKTzxxlnq9y5/8ybc4fGiCSq3Ai0+dY+78KrZrMntqjQN3jbCSnsczihjfxQLzf43otgIun13m8F0HKG9z72mtyXSHxfbnWWj/Ge345Ktq9PnWQW4e+W0Kcs917ymd0Iie42L9o2yET17XVbERfoPLrU9Tc+9ib+UfU3aObJvrbYRPcHL1n2IIl0NDv4HWGefqv0k3Oc+1dLbl7iO4zQn2Vn+RUf+HMeTWtVknOc1c8/YD45kAACAASURBVI/pJGcI08vE2Vp/v6JsgeMr/8O2x+maU9w2+jE8uX3eeL7+EZY6X2C7XGGy9BMcHPg1hBD4poNnXJlbFtttztc3uH18AqUXOb7yTwjSS0xXf5kh734uNH6H1e5j143devAVLrc/zUjhh5iu/gqWMUSSZiw32j32C1xqfpzLrU+iybhp6F8y4r+z921BplJK7pXiVJyt8vLa/0YrPo5n7ubo6O9RkHu3/GY+DwRcbv9HZpufIEznrjvele7fMNscYKL040yVPoht5K2QcZLx3MtzHJoepVJ0OV//Nyx3/gqA20Y/Tqe+i6AVEkcJthYkOu4XIMN0npdW/jvCdI6ifZijw39EoeTR3Niqtxdna8y3Psl861PEV0mrXBsCg7JzCwcG/imudYAo62JKB93Th2x1QpTSDBQK+LbN5UaT6W1yJSkFQTvsPdOuB3bq4dOcWvvnZLrDqP8uDg78Gpbx6mqyiVrn5Or/SjN+AUsOcsvIRyg7t77qd/628T0PHAE45h4MWUJlbQQGnr1d77qgYN/IRvfzgMaQZWxz97bbS9U63eh5Mt3a9n3I3cqU7m6ZW0w5hNLbi5P9XYfv3Ml45X9kqfk7BMmJHiCx3H9fYFMpPMRY+ZdoBI9uDxyZksGhInv2bK1yO+YUY+Vf5nL9/yZITpCqVZrhl7d8Rgp/i6j1lveky0jpZ1E6YKP75yjdJUxOEyant+6f+xbGKr9CO3qixwLLF6aVke9MTtmWFkq/xta271IILAb89yOEw1r7P9CNXyBTdYJtx0hgykEK9k3b6mMpHdKJnidVy9t8Nw9NRKq26s3ItETqr+bva0U3XcQ2KljyO0uSwnQNKSzsayYipdNc9UQYRFkDUNhyZxHEH9TYM1LlwvI6RdchSlOOLy8zVi7RjmKiNCPJUgq2jSklrmVSdGwurG1gGrlOx70zuzFsk7PtWVzpsL+Uz0MDAz7Fkst0wcayjG0Tn80YHC3zxodv6YkMCpTSyJ5gXxwmdNsRA6Nlpg+N024E1NdyFtLNtV3cUL7CxvRLLjfetS9vW8l0LtBuGH3hPynzSsXMTVNIKTAd86rWVhPTPITWCiEc6LnEZFrx4sYcBdMhSGOCLOm1wGgsYTLkFmkmAZ004sbKBNPFQWzDxJYmQuRUXENKzlxc6dGdedVFdqJSznUu7egocnVcq3ezst7CsgyGa68OdO50jRtCcHBwCNvIadn19Q5zF1ZBa1qNgLe9945X3e659iVitX0r8Ta/hm3uRakGlrkX27qZMDERaQNLmpjSpGj61OMmAoizGN8s4EiLbhoihKRgOJjS7Fcii6bPLdWbMYTRTwA3ha4n3HEMYfTp/ePueA6yCfM7TnpyTF/z9NpZpgqDHCxP4Jc9asMlhBQkUUqWZrieTaHo4ng2lm2SxikIQRQkSEP29Ac07WZwlaNS7rhnC4dUJ6Q6wTMKFI0yJatKN2tjCZtMp4RZgHoN18n3UxjWraTRo2TxC8Tt3wPhYJrvYlBbHChNEGYxJ5qzRNnO11mu/2OgtWQ5aHO6scp6mDt7liyHA5UhhuxCzwFIsGfvED/3c/fx4IM3ceqVRS5dWiOO09zwoeazd88Q+/aNUKx4rLa7mI5Bs9c6VvFdUhN+9X95JwtrDarFAoYUrDY6VHyXExeXuGnvGKlSdIMY37NpdkIGyz5OweLC4jq1UoGVepvxoTK7dw/yr/7lB9nUCcuPZ+fxmu80eW5lns6g4mTjMq5hMafXefjnb8EXDpZncLI1T5DFFE2XoBbzjz58P/W4y3i1yrGNSzSSLo5hYeyWfPj/ephdhUGGB7cCZA+/4yj3vXln45LNSJXi+bXLdJOEuyf2kBpwYa1OnKbEWUbgw4f+Se4wODZQ5vm5yxhC4lgmhhAESYJtmLSiCKHhv/mFNxPHKcWCw8nlVYSAMEm5eXKUoaLPoUPj/Or/9DAvvTTHK6cXabdCTMtgdKTMkSNT7Ns3gu87lEoev/F//ihSCmoVfwsIk6UZURjjXcNEPnJkig//6sO8dHyeM6eXaLVDDCkplVxGR8scOjSx5fMKzVxng6ia8Su//lDfKno5aDHkFtFaU7IcgiwhVhloGC+UKVkOIChfU40vlVw+/OF3kqYZlmVSrV5hxFuGpGTnzyiJwPMsPvjBe3jf++5ASkmttpU9/7WvneIv/uI50kzxsz/7Zt724BGKRWfLOGidO9sGQcL4WJVPfuoJ1tbanDq1wOvvmO6L645NDXD09ftYVudwjAKNeIVu1qYoqj8wTo9hN8avFpg/u0i7UWJgdGsRRumQ+dYnudT4fZLeutiUZTxzClPmhetUNQjSOVLVwDN3Ye+gQdsIn+XMxv9DKz4BgGtMUrQPYkifVHXoJmcI0jnWgsdJVZuDA/+Mon3jjs/xTAcsdx6hHb9MNzmPa07gWwcwpU+murTik0TZImF2mYuNj+KY4wx4b9jCgI6zNerR0yRZfmyG9MlUqweOSUxZQnD9c9OS1VftUvGtA1Sc28l0h0x1SFTjOut2DfimR9G8UnDPtKIehiRZxtX46UbwJK3oeC7aLcv49gFsYwitU8J0lnb8Cqlqstj+HK65i13ln0WIHKD/dnV5pTNiFeBI7zvKC5SOWGj/KRfq/5ZEbSCwKdoH8cwpQBJnq7STUyRqnbnmJ0hVg5nqf49lVAmjhJWNNhPDZSrF6wkHftWnWC1gWiapjjCESbZDvrgJyFeHSn28RumYy63PMNv8GKlqYcoKResgtjmCQJKpLlG22L9utU6xZA2JiSIjznIQSitNwXP6BY6hYoGxUqnPcL86pCHR5GCsJhfrv7qgU3FupWDtoRWfoBk9T5BewjKO7Di+Wms68Wm66UUAivYBfHvfdy13+54BjhIVsRrNYgqbWAUkOkZiYEkHrV1q5V8HEoKsTSr2c7b1LEWrRidtIJCYwkLJo3jF/4PJwgGE8GjEo6yE9dxhI0tJtcqdv/QRKsV/RzsJ+slImKZMFEtUHQt0B0QJrkn682R7astrhqwyVf01UtVECgvXOvCqx2kaA+wd/E2UDpDCwTbG0aoFwkOIqwXSbGqFH8a3j9IIvkQ7eookW0IIC8fcQ9m7j5LzBgxZxTKGKTp39d7b29/Gpv6DaW29cIUwKbtvxhnaQzN8jE50jCSbR2uFlD62MYpnHabo3LnjcdjWbqZq/zu1wsM0wq8QJefJVB0hHBxzN2X3PnznDixjBNscxzH3YsjqNuMjGCv/EoP+BwBwrf2AJlNNRM81b5c3TsHcdNBzmarl473JRlM67lEbc+viYf991Ly3AeBYB9C627NK1IAEYYKO8xYwHTBR+XlGSh9E4OBa0yjVBJ32W3M2x9IQBQb9H6Xsvolu/Dyt8JtE6QXirE6uHZXrcRXsW3CtG3DNaeIsZxIY8kpvrGmMs2/4d+lGXebXmggBk4MVHOvVb0chDBxzL5kK6WbLrARPM+gexRRubn8sywTZKlJYOEaVKF3PHW+MMnHWzJMvWSbM1nGMGolqE2cNQGIbJVaCZ3GNIcr2DFG2gSlctFQE6RpaZ9hGiTjLe/hdYwhzBzrq93sYMk+eNjpdDhpD3DO9G8vIE+/5RpNmGDE9WMMxTUyZPwBGS0WSTHFudb2f3KQqpamSfq91faOL59kMj5SY6mmG7BSmaWBu2wpoUSi6+YOvF4Wiy8hkjUwnOBhY4srjXxoSb5sK+GsNpeoIYSCNkf78JIXktoHdfQJgPQ5ygWMnd/ExpUGYJphSUjDs3F67p0MTJxkLK002Gl0mx6poFCCxpL1jm1qQhZxqnf2O9nt1o02UpCRpRrW8qV/wnYcQgsJV9tvlaoFbXpdbAK8vN1/lm/nC7njz9HVOVK/2W4YcRcoqYfw8UhQYdR9k3LsCBAoENbuyRSh88/XN37l6HA1h9FsFrw1LXnP9/RflNwLXyN1fLGmitaZcLVC+dXcfrHpNWxEC1WOruK6FlJIJd8/mT5D1TBKMbdhQeWtDhCV/sBzWDPtWpDGOct6CEBbC3IuUA5hZG8ewsKSBKQziHQwkNkNrzVcXzvOvX/ga55vrKK37ifzPH7ybQ92BHAx3LJZXmsxMD1NvBoyMVTh84yTNVkC7FeL7Dve8fh+OY7FSbzO/2uTyapNMKYaqPkGUMLfW4K7Du2mlMbZjEcYJidQMDpdwVjeYnKyxtN7i4nqDxXaH3aNVzqyuUysWmB4f4Pkz8zQ6IctrTWZKxStAkRCkSYpbcHLQEbAcizRJsWyTgfEaz1xa4LcvPcFN7WF+Y/whLGniWC2qQzaeYZPojDhL8E0nt3O2bTzPIooTYjOFNL+HHGniFxwOjU3kzoBaE2cZhhQYIgdLSq+BOT3XbvDvnnuSxW6bj+35cXztEKcpqVKkmUIakoGhIrWCy1DR5/TSGhiw0Q2oei5Vz2N2o4EhBaOlImZFstruIg3JoOdxemU1Lyz0BklKydhYhbGxCg8+uLNAuOOYjOxQxMtSRdiJ+u3oiYp4ZuOr7CkcZGx0N2NjVR58687b1lrzQuObmMJjoePhGCb7pobZiLp0s5gbR8YJs5SVsIVbsNnoBgRZwqhXYqhYouYU0Frz1PqjnFl+iUlvhvtG3oVhSIaGSjy78VVeaR1jKBnjwbEfy49bSG4Y3OrmlwNL1wsKB0HMY19+mU435tChcd724E1bQKjNEAKkNCiVDO68c5q/+MJzrK62WV1rs7rUYHSqxsh4lRefOk/3lgjpmVwOTpHphJXoEq5RwBY/GOukgdFKrouVZDRWW1va95VOWer8FRcbv0+q6hiiwEjhHYwV349jjGOIAlrTS8JXqAcv4du7QHn9HMww8g6JKFviXP23aMXHkcJmsvQTjBffh2fuRgoHpWOC9BLzzU+y2Pk8jeg5ztV/ixsG/znuNRImm6F0yHLnEaSw2VX+B4wX34Nn7kIKt9fmdZrz9Y+wFnyVIJ1jofWnVJyjmLJEO45Z7XSouAc5MvxbfUOhRK1zau1fEKQXcM1xDtT+GbYY7XdGaPL7wJAOOqkQpTGGeSUXi3otkWPuBxkvvh9FjNYRy53/xIXGv0XpsP/ZmlXkUmdpSyuyKQ1s4/q27Eb0DCCouHewt/LL6HQXWVbAdw0Ms8l861PMNv4QRcxi57MMem/Fd4bZOzzwbY0mTGEx7OzGfBXm97WhtWYt+AoXG79HojbwemDVcOGtWMYAAkmimrSil7jY+F3q0VMstD+LY4yzu/IPQcDM5CCOvf16WUrB+lKDsBNSrhXye26H9aQmZ+G3GlcIIEE6x0L7P5KqFgVrhv21D1N2bsXqaQErHZOoOnG2Qj18GtsYwjFH0AhcWezLNaSZYqjm982S1jpdZgYHdixyRN2YdiMgSlNONpeo2B4zpUGkEDjmGEOFB2jHpwjTJVa7j1KyD+8IQCodsR5+nVQ1kNgMFR7AlN89v+vvIeAoJsy62FIRqZBOusGQs4tu2sgTXmMapTMskZFhYUqbMOvQTZuY0sKxhjEZwXBrFJ0DKA3H1+ZZ7LQZ8PKHX6Y0A67HQqeLEOOMFoosdls0ohDbMNlb3U3RTkjDLyONcaQ5g9YRWteRchSt43x5rnLhV5UtIY1JXHMXSi0j5RBax6hsAUSuyaOyZYQxisDMNQpEAc8czoELTASaLDmGYd2K0glabSCEj5AVVDqLCQyXfprh0ofY4nfbEybTOsGUNUruyHVjWiw67J0extumHUkIiWPuZbj4DxguKnRPBG3zRsl1e3aeRAQCKTx85zZ853aEcFCqCyiEsBHCoRN+Da1DTGMIpdZwrWlyUe4UrROEcNA6wjH3YBnDgEaKYi7anc5im7somyWKhkATonXucmebk9hMIkWBNFumnTyJ77wBQ/qo7DIiPYVrHcrBH90kSy4BFlo3EaKCNCdR6QWErJGlZzDQWHIAlS2ikg20KOZAifsgcC3oJrGMMSreD1Hx3o5GcWJxmaVWmyRTFB2Hbpwx5Pu4VsBad40hv0CcZnSThEwpDg4PMejfyheefIGPPnIcQwp++V17ePfd394FRmvFaniMdnKRIF2hHr1MrNoIoGLvZzV8nkH3FpKsyWr4PGV7hkQ1Weh+nWH3dgzbZj16kbK9H6Uj6tErWNLHt6ZoxGdQVoJvTVKPTmEbFRQJy8HTmKJAqrtE2QauMYxvjTHivW5Ln/UPCjOp6NrcPjPJRjug5nuUPIdMK7pZl6khD/B6d0bavyNtA6qGx3hlT95eoBWGNFBXJXO2YxJGCasrLUZHK33gKFUZnayTC5tLqw+SKq3pZl0udS5zrnOJetzo630N2gPs8SeZ8EZxpQNoVsNTFM0xmsk8Y97NgOhZuOa6JpY0KRheP5EPspDZ7gJn2xfYSJqkKsEzXGp2lb3+FBNeDZldQveqX0oOI6WFFJKaXUCj6WYBA44N2GzSM5XOyLs6MrTI+ol8pjRnLqwwe3mdA9MjlHwXhUKRkVwFsOmerlmqUxKVcKp1lqUw1w7Q5C6L9Xh70EYIgSsdLlxeZ26xjmVKjh4yKLgWW3rm0YRZRCNpMRcssBAs0067JCrBkha+6THujrDHn6JqlbFlLoAddGNmz62glaJZ77Jr35V5V/dsxVOVEKuU5WiVue4VN7tOFlBPmjsCZK5hY9ImTWdznTnRE9jnCkgYZCFRFm/7fQBDSApmgTRTpEphSonTc2zRPYFLedXiQ2tNpOJc4F0IPMPBlnbfKfB8e5YLnVkaSRuFwjNcRpxB9hf3MuwO5GLeIgetFoNcdFJpxV5/eMt8sHnNhVnEQrjMxe48K+EaoYqwpcWEN8q+4h58o5e8WRBnUV+6z5YWnuFiXgt2XXPunR1Asu/nyKLHEeYeTPuu/IXeuC4GG5xszgEw4Q0w4r66eUQzifjo8Sd4eWOF90zfxJvGpzGkpB4FHC4Os362hV90cB2L6b3DjA7n4q0Fz8b3HVrtsN9yZvWKUZeW66w3O1RLHtWix9xyg5v2jtINExbWWqy3urS6ERODFV6ZXSFJM0oFh+WNNqfnVknSDNs0mByqslLvkGQZ660utmlgSImbab75haf7CdeB26bJ0ozWRoe1y+uUBop4vksUxAxODDAwdqUlv2R57PFzRsO4t2ntrq/5y3Wv5fPQZly5W9fCLn/8yrM8MLmPo0NbmTWvFq5psrtYwzdtSrbLnmI1b9kS4jptWAEM+oUr7/Ve2zc8kO9H79zvGx7c/ApF12ajE/Sty/8uwvUdbrhjuu+MJYXBmLubgvna3WuHnHFs6XCoPMamf+OYV+7vd37oY4DgQHm4d7hbZ8fbam8iyLo0k63aMTdX7iZVKbPd0/xtotUKqde7QA4ulcuv7uQFkCRZn01gWwaVms/pF+e4fHGNLFOcemGWkdsUqY4pGGWmvBuw/h6Ndf4+ojpcwnYtzGuKnGE6z3zr/yVVdaTwmCr/FEPmh1i6lBCFCUo1GBuv0qgLGo0ifvHNSN/hhdMXUUpTLnvsPzCKEIqV7t/QiJ4FYMh7gL3Vf4wpSv3niSE8fOsg07VfQRGx0P4sG+E3We48wq7yz+y4DtWkDBfeyXT1lzCuMrYxhEfJvpk9lX9EOz5FlC3QjI4RZ6uYskSaZTw7v8DR8TFmBq6YFEXpErJ3fiU2Mpnk3AmDKEqwHYvJPYOsLjVJk5Sge4mB4RJRmFCuFGhsdHA9u7emiJjYM8jg8CBCChxz9LpOhW4Wcu/QTVzoLPZfi7KUIE2vW01oMnxrP/tr/zO+dZiXl5Y5vzDH7QcmmRgaZ1f5v6URPUc9fJIwXaAZvUirey9x+u0LD610nfV4nqo9tqNpzLV7k6g6861PEmVLGMJnb/WXGPXf2S/2A9hGjQHvDVhGrd9yd7n9GYYKbyGOJzh1YQnXtRjheuZ4EidM7h/F7mmRdtM2uvfv2hDkDMLNogNAlC4SprmcwKB3HwPevf3zCmAIF0OO4RijlPrdTgJNRpC1MGX+u0GYsLjaZHS4TLVWIEozwmR74ykhBdXhMuuLdRpxQDeNGXL9/jgLLAa9+1ho/Rlhdpn14GtMlH4c1xzbdpRjtcZ68DgAjjlB0b6T9WgJU1r4ZhmJ7Gsk/V3E9wxw5Bo+494+pJB9qrkpLDJ7gqsf7ALRe3jmr426WU6vZtNiPkMgMQQcGRrjpqGcAqZ6yYJjGOyt1BCAZRjsq+ZOSEprCpaVC2arNbI0ResWWtUBjZazaJ0iNxlCupuDQm6RLH4elZ5FGL19VU2kuQchh8niZxDGMNLYRZacwHDuIoufR6tlhBzCdN6IymaR5mGy5Hm0auR0OusAWfw8ANKc3sJG2oxMdQmS40hRwDX3oXTY09vJSFUTyy4xMGSjVAOtK2iSnOlEXgHJdBtTVknVBkm2hGvuAyEIkpP49m35drIWhiyiUSjVRcoCUjhorQjiFwmSlwCN79xNM/gihqwhhUPBeT2N4K9xrBnK7ttJVZ1W+JXeQuc+wvgkRfdNtMOv4tpHaAZ/jWmMUHYf7Iluf5VK4UcQhkcn+hZxeh7H3I9t7qIRPIJljFFy76MbP0sz+BsEFiXvfoQoII3dCOHmQBEuWjigE4QoAAp0AIjeuc2Qcjg/rzpDSB+07q3Ptp8Ur0yWBmjJcisiSgVVz8cxTaRI8R2LbrxpCSqJ0og4zXCt3LUgyxRPnpplvZUvXp4+PffagCMUsWpQdW4ABJ3kMkIYeMYwnjlC0dpFkK6iDUXBGmfIu412fJGCOcagezRnUJhjvQeTwLcmsWWFTAcUrV1UnRtwjAq+NU6mY8JsHc8coWCMMtf5z5TtGcrWNM3kApBT5NcaHUaqxb6t7fd7HL+0RDeOWW50GCoXKBdcFoIlfu/cJ2nEO7S3CvjQnvdz+8DNQD75V61yfy7TWmNZBuNjVVZWmiRXuZLMBQt85PTHiVXC7QNH+NDeH0Vrzdn2Bf5y4VFebp6llXb64IohDBxp45se75l8iPtH7gUS6vFF2skiBWuIzWt3KVzlt09/jE4acKRyAx+afj8WFhe6c/ynxa9yrH6CZtImUQmqt21bWviGx0Njb+LBoWEM0UXrgCT+FrZ9N8LI9XOaSZs/vvCnnG5d2HEs3zp6Lz80fj+2tDAkjA2XuTC7Rr2ZV3yGBn1KZplOmuvLtdMOp1sXuBwssRAuM9ddYCFcppnk4x5lMb939pM7ah25hsO7J9/G4akb6fZ0Ua5OqLTWNJIWp1pneWr9Bc62L9JMWoQqIlUZCoVEYkoDRzoMOTVurx3hwdE3MWhXEQK8go0QuYvQZoRZxOnWeZZ6YNFsd4GlaJXV6ErC86lLf87n5v56W+BICMGDo2/kHWOvw7YOk2YLZGr1Our7l5Ye50tL39ixHWvCG+Hn9/00C82Q0+tr3Dwyyt5qnkzHKqSRLFGzJ/pgnkbzleUn+KuFx3AMm3dPvI17hm7nZPMMf7P0OCebp+mkXZJNpo+QONJm0Klx79AdPDByLzW7ikRwc3UP59tLTBauF05VWjEXLPLFhS/zQuMkraRDpGKUVkghcKWLb3qY2zznAO4Zup0f2/VOjG1aAX7QI0tfwZQDYG4d04rls6swhEBgCMHLzTlsabG/NL6lMr0ZC50ml9p1pssD/NyNdzFT3qpDE5YHcWyz16qT/1a5XMh1p7RmoHZ9S/ThPSNMjw1gmQZKa27YNYxlmdRKBTzHYmo4dwU1DMkD5QMUPYeBcgEpBGMDpbwib0g82+LNR2dQSqO1xrZMgjjBFpLdw1WCdojWmtE9wzmwnGZopXOB2Z6+mmVb1z2yr3YI6r1yzd/tXtv+uX+xvcGfnD3GwerQdwQcDTgFfv2ut5MpxYhX7LGX+1TB62K794xXSdBGS0WGfB9D7vyZqyPTKQvBJZrJOiWryri7B1NadNMWjWQDU5isxguUizXK3m4iFXK+c5JUxQw541exTDJWo0VWo0UynSCEZNKbpmhWuNQ9TTdtMVXY12/BWI+WiXVIkHaJVJchZ4JBexSFYi1aYC1eQiAYdXdTs4d67ZU2juFAuvXYLGnjGG4fSNNa00zW6WZtRt0ppDBoJw02khUmvL0Y18wrRq9NG+i5/iW47s4siixTvPjiLN1ujG0bDI+UcVwLyzJoNQJGJ2scvXsfy+osni7imxWk+Nu5Tn0vxw13XBFmvvrYGuHTdJJzAJTsG5kofQCSMnG0SLsdUi7nGlOtVq7r4roOaaJI4gzDlNi9lj8tOqx2vwQoDFFgvPS+HvNjawghsGSNUf9drHQfJVV1VoMvM158H5axvfC8FB5jxfdsAY2u3p5v78ezdhFlC8RqrScyDK5lkWRZrnH5KpHEGY2NkHK1gOfbuJ5NHCVkac+dWOQt2512SBQmuAUbBCRxSrcdMXh97b8fo26Ny8E6Q86V4kCmFN2kt267+lgwGCo8QNE+mGuoac1g2We4moMulqxRtm+mET6D0gGxWkJKuLzR5KZd2wMTm+EZRVyj+JpZ1FpDJzlDM3oRAN8+wJD3wBbQqL/fQlK0DzHgvZnLrU8SpvNshE9Sdd+HaRroHfSACiUvB3QFfXBkp/WRBizbotMzNikUXa52UMtUF63VtvNyfs1cVVLQEs8sEfVa1TzXQiC4MLvGrVWPiufuCOZvPsOSOCVohQy4BUa9EkmaMb/eZHKgTMHaR8m5mbB7mXbyCu345R2Bo2Z0jCCdB6Dmvg7bGOV0+xRh1mHYnWTUnaJo/t25kn/PZHpSSNxtBC3/S3bQtyySKKcwX22vaV9j16mUornRRVTyhYcwJhDGCKg1QCFkLQeLCFFZXo0S5jSoZVR6CXSANPcijClU/CyIAiqbR+g41wXREaCQ1kGksRvFiyDHcjBIJ6BC0HklWpoHUNnFHH9Sa5j267iW9QL5Bd4MHiXOFnGtGcLkNGm2RtG9myRbQGtNwb6RIHmZTHVwrf0k2QKZalN07yZTTdrhE3j2YbTO16KEAQAAIABJREFUCJNTWMYYApMoOUvBPkI3OkailnuC3AGZ7mLKKmX3PjQJnehJtA5BSNJsBYFJxXuYevdzmHIA1z5MyX0LlhxDCo+S91Y64ddIsxUyVUfrhFSt9YTHR/MbFrDNKUxjBE2C1t3+Z8P0ZVz7MJbM2UlCWHj2LcTZAiXvfkAgjVFsY7TfGiHEdZwhAAxzH0o1kGoMIYcRwscwp68Z5SuLkrVWl3YQM1or4l1DmTw6OYZjmvi2tbUtI19x9/4rrtqnnNa4e7jar6zOjG0VBlaZot3oEgUxhiEZ6LmwCAyK5hSr4TEyHVJzbqabLmDKMqlKaCUNLGlRsUdYCZ/lfPNvKNu7MUSJUCUI3aUZX8CQLrYs0YzPUjDHGSvcQyu5xFp4DIlJMz5HqkOG3TtoZ7NE2XoOPGFgCAdL5g+hVjfi+XOXeeNN0z8wwNH+iSHaQcRErULVzxkMqc5YizZYidZ3fGgG2RVqsdKKetzss4e0hkYjoNOJSNIrlUuARCUshMtEKqbWqZCqjOPNU/zR+c8wH1ypMG0mP5nO6GYBsco1bgwhkcJj0NlPM5nfwnJKVcpiuEIr7VAwPRKVcrZ7iT8492kudeeva2/KhZczYhXjmT62LJFmEWlyFss+itItJJtgu6KRtFgMl9mpO76ZtPv3AEC55HLPHdM5gNMMe3bhFql0EAjOt2f5yJmP00i2B+g0mtV4Y9v3ADzDpZMGuEWTZjuk6Dt96vDm8T22/A0+O/9FguyKhphAYEkLQ0gynRGrhFgltNI2FzvznO/M8rPTH2CkPMj6covZ8ytbWgBXojV+5+y/Zzm63lVlMzbixo7vCQStJNfwk7KIbz/YA7u3PhM7acBytLqjbpIpzZ5YsqJo24z4Vyp0SmfMd09iSZeanVf6NdBKO1wOl5BITrfPY0qTj53/DGu9cbaEiWs4/XFJs4BON+Dy7BIXO/P8w+kPYAiLxXCDRGdc7KwwXRy56ncVLzZe5j9c/DznOpcAcHugnCVy/bpW2tn23pK98xJmW/Xe/v8U0jxIEj2G1gH0mMCGOc35zhKvtC4z6lY5315itz/EsHNVG+NVobWmncSEacKByiBl60r7zCaQ4Rd2ZkfslAAXHJuCc30SUOixKb2r2nH9XmJum9snYNY1r29+t7iNrsXfd2iteWZ5jiDd/r7brk10M6QQjHjfPTMJIQSm8dpBo2P1b3Cm9SLD7iQnmk+zxz/I7bU3sxIt8J+XPsOkN0PBLBFlAcPuJIK8Ve3rq49QMEsUi3mBayG4yNdXH2G3f5BXWscwhcWU1wMW0Dyz8RViFXHHwH0AvNx6lhfqT7CveCOZznhm46u8c/ynKJglTrWOkemUVlLnWP0bvHvyZ75jgftmusE3Vr/Iw+M/TdEs80Ljm9STNSa8vdd9tlLxmJqsce7cMmfPLvP446e59979eJ695VpP09z57ZvfPMPnPv8sYZiwb98INx+ZYmOlRalawDAlSikc16SajvSYuM1eG/YPWEhBmma5k2svtFZshE+idD5Hj/gP4RijSNPipptzfcfNIa31hYLz+2Xf/tHe+/kHGtGFnnA1FKx9+NbOjnpCCEr2jRTM3TTjOp34DN30EmWjsu196Fsz+NbMlvObpBmNVkC17PUkHsZ6x5SRqnwN0o4iyo5DkmWvCgT6JZfX378f17P6+3fbPft3bNPe3Fa3E2FZrw4yrkZNZvwxzncW+zSKgmVxcHAIxzBJt2jxVqg6dyKFlbOODUmaZf35Ne82GUX2dAEz1cYwNFXfe9VWNSEESmdXntGvacpRNKPnyXReGKy5d2MZOwMYUpgMevex2P4sme6wEXyTgvcQkyMVCq7NdgLiWaa4dHKeiZk8hzSl1WMubr+Dlm1g9wxlABxjDMccJUznWA2+hN+eYbjw0BYns22PTKeEWQtH5sUA2zIoFV3GR8uYhsHB4UGqhU1g6vpIogTDNPA9l4vdZSb9KiTwwsUFfMdipOIz6j/MavexfqvloPfG60gkmQpYD75GqpqYssqg9xYK5iAzxRtZDuexpYMpdmZq/23ieybTy9KMOE4xDJmLZia5eKtWqj/rpEkunmc7ORCU9qw9LcvsTd6aOEoQCGzXJOhEHPvmGY68bga/7CGlII5StFJYTn7hJHFGtxXw6Oef5S3vvo3aoIc0DyJkAeRozjrKljHso2i1isqWEMYu0CDkKNLYBcY4WXoOKQcRzj2AROsQIWxUZiGNcYQc7C3TJdI61Dvqnr2eMYLWAcLYi5DFnNGjVhGigsoWMXQXrkkglG4DAsfcg9YRSbaEKWtonZCpDr5zB0KYKB1hm5Ok2RpKdSnYRzBFmTB7BU1Kppo45m6U3oUpK71tBygVkKg1HHMPYc+BINfsWUCTILBw7RuJ0/MYchDLnCBOzwNGn+ZniDKd8BsU3fswZBGJhRAOUpbIdIdW9JVeH28+6+VV9g2SbJ4wOYUQNq51iDi9mNtTCwfIQAjidK6ng+SgVJtO+AQF+26SOCXLFFGU0FjvMDSWH1MUJNiOiWkauQic0kShwHZGyFJNHDWoDhUxTaM/oWxGN0r4/S8+yZOnZvn1n3o7t0xfpTkiBIN+TnMOg5i1pWZeiXJMgnYuGCylBDRxmGJaRl5xKdi8ac8U5j2KbjPgDdNTNNbbJElGwXewLIPF88t0WwGVwdIV4EgIyvYMRWsXK1GLhaCFJUdZ6HYpmNBKDmFJk5U4wZY5+2UpgkwPMR/OcmttN/vK7wME69FJLLfIsHs7Colv+TjmfjJMBr23IBCkWoN0GLDLGL3JRwOuWaaRtHoMQU2qfnAWSVppvnV6tt+mprVm0K7xvql3UI+bBFlAJwtYizY40TxNpK5vHTKlAUJQ7zGUhOiJY/sOStUoFLavbnbSgBPNV/jUpT9nIVhm2BlgX3FvvyUtUQnL0RoXOrMoNLsLE/0HZJBtULLGKewgOBlkAWfaF/mzuUeY7V6mapU5UNrLpDeOZzikOmMlWuNS5zLdLGDG34VljSOlh7Y7oCMMY7S/Pc9weWDkjdxQ2k+kIoIspJm0eKnxCq0eg+i6fQhiTp1bQilNsxUyOVUiyuI+tbho+RytHN4ypmtxnQud2Z4lveSG0j5K5vZJhS1tRt1BOt2IOElptjTtbkS5l3wawmCPP4Uj8/bDmlVhX3EPU4VxalYFx7D7LXxPrx9jNd5AoThWP8mXlh7n/VMPM3d+Ba00jbV2v/LqSIcjlRvopN0r5zILeKV1rg/yHChOU7Mr2y5nBIKpwkTeppucxjTGEWy2mF1ZeNxavRHXcAiziG4W0E0DznfmuNid639GkzNooyzDM69sI9EhBbO644JKoXixcYrnNo6zHtcZsKvcVr2Jvf4UBcMjUjEXu3M8u3GclWiNVGc8vf4Ch0r7eMvIvVSsAuNejU66FeRZizf4zOxfcb4HGu0tTPHg2Js4Uj5I0SwQqYTznVm+uvIkz2682DvPBncNHGWvv4uqVWKmuAfZe15qrcmUvg6AdWxzi1vXD05o0F3S6DE2V+ui8GMEWcQdA/vY44+Q6oybq3vZ3WMgQQ7YnWuu8/zqZdajLq/UV+mkMRdaG3z0xBMUzHw+H3J93jN9EyV7K0ATZSmn6iucWF9iNezgmRb7yoPcNjRB2Xa3JDpxlvIXF08y167z4/uPMl7YyhKoRwGfO38cQwjePX0TFdtFac2JjSUeXzjPg1MHGPaKPLM8x9nmGnGWMloocXRwgpnyQF+L6eoI0oQT60u8uL5AN00YLZS4a2TXjvmM0ppumnCmscqp+jKrYd7CW3M8DtVGuKk2imOYW35nvtPgpbVF5rtN/vzCCYI04S8vnuSV+hXnnQG3wHumb0SKDN/0kUISZyl/efFlLrSu2Gz7lsP7po8w5O0MiCitWQ+7vLi+wJnGGnGWMeB63D48xUx5AFNs1TS50NrgkYsv87rRXRyujnBiY4mX1hfpJDFl2+W2oQkO1Ub63wuzgOONp3jD0DvY4x/kcnCRLy9/jkOl2wFIdcqdA2+hZg8DIr/nBBwo3cLLrWe37OtiOEvRrHB79U040mOuexbfLGMKixn/Rk63jl1zbIqyVeNNwz9MplP+/PLHqCerVKxBXjdwP0orgqzDFy5/nGay8R0BR0IIRt0pLOkw2z3NdPFG5oPz3FK5B7lN4dUwJA8+eBMvvjTH+nqHj/7uYzz33EX27x+hXPbQGrrdiKWlJmfOLnH8+Dzdbkyl4vHe997J+HiVumVSrHicOT5PwXdQKOrxck9Co/Ff3dDluxFKac7NrVEsOEyO5Fbfme4QpHNssoQsY4ZOqtA67LefbwISmy2aV9+jljSwDRMBBMkl0p67dS6q/eqtkbmb9jjEL5KoDaJ0AewjV5Cqq8IxRq/TfekGMd967gKvv32aatlAXpVgb2oZAfiO3de53NFIw5Q4pkXW0467umC2qXeUj1fP2bL3fuE1aFCWzQKnWrN4htMfuzBNuVjf4MDAIFenLIYs4Jq5Fq8Q4Dk2o7Vr9Hq50rqvdIZKElpB9Jq0CDOVXMfg2yk0uWjzZvjfRgMYwLMmMWWJLOvQTS+g6FAsFBnYRoMMIGzn4v9pknI1pLFTgVcIQbnm9x2lXXOCkcI7mGv9e+JslbMbv8lK91FGCg9Rc+/GNSeRwrnuvGs0BaOK1zMZklJiSEHJd2lGEZcbTQqW1ZcKuDbiKM8HDcdgca1FnGWQwli1hGkYgKDs3IJv76Mdn6QRPUc3uUTBmt6yL2G2SD18BtD41jRl99YcyJIOiYoIsg6e4X/HguavFt8TwJHWmmcff4X58yuYlsmb3nELJ5+7iOvZ1Nfb7No3QnOjy8nnLmCaBre8fh+7D4zy1GMnWVtuMjJR43X3H+bU85c4dewS5WqB1z94E+dfvsyjn3uWjZU2d953A6Zl8uSjx+m2Iw7cPMW+w5N85QvPE3Yjluc30BqE9DDk/qt3DszD+R0oxzDMfFLSWvdayDbBnxxZ34LYao1hHu7dn1deN/rAUR7yWvc3Y4QsSZDGWF5x3obWZ8gqQpikagXPugkhXJRqY5sTKN2hEz2D79yBKStE6UV8+1ZS5WHIEgiJ1hGyZ/FuyiG66gWSbClvSdMBSbaMY0wRJmewzUnAyIGpnoOAEAa+83occwYQmMYgZe9hDFmk7D2MFAXK3tvIVB1TDlBy34Yhi5TctyGli2WMoXtaSlL4FJy7EI6JZYyQqTYD/geRwsUwatT8DwAKKfxeG9xd+I6FZeRV7QH/J0BI4jjlxHMXaW7kgNH6covzpxYZHqsgDEFzvUsSp1iOSacVUh0ssrHcZHi8SrcbUa4WOHx095ZzBVBvBzx7dp6VRpsk3XkxEIcJl84sYfUAqo3VFtM3jLO+3KQ6WOT08XnKNR8BVIeKJOtd3vv6m1hdbBCsdTi3nIMMw+NVJvcOMTEzytkXLuaaLFc9sISQGMIh012CLCXVJgILU1iMe8M0k4BYpTSThBG3Qqy6OZNBSKSQGD2dkJpzQ976JB3mOwt0s4BOGtBOu4BgqjDSZ2VsxAsIBLFO2IibjDgDlEyfKgOUC+63FdX7fgrVa91Ybra5QeVim2WryFtH39Br61AkOuVCZ45Lr1wmirfXnCmZBVwnXxQIIWg2Ak6duoyUkqNHd2+rPVaPG3x69gvMdhe4Z+h23jn+AOPuCAXTQ/Sg5zALaaVtNuIGo+5Q/7qwZZHV8BQlq0XBvB48aiZt/nTuEc61L3LnwM28Y/x+9hQm8c1CDhKmGWEWERCyFq+zqzBKmpwjy2ZR2SKO90OIq4Q+JQY1Y5JqcYKD1SEynbERN/mt039Eq7U9cFT0XQ7sHaHg2Ww0uj0HMMVGskGiEnZ54/zMzI9vWcA8tf4Cn7jwp6RZF0tavHfqIfb511v4Qg7AOIaNziQzu4ZpdUJKRfeqe0dwQ2kfD4zcy6BT43B5PzW7SsFw2TQT0FoTq4Rbqzfyh+c/zXK0RqYznlp/gftH7mXfjRPEYcLG2pVjHHRq/PTe9/dZkwCXupf5rdN/xHqcu7C8fezN3F47slMDLLa0QARkaoVm51NIWaLovRONSZQ18MxhBp0iR41pxtwpDGESq4S/WnhsC3AEMFwoUnG2AgGmsCmatZ597PYFw9nuZQQ5yPXeyXdw0J/Btz0MKft6SHfUbub3z32K5WiNRKd8fe0Z9hf3E2SKYadCxdq6wHuhfpKz7QtooGZX+Mk9P8LR6uEtzm3DzgDT/hTNpMXLrbMorZgqjPOeyYeum1uSNOOxp07z5EsXia9q+fzAQ7dx8/7X3kL0/RKm80YM6yjoFkL2mFzCYaa4wteWT/D02hnKlsegXdri4JQqzTMrc3zi1DNEWUoniYlVxlK3xRcvneqP60x5gLfvOrgFOGrFER879TSfOfsCzTjElJJMKUxp8IbxvfzSkXvZVx7sbyNWGZ899xJPLF3ivomZbYCjkE+cegbbMHjL5D4qtotGc3x9kY+89A1SpVjotvjS3GkUuQh1phS7SlU+fNtbuG98Zsv2OknMH5z8Fp85+wIbUYBnmkghmS7VuGds+7mhEYf8q2Nf5dH5M7TjKAf3yQHWquPyE/tv5UOH7uw5euXxlflz/MnZY3TSmMudJonKeGp5luPrS/3P7CnVeGjXPi5HZ9lX3E/ZqpBpzUvrizy5dKn/3arj8ebx6R2BI6U1L64t8JsvPM5L6wv9BDRVimHP50M33MmPTN+0Zf8utTb4nRPfZL7T4CuOx+cvnCBMExKliFTKmFfiF4/cw7v23ohjmGQ6JVExRbOCIUyKZt5OHaoc8C6bVXyjdF1iuF0Ff9id5OXWcxxrfIPFcJZxbzeG6AFvWl/3eSEkZWsA1ygQZkEuRaEzWmmd5+tfp53Uc9ZRuvG3Al1s6TLjH+Zs5wSu4aG1YsLbu22yJMT/R957B1l23fedn3PzfTl0TtOTBxMwCTmSAAGCkkiDokVJFsWyLLvKcrl2TdmuLa9KJWur1q7atUtSKXi1lmSFsrSyGAQCIECABEkQGQNgMBjMTE/u6emcXn43n/3jvn6dewYgawvEflGF6X793rn33XTO+Z7v7/sV3Hbbdr78y/fy9W+8xdjYPM88ewrDUNFbyumgtZgtJRiGxoED/XzmsVt5+KFbUBRBoSsNpOnszaGoAgWVDrMfgFpQQttg3P5xQKXuUK416e+KF2XDqEEo4+tHVVJcrnh48jpVz6UR+ICk004hiO/BjGG1vGh8EprO8c6BdhVIEFWIZDye0pTMKq+ZjSCEwFALtNxr8KMFaAVurIWqJBBrzokiBMV8ckvCxNZ1aq5HdANSZdFpUvXmKLsOtq5jazqmqmGqKgtOXJofWw3EZWs9yRQp/eaukZCIRuCSN2IiTUpJ2XU3pEYU9FWEW6XuUKo12T3QsfG9AJi6Ti5xY2JBU4wNSbnNIfHCZRX2Zil6K6FgoitZ3HCKMHIQSp25RcimLLqL64lEwzbo392LbuoIEQdlhWz8/IjCCCllmzQCUJXYk0sSMFX7Bn5UouS8TsU9haX1UrDupSv5aVLGLWjKsnJUV0x67GUiTIg4cEAogjCKWGg044X3jQ8LyYxNZaGGQKApCgtunQErjx+EOL4P2JhqN3nrLureRZxgnJJ7Alsfai8qQlym5gQTQOzRpLcFILHoxlSsTVPmPiw+EsRRo+byw6ffZdueHuamFqiUGuy5dZC/+cPvMLSrm4Htnbx2+Qzb9/YyuLOL177zPqqm8ub3zzG8t5cr5yY5dOcO3vrhOb7wTz9BusVM7jk0yMiBazz0+DFMW+fV505z5cwkxZ4sl89MkErbOA2Xhz5/nG/+xUsb79zKm2SN2eeqeseNbqYfYTKt6gdQ9c09bxRhkLUfbW87fvDF5ILWLuUSGGp/62cFWB58ZayHV+13PvE4EK/qFex/huP4WJaGnTjQfh3AZHjFPpgY2nLKnKLGA09NjUtZVJFpeS6B0kq20Fr1x0vvXYKpLA/2NDWPpi6bXBpa36bvBdBbSQohEX1DRfqGiqQyFr2DRdymR6bVMdSKTSIpMUwN1/FJJE387Z0YpkalVCcM5aqSxiWMzixyfa58Q3LETprsPjiAlJLAD+kdLKLqCqmW2u3oPbswLYPADxCKQkd3FjtpksklCPwQx/FQFCWWusp44KJqKuYmJQR9do4+O7euDA7YtCNceZ1qyrKRbIeZpxnaJNQGvgwYsLtJ66mW9FOjETQRCHwZsC3RR0KNB/6NakQ+Za8rM/hJhqGpHBiMyx3tNclncVmHioqKpZqbRu1KYLI5S0pL0G3FZYjZXIJjx7Zz7docqrbx50p+hWpQ5+7iMX5l+xdJa6vr8QWQ0GwSmk23tTpBxo8a6EqCUHqwATVQDeqcq1zkeP4Q/2zHL5LVM6uul9Hz08zPVLj1zh10ZYots+duhDCR2jaEWF1uoQhBM/RRl46JULFVc1Ws6NpjF4Yhl6/N0dedZXquSk9XGid0Yv8mKdFUjdQafxZLXV7tEQgSqk1a37r0Y3K+jOcHVOsOC+UGhh6rUYQAS5g83vMZVFWNfUEkxMLW5Vp3Q9E5kj/Apxr38bXrz+BGHvPeIuPNKbSxBEM7u5ifjYneKIqo1l1s08A0lvc92SL7lmCr5rrzuRaxv8ve1iLADgQazXCBeec9+pL3IwAnbKAIBb1lVG5sYBhdcR0avs9gJts+9qrQaYQVUrK47v0rUTQK/PLwF+iih5Nnp9jWW6CvI4PjBWiqwuHcfh7qupevXn+aQIZMO7Ocq16h5is0A5dOK8vO9JLkX3KydJagNXC5Jb2LA5k9q0ijpf3rNIvc33kH56qXkEhOLJzisZ5PkFlzrqfnq7z4zmUevWsvnflU+wj3d23sbfGTjii4gt/4KjJawMr+FoF3AlXbzlBiiE/3Hm2Z3pvrSvA1ReFTA3s41hFPZt9fnObfv/k8+3Kd/OsjD5JrEUWGqlGwlsm+RuDxFyMn+L/ef5VjHf18YcchBtM56r7HS5NX+B+XTjHXrPO/3/kY29J5flQ0fJ8/HznBUCrHvz36CYZSObww5MnRM3zzyhl+/9TLHCz0tM1DgyjkmWvn+OMzrzGQzPIvDt7N7mwnjcDj22Mj/M2Fk9T89aWNhqKSN2w+NbCb27sG6bZTSOD0/CR/cvYN/tu5E+zKdvDY0N72Zx4d3MPtXYOA5H878R3emZvgnx+4mwd6l8vaDVUjbZg0G432AN1SNb5y+H4avs9YvcRvvP4si26TzSCl5HJlnn9/4nlGq4t8fvtBPtm/E0vVmWxU+KuRt/hPJ39A1XP5p/vvQFeW758wkjw1epa+ZIYv7TnG4WJvfP/MXOdPzr7BH5x+hV3ZIkc6+tEVg6SWZtYdJ63nmHHHYwNVNU0jqMXjxLUr61LGqgQZtf8VCLJ6Hr1lDHs4dw+91hCKUFr9xtJ/IZGMfUcFtP6/Gheqp7jeuMSne34BN2oy7V5vb1cSL9TI1raXTINXvh7KsGX+KtieuoWR6kneXnyR4eS+Lc28DUPjocf2su9QD2+euMyFkWnGJxdpNjxCQizLpqsjS3dvmsMHt7FvXy/FQgpVEXgtRWwoQxRdQVXiPqrkz9AIqlSCebJ6B9oGxrg/yQjDCFVR0FaMXyLptckeRRgMpjpRlA7CKA65mGnW2J4poCux2t+L4lARP4qwVY2sYbWvilA6SIJ2WxvF26/FyiCfMGqw2tx+GQJ9w/Yajr+lP5jaUhAKsXnJLsDF0gITNR9dUdAVFS8K0RQFXYkXbFO6Qd336LATlFwHAaRaHrs3wqJXJZKSieY8+zPbWvsl2J7LoysK4SqTfQWxQjmlKgJviwVvANcPqDruTXkXmcoHCaCQhHL5uafehFm8EOoKwjCk6dWoN/VNF+1rizVG3rxIoTuLVdRJqMnYC3GDryKEIAwimvXV/YOl9bAj/xUK9n1M1f6ekvMmXjhLw79Mw7/MdP0pctZtDGS+TNY8ukqZtoQwkkSRRNdUfBlQcz3OTc9yx7bude9FxPdSquX3p4rWorDvs1hvtpXUQqh0JT7NTP1Z3HCS2cZzdCQ+ianGY/9I+sw2nkfiY6idFOz72qSSrsSliDPOON3W4A2P+wfBR4I4UhRBppDknkcPYdo6qazN3GS5XbIW+GG8CuwGeG6AoiqoqkJ3f54HP3sETVdbpWgKvhcQ+LHhGi1yx/cCDEtH1VR2HRpoqY9U5qcrRJEk9MO2qtBzfUbeHWP7vl5SGZsojLhybpJULkFXX27TB8eSwWQkZcusMX6UCSFisdIGUuvN2pHQNolcakcRAkVZ3cb6n5dW1Vn385K8f20K1pKyf0k5JaWkVnM4PzJJf3+egcGNJxlSxjdJtK695ZX9m8ESwRG12ltuK/a9utHDeiVUVaGrb3nyYCclkYxjQCVQbJnaCiHWkUCZfIKNuBYpJaeuTraNrbeCbmgUuz9YBOJaomfpd8/xufjOFVRdw0qul0mu/ez649e6Xri542drJrZmkjcy9NidaEJFStoPMFMzV1/HrSarUYWpxSrD3T/6BOKjgrrrM1Ouo6kKCcsgm/jgHhtBFOLLALdVprRURruwWKdWc1eV2KxFp1ngM72fvCHJsBa6kqDqT6BtkYiY1dP8VO8nyerxdbpSyaYb2qqY0njl2MX33kZRu1DV1VG3AkHd9/BluKWEeyU8P2R+sU7T8enpzICIY+GTagJDNdr7FEayfb19GBi6Rj6TIJ0wmVqsUnXdlreKwGiVo86X6ySsmOwJw4ha00XXVDqySbpyKVShcDC7l2enfoDrxUbOi16ZoWSeC2fGMVueLZ4f8u75cfZu70KxFfwopC/x4aJQw6hKEFxH4uL6HoqSRRUGVntVFWbdSQYTOzA2iZ1fKlNbu0Iar3ZqG5ZurMSh3F52p4cJfEm1HpsS15ou4zNlMkmLoZ48h3J7eWryO9RDtOv5AAAgAElEQVSCBm7ooYqIR3qOYqn6qlh4XwZtxRXAQKIXbZNIWYB+uwcFhYiIil9j3ltcRxwFYUQxk+DI3n6yqY9fitpaBO4rqMZxQu8VQCCjMlE4zsWGzven3+Pezlvwo5CBRMeqZDVFCIpWgmKLFCr7buxlpBtszxTaRMxaXCzN87XL79Fpp/h3xx9ib64TtUUIHO3op+Q5PD16lqdGz/IvDtz9I8vfIyRSwr88dC8P9u5o3/MDqSwXSrOM1ha5XJ5v72/Fc3niyvtoQuFXb7mDz+84iK7EZSR7c51cqSwy3VyveExoOv/8wF0gwFJ1lNaCy6FCD9PNGn969k3eX5jm04N72t+pw062FUIp3YyVAok0u3OrSftFb6EV0rI8/knpJindJEJiqluPHbwo5JlrI7y/MM2X9x7nXx68J55QC0EkI3oTaf7nl77JVy+d4tHBPezIFJb7fyRuGPDlPcf5B9sPYLW2dbjYx/sLU7w4eYV35yY50tGPqVgcyz/A24svcrF2GidscCR3D5aaaI2L1t+bk84oZ8pvMuOO827pFRa8GW7N3Y0feTSDOtWgRNQIcUOH4eReyv485yrvMNa4xII709rGfXHhW6v9eEwbH6+C0Y0k5M2FFxBCiUsqEDTCKu+VX+dC9T0aYZWX5r7FvvQx0lqO98qvc7H2HgveDC/NPs2u1EH6EztIaVl67W28V3qdBzo/1y5v3QihjLjcuEqi0+Lww0V23GuhRrsQKDSCBhEhlm7iKx5ZC6rqAq5TxVItFlvPNCd0SGpJtiUHsVUbXZhElOg0B/hxphh9VCAEeH6ArhkrXlOWCT0ZkjcNkvpy/zeYym1p7r7y+aGImNxp5ZMSq4e27q+kjFVN8edNbtJ8B4gn+4vlOq4XbrqdpblIww82VeoCHO7s4fae5cqTquciiBVLS8/PpfnHXKNBpqVGXzv23wj9dgemYtBrF9rvTRoGJ6cm2V0obqpRkDL2lstv1U+KuBQP6hvOgVbCj2Ifz5X+XTG9uxkxJVYZYUds7BG3ap+JiFgaQyjU6hEJW8e2jA2PUaaQJpFJtHmiWrCcXCuRyBVqG1VT2L6vd8PxtypMCtY9ZMxbqbpnmGs8z6LzOg3/Kn60yGzju9S8C+wq/BuK9ifWkUeu67cWISUp0yBlGliatvEYVsbqp/J8lQHitMm+RIZS2cH1g1Vjt4S+nZx1nOn6U9S8s9S8sxhWrNxq+qPUvRGg5fe1oozNjRw0oTGc3EM9qJDRfnxztI8EcWQlDI4/sJeXv/0emUKSux7ez9XzUzz0+HEWZytcvzKLqqqcOznK7MQix+7bQ99wkeG9vfzgyZNs29PN4bt3cedD+/neE2+Tyia499OHsBIGAzs6+cFTJ7nzof3sO7qNl585xfefPMn+48Ns291NrpjiledOU+hMo+sqgR9ydWSS3qEiqYyNlJLJa/N0hdEqUmIJkZSU601Oj07zzqVxRmdKlGoNvCDENnW6c2m2dxfYN9jJvoEu8qnN5YBhFDExX+HdKxO8ef464/NlHD8gY5vs7C1y594hDm/vI2VvfAMBOJ7PhYk5XD9koCNLVy5F3fF468J1Xh+5xuWpBRquR8I0GOrMcde+uM1iJtFuc36uFl/8aQvXD7g4MU/T8+krZujJpWl4Hm9fHOeN82NcnJij7njYhs5QZ4479g5xZEcfndkbT3qllDRdn/evTfPK2VEuTc5RqjmYhkZnNsmxnf3cvnuQ/o7sTXtXCCGoNV1Grs9y+toUZ6/NsFBt0PR8TF2jkE4w1Jnj4LYedvUV6S1kUJVl3wApJUEYUWk4lOpNFqtNXj07ShBGhKpkZHx2Q05eVQQDHVk6sxsrIVw/4NLkPA134wfnYEeW7vzy6pimq/Tt6kGGEmsTxZGUkkrD5d0rE7xzaZzz43NUGg66qjLYmeX2PYMc2dFPXzGz6QRcSsnVmUXmKw3StsnO3gJEgnOTs/zw/Su8PzpNqd5EVxW6cmkODfdwzy3DDHZm47hkQ0NVFPzg4+NxVEjZdOdSuH5AVzb1oSZGqlBIavaqVY9G08Nx/LiT36KDvjW7jwG75wNvN4gcnLCCoWyuxtmb3sFwcrD1/gqKYqHSipRVRFuBswSJg8QhDK+jy4OrG2sRO0F48+c+YRsM9eUZny4xPVth945OdKVVby+X/7k0P0/FcTnYs8FqzU2gVG1g6CrdxTS18Vn8IKLp+vhBhGVopGyDxVqTIIpQnbgMJwwjphaqWLrOknilYOSw1Pj4RFJS8xp09ubQDQ3X8WK1H/H9XXc8GnioQvnQxJEQJkJouN5ZzFYJsh/OsZQWaigWHWbPpko3iM+JoaqUHWfNQFdS8WfJG32bDn4FgmP5Q6hCJVLCVt2+QtIyWKg0WiVrkNZSpLQktaBBIEPm3BKXalPUAgcFwUM9sb+aH/kEK4gkUzE3HXnHSTlaO1k1lCH+Bibg2ZSF4wU89+oIt2zvbj/b+ruzH0siaUlrJ2VIFE4jwwnQtjHWmKPLyuGEPnNuhYJ581HpW+H1mWtMN2p8bvt+9uW62sc3NqM1+dzwfr41epaXJ6/y5b3HV5VOfVgc7ujlaEc/6ooUmoKZYHe2k5HS3Cq1zlitxOXKAj2JNA/07Wirb4SITajv6h7i7bnxddsQQpBYUxoihMBUNQ4WeghlRNVfe8/cHDRFx1BMArl1pPVmKLkOr05dJaUbPDKwu00aQTxpPVjs4fbuQZ68eobTC1NszxRW7eNQKsed3UPMNGsoCAZSWSxVY3+hhxfGLzHj1FttqexMHaTD7KUZ1jCVBDmjA0WodJuDfKr7H2IqqxdKMnqefZlj7MvEPkiq0FCFxpnyCXqsQQpGN17k8Nr8tzEViw6zl+2pWxhOLim3BKZqcSh7J2Hr+OiKySe7Pk9STaMpBhn9l3CjJgk1voaTWqwG25bY0zbcBhF7KCk6Q4ld9Nnb2q9n9HhCraCQVDP028N0mFv3oapQyBkZQhmhaSHJhIWhGGT0NLWgjibUFoFdRQiFnJ5BVwymnOm2b09SS5DSlkudOsx+Cmacrnwjgv4nFYauUWt4LN0lirBbqh8IZZMgqrfe6SGl31KZyNjHVdisLiOLSWMpfYQw0JQ0itAJpU8Q1Yikhyo2f6ZLGce9Lw1aNCXD2ij7rRAEIZmURbiFR6cTBJiaStq4cVnZTK1GEEVEMk5zVYTA8YO41FdKqq5LZzKJ4wf4QUhd9wmjCFPT6Ehs7OEDMOUskNNTq9TctqaxI5dHV1U2G35HkcT1Qyxzc+VbGEWUGk0sXb9hkYymmATSX5VaJmVAGDmbfEKgr6ge8cPSJu9bRhyaFKu5FaHTle1mbMzH9zd+tvpeQNdgESsR+z/pio6qaPFYUvpIudpKQtU2z2YVQkEXGQr2XeSsozjBFAvNHzJR/Tvq/gWawVUuL/4eptpDxjy06rOGoeEHIUEYISNBbyZNbya9abKabsTezFJCM/AIoohiOsFtuwYoppbn45qSocN+iLnG9/DDReYaL5C37kYgKLvv4ASTKMKiaH8CVVleDDIUE10xaYZ1Os2+D96pbYGPBHEkhODovXs4eu+yg/49jy5NUvqJooi5qTK3P7iPI/cu1xTe/9OHV7Wz7+g29h1dXcZ032duXfX7T3/pnlW/P/IPb1+3P5/95XvbP6uauq4NiB9YXhDy3ZMXeeK107xzaWJLOaCla/zWLz3Co8f2rGPfl9p69sQIf/XCW1yZXiBcw4i+cnaUv3vpFA8e3MGXHjrG/qHuVYOsJUyXavy7P3+G63NlvvzwcT53137+/PkTPP/OBdw1N97rI9d44rX3Obazn698/n729MeraELE+6RrGnPlOr/137/NxYl5fuGBI3zxgVv5y+++zbNvncPxVrf3xvkxnnj9DIe39/KVxx9g/1DXph13JCUXJ+b46++9w3dOXqDmrPeJeebECNu783zh3kN8/p6D2K3Uss0QhCFnx2b4i++8xWvnRjdscwmaqvBz993Kv3r8/lXH8dpsib964W0uTswxXaoyX2m0z2vT8/k/vvr9DdtLWga//vkH+MK9hzb8+3ylwW//9fOMXJ/d8O+//vkH+PLDx5dfEOA1PaZH55BSksisJhzDMOLMtWn+7Pk3eeXs6Lpz+/alcZ564yy7ejv4lUdv5xOHdmC10uAiGeGEdUw1AVLwp99+g6feOMue/k7+wz9+jBdOXuTrr5xmanF9stWzJ86xs7fIrzxyO48c20PT9ckmrXUlXT/J0DWVvf2dN37jFhDEyqElY2RFEeRySSbGF/GDcNNSNQXBkDWAEmpEynLnHIaSMIzQNAVFWV69WtqaoghC6ZI1Bjb0N4rfJdiWHCCp2XjhHFXvHDnreLtDCcOIfFcGZQVJq4gsyACExVqvNSnjZ4W+Sce4Gfp6cti2wdWx+bg8TWir1DMNz2OmVkdX1VY0+AfH+EyZhVKd7mKG/bt6yCStVSWdAHsGls/x0t9mSrX2fQKxmbbRXlmS+H7A5dHYp6pabnDLkW1EURwfrgiBH0Us+g3gwxFeijBRlS4UJYOm9qAIEz+qEUYukpBQBq2ywM277iWPET8KV40VAumT1bvRhL7p5DipJei1Yh8dKWVL5Qp+EJFOmBh67AujCgVTia+HpRKWjJ6gy4pTAZdgKAamunzdLPql+BxstAgnJWW/2p58G4pOQl0/aQijeL9ePz3KyZHrrfAB+PlHj5Ld9fEjjjTrYfzmk0TBZbz6H6Pqh1C1fWxLLPLd6VNcrU/TaxXI6x8shWozXK0u4EYBu1Z4GK1EbyJDxrCYdWosOI0fC3E0kMyS3oDUMVSNSMYlL0uYalZphj47s8V12xZC0J/MYijrpwZSSuqBx6XyPKcXpplsVKh4Dk4YcK262H7Ph4EhdLzowyf/NQKPsVqZvGnTk0ivG+dYqs5QS7lxuTJPtGTA20LBStBhJ3l3dpJ64DGQipVnpqrFeoAV96QiFPJGJ3lW93GmasUR92uQ0rLropwjGTLvTVM0u8noecr+AgrxcymhpUho6xcvjBWElCIUCsZy8mLR3Ph52Wtv2/D1Hnu1L2j87FhktH6OC7X3uKPw8IbqqZUQQjCYGFj3etl10LV0bG+qKORbVghSShSp0KkPYqkaUUvppQqFhi+xVBl7SG651Z9sBK2Fl6S9lHAl0JQMptpFlXgxqhmMkTUP43lvEYXjgIKidiNQiGQDRSSR0ieSJZABQsmgiBSathtb29YyRm7gBOMEURV1i9KoUDZa/i4STUljaX0fyCJk6X7fLOkRIIgivDDEDYLlQU8LK7dU8zxeGxvDUFU0VcVUVSxdbxNJHYkEc/U645UKk9UqvZkMBctirFxmMJvdkjjqsQrMunEqa9HItPdrZH6evnSG5CaclhDxXMtx/S3nTrqmkrTU9vdZqZZbqSay1TR99h5sNdVW9YRRreUttcH2EaT0vczwLQDq/kXg0U33A8ANpvHD+Hlsaf3oahZVXYwJGSlXEYOSkHQ+ya4jwySzCQJ8cnoxHh9J8KMyflTZcnubQREmCX0btj5E3r6by4u/y2zjeRr+KHONF9YRR6mEyd6d3aRTFmXHwTb0DeforQODlTBJ51MIAc0w4Hq9xLBdYLZcJ5+02yp+IRTy9l0k9Z1UvFMsOq/hBBPoSoa5xveRBCS1XRTse1ZZI/hRbFkxmNiNoVjrPOd+FHwkiKMbQSDo6s9jWh+dyWkkJc+cOMcfPfUqs+Uakvjh05NP05FNoqkKDcdjYqHKYrVBJmmxrSu/4UDMDyOef+cCf/DUy8yW6yhCMNyVZ1tXHlPXKDccLk3OMVdp8J2TF2Jy6IsPsXdg68ntyUsTzJRqvPDuRXRVYe9wL525eHA5tVjlwvgcXhDyxvkx/uy5N/nNX/wUScvAMDSqFYe1sohTVyepOS7PvX0eTVU4sK2bnnwagWCmVOPc+AyeH/LWxXH+9Lk3+M1f/NSGEkkpJVMLVX7nGz/kxIUx/DCiO5diuLtANmnh+SETC2UuTy1weWqB//vZ1zE0lcfvPripl46UkjfPj/FHT7/K+6PT7YFVVy5Fdy6FZeg4ns9suc50qYqpa+zq61jXYZRqTU5cGGOuHK+a6K0UtiUiz24pbNYiaRnom5ABAKautsm+uuPRcD0qDXcd4bMEIQSZYppaqYG1QerCmbFp/vM3XuTUlUkiKcmnbPb0d5JNWHhBwNWZRa7NlBgZn+X3nvghUSR55OjuuP42cjlffYsdqVtJqssquqnFKl976T2efOMMTddnR0+Boc4chq6xWGtyfnyWct3h4uQ8//XbrzPUmaMrl6JUa25ZevX/RwghcENv1crM4mIdRVXYvbsH2974WaYpOlpgcP78VJy+Z+moqkIYRpTLDRIJk0TCaCX/CVzXx7J0tg0V0ZUEC85llE1IBU2opLW4ww+iCk4w2Uo1jAcshqUzM7HI7gP97WetxGupXXxYI0cWxAa8H3SuNTlT5uyFKfp6siu8i1YeA4WErpM2zQ9MSi3h8J5+ao3YPNLUl5OS1iVjSEkgAxbcMlW/RlM4uK5H4AQEUUglqLXM4mM4Ta9dipzvjFfHIynp7ciQMA2mgypu9OGNCMOogh+OkbYfp+m9hhHtw9a60EQCVZgEssa8N0O/PbxpqRpS0vADJmu1da/rioW22eeAtJZcQfQImq4f39siXpnMJK3231Zi3qtyunSNtG6T0W1204tAoAmVoUQf5yoXYyVZdZRa0CBnrFdkRTLiTHk5gSVnZCma6+XVhUyCX/u5ewmCiLrjYRkahq614no/PpBRBVAQajdG8kvo9uOxf4WSQwibHSmTjJ6gEjTpNDNk9M0nHh8EfhQP0Nd6Ji1BEQJdUQmiaBWhs+V3af23GQxV3dQbbS2WJmKGom44FDZVdcO540Sjwl+OvMXTo+cIo4gOO0laN9EVlVqw+SLTzWApWTG6yeOxFpKY6LU0bdPjoCsqAoEbhqwdm2mKgt06X5utcP844HlxuY6hq9xZ/BQXqqe4WHsPQ7G4o/gw3dZ6Iub/K9SDMlPONQ5kbmMwsfOmPrPRZGpkcY4Fp0EoJSndwFI1yp7DvNPA1nRqnsdAOkPWsLhUXiCpGaQMg/v7hn/M3+ijB8PQ4lIad/n6U4RKytjLfPMHSAIWndfoTDyEIhKg9iCjOsgQlCRChnFamRAoogOBhhAWQkmCUEno2zDVbtxwmoZ/BTecxtS6NtkbSTO4hhNMAmCqvZha7weaIFuWTlcxQzJhAhvfuwldJ2WY6OqauccKPzCJxNY07h3qpZiI1SJLNhFuGLLYbFK0bfZ2dLStJZba21UsYqxtew367A767NW2IQndoDedouH7mxJHihBkEiZ+sLmdgCIEpqHTdAOW+nW1rQwL8aNKawwYKw2T2urKm7p/iTDazL9NIW3sRxEGkfSouCeJpLeqfG01JGX3nbZnVtrYj6pY2ObyGE4Vy/2cH5UxEkbbB1aRCr32EJrQgIi6fxF5E+VxW0EgSGjb6Uo+xnzzh0SyiRNOrTueqqpQyMXqw0IyQSG5tJ/rty+EoNiXJ9eVQTc1Ptm3GxVBue625purr0VdyVGw76XinaLpX6Pun8dUe6l5ZwHIWXdgql3tRTtNiT1HG2GdK/WzDCZ2kVVuzk/rZvCTQRwpgt0Hb75DCsI4sv1GHWgYRUxXa2iqSlcqlg9OV6t0pJIkt5AlRlHEa+eu8V+efpWZcg1VUbhz7yC/8OARhrsLpC0DRVHwgoBSrcnFyXlKdYedvcUNJy5vXbzOH3/r1ZhpTNn83H238jN33BITUIpC0wsYmy3x/7x4kmffGuHdyxP80VOv8NtfepTcFtL809emOHt9hl29RX7tp+9m/2A36dYNVq47fO/URf7wqVepNBxeOnOVExeu8+ChHWia0vZCWolzYzNcGJ9je0+eX/vpe9g3mMQ2qxhqBw3X4AfvXeb3n3yZUq3Ja+dGeeXsVT5z2751ZFnD9fmz59/ktZFRNEXhc3ft50ufPEZ3Po1t6IRhRKne5Pl3LvDfnn+TxVqTP3v+TfqLWe6+ZduGx/Dy1AK/8/c/5ML4HEIIbhns4h/cuZ/b9wyStk00VcX1Asr1JlOlGhcn5rhn3zaCIIq9DohXsXf3d/CffvVn2uU3fhjyH/72Bc6OzZAwdf7tFz7BLYPrOzJFEXRtUqYGkE8l+Mrj9+N4Aa4f4PgBf/P9k3z9lfc2fL+UkspCLTYj1NRVpXQL1QZ/9NSrnLoyiaIIHj26h5+//zA7eorYpkYQRsyV63z77fP87YsnmVyo8l+efoXObJLb9wyiCg1LTRCtkdVXGg5fffk9ckmLX3nkdj59bC+FdCImQV2Ps9dm+KOnX+G9q1OMzpT4+qun+dVH72Cx1rzpCcRPArzIpRk0SOmZdSa+HwRO6GJry0x/ImEwO11hbrbKPffuIZtd//jVhIISqVSqTZDxQL1UbtDVGU+0a3WHUqlBKmUipaRac+lvldAG0iWl9bSSvdZrSlShtkkBRbFJGbtWmSiaps6u/f1rCHqJUNIorJWYx+iyk1uavm6E2fkqCVunWotX6Bthg0V/ob27uqpScV38LeTjN4Lnh4xcnaZcdbjj0Db6urKr7iEndLlYu8q7pTOMVC9T9qs4oYsvfYIoJJQtc1cZrZrwGqZOebFOV1+e0nwt9mMKIxzXR80l6WiVWHxYCBTCqETTexU/uEbdeR7TvA9LKyBQ29fSlDPGztT+TdvJ2xZDmdUqAU0xqQUL5PTuTW0jDMVoexC5vk9XPi7V1FWV+VKdYjZJR269sqXPLtBr50lqZuxx1Lr8hBDcUzzOq3NvUwlqXKyP8vfjz/FYz4N0WoW2B0k1qPH24mlemnsTiK/V+zpux9qA5BICrk4s8tSLpynXmuiayv3HdvLw7cNEYRmhFFibnvOTiMB7HSEyRMFlNOtBFK2//bcXpk5xuT5FEEVYqo4fhTw+cCedVnaLFm8OedOOU16cxoZ/d0Kfmu/Sk8iQaHmdrMzc2uj6bwQebhhs6vXzQSZ7Sd1AUxQqvku4AWtd89cnIDmBz5+ceZ2/vnCSh/p38Sv7bqMvmcXWdFQheG7sPP/La9+66X1YCylpGyZ/mJVdXVHJmjYN36O+AYkVSUnZbRLIiIKVWLeNpd877WRMMK1RVzYdn++/cQFD1yhkbVw/xPdD6o5HPm0TSag3XLqKaS5fn6O3I0ul1oxtAwopzl2Z5tj+QcYmFylkE/hBxMx8SNI8gmw4ZFI22/f0tZLqbh4yuI50v4uwfw4hVKTzLOjHENrGZq6y+Q1QBxDG6koBIQR99jB99vAH274MIZpHqJ0sdUAHit34YYjfMjc2VA0/DHHCoD2WNdSYtBxK55htNqj6DqGUaEt9TOOvQT+M2CLg5icSUmIaOtGqvllQTHyCydrXccMp5hsvMmfdTVfyp9GFipQuoLbGGhIZNUEYCKGxjgCVIZ3JT1PxTuNHJSZrf0/K2LNhuloQNZipP4MbTiPQKNh3Y6k9H/D7wPRcha6ONJ3m5n2GqarrfGpVYbX3K4iq6GqTgWx23fzE1LS2n9FGsDaJa1+J+Lpb3W7Btrl/aBiAhr9xCZgQsWfnVtUwAkHGNhEYbcLd0voRQkNKn4p7kp7kZ1HU9VYtQVRjpv5M29B8/fYFaXM/aeNWyu4JKu4pFpuvxibOa8bWUkqc4DqzjeeQeOhKnqL9IIZmk0laWK3gEXtFIFPJeYvOxKcQGO3tGa1z4gYzzNa/ven3Xt5uFB+FLZVqK4+/gio29z1dIg2XsNGauhACTVfR9PgYJFv9qKPF80NlhX3K0jY7Eg8xXv1b/GiB+cYPSOg78KIFdCVPR+JBIgzeXRylz87Tl8ijCBVDGNSCClJGN+1DejP4yBJHXhDS8D10RUXXVKIowlA1nCDAUBUavh8bfxkGThCvDquKgq3rnJqYRlMV9nQWgdi3I5KytdIe4bZqVjVV5ez0LPP1Bl88eggvDPjO+UvcNjjAwd4uvDDE1DTcIIhXxFpE1Hy1wV9//x2mSzU0ReETt+7kf/rZe0m3lCEV30GRAsPQ6OxIkS1YRFJSj1warodEktAMTEWj5nj87Q9Ocn2+jK6p/KNPHOVLDx3DWrFKbugamUQ3v/75B1ioNnj13DVePTfKC6cu8rk7D2zq/xNFkv5Cin/9sw9yfFc86FxqszOb5PG7DzJyfZYnXjtD3fF45/IEd+4bQtNVstnEOsInkpLOtM1XPv8Ad+0dIohKLDZfwtSP0pHZwWfv3M+lyXn+xw/fpeH6nLw0wSdv3UlixQNZSsmJC2M8/855QHDP/mG+8vj95JIrSrE0FdvU+fkHDlNpOPzld99icqHKk2+c5dBwb5v8WrlfX3/5Pc6PzwFwaFsP/+sXH6IxU2d+tMSCEKQzsWGw5/lkfMEdPT3gRpw9fR3fD0HAzt3dZLMJdvUtl/q4ftDef0UIhjpzbO/MMzNZwk4YhGGEYWgIRUGTgtJCrXXTx4Sn0/CQrTjXdM4m0zJaDsO4nnUzyEji1F1mrs0RBSGGrZPMJIik5AfvXeaN82NEUnLbzgH+zc8+SDG9XBNraDDYqfPlh49jGRq//82XuT5f5r9/751Y9aT7Lbnn+oeIKgT/5NE7+Nl7DmKsIKwMzebOvUM4fsBv/MUzNFyf90enqDZdEmZ8HD4uqPglrjWucCBzBPUGK0FbIZAhVb/eSvkQ+H5sWOw7AeGmHbkgn0tweDguyY0iyfRMhXTaIrWB8mwlkloXzWARS82wWUHz0qt+uEDFPY2tDUIrutX3AkYvTNMzUEDTY7NZGdUw9KNEshKvGq5sS0DVd5ndwIh2Kwz2FVgsN2g0XUAwaA9SMFavpmUt66YDBTaCbemkkxZDvQXsFUqUSEaMNSZ4cuK7vLFwkmYY1+arQkEXOpqiYaoGCkp71WbOXe2gi/oAACAASURBVGyXT1m2Qf9wBxOj8+Q7YqLYMjQajofrBURGRN3/8OoFIWx0dZAoKpMw70NVuwhkRN2/TNE8iCo0TMUmrW2eIBYhqfv+usGqJKQZVrZUfiwZ1kq5HKcdr/bFffJWysJbsv2M1efpNFeriXalhvl0z4M8PfkCjbDJs1Pf4/3yCDtT20hqCfzIZ6w5yaXaKM3QwVB0bi8c4b6O2zbczny5wZMvnub2/YPs3tbJfKnBt146w1C3x67eS2jWQx8L4gg0QvdlwuAy4COUZYXx8fwB3MgnqyfZnurizYWL+D+myN3DxV5Susnbc+M0Ax9bWyaSpZScnJukHvgMp/MUzVZaqqK0Vs3lOsIpkhFXKossuE0yxgcPGliLbak8WcNisl5hrFbilvxySXwYRZwvz+KEqycyi26Tt+cmMFSVf3LL7dzWOdD+jB+GTDXXl2WvxdICkxeuP861oEokI3RFbz/vPwiyhsXBQjffGj3HSGmWXdniKuXRgtvgzOIMhqJyS75rQ+U6wLzTwA0DtmdWK/VmFmqUEw65tEXD8Xnv/AR7tneRS1lMzlbQNIWDu/u4NDZHFElqDQfT1HC9gKUwEdcLsEyd2Va4QW9nhlfeucJdh4cZnynhByHazaSrSonEgagO0TyE14EgJhSUPLQUBVJKkHWQTRAaiAwynEJgIMPZVvl0i8SWVZA+KCkEJhIPWj4pKOn4M7JJLBdtgtDj7QRXkc2/g+SvgkgilCRJLQSlvvwePFADkG57P2IDlXjfimYCRJGYFFmM/w3HEOrNqZ5+kiCEQFMFydTqsWtK301P6nNcK/85fjTP5cXfJYjqdCYeQVOSCKEgpRf7tEkPL5jACSbIWkdXRccLVLqSjzFTf5aqd5rp+jdJ6NvoTn4GTckh0JCEBFGZqfqTTFT/DohIGnvoST2+joy4GaSS5g09VCuuu25IpYoktjZIxT2FHy0w2/gOtjaEpiyNv2LTaEmEwnpf2nhxPkDKoGU23UoJbCXDAUTSxY/KLSPm2IQ8NiPXb8p8XQJ+EFKubb64J4Qga1vkEst9Zto4iKHkccImC82XmW48Q0/yZ1BFEhBIfLxwnsna11h0XttyH3SlSH/652kEV/DDea6Ufh+EIGsebxEwgggfx7/OaOW/UvXOIlDpSDxMxrwVRagc2NXb3teMdQRVJAhlg7nG8xTse8hbd7W9sCQ+bjDNtcqfU/Hea5+LzTDX/C5h1CRjHsZQCyjCJE4mE0BEJD0awRWmak8QySaakiFtHFx/PpEgZUstFcUG7zIiku6Kcj9JIKv4Ybl1HlVAQQgVgYaiCGqtipRcYtnnTghBUt9FzrqN2cbzlNy3cMJJItkka95KxjyMBMpek4Rq0mvn8CKXQPpk9Dwz7jiWmsBSfzyq5I8kcRRFESfGrnNtMa7pPNzXw5WFRW4fGuDVq9fY3Vnk7euT+GHIHUMDvDY6hqVpeGHIY/t288rVawRRhK4qzNUb9KRTzNYapEyDIIq4MDsPwBePHmK4kKfmxgP9jGUxkMsiBNRcj5cuj3L/zmFevHSFB3YOk7HiQc/ZsRnOXJsGoLeQ5ksPHWM2qnJhbgZT1dppYKO1BdK6Ra8dx15XPIdFr4FAcKTYz2Ayz9hsiZOXJ5AS+osZPnPb3lWk0RKEEORTNp+7cz/vXpmk7nh8791LfPLWneRTG18MQsB9B7dzYKh7w/ZMXePYrgGee+cCtabLxHwZ1w+QUWzku3aSIIA79w1xeLi3NZiwkDJoD9INTeXYzn6efvMs5brD5GKVphesIo6ars8rZ0ap1B3SCYvHju9dTRqtgKlr3HdgO998/QwzpRqnrkwyV6mvI45mSjVeG7nW3odfePAIO3oLXK75VKsOqqJgmjqu65NMmpQWG6iaQiJhxP4khsbUZImhbRt7w6zF9Pgip05cQdNUuvpyzE2V0U2NvYcGGL04Q7PukivGk8pqudEy0vW555P7Me2bk5GrmkqxL8fk5WkqCzWaVYdkJkHT9fneqUuErev7s3fuX0Uatc+ViNOjHj6ymydeO8PFiTlOXZ3k8tQ8uwbSpPXChj4pQ105PnVkF4amAWFblaQIE0UR7OnroLeQ4dLkPJWGy0K10S4V+bhAoOBHcSTvjwJLNWIlVkt9oSiCoaEioy3fqi33YUmWqwr6em8cMx5ELgKFvDGMG914EqQpOVgzyIoiSeCvLIMICMMJwnAaVe0CbXV5XRBF6EJFV7UP5CtQq7uUKg2KrSj1td4aYctY8sP6jSx9l85CmvlSjUxquQOecmb5q9Gvc7o8QigjFBQGE73sy+xiMNFLl1kkraWwVBNN0aj6dX7vwp8y7cytat9fkcQpgen5GkO9Bdww+JE8X6RsIGUDhCSSVQxlH15Yp+6PkTf3ois6Q4kd5PSN0y4h9gAqOU0WnCYHu7pXjHcFCgr1YJGUlr/hOXM8n5nFGrapY5sGmqpsumrVCFxGKuM0Q58Fr8bO9PLKr6EafLrnAYQQPDf1IiW/wtXGda42rqOgrCpjyupp7uu4ncd6HiS1Sapgte5g6Cp3H94el4D3RIyMzjA9V2JnT40tned/gqAZx/CjWaT/DlEwhlCWCdq0vZ+UZjHrlhECSt4HI2+3wtGOfg4Vejk1P8HTo2f5qaF9JHQj9iUsz/G1y6ewVY1HBnejtYh1Q1HpT2ZRhOA71y9yZ/dQexX1YnmeJ66+jxt+OOPotei0Uxzr6OebV8/w91dO05e8h5xpE0nJ+4vTvDx5lWCNWlERAk0oBFFE2V02wA6iiFMLk3z72sgNt1u0EvhhyPsLU/zUtn2r1FOWapNQEx+6VC2lmzzcv5vvXr/IVy+dYlemyL4WQeQEPs9eG+G9+UluLfayN9e5yb0rlpN916CzkKJoJcimbHRd5egtA0zNVVgo1envzmEZOraps72/wPmrM3QV0zSaMRkOccm+54dx+a+EjnyS2cUaB3b1kM8mCMJwVQm/lJKq5/HuzCR+GHGoq5sOOx6nSFxofAMZjhITOa0yaO8lpPs9RKofKIKsQeNvkLKGUFJgfwGkj/ReguAMoCMS/whkBek8A9JFqANI62cgvATOC7Ehs34ErEfAfR7CWSQNhNoHxgNI5ynw30Y2EgjjNqRxHJrfRIbXAQVhfxaCa0jvNRBmvI3EL4Ewkc0nQZYRShasz0M0jWw+gRBJCEbh48Bdr4EEFipN/CCiv2tlgqNNb+rnqHkjzDdfxAknuFz6HWYb3yZl7MNQ4xKtICrR8K9Q9y9hqB0cMP7P1cSREFhqH0PZf8zFhf+MG05wtfSHLDZfJW/dha7mCaIKi86bLDqvEEQVdCXPQPqXSeq7N9jjrREnQSubrbMBsWLo/u3bMNcog4RQKNoPMtd4gVA2mKj+LV44T9Y8iirsllF4GSE0elNfQBOrKxIi6TLTeJaGd4lA1gllgzCq0fCvEclYjb3QfBU//A1UJYWq2Ggiia4W6Ew8QkIfvqnvGIYRNcdrzcU2sXsRq8v4E/ogBft+JmpfxY8WubL4B9Tcc2TMQyhCxw2nWWi+SsU9ha4WMIVBw7+8cdNCULQfpOaNMFb5Syre+4zM/zYd9idIGbcghIYTjDHffImadwaQpIz9DGS+tKG/VVLfTdY6zkLzZdxwhgsL/5EO+5OkzVsAgRtMMd/8ITXvLJbWRxQ5OOHEpsen7LzDZO1rGGoHaeMgCX0HptqBIixC2aQZXGW++UPq3iUAsuYRCvZd6xuS0AiuMl1/ijBqEEZ1gqhOKGs0/NH4XEiXy4u/i6l2oigJNJFEVZKk9N10Jh9DIEhaxoYLA4qwKdoPsNB8BScYxwtjv9yC/QCqSOC3koiXqj+0VohBRDzOdcLmx5s4coKA10evY+s6AjA0jarr8droGEnD4PL8ItcWSuRsm9lanUhKHtm7i2fOnkdVFPZ3d5KxLHZ3djC2eAnHD6m4bqz68Tx0ReHdiUlYmxK0Ahkrrml9d3wSU9NIr4hOfOvCdarN+Ma+98B2dvd14IsANxmiCtEetu5Md8QTeEWNlQatEghNUUlrcXtxalW86n1ouIfeQmbTVXYhBIe29zLYmePc2Awj47NcmyltShylLJPjOwfaEr+N0JtPY7TY9mrTJQzj+kqn4eE43qoHasIyOL5rYIURssDWd6EpWZbe2J1Ptx+wtaYblw2uwGK9yduXx5FAMZ3g1u29W6oKhjpz5BIWM6Ua89U61+fKbO9ZrtWUUnJ+fK5t5Ly7v4Pb9wxg6Br79vevi7pcORkVQnDk+DBhGDEwVCBxA0XHEjp7s9zxwF5MU0dRBcO7umMiKmliGBpSgp0w8L24lhspUTUVw7z5203KOE1tcE8fUkqyHXHnen2+zKXJmPjsLWS2PH5CCHryaW4d7uXy5DwL1Qbvj04z1GtRC0obxjPeur2XjkwSIcAPF6m5J9DVHlLmYUCQTpjteHrXD2i4XlzW9/GYq7VhKOaPZCYnECRUe1UJnxACzwvo6ckyMjLJIVMnnf7RV+ABQunhhGVUoeHdgDhaktImtW2rIkXthMHuQ/3oreeFEDqavo8oqiDles+zSEosTac3kV5nGrkVKjWHpuOzsFjf8O9hFCEEWJr+ocmjWsNheq6C4/kErciRUIZ8a/J7vFcaISLCVi0e7X6AR3rup9MsbJhUZirGunLFetVhx75e5mdi08WZhSrppImqKZRcZ8NSk5uGiBPmPP8Spq4jhE4znMHSOhGo1IMKC94saS2HxsaDQFNVOdrTixME665gXbFbHlhbnyshBOmkRTZpUao1CcKQIJSo6saEXkIz6bHyCATRBl4RaT3F7YXDnFg4RdmvUjBydJh5/MjDUHTyRorhRD8HsrcwnBzEUGLiOt5UHKkrWquutmXguAEjV2cY6slTbbhcny5xaMePXqb1UYJQshiJLyKEjWrcgaKuNhA+XnAZqY5T9Zvc3bGPLvMG3/8m76XuRIp/dfg+fvONb/Mf336BJ668z3CmQMVzeH/h/yXvzYMsu+77vs85d79vX3rfp3t6FswCECBALAQIggQpUSSlSIakSIpLu2VbcRw7FVfFdsVJHCVxVWwlkctxUlYkUbIWipLFTdbCRaQIgtgHADH70t3T6+t+r99+15M/7uvX3TPdM0OKpZDMrwqo9+7cd/uu557zPd9ljS2vzY/Pv4Nnxo727yIhBB+aOsFXVm/wH669yVKzxnSmSCv0ebu6TtF2GEt9a66Pqxv8xLEHObe1ym9deo1zW6sczZVpBh4XaxUm03mWW/sNUYuWyxMjM5yvrfM/vfp5vrR6jbxpc6NR49J2hbFUjoXmnRN/nhk7ymdunOd3r5xjqbXNkJOhG4VkTIu/dd+DGNJI3hkKLtcrXKht0Ap8VtoN1jtN2oHPH1x7k+lKkZRuMJrKcX95tJ++9J6xI/zk8Yf41fMv8Xe/9AecLY+S0k2WWtu8XllmJlvkF08/zoCdOvTp1aUkVLe3Y65t8PgDSTrZTn+hmEthmTrlwi5Am03bDJdv9x+bHEn6CrMTyeTaznO5s72h0v5EPwX8X6+9xL97/RXCOOZDc/P80nven/QNo5uo8CIi/bMQfB3lfRHQwXofhBdhJwUpribMHfdHQQ6A6N0/2iTC/QlU699CeAHlv4yQBTBPoNq/g9CPgj4D9rOIeB3V+WOE/T6IKiB0hPMzIARCOOB8H0o1kJm/l2BY4Ruo4E2E/T0Qnkd1PpOATMSI1M9B91MQvJqAWqqFsJ5CdT6J8L+GCi8hzAfBeBAav8RhnjnfySVI5PNteftEpaNPMF/8p1zb/hU2218kiLeodr96KCPFkDkOkr8LIRlwn0UKh+u1X6Hpn6fS+RyVzudu2RcNV59hOv8LDLgfvC0e/V6r1fbuyKSVQhxqXVJ2n6bhv8VK8xM9ad3HWWl+fN86aeMEg+73ostbgaMOK42PU/NePPRve9EqXmd13zJd5nCNmXsCjqQQzIwUKedSt4Uy3ak0kWEy99P40Sabnb8giCssN3+b5eZv99cRaLjGLHOF/4qt7l/SDq4duj1DyzGV/3mkMLnZ+G264U2WGh87YH9tys57mc7/bdLGsQO2BIYsMJP7RZSKqHW/RjdcZKnx67Cn2yswyFqnOVL4L7lZ/0267ZXDj1WmiJRHK7jcM+8+uKRwKNpPMFf4h1jayIHrtPyLXK/9yqHbgIht7+XblhbtJyi7z2BoOnnX6U3c7y8hBAXnMdLm0Z4PlIelDVF0HgckMQHjbglHS56DSIUIJMPWBK6ewZDfOiT72xI4MnWdUyNDbLbalFIuI9kMs+Uif3L+Ej/zroeodjpUmi0yts1UMc9aI/EZ2tGK5hyb15dXKaYcCq7DK0s3afo+p4aHWKxuk3ds8o5Dy/c5t7zK5com17eqGDKRrlVabSbyOY4PlfkPb57nB06f2KU1hxE31hOHd9nz0nFMA3dPo3UQSLGzfC+QESvFpeXdlK25kfKhFOSdStsWkz3gqN31ubq6ydkjoweum7JNRkuHA1GQzCTtpXkn/QFFEEY0mx5GZvdmcy2D8fIeQ1uhEcUNgmijN2sgMDTZHz9GcXxbX7Wy3WK9msyOtroeH/vcK/tSjG4tPwipthKaZRwrqs1baPCxYqlSo9X1EcD0YBHDWqHW9TC1IoIkbUqXLp1wDUsrEcZNZM87QxcOIDAzXQJiiDNEcYdYBTj6MLc+IoGK0G2Nop3pnYP91zQ3kOrPZtp7dLcAoYohVhjy7o+dihWtegev45EppDF6YN3iRq0PWo6VcmTdO4NdUghmR0pIKYgjxaXlCt+jjuBFbTpxG0fb35k/Mlza81sbXSvvmxHSpOwZMyfAgRCCcjaFpn1rtLPfDiUQFM0yh4d23ts2JtyEdbFz/Tsdn+WbVdbW65w6NU6t2joUONphYOyAV7d933mwRLLMlGmyxghV/zpp/R4SvUQy+7FXthQEEZ3WLuihVEQUXkPKLJp+BHGLz4AhNRpBl+uNKkcyRTL3KEMZHcxxbbHCxMjtwOXOMXaDXaPGb6aKuRQpx0pMKPVEdlfxqry1fbEPbNyfP8mHx95HVk8f2kb6cbAvkUgpxZHjIyxcWafUM8cu5VJJRL1rYQTyr+T3JYWNYz2Mro0gZQopXFx9mG3/CoqYdtTCkvaBINfecg3ztuhxUzqMuccTQ/x7OLUqVpTyKXLpxKfLMrQ+A+GguthYoRF0OJLe7wGnUKx01/nNG3/ItdYi4+4IPzb5/dyXnaETvIGKG2gyg6HlQbVQ8QJ+rBNEN9FkliiuJ/IT1cYxH2Agn+GJ+2f4+J+9RhBGCCF46OQk89MjEF2+TVL5nV66/X5UtErov4RmnE0G1sJEodCQ6EJjy2sy6hTv6DGjS0nWtEnpJrInRwyDCKnJfemFUkjOlkb55w99gN+99gavVZa5tHgRQ0mO5Ev8wn2P8r6xOZxbANUHymP804fex29deo23tlY5X1tnwEnzxMg0PzR7hl9+/UtsdFu7fRzVM281LewDvI8ESeR0zrTRxC7bTQrBqeIw/+yd7+d3Lr/Oa5VlbjSqDLsZPjJ9kseHp1l74Y/7/ks7x/5j8w9gSslnFy7wHxcuYGoa05ki/+nRB3hgYJTKC619srxb6+GhCf7rB97D7199k3ObK0RqmbRh8q6hKfzYT977vYnD/7hwgV+/+EpPjpLI2zQp+f2rbyCTRpsHymP8y8c/jN7r0DuawU+feJgRN8ufLF7k5Y2b+FFEwbL54MRxfvzYAxzL70+q1aUk17umSbKURItFv19iazo508HWbk+kTXzfuG35vciDk1UOXy+KY/5y6UZftvv62gqVTpuxTLYnFbNB5EGb5Na0zn5powjrSej8EcgSuM+BMBD6kZ6ZcjaRu0U3UaqLIEYYZ0DmoPsnENeTbaudCQoF+hEQBzDcd16D8TbEaxC8BUIm24u3er9Lgyig4i2IVkBVIXgToU+DPgbBKyBHELKEkuU7np/v1FKAbelsN7t0/XBfmq4QAlsfY77431BzP8hW58s0/Dfxowph3EQIiSZS2PooaXOevP0IhnawYa8UJmXnPbj6NGutT7PtvUo7uE6sOsk70pgkZz1IxnoSRz9GzY/Y7G4w6KRphwFKKRzdIFJTDKR+njD2UeII236XnLnLQEbQ9ykUQmfA/QCOPo0QEtc4ctfzoYk007m/Tdo8QaX9p7SCq4TRNgiJJtJY+iAF+2E0eXvfSAqH0cxzFJ0nD9y2H4Rs1tukHHNfiE+nKzDEDNVGB0NPpOVNz6Js/WfU4wp5a5DFtQ6upVHMunT9kFLWpdVNvI7SjknWOstM/heJVUDOOtuTTO1WAgROMl/6J6y1Pkm18yKt4BpR3AQ0HGOMgv0IA6lnyZon0GQWQw5gyDS6zBNGMdoecFEIgS4yTOV+jqx1P5udL7DtvYYfraNUjKHlSRlzFOxHGUg9iymLKJW0YbvpYrv7lzFPcbTw37Le/iTV7tdo+0sgOkhhYetjlJzHGUh9EFefRmUCMtZ9GLKIPMCbaDj9UQyZp+G/SStI5HSRaqNUhBQWplbE1uawOcNY/gM4xgCxUjRaHTQpcCyDtWqTtGPid8fJiJ/HtQxilQTHKKXQNUnKNrm9TUj6io4+gRAalqFzZnpHIbQzfpb9c2hpZUrOU2x7rwGQte7H1acRIkn0bQSd/ntPFyYxETc71xh3Z7/7gSNdSp44MkW13UlkRrrGqeEhJvI5Sj238rzjEEQxpZTDs8fnSFsWzx4/Sto0ydgWw9kMOdtiNJtlNJdFCkHOtrhveJAgjtGlxNJ1Hp2e4MGJUYqugxSCHzxzH5pMfJEWq9uM5TKUUruMnm4Q9mPebVOnmHH7A+mDgKLbau8iBVuNXe1pKXv3OF1Dl/2ksiCKqd5Bu2roGulvImlG1xMjuFuPx9A0Mvbu4FGpoHc89/6C3Gq2+zTy9e0W//6Lr93zbxXQuWXgEsYxtd450DTJQC5FN1rEj+vYcRkhDOLYw9FH8OMqYdykE61hyCw5c54gbuJFFRQxflTD0gqEcYsEBMsC+2ffztVuYNaifuc3azh4UUg3DnA1kw2vjh+H5IzEI8qUBinNQghBzW8RxBEPle6ufU88RsDNumjmbqNeb3sEPX+crGth3YNErJTd9avabLTwQo+UnsM64GW2wyZK9iEgjjtwSydo79WOopiF9SrlbGofE+w7uQIV4MUe6q/Q70tYPfs3UCikmD8+wumzk2zX2hSKBz/vSXxyA4HWl4FGcRNNphMzT5F4icSqi1Aasgd+1vwFTJmmHtykaM2hcSdQUdCNVvDCVUwtAXA6LY+laxvMzA+TYA4hsWoSRxso1UGpNro+h9gjcWyHAWndxL7DgKt/XEoRRjFLq1XecWqCytbB8hpNCvKOg9bzOLr1MtwLb+Lm+jYLK1vYpsH0WJGUY1IL6tSD3WmpM/kTh8qhdqribeHHu6kYvhdwbWGVbsdnfbnG0VPjaJrk6s1NbFunbKcJ4ri3TXFrc3/XiuMGXvB14nibOOxiaJNJmlrcQqmQtJ7hRvsyI2ryEL7R4aUJnZxxWELN7SWlZHO7hW0m2vuxwXwvhvmwUpSsDGljP708VjFf3niRt7YvoAudpwce5Wz+JJIuAU2EZmJoRRQxYdxAKFBoBOESShtI/BykBlgIoaNpiRn2ydlhWh0fy0hYE6ZWIYgEt6b/fadXFLxB0PkkKq7i5P4Hwu5fII05XqnGvF1fQheSII4oh2nG82Xqmy0CL8B0DCzHpL7ZJFdKk9mCf3HfB1CdkOZSHU9r8fUXrzB7ehI3YyWDpYxNdaNOKuMQvFnj5+cfpDF8io3NbVrVNilpcDY/RXe9xdJKjbkzk1SWq+TKGVJZh1OlQX7BfggvirlUX+F0YYJG2MLUFX/v7OOsdrdxdZ21Tg0vDknZiv/x0fcxnS6z0K5gSB1bGrQjD1savH9yhqfHZwiUx43WBuNuCV0m/ZOHByeZSOW5VN3EMQxSuoEhdMquyz84/SSDqd0Z/nYYUO94PDd7Pw+VJwnjCMcw8MOY48UylU6bf/yO92FrBov1bVKGQd3zyFo2URwTxBFZy+JYdogfmBA8NDqGQqFLjaxh4RiSZrSFRmKa/Nzc/bx/Yj65frHi1es3KaVdBrJpNpttMo6FjAW1Rpeq6hBEMWnbpOuHPFk+wsn0EBvtFvmUTasTsLHVoiRTrNUapCwTxzRoez5lmeJ/efBDTBYLKBKT1U4Y9Dw94cNTJ3h0aIq8dbvko+Z1uVzdJGWYHCuVv6VpbEIIMubu+ydlmrugnHBBdSGuQrTQk6odVAGYD4E+h2r9GiJaIul9aOz2QgRoUwl4Y723D0qp4DWE+9OJ91Gwd4b/1iQ+I5G/qRZC2InHkjYNzveR+CdJ8D4P+4JEBEIfR6kSOM8BXrKu+CLEN1HxaOLd9N1GwyZ5N5+aGyUMo/6ETOCFNGstcuUMjWoLO2VRcp6kYD9MEG8Tq26SpIZI2m/hoMts4iVzh3evEBopc5Zp428RRNtEqtljnmpoIoUizcsb62x1rzPsZoiUou4nk2HdMKTSbRNEEUdyH+ZirYImBI7RIbdngsu1TR57cBZNlz1fnacou08B4EUB9aBNRt8PNLbCLq6WhEAIITBkluHUhyk5TxHFDeLe/SyEgSZsdJnu9dFuPZc2w+mPHHr8FxY3uLm4wtx4iVDTiGOFY+lcvbZCySpy+eZNTh8Zodpo89b1CpODT1NrdkgN5NioNRksGKxu1bm2UmVurMTCWo2Ma/H46ZnEsNo6PFwj2X+BrQ+T0n4YYb4fm21iFeAFESJyCLpFfD1Pm4hWZwbfGyOXSdPxBMu1CkbPUHyskO0n22o9uVXefidBXCWOEya7FCa6zKDLLEJo+H7IykqNOFbEPQKCpiW+we22j+OY+L5DFH0/VvgkImwxO1vCMAw04WJohZ4PCFiPbQAAIABJREFUlKDoPEbReezQ43T0ccYyP0qkEmlZrLze/apASKSwWFjo8n//1qs895EuD54RhGHE61eWiWLFsfEBXr+6TNB7JrKpH2DlZrufxi2AicE880fHbkvmi+JtwvAmEBGE15AiBcQIYRPFW0CMJotoskiifZV98EsTLiXn3f3vptTYDtrEKIZ7IRlB7GNr7v8/zLFfePEq80eHyJs2K6s1yq6LbejYxm5HYCiz+9kxkhdS3klOoI5kLLc74B9wXBaWtsiPWJTT+wdrw9kMi0tbpAvJS87tURK7QUAx5XBiaICtjSbVWpsTx0YIwqgfoa5rEksXhNFmT5MqiFU7iaHsmWoJoSMQCGESK69nrlXE0BLKb8fbneG37wEEkEL20ec4jml7h0cNSiH6HgTfSElNksnalMsZ6v7u/gkhMPYg37Hqook0mswekOF0cLW7QR+Qci2DcjbVB97uVoamkbll4BLtOQdSJOhvwTqRmK2FG5haEV04CKFhanmE0EirI0hhYMg0YdxBly4CiTIUSiUymSR60r5tCJLSLFK6RcWrI4WkGwUEcYirW9T8FpGKcaRJznBZ6VRxdYUXBSSmaPE9Rw4rpfA6Pt1WF3NPypUXhP2oRlPX9vkKHFZ776uOFyDRCGIP7QBqr2nsuV+ERhhXkLGNxdSB0q1cyubU1PAdWWPfaWVIAy/u3rO0416rXu+gFOTzLhMTxUONRL3wBtudxLNLChMpU8RxE1MbTTxPeowKQxsgjLawjAlsfQZNmmx6l1BEbHlXKVlzh+6LQKL3Zv/2LCSb3wXJhbDQtFHiqELU04hLkUbbk2ox6KTRXYlxD2k6SsF6pUHXC6hsNbGt22fBe7vBze1tUqbJEYqY0khm6eklb/QMre9UtqmjaxLb0smmk45fGId9k2uAtO72t3tQRSriXO1tmuGupM60DEYnS6SyDo3tdn9/m20PP4hwLIP5XGJinGjMd89LN+re3ThXaITRTQx9Bq1//0kkOiAxpEnRHPgrpf3deylGyzkGCmlMQ2dprYZt6QwWMweubUidi/VlIhVzLDvaP8522OVK8waBCknrKYadQTQhEcIl47yP5Az2jtU40f9s6tNIkTlQAWnoGsOl5B2vlOLijQ063evcNzOaeIx8F1Xkv4xhfx9h948BiSJAxRW8OMtsZhiJpNpt8ObLl3FOGnzl068yNjtIp+lhOQYDY0U2V7cRUrB2fpk4VtyMYqaOjRDHCq/jUVmpEvoRmi6JophjD0yzcm2D6RNj0A5ZertG3A1wxou8/cIVZk6O43cDLrxynZtX1sgPZHn4g6ep+k1utNcYdQqkTMl4Ks9XK2uU4jQ5y+Vau8VLW5exNIMJt4wUkLVNvrr1NkN2nqrXJGM4iVQ6jhlzCjSjLkEc0Y3233dSCLbaHVQEm16HLToMpFJstFss15u0/ZCSlSJSMU3f47W1VY4WS1zdqqJLyXyxxGKtShjG3GzWmS+WWGk1sHWdy1tbTOfz3GwsoglJTBK3XXRcDKEzm7vdY2w+c7z/ecBJMeAk92EUx1yKK0y4ebRYUmm3KLgObyyvci3eou0FCWgUhNiGQaxiTF2nmHa4vL7JRCnPRrXJ+eUNNCnYqLd4eG6CNxdX8cOIyXKeETeDIDHvbgU+6+0mw6kMBdulYN9uZaCU4k+vXeZ/fv5LnCwP8ncefAQpIG87FB2H1WaDvGWz0W4TqRhDagRxhK3pdKOQlGH2WVSyB+63Ah9L02kFPvcPj/Cz9z9ErBRhHPM3Tz9A3rLotDzisIijn0S1fx1khlhOEAYVNP/P0cLrqM4nQL0f5ACq+2mIm0nqmRxAyDwIJ3kHymzCHnK+B9X5VML+0UbB+SjCeAjV+ThCGwRtHBA9420nSeLrM3YHiOU0ovGvwXo3kX4WIY8imr+GEAbC/iBCpOmnj8o0gjgBtLqfpFv9V3hRCif3HKb9LJ3G79GOzpHWC1jy8JTd79RauLBCtpBifWmT+lYT0zIYmxvm8us3eOCpkyxeXCFXzuC1fRrVFpqRgAembTB9cgzN0NhY2GLx0kVGZgbYWNrCSVtki2muvbVEYShHs9bGzdjops7ajQpjs4OsL24xNFVm7uxMf18iFXMkW2Q6WyRrJOziThQgSOLIvShEE4K0YTGezhFE0W3PgpQC2z6471r1m7y4dZEhu0BGdxhxCiy1NzlfX+L+wgwVr04n8pnPjHG1tUrBSJjLrTBizClxo73OqG1RDVYSuwkzxUJrg+nUEFt+g1DFCaNZxUy5g8yk9zPFgyDED0IsQ+dmpY6uSfS27I8/x8pZMq7F+YW1xJhek0wOFdA1SSGT+I4ZmoYmBa6dJKa5h/jn3Knq7ZBqy6AT5JLJbMtkbbuJqXmUMwk7fLnaoNbuYBsGlq6xtt1ko5dYXkq7+ya4E/aRiy53r0UcK156/TqO0+b08TF8P2RxYRPHMXEck/WNOrZtUCymWF9LPjuOSWWzQRzrpFKDuMY0hnHv/SKlFBeurLHd6PDOs9M94Orgvo0uNxB71BaalMyPD2AZOinb5MGj44RxjGsZBFGMF4T7woWsHhnj1orjGn54JQmfQSb9a62Mro0SRRVAEkYbOOY7EcLEjzbYaP8ZoHCNWQr2u3YVTiR9sJ3xdagCulGbTtQiqxfIGt+6if1vG+BIKcXC0hZLS1tcuLTK+HiB1bVtOp0ABTQaXd74+hJSCM6cGmd1rc7S8hYDpQxDg1kuXV0nDCPmZ4cwDJ2vn1/GcQxOHBtlbX2btfVtRoZzbFQaXL6yRhjFHJ0dIgwjPv6HL/Hux+a57+QoKys1lldqjAznmRrO8+bbN7l5c4t8zuX4/AialP3BehwrgsinE7xNN7iKbcwDMWFcB0J0WcYPl0CApU/TDS4AOhnrXdADjsw9/kPBAWkdB52nnaj4HYPrO9Y3ATAqpXBcax99/aBNSeEQK59Y3X0gt1N70wvumxrmH/2Np8k4d5Zbqd7/hRCkbXOfNEwI0R+AKyCMIiy9jFIxusyiCZtYiX7n5tYypIHeYxUd9O+3GiTPZUaYTQ8njXzfHHcPkqt2PWQmUwP97fYtAe7xemi6hpt1ufbmAtlSpo8WG7qWNF5Rwra6kzZ7p/beV6auI4SkEVYJ1UFeLPt3MEGqvUO3bRo6Y+XvLm+RIA5oBg1CFd6Rs/ONluuaXL60xnatzekzE4cCR5rMYOoZpLCJ4zZCmGhaBikddPKApBNcxdDKCGn3ZriS62Zq6URqZ83eUfMvhIGp7R/8xJFK2IZ7ZIdSFJAySxxHQAR72EZSCAqmgxffm+mtEDA0kKVcTPd/f1BFSlFKpfqd+4yR6cs7IxVxo3WTM7kTB4JOO5VyTOrNLhvVFiMDOVzbwNJMTGnS7gFPFW+LSEXoB5jEK6W42lzkq5uvEuwBm4QQVCtNcqUUgz3TckXSAV3fapIxExP5ISdDSnewtF2g+0brJlE5uqPMTIosWfdHEcJAqQAhbGwEo6n3IIVBjIejufx1yCDCKGZzu4VrG6Rsqy9rOegvSyF4oDDDmFNkw6vva+ukEH2gqxN1eWHzVQpmlhF78LZzL3rSSylkDzTa/Wt+EKJUz6g3DPe0v3B9ZQuhupyYWEEZp7+rRCJCFonDS6i4ShScIw4vohknOJkbIlYxL29dQQEZzeHKuQW8js/AWImV6+uEQUSn5aFihd8OiKOYdD5F4IcJM2A4R6aQYnN1mzAISdsupeE8hcFswiDYarJwcQWv6xP6Eb4XoGLF5kqNjeUqpaEcCEFpJIcXBVxuruLFIbrUcXWbUEUUrQx+FHDNW6MRdnG0JGGoG/m4usmYW+JcbYGCmaIRdBKJmmaS0iXjqTIXtm9StNJ4cUB8C4sjjGPqvodE4BoG45kcy406SsFwKo0XhYRxjKMbBFHERquVgPeWzfXtGu0wSNhEpsXC9jamrhEHCk0KpvMFlup1lICcZaMJQTcM0e9xomunNCkpZ1N4YUjKMhnKZSimXZRSFNMuuuaRtk2CRhulFOVMwoIcyWdZrGzT8QN0KZOI+DB5Br++tE7bCxjIplmsbDM/XMYydMI4mZy6W8S3H0W8VVmn2u3Q8D22Om1MTaPp+4BirdWi1u1SabfphAEF26ETJvvRDSPyloUfRQRxzIMjo1ytVal7HoaU6FISK8WjYxOcHRxGAa5hEAYR186v4KQspo5+FCG6gMFao8n1tSZzhQ8xmPshkoZDR0XXUPp88l0OouINhH4MpRoQvIYwToJSqKiCsB5HESO0cRAZcD6CUB3A6LUFOjgfohXEvL26gheFOLpBxjIJo2dZb23hGGlMbZuC/RS1zpkkpKGTYr44vXs+zYdJWhwT4f4Yry9eotEJKTcj7p85yvXox1hs1DhWHGZKv3d253dKKaX42p+cw83YNLfbpLMugxPJ+3HHuqC53aax1SJXSvPCZ1/HzTrkymmcjE0URKxc32D86DBX31hAxYr5d8ywtrCB1/G58PJVjj94hNUbGyAEoR+ydHmNKIjYXKkxd3aqvy+akAy7GYIoRqEwNY1cj31xq5ep22O7RXHCRoLkLjO0O0++Fs0MfhxyvrGEo5lUvDoZw2HUKdGNA/Jmmoq3TSNoE6ukdTqaHqUWtMgaLgN2jiutVe7PH+H12lXCOGapXUGXGkvtCgpFWneoBk1mGEq8cMMIKQVSSmZGiuRSDtPDxaS/JODB+TEMXSdWCikFD5+YJI4TOdSOhFT1TPKlFMyMljB0ydhADk3utxIJoiRk507nYKqcZ7y428cXAmI1iED0x3NnJxPPnx152qNzTnINhNgnszus2h2fz/3lBY7NDnP6+Biua/HwI7N9oGbuaCLfklIwMzPIznjwaLwbwqHr3xhj0g8inn/pCrGCh85Mcac+1fREiX/8X3yo7xesa5KR0i45ZbBwZ5D4sL6qro2RcoaJ40ZCMJEpdpLWDG0kYT/FTYSwUEqx1fkKreAiICm778HUdtsYiWAmPUDOcNClhsJizDmCF3fIGAfbQnyz9W0DHPl+yCuvXufo3HDis4rANHQuXFrj1MkxKltNbixsMntkECklYZTIAf7iLy/y5BPzXLm6zrH5YV585TqmqZPLOqxvNDDNdUqlNMsrNY4fHWFltcb1hQoz0wO8fWGF+89MYOiS8bECvh/xhS9dYGQ4z42vXeH4sRE2NuoUCin8nkTKNHScHrvCC0JaXYVjnEGTWUy916ipqCfn0HCMY326m23MoVSEtjMTIaCQ2qUw3kl2tlNBFFFvJwMfXZN39bj5ZiqfT3H2/kksy4DO4aCQEAI/WkkGsPe47VzK6TdSHc8nl7JRBnTCIPEAAgwp0YTEi0KCeFcW5mgGa16TuKvImBYDdirxbegBT1FPuhcrxbbn0fRDWsEW291ufyat5fsEcUQYK1KmgSE1at0umhSYmoal6Qy4qUNp27qQd/SRuLX2yf0O8BM49HexotPoUNto0GnuXoOMY2HoGl4Q0ej4+OF+nflBVWt2+vuRS9kJ+Cl0ort5gaidSMlbRTe7FcYxfhT1DT6/G0qIxFhurXuTI+ldgz6lFJGKCFVEEIc0w9a+FJ1W1KEeNNGFhiETtsleyZreA/08L+BWGejeCpVFJMbxVAgiTxAn/l0GFrGyUETU1TBRnCFSNsQS24iJYh9by1H3l2iGa2SNg73PYOcVuf96BX5IZW2bKIzBSkBDIVPo+nHieAOpjSL3RIILIRh00sTEeJFHqCLqQZNwjydQN/Koh00c5WAIDU3qmJp2R9ZNy/eptjtMFRNgZtAqUbKKbPo1AhXy5cqLnModYyo1to95k1yf5L0QxYpSPs1KZbsPtJfNAiWrQC1IjHOfr7zCfbljTLlj+8CcbuRxuXmd31n4JMvdtf5M/k61m11e+PzbDI4UOPmOqb5HQjZlERKz2e1wNDdA1sgwZA+w0E7YWi9WX+dM/gSncvMYcn/E+Y7vUnLPpHrndxd00nr+UpZmMyjHCFVEO+zgxwGdPQysSEXUwyaa1NCF3mM9yW+KohzFCtPQ+2B1xrUxTS2ZULil6VAKrjbXuNJYZS6z3zjS0WxOZOd4Y/s8Xuzz5UoiW3N1d995FyRm5CWzwFRqjDP5E0y5Y+g90PAvXrlCvdnlofsm+V9/4/OYhtbzckkMyn/0AyUg3DXX/S4p3XqGsPunKOURdv8EzXwMqc8x2mNaD9kFQOHnfVqNLicfniWddxmeKqGUwu8EOGmbdrPL/APTfYNzwzJQcYzpmDipZMBluyayNxi4/8njKKUoDGaJwhipCaIwWR+lGJ4qk8o6TDW6uBkbWzN4Z3GOSMWkdZtJt4yl6ZzOT+JFAZrQmMuMYGsGQRwhEIy5JWyp84MT78LRTE7mxpFIAhViSh1bM8kZTq8/ENzG2DWkxlg6y2yhiN7zuSzYNmOZLCnTIN2TS7WDgKenj5AxTTph2A8xCaIIWzfwoxCj1y55UYilabiGwfcene8PwixNpxn4B/ox3a3OTo7QDQJcy0SphAX83vtm+5NfO/vj95gDmkyY5QPZFH4YcXpiGCmTds3SNVqej6nrKBRyzwRiKwiSAfRd/Oaq3Q5vbayjgLRpcnZomJSR+GZZms5wKpH+KBRXq1tMZHMsNxrMFJIBiNbz1JAI0qZJ0XGJVNz39LA1HU3KfqgMgKciGrU27WaXqaNDiJ7PpJQRphagabm+/5BSHeJolSSq2kUJA+IaCnqAegwiSxxdAdVIDLFVMzkfiMTbSJj4YUS965FzYgzNphO2uFGvkbdsLC0JMqn70I1dKo0O88UUrQA2uhpKQcmRhLG+x99vL+Pdxo/T1LtNpOYBkoxdwq/7mHoa/goeid+ulSul8bs+Z544xuXXb5AvZ+m0utQ3m2wuV6mubZMpprAck+raNscfniWOIizXojxa7EtmFi+sMDhZJg4jDEunvpWwekvDBQrDeTotj8rNLfxuwNDUANXVGoMTtzMmhBD8689/lbVGk3/w7BN9Rclhvl2fv3CF33j+FbwwGVf8wnse4cn5mdu2C2BqOl4coAuNMafEpeYyBTNNBoelToWCkWaxvcGgncfy6gzaeWIVkzYcdCl5e3uJlGYzaOVwdYtJd4Ab7Q1yZorlzhY5w8XWTFzdIqMn48C1epNf+swXeMfkKD/2yP10/RDXNtCkpBF02ei2sKRGFCi6UcBmt03JdnF0ExEm7WG7F86RtCkCPwoRftJXH3azZDUbAby2sMK/+eIL/PxTD/PQ9C6LPIpiVjfqbFVbhFGMYxuUi2mK+V11SBzHrG822dhsEMeKYj7F8GC2P7bTpOTqjQ1MQyefc1lc3qLrhWRSFhNjRaweAOMHIavrda5c3+DNC8vYlsHL524AkEnbHJ3Z9XTzvIDVjSbVWosoVriOyfhIHtcxb7verbbHyvo2zZaXmJunLEYGc7iOSRTHrK7XWVyu8uJrNxgZyvHKGwsJ+8wyODY71J/UrTe6XFusEPbsQWanBsjn9rPWqrUWa5UG0xMl1jbqVGttDFNjdChP4ZZ1/SBkabnKdr2zL/0y5VrMTg9g7GOSa2jCBJkQB9rhdZabv0MYN3D0CQbc9yP3+sMJWO9uo5QirdvUgypb/hopPUsz3MbVvnUMyG8b4EipBLE2TR3D2O0c+F5Aq+0zOJDhyHSZCxdXGCilee3cDfJZl2bL6wX6JEBToolUGEbiyxD1Ep+iSNFodpFSMjiQY2gwx5Wr66RcC9e1+hS3IEjQ3qNzQwgp0HSJaeoEQXLj2KbOaCmLlIIgirm8vMlTp2dxzTOHHNkubf5WYy4BzI/vDsQuL1fuGk7U6vosbCTpH65lMDP0rfeV0XWJrt/dG8kP19FlEV3ensJxWA0X0uTTDo2Ox8Z2i+WtOp4bUfM6tAKfguUQqphOmFBOK902A06KYTdDJWqz0q6jC8mRXImS5aJrkrFSFlPX8MOIG+s1Gm2PipfMlK00G4ykM2x7XWxd49W1FTphiK3rTGXz1P1u/95rBT4ny4MMuneWOiilaHV9/F5jEsWqPwsZxjFRpHrLY3RNY6vRophx6XgBaceilL17JKICOq0uA2PFfffD1ECejGPR7Pgsb25Tb3vkUrfrp/fu65XVLaIeM2lupIShaThaGv2uKRSqp0M/vJm40ahSXe7w8MD4gZT478QSJAPtHVnTy1tv8HL1HO2oix8HBHGAHwc0wzbbwW56zx8t/xlfrryIIXQMaWBKA0uajDiDfGjkGer1Dvm8S7vt43UD0umDO/db/jrXWz6W5tAJm+TMMrowqQYV6sEmBWOQQEma3jJKKTJGArBIobPWfRNN6OjC4k4zKLGK6ISLvQjTpBzXJJ1z9vxMEYVLxPEGcVxB02cRvRfbcmeNz6x8jnbYxYt9gt556cQeN9u7KSAvbL3GtdYipmZiSgNDJMyftO7yoZFnGLRvl3ykLQuFou0HvZegyxPlh7jaXCBUIddai/zyxX/HsewsZavQG+x5NMIWkYp4dvhJJuxxJkcKhFGE26Oi54wsjxTvZ6m9ghf7XGpe53+/+Ks8UDjFqDOEQFANtrncuM6l5nUaQYOT2aOY0uC12tf7+1ccSF7kqaydDCpjRSHrYpk6uV4nD5IZ0ScHHubN2gU6cZe1boV/c+VjnMjOMWIPogkNL/Zphi1aYZunBt7Fg8XTB16vbuTx2ZXPs9xZo9s7337vv4q31V9vw9vk/7j0a9iahdm7Bw1pYGsWj5becVem1t6yTb1Pt47jZFY37VgHAsSRiqkHHeazowxY+wMZBIJHS+9gqbPKF9e/SqQiKn4V/Oqhf/svN1/ij1e/yAeHn+KZoSfIGmkePDFBGMVsN7tMjxb5nsdPomkJcPTiWwsI0UQzjiPEd5tEJMZwPozpPpd8PQA8ATByOqk9HVVrR9adNA+4d0hwLAze/g7PFu5N8ufsaceK1h4LgT19iJR+ZyBjQDv8XWT2gMODtjGTLyQSdWP/74fS++8B1zBwe+ukzXufbDNvAYn2AiHfSKVss2eOululzN3P72H2BVn39nORgFvaPpD7sLrZqHNpK0ln1YQgbzukD0mO2mFble8woXard8dBJQSkcw5hEO3bQz+KqPvdfQm8Qtjo9jMc/A7b/bXUJ5IlSiFUI/FP2tP2rDeavHh9iYemxhkvZMlZNh88cpTULfdLGMd0o5B0L1DgZLnHFr9LWzlayDBRyjGYS/cmUiO88N4YuN+Jtb3R4MTDcwxOlBie3h27TJ0cQwjB+NGEAXJrmvHemjk1wcypiX3/fv9TJ/Z9zw9kOP+1K0RRzOiRQc48cXDCFsDN2jZL1TpBdPdQijPjw/z8U4/w5s01fuP5V9m+w8R40czw9OAt47o9qmohBKNOMv6aSQ3t2/+c4TJo5fctm8uMMptOJlVO56b2/dtOBWHE1Y0txvJZDF3bZw2y3K5zbmuZ47lBVjp1xtwcjaBLI+z2JuUkad1koVVFIskYFvWgy6ibw5Aa690GnSjg/uIYAA3P49L6Jo3urqKg2eryyT89x59/6Tydrk/cY/i++5E5/uZzj2JKnSiK+fMvn+cPP/sqrXYSCuDYBk88PMcPfugdOHZiQfCrv/MVwjAmn3W4dG2dbjcgihXve/IEz33kQTIpm61qi0985lUuXFljZW2b51++yqWr6wAcmx1i7qcGk4ncKOYTn32VL331Ms22R9wbZ91/3zg/++PvpphP2lKlFNcXN/m133ueqzd6gE/PwuQ//5n3cvbkOJ4X8qk/PcdbF1a4cmODtUqdlfVtBIKRoRx//+ee6QNHm9Umn/3zN7m2WOHStXX+2T/8CO9+ZL8FxOtfX+L//NiXePqxY7z+9SUajQ5eEDIzUebv/tTTjA3nEULQaHb5/U+/wvMvXSWdtqjW2lxb3GRyrMDTjx1jarzYv95+VMELN9CkTax8WsElbjZ+l7p3DoHBaOY5UsbcLf2shK3fjXY8tgQIQawiMnrunvt991LfNsCRZemcODbK9RsVxkYLmKbGtRs9Xf76Nvmcy1a1zchwnnw+xdhIga4X8MDZyYQe2fRYXq1x5vQ4lmlw7s1FLEtnZmqAK9fW0aRgbb3OyHAOXZdk0jZjowU0TXJsfpi33r7JQw/M8PijR7m5XEU3NI5MD1Cttmg0ukxNlpAyod0fHx/E0nU6fsCLl5b4gcdOMVzIfFMX5tjYAK5l0PYCzi9tUG93yaXsA7ellGKxUmOpsg3AQC7NxED+r3zuv9nStQJGXLyzZ8ctlU85zI+VWdyosd3u8vKlJX7k6ft7M0mqT3Peob1udltkDAtHT1zqT6vhniFdT1svBFODBfJph/Vak8vLFW6sVzkxOQgC5ovl/mye7Mkgtn2PmVwBU9N60d9Jwt3ObNte2qYUgh1mulKJLC1Wijeur7LVaJOyTVa3GkwM5FAKaq0OjbZHKZsi41osV7ZpeT6np0e4sV4ll7J56vQ9pDVokvGjI6hIkdsTjztayjFazLKy1WBlq87CRm1f0t3eUkrR6HicX1xPotMNnbmxMpomaIXbd+1gCmFh6eM9o7iDy9Z0trrt7yobSEta6MIgpSd658vN63xu/fm7MrTWuhusdTf2LRMIptwxPjD8FIODWer1DoWCSyZ7ONg3ZE9yIjuPFBqxipAimQmPiEAphJC9Z071FEECiaRoHSGll1nrvkVKH0DcQRJlyCx56wG0PWD26HSZwbHCHvmsRDfmgFniqILcMyDf8mp8bu0r+2RcB1UtqPcZPnvL0WyeKL/zQOAoiCJqnW4PoCijCY1Hig9wrnaeV6pvEhOz3F1jtbveYxwJFHGP6ZDikdID5HyPF9+8QRwr2qM+hayLEIJ3DzzM1dYCL26dI1IRi50VVrob+6RwO2bYJ7JH+Ymp/4Qb7SXO1d7u79/itQ2iIKbd8picHcQydc4cHcU0NGzLoGzvDghP5Y7xxMA7+cJMoVvVAAAgAElEQVT6VwlUwJZf4yuVl9GFBmKHUh4Bgvuy84eeRz8OeH7zVa63Fu/43PpxwNXWwm3LBYIha4DTueP33F7rmuT4zFB/sDg+mCeXPvi+rXh1HNnC1a0+c1ShQMFie5kvbDzP+frlPrNKF1r/2tFbO1JRwlroHd+WX+OTy3+GKU2+Z+Q9FHqAu6ZJ3vfIPPNTA+ykR3X9AM/bBqHgr8X/6a+vws6n0MwH0Q6dnDq4dgZvrSDgWq3KaqvBtucR9sJBMqZJ2XEZz+YoO7sBCnfqx+xss+n7XK1tsdSo0/R9ZA98mMzmmMkXMKS8bVu3ykd2vh+k4K52u5zf3GCj3aIbhlg9JtFsocRIOoPsbePWmOy922TPdu+Z6fsN/j5JTUsYPJe2NlltNemGIYaUlN0U07k845lsv6/yjVbColTcbNS5sV2j2u3QCUOkENi6Tsl2GE5nGMtksTSNU6VhYm43Qd177mOleG19lU4Y9I91h1V1UO3EeN/JPHvnPBxWguS5zRZStOqdfddbl4lH3n553e33TRjHXN+usVCvUe10iVTclyfOFopkzNvBz7xjM5RN9yU1hpQYUu6754QQ6FKiK8nXKxtc367R8j2klGRNi/FMliOFIpaWtNl79/3GRq3fX0/ZJhnDJG2a32p7xG+bGp0dQmoCbcceQikaoce1RoWylWbUzfVZdIfVgb6GtyyzXYuT7zpKHMdYBzBKvtkayqYZyqYxNMlvf+31u65/t4Cjv+pxfiNVtlI8PXKUnGkzlx1AE5LZbBmB6D+7QsCx3BBCJGOXSKnEDl7AkJfGlPqh+xDHMV/+2mX+4LOv8Z5H53nPY8fIpC1W1+s4ttE3Q7+2UOHf/8HXeOD0JN/3vtPYlsFfvHCJP/qT15kcL/Luh3cBjVfeWOB7nznNP/n7H0LXND7x2Vf57Ofe5KEzk5y9b4JyMc1P/vBjLNzc4r//V5/mw+8/w4eeSSbPdH1XVielYG56kJHBHHMzgwjgi89f5Pc+9QqPvGOGpx6dRwhBq+3xsU+8wI2lLX7kow9x37FRgjBiebXGxGjClrQtgx/9/neyWW3xz3/5M5y9b4If/f53JkoMTeDsAfgnxor8nZ96DxcurfLf/ctPH3ptVte3uXh1jZ/8kccYHy7w2lsL/NuPfYkXXrnGRz9wFl3XuHBllc9+7k1+4ofexRMPz7Hd6PBL/9tnmZ0Z5KMfOIu9RzlS677E5eq/IFaJdDhWXcK4hUCj6DzOcOojHASq7030dbU0sYrY8tfJmSUc7fD04G+0vm2AIyEEJ4+PcvL4rryiXNpvVPU9z+7Oxj7+6NH+54WlTc6cHueRh3YH5O9/7339z/efmeT+M5O3/c1yKRkIPf6u3W2dOjnGqZNj/e/PvOd25/mH5yeYGipwfnGdtxfX+L0vn+NnP/gItnH4Q3lQCZFEpT84N86X37rGtdVN/vS1S3z/u+7bhzRD0kC3vYA//Mpb1NvJoOqZ+49SzPx/x/KQwiSINtHlvYNXWdfmvWfm+Or5BVpdn0++8HUeOTbJycmDI8TddLLtJNpQHajFnR0pcWZ6hD977RLVZoff/MKr/KO/8TT5lI2h778ek7lb9vUuM2W6JvtSsG4QsNVIOj0nJgaJ4sQLwZ+M9ul412oN8mkHS9eZHSkBCsswmBzMY5n6vk7zncrNOBx75/4EtrRt8oEHj/Ha1WW6fsgnvvIGZ2dG+h4keyuKFV968xoXbyZgxomJQU5OJMyKsjWGLQ8HL5IS6LLArVGd+86PlHsgjO+OilWMLjQKRgJqFM08c+lpFPGedRRx37gqqUTStGeZSgxeh+0hgkghpWLu2HC/Ix6rRGZgaxZH09P4KsCWNmktJo4uEPeSSKQ2iK5PoN8lR6sVVqgHN3G1IrueRyaz6SnaUQdLmqQ18PyvY+iTZKzj/fWEEGiauMXXzMP3XkLhEUfLWPYH0EjuGUe3mU1PE6uIMFohVi0MfZoo6smietRJKUQi5+uPxJJEClMaaLFOGPYkWtpuZ9PUNMbzuX2Dz6KV56dmnmMqNc5LW+fY9Lfo9uRxmpDoQiej2wzZZVzNIe84nJ4fJeVYpPcY6pfMAj858xxT7jhf2XyZql+jE3l0oy5SaDiazYg9yJn8CZ4dfpIhq4whDc7mT+LFPkUrx/j0AK985RLHe5MWhq6Rz+x9lnYfxLSe4ocnP8ykO8oXNr7KhrdJJ+wSqgiJQBc6Kd0ho6fJGoczZTQhmXBH7smE/LAqmLd7kQmgZOaZzyR0/VF7uC8NE7f4E0wO7+rkDakznRrH0kwkktn0OI+Wk3el3ZPhxUrxdv0S/8+132OhvYyj2Twz+DjvKJyiaOZv83qKiWmHHa40b/D85itcay3SCFt8bv0veWfxDIN24gtYzLoUb2FtHp8eIgpjouB5pDaMOCB29zu2ZA4Vb6OUz6785c7yw1gpFuvb/Pn1K3zmykWW6nUavkc3DPvtjqXppEyDvOVwvDzAj5w4xWPjt/eTdkopxVa3w6cuXeBTly+wUK9R63qJFEIIHN2gYNv/L3fvHW3XWZ17/1bfe+1eTu86ko6KVaxmWZIty4ViDDiEFkgIuZBACskINxmpjHtJSEL6F1JG2kfgXiBg08F0N2zLRb3303vZva3+/bG29tG2jmSbQb4AcwwP6+yzzrvKXmu973zmM5+HLW3t/OwtW9na1t5kb+/aZ/HcEpK6BQgwXynz4aceZ7pUYHNrOx/ctRfHdfnm8CUePnea8UKekmliu34rdFBRaNPD7O/r5+0bNjMYT1x3DSqWxV8+/zTH52YBj3v7B/mFLdsIKS/PXXaikOdPDj7JXNl3fPylrTt57eCaFbd164DOw+dO89jYMNOlIsU6MCfWQa1kIMi+nj7esXEza5Mv37nM8zxKlsnTE2N888pFTs7Pka1VqVg2tus0wA5dVohqGh3hCK8dXMNb129qMKuuxny5zHghx+VshgtLCwznspxdnMeqm2wcn5vhHV95uMk++9pYnUjxZ3fdd1NW0b+fOMK3rlxqSlyuja2tHfzGhp0UsmVK+WrTaqFsmcyVSlSSKVZSS6zaNsdmp/n0mZOcmp8la9SoWr5znCpJxLQAfbE4b1i7jgdWDxHXlguvFcu3IC8bZoMR9E9HnueJ8RGSAZ3f2r2X1YkUz0yO85nTJzhd130ybQdBgIAsE9MCbGpt4+c33cr2ji4fQKpHRzLCTKbQeE/WHLsOEt7wUv1Yh2/U4nEt3KsIEhPlHJIg0qnfXO8yX60xmcnTk4wRDS6/o4s1g7GlHF2JKAk9iOk4jObztEbCWIbJbKFEzbIIaRrt0TAhrfl5FgUBy/HZOiXDJKjIdCdirzgnuzZsx2UmXyRXrfpr5rBOSyTUlIN4ns+MXiiWKdSZOyFNpTMeISBfv2/bdVkqVVgqVbBdX7w6GdJJhfXmd4MgkK/WmM2XMGyLsKbRHoug12VSrq4DXE8kW64yVyhhuy4RTaUzEfXlAF4E2suOzFyuzIxXoi0avg7cLJRqPHHwIr1dSd79tj3o9XVTT+dyV4tp2Tx3dATH9fiZB3fSUs/RH7h3MxeuzPHtx8+ya0s/wfrfppNhfuo1W+ntTuJ5HvfduZ4nDl5gbCrDlo2+zmcsGiRaCPgi3kGVRPz6nFYQBHZu7W/67MC+dXzjsdPMLS475V64MseJM5O89x37uO/O9Y317Or+a7SARIFoJIjjuEh1A5VETL9O0xf8/C8SChAO3dwBMKApvObARrZt6kUUBO4Ir+Hbj59leHzRFy6XJSams0iSyC3ruojHdOIxncH+FjLZsg9Iv2h8161huVk8XF/qRu6kRb+X7sg7UeXWpgKghy/TIOGDbYIg4HgOmhjEdGvY7o1NtH6Q+JEBjl4cxYrB5ckFbl3b/ZLbdrTHiSX8m820bCbmc/S2Ja4DX64Nx3E5cXmadX2t6AGVZuvMm9FjBTpTUd68dzN/++XvU66Z/OcTx7Fshzft2UQqqqPIUh0FdjEth3ylxtnxOdb3tNHf1ixSFdUDvPXOLVyZXWJ6qcB/fOcQoiBw95bVhAMqoij4IqXFCl985hSPnbyM43ps6GnlNduHXpYd+39VeJ7r24YLMi9XrFUUBfZtHOCOjQN8++gFhmcz/NlDj/G++3ezvruVoOar/l8VAa9ZFlNLBQ5fmmBddyv7Nl7fj6zIEm+9cwvHh6dZLJR57PglwOMt+7awpjNVF4T2UXnLcajUTC5MLRBUFW4buvFCGfwHcG1XCwfPjmG7Ll9/4Rxru1roTEWRRQHXo2G7qMoisiQRf1FV3nU9DNtGkgRc1yNfrvnHcY1jXaVmkilWfIaTKDTcSrR6y+XVkCSRA5sH+e6xSxy+NMHTZ0b51289z0/v3URbPIwi+7adZcPkmTOj/Ou3nqNiWMRDQd68bzPJiI7hVMlZCwSlMEn1xjo4HhauV0GVb/wMyoJIKhBC5Idn5fvfHYIgElFiSPUWvfva7+C+9juatrmUWaJg1JgqFAmpKolAgIJhIAh+kp+pVolpGkFFQRQEjs3M0aKHsBwHx/PI1aqsSabpjcXo0Tv5X7f8ZmPsqnGIpfzHMO0RbGeGWOhnaUt8+CWP23RLKGKQmp3l6sKuPdDCH2z4QGObYuVrTC7+Om2JvyAUuOslFrgKsrIOQZBwnG6udasaDPfxx5v+J55nM5/9EDXrIgHht1la1CmXDFzXQ9dVDNOmXDbo6oxjmDa27WJ5Nsl4mOJIjWPmKB4eQ0MdxOptNooksb37+vuyJZDirT2v41Vt+5iszlC0S1iujSzIaJJKXImS1pJE5CCSIDDQlcDFQWTZvU0QBJJqnJ/ufi13td7OdHWOuWqWjFGmXY/4wFCwg5gSaQAbfaEufn/DrzWO4+ilS/QOtpLPlhvaL82LaQ8wAAUQico6r2nfz57UrUzV5siZWQzXRBI0AqJGVAmT1BIklBsvukOyzgfWvJuqbVGxLOJaAAQo2ZX6d28hIdZFpUUcz0ESJDJmnrCs14WIAzieiyj4Qrt50yCqauxL38bmyNYGoIkjkLGquJ6LJstkK1WCqoJpO0iiL1xdNl3e0//O61qEro1FI8Pnxr/GWGWKgKjxpu7XcH/H3Q12141iU2wd66Or+esL/0bOKjBVnWPeWKJFSyEIAoVSjenFPGt6WxqL+JnFAkEVEpEhhB/dZc0PFIKgYlY+iWB8D6GusaKE3o4kD664ve06PD42wt++cJDzS4tNTBIBP8ny8C3qK7bFQqXCfKXMPf2rblyN9jzOLc7zsUPP8eiYL/IKfuFAk2XwfJ3CctFksljg+alJfmbjZt67ZTtRTasXS2p47ix460EIYDoO5xYXGMlnydVqvH71EP955hRfv3LBd+wShLp4rey3MxkGBcPgSjbD4ZlpPnrgPoaS6aZj1hWFdFDn/OICNcemaJq8atVq1ibTL3mdHc/lmclxnhgbxXBs0kGdrnCExVIFVZaIBpaTB9t1eW5qgr994SBHZ6cbq0NV8pkzjudRrB/vaD7HwclxfnnbLh5cu/4l27pcz2OykOfvDz/H1y5faIj5Xo2rz6npOJiOQ86oMV7I0x+Lr1iQeujcKf7pyAtU7JWTh6Jpcmph7sbXxfWuEyV/ccyWSpxemMd07BW3DCsaeiRAuj1Gur2ZIR2UlYZ5yYuT3Wytxr8cO8TD506TqfkaoKIgoIgSouAXZuYrZeYrZY7PzfD81CS/tuM21ibTDVZWNKAR0pQGq2qskOf43CwhReHk3Cwn5+f482efIlvzi4KqJKHJErbrUrYsypbFdKnIyfk5PrBjN29df0sjyQ+qCpqiMLGYZ1OvD5S26uGX7Z774xjl2jMElA1IUpKq8QKCNOAnrS/jnI+OTfNHX3uUD7/x3iZtoVNTc/zuF77F7752P/dvGmKhWOZ3v/Bt9gz2slSucHZ6nkLNQJUl7ljdz6/dfTtxfRkgNGyH//vsMY6OT5OrVJFEkbvWruJ/7NtOe2xlp6ybRalm8LlDp/jG6Qvk6pqybdEwb9iynp+6dUOjhXuhWObvHj1YP74aruuhKTL71w7w3jt20hJZbqEqGSafO3SSR89dYb5QwvY8VElkQ0crf/DAAVqvcQqfzRf5k0ce59zMAsWagSb7Y/7KgduIBf3zthyHx88P8/DhU4wt5bBdl5Cqcte6AX5u96201EX2Pc/j8OgUf//Ys0xm8yiSyEA6SW8yvjznA+WKydxCgd3bVzVAoxeHaTpMzmRJxkOkrmll1lSZgZ403/v+WWqG1QCO2lujRKM+QCgIApoqN0m/vNzwPI9MtszZSzMMjy1SrhiUqya5QhX3mhbF6dkciiwy0JNaEQj6r4qwrtHdnmiwdiVJJBTSsOqGHgBd7XEcx+X85VnisSClssHYVIahwfaG5tPViAd2sr7lo1hODg8bSQihKwOElFUNuYim8MBwbMx6sdnzPBRRRRZkbNdCEX94rD34EQGOPA8uTy1Qrpqs6kyxmC8zs1Tg0uQCHakoyajO9FIBWRRZzJfpa0+QL9WoGCbhoMZivszFiQXu2joIgi8G3NuWYC5TZGapQHsqgiyKTC3maU/6+kQT8zlOj8wy0JlA1+o8Aa/CsvidAygg+K4MnlcFHAQhgiyJ3HfrGk6PzfC1589RNS0eeuoEz54bY01XC+0Jf5tyzWBqscDYQpZsscofv+vV1wFHANtXd/HWO7bwL994julMgb//6tM8d36MDb1t6JrKUrHMscvTnBmfpWbaJCM677x7G53Jl68t9F8RwtUWEbfMK7EMi+oa77pnO8OzS1yaXuT02Bwf/vR3WdOZZlV7Cl3zk5TFQpnxhRyz2SJVw+L999/Ovo0rj7lloIO33rGFTz1+lEKlxqPHL3NieIYNvW10p/3KQ9W0mM+VGJ3PMp8r8YbbNrwkcASwe10vX372DIuFMs+dH+NPH3qU24Z6ievBuli5Qc2yuO/Wtazvud5JYz5f5EsHT7NUqFAxLMqGScWwGJ9f1vj46vNnOTY8RVBV0AMquqYQDQZ4877NdKaav+d0NMS77t7OfK7E2HyWh586yfHhaW5b20M6FsawbM6Oz3Ho4gSZor/ofWDXugbo5uFiOFWqTgnvBhVC8LV+TGcGDxdZXVl7ZTl+chhHETmKHtJvqgEVkGTkgE6+ZlC1LaJazGdfCQIFo0ZbKESwrqsxVy4R1TRUScKqJ0QhVb2hpoSmbqY9+deY1hVmM//zZR+35zm4nkVQvr7962oo8iqi+oOodW2Im4UgiEhSChAQhCisCA4KBAO7UeRuXDMMuIiiQDCoEAiqOI5LIq4Tj4dYWChQq1kk4jqiKDRcP4LBm1dzrg3LzaOKBXqD4Hkaspiqu8p5OF4RRZAoW7O4nuW77AAxbQPCi9hagiCQ1hKk1DiL+RG+cPIF/ubALrojL/1OdR1fBDa3WGJqdIGu/jSiMIfnVgDTv05uBkFqw6sfG4JORHQYCpQgIIHYgyD13rSdcKV4dOwKX7p0lj/aey+xoMyp3GVMzyamhAnLQTJGAVn0ASNNVJkzMvTrHdieQ9musiu5kbga4UJmkb86/DS/se12WgNhnhgeoSfuA1eW41C1bKqWRWs4jCjAUqVKRFOpWBYlw0RXFRLBgZsCRxeLI0xWZwBoD7ayK7n1JUEj8L+bbt0H73JWAcdzKFnlxu8XciWeOTbCQGcKSfVbm09cnCIZDbJny0ZWvk9/fENStiBIHTRpu4grAyGO6/L89BR/dvD7DOf8+UUA2sNhBuMpOsJhdEXFdGzmymUminnG83n6YjF2tN+4iDBdLPBXzz/DUxN+AUWVJDamW7m1rYMW3be8H8nnODE3w3Auy2K1wsdPHCGsqrxnyzZkQUIUE7huacXxZ0olPn7yKI+ODuO4Lpta2ri1vYP2kO8SOVbIcWRmmsvZJVw8Ts7P8smTx/jDffsJiIX6NUkhCCL7evr4z7OnmCoWmC2VeGF68mUBR0XD5LmpCQzHB2q2tLXTFopwcnKGkKZy28DyO/PM4jx/+dzTnJqfwwOSwSA72rsYSqWJqCpl0+Lc0gLH52aYr5QZzmX52KHn6InGuK2z+6bvu+ligb954SCPXL7QYAXpisK6ZJrOSJREwE/EcrUak8UCo/ksNdtmf+/Kz2M6qLOlrb0B9gGM53PMVfxnKqpprE4kb5j4DyaSvuD0TWJPdy8CkDNqlEyDimVzIbPYYG4BVMsGIxdmiCXDtHTEubperFgWXdHodUwpy3X5zJkT/J9TxxualwPxBFvbOhiIJVAkkYVKhRPzs5yan8NwbL49fAnTsfnfd9xNZyTKQrFMtlJtYrdcjYpl8ciVi4wX8uRqVTrDEba3d7I6mSIoyyxVqxydneZkfeyZUpH/98QRdnZ0sTqRRBAEnzUqiQTriV/VtlislmkP/aTprPlhO3NUagcBD9ltoWIcRFTjtAQiBG6iU/aDhOt5fP3kee5eN8jv338XsiTxhaOn+fqp8+we7OXe9cvA+dhSlvZYmA/et49IQOPJCyM8fOQUqbDOe/btaHJzfulzdPnO2cs8dPgkr9u8jr2DfZiOw+ePnObfnzrEQDrBrgG/mCpLEr3JOFt7OhhqT2M5Lt88dZFHTl1gXXsLP7VtOWH55umLfOKZo+zo7+Lde7cR14MsFssUaybRwPL96boeh0Ynec3GNXzogQMIgsBDh07xleNn2bO6l/11wO3C7CL/9Phz9KTifOj1dxPRVJ4fmeShwycJayq/sHcHqiyRq9T4l++/QMW0+M379jKQTnJ0fIrPHz59HSj9Uhq712x5w0+v/Y0sSSu6yL6SbMHzPDK5Mn//8ceYXSgyNNhGMh5CU+Xrvld3md/x/2tc1UO+Waxb3c6dt6/lkw8d5PFnLlAzLMK6xqv2byAYaH52VClFKnjHDUZaOXRZI67oBOtOvp7nkrcymK7xiuRkXk78SABH4FE1LIanl5jNFKnUTNqSETzX47mzY6zuSjMys0SpahLVA0wt5MmXqwz1ttESD/sOa/ioZ820uTSxwPq+Nk6PzFCqmsTCAU4Nz4AHZ0fn0AMqLbEQjuuCM4NnFfCcCQSpE88tAhaC1AleGTwb113yf3YLeFIrgryBqK7xG2/cRzKi852jF5leKnB5ZonLM0srnmFAuf4mvxq6pvK2O7YgSyKffvwYc9ki3z12ie8eu9S0nSyKbOht4+fv2c6BLaubmCj/HeHb0MuIwisTjBQEgXU9rfzvd76K//vYEZ4+O8pCvsxCvszBc2MrbA9t8Qix0Mr7EerMnHceuJXOVIRPfu8II3MZZrNFZrPFFf9GkcQb2qG/ODb1d/Du+3bwye8dZiFf5oULE7xwYaJpm1gowMa+9pWBo1yZh546eVPXvOlMgelMsxZMQJW545aB64AjQRC4fX0ff/D2e/jnbzzLqZEZTtb/a94OUtEQb9m3mbfduYVY3TlHQCQohUiq7Yg30QPxPAfbLSAKYf+7XrGn1iNTrWD/BDX1+6yNm7c29MRieJ5HVzSKV6fM3ygGE8tU36sJw83EI0VBQ5Racd3SytWFG0RbcDP+U3ljEDegbiSg3gB9vS6Wx7jRcQiCRFR/I+CfUyp17e+EpvNMpZsX0/39wk2vw0phOjmK5iVkUcfxDCS3SNWeRRKCSGKgXp0JUHPmwfOQxZcQusdnXsyUik1ucDeLdVt6qVZMVg21owUUBBE8ew7PzYJXQ5DaQEzjuUU8+zIIEh4BBHkQz10Ez0YQmwsIOaPG05Oj3Nu3+qZW2kFZIRnwae1BSWNzfA22Z6PUWZ9pNV5PAHuwPYf1Xj+qqNTbfCXC8jKrK6n57bTxYIADgwMNIeCr7SZiXTtBEgUMxyGkKFiu6+tteS4h9eaJwnR1lprjsyrbAy2Nfb+cuKo5dzUEwXcCW8qXGZvOMLtU4MrkIqoiYTsul8YWuH1L/yt6Xn5cQpDS4C7gWpdAUJGUW+AGAuAXM0v8xbNPNUCjsKLytg2beNPQBjpDESLXgNU1x2axUmGkvm1S8+n7juPiuh6B+mLWdBz+9fhhvj8+iuN5pIM679u2kzesWUdCC7JYKCOKAqmIzkQxz78dO8znz5+hbFn867FDrEmk2N/b74O3gsZKwJ7h2HzzyiViWoB3btzMz96yhfZwpFHBtV2XkVyWP3nmSZ4cH8H1PB4bG+GNa9awNX0Fz7MIBA4AAVbFE+zt7uWhc6epWKa/3dr1RF5CFHs4l+H56Un/uqkq9/QPktZ1LMdtSn6ytSp/98KznJyfxQN2dHTxGzt2s72jqwn8qFgWJ+Zm+MgzT3JmcZ6JYp6/O/Qs/89999N2A2Chaln887FDPHL5IlZdi2pbeye/dOsOtrS2kwj4jk1XtY8ytSozxSJjhRy7OrtXFK5/w9r1vGrV6qZk7S+fe5qHzp3G9Tw2plv58wOvolAxkESBmmUTCwaYL5QIKApBReb89AIIoMky+UrNL3CpChXTbwHTDJG3DG4kU66yui2Fpsp85JkneOjc6cY+gyGNUCSIUW12PZRFkalCgVWJhC9yLQg4rsv3x0f5+ImjVG3fTe8Na4b4le23kVZ1HNsloMqEgiqL1QrfvHyRfzr6ArPlEo+ODtMZjvJ7e/ejKTLzxTK9yevlFDzgqYkxJEHgvoHV/Or221iXSvsMOvz5bKFS5uFzZ/iHI89TtS2uZDN8e/gSq7btQgSqpkVXMkrV9NlcoiBQsayXJdT84xi2s4jljFMxXCQhiqqsQlHayZtThGT15aIOLzM8WiIhfu3u20mGfLe9hB7g8OgkJydnuXvdqgZjRpUlfmHvdrb3+TIjgy1JTk3N8ui5y7xx6/pXxDpaLJX50tEzbOxs4337dzXuh9ZoiA985ms8cuo8Owe6EYCEHuAX79zZYMt5nkdCD/L05VEuzy817udcpcbDh0+xra+T333t/psejwd0J9GgW/gAACAASURBVKL8yoHdJHT/vCMBrXHe+9cO4HkeXz1+Fg/49btvZ02bD4yv72zlzPQc3z5ziddtXkdXPMaxiWkuzC7w26++k/s3DSGJIqtbU+QqNT7z/PHGfvWAQksqzPDYItWadR2YAX6O3duV5OzFGZay5UarmmHajE4s0tURb9LqeSUhCHUzqxXi9Plpjp2e5IPvu5d9u1YjiiKTMxm++9S5pu06WqNYtsPEdJY1A74D+413yLKB1g8hXurOVxX/fbVhqJMH7t2Epip0tEaJx/QVWxqzRgXLdXE9j6pjYbkOQUkhICvUbIuA5DNbr0ZrMMTG+LXdIQKa5Od8kiDjeu6KIN4PEj8SwFG+XOPc6Byu51GzbMIBlVRUJxIKENE1Dp0bZ9tQN8+dGSMWCtLbFufcmMlAR5JkVKdQrrGULxMKqP4CWRIxLYdbBjo4dH6cy5OL5ItVQkGNntYEmWKFZFQnoMogRkBO1RfyEoKggRABQQVPB0Q/ERBC1yx6fGZBPBTk/fffzr1b1/DU6RFOjc0wmy1SrBi+GLGq0BoL0d+WZFNfOxt6VtbxAQhqCm+7cwub+jt46vQwRy9PMZMtYNkOoYBGb0ucbau7uGfrGiIRjUu5RUKqilRvwfI8MF2HnFllz45+PAcswcESHMYLORRJomQamHX72auUXFtxec9rdzGcyTDYmqLsmuiWgl7XBIiFArz73h3kSjXCQa1JU0kQZMLqZlQpzdXHpjUe5n3376ZSs0jHQkSCGo7tIIoiwjVAlygIrO9p5Q/edg8vXJzgmbOjXJlZYi5XwrRt1HrLV3c6xurONNtXdzV0kDzXo1Y18VyvySlG1xTu37G+fg1HODM2y/BchkLFwLIdNFUmEQrS0xJjc38Hu9f5zgYr0buvfZBVWeIt+zazqi3JVw6eYWQ+Q75S890EVIVURGeou4XeFwmVXx23IxnlVx/YQ818ZW4bvmPcyu0rsiSyY003f/rzr+V7xy5x5PIkw7MZSjUDVZJoT0bY3N/B3o39bB3obGrbFASBuNrSoC/ev2Md67p9wGuoK33NdnKdqbGcUKuyxFvv3Mr+TYNoisyq1iQdko0u/3CrTf/d4XkutjNHufYklj2MIKgE1FsJarsQhXCj9aJSfQjPM5FDb0aqgwGuVyVf+k8AoqGfRhJjlGtPY1oXCQdfRdU4jGGdRxTD6NpuAuoWhJd0uLvuCPE8i5p5mpp5AtuZBkQUuZtQ4ACitKzT5nkOVeN5SrXvgeeCIBEL/Qya0uwOYdkzFCqfJxy4D8fNUjGew/MqKHI/ocABZKm9wZBx3Qr58kNYzjh4DrLUTiz8s0hi84KoYnwf0xohHLyXqnEIw7qIIIgE1C0Etdubtvc8C8O6QKX2FHYdYAFAEFGkbqKhtxJW+ggpvQ1xcBAw3SyyGEFs6L8IxNR11xzFD5eBEo4GCV8jbu55HihbGgpXTUsIMeXPKWISkBAa7UVCYzvP8zi3NM8XLp7lzu7+mwJHB3pXsb+nH0nw56CoEmoGWNCX32fXingiNG23NpHiz+96dWMcXfUt1q+NJi22q7pVP2CYruVrXb2McD2P0fIkOdMH0gOSRkKN4nkwMZvjiSOXuTg2T7FiIIr+ndDdlmBd/43n1x/nsGvfwzEPI8rr8Nw8ZvmTqKFfQHrx8+s4fO7cKc4u+q40EVXlF7fu4D1bt1NcrDA/lmW0amIYfpLrOh6iJNKTjiCKAgszBcqlGpLsF1VWDfpzwon5Wb555aIvtCoI/PzmW/m5W7aiSb6T6ch8BtN2uGvjKgZiCT54216mS0WemhhjqVrlk6eOsSHdSlqZA6/CtfPJtSEKAm9Zt5H3b9tBWBXrz5N/D0qCwGAiyfu37eTUwixL1SpL1QrH52fZmlZ96wBnGknqJ6xqHOgb4JHLFyhbFsdnZxjJZtnU2nbTVrzHx4Yb7VB9sTh7unt9wf2A2tD/cVyXJ8dGOTg1jgd0hiP8zu472N7ReR1ooysKu7t6+PWdu/ng975J2bI4MTfLE2MjvHndxuv0Gj3P48T8LF+/fAHLdRCAfT29vHPLEGuSYQpujmLFN7XQRIWaY6JJKkHdYms4iSlUmKyUAIGiXaFVS1BzDCRRpOIYKKKE5dp0660ErnGLU0SRRCBIrWpTqbvFOrJLrWYjeyKLFQPLcZFEgZ6k3w7nuA6xYJjxpRyKJKLIIvlqjWzZv35hVV2xmCIrEpZ5fTubXW/3vxpL1QqfOn2CTL19bHtHJ799+x206yGGp5Y4PzbPhoF2Vukp0kGdt2/cjO25fPTgU5iuw9cvX+C1q9fSF4rRlYjdNIEcSrXwe3vupC8aI1utUTEtogENSRRp0UO8a9NWDk6N88ykbzrw7NQE7926A8d2OTYyTWsshKrIrOnw104xLcBPEgP72tCU9SQj70OWOhEFHQQRyxVJajrXnrPn+e2NAsKKYObLC4HVrakGaAQQCWiENa1J5gGgNeKLXl8NXVVY3ZriG6cuUKgZrwg4WipXGFnMYjoOf/OdpxufVy2bkmEyspj158V67rVQLHNmep6xpSwV0yJXqVKs+bnW1RLeVK7AQrHMg1s3NNrXbhSiILCmNd0AjQCi9fMuG/55W47DuZkFyobJp5473uS+OJ7JMZsvUTZMPDymsgUkUWRVS6IBHGiyxFBbs+ZaNBJk767VfOaLz/O5rxzijt1rfGA2U8J2XDYOdSLLIrfdOsD3njrH579+lNfVxbGfPnSZc5dnec/b917XdvVyIqApRCMBTp2fYt9tq/3cHKGhd3SVoV4qGxTLNQrFGt958iyZXLlpnLWDbQwNtvPw144giSLrVrdh2S6z83m6OhJ0tS/naLIk0ZKKcO7yLBPTGWJR3XfIjfmMeM/zME0by3apVE08z6NaMymVfYBd05RXRN4oVQxeOD7Kzi399HQmkSRfj6hm2AS0Zj2sqm1xPrdAzqjSpvv39VKtQtE0SAZ0UgEdEYHxUg7TdYgoGrrcTVKS60UF31RnbWSrX3Az53DxSKotNzq8VxQ/EsCRKkuEghqSKNASD1OqGozNZmmJh1nVmaJUMRjoSGFaDkuFCpoq05WOoSr+xJSM6li2w9hclpppUzXsRhuQ63q0p6J0t8QZnc0QDWnEwgGGZ5YI6SpIEWw0XCFS19bpXO7nFmi8+FzPQ5HSPqB+jV6GKkts6G1jdWeaYqVGzfJvNPCFnDVFRtcUdE19yZtMkSQ297ezrruFXLlKzfSFLGVR9FuX9ACKLHF2aZ4zS/O0h8IUDIOQotIRjnBmcR4Pj20bu4lpASq2haxIXMgsEJQVKrZFzbaJqBoVy68UqaLEge2rSS/M0hdNMFzIktSXwaFwUOOBXdcLhAOIgkJQ6W/6LBnRedOe5ram80dG6F7dRjjWXHEWBIFwUOPA5kF2DfVSrhrUrGXxTlWWCKoKoYDaBHxYps2Jp84zcnaKn/ng/U3jCQL0tSbo2h+jXDMp10wsx2m4fsiygK751rh+Jd2ibBvosu+EUXMtAqLi92yLIpooI+Azmm5f38fpYxM88OohentSDbFuVZEJaUpDRPtqTExn6e6I0xIL8eZ9m3Ecl+m5HN0d14t6/iAhCgLtiQhv37+F1+1cR6lmNoQ5A6pMVA+sKA6oCBqtWg+y6OtJ7dnQz54N/deN73kWkhjG9aq4bglR8DWU7rt1WSx0vlLi9OwoPeHYyxYg/XEIwzrPUuGvsewxJDGNh0mx8hUi+utJRH4ZSYjUwR6ZTPEfEQSVWOidAJSrj5Mt/guJyPt80AComScolB+iah7FcZcQkLCdGQrlL9IS/33CwXtf8TGa9jDzuf8FeIhiBM8zKVa+TLn2BG2JjyJLzZOE51UxrAsY5mlC2h3XAUeOu0Su9H8wrAvYzjSiEMD1ahQqX6RqPEdL/EPI0rWMOgfXzVMxnkMUQkRDbwKaF2hV4xjFyleomi9g2zOIoo7tLJAvP0Qi8oskwu9tgFEV4xkW83+FKISQpXZMe4SaeYKgth0l6INFvqNcc2iST3OyXZeJQo7pcpGqZRFWNfpj8cbE618Dj5xR42JmkYpt0RmONAEaBcPg+PwMg4kkXeFlpl/Ntjm1MEtIVdmQam3sbziXYbrksxr7onF6orGmxdiprEJEVWgJupzPzFEwakRUjQ3pVnRZIVOrcCmzxLdHLjNWyPHY+DBBWUGTZG5t66gnIb5o78XsIrbrossK29o7G8+bcA0ANV0uMJzLUnN8MLczHKErHEWRfN29vFHj2Nw0huMgiSK3tnaQCtbfy/V57vDsFD2RGI7nciWXwfOgOxJlIJZoSnbz9euYNWoNgEkUBFbFkwzGk6S1JKooYzs24+VJxivT3KLc3H3U8zwmKlN8a/YJira/KOwOdpDWkoiiwOY1nWiqzLMnR3nt3vW+KK0goGtKkxvKT1K4zhRy4H4kdRdgY1UexnPngebnd7pU5JmJ8UZ70+1dvbx1/S2EFJWcU8K0HGzLQVFkBMARXVRVRtdVSsUatuWQz1eIxUMoSl07xHF4emKMxYqvpdUfT/C6wbVodfHVqyzqq/8XBIF0UOct62/hxNwsOaPGyfk5js1Nc29XHs+ZR6y3kL442kJh3rB2HQEpR7l2mqC6FVlKUzAMsrWqbzaRSLI2mebZqQls12WqWMBwZBShhOssIUk9iIL/fPREY5xfWiRTq/Lc9AS3tLbdsCqcq9V4fnoKs24Jv6+7j1Y9hOf5AsnJkA8Uly2L74xcplZ3NtvT1cuWtvYbJseCILCtvZN1qRaOzE5TsS2em57kNYNrGs/21XA8j8dGh8nVfE2VlK7zc5s2EQiYXC5NYjgmkiDSpbdwvjCGIsr06e0U7Qo10aA9kGK0Mo/tOSiiTFKNMl6ZIyBpyILEXC1DRNHpDLa8aL8OOStLIqoSdnwGuenV2BBOIAh++4fjWYCAKoMaDCAIHrZYYmNPClfwdeZEQWBtR4qIdmNh+lA44Lf2XrMWDqkK6VCo0eoNcHphnpMLs4APQr1paIPf/uUbNRKPBOlsiTausSKKvHrVGr5w/ixnFudZqlZ4fHSYd92ylbDms6ZWeu+oksTrVq+lLxbHdV1OzcwRVlU2dbYh1Y8nomnc1tndAI7mK2UKRo1kQOeW3jZqpk0q4rMGREFgdTxFKng9i+AnIQRBRBJjVGpP4no1REFFVg/QEoigilJTr9NitUzVtukJx1Y0tWkKz7sOaxPwhaabrqNwtaDRvK0qS016PYIgEFBkX1PSfWWMkqpp+fpWhsnYUq7pdxs6WhloWV6/n5me42OPPkuxZtCdiBELBur5RvOYNcvGcX0Q+qWANEG4/ryvpp1Xh7UcF8O2sRyHqVyhac3RHo3Ql4qjqz7T2LBtP0+R5QYhzM9dmx2kJUnkwN4hMrkyTz57ke89dQ4BAUWRuG3bAOtWt6PIEv09Kd7xU7v44iPHeOHYCACyLHHPvnXcvmPVD9QJk4jr3L1vHV/99kl+5yNfRFUktm3q5VfefRfgA0JbNnTx2a8c4pFHT6HIEuvXtPui19fsLhoO8q637OYTn3uW//jsM3U5BF8A+/3v2t8EHOlBlfvuXM+/f+Zp/vAvvoqmyKweaOE3f/FeAgGFas3ike+d4sTZSRYzJUplg89++RBPPnuR9pYo73jTLlKJl9+SKkki7S1RvvDIUf/aCj5gtnlDNw++Ziu9XcudCbqisDXdiVs3YAJ/fri6+r3qPt4X8YvViiihSTK2azNcHmWkPNoQxI6rcWKKQkiOklDTP5S2tR8J4EgPqDywZ2VwAqCn1f+yd65f1qNZ0708+SUiOm/Yd0vj56HONIIooKgymweX+/bXX1OR7O6KcWRxgsuVRaySg+25LNXKtAcjhBUNy3UISAqT5RyKKKGIEjtaerEdh7O5UTr0GGXbJCgpzFWLRBUNRZIpCTVkTcL1PFJaiKxVBnQmC1liapCaY9f1JkySqr9g7w03t7Joikxb/MYIeV80TjKgo1xjXS8KAnu7+qjaFplahVWxJLbri5sOJVvQJKlhwysgULMtJFHE8Tx0WWkkQ2FVJXwDAKCYLXP40TNUyzVau5Ns3jvEhaMjDJ+eJJIIcfv9W8nO5zn2xDk8YOc9GzENmy/986MMbuph+4ENdK5q5dB3TzE/lWX9jgH613fxnc8cRJJF1IDC/gd3MHl5jovHR5m6Ms+dD+7AMm0icZ1IIsSFIyPsed2trN85yJXTfruY53mceOoCV05PkmyLsveBW7l0fIzpkXls02Hr/nV09LVwJDNCzqygOTJqzQeEYqrOmfwkvXoKTZJ9oE6QKDsGnucRV0OsjrTWk1YBy7CRHAEMl1QiRCiksZQpoSn+S3hhqUgkFGB+scinv/A8P3X/VuIxnWQ8xKWReb7+nZO8+YFt/meJEKWyQSZXRlN9mihANl9BAIplg9ZUhGBQoVQ2WMr6LQFt6ahPX6yaGKaN47i0tkSJBDXmF4tYlkM4rBJQ5Mb4kiiSToZxXJdS2RcvliTfOSSVCK/4shcECaHB1lh54pfqFX/rFU7OP8rhuHkyhY9hOzO0Jf4cTRnC9Qzy5U+TLX4cVV5LNPQggiAT1R/EsM6QLf47mrIBkMkUPoYe2E8s9FbEa9ydLHucoLablsTvIElpTOsSC7mPkCn+IwF1G7KUvPFBXRcCitRDa/zDSFIaWUzjeQaF6ldYyv81VeMFIvr9+PpEEnpgD3pgD8XK15jLfuiGo7pumZpxmNbEn6Brt+F5FtnSJ8mW/o2w8Soi+usBEEWdROQ9eJ7NXPYPqZlHbzimZY+hKmtoT/4lstSJ5UyxkPtjCuWHiQRfjyJ34LoVcqVPA0J9ux5sZ5qpxZ9HU9b71/ImbWee53Fsbpq/OvQ0hmMjiyIl0yQRCPLhvfewJuELK48VcvzFC34rT0wLEJAlouryd1S2Tf7uyEF2tnfz27v2NRZWw/kMv/f97/A/Nm1nQ6qVqmXx2fOn+Orlcw3bW1EQeHD1et6+fnOj2v7xU0cISAq6InNyYY6q7Qtb/9HeexmIJ7iUWeKhC6c5vTjHQqXMVy6dRxZF4gHfKehqcjlbKfHExAjH52coGib/cf+bGIgtv6ddz+PI7BR/c/gZHxQSBEzXoSMU4fd376c36s+hJdPkmalxzi7Nc25pgX+49/Xs6VqeVy3X5U+fe5LBeJKCWaNomOTNGq7n8Vs793FP3yCiIDBdKvBXh55mulSkJahzMbvEZDHP3b2DvH39JgbjSTZE19AZbOdyaZQlM8cnRh/m9Z33sjayiqAUQBIkqLuBmK5JzixyKn+eZxYPMV2dw8MjKoe5u3UPccUX1JUkgcHuNK3JCMmo3vTeKmTLVMoGyZYI6g9Il/9RDFFqx6p+BdcZA7eMbR1B8gw8N4+s7UUQ/XbZS5mlRotaUJZ5w9p1tNZbojq7EnR2LbdHvriN9Oq/19DR1G1SMAyemfTZNaIgsK2tg95YvCmpieqBpjZsQRDqWjFJDs9MUzBqfH98jHu6oghi+YYL16FUmnWpFvBmcNwlbDeDJCYpmgZHZqdZlUgylEzTEV5eGxXNGqbtoSggyd1cXdK26iHu6h3g/NIiAN8ZucLb1m8iFrge1PDqwt9nFnymViIQ5EDfAEFZbiSQHjCQTjJRyHNy3gc0NElif1//TduUAZKBIGuT6YaI9umFOQqGcR1wVDBqHJxaboHf0d7Fzo5uRiszDAX9FgRJEFFFhbaArzukywGqtoEsSgQkjQF8Yw0BAVWUWRXuRBMVZFGm5hpIgnSdA2rVqTJbm0aSbIKSjiqoFJ0CvXo/C8Y8AgJhOcJEZRzFlpEEmZyVIaGkCMsRak6ViBylYpfp0weQ6uvS6y80ZJeKFHJlNmzvb9xDsigR1bRGguR5Hs9MjlMwfJeq7kiU27t66u9ZvzDmOC6y1Myi7ghHuK2rmwsZH2B/emKMe3sHuTi7SDq88twR1wLs7+33i8aCQFrX8QTvuuR+IL787Ji2Q8k0aQ2Fmc+XKVRrLJYqDLQlcVyXmXIRXVFuyhz9cY5S9VEkMYamrAFkJDHAVGWG1kBzzuIBp5ZmSQdDhMV6kaMOflzLLnM9j0KdofPieLngW/FFf+95HtlKFV1VGq1mLzeiAQ1dVbh73SC/fs/tvLgJSRD8TxzX5T+eOcp8ocSHHribrb0dyKLIeCbHkbGppr+JBFQUSWKhWMZ23ZcUyH+ps9ZkmUhAIxoM8JEH71vx/r76PIU1FctxKNXMBq7nu8H5heaiYTKZz6NKMrbncPerNrD2lg6KxRp4EAgodLXFyRo1dM8hqmncvXcdG4c6mZsv4DguqWSYrvY4irJ8Xj/35t1+V0jQn4s9z8MJZ/mfv3w33W3Lmgae51GlyBtfvYXtm/rI5Pw8pzUdqV9vgdZUhPe9dxfZBQej5hCP6vR0Jpiay9XNrfwQRYGhwXZ+79dfy/RMjmK5hiSJRCMBujuWn+FSsUYmU2L3tlX0diVZWPK12EJBlWrFJBBQUGSJzRu66elqlhWwTAfLtNGDKlcuzzEzluG33n8fHW3L3SGKIvGut+z2yQeqTM2w+PzXj2BaDr/6C3cRiwbBg4VMiS994xim5fDBX7q3sZ6RBJGI8sokYADma0s8u/Q8G6LrCEp1B2RJo+bksb0fnrPaj8ybzTJtFmfz6OEAoUiAzEKBYEgjEFSbPl+az6OqCpGETnahiBZUUBSZUqGKJInEkiHGLs+RaIkQiQYp5qsIAsRTYbKLJWpVk3RbDEWRaAmESQdCXC76E013KE5rMIzpOMiiRFhWadejgMdMpYjruZRsg6lKHlWUmTeKBESFglWjYFVZE21BFWWqjoUqSsSUAOfysxQtnyZcdSymK3m6dJ/uO1HOkdCCzFeLxDXdR+xfRuiygi4vOwVd22rleh5d4UiTbXNU1a5bJHoEEerbCwhEVY2aY9MSDPlAVP1vrw4tigKWaTNxaZYDb9lFa1cS07B44ouH6VnTxoWjo6zd1s+Rx84wsKGLNVv6UDQZBIG+oQ723L+V9r4UpmGjBBQkSeTM81do7U4xPTLPWz7wKp771kkWZ3KcOzzM6s29lPJVwjGd4TOT4HmomsLcxMoaUqIsEgxrXDoxzi2715CZy2MZNgd+eheKtmwPnVTD6JJat0wVkAWJ3lCKqKI3HGcEBMJKgIJVxcVtWuhWqhbPHx0mEvbZXw/efyuf+/Jh3vz67bS3Rvnslw7xwH2bOX95lvHJDEdPjtPbnWLTepUTZyYZn8pw+MQYQ6vbCQQUHvneKQzDploz2bNzkL6eFB//zDOs6ktTrVrcefsaopEAX/7mcTzPI6Cp3HvnOuYWi3zrsdP09/jixffdtZ7h0UUOHx9FUSWCAZU3vHoLV0YXOH1+ikKxxqq+NCFd49ipcSpVi2Tc1/l6+4M7V7TABAkPB1mMId0gaV+olimaBj9J1GzLnqJqHiKiP4iqrKlreano2h3kSp+iYjxNNPQgAIKgkwi/F8saZTH/1/VWV6XONmq+ZoIQIBJ8LbLUXQeINxIOvopM8R+x7MvI0q5XdJyCoBNQt+JhgecgCAoB5RYEQcNxM69A6PDaMQWC2m5CgTvqjCqBcPBucqWPY9rjr2ywxpgysdCbUeRBv9IlDBLUdpIrfQrHzaDQgetVsJ05FKm73hInIEudyFIHtjPtVze5OcV7IJbgA9tuZ3UiSUhROb04x4eeepSnpkYZjCcRgc9fPMNwLsPv776LW9s6mCwW+PDBxxpjtAZD7Gjv4sjcFAvVcoOtdHBqnICssLuzB8/zeGF2ks9fOM0vbtnB/p4BHM/jE6eO8onTR9nZ0cX61DIz66nJUd6xYQsfu+d1BGSFbK1KZyiCAGxr72Rjuo1Pnj7KI8MX+ej+VxFRVT/xu4Zlua21g03pNj53/hSfOrOsS3A1LMfhqclRDMfhH+59gKiqUTANZsslWoLL160jHOG3du7j8Nw0f/jUd1e8jo7rcnx+ht/edQd7OnuYLZf46PPf56Hzp7izux9Nlvn+5ChnFub5yJ33sTHVyunFOX7nyW/z2lVruK3DFxFu0ZI80HkPnxh5mJxVYLwyzb8Pf5akGietJQhIAVzPxXBNcmaBnFWg5tSwPT8BCMshXtd5D3vS25sSOVWREEWBk5emqNbbrgRBQHcEZq/Ms3P/up8o4EgQWxDlvrr+ooekbAJcPHcRz7Mb8/iFzGJDnyqth1iXTGPZTr0NyKVkmIQ0tVGdrlk2Ut3B8+rPAUUicg24slgpM130WwZVSWIodb2lvF23Lr82wqrK2mSawzPTOJ7HaD5L0VSIyHZdL685REFgMJ5EkSRMy0IQQlj2BLKYxvH8aurqeBJJEJqAGsvxQEgiih6i2MzkPdC/ik+dPkHJMhnOZji5MMu+7r7rklHLdfn+xBhly28DWZtMsTHd6m/nQTocqssBeFzOLtXnO58h3hYKU6r/fLMIqQqSKPp23JVKY1/XxmSxQKbqM7sEfO0kXdZYHe5Gk5S6Xoq/JgtKflLhAZqq+Os4QSChLifvAhBQVb9dRhBYF+lHFCAgNhcGVVGlPdCBJDk4jkNICRMUdQRPJCAEUUX/fdQZ6EISZEzHICSFfTdLMYAoiMiCjOeBJt6YbQR+1d02mxkZtutiOQ41y4agz+4cyWUbYt6DiRQxbTmJ0lSZtlT0OnBHFATWp1p8CYa629pEPk+wzjxZSZKgKxIlGdAb17JkmoS16wunwWta8R3PbRTKOpNRYrUAxarRGKMtFLlO6PsnKSQxiiS1oCpDCIg4qGSN6nXAUck08PCamEBhzZcTmcjkMGwbRZIo1gxeGJlc8ft5ubFYKnN2et5vS0Rgrlji3PQ8fakEcT340gNcE+lIiI2dbRwanWS2UKI96ksT+K7MLrIoIEq+tt5isUxrNExfKo5cf74vzi0xX2xuoepOxOhLxXniwgj7RVKMFQAAIABJREFU1vQz2JJsdLL4jszSK2rpk0SBXQM9fPbQSU5PzXHn2gEk0W/utR233g3hp/er6vs6MTnD+o4WNEWmWDM4MTGD43oMZzO4sr9YLBkGsiQhCwLpNp8F6OExXi2Qy86xobUVIRolommosRqKtkCrmkagwsXKKEm1lapTpuwUSba3oEoaF8rHSWvt6FKIqpRj88bNVOwSZ/JHiCspREHkeO45NkS3kepMYCSz6FIYQchzKn+FgKgTVeKcNg+yqmuI9aF1KPV32GBfC7btcOH8DMVCld7+NHMzeUzLpr09TmGpjOFaxNriXLowQ61msXpNO/NzeWzbpUsUKOequFWL3r40F87PcPrYGPvuGKJcNsguFOnoTLC0VKRaMenoTLCwUGBsZImlVW2k0hEiIY2hVW3MzeTp7EowNrrIqsFW1q5aJqrMzOV5/v8j772DLLuv+87P7+Z7X07dr3P35BwAzCBngKRJQqRJiaRCWfJqpbVEW2vJ1q5ll+W1WJZl12rLVaqN0lIrycoUSJGmKEYAJIg8wGAGk1NP5/xyuHn/uK9fd0/3BIhaL0idKmD6vXfffTf+7vmd8w1vXuOpR/byvkf3dQtEtbrNGyfHKVeanet/85x+fdyoXhqGEARB10HOC33Saop9yb3oko4Q4AUuC7b4W9M3gvdQ4WhhpsTrL1zgyH07mJtc5tSrVzEsjQee3s+5k9eplZo88L4DvPz1M+y/Z4zScp2Z8SVcxyOdj7M0WyEIAh76wCGW56uomkJluc7lM9MIIdh/zyinXr2K3XZ57MOHSVsJDmUjNFJSM1GFhC5vhrMOkcENfEbjESIlL8V4vG8XuiQz7GdRJbn7QBcIeoyo+6wICVWWuSs7hCrJeGGAJslk9RiGrJDVLSYaJfrMFLqsbBhcbxc3buP617IQyFugQ9Yv07Ad5qt1BNFAkTINJCGIGRq1ls1yvdnlBK9y2wezUTU1kY2RysbRTY1200HVZY48sod7npTJ92WwWy6xpIUR0wnDqOCkagqqrqCoCtfOTjN7dYHRfQNceHOcMAxJZmKkC0k0Q8X3fPL9Gd58/iz9Yz3k+lKMn5vG9wIaHTj9jdGstTn14gWOv+8g89eXCHwfWZbI92Uw42uJzMH0EJqkbEpaR2K5TccoDEPcwO9CY1dDkSWOHBjk+F1j/M5/fpGJqRVsx+ve6I7joesKDxzbzumzU3zsw3d1xeIeOr6d+YUKn/jIPQCcvzTH5asLPHh8O5MzJV56/Qq9hSTNlsO9d43RW0giyxIvvX6FIAj5sY/fiyQinYCp2TJBEPLUo/uImRqKIvPcd88zOpSnkIvz1efOcO9dYwz2R9Xy8cllzpyf4ejBYfqLaVzXJ5+NMz1XplZvb1k4CvGiiYlQbiqOvXrPKHdY9Px+CD9YxPMXqTWfpdl+oft+GDr4fvTZagghUJURMsn/jtnl/x4I6cn8Gpoytuk+laQ4srw2uRFCRpH7CUMfPyjxbiIkJAxbNO3v0LRfxnGvEeISBHX8YBn4myLANFRlBLFOHFwIDSG0jkPYuw9JSqLIfev2W4r0EYBVvRNJmChSDi9YwA9KyFJPhDrwlzC0Axu2Z6sQQpAzLfbmCsw16lyvlKnYNglNY6HRICTijZ+Ym+au3n7uKUZCtrsyOR4fGuM/n30bAFmSeGJkG1+9dolzy4v0WnEanssLk+Pc3z9EXzyBGwS8ND2BE/gYitK1ss6aJsvtJudXljYUjgYTST66Yy/9Hepb1lhLZFUhI6vRs0ISAlNRuvpy60OWpAhufpNOpSxJDCVTlK+2+Pr4FY71DbAtlaUvtpEaJgmBrihdqtHN4r6+IR4cGCah6RiKyrG+Qb50+TxuEKADU9UKOctiKBG5Ie3M5NBkmaptdxNgRVI4nj2MF/h8ZfY5Jlsz2IHDbHuB2fbCTX87rsQYtvp5qvchjmUPo98w0a3U2/zfn3+ZlWqTxZU6maSF4/r86GOHcB3/v6oN73+NUPQHUfQHb7lMQMh0bc1cIaXrZAyTNydmKDVbSEJQaUUUqO09OYKO6GYQhkwsV0hbBrbncXSof0PhaK5Rp91xGVMliYEtXAcVRSZobUx0dVmhfx0yaLnVpGbbJLXMOhTrWggiqpoAJMkkDB0QBkKoyCJEV+SuLNj651CIQJILCFFifUothGBnJsfBnl5enp6k1G7x0tQk9/QNbCgCrG7bi5PXgej+eHxkjHinUCGEoOW63bbIbL3WdSJqOA7/4rmvocu3T6MXm40uZcYPA+rO5sLRYrNB010dYwU7M1lm2yu0/Da279IOHHRJo+3b9Bk5/NDHDjwSqknVbRKEATWvhRO4XcSRQHS61xZLdoUjmR2b7ntN0shoOeKaxsW3ruHrLRRNwdVDYlIKPwhoN2wkSY0KdaGGZcSjvFeWGB3tp+k1yGq5W44pjuOiKDKaoW4oNKqSRF880XWEq7sOpfYagq0/nlgT7vcDJhfKtG2XHYP5TW5v/Ykk6moh1PdYtpvsjOVump/kLSu6tojOfU88xny9jh+ErANPbNqv1etBErB3sCfK/zvNyHcW51AlqTve/+CFFFHiW19HkmLErJ9kKJ4hq8c20quEwAuCDZPgsXyWkVyGP339NLbn05eKc3JyjlNTsxjfQ7HN0jT++PVTLNWbpC2D71waZ77W4BPHDpLuOOrZrsf4comG7XB+dhHb87myuMKJ69OYqspgNkXS0EmbJp86foj/+Nff5le/8A0e2BE5KC/VG5yfXeSTxw5x37YhVEVmT1+Br75ziWffPMPe/h7Gl0q8eGkc4wbjHUtT+dF7D/Pv/+p5/u0Xv8kTe7aTMg1WGk0W6w3+24fuoS9959eLEIIPHtzFievT/KdvfJd3ZuYZTKdouS4X5pcYyab5yQeOoikK+/t7OT46yB+/+jYN22FbIcs703OcnJyNtI7yeR7cPkLTdanaNj2xyEl1tdAN0cgaECFZo0aFz5XGeYrGILpkcK1xnh5jgAu1twkIGTK3cb15mbiSpOHV6DUG0WWTkrOEF7iU3GXK7gp5vYgqVEw5RkJNM9G8QtUtMx9OIwmJrNbDirOAJmmYsklGzXeQymtRLjW5fHGOY/dtp1JqsrRUI5+P89KLF1BUmdGxAufOTrO8VKd/II2iSMiyxPTkCrt293H58jzZbAxdVzBNjVw2jiRLvPnGOOm0xcT1ZSRJsGdfP+fOTDM0kiOdsUinrc4cN1rf5MQyruuxtFhj2/aNdGDDULFMjRNvX2d4IEs8puM4Hm+fmeLa5BIf++DRDehp1/OZXYq0qYIgwPF8MkkL2/HwvAC1sw9hGLJSbZFLWaQTJqasM92a4dmpvySv5zpoUYtBqwdNevcIppvFe6ZwlEhZZHJxpq4toBkaibRF31CWuckVAj+k3XYxYzrFoRyTV+bJF9OkczGW56u0GjYDY3mW56vYbRdJEtTKTTRDpW8oR73awnMDauUmA2MFTGstKQBuCwmr1ts8+93TPH3XLkZ7s6S1DgRMub2mQkbfOCFffaVJCntSvShijdfdtF3+4sVT7Bns4djuW9tl11o2n//uacbnS+SSFp9+ZnNiuVJr8ux3T/P+u3cztE64uW47XJxbwlBVbNel7XpdJ435aj2yra82cH2fYipB2jK6ouOJtIXUScwTaYt9x3fw5nPnSPckyPamOfLIHk5+5zwX3xrn7sf30TOUY2BHLy9+6S3uemwv8ZSFY3tcvzBLMhtDVmRS+WhyE0uayIpMu2HjOT61coOFyRUGtvfy8ldOoscCFFNQq5R49WunmZ2Y5sTzJ9l5dBgjJjjz+hlUy0PIDpoVoGjgBy2EkAhCF1VIBGEbgcKqcWRIiCJt5qkKIbrJyvpQVZl0ysIyNUxTpdG0u/DbIAjxbqXQ30WIRX82Ww6u6+O4PoN9GYo9SVRFxjRUevIJtI7IXFTYiW1yOchlYiQTRndwKVdaiGHwvIC/9+RBdE3hi199m55cgiAIcD0fSRJYpoavBVhWpPO0JawcEERUkuAWBQNVklEk6V0VPt/rEYYBQqjEjCex9Ac2fa7IN4rwhgRBldV+QBCUiR6zN06QNjpFbfzk3W6kR6n+u5Tr/w8x4zFSsY8jSRk8f5bF8r97t2vrRqTpduO49r2dW4HSuZZuvlZJskjFP8Vi5TeYXfklNGU7rj8R0QFjn0ASt0YbBWHIcxNX+f0zbxGEIRkjQlTON+vdxLXhutQch4IVw+zA14UQFOMbO6V7sgWO9PbxV1cv8tDgCO8szjPfqPPzR45jKipV22a+2WChWee3T53Y8N2dmfwG4VmIUD4x7f9b/R1Fkvjg2G4UIfPspTM8e+kM21JZPrnnIHf39t8WGn9j9CeS3QmxJARmh8a7qv+3I5Pjq+OXObu0GHUzF+YitGsiueG8qpLKI4Xj7E/t5Er9OudrV5hpzVNxa9h+JN6pSRpJNUZOyzBk9bMtPsyQ2UdcjW1ZrF6pNGk7Hr/0E4/zuW+c5AMP7OXlU+OR/lPc2GDC8HclwpCuNg5EqOSUrjOcTTGcjcSBbc9DV5QudcEPIjOSYjJBXNdoex652MZ8pebY3YKHJMQmZ7IwDKk229wYshDE1y3bdF3sQBD682w12gkhui59YeggkNCUMWQpgSY3sFT15toMoYckEtw4TmUMg8dHtnFyfo6W5/Lc9Wt8Yu+BDbSjMAx5ZXqS65VIy6QYi/PYyNiGNTUcF88P8MOQim130SZ+GHapge8mwpAtaTl1x8HpODvKkiBlGPihT0FPc7U+ExVwQptlpwod7U0v8AkIyOspZlvLBGE0UffxcTwPU9ZIa1F+Y8r6bfUtfMdnZmoFu+2y/eAQ7aZDdaVOo9LE9wPS+QSaobE4PUWuL00iEyMMQyzl1uMzwNzECgvTJQa39bD+XM3Wayw2GuwtFDqaLD4tby3niHdMYCAStO3NJlgs1bfck4S2piHjBwHz9TpZYbJNbE0DNxW1i6BbbRbKQtwxUne2VGNiqUJfJsGB4SJe4OP40X+rjlo/aBEzH0eWc2jKDoSQ8Umy0BrH9X2yutVx9QRdUsjo5oZjkItb/PIHHuaPXn2br7xzEUUSHBsb5BeefIDPnXiHWAftpUoSA5nkpvFIkSQG0gly8bX3C4k4n7jnILuLeZ598yzT5QqFeIz/4f0P8/ieNc2d5UaT//35V5kuVbF9j7Rl8M1zV3jx0jjZmMXPPnKce0YHkCTB/duG+MxHn+IvT57jm+euULcdMpbJ7mKewUxU4JGE4B8+eDeqLPOt81f42tnL7OzJ8dMP38Nz56+Sja03EhI8vnsbCV3n62cv8eW3z1OptxjsSXP/tmGszn4rcrTf2Rv2W5Y3H4/BTIpffeYJvnbmEt+5NM63zl1BVxR29ObY19/TRZJamsovvu8h/vCVk7x4+TrfvniN+7cP8+nH7+dzJ06TtSyShkHSMCgmEvhBwEqpSRgGZDPxLd3AvcDrmBasNaBWabCmbJHTe1iwZygag0y1rjHVvMbOxH6CMKAVNCnoRSrOCpPNq+xM7EeVNCQEfugDIX3GEC2/QVYr4AYOilDRJZNID/OGIm4YgogaGIgIrCDJEmEIuVycXD7B8lKdw0eGOfX2BKl0DFmWaLddWi2HQ4eHOXVygqmpEqmUSbNpR0VPz0fVFHbs7GV5uU6xL82Vy/OkUiZqByFWb7RpNmxcx2d4JMeZd6bYf2Bwk2N3LhPjZ3/iYb72wjn+4HOv4PshiizRX0zxMz/+MMePjm64T5ptl7cvTNOXT4GAVtuhUmvjej7lWqs7TzR1lbbtUao0ObizD8MweDB/P02viRd66JJOUk1Q88oE4Z25Bt9JvGcKR57ro1saiZRJ31COC66PYWlYcYPSUo1tu/vwPB9Fk8n2pBje0cvF05Okc3EK/WkkWUI3VKKJbohje2R7kiRSJvG0he/5aLpCrdykWm5SMO88kW87Hm9dmeHY7uHbL3yHIQmxyQrd833eGZ8jad0a7gtgqAoP7BvFdn3euDS55TKyJMjEzQ3C0hBBMR/fsw0hCYJgjc8tBOwq5tfSuhBkec0VIZGJ8dAzd6MZnUmXJLj/A4doNR1kWcKIaWw/OMTAWIEgDDE79u/3PLkfu+mgmxqKKvPMf/NoJFStymi6ytM/+gCKKnP86YO0mza1coPHf+Q485PLXDszxcMfvYdMUaXhnQXJwVevcvf7+9n9SJuYBb56iUd+tJ9G+zqqEkeyLjFyl4IsydSdc0hCxQsaSELDD+odtIaDLFkEoUfOevQOzxq02i7Xri/RW0hSrbbJ5+JoqsLCQpVWy6FUjqDmqioTAiulBtlMDENX0VQF1wuoVJtYlk42EyOTttizo0g8Hrl4dCc96waRYk+KF1+9zOJyDVmSsFavXbGWBgohGB3KkcvEOXJwiFbTQVEk5hcqPPXwXs5emr3BdvL2CY0kLFLmo0TG3Fsvn9YMdqXyd0yz/H4IRc4jiTiyXCBhfYRbHaswDLHdc6zU/i8s434EGuX6H2Bod6GrBzY8DIKgTuCXuwll5Ny2AMjI0tbueTeLIGhQb30ZQztEPvXPkaUCIG6pNfTeDoGuHkRXdnZFN03tHmLGo+jqXsRtYLZVu83/cfI1hpIpPn30PgqmRcW2+Rff/mp3GVWWUSWJluvhhQFqZ/xtuxvdDuOqxmNDY/z2228wXinz2uwUvVaMffkIRaRIEjFFZWcmz289+eFNGgo3Ogze/O7ZHN8L4dNSVZ7ZsYeHBkc4s7zAsxfO8OuvvMC/e/gpDhaK72pdkrj1Nj84MMLzk+P8+qsvMJJM03Ac3j+6k6M9fZsmS0IIcnqGrJbmSHo/bugRhGuGBUJEqAhZyKhC2ZCMbhVCiqyDNVWOYPeNNoamsFCqk3BDfO8HR2/t3YS3TuRdkSSE5JFNuCiS2SncRvkRhJhIhPgINPzQRhB29Nh8vKCJH9qoUjyiaHXXutkhyfF8Eqa+qWESndP1yCAIQxlxi3FudcIpiRiauhPR0YezfZ/FRoOt746QkABZ2caN6awQggcHh/mjM28zXikzVatwYm6G0XUaTQ3X4ZXpSequgwDuLvYzmNi4jUEQUm/bkdvYOgSFLssMJ9No7xLhFtP0rrD9moRAVIhafS13jt+gWUAgOJja1r1fpltL9JlRN3mVXi8JiZQS6xyhsEsFW723Vl2uVCHfkhK08+goQRASBAGqpkR/d85tGIbISvT9HYeHkWT5XRVpB8YKWDGdfF96Q2EmZ1rkTKtblAw7OfxqrIpUr0az7XQpqjeGvG5iGQKarNCfSnQLEjfGqjTBatiuTxDeuYHkcCHDfLlG3NQ7BbRIU+cHr1y0Fs32izTaz5GMfQLHvYKqPYgXBJSdVlR4W3c/bHUY9/f38q8+9DhOB7lnqmpkDDTYh9lpluYTMT7z0acJREDbdyg7jY7QseBfPfMYsiywAxc5lPjE/ftIaxaqInFouBfXDzAVlYRudHV+AHqScf7NM0926bzdbQxDvNDD0hVafhsJgRd6DBYM/slT92K7Hn4YEOChKyoZY63R3JdK8E+euJ9WBymoyTKWpnGgP3Jw3NBEkWXu3TbE4aEi71yc5cTp6/zwh+4iYerdHKI3Gec3Pv6BTcjiXMzi1z/2/g3vCyEYSCf5ifuO8rG79uMHEfNFUxRMdU34WghBMRnn04/fR9uNqMKmpqJKMoeHiljaxnzFc31OnLrOuUuz/IMfuZ9cZgv9JCEzEtvBfHuanFagaAyxZM+xI76fgABDMikaQ9hBG4FETu+l6TfQZYOqW8KUYkBIQS8SkxNktDwld5mR2A6mmuPIQiGn9xJXEoR6H0k1TU7voeQsklazG5qb6YzF0HCOi+dnGBktoMdkTs6cJb8/Rlaz8DyfZNpierpELG5gmCoz02Uc12dursy1q4sYpkoyZeJ5Pp4XaRgdvXuU5aU6sZhOLK5jGipjYwVyuQSmpbG4WMW2PUxLY3m5TqGQwHV9Cj3JLXOgfbv62T5awLbXDKBUVcbQI+kWx/Nw/UjDLSRkoD9DJmVhqAoJQ8PzA2zPR5ElFElCkaWOrIyg1rQxdRUhCUzZ4GLtErbvkNbSjMaGcUMd9Tao/XcT75nCUaaQ4Phje7uQz/ueWhPLHty2Bvu668Fd3WWOPRpZLt94ku57YrPQ9vx0iXwxEtjUja0hkWEYslCus1iJYMXpuEl/dg0+2Gw7nJ9cwA8CetMJcsnIOcHzfeZKNUq1VsRxTsfpSUe82MVKHcfzkYRgsdLA1FSGCikMTSUMI22KiYUSTdvtDprrt6dUbzGzHEHQi5m131QVmR39eSYWyrx5ZWrD94IgZK5UY65UY0d/nqS5sUsoIag2beZLNRRZoi+bJBUzEELQdlxmV6r0phPMrlRp2i696TiFdATfM6z1FBaBqqtdDaHViKUsgjCg7XnYrg8SGEmj2/WO3eCuZsai7dNNDVmRyBXTvPn8OVRN4Z4n9iPLEvG0BY5JGGoENDHiKWQrjSxU/NBBMVvE9MgFSpJjSIrS6cjVgQSaXGCVvhOEbYTQI/71bTj5N8bYcA7PD/j8l9/i6MEhhgayPHzfTr7zyiVSSZM9O4tomoJpqNxzeIQvfOUke3YUeeLhPWTTFvt29fEnn3+dY0dHOXJgiLsOjfCVb72DJEWOBn29KQb7MxsS9H27+lhaqfGnX3iDmKnxwacOEI/pFAtJVpMkIQQfePIAX3/+LGfOzzA8mOXxh3Zz9OAw/+Vrp+jtSbJttEAqaWLoKn4QkEyY9OSTGPrWw0D04Lv18YlrOnf3DL6rY/heD1UZwtTvpd76Kpb+IIa6HyFUgrCB5y+jykVkORsl40GJlepvEYYuueQvIZCYL/1Llqv/id7Mf0SR14sAtqm1/gpN3Yks53Dda9RbX0VVhtGUnbfYoi1CQLjaQehkuZ4/S631V++a9vZeiZb9MrZ3iWLmNzG0o++qW1tzHJZaTT64bTcjHSHoU4vzXK+W2d+hjSU1nR2ZHKcW55isVhhLZag5Nq/Obiy8y5LE8b5BfvedN/nm9St8e2qcj+zY2zUNMJTI8ezlmQnOrSzyQP8wmizT8lwqtn1Tc4GbhSBKONueR8Vuk1yH1LjTY+AFASvtFjFFJW9aPDo4iqWo/PLzf83l8sq7Lhzd9FfD1X9Cmq7DR3fs5dGhUUxFZSCe7NJNYB1PvzM5E0Kgyxo6tz4+YWcCfbN9TydMBnvSeF7A2ECOP/zKCcIw5CMP7KMxWSEIvpfy2/dnCKJCxmq0fZ+V9kXcoIQsNEJC/KBNO1hBFTF0OUNIgBvU0eUMjl/BCWqEoY8uZ9DkJGltN0bnOQrReVmlaa2G1vm83GjheD56xxY6CIMuxQ0iBIEiCRAyt0s7PX8Rz5/pFtNVKaJy3vS0hm087wqqup8bUZ4jqRQPDo4wXinTcBy+NX6VD+/Y3RUunqpWu4LUCU3n8ZExYjdQZlzfx+3ohmgdSqkfhuRMi888+iSDySRhZzIaEuIFDpKQiRwgBUHoAVJXY0ISgrSu4gcuCIEX2GiSiSbJ3WW8IMDxA0IfxicWUWSZRtPuWNmHBLESKysNRkfzNJsOjUZk5pFKWbiuT7PpkE5bNJs2IyN5pqdX0LSocFgobEWJCWn7TUI5ACkEIeGELpIsY4sWumQQhH6EMAk9kAWSFDmyhaGM49soQsEPPXTJQhKbz7EkC6rlBqqmUOhfQ8C3PY9yu0VAiKVGCKD1E+SW624oJCUtA1NTtyxatT13g8MjQNI00BTpjsZSx/dpbEEjvFlcnVuOHK5cj5F8JCCsSfIPkOLj5vD8JTRlB4QeQVgHPNzA24TSCgg3UQkhOi9xXYMbinnpdQ1zWZJoixYz7RKtuk1MieQ0pprLeKGPhECTVXRJoe61MSSNduCwPV6k5TvsNgc2IWUUSSIT26x3VHPrTLXmqDsKi7UlEmocS7ZoeE1qXpW0liarZig5K0iuoOJrjMXHkIkaHKamYt5QfIkba89w2/GYX6xiOx6aKtNfTJM0dEInZGG2SsPSGexLI8sSS8t1VspNsmkLK6+yXGrQbrtUO3ISqUIS1/OZma9g215XkLpeadNqOZimRj1oM1BM02q7TM6sIEkSQ/0ZZAT1cgvb9QDBYF+ahK4zt1ilXGmSSpj0FVPoejR3mZxZIbiJ6Y0QgqIxSNFYy/0HrbENy2yPR/PzIWtb972CvpaHFM217+5LHu3+faNlfEqN0IIJNc1WoaoKh4+MdF9LOQer3UdOz6G1Ypw+NUM+l6DWtElmY1ybWCYe09m+u0ip0iKQBJoq8865aUxT494HdpKIGwzc8DthGEYu6TGdBx7chedF2oGDg1kQ8OYb1xgZyaPrKp4X6bj5no+iygR+SBCEGMbN3V9L9RZvXptBEuB4AT2pGCv1JhNLZWRJYjif6hTDZVYaLZodzcIHd48S77Coluxl3iq9zeH0IRJqgonGJC8vv8buxCBJ9d2Y79w63jOFo1Ur9fWvt17u9stsFb0DGXpvUEdfH74f8PqlSf7k+ZMYmooiS8QMjU8/E1FVmrbDX7x4GlNXqbciHYd/9vFHGcinmFgo87tffx3CqHvtej6ffuZBdg7k+dbJy3zz5CV6UgkQsFCuc//eEX7yqbsJgS++coavnbhITzrq7k0uRZDpMAw5P7nAHz9/Eq8DKw/DkJ/94H1s77s1lzwIQ2ZWKnz1xEVeOXed//DTH2LfcG93va9fnOTzL51GliRs10NXFX7+ww/Qn0syV6rzP3/uBQ5v62euVKVluxzfPcwP3bcfSb7z4111HL41cYXlVpO4pjGWynJvcfC250xRFR565q7N70tx0saqePCqC8z27udCQBC6uH4JXVlvGb7+98J1//+bEXB++Jm7N713eP8gh/dvLp48+chennxkb/e1pil8+H2HNizzyP07eeT+jUWDH/9bjg9+AAAgAElEQVT4vRteG4bK+x7bz/se2999r1BIMjiU7VCfoj0ZKKb5qU9tpFZ94IkD3CoO7r1xeIwKj03XxdLUdyXY94MSspQml/ynLFf/FxZKv4IiD3QLR0HQoCfza1jyvYQ4lOu/T9s5TSH9r6NkCsglf5G50i9Trn+WbOLnkDpUSFnO4foTzJV+Gakj/hqEDQqpf9ktMAVBg2rzL3G9q3j+HJ6/SNN+kYXSryJJCSzjYSz9PiQRJ2H9EOX67zBX+iVkqYAfrCBLeVRl47XoejPUml/E8+ewvQsEYZVS/bM02s8jyzmS1sdQlc3Xwc0iDAPazlvUW18jCBu07FfwgkUWy/8eWc6jq3tImB9EkrYSXL95CMkiCGosVX6jQwcUSFKiI9b9yC1RWQUrErX+i4tnuF4tEYQw16hRjK3R0CQh+NG9h/i1l77Fv37xG2xP56g7NjXX2WQX3BdLcG9xkD89fxpdVnhgYBhJWpv4PT26g4srS/zmay/yF+kMhqzQcB1USeZX7nvkXelbCCE4VCiiSqf5N9/9FsOJFHFN4yf2HaEvnqDhOrwweY3peo3XZqdYbjf5w7NvMxBPcCDfy13Ffpquw++98yZXyisUzAh5MF4pMZJMd4tGbuDzxtwMZ5cWuFxeptRq8YVLZzm7vMBQIsWTI9tvvaHQHTSbrstUrULNsblWKSGJyIb9qZHt0b7IMjXXZsluEFd1lO6E2I86/CJCB0QFjSDSUui81/AcCkaMhLp10TodN/nhp49EduiHx8imLDRVpidmMaeoqNoPDvrxTkMIQcFa6ww3HAfHS5MyMh2n6xDXbxKgEleLgIZESFyJ9MtU0SAuQmShEJkiSMhCo2DFumhSLwxYbDY2/XYQhrQcb4NTkuMHLK1bNqUbWKqA0OF2uDpZzhCEVSRhEoYwXatGWj1bolsCgmCFMEyzla5bTNV4anQbX7x0jprj8ObcDFfLK+zNRZOT70xeZ6EROepsz2S5f3B4U44SFR4iJEHOstBkhZbn0vI8kppOb8xgvn0NO2jihy4OLeJyBk22cAMbCYEpx2n6FTTJoulXWHIUnHYLP/RQJJ2x2GFSRuTy2HCjYzrXqLE/U6BeaxNPGLTbLpVKk9GRPJ4XoGkKjuOxuFjFNDU8L2B2tkyjaaOqCo7r0Wo6JBIGC4s1spkYtVqbvr7NE7AgDJhvX8eQLeZa42iSgSRkclofc/Z1EkoGN7BxA5uM1st8+zq9xjDtoIUsZGy/3dl/j7HYfmLK5lxbCEGrbjPTWoqauJ18UpUlJioVhlPp7jlb7zi31Gri+pGenOP5TC2W0VWlQ1HZeK4Wm40uokSTZPoTCa4trhDT1NvShVfFC2Qh0fY8dOXWyEeA3nSCk+MzkZNzR9NnslZhWzr7A0lTAzC0Q1Qaf4btXcRQD6HKBYqWREzR0eW1AooiSZuE9OHmwr+b7jstGs98QpKqiYRE0ch0c97IWMFFFjKKiAqzSdWk4bVRpTuf2pqyQV6PTGaSanId5coko6VJqcmIOivpOIGNE7hIN3EZ3iqWluv8b7/3Ag8d287UXJm7Dg6TjBtcGV9ksC/NxPQKjz+4h3TC5KsvnKEnn+TF1yo88/RBXnrjKlcnltgxWmBqtsSnPnKM0+enmZotE7N03j4zxeEDg3zlm++AAENTcVyPT/zQPbz21jUcx6fVdrheTLNvZx+/9bvP88CxbcwtVNm7o8i9d40xMbVMudri4rUFPvr+w4wM5m6/U1tE1a3iBA65dVpnYRjiBBEl3ZBv3oRu+S3KTpleo/d7FnHWJI2yW6Hlt9ll7GbnjiKaKpPPxVEUCdv2iMV0XDcq7gwOZFAUqSs1slUjvd12EUD/YFR8mZur4Ngutu1Rq7UwzYgiq+sKb7x6hUTCoFZtY1oalXITSRIU+9Ns39HbHfduDFNT2TtQQFfVyMRAi7Tg+jKRQZciyQgpqlVkExau55M09Q1DoB3YqJLGkDWALulIwHhznJbfxJRvTye+03jPFI7+/45yo8WfPH+Se3YO8sHjeyNhMN8nbujUmjaO6zNUSPPjT9xFrdnmN599gXeuzzGQT1HMJPi5Dz1AzFDx/JBf/5NvcHp8lh390Q24WGnwU08fY/9ILy+dvc6XXj3LR+7fjx8EfOPNS/zIw4c4tnuI85ML/Ic/ex6IhNy+8sYF+rIJPvXYUVzP53f++lW+/uZFht5/vNvZ2ypkSXBk2wCZmMmFyY0ipI22w5dfO8fR7QO8/+7dVJtt/tcvvcS337nKpx49AsByrYmmyPz8hx5AVWQUWbpJ0rYW6x8GQggSqsbOdLT/OcPa0kXk3cdG4b3Nn6odZNHNtlXcsJbv3/D8gKlyhYF08m9dP6XuOHzzwhWe3rNjS4eRvwuhq3voSX+GRvs5bPcMYdhGlgoY2mF0NUI0BkGNEJdM8h9hGQ936VSmfg+ZxM/g+bP4QbVbOBJoZOI/g+tP4zhn0YxRLOMRTH3NTS0kwA9K+EEFITTi5gei3wobhIFLEKxOxmTSsR9DljLY7mlAYOkPEDMepdr8PJqyfd06HfxgmSCso8hFEuYzIKSoWxiISIiWqGCWsJ5BU/dsOBaSSJKwPoyh7e2uMQgb+MEKEGBo0biBkAiCCn5QIuyIXhvaXoLwQ0jSxsmKpu4kbn0IWYomGH5QxXEvochFEAp+x0HK9cZptL6Jn1ginfipLq2g4TVpeC16jGiM0WWZf3zXvfyXK+dZaDTJGAYf27mPhVYD2/O69/zubJafu/sIb8+t0HBd7usfYlc2zzevXyGubURTPrNjDyW7xVg6zWB8o3ZPUtP5+aP3cqDQy6nFOZqux2AixV29feSMtYLZ8eIgQrDB5XKrOJDv5X+89xFemLqG4/skNL3bdXf9gIlqhel6lZxh8sTwNpquw7VKiYIVi1yWVJXHhseQhGC51UQIwUODIzw5sp2xDgJrdTI6Xo0QaU+ObEOWJK5Xy93PZSF4YmQ7uzL5NTFzIk2jp0e3o0oSru/z2uwUPVaMtGGiyzJ+GHJmeYFXZ6f4zENPsT/fw+XqEkt2HVVEnzc8m2W7iSFFMPqUZpDSDOaaNYpmgpLTomy3MBWVB3vHblo4Wk+D0jWFQzv7Wa40mBxfQvbDboHv71IIAcPJtXusbLdZbqn0J3qZrdVouC4tV6Xl6WTNOLbvY8gKVdtGlT0UySSmqpE2C9E5357VKMbipA2DhWakebiqBbQ+ZEmikIxtQEzbvsdEtdJ9XbBipIw8srYfxK0LykHQ6AjxR+e4EItTdeyb0IckZHkIVR0houLdeFwEB3p6GUmmeWdpgYVGg1Pzc+zO5nGDgNdmp7p6Qw8NjZAxNiISgjAgrqsYnXxrKJHCVKLCke15jFfK7M5nkEUkRG37TSw5iaWk8EMXWaioksGqsneETBKYShLJXxVljtBJxVgcS9VYbkXC0OeWFvng9l3s2dMPAgb6owOgdOhiQRAgSRK5XKJDDwo7NLOIsiJJEn4QoCoy6bRFudwklTK3FI+PRFTTxJQUtt9Cl61OUUslo/WiCBVZKGiSCQhyej+ypCKHLhIyWb3IfPs6fujdtCwYhiF22+38vfZ+y/UidHoHoRZTVYqxOIKIsjFRqdD2PRLoKLJEJmF20NCbM7nxShmnQ60zVZWUqqOj4N5Ke7J7DKLG71ytxmI9SVLXblv8cf1o8raKSAN+4DQfbwxDO4gi93TyiX4kkeDuXGyTfqPj+1TdNcfBMAzxQ5+qVyehxAgIaXotBAJT1jsFg6ggFGnfyRSMCFWvdOjLceWGZ4KIrt31rtGW/O5EgBVJIa/nO6tbT68NN7y3SqEMw3dLhQ7Jpi0evX8Xp85Nc+nqAncfGmZsOMeTD+3hhZcvMTtfYblUZ36xRk8+yfxilcnZEkJEjIOH793Bn33xBMulBucuzfH+R/dRyCf4/T9/hXKlSS4To9iTwvcDFlfqLK/UeePUBEf2DxICJ9+ZZN/OPjJpi8fu38XZi7NMTJeQZYlMOoYfhLiuz9JKfVPhyAkcplvTAOS1PCvOCl7o0W/2M9eeo+pWGTAHqHk1DMkgIGC6OY3t2xT0AhfrF/FDn/3J/dS8Gg2vwYA5wLKzjBd4pLU0Lb+F7ds4gcNsa5Z20KZoFGn5LZadZRJKgtHY6B0dbQkJVag4gYOiSQwNRE28re7lO3Xym58ps7JUI54wGBjJEXaQWKoqo2sqnuOTSJoosoxlaRE7x1SJxw1kWcL3A2IxfVOhe30kLaMrU7P+ek4Ym6/nVXf0G/crKnIKvjL7NSzFouSU2RYbRZWU6DnztzT7fU8Ujmzf5WxlgqIZ2a2qkoLtu7R8B0NWsX0XU9FpeZFolSop9HUqz7PtGZzAQUKmaBTR72DQWK2CLtqLCAEFvYeZ5SqVRpsH94+RTWxObOKmzrFdgxRSMUxdYSCXYrFS74j5ebx45hrXF0q4ns/VuRJHtq918Lf35dg71EMqZrKtL0cYhtRbNsvVJkEYcnhbP+mYya6BAiM9UfJXbrQ5dW0GSUhcXygRhrBUbTBWzOL5AbdyG46srAWautklbqFS5+zEPIuVOm9dme7S2gbya938hKmzd7gHnxDHcQjCkLrt4Po+QRAiSxKGptBoRxPOIAxRJImG7dCTipMw9IhKl8nR9j3qjsPebOGWD2Ev8Fh2lnECm5yWx5TNTcvXHQdJiJtanUbLR9/xg0jGVZEiHmjdsUnqGx86YRhSdewN3a3bhRd41LwaTb+BG0SJjiJkdNnAki1M2byjinkQBqw4yzT9Fik1RUJJ8Pr1aa4srZDQdfYWC3z78jiyJNjdU6AnEeOlaxPMV+s8vWcHkhCcmJzmmQN7mK3UeOHyNWQhcXiwj6Sh89r4JLIk8djOMc7OLXYFAw8P9PGti1dQZZmjg314QcCJyRl0Reaxndt46eoEy40mK83mpkE1DEMafp1FO3IViysJCnphq937vg8hJBS5SDL2CaJrauOxCEIPSUqTTvzjzvJKR0RcEIY+ydinkNiYeIYEKHIPcfPJm/6uLCXIJT99B9snkOUM6fiPbfosm/y5Da81ZZRC+l/ddp2qMkhP5t92X3v+IpKURFX66En/TwA07bfwgmWS5pPEjEduu864+X7i5vs3vR8zHul+PwwD6s0vU218jp7MZyKaWkfA3gsWmV/5FRrtb5GK/yghGitOhenWPFW3jiopOIFLUo1TD5d5aCzDrsRR2r6DHTjszQ/T8JqsuGVM2cAOHPbksjw1tI+aW6ft26S1JCPpGG5gU3UjAfOG12Isk+QXjh9hvDGNE9q4nkPdbZBQo85NK2zw+Mgwz2zfc9Ox7cf2Hd7y/WsXZqlXWhw8HsG4VUliuxvj0M5jZAsbxbrThsE/OrJWXAz8gMmri6RzcVLZtS7SseIgx4o3p43qssLf37mPv79zI5X7xqL/L96zEbUoSxIPDY7w0GAEB79UWuaPzr3NPzx4Nx/atjuy7O0Ujv7Zc1/hwsoS+/M97EzmGQ4y3bvHDXxmm1VkERWNYqqGLCR2JXvQZaU7cfTDkLS2eUyOKGxscB9ZjbNX5pifLbO7J/s92Tp/v4aEYH++QEzVaLgOS80Gr0xPsidXoGrbjFfW7K+9oEFM1bhWK1FzHEZSKcJQZsFxmGvUyZtWVEQNIWUY3FXs5+LKMm4QcHJhjord7j4z/SBAkcUmqsZMrco7i1HTSpcVDvX0ossuvnsaRbsXxGZDitUQQsX1p9GU0UizQ5LJmzE0Wd7CXCAkCGuEQRUhb12QypsWj42McWZpgYCQr1+7wjO79jBRqXB2aYGQSGfn0eHRDXQ/iCgDi7VGRyMnZG++QDGeYKXdwvY9Xpgc533bdlA0o0K9G9goQuvmABsVoobX0Tdv2GcEA4kkI6k0k9UKIfDdqQl++sjdm4pZN8ad9I00TUHTVGQ5ooyuRxIHYUeHTOsDYMjaveG7qfXUkXXaSTeGJcdpejWMm3S2hRAYlo7dcjbsvybL7MrlSWh6tyh8rH+Av756mYbrcKW8wtXSCgUrFgnfShKlamsD2hoiStuJuRmczjiyO5tnJJ1mYrGMdgfooRDQFYXeRJy0adwRYqjedujLJplcqjBbqmHKSqRzJCs/sOLY9dbXMPVj6Moe6q2vo6sHUZUiLc/lV1/+ay6XlxmIJ/nkrkOslzJ2A5drzUkW7WUSSpwg9DFlk5CQufYCkpCIyRaqpCILiZrXQBEyQ9YAvZ3CzmKrwWfPnuByeZlP7jrIY4PbImfrIMT3fFRdveNj7touTttFN7XIPMb2MBMmhCGNShMzYaCsa9ALBC/NXOe33n4JWUh8dPs+Prnr0C1+Yd1vuT6eH+C6axpQ8ZiBqsqoamRuo8gqyYTBYF+agWKa0aEcUzNlUgmz46oa7ZcsSziu19EsXdXKUSLtG0WK7m0Bph6Z7GwfKWB2JoyWqWHoKqoiE4YBE9MrfOu753nw2A6kjubtjVFza5ypnuFY5hiL9iKX6pcwZIOyW2ahvYAbumS1LC2/Rc2tkdWynK2eZdAcRBISQRigSzptv82p8imSapKqW2W2PcvR9FE0SaPu1Zlpz5BSU5yvnWfYGuZ05TRu4GIHNj16D33GIH9+6TRfuHJ2y2P8C0ce4KH+UZzAIa2lsH27Q68VXfr7qoHNmsNv9G8QhryzPM//efpVTEXlZw8cZ1cm3113LK5z5cIspeU6tWqbfYcGMdbpJN9YxFlLQTYez+7nhHhBgBf6GNLma3Y9YisIAySxmWq71XUek2M83fs4U60Zmn6LA6n99Op5TldeI6Yk/tbGpPdE4Wj14fra8kVSqkW/maPl2/hh0BExlbhUn8GSDZzAQ5VkikYaO7D57LXfZrI5SUJN8E93/nOGrFu7ka3+3neXX+RLM19AIPjY4A8T87dH1WvBlgc3oq513Ng6F14QhrQdjz967i0mFsr82ONHySQsSvXWhu/GDA1FXu0udbYhXC1uhN0ipCSJLi836hoJnjy6gyPb+rvrSlgGhvY3P21BEGJoCh86vpex4hrnMRNfS04UWWKl3sKZWaTcaGFoKpamsVhrIIi6LJamYns+miLTn0miqwrLtQaL1QZHRvsRksD2fXakc9Rdh/ptEEdnq2f4i6k/o+yWebTwOH+v70OsNFwc32cgmWSp2eTN2Wl6Y3F25fIokoQXBri+T8126E8mqLTb1B2HvBXjWrlEudXinv4BGq7DtXKJ4/2DlO02S40GA8kUju9xdnGRewcGWWm1qNhtemIxQDBdrZDQdfriici1LAyYa8/ynaVvc6V+iSV7iZbfBkJ02SCpJBmyhvn44I+Q1W7PJZ1uTfEH13+P2fYMB1OH+EjvJ/n25XGKyQTjyyUyloksCR7dMcZr16cQIkKhFZNxisk4pqpydm4h4uTbDqaicmxkgLemZmm7Hg3HxQ8CLswv8Z3L4+wo5LiytMJwNk3b8/jQgT1ossQfnziF5wcs1z0uzC+xWG/w4QO7efbkmU3bHBLyTuUdPnvttxFC8EDuIf7ByE8B7442+v0SbX+JinMBP7DR5SyKZOIGdYLQ7Wg4CBQpSpIFAsev4ga1qEug7SCu3H4sujHCMIjQRWEbIbSOW5DfQeBEhaWoK9ciDD0gRJLiSEIlCNoEYQOQkKUEIQGEPkKYhDid5TuuRUIlDF0kyep090PC0EYSMYQw8IMyS/XPkjSe6rgbpQlxQEgocqGznpAQlyCoEQl8x4kmcqvbJpClBGILvYsb9hrHuwZCRZWHkEScKN30IYwQU7JIIZApOVVeXzkV0SMCh5eW3kKTVDJakmWngkCQ1zO8XT6PJqmk1STjjSl2JEboN3pYcSpcb86QUuOcKJ3Bkg1owJJToqDnGLb6aPotrtYniSsWmqQy3ZpnNDbA1cYktu+gSSqqpFB16xxM7SammNH5tz3OnbzO4kyZHfsHsNsuhb40y/MVkukYC7MlFqbL9I/k8Dyfky9fZn66xP67R9FNjctnpjlwbIwgCDj92lWW56sMbitgWDoXT09CCIfujQpNV87NcOS+7dTKTd787iU8z6c4mGVgNM/br1yhslJn312jbN/Xv+nedIM2Qeh2C51CCPzAASGQhYpAJsDHD2wUyUCVLCTWmhB+GESCxc0GK+0WqiTR9Fxemp5AIBhORk2IuKoTZ2N3rGhuLIqx7vPYbVxKXz8zQb1pc2BHH3/+9ZOddUZlqfGZFe7dPURlpfF3snAkhGBbJsvBQg+vzEzhhyHPXjjLPX0DHOktsiuXj3KNLTqOm8via5O9mKrxxMg2vnTpAg3X4fTCHK/NTPPk6LaugHqtadN2I9RO5EwV8KXLF7oUsGIszqPDY8A0kpTr6Bzdcm+idRGhU2RJcK1cYjiV6moTrV82DNs4zgkM832wlXujEDw5up0/OnOKlXaL88uLXFpZ5uLKMrP1aBv3F3rYm9/c3IrpKnv7errv50yLJ0e3cW5pAT8MeeH6Nd6cm+GevgEkIdBu0Ey8WYd3q/d1Weap0e28NDVBEIacX17kixfP84l9BzCVv7lV+WooylpDK65pnW0IKdstbN9ntZR3y2f5LT7SJQtdi4p3W8qYByGyLPBcn6XZMoX+DJIkaHub9XHu6x9iWzrD6cV5lltN/vLSeQ71FNFlhbjZMRJZtzFBGHJyYZY352YIiQrxT41uw1I0LF3b4rrZetdsz2OmUmMslyEXs27bn3c8j3NT82RiFpFDb4eS+65RKd8fYbuXadovEYRNZClDy34TvYNQ9sOQcysLvL00R8lu8VF/I/pMEjIZNUVKSeATEIQBSmcsSChxFCFT9erUvQZZLU1OyzDTnt/gBvXy7AR/cvFtynYbJ/A5Uugnb1hMX56jWW1iJS0Wp5bJ9Wdwmg6e55PpTTFzZZ7iaA9L0ysYMR3d1Jg4N4Vmapgxg1QhycUTVykM5RjdP8iFN64yuLMPTVfw/ZBEJkYsZbFiNzmxMB3pIBbvPL+r1m2+/M3TLNXqPPXgHlx8ZE1gBx6BHKIImbFtea5OLfH2+Sl0Q2FwIA1KiPL/kveeQZal533f731PPjeHvp3jTE9Ou4vB7nJ3tcQCCwJYEoAokZRtUSrRVChK+iCVLNvSJ6lklV1WSVbRJUuWmCRRRVm2KQYQBEESgQARFpvD5NjTuW/ffO/Jrz+c2z09092zQ1JVJsD/hwndJ5/3nPO8z/N//n9DprpOpkAY8MSFKb7+2g1iFEfmRyiUHFhRGMZQc8k2yLgWLz57jGu3NjB0yalj4xTyDplhsiMVZTaxzPT8Ll9bw7FTA5/tZo8vff0Kd+5t86Xfv8qFiyPk9Bw1q8bN6Cb9uE/VqjJijbA6WGXWnaViVfASDy9OHTZP5k5yrXuNolEkb+SRpImPftxnxBph1B6lHtQZd8aRSDqiQ6ISQhVSMAqM2+Ns+GnhoWAUmMvMkSjFtjfgeqtOnChilRAkcdrmDtS9tNhdNAv04jQO2CWSKI8ougQk6MYZeEi71Y8j/vON9/jc7SsYUjKVLXCsVE1ZcnGC74ece2qOKIxxMxa68eB3Zv87UxEmqai6IXVCFaMJiVBpAU0A77eXcDWb+ezQdEVoBEmEHOYWUodjwXbQoWzmHkgghUmEJQ30h5jsjbDJmrfOyVya/PeTgPfblzCkRSPYYsyewjmkwPIHwR+LxJElDU4WplnIjaMh0YYVzJ2wUAChilPKOyq1sEeASpkbyfAl9LieNArF5fYlOlE6GbvSucLHC+dIEsWd9QaztbR9Ir15e3DAV8QPI26ubvN9p+Y4Nz9OzwtodPv7K2MHrFspZIjjhNXtDtV8hk7fZ7OVtqJkHZPxUp6+F3Biuja0XFe7aux/WBSzDsWMQxgnnJyu7dKZH8ZsrcRsrUiSqN1k1o6GwVanR8G1d6s4upTESjFeytEZ+NiGztagx+dvXSVrWHQCP0347MngPowbveus++tEKuJK5zIvjrzEzWab11dXeHnhKFfrWzQ9D9cweH11BVPTyBgmb66v0vQGvDR/hMtbm5Qdh5LtsNHr7jodREnC3VaLixNTvLG6SsMbUB3qQdxpNbkwNsbv3b2NITWW222EENxrt6i6GSZyKc1x1Vvll+7+Ile7V4aWkfcRRiHdqIOlWY/d+7w8WObe4B5B4nOtcw2v1mO8kMPQNM5MjFJ0bLKmmbrjsJOYiJkuFdCkZKnRYqnR4la9gW3oZKw0OakJwWguy1qnSy2XYaFa5srGFrahc7Y0hmsYuIaJpWloUjCez7HV6zNbLjJXKfLu6jrvrW4caBecQpGQIJRAkVZQGvUumaxFNvfoyuh3G6KkT5QMMGUeQ2bQhI2n6gBIYRLGbfShjk+Y9JBCx5A5EkK0P6SDQRgvs937DygVYmgTlDI/Rt9/la7/NUCQtZ7FMo6y2flX6LJEnHTI2s+RtV6g2f9/CKIlhLApup8liJeI4i2K7mfp+6/hhZdRyiNKthDCQimfjPUsXngFpXwS1R/u80fpeL9L1/sacdzENo5Tyv7XBOEtGt1fxDEv4JrnUfi0B7/FIHgLgU7O/giaLFDv/jy6ViFOuhTcH8I1n/qAxKLANi/Q6v0n6u1/hmu/iBQ2UVyn5/0ucbxJMfsXAZ1u3Cdv5KhZZV5rvIefBMy5k+T0DKY0sTUTUxpsetvMZSaxNQtDGpzKH8WUBlJI1v06gzjVQTiVP8rXtl5DExoncvO4us2363dJVEIr7HI6fxQvCSibRb5Rf5O8nsHVHKbcUS63b9IKO4w7aSJte7PN9feWef7jZ3CyNq999QqOa7J8e4t4OmHldh0hIZOzaWx1yRVdStUcNy+v8uRzi4RhTLvRR9Mld66tc+7pI7z76i1yRZfQjyhWc9y9tsGJJ2bwByHd9gDLMVm6scHTL53k0ht30A2NtaV66pZ5iNtTK7hLrHwGcZMw6eNoRUyZoxut4cdtxpwLeFSBVJwAACAASURBVHGDhBilYnLGJHnjPoN2Nl/k43NH+bXrl/mtW9cxNIkfx+hC8udPn+fUUD9GCEFjMMCQ2qEtr4lSdDyfnGWSDK24H3Z52YHrpG08ra7H7ZVtPnRqOj1HpWh2BgiRVpH/pLqqlWyHTx45xrubG3TDgOvb2/zzV7/B//jsn+J4pXooE1YpRdMboEuNvPUgY1vAbvLp6/fu0gkCfv7t15kvFDlSKmPoGmdnx9NYaRjw/v69u/zq1cv4cWoK8rH5BY6USqCuEcebSH0exOHfCoHENs8hhq1nYZKQPDK2EyBSRtBhmCsWOTc6xpfv3KI+GPDa2grvbW6QKIUhJS9Oz+1zQ4R0DI8V7ic7LU3jEwuL/MrVS9xtt1jvdfkXr32Lv/PM85ysjOzTStt7jTuBz61mk/licR/7GVLttBemZzlSLHGtsc0givi5t18nY5r84NFjWNp+BvnOtr0oohsGlB1n153uUZjM5dGEIFaw1u3yzsY6L87M/ZEKQA+waw9I3hqWztEz00RhhOWYu8kiXcoHRNgBxrM5fnDxOFe2twjimC/cvM5zUzN8YmGRWilLGMW76yuluNdu8XNvvb7b5rdYrvD89CxZ3eTWVkJz4DGS+2CND9vQqeUyjOayjxVnJ4liqlJgvJhnslwgShJKtoOhCbaDOrbmkNH/y2mL/P8NASgVEsebKBXg2s+iaQezzqWQeHFEvNvCpzFiVe5viAfbwAAqSYlYxVgyfQ+VzOID761Y7XHc2zPGDMug0+jRafRw8w7vfu0y1ckyi08usLW8PdTX8li+toqQAtM2qE6WGXT9VKOt52O5JipJ2Li7haZLNE1y9bWbSE1y5vkH2/f/oBir5Zk7W2bQ9/CKAS1vwNj5HK81bqMmEwxd59u9Gxy9WKXR6bPut2jKHrljJnPlMu9075E/Y3FP20aVFcefqdEaDDg7OcmVziriaMLx8VFczcQLIhw7ZRvNTJUIowTbMTCGGqt+FDMxUWRsrIBtG/zop58ijhWubWDbBlIKXnh6kWc/tICuaRjZhChKk+rj9jitsIVSin7Ux5AGrbDFze5NBvGAftxnO9hmO9jG1VxcPWWQXe9ep2JVWMwuDnV4DEbtUQSChIR6UMdPfDphh6pVxZQmWT1LO2yjUFztXOWZyrP8maOneWJknKbv0Q58fnvpOl++d3P3zR+okE1/g+1gm2bQZMwew9EcEIIk3kDI+4ZCDyNS9zVwd+fvCu7e2mJ9tYmUgu2tLp/87H4N3r3YGvToBD6rwQaRisnqNpt+G1ezyOgW/dhn3ClxrbPCifwUbzdvEyUxZSvHUn+LkplhfdBCoTiZn2LVa9K3A+70NzCEjqObrA62ebJ0hEl3r/mOohN2udtf4mTuOAroR30ud67x4siH6ce9761WNSEEtpYG/Tt4mPr1X3JKKhCM2qMYIqWITdgTjJayfPbZ0/zSV97krVurOGbqPPVjQ92fw+BYBucWxvnKOzcY+CFrjTZBGD/WDZqqFHjiyBT/+vPf4vTsKK2eRxilE/acY/GZZ0/xi196g5/+la+Tcy22O30+cu4ITx6dYrPV4Y0bK7x5Y4WNZo/PffsStUKWcwtpEPf69WVurGyx3enz1XdustHscnZujHLO5ZUPn+Rz377E8lYL29TZbPX40993huNT9z8Alq494AywA6UUuhnTDJoYWo6scT+ock2DcjadSJeFy48eP4spNfw4oh8dbJ+6gxFrBFfL0I97TDgT9PyYjV4PXWr0giB1RBgmhe62W/TDkA+NT6AJycmRGiNuhuuyzvnRcUqOQ83N0PA8dJlSN9u+hxdFzBWLbK70WO12mMzl6fg+vSDEMQym8wU2et1UoDUMuFicTPveY48vbfwOVzqXUShGrVGeKl0cUjE1BnGfDX+DilklbzyeKG7RKJLXczTCiFF7lIKZ45XTVdbaHUxNYzSfYySXoWDbPLsww3srG1QyLkuNFgXHZrKY56PHj5C3LcoZl8lCgZxl8sLRObKWxd1GAy/2yTkaHz01RaMb4Jg6hYzBc0enSESIAp6aG2G52cGQBllH4yMnpul5IS+fnkNqMd2og6O5aAdViRW0WwNuXFtnfLL0PZE4iqKEer2TTmIDE007TyxNQlOj60eYxjk8PyQeUge3vZCZ6QqGlV6fB1sT7r8DdFnF2mMxfRha/c9h6QsUnFdQxCSqR3vwBaq5vwxCsNX5GYrCJYxXqWT/AknSo9n/ZWzjJIPwEjn7JRzzNJos4YVXiJM2KQuoP9RO6pC1n6c9+CJ55+N44ftEcR3bWCTvfIqN9j/FCy+Tdz5BP3idavYnsIy0DcM2T5K1XySMVwAIoyV6/jcZyf0UUbJBs/fL5JyPESUNRvI/hR/dpOt9Gcc4g+Dw/m4hJBn7I4yW/iHdwW/T7P4CqBgp89jmWSr5v5lOJIWgYha52rnN7d4yM+44sUoQCApmDj8OudS+zrlihiPZGRSKopGjaOaQSGKVcLu3zNpgkwl7BEPovNp4hyPZGepBA13qKAV+Eqa0ed0lZ2RptTqseZscz81T9xvkjSydsE+oYiKVCiymbFUBShEGMXai0HTJxkqTrbUWI+NFTlyY4cb7y7z1zRssnJxgZKxAaSTHvZubBH5E4IU0613yRTd13AwilFIYpk51rECu4NKsdwn3LFsdK1CsZqnU8gghESKdzMwfH6c6drCYeN6cJE58svrYri6XQOLoRUDiamVsLY8QGv1oC0Omz/UOE9fRDX7qwtP84JHj1AcDYpVgazpjmRyubvC1W3epZTJ4UcTN+jZl1+WpqQmWW20aAw9Dk1QzLsutNlPFAt+4c5cX5ucQwEa3x2K1wjfu3OXkaI31Tpd+EDKez7EUdhjYMVnX4pUXTvGnnkzp9UopXn3vLvfubD3QWvAnDVIIfnDxOFe3t/iPl94lShK+cvc2y502H58/ysWJKSayuWHCQNH0PG61tnl7Y53bzSZ/6fwTvDx/dN92y47D33jqaZbaLe62W3xjeYm/9duf58+dOsvFiUlcI3WI3W4M+Ord2/zSpXdY6aZFuSdGx/mLZ58gY5ioeAFUzAcW+IRGHG2h6UNtCtJVtvp9pvL7v6+aHEPTYh52VNuLomXz0uwCr67coxeGfOn2TW40U62vyVyeF2ZmHytpIoTgRKXKX33iIv/km1+j4Xt8dekOd1pNfmBhkRdmZqk6LppIGdHdIOB2q8lbG2tc2tpkEIb8k49+4sDEkRCC+WKJv/LERf7Xb36NjX6P260m//D3vsSX79zi5fkjLBTLu+wZL4pYaqftdm9trHGuNsZff+ppXOODE0fnR8eoZTLc67SpD/r8029/nUEUcrpaQ5caCYowjgmGyb+jpYPFnuMkYW1YpIuSZFeaYOe8d7DZ7/GFm9fJmCY5w8SOdfS+xNJ0vCjaZQ3swNJ1fuTEad5YW+ELN69TH/T5B7/3JS5tbfKR2QVqmQwy9BhEIW+urfJL77/Dm+trQJpA/atPXGQ6X6Dj+ZRch9xjaDUKBBXXJYqTx55eaVKy3uzuapPoUpIzLZphk6XBXapW7XsqcWQaR6nk/yaarCLlo+M9Q0pK1oOSEym7NWGz19stRCcoHF3Hj2MsLW1RX/Eb7NjZD6KIiVyOgmXzzNg0nz1yiputbf78yScoWjZJnNBYa1Bf2aYyWaEyWaax0SKOEu5dXSFbzFCZKFEYyXPr3btUJkrEYUx9pUG2lGF7tYGbd4nDmM52N2UtLdUpjxWRetoGly384e+hYWiMjeRRWUVWt4hJ8ETASC7PutciY1tU7SzNTp9SzqUtBpRiF8cwOV4dwzR0tpodMlmLgumQM4ZtlANQWoJtGlRllrxr0+r6vH5rORXzt0wEqczK7dUmXhAxM1JE8z38MBq6q4GuaRwbr5Kxzd1WtWo5syd+VWRlWtjSE4Mz7jk0XbIyWMGRDprQKBpFjucWaYUbBEmXI9lpIhUglIejmZzOH0UKyZHsDEEywI97TNplvLiLpbmcKZzhTOFBI59T+VO82XwTgKyeJnInswUms/fjmlbg8ZXlW7v5AlMaVMwyeSNPL+rh6jtjNEEpD107iRD757WWpvPp+RPcbTcpWBavzA0ThQKqIznKlSyGqbG10XnkvQ6TmH/6xtd4e2uVP3NygSeqU7zXukvOcMkZDr3Y52h2nKLhMpepUTZz3OvX0aXGhteiauYpmC7tYEA96AyTPx7bYZdxu8TKYJuel7Ltcsb95y9t91znt9Z/h7XBGquDdSB1vTyeO8qEM3vIEf/h8MciyooTnyDpoAkbAUTKJ0zamDKPFPd1QuLEBwSaMHdbRPaiGQwwRZtu5A8pkApDanhxtCts5mgGCYqz+adB2YSJ4lj2Alc7G1w4M87ACri5WqcZKo6OVln2G0w5JX7o6VPkMzYd3yeIYp46PkXWNvGiiJeeOIrQBZ2Bz/mjk3zoxDQJipbnMTlaRBoSQ5NESURk9njp4hz5TCr09xc+9hRff/82rd6Ai8emef70HCOFLKEfcXKsyk++9CFev3aPbqPH/EiRqm3T7wxoNvvcWtqkYJl89PQCSxvb+EnIbFBAJYpbW5v0Ip+PffgIsYy4Vd9ivJbBcTWePTtNuWxx7V4dLwx5cnGSiUoakJWyNp955vSBOk+QDtDvbL/Kr6/+Ki+P/gAfG/34gcvt2Ev/2u3LHC1WyBgmI87h/ZVPFJ/CkhbdsMup/BkclaVidSjbDvOFlAHW9n2qjkvJdqgP+swVS/TD1MLaNQxOj9TIDKvVU4UCLd+nH4b0gpCJbJ5OkFrWjmdzlB2HTuAzlc/jxRGL5QoFy8bUNG42tnF0g+VOm9liiVbY5FL7fRIS8nqePzv1Y5wtnBu6Agm2mh1mkhAVK5bWmgRhjGubxHFCPmsz8EM0KdlsdLEtHSkFmirzkcynWe6sMR3Psb7qM1q2KA5tR+vbXWrVLCEBeVdnspLB3wpIDKiVLIoZk2LWIEpiYuXj2AaJiHBtDUvXWBgpcL17lX6i0VQrlEslIKIe9pC6xo1eh1F7jHq0SSZnYGuK2/2rZK0cjhnQCltc762hlGIxd5yMvl+TQgG+F5LNWpQrh2tWfDchjmOuXEsD0ELBZW2tnjKzxoss3dsmn3NwXZONzTZRlJBxLaan7rcmHpYwzmd+mJz7Qwd+tPYiSupkrKd3HcmiMKXrGtoYigSBTqxaGNoEuqwR0wJipMhScD5F1/86g+Atypk/91BhJZ2wSemiyQK6rKDJAkpFCGFiaJNosogmS0PR6w9GlDSRwkHXqggkSsUkSR9Tn0WTFXTZQimPOL4DMocQOVLRbDnUMIrT/6sYiHDNc7jWs8NrlCbiYpUghMmO8K2rSV6oHEclbTStCAhi1UcTIAyHMesMmtQZMY+SqAhN6NSsxaFYqeBC8QTnisfRhGQ+Mz1kqw6omiFCdWiHG3xf5Qy6tBFINCH5+Nhzu8KdsYp3WYXz2Sk0JEmcBr/lWo4zH5rn1tU1Fk6Mc/z8DNffXWZ6ocboZImlmxsYlsHJxVEKpQz5kksu76Lrkn7XI5OziaMY2zU5cWGG9eUGZz+8gOOmAZ1p6WTzDvXNNm7eIvAjTMvgxPkZnIzF8fPTtLe7WHbKQjIsnSefW9x33wzpYgiXUHl7bMRBHwr8DuLu0F1L4mg1/KRPmGxiSAtLyyDRsHSdxVKVxYfMk+40mqDgjZVVFsolRnNZwjjhd67fpOzYNAYeOcsiShLG8zlmigWubrpM5HN0fZ8b9e1dmvZGt8udRpOsZXJpY5MTtSq36g1GKzlGStkH8pCnFsYwvRhL13AyfzIF/QHKtsNPPfU07cDnN29cI0wSrje2udV8lZ99+3Uc3cDSNMI4ph+lVPooSciZFv3w8OLOh8Yn+dsffo5//urvc7vV5J3Nda58bYsRx6XkOMRKsd7r0vZ9oiRBE4Kj5Qp/95nnmc4X0nZEIRHaxCPZRgCoMH0faanT0WgmyytHjyGFOLAFSKn2btvuYRBC8NzUDJO5PFe363x7dZkoSRMEz05OM5U73LHxYWhS8pljJ+mHIf/mre+w3utxq9XkX7/5HX7hnTdwDGNYMIvx4zQpEiUJiVJM5R5dWJJC8Kmjx/CiiJ/+zjfZ6PdoBz6fu36FL966jqlpuEbKzvOitJU/GrZtTGbzj92mOZsv8pljJ/jZt15nEEW8s7HO3/md32Qyl8c1TKIkphsEtAOfD41N8q8++ekDt9MLA/7+l3+bS/XN4fGk8gs7LYs7uLq9xd/67d/YTQbssIzGMjl+9OQZCqbNeC6Ha9zX+yjZDn/7w8/R9HxeW1tho9/jX77xKr/43luMZrIYUqPhDdjs93ZF3UfcDH/5wlO8PH80FVO2Lc5PjSHlfo2Qh6FQtH0fXfvgZXdg6JJS1sHQtAckLvzEx5QWxUMsxL+bkag+QvXoD74JRLj2C8h9BTFBwbRxdAPjIRZePwz58p1bKJU+S4aULJYrvLOxTtV1U0dfwyBMEjq+j6XrVN00HhrP5Pj7Fz9ColLx7JiQetRk7PwI8+dnudVsojIWz3zyyd2uFakNWftS8OKPPDv8bohdcXmVJAgp0gKQSpm6C2dnCP2I7bUWsycnER9gDvQoVMs5/uwrTyI0SFhgfdBmLlMla9hkuxZHc6NIBAuZEaSQzGfSAr62W9SBT06eI+2zEUNBcJjLVJBCMu4Uh99vSZx4FIYiy65p7Hao1ArZtB18qAFlGXoqA6AUrb6HlILAC1m5W2fQDwj8iDCIMAyNwI/I5G2K5SzN7e7udcoWHS5Ofxjd0NCERitc507vHSzpUvfvkdVL+EmPMPGJVUTRGCVSARv+HWyZIaMXaUebTDrH0bT98wdXc3m6/DQKhRTyQDHohyEQeLHPdrDNur9BVs+mjCMFmj7FQa3MO9f4ydok/8dLn0Egdo1JALJ5h2uXVhidKNLteKhEHToe1vtdvrO+zEqvhReHBHHEi7UzbAddCkZagM8bDrrUmXZHKJlZvCQkiENmM2nLmiUNtJxkPCphSp2imaFoZKiYWaSQ3O5tkNVt6n6HvHF/nl6za7xUe5Grnes8WUq1NQ1pkNf3ywP8UfHHInEUqQEN/wph0iXVjCnjx/U9vukCTaRaC0HSQSCYyLy4bzvt0KMTbA6D3wBL0ykaLs1wwNqgxXy2ynK/mVoHW1nm3fP4cYQXC1phn57wsUYEZ2ujTGVSimQv9sk5Fi+cW+DdtXWu3arjRxG2rROb8BuXrpIxDY7P1+gHIbVclqubW4xkMvzO1RuAYHqyhK5rQILtCqYXFYnhAQ6FjM2nLu6nQV5+7Rbt7S7d9oBjrkN2poyTsbjx7Zssu6kF3wlsLNPAtA0GoyHJiOJS7xYlM8fYMcmcVmAQp8kSTWqsiFVarQZ5I0Nc6DOft+jH8ExletfBppBx+OQBx7ODWMVc7lyiFbZ2+1kPw7bXZ6XbRhOCvGmzUNhv0bqDjJ7hYvlpNtbbeOsBK/U1CgjmFmqsL7VYGCkQiJjmSg/T1MknGmthg2O1MradTioL9v2PV9l2eGEmzbKWHIcj5XRyX3MzzJfKuzTkqfyDAWPBtmn7HiCoOBk0IWiFLVphWj0bs8dZzB1D37H7VIqltRbFnEO92aPR6VMpZNhsdInimJmxEiubbQxdo9kZ0Oz0yWdtyoUM52oX0OtrbG10cWo9Bl5E3wvwgqGjjBvxfv8GiVKMWEXmJi3aYcRquMaVzZuYw2MomXnCJCRIIrw44MOVUziaiau5CCEpmiUMaZLXC7TCJu2ohSZSdkXBKBGpiEHcRwAVq0ov6jJU8kKpBHmIJoUA3IzF6vJ916rvdhiGxoXzM+haan25MD8yFB/UmJutpoGIFBxZqA310Bg+24+GEAZCfLBOhS4r+NF1LOM4EKWObEISxCvp/SBEE0UEEh5oSYixjZNYxnG2u79AP3gdKbJDl7MOfngj1Ty6f0C7/1TKJ4huEyfniJNtNFlBoCHQiJP2UANpf5uELstp61u8QRRvIoSGlC5iz3hRKFTSIoruIIRNEt9LE0Mig1JtUAlSG0fILEp5oHw04zxClunHTZb77yOQzGTOY2kuYXgFlWwCOmH0BkI4SJlFiTxxvAHCIEjqCIx0e/o0SbyB4XwCISz0vXpLAkCjHaxR997FT1qUzUUMuYmjL9wfE3vu24NtIBqeF/LW63c4fnKcYinD8fMzHN+jhz06ef+d1+n5nLgwi+M+mNgoVtOgaXzmPu14Me+Qr2YpV7JYe5wQBv2AL37+bc5emGH+SBpo7DCLFs9McuP9FdaWG0hpPrDvvRAIYiJudl9Lk2PSIEp8+lEbS3PI6hUs6dKLmvhJn5xRQSAZxC3K5hRlc/LA7UIqUOtFETPFNFngRRFHKmVu1Lc5Mz7Ke2sbFJ2UAVCw00R91XW522iRqIR+ENIPQgxNw5Aa4/mUIZO3LZZbbcquQxTFNNoDKsUM+lDsN+tanH1iLtVR0R+vXfh7EUIIJrI5/t73vch0rsAXbl3ndrNBrBRxlLpX7VsHGHHdR5pEGJrGJ48sUnYc/sVr3+LN9TW8OGK522G5+2AVNmeafP/MPH/x3BM8MTq++96Q2jho449xFhI/uoKQDlIbQwqBHAby0UPuWAINXT+GYWjAoxOG49kcz03NcnW7vtuKXbRsnp+exXkMDZy9yJom/82Zc0zl8/zsW6/z7uYGgyhkEEUMDrnGFcflybEJivajWacZw+RHTp6h6rr87Fuv8/7WBr0wHCaiYjrBfr3IjGEwms0eKBx/EGxd58fPXKDjB/zy1ffpBAG9MOTqdn3fsjui9QchVorlTpv1oZ7VYVCA/1D7eycIiBI1ZOmY6AeIvx4tV/iHL77Ev3vnTT53/Srb3oCG59HwHow9DSk5URnhJy88xUfnjuwaqOwdOx8EIQSVjMtWt/fBCw8hhcQPIwb+g0lXUxi0wiaCucfe1ncL+t43sIwTQ7fZCMs4gTxAzzFrWMzm9yfOHMPgI3ML3Go2COKYEddlrlBiIpdDlxpREmNqGvHQHTBWatfxVAgxZCWlCOOERrBNzR5F03SW2m0iYDSXPTDBYDygD6s99Pee3+gaUpOcuHgE94/IpE8F3e/vo6AcVpdbRJmErLJZaTXTNusoIYnTplxNkzh2yuSMopiZ6cq+Z3u3hW/Pj0fyGUby7r6W2cNcuOIkIYwTTF3D7wcM+gH9ro+Ugnazj2FoFMoZbMfE90I0TcP3QvxBQHWsgG1Yu0kUU7osZJ/A1fIkJGjoxMT4cY9msMaIPYcmNHJGFVfLowmDftxGP6SYKoRAF6nIfGfg0+552KZBJe/uO4+9UCRs+Jt7EiYKRUSStJFy7NB9CdinJSeEwBsE3Li6xvLSNtVa7lCdN6UUdztNVnrtIbtqnAvlBQyhMelU9h3zTpvZYnZ83++K5n1izEzmfidQ1nCIVUI/8h9YRpCaCozbo2R0l1E7jQ1TE6YGutDJG7nHMm56HPyxSBwZMkfRWiQZ2kLrMkOiZgnjHrHyU30RaSPRhvoh9i7Ffi8Khk1Wd7E0HV1YhEmCKXXKVoaFbJWMYeHH4W4WNlZq1yZ51M4jhGAmU0aXEkvqKCBIUuE+S9eYL5cIh9RdXdNIkoTFagUpBJaeUm4dwyBnmbiGwZFqmUQpLF1DCkE/9jGlwbQ7Se4ABsdejM9WKY8WSOIEO5O6TSRxQr6cRTc0vH6wq+9g2ga+EYELtjSxNIMpJ7VTvtffJKs7u9ofqfBWQsXMp2J+QsN92OLyEWiHLZb6dx9r2dFMltOVUXphwGKp8lhVnHt36ziuSaHggoA4Tui00771KIwJgwjfC2ls9ygWM4xNlA7c7mH72nlBKKVoBT7dIGDEcTG1VK/J1DQ+NPGgM1E36hIPK50ls/Tg5BOYn6iQJAk512JGlXAt874DnSYxDR1D1/DDCE2kbne6rmEaGifnR4mTVBvBMnT8MMLQNTQpcTM6jnViKLppoIaCa2li1MfWLGIVo4vUbSZSMaY0yOg2mtCYdYeT3z395FkjxyTTe5KyD10fBHm9wPhD6x18MdNESxBEB7oxfDdCSkn+gEDBCyOULsiYxr4Pb4JCPkbqbEdL7OGPepwkdP2ArGVScD/FVvc/sNL8R1jGNPPVH6fgfIpG75cAQd7+GIY+jq6NIJAIYQytcQe0Bp8njrcQwsQ2zyBFhr7/bbY6/xJFjKXPkSgvZQnJ8u7fcdLEi67htX8aQzuCqR9HYeCYz1Dv/WdM/13y7mfxgq/RHvwuSTLAT36BsvsKuvEMm52fRxMGBfeVXTZTemwWujaCph9BoEAYaGqRJF5BiCxCZEDoIGxQISiPJNlGiPR9N4jbmNKlF22zHdxjxJpDCBddmwdhgQoQ0iEN/CRSllPmEzu0XB0piyjd51GTSksr4OhljCRDQoSpPV67KaSCr2srDVaXG0xMlcgXXJbubDE6VsCyDJbvbVMdyTE+WeLy+8uUK1m6XY83vnMLlcDR42PcuLZOPm9jWgarKw0cx2RqpsLvfOEdTp+b5uSpSd59ewkh4OnnFhmp5VPBxnhArCL8xEMXBjmjyNHTkxw9fXhiZwcSybR7mpRFFZGoGE1ou4mkREVYQ3ckfajXpUsTUz7abej4SJXjI9V9geqJWhr8jOdyuz/bwTOz07s/OzlaQynFdKmw74k6Nw4IwdJag1/76rv8+CsXyWfuf7s0TR5WUPyuhlKKRn/Ad+4uM1nIc7RWoTVIWbaGpu26mvpRnOoNJgl53eKvnPsQn5o/xmvrK7yxtsJqr8Nap4uUAkNqVByXUTfLhbExLoyOc7x6uAYhpO1Df2pmjpOVEb61vMQ3V+5xpb5J0/PQhKSWyXC8XOW56VmeHB0nY5pEQYzSU4t4KYZuNsMbW3Vc/tGLH6UXhUgEJ6tD0X1C4qSFUgE7Ljg7MKTGj589GrzkqwAAIABJREFUz0fm5vH7AY4PK+9vsxIlGJaO1/NJYoVmSCzbRDd1Bl0Pw9LxBwEv6TUqtXNYrklzs03GshivQzCZuis9jDCJ2PCahENdw5KR3a3ymlLn5fmjnB0Z47XVZd5YX+Vms8HWIGXA2LpB0bKYLRQ5Xq5ysjrCqWrtUFfYvbB1nU8sLLJQzvPu1jrXt9pc3d5ks9/Di0J0KckaMJkrsliucbpa5mxtCltLmZ/3Idn52Kd+kUla7RCSqmvyd5/9Pr5/dpqv37vLlXqduuehlCJnWlTdDDP5Is9OHiwCHMQ9EF3++++7MDQ/UWnBQQgSFZOokFhFSKENmY2CnDH6QOxuSo3TQ6mBvVCJotcZ0Nru4sSKv3bkAq8sHOPrK0u8v7XBWjeNyXKmxWK5wsXxSS6OTzGZyz3y/WRIyX97/kk+eSRlYo5ncrtJQ6UUrYGHH0V75XMAOFcb5V9/6jMo0snlZC51KJquFpgsP8j0EkIQk2BJe7/W6fcAhDDo+1/FtZ7HD68MWcQHQymFF0c0/AFeNGy91jTypsXFickHCjHOYzwXD0MXBu2oTS7Ok5EFdE3b1/b4MPwoohV49KOQWCWYUqNsu7j6g7Gdpmtki4e3qO0sGScJrcCjE/iEQ7aloxsULBv7AF2yXjdgdbWJrmvomiQMYwoFB6XA80I6XQ/T0LAsfTeJNDNd2bf/A6+HlrpIdwKfVpC2NGlCkjMsipa9L/ZM3drSnzkZi5MXZnZ/5w0CdENH11MNWwH7dL/2nltGL5BhP3PT0bIUjBpeHNHyfQZRkW6SIESEIXNIFVIwtfvfhwPQ6nq8dX2FStZhw7aplDKMjhwcpzmay2L2CI2wibE7V4tAeSh8Hv6mfBAc1+TMhRlGJ4rUNw9vVQuThLe31uiGAUXLZjYzslvcfxT+ILpyptQ5VThclL0VtvnNtS8yatc4kTvOIB7wZvNtBIJnKhdZyM4/9r4ehT8WiSMpNBx9v8Cao6VCvDuUQwAdkSqL7/D19gyAvGljiB6DuMukcwzzoR5cIQRKt3b/DQeL+O0uuyfwlRrUsge/RHbEuV0rbY9zzLQ/VAix29YghKARNOnHfaIkYtI5uPKmVHrObtnGxWbXPvCh7R2GnfNytTSgzuYd7mcJ/mDbOujYlgfLtKP2Yy1rSI1nJqbpBP6BVbiDcPb8dJrcGWaxhRAUCg4gHhDDU6QvsYfV7XeuXzpm9tgv7j1nkQpmv7+1weX6Ji9Mz3G0eLATmlKKaOg6BaAN2zl2IISglN8/znbWBRB5d98421nGru55+Q2po3tpkEXSKswD75Y9CZ2d80xUsnu+yTDJtTv29h7v/ZUPPd9ExakY6VC8RQq5Z6w8uGIYxniD6HvWyUip1Dbz2uYW9f6A5+bTD6tAoEnBcqvDIAw4Uq2gCbEbtOhSongwWXRnO2WtzZaLu73+kCalvnD5Gi8dW6DoTFLO/BTvrF2l2Y+Yrzq41tPoWmqhbuo5BIJq9idSto3mUsn+ZaSwKWf+q2HrmYEULiCo5v86ihCBkbKUUAhhYOkLCGFi6QtstP83XOtptsI5mpHGvUYTKVo42nkM/SQrXp9udxlDTDHm/DU6ic9aq8cxEja8Y5hyjgvlaXSZRZFQyswghIWlL2BmphBiR+dAAXmErJGOI7H77hRCoEkNTRsHUtq/q5Xw4i6GtNPkBgmGvpBe/QM/tjv7GN674R9KuMRq7xhNnwIxHNex8olViCEzVO1T2NrhzMid90vqcCFQpBpEi8fHeOv1VJz6qYvzvP3mXTRNUhvNc/zkBJqupdXDMKZYypDPO+iGxsq9beaP1Lh7e5O7d+scXRyj2UgdQaZnK5w4NYGmy91lm4371fA17y6DuEcv6jBqT5E7pC3i4WOWaAghcfU9y6vUCUgb6iQB7K1nAWT0tDXwUZauD+tZJCptWxHD/x8o7osiVtEjOo3SyuPOukmiUgHs781Xzj4MwpBfeesSN+vbHB2pMFbI8e+/9SaLhTLTlfQeOqbB7Y1tco7NSCGTTmZ6Hrom+cjEPCfcCo5l0Bp41ApZ1hqpIcdqvc1oIYseaY8lqgyQ10xOyxI1VyfJzmKYOr4fEQURptQw1xOury/juCb9ro/tpnIDxXKG1nBsm6bO7JEaz02nSd60vWnYmoo9ZDnunwBKIThVrXGqWqPT6HH9rTvcvXOP2ZOT3Lm0Qrbosn53i3wpS7boMnd6il6rz8bdOt1Wn1wpw1lZoFIqMjDLaWVf6Pj94MDEUSvs8W9vf5G7/Q02vSZ/bvYj/Omp50HBUrNFvdfH0nXG7RyVKRdnQWejm7rP5qxUjqA98LF1nbl8iYxppk49w5bXR00a/CTk19a/SpTE/NUnP41MtojjDirpo1QbKQx0KbCMMTRWEaJDEvggcim7VRSIZZl2uIIft1N9SmljSAeJRqRSu+oTtYBTtQX64ShBEqBIqFjzuHoaexiHiOxv+ddph2scqWZJVESYeJjSJW+M0w5X8eIOWWOEbriRxknmLDX7yKEM5r3YXG3wf/3vX+Ta20vohsbiuRl+/L97hSfGJ9L2PJU+/1IIDE3D0rS0He0DtqtJyZmRUc6MjBKFMZ1mD7mrtazY6vZ4QBx3iBE3s0//q+8HfPvaEqPF1AV0fnRvyzpE6vFi3u82ZOzvJ4huYBknEdJFk/tjZ234bXxra43fvHOFb63dY7XXJlGKomVzolzj+yfneXlmkaxh7ouZd3C30+QLd64deBwnyiNcHJ0gSAISEvw4wjUMCgdos0JqxHCtucWv3bzMm1sr3O208OOIomXzodoU07nCvgLgkWKF5ydmH2A5QXp/NSFo+gO+cOcaX753k8uNTbphgCk1pnIFnhyZ4IcWTnKiNPJAwqVWy1GpZB74fomhVt/Oz3YWT3O8goNeE/st4BVBEvOV5Vt84c413q2v0fAG2LrOsWKVT84d56WpI0gh+PydK7R9n49ML3C0UCZ910rCRPG525fZ7Pco2g6fmF3ESnSut7ZIlOJMeWx3f2Ec8831JS7VNzA1jR+YPcZ45sG2qEQptgZ9vrl2l7e2Vrm0vcFyt00/CtGEpGTbzOSKPDc+y8szi4y5BzPFEqVwbIPFmRHu3K0TRva+5O4O2mGLa93rJCpJtXNxESIzjIPvSyB8dfk21xpb+9bXpOSFiVmOle7nJNZXWwRBxMq9BuNDJrdSil4YsNbvcqfT5HZ7m9+4fQVIWZr/6dq71Jz9OYMRN8NnFk7usn/8OOLL925yp93E0nQ+MXeMUffRxBKlFN9Yu8t79Q2EgOfH5zheqhKpiDAJqVpV3mq+gyF1zhfPolDc6N363kocwX5WQ6ISNv0N3mq+yfXuNZphE01ojFgjnMqf4XT+NKa0HhhkQTwgEQGaMDD2/C5MQpYH9w5trRqxalSs/Rndhwfww64R3ajD9e51bvauszxYoRd1SUiwpU3JLDFmjzOfWWDMHqdgFKhaFZb6g1SElf3WuN2ow43uDW50r7M8uEd3uD1LWhTNEuPD7Y1aY5TM0iNdUnpxj5XBMrd6t1ge3GN7qFqvC31odzjBsdxx5jMLuw4GByFRCYN4QDfq0g5bvNF8nV7UQwrJlr/J5falA9fTybDeUdxuN+mFAROZ3C5tVSnFqrdKO2wduG7BKKTU02GAIR8jawswiAcsD+5xqf0+S4MlOmGbSEUYwiBv5Bm3J5jJzDJhT1I2K0xm8zS9AWX7fuLHiz36cR8/9vATHz/xud27TTIMYlthi6vdKw+0r0DaSzrtzmDK+8Hn3vGyo/R/EBzNYdKZorHaZmulyfSxMTJ5hyhO+MqlW5yYGGGqXNg3HpVS1IM6lzrvc7N7nXUvdaVzNIcJZ5KTuVPMZ+bJGrnDWUM720IRxAHXuld5q/kma94qXuKR1bPMunOcL15gypneJ5ItZdor/j2aN2Kt3eW3rlyn5XnMl0u8vbLGlY0tDE3j+YVZfvvqdVZaHT5xcpHJfJ4vXb9JGMd8/9EFGoMB766uU3RsPjw7zRcuX6PjB3zq1DHGclm+dP0Wbc/j+4/Os9Rs8evvXSFvW3z6zEmO147wxr1VFII72y2+eXsJpRQfPXaEsXwuZesMoQ3/rYn9vcyaODjZvdM2J4SOqc+iazV6A5tWMMCLI0qWSyuMsaRJKxy2u2o2hlZms7tBpFz6cYgXK1y9gCbTBOjSdpvRfI6NTpvpUmFXq2m41+E+0zHkxQO+uvl5rnXfo2qO8vLYD1MyKrsV6260xSDu0IsaTLqn9lldH4xhEIWiF3W4N7jNjc57rPnL9KMO4fB9kNUL1Oxx5jPHmbAqSHQcvYQunQPZrDvwkj5f2fgNbvQuUbMmeT7/CrZtkMs5SCnQdY2tzQ5SSnRdI5d3MS2dfs+n3eqztdWh2eixstzgyYvzrK+22K53iKIE1zHJ5R26XS9lzyBoNvp02gPqmx2SRNHvB9TrXYQUnJmZJDQGdKPWoUkjgEHc40ubv87t3lXG7GleHv0sOb34wPukFQ745Ttv8/LEcSYzh21Lsj5oUzJd7Me0B98YdEhQjDuHs7iW+jf4v+/9zKG/zxslfnjyL1GxUvp1Oe9iGhq/98YNFmfuW6iPVfPk3EdriH03oh+EbPcHfPrcSd5f3cDWdVoDD6uq45g69U4fKQV5x8YydKI4ppR1CcIYx9KpFTKsNzpoSpAzLDK6iYGkls2QhAkqgUjFu1biH4Q4TtA0iSU14ihGJArpJVipiAd+EqLraUtDvx/Q7/mMjBUY9Hzqmx2kFJQrOeI4YZdkoOBGd5mb3VWeKxfQtQnieJtEm0Ae4lDp5myOnJth7uQkTs5m8ugoSaI4efEIUpNousS0TbLFDHEUk0QJUpNEUYxlG0RhnC6nSQzr4BijaGT5ySOfYtNr8j9f+iX8OG1JUgpaA49+EFDJuDiGzmq7Q6M3wJI6hibRhUQlKcsljJPdyWM76vPFtdf46OgTlMzD9Sc0IZl0qigUGcNEjz0SuY1QMQgdoU2i4hUQdVTiD48rRAgflfSHncxlvKhFpDyk0NGFSaIigqQLAvpRHUcro8sYXesiZIQhXXKmjqM/+lmqWAuUrTlADBN/qV20FDquXiG1k9ap2cdRKDSxU7z4YLz+1cu8/51b/MTf+zRjMxUMU8dxTaQmsf6AbYWHYX2pzuf+7df4kb/+MUojeaSQjOVz9IJgqIn3aBiaxrnZcbKOSc5+cC5iSmtYdHq0Kcx3Iwx9EkNPWa1a0jjwnpqaxu+v3uFn3vsOt9qN3UJyrBQbgx5Xm3V+d+kGVxpb/I3zz5Iz07HWbfTYXm9SnSjj5h2ut+r84+98+cDj+LFj5zhbrQ31b9IYVIiDGRxRkvDt9SX+p29/iUuNTTQhqDlZsoZJNwj4j1ffPlA/7bMLp7g4OrUvcSSFJEhi/pfvfJVfvXWJXhigi9SIJ0hilnttXl2/x1eXb/EPnnmZJ2rju4n5vSyfHewUWHb0/dLnJS3ixIka6rGlZImdjolBEOFaxu5Y7Uchv3j5Tf7lu99i20u7NDQhkQLudlp8a/0e31pb4gfnT/CPX/0y7cCn5maZzngE8Sq2MYcf5/m591/j7a01jhYqPD06zWQ2RzcMaAUeZ8r3W72CJOYLt6/y76+8Sd60OFWu7Uscrfc7/L3f/y1eXb83ZCWmx2/IlCm71u9waXuTL9+7xZfu3eR/+NCLHC/tJ5FoMnXvNnSNMIyJHuGcWjSKVMwKBSOPMyRQoGKkyA9ZrOn1/rWbl/h/b7y3b31L0ymYLz+QOJqaKXPt0ioTM/dNApq+xz/69u/y6sYy7cCjGwS7Y2gQRfyb91498PjOV8f4wfkT7O08vNLY4p+98bWhcyb8hZOPdm5rBz7/57uv8uV7N6k5WS5UJ4BUp3LMHmXcHuVy+wpBErCQnSerZbgbLz1ym38Q/LFJHO1FlES81vgOv7n2GywNHmyLuta9yre2v8mZ/Fk+Mf6pPVS0FIa0hx+s+xPcdtjm3935Be70bx+4vx+Z+jF+YOyTj318iUq42bvBr6/8Kpc7l/4/7t47yLLzPO/8fSffc/O9nfNM9+SIGQQikYgMIkWCJEiKlCjRpkzZ3pVKDlUur112abe83pXl2t3SuuTdpSKpRDFBABhARCIQYYAZYCIm9Uz3dO6+OZx89o9z+3b3dPfMgISqQL6oQs29fe6J3/nC8z7v81wzs6BKGruTu/m14d+g7jdZsBcx5LULoCAMmGhc5u+nv8vpyinccPPBRhUqY4ltfGnkH5PX11PLgzDgjeIRnlt4lvH6Raxgcx0iY87gltytfLjno3TpXes6XMu3+N7MY1yon6fklCi6RZzWBMUPfV5Yep4Xlp7fcN8Pdn2ID3c/xN6ObkIiEcXl8EOf7808xsuFlzb87Z35u/nc4OcxlY0Fuq+OMAyZt+d5dPq7HC+/Rd3fvEZdQqI/NsCvDX0Jwgzv6xskpa+UX7xSeJkXFn9M1a3S9BtYgYW/ivp9snKck5Xj6/ab0/L86+3/hq5WbenV8ezCMzw590QbgFodY4lt/LPR/xFJkqiW6q0Mg6DhuIQh1Cxn3bOxfZs3y8f44ez3mWhcXpchO1k5wXMLz7AtsYOP9z3ElviWawKNS84iT8z9gJcWX1zXZo6X3+LFxee5t+t+8lp+LQglorrxd6l09j0XlwpF+jMpdhudXC6WeHF8gpiq4HgBlutxoK+H4WyGwwN9PHn2ApeLJbKxGJeLJS4sFrhnbAsj+SyEIfv6upEliT09XVwultBkmelylcV6g4Sm8bE9O/j742coNVfuvx8EvD45zUKtgSJJzFZq9KTemdhdEAQEfriOnbccucQXANibXUu335BFJgQjyTxXI4VCCEpNi2fOXqQ3nSRrxhjMbi44G4Yh083LPD3/99iBxQVO0WMMcFfHB1usPom4ko0WIXJq0zr4DfdNyExzgsdn/obztdM4m/WBZdidOsRnBn6dhNpL0y8QDyxUaRNjgDBksjHOMwuP4QQ2F2qn6dEG2bnvMGYq4PDtA2i6zOxMgf0396DJBqoOTlClapXYvqubUG6g6CZDYwm8oMm23V1MTEyxvbcDLQZmTCeR7sAwYa/WS8OqMTCSRTdUdF0hnTYZHulA1WQcz+GCcwI3sNka301KWe98FIYhl+pneW7+e7ihw4XaGYbMrRzO3s1Mo8Lri5OkNIPbOofJaiZeGDDfrPLKwmVMRWV3ppdjS1fQZYU+M82jkyfYnelhf7aPo4UrZLQYg/EsZ8pzKEKi00gwXl1CliRu6RjmZGmGPjNFTjd5bWGCuuewJ9vDqeIskhDc3DGEHdjMWlfwQ3/D0o6sV2uxPqOwHJdCpcHJi3M8/8bFtvbDr3z4EPu39d1wO/l5CV1RSBoar4xPMl+r882jJxjOZ/nA3q1osswyt9YP/FYpdsTky6djLXY2HNrWz2qKfn/LDCOfirNYrpOIaTesj2PGdYa2dDI4snb+sY5pu0EfsnV7z5ptlyMg5OWl0yzZFe7suBdNkgAJwmBTdqysyKRyK5lZYxPQcLN+70YishBPowhpTdmBIkscHFjLGh/rzF+XeRsScq56hZcWT3JHxx425zaCKil8fvi+lS+k7Uhsv+oER1v/WL5Ja48vIeiPH1r3XolV7Mzlv3Swrb3dZokm1/GoFus4touQJGKmRiJtIsmReK3v+ZSX6riOj2aopHKxtgZgpVjHFk1c20UIQSJjUinUQUAmn0SSBfWqRb3c4NLpaRKpGMlsHFmRMZOxSJqBqH01qha1SrMl0mtiJlbmcGEQUliokEjFIqmDUqTfuLyd63hUSw3efPEsZ9+aYObSYmTHbqhs68gjK61r8QMqhRp200VRZVK5eDTXabVrz/bo0nQSCZNqqcHsYh3NUEm3HKm6jR50+cZlIN7r4ftRGbnnz+L5ixHwaD1PKv4wmrSWITFbr/J/HH0R2/d4YHCUewdG6YiZWJ7Hy7OTPD5+hpJj8fW3j7Etk+eTY3siJ0LX58yr59l/9y7MVIwdmU7+3S33UHFsSrbFpUqRV+cmV+llRcx4WcgYasSabG4g8j9eLvC/H3mOk4V5BhNpfn3XIe7qGyauatRdhx9PjfOnp15ntlGjMxbnIyPbGUvn2Z3vwgosPMchq65coxv4/P3F08w1agwlM3xwaIxduS5USWaqVuaRi6c5vjTHycI8/9exF/n9uz6yDlRZHRfnC0wVy3SlEizVGtQsh529nUiS4Mz0Ar2ZJFXLRpYkPN8nCEMcz+d9Y0PEdQ0vCHjk4mn+21s/oezYdMbifGzLTm7u6keRJCaqZR4fP8PfXzzNyaU5Ko7dPrYkJfDcImE4sOG5LY8pP01k9RhpzUCXFXZkO7i1e5BtmQ6SmoYbBJxcmuP7l85ysVLgualxsnqM//WOD7WdI9sRQkcmHkm+NJ2Wpt3GfW3dr7NgL9DwGyTVFDE5RoiNkBJIckQQkYXEJ0f3sC2Tp2RblOwmJ5bmOVmY23CfU5MFXM9nab7K6PaIdSUJQU88yZ5ctObzw5C3FmeYbdRQJYnbeoZIquuTHiOp7BpmmybJ3Duwlb96+xizjRpPTJzjk6N72mDqRjFeKXB0fgqA/R097MlH6/ecliWnZXlx8WWGzAHCMOR46SRu6LIvvWfT/b3TeE8CR+dr53hk+jvM29FDzKgZBmKDxOQYDb/BVPMKx8tvYflNGn6j/bvICcZftzhXJZVhcwSBwAosLN+i7tdwf8pswJw1x3emvs3Z6hkAclqOPqMfU4kThgF1v86ivciSs4QbOGiSjiZpKEJFFvI6dsaivch3p7/DyXIESGTVLP2xAUwlsmWu+zUK9hKLziJu6KJK6qYDkkBQ8SpcrF/ADmwMyaBT7yKn5dBlHTdwmbVmmbNmsQKLl5d+giKpfKL709SrHrlMHLU10LuBy4X6eSYal1v3V0IRShsoU4SCKm2ceV7+/q2FWZzAZ0tqZYokhKAv1sfW+FYs38YKLOpeHfsaINe1wgkcvjfzGK8WXiEgIKkk6Y8NkFSSLa2UJkWnyLw916a1JtUkUw2bK9UKe/JdbdeGqlth0V5oX6MmafihjxM4rXsgowp13cQ3Jhvr6n9XR5fezWhijIbfwPItmn6Dpt9cO6ETsDhTxFu24ZYlejIJTE1dUx4ShAFvlI7w3alvs+QsoQiFbqOHbr0bWVJoeg0mmxOU3TKnKiewA4tfHfp1BmIDG2Zj7MDmh7M/4MWlF3BarLS+WD+deidhGFJwClxpTvLD2e+zPbkDWchtMM1zfIIguCGB6J/HSMcMTs3No8kKKUNjOJuhatsMZeN0JuLUHIfziwWGsxl6U0mysRhdiTij+RzlpsWxqRkWanUODvRiKCrHZ+boSSaZKJaYr9WQpWhItjyPVy9fwQuizPTpuQXOLy4xXa7Sl05StW064nH600kmz0ZC2WYqhpAEVs2iWbOIZ0xUVaG0UKFrqIOFySViSYPAD3Ftl87BPNPnZ0l1JKmXGkiyRLY7zdzlBTr6c5QXq5jJGB39OYS8eb05sMKlXhUJXWNvfw/lhrUuA7tROIHdBqJDQppBoyWqGPURbmCzZF9GEfo7mrbYvsWTc49wuvJmWxBcESoJJYUqqbiBi+U3cEKHvtgQQVij5s1ieUtktGtTee3Awm31BSEhDk1SHR7zzSOIjIQtZPJjMgHzaNoIll/Ad2MIs8n2A90sWsdJqMMoHSWghCeV6BirkNWylJ1x6qGHkjJphE2SPXl85wKenGfPvve1mVAdndEEtOnX6RR9LNmzGz6P5bACq52MCAlo+HW80OfNwhQLVo25ZpXtqRXA+0RphplmBQmB5XuokszhjsHIFSqeYXemBzeMdB3e3z3KqfIccUVDETIvL1yiJ5Yiq5tM1IvEFY2KY7Nk1ZlplPngwC4kBAtWjf54Gl1WyGtd3Nf18Qio9xtYQZMle55pa4KNJoe5dJx/+pm71i3SE7+AbCOAuK7x0b07eeL0OXTLQhKCD+/djibL2IHDnLWIG3roUjS+u6GH3GIBOIGLLmtk1BRlt0pAgIREj9EZaRuGIR2p+JrmE4QBc1aR89Vp6r6FLqn0mx1sifegSit6MItOhTOVCRqeTU5LsCs9TFxeWcBfaSww2VjgQGYrZ6tTzFlF4orOtuQA3UY0H/DDgPHaLBONOV5ePI0mKTw5dwpVUonJGrfl+4ivSkrUPYszlQmW7AqmorM9OUCnnmmxFAOOFs/TZWRZtMs0fZv96a0UnCrnalcYTfQxZEYMhTOVSRqexViyn9OVy5SdOr2xHNuTA+jyO3flc3yXy405JhsLOIFHUomxIzVIXku1gYYlp8K56hTPzb/JbLPAs3PHSGvRYvRQdhs9RqTX6AQux0vjzFpFALqNDAczYyirBHadwOWVpTNsTw7gBR7nalO4gU9fLM+25MAakMsPA6Yai5yvTdH0VxJ4ipDYnxmlN3Z9V1AA23J49juv8+NH36BRjeZqO24a5gu/+xFSuTiu4/H8Y0d56puvYTVszKTBhz9/B7c+sAdNV3n8L56nUqwzf6WI7/nc84nDPPPdI/hewD/6nz7O0LYeXnvyBM8/fozxU1M0qhZf/Z+/g2Fq3PPQzdz/8K2EYcjsxBLf+n+eZvzkFBCy/eAwv/yl99M7EmmrOY7L//d73+aODx/g4qkpTh8Zx3N9Pvobd3Hfp25h5vIi3//6ixx9/m0Kc2X++D89gqopbN3Tz6/8zodIZeOEIRx97gyP/cXzlJdqqLrC+z92iPs+fQtmMpp7v/nSWV7+4XEO37OL5x55g8WZIn0jnfzmf/gkajYC5q5maP88hx+UkKQkteaPCMIGspTA9acicPeqWLQaGLLCV/bdyhe276czFkeWohL0u/u3kDNifPXkEequw9NXLnL/4BhZI4ZmqHiOh6JG7bc/keJLuw5H7o9hyKuzk5wF5xFrAAAgAElEQVQuzGO3132RlELRKZBROqg7ThsAXw1g/3j6EudKSyiSxOd3HOBXdxzAXLWoH0pmKNkWXz35GmEYcmfvMPcPjuGHPifLE9Q9i7s6d7e394KAiWqJPflu/sOt97GvowejxUrywoADnX38x5ef5PjSLCcLcxxdmKY3voMwDLF9HyEiwKBtHCAEhqqSjhkUak0s10VXZVw/wFAVcokYru/jBQGqImNqalvTDmC2UeUb596i7NikNYPfOXAHD43uJtG6RjcIuKN3iP/y+o95bmp8zbMSSJE22TXe/ZSmY/nXXi8HQYjVsCMXOyGwmw5Igi+M7ueDg2PszXZj+BKJmI6iyDSqFnd3D3Ows5d/++IPWbQavDE/xeVKkZxmEoRhWx7Gcj0Wy3WGOjMIwHE2J2ssy4nUvUYboBEihST3I0SSlsgHd/QOcVvPAF4YYnseXztzdFPgyIhpzM2U20ZMAElN58u7b2G6UMY0NHRd4T++/CSzjfOYisbvHryDscz6Sia5xUxbDiEEY5k8u3NdzDVqnCkscL60xMHO3k3n0K/MTlJ2bARw78CWNiNOkzQO5w6xxRlp9z2Dbg1DNugy1jO5ftp4zwFHNa/G92cfZ96eQyA4kLmJX+r5KD1Gb3vSP2/P8fjMYxwvv7mGDSIJBSFCbL++ZqGdUBI8PPBZnMDGDV2cwOXp+Sd5buGZn+ocXyu+woVaVHd7e/4OHuj+IB1aJ5qkRYuJwKHu1Vh0FjldOcW2xHZM2cQNXXqMbuzAXrO/N0pH2iDUzdlb+VDPh+nSu9fsr9ECo05XTzEcGyYub1aCIjiQPsjF2kV6jB52pXaT1bKYchxFKPihT8kt8WrhZX44+32swOK1wivcmrqDxoLOibMzHNozSCYVw1RMvjj8pTZoEoQBzy08w/OLzyELmbs7PsDdnevd7QBMOc54ucRis862bEfbFQEi1s99XQ9wR/4uvNDDC13eKL7O92YeuyZDarO4UD/PsdIbBAQMmyN8bvDz9MX60SUdgcANXZpegyVniXO1s+iSQV7PEyYaKA0JeVWm9e6O93Mgc3DNcuVk+TiPTH8HL/TYl9rPR3o/iiqtnVwqQiajbp4/vD1/BzdlD+G1Jvbj9Yt8Y/KvqXorYmuKqiDLUqTfQdRZnpicZVd/N0MdK2V+ETvtEZacJXJano/3PcTO5C6SahIZGSdwmLNn+f7M47xZPsaF2nkem3mEXx36Iil1LQskDENOVk7wUgs0SipJPt73EPvTB0mq0QK17tU5Xn6Tx2ce5Vjp6Joyy5AQRZbx/WsLEv68xrbOPHFNIwgDsmZkuztfreP6Pq9cmMRQFW7q6yEdMxiOGaQNHdvzyZox7t++ldlqLaImC8Ge3i6yZox0zOBQoo+BTBpJQGcizmAmQ8N1uXmwn6Susaeni635HJmYQV86yUA6hR+G2EsNjv/4NLIi07Oli7NHLjCyN9IFu/jWZay6jaKrLE4XqBbq3PLhg1QWKyxOFViYWmLxSgGrYdM5kMf3fC6dmKR3axeL00XOvX4RI65z20cPEU+ZOIHNi4s/4mL9DHd1fIgdyX3XvFeyEBTqDQYyaTKxazuRRJoXnfQZQ8zaUySVNCPmtrbwvB96LNkTSEQuHJK4saEqDEMu1s9wunKMkACBxP70LdzZ8SBpNYckZILQxwlsKl6JvNaFIjziSjeGnEWVriGGKQQdWjfdxgAL9gwpNcuwOYYbVAhCF01OEhKSVAepupNM11+kO3YzQkjYfrElwt2FJidxgypOUMN1q6S0EbywieUXCUIXnYCsvoO6NwOEm7KtJCQaXrUljr1ehHP5nLv0Xrr1PhadOTJqjqHYKDIyhqxiKhpbk3l0WaZg11m0aihCwpRVRlMdqJLMdKNMxbXIaCaaJFPzbExZI6UaZHQTU1aZdSw0KSAmq+R0k6SiU3KaeEGAE3j0xJL4YUjZaZLRYhzM93NsaYpJs8SOdBcf6f1sW6/NC12OFl/i7658dc34vhyKHE0+q3VrjctW+Asi0H91+EGAEPCl9x1q63Yt1OpReQMhs9YiFbfGcLyfBbuAJqmk1SR+GFDz6lFyQoVFp4AiFCpujbyeAYyW7tTKsZzA5fHpV/jBzGsEYYAiKQRhQF8sz7/Y8TCqpOCHAUcKb/PXl5/GCTxUScHyHfpieX595EFG4lFG9mT5El+79CQHM6NcaS4ShAElt0ZOS/EvdzzMoNmJG3i8XjzL2coVZqwlDEnntcJZZCGRVuMczIwRV4wWq7jE/3v+ca40FzBkDdt30WWVL448wOHsdvww4FuTz6NJClbgcKk+x+353RScKlPNRUxZ59/s+hX6zQ5+vPAWR5beZjTZx7xVwg5cqm6D2zt288WRB4m/A8MQPwx4ev4Yj079JBJ4RVBx62S1JL819jF2poYICLlUn+W5+Tc5W71CxWtwrHQBowVSjcS76WmBaUHrWo+XLvJG8RzbkgPsTW9BWaX8bvkOfzb+Q7YnBli0y7ihR8O3qXsWH++7g88MvR9JSARhwDNzR/n2lRfIaSkUSeZMeYKmb/NAzyF2pDYXWl0dnuvz/KNHeeRPnuNTX7mX0b0DOJaHY7vopkYQBLzyoxM88sfP8enfuo/hHb1cPDXFN//oKcIg5K6PHaS4UOXCiSv8ym9/kO989Rm+9/UXefif38+P/vYVXvnRCbbu6ufwPbvYeXgLj/zxc4yfnuI3//1DJLMm8ZaTUnmpxp//b4+ixzS+8nufQhKCR//sx3ztDx7nt37v02Q6koQhzE4W+MFfvcS9n7qFex46TLNuk8lHIF1Xf46HfvMe4kmDoy+c5cv//hNkO1Nohko8FSMMQ869eZm/+cMnePCzt7HtwBDT4wv87R8+QRAG/PKX3h+9K02X1589jZkw+ORX7iVmajiWi5k0aIZ1JhqXMWWTLqP7htvSezk0NTJbicfuRZX7kaQEDesnSNLGZci39Qzy5d2HkHibuuOQMm6NtM50g89u28/Tkxc4WZjnxNIcJbtJ1ojhWE6UuFqW4mxpWKmttm+u0kOCiGenCIWmb1Gxm1Rtm0zMYLw+R81rsjM5gCRk3lqcpeG5dJsJ7uobXgMaQeRkeHf/CN88f4KlZoPX5q5w/+AyWLuxpl9MUfkf9r+Pm7sH2nqHAKqQ2d/Rw0OjuzlTnKdi27y5MMNHhrfjBj5vLc1QdW3u7d+K61tUvSVyGZVEKkQSFXYOq+QbLqZpI4TgplQKyy+zJa63rzkgIKHkUVvgwE9mJjhdWADgwaExPjG6ew1jRZNlduW6+Mq+Wzm+NMtSq5QNIMTHD6psLnxKW6PwWhH4AaeOXKJebRJPGkiKhGt5pDJxpHJIYXoB1VA4PVOOjA3iGjffu5t7BrZye+8Qj42fYbZRY7pexWp4xDS1DRylTIM5uRaV9Jv6pnqJJbdE0SkwEh8mriRWqlZCi6AFcspKlECXhUBGQgNUIa1nORGBYdOTkdPk3oODZHKJNWCfioRlecRVjZSmt822hIiApYx+Y458MUXlo1t28tLMBItWg2enLrIn34Umrz+nuuvw1OQFAAYTaW7rGWo/ubrf4InZJyk4xXZpZK/Ry4d7H7yh87jReE8BR0EYcLZ6hnO1s0B0wZ/uf5geYwV5UyWVYXmEhwc+S92vtwGXKKI6a+Uq/RlJSJiKiYnZPk5KuXHnnLXn6HOlMYkXeiSUBHd23M1gbGhNI1YlFVM26dS72JWMUGohBHW3QdEpkVDiq/YXcKUxiRM4mLLJnR13ReyoVfuTAhmvFtIvDdNvDuM6HgvTJVRNRlZk7KaDqkUK/LqhIlk6vzbwG+jqioXlcigodEldPNj9ISYaExwtvU7NqzFRv8yWxH46cwmMlv2zLGR6jJWaVj/0SanRfRMI0mqaIXNFhX91lG2LqdoidddlslrGkBW6WoJfQggM2VhTspfTcj+VVWCUHZ2l7tdRhcqh7GG2Jba3aoFF+5pjcoyslmMsEblpuEHAeLnIT6YmeWBklEyrXC2tZUhrazU+Zq3p9r7iSpw+Y4Ar1RohkQjySCp7TbYRgC7r6PJKR17zquuyUbIsYcR1GjWLbFeKZEynL7tW28gJHJ6Zf5oFex5d0nmo75Pcnr8TWHnOiqQwIm/h0wOfpeKVOV87z8nyCU5XTnNb/n1rjukEDs8vPIcd2AgE93Tex/s771kj3KlpGnd23E0QBnzzyjfWgHthCJ3dqRZ19Bcj6o5D03PpMOOossxwbm172JLXKNabvHV5BiEEe/q7iBvghBZx0ycpJHzRRAhBzLRQJJWmXycgIJMMUCUXISR6Mzpe6FH3C2QTWboks90mhrJrjzmcixYVFaooqkKmK4WiyuT7stTLDeyGE0180yZCEnT059D0aCI8efoKC1eW6B6O9GB6t3ST6UrRrEVlAUszRXK9WSRJonOwA631/te9Kq8XX2DemmZf+pYbuneKJHFxscBwLkNPKrGKUSfwQi+q22+9Sx16N58b+qdMNyfJankGzdFIorGVKTSVNLbfYMmeJK12o1zHbhvADR3G629jBVFGstvo42N9XyCvrS3FDcOQSKUhZKbxGg1vHoCE0oMub15i123084Whf8aCPUNO62TQHAV8QnwUEUMSKqoUR5NSpNRhTKULyy9iqt0YcgZdzqDLabygiSHn0eQUTW8BSWiktRFkYaBKJpqcRpdTKCJGTM6x0aTODR380GNbch9JZXONo77YMF8Y/uetc+5mMLYFIeCmXD9nynPosozte4wk8/hhwO5MDzE5cuAcSkR923yzRlIN2ZmOU/cqZLU429MGll9iKKFi+SEhFnd155CFhioJdFmiaLvEhUlSNdib6mWiVETOSDi+z85MNwOr9JSWKfGa0FGvobvXaDp8++k3OX5+hnLNQldlFEXmtz59B9uHNy4V/nmNMAwp1Jv83esn+J17b0cQ2Zr/zZE3+fKdtxDXdA5l92AHDnE5xrbE8Kb7ymn78UKPRbuELq1/l8Iw5ETpEn99+Wnu7tzHw4PvJ6+nqHkWVbdBUokmwXNWkT8ff4Ihs4uvjH6UtJZgsj7P/3n2W/zl5af53R2fItHadtYqIEvb+Le7P09eS3G0eJ7/+vbf8fLSKfpid6NLKp8d/ABu6PHv3vpT+mN5vjL6MWJXsX6swOFbk88zaxX41zs/y9ZED4t2hf9+/jG+fulJRhN9JJQYASF+GPCvdnyG7069xPdnXuFf7fgMHXqa/3zqrxivz9JvRiV2lxtzfKj3Fn572yeRhcRj0z/hry8/w770Vu7svHFav4TgpuwYo4leBs2oXOVY8SJ/cOYbPDv3JjuSg0gIDme3czi7nb+8/BTPzb/F7+74NL3GemFhXVL5cM8t3N99E//p5F9uqL0SPTB4s3yR3972EIdz26m5Tf50/Ac8MXuEe7sP0mVkqLgNvjfzKgNmJ/9yx8OokszTc8f42qUn+WDPzQybNwZq1CtNnv72a9z5Swe491O3rCtrrFeavPzEcbYfGOLuXz6EEDAw1s2JVy7wwuPHuOX+3RBC71CeQ/fs4ujzb1NcrHD4A7s48/ollmZKCEmQyiVI5RIkMyZ6TKNrMEd6VTniubcmmDg/x7/4r19gbF8Een38H3+A3//tv+DCySsc/sAuADzHY+x9Y9z9sZvQY2vXA4apYZh5UrkEuqHSNZCjo2elH3Jsl1efOolhahy8azuSLLFt/yA9w3lef/Y0H/zc+9pC6kII7nnoMNsOrJ0HW250f5xryE78vIaqDCNQ8IMKurYbaQNtRUVI/NLIDpKaSsMR+OHaZHlGNxjL5DlZmKdsW239G1lVaFSabeOQGwldNkgoSbrNDIcHFExNpR7WmWkuETlj+9Tc6PiGrJDSNgaF05qBJssEhCxZzZYzoowuaxScFWfl5diZ7eTm7oH2yFxzIxezrBZHFoKxdJ6EqlO0myw26233y4Sqtdg7AitoMNk4w7JxSVqN5ikpI8G8fZkwDPBCF1koJJUcZXcBp+WiOhjfhapEWlovTF/CabGDlwXHrw5JCA529jGSyrFkTbW/X0kCb9JWBVRd+/rmCQK6BrKoake7rNR1fFJZk4WZEpquYib0CHyRJQLPR9UUFElme6YDWUhYvoftezTxaLgu2zojxo7n+yQMjRDIpk1MU9uwLFgRCtPNWUJCslqGuGK2StVcQAeh8E5c1aIpo+DUW5P0Deao12zSmRUpgyAIcX2fjmT8usDa9eK2nkG2ZfIcX5rjyYnzfGZsHwPJ9Yn+44uznC8tIQnBrT2DDCVX9CrLbpmAgF8b/pVrahf/rPGeAo7cwOVs7SxO4CALmV2pPXQbPeseiBCCvJ7nYPomzlfPtkvTQkKEkBEtl55/iFjdVP3Qx95EcHuj4xuyTpfRSW4VMLG6VCkIgw0FvOvVJq8+c4pMPgkCAj8CyArzFfLdaRJpE1VTKMyV8YOARtXi1nt3Y+Q3bjhCCDRJYzQxytHS6wAU7CKZRgNZkujr3nwRcqORUDVu7Rmg6bnM1Kuo8j88sBASYvkWIeGGjnGrn4ksBD3xBPu7usnHbkxLafVxZhs1Fq06/fFU5MbwLlCSPdfDajjYjWgQlYTA1NQ1jKh5e64NrA6aQ+xK7dmUaZDTcuxO7eV87TxO4HCmeopbc7et2X7OnmXamgYgpaa4KXtoQ7cXWcjsSx/g2YVnuNJcEVnz/QCr6SDdoLDqz0NUHZujszMMpTOMpDPEtfWDsKmrbO/poNK0ySZ0Ljcu0fAiba2clqfuR5l+QzKouBUafp0eoxc7sKl5NeJKnC69m5pXpebVKDpFRuJbka/zniSzCe74xM2AQFYkRg8MM3FmmsD3GdzRjyRLuI6HYWoEoz1IssSW/cMM7OjHMHW27o9ckBRNJghCwiDEsRwMU6dvazeyKrdp4vP2DBW39I7u3bKTVsQ0cVl0StQ9C01SqLqNqDwGgdY2CDCRRSeCBJONebzAY9DsJqGYyEKl4Zdw/AZe6HIjw6AXuBSchfbnbr2fjJa/htGBIKNvxQ9tgtBtC3dvFpKQGDS3MmhubX8XhpBWOhEiBrgIYbQcPSO3ElXqBXoiYUahIAmFjD7WOrogJre0YpT+deeZ03dedb4roQoNN3Q4VTnC/vTt6PLG2S1JSAyZo8SkDuq+xXh9Hk1S8AKf4WQa23fxcdmVif5e8WqkdYWMFmfeLtJrJshpSeat14jJDeKqSs0tIQuFRWuJplekz0xh+yXiygBCWFTcKVQpZDTVT6cxhOeFVC830WSJUAq4qXeAsOUiGQZrnSSvF4vlOqfH53j4gYM8/epZ7rppK6+fvoKmvqemM+9KVCyb586Nc25+kceOR0myqu1EgtmFGmUEqqqgqjIFv46mKVi22+6P5ZZge61uoesqtbpFNp2kWfNxZQvLdgmCEE2VSaYM3iieI6GYfKL/Tnpj0aQ9p6nkVok4n69OMW+V+PLWj5DTo0TScLyb9+V38+j0T1i0yiQSUVtMKjE+0LmfrlY52fbUAL1GjjmrRECIstyuw7Usgqvbe9mpc6TwNvd2HWRLvAdZyHTpGW7ObePrl55iurnE9mSk0TES7yGlxhkyu4jLBlsTkRZRXIlR91Yy7R16msO57cTkiMXwvvxufjhzhBPl8XcEHAkh6Day7fI7gLFEH11GhpJba2+z2W83/e4GCHT70iMcyIyiSQpZLcHe9Ahvli5Qcet0GRks36HhWfSkR9rspm4jiyByjLvRebJtucxfKfCx37gbSVqf7beaDktzZW69f08bVFIUma6BHMfG36ZRsxGSwDD1yERAk4nFDYQkISRBcIOM5fkrRWJxnVR2JfOfysZJpGPMTiyt2bZvSyeasTET81rhtK714qkp/svvfK39faVQY2h7D57js0wkiCV0ugbXa8tJQiKr5chrG7v1/jxHw3oOTRml2nwC8MnEfxVZXnuduhJp2kBUHi2tS+YL4qre+nuI5UelR4EfoOoqTvPGALcQSChJslqOmuNwcnaOLfkcpilo+i4BAYpQ2qU8ju+v0VtdHVXHxm1pJ8UVte2MCyF1z6Lhr4BfAhhIpsnpsfazr3k2J0qT3N6xjYRqYKoqqrRcMhw5AapCRpYkml50fQklw67UHe114PI6QkLC1/pW3b+o/Cqn97X/LrfA/7rrMFWrEAI5PUZ/PLlpIjsmKwwlM7w+vwIchYSoch5xDThAlxVkce13VFFkBgaixM1qUEcIQXyV83SmYz3QGFPUtimuHwZYnrcG2mnaLo7n4/sB5WqTdGrjuU5cibMvvZdpaxpd0lsM9jBiHAWLSPJ6XeBrh6B3IMvNt4+R74iA5uUIQ3B8j4nFEtt6O27YMGSz6IrFuaN3mFOFBSZrZY7MT9GfSF1FHPA5Mj9F0bZIahp3942sWVvrkk4YwmTjChktg0CgSipp9acjymwW76mZlh1YXKpH9ZeqUNmV2r0pC0URCiPxEeJKvF3us2xDrsmxNeU072ZEC4chjpffouk3+d7s4wSEbE/sICbHrjtINf0mth+hoKv390bpdazA4oez30MIwc7kLkzZjCh1iszIjl66B6LOOQzCFq0fCEHVFQI/oLM3TUik2p9IXxsMEYg1rCs7sOnIJlBawoA/K/AmSxJlx+LY/Ax+GGJ5XgtgeXefiRCCHqOXuJyg7td4eekluvRuDmQOkFCSm7YfIaDhulwqFxlOZd7R9QogpiikVJ2heBopFPief9UWLYuHMFz5DO36343C9wM8xyPfGwF3laaN5XqkTKOtkzBev0jJLSIQjMS3kFY3Z0coQqHP6G+Lpc5YszT9Zpu+GQkUT1N1KwAMxAbp0Do3Pb+kmmQkvmUNcKQqMgsLVbY6HjHznWtDvBcjpRskNI2qY6/LMi1Hodak1LDIxmNUGy6DuUjkV5M0JCHhhX6707ZaWlaR00oQOf1JGqpQickmaTWD5VvtMq1rhZAEiczacqrh3QNRzbwR3f+rh1QzGYPWWK1v8IxiCaP1txVoZllUuenX31E/arkejudTalq4oYcTeJTdWmQFLWTKTo0lp8xwvJcFu4jlO1i+gxf66JJKzWvQb0aTj4SSp+YVSKmd61ikm0UEvkdsIwmJhJK+7n2VhUbTLxCEDtJPMST6/iSW/RMEErLSi+8vIVCQlX58b5IgbCCJGJLciSIPICnDa+7ptfqea/3NCz1iUpykkcH2m9ftt6eaS8zbZbzAZyzRiyoplN0G81aJuGJg+Q5xxWDJriIJQU5PUvOa1LwmSdWky9hLEPoEeIShj9RySQp1H4FEEHookkEQ+sSVLiQho4hY1P8EAbW6jW17mIZGdz7FmWOXadZt9t68BTNpYAfNFpN3/STHC2wa3hKGnMbzfHLpODtHujj29hW6ckmGe7NMzBYZ6fvFWqgpskTS0FEVGa+VhU/HDD6+eyfTMyXm5iooikw+F8f3A3RNIZeNU640GejNMr9Qww8CypUmpqnRaDjomsrlySU0TaFpOTSaDh25BGNGJ1PNRfrNPBlt85LNGauALqt0GmvZYgNmB3XPouyumFOYitHWIAKQkTBkraUheOOlhUtOharX4On5Y7xZusByxrjk1qi6DereSsLNVHQkEYHThqyjCBk/jLTjVvfnCSVGQlnRZDIVnbyeYt56Z2B50NIvOlJ4m7crkzQ8GztwmGws0G1k30F++51Hf6xjVXmEQJc1BCvXmVRN+mIdnCpPcK56BV3SOFI4S0zW28DgjURLKWRT99TIkEasKxcNW2WWQrQAwRbotLx9e7sbPA9JFtE6cNUvwpYj1dUsKFVfr0V5oyGEYM8tW/nC7354zfeGqRFLrIyTkiShbghYhwThL6bbrOtN4AdVFLkTPyjgB+V1wFFGM9qlUgIFx59Zt5/l5x8SvUMQPcvOwTxm0rihZycJiYZXJwwDOtVBQojK1RJxBuMdLU1SiQMdPTwzeYFFq84L05fYms5FYEUrGp7Lc1PjFO0mCVVjT74bVZJaDMYQ76pnKUsS3bHEGne0yIHaaV+LWPX/IAzbjXwZTApbGliLTZeiba0zHFkTArK6SX9i/dhYdqy22HVaN0hfozxKCEHfVSLdYeggSSbiGnMsRZLb4N6NxNXPruG5zNarnCstca68yFKzQcmxsLyIYXSpUmyzzMIQupJxavYKwKepCo4XHd/Q1fY9XndcBE7gEIYhJbdMzsuRVdMgdCQpwzsZc6LriCpBSsU6qiqTItI7iiKkZkXn6Hg+Bj8bcKTJCg8MjfHo+Bmm6xWemjzPfYNb1zDkFpsNnrlygZCQHZlOblnFeANael9Fnpx7BkOOjMJ6Yz18qOeBn+ncro73FHBU9xuUnGjQ1mWdTv3aYk4ZNUNMjrWBI0kI6l6RhHItr4qfLQSCQ9mbOVk5wYXaec7XzvEn419ld2o3N2UOM5oYI62mN9SckJCoew0s3yatpRCt/w5mbuJE+ThnqqcZb4zzZ5f+hF3J3RzMHGRbcgeZeIbRPQNcrx+92tXEDVyafqTts2gvUPfqkVBq4OKFHpONFcc623aZqpTo6Xx3kMkwDMkZJlkjxnAq066TfbdDINgS38q+9D5eLbxC0S3yN5N/yWvFV9ib3s++1H5yWg5NWlsbHYRQcx1MVWvXtl6Nkm96bUTieCmhMXV8lknbx/f9aKJClJ3L5BNouoLn+tFiKa7TrNts2dVHYhO0XFFl+rZ2tanVMU1FU2RkKZqw+aHPnDWLEzgIBJfq43zzyjeueX8W7RX2he1b2IG1UrJJQMFZagnnCnJafp3j3+qQkOjS15aCiJa48zuhFr/Xw/JcpqoVskaMhuuS1NYLPdueR7kRCdUO9HcTVwziYoVSv2ypCmBIUUmaH3pRKVZrgA7wMGSduBLfkCFneS4nirNUHJuD+T5yxsZgsB5bAYPCMGzrhgUtfRiBhCxkFEndkE22UdhBkyuNcbzQRR7RxzUAACAASURBVN3EDnujSOgalwtFhnIZ4nIM04wxbPZcNVxHnxJKjJhskFTMNiUYotIPBFh+FTewIrHDG2D0hYQEBGtcLm+k/NUJaphKJyl16JoaR5uFECk0dRdCSiLQkaUuEFqUPeMKmrYfgY4kJWCTyUUYhvjt5xa0286yQ6gilNZ9WFV2LFQUScUOLPI34N4zEu9mR2oAVcjt+xISsi3Z1z6HZYBaiEgyM5mJUfMsYrKGJPT2os0LXfzQbyVrFEAgSQaSUFCllfNslwvLIdm0yelzs+za3oskCWzLxUwYqHrEPpuzprADi23JvevOveEXsJoTDMZvJWHqxHQVy/HozCV55Nnj1JsOH7v73XMNea9EXNO4Z9sWBrMpdvd2t1n2ruszI0pkUyaappAwdZq2285X9HanyaRNHNenWrNQVZm+7gyO66MqEtmMScxQcT0DRZHRtUhfLyoXcK7pDrascxRcBaq7QQSWry7BlhAbvoPXmr5v9LflEtcdyQG2JwfXzIUEgiGza83nG4mrryEIQ7zQXyNCfSNRcCr832e/w6Jd4QNdB+jQUwRhyFRz6fo//hlDFtcWtTVlnU/038l/P/8ov3/6GyTUGLqk8MWRB+l7B8CRbmoMjnVz5vVxbrlvN6oWzZmCIAJszIRO/9Yuxk9P4zoeiipjN10mz8/R0ZtZwzj4WWJwrBuraTN/pdBOpM5PFalXmgyMvjMtIVmRcF2P4Cprbz2mMTDWzevPnqajL9NOwgZBy8FLka/rnueHAQWnwKKzwJCyefnoz2NoyhhN5yhJ86NYzluIDTT4IoZKVH4VhA1udPavagphELTZqNebqywn4iQhEVMVsrEYqiThBj4lp05oRmzWB4e28dTkRV6bm+RPTr1O2bb4wMBW0ppB2bF4+soF/u7ccbwg4O6+AT4wsCUCOMOQmKyRVk2S6koblhBrgCeI+r8eI7MGTNooXN9va6EB/NFbr/CtCyc2BUMg6tM+s20f/8vt67VqbN/HCaK5nibLaNdhrV+t70Rr7ik2mIMCEELTc7lQXmJHpnPTUr+Nwg8CThbm+fqZo7wyO8lMvdoCzCLdz2UQ2Qm8FVEDAX4Q0nRWWGcxXcUPQlzPZ2qmSPwaSWpd1nBDFwmB1jJpEiJOEBQQIgk/RXFIKhXj8sV5ch1J0tmVOWIqZpCKXdsYaaMIwxDX8YGw7dQIsCvbyeGuPqbHK7w6e4XxcpEDnRFjNgTeXJzhQrmAJAT3Dm5dVy2TVTP8oy1fbH8OwpDSO6wauJF4TwFHTb+B35r0G1LsugsWQzbWZKJFq0U4QXOzn7wr0WP08Kn+h3l0+hHO1t6m4dc5UnyN05VTdOidHMoeZl96P71G3xrXsYAQTVLXvaCdehef7P80j0x/h7erZ2j6Dd4oHeF09RSdeic3ZQ6xN72fgdjApi5mqyMkZMaa4UjxVU6VT1J0C9i+3Vq8RpbHqxcnENEMjVBBuQYj5p1E3XU4vTSP6/tMVEoMJNPvyn43Cl3S+VjfxyMHhMoJGn6DU5WTnK+d44WFH7M7vYcD6YNsjY+uAZAGk2m2prOk9JWOsOQ0cAKfDj2x6QAghKAjFufC7DzhXJWObJJ61cIvNjBiKol0VJ8fBgG25WJbLh3daTzX31zANYRqsU6zZuG7fksUsOVO4LYczEKXmldrbR5yvnaO8y2R9huJaGHqr/rst10JZSFFAMY1FtqSEMSVtQtrSRKk0rFfqFK15SX7QqOx6WDemYxj6iqLtTqVZsQ8ujqCMKDoLDLdvMxk4yLz9jRlt4gb2MiSQlxOkte6GDLHGE3sJKt1rlkElB2LJavB1lR+jXCf5TcZr7+NH3pktU56jAFkIeMFLtPNCU5VjzJRP0/Vi+qdTTlOXutiOL6N7Yl95PTOdYuNMAxxApuqV6bmVZixJphqXoqug4CpxiVObCLID9AfGyGj5ik2mvRn0miK3JoURPfS9i0uN861XdSuDoHEoLmF1CqB+WVxa02KbXq+Db+G5TexAwvLb1J0Fqi4xdbvoeDMc6J8ZMNjZtQ8vbEhVGHS9BapuzMMJe4jJuXa9/lS/RxeuDG1XRCVgCXVLLK8PlkRhi6y3IkQ165/r3tVppuXGa+/zVTzMhW3iB3YSEJClwwyap5uo5/e2CB9xhBZrRNJSIQEJNUsgbNE2CrPtvxG65w3pvoLJIbjY6tYPWtW4Ou+0mUVXY7GHDdwWbRnmLEmmWhcYMGeoeZV8AIXRdIinQl9gEFzC1vjO0mvKtNwPZ96w+bu28bIZeORu4zlksrG0XSVoHX+9iZjt0DgBRZh6JFLxXn4gYNkkjHu2D9CqdJgsCfDji2/WPpGy6GrCrl4nOfPX2qXUiR1ncNDfdccowD6ezO4ro/r+cRW0eyzmfga1yGI+quReA8/nHmNWavYdvy6OobNLoIw4FJ9jpF4T/u3F2szZLT4NdlK1woJ0Rqf1ve5eT1FXk/TaWT4eP/tG4I7bnDjGXGAklOj6NTajKiKW2feKrE/vfX6P14VE/V5TpQv8U9GP8qDPYeRhGDBKvO3E89uuL1E5AB3NfD2DxFCCJq+janofG7oHnqMHKaik9WS19csWRVmwuD9Hz/EN/7bj+jsy7J1Tz+O7dGoWtx09w7MpMHtH9rH1/7ge/zgr15iy64+LpyYYnp8gc/99gfbQNPPGiM7+9h1eAuP/fnzuLaHkATf//qL7LxphOHtPdffwaroHspTmKvw6lMn2bq7HyOuMzjWjaLK3HLfbl576iTf/KOnOPT+nSBgYapIZ3+WA3dsv8EjhGvm2L8oETNuR1NHEUInpt+KLK8HIJcBAYGMLCXx/BtbvDq2i+f6yMqNt01ZyC3Ti5CG67aEqteub4aSGX7n4B3859ee5VRhjq+ePMK3LpxElWTcwKdoNQmBW3sG+Z2Dd9JjRqycEDBkjbyWXDsDEeuTy7KQmLfK7A77Nz3XqCxMZjiZbe/PCwJszye4TlvxNknOhqvYTILrQ3TKVe+9JHQ8v0igbN5/eoFPUtXXgGUhcK0eLAhDji3O8HuvPMWJpcitbDSdY2++m+Fklm4zQVLTMWSFJybO8a3zJ/CXSQ++T9JYAST9IMDU1VYS/dpvVRAGdGodZLVsVNkjBIQeQVBEVsb4aSgMfhAQBFGifBnQXF6j9WaSaO/QVToM4dL5OaqVJjfdNtpOhMRUlQeGxvjB5XMsWQ2en77E/s5eBNEzeGH6ElXHJm+YvK9nEK01DrYTjUKgrsJE3MDljeIxPtL7wXd8zdeK9xRw5AZu++VRJfW62WJFrN3GCZrYfgNdeneyG5uFLGS2Jbbzm1t/i6PFN3i9+BoTjcvU/Tr1RuSm8NzCs+xN7eP+7gfoNfpaFLISAomsll6zEJKExNbEKF/e8hXeLB3jSPFVLtXHqfvRvtr7S+/l/q4H6Yv1b2rz6QYurxRe5onZHzDT0q6JK3EyWpasmiWppojJMTRJY7o5xVvlN4HoxShWmsSMd6fcSJWijrxkW+RjJpPVMtsy67VG3o1YLlf74vCXIrBo8cecr52n5lWZtqaYtqZ4afFFdqd28/7OeyLxbCQmq5Em1M58J5ocwwsDLlQXsAOPhKoT30xcrFWHm0zE2HXfIJlUnGY9WhCrmoKygVC0EIK+kWvX14ZhVP+raEr7cxCGpEw9QuHDoO1wJxAk1RRx+cb1mTr0rjXtJgwD3MBt70+9bjmQQLsKzE0kDUa392DGf3GssA1Z4VBPL34YbpCdicLx/FYJTkjDcddlx+asKZ5b+B7j9bdZsGfbgPhG8ZOlp+iNDXF7/n5uzX2g7dinywoV1+KNxStktDFMJfq+5C7x9ct/SMOvczBzO58d/CcEoc+z849zpPj8/8/dewdXlt33nZ9z88sBDzmj0Y3OYaZ7evJwZshhGHJIkZQoipKssJal2i2Xtdoqu3a3tFWura3VWq6yyxvs2pUsWaQomQoUSQ1Jccjh9ARODp0z0AAaGS+nG8/+cR8egAbQ3UPRZY5+Vd0A7nvvvnPDOfec7+/7+34pulsz3Vc5z7vF1/j8wK+RvYXJWfeq7baW3SJVr0zdr7YfRr70OLXybU6tfHvHY/j5wd/kRPbR0Fmt3mi7YaxF2Svwn2f+P1ad7e1OVaHxy8P/lMPp+9rbDCVKzVul4i6T1ns3CSa70uXFle/yTuFlmkGjzabbCJhIAs6V3+Zc+e1tv/N45hE+P/DreLJJm6+04fMlN89Xp/8fyl5h289rQufXRn+HffrR9e9sU+5BCB1lGxHi9jEEDleq53hh6VlmGtdotC2Gtw9VaBzPPMJnB34VQxg0/TpFZ6XtEgeQd5b5yvT/RdUr79jm3xj7F4zH162F7+iWIgNm6tc5tfwsU/UrFJ08AVvdzgDO8w6q0BiK7uKp7s+yO3EAVWgoSgiCn75wk8P7+rG6UvQP50K2UcsdTBXqbRkUobucoOF4LKyU0FSFXDrOrzxz8rbt/6BHtWnzZ2++h+P55OsNkpZJ3DQ4OtCLpt75earr6rbmBVt0vxA8mDvAC0vv8e+vfovPDDxIt5Wl6tVZbpZ4uPMgCT3KnsQAx7MT/MX0KQxFo8fKcKkyy0srZ3i08whd1vtnfCsIhmJdvJm/zFv5KwxEO5FIBqKdGIpGWo/zyb77+dr0C0QUg0PpENxZaOapeg0+0XvyfWd9q16Dr998mY/33oehaPz17EsAnOzY135PzWtQ85rknSpu4FHx6iw2CxiKRlKPYig6lmqgCZWp2gKz9WWavsMLy++xbBcZjW8FM3ojHdS8Bq+snON4doJABnRbGRJ6+Cxv+g4Vr0HTt2n4Dr4MWGwWiKomMS1CVHt/z9orlVlqXrNVwuZTcRt4gU/WSGCod1deoekqD33iCKqq8OLfvsOPvnsGTVfZd+8oRx8OgZTDD+3hSxK+92ev8qPvnCaZjfNL/8PTHH5wN7bv4yc00i22QrozgR7RuVktk9c9Ep2bQcpUR5yu/iyqunkNEEtG+NJ//3H+4g9/yO//q78kquk88dhhPvKFkyRaTABFCHpHcm1m98Znc9PzwoVXJML+42M8/csP88p3TvOj757m4MlxugczqLrF4EQXv/q7n+R7X32dP/s/vwtIeoZyfHj0PpzARkUjkjDoHV0XAr413MC9iznVBy8a9mvUmy+HzzcRIRn7Ioq68z0ZtJMBdzFWGRq1Uv22kg6bY42Rq7ZBk2KzgVOt0mGtg6OaovBAzyC/MHGE/+2NH6IpCh1WFIkkqhk82j/K/T2DPNg7TO/GUi4psX2Hhu/csT2KENiBt6O8QdhaQdV1uFJcZjzVgUDwybG9TGQ778BiE0xkcttWRRiqhr4mRh0EuHdg/4fC3BtD4ss63KbdlqZjqOom0ElKib1lX+uxVK/yb955mdMrC0Q1nc/vPsSv7b+XwXhqS8LjcnGl9dyX+IGkUG9sYpVW62GVRTxmhoBVsHMBsCc9rtemSDgr7InvbklzmBjmYzs6AN4pSoU6jYbN/GyBvQfWgUEJrFRqDHTsLBmyXfiej+t6OE2XjWLdqlA40TXAnpZw/PdnrvHFiSNkzQhz1QqvL8zgS8mhjh4OdHS37wHHd3hx5RX6rF7eKLzVlmcIpE/Vq+3Qih8/fqqAI7XV+YE2M+Z2Eb6+/h5FaMT1bEuY9MeLzZ133REoxFaVDdslSS3JI7mHuSdzjEuVy7xXfJer1cvknTx5Z5WXVk4x25jh8wM/x+74HnzpUffrJOT2mbyknuTB3EMcTR/lUvUSZ0unuVS5xKq9QtEt8MrKy8zWZ/lM/2fZnzywLbB2rXqVb8z9NXknj6EYHE4d5WT2fkZjo5iqtalU5eWVF9vAkSIEnudTrds/EY0jU9M4kOum7NjUXYdd6bunRW+M7QbTNXBRuWXgsNQIB1KH2BUfZ7J2nfPlc5wtnWHJXqTu13ir8CZTtUme7nuGB7MPMZxMk2820BW1PQgqQpAyIkTVnRd8EknVdUhGIsTioa5VNH739M1tQ0A8FWHy3CwD4z1YUZNABtSadrsMLLSQDBcAuqLzRNeHeST3KGv36RrrYLsBVSJRUYlqsU2I+dr+JHJb6+tb49asiKapdGwjdvdBjquFPOeXl0iZJgnDJGVunRRFDJ10zGKuWN4221Bxi7xXfJV6SyNIExq6YpLU0hiqiRd4FN1VbL8RMnoaU3xn4WvEtSSHU6FtbSj8aGD7Pnbgb9svy26Bpl/nR6vf54XlZ/GkiyZ0DMVEV3TcwMUJ7JbuTILeyOCWhbkT2FyunmW2Ptnepgh1E9iloN6BjRa+Zmga6YiF0srMrIUqNLJGDidoYgc2UgYEBLcF1Bp+GUtN0vDLbQOEtZAyoOissmRv1k5QhUaw4dmxVqa3Xaxt14RJxtxD0b6GWCvhkrLV5k4CApy7bLPj+kzP5wkkDPdlMPXtxVmdwObN/Is8t/h1Cu5Ka6tAEyqq0DAVC0nIqvKkF2oLSZ++yFC73TEtyUB0DAU1zG1LiSZ0Mnpn+zuklNu22bY9rl9fYmQkRyRi3GG8lyzb85wpvYkrndb9rGOpEeJaEl0xcfwmJTePHTTxpcdk7RLfmv9Tfkb5FcZiocC3oavEowa27eG5PtVyg2xXsn2+DcUipWe3fe6HQHkPigh1eb714jlcP+Donn6O7umnpzPZZrn9Qwu7xTL6xMEJzs8v8eG9u/jTN97DC4LtBeNlCIICm0o8pQwAl7XSQpAh+3mtrFAIhmNd/ONdH+O782/zh9e/gxO4mKrBgeQwD+b2gpTEtQi/MPwEX599mT+4/m1s3yWhRXii6zBP9x1vW0SrInQk2nRNBBiKjn6L7pgqFJ7qOc50bYn/cO2bCKEwGuvhtyc+h2Ek0ITK411HsX2HF5fP8Oz8a0ggrcc52bF3fRGlaG02kioUTFVvsx8MRd80FvRHcqT0GP/h6rcouVU6zBS/OPzhTWDP84vv8fzSu9S8JkWnyqml05wr3SBnJvn5oceZSA4yHOvmMwMP88Old3kjf4mIanIyu5f7s/vRhIrjeZja+jhwNDPOY11H+Nbca3zj5itENYt/uuezHEyNAPBe8TrfvPkKRafGfDN0hvo/Lvw5ST3KUz3Hebz7KCAwVX0L80oVCqait0tOfRmQNuLknTL/+4WvoggFBUHWSPBU73E+1nvfJgc7x/U4e22BSzeW6M0luHffIIloOLcxTJ2Hnj7KiScP4Hs+QmkJs5saby/OcyDXxT2PTHDgxBi+F6BqCqalIxTBlfwytQcz/MLh4+G9/EsP4Xo+35+f4syYx+8cf2jTcTz1hfvx/WCDngjtezSdS/CR/+ZhZu+18IKAn3vsw2jGet/XTY3f/v0voZvhPfbmwk2O94TGAzfKBZ69dpnfOHKCaMzkU7/yKB/94gNICaouKMg5io0AO2gQG9d48nd2YcrDYWmu4hKLONysX0EVGpljAb917NNY25TNxLU4h1JHyBj/5WQz/muF414jEX0aXRsLWUXi9snL0LTo7paaiiJYnSvgNLcm43bae8Ovk9bDErH7hwcJpGS2sUzNb7afJFJKzuUX+Y/n38LUNP67w/fzmV37UYWCIgSaomAo6hbw2ZMBTuARabmX3bbtQrSTe7eLpL5m0x5+1wM9Q5zsGcQLHAICAinb5fpSytY5DtnEVa+ELz0MxWqt51TiukGs9b1V1247yO0U+eatCSqBIkzWXNW2O+OmqmHdAjL7MqDQ3N4cCuBSYYUzqwsAHMn18hsHTmwRe16Luuu0n/tr7nN2SzdWSkmlYROPGmiayrFDQ5i3McJIaAkSWrxV5t8qwxMqqvrjM5J7BzJcv7K4CTRai3QsgvU+GZVhpUaUwN96T/XEEjzQO8SlwgpXS6ucW13k4b4RLuSXmKoUEcCHh8bbbCMInbQPpfZT9eoktQRHM0cA8AKPN/Jvvb+DvYv4qQKOLCXSfrCvTUBvF+Gken2xu5axdOXtO87twg/mQXpIaYPQkDLsGIG/hKYNI1uijlJWESKOxCYq0hzPnOBg6hDL9jJv5F/j9fyr5J08k7XrfGv+G/zqyK/TYXSwaC9TcatbFWw3HENCT3Jv+jgHk+H+3iy8zhv511i2l5mqT/LN+b+h1+qlw9zMYAlkwI9WXybv5AE4lr6Hnxv8Iklta2cNhfvWb1rD0BgdzOF5dwYP7jYiqkYuEqXi2KTMuxO7uzVqXpOyV0MTGo2Wq4EmVMpuHU1RUYVoZffDyWFENemJZDiQPMhEYi+Pdj7GmdIZXl19hen6DVacFZ6d/xb91iArFZ2rhVUsTSOmpzFVnbLbICPuTLWvug75ZoPxVMcWyj9w19tuDd3U2kLbQSCJWQZuqwZfE1pb2NpvLSSTWhLPX8SXVSQ2Kkl8WUMRUaRsIhQTGThIwr7keSaqkkZTsyiobU2jUNjv9gK7ofvFf9ky0J+G6E8kiGgaEV2nI7K94H3DcVmp1Dk5NkhXMr7lPb2RIcZi+5hvzjASG2c8fpA+a4ioFkcVKoEMKLl53in+iDfyp7CDBlWvzMsr32MicRhLjRDIAEvViWg7iwGW3AJvFl7i5ZXvYalR9iQOcih1nE6zF1VoeNJl2V5gsnaJQPrkjK0Z8KgW5yPdP0OtpRUHIfD1g6VvUvMrqELjROZRxuJ7dzxno7E9bUCq4XphfbqEaqmO63jEO1L8/OA/wZE2TuDQ9OvMNWb43uJfU/cr2+4zqqZoalWcoLHFtVBTdI5nH2E4Nr5pe9Nv8Mrqcyw0ZxAI9iQOcm/m4W3332F0oSk6XqASVTvRLAtFhDavfrBKXPX5xeH/FjdwcAKHRlDjZuMGzy1+nYa/fRZHCJhbLmOaKkknIIJBSs9sGQfOlN7guwt/QdkLKfyWEmE8foBD6RN0m/2YqgWt/jbXmOZ67SJVr8zu+MG2HlbZzTPXmCKqJYhrKSwlBo0YP9vzW2iGxA0c6n6V2cYk31/8Bs1gfcLo+wGLiyU6OxNEIref7CpCZVd8H32RIZzAZig6zkTiEF1WH5YSaYGMPqvOIi+t/B0Xy+/hSZf5xgyv5X/IUHQXUgoQgmwmRiDDuv5SoYaqKfQNh0mFmlfeMVnkSxdJKPaYS8f5zZ99mBvzeV4/c4M/uniTzmycZz50kIGuv78j6E9bmKpKTzJB1NCZK5X52ttnsT3v9gwb7xrIOhhHN29z3wHjfhBRoAFKPxuXCoosczwVZW/yZyjY83hBE03tIqn5JMQiEEMIlf5Ijl8f+zgrTgk3CLXaOsRVDP95BM8ABvd17GV3op9Ocz0bG1FN/sn4JykulFmdLdE9EF77IAjYFe/jn+/7AiW3HjIBVJNki4UjhCCqmTzT/yAPdx5qu6NZqkFcRNBFmPz5QuoxenJZNKFyLLOb0XgPaT2ORPLbE59r7w9CkOnT/Q/xqb4HsAOXuBYhayQ2gTEnc/vYmxzccnpVobSZVTHN4rMDD/NY1yGavhs6nGkJnn33PAOdKb526jSfe+gQ0RabO63H+EejH+XpvpO40kcXGl0bhMb3JgfpND+2ZcwXQpBpudvFNIt/se+LJPTIppKzY+lxhg50tR3e3sxf5rnFt/nS8JPsSQyiCgVXeryw9B5/MfMiRzPjjMbWnwkXbyzxe3/8HNMLRdKJCL/+6fv5/JNH1o9bVYhsYBd7QcB7ywv8x7Nv8fTYBAOJFPtznSzVasyUS1g1jbF0NmTmCo+LlVXidpU9mQ7ilsGHhkZ5b3kBvVVGKaVktlJmrlqmMxpjOKIjJCzWqtwoF7E0jT2ZHCMdWT518ADP3bjaZi3WXZdL+WUCKdmdyaEo8O7CHH9w5i2ankdXLI4fhCz408sLRDSd8UwH8UQ4GQ+kT61awpMOmghNLoyITlyL4UmHmldCUVUc36EZ1NFVA8vavJhu+i6LjRIg6I92vW+9rA9CKEqCWvMUll5ACAPLOHpb8EgRGn4LqL4T60jVNYyIcdcuexBKnAQEIEBX1VbSR2HVLrfKQVVqnsNfXT3HtdIqHx3ewzNj+3bUjNzUHqEQSEnDu/OaUhMqMc26bQmoF/gsNaukjPUFoKooyMBjyZ6l4hUpu0ViWjhmpbQMzaBJRI0yXb9KWs9hqRHyzjJ74ofJGDlSpkVPLIFYmWelUWe+VmFPOrftvNULfCbLm8sGbW8WgY4qwnms0XKga/ohe0oIQVw3cG8R3S47NrPVnUsQpyvFdtLjaGcvHZHotm3yZcCNSrE93qmKoDMSo9hott9/cLQbEKiKoLfr9uyekltGVVSafpOSW24b1vx9YmmhxJHjIyzcLOA4HkYLKKo1HaaWC/RlkiQT1h11iNfCDyQ3p1cprFY5cHRo02uKCDW5vn79PCuNOi/N3eCezj5+ePM6XhDQH09yonszgKUKlW6rm7RvkzUyZFrO7f4GNvpPMn6qgKO4FieuJVh1Vmn4DQpOnh6rd8f3l90y9gb7eoFCw6sQ3caV5W7Ddc4iZQ2Jh6J0IEQU359HygZBUGgp0KeQeEjpoKrdiFaGJ6JGGIoO0R/p52DyIF+d+VNmGzNcq15lsjbJRGIfda+BehfqXEIILNViMDpIf6SfI6mjfGX6T7hRn2KqNsW12rUtwFHdrzPdErzWhc6DHQ9vCxqtxUbRLN8PqDrNNhPlJxHz9QqzlRIjyQzqj7nPitfgSmUO23dbzimS8UR/2/kkaGXadUXdZIe7VuvZY/XSbfZwNH2UL9/4T5wvn2PZXuJC+Rwjxr3oiorVGih9GWCq+l3Vpcd1g6bwaNZtVldqeK5PIhPDabqoqkKjboeTrBYTybFdyvkqmc4kud7tFzfJjgT3PnEAtcVgMXSNe0fXVfN1odNldqMJDU96zDfmsAMbz59BSjvUASGP5+fRlAxesIqqpPD8PIY2gOsvoipJdNVGU7OoQiVrZNv7K7h5nMDB3IF2HMiAVWdl29f+oUTddZgsFnD9gOV6x1gfjgAAIABJREFUjSPdPYykt2YNdVVBVxWm8yWihkHQ8HEcF13X8DwfRRHc638MxYJusw8cBeEK/ECS7U6iqAodZhcD0VEMxeQHS98EJAvNGeYb04zGJzBUjb5YkpVmjZi2vUNMwVnh+aVvENdSPNP7S3Rr46hoSF+GExLXI+un6U8eYq5UIF9xgNDOuyeVIGYaGIrJ/uSxTftdas7x8ur3qPkVFBRG4xOcyD5623MnpWS8s4PpQpHOROjyNH1pnkatyf77dpFNbM74JLQ0p5af3RE4avpVlprXsdR4G5QKHXQagM1QpJeR2O5Nn6m4Jc6V32oDRyk9wVCkC0tNkTJ6abpTuEGeiL4HTYkjZUDBvkrJmcRQk/SoaSDA9qbw/BWykafaLCSAmJrg1PK3dwSOpAxZR2ODHUi1wlRtmsPp45vav+Is8P3Fv2mDRlmji490f4Zj6QdbgNHmGInt4f6Ox2n4dSx1ffKlCBVTjWApEZJahmKpzrlLi+wZ7aInua4pF1GjnFr+zibgSFEEsVhY+tU+r62frudjaNoG1pYgoWb5bN+vE9ViJLUsvi9bbKp1IDxndtNjDfKNm1/m7eLLBARM1S5RdFfJqF10dybw3IBkwsJuOETjZlsbTREKlhqlsMP4oikmNW+JtDGEplpkEhGiVjfZRJSX3r3O829c4ehE/z9I4ChumTxzeB+WpvLpw/u5vpJnrDO7bdZVBgWwX4FgCdReZPN7IF3Q94LzQ0AFoYN7DpQUEhWcVwEF9MPgLyBQSepdJJQ3gBqYH4NgAYIGMkiCPwvaGJZ/lgFVA7EE2l5QRsGZRQZl8KdIaHtJaFdAdIVZ42Idz/HIpZOUCxXevXiZJz57gsXpVVYWiuy7dxQrMKAuiSUjuI5HpVZH09WW46hPJG4SlxZqTRCJm9RLTc6evsbhB8ZRVRWjqBLvilCvNolGTYQHXtOjXrXJROJEI+v9SwKmopE1t84XXzo7SdP16O9IYruCm6slRrozNByXa3OrJKMWdo/KlZvXGenO4AUBy6Ua3ek4murz7sJ18qs2T+7rZe5mHT8ImF4qcnZqno5kjM5UjLNTKwx1pckmdL57+goDuRSHR3tJ6TFS+u2TV6pQGIptzaDH9Qhxfb1E683CJSzV4MHOg+SM8Dg96TNTX+bU8pktOktXZ1ZYWK3gBwGrpRoXJhcIgsNbHMva51BKSnaTYrNJxbFpeA5+IJkqF5gtl5kqFxhMpjjY0c3F/AqjqSyzlRL3dPfx0dHdW/Z3rZjn61cuMJhI8ndTV/nM7n2YqsafXjjNeDpLVNcZSqSJ6rcKEwf85eVzlOwmpqry+vwsv7D/CPlmnYpjU3FsEqaJoahcWF1iKJlitVHnUmGZz+05iKYoKEJld+LYjozHjePhxt83notLpXlKboOcGf8HqG4UhqlPIKWL402iKpl2UnKnCMWzg7uAjUKXuu7hHGZ0qyHJdqEKjX3Jg1iKtflaIDdoLIYlipPlQrsNTS8ERRRuv94RQrTUku5CqBvQhUrDd4jr21cgaIpC1oy2XdCgpddYd8koXSS1LKa7GDru6pGQpe5LhFDYFbEwVAMFlZzRQ1JPgwgrLx7oGeQHM9do+h4/nL3Oie4BYrfILEgpuVxc5Vpxs5RBRB9DV1MoSgxVhrbwAKvNOnO1MmOpLP2xFP2xdcAmkJJX5qdZqFdvcz42Ms63L0CXUnKlsMrplYUN9T2ChGXSlQjbIYRAu4Pg98YwFQPHd3ADj4XmAnEttkWb9f1GLGaSX6niuT5T15YY292N1nI6tV0PRQmVi6Pamh5kQNXdXhsTwkqNvsEOXGf7vjORyfFAzxDfnLzIS3NTPDU0zttLcyhC8EDPEEOJ7Z3AdUXH9yqcLZ3DacmQJLTtK5z+PvFTBRwZikGv1ceN+hRu4DFZm2RvYv+2J0hKyZK9RO2WCbwvPWpe6cdug2k90qr1XBtQFCRHNtR/ipbYVuvXbUAgVaiMxndxOHWE2cYMvvTJO6soQiFrpFvCpnJHLYdbQxEKQ9FhjqaPcaM+hZQBeXurjkkogB3eLGbLrWmnwc4NXKYb665qhq4x0tWB4+zMOBKITe5Pa+URO31HVNNp+h5LjRop88cr5eqy0qSNOLbvUveaBEg6zRS7E/3tmti2G1PLxWFLu4UgZ3RyInsfV6qXcQKHgptnPCIYTKaI6et0ek0o6Mrtu4UQgg4rymqzzsL0KtfeniYSMwmkpKM7RWm1Sr3SQEowWhmpjp5we63SJLODc52qKptq+hUhNpVBCSEYiAwS0+KU3CJT9SlWnVV6rSPhcbczlC1rHRmAUFolbGr777XzJYSg0+giokapeGUWmguUvRI5pXPba+oGLrON2duemw96uH5AwjCJ6Dqmpm4Spd4YXhDQlYyzVKlRrNa5enGF1aUK2VycUrHOwWNDBMUIK8sVbtSvoigKnd1J4kmLjq5EuxfpwuBw6gSvrT5Pza/Q8GsU3TxSSs4XFql7DkuNKl2RBB3W1odfgI8XuDzW+Ql2xQ7y7o0Fqk2biKGTiUW4ML9EbyqJ4/nUHYcbyxWarktE14nooWuf/j4eyneK0zfnURUF1/cZiiYASSwVxfoxNLAkEk0xWuLY62XCrj+H7byHoU9gGVsduDaGKnRcaaPLsJZcCAMvKLMu6yhIG2NEtS4kfstVzUfBQNtG9PNOEQSSzmyc0E0m2MKUCgi4VD7DYvMmEILBD2WfYn/8JCoGTdejbjuYuoahqTiej+P5mJqKqkRwvYBAhDbthmKR1DJUvTJVr0TMyhG/ywl3EEjqNQelZY/dsF0WVivUmjbVukMyZraYnC0QXlOoN01qmqSgFmg6HvGIQU9Hkoi5vohLamkOp+/jbPktnKBJyS1Q92r45Rpnzt+kK5fA9XxSgx3E4hZWdP2+CPX/tteBkzLA8StIfGzH48zVOd6+MMv0QoGejiS/+umTHJ3YWZT0gxqS8PzHzXARMJbLMJbLcOrKFBndRASQTm/I5HozIFRQh8G7BPgg0uAvg7orZBopOVAWQZZBRkE6oO0B7wqoQ+DPhOCS2g+qBmo34IB7Pvy8dyVkMwUl8KdA6QT3DJgtYFlEwF/gVqnW6cvz3Li8wOi+fnI9KeZurOB7Ppffm2ZhZpVsd4rpy/M06w7pXILCUhnD0unsz3Dl9DSmZRBPRekf6+Ta2VkS6Ri6oXLutWv0DGaJxi3ee+UKud4059+cZHRfHxfemiKWsCjlqxiWwX1PHiCWuPNcZHq5yH0Tg6RjEb7x6jlSMYszkwuYukrDdulKh5NxoQgu31xGURRGujIMdWX40YUpRnuyFGvr7FzXD3jv+hzVps1qJQRw67aLZejh2Nx0sAwNRbgE3ly42JYNEFb4T9ZA+oASXl+C8Fqs6bPJBkLJQZtt74HSxVisl5eXz/GXM6cYi/chpWTZLnJq6TT3ZnZvYoMBdKSiGK1jNHSV7o7kbbPouqpytKuXwWSKJ4d30RGJEkhJbyzBaqNBICVX8qsc6OhmOJnmk7smmCwVeG7qGo8OjGy+16XkrcWb3KyW6YrGWG3UeWdpHl1RGUgk+cLeQwDbCsJXXZvzq0v8s3sfJKbr/P4bL1FoNri3p5/v37jOx8b2oAjB+ZUlBpNpPjW+l/lqhWevX8b2PbQNWnQb5+VBEFBthAvAWMRof/d2c/eG7zJbL6AIQUK37lja9EENVckBLoGsYqhjKOL2/UkRJspdupXqpsbeE+Oo+mbnuo0lZ+sbQ0An3TLU2PiaLwMcf92py1Q1BlpW9i/N3eD33nqBw7keTFVrX0tdUUibEfZkcgwmUmgiFMMwFI2ab98xoezLgGW7jCIEndZO5AWBGwQs1Cut/Qk81+fq21OkcglUTaW2KLEsgZZSqDXr2A2HVC5JvRxQ9esoisLgRB+qud72kz1D9MYS3KgUeW7mKh8aGOORvpFNrNSi0+Svrp5job45UScJ8IISmpJGExb7sl387dQlbM/jbycvcayzj6i2Xv4KMFnO87UrZ6g4O7NZ+mJJdEWhAZwvLFN27PB8i/Vk1WKjyp9cfIfZ6uY1e81xcH2fXEsr89a+JG/5ufaeQEoSWoIDqQPQWmNvZyh1670VEhA273+NASWAXXt6GN4VzhlVRaC1yuIjhk7E0NFVFVVR6GvpYzm+x+sLsxzr7G2XGm66P32fwmo1ZG9uM8AmDYtH+kb4/sw1lupVnp26xFKjSkI3eaRvZFOZ2sYouxW+s/AcutAxW2Nap5VjPL6r/Z6fBDHkpwo4slSL/cn9vFF4DU+6vFt8h/s7HiRzC9UfoOpVeLf4TlssGFoAgTmIHdTvEpLZGoqIbIHFRfs/2hmau2HmrGnGCASmYqK0buJN4Etr8Fu7ue5mf8C2rJCwhj+8pG7gtrREtgI7ofvYGaZq19vbbMfjytQymqrQswOwIQhZUGsii0W3SICPusNtlItEeXp0gobn3dExYKdQhUJENYioxrZuLWsaHne0GRfhwL7WeU3FRBMq89U83dE4SdNEFQpZM3bnayElZadJvtmgI57g2KMTJNIxfM9HNzVc22tTbYUIa5UNU8fzfHRD21FM8W5iKDrMeHyctwpvsmqv8MLy83yu//MhU+HWJov1n770UcTWczQQHaTb6qZSLbPUXORs6QyPdT6+ZWIkZejiNluf+bHb/kGIuGEwkExSd90NlrIbXAta58XxfCpNG0vT0DSVQ/cMszRforMnnIgn0xGcpofvh/ecaD1spJSbJr5CCFJ6lrTRQa1RwZNemxmyP9NNvllnMJbeFjRai8HoLg6lTmBqOgf6u/F8H1VR0DWV7mQcQ1PxAxmCAK1sqR9IIob+vgVl7xT3DIVGAJ4fYFo69UqTWGp7ivKdwlSjpPQe7KB2ixhzgKIk0LWhHT+7FgoqXmDjivCcCjS8oIRsAexCCDzZoGBfoennGYg9QlTLIZG4/vztdr399ymCZMwkk4xSpkZC32yEYPtNzpbfbGs29UVGkKURXpifpjMRo9Kw6UhEKdQaRA2DQq1ObyYZJkpKVQIpGe/uYLy7o+WyWCJUtPERIlzo+H5wV+e7Wm1Sr9tks3Ec12NqfhXL0BECCpUGqqJQqTdRhCCbjFJp6d8FMiyhrdSaZJOxTcCREIJOs5eYGscJmjiBjRPYZCMGu0a7iEUMspkYmq4yN72Kpqv0DGYwTA1ByHTrMrcCQGV3gW5rAFWYrJTrPP/GFfaOdPOJh/fTmY3TDDwavofwttokf5BjaqVAzNRZrdXbmg8A3z1ziWC+QWcmQTq9oeRC6CHggAutTD/aMGjjIZAkWtoasgLBKogUKGkQCZCzre15pKyCiIO/FIITQQWCQghMqL3gvAnWx0Nmk5INgaogD34eZDX8Pud1MJ8IM/wNl+W5Ik7DZXFmlVxrnNQNjcHd3SSzMdK5BD/4qzfo7MtgN11cx+Oex/YSiZncvL7EsYcneOU7p1E1Bcd2WbqZ555H9zJ2oJ/Rff14nk88FUU3NGIJiwtvTqKoCvM3VvD9gGRGIFt6gT1Whl3xPrQdkkSaqpCJR4iYOhFTJx4xGenKcHF2mVwqRnc6wfnpUOjf8Xyiphqy4EwdVVEo1208P6DasClU6+QrdUxdJZAGw10ZerMJak2HCzNLnJwYpCeb5OLMEoM5n7ge6hpJ7wpC6QglE/wlEBqINIo+gXQvh9fBXwChhuOZUgRZRwYlEAaK+TAf6jqKJlTeKlzmuYW3UBCkjDifHXyEkx17SWiby3WOTQzwK5+8jzfOTzMx0s0zjx5432P3cr3GH599h4cHhhlMprhayAOSaEtgN6rruIHfdlDaGA3XYyCRZH+uiwO5LnrjSb4zeZmkYd42weG2nrNRXW8/t5ueR8LYOk/usCJEtfB9vpS3BXgm5/L86y8/j6Gr/PN/9CS9uZ3LZAxFZSSe43RxBk8G7E3uXC3xQY5a8wU0dYCIOUi18T0MbRe6NnDbz+xo9X7r+4RAbz1PpsoF3luZp+o41FyHqutwrbTa1vB5Z3mef/POy2SsCDHNIKYb5CJR7u3qx5cBnvTb67WYbvD53Yd4ae4GNypFvjl5kW9OXtz03YoQWKpGTzTOk0Pj/Pr+43REIjiBR4dxZx3PiKoT00xS+p1L4DwZtOcFiiLI9qRDR2WviSpU7LpDNBFhfnKJTFeKWrnO0vQqMghI5hJ4jrcpGTeWyvCFPYf5d+/9iJvVMr/76nN8cc9h7useRFMU5msV/vraOV6ev0FPNMHN2rp5RiBtGs5ldKUDXY3ySN8IX774Lgv1Cn9z/TwATw2N0xGJ0vQ8LhSW+YurZ7lWXKU/nmS2ur0Rx75sJ3vSOd5cusmr89P823df4fO7D5LUTdzA52opz59fPs1bSzcZTmS4USm0x4S1+fVYLnRm9aXkneU5ZiolKq5N1XGoew6vLcy0++/fXL/A1VKeuG6Euk+6QX8syYmerffmqwsz3KyVqbsOFcem7Ni8vRyaSXmBzzeuX2CqXCDW2ldcNxlMpDja2Yu+AbSxdI1MLIKqCAxF5UTPIMkLb1N2bP74wlvoqsKxzj6MlntfxXHQFMHJrkHiCYtKqcF2JZyKEDzcN8JoMsOV4irfnrpM2bY51tXHiZ6BHcfkml8jpsX4RO9H28DR2jX2/Dya2oHg72+A9VMFHClCYW9yP0PRYSZr15mqTfLs/Ld4qvtjZIx0y3IxoOpVeX7p+5wuvbuFNtppba5HD1rOUaHoWPjPlz7NYL3EreE3KLulkE8jlLaAoL5NbWTJLfHSyil6rV5GYqNE1Ri6oqG03GB86WMHNhfK53m7GIpSxbU4g9EhAiQRJRLqMrXulbJX5sXlF+g0uxiL7yKmRtFaIo4b93e1eoU38q8DENWiDEWHt5y/mBan2+pmtjGDHdi8kX+dPquPmBZHEQqBDGj6Tc6UTvOt+W9Q99ZLFyKWzt7ebmqNnel1Qgi6rR6iapSaX+N8+SxXKlcYi+9CE2Fpw9r5bXg+k8Ui05USxWaTzmiM/ni4AHKl29LoCZCt61L36+3r6AQOZa+ML70Q7GhdF13R2sDYWjSDJj9aeRld0dmdmCCuxTEUo33+gpYT2aK9wMsrp3Bl6HQxHBshqpk8MjBMoiV+LICb9SLxO7mWCEHGjJK1onRH45sGEgA2mTJIPOnhSQ9dCgI8qr5Dzau2Ee3Q6aTcos0qKK3j1YSKdgtaHlEjPNH1Ya7XrlNw8ry0cgqB4KGOh+kwc2giLB8J73uPhl9nqj7JfGOexzofJ21sLuWIqlEe6HiIqdoknvT47sJ3yBhZdsf3tPWP3MDhRu0G35r/BlVvZ2rqP4QIpGShWmWyWKDqOsQMg05ieEHAql0lY8QwVY2YGbJDGo7LYDZFNpsge4tIuH4bAb+NoQkNQ9loPeqFwsi+x8uLU6QNiyOaTtrYqhOmoDAS20OiVZKajGy+d6PGunYErGcbNmVcfgJi+AAN1yVfa1BsNPGCgN5YDN3Q3pdewVr40qXmFVluThLREqi3uNNI6SDEnQUrA3yiWgZDiYaji1Cw1EHEBndAXYlhqilSxjCaEgmp90LcMZN6a6w15dy1BQxjACOpseTMI6Nj7UliwV1mqeV2qaAykTjEkfgofhDQdD064lGihk4qEgq/9mUSWLqO6/ukIha6ppKKWCAEBWeJsldAIMgYnbhuQCoRwTL1O15TIaCzK4nV0l1JxSN86J7x1ms767IVqw1s2yOTjFK3HeLbCMOairlpnPalh26oNJsOi8tlanWbPaOhI0jvYAeGqRMQ0PDr2EGTirdVNyFp9IaLaQK6s3H+2Zc+hN5iY3pBwItTNxBAVyzO8d6+n1i59X/tuLi4RNKy+NrbZ9nVmW1vz9cb6LqG697CENZG8PyV0B1QH4agggxqBP4SqsgihIUISviBgwxAUQJQ+gANRR0NJ4TqIJ6fB5FFsgT+HCKoIUUG37sOpFH0QygiQ6AdIvBuoClNNGGFJXGyGYJWwgK1C4TAdTzKhRpWzEQ3NKYuzjN3fYmbk8vEUxEuvDlJ10CW3YeH8ByPgbFOPMdFNzSEIogno2iGhmHplPM1VFXFtAxiSYtyvsbVMzNohsb8jRUmL8wxsjdkG5388AF6hnPMXl2kdziHbur4fsDHuk7yqb5QFDkIghawLtrJ33vHB4iaBqau8ejBMRYKFbxAEgQSQ1N559pN7tk9QNN2SUbDfplNRDB1jXvGB5hdKXJ8zyCqqtAXjbI4k2dXV5rZlRJew6GwUkHaHuO5NOWVKkHDZSiRoLYKtpalozuNomRBSUFQQGh7CFmSOigphD4RNlRohNN4HyEskE2EOgLCoNEQLCyV2a+PscsawlE9dE0lEjEIAsniVJVVrYEAIhGDRtMhHjM5MT7A/RNDSMC3fVbzVYrFOkEg8YOA3u4UqdT64thUNUxV5dzKEuOZDrzAp+Y5ZK0Ip5cXcIPwHr1aXOW9pQUu51cYSWXQFIW5apma67Bcr9ETS3Ag18XfTV3F0jRc3yeiaezr6OS7k1d4b2kBTREMJdNhyW+jTtVxWKpXiek6vfEEL87eIK4bCAEDiSSKECgCziwv0htvscTex9jwzqVZzk8ukElEcW7ta7eEpqj4MmAi2ctsLc98o8RgLLt1fviBjwAhdIQw76JqQuAHFRxv4X0n838wc41/+foPdnz9cnGFy8XNpc19sQT/9+Ofpice5WBqBF3R8IOAM6sL/PXVcziBT9IIbeU3ymdIQmfpuucyWS7wB+fepGg3+Z/v+xBZI46uaLc9ViklVc9GVzQ6zJ1LgwTQG01gqVp7b6qmMrBnHWTcOKcZnOhr/7772OiOOqmGqvHFiSMUmg2+culdpitFfu+tUxgtHdim72GqGg/2DnNvVz//6u1TG9oUGiTIVvJpf0cX//jgCf79mddYbtT46uX3+PMrp7FUDcf38WRAQjf4xb1HGU5k+F9efW7bY+2JJvjNQyf5l699n+lqia9cepdvTF4gaZg0PY+C3cBSNR4fGOMXJo7yP77yXW5UWuX7sQh1e92xrem5/Lv3XuHUzakdz+0LNyd54ebkpm0nugf4k6d+DlXbvI7//bdf5K2lm9vux5eSF+emeHFu83d9qH+Uf/vYp0iZ6/05ETF5/OA6m+fezj4+t+sg//nKGRbqVf7X15/HVDU0RcHxPdwg4OG+ER74yDCBlKws7Vwd1RmN8ZGh3ZzLLzFfr6AgeGponFxLm2tNXkARAi/wuFq9TsEpsNRc4tTSS3RaOQQixB+sCGX7VdLWh1BuY/x0t/FTBRwBZPQMj+QeZaE5T8Nv8MrqS8w15phITJDQElT9KteqV7lavUKH0YHUadvObxdFt8Cp5RcouSXswKbpN7B9myV73Rb6ldWXuVq9gqEYWKqFpVhEtSiPdT5B5y3W1U5g83r+NcpuiayRZTA6TM7ItcAZQd2vM1Of5kr1MmW3jCpU7sncS7fVgy40FKFgbgCjnMDhzcIb5J18a3+D5IxO4i2wp+HXmanPcrV6maJbRBUqR1LH6LX6uDUUoXAsfS9nSmdwApvX8q/iBA4TiQlMxaLu17heu8aZ0mkafoMDqYNcqVymGTRp2i7XpldQhKDvNuJjw9EReiN9XK1eYcVe4aszX+FY+h5SeioUkfPr1PwaE/F95CIjJAwLVQgaXjgIrF3T+eZ8y0Lbpuk3WXVWsVvi1xcq58lPrhLVopiKhamYRLUoR9PHmEhsFuj1pc/l6iXOlc+S0tP0WX30WL0k9SSa0GgGTRabC1ypXmGpGV7zXfFxhiO7WKk1WG006I7FmciGLIOMEd1RiHgtAim5WloloRt0WjFul0zxpMfr+deYrF3fdLwlr0TND0GYucYc/+nGHxFVo5iqiaVYWGqEPYk93Js5sWl/Qgh2xcZ5ovNJvrPwLDW/xqnlH3K2dIbR2CgZI4NAoeE3yDurLNlLlNwSST3J/R0PbtvGw6kjvBl/nYuVC6w4K/zp9JfZl9jHQHQQJCzZS5wrn6Hkltid2MNkbRI32Blg/CCHLyUpy+Le3j40RW3bnAKcLdxkLNHJaDyH4/msVGokIuYWy+Dt9+tT9cpU3CJ2i4nhSS90WvSq29qnKyJ0+ijYjR2nLLpikDU6EUIJQcogdOVYm+QErYxq1XZQFUHMaD00BFSboZOFqWnEDP3vrW+2JgTam0pQbYZ92fcDTO3HYdiFYvf90X0YSmQTS1NV0qEgJ+GkcL5QwXZ9+juSW1h3vvTwAhtLXWdRBti0nQhbfJ2EPoAvHSQBAgVNyYQGCe8rJA3bxfMDmo5D3S1s6ScFZ6UtVqgIhb7IMB3xtUXYWgnpxsnjWjnuhjPTukZJPUPJzRNVE9h+EwOo1m0y6TuXBQS+ZHW1yq5dXaxlMba79rduS8cjEAcvcAmMKnPNeWy/gSMd/CC8n0tuATvYKKIvEUC1ZtORjmFZIavJc/32Ql2w7synbFP+LaXEDeogJYqqsLFaRQCWprFYq5KL3jnb+0GKJ/eOU7MdPrp/Nx/aM9re/pVX3yWdjuI3Ny9mfSkpSx0nqBJ419HVFKCj+IVWki3AC2qYahappCDwaHg3AEHMGCNl7CeQNjX7PdwgTyBtNHkTRRgI0UHTuUEgbVQRwWKaunMFIXRSIoWuDwN7kEE9ZBvpRxEtwDeejPD4zxwHCZqhEvgBu48MEolZaIbK4z9zHCtq0D2QxWm6GJZO92AHZiQEAe55bC+GqfPYp+9BSvBdH81QiURNnvjcCXRDRQiFT//aY1gRAytm8PQvP0w0HpYM9Q3nUDSF5UKVYrmB5/lkUjFK5TrJRATP94lFTJqOi6Fp6FJwY2YVTVNJJyLcM96P4/mUag0qDZt9Q93s6c9tWzbVmQo1jNZizldR7IDlayuoroe0PBYqeaKWzsr8Io26zfDuHmYvLrIqYdeBfjq64wgtQShikmJ9DGh1GLWSWboFAAAgAElEQVRnw99ybeRobQvfmy8UqNcdSq6H4/iUKw06cwmWlsNSlXyxRjRiMDiQRQiYXyhhmTqr+Sr9fWH5T71uA4JG02F1tcrwUAerhdotwJHKUyO7eXtxjqpr8/jQGI8NjPLS7A3GUll2pQVpM8LHRndzubCCBD4yMk7FsXlhZoqIpvHa/CxRXedgrpui3eT7N66FjIFEkiOdvVQdhx/OXCdtWnTH4lzKr3B+ZQldUfne1DU+OjLOp8f38XdTV/GCgM/s3t/WeHlqZDcvzExyuLOH3ZkORswk1VKTpGVyqLN7RwFr3w+4dGOJpu1tSgbeLtJGlFeXr+IGPkWnRl80jX4XmqYfpIia91NpfJt68xUs4zCqGpYXa0LhQwO72JXqoCsaJ6brgMDUBhEblpqB9FFEwMGODE+PDpHQ42QtnUD6YbIXDxCMppI8MzYOCKT0CPBDcBqJLx0UobX+Xo+0aZG1ovREwgSplJIL+SV+90fPcb6wxLHOPr40cZTd6Q6iut6eJ3lBQMWxuVhY5g/Pv8XV4go/nL3OF/cc4VhXX7tnDcZTfGbXAVQh2Je9dW3oEdestr5RhxXlEyN7KDs2Rzt722XhaTPSclXbPu6kubRTpA2L3zp8ko5IlO9NX+FaKU/FsVEVjd3pHB8eGufz4wd56RZAZG3OowgDEOiKys/uPkTGjPD1a+e4WFimaDdp+h4pw2I4kebp0Qk+P36IlWaNL+w5HB5vZPOzV1UUHukf4X+673G+fPFdLhdXWG3WqbsOMd3gYEc3Hx4c57O7DpA0TX5+zxGuFFcYTKToTW7ucJqi8lDvMLnbMO+3i9FUFnUbfbbH+kcZTrw/PcS92c47yjpEdYPfPHwf2UiU56avMlnOU3MdAhkQ1QyyVoR9mU6klFiWEQrz77Dc1ITCQ33D/NGFtyjaTToiUY53D6Aragj8lyqoqkJXMo4vfabrM1S8CgktQd7JU/bKgKDLzDEU2RPOaX9Cyms/dcCREIKT2Qeoe3V+sPwceSfP5epFLlfXaYUKCmOxXTzT/xkuVS7yt/O3AY6cIi8sP0/F216AFSDvrJJ3NmsGGSK0sr8VOFKFRkSNsNCcp9aoMdPYuXQnrsV5IPsgH+35BBE1HCgGopsBH02oRNQITb/BbGOG2dvsL6bGuC97Px/r+XjbXevWOJI+yhONJ3lp5RRVr8orqy/xo9WXUVvON2FNcJqPdn+c+7In+cOp/5fJ2iSGoTGQTdNoutvudy1SeopP9X6ar858hcXmAjcbs9zcRvcmqSUZzOzm2lIeRQjG02G2tBk0eC3/KpMbyuRujapXoXrL9VKFSkpPbQGOFAQxNYYTOCw051lo7lxeYikR9ib38Uzfp+kws4igTlQz2ovpqmtTchsMRrM77iNsi+BAtouKY99xoR2WXL7NO8W3d3yPHTS5Xru2aZtA/P/kvWeQZel53/d7Tz43h865e3p6pidunk0AFlikRSTBHGSRpkyWWDRtufjB+qCy5LJUUpklf7BpuURREkAzGCQMgAQILrgIG7A5TNjJqWc6x5vvPfn1h3P7dvd09+wAhKoA+Nma2bn3nnvuie953uf5B/zI21U4gth68UM9HyZn5Pm7pb9l3pln2V1i2V3ac/260MnpObQ9LFGFEOT0HD879Av89eJXeLdyjg1vne+tvwTbbomkmuKp7g9xf/4B/tPNP9pReP1Jiqbv89LtW+iKynAmw0Rh61rworDTPRUC6q7Haq3Bge4i7FFr9SOPVXeRC9XTXK+fp+KXaIVN/Mjbslgn2mEfvz0kkprv4ATBvvRJRahYajwWeEHIu/PLLFVqRDKmox3sLRJJieMHmJrKRqNFPmmzVmtQSCbQVYXVepPhfJaJnsIPTPGFWDC8kLTpTiVZrtZRNYV0IYnXuvuYsleoQkMVcTJpq5ltyE9BFDXwwzk0tRdQuLVapul69GST3AFMQhU6btQgRXecJEqJIhIooo3MkpKye52ydwNTzdGfeARQkQSE0d4Q7LseA00llTBjHrxiYig7E9t6UO3o0Cki1vR574LN3mclreUwFZuSt0JXug8TjSCIkNF7JweSWFMtuodlN8MNHeZbM1yuneV6/SL1oIoTtfAjj7B9PYdtFOmd17OiKiQTJkEYYZs6vheS70p3aLuhDNGETr89SlrffTMpQiWhFncIlW+PsWyOg/niD0yJ/lENQ1XRbIunDx3o6N1JKfn44YPcvryCfYerkypM0sYhIhnExTjFIpIeAhUhVKQM24Wf+LqUhCT0EQSxhhYoKMIkaRyO6U9CoGAS4QECW59AoCCEhiIsbH0CKQM0ZeucCSUB1lM7tksogkx+/6Q/W9zq0tt76KFtvpdM755wbV/vdmv07e9nizEd8/bVReoNl1TSpNH0CMOIhB3bPFfrLao1p3NfeH5Ao+kxMdpFsZDE1DUePzK27z7sF1MnR2IJQiRRKFEUQRhGaHpcQBOKgmUbDB3oIQxiC3qh7NSH2p+Hvv9nPd0ZEgkTTY2pw4oi8IWH6/ukjSRRIFE1BcPQYpqOZRBFEWOjXdi2ThhEnSZaFEkmJ3rQTQUncghliJAKURSBEDw+OMLjg1vU4U8eOLTrOPzKkft2vfcbJx7c9d5Hxib5yNjkXd/rGkzyxOBO1H0XSX7z5O586UOjE3xodKLz+mMjB7lycQHLNvjEiakOcvHO2Kg2uXp79T0biduj5DUx1JgxMJXp32Vh/pMQQiTIpX4VUIi2PSMtTeOf3P/EruV1tYi+TS9wqfkWofR4pL/F+wbHaAZrJLQZnFCjGawSSp+aP8dUvsg/f/Q4zWAdRagoqDTDdUCSUEdI6QOk9LvTAf0o4j+cf5Oz60uMpnP8T6ee5mihZ9+8/b7ufoSAf/Hat1lrNbhaXuNkd19HsmAiF/LPTh0lq4/sWIcQgonUTrH68WyBf/7oh/f8nZq/yErrXUZT7yeQLSreHN3WYZQ9cnQpIwIZIzr3ev55YZ2rtWdR0JjKfoL/5tjDfO7AUVZaDZzAR1MUilaC/mTsGPmt2Z1zDVWkSVuPoipbzTVTVTnVN8QHBsdZbtWZrZVJ6AYF06bLTtJtJ+L8xbL5l49/dJ+jH6MRPzpykFN9wyw369R9DyklpqrRbSfptpOd4vs/PnFq3/VYmsZvHd//871i3Wm0mSq77+/fvW/vJrqUklZYw1BsfOliKGYnDxVtORQnbKAJfYcu2vboTaT57eOn+LmDx1htNnDDACEEpqqR0g2KViJ2gWy45LtSe2ocbUbOtEjrJmXX4Wihh6OFGKkdRhHXltexDZ3udBJTNflo39MEUYATOSTUBPWgTsWv0WUWgDpBWAJ+OK7pP3KFI4hFsp/u/QhjqXFe33iNW42bscWeUCkaRQ5npnm08BhdZjdZPUtay2AoRkckbXsUzS5+evBnd2gh3UuoQqHb3O1akTfy/MLwL3Gldrlj794I6h0Fc1MxKZgFRuxRpjNHmEofQr9zNrMtMnqWnx/+Ra7ULnOrOcOau0Y9qONFLqGU2KpFTs8znBjhSOYIU6nDWKpJGMU6FtsTdD8KMRWLZ/o+zXhygjc33mC2OYsTtQilpGDkGbaHeSD/MIfSh9CEyif7P8uau0qPMsDKem3HQ1RKSbPlEQQRiYSBrsX0r+nMEf7R+G/yTvktrtausuGtE8gAQzFJaymKZhfjyXHCSHYGr6xpMZjOklCTfLT3Y1T8/Sdlso2SUBRBFEkUIZBSMJmabE90Ylh5EEaYqs1Hez/OgDXEzcYNlprLtKjjhLEeh6EYpNQ0Q8khjmaOcTgzTUqLHS9MTaPue4TRJg861qHyo3AH1WMkMcbPD/8ioYwYsAdQhMpSs0zZbTGQygCxdk3L8XHuKLyFhNyXOIW7kqWQSZC0DJZLNTRVJYyiWH+mnUwCGLpKpe6QT9vkmj3cXNzoJJqqojDcE7slaeicKjzKuD3B2epprteus+gs0AgbRJEkodtk9Rw9Rg8H0pMcSBwkp+dwvQBNVeJjK+NjGwQRw4lhfnXkH/Jm6XXOVc6y6q7gRi5JNcmgPcRDhUc4mjmKQPDZwZ+i6lcZsAaQbSF5xN6CkT9ukbcsjvf0stpsYOv6Do0jQ1U7mkCWrnNyuJ/ZjTJuEOyiBlX9Mi+tPctbpe9R8lbbOmYKeluHTBMaqmqhCx1FqJS9dXy5c4zyw5CEZjCcynUsUu8MgUBto3E0VaEvm6LSchjMpTE0FU1ViaSkL5vECz1MTUXXVDQVkkYsCu9HPogQiJDyPbTC7hJVx+U7l28wkMuQNAwGsxmiICJT+MFcHRJalrnmLG5UZ1A9iiE04jPhE4ZrSEKkVAjCkIFChrRtUr/DNldBpRlskFBzZPQeIlq0/KvY+ni7eCTIGhN4UT0Wkd9E+Miog5a419gUkVaEQNc0UnqevF7YcV/4kd/RXYivA70DOd4rNj8Td9xfor2uelDBlx5u5GALSanaQlsuU8wn950QAXheQE9PumMre7eQUrLhrfLtlb/iTPk1mmEdiUQhptJqQkNv04N1YYKIkVWh3HIMCYKQ1fU6iLi4VswlUVTRKQhEMqQZ1khpe3cBvaiBr+yd9Gw4LV64fYvBdJqcZTOW/clyVmt6Pq/cuM0TB0ZJGDp11+Pt2QW6VX2XXp4QKrq6tf+CWJTT80PcICQIQ2RkxEAWIVBVBV1T0LXtDnkqWnsdW0iWO8RJZVxM8D0NP9jUEmmiKgqmobYd93aPI1JKvFZchDLsvZ0idyzrh7h+QNCmu8b3loppaGh37Pt+NI7N0DWVRx+YYCd6Z2vc3qoPbGlOLK1WsK17h/ZvuhK6XrzNUkqErqCrKoauoe+DvhRCYNo7tcL2W3+MaAwIgrBDVVBVBat9TDa/q+sq+W36VxERN+pLNJQWBXui08zcFG7NZXcX5YQQRDLAj2ooQqfiO5wuvctj1oNIR+HC1UUyaZvpyb67nssgCONtDiOidu6qqkpsAqCrfy+kq5QxhdDxgvhabOv5qaqCoe++FltNl97+LLl8kv3a/VJKltar3FoqfV/bUjSSWBmdm43VH7p+4I9K1Ft/Q9J6Cl0bo9r6f0naT2FoY/f8/YTeQxR5CKGhCoOU3o+m2Fhqrj05V7HVAvG9qZDWTQQKkghNsdGVBIaSRLmH5/Naq8GZtbiZfKzYy1Su+J6InuFUriPq3Az8eFwRMX3eUjMdlFMQOYTSj6leSBShtV/H8zJdseNsJWoiUNt0+Qg/atIKStSDlVibEJW8MYZARcoIP2oRSR9VMdGESc1fZKl1hoHEgyS0IiBi9C0SXUmgK0n67fu53Xi5bYQjKVg6PYlegih2W1a3FTnuzDaEUDGUnQCJhUaVP778Nv/dySc5kC3wzto8p3JFJrPfv2HIvaCs/kvEs7evktR1Pjt+lEiGeJHTKUAG0uvQ6WNJFBVVaLhhi6u1NxhJHsENHXJGD5GImG9eptsaxVZTVPxV0loBRajtdSrtdcbnXldMAumSNzV67O4O6jyWvolzsjCMcFoetUqTg0cG2Ks5GCE5s7rIQqOKJhQ+OjqFuQ3xpCoC149zrM1n34ZX4mzlHI8UHuLZpeeoBQ3Gk6O8r3gcW5+INZx/CPEjWTjatFI/lDrMaGKMZtDsnBRDMUhqqY5S+qA9xKC9vzBbVs/y/u4P/NC2TRUqE8kDjCXHqfhlZptzdJvdnQlmTC8xsFUbXRjv+UBUhcpYYpyRxCitsIXXRiMsNissN2uMpgqUPI/hRBcClflmnS5LMlsvkTNs6r5HICO6rSSn1xd4X98EC80644lp+s0xLpbnmcgUuFpdYyrTi0DDCwUVL678DpiTnMiepNnyqEYOxW1dumrd4d9/4QVuz23wCz/1ME+emozpLAhGE2MM2IM0uhv4UUx5EUJBExqmYmCpNhXXjSkKntcZrCzV4uHC/pXjIAyZXSyj6/EEt1Rpkk1bzC9XCH2ba2urSCkZHShw9dYKkyPdeBWTwdYxDmSO887iLU4e6WdpPeaOdufTrK83Gc33ooQarSokcpIQya1qmaV6nbFsjn7ShFHEhtdAAgfSWwNpn9VHn9W3tY1RxHor1mTaTDY9L+Arf/MOX3/u3I79sUydX/ulx3kk9T5swyD0I2iU6MnFE/wEOpWGQ8o2GejKoGsqby3N0a0m8cOI6601pkd7uXhrubNMqVSn1fIp5BOUFyVH0g/TyxSO5tA9mGJ2bp2Bnjz1qk9aS9Jj5ZmbLZEa9DhzbpapyT6klJQrTUZHurh+Y4Xx0S7chsIRHuK+oYdoygrr3iq2amMoJiktRcXfwIscxpNxpy+SIavuIgktTVK7Ryz3j3gIIZjIF+hOJGkFQcf2V7T/c8JNUeVYGC9pmmh30BW8yOXbK3/Fy2vP4UsPQSwYPJaYYjQ5ScHoJqVlMBULRSg0gjpfnP1DFpxbO9ajKTFVLYh2Izj2ClVR6M5oMRVIqaIJC0lI2HYUI1ijx+5GEQZShgjh4YRlekyJoSZZd1ZJ6j3Y6t7OVu8VCUPn5FA/g7kMCcNAAOuLZZymx/BU33t+/87YdB3xI7ddoAQIkbKFomSBuPiZScSJ3F5jbURIWu+NqXzxUkTS68wShRAYSoqccYCSexU/amAo2wtd31/yryjxBK7l+ojQpRFUyejZznq2b6KU8Xldc2KtDi8M2w4/SvxHKG0ET9y5a/geuqKQ1A362+4deaObkrcKHQF2hUzS2pNCsz0MQ6NR39s84c6o+Bt8Y+mLnC6/EovsE1PsxpOHGLRHKRo92GqsLacIlQ1vlT+7/e8o+1uQRV1T6elKc+HKIocmetE0hWbdoVZq0juYR1cMDMWkGe6NDFaFQc1fomhOorMz+UkbJtNdXThBQNG2f2L0jTaj5fmcnV/kfZPxuKupCucWl/hQ1wjOHmi+zQKjlJJq0+H8jSXOXl3g5vw6yxs1mo6PokDCMihmkwz15jg82sPYQJGx/jyGvpeex9brKIqYX61w+soC71yaY26lTLXhoCoK+YzN1GgPp46OMD3eRya5E3HnOT5nX7iIUBXu+8ARNEPj7NUF1soNdF3l+IF+sikLzw85d32Rty7OcuHGEqvlOlJK0gmL4d4cxw708+jxUXqLsY6NjCRryxUURaHQk8bzQ05fme9oNg72ZJka6W43aPZG62xdNnGj5ubCOreXYs2NhKVz4uAAiX2KSFKCFwRcuLHEmSvzvHtjieX1Gp4fYJk6g91ZDo/18MjRUSaHutD2KOpuv27nV8pcvrWKqgjGBgqM9hcIw4hbSyW+d+Ym564tsLxew/UDbFOnt5Dm+MF+Hjw8zORwF5oaF2OCIGRjrY6mq1hZlUCG2KqFpZqsV5pcmlneV7tHUxUmhor0dhmsOWdix0kx3tamlGiaSsI2Sdr7F9aCMGJuucwbF25z5so8C2tVGi23UzyeHuvlgcNDHJ3o37EeKYNYjF3JINro0GrD4a2Ls0gJfcU0h8d6EQKW1mu8eeE2b1+e49ZiiabjYegqXdkUB4aKnDo2yrHJfqy23l+l3KRec8kXUjvOQxhFVBsOq6UG65UGL52+0bl+Wq7Pa+/e4vrcbjfjzTh6oA9fD+m2UgwnCz+RaCMAhIYfLqEoGSJZ416FrzcjZ4yxRRXftloEthYjvE11P4Oe+O97pdw0gvi5CmBr+r60xO2x3KwTRBECyBhmB+3cCFa5XPkavdYx+hMPcLP2LWr+EnHRSMfW8jSDdULpoQqdocQpnLBM2btNKH0m0h+kHiyx3Hq3TQ9TCaIWM/XnCaXPdPankETMNV+l6s2jK0kOZD7MfPMNZhuv0QzXOZh5hoa/zIpzgUA69FjH6LNP7DgHJfcmZe8WE+mnuVn/LjljlG7r8N47uy2klCw1a1wur+KGYUf24GJpBVPVyBpWZ7nr1Q1mqhskdYOTxX4iJJdKq1Q8h9F0nvF0nmuVdQIZsdysczDXxWAyw8WNFUIZse40OVLopcdO0gh8zq0vEknJdL6HrGFxq15mploipRscKfRgqTrXK+voisJMrcTBXBdDySy3amVuVDfotpMczseo8oulFUpui7VWnYSWj93bnBmWnBsUjD4iKSl7yxiqRRD5eFErdqjVu1n35qn56/RYY9xqvMth9VH8yOVS9TXcyGEwMcVM/SwHUg/QDCvcapxHV0w0YVD119AVk15rjFV3FoFCjzXK7cZ5CuYAQ/YhElp8XUdRjAz3/XBfqlrT9/nm7WuEUjKazvJw75YotpSSlBUjuEu1Fi3Hozufwos8nNBlsbWEIhQ+1vs0L629QoSHG86hKDa2MvWe18J7xY9M4ShsT442bfC09s2qC5OsbiKRhDJCawtkO6GPqezd1fovHUIIVGIhvPPVi3yqf4KM/oNPnDfXl9K2JixvrWyw3AqYSKVYbS6jiyaWqnG5vMJIKs/p9Xkms134UcwLLpgJQhmhCMFyK6aq1AOXC6Uqh7Kj6DiYSgon9Cl5DbKGzasrt5jKdtNtJZFScmN2jXrTZWo8RlotLVd45c0bbJQajI908eSpLaiw7wZcensGw4z1KkzbYG1xna7+HNWNNayEgTQUjmSK6Okeqpc3uDDTwnN9ugfyeG6AEOA0PcYOD3QcAhpNj4WVChPDRW7OrREEEaulGrOLJQZ6clRqLdIpi2zaplRtUa45vHNhFhCcOjlGRstih1lmr62QzyQYy+ZYa0VYisU7l+fIpmy6Cik0BJO5In3JVMeFRxGClGZ1rr19zxeQ1PVYe6Z9+SmKQk9XhpHBAtWaQ7nSZGG5gmFoOE7AA9MDKCKuEEdSMtiVZWG9ynhf3FnRtbgjKaWkmEngBSGqItBVlUzS4uHDI+iqgqoIKtUWN26uMtCf48KlBUZHumg2XYSi0JQqty849JgmXjUifyDD7OwGb74zQz6fJAzjbtzZd+doND2KhRTlShPfD1lZq7K0XOVD7z9M6LfwZZNurZtqsEHLjRF/kYxotGmErbCBpSZI6/mfCLTRZihC4IYB660GfW1BzUhG2KreoYw5XoAiBFnbJG1vWaBHMuJS9QyvrX+nXTRSeCD/OE/3fJYeawBV7E5cdKW0p7tPKCW9dpoD2SIZ/d7s7P2oTjW4iq7YKGgE0kESoQgdXSSoeLewtAIJtYuaP0/dX8RUM2gImsEKlra/vtl7haYolJot+jJpgijCVBRM26BRaRIGMTXj+wknrJFQczhRbRt6RUURWQTrCOig9pKmsadQdiQDFNQtjSPJrq5LKF3WnQsE0kVTbOJeT4ja0Ra592s7iiLKtRbFgo2UYVv0fOv7pmKjChVfxvQsJ2xScuqsOc3YgjYKqXkuWdMiZ8ZuHUlNZ63VpOa5uGEQawAk0236boFeaxhTsXBbsWZQJm11EIz7R9xFdd+DmhzKkFc3vsOZ8muE7f15tPghnur+JFm9sMs8Iv5OsOt9PwipNxweOjlKNhM7exmm3kFf0UbG1IPqnpMCVehoSmrPM6EpgrLj0JdK/0Q5qm2GpipEEi4trzGQTTNXqiCkABmjSu4MKSWuH/DiOzf46xfe5dy1RRrO3RHXqqpwcLiLf/nbn2S4dzdye3O9Dcfjay+e56vPn+PWYqmDBNoer5+/zVe/e46TU4P8+qcf4ehEH0q7kKmoCoZt0KxtaWB94etv8MI71zENjX/125/kyEQfn//aG3zj5QtU6s6u9Z++Ms/fvHyRw6M9/MNPPcITJ8dRhGDx9jpOy6fQcwgE/Pk33+Z7Z2Kx1IeODPO//u5n71rk2B7leos/+IuXOt9/5OgI/2riU3suG0WS2ZUSf/7sO3z7jSuUaq1dy1y8ucxzr1+hr3iGjz82zU9/8AR9d7G6f/XcLf7NF76Fqij83IdP8lufe4KvfPcsX3n+HLeXStw51J2/scR33rpGXzHNzz19Hz/z9ElsU6dRd5mf26BvIEdS6FiqGRuHCMGlmWX+5//wLOU9thfiwuLv/PyTfO5Dx+JCDgIn8iiaeZQ2XcIPQhR1N0pVSkm51uJL3z7DN1+9zO2l0m7K1+1VXjk7w19+6wynjo3ya59+hAODXe2xSyKjVaT3OorxACh9zC6X+ad/8DWiSPLI0RH+9e98mrPXFvjDL7/C5Vsre1yLK7x05gZfef4czzw+zS9+9AEGurMMjXRRr7XQtyGdnn/7Gn/zvYssr1fZqDYp11uxtlE7NqpNfv///s7eJ6sd/8tvf5LBQynOr87Tbaa5rzCKuQ9S+Mc5kub7qLW+TtP5DpZxH6q6m5mxPZxgmZXmdxlMfaZNh4XtWEaImyhOuIqpFhFtY5u7hUDQ8GfQlTSGuj8KZlMIG+B2rcy606TL2tvlVUrJYrPGV25cwAkCuuwkB7KFbZqCA/RY04TSAyJ86VAwJ6n6s1hqlqo/T5d5mDX3CgVjknX3GgvNN8kZYzhhmQ33OmvuFSbTH8GPmsw1XkNXEgzYD3Kr8RKxdpOPH7XQFJul1mnG0x+kP/EAftTkcO6nUIXO2Y0/QSJRhcGGe40ea3rHfqT0XhZa77DhXqPhrzCUeOSux3IzSm6Lz196i4FkhuVWTCvbPEcvLc5wKNdNt51kpVXnTy6/zUM9Q51mly8jlpo1vDDkC/Nv8ZtHT/HHl98mqRtMZAq8uHCT3zx2iv/z3Vc4UewjqRu8unybf3zsUb5+6xJBFKEKwRsrc/zG9MMsN2u0Ao931ua5XS/zzMgh/ujiG4yn84yk87SCgNl6hb+8fpapXDdvrsxS9Vw0ReEbty5xotjP+Y0VhlI5IkLW3DlGEkdJaXku117jQOp+LlVfISRgMvUgt5vnWXFmGEsdZ755FVtNkVAzSCRpvYs+e5zR5FEsNUlKyxMRUXIXGbQPUQvWWXZmGLSnCGXAjfppQhmQ1gs0gyq6YjKZenBHTqRpKoeOD+E6fpuWvDOCKOK52Wu8tjSLrih8bPQgo+mt3DyMYgaKbYPpQUkAACAASURBVOg4rs9KuU4unSClJWkETb63/ipPdj2GoRptJ3QdgYYflrB/CCnSj8yoVvJaXKuutvmIkrrvYWkauqKSNxIstaoYikY9iFEsvXaayUw36rbu2nxrgSv1q0gZcTJ3grSW5lLtMqvuGsOJIQ4kJ3ZN0Gabs2hCx41cakGN0cQoS84SWT3LpdplFATTmWnyRo5r9ZgbOt+cp8/uo8uIByyJZKZxCydyOJCcYKZxi9vNWfJGjqOZIwQy4Hr9BhERG+4G9+fvI2/snZxtxvFCH72tFF1WkrF0gZRmYGo6o+kCo6k8Qgh67TSRjGG/GcNiOJnDj0Kyho0iBBnd4oHiEAnVIGPY7cJbjGJIaDq6qjKQjKlPm5Swlut3utDFfIqD4z3MmSWOTe+0SPZcn+W2PaSdsskUkli2Qa3UYGO5TCqbYOqBcaIgBCGIrAa+52MnTUqrVWqlBom0jWkbOE23UzhK2AbdhRR+EDHSX2C9XKeQTdKVT5GwdC5cX6Y7n8I2ddJJC11TOHZwgCCMyKUt+ruzJCydk4eGMA0tdhGxdAxdRVMUhnqzKELghyFXSmucX13mwb5BDhW6UIWCKhQM9b0LkkII+hLpDtJM0xSeemKKJ09NEknJlevL/Ivf/2ua7Y5wIb0FGc+nE+iqSk8uhbEt8d/8zZS95Ra1+V5hm7ZDNmMzOJBnsD8Xn/u0xfWbq/R1Z0hnbKan+ukqpijkE2TSNvW6y9Ejg6RTFiNDBUAwfWiAesPBtnTSKSvuIKoKg/252FJcz8UW74pNP8O00brttqzs3HNCiD3FbH+cwwtD5ms1UoZBy/extNi2t2AmSWrxuZlZK1F1XMrNFpauM5CLCxNe5HCxdhqnLQ7cbfbx4d6fotcc3PeaCmUQ08XuCEvVqPku724scX9xkMI9JKEJrZuR1Ps6HajtjpNxpy4WQhQIknofHWFkBJIj8bn8e9QAk6bB2flFhvJZDhaLqJqC63gEXvB9F44Sao5bjdPowt7pqia27ZcQSMmeAogQI1UiAoJNqnL7+pVsTTJioU0dDbaol4CmdPH9HAwpJU3HJ5eOi0Oa0KkHtTZNsZ18ajl0xcCJWkgiVt1FHioc6lDSgiii6jkUrMQ24eitDut8rYqhqkjAlz63m1fJ6AX6zGFabozSlJF8TySRqirouoaiKvG9vc+ijaDK5drZji7TgeQ0H+z+FFm9sO/6YzreThSDEAJFVShXm2TSFqRtkmkLs4PgkOiKialYe3bgDCVJVu9FV/bS9hMoQrDaqHcQgj9JkbZM3jc5xtfOXeqc1/dNjKLXIV9I7jgPm0Wjv3juNH/8N29SqbU6h1PXFDRV7RQVoyimPQVhSBhG9OTTZFN7Q9mllFQbDn/6t2/xxedOd5AYhq6StA3SCYsgDKk1XFquT63p8r0zN1haq/I//OpT3Dc1FFPL2si4roHCroTZ9WLEzivnZvirF97FDyIsQyOdtEhaBq7nU647OJ5PGEacv7HE//7/vEDC0rlvchDD1DHamk+GpvKBByZ5+exNpISb8+tcvrXC/Yf2H4e3x/W5NS7NrABgGRqnjo2STuwu3kspuTq7yv/xxRd56+IsQRihKgLL1MmmbAxdpdHyqDYcPD9gab3Gnz37FvOrFf7JLz9FsS0yHcgIVSi76E1hFHF7qcyXvn2G//y112k0XXRdJWWbpBJmXKiuO7QcjzCSLK5V+U9//RqphMknnjiCogrWVmsoqkJXXwondElrMarcNDQKmUSH3iXbunh+sPPejSlCCQQKRSNHQrWwFBNXhJQqTVRVYagvt6MbXqk7/Psvv8Jfv/gunh+iCBHnbAmThG3gegHVhkPL9ak2HL71+hXWynX+2194P0cn2s8lYSH0aRC7KWVL6zW+/eZV/uQbbzGzsI6qKqQTJtlUXGyvNBzqTZcgjKjUHb78nXM0HZ///pc+QKPusLxUwbR0kqkYRXHl9iqvn7+1w6VQVZSOjAHE98/dChpxzm0zkiyiKSrXassczPRi7NEU+nEOTRsgZT+DjGoIkeDOZ6SUklYwT92/hq0NktRH2WzGOMEyNe8SptqNqfVSdc+jKWk0JcFS41m67Cex1F4a/g1UJYml9VHzrqArGTSRoBnMoQqbjHmEhneDtHmYMGpRds8CIVnzBNo2xHDBSnBfdz9XymucWVvkD999g18+dJJuO9m51yIpqXouZ9YW+fL187y8eAsh4MmBUaZyXZ29C6VPEHkdFLdCrMOoibhJB6JDL9MUExEKElo3E+kPdih2JfcGblQjjNw2YyEikG68bulS8xeo+8uMpz/IunsF2hIHcUGpgapmMdUsRXOSonkQVehtipxLJH1C6WEqGTL6ALcbL5PRhzDVewM1rDkNWmHAp8ePMFsr86dXT6MgOJjrYiC5hQDTlDj/qPsej/QOkzEs6r6HremsO03mGhWqnoupajzSO8z7B8b5/Xde4Hp5nYSu84HBCSYyRf7FG89xpbzGd+dvcDjXjaYoHbRQWjeZq1eo+x43KhtAfH891j/KiWKsa/XN21e4WlknpZusO03OrS8ihOBEsZ9Pj0+z1Ky1KfUKab3AfOsyBaMfW00y0zyHrliYbYqaJkwMLcFC6xqtsEojqLDhLWCoNmPJYwTSZ6F1lS5zmDVvHokkoxdZbF1DEQpZvRtdMSCS5I1+nLBBRusmredphtVdjTQhBHbCwE7EDc+m7xG2n+1uGPDi/Az/7uxrVLxY2+izE0d2ajWJGIkspSRnWmxUmoSDEVkry9O9T9EKWwzY/bSCFo8UHkJVkiSMI+jqTkriDxo/MiOaG/q0Qp+MYeEEMbVioVmhaCZJaSZOGLQ7/nEnXrujKl0Nqnxn5bscyR7BVm00oXO+eoGZxgxT6SneKZ3GUAzGk2M7frfklWmGLSp+hUZQx1AMLteu4IUeA3Y/IHhh9UU+1vcRzlXeRUHhaOYISS3J5oC50FrgXOU8T3Q9xoq7ytnKOaYzh7lau4ZA0G/38Z3V53my+DgD9gDGPqJa22MwkWUwEVcYs8YWzaPfjgeBohknHNuToOOF/h3/l1IymYnFVwvWVsI9nMpT9locz/fvQjIY26C7xUKS3/vtj+J6Ad1t8crNCY6dtHjsmZMdq23daBdbBIz7QzF/3TY6GgymHQsvappKGEQEfoDWnrhoxtZv6prKwdGti7u/O7YZHyROokxDp7cYu43cPx1TFIttFyEhBNMTvQBkUlv0le5CiqbjMTFcpCsf74eqKPQmUuSGLHJmvGyEJKW9t8U3xNznctjikOzu/I6mqR3oc8I2dgwW28+T2bZpN++iL3Jncrv9dXdXmu629Xsul8D3QyzLoLsr3rfNgtJmjAwXGBmOJ3rJsd00pJPHhwmCkEbDpasYr9dWtwuZKnvPn39yQEY7otRqcX51haSuY6oqw5nYMbDitbDbVpaH+uOiQt1xdxwHL/I6dusAA/Yo3Wb//poVSJyw1UFxbQ8hBF4UktSNWG74HmhFQigoUu98f7fuxzaO9F0h5j/YyY2kRFWUNjIrRhgMHujF3MO2/b0ikLEbhRTBtkKPaI/7PhLQVZVyo7Vv4SOUHs2gjKXGY6kXLGKo3Vvi2MQCwZpiE0mt/TsKUno4wSy6utu98m6hawqHxnrQdAWHUmdrN6No9mKrSWpBhVCGzDSu8kjhAyjtpMBQ1bsWP8ayWw0HU7EYtMeBmCJdyCboLd6bbpEQAsfxdtu53xHNsMGGuxp/B8FEapqMnr/r9VwPKrjRTqSIlJKRgTzrpQb1hovapzB+qH8LrSAVLNUm2ke8UVds0vredEdBnFTWfQ9nD72xH/fQFIWHRgbJJSzCMKI3k8JE5cbVZTwv2LGsH0T83auX+cLX3+igdSxD48TBAR44PMz0eC/5tI1AsF5tMLOwwfkbS8wulXjyvok9iyPxekO++vy7/OW3ztBoeShCcGi0h0+//yjHJ/tJWLHF+0alyYunb/CNly+wXmlybW6NP/jiS/zeP/gQRyf6kBIalSa1jQbjx4d3/c5XX3iXejPWKfvAAwd4+uEpJoeLGLpGGIbcmN/gK8+/y1sXb+MHEXMrcVHl6Hhf/PurNQ7FJj+cnBqgv5hhYa1KqdrirYuzHDvQh6Hf/f4Io4iXz85QqbeL//kUp46N7YGqgVKtxR9++RXevHCbMJJ051M89cAkTz04SW8xjaIIXC/gyu1VvvnqJV4/fwvXD/num1cZ7cvzX33qYVx8zpTmGE91MZTYfW+du7bAu9cXaTkeh8Z6+cz7j3J8coCEpSMlrJTq/N2rl/jbVy51inZf+vYZ7psapJiyKXalMU0NBYVG0MQNPYYSAxyd6OPf/O5naLY8ak2HasPl6uwqf/7s27S2WWHH8CYlFudF4LabHLHOVbSr0OR4AV/69hm+9tJ5PD/EMjSevG+Cjz12mOHeHIauEYQRy+s1/vaVizz/9nXqTZczVxf4j3/1Kv/01z5CV06DqIyMKijWBHfG8nqV/+tL32O90mCoN8dHTh3iseNj5DMxmqTWcHjhnet84+WLLK5Vcf2A516/wtMPTzHRnaN/MI+uq52x4tSxUbIpa8ssQMKZqwt8640rAGRTFp/74Enymf01QqZGullqlbndWKfsNzmQ6qHXytBl/WTQ+Dej0foWjncaRUkjhEVW7QZ1a9yIpMuG8xpIiRMsYKm9CFQi6VFy3iSIGjT8GQy1iKEWSOjDRNJDV7JYWj9OsIgXligYU1S9C6jCxgkWcMM1LK0fL1zHCzfwoxp+WCGI6rjhCgXrkR3PdYidqX718H28s7rAtfI6n7/4Ft+evc5QKkveipvpVddhoVFjtdWg7LbQFIXH+kf5reOnSOhbecuac5mydzM27NCKJLQilpaPC6sibr7aao6MPoil5lCFTs4c41bjewgUJtMfYTj1OAvNt9CEScGcoBWWWGi+hRtWmG++SY91BENJsth8m4JxAFWY6FoCS81yrfosBzPPMJF+irnGG5S8mwwmHiaQHvPNt3DDKvPNNxlNPkG3eYRb9RcZT30QcY+N3VDG7oyaULA0bV+NroJp8xvTD/Pi4gx/dOEN/tGRR3hjZZaVVp0n+sc4t77Uli4BXVHR2g35QEYoCDQlbuIr7fzWUjVO9Q6TM22eGT2MH4V88dpZPj4yhReFLDSqnXOZM6y25q3Ei0ImMgUe7x/lyf4xinaSr944HxtKCAVT1dp5qMKQfYiG0YcmDHrEKK2wjqFYbXkVg6lMGgUVJ2oSS+JYJLQMmjAwFJvpzGOxZq5qcyL3wbZJVoqs0YuKiq6YbDobg8SLHCQSS02S0e9erHHDgM9ffJvnZq+hKyo1z2O+UaHsOmQMk//66ENxAXNHgyg2o1EUhdnlErl2k0IiCWRA2auw7sYFt7SeJoxU3GAeVaRRVGu/Tbnn+JEpHA0mcwwmtywUIyRrToOUZpDQDA5me7arFwJ3cMJbC6S0FPfnTqIIhSAKuNG4yXT6MEcy05S9MjfrMwzZg6x7G0QyIqklKJpF5spn0YRKUkuy0FpEEQr1oM4D+fuRSL40d5MNrwRScjhziEOZmCO46q5S8Sr83fJzfKT3wwxY/by+8QY3GzMEMqDq17BUi16rh7SW4lj2KGZ7gI2iOnE1Wd3WPVfaAAAN2dZG2d4Z3wolXhaJlCabSvt3KzbcGTnDJmdsQ7GkbR5/cOcDWgjRKVBsRhBFXF5awzZ0xoq599TS2Iy9nFL2i/22W1UU+rp2c5/vdDfYKxKWQaJv6yGgCEF/aue+6agcyw/eU+EopRs4QrlnrvUPM+7cR8PQ6OnePzm5l+OjaSpDg3d3k/v/SyQNg+5EkiAKyZpWrOslwFR1wja15uZqiXLTodJyGC3m6ErFhTZJ1LFbFyjYamJPOk8npORW8yq1oLzrIyfwqfkuNd9hOJnrFIv3i0a7EFBpOPhBQC5l44dxYm/qGl4QoqlK/EdRsEwdy9gbXacIpSMeGBERRP49TcjX6s2OYN9ELkt5tUbqPYoT+4VAwVQSGKqN0ilyybYApMmm8G/T9Ts6R3utQ1MMojZixtQGcdr245thq11II2o/6ON7QAiTSMZj9L0W0YQQJG2TpG0SRD4bzUbbyXJrDRk9x1jyICvuAhLJ1fp5FlqzDNqj33exoxnUqfllbDVFI6himykOH7g3LakoijVKbFvft+gGsY5Z0BZtV4SKre4N898MP/K43rhIK2zueN9xfRZXqiBBVf2447btmSCEIG90k+f774hJoBXEwrtlZ2/azY9z+GHId6/c5NkLV3h0fIT+bJpvXrzKuJKmUd8pBn/h5hKf//rrnaJRPm3zG599lI8/Nk06ad6RfEoeOz5GGEWslhqkE+a+5/bCjSX+/JtvU2u6KELwxH3j/M7Pv4+x/p3Is9H+AscP9nN8sp8/+IuXuL1U4uLMMp//2uv8s9/4KAnLYOTwIAvXl3fRrQDWyg00VeGXPvYAv/7pUyRtAydcpupdxlSLfGhgmiMTffzbP/ku333rGrI9wb96e5WMrZPJbzXUBruzPHhkhKWXzhNFkpfO3OATTx5hsHt/Oq6UktVSnTcv3O5Qnx47McZgz+7vBGHI1186z8tnbxJGkt5Cmt/7Bx/k8RPju4TpJwaLPHZ8jP/tT7/Ls69cwg8jvvr8Oe47NMShg900A4+a7+76DaBzzJ96aJLf+tzju475SF+eoxN9JCyDv3juNF4QcnupxCvnZviZp06QztgoCiAgpSVphS0kkoRlMNq3s6A/2J3ly985u6NwJISKJizcqIIb1LlRv0Vez6CqKn3dGSxruzaR5LV3b/GX3zqD6wWkEia/+NH7+ZVnHiJh7hRDH+svcOLgABMDRf7jX71Gw/F49dwtnn31Er/yseMgTISSYa8x2PVD3HKD8YEi/+OvPc2JgwO7ctGpkW4OjnTzr//zc1TqDk3H41tvXGH04w+xOF9i/EBvZ9kTkwOcmBwgCmNNOaEINFXpFI5StsknnphmtD9+PkQyJIiaKMJA3aZlqrcEJa9BSjdjp7qfQJ2jMFonk/wZDO3QPuOFbGsoaqT1aUASRHX8qNZ5DqeNwwRRvY1+VtFEClVYbaS0wNR6MdVuBGqn8KFgYKsDuKwSSQeICKIaqpJsN2h251lCCI4V+/j9932CP7t8hhcXZlhs1pipljq5nKooWKpGQtO5v2eAz4xP89HRg/Qn0tTLDXw3dpc0GWFCDsXGPQ2VwIvfT8peVE1B98ZBwIGhndSxPvtE599JvWeX3tCJwi/teH288IsAeH6ApsScmsnUpzvC9wmKbZ2orThZ+OXOv72oQcm7Qc4YI2sM33NeUTQTCAQvL91irl7BD2MX4ZVWnbLbYr5RYSiZIWxT+g7lurleWafqO9R9D0NRqbgOy806EGv0vLp0K0ar+g7jmQIVz+F7izPMVGO37alcF9P5HhaaNVK6SRB5pA0TLwpRFYVbtfLOO7+9L0IIpvM9nF5boOn7CBG7Kh7Kd/PK0m2KVoLzG0s82R831jTFILtNANxQ7ygAR/E6LQQRIZEMccIappIklMkY8S7i3LMRlOi1xnHCBoZiogsTL3LQ0NEVg0D6GIpFKAPW3TlMJdFGn28QRB45ow9VaIQyQFdMJFDxHM6uLcXaWkJgqzpTuS5+/ciDfGr8MLq683miCMFGo4Wt64z35Lm1WCIMJSVZ5rml7+BGXtxelT5D9iBD1gkUYcB7yLDca/zIFI62R6z5E1Ox7vhg/+8giNo2wJsT/1iHKE7cQ2L9n0bQ4NX113Ajl8nkAQ5lptjwNhhLjKIIhbnWPKOJUdbc9c7AEsPdRDx4qDsLIBJJr9nL7eYsB1OTKEJhMnWA93U9iSIEpmJSDWoxHG4bZNX3T7cLYSHxaYjQtDHCaA1VHSYIriDa70fRBkJpT2ja78UvNDTtMJDgjXdmWFyu8P7HppBIzp6fo9F0mTrQx/hoF0LA3HyJ85cXUIRgeqqf4cHCDi2MKJJcn1nhnXOzO/ZRVRSOTQ8wdaCXStNhqVpjMLe3eB20nUX8kPmlMrPzG5QqTYIgwjQ0ioUkg/15+nuyu/QZGk2XF1+9ShRJ3v/YFMmEQb3hcvXGCnMLJcIwIpUyGRksMDJU3GVFvHneqzWHazdXWF6t4roByWT8nfGRIsYeE2XX9Xnj9C3WN+o89vABuoopVlarXLq2xEa5iaYpDPblODjRSzJhIAQ7dJH+vrG53UEYUS43uXF7jdW1Gq4XoGsK+VySseEi/X05VGW3LfvVGyucuzjHyFCRB0+M4HoB126ucHt+A8fxsS2DkcECE2NdWOb+TjZSShw34PrMCvOLZRpNF8vUGejLcXCih4T93mLvPwnhBgFDmQzjuRxW+zxHUrLUqnSoibmERdoyybYskua2giRqB60liWL79ShAU3YLzsZuVWu8ufHingXIlG5yJNeDGwbk78GR4ubCOjcXN3D9gKRlEEnJQFeWcq3ZmcislOr05FNkEhamoTE1vPdEXRMaVluPIJQB694KgQzu6hAJ0JNKst5scnSglyiS6KZGFP5gxdWklmM6+wEEyjaq2ib1Lj7mXhiST9mk9hGtjWHIRsfmVsoQN1ggoR9GZdNVSGnT9ugsI6VHFLWIZA1VfP+6T6rQKBhdNML6jvOuCZ2jmQc5XX4NL3Ioeau8tPYsH+/72bvSv7aK2W1qoRA4UYOyv0YoQwYT49/X9tm2wcMPT6Dt4/K0fXtNxaYZNohkSC2oEMlol1bX5vbdbl7nXPmNXetJJy26iylW1+odNOgPKwSQNU3O1ypMFvY/hj+uUXNczs4v8ZHpgyxWa5i6xpWVNR483o+t652Crh+EfPPVSyysxh1a29T5uQ/fzyeePIKmKjgdB5Y29azlkDRjOm46FU90W56PIgSGtqX/EkWSZ1+9xHqlAUBvMc2vPvMQo/17H2tNVXn8xDhL6zX+4Isv4gUhb1y4zYWbyzwwNUiz1qI4kN/lCLcZRyb6+LkP30eqjX6SMiKIGiS0mC7fk0/zzOPTvHJuBtcLKNdazK2UOZDJoG67nnVN5bHjY7zw9jUqdYfZ5TIXbiwy0JW56zVybXaNmcW4W5u0DR45OkrC3D3urWzU+bvXLnfoaR977DBPnBjfV/g6l7b5uafv482Ls6yW6qxXmrx0+joTYwUKZqrdpd97u3oKqbsec9vUeebxaZ5/+zpzK2Ucz+fa7Cot16fVdFE1lSAK4+0wvv/xLNbIU0Bo5PQMAoHnhzSaHvY23SjHDXj21UuUqnHh+OEjw/z0UydI7jM+26bOJ548wivvzvDmhZjq9/zb1/jUkyOk9QW4C1rCNnV+9umTHBzvphX6JNsFnM2xSNNUnjgxzuHRXl47fwuAa3OreEFMR950d9uMKJI4LQ+EwLqLFpaUMtbFcy9iKjny5iSbhS03DDo5wsn8MEnt3humPy4h0CnXv4ChTaIIm1TiGVRly8lRERYF+xR17yoCFTfcQFPS+GGZnPkANe8KAoOMcYSqd4G6f52McQRLG6Thz2EohfbYIMgY01yaewHPNVEYIsoYBDLLut9go1nGc5sMFx+iHkgonqcr9eAutzVFCI4X+zjwSJFr5XXObyyz2mzQCDzUdnGvN5FiNJ3jQK5IwbTRlLhIc+PMLVbn1sl0pREIKmtVNFOj2JdndX4d3/ExrJhNke/NkS4k6RosdK7DetNldr5EdzFFqdLCtnTCMKLpeKSTFl2FJJWqgx+EtByP3u4MC0tlsmmbqzMrDPblKeaTzC+VmRjpYm6pjG3qhFFEs+UxOlggeQdKtOGvUveXGEt9oOPitRnNwGO2WeJwschD3cOxhlP7s6Kd4DPjR3hrdY4eO8VQcpJ6w+GVxRmSkc6llRVMR2EknePS2jL10ONktpfeyObR7BAvLc9w6fYinx2ZpmglsDWdtGFypbzGZ8aOMJBMk27rFd+slvjZA8fpspJ87sAxvjV7ju8tXOJY1zQHc2N8cGCYM/8fd+8ZJNl1X3n+7rPpbWV529XeG3TDEiABAgREkBStOJShRsOV3ZF2Rzur3Y3Z2DERowmNYidmZ1cMiRo5ciR60XsHgHCNRjfaN9pVd1V1+ar0mS+fu/vhZWVVdlW1gagJEudLd1WleXnzvfvuPf/zP2fhOgeyUUzNRBEN7uvqIKEbON4sSI+heJTHBwY5uXAV5AQDsSc51JGk1EhzMT/DQz1ZtqXSeE2RhqLEGK8UeH5mrBVyA7Ar3c1Q3MNUI0zXL+FLF1XoVN08YS3BQmMc27dwpcNwdC91r9QkhSYIa3FqbpmCPU1Ii5MxelpqpnnrOr70MdQQJXeBmJam7pVxrAaaYuD4Fr3hbeiKwtsHthDVDMpOcE72x5IcyPUymspgKOoaMYOqKOiqQkgP0lAbtoNEUvfqJPQEQ9EBXN+lP9LHi4sv4/llXL/Ahk7cd4mfSuLojaA/3Mer+RO8uPAyYS3EUGSQrfEtnCudp+5Z3KhP8ZaOB0nqSd7Z81TTXExFIUiuMVSDqBrlfOkCI7FhFu0FXlp8GYCEltjQkyhlpHis6228sPgSrxVO0h/u51L5MlcqVxBCMBwdXrderShZQMH3pkEIFJFEUXK43ji+P0cQRRlCyjpCxFGaxJGUNaRfJjD6qCNlBSEifO/Z83z/2Qtk0lGeeeEizx+9jG179PWm+K1ffYSQqfPxv3yGK9fnEcDwYAe//1uPs2NrT+uYpJScvzTDX/7dC9jNaFMIDDg/9osPsbVZoelLJ+mIRtYlTTzf59r4Il/46qscPz1BsVTDarj4vo+qKkRCBslkmF98/7383Nv3tD23XLH41Odfpla32bmtl4kbNn/z2Rc5d3GaUtkKpIK6Sm93it/+tbdx5MBw2/Ntx+P4qXE+86VXuHp9gWqtgef56LpKMh7m3oMjvO/pg4wMdrQRZnXL4evfO82rJ68TsKT+GgAAIABJREFUj4cwdI1PfvYlxm8sUbdsFEUhEQ+xZ0cf//TDDzA4kAmSrqRca/j4BjE9W+Rvv3iUMxdusLhYpVoPjl1pKkMy6ShPPrqLdz+5j2S8nUQ4dW6SP/ubZzm0b4hcNsZnv/wqL75yhWK5juv66JpCMhnh4J5B/tlHHqSrc+3C2fN8rl6f52+/eJSTZyYpVyxsx0PTFOLRELt39PKh99zDrm29qBss+N9MOL8wx3ixwJG+frLhCKaqYTarUkIIkpEwVauBqoi268BQTPrCQ1yqnAGCjfTV6gW2xHa1jbknXSZr1/jB3Fe4Vr207jFM10osNmpENJ2CHfR93wrDPRlMQyNs6CSiITzfbxrrBr5WgoAA0zWVYqWOrm4cgxzRYuTMHibqVwE4UzzGtvgeRmM720gDiQS5nH8m6EnFubaU5+LsAg+NDnHgkR1tSqVg0e3hSRfLr+OvUlQ2fAvLqzV9A1SEUNDFzSSDxPOX8PwiEomuajiet6H5rxAqUS1LpJnWoqlpEuZh1FvGkiqY2gia2tmcg9uPWa4qKDS8OpZXbxJUKohm/p4QpIwMKdpVfIpQ2BLfxY74Pk4VjyKRHM//mJpX4eHcU/SGBgk1VWrLRRDLq1F2i8w3plGEyrb4XlRUOs0+OsyeljKsbZRax+xg+VabcrXh17GlhaYFJPpqD6abEdXi5EI95J0FJJLzpRPsShykPzzSdu640mGsepGvTf0dc43pNa9TsxyuTywyNNBBJhVd8/fl88htHrO9qtUtaOes0fAsVKG1zH2X4SNZrNfoisTetBHYyx/LchyuLixh1R0uXZimrydNZ1dQxJldKrd8dgD2bunl6bfs5MrcElP5ErGQieN5LFVq6KpKxWqwtSdoXXY9n0LNQlMVtvXkGMiukAszSyWOX5hsKYTu3TXEjpHuW461aWi87dBmvvSjU4xNLVGt2/zo1cvsHu5iYXKJbF9mjYIcAjLgbfdsoTOzUjT0cfGlS0gLSG5FEYz0ZsmlYkzOFfB9ydxShZ1dWZzGSjyxEIK9m3sY7evg+OuT1Oo2z524yiMHt7R5C66G5/k8e/xKK2lsqDvN3s2967SpSY5fmGilrqUTEd5+ZOu6pNFqDPak2THSxXy+gi8lpy9PU642iOg6oVt42N2zY5Dtw123HPNcJsbmgQ4m5wpICYvFGo7rEYmaKM37lJSShmff0qtnLSQ+LlJ6JPQofZFuDNXAVx2y6WgraU5KycRsnpMXb+BLiamrPHZ4K9nk2ut9NZKxEPftHubYuaBgOTFTYHymxK5+B9jY1H1TX5YH941QdhucXprmga5hYrqJjyTfqGMoKjHDZPfmnhZxVKk2KNca5DoTmDeRgZ7rMXZxhnDEZHD0dobPS3i+ja7HWK2GqnsOKSNC0akFapE34XwUCT2IoY/Ccvs87eMohCCiDVKvd+C4korj4jiPUmoomLpGqboP1/VJREMY2hFqdZ+iW0fKHeiayly9QWcqjhH1MbUcMfEw06UirucT1ZMIUji2S4c5RMVtUChIVHU/ca0TXVl/jaQIQUw32J/rYX+uZ93HLEPK5bRkyej+YQZ39LVIbtdx0ZvpfH3NvZOmq4GHo6Ghrrr+pYSx8UVMQ2N+qYLreoxNLGA7Lr1dScYnl4hGTTzPp1SxUBXB5EyBTDJKPBaiZjlEwgaqqpAvVhmbCObpa+MLNGyX3u4UV8cX2LO93X82bQ6TNofX/WwXCnN8f+Z1cqEov7PvPnoiKwIAVSgcyPVyIBe050+NLzJ3Kc9QXmeIHLFkCGVRsDi9yJFUJ6ViDa2qslgtsjhbYqCiEo5EOLhzM/FIJGixzvVzuGugNa6GqvJY/2YGYitEY3ckzoc2j1KxZlCVaaRX4uGeFJ6vYTmvYWjDCGnyaK+CqsxSa7yOIkJoaicHshr35HZRseZIhMM0nIs8OZjF9WZpuOOEjQzVxhlMfTuGEmOmVuKr42dZsKosNWoU7Tof3XKYj24bpO6VKDpzpI1u4loHi/YNOrRB5q3rqEInoiXwpEPdK2M3PUzLzmJAFKlhknqO65XTdIZGKNizuE1PrLTZTdUtoAkdy6uQNfu5VD7K9sSD+FIyV62S1sO8e3hnk4vQqNo2tusxVS7j+T6u77c8jgaTSZZTnmt24G0nm+d4WAkR06Ik9QQvLR7lRn0aX/poShJPlnhTEkee9Km6DRTAkT51t0FCj2BLF8/3iGohCk6VrBFnxsqTNYLFRcN3SelRDqSOcKM+yZXKHEktx0h0E2XHBhy2xnYTURNYnk3ZtUgbMc4WJ+kOpTiYuoekniCmRXhL7iE6jCyP5B5umWHfmz1MSA1xJHMPaX1lIxDX4jyQvY+knuRtuUeYa8zRYWZ5rOtRJmoTqEIlrIZQhcpbOh5c1W4But6UM+rL0XjBDcY0H1o1IrLtb2u/9JXNGAQ+BF/8+nGkhKef2MerJ69z5do8f/2ZFwmZOvF4iPe98wAvHL3C5atzfPlbJ9m+pbt1c1MUwf33bKKnK0m97jA7X+LvvniUYjN1Y9nDqFS36IqvXQz4vuTo8Wv8+aee49LVOWJRk4G+DEP9WUxDo1iuc31iEdvx6O/d2Bzctj1OnB7nm98/w8JShc3DnXR3JnBcj4kbeYQi6Otur5q5rsfXvnOKT33uJUpli9GRHDu29BCPmcwtlDl9/gbf+P4ZLl6d5X/69bezc9ta3xnLcvjad04xPVskEQ/z1GO7iIQNJqbyvHZ6gmdeuEixVOdf/NbjDHQmqbsOxh3Ee94JlgpVXjh6Bd+XjA7nGBnsIBYzKVcsTp2bZGx8kb/5zIsoQvCh99yzbprOpatz/N8f/y5XxubZua2H0eEcILhwaZqzr0/z7R+eBeD3fv0xYqvaRKSUHD81zsf/6hnGxhcY6s9w3z2byKajLBWqnD5/g+dfvsy18UV+7zce4559d99W87OEsK7TEY5Scx3U5mJbEJCiNdcOEmNqdV6fnsf2fAYyK+eioZjsTt7DicILFJ08RWeJz0/8V/aljtATHmxKVktM1sd4vXyKgr1Ib3iIkBLmavX1tg2+AGzPJazppI2NY8YlULEbWHj4EQG6wBYejvAwhUatWV1RhYLluUQVnY50DP0Wbaa6YrA7eQ+XKmcou0XmGlN8evxP2ZHYT9bsQkHB9hvUvAp1r8aDHY/TqQ9xbTFPKhymJxEPpL9Rk2lrgu/deJaGb9HwLRzfxvFtql6FqhuoIzzp8YO5r/Jq/sdBNLswArNkNUR/eIS9ySOYaghQMfVtaGo3gqDaEg+bdMSj646PQCEXWkmD1JRYm3nmRrhhzXA8/0JAsviNVcdcpupVWsf83dm/5+WlH6IrBrow0RWDkBpmMLKZ3clDzVS1doTVKE90v5+aV+VK5RyOdDhdfIUrlfP0hodI6RnCahRfetT9GmWnwKI9R9FZ4v7s29ka2w1NYm3Zp0o2Wx5PFo62jtlePma3RN2rNo/Z5VsznyeufRdd0dGFidEc55HoNnYmDqIrKxuBiBplb/IIE7Wr1L0qE7WrfHr8T9mfuo8OsxuBoOAsMlG/yqXyGapumc2xHVTdMlPWeOt1ZudLVGs2M3NFQqZGKhnB9R3Olk4wVr2A5dVx/AaOtLF9m6KzhNc02K64Jf7+xl8TUWPBOCtGcMxKiN3JexgIb0ERgqLdwHLfWFvkTzMSoRAPbhria2cuMF0sM1eu8uTebcQthWiz/UxKyeRsgYnZgMhQFMEjB0fpSMXwhCQbC8zWXV+2/NksxyFs6K0CyGBHmrG5pbZiiJSSsRtLzCwGHmxCCO7dM7Qh8bIaXc3I9LGpQL1z+vIUtYZDuiuJGV5f+ZqIhji4ra+NIFFFiIje19aKEg0bbQlplVoDx/EC7wvZYM6aIKYlSSeyPHJoM69duoHvS05emmJiNs+mvuy6739jvsirF1YU1285MEoythJDvYyG43LmyjS1JmG9qS9LZ/r280osbNLbsXK/mJgtcCNfwEt6ZIlu2A587+6hVgCAlD7IGggVMBBNIt/QNNKJlXbmesOhYbtUKw2SqcDc2pUu2jpE863gNW0TFCWE7Qe+c4JgY9yw3ZbiSEq4NDHPQiGYazLJKKP9HbdNeFQVheHelXV1vlxneqHCrqFekNUNn7d3Sy+ZZJTJeoEFq9oq5jU8lzNLM6SMELsy3eTSK2tVx/O5fn2ezkSUeDzUGm8pJYWlKuVinVhi43vtMnQlisQPzPqbD5VS0mHGWEAS1ox2M9s3AaT0AIGm9rSS1GrW8/iyikL7fkAC00slPE8yvVSi1rDpTMVJhE10TUUIuDAxRyxsMJMvY6gq/bkkuqqyUKpSqNY5MNqHrqqMDuXYNJhb9cr/+FhYKLO4WGF4uINU8tYWARtBCDBNjbmFMplUhEKpjqoI0skIuWwc34epmQI7t/bgT+dRlSCQaH6xTKkSoyMdZTFfwfN85peqxKMhShULRRGkkhE6szEKpbtrze4IRdme6mQwmrptAmkyHUVRBB3dSQxTR9OUIFDB9YgnIyzNldAMlXDEJN0R+Ll5rtdS6717eAfd0fauoQ9u3ktmHcsFgYqhDSIQeLKC5VxAUztRlSyqksTzK7jeHIoIAR5C6Piyhu9V0M0eDG0QRYTwZAXp2s1ClI9AR1O70NVgrXKwo5//78H3U3EafH7sJH/x+lEAskYfJXeG4eg+oloyMDgXOj4eWxP34zbtJwwlQtboR6AQ0ZIoQqVTCWF5FRJ6DlOJEFbj1NwiHi4CFU/apMM9ONJGV0wUoZHUu4hoCXwpuVYokK/XcX0fX0pGMxkuLy0hBPTFE4wXiyhC0BmNEjeMpsczxEKBH6/n+pSrDXwpCWthukI5QkqI7fFtlN0KOxLbAj8pd4qQNnxX58tG+Kkijhqew+XyFJrQKLk1HN+l00xScGr0htOMVeeYrC1yJLsl6CPWwri+x6xVQI0qTNXKRLRO+sNpbtRLTUluhP2pEa5V57A8B1WoXKvOkdDDzFtFcmYcTcQYq+TZk0qwNb4FgKgWZV9qb9vxDUYG234OqSGGm2bbuqIT14OLpCvUSVeovWKxKbZRK8GtblA3/+32m/XZuTL/6vffychgB7u29fCH//mbvH55ll3bevi9X3+M7s4EmXSUv/jb5xkbX6BWt1tSRyEEuWycXDaOlDA7X+Rr3znZIo4AQppGsW7h+WsXOEv5Kn/7hZe5PDZHd2eCX3z/vdx7aIRYc3HreT6lssXsfKlJaqyPet3m8199lXQywv/6P76DraNdmEYQVV+3HPKFGp2rvI6klFybWORzXznGUr7KYw/v4Fc/fD8d2TiqEkiqr16b57/8+Q+5PDbPZ758jD/45+9YI/EEOHXuBvceHOE3PvowHZkYqiKoWw7PvXSZT3zyWc6+PsUPnrvAL3/wPtLR27cP3QmEEIwMdvCxX3qIzo44m4ZyhEI6qqrgeT7jk0v8yV/+iNfOTPDCK1d44q0713hPAcwtlLBtlw+/7zBPPba79fkKxRp//ZkX+fYPznLi9DhXr8+zd2d/63nFUp3PfvkYV6/Ns3/PAL/9a2+lryuFpqm4nsf45BL/6U+/x8Urs3zhq8fZvrk7SGJrbjgUsbZ97mcZpUYDH0lI05gql4nqBhKI6mZrMdiZiJGOhPGbpNIyhBAMRjZzJPNWfjT3dRxps2DP8Mz8N9AVo3lj9HB8Gx9Jl9nLz/X8Anl7nsn6NRr+yrWWNiPYvsdEucBQLE3cWL/FRwKvzc9gqiqlRoO4YWB7HlPVMrqiogqBoarUHIfOSIy667Cro5Oe6C18sRBsje/hQOoBnl/8Lp50yTsLvLT4A1RFb24cfDzpowiFPcl7UOwOXp9dIBuNMFkoMdoZpE7OWTd4duGbgdH1hgs/yYw1wYw10XYMAoVdyUPsSOzHZNlvKobSIn88yvUgabMn/ZMzIZ2uT/DcHRzzlDXeRpAsH/OBdInt8b3rEkcA3aF+nu79J3xn5gucL5/Elx41r8LlSkDuKigENovredytj8na2G2PWSK5Ub/W9rvlY7a8Otvie9BXVZCFUNiTPMxE7Sqv5J/Flx5T1nXmZ6fRmgRTkAoYbKC3xHfzVPeHOFt8tW1cunMJKtUGWzd1tsyJXelyofwaRxefQbLxMbvS4Xrt8ppjVoRCQk/THx4lrOks1GrU3bXphD/rUBXBvv4eRnMZGq5LSNfRXLh0YZpEU30qJUzOFVtqo1jYZKgng6oIetNJbrXhWq0GzMYjbQmFvpTMLpZanjexsEFnOn5HehUhAmXQMopVi5nFEtW5IgtTeXpHu9e0q8WjJtlUpOlj1qzy+8vkwSoySVHaCAlJENDRqDaaCYqBik5TFfZt7SWbjDKfrzC7VOb82CwjvWtbvnxfcuryNPP5gBiOR0yO7FpZ89W8CiWngKGYCCvaagkEqNZtvv/KpVsGXixjfCbf+r/repRqFiWjRlhbvz3K0FV6Vns7yipe44cIJYWibQc1KBAJQZBct+rzCAGhkI5lBYEzuqITU+9uIyxQm+0/gqpX52plnAPpXei6RiRsUK5YwfrO97jWJAkhKGS+dPoaZ6/O3PY9rt5YbBE4ywl+vuxAVdbf3KqKoDsbx9BVnFqQ2rZsLbEcy50xI2giSBJsDZ2URGMhIlET/abvyjA0EqkIdsPFlxvPu8sqTdev48sVRZQrfU4XJpoeqx5D0eybKlHN8cZRRBzLPoXtXgruGc55DG3Tmo5CAWzty+FLSWcqxlK5xqbuDMrydSslQ52BEfzuoSBaXFUVfCnpziSoN+w2n7CVS/WNrTOXVURC3Nr7FcBxPGZnitRqDaT/xogqKSWO79PflyabjWEaGrmuBK70iOgGhq7SkYkxPJJFqIJsLgpSBB0VPSmiIYNsOhp0TGgqb39oO6ah4TheoB5senB1reP5eiv0RZP8xyPvQldUYrdRsEdiJpHY+o8RQrT9LZlZKyTYlW33WxRCsL9j/bARVUkTbgpBkD6+bKAIE4kfEONSIvVtKCKErvU2VW4CiYciwoSN/YBK1DgECBAqYWM3iggFr9G0NtAVlYwZIWNGyIVWFMqmGqFb29w8z4LfRbTEmhYxIQTxpngk3iYiCZS7ITNYl4bVROvxNwfULDQm6Q1vCdT0Ag50B8q168UCuqIykEyyOZMNgmaEYFeuM7DvaT5fVwPfq3Q0jERiqhqp+LKthM9UfYZLlauEFJOwGmbamiEeTTd9wX4yZPZP1aymCgVPShYaebJmnN5wJmDxFZXuUIqG7xDVgs1b0a4RVkuYis6iXSamh7E8h6QRRRCYOHvSp+TUyNtVSk6dmmvTHVYo2FUszyFtRJvPiTBemyeywY37H4p6w2F6vkhPR3KNLw8Ek8xiscrMYjkgb1LRNqn23WBoIMPWTZ1omsrW0S66O5OMjS9wz/5h+nuCJIld23oxDY1KtUGxVF+XQNloohZCkK/VKVkWHbFom+3US69e5cz5G5iGxsd+8SEee3jHmramRDx8S7URBAuORsPlNz76CLu3t8vEY9EQuezasfnuj84xNVNksD/DR3/hfgb6VhaGpqmzd1c/v/IL9/Mf/p9vcvT4GKfOTXLvoU1rpN/pZJiPvP8Ig6uMok1T5+2PbOfS2Cxf+sZrPPfSJZ56bDc9XXfvFbARohFzTeveMraOdvLOx/dw6uwk4zeWKFetdYkjKeFtD23j55860KYo6u5M8p4n93PstWsUinUuX51rEUdSSl47M8HxU+PEoia/+uEH2DLSuTJ2aGzf0s0H33WIP/p/v81rZyZ4/fIMh/YNIYHXC/PUPIfd6W5C2k/VdPKGEdEDkrLhesxUyuQiUTKRMAk91JojNEVBM9ZX7OiKzqOd7yalZzmWf445awrLr1P36igoGIpJxsixLbGP+zOP0hMeZL4xzWhsOxW3TFxPBsZ2vkfcCFFzy9jeSlqULgz6w5uw/DphNUJMizGS7WxZiCpC4Pk+O7KduL4fxLc3CT5PSuZqFWL67f2qImqUd/R8gJSR5Xj+eQrOInWvhuvbBKbTGhE1TFxLEVIj9EUTbMqmiYVM6o7TakUJq9FW+hesJDMGn6e9Y2XFyUeynEiW0jva1JqrIRDoqspSpY7luKiKSqfZS92tIn1BUs3irVKhSAmKKpASXMdDEBihLm9CFSVIEIxosbZjvlukjVxr47seFKEwEN7Ehwd/k9PFVzhVOMqsNUXNKzdJRR+Bgi4MTDVERI3RFeplb/LwGt+CZUS1xNpjXl4t39Exd7CewWhcS/Ku3o+QNTp5rfAiBWcpUAh5NqpQMZUwaTPHnuQhHuh4nJSeRUqfS5UzSCCkRtE1laVClZePj7F5pJPBvgyKUEjrHfTfpT/TasS0BIGZqkZvPI7XVB28mYjsQt3iq6cv8IEDu1sm/LVaA9v2mJ8rMTCURUrJ7NJKMmMsYpJLRXEdj2vnJlEUwcy1ecyISThqEomHA1PxikW2O0WlWKOjJ83F42N0DXWgagrprhRaSGe+sKL6iEdDxO7C665r1b3aajjkSzX6h3Kkcol127qSMRPLO0uxoZEK3QMIFKHT8BaQrCgHb94/CgKVlaar+NKjYM+TNQJV8ZaBHDtHungmX8H3JT88dolHD29ptVgto1yzeOHkVaxmUt2+Lb0M964ok2y/wY36NXJmD5ptslBcGZezV2fuiCC5GT7QaLikjQjmBiRDJGQQXu1NKG2QNaRXRqqDraEQa4ZF4vsSRRFEmsU713dpCPuW7ak3QxEaNXcGx6+gqWEc30HKgJhyXK81d/q+bCnTIDA6/8+ffvZuhqOFmuXh+zUUsX6rmq6rJKLh5mcWJI1w21quaFv40eUNW/tzLctBSQYExvKYCiEwQhr5+TLhqLmSrrYBfOliqknUVa3UihD0RTIsWGX6oukNv8+fVahKBoGB589j6jtRlQxSOi3Fm+U4zBYrwSZXEbi+xHZdoqZBKhGm1GggZZC6WrOdFsnZnYi3EZ4hXSMdW1uUvZ4v8O9/8Azv2LaF9+3e2fp9KV/FqjXQNDXwqAJUVUFRFXzPx244NOpOK+k505XAbM5htufxtXOv86Wz57A9j5Cm8bv33UcqZmKYGpHbhPpMFor84Q+f5ZFNw3xo38r6veY6nF2apdiwsDyXmG6gCgVVCFQlSN+yfa/p+RNgplrGRzKazLBdz7WFqiwnpd4JMX0rqEIhG7p16+gybjfH/yTvsUKsmKAjQOGm73/VW63nObn8XCFWfbZmwt6dHuW6ivU7/Iy3Cqa6+W8dZn/bz+Fmiu72jtXm3bcneAY7gna/+XylNQcvJ8cv2kvcqE8x3wgsBvTEFlLhJ9CUn0xx9adqZjMUjUOZUWD9L3tbvI+t8V4Egs5VKRdD0WDAh6OdbU1dAni8ex8AXaGVx3d3BwN+IL0JCUzWFtmXHsHcoLqxDN+XLJWqpOLhtirG7TA+k+c//vX3+f1ffpQdI11r/u5LybWpJb723FlOX5riqQd38rH33n/Hr78a3Z1JxPJJZGgk4sGNrb833SJxYs2ed8/1Wr38dwrH8xjKpDA0jdVpF1JKXj4epIvs3NTF4YMjt5UobwQhYM+OPrZv7r6jC7duOZw4M4EiBLu399LXuzbSVgjBjq3djA7nOHl2khOnJziwZ5DQTX3uo8Od9HaluBkhU+fI/mG+9u1T3JguMDNX+okSR7eCEILOjjihkI5tuxt+Z5GwwZGDI0TXiT7v6UoSi4aYX6xQqqz4h/i+5OiJaziux55NfWwa6lj3/bdu7iKZCDO3UObS1Tn27x6g7jvM1StEdP1N5S0SN0weHV4bAbwzGVRL7uScNJUQ92UfZUdiP+O1K0zWr6GiEFIjhNUoXaE+TCVEUk9TcBZRhMr7+v4pilCa5tqCiGagElRSzVX+Fxkjx2+O/u+rrr72m9TqCkfQqt++CM6Egpuy43l84tgxEqbJe3fuXCVdbr6ylBh+iIezT3Ew+SBT1QmqFHGlgyJUnr82xakbBf7F/U8w0lRedsRjvHJtgj19K9WmrfE9bInvbv18pbjIglWj4bkIIGEESqIb1SIhVSOqGYQ1nSWrhqFq9ETihNUoM+Uyf/Hqqxzs6+PJLYEyVFUEhzevvhFrvLfvo1QrdW5cnSd/psSZ6FVc26Ves+keDIgkz/Wxag0yuQSVUh274eD7kq17B4ino+xKHGRn4sBtv+eNIGUzsMH3V30/sqkCEC1yI6zEuC/zKIdSD7Fgz7Jkz1Hzqni+iyJUDMUkrifJGp0k9BTKLSpG+1P3sS91b+vn0lKFerVBKGK2nRdSSlQtqFp5TYWKogh0Q8dQTHzp4vgFkBLHL2CqnejC4+HcW9mf2s+sdYOSs9Rse1GI60k6zV6SegZwcfw8w9Gt/O6WfwvQ2qC+5cjmZppbcB/ShcHjXe/l7V0//4bHOUgehW3ZDjxfUnM29kT5WUWQHGgHGxu5bPIOsZjZIl8kkmp9JZVL11RCZtCGtjC5RDgWwqrZFBcrZLqSlBYr2A2HzoEsqVyC6xdukO5MoJkaErh+forOgSyelG0JW4am3tbHZzUiqwplrudTbzhYLhRkkWQugXrTSi9kaGhqBCmrrflNCBV3lbfYehCKYGm+TGmpSs+2GI5v4zYTnDRV4bHDW3n+5Biu53Ph2iwXx+fZt6W9KDU2tcTJS1MAREMGD+7fRGxVO5wnPRSh0BXqY7ZkUV/lq6Yo4o4TZlfD0FQMRWNbsmtDI2VDU9sLcMJAiBQoYaR3AyniCDW77nNVVaFuOdiOR3d/klozUe3uoRDResg7XhD0IILUpyvX5unIBFV2CVRWnYOBAuqNVbiFCPSWyDrr7QZURWm1S6bNMAuW2TKlThgmVddG24C4FwLGry3Q05cmHFkhQWWTCNs6mlujRlqNoAijUXGnSclRINiMqSIob/hIrlcX6Iuk0N5E7WqqEqx3Y+FhWuVoAAAgAElEQVQnUUQEIXRUJYna9F+dLpT50YUxwoZGxNDpSycZXyygKIHiORUN43oeiXCIawt5Qk1j/64dwflTthp8+dx53rpphP7U2rV13XE4MzPH/t52j6K5ySVuXJ3Dqtk0mubm4ahJIhOlWqzhOh66oZHpSlIdX8D3ffo2BR0hmhDs7+0mrGs8N3adH1y5SsW2SaDiurdX+1quy9mZObZ2tK+bdUWlOxInbYZREMQMg7rroisKuqLS8FxqrkPCCLVIpKimo6sqGTPMzef82fwMX71+lg9t2s/1Sp5X5sfpiSR4amAHqhB8Y+I81yt5dqa6eKJ/G2F1hWgu2RZ/efEo81al9Xoj8SwfGtm3oYodmmE5nsuF4hynl6aYrBaxPZeobjAUy3A4109v2MX35wnpOxFC5cTCDb45eZ6nB3eyJZHj9eIcL85eZ96qENF0dqa7eaR7lKjevkepujbn87McX5hkpl7GVFW2JHPc3zlMZzjWurYhMKD/3NhJio06Hx49wLxV4cW560xWCkR1kwPZPo7kBlt+pP8QSClxpc90rcS5/AwXivMUGoF/WV8kyZHOQUYTHW3+dI7v8e3JC1wszvOR0YPYvsfzs2NcLS0ikQzFMrylexMD0bWp5FJKGn7Qant8cZLZWhlT1RhNdPBA1zBd4fiavZamKpSrFp7n03BrPDP/HIZiMBrbxECkj5SeQhEGivqTE8b8VBFHQty+BnKrR4gN/n/r14PucApNKLc9yUpVi099/Ri//PTh2xr+3Yxls7X1oAjBvq199Hem+ONP/mCNPO5uEIkYKxUoQYu8CRKxmu/XzGZdTle5G/hNtYB50+LRshxm50pBX2ZPmmT89n3iG0HTVPqa6qg7wVK+Srlsoesqfd3pturFakTCBr3dKU6enWTixhKO460hjjoyMQxj/VSUdDpKNGpSrTWYWyitecxPAp7ns7BUYTFfpV63adgurutx9foCnu+3DC7XQzRiksvG1h13RRGBFNiXrXYGCL7/8clAXt6wXV5+dWxd8+ulQrX1vov5Kp4XVI4yoQiGqt7SL+dnDT+JSkpQ3XWYtaZwfZeMHihQOs0e5hpTLDTmsLwaSSPDYmOWiBojY+aYtaYYjmymNzyIK32maiXqrtts0Vqpjt5qJmyrdgS/WPdxnpR8++Il0maIHV6cXDKG63pICU7DIZGKYlk2ruMFyWh+kn27d7YW1S9ceIHj1xaRh6OoIlA1XVtYIhuNkK+t8kW76WgdP5BxhzWDqhPEr+YbFqVGA0t1UYWKoWpEdJOFejVYXAEFy+ILZ8+hCKVFHG30XSlCIZ6M4Dk+mq5SdjwS6QiJdJT8fIlquU4iHUUoArfpjRKOmrB6nAmUW+VGg5hpot3hOd6wXeYXyziuh217LbKkYbuYhkY8alKqNIhGDBzXQ1dVSlWL/u5OepODt3+DVZBIKk6NseoUW+KDhFelfl4+Ndn6fI26Q7VUb5JGCulcAjOkYzccKsU6qqYwunuAcNTE8YvMV7+HEDquXyGkdWN7S8SN7ehAp2HToeuoIoEvbRx/AduZYckNEdJ6MNQMhppZc4bqhsrl8iQh36Bf7Vz33HgjKFp1fjx+nYHEfx8i/783TE3Dk5K/fPFVhjMpFKGgS8GWWKolX4d2fjggXAIFzp63bG/O/T43Ls/SM9LZ+lk3dXRDY/f9WzHCBtvvGaVSrNI72olu6ni2+w/7dm66VSmKQjQRJrzB+iD43U3x61KiCL2lbFgPjh0EcMQTYWy/QULPoAmj9Zo7N3XT15nk+nSefLnOiQuT7N7UvUK8ScnRM9cpNosqXdk4B7f3tx2jKlRCaqRJvLTTL9uGOjmya2j9Yllr7SfWLEx1VaErFyffqBHVzA3G5KanyQrSn0O6RRR9K743jroBceR5Pqap0dmVxPU9oloEc4P22VtBFSZSuhiqTkwL1r6KIsikoqRXmd2vXptkk1GeemDnbZMb29B8+u7RPlQtihDrt+IEytrlQgksNWosp9IJBLqi4mxANPq+RPg+/k3rIN+XxBPh26qNAFy/TkTrbB5L+85DFyquePN5rS1jmUAC0LWVok1vKsETu7cAknjIRFMVtnRlKdQs8tU6w7l0S3GzKZeh2rCZKVZa19FctcqnXzvNnu7udYmjjTC4tZvuwSzVUh27EcxXqVwcVVPxPR+hCKQvUVTB0lyZZHbFD1FRFDZlM2zKZqjaNs+OXcPQVbyGJL9UuRvBbhsMVWUg9sbuR+vNAROVAp8bO4muqPxo+jLTtRKu9Fmwqggh+PzYScq21QpxeaJ/W4uU96TPVK3I5dIiJdtivJLnUK6fdw3uJM7GxFHBrvPfLr/K58dOsdSotVqnnKaK/aNb7uHXtgygkmf5wh2rLPK5qyfpjSR4bXGKT146FviPSR/H99iR6uRQtr+NOCo3ia0vXjtNwa4TVnXcpg3CwWw/v7njAQ529LcIE9f3eXb6ChcKcySMEF+8dorZegUpJRXX5nOawUc2H+SjWw6TuAUxdqe4Ulrg/3jlG4xX8nhSEtY0bM/D9j16I0k+tv1e3ju0p0UCedLn2MIk3xg/T9aM8oOpS1wozKEpCjXXDtbdk6/zP+95hEMd7eqjstPgs1df41OXX6VoW4RUDUd6+FJyINvPH+x7lM2JjjbyyPH8YPSFIKkneUf325lvLHC9NsHZ0jk6zRzv6Xv6HzwOq/FTQxxJKSnXGuRLNRqOi6lrZJPRViQrBBN+sVInX6rj+UG8e2c61tr8+77PUqlOsVLH9yWRkE5HKtbyx7Edj9mlIOY8GjbozMTQVBVVKswslomGDeoNh0q1QSRs0JkJJJSu57OQr3D8wiRnr85waXyehXiVcEhnoCsVSB4dl4VCtWmWKEjHw6QT4SZJE0w+lu0yPp3Hdl2yySjJeLjlD6NrKtGIuWE1z/d9Fos1ipU6qqqQS8eIhdcuALRmjOVqLPfE/iQ2xCFdx/X9NYv9uhUYMaqqQioZpupVcN1A8q2IZWmm3TI8VYXaquDpik5kVd+9ooi2VqvboVa3sR0PRVWIb9CTC0EVdvl1S2Vr3QVCKKRvqJQyDQ3D0KhWG1RrjXUf80YgpcRqOBw9fo1nXrjIlWvzFMt1bNttbQY8z6fRcNdtdVyGoauEN4i9vekNW/+1bY9aU9579sIUZy9M3fbpluU0mXGPC4U5usJxtiU39qz6WYTrehx77iK9Q1kGRnJ3de1UynU0TcUIafSEB1q/96VHRI2RM7txpdO8hgQ9oX5UoaIJnZzZRUSNNeW6ENMNTFW7rQm7VQ8Inmg8dNfXued4LM6V8CsO89MFzJDRJBVcQhGT+eki0peEIwalQg1dVwOSZR3cM9TPfKV6S0J6RzoHrIzp8iN3poOFuKGorTjcvFUP/n9XnwgisRCRWIiemxR0Qghyve2KwqFtPWv60JdxrVDgE6+8wu/cdx8DyTtbCDZsl+m5EtVaA0URLBWqhEydRCxEKhlhqVhrEnSSQqnWvP5dejrvzq8AAAlXqjf4syt/z7/c/kuMRFcSVjbvGQiK9khqZYtq2aKjO4miKmi6itMI2h8RgvGL060Fsq4kyEXfDtD08JAgwVDTTW+PSmsTL6UXtLcJECgowthQz+BLyWcmvktfOMcvDj2JcVNsspSSK9Ub5MwUSf32RsPL0BSV7lhwzfTHbx21/rMIVVHY3dNFwbJaF4uiKliWg6kvL+EEIXNV1dPzaNhui6hZfszovqF11wLRpgGsbmiYIZ1c0wNIEaKtpct2g0LGnaLWWFHlaGqQEKqbOoW5ItmeFKwzr3l+BV8GbaRSSuruLNzO60sI4skIHZ0JhLAou/m2z9jTkeDe3UNMzhZwPZ8XTo3x1IM76W620i2Varx89norSfbBfSN0Z9uvR0WoqM3tmKoqq8YeNg908NF3HcBTykh8FKGhCq3plSNp+FV0EcaTNopQUYSGJgzCaoKCXefF+avE9RAd5vqFn/bPmkCo/aCkkNJHUdaqhFtj6frQbAlWm+swVSh3TdbqShSQhKTJtcYkXsxHoJJMhOlomk8LglS8ZWQSEX7pqUMIP7j2a7VG0MakBomRqrJSrFXVgFiv121SyQjplIp0ziG9AmgjGx6XlJKpWrE5f6/obzNGeMNWMdPUUaSCorZfB/Vqg1r1znxtdDWGtL2mEm6lNTak6lwuz5IyIm2tRm8WWI7LRLFITzyO6/vMV6v4UpIJh8lGI/Sl268Z1/exPJeQrzNTqZAMmeSiUcKKTjxk0pWIUbFtpstlnr16jZlyhbGlfKv4G9F1htOpNddE0bKYr1RxfJ+4adAZjRG7ycTa9X0WqzUK9YAMToZMuoeyt1cGisAvLBZvJx2klNQch5lyBcfzSIRMPN9fV6Xg+z5LdYulWkC4xE2TzlgUfZUCz5ey9Rl64jEKlsVitYYEEs3Hrz5WieR7Uxf5vV0PE9F0/u2J7/DJy8fYnuzkPxx+J1O1En986od8a+ICD69S9SSNMP/ngSeoew7nC7P8wctfu/XnBypOgz+78CJ/c+kYw7EMH9l8kH2ZXgxFpeQ0OJuf4XCuH1P1aTiVtud60ucr18/iI3nX4C6OdA6gCZV5q0K+UacjvEI0W57Dp64c5xOvv8S+TC//275HGYylafgeL85e468uHuVfH/8Wf3zvu9meau/Wma2X+YvXX+bnh/fwUPcmQqrGldICn7jwEn/++sukjDAfGT34hpSgqxHXQ+xIdfHUwHb2ZnpJGCFsz+Nbkxf4m0uv8IkLL3E4N8hQrN2CpWDX+Pj5FziY7eOP7n0XuVCMqtvgU5de5ZuT5/mvr7/EtuS7Wl5Ttufy6Ssn+Pj559mX7eUjo4fojyapew4/mr7CJy8d49+/9j3+3aGn2lLpdE2hpyOBqauU3QrH8seJqlEGIn3sSuxoej3/ZPFTQxxV6zaf+sYxzlyeAiFoNFz2bOnhn73nPuLREL6UHDs/zpd/eJqlci3wQ/J9/vmHH2bnpm58X/L8yTG+9MPT1BsOQkDI0PmdDz3Epv4OyrUGX/z+SV45Gxh2CgFPP7ybt92zBdtx+ZPP/ZhYxKRcbVAo1/B8yYefOMBbDoxSrTf41ovnefn0dcZn8nzmOycwdZXh3gy/8f4HAXj5zHU++90TeF4gd03GQvzK00fYuyVob3E9n68+c4ZS1aJcbZDLxPjldx5m6+DtN6Wu5/PS6Wt8+UenqdVtPN9ny1Anv/r0ETI3TZb/2ItmKSUVq9EyIlx5X4IJtNn7fr12Hcd3cKRDSg9O8uWI6YJdwPIs0kaavJ0npsfoC/UBKz2pd/MxVFWgKICUeLe46Uspg4meYKGy3oTvef7NnT0t+L4MFhWCFiH4k4Dr+Xz+K8f59JeOYjVcRgY7eOT+reQ64sSiJqahMTmd5zN//8otX0cIcfftgWJlrIcHsuzfPXDrxwN7dvYHC2ehMZrI3jJG+GcRUkpsyyEUMVoJEa7j4TZbnIQiCDUVfHbDxW4SpqGwjm25PPedM3T2pNi2u594NInfTD7wPB/PlkTDcWzbxTCDOPSGFfTfu07gz+IpgSFwNGTyePcWGpaD7qn4vh8oxpzVx6HjuT6njo1RWKxy39u2E42aKOrtFZTLCEVM7nl4G0kzRDFfIRoLoS2r/YRgdMeKNDxogxUbXp/diVgrcXGj91/TRtr892b5MkA69A8zoL/TvvX1fiel5LXpac7NzdNokuB3gnjU5MDugZa/UKlcJxwybplEJaHNlPhuMBDu4kMDj9NpZtp+n8wG5IsQglRHfN3q6XILWzIbW1GkCh3jJgXDypZMoimRNnIoaBe7/WZLQfBk9/3E9ci6yU627/DFyR/y7t633BVxFNI0ao7DUqmE43n0vcnII1NTuXekn+tLBQp1i0wkzEA6ha4ogRpFCISQdKxSQdcsm3ypxmB3mkq1QbVmE4+aQZiBIkAGnkOKIjAMra1lUVVVQk2ViKYpdKxSlFRqDaqWfcc+UnP5lU2FaWgkIiZLMwXCsY0qwT6etNruwWGtC9sv3PJ9DFNjaEsQolF2A0LU9Z2WF6imKjywd4TvH73IYrHGlclFXr8+S3c23kyOW+TK5CIQJLvdt3uY0E3tSlL6OM32N1PXSCfCXJsO/pYv1ViqLbAkTqGgEdXSmGqMhlfFx0UXISruIhEthePXcaVDT2g7YTWJJyVV16bm3mGbpWwgZRUh4ij6LrgFcaSogrnZEoqqEEvm8KRP0Slv+Pj1IFAJN9U1S/UqSSOOgoAmKS6lJJ2MIISgI7Vy3dYsh/mlCpVCnWqtwVK+Gqgv1cAIuSMbw/Ml1WoDKUHXFayGy95d/YABIta839ykQLsJSSOMFXZb6jtJULm3vPWN8sulOoNdKXS9/fuNJsKksjGi8VurFJZVcTV3noiWax2f63uMVebZnuyhL5JGv4VC7mcV44UCv/vlr/PBvbu5tLDIialpGq7Lpkyaj95zgAeHh9CaHj4N1+Or5y/w9fOvM14oAtATj/P+Pbt4546tmJqGLyXfev0Sf3/mPFeXlig1GvzRM8+1PF729nTzn971VOu7FQKmSiX+8AfP8trUNFXbIRuN8P49O3n/7l1EmimRluvylXMX+Pr5i0wWg/fuTcT5wJ5dvHPHtjYCZz3Ytke91iyaENwjJ4pF/vrYCZ4bu47j+fQl49w3OLimdcj1fL53+QpfPHOOK4tLeL5PRzTKe3Zu5wN7dxHSgnWf7Xr87YlTjOXzfHDvLj576iznZudwPY9D/X3868cfJRFqL9CNxLM83r8VgWBfppdvTV7gwa4RHuwaoWDX+dTlVxmv5nFX7c8UIYjpJjHdZCFUvSMi5Wx+hi9fP0NHKMq/OfQk+7K9QeunECAlD3aNIITEcS8jaV8bedLnemWJf3PoSR7v29YqekpoqpZW3v9qaZEvjp0iF4rxB/seY1e6u9VVsTPVRdVp8FeXXuFzYyf5V/sfb7vnuNLnUG6A/2H7fa3WvG3JTjSh8gevfJVvTpznHf3b6Qzf+VriZggh6Ikk+L8OPoHS9KhaXjP1ROK8tniD1xZvMFsrMxhtJzg9KemPJvlf9r6NkXim9byIZnB0fpwLhTnmrUqLOBqvBqqypBHmX+59GztSK2OxPdnJWGmRZ2eu8sPpS/zy5nta75WKhUlGg4KxQYKf63kShSA8RPCPE1z0U7PjM3SVB/aO8Pi924hFTM5dneHPvvACjx7eyt4tvSzkK3zya8cY7s3wsffeT8jUKVUsenMBw31jrsAnv36Me/cM8fYjW9E1lXK1QXc2ge9LnjtxhZdOX+M3PvAgXZk4x86N85nvHGegK8VAd5py1WJ2sczvfeQRUrEw3z96kc985wQ7RrrpSEX5+bfupSMZ40s/Os1vf/BBssloW//2UE+Gj/38/eTSMSr1Bn/y2R/z7RcvtIijctUiFjX5lacP4/uSj3/+eb78o9P87j95ZM0C5WZMzRX5b984xoP7N/Ho4S0UynX+y6ef5fuvXOS9b9u7YWvWPxZ8X9Jw2ieLcMggZGh4vk++UGMkvAmhgi991JtuoN1m4F203N6iCAVFKCxxd9GSy4hFQxiGjuvVKBRrGz7OdjxKpaD6kEqG151AK81e0fVgNRwsK1CKJDZc+N49Ll+d4yvfPkndcnj0oW38yi88QGc2jmFqrZvSqyev87kvH/uJvecyDF1tqfo2DXXw67/y8LqtaquhqQqaplB3XObrVbKhNxZX+tMK35ccf/EKP/7eGd73Kw/R2ZPiwukJXvzBOXRDw264PPn+e8h0xPnWF49RKtSIJ8I88d5DXDg1yXPfOUMyHeXS2Rs89YHD3Li+wIkXr6CbGgJ47N0H+NbnX+GpDxwmnorw+b/6MYObcoxdmqFWaZDuiGE3XJ7+0L2cPXGNy+enURTB/Y/uxLVdfvy9sxhmcBxPvPcQju3xva+cwKrZTF5b4J0fOkxnz1qfrvlqlbF8Hstx6YxF6V3eYItAwaBqCpnmfFq1ba4sLVGoW2iKQl8yQV8ise41s7yomiwWsT2PjkiU4XSKqBEQQeVGg1MzM3TFYmzKZNoWWr6UjOXzTJfL7O3uJmEG56Ln+0yVy4wXgg1jXyLRFhO+DM/3OTM7i6FqbO34/8l77yg7ruvM91e5bs6dc0I3ciYBkCAJgkkUJcoSFWwrOEi2ZS3LHj97xjMe2zPPM/ay1ziPNbYcZCtYGiVKFKlgUmIASQAEiJzRAZ1z35wqvT/q9m00uhsEKdqL0vvWwlro232r7j1Vdc4+e3/7+2JMZrOMpFKUTYuIx0NbJExAWwq+MqUSo6kUc/kCpmO7/frhEAm/v7o1MW2bsXSayUyG7/cPMJvPcWRklGuVz+JXVbY3NCwLPk3bZiSZYiKbwbRs4j4vrWF3DKLXbbwLhsHJiQk6olGiHg/DqRTj6QwCUOP30R6JvGZQWz2WVeJCegjDNggr/hUV7v7sKAHFx0xpgZDiJ6wEGMqNE1R8NHtrsR2HgewYUTVI3iwyU1rAJ3to9zWgVfrh50spxgoz9ARa0CQVx4EFI81ofob1wXZkUSJvFhnKTdDqq2e2lGS2nEQVZTp8jfhkN/E3nJ9kojAHOMva6dyxsxgtTNOfHWUgO8q51AApI4siKvQEWvDJOlcyI+iiSpO3pvo9i1aZ/uwoUTVEQHNFRk371l3oflRQMk2+cfoCFyZn8KoKuVKZbU0NPLxpXdXlURQEmmrDiIKA7ThkciVGppJs6qrn4uUJFpJ52lpiXLw8iaJKhENeVEWmVDJobIgwNZ0mnS0iSyJd7TU0NoSrx62PB/F5VHKFMpl8men5LOs7XlsOwE3ILLlsBb06tVE/U1NpbGt11pLjWOA42LibfkEQsJ0ypp17TZbMYjuvLCi0+HrQJc+y3/W21dLVnGAudY1CqcxLp4e4Y2sHju1w4tJYhS0ObQ1R1rXVrAi4bVz2kICIzyPTkAhx4tIYAMOTSYoFaE1sxyO5Ns2ug6bJ4ubTdkxkUcV2LNcNR3STLWXb1Qozb6LhtBy2OxKiHwSNm4nwW5ZDPl+mWHDjllZfU5X1fasQBAGv7Fb7I+o8MTWMJqkUSib5QhmtwjKSRIH260xFktkCs+kcW7obMC0bQYBstkTZMJlfyNHSFMPrUTBNt2AiKxKWZaPrCjhFHHsG7ByOnYa1WtYEwdXDK+UxKs++g6sZkyoXV10zVFV227ErLcqL940gQD5bpFQwrmPprQUHVfThkePVbHzZtrAcm3PJMWaKGe6p60Naw9ThRxW24zCfL/AvJ0/zjvW9vGvjehYKBf7u6HH+/NDLNIVCdMaiOMDzg0P81YtHuK2liZ/bvQOAH1wd5FMvH8Gvqdzb5ZrT3N3ZzraGep64cInPHj/JfzlwF+sSbjLUc4N+Ztm0+EH/IPd1d/JfD95NybT47Ksn+fSRY6yvSbCjqREHeG5giL9+6Sh725r56G07wXF4pn+A//3SUYK6zl0dbTdNoMiyWC0yA5Qsi386doLvXLrKB3dsYUdjIyPJJF84eYaFwtK+xa4Um/7shZdYl4jzW/fsR5UknhsY4u+OHiPm83J/TxdyJflo2BanxiewHYfeRJx3begjXSphWBY+dWV3QZMvhFhJBsR1H6oo01ZJSiiihF/WmC/dnPF9K3hlZoRUucjbWzawNda4vE1fEJAFocI2VpCElYLLHYEYd9Z1LNPmdNtLl8+pp+bHmSpkuK+xh+7QUguWIAhokszBxnV8ZfA0x2dGSZWLhLXlz+Xemjb06/ScZFFkU7SeRm+YK+lZ5kq5HypxROUzqzcUxwVBIKx6aPAGOTozTMlevbC4r7adRl9omcxEXPdR6w0wlkstc4G9kJxiPJ/izroOekI1y8bCI6vsSDTzndGLnJ6fwHTsamJ6mYwFAuq/A9PxLZQ4kulrryWVLVAsm9RE3GpDNu8q8Q+MzTGbzPKbHz5Ac51LCVu0KHUch3MDkxiGyXvu3UJoUZG/0j2TyRU5evYadbEgQZ9OsWRQF3cTSqeujNNcF0ESRTZ3N7Chw01q3LWji+dP9HNlZIbaWIBwwIPfq6HIIuGAl+gNGkeNNSH8Xo18oYwqS9REA8wms9UHWNcU7treRWu9+5Dfua2DJ54/x0I6v9xqdRWcvjpOtlCiqzlOsWSgyhKt9VFeOTfMA3t6CfrevCTGrSDsWykOrmkybS1xLlyZZODaDOmFsivUvVq2c8190RtLHMWifhrrw4yMzdM/NEOhUMbjWcleSKYLDFybAaCns3ZVLaNro/Pk8iUCNySGLNtmZGyeXKGE36fTuMrG/I3AcRyuDk4zM5shEvby7kd2LHN0g4ooezJHqWwta0d4MyCKAn3r6jlxdoSxiST5QnlNnaQb4ZFlyra1pgjljyokSWT3/h5Gh2ZcsUWgmC+TSRX4yCfv59SRAU4eGWDj9lYmhud55AO3E4x48QV0tu/pZPDyBOu3tbJpRxuCIHDpzCiz02l++pcOVF065mcyrviiA/PTaUIRL6oq46vT0SqB87kT1zh26Ap3PbiJqfEk3//WSTbuaCOdzPMzn7yfs8eHOHm4n4ffu5vb7urFtmwOvmPbyg2P43B6YpI/PnSIa8kFFFEipOvc3ty8YqNtOw5DCwv85cuHOTM1Vf29X1V5+7p1fHDbVryKsmwh/P7AAC8Pj7BQKFAwTURB4K72Nv7Dvn2EdZ2SafInh14k6vXyB/ffT9y3lGhMFor8r0OHGE2l+bufeBdBTcOwLL5+/jyfP3WKuXwBXZaJejzc1ty0QmfIsG3+4uXD+FSF+7u6+dzJk8zkchRNk4jHw3+/9162N7rJ+7F0mj987nkuz81SNi3silh1YyjIr++7g+0N9UiiSLpY5FNHjnJxZoahZJKCYfC3r7xSTei0RcL8yUMPVX/OlEp87uQpnrh4kaLpOg7JksTm2lo+sed22sJLlam09H8AACAASURBVKi5fJ7f/ten+amtW7Bsh29fvsxc3t30rIvH+ZO3PUTEc2ssq7JtcDE9xJXsMEO5Cf7bxl+gxbtE5/7c8HfwiCppI0fJNugJtDCUm8BwTH656zHiWojPDX0bVVIQESjZBvPlNLuifby/5X5UUeFCeojPD3+H393wUWold146nx7ic0Pf5n9t/SSy6GG6NM+n+r/CnYltXMmMYDomlm3z4faH6fS7PfzTxXlOJC/x8uxp7krs4ENtDyFWWEclu8zJhUucTQ+wUM5wKnWFofwEftlDgyeOT9Y5On+O/uwYv9L9PiJqhSWSG+Ovr36Fn2t/lL54M1PZLGXL+rFiGwFkS2UGZxf45D17CXt0ZnN5/u7FVzjY11W9BwVBoKUuQl0swPhsGtOyOXRygD2b2+hoS2BaNj6vhq6ryJKIpsmIooBp2QQDHjy6giAKXBueW5YREgSBjsYYDfEgV0ZmcRyHw2eH2LulDX2Vjc31mF7IcmFoqvpzX3stIb+HlE8jWh9BWGXjJggyqpxAEhZYrPSbThFRUHC4tYSH7djMlSbxy2EUlpKUQZ/OPTu6OHZ+GMt2OH1ljKm5DF5d5fCZIcBdD/dv61w1prIdm6JVwMHGo2ls7KznmVcuUyyZTM6luXA1TXfDxmX3381HyIVP1qj3hvDdqquvoOHYOWACpGbAdRZcDVLlWju4BbqQ8sO56sSvYzWKokVdIoiuL2lJ9bbWEgl4WMgUyOZLvHJumK09jYR87pwWrNhGt7WszZICcBwJkK6jst8cWaNEwSwT0TxVx86iZWCvwoQURQFFkVZqLzlglC2MivnI9QU0B2dZEkpEQhCUZYLtXlmlzhNCkxTS5TxFy0AT5R+7+ch2HLrjMT56286qI5Quy/zHp77Ls/2DdMaiFAyDz584RXcixq/v30dtwN28b29sYDiV4gsnTrOtoZ6E30fCt/RPEkWaQiG64qtrdtmOQ2csyifv2EtIdzXBfKrCJx5/klMTU+xoaiRXLvP5E6forYnzH/bvI+Fz92rbmxoYXniSz584xeb6WuK+tXVqNVWuGjA5wGgqzfevDnJXRxs/u3MHqiyxs6kBy3H4/aefrb6vbFp8+fRZgrrGb959J40hd2+3o6mBs1NTfP3seW5vaSLqXYqBMqUyB7s6ePv63tfUUfRfN0dIglhlEy1CFIRVFNheP65lF7ArrJ+bfSZR8CBLMa5/RgUE6rxBgspr70tHcymKlkF7ILbqPiLh8RHRvCTLBWaLuRWJozpvYMXsENY8RDQPw9kFFkprEwluFZZjM5nPcDE5zbmFSaYKGXJmmZJlcGbBddJcLUEN0BaIVos7i5AEAUUQsRyb6xtkhjLzmLbNuYVJPv7iV5a9x3FgspBxtT5LBUqWiei4yW7LqnTBVJLfjuN2zgiiUGEcucd4s+Rq4C2UOEpmCnz1mVMMjM2iKjK27bBQ0X9wHMjkSmiKTGC1Bd12K2weXcW3iu5P2bRIZgqMTqX4s88/677HcdBUudqTLYoifu+SOKFXV1zbyMJr04dtx+HQiQGeOXoZcFkcl4amlyWEVEVGV5cWkaDfQ8kwqz31N8NCOs9cMs8/fvMIakUDqVQ2aW+K/bsvSpoiUxcMENCWBzmCIHDb9naefv4C10bn+MGLl/mJh7ehqcsXTvd6uk/Lm9XuJYkCd+zu5Oirg5y/NMHFq1Ns2dBUbdtaFD48enyQ8ckkkZCXTX1NKKvoSY1NJDl7cZyaeLDaBuA4DplMkZdeGcBxoLMtUXUS+WHhAKZpV1sIbhTrdhyHVLpQOfcPtxisBkEQ2LW1jW9+5xSDw7O8fKyfh+/bjCSy4rrZlXGUr2uDqvH4iP6YMY7WQnN7gkDISzjqY3YqRW1DhPXbW/ne48epb45y7yPblgTdb7hUdY0RwjE/kiRSzJer66yzaJksifiDHjxercpqSifzTIzOc/7ksCviuM6lrja1xQmGvYRjfibGFtyq6U0+92wux18fOcJ4Os3Hb7uNjbW1TKQzfOX8OYaSSeqDS5uJ+UKBv3z5MJfn5vil3bvpiEbIlst8/fx5/unECZrDIR7s7q5Sx03L4qlLl3n/5k1sa2ggXzb455Mn+OaFi+xraeWB7i5iXi/729v54unTXJqdIe5rrZ5vcGGB05NTvK2nh3glmLo4M8NfHzlK1OPhd+65h1q/nytzs3zm1RPkyqvPx+empkkWi9zf3UVPPI5hWUxms7RGlhK8PkVlXSLOvtYWuqIxZEnkzNQU/+fIUb5w6hTrEnGCmkZQ1/m1fXsxLIs/fuEQx8fH+cMH7qc17B5LFkX82mJfusW3Ll7isydP8kBXF/d1deFVFY6NjfHF02f4q8OH+b0DB5axngCevHSZuNfL+zdvoiUUJlkqUjTMKuPqRtz47AuCQFD28Z7mA5xJ9fOpq19Z8Z68WaQlUMsHWh7gjy59FoBf6no3n7r6VYZy48S1EEW7RN4q8onux4irYU4mL/OF4e+yM7KevmDbqp9lNSTLWa7lJvjZ9kcIKj5KtoFXWlqrt4bXsSnUxURhdkW7nFfSeah+L92BFqaKc7y76R56Aq1u9UyUERDYGenjpdnTDOcniagBLMfmfGqQiBogocY4NjGGYdlIgsDm2jp+nKDKEgFd48TIOLUBP8MLKURBYHB2nojXQ1PE3eDUxYJs6Kxnci6D7TgcvzjCi6cGefiO9ZXqpYDPq1WCy6X7SRAEahJBHMchHPSuaHeOh/1s723m6ugcjuNw7Pww/aNzrG+vXTP+MEyLl08PMTHjtomoisS+Le2uy1emyIUjV7j9bdsRb2Bb204Zyy6gyQ1L318MYEgRV0vrFmBjkTRmCZanqfcs6eNIosDm7gZqo25ybWw6xeD4HIosMT7rfs5o0MvuDasL1CuCglfyufwhQWB7bxONiRD9o3OUDYunXrzAnk1tJCK3VnipxkGCgCxImI59a/GcUwCsSsta/qZzv2mYKKpMciFPNlPE/wY08NaCYVpk8+WqrqIgCDTXRdjQWc+Lp9xY6fkT/ezd3M7O9c2va0yo+JMh+BDEmxdW/bJKVzBeZWzJgoguyWtKDoQiPoyySbFgLNexEcAX0JFkV1Pvem2vUtkkkytd16IpokkBxBvY9KIgUDDLZM0SV9KTbIo0ob+JbkZvBciSKyity0vPbls0TMTj4cqs2+45nc0xmkrz6IY+wp6lMfaqKlvr6/jiqTPM5fMk/K/PZEiWRPpqEtWkEUDU60WX5WpsMJ3NMZZK89jmjYS05efe0lDHV8+cYz5fuGniyMEthrs/OMzn86SKRdbXJqr7BkEQ6KtJLNM6yxsGF6ZmEASBr509v8xWPVsqs5AvULyh7T3q9bC9seGWzDekVZIr/xaOxoZt4fBatvAClpOlbI6hK+uXXhVcV7lbed5N25UGkcWV2rzuGQRkUXSlHlZhZK6mfum2aQkVPcYfjoFsOw5Hp4f583PP05+eI6p5qfcG8ckqqiS/ZtFcl+RbVpMrV1i4RctkPL+6+VJvuIYGX5BCweDCwARBv+5KqABBv858MofPo2FaFj6vxkIqjyC4SfCOlniVHfrD4i2TODp0coAXTvTzi+/ZR2dTnGy+xKVKtUoQwOdRKZkmuUKZcGB51lEQhaqwdaFkEPAuD74VWSLo97AzFuQj79jN9YoNPq9WSSzYyxaGQsnAsuxli8fic3BjED81l+FzTx3jnl3d3L2jC02V+fvHDzObXOrvLxsmxbJZPX46W0RT5FWTFzci5NepjQX4+GN3UBNd2uSpioRvFWbNm4HFhN1i5nox4SOLIp2J6Krv2ba5hV3b2jh8bIAvfv0o6UyBfbu7iEZ8SKJA2bBIpvIMDc/R1hJj8/qmVY/zRrBnVyfbDl3ixNkRPv3Z5/nw+/bQ1hJHU2Vy+RKnzo3ypcdfwTRt9u/pZl3X6kFvsWTwha8ewaMrdHfUoioSyVSe73z/HMdOXsPrUbl7Xw/h0NrJkqXkmAOV/6+lCSEAdbVBfBV9rR8cukToIQ9+n4ZhWIxPpXjyX09z7MTQa7aQvREIgkBfTz0H7ujl28+c5Z+/9DK5XIltm1sIB13nm3LZIpXO0z80w+x8lvc9uqvKyJIr1qL/f8BiQHl90mf3/nV09Tbw+OdeZOP2NprbE8iKTD5XxDJtpEpVc5nmUKU1bNEGPpOuOJBVqgOLf+bza7R01nDgka2Eo34cx+HS6ZHlldLKXCQrEtlMEaNsIivyss3fiYkJXp2Y4CPbt/GeDRtQZZmNtbX4NJXTE5PLvuORkREOXbvGb921n0fXr68GJY3BIKcmJ3ny4iXu6ejAWwlyHODujnY+tG1blf2gSCKnJiY5OTnBfV2dSKLIwc4Ovn7+PN+9coXbmpuRRRHbcXh2cBDTsjjY1Vk91/cHBkmXSvzGnXdwX1cngiCwsbaG6VyOvz58ZNVrM5HJ8Mu338Y7+/qqFPQb5+mQrvGxXbsq2jDuuXoTCY6NjnF+ZoaiYRCsuKfV+v1Yto1XVZBEgYTPR0Nw5QZmNpfjq+fO0RoO88m9ewjp7sasL+HSXf/26CscHxvnrva2Zc9/sljgd+65m421tcvFWB0olQxs20GSxOp1NE2bcsXhyuvTECuJXVVQUAWZ1YIuURBo8tbQ5K0hpPhp8tbQ4ImjSQp5023ZFRDoC7bS4q1DFER2RPv4yuj3GciNsS7YuuKYa8HGZnd0A/Uel0ngY/kaLYsSMtKqgrEuLV1FExVERFRRXdHO1uStpcVbx5G5s2wKdZK3ihxfuMhtsQ2ogkpY05FFEU1+y4Q0bxoEBEqmyTMX+9EUmULZQBAEvnH6Ap3xKB/YtQVwhYkf3NPHqctjTC9kSWWL/MM3DuPTVfZuaUdXlercsrwo4GoipXMF4mH/inVGFAUe2tfHCyf6GZ9NMz6b5p++dZRPvPfOqjnI9bAsm1fOD/PF771KsWwiCLClu5Gt65qQZIma1jjWwGpcEFdPRxQUxOuE00vWHDljmJC67pbHSxYkNPGGOFEQaK2Psr6jjom5NMWyyasXR1FkiWTGnYO39jTSVLs6m9jGJmdlqyyTxpowD+7t42+++hKmZXPm6jiffvwwH3zbTppqwmvqDVq2TTJdYGwmRXNtGNUrEdG8q24KV4UYRlTW4djzCGKEmzFyFFWubhy0mxhrvBEosoTXoyBdt7b5PSpvv3MDF4emmE3mGJtO8amvvsivqnexoaNuzRjGcRzSuRJj00kCPo2mhAaOARVR7pt9x6CqszPRvOxYmqRQ6w2surErFQ2KOWNFAhsqbR+V/9dE/WiKRMmwSGaLnLg8yrq2GjduF73EtD5k0bOshbLNFyehBVzNOkFEWUOg+0cZIkJVp2cRiiihK0vJm4JhYFgWAU1b5vwoAEFdx7BscuXVNahuBkkQCWrL3QcXW6AW1/t8uYxh2fg1ddkzKODGAGXTes1zz89nsSyHlpZY5fuYOIBPVZfdiZosL0ugFU2DommSNwwODV1bltQJVoTBb2SgKJKET3trJRdDqo4AzBSyN/krB1Hwokg13AorcDVENA+KKDFfzK/KkipaJhmjiEdS8K+igZkyiiteK1gGedNAFkUCyut3kLwe04Usf3r2OS6nZvhw9y4eadlAWPOgiCKW4/B7x7/LSO7m+nu3iojmyqfcWdfBr226a82/0yUZ1XDberO5En6f2+lk+zSmZzPEow4Bn0ahWCadKaAoMplMgZbGKD/caCzhLTOr5QolPJpStbk/eXmsao0K0N4QJez38PgPTvPoPZtQFZlcoUwooBMJeOlrr0UUBJ547ix37+xCkSWy+RKxkA+fV2X3hhaefOE8U3MZGmvCWLbNXDKH31vRRrBsTl4e5cLgFNGQl0MnB5Blka7mJUptyK9TKpsMjc8jiiKyJBIOeChXmEM1ET+6KjM2k+LK8AyR6xJcxZLJCyf6qYn4cYAXTw7Q2RQnEvBgO66rVjZfwjQtSoZJJl9ClSVURWJzdyNPvXiBM1cnOHhbAEkUK85xKkLwzcs2nzo3wvFT10hniuQLZVLpAtOVlprvPXueq0MzeHQVr0ehJh7ggQMbl2n9REJefuFD+9FVmZde6ecLXz3KE989RTTiR5ZEiiWD+YUchmnxqx87+KYljgRBIBEL8IsfuYt/+MKLHDt5jd/7429RXxvC41FIZ4pMTCaRZYl79/fxwcduX8HsWcTeXZ3MzGX4n3/2bepqgui6wvxCjsmpFLIs8Y4HtnBwf9+KAOjFo1c5d3GcXL5MvlBmbiFLKlPEMi2++sSrHH11EI+u4PGoNNVHeOCe9Wia2/Kzqa+J27a389zLl/mXrx3l6IlBwkEvpbLJ2MQCtu3wyINbOHT4ClMzq2eifxh4PSofft8eVFXiuz84x9/88wuEgh4iIbf6XCwZLCTzFIplNq9v4rF37Ky+13ZcPYEfJ2TTBZ596hT9FyeYm8lQrIhX+wMeEEDTFfxBD3PTGV56+hyFgkFNQ5hoIoAki2za2cazT55ifHie+965Dc3j/v0iFFVm884Ovvu1Y/hDXhJ1QXyVyoHuUVAUCX9Ap7kjgQM88X+PoCoyO/Z0oeoK3oCO6dhoukIg5Cb3uvoaeOKLh/nS3z3PQ4/tIn6dQ9fl2TnKpsn2+iVdHlEQWJ9IuG1RlfXacRxeHR8nWy7z+PkLPDs4WD1G2bJZKBQZSiYpmxbeCkVdEgT2tbQu0+VpDAbRZJlMccl5sD0aZV9LCy+PjHAtmaQjEmE2l+PFa9fYXFdHT2xpnj0zOUnM42FzXV01QJREkR0NjWvquTUEg+xobFymW7Bay950NsuJiQmuzM6xUCxQMk3OT09j2vYbqk6NpdNcSyZ536ZNhK9rMdNkme0NDQiCwNnpKfa1tiwbo021tXTH46RTBc6fGyMcdhPRqVSBaMxXYZEJVeH1RUZiqWSiqjLt7Ql8r+E8ubhxEQURWZBQBKW6yVnctkuCiF9ecgDSRBWvpJMysmtroaxSyhcQqNFXLyi8GfBJOjujfXx34jAzpQVGC9PkzAKbQl3YNlyam6XOH0AUBPpir88F8a0Ov6by0X27OD8xzUKhQMznpa+uBp+qrPieu9Y389jBrfzDN49QKBmMz6b5g888ze2b2tjR10R3c8LVtHMgnS8yOZdhYHSWC4NTqIrEb3zwwLLi1CJ6WhJ88OFd/O3XXmIhU+D5E/3MpXK86+7NbOisw6upOI7DQqbAS6cHefzZM0zNuyLMbfVRPvz2XUSDXoyySXo2gy/kWTWxIggyDiZFYxSv0l7Rd6gnZ45zq5sTURCJaQ34lZUJIEWWuHdXD4dODlAsm7xwcoCAR8OyHQJejTu2duDTV3+uREEkodWhVOzsVVniHXdu5PzAFC+c6Me0bL71wlnOD05yz44utnQ3Eg15kSWJYtlgIZNnZDLJ5eFp+kfnSOeK/O5HH6SpOcRYfoFaPXhLouOOeRnHMXCc0nXJozXGQhQJVtZxRVliASyk8+SKrhttvlAmXyqTLxr0j85SruhXmpbFuf5JIsHL+HQVXVPw6SqaKqMp7j/DsJe50wqCwJ5Nbbz3vm185okj5IsGZ/sn+J3/8xR37ehiS08jzTVhNFXGqHQCTMym6R+d5eLQFNcmF/joo3toursbhwKC/frdawVBIKTpa24ai0UD2bJXYXALTI4tEKt1187GmjBdzQnODUxiWTb/919PYlkOB3Z2460k4Sw7T7kSrzfVhAkHPGjSm5uge6vBcmxSxeKye7VkmuRKZUIVdlFQ19Flmbl8HtN2kK8jYs/mcmiyRHBVB+DXZtW/1vMR1HV0RWY+l8ey7SqTxz13Hl2R1zj3EjRNYW42g2XZSJLozrVAqlhalsYsmeYy4wyPouBVFbbU1/Ff7r17VZ2i1ZhFb7XVanO0gceHzvDy9DV+upgjonlXGXcBy05SMM6hyM2VBNLrw6ZoPWHNw4m5UZKlAonr9Ihsx+HErPv67ppWajwr16WTc2McaOiu6v04jsNwdoHxfIpGb4iY/voYbTdiIDPHUGaB7lCCn+7asezzTRcyTBdfn9HAzbApWo9XUhjJJtElmbjmWzHm1TlLg9u2t99wBIHmhijXP0PdHbWUyiYLydxSN8SbgLdM4mhHXzPPvzrAX37xeXxelUjAS9t1Wi81sQDvvW8b33juDL//999DkSVsy+Fj795L2O+hpS7Cu+/dwrdeOMehUwPIooiqSHzsJ/bS7Uuwf3snU/MZ/uZrL7n6PI5DIuLnY+/ei1dXUWSJgFfjM08cJV8oUygZPHZwK/HI0o3S0Rinp62Gv/3aSwR9Or3ttXzsJ/ZSFwuyrbeJL33vBE8fuYyuyTTXhd3ewwrq4gEKJYM//cKz5Apl/F6Nh+9c77qA5Et86buvMjqd5PLwDBOzrlDlxq567ru9l6aaEB94YDvffP4sL58eRBJFHOCnHtrhtsMJN06mTjW2rwr/sTzzvlrr+JnzY3zliVcpVirei6wjgIFrswwOzyGKLg2woT7M3l1dK0SiW5ti/NLP3E1Xew1HXh1kbCLJxGQSy3bQNZlEPEB7S5zOtsSqrVcuo8OlXazWnnGzdq3ujho+8fMH+OZ3TvLKiSEmplKUygY+r0Z3Zy3793Rz8M4+4rG128xaGqM89sgOHv/2SU6fH+XayByyLNHemmDfbZ08+uDWFfpHAIePD/KdZ85iWfbS2FV+d+HKBBevTrpjJwqs66zj7n09Vdqgz6vyMx/Yi64rnD43wtWBaWzbwe/T6Gqv4W0HN7FrWxv9g9NMz64+US2KO94MVRG1G1rQAOIxPx95/17ammM8++JlhsfmGZtYqF63mniA1uYYe3d14vEsLYYZo4RXXrmB+VGGx6dxx/2b2HPApd/qHhVZcV3NJEmka30j7d11yKrMQ4/twrYcNI+C1+dWwrp6G6htiAAOXr/Ouk3NeJr9XFyYIaBqpEoFmnbUI7V50VWZmNfHVDFLrddPslzEp6jUr69h1iiw/o52pE4fTf4gtiqSNcu01bfw/MQQm1praYvLXMunaGkI8YGP3Y1p2gRvYMPlymUk0bWPvv46eRQF5QZ3wXQl2WM5TpU6u4jdTY3UBwLL3L8EQSCkL38exErV9von1SPL7G9v45mBAV4dH6c1HObK3Bzj6TRv7123zD0kUy7jVdUVAZZfU9cUyPWqyk1p1bbjcGFmhj947nlm8zm6ojHiPi+hSpCZLd2io9ENSJdK2I6zYgzAHV9VksiUSpi2vSxxFNQ0RMAom9i2QyjsJZMu4vEo+Lwatm1TLJnIioRpWGia7Fb2cTc+a/ZhrMDNn0s38WtUNwE2NmXbqLJ/qLY1VZinOBTt1cfqzaPMr7IuCAIbgh08NfESVzIjDOTGiWpBGjxxVww5FMawLPzqW6ty+2agYJh85cRZJtMZwh4Px66NcXlqlse2b6q2ri9CU2XeedcmJufSfPP5cximRTpX5Omjlzh0cgBdde8j12HUcQtVZZOyadHbVrtMc+F6yJLE/betYy6V4/PfPk6hZHD26gQDY3Ouq4tfx7RskpkCqVyRUtndTMXDPn7mHbexpbsRANuySc9nCUT9a+4RBWQsJ13doNmYOI75muLYi7Adh3R5noTWiCqufC5722ppqYtweXiGkckkkuQetz4eZEt3w5rrqO1YLJTnqNWbUCrqReGAh59/5+2UDJMjZ69hWTZXhmcYnljAo5+stHW7cgqmZVM23MKgbTvUVNrdF5+bW35+hCCC5ApjI9xcR8SybEpFo6qtt4jPPnWMHxy7Uv0stu22ohumRaHksjHKhsXTRy/z/In+aty3WDBtrgvz6z95Dz6vSia7vOqvqzLvunsTc8kcX3/2NGXDYnw2zVeePsm3Dp1DlV1GrOO4Y2JU7kGzskl3pxsHQQgiCAHWmsPWmgFVUWJLtB55jXaZRE2QqFevajMtQlEl1m9tcQtEQCzk5d7dPQyOz5MvlplZyPKZJ47w5KFz1a6HfNEgV3DXzN/96IPs6HttV9ofdZi2zdXZOTKlUrUF+9LMLMlikd4K0zbu89IVj3FibILZfJ7GSjt8ulTi5PgEPYn4Ci0/tVIUypRLK1ppXw8Sfh+dsSjHxyaYzxeoq+grpUslToxP0JuILyvy3AhBgGjUh2laFIsGPp9GzOsl6vVwdnKKomFUWUYXpmaWtZ55FYXN9XUcGxljIp2mtyZR/d2i9ta/J9Y8n7Py99eP885EM12hBGfmJ/jMlVd4T/sWGivC3JZjM55PY9gmdbqCItUjiW/M8n19uI5diWaeHb/KlwdP8VNd2wkqOg4OV1KzfHnwJA7wYFMvsrgyvntm7AoHG3rYEmtAEkTmSnm+ce0syVKB+xvXEb8ucbTWWCxGN6uNhSZJSKJA1iixUC5Uj1e0DJ4aucj5halVjvjG0BWMc3ttG89P9PP5K8d5b+dWaj2Bypg7zBSzDKTn6A4lECwBr6JUC6mO4yBLEmXTomSaqJJUnbNNx0bySDiCuza+GXHaWyZx1NEY59c+cjcXpqaJe7zURgPkCmVEVeT5iUF2Jppo6Y7yS813MDA9x3Qhx+0NLZi6zXQhR94s07E+wSc77uLI6DAhRScW8BGJexnPpUkbJd5xcBOtvTGKRYPuSJx4yEck6HXPIwps7Wri/tvXkc6V8HtV6uPBZRXugE/jlx+7g8m5DKZlVUW4NVXm5x/dw8RsCsO0iQa9botUZUFpqYvwnz5ykHDQy8xClrJhkoj4iQTcLK6uKty1o4uyafHYfduq5wv6dBRZQpZE7tzWyYbOOmaTORzHpQTXRAPVIOdD793DOx/cwnHjCEcXjrM7uoNQwMOv/sJB8oUyLY1LVam6miB/9DvvRhRFGmqXHvgHDmxgx5bWFQ9Y0S5RskqElCUWg6LIJFZJwIiiQG0iyE++ezcP37eJVKZIsWTg2A6yLOLzaoSCikAEOwAAIABJREFUHjKOwVh2KThMlYokiwV+9hf3M5FO091Uy0QuQ7ZcxgHqfH6CqoaFzVxpAaniWmI7NqZjYTquKKoalXj4sXXsuKeWQt7ENC00VcYf0GiIRol7bq4/YJgWPZ21/D8fv5+ZeZdtIooCwYCHWMSHvEZr4QfetYuHDmxY87jXQ9cVvNe1UwqCQGtzjE9+7F7mFrLkcqWqBlc07CNUaRn7lY8eIJMt0dq8vLJ/z751bFhXjyxLa2oveT0qv/XJBykWDeKxpcz9hZPDTI4ucOCRrQQDHt7+wBaUvMm3hxbY0dfCO356L5oq4/O5182jLyWJbMfBI8k/dq1qkigQCgOCirBsihQBC1UBFPf/4YgKjgmCCJjgCEiSQzjirSbobMGhKNlcmJ3GdGy6wzFemRujyR8ig8lcoYQiSmQwOJeaQRQEgppGRzCKT9cQ/DJiQCFXLnEpPUtTIETBNClicTo9jZgRCKoaNeHVr70uy1iOg2FZy6qEhmVj3bBT9CoKmiTxK3tuZ2Nt7YpjiYKwjJq9OF6vBUEQ2NXURG8izlOXLnN3ezs/GBgk7NG5q7192WLmkWVSxeIyVxOAsrn2fbZar/v1yBsGn37lGKOpFP/vwYNsa6jHp6pYts1vP/00R0dGX/M7rAa/qiIKAtnyysp40TAxbAufoq5wcBEEgYJp4gnq7NzdjqJI1NWHq3TtFbpwuPOk4ziUSyaeVbT83ggsbAZz46TNHEHZx3BukpxZoMlbiySI+CQPeavIfDlDjR4lbxU5nx587QO/AciijIVNwSqxGvMipoXYHlnHM9OvkDdL3JnYii5pWI7rMpUul2gKvrEA9q2MfLnMTCbHx/ffTlDXWMgX+PSLr1C2rBWJI0EQCPl1Pv7YHbTWRXnqxfMMjM1RNi3yxXLVOexGqIpEPOxzE8lrwO/V+ODbdhEP+fjyM6e4NjFPrlAmVygzVtEyWoRHU1jXWsNHHtnN7g2t1ThKlER0r+ayjVZ9ZB1XcPU6XRsBEb/ajrRKEmg1SIKIKEqkjDl88sr7oSbq5/ZNbfSPzmLZNosmqndu7VhmJ38jXDF3h7SxgEdyk/OCINDVHOc/f+QgX3v2NN9+8QLT8xlKhrnCebb6fQSXvd7bWkPYr1OyTDySSlDx3NImWRAD4JRd/Z/XCOElSSAQ9BC4wSVsZiHD+GzqNfPPa30PqSKsHg17lxVI3e8nEPTp/MK799JSH+FbL5yrMJksjFwJWJ1F5NNVmmrDFadkV0cI1p7z1xopQVjpgnQ9AkGd9taV7AhZlmjpXHpdEgQeuXMDZcPky0+fZC6Vp1g2GZlKMjK1vD1FU2TMNdx4f9wgCgID8wv86QsvsbetlYV8gX8+foKueIz97W57syZJfHjHVv7rd5/hfzzzLI/09YIA3786wEQ6y38+sH9F4qgrHsOjKHzmlRMUyoZb7JJl9rWtrjm2FnRZ5sM7tvG733uG//HMc7ytrwcceKa/n+lsjl88uItwpdCTL5eZzGQpmiajqTSGZTE4nyRQrxFqDaLobrGtMRTk/p4uvn72An/14mF2Njcylkrz5IVLy9qjVUni/Vs2cWZiit/+7jM81NtNfSBAplTm3NQ0O5saeLi3Z4W50L8VDNvizMIEA+l5smaJa5kFMkaJ0XyKf7pyjFpPAL+i0hmIsyW2pCvX5A3xqxv384ennuHvLx3hqZELNHpDaJJMulxkppjloeZefr6nDREHYW3Ho5sipOr88vp9TBUyfPriYZ4Zv0JHIErOLHMpOU2qXOT9ndu4v7FnxfPukWTiuo//9MqTtAeiBFWdgfQcl1MzbI018pNd2/Fcx/7LmWUOT19jrpgjY5Q4PH2Nsm1xan6cv790hICiEVJ1tkQbafa7bNXuYILtsSa+P36F3zjyTbbHmhAEgavpGXKmwc54My9ND72h734jwqqHT6zfR8Es84+Xj/LU6AUavCFUUSJjlJgt5hAFgU+uuwuhIBL3eykaLuPNsh0CuuruG2UZn6oyMDvvFi8rOQTLttlYX0vYe2vmKzfDWyZxJIoCc0IBT0ylgMlwKQUiNOlBSmkTy7G5kJym3hugsyVOqOAlENR5ZvQqiYIPAVcMS5dkojU+6n1BSpbJfLnAcDaJA5Q8JlNijoZEkHUNK+1WRUGgsSZM4xqfURAEAj59VYFun0elqzmx7LXFqoRHU6pOcDfqL4EbtPWsspDdOD7xsH/NwKaxPkxjfRg1uxWPpLt9/rJIW/NKdwJNU1jXtVJANBELkIgtpwM6jsOJ5Gmm86PsaXx41XMXrDyyIKOIKqZtYjgGuqQTCfuIhFdSBU3b5rmhIQqmgSSIbIrXkiwVSZklsmoZvUbH0mAonQQHRrMpvEoLQVUjZxY4mbxARAkhCRJlx8C0TUzHDTAcKptIn4Dog7jiZ6GcIinkiDuvLWjtbtIcvF6VVu/qzg6roaEuTEPdD+e05tEVmurXpp03rvG7aMRHNHJzSqYkiXS0Jla8nqgP472OQSUKAg++awehkJcLr16jt6uuqtNzIxRRYmei6Yf0b3jrwXFyUH4ZpBocpwzIYE/heuQIICgg1lRek0HwupVfa6JSBQ4gKBurxyuYJhcXZjBsm5JlMp7LoEkys8UcPkWl1usnonkIazqiKNDsD2E5DhP5DB5ZYaaQw7JtpgtZDNvGr6gkJVdkUZUkwppn1f7vRbRHI8iCwIXpGXZUbOQdx2FgYZ5UsVh1/hAEgY11tXzz4kVOT06xo6FhWYBjXyds/0YQ0jQeWdfLn770UsWJbZi72ztoCCyfc3oTCc5OT3N1fp7GYLDKNDw/PfOGxQ4Ny+LK3Bwt4TA7GhvwqW5rTbpUYmB+YdX3CIKAJIiYll0RcVyZzGgKhWgKhTg5MUm2XMZXcZwzLIvz01MYlk1PPI5yQ+Iobxi8fG2ExmCAkmmhShJT2WyVZWU5Dl5FoWAYtEcjFE2TmWwOTZZJ+H10eTX6s6MM5ybpz42SM/McmjlBnR6nL9hO3S22jcmCxERxjn8eeoqQ4udSeohWXz0bQx0IFY2kRk+Cfxr6FhuCHaSNHGP56RU6DTdD3ixyOnWFtJFjqjiHYRs8PfUKMS3EllA3aiW4q9Ei1OlRvjb6LOfTg/gkD3fVbCequvenIsjsiqzn+1PH8EgaW8LdgHtfzhXyWLbDRDZDT/TW5+0fBWiy2xb05NlLNIQCDM0lKRkWx66Nkgj42FC/PMErCAIBr85779vGgV3dHLswwqnL4wxPLjCbylIuW0iSgM+jEQ/7XEHj9jo2dNQRWcWGfDFxoMoSHk3h0bs3s3tDK0fOXePY+WFGppNkcyUkSSQS8NLZHGf3hha29TSucJ8FKBfLFLLF6lzy3vu2sn97JwB1sQAhbxBJKlUZRpZdoGTOYiltVe0jn67yc++8vapN1NUcryanvHKQjaE9a46nIks8etcmOpviVXMSQRDY2deEoohkDTde9MnBagun7dioosrW8J4Vc4AgCNREA3z00T3cu6uHk5fGOHN1nGuTC2RyRSzbQVMlNI9NZ30D65pr6G5J0Ndeh1dXWCjnGcsn0USZGj2AIAjs6Gvmt3/ufsCNH+PXxVG2OQBOAceaRVQ2IEju9VcUmQf29FZjSY9HwePTaKh1pRmypbLL2hQEHr3HvYZvdDb36goNieCqsfDimHh1lQf29nLntk5evTDCyUtjXJtcYC7lyhUoskTQp1MXC9DeEKOvvZZ1bTXEQz6gjCD6cOylubkhEeS3PnKQTLFExOehr/3WRPC3dDcuG8va2K07y4X8Hj708C629DRy6OQAV4ZnmJ7PUjZNNEUh4NWojQXoaIzRepPY7ccJmiTz0LoeBAH+/IWXKFsW2xrqeff69dRX1nJBENjZ1Mh/f+Bevn7mPH/x4ss4QHc8xu/cdzdbE3WYhoWkLa2L62tr+LU79/LVM+f4n99/Hk2RuaezvZo4kkWR2oBvBatUFkVq/L4q+0kUBLbV1fGbd9zBd/qv8ueHXkaonPt3D97D3raWaqHq0uwcf3Ho5WrySJVkPn3kGJoi0xAI8FsH9tMZi6JKEj+3ewcRj4enLl7me1f66YpF+PDOzXz74lX8GjiOCTj01cT5/QcP8M1zF/nWhYskC0W8ikxPIk5LOIQgODiOBTgENIWEz1N57frYZvHJdD+nLknUeQLLHNSCqk6dJ4AmLol1x3QvbvLdfV/eMvja4Gmemxiovi8ga9i2wzeGzlbeBw809S5LHEmiyB217fz57e/im8PnODk3ylg+hWnbBBSNzdEG9tS0oojmsoSpR1Ko8wQJq7eWnBAFgZ5QDX+0+xGeGD7HS1NDHJ8dRZNk1kfqeLh5PfvrO/Cu4jhpOg4/33s7g5k5np3o59LMNEFF5z3tW/jJru10BeNYjuMyu22L8XyK/33+EBP5NJLgtv9HVA9juSSfvngYXZKRBJFf33w3jb4QAuBXNP7jlgPUe4Mcnx3hBxNXCak66yN1vK9jKwulAlOFDPp1iWoBgZCiU+8Joq/StioKIjHdR840lsWFgiCwLlTDH+56O98aPs9L04MMZ5OULBO/otEXrmFfXQeb4nUYZRtVknCAq9NzeHWZuqArCaNJMrbjsKG+hkxFkD3h95EtldaUeni9eMskjgDqvQFKGZMGX4CpfBbLsQmrHgKKhu04xD0+VEkmonlJlUtYtk3c46POG0CXFCTBTR5dyyTxK241uWAZNPgC+GSNgmmgihKq6FrhycLNg9+SVWIoP0KynEQWZRr0Omr1GkRBJG8WGMgNkTNzxNQorb4WFFGmYBW4lhshpsUYzY9hY9PibSKuxshbea5kB+jxd+GV3Qcra+a4mh1gXaAbj6STNXNcyw2TNXPEtBit3iYUUSFlpJkqThNUgowVxhCR6PC3EZTdQGO+nORqth/DNmjzrczQp4wUQ7lhilaJer2ORm89kiAxU5olbWTQRY2x4gSaqNHpb8cneSnZJfqzQxydP07azPDi7GFEQWRTaAN+eSmQeXH2OVq97XQHepksjjOQu8qdiXvWHFdJENjf1F6trntkhXp/EHCwKtoecuUaAWxO1FVZDn7Zw774DqRqG4V7zMVYbjEYtXEQEavq+o7jvGlChbZlM3B5EhyYnkjiD+r0bm5G1RQsy2bo8iTjw/Mk6oJ0rW9EVtyN+tx0mv4LExhlk6b2BC2dbiJncnSBgYsThCI+ujc0ontVbNtmcnSBwUuTSJJIa3ct9c1RHMdhejxJ/4VxAFq6amlqi2NZNteuTDE+PIeqyazb3Eww7DLarpwbwxfQGR2cRZJFNmxvRfeoXDw9wuTIPA2tyzdagiC4Gk7XxcfzMxlmJlN09rnMpunxJKmFHN0b1kqz/ohD8FGdHp0MOEXcSqkAQhSw3d+LURCkipCnF+xs5XdLnfB+ReWB1m4cHM7MTtHgcx37ZvI5eiJxPLLiOuuIIg+29iCL7n1bNE08ssI9TR1VMWkHB4+s0BaMoEoSTQG3oq7La+sqbKmroy0S4fELF+iOxeiOx5gvFPjaufOkisvbDPY0N7OprpYvnjlDwudjY20NqiSRLpUYWkjSGY2yLhF/Qz35oiCwvbGBoKbx1bPnyJXL7G1pXsFguqO1la+cPcu/nDpNjc9HzOtlNJXmycuX1rQ9fS1Igit4PZJKcX56ht5EnPl8gW9cuMC1ZBKfsnL8BKDG7yNXLnNkZJSQriMKApZtU+f3I4oica+Xh3q6+cfjr/Kl02c40NmBR5Y5PTXFl8+eozceZ1tD/crNJi67S5EkbMdNJJVNi4LjtonU+F1mpCSKhD06E+kMfk1lLlfAqyo4wHhhlkuZa5Rtg+2RXubKKZJGllo9Sp0eZUu4h9pKAmlzuIt6j/ucbwp10ehJVK/J7uh6avUog9lx+kLt3JPYgU9y16eoGuTD7W/n8NxZ0kaOFl8ddyS2ci41gFRZP32yh73xzQTl1ZPXJbvM5cwIGSNHp78JAYH+7CgLRob1wXbUStuPR9L4yZYHeXbmVRbKaRRNXsYkEwSBRm+CsOqnyVNLTHXvfRGBmMfLuZlpuqLRH6u2WXDXNo8iM7KQZDKdoWxaaLLEiZEJ2mLhFYmjRYiim9B4cE8f+7d1Uiy7Wox2xYVRkkQUWUJXZXRNWZPCPp3OYtsOLfFw9biNNSHeGd/Evbt7KJVN1/5XcFvaPLqC9wZh2usRqQsTb4giVZLSuzfcXIRdFn0E1C4kYWnTpKky+7d1vtbQrYmm2vCqIthlu8jp1EuMFQZ5qP6n8Mvu3xSsHMcXfsC++NuQ1gibJVGkqylOe0OMB/b0UiwZ1U1V3s5wdOE73Nuwi5gvgnSdQL+DQ40eWMaSaWuI0tawevJXVPpwrClARRCXvoMsiWzqamBTl7sBfPr8VY4MjfDgxh7ShRKPnzjHh/ZuRxIEdvQ2s6P337atqlA2+NrxczyytY8H9/RW7kEDY9FFVhCQJZGpTJb6yP9H3psGyXWdZ5rP3e/NPbOysva9CoWlUFiJhQDBXSIlUpIlkbIty8u43e2e7p7pjpiI6Zj+1RETHdMxMTMxET0/bId7wmqZtragJWoxRUrcQIIgQGLfl0Lta1buy13P/LiJAkCCFO32dKjVH34gkZk4efLem+d+5/3e733jZGJ3dFSEUJDkHiT5TrErHY9wZO8o/+nYB3x+9zbSEQvH85nJFxnMpj+yKVoqVbi5vM5EXwdfOBIWcor1JnXH5X68xKbrMZsvMtZ5R2/PDwLqjsfOTT1sHeqk3nRYLla5MLdMRzLGlp4cuqZg6hq6phAIwcxakZVylR39Xfc4bv26hECQNA1+b+8ufnfPrjC3Rubo0av0WvGNAqoiy+zr62VLrn2jFTyiayQMgx/+9Ax9PWn27hzcGNdUVT6/ZZzDgwPYnocsSUTvAon60yn+w5ee+Qhw1JNM8H9/8XP3vPfmrTVKN8r864cfwm5pcEZ0jaRh3LPObW7P8r9+9gkCIZhfLJJKWkRbxX1Fku5xXstYFt/Ys5Nnt46HYLDSwFRXmcjlkKU1XG8GP1gFVEYzaf54f4av70zi+g6S1CBh9pOOdOAHazT8FQTw7FaJz24aQZVuUnV8IECRYsiSicADJIKgxs5Mgj996Hnid1nc/+bwLp7t30bGCNmPlqLxb3Y+iS+CDYAprhn8q+0P80+2PPix51OC+wIzkiQxkmjjn255kKpn4/geQcsBzVI04pqOEOuo0iZoOV4+lEuy7fDDRPVPxxITQREhmvRFO/jD8f08P7QTO/CQkbBUnYRufKxpgBf41D2XXW29PNQxws1KnpRuocgSNdemYDdYrJfojaW4UlxFlWX+/b5nuVxcpsOKs9yoYigKpqIxVVnncOcQlwrL7M72MlcroUgSKd2iL5riX00coeLauC3NrKiqE9MMbN/jTx96fuMcQNgq+/ubHuArQ5P3tMrdjqRm8m/3PIUb+OTMe8kMkiTRbsX4+ugevjCwjabvEbRMqczWMf/w8cjGIsiS9BGphtsFTNcPMFUVL/D/wZhuv1KrWl8sSV8suXHBwt0oKuzL9bX0eSSyred6W++/HUIIxpLZ+yaPFcem4bsk9HtV+SOmzh9/9dA9DmV1r8H353/Iqr1Gp5GjGdhUYzU6zBwFp8gPFn5C2a2Q0pKs2KtMJLfwZMejFJ0SL8x8j3YjS1JLUPWqvLbyFt8Y+BqqpPLy0s8RHYJd6UmEEJwtXuD4+kmGo4M4gc33517CCRziaoy31o6xOb6Jp7qeYKGxyF/OfJcus5OklmDdKfDu+gl+q++rpPQkILB9m1eX32Bf2266OjtDFpYQrDl5vj/3Q1RJxVQM3lx7h/2ZPRzKHuBq5To/XXqVbrOLpBZnqbnC6eJZfqv/q7iBR97JU3CK2IHDulMMK/CBhxd4rDt5fOFRcou4gUPTb9Dw63SZPRsMMDdwydur+MInoSVJaEk84VEP1rEDm7gaR5HS+DQpuQUMxaRNb6PpNwnwialxmkGDut/AUiJUvQqBCGj4dbJGDlmSKLjreIFLSksTVWN4wiPvrOIEDjIybUYWTdbIO6sEIiCjt2Eqf3+6nucF/OCbbwOwZWc/x35+kXKhzoOPb+Xs8Zu89uMzjE30cOrYNdZXKxx4bAsL03m+/Wdv0DOQJZGOsL5Spn8kx/UL8/z428cZ3tzF+ZNT3Ly8yDO/fYC1pSLf+bPXGdsWCohH4yZdfRlKhRrf/tPXGRrv3KD99w5mEUJw7cI8Qghmb65y8YNpvvE/PImiSPzwW8eIJUz6R3Nomrrh4hSNmczcWOHG5UU2TXyyUHmt0uBHf/Uuv/PPHqctl+CNn5wlEjN+LYEjSY6BcW/FWggXuF2dvn3zvr2G3KkOiaDSel9I5S+XG6RTkQ1b2N25bgxFRQhBVzQe2nXetRbdbR9rtcCgDwMrEFb9AO53Fa+tV2lL3xHW60sm+R8fPMj//tZR/pdXXiFhGOiKwraOHHt67j1/3YkE/+aRR/g/j77N//X22+hKqBNxG7T610eOMN6evc+n/vKQJIn+ZJIDfX389blz7OjsZLLro6DK3t4e/nDvXr53/jz/4qUfETN0VFlhf28v6/XG3+uzo7rG13fs4N+/+Sb/88svk7ZMQKI7Hucr27byt1ev3Xe+T42NcXpxkT85cYJvnzuHKsuMt2f5t489RkTXMVSVr23fTtVxeOHsGb53/jyKLFN3XXoTCf6nhw6Tu4/tr6VpHOjvvYfm7gUBbmujad3ecLT0om4nsKVGE1kOn3uofScPte/82O/8fN8TG4+/0vvYxuMv94agftWrh23PqsXnug7ddwxZkhmPDzAev3dzf5vtA9BupPnvR7/6sfPQPYPnO5/E/CWuTpIkMRzroS3IYBjqRwwMBIKiUyUQgj3pzRvOawGClXqNtGX9vYHFX+WI6jrP7d7OYqmywbizNJXhbOYeB8Ga7bJUqqCrCl2pOMVag4iuh7pUvoeqyzSDsPUoG48StwwKtQYLxTKJiEk2FqHUsKnZDnXbpScTMr1czycTC/OtIAhYKlWp2y7ZeKjRVnUcHOFjaWoogO0HTK8VcP2A3kwCRZZZLlXxgoBcNELgB6zNrxNLR1Faa2jVK+MFDg2/hqVESWghaFJ212n4NeJaGgmFpl/HCZrYQchYajM6CERAwVnBEx5xNUVMTeIENgV3BUVSSWlZBIKaV8YNHECQ1ttRJJWSm6fh1zEVi5SWRZdNJpMPstKcJ2gVrtzAoeSu0Wn2IbU2SBW3QNkrtApSOlmjG9Gah49PyswSj8RwgiYFZwVdOJiuRzSifSR5VySZjBG9b3X6vhHUCJzjIFyEkkUS2Ttt0UKwVKpQtR2WShUiuobteiyVK4x13MmLXd9noVim7rikIxa5eIyqbbNUqoIE3akEuqKwVq3heD5+ENCVCgse69XQVECSoCeVRFNk8rU6+UqdmKnTlQxdxRaLFYba01iaiizLNHwXD0HZs0laJu3xKAvFMi+dv8zOvi7Gcm0MZjPhJr5YxvUCulNporKg7rjMF8vYrke+Wsf1fRYLZdZrDd66MsU3Du+mZgfkq3XipkFbPIIqy5yZXSQTi5CwTBzPZ7FY3lhz/SBgrVKj3LAxNBVdVfjJ6ct8Yc9WLF2jPR5larXA21dvcXjTID2ZJLqucnp+kVKjybbBDqIRHdvzUVWZfLVOKmqhKjJvXZlitKPt1xQ4YsNhLKrrBIFgfrFAZ3uCWEtLq1iq4zgelaqNZWl0tofC7ytrFYpOjfx6lfZsHM/zWV4t07Q9ctk4EUvHr/tIvqBhOyRz4Xi27bK4XEKSJfSsRdN2KZbq2I6HEKH8hqGrrK1XKZUarKyVqdcd4oaBVGtSKNZRY4BhsLZeDbXWqk0ScYvOdIyllTInjt5g2+Zuhgay9PVkPgJESq02/c4WqyoIFJruTRL6PAIXXxTw/JVQ4F/UUaUK2UicQFQACVOPIUtgB3kcfwEJjagmsNQmDbeMIieRJA3kAI986MiMjx/UiBoTJI1wn9fwFvCCMjFtgJRxBwKVJYncXeLNosU8ypq/vNMCoOEtoctJZElDBKthsVSKY8oRTN0FyQIR5l9CFCBwkZVOFMkk8G8hSSYms3QqN1FUExF0AH44lmQhyTlEUECIMpKURJKTBP4ySCoSAp0ibVoV0JGUDkSwjvDmCSQDSelGug/R42Y5z0AsZPrpikLdc2j6HhFVo+Q0uFxcwVJ1ZEJgzfF9pisFMkaEqBqCMH3RkJHZF00xXSkgSRJlu8GV0gpP9Y4jSRJRzSD6IbH9huPy0omLzK2XOLhpgP1jfVSbDt9/9xzlepMjW4fpH/ooC1GRZTruI/QNULcdvnfsHF94YCttH8obry+t8cbMTT63axy9ta7YrscPjl/g+lKef/zkfnLJe8+1pigb2pr6P6DD46/UqnaPxeJ9Hksf896PG+PDEdN0dma7N8Cn26EqMiO9dzZDQgiuVK4xXZvlHw19g3YzixAtzWYkzhTPs2qv8QeDXyetp5iuzfLC7PfYntyKjIwnPCaSWzicPUDZrfAfb32Lqdo0e9O72BzfxOniOXakJghEwJniOSaT24iqEY7l36Pklvlq7xeJqVEulC/x2spbPJAJdY/cwOXBtn1sT25lxV7j/731LeYbC6T0JGktxaHsAS5Xrt1zpHzhc3L9FIqk8Gz30+iyxtv54xzLn2QyGWryNH2bJzoeZiQ2tPFd8vY6nWYHh7IHWGquUPcbfL7rM63zIHG5coEzxfdJ620sNObYHN9Gw29wvnwWP/AYi48TiIBzpVPM1meIqTF6rD6iapQzxfeZrk+RUJN0ml3IUYXXV15BlTWafoOJ5E4afp2is85D7Y9xs3qdZXuJbYlJfrL4NwxGR/ACl93pfXjC42L5HLbfwAlcnuh4iuvVq0zXp/ACl7yzxrPdX2GxMcdcYxYZGUMxeKT9SdSSdo4TAAAgAElEQVT/jB9SIODQk9vY/8hm0tk4V87N8cCRcd75+QW27RnggYfGSaYinHzrKpP7hzn97g3aO5P8xu8dQtOV8ChKcOKtK7R1JHjos9uZub7Mj7/9Hkc+N4nvC+ymRyxhMbF3kGSrFU0EAtv2iMRMtu8dIt0eLkCKInPk6e04TY/ewVVeeuHYhphzEASMT/Zx5Knt9wip9w5lGd7cxfWLC7/0++a602Q7Elw5N8fWXf3MTq3yld8//Pc+fr/60UqwXT9sVfIDEFIoXCwE9bpDIAQRSw/7h/2AesNBoGIaFooimJ7N8+pbl3ju2T1ELD1c7F1BtdFEUxUsS8dxQzDK0NUNQVlFkXFboqWeHxCNGAghaDRCEWPL0tBUhUYz/LfnBZimhqbKlCpNXnjxPX7ny/sxDJWIFWrwPDQ4SEckxrnlZezAYzCdZrKjg4urq1RtG6uVTEtAXzTBv3viSS6urTBfLuP4PknTZCidZjx7h210ZHCQNitCWg/dJvWWa0/CNPnnBw7QnYh/ZD1WZJnJzg5evHiRh4cGSRofbd01FIX/bs9uHhzo59LKChISm7JZhjNpJjs7aIvcqe6ossxvTm6n4Xok7jPW7ZAliUeHh+iMx7i4soLt+3TH4+zs6qLheoxk2kjcR+B6tK2Nf/fkk5ycn2e90UBTFIbTafS7AJ+EYfDP9u/nkaEhrufXcXyPnkSC7R2tuUoSflCi2nwTXc7yT/fvpieRQW2BckK4ON4tNKUbTf/kltPbrjX3CyEEXrCMpny6Fo67w/cD7JZQt2lq4TrjuKiKgmGoOK6P44SObqoi02hpv5mGhuv5uLfbmXQV1w21tIwWi2V1rUIsamKaGo2mi+/5SLKEZerYtosfCCxTQwhB0/Y4evw6YyM5Ng3fYdI0fJum73B07TRR1WJ7avSevEAi3Ax/WBfr1yFqjsNfnTzL7HqRUqOJoal0xGP8y8cObQBHnh/wo1OXaDgu69UGj0+MUKw1WS2HgM1Qe4a1So2bK+u0J6LYrseTk2P89PRVVFmm3GjyGw9McPTKFOWGTXs8SjISVudfPnuNoVyaz0xu4tpynjcu3kRpMSAfGO7lu8fPsWe4h1srBX7zwR2curXAaqVG3DRIWAbn55a4tVrE8Tz6s2k+MzHaEo2+szZcKB1nrn6NNqOLorvGQ9lnaPg1LpRPYMoWDb/O/rYnWbUXOFt8my5rEE3SSGhpblTPM1O/SkxNkDP7GIxs5mThNepehUD4tJs9dJh9/GzprxmIjFPzy/RHxhiP7+ZG9TwNv0rBWWVf5gk6rI9Wyj3hMFu/zs3aBUZi2wGZsldgrn6ddWeZilvk2e4/YKp2ibnGDVRJRZFU9mWe4FTxTcpu2G5V90JjCyFEC4i/YyNechqghy2q0odaYm+zqGXptrmFiaxuIfCucLvSfztm8kW+9e4pupJxzs4u8dCmIZqex7nZJU5Oz3NguB9ZkTg9s8hrl2+QS8ToSSU4Mj7E1FqBD6bnKTVssrEIj20Z4X/7yetM9HRSqDcYbs8w0dPBf/j5MXb1d7NSqXJwpJ+tXTleOH6GiKFRaTo8NTHGWEeWy0urvHLxGuOd7UQMnW+/d46VcpXeTJLlcpVvHNzFXKHEzZV1Ilr4++9OJ3nv5ixn55YwVAVFlnn+ge386MxllspVZClkDc2vlzl+fZZM1KJUb9J0PV4+e7UFoDp8+YEJkhGTttide4XjeZy+tUDU1BnOZVgpVfnp2astVpjg6R3jLJWqnJ5eZLVc40t7trJUrHB5YZW+TJJsPErVdphbL9EWi2DpGleX1pheK/LIlmFeOnWZ5/ZtJxuP/MrZq///GUIIbs3k+dvXLtDdmSIRtzj67nXOXJhlsD/LwlKRL39+F5WazetHr5BrT3Dt5grjY52cvTjPB2emsSwN1w343BMT/OlfvMFAXxbbcUnETL749E7eOHaN5ZUSvh/QlomxeayL//iXR5nY0k2x1GDLpk52TPTxnRdPkohbLCwX6epIsrJa5pXXLxKxdPKFGk8/PsFrR6+wXqjR1ZlkNV/luS/sYWGxyPRsPrz3CUF3R+qXtvVIUgRL3wc6bDDMtQ8Xcu62CQnHs7TNWNr4xmuBcAhE8y5dN4m7GesbAhxS2DJbc28xW/k+w8nfJ2VOfuw5aXhz+MImpo18KhbudOlbdMc+T1zrxqn9FYo2QuCvomiTCH8JWdtG4J5FktP43jUUdROKnMJt/BhJjiEp3SAcAn8WbAehDCGrvfjOGQJ/Ci3ye7iNF5GkKIo2iSRH8N0zSOjIZhdu/XtIcgYRrKBZX8Jt/ghQQPjosT8MH38oRhJtSEi0W1Fc4W+AQZqsoMkym1M5krpJTNOpuw6aorA/NxAKfbeOSVTVsf1Qj7LdjCIR5qD90RSmEl4PdTtkhKuKTNTU8fyA60t5zs8u89UD2xnIpbE9n3MzSywUyjx3cJKudJwgENQdB88PiBghwzzUFg1NAZAgZoZmKNWmQ8NxubVaCNnnjovtesiyRNTQqTUd5vJFivUmlh46XOqqwpM7xrixvE6zlYMJIWg6Hk3Pw1BVLF39VOf/7xK/UsDR7QiEYLlUIR21MO/TQvBJ4fo+SqvV4+4o1Bqoikz8Y6xW7w5f+Cw2l+kw22kzMiEl/67hZhvztBttpPUUiqTQGwnBqKXmCt1mJxElQn+rxcxUTOJqjJpXR5EUxuNj/GDhxxScIk7gUHRLbIqPIksy07VZFhqLfHfub5CRcQIXRVI39HsSWpwuqxNVVomqEQzZoO6HCPCHndNuhyd8puuzzNTneGHmu0hINIMmmqThtSpq7UYbOSOLIinEtRiqpNAMbCRJQhYyt0e+3e8vhOBa9TLjiW1sjm9lxV5GIEjrGbYltnOxfG7jfTWviiEbbEtMktGz1LwaN6pXeTD7MF1mDwLBrdoNAB5pf4KFxhwXymfpNLsJCOcXtpoFCASBCNiV2kNMjSNLCnbQpD8yQNEp8n7hOA2/wWJznuHoKAk1wQfFE0SUCGeKHzAQHUKXDc6WPmBX6gHajL8fcwLAtDSS6dDm1rA0XMdDBIL1lQrHly9x7fw8ju3R1h4HAZVSnWQmivGhqnthrcrc1CrlQh3f84knLUQgyHWnePbrBzn26gVOv3udR5/ZyeS+YZLpKF/8nYO8/coFzhy/wcNP72DP4TEqpQav/s0HVMsNysU6lXJj436lGxrpbAz5PhWUXx6iNYbKtt2DfPDONSJRg2jMoL3rP0/T6b+GuHRtkZNnpzF0DUmW+MKTk8wurPPW8ev4gWDzSAeH943ywfkZTp2fRVVk9kwO0N+T5ie/OM/5ywsgSezfNcim4Q5efesSSytlJFniS5/dwaVrS0iyxJH9Y5w4fQvfF/T3pnn1zcsk4ia1usMzT2ynUm3y5rvXqDccRgbbeWj/KH/x3WNYpk61ZjPQm+GRg5v42RsXOX1hDsNQGR3M8dih8dbaAH7RZXs0y+aRO8DCgb572xVc1+f1Y1fp78lweNPAXa0DgvVinWbDxYiHt43d3d3s7Ozi7ZM3mFXW2b9zEEWRiOk6z22f4H7hBQFnFpdIGAaHBgbuew1KkoSmCMbaXLZ3bMUXDYLAxlBNntq0aeN9ftDA8fM8OTr6S89jqFckMdHRwbZcDttfQ5EMtJbI7VeT9xe2lyWJznicZzZv/sSxVbnB1myJzW0OhjaCEA6OdxnHS6MpnVSbb2C7l4lbT/OlLT14/jKuewZJGyUIynj+CqrShRAOtjeF76+jKu2oSg7HvYkk6ejaKK43hxBNQEbXBrDdawjhYGhjBEGVQu0F4tZTGNomPH8Rz19FU3pR5ASON4MQTTR1AOVDFt7zi0WOnbxJPGayd2c/N26tsbJWRlVkDu8f4/gHUzQaDptGOlBVmbMX51Fkif17hrlwZYFKtclqvsL+3cOcPj9LxNIZHWpnsK+N907dYudEH9m2GBevLHDx6iIRS+fhBzdx7MQNHNdn23g3Tdvl5vQqC0slhgfvXZtfmn+T08Vr1PwGz/c9QUq7u7ImYakaFcf5tWQcNVyPuuPw1V0TXFvNc2R0kB+cvXTPd63aDu9em2FLTw5ZlrBdn12DXfzZaycQAr60dxsvn73K1t4O9g718M23PuDy/CpnZxbZ1tuBJEk0XRcE7Bzo4oHhvrBQJkls7c3RbAHZl+dXmM4X6WtLokgyDcelMxXnmV1b+Is33ydfrXNlcZVvHN5NWyyCG/i8d30WRVGIGhqe74dt5B/S/BIEtBldPJj9HO/lX2W2fp01ZxEvcEiZfcyX3muBMhqmYrE7fQRNMpAkmYZfRZN1RmM7aDM6KDp5Vuw5nsg9R9Ov807+p8TUJJYSZXf6YUruGhfLJxmP76bbGqLsrpN3lsk7S3SYHwWOLCXG5sQeFhp3ROF7rGGyehfH119hLLYDWVK5WD5Bp9lPRI1zrniM/sgmlpozPJL7Mg2/ypurPwTC/Pb66jqKRHgcJEgQAVtixi1Sc1wiuhYCes0mXiDoiEXpSt6uUgcIf56wCmRssI2EEJydXWSwLc2X90zgB6cQQpC0TI6MD3FpcXVj/pWWrfiegR6G2jNoikJPKkHNdlgoljl5a55HNw+jygpPTWzC9jxeOH6G/kyKhGnwhZ1bmFkv8trlmzQcl6Rl8rV9k5yaWeCd6zOM5to4PDbAB9Pzd51jwVhHG889MMmfvfkeM+tFHhjs5diNGT47McbmrhyO5/HqpeuM5bJkohY/PXeVnf1dXFla44+OPEDVdvjzt06wVKzQnYpzcKyftffqFGoNzs4ssaUnt6GJ9+GImQbjXe3MrodC7q4f4Pk+HckYyYiFpih0JGM8OTHGDz+4SN1xGevMMpzL8OCmQUxNJRkx2drTQU86wVB7hlO3QmaaQOD797ao/7cSiiKze0c/Zy/ObUhHBELQ3Zniq1/Yw49/dpZbs3nmF4tMbOnm4AMjrKyWEYHg6LvXSCUjdLQnee3oZWbmupFlmUP7R8hmYvzZN9/i1mye4ydvsmt7H67n88GZabo7ksQiBp99dBsr+QpH371OJGJgWRq/8cxO3nnvJvOLBc5dmidfqDHYl2V+sciZC+EchwayPPvUDr713XeZXyywY1sfJ89M8+ihcUaHP52tvMCj4lzF9vMYSpa4vqnFilym6lwDSSGlT6DKUereAjV3CkWOkNS3Uvdmiai9qHKUmjuDKsepO0v4oo4mJ/CCOjFtiIa/SEwbRgiHmjNNTB8hY+6mYJ/ekPoIhEvFuYbtr7XmMUbTW2au+kOCoEna3EXG3IMiR6m6N7G9FaLaEJbahS9sSs4FJMAT9daYAkkyUPRDBI0XQdQBF3ARwgYCZHUUxTgSPhesoVlfQJKjePZxFG0rsjJI4N0g8BwEDoG/BDio+j589wIiWEOSJpDVEYS/CIAkWajGITz7dYSogxDIShpZ3Yok3R+IbTejbEuHBabbukp356wZI3JnPWjV3HoiyY/knfEWm2iyrTs0XAoCtqQToWSE6/Hy6avM5kPN3a8c3I7r+fz4/cvcWMrz8pmrfHHfVqoNh5+dvsrMWoGXT1/hi/u2sbBe5mdnrmK7PqNdbXxmxybOTi9x6uY8qiKjqwpfPbidD6bmefvyNFFDo1Rv4vo+x65Mc35mGS8IeGxiBEmCqwtr/PXR0zi+z1cObGewPf0RvKNQbfCj9y+xXm2QiBh88YGttMU/uSD5d41fOeCo6bqsVeocvXaLAyP9G8KQsiQRBAGqouCLgIbjoikKcTOkW2utk3D85iyjHVm6kjEK9SaKJJGOWqxWai0gSqVQayBJEDMMqnaYbLbFIh+yfxYbVOUPR9gCdv/nARRJQZf1e14RhFWkwWgfpmJytXqDut8grafotjo3/u/m+Bif7XwCTQ4BBlmSSWkJym4FVdLQJO2ez7tj+v7JsSs1yeHsAeQW3U+VFJJaSHPUZR1Fun0p3PnRfVLYvk1MiWHKFlElcl/QSpEU9qT3c750hrfWfsFQdIyR6CgBgpgaR5XDlp1Gqw1Nlw2iagwncPBFyMQQCHzhEdzWQ1Is4i1hbCEEJ9ePU/UqdFu9rXkH9FkDnCi8S5vexub4BBISdtDEUiJElCiPtn+G6F16HIoi09meYHggS1s69qnsCjes7T8Ug+OdpNJRDjy+NXSS0xQiMZOOngyXTs+wtlwiEjUQQCRq0D+SIxo3+fzX9m/oNKUyUVzXp3egjef/6GF+8dJpjv3iEpP7hvG8gI6eNM/94RHefe0SR185z57DY1w8Nc3MjWW+8S+eZPr6Mj/41rG7Jnt/kEgIcceKNxAbGwUhBCIICAKBEGyIAo9P9nLizcv8/IenOPj4Vqzor39VrVq3WS/W+YOvPdhylIOXX7/IxOYeEnGTn7x6jpHBdi5eXWSoL8uuiT5iUYOIpfPkkS0A/OOvH0aSwta1ndv6aI56/OLoZa5NrVKt2RtaINW6je8Lmk2PucUCf/zkEVJxC11XMQ2Vw/tHKZUb/O1rF9gzOcDSapkvfXYng31t/Ml/eosjB8b4zMNbmZrJ80e/fRhZlilXmhw/PbVh1zmzsM7MfIFtm7o4fWGWRNxiZKCdC1cWkGWJvZMDdHUkQ/r5UpFSpUEybjEzv87UbB7L1Ni/a4gb06s0mi4T4930dqaYXy6xVqiyvFqhtyvF1MwaeybD9iY/CGh6Hr4QnFlc5LWpKR4dGWYkc38ND8dfp+mtkG8epyf2RRx/HZAwyOIFdRy/gCzpBMJmqf4qnZEnUOUY3OXuIQiQkHCDCopsocspnKCIHzRQ5ThF+wySpBLXN2Eq7fjCxvVLqHIEVY7j+kUCXFQphip/shMjgOvNUW3+nIhxACFcSvUfIksWnr9CKvo8CB9FTqPKbTjeLRrOCSL6fsJ7g0fDOYOuDhFICtXGy0gYqEoOSbLw/AV8USaOoGq/haEOo6vDCOHh+2vY7hWCoIqhjeEHeSRJxQ+KVBqvYmqbqTRfIaLvodL4GVHz0MZabcoGvz/0DHE1Qn3JI5kwefyhLTiux/tnpslmYniez+JKibV8la99aS9BIHj97SvsnOhjNV9hei5PudJEkmDzaBeappBJRZjc1svFq4ts29xNd2cKp8WmG+hro1CqM7G5m+nZPNdurtDVmWKhda09sHOQE6enP3I32dc2wVi8n7SeoM/KbRQxwnMd3hv2dnaTjfzDJki/CqHJMm3RCHHTYKlc4eiNaUqNJncbkpuaykhHhr3DvUQNja5UguvLedIRC18ETK2sIwTcWl0naYVMoq5UnLHONg6PD6IpMr2ZJKduLWDp2saaVKw3WC3XsD2PUqNJeyLGeFc7B8f6w5ZM2yGih84tt63U01GLi/PLdKXidCTjDGTTJKMm413txE3jnvbM2yEhEVHiaJJORI3SDOqttrUYumyyJ/MoOaOXpeYMcS2DIUeQJRkhBNuS+7laOcX7hV/QbQ2TM3pRUDCVKBvM0cAhoWYwlQgN3yLAZ7F5i4ulEwzHtqFI6kbO8WnCFx4Xyycw5QhDsa14gUszqGMpUSwlyqHs5zCVCEIITDmCgoLW2vz4gdjQLKs7LrbnEdE1qrZNLhZjrlhGU2SG2tLMFkphfnqXE47wZxDCQdYmCO3q70TNcUhaJpam0haNfGx+eGAkFAj+2YVrdCXjPLtzC985cY6OZAxL0/CDMOOK6BpRQ8fQwlzN8XxipoGpayQtE8fzKNSbJCwTU1NJRUzqtvMRt04I2ya6UwlMTcXSNBzvo8fbDwTVpkPCMoiZOr9zcCcJ00AgiLUq7IamErcMri/lmV4rYrseUUOjP5ti/0gfMUMnFbEo1poUaw3WKjWabhohBKuVGoVag1K9iWVoNN3QkWg4lwkZlJqKqoS5nUBsOIbOrZfob0t9xMXQ0jXWqw2uLeUpNZoIYLVSp9ywWSnXSFjGfzEHrf8S0ZtM8n888zTdiU8WGJclic5cEkNXsSwd2/FoNBwScYuIpZOImwigUm3S253GMjW+9PROurtS6JpCNGIQjRjIikSl2sR2vBAYMjVGBtvRNJW2thjRqEGkZuP5AfW6Q8TSMQyNTDrCwlKRSrWJaWgYhsqBvcP09WT4xVuXN+ZmGCqO05Ig+Dsei0A4NLwFAuGx2nibvtiX0OQkU+VvEtc3oUgGgXAoO3PMVV8kqW9HEU2E8JitfI+B+G8R04eZrXyfuD5K2b6MJKnILc2/tLmbfPNdxlL/HC+oMlP5NpvS/xLlQyBKINzWPFzWGu/QE3sWXUkTiEY4nmSAJLPePMF68wOi2gBr5b+kL/4cRfsMNW+aiNpL3bvjLCtECa/5EpKcRlZHcZtXEPbbrZaxGJIUMnE8R0JWt+A2vo+k9CLLWZDTIFm4romq1hGiiR90AxqBP4cQNq5bpWkvoEonaFTXiKSGkJUsSDqSnAk1Q/EQQYXAv4EsRpGk+5M+Pk330S/L3+5+XZbkDVc1CFvgdg93s7Uvx6tnr3Pm1iKf2z3O8w9uR1Nk/tET+0hG7jDBT96Y4/ce2YOqyPw/P32HHYPd7Bzq5s9ffY8LmWXK9SalepM/enIfpqbieD5vnL/Jcw9O4vkBf/7zE6iyzOaeHN2ZBCeuz/Hu1RkOjvcTNXWeP7SD8zNLvHr2Or/78O57vocQgrcuTVGsNXlk2zA/PXWF49dm+dzujy98/n3iVwo48oOAM7NLzBVKLJernJtbwg8Eju9vOHuoSiiX6QcBkiSzqaON0zOLTPR2kEvEODu3hKYoRHWNc7NLrFVrPDw+xKmZBTZ3teP5AS+ducRkbyedyTg/v3idkVwb+4f7UPUwGVUkhW6zi5Prp1hsLtNpdnA7PdUljeHoAEfX3mWluUabkWaqNh322Zq/HKnWZZ1tiS2cKpzBFR4PpHdtiIwOxwZ5fSXUzsnoqQ2reVX65NN02wls448INoSlVUllODrI5co1DMUgrsbwRUAggo8VHftwaLJGw7FxAgcJCUVSyehtzDVmiKlx1pw1xlt6Rm7g4AUetm+jSAoNv85YbBxVVpmuTbEtsR1TMblVu7FByGzTs1ypXGTFXmK2Pk1az5DS0lypXiJvrzFVu3kH6PnQArBmr9AXGSChJbCDUOg31FaykVFo+DUkSaLT7MZSIvRFBnADB12+swjFogb/5HeP4PkBmqagt8Ssb7cnhsc4fOz7obuSdhf9T5blUAAbwUNPbedvv3OCF//iKIois33/MLsPjrLzwAhTV5f46z95HdPSGZvo4dAT29j38Dgvf/99Xvzm26iqTP9ojkef2cni7Dpv/OQMvhdQr9ns3B8KgeZXyrz6g/dxbIdGzWZiTy9C+HT0JPA8jx9/+x0kScKKqAj81lwFsixaOj0yIKjXmrzzykXOHL/B+lqVl154i70PjZPOpnjn1QucOzHFykKRl144xr6HN9MzmCUSMxne0s0rL77P+GTvr50I7f1CCOjqSJJKWiiyTKXWZGG5SCxqYFk6W8e7iUUMHjm4iV+8c4Xp+TxH9o+x+UOuhWF/f5lX3rxEOhkhX6httPfcfj0IREvtXdCWjtKeiaGq4bV44sw0swsFIqZGtW4TiIBEzKSrI0kibqGqMo7jo2n3Jqm33Wvml4pELJ2RgXZURWZmfh3H9dE1hbnFAsmEhWmoXJ1aRm0lx7W6TaFUR5FlbMejrztNR3sC2/G4cnOZXFuc6bl1unIhxToZtzh1fhbH9YhH7/y+lms1vvnBKVZqVc4vr5CLRnl+YmKjPe7u8AOb+eqPUeU4tr+OEC5F+yyypBPTh8g336PmTBE3xomo3TS9JfLNExuOJtJdAHhCH6fiXMf2V+iLP8d85YdoSpKUsYNA2DTcW1Sd62StB6k41wiEiy8adEYfZ776ElF1kLi+qQVK/fLQ1F4sfQdCOHj+CpqSQ1cHkeUomtqLHMRQlc6wnUwdwNS3gySHm0A5isBFJkzINK0LS99FrXkUX1TQlG5kORTNNLSt6GovTecStnedQDQIRA1VyaHK7ejqEL6/joSKro5gu1cROKhqJ6a+I0wkCYsHm+Ihw+KmvErUCrX/DF1l00iOpu2RSUXpbE9gGiqvv32Fwf4sufY45y/P43kBO7b1MrdQaAHpoQZOJKKjyDKqIrOWr3Lt5jLJhEV3V4rXjl7G8wNW1irk2hP0dqeJx0xGhtqZnS9w+sIsxVL9I4yUwWgXg9Gu+x53GYnhdIZ8vY4nAjpin+58/aqHEAEBPlFD4+ltYyQtk4PDvVxYWOGpbaPoqkzQcufRNZlndm/i1K1FQCIdNfACj8/uGA1d5yqNsACAxHyhzJPbxxjuyOAFAZcXVklFTLpSCcY6s2TjURzPZ2ppnUASLTFtidm1Itv7OrFdj5M358jGovS3pdgx0EWlYbOlq51sPMITE6O8PzXPQqHCI1uGeHxilL89fZWlwk0e2TbC/aS8AyFYtqfIO0usNOcZiU2gSCpVr0SXOYgb2ETVlmPTXWUqgaDh1+iPjKPLJjeq5xmJTQASi81pGl7IRoqo8bCActf2sOIW0WSddqOHi+UT4TwIcAIbX3gbf0Momu0LD8dvoisWc/XrLDVn2Zd5HC/wkCWFDqMPXbbotoZwAhtTjqDKOsvNGep+FTsI2eGaInNo+A6zSdwmqQiQJTbEzhVZYjibQQg2AIwwZCQ5gQjWQU4hcQcg604lODO7xMx6kavLa4x2tOH6Pk03bMWoOy4RQ6PUaDLa0UbCMnjpdNjiuFAq8/D4EPPFsD0ZYKlc4fpKnrrjYGkacVNnOl/g1lqBqdV12uMxxjuzvHX1FjPrRS4urNCbSYb3yqaN6wfUHRfXv+Ned3coskxE15gvlOlKJrB0jbGONiK6xtbuHA3H2wAbry3nqdo2tabDUHuaIBAU6032jfSRiUZ4YtsolxdXyUQtsokoy+Uq6ahF1Xao2WG7iO35JCyDlXIVx/OxWkpnMkUAACAASURBVEYDr56/zrO7NreMHyS2dLWjCZlKocH23k5uLOfJRC2SEZOuWIyORAzPD+hMxOmIx1haL7OrrwtJwOxake5UnMVCmf625K8VcBQzdA4O3MtSDoKApu3heQFN2w1blfnoue7IJbh5a5WOXIL5xSKjwzlGhtpDV+mRDhpNl3jUpFRucGNqhUKxBkBvd5ruziTpVIS+7jSeH1Cr2fcCA0BbOsqlq4vMLxa5en0ZEAz0tlEs1hkZasd1fdKpSKtAeu/3UhQZ09RYWimRa48Ti5ofK+6/8ZmSiirHqLtzYbHLX6PuzaErGXpiX2z9ImUWay8T00bpid12pb7/vstUOoho/bhBEQkFNyjd8/rHlfI35uHdnscqCX0zUW0YRbLIWqEw9mr9bdygjCpHqHvzVNyrlOwL9MZ/g5g2TKF5euNoykofqvk0kpwAVGznq8zfWiEaj6NqKuVCjUR6nUunbjGxdwfNxiiNmosVjVApWqTaIty4mKZnqJ1MLsLCdJ6RrTFmro1hRrYSTya4eWmJyQO/zfTUFNmuNKaVQ9UNFPlBIrH1UFNJiiCCBkJ4BC24QpEFj3b3MZ7K0hGJ4gsPCYm6X6PuVdFlg7iWbK31EoI7jri3+RCyFN47ZUmh5OZRJJWE9lE9ottg84/fv0w6ZrFarm5o//2ycD2fcsOmvz1FwjJIxyKslmsoskx/e4pUxESWZep2DdcPaE+EpIWYqVNu2Lx1cQpVkVkqVkInZKAzGSdhmXRnEpy5tbixrt4dS8UKS8UKH0zNk01E6Ex9ehfJTxu/UsCR5wcUanUmezup2y6rlRpPbhvl+M1Z8tUanck4N1bWGcym2NHXxVyhjK6G9NJCrcGmziwj7Rm293ZSrDcoNZoU600cP6A7FdLOAhGQjljsG+7Ddj1GO7Kst8T2rBbSK0kSm+IjDMUG+c7si7QbbfgiYCDSx8Pth5hIbmW6Pst35l4kocbJO+vszeykw8yx0lz9xO8oEY792sqbaLLKcGxo47WJxBauVm7w/fkf0qZnsAObjJ7mma6nPnFMAVwsX2G2Ps9sfZ6SW0aRFMZiIwzHBtmb3sWt2gx/NfM9klqSht9gMDrAY7mHfuk5kSSJkdgQF8tXeGHme0SVCE90PMKO1B7eW3+H08WTDESGiGtJrlYucaF8jrpX4+38G+xOPcBCY54btWv4PnTp4zRdia2xvZwuvs/ZwiUGIyNsTWwno/bxi6XX0SSTB7OHEUKlYV/hxemfoMsm2UQGx4WUmiNfb6ArKg3PZSiylVuNyxTdAuPxLSBJYStc28NElCiniu+R0jMcaDvM+4X3mKpdp8fqJ2vk7vmOHxZuDQLB3FKBVNyiXLOp1m262hOcu7LAaH+WI1/YiScJKjUbJWFw6HOTLK5V8BSJZ3/3QS5dmqczlyRfazC7WKAtHePBZyZRA5hfKtLRnWJuuYjteDz1m/u4eHGOdDKCETFYyVfo6svwmS/vxW44mBGdttbmPNuR4PEvbqFQ/IBodJB4Zpmme5lcv8dzfzxAtbpEOt0FQTe2/x4Nr8rTX9dIpgpUmrdQ5TR+UMQTgv4tTXJDIJFCUfPo0Wvo+kE27+hneDzcpMmyRLItunGcLEtnYs8g6bZfj83ZpwlFvrNVURWF3q40+3cPMTaUw7Y9ohEDVVX4yud28drbV3j3gyk2j3aiaQqu69O0XXRNZXpuHVmWeOzwZpZXywjAMFTyhRrVmh0KTOZaTlGyhNRKXARw+sIsTzy0hVjU4MylkP4vSdI9yY1AoCoyQgTUGy6moTIzv87qeuiK5PsBhVId01Dp7UyjaQpTM3m6cgkatkuzqZKIm6zmq0iSRG9XivVijXrDCatzLfHJ7o4kUcsg1xanvyfD/FKR1XyFRtOlpzPFtakVHj+8mXKrFzsIAsqNJivlGgd6+3hqdJThdAbXD2g6Lp4XEDE1TF3DE1UEAbnIQ8xV1pBlk6g2RNNbAiCmjeAGZVy/iFC7sNQe2q1DLNVfQZFMmt4KABGtl7o7CwTU3BlkSSFpTFB1b+IFNWRJJ2PuxQmKNL0lys5FTLUbGRU/sFEki/bIoU8NGslyFFXpJNQus4hbT2C7V5EkDVmKoshJbvfoy1IcVQ64vdmz3Yt4/gpN5xy6OgQIPH+JpnOBiLGXun1i4/9pSg9gYrsefqATBBKQQpK6sF2B63WSL/+YuPUY0M5a+SdoygDQjqaUNsR9N64Z4QMe3TmX9rSFIjsgKRza10+pVERVDeJRl8cf6qNS80nELfq60+SyCRRFRpFDhmIibjG3UGBsOMdAbxuGoZKIWyiKxKNHNtEQTVQD9j7Qj+u7RE2TtmSUhw+PtgCqCJmMxUx+lU1KG92fMjkDqDgOi9UKVcem4ijsuL/J2H91UfVWWG5cIqX3oerQFAbDnQ6bu3sQ+Cw2TqPKZgs8ClAjHocmoqiygePP0t8lCFhHl3Qm01uYXiuytTfHwbE7QueT/V1M9ncRBIKp5XUWVkuoSFyZXeHGQp7D24bY2ddFsdZkZqmIFIDwBOMd7TRsh+vza+wf7+PcrUUG2tMsr1coVhts7+nk8uwKU0vrICCiqOzdPMRwx/1Zhrc3cicLr2HJCaJ+Dwm9m1OVY7zW+BFRJcXO1GFwDeyaSSnSxA9AUySuFK8xXb+E58Ou7AMofpRBbQ/vLh5F+DJ7cgdJqAmyRjeSJGPIBlnVottIsNrIc2r9J3RoUeKKRL5xkXOl93D9ZU6uvchk6hBeUOZK9TKuv8ixte+wLbGHVXuZmlfivfVXiKgJ9qYfY3f6Yc4W32a+cYMOs4+J5EG69EneWT1KVE2RVgcpNh1spYEvBIYSFpu8INgAh2RJwgtCtqTte5hqaK2sSDINz6U9EkWV2xFBEaQIiGrLsl4GfHb3JZheucRLp96nPSbTm1K4NH+dN69M4Thlvv3uazy+dRPrtQrv3FgmED6Pbxkgafx/7d35k2TVldjx771vzX2p7Fq7uqq7oZulaUALQgIhEANiJM9mxczYMeEfbP/kP8m/TsTEOMIehe2wxzOSNRppGAECCcTaNL0vtWdVVuX6tnv9w8vK3gpoQgwB+HwiiKCqc3mV+d5975177jkDnrp3ib9/+ywLjRoPL86hgGoY8Nqla6SZ4fcfOonnakq+zy/ev4hS8OyDS9QrLoe3i/z1q69RKwb8wSP3c7mzwd+/dY61wTp/9eorPPfgPUzXCoShJTYRU1WfQggpI04frfHi+xe51Nngz776CE8/uMAv3rvOOysbHJtq8MSRRb5zfJm/fe1d5qZqLJer6BROz0xPGn0MuxFzxRLTywWUgsHukGONOsu1Go5WedCgWuIPv3L/ZJ976YMrNEoFjkzVeGcY4aCZ9opcurSFjgzbvR7d3pDD801qxmPlyg5Jq0KQKHyr+O1bVyiEPnO6QKtVwWSGDz5Yp+I4nCw2mK5UKf5/UOtopzPgxz97l964hlGSZEw1Svh+fnvZapax1rK82OInP3+Xf3zxLMePTtOaqnDfvXP87MUz/Oh//4aF2QZPPH4PYehx7sIGozjlmSdOMjtd5YVnT/Hiy+f4zZtXeODEHEuLUyzON3AcTaHgs7jQ5PjRQ1xf6/Djn71DvVpgYf4QD92/QK8/4n/8nzcIA4/ff/YU87N16rV8ifr8TJ16vYjrah7/2jH++ZVztLf7vPDsg5Pt33d73bHO6C22hi8zW3qWfnoFrCGzMVoFk5UdAIYEX1duK+683+05w9gYUGjto1X+3/4E4n5E2ZKMJ8duZa1lN3qbreEvmSn9Xn7NM1kpc3Pgy2IxVIOTNIJHaIWPE7gztIev4KjgxvsCqDJu4Qd5AevxNrc3IkZ9zbULq2hHsbDc4ur5dUxmQLmceX0drTVxtMnSyTkunlknTRSVeg0vCOl2Nhn2Y/pdy7ULW5x6rIa1CqWLOG4ZrMul99ukScq9pxcpu4t4+gdgM9Bl+lnClcF7GAwtf5bTLZfvBidZH52nt+cQOAU6cRtDxjDtc7z8ABZL0SlzefABrnJR4wxVgLJbpR2vMzdemqxQBwaOAK61O0RpxguPnmS7d/fNWXzXoV4MubrVYapcZKc34NSRGXb6o7w2odq/pxhPsu31MeMmF+3ugCtbHf7T9x7nH94+z3onr4+32umyNxyxur1HtRjgOc6kc+e+2UYF33P582+dngTHP22fq8CR5zjM1au8cWWVzBiOHWry8vmrlAKfE7MtfNclNYZmKS9MVwr8fJ2yMcRphlYKz3H4zeXr1IsFoiSlXgzpRzEXNtsUfI9Hj8xTK+ZtlaM07zASpelkhmVf0S3yp4f/mKuDa7SjDte7fabdI2wPR0SZ4euVp1j1VxhkA44Gp7ivcpTN/oA4c/l28xmafr4TBtrn+ZlnJu3rlVLMhTP8xdKf4SiHlp9fSA2jhMEAnq0+z/XhKuuDNqETMKtn2O1F+FGVp6vP0O8a0kEXqyzfKD7JcmGRzBp6SUo/gW81v43B0ksS2tGImtcnNQ7PTr/A1cF1IjOi7Ba5r7qMRnNf5QTTwSFCJ0+1q7pl/mThXzETHiKJUnrdIYeiGf6g9H1WumsEjs/FN9ZYXp7nm9530VqRJhnZjsH2A/5k+d/g3TTg1v0Gp+uPcmZrk6t7u7yfbuHogBPBk0RZRhJn/Or6KqG7wOnSCTb6fc5u9umMRmhzkrIxBK6LisusJCnz7qP84+VLBI5D2fdZrM7xxwsPTd5vlA0ZZSMKTgFUPpMZ6ICZcI7vz/3RJ9ofN7a6JGnGytou2lEUQ484SVFacWVrl/4golAI6A5j5o5N894Ha9hxtshab8jyvbN4WYrWmmg8GCzM1HErAe3ekMHmLsXQJzOG9jCi0qpQCly6/Yi56Rpzi3deZHu+y8xCg8qhJr7TIMmGQEbgHaY1e556FuDoBOiTZBtoVWRu/l4sKZmJyWwPS4bnFVlYLpKaBK0qKBy0DnA9h8PLd9Z+2t3p8/5vr/LGK+d54Ydfv+U7/jIrhB6V8o001MB3ee6p+/n5Sx/wy1fPs7jQ5DuP38tLv77ApatboBRffzi/MZtpVZhuVfjL//oKTz52nKWFJr958wo/+tvXUVpTKgQsLjT473/3Bv/lf77GcJTQqBXxfZd6tTg57WulOH3/YX7x8gc06kWmWxVcR9OslyYZmPv/XyoGnDg2w1/+t5d56L55Hn7gML7v4nt5m+ytzb28za3rsNuPmasWONKqsr61h+M6TJVCett9lFaEWrF4qEaxUqDZKOG5Dmsbu7QaZb792HH6g5gw8JiqlygWfNLx0oPpVpVyMeAXb12Y1BZ6fvYYzCoqxYC19S6/7FzCcTTlcaHB4/NThL6Hp6u4qsh6/6do5ZNmXXai10nMHtXkfuJsB2NGZGQoNIEzhVIunq7h6wagsDYjdKbpJ5dAaYruAtYaomwLY2NS08XVZRxdxLUJWntMhY+PawRM4Tk1fKeB+gSnR989gjFzfLC6k59f9AMYcz++63Ct3aMYLNAbxQyiLRaah9npT7HQBNdRlMInKYV5oflR/C6OrqBUgNZFfHeZwLvRwaxSeI7+KOatc9dIM0UheIFOb5S3VPfXCfzvYi2c6e7ge6eIkvtwHYckHfGth759R2cSm17EZit4yuD5GSZ+D6XKFBxNWO+A7UHiUyksUvavoNxjaPcB5mbyAGeWGb5yeonBMObEsWmmW9VJMNP3XDajNjvFDTSKc8MOlXKZQToAt8DOYIuRigiKAanp0036mLLBKBftTt31Z1/2fU40pwhdl36S3PXzPu/suIByaiNGWZey2yI2PVITYUiITR+LITUxqR0S6hqpjRklXTxdQClFN16n5i2gleKx44sf2uUpTlPWOz3iJOPty+s8tDRLe2/ApY2d8ZJli+Pk9R7SzHBqaZYrGzsUA59SGOBqze5gRDHwWd/pcn61TWYMO70h5TAYd3v58OXvCsVS8SSPNp7i6maH7d384tpunuDEdB2TwvaOIUqa6L2AV3euMVOv5LOzay1O1F5gmCWEcZXzu9tEaZNa71tUCyHRbsBUfZapIM8CbXgFvlY9AmrE083TYFOsHaBUB+W0eHrqEWzWQKk6yjuETdosNB/CZnWUqqBcn+nCM3yt+fwtf0PJrfDMzA8nP2fWoM0cJ4tzZNYyXSjx5uYm3XiVxUqdyKT42qHi+QzShMxaunFEq5DX5XivvYGr80KvBTcvNP/43CI1vzrucNRHcQjSc6BqYLYouyH/7useuPdDdg1UCnaPh2YqQBWyFVBnONqY56tLj+TPZRPMBZ574CmeP3Vysv3Xd/aoFQv8xeOPUB8vlTuzusFCo8q/f/KrlAKfS/0LnO1f5KsnjvDwvQUSkzDSbQjhD79xmIcHCRW3gmWHR1tNoixiL9nl9D1FLBkb0RpTUxn/4elTbMdtttN1SmXLf3zqq/jaZ6fd45V/OsvicosfLBzF8102+rtcfHuFxaOH6PdGxKP8mK/Uiuy0e7Q395iereP5LmmSopTi0GyNUiXvpLnv9JFZQt8lzQzff/gkvqPZ3RtQLoUUCx6u61AuBXR2B2zv9FlemqJWDTl/cQOloNeLqFUKGOMSRQndbkS5HLC93aPbi5huffoz/Z9HU80y//aHj2GtpdcbMRzEqENVlFLsbPdZmm+yvd1FY3n2iZO4Xr6sNc9Qtbzw9IOkmaHZLDGKEooFnx987zSHpm58fvccneaeozcme9OszfemWyi1S2sq5vvPzWLp8MKzM1jbYpKXqPZ4+slpvvvtZbTOg0XPPf0AAL1kxOnHFvJOhMNdmosl/vXSo6TWMFQJK/1OvsRfOygUdb9I2b3RkTuzAxTOZKkYIVS9k1zee5328Fdo5VPylqj5D7A2+ClFdwGFS9k/hu9MsTN6nWF6jVG6QS24s8aiM+7e24l+Sz+5TGZHWBKGaZsk22WUrpF4R8hMH9BYmzJMV2gEDwOKwJliN3qbbnyGkneURvgIe9EZrJ+RMaSgPEreUTaH/8wwW2OUrgOglINSt957WGNZv75DsRzgBx7t9T1mF5tkqaG3N6RYDnFcTc0rMz3fYNgbUa4X2VrbpRantNd3uXZ+g7UrbcKSz/bGHltru2yu7LBxfRvPd/FDl93tHo1DFZTSebHt/e873WJ9dB2LoRXMktiYbtqh6tXZSdp0o12mg3l66R6+F1Byq1wfXmSUDdiONzlde4x39n7NQmEZUFwanKXuTZHYGOx+iY58FdPNlFIcnqoDlr/6xesAVAp5xrbnODTKhVtKm+wv180ndTUvPHqSv3vjLK+eu8bRmSYPLs7y+sXr43pouXIY8J0HjvGjV96mXgqZb1SYqZWZa1T46xd/S+i7NCtFAjevsfY3L7/FMEr44eMP5fWMfvMe17d3+ZuX3uL5R07wxH3L/K/X3uM//+QVir7HHz324KeedaQ+rpbNZ2SyEftptUopfMchSlPcm4o/ZdaiFbjamXRQidIM11EUvHzNsrEWb/zc/WBSlKSg8tbWmbWEnktm8lpJKEXR9z60ts1ar8tL164yXSoxSJJxIc6IYZpyvNEkMRm+dni/vQUopktFvrFwmNC9+0jfytYur569hjWWSjGgXAjY7g4YRglH55pcWd/hyEyDi6t51sJUtcheP+Kp08dQHvxq8zIF16PsBlzubVP28u5PBddjfdjNAyiOQ6A9Cq7H11pHJi29P8zmyg6bqx2uX9hkdmmKtctt5pZadDt9+t0Rh+brmMzQ7Qzod0cUSgHffO4UQeHOWZbVbpe1fpfQdfMuTUFIL45JTN66vOC6uFoTZ9l4mV3eAjzNMlJrKfs+qTGErsswSQhch1Gad0aaKd3UgtJaLvTPcXG8FO5IcZmjpXs+cQe1NDOcOb+G7+VdhAqhRyH0WNvco1YpMIwSut0RRxaajKL8hn99q4vWijhOWdnY5b7js/T6EUpBqRhw6eoWS4enWNvcoxDm+1sh9EiSjJWNXeamaxhjGEYp9x2fmXTMuV1eKDxG4WDJyBdruFjiyZo6Y/pE6XkcVSXw8mVudtwmfr9LQy5/PtaA0pNlLHd8f1fa/PKn77Kw3OJrT57A9T40/fqLvn7tlgExTlJMZgmCG0sTjTH0B3nqe+DnbcNHUUIUpziOnnRay7tEJYyilGLo4Xku/UFEZiy+5+QzDZ7DYPxanqtxXQdHK+IkG9dUUpPtGAzjcSeuPKA1HCWTxwyGcb5Pac0oShiOEgI/76p2s5WLG2ytdhgNYgBKlQIbK9uYzHLi4SOsX2uzt52niAcFn2qzzKlv3F1Xjr3eiHfPrnLy+Az1aoF3L68zjJPJuBp4Lp7rMIoSPM/JU/itpVwIaIxbewOkZoi1CUo5aBWQmbxoo6sL42W4EVp5aBVgbTKudxSD0mDzNHmtXIzNZ+mU0jiqSGr7YC2ODsdzeTe6l1gsxowmjzU2QqvwEy3H3BuO+Omb58gyQ+h79EYR07Uy1UKI62qssWx1+5RCH1c73Dvfohz6k3apQJ6SbQfky+4KKLw7tqE/jDl/fYti6GOMIUpSioGfn/dch94g4vLaDpViwEyzMvn90fnmHWOKSc4CCUo38qWsZhNUBZQH2TrYOC/Aqxtg91DOEZSevjPX/0OsDNdpxzvMBC22ou3JkmOLJTYxq8MN5guzBI5PalKqXhmNpupVcPXdLfG4+Rpmv1PV2Bd6LDI2s2achm8xKOVg7Y1MtRv7rrlpT86XIis0mY3Zji/jKp+p4KOP4ShJeefyGoMopVEO2RtEeK5DtRiQpBn1coHL6zssHqozjBOOz07RHUWcX21zeKrGxfVtSqFPMfBZ2+myMFVlpb3HdL3M3iCiH8U8cGSGqUrxwPe/1D+Dq1wOF+9hpzdko9PNx7UophT4ZMbiupokzfd3Ryvmm1XiNGO90+NQrcQgSgg8h/4oRitNnKY0x++3MHWjbbW1Q2x6AfBAl7DZWv6JKh/lHMVmV7G2nx97zgzW9vLP3HRBlVDOzHgJx0ez1rLa7zJME4qeh6cdLu91MFjmSxW6cYzF0ggKGCy7UV4UdaaUX+Rf6+6iYDI+pMZwsnGIoueOMwUNkIHZAVUaB4pqebBX1yDbGB+3/fx4tlFeO0QVQE+B8iE9n/8M4B67pQjt7mDES+cv852Txyaz1uu7Xd5Z2eCJe5cIXJd2tEUn2aHgFMd1JAtoNMNsiDNuXrK/vwY6IDYRFa/GIO0TOoXxzZrC0z6dOH+dzCZMh3P42mfQj7h8YZNGs0R7s0upEpJEKY6r8QOXIPQY9CPC0KdYDlhf6ZBEKa2ZKv3eKG/mEHqEocf0fB3nIzplGWOIkww9zvzalxmb16t0dZ5VkaTjmqv574yxoPJLKO0ounsjrq/u0GyUWJifZDF8oceisY+8Wcwyw2u/usCVy1sEgYvrORxZanH92jZpavB9h9EwodEs43kOpVJApzNgNIwplQO+8c17yDLLP738AY99ZZly6cM7iPaGPyYze/juUeLkg/H5yJmMf3kXshClXJTy8dxlfHfpltd4ffsyO3GfQRozSPProaKb7//L5Ra/3r5EwysyV6hzqd/mkcYR7qlM37gmyzqsDX6CtSm+blD276XkLdGJfktn9CZa+8wWn8PTFbZHv2YvPovv1JkpfpfE7LIx+DmuLqFVQNE9TGYjfKdOZvL6dZ6uEJtdOtGbBE4LrTwawSNsDl9kkF7HUQUa4aOUvaOsD36GMTGBM0XZP0bZu4fEdFjr/18yGzFf/j6O8mkPf0UvuUToHmK6+DSZGbA2+Ae08nBUyFT4dUL3zpTdq+fXiUcJC8em86zINCMIPZI4Qzt51rHJbJ6F7Dlk40lEkxkc1yGJUlzPIU0ztNZoJ0848Hw3byxkLefeuka9VWHpxJ1dYUfZkJXhJSAP0G9EK1TdBlWvwTDrE5uIUTag6jVITEzghAzTPkMzwFUeC4UlVoZXqPv5hNQw6zNI+7SCGXaTNlq5LBaOH3ifaKylP4pJsyyvt6w1geuQGUuUpnlNwPE+kWQZSWomncyMseNlshnFIK/RFqfjrrOeO6kpm467qu3HOkLfZZTkiS2B5+bnAdchSjLSLBsvaQvypJFRjDH5OFoM/PzeOEkZxgmOzidnD7iX/J3Go89d4OjzKM4y9qIIdzyTup9ObKyl4gekxpDZfCdSShG6+QzRh934H/geaUZ/mN9Uuo7G0ZrUGLTKYwGbnR4zzQppanAcNUltLoUBKMtePMJRGgvEJsNR+cnPcqO4sbOfBq0UFS/42BpHybg9cxzlN39pkhEUfLIsIx6lhEUfk5lJ9NQaS6VRvKM+Bdy4uL+54v3Huf2xBz33s66zc9B2374NN2/v7dt++78f9POnt50p4NwRRf8X9kW/QPpcj0W/qyROiYYxWWpQClzfnczYlqoFhr0RKDVeAmBxfZdi+ZMFUPZ9kmP8yyAzhk5/OG6xPc44UHnThjxjQ5OOW7E6WueBtPFY/7u4/XO21tIdRBQCD++2Yq53ft77z1W3/fxR7v47S0we+Nj/G29u6GCsZZAOCJ1wEiQ6qMnC7+CLvnP9TmNRnrG0P7FwcDOH2x9/K3VLfPCTnstuf727PdY/7nl38x4ff069eb//pMfA3e9Wd/P5fNQ2f7LP8ObHftzfcusyljt/9+naP+ZvPr4nf5ti8m8ft4992Dnlbj7Dz+pcY22+LDzPqvlyBLHHPvLAsNayuztkfbWD0opKJaTZLBMn6eSZ1ub1hLIsv/6w5FlHvu9QKPh3/R1lZg8wWDsiSt7Hd4+jVQnGdV4Vmv0GGSgHhY/WhVteo5eMSEyGxRKZlL14SDMo4WkXXzv00giNxtOazFrKboCnnS/VNcvdSsbNLf6lVhpkmaG/N6RUCXHcT14T7Jax5La99MO+r896XPickcCREOJz44s+CstYJMSXg4xFQojPgy/6WAQyHgnxZfGlnDK4hQAAAWJJREFUCBwJIYQQQgghhBBCiM+Zz3QNixBCCCGEEEIIIYT44pDAkRBCCCGEEEIIIYQ4kASOhBBCCCGEEEIIIcSBJHAkhBBCCCGEEEIIIQ4kgSMhhBBCCCGEEEIIcSAJHAkhhBBCCCGEEEKIA0ngSAghhBBCCCGEEEIcSAJHQgghhBBCCCGEEOJAEjgSQgghhBBCCCGEEAeSwJEQQgghhBBCCCGEOJAEjoQQQgghhBBCCCHEgSRwJIQQQgghhBBCCCEOJIEjIYQQQgghhBBCCHEgCRwJIYQQQgghhBBCiANJ4EgIIYQQQgghhBBCHEgCR0IIIYQQQgghhBDiQBI4EkIIIYQQQgghhBAHksCREEIIIYQQQgghhDiQBI6EEEIIIYQQQgghxIEkcCSEEEIIIYQQQgghDiSBIyGEEEIIIYQQQghxIAkcCSGEEEIIIYQQQogDSeBICCGEEEIIIYQQQhzo/wEoPGUmDBopaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1152 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install wordcloud\n",
    "import wordcloud as wc\n",
    "\n",
    "word_to_id = dict()\n",
    "for i, v in enumerate(vocab_list):\n",
    "    word_to_id[v] = i\n",
    "\n",
    "limit = 24\n",
    "n_col = 4\n",
    "counter = 0\n",
    "\n",
    "plt.figure(figsize=(20,16))\n",
    "for ind in range(num_topics):\n",
    "\n",
    "    if counter >= limit:\n",
    "        break\n",
    "\n",
    "    title_str = 'Topic{}'.format(ind)\n",
    "\n",
    "    #pvals = mx.nd.softmax(W[:, ind]).asnumpy()\n",
    "    pvals = mx.nd.softmax(mx.nd.array(W[:, ind])).asnumpy()\n",
    "\n",
    "    word_freq = dict()\n",
    "    for k in word_to_id.keys():\n",
    "        i = word_to_id[k]\n",
    "        word_freq[k] =pvals[i]\n",
    "\n",
    "    wordcloud = wc.WordCloud(background_color='white').fit_words(word_freq)\n",
    "\n",
    "    plt.subplot(limit // n_col, n_col, counter+1)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(title_str)\n",
    "    #plt.close()\n",
    "\n",
    "    counter +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute topics using TFIDF and Kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the other method called Kmeans to clusgter documents into N clusters based on their TFIDF similarity. Within each cluster, we will count the top occuring terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_d = get_np_from_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_d.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_d_backup = tfidf_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17998, 3109)\n"
     ]
    }
   ],
   "source": [
    "converted_tfidf_vectors = tfidf_d.astype('float32')\n",
    "print(converted_tfidf_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#threshold = 4\n",
    "#filtered_vectors = tfidf_vectors[np.array(tfidf_vectors.sum(axis=1)>threshold).reshape(-1,)]\n",
    "#print('removed short docs (<{} words)'.format(threshold))        \n",
    "#print(filtered_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(filtered_vectors[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans Model Training\n",
    "\n",
    "We have created the training and validation data sets and uploaded them to S3. Next, we configure a SageMaker training job to use the Kmeans algorithm on the data we prepared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tfidf_train = int(0.8 * converted_tfidf_vectors.shape[0])\n",
    "\n",
    "# split train and test\n",
    "tfidf_train_vectors = converted_tfidf_vectors[:n_tfidf_train, :]\n",
    "tfidf_test_vectors = converted_tfidf_vectors[n_tfidf_train:, :]\n",
    "\n",
    "# further split test set into validation set (val_vectors) and test  set (test_vectors)\n",
    "n_tfidf_test = tfidf_test_vectors.shape[0]\n",
    "tfidf_val_vectors = tfidf_test_vectors[:n_tfidf_test//2, :]\n",
    "tfidf_test_vectors = tfidf_test_vectors[n_tfidf_test//2:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14398, 3109) (1800, 3109) (1800, 3109)\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_train_vectors.shape, tfidf_test_vectors.shape, tfidf_val_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_train_vectors.npy', tfidf_train_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_test_vectors.npy', tfidf_test_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_np_to_s3('pilho-sagemaker-ai-workshop-kr', 'tfidf_val_vectors.npy', tfidf_val_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmean_feature_dim = vocab_size = tfidf_train_vectors.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train\n",
      "Validation set location s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/val\n",
      "Trained model will be saved at s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/output\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "import numpy as np\n",
    "import sagemaker.amazon.common as smac\n",
    "import boto3\n",
    "import os\n",
    "\n",
    "#trainVectors = np.array([t.tolist() for t in train_X]).astype('float32')\n",
    "#trainLabels = np.where(np.array([t.tolist() for t in train_y]) == 0, 1, 0).astype('float32')\n",
    "\n",
    "bucket = 'pilho-sagemaker-ai-workshop-kr'\n",
    "prefix = 'amazonreview/kmeans'\n",
    "\n",
    "train_prefix = os.path.join(prefix, 'train')\n",
    "val_prefix = os.path.join(prefix, 'val')\n",
    "output_prefix = os.path.join(prefix, 'output')\n",
    "\n",
    "s3_kmean_train_data = os.path.join('s3://', bucket, train_prefix)\n",
    "s3_kmean_val_data = os.path.join('s3://', bucket, val_prefix)\n",
    "output_kmean_path = os.path.join('s3://', bucket, output_prefix)\n",
    "print('Training set location', s3_kmean_train_data)\n",
    "print('Validation set location', s3_kmean_val_data)\n",
    "print('Trained model will be saved at', output_kmean_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part0.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part1.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part2.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part3.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part4.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part5.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part6.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/train_part7.pbr\n",
      "Uploaded data to s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/val/val_part0.pbr\n"
     ]
    }
   ],
   "source": [
    "split_convert_upload(tfidf_train_vectors, bucket=bucket, prefix=train_prefix, fname_template='train_part{}.pbr', n_parts=8)\n",
    "split_convert_upload(tfidf_val_vectors, bucket=bucket, prefix=val_prefix, fname_template='val_part{}.pbr', n_parts=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "container = get_image_uri(boto3.Session().region_name, 'kmeans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "sess = sagemaker.Session()\n",
    "kmeans = sagemaker.estimator.Estimator(container,\n",
    "                                    role, \n",
    "                                    train_instance_count=2, \n",
    "                                    train_instance_type='ml.p3.16xlarge',\n",
    "                                    output_path=output_path,\n",
    "                                    sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_topics = 128\n",
    "kmeans.set_hyperparameters(k=num_topics, feature_dim=vocab_size, mini_batch_size=1024, \n",
    "                        extra_center_factor='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.session import s3_input\n",
    "s3_train = s3_input(s3_kmean_train_data, distribution='ShardedByS3Key') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: kmeans-2018-11-14-14-56-07-320\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-11-14 14:56:07 Starting - Starting the training job...\n",
      "2018-11-14 14:56:10 Starting - Launching requested ML instances......\n",
      "2018-11-14 14:57:38 Starting - Preparing the instances for training.........\n",
      "2018-11-14 14:59:05 Downloading - Downloading input data\n",
      "2018-11-14 14:59:05 Training - Training image download completed. Training in progress..\n",
      "\u001b[32mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'_kvstore': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'init_method': u'random', u'local_lloyd_tol': u'0.0001', u'local_lloyd_max_iter': u'300', u'_disable_wait_to_read': u'false', u'extra_center_factor': u'auto', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'5000', u'half_life_time_size': u'0', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'feature_dim': u'3109', u'mini_batch_size': u'1024', u'k': u'128', u'extra_center_factor': u'auto'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Final configuration: {u'_tuning_objective_metric': u'', u'extra_center_factor': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'feature_dim': u'3109', u'local_lloyd_tol': u'0.0001', u'_disable_wait_to_read': u'false', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'1024', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'init_method': u'random', u'half_life_time_size': u'0', u'local_lloyd_max_iter': u'300', u'_kvstore': u'auto', u'k': u'128', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 WARNING 140392253105984] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Launching parameter server for role scheduler\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] {'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/fa885cc7-41fe-4082-9f82-079090e5e063', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'SAGEMAKER_HTTP_PORT': '8080', 'HOME': '/root', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/738fe8ad-1abb-44c6-a293-67a85e2c25e6', 'PWD': '/', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] envs={'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/fa885cc7-41fe-4082-9f82-079090e5e063', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'DMLC_NUM_WORKER': '2', 'DMLC_PS_ROOT_PORT': '9000', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'SAGEMAKER_HTTP_PORT': '8080', 'HOME': '/root', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'DMLC_PS_ROOT_URI': '10.32.0.4', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/738fe8ad-1abb-44c6-a293-67a85e2c25e6', 'DMLC_ROLE': 'scheduler', 'PWD': '/', 'DMLC_NUM_SERVER': '1', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Launching parameter server for role server\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] {'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/fa885cc7-41fe-4082-9f82-079090e5e063', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'SAGEMAKER_HTTP_PORT': '8080', 'HOME': '/root', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/738fe8ad-1abb-44c6-a293-67a85e2c25e6', 'PWD': '/', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] envs={'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/fa885cc7-41fe-4082-9f82-079090e5e063', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'DMLC_NUM_WORKER': '2', 'DMLC_PS_ROOT_PORT': '9000', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'SAGEMAKER_HTTP_PORT': '8080', 'HOME': '/root', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'DMLC_PS_ROOT_URI': '10.32.0.4', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/738fe8ad-1abb-44c6-a293-67a85e2c25e6', 'DMLC_ROLE': 'server', 'PWD': '/', 'DMLC_NUM_SERVER': '1', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Environment: {'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/fa885cc7-41fe-4082-9f82-079090e5e063', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'DMLC_PS_ROOT_PORT': '9000', 'DMLC_NUM_WORKER': '2', 'SAGEMAKER_HTTP_PORT': '8080', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'DMLC_PS_ROOT_URI': '10.32.0.4', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'HOME': '/root', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/738fe8ad-1abb-44c6-a293-67a85e2c25e6', 'DMLC_ROLE': 'worker', 'PWD': '/', 'DMLC_NUM_SERVER': '1', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Using default worker.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Loaded iterator creator application/x-recordio-protobuf for content type ('application/x-recordio-protobuf', '1.0')\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:06 INFO 140392253105984] Create Store: dist_async\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'_kvstore': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'init_method': u'random', u'local_lloyd_tol': u'0.0001', u'local_lloyd_max_iter': u'300', u'_disable_wait_to_read': u'false', u'extra_center_factor': u'auto', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'5000', u'half_life_time_size': u'0', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'feature_dim': u'3109', u'mini_batch_size': u'1024', u'k': u'128', u'extra_center_factor': u'auto'}\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Final configuration: {u'_tuning_objective_metric': u'', u'extra_center_factor': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'feature_dim': u'3109', u'local_lloyd_tol': u'0.0001', u'_disable_wait_to_read': u'false', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'1024', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'init_method': u'random', u'half_life_time_size': u'0', u'local_lloyd_max_iter': u'300', u'_kvstore': u'auto', u'k': u'128', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 WARNING 139789377914688] Loggers have already been setup.\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Environment: {'ECS_CONTAINER_METADATA_URI': 'http://169.254.170.2/v3/2ae6bbcd-4c52-4fc6-a0c2-180b3aa8f0f5', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION': '2', 'DMLC_PS_ROOT_PORT': '9000', 'DMLC_NUM_WORKER': '2', 'SAGEMAKER_HTTP_PORT': '8080', 'PATH': '/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin', 'PYTHONUNBUFFERED': 'TRUE', 'CANONICAL_ENVROOT': '/opt/amazon', 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib64:/opt/amazon/lib', 'MXNET_KVSTORE_BIGARRAY_BOUND': '400000000', 'LANG': 'en_US.utf8', 'DMLC_INTERFACE': 'ethwe', 'SHLVL': '1', 'DMLC_PS_ROOT_URI': '10.32.0.4', 'AWS_REGION': 'ap-northeast-2', 'NVIDIA_VISIBLE_DEVICES': 'all', 'TRAINING_JOB_NAME': 'kmeans-2018-11-14-14-56-07-320', 'HOME': '/root', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'cpp', 'ENVROOT': '/opt/amazon', 'SAGEMAKER_DATA_PATH': '/opt/ml', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'NVIDIA_REQUIRE_CUDA': 'cuda>=9.0', 'OMP_NUM_THREADS': '32', 'HOSTNAME': 'aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/2ad7aae9-3b29-41db-bf4c-d8f64a8b205d', 'DMLC_ROLE': 'worker', 'PWD': '/', 'DMLC_NUM_SERVER': '1', 'AWS_EXECUTION_ENV': 'AWS_ECS_EC2'}\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Using default worker.\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Loaded iterator creator application/x-recordio-protobuf for content type ('application/x-recordio-protobuf', '1.0')\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:07 INFO 139789377914688] Create Store: dist_async\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:08 INFO 140392253105984] nvidia-smi took: 0.125995874405 secs to identify 8 gpus\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:08 INFO 140392253105984] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:08.809] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:08 INFO 140392253105984] Setting up with params: {u'_tuning_objective_metric': u'', u'extra_center_factor': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'feature_dim': u'3109', u'local_lloyd_tol': u'0.0001', u'_disable_wait_to_read': u'false', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'1024', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'init_method': u'random', u'half_life_time_size': u'0', u'local_lloyd_max_iter': u'300', u'_kvstore': u'auto', u'k': u'128', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[31m/opt/amazon/lib/python2.7/site-packages/ai_algorithms_sdk/config/config_helper.py:172: DeprecationWarning: deprecated\n",
      "  warnings.warn(\"deprecated\", DeprecationWarning)\u001b[0m\n",
      "\u001b[31m/opt/amazon/lib/python2.7/site-packages/ai_algorithms_sdk/config/config_helper.py:122: DeprecationWarning: deprecated\n",
      "  warnings.warn(\"deprecated\", DeprecationWarning)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:08 INFO 140392253105984] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:08 INFO 140392253105984] number of center slices 1\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:08 INFO 139789377914688] nvidia-smi took: 0.125991106033 secs to identify 8 gpus\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:08 INFO 139789377914688] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:08.809] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:08 INFO 139789377914688] Setting up with params: {u'_tuning_objective_metric': u'', u'extra_center_factor': u'auto', u'local_lloyd_init_method': u'kmeans++', u'force_dense': u'true', u'epochs': u'1', u'feature_dim': u'3109', u'local_lloyd_tol': u'0.0001', u'_disable_wait_to_read': u'false', u'eval_metrics': u'[\"msd\"]', u'_num_kv_servers': u'1', u'mini_batch_size': u'1024', u'_num_gpus': u'auto', u'local_lloyd_num_trials': u'auto', u'_log_level': u'info', u'init_method': u'random', u'half_life_time_size': u'0', u'local_lloyd_max_iter': u'300', u'_kvstore': u'auto', u'k': u'128', u'_num_slices': u'1'}\u001b[0m\n",
      "\u001b[32m/opt/amazon/lib/python2.7/site-packages/ai_algorithms_sdk/config/config_helper.py:172: DeprecationWarning: deprecated\n",
      "  warnings.warn(\"deprecated\", DeprecationWarning)\u001b[0m\n",
      "\u001b[32m/opt/amazon/lib/python2.7/site-packages/ai_algorithms_sdk/config/config_helper.py:122: DeprecationWarning: deprecated\n",
      "  warnings.warn(\"deprecated\", DeprecationWarning)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:08 INFO 139789377914688] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:08 INFO 139789377914688] number of center slices 1\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2018-11-14 14:59:37 Uploading - Uploading generated training model\n",
      "2018-11-14 14:59:37 Completed - Training job completed\n",
      "\u001b[32m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Total Records Seen\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1542207565.199769, \"Dimensions\": {\"Host\": \"algo-2\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207565.199612}\n",
      "\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:25.208] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 16411, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Total Batches Seen\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Total Records Seen\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 1024, \"sum\": 1024.0, \"min\": 1024}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1542207565.199189, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207565.199021}\n",
      "\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:25.215] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 16418, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:25 INFO 140392253105984] Iter 10: Short term msd 0.944447. Long term msd 0.969429\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:25 INFO 140392253105984] processed a total of 14398 examples\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:25 INFO 140392253105984] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 14398, \"sum\": 14398.0, \"min\": 14398}, \"Total Batches Seen\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}, \"Total Records Seen\": {\"count\": 1, \"max\": 15422, \"sum\": 15422.0, \"min\": 15422}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 14398, \"sum\": 14398.0, \"min\": 14398}, \"Reset Count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1542207565.919805, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\", \"epoch\": 0}, \"StartTime\": 1542207565.215204}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:25 INFO 140392253105984] #throughput_metric: host=algo-1, train throughput=20429.5923567 records/second\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:26 INFO 139789377914688] Iter 10: Short term msd 0.885745. Long term msd 0.911325\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:26 INFO 140392253105984] shrinking 1280 centers into 128\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:26 INFO 139789377914688] processed a total of 14398 examples\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:26 INFO 139789377914688] #progress_metric: host=algo-2, completed 100 % of epochs\u001b[0m\n",
      "\u001b[32m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 14398, \"sum\": 14398.0, \"min\": 14398}, \"Total Batches Seen\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}, \"Total Records Seen\": {\"count\": 1, \"max\": 15422, \"sum\": 15422.0, \"min\": 15422}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 14398, \"sum\": 14398.0, \"min\": 14398}, \"Reset Count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1542207566.819886, \"Dimensions\": {\"Host\": \"algo-2\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\", \"epoch\": 0}, \"StartTime\": 1542207565.208561}\n",
      "\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:26 INFO 139789377914688] #throughput_metric: host=algo-2, train throughput=8934.7827426 records/second\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:26 INFO 139789377914688] shrinking 1280 centers into 128\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:27 INFO 139789377914688] local kmeans attempt #0. Current mean square distance 0.104014\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:27 INFO 140392253105984] local kmeans attempt #0. Current mean square distance 0.104132\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:27 INFO 140392253105984] local kmeans attempt #1. Current mean square distance 0.103555\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:27 INFO 139789377914688] local kmeans attempt #1. Current mean square distance 0.101557\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] local kmeans attempt #2. Current mean square distance 0.105264\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:27 INFO 140392253105984] local kmeans attempt #2. Current mean square distance 0.105501\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] local kmeans attempt #3. Current mean square distance 0.104317\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] local kmeans attempt #4. Current mean square distance 0.104273\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] finished shrinking process. Mean Square Distance = 0\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] #quality_metric: host=algo-1, train msd <loss>=0.103555038571\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] collect from kv store took: 57.5370%, (0.409017 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] gradient: cluster center took: 22.4411%, (0.159529 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] batch data loading with context took: 7.4649%, (0.053066 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] update state and report convergance took: 3.5334%, (0.025118 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] compute all data-center distances: inner product took: 2.3498%, (0.016704 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] compute all data-center distances: point norm took: 2.2354%, (0.015891 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] compute all data-center distances: center norm took: 1.1835%, (0.008414 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] predict compute msd took: 1.1579%, (0.008231 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] splitting centers key-value pair took: 0.9905%, (0.007041 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] gradient: one_hot took: 0.4720%, (0.003355 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] gradient: cluster size  took: 0.3121%, (0.002219 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] update set-up time took: 0.2500%, (0.001777 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] predict minus dist took: 0.0724%, (0.000515 secs)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] TOTAL took: 0.710877656937\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 1842.1428203582764, \"sum\": 1842.1428203582764, \"min\": 1842.1428203582764}, \"initialize.time\": {\"count\": 1, \"max\": 16377.634048461914, \"sum\": 16377.634048461914, \"min\": 16377.634048461914}, \"model.serialize.time\": {\"count\": 1, \"max\": 1.4910697937011719, \"sum\": 1.4910697937011719, \"min\": 1.4910697937011719}, \"update.time\": {\"count\": 1, \"max\": 704.3590545654297, \"sum\": 704.3590545654297, \"min\": 704.3590545654297}, \"epochs\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"state.serialize.time\": {\"count\": 1, \"max\": 26.31998062133789, \"sum\": 26.31998062133789, \"min\": 26.31998062133789}, \"_shrink.time\": {\"count\": 1, \"max\": 1825.7570266723633, \"sum\": 1825.7570266723633, \"min\": 1825.7570266723633}}, \"EndTime\": 1542207568.685949, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207548.795071}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Total Batches Seen\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Total Records Seen\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1542207568.694252, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"test_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207568.686329}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] #test_score (algo-1) : ('ssd', 1585.083984375)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] #test_score (algo-1) : ('msd', 0.88060221354166668)\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] #quality_metric: host=algo-1, test ssd <loss>=1585.08398438\u001b[0m\n",
      "\u001b[31m[11/14/2018 14:59:28 INFO 140392253105984] #quality_metric: host=algo-1, test msd <loss>=0.880602213542\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:28.697] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 2, \"duration\": 3480, \"num_examples\": 15}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:28.697] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"duration\": 19898, \"num_epochs\": 2, \"num_examples\": 16}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:28.697] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"epoch\": 1, \"duration\": 19887, \"num_examples\": 2}\u001b[0m\n",
      "\u001b[31m[2018-11-14 14:59:28.697] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"duration\": 19887, \"num_epochs\": 1, \"num_examples\": 2}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 21882.22312927246, \"sum\": 21882.22312927246, \"min\": 21882.22312927246}, \"setuptime\": {\"count\": 1, \"max\": 33.61821174621582, \"sum\": 33.61821174621582, \"min\": 33.61821174621582}}, \"EndTime\": 1542207568.697918, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207568.686045}\n",
      "\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] local kmeans attempt #3. Current mean square distance 0.103356\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] local kmeans attempt #4. Current mean square distance 0.104562\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] finished shrinking process. Mean Square Distance = 0\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] #quality_metric: host=algo-2, train msd <loss>=0.101557254791\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] collect from kv store took: 87.9803%, (1.414492 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] gradient: cluster center took: 3.9816%, (0.064014 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] batch data loading with context took: 3.2446%, (0.052165 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] update state and report convergance took: 1.0517%, (0.016909 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] compute all data-center distances: point norm took: 1.0351%, (0.016642 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] compute all data-center distances: inner product took: 0.8792%, (0.014136 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] predict compute msd took: 0.4879%, (0.007844 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] compute all data-center distances: center norm took: 0.4694%, (0.007547 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] splitting centers key-value pair took: 0.4390%, (0.007057 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] gradient: one_hot took: 0.1894%, (0.003045 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] gradient: cluster size  took: 0.1134%, (0.001823 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] update set-up time took: 0.1029%, (0.001655 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] predict minus dist took: 0.0252%, (0.000406 secs)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] TOTAL took: 1.60773682594\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] Number of GPUs being used: 8\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] No model is serialized on a non-master node\u001b[0m\n",
      "\u001b[32m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 2047.4038124084473, \"sum\": 2047.4038124084473, \"min\": 2047.4038124084473}, \"initialize.time\": {\"count\": 1, \"max\": 16378.751993179321, \"sum\": 16378.751993179321, \"min\": 16378.751993179321}, \"model.serialize.time\": {\"count\": 1, \"max\": 0.08797645568847656, \"sum\": 0.08797645568847656, \"min\": 0.08797645568847656}, \"update.time\": {\"count\": 1, \"max\": 1611.1421585083008, \"sum\": 1611.1421585083008, \"min\": 1611.1421585083008}, \"epochs\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"state.serialize.time\": {\"count\": 1, \"max\": 21.65389060974121, \"sum\": 21.65389060974121, \"min\": 21.65389060974121}, \"_shrink.time\": {\"count\": 1, \"max\": 1966.864824295044, \"sum\": 1966.864824295044, \"min\": 1966.864824295044}}, \"EndTime\": 1542207568.890104, \"Dimensions\": {\"Host\": \"algo-2\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207548.795499}\n",
      "\u001b[0m\n",
      "\u001b[32m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Total Batches Seen\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Total Records Seen\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1542207568.898782, \"Dimensions\": {\"Host\": \"algo-2\", \"Meta\": \"test_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207568.890477}\n",
      "\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] #test_score (algo-2) : ('ssd', 1578.9114990234375)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] #test_score (algo-2) : ('msd', 0.87717305501302079)\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] #quality_metric: host=algo-2, test ssd <loss>=1578.91149902\u001b[0m\n",
      "\u001b[32m[11/14/2018 14:59:28 INFO 139789377914688] #quality_metric: host=algo-2, test msd <loss>=0.877173055013\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:28.901] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 2, \"duration\": 3691, \"num_examples\": 15}\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:28.901] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"duration\": 20102, \"num_epochs\": 2, \"num_examples\": 16}\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:28.902] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"epoch\": 1, \"duration\": 20092, \"num_examples\": 2}\u001b[0m\n",
      "\u001b[32m[2018-11-14 14:59:28.902] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/test\", \"duration\": 20092, \"num_epochs\": 1, \"num_examples\": 2}\u001b[0m\n",
      "\u001b[32m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 21399.761199951172, \"sum\": 21399.761199951172, \"min\": 21399.761199951172}, \"setuptime\": {\"count\": 1, \"max\": 22.38297462463379, \"sum\": 22.38297462463379, \"min\": 22.38297462463379}}, \"EndTime\": 1542207568.902478, \"Dimensions\": {\"Host\": \"algo-2\", \"Operation\": \"training\", \"Algorithm\": \"AWS/KMeansWebscale\"}, \"StartTime\": 1542207568.890199}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Billable seconds: 106\n"
     ]
    }
   ],
   "source": [
    "kmeans.fit({'train': s3_kmean_train_data, 'test': s3_kmean_val_data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy Kmeans Endpoint Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: kmeans-2018-11-14-14-59-49-524\n",
      "INFO:sagemaker:Creating endpoint with name kmeans-2018-11-14-14-56-07-320\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------!CPU times: user 311 ms, sys: 1.7 ms, total: 312 ms\n",
      "Wall time: 5min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans_predictor = kmeans.deploy(initial_instance_count=1,\n",
    "                                 instance_type='ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference with RecordIO Protobuf\n",
    "The inference endpoint also supports JSON-formatted and RecordIO Protobuf, see [Common Data Formats—Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html) for more information. \n",
    "\n",
    "At the time of writing SageMaker Python SDK does not yet have a RecordIO Protobuf serializer, but it is fairly straightforward to create one as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recordio_protobuf_serializer(spmatrix):\n",
    "    import io\n",
    "    import sagemaker.amazon.common as smac\n",
    "    buf = io.BytesIO()\n",
    "    smac.write_spmatrix_to_sparse_tensor(array=spmatrix, file=buf, labels=None)\n",
    "    buf.seek(0)\n",
    "    return buf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we specify the serializer to be the one we just crated and `content_type` to be 'application/x-recordio-protobuf' and inference can be carried out with RecordIO Protobuf format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'distance_to_cluster': 0.7774367332458496, 'closest_cluster': 64.0}, {'distance_to_cluster': 0.9791327714920044, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9863758683204651, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9120752215385437, 'closest_cluster': 34.0}, {'distance_to_cluster': 0.931437611579895, 'closest_cluster': 124.0}]}\n"
     ]
    }
   ],
   "source": [
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "\n",
    "kmeans_predictor.content_type = 'application/x-recordio-protobuf'\n",
    "kmeans_predictor.serializer = recordio_protobuf_serializer\n",
    "kmeans_predictor.deserializer = json_deserializer\n",
    "results = kmeans_predictor.predict(tfidf_val_vectors[:5])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Example of Calling Existing Endpoint Server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is for an explanary case example when you want to call an already running SageMaker endpoint server. FYI, the complete list of deserializer which SageMaker supports is availble at https://github.com/aws/sagemaker-python-sdk/blob/c758362b3fa33bcfbe6d72be2c0f9b97b85a8358/src/sagemaker/predictor.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer, numpy_deserializer\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "kmeans_predictor = sagemaker.RealTimePredictor(endpoint='kmeans-2018-11-14-14-56-07-320', \n",
    "   sagemaker_session=sess,\n",
    "   serializer=recordio_protobuf_serializer,\n",
    "   deserializer=numpy_deserializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'distance_to_cluster': 0.7774367332458496, 'closest_cluster': 64.0}, {'distance_to_cluster': 0.9791327714920044, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9863758683204651, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9120752215385437, 'closest_cluster': 34.0}, {'distance_to_cluster': 0.931437611579895, 'closest_cluster': 124.0}, {'distance_to_cluster': 0.9885457754135132, 'closest_cluster': 9.0}, {'distance_to_cluster': 0.9379200339317322, 'closest_cluster': 6.0}, {'distance_to_cluster': 0.9216263294219971, 'closest_cluster': 80.0}, {'distance_to_cluster': 0.9740128517150879, 'closest_cluster': 6.0}, {'distance_to_cluster': 1.0031163692474365, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.895906925201416, 'closest_cluster': 105.0}, {'distance_to_cluster': 0.99403315782547, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9922012686729431, 'closest_cluster': 24.0}, {'distance_to_cluster': 0.9320039749145508, 'closest_cluster': 33.0}, 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{'distance_to_cluster': 0.988842785358429, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9637194275856018, 'closest_cluster': 11.0}, {'distance_to_cluster': 0.9709932804107666, 'closest_cluster': 6.0}, {'distance_to_cluster': 0.9828143119812012, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.8792905807495117, 'closest_cluster': 10.0}, {'distance_to_cluster': 0.9402240514755249, 'closest_cluster': 6.0}, {'distance_to_cluster': 0.9137190580368042, 'closest_cluster': 4.0}, {'distance_to_cluster': 0.9818128347396851, 'closest_cluster': 26.0}, {'distance_to_cluster': 0.9896631240844727, 'closest_cluster': 14.0}, {'distance_to_cluster': 0.8783559799194336, 'closest_cluster': 15.0}, {'distance_to_cluster': 0.9945505261421204, 'closest_cluster': 1.0}, {'distance_to_cluster': 0.9636374115943909, 'closest_cluster': 49.0}]}\n"
     ]
    }
   ],
   "source": [
    "kmeans_predictor.content_type = 'application/x-recordio-protobuf'\n",
    "results = kmeans_predictor.predict(tfidf_val_vectors)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Customer Review Rate Prediction\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(999983,)\n"
     ]
    }
   ],
   "source": [
    "#filtered_rate_category = np.extract(~pd.isnull(raveld_source_data), source_data['rate_category'])\n",
    "#print(filtered_rate_category.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train, X_test, y_train, y_test = train_test_split(tfidf_vectors, filtered_rate_category, test_size=0.3)\n",
    "X_train, X_test, y_train, y_test = train_test_split(tfidf_d, sampled_source_data['star_rating'], test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pilho-sagemaker-ai-workshop-kr'"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12598, 3109)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3109)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buf = io.BytesIO()\n",
    "smac.write_spmatrix_to_sparse_tensor(buf, X_train.astype('float32'), y_train.astype('float32'))        \n",
    "buf.seek(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uploaded training data location: s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/train/linear_train.data\n"
     ]
    }
   ],
   "source": [
    "key = 'linear_train.data'\n",
    "boto3.resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(buf)\n",
    "s3_train_data = 's3://{}/{}/train/{}'.format(bucket, prefix, key)\n",
    "print('uploaded training data location: {}'.format(s3_train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buf = io.BytesIO()\n",
    "smac.write_spmatrix_to_sparse_tensor(buf, X_test.astype('float32'), y_test.astype('float32'))\n",
    "buf.seek(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uploaded validation data location: s3://pilho-sagemaker-ai-workshop-kr/amazonreview/kmeans/validation/linear_validation.data\n"
     ]
    }
   ],
   "source": [
    "key = 'linear_validation.data'\n",
    "boto3.resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'validation', key)).upload_fileobj(buf)\n",
    "s3_validation_data = 's3://{}/{}/validation/{}'.format(bucket, prefix, key)\n",
    "print('uploaded validation data location: {}'.format(s3_validation_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "container = get_image_uri(boto3.Session().region_name, 'linear-learner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: linear-learner-2018-11-14-16-03-42-248\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-11-14 16:03:42 Starting - Starting the training job...\n",
      "2018-11-14 16:03:50 Starting - Launching requested ML instances.........\n",
      "2018-11-14 16:05:20 Starting - Preparing the instances for training......\n",
      "2018-11-14 16:06:25 Downloading - Downloading input data\n",
      "2018-11-14 16:06:25 Training - Downloading the training image..\n",
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'epochs': u'10', u'loss': u'absolute_loss', u'feature_dim': u'3109', u'mini_batch_size': u'1024', u'predictor_type': u'regressor'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'10', u'feature_dim': u'3109', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1024', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'absolute_loss', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 WARNING 140494836311872] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Using default worker.\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Loaded iterator creator application/x-recordio-protobuf for content type ('application/x-recordio-protobuf', '1.0')\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:06:53.396] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:06:53.444] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 62, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:53 INFO 140494836311872] Create Store: local\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:06:54.479] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 2, \"duration\": 1034, \"num_examples\": 10}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:54 INFO 140494836311872] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7fc73aed3710>\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:54 INFO 140494836311872] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[ 0.01467489  0.00982118  0.00591055 ...,  0.00412626  0.0082073\n",
      "  0.01101616]\u001b[0m\n",
      "\u001b[31m<NDArray 3109 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[ 1.45611227]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[ 3.0320313]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[  6.99623779e-04   2.61946319e-04   1.17951829e-04 ...,   8.91990130e-05\n",
      "   2.15159351e-04   3.61843675e-04]\u001b[0m\n",
      "\u001b[31m<NDArray 3109 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:54 INFO 140494836311872] nvidia-smi took: 0.125710010529 secs to identify 8 gpus\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:06:54 INFO 140494836311872] Number of GPUs being used: 8\u001b[0m\n",
      "\n",
      "2018-11-14 16:06:51 Training - Training image download completed. Training in progress.\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}, \"Total Records Seen\": {\"count\": 1, \"max\": 11264, \"sum\": 11264.0, \"min\": 11264}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 10240, \"sum\": 10240.0, \"min\": 10240}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1542211646.637813, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1542211646.637776}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6140756979584694, \"sum\": 1.6140756979584694, \"min\": 1.6140756979584694}}, \"EndTime\": 1542211648.112217, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.11215}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.603150547792514, \"sum\": 1.603150547792514, \"min\": 1.603150547792514}}, \"EndTime\": 1542211648.112308, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112294}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6373583637177944, \"sum\": 1.6373583637177944, \"min\": 1.6373583637177944}}, \"EndTime\": 1542211648.112351, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112341}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6029378771781921, \"sum\": 1.6029378771781921, \"min\": 1.6029378771781921}}, \"EndTime\": 1542211648.112388, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112379}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.048685248941183, \"sum\": 6.048685248941183, \"min\": 6.048685248941183}}, \"EndTime\": 1542211648.112422, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112413}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.1992080969115095, \"sum\": 6.1992080969115095, \"min\": 6.1992080969115095}}, \"EndTime\": 1542211648.112455, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112447}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.135927124569814, \"sum\": 6.135927124569814, \"min\": 6.135927124569814}}, \"EndTime\": 1542211648.112488, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.11248}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.09365439414978, \"sum\": 6.09365439414978, \"min\": 6.09365439414978}}, \"EndTime\": 1542211648.112519, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112511}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.55738793934385, \"sum\": 1.55738793934385, \"min\": 1.55738793934385}}, \"EndTime\": 1542211648.112551, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112543}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6223033939798672, \"sum\": 1.6223033939798672, \"min\": 1.6223033939798672}}, \"EndTime\": 1542211648.112584, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112575}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5779169263939063, \"sum\": 1.5779169263939063, \"min\": 1.5779169263939063}}, \"EndTime\": 1542211648.112616, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112608}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5692392773926258, \"sum\": 1.5692392773926258, \"min\": 1.5692392773926258}}, \"EndTime\": 1542211648.11265, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.739703077822924, \"sum\": 5.739703077822924, \"min\": 5.739703077822924}}, \"EndTime\": 1542211648.112682, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112673}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.729246582835913, \"sum\": 5.729246582835913, \"min\": 5.729246582835913}}, \"EndTime\": 1542211648.112714, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112706}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.653516359627247, \"sum\": 5.653516359627247, \"min\": 5.653516359627247}}, \"EndTime\": 1542211648.112746, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.789816877494256, \"sum\": 5.789816877494256, \"min\": 5.789816877494256}}, \"EndTime\": 1542211648.112781, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112773}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2514994697024424, \"sum\": 1.2514994697024424, \"min\": 1.2514994697024424}}, \"EndTime\": 1542211648.112814, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112806}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2704820483922958, \"sum\": 1.2704820483922958, \"min\": 1.2704820483922958}}, \"EndTime\": 1542211648.112845, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112837}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2374682445079088, \"sum\": 1.2374682445079088, \"min\": 1.2374682445079088}}, \"EndTime\": 1542211648.112877, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112869}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2350883278995752, \"sum\": 1.2350883278995752, \"min\": 1.2350883278995752}}, \"EndTime\": 1542211648.11291, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112902}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 2.108867426713308, \"sum\": 2.108867426713308, \"min\": 2.108867426713308}}, \"EndTime\": 1542211648.11294, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112933}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 2.1342851780354977, \"sum\": 2.1342851780354977, \"min\": 2.1342851780354977}}, \"EndTime\": 1542211648.112972, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112964}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 2.095933390160402, \"sum\": 2.095933390160402, \"min\": 2.095933390160402}}, \"EndTime\": 1542211648.113004, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.112996}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 2.1103514954447746, \"sum\": 2.1103514954447746, \"min\": 2.1103514954447746}}, \"EndTime\": 1542211648.113037, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2629124422868092, \"sum\": 1.2629124422868092, \"min\": 1.2629124422868092}}, \"EndTime\": 1542211648.113069, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113061}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2742233561972778, \"sum\": 1.2742233561972778, \"min\": 1.2742233561972778}}, \"EndTime\": 1542211648.113101, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113092}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2803541993101437, \"sum\": 1.2803541993101437, \"min\": 1.2803541993101437}}, \"EndTime\": 1542211648.113133, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2861228914310534, \"sum\": 1.2861228914310534, \"min\": 1.2861228914310534}}, \"EndTime\": 1542211648.113164, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113156}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8258562150100868, \"sum\": 1.8258562150100868, \"min\": 1.8258562150100868}}, \"EndTime\": 1542211648.113195, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113187}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8147703607877095, \"sum\": 1.8147703607877095, \"min\": 1.8147703607877095}}, \"EndTime\": 1542211648.113227, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113218}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8173214296499889, \"sum\": 1.8173214296499889, \"min\": 1.8173214296499889}}, \"EndTime\": 1542211648.113259, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.11325}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.800127776960532, \"sum\": 1.800127776960532, \"min\": 1.800127776960532}}, \"EndTime\": 1542211648.113289, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.113281}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] #quality_metric: host=algo-1, epoch=0, train absolute_loss_objective <loss>=1.61407569796\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:28.224] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 1, \"duration\": 34828, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8962068684895834, \"sum\": 1.8962068684895834, \"min\": 1.8962068684895834}}, \"EndTime\": 1542211648.414304, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414242}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8423270444516782, \"sum\": 1.8423270444516782, \"min\": 1.8423270444516782}}, \"EndTime\": 1542211648.414389, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414375}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.949754751699942, \"sum\": 1.949754751699942, \"min\": 1.949754751699942}}, \"EndTime\": 1542211648.41443, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.41442}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8484275987413195, \"sum\": 1.8484275987413195, \"min\": 1.8484275987413195}}, \"EndTime\": 1542211648.414465, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414456}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 9.94119674117477, \"sum\": 9.94119674117477, \"min\": 9.94119674117477}}, \"EndTime\": 1542211648.414502, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.41449}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 10.046780870225694, \"sum\": 10.046780870225694, \"min\": 10.046780870225694}}, \"EndTime\": 1542211648.414537, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414528}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 10.051398473668982, \"sum\": 10.051398473668982, \"min\": 10.051398473668982}}, \"EndTime\": 1542211648.41457, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414562}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 9.986575339988425, \"sum\": 9.986575339988425, \"min\": 9.986575339988425}}, \"EndTime\": 1542211648.414602, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414594}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7702626376681858, \"sum\": 1.7702626376681858, \"min\": 1.7702626376681858}}, \"EndTime\": 1542211648.414635, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414626}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8703401466652199, \"sum\": 1.8703401466652199, \"min\": 1.8703401466652199}}, \"EndTime\": 1542211648.414667, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414659}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8585141725893375, \"sum\": 1.8585141725893375, \"min\": 1.8585141725893375}}, \"EndTime\": 1542211648.414699, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414691}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8424175008138022, \"sum\": 1.8424175008138022, \"min\": 1.8424175008138022}}, \"EndTime\": 1542211648.414732, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414723}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.950449670862268, \"sum\": 8.950449670862268, \"min\": 8.950449670862268}}, \"EndTime\": 1542211648.414764, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 9.226294578269677, \"sum\": 9.226294578269677, \"min\": 9.226294578269677}}, \"EndTime\": 1542211648.414796, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414788}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 9.05857765480324, \"sum\": 9.05857765480324, \"min\": 9.05857765480324}}, \"EndTime\": 1542211648.414828, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.41482}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.98316953305845, \"sum\": 8.98316953305845, \"min\": 8.98316953305845}}, \"EndTime\": 1542211648.41486, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414852}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0817395302101418, \"sum\": 1.0817395302101418, \"min\": 1.0817395302101418}}, \"EndTime\": 1542211648.414891, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414883}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0984759577998409, \"sum\": 1.0984759577998409, \"min\": 1.0984759577998409}}, \"EndTime\": 1542211648.414923, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0851905596697773, \"sum\": 1.0851905596697773, \"min\": 1.0851905596697773}}, \"EndTime\": 1542211648.414954, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414946}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0501853208188658, \"sum\": 1.0501853208188658, \"min\": 1.0501853208188658}}, \"EndTime\": 1542211648.414985, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.414977}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.665635070800781, \"sum\": 2.665635070800781, \"min\": 2.665635070800781}}, \"EndTime\": 1542211648.415017, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415008}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.6936842176649307, \"sum\": 2.6936842176649307, \"min\": 2.6936842176649307}}, \"EndTime\": 1542211648.415048, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.41504}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.6038182576497397, \"sum\": 2.6038182576497397, \"min\": 2.6038182576497397}}, \"EndTime\": 1542211648.415079, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415071}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.536140679253472, \"sum\": 2.536140679253472, \"min\": 2.536140679253472}}, \"EndTime\": 1542211648.415111, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415102}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3386103764286748, \"sum\": 1.3386103764286748, \"min\": 1.3386103764286748}}, \"EndTime\": 1542211648.415142, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415134}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3093258271393953, \"sum\": 1.3093258271393953, \"min\": 1.3093258271393953}}, \"EndTime\": 1542211648.415173, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415165}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3284917082609953, \"sum\": 1.3284917082609953, \"min\": 1.3284917082609953}}, \"EndTime\": 1542211648.415204, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415196}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3219938320583768, \"sum\": 1.3219938320583768, \"min\": 1.3219938320583768}}, \"EndTime\": 1542211648.415236, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415228}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5514048597547743, \"sum\": 1.5514048597547743, \"min\": 1.5514048597547743}}, \"EndTime\": 1542211648.415268, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.41526}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5596456457067418, \"sum\": 1.5596456457067418, \"min\": 1.5596456457067418}}, \"EndTime\": 1542211648.415299, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415291}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5687198554144965, \"sum\": 1.5687198554144965, \"min\": 1.5687198554144965}}, \"EndTime\": 1542211648.415331, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415323}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6021806787561488, \"sum\": 1.6021806787561488, \"min\": 1.6021806787561488}}, \"EndTime\": 1542211648.415371, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211648.415361}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] #quality_metric: host=algo-1, epoch=0, validation absolute_loss_objective <loss>=1.89620686849\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=absolute_loss_objective, value=1.05018532082\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 24, \"sum\": 24.0, \"min\": 24}, \"Total Records Seen\": {\"count\": 1, \"max\": 23862, \"sum\": 23862.0, \"min\": 23862}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1542211648.417058, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1542211646.637996}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:28 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=7080.89799992 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:28.417] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 3, \"duration\": 1779, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8856661897152662, \"sum\": 0.8856661897152662, \"min\": 0.8856661897152662}}, \"EndTime\": 1542211649.656071, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656008}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8615300630529722, \"sum\": 0.8615300630529722, \"min\": 0.8615300630529722}}, \"EndTime\": 1542211649.656151, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656137}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8857211483021578, \"sum\": 0.8857211483021578, \"min\": 0.8857211483021578}}, \"EndTime\": 1542211649.656192, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656182}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8700521495193243, \"sum\": 0.8700521495193243, \"min\": 0.8700521495193243}}, \"EndTime\": 1542211649.656228, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656219}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.765572806199391, \"sum\": 6.765572806199391, \"min\": 6.765572806199391}}, \"EndTime\": 1542211649.656262, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656253}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.800192465384801, \"sum\": 6.800192465384801, \"min\": 6.800192465384801}}, \"EndTime\": 1542211649.656297, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656288}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.9904094735781355, \"sum\": 6.9904094735781355, \"min\": 6.9904094735781355}}, \"EndTime\": 1542211649.656331, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656322}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.892271647850673, \"sum\": 6.892271647850673, \"min\": 6.892271647850673}}, \"EndTime\": 1542211649.656364, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656355}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8355123059203228, \"sum\": 0.8355123059203228, \"min\": 0.8355123059203228}}, \"EndTime\": 1542211649.656403, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656389}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8596385338654121, \"sum\": 0.8596385338654121, \"min\": 0.8596385338654121}}, \"EndTime\": 1542211649.65646, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656446}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8487283575038115, \"sum\": 0.8487283575038115, \"min\": 0.8487283575038115}}, \"EndTime\": 1542211649.656513, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.6565}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8484976049512625, \"sum\": 0.8484976049512625, \"min\": 0.8484976049512625}}, \"EndTime\": 1542211649.656563, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656552}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 6.209652359286944, \"sum\": 6.209652359286944, \"min\": 6.209652359286944}}, \"EndTime\": 1542211649.656613, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656603}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
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      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6095322494705517, \"sum\": 1.6095322494705517, \"min\": 1.6095322494705517}}, \"EndTime\": 1542211649.656914, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656904}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5930036927262943, \"sum\": 1.5930036927262943, \"min\": 1.5930036927262943}}, \"EndTime\": 1542211649.656947, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.656938}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.596651711811622, \"sum\": 1.596651711811622, \"min\": 1.596651711811622}}, \"EndTime\": 1542211649.657011, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657002}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9184569188704094, \"sum\": 0.9184569188704094, \"min\": 0.9184569188704094}}, \"EndTime\": 1542211649.657042, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657034}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9059850244472424, \"sum\": 0.9059850244472424, \"min\": 0.9059850244472424}}, \"EndTime\": 1542211649.657074, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657065}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.909389757240812, \"sum\": 0.909389757240812, \"min\": 0.909389757240812}}, \"EndTime\": 1542211649.657105, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9043362041314443, \"sum\": 0.9043362041314443, \"min\": 0.9043362041314443}}, \"EndTime\": 1542211649.657136, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657128}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1366829654822748, \"sum\": 1.1366829654822748, \"min\": 1.1366829654822748}}, \"EndTime\": 1542211649.657168, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.65716}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1439626260350149, \"sum\": 1.1439626260350149, \"min\": 1.1439626260350149}}, \"EndTime\": 1542211649.6572, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657192}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.12097236700356, \"sum\": 1.12097236700356, \"min\": 1.12097236700356}}, \"EndTime\": 1542211649.657232, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1378893194099267, \"sum\": 1.1378893194099267, \"min\": 1.1378893194099267}}, \"EndTime\": 1542211649.657264, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.657255}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] #quality_metric: host=algo-1, epoch=1, train absolute_loss_objective <loss>=0.885666189715\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:29.714] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 2, \"duration\": 1489, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2927994735152633, \"sum\": 1.2927994735152633, \"min\": 1.2927994735152633}}, \"EndTime\": 1542211649.911883, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.91182}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2425278896755643, \"sum\": 1.2425278896755643, \"min\": 1.2425278896755643}}, \"EndTime\": 1542211649.911963, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.911949}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.289198489718967, \"sum\": 1.289198489718967, \"min\": 1.289198489718967}}, \"EndTime\": 1542211649.912003, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.911993}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2340746053059897, \"sum\": 1.2340746053059897, \"min\": 1.2340746053059897}}, \"EndTime\": 1542211649.912038, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.195166060836227, \"sum\": 8.195166060836227, \"min\": 8.195166060836227}}, \"EndTime\": 1542211649.912077, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912064}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.134290771484375, \"sum\": 8.134290771484375, \"min\": 8.134290771484375}}, \"EndTime\": 1542211649.912135, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.422452076099537, \"sum\": 8.422452076099537, \"min\": 8.422452076099537}}, \"EndTime\": 1542211649.912167, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912159}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.336762966579862, \"sum\": 8.336762966579862, \"min\": 8.336762966579862}}, \"EndTime\": 1542211649.912199, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912191}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2123265923394098, \"sum\": 1.2123265923394098, \"min\": 1.2123265923394098}}, \"EndTime\": 1542211649.912232, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912223}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.233937767876519, \"sum\": 1.233937767876519, \"min\": 1.233937767876519}}, \"EndTime\": 1542211649.912279, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912267}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2593010513870804, \"sum\": 1.2593010513870804, \"min\": 1.2593010513870804}}, \"EndTime\": 1542211649.912313, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912304}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2648331423158998, \"sum\": 1.2648331423158998, \"min\": 1.2648331423158998}}, \"EndTime\": 1542211649.912345, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912336}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.033311179832175, \"sum\": 8.033311179832175, \"min\": 8.033311179832175}}, \"EndTime\": 1542211649.912376, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912368}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 8.171727566189237, \"sum\": 8.171727566189237, \"min\": 8.171727566189237}}, \"EndTime\": 1542211649.912408, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912399}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 7.930697925708912, \"sum\": 7.930697925708912, \"min\": 7.930697925708912}}, \"EndTime\": 1542211649.912453, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912443}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 7.937023744936343, \"sum\": 7.937023744936343, \"min\": 7.937023744936343}}, \"EndTime\": 1542211649.912486, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912478}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0196547049063223, \"sum\": 1.0196547049063223, \"min\": 1.0196547049063223}}, \"EndTime\": 1542211649.912518, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912509}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0131344378436054, \"sum\": 1.0131344378436054, \"min\": 1.0131344378436054}}, \"EndTime\": 1542211649.91255, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912541}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0088306568287038, \"sum\": 1.0088306568287038, \"min\": 1.0088306568287038}}, \"EndTime\": 1542211649.912581, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912573}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0344279423466436, \"sum\": 1.0344279423466436, \"min\": 1.0344279423466436}}, \"EndTime\": 1542211649.91263, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912618}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.3040809913917824, \"sum\": 2.3040809913917824, \"min\": 2.3040809913917824}}, \"EndTime\": 1542211649.912663, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912655}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.3466377653899015, \"sum\": 2.3466377653899015, \"min\": 2.3466377653899015}}, \"EndTime\": 1542211649.912695, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912687}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.340053586606626, \"sum\": 2.340053586606626, \"min\": 2.340053586606626}}, \"EndTime\": 1542211649.912726, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.282386485912182, \"sum\": 2.282386485912182, \"min\": 2.282386485912182}}, \"EndTime\": 1542211649.912757, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912749}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3418799845377605, \"sum\": 1.3418799845377605, \"min\": 1.3418799845377605}}, \"EndTime\": 1542211649.912802, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912791}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3449976151077836, \"sum\": 1.3449976151077836, \"min\": 1.3449976151077836}}, \"EndTime\": 1542211649.912835, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912827}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3510971634476274, \"sum\": 1.3510971634476274, \"min\": 1.3510971634476274}}, \"EndTime\": 1542211649.912867, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912859}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3253101716218172, \"sum\": 1.3253101716218172, \"min\": 1.3253101716218172}}, \"EndTime\": 1542211649.912899, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.91289}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.484063975016276, \"sum\": 1.484063975016276, \"min\": 1.484063975016276}}, \"EndTime\": 1542211649.91293, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912922}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.483628087926794, \"sum\": 1.483628087926794, \"min\": 1.483628087926794}}, \"EndTime\": 1542211649.912974, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912965}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.4787379851164641, \"sum\": 1.4787379851164641, \"min\": 1.4787379851164641}}, \"EndTime\": 1542211649.913007, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.912999}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.496393296983507, \"sum\": 1.496393296983507, \"min\": 1.496393296983507}}, \"EndTime\": 1542211649.913038, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211649.91303}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] #quality_metric: host=algo-1, epoch=1, validation absolute_loss_objective <loss>=1.29279947352\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=absolute_loss_objective, value=1.00883065683\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 37, \"sum\": 37.0, \"min\": 37}, \"Total Records Seen\": {\"count\": 1, \"max\": 36460, \"sum\": 36460.0, \"min\": 36460}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1542211649.914758, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1542211648.41728}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:29 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8412.1885705 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:29.914] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 1493, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6502645748356978, \"sum\": 0.6502645748356978, \"min\": 0.6502645748356978}}, \"EndTime\": 1542211651.135207, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135143}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6413209040959676, \"sum\": 0.6413209040959676, \"min\": 0.6413209040959676}}, \"EndTime\": 1542211651.135287, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135274}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6469226522992054, \"sum\": 0.6469226522992054, \"min\": 0.6469226522992054}}, \"EndTime\": 1542211651.135328, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135318}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6436338567485412, \"sum\": 0.6436338567485412, \"min\": 0.6436338567485412}}, \"EndTime\": 1542211651.135365, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135355}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.676848302284877, \"sum\": 5.676848302284877, \"min\": 5.676848302284877}}, \"EndTime\": 1542211651.1354, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135391}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.794928376873334, \"sum\": 5.794928376873334, \"min\": 5.794928376873334}}, \"EndTime\": 1542211651.135433, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135425}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.688383777936299, \"sum\": 5.688383777936299, \"min\": 5.688383777936299}}, \"EndTime\": 1542211651.135466, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135457}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.750594183802605, \"sum\": 5.750594183802605, \"min\": 5.750594183802605}}, \"EndTime\": 1542211651.135499, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.13549}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.621600367128849, \"sum\": 0.621600367128849, \"min\": 0.621600367128849}}, \"EndTime\": 1542211651.135539, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135529}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6308902675906817, \"sum\": 0.6308902675906817, \"min\": 0.6308902675906817}}, \"EndTime\": 1542211651.135573, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135564}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6345857630173365, \"sum\": 0.6345857630173365, \"min\": 0.6345857630173365}}, \"EndTime\": 1542211651.135606, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135597}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.640562537436684, \"sum\": 0.640562537436684, \"min\": 0.640562537436684}}, \"EndTime\": 1542211651.135638, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.13563}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.624495312571526, \"sum\": 5.624495312571526, \"min\": 5.624495312571526}}, \"EndTime\": 1542211651.135671, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135662}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.740440790851911, \"sum\": 5.740440790851911, \"min\": 5.740440790851911}}, \"EndTime\": 1542211651.135711, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135701}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.6707657525936765, \"sum\": 5.6707657525936765, \"min\": 5.6707657525936765}}, \"EndTime\": 1542211651.135744, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.816484798987706, \"sum\": 5.816484798987706, \"min\": 5.816484798987706}}, \"EndTime\": 1542211651.135776, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135768}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6086672926321626, \"sum\": 0.6086672926321626, \"min\": 0.6086672926321626}}, \"EndTime\": 1542211651.135808, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.1358}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6094706927736601, \"sum\": 0.6094706927736601, \"min\": 0.6094706927736601}}, \"EndTime\": 1542211651.13584, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135831}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6075823170443376, \"sum\": 0.6075823170443376, \"min\": 0.6075823170443376}}, \"EndTime\": 1542211651.135872, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.6090717477103075, \"sum\": 0.6090717477103075, \"min\": 0.6090717477103075}}, \"EndTime\": 1542211651.135903, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135895}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5661382625500362, \"sum\": 1.5661382625500362, \"min\": 1.5661382625500362}}, \"EndTime\": 1542211651.135934, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135926}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.579451239357392, \"sum\": 1.579451239357392, \"min\": 1.579451239357392}}, \"EndTime\": 1542211651.135966, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135957}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.544090386480093, \"sum\": 1.544090386480093, \"min\": 1.544090386480093}}, \"EndTime\": 1542211651.135997, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.135988}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5657860599458218, \"sum\": 1.5657860599458218, \"min\": 1.5657860599458218}}, \"EndTime\": 1542211651.136028, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.13602}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8560312415162722, \"sum\": 0.8560312415162722, \"min\": 0.8560312415162722}}, \"EndTime\": 1542211651.13606, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136052}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8572143316268921, \"sum\": 0.8572143316268921, \"min\": 0.8572143316268921}}, \"EndTime\": 1542211651.136092, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136083}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8623331959048907, \"sum\": 0.8623331959048907, \"min\": 0.8623331959048907}}, \"EndTime\": 1542211651.136124, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136115}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8513687724868456, \"sum\": 0.8513687724868456, \"min\": 0.8513687724868456}}, \"EndTime\": 1542211651.136155, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136147}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9370319470763206, \"sum\": 0.9370319470763206, \"min\": 0.9370319470763206}}, \"EndTime\": 1542211651.136186, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136178}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9400700305898985, \"sum\": 0.9400700305898985, \"min\": 0.9400700305898985}}, \"EndTime\": 1542211651.136218, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.13621}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9303126726299524, \"sum\": 0.9303126726299524, \"min\": 0.9303126726299524}}, \"EndTime\": 1542211651.13625, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136241}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.935458658883969, \"sum\": 0.935458658883969, \"min\": 0.935458658883969}}, \"EndTime\": 1542211651.136282, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.136274}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] #quality_metric: host=algo-1, epoch=2, train absolute_loss_objective <loss>=0.650264574836\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:31.194] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 3, \"duration\": 1479, \"num_examples\": 6}\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0839646968135128, \"sum\": 1.0839646968135128, \"min\": 1.0839646968135128}}, \"EndTime\": 1542211651.390658, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390597}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0697723219129773, \"sum\": 1.0697723219129773, \"min\": 1.0697723219129773}}, \"EndTime\": 1542211651.390741, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390727}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0713211229112414, \"sum\": 1.0713211229112414, \"min\": 1.0713211229112414}}, \"EndTime\": 1542211651.390782, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390771}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0336053353768808, \"sum\": 1.0336053353768808, \"min\": 1.0336053353768808}}, \"EndTime\": 1542211651.390818, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390809}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.857061089409722, \"sum\": 6.857061089409722, \"min\": 6.857061089409722}}, \"EndTime\": 1542211651.390852, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390843}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.755039559823495, \"sum\": 6.755039559823495, \"min\": 6.755039559823495}}, \"EndTime\": 1542211651.390885, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390876}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.801027199074074, \"sum\": 6.801027199074074, \"min\": 6.801027199074074}}, \"EndTime\": 1542211651.390923, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390909}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.7984951669198495, \"sum\": 6.7984951669198495, \"min\": 6.7984951669198495}}, \"EndTime\": 1542211651.390976, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.390961}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0366321874547888, \"sum\": 1.0366321874547888, \"min\": 1.0366321874547888}}, \"EndTime\": 1542211651.391032, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391016}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.064027065700955, \"sum\": 1.064027065700955, \"min\": 1.064027065700955}}, \"EndTime\": 1542211651.391091, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391075}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0540457492404514, \"sum\": 1.0540457492404514, \"min\": 1.0540457492404514}}, \"EndTime\": 1542211651.391138, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391123}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0646829845287182, \"sum\": 1.0646829845287182, \"min\": 1.0646829845287182}}, \"EndTime\": 1542211651.391198, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391182}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.540994737413194, \"sum\": 6.540994737413194, \"min\": 6.540994737413194}}, \"EndTime\": 1542211651.391245, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391229}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.833606770833334, \"sum\": 6.833606770833334, \"min\": 6.833606770833334}}, \"EndTime\": 1542211651.391299, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391283}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.528976530852141, \"sum\": 6.528976530852141, \"min\": 6.528976530852141}}, \"EndTime\": 1542211651.391356, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391342}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.7889846462673615, \"sum\": 6.7889846462673615, \"min\": 6.7889846462673615}}, \"EndTime\": 1542211651.391398, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9251904664216218, \"sum\": 0.9251904664216218, \"min\": 0.9251904664216218}}, \"EndTime\": 1542211651.391456, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.39144}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.929075540613245, \"sum\": 0.929075540613245, \"min\": 0.929075540613245}}, \"EndTime\": 1542211651.391506, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.39149}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9259990833423756, \"sum\": 0.9259990833423756, \"min\": 0.9259990833423756}}, \"EndTime\": 1542211651.391565, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.39155}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.936161538583261, \"sum\": 0.936161538583261, \"min\": 0.936161538583261}}, \"EndTime\": 1542211651.39161, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391595}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.225078317147714, \"sum\": 2.225078317147714, \"min\": 2.225078317147714}}, \"EndTime\": 1542211651.391667, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391652}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.3037041445131656, \"sum\": 2.3037041445131656, \"min\": 2.3037041445131656}}, \"EndTime\": 1542211651.391713, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391698}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.17945398401331, \"sum\": 2.17945398401331, \"min\": 2.17945398401331}}, \"EndTime\": 1542211651.39177, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.283791029188368, \"sum\": 2.283791029188368, \"min\": 2.283791029188368}}, \"EndTime\": 1542211651.391812, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391798}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.205255206072772, \"sum\": 1.205255206072772, \"min\": 1.205255206072772}}, \"EndTime\": 1542211651.391869, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391854}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2139983113606772, \"sum\": 1.2139983113606772, \"min\": 1.2139983113606772}}, \"EndTime\": 1542211651.391914, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391899}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2002134082935474, \"sum\": 1.2002134082935474, \"min\": 1.2002134082935474}}, \"EndTime\": 1542211651.391972, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.391957}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2064755192509404, \"sum\": 1.2064755192509404, \"min\": 1.2064755192509404}}, \"EndTime\": 1542211651.392018, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.392003}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3197932942708333, \"sum\": 1.3197932942708333, \"min\": 1.3197932942708333}}, \"EndTime\": 1542211651.392075, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.39206}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3558873946578414, \"sum\": 1.3558873946578414, \"min\": 1.3558873946578414}}, \"EndTime\": 1542211651.39212, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.392105}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.333040991889106, \"sum\": 1.333040991889106, \"min\": 1.333040991889106}}, \"EndTime\": 1542211651.392176, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.39216}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.3420656161838107, \"sum\": 1.3420656161838107, \"min\": 1.3420656161838107}}, \"EndTime\": 1542211651.39223, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211651.392215}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] #quality_metric: host=algo-1, epoch=2, validation absolute_loss_objective <loss>=1.08396469681\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=absolute_loss_objective, value=0.925190466422\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 50, \"sum\": 50.0, \"min\": 50}, \"Total Records Seen\": {\"count\": 1, \"max\": 49058, \"sum\": 49058.0, \"min\": 49058}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1542211651.393993, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1542211649.915003}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:31 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8517.23734939 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:31.394] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 5, \"duration\": 1474, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5690498886009058, \"sum\": 0.5690498886009058, \"min\": 0.5690498886009058}}, \"EndTime\": 1542211652.93093, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.930831}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5708643887192011, \"sum\": 0.5708643887192011, \"min\": 0.5708643887192011}}, \"EndTime\": 1542211652.931043, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931024}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5704087779546777, \"sum\": 0.5704087779546777, \"min\": 0.5704087779546777}}, \"EndTime\": 1542211652.9311, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931086}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5706162275746465, \"sum\": 0.5706162275746465, \"min\": 0.5706162275746465}}, \"EndTime\": 1542211652.931154, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931141}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.3489044060309725, \"sum\": 5.3489044060309725, \"min\": 5.3489044060309725}}, \"EndTime\": 1542211652.931207, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931193}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.38627915084362, \"sum\": 5.38627915084362, \"min\": 5.38627915084362}}, \"EndTime\": 1542211652.931267, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931253}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.277053038279216, \"sum\": 5.277053038279216, \"min\": 5.277053038279216}}, \"EndTime\": 1542211652.931321, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931309}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.567520707845688, \"sum\": 5.567520707845688, \"min\": 5.567520707845688}}, \"EndTime\": 1542211652.931379, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931366}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5597955904280146, \"sum\": 0.5597955904280146, \"min\": 0.5597955904280146}}, \"EndTime\": 1542211652.931431, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931418}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5717880794157585, \"sum\": 0.5717880794157585, \"min\": 0.5717880794157585}}, \"EndTime\": 1542211652.93148, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931468}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5672659914319714, \"sum\": 0.5672659914319714, \"min\": 0.5672659914319714}}, \"EndTime\": 1542211652.93153, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931517}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5707896851624051, \"sum\": 0.5707896851624051, \"min\": 0.5707896851624051}}, \"EndTime\": 1542211652.931579, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931567}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.184097662568092, \"sum\": 5.184097662568092, \"min\": 5.184097662568092}}, \"EndTime\": 1542211652.931629, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931617}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.414305741588275, \"sum\": 5.414305741588275, \"min\": 5.414305741588275}}, \"EndTime\": 1542211652.931685, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931672}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.181789681315422, \"sum\": 5.181789681315422, \"min\": 5.181789681315422}}, \"EndTime\": 1542211652.931735, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931722}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.480696211258571, \"sum\": 5.480696211258571, \"min\": 5.480696211258571}}, \"EndTime\": 1542211652.931784, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931772}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5706484373658895, \"sum\": 0.5706484373658895, \"min\": 0.5706484373658895}}, \"EndTime\": 1542211652.931833, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931821}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.576972202087442, \"sum\": 0.576972202087442, \"min\": 0.576972202087442}}, \"EndTime\": 1542211652.931883, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.931871}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5695473589003086, \"sum\": 0.5695473589003086, \"min\": 0.5695473589003086}}, \"EndTime\": 1542211652.931933, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.93192}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5784196251382431, \"sum\": 0.5784196251382431, \"min\": 0.5784196251382431}}, \"EndTime\": 1542211652.931983, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.93197}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6080701972047489, \"sum\": 1.6080701972047489, \"min\": 1.6080701972047489}}, \"EndTime\": 1542211652.932032, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932019}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6216195498903592, \"sum\": 1.6216195498903592, \"min\": 1.6216195498903592}}, \"EndTime\": 1542211652.932081, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932068}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.5930963543554146, \"sum\": 1.5930963543554146, \"min\": 1.5930963543554146}}, \"EndTime\": 1542211652.93213, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932118}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6111862572530906, \"sum\": 1.6111862572530906, \"min\": 1.6111862572530906}}, \"EndTime\": 1542211652.93218, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932167}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8140964011351267, \"sum\": 0.8140964011351267, \"min\": 0.8140964011351267}}, \"EndTime\": 1542211652.932229, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932216}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8166430629789829, \"sum\": 0.8166430629789829, \"min\": 0.8166430629789829}}, \"EndTime\": 1542211652.93228, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932268}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8153458877156178, \"sum\": 0.8153458877156178, \"min\": 0.8153458877156178}}, \"EndTime\": 1542211652.932329, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932316}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8181997556239367, \"sum\": 0.8181997556239367, \"min\": 0.8181997556239367}}, \"EndTime\": 1542211652.932378, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932365}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8582507309814295, \"sum\": 0.8582507309814295, \"min\": 0.8582507309814295}}, \"EndTime\": 1542211652.932427, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932415}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8600603565573692, \"sum\": 0.8600603565573692, \"min\": 0.8600603565573692}}, \"EndTime\": 1542211652.932476, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932464}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8661444100240866, \"sum\": 0.8661444100240866, \"min\": 0.8661444100240866}}, \"EndTime\": 1542211652.932525, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932513}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.862248782068491, \"sum\": 0.862248782068491, \"min\": 0.862248782068491}}, \"EndTime\": 1542211652.932574, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211652.932562}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:32 INFO 140494836311872] #quality_metric: host=algo-1, epoch=3, train absolute_loss_objective <loss>=0.569049888601\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:33.000] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 4, \"duration\": 1806, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0261071042661314, \"sum\": 1.0261071042661314, \"min\": 1.0261071042661314}}, \"EndTime\": 1542211653.189212, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189136}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0237240713614004, \"sum\": 1.0237240713614004, \"min\": 1.0237240713614004}}, \"EndTime\": 1542211653.189303, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189289}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0087868358470775, \"sum\": 1.0087868358470775, \"min\": 1.0087868358470775}}, \"EndTime\": 1542211653.189343, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189333}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0112495309335214, \"sum\": 1.0112495309335214, \"min\": 1.0112495309335214}}, \"EndTime\": 1542211653.189379, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.18937}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.71644840946904, \"sum\": 6.71644840946904, \"min\": 6.71644840946904}}, \"EndTime\": 1542211653.189413, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189404}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.934451678240741, \"sum\": 6.934451678240741, \"min\": 6.934451678240741}}, \"EndTime\": 1542211653.189446, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189438}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.520086149992766, \"sum\": 6.520086149992766, \"min\": 6.520086149992766}}, \"EndTime\": 1542211653.189478, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.18947}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 7.055417344835069, \"sum\": 7.055417344835069, \"min\": 7.055417344835069}}, \"EndTime\": 1542211653.189514, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189505}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0040409455475985, \"sum\": 1.0040409455475985, \"min\": 1.0040409455475985}}, \"EndTime\": 1542211653.189545, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189537}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0189653806333188, \"sum\": 1.0189653806333188, \"min\": 1.0189653806333188}}, \"EndTime\": 1542211653.189577, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189569}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.009063229031033, \"sum\": 1.009063229031033, \"min\": 1.009063229031033}}, \"EndTime\": 1542211653.189608, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.1896}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0162907183611833, \"sum\": 1.0162907183611833, \"min\": 1.0162907183611833}}, \"EndTime\": 1542211653.18964, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189632}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.532737336335359, \"sum\": 6.532737336335359, \"min\": 6.532737336335359}}, \"EndTime\": 1542211653.189672, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189664}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.624273952907986, \"sum\": 6.624273952907986, \"min\": 6.624273952907986}}, \"EndTime\": 1542211653.189703, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189695}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.357586624710648, \"sum\": 6.357586624710648, \"min\": 6.357586624710648}}, \"EndTime\": 1542211653.189734, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189726}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.636773726851852, \"sum\": 6.636773726851852, \"min\": 6.636773726851852}}, \"EndTime\": 1542211653.189765, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189757}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9098846605088976, \"sum\": 0.9098846605088976, \"min\": 0.9098846605088976}}, \"EndTime\": 1542211653.189795, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189787}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9170480205394603, \"sum\": 0.9170480205394603, \"min\": 0.9170480205394603}}, \"EndTime\": 1542211653.189827, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189818}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9069122144911024, \"sum\": 0.9069122144911024, \"min\": 0.9069122144911024}}, \"EndTime\": 1542211653.189858, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.18985}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9105793281837746, \"sum\": 0.9105793281837746, \"min\": 0.9105793281837746}}, \"EndTime\": 1542211653.189889, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189881}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.227111737286603, \"sum\": 2.227111737286603, \"min\": 2.227111737286603}}, \"EndTime\": 1542211653.189921, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189912}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.296445154260706, \"sum\": 2.296445154260706, \"min\": 2.296445154260706}}, \"EndTime\": 1542211653.189951, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189943}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.247714165581597, \"sum\": 2.247714165581597, \"min\": 2.247714165581597}}, \"EndTime\": 1542211653.189982, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.189974}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2666596363208913, \"sum\": 2.2666596363208913, \"min\": 2.2666596363208913}}, \"EndTime\": 1542211653.190013, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190005}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1915805788393374, \"sum\": 1.1915805788393374, \"min\": 1.1915805788393374}}, \"EndTime\": 1542211653.190044, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190036}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1871957001862703, \"sum\": 1.1871957001862703, \"min\": 1.1871957001862703}}, \"EndTime\": 1542211653.190075, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190068}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.194541524251302, \"sum\": 1.194541524251302, \"min\": 1.194541524251302}}, \"EndTime\": 1542211653.190107, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190099}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1926234944661458, \"sum\": 1.1926234944661458, \"min\": 1.1926234944661458}}, \"EndTime\": 1542211653.190159, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.19015}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.246821226897063, \"sum\": 1.246821226897063, \"min\": 1.246821226897063}}, \"EndTime\": 1542211653.190191, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190183}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2775580738208911, \"sum\": 1.2775580738208911, \"min\": 1.2775580738208911}}, \"EndTime\": 1542211653.190222, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190214}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2621746543601706, \"sum\": 1.2621746543601706, \"min\": 1.2621746543601706}}, \"EndTime\": 1542211653.190253, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190245}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2496477310745804, \"sum\": 1.2496477310745804, \"min\": 1.2496477310745804}}, \"EndTime\": 1542211653.190284, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211653.190276}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:33 INFO 140494836311872] #quality_metric: host=algo-1, epoch=3, validation absolute_loss_objective <loss>=1.02610710427\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:33 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=absolute_loss_objective, value=0.906912214491\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:33 INFO 140494836311872] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:33 INFO 140494836311872] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 63, \"sum\": 63.0, \"min\": 63}, \"Total Records Seen\": {\"count\": 1, \"max\": 61656, \"sum\": 61656.0, \"min\": 61656}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1542211653.191982, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1542211651.394279}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:33 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=7007.3984759 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:33.192] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 1796, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5415827414641777, \"sum\": 0.5415827414641777, \"min\": 0.5415827414641777}}, \"EndTime\": 1542211654.646072, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646009}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5524750060091416, \"sum\": 0.5524750060091416, \"min\": 0.5524750060091416}}, \"EndTime\": 1542211654.646194, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646179}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5467489697039127, \"sum\": 0.5467489697039127, \"min\": 0.5467489697039127}}, \"EndTime\": 1542211654.646235, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646225}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5514312982559204, \"sum\": 0.5514312982559204, \"min\": 0.5514312982559204}}, \"EndTime\": 1542211654.646272, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646263}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.293975353240967, \"sum\": 5.293975353240967, \"min\": 5.293975353240967}}, \"EndTime\": 1542211654.646305, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646296}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.489866241812706, \"sum\": 5.489866241812706, \"min\": 5.489866241812706}}, \"EndTime\": 1542211654.646338, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.64633}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.27403911948204, \"sum\": 5.27403911948204, \"min\": 5.27403911948204}}, \"EndTime\": 1542211654.646371, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646362}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.642685318986575, \"sum\": 5.642685318986575, \"min\": 5.642685318986575}}, \"EndTime\": 1542211654.646402, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646394}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.543403639147679, \"sum\": 0.543403639147679, \"min\": 0.543403639147679}}, \"EndTime\": 1542211654.646434, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646425}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5524457634116212, \"sum\": 0.5524457634116212, \"min\": 0.5524457634116212}}, \"EndTime\": 1542211654.646479, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646466}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.543756591466566, \"sum\": 0.543756591466566, \"min\": 0.543756591466566}}, \"EndTime\": 1542211654.64653, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646516}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5542028940593203, \"sum\": 0.5542028940593203, \"min\": 0.5542028940593203}}, \"EndTime\": 1542211654.646584, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.64657}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.315276478727658, \"sum\": 5.315276478727658, \"min\": 5.315276478727658}}, \"EndTime\": 1542211654.646639, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646625}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.53738959133625, \"sum\": 5.53738959133625, \"min\": 5.53738959133625}}, \"EndTime\": 1542211654.646689, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646675}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.3834865142901736, \"sum\": 5.3834865142901736, \"min\": 5.3834865142901736}}, \"EndTime\": 1542211654.646746, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646731}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.481137126684189, \"sum\": 5.481137126684189, \"min\": 5.481137126684189}}, \"EndTime\": 1542211654.646801, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646786}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5658795274794102, \"sum\": 0.5658795274794102, \"min\": 0.5658795274794102}}, \"EndTime\": 1542211654.646843, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.64683}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5687204459682107, \"sum\": 0.5687204459682107, \"min\": 0.5687204459682107}}, \"EndTime\": 1542211654.646901, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646884}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5688388664275408, \"sum\": 0.5688388664275408, \"min\": 0.5688388664275408}}, \"EndTime\": 1542211654.646956, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646942}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5725731722389659, \"sum\": 0.5725731722389659, \"min\": 0.5725731722389659}}, \"EndTime\": 1542211654.647004, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.646994}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6535212695598602, \"sum\": 1.6535212695598602, \"min\": 1.6535212695598602}}, \"EndTime\": 1542211654.647058, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647044}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6513688787817955, \"sum\": 1.6513688787817955, \"min\": 1.6513688787817955}}, \"EndTime\": 1542211654.64711, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.659712145725886, \"sum\": 1.659712145725886, \"min\": 1.659712145725886}}, \"EndTime\": 1542211654.647167, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647152}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6531584473947685, \"sum\": 1.6531584473947685, \"min\": 1.6531584473947685}}, \"EndTime\": 1542211654.647221, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647206}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8017879525820414, \"sum\": 0.8017879525820414, \"min\": 0.8017879525820414}}, \"EndTime\": 1542211654.647272, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.64726}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8017673287540674, \"sum\": 0.8017673287540674, \"min\": 0.8017673287540674}}, \"EndTime\": 1542211654.647323, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647313}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8010055727014939, \"sum\": 0.8010055727014939, \"min\": 0.8010055727014939}}, \"EndTime\": 1542211654.647377, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647364}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8014675521602234, \"sum\": 0.8014675521602234, \"min\": 0.8014675521602234}}, \"EndTime\": 1542211654.647427, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647416}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8216375056654215, \"sum\": 0.8216375056654215, \"min\": 0.8216375056654215}}, \"EndTime\": 1542211654.647479, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647467}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8314466035614411, \"sum\": 0.8314466035614411, \"min\": 0.8314466035614411}}, \"EndTime\": 1542211654.647531, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647516}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8240819411973158, \"sum\": 0.8240819411973158, \"min\": 0.8240819411973158}}, \"EndTime\": 1542211654.647585, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.64757}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8264480146269003, \"sum\": 0.8264480146269003, \"min\": 0.8264480146269003}}, \"EndTime\": 1542211654.647641, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.647627}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] #quality_metric: host=algo-1, epoch=4, train absolute_loss_objective <loss>=0.541582741464\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:34.700] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 5, \"duration\": 1699, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0173538208007813, \"sum\": 1.0173538208007813, \"min\": 1.0173538208007813}}, \"EndTime\": 1542211654.885229, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885168}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0021473185221355, \"sum\": 1.0021473185221355, \"min\": 1.0021473185221355}}, \"EndTime\": 1542211654.885308, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885295}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9998319668240018, \"sum\": 0.9998319668240018, \"min\": 0.9998319668240018}}, \"EndTime\": 1542211654.885347, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885338}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0177509166576244, \"sum\": 1.0177509166576244, \"min\": 1.0177509166576244}}, \"EndTime\": 1542211654.885381, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885372}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.745287814670139, \"sum\": 6.745287814670139, \"min\": 6.745287814670139}}, \"EndTime\": 1542211654.885414, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885405}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 7.00295152452257, \"sum\": 7.00295152452257, \"min\": 7.00295152452257}}, \"EndTime\": 1542211654.885445, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885437}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.376802797670718, \"sum\": 6.376802797670718, \"min\": 6.376802797670718}}, \"EndTime\": 1542211654.885476, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885468}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.836904635959201, \"sum\": 6.836904635959201, \"min\": 6.836904635959201}}, \"EndTime\": 1542211654.885507, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885499}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9922727401168258, \"sum\": 0.9922727401168258, \"min\": 0.9922727401168258}}, \"EndTime\": 1542211654.885538, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.88553}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0175494836877894, \"sum\": 1.0175494836877894, \"min\": 1.0175494836877894}}, \"EndTime\": 1542211654.885569, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885561}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9976318868001303, \"sum\": 0.9976318868001303, \"min\": 0.9976318868001303}}, \"EndTime\": 1542211654.885613, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.8856}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0037234723126447, \"sum\": 1.0037234723126447, \"min\": 1.0037234723126447}}, \"EndTime\": 1542211654.885664, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.88565}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.258868317780672, \"sum\": 6.258868317780672, \"min\": 6.258868317780672}}, \"EndTime\": 1542211654.885717, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885702}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.7186038773148145, \"sum\": 6.7186038773148145, \"min\": 6.7186038773148145}}, \"EndTime\": 1542211654.885769, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885754}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.536196944625289, \"sum\": 6.536196944625289, \"min\": 6.536196944625289}}, \"EndTime\": 1542211654.885825, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.88581}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.80003015588831, \"sum\": 6.80003015588831, \"min\": 6.80003015588831}}, \"EndTime\": 1542211654.885862, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885853}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9019952025236907, \"sum\": 0.9019952025236907, \"min\": 0.9019952025236907}}, \"EndTime\": 1542211654.885894, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885885}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9006181646276403, \"sum\": 0.9006181646276403, \"min\": 0.9006181646276403}}, \"EndTime\": 1542211654.885943, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885928}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8982127973768446, \"sum\": 0.8982127973768446, \"min\": 0.8982127973768446}}, \"EndTime\": 1542211654.885978, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.885969}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9103806333188658, \"sum\": 0.9103806333188658, \"min\": 0.9103806333188658}}, \"EndTime\": 1542211654.88601, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886001}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2397932490596064, \"sum\": 2.2397932490596064, \"min\": 2.2397932490596064}}, \"EndTime\": 1542211654.88604, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886033}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.332042032877604, \"sum\": 2.332042032877604, \"min\": 2.332042032877604}}, \"EndTime\": 1542211654.886072, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886064}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.229149192527488, \"sum\": 2.229149192527488, \"min\": 2.229149192527488}}, \"EndTime\": 1542211654.886103, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886095}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.297139576099537, \"sum\": 2.297139576099537, \"min\": 2.297139576099537}}, \"EndTime\": 1542211654.886153, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886144}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1626728425202546, \"sum\": 1.1626728425202546, \"min\": 1.1626728425202546}}, \"EndTime\": 1542211654.886186, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886177}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.166406724717882, \"sum\": 1.166406724717882, \"min\": 1.166406724717882}}, \"EndTime\": 1542211654.886217, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886209}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1664228651258681, \"sum\": 1.1664228651258681, \"min\": 1.1664228651258681}}, \"EndTime\": 1542211654.886248, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.88624}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.166345864755136, \"sum\": 1.166345864755136, \"min\": 1.166345864755136}}, \"EndTime\": 1542211654.886278, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.88627}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1931010719581887, \"sum\": 1.1931010719581887, \"min\": 1.1931010719581887}}, \"EndTime\": 1542211654.886308, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.8863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2037842644585504, \"sum\": 1.2037842644585504, \"min\": 1.2037842644585504}}, \"EndTime\": 1542211654.886339, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886331}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.191694002504702, \"sum\": 1.191694002504702, \"min\": 1.191694002504702}}, \"EndTime\": 1542211654.886369, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886361}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1911386503996673, \"sum\": 1.1911386503996673, \"min\": 1.1911386503996673}}, \"EndTime\": 1542211654.886399, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211654.886391}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] #quality_metric: host=algo-1, epoch=4, validation absolute_loss_objective <loss>=1.0173538208\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=absolute_loss_objective, value=0.898212797377\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] Epoch 4: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 76, \"sum\": 76.0, \"min\": 76}, \"Total Records Seen\": {\"count\": 1, \"max\": 74254, \"sum\": 74254.0, \"min\": 74254}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1542211654.888364, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1542211653.192216}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:34 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=7426.91088102 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:34.888] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 7, \"duration\": 1696, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5396489559983214, \"sum\": 0.5396489559983214, \"min\": 0.5396489559983214}}, \"EndTime\": 1542211656.173626, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173561}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5475766146555543, \"sum\": 0.5475766146555543, \"min\": 0.5475766146555543}}, \"EndTime\": 1542211656.173706, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173692}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5399686169500152, \"sum\": 0.5399686169500152, \"min\": 0.5399686169500152}}, \"EndTime\": 1542211656.173747, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5422887277478973, \"sum\": 0.5422887277478973, \"min\": 0.5422887277478973}}, \"EndTime\": 1542211656.173785, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173777}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.389939268430074, \"sum\": 5.389939268430074, \"min\": 5.389939268430074}}, \"EndTime\": 1542211656.17382, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173811}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.662993575135867, \"sum\": 5.662993575135867, \"min\": 5.662993575135867}}, \"EndTime\": 1542211656.173854, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173846}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.273567825555801, \"sum\": 5.273567825555801, \"min\": 5.273567825555801}}, \"EndTime\": 1542211656.173887, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173879}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.711837341388066, \"sum\": 5.711837341388066, \"min\": 5.711837341388066}}, \"EndTime\": 1542211656.173918, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.17391}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5388607208927473, \"sum\": 0.5388607208927473, \"min\": 0.5388607208927473}}, \"EndTime\": 1542211656.17395, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173942}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5479235906774799, \"sum\": 0.5479235906774799, \"min\": 0.5479235906774799}}, \"EndTime\": 1542211656.173983, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.173974}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5388472070917487, \"sum\": 0.5388472070917487, \"min\": 0.5388472070917487}}, \"EndTime\": 1542211656.174014, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174006}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5463137676318487, \"sum\": 0.5463137676318487, \"min\": 0.5463137676318487}}, \"EndTime\": 1542211656.174046, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174038}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.285439516107242, \"sum\": 5.285439516107242, \"min\": 5.285439516107242}}, \"EndTime\": 1542211656.174078, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.17407}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.701690380771955, \"sum\": 5.701690380771955, \"min\": 5.701690380771955}}, \"EndTime\": 1542211656.174109, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.1741}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.499181563655536, \"sum\": 5.499181563655536, \"min\": 5.499181563655536}}, \"EndTime\": 1542211656.174191, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174182}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.765036717057228, \"sum\": 5.765036717057228, \"min\": 5.765036717057228}}, \"EndTime\": 1542211656.174224, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174215}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5646100668236613, \"sum\": 0.5646100668236613, \"min\": 0.5646100668236613}}, \"EndTime\": 1542211656.174256, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174247}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.572429825241367, \"sum\": 0.572429825241367, \"min\": 0.572429825241367}}, \"EndTime\": 1542211656.174287, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174278}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5657616959263881, \"sum\": 0.5657616959263881, \"min\": 0.5657616959263881}}, \"EndTime\": 1542211656.174317, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174309}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5684704249724746, \"sum\": 0.5684704249724746, \"min\": 0.5684704249724746}}, \"EndTime\": 1542211656.174348, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.17434}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6979060396552086, \"sum\": 1.6979060396552086, \"min\": 1.6979060396552086}}, \"EndTime\": 1542211656.174379, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174371}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.695726132641236, \"sum\": 1.695726132641236, \"min\": 1.695726132641236}}, \"EndTime\": 1542211656.174411, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174403}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7012951051195462, \"sum\": 1.7012951051195462, \"min\": 1.7012951051195462}}, \"EndTime\": 1542211656.174443, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.6976775800188382, \"sum\": 1.6976775800188382, \"min\": 1.6976775800188382}}, \"EndTime\": 1542211656.174474, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174466}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7926436110089222, \"sum\": 0.7926436110089222, \"min\": 0.7926436110089222}}, \"EndTime\": 1542211656.174506, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174498}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7927360311150551, \"sum\": 0.7927360311150551, \"min\": 0.7927360311150551}}, \"EndTime\": 1542211656.174536, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174528}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7927044915656248, \"sum\": 0.7927044915656248, \"min\": 0.7927044915656248}}, \"EndTime\": 1542211656.174566, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174558}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7918074037879705, \"sum\": 0.7918074037879705, \"min\": 0.7918074037879705}}, \"EndTime\": 1542211656.174597, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174588}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8068843185901642, \"sum\": 0.8068843185901642, \"min\": 0.8068843185901642}}, \"EndTime\": 1542211656.174628, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174619}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8185402452945709, \"sum\": 0.8185402452945709, \"min\": 0.8185402452945709}}, \"EndTime\": 1542211656.174659, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.17465}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8185606511930624, \"sum\": 0.8185606511930624, \"min\": 0.8185606511930624}}, \"EndTime\": 1542211656.17469, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174681}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8086485763390859, \"sum\": 0.8086485763390859, \"min\": 0.8086485763390859}}, \"EndTime\": 1542211656.174721, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.174713}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:36 INFO 140494836311872] #quality_metric: host=algo-1, epoch=5, train absolute_loss_objective <loss>=0.539648955998\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:36.230] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 6, \"duration\": 1529, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.004646917272497, \"sum\": 1.004646917272497, \"min\": 1.004646917272497}}, \"EndTime\": 1542211656.425513, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425452}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0144061731409144, \"sum\": 1.0144061731409144, \"min\": 1.0144061731409144}}, \"EndTime\": 1542211656.425593, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425579}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0122907624421296, \"sum\": 1.0122907624421296, \"min\": 1.0122907624421296}}, \"EndTime\": 1542211656.425633, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425623}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0080643830475984, \"sum\": 1.0080643830475984, \"min\": 1.0080643830475984}}, \"EndTime\": 1542211656.425669, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425661}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.495678846571181, \"sum\": 6.495678846571181, \"min\": 6.495678846571181}}, \"EndTime\": 1542211656.425701, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425693}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.958627319335937, \"sum\": 6.958627319335937, \"min\": 6.958627319335937}}, \"EndTime\": 1542211656.425734, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425725}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.29320793999566, \"sum\": 6.29320793999566, \"min\": 6.29320793999566}}, \"EndTime\": 1542211656.425764, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.77128820348669, \"sum\": 6.77128820348669, \"min\": 6.77128820348669}}, \"EndTime\": 1542211656.425796, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425788}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9965823364257812, \"sum\": 0.9965823364257812, \"min\": 0.9965823364257812}}, \"EndTime\": 1542211656.425827, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425818}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.010220043041088, \"sum\": 1.010220043041088, \"min\": 1.010220043041088}}, \"EndTime\": 1542211656.425858, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.42585}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9985812773527922, \"sum\": 0.9985812773527922, \"min\": 0.9985812773527922}}, \"EndTime\": 1542211656.425889, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425881}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0022941363299334, \"sum\": 1.0022941363299334, \"min\": 1.0022941363299334}}, \"EndTime\": 1542211656.42592, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425912}\n",
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      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.297357335973669, \"sum\": 6.297357335973669, \"min\": 6.297357335973669}}, \"EndTime\": 1542211656.425951, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425943}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.568202944155092, \"sum\": 6.568202944155092, \"min\": 6.568202944155092}}, \"EndTime\": 1542211656.425981, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.425973}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.463601910626447, \"sum\": 6.463601910626447, \"min\": 6.463601910626447}}, \"EndTime\": 1542211656.426011, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426004}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.768782280815972, \"sum\": 6.768782280815972, \"min\": 6.768782280815972}}, \"EndTime\": 1542211656.426042, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426034}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8999143586335359, \"sum\": 0.8999143586335359, \"min\": 0.8999143586335359}}, \"EndTime\": 1542211656.426072, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426064}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9052871280246311, \"sum\": 0.9052871280246311, \"min\": 0.9052871280246311}}, \"EndTime\": 1542211656.426103, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426095}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9051073003698278, \"sum\": 0.9051073003698278, \"min\": 0.9051073003698278}}, \"EndTime\": 1542211656.426153, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426144}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9101697766339337, \"sum\": 0.9101697766339337, \"min\": 0.9101697766339337}}, \"EndTime\": 1542211656.426185, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426177}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2208044998734087, \"sum\": 2.2208044998734087, \"min\": 2.2208044998734087}}, \"EndTime\": 1542211656.426216, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426208}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.4036949214228875, \"sum\": 2.4036949214228875, \"min\": 2.4036949214228875}}, \"EndTime\": 1542211656.426248, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.42624}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2799176929615164, \"sum\": 2.2799176929615164, \"min\": 2.2799176929615164}}, \"EndTime\": 1542211656.426279, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426271}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.348324019820602, \"sum\": 2.348324019820602, \"min\": 2.348324019820602}}, \"EndTime\": 1542211656.42631, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426302}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1605500849971064, \"sum\": 1.1605500849971064, \"min\": 1.1605500849971064}}, \"EndTime\": 1542211656.42634, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426332}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1587533399793837, \"sum\": 1.1587533399793837, \"min\": 1.1587533399793837}}, \"EndTime\": 1542211656.426371, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426363}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1586071890371816, \"sum\": 1.1586071890371816, \"min\": 1.1586071890371816}}, \"EndTime\": 1542211656.426401, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426394}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1589096069335938, \"sum\": 1.1589096069335938, \"min\": 1.1589096069335938}}, \"EndTime\": 1542211656.426432, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426424}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.172313407615379, \"sum\": 1.172313407615379, \"min\": 1.172313407615379}}, \"EndTime\": 1542211656.426463, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426455}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1759507977520978, \"sum\": 1.1759507977520978, \"min\": 1.1759507977520978}}, \"EndTime\": 1542211656.426493, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426485}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2524304594816984, \"sum\": 1.2524304594816984, \"min\": 1.2524304594816984}}, \"EndTime\": 1542211656.426523, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426516}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1843051260489004, \"sum\": 1.1843051260489004, \"min\": 1.1843051260489004}}, \"EndTime\": 1542211656.426554, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211656.426546}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:36 INFO 140494836311872] #quality_metric: host=algo-1, epoch=5, validation absolute_loss_objective <loss>=1.00464691727\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:36 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=absolute_loss_objective, value=0.899914358634\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:36 INFO 140494836311872] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 89, \"sum\": 89.0, \"min\": 89}, \"Total Records Seen\": {\"count\": 1, \"max\": 86852, \"sum\": 86852.0, \"min\": 86852}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1542211656.427302, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1542211654.88862}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:36 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8186.99528038 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:36.427] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 1536, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5350000631685058, \"sum\": 0.5350000631685058, \"min\": 0.5350000631685058}}, \"EndTime\": 1542211657.656639, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656576}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5477412572751442, \"sum\": 0.5477412572751442, \"min\": 0.5477412572751442}}, \"EndTime\": 1542211657.65672, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656707}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5336658591404557, \"sum\": 0.5336658591404557, \"min\": 0.5336658591404557}}, \"EndTime\": 1542211657.656758, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656749}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5448938493306438, \"sum\": 0.5448938493306438, \"min\": 0.5448938493306438}}, \"EndTime\": 1542211657.656792, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656784}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.285907492041588, \"sum\": 5.285907492041588, \"min\": 5.285907492041588}}, \"EndTime\": 1542211657.656825, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656816}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.539719253778458, \"sum\": 5.539719253778458, \"min\": 5.539719253778458}}, \"EndTime\": 1542211657.656859, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.65685}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.416157990694046, \"sum\": 5.416157990694046, \"min\": 5.416157990694046}}, \"EndTime\": 1542211657.656891, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656882}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.67022090156873, \"sum\": 5.67022090156873, \"min\": 5.67022090156873}}, \"EndTime\": 1542211657.656922, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5394577383995056, \"sum\": 0.5394577383995056, \"min\": 0.5394577383995056}}, \"EndTime\": 1542211657.656953, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656945}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5414873246724407, \"sum\": 0.5414873246724407, \"min\": 0.5414873246724407}}, \"EndTime\": 1542211657.656985, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.656976}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.537113758114477, \"sum\": 0.537113758114477, \"min\": 0.537113758114477}}, \"EndTime\": 1542211657.657016, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657008}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5501783862709999, \"sum\": 0.5501783862709999, \"min\": 0.5501783862709999}}, \"EndTime\": 1542211657.657046, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657038}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.286879852414131, \"sum\": 5.286879852414131, \"min\": 5.286879852414131}}, \"EndTime\": 1542211657.657077, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657068}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.791532874107361, \"sum\": 5.791532874107361, \"min\": 5.791532874107361}}, \"EndTime\": 1542211657.657108, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.6571}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.4184693445761996, \"sum\": 5.4184693445761996, \"min\": 5.4184693445761996}}, \"EndTime\": 1542211657.657138, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.65713}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.740962838133176, \"sum\": 5.740962838133176, \"min\": 5.740962838133176}}, \"EndTime\": 1542211657.65717, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657161}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5637406532963117, \"sum\": 0.5637406532963117, \"min\": 0.5637406532963117}}, \"EndTime\": 1542211657.657202, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657194}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5713663464412093, \"sum\": 0.5713663464412093, \"min\": 0.5713663464412093}}, \"EndTime\": 1542211657.657245, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657236}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5664497269317508, \"sum\": 0.5664497269317508, \"min\": 0.5664497269317508}}, \"EndTime\": 1542211657.657283, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657274}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5666092199583849, \"sum\": 0.5666092199583849, \"min\": 0.5666092199583849}}, \"EndTime\": 1542211657.657316, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657307}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.738894809037447, \"sum\": 1.738894809037447, \"min\": 1.738894809037447}}, \"EndTime\": 1542211657.657346, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657338}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7546012525757153, \"sum\": 1.7546012525757153, \"min\": 1.7546012525757153}}, \"EndTime\": 1542211657.657377, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657369}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.744964358707269, \"sum\": 1.744964358707269, \"min\": 1.744964358707269}}, \"EndTime\": 1542211657.657413, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657405}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7464676623543103, \"sum\": 1.7464676623543103, \"min\": 1.7464676623543103}}, \"EndTime\": 1542211657.657446, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657437}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7879291319598755, \"sum\": 0.7879291319598755, \"min\": 0.7879291319598755}}, \"EndTime\": 1542211657.657477, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657469}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7877649385482073, \"sum\": 0.7877649385482073, \"min\": 0.7877649385482073}}, \"EndTime\": 1542211657.657508, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.6575}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7878351466109356, \"sum\": 0.7878351466109356, \"min\": 0.7878351466109356}}, \"EndTime\": 1542211657.657539, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.787715932354331, \"sum\": 0.787715932354331, \"min\": 0.787715932354331}}, \"EndTime\": 1542211657.657569, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657561}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8004828200985988, \"sum\": 0.8004828200985988, \"min\": 0.8004828200985988}}, \"EndTime\": 1542211657.657599, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657591}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8129855338484049, \"sum\": 0.8129855338484049, \"min\": 0.8129855338484049}}, \"EndTime\": 1542211657.657631, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657623}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8391455418119828, \"sum\": 0.8391455418119828, \"min\": 0.8391455418119828}}, \"EndTime\": 1542211657.657663, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657654}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8053223403791586, \"sum\": 0.8053223403791586, \"min\": 0.8053223403791586}}, \"EndTime\": 1542211657.657693, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.657685}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] #quality_metric: host=algo-1, epoch=6, train absolute_loss_objective <loss>=0.535000063169\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:37.711] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 7, \"duration\": 1480, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0057015652126735, \"sum\": 1.0057015652126735, \"min\": 1.0057015652126735}}, \"EndTime\": 1542211657.908878, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.908817}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0015469529893664, \"sum\": 1.0015469529893664, \"min\": 1.0015469529893664}}, \"EndTime\": 1542211657.908958, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.908944}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9899052316171152, \"sum\": 0.9899052316171152, \"min\": 0.9899052316171152}}, \"EndTime\": 1542211657.908996, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.908987}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9967999324092158, \"sum\": 0.9967999324092158, \"min\": 0.9967999324092158}}, \"EndTime\": 1542211657.909031, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909022}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.3553792769820605, \"sum\": 6.3553792769820605, \"min\": 6.3553792769820605}}, \"EndTime\": 1542211657.909064, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909055}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.691603031864872, \"sum\": 6.691603031864872, \"min\": 6.691603031864872}}, \"EndTime\": 1542211657.909096, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909088}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.181765566225405, \"sum\": 6.181765566225405, \"min\": 6.181765566225405}}, \"EndTime\": 1542211657.909129, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909121}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.6547677273220485, \"sum\": 6.6547677273220485, \"min\": 6.6547677273220485}}, \"EndTime\": 1542211657.90916, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909152}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9978581915961372, \"sum\": 0.9978581915961372, \"min\": 0.9978581915961372}}, \"EndTime\": 1542211657.909192, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909183}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9960560155797887, \"sum\": 0.9960560155797887, \"min\": 0.9960560155797887}}, \"EndTime\": 1542211657.909223, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909215}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9928935976381655, \"sum\": 0.9928935976381655, \"min\": 0.9928935976381655}}, \"EndTime\": 1542211657.909257, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909248}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0078102620442708, \"sum\": 1.0078102620442708, \"min\": 1.0078102620442708}}, \"EndTime\": 1542211657.909288, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.90928}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.023321261935764, \"sum\": 6.023321261935764, \"min\": 6.023321261935764}}, \"EndTime\": 1542211657.909319, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909311}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.638479682074653, \"sum\": 6.638479682074653, \"min\": 6.638479682074653}}, \"EndTime\": 1542211657.90935, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909342}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.228766072591146, \"sum\": 6.228766072591146, \"min\": 6.228766072591146}}, \"EndTime\": 1542211657.909381, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909373}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.930467145001447, \"sum\": 6.930467145001447, \"min\": 6.930467145001447}}, \"EndTime\": 1542211657.909412, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909404}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8970878431532118, \"sum\": 0.8970878431532118, \"min\": 0.8970878431532118}}, \"EndTime\": 1542211657.909442, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909434}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9015782476354528, \"sum\": 0.9015782476354528, \"min\": 0.9015782476354528}}, \"EndTime\": 1542211657.909473, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909465}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8958964369032119, \"sum\": 0.8958964369032119, \"min\": 0.8958964369032119}}, \"EndTime\": 1542211657.909504, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909495}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9038631467466001, \"sum\": 0.9038631467466001, \"min\": 0.9038631467466001}}, \"EndTime\": 1542211657.909534, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909526}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2281257233796294, \"sum\": 2.2281257233796294, \"min\": 2.2281257233796294}}, \"EndTime\": 1542211657.909565, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909557}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.4022146832501448, \"sum\": 2.4022146832501448, \"min\": 2.4022146832501448}}, \"EndTime\": 1542211657.909595, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909587}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2625428715458624, \"sum\": 2.2625428715458624, \"min\": 2.2625428715458624}}, \"EndTime\": 1542211657.909626, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909618}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.337199548791956, \"sum\": 2.337199548791956, \"min\": 2.337199548791956}}, \"EndTime\": 1542211657.909656, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909648}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1537434613263164, \"sum\": 1.1537434613263164, \"min\": 1.1537434613263164}}, \"EndTime\": 1542211657.909686, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909678}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1542860469111689, \"sum\": 1.1542860469111689, \"min\": 1.1542860469111689}}, \"EndTime\": 1542211657.909717, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909709}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1538133465802227, \"sum\": 1.1538133465802227, \"min\": 1.1538133465802227}}, \"EndTime\": 1542211657.909747, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909739}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1543482632107205, \"sum\": 1.1543482632107205, \"min\": 1.1543482632107205}}, \"EndTime\": 1542211657.909778, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909769}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1673926798502605, \"sum\": 1.1673926798502605, \"min\": 1.1673926798502605}}, \"EndTime\": 1542211657.909809, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909801}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2021837531195747, \"sum\": 1.2021837531195747, \"min\": 1.2021837531195747}}, \"EndTime\": 1542211657.90984, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909832}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2232739031756366, \"sum\": 1.2232739031756366, \"min\": 1.2232739031756366}}, \"EndTime\": 1542211657.909871, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1623504073531539, \"sum\": 1.1623504073531539, \"min\": 1.1623504073531539}}, \"EndTime\": 1542211657.909901, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211657.909894}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] #quality_metric: host=algo-1, epoch=6, validation absolute_loss_objective <loss>=1.00570156521\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=absolute_loss_objective, value=0.895896436903\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] Epoch 6: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 102, \"sum\": 102.0, \"min\": 102}, \"Total Records Seen\": {\"count\": 1, \"max\": 99450, \"sum\": 99450.0, \"min\": 99450}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1542211657.911511, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1542211656.427503}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:37 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8488.62390737 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:37.911] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 9, \"duration\": 1482, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5346275167539716, \"sum\": 0.5346275167539716, \"min\": 0.5346275167539716}}, \"EndTime\": 1542211659.120186, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5454272699231902, \"sum\": 0.5454272699231902, \"min\": 0.5454272699231902}}, \"EndTime\": 1542211659.120266, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120253}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5348843621710936, \"sum\": 0.5348843621710936, \"min\": 0.5348843621710936}}, \"EndTime\": 1542211659.120306, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120296}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5433238207673033, \"sum\": 0.5433238207673033, \"min\": 0.5433238207673033}}, \"EndTime\": 1542211659.120358, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120346}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.346588124831517, \"sum\": 5.346588124831517, \"min\": 5.346588124831517}}, \"EndTime\": 1542211659.120393, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.641646350423495, \"sum\": 5.641646350423495, \"min\": 5.641646350423495}}, \"EndTime\": 1542211659.120427, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120418}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.2789599398771925, \"sum\": 5.2789599398771925, \"min\": 5.2789599398771925}}, \"EndTime\": 1542211659.12046, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120452}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.749685342113177, \"sum\": 5.749685342113177, \"min\": 5.749685342113177}}, \"EndTime\": 1542211659.1205, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120487}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5344675630331039, \"sum\": 0.5344675630331039, \"min\": 0.5344675630331039}}, \"EndTime\": 1542211659.120539, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.12053}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5467684144775072, \"sum\": 0.5467684144775072, \"min\": 0.5467684144775072}}, \"EndTime\": 1542211659.120572, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120563}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5385221084579825, \"sum\": 0.5385221084579825, \"min\": 0.5385221084579825}}, \"EndTime\": 1542211659.120603, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120595}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5469434096788367, \"sum\": 0.5469434096788367, \"min\": 0.5469434096788367}}, \"EndTime\": 1542211659.120635, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120627}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.282789389292399, \"sum\": 5.282789389292399, \"min\": 5.282789389292399}}, \"EndTime\": 1542211659.120673, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120659}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.876590505242348, \"sum\": 5.876590505242348, \"min\": 5.876590505242348}}, \"EndTime\": 1542211659.120714, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120704}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.354133889079094, \"sum\": 5.354133889079094, \"min\": 5.354133889079094}}, \"EndTime\": 1542211659.120745, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.821590344111125, \"sum\": 5.821590344111125, \"min\": 5.821590344111125}}, \"EndTime\": 1542211659.120777, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120768}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5611089281737804, \"sum\": 0.5611089281737804, \"min\": 0.5611089281737804}}, \"EndTime\": 1542211659.120807, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120799}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5692643945415815, \"sum\": 0.5692643945415815, \"min\": 0.5692643945415815}}, \"EndTime\": 1542211659.12085, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120837}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5630490460122625, \"sum\": 0.5630490460122625, \"min\": 0.5630490460122625}}, \"EndTime\": 1542211659.120889, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.12088}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5716710081323981, \"sum\": 0.5716710081323981, \"min\": 0.5716710081323981}}, \"EndTime\": 1542211659.120921, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120912}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.753183275461197, \"sum\": 1.753183275461197, \"min\": 1.753183275461197}}, \"EndTime\": 1542211659.120952, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120943}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7852329735954602, \"sum\": 1.7852329735954602, \"min\": 1.7852329735954602}}, \"EndTime\": 1542211659.120983, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.120975}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7635074481368065, \"sum\": 1.7635074481368065, \"min\": 1.7635074481368065}}, \"EndTime\": 1542211659.121021, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121007}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7764246761798859, \"sum\": 1.7764246761798859, \"min\": 1.7764246761798859}}, \"EndTime\": 1542211659.121061, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121052}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7866176621367534, \"sum\": 0.7866176621367534, \"min\": 0.7866176621367534}}, \"EndTime\": 1542211659.121093, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121085}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7863788163910309, \"sum\": 0.7863788163910309, \"min\": 0.7863788163910309}}, \"EndTime\": 1542211659.121124, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121116}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.786154255270958, \"sum\": 0.786154255270958, \"min\": 0.786154255270958}}, \"EndTime\": 1542211659.121155, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121146}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7865731418132782, \"sum\": 0.7865731418132782, \"min\": 0.7865731418132782}}, \"EndTime\": 1542211659.121191, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121177}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8002524338662624, \"sum\": 0.8002524338662624, \"min\": 0.8002524338662624}}, \"EndTime\": 1542211659.121232, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121223}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8332007179657618, \"sum\": 0.8332007179657618, \"min\": 0.8332007179657618}}, \"EndTime\": 1542211659.121264, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121256}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8292113691568375, \"sum\": 0.8292113691568375, \"min\": 0.8292113691568375}}, \"EndTime\": 1542211659.121295, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121287}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8164291394253572, \"sum\": 0.8164291394253572, \"min\": 0.8164291394253572}}, \"EndTime\": 1542211659.121326, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.121318}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:39 INFO 140494836311872] #quality_metric: host=algo-1, epoch=7, train absolute_loss_objective <loss>=0.534627516754\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:39.178] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 8, \"duration\": 1466, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0013148102936922, \"sum\": 1.0013148102936922, \"min\": 1.0013148102936922}}, \"EndTime\": 1542211659.365634, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365558}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9948150691279659, \"sum\": 0.9948150691279659, \"min\": 0.9948150691279659}}, \"EndTime\": 1542211659.365731, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.999575828269676, \"sum\": 0.999575828269676, \"min\": 0.999575828269676}}, \"EndTime\": 1542211659.365769, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.36576}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.003315700954861, \"sum\": 1.003315700954861, \"min\": 1.003315700954861}}, \"EndTime\": 1542211659.365802, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.513574490017361, \"sum\": 6.513574490017361, \"min\": 6.513574490017361}}, \"EndTime\": 1542211659.365839, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365827}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 7.166567224573206, \"sum\": 7.166567224573206, \"min\": 7.166567224573206}}, \"EndTime\": 1542211659.365883, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365873}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.190581574616608, \"sum\": 6.190581574616608, \"min\": 6.190581574616608}}, \"EndTime\": 1542211659.365915, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365907}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.888686319986979, \"sum\": 6.888686319986979, \"min\": 6.888686319986979}}, \"EndTime\": 1542211659.365946, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365937}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9917612542046441, \"sum\": 0.9917612542046441, \"min\": 0.9917612542046441}}, \"EndTime\": 1542211659.365976, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.365968}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9965524800618489, \"sum\": 0.9965524800618489, \"min\": 0.9965524800618489}}, \"EndTime\": 1542211659.366013, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9888242481372974, \"sum\": 0.9888242481372974, \"min\": 0.9888242481372974}}, \"EndTime\": 1542211659.366055, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366046}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0048772289134837, \"sum\": 1.0048772289134837, \"min\": 1.0048772289134837}}, \"EndTime\": 1542211659.366087, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366079}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.354279446072049, \"sum\": 6.354279446072049, \"min\": 6.354279446072049}}, \"EndTime\": 1542211659.366136, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366126}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.840874113859954, \"sum\": 6.840874113859954, \"min\": 6.840874113859954}}, \"EndTime\": 1542211659.366171, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366162}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.210918895580151, \"sum\": 6.210918895580151, \"min\": 6.210918895580151}}, \"EndTime\": 1542211659.366216, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366206}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.717663257740162, \"sum\": 6.717663257740162, \"min\": 6.717663257740162}}, \"EndTime\": 1542211659.366249, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366241}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9008775838216145, \"sum\": 0.9008775838216145, \"min\": 0.9008775838216145}}, \"EndTime\": 1542211659.36628, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.906311026679145, \"sum\": 0.906311026679145, \"min\": 0.906311026679145}}, \"EndTime\": 1542211659.366312, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366303}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9011958793357566, \"sum\": 0.9011958793357566, \"min\": 0.9011958793357566}}, \"EndTime\": 1542211659.366342, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366334}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9069633766456887, \"sum\": 0.9069633766456887, \"min\": 0.9069633766456887}}, \"EndTime\": 1542211659.366386, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366377}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2096924845377606, \"sum\": 2.2096924845377606, \"min\": 2.2096924845377606}}, \"EndTime\": 1542211659.366418, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.36641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.404197139033565, \"sum\": 2.404197139033565, \"min\": 2.404197139033565}}, \"EndTime\": 1542211659.36645, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366441}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.2388480970594618, \"sum\": 2.2388480970594618, \"min\": 2.2388480970594618}}, \"EndTime\": 1542211659.36648, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366472}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.3607561125578704, \"sum\": 2.3607561125578704, \"min\": 2.3607561125578704}}, \"EndTime\": 1542211659.366511, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366503}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1550567287868923, \"sum\": 1.1550567287868923, \"min\": 1.1550567287868923}}, \"EndTime\": 1542211659.366555, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366545}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1523997214988426, \"sum\": 1.1523997214988426, \"min\": 1.1523997214988426}}, \"EndTime\": 1542211659.366588, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.36658}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1524304481788918, \"sum\": 1.1524304481788918, \"min\": 1.1524304481788918}}, \"EndTime\": 1542211659.366619, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366611}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1539770620840568, \"sum\": 1.1539770620840568, \"min\": 1.1539770620840568}}, \"EndTime\": 1542211659.36665, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366642}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.15783770525897, \"sum\": 1.15783770525897, \"min\": 1.15783770525897}}, \"EndTime\": 1542211659.366681, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366673}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.17986730504919, \"sum\": 1.17986730504919, \"min\": 1.17986730504919}}, \"EndTime\": 1542211659.366724, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366713}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2324451135706018, \"sum\": 1.2324451135706018, \"min\": 1.2324451135706018}}, \"EndTime\": 1542211659.366756, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366748}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.166843499077691, \"sum\": 1.166843499077691, \"min\": 1.166843499077691}}, \"EndTime\": 1542211659.366788, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211659.366779}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:39 INFO 140494836311872] #quality_metric: host=algo-1, epoch=7, validation absolute_loss_objective <loss>=1.00131481029\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:39 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=absolute_loss_objective, value=0.900877583822\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:39 INFO 140494836311872] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 115, \"sum\": 115.0, \"min\": 115}, \"Total Records Seen\": {\"count\": 1, \"max\": 112048, \"sum\": 112048.0, \"min\": 112048}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1542211659.367556, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1542211657.911719}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:39 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8652.80638043 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:39.367] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 1453, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5320928947379192, \"sum\": 0.5320928947379192, \"min\": 0.5320928947379192}}, \"EndTime\": 1542211660.701844, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.70178}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.543377493818601, \"sum\": 0.543377493818601, \"min\": 0.543377493818601}}, \"EndTime\": 1542211660.701932, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.701914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5300816415498654, \"sum\": 0.5300816415498654, \"min\": 0.5300816415498654}}, \"EndTime\": 1542211660.701994, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.701978}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5444480230410894, \"sum\": 0.5444480230410894, \"min\": 0.5444480230410894}}, \"EndTime\": 1542211660.702047, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.702032}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8348924362411102, \"sum\": 0.8348924362411102, \"min\": 0.8348924362411102}}, \"EndTime\": 1542211660.703546, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.703532}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] #quality_metric: host=algo-1, epoch=8, train absolute_loss_objective <loss>=0.532092894738\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:40.758] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 9, \"duration\": 1580, \"num_examples\": 6}\u001b[0m\n",
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      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.4223096042209202, \"sum\": 2.4223096042209202, \"min\": 2.4223096042209202}}, \"EndTime\": 1542211660.960399, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1521085555465134, \"sum\": 1.1521085555465134, \"min\": 1.1521085555465134}}, \"EndTime\": 1542211660.960446, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960432}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1523763812029804, \"sum\": 1.1523763812029804, \"min\": 1.1523763812029804}}, \"EndTime\": 1542211660.960493, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960479}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1510620964898004, \"sum\": 1.1510620964898004, \"min\": 1.1510620964898004}}, \"EndTime\": 1542211660.96054, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960526}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1520886173954716, \"sum\": 1.1520886173954716, \"min\": 1.1520886173954716}}, \"EndTime\": 1542211660.960588, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960575}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.217443276864511, \"sum\": 1.217443276864511, \"min\": 1.217443276864511}}, \"EndTime\": 1542211660.960636, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960622}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.223172720449942, \"sum\": 1.223172720449942, \"min\": 1.223172720449942}}, \"EndTime\": 1542211660.960683, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960669}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1681806889286748, \"sum\": 1.1681806889286748, \"min\": 1.1681806889286748}}, \"EndTime\": 1542211660.96073, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960716}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1831118548357928, \"sum\": 1.1831118548357928, \"min\": 1.1831118548357928}}, \"EndTime\": 1542211660.960778, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211660.960765}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] #quality_metric: host=algo-1, epoch=8, validation absolute_loss_objective <loss>=0.996981794569\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=absolute_loss_objective, value=0.887881028917\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] Epoch 8: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] #progress_metric: host=algo-1, completed 90 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 128, \"sum\": 128.0, \"min\": 128}, \"Total Records Seen\": {\"count\": 1, \"max\": 124646, \"sum\": 124646.0, \"min\": 124646}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1542211660.962502, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1542211659.367788}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:40 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=7899.36292015 records/second\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:40.962] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 11, \"duration\": 1594, \"num_examples\": 13}\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5331029997517666, \"sum\": 0.5331029997517666, \"min\": 0.5331029997517666}}, \"EndTime\": 1542211662.253655, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253592}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5410306903844079, \"sum\": 0.5410306903844079, \"min\": 0.5410306903844079}}, \"EndTime\": 1542211662.253736, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253721}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.532270352045695, \"sum\": 0.532270352045695, \"min\": 0.532270352045695}}, \"EndTime\": 1542211662.253776, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253766}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5456249509006739, \"sum\": 0.5456249509006739, \"min\": 0.5456249509006739}}, \"EndTime\": 1542211662.253813, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253804}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.12220258017381, \"sum\": 5.12220258017381, \"min\": 5.12220258017381}}, \"EndTime\": 1542211662.253847, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253838}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.757430210709572, \"sum\": 5.757430210709572, \"min\": 5.757430210709572}}, \"EndTime\": 1542211662.253879, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.25387}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.196284279227257, \"sum\": 5.196284279227257, \"min\": 5.196284279227257}}, \"EndTime\": 1542211662.253911, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253903}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.707995822032292, \"sum\": 5.707995822032292, \"min\": 5.707995822032292}}, \"EndTime\": 1542211662.253943, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253935}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5335115408524871, \"sum\": 0.5335115408524871, \"min\": 0.5335115408524871}}, \"EndTime\": 1542211662.253973, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253965}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5419991537928581, \"sum\": 0.5419991537928581, \"min\": 0.5419991537928581}}, \"EndTime\": 1542211662.254007, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.253998}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5332078722616037, \"sum\": 0.5332078722616037, \"min\": 0.5332078722616037}}, \"EndTime\": 1542211662.254037, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5520354304462671, \"sum\": 0.5520354304462671, \"min\": 0.5520354304462671}}, \"EndTime\": 1542211662.25407, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.25406}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.211422478159268, \"sum\": 5.211422478159268, \"min\": 5.211422478159268}}, \"EndTime\": 1542211662.254101, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254093}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.866023972630501, \"sum\": 5.866023972630501, \"min\": 5.866023972630501}}, \"EndTime\": 1542211662.254223, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254207}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.3707813421885175, \"sum\": 5.3707813421885175, \"min\": 5.3707813421885175}}, \"EndTime\": 1542211662.25428, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254266}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 5.812790344158809, \"sum\": 5.812790344158809, \"min\": 5.812790344158809}}, \"EndTime\": 1542211662.25435, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254336}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5611325533439716, \"sum\": 0.5611325533439716, \"min\": 0.5611325533439716}}, \"EndTime\": 1542211662.254402, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254389}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5750888182471195, \"sum\": 0.5750888182471195, \"min\": 0.5750888182471195}}, \"EndTime\": 1542211662.254475, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254453}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5650608399882913, \"sum\": 0.5650608399882913, \"min\": 0.5650608399882913}}, \"EndTime\": 1542211662.254547, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254524}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.5746793911481897, \"sum\": 0.5746793911481897, \"min\": 0.5746793911481897}}, \"EndTime\": 1542211662.254608, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254593}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.7693880759179592, \"sum\": 1.7693880759179592, \"min\": 1.7693880759179592}}, \"EndTime\": 1542211662.254671, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254654}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8116518842677276, \"sum\": 1.8116518842677276, \"min\": 1.8116518842677276}}, \"EndTime\": 1542211662.254724, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.25471}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.8095853539804618, \"sum\": 1.8095853539804618, \"min\": 1.8095853539804618}}, \"EndTime\": 1542211662.254776, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254766}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 1.833429228514433, \"sum\": 1.833429228514433, \"min\": 1.833429228514433}}, \"EndTime\": 1542211662.254823, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254811}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7858119731148084, \"sum\": 0.7858119731148084, \"min\": 0.7858119731148084}}, \"EndTime\": 1542211662.254864, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254851}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.78594419422249, \"sum\": 0.78594419422249, \"min\": 0.78594419422249}}, \"EndTime\": 1542211662.2549, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254891}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7857165914028883, \"sum\": 0.7857165914028883, \"min\": 0.7857165914028883}}, \"EndTime\": 1542211662.254934, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.254924}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.7859176521499952, \"sum\": 0.7859176521499952, \"min\": 0.7859176521499952}}, \"EndTime\": 1542211662.254979, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.25497}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8167954428742329, \"sum\": 0.8167954428742329, \"min\": 0.8167954428742329}}, \"EndTime\": 1542211662.255013, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.255004}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8536119231333336, \"sum\": 0.8536119231333336, \"min\": 0.8536119231333336}}, \"EndTime\": 1542211662.255065, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.255051}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8260318587223688, \"sum\": 0.8260318587223688, \"min\": 0.8260318587223688}}, \"EndTime\": 1542211662.255107, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.255097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8335811843474706, \"sum\": 0.8335811843474706, \"min\": 0.8335811843474706}}, \"EndTime\": 1542211662.255145, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.255136}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #quality_metric: host=algo-1, epoch=9, train absolute_loss_objective <loss>=0.533102999752\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.309] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 10, \"duration\": 1550, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9990056694878472, \"sum\": 0.9990056694878472, \"min\": 0.9990056694878472}}, \"EndTime\": 1542211662.498362, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498302}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0067704546893084, \"sum\": 1.0067704546893084, \"min\": 1.0067704546893084}}, \"EndTime\": 1542211662.498444, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498431}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.983834386754919, \"sum\": 0.983834386754919, \"min\": 0.983834386754919}}, \"EndTime\": 1542211662.498485, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498473}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0028006716127749, \"sum\": 1.0028006716127749, \"min\": 1.0028006716127749}}, \"EndTime\": 1542211662.498532, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498522}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.3261348922164355, \"sum\": 6.3261348922164355, \"min\": 6.3261348922164355}}, \"EndTime\": 1542211662.498566, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498558}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.683821885850694, \"sum\": 6.683821885850694, \"min\": 6.683821885850694}}, \"EndTime\": 1542211662.498598, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.49859}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.020841448748553, \"sum\": 6.020841448748553, \"min\": 6.020841448748553}}, \"EndTime\": 1542211662.498629, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498621}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.8739521393952545, \"sum\": 6.8739521393952545, \"min\": 6.8739521393952545}}, \"EndTime\": 1542211662.498667, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498654}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.004711043746383, \"sum\": 1.004711043746383, \"min\": 1.004711043746383}}, \"EndTime\": 1542211662.49871, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498699}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9914231703016493, \"sum\": 0.9914231703016493, \"min\": 0.9914231703016493}}, \"EndTime\": 1542211662.498748, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498739}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9938106565122251, \"sum\": 0.9938106565122251, \"min\": 0.9938106565122251}}, \"EndTime\": 1542211662.49878, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498772}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.0018803349247685, \"sum\": 1.0018803349247685, \"min\": 1.0018803349247685}}, \"EndTime\": 1542211662.498811, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498803}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.0632917390046295, \"sum\": 6.0632917390046295, \"min\": 6.0632917390046295}}, \"EndTime\": 1542211662.498842, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498834}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.415393156828704, \"sum\": 6.415393156828704, \"min\": 6.415393156828704}}, \"EndTime\": 1542211662.49888, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498867}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 5.993948816370081, \"sum\": 5.993948816370081, \"min\": 5.993948816370081}}, \"EndTime\": 1542211662.498921, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498909}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 6.726938860857929, \"sum\": 6.726938860857929, \"min\": 6.726938860857929}}, \"EndTime\": 1542211662.498958, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498949}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.8990551503499349, \"sum\": 0.8990551503499349, \"min\": 0.8990551503499349}}, \"EndTime\": 1542211662.498989, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.498981}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9138184413203486, \"sum\": 0.9138184413203486, \"min\": 0.9138184413203486}}, \"EndTime\": 1542211662.499021, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499013}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.9004813611065899, \"sum\": 0.9004813611065899, \"min\": 0.9004813611065899}}, \"EndTime\": 1542211662.499052, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499044}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 0.900460442437066, \"sum\": 0.900460442437066, \"min\": 0.900460442437066}}, \"EndTime\": 1542211662.499104, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.49909}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.181623648184317, \"sum\": 2.181623648184317, \"min\": 2.181623648184317}}, \"EndTime\": 1542211662.499141, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499132}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.388480382848669, \"sum\": 2.388480382848669, \"min\": 2.388480382848669}}, \"EndTime\": 1542211662.499172, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499163}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.1986077089662905, \"sum\": 2.1986077089662905, \"min\": 2.1986077089662905}}, \"EndTime\": 1542211662.499202, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499194}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 2.393094312879774, \"sum\": 2.393094312879774, \"min\": 2.393094312879774}}, \"EndTime\": 1542211662.499233, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499225}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1528546820746528, \"sum\": 1.1528546820746528, \"min\": 1.1528546820746528}}, \"EndTime\": 1542211662.499286, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.152121231644242, \"sum\": 1.152121231644242, \"min\": 1.152121231644242}}, \"EndTime\": 1542211662.499323, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499312}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1503106067798756, \"sum\": 1.1503106067798756, \"min\": 1.1503106067798756}}, \"EndTime\": 1542211662.499361, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499352}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.151939346878617, \"sum\": 1.151939346878617, \"min\": 1.151939346878617}}, \"EndTime\": 1542211662.499392, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1618346037688079, \"sum\": 1.1618346037688079, \"min\": 1.1618346037688079}}, \"EndTime\": 1542211662.499423, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499415}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.2887738037109375, \"sum\": 1.2887738037109375, \"min\": 1.2887738037109375}}, \"EndTime\": 1542211662.499454, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499446}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.22874102557147, \"sum\": 1.22874102557147, \"min\": 1.22874102557147}}, \"EndTime\": 1542211662.499489, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.499476}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_absolute_loss_objective\": {\"count\": 1, \"max\": 1.1988290518301505, \"sum\": 1.1988290518301505, \"min\": 1.1988290518301505}}, \"EndTime\": 1542211662.499532, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211662.49952}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #quality_metric: host=algo-1, epoch=9, validation absolute_loss_objective <loss>=0.999005669488\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=absolute_loss_objective, value=0.89905515035\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Total Batches Seen\": {\"count\": 1, \"max\": 141, \"sum\": 141.0, \"min\": 141}, \"Total Records Seen\": {\"count\": 1, \"max\": 137244, \"sum\": 137244.0, \"min\": 137244}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 12598, \"sum\": 12598.0, \"min\": 12598}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1542211662.500288, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1542211660.962734}\n",
      "\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #throughput_metric: host=algo-1, train throughput=8193.01107889 records/second\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 WARNING 140494836311872] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 WARNING 140494836311872] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.553] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 11, \"duration\": 243, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=absolute_loss_objective, value=0.89905515035\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.740] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 12, \"duration\": 186, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #validation_score (algo-1) : ('absolute_loss_objective', 0.88788102891710075)\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] #quality_metric: host=algo-1, validation absolute_loss_objective <loss>=0.887881028917\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 1, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.005, \"lr_scheduler_minimum_lr\": 0.0001}\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] Saved checkpoint to \"/tmp/tmpMOxLfa/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[11/14/2018 16:07:42 INFO 140494836311872] Test data is not provided.\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.851] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 12, \"duration\": 1888, \"num_examples\": 13}\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.851] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"duration\": 17287, \"num_epochs\": 12, \"num_examples\": 141}\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.854] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"epoch\": 13, \"duration\": 112, \"num_examples\": 6}\u001b[0m\n",
      "\u001b[31m[2018-11-14 16:07:42.854] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/validation\", \"duration\": 49447, \"num_epochs\": 13, \"num_examples\": 73}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 49554.291009902954, \"sum\": 49554.291009902954, \"min\": 49554.291009902954}, \"finalize.time\": {\"count\": 1, \"max\": 327.33607292175293, \"sum\": 327.33607292175293, \"min\": 327.33607292175293}, \"initialize.time\": {\"count\": 1, \"max\": 33217.731952667236, \"sum\": 33217.731952667236, \"min\": 33217.731952667236}, \"check_early_stopping.time\": {\"count\": 11, \"max\": 1.1150836944580078, \"sum\": 7.912158966064453, \"min\": 0.13399124145507812}, \"setuptime\": {\"count\": 1, \"max\": 15.397071838378906, \"sum\": 15.397071838378906, \"min\": 15.397071838378906}, \"update.time\": {\"count\": 10, \"max\": 1797.5621223449707, \"sum\": 15858.712911605835, \"min\": 1455.6968212127686}, \"epochs\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1542211662.855205, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1542211613.381231}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2018-11-14 16:07:52 Uploading - Uploading generated training model\n",
      "2018-11-14 16:07:52 Completed - Training job completed\n",
      "Billable seconds: 95\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "sess = sagemaker.Session()\n",
    "linear = sagemaker.estimator.Estimator(container,\n",
    "                                    role, \n",
    "                                    train_instance_count=1, \n",
    "                                    train_instance_type='ml.p3.16xlarge',\n",
    "                                    output_path=output_path,\n",
    "                                    sagemaker_session=sess)\n",
    "\n",
    "linear.set_hyperparameters(feature_dim=vocab_size,\n",
    "                           mini_batch_size=1024,\n",
    "                           predictor_type='regressor',\n",
    "                           epochs=10,\n",
    "                           #num_models=32,\n",
    "                           loss='absolute_loss')\n",
    "\n",
    "linear.fit({'train': s3_train_data, 'validation': s3_validation_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: linear-learner-2018-11-14-16-10-25-589\n",
      "INFO:sagemaker:Creating endpoint with name linear-learner-2018-11-14-16-03-42-248\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "linear_predictor = linear.deploy(initial_instance_count=1,\n",
    "                                 instance_type='ml.c4.xlarge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate model accuracies\n",
    "We try 3 different approaches to building the review predictions: Logistic Regression, Naive Bayes, and Support Vector Machines. We also try a final approach that does a combined \"vote\" of all three models. This means we are actually building (4 approaches) x (3 ratings) = 12 total models. Since we have limited data, we will use cross-validation to split the data 10 ways and measure accuracy in an unbiased way.\n",
    "\n",
    "The accuracy % are printed below for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3, csv, io, json\n",
    "\n",
    "def linear_serializer(data):\n",
    "    js = {'instances': []}\n",
    "    for row in data:\n",
    "        js['instances'].append({'features': row.tolist()})\n",
    "    #print js\n",
    "    return json.dumps(js)\n",
    "\n",
    "linear_predictor.content_type = 'application/json'\n",
    "linear_predictor.serializer = linear_serializer\n",
    "linear_predictor.deserializer = json_deserializer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str = ['I bought these knives last week. I immediately returned these when they arrived damaged.']\n",
    "test_new = tfidf_m.transform(test_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'score': 1.4680500030517578}]}\n"
     ]
    }
   ],
   "source": [
    "result = linear_predictor.predict(test_new.toarray())\n",
    "print(resul)t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str = ['This is the best toaster oven I have ever owned! I am glad I bought it.']\n",
    "test_new = tfidf_m.transform(test_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'score': 4.25839376449585}]}\n"
     ]
    }
   ],
   "source": [
    "result = linear_predictor.predict(test_new.toarray())\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thanks a lot!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# References\n",
    "\n",
    "* Original Jupyter Notebook: https://github.com/zuenko/python/blob/4db739a658a550cfebbc4930002d925777f2edf4/yanix/Contest/ML_track/AmazonReview.ipynb"
   ]
  }
 ],
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